From 97b7780d6747056e2e6ebe4541dabf686bac190d Mon Sep 17 00:00:00 2001 From: PitKoro Date: Sat, 18 Mar 2023 18:42:35 +0300 Subject: [PATCH 1/6] sem1 --- .../seminars/imgs/sem1/sem1_1.png | Bin 0 -> 121194 bytes .../seminars/imgs/sem1/sem1_10.png | Bin 0 -> 81863 bytes .../seminars/imgs/sem1/sem1_11.png | Bin 0 -> 30785 bytes .../seminars/imgs/sem1/sem1_12.png | Bin 0 -> 78342 bytes .../seminars/imgs/sem1/sem1_13.png | Bin 0 -> 173581 bytes .../seminars/imgs/sem1/sem1_14.png | Bin 0 -> 269585 bytes .../seminars/imgs/sem1/sem1_2.png | Bin 0 -> 175147 bytes .../seminars/imgs/sem1/sem1_3.png | Bin 0 -> 6827 bytes .../seminars/imgs/sem1/sem1_4.png | Bin 0 -> 5546 bytes .../seminars/imgs/sem1/sem1_5.png | Bin 0 -> 10339 bytes .../seminars/imgs/sem1/sem1_6.png | Bin 0 -> 60074 bytes .../seminars/imgs/sem1/sem1_7.png | Bin 0 -> 178385 bytes .../seminars/imgs/sem1/sem1_8.png | Bin 0 -> 75078 bytes .../seminars/imgs/sem1/sem1_9.png | Bin 0 -> 13126 bytes ml_system_design/seminars/sem1.ipynb | 3249 +++++++++++++++++ 15 files changed, 3249 insertions(+) create mode 100644 ml_system_design/seminars/imgs/sem1/sem1_1.png create mode 100644 ml_system_design/seminars/imgs/sem1/sem1_10.png create mode 100644 ml_system_design/seminars/imgs/sem1/sem1_11.png create mode 100644 ml_system_design/seminars/imgs/sem1/sem1_12.png create mode 100644 ml_system_design/seminars/imgs/sem1/sem1_13.png create mode 100644 ml_system_design/seminars/imgs/sem1/sem1_14.png create mode 100644 ml_system_design/seminars/imgs/sem1/sem1_2.png create mode 100644 ml_system_design/seminars/imgs/sem1/sem1_3.png create mode 100644 ml_system_design/seminars/imgs/sem1/sem1_4.png create mode 100644 ml_system_design/seminars/imgs/sem1/sem1_5.png create mode 100644 ml_system_design/seminars/imgs/sem1/sem1_6.png create mode 100644 ml_system_design/seminars/imgs/sem1/sem1_7.png create mode 100644 ml_system_design/seminars/imgs/sem1/sem1_8.png create mode 100644 ml_system_design/seminars/imgs/sem1/sem1_9.png create mode 100644 ml_system_design/seminars/sem1.ipynb diff --git a/ml_system_design/seminars/imgs/sem1/sem1_1.png b/ml_system_design/seminars/imgs/sem1/sem1_1.png new file mode 100644 index 0000000000000000000000000000000000000000..e4c7833f43c035530599b918115d5db63054e8cf GIT binary patch literal 121194 zcmXtf1yq#X_caQlv~)L;LpKbaLpKf*(hNNa(jX}C(lK;*hjfU5AUVJg0wUd|ASDgo z!|%VoSu=}R&&)IT-gEZZd!KtFb+lCQu&J=o(9rN8s)~AOXivw1*XtLUz<)fZ-+BN) z)?f+>I`8yUQ&HFlNZG@X!WWR>0?7rt=Qy#5;45@g~(wg)Sz3jk&}e{lqDDZ zOA>}5jy^9f+afPPB#9N7lVp;OYqNtkfBd3*3uXWI^JQ;O515cKRVX&Z(q9t&<9FIm z=_B%(nD<7Q;Vx-}R(W^d?rljV4U$ZBpAsEUd)u(9g48h71wTF&;*JejoA$uOTvo-z zT*JY|HJyCv{o#odKVLosmkk(}$4`+@Mf56-z+kc5RiW?D(D3^nUrz?z%5Bil7|xQ9~O3z@C4|_h#bIWsVhU^!IlAzVzSCL-4n|6D!T%;|`mz)Cvio`Jw$; ze$EgbX=5lU^Z|38EmV%kL0^RH)W#^4bPyiHxGsiyBPs|&PTh0sZq6%>$R4d9HG-Tr zsm#n8XZt=?pCdiYNR%>iZ5LWNon{5}mm0eix_AjrTC69J?V+=(d93H6b)qvW=6OaU zX5!H?8J#Fqs>TKjnDd12Szv0%{<1KRj(8EfJc&|ew+-zM=?{w*m#Pk3nc;TjKx1a= zO1>a0y?+yJw5>5pHkT|tTkLgCbXSh@xgs5?Q|n(F;_=e__zjDBrCEEqiOr@ERO!{L z!8#=G?#0cEP=;xw2CtT0HLLjW6~?oKO4(1AVht~2zo2DFR5;|EV&gyuo9xLAv&$mE z=qoKQC8mi}-(_%bM6YZ4qD1!_+_pzQ?Vm!f?TXZ}&+D8LfXKUE_z`p*4^HE}YC+ zV&^%3f4B1CK9Qxo-Wb(?Ix};Y4Z1F=_AUNC^{ThUnOp?yS zERXpCTp1T*8Kg;AJmZptn|iopN#STqoG{vuHK0NmQ{}$t%gccfvM34QTsRelohWnm z1!#H9D=JU6xtP78@SA!U^8XgYry1lZ zarSW0>Sg&g@k3jeghS!mDOe~=Q-~_|O}{vwxJb;?R9lD7P5yP-Bemgp{A+yU>b0vFaex~Ca>j-cZ@qX zT@C-WTe5tc=kbf^Y~chwk0AM3d(eB@DLF?XH_Na4F971__7X$Q_dU(?+JnkxQT{j$ z(%tL>hJ1~wA!KX8Js0~lN zlH;_Ou;s3MIkaF@DtC0#Wo21mfovf3FXqU$_nlBSkt)Fl;oy+Y5vTi3?GheI7yPWoieg&>axb9CSDcQK7fwxU%d^;VLwXwby1{%HLZpt=JlH;+d*ukl)wA_L~&PKxK8y`f;LOMs_#@=SRcg6MR2yif}atNPhuWgkOnFd#oWq^ zCS2dH%aXx!m;v#4Wi+g$X3OZ7j%&dleV#DHIX%LH&2AHN9Zh}iyZ`#fmlR=7C2u!E z_jygVGK0^aM`OfRLavn43{uhtbkcJUxhEb|reh1Oh93>GLl)qpq}-@eBGR4WtJJ}^ zbQbY4^g6ELA9jru|B2Nm!0Q}RFE%M#H+<+kG4mD;2xKXPn+En{u9*@JJP7T;s0--f zR`*8+3M}SE5RSKywRvQ<xThc|r)<+wc9{ z*6m)3|Ir{3raBeXM8)oZ`8aZr>DLJnj=TS6lXXjBp>>G?H0t76hT@zJ^|eD@kRd@W zF+#=_UdmCHUEC;m>{j(gR9YTNvKIXb1wVe9DW?K_Hl-DpNQB@C=eZP=XIYDxf1HGV zM0C8wFrQP^I>2SYh_+6jPxZqAs28OCv6gudl97^joLX;mEB<{}HyxW>18iqNP7cO- z)=|EWyXe|e5VU>xYN>JhnkC+fd&vZPIBDj_NpF!*VGb4w;~i~yDCf3m!wep*CVxx3 z89JKir7Iwnw(_R5th%}-Ih-efrYfy}LR+5fS<~-kS-VL-%KQ$z*AfJyLT{7KOS>j1 z8xbeTqu5jBI}OGH4IaxLN?|MLi?oQ1hC~=U5fh}ErHRh?HuwV@+f@qpfL-f~Ov0j1L*nvd-m<(tB%(EeW&|bZeuAvsOAvVJ8=y7aB3%l!^K7w!Xj3H9bY9Xx%SFlHE+(4<#@ujw5 z19hxqa9ilmP5i^e>r`toB$d3tXVrG(PqyUu+=(%z&7u2w15F1b31#5Y_e`66_U++G z{IXt&Sxv~ri2N$SnJS0W93fRV5O4JFH>7&3yqhv$uQ-q=S*nJWp4>46_3DXT<`3J3 z%IR^VC&fo(DIm#}8dM7ZVd@$+?Td!kK{(zksUr3vGfuN>*0~CU!oelyft3QMh-zz@ z;S*v0RS8VlR`+?Ipvp8i(bdtWB>TZTBP;uz~cvGm5VQxEuVW`6L1@?Ou!e+J2$ukBsEMzpd zK08lzZn8XMTO8tI*=TPJSI~Ng2ibq4FWz?h>geU3mcUryo_VNiAZ5fSh(G>AHW$jH z26B>+yoROz#`sFY=sR%=rn$wkH|tTg%YwJ)^L-Km{Xz8u8>0l*m(mW1mr9eW+X>a^ zU>A5sX;OCBzWyb{X7;Lf2x?;Y={w$;Su{(#@3Ur zJQA*sG-^o4xvde#Cs?=k6cf&(?GMd4lK!@h8j$E0t=vo-mh<+C--v%io4)p`T^k;0 z^1C12WDI|`Vr7+xE!g(TD6TgMXKRn~_9(9D-(VVcnBGf*)2tX~U!~bVrdAqTT&Ecn z@OA|eTn4UujQ#3Nu_4>#R+7i}h$y2Z0G*yIaYmJD>=D|}H zm}<06w*|Iq$@ZwW=5`N}Pp98SeR7LoX0egr3M%F{7E6j@Zs$Y(JYsTT%FDvYIaaYP zJ9(Fu?+VhgGT$E<73xW`l@7Yvxxhobj%oNDa`FXqxF=xgM{n_+t-yJUBl=KcTp8Jd zfOntuP5X>3_z7^3*akL&nemhc8pZ{+1Ca%%Mh1qidn36vxat3uf>m*XGmAZHnJXL4 zV)e2S>n0i+EM~^mB&SR-rbooJRx;BspkGX;!PvHK(g+dUy=AE4q$Upex;a_JS?1y@ zROjE%+Wn^j@%dfDQz`=4DiKTtQoe%-*R}OEs*@#{llCt#C)0HCZ`CYGLq^0l7G%ST zN%FWv7O| zCCk>XG*xF~+;KLG&k$k!P0BK&VLJo1F~+h}Ki(&w{ylu6`aprq+N5CxVjUTglrnEG z%o+hf4i(p1ymIg3S0BAE)-ATsxOo+|-?TsEay@4KxlLYk1tReUJBNpw!AU_QG^bpb znYWbjzohhjC{uLEw~g5?ZPEiF{1cFtZ49;9jb@Ztkm{=1WHo6lR9@6R#BmqTc1;?E z-cBABEbS6AJ#KrUYgLYUS1Z7!z^)I=^?Df)>#3Tm;+jq!G0)M!QLG#bPgH@5xIE1k zO@`jXD5){pp#sQ(zc;RzQl2bfn@_HP>l=nutCRlRuIx!SL{FzLp=8%)*e|~^X_EqJ zFtb3gCt&Qd=kii{t1gKS?7b>6YHo6KQHa@9CtYl|%aJ?HUUf@NY9ywXerprv+u0KCP+k4I`b;cjC zvnh+8fjp7sUN_XeWy8z)67q%igQFk5V@x6T-Ur7~vW}*L3;3ayqqDYJ`_@*8b+lKT z_JO_8A{;*Fq*bCIa^ zsY5yTCWMf^(Z@*RAWbYhB=KqWlz5f*fU;t2Af+tQAW_y{V%YVJDa)3u*@S&nk36FK>9f+89; z<6RbZGJCsYJ3dCy`AYnISy zmS^D%*eVw}uKUIPh<@sNvgHpU!P5(t#WeWc%fw;_9W7{nW=2+}Q7$J#6jREe#8KeI zGl&g!>;~sB$o1zgkpc_tcLOgX92Eu36f@$Su&(tSxcbSba=Tpg*(Zypc~F@ z)~bPG$#zxAQS}A5fmg#WF4=S`2Jy*2@tE~PN?0Y|>1l3Fsd9T8Z&n;@NO85iVY(pn zzjU#<8CLlYX;djFj`K(Rz-9VY0Ix*&KDti6j3c;4a0fm$i=!6{V?xOr$B#u|<=~{} ziHM=h>zO7flS+>^y$s(VSeLvJFBrL#YzqeZKH`j)5xpj}8KKZOCE}R&g-(Evf_H}+ z)*$wq6H)@3;cltSjOMKuXr*xQpMMbbyVuPmTEbDxb&Tf|DR%czx{S*Sz6z<=&|G6= zX+$L9z*T{^EdIE}+#dlmn=y*FCJsqt41TqH9XD}sJ-IDIn-No=%WZAFN+7jnh6r4L z&K6LitF{C#4~QT2!Orasx4s4b5`Cqs{I&9@JuDqs9?oMs3(}4*^k(B2EyeFb5f?e~ zm2^X_vbXL|#dMK9ZCCopk?!k>^zT$fSN@Fyy-!cu%j3|c2lsy@Ug?&eWV0SMoDFtv zR@WxtR;^$2RgJvmY@|_QJ78Jp{ULhVK=G>o=E-#nvQG@dn>q;F)QY`{l#glz zFJbdVm<}Cn+2ob>%Vj-mhRd#sEHXt;)p9j<+Rvtvd~0zLi!0jK^zx=rnYrrXW=F`+ z|4CLFI$`z?kqbVduvmJ+S4sg#HtcuR7VvR|G7Y;k?}{9aJU}vd)G3FVT2H0xV*X{> zI=g1J7XBu2_xM_oSvY)W!z+{%Un)#TvJ2!du*(yzSa9VkULG(mVEt__g^!#A+#}* z{F=c=;Rt+2>6bLuq(Ef7;a2A+wDY&EzccGqHJssVy95R44mdh{&*2dEC43sx4s$iC z2-PX;k4Q(?%M6*+iG^A%j$w2L3AJ;8!|vlYC~|jFpuhDJW)yny&@%PU`fh%DnYFpX z-?V{R-^HHvP%_lMHG;HLJkRc#L%~_bF^nx|9Qn4Vi>;s z{bN$@Wz6T#i-BTp6PiZjJyQgl9##ugV#AG`&vx=#e*yf-4r;q_hjn(p1!D9|<3%|>E2=fGqps%>BAcFp(>UwR`r zEzDyY+j)nro%Xq0(NMqM^{!+Ik=y=9cxMrQ&*yaIoupayDqriJ zb=lWE!N$fz2Q1DkNV%cq+?h~w@8#Z!-8ifdY0Xxq;r;7#$a$QX9?q?XSbe_|wV2PyE zyqH>kV`DMblu$~X%*|WH89Y58R}s}Xe$H!A{jQn#^6O?M>n0?h7crD2k}))#bN0KF z*wF-?$wjUtAxtdJupE1JMb1QgV;HTH7ynK-< zUo#>*o8AjTP2L$8{?&SFXq-9t#GYMqjeY9iwQxxhXs82aEp_!v&oeI0i9NYMwM=ej zYKPC3dgEcPoXlo7-Su|cDBCOAm5TYhMYga3Ge=x}@HiVgL2Ld7+XA1M{fN=_75 z=a|OGGVs$FY19h&!4UzKP}92gwaoT99~&_p74-R9(ap{h))4VmI++eT4#pD>B@5!@ zLRHE=oH{d+l*)}Zk}NkW>WLpBDL~Yv($oEo_IQHGkB|l{=s&acA^howPcUlo#@shh z{%DW=PHY4S*<>4;JT1gKEpKpXV$gitw}s;_NnbUZ9FJ};bJf}zNBo*Os!?OAHhOq1 zxQzcD6g1qKW7+VQBd@MgS<7gTN@yk}jd$;+VP6>Ep^Pm*o?~Z(MF!2*Xp5U#z#t@k zt=OOCdc%RSOhYz4lqqZ@D?W}*VhHM+M>rPapd9jq{ z=V8O%$OeOLR(-P_%bH;cX?DZ%it$_D?1?HhYIon>jCV_!3c>GU-o!&A{L_b?=eQz^ z2$R>V7n<1UIjME=?mWpRx21eeL!Az1QUO`;bH_lUi<9BE~ z%?*rUf6=@bIO3^WEB~;N+vC$YvMk8kn~pz){izhh*NE09GzdEunJJ2q&>~h^(6^UG z^RxcI#c)E&s$q4%KZ|chJ3?b^8Xo(R$+~j)KEYQn{ZyXnOkzDT->Dmla;acJdg%*{ zXMf=O*pz;ro)E4;s+B?sq#m9R-? zqTT2a^}hu0A@+gAGq6nJ&^Pi8Jjq9CWHi!0AqAPj+A9N)7xvw<~MEY1b;=#paRO4JtK$?~CV07O0o)Yz5A^6~=c6yQ32Kp{46)`7+ z>M+|h>*ReB6SL5;l4g_&S>d|Kc3+Q0a#X|^F~-D=Fd&sSk`AtDPGIQ=xsfRS4Wz#d zvIso9>C#=x_Z~W$;`{UDTL#6`bFLXTJ)EIx!ohk8<;9D@Eoo-E9Bu6Oe8MEMB}l5| zpFoxmzA-=}$q{$DegjQ8#w^IQb9_q#Vq=((*e2rjCsEox_XD12AC!yu*M%vcHzn8e3)VwnKYBs=w+?3{^(=Fo_wcxP_pTqkjjqbbk+2{LY`KWh#Qug)#LHgV04hk0j#KmY2i@meOXmjDJxQyv@+;TCV z{(_J$>W|!4&x7bt#9uNup?NQa|98!HVU47m^Hb!BH*CU1sTiS2fP4FdnS7hr3dxLpKkuSpFh zwZ{%AK0dhVGI=-hPeAJpJunw&%Br|}`Fm94ZDuxClcTqy#?|eAw{tWludy}+$n>Cm zctZEI#`{~14h)E@G^$Wcw52-#c$-D9t(0_Ogwob@7_VxpV(!kK4yL5x8Pt>lF3M{$ z;S=claO|;*(NjEMS$&kejA2f0_H{RxWvid0rQjw`hE3ZPpaOOo3VQHuwdYvQ9L!U> z71VD!t~K5rddQ6_=$4~CGPG?lu0_3)`uMPoDm^%5s?$R#va`gZ(}ngm_WpGzL9oSk zs~8W$K96N5Bt!|uqrWmrO2+qqBb~)h+=x#;r*vqZmfEx4)u9KovZeW`lG0C??x3Q2 z_?scApD9o7C$(t?-OY^8YqHZycapIVbv4mNd$;ZkL~AvsOz}cxX9VLJQ&Utq3S-Eh z9Fey_M8=qz`9$euW1XQ<-fH3W^nIo=?F|GQ0A^D_!n@RRcFAhJibyQ+F^9nHw?Yb5`SWMb>};x)%;is3a^Kk#+bbTflP3B!rSENO^FJJflu7s~Sw$=ShU_zb z>8tRt_~Kl7eK$|!|LNGQZ1qnwsa~9a81X%mjy{)Mvwcmh8}!7ukO>WN%Gy zkepndEf#L&vJN>G_s3knwufb1v$=I=_Ts%zn%5$BNQ$s5zfPpaQX>5Z`)8Zj)~Fk-e?lJ7u~Dy?SZlm=nNB~ymlZT-!1A8e8>Vt>Ta@FE2I z;vHq4wVI}ZLB@Em7+e1Yt6A1Fn3|%l#(IWrD77LuXo+N{`b%E^0yRUT@!MDJM~IQl z8|m@!ad#h||5k!-@Z6R=za1a*cLhJhg@rw9ek1eZ=@z$RvkCJ^Do?2ZS2Vv?w>|9_ z*l7&X;!t9<_6MwR_eS+zZC$SQgW8piUT$L{wxy)Rv+kI#EQ5!gz*^Uut?E~ST9|~4 zkqKEw-L_`Yh>^aH4_8!KXFSqr$PaO{$*0^n#aexHl`istEusHzuijFD5?CI+4(aH2Le)u`b_ly-;RuU=DpOzi%G zhb>_!(DQ$iJ39y67@X>V7$yvKt2{z_gy}Ju^c6N>d zS5?D}HprAnxZ2$_2a2?g`NozSD8E2L=VC>qJQT<$c7D*?bJLbpLm8nG;wx>{Nowg> z)28%|&V}k+1T*SH6f7$3i`#)NN#A@e1&mI5o9O;}Og(tiY|YsRw%meYV-x*v$t&XV z!HQw6)+P_-)l|^xQh35YusGI}(|pbS7l(!0dB<`!>K!O36w0$CxufZzJ`1&fn@+~Q zv;%oMZGgY@6{fl{!4!tb-UZtU@v_z!ypqb>A3IRZo6N;9m$}Z3C>^4BPt2TP#M8m1 ztuFsfioDXWs5}P|dz1)v%^PEQ2*|m(`9F%4Kx+1*8!Q zAAc$k-sXi;@bJO4B(fWmV2!9^V&VI|BN%_|U;Lp4!9fYZDhmm`Ik- z*)E|;4H*wFZ+$~U=wLj3UjyiI=?~`WF7GZ@W|X;*R5pu4ew=osi&sRfcP|AdXw%nY=#w_BiH01aTWY2v2%&*Stua)-@QX0gnzED59#k$>gTMhu72z9 zPi<^$43O@!vNE5G!=)EEv#zaz^~JUV-%C6863*gS#1&Uieu?~J+#LGF0R`~NFS%vw z2$1Amv9FY!k`{?lZr7%hPR1Q-j;FRqeS1MMVfS$C+I;#!;=L%Br&HtJJOri5e9BUk zTTHK7#P0OEdi;h#hPQVpv*&9%_^w3Dn^WK);+J$ZJdq4N=L|%fw24*M`U%Oq&YLo; z1Z7|6tmUUnQ0jls?l5}Zm7a0!k`$d0gXAvE-@%j2z|{DS7sx8b0tu)8#=>$bUrqcY z`xsqW`{{Ijcmr=rlzG$I*ZIG_reM_=A`bn8^j`0vPnFTHmhMoZN{o9M!V92Uqz7=dC4KJ zy<$$J7WQ5f9C*+JV>`)W4)%cYgJgS%l}7Jhb!y`5`JL6M>ce;VW*J_i9koGcP`&af ztWB7jpBprnEE7?>`P?Ii_QDKfS)|nIq|+_?m{$r@qOE~(gh!EZGteSe>qon;#_wVv zji&Yb6VRr4ezG9DaEs3spnqNtUXwV%=srPG?0@l{S9yM!TsiBrw*TL5HRk6oFWWwE zv$^VZIpkWr=Fw0hFJyS(?&}+gKoEb2$22z!SJ&1S0_1+)nX{m)qa(_$F%q}{4iAyJ zzeHc@3RL*AG%z(qWMpJC;Vgo4{s&}w>(MzKg3tFsITh`VXrWHwN63y=tmoo2Aj=5-tI zz>>^Az=SbHnp*@c^C;6n&$;vj zZ>hOvH%fue?0a?{b-NPk5@@0$8gvcdBTcF=5}{FClhA#ply|ja``|=GCfjs+FcfdI z0cnN(pbBhaJNq`orZcr2!jfD9A`Q%=Sq*rq>r9)c2-(UBZ??c}`;4Y+_PbOr^A(Rl z(BM+-S@4wL$RcT0OIC)UvF}Ps39c=dU5EG7q04n#(DcBw>$p75!6O7CDX=NIsN+ib z40mKy4s;5XJ$wQJQk+fz-2eP>0daH3jute%wu8M*oK&zLYyh1ByjX(P)YP=+6b$w| zdl643)w{L^?50+Ji2;B_s<2oa%*6!{?J=0xqbB~mC&YNtvBWcuLd{$VS3cRpO9Dwg zvbgPt0{)eHtGVo`cHd9)Vyg1y|qTBrPK$L z?Dp8qwXY7r1tJa3x9)6jKa+p@4=2d^ra0MNUy?K=vS~x2boleCli~ct9!IJ4JH%_y z{f9zc-cV4e+=cYgW@0VglD+SIY{R-AE8a9y%J#_mP{(mRucOkZ!T|G(p}uT`{@t|% zGG2Rb&+yRB_(syscn96$B1J&5o!$YYATB=s_;5)w;OgXA?ZNtdqy1|$GsBBLeC_qs z4K1qWrvIMFeKqsBQMw}*?IaaGxC9TJsg=2a_XwMFLf6(%g{s(Sz)NzX5W{BFBEM6 z_U-KIY6XW+mbaxnLDWmaFvaRxUvY_jV_DI*R&1Sp>iq7lc{*r~g@kY$7R#~`?ulCz zgNJaoA+ysf#h>EXG(_+JsQ2}<*2~NyV?WQ<>MunBIQ+lbI#&L@XdBUT36?%rtALUa z)hJF5c3_-=TGj7Jl-fEwzh4}!9ABS1b^4xS=4d@vS6Bb}>zAI&-1I4AayFLR^)=+1 z`c*jua^xuiT>WXs_vhl`_rpUT0Att0@H59)0iLdGRrBPLm`@g|xSNs7y?pEIO9^~X zrFloos*7rR$z-vh^cJShKE3?Z&z$BbsyLOA;zAs6gdR$Ig^$!4un>A4BY&eDHpX-K^52`a4rB?kfI{yM?nR+9l*CSKoz1F zjQpmbZq&$nF?~2D|2acIQM1ro!s;ns1npCKIICp|@lTJ7>cdx%Xd3<+c>(ms-^XAAf@RE(n3|gU^_w>eRWo{r zebtQooz*N{|812v=wf#O!2u4sr2r(cn%uuYdNgIz8j>v9!B*=3{-?&hd+Vlj!U;_c z$`4GtN{@ErEFtKzPY~;cRc>_Jo1F~NgQCbgBG{kgFyXCJ(1AZ&2>kl?`kKn3E8yGw zJY^w!Z*OntFZa&2w(@_PBube(*4giqtUnTytaJwte(^q<*zh}Cs<)^Ipssx43t z;%dMLo7zTo&9(TxGFc@tMa>`%4UW@o*_A!MS#27DCiz)IHnbvkJw}4>9TQ-NkjaDdN5B9OmWH0b?;`N`;oPYX%>P`#r&zaqVCAb;nzXF+J;*ige>*Gjmooq?+5rD286>mI}=bHfNstb zb^E(F%?4mwb#?VBaO0mVD}%p&QRPYpW?ti)Sq!~)&xzB;6yqAXDO&imes)Ik{Q2|$ zHYiYeGK0&@bRA2zdbwr7+9y=_5=Abi%F@~yBR*PdI(SQcNcP`t2vvL&NT$pFa1(qvD03g;+8SM^owvS)O!GNje+EDdP=HmI6YWoSEl~Gza9^34jM!)e-pA@$k!cb!#P_wgUu2_8TClzpe zN%(L**ZmjBSODbFdVmn=QEZ%c=pC`nf+4;b%^iuBxED0aVRS*>oH@2 zXeOB?#0XI%9Eto7QaE%uCTU-_3*%IR&lWh(>65W=d^LoUoNg9X6ID(H36C;hzQ%S5 zwS{uFv`_D?PM2vvrUU?(&I<#;ACnG3k8!uw_x$k-0afid|cN0a#;FC8FTHXX9#%c%%3zBgfx#Zr0xX;*Dq`EYgk zbW^;lxv@>n4iw=)@SN{OQ9<^36ZG%?zS^HZ`8v%Iu5luzl*OjzBFB-_?<6}7i8Hn= za@81K*zHAE;o`20c33iNu6vwb+nxENfAPmXGlRDK#W>JCz9mst`+Rww8pdnUwYt9k z6yTZvXMTx^i+=-h@6n&w8eaq=@!vl{a=o_-a9nP9+DeVFAvRE?692iSB^+R7fE)#u zs^qu&fUh1k#7P4mCZJztaDqC_>io*X z%?1000-*(kOc=O{I z0EUFtbAK-lBvw>TP7Yy7yK}kk_ahFs-D<_H>dH$*;qk%-%wEoQfAQ$nhREG?8 z`nj#`5mGt>Z^!`P+VzwWlaT1Wx@!ORQLpX@p}^YTUQT3+ff#A6TIkOy0d-uAe_Y+h zzGET1fwqP%ZdTmzTh7|%VN8j1=pBv$;Q%1a!`;e5*pBa`DUil%@lxp{b8{ZBsP?E_ zOK)%ElDsTM|6_s7ZK@+{vuuah`Jy3Zn+gL}=BS;pod}|rhgEt#qQx1%nk&81!sBhi zu)zI)+VbtMy5hCUK0eyOXJ?*@aW1Z|h4aqK^ACwFB2$g+b00+*^=faTqj7+PRHRSU zGCt4@>ar;6PTUiqak6~(G6P&@5D3Ix$4E-YAGqlz-g! zeZjtT8P=FS-7%J|+=T>YZ0PBhyqV_{Z9TFmR|fV5j;et>c@sOF%;zjIX{o7?{O!@< z7|Rm%dAOGXXzZiy0pwjx4L48~i)I|Z0q3qp#+$(0Q*R32{8trPXgft2yhuCR$Ka{9 z17;%FkZ{A6r7T~+Oif_KEmcrreKJmQaTECXp@09r1bh{MXZYM+^3%x##i+#7Rq!=- z!n>zF&H)t>kXyiDfumTfHy201OzTITYRlYx;=@hBj0Yo261_^g#%2EA?{xQtWr}`NeX&SXY6UM;WSl&WNB$>V$#wBfJ^|u z1EA59d!EVbxVZ8hskH-h8@Bl&rkl!amwZCJ&;A9@A0eeGp&8$0CT{oyU7@eq% zG=tb&l;;A?MI1gZ1LCbK=q_qw!y2e6KxBCJa-<{<-r5QGLcZaLq5*UEi2Tdl!N61n zl_S)E`2PI)bN=!JKsp{N9t?)Hu&`h~qwR-jY9eFq4HY z80yc2w#eV$na(vJj#QR1hq^*`PsH@A%MOd_m;A2=Snj+0+-K9tU*1gpc@L~LP|P1a zWri1@TU#R@F&aQ;0EQkhsLHtRhn@VdOTE;sJVWT}yUe&1%*ryw9~5Iux;EKhMeAK@ z3Ob38sSmRyu3Xmldg%kh!&pGm1MsOn=H$>mW;h_kfycgq?l{X7T^Z<{DG=yLt^)}J zz#OnJKbMz{I1V3c&w^X`T3-agV|4{0QA=A}<%BaUH}9Dz8g_SlgFlFu*BYpV5fKrO z^$M6uK(&T4w2@m!7yW+8YPQRGmm?(2RVKF58V78aRb%MSSoEti|Jnwd9S>jt8i7!G!zuBAqn_xBkFzB%9^$g_ztIS7(c!)I6b#Hf z(aPmH`78_sNli_`q{AwpNiVN<@_?IbX=(kvyYZ%>rDcq>PARO_P@T;HGP}OBGX@Zy z0EGmEDgdG>1i|K(CyqvzwTrshJM9JbAx*E-g?=163ztTk*FB%}f&(>D)NOecXo>(P zFc6gek34KzTgDO&hkvrX0^3BnsidK#` zfT|2coMPGZw~>)3Y%_)7jhYz#{e=aQ7!Saxva+aw zez8$i*w&UUu%2boGTH&%abEwx1VD%YUd@LMv8b2r?d@_D;TP{eu2%18Y*Fm6;P6H3 z$?*E39pUVYRqit!v;ilP#iXSA0dV%K3s z!i@A{Ead}^L-LmZTo!PNfkVtjWrWfIQElfwi zK1CtXY}3aRC7#qTxfV@2lz-&{AjR5EqxxxO2iynL@B52%s{Ts@ z?k!aYc}mxgirtCe&K}*BbBB59mn#0APFLr4-XY@aiKC0-KMSIr{ulTb-9d&bQ__Lg zB-**)ZvY|##tty-&^92aHa8Wy@d2s2{}oLc|EZXmSkDeB?|2JI-i#9nWMS~#fyHrJ z_w6&_os=Bjzn-c^(`Yj*#SoxP3?Z12XYGE6pWCZ_&VsSHYy;CSl#EU&S~qT7eyC71 zL2%JhOYaITjXu6!(IWgX^whD@p(}x80}Rnw-Ub#G&~=vf_RoQP17ynG%$wx_JleFn z^#At)^gly-WXkS<6FOndCXRK__t2B$4gr@QSj4amu}2XpA}Y!zzY3Y1ZvonCc#jDQ z@C5BOfX+U;`%O&-ew2;|3Gm#~W~n$PHUg$qY+-F*eXd_KA+YC{fg!H9gbp-QgbR-? zF(BVtT3Y@hdAr{R2V3wDSyBK#DS*`gx7%1>m;dtvkm-O05~c&FNx(_Iy*x$-Fy*rQ z;YQ|BY5>2#=z9qiYe&L@YFgiK1e}EwJAPVY^uU4d*xyk|1-T{s`Fz2qZh-0m3BZ7> z0Jx16njru~2hi*XXrKI?T030Bo3v1X7q&;A5?gYJ0>A9FR&|FR{6g%KFG~mdwLm$SYhVXHi z!3r1~roGfOW4};{#zKik0%k!_&pq175+~*~TOd|}DSNCj!S|u|>wTF3hIriCeTN75p+H6qY;TwP@vcnXKbk=R4Fx!!5=A&5?Bp8f zA59{{a7LgiuK}AlfUQB$U-4uTXztw9wSUT2`1#XMSa1FjFSH!-q0U^KH;W@yb|^Sg zZTm*y1hOlo{Oi;5x+P!LLW;B&xN8?bUgnYReaBr|E#B1 z8i(Y;tguKR{maK$Q@3W=XXwMZH=zT0?)?55D1h(}4wpqimH7{ZgMmh?T+I?f$}Sa) zoh_J=_Pw{khH(F&r9yU6%2PhnI%)8fM@HBptvR^}a=tlnMab}1Rx4MN(8{B-?U;t; zXN!Yz%Fj=q;!8vGZe#itrKY~#{lx?-7xV2p(52rZbal`vM3kwS!QknF)ZpRx+R-nbMvg3B)7asgWu%T98u#2D&kK((3;2)x z;8zWRW`W=2HI1pnE0uayH0s$-3A9{5V_;)r+k;O6RIN7mQzUlXHekSpSKrEcWDZDh z1E94oo-_TixLiLSZP@5?wO(l8ru~*@9LufymC7J1sd)b*!R&!_m;=$v>KNg6;0r-J zO45DE$;0Sp`&Oq&-itT7rQu6~s3>G^_ex)0le{M92_>J2*H9A4Qfsyu;y`Y}OK98t?AzpvKX!{_TE{%oL0Y0LgYf*DFBP zgB~9nWn374_=(C+K!scG=&)=8FIRIgc+|+GCTH(i7;9#6+a}+f_bZq)OjT% z3799KC#(Wit>f6p#zw*BnRO$K&NIAeN=I#R0r=_A0>IDnr)UzA~pDN+VDTC72OsaRmAmq6w*QTjbrpj^P!>)EFftrAbih0Rseh066&j zWsNj(rKj7Fe3a{H&b@fpZ;v zdy(`{SgJ$?g7=mha z6q?2&IB#-!6CB@Url%VKbqOvy0c@gGo;dsr1yhW~{2_Wu$9S4Q!7s#|oSZysdRO1b z2pP`KK0I^`Q>9-voA^&n;cZZ$AcFP;Q2l!q`>p0-fh8VeM7ua41L_1hsW)==ZuPbw zPBrXEGnw8fD_V7LKtqpacx^wQWzstA#}!RO#_0{DmvMDhSeZgfSb3mg11lfBz5<*E zT0(VeYdAprmzOR8cb4|Q9GBF1H~{^j&FcmqU@~UjX;2n{#|?G-{N(<|DPO}g!_BNx zl+2x3Ab%A`L_Q++b74;K#-FLQH+qtH%cJ1#kLN2&j>!w}iZU`zA4je~&DAd2YPTW{ z8gGp;Lf!isY-mt%v3p~H6#)BOo)k~w6LvG``buWrK82X z=tjiViZQI((g-W>Zl!D0(&VA!VtA><^bv(=6d=BSFjVz$^zH32ySll-<*5bJMFPg& zG3ZacXGZ7VRDhWT0{}QoDxLy=|Nd3Az651+Nt$7rRMmqZbt?&qHw_SztasVa!N`h1o(*t<6Oyt=}65Cs$~X}Qla3F zza&q2_?TKzO$cZPG!r+n%fdXmjW$deh{9%^ok6_7B)`PJjzpHf84`}ZM6r~%m2h=( z{L(oYVj5|o=^}GXYPPmx%V>L#`;n>l4SmTBu}2Ia`>xZx%O@}DkmEbLgU~<%%FWr; z@M++&@u&X48DeH;20AWKey*o89|FPgVKD%n=LSlzqap{fH4+CD5lp-r#C7UGE0{g& zQ%Y<1#K<`pNEYRWK~`B1%=Z@r6_+kbOyh5KPF)Do3)ZKhV&bP&wgk0;Xpjz*z~Ttf zkg(mnZow;@$8V@u2qTnj+%>+qoMN6Iv+D(QBdt z5mA}%Xy{BY#Q7$^yiwR$B!2khz7@P`y%+;XAzs~xQ zh3ifbqS~O>Jilu0mj9x3c$cLP9d zCXDmQ>5aU7mW$@y`R%RT_A#Vkm?_epL_M1iIaZPs*&)LHdKp5#`83!DE7^{QBcGO$ zU`K`~>ZwQA2BRjbI#wgS=ofw$`8E^ns&(fr?WcEsKR53D9X8nx?GS8DuqaCE!1Xdr z<<~Y>35Lc8Oj)=JPMRza40z%TASi@I=UtUNz8D#N*kL1EKkt zvC}~EVg!L1JP ze1r3Xq9BY526pgJR=k5xzsH@AQEtb}fNnAb-A3f!Mckr^Vyab5TgW3rpFZO3C`zS1!i#`lnXwYxJDfBl<#uk>Cz&e`dn;QjW z2Lif=5~eq{8)aL>y(^?pYfU2Dop84Th}~u=OD4~h;;(WO)vwX_%|JARcY1(yTJ8u?9|eSSRGgNQN6B46)C8(~4mcR+rCyMqDi1*qYfdSGt_V>BFw!-13qVHX9v z{^FXF2Q}#qgwhxh1O(gL3f+Ndf=ZS^m)j5$uDD!FI4YTx2gW1dodkwuKoBEhu9sPy zkAYbJFjG4y1Vp54KRzX5j7VJr5&nCjzdoyrUN-$gps&% zD0LKWkn?&PFXoj!UMm~z_9oc4zz5+Az-7Da^6aIRH(c*%+g3z2f5*#u@s z{($QD`#zVl&qd?YN#G9*G5x^uv4Ozw-j4Km*YsDk6><<4o^&zGd0(x^J5s17IgW~j zuAUy~kfIc;;C91pGYRD(opB4jT-yUZJxBuSQ7W#vj6Glqfpda@KLU1IKtT}Zz-@&9 zBjk5Y?KnqnKs2B!1Z$YxEdx9$Lj3yAUf%0=6Xqk6t{I}ZGt1Oi5#w&~ zP&R0pTvKsmKUToV?ynIM;Pm$3rdDFm$cP+J8{m`^K<={H48(N(1o%HluzPT$Y4FK`Q>-q%o5eRK?vj!Ma^a1$%ZA<*>)hoa* z%SP+z=w!hTnO*Z>hqO^fpx}V50ibs0%vwfTBgL9T1+Nhyo6ZGqErzDO>wFVTP$4U2 zhI5;mwB>#cb42C*S=Svyl<)%wPn=U{MABF=ROqRLG4hfAhIZ1FF(*Y8$GzUbZPZ+U z$9X%}i_oXl&1z_Y2AJ$f2h_n^G_{v;I=lohZtG6Vwdw=6&EuN2iV1~`y&LC6W| z9nbY)A6^@^7)$6Xc5R9{-f9pZ>qv0FVN0?TVl7d1%fh5>W9fjf(maH{aA=#Kz-p?> z<^}j{z@`H}9zY&eR#yL;sGp^bZvXImg}>48rn*U6TN}>xfQ|yZ;eb2?Vo@X*m4H1C z93DayFjz_Pp-plBsbT~euBi#)NUf@T61mzZ;MC6#4CGH@Y+{JC;xGR({0w;bNFX`j zLCN5rJi+wPveC#No1{K%hX09D=M#Pw!^t&FQ+x80^Uwfkp%jD*$S!&EpRzK7!?tsLkLii7$hZ8h}xF{Kdf`8{3E0^!rHc z*G?P(1}|_2;mjE*+n{rULFbFoV$#Iub4~TwS)tsgF(^Ee`9hVP&@!7iCe;9qR*%e+ zC4sn8b%DkgA#7}#%X)kpIxK$uAA($2tSbIs@2}|ADFTS>k;1^57 zr4T3rEEt*SMWnKXR`8qRhaJ^&6qqsb%b!%q&&HfzGyQNhmX%yQH|mGHNkg;o>FRjX z^RA@Z7Zb~|_gkRjf}0SMU%FBZC50_^4~MVKFElTz6{XJ7zgB#y=InKAU)XZYtI%CI z_+E<}V2&X9b97Cvp+A5~$i~SZG~f;yEqA%UKOf(N+}@!yo67bo#*&^bA;%r8(G#Uh z-U7cTydnTMT5D@7xG9vJ?qLR_sS1qYIpQ_eKm_-gyO!(*?6u1R+7O4pZgd-t0f`}a zuHXs*>!3vI@3DGG-{g2tf>jn4`y*aHz2pm_{fP6iqc zPhJE#^2;9$lzGRWW}cPBJQPAg7^iGq+cRHp2qk!askqxBmIvBAFvh!r9tR#>GZEG-Mw?LGFX_CAYj~xL<+L~01;_f-cD@kx6MpZMj8l0 zgA%} z5z(4uiAr($8k9McBV)^bB$le?>pV}VH1hmK%emz25n?k!7XQ6}A7{@k)s>xW5$!Cn zcVdd^w7NwsNE(xOC*}+@^E>Nt#0q8xd&karBkLXCF}E)x_L_t!?tpzRZU#k9_4_8G zU`#Je7RrILY3|^yZ-;0Os0HGUZ^8y~RH zu-1g#V;(KMeF$s39=Ch!257Zqrmq`2oTi?B0#*(IRRhj@Tp&3PTJK8n4&~n=~YdKIp}L58Zor zC^0WB%?@ejX5!268GpYvQ?O?c!7VJpl85~qJ=a_1N9nhSjjcDNXcul%kYR$7cg1p0 z>>n~(XkH(X8*Qy`wY5y9Kb%Z2t!P%n%@cXdvx23|(^~TGMaray8C%{ay@t?up1{rH zvaMkOswTYOwvDr8S~<=FH=22c!=eKHMvZrMjN*r5gM!p^`P0LyT(SN0V|avvCBvB@2dO}EXkmu zDy(74hT<8%Uq_5?UCm%6TLa8R=Yj=Stsl%jxYvm5jDSuHAnzy?!3{>_d&399Y8)IB zO9l_e5J^D!HCC>ito?fm_p3Vz9~>O4!u^7`7fb+%A6e6PDou=1rCYC`XX?EoOyt1) z#5@9E>G4V@U6fHi4?8RCk_UEYr?tI`Sa5ml{f0xIs(MRQy!98N-??;m^5SXtich~# z#XRpgW*JEKJQ{end^a+oG(6mu{eJQ+l5xx;!f{fqw5 zDS1FpJ-F6CL}kd0o)(JxG@w_G79*4#BiraVS8s1WD|U!ORBthvcKnbZqasE%FNA5r zfm}luQ_vCD6WNuK(sYl6T!y%GBH%|07JHMnYe5rN!N&&gh-*F~%}vsRv&Fx@SjVlL zx2M#fbvOHciF;i_T*6g9;V#Zm?};&ux%CdtJWl@Ep<5GSbvCTCy%iXwmFDl(C5cZU zu^k=~Vhtz8iXzp+WYu_})^qEvG4SL&_#tTy3tjii?5bh?!Sk!+_p7`RwmRaf*4ps= z{+Do97+}-v_?Gn@EKXhDy5qs^Vt{+P|9fY5eX;u-Nu=v&nN7FN-H(`ESKr(Fb9H!2 zo&I?g{r67eL;9iqeet|zAVvYbXOmFvpQFx;atgVV2}eY@2yA-~MKhyvi$ANsuJ^0~=?6?=Wf4HTd0g!e=TJ zmFwq#hm!z>P=@VfFi0a>@}+9%X!V@~H`U(lGvGk$X}vnGn$RV27b@$HIf5&%fC!HS z9tL1ufm_J`Bh%m-nP97+h7P&$S2TZ7rm<@hx2ebleOZjUWLn7YIAZ#fbfR`Or>EWuJoEwA1!8!jG7%4`fgAgbGI;($E1w>eo{ z_)8rgAPiEZL;6-fvci2$A5@>0+ad&cT^s&;6|9**^kQn$mczWUK}2WO+X~nIP|BBz z#u1U7td9f6i7OBg=_dS&1id$g@#D(39D}#|^W9VH_+WwvCuWDU0w8t%i5B=T#+n^yy`>NykZi~=0cp`{IdR>gTgywiB z%g-TQ`u}?Y_HVw1g1HSiUjQ#tLf63L#_cOuV~syy60pkkJzj;)XV<5qPnLpabVQvX zuQ4WK+hpWir$w#iw*|^xQxTV6zw)2WuCjw=o^q?_o13#PZ9`9TTQfW2;?%yE>o2}I zp5oKUPbif3iPRpnP$2jnwJwu|nJs3L;LWgcUCRs;7@#kI-9$4SRAH-&4Fv-UO0@Z}4sfL=+-VMqjB1E)%6_qP_yP5#_x%q-mM!Os_XNVPbqKZmfg8cpW9 zSNU;5#ZbEcC`56WJ%?Ejju;x2)tl4=hx5|a8w;ID3;*(VAb-)g&g3slHlT(Y&~p8- z8XlZ^M3Lt-OINj05xkR4CA6fE1}9tgG`Er;^3yBG;R;g@a%jd($OaJmlyG>@N7jFH{#` zcCfle}o8u~M zVS==3zS^XB`^rKLyiGzylR1K*C@78D9;gnl8HYIGq_oZXc`>qR&`%DP|n zKyz;Gx?;(K1ts zw_d=ufKb)aBG8jlKvQ|9iK42jE2W}UOu=YFfsn6r-a-B3%@7Hv-(rj%PhMy7KaV0* z;kW^;Z@>9=9lg>MHZ=C|x$Z)v?q#L~4n$B#%W$QN#{cdF{@iV3@Q(+y>s3~K4gGkIYH*XrcSs_o z7@H}D#wGaE-62F_+%J)6N?#WCi|j;;`C>?r|9+BNtqRPWfu&`WQxkG5EGLFHMHTU@ zfqAs^i(yfP<%<`OeP6tC=p_jTadGVfpEVC$(uNL@_4*gy+@8s(y?z@jzh0J!<@!&@ z^$~Y{*pc!zu30fLk!rbPydn?xtl5hlrwB>9Rp-zfFYGHX)Om;1K0#nfMq@BhwL;1+ zWy2M4^-x)7CB6)EHB2xeC9KV!j&r`AkZs^#P5o-4=%( zM{oTkKNi0!s`SM}&$N_=>6W~aJZ;9e78|M2%ukSk|E>wM_z8P-dJq59)v}WdHug)D6 z%aEp{#k>PD&~1vagvVQJAk&B!`fzd}*P}IttWBGOKhDeLcrgE1VKv5k*A#zMEpNY#N8fUtNQHFHCLp}<} z(Q77skYAVI8Qm|Gm@6^q`<2#~7<1wB&PG)HRRrD70E7+^DS}qCKctBh4r!!D_TpM^ z9O_^6PZQhcH^0tzdhr}z>>0g$jCS|*fqkO5FWxWt$HM)W5neAV_C<&X@JvHmgdsU0T5fogQsY-I}~?K0gtMfsV@s{H zIwq-Z)o*|2MX!xC!H+(5$3!Ta-*f-B%`2IU_x0aZ<@Lrl5e~|4zzrG#_ap&#`)^(1 zC1r=KAY9S-hWPlDz9cn)xU!_RpMOYmj;)PwDb&T&M&EwFY!7q6Vw4E3%c)L^()(jK z;8}9+EYPpd(FKTW)Br?KvOqrUL+LCLYkHr04ceXp`EOe!r>k#!Qdd(Eh6c;ipQ`q3 zD=ki+RJbN77%{fXR~R`7*5VlB>Bmu}kWVguLa6$n?&;RuAe!n@?Jys#?kSZ2RpQt} z*+?7aN60faLW$qgk~r8(OW&Z{%g|Q3H>CF2gt$Du;dhOPdec63<`XBpD)iA+qc*p{ z#^M2ahnuohGA@(+;T0BaLi6>I%Ed8kx78+#d()8j{Z8A`IUMx`NYV)Jii=ESZ}A;o zvP*MgBKdxobisA798!2aVT?#rY>bSNBH>`7tnkR?VIrXxcz%l=U*pVeL&L}0H}f&s zv%a2mkQ0T`z#n6bG*IJUp21;GOW(trc4iO*sum+jKSVR8+4u3sexK~CM4BPVTmH`5 znA;bnN@*BW0o@4oBLDe+;~$#(y!d(>lW8;Gc|<{=s}y`ydUn*7_K+S4{OMW+yf+oM zTxR#Ov`$i4`|*}9!AzlV&))@XF?FZ;x}_Ui;olcvpDeZ{Xo+~Y4_bJ-y^_zE&r=x8 zv2!J=Wq!)z8!uB$!Bl!8g=BnRf6n7tw9%)v6VfbH7_AOj1}~ENWgQG?G(ZdkSMGnl zv5snc!6V*#!>g@7Z9-!JA}h`GpOgXZJe2uRC{+Dhq5wbb5ByQ^HZ!t4{;Zp$1y zf`Z(G*BFk;-CUGxK6hAzHCFCUg_u62cB&&~nbbX1BPGqDO3*c=O=y>hjStJ+Olmswf^@OX}gWv-Ut<_ zvns`bD#gyZ5ua0ON8hBY)@1OWd(BiT2!AeCol3r|aee-$O*3HAX3t8y>JPa}$u)&$-&`=m?fUR;G@^YV?OBu!4e|UShlP-l1QBuV*ICga+>$}M2&M6f`;}uf zwz29f6DR;@AKiQ{sGfh%eesFo&H`=5^d#j|mqJ%=ostYO?Prpv3XJhjW1b~CjSHOG zh7P9k`Cq=P*nCuHCj5<>=D;x)!E?g=vbL8Z{~Iwre%M#tF!9NA*}>HDdW&SmSd{Y{ za`2-fD@?_5GnH`s%!cVX9{cx^qFKu#Rc82)T4~C}7T!gUhj%g_la&zXxP;3~|4DNYWKm{Lfa|zm&;_2IKYV!&95Q?FeUC6SzbmX;%hIXQj7b<7EV~s1!O~WW zBSWW?+4qpQ4Ts4GYgq>i$OB#ZWY~-o{l0$B&(s}UPy4i@;3VhymB;P2%$qBk=L=~}d5e?0Pd6L;xx3$m| zBC#4qTWn$;*>enZOCT{-XlMVlca_-S z#4{@Yr#Nz$H-#D1&-s(}yYO6 zehpS^1SZ0;Satsmf#QC4-DuINS}S}TQQ_&1<9%z7+(|Ux)e}fJV4lpl5q9}l@IJ(pqecE9-N{|O7{F*xw4FYm{nR=A<5-Lx0m{oE7LEOm0KmV3_Q zYZahL1SP`2QAjJsG`4XOR??jOu^u6@eqiXZMw6o|$Q;;E#^q|lPbB6`3Y%55bVw{7 z(5$Na28xMIbAC0$tn6R}>KdeWbji7h2fd8h7fO`lsgOpk_-rBHG@UhVHT&aoL=L8=P`hcrslA7-cM%p;p}(De#^Od&i;@T0Z{RLD zOr*>F+(6qQet%;5yjlc#9hb3Lv>sYfILs}D$%(TnMMF)$t`fmD+@kc+lWoMr0izluR7%-mh z{1Hz%;pvpdjfF71O8~?vAVmUi#d9DIgQWB2RY5lWxVcH<7MlTEaFJr)OY7#Ke?r@% z-I|v}FIq?c$P^fg0&t`lB6BbQV3-Nxqf}XWDaTE^E^iwnmF9QQM#iVfUh?=k+O(F{ zqmO^YQpsyw(jmnj5fsRj#`+-r*zk4B006xGAK;LT>95vyJ4b!*w%Nr87{+{=r==WY zjkNNdOT!cK3-J``-nC-&+Wq1~`(%a}(srFJzj}x|HpQNsjZrA15j)14PtjC}#l;M~ zemN#zbP&Yn@KhAj`$FxMt03W_U7Fh)^F`^rU$ z>+=6lDCBNZDY5E9r+yNP0?iv3o@7Ep3Kj1vjgJmRiE<_iPx!oV)kH?uuW}|&bP7*r zf^?OB+8WsUWUnKo9ok9Z(`)jmV%Fe9Q z?|tUcVw^U%9rGb@F%(Gex$*|WDcF0#{zK#p^R4D_?ZbEq`%likveX-&jF&NArA*Ss zcNRGsYik1}bFId0pFA-^>1&36~`JM&LR^3}`*VH2LinAmdP-G{*2x6x9TcJDK6ohn$x zI)A2^v*SX?8uwhjudzv|*bt7~S|>ELDaEEgaeDf4WL;kHsq2dhu9!_MV~MMc3Yd{* z@=Lu4Ynr4lFVmWI1p@O6HPMdx5RU|Itqgo{bi#-AUolQ*8gVY7*~@8wes2qrN~EHwYC+Yl_NkyZ|}&99@lH+|xy*Dr7X<(dGZ@b5*1Z){UYs#GjdJ zWO7^f#2eEpPP@nDNocRe&Eyai&f9w6PBS;DOEznTe5~{hF_A|`9DFjS8OQx25cN%}f#?OF1o3?%D8vL5eadLg3dk7Vtgf7|m-Ch&2% z%$*vflXkr=(RDp540f@7OY3?nvKv*kx=EIbvtav%g^dA4%IaYsB0gBDFv_`f`h;~p zt~`cI=0;`GQB`c{7sB3CAY91~)K&|fuay8t!p0B@a8z7>xhDk6OMHB&eC}W?fUKW< zmM&iwt?J|^ybH)9G+ zxXZ!nX0W^4&lQ)a+)uRvMy+I)ZsO43V>B0(Mn1T~8Qe=#FwF!){w<#qf;dN>T6`oL zb&2L(PPwGp#Y`A&J7&1ad$i+HOFcBxYOpX6e@shE}fE zrfRR5kDp(Np3=~9f#DB&EVuMXqE&-JMuW?*=(e%C`h`d$ACH6|o2GFHmyheW?^tE--}PyImtr22HX&82Uz?Ifq5Cu^{;Hi;X(~Lt&MOXKj2>wl zZ`~tMlpYuRy^heJe2&f z^5GFD2n4$V9{dA{*}F=ZX^?{ob6wjcy0AXo523tTiT~umm7FzyxOUjuc)j&qZ^D{q ztr;F6G3SB%w;s-6KH#7HByTpbmQZog)}R;dL8UV<(XJZ;-i=tV_2p}cDzjEbb{<>I zT>O;BsQcIj1)e$6mj5xbCJk{$`cqmq29KPUNJ+Xwl4kJ4U0ci3Gj<{R%};zjR)tmE zd|yr#4A9hm*lw;F)-lG^s`uKyR45K&+NlmZ{5<~pZ?g^QI$u}YtH14BcMQMH_G(2i z9hTMb>&uW|znCd{+mJYPZ}ru-;!MFT)@8&bgT1uu>i3uj5ruZI$(%=Oef#)_symh{ z@9~p@DyT~=wWA^5PLSy|ijY#cVS?XSYlQor)lNDe8ZL_8gSMO@T8zMP;t0g6IYg^A zY&TOc+tnHE;)`hgvqDuQl7NpD)KP`fQ=U|>&^su7Bz(A#-ZTW?bYmb$(>;YW(mp9T z@_>-9pHpQzeEk{)jy5s7Ue@#xP$TJSyTiY;b0jOC9B#fC*x-EY^AG{g@?N-pVEaD# zy8hGorbdlCOJba?Qz$+D+Dq?Zn@g^{u-BPc#QgXvWNJNptiz*@as3_w>9kPVnSB?+ z{5;jg7PAM2!$kEE*vxAy_2ja#v~#PN{6v}CvksQq-wN9w7AZWWlUkAf+Ez5 zweU}!>rdT;2T@x{e@a-Ew4%J0*^y`!PXV_RP1mfMw2L2?QJek%w>-KIX2wsFX@kEI zC*qEI<@eHetg1*juj)BG(bM_r8TU7)$9FxMRKgpPQ4nJ_LjmJ8Hcxs#{kCrlWk^Wu z2fY}Z1I=@1S@Ja)r6s#w&p)3Pgm%i<6+ltNd($~B;NMZ|L)7H<#f1&0^DhY4tmn5f9`{8Q)s83y(@?8#l% zRS4TUW6uaN`|00N?F;r>63nO9C#M0i4$g~3v&>qu)d$JB53GUE9ihx|V%9#@H|c-h zyqw)UkA4|b^?o>oXvijsSb<-ZK!W7kAgJ_U0?@6@)vetBt1WqZ^h~oa?CmS2K#Kk! zZLhXHj*$2~3f_v(nQFPSro!w5*^NopD8sF%TpQjP9P`l2oLz*Se=aW>#deGvYbc=v z?=eo^scF}rkFiV|+_6}Ij-{#>Dqxd)VBlzLHyrl)5SGD0sGf7`2Ag1Xrd||>G}Qdk zkI*@Y@Kh$_9EY z53d8c#K{Jl7`=J@qNv@|Ez^&hDW9KeByCnoXMAy>4-;Jtf$-m;?&?~2%jM&L5fpyy zpd2GK=hGoU%Yk7AZ<}x{Fo@lAxDqNgsWRs)>l%VNhbILK-bH(--vf#?V$jG-@Fuu7 z_kEof76oAsHZTT;cjQ7wK_)?foFYb!_Feo^U>d4by;cR!YdPRuojzC*A^>|0jJe+7 zq6oyI^=B^gCAB6N;7FFMy)RZBet9Di<^9Ib0+TjY){(LXk~)^R?V`pTC2W!;^vW)y zJ|7Yx>^so>he0?<=HzY6R=t&vEtTB}FSHCtlSSojfz|5jGiHz5%Tjmqr$H{&d(>uw zV@ttdrwmn%d6~3%1im`TIqwoT(hRVSg+hDstjQw3*%5^2p|qkrr4U>0 zw#7>7GKcu#t~7KA>%fBYuPd_bKz-TTpOBD!0ef=nH*2B(Xs-rWR`ZBVWe2*FWyv;9 zwk*PHsq_2Xg$Cg3dg1$N!So1^8-f)A_EkQ^*mu%) zyu|Huifcs|iCw;tD%m$e)jJb1z4W$tOxS)zQi+~U)A{oCx$%=if{ia`Qb@|U`dnlN zT*=faKRJ@+IbK9^W{ys-Lr?0p*xFRkhklV%>}QibF>i#0mVBbdA`RsbCkbLauc19N z4&pZdSZm z2h|KS>;q>X#XLN#3u4u$H{XEiga#}RYmBpVzdPau(d7*YhD;q=fwRBZka9y8*k%Fu ze+#5r{(ABnHx&01Ga-l{r)#z9uz})Wh41a`T(?Ru*_rFv@|t>GD0MWN9)0ifR*W9E zwbHKlx}Sd#m>##!`Aus{198D&0(u1Kt{7ij1RmHT5$cTIomSNnDSMzMD3GdChV#(j zNRZh*hc!N}dAAfq;`8yxd0YKylHeM5hJ*G8W{0un<$q+bh*9)8)@{t*LIWpTA zO1a10*nNg#OaHH#(OYc)-YS;Y`7?5xA<#QRi3egt3nn25i6mP_@`I?^X9LCki)+5p z_*LR!Vzeov9F$5NRM?!9-#DovIMNl@WV3WDvzf`=>+wro`q`uXup|wA4-q*=k_!3L zvGf$ODwPdQi$&u=kI%!9?uucK{4(UU>KLrn>L*(tXptq`1Y7Rzet4_f{v}AU5rBe7 zz(qooqfCG63ZB1>Mj6l%qNja_|0+qb00TdA-cA=}H&(Le53{wD9r54IwTO4Q5cqkY zo;`W^+xX~@i|$V?+O8(mtnVg(@?M3WB`40bgpj1G#+R zM}>hn@$^*IvLCDDJ`zmSFMdv73;;pRwl9~@+I8*T<=P6kP2Jd9{h;PjoHKkMpdy)>;&p7V}H{`s-L#&aqLhz03&KEjBDWgByVKz1*u zDud?#_X2pN3@PWM9$&F@l3;QO2!0&1HozRC;MsFY_?fHgCvvq$|FLRP+A48mi|a#W zFHIy50eV7%DR*SpK3FJ2FG(T^68A-o+R-ZOC$lW%6qIXVDFp~AG3TWg-ioK`G@h7t z)ANET&HcL02+xmLuj?~hu*|QbvFR{96eYsJy&?oqXm2W4z^Y6jXXX3Y@_Z;(epoCPF6jkn4cooNI_b&YgTP$?c>cCBY8)zlaPx76Qr}c*Y3e#ofT- zKVvb)lJIQr@{0ju53DQA4MY;G`2vpbq+`Wb%(P3Y_guK$>eP6yU-vfrDYYZ(x#~7c zSHMjyi{RD#VTmT_+x(mKD8+oG;K7yJ<6~|7EY0XoF-;gBgS8aW^X=-9w5Nwo%LHx` z)>O|8mc{j!wr^GbS^xIRSDD-dt5pBx9PSTd*8Jfbi0R|RXR6OnuAzUHw-j5wzqYL+ zR~MCIpb15hdp~0lS(BGp+^Oc1g&Ar*L?nHOxnoZroWw1T6l&~G{fvVI%YmQFkdO2| zJ<_2p+=}0t&IhI(jzoL*yRI8tIv*q-E=ltKqAR}GWXh!DqLG9?=M_14 zI_!Y0R5O;mO%>o)x8o`TB@-wi9GGASQ7paR@f~VbAc@!|1$3|b0&Y$6tqC5HE#N!wqh^{I$ShlR?SP;6mx zKE1~eV22DZJz<&e(!r_Mq7eK|F$3N#K^SJ(mAT<4fEr9Ee$G$+Jy)fYZh05sut2tQ zlmab7%YhDE9DhsN--&SPJw19J0cZ#A=hG|_@+>dq8J?%rFNWSDOrN4;Q)Qjz1gprV z&GuM=_tD{9Ak_ZC%zV6#05b<28WK4r#d=}pSFyre{EUfe34382mwC--IcX;69x}m= zjag2|ZVm&fKI(7=Bl!{5KZ3N9c@E$`C7nL!+mU0`8pW!BDyhG8dLimk7!OlrtKr+x zlsUFqel_An5)6WE|1Ho0;U=(M3UDPLj<*{`YlQi~g^u0sLUYD*bq6Zm=94}!&V{u3 z?epfj-s!O?`7CdU$x^4(?4IHLBCd~fdgn9Dyb|zy=T0n!@$LR`&58~8*`A>m-@!s4 zLya*REY?vF#{&bUHbTS^DSi+N9==w2uvR%L0#IDQB?j_`CT}M0&mCpyFj~*s1Sf4p z5k5+O?2q_8M4h@^W}E}@aRw!C3dYIj{nXDUV7oM7&+W6@u1o7nxP0#37Q`|6Zy2Rz zrf2h|BYq&#gg*GH$Y`b|Pu(&U+kQqvMDrkg_N|oFNVhl;49a~nn7U`HEtIaVUjS(* ztuoc${7iiZg59(sI&4^4;^?j#RS0%Oz^$YJ`|i~X0R0@U$!oPYROvH8u|`P)Hkd3V zY4JDS%n!cjH83*R`RNsN`MVA?B~B=YhE~ZqJ0H?RkRiV!H9}*r?P@J8B7erRYKfTs zlDkPMN8(VB@|y!N>;d?k@TGme7xbhEkop@3APsC}kP_zytCPImO2yL8hjw`5*lH7> zzrE}D2Bcs}luL`NV9nEirs?~pr=exzlyqm;D_l`0^r7ePJ$!9*YOP;zvd3B)Q=~Rp zwvR*$mZk|>8C2QWqzAQ5k-wecc6=dSyq=?0`lD5WM21w6s}aMWM!#Ae4PqW24V6k}c@lz92k3CH17DHi48K5$ zOXAIJ7|#{;kp%rxH*KE(c8Z+^Dj5&hLO;mNwpdrlbk@h=geBTc`01L&-Py)Zt;nKZ z<`}Tw&$}hgh0ipoXs~>6$!e;~U4_i%JiyoQ`J#>kVL6KrB5_0B>}7 zL1Qw&Z371Rs%btC-m=Q-W47Oa?IJ8Q2rOp02c0SjznYJngz9(-%W&L4P*ihjJ9R>N zRtML=UN?ooR~Klkq@e5)FV~#02NyA?j)s{OS6q8j7!!#KR=8lNh*;cc*%CCsIekKY ztKr4Xf+LhzR6j?pwCMznbYBQ5k`Wu+0R|-p;=JnaaV7($!Nc_{fO46s5#%Gk>3O>CTYe43-JVJ{bZR*9(BC%WfuI5?1!um zTRRW1iHGo6PRU%rv|(Febi#)cpHcO7Y^3604cnI=j9xF*#a+RgPU#%%_i zI`YuJvq%47sv#t#uRD6iqD1#19kepINQs#rBLbE~uuwoRUX0MNT^)8k%U9i%yCny2 z+71*FEOjGUJveJwWAbD1F;%iCF1CxSDBIEYwR9Fh>dyeTeQZDLaYYp;x1q2yEHKJ} z_&?xD2Fcz4_n19gJ8BcJ6rEFXK9=>%cBykeB+pUIXoSjw*Diz z@{)q$7qbjgT03NGOci?T2}-(dLr9#F0Ys_vk~_pnGk&%ymol7MK!f7)iB8e?YMKnm zvW|&#vd8uQkk)q>M+wMrauF7A@{#Xzs%%Vz zcj2{nuNICzYs$BDv)ci^OPiH?ZpS!2n zJEZ_wHSXDhfn#8{Wny`z3?aMnJiF>Ef!5ILiM05Z;7aom*s)ePDm<&4dr=OeEfB_Y z?uhJrS~#r86!kJSZNi+v<>#C(Q?lP2j<%_qE2(yBstZPHyF3oeFs?k6T9wwkgTR~X zKbqN$%KcGnO}{~uDF_#a3L?Qdm8^&WJYwL0L|t8{#hoFsHOhaxVyC=mF;2J7DPziy z$~XM^_im;jg<1njfSpX|XxsitlX}N*_404N_cv9F?w30VAdV1#K|mJ;7?^XA_XKrm zg2(`nLkEqc(yLks;NAzU<_j4)b#(EQnfRn=aDf$`1$1xXVJ?R{tRQ9xIIP}`ms2V) zDZf%(-N|ZXHQ&vrQCOq<&6-8+B{l6HyUdnJJ71A=%z0zBb=ovVuA!gS*XK$An^#Oy zME8IlUJsm9S&Jw_8T9O`b8pZc1` zE0J%h$nq#d46*{*G;J+?zbY|BZ?^?V>lTCBSEc%Jq`kwZVtYo+#@g`zq9Y{}#c^Vl z^zW$)a&sX&A|oRT7=(W>hLP4hgz%s7iwAKM*H(IGEpbr)bQy)AL;elXlX|9%qf`)eX| z-rkF9BPE&l?7qFckk~xN=yS>Hph$?LD?sLHx~D#TdX&GlPv-n*t==GZpp64_bZ@jW z&HDLaves7d)4*Jp6KEiZtvxg0Xz2qu&C{0{`_?GystA>@aLwzoG$`2cRC;vKS^@KK zk&2x$f)R!99o_N2zj=Y;cyRRe28FP?N#FyquYiCRQ(6Rya=1wyX$aY%L}zt@CG^*Y zhOz-yVwC>dGe+7qyGr-WxVOSL7TtU`szG4_F{vTMoi=fz=^qC`FIlIQa?!uV55E1I z&rco?2V&14QxSwBs2*;=_J-w@gqs{) zJvyhBZthm|Tsst*M+k-ceO=Rx_lwSn89sNGUIW2%qV##>qc=PW1+nYyN6Q>g>z(7J9L}2ZuWmU@!@^7a2*zE9WRe77I(Z^1&*uJUc&y0M#RR%HxNLwJIlYv8jLTRJ(bEuwjasIc(VO@yNzDz1KDaQ%7N>}HO zqU^SUrq8Fd$Hx8w4j+$yi8>{+#tG{N92Q9|?3-mDGGe6*qq}IWJ21`bT;EeK1v)SA zz2C6Gx8=5?xBXbbZgXBgPB}Vm%Qw4_1kf4l#=Fvi^3?#A(IVrNGP?9kLrF#NEDmyu zfp=);5J%JU<@sTmzTR_J(HQ!b?SK7Vrku{4(VZvrMW>@j@}1QHo_sTpHi!D zlds_b_Y*O}9zmrMKFKgoH^4P1#|s075%+39oK&H&7J}1_d(-oL2;!2%+NsO1MofI6u*hs)f-8^&ps|~7oMwa!tmIuMHYWF=3hG`-d-tF7EAw!XsO{H+Q}=@-Q%4t+ZoHvGmN+n z4iPKHX}r4=)G)~(A|@M9>F=cUgqI%&_ri zXrv6znir4hsmx$e5Qn1}2nJLV%bX_KtB>1O%9T{!6`Any-jv&&PTu{a@ng9gH`fSt zKn)O2RHX`XzoSLHG8Dd*LF7(21Zn32F`Sv>&^+$h+1X%*mpKO@Ru>rgOhj{O(4;`1 zj;x&d^768-P0n~`s^xd`Xj#1l5V;SW^I(X%mYhm5sD9mLL6%#5`+?AFa>9E;!~H77 zEuL}SGYYQPPWX3y!*~gjHMafd`rKDf7Q!?XdkoSjZ^5^Nv@KPp)%&$YIEL&rI!a6TfXAbF9Qh?Qkgk1Qw(~v6@RRPW-jkO^hDQH^ z_5<+j%;HcAg}8Gq1lu9dZ^i=QFo0+{20*?y&;`1dXmbCOz>esh+CXS3-rNu(UDxPo zLzhrNYLJmzKzO-tJ=bSqU@D9+Z2soRkY@BB)$wN`bA9Z=bICRH)#-iW<)yj$cm<+e zcz@u!;p_i<1wRXf5Q+%Eu8_QL$n#RUMbpd)k)Y6ZN?@cOIU>nuhRAo3R!+znQ43RF z%hhMrvkryYST^|Ibey(D9W_{{esW5STGSJR9UJUmB+&zsr2?~Gf3g|L85bN2K`S_d zp<6MSAMWa%ThV}Ijy4kfPQcpmko$b;3(edGiPZUv$A>aqWH)T6`vIdVj0teX02KsO z>f}Mj+~TpDXU^A;#k=-W_2x2*o{H_w4W&)q{W+vg?~%MntTJu!+}bn?->xBOIrY{2 z@JQEJi!E~zE2-O{^J!rHNCgjw04#VgwM^bql%XrtyxSXvB=^S%l(>;1xwZ&i-ouZ; z{84<#dUf8NTudykXrJtY9f{C_Cr%wdwlB~23Yk5mB`rE$-rv0k^Qn05%8sIY`%On$oz(V>QUB6(tvO+UN^35rSXQ z(S*GahHS7$xT;qS+xo zO~u-h@EbObI26}~g~@wgQMhqh9LEZx{madHu9_H z6OLmS+^;_=V2a4Vgb7lpz`qI@Mz`*vyE!yo81yr2Jp~Y@rI=rK9(W1iAVUCr_Sm{B z0Ae@ht?>c(2<79!%??t#p&_M!)r98vQnWL9aJpctt~2%ydJ2OORq!YQ77Pfhh0ce$ zPZ3F5^xT9)!msFc7W~H~Y}4^}z5aau!qMDeyQdqNLqdLBpG}Jb7&L%+(N8q$oumtV zbu9X%#`A(3vF;TyX%Tx#<(L61#*C_quyRx{e2`D^on?x6o%C^n87UQpH+&(sd1`7} zY)IHXcgs(lqbfw}PQEpFvPwNf10+sbc*bvnaKYbA#$YS}%QQe*b0yMEg`i)P@PqQs zG~K7N#^W*P029IGKC=h|90YTA7HT|9+w?HricnAQ>3Z4eQWH?TMnH!u2yiVW4DTdmx1+*BCmA`Mg z3!lz7AbZ`JyV3OL!+=6 zLqKD7+X=@+G}8oVRGb~d4?%(q?I*+HRoliDzxI|#mMD1wTYuqt8+N1VqcP(47w6|; z@p4JI;g{55K?!&tfr<;P|3JJ9Em8soVZ^{5N&A3%@f@133TeZCCAVK#rDjN4+fI&~!Jae{ixs9v?4JC}0aESS zs7NCfPo_SDBOXQei8v1YMcsaPtxb-yi8FbaSMvgLE)?}W$l!}BN_8KDsgv!fz1=Yb zJ#P;N1Bn!I-`Nf%@A0z?^{Oo*906*lw0ElO>QT(_DUVr|p5N`ZF{WUK*i zIt&&3)8AdpFLzfcfj#2vdH(k=FYPX}%&bZX1%(Ej&<4^}j?~#wIRQnTIqv$xVTS6U zCuTtMAb0Wr9B+ zMEsdM+tq;AHD~Z)KBUS$^6OAwvFF{iFC`3DUH$Prw)KoA23MdOWDrHFx1%RTDf#WY zMI*$J0k`7#wMmG`jV)^JGC`W1#Igh<)rg}&A2dS6?P%r& zq(F2B0B7sQDa+ESC^bA_WoHc%GeE2})kvb+yyeVR=@DE^v3nsU$A#srXQ0JCqr94; ziM#*N442?}aDtJ1P4EvDQtUyERt2VTh3l2DMY~y#cSFrsU50878o=CM*I!0+Q?zOmtUv3XdyKfO8%0iCm-1#t+MD9ZG3xvuo%yby!G8XH8K5J6tlGhuA)&nOCdJ@DLPKLwJI^&ka|~_bBktwV zFiy)DC38E3!x6ih`X8c_~aa?*Gm{rKy?m$@?izYV4sFY?14%O zAhre{1Q_~sGM@@^RK(mvZZdilv>NZ_d{h7ViPvOmdRgj>Xb6ILKzo7_3-Ly zHkPP6xqXFcj<#>tsJN~z?BX&=^MIpl(c)KxMfc$lx@TIof)tDYj)#VzVV13%A-O(}! znS;E(Wy<1>f{ur_Y4YZpyTR}qahFvaWCoO4t8lX@xCE|e&5#W`A7iQ(=&a^8<4959J3IRPPFCF+gWdgqlXb zVy^4FRJ2PZ?47q2#amq7ebX_~)E9P^DD%oO_u&Di&X8*+u z0zFSEz;)$!;HzYDPuzuM!wdeu7T{&{C%Dr2q#SCxKx%9V5$Qx@lC&a9Da(4WS)3$G zNWtfULPYFQH(FEp`ezWF{c!l!cji0`DG&z4szb!1k-!@Q(91uvWJLPEyLNeQL z)Q<7}%a!b`SHv3OYS7F3zhE+;VpRLKn5rlpt{o>G{XXgaJ;(riBMM$AV0Efcs2B99 zq}JylgzP);^{3|?tyQLqxSK5VZ*g|BebvqF2)|1IE4oWnJ4}|N%#Ou>Zo76vTGGFq z(;eZMD09L${E?4$f8WY4IS6)3(vKS-Rgbo;Ust|H+%MYu&nRQU8Ju(=V|g)7MmAf< z3AuDV;(K?hY#w4b_#!xt;Ur$t$lLG{i5085B#TBt6Q%T7!N8in@mM>TR{4^1o&9+? zqcfi>uJ7salV2u^lK|rfLegha7NI;RIJASXMs#@r;|yqUK)t*X7&~U!W#7LooR*h6 zp^9{z_U5W;i7K1h22%~7!vTy6)FIGsg|=aVRy%0-3$*a)`cpcFrz_jt8$>wkNN_Kj zk$_VKd=E4%5@?#~_jDKbS<+0(!Ll}@Ut2BFGgi`Z@-WS^TE5$rgmcPw%+QRjThU@i zzv*?ie?KL-`HH;az0GW(;zoT3O4&g9ZlB$e>2dq;h2&-x_mtVZ2d?zfcgGL7G1hX_ zB(ULFjscMRL$v?gN7C_W=D3~qgwe8w{kADMQBFUNhSY~i7uf5qteV0j-ZLC#ZB4q& zYeGK4jLHGQ0?J9+C!K;U=ZKcyti{cX&*Zd&;Jyb;njfSd5{&Oy6q zlHyNRxi5j+E~)y})c>#g3rWrhHBL+2ANA-u{YZZ)h-5D|n$in1?3j-=wXu87QAJ`1 zAu#39FWg@m&aIN=ZHT{9U!H}313?N)ug|k{XWgzF|Ga+X5WrI~Kyy{)H9&2D|4=EJ zV}<>Af7WFpOuh>eXzuvIzzXV0+V*LiF~G>%%P}`BPC6(74$No?VZ4oTk=_)ur%T+{KlRWZ z4S)VlWssA!AF|-9%`%Q+#^v7iE1vfmYWg%V9_Q9|@qnEYe*oP^oPLAj5s<&&vMdzM z0id33p{2#V>nX0}7BuV}l=Yn5XS4vS5@4Y~Sdtw-8I=Xqi)E{Tq(3Oj0g~7}tbuaH z=kD;;!>#W#KNx-0%HCDaFNfH{fC>?4R5hsc0#g#$;;OU&fAT$stIpoi&}Cw&PvFCq zlKti7r`a|!;++TEd?+;)qHN`_?S<&z%*q|(vf*w@p8tu(xyVdt#B6A^+Co2Pm^zB5 zx;~Yms{R#(kvLAiPlKXheW&|P8cbknNlFoG$w`>9ReaN?#n^2qm|5-(bt@9bp8M-ct_W5?8T}5JTIyii88_)+i9E8Cz%qJnK+)3nXLQ1E^FGtia%Fm`RfxUxD=iIXW9#!EMoAN zUv#OAFaN|nDO8YX{=UE53%lgbIge`ZVJb44-J5j9X{e&&XQkP0g8$7HcFJ^6WI}-G z-Oi-7el^!eP^Bk~ZItDYiU>MX%(?&0a?)R-6wpdnEz8<9!+o8FWikU^CD96J1MHtR zsTPsBsK=MZqCS&|aTFN*RFmLT2h7*s>%|Y}ATROZ>Hdt#c+xZg0)IXvnt#o#zyH-Ttkm&gv!!C-mPXSiWnG$CIiS)@e z;M}udcfuVMYUEcLAR5S-T_rMYtpSPYOVen&PfBo&1u6{HsSKaICmJ-XJ#RtrXVwhb5daDMSiehK@gZaSd@ zo0T{VVZF5!;r8lC!0Uj&4c=>@{R4*-NF(7GAV4nhB3mV%pika&w1^Get3hMvB?-x= zum0&wT&!+YW;%vZtFV_d@pci^h(SF>ltCbcyB~xu0VI+ME_;^L;a7)w5Jq_>N}cnp zgHnQRbF^~PxXIXN=1IHC&D>zXD~YVsss=6pb>f_J&sq%KW%rLJ7&SpGljffs7isV9VZ8( zXbp{c0fOO=e3|~7fL6>eCY2f9u*Fec0m{Dh>y zglbiF!U;i==#YsQK)nU1QgE$p2}}g^(N<(%Ud=_-Dg}@Y*3wTlW%WOdslNM~}qvD!{Ow@<49U+<$E z_-t3h$mN|m7VQzt5CM<|0I6=jEdc_vMm_xNPw3UV5MejuJth6HVmu?AEF!{Js`vF2 zvDB+-AL?qN+a@&~30=0z_lR{G9x{R&^{PPLbe~fcL&Vhq{qVRXwnE9~3K!=6t*a90 zV7kgU{UQHwwSrzDj-PH~cu#2YQai_=h6q}r;<5mfblDB^FTVmuPYQw>KOYg?kwDF*BslcnNxGwWHnuY#d4E6fD_o$LB-f2*= zFP#O%#K={o`n?jHy_XQ2TlVc+cI;PZNbEOL z_lFRbde~-7B%(l90eIRXqc|Lf9=OEvH7wZh!Xv8q5%!sq%b}|*0y%x8VS;f|2x*j{ z5zFHf;syC#I1BP(&)@6I_n5MEg48yN9hHksv0`2|2jZ99i5%%BRSGLw+0Duq)=!P! ztVmS;Ybghb5U87G(fD4wW!~$R7mAE-0z%GM4Xi!%#=N6xGGE3`z~_jA++z?_h0{uH z6H}um-Eer{k+gavoG7zH;X=`Dd~=Kd6blhNuUPaAau(yKp%-0hw^gZ6R=QM6?tC*$MgD3U0ddaQ!bJv7itDe0 zg$25c1q7omb%k%q()D(6x|Jc=v{(|zd|6UAU$^}GRH~v+pI}JV**q&?9FaJ+RpfDS z#TGf7{&M!P_qdAX8R%N*^JB2gQ53)8~aoVNR~?m~p;ZJqJL{Js+H z8{;Te91CN#g(6I@X1VcseJv&FdxPeFWg!yfp?$%yzZyvSh#ap>>&JA-1`TAYE+89= zc{IJ zf*L5E#3BnUjq3_F(`aYuy^iO+=kME&1jkVZ$g>h@fin!`SH50S-7LP(Y>ewXDR5u6 z*eRL(mrRpKJq`vKE6WNFG#IC!hGzm8oGL@orh4w*#O|#=J0qSt4)LI6wZ*2FnTFsOLAVlNs zUW>d3+8NqP)ut(oHf$@Vy7sX3EPdIf7!X$-5vrs@n$EB{Y7F05eo&j(TBUW^=?mk8 zLaGIV%YK&JS?<{SNPNm>x$G3Qs=Cjs3Rq1@6NusF6`X^yRKtSrkt~dRWxLQ6V^?KR z9JMN*b<&oB3G;YEzl69G?}i!XD$htrAaw;vTjUvSwZr@t&JxW%91f7JjkPOp0w=zz zDH)+IO(%`WSoGPhZiBy5y?*Kk8N}_Sq7608tf%{&$0@#vZu*Ga0f-(+JNyweeG*|D zQIv`B;KWf_cA3c+QXr@tWs%CE437%H_;RXgvu+r?!$AH zq8wd<5QrZGTbYG`Adc=ayj1b8sR z*btoXuTwQ0NK?Pz;Y*h#S#(3a9+!FjuKOg)iQno#v-(vKnCifT7u(se#XDG}97W-E z`%zbLvF!D_n4K=KC+_;#vaYqQbQXleE4C*Qfe$?y3Y!b-sFtnj8c5#Jg{rNA474{v+wB&fKvA zvpXNjUPYUkwJ_R$_HLt)E&<*%EXh#Dg3oeAEN>F(SrfGhE_k#XR$lGc>eZZ%?4a5D z#5E1#uaI#KBVK=y)cOeP(%eHovx-a>6U{9kDb8Uj;(;e8UbiF`P8qqR_jgZzZ)Z-D zg|t|ipfdS9q!$pQsr6Z*sxCSI!r3B*wDxK{2QJ}RTv=WBRh%fQ!_!ldxV2)1VC!i@G`>_->QlN-*`hBX9mJ@fM_ zUfowZ+ZMhHtd2jpeqrF%pW)VYt&_>{gFdEf{Nw2N*sROo?ps&elwi}$`0J=?i@DS? z(RZ8FI~^0;Hodk7nMv)xS_7OyPdA(K@@XnXIQK4Ov3!4phO)XGoAR&uaQzNh>8TL^?=z zstz+Fu7F}w%ac+}J;G+*1K-r2lOKg${=H+EXkegI2%uGn!2P`~{mo_m@9z*~kGd?~;xj9*xI;fY zG%gCYzZp{QB19kIC{x6p;=9$dA*sk_3aaq#d>LF1J-#oM2uA4UehXrZ5_AG zrf*|2`RTp$eB29Jh|qZaWCwLw_B^cu4lt4zF*&heS*JH98Y!Y_lt+4wmWkmBU>fKO z+xYU4Yy;@-fWKKlP{jY*L>|*X8l6>m*4A(*am^B$kt#{-KB3KtmvHp^-!fyfKcR>) z;-9NUuBH2o;@akh465yAxwTn7LHkekN#rv^clU69+9_{wKbCt<=_Uz+g{ArIFo*Da|?MYMZ z__^ex+!)8=*OmhLHXLu2!~VA_Dk%oPB`YK=(c7p4QP%YRzfm<*Lq_`y5@_XH!h=PX zW*g+qh8=c9ncKw;0|(r@%$|53(&rAlFRY$eMqdLgS5-z_9#{CJrJADGNuFEPA&qZQ72!F~*}rUwPD?P*?j%n~#TwBS-5Bg_nYZ2*95Rjg zRTh>Tu0xsA*R#h|NG5}}NMi{L>&Yw_%Y^5>bNZ}h|`0YZ! zu_}K~63I`k$H}*SY|Ins&Z0xoRg4=o?K7VpD?VEuZGR86^}4oSr8r@kAC|5EJpbzz6EeqGt;gt1Jv z;rT^QmlsRa1^dN#bAB0}*87aLj_+$MJ>IU8JlR*dhr6WAm?(@J4s)@tZ!O>JBON2x zm!D6Yr%rgsx3FuK%AdDYGdJ8u3(ZnI$25K4>HURMN}cLf)Lfi;&k901RdHk?OAw@i4Nroqk6MWgg2F+3qFjvyd0_O+Ho3ct zSX|WBv2pt39s&h4fucnoNjyWc)c^7;2g!ntPV4UtTDWDV;A2#p$B&1jQsG(5gDu7pXDpS5371UptoVBI67D$X>lZkJ6g(Sj#o9mOdB_Qa$W%@H zyGmP%aE~Rt5lM`qA?63zU*e&PzMzuiv)y=ZrsB-n0Hcm=HdGj10V?$$rJXJTRsWm+;|Ktlh9v_WyP%!7| zR(2!XS{4TZN7Q59_v>fGtO7KBk>^P+qa5PWRK`DlV^R++lr}c=+1@$4K2DCW3!CbC3e+%1xGb+3YVZGSlj&*citaY zP`qB6@;FarKM0$8M7%CJHG*rJrKS5!}iT zI-c>9)wYS@%BT)w+JXZO1FGilE!!hk_;;0ZLtht9IoF)NM%mtOSDtQIx1{OYRS(65 z=rq8fG)<9A(`n#Xlp&iv;?<#M_3Wg;vvst@8slQ2)}r)sBav>0u-&lXOnh0J9~bjE z`5x?Crmp`m+O1bxR7CKiZW?PK)^3~FXq@k+_g+Aw4_&=9n8AS8HD&wy8lNII_V438 z|Hk_dR6%{fEN$QA?P5=9b(CaYtaYVk6Lo1t04u3(iVjg^DPE`!IA%X1AYk}DK(yD2 znn-0AH$8hd=EM92Kjny~0)BWU=(?4D;s2+{vd`7`Ps2<9# z6V#-OU9~-0lzHieKhmdcK$j60pt(4e(eU;U7f)?NJPXDk)1lu(C04ZI6LF1_u=8yw z8;ya96d8NTy^MAPmuzSaP!)A1Y6~>I*~$%dvnmj{_>56?c|4VTSIO-7ugng@{1UdD z!kc`6b`#|Evj+8|F*Fxo$@@7a>PC69n}lX01$Qxr)b*DQ!>Tve1ZVm)EtE&OZ6IX~ zVcArQ?Hzb~Mw=vyDCj@v^c9xlv!UQ!j9cm)$81OEeC6<%Ondf@{k=^LI-jB4O>6JB zukLxxcj4vnXXrfmyH&MBcWLyI2_-QC!-YlP8^xS%a$;5SDTt=N;a)=6acPl*&dcA{_{yTNF>aA>J;ou{^b=*y9? zaY_$Z-Rm?%9^EW?&S(+nBf4bbPb$AzYkYS3z3jFprSqhdU4Cam=CvApKFM#8|I%TJ zptzc3JGfQ_ov@m(B)MFst+92MO~v^lh;U6I8LgH_4m0BUux6;X?7QA8kPwrMBG4n8<2)TSJAAW;}`V0CjVkBdS|keCuzrHSG?C z_LIbPhx=2-{`3zu6GfP>mPAQ{@dk)SNuPq;qFSx{+|%6t+G>nQs+qiPAoLLjU_1Fg*G)2v4BBrTh8> zeeMv2((empK0@9XRC$3#WF223dHI7y+wCv82oR#Q-t`_jWJ)tVRfMv$cj)o3=q9{D z<7$6~B-?V!Z*=2LdgyJUmHfMJ7AmCTS=)8Krhp+W>v!3{dvtGD$Yp6u z+Sr3kc!d3W2{+S4byk7kq+rDn&D zD4@>idxbmWYtX@@8%blu%e%vUobewlQB{=oQnQ5aiMh|i` zUdMrxw=y!O3?SSOzc(sHU}2HPg&T9;x`nU5^?3N!E{C>HvO8+?0E!FQ?7LtJIg{jJ z;voxWj74K#THoHL!jIKn*<;O&5|$ahi|(*QgM(53*f05t&_FhG$1yhuChNH2eyl~+ zrI%!}9O1_)f9n9gOoYs)jt!MJlcO<9U+<+C_Qp5&7M{1r4XC6MEGzgUO8?A`b=K?M zYft&eaLw+dmH1H>>kX+Ed>S*C^L{IzZf{jyvxUzpOPDb;TWBu7!MCvIvkOpu-KG2> zK`uk}E=S5&%NnozZB*;d^ZsqftHRZ^zhWAc14)Xf6?3L0j>xdW!e~e=%*Mw`7vF*= zwbDkhZpg%h@pGmy*5m&!l}uJ_OuOmWplTzVq!e1?Rod^7`~3@hgJ1=*%f?9?Klt0y zH3%r%KQ}i(t*NoLX0QvGI8{PIw&O5GKZzsnz>0%NJlKx(s#>*80vkKVbA3I+AIHa| z(*wzt%Dshy?%l~nY{Jd9i5pjUO$;t?P?Br;Xou)^4#0$;zds0TJqS2DR!K?UBXxpQ8wTc{h0ZcD~Lrlb&Wt^M6{FEY@A#rau{1h_vW&)?vys>N__&{E78QbGLdhKc}QCG$$r!cPVe*St+{%B=Y z)0WF|i5vQpE@XYiC@CdMU;Vp|YPZ(2hQ@*O#sx}Fzj*?eg!TaoOf;in|NP0|ZcjFj zOtbD)(ocLpjwfGtJhlz8WSV^W6tI2ET(rsNpQ&NQ0Z}JB9mm(98BaM*r5imF$1iZF z-cL{S#1%4Z4hoJGw6nvoX^%%UP5qTY-5M?&CR22$Xz7y1((!HNxmE7Awm@LefOvt7 zaLaKUr!q+kH>bMQ)$ft^(dDq z1%>A)pWfA7IAFaR_(4_eVDI?(bL9Cy!8lqrZp!hpkJfJOWQ4mb4n;;rTNpg2fqjS5 zXrXL@nSKNBHCk6jD}RU+V-K1sSH)g_KPT?x+DIaWwEHo$(FjR+=HRfgpd?QqCslzk3)8%t<00 z|Je#u1K(#vCl`#YSmOz#IHn#08zLJ{Kh#uB>DHRaTNrT_w9g!Pkx7eB9`B_mmnz9p z2uhejf02s_1(cZTvAm=A`1xy^Y%ad9-@NRI%1|xu2h0ksik;y@?~;dWotSCUrzWqi zzvU;BGzCGH-2Clav5PW>SvMxKcAGy*1}ve%4SHPgr#vHcEBdboGaJ_G#UXC7Yb|;^ zhv=fU{pT>S!mbBU!$!PrUtfvwm~;eWmhJ4}93$0H$pTpUHc1-HV8FTR}6V zQBN+c)2?;Q)Aa(o`-?bJvq>RSzUzv@;huEER;xL)i$sZd$=v;!`-ijSv7o5!M)%~_ ziQ;D%qa>3r#qddZHCoiuy)tV<&O~-bK4QE7PBCJApCzAeV$O$&HtoB%?{a;`4RdHH z=;hVJZ5fQCF7re1R7Kgic+If2g($u8E!FZ7k$qClpNxo$gxw4YkLO7cIa{abKt`yWLNySn!?S;`!_mbvZ$NlLY|=f>~X-=$l!1NqM4df1Dyy zuaMhV4`ES}ou9W3L?(`)KOQP#-8mv5D!Ot!)B0-M3+f}E)XgzUeJ-Y_uXq3QA@M{t zyOY!B=<{1yjfzaVKY1Pcm#=HxXG- zDED>K@xnj4!Ydo#_VVs^ya+rl*;IBDcs3Mlf1~<3{e*8!MJ!^H);P zVdM-A4w9`#C`m*y_lWg;I3~Qf*Dg&TxIGhd1g5madhV^?ctmXnZ;KI(;&o-IRmUu; zB0k{}L%eG>2(&W-j})#;?ibG?j4YH3y?=jr%?ODd^d3<|x~n<sDu~Sb0tK93P{a{PQCS zc~bi`C2IqeBzu!@JwJVtV7PYD=N5fo>Y> zq+pgu>&zSSRf)0wYB_%FHtl5pQU7TC13RWzGpfCj4MOgJq2EXH)5D%S?S;45s4C$I zud=K#)9O01J`tx_s@NARow-A-gP*R8z_y42m ztfJcL+AWM1cXudIB)Cf{Qe27?TtblI4#g=>aCdiiEl{*LloH&vxE6<#?;qpbCN~)w zYwxw*x#oQ4ODlYna!tj!ukYX2M0(C`J?4>iazT@pPYh{a(URahT#|3JnM`AT4sB>o z1bo`r30T{?@(%MA8nhOZdabifuVOo_!b`@#C)Jn#yUk=^8!a6a`?obvmpJ;0jNH|- z!if3I<&5ZpD+)!05wAU64HC8eKv+}M-v9Wm>u#;bU~|pV@vGw|{C^@%gROb)JZ{uax@(ZSipJ#_a5 zNtOkbE$^-Rl*iUEZia?#cb_`B znf<{ey8lO<@rHn+L!w;$mOZu|peu*$A>I$w2ilmzTf9fMWw7j_UGDVER>AtMwY5?; ze7u5m^$Dl%?KNg}gs`oxjr8@JV(RrC@!=NDrGkq}1Vg$u1m_Y(0+ku{ZKIG{=_=-k z2D3!WG*!B~SySbQu;Cjy5L9qYxlAe#hyFVUnuMSemBaj?jmcLJSOHzD*!sC$mCD6Y zWAM>ch|~#%?;l-sx#uDRFy!^K$IU(dL9Vy9fEfj))e8k|^TlFmXbsY}dAR+I^y-22 z#KhyX{>d(MyUhaW%BAhSy>j0?!$;lzgXgTQ7-aiDp?LdYv4qA2oDc|QfbRfz@8NA} zLqi%$2`oKW%l<);c08GXACQu0G|vsUK`K8hHN?k_usVLbIc$LY>z|{W9pptm&GU15 zjbAD+Ma|ox(<{UbeAr>6nJ>+Ir~6qik2*NdbqS>boqITkZQ=Pz`obIgWz2)>F(G{} zUXn3Hd@3Zqqquiq`mg_3yOr379~TM4^*R!y7)AW5`@|isq&)}ell!F4dAbY%8=%Mg z;DiLymmT7Rw@1XKIJQl@h&aA*twz(vl2%+e4LW!XhFsP*rvPb@J+fqLi|NRdsPE7V z@u~i|`>W8R`TM%R*G5%UtEX|}sZJd3e#WN=$6?9GM#kSLMX;ru*9H3r35|4mz5$%d z8>EhjQ=JlOuV+8;4FbfthaP4g`B7}KLgLl%xn%pLq;0>)#i8Zq=3cxWIEsmhZAzi8 zuCH%y)4WNzxMB%YA`A=Vnchz}>PmzX;sWmz<>^e3TDLK}RY+~kKJEZEUot{(Yc>aFm z3=3t9D)u{aKKB$Lo&s}Y-z(s)Z?m^9{iS>0rq@$#j^$=gSoQT1>wW>tiNAjZu3qQ8 zW`$KIyhJ08803Mq7kYkK>g;BQ^pEQ_~%Yo7V|% zqN%!pG!|Iz2QDCa?zQsaQLI%^ZaX)h8#oWgzsscB`&?Hl4o{S(+Q0Za1TA3BtRJHl zBU8Ag(0%JBRCm9h=qj5bK-AI2kKx!wSXW}PVkT6cBKOh7@`kJ4?aXSU{`iejKx9a$ zuIq_u&f}NB$i%`Hy%AAW9qgIvlKaJX?LGQwBP`GLkivj#>?dg4Fd!*$B9IQ9W`FZs zh7bD=C57V9H`dLP)-ASagv|KOU-I?f{V)y|-n})fBdHbPUy#YX_q70S75!T5a9Q#p@MYeuw@@vr->ULa3i9Ri5G5is)f zBnn%9@gK#_#q)L^|DnK6qd12v2AHbD^7te>izpOm>T@0#_-1cAl05hW_j!eHC2-GH zCKYc!H;2wb%!CJiGL+VZd?PT98bPz^h!j-MAXoos@p8{$jFHL^#Fuv%I~V&fqh zt7*FS=${42wxz^_Ssz*9cmwAol4q(>QPd;{Iv50#$6*{J`R?d1xSZiw0+ncnx7)EL#Zy_^2$qA#(s}yV7Sl z-+b6lyKGVG6iLgFwUOLt3s`7PhVp>L#4KdaZ4>9Z<>FK>Qj+(%a=(#kIO|5arqPCs zW8XhNL!O@Ab$545pa)eQv-!eeUhSR3PC}nEi$j%`=|ATgs?&~IE$TO2%`t}P8L&vn z?>vbTGZxFmfuz4+4=2DKGu|&k&7lmfcFNux9^tJqIV(jY;pxm6{8Vx%iY=8p`yWw^YZ=HZ!JFJ#n*p2L{!`B zhd4D2@3_V*K9H+{Pya!7$qMu4G9N9}hvKP=?ZpV4HLpe8V^!av-PyObYM8s)H80eN z2_S-`HQyE8XuHC&%WY3SR)1Gce(bg^Alk_h+s;Q7hy3ZJYs8pc_ z!|y)p7_F{0YavFx0m6p(fk*kDJd9BO&q{=lA8%Qd50HS2)Uyq72!9A{jMPsCrHWsF8i_@kjeF1P9N4+^_t$TAF0B9OUuQI*R`F?8Euu z-4OO55B#vWsBCFQJXjbeAFP`g!_=`bUhiO7rv{9U4F5f-OCHtJGPdvwmp~lv{`!Uwu=FI_5yYy{$o5({2jeEz1r+2 z;mu~tI`R`K@t%^D2Ws?~C)=*JDqC6I?HgU{sCjs5@!N-wJo63->K$F&p`r6A=9zTU zuElTHUm$PK6pq)zJx0d#Z_beq^Nhx?La_V?+|oSUcP**1+ST`1>E0-(d1Zj^r(o$b z7BKZ%k<)khe8ukXfAftNJ5|=`_*Q}FEAO06hga+2;iTCU4)B*1;TkXOnMln{w+ih6 zDI?^lKwp&WZdJYy{!YBUs$P>;rfGNu0t(W`DO-2A>LtoopJlXxx)tj(6b`mV)>5%> zUBYdxN}q`iX3FBlcY1?P`5X-38>?pA#PnqWLw7wy5cj^6Q7$II5rJb8C6T$JF4Thu zt2@1%PqcBD6DbGodJid>g)&l!dmGWm6@X-Vk$dW8mbBeUv+ify=wjt%Qi-g>37Bg? zX~e3#r$_4T{B860l<3IqzoOytD+8u1$>Lwj|z$H2S28{r}x!l>D1Aoi^F3z zxGpWhqW!;@&2(1SqrOo3gyP($8h6yv8xrMwO^X_2Qasm{(axgEyhXBZmA$q#|Ay!o zeQ^#NPTW@R3DHjSUf@o-3zDY10b4I~7DMb@^GDKCRE8Do3WwJf&=n2|C>)*-y?qbX zOCqU3gdY4GUQCmI`ap9~ykk$fuZALXi3gJ>63cK8%da;b(zlM%M)ovKB{Vd6=?wUq z(1ch2Eq@C8_zBmt`va6Kzp&?5{tq3n9k%QPZRhkhI zv*xq-9M?7GZ=gKj2{jE`1-vOYH?QdF3cKWBLKz1E$t1{ko^$Cao9seB84X8AbbeY` zjku_l1DOLq!76t9vUrv`djSiR(;$6=%)%(|n#Wh%*;WpV+4Z~5CIy<4e)rhz78-Q& zuCFIuMOndY8V%=CgTGxzdTf^~z$ZpYlC5z;bD;{D$qPTwc=1K$iJLM9&sLBH6sU0Q z(y+eroS842C`E*3QGqBml@xhBhnB)NYbj}7h>L_$K4n$spRu4&c~?gJUnor<3hjV(MP<)0%c@Y#>AN>-Tg{_L&+i z6mEB|Gp32{PIZ|vCNqQKWRvMN#j#-%h;!+*G}L>IOHyU2xE8Xfa=CPy--8t@OoIWw zw%1e}{=v0FBT-R+SR$lWTw$l*8Sm8La#DdC3)ap+36cDY2|EM%`OE#gK)SxbyuAv# z*(WSIPb2m14eJ(?7SQ?CNSRSDpM(U;kN~65^QyrWUr?Z;k^%}X<+li53XR1{uUxcZ zv}txv%VU1_AEEE8+rUnA_v0E8Kn!@7wuE(ECVDxeOTSgy#Ew?J&QT7vvJ=52Os$+D z)P_hg8|ewVqfD8d9t^#Ju3&-NFa_*Z7r;;r5zzA6P;SIq)4Gw(7Vis z>*}4}AJeU?C8N0*Gpu1rY-3v-f~8_3&+Q#1c5^W^BITgbW3_*0?k}EIV;cOa-XA+% zxCB=3L&kXXin2@_@P@A(Ltg#PTv^~jspGqaT0m4fI;8yKkiekX8#^?R(G8?+L{4n= zlj-9tye$@t+TrZ(9Xb0Tyz7Lzg+s=~`qN1W3C_BxfVLp{v()X~!1rIqO@CCQ1@M9e z;Fx>L{=F!pvQQ%8OpcjGq0(t|T5xP-lW0hgFVv>Sn9wm6!)hi*aQ*WJeNAxMR>0t^^~LTiETf--#Yiscc1%#vi+%L*4$!I)HA zU2w*w_8C#jtnclY3;(tZ#xJnn7;ZUyl`6DqF`L6lP=~{Qy7_h}J(jX>UjUSdmgAT% z>*p7;Izyb`m%Qix8sd&#>4wK-iV2w~)sp}QS=4d|_zyh6ME6f(rb4{!WZ9Ls@hc%d*9@=dX3ue&uI}=AgOdk;G z4woxGlRN0ybhfs#*v@$X)4<|PcJw$f&sH|1>g$sq=nLndMxJl&)WDTaX{-8PD7GqPf<5EqLPVgy)ZwA1dQ*_ICX!VYRHj{b@!t zJ^>3z2ET;PWA&ot^zymvOYX>a2*m-ug)#hrQ)#PVmQBLOu+91EJ|u1w$!dVt(8$Ql z%{ge+8ludK$^v7GL0ZxW9?D8tgS5V~khETKFhDu}n6kEV=gzV}E&!guE8x5XwFG05 z=lYzBiL1gGLoF;eVk_Bd4@kES7qq4#R_O;@NR;XlS;&k#T{1tN=6HYr*>qQ`!m)S& zl@i-XL2VWo_36{MH@@qi8xu)mUur2T~Co zAh5=C^*M7pF94AaK75OS<1 zN_lseAB1p1Lijk0o47c7xI;n|D3xlz|5bc$EWV)qEMrg#Xd3X)c@Um!Je88)5aB;s8J2S3v5T^A2qf-Fkvy0a@$Ra)ojoZW(8@bnK9cplE_5CSdA`>*N!9 z&7wAAVq8i3EiX9d|GfaXr80tIn`D{3(lCKJw+h?uj16Q444ctVD-j|XXsa1=DL{HS z0P$DKp5!{!64es);53QBZZ6rxsc9}=|Dn(^OifQaxw^(|P4aV5(yM6v+M3Cs8+F~) zAIJoo?8)@DOGiLCx)R$b{f#=+;S#G-8~Az->N!AtV&tK=I66l;)ao;cUbZQw$e+~FwZhl3C8&pQ+- z0jqE%W%XrfT;!6uv0Qo6305d5skaojWdXexijD>4o#I97#i@o6j}O}z2+Jl48fm#E zItyGmf~jn(&|y9)~Buc&zG zsKnaLE7mEYyHzv>D!*yN8@527AsVcmTlVPWK3&OR?J?>%@oGoq1avS_P@Q_i!;i@M zl=_ISB1y#?!O(j+a&Ns>k9{A1&w#_*P66P&xOU$heO0=^$s^wCi!E- zJZoPT90zW#beVZyv{MSkEZWt{$OUk$h~NpiAp;Mv?bA)<=kp#i4o+NpcaM}piR0Z@JHlimjvG7qLRk=LQ)^kH7hM@GCmCMts&7U#8`8dBUxuZC0>^Xo1HX_NFEtt zCkhOI&g8et*Y)4==k1pXO2kp0O0XAt3KjvZ>?WAn?^MV4vGej8oJ zR5w#wj0NKQvp`l?d(`7@Y|(m_v0MUBW>)%y7uEQLjFjD7{9z&5mH8i;M`iAG+?X-WVxd?C?J8K_YqQ$1^Og{WFG3I)E zfP1-f=$M z7tzq9PJ!{KKxGH1sOk(bKvnTxxloFE&`Ln4NY=k}cDO!i zyyKHeBmUKTf6}-R)50{iA|TO_fq^{tDHW(LLI^D0CO%v9&g~j6aSs1;MG52gBpq0- zuC1B*zXie=1F!&l*0C+@G%FP=8<%I3B^vo3AhK!S*fO{Eus7{C}`oA)-R z$W-!IQ8$gby)NYqYSn_6pGi16p_#!%Vf^*=GFn*dn8nc0YW`ukS4R*;v#`cJ&j!uj z&6!zE@vX&an%D`W5$myL?1Sh&Z1MPqbD(c|8rCMRzQ+JsQ$^L#)+BZVf$8?3CnB z4fNLUo&+VA((QfPTTVsOUO80T*u%Yfb^3tcA=vZYikjd2T%ElpB~@Jy+t5lqzj(cP z<6K`r_|%1Z#Yb)@`Tq>DaK+!tulYUr9`#S^ae_8U4V5OUofd4)_03ERT7v%NaW1P6 zSF?lJ4Qn^A@`mMB@{e`4xvo336EqBbzzg3#jToY$Rwa0u7_$TTFioF%Su(=}>h+~< zv2qT6HKRa+W`&Chf3;|GwCF-=Jf?Yn+iMYaI3p27HymK-CPy^Z@kvt;O)Dk_a20I3SSP7 z;5)_{9e)CY1OX&(u>zmDO&oiKt1G2bVm;;U`07TYs!;L{!7;cXzVYOf#n{48)moN* z)#!#{vyp4~anI2XtAJ#UU)Pj2nZyu{%u=Q4iEB!Q?aa8^C6r8_N3i1il!~K{t-gcP z`cZ5rs(%dG)$Rc7wSh?$MHcIt1Ta}6;2JXpuuM;A_w1)~{F_*XsMFmq`&tYJU>tB0 zG0KcK`y3-uF!55D^~YwVKc^b8`^We&tC}Ox&O&O2_`rbzOF|YUbTPO?OObxsy}*{8 z+6*I=cbcD8n|mFu@94kwaT=O)FK_RlOyB<)k}&EJXiioMqm4qhxr%{#K!W=H?D~(2~_Y*t-cKx4yyL%7!_rS_~SPet&0oO3?I8U*S z{t0>uG+qN%%0-MNlB1fo&Q=2BE9g~#h%z&+GF6MyHEu31)s+!!+4rx#=?0TEU<^w6 zx#mAsAARM>JovfCJT|v#`S&X&qZo9PZ8Qm1P-_js?V%q9Ncxe}dv+*vYWQJP7t5xl z-(d-yzf=|>0(K)DkA{kod@5<{ju6-;2WvTE58)3CZrP!jS8H(4(6ME&UWkt|;tGIW zfQh*6lTCH#R?+{k3`1DYe;naN>F?+@Yoae&NhEMCSiJe!1&%K2>Bv+KXU4s z{o0Noi_W?9?g}=KAFY3)fFzWnC42bs=S;@s8{SL5IxGs zCrn;WIMm~0t)z#4UksmE+HaOza;4?r?b%&w5(Z!z4yKiAWb?VE@;VbPROrpYKG2Y4 zOeYmyFSWyK8Y-2v4S30(aZ>&mA&irLz^>1h`RgaEo{USnz&}2ybxsA_NDrXF{wH`2 zw!(eI-_={k_1BEnHqJdsQQ&j7`d@S=uFmU|;Au(qf6pBtI`E?}-3h%dZnV-18?^{t zveUt(M*2;$a_I1cV(%6R-cH_UxMfSISXE4hV@@7<@HOT`4`7zlvl;=?vl>?S9p|Gx zp$}@ByM#r04D#8)G8wbhzVCmA>NC@h6RuTuwp_BWe6HB}o<@jO>+7^%c4nkv$hWNg z+Bqqm(l{b0TQJRs<*QK};k++8ByuufUli-Sj}Ups9%IH)UtzS&HAQy>Y%xL|1dOMS zm9-3yjJ$&}EMOqqRKRE0-3g2xEF-|5@oD^pf+oCJ2&1pe4;x2RPl$g~SewBHoj33O zc=pM2d)EjoFJbn92G*i=^hho!C@4Pyl8B;jcz0U`^;Q?C!D^ue3)*v2@g&btb#H|G2C2wC^-i+ zBH^RF7OsL0RtPg4VYhyDB?d{Y6+&%hb}M#bpeWbegV62jWL8u%{rC#8mHKCXNlkBD zht&a?9aX&2ifCum!pJ6gT1~9#ImNqIkp&}KDhFds0Qy@6@1N09I>wW`c%C$ic6>~B z{`Rc-kr>WDEc3CBsS!b8y;N9{8hegr{uBP=yNtMUNf-w*Q9$+AwV0xGF=0H9U7_3MsNxxhVue&tu@U`Cv$wzYLu-jL-i}F` z(46P<22`IxLE{g%iY9|XFrNxV*1%Eyv5{N*PMLVowA4^WRtOAEhVosKamTQ&gB?WK z-|?fyBmZD#_`@pP45_vv(d~P)$@j#`DJ;*BadxBZ+-v2EH(qWCSuk^<-Y(+7r>c^> zwG@uTffn+u0rK)Y%B2y?xisDMI~<4wtm8dooTiO5JI7R9)eIjw3qV7hq#;=$W3(5A zS@@aJY0O1UMLfD{cfD0l2@Ou;0TPKXrGUD#!>xo?^ehyDP%VQ_tBs}sHqB39KvzgK z*SUGRk_9UEu^NQ5DfxE)&9(@+0S%th@@@N3Y5rR_r-1OL4fy6@rHmO-r?OSa8NFmS zMik6P)P9+3PH)@d%@S*4&lGc{vXx%?mJS=p@BWtcU>%R_38qc1WT)A$B#gUqxCUNz z{w|G!>gR$2hXr~)7Tgz+e48YWv8=LdT0)~8qa)NzAfy3KgkTi9U6@7@O27(Uv1?@rP~MEymJIu>ABa35L(s# zQeuJ8r_(}!_tmY+NODFwnRZN4LHGpA-Kwi285qSde$}kCRy*Yo_I7o&QPzNWPL*%= zgBJy?cI5HI_4%<<1+~DgiKNvpgUBk2me^E04+bJ7mJ(4&Ec^m%Z%$;Ws3ws6BgJ(1 zZNqq(x6rZ!2eMEubChAOAii}b9GM{h8;Q8jS3F#R97ZuC`S5;qb$$El5#{Oxv*;2| zUIU^z(l_O&u9E2`%wPry(_qosP9ES>;HHk{BE>XC1`dC0+HNx#A-1QO(jMryJ|E9w z3gv9ZMfgSBDCZ-+4yd=h2DJ81j1$Dl=!7iH3ty$qxk+Wu^>NKFHk+B`uZ_6XN8p{#?sn>;`D&Hoi>4D#W2rSZ&Kg2{!=$dkfA*Y}k4i5KQlR zntkt_QvqMiABd->0gr`!*W#Rj(OX6DD>cmF%arz`nXb4);mT%h(s%Bm`|SF78Su}) zXCD?r{KfKm!pc~6@}o;)rFC}?3+DwZe1NEf$Y;2=OaQC@6kpu5h zD117OWxvYAb+?F3@>|-z_OsJlLBtFkn0T}>C; z>P_{JkD5;>^N^yfi7|M~XaW@bk7O9I|Bf^ISxWp|HAAm5NkoVY)iqu@g6TR8p>Ti2RZ|Gb$t`Jcbz5)F}#t5baBVUqF6(Moy<13I*xxL}cx@2~k zKV8p`uNb*nKn5yd9bK@I-j9e^~Bsq3XwdC00+dQPTba(niV{!Ey?-Ibkb zOMp!xzxq0eATt-QlQ498B`wkLT@TFl0IEUDi0kB@ICu~k(=0{02#ry;;Py?ePthPQ zyFfBzwf^=7b5Y|}UY$Hs3JNX=eH0E-CZJVFz|4st>X%Xs5?i44gb9wn{{%SZoVyef zHfwN6Nw6Zb5HX0_4XzlYpm_Ow%V64JJ<=`ncr~ht!k1#)=v`i?C~ipD z)C)bd5nE-~2y#);RL*DSo@Go>D+oq+J-KE=KP=r4+)ejhUS7jrxkoi^!7W%>$=6NO)lGBvQcI&oKV)1dj5kZ>8bG)M z1L1h!QB5;dR!OxGM6CY8AULwH*hfLV2Osh^`ADbz`k=^r0GtgiKxeGDA6XNXX8jAC z6o64P68vaDVh>hLiSyOeqtxV*PNR3&+f@XUnKk|Gu1tKt(%fo4(AbFqKH}YFp3jlH z-?CLz_D@alp(E&;6tHO=cD$P6xf>}xKI$~aSb=N%dc7g>!qgs7iMK+On&Ew{W~pJ) z=32>QHQ`Hqw&?xw{!6J8 z9K70H&}BA08HSVJ`X5x7WMg`sr!d4&UC|4yW+PK8QMK#)-L+M$0uwDLjlqXqj(Y4koyW=s` znJ=bfe^pe2j~0&_VyJDD2rKo||K894ZH@PX`<_Eo`iaev9QBx_G07vE5m9CRv^&eb z3w(8_(I6DJle0AZazm=R6MS{-Nfy?C`R5C_NA@stjAjp=hGGrR6qu?zW~jR+3!YOm zDH7kq{C<5VI}fafr&@wt-~_|kNtJY;J#4*X(Hq8RvECf3x;%EraDJU{>hHl!vB2i2 zFE$+fT@8x-CY0Pj7otMK`Z`g0)8#&G{mpO9o&G)(dugwStQfhNP)!G+Lq5&=(U9?f9h%w6~8zP=7craNVh5qNaC9Y|q!V(U(}=*1yoUEu6rE&N61$a~JFm zt!|y(J5@_zE2wz>`a4gB@SyV`xr1q@SHT&3=cWx-AUI=5#r!_4^08x5-x&O2@J?mL zE$MC$x>@679Q$hWq;r!w<7$e{_e(tO>~j|?b_ zgHUzTI)U51YUtVO=DuX0zQ|V1Fow0-kx({H(Yfk_9Ui_iuf(U^y4p42i%0iqn&?VK z%nRkI;PLWn2rp+YgUay@pg>g8L$UA~@@4B?N5}TAu7RDM_b@r2$7Ew0Cd|Of()yOj zEHV)^h---mQ>0N3ngA~FK!;J6t-rs*J8_ql8siqRk=atgg%(k+m|1{z?F}>qupWa% zfn7N4q+?^04Q8Q*T!VEK;Ui_t+4J}1lL(W%3MkStKm=8mm|zYwa4q1*D20O3s$S}k zJTR6S!hM2ROXB>m z<;Y0v)@uRpsDDdr?^d}Vr;?hKexGegc6K>cgZRwSd}Hpskp`OsF6*qbD>%JM(TlX; z@5^zw=wNN~Lg^l)AsOs|_$2$Q)D@oM`g|V&&cEHB5=t{tMH}+q(yQ-;u)K#)4dmu0k0t)0Ye#h?d?B)KEM>nJu)uEgfa)Z z3>I^c5pxG23rx>I1|U`cioSDkM6t(g=B|gboQO=DC9@h*M>rE}^yybKNJW~hxuVNZ z$UH`D3)vzv|20&NiXf4KgE&KnC4N5GuDFQ56q5z#^XdJ2-KaB4XDG z)U9FM;`bfUYgyV=)>&)H@?Y4`br%4t)XX%%mN{+@$B+eO0F=ppdFAtbS0=v3lN$L- z=WGgIfVyow(BrhK$<&(82$@}D5>}q%-9>ZH>dj2Cvb)23FyNOJ>(q_-*>AiR)Sm^Lsb&1#W0|v~xLCT0;W|anuB@^$AE18NR(9Z&N4&>~D66=jG zHGhZJ@j{2BbrcZjknvN5i1p1`8sv=-k_9OCc~Cy}lq2YfGU+KIXD|bsfKHG%Ra7)a zcos3A_e8~ffyrtb_N#K*i#JaAY2UTO;RfzP??SI4#>-0^t*J8R-D+#uipHmSocYWw zR!kQZ7X@;^JbqqSAnQ@6m@^7&XOosMZ*Bm3$`o)kF*t=}g)WzqH^|TmkYxf4jd*fK ze1Sq$&57YMZc+SP1%J zy)5KrW_#encdHGepg64junuOD9I5!kxF&c5=5J zPdg6*AZZBox}8%s>N~pV+33BnKB!rB!nX1Q`-zX77Ki^B^IGYu#swm}25X5LTK%WN zE?yhQs!4ZA6HU-m6sgUHkZ)hIT1ujki1X?iBrvo;DZ!kWga)xp1moE{)zeG?=Lq$WSw?;3AAgXnVE(acco$vo;cF!WN>2v37usrn1aXD$N&@-prYt$jL~7 zq&jvAh`knQnA99x`8;98_Rn+4&%M0L(@TV(4<35z3$h$;-TPW4Z%?7^-?NHbhWWI9 zJ6wOtQ`2MN zw=!+wb$`vNoM+NFHb7J@EZ;@q-3Mh zbS35jhbT9t&3uVwO%bmDNe2-HKQM%d51~6tSx5D{<%oU^=~^U=czF=Q#D|KUWUAW9&Q zs#Ukl0Q`ijS4%W?tOlE13m7zS=;i07PtBY)N{U3qDYY$mxy|J=NOWp z)qhppD%al?k_oV821Rzf_oW!_){Vy@u=k_-gxF;E>qwnins z4$EDA3(Lx~U0q!@ck;mY8uq3W+zgoe`}tASBVQRoAUB5H@<9X%0H6&th3X&dOkz_L zozAU2Qt7YU2>~d#pBfK7NOjH z9N~9(Z4d1fekY5lJdCfwdiWyc$ak1Y+Gw~LEv9Xt@Q z;7oH^D%3h@gK57oUmJHy{jpN5LdbPvpNBG9tvu^K+j*UjKsWC8l8hpcqkful^$Q?X zxE@^vVTFb#!iGUo7Q#8N?{yW;J1w@F9dcbWq%5sm_A8f~`$yNn(>1~8T#%bSTj`%IZo?tpFxp;@9bQgO57uaR@6mO~XLj1{VQ1!vE=RU?b&{Kh5s`Bg=_e~;@&pF zw)UPLcvaOvzEDNV@l58wq`Co5g*o3X;D_#_j}3bP;qOJmjK3D6y6CoJ9boouC@!am zkkoVw=;t;%g_$6Ovw|+JD`OBxvlhd|-Qso^?hc);VbAW0<)pA|GTJLsRu~8Cmv9t+ zQda{I;Dlt^OyF{;idFgq>49m{a$h2stT9}gui2zF2!Lpud-?DB15AWMdwLAJq#c@^ zBnk^KL`(ziTA?LsnP)!sTuzN=cvfl;v;FA^Y>`Ox`E)BadZFw}=Mr;`MJ(Pai?+Aw z-dK1s63+4*Uy@ueB^@YJg9}Hm&hzWtYJiai3IkyVVuP}Lm(VI(?bJsPNUH+Jw;Lf@ zLYHcQl!Y`{V|uxV{Z5Cj)yJU-^S0~!j1Ee3JV<{Y>f1(JeVxX* zVRriTfg~_AG~|h+jhIzk4OvuSqE2^zo#_`W_Vl>m*!|`km^5L3Z;p44c{$xMsFB1sJDc5^nFTtFy9KSIaKBX>J1=A17tVnTmfbLKKqWq#c2>@~gZkDr1)s!W zNdAbjr+3OsH0`80oB#fy2rex2jgl&|N6Xu?Hw&mf6wNuXt9^d5_THeAd<^+O7i(iY zXrW#0-vB5N4+H-2O4oU$vh=R`M%^$KzRdKoM<5D`SzE}mWQla{%X(~~?M|(3?k5`j z*wEci!Tk25ZMr^|!b##f^~l8^+-s(Ht;`ap%V2F3q_4ig54Vt$7sL-dL$DlE&)X&9 z(LO#Yc0rPin3*k>%X`z%=#dj_NfuNgOD)tf!U)C#gE9tAaa<{hZ`e@67BNGz&$Zl? zD!kDwZw);7T=>7=bWrMF1vg2pQ17y+_dZ2Q-OSbDK~prr57_rs+4g5m@BRrf-PDr9 z_ZvC&vYq{$mGwsXH`3^rB2y`(JoefO<+!q!haYVW`U)~_{(1BApVwOc{QUCu61xQ7 z&@g-RKTe45LZ25VDjJJ4aZD)FcI3yy6)tJ&dbb}>Z6Ub-4j&_6>WU>4hRx733=reM z=B6y-bc1Ctp=#N2+6kB>4aS>(;^ul~{U@GAY694MeVg1}JI$v<>b9Qc%W#Lx;K4n~23Vj5X_R8-8( zU71@49yA;|$VG!_`(x5`8WW92&Fj zvnv1zX&;Lh#ot5huyMv$41cQNz1RskVqC%sKZbc6^>Lw9w$BX^(VjTSzJ7Wuc*PBV zf9s`(yt;ua@wbhSE=Enlo-h-PJmKdTdhZ3R{u}xR z5{4*0_eJDvXG=2V=(@PYwuh=hw5HX0m6KAJAjd27(f9YB4R=&*cndr2vuslzMdI8; zk83k>TKBf2W#<~_S$b4b4r*Da#KsWh4#(|k+-)*qLb*`>vvB5%6;+kkzQ;O<-CE@b ze0G3i;ZE;r!aV3L`!6b|`j*%0>>Ysfk=oqLJMy8|fvck@i&yH8 z*m!bvLL>AR7i$-#1nnE_0ru5Ze?|zGfU-3+6QcpwG_Qs{al>Q8<4CPvfL#}K#8RJe z@W|@;D2hnJW2!@=(xbe6fIw884VYhQw9e%Iy6zLVZ-888X?u6i;2q6WowqN~u$V$Y zB9cuaAoNdT;$2)ws4nT)4UI;Ak-Me%BGz(w zL>xD?g6(l5wGiBBdGg_gNob~w2A*w!3kGB89SEg55f?#ixt}%Y^3S~Iy=DBH|IdNE zUhbzCqzpaSOapm+p*lK3-!{kOrIf%S_@yQJ#7s#Z-H6M3Lh2|XC1PTTA)oiP%T$RG z4wZE8sO(2vlVxXK3rb`8wadwhv?g;DX280J8W1yLNhVu+0Y6@YR*5j4A)U=MKTuh5 zH6tVlts0f_Nj>yx@yXz+!L6EOQPkNFqP^Ul-D z-&-=*N`4LrX6S+KkEBl%J$9}q)xa3fYr^%ee!mZ0Wp<=5qx<~Wg`op-TVQ+^}%W?zaw)#1K?JQ_%Tq;CPg#2 z1im^b72og!)3EDknaA6=xhcsLUP+OGpeM>cEd&Mngby6@uB8ki$?0qoKGEM>h$}qT z0^MwXuRcKx8oD3Us$>o^JM)=G)b^&dnWp=bSDphca^{P1<`#;M%d@KFj$sHA+@L2@ zZGP;$)gMwfEqEOG?hF4wC1fGE^%F$F0ml0S_V2e=qRy*BXH^@KG@ZDezmT_ z_)Wk69-VTU$v72Ph_18Jmzp3GQqc+fU0vdP9&ys2Q>umahdNm9D>kU};?ikflox`H z>S=yltx|$ht7RJFy%_Kjg*Gg|=&_ylP)zeTPjP2klh#~bzP*N?+)-WS#gR^9$qJI( z`@2T}oELTy7=C65QqY|EsGX7Ukhh*kP}F+TDml@BzAb30(3_LjRC0)W1Ju#=>03ew znhzafkwbw-ZEpm+wgffQ2XiMt`^k5T3g9~2?9+ZqDZaI}?6RJ*6A;Ir!BvF0C-9Zi zq;vAsn|A1?ieI^}yBH*oCX}iRbV7jM1Grg+EVZEQ4#uvuB-pzqblZ@GHTs+tXUVEj zF3$Z@&w(O~gJ}za8Whq=To}t+S!>sL0g6dUdTQ$oU0fI;x3hJj)1SLhfk>=73UQ)WdFP#F? z4PUyuySuwnIxi*NB3()&4bO-FTEDd(7O(Jymvas?d(Z5dtq*2&iro-G+(Pq8p|zcQ zeXhfl z9?P!hG#`Y8%A)9Svx6}F`hw=w`QBF1zYj*c9X!EfozE(Yc!o(q5@>1NXQ8rk*b0ow zr1&u27cdv_mlbsN1{~=QVsL9wl?gIZpL3Aug8vxjHweaX{N8q#{rm9sru?Vq{)!<6 z#(S6--+$eWJTa6j|C%xhqWlpKg(CJNI%jRIt=C^)QEb;ADO@!%sb?YzK*IQF!%da9 z`Funx;T7B_ez2^dcmC-)r^gKtP-8=b8ogu0e?6|TC<=3x5#p3%yh(f0R6V#qbY&Tp z-*ZZ4@PCqfex1cSXoyC2;f&a8n7Q9#?;M$6F#X)xp|P=1>FG1O$%33>VS8hkz<%75$WRY9DvU7&V+U4VV)Cz(0Y#|P3Kx)<#*J+~5{9`zRM zz`t>Rre9pcO#S5YuvHA20zU2s)$61yGLeq(QslcJm4DVaD3=8Z-vTCbvx6!5PF(x9 z#E@%D3X84D)RaQuP`{q-?CuCk7@%Ra5*_}r75-~8u&EWedYZR#jb25S2}@U$4Tpeh zO%?%*9^d*?%DeQhlv#>w#3{`CLCb&J^OC%LN6ZY%M|9tx{;N{@4~#&9IFO3Hce!l_ z`V3h=km`%U?m|2-7^EN-_si5!=J>JMz$k|CQ|Tx|EGf8 zK8s@%KK>Dlt>)O#K^@+$3gf#2HhBx3@({xD1Od z3tnrH?x)d@X5*AqQ(gs2chftTp_Rqdn5@*YGc!2|+C{s3KFe$^w&2miLuhn7E9(@# zt+@8L4%aWwdK~3%9*jVW7j@K$*#a z6N#3>cE$`^!ZqbrC?iy+oYRN(x5St)2$*29{o-|AnqM zoU-WRDM7+fw^w>w#2oaA@VH&B!kn$F6kj58UZF97pVu?oQ5VF}iFUO<=lrX!X%G^6 z@{6q&llc*|2`y5>6&FMNX21k&ez9zW0(yr1060qlCw(4^hB3mn`h&ZrwPj}gmqh1= z|99@+?Vm+Pg8yda2x;zNCk&=)xCsd8d&ZFEZdQsZu=7o-bH{_e^qyvEu);;@U?M1TFqvXXHYw0e zs-3_l{JxOcgSm6l)W2I!Om>J~(!l8=V~%x<+ju!FUo8wDBdm@$H}<#hQy7cgrjffd z{F=*8os<05AA!FXxdSFYi%bvIosioLL}`cHH5S4MX`Z@D z_|LPqp_&8XR4u&5L9f5%QUKo!Aunm8b#O1l@`Wz5M=t1rLh*YI`&R?2ZQamb#*UC` z9VnNPKKX13Mua0J@xAMm$l-B+{ZY+{QWM=bm82oL-1qKOA(iZqqvPZAbM#!*o_*Gc zy1BK%{e2B%8-0Vo1^caKZsL*Po#k-fMw@{M<3VLz?XN3#GHni$pq+~rvTvPgr-Lu!8 zko7}enW!w)3MjQ6&oTWD4R&;>xbjb2STRIU{N|jt3C~oc01eYwL3PtqKoamvb&ZPQ zvtkLpK-{j&%XJ%e#_ff#1oxmxwFS4wQXNMbN7G6FIMZ;*xx}C1ZbzvfC)E9$$qj__ zJnvo$-1M}lR?@A2&rl9*@}(87`Tk0>?ZHWBY9GDEVH@y;Yj}6$ zbA_12+oTp-@d@D@@e}sldS?-THsK`fyHVxA0STTe6roq5cjHh*7?VY8wW0lYw8b6& zy*Jtbm2!w2li@gYf`2sP_8UuBdNOajJ%n5 z&^ailVs@}!rm90_vx2F_&Elw%Tu*b5PPmF4?LQk$RqWQ6ot?Mk&*e>B9dJR^nlBlT zch%=x8gx}8w77$d`1Jk(%N6RZiAF|7+;5I?3O1!F?HQLrEOF^%x$^qxvU% zu;WjEfDPYyL6I*!eXl#e#P3Jx#}fEqAa=PZY$lIs=p`k9qxDlej^w~85gODkMOySN zDleITCB9#jwof`_nKLY@a^^RZWQ(i2UF=64{;z8520Vk-k z5<3T>h)n@+#p2sw`^2;*hBC+iPx=WM8t8VKeUMs2dC|cz9f{M3FFy5D)Y{>@6Q;X&nm;%+MM5fvHni796 z;@vwrbmZgG78~*iOr6etOJZ{*GaY-v&%|2i+Vbu(a7zwYX3&GA!xsP%@%z{X7+_1Nh@WLu%E=h!2bRR+x4#C z$!aEWHuvxUgjScVN2XGiQf=DtM@`eGwzFE|Pr&vh2r)~3W-Au%`i`rGX4IkcVz_z6 z;FMqqbTZfButG!`Ix%HLz9>T;3<{9LNqEEVTq1?Z&^qT`ggV6^;H!f;R%k-kx9a%Wqrj#lbY7_=2gRRec_xEXtNBV|`Kfq*>PcIQ9aKinU zIx608mJ)b?<5D;ZgMhfL+k!ptUMLBf?p=H$MdcR7pvWQ;Gc+@+H!^6Zs+Vg{1hbE^ zcTNk_|K+OMSe1BOq{*`7<55_v=jh)dJ6cj;3{yg0nKj>L zV^Kz#eK&oQ$e)oM+-MYE%^BBNogjoC12@8RBM_s8N=w?Z*XvllD<-t|z6xJs-jB<+ z*D((LZwSJ8J!~>@-nNzo=E^_=I*;_LBPEOs^+h2QLp0Q(10#-cGD-1QB-41vkrz(( ztm2|+cM;2L+YlrnkRJDd;@<2(L8m~YhOaRuK#qAc#%ta5833G~!};7!4NXkoe&~Oy zIRU)&V37K{Pie1$nFgA=4m+lVc{LY0;+_;Q88)8b@qQC=V;(+1KbmFK=yYTd-jX z@|mnbVLfpq#@d6hZ_C22h5YR}XMCTk!&Po&>dnx6UnF?YVp8b;0Wj_PgISh$Ar(J{5q~6UkgRGiyN>|)* zF@uq4@IK}ks0toJ(hcTZR3UOd@j}~-W#Tfa0NhL0%-etRSrWstl*y^Wup!+~fREM2Z zoJR~=8%*~fcFNM?6U8YYjlu|mWgHi2+ymd655Q;C;hdqx`>Fj7=0z2Ax?Hi z2_9>I3SWi}mrl{_KIQi?b#K>-UA!sgbfnE1;EWoxlectY1Td~_1%$`PNB8?nYOgob z)a8|xJ}49_r_0UEXPM$NDSmsH1dnA<(a^vvdpIljsko$uXr{`D9gK0C+!c0AM>*Aj z>NGpy3o00T9~5rtbeIxSBAy*l;+ByzMn#ZhmZJ$u$Y^~sXpZt4%eNSdBjYmF(-voi zWXs!FbqIztNI-m?_XO;W)DX;e{14`^qXQJz#bpMlOcW1vXdWfwbwbc+MDmkFutUOj zXdA(y4pFR#4(#}b&8-ncUu=Ib{#^s?c+bdM`s4W;t_#1_S>+L_Q(7tJYj_#Q2hQ=1ou$}Kiw|9|9H>np6$^i zg8}W=lWqfSIa$pnV)5PFgH-xYV4VNFR>n-bS+pHfSS?*u%Er77XMe`)=%3O?IQq^L zm|82Vnr|^CKlTtk^kw2Qg638{BcV9|%dv z(5YHShVjIKhPqzIcx$#}E#EDOqr)p2-dnY|CbFNC;)LQ-Nk*kQBmLMoEjEi_XGaja>t=T}&Q7+0i`n^~7J#K* zrH&EgR$cbFf;LEnS&2`Hw9bZa4N+FGw1oPFqFt?Jb!hV=mCpdAgSY4Xeb+}ag;k0O z(Lv!L+7AJ=L50LY@c|JD@}Bh#=nz~+E&H&(RbA%yh=J$>Gl+q9ATa-ZO4hjr9X$8X z1{}sgKQnTXDAp*}Bw4g0&CA*5Dyoo1$_il!)uvcFs@dMn=yOOrYKkqYSCs~NW(kj2 zYACx@!C41$k_zz9+I(LHy*5KnI^1thNdV~N^Kx%aOicV6@MdpwJNtaP-X7@M8U3vh zX-Pn@cmImIhxy9-a)((2N4$={ggUaMC9hrtimBIQfx;j>qAuse@Txy-AlIW<@q0pq zjsE*i$1o>hA@jy4LaWUhgbG0+Cydo`YZ8kwqD5(5C^1ha_|mbx24$@U173Q48XU9b zp>OPBXhtn8LdcPv>NU?e4uzmaJS`%M4<>x2WWrG^EqSC9C|_3+lolH2hq|&iN6+`( zy|QmEHuxB-e2}B1Sfm-}&rPbh<~fE_Kg}ExATD`2S`~e9AeA}Jd~&P#lYU$=nyUAY zhtAo>GGnze!WcV*e0YZ~WPrS%%+G|+qV1!5P2^;>ytSPjHBmA@3;9@HOUX_eFasq@ zfu#E4`ML}LLmw}&52=6_m_#u|mWD4@c-Fng591ny)CcN>(&h6-%}7 z-LfUis3%|0jvIEk($s#qKS3O#5<%6>P6(wH>&QT-jG=%x3R_l4DrQAA48~ww6CnmU zF2zjmQ=(GB3#X-MKEO#L4j^ja_to2oS-S3~Q`5_yU5fmQcp6)sSn$u@Y|RdmzFM`Z#N48BVHF5oLoWg{;e%?{VsQZ#azLy zl}7Wsv)329^>!x&K0dy~oBz_^NnWAdhVF-arsJcbmP)h_r~j*glSPECi5Nh}P|7JN z?NRfni_n;69#J#qBX=2gYt?Jbx$Cs(kq^~H-qlkt_9||O|5Gd|f7=e@h$Dg{ETW^( z!Ryb&p_;S>Q8!#bOJrc2^Rf&8q7sZFVPg#cMMs=Na`B#D4bGls>DUYx!J4Y1qetkz zX?RX4`v+k^GDJtVobssI3`S*Bo5lDi-V}2Zp2`Hg4L9Z@_YB?k@*N4*NI~tT+3)l{ zYH@yZ>R6bwoMr6`^%g@zNeCO}-&I%eT)Z2g5s{}q_~k$0qKnxNWBgGLgB2NM!khd> z-_fa~wWcN6xfZ23GyySV@&iXM~>vNl3<(?HJYs*vUI*%Oj{|K%m!Sq9exe z(>=a&60xobLL?^z->u-dbc~RuhKMc`Ew~PSMJNhiBWs@}LdfX-4%p^I%PbSggiquT z&$%8-O*IU|g~#XOza&MI!psCo@(c=$`;fngCp^ssR8vrF4N*TL*0LHo6EDI5Ww)3; zXqt0pQL)wZRb6{~IAD^#u%O=Mb&qM#?bX-mcJ_ZbhLn^tf&$>hIIlMbugS!MNeSk~ zY6{%(uT6haucxjCtHL;%nI;=~;ofh6M(CAJ3oQOSpz3i*N>&EF!kX$*AO`&LOh#lq z5Y9TGhv)T>>zO0HzlJMySRP=V(8&ttoQpQS`OS)9zM>38w$B+1QsmD$FoV0>-GvYA zBeOdE)|d{-^WL0>4pzhImYsV^$@)vPs*xx2GbQ%0`(F+=|MxpdDB+b9!`vtLdUJ74 zv^(-eQ6?*rv#$f|iyaoPl%G}D`l>>I<`P8tS47H0rLo3fdd-+R;%UHurktFmEPF|( z;%z##KfJTt4rW0F3~jjVUT)F@?7I{DkU|CkJq&pPYrsLfg&VKEE}jD~9h_e=rnpg+ zh?r5(vJB3&b(Bp1D5z<-^}-z$N2iX2kFT7S!AU0DSQ*z$_@qE6NLxYhsiz9THh2dM zi-6Pbi5|06E>W>U%~2k?70qP?5RsdEWgRsMcnF9@p_kqq1atS(25@~Yy{SRhL3Y5V zL=;&T_FJysN@TpkvT}a$!wLSWxX$IZsi;8+-~vg+Cm0h?)Z~AW@tJY|eggfc!4;Xc z7;XgZ-4i-IS}BYMg-4VgG_uT3W{;QcfxE1e;_KV|k@s~~8xi~H>E`TYGhQ=)T^$al zNS0foGr!mw+t_@Z&iy(9fXKC0I|nbXC?IY3JngZxc|US%XlepwJg?2^B##k?-$fwz zsRVHDm!p(3nk9CMc$%TKaf5V;4L5~L9RlQ$4wvJKy+@zn*ims*RxI;wdV|{^jibH< zp?RLp5+YNfBqL+h%%(J2JnhH2oM49(mUseoGk07;2B9Axo!0-Hg3*8hK0)t?pjgtc z#$aOLC9V4tBncv6V3Kj3x9??yac)T7D!m+jiSU)a)r2G@Cgz^{NbepIwi;d_rC}qe z{&(47?WzBjH|-&)OP^}J@*jyFh%|O2-AVx+9uhhYX{W(vb*aX1nOj(hu9@JCY}^4` zaA&r=lJB)d@F`<#Rf|>}&tv>Zyfs*o30tW14JhnG`rTfa@wxk+vTO4-Dp?;vvJlIi zfk~?+cvNY!qYMG*5?BfC7@*LKGVYj=c%bIE9AN_q+r~#r6X)l9x6WOnC|D1ltwP5n zpJjNV-Xl@yf8m~$2lh1qVVGfQv}5NDmN{WyQq9v15{9`G ze7B!SQVc(c2De-v=s>7aM?mGWjR0$$(h^-HK>X$Cw*I|)PH5Q?**Rkno#io(#!vF+iFa=*sz+PQ>-f{RKWk2ol?=1(bGM>p{i z2m8l=HrJ2d&b%a5wY58c*IM15AGo@`?h{C2rw*j_CD77)cq{$IuUj3O2%Dh^-5nGyg5Edk?6a6W%trB zY*NWKHi|4nBy~$r;iI9Uo$pT)b$LJH7S146YcL#C^npHS{G=7o2dQaZOZGT5$V(>w zmlG;c7|-`T8O!Q{^4-)ct^5n|UUaJJ_l_sUmij7L!TJ-%jV{V$Iw9VKVLnpDTE^^x zSAK!bH(OP9Sz-+2(+c5!J7(00MZrEDjL}`p4 za*W}tpl42gQ@H;f!9#lM9Nn2Q_LZc5+tMr-hk?UNl_Kxj>-TPLs|bp}jVKppu1m7^ zJ;|B^8h<@WTMgW_6S^duu4bRBHRs$y?;nAqkwHyfh44m{1*zMHpPo!8^(si-8f=Wo zP``P%4vd_C$Ug92&e~IKgzHbtB#7DxL3?D)rBtbN6NTb3OsRXkm0&*(M`3Mjx*mW` zXJ%%;(V^2*W}r#ZXg+Q4>>Om^`^<%++S=S4tWlW^*|E$?f@*Et8|vdONeell9EhkX zdevgHTvefl26;HLP)zlf^TnQYq{AlMA^T;qDO?spXhRym>twSJTrnT8eCKF4-hSsH zOOIqh;htbPGbrC_cl7=sx&qgnhv2R|7PxEmAQk#6#RB<2(~GTa{qIBb1XFUfB2pLO z$_f@@#yZuw09gjU38N*r@9s>cmrS*@>{T;NSN5qQ5lsqbMs1X~?u*8fu~@RV#r9HN z^5-V*O2R4FC&$R$T7N?1?jL!!h+{|aQ9nRDkHa`%m>hC~J%_3CE-xKN8^${*tvHyL zupIUmnZ9?j8`m&cC5NZpqNwJw7E3NU53cwJlc54PAn5fJ_WZ1Pc>Abj$+>m23MPn6 zcxqGVn8+>Bgw!C9TOW7Kd~e3^en=XxJn^X8Ml`~v*=1DVE)-XeO0(hJbt$C$qayf| zD}hvZrlf^>d;NR!PYX_V54oMJhmT{Qm@C^anKh?&zvGR8+A)WvTa(0Nc6#eE`>ptZ zvb=oEh^wCa=dt`E{+v{8rnz%F24Ru50SP#T0a3*FYYN9qAAZ)+36Qm5MfOzB({17C z(FFy-k+~*xRd8k?{~4sW<&n;9NPcH%UPI`79-jJLKRq&JGFg=G%8Z6Ozrv0qmnh6W z?y1>^Pc>p{Kwb0;%Y|qA2_EQKWri{}FF6?p2ouh{*>WEQydSU;5D?rSuIUyR7k{4I zH1I@EtFPk^y=v~EH@1Kkb#A@%^Lav7U1s@{^J%9O?b%fS6U%DXCASqr_H0~%_aipX zc<+!?oL_OcK_%%j?X*KRW{?oLGUmTtAniNr?Vov8JapBi+-lrT29S~E_4PqR*51`s zZ4C+S{R3Pb3i{op0VX$IcG~x_khVfv=KKxoc9eA5%&>%X8`H_W9-L4K zdi=JKS<<_Nuwv{scr445LB-alfo9tdi5t;Z9=3Uit_%EX10%e!-;6~P6ITj``HNRP zpC|#_e7?eKl?b>vM9xLbcm7m`F7dB9H1$aD5|SMmzs{(GL$hs-Fm|h78@g2&ONRLK zdre-pb|cJ05IAv<6M4XNN4qncHXBBw5QBdy@FPNwqmx6ZzbPe=B7)FqFOIZ=vj!Kl zRT+sTHLOqx_Jtrbla|*_7(=!r|M&uH4NUx>t=n_K!En!#-qMTsZ0C12ocx?!9|&tw zF%ZTZ46CV1@7pypY_qCp#mc8^&mQfGL6Kk#33B2k;VP9C0*P=Gd6_P=wzg3P99&Rz z{vWEn%WBT1{A{g=@~)=x_yX&)(Gath^!M4iYzdEvS~EbPQ0BLotow4~$>S>L+rddg zB74c<+e+NIjuUvC+X>@vIg;$yed%%bOCUs*4 zJ={;^HLL#4S{Y#(?Zep|RQC%+XuqiwdPRKJKJF@fVb9cBrb7v-FZC2Hl8JBbdRecG2oLP8hl9WGv zkF0QM=93F;16cMes~)iPa!G7kT3U)5WoU(2OB;F5TMg7T4&_2pu9=mjR-Tb(E~;`A z=|A}rQ6T5PZ5obcUXD7`>EW$Lz0w#E15V7l9DuR=)#dMK(^d_DCC|3!b8_2_u5@}I z=5265FlSSN#6B%fV1b2}3~2yLJz#6S)$y5FlBG2Q5iPiQ4g*g*%>_A)cxhk@y>N!M zk;dtL+Adr)Xaii>-G}2zu~}l~0O4+jj;5{@ub*2Z=oi9HJ8iXp*65OG(A~5hxX~S~ zfg5l6X^%Pk1CzSa=idza&ISG=>xy;Ib8I-q&K#k3HO`%F0l3YGu$EWh@~mQzORU%o zF{|jg-Rgs%>ZaQaOT)P31qt|`&4k*a*M{9#D_nhp9UsejTD~f3Hw67aQ^kTVGm6Cs zzu!ggt~-~G+_CF;f11L$O7)VuqAhi_J)~wXgq32ni6CZwaAQNy>-G8e?%{?3fDZux zj@y2Iyj@;i9;I-!f3KL}=249%C*!kVzWb8bA(n*5F%5Sh$8VrgvNXgA-zoE7#{oE& zEMe@U*B~4Z3;bn6(C#cFtCc9z-X^E-Rm;vTemDn$L3jM1q~?lCQ>3)?zapGbt^pux zv`e4w;XnlG)SR&jEX%WL*lfvnvG5_u!#fx0=MG}aM?G?(zXskD#m4mK7IvigB=Dcj z1_&aVO3q<8`H*_2_^cG?``GE@22}6$WU$mmVw5ZWlPNc z-boxMrX!WIwNIxU zVQN_OJ3(tURn%wil1uya4=}DBJTq3Dm~J>HNr(yCqK+xYDXYmEFKtib#goIqf4-oO-E+%WPwhM{{Wi|{L2bCYz zTAaGSZPCXFP)OU`9R;y8nA%&xCepOQOFY!zg~ll~@oy@w@{~HRxXUjBQ&}Ozl$ih2sfKOSkH# zC!ESVHisSr|B}88-Gwp0pu8X46QfCdf*IHXT(1y)ep3>@M;w4DR4!@N}7=f3*Oh;A#MI-7FqRln>_rF0UDv?*$5{7++4vgBWCJUqcMfj}y+wu-VO#)V{! zYoJDqa%lDzZ5OL;_X`Jka1!qA(-mX?`}gnXU)Q&*<^S&*5y0s^uP=PRdVFKEv)`zK ziIo+KkP0aS1A~?^o6^vYXz5|%UOK}vujqI6p|UXI>D7MSn;+Mda6LncnU3F?0yyc17-nBQ z`7FB4>KRtb=$eE46^nwoQ^VpP0TKhUPlj;sQIP6g{&>BT8E8a=8SOCk!T13>YhJW> zRLTU-zf-JbtE%JaVfJ}Ar*;d+$a@DuE6*%=l%2zy0gG? zOX35tY%9yGfj=>}k!ospO%B&wrHG=q){|mNV`4ZY;%c*9(KG2QQ|jY90E*Z7`dI@a zE|ovHlUz>zQXkPL+15=;QLsH;=PWW-c=m^iULg{^q2krQ+;4Fgk467CP)`BC86=7F zRI_JryTBqQNn)@-&?CNVbvo=R^|^+FRqy+@?vT6~^GAotT%X8-!ziJ2u5?<;Yv;QN z=1=N;#eYVhoWlHJeNCp){IFSKLDQw|dAKZd?)VGp?k!mww#ds%hj;YX@y))?S}T){wgj{3QRE?Y zG|?dC(Hu%&yVX2mbJ?>Mfop+igsMpF%A^33H825c!`;LfeUxr@WV{VMHihIEUaH@% z@P?h3%xHyZ1gI9TeYQWjNZM3g`L*!&m|Fy{wB}nrDH*b;Sox6GVJrqb`LMFzmPL?Z zQdT5{Cd31Q;w{BdGjO zopK2iFI0N}-3Iaz3@Nl77T)pmMr}lbLf+P`n}Ur%e@d zfr`YV=N1pO|IvrqVL0sHr!GoZ9vztNW0h2OLWEjaG?%5w_Z%WT?swD3F26x=|2WHm ztJYnQ&A)RDF-ngsc-fd#eBCPPtD+Tm;g9V2o#!^%&3v{4C2Lnu^1S%y&pxfz(}5G& zfdxr7u1!=#I)w0%}bNII@%w`PMQkFi- z$6EhU*@2ZI>2z&6RoB@w(~IIsf3YV2!Q-qpi@~t`RGryvxxwWKMW#syV)A}VPMl)3 z6y{O0ZlCwKV+wvlwr?y8jCpLf6on5mv)!7jLFq zLW%V88HWjP#))6PdoYb;D2txKbaDyc6=-^$aseKwrX+01f=6Q3Z2?BBVX7e=JL#?D z_iI?fyQS8bNQpgxiQbrPG4e!3Docr4ea$KX#MCK$*Tp>j)^tKk@5H^KEfdJj+5hHm zo;l7i|MudzKTMcasrlU@M<%@&KV!NyPcrF3J1SArQ(BW)aan6o-6k~O+WkWo9=su? z$1*&C(e3>hl3$oG*;Sbx1YQ%umOL+@`uZ1qvoW0&nfSUhpGw&f&-YH?9c2fh3N&`H zg-LeJ983{7^d*LJP&ulKZ}yw?K8xa`ltek7?BdABQ%_!ONLD-l{G^?i?N0%QWXE=X zCeiGK=JeX%K2LWTZxu$M!VCjLL`1Z_u_4gtCVK0m(2$~Koug;W9kTNo62{ zY4Wb6clPMP#61ufO&>}T93QJY^s%hwfVA#%b&rKIHg+gbA;7hwj=pB09_4UBFrBfw zq&;{0o5ITG+@y!wW=AOHe%j4`=aZG=KuUsiSW=8b%x(DGjBzv*z)*Ne0;5Gph(}rz zIOgIY*LVB;oV8J{8pvfp85BSpJeWMZL?c-`(DEp86WW*^#$W7u_rWl`!z<;)UHjc$foX9je_;T-BY2zLix|u$bVC+;CVFuOLAT1QpX2* z@`r(2wZm&UD75n5FUi~%idp7rz%cU7#4w`dWQtwaS;L^Amo6GI*epxY3AHi7j$)XP z%M{iu-#etJR-vqN>f9LC;Y>wgL`dW(K}o(*Y9cxRwP*lm{tFnbm$8(BOqx-Jjv-P; zf0vY$1PcldS8CRtUmvLgiV2{xv%OfY)8=_=J0+$14IN_az=i~=31I)lf#jUkKc5R4 zDyEq#x>#(!#F3wv6H3tW*$_i_VM~SezDE0Rao5$5V&BUN0W@jWJuus^j;9`09wRr7 zCU8{oAy??wIHQkYgBIF*WzjGSQwoKzMaf1%chgJ9xf=w595!zq?Ct$>eIAg2EsY~% zV@NQD=H>`r?0yXbIw4m#xB8Bbh~(sC4k{(Q2o#6!I8i24rAAb~E$^(g2riTXdFl#P z*}SEfL3Zza{D&8Va{LsRl%{jaRY3|IHN%tLfoq-Q`YGR5 zBUp!ROrv(RFdsUG8ENA|p{TjSHrPR>Fco70KHMrml%lLu6SyqR{lH^yw1!5gEGXbe zr&z2$1x3xWMVpWV7S)B(wm$|%Lk=pY)&_Bz35iq8`T{bElM=MEFmmae`Dxho3bFQ! zwzb4h40i@*nau0gBsrB7jGVmR0wc7*=Jm(v3 zn_G`(JtM#Y0AEctjoN}ZeiH*s^zOhrth*T=A9rdwz$R0)4^po{~Wy47$P2+XP1<;@$Jgm{vah`Z?U%<^D4n&YOENZ-d*tZ{< zo=q0C#68LBiIJ@;L2XVSWtVofmJUQW4cZ;mxj&^rEfY{U6{L~gmICtf_5+XS<<|EV z5BO~cXJ#+}k=bWKudp8Ac-uU#zU_bdnDuX{c;Wsyzk)~%(!;1nQWY0lQCTv#y^3+* zaSxtG_9D;?Pz<7Z*NfxFiW+@1TCAB9@?_8guL}<~Q7)}%V2j-Z&20tbCU&yRNac{%__<DpZH?4C*cx^sk7Ua24Ud!!|6N~y% zb=ddB<&KF5>Ut$eawb-aPs?*(tkT~{s$=r4)?0%Khx9?2<20Td5k@3R28m}y^}{%A zKx4=4?AO-bc(140o~Iay!d&ZOS}#9hS>}0 zP$`<}#;Fe5eu}t#`q3~EnF0?+yMD$nj@+#9rcWQ>x;$^udR`tFa`-(0o}ay&w3)1} zt$Q7a-*kJ2hls#GS-G-K1423LwJ92n8dQf9j?HkESBvn>ONz(37^Tc^E9qHmR&B$N z(7Gmc=t6);-bBB3g9WDJjyk6K1-UH!vz?BRb#B6r>YksC1gIU3=vo^&)tlxyN*i!k z_;yofb)*IB+A_^~Bdw7U-OuBe4ZdAzxWxT3-T-qlf0&m9y;n3RLfq-X`HwIy3VE(* zNI2pYczeIPJOWD$?%rG#WcN5Q+55{f81i>E6$PiOUH`Wc+ zC9XW=dWLbh)|OemJzLvT9T`BVWz7PYiGQ>U1B+8{Yap zO>2LNSBmE@%eqYeZrd>6P+j&gLit9j!esQYF^uQzcuxwcev6w_M!^`s8}HK9G}!OY zOL}~@m+4(d|Ff27L0<>Ti{CTX?Z z5GkGW-vL9U#P!i+VjsO(9C!>fHuogVe12G)8Ks0&Ud2o_*bjRA>aN4g(#oJ&x$?8! z!4uve$8Ih_R54<;si2u?4w_K-|bOhDUUFDfTiS6ZmG(pK^wf4Zbr z^LVY&<7z7q_TT~XC(s7H__MI=V7xzg;(4$oVKQLx=w|nflaUNVH*+(U?Qo8hntXhW#+EV1A@fSUh`41yqX9pj zyE<-28GgC+6t&}}a)5tdO0`E>W((kXZ~2v_=PAIz=l(sw`7B!3-deXWz}B^$Uq3v7 zmX2`int0>NM7EfYT1+J}T`efR77x}@?gB@bId#{Z% z6$)%T<|dB~+-7sv=XVFr1URFn4*R*q?=xm$ud&q9)kiqLd1OQRQ`+Azw|Eb$peC2Z ze*-lnK0DPX+r87FUNktulHYV%v>X>PHgR=SW=m3$U})hdHpf)5__5A4GKF@cOiu^@ zMJbN`H}6eq6&UHyfHNIHieg z7UYq0w)c`}8eKHEE>(vGn@FNxF!9<(ODf*k64^a&MZ?~^fjvq%R>-9!c<(SzGhsre zu+5;~!V`zW%-rN0G}?4+70|S<(jknVw|Qpb>q`o}27u$d1%j5=*80ZAK%h@y+nRaL z283YZ&!4wTbA9>&+2{q(i2d{TuMuz&x6Cq`+;9nxAx-W|uUvga_)G!K>7p%x;o!_9 zSbYWQZs#M}c|8dxotv>|*D0)Y!5%y})>PfU5tL5)WzXPt)@Y-L=&xTYld;Rw_+kP9 z*uOKD2zrxS#>B?H=?;L9!|(Hi4-DD&FD`2AKOm41^?0pA1-+7$HKwlgm2+{$i&Do) z^_ob_)9@yxiLa98)XlUAn6tpsos`+BDVp;H$Id|>8KacFNpeFFsxLNO+|5eWGxw-U z^dVU>Pk;qhv+ZBczEqS8j<=u>Sx(q&?O2y{K8QW&;TiCFN>*v{e^X{rMn(BYFn*KX zRdS7~V5&K6KEky-|^M@NN0KJRS1^@kP zv)!?B*%LSD05$Hyhol+9W{5tz7ytKU0-oc=WKCMI)kLhnpeni}YIOa^dx&3WY zLDPOPucd5cBcmiiTd^;$8S()7n^5-gAJdy816Yv^yd6M-Y6uYMANnjju*`ZbH~gP~ zwlVMxQ~<$Xa4*oEbLXGZ<+y_)RAZj=Cnl#qaw=-4h~r*}jk@ zJSFV%Sfa)W`n%S_|1-_1bqyeeLyL>JKxhSuB&pFi5`_-KgmjucDOz4~##-|PVCGqz zy;VE3ERb^`q?PzTjGs&_se9!y&9cnTlixk;^tG;^^fXaJMZEyFNh;{o3t(>a5jFsb z)wQ;U0y>v(nJ-}eY0Kx$@nlzaZ@jCHd8gOr0#HVYYyU3 z$q5qYzEb|Sss11kAftc`DalpkiFt>{fw;mBo>4B7xsY_7}yc(}FmG z#;GrEq3z6APgHR=g2L#AtoXkHcm`y}$!}NylmGu}TJAZi7|f?TQl{yO9qZJAr#m%V z%Gi4k!BgA1&8VsG*Oxq%^D>>;zh2I7-d5Rw6<>99Q&qZ0*nNJmaal+;92)08bEY_7P4670I9#|q)#ikEUK?R{?JlP3)NS#R}(X>g0qGO|HJT3SPkZ0lZmv?=3jug z=s{Pe_Q-kYiRli%v`dkRh_Mv{RKecM3$+;g z?MVa44nP$qz|g{9SH4!vL8e7h;S%X=#ZyPsl47 zmvTpAXSYh8?Xk%-yF7^9DWrW3TB+{#!{9e^ckiHTT%`Ug;D*v#tX%zpWL_~4Ag zxLPq=&`ULbOz(S@1FHEkbkEOwR&>@DAvm*EL$QTR*hY$2ahfM;oogkK?bh;*@qNRh>8GMqIM}z z1sNcbOD*zKC@z{#2L!f%*$b_QDr~iB-U&vsdC-M7qULf`E74}mgUSz!<1)}nq%CIK znq=3sQw}^JT0A*4&j-`6Lcm|A>oFcV9!VcNX`a(U-&&sB zeHwx?1Lbr_n9vfAxF;7?nWAFlveLv}DvQB3Ky48NU^abU1%PZD7tOUn40=S?)J-J^ zbn4$XdurILP#dWxpH@8L4`ijY4uT;NzJq#3HxjnVm>24AfnEO;bE3s z8q+2xzj07bHxYzO&VYG%`T)QWsGfnUIsoh&5s7(6sXb*BZ{4!VHVk&T=6)mw5K&iX z`tm>RBpgnn;nfp5 z5ZGf}anXe*8k2`RB`+tL-72oqogj8K?AVwK=ba=CU@zK)Q_}zvbcmeh=Iz#_4>#ls zVf4hT9N&Y#mnmY#&&Ov#nmuxS{N0txt#O6ZR${w`$n-fuqpp(uVj{p$rk}UcxiZc? zv2vJk?-KJHZ89-uQ%ehANf-`TFuY|%x6^eX9lGL5D70cM-Fin!Py%dl&;fPCft{iG zz(aPsYp}n+>FAEXE^hF?!U)qXVGq7>u)vR-bf%9QrePFcEGv9>R)XqHo(=5Xe>YZ2OePs zqiys}VlIpiclrf_%nN0-$9{M}U<)QRD)1wajDYGWKP^BR?haoU3 z86{B0HX}*_Bweh;XpNL!;hcE)&0~j{vbp1t;_83>*%GV_arvvU$8o6d9tcpZYiFHvs~aXgg;sJy)a44~~<-_infFSk5?wWsXS8_`u( zn5f*d%9AK|_WYhjKj2Mv24G8ytXAT_X2@5Ho>_Lh1nQKs2#G@2&07u%IoXb!gEtaB z-k$YG<&G!f@o8@y5A_2akX5e#6KGnq0mrERH;d&zjPMnHuR=j?eKOcLHCLKvt4F$k z_k?#lWsW`9{6P+R!G(Od}zVSdP6#2g&`rOQ4f#m+y zFM5N5hOB-n$Uub--ful*v%vx^%gBb3wmZP-IKbIm1+p%jV3ASI;N1;HD{bn zzg@y;T8Uj}JDhH@X{*U3=|jn?DL^kr34j5D1@Ir8d%raY--J8Bw3~*8h76OoW8iO> zfG7$T@y|J_uU|kv&4@k}2Q*O`HNw@}jW+<)^i7${?{$a%#%kV}*zrQ;1Q*nS7)b4J zqX%#0v`9RbH>q+}Rn=Qb5s2$;4m%Ph%L&Hc9EBm<4`an1+x$rqRHT<@Qh6E;(@q_SE$ypwXs@Lz zb%3?+8T(q`lTONk6MNW#b-{Htjt~*ln_LA{6X0#__Wt_%L3w~PrW|*NqHQ{TAHh(> zf_?))_MJ_+tGhb}_|EQ!*axhb#$QpAKf2F{GFf#f+FWMK@LmiPT+EhJu`{@)AtUET z<Z+f|r;6rcq25$yv1|D)&V z_Ir(BM)(NT0^O30tu3yIi7w}aQJZcF)oRfcr7$;lQuI4(TVJ@tY}ZRyq?hEF=WXjX zE&d{zzkcEDM-1_!)A}%J=)XT<11RU_=Jp{R|8K49b@c(1_@i(EX}c|;kNoIqIKO`f zIQ*;yd&5n@phZXJfBPqv97iyA*?#7vlZ^w(R8u}QGDb?2=Tgn|jDKO;<8;BXo6d_d z#>w6JDr)WW9V?~LyB^7k>`2s|n6^lK+}r^d&&v?hUjXcl4s;~=Pym4I^+7s;cA*j0 z7r4E^0{S%eOq5&Y>ZzM_#j79*Cf!euSzGv#e7JWYxc=JSg&8TCa|ktG0sqdpZf|z0 zN7ORwNbuoB`0WS@u)vT0{Bpbh>&x`}3o_7r0!_ik@Za}k+=%mk9_2;2H}ZR)zQ*OIEDPsP*LwD(WC{eDLRVAKCQKi>gHg&+Iq zBmU_32YJ}B4#0AtfBu-u19xy}YD!){okH?^9tDJ+5vn42<-xX^Xr}SL;SZS|9SE^L zM2AWp%?oT9J6nT!{dUq&saD#aH%BIUp}+1kic_SaG7oAFihb*@&g!m?T28 zJN;hq{9mrV0?-arKu5s!1+-(U4yJ%2NLa`LP1`>M~{tBIp> zD>ziLwF$ZM5PsYMKyVxc5c2Z&RQ*%W`MF(7z~xjnFAx_N1Ed|m3J>lkA74~z*%8uc zw^j+2l0T}!ra8IpN#N3F9t--J3|lfnD5jNjyyE(8n!xE^aJyZCg$7<1Cxj$P8L-@Z z_*DTJ`=f$?fEb`~1CRr35_@}OfS&-p?+4U<%tL?51>38I_0=CknCG)v;jbf2Zk^*DyHNXkC@;S6GH0R#CBi;ucoE zlW!~E;|T~=>yS=#Cp>!kbB@QLG|PGcc;>FoR=*E8{;!MxUb=Tc=ne%g*oQ42aD@RY zM-yTB18|u9AK^bVh34kwe?8E+eKEDrI!l}o711(&D6p@o@tiQ%ig9vkjRlB-C7q*P zFgj8dq=iguu<4;Ms?vr4YHV$N0sv|P={sycO0+l`uXM+vmFf1kM-RG85%QwB0blW2 zc#=PM;$@Ye?V@H!@wy+?a(+74hF_ZohR+;*qoO{J5eogO<7!QKFO{&=Ryl+Q8{vaH zdIGptKf0bzBSu}_fMfHU@Az1GCDV|6TF_ zi3PW8@gzqc=R9eL(3N6A1pOu6duBBaH(W)}@P=r)hYj8)M76XuyYY;lx(a?a|5MK2 zi90)c763TL|2*y_6TNpzns!;JU~;P1FKp^8eJnwsW!~W$ z+SnjMLPovAnM19#K!>6q%}dGlTT z&*n!#b9{gE1=1q`xSGY={a=$1&~Y{Xo)q#EYmiR4{mc?hVSKf<9MK+mpX{a+xZDYi*R-yeZ$4RFaJKmy_v*ac2D z6${(t(pE~?)1);@`kE+sl)%Tt0(%0?Fs5aBu(<8Fo>^+x9cz>|RVgc2ow$*~nv4GC zXv9O6k|~wcuwOFBYmE38AT}S&!BAi28Nyzezf4~@Kq>&tXXe;t>sf$c_y=H77xQA# ztbJ&%Ja^XrniYpR*uja~TH!YU7C2rSGgTx6>$ra2#gfi02Gib#7K})^?v7q_Mi#&T z%O9XD54TJDR~P>8_!k>pfS54=9KUeY`sWqV|MgVAF*y$?F96T`NL2uJ^W#tk0CG9a z`kC3a&OHR9cDXv#?Y`{QI4o&)GqT{0-PvRIy2OU_VYk_0jn2@?WtoRJnC?n(oHoQL zQp%R1%0`M4n5SbWHXB<)a7U6x>xj2~izIKwX$QDojBtwS=Dl-@j92V2p_oRI01xuf ziNyI$N{wo#uFF8XRkkV%j0%N|EB&;3Fa~xT>gdroN~kiJgeabvMEFfEqS*YTKENb- z3DlbpX&3-*fXO{+T!IHOMidE1u=0U7`{Md{vaK7iqWd@v#yy%wM$fY0ts z7loZKW*hfB(n2+#H_$T?>}ZfDqf*NL)IpnB`TO*fgi;Y=U=wK8`Qs3Y^pjuo(sO5l zq`xKA5gND_qyIA`O`z0ic^l8?{U$0=@moJz#ZI=UN^h0#+-S?M=-B@7b z01Z#TX}cyKz#KeY9$0v?kpReR*pz#u9CQbyBA~c^j7yS~fghc!T720kF7^zUs;Bmq zh~ape9zJU(ooejyZ2vC#E<)%c6DWqhM=H{(>V+eQ!r(Uo<1+TV-;m*6~s>b}V$X9|{U&Yf7hU)oE+ zXmI|>j_lUb2%~AZg(IcW?5TP zvjY%(z`xoDOmzWc9q?n$SBq`%D5!^pg=Bm{^5M_x9sZ{+fZ&Jjask9_0)sbabDVu? z;mE;y+}gfRl}50+mj+3%4sG}WQR(XK*yU3E>Uia^rHc1z3?QlVQd zv)|OD$v5=zN=Petl}R5$H$c%t0VRa+!;Y>D_o1(UOeTT08c^oC$RY|Rg2!BSfvS+V zXqCTgV^#_a<{Ng*x^_hZ1QcL;{n2Wz+W+Zopa=3DfG$Lrs*r%*z-SiSZmFBZN|F2~ zmZG5A7oZl9_5C4)mz!$jnNl}-0ZK7$za0H7>07k@9L!gou*T8eT&DgLR%SVj$O7?< z+fQ*T^++m)t}oD$z*A={g!%Rk5+f1zB^1Z1a9Ljh%>5 z!S9=xtZ08zy0gnRZKu{}RhY}`8(ryKaZliu5l3d>XL_#5Dk_)rT>kZ~2LO_fhMvz8 z`~_GJAJ<9n?FyOC07y(KNXpY?Qoc`*i^FK=GNB4hi$Q1o=sue~Z!Aum(wj&QZ z2bKU};X57X<0k^itHU`9WP5%vCs;h;rRbv0L<|W*mk%jL^y%A%YEkZZD$f?SJ*}v z*Q#Hx8-`k}$>_Cq!j-F*Q;{CBXr=+JsFrYjdY%;({ikoz`p+P#Q{|M@P_Bj1`ifWC z7;iscEYEC=Bx2IM1@QzINfE^TY(WuVwESVg_JQN8dKuXz}o<<*>j{1<$Rt#&)pXm31~^h?=`IgZ~zDC{r+X|vOZ z=SQK8x6(I;ATUp;<<8=EXKIJptbGNXHPPH@fg;E~pmQ<2X5PJ9b}UtW9HX$ojo<#q zVD*ma8C7-Ne|l0|ait3kfw!9SP2}d>0Xq%qHAzxpA)8d2WceKuT6M5Br)DX3f#q*g z{mC^Wq1xuYHSMG%ERh1rhuK*w^X?~yhByZW+JF!Nd_ZD=Kt5az6My)lf%e;!%Ef8U zRx3DW6PakXfh{*wS`p`~nz~7Qoik8%;DBDg z_s21iyZjV*T;jaxw3??s=Ahmft+aZ=iP)|zrAmFsAz++vaV<)J3!4~=C~I(0$97#8 zj1V}bBu#XUv`@+_R>}@8q;;{JhNIk8FLKe@3(2r#926%cktzCTMfB)PMrREst;PfaDr$oASv4r_(e)mQ^ zHNj`0G1cUQ|5jyD%g7+-|LYi_$5)|3Ffpf;0TR5IzJk2_Q9=LN9Y-xiVu0o~m?|vT z#b)GeF!WR38H4fEQMe3g#(u85aqQRA%4S+cD2h3xpX?MQ15v3d0uvI!YoB=&yb!}X z3Cqi0sAKfZre|FChGk^qLBLxS6yTP593X>08C#RWk{Xc_{*n2cT7dJvthp%$UP{km zvLwn!ji7?f$^}DJ?%roxlrIjt>fQ+yU8%L*M+lJZovJKa6MaYh!>nDAU|HkjJ;vDI zN@9LW-Tc%2D=T>Cn}2e}CO+IsZX>X@B4hBmjYTA~RA2WmbOe-EFox5g=%xi9g`J^N zSJIw!)UTLw?md!sYZ=R?q>!8o3G^}dEKfJsEd%|73M zOl*i~b&m&o<$%-g?R9f3CO(f$@&mTD;?GyGuw1%1yTLTxv*e&2O(A&%9w(V-95K|8 ze#4UVd1VYHbjwE_$;_&6s=dLYvjb)s50N|=u(UOp5T(Kq@{Wn)+pbCfwWE)FqGOCV zY(4v7O%QXr>Tj7Aw=CLPwOL#`4v|m^d?Uf#z2eGj3Nuh%J;i{E5p!PrHiJ|ACOr@# z`BhHk<*$opLq4NixMd2zITWE|$uF86uBjxinzifXhKhlPl*qL`U9QDI}j!&KrS-V zziiVC%Y3SA6x{23_TZ2AlF071Xl|yvyif-4Nh-#8PRT%Vi5_#YtjH>&Ipr#Ja?0@t z3?I<~u5{8J!v=6N>Wx$tOXD{Z)su`ROgVY-B1_c(I=D_^nLpc}1yy>Hl&I*AC19t9 zfmEgaYuAx$PgrIFDRjx9IQ`Od1)Ah|$Tkz7SwSpk z5`HsFYhOZELIiPFu72vXo$NlVFH;{mof6M}AiBAe_g0Xwxo+w}79Ku(44Vo z@*jWiw(6Zv-P1gTWI!|pTo!;}09Z=^1p?^5mTd~w?aY1E?!?AoYjhS`Jp~!GX)1V2 zN|pYd+@SABlZQS&w=E`@(lOM}Q=2ymsY2r5GL+eS=A`}vnP{4}VPh))*m9=-zU8oM zTwR9eYGAchftPUenz5(L$InRrU@owHQpLd+t!Wu-+U=pN8NWZXFO~Ye2Igx0l$_Z# zgL2z7htNO6-SKnJY4AI$X2}!tBaT9#Co1cD9>~^aKTw8hfWoZaM9nlIvq;ZG+X+eu zuD#+y6QfTyZeV#Krv2h3U9j?*N$?c>ZU0l=y(g%cd(vak)@?7MO6u;4@6vlLsg&nT z-}P;lra9s7HHCLE82`}4m2n?c&_?oY$dvn@2GmUKkNmxAggdSyZN`Iy zw-7v8^Q#2Myj^y6OEDI+8)feoZf6CV-2X_K5arwO9SJxW<|oNfzPrAq*IFrhxhQCQ zXVgr>t9sHbN{MGZ_7wH@#I6*-7X_TK@6$pSAY50_ZyW)=o{-UKz93Qje}}2|NDKL6 zc{HTdVnx+f*-~f|*HittfFTBGAGDu}&CP68GeYPW+tktEteC*PyJM*8M87xnR8LRX zP3Dr`NdrpAkdo8|ILj5jXHM#@${YJu^6Hrv>m)WWyRH+&@APGimsZf}M~Y`abPcA* z>H=CU=YE6>W~4K&BcpXI=?=v>Gfhz}t6-GKl6$#Lf`K$e8+9&^5#^?_@VbEf*?2g$ zW|kDv>A_#AJZ*mZN_aoyKkAISGLOp^?0_$-QDfHRmJ*(AGL<~CJgbw~85^^LDtoS| zpFWDN@v|UC#DO~8SB*V5E=%6rR)R#)w5R`oiQuzyBpZJr+mmCV1g4l|sOM#Wf!i1- zWQlK@M+5vGFQco2mCsg{>3E-ek4E9YssrgoAxbVcd`itEtt0v7rV^Th$MTTqM4UZX3TA! z4N#eUgT+@B&3fdO+dNI_O#&5RnRk=+gtNRSqF*&Vc1xy(vBe{0WSyXyuX~eE&1V>Y zcBcs2eL!$Li1?I8H3iNX%Zge`>eExm!wu5R{})*q}XAvkM+ufFyNOig6rFPwU8dA`!C-oQ-2UQB!6}CuOs<`IC1)WYJWcJ$a z)5-K5vM?%`zZ2=LM-R=EW=;1b6R;n3P|axeN=R6EjFdq&B`x&$ve!8dR8qM-yzYC4 zZ|m*uX;rk>Q*>Rm=S36E2IrY&*5&6r#Xd%q*fkRznnPM z;hW)$9k-<(eVF(5QpVQ*cH-*#ddC~^IZ6(&S56eK1R1>g9OI@p~LzHRjD|62zSJUgt zGm<;Tr-5oaXe&UCc00-V`m)nAi?R^A>^-fJGAPbANiHv<}OU@t6{JIT_;Apl2YjT9bC{@IUBUhrkBK9u64!N z=1q))HLxj*S!0ZW+;Ff%vn@Vw%7?Sm7I4&^@Bqo`ybIdCSlp&%PV%m**22CwV+5BP z4c#=hI}0mKUxInso=x(-`sgM-rW`ucPL@)8;;gx`3S#yqs_IVXi$UaErkiG#J^Rl4 z_%|qs+`6e%Wy`<`;`m-xxjOmw>%PuSL#@DsKwGsktyb0N_Oo(h*m$JU*GS#ZI+S=hErPlH`14V<28i@7eyd% z--+kn=U(H?y*rUhj~ZnY(KtkdFhHLXR#_s@2BnhNLoXx5dYrOE=G_Sus(4ASQ^#hOMoMP-*3WQidmIZe&;mGh|QV;%IwrW-NS| z!j|=OB2gh_-1@)%Yy&Rpp1 zT>>~&eBD|E$v+>WTDL;p&ZBo8L*8f5 zTxHy$$WVe=(}?*5L~MSJF@00err&e)+tVr2%9)>?4fy?S^Suxd>VAd!P15zMmK)uk zvDj6=tpxVc2l!Otringv7N*pLU`*+pcsK*Or3UE)SNg5gEJlOiryKVbA?NZaep}1h zBSs#AM5JCVkwnXUauh6%Wc$3UQ-|<6lluBSvZ;_)glkykqg#wt5cQ~z``P1g-fG6wurERU2SaX!r_!g>-n6??z|pGEw)x@ylu=kc2)npmtF(wMGg zRS$X_Gz#L!>_i%HImu>3vAHrnZ`Zl+MgG87rll>dhXw`@BhFL(sBeegI4_3!(5B$# zncP#!p7t*d4Zga%(emf>OdKDQyrtfu&szI0DmyeeYvnG8<2zZ71dIQayxVy<7-&nj zD1f#P?McrgP(l);V&B74&u488TIp-YtgXjjxE7r4^jYX{>=IWyJ>!Z)mc#X~F&!~o zNXd}2ygJirC&4g)*{9yk7-ShvL8P-aVGaSgQ1;5PUc zQ2TeeOAfcXG)HkfdKSg~9kPOD=bpZZFqBWv#W&SKuSXTPe6UjD`B$64ve6hdHzr_? ze>m2rqe0`Z{M8qYD7L2pPj3ONp*-!G8?9H9{OQ6IdiE~*Si|>*a;wgHIvj0~m`-1E z*-8Wic_B%ts|C-Gr^XS`2>N-_qje`kdLSD!3yWV51sSGys;7M%UMoTL?lN3&D)~H0 zmdlqo&{c@Q06{3^tcoa(Jdj`45B5SR!6=l*Gvt>=x%t){?W`88fhU7cEOYfAJ1zO< zJ$0P&maC8PY!wm0Jkb~zm2#9hg2f1orL_NFr_FGZq|l7doQ!*5)0K?H+^OB_e%|Gu zeQ?YzNd*a?qp@6_@^0Y5{{`JO(~+`ktMW1)?}o##@TRpOzY^+ z1HPbaDrZAo@<>zBbfG@GraGB>gPU*2LwhRcjp(IPs#cRF*a{xHsl>n8ZSies!PoiQ zosAr)rCR|=LEFN4+3W$|w2n`HZ^AK$EkVOL4gV+20~ekc>!OiNJX9NN5GN!SmE?}S zI9>cs3iBa(epXo*rY^`M>yI`|6vF~m4mYc{oF?;=u5S^@^3>PFcHk+LuNHTlz|qdI zv?qYM?tQC*njo*PX7NGvgj^@M_#X9{2BUtlM;zO(B3{@>vThLvZA*MMWo+BKMW87A za~r1}t=1(0uHKtiD5*LBCC;DF&J&fwbk8ipAJ!toFOz(mjfMy5<=vYX$4+gXy8719 z_W_UbiSq^4s?61@u2VwS(N)%MJK(PEIQjv^$B&%%n5R@ zC+;`Pw)LJWmW*3_L(Ov)%j(okEmg!N=o*`pM#<5_%B_X*O9)uoY6y?c*I1|i0eyju zrZQ!8KYCKpfNR0xPdo3u*vVoOAhOa1ERlFnShXQF@t#llEm6(G8N6^~uz8Z&Wpdi5 z{SK)^liG@c*hO^=fBxy_3IorFh##d1+#4 zHQ^dI4}%E35b3>5&+VD0<&amE;|j~eoMUE0_AP1Vs@c_S5h&*62_*$6y*6{)TD{jf z{Vs2Nu<9wGp3Cb}$aYl&#PR)!y!6t3fKOKUgt{eVVq)a9Mrnj-*qJ2hI_?NF!qDmL zkI9t3S*X|`1Q6;MG zW6_u{TA|)^s6_feO|Rd|Pj(eJvo>@KWRc8fl2ZeNJavo0BGLY2$OL*W6`3k|~ zXe+uSkvAl6a6f8iHZ@??5#;Z=Y!MilIVfRTkQuD1vSlFTg>CAYWC}guXWJkM(}oEX`XPP$R~9UrE`(wg$)@t_ zo_m~T2vW3oS(>fb2lHkvtB05`V^UVvr5U!J{}S(+F>4QO?FIlZPsIYkkdAx9qs4xO z>=96fqXwLmy4|7u^0!^;qUIRqano9;wlCW~z)Q$CNSv*D*FF)_5e-QkyvHqD#i(Q= zBN{E7h~V=3Yoo1KKXodq+HgG6851ObZPs#7$CCI|QQ^u(?(=f~-M=ZZ<)z&>2HvErZUBKfj>k=T(cuFgG#!Sofg+hx2M^0KAAi zZll^NLa3Y;;hQ?A6mZT7EwG6oVN%V6 z#OU3B6UZ)UaAupins|iY4vtvybNFqRUZv3aWL6DF8Ug|Y^3HM|U|H=V|Dx~1UXav1 z4_LJNk*H_D!0JwV++|rKiqs<@AlyXc+qc5H9tzQwd&i98%P_0akMgJ7oaeiSOVQWg zkLN$4MB{|pU~V-0Zynb1-&}aMa!LnUua(I5#dnz1$O(!RDkbv1tUEo*T-=Y4pFQQE zX8hMbeBk%KHhp)LNtAyq{U#qj$T3!jRV2S}uOp8B=g}Q`-16YB4A}2fhc3=rQv<9v z#|Lt>!*Oy>R=hzB-P*r=#R|G9Efn9wRL4JNNLX!8z}5I#&-bPfBC&B#`4t-4ip<>p z*H+3Z%jI~8a%HTtDGF zCuh@&eQ4I}Zbr3wR&s}gblj3}=HfvKE0N1oHT#2e|+FsgHnP_GnmGry3pi(a(oo&>@dUV!dMYSu;e0p2l*Uo-H)4F zvX6ylgV!lcEdt&-G&`U)&?D&|RlrCE?&jQ0yH<#}AD2Jp7YHkV88KyYrenQ`5Pmcd z4{RwPWW&G>_U>5M+XIJjsSCx1NXME^iH$4ysJIb$=KP$=tFvy?!=Wjs%MHtFN{(ZD zlyWkpyR?Zh;ok0Qbw72+5Z7~lhOuoQ+?-Rw32`Rz!sgSKxDbZ#+I)VM5AEOH_ffFK z#%r=|u0~Yks~o?HSx~B09$ag90dvX7Tm`D8V0o|%m)o?he}x8xEDCa(b9rOLq*QF7 z^7u@ma6-zE1TSeRZa9;0_BwAh2E^>^OkZG%=re?p#6Cfl_DKaPk8m1uAs^N8xJ!7~^_VUkKscKff;R2^Bni>6vM3 zMvdeeK59x>-*-NYaj@7>I2nLbAoWL17-$|zya$38ZsV(UY?De(t4f1p~0;`+-I@qnKOF*X% z(O@aPQ%l_&w4%Ac!o>8psm{X4?eQfhy)W5{= z-2}Ew;h-M|K8I-caskP@u(xCz{|s`|BRMOSd}3E`He_?Eg{LdOOV3|XMDE9ueUp2| z<-K~8>}mDe0nL^oS7m7n!tll3e_*sTmh!(ZW5?*|Z%Ug3*%X{Thm@ho+1 z`PI4BG&MvC1qpHMKw(%Cc)+7IDU?5xfkpQY-sSQV=pIa4{p}5It_N$2sxtffKWH@~*j8tw=aw=JuNH>yyE1W{j zY$?QQE%pW?NsGw5)`dRY(Tl|_0UuK#F`m(_nn+tIk9Gdx#A^%($#Vr(Tjebhvei8e zJMF`t!C~jyLyqU#tiRGXJU7Irl`L=a$KxKh)qZ4WP%;AB&sXc#<7!M!fvnSEY;eb` zXX2^j`<|%8xh2P*Zw_2(&?SE(~DbcZ+Pfl%AFZs*damrG1q zaB*>A?uiuIj}NI+yOgR*Ev)yJ6@_++(;(Zi9#gYKmpy$*)fGGZt7hp`rqnLEt(hmL zn%S{S59n}aUj0hP_l1F7ZweFA>od`*6NpB#AI_yc)&J;Ia{c(iUh?sWk+pp*#Rs?t z!)M3x*JFK8fnPjx^FG~OYXMGI372uX?UIaV>Sk_) zmN?4%NW+5z7vD3(wQMw}9;+bHDZ1<0t(Wf!v7JoHD1|BG_GX;(PB~RK@nigB`fZGx z?uoZkUC?~<=0$D`S}Wa@di}qmwsEz2hoSC~riQgPy;^oTLAjCfeQ?1~6O7iPHD~Av z6E7URsio6f(|G94J4-I-Qt{#-Y(b*fL&&0?$Bb(9=w~H4a zhelE?M;l%YJoc;vGg^-3Xz#VWDI}|TCI|<^g@q{bv)iA|`74hi;KCkWKb~nGUZ(>W zaUOzxa79VZWo&uY6n6qTEb545J!88QgP&usfA=a6_$UZ-%(i9be;+4jSypak#tcF6 z|MD7HxaH;(&7?A^;!L_~p_NalOt}30{pSH+k6h{eaWR9;M(St#t|Vw=4N6Py27
t}8Xnw^^$mKh~f zufPHT@HH#1m-r@Zc0`sVi$7@Tcg|)l?$ir?No2GXJLg}AlzzlH1My$P;;$Lm!d>OU>VEhSs9}U`sUZPbT zR~m&5d0Zh%vY}>=n*LPEmUK7E*FF60KY3Ih|0Cw=vo!A>C#Zm?ZP$NC;4scrC4#EH zvg)X<5~G?eT1Z>cJ%v4MOKi4SL9(g8TmNngTOB*rEiy_zKTT~48YMVJXX+(x}5c2#BkHbunBy1DpH zk27gTQS=)>pKOS)8lKie73dqkuT7(6#*qhKx9%`|jyx4UCm1W^VGD@qh2AFh{bQk;-J(6 z(_g`|xDT-vHqXCtv#+0$p4s0Rp5O zz*xp_nRG3}JcjOdx8l3(B?xV*L1ZXTJ?rh2MYZ#fM#Q#Iz&0I3`bKWtD6XL5%y<8i^Bi|}K6RLSrUE5ExE3QZCz2#JtT&HMg+;pMX*dukl zZxTAQTV=o>Pb^t8Cxhg3c7B_@tZ%5Qw!z(SV&>9Ege7>Q;F zMDPVSz2|h6W%zw=3x?aDp+6z_p|^}Mclfy~+PUd)Y%||G<==BwqrB*}+al#;ic$Gd z$sIn~luuSNb8Lmi^eF_r@$9Ev7rFyO&aBj(>f)0)s<`@W2R@?QCsk;ipMiEVV4)F* zp@!W78>Axn`J053{44*M!pikjCwn->(6Kgq8-=>;O9U0vu8aO?LVS37^~D}+?PT?c zzZC>vf)of$L?r%^ZB{pwDktNCHtam~|IQMv!tOpFZx{Zc_Y6E0kJKyfw3geNNxKPS zSlHQAwDW;c(+7R2T4Zdh9Ve3Q-D++gxR_OV`)slhhLgDjsPjSJLQc>ZR`yvSwYT zt9_v|u_(<;`D&Le)>2DAguK4=S@{IVxwdFF;ATXpWM#j+9E09ow`5z=5Ad6>iD40TJ}QxDzl<3FuLl$D!Au203jH09f%5`7GE}IEP4vCh zbUK+TnTp=@)xyZ1+itWK<)=hS?>+y??jF`DQz7r{2t2SzWAOYdYOJ4rwu@^-vqc(h)ZyXGG;p|ozj(A!=k`ju5)on zvwvDh<;5!U>~XoA`N^v|zS2XFvkW+c`JHiH*F1|MO~g2@=xC;$Q+9F*GNhxDx$~gt z6_qSmEuz7bunfZL&yUVxeARxX(F7W^l4so-nujjX2mcNBJ2&6Y)!D!*D6-t}o%%D3 znEtJD4_5mPdn`5{VpN1PMFJ`K)q?rDRcG<5L>Vq13|fU0nd*d~v`@{?{^UtBoiSSQ zk>6e;W&g2Gch)u)v8NA6b~H|#*^2D`AP)BHD z?>CB{p+WFtRHR-qx;+?dh*>cuT_Vr&G`l)8$`#~IVQH$IXSiw@wn!SN22lo{TjkDj zDN8V4Jmw*O>+Tgj4)0zi@KCCAX?>QtF`mq+bE#6>q;ax<^(rDb=s~!ykyVfT)0{lY zZZ1_g$y)LANWKlkI>WL;A8qP09ONI?mkk}Q;7Ocw4M$9u598%X8yvYnhhQhgw*R(lx z{?5~2SN;=128wfAI042VMqLBS6Zvr+U)AT;L_bN^eD{Amr-Y#qiKEhsD^ zOJQ0;xG2q`;T%OZkfqc{@QVN;8Ny0LHSTQept zd-r%Riqh|P_w7}5F?m4*!BT2S@52;oaj*}aDi++L9z2Qkor4Y}ySVAG9SaVl!9x#!WAM&oQkXEgUFlZGoZ`~3`Pge_<58VZz3kMb@b8v1Jn09qI$GTZ zxYg8G!cvN>tW&I|@(B7zIYnzlUYwd@YY)vYZ%Klp_KF{~dZ8(hV8^RGeCwol=mYADp?lVpRz+N6EqE}cl(A1ZT#9=Awaem$DEO$^v$X%C)9ooUNRJC|? zJ!cEpKnIQ%l`;41!ISeTkET)^D?nen}-4}xbLc6(560wq!UwF4iHF{0H6p8l- zNA2i+@ufXLK~oDg6-g&cb>b?(Olt8w=`afH#PlHTB{E0hr#b!XLB~FO;@Bpb)IOnG>#6YzNj5N%XPAp3 z6R97$&eEu`W8mS~9oPE2h<>C2Ph*P>?=zYKJkQ-Em=OQ0ccZd67))h+HE%jF8mV2E?XS5>g= z1wYO9>*=kuzP|qGMFhV)jR&UmzxmIdaGZzT3SZy^Y!_e*7Y*giDJ5XMb63gs6wSsX z*uOTf7AM`eM4)xUa#M6gn^nO^k!z2qu{5!FD2Q1E4Z&-MHY+C^O{W(@V6s-Q*OoV3 z8Q;qk@3d|3Fdh#O?g@*)Kj2#qITh0rsy6mp@a}H=-vvEt%L|d7*q^EenPGTk{YuG# z7Om>HdR=&mz1JAhlv+LXxCaZyFdAw$-!;bE!r+?DdSJ#{Dpi6k??pF&dH3MVyj8cO(sQgY@GsF~HiXYtQ;vIj*;k4VRj zdusm8kRZ4Qw-!Y?p`cq>FU0EjP5siCBESBj5U8Dk*_uOi1h1BP6}CzBDHV#4uTwYZ zfKW=0*r!flCIILMrvEE6{qC6QYRHBB_Y&`UC2m{@#~EjVXfWM~Pn(RDt8eZ^w1^`# z{i|Q!xOuM8)>J@mX)XkvgGAANBhhnG)(D;YQ~mjD&MbUW10V~9(yEkbdz_1e$Ss4D z%8&y}_pv=g2%-H7+%Q1>Y(8?;Zhyz!=kFf~TVx-4 zv&TTsp~x)OJ+*UIfJlXrevDpg3EAZ2hcB zK>Uw;>9*c+GeIT_|4&JcP_s#}yKR3@hN_&2w+YEM4OL%0X(EIh$Ftoz zn_Gu2$F66kr3uNKDPfaayYlEON%zgjHp91tZV$vh`(DhDO2qXo>(#jpve53JIb;? z;{Uo4Hq;}e0K*OXl?%)_#OV2p;rlg(BlAXge^0U#MOF*UM8@;g(StY{MZ##dH^7$R zPecc6JXUv5=~@)~*PsW3qqKl+_GYN_L=ygGpBT1-%(4AG_JHc(w?{8r+goNE+MoVT z18(p=I`eaGRrx4Cqu1g{r!-gWwi=mDmYi^)=kHLYpYi3Db1(mXp0rxT2#J&n#Gl|r z5vhd^O8L1&$3PU-y~bp6{_U_F8Q~^M@rmlN!TN7Q);VmW0us5GX+p76vock`#^{kA z&rkFn?Qrs~AoR5ee5%3`*k2FpY|;Cb{;D_(S3Fvp0e~Y#|$*X)PLxpiQ z@U-Bd&kZ=36(hDikbOu)V(ihaqXmFXchANCos@OFV2Pnr(AI?@n$SXyC_$~-XzomrA8D&R8K7O9kM!TW>_NmTeo-T8* zvC4VHmAfu#hv0CfA)ei9G0fS9

l&l(^=kLF$%M-x9!q^3|;%&By#Tr; zdUoTrq|(CRP?xb%Qj}@9Mc5w6q-Y~@+o9?g!|k+Q{G9q;GYHxS(sIiHdHfp9v#EWX zU{z@EXA~-iT@9fL&novNI!}9Z6DA~LZFRx00NpB_L^rdXu|Xb{PvFEE1UJ4e%4}wT z%#qZ6EtICc_7U>=Xn^)NH%3Y(T`C6}n|wqDi|n5=+A)VV;zN z+^2YdPA!T?sZDn|7l3$;o|>U>lOof6ETh$A%nLiEf+^e?ytG@htM10?N$8)Icm+d$ zWoZo)z3p3(FBy3Lqlq-!Al@t{a|9i*WG7`Le~1O}iuKruZQW&cCpy2M z^x8@}Df{-I8KxmM%&ErR#&nR}60DRWGY5c~roO^!Oubd;S>_2S(dr(p20*izi55&J zs^=TDJgF=GQ!N-~@OyhXU3dd-pWKESusmZVe?3XQ~u$e{#|l^uU4 zN$@}_>uaI~@L%kbiFsZ~=7D2imCKe6Zc$L2CQPAmI3t^< zVp<|{+%PvlZ}Y$M&9)z5;+&X@bXdJ9$<8A!oF-gtggxB^qIs@3sM2xW@uK6x9>F4f z!4FA&YPGj+8}NppBj8AS@`R8Hc8O3=dqpCp{dwDAv7f#NT2BGAX5wOZ0y`DPxUM!# zS@ziceyigleQ497jc-STFyRZA4*dBp)tc@^t;}gUki7|Gv-&EoK$Fo@x@G?Osk7K! z=G$r`t|mttO(*|fco!(cZ1gdYkFnR7TYT822rI`A*H@N8+PpDj_9|Dwi5-$0YdpA< zb}KxwBDxczPSoPj+y|#v)@``KSTbXpxN9k)wX?IJ*ZSe#)_UCX%shRr%Z+43D>z9% zw3IOVocyBkp(OA2%Y;zjr&$Na7m!l-#&C(N!JW+gzIrwUOzg?FLDXBku~Fp``pn{Z zblljseSYcuV%zlJp+_B?tgCfS*bWPm-*wAxh@i&S?%9oP{liKXc{)mPlDima4>s(U zwDahmKKNPvm9)B~O4d^L+uL=hSLoR$ROBjp=syp~eHR$N7ysq#;@Kaobk!3VyTmq~ z{2lRHIc>%)`m{T3rV3`q2P$bZmIY0WV~f62m`PDwue>HrJ1uO9kAI?Nm--k(Q}tZv z(C9~kr1|wAslao8Im8l;*Ea~ki6NGQ%f-AMCxl+Pgj34ga3ehHeRlhSeRh_C3J+J^ z{;&lM{;8(`z$`I+FC=}M6!foRImI#bca!(OXN5;D?O#dUrS#5em82pO z6|>^0W;uB+ckUwpv0x(Bi<+K(?4oWxyGz4>l01l&#fL*JWjPvfZ>B@F1YRd}TkMoH z9ZE`*a*aA`6EdvjE+fPa`8V4PBV?FKIR3L3*|&UZDz2&U`a#TOCliXgq^>vF%3)aN zT2s^h=^$Ba6)s86q(9^@>x)U*(#80N0r(JI>h>J__qfDp>kl zTmQ?$pK-EeJt2uN3`h#iA*iD?UK7}z!BX96NZZLTh!IFW?(qH*mdxl*H_xrPBa=Mw z^L{FBGU5$GQCGH0l(xOejEeD)8CyeZFA4+`>nezZfan~b!IJCb=HUu@MS5^YcLau! z4sD?GO?D6JEl-k9-YK}9@noE|wt8mN@PYJo)lE%eYOI*DZy|F)T*dDl!sAr&DD*kF zY9MNL^G&5)b#qVb;ku`PsIa2-?SlII*w03waj-cbuU%rS$el%r`n84KcxWmpMs!+2 zc#2I~&NcDR*{4bPpyg4xW7y9frm_EM`lR314iuNg3W&fYjr7D^8rv`(`b4dRQ2q~z zWKMKG)wYE?gY&F?t3_Le^5#B}{3lEYOrY|ivjIk5eUE3;S02B@T)hj+Y-Z#_vWt7y zOjV!xrR7*!@gScvnYFU#Hml}RhrdPTfBu-0YPXw)k zte!PMo_Zkr1fq?ZVoKm&lHtoAFUI+kMSdjIPJ`{C#bv8WO)*ZQU8aa}xA&qn!k;7- zj=kQiuyIrR8~qeqpOzmV7aEU#3GUxqr%Xq45D0`sjIZ_?>) zj(TPksD(CyRE}aD&zC9gpqordJKBf=-_?DaF}HEA&x88TDOL$oU)~IA=eA#qAgzu< z1wcrVcItP{i_(&mo1K!6R=tTSUuv$M1|l~OD`aG8Kk5ZI@db|$vXt|KSGzcSpseT| zGI>8xq;7ZZgt;>}y&n9~-c3UvTOtM*RH58r`0~WzTVz`5Vc!DSnPFZQp74IaU#rUC z%lHBzD*bF!;I$q&iTLa__Ko}1Dwn}}9`{&M1ykZUowF@?LXa)vwK$H>Pta_Y>8DrP zD^WY+>m2+f)%=`^>QzJnB^J2S@D#Pya7A2PC*fyo`9TX4;eW?fkUM%q1Zk1!nkoET z59sa@36wpG33WWdNz_G&cO^v^m^u~Vxqh)3Lo2#{C1vx;(QwV^m1)LR-oy(S)vWSO zmh@&+jx+p(@2c^w`L8~9&Fl)~!(Ee|#>L;~oWm)@oGh+4v&2r#8q&FJ_Vja*JO*q~ z(1@V;Ay!#|BB9UB!70h`*yY%>yXi}fHhys|_)*oz*mf(5`lQER3Iy%6dc`!$Cw}$5 zTjfl3+>hcLMUPIwUZ)lPFm2QZ#17Z#!=a)^zj?!Jhai^)$ifea>372dndggn=fgoM zaOWJv5#F9H>*ZXl5xD5j=f`$dI{HzfnY2%)#il3NOe||&w^`Mk+}zxVhfZ!EKarpQ z^S$Dp20=<_%qBFz+_?uLm6?uw*lM)_5aD;Bf4d)U-IaAb zZ=#rd^gHs)*l?IALHzaTrX4d08exyAjat`LHMkVaeDX#x)5!wpjmZ>D8no4RusoJ| z?YUrhh9Jvb`MpiL^?&?&>({Y|tG#%evBR*X8*LR|;$%wK-svU0&;9T%S~R2Q1MLs) zd*^ci>Bg=D*bbOcvIN!IF5@i44C(LOgodr#cL_Q*_8f;^`*#LE4Q@@WX z+!FOCoRBD-%S2hj0ioz$MKbx_76jiqkt=p8B~X7lE|B7MLZ?4lXlr6zeSY2ZAL2dg z0r1_+*-j<(FaMc{;eZEW;&~#x{So&I3i!SV;2`@wx9Zyz}6| zqiyZrOpY!(&tOVJ0zYh>ff>Q=T&W6y519?l@{F>tOn4mHbX;`GaBg!<@8MpRD*cU+_Dl$=;z+XY9H zQUo5!vlzs&%!r~OQ;~y3xOA22eGH(7ujzkFl-M3!)a})7xSX3~%!#}hOp7YYJBS0| zbZ@p+SiV+j=G*SC?{iQVE%$1beb-du^y0su|F$an;J?PhYhW{x#`Mke^RSHiqCB3! zisdNza5iGTYq?ZqN8tbp`5UVq9 z$}G4m0)$S@pH(c* z=3Rzo4HbSv!8{`3!L2MJ#VRNCBHy+1%67HR-CKfq-GAX(m6$NV_~WEDWNY29e@JB? zeywhbnV5et(~B#ToaCvhu|XvuW5EWbdOlQ67!`S1pkMB^<|v%FBW}yCuAV1R65zZ+ zclBdoVWEKu^A~t@s7cx~#9Uf(KX@tAI$?}wdgCk?2YE?>5vu_}CU?6sOw6uQ3oR`@ zA$1uY0#D9N4-qKI-Fbayx4L0DQvT!SISJ&bk|v7KNm9maEjT1({Bw*%1QSQkBV6D1 z6`e4SK$7KIL4(BYBFxyDlyk;f_$~os$PJLGR$@dy@gJYqs1`bT^DKInOcKplapxH! znQUn=bAmmMGpJVvG+&e2b%fjS_v+yis>&x-#aPq`P(?u7c@l!{WGvIz>PfWZojrZ5+=sq zxDf$usS_U*9uK(*WO%xFTUvA#3AR}Z3I$xCVlC!4tZeQ@gl$AQmK}P_P#y3(_1Zt% zT;98n`w2K9UffShYK$f4ekDsFqJ7gG70GT0r0TkC^Vyu!VGZgH&kMgt`&$1phw9TN z2iBF0U5|wu!}S0`PKdOOWU=&RuT9^#Bl0ck36Qtxv8(bfTktioyb-yL=V}U=5J2}v zpt#Bj6L{!v%h3M555pVMa>*)PjGe&4rFYmwo1xj*0980en!YDh(#vvuLkhvLvo*pk zKN^`yFVl(1O?}OapU?Zq;MS-zA^*KYMl#Q+LabopMxb}5#-a4XPNr>Lwz$-i={@dh*cyS=X$=A}jbsUc6&wZ8ft zcA=ZNFzuGcD{uHZs^uNxGftdo-Tu?_S7!sg-|AoI;;d-7m#8j%Kc>dQ;@iaiUE!j% z+NNgaubidwQG?(rIaj&~dL~5I*XfF_Jb$q~0oJ;RcVE4$KA<+dP-kJ30%$kfy!I?=~(^`oY#up)Yg-&?0Ng1Xd}Qa^k-7Nl$4@IZ$g zB21~E6_%+aUQ6>=UGTzFqp_VMJ0gXYBNd2AM^eT+f_!vKWvQCJ!>qqlOJZge@DdR! z^W3FtGPTZ>q56e~o^ZFQ)Fe%&KpzC{1oU#B&Uq)v!7i@RtZx;`zFn3$UMaI8CvlxP z!I&xp?+=s0CJpeuc5xSN;4`Mxvo~Ve&ooft0K4&nC~3UFQLk*F-mHVQKVFO+`k3bY zdS0v<4-#SH(Ic%)SXLE17YrX9!}gGVgdWYA^08A1%&Tw3f` z4lEY;z@d7IogQKblQ1elYDpnRs9aTt$d@b%hawH7RpPFhb2_hQS(O`IDE1q#EFvVb zwUfDy!V)^rGChG(>qOnOboSg1zbN?W+;#oDsW8pbWHF3drVm z<$4z&5UOTjBx^wF`^N&xXeaq7)RHBhtaq7Nz0pNBHj%U`$ARB)Di3&?cv&O(SopuiXjcX_S|NK~@qTA>4Zk1%kLx(26a!?Cjg1a% z$bTLW7v9a+o)%4qhcnk@;N9C6*>DdGwwylKB76O1d+-3UWcg%tI3rh~GVw7kXb7+H zwja3cX3%yYcYL-**}7+vWNaB$d-6Jcyg0;;VbR-M2ld!& ze_)SNtWi9PHW9rLK9}^jz|SE_U)Sg|5b`H|eCD-9*+*_Ss7(brC<>`#SFfc|;?$3I z*NtiV{ugvc?p_}yo8uxo7l78CANWQhf%4VbI*o)mQLEv9m>uhEwkMbYl1XCM`$m1= zMz3sKd-r}It3k0gM1;ZgXID331$4(*?{L!l)|G%gLM!pl(C(*3BgBR1^a!y0>mdBv z^;WeN`^^_mi665ra*pGiPVt8QE60;2yoH>O_3?uSPCj5^h@n_-y0TFDQW=NgEx^!s zq!B24yV;T_MLE@DjKwp%fUT=EzTufPATABuUfc(5+|Ua_?t-w+dNIRmnj{8MjlVsJ zSOWj}>nf0S%FyZ15h7KNMQ$Co7wQ>6Zq^2^$_&r7n1N}BlFDa@YmaYaUYX;{<75e0 z`%yz05mxumfwBoxNlDZ9>V7jx=eV&p_r2WfFlWfh>Ft(wk(eUs7|TBES!>njJ*uA@ zYT?X8GSl-g;LyXxM|DW`=2F@p*3+~~BqYfRPf?3YM-F<8MfiNDftrv7&WN2~$-De1 z+sS4&evDKZlZ=3G$O+d{xsIX7?-8^uiYY(F-GZG=N!LgAm2~ypDP4aX+i3st&!!A@4xiM0!abcaV;R1d;wi=0&_C#KOE`-IWNlMV z9UeAuak=X!VA8@@jZ8StpRJY1J_g~>=&(s@;Uzl*hL*hZ85NwZY0d*TvLfgJ`9)At zqe~hX8zAH%WImsIh2bVv+i%po%&9mGMH3z7NxX1$Ta-_6On2NKu{UjR=8rMKrZtP_ z&d-zA-5P!%XFeK!7QlGmmL1c%`2n-Y8-iDbvSMnd8WI{Pqb1u0oH%oZo86<{QNXrF zw6%f@DQ=_j(n9isPRdxOg@_WV=p5L_BCABtMBQ=-mzxjAix6;Xz#pBp--O;-ir^6K z)opn^ouVvQ;J!XN+aLFbV_xlk*Dh&&eu)cL6LS6#k`86@Pu#Mi_p>Q{{{7*9KzbE*lB3w%U@hg&3lx*uN#gRy?^V+|tvDM4|GW|ZE~%6LMj^Y=_&#)W6`m9oWNYcWrnhxTHF zL_a6tz#>#D2h!EY+n~-@?v6qyK&EOAMQ@2mwyAA1_{yBHhf@t8rC9w7MYS`R^U3H` zCo%b!rTtCCGXovwH#G}C>L=sU1MyGC;TM@?e0w>8837PM&kIKavSZ=U)!VW)s0F-R`yM{`N4gk}BDfuJDji%))kHOr# z&Cg%c5X6>MWejB=MM=lxtHwO2GpH&Ijp{iT2}nQ##x>hwuG=I0F2 zf#s$Ash|^f&AnwDjjH1S;a&pIghkPV&`*{gWP>Li(fqTeEA_~4}S;23^*3Do1V+=i!A$ELIxC{c1OQ6cuEe$26uzfmS(%=<_% zT(!oCZ}mmW zC`4ByO5`kXlhfyv1vUZ%Hp;ln#cfn9m`_Y>Mi%i6j;5Yz{Bgh<$f}=YQ!PGdEPB`q zZaFjeipb+i7Jo|%#w|WAAQIJi;W}RN8&i>bJuL3o2xwx1tD8SY9H@N}Sej~`fvN)f zbsbgaWRk|mRZMa7&JF01mU$cG^Wh8p&PC1tc~fJLV@lK>FeRXQ{ zq+1#Db+szpkCr|Aw+>UU6wB-MU9 zWY2Qhv00F&FG>9s?*|zP!3N_?uT6DSwzW{ys5GaHO= zvGOE$AMsK?hn*yq`ao*`(oMD`W#zLc5By>;mc)UQa|eB-w#-w~SKq!j87oEdAL$;R z1Zx|ppDa0M)P^plAeK1_{*d?E)bH>FjuK16Wx`=t1qzh&(f20y*Xa`y5@hKVG9ihd zgQQuK{H&7OZOH1xAYo$a!d5CwxMQzBP{!DqxX!nm+Bt1uj7kEv4FmT$5uu)p$M%!O zapV=}Ih7MbT=+hh{#oOq#S}HKe)>3ng{y~PxgFYg0o;uNE2c}(!Vu3w&iSa1XL)M>j^<0`<^rU+)t~G5huEHVCoZNAGbS$G=+TJPxt1H7vt+1x*9B( zbZA7GpONcaqBsIr(s5nBr(v}xG@r7`NE>!(ig|))MbqQ<{pBR-28x%&jF68OjNhD_zt=1VD7^)~{bLo-*lL^ul<1ibw&!e-oW&~T;y6iiR&U2&-v3b*F1l)YSUEZ`(~{6uV_^}esa`#FE40|c|hs$=fLxMXx_VTz5fiQNYb3w zHoF?pB+!p8l8v{cmL><6f7Obttz zp$_4e$$81|0;CJFcO*u?_!KtTYfEpXSewD8G{Z{;s48(UK((+YNJeKV5kyL#_Ujb* z$9we}Vk5k-%_16-;xy$>3f0l{^%YUw{}CYtSbV;?w=lrO+>MyxO`Df@TV0QGdn4i# z$!^_4{(DWpegM^z$17g!Y5Q>KGS-psK-)x;8Ihmc#liH}DuB%)$|b4(G4h7}b!egT zC>_V=wyz-T$<38THWn{d$5%*nlTe>%9^{X=_LW_s-tYM~_W4<@O6hY71I=}<=U~lI z324x8)eL1-#;@@NX#2_zVB*`qB3{XM7wn4zySg(Sp>p zdpYyaQP+$PvlpaY)BMnIqiM3d&9^5ILeGeM^$O^tN)vnnNg%a1wMqHu+I9GJSyOU{4D+;@h}5+tBQ@qlgKfNRlA=pwVcU zIVzb^5&_Y`m;^k&V$Gxp05xy{;U4fM|8-M&1xDo`cNUELte^V&bry64R{2))9@Thc zJP#Dh;Seu}U6tsX*BS=-HFL0R3MRdGhNU#u$NDwiHv-YL4z^SGaK8f!NUz7S4-k&0 zh7PzzPmn4$_=Yw8v~_1n@}32`G``l{j^*w^mC``QP*#h=uakz)QqP1jRbO(1Rg<3z zgo%rf$v*O;eLDSj^L&_*zceFxWF5b&I@25XiRc0;6;(k^Hi>!r8x`qx>aa2tYbwY} z@&_F7K&uOhFG}DjHZ4m)pPH;vxzwa!P@4_MV(25Y^I2v674u+*AKRv!k70$Ig^){J z$3H-~PfPFP_^SIqnKC@>z~cq?D6XZFY6ed~sL2%8T{QF5kfYfa~%GgTC$Sxenn z-q^RX@CDlj#+%Om6(*d^SzOh0@ECIXOl)CE{iR z>e{)v&xB4e6Uy(3!c%);^7C!=NA@r82JbiyoJt$-=wA>Xum|iu{FH_xvOx2wEy3&v zN5za66#sxgWskd-Ir;G$K^mYj?hVwcZ#xDsuF(DHpOTwy_Wi9d2w35Op9ab?djqD5 zRxMPNa$mc|BESw)`fRTG-3@O4s17^lSpA(h&I6Utjb`OknG#gJk-9q#?3@PnRRA`S zcc1AET<1D>dv-RpjyeRCQbCQC z72)o?fd>Qv-9*2kS%0>9h505P$2=VV%P>OLf~Cof$COd=8jS{#gO1!B?b*25`W93F-b3!&^5Y>=C(sU5Im0I3~c z=mM@oq-{TW;6MxMEz=wik*Sxn2Z{to!R4E!3KDeoFAMbp1d;8C&C+8LiKmJ4f7!#8YUXvfn2X#eJp1t>sZ6hZ-3_-r+YysaGK|DPMMUab zXl2hoe9Zq<-+}q!Fy7Zp$QCOcF{ldo`8MeR?7KELuh!2*?3Bg;r^)JIo96%;1ep)C4uI1>L<>%r~WlE*)W12_tU z`5I0;j5%_TIv9mRz)}=9qxkcL&nrt!Vq|rkZOn`1Cq+|gg+c*);Q9Hv1y*a6bA0`< zR3Aw@!ILr=LG9SzHN4N8_z{5c3X`I8D#UstK}Le_YWIOlY7i4U23(?5=ya2&KflyA zPj_ZD`K?oDWHW_~t*E2Mk*4!Ed>Tz8kzSF~zZuf@K+zVwqzcGHts8W6Qpp1X;fG zHL54B*2E6FO)_q~H{AD-!U<}~*if~Y&O~;vjg(di9HOy8e29U(Kd00Hcxob=@*b}# zHI_cntCyj(Zla_XpKj7$pv6cV0j@5Rdu)95(nZ?h=-R$9G$aIL9d`52boOta@g)ER z&e#~UL8DUzIA2;&6{QgwC2xc4BrsoP0~#KR+SMEu&(<}O&IEX=oV|9nklWhLh3i`_Rip<#Z*tFcO$MCm z4U2AeZwt!efPTD~Pv< zBrZ+%*r`m@bF-}oTurA@|IJ5~TPY7J+^I9St{niyw(BjE-EgdOz$=|ROyy54#DZQH zGFJj%M(Jzy`+$?p_-y&y{4bEFw)1Zg)6K@yyTZG_$#;do80`4lzZ)dLIS@5Tk|W!O zKa{4Q_^Jr>HvltUxhq^;dv%J?J+3vwQ&(u1OkUSoDqkP?$0A@2!SEKnZBM~YpE&Q5 zG+{jxi4EzOa6_g!z*>k+W%CAT8*q&At{)B^wfm)CR=2WTvCoVE0%}PWA8XmmU+dQ1 zHX`5I*(R{A~u}xUXz5)WxlyU)+;I(KTz!Sxsq7#V|9{iE;VwlK@PLAMu zE63J1%F+ldQ}D7L!a}+U(dfvqZJ;14ivFbFQaj9u%STI;3!|ti?fhk>`i$XVDf@P4 z7uMJjeZOWs!HDr!LynRVj|#EN+}yuZ_REFtTeW`^aWnvLGf)kanZ9!gsI2v0BF!#` zy1%`o=pl3yN8jr3v=T)FY(_k8;ZMD3ySlop^4{?M;NNTyZaM3Iy9QVv#D5+>pVYF+ zH(_I~&bU7&Lj%w&1`t4tawYRHJ<5G_CY~<10#FS_xq}%(r|gY97pWSm!arTF8_42z zIQC)t_|5r1>n-RLZeTxukAt=f>V0Lx-Yc*%7=~CEzJ%)#M(0f%g?>W6hkiigq& z=bg|AE#+kVn*7{WbaTR+lKiw$^2uZ2gN;K^qN^<%o}yp()$_7Vv%6B3n$ffq|E4UF zk=+1y|391ehpa1}QMQ^RJ)e1g?8(5C1V2jU>E4Z1$v2BK%YJOLFLBHtBx5rIt(J!t z?O(L&;(Pl(kmzN?{Go^C<*S);`TImFzvKThY*O5=rmiQzXr_6HoX`h@jKLlAGBmuo zG`f-Ec}-9q&}M*v%uo6=816TP>E6tf5*6^46eNO{j>9yI(rnOk)Jr8o+_#|I9v0P2 zgCwK~?0%&{xBk(wk}0n8U1A4wev##B-(?(6uUUW#!CO5|khongdUHf55rFH!B33@M zv0ba6VI2oge)|QPQs+_cIe*Fj*%&+@pd-nDKT+~l$a&~l^sgGL3wO24#=B{oCnFg0 z3u;J#z-I}9H`4s)w+ zA+l%4TCC5m*tROM^`_iXq-AFwLm`10AOU2nY*UIXwFu?p`C@Ek+F+87$FAkzLRb2k z0Q0KYTSfhoZ@v#&Jn+yzmaNvxpRzZV2|0_iKWi0gr?Pqh1M_smYA-Js%ZR?**Rj$Xk{|GVYXpE%g8R8YaIXIdZoju6z|wXRjh7xQ8cv-vwA zS}%OId+3GGk47w4@0e%vFeskNucv<%eM5e=r4-fRKR@_#`m$6V+ z!tP{(rQdFHHa&S@4+MC)$syI1FM^fR+STcSl-+&4ro-hIr$} z&Ub<`WVaTJus_|dG(ZREsBE-$KaXO39w%MW#yl(P*%Uf7H1Fy(+1}B{rJ-^9K2_9! zTU_~Ruz8YSMtq84-^SPB)g9pp%8XM|QPn2ROvefvbM&47*S(rVH6`@X&|;%6!G1+HT^86KG0ds*p~@_{aV1~eSAO~ zu`L4xZ%#hBX-OI){PFeJ&Jof&-ZAxaa4Amvr^M+SFV<+Qn$OPR)y#$qu9Y?g_m(Gna>ZYr5Ft%Z)ZuLIf>buwd&U`MA^f`O?W_exdH@%i;ytFpo8l z-C(Vco{spe|Lb9Acx;&qnEBM$%tQ$}wJ%~NC#|~Z)ojBUWb^|)DC3i4yGqflIaB`P z2p#sDz|w1m}7`&TuZa zdbUE5Z+a^cIwbs>Vf!I3%BX-+gPjBPe2N&FuN_d`bVj0<7nD&`89VYipz9Lb!(oFj zbkO9L&gCsAE{!#z6^r741q(lI9+EMjyU~oRJ_pgL`j~QPZdT?{gf=#Rl9KbOvXX=#<@<2|hOwW1aUY_9*5%imgtPBm(6}p4j!R*0XNe!M>8x z-ZrNvpY0bJ#beDUQx~*`wc-(Ra;qCf_3N#qodVulK+}N4drNu4AW>nNbq*5MWGrOb zZPNxA_$2avH0_Y6v0kvXv=XVQK^--ANui^iEYkeRLkRhn)kDO>Wwwu=?!gIO{H+yO zK)^(o_*f*5(QKnrVjYuhbwj`RL_k@vx`%=d$v=4awm(2G0)nSIwsY$1pm31y5IZ@| zI%?Y3@OaSW5t{*j0wY^J)uiG};ykICg7DX7H}>OgpVpVbwEK#NIIEb}bn*Idg=ZQM z?ABt-*Oxy$bBU&Fy2PV%H0MG(aeiU(-a9tPV*yl)eKm5U zdO-$p7T!*{Hgo(e&DnHeA>$~4ou0q^Zu)Ui-*lAYnDlr8d>3kade;(rTrLUKJ?uZS zw}*M{oRh12@9l#?PesYU$#jbNGooH1T&X(Ky_~_KYDunIw|~?(yqHAP^*_MQ6q{-m zuo+WW-Cq_*-KUHKA>85w!hgHMqP+E8{i$bc%~+60oxRBp1tAG9yD% zEW%#?S2kVJwihBic$wV7)Y#m=wknxlvTq;4?^|%nsJB%;(A`8foo{N<@;*m&x}FLJ zap`QKmXn#OvhKWj>*f^Btw2tF`5)^9JsXfA7#me!yJv~jdRv!E3H)-AyjKe^f_h*Q{U^9HdS?x4tc~#Kvm`Ls%LN-CoHYI^E z*4Eb3RXtoEq{eGL`mkAR=H6Aiy#lhb=I2_VKl^^r#1-eNS9uD1iKE`$9ew+a5BjrO zJ&ecA10wacZ79%#9}3d0k_=OYTUQ4$1Ws6{9GR=rpwEY3L6C)Q;C>A0^|B^X@!MJU zLuIR_L~gN8z{BOvWld@fMgkfp#zjUT-kMC%?VfqLka!^!hQJ_nzDJUbq4$VBkcY>y z&M5$siYd%s;|vq9{|YfXt~34lJ?$ffH|>5ilg)S0S+1rH3Ux(${O|KB+E1*Zoe2+h z0#WQCiSE}zYe;$Pc1~*BDONeeN#fT-!}OJ6iL+~tXW5cvU+R*O;m%DEmvS?Py0bd} zl^-jJ^^XRS46WOFQqhXi<;srhuokT3DapHc~uT!}i^(%-E`4W_2jMp(!E@m>_ zF;tFut35DcAgy)3O}xXA;M9J3g~BgO<$KZ?dSoYMxfk*WwBORf)kFwUZZ_9klVhG3 z35S$yIb_3A|9Hj30r!lNCgj5#FTsxU(PPB+IRWJ$y|%xjjg|j&yK&Nhzsy39&#4& zy{N!|=?-2m1PmsSi!z`NMcXBIWitd+QIVQ4Oy4`&k}8*TDwI!G&$FK-I>d18pn;^g zu=)J;FR^;WC(C;Sq%a)$XBwn6EFBtm0c!h~Stjb6atw~AaK?M=nhZI` zDH4YuK*7^8GB!>-fmr|B1U&*dTnpe^m#PH@SaE^*KGu+9r>>lRX8AxOQWD7ex2@V2 zeu+oP3JXGZMjOD-=IoDY)zLx-yx%BVS&Eek0Gnp;-sJbuz-@&igMbN?+$bq?P#{?(`}y zePdBVVzKe<(pX#l<*Oa5eMNm{%iOT-2=z#g8c?Z3d6?$v)QSGE>BpNuHw|8PFFRQ2 z{CumU8HFhWsNgN@1Uck2cG;||p?_ZI4ZShOjJr}u;B}?Xt&3Z0hcua(u78=#gqgKA znUMKiszFnii<+j_g(HvjY(yF&-Z1@*zBi#-E$|w*;@%SQL{^xgv5Q=`x!Figm?TFc zZ(BJ|W7dUh$B1qYmUpw)im1qMdxBO{QuP@9c8oFw?@{T@T5ErL-%PKzKQdjE-kNeu zS6t$#a5DXDa_BM(RW<*B8Yk3$(`{5_oX!GA;CviNw@Y&;+@>dDq&L-RRov_{P28t0 zr=g7nGGUFh9rytBi|uVy?I`v5PeY47ZXDEO z?@leNFzKlz?Y>M=q&fe+AM%f=@-?W5Jv^WWJN)6KqbE5Ii!*yVF%qEv@w^CvwEsRQ zVI}=sW63^SqI@Z}rU-&{hg$rwj=yDgCZg*S>5N?H2LszxykAkj9yAyHbC3aF&bu$q zItv4J^3%Vo9$+_qc3d~>M2LFwEy>IO*YZT&Ch>wYDj3I&ZBUV8pMEhZAS;KFm!b4HgdXVgq}{mf8%c$kg!Ls{kn+ms3|Ocxc4{V_WabR z@Y}cvl~GTlv51;e?u#C|DMo~JqCm|L?)4T^SV^Wb|IsZEpzcu6+dKD>bsyI z>JRs}sg|9{TZ>StO+VdXD2PvQ_HfSf)|&nOl&9o=A1xjD5eLtcf)85iyN#;v66n*3 z6On|^hY$k`haSr}Bn3r!#ElC6`GD-bq%Hk>pUfDYDM1roiO>CsGzwrE*oIqNmSND3 zD85kU)dg}?lbvHww1Upj_Hykda&QRpj2)tlKsHqdyN|Z@d9j@!%oeqD&&YSjUQWEQ z_$em8FqRkuf-72ShNjE8?9MfjS;WiXjthSGNN+!qfuy=VnQ~Pc_f1(a^G%JW2SzOP zKyWt7xqt9Szkj@#fxy2FfDkCmQIxqd*Ow2<<*Hmn`2{VW~0*Gmun?8!{~l@~-V zYylO=5_>@U7M(x!_pWJMn1d^CaB;<5d9-t$WWxrbT4jcz_&o@gW+qcIt_YfU!weG;`SewN06Kt9_?UmY#y)>s@IFS|cWvK{K! zof@}@O%J@_P}&KG7|RcCHdd}Y$^L~QuHg%EM*PlT8*i`i^^LFX(gY*KQ}cwJiFaQw z(kMn0Y_r1R+k7jL&n%Q;1yM1850gRkh>a!r^w~p2pu$Y2T=J{k9A+O@FDOh2sWT80 zov$hGqXaDx3| z?E%C8EsXHE_Rr^K)l1&2`erP^5Hl1V9aSp}IO2;arWZj`98S=sropSsF6-$sy{^oI zUG$0(?nqopC#wU+z^@PgG72kVIUZ^KYzJBRiD9Q)zyg8mBSJ}YEzK%41mL{7g5&Fd zh|?I%mmDGrTbKKXdg=NKnRf1T;F8Mzu*fQu`94(6Fs|AMlv~AzucRwIXCet(|Nds* zZU!b+6Zt{fF<7oq&}H%~tDbyG(@8|QOhtwa+3341B>P2fv~3c3nh;QVGnZt5L;{8TdbNFaK?YKpx1j|s6^&Z}cwL-V=g^QxS7HA>;{$E0^ z5>u|GwRTG7qZTe1cDpm19Wj>m#Z)#6jo3kfsYx9i|A1fsg)xO7gSwLNXrhCIGO zjX(O2kxl#C(?-b^7g?7hr#-M%*Tk!X3a{AEkqvZnLut#a(w|(d%CXDijgNC6?>B|E zPYhHK)a&y`#$l#}BH`ZyVCUx44vk3+3Y3_CPnAw9>zx6a^`M#sK41^R#iS#g*aRhef3s>+uEKRzaWT62F6XuBsN+Kj>I8viSAl)eeoEMTZxw-t<6 z-vBt>QG+Qbx@NmjwJTt4Y(I%u=y&~goN2Q|tY{YVykdn#vp5NBKiik~^Q(LSRxu?H z{nR;}OLxoZqu(1Et`X%(zv5nGUG2)AIhWJPg@)MLsFh%T1_9BI#4(XNT`V;%UpBad z;zAdRRR76%fNaO^yW6ECDGQce0qg?{@gLFw0vMsbN1at5ZD$^E{waXS#Px)R{w}e_ z$}xF6gT1xwMPGI?FB;<<|IhrgK<_NYQ*nb}j2vm3|M7bN66Eb)5cy#fxLyJVLmhn1 zDM_KfI_r+i!lPTm8*RfJt$sIIYis6jeAJSE&twUSB)qU@ej?xUu+ry4+Z$Xm6~|sB z!Xr2ENoe(%{>O~Edqk^|C$@$eI3}%*E>zRFsuo$1o)qf7W}blbDRa~^^6%n7KVA<2 zS%56XmL}Jz&PM||BCMzov=Ljy*;NRw@p&wSm;=ozQ1mi_s$&9ll^Ne8*xEStfKj#x2+E1+3kSS277rh@WidzAy&9HubmWlr;U+LR(f5NG zIk~?EEJKfmfM%TqFdmpBVb+{Sqf_aKnx2V2lQ>4@wClgJ^#myX=7hMON6DL` zx%_NiD!3ZjdP{o)2@*LBsaCfFyRjUXW0!H#COY#JNjc9HEmUGH!W~gIOZ;?m+GJ;S zjICdj0*ji{H!JG;ta+iuuT^WDC5xbw7z77}#mChtc;AtXB>W=pS9hlhE}I>+SwnLr zHk@lK`m6Sfc`+{|`~EQ#+*XHDs^~X52mPzL)7wq=EWY^ue8jR1bK@!ls{BSg@1h^b zP6RLnBXhdefsUQNy8VyV-g4)_L()v{K4TlM*jNO!9ZJtpJ{>OtNhrxo*BdJ~J?2>V z&{tb^C1`xP(Tg%B*6MO^dh;1?E?9#y$xMNnB&Tebt}drcOV7IYHf91%hs;QU#s!!$ z9G%kJc#XIxCn$kWzmV}uDgaI@vCmx`g`YF(ou4$iu&Ii!nEXUA6FM14*S@(3{M~^5 zRbj@uQOY=|`46B*dS0*Se5i8#r^f@p48SjTfPu<91)OJi$wO@a7NLI#8{m__5JXPF z#51hfOff3*+D%-MIT;j~ns0xZ*__-;gdfqqQ{QC2Sg;o#g4KdP(TL|EYbWTK`Cr7X z^Or!6=)Uy1N7Q%%Njp5>9Dd{>-MF@RM_GIA$P~XzkaxhB|JE%@Z?D6BZVj;5 zc&X7g#&H!}Cx*!61EH9o$nvdNh@2D#A6Ufybj4>JdEp^Kg%*=nQBOw?w{5bV<0ec6 zWIoQJkjP--Wu;!eUMFL{Sk3F`gnLRcBGUZgMq#HxMFpF}W98@_Cj2~Fu9=5HF>rXF z%wx7+=)JFbMlZK4q{-QRCP6ot+=;CLalJ{#-y4T!#*J^o!xzopr0HpeY+W-In literal 0 HcmV?d00001 diff --git a/ml_system_design/seminars/imgs/sem1/sem1_10.png b/ml_system_design/seminars/imgs/sem1/sem1_10.png new file mode 100644 index 0000000000000000000000000000000000000000..d831bf6333855b36b52e081b359955a0c1e0a2de GIT binary patch literal 81863 zcmcG#gscGXGABmfKBe^DI?j3Hxg77wr;dms zAiSs}gxaQH8D&3rznJ3ksm6np;jquA-A(AFfwJ&&96@lL%+Ues(@qEotI`Mv>nNzG zI=?a9zrtF7V9Sv~rGv8Nw7?adPbO6lWfsXnTFV&*2EF(1AMBugi3to0IgE_Bh^j}% zQHOh+$*}L*{jg7!kA_BLwwtlnkL-{2d_iyBM1Vk%ujGIcFkB&zt`6JPU_XPxJ9mHR zfFTfh|D;4oI?u(OH`JqzJP?5#$tY#Zt77c~pFoxQlEUN3!N~#3>+0waA-%@;^~r2< z6hcHFwRLIrJAe7$-TMmBHOvtPbsMk|pd$SJc=|=@zeMuiGw8)$#dlPwX#afo_m*{Y z;{X3nAB$H46bTQqx}u%9{-uWi^|jGBcpb6k3%#EQ*rWQ#(;oBxzJd~moUbYfPxzem zrm*oP;->$9d=U7}5ExqgA0I-S1R7J`n7N{w*qk{ZrLjCiJ7a^<17RRd%@tvy(pLhj z1wGOZ{}aR+Qa_;ATZysYN2h|JC6dsYqs_ZQTo8zyYQ4A-ff)-f@y3z)-Jom4pfOLD z6mO=a^dz#&7tF})jZg3YBgns^do&DQM{k%*G` zW;o}^V93wKq3o*{sH?1Wad)2<;`v$n#az00xg0iY2Y)+*?O z2bOlcsdauj)_+B7Te_g$z^o65vkjpCVD$TcV)LP7!|%-m1iVw6#Je0a=fIs$_S|pP zHtCBZqM@T67x~wqFdX{qV!Q@hwGQPbn0xMlr52Nh z^2BkgT4)fW={f(WuWZb3Y(Fs2dU_%}`;0ySM``h+u&NRx`i%;(k#t~Vj437;ro{RL z+5|8lOM!-(lL75^@x~w2%9_S(vVGKwstII^?wGE z4KEnawyYH(vU+{sJ(S^{|E^!4`q<}1Mp@QNican86fEeT2ecXdhu(p6;d4lF9N_pX zdJS=yJiDT|HA4E=9#BASgk!S0dI^)m0)8lAbcnEZ^uoTzL#?hnUdax*>na+@S-!_*x0kqg*J#Y+I zqEytuqU+j!pX28relS>=Yu6VB38xqJ-~4W)n) z<&I^*1x)h(Xh+CH04R-}Lww{`E*COnqiM;7*P5Bs_t<_&d01d}uT-zh)~v239%9Zu z*_W?m20C5VZXTu|8XsElQj6o{HNV8bGb?5IkX<%Us;mLOEpmmAeC(yt`R0RH9aSD+W0joU^~y-}7e26Pv)Yg6 zuKInpMVDSUGPr#FE%zOP0yffOHgs$)nZ#EOxe~6&3#lP#2{-q4J>%;#BFj4P787cP$wmPV}f zRVBbipkVyAtY8c2b|Nl-kpm5x6?#`vCFL}V8sv(Sp)JIC$3}?ZU{hj(7vk8bL~mUg z>eP|MuTFjA^(iZcByIwBFNH5hie#rPTnbWz0-q%~g{m8AQMqYyzYYAX`@Wgetc|(= z0>cG}QemC?KZ@FoE4!eJ9uyTGZl>mZEWxbg1WB7rUoiHgqZPK;+ep+CQHX;Skb&?4 zQwu8M#w>4DL`yyfOz|X>Sd!yid&&*lkB=4oQ4^F!YKW;w&8clzIDj+_P49jF@+a(* z>*oR$8uIOxOP0KcbjIamHPN7W3jCa;ACClio}_at)iMj%7DIc94OX3WRJnBD-3fxj z1#{M{eNTk#ni`E;CUZhoa|xm|C;tLJY4QKjRt~f2R#<>;bO5)(e?$&DmD{ z@D53DmJ|UjyF{L>Dm*+s+=b{rA$TK(F0{9kS+v#Ls{7Ap;|fiYNW=z^cBF`y1JgLBVrNqh~|d) ziCv4+gFL2`72-f!Zb?ep=2DD;sk@Ka*U_$$bdp+3_?9yV)HpI1_ii_6B-HrM!jeXE z<#87mjO8Jw>Z7IwarFC>i>orcNwna|V`8r!@|>2op#ur);d>UO_H*9 z^YWB?_Nc}$7peDaYatCj{=BBHZlQWPShjEP;DmO5N!D5pnH68F`rtY)snHWT1kR62 z95H^f=}afxW{M_zc$0@EBngga+m*2qG)-^2u1Q*|8b3bnwow(Gh%YL~DQH+Jx?<1P z^<(Jhia$COV~T=QF@AKLVDFeJYk~BeS7Tq_75pp+kHNO!Cj6+%H8>JJMHB}Rpo$KV z;m@SVo=C9#Ocf%0P_&2abI^~ct|13xVdc=)q-&tAWM(#Twl!Je+Z&_Fv$P-g@6a|zVyFhHTEcKY^1|LD{D>+LK1r%&C>BZP9JOu>L<@r?9*s;vvp#LPd_idJpB zj}e2E_MZ>!m2l$_D$JCjH_7&g5i7K|4+GUYMO%DB)y5 zV;q$zGBlM>L<6E#z--BeImOwltGZVw2ja8ytskADexGBh zbIUF?$!Ys>+pc8tKf9oFC;b!&0-Oi&HcN%Ix?SU!TU)Cpi9f)KgSRX?Kl=JI@f_;6AQ!{} zj)^!@)DhwnVOjd6l-6bXtQ;JCqZn&#Lpr&z7CLf&FxlMA17l(oZpiq5R&_)7H+3K& zo989trm^w2wJd`UECZT$%ZX;EhdoRu`)?*X96ra$2{_FJMC}rVJH|A8`y>m!St~&q z*28fw)ID_~Og%XxJsTtgbVV&3jeV-Q;l^?{xJ_Sgn%wxQb=w~ziggo_wY1uL>(`#q zQRK?QO}O{Pi6Ph()#KQl@RXUJA` z%UP-UHFc7@+QA}dT_*`cCJW9-Jd5?aGBhxy$|L5ct`Xe&{z)PGGCStxwJSUwVG8Gk z_O5ZGCepV3CyY-I2=0>A7r(M!Y2-$&hctu6T{}je%pVwQb7O$?^%uD{}g;q%ow#c>=SY+t`md zQ7QlK((Z_c^hc9a8TzA%xbG59{bIRSJ%XGHef0$ zq&pk^QQyYK>g?D2F7Em5er?|z!%$c}2qTsFXuN0xDN<$R;N=~$uI>iMj{-Pu8IXl$NtJ|$-dq@`z?J6T2YjgDq~(%oM8-HUpB+-Y3^ zG!(&{q)%z}dq%L=Yx_C#3%K$u=Hl)aCo3%}CDrP3k6q3?hNGCVd$fon&E&+yA&vbQ7L*tZ&&Dg7=7?tyfI_+-Jr#`!Us#dUXycM0U?vNU$G*!5w=f2A}j*O*qdtP3n^kFl^ zWP2p`mIi$lX825{G;`=0*-xoat-C!?iG^@W@<;x$s=ZpD3h@7q)Z1q91QZrCbDEl| zoBB_4LYvpSEOjcSlwTj_!rrRGs4VS)hPH<2Q7-WimToSryKc%+PwXh(M=jxuJpRu7 z9Se&#(;L^=_;Fy6&SIlD8sp-27Kx2{5e)>(Zb?4KW+Bl`JEs@VlUjJUtvc|VQ%Ur%^$UN_$z`#IX|ed}I%7GSXsE6u9;r&+rYu(E&z2t5D1?F{ZF|b(1}HlU+pX zKdJexzw1(Hs!nM^E_t`>c?_kXxa*$PbG>l%sb{`(#QrJYKEX;M8-}>@EJ~tEueHbi zxqQv2wXXaAGJ6NI_7c`W{Z_;>v-UUSX)*id!|S^H3kmm{XV}Tw!`j~JY~Uu33+nOq zu+8_D3)Q$Kb;mD|FlAOn#@E4}#Zy zpLBAC3JRrz(0|K*;1leze}%jTMMUuV+&Hgo)lT@u1Oy1ie~8v!{WH*5{^MeV37Ya= z7C1d;@O!2<#c&ER?=H>@4ILk*w0`hQzljkPyxcGLJo^2`LW63O|2l1Aa*1#=WH&n( z#DJq{c6T+cSnt242ik-y6;++gY2lg}Wfd6gdd++dKBT^zO7Rb|_q)`H@FbTI_VtZ$ zXVu^7dRjeoHg<{h=d6h`S*B4EY?{g7_4~GVNB5feqMIqN814LcAhGjK_UcGzc>Z;c zbsDl$%j1=>tM37JuU^aj0qRltBmLR5a;UtTe=v%*bLi@cvN-GRli>3{9gmlXQ1mt4 z>Ynvx``R<$IGo#G@8C}<_F1|W`i6^(`;e{G| znmW6!Z4Sj*IZ*Mlez(u`Y6B41N;^nSbpU!CUQy8J!)``nL97S+L_tN;$)#iic zzv<0gb!aS}F+i~ChGBf!@aP)FM%7ANn-zr!6TqzG#AJfjKn|=tU+U-n9%x~OPPzt47CeIIRi%yx#iaSBw> zr_STJsEsL(Z9mW=I}|tky1tj;B8+oxcGhMTDq~JlFkhe#gU^j_$Un8l(`qP81fY3M zUp(ZnqvujqXlQ~6c2`DwgLzy|g=D>_A4M3!tOULMVuy5-jI%{LlZQu|?75mQ9ZThT)@|Qog!&ab44|qZ!m$wy_b(2+beSah#MwKdY$d_D@eK5?sPP z@kQ-1BVBE{Wq?^lRM|A8gg|B%Ho=e4@>Zfql&Gb+?^?aKDY>QWrkWn%pS{N?e}4>~ z`QybBr2FaSw%O78j>HaC-}q>Tc|4O3k0!g8c{x>%cN4ratFWjtLnv_~kFUwMO1=lf zH*=T7D2EbdQ&9@vt_RvcVv0n+GK*M5CCj6?$zH2K%O$;xHW`@P9~rro02b| zuCm#N-l~%|HwR2}36(E<>pj}=w$Um{L(elv=xOs5%PMMfaKhnX=Kay0Ezoqc5Buog zlnMg=GyaQUqVo*@KE@B-Wy!{j2yg5=x~x=$_>90KD3=P0>AUH>#JF4Ah_`<<;b`DB z=iFAw7=A`l6b7jTLh|#M@0wpT4tuMeFNB`4>`)A}c$y@DCmYo>nyLB)>h?L)=ghV0 z)ybZZ(cGRph(b9V^B#9+%IIW#F*NCpK4{q{lroBnte++fP2IpLdVV1;=E){y3F7rB z3Y5cD0U|%1SYGz3^|lTQN9E;NFv$-3R3aai7>%j8rTZ>WkNHMK*WqLlEU>uBNy4RZ z?dE=XPejZHt7@f^ZO)Q){%TK=;<=yHa=S*4ll4^2Q_=sx=Nh(lfBOoaU-gJ$TkS4& zMoBCqa*2yrvTTdW!}TM;wvZ_KSVg8%t?IEG&F!%sX0KF+0H51;&d-V6n9tcS=@_z` z<)56Q??W>G+40X$EVttm5!pCK16$854Zk~f|FcY?Khe#4n{kZ#zV1SIma~ps*0Wv>#JiET2h%#oykH4KVfJ;Wiy=d|+iP~- zPlHLjdONDGFoU;O!kWf@X2FN^tXS~z-2#ZI`g#&!zd`Ehi~8xC6t?N(og(AD2mBLH z{mwrw=MEb%j&j?n#H{PkO7R*&X=#g=b8>j&#C&p!=2BO#`S<_g$b#5ptOH8c>gp&^ z+(DaRGbF~%D7mJoFC3GdaTG>a6#=@@#hWa=GWc*Z&pn+UA8B8>wJ3j}gea(zb3eF;d|5j+8*Al(Fjh(*Y*r$?5Q zCW@A3Dl{X(TUmVB{6s?0#3*p&fd#o-n4RuFS;?$GklBdf5tR)5QXW^MMZ|_GR)EX- zM6#hT)}dl*#WJYT2&_|uH-8CG^O!FU&vZWXvyjkas%>c*`gO(G zWSgK+EJKs9z$IOY^!!z_w^*ezr-3$2Uc5H` zhzSo{UVd0nF=M%ZE-b}TpS2rrKbkOR#!xY@si|q;l$;qhY z+sMhL0xjh#8->$XH`np@)|&%Y$Q&HgxXCC-$M#~@60JIBo~QD9O|Alu$ zF}A3XddE_k@gyZmh_1a%aApE4;SPC)qjK&uNZe0oECQGkH5BbvE+AH2H=Qa^HW=F*Z*Lfuqu^J-Op2Q)&yx%6%xVw!8FE&c8$#R zuz$o;sa8`iR8uVs&&eEvturyMXS&6Wp)2cXW{_@>45w!pG3$2C{Vt>MeOO`g;Uqp3 zB)n(1=!s71oAG+mb$^nTaY}WLo8GqKOZhux+1y0YQ{Yw=o=8W}R1d#g z6v|zi-Q37W+TvXq5QU?tPNfBBnLIOo$vt{!-B~#eW4~(;Od`!>a!@si(Y`u}gL!KT zy)hbYlw&$E=48=iC>= zvJm{?-E2kUZVT6NGQL8FAN+oy%r+u)lN2kz)NAN`jDnetw`y9C8YDVDn#eV`P%^i7 za#AQD)U*6Vi4CAB+3`{q8G1M%l)L0mWfDA}qdr$9q_Z>31y50C4%L>slZC}(nS4vN1)th%PQHk3)qMiCYN`1_dWdW8;}%-_a4Jzx1ivPXldGcjAotJWjg;fT z%UI1KjE;_$FFvJ4c3I(>MO3K_G(Kb_yQKOn&=tIv% zj|^kC-;rbxG`m4&Di2=D7f7I8tst(yPQT#ZzWuk3wqE>8ibC9V_QhMv68N5k!!@bMOu4mgH9?A8wevv_$%m6Zn zynrRfx~kc^%^-#C)w9nXYC^he&%U`LTyax*G}-GZ{@WU<89HWuj^MTDmDjMGHWATz zEj>_s_h1l`WMk}RS+VVWXu3dP_Ueb%kJLK~FME3e8+vnfzSBSYtGeoPtJPU`#F}0| z$W4ULeJl#|Hnv-LGq<~%apX`J*>-S6&Q3nwNoHdq_WflxI^Q=3)U zQgx_E7dwqw5BPZt-v-Oxau*zyy>YcqM>(%gL<|vmQyXK)yVMTo-8uN8oGFppQ>nQ~ zKgaIHnr69vl@^8+(t}(!Ga@mko?K$1I%yQtyy_KJ+QYbC5H?M3R9bYc8glgM0rT~4 zDJC zQB|Z~1M3cCN^VP7dbB1Ya|?@bV=0Ww=To84*(iHwhgZz(hhJZ$*UXJvGWPj6P|-6f z(q)VzNK$IBLjGHw`X0M2%UD33BoCh06})iQou^6}1&;}8YWhrJEelwj((vMvM~q;? z8%h%(XeJkwmnU#wWbQ=izr00&Yz7;CWwr_y;YgO_nq-$`Oryz^p%UstjfhSEA?1(_ z(Jk?bw3c{zdh{PSOLByoctKnH{;AdOP&ZXYh08_IX>(1Ri@)>tp?+0s34+juZ3vI+ z1+}98Nj>rMtZW91B9kb|pF?cfMk>sPh8@b$1$BgUi)b0Scxmdr<6|1qSPlV!v4sVC z+eK8SaweV_F-q{yrhK@4GJ62LXynjF8K+@M9>MsT-MlKA^u!wqu+^jAfP%dm16NY` zPy1jXbCvVs9P>dVuh`>FeJy+G1GCkCdjTSl#Z(>KA=-nJn4yG)c#Pld%1W4{Di9bj zoBEXP>TAqga6}~(u|y3ZTnBG_EJ7BZD*ECBl_Y9Yp`}l}cxhUXr}M#2=L^OSjYW<@ zu0ZBmi7i^9rNGI$@b(WXmaI<9H)1{HeRaV2Vd~0CEym2uf-jaM+w3S&D4P46g(?xr zbEu^zWehJ@-fi*7STrdSgPU7UpH%ia8@+;(lGH}F8SRwJEiK;*0nYf}F{I-=P$B#m zbzvuyA+WPkE^Lq~TJpRD)9vk#ecFMs4NI=#AeC$G>4aYDaDL!3+ z51a-}TpR=B4U_=5GPQJB8uFyYtLQIdFGD5vpgd(VSzOuA)5&r$pm$0?s(_Ff{YyUfpyLd`3^RW755aJs0 zyPa)_7`iWY0L&o%uqvDH0iA9@VMe^xg;2N}z?*)n{NQ+Wm4UMcQMp-pQA!Kjv5Ue9 z{_&nW=FZ2U-Meyg=kT1Np*zk&3}Tve%WhH3JLKr7IRz7v1oA60kdBgy{pZsTFQFl#nzO)a{cblR zR{2qRF#ppm<$(qPFK3NOp$W7C|`zq zuyfsLIc`TPodfQn1Rt4L+`=l59c_l4>TEu2&$s#=5dLS^>q8)zoF;)3fTuV$-IX3j#wW!Ki;j#K(&U4S9RhDll@z7QVra4M#US|H zx0%7?7oJ@34k@Iv;#)SR+Ywbr%(65y-f%>$4!t%#;DG#Nn#=c8ni9m1vW@JAjqH@) zbkrK*DL6$f8qiBCGzdsZ%^V#gge3|Y8?0&ITgnX7Af1()s+TJv9UZqw zljI~}w4s;DRyO>C;+U-*jbE)IC2~bdO6&u^x-><1zKHZhQ$}qX%SV6vnn}Jk9aZ?I z;HDO!Oes9-AV<4jG}prIeYg^2V(6{Kp60fsy&^`K^Hj=|OFsgd(W{#5z$N4czMcWY+El9QveJ0_Gkaz40EM*!=24I)sdfy&xKeale zUb$jJfRQZsTbd|qVX+V~3Yx6l-`{O%z|u;UvkMCgK zC`?UHH}>#IRb~7vB{dkkHB)E{Rwy2B+yV4r3Y{UX_O5&>jT6oNd2KzfrPH>S%>@um zPD$3((J?W|$$s+~COu$l(T2xVc;fs8ExlimnZnZEnkrLa+Je(!=8*GgAO;rJQcBt5 z@K9o4KvGLnbKyhLY3zbyYgA^D8HJ-xn2lVrCV%MZBQ7#i;^;QBV?~I;^a7;h@z=xz z#|Ob~kmGUpAaIt!1 z14UJ1gc&4q+)XUDyOAs?ftZdP`&?OG7nd2{TeycPm#AIWv>Gfwg2KP?g2`0{{gjF| zbxhstBB7}tNhXPA%$wx?9dv;a9!4yc4bCVUWY`{TX-kTbS===8!jQ%!&M)Fd!T?b` z>@rL|#-=iU3xI1-VXIc4moP9pzcC+&6}DCyPX$^^nBueN;Oh$4wVavYekl zG*Z(+@Z7@@QbPUbVdE;Gw9SLU=9ZU}w?Nn?ni_sYL!?B&0{u9L07v9u-`>&QKr(ja zN54P$aM8%{ptinCfg?V*_*{YKiqB(gUiXZ}3Yo?es{$U$11c0v4ZrIGuwR0xj;>ac zOKstkccJVQinpMf*Kj~ zi=!3i)ZQ=!OlR97I(sK`r*Bh|rD1EA^P@g9KK=u`wR~c4;GdY8@rU-49XTyYt`WX$CIH7-roE`fEZ{$kCNH>K9 z%i8eI69JFmp=(v-w`GJ#jz$*i6j%$Qf0o8#7^9CppjDDz0dK<28WbJN^5@oTIN(=?w0KbCr39?8Lb%>~0s!#?7th?afzpEV=pn+kV2WCQEP}wKiz~WFbVfzOC-p z(G@OpDKS3!wrogazXa_7t;c$Q3ZMg+{n|54oq#P=qO4cW&doPiUfns=u2EI{L$A2F zJnBY=$j>!;yU^6E=!EU#hml4isvjSGaKOJD_U5(iI zC*b)uhBtJzWpQ{&3wE=R+u1uLp+Sq>ELmAM^4BN=LcHf7Cl-HsV+G+H~oLc&B$5P5@y zp;>6No_`P#x19maM#kfe`Ot3skgTw8=g++{$kU&D^Hk7+!NkiZF`rAE4Ohr!Kfv|p zuPgdyd-Tg?lnIWmhHoh+hBfw>^XC@UV+LA+qOzcA5|`$tFJ+ZqG<8kA@oxvy5&RI8$^scYXfc- z(LayNX)XQRqwhQpj}7PMqNKSc&E5HqZ9OX_(R9DKTwgg{9ckg4pPU_xPH+ec4h#(N zx|L;$Q;HOQ6ywUGO#i&bO@;xo{9ZB>Y^+_dDPsdMbaINEwMt9sK_=>VH5(j+Vz>n~ z>829_6c32rwTVWyp3#urQ>XovD4llKXseYTg~s)Mop_`$dD`eihwUjMRQ7xY!gnz(ET9*xW*sMwNV3#@aR&13R;fv|$-pBAU<}5+ef( zGG^w8c3Dn%k)f#`I1PJr=gp;$1J$}T4g9LW08_07>Vz$JOPzTM*~Nx%Np6dRL~0x) z{=p(>bN$CeRYo#re;3?c(fj8O9Uau{3C*q_SHau1k8QM>7Pt?$U zh9d#a0qs*AGegI$>u5MfENJ8JC;yW#E@(&nmTdz_urW~ga74Hz&aJd%?N@@PGty=z zoU78oUtQ#q--N|Xer(B}lmXvv@PKl}vM1+S&Ity|{K9>uZt@#`S`K5)yBg(*Od*hH zx||A(C+N%9Ho!VxzCG{@GSMvV@(&Lq^GdYgl3Q}c`Va<{Z=Q8Jy`rI*c%ak!(Wars9Ujjow&$QAzGaxVQ?hXu#~bT|6S;|$ zv+)!~M13?+0Pm+;f`?v4@VAYN4EdNKIx?eIura60H+e2G(NInHx?qOuZv2AJx~AG( z$C)GKR*jpGZTYP2{K`m)M53`%)5wGcuo*p+@oeyrbwm<(7LVzVU%Hit4oTwqvohee{Q|8rP3Uv9HPWzkzS3_%Ud>A3O3{ z@|z}ul{HG#bj zGi^px%R4k6qRnWum#YSZ5BX{3%OPtS@#44bFTA`xbHsLdWx}@o&-?W8s@$XUm|C?& zuGws;fW+8CEzPEAZaXq}sq@;xWtpBrF=P$3 z@TZ#Mk}dG77aURH!_DFg#^l?Yf|hKmwB(%Xd823eu-A{m-^{Au+SI6zl|V{RMPg{w1y+%Vm+~~#KEzy2fF3=iBK-PY2)9%-}c{v zzUpJN>bCaQ1{vgg8zscvFvl5)5N-8_L>;S^hjNF)RXSfaZ?$APek^|6ByOq?_g~cG ziT(}@=)Gl`uZey-Kcevev21!5kAd&N$29`^(L4hwL;u~tV%ebojMkJ6bJ(-z=@;jW=NsWUARhh9fy^V zTVqq`7+Wu`o|85bH~cG{!CGVTX|F=w_Dc{+w<3T2#v09K4dp~}+hm}p`?8ytVsXMS zIY&2jn#k8~1B82E#a$-`eIeIDTtMJZJ zHrcdt?)lBkoG89xCa;>W{_ZMp0n;GRQ*Y6`8L>VS0}*vjZllQ!H`PB*4YTq5xa@An zHV~%#RS>I5qO9hS3yvsy8pQ%O5Ed}f%xY}Mp9-1RJgTl@vSHF$*T!f*`}Jf;CiWF= z0dmZC*NViR9em8u&^(`WSpe_$#^(_Xp_u1t@WRvgy?HPAGOB;>@i7`ay4HOQZPn`Z zUgt+9N~brCYVkN{9M9%MEM03?z=Rip9!F7cutL2rJThrT!e7`v^P4|;U*6jy?p}9F zIECH>Tr(U!vc2SAMbrDtfyk(X#CN-T>buqiLw^pM^lrSt?R?&OpKA#btybEry=|6= zK0C(hd{2hbmvrxqpjZk;VNY?8MD7NdXki@AE#$Y;u^@r{KJfxo`6MP!S&QhAri0KL z?PZSQjXn+}>!+;HMrA{bM=K0G?u1&W)?Lr5j{Wzr;xMwOkYM`> zbL|2Q57cl-o{%(c2d1NOK_ccSFgQ2 z36xcfhC1n80Rs&o@~t72p)r>UhV%#Mgc;<3KniBXqnfK)Gm)VAVjt{$`e+*oU^MBMLTAW zhA)(6W<~2NC3~oUtp>ACMD&D@Opjm1k&*ZWEtaiSzF}=k*i;W-s zMsIGX=F@jDs?Ba~wd0kBn$yz~ycaeeGpu@ZlzM)sq;42>1VG9sZiNwE8IBrujq7RQ zr68e}_aAkTE6`D+Dl$@|B7WO6NqwP4z2%D$Ko)yU$F8nvV)_%#Zs2>_0lfVY9Zc~b zlDdU^;>g1jMp5kI{$tyik3mkTNP#@9aWK^XOv!^?qWkuroBy)#KJ4Eo|8pWL1HBb| zq*P{RASC{e>;N0vbVF1LS7MJ-Crs)u71h+#P#(#{aRR2PEVS^QT8U3q z+QZ!1qf1K}pzm|OuLFw37Z?-M{4?SAIJUo%P{1v+%+myJ+#Eyh_VQy52oQjyJy%eexZ|w!61qKU3kGjPgDdGTEcVDm7-1P-p%%pQ53v~cRyY29~uI{i~Ly?-Sz&D zXbGeVu%S;5hLQtnfGDAcIg2(%o}TZ!UY}3Tq5Y!h>kb6|x9;TtfncseR7Qrwh5|R%gT{*|)~V#vYHy%^$eA!-S#lAjc2M{f9ny z+pd3Bi!>zA1c7?0{jQmO?$*OB8bNn2w_UONPaE%ATo3V)ME@$12X4st?_q_ z)tl#=gDvi-q|mm0xB!?cy^hF;2&C1#e^egc8}bq>_s_jh7IavQZEexY%gdqjO4BB^ zcwIAMM~KI=6(HRHRlK9ef5aK{7Sg25l5&1NNVXY-At+p`+A}kQyXt$ZtJCI!Nu$I@w%IA zy^6Bo{v%ka82ZQ>TcTDSbbY^j8Yu0dp`i}v8xjR7rRK=vaUy?bZG%RmK=QLDsQD_b z-XVomq5rcM8`%6ityLqTY$3LbZrJ>x1V8C+B?DCC;nQKZkQ&=`dl>oYT~G%{6M&VBmKt> zhW5DOGjm-{s7YB??A{%_5jENN9=nMtg0>|$^)lSfRU7{YTL&VP$fgH&P1zdcMK`C$>gu=8V7~M-h_eUrTIOe zemFOW5A^}UXav+=@`-h)0l|PH#-XM`*0c^w7!-tnzFyEauTSyt zR=<5kvJvza=I+_AW#z;igoQZ!ZA(tH3`}CvGsPw-{qDU8r8EEg##$<{P3baJPkdG|*H_KrbM_q-VJAZMt)E?|@*!`+OT88)jm)ZYpyY z=Er*=zP_U#ih0cXsOqRIvK)TX!m*q833C%m#-QLYelzoh3d`?osBh|kmydx0hzk$K z6JfoY7>Li`Rz?Y!M>ke`+}hEaA!tzTDC6E20c>S6<^K&<=o~ROQBZtBl~Ge)U#eC! zxa!cMeFYHK3q5`)Za}*aSsk1S7Mh}=@zjMk8Y*?@z^*w;6Bug2iQLPk#FFycsu3C= zp-l?VMq$LRHUA39DL;uwasZT|YcG-zQCO(QLI*4(UddPlC_@6ZQxw7z7>TMoBb)n4wq1DFdLVGcpbLf#N!2ne?0Z!$i;8M%)lbmSKP)bBZ zw6L@kNFNF1O_~}T8Y;B4wM%BLpaW=#s;{qyDz{pGy;8kA1ZrAPuL>5WP%T&Uxt`V7 zbK!-C9LI|&(o7OT+nw);bI4aR2knw|3ke;wlL5~Fm`?2aJBuM zlQ6h~`v2kUJ;1T<-|%6TG{{O;c0wg9*)y~$m7*jgB(lkh5JG54c6g%fkrk3%-MEvd zknF7NWUu%9>iHe-@xJfze>)sK)x&*%zn|~tb6w|kp67MlD`?N+Qu5fU*<3`Qbz#3y z_{mo-BY4T$j4OrRb4zrl9M4Qun(mtJ3EGgTWUIA59vJ^{^!UUA{X^mchgL{$C>+$HJaz?dHiWQ=CV95e(p8X ze`)A`;(a>hW%}~PuUTjJhfnr#ud&*c%`kf?b!B!2b?i^cRoF+N=>OnWJ04|a%QY`t zVbIi+U43ICPV1g0AG`a)lxvfYg-yb6ZKgS76 z6$+<^`00!vei{}M*l?%Jpe}y(kKppe+NvlnWj%3~dvZo4_YeS~Yi;FPdU|?yfbuf0 z$kn%h>Sc5KD7xG{Te}XDer@|`GIlzK_r~a3?AP?Htfb2s#%5+zd-m)B2q0jB*Gh|5 z&l#`9ovXv~tJ`c!r>K;Pi?4qLkWOQU9?Be_DMzYF7T@*sky}q|LAGY=$Eik)F7Y3rA$HxyE zMK_W;|D?B(XN-)}4;(mv`4rgeMV>2i2)as(LwlCTZKZ!2mn#D!AsD^=ep~%UJO+v{jDdI{>t3MhJYc@Y@Fv}?|rO|8_ zQ&tZ9AC#d~eNP~xuGQJX`O!QweW1G7;lp2d8rN4^#m+72m0kkHcx&fbrxir@!KSc|OO(zubQp4F~cpZxUvZT2kT+c{lT)e7dgbsW(t zml$_p-L#DZ4EO8$o<~M{r>5e_mKL&*yc`B7-s)#=BGuUW7mN2fDEqTS(_L+WW}ox+ z?RHYGn37UsnRud5WxxHDB@WL*vDkBlj1f6X)w_v8iY);lDeYs4&BL|~BU3Kj_Qh_Y z%~nzXG{}aabm_NJ;=JO|G*v2TUwaeoAqZ5>y_-|ye{$l%g9noi5}G^TW+ru5tI<=g z%DQJHei$gcc0o>>xz&a2RuQi1(EVep?e@Z0LrYUjOX|7tj;g8vI=lW)yY!1}Hz3iM zAJt}hmAbvWi!Q5$I)e0d&yl62SD$j1&vhR0%>S$>up82|zqqEx`#13Fehex;am7NCv6!ybp;% z?jhwOB6}!tV=v)%Y$u=#tg2hJ8q#*c*}JBCtnBQ)KF;M_Gy->bRCON}+j-?QrF6y)zHpd%~xE46#*BBJ!sJl@wL8q&9Ap)XzhbsI99j zuGnftU5RjeG|4+7;~)+uqVI8j_#yny3Q^+zn(iF3RBt;( zXsWuq=p=IK4sDs0vF)_6ifd(Qr!d;A*Q!{3&%3!qdTW8WaSO35RhzPTda@3^zz&`d?6(#B_(a8^5v%qqyO)K`ufp{2n(YjUReWM0QtXnO6G^P z^yy~xjEia_5k&C#`IDajh%;`yCm1C)@sCx=RLm?a+CofN(8+;lo8B)kPu)M7gf~KJ zj66++P()xV!OCM#jWr5Nm-Om^8eFf)tNhVlV6uuf$s-cS|ovHr3bW z<>YvWhwmavA>>}=wFR=cvU0hgZ_b7NJt*=}_#An8PPMRoY{4BE4dZ=Kc*M%zrVp!I z&}Bmkiag!>yPxztDyrAjrE&VY!81R<&C39G{)s~KAJ2IDP_u~eu+nU7oqeEiXORi8 z?a-k^`0gWk%5h(@-x?Y;5vaTK$Vw27Bv)syt?j~so@LsMGD=t2*wl3M+sr+zG)HuF zh)XQA={hRIQA_7FuU5LcNZ_dBFVEqy{y6aQh~uS8mw?7QUHW!l2Xh4_PWq^i#yDOb zj0P&YPaV9rRK8Y)jOEqWAl+3>g!rDG9`EVKBR5-GTBc@Nu9-MF<){2Q5S@qh5Ed1k zKHS4pVI_=AOY{{ZdDDi~Iozb=n_kV)e|tuG<>}x<*)p$mAyRKIAC~>ww{Kz!3d{8a z_xMi`>AYTZwyL&KQXt5gO>ZI-}kdZ_f&Z978e)a@$x@Z+3or~IS{C53mw-B)Z-!2o$srw zDZ}^;Yf@3Hpnipwb)o-Q)Y(>txH$;%#cE4 z?Q%z=9GtGm6-L5vQHV4D_FR~RC{kKe!Q*(f4v-O#2GmDSyOs@88pWu6F@h5UJUM4( zX$1)x;I~wlhIGq4(!qR$erQ(}&_7Y~7UpR-1bYkw7tFF=*mk>o*ZsMT!2*b}^pxK2 zzx4PaKq4o-x|oKzcvfc@f)jSmGgDsR$UAp9fdDA98U{Y3ZTw5j{M$6<{HVdqZlY81 z>B+r6itIMQ+eXcKk@lUgFdql4iL*0vj&8bO{iFQzD9~$+A4#`6v#SWxkoxU$bbp^s zAKaB@7=Di0ZzFCAR1O9Qxc}ye)v(eRVL|-Cm%4T}YDOF$mR;^`;&hZ7i|qQSL<~x0 zKEFpZJc0HBFYLy?AFgj`*kQ$8@q5#4>T9OERDx^xpyz9HQGI$akF)ji<=zju58B%F z)RVNwYmEGa@zQuvq(U3Z9wsZIaOn1>6PwXI6^ITfY#Jk6`k6R|ieJC>)2)qgn>ABM%M6$~&9+C&`=Ekq$^dQMjs~kIa3?MEiJNs^nf%^{BGKfB#vP$au zDiFBNz5e%*PfUOF%J3O4KyZRjno8ncc_qly#wN9o^jn3ljl_I>n1#uG=G>UZ{p_V6 zwt3~{9=fTO2`?|hu!_qW{&Y(76xe+eWpi8u0|P~uzOn_$3w5<@o0fTYa__w=qx$;# z$p&u2rSI0()&Nr7h=;7Zcl#y^Eg!m|Qv)&x0F>?+7k^@FQ7da3d6dh<)pljVDEo*o z>IFc%zP>&r3`OltjpnLe6?>()8_!;m5_NBr*&jh7DpSPvw6EW1^5v0puykN5#3dvo zCY1~w2zzi1j$1aeNPoX6T4Ylz*E+R)l*|YmTngz|fEot`#Tc;GXA$ zW`!Gt>*+O-5soIEW%AoyD;yDaRXIh_$CbEkKjtDCj8-o(r*u zBz9O&w`4C%Kck*2)t9E)`ETP)b#0x3U%h)xYTqo&<>9R7#XB26g}5h_FAf;gBs4GO zN3?8cpB6i=AeNJqdh?9Y^H_zPl8}%N8&uz@*?Me?1f_MGC9Q7Vs*u!t5E4=`IGvy` zQYEQL>01$Zwlfyxa>hmKuQzu+Gp!hz`E7g@aqgKZzHv%lXRx&`%1SEwL%%(0Vgv<` zgWn565VH_gsdI&beB8qVw2$$z((ti|{9YP&#bbh(3s&C5%Na^@Grl#{s5*Vh+Zo{tO--3U`% z%5~^fh+i$24W5uuGrPt#&}Lt(f0pTGoywS_+>dOhk`BZj+|#nTj?R|Gw4Em&+m8Q` z<$5e5DM@+av737Ap@`;TO$H5y8MoNM^CurympS+w@kBT9MUZ%zgN?3Vr$o5wT4=oe zdDzjV^Cka@Qd@?dO(hLa95T1K%ISVRHTYw0&{=NKr0`+wsc-orxPt%QV0EN1I%a-{&<`~g7akQ8!*PRY>}L(^Tsy_ICOSQ7?9C(vpPV>-`t+X*yG=J+b1Ayr zL%pOnfOV48tb&6nlU^h;eBl;}KZXsL-^9nF*p8BnAPAKZPG? z{qf`H%ChcP6Ky6uB;+xaPvQy+^}(!q&-{SUP&h5kP2d#ujum#*)&#TU6cp43-_qb? zm|s~PjN3xp{tnnLemb_&caBl2G@8|v>~AacKv%dn{v$9;3%Z#O_JE1;@0zG1d(Ix} z?3*%;yIoxYDUOgMoPe1SQjzSi`Rqh@9eSm!qh+ouNHMNQ5x48w(yjnuo0~w_r%s(h zfpZkquZFm;r)FN8I^rZq%3C&ba}La@G6!XX=lB`7Aqm|SosAk=q*dSa~A_4PE^lpw?~D(&6kuLD-r)(B9EXN6vzdp%h|k?x^Hff$8? z%SEBFDsag`4P4nog9|bVZ1)a*PUS5@WfA?il+HYzn3eU(?l^A^T;jDda?SP1JygAM zwS6mGAMA@CU}Lazd(QLXq=FEC7@XT5LM|N7>e@q`Mia@#Fx+&2s(| z1m}$!knDb4wj5sqZ>PXN5 z3Lek>&RyKUOXRuk$o6mFP>FO-oYB$2qF{Xi-#+BlD`vi*t_q3Gl6`QI)mJHaR}4ER zrv}OQ!BQZ9CcKgK7mF5>2~t>(JC zxGgO$LAc%?|Is6R6kGpCYx^mst`k-L_C1R;BNTVR4vCg8KW53*)(MYTmZl+?RitTv z2{lRfj{Jv>FJ>Sb;R%j+m4hz{j$d0IM444ut|!b~H8Vo&Vj8_?XhGi!TGeZ*(5p{F zOlfuA#30bg!y~3}*RUwXmz(*MlRat*Zhy8qI5-eRjLoSp*JPct{!qX1y`c&I2cSVr zUY=Crn|EjXUGr}l=sY?ta4ogGOSNyt4Re!xiTlHy zspjZbo5$aw0zSYMVlvkznA%(x|LntM<5?pz0d1mwcDI*Yny?h;^{wE^vL#h2;=e$j z(5VpdSe78MjLaC@+dmHA$@@H0zS3S^14afff%sB+<#0B$lvY#9h#V*ys3ny>Vg!K- zUJ!gJ8dDTsthBfbhW=itpd4CWR#4!g1I?%X`oiRJN_=9|jrWq8Ki)b~V-!~vurJ*y zr=ySQ>!|;Sj!G|97}8@hn8NkIZ96Z$m-Yw zWYTu<_ySK5%YdCgksXm_?{E3Qc4W9-&@%rrD<@~a>kTyQPcO1~$3`O`&!1l7C>8SI zF|@LxA%HBCMg^jLW~aF-J@SAZq(mZ9dWOgYihx3}TUm4>YV``k$wY!#p4OFA-yiOinu?sSkgmW=W1n?^bl7ZS*ef|#^Xcjj{a#z%zgc+q(k>;d zWASVTsi{YrCAmCnXlx7HLtGSS+Y1=B`^8z1yJ@)d34Qv{(?D~XQ{UzWy~KS%`|>gd zzGxm+(a{-iLPr#63t*v#D2dBwSOVGj&&B%uF;&z|-)IhQgldSy5w>lM)Leb*oLd=$ z5pR{dEInW*Hz{(YzZE`^SZP?}0jL6H;n$2DHpayH7VE+U3wGQn>Hs87RKqUemoM6V z9hpI;ImH#K6dxuXdOP%Ghc%juc_T=wkNIin=)kS~=DN)l7C`-p#qX*b4wJ16O+0z- zivyjNw1~OE%?MIW%^$5gr`hT2Wx}_o@8*h!g@kON5S`8IavIVfJZ<Ib4yxW z@r3NrNvG4|glI~AC?{m2z;V5a^8yOHG{8M+01P23VGnmraL>ocZ4=Alb#H`pbR&u? zg0wlSg*2G%)!8<4h-MRon|;CwdM=pi*Zm4r!R#A^={}4(&#|UkMaTjx({Hs63)$IT zkT4>bMgJ-2iDa~$q<7qVY~YY@jtLi(@(Ps^W;qhDpmx1JXP5VjZZI zyE}j>po9HLLfj*Y%E3lXByWIsV?f%dWFrfUZD4btXAtEti+g>^7LNn9&o_(P`px<+bk{prT$p09Mx1$av^e6ub@H~iF};qzc0K*K=!nHCcq-~^rV z;kpD7l3t$kP81?=R>SAp+rxgm?)~|~!6Yv5az?E~qFgMk=qqDZstN9bYkkKb|Km;d zo5X%)#ULg2h;`dsvc>P^bmT#3n%Yb`@7~d#cw97?e^#g)VwB78pWC3eAQ4d5W{K_m zRKN7C%l@9L%Mv-i{0{PY9VoIXB)6frv|zB!KTB$!*3z1FNiuNkY#n(%q!_|+Pqi_$ z!hWuGZj0$?o@GgUURAM_u2(q+pUtPeSyt`@8G{4lA|b{ChGx{sd&t1shR7jX+@S?)d=<7GA?6~S@YqEd0qwA9L`em5mGefdui5xgT;K^b9ahuWZ<(PmHGSDnEw zT8m}Rk+KHK&Hy%BD z)a9M6*V+e9a;vq;&|2HRdEJTg8rm)zU3eZ@E$)f?tH=Ff2Yj|EF*6WINzGNyJF&U- z+u)o3aKA%#sY+qHqF5<;*vWXo^3n01pX)YRu{09YPxwNNto_nQ>gzE~eL-y9CEG;4 zec!}7x{s4z#K6%&UHe{LUk}xqR_D>dnE0o%3d5sPX95lfh%$B_ApL${s$BpFmzzLX z^YAUmAPUFqQ$hk)OU(z%}hs#rZGW~bEvkP0SPkc$wo68Z2G6R~Y7AOFyjcF%Q@68SYavCxUva|8%J()D!v+J{=a{@Z{i)* zy8hB1|1JmdQ~z&!;4!7~4ePHDIMYdbxc>>^=c;Z}T{j4=zx4e>w12m6D^J$CT`-2` z?|ll~&xqdjY?_l+U!0Z}Z{=$cwoiNT@= zu~wM$kbk%+;OOXI7I^(Qsqt`YJ9t4yjqL~864gd2oLif@6<>$ z36`IOP4zyQKZ2~k*By#kOgQ4Ay;qQhP&r(@coBH|M$77bL!%@B0fJG2kb+~1 z_n?N*)jW(KZU_aY!ECxHK%E1e{FG&fl@wkbf+e&;#fUS1H`V=1+s&zIdapn%gkMT3 z8dGWmD|`VP=gAWOWE_mxz~;Y8nPvVfcEfHf}vm!fu$>0#_;)Mo-p+~8wIWr zcP~OSTaAhVFzVDy<@=$>(NRlQgBJ;QlmJm=gPAWE{uk#(&^ zC}<-Plm$C(57pGVTqFC~n7`oGH0QEbN^`Taefffo@R`$08U}%9nX=g7#Z@Gtd-(eppWYdEJOqF?dz`=^koJOE~eN45xjS%kvk%as_q3 zHQ4Rwes*3rN{vdM8c7%0el6fJzv|_-d9Ce>%BQK?|_^(p0Tj`*PK~0AmWGDvk2WT^Y=XL7-U5TcztL5nY#0CBYj0xM#h6O%2VrQkaNcj+}4!26=8!{fZLKcVRck~}`y19|U! zv`sA~@6MP74=$)Q(CMK2^i8Qe=3mWAO%0Hu9SBLqi!cYT%QPFna3axyc7T5XBrzl! z$g(-QG@j-0mF=O=eVJ;l`rz3_wF55~z^f-YuU8NS(BL%Zq8a8q>$4VIK8ShK*y6_@ zR|n^SYk{@^oYP(a(=gX@UX#r0+)eyz#->90c}=YBJv)2 zFllLmQuQyI_?jI%jN?quYbaHL^1M|j!nl=deYeno*dRzfxM9mtA<8|CUBJtNj6eXm zoI!=c2>yT{sKAvC6<5UoI<+zVLj&^YMv$LYo}cM)*=ef z=KZB>cL*TI{EYCPOwdteN*rI z?Tv7h2-b)Jat^p<9UVBqd?0f2ff^K4Uf4#6M<0GSMXH$}t}j-ei*SvmGbWH}oYI}3 ztapnj&Ne`%28h8kng5lRl?~Ac{@v2G)g?F-?sZKlOWNINmqPJ{(~aLzl_v^erd{{k zafwKEenwRY_AsD8E}EiSo1}A@>$pwlp)c$M$Ul3*=guQnbL|be!V@QScl#6Wi8%7; zl*`$Siwnt3W$ts?@7{$@+?jAEyu~QxDE41)V*DTdyCwWuFHK--%Lqb9H7o%@O@s!i zq(qB#hGC4_6GAcPsh@KJKDmIV^}dG* z31=9nqbMNNAPZzjrPu>+S@V^62m1SoAF6&Www(!6yxKB_#r2qNGpD!<(;zAywH7Bh zK46Pe@LXnvuB7lLwBhxkr{R8#xy9x!ABPp&EIMFV#Xi6e&}>GfPee#kF7A=g&+zO^ zryJ$5l+{q+K(`?6BwKilx4Gt3vLyd8qlP&)7Cd#MSZ!28*O9nmJ{tohAp0}25wilPFQ_`lFp zuj?Jf)&^x-TT4|?PypR(0~kUG@_%(+xFosUJU<)tW4Ul%@fE8zSlq`AP5(z4TX2mt z&5CKuQw>d!n4V_@)|qjX^Eipbn}%^I!jpaB%<X%3TfKd!-DrMe%VG8%IL;I* z*j3~Hr+VFY$Epj+0 zUq};ja)j&+Qz{@n{!w3K3pN{Y`CrEG>gCxO;=Q`#E(-*2NMc06ODqOIBO#q4e}Xlcm$S*gO!0ntUUx{mWI(GTQ)s)s zNXxt0s&6hO9aI$Kugw-55k5iYBP1dof_{dv4c-v0Fg5L`o5;n`?yZEP1l! z5|aji2gTj4i7xR!u~!GN7r`4N6oMm#&-FoA*q+GK@T&3R=bxGWtuR;Jn^M z>X&U3S^j6&*~PXm{3Kzzo9vIc*845vN|Ecd0PHzPH0#!+yO|?c%_HEBAodFdDXa;? zCK*9WXtPQ*$Wmj{fU4I6E)LpP=}$N0Yh-O0eNbKyVx5Qg+#K!&P=}BdgXv$M z+ZRVBNpZHWpr-oUc>v9VAe+m()+%{x>#x*)6AGp`ByT9t6F+>|4e{2 zAF?FN9{*dd!Z91C;O~1cfoCgjuj$60;>5OsTUhv>rlez#ZwbwbFuHPy*hC4GQ(=1! zJ4U(BpL)r7jT;w_NQ{H{{Q2`2`qg{>^Ez9NN~b^FRshK^BxDC55lAk|jT=CU-5!g4 z)TrR%!vkI!|Bi!-{SF}ifd4~d1K1_N%t;U?3NDir{q~W;`Ku2_@#IlJhX{V- zf{cm;R0+Ac!v=1Z-R^l0YfrwcR37OpbBEyy{1Y;sd(;!0^qCna1hw(KPK`Oq76c&z zpVHi$)YQe(MP~3{{q?<6Zw_iEeX7+*{)a^bSY(|CQQlFdvTkz6i~Uf8Z5`n{>*9x0 z30Q?d;h@w${t&CY0h#r2zb8-$ri9=f&ikVfDR)fMU|m(wS^#efg5n5$9t|L3BeqK(?IBQ ztTedy+2z=b1Z;W}d;8xPwy8;Zt`mRRzz3%VrXNH+>?njziY-P2+x*M@yx*-dKIJ|@ zf`Du~E<}TyE9oE%&p3c{Olhg#`x35q%CGxMNj1iRK++!1kP<(9lJECZ5pOsw< zn?tU!@+INCrNCQn0M_AE95=llZJ2*Kgj&5o{8HN2`J>?NG18hBDIDU2=yeSak-v59 z`2dS3)H3t%DG|WP3z_V$bjpy=FcmW0aGgFS!%RJ~y85^=8i&<@pU@lV7CnSI%tC>%OPMh0w1M^T2f*T8em1FwRNHxx40g!7WN2 z^u5&%TmfoIzx#P|u^`zm@58-AHjmiug9I(yYG`71gv~|jhs{|R?n4GnSL=XZ2#t^^ z4hcwyV8P;rj0-gib|T0|$ZH<7(o#~B5#`I9fr1OIJ1W7=&^eUA=t!4#fH{~bq!2b= zo{AtmD7e#;WHhW%NH7KvP(-Jcr8SDRbWhA{BAdr`pdk{Tc?HO20IGIyfwjQ`e%-a z(e(M*bA&~!8YPXEvI%pv4>y@*m@fM9ULlptxWl_a^ovziZltMt4`m1(s!OZHPB%aR zB(B#Nf-co$e7#xZJ|{xa4eun8JBsydSC;0cdII21?W=QvbYeR_&|ogb7$;5F_^agf zJy?LSi(&q77;H2`F=}176gg57VXUz9TKE@D_#N$5hm(*0QS@_!RRkR*2p+o6YNi?6 z%px;K;5LVEuL9$Q$eYR6B>)O{$C0q1yuoJ zqYJALb@)cN_Dk6waY*-s+4vu%Q81@a2}rqwj<=Q4rD)HHv>=Hf--Jm=WH^qBM6`bu zvs(@EW0nYe2^*h$XT71BKd&Xl*z^JlG_*{(Dt$Hxk%fE+R1G0@=)%T24#+#H`|<7* z8Q;0h+*z~TLAfhQKXnuYdR{kVs>yJ49{%|?SNNUlz?6$iFul1^3c@NN8`>JnyU2=v zguAY2Gp$O0n?2I}MsaYNap`Y&s@*Tc9`{(SfPgJ;JFMfOy{=ihq+N@jtK%SgYr|7^8q-bh3^LYaVUTh#JJ;Ef2 zV;-kLDa4$l@bL7up>7b%deporMQA&eRyr25KE#v0TkAA9(F_T6!i{+uCUyfajq85l z+H7}GCYXoFDBkro?0x80A!k(SgjP}HZ}+U$e0Ill76!l5|0s}4idtI6{uo^38FDpo zz;kD3cRM@V?FoHS6c}0FScz`#4GNLLN5)nkh3a~km13KZa`)>g4r1K z1P%zU3RSua;4D!5{QSJPbO*D>RHR*=u>{x%^nfICukZ%1bUs>5jY?;Kg~Q^ap#Suc z_czFr7M{;ibY5RT#|I%{K@>)A{hgZg-WgMv1_aD-DQyvnu`JsT~C$YZG~N59?l3m5V2vczSXXHY?N-z-n-(Q-G?2 zQu_hGkEk%~`V@PkWfcCG6`+&xT<>p=HHr#9=@%g2cZ8QqqW1emgg0w?NLcNhK&3EC z1946pv&!c)8TQy=3xmsAE=xbgwo|I!_IiBHgSe#ayB$Nz*QOV*T>i86ZcPSm=yme8 zV$9j$5gr>fZDr-zLYN+%OBxgfPFIWZl2f0ecoM0ODUKD!X9RgTu)j{)4zZtNq;N@?zFvwC*vq-pdXIxdCo zmKeW16KJ04_j7e7wf)#Jpla^=s3gN^U}cn?a6RGQ^j-ESEs@V)3?Yoz57NP7m1dH9Qcb7w}@`8OIZefu-*eDOcI~)jE++RF7a&Lh!Vv( zhZXMets2^37(WIZqinijjIBx0zA>Dw4^?8k*QzYM=%QW=gqd&ZrC)+WCLMUGNtinO z(1X|o)_bx!ewE3ld_@Yq@6OAksKkitnXlqry$jx8`FBKlbw4S>JvB+LNpc5fyxjiQ zan!9dc=g(5rU6$fnV^4;=Nx_`{JMRdL*BZst#QgOw_r{y(|$1c2miIouKv*&8R-+D zmttayT7Ex_9T)1jIrAeUkV(Ubas&Oe!iBHJXUlC^c(oo2J!V{07Ja*yk#f6^-E9 zY>l8ma5p`+_fHF;g9)6Q9Kp)m{T>_?8prTy)wA56I ztzVs1K3FOTF^=}pq|=g^MGhYPxYrP%ldzHEZHb0U5Rydisr8Q{@6!T%e2>{FW}-RV zVUU(ETw=d0_i(TFcz0L~yNHYN^CceYd$ma;r?CxlBtX{^Ln!n`1P4=L#elEQ=+-UG zM(I;2&B)xjJwZ42bluGVnY)Xqv14QXNd1r5;+i&myz$;FuMOQ4a0r39{>dJsm2%#> z<2=)52s3b`*viEs--F10JhCv zRi8a`{iyo02*JnY;0r(JPrkf4p>`vqhEzRz^4dYxWLM<{At z84g;arvh*L+36}(9G@8MS-HHSE<3d4_MN;-7`)Iyd{*d>zG=K@(1*+*CqX)PnPA~! z-9hp2lOJDRoi%~AdE!1}SKiZn2F+mDP=VThHY5P->F9vC*oGNk!@sB#HI3^|P2mpvR*#c%O6C(MkEyFWQhxK_n&?eK z(ME9{nF$mLSQG2QE6(rU6evv0xB+qjtFK9Z{O5mO0Q;$h>k|96Tus{hENO>8^&BeU z%&R2=hl~?X^{+Q(FtLaDd%QCg6Dl;36!OqR4TZ%>yOf3gAI)dOiylhAMrm+#24hr>r_Ap5HCFDO1e<*Kdjpqc^Vx< zL?c$4)kUeY6a0)4&vU8bQ&T@tsHT@m$;!Ikc<;on?(cf@=5|Du0{u|a^7?q0LM$w1 zE*R#uq3y((GptM|RtxB$7fch55vpJ4rJ_HQ(e zR08slt3ePs_Ia4l+b=;gGBvV717Dy3vFQh&`{Z15Fq$=RVzaL*W`vw*h0i66Gtip@7_Fcf<@oeB1g=)DHY0;EKmI+DP%X#xL5 z5e}7nw+kmN9UHNGHmWpV?^So~Xc(M^#0XA;u#Wxy z{X28yQL+WRRfz9jGRYr@if`n#L7uQh6BNQ8f&DRFqN;};H%QPG>y#sbQ%z5 zh-^lJ#*KU&&@fhhkQpG9nP$uut(CH;MMFnN=b2ZS*}`y*q2Y0L^_1GT={`3or-g3^ z)WoQcbbn#@2r|@I)=z(#k2H^Q2r12h5E(R^f0pt-^3D+nMk(rFl|Xavt_`zKmnB!& zb3tkLA7h20=}YRu#QTS4=pEZ1j`~W4hyLNxclJ@m;4--vmm?ywJL9dGP8*rLY?$BH%s>0voAG#{Nlt3 zo|%BL;U^`6_Y8!x`|}R|qd2nK;=~ELR@(Xp#2MG|FEVxp+m5vrtgzFKnL93o_wli( zAWG=bE-R2ll>)xKC$n40anQOhYtSc-$N}kLdISt^$lAbC1ZxR#0?U=RXsoEMy@N-> zodQT7o(v?qAJ&Cd&ivN)dM&L?-c3ezsc5Bdx?w280n`ls4!Suq6QC7XK0NJ@BBkab zMbGY!6cwpbDYzaw@-&Q=Y7=NbL-_q+Ny695b0?%Sg4FqN_yyO@YUwwr@$t!Nf14SW ziw%vj>6=@+-B2uFKV&jXveg^Rp$IGK>%b``9KFP31z>zO{pZR1c5~tW{9%lNxy^YQ(R((Qs>XC{W$|j5UPKG@vc6{3ZYh@s~P6>uxl&7 zF2OMGt#GV50)6G}Hsv9ak&%K04RZ5uq_W#cwleBU@pV~VE0zRqdKSA!!{P-f)0LSP zCoFO3QW9P$aAcOVI%D4%yw%9wR0yTbrS9IjOWpAGSMX4^7r4Jn8? zqr5h)3^~L*b7Uzq>tvm;H!lN2tdMv%eyFf z**4}(%q|fY{;+U?PX+kG{-AU`c}q#hyEG2x@)pHkv%z0vV1WQ1P7KmsYS35B{zlPeVMvdsamUpbFkMK<1j`CmVdNUd`KRybp3m@UJ!E0LDi}<)F>d4arpKA)^13u_p9-IvP zEzy*5;pyE#<}V zpar6WOy1A;2X+!~6_rdy=ZAv_Rd*j0&8NEEEB;M+;!>`YzUs%Jk@EUkd6A@viR?LH zD&M58-AK2*-_8Atz*@0P3SGfQ>5#ID4vJv}us^E~ge+@iv) zd%3&oE^<|#|0QMOt$W8k(C7sA1#_Tcv;>hy(b1Z@;HG}8qJod1Y09wn;r;ts_k>zf z;fVN&4+a}AnLt}lccmZoeBhf=$o3_ z;x)NtKCOK503hXRpsSZUkfOcJBVSyTdSN>;O9ku;;Wt1sg}|-h%)6J5FR{7m0S-yo zS!8D9K(u=kTnkhOq`*$Mk%K_rYMI(j|9*X9Q1fj8FPRcT9I7%gd_NlfpokDBeUcCc zSVZ*SUt6xZW{55I=(u`;p?Nj|R^4<@o>W&CeP9taG`9q33e5$X|3C5{`+jg2BMvD8 z4Nc(vAwTVP1r%g5K}3{y)peBIo@z6XH*$C1?OUNDVq*92xpQXBf%or+a-TXu`~b!t z0c#_}phs(6eujWwI@jrrHKYyL1V3F$StPF7By9bv&*FQrWr!;UoirK0wh&Lu-2;0I zmB#zAWbn>yhSf>I7u5U_Ui0@lrO`*U&BbUXVUC7H3?kaTm3Jztaxu1w|ThJ1FtRdQSv8txRM%;${aJ7#Ko0sr>QYi8NJr zEu#LC!GKD!T3et8fV^b4mHx-%=lRWJp2E>Om*)J zpUS_IJ23CBeJu9?R;X#85rKsSF(d(*1zy5{stsTdS!w?IL16Wle?n{?vGwaPLu=%V z^`|GcUIwdD`VpJzz7`j83HX&LUiTjVAHQzY|2;)@_S*R8JU?P{;8)Xg6XN~wOLE}v zf*P|Cm(f;5{G66wq7a@ZmP9IAbM>kR@dF?KyFg0MWD8>%4jds+)UWohoxt149bCTj zocJ+|C=fOvubU-XtqPhu`SF%HrpjYGc=!^h{t?=arUUemBc!3)Mv()O^Nf2lF0z*P z5V5>B{i}GPEnv=`TNxMr?08jqtN}02F28$LFTAz8(?Jwoa-a zI}Zopj;+VMJfEtZ(|#)WHkoVoGG0b>=lUuqkM6q1`A^pb(HX9zgF@#IMueiV-=G^m=DFO*4OmLF z#`H~bv?^lIU@uTx9E|`z?e1Nha?_Y3&M4x$5_}X;G&pGMrpotW>zuD9Hlx8wV*_JM zWK!UYICYXk>Jr9Km>}-LNw4gSCR<6bt^d#bm+7UES* zr_s@VnsSq4Puv;dGZlOHBkbv{zqHoVt(iy5Hlz!ek#72*5HEnd>x(z3d*z%_W z{|~2y_`e~t#QPAxOhW&k-zFFuT)h)#_2Bxht+vxt`Tsq6uj%{$Nw%}Ta5PCn=Q2`3H<$N%$?Yi%a-!~I6u|I_v068|3Z|HEa+o&XjR#c5{s zw+p&5b2B%7mWrPEUWT!nw{O6|BaWfPSVsp=U=8h`EQ;Lg@5Db*>tL&)ZmyHnLJXBE zY{%)(ZnHwh!z7ICcD8Q{qT{MKy^m?C9#-)BR_9e+J}RAA=eJ~7I{Y>ift{%I*JrgN zk)d~X`A>>+O|;EBXey-XURh$t^RYV^uVNpa)hXbX36|;ZSKXcd@-_^qf~gfUG13Qz=a(FYHhYA9CI&alxsX$>+XnU zR?n#E=zszzrllf@0Qe*FgI+=hxioF)1|l3uGVA&~XM2{1UCt2La)maauk~7=X?&f_ zm%)du^q10fQ0agw!tBedY@K#Ioi@0~u&{pi&L6V&ZO?-*lsBH- z2U?V8s=EkP&xQF0p%lqSN;hZ19)Av_Uw3TR9+Sym5pZSKKSM3}~G zY~V2K?yU(1?SN-%;#&2yD5OOv9;Z^)_%kNk;OGOpfH`U~^G&w+EP1P??~F+|&&inT z0yx0_EZxD}OfZ`_qGqdSgD2vvXD=7-xT`N%Zc86^1->&Nz`fKL=5e@xY|pw7n$0Fr>=8{O+|R>ZU$pc$^P zLEVjUDIG`Q8A8d9nV1kDG1wugYXWiJ(AapRK^BX=ygc+KgloVYj6&x_oEmjr3f$os znl^f#Qu1#rAYsW^Z@UFQ5562LZ7ah;;6jN=g{eNif;@~C1DgzX9Nl!_PXSaA{)n-< za27W<(jr$vcSNWKe~7#)Lvg1fJ=XqO^*iQ@e~B6h{oUs}nf{hl*(j~m`G{G5n36f$ zqP$&ODlK(C_y>X|0{;eLM3Q|6c6JPm7szzP4M?(sWyK}Ue=B_Fhe1+;)(^{smafW& z_O-rF4T1v%ZT25_9|ku&<94-E0UwJ>%q<4WRE{R!U5BG@s$W=}n{OfJ2ot^G*cG6n zuyqZw^VTrs%MZL5!P%o54a-_x*u36-0A>NkEx=JsI)FkzFa{u-VDiFE5E=05)$slp z7(jb$=hc0Z`}U_WkugSaituF;1E!u#`EoC-JoAl}T%T2f z;fmrMw6Fz%b|t2oqBOCU5k6D`VuEPBC&qKB@bGY-5*#U_TE=Q%@o|^jcm6PdDa7C# z2HJrCMm^xT5MEl)`dTT|7fz;Y7%Fhk_G>c3)pB9KA93Ky+*ydL=V!!I{_W6$VR%pi z#l`JLr6Xobyx(+L>K-a3pXUT`uDw4Ty{BlAKO1rV#6Zaj@|Fn_ zK}ObWw#A$Z7@bjt6T@O!Mu-WVLo;p?=M6dUDURK}`R2*MjfH2rRNY0-j$}Vm%EV|9 zLL0+165c4wmQ1L$gpKdNY6|7W_p#ZW9hWoO%Ug1?88^6Nyi95;Hk>NV@CbK=lOF>P zh;p15Uy0E{lM93K2s-y4U=UyGbyy?PH?`w;ic|u{upwURN23 zEQgt-`*2M0Og~S|nSKU(fw&T*H7IVqBHfN!7w~2Sg;KoDrM7S2t6&OVA{gM7 z9OkQkJO{CgCv~L7Nmk|Z<;&>1itys5p+eIM4k-=>0u4E~BKz5|kj%)!7Q^4!s>zsI z_!lo?e5gFJa>N*d_2DlK4MxE7&BO0UWb(PAmwipexi5^er1>^#Ppu$f5~HuyOMTc^pTsXy zcCD?>;h^=Kf443Kkc9P7Z_$HY{cI6N9za7NMTo)#27H3b34|_z5GLmO@QmS0`Rm$& zpU}Ii=izjAe1y~g#ok*6RoRDMqX-BHo9=E2X^@gm2@w<|q(MSLK{`c1x|9%*77!JX z29c2NR6@E%x*N{5edhn3bKaTr<$OCc$6=mvcx1Epec!(;*0t8U(p>-pgmEz(u#DC3 zE?^n>&q^Z@BwqUl!W0OI2=Hhl-K@?#GtO@yBPw<<3JTzYz|lpXH5m6o7%@5N?{WH9 zt?|-o$^Etw0{?~J|3(C2dZj$CB`FYa$ez63tfuCNGzj!rKwObFE#QVj7bDHjtBT-L z^ge)GY+M|ol*3rn4>}-7V5=@&rAmNx%Tzr23_KeERTyaS79aydUQqts2UiEUe$YRD z`UIs9vM(ODZ{kBh#I80hVmnL#YUPs48Sa}_ql^DYyYR>X4BYV}^>z+0s36c;^10EV zi4y+YSYHSKKiJ)x^Pm8#2T!8NYA8%CjF}{5d)c=qHgPZ5TYdo&kSuQJ<%RMbUJuyQ>3)}bokGO1h z_z0!v-uoW33EfLb6_Y59)X=wrd8HUL3IG=&jlg@_O_2tWZS4Rahov`nc)!X1{JgyM z-HU*5P>&if1X4dzX}@!MwgqNgNGW`p2ITgRJL}MXek($?KRzypJ6gmr* zO-Kn0GV1^aOJ5x|;wcG(d!$|6*Sb3H#}|*$-lnA?Ni{H>M4n^lL3LF={6X~q))cTI z=EB4zMu<9Kym7wPaEYXX`vFs79!Ajcd3WoOu28V%@YR%K`~3WTT;PP3cLM3(Z_!BJ z6yz32T}U|U-MhxKt%ew6Y(^RoAiFn^lYvIf)YDTRgM^9Q4;U&@E)4GIGSlk)+9enzplLtqefj@ zTSJn^fR(@$=P{ob2w_8luATbXcNnF$CJBg=1pHQ}_YI1Qiju62F8cDt6o5M@gg`I? zwf1xZEs6d<J6T0lR?&22dRmlte*6nm3=HfA85p@{$Bz6r2(OnqerF)-`GA zwAEV4l^5)YbE%Ue=Y?=fwPS zrG=aeuupilDHJOudg{{ygrj_eK0Q{yVUT7bnv9EM(5RkhT3@^uNPCC%lZ{rZ!eFZd|dy$s8GD!d+5vLhls)*-Cv6a>E#fc1CLrW14vh7MN;OP1LQqI|x$)3jlP1S!>^Vsa(r!U|B{f8ZB;PyjbS9{6(!c(s(agHU2aGlNu^5Dk&N~ z@CAAjRyXG7WJHO;_^*AfE$ws>`mYbjS^H)$2M%-aG2AQOfISju!C(Y|M|S((+dvA! zmy-8i5D+Q^H=Dtu1(RXu3SpYfw!Ei@sbP+10`IVFe6qr7+OK%&xhS%3fTA$-$QJVm zW~WfYC+$%e7K1Pl(i^-DsQ8|*4zaeFml&2mQARr< zDj3NEOax5=^fV~-9GDVcoFdtY>1g>Pcq%}2CI?J_+zgncfXO3tP#1pwqm#^Q{NSJ| z&=CU^Ag?-at8@U^;0TAeX7@aZSXcxeHk5_^D}c;F-%Q-k*azcQKHz==O#;o=PQ|u% z>a}eO)B(s229^#Swa6H7ok23`TTgKf0q<~RdpNNH(w)h@bD|gaE6dBs&jD>Po0K(E zS?$a&B0EkER89>uN$qNr6hMK4WCX_^h2h!n_jz?vnC8*0RTBv!87zI$}l z1xjx~50I_;(HXe3!R0{S_=r}dGez<>;w2-x80&s6#;>*VxyQ!EqsY!31|+Y3WcK($ zM+1os=B`kLK!H;8vf|SxXFkhbbUGyjrz9OdXtpeq6DsD^a|S`&h-{~7r(o_8yBDH_ z`GHA>#J{rs^}!ed0}Xx{#;D0oFdqPX6a4HI0S%C#gGDH^;7bafHXscDjG>{a75Hdh zH311lpq3N07>bX7rVtU3p~c)adv+q=yi;>5M~8{ok3?s@Dq4dR-qcR-j{x2e^-$;! z5T$@68B(YLuI{j%0bwzE?*`#V1M2p5cf*(M`b8k~yUS9#(hmUx=9?%ekm_KVA-?}^ zOR;B;xc#TsC5=0u?Z$|g-b>!l7cN3L?CC&-fy9j*F2ke_rUK3yB?ptl?WdWMb5PAz z7=ZgNFj;C7X;*=~LxK$*TrgxKaD4^gI4DRK&ws<%8-~LG0(3{TM$t9%f$b9@jv(np zy76osL6rdyCTO#Gr56C|ga;g&?3BFD;NO^m0fwoX@9oU#Lyy>0)4l8H0IUQR2zznB z$P$f$k{?*XL+=Jq0mLV%jQf3~>jz%&xskL9{gvkEQFwzujO(>d2PhNddrgod>-Q`Q z_xz}%Fi<+eF`RmUm~6 z+zTE5!1Pud{S~cEQ5+-z2(BgAFLl=32FMN1k13TM!ve}m_z2L_!|jG-4>V9`O>~(= zMu>pO$}JDsG&=VU`q^!6+(se$5&DM(`0PuKaNgioA?tRchdTz@QD;sfQDdJCb>hDm zkPl7UJ-10Kw!kI0z9V^W7+Hv_BzgFOG8z=OWo2lJT9%1m!Pl`n;#F7wx#vEY4=<`G z&@6z;f;j}E6aln9Q19({SOz9T1fUK<1~m*cVEq7llD%tzRpB2QEY*I9aoI0i_Irl} z)N7Dmk+Xjx3;6ID#eNMYQr5LoiL~S~rR=AExgJbsJ~V1!dM1F}!B_VMYxI=gN=ip| zavn$7L`bg%uw9^ofuJUkDZcKGhr>`fuI4o*N?f9wzq0Q2&SUMhg^MO2ZJZ(~=086P~D>_aVc}@87^~ zdLlpxL@-EtK)16%)FjRXP}scgADDyC13Jgmdv$p?{TVOBTGng05ot&wj7iL^gi3y{0=^wn8DaW z`gAY`x@1dWAnt;I3kE}gl0x1B84B{=k%Tmxom|4bbeyS=0!mfYj307Y2UISe%TMqW z2$)M_Lwq0M-Qp!1FLj^sCBHg+cJ&(R)A>hP`VCIzmNl3HAO&6!;gNm?#$-po^29yZv=q`sXQ&WXUP@9HQOhy$B5MY@wguB+b+A@@uANWVC~gsUc4i7k#%?8BBd4>%d0^ z(Q2ATcTeo_sse0n=LNXje2Yq28Js^@CE!qpc+C{k{p~}Rut!ACrQqSp$Fwp z1V*5IKP<{%d}ayH1kC8Rye(vV_(}C|4|=-sQM|VqP7O3x9u|Un68c^-@}Cgm1t2zZ zuZ%&afe{Cw14I-|rZAv|fdve~;W0||^sX~bE_AFg*qtmNw=b0dH?NK*;g&bJOttoD zWo#*P0E{55lL@dg!H(pgo-F%80W?d{lKq%_4vuY|JQagB+KC+zvrhkp3LVsfQ05Ak zirY8nOWHLjTbrfL&_pT0!w78*vSelkc;YpZQwU}H+AF;WKzW8CWT$|OmL$V+&g*6~ zKqQRL%=Wzmc>dL61!C+AZUp?tT|f&bi*S|{uvN6_7G%Gy)(aRvbTS|aU2NI|=r-=I<>;(>{OfSX(ngW?TVNAJ7SWlh#AVF8 zY^z_TIO+;98fuqDn9>4#RJxAu;qwBlrvFg^cLAoWKbzX>Lisea)m>v`j$1SMLI&Ls z5bt{Bl9nH3LfF6U8l-r4K-ov7<>wn;stGshH1+KELP;s4HBq8~pJoj&9$d?@p-Rxm zM@m4DbVG0N$2?ZWwp6h61p>|@(TFp1#}JYHmE!RF5UQc;xcI`UC;F&iJ&x*hBKTgH4xO_04eW#)yZQHnQQGr zMG9ptvO+lX1+ySf8&FdMun$)ouxemfk88>Z84}*Rp+KnxkWh(UJT8lZQZd*BYqED# z5V#uoXTQj+D9f#gb6Fdl&;7;GZ5VsepJ{;fe z1QQ3y6re;E)9nzt2ShdyVWOY_lNRCx6qOL)xCO6ge}MuS=|v9HAP9eOT>*WV*#dwP zx#tO4+owq@z8-@Z1Q;8bz4IZ!ye8R&c?S^%p13MJht0m{RWby2&WF;&_WAo_JVW+_M~ZjfZ|Q4Uk-#rc?>-! z>Z|W7pJ3kAzA*kPXw?>o`>rSe86Y8`E)4C)1z+Zrc(^MN31G`MEa>P)Frg$*;fFLk zZ~TP!0s|IEqaG4GNh+Nydytc2d!4_u=ff~-VW)%W5Bxuv4nY}+gm~d0g|q|Dk^jE3 zf6%|*?*zdJI!>hc4*C=zQ|l1UThCLR_(;Khhno#m4^+wU1%IhoUfJI-KR49>%+ZHh z(f|xIq~Zgx`SGu+Ukb&=`yafg2KporJqol{i+EGJm*6-@K{$ng3B?d(zs?H&x1Aph zqZcxBJ&hHuS3VL0VE5JPh9U-pHI$bGE_2?5A&-=ttIpWR!3VL6lw#O$khYFsp#=@vW$v zl>~)mY^+(FO&Q_XN<`5Ac!+gGC9-|tj3;fUyq|3uJq%F@^6}bz;ApB0*qlH=wcRU4SQ(0|B=MNP73lbTs%0QAh4;g$8uH>ZUva} zg~EHZY?j^>jxszF#e%1hfuVriyWiTV^Zm#=?fx1trPOZ%q!hzi{eVOf+B~^;0XW5YH@pf{(;!eN zf`QI+(fIMZJ9OwyXwBlI1CUW5@lp%TzmBc0Ol!T3qIjt9wnY2QnW_W1*=7A8&}pR;UUx90uPa02NAlo6M}EgY;YPUwM)=2pFQ# zI6wb8H}N#*)f*_zeQ|#fV!w}DRT1LuZf;;Ds$cDrS9VhtJx+AE#7X?_MGldfF zcxuZp1d9Kf#O#g-`5?IilA)Udss7sFdNCHpbvq>r4;ltIAjoP9$Ym9MszCZiR0avNj zKq2kZqiJ&sIqv1;R?Cn=2!Zyq~Efk%#W4I%%tJ)!QC`#4C(in!K1UNL)_dj2qY z9TXWh$@UC;X+?3i%oa^-bh*~Id<&X){I1tb@UrJ;XzeLL{U@Q&8bkAo+>-==CAcTR zi0w&DmWShP=a){uEr;T*h&RJ9q_06J!(0(&po`}@E4XBbk2PjJm#OLYB};e@|I%@g zeIR$=Q{2P1%TwzacZPfK7i+U&0vi@Vfji!!gm~Y|V9-NdVWo8V+jvb(^wfl3THB@Z z`qx{>Rd|gV31d8QwBmoaXqNTW_%d3ZEJd$F*Drm)e=T$4_p|A*|7L5T6m!<{v;+TT zXEVT%q1H~n11&JdI~W$|hIxPvVJ}QEmK=cr)4eC1)g3|lG2^m(rAOjPicbQ7dBB>) z^qF@=PzWJ!MSEnBzt2VGTby$yZ)vGGdrNhvwU=d$#?+~okoKvf%1N~=t4@!TeT_UY zRQvc4NESM*_xgdH$s+0KFnVh>xm+@tx0XIjws+fMGS;=61Pac^cq{Eb7{7t(ZlL(T z-HgD3v?P=5c>+)j_|}y=g<(<2XAv;Pg(BpFtSL>yqLbkILl_3}PvQ?qw%}MGnS|$| zA6aBw0~CIEC~Sl{1>czu!YVNCq(@MhK<@`%5YC{v0kU!*BM~L#Z0boebapVfh665e z>)WURbI#PgBPP|M7B8ETBZiR_VjP>tweQk@$qLhIOlor`b%OxTt%l^q2SrR{e)>U} zB&>%IuTJWC8GQZ)ZDY@;2&LOlgu{~v+)ps>AB~FDI>Vh)roUAQyC6XW(fzU#%HOr5 z4yEwD1NMx7GAkGV(4zKi5(A-h4iao=IRJwaK)WR_A2H-qGQH{)FVmp@UyVu-kv0Q1 z?G4ARyxaZY7z9%{q|0r1!{u57>`HqR@r+)ina}bqb=({wYY~%XNut%?Z#;qg{Qg>& zH@@_mDv?i5+iCokJ+o-|kx@M?`K_a-L*TnA?Y@GpUL40De6WWuqBPEpxH5g@8%JjX zk1=KX9@08svn1jGpi^7Qn8Rutf*antpm_56SklR-rG&h_UEE|%M@(bkt~Mg{frl0) zLnt-jNU9L9g!^uhjN;iXcX{;^sQLa!w$bl=K5)4HWS@q(ScC$xc&;B8M*d>8k_pxSYBb6ecS z<9+q#>C+0`K!javwXTw5!(tIe3_XA$lCe87q~FFxM>x3mkBXV!Cxn!OLZO4)F9LHW zB-i9WF99IU!jjK}mR)=yKME8g!a9oc(F7owP6E89kxvXFf2fNkzy}Oz0}g=Ec%|>; zNPIS1N#xtmKmTd1tix@xq(BP_r3ln>&qaG4%>z4-mw>z)NuNXV?F@^M^y&ZM4gdGO z|0m|*|NqbaH!9};hc1ZQu+V*PYUG_piU9a6{;y>4|9GN_Z~mjfMv47nE5g6~-NqiM;?-Y-}}ei{P#E^`PlzGL_MGt`0s`PPo8Qn98TmPi)f_c1^)c+y!HQn z^!@*OcSwc6f8YB*pUHphSNOGUG)UIwe=qlcC*;ESgqB6y|JS?wUoVG@`Ts9JY#ESb z--}_kc6QOlJUsMbK)y_x!_9P>V~*L`=r&TcW@dr-Dc7pD(NV<6Jo4AnMN*&>>6gU2 z{FJJ-*iU~Wwrle5vt%cc5*z2J6X%(Aq(WK6i9&($8Y4564`d~F#Z>?UvQK*JF?66Vn zH8Y7$aMs$#L^qIryo%$?d+*r#}mDhquXaEid71Zcox8t*Q_x|XW&a+I_|pYhJ7r3IeQQ0CgyH8Z~VdDpCj1IVBMbfci9G| zHLhyS8GB;5sJq{dADNiOzwquL*ag@vmw4b^ZS-rS5Z@%RcTL1ac^o+7 zfZSc?$>OeyI&p;;E`a(Ub?u;70&Hz?&93_S68Cu)TeRg*4AYnGW{e^2Lg^mM(m!6B zBL+S#?$p8->*Eb&_w8lsXY*`V7yk2C=YvOcZk#4FS!kvydMRlw2cuSTGltC}^wUij z3HLnSQl9^5xIjrF6X4@JKlS`o+$@I?0=J^Ps-5yPqrsAdl-HIdtj^lKGOI&92SstX zTmMV;{Ris7pSfw`dW+@`x{DWvf}b@0S!kHPm^q%fI`Fs19}1jz`9Y1{wER^zAgcCl z8*M)%q&%*3df=+6(F~^x6 z#*jaPa}~MzbIl&sC$HOY*2HqcMT<)aE9|8K4JcXt@j1?)>7E6xpp#yT;O4L#ukSh9 zI@j0T-pSLZxlvab(9+$f2(v^ruEN1qe|}eeVY6UJw{7%_cuF)Q-Rz`EL+mh(}QekGA%C?>cY0XXnIU%v@<5^*nUg!g-3Y*_ieaj=q`hz#M(* z2^S~xK+xqw|1jnH3tPWE`r%W_7yGgy_FE4P?`vF?ma)+;X9h=AoFQpLC6yIXR>k-a zDI2x-jV8|Q#|s5L)=k(;lGHy6lM1JX8&pIZaNrlSh2FJCO=-XWkTU5)w!9&qi}iN> zrT;-#=#Y>r%1d)pK8G0TkWbOiUS+k%MeZD!=pe4Oo^n~|vgYeO(}Pa7qxNNDzf?CedS_Gtii0Ncs&=v|+kAdeAQF%XKZ3sdB@q zhReWG_dlj`A?CjuF3WaZ$%r(22HPjxHcpL-KZjF!QJa1Wx#~8TkgEK4+RIP$b_&<0 zEMlFqZx?iXZ*0EExpNz&tJGuxS{>}(ztn%)kNh-EpKMgoJ zn7Q&loW7-3$}Bl>CJ@81wf4wp;%NGC8s**Vc0?ZLd&VSr$LFo2teZt2F%{YOGueWf zNU{b3nfAM|q&jX{KKh)7o5C1EqlOR@Y;1Hvv)QVBc68ud$L+3e^>EPy-tM{y3d%Pkg%?!iwh;O%tYfr~8{8r#3$P7$1waV$E>ol|tpxFU^<3Z2__t zSl7r0^X6@$r)I`}+!gBoliK&56@&bjpz<0=vtP;po0r>tUQbEwmMmQXUnV1ITPHU! zpP}08OxRa)fmmUDHrGDgnfP;LER}gJK;bY{B(pYc&8-#-rzzB?n#r6uCSW|P;-l}r z1-iqJyX99wTDCYA?gP_p@01uO_98}FOn-Z;D1EQHV{%5QzCZirLKn+L1AD^7IbZ#( zzwgt2UtRa>3CFkWY-;O5e@<4~9av>ojHn`m`P1Am7~LQBze{6lC5}5q=TkU;6lxOc z?)ovW7OjPuc~k!xvk(=vncH!D{x1T$-2RY99Te)Rp-d@J1{4S-h1&c*amA4S^?i2r zl`V~swCjX}_e1`OKNG4G8Vn57c+4ylarnEEykLcr*~=!IoZJXCblUpcg;HTf=%IR2 z%)!9DEw9kShJ9wkD_zu^ksbjhvJ;t^6rgtLl2!FP=QoMbB?2 zO+q`v=zPKz6lzH|Xysv|uHLw%*m!A92ZTz4D=vaKO96{Ficg!GTNRY}?a-9v^>~|I;zo zhn+D&NqU!Up6e9D@(uUBFXsr8W3nC(8zm^SPf8THo|UC-JbnI2h*1D0%@L8U+*Jf5R`+9-(2TAW438Bl7(aW6~x+~B4KB?$W>6LHO zTY}RkSKBMS{!(Y0#;;yfx@)8ri=Vf>c<;+N-;S036 zka9+jtuAn2pRsB7$`h}fw{Iucb;Xz;XBqb}kBQ(*@335E^WTwk;eF`sf`>miNNiHS zCrj=hBQ>!X-R|xE_l!=G_n!Oyeb04{vy(Eja}HClo7M>S-oLv7qeaHx)(%!?t%<1P zMZ#so_fYP9uXeZ0a}U4oy}GdHWz$PHm3-Ba6=L=@*H-8$K*6Z5e5CGkQnJlotMfS= zHJvtTB;Tf-6(r4wbHH(1r2VQX&Y~>^U~EDwX=36Cft+dU{NnHpnC2v?UK2?-ZTB}j zI_8m5HWiYS^~+Rs(~ZkA#29@#vF=S0S|q@T_&sCpwk&+Bdzj@tS35ra+`x35C?2gp z6|9GGyOx8SwUra~BdVPsW;Z(oJ%m<*gJxZWIg8+vB!ijcxjnx0sHCq7qfG7n4F4_f z+Jy?b)65qws_1#63s%{`bc_@qFe7X@2<6H>%T^6|6R=i~z1@cDa{Ou+$nPv%<*p|h zO}?viS2T@pq#&lP-f(wyQkt0KMs#%Wa=LzyvE`UZw-~e?_l>g0Ni2OqpzmtGSn`Z-F_KMF+O!LrTKR=vsN^bg772)-C@~W2=zIW`@=KButtZg>BGFx zK99o6nCy$R@tx~#-%(v2`-yv`wA-v0y$TV@_Go&W!NM;2bAgy9Do;{ZB`C|yo7mI; zOEk)KYt$dk*L8v93U&lH1PC3kb26!L{Enl<_ol4HjwhH}xg$6vup8oXfv1$IC?pkN zJDgr2nD3bXCVESUI{1+k1~p@0yVFEO1YNw_AC>YG<@Tkrg@V5l7Ic2)Q~8A1YN<^M z-}UwDHr$pe4@$~J&>#JbA#ylZ{Lbn1wX7YHc8~oV^Ci0Ut7}=fHc^DH2xKXgynmIl zIBpqL)%BGcZ|UDR8EmJee@a4XdcY|uNw;u+Ya#TJX5|X$oQjKQGUL1nE%UQci;=d^ z_QgrEnfEN+iQ7!wv2!GZ@b8^zj*UNiu(6%$xD|)_xtrP4S}b!-X5DAvz+-Rw?>prI zj&J>H>JtoPB^T@!GUfQkMe@VzHamkV6?>Z|2_h;MITPM$M6(sbqE|m^-%EQZ%Xxb5ON0R z;zljsJjy2@FS_NtDjQ8N-gMki)Lw=iEPGw5o_@B1WNmHYWt)?az^X*2B{o~4%xAYZ zLIgL$u%T(;l({a<6wiR3PH?x-iKbS0RmjGNON}Uq3`@nPhJA`DA(S|YQEs9<<(fz8 z-3i4`(X0`QQKmTqy$9KWWKo5#;4Djx$^0g$4b~FwLP-bgd*I&W)?B8%lmz7Hez z4~j)>Zlli2fkf;1f>V%%S-bh*V8QJsySb_qb|2IQQ+Iwv4TA4?2g`(~-87-`2C6h%Pr5IY6; z66=RWpM2G5;hi7eMW^FN|(OaOBpqjL4KA0a9-l=A=a&5A}9SZFTB{~kOpYe_2m1TTxk26u~ z^r59P;|u2|I~GQ{-1Te5vu5fq8-J9`i&UWI`qGSI$VJAnyiwtr*cc@CCT+==wH(w< zS03193Ji-6l4Ee77NJxo=IAz{7Q%k4T8@pC!mZ7){b5+xfP&}-`Dn=iXUh;34K?ZU zPb#|9)QLX~EeWsGhZy5jnTS>5H{6tMW$%>VCw?u}+a@e08q^j(&4?%}ydj4rheKS5 z-4rzDM#J_l{J~WFJmm+LIw7MwbC)I*ykb#-yhu?oR zb4oO$Ww673VyI!-keZ6&{Vv_9km(BTqsfxRLM^^-c5Am0i`t|b9g9XSR->gPSN1w% zz-Y@6TIAOf8gg!0Ikd_rbE0HL1A0ciEb|fEq+d%bWb1w@Fx_o6EKR0awZvomqm;@N zI)sgSkDweY(5j`(hhdrNjnicHgpMYYvrwj__Hg?VBOj4N&0Rz#s|w2`TL>|6L7-8v zLGZCLTJiY$rc+SF2u3$irf(#_USgEPooXlY?!V(?7?IdF0?F#h3zJE1$aTw#lT~s* zcaXVmQyjb_%Mn(Ip8iC$X;vIB%mbmOkjKm@)OB=2Qo8GuoB(?mPi#a_P5}d9k@X?s z4PGK&b#!B5ToY&c<~#?MPvz6wl}}wGN5D&TLG*)wzBzb-gd0D^H62J-b|`B*eT2)F z&Y;U-)^7h{>;~%9FBulXqqjn1f1Mo!75=uGv-gEN?Za| zg<&48sMfs6d=2wxt9^dYE@p_crPLmeR!W`gp{@6I@5k#{ugTtwj8H+B`^3?a&Q>3R zzd1ivVxfK39mB7i>qC~5sAn=`>FJcPQRua(^OjqnfF|K-aNQaHf_NYEeQckZg|=R5 z4_x`r%Vzq*^{gtt@rK?=W)We?%Mf^Y)GHVWGsS(-RDa01NXc%cXQFTIY8%#@pdU*% zk=_{9dUtUcaiIG#Ud_I!SU}7vwGscL2wJ&{c@58wWh7dN`@;@$=76j~Tnn4q)L(dt zoGhNcdXy8e;@R@GbEPbDV+QyVEOD?7{8|<^%zmCYcm_Wzd@W2Aq|h=!imBH6q5 zX&*?j%cB{&RG5;iJWO923ggJWZN)6JcX(C?b3;NE?aAC&l``whjHX7uMAq0x6R7ka zANedjUDHxU90%-$;<`*9ldWop&+TYAqmd33AW-}$zLspiJPB4ze2N>nWPT&e0%4}v zEU$!F@4)O6Bg28(on=UCY4bHB;8K;Pe6{0N=Bt;#4qUCQ$g8hRNoS6}1~H}eY0|>t z>-rp)d7}Ic?L*&;lNzyQax(pyFJ=gq7uOHhr|#z#M{+O8Gpccer~f=pcek359Q^{z z-J3*_xKVN4dDmKpn$Sj)=s?;KpkzMDB0$HSpk8X-FS;bhNOX#lqaIYeL~50vzb#2G zy=_YwOd9c!ldNF>K&y-N>u^{M8#?25b8JGZB3TmI*q9!xHp7QN2Qj_Gz*w#Nl~mSu?N6fOlacMDa?@DcWp&_8;H*8QZB$)) zK3TcbMgi-kNxxRqcSYTSmp~KJSfmwX6t{U*9_NNVL1Z(`VsksR^#RG5+n{dY8y9o? zqQb8=-x{AfpfR%SXh+MLB)nTIt^VFPf6iieYfW#+%ER(~AZD~?hxJ{S_eFck)V&}@ z0$Pwwu5E?fdE;y~&#F)2-CY5 z(SoWJp%gk)6{fUSjW!^Yw9qE~J{4HA`;2ZDN+$f<4LHt`TU2@Py9wD_1CuF*ACY}@ z7{$Up7Ko<3KcLIWCncaRzFPXNw5jJprE<+R#V@p+_Pq!8MASptKh{1j6pRzbe1(Iu zp}NlmYRelpR#hrfUAfSmCbA4a@kzPG8uA1#jJeQ+wox?kfoLgtb@j&g*UA)B=Bo%a z?>0(G+VP!Nm36^cD}sWOoj-q~wmAX5aa~>7%nfkr1_(e+f*fqZN$324kH`x>R z_I1Ot?<4kU3(DZ}rPBSd{qFe%hh9by<@CA*% z45m~-@itmuLJz_HrMtOrA`v?5%fm)K<_PZM)~0#1JS!_bWBn)uQ=%egg1NoDNNro& zbEBiVdque2w{98M`Xr2>C_WgcIg6pSwzPj|`PU-bQ3{Ph#_rw=@6@xmZ-we^!ixF! z#D;eQ)o5`B)achL4ALqpTtQQ`AC!a}rT z(phuniB8J-_W%P_0LSUdw$J0?Czji8;!vwWP?%#bK;R9V6cUuBnaFdW;2fh$(Me6X zk}AE#)-sN8pT4A=xk!~>0`sKDRJ~-^-e~x#*!1%UqcWb%&Xc;)`v+VdHe41{jNex0 zG;_sy5!$asQ(MWzJMlwa`G(w}h60%fcZ}UAPf2}=1_fs%A$ody1%AI^gGWrj4{MLr z1j+YaJGW@0YCc2GE~moWVdGj- zvC;g>Gn4E|-MN7>-rf84Cm5^hDSviH94epgeZMH9-yKvQeW$-C>hza|*KbK@cNN_% zOXg37&Ckl@wB$MebCXN!_&(=l5x)@Ybg|Z=!C1rJ%^ehPVy!F^BFLrZcXK~;-5aHt zY)HDit?Z=nR+NY{QA}cjMJMwvnYc!Hx z@e9g$bFUv8);I;0jot*h%No-8_ge5%`Wr%WxAh+DFF%8YMLg5*8^iKNPj)F!o%Ivh z*hJmuh;Zzr=Lc3tJ?2hUM7>Od-jRy&egBP7R>iY3wRMRr!j`u!7VC8AY#I$6am~tOlFI}hRD=;h;{yUg0idYkcU>V8DKcIhN}%TYg~+czCu-Pf6A5i4 zj(?NPwrn7I8a9%ml+(9fcXqztF1`3SP34P1^a9&`=h*?qu5rW~^{vTj&#>ScMb%bL zldHBZr}^ZZ_-@aaZdz~hcWy-q)A`NL-4;$xO`n|{FZ+J7kfGVUT(e>_FMY*(MSU2J zAG^^(b2D?=aIvt;zlHwF-9M6?g;M%}tKWB3Ki%^XUEW-mWZ914M!|F}J9wx4j=aa#>c(yB8OQw(8xZ4fDFXO)dp^>o2iat3#Vi655^# z>!&9#ff>Pf|3hZ0q*0H}U~;j&)Mv5k1}%o$k01AF%q-{oAKkR(oi^=e@tkWVhnr5` zLGqd8!7I(%V|r;Tb?(!1A^ul*VfF#TCSuK<#LA(p=G6k0Zl`$C{%6*P)h-F&ww(8q zlRPi?ZBlQsX;&kJrVXhsu=@P-d265EFTDZyMJ2r9Qi)ecX#7kLe~TBot(K$G(Vb z=djG>w*(Yr~fC%yiM#m6~U zi!tn^3vuX8~ir0Ck3wM)yRqwT9m9ZH8aYS>9=ZSR_%oI$*w+f4+X<&ap)`|7V973vskLy}0}9R&^MH_x%aC z@$ryt+oxdmdxwe9qb0lTOr+3$FEov3@cUlj)u<=dBu;%gI4XxrT(BoX6m|KKpaqEKp0-ArDD_S&; zTUX?@FHq}W`tu#z)5)7PmT!M|n7-UOI+>j+`j| ziD!a*zhrac<&@wh%)}`FS>+1!aLOynV8T6p0e(%4+Tm-wm6N+{Za@Zh;u&zEmU%nY! zBYI@T<8+~sW<6^1#3nPtQlN8eOq3Jh^qytS4p*S&S~QJR*Kzuk^b?G zdkyRq+IrTW%XsAdm+#L6U#xo`dg8tJ36NBdo;BbOuN@i_dFFi4W>jR{ATRKiP3!?< zI`2@!Ez7NXC(w!3P~I&q5XiAm9=|OrCt*e0LK5a{A@n$veQtQyN9nT#&7ECDYyCA# zu1^GiIeXid1+1k#BHa6ZWoGwx{@!Y6zVxl_%$M=JN4HounOfhzKBY1eeY<0`>y=|E zx18MQd`J2Mjr0X;m#=5umS0QSgb0>)gq-b5G-VAfmB2(idZW6g!)1&@D`H-umi3o1 zqcyHqWY6jkcyQC^a+B^qpYI?fQt9;K2@?(vK9ZiDJd_Fb!RMa^5 z)&46~&z^LDQ1c~G_2knUE86V5^B3H6!YQ0av$VfFx0&%QoLIObY3_Aq4b1~e zirK}iw~Nj^{OzsbxjRb2H4o0Q&dF}vX&;-t5l!cre))rl(zZjq_O2-2i(z+yr-AQM zE&A)5*QbsUznlYHdKiv`lj~lD+#)YFjWqk}9~u~fH#}p)x{Pfj?p}hM0M5&$7;`q6K@><=I4?_xYC0sW^OCu+`CuD?6hg zPP=b%Vn)+0w1nwWP*5;f=^s<81*Jaz?zzz_}8q3<*+phO++zY?nxjWCYRlipyBr^K(5VzFd z1-;w(ZZ39Y)W#EU->9R7tsJb*6+xQGh8X$Uqqoue?*^D_t{4RO1n;6%c-@`ker|rn z*rxniWvul%_saip0mz0FelZ$}v~3?#)bHv|PrmkuVD6Xd?_@sfXq8;fBl24 z(JXTQGj`+C5^Asu0tZXyZcb%e%c71{^*Q0*8gBIW(wIQD`=&V@OXeQb5n#TNRtBqt zPn@M|%+up@!mEr!cQQkH=$yK#A&0znUi%X;ve?2!|O|ao>S4(fLWdz!Lk4=_jL#p=aPBzk* zKE;=Vj0Pt`U2F)_tkb21M!JUV{I{c=OqZu|r^?SH3_AKKN9gPI~)=)SwCUDnt2lVs*}*gvA!r~dTxGs`FaSzXHf zt6L~FZX*taCfxo&)#^80qDqxkjpAWHOU|<60T{QX! zIvo|r+Gm^2=y$DWF0VNaOboKJny0IxN2d|>f(ub2XVQ1` z@rLzRV^{$dtIitQYJ`D-F?L(8Wm9QAW%B@YaBHJMIt;UFFIkh3T5$M$wdyh+pV!c8 zW>xU};jdz+{cS1iXxTFY_TYy4wx2CVeSUl@5(!q8-{~LU>`c5B|2gmjF~9i(t)9F; zYxE4s5f*CqxI%}Y+Lu+7H~#LzP+I9&-qlv~U3{n&jf(v$rM>W$?up3Na({s*WAWuC z4vnN+yxfl;zpiyXEmeqEs?pCQql(8YiVG#bi%qT&-rU~MvUPEG^~Q)4s-qkz6PNRktNc%B4UNE;+gj|KH`XYBi?b(ouI%aOz;!xmYsX&;^EEg$leIC! z{TWR|TC$&>d#8@qcv8v57Vggls|a)oo!F-+AZXC_w9>V0a};hSFw#ZDspfJ#3Mc8( zf8*p9eyd_s%ixXn&EA0lz8Ai%yQ?IrxEb~)aep^j(%f&|0i4abM>$2&6@vFl@WcMf$~ zq`S-546H=*6hfYSFTjI+dTC^xsG!Rj&%ZP4ood|OwvJZXWox#R)U8WW9O8<wd);~?wlPC=~7|M<1)#M>5$yH>c%P)SATig)iqHeAh zg^(XfOgdSexr%Q%o{q7tzK++h&2)xO-&oNox3GvQTJIYFd1Yp0GK9WlD*P=S%|gfJ zRQv6%~9ud|Ry_$Y}evsjpirIb7_-vVed%n9^U|=T1K?sc_B;Ju^}? zEJ}gg_l@WZ3R>x|TgC6^F6+)9V|+gyidd(lXwllRSS23`^diAxmU*l-l!~SK>BrDK zY7(zeljx25(~6ndmV%s?heV7_OGfWLuE&yTmE?yc<6-?DG@S)kTus}o6X3xeg6rV! z5}d(Zg9mqq;2LCbcM0wxxVyVUaJS&@{_Xdyb^gF!3_a7myY8y13fPQ9e7!T*p=#9*0DPVQr54+0jn3adYd~fHKK|+yx`FW1RFx$9@vX6|ClDfcj_9KU(R$B zoin1W9Wo97bBdfGwvp1$pwytd69|gbh(X&J{ZHAXo&n$Q1bUzcDB@5<_~_HRE?#}om0WiP{B6$M5Q4W zYFkY(77V?qmo;Jq`S9)4^VpOLe?3wFg5FTk{?p$Rlq7b`a;y-7eHpx`YAyvr>3_n9HwY5~zr6@jc+yVW^+s zUl0&ge_};mURp7=c23Z#7_|KOwCsX2REr=Mq)cv8ZUfLKL&s8?Yb4Um%OMAz!FiEb z_3-L=U-nh#XZKyikwoDe8xkpI>lky)5!e9Luweoqxi7CR<*G9$PMB3Q1#xGX*G*RF z^861<07MPQQq}>lua^rtAv#EQ4;PBjh`gw~mZ_c%Miz_vWPECp?JJi8kn``q)|v%N z&!AYa>!{i6TDiJL%h$;BB~xct*A(^8{twEN_;B&#Xosrt4ew3|zbQ~EgPiSaAW_}rzkMzm^eCy6)zhal~0ad^Px<|MFt=O-E&zJ z6$;l7D8I4ldi?dsc{;yb@ix?4djpi|^#@q;eZ@P4!^tWvdb6k=%V<&<7<&5BpKQbT zd{$=Y_q#OrCclajLsyGU>^K{-RqVC+#m7#KCQ4bueG1EmhHGdZ z#tutWEd)p`939~)kZrhffc7Je#RcVJL~%}DVV)u2pONko@-GEq$FG$V^^Z8jP)_}D z*PA3-GAO*wJzSH;dFS>=Ay%8y_vyjhsNOVM9w6(VuO5c@9~fd;{zZSlWP86r!sf|4iV zS@v4Gsz-oT|61)%+TudMZ{>RNeg5oKvw|xR@iW%!KDejWG&(z}%Wi7T-4*Ts+jR_nW^x=<`0AW&o zt!>D$L7YwH6f-3$P>*|TYnm)it9ag$O_(15!U&C)K}p&)a`R~G>JCJ=6snHUwyRk6 zDi_p;M@YL4zaz92j1_nhNPN@&FQuW*zX3-})o_`+^2Au1du5KcXj)J|8?`G%Rq{aA zn^_h=DayGRHx)-4U~Sy^B+UlPV!_h4Zj4y(wg{3`6r>T4)mGPk>ChyskLh*$4_0oJ zct3XnEehS%*4`0NIDvjcg-U_J;Y;{*`GaS=AUl3jXKmFo%4qY%mKN=l)WzWvILAU! z>xsidS_hvqlyikOisUNi*K`YD4vSHR8M||5vX{2;<227a#H*m^Y}I}Fx80Aet8K9VfBrE3aBu@=(=h<1L8fHR zqcijZ?)HMf(8uSO3;nX+RANbO!LA#3AQ`-vK-FFK>@)t>(RJym1_v6L)NbWVS`O!A ziEADT0NMabo@Ok$Wpr?W?s(@i`8C%bT0Uz_^blKn+v?WFh=Zp5hzkCpc?y=7_L0i} z?WJQqB{z7DO#3=k;3eQd95`B_6?a(J3{bv2?8Ijp6+Wn*jOPM2{~ zAeYxGaJHKnGhIMcWE?)qQM%-sa>vOSyzKxW3~FUN{#z#E{h0!FL`i%9&8iFr?cZb_ zx3E}8rOW`OEX1~|s&3|%PW@pE0@TBkejXJ^012^KHIKmnOk4OJ{%l)#)!e&U1lhx z@DSqhi?ZvA{Ph(e-hu93p0UJjudHYYq#^HU)bQO!rL;V;G#vL ztN^Ach{#$QgbzO}ru`N|i}MnuO^^pR2K8fRpoE@ zvmA;QW(Lt?2k1;^d1tFzTL#D11Wp~epaI|m&_Um_ltbCcmurw1vP`88^ zgP8FzX}q)}t@8n`(1Yiv38Elj4&t5sk!} zj9~_oP6Ou=#F}iyafk3kX+T<6Rp+BXg7X^z>K8*QXDu<;Ki#x04 zWRXZnfMA1KWIO0~F{n@iua3U0Y?H+PC@KODRXWwNA;u^poh{{YRFk=e7Aw42KEJyK zKfzQGOz5E08(Mn&I_`#d8ZtAr#2o^rIEV2Eyq>45E#f9Xy^wR_%9@Qrz%F+SG@h4ULj__$`czv>uY?3Kq)q}hRFcBvGb}ku3PQ~{ zEDTN+{Y3M=0t|W|{Hw;P#9z<<`F#FRzAXFF7q+a+*{Ey#$8N4AzFd=XP7uxIJ{~Fx z)oQ*;`&W*Mok{tb)v0-$uc8Qre0P@aV%qmVMuCjU?}$M`Um_rQit;Y@)cts$M+D~M zp?!9vc}gATn|*vUgg=BoxV8tW+dd*c>a)}+Mp9oq-MRLqjvkHzQ7xMyR2Bd=)7jdnEnYC26}LE8NBN{Q+2Y-S8u=I?_;DSjmhH?_T6t zMCCkVGpB&A*L!zIOgeqvGZeq~L^Mn5fazj>_oQeGg21V`2q+u~!+h3xOU`aLH;l!h|75%YmRisU$P=EdA?wN(bU>2yn^OLr!3+hzlUAIh@00O_eZn?h&m0oyP_9QALh(xnEhvy4BBys49 zP{Dq!rEhi+HWZDxq)|mo2PqyU??nHIT(w|Y$0!Pnu|p`<$3a;iTLCUKA%{fQ*hZm9 z)?X}hAj-QS6&9^VbkiVoDLsBSMwiS3M{inB-xB_{;R>@@TG2n49k9{s^uy)P-f#Li z(tKPQtBOk!aAPrAw0xf)6ZhU=zkVIq0ko#u2ef=0Zqlr_?tZC*Z9DOFH_7cAnAG zi+E+L;5tcZq_O1YetZG~P}Yd@*~Q7>H*}*$ex8}^RE$95h8#@;?`Z29Fgr4a>dp~* zQY?fzU0#*>n{)}sOf;)xL$v11+n*;N{2sgW^^U#?-KMW@9xNJMs4BT4>QRzE6YMB{ zqQVCc&0Y%}2)75?74dprhe%7BGvU%ccus1+6y3`@@!^?$&cqaWvH?VWcdC~QT z<8HqJQrVbV>PFvpNx`#Q?-ozrwP%ztss?we8vk2>YGTpfl9NKszdr@ZB4Oi^%b3;@ z@%jWGD?^$#_{ZjBg?4VQ#D4LyPP&zk%ntI|gC??Td>Pl;A<9{Yag&fXrL60@o}V-5 zA$%GBd*v^i#yIm{nc3>|dp}W(m)6vlQ5CwaDdNE>7;-WXDX$Icgy@N$dH@{&ZJgn-W_OF%SaEyX0midv5n2|qs%g26Nb ztYEm!&QF+V3n_;;P0bvx$*fvn5Cw%=spTI#RR>ztbmZ(!!yh(Z>|jCvX$GC~CB2km zmw%~`IbTW!-|9O&9`(%rL>n#WoZl(&|G?RN+jzhyUX68`ZU5L6X;Yx^_<~l(b*Hr_L~%_xLEuhfo|d6MR2%Wj z@BWLv@B4qSviLigg*BHx6ANyMDJAI7A7&R5fsA97H|-7=9BZDVo|B`$M+sU7&wrCl zuZuv#h#>u9G&DY)k zGJX)SeQ;0u^G$t^18g%}hF!UPYO&*~FZx~Jh2?XK$u70tTK`n)`O|?30r6wK^*844 zUYRycqy>FYG>SyK6Fde5j_(k`Vd`ocY9=*0K(9VJIiW~j%hNBfsjmkYZ13W%<9X5n z$n*C1XWXTgjp8poZ{81A&^eN>iT5i+nVE!a;(Jw}Gswnau^xq1SNThouX@W8O*!xx zR|Dhl-ld`pRA?&>>-k{8`CPM@HIhbQ)iAvw>g)rzH73|t+DV!Df7QUUVc`3q@g=@9 z9Dr3^_9VKy`V&9BL`W>^F4H*Ovh0#(+5Y}!P_1|nFUZMK46xG;q4xLAltLpVr^soy+%AO&W(<_Gx5zQ7 zr!0OE@gGUWoWL!=h300@+4J`RB|kr*UY9Eb00#{Y4L$8-8ko9x!1Rpizm%q9W&T?%dU#+HVktVHo(g|~O0hFk%+Bh}Hr<$LT^ zU0qub;@(l5y9rku+*H8d8o%S!TYp+d$f2*K6F{Jwf!?^-GoniIP{QVe!CvL zsNXO->?lnKK>m=?Q0i7AN>9&Kmn-6`8TlSLg%6mZ)#$~csJ1r zn-94LCOC`lWFMuVtJR&wyFuKiN~~j}`$0^#x7W!wI4(F(<~n^tJ)qi-(Q11MGaz^F zX!@RSeTe7-oi|1d2=67k0W8-)%(j3>P^_GI=Fei;;>q64S3Mve+?S&O^WE_G4sdg@ z=GJDYa{Aa1WRJ=JUd8%wiIXe~N!9lX$2E9C9Eu?^GCr|g z^CXnoBzmO5ypB+dv|o1MgpBaEqzjCmtmGvk+!&28VE{Mr*t++_lD0M{>me^|;aNcv zU?8`mMk59#j^J6+pR%?5#%rW2It~2nH;6af&ac09b9)2@w9EiZOdxLp+_ud>aLL{9 zs@pmwR!{m>$w}*=;j8T;_3}P7io+kImjJKLS`4W*g?rO`%`Y^Lp+Z>B>p;g%`a}r|DwAMg)+f856KN61Vp$cq9a%Otu6qo%Dx|8$p5!Sih}A zX-Ycm2ujQmrN@M+vmY<3W;qo@%9iM`A@TA>c*KOUM*Qa+`Bd<6PVHsE;9;%QZ)plN zsdSDW$9O?Of~KvZNZA#SAFk;O5!NN6tEv9|U5{PwO4;_D@qBWd!09$`r|)&N$Dz#r z3BxM5f)4q1ntwL-OvtdTb#OO1^i7)+r$}Xo1i8q=(3!v>Yz!zQwd82yao;u1Ajy1I!LO~l z0qQGZS=FzhO)-9lQ~l;7M9CJ? z-Wxn?Lq>_de)y>SNZBoknc2)7#3WCtMMa;v3-`j=E}s z`w0%WFGZ$8+ePybK9L58dV+}SQ7sz3_OEy6^Sf-T@k*(i2sJLtFFT{ zRdY!T7w&#TSBZA}+dV{qnW1dSm{-%#8!vc!cL$i!zF0h&NGD6jC4?^tbLDaNP-93} z#I4iA8DCVNQ!+Zv{c|@8`+b(*$5&R^xAchT;I|KA`NbXo4>rFm%6|Mvno2X5yXNj}RF zePvfLv&ht=j&$H(QXTxJ-6vlL=~gIloU_{1<%=Tkfb&j8VdRL|c56J_!O^>EspfY_ zmzJTW!pKl&097c$I+tX=t^1#^CZ6ih(tf zDWsB-AVXoZf*4Hm4_E?ICATuR&4x)Y;g{^=boxO$i9e416 zNFoAet)oTq#S|)3i`r`>7iQ~ET#x6uJ4v@s;$%ByDmHqNvi^E?Eu7m8*v`O z19@dItk56iIfp`DzRsE1hX3TFV=gBQ-f@Pg2W$F1#c5d*!38IY_loi|r`HFaz=v(} z_xEV7kAD+yhg`L5SqsZJPDedx!NahJ=xlaAr3pW-Z|)!7I=#;bzh8_LJDZ<^F|0UP z+^agrE?&z~3EP_F4gSPTms)szR zHan-DMSmtix=~8cGWcnlfn{N^w{#^V>xl$M98}BMrn|b<_429e=*QkAn_{+rq5)fI z(XRGF&U?DQea`Bp`pc1)Ogvzb6r(WNb4JZ-jqQ%fLuLNm#tYvtG)!a_F0ZvGh05W# z!wB8t_lMb3=$zrnhNl9ff&MHSWn~^#97l&YIKEqxI3JJuUjD5Q2VMHla2!&u0$T2% zm=yKatV;jF3Z|xTbAM4&RU8Yp=MfUF$8PlEh3+}UkIbMz#bAvXvMB$}Po4|K1iQQQqADC4Xr$mJOVWQlgej{0UXhKX(D_yNd z1wBG&auioLvm&OZ&Y%)1(ce~)hBtIgEXZh_ht`YQ;(C7D zRL=Pn^oPHLUt1=B+NqPNDA_c-0yTfX$R;hcOm)!{s@mF|IK0@oVR{e~o=5I1fs!0w zUaul$x)A5ccvjo#ALi;y(*VO#1t~hOXG!!%D7m4I08q$$gok9lM^r=@ycJSN$jjH4 z9jnPKKG7c3tKa;Eb2u06-%Z?{VwyI6Q{}0Ls*CyQS%r>b@0p@$|)R`ep*a%BP-(ckF$0WbL3gqSzPggU@t&s_CC)eO;eJWb51M zk$&71+Q}4=u=BZvsn);+#KvuJn6<^KmBlHVWI{rl9%qV39iM{u<$rY!k04b!Te_t6 z)I+cu(*0GzIyjxAI{<#*XnFa`(a=y;8cce*h_?d}%GmrC1IZ|k=Lo&sas4W7IwCYUzCGwZ&_J8^w z+q97S{s^NM{kq=se~1XHe*f0z()#n`5Q&0h(aB4@DJ5MW)SS8s7KHMS(dkIZzf=~) zHwOI8_?0oJ^0`r2OEChR7dsOmT8j`l>XzODkyxpqw=-~V^bb4!2PISH$OtVO9n2{> zR^lf`K5j z!ZyOl6g63;j$Zb&V%;v#4I&~F@#b6TjLL2ID^~(^Y$&{{{4w4U;wRFfQLuiGu4)|V zOZW9lTUPfTQ5K%U?>K2IBP1hfe@aR$RETj*K##uO64JB$g$2>2Rn6kIM|FvM_<75a z$TSn@f<=lMt3y20^yk(&b36NN*#?=ZVVI{4A)+?I?x9TOy)EkRj7a^Tcgigi51oX~ zee9yUdzZWTx3Pjr_>+mEb`hbLwlCAcf-pj9gcf(^C$9F-Q6 z6GT^3rom9ZN<5j(A9VPi^sPB9CpMiXfeDn}4E-71PP31VgZMK2WAf^e@SfVYVopFekX>GIQs z7;|=q%tH>^zSqpuG`Mp^KU?6tg_|4d>MU^mfqi~a?%|smdYss3FwT#)WtQ^yC-r@! zieMIRK|Yx37{0$pMcU`GUuz$k-Xa?s8k&|B7JI#_%P`ZXoWs9<5*RQJFjmS23x)R~ z)e~?5VN1`8Az^BM&JkRFe~3KyotkefIZs@YR|GN&+LfbUCO~mcKtS4Wz#}9ba3*H7 zB1aR+YLg*liCS7(+xvH>yVhcI`>6PfwM zh+pTWXzo25igS2n8@hNU>3eT{3%(ue@TQpfJa5hK(0>sr&<8r810)N29uM(41O+)8 zt)-2~nXC#$P<2wRV9|R)-x!_im3O9Z5mxjwrDbe~23GY46{tegaLQ*#0ep2sZG*QaDI)Q8h(VjI38@U%56Mfger!SA${X4yKQv})zhWf z4BL-NySddlxf(4~_om*&7HvilVmjjh7-Osv`Q&&i&(f%#mq~9Cc%bU>5u5WD`a&9$ zAC|}H@e$2G4w%J4tFC}XZ$}T?%jLqH5ZZ`Kf!MPOJNT0*)o5Y8G+wayRb7lGqaz6p??# z>~da35$#R2K3ZDO(*s>H$S5f6zPCU=cgL{&B^+;tVxMI9{6`nhy;=0*WsNDzGDZlj z{<>8I5Yfa&#K%1M%f=2<5ytxuWtsRQ^f%<{!Sz5QS6g5E9{?a0@Zy!~*x0H1ZR}C) z?D=n8yYs%GiX-`#xa@&R5SNjCIvM=(@-k3=8$V$VD7BQS6N5K@(D}e+an3E{y}cm& z+SrkB8Q5oUr>vawwAU72dw;bvj?r&v+1O0B)rrW_q&xnvIq$gdTbplPujdyZai1U( zP@>}4QV<+N1j1zbAIHf&j}{;64s6^yg_=Yn;~!ApwyoM|tj#tRJ`jk2znx zOe03F{OaiFqHo6Etkv5lR%Fv#Xpn3*GH;`Z{yo;HVLN*kRxHt8I3Fo}5NmFB4Ni|N zdYss7ZtX>~q#;JfSaQ14RoPWnSHr9joz7Mc+CXd_7!z6U6Q@cL0=Zudo9!V(BKr3c z%A?)&sNlZ)ZPj8`6GI+yolx@R|&=nd9$bkVoRy|r%=mJ?)+P}N z({;~au)nsf4xBhp+ zq9A5!ZHKY$h5^{HO?;f4{h@K`shwE~mqmrQ$QTt5-=MpwWK3m=`1mG9|Gk{r|Eb~a z8m=i5yDhnCPUv8_!iJols;6h=n3UI!&6u#TvI_*WKALcHrzDbTk2^gN+z4d&j-vTJ zvMfCn2%Fy&-T2%rc8)B?)1_FXAV>t`u>JWucw?CtZklSn{;6DV3|tDY)C7;9B5SOh zFq20z*}*!ImD%*PwZKKP=G}XbyIRjB-WY@}>Vtl!k=@GDJ*2G{G7Y0g%p!8=G- z*T4JW{qe!`b_)+z-!IzWsT7=hoMc|OfL z4<}Hkt*1i@Z6qe@a&%zvtLG_v48^z?)x##0m`42+SXSI;Hr2(7lSwXNf?7p37hJc3 z@dO*f5*&b0{&rj-_Z`LG-?<>5; z6w&t3;`wE0*Mm2*g8WUi!4qGLDhZwZCBF4#5rhUp?mj2!e)Gg?^W(ysVtU;ZKHL~q zezmr`5CIB3(3EdvyM|3MTB*9hE6BUa!SU|-`}2#esU89!`wat^==Ap08j#OHHt;)9 z0IYzL{$)Sy;NUR~x1sX_*oEq(25{);S6vrUxO-&d7_gmMlg+b}?V+MEVYC0Z1V@`Y zdIXo1u{F9||7Ac~7;CGKf#J1^R0gcBx@pg+lWq|2*ncwS*T49_64P98sv@)_W z?|<27dj;ZC2+W=Lu*`;5b0MJJPnY;rGkhuj;dpq5z--rGAA+m6?R{(#^qModgQDY( zK@-^7DHIYK;`E*HN2ljK3o3{SCYJ)T`CDy$c|eW1tP|lmlzO;mPWnqbMA%bQ)pR2t$@u>~7r{1LXrltDj|PP_kr zW&Jmu2`Biu?zT}# zEIH&kW2&QCFe*Qpvfi@fY~cAk?g(y_Qnl$EW-*xOfS3xqYlylImIO*~DcIL0OG7t7 z`alLZ0)A0l9ZWoP#HppNZDi&NVf`DNM~%oe(K>W|jX0|qH$qvHl~yg~A60w40rgBL z$m!ccA0``Mx5}3EH%>|`W$bVmrl5dg>0S)rQFW$wL#y)A4hYA@65hVb731m)6DIpG3k=SfRunnC(s0vkvc1-AV zt0Ftr>x^Y;9RIIj;`{L(B2XGx&V$P_WRovV0U^6YR*|UD>aIa9YK|}#JEws#HjY7~!E4nQ*-EiHHU<0(Ba|6)HSlmM%tLf5KMHnK#wS z)y+r}n?}z+r@Qz2ywV6BKzA)d@x+_E9J4-!DqUur{B26TB3KOueMGUgt4gX>t=sOd zcw)NYXtt91Ff#Nvh`|(VawMPR`CH;yF}O8ymuXnv-ojf_F#BO|+2q8LrA+a~WxfA7kvb)Xnx=;VQGCp1iIPj} z;Lu4KBL!&m-Y$;jFw-!@XjMv3=a{&-1a26|=swsd$kTLt1=;c`*b&yp(KdX%%1`Nv zELrXH5C)S-pKWQ$)Xq{9YI1;xfgst^@=D>HJ!p;!8zY-^+H&FOuX}D&@b2Dn`RAc@ z3=GXvl#{A5?)Sp4VuZp7f!(aF&qDLo$vPdP=mCVO&h@kPge3gBC}6W0egUw7KiW}n z5b*=4`#K07y3yua$SZ1)Sf6}MO#GCo@`{wnGk)?R)Kj*J&QBmGvie$RH)q5IRUZbQ z%(+r+RL_%S!F<1sge0G=nqfAa(BF~a>_6FxRqKuro;l7Y;FeTIF|=eqp;BI{QgwkT zX>ujHsV>x^C|$IYw~OGsEHoI3rXr8)SR^71PZ5rpGJ}T96;nTQmxL$G-W%QaphZ_T zC+rjKBZQ=iiI6&+;_a(E^Eqw5gs0A(Hdx!ckbH7EmS{I89xO@HA0GJ$iHUJ1h8g-x zBb8lObXP+_TtfU6!gUc;Bg4)M)?-SciSA>H&;9q}%u^MY6{Z!Y9Qj6RAzjv&@UXmG zIH5|I;F?t(Cl=dEWm6aDsL)s(OKu(8KMtxk+;oLETD_FQFp|1X?(Ugxa#MQ;Bo1S&4_Una3S`;*s5$G?< zOeXBvX>y+q-M%AY)7ovVe|L|q($_WEJzq_I+<0KAnMd%vtEf^vrkhSo`cqXoTdE1P zGB&5H>&?VK3nA=Z(*t;tBuSd6Kk)P$?%BeUVg#n2aUidsmdA&%_}+YMyNrLE_yyAf z+VXSaFM(`Qx}FH{dkB*>4c4BjmEIWnEKX^dVxI-@irUa%m$2+djjF&tol0&NP*m}@ zlr+^j@AE%Fjq~LQO03&bN7TwOuF8Lx2WV5~${wG*zPuvRtnw4t^4yd292}%h%N813 ze4pfTT!+r(bpI$%eyz_NH>g1f$Q-|(UB-Qi^*cLh_dX!ktkgf)kHn>I_Ou=A0SN_t zqMaDLM{U{ws##v&4XW!-ejGkZygv!QNd5ZiO&}F`Z)eN@-xE>qtj8J7oh+o(PE2*K zl&kq_vQ4k`%ET=nw$jW53*7~5-PWewbSxiLLRQ<2X=#^vk&1~g=>#1~Trz|W4-HYz z1B0$Q;>aT4#R^bLp#!G#5V}DUcnu?kv+eIIHa;)9o7Aanx0|vrWSBprQxc?C#YbcF zaG|7H$+)nj6t4lvo;sCfv#9nlZ9l#3uO9$Guv)xz&_Z|N8y9FF8tkD$a z-=zM_JTW|o2I?gP-9m?TV^RmLT)Q>BjDp~*?+ zgDHdDrX;Q#bjh$`Nf?|IMUyeiWuK5;8bzn)HM;XjmYLO@o!gc<)3O@dBIJgk{y4hr zp>U0W0xBJ(ddI6|>RCeu2}NDX--&qeXKA^b#+Jz<22GFl=CCt9+Amw)Oay?O z{7h|BqTp}95+k4SQ(l%6(M(soQS8tP%E0K60-L>ZXUIYdb(U=(|F4Pv^k$T1lGbD)V_%d8DG&uj(pro<2$m+xw#6X8iv*Wu0 ziI7%NMaoz2W6q%%q8=QAG8p>$s7`A36#~{ZpiG?g!Es>dJB>3 zCFs&!%Gao!&SUylf2d|_2jufzNhirO|M=>w-S|lr%rp5|_C~d9iALw^jK6YXO zi2B1vbjyH3#9M*SQ~Bmj*DGAtNI9-jx=?@64Pl!ByyP+H7K87T5mej4BzMGAepDLwAPyq!a=cq#+fGyZrK`fc{4L~X} zpd=RM8L{FN5yp43Tm9(FOUZFmTE8tom)umbK@^mT>2Um&XX9=;Kjw0LQV)g3lT}>P zHuj=fb(wYHliKL^82T~bKT-8Ms~hz%+l=DPa}g}uGQ&uzN<$ z6-MncvMih>c7rU$9T4%y{GXtGSPYLZjr4s^#(@6MlpYmd5-{}dsV7Q}uVI_a5uR!wY|_%wGxhbwa`#C}DvB5Fo}5Ij zQ_B8?FgP?AwHF$~ihNYRB3@%yU(J}L%7)C5uj2r;n)C;SqFq7dEN-+9GgP(rPKyZV zc%|b6nsN_CvUK(TkZG&q!ob+XB#7Vr{AOXXOU9r_LR?6{ZDR14ATknZ??&SJ8P1Uy zPF);u*0{T;0v&km%0Lx$%`eb^YWPe%h6V!)Y$&WFar8C#RWX#6^Gz-LVEct!-w;(L zrb5N&=dnvzK6()-pSEzr5@=bMSw_fcpa-z40%@KiMLN5GkJNs7ZDWg~1JB$&u>J=` zcJjlTn@$pcv=oDh&hI+uu>J71o=CD6^Omi)-B$pvq0_WO(-i*_DGQBC>y*15B*rt$@l9KMy$8F!H_fgAxssAPdr<$o}pN)0-B8=;QLiUrmS`4-Lbc^tpEcyRn3`mlS*feXQhbjWphzqZ*v zkTuMa2)RdbtKjt~?Qt&|w2oaYD?9p)eU)3=a&dd7BG7=^$b=q9%PyE&p^>RkE{YdI zTX88sI)!xPfvOs~3ueyT2YwOt^j+efZIiEaCF4^QH`fqc84Fd*{TXY`w5*&WJ!TGG zpD=NH4BQe%b!?#X{;)oS7Zb#yzgTdI_0oxAVj(?kjae2}am@$Nx%Z`j5MfQrq$9gj z`F>4@ZlBDmX`+lc^8UQeoaKUY_SEDcweIRF;68F0GBGhpyk5Z>kk3f6NtC0BOQD2^ z^4X*Ss4~yDbc(+Odd|<8f&C?JZ2$1&!pzSMi4MjHJ7R^WDJzch@OYTU#0t9Y%~8dwO1px~XAqj%t|*eX6JqR0RmtZJ``3>%`w_ z^ahqXX^fh-SWVCSLNFy;U~Bu7Vw!yZ7*$u7P{8*(>15I25&((e$L28qcwO`Cd~K+@ zSL}F^5C#83jYca_X;wx5Tb`7v+2VOaphPk;YiR}h3`d&;OZyLzw^5>@cHlK} zXstuI4~?Zm%usHSW^jbk&gbPvfl{`>&e1Eu z?rx~Z_FuL*#3`HF+U9{isk-);4evj8e|DRh=V)T`bnLh_a6R~h$5vBWew_Uigq0e4 zn)>K*xSnnKv8PJieb$RP@pSby8rCUUS~k*lH11`5twW-sHS)_-l57yC^%wI4k{<^f zf&k1Cm!(tI-#}PZvc7{!Vr&QJ?79&r3FuliHso@dLeNsNQ*6g22+W8>nM!>bE9utt~ z2WDDYSrL^YRwUx@Il@(}YrG62B=zYYAE(e*Y?9u)zJ0i5o#yyiLG@eP%-bZfWtpb^ z$NmswGS9SPh;5_vmMg%u+uq2M(rdkCMk8f1rn>PtV=yptwoP04hxl-@vAQ$GNWs>lgD)w59bG!|fxr^Rl1CYUxgnd2?Y!LNM&=~tgLc@gQyb2ArTb=-WHgPndL! zI(Nh*!fD$b4;#BZyI*xWi>v(8vcVx>Zpg{f%b#F4LswYcQUrmVtO_Qae-65Iy?DEtn%EM-HD|xDAtims0 z=`Rn>?>$VQF*8S7D60Sy?Y%jC7i`f&K-Q4vFp=OPhG-=7@!1?71@ z?Q448?MO(5dST!NmXjd^2$9|EeTKDmr$}o%+*LQ}Vx&Sf00#g|ul`Hu1{4G>2&C&A zZ=PS;KYCp2T@HzfD316jZBuPm;)32xEw~D4L=Wp4IwupCX#_k3#wxtg-2SXXy~dc zr*!!)K(n`zQRjH?+&9Gk*?@i(cQpkwiP9ztcwRe(*jyMCEnmwN*2xnK_p~4#;1iL2 z%HA9yQ&{*38RBSvn2c@qx?}ghdb_xJlS@s@$}E{V--0XNbhYsgm8_64P$EU3`7Hj& zA*scAAMwJVrT0aX`NzSe-ElOkT}5e=&0l&TUR6=s&>~8mm9NZLTh}l#R}#r7!0UPs zQVT6f&(zJ;cew6NuLHOM)0LVtcCo7LqJL4F{T~p}NElh!g$8bKIqx~5#^5pqKd3X7 zLPcz%<8Wjaja@whAOk~7m?6(0HOYN1nw8GG&&0E3m=3;AC+&|rR;u_GXndOuT{Cl6 zz-Tn5ryuC?Q#!ctwso(`R&&2mfg{ZO%RxiyoBN^QPaFM&^qC3*T_hik=xbd;4LI}v zBxwT53y>6trb8B$XU^PRx>CEnM`dO6Oj8F4>tQpJ>&12$`SpXbFcW8X8{QA!fxI;W zSfO%yZ0|26OV>O7lX{?H`N)~g#gB(=F}}{d!Ww#oaBqTXBbm zFWh&ndw+fB*Pc0NX7--7&e}HfOmDv?*LKrMr?cBsZcAIo;v#jx6yq7OlF>Rd2#Ssy z@xhc&5uB&i1S)K)i)Q+i7BtS;_ezl>ll+4d@96dbwySH*NedpmCfOV?UY@Z(x1R}l z`E4mmrtVjh_&6P_bQ4w7;J&LlzGF1&J9l0{tp@Ifi^Y+(<0g|T%lv0twpO@;uWV18K<(`Fq z5fp52adpWj2Ix5%hU2}3UrV;Eq(+GeG9)+3 zDonUi2fHr?n_7&&Te?_@KUJO$Of5IZwqkAlDU9mcn$6LE;!IWx7@U0wsEwQ4AIgp!+>7*BAoM0rD7HWiAefD@iJF*&P zK%T6Bjha|`!I-uMI3T$oi4^zLhiFtK4#<+4MqXH?FI@|_cW|t3s*x!(R$#(XW?H9Z zF|x524?Do*pQrx&oaurT`_TRq@8d3>CZ-9&s-=0+1c^^Ia#=dIA}oK!3T|nBoALSB zH*r>%t)gtaR7i87D8x=lEI&$Zu8?jlE?>*Q$Jcl74`-YUjs4&fKJGOoi`1R5Av@}d zwXbzV5zaZXz(g$3pa*->-B(HeC~QCEOyB<@;lo+{(h|p39e-wW%nAx)ioBSDZDrR* zl`W|EA1K4X0WWfoxOH7o^S+FsU|8w#nKdxu!~Ptf+h$D_U^Gv$M#9*zOJxg*wF?Gj z`UKVDa8ugL(P|b?>8BZ46;+4P0mhh53nG|4LGRH;0RCT*aiQciw2_K@7qq*#sy0-0*i{uh*?`1PhH*RXi*fqTO!%+xB!_KTm7E<{DTM7 z11{%{n)@mLAogI-(#$Kv4WQiGHnP3X7!|(P`%(clRE|3xjKtpvLJ;+DYLvU&`~jpN zfM;e0bg0C?D-qhJzspC4TBre|CMQklcWdYM4K1u)({<}U#b%U;Cb_YbMW{t<#H$p} z%`aIwn+u2vd#b{i<~(LsNW?C%kf)@&gNte2@@1M#;evZQ(9fKZ~f&UG7(z^ zq8=U%Sbv|vm2<(Uv-L;^ncG{SiJzXf(?=k=f5S; zusUu6g$t65sfzq3`={2F?ct1(e)SCi0~3>PT4hAuv>a#BVr+Yq06nzm4K0ofC$Eng zh-KObUD)OH(-aQ=v0G296GyDHynNCgpj=dg1OlOf zA%e`g_RN}>X8bLGin~#K6N1d?$9-#SRxes&&_XRmn^#Ly(?F+S|HjMwQRsVvQSI{x z5VvAmqC$^4j?5T+b?Dw-X##BJ9D9&zeBI;Y)yQ#g{Kk)sZ0%`(>!SyK-$0Sq0N~~v zecTs4=3q32a02?%XUPvd^bA{%sI+~m(s2YR3_%da$V#-TSX6vMwrGaaqmhvqxUsv} z_x(2iVVG;Z;USk z$QOvr+CHUC&Nn zxW{TXi&Jo^Z-C?6X=-BjIk~y?jp891_I4YO^J3}mFy`&ALW!6kn!=63 z<^&TPL9xwvQlifo*~N#pFUVAW)evoi^KkBE__#1el$w|d}V`9RU66$qD}^M{o>hGviH_edu`O;PoS$x(*nf7rC zlNL4YT!KSTrL`$^3yM2=&z9BG)8LTb&n3i-;LV3eBlGjA#B$`>#pzVi&V7@fZ_5-c zxOQ43y1|q3cN?N>xt9EO9<^d+C)sPk*6gi6?_CcO1EtPvLZdD^{1oxa<&PobR3A^RLOy6-E8ec=f z&!#4XfATPGQib>Ir@Bq*uWlU%?$f(BQq5aj@)~YyV}4aEh5BF*f8ajn?H4%Mwnrsr_W@xy*uxuW@tR!N3l_IzFuNv<5~Jw7?QxU}Q^fubz-cycG8| z0ec3f@C6S|JZx{XLx1~T;<$JYZTx1H5HpNjnp$H_-?TtBZeimNlr<1 zCxB19_PrRbRogpV9!DJrTf9WS=$BCR|LR)O{$60#%+ZQ5%fP(Dm1ES!9XXU&5UR zxQ+YyEtxx=(nizIPN88wHuOq|NZXX<5`B;cXPE)DV3vqm-Al{XgevKx6{Ww15My}n@5^k zVkjq`g2(XnC`rJ$Z^Y*>Rkv`ns`eMrU|DL_^2}piH5nKmol{1XRm^<3w3Z|UJP!Nu zrfb9h@fMs7Fn$~n`ZJz>?K7<=zN_FddKmtnHQic|Y z!5XV#L$IJVOL2l{5wnBdQvAt|qOf;hwS6ri`?Lcek%ceyfWQ1OFnd$Ca^v3Sbu8)i z15XqBDorO*oV6GBaI%8P{Pj?BT;;lha@_hT>FBqH5~ck{NafCQ$5O6U-GNu-1 zwMDr*6>$f(GHC6bI&inYI$IS<7K!}R3A<&Ss=ETIH+HCQ@fzQ`gqtRrS!;CsRtNq; zWL0f;b2{+GCslVPrSFHY>Ja{fij5oG{o#!^i2AdY7|y93@rq@6zNpd~FGP^Il58jDi5( zYGCY_27Grwq2X9sjl5a+2Tqllua*EoL#(sF0%LU&cGQiV@DLEz;BJF?qX?+x8_5hm zo@@uUd!dUyR8IfZD!PH+7eNW{2HWIudVqDyAlR=e1~p~Y{m+_aMRxN- z$zek-$GH{x@E+kRS5Pk-rQ6@~$!$Z()tdV6v$W8vB}2A>xc8$$0G}#5J;~NJca3r| zNV*uyhZJPrm&1((w5&I5V-rDnbSaqo@YSGWSr1cVe*Ha?VKvk7h(Sw=U! zoD`;-xH$DM@}55avR214KPC;6XT^rSe^KJGbyo=W2K}_mKI)1Yh*HllgTVQYaJ2Bc zPw8~^l3sK2nj6G)w|^L_#Ow{hh~a2SS77W;tfCeHs92P`_=NYT!eu*N z0~lt}@w`nMZ)-)Ovpwg{+_i3R&IC79|C<~OJfDvQ)?UJ6(SD(nQ*)Pw1j@FXI`y*I zsrqqBK~*xjRCPX*XCk0Gb5}aG$5uG=lU52WF_`E<{;*Fj*vj1Xa(h2~P30IskqNEu*M958$3XdtVl^yr0a?gZ#+)#h?Q%&f$dRP80XTsJ57{NHcTZ{;KtC?>v@i zuEc?GQ&MJvyTau_2H%1@d?BZQbbAFyr@YgoSH|8=u{Fl5!l^(C|bYdgLGN@g*L ztS@%6pcj~=K}%!iudy%C^b*3$W|P8kwbMnhvbR3c1E$=%C@)Gy%&NL++Jql;PDU>k z^<>pKA5QTycD>y3#>i=!$q;f{PV8`7T@>)mRt(_DL1Y0&(=Vb-r-}o(zSSwuC6Uh zB-o5xG|XGfK%Af7fa$IQ*Gr@Ii#)3Lftrix6u>Xm1qNC71hZ+Q=K=v_w!hk~Jj|&A zQNZxTt-IrZ@w(BHL!Q%0rzZD{I*jtGO$uAkc8_&Q%P_ly?}FZI@nsg5qusl%4Q{8# zzWi=1Rt?%J5wUN_tr_zMJVy&e2-kF%tk>B_b1|P&O8m%d7iii}7Sg42GV8oXM1FVd zectKVypG&SPi}3#o?kZRO?Mn$9jz$y-v+f+{A$-+gI1b^*cr`FF3G*o2sirdfg4I7 zo1r?A3d@$V{Z9Q`+8$&^ZWYG zjaXO0Avut^vEKmBI0w;uD8c%CWsDR;->yU!lYzUd)dxCMgnGX^?b$YRh{*sUm?i#0 zj?DmRBw+!?ltI(T0nYjONSa`lk{$Xa0n;A!d;i7>chi{kA9fg$(^aQnqKxzch&F`u zn0teecbMgg>%J|jSVoL&K?r0qPpcWUwU8~n-|ZnIub$Kiqx{DV@*P9h8%66UH-adJ z8J@^tY4ImZPjb$KaZjWD57j`O8voQW{xmrx=SzCfe|!(3NKk#!y#5b;_q%_E7#u{k zp7cFY>&bZn|5G*|o%=6acF`sOX_QYC(tKh5f9?H$pM?KU(yixzh8Flp{uTXV6ip)c kuR=)UR$rpdfBW)?FVmPePrZt3{|xykD*)xoWzB;B57kcTGynhq literal 0 HcmV?d00001 diff --git a/ml_system_design/seminars/imgs/sem1/sem1_11.png b/ml_system_design/seminars/imgs/sem1/sem1_11.png new file mode 100644 index 0000000000000000000000000000000000000000..c79c45222741b85a9820864536b8db0299a15616 GIT binary patch literal 30785 zcmd>mhd-9>-#;ZJE4hX2R8m>V&fdvNiX^L&BrAK%ESn@DD@l@DlB^^vNl2t@lI)eu z?|pVZ&+||G?w4<0_f6Mzp2v9{pU-=Jt}tC~H5w{bDiRVB8Vz-2eG-!GWca>_k{o}E z|M>nd{%_!#l9H~IzM3Kl33Gy_nVC#837HlxHFc@`0TqQQZK~ZddQ{ZvT*9TV{Yb^l ztjv~OXp}{SOuo__H6N+ZWZKt%bel0{mK;f;y43yVhHVyy6uKC=T--Gjc1GuKw>Y$y z(Z5T6@9`PNATr7A-(=-qDM~R&?+i<#%u2%IxO8CmP{z z?kT-J@ICwO`yUj^$u|wjgKs46wn*Ek-n`5xZ4hg$w~b-8-PMXu{ir6{spEm$M2|)I z541ayllQ8VlMn2orZ#D%cl9H^E-ds&gZdC|%dt!Bb|$A<5pH&;y}FST2?=ct@jue~ zn@_ArNVrHelobrz9!^wTbGk6X^cPl*Rm-rKjNOU<_~!WvYmciOOyTmTk>0L zf4EgXn8?qOyp}u8dWdny!;#+Slb*T8`G&iw$pfp+gqW$xGY{#dE$$$``}-g676r19 z{qGM|>1wFA{ogN!n3I$K&tD2`tq}_J`|kw;tACl_S9tsP#qi?=p^seu`@N6{^A-C4 zd+C$Z$0`5sZ8(DezkeH#HZJR8|4Q#lfn< zz!}s*NrZ*W(HmpOK!MQbpHH#d0vLQ#uA1+=Nc|$Uopq;J%+ILG9 z3rJ2*-tR1x-fG>Y>axvBgyrL>PZmBt9NIa1e5$sd35K=qi1yx}+|0vtC*3fwdca;N zus{AuhfSz5&;8`&njb&dY{m9He*E~nvtKI{)=V*-UQAp(Ml<_!U*DGEJ$1pT6H{rM zWB7U|ILND;Ye0PEbwu;-Y_(>rT4}yH(|9ONo}SF4%!MXV;9@|ng?ak^xd2M#Zns+FYhq00af(6SSzBA%{u(;D_kzAFr|vgjDPNl^yZ`Xv?QgbM zT9O=ihi_f>sNfJwrfEq%S(B6I6Kt35H zqxPjHZi^k~gr%gY@H;=nhR)+AXJn#m#qQwod~9jSG|pEn=&Z3R4j5fY<+Qvw{U)Aw zCmwC#dFm*RlZv?p@4kFtR8>QF|*?JdCXZfCA3FGmihA+ zFV?oTG2mh1kw)+cMb=MUJ%5yGH&xJ!H*cu)7=I<^mz#;Mh0(!SZ25B)Rj~ASLO{Itd<*khJujDA`p?om83Xj~}TwR|~dSWz(WOygYjN zt=KpdGp6!qqhu8e&%a-) z+VU=6>h+`~5A2tSJRBnwNfX9D?kpw2M*C~g!)s%)^L|Q-N~uY3(?~`nuh`efkoA=t zcz8O#t9xYECXVB{{&?-MeY$*}HaIx=QHPCD_kp3VXiEQ@fw%qrT+a-TO!ZdWQH$cJ zo^+|PD>+lokly*z?Oe;hSmB5W8sg{-*fV>3d*f6F_1NVMq?>vU+>Hwyk~p7lNKHe- z%}lKr%lj-p-~7f6njl3~ih|7C4jaml8n@*cZk`xQHd=~LX>xQSH4a}B>wf7UW!iP{ z;KBYykKGZf*WMSH7Njj^-;#0EvCTKESzVPPo+1uyOw4XAZS8ks&LW4XGV@V*@D%Y( z@P5@}&OFJ@7MCyY2vgoR;>Z%qo3eO0mF40MdISD{jvhVQ-xzm@kLk{9w^<(Smt1A; zJa)5}`%*^-X5ECu#DXg;Wrye8Wjy~-Cuu#-+4~@qYcDx|{rrUsHNSuJ8|O0xRCD3$ z7^P(^sjBp~V&l&?#lObK1N)|Pa+Qu8Il`Bqc{0uTvw*UmvOr?Z+?)ssfVmK}(1{a4 zxMrLbrs}IN*%?TkrBaw#Sp!T9>Wi+ny!iLu0B0#0Jx0<6Q)iN2ZSmY1vATSW_DfS2 z*S}Y7>|+Qc6S(;1cG||oRjhJ0cY~=HmRKcQzuHP9y`X^ZVBF3N7cPiW%Zi97o(W;@ zqYdl7ar5R-QHyNNB6y)~|t^Xv;tOG@-)f4o?3gk^HN8@ILcI06G9iG&fCAVCVcW_1Rjgc zm!i^-5{JLooOq~x=-{D4!otG-7KNepQJh7kURt3~KX-Sta>#mxddWs@Zu*b~D&Pbd ziocc63)REB;sNZ)H@s6^d_u;3jx5)JDnd2$P^RC17uwa^TNlFV6RIP-LZ+Iz`)1XK zm|bsqSar3+y|@O6f80_-zS*kgbtp=(sfx0yT% zYT2@v?E1u2xtfut!~uJWE|V({Dc?8nTAs@5csJsR9?(BKRR7v(lop#DeaKmgJ&xxj zZ&qP{AO+pb_r6W0OJ1`(w)vCv`fhofRC=)(<|X7u-QA;>t*?MrxKfmEz)zf_J9q9l zM~NkAoh+$$?s>LHWjA&@YKekWO5Mzi5Oy`*+v9hw$S01}(bmq+kl9%WJc?(}o^dgR zEj(Kq5K@o5?O1vj#qf5Gd1wY@LFXN;FLQ*di<#L&R<`!bSi>NrBEfjG2iG$?9bvYhKaTf0tYFyD<+Mtijt+fPqvhK`5|+SJr^@(9Q0vt>3| zxO_t;QPx_V24%hX@86TDs;c7A(=JN(Xo~kGx1radR>sA}y*Pgq@72}S_2@zU-Hn(f zwyqyPZkrZ_w2TIJjt7|*oO|h@(|fGFc#1d-<_Pi@x`XN zsIoj#kx?aH^T4x$g8eb-Z*ebfZueQqdh90RZVg<%zWstYvGs&RMQ>wWx+Yz2D{>PJ z=KRrZzw%vMPaIYP9Lm<;iA7%S+uFFazS81bhvzvf?mI8e%*OVkHOQ`@hD`4`_nnwn zIyMT0UQ$Ovsf|tTJu8FW_ea0l6mwB-n{bhKEM==){e1+j5?is$R?J%T!SlZLNKLFm zJ*V&8zRf=+>N)MN-RBSb%mi|B(NjAAI3CBlGv6@J^7-2LI*%NFDfW>!Be_awMFCuo zOav@}3(!x#=oX(pddQ*iVhFEBX#LRg`h3f5s~TszasJ@y;`s9BQdN{Z7fI@9)veU% zrPrMb`R#QhB^Phz^t2egNaX)){_{<_bg61)bx)aVVP$2c)95Eu%s=t=Dpx0TDg4y; z*xA~ed3&>y2a@{BlNjbL%1fs5C;Z;AxOXLBf64CzJK?EHyS9sQZ-)ixC=Sa@eF%^j zjiMkY$McNV%zp9ub*KSyZoU(mhn+0-KVvN@iP%->-%Y>*jO4zqNm)4ViJ zTQjx2MqyfqW0reIol6;kwy@^7r3Kx0Zj#tRNm}G+&kZAvKnKhm9KkgMT>~#YSsd$o zr{q%Vn(E9C_2Qn+rEZz0vBz*Pu5AF;#Hf?CTE864Sh;wHCZT0?Hb-t_C#Uzq?t6)e zv#a9;LqD67?IqYK+C&ZtreyKk-nriZ;ca+HHwVJ5ono&p*;kdR>-3i2kN^ zFc1~Sra0WALi>bUn2N1fauV72DL~MM(RpA_q2y+Jsgw{8L*?Lw^_6+zVajeSoLOHV zl~coUi1J;xq^6}!?eB2q$l&8i&>WcRDSLfuoT+V0)YNnGMERfY>-J?m;p{o@xnr6} zq8qKETSiGqP=RPewrl4U?YH4X!$Bv(`R9p|=ZW#-3;%2_3W5V*^TIg4-@S7GT{b;N z?r@&iMqZppzneU+#3M6anyYdu>v5ZJ7N6~}-u|(4bc$rPattzKEz|rD1$GmiZh9&k)6_ zM<_RVFvZ3+Om}vm@32M$OYPCjX284SOybu~3#LOQU71iR&_`!vbXvqijIN*M{|86H z)P3Y1lTybWzRNY7Gxc(tyX{K$6;!U;w0wEw()|0ZKw{yG7k+31DDhfaT5RfPLz%b5 z@?vN6q-gJFqutL)Wq$2im>maJ2mjnpO7iaYX`{oZ!fsal>LNvP_;n@TbqYK7;q zW~3xQAZ{`oTBlDhDu4QE&drewD-j>V)RS2W3LoxY(?9>!e0Ak%k*}Bdw9!eP0+5r zLQ+ysmrqx;T64uUa&bfv=M@|JN#{7~n^X$znKNfzJC0DvaL_GyY()Cz-FD@X^Abxw zjZ&qYsfP=~Uz10z%xv`=lDK>KE>TCNQl>7@u!&4zMNsH~I{?T2{dN?10PD^(bt$Z2 z{}sSRTnYOBYx{w1fN=dTWmPAn^9R+iVE7=*GE^?N0-+@)xYwFA*wz`>tK5O7)TF(pc6q}C*BfwHy6(an38N8 zDf*msarbuLd%*QA^B_M!(DY>fRK^dbU7uf#FOc%FimACN!%+|1jID{)MOOpu@@Kjh z`&eG~s=GVOiWk8Tz%j6fxc>3+aU5T8kawRyM`!Sm%{|oAV~o+xDSY)R`210FJw|{i zAcvSntC``(J!ogxUAQBl70LknI@d=8HU|On4h9%a^I1KddeV~|xZ=)UK@O>mHk;x{ z?PEehLL?zI{+>%y<_-?Kv0z`;KDcS4HDSYuvIg;L;8fu&oIQ=LUhi@dN*7q3JsT=R z0J*%6_kcPE>;ZgF9Ea70m{2R2$UT)%#u=z1MC z-qq?BG9g*ROm|d_Lnr4~`sY{7L|LmAzs3Cm8c-}Xsl(y|T>{=cQMQsw0W_%vQ6uR` zB?@ZeMLqWVK%d&ciN$R8_^9!S}~tHT+>k+oJ;- z0es;AYt<7IM^P??MMcRnTY(Kc`2tCnRfE3_&6?TU(`j#+l_=%O92A z`>NJ@reB~)EcN+v7F2D%v6vcq1|e6|^`P8nLt+&+e-3 zudA;Y1^~ZMVrOn{PKuYW|9-P7|JRS@_Ta2t#{>l4O}Y%#MeHLu0oF+(@{EB(98nz!Y-Y6&O{XJ%2vzt;KF|)vD!M!5P7=Il<+*Cc)Wi3eNtyk*4t9UEgiR zP{dI~Q?ZoNu2ZN01gE=y|Nf&-X$Jg3QF5Eia$74c!m_fF)85mbAg1nPSK|t=3NhQy z4yu%I{P_-&;z%VP)LK6VuWho*x0o!{li{!jfaT42P{2leIyKJ-dZ$Vm+y9AVc}j! zIvVor1l6f7a9I4^ky>y7QV5#qi*L5L7gR8`^H9~y^Tx(fpRZ0_b&fUI4meO@ z3JK!WxPkho%=IPD-5RmcS_g?HrI=2V))JDQp1zm*V0IQwt^q&F5vn-q((;CnTZO4d zK8jIbU?5Qmn3$MmLgd!}?z@ErQLx)jKe)JBq9hpbAgF@2t*)&JgSgSzKpX?iw-OnE zU{sr_7DZ1RA}lSvE3JhhuF?7Nk^&dSfwrF2&5bo&>uaA4DTqhblZmRC{@*86u~-B( z4hq`wpwjU(O`lx}id4;&B5pQXO9zJ-m$rL3xv7iHXBhx(^YZfe7^!g2xOvc53LjNG zdYro`PC0TCbe=~A1yLRW#m2{g@1arMyLV4WTs#CTjcQvvabu40G6^Nww$I~>Uw%C| z%o9q|0xW)y257yY`CL8c_L{^I6yp;gC@3hvKGgQFf(q0N2%O6`z)>K892BynOiFx= zkZl5ZHP|nd`{+vbazXJSj@!oCQmd_)!_*H+lM0W;w=02w<7g4PcI|?E2wluD?^gY( zy=0^)n8%MFf9*2)m-NxetG^Au@6mtub`m!#o4?wP|sxgwMbpmBuMiJ*E zBs`S;I_O0U9u*c=sf`cR*vRoy3D+q$t%G~89e`j?@HcWEFezbxOckCEfJZ`NXlVGN zu+YD`x!IDvtFh`D)yx_-45)mU+@gYEt&mvDliXadv#e&C=; zr9^Ieu3Y`)ECmN7G$RyMtfEXa(D~UR&q!$L)n;9K!_VUzg2BBbQ*U(N_!U!R>gy|) zzyAOWpQcff4%Nuy%Md?CW+;V#rEhJDEv&3=|NI`)$32mH{{60_f*iS>*T?LJ~;Fs!Mp9H|5JWG(u~*=NwS zq5W`!FWtYn6H627@o())pdH{(OHa>zh3D^b@OlVKDT&SF#q@J;J4;1C3ikIWC1f2O zidT8v3Ha*}%K?^P?^8 z&RqYdQU%Dj(>JbagtI5i*fk9J;O(Fzv(-=LQRcK-|Ed5SXQT=`Nt0aRQ#UVz288C1 z9+Y3GelX5CO84Eo?l!2$pg?dHu-4GyYTv$n>z|o{|B4qvsRB0wZqLqD&ERWWc!BP1 zBA~WkSyP^aJ9@fouI|a*KyF{tVaZoDl2EI!b=!+1Wqokyc}~?;m>K2v=S(hRLVPj~ zQ%Bypfu%LNa^^izrLERkcRo%9>sdJ+Iy?Dx-b;lc*nkS98C0?J`NbN!^%}YUKswn+ zZF$4ftZ}(C~4}KCf9$v^eZ#WoWBJ!?yeJniO60d&$iq0GKC+c_+U=x!bUt zApKBG(PMq8(wNxzXU$Z;Qe#7-|A7Wu6uusnj?c4*-u^T;!*ST&qUakOw_n9mL44t$ zHw!a7l-tM@bU!KtFR*cqZC_KlKRjM(oUaNy2AZj6c3WlIG0u?TqMXN%?NQg^5rOTP zfgXLcH5A^x=KxnY&)3dQLXE(ijg5`5*ZnMk-H}@&7uTqJbJb^joA3ha2UrRYANCsK zindGVEL<2ysvUS-DlFY8^3r_;-)!p=j+`R|#njQlmoIN;XCE-g>v-iI!k||d=}Z=C ztembZFO@?00xMnw^Xs;jj12rfS$E-PMG(>5dZzZ;P!9KLIIdHZZ-+_7<9Kx>I>N5% ziZo8w_wV1ky1TcLXzA$ehoqg?;d8x(yl$3zH%me1E>HyEC1svjMe#AyfQAW`Og;Xf z>m}boTAs+3wu!0gce%io>YkYfIQRS{U2J^1;vt@20N2D=vFs7JMd%O?lieqP9w14f z98HwII+1+(%lIw)jMezu2XA`yg?lpN6+AcJH`dI{pvvVMC<-KIQq(6#oS1X{E7}3Z z5|t&fiJKz|^c{!kh}}(zBTqZ{!NE`_CmSuqlIhrXANceXf(E+`7N1RmnoX|B-&K8Gyf&lM@P>+epGWAz4}4ypGkjms#yrB@}l| zJVCljO;iuY5&Hw0NC@~9z~p?uO*(_Q>V52Q*_0SuSarJtu>*X4N z_CR06m%&JrMd8ENvsIy**#S_d_uVWfB@sPzuJUVas62ndI{?Lms~qg?XMa?eme%~h_Q#`Gq{@SxL=&3NrH8d-MO*Cnco3-=JzJFh2wFmV}OaE^c;F>WRdxUDM zO)<`m;(neUsn-dVv&L_F?9h8jtmhY zit4^(lDs3qjJxA1q1e~FluU}XQ61(u7t5+aD=<8bhXV}-a+bMRvctf;puJy0Ke7HR z9<4aKkt!^CKV~uci!<9#Qygv-1sFm2!g3AHO2Fjm%wb|BEO_WuDcd!s2=^Jbx^6p41e_+ zucAissvV38pgL?@FwKlsK1jT{Y+#Ou+M}r__Q3RFd!Zh4xMlujRIvWMi4H@li5*r9 zf==_5??py2vEtP|;}nv%;NY-N&8}bHWtjI5WFb7`FM+wrE}p`T=}(RGEg-)FjYAwG zB=PT);TN_y4#YS!^=>>I*)2Ec%1h1zvtM8`{G^>dQ?1bI2oz#mkiCM#}4`pY7D{uQSWUg1O)~6GlV@d&gZxB z)&0HNA%OjI;!jvRe_G3QsI1FA_PyLBsr$CWGX_PdgX_HDaT2GW;2(iGfo>aBH2N!P z+Bpu>y(bCh47hyxlWV0?LTLOemJb6Gkz$gw9u)+;#%3TYGgu1YA9ojDhbGyVzWAJM zIF)^lV|Z&zqJd62XJxZtzQbA+vM&lXATvrYLGFS>h;0Nat26oF6YJoCT_H8_!GYI7 zMnYm~?F>bzj&`E}&5Tb5*R^3BSffCZ?5 zidjAu7TaLhl-%rlym;)~a}zzSH|a9(l^l6P;rMcz@9PiV5K3L{=B=f zft$(x*im=Zv)>EMF3}og;<|CL=qbtUM?U<6`%!5wx(P2k{rpiLXt@9YXo1))1WO>= zEjtgz$@qy#zXo3yW_76$lvNK2RlEQSA*d5z)%X; zDdJxr zE+-vE6>JT7u>YBaccY@9#~l$H36qdl-@TwP>=hRB=gBR0-rwq}8W4>E__FoWLt0I> z_VE-eKv0CAJtY~ELNOOuR6etDc{)KkuCYq*gXyupw6OADKIFS{m3TBSI~Mb1|Dc;W z-AD0nU2%=kQ~i_YWTl(jGxaBJ`5GPA(8PcaxB1JTQ{5?L84|0&E05ZEQW?HJXw-ww zTR*D1Y)(%5v4tsPCz!*rhmu-ZS$Q&VJwZe$lsm4`3No5mvGFDKJKUYxGKYYbL4xpw z@P$;0_HJ_0ZzRwFU|tX=OE~XVN{tuCO!L4eyOMT~xFSfM#QPl43AedZJD7B_{hAWh zj$J|9a9t$2DiN6+sb`;#Hm|_gFe~{^U^s63c!T32mqN`~Zwy>#s4y`yA|<&S8EN6{ zywG6UT7<9(HU$(~;9zs$CG=OwQ@0KCdbkIkWYz3!+&IBZ4f#v(^xpl&$j7>@|Mhlwe1uAP=N1 zm0}ogz#(j`F5YSvKl~Ch95XXXy#t|e;{+gVu`)mU&!x0B+R&qRNI38(Y^!%b`-N4i z7D*q?h~Sp}(=oFw0m8WXFYQCrY&;W}1%7V!+yo1^>EJKg|J+f5MPhmF+D^#cgc}7h zeK9~B7FW~d?kv5XA&mVi13w1r-;Ow<1!U;wP9!(!uwJ;=bQ%7*n@sB7j9>~4J>FIb zBRdr6txxNM8Ox2I2vsO8Ee-RMr2`DC>WeZHdOgI#>FnJ;=8Wq@`$x4C9Dce-J#i9B zfn%xG_1bublH1Lksv}YfY%eeIG$I*hs?-!>Xzpe$yL`06dKdp0O z@cO>NVZheU7j0csKjhzGQw!Dq=jbT(_R5>{b=s;dMZ@&arVGz!^;)E8ci!73&Xd0v zC!%Sjf7T5dl>x`nC&KaudTnD1h2@%)kH6Wr?Qp|lL9&^3^Xe1bS(+Gh`)t|S3+Hm9 zb@R9=U*0(R?bj!dYB`$Fy)m}EFzD#X9;|k1wD>cVp-=$y3=Iuo-)1GtX#9iS`qD7K z*}{;4m)!}dam<+&$q*O6L~(8Y1Wo7?qGwe#VxSqyU)P0ec{aJbLJpb_NEPfku=u?V>iRVl38dy`Pt1b7HeO=VXM|yl36>e2I z54Nl38@8*9YmE04)WtZ97j%we*Q1JsUl7zDO5#|M*0!M;l%R!64a@AReZHZzBh}Mb zVk<9@;jsWUpzUI>0RT&XB}Fa++Z}3{t)1OFG(cqJR5Kma><-6pd(RE;1fC^Kvvew@ z@=ci*#Is4Dl@b<6@}iyk)GJ_n=-fmT4DOnI7{8=Y(Aoa{ahAIdygR<|@UOWy_dl$v0yu=`y`Ko zyF;g^r|chV&w2S%v!HXD*%QA|Y#i~F75kUgu4L_V#Aj`WybjImka}2I!or10+%dX; zwMZ@v22e|TXg$VGb<4pUe}1}D+$7$#x6VSCKR`iQJ6`BHOrsh{*mOOdC&*g26&?`~iDDyJl=cm|)e=) zrHEzB`9ZiTbRhNzBRwCc3Xg{KptHbr!JdW6lV2tI`_1G3yy3(ql?cg@8U?3`#_|uN z^MtDV*ASMDpO1g>_(Ag(AXxQSo0m!OA!?>MNtL!LS;hw^hv5<%RI6LL5idV zXoSfN`>WS|lzqLQLe8&mdiqJ=`7-XM)9-#L>v#bfL$E}Q>%6HcVH~3ui?TM;F8XIe zU%|-(i~YSQg>^Z0^k6H;0b8>}J0!K#cy=QmirYq%ZbruUXwv6)zpR5ls(d-CRyzui z?@@PZY$T1oyDQ;$>215h^Mp?!JzX9M(^ z14~6j52%&z3JqCmUtIH*8suQzQtJs+Dz`DF19S#I0nvgl>Y#9eeBnft1rm_cuh59~ zjoqYV|DqWjK%WoI=vu6MYYG(*S)NP-egtrliEO=WvofdQDjYtioNyCe52PJi7!?oa0(`(A+%&I)qv;z!78)UJ=#zWV zpC5>2Wd$q_wds+Qz9=%kZ=C;_e_Q=dqftjm^v?P$7g5JX_ESRQts1aV%YJeb6vVoQatQ*{y9^ zz1CM|K@y%W2frRuX_hWqVovm0qM8G@(MS$s;mwb;}OhKe&N(FyBwu%i+i-r8~ z2yye!7kM4O_eZ&l{82ys78!3aHf&NcF)`$E;1lkSswezdpnaldBfzhksi!y2r+2h{ zd=ZZTaSWKcFtgx|AVjC1%Z19u5rrHx!Ra?PHp25dBzLLbj0c;?T7gOs`UPC@h0>Gw zCV&f}{r(@NPI#d8#y1-!qlYAZ)5H0oh!vd@mHN%7Zc2WbY=5mueQVsIfdd(yr{1hi zEoDeXDsj;xJpCx+%Pzm0!{5L-kyj;>EJViDLKxx{iVMC$BopC#xNn1P*{5EvXb1|5 zz7?m)L_P^UM{n}#U*{Wa`XiU9i$l34rYlVG*kW8TfscWr2Aynqw`3njm~41fbX|o7alfi6q1zv{r#0y zr|#~{X%8zb6ju2ux+f}?i;^HKcrTP!R2;}>xyu1yg2{X*We#VP(MN4uB>P=k|Kt6#M?mdzAg@jzzv(sx)S(I_^n`{i;B9M`Oz$S}ws*;TnmiIRR_Stu*Rb$KBVSZAc}@QUC;S zdd-mH3@qC0rc2Yz&gk`w9D39_e(e~k{qpG$>+jMj+9WM!tPzP@eBgSVcjPg0bdxqb zN7NSv5`MbhQ+mEsu=%@Sraf)bS*?1~E|if+Ze~kO5#11Q7B%!d?5k_lhM)e5GL=Zyc6or#mlDnIEX9at zX>1*z#;iA?*-J`loUI7uL2=I9ee8hBo&>*|0m5>`CEj}3uy6Oau9ymkhofqBQLWZq z=~w9;tPWo@fifjRIXtFVcHnpvm3#X8rc*Lza~TO6n?0smYtLdr&LIhyUjW?hY{_GO zW3bd9wq))ZcPh!h^wBkkCCQ49hd8<3zLS+TnUmamEIRP`;V%bU&Vh>Y5B?DuPI;+e zD*9r{SpM#g6;c(A=<9KL*^?!I4qwZcrhM-(wCI640xo`f`r7FE?>Bxy$zpH2UqNPc z{CY8Ke!BHix?wLhSZ8Ld_2<$j3)^3M$>1119UPfof#EtP5$notch287M`(MiQSK*t`!}*~Wd0waYl}(znH#@da7UUrj(jz##RS`?_f)*DLO3dIah08Pwzc zeQfyZ-HI5&a>p7?fpc9`(?lUM&Ie7wl4Dn4$#X0iqpg`W&WI#wy^LPS;&x2=Z&HCg z(BH!+)K#sKBDZI}rs-$Gx@&^Omb%p}A7AtQo4s?!MR6I>HtO#*T4C@bD=0+MS9Vff z_8dZ0#BOuj6(FDKJ303xwt~jZM`Hce%hLxcwy(%4ZGPA2EMRo$bo=1j`)Qgab#FkK zi2%w8+UH-ha|dG6ks3}XDL5K=iRugT&0~kwsY3%c|pO@e2W~B@}mlR7v?KC(`+J;pedRX{6yhJ=-JV?UlUV4 zZExGSNCdL8pWiTBKt%fljP7oOCTr1DjRl=t&qoXeVTE#8vY`@`a4x}TI#o!d1r+7m zs6($lP3BEwuBf-mym$M%&h?lW-kk`@!2yE7jqxah@=D%~s4trIQJhB}S~~Oa9#iG$ z;i>Z$GsktUEvsi24P`{M=QbCc5&Z(3Qzn(DIjxdpRcdZ#opPS>7v9#+_?tnW7 z$`-UKo8kw5j*qFjzw2pOx(^Kzh{2*zJ<~Vq(dM&1TnDE7HOmjy^&%aR*{Yr#hoC8n z+w(glE(W(rmLLC0RH0<W2_ez)pooQ)8y{NR?kCrH&Uf{zZwVkuY*c{pB>0aTaQsx8u@va)(B+IcU6Qn zWtzYb`P%TX?pCy1&NY)g3CPi^js!N!96Y>NbK2me*+ej3U$mEdEQ4D4+xinTBmcgd zlrTw)uTkg?)e*3*{$s6rNHOa)s_)tOxSUNTf11hA-G+3YS44`%apu6hk&nGK1ygDdi$POO(%xKN}enfbL6&;QT0<-Bpf59TZSo;U^Qbw$|^oY>}NV)#+Ov_vtg?8 z0K}_BkBu%Gt^*p1nER{nTq1^WkQB1a-#vF-oSB-)cne&r)RJE3*kM5lbliO-Wk>=c z3&c=|uU{7W#Om%HZKCk(`@>fDtMm32D zecWZ&nePj~{iQtY$-vS#$=p=dJ{GxFI%jc}A;vbb{)~@z&aupoWA5kJiL_K5E{nUq>RV; zhJiFUqtJ5+Sz9qRWKEJ+oEdm8|Kio(rCt@Al*B^BtuJg(naH-HEB>2?yAifIsXg9d zQ~Z)EuKJhN@O)+pd6oXJ*DRW>iXT6h=dO75DA>Z)Ab=N=og3`Gb*U5)OxjoN_iyhc5^_i<{y#Fl|i)zW|$U-g0dE^Z7 zCO`_WMyE+yE^C*(dWHK&&KK{CYLuGR;flFmum4jmcAUtU*I_ zBGvwJ0nJXT;;pA!NgrYEICivLy4J7ymx0TXjfVPVJ-Q#*cnDY^S3>BvJ{zm4RcciC ztJddE0TEI_x_6r_?nElrbortq<$we&0`asVna6TH<^tPIMcz@X=5R%kzx14lZDCZ2 zVLBX_#<`crM*gQ;*-z2$$t*ouCE2@3*{{&kcPZBVa{d!bazO58{qn)FlX%Yzz9~-{ zs2R}OkZgef4mAjTTd;!G(O!;rPVr|?&qbEI+PAa%V^+RS1v(Ucd;jz2L0FRf2_Guw zS6?9a1FbL}{+>+B^!Yp8G^^aiZdKB%NLK5qcG>en_84+GN^B_=TJ&f!r} znXjpobV#3Ssc7~adnbR$g(+ds5#vL&dj+H-cA0r{bW`PcX;#fb87uohL3Z08rYFJ!V~f$9nz(Ww(HRPE+e*R`$+ z9VqFnq!W)v_Po+FX01^g_H#KTp~pqgAN!mc=PK>{{0I+(TsmX+|XWYEn>vxxGlkW zt8I!?R~Ac*k3IPF7%wW8j0urkuM#dwBF2bl9z+!rfzI4x_L$*<)ep7?0*=SjVYB#= z@t(T56XsNqNkU#xB#CUsB(DcA336;xteDe&tAgQ+0(WaZWKTrMfI#r4VIFjGBBX$6 z0GG1dR`&{~S(f#VyLpEX%9Qt8Tl(SS%WPG~am6DH7k@@4IgopP@@+g8@ zxFyJzZmDwPr@qJaLFo; zjD+}zKt2A%%OFPt6_pq^o?e1V3MpymOM%|ChK^fA+68mrkYubxG|I-{%@V`laC$Ky z4Lu^qV&=njCyYR#R70%8h7d4$O%m7m<<&G#ND_t?^ccrXJ8p2izZv-KtV~Odm54%S zD`XIi|CMAHIbzKHzd?3+@O$ZsbKUJAxOOV@o1S&4{&5h-rOUWoZypSid+1lZbX zbau9+|BVb-;O_zZV^&vHi1|GXs{xlXy?jGN{0VoANEBk;0_mtM8ei=_j?p}N0{YLC zWsKjymb^x$@QC7NPoFEpbo}tS3rCso@TBVTs4%9{z3EMn{>>JGY`gG~vlJ0CbM1Af zz#)a2cgcJ4pUuTfTmQ(k3=#9sbiT_tM zghDYnLI>*vwDY8SbXW#4gh>oYEdS0cKo$cwD#RERL3~hx>7#G9D0tjRV<2<{JqETd zB+O+@72C!@JS@`)F9e#_s zaY$9dLPGYxI}t+T-KHXT!kzzvQA1Sf*CqN|p+S{r2K=aiFZt$0#pm!oP*f1~#$*`g zy@|y6Bcr2CFk6XvX-296d|ZHp@1{(|Gv_kLk3M)Z@as{JpwRQ6NDa;EPM2Xq(zHMg z#q4UDH#W3C=8BQrfvHp6Lz#HwazfK(*!*DItUo)gMGxJ(aGH7@BZSu-Kk>9>lc`^f zg$CS`jlSG!&DLbu?7()b%e}%BI{{qzjn0m3q!_BB#aY3O$E9-3ZHPq2o>>HInN>l73&G!GaG%M(Q{)~Q)-wC_W*4ftDitB{j~0DF3bBvrg= zZpDj`B#kPq2H8`iqZ_{859NS;l(F~(qHW&ksr6jS8u99WT=qW4IU)`)yk06@ zGWzsKP8Ev2TSf2luga)Vvzub9G&o*`T(s{L&}gBKVs#)`QVQ<&doJ(_6c8H~3xM+j z0W{;u(4#?Nq&x|+4bt=95j0FT>&R^|q6R>mXAK?gh~(8kI+&QJfUk;vfF|(gMAS>n z*NiWs;dXudR*g?$m?b9N5ER2Fas+kQocww{;}*hL10R!)!)by_jjD6WcjE*GhW>0! z=vkk9fy@0b=8WBd*3BGI*BNP?cExx7uDX+-3|)(d@c!NdEK z*dgh&v5Zz4beYi24%%qX)p6z&Od)NZ}T(fO0+)bz2uh>LI~XkvGq@sG&{B&v=X!qlVPA>ScNt;A0}x#FA@&{(S6q z@L=3nQU=aR6y5`;mZ<-KKl39aue19U9CMtviHQjkKfcE|`gkF{pfw^uI6E_-^j{mt zqHvTcd^UDPy4S4mJc(J~dXBH$^AmS|^EGfi@(}wV%%O~m3pe!1(vid_w0yN*!NZ9c zpq?Y!_OK%SL@sl3kxsDxd!(4dE8Yb1gjR zaAz476Jd8N5m@L8rxm#WF%S?GR7%(F$FWlUmwMSwF}4s9n@7;$^9_U4O7`j>1p0(l zf}o3RTHiT8+Ww@|?@=EMIxTPBqyq~8(_YKqNeQOn%xrx^>3$*qtAB&%0ZZ&tB`|b8vFYsOKlHse_D~eW(Y6gFFC6fCt->n_sm& z$$Y|=?G#P*QDZENv-5}D1{aiyP>;JgFpdg&8xJEL}{AITCWj)h)x5_uv zH}&G$tv_swMLPS@pHZCfQAB`A7(co=dC1A+v(xyA4vtt0oos!@4_`+dcjo8`UL1F@ ze@eaWDfc7A6CUTWdg^y*##p}FzS=Gq*rrQBpS%u0W#U7vz~wIAesuB~Xe9oX2tw=f zA=CnY=Y7j7R@DSem6z!`UyjXveCYg~b4+M--d#PGH@vgE;EvQh@GZ(xx(xypY`)<(sT8XskX zjR*3v#J;TULHm%*v|umsNmQ_emzS4i&$1g!TSxFxY#Z^|PQSZzXwZE~r?V~WvG0OA z;$_Id;qn&pnaPGWx%k+l|6q{dXvPb*^ooIdyc#_3@;Yo({>TfSy1_wVAdRdJF5q@I z=c1*=vC-JC3y(}IP`UVfg*8*AMmA~H1`Jrp-nQ3v;v50i`}ml;1szZN7{j#sr!V{I z{{RI{DOg~%L2kL4Zf0|B+S1l`;>+!yrey5F#2_&7ISObWnEqiIVU#M5{SdC7i_bez zZm})|ym6*f#974T3Dp} z|NAf%=s|eqAf78;rpjY)YGIYbd{$|p`EcU~I zVxvXKpj9ceqU)7mUYHLT0+;_KY9t&YW`{D)W*6jG^pLYrzX!!brRN>EYPZ`4<-+Y6ki_=Kn(#G}B#Sr#GvhYtDUa z2G@_YR-j>CEfQM7Vq#zSZctEu7}~#m)SaCSaYSs`nC2^B?bx&Lvwj6>Xz6YHaP~$b z`Sr;wr{&c24t76z`dN#=x*Ohu+7gpE)KiE<)RGDVp}5t4Z(V|KXD%U%C-?^^esWu4V3``h3C_WS;Z z=Xrk5n{4JttM^CZk1BxjE8U!>{*R~X*Ly9+1RW~Gce689v9q(!YNif_Cl^MavWZCE zT@#)E=I})y$X(x>_XlMs&97?lvy}T<^!bAtyqw0K@(H`vBIHr%vuL%_C0`hTE7ds& z`4HMH@X=t;!7pQq;@aR5n$|?l`-O$dp^pFy!0_w(PgVC8;j=mT7G^Ux$G&B-lVV+6 zcv{BfzIeDUvJ}=Oe^p<&W*aVr{{Uk?D<}v;1V<%l3L#zenUX7ZrW^|i8(&S2u}KYL zlTvOX{y{|52`o~*G-=}&?WDSOBc)ze?2Z{vc4VZ}jx5mRYps9_8AmyOO&xvuLcHvG zc&;F?Zf`RL7=79%HEq3GE%ksTz(0}B3PsA7FAuDpnuF^2jfxVT3lDeL)tb~jVN+l4 zjam~)4y8OyBBH7qLm%%OdBNO90dj~f|8N67OuY%i`oM0?7u3Z{hw9)XA&V3`O+hxf z)`~6N9#h%ea5GDlEO0*WWA?wE8YqV5PJ}Ps)%!8 z$42{n75p7U5g_2Bs(g_Ip$|aAayW5pWx@nwz-Qnj0Q`XXrlE(g&AA|x{Jm~q%w-eq zoj43Ykx3wL1iLZMB`Q@hHr@%vIiOVZK1i3)1Ypx>rBc;^0O3gmNSMe)zegVob0uh* zY{vL-ahN6mh*|H>b~E*~{gb!#L(_AOSsuP-OY_aB&d6ZpUa3Vj>i(gJ(;^J5h6%eG zzzd_1F_)dI=KVZI)!u_n9r{2LTOg$uTBj<@12Jnae8+CGtR57U0_)pxvrADEpS|vK z_&Y7MuviF0l8s6i6%+8E@_fUvRjO?h!A}j+cKaDDAQ{56d#|vWDZ9M!)GqzGRZLPp-ykY*7GnX~g@vq8 z4FH#b>Us8(hg^mLobUX;i2~kXyP+LF8;i$8%q_B-cPJ}hwQy^>d8duJ7mNL(1N zts#5V=^K0US-IxIq>lX=qg+qbxlNNI`a*FdFeJ21fT%DVh)0S391&ML(pjQs%GEvK zw6mvh9rO1B2{0!R#ezovB0g5-KtIT#NnIbem+JUUKTZu~Fpx02nc)+$%5G%v9LNW% z5OttpAJ4mu&?;WO7_oW3LMEB=`os3yKVBFj0jAmsKN%iAz9F4X9^Z7_x*dokIPVB3 zWaWRo^bX!%m=Yj@3h%eynUBcoyztB4fX(u|OJz|M%Hf*FFKd6aW*%!3Y`XjxUl?YH zr6L6kg|*W)4VIUBV)5LZJXQ^Z-eZXWP&pI9%iEZx2-oH&V?!Rd5@Cf&Q-8>+Lm1pB@<= z8R>+`iIgtHb54btqcX&o-g{K%xs#O@12!d&v> zI4#YMvIT~EEb%qX&VDITb*GOXH01bMk=x|%z^C{bN+jUVVAha_OJK3}I@S_29VY6w zgacqzzF(6(|0GIl>()pns5G!t!Bt_V>RsnES^9ZNMJ&S7y(hOvp*#bcp*8hS{OI&E zgIjv|-Y@+Lp%+Sh%oRY(6ck*V6<(z0xTEG|YUQh><{>0NbQz%-+I#4&bGO;LNZ?Ps z1*No3$x63|U$?rmC7HB7T%S^Ah_f)^%X4bzbbNS0nQHjK;FT$NwB)WVs+~0UfGX+} zVBeuC0Dd*q&bTp8MydJQVm_G3SKTG3mG2Q4CUkZXkVDQxL{;cN1{?&b3#;^*xBL0| zVNf21iouuG8K^l)aC(rAipaJlv?aJTb_|t?K_&FA z7A6gF)dCoS|0{de4%0>04m&8$>b`b7xh-jTpeEl>lp6p#U`+wttLBbNGPaBnN6BWz z9Ys|2h`I|3>_P-UuU9(%MQ`Xh;sB&^WHJaL4MHPrp>_Rx&)!TZ5lUG!yyBKsv^nl} z`^G>iB>zo za0G;#?paV!fF}WIw7OKmbbI>Z6R6t_B#;6bwMC}e26_q$q7v*rTFL=5#YDP##qFkS zkJygXdZC0Xc4R~q`Uwmbc>$**$kLU(pvNXMNnbBfFbfEPk0=+hK!G+E0qTPX>b@L* z?2pjAyqp{#v}UHSU9vgyBq$qDVyQ`RZfJIkh`Q&obdDYqHN-Nr=vOZvPy~z?o8~*{ zcAtOwbUG`Vv?HtWB1%JIB!glG6_GcJ zQU5;L;Si~rhtC-*x3RIZj@XO59-S75IIo@iNpeuf4Tv%1k8n68N;pcpKZ~PcLfmU<7`=jBGE1p;{T-bHM}d*TPJFtsd{1&oM9 zuJ4FbK}E+{uGK>$Lnmiv(piBmaw=euQ&b%=0b~lFJ%l^-5(5q&A0QaFyB@^~p&0E<#9CVUJlrdcJ z#O8bjV;~5DFJFku6$U6e##C|-KZ#HD&#mh5MnR1#4B; zH7%&#=DsDR9x`0KVWxaW@cgfuow<^VeNF`!)-9cTp}ilXS)_3wRM5U6v18}s|By9k zGn@`J-7e1S$IC*G>5({Pn8*Hx7>PusJ&k`;@}nnIe|6; z|3bIg%e^itygEi@ZCYhByb6fS5i(=!d$I6JD_%24ribiU$q>?NN^|=FZ-q_OHm3fQ zNq-RwYy2%_rPb93^(zlJQ43Mp0mUYUU65Wt4NDB3>MomL9TywHmIh9Dtm2rh%P2IJXxr9m5az!Ej6wBFHu{&eDWk>m%MNZED~S7spSm1+5p!YN>nB}@|7`ic--nH1d4tKefF|0MKjR(;OY+h7Uv?f`! zt_V}yP!z*L?<(T+YwBnL!Hj)()CSf4)1;uYTco)1)26^%7*rq*g#g;YH4smc#)~7Z z4#M9#`RL@n9_F*OS@#;u98%g1KSo`G0hXhDk7bfd zcasqKCtc$z>O3r-(t#1-FJ1e$xV3O8#2sS(c5PTAB<)XsRKT@0ZQjua?_gpjRGO>YmWIk&1-2|-eosDItq9nruF`_(Schpu>O_W zfDQMiw1+R1{En7zf`{%u!(wb}$X|Zk56=7*$qrO}F zQ^cBI*R>oyI~W@us5b&~)pf&eDVn7itrz^+RCcPcx++s2yS-eaAUuTfJ>vw6vAH|1 zQcK2##Ej>a;<_-CgaK2c!A*dx4FjA*CU1DDsVzd<7Dh`nG~7SP83AZ#;SNPs3Nnb3 zlM_HH?EYKFs9KNqGL^b234-BRRlM_*|4DnTozX1Vpfq4h0KqvlmNZHNU_)1jxf1kU zsBTaPA!S2%S7d`w_`^!KFd+(LG=d}MWYtSkO}8(9lACvc<-v1{9nnRR-Kk}#{ws8S zoITf0bbp$Ca_d|kz;rAT0O-KV(~b2c{2XZ!E3w6335YF*NQpcLBU-E6;-FFy-1UsW ze){z$>6Xkc&dJ_ODu**KW}@cfn)-#6Ltqq$yU2by zb2z_R&4yKAvGVJYtY)n-O5ehvtlE9?CT_;4PqLl(rzYdPn^>*|!viwZTZ>*zCS<@+ z{so}V&o+R`#<3gEVzrYIS?KjY#VUv@t(Bo1QRsrv3n5I=N{)VeCHgwDdyNTZI^dQ^ z*qFjGcXr|w2W}XpV|-=TUe?^xR;U_}nYsJIs;#RoNn7MdlH>3GzRClG6E5Sh?ch<2 z%S`1y`N}jHSUSP5u-qWDBsQHSs}m*Bap}>mob%%)>~@&sMx|xJlIwRVgxSQB6c>iK z=E&P(lH0wof}h@~k;wWnU;_f@oOsiJ;-$7RM*tm|XngB*G$JWU$M~8YgJ#^sD+eKVgWb8tRMV!-n^hcc zox%><{StAW47C7dLxx8P!(Kz?Cqp%uq6X)>Z@yoY;4sGfLS}~CA8Fhh-3v2UcsXOn z4X6p}%;2*{L?uX#5S7^5aHY?o|A4C#mL-{q1h9;LDDO&}dd^o)$G|D8R8c{oxOjKG z3p$;fV!9}aQJcDK3G1+py$JTbJ@Xm}&DZ6{nijOo*j=ujA zI-}S)hye(+jQ|Om2TEbyj2vKrJH^^Vp}s$9BE?44Mv->+HYrXfKwa?JRd((lH3%{T|Twd_m0_NZ()#s#k}TkiTT0E?7fm{*5e>Sz&4hG ze6*v^ToxYCj`0vj4}w9QIX}-ihB`iPex{mM8Zcd$2U2YE>CQz`A9wbgb)NfL0FCl0ii0J;(Zvs4p4#yaA66FpGXo-Zt0N!_- zF~Ua4e0Df4z-?`qIRg!tHCTR{?dnV3C31KFq~3;!+v+FTM@*u z82tN(p6Bu|^`70A<*mIpMq1hp z%=sF;(G7lPAo>q=e=lM&5uaZuo{+0JF1qIVMm2`%L)?n>&IWs^KHVzma}lo>xEGCNF(lpJbN;KO3MC1Zq_BNLHAcIySgY{{sjwcF0sc z-kAN(PVb|ZTsC!mviMkva&yu27$97bofWS<1q)}c)aRoSFBSYnamToj!?~>Lo@*GS zLgqW8Lb()rD1~E|DRogyeUMmjB7(2bQm9WT{sTvl*|By}E{iCt#vI(k>UBlkueZ7G z?btcjgL({yA@$(Bt1cN(|0qo;w_#TG<2h6aNM;Z$BGf7g)~LEtv^Lm3Ir{Ci@KTMK#~=aj&DGxFGNsjwvCe7Q^NnHwoOvxpSv}_KF<@ zSqKd5g5N*I?M3xV4l;>7WOeLeo`A9Djdcs7+Z3h!0@!jJ?(Ql%^qH=*=Nht~()B4u z7|873w_KCMg_S9!eCPO>`qmM901*~WPTYWwwea}J!2+0aL{vsNKPJZ{G1!s0%b|95 zMs#HC>jn3*3WZsURa1r=#^upU&ix3I8K-tE34(+pV@?pd!_%Z!FHfK zJRTC+2eS@56(5vca(vFXu&Z|Tm0O)>4YjR-E`^zkX${E<7=i`;#mR&-N9+eXmL@6A zH@k7T$R1>;6$KSrB~Skrh`Y&K(>v*EXy5NtfTLujE;7)s zD#ZacRc4Q-wD`I7SQGb~Ad~*%HoF}|v;Y>Vr=EODSwJ7T z7C|LaA-?VAd=QZhLA(ywLH*2la=|e1_$a4en>x-FtqkrjO&4pR={g)T#8J@>65VXK-rxR-)9Rczj zfvG>9?@DXjimi+E38z-5BYl;&16is9nSFhIQ2vqAg7R9Guexi^ zi=1Pe%akuQm&GAZ=FkbQbPW#wh=xe(Z+UzYORDqD;?SF`aKnN4;PB_s5RktiZ?V z3flI`DwLIb;j0Fb7TSPdEG(=SXT9k$A08xL_$}X&naBZy$pkUM{};(|KcKh=sbx+h z64c6u{eiUbw*jgIMKKCW$O*iwdU*fwtBRFI{2_S2sZ&~j@-TP@!2MBO#b~O6ORT|r zTd=CAf&f0E23>#Z%2y%teAqi-9}|t_ilaGZ^w9>xukc5hf)cn9Aou8?2-63H3m`Di zP`W*d+1}upJ6nl`LWaXpL;@SZ!e8xGoY);fWi2Hhx=TNkWq!z`!@$Mhc?q|4LNZ_h zpbom)TOIBGF_-5tN3BQ>JW(_gu=>GsCC9jc zw?zJd0=PV4KqBJdeekAGW)XS}23M%~%82(D07G`myJlvO?jX3V&~E6Ah}$H}exyez zZo-R{9@hx_n1dI9o&#MbHWJ5Qt`AB?p;)-~>ME=ov24gY!pazh_&u?KWSNjnH0V5V z!$5ysqi@Z}ecSFvN5O-D;GU=m(Qu+w0qsr;*?1}KgWx);$k?i$b=b!Tw?G_W zhijw3VolybLlfvZpXxc^qigowPGe0O+QMXBgCJL!$_ABQtB7rW7*b9woo>v0 zwIZvPWRv*c-QrZs{m~_))R&8f!V?jK{1V<5!nVK(cr(WgC_#G_U(=QMW?`Gh^iwlo z-n&0wzebFMQOkbekrzQXf}Q~V50vmL2GJ;v@!Tm#{VjsSfrsG-ENIO>D$CN0?GinR z9dd@1%I{8g$VnNT;zwJ#{z4A{R_gqDdh~(_F(lH8gjQ_bqh|4-1dIkYEgU?@0f-R? z5Kv<9HUN>>`*CEqux=-|6||M1ldtmf{7f>O>Z8i1B#}H2-2lKs_XYm47@Asqg*82A z)xnxV1CFuaF`?J&*()5(#Rd&PZi%F9G?kcSYw&w;HU%ad zk$U7XA1pX*DL6d9u>qE6eEt6R9b8;_>RJIv!ce}EGf|eADEMrPh$&Ra(AeKVFd}Dm zdBT$(Lk-0IgLoL=cp|7REh}4_Ay)vpLu8*A^oAuSR^J}mc23udjC2j9vT8%XC#Z1a zJr@<0)y+VPJg1L&`r0&S;tV{81ZDp?RUihip%|OR4j}FcgnwKHi(sVG;yF?xU>-Ep&KfHH;O`3S#1+IOx;VIaam34m<|{EL+N z-uTrM^`*5HPT=n#`_ST$+m|dTfU+H>JglvtC~Ss0GG*V{CtbdBxX*AtG^?l!YM3l+g}DPY9w794{g>yi6M}wl(MZTAXIk8z=<5 zO&|zzqQj?8KX7CL<(^Az(nng0b&wFFRDzons_8D|Cek8ae1Tr8(}!-d#o!4VuXpJ5 ziTxVzUvgX&C{4<?!`8`qxxhT3@%9|2Uofw3`CD0v=HB^TR^}Oj=72sQjwDbni^U z8#DJeZbp;j3oK|O$M;y++UmRTRf=@4iE|Y|6b1GMRs?-3j@y#mv13(UE3402A-1&N z9s&YPJ%}p8>tS@PnOCWo^6pn#^;Qixx^d%p(=ah4Vv?sZY9LF}^6~QB_7$kSnI|H|9w|QDu$Pz(@ce@$bwNfZ z<)3bit`bWz5 z*~wBOn~|$Mcp`H21ii>Pe~UvK9-WQzDvz|_x$Vl;!b;PwUF6es6~9G(b{0QVE0(QJ zzs1bD>9z$=uZy|QP5dJHq36jIHh*xoJHdnYn@@s$u5-{$FkU=gx=`I4z?4{bU_UiRy~{T*_h2d8h7U*J)YaQ8iIvV84tfqx;YOUj49c<) zU-?Do-@mr`9~Wm_^`BQ>TKC`AuBIuG)cE(}xBmU~|KqkS{`W)q&wGmgdt2l&u3Uze zC6#5z${%s@_5b&6;fbt#P<-b9=eFqoy<1a_|6II!Y1O(qvE9m}%o}k`E{(3%Ay1To(LqJ4Cq(MOHA}!tBd8NEux;v!nZ9eb) zzW>7;LuFjSbN1P1ueIiyb1tKl6{WB-NimU-kg#Q>#Z{4zP-BpgkOeW&!QbRip3{IY zOV(my%C4$XqDV-@iKa$I0>emX@;F#n_1-TfMD`Ui@#9r6v81Wk>g$4#xsA+>EzC5BH&%(2L9vP|0F8ot4=zW*#^?=5k~9UsHIW)MMqnk06z71&e&jMUw+ikt`}j+ z1ET2YcN*vs_DT3AS-0(X<`90huR1Cy1V=DWGX`lUSu{D0P!vw)*x)6Y3p)C|G&=gy zQ!FgKaXil;WLq}YA2L|<;IUk0xWaNNrK-TgK5>%Pc11$M>3sN&-0N6qhJ-|gBqJ`O z=9RvS*z$x+re0jXMIxZ9dz&L3^@m07n1;V*#jW8wo%bX^@LV{P`y`FOwvj%8XAd#e z@`0>cnSk3FRr&mpw9I$A-eZDzMSmA!U3Q`%v|t}-rjIPXC&m+zOeD z6H|1qpv8iXnxM@5Z_kCok%E7y7JcbA^SZrycj=c|Q9(A>;!hSg9b@MxwqX)008u`B$qOZ>3UN&y^W!U2KblUK8Ey?iEOW>c| zIg;q-*Rn4})YTLR=!fJ-jb;fkf|uTHZf%LUT90I`wlm3m{TKPt@|E;gI{k2@u(n^^ zJTt1Y(BoeN7tzAu}U_E1G9!r5lQnc2Fpi0a5((tU#l>iAcsQB zm)@GaojF;soFeT1j>MMfyQiTuH9z!Ns#v*uM}=>@{thteR}h~c%$u7zA!TV6@ZQW& za|@k#EP4O@{DZ;B3mg6M<9gq7mI|%(#Kh+m!U03u+l+~_kAlBs6+P@#vb;J9~%Yvfj9qfD_Wrowdk=eV0I!Z^=vORcKIu7B5xj2-j3Gz7nM3({o#QhsIJ_ zPETcI$a79?&PUk|@13(hDh1kX?R-#HRtuNr=_rBM_Su!{u&2-w#>UI$ zIqf(UXeHb*fuOi^`Y#;Z9$7TEx6f0Q88)qUpAe}jK?Ul3lY|j160)A{J?v_jKW2h3 zYdW;c`@$EM>n~z#kE7sYKFKJBq+ZQq&!wU~f(IXO*WZ5st8|h(F35L#d+T?9=sPvd z(d2jewKt9uhCsxEqeBswYfT!>h$;z=l`!dlFV`OnxyJkh6Jbh5%2GjAfQhYXX!eqE zfY-&xyB1Cp%b0<(`=3o!$k343r^;!xPMFck^8unf`e{c#ze6VDMk$*iZ!m%x?cCHm zH@Dhv3n-ULDMJiaP-g>pl;vU<1? zRo~_eajhf5!rnf1DB>9>4mLJE2HN4JpUXT**%Oo>|4SSJV$CX+zP@SnN?AwSr<^rG zQE9bx-q&19T3C7`MQYD}%m&%7s6ClXr+MqR=Gam^Hr!h$>Ku5|J}e(1wG!hZ=-fdqowK?r#&u}(zjb$R@}ye@=N2V#m#r6aqqnWlx+zL~ zF&eX`K4MuAh98bM^F7v{chCnz*9gv4{nF?X}&~ zZrMw`DegKCBUeh@$ei-D>)GAf3kJ+Ws4Oe-YR?R@=K+G2AhtYHdt(b$@axwvWtOb; z7xWLgm0QUJ7u2cdxFDPAf%WxQWd%48hYH30y^Fr*oq@KaQEf3ZI zn%*8MQR(GWu!BpgtLw24HI%N53vM=ihWeWbn>2Rep~%C2YLuvyOj>c0&eX)_1m)X+ z?@sRs!lxA}>>-NuLFPJt$WCGY)%L+T44V$z4N|>(E`f6sM3`2ol1vn)E-oSC@hTow zjYC5thsTq06Sub)enN8%c0wTa-s1OFHG@_A&UCC_@QkKyS!W9QO764v9*(irIx2mY zr9UEeP?qo=9k+GRXx5-lL%dATrcWDIWlUsMc&4s?bm=Fat8xXq{daasfEn)8h|pxP zput<-^(aA8V|;#gem47eo+fSh>*xj>*()TL+88GuQNBc)L`D3V?s=BE36Wq^TeCWi zlK8>3zJ0!H8*N3SPe`?J1v=7~DZA95njHm*$CEz7%4%c&0xle3{QKVb0WhX(6UXc#z$ ze^yd2o6cCYvW_yTkTx`=1Tm+~5QZ1h*(pXyMCg6D%zJY3)XvV%#m%j&^%maB#x4+I zOw6DYDK3n{1fyw8TU#qmGGiwJHz&y=rk_j_J>UFe^Nx$1tjX{6>sE@jA|pLRwCMeN zVT!no(B3R45ADb;>g}hv1tSlSh~{QNu+lo$4V>oYW-kE>-Ufgd|HUReAj*kH9faUY zXJ=Ivu!1fDaq-J>m_Mj%WRwqw)48kEKG4n?ls$(=j!R+zNhGquOFLnvHu}+ zkBE#QAt$9H`23^(9TP04u`x}YMue4kQTT2}I5{Julbso>^Zk9XVh+EvgQ2h~ti=V( zB8Bkr zhfS8-9f!>g8wPr}8lQX|ZD>ktmmYQ}oK&zf%y zvm4!S0)!EQ2t}#3$D0j`N}jm24)Z2g!<=CKznI>htPhh#L_{1uMj?)vu6K>>nt4b> zlm9OsYO-kmBAeII)ALGBZV-a${k5NNWo6~x`T5HErAlH9_THHd^HXYutZ%ajc%Dwl zSNT^Y#N<>2Q9p`EF$!kn+9=;EZDHs-CG)Q?E(TqOdoK%n0+8|~vXe5=q41*;h2gDV zP6~Td_3+3D4g!I|!onJxoD5%|P~6xgNqyLfA5Lz5LsL_PwzkCr0XNBNj3Bmo(~4&e zC^sI3NH&j1_GqJ4s4*QOnh%JxBYMo@4Gb{H+L_H_}<>$nLVw^vpE8)MK`o>vM;BmrnGc*#i*p+=s$|58=c=c zZ@JWCgp0X(dgc~l78e))`o;8tkubwVy!B{&y8#@=_KOSbi7n3W=2Z<1105YG;LUSx z$Cg!3gIBFe_T|6h&)Y%xZP0vbdK#pGm!4l}@i166zO5^UbatHi9TL2gGoDJ2D)u3$lu5?d65X>fV- zB6+g<-DjLQ03gD|jOQAzGkWu;S6))YIqN;}Sp{m0;|N6O%nWf< zDH@BWrP;w#c>Wx@{}t#%F0L2uN9N|p^OnPV{5if%aAL>pJ4;fk$I4dA7U|{87Y6=w zA)bCN46hmPm&wx(GEcp~%nCdP!Gsx(^4*@-ldtwE6uS7XDXha7t25GC2W+lQ_21LC zw@1_)lEiVJar(`sc#G1Fh|vu?ujlLY91sc&3K&I*6zFn~Pu zwV$G=M^c8409=HSkVsp{;FV$D;6oF;MRvCLD8}9|Bk5-?21{O&%(OHQt;>i{m^L9H z;mqv3$oVD~Ha5F}fG8*7b*#}*2i_DBcGkDJmeMCQL*a{!`ZRRtqQ^C@mot%Q~;TQe^Zip48>m!`#oMo4K&^+XuJ9~NcjUOF3K8_hK z_@JH$8kw<0Jb!>(5#^JAw;^j9f!Pet5DP7S{Pz} z$eN3%C|$X1`p~CYoCe=7V)XSL%;8&YV7Xn)u}q3;5n9kR>j>sa)5cP2e$EbGG)lN= zaBi-cO*Lt-(PFcR#ZEgWX2j@^+DrY9k_N8e$p?vv@ z)_0QY!gYU@lsF$;P%JqS>rcb?eGen)(%nrSDK-`&;;dz^)<1gQ=+_@wKHl0jQVE)6 zeGQ38Pycr0(-cNSy%F+KX788x?)QP0U^iKkp0lzFNdLpi7w%L8TK_bHRVh(!R^SBl2RbM zDi)YFs-};q6dN0-zN)d$g)%~OayajO55&^CZIHH4Nhf8W5B?VNfoQ2b7tO{)MMVVw z5MUZ!A--2t@y3CHS@ZJ--h0g~L=(SuPWi$`+lxYWIWQtS6|el#(lhC{;@a$zL3~H& z(Kytm8L@seCVug5Ej!OOR08?bC)Ca(611-Xoe&cf8`;=IdkL^8MXTyQ6LoTN=@>Z1 zEVeE7a>M2B>D@X|GWCkBDXY00RzX5rQ;j;UcAw(dQ7e89Du9@n*uQ`O7Po#!lt)+D ziF3prG7|;-W{UX_!WDqL^Y8S``C#7j=Lc)c*Aa?ipC$usA%7~FapG{ab#yEo9pk(- z)8Vy_{4qnK(p^T zhE&INLlj_o#uY z=R!m|T=`sMpobomaL#Vo4d!bxRDxo8e9X==+!Z<9U3y}_+-e-dS9A_=l2>;Dn$$*xufjU^3zIcpTw&T(Wpulz9QmfhT_WkHT+H?>25JBfnfdAN1CqTqi6T*%*KCDTfu$`^s8%aUEQ^< zsshDsXLWNgFSOQ$IH}V^q&o$^)5Y>>PaOCL9;w)wQ|F5HVV}ukh_B zu5CU;oxX!lgYQF&0pR!StaiZV8c~x0YOOB)EG%^Wj!UZQ@8=+wdihrg#-^sBI@PjG zZxof3aKZ6wia1Z$AW8l%0pNueQ&aQjtVI~epBfrowi@(Uu|t|wI7l~7p_7yE(9y3h@0QN7aIitIeJP&^i@WeI zD90t5N&M-UGy#2I0sz)92p?Vnfz`mkJ43*begc@+@?w*cxGi_PAkNzI#d9W1c*;g~ zL(RYnU`JC@z`gzMH}S!JUxAj2k^Pb6HWH>4ORO|q$HfH?sIj4;sO%|NtwI6v48&cB zK2be6FOuN%Q97>+$x^TZYk-fDGtL0o3A9j=xxe(8jF0{>sQT+^gow+pLP{DM{_wA}xXc1o>xQ7_`UgRAYK*M8>+Pmo4O3Y`le-&E6~KU3v8_dK%SZ9J|~RS_1)2sY=RR1&1X z4QQpjeJ1(gxdb&a#>Dirl#(ZR8X4JY+zH^|o8|epya=-|U#?RDcc|pvUajf8o9EY5H+|>z^F72_M&^X102byluUa2h`Z@HSWcZ z;r+na;g^Jjw~wFa;jFqe0)Fx#V*qX9p~>AC&BBk7%uqe8TpZSX8TLbsrJ}yN|7?m7 za8p6T?+qCx456bbnoewoK0D&2L3K^!5;4;&dBTV|d-$igIPQ&VV(-n!sj6DI5rZ^iVopK9V??g2DzCArYXX2&J-xkbSh=oo891tC)3d|-$IF*b z?@r4CRhS@``*)rLm9`dJ{`0)DzPN32;ZM1bC)gPVMbt}qsq(68xcn&Adls{I`Y$K{kBhYsUMn{ zAc&(Bw($4QaN`@!m0IB7wyV<~b;kp&jxPx<(rG|`7ujr=HWTxeEYtXG|1`^v%X z>h1jqP6!t6WVww4m;;K@aEVFbv-HArY?hBxJNgCdJJUAmT-;pAX&HHXj2!Ij9e@6O zs}di+4C-;H3FQBQ4Q0Rt-0%7Ik(Zlgv-qH!u_q8XLD{&hRkMczAfI(h$agiwEtDH0MOTZIn9*cK|Oy%OT)>6$Z-7;v&_8@J(jL zINJ^K{aT2oWh-o~`lKwwb_`Spc9#!at(N3`+mmcqQDG$)~Ug|S6LN1EQ@{2JQbyi*OKH^K@RoojNB z+1+R1pJf_+^G3k;>Kgz93kL~_0j&gszxy^o%-dI{4V&mF)?bQih6D*aUEa8iTL(F_ zOz`YI(o}@S`j_r7x0u66=JLS<1N|=6CnGA#8SuUt_%ydn%8;5L8pXzNE`ze^C+~${ zy*m?YpE{T&p-;_aX>0^CiZTNs2?fd0$Q~T<*vqHs>XELXzDWK@%`B|d=orKmDNYefQw-HSDkaai1@=wZr3k-^|=! zGiU2~Ac`CwvOatE%yh+uXpKf$RdcggvwqE;oqbMD{9b?sX?DZPZbpe$+D;5i2t}?__AePFD>k1EA zA;69n7Ckas2m9?C9ISqly6B(mUoc@(^Y9jxS5|hE#RZz1nc85VYSpTsq*Scw(i&)OWSzj+Qsfg*L=E>mZe;P<^vxIhwh za~lG!*7f7-e`jaxLP7u%6V&QT&5Q_)PF;T}QJP@l zcEw301n5#iB2|>M5+@k@>!d+IQiApyP)g#aDs!65;;t;yGdo8FalKLEG(X`^YP4uPFH*w&8|wNsZ^h59R(S%cR#cD|!9H9VuW`AHVynM5 zY;SLyfNuTr(mUYp(lYaA=Lza9s@uzk&C`E;+Q{}EOttRiGgh24v$N~7RhB@*Ab{5- zC#SEjm*|GkF0IdzBX^(}XDWQhUltv^z)uKd(Kx>{fz11p-h7#QbT8*HR$$kw-t^9N z>*-~44N1$DM=c+xPFWH3N8MZXS+^q63a2pLgakmuR5UjaPb?EW#C3-3GmMK2B*`lp zK+iHJ_3vBo3JLX8GP~*1lSXC6vbyKEM!Ghu&^M-mV-#+ZE$!*a4LB=6fuSwCy~aiQ zR`v3oOTCySr1Tl-&2X7|Cujdg+yg6YYa3(4+6N-ELJKSG$9N$>E#Yt*;IIIPI|GNK zc56#(PYSWDo&Du3bB&h{TUw$vX!)ed0GU}>D9|a=C&jL{Pg5=GnxChbv`K7f5dz9B z#sz8?p&BP;j;|ZQ^zqVY!+9!28oWa&$5rH4^KN}bFWvLqS_88tC)K%XxxBCby#|~kXf8t9 zjo_1oR`Bm%?4J&-J^P-NX@nZ z|9+Ehwyw!N=3~Yjk@;sjY8FyPUmJUqFZ=q}meUY4ZB*xg+8z%6-Q?UFbev^q;leU%|V=o@+n4NxX7bN*CP93WrE zc}*CNPj*9fHt*< z1NIp38ng5B)QQ`jYc3hOjphEYG0{*0dL(;^DJkg_`pau-dSc1AS+NeRBfpkE(&${? z-27Tv_NcWX4a+sb%O)*2hR>Oz;Gd;{>xXji|hvhov!NJ%vs>w7SMGCN>A~Kgt5;x5?AbXDxI`gw)F)L4D>Suv)vXONnn!R(U3DhCj@RjqN_n!C3ri%4;Ottz<)uh|jrH0} znC-|1PhDF(3S#Hf=Y{?`UQG8#$(#|Bsglec9JAC{vv%JLMl+Ag4DY+A&IlL(*+8K_ zd?t)60zI4xD9FNs2ti_lm8WmFZ=Yk(BMAUL1pw)Xc5!XZ9Eo#SyY6nHuh6Md(+=K2 zd3XXEQ@;R1Uk%ab!bG0jLcntD&o{?dr-II}vhMfC^$Ebay(Gp0P7I)S8#y~iDKm8a z`2+Dhn14wS<>xV44TWA{QC3)eQf9zn0K(;$!8INsp`7u;m80zpe)a_mror{i%`f%! z+7=cT3`v7?=}rBsU36fPb#Bv|rOj07L(Ir1lh&HmLVwSj_pbx)OU0J;&jp3`3VBq@ ztY4yz(Lf+jBrs4o!gx_d(B9RkRv#!MmTJy{ zj&Xob)6mcaJ`wr(^wpjVo_6KKxn_I3SKntB#e+P(l1rt8-&f-PTbGW_ajDmv%Cr9C zczdLvJb&KFT~}tmrsr8;B|$QBgSkt3HbU4-E&7N#Q9-O3RugymcTBcTxe61O(NlLgpIWW3sce%XG@$9JNC`u_1~Y_bkKa34=0(1`N<(XF7Ju!* z;ngV_;rfO*s5IP;h@Xw{BGq}8+!%Wjrth*Z36luDfp@A~G@cCb;PUb<`t@<^m+k12 zgqzR^{>?}=QgkVib3R%wXfbabzE!f3HU_+hP*F@&MIEAms$fg_V##iP<+9b204~%V zwXT2+QMcbOSchnA{l2xaEa};-pVy?9C~$vFHiQWs3~l)x(cB0Z_&9pH&n<}P$?84lmK(YnJ`NB^UzEiEs|h5OqD?|+;4 z`R080V>W{=^jeUQun^QGW#YK~t-5$t{pkn7^x!-}F)t&JbHLmVPJ@Rr23RL1LVD8J z#q-A_j6VW`Wek9S$!h3w1jsbqs!=!2nVoag^lJ%K_(P+98N$CT#H40I{Dek9g#&&a zAoGI)T<^RRQ6&l0b=1JQ^+zd)^NE_uNFA)lpe|>As{0Y96ISB3ev*Qt7~J9Mxmjx> zHamUm9R?Iq4o6*)_DbfB3+n)iRA_b$J3HU4@0--fKlOvj<~my6*Z$Crl{!9FAMVEi z9v34Mzx3)!b#UsvPOh-IdmI+eugBG!nfVZZS+qEr6Pj-YE%Ge^EiQGwIakNlGqcH% z?X>y(BFJU`-Y9$QJwweiENhmHsUcf{-+Wqt@pC5AU%`{m5C8EO^xNiG1DFCy= z(yX0b>26f?!o`9h2rN?}Z=2~+IiO^-m4S+cp$pVbrHXT}IX_xZ7GMw(dr6!=P>FXe?>l6to&3^?Fo3 zUtB$u)#t zq{?+^eI<=gq-fAW zkbS-i0-EK)gZIu&zjx=9Gv?fH{VpSmGXFum2j1y%EeIl)Hk#{FH25|Yb%1_to$Gg? zr5N0%WdR`Fk`iXHzcYXT_8fRlOrxWtrw>GJpdNGa2av_0cmGFspPiq-)>JG{6^UNr z_}JUHxvz+a6F!D$fEC>;P8<5?BxD$dRs;VHe_urcf;wr?8T2<$D7ZOIJO(fmS#d^> zZg^q>^)+I+K2$;l30}WGP38{R1>oEE5>WK?JfHR1n|?p-u37{EVZPcuNMz?=cf5W2 z1{m3-a(ElOGLY@%bRWft@;sHYoZyu-`3elo8X{Q-4ZfYK#&;^z`H_ zW@)DjCqd6f4RBxm)+Z`Rj0AAu#!7a+w!+6@V`m>&U8Ms?VjwcBozD4{KcyAglcp-1 zhLV%7g4AeaY1yld*~VGKO$rQI@-T4~2*Cac3D3acQxu?(avh{W%j)WRGR7REQzFaa z*#~0&tznbsOvwF7?R})vDob>l1*6-^r#!76Gs9_XXt4)YZEv6<`$u0kWm);yWh?3X zceu;6ld~+ny;GLrjRg#pq(0sJezBw{U|UTJ4q8G&f<oRz4=R~Qr^r_i!5x^;ni6~L-Mx@tUE2Id&~Y^b&xdUgo}oC8VgV; zRhibbwYLkcRX$^q%>n@~+R}D>j9KMcMNY9!Oipa(=Ju(jrNwne;UqEc^4z~(x3%?f zx#kBDAFHZ5&(63WU{j0)V>KfXdnBv$O^DG!c9WkY288T0Ec7wGQj4n#xRSTS*GM;s z=$v%ybUXVBfVB7bSt27N)6>&I!UJC!M&$c)*9bGp&5uq<|MV{GJOO#&z zaEvpm@XoA|k9qL!0aJ?M=%S1Dr`T9FHX)HZ-ryDqYr)df1m(0_#Ss^h4z8kiOf{Y! z+6FqyhnpCNRBZ^gw-LP$0|dhs+Il)>4#vS-v%G{!-!G0vh1uV|lMtW?yzaDKsRC{( z5F-eL{`$to?7~9t!3l@Q3VOD3pEhc7eJcly*Tpdi9gv&VMD-~KT$ z^X*^*nI$rk7yKzDC1vMm53FvS9930xjlLI4O2S4ugbtp?sb(Cfn)o9OJR6Q1Q?&e^ z!D{ak-&4JBXw3p%^#>r(zt0NL@*n*T9{b2=GE(@88?q()LmP~Qn15{FaVAA99CRM~ zjC1Zq7g5?1Gu&l%g-6VF*Dl*qX1y58K5w+jKf5(3%WL4o$`S#FQ(}ze2|ci~=x%~F zRg%F<#e+BgsS&e_z#i8f{)mf<3xFHYJ#q*L{PXDUT38b#wlu-1`TL{3=CLR_MYk3dut;>_F`=PEbG+??=sMl*#KvugHyr&bz z07C4}4hegTJ2a88x!(Y2#&{h<_F^o|%ttqut`?RS4^s?*cQGd^7dIIkS!96wf?#&T zLUe&*02GY%^>weaT@r(`r0gYNtnI1Rl%5cL5QSg8dXAd_S{E>%;hx93!K$te< z{vjA5k&apq^c8T7J8lF(JrM6O20u$oP72)BZ~c-FUjNBi{B-sQh|J`bzkl}uEgoo~ zz=-&<+tf?YKz@)eYcxT&;NroGwYFh+(JOo}Q?1c`c*WX;-V$_hK6?pA^UV?N9GChuSi>Jk! zfJ$K#79PF2;sZcGtaGWXx_E(!dPMU1AU(ZSO?bYISF?eD0dDFQ|MQqHE#%%;SBRqF z%aW1vo){=J8RT=5@f4JCDJB7{@Cge zsae~v&f-#~*Dp@WCdS5MgAib2PH1lz0agz1f)RT^Z{Cq&cP!zpxkP2%ZO}h(OkqgC z_`g=UteEdQSpo7 zgKdQAZCUy6-vHMVBj)#Ql{OsHjK;a_WxENAi*0|EmP`~*D}`o?(**DMufZ4C78GZ4 zvvCf-IQINLey~*H7x(!@p9W(6c!pefhh?e4ZdyVJV}0r+;1gUG2zrGs6BXbnk;(gDXXj8}-P{eR zGT>X-AA-c&uB+*nf+?0F4Mq%Vr+PhLe+N#mA|?#OpBNwM{0D*S9&{@99`aoB34V9U zow;JrNhpjs(UwdvqhsqTJ0%ty_%Ccu`9K{8hupF>QA)T`ldciYr=MM~8N??k_fkm10_6&LQgfj1UM7Y+QZMXR#8_ zJNqzB@1VTAmkuo@uIKo#_ekd zCFn@A>1RV1HY|Zl0=#pO!?)b7Qayj7rhDWt0tj8C7A;P*bfc&O$U{qK=O?#vd04-r zwF&`ZlQMLq>=f$+miJU(%U?@02jq@S0nCeYG|78d7$q+yLZ{f-&rQBw4?SICB($9> z`2FG_1T`MaW+n|vP~o+0oMhWD=9>d%vTw~r?ad^NF=pcVlfr4+-Cet?rlzjDJ7kau z-S`v$OTFT^QV~0E0Izus!Tj~B^bmM%8XA`A2%;>;S-DHsUAO?pbLz9Ty-h?ItGW!F_DU41nYW!=p*X0s3MLCHlH22hC#po_1S&r z-UPfTz+VgIdVqr+7$#g#T!HqIAe%QP7*(aXYwL8|iH`!DPOA2K%tOvzZXbm=3j&RT z_Qkn@cV4zC)qXJN%V=!!*f-OcoOw^xn=ZJlp&L^=W}_sXuiLEil>)?;(&O) z3%uSE0`n4d1fY51ZXGfVfc_rbd$7-skE?)91a3fWLtRVSDv^3Mp)?^He{x`i%P=Y0 z^wSIjqT979Yj^q(95Ue1oJ$St0_`Zy;xys}8+%`nwVsl077r*y|bV6|y7cxzC`!N{EY zXhnXot7caYMw3^plKQN#c#^Tvx6#hgv8u6gaQuj^+Go#AA8|s$SyKH0AhoFOWze^Q zS%lra{qaKXzm}Z)+#s#BD}T|_9D4G(6CbP7X&XPiW;}qlbI0?i4P&;^$M$yW24A0Y z`IysFcVO%W?dr%ma;g0XxQW?#&7*LWOwb(wYE?yfb89~vwB*v9K5G#E0?XpQMH;p<-}i|>u zc=jYBGV%#}2vt~BPY#p|=4wAAi3+phxd!r=puY=bO%{?quH#&aQdU0dQ`8cnQ6LFi z2aJgp?(T`88+?F8!ZY;5n9DAWYf=~ScJO?iVn8J+lCh=A*>dTPJg z>y7D!VG$}kIA&ddThzcYhF2t`uTs81|95>qs51H11aY3@aJUpJ#7#2%@4uVXlS@#l z)*F2nmOL6|3MMXY11!B$U2}&@7RwDBjZz5>;)ps~of_RC3hogct4pr$HWuD?*H2AC zM}GeTEE3P4)SqqQ2%@syn05ba9#x2C7dL4vK~z|UJ>UUp!Il(G?x6i;TD$bu=}mz` zjkuGPZ8kz>kjs`{#&6P0PDd_r^igii_Sx~g5&H?Nn=7>)oT$4qQgJ5z+bg9g0|yrY z&8(@ZDHbj^J{DT(onLf_Ij`+}$**5v)UNIx;osxtyf^XewiqV?6+Kym6wN~QXYx0uzw`RS*Mz5#HqolqDbPv><7P!?cqs4KnvOj5o%DzXYc}2Zq#F{tIB8KGInR!VR*rbS zn*)_-+zOJbd1qKCZC<2i;_Xe+`+=d+XN_?-dpE@?dKASkVz}bKQIwgkv)&l=`@{ol z$!WeL!1FFQC^u&}hc&r^gZiZO+f0o_)$EU1J7c))Cu!KboC=0Gtwlg#_7jQFx8+_#0vQIp&=}QB|}MwvABdp zI7z`IpK3J~Y7YKOjTLTBA`J3rbx3bschHN6O^N;>@wI<-)(%bB>JTg5igN$}IZYNr zV^`aR@d~!DGG&2=W&SVz9Lh{SuFA7o9k=5A;S`{J;$`{#_w@gH0oqXTNl5{R9Ye&z zs-AxA^R1L!xV3FKjb9c_6%rMjARMNStm!Mww67L+$of{D$(e4*9Q#6tUMu%^K4B0F@Y%W zr~GU+19Lrzz?(>!M`3iIOHwOFMn|h!Dnx_~7pq2vHU=i&{QR8SjD2L$w57s}Zqt-+ zZB|K=pI^WLIUW)W99i#q1+0E{2X-6+>;hin>&Ri>SoUgC4^DMJX$UHXnkqkalicK@ z7EQ-&I+dSf>iI~&JP?I%#LqQsk)Tl(wZgNc#)svD?V&kg^=B41dwng*G#gf*dPjG^ zW>5Jw@q$?S;w&;hP1gDZ+t;ArVK_De$Vs3^)_E<_fW&PKV3ik5X?iy@$NDHa+Gd2v ztF*PNSTee=Pe^0YbHi0YipVFKO=J0ILvBBquUD#EJdKx1l`#C}#Q;HUX2ujrWH_FQ zkx+v8SLv>ll!rl^h-+L5WyZbAx{GKqaub~(K@;CsqzB)!Ak52o7(@XHoFP$GoaR;d zI10#PKfk_?%ik6pjnv}_D@_<#tMlE!`M#G!#s~CPec`%y;>8FH@V=_6-8r+tbdroS zRGKD}*iNc~D0Lti6C>Se+8LO#Jqi@^^p?6~hy>36@U`AudEOkw1%mN33)b9kp&#d^ zmP$sKkw@hh8TNg11)3gjO&6@6KpkGD$f`R2c`%oQJIuOPb;Wwk>@V3Ls(|uP0n^V%XG%{d&04MRo z2N)JEZWfeu!`T$imiQ%s{xr#e1qt(~uZji0<&C8l@>aje%|`ni=1M3SnQy8MST*)@ zzt0&NF90%owj#T)!Tql^*q^SY;yuiL`3S>V0sgE4ej@0FnFDX~I|>1_s4ktIb2tB9 zZH#7Uj+Y=u@sMNA=QWhd70>3V7J>PuS;VZdxqCPO^xMOH(`5?}hkj)sagZ({YK-_U z7SB<6TX5f88bgGR)R>2RXmu5B%Plqb6nkn)WA^Xg9N0cyOpmDXcTW+hw_zE#Rjn@7 z?Jtv|52EVPHPoVEUi@KrvbPgsjSvR~%^cD_!r=&;Fy_veTTX|%FSK3&!a~chTDnAs zHc|e;v_CfXWne#D6)} zVcR>hdbfT!@OOk00-Atn=&f>*8UtaB-_;hjq?DAHeay3n2m*K6ybnNN05X)8wze^_ zCFHG{ry3}2!rF=~3HdW{fRz^5O<*?F06OHe>KHL_LzswtKP~u6EZ%XW1b-ow6_P_O z5-ZEyJl4s@n|3dn-f8lwZUf9=&7RZRvAM&1Lw{di->jYLfw}FQHO#>l`Qy(v1u7-# zGA}=XDL{t!Z{jA7KR17w|HN^+i+j|1bn>`mMMOqy>t21Q<+%L&H|0~2E&0@=qbDx) z#$cFs=I@*da2x8BNV2DNjl~KcJgFceD59AwA^e{AvrPd(M;b-IMr(x}yMz&XUJ>US zM;NYI16igG9^-V=pX`^SRSC;qYJkRB=gziy*mXc|sh8}ZfQ*y4_8h=OFqLx@rNgC&Xz&5s#^p23N}8zbP$M=Fc>(eaX@kW{3{^P!fE+@ z`NhUz!J@A0|3lMRhE=&fU0ekTr9rwuS_vuXF6l-Zq(e|Tl@2KZ=?)PDq&oxw1tdh= zgmkxb!#kY+^?o`Z&UIiPo@d|p%$l`+W09Y5`~KF_#WUJrCa9ejdlOmnN7lYXALP>{ z@ZrbwkdRTCzj%SHWPMZgw${ot7+Ct4kt-q2{MeOiEQg)Ri$_`hm#G1R+z+YuKCR$R zQhP*&#Ywq-dMxmFT@Vk%Oy6;zyOt%F4!Gm>*>MUXkdO8PLT4OmA8JK$PfW zNTh@alTrj-W7s-Kkyo<8piq;gqP{+Vy^=dB0MVl!_Bi;EO-v!&MJ=RdRg3y*7k?-B4>K^BVM;m;f|DsBr`#^pjoJVG6=?0HO#gJVJN=qGHNj^OY z@^f1q`FI;$yC-+t*39!TqLK05gK%>3-|L_6>YTmd6aGabVt^)w;L%z)B}SXuDlg?d z^nx5m=R_4p5vU>Q2iSK4w1tnuv3V9tHcng|tf$L(zW;iVA&};WZb{ zol}to%_oty7FAVw`8n?H?wm?+bF1A84&a)=+HY?|HPHFB<}x-tK6m=}^|DbvZcNX~ z#XkGVluny@HHR0kRu(dv(rCB|eQ~iLb1w>4JH`=f7DZND}HA8?S&Gvj`^b z;-T>#5{`fZR-2j$my7PotDS;_-di(SQDx}3zZ*ykcehgqGBxMrC#59N{)?f2-v7>& z6pVD>j~Snym4s+8%Y7ag>e;L`q zmA*$!dyMGScgP*+>FR=i+WGv5qPx4N*=nsbV56QpO-cIak(Xj)*}3vZX>#2S+T$@1 z2{eqUb`Gss4aA}3j)gi4LpvBj0Tp5bBZ43E|7H5m4mTEGHQc_hPELnXDjcbK%#|S@ zKS%`6d(*x|y>fK)!Nk@^{;yBMTgqo|HPWJrjVcz@@ClidSV5G8RA@sEO@YB#?eTu5 zIlb_ePeJbm4Ddbokb9i#T{mhYvePvb46H2GVFqGjBBRt_MAF>tOm0>|T((gO*?;4! zJ$Mq!&g7y0<#|tx&T{=XZ4SLonFm91XF?6bLXW0AJ z4~)1H3KBvB`&EO~#}P|wroNN08`nlL<9tPy=2Iom7>L-7|IL0y0wXl2Q%im9t&6`3 zp3!SqeQ6tPMLp*!esgPT2(&^-emQSV@c5mr8Q<8$UbdcwRHzjQ2_}#U;NK5e;J1Id z^AEl?d+?(`f^sGwW!xnHPJC=Vycybr7C8h*ShZjYps@ z>!;~MEN#fPCN^~3Tg0sX7p9lor~anmCa#~~$3JUe;m&K)3~^`3aZAvfp=?2q_jO$t z@K3!6cehy$k7rAGaFarj^EkxJ^Nxf1dM#2m9XQu6Yo{$$TIC$$CjS+}~1WA4d>7--a{@^SP>ChGS$Y zbVxB3YOr;$TECT`8N9f7#*)YdSI6&SE%Hh6m?NE(BNv;ZT4@mDpO-=)4~R+?6`Y_` z%-B@73|{Vg3zV^y|2tUIU>Utf#QteogoxdMK0}_WnHbwTrLC@PHBmcZWJ_03c zIK<#?Bq5{7Gb-5~i4o-#d_wj@6KoM;{@PR_Io__=bW*g-6)aNdIk^JGzR&P{+r73= z5lIxiKro2IE3P3WBil$&XjuRt**A-1#c??G88FslXj45 z$3u8ULBleuD5{){UYhM~jqEam|3Hd1R5Kw_wN%^l>f&h(rD(S^#rE{&J4l;AgKaZ9 zva^YOsn2OmPfK$#UE{n*=Lv!05JzaInUZ=FdMzHjSlt(J5*WyBqyErq|GC8Iu>=}O zg?Z+AFRI2Dj0qlAPf|d)aC$fgb#g5{)|;5l{>osyTyp`TNo;1mk%xpZlqE2qgVP!c zfWJ=Bx@>MH;#H=^7#3BF_vuzc2F|(#z|sJ-1db<9p44a6poW-o@bY%=`1{A`*ExD} zGd3Q4ok3F?8FJ0gWs~8G+x)}Udgz(pJ2@DH11?_p#3_m>N@(y4;9L8=w2=1HYE~o@ zD}VjiV!S%FRBX0;^+Elq>gb6?5Wg~7!Y+GayOIztBV|m^2I((5l@Z&zrVypDx=wSE zQCl9j+PY6#iG~S3vDE(ACJjhrHMuoOmTKo|m(B}djVpfGRIQzK`|9o3KB5~YLrh9a zE6Q~T4@$0^FbE7Kv}&_YO+WWEmT52%oLoXcE2GK%i-RCj##5}5pG!(3^eKuga~*ZW zcYYn@!u2 zPiywiBi`48SydGkUsqQF!89`u&*^R6>d3J{kF@ z<&yH&`HP1AcXt~9!#$Q-jB`Gvi^1TbwUf1+U6ewP37&aWiFCph3*ZkYQ^d46=_y%LUaXv;ih3Cae67vza5 zt3fcd!pP{={z#f499p1{;H`$2%B_taWO%k2ZtmjG>{X5g^6~Y{9qU05k6WLGS(7Dk zRX4*i!y?PcmRobO_S2tYsE$YMi)gnM1thsryr54lTID>_3>vY7K}{$&*zsM1hb7*s zh+ZbVUKwQG5%OsHR%s8(c6iqIyd~cpQ3%u(p8sSR%o1H`yV_{;0E8Hy<6r8m!IJN> zHm%At|NhV`X}D+=B-qY*tQ%O}Ij4>9d1{J`Q*FFUF?8%oc_jNe8r|=JBTf$Haq{2r zx%FXUS35pFrRMu{;LV7Cs*_;>&SuFk8;Mc2)It)0NE!)pyh_jOFG;j4i5-`4FI~eN_5>!(??$(arkxfR45fei zIvJueYrQc-Tf#WEt1N%ICtms;bqu+ub*0&Kh#s%U&AR3}NNju1X|p8WTNyN-1t0Lc zm?+nH9yIdFuaybJ={Nx}8YDUWawO->v{NyN(`usjPLV;9HtvPDUD}QQU0Z8iHz_Xc z)aE3>8Y3$?HT9^Xrbaq%90G;V5z5iePuWlFA0^c}id<3OE2!5h(E|L333%sxTHAl> z^QB~H`I>9Kc!qLcvq0VSYDowL9~i+bEF@4p$uGe$>Z`TpC4-^70pcNH)Yj`a-ZN5S zhBa)FZcTw@Wclhg^Wn(wrUtJeF)}t&U8eut;l!FEq)h)_bB_k5)gS=GD}QV`t?mQY zMTYXqCjZq(6q0^l|Eu$H>B~Bl5{aM_xmk^w1g5~G41}+h+FjbqBPEbfCo9LNrqHD( zK?wleOqWe08Ku!h*hb?pkDa={s!Fo-xz@p=$7iyjjS5!!UP7q+-~(CcLG`Opknnm_ zEw!tij80Ze)G_Dp@n!2=m3`#@M#P^G>;3)xPM2ql@1mlQ_#=9|9o+?PQa799PrxA% zSQ%Ayk7jlJ7~4af+QZHidp=k5OBCJn7zRgsDr3^BXR84$!lG(2O1u=*B|+}#rl6Ivvd>F#%cov zv&9kO0SPxo!NlGK1q-%k;a3GCHnd17KXKn+b?84aL@%Q!QQ^>Hd$lnFqJ?zp#l`)5 zbad7%wAjxPHVNt``dBrol}+4+=4NIAuU7`*ls}GMErlNk^V3i7C6CprzxS^>iLfmB z0m9f*_bZ`b38V3MmB$=+dTPfXZB;fjeDd$mQhc&i|Kswulryf)Ykq9N)5830^aCQobldbW{YLR@0R0;?<527^qMkRQh zBC7))bI%X+<1fVjJy}GdDCrEoPeejOIsR zCtFk0d*=1;7~kg`r<_mSQ6;v|-LE~pkskb-`JE6Ju}~b+biRRF5c@s*`*ZTs)AiDQ zIeNTr3oK+aYc55aza{1cw%;aX#ZVL*O%#qgnohDPu&64$YII~;A31*!RB-K2Ri{SR zL33hF6Z9|Ok?s6RL~_}N$J;8Lmq9vhi0x|TL^tLn1<=f4#Gj~)A5LEBD{lNwUzDkk znY5T-Iw_I?SuzD<#nST2Bt>C_v}NJPANfx*pU}2%8Rcy#z*HVON&qRuDHb%mI%HWH z$Y9JEV484<#Zf&k-mrRdY^WB7G-RGPR{Qhli16lyt!$RyD)7=vEfmii&X%w0A&i^e zsJrPhhpjmT1viER&h=T{+0Zhn+Dgolv=$qqkA*O&t++5Uz-Gr*Sh-nST5SYgxaf_8 z!}sSqY`takh~+i3Weid##w=U}hbOX7x8|zp(EBpZ{e@ zSJy*ea5YUf2`LC`SEF1LPcJ$_{C8?hmoY_=ljmvprwybHrn9hQ+z9;m1a@QKAin(> zrS#`@C$aP>15yrc%P+0NDAs3vLMNvpC4GJUib}iZ-pBQx`wWufY^l>4K3KDYGoc;K z(Hf#rBbh}6>eRlZN>$P|28HS@!?rwYE^N6f;HQf5ap%UN zrMQH6utkeKja*Vsd0rZ_HI}^Rgy|{DB4?4!A04<*KRdjdkW)fuoUZeXfOaZnjQ%G)-k{%aDeA^Dm|^)j||+*6XV=%0U% zc(J!dXD4@B3p81t2&J0?)DkF)zeCSdF}Sr}HbP4dChXK9g02e;C9?-QPRvdn>#Zp?^-i;yobJ^$Y zjmA`h)R5;MM;*=-saw3bPdapreS>ksal$Dc_j%It_X`xgwOoA>62kAlOIxZOy0rAr z&u=JKlNqGiVy`D_q#16ZH8nT=E2E$1NvsQRU#Wgw{qecCA^{`k!tl1|;K1`CV`Kv7 z2d*XBC7auu>LmzBs?9-CP005u{Z-99iG@%qzi;oPh%?fu;}xEyhH0Lo5H%8@xUZz)yFb5M|Ee0L zlAZx4R5f2yi4HeLBRyG4?xuJGJc{mzE^wKS=a}XW^FLO7$qhM&+4G$?D^Q`K2HQQK znOPW7c?kp&4oqM6yQA2OC$7shSePZ|fGr{Szq-Ea=bO2R0*&C?JLJ;bBf&_KhZC*_ zuR5Q~goGw;ZMs(rBDsN$0FaO1>3|Ofz6@{~fSn46ixlLDIfSvlg^ztYIBMXMTGLFm z*FG%g?H@rr=D>{Cl@hc!lmvoYl&t={bx1!j*Y#)w34+yzLMp< zHvjPbZjgX3@n=DD`f4(z4}?ZH^cY?~zEA(=VN}MsJJt6hDqYwmV<*vebvPc!A%F<&FpGswpNaPkZW^)jurK zJB=IqkN;uVFCxWUE+BT;_L`iMz5kMth;DjM6G?34q!!wyHuj5$Z>XUrPFS3nP@?4! z>qL10DkWrejT#4-aRp*ezq9=G-b8s@wgy8o@X0$B^z{+3Lj)1Nq!}&@l0uo;=7O6a z6mFa|H7!3ND09;@t;1_U7O9#Ay*i+$mr zh|o5m1aYzpXVo8ld zQJRNFNyugu{P_3_=~QGWbz9E6B(pzqXz0Ju2BcKrGR!lOoznb4@FkAP+l0!rM4L}8 zDrM|v85HpIbRG0#obEb@N!=z&&wRU8@SO7OHC2YPXCaPi=EIIedTxHc1R7pJB?}KG z>#x)lL_KbrW%_(WJeZ$YdsnS#l@)kb<-QoA)em)OMK7q8-Q^H^IQfDrJZHmY;)YGM zq;T3_@*#u%g;@OSQ6e0nw!jNCZv=y(^v?cTRV(gep}DXjrt7G&6@s%A3?=ImmcJ4a zBl>$-8yAPW69iHEvM1(?vSuB(7C|q~AGTI48U14a>~JJXx@llu?2+=zh6;({m0xQZ zNsqfp9ueHkgIryeETmbqo_|pPLD6g3;B+Mf6IK&T+X#E!K$0jFyOdX!F702fV$n^D z2+5FmD3OAUw1bjrqGj?78eXNqbyBeBpWy2h=E)pHogx2v?&2;?o`!i^yrXYZ`E?Hc zc<~DgYN)!uuz6$iP|~LsN;Tvs5I2IHYGrMW*2$5$<=>kHA`o6U|E?5yC%03}Hx-y* zjN8sPK0Z^kvDpc~5{xWksdlKFtWGV)ZMI3JpO=lw^vujlKwC58r#NW5pQdj@A4bZ6 zC}eEleVSVtm0GCWYV|Sk9E(vL@s%UH;(lqwaps7>^)%YJU4@)tPfqd>bM%T+yug-1 zq8mem6dtD%-Z&d=64F%2o(x~jt07c^oGN0m;2$kJ4^tCfIDLO-X+r;LmCG;Up1A74 z4yq_Qsy%8gOPCi~r}L;cxz451&YF%Z8X7XNM)%*LcXC_TrUKd&YU&RTbM=qUvJaO8 z9OUlA1sMs%pUOe+3nI#U$zP#GJI6~ zRD%t@m_h+0PCvpVuY}LKdV(4m>@_*|A0QSbG(IA`S0h>cD$f`93ZddZ@%kkW4Rwkl zBs-ruSB5R{8!^5K+)2w8487!`!wY9wQmS})f$Gr=qD~e&2RHX`jSqPjDE=`-skwtC zjkX1=n;Ng*YGAvpH7Y~W2NLp8Qn%T&HOuG5l#QhW&NtY49b?+GOQc)Wdfe>i(Rqk> zbuZ8&bN_L1QAeRYQtT~xw(5Uw+^b>-#+K#PvxRDBq3<9Oabp0f>@Q&Sg zaigbxbOD^ZiC3%6+|6zBD#X$r_nb)uBt~N2Z1Ar>{;rU?yvH)|kHc9K$)_HTQ8lX72WBGcG1 zFkvHa=+z*HxUdD`0NX4iR2CrXreH>g5f$z5ccWBXjDuwnrr5IlDaV_!1B9Dy$YmgH zTXTykxJbfZO^uDP0gMm;b)bJAKEk=`&3nIVLJhMH0pwQ5mTf7gy{=l}cfTV4p}7}S zsbR~wXXp1oOKFR}c8e)*S&l6+2LD$gH<=Qcd7>9gx)nkDAJ#pXn&<8DeKg(X%@lgo zO6Tj{O_UjT-s%-S|CD@KH=+3{hJ=ZJt(lC+J|tgVMl|1!PRE}|)-be~d_Eih7f|!| z_QBhpPce7zybBvH?G*gxA&)4L-}c?oBel96^2vPFD+%{k6V=(kws_VLEn3lYyQk-v zqm+a2#+SH?ja0BjT2{&Tth!1+$23hYLrsq;pJ&8Cc4@T=^CmDu__Q?HyxNZyY;;o!bw*`GJw zJTt~z^BEapNX&*4TZA=UN)JRshc8p#KK#1?V*RuMC>pqT=G>vMtMvB_9{OvbW<&y!@Pv@d_&T7~;dK)P#od3arhefTO#>?L}y!yS5 zxs>KB_;L4R9iAxPX`RC%mM<<7q!oW{@a54Lfow7l+(%2-V)F3jnKWg;joayN52{S^6EUwaSC z-_Di8nk6PZ8{!~{$nG)rpT>^AyH{yiXan}1aG5-`98*`eiBjGS<&Wj}f24@3$f%9D z>^v)8t(|T|sS6EeHC%Tsn6GVaeuonPLVX-rFC3LlU<;TsG-^&>xVk*QIO{8i zT>(GdLhmp5W&Wx<8k?WRw@ho7{5+c=1)U$-X|>~olq?__g1rLD6Yez3*`N(w3l+C% zjsUj2ZH*?Cv_YQP*vDfZfZX!YNav14oSf#N4q1?2!`mwMA^PMGfq(2JThL`j`tG$( z>23b0Xf(3HS@%svl;es%VQ=D%%0hodUW6E)bT}jreV1}&Vyu0d*Fo)L$;^on2#q#( zu>v}IPvMl!$aXc7fV1eGj!RdV67UR|hp}oZ(1%>)zReoo z{&jYu*kKRp9XqjE6uzi(ciI}nxR9=~auyCt8zy?TYwivMu_Giu#)pjwwpk;{tKR3- zt%P)*`a@E9OdlT3hnvt;*hgBHd|Ds(A~U7p!*sshjrO|WW2{r5#EbDOMRxRI>7llq zwePb8L~_k24|&gx`3p$spo~hfGfX}LK$cv=FU=Ajz`VQ}t&n4Q4`nt(WPF4d<$Tn? z$vL3n6A}>tvC+iCBM9R299-{@73h&`9iC{Vo6(B~IOtuR4*D-ozHIB{R)RUs?uIko zk1LDUk4hv9p64tcx*ysZKt$1*+?j(X#;kfs0a41;xbH z(FX4{dfpCCWOfkJ#Fq9yam>zkZY60$PuXR}11{=Q`7ES7JB5VqLocR#Vy$$fi~;va z70K6s9a-VciCI-pq1ojc^-OYwoMV>JSva@Up}jRt2D3XYElGDeLuayp3FDJdH z7v4~%y>W~xFZ?t+;63fVCTF1XRbxYP@?QA**F}M;kh$|Vl-tn@z+Q%!YaU2BBAm(< zy1>x9TVskAwsGUMsdt_-b#O=wKJ_#jXj}Z~fB6mMF=9#*?L*tZFQ=GH$anfGZ^DZX z4w#0B6k@86s!U8Qy|bL~}R zhl}dxozB0R$An@Xka6!Q=2~1&eE3}UclPCn=eKfxH$k!!(J1^DhS#p-(mie}-D1@f z6mHJ|#4Ih4$dIDILdqEGe&j z@x5(nZ}&0^l!hQK{?=7cnf<2S?E(s=*AE(q_)&3fcCEnrYy^Dk1I(bcWgi>%cvVi5`DeAncL75OP@ByifD%#~l zJlP~Lpw8iM%fO`g4CNoD*r`V8PHr(5Ox{UnuXoW4!K@DxZ4+V3PgQFxd?6M)g71U3 z31h_L$=&jmS70i#=e}|FZFiAJFF5VaKfH15mSv^Og>9b3P$RvF}3Zi7xFYhcM62@83E4py~klWJ68xXa$gV8U2Uv za2wL)O8;)gk6`y&$rjoPu=kWym0!+HWo`g*>}dbxYCS zqL#s1RDWdfNTtjNIyg^X@ysSJgtC<%U|G$!NPGiNNvqk0MID85PdJur~3nX`#-q?wKkIEv;Y=bhjU z!yceE(g@3&Lyme)^_D-<>hpMQ)WVoYut$$Srh10Con0E0|3%!&;7ZZx`i&0~%tMy+ zd)-`mZOo2&!?U9utbQlEs5j^}aCq25gt*{{-jL4iP0_~|D7A;JL529Pa(yQMl;acq ze+loaRL^|$D7YCxHzkdN=|7lqTm&*&eOsTDynd*4IWsW83zpr;_eVql;XlwTfh52c zjQ3Co!nu&Zs8>JyzEM5NAMO2x!bcrvlatVAYeS4}J80mPpVE;lLc(c)0lJR*$>{HX;G% z<69A7_trgXkI1lvS^nq<{)~2MG$p!31x_uty@NFzI6dpj0t?X{|Nf(pdA*Y+6gcdf zpw#LN%PTK_Q>nMZro5A2aD-2IiAAO?tp_jO2SXG^+ywNe6 zOu^xP`gc!y|Qo<1Xqu4xmE@`MYXgAGj^kwJL4~P3?<5P?>SV!)>Mbj*JU@mp& z=WdVMQ?d53*TpzPddA3^qU&eUFKIL$9LsM9?^6AY)WUGA$Wn(mT5^_eT_!U?ECEc2;GWTxwhH~&AQPD3_Xnx2egJnAr+rG$ z{bYWh9dc#Zt1g#g*WS|nD4bQ+sSNS>_!n2l$HWwmV4hm|}z;;lBx))VJ^ zjM%vI=0QW6$P?ubag@@U7TZ(!Y08ad@nn?C!1XQGUcY|YKiG!hRXPQ%kh!?IY>Pk_ zElYb-)3-Zq*O^&6Gf)tToNqoh_P^w*s;Yvle0i-A>5C;Z0f|q)FoHAG&mz`pX#2v$vR&@H7xkzIIf+2cD8M| zsOma=Td>nE+JRUg#58xKNB?@`iG+QVzTLEzB*j1Z^IBJF@_)y9e(ATbPhhUegzSxX zr_7~kvjSTiQ_IcnLxS0JzHe5^3^K`j7`XFtXutQkf9`v};3IzBK^g04y%-W&4{#Bd zdN&FvC{KY%8(}U8AkFyU4kihF?zG~g?NBQJ*_AB+8$;3GhEA9)bR8UUL-Xv%lO)WF zBk{>lCQt2QNsISV5eLE|~F-h(v8GOOz{iFib z+KE5<#xE8ed>3>=IGOH4ScHo!Ajf}G_|Z9;XN5rV(htbBfuimrboTG4S8^)uqw+{e?Xl+fAMDG2yvUkDBBjD&P;zTkOJhm*G z={zLRYaQI5STgb_xhPq^@Ca&h-^sNT@IRet`?t6B>~r+pJ2$oyAoGAP^uM>q?+jh1 z(Z}uhN$c5^4otK0BD*@er(SlDG5LMnY4QGR;||MO?i1Jof}jDWY~aNHmg}p!EQERS zL3r;LDvrmqN7lzaWgF@xqc?hoN|vxR?dd`HN1s<)_`lW;CS%}W1dFe(kC(fO2xoF~{lmBT(`H3dx4+TcN0$gfqd~S# zjS#bwxc^(&P*(oWF>8DI8Ywu?e7yEGv{={bfmXJA+;$C3s3g!R;X{Fm5Sb^+*!S=O z1Qi(L6pDCEZrNl9qVG<`KwbSw`-UDcm?;dpn$ya_%^M{tk|pZR;}R&BbeO5ati@ml zK20Fv-*5^y``L^yw@b3Kmh7rWaTxD)aziZAXYU6ipv0b+{;3d*lt2SxAdok|E%f-r zSg6D(ATS4r&=@fPzTm$V$ZsVX#J?Zg^6u&lI|~fOQ^Z$-D`oN6{{+d{=jtw9 zC*@t&p`H_@K%3P6E8a}wHfTygY64fmowKH z;N6R(=ym&aX+{524?QDs65*9jW`d+Sq%Zu&JC|5vIk3A_-^9Y zM>@Sieh10^SN~Kdws>#0rCndFsStdONG|mJk}Vsy(9`P5==cqssEpnN@r~t_IlV*| z8xCD~)ly!Q$bQBJ!wVgLT_gX-;oQIc;=kR@yVX=?)^>c*DiDdcHoVYS6CuJkbImRV zE@m+m=4Y|e2h8_7lFE*kN75E&V(#CTR*`On$q5YGUp&zhGtnbVEdvKnr#4m(+9br8 z$8L_jWmwGzrEcrV{C$z@i`UR3+<7=rwb%@k;fV>-;~ml2@v4uSC8LN+k*|Y0*;tgw zG%(HgX|E6 z6-dy4hKR1+Vw^x=+G!9XH)1<<2zhMy-(w;_mQ(CU){ zXVNlwrX!R~!*9Fys%YQ+N>sFmB;y-QWsUu=$X6;dk1}%m_3|WBOL0;0LN_O(-#VhV z{9CV-37P`^FD;9^p^d(mepg=1+p=G6tbnvZ?1GFgrIohIc=t~5ZxO`S;fx*uaxd8S z?RyGCAAn8u_Lm2!f^H?mO9T+BumEr2=)x|GZB0y}Xsj z;ratsR#u<=pEFK|NjJv4;f|tpdBjRX88OzL2YI(&XP&Wk2@FpC`a!ycp2FaKk+cqW zTe{d+r>Si+zDUB{D%oqr#*hq8^x0h^f&tOcc6H$S!8A{VFixtiEM8b5{rpRJg1GAE zk8ZFwh&De(HgqVn9cMUEX)Kxi(~<=IsCePzb{yR=6HQnBVr7w!2i)w*5np3pqTRym*mp~? zxxL}A?qC#c!p4B8-YR9)<6iGKOQvGXbCjGh12TswYTHe)>aZCAM{SW3POwF9kjnWTh~+tVwz9ZZ`yFMj03 z!p`+9)JtNtrx#U&4emRXaLp%&$kTq(eA;$-r2ir5!|C?q4!miTbvs~v4l!MwGnk2? zT+Wu7AHy28jXFI|67oGGV15Xa>vFt`2;hx&at;-kA=j3&P`sCtPqwk6 zW;!HACTuQlRh6v)$rlu|5$?oCyP2+~E#q>-`2B>bXz2~{cn!mthIgr&3nq6emM0aE zMBOEWKp9%y65blaiZ=H)((iG1u2(FLn+IFYvW}uQb_C0+7Wp<4%8dVHUkc=PwV1}SO)YNU) zmpg21Y$n&8N3<(hobRc``9!pl&L925l$Etm1-~-buX}189ve1)ODxfWazH#m&EufP zGVe!sH_G7+&ITBg-^bVMn8d!1d_p>Spy%%9<_U@fq+AwdW$NLDt6H&?FI4aKIEL-5 z`mmg;ygXi!Atf`Fp{%FV;WKuu99;QOdBC&-*zF7aJ${rlFgr~5@e5L9c)i)e&BKk+ z**+9CJNC=fnOwg{LLf5X6bF2?6vEz_#_SH`EdNSYuV}Noj`1&F6SmYmRz2N2>$uI* zvffy~y~_^fL!Y95c01t5ZY(j~o?z(KKoat;kJ-adFZaW1ZSfWbE@5 z)*mIaDVxdkXJ=(Nu0r8cX56;@v<{24kJaaR%bhsn>(*o)Y$Bkd^4)Rxr;A-6t2d{tACBnHcm6ist)`sB{ejCpHo_CUGuKU=(Doh*vT zp5*lO7hrl^xN>^iSUrP%hFs|*p`oeM$sMrL=Vq@JCPS)NC;OX6b6q? zGNqYG(v6_w^edQ(%~}vj4>@U@O%516s2TbKXVwM(zU$9_Z4g=VzAtnYZne{g=D*8g(D*x4`%&JHdv zEgKn$V~y#(At!sllu#s-A;jgk!CS4f`;sQk>+Y!t)zm{60BR?e2f3m znUI8u$?r%4_CGJQ5k39y{(Mq@rqqQ}vytRRqoWRb^5aLmP?At_7^N-a^*C2hpdvng z^DdAPhk8I`7RAL-?XimrdAqA25L_#^s)_}1X{D~`&R&CpWbm)@D8$r+R7Ol*$dRto zHBtQXVC{+D>7fq2({WYPO+^aDaB`end&!JBzqi77Iyz6-#Fq2T(>@g3KTwkD$lT8Z z*&R~;%p^5|H77;d^b`QEulK(edCk5oRG<-FXa|I}wq;#bi|=5H?EAgJQ)bvA!%Zrf zrYs@KK5jRu#;oH38I$X?Q{$T`2d@AmH9%|H&dEG;qDcB#ss!X9;N?is4#y{~jW_(i z7ofyWa5mMO0zK=!qdp-;N#NEP zT0agK#eJh)Xz_VI0pE=S_gb!21}bmyy@L{PRHw)c<)IZl1sKyJ?^pJF_S{AC-N$#39Sc2qus zSy*&cSuR3%>vFoU*C&B%<)!4#*m=OKb5cC2dK!N;$uaY>lg8$bnS4~u3`Al?@!}XS z-VkP>E;qa;Q>@nW7BtbFw@0|6fKQ&ts*{qQe8X^qNSRFDY|Zog-{hxFfU+!4W87YE z!6m{e-ZW+Q;zf`yn->eFm2c5m{#BMcb~9(yqckwAW{P-*n zN@UAGdp3p~JDtX{(;0<9FCR-Qjwcs8W@m^=?Ma`rl{l&SY_8J<{;Kh{7-X0&G&?ivI_YNpOES8(Q_7OkrbQT02)~|EN@? zC!gCU0tS1g_-OTpykv`%hX<3N%2v{GD}CC}`Fcl_okf2p4h1Z~fqhl2M5v-~WEg(( zR+q%aVar@m;#p4^E1VonzPzcvZh>)6jz7Rb5YlUnQ&`cjpWY5zVdNB{G0hwIo=f-`~#^9}s>}prlQHPw!-(bd}A%sc~mUgG5Pd z*kx;vX`brNbP8A1M&nnH3z}yyK5rrIbgftm?Uomlp1G36vvz!c=EWgoF4h}C)mM~? zlX5;J&4}KHefds!xe=D2qPc}xAn_Y`AL2cC*fF= z-(arpEesb!QGMiQ1W^$CUIsOT!U)%{!LX?N@MdXluWkhMfHQYJmGiqU(zAK?@B!V@?Kbtru@Q*;qR`L#HrA*6L7pX%mf)Ttiw2P7@15QWG5xi1h#ahsXP+`$ z#ifoZjo0!xPA=I)$ggx|8<)XWq?PHSM_!G?KeBPd<}wk5kP zvc-dmTte&dORVdYuk+8mIpp6DVP-fnu@$4e|K`M>KWhJL!*!6?VMp-RwA^M+*N-2} zp`obolpCkY*wN;Pw%pcSz4^-|X)|HOtaS3h;(}=JwWoVGm+q)-;$@33tOjM$>HSK4 zi0XTGDMB6hBfm>`s__Oe! zmabj(9@yHr3{+m9Z+Z(4-e<5txXwH_J-8=)igY*q>X@Zi0+^@dSg0$9miOwZ_|H;g z^cw%Jx1rYOy*uIwDzkY}r3YLBF&l6Fce+oK8t2~oD0py7KzWa6c;d!WZq%t~CjMb* zgU2c>6x|WK9*tXz?nN*MhGbY+&2w1v;&nM4>4k;ZeQxZPDAQqIy9K5FRxQqw*WL*y zfZ9O6x!OzeXCMuau$>^w)ar4D{kEIiTQ@ZDl2HkStKtYozz{@Nfl%GoXGO>#uT9|O zQ1JoVAQ0j!jS8>_T;{i!8J=Q#6RV${w7X z!U2@0c31vMt4T{-;zlE?smS!fh@)+&0a8FfqgI5BBd)>nr3jP5vH&3vu8XPEJYrUZDUSxHR#&E$)|BZvq2-GljjoZ(y!Q zN$2anK$uR=&lnaeyJLQdIxv(ov+;x+D~_I77e~|Z(j)Dw=(^;YiA8_REs_^{MdB!l z($}Oix5uRpQBTvnrL|R?>sE!E8Fpr+%y9Esd_&flZYc>gO zej?C(Kv4Py8>!%9AmDcy3Oq5Rof}NLbj<3!l;o|eg@j4+hpr3>$`Nk3kKo%6;8h4T zFmDY0@dO3^Mv@-joACkh{CXkcWHLQskaFWt`fK;T5Qbz=wc%$4CSH$4Lb z+V~&;{I3SDZ^9wfsgGbdzZhv_eKt8vS4Ilo8J0v3An4y99PpyQG&lFdBoaNelPLm> z9jlEpRl|!(#{M^vi7sv>3GFZsS`kh?&&i54m&OAxUwZj&4h;BJjm5@WPnEm>H50OH zO4F=v92mTOg*1guHA1F{X5^>b{r*{!QN}%%DL(8SGMcxanRK<~m_}Ldl`Z_*IV3p| z_Q<*?qcyTJ=XdHb? zjssoWU5)&iovNCepDLp0=UGke+czt44h}FrSloe2{OdmK)3EftBxx+f#R^VI&1}rc zr`?wQWUzb+(w|qLrofN>eztLY6hj2Oji63nTnK%z*roEn#P&PeWjt#U33+^E5-v4A zzrasx|9>=ncRbba`~SO=y?2Prgskit*&~}|?+`LFj#c&u*&#cGviHmm>5z1iO;+~a z{I2u)J-&bRUuB%v>%On+IhC1GRu2&H07?)+N*^7e*=fMp0#-E7@Tgp0A&V+?A+@Gq z$}Rjb0h$=|AKqs2qDTtiUHC#o43}X};(y(Jw3SD6^6c|&>5YaCKQ2ct!l!r+!_@ii z8qYVLZx6)92hY!28bV65)TEdsLjL{f@5h1eFX&L--6ef6+PQC}MInLe!Zn0RnQ4yq zA|QKolr+Q0=m}#q7S^1-!zG zMz_d-ueS$8rv0{6)P=7ps>=4o9vTl0)Bo74Sv^90jFN?y`IPhS9-FbG>52i}h~`cR z*Fm>JlyF0b@BEL_DAFpHvn15u#pW8ee0-!_C-k0pJfflqHI4Huy}i#Fi0eyYm@*=m zY0dw7?K=*mkg_FwH}kIXkL$^5dRtzl{*;vL%YRzDJ5k;ahGk~A6FO+igqRPDblWfZ zpeXu=9MXYD86C9%k21KeRSH6$3K)E4pSfaueHIo(3%<^orA6Jn zdIHW8s9%K_?lGg8Ign+~g~BZ&{A*d1;rmNTNfnV2wS|V(YzW(sW0H{|Q54?S$P4n< zdK1MAtRWEa+QsCKrH%js37FvdX+F}Ac*YXNg>E{Fy~?%@xUM#K>b-*8j>w?CxP);{u=q@``=hgr5|Vv1_*>|7*_5$=Tf4 z*o6WO2y8pgENYi7cv>2zR>ZIoSMomoMTj+1B-bF_ZTcO*WziS~=89j(KSvnw;qirt zCXy;y;7}O%7li-BA&l|6AP?z(S@E3`!7|x!ckZstF%J|}eZ_eE?}bl(LK_uEG~g{? ztG>aDup?)@eQ?p4z@_{SGVjnt`RJp^}Op=T~ZGiwJy?9t0}Vp0SLZ00Q;1l%DzUUnaZxH(-mMbFmf4E$`A+2~>+?`l}! zHtJyI5K;5T3G<@kFF*Y>NdgN=YzS`AJd^_q+1cpLwP zIylHI0u7~3=sm7M(rX`Gq6^t^^V2*A%>NcUIJIV5=&aP*&TAi z>iTx%(l53H#1z%wncrMf9(HJf3(LKslA$j+nm_ub?g+`-xhv#$G$;jJ6 zL=*Iw7pcH1J`(`Ah&tmf(#@fYi;OT_`HWx?9`5z64V`k6osfT{tWHMkUk=XvjZsja z%*7dE|BijvNa=ZCA+?sKnRmM+ByS!?CH@C*`peMeSRc*UVz$zfp#8M{D>L#euR>kg zNI6YC3Wzc|Xp-UI)={=pP6#Pm>$c?UP>JfPwk1~Bq}2DWltiZ z-+DNl{nt*ll%c#%hGe`QhVcXqN@osGL9E8nG+! zmtbI(HmA8K#KpbKRK=3TbdCdY(#uFSx|LbxJ(_RBl`SGBmWNuK6D6CB-HErD2wHu@ zMGpzfU*Xt{LEPvI5Ujk@4-Xs+x`0IGmvBH^;`+J-%xGDWu_hR8N_OotcEd;a#aV`5 z-tH23OJOL{oNbA;%-Jns2X!P<7V+*`Rv#RTkavfVxpgI21^*Rn;;&uWF2WpYd*Sf| ztDn062$8cu8a~z|W3Ui;Kov-#tK{G08LHTg;b%g>CuX3?JaGPG(_KJ%awr_+60EouI@bA}Lv*wr1$Y=ieV=ioTTU!sb`tiLQUc zfvjTD5xbB6;32H8Zliwp)hYEhv8f9j0SfubMaz`JDOKfgwhy+fHF3ibcX8qWSmSZzT~gpSW~Tq0+YRxns|}Jn2^Zak z_NpJw^*EuQAGq%g_BwXY2T|fEclr)C4DHGN=kMX&-QUj#bPZ(roAYc(JxsuYi?z9I zEz0`%@)}Ttd1BGK_T#lJtj4HabuyzB9_rm;5t zetHaMSMJ61G6Wc9J`VRRig6PEn()RxT}Jj^Lw4$mxH5CadqNHXPry3yyf)iRfpQLmrVh!`jA0lP|pjq%>sfTL$wyzCkRUujQioc?{f99vnlUtFL^r zzV4m9J;%NX%4h`|JVk1shu>oB_!>G&Nw7;#q>+Z zL?lIfUK&GVK%q~73mx&pZVW(P9U?Q=Dc|=L&)+5WCSMzAt8*Omt5lEg zlidzm5s!#&3w{-T8Y$hi6=i-y+!JZiu6<^j_JwwG);$y}cI|j;IPGHWS?1ehjV~n& zR-Hw;VX};1%m};pAQBkym=X@rAS=R-$-zw^>}s#d|&=<@SgE>}Tudi$3a zkIl{`d_A3;t@8Tt8xZ(M5Y}}qi;8sTw+)unrJMjY;YSpRF`ZoR7b-*O0Zq)Q-{B5& z#vH?twD*9yB;l)jL&JCV3h!WAWAZZQX+{ZTc}nC<1HG}xy_yU;_NW{t1uHEzosas5 zh}Im*d8mzgkKr=2w_y9@PVs8$hMHE(eAX|3@VAEVa(@!u zJhhzu_eo;s6NDR~V+7+J}v1~C-INz|6GRUNr!1wpS)?)NU;Vt1j>WX zNH-SK-%QiBlX8&6cf|zr>t2YHLeW%*E1j+$K}nR%@6}Ah-#*@7tGE&t6XakK8FRiF z5RlEOt7ijN68`X(9ga z)UlSUpS>h%fi{z9gHNW%kudlDc+tcfrpl#dL_uYso2@E;Nz-YNY}wimfapA%A?aet z-awk$pdu}a0zJ)dc3_ig2d zoHx_<@VpWjs^)hvN`$T@p0jke+e9(tuVJPa<{zg?qh3~oKgt#Hj5j)q+*shgj`LRX zt#j}EgOG5i2`dj}97C;PUA-17p1WhtOGO5Zk>-5KDhb~|@Zl%U4HueH(f^FK(pWk@ zWouc@YFDfpbA66zb-okv@fas^M{~zUDWbcaX;36qy?urXxyJH%dU}OndcxquPdzMP zh9R-O=98B7O+$T*31?p7fs8{8P4~X>SE>Fq3D0bFrZsQv8+YhC*8hPm)9tAkD@}~s z^5JcGA9(S}`hBHSGM&UBNm?v3b{G*U2OiD&-}yJpejFPcHfbc-$jv8m2V8YIx1FbcG%bZLh`#CrI{*i?CB%B`ZYLCzL(|5GQG1J}(_0^N+WuP|n_-G} zJ0w9Kti1GvX`}+e$%~DdGKO}?49wubQWBxtEml-pcUThR?wWf$_k#TsPp>_P%ME)u zyVgHFa9_hGGo+A+Xk|nQd7xGNB6Lk!^b$k?ahMUlWyKung_wnPkde=@AIh;C8p6W% zqRTZ6#H7-DbH|g!&jru}pnM98%*liOSILZ0-UUUXjoT>o9s@b`qEYIw@8-i`m)0yF zgKy6p6?rnd)7f%%ecC2_d-oC}cV3Dv;;*SDEA82t!4acu;KP*AinNNXDtS!-dm$1j zJpoOMfQiIJ2q}KI2RjxX$m>0ijh@7U zJm{Ts{osaCJ*3NC1IT`*iH+$~^z*(Jt=FZG^0kUaSD6NJBdsA7j4{@~f1;(dnCmq8 z(=!%X?rdh7NH;e>+1BYCW9|`$dPsbe&HdNqmr2Z_61Mq z%RaC)=dztNT5+u{ixAc#Vt`wOR0~KNgp|d-4dEGny?33wZydYar85F%E}Zb*XOwMg zOCs}Uwsea1A!8RZqAcFQiVMtn+WZ!Qsa!=ou`Y|2A-R7v{KnS~Rt|iHbyE)YM|Sb z&#V!4mrEv?m)mMB_2_WDwN`-`-D#onH4Q#dm3@z=S(bEiP;Z8A;L9G| zd%|^SDt{M7FVAM(0Sd6~PdQ`hX+ducT#<~Qk9h-%G|qFX8{R02F_)zb)C|bP;P0l$;)_05|>Pc^&jR%+-ezL7XsT7N)kJFJ?m@ zTsQBK`EF*7=rR$XHsW?RPVX#U-f8CmNt60x|2d?QHN+2%if%ZYPhOLPK zp~Q60`!EU$d5+nXNC7PRTE(lLs9zfD*bF<6nbJIhMbi02aO5kmx`6Szk6bDVAnrQYnTY6^=lThPG_+)w2EhA>Eux^ndj|hZ$O%L?~S6xMD$?gqH73-Hm zIB*DfukzQ#X8iUD;DEn<;zzI7oJ0EWx6|4I;(b<@c5%|`(J8EiT$E(K67sFO6Zj3C zS@P;*eu;zRBhO%r?3qn^47s}u=dURegQ5ycWaDFsyk%D953`46zMdaHn4pVkBgob4XTcMR z`Jpj`7USLJJ(?} z%3yZMp$t?b)I$&cm}i2B4tee4w!MF1MsFV@I$*mLLxFg#(BkCmzK{DS~iJr&u@S-3Zn?GLa?Ho9p}T04^Ag?1BV3 zu&8NSb{NXm0PfFxoB?rs=xn8?DQ>u!XU#4b&7M3p?d1O)y@A(kuU0j$7a;mgbS3mSzv|1E{v3VG$(dbyil}B)Yd1DR||2?$Z$h zDBA^jO~)Jc;h)N-yLdf-n^h_rm8)F|mkxKI6(Ix@H^bEqbNl5XXKNYnod3)_pCra4 zn>leG5F=YwvhR4G$5FWw0iDyczyvZy&7?mdcs@H6-<)aJ5X+RB>9V`-n)0mqn3nQg zqgerd3({uGLku0XcOaHt7SBTtY9FRGt_HyzwP_~r0&1gYy6Qc=1iE^U60Ct&L{SOo z_PLg4zwIV)e$i9mWCX$~EM@R=4GUg8U+~$JyrrQl`w z8~ttLG7jEZs4qS~o4NW98ow#$qhPjWfb}vT?~MhQE3wFhg4glFe1>mnMzpHuG@oVW zBk_a0+*q}r(yVXHMjQBdXy@897W9PV{l#71$6XJMn_fHo?cX*ScXb>;^!fg2E71fK z(;z?KrhPBJei(g{^72yf>>Ix^4$A%3{uuyWJKOc)kQLr`KCc>ZJV-S8v{_z+j`H#K zt;l#%hhkk=IP<^ao)~Az=00R6Z~A?9mDNDAhCdS~lVSrFAa{3n(CLt{rwl+i6z0XX zxfjQoagq)ax7C@z?~rICa~tBom{iB3Q`pUQ1ws8Z}7NOcClPJq-;jJ zjVLS2dW*6p;QW7$-Q4!4s$TAG{!!-`R+h=}W#Ew3dPHdcpGs z0eb4Ji9;1FkjqSlc|rBvx90Wjk*pa{|Lkw;kxBkql~V?QTC3>0K~{?bWX6rggo8BVDAnhIP3MMw zZxrqi@`GIj6egU%C&j~4+9eXc=Sf}{Le zf@52Te{D@qnU!)JVB3JENe%wA+5NvE;HH6qq%j8^B89~u1TN`H`uF)xrZly%Idkey zGnTjfvi4uZZo+qPvV48=)y>h-TdA?L~9f2nmK!mLf;~{ zviAH``h8R$F>W}vVkdhQQ;q2R%G&2Omp7F)iIDEAM40$aw0e4c>UbujWeQpstUIiu zH4q8Ml06vx&{b`HXML z@M>&KuQPu80HqHOQf|bD0!ciX5`Q>7Go?T=qf{7O+fyM;AxH2wSB0a;hjrLAamge7ud} zG2bu8{j&?ay~7!b1Qv^TVoh25Xa6C03Qx_=maa~AD=)qvU5)?MiWwPAtZ!|B7^tDv zGuo)$1yuIPkF;gjrpCxe3GXJS$?fGBlfnN0nm?N~P+-=hB-`z}u0&a~563rV5!`c1 z?_RL@o$OD{PKyW_-AA{i>6gIh%AIY8K%{$1Ft#L@o#Y)=l3+y75vP~o04aVnOSkVj ziFbH&Y8Eq_toIiYimXm9_%*Se4*5-!Q9kw2^4ZC6$}Tsh?d)mT($v_7DS<XjuahtRxUr==i(NJnVO! zdu&y41m*1s&=8ES9cYq=0tZI1o7hWMXKVWRxvo4bX(aGHn!0+}k41$7!jdYVX_+VC zGhnwXtB>M@a^ZJjpVISCEKe@H5RqdSuzh%?&41OB?VGx_V{FcnwMX-2sV;GW>{qsA zzA-lhw+#XN3!Xd5*Yhhm`SblxAab+Qo#aznq7|qLwk3cWANr~LT8`h<=ChdrC0DFU zUwR3e%Sp>(B+-CP3=~>2Z;#5ZMQr>AQ3buU&o{HH1!)lpi5y)HKFJ3|`}^dDHZ7-b zm1Y04Ytd#K97xtAU^PT&F&$gASNw>++H9nUxBv5oQ`XkH`BSz*)jjDG#H5jsP;Zgj$}2QyVvB&C7Uw2Ap;|7 zGav8vhSzEEwIN$Pq`PuKLZ{;-|4hut5g+d_sh7@Gb2loBSYD;xSg2HVPV#^Zg6B>y zT4hPlgN@FyKWS_p`{&emQ`I`EqUdks6A9T+}nWd>d=0UN^^ zWM-O7ZO>!-;2<}-v(stoB*1gf)Ou$;?<;PKJqe+r+|4x(4hj^>k6U}tFwZBGz`L@H(9-9Xz=Cqqcv3w4DYig^khD@Zfyn64LD5FQ#JmIAeV0Bt|zZ8sJ zF9bHHqy*|!-(_V{cFgOo?_B;jUo*3T8H3QWv$cgM8D>8?DKAW~Jia_GUlFDfMkhm70NCSW?Y^75|M%X17xs5Pjm zSK9gb)tt9wxG_5DVQ@ZkM}+`EaN77S@aW0EK*+y7PnISr$xBdUMc0rcY-tQripNTr z{wqekc)m=;8n5`4g;Cq`ALgn@*h#3*m= zmfOEm7z3sydolPTt7*49Oo{;0d?xgrGOf~XXioysDpmXRcAqOewrUzkycX3I-wh`nPlsv5S)SX^ajlmKuc@8|Ak-uG07j68#=y=k817#W+aEt zVJxaJ`@L1R<6c_uG1Ic;!ofhzPfddlM zZSnoqyfO7fd1$F3jfI6p7SCn?Xot{7Zq@GZ?4PZUkl^7Q!fZx*L7{CIFFvRR`heE~ zgpgr7=q_=G1k3d$7`n400%Rdv-oYVCNu(2qiT%FZpe1DTuN0&=!fTXcwb>sE?cO8#$V9Zj67S{BfMY* zkt%?WzHlOO+Q6v-vU(&{1=rf{6rMa<%oEwyP&f4}`?I^7 zTj#Gs%7RhQ^*oTZ@gzNyoF>Vi233nc0fqtgFOk@CUG6*<)Xuqec23N{u-lL3DVj~UdU3uL}i(K0Oo8(rk2Hlg;W$niuS7xHgzVc}Py;tYKBvsjr>+P9# zU4#|Sd*_vJ--oJNYScgm_V*Y>#FOTdWodZzx*4B3Urs+tThwFpvdh5@8;D> z_EkSm`(@Bt6CFj|@86TeLp9gJr->*{QsJxshsVrXbzL2%5~{OWDS=gXju_;FekVPt z5`Oz{A(BYnK)+HAZ?sC2{1%dgX9L1nfN7ND@0e=#0!3QKcFZIX*#h~`si|d{Etux8 z@%Hf4C~&F6`iHpJH|{W=7C>YrRNmr-u6oF|>ordmPOJhVQBpYdX60pv(tiV<4@vWE z62E&Kf27v<~JP@h+kTz7~{E4~qje1;?}SBL#?T1GZAWgO74A`&8h z|NadzZNGacPj8@a-@x08!0yoaEu0Q(0=!RPf49w7h7S$3k-OHkEu6A=XZV((nYFti zY%HAc{)*nutg392;pRLKduf6Z1dCQz7c+<@fYVM+wsD?nlV?hM_dY;AdvR&bVnQ~p zdHgvDs{w^nQtEUs*H#eTe<+}$H%9d=68zdoss<4H7Znx#{AWodAt@Q0_pwbRO6ib+ zjvm^wV%M7dc6v)_Bbz4P+$uMBW>ie&txm-^G82cYrHLZd>0lX z<;$ON!F~9!9A#x|8`e`G5Bj$B!hqe#7$O{b{UCGKzeazX=;3p5E{}eo9iI`A*{#^= z zx5KF;uZEC3yn=%AjmC4`C~RP@L7O44i1^gJ0LoJ5a#es(^gjc22Y!YVX!Jr$>9vcu zp&kwmnqdZCLwDOtJO+~hWem=!w>(QQTxj*qQ-xYC+fc-VuDOSd_NWrj`h!9e^oD5A zWPAHr)==VEd`zF~w!ia<;AC0LZT4Pz1B?5NeL6aItDRN)D z18$*B^A2TP?lBWb0tRf^U>^i=jrZ<8PII#_;5f!8M#|{9MtZ>1pzP`e!fgNqY-qa8 z*_p6QKx(Du`U!_0Y(Ra@YjTID5AEdXr6ZS0E^3SEeHH%?#~~%5IJ$! zR7PaIf1ksS3wj#pd`XD7TNXm;o5|Qu3aP?zJ#eONgDr^t>&wuf$-8lTLEUSo=Sp@# z_FL9iFHFjhUW)KM+&4YlB0>z&cg?+0sD?5jb6z@(3(tP*>s|GB4IRyo-dKe|1YZhe zDroQvq3Q>u{KdLV*+{Ih>NN}$S?dwVE}>)F2An0^u1b zY9^arMlTlFR8s9T+*3zE53t|S6xvbSF?a3E*BAfd&ZsLL`n;0jP8hNQ3)&pq`9azu zGHUF7FWFNE(J)(Z@C~S=f&~Zl5~PIJztr9SCHzZNO0-u%-V(I1T!V7-<0k*XTPrBp zQ$OP$O;H7_pS72l0mgJcdDgP4pC4o;**OQqCMWAb9)P>;_+eVtfiX;aFvKW{K(1Gp zYi503QZE?}ga?3z#Ez211qW2ff<_X7+)uXwwSmzJpa}jnuCPnzzO&(X5$0Mc-MvmQ zf$+2+2bl$2re!XR1KH{N!uty>vorUeU|c8aYdrCRJo*z^DQ_9X6#d3y+6hmv?7)Ww zZ|czee2```o#3(Q&%Q+(nFJP)@5}`3-3McEjra!D<>uJ!0_-|#d6)%&{|%LWtt8;) z-`nqksnJa~+31_W)EWw%_{CcDv+~EM-XcDmSrF@7u6)8( z1s0T6Q|RY{JFs_ha#FmVHe2#~Qs(?oO)F)KSm zwvRq){i)-{s+X1P1b3Wp1GUspjjDt>t_==%?diSQgJCtoe8*{8Jv(!*i5u=MD19DS)YcRP+}S(_7S$KAt-VFKh6y-|OrOd=S2onJOH_Rj)-CQ8HcDu}7GKbxqg%hdI} zm}ZYosGA-O&~)&tzYQUAt{&SjSe1OyktltQATqU7?Y}Y?ywO##Lh<2eK4U=?e)M&6 zJ){?jg~-F)k!RRf?OIi;DUiOb#1drlRQ7ni$+S99`gR|DJsqevu1#;tE8iv~2<6I) zXP;B*F5340Ep@-o6$+|APX!eDsK+NwB^HaM{@<3y2J9uWkn&_>lLp^nY+M{%QlVME zx5zQYgGy1*CoEJE^dANCP{|prpBgV9`?h6Q{y^bMO2zYi{jZo$3g9U^k12CevqfMC*6q3A68nJpb<8^nxB5RcP=Qio;a*dPDHWee+f^<(i|90B=1!!) zcRk9+#U&dmC;-6#I29^Cc2CabhJ)`~v8A4?(Q-ru-ifCgt+*GI9zbD zxtiN2UScg8s)G2zFId!sQVCACtPOWuHb1`UIw-ViSvh>@<8TVGv}w))v&5#kw3H-zry;M_^9MH_S37!X1p~>_JB?X_Y0@9h zEPjEs7Q7lBQ|2Pn{3}Or*aD+(k-dYX>2BOtAWe*7MezGVECSlig(l3v4%L2f*6!-N zad>w)`#zLC1K$J=kw=6?gkWP-V-05V(W!yS)q@);|B ztxX+ZeYjkJ@qbzXs1*|xlgLADfDs=C4oJxvNaoL=rG-Z_1mL!|%Tq@*^9IqEO(y~S z2l2SGInv%8ZVGy#eU>bwCm0-K2H007KM67+7!IUNqoeKE1;+ zAf}@4#9TJ`=s1U&-Lrr=o+IT>=^8^?>J{|GpvqAt4~~atz6g!fSJE$@>Vn1EZT&TFu0L#D&ynPu1ss zl2r0dOEBwsUQE4?NWnB)*GxVcCy3XQrJ- zvn!hglaeU{>jT42SH2<<`Zot= zDB28KH1eX+J=5HDjQMAWK*~$)iH*%LgC-XxR1W9;FGJx3oQJTLcGZ02EMhY@Q2KF8 z9=dBuqkF;K3D!OUN)8agbLJwhve;CwmwjYg`igL4KRU%Lk%I?{Fgz&TV~FSHUdTZ| z)JaKS?vo{}B>Nt1R@OBsuqEyo-kARrKYM%}a<$cd8SPxJ%9OQzcG^jH_1H&h!JK8@ z6@gIUPqTzVM|kGhg_>hk*)kcE)pG4=uFgPAA>nh5@fQ5pVC*4{><&dflC>16DIP<| z+J-#>0t#Rw*)e_^pVvL^Lk#7EJ*rkVG;9y5A531%5!jsvzcE~JOH#a7l$1_wv90O)%G0FDn;Lfbox3TgM2KDU9!72Ef+|Lf3 zb{i#{Jk_7MlVKzGuI~gJb1%~ha7xKntJOXw$zz{pD9vg0_#5|ffGRAH9ga|l$q*w) z&q;0x9$GlgN@^X<$)zf{|I5A{e=Z;@DJh@80#O}}#<4F%=)C=k(;+a^&c`OrrTroW z_Okf~u&qh`1)B{hbV8gawp=;r!D#>zc=(A#=yL378lOLYrxgCk?qXVa@29Vfo*qmh zuoGF>*xZs&fwLCv>(wsi+FbanE@=>+vAltqNEm3ke&7q<;N7#)8G8X5z;T8zk79xJ!XIm%qLQKo#@tvPmc+c5 zBTsx2eI8gFf@7IqSiEmwmmjP?@OPoW7+T{nLUS|4n zBX6Qt92hqLrPuP z|8rjejrM7>4?EY6PyV*{IuaeSb>Ahs&2S^JGOn%T3OVh6zdqh3a{9`SjTFZPoA{2* z-KeA^y%Oh_M{3>1$kA`iendi4Gm)EFqoXDY96phGSPyRyPcyq`M`Oe&sjwEyk$(8a zlPCL$BS9tnTdP}nYs#@7jfj6Z0Ar>t-W>z~aHX>2GK5{+76&LSD~~m6AKWT4cikR# z^ZnZ3|JQlCB@(#{1qI&*Q+dY!{P`fQltDV52eiGmeK^X+bno97C#+fMd`5V6tod)* zdtOkigvs2YW)V&Iu%i^r)GzJvw8#KeN73k1=^aGY#v#E0qV-8iC~B?83NgH2J|R1& zc2B-}F@@gDyZK@H!~|!-mS~}WaM>Eh4H*a)RJjKwQE-(Pn10?m5Tvzjc1$yC^Xr$s zI^fBY41ApXM5JjWScvbn`Kyr;E#UA?UoKkaDiuv#&Zp81xCZxV7ehf*?o2%nMi5J4 zez%qLRlNqHpGgWDmb$Ulw98G}!&U_iO$}}5Hm(DtvF|(cdwpKM(JRs=!Hpb>es(Gaad{+!Uz zWs38g`HbA8JaiXCWj|CF)1Q4+dJhLAABX_%@Sd-ge7~XYepaEtMB=l z?;?wm@=RT}mxgG;I(&jgO;5zfpZ_umL=2mwBp$T}Zr_KH5f2ymWh96E4B!2q(tP-5z<5a#Gei2uwJH9pzmns|1! zNrI1wwhE$(+JUYDDAKOAR`z{F021s5yQf2rrtJZt+UTk;*R@~wft6dTe{!-J;w|AE zH}pU2^rJ`lqnnzg`oQw+Z}6Cn3JMTT8cY@bqT&kZ zUiwAL+*<X8ijjYD4Q@>iE6Qb-g5L9RDzS?>r#;O#j@nEmedzy?I(FUd`>`k6@W;YZPh? zT}ce(_La4bwFaM5u=ysT%BLEs~?d4 z?{VsEz6o)qz12m?2G0W;<{c8h6qokO?H9KV7^RT2*IXuqAT|STpxr$U>1s&yJN_+# zKCuUqIYYWgJRW(WMj~hIgynf=3s)fFM6F!89#vp`4@7kV2G`!sz1(q5QE42L;Y`Mj zT_DECto%f@e`o9=YI54D5G9B55W_Mmd>=f1t9;caz&Yy*7UB<2xFDxh(N-e3Pogc_ zqkYEf?0KCLSz8h0q)iY4!o_ai}%cG&&nDNd;cs6#^mau>zzd zfnPv02r9pukk(su%Y-p3DF}+3JS7Jc@5TX5$U+nO;%ZtFBH5t% zN!)X_+mN>HzSx&-YS`1@*E`@v)Mn-A9$kANm?h;qP%y3MB5m@`92|Ts8g17 z;P;8pdT1z|^7U%;J>u>kVF^=A749<|ajtl$#x|8v)Ay(xm2X_uu<>7$JB@Z zd-CQFoRrEiyTDq~7A{p|Vs_vtL8F%X9F z`r#{0b`QS=Cjv;KD9}y=;GBUZ zzlN(>snyDw=3l55r^-8*LEbkD?fFGT`-uXNUyFNfJn&`5iX#Wa|0tof5!xTKM#{+r}S3dPzk0w{Q3%3m^0wR;)|Yjs)V* z0>&gbqRUZ_Oul%C)!zti5_#}*ATxQ(vpHLc_EYonw3Ron1ZJ#TaL*KO!y{VuPE!rF z_8HJMvL*Jfx2}ks`_E1rzzM<0Ik-;l716hVjxGIZPWew&FrA=k{AFEz{R_H2_2tJ! z5k2#dMwL!we|&NDNqe9Ew;5~3_2Tp;{6?WpbPiRdagV$pIgGfg1PCE8bx2>GXaMR6 z5a{_LPvG32F-MGGtbbbgv^@Ked(wE&6^)AII8?Abg zD{^O>4#4%$ijhBm#3JQ_w%X2O*HA9R(FBwdsxLdrRYnC`QZl53MMY(}=+QcLuFZWI{*-4Im$N=O7q+h{)ZHhFsGFLdfyks&+!_BYs4-!;kL_u+ zEda?Kb5ot-&-<4C)jCc#`iGBFHlUcvS;&<67j7}l@%9oY z`?W`a`WD2r!qR;gSVZsMy)%aqs4p(i!rnFBHs8n96a&X~nm{Tl(=*kWyAPsOyP>FN z2XP-NZN6Nb9Vr)0O^+#X-?GjP5SeZF5E00Zhp%R)$jN z0W-t$^Rnk`i-aEH8-#1)2Oa8vZ>FFIgU^V(!8=g=lf9|TEJBFzks`q`qpsv2IU(I^5W6L!>kOxn;!@ThOgh_j0jt3kT<96Q@AHQOXtAQ zN`F+!QU3Y!4%AlF)z!mqVs`Fm=7F$q_{`Q--YZ$r&C_9cL;(_=a<)RKLx&9Y?b|($ zhQaF8hab=UMhP;3f5;TChan};w0v)KN*q3PH3V18VzqTzEU}%FS8+%_J0%`^J4N?F zfa^GOqTUDe_AtPDOJp5!>JW{$x*yw861CmCEwTN@Bp=$RPcF~4&>2{OBf~$6gbyaS zrkeS)K=+Bed5#fBG8*|gS?$XPy56|mH8b$+C~yI4yEji6eCD(92|U#Ag0xujfx7MR z%*XNP34_bPUP5nluJNQ}Zx4XT>P3UxJ+aU_+|a zbPJDP9h1URP`Fj6`HpBGd>fgWr0AOkB5wg>XSm!}q`z1tfm6ch-XR`)!83t$@E^cX zu;L+xaefWjJa5r5^i{Du6eXacfk6*O82XEyxXD&`bTTbc`r_My8NCh{Wo0Jbk5k16 z_iwIrz?lQcbPi(e)OAG4__*r%;>=~|!5VasF+D@)_+Rp)nXMVufkuNPZ|EJawfYJJr z91*_sGLmi3dDGfCAkvjsIzfXD8em`w{~gebJi~}vIyslA!1OQD_^h9=T?Pi%_S5M5 z5aMy-4_OT0!KNR5P*cox_g{3db!}<_WN@bkd^a1y|K;+_L2$a&2vTjAAtc{x#=kH7 zaz7$6vVTVJw?ghS!3@{~;7o!%N`QpmY=mdubZs!O{un>FcCAD6EtGk~?_p6T4Z`&y z5<>BZl~qU(qip8DRzdqBli zL4XVw{c<=1y&H@{3iHe>={5Z#1WmbQ(LL|pzccsnNCHGUN?ab==h2>&WR=_PgpR@w zT``N_H5_q4pDW>S!Wcv%qy=w7AOl=E3e97e4ild-Uq9xL|JxCag|@HAG40Gz;bZ;K zoV)P&Ig=a~)p_lGN4CQiedk@7??W}m-sfydEEyy}bQngVVg+$W?(C!{Brc72p zPA+r|VYVnUX;(mVm8I3t9|DK95=dC#jcG5Hk0PGO!>13Na!!qjkwDXr5S#LhX0p{c z6{0Wr_z5vk)fwFjwV-xRS49^p z*@;ExJbn&aZg9!Cgumcse@Ge-_^|hBk@AheFGxAIuKha{wNF1+RaSQN zDr707ytlavL|6@OHjwqC1kH3aUcrgwRpaU6iA#GY)W|o@6UP~kbGN_U`;*DI z|E}QfHOllawt#%;prsCvOmNkA%2< z*OOfvVa9- z051U)xg@)V!fGm&U1VAwDABsd47LL|%Y>bsog4SEw*{{&c=-xw;!X`q`Yt)JqA7k6_S0g@U zvNoJea8>*4EfJg;PdiNp?6Cnv4=jj3-Cwc|Y9zXlKbPwOw>S zb9-m~Z{o-zdnvbmeeykZCKI2PA_{j$))HFXx z*|ok;gfU%Fsn5FBT2*}t=gu%_+3@|P23>_Y*T8*vFw7(}^i5NPymhM2%br4F*aD9T zfO|I%{1txx%{lX_`TqCs{rY{HY5Rl`%jh)42R?r1;U{nWZt)TZ=Q6-J1otNZcMFk8 zF1I?YejMms+$MR zj#l}S!Lxj#telg(p|$?$RL-Z*DqXg2(k%uYxd6J2O3BoMzliP@7-ko%=>t8w-;&8jThW_#D5BK6z1#{lJh zYq{9x0y-Rk|DX=}Lf*|L2Yz#R$UHzb3ey}mk8cnyRBgI4F9?<^HP&&-qc@?Qud0x! zP&Aby)SwKuD5@&H+~3NIwMuQ5_$~SL08;Z5?qc02RgX3=mEn79=HwLRTp-Cyviv)_ z6rQ<>m*1=4M}L+^aGe^6ECw=ZCX_vm>7&_&=X7Ov&(3xSfWZ!} zVOZIpR@by_l8iN1jh%~jW-`I@ZprcWrVU-fj+GP|!igsM7d z)%_@(Y6&G?@FQRP;#n2kiSMl5!7pAuI{z*Rv1kl-%*V%fpMMcX{Q5;kP3=WSM)o;i zOBB?eAL{<@vO$FonfDVLwUY*nsisy|Q9qQUpfL!nvfP4KU@TxA1AP%ib)c~4Atwcp3EmTg2Yy}OzwhA{U*F>b<*KWa*sy3a zz1v*bC%Z|Medobp%T8U(1(cy5pUw3xQfiJ19r5Y8#GF9ml%RcXC|}M~iHmN4l>i+Z zIRJGDoC9DU%|fysAv9&pj$*y#Z`4dBGHUlrUBMc_2yDW_+;DMf z4!qoZ=h2Pvi!_h9Mh6F7Gr{`-v{8R`EW;1pS`{nYq1wlW=H&;Dib3022;u$(Cl>qP z4zlVM@^xV4!U zBD@O6RXyc>vwUnzys(f(HX1qTVpn-)=mkyo4h#V7nu7ega%i0XYo<3{{rkPZ{1MiH zS}Tj^FCX6tL;gh-TZPCac-tT|heOA*Go@09Y<>^~21CFYbfdvkt!<8>=7^^0s=Hpc zI0xC{OtVjHEHmJ%t2oiDO|uA%$-BihLN@-;lakCGWWBjtb;>7HKub0Q`E)DW=`0YF zVGeTxvTjPmIYWubGp3fqh!=?UhjB#6E$W?@+41qW#wKPK`X>3O2Bujv6^y-w?R6$G zNkTWB(Hx~zX#9)CE@yW=T*y3TX$857;F>1X!q zP$^1tlb$Wn4}X&2t{2icqdqN80@E_!6rw6Im`DJ-+dr9^`#yS?ndEVr7K|Q;zz?WF z6oUPLy`k_%jBLI8K0Ckt6hmrXRpKlvj|DPFGLPhGF70^|+x$kFv%kzfTI|&`qqF3$ z7zw)5I&CL`{oh=GvH{_<)5Al*3ODS=^@jsS1J}-y8{`lcN0l7( zIfF^zKW0=)r}grWChy@y^jKhj@;UXIIzA5s@;q!~&||y}dK41+`Ja7yDED35qeDYc zT*lDPmHjH4am11t;cX|JuG=_gz0cE8O;Omv^D+l$y^uBOlAA!Ws`t;3-7ejYzM`e*VLCxpEZ! zhxBC^t8mcV@rbh7-h#z-;kA((OgW=E+V(zb{(7G+I!M&r-{%;}5;OI)>(l4@)oS$U zujPj-v9wzq^$w+*aAF436D>3V2hHdT( zK7=I4Lv3x(o(ecA{T@{``0_=D<$;8C@A^exzH0o0qUp3geOJ&o1pd5K*@Gl1-}>xe z9~P_Pg&soXRJ+L}cZDXu)0D6}d^R~Av4Ak4I)?|MeBo<6x6d_k8NXb=U(FUu96^iw z2ZiwwIRO-l5`#0PK@{@tQK}&f#Jo}4;Ke3#Y5kNR}QOpl(;zFvSKV#~^ zYk3x6W^;lJ1}hl-KcglR3j*7AsGlT!)Ao+tYB|kT04bg5-!6pbEBom|Ky!`ZBKhZ8d|rm3}fg~mwY8$Yc>Nh z-hF@~!FdrRs>Vf{3!OdWAIi#q*z>g|cFSd$hOn2!$%a5Z0;s!kfv&l=^{cIxfbQgD zFG)t7Quu~hDjn2KXNw~r0%wcgVa5d7YL~lpExW%q^!&Zf@Jrw@V^7U-kAaCRoCK>GZA3m3gW;Mwnu3IA?#jF}FtcRS_ll!UlsFiKfT zMI@y0Dcvt)sine-rJ7aU5)J!`PIIWzCxOxFA`RgS=8$F$=pei@o-E3h`D@o;-Nb7X zAKVaOhcA44%c>YIo6@!22&w`wOM!La=+yAC3znld`(8Z01|JL@D&j&F^*?-G!?Y@n z{}mpCXO-zRv)3R3=r`D-{sPh|R)_?hsBbr*S#pm=!jXR)Y^aufVS(e9`!}c`SW;;l zZ~;v%VpeSFAX5lFiy|B~cFO2(9PkU_-^%ScUK}F=ZVa@lb`}%F)GLsvY6A5XOiVDc z2TrifyHLw!R*N5QA#msbZvMV?*g-#^ki$&n^Lq~-CD(s`z>V1a$)9N9!*d6A z8L)8JUD~+;ht7J}HIf9n^QgPK_9rDU+Z^8h8>{ua+nxj_Rk+V8^b0=Lw3RwXZOW!7 z*M+!nYin*@PKD(jwO%|Q6hFpLXM^+X`nnFVo)#AuQOsaCj1$Mn@CbKxHkhL>jql&+ zGL*zjSTlCz{pK+%!eDl2d46^y<#)aj1&>+^HfRW~105TO}oFpcY#} zZW7UV3f}}_)9~cR+NaQn{Or7s2S|a>;a1`k<{gTxwh4{!-SnH1xK8XZ%ux$Y55v(A zni7H>_r8l&iW8x`xBj2x+XSS0K1M=;o7l1R?_*CMbNvx5BJ3oWewW?Q^;dqI|46XF zRN&--$PkE+p{m8GlUYGr1qjV)l^Rh;j$q6Tp8KbE?Fx(@{+RhA^uOzm3?h-r6V)fO3!nC z*I8z#hQGhF(ZO;sGmCfVFhqkhy0xfOxWkNS{d+5BI(#bV>fh#yWmk9}0@A^#2|dlb z9kw$t&A6D5^V^NLb%;jw5!H!7jwiSFi>>w9JZ$tuhX88J49((V+SmH;u^&W*mz#z% zy`=L9k@^eR(j%2pes_Jh{gD>XVa!{iqLm#KfhClYjRy35<-u zi^*9gn9i@eKg7~tGQyuQ5>YR*1IhY86Y9c_dREjtccSYq(}imxE_z~42BQ}BM@bsS zHu(0)3YDLnc<}d?EYu_mJvtdVHB|Tmi;rZ6*uU-K-%l^#Uj08l`0qO+Z~eQ($Q!IG zRH)y6_HP;d&o7Ysw}AiGr%`YJ-zRXLLQ$^^z8?N6Ac=a&|Cb;CH>v*XqiIl+{P#yu zv-dwAh=0ke`Tu{kyBF@{-~WibC2Y}*4u9?lbLS3{6RIvl{SVQ%|M{J)kA?P%;6FGi zcc-I%@d9u6;#2rp!CMdh#}}g>;J=INxc({}7R!I%yny?^UC00U@sFr^`d=UY-<}=n zef;NV;$NcnjQ@Q7|BHvU@_}C*HvOxJ+fJ56U;gv_v!DE1nR#hEO10!$QExdi_x8Ub zLM2K{`u+3~>VgRF{F|)9&fG>x4FB#(O8I||8ygr33mn~L&v1ESU)-x9UO@Zbo(;UC zj=IIUjm;0kAC`XJ4W*EEb|8_YVfa5D_s+&3(fOT!k3v))hD1l54934l6RE=VKY!WM z40EVJtIJPnnD#3Z+&B9Ktr}0?JDmmo&D**}x(@k_(>6VgKRt|D$jg?4ZRkI>m5pjQ zwb#y?D5;|%gTfuv9GcEBWfS#?3&AbsqnmIsaQg%ynDkNwqzZR@YXP;YCQE5t917rgK73d1M|sRA1s`a|miaenLpX1E3!c;FOdcqi`JU{$p!;Mr{8>z^&u-5{Qnzj`=*t{MMz>k!(9 zy!w;$wuBn3p6ZkLIZj!XQ!yrM*Y;2Ly!CmN+E)(ehq!(&YUi%*PYrm@ZUW63+J90H zI6G<_4((8ot34$Dwr?HgQj^~yafi-*_2@g`Uh9yd2cS*6>>Oy|?iiD=uQ7KB79{0O zZhX|3GA?QgS>vIEj-|zMtH65M>B*yM&dD%4>tw2V%QvpeS(6Ob4i4o~4w+B7qSPszbvVEIrN64r94AFU1XLIIVqmgtj5JR2F~VR2)jNew*L*lbaP zP-7n(i0Nn%n{;o3;t|0;ILMZ)$ia0F>;$a%-U`$@Bl!h0o+uzwKioYT*%Ij^<#k&f zFm_b?X_LuNK-%a18i>OaCsg~gv{L-r6f3-{XWhp?(c1G(y_+GV!+D%Tu}2Z)wltH1 zHA-jJyl67O$k1l{*!1~{Xp2Mz62aTNa%ga_m#s(WngEvhw^IA6KQ&1^70I_CQwJJ} zXK`VT>W{!oErIhhg6>1UBl2vuA>Q25a&@)A{fz3Bu~!QHL?OsQtF&KOd8K7$GNFQf zIl_^9_mi>*+Bn`)k(N6qHS-@$X5JfgC6szFNU$-cBbb1Y0&`%JipnpwC;+L!m>c>` z>ko(a+Kf*o8ZYZxwlt8p{MQ;fL&Scu*&-;hdWi69QiDHGKMJ>RY6;icnK>lDH)~uU zaYsH9j1RG=SPhRaw_0rw14=}u|KK<{3lNlsIe~I&;q0cIP+gU{&tOa_V*; zqH9`tS_&Iwlt{Pp{OYFkLTICc>n9ki07l@~m;h$o3sv|6w=TSrsHQ@0?qz=Qp|9I$ zyBwV|YiI~Un9`hoAXq$-jx_b1%yxDX{d0vi+P^}jM%T=zQM}}nRO$FvwapXJ7BO*P z{Upi*6uRx*yoD3j{!+oylEe7W?tL;lU}TcU)Y;3Xyn??AtqFH*rCMf-49cpTo0Z_b z<Qkan4RAK-`k#@QVy z&`c^7l#jlLjsbWB^_kxY192TdQUEG|!7u|wD>tW+|GpFUVU}{E6lba_Ae3O3cy#pP zt@Y+E;ns(dpg#5Q`Zmu?G}$2buMmLn6U&p&g==q8QUMYH-y&!V)$F_Zii#Clk>Fy5 zd(2)_Qy8+Y85CJ#J#hAbT<0q~yM2#dKnc$=XL1YJ<;6*N!6hF;xebh2d;UW59p&R# zVj{V1PhooDpOOTn^R#hzPJ!yt%8fk-Kgrg|-GB}bO&nnRH#7r9fj)_e2JZoAx#;S{mj-sy$CXe@=iiX9wV zfPkm`p{$}BVByv+*4N+W_G$gncwLix;>hu?kj-$ami~P6_ASJ&E$$wNyM$|Q^fRz0 zIZwHaS)=GhC>kuV>!C-5B5^|a>_sAmV)`bw$hQ$Tz|ymI z)Z@Y69~H&>tBa+;InBK>Y`Si3-v>@--pzD{NajpsAi2PMNjiPCL@jKvaCZMDp0;Ju{$u91Wyar71W=w-iE`28kGi%800s2|@J9mh28V&J*P)+z$mi zS6A6!ssbzKq``*{gw>8TVYBqDTf5Ddfv%3o_EqqVi-5!(;A!VNMyj%X5<{c@+FL%W zv@yV5QC|M>(otL953aWrC=$h^@3GGOVG^Gho&PR*w7g%?=RHho z#C6Y9hpal43%uMRKa{P1-Z(xV$Q&TiPV6%W+~>oG58J4e$m5pFrSV~lHwLc(2?tWf zH5|o|#Sd0gkk!E!;annJqAAHwz?4`?ZAq{wAs{XoZC&#n-mH$k{%uhbi;M3!U*$i3 zGECF&VhJ|3O3`7#jk4}`aVXeB)bIP{dG=+`!U9R#%TskOswfjr(Z9WvpFGBu7%McV z{-7_jF(pRo)tpk}`zLJ}+yFBwP>iTK`~;#pY|AY7Z@km6TDaxp?VixtDPz#&+otS< zlRYcPOwu`ZkJ$Bkw!^|#DC>DLh(5etbMuCFV--B9#%cH2??uA^0s&0DIXUth8n!%i zV#i^K7GdC&4{f;Zoce12)WT?MP224E;q}-CKi9FTi5HEPafo$e-ZZBB@*0A>ZHU~o zN6Tz~z9^Nbn~2mYm#N)KlfR!uX#wlhFfSAi@Wmj^goY6~W$r@m5>NsP*V;Zdff@U` zxF<1Ncst|$wAi;ZHCblRu>iM5d6SOmaE(V~@f%IEvn28KXnqA7R&|pQMFuEzM4=Tx zh!6NgWtmCZ_TE6ZY15U0k`mN~+n|zp4Kx;LcJX>%U8w1Ie<~}u`RC~1fUMq@Xwn_h za9xLQU`h}SJBnr3P%SP}s%{Sth*ecrhd7hcB>>gh%GaO1!QHv~!FS3JLq8lfGE{bU zN4E~|km7$RFNd4&e)8l`L3Y;h%9#LOk~7e2ULdPMX8FUC7ZQBc>pP&|+nJfC2h9Qc z*@!kHZ4?g>NI4QFylu)}5q*7xKh{Wdjd0tkez;RLS}lZNycoAl$(p|PFrA4mq4B0f zL|>2@H7GJY6t}u9c1Nh)Y$tO;jH}N!xb6n&I$PxDCxe0%8FCeCx38;kXVFdeL_+y@ znvy!Lp-~G4)5|^n3-0&~_tps6Gn;c=~ zkT836T$Ftb3^gER!=78nQZyB`|8gpob{U8O&~5?Oc@`EHiYP>cDRsh~NSGSB&0Vto zArRMV>6`?u1@IpzL{3o$;$Wal1`(sd-J(cv(5M5AD`fs1`TG0%n{w?B>Yr5r#|bRr zVg7-8e+P80=jD$^%m-)?e)xuEKL_3T^?!JHvh20)95usb>z01}^tl7njG&VO+@9oC z)cM1#XbJCU;q`Z2n!HZKp(6pF!x|_L&Ulunw-(ww9;%&n&)HGcsK*4X;cwE@ClrNd zMEu)~UK%k(I@196G$C7fJ^<*Vc~xwQmZ6`x{G8vGkOEq%TNb`tgBx*(^uu8F@FNtJ z0&k7yd~%}Mti-seh3ZNOYaQp|A;mk+O)|o2Us1U0DoX;0ZQdmPX6CZwTz}~QD<2zy z93+HIYjMr#<3E53C{D4lv2#1#+~HMngE>mF&1x1Rjmpp;280jH_qi!3zVB&VaRdQ2 zM4skdhba#u_AT{Wpm}Lb-SI!z`srH7x+-r1a{6xA8lXYQ!g62R-)7Y!vIXx-?S|^T zXW9n3=1x|LORH=kxAYVapWT|Z$z9m@PU9v(b#qHoD^UDHlsW-WKQJ~&-sgHUtL9Yo z_@O4xrzy8++UQPr1z`)&E7Pe^%(e6T7rudvqjYg*z{O7-6ymek_m<~nG_z3(Lzs@hvA!|Wv{ z0JF_@m`sX_iqKixIT^JvFEp2rE8O<(yLH~(RG9jL>9GK*xt(7ei1WJ;4?Km7T>4L% zMPWk&7}7YMrOtlF#MbxE`R{z3c*OPWdh;K(cI?Y|@O>!KICMmpmYAawvBHi2T$6kV zo!X%#HYn|((uZon&o2`YR1%_yZA**Yeia~fBMU%p!$G!lJSFjNb_HijQ&wp*fDw!g zp_RWl^GjsrUb87M2E;BiQ07-J?t7VFI!x9=v^V(WgX0~<1bb*St9XVFm682{Y{G7b zC99G37xx!qV>@`7e>wX0UwicO;2J2a)6&y{nX+*O37T{tnNnA!*;4n?jA`HDgv*=Y z&k!XGx;6V~8%{aMVxl3sW;S=l-bnS%v%}5NMbpI&ldAdpX90nTMMe-O2|b7rv4V}Q zA$Rd%Ggb&AjODqkt?2;&;W;|`C}|3MdJ7+)w_vOT^37aTOG^#s7WOOW{t1~sh_+^$ z7ms6Bz~M~x#$#EH$G#ARTQ%akeLTG&zLO{b#7St$!&8F#ii1uL8GcAbVuEy>$qb`_ z(=hf=ID!7I!3L?RL0z4gfS@?PD@dzf-*^oLT9W$G-%aZCr#)rqj|h^~!MFlgrNx?e zLFNH|tp2kvzm&L>Zj|eduw?gWu#rKe12pDs5u!hez{MGONYof&O{GRM1X0zzP=7y#`~Jz23E%^f3ILNBb)$P1`eU~ zI1PaC%5}RJ{2Tq-+GNlxb)tdeKpBe66XDXkGWQA8cXiVRnoh_Z$dagwF8w&Yz!DJ; zDh?3Uaq2g8tXcv!0ixCL;K1VE=;u6XB?KNX&@b09^sxAQ>Ua8r>+ThTi5p1@xA7*g zaP&A^+>~P6GtSTwHsZJAB}+)8QUs0w82D+6;LzL2x3^d;^wilbttj zso=QSIGWTl_i}v|$hzwJ_V16Cog$!d)vV^RmDcV-)$*u-wAOTZ^V9g9jhB69cQ#4a z+#BSA?Fk9nO+U-54+4k4RY?EDot{~hbS3m%ZuI;^&yZ)F$q*q3H0gY+&*<|@ONnXd zN-osm5$(osf`kN@J6ddH9+ou|_W6g56#4ZfH4kCHxeu_A5}&zjbbc_h|JyZD1_R zYq%E}sY0E!kOQ7z*X%?B35mmNa{}?ySlM%J$eOk%M|-5ZolHaWp>V+R5O-qxuC6dY z23eU&Kfs`S;ry5jR;MNu-ZnUcI0{dhIa=aUr+C)K3?_#mr z$Ef}6f~OHZzueg9&j8&=G0djZeGC1|kXb8lCD1#=wlfv}HpLf8Z4jb4(x+B*a1($% z0?a6IbponOm8ETqJ`%mrF5Y&sj1>4OJGusS4x4q3l^tOWZFv;BcplR-%|nhoI}Y}s zVfyU7!4A}JYDXGebzbPGHKl|NtRNP52JP$C4!|Wyg!lHzMw{)LvK5XJGEz40=*-{a zd^tDAhJtNM^8=))=T`QC#uNPcOV;kdRzGF5Ge}`LJT)#^fm|HA(9B1wU{2%9Q_;75HAx3Bj>)3NJ0yeCPB`4x9Si ze^%e+Ss6MkoCiMxvRz4l^qmWQ5*92Nu{=D8164}V7T%P@g>=-f*w5~^>5l@%B(L0lyR zafhkr0aBYrTdxQk({EWoAS9>5G@T-3JX*yd)hU=pu70^UMhImcnad4c@)bzex}7Uiyr$czH@J22;lv`6&2E7nfiM%&6M;!)D2S$Zr?%hmKR&-RwOy;L_R zC$xoS*&lwNI7!zVDSmpJRP;EBD@;=G)P_sUhIZhYDYor@a{@gUx+Pwn{>`3ZoK5VENIDGC|~x}m|w@WLDi zs(xJ49|*bd160X*Dw|(~C42=F+4*duzj@YYu4%LHQ%1+U3wD{4do*y2Xb_OrL?UpM zCMb6hpZmzOx#Zku(_%Bh%nQh#OlCFK$+|VNEl3Y!Ub`bfUh-t?mr8vj3k#eIq$0;J zGH$^Vn|mt1p@pxyTKYsL-g+ zyf;}=B`K^A+dZdT*7$!IxJk|4)!p3!?m0^02uw5&BO_YFI1RyQg9=DRhRjmf*TEe{ z>EVO;fZe9Ic>*N#5|}Vp!96uLAt7LG#-DDZHM_{M3{Y3A^ez3KM$Cxm9eC-c7DdwT zs>G1v=mOLgkh|cBob+$z{dCK8WbOqT6HfKfJ($w+aq*kYIf6feW$r*Wdz?9DATUuF zmJDt*+9@h`P|_3`%!+t`fKZ%4e2|_dzE;3O2{U1ykp}Mu0hU#b9uUVsc?FE{4|SU& znFgsq-76hsdHCpeZB13b%k|XrE5Ckko9wC-)Kg*$_xXiQWTK~x8>LcQO3lfl-hjh_4P7aEz;X^@11*C5D2rJ1UbyLJ>map3f5 zTMK9x*kaZ>MlB1S3<{Jgor~*c$sIx!57idPXv!P4_C-g!>ycX;?5S*|(U5nmUUFSl z?zMe-f5guMt4-ZEMr^Bv+sWVI>|=}QTDsd(cJYQQotD}B zgyjJbZ;8}DS6mpjd#55zh2+C~Ik|q#NuO2jyxgr1NcaWWYyP_dQ+&{!p##P+Dc+=Z zN-9V|yB;iV4hZG&x-Aj@-4yQ{BqW&5Q7?sk)ya0HV_?z;v!qSxvIDaVvqe|X=UvxO%GjFD$=|>$_{5&zSN7@Y`T7$wyO&v zxy>U_`XF&O0z+J#>GrEaW`($o(O2o{#q64o+z!T92;>lQ0=@K1kR0vfLk&8^wuJ?g z)Q`~Y`K~6x=J#~YO0-w}{*2G(=H+TTYS!f&z87bd$qH8>_7RFWI4K7$UAX;Fy|a<@ z{$az#?rr_=W5?cpLn5@~8}5Js+^n^eiee+aUG>&}zUk#?>>`GilRDll%WNY`_7UbD z5{^m9W?i|`@!*?vZG?h>4OV0EaqdM*TB7DE_C+$MQ*5X`mtL} z$Dv_a*6497x%~ASrcMP;^R#t4*qIhCwFurMrh3{|{ZcxL&UXLZopcJ$IEPWf_pTF` ze2&h}!TI^7;9M>d-#zAA2`BZe;Uv|pBc$DyoMb4)&0Gtfj_E-DBBGJqZ5I9Z`)Z#L9clA6*J+@LxxZ+1s`FqR^BI{^z5`#9(q=AmpyuZ=CW!L ze0uQH5AVF)jTQiF8ag#bRxuCbvZo=f_@JC@193JG`EuF5yE>~V=Nq2dEpF9?d-XpD zgJ!s)#lE*-IW^(=`J5Q^^-?Ua>z@wftrt|TPQSS#<65C|RcmXt7-{tgT_>ecp)PgW zS-XX*lUmR$j9v$tN zIu0&WSgf!QUXFbWMB+@P1_p8iu!#E<`Lwd8J`AF_(*n~C`BS@$hsXY!f^FT+jvyOa4h)7o zcNPTc#Jr!}U`x3BZC1+8&6f2u1eH8d*GQJ7L=z>nE`O_M4f z83zlzbKcN8d0J-d;Xsaqb4o089hmB4{zuyi4Y}D}fu@4;S?bRlr^AgY9BEd9GcNbm z=ZO5h24jY|2?#$&Eebf=>LS7QM!ssjAzm^UO^p$#d;(cR;Rv!2`y3YQ2{j_7 z=f4ZUYorrgMZ%k#Mc_Poc3diEYO{_`Dnohs^1XE#w31d$xs&bhY%p{ ze(`9#F|lGLy*(>nr%ZuOgODPY-lnjik9aKdrR)oAS zrn9n}51mebZMPj-=#W9@w5gSmKO4G*Z-onH>T;rL!ji?i_* z9}yu(jHO`qRD1baE*15{1p_ac)RdP%ZHRYSJdn5+rX99GM@}c^A4|?Wb}uRZ1n-Zs0T8l+o*v z#vqo^tFauCXMHTqv#qa@GkvRXSjXa}noV(zG0!eGJ)k?fAvbySYuy17;{ROzNN{{V z*MM&J)^~0Wk+O|XA<~GTT!EYI6SGO)EwtIeRK6V=KMRD|oH?@!58H>g|3bP!Bvl)D zK>=+YI@=sQc{5$@QcXUGM2(F;W7E@}yDI}#`*Y~LN|~z$wPyLcWecAjW4)V+lt#J5 z#2_AvPt$i*ajtL zF<_ENJG0or(i_-hRCMItxspZQ^U-qF&GN&;p17CKHZmCJ?q1c1u=9vb(0P;dKL4v3aV zs2)N$V}8=1r4y6@U`_jOC$J&c)MDkq1>}j(0TXhNm(Z~J1TO$Ay_THud3kmPI^CKG z-x;^o+Tr^08!Y^AT5Vn)BYH$j3p-QU(;MMdtq1BFSZ>k-ckbunbGLI(E9D(du57v< zj=H+^{^ekD4t+oM?LI{~?eV8^ZUQhUUjTEoHm4m5K(>xfN-Ln#C)+Hy^ve67sn^-| zr9vpxPxCn)n$i4~F`@!=zTcL0lOp{;U94_2HF!Ltg)=}#QqSBKx`+s`$Y8Yim6O-6 z52>O;16Mq|qY1RM}YcKG90V~afQX+nDhN{CYtlq+>|XvZ_dZje29k0KUZi512wezJ0&hIxKWp&w>q^M zE3`aIqbmlFSuL&WQPP$)({Hjkhd%*nf(E%marFuEG+^A(V=d#UNdAI=zr8wgzp2f% z-*hE{20czJJzHtG99gU6Vg)cnn^GmjUhr(9alwQC7`_zAdMZ@6E1(jzUJjX+r$ zoSpTkFfbeq$B6xV#ovEhw`zX=_(bG#YmCTMKf)^Ahyq)90LMzDJiP+=90P;-$VVx| z8g<6}8)?}-UDA#M%YMOYg1k3muBR7D41by@9nTU4W#I8;`D!XT=XwJ#Es5OjTzVmT zGpv?rj?>>X?aXxY*4RGka9vufZumcqin(A^!EO?LcMmPFvO_OmoGXUbwGjzih4=3d zXr&v<@pV!F03_fRk~N=|`^nn|P(J~^jren@+}utCek z70_R;+pK%;oLvE10$inGmV+c%^UU+3P;1`o&1Gs-5VTx|~7VbSc%}DyyS>Z0ilA6?e8Oi(@x~gp;;l=>i zT~%v8dP{vQCC(%_($L>4?#!63K8(ho**7d=^4j$nM6va6ZtI`*Zt1mGALiZb!Q<=6 z7nQTt^l)_b3^fcmzv*-8qggO;$Vr$}on9CunHv~#hVB^;r?s!^h6Wlc7&~P)b%^%I zt=w37Y$i@l*MSn>3B8K_Eagc2mAO#+--|l}popxjtBYx#)^GAZr`qwPfsjHgFRLX1 zc0?|A@7mhcnJ<1ZmuD+t-SkUp0-OwVai;$MS@D$S-WazU$`|M6(8Mm!ZuIxFdtRIg zUi?jMG4iv^q+5!mZIs$g+WhlpZu-;%&M%c5)syb}tR`raADw(%W5Fkveb=xA%Ozx% z{DklOOl~x+H!e0eROrPdyg5ZQk3A@85aAnRbj<^Y+lD$K+J#|X)zHN*j&99dUeJN< z(pyYdg*i{6HBBmDf_DJ&4?f#P?i+XKYaGPaJS0qS((-!-(0nH9GpmEUqxV0e6Fb|+ z#AS4(!Kv@00i_96V#`a8bKf*h$nP$BQ(U!`e8_&Sz(SZJe188lduMH!6*NuOXM2uC2CCcR#&M9XZst9#?-$He^lPSeTn+jO=fxO^dgRq~57U)_wy`CO+1XNDxi0Y9^n z+_@UF!ODDX^=VL{SUE4nJt)%E;dwO2c=MWo16#bRVK(;fR2vRrFfis#CrncL&PIxF z$v-KAbU)>LfmYTI`#V(}i$DsPc*_>$hY-ffxT>r+AH1Nz{>UWt>Rl@)uX_OXoY`6_ zo|MRQIDEv!#6-%P9__8&LYa7j0F5zG0sOn;M`Q`4S}QwxWF{mUM`cT=CE*{rEzj`2 zBm$Dut~)6`te&Uq6>MaRV4DSo$^J2rooCIaTq)uF9H-C>=m@#K>atbZfyNZ6{)k#T zz$O6)ST(>4m2F1&dATkb@PVPwabp6Nh<3|p{tW0)k-wCciQEEF9iCl#hrWv;EfUQ-)rX0h{a_y zFb5H1s3lc$w*-RKU+{_`+x078Mj&I(iJ!H|@`~z^oQxceMrE*r^4`Nxrn?|emgFF7 zH@rM}HkLw*OFDS}T0_yH!M3TR{+a_eO%GVRb}a6+D!S{8`%1i>74vPKxu7HY#tcOM z$g(mea1H&<=o<0dbWl;Wtk32~i|wN7G%hYAsR0`e&JhO9-lOE}GHJf&$RGPeq9AJl zNrfb_r?Afffudp$G1ixrPc4UZxWWO}(vbHZ%7B-z#;$+zoTDeSeBDu>JdXi835g$_ z7g|^Z>@a!eg-Sc><8~=9vIfha8`jJiWY3oV;Kiy3162;^8XZIS0K^j*!=eX=1o^Jl zx9B;ZYl-!IQsub0bCQY_5D*aOtedM@BT+gHDxasNkht|HmEtQyo-_)pfp$-G8Jtql zRu&Z22+>rx_O|H#5#aoR`f`hbY-3IlIjdqR``^PvHO1lJVr!S$Lwh{U;&^(E(04wW z!Rhd}68Mims!J~0CDg}FxXwtW;c9u@An+Vzd96Cyojl;ncID>lA=NttKMzJ!POW@i z?}@1qC+hXiue~mE zY*cy$xG#lTKEaRcBz}-ESWU6e-&SjvvdF^8RA$0j^} zq=UwtZjG)iDP{=rA_mv?4ULC(hL1;mD)hqb(RIsMz{9yp8%7lH+*-jtf&tH;X5qG` zVS?yG>pxK&V-T0ry|q$R*MbvnR$2cgs03oXMRfbr0~jRdUMj&WZ>+z zV&}CN)aGA_BR|TUj+k9VrNg2zBr7lqWfw#U+z2+)=H|deI*RO&4U7uxEyaE5`^W)C z54>9Y9m-z_q&juQ*={Cr^E)_x)^z;#kwc&yGl-l47eo6#<-Pf@LW=zNxH<7m9jJfV>E12gi4&N z-`w+N&XuUz^n1Q1dsnkX{X*HI#wMn|0tT8_Woh^))>J6y2P491X2I7B^N046z(Z-= zE_=s;bwnk>kpY#?mY>hX(v=xS*K5SwbDr^qLd3GYrF_^=hQAtL;9j0>25b)}hy|~B zoozS-gna0RMtjT1>Yj^Qum>V7qU7Fz@B>tR;Sp^ZA>UueroOOfWU*>lJp!slEb`IF3q0^awd^2(oV0) zJtLb|mwgJp7nd&vS3Xl!(?1DeB%tue>pIek4Z7Phqtcj-{Tvy#F@}ES>D-m=uS>g; zrb5M8ne$Mvd4lU27(m)0vff|+lh9(x8!eYWKR>R)HE@@b5y_K#daa$?(bY9J!0(XD z_cD8{?IXvR;{)omrh)YXQ$jJgzt>XrY0V28P49^!#VEo?76}1s2m!J`7JyG^R|KH~9Ue;6q4H>7=lL!v!2XAh!22#gTs9r2`_Db^koi z=QW~D<^=5%pBG%(ba zK_2oVohn-r)JsBPmjI8yB{JuiX7$+%3V}f2H_62X!0DSggF^EINPj7R}NwF&16& zaCDLHRKd}~&|ot!NBMdnHx>lbR#iP3zh(;`ft`Pc>*7tgD zpu0aJWqP;De%iC{n+cg+513cUHclT9g}vC$O1$1NtrQ*O+qW~1A>1RS#`Ka|u-nTz zorPv{?T|*_Dbq@LJ#ic}>X$G!{U43EI(mstji4&~e`1Y8B(v zHqZgTCz-}+B4J03Ao^f5>kpkIJiyCgp#9FaJqf_4d|vE}`$11wLcop8GF|pT8Cc8zkW8v9`v2Std*grb)-r9hC%T6ObwL+X0>Mp#uSyd>peZLQrrZXVJ7IP$>WB( zyQ>3CoX9-+D;~aXPZ41o6Wp$tHM)eO3ZoCsIE!}FZ+8>z5owjhN;B>4d8F?M+!AoC zxW*|EFEgdV??y`w%?pY2R}u!2VzPyr>`?TFx9)0xzh?{{uF-<}DI{~)bJYG35pn{Y zG$9b2VNnf0@0=%D`C{weSDKAP4}SvCeV(Tq!mKboR`285%^5{PvLhg^hD#J~JBfYm zqBe%_`U&4B7^>dJlY86wLzZX^k;T8~JS!$Kc64qtk?IgpKv>OQl$n0?W7B}5FvUOD ztaJAKx1+Ut87n#PH=a@#T1gOxa=k4T@vbj?ugW~}Yq+oo}J=y^t|g zFVHLv&i~K@Wy#YW!B6|_+2URum!B_{k&$#q%iD}nv8N}pI8iUV1wgg`ESd9V{1>u< z*#nCJm_KAQ127jy%hav8gdEw8&TTT3B{CwPqf>171K~ z>87VMvGBmDu_i33Cq==+$;merwrC<=3HPs*2d|Xm3vM2411gx2naN0(AVqSUqG6~^ zaX^F?Ap;gZ5*KJ?viRe+zhV||Bhb;)`g4bFL(Lz|hGhrW&+G3^#=|T>@*E9~*zmEm zgl4bo>i$t-uW5np_sOZ*$fcm)x4mu?2Fr}e!Z5NN07V}$k_wmN2ezB z@IgQH%jE^m*}CCu?sHD5r!U}ej~7N@`DC`;h5}o?gqO@2pDxZq`~scpSJ|j_{?pPB zQ!ki?9eImGp)BPKf`VR$!ge;%MidxYT2&t#Fxq+HcvRKch(GCG?8Kze54Oj-z;fb^9Nt^Uf!7JQxL>!W#Dx)stldBzn-g7jNIDuL0Bn=bQ+WDf`7`QF~D;GpK(KZvxs6mPtqr$Tyy>!Ya@+#Qq zFCKSE+N0CzBOcesP7gG~A_fSQ0<&attAt1efQJqNekdrdJ36>XC427MYgYLp4I{yW ziIrSp#=;;tG?|#sBM`|7w|!@hL*WNu5v?Q;a%J2txr>#={;JQM@v(YK4xXb8E0UmH zGUaB+ViZG~Pnm9rRT?pa>+W(Np72&;`LICe;nwW@A|e60*t``<97N(iR903Q+#|TB zrI9O-hnAyTY7%gX2G-#(rLWp>{oMJxoDH5s(UBzRJ~5wAr!Y&tmtx(E!*Y*A^~%T! zw)KCoqD>UR?g6C7m$xZ7_yy#~Kg|g#oPGNLin{iAruR4QmbO|>`1YGW|I&{9QySzPn9Z2CEoN;ItbZG(k|QVdxb z@q%qrCN>k5L?8)RI7bEofWU?unfAaD#AND*1@nfW*VkeWymwArCQ{_n9Ci|G0)L6 zy08!m@G2CFu_9=I{R$7cu<#icjd1+UozGnbMLd{0SRP`+PPJJ^@NP(BrTBQ)bLVJU8*>9&Z zlphzaK9~sPbBY~m_xCOumOPu}o)%eJ`nwySSnp5waTE|OZ`oV<95xEWU2b%H7{7kJ z9z)}@gLP!b&i1&lb7P>-FAP$hVn{&?F!|vs5Pl)BG+Gv*YdvJr?dPE52TuuJYO`^S zM+o=fBMAjMaYo`!^|d45MJPtKhEvgAC_0j@BsEqfF{Es-*6iYKl7;e`dBbfyi2Y)(O-ijeLn_ zJuE^(et(#kt*)+u&d3K0KISj+5gEeIU`469;5BS4KqGBLuk0j}J}bRSs5a%cW0two z2+GFHp3~UO15ZK%q@xYdI&>xJUKJ(fUhhZXY6a1jXqokukrF<$r^cglRfA7FxI7WJ zheVyIO!F>`xjJZEY2|8VtCjYr5nzg4@J}UvqE68f$RMk|~S~ihQk$ zE{COX-QU~KdpFU(*ik!cFwd27Q9PfvN}_cr)mjQ3MH88+9@BT`91sCZK3`buixZb-sT*{d<$qu!;sE zGZf|$*-1Skym}wq2kqyyV+o_xJpIv14U{n@M*R^lbYMV*=4p2;J)N#|t*W=eK+-St zext+jp?v{Ht2+q83QZ9memt^keTcE*(m7}Jhe?|uqUE~MU>KOWhOfD4$Mvw>{Zah= z-s@0|yZNwl-vN8Q?KH8S`6hw5mM)dMM0d9rM(Co+=(q;a=V}xDHl1qzec|J8(E_%= z>x^SL#AmtWEpg9IQ@?+G6Z}B>Af}i{khQBE^fC0L8y1tW+oXv7kR7ZlM(bEp*0dD` zBib;U<(-}%oc7`=MP6r)iD@l=S@n2GxC@cVTy99dZXxT?sV@qqm$Jx1PhYJ)&Pp#q zCE{U@H2F#%lcpKX)$CQc~C|jGOG>s z>Z5PKY-}}_-_ObH9_VgqeAFB!)=GsoBja6lH zQ5B}gPe1B2(7Ge&w1j(JwzL>0)-FCC-Z>oeGzh&}4opKJQ=2~QseS)K3qmseoXq_* z^0L~0ZEL`uEdiL_CKm)kiikMEjSjrDuZmlRM}!-Tm6>b@(lZzXMP7;rS& zD}m#)K&!V=O)bm02vqAD$!1t3HEe*$NS*fr>F;Fvn0DY{I|bSRFs{flZii&a6a!Xp z8W;O0wWeJp2l@u9Lsmd}kW47LQKPy4$&~t=S zV&;T2nGE~Hxg#q#qN5I{;PPV&i647^3>-@@{}uES3R1vIz-JN2L|9B**u=t(Sf^X0 z{*PL7A|G);$vh}E&^v(Rg`nm2wqKh4Mt$Dz-qkZAhhx|KVXAp3N}2NZ^I5;58L)LA zd9j8rEvdlEZcw2K6`mli_+b9)Ik49@&tU;#AO@Kd5{yvFyR2hnd6M_-7jE&*0+9SJ=(#e+g%B>Mw8|C8~il3O*nS;(A9I!yao!|ZTu|dP`7_95_)^Ny-)?a8h zH+e`wtCMIq9qh2w=+|jkXa_xS!pRFMGIB0WsTnJ76QuYWuDXvnb^RJ*Fo!k6zNp=D z43#`b5}sclQ>OunNeB|qQ8G1@`}BJDd7Xak9C z<|NR=CpB+{3A9js&aR#X-ElY$`S9SVq3rf9OK;t%xze+scmjwU&h?79K126B(CF?m zlPhBqbExfjy!y2l{hxT3(O9w=!aWcBQ7N~}=~Q;#4pg{8xKMvn5F(OcE3VQ1c^?`jUta4Wuz9!a^QIoS z@AI!Mq<31*e^U;fE}v9X@h5!<4ZhPnuKwE>s@4x@BskX>F%;o6(6*a|?_ddbm!o-s zrrfS5i>A{@wg3qQ-{9izjsz-67@~*T!7*VYM0M(ChN{*?1#v3EEAB^ z*5l}HUwmZ0V^t=$kUs1@-4Lmch~NC}W8I1Kt#xHy!FTUXm+0^%u1-8TLJ8h`Y2~y& z3P!OGxYrP=Y0i!3Bj>;E+d<)=tAP34_v5^pxRiROY9$)`aToIy74_-U;>N(^^^(2bm>kS#idY$nC1228h6^vD*aH{-(zR%1rZlY`xj4c#3Q+qY4B>W!aGP<8w4sVl z>A?K^fIumJ478?kU@@=}ao7E^UJ?O%D*=(j0y`I+9DA?;fOsk|Gje2 z0I{YHjXiLHiI4pE-oGzaBcPG>-;K*pE54*AJ#mu!BN`(u20sV)TTu$hbk_d>D3bSt literal 0 HcmV?d00001 diff --git a/ml_system_design/seminars/imgs/sem1/sem1_13.png b/ml_system_design/seminars/imgs/sem1/sem1_13.png new file mode 100644 index 0000000000000000000000000000000000000000..9c990b4c6d710f82fd795c1fb5268ad5e741f096 GIT binary patch literal 173581 zcmdqJc{r7A8$L<{5sHeEkWfiTNHSF_nUgtFhRT%8^IRbznL=7k<`BKeOp+WLKCdsNtyRZ>FYq+W9C-of83ds#KnxiFbiKEZg|GNdq8-d(Wd7LGzH2s4_X`7S?oXg zm5tZk>-?W!;+~5gByZ=fwJuw|j0rXg))58- zJ**BW3#)8UJsPr3;&5cpV7oINU7s=?-QZ>>CgZlP9)YxWN5meVXWEb3a{elH^BK>% zSGZY*8_I?*G&C%A)c?^oIA&VW(D2fnS30TV`S4e7m&@+ac=^?gf@2G*Cl1<%@9}vk z9UqaE+)|oc?VT-nw8=x<{nyuUtHV9C7X59^kAvuzJf8G>^#&@3-&Za;C{GZIKajFv zcVu55xnh*7)rpIqkm6Cf&tdw@T4kW)Sbvpj6{)Urh1~zvarxnkDY>b`d^2~WE1%hR z7Vc0|`f+4tdFh=?PR}vVl{IM+*P34sQM7M*mMeNIE56!vH(vKyUlA4}*wtw2hRQHIZCc|JLC5KeZBwtTiIo%=KnqY?|*y6=!XCE3SnW_cZN9s_a&5$-935y zzc2B0_q)ej|9y=%W=pOA4_}5Ke}mJ2^alq=y8qmi7j+eQ&bLckOHN;HrP=eJU*$6P zEYNNB6WMw9KP&sR<^SA@i%~z5U#l9h{pY!*NB)1kVRKzBT=Y>xJquU0 zgBlV!C%`Awr@Oz?SH8ibAaF?f_oDBn4eM-q!pdK6`MFy9YuRnBa;b5;r<7neWKjat1BBM=8?7q>FeCH94KkQk;M%Is;o{l2s0 zHM=CfzudU;XjFmKi(8>h zb*@UL^To6J!E*1dri&MS^XS^eyM~Hr=-Y5_t}^w$@}bs-A0?P$lsEWS^9dNIW*X$d#PFcRSM^=t5|PTTbz_os{6b)lVYNJcm%)yK!j;L@e?w8Q*-M()*l zpOln%vbyY_8jCzL%H`vX{NDEL9JcF`(D~uKNd@lcBb<>mG;PzA6k zM!I%ZcTdIUCP}LTgU9V-yjwT%#whc$h5IIS#+M`!N3k};&b#Ucw@yquWp`6IJ2L8%O^m&D0s~Ej&tdY z;D)liqd>4nP62rzm6SRkciDd*7@#?K?p&jl_@6qSRho7Q)`B?wKj{ z^v^PumO`4Ei#eGb5gV_(+Jy%->p$B{($p+1$zYWg#*5NE0Qkhzx77{)?qjweo4SAg zN_X**olGCEm*BfjR$_Otw@B*4hXa59>_mD3r`8r6*KVr`K3jVF`qgkC(c@E3v4fi6 zHpEe#m{_WYZc+`gGH!pbdi$78?ms0CyNexi?&{~?bII(UL>dhd)|U6L3kq!J>+0_( zE1j*$;3InE>TT|@-O**wo^F$^W^8&jtU~iq1keu~I@;Oqu;?FyRu?&HoT^6@5 z1KO~A`*t$%ZQbNK^29?t>9JnUAoW)K{|wSscuudob&3{OhGYYD@#s!D$fR0`?KGF* zyeyNNEw8U+xE(2U+J+jRWHMY;jA;lQA3Z7a-RZo zs#hnff3L0fuU)JRWV{v3xVoO;Skte*Zn@Zj8g>%ue|+V2MH0g*nkvQ4KbkelWjC$z z4M&J~7f)0A*SPKM>=3SDN_^z(=Y*8Ix(?lX!_4aKIzi3k(l4rB&C2{w*`FehP+%UU zhp(*oA$otzdd-&y<@9QDHC!O1-90L>>9v6{N^i#m*0MuEj*A6#4;Y94hj)K#QLrUD zotK!1Jsy2uZ!sADDPyXDJUxAcg@xsJXZ7k1g!7+~6vy8k`ev6cEl9^m$B_|(P z{@7%v-Yy%ft2FmSZ~r?-m#4l(ckj>Ly4M%qdS%<~%gAcn(hjDz2lo{{TF*pw2X%%x za%AirDut!vPW$EJ}vEPat^gWZ{nrHrFiK?O5R-;KY5;~Z3nR2 zm6QBi7mrjpYu@0Z@4e1}#FUnmRYFu(_cxe})p*L9e&Zp>4@nD|ADU`8%FNmeTEfce zC)krFxdp2|H+GSWi)#Qy57gyZ8?{_vSAsa&{Pqbr_09Lo3ncLZR$e;np7-8EPf!16 zFqG#v>inFy99979Aldd(hP7yE-lT*xv*PEPkIl&qF`c zb77#i*Tq+w>-&!%8%9S*fu*>VOP4O85I9-|RoVq~6-VBXzH3ns)@6UY$trkg$P!Qv z#OS)a+f=D93`f{=$uEkfL2c*H>!y~3;7z}7>Mc%e3x%tZ_VfLNT z4L~)J4x}*3q|gJ8c;ONchkl1xqPB|W8sMTBIpbSX-mpbU z$@C4n%r?og);F@QM z!EwM|vKcpE6;WUGMUF63__J@O3)w^$f?A1!b*-d3o99xl-f0QWGtyk9-ljg?B*Xv! z^e34jG*r-g{aHT=75cK+&c_DAr`jbzU35AhrH8J8U#CPUWi6mEKd zD592s-`wxtTT$~fb%gLTz!%&1=7#Lx_m$(!6YmOWS$j89BeVD96x~Y$;F3d7LVd$`h-AL|G`G0g|>c#xi@;;Nj`gP8D@ zH{-PWeL1ypH_G1)SJ#`)rFYLhqXi92{zklV)F2^E+~D=PxUjxXi-H<+F~uyMQ{}zy zJZ0a3hhru3V#g8Ix+mGHW`Dz?V>!1hz(NdRw7 zWbC&|DfB=>@r=%;51Vx^O*&4A49O>{Y31s*T`ytXwBepcMvO)VD@!PNidKH-pfn4& zocB$8sR$s0y;M$ce!9(5tpfvy4*bw|EmQ4xd7qiO_j5^3W`KnRC$p~n=h4|Lr|P@2 z!=CpF$>c#0L zCUHt8b=9i-yplc$;f0(I^KL0%G+)dnuwuqcGR1G_?Vv^94^7T|(f7l}45zD;v)tRr z>yNE1^TxUszNzXb)s4>X<&HkxE&^umN4;IF9!`|8 z_880FVh2etQCjwcKBFBjwR^0PQ`zT|9l9GwXAR8EP%bJxh^i6w>wrJN7l1c@Uh(HC z%gYf*M&zhvPdvBbL}>xZM(kjP@%;mlSWsMiTZu17nZGdZ)?^h?IjME;jPzSH*Z&)LPi%5w$jHoz_9 zC(01Q^_m3d_-yHc&6>e==omw475BtC6P(F1^OO}NFt8Naiigm8%_zgMv?=K*#t)^h zYDmfT7Ejo}y36bVRRQTh83ld%TFi|53Qk6qILx?YJY}b(MA(NrWUUO15Ffg3StGRN ztGJW9yZdb(|39=~yC_kMzT9g-J@f;gYu<`6Ne+Omf(0O*&}d7`%ikuB)^(Z%M9$B< z0il8W+0K$cnb;&Rl2hVc=c^{U3oh{_w*bG3#BJhF$iSQB+^s<8;Ir@JVM+pU`rQr?HJVR)k9gUySI0|dHI(CAe0lWQ|#`YsO=(lj2TUuIX zB|c0nUJ-mzm2V}^fN(?Tp=ZW-Je2u^IYdo)6I=Ff-IRH^bmIH$qE4Xlx+|at2jW$* z*k8YXRTNM1ebzwmorL-mQLmH#TA6=cckvS&PC(M_y1n8_Ox&`bfnW^wQkw@_ogaYm zATe;KU_eNZUlrx9>aGe3XapdjP%Dpi7H_=DV06IpKHDNr2qvhs>Fv}i0n8NPI%LfL zxcm?$BFp&_eJUsIo@cnUO{-bRXO@-;MB#n&AJL$fB`SMvvdF-6PmY*e+ zD@t=qG8TsH$d@$hp=oK-V2&%jfMj{ITZ~@}D1q z$cfG<^6YxFR)7Ii6QS4N>n)WFpDg<;U`g;C^fzqb8vq6%5PUI>pIq*9^f$Q8F8T`b zvNPU#Sx^w9b>Mj)#iBs*z;^9^pWaTHML9If=(Esmq2~gD0s91(E;VME_FR5LKoU@M zrC}(W-%@O+(yK09{29I&YP(rnQW8TPeKN81$*wV&t?5s~i%I~sGQacwXxf7Ua-5Mm z(eezYD(sh01Ejh1b~vTCXN^Dc;5m9GJZy^b!^l?F%8UrTh2DgEiy-Z+o8aRH3AYeh zmRGORE37Ra1>)ktRHr4E)gvH$m0A`Da<41&vgF0h(bb8W@1)D_5NaM42VKIByeuz| zFc%ARm5DSEzO9k*jP9%Un}$MRy5XO(mkytByRcAurZbUp!@-GusArcmN$BcWUub7R z@Y+-R3Q*43Sr?=+u-aH8lv>4aUmh51&#?WZ=dIf060Anomv~+-|H?)aE16V2ws3Q? zoda`o+mWH0LhJr~j}YCuX~UF~dCc|16PtR+Tv5B6oSoOziT!PSBslkSMtUzSn+dan z6M{B@*x$5CM*nb$vAOryzuhl306lPW`bo$VyMUED;>yGAQcfI5R1*Zfy{D3f_5uK* zm8An}?KVbQfCNhyqPpt_AOuO1*QYTJ;%w;LJsjJ_qRzC`3t$m$<9M zCqy%3DmHz*{Vyp;sj4;CvJN|6{EV{JLhj$dKt~sHGX8MJgba8M$|N3Y?G}rt&)&?v#rwt~^AG4cJr& zWC@Cx(COcKcm00I%M*wwpXn<9ToK>(i$t2U0z;}tj*4=$fHWwNE_R&RgkGJ5V#~Mto=&SU2 zxzW~-KRBZ@nVcSuVSsx3y(X)BEyIr|%Dfd!y|alRDh?042Z|Pyj&3piOj7 zq64gI+omBeN+7wN%m>dDQc)gjRpO&H>C_z|_~y@@6;5oSYV~v>NZhb`i|mIPJ?%Y| zdew$7A6Y?5GM?f+v$*2_T>GG|?s^(PDM+rjCV1EEqVdK#j0iIN_OY_A+`%mN=?NwA zD=5CuEC^lHxlX)AJ9BJ>73Y`sxdTJ7m97B!b`?kREq$s`J9L43D$?> z1BGcvJ0xJq7XB{7wM>1 zu8}e4?L8KA#$3HyNVi4y5oZ0W7ux3`4MfyiWYMSVlX9YlY9!Qk6H4@QXlVe4&ZRxt zBry-`P5D)XlsS3QqKs->^9nyS_$0Nb2EtVF0@V>U7?X`#rv@V*KlDifdhIFwCiy=v zz{v`X1i-aDWuve(i0G0|CoU$s^=Ox%v_UmPS80J9E-+w12}j)GI|Auo%BRf@4dWK) z8L}fAEFY$(UY6yi)Wquk(W-$tG6aM*Edg};-42E)HPU)-5T!T)Y+t2$B|#2BH4+n#?x)e(!%tXq5E zIA2AaT#S^L^Llje?(xC2(YlUNA;V05xnWS3`%OQz6{o6}?hz-`^Y+a!rTndaA1B=} z8?xW7d(ymor{03&`YVU-@h(JA(F2JX*mmMrW-N`P~WuD`xdsivQkdgdu|h06Ag;L4gm}Qv{_$eA@1@{ z+ouk01Ag&jzC&b@Z}cTAiarGTJ(#76?aaI{h8>5boM4@GUcbq-&23LgN(!nf0O?=7 z2r?v<{r2EOHm*7ZygN22qZixE*wOKB_I{8>a{BnRRkC1laq;h;ZTln}*--=V0x0@# zrl$*za7^hiGh1eI}7n z-JvN6u1gCL@T#+=D?V{3_-FQyKQTjR+y6eUA;0ue2MN(6yL!A|?`cM!wSC{Zb}(zCYB z0hj?nx(+3kgka}?hk3R%6C#c(q^pv@57UNUFNr{R zj_-=RfsjD(uo5qwtDJIv=FF|SWk=JMK`%8zu+;#_B3_?*8;}Sf8$;m(VONbcz8{%u z!-=i{f-OKBy6nL0;_u&sr)kX1r;=a$d~95)LJcv^2dxJmG57P^gJra73o=&dNph~4 zuuHURF38|&8zt`f>PAkSo8hlA&am&+tK~UJoGSX2QEuh-q`iTAJ%-{)D0>mFv-hTd zj=w7G{wt&e*(BT2hF2u?wEU2lN@*42j1)^!1BYdeZis2U@?@fHe8|4Lm~&gH>Y^xn zMB315aqFlH)RLl)p9+PoUKzf%IHDV_*f=^YvWrDAb$6jcSXg7qUm?eO%AdYw#;U4( z?zx0_o{kX4%Sb{97}+9KzJwuZbh{7^&;SNSFqvDwnR78US*fbMlk)T6?}K{A$>|KB z<>MWP0I}4`9OM>61?JfGsM9EYoG+N^*6RrEfS8VEu~zJE+T)&p<%R%r4-b7wrD{ic zsmA%@J^?5%12!N)H>7ncMGCuK7#(I(+^Bi_$vvT?jOJOL+vvOI4l28kVkw{uQgwE` zUZzhgWH8L=z{#=kSQ4PNd9vUmdA~ba2dK;nL(x~Sj-s4GFb;)h&WxUK(bwOm_HlXW zxTmZh`2{e|=!9~X=U6rL-K~tbbl#_FH zhM>{8xw*##yJ#}ijwgLg6yB3XR7h!sn&kbmni$fV^829R5lQtBcn@@hJg2{JL^$hM z&hFQIG%%50{cdV1Psr{&nbI~fPop@`FMIs>$&B{e8`4U=TWg-3i+)*I8Hr9q<>{x6 zMlYWWFE4o$sFDE2)BU7<=vG`@w$)o)NUhU$zao`#cs@2(_}uYOYquk9>MmpsN`t~` z&?u2Q%HEr#_Ps^4`fcof&EKuZ`Y~+!;d~qw4zUChGdSFm2(23g0rM4bE(9VvbgTx@ zv~OHQGubj%XaHzHA?QBXCvqu=CM4+=P?xu(4+OsqdK^RQ-gJ?Z<^)5n@rJ8L@b zQ|@n+NY$WKJS@NY)+xuh8_&D<5mMql(m(cY(-7PyjKp~_daNmKcjQ^r4FDV@t#P*= zeoO7(Lf9|BEYfW(07l%dIccK$hm1T29}v_8f4a)x>M$dr>d+bO2>)#Rv`b<;Z{|(d z_K0pKTe0>xSXG$0bLY|(g@ygBqfViJuyV{_jn#I8zyb*pLL|*gY#EjxgM)0^TjhdP z8!Q0Yt|t1ma)!D>)`#Dok>jXnEEN97=0g=hK$>EBrkbXfc2%p? z_}vzTRaFh>Y^wmY(X2sb{?U!Rg!$T{TLFmwb$3g{u=kE`Kifx+9pK5onc zc5h>nCulMk_EzqNr~ut2mTB4er{G^AfRy~5SiQV)ZIQ4Bq@W5WsPxMd)vJfN<$a+p zFb~YnALnApVd@pF`g6+U8MIcahXS&7bc38nbm_Ihy7Hy>-jV4xSZ8Xn!XN@_PNxx+2y{Nznb6p^a z3}1|*U~Eu7Nq=)n;|Z*@<$aJX@XWtDtG7*-FGJPTdh+VYi zp?0^S=LM7IiZLBh9Xcx7qwpgs2iT7R5U79?7ozw@e*V6AmC6gjM(iZ(SBCT4uuZwj z=u-YzvY+hj8Baqm5}@cd?&)jMD{_7=_q43CS7BvEiPZ;#OQJ&-ucw4Ua3 zUD=6C8lXd(+2wQQe*XSZz%}Zg0?ukuh7}ZUb#%l-c0%Db^IsGIfrJwx(VN@2ffQT4 zr=b=T3aAuN6p`js9iUsPYH9oswG;f>-F;dnRgX|sG5?#f;jwjFGrhXDbxBq_YJIx|I?mhGYbzw`SRWxtHMA@&{O;~wW#7Uh z56g$a_*KD^kFX3FSQaYnX*em(bru^Yo>bA{AD?Jll&9g|TSR#_B#p#_5+J62G5+<3 zVdwF#Q;m{m#8s`1gbT0U;|}hOiJ$0;c2(1e@vThMx1>H=~S!Z07e4TPHy3z@Nuua z1r?s-$y6%jDgzn?YXEYF|MXjqxhjicXo8N)pfLv@oxK>i__xL%8&fhIqxk88BNw_ zfeA2JGr3{7r`k@QurH;Rk3fVp1d?GgKf3s!)1CfWkJ+V!ThRQde;lwnOMn9hGVjY* zuR;`7r=vaRe#RJ`7YaGL*aiLrxfr?}m0^F{!MO3))RYBC>cIo#s_w(XAFbZZJLJ2x zjSfG09q)f`%8$2J>v&(KjLnok59E9x7T^kq3p50|1tJ6X9Ido%lW&`gWqQ+=(RVqs zv!DIg6IZdpzfC-;pG??d8iKD(Qv3_+kFY0i!1--8{-o~ z`9pJKnDYKiXHLiC>~-JBChfGuAVz zdbRZHg3{tipHgh)2|muBrp3$h_Cb1Kll*=$G2b|KC(0f|*Ea3Dt!BiQ-l~q;sJY9? zE9v9MYX=ASLe%r0P3cEn`d%ACj|L$7$CDeHr|goc@)vS_iEd-aNwlQ=u}zyPPR4GO zw2E{l&wTMQ?=HKRs`#MCl%9KQB)2JT2{e?j38iXSf*HdA0>usOkW8w`#v|5js^WWk z(`?c*9zGl*ImfR|aKBKE&Z7Jh;ktC}ytp`#NTeD}FeHV-LDkz3&8#4PU^=4AzRJJP zMcI_<-9EJ!@Wsy-uIkg+vznor7UcEXRdwnSM2B9ClA|8NEtTPq+ zo@mj~#HSV~AK6db6r-%>r;0x?m=km4)v#zTJN&(BxZLM8Pxn-1z9 zT;GgxhPid3sM%9+t(4a9TLS*92f1eSY&>1Us>@B5o7x(pSx-zLP?sP{o6e`?pA^`${T!ya@WOYReyYEKN1N zjrco?X2J|@VyP&;!OgLJNP4_)7IcfMPPtJgE~sjXMwn8n(_xs$4KPOc6krokJT;#Cp#e2}go_UCcdm}&neT3D#I-+ig|}SorIfQUd(m6J?u=2>21jIxztB4 zqRm{PHMgN{q6llh7MW_^cFc>rEIK8^_x;sTQ`x<`racj-MJbigw$YlbPzr^1O&xh5 z{PBh(`HY#*E#ngAx*BQ}!w7V@VJK}nYN$eFzkd=&Ul>`S*J1B#B*G9;WI!&g*UBN9 z&Xg)^Y3A*Qc^b=s7u9?ud!Z{LC;udD^9|_CV(1$cPttJ#Xt1b=roV2w%=21F_A!Op zIU&0moUnJd>jrmkQ_!yKm&;l!2zsG;5IlIty_}G+h*f_xo)ek(Xo=;5o&EKdcP%7}S?<=G7Eb?%fuI zfX4e1AIV*e?6JNzQLY?t`e4WShY!dc=@TbTV+JYA9^%lAmr|u=5M|k5z3xrw@HoqJ ztAdbORMNXb3Xc#DY;+r!gm&TmGfmF%vc;a%cwSTQM0PItE=*>DC{Qj z!DT8n4JIQR!D0v#jMLxAx=LFfQ%dY&@0f7NH z9(0m=CS>S;c#5lUYiMYI^+*wRWn{5{enkD#=FIp>+Q-EVo_?uG=)7RN`n>}_7vksz zVRj7O`lJK&qu1b*2tIt@O&_*IFy*VnM_IaaR0;F6Pq*Yr!EB~F#b<4bn7Zb7UP=xV zj#sfg)$GLOyEvTa`FnsCtRaE@wP%ppDLAerr@z4Do22%+xOv?DxMtnp%Vy!2#~P5+ zcio&~a3~#B{-`ZJ_QPW{ncTABkxerEOXItq!A}IgX~61ez{`@7P=t_ zh8Mw0=|X~x?ny%Z`D;Gz|Eb{G<^Fn&&Xj&lVCd93SJSUo=MEj$7ufvHgyD3aS)rCi z(Ynx{(>*6k;Wr0CFwM_+wB=GRJ6f!cVBW>boQ7Zu=hUiPq31^u*^Dxqyuta$;;2j^ zr8j~R=0kHat)Sx`FQ<#&si0Q3U7LH;XJIBF|(9e5MLyPV252{`5g*?!{ zv7mp?ALwt6dA^QntQx#@*JdbZVzi^bb#qEQ9z z_=l>kD_t+-v3KVvi!922kuz~+tv8@wC%swMzb}>9bUNyzNHdbhe$ZwfXQ^W`L9s_DnjtXi_R>M;JC$%1KPpv z-MeWX>t(AFq{+mlwZy{2$g`)d>zY^H6Z>MDM7<&-3fTlUdBEob>=z`p42x5}A24Cv zJhc}N5L}*tuzMQ$n{APfqGI7wOcOLTG~n~nwdurEq+|1}$Sx*2n8m0b*OG3(t5RRi zm{mvTY*>CSaS_~_?3P~%L*7X%?ion9O zUlxW;5c85-)5~}^m3~py5sKG&d`&!Q0ICo`s}3_*rgjPH@OPjo zc0b8CAi)~`MT~@CqWjl9)WM;g7mLVEupXq4s?TNVQ0=r3i6zwQKqH>E{XAz)G(Kj? z>*no!r|sH5af{?ULhNYq4e1x`)Kfy>B+!B3LZZ%$p?8zyF5qYbE)N50jHy9CJLJe^ zs+ONZT?j7P->cx=H_gx4$ULRC6rmDcuG*!N^$o-vb7EM5&~rf>$BBctL(|9daycAg zeaa=&Pm3ohVG>O}A`#(l!XI{ch==SpX4xQ#aU_&2c(pOaKyx`h-y77R=>XnL9ZvN3 zSI6)+sz+_?jqvCR>h5^|LD%t8rfUbuWH_x}^3(orVoX%)GVDd5W*7Vh*9Xz4wHr4} zRN9;E;o*6PQ4=qrVhY*4YbHle!KFzlQHlYRe>9k|hUs=-oM+}Yzhh~oDw}_RJ9H~+ z8|5;r{*9-fTiw~v2kdB@bcewvv++l1+|!tFFOP&kCU2`7hj$%=in?jW#UtDvzCuC; zPh04p2Tk1_P0UT6uv5T#mHom1O+3^FzvRAoiq%Ugh}a;^RdIJ?JHit?I%9>^4ciX- zZAnYK@T^U1+jEkUiRiYs;=GAX0u0NB3AbQEQL$8ccmKpbG<-Lwf^vTi{XK+o6BBey z1hG}vF#G_4ER8LErf5S-FYO#~4>-}xuHqi%OS0ak#WC5BC&7w42$J z?Zm2Ky95Tm0AI{jhRAYf6j32)x)ZIUo}D1uce{!STT#QC`H*`sJ-cQRRE z=#HWa!#Adk@gW;buO5UpnYK})q~0$8byYpRWnRB9zIWF=b&j*d*Ckn&hw}}t|QP@>#g#P1jE4p;howZmR&8t?+TbnV;3qk;tM zO0vs+;YR9dqs-0bS=$eIwV>8rlhBT5{IRRwPt@aTZ8ISv*cpao91Kg-WZOHCkjwzp-aZ1%)N|iv68rk9@W;>?&C38Jqm#ESp zQVt^k>anMIopkbk;wSkqz`uUuPuBtA1G9d%yHJ_ZnkR@8UZ-qqM^376jG%# zXzCUPo|oO*$9Xk^&p;l+SR8H~5X;(;k&#bUl=8lm7k5!PaRd*!N(Err0TtQ@9gm^9 zpM#A8i8cHa^C?&(yz}*2E6NYEeZKLqQ?8UhP)J|~kQLZTNK>0Llej!GMJzT-K3{l` z>apV!Xx!WVa;d~;EiX5>won*N4Tyr^uTTt!KYJ3w_ZzlOurL*&L*wSo9(WF&>enX| zzHOZ1C-(}>^)0M?PY-rx`}JXTHe2DFn`u4{QlY|7#|fmZ4B1eZAPl61ueJz=qf}kW9I|UuVY0EyeA(LraXc4=EVtesRUKNR-Aa5^*=8F z%k)sR0`<@kY)hCiP_;s|#iO@o*~_N|)y&0^2<`*r(X`9_r*YsAaP{VLVZ zH_d7)hYK&wz6Vk#gxC0bj!he0!Wn_m3pb>jePb|9Q#z%cuzK@)NzRfJoHUS7H4l%( z5Mg$C0y@}Fs-C3;neBJ#CHk;cBoMPPJBN%H+f?ClqKBxb?d&C$@DJtR1@R0K-oq{hLG4~fS%i5(#Cx;IM(CiPy|mlJKUIMQ)WUxlmM?ugdEHbTpF zi-v{|XGLa~V4(nAhZ|%*J#k!U#Xn%IOTBQkQks1~U)iC*vclFTmix{(pkH9C=FJT! z$pTy2X0(L2kN`y14KSkVjyLuCoDy`snYiE7YT(xgI@h&HfzIPM5{XKM=P)?Ms^e@< zESxpISym(6n5dy9>KX~dwaRu{-zyTrBt`=8rp+Bij8oMD^gMy&ZhCRAqc{&_caD!0 z4JTDS)ZJEJBDZ%&YRcuDdNMQR}40kxsCWTs`W1lwQEE z$7;umxb*TcpEYa|b`Bza&Wr?Zn7OdOM9EM*=_Z`Pu-?M4&d(No z@Jj>?$qf4Uf67qvW|B%#r=IlsJKKZTN_0MUeIFA){W@BN3oIQtd=Wptugla~AApDIVcRVyl4!jdgQgUo~v-MN1VYQ?~PZ1Psz-yyoVO9%nt(b?T(`%L<3@ zY71?cz@}-Rg#QBr9jO%Rh#jMfd!>OoQSmYeTS zTT8L3H(v1Xp?KKYZH5Qmx+M+9w)9`87qVWAMAlmfy}Gc(=5=}9vL)izN_^LjP04D4 zjw`DvOwF_{!^+Rv4tyH>r&(j!V1^Sw+lQ-XNVnmG`pgR>jBf4Ah^cMPT#fvkaE+cg#PkIZE!O<~pF2hm#zAD~f9GR_$d7S`Cs5=uSb^JY*Cg5p|3 zpPyujQ~Fq4^}V*phqS?lal&5q(fjSy9kRbYTUocJS$_3m(W@fKBD+g=E%%j=#PXix z8~wP66x2s?TEFhRSm1`H3H`L>Bl|W5os@zv$nlHoAGR+sx%oY|qJ7z8x*g2uw8B7RR-Gx2` z5=`CTs9{{|6&DLOqBO(yru0;uLhy;A(*5bZpA#12z)&E|dCrbRGO{I)m9eO|<1AE1 z`h0CIt-RmTO=R)_5u=?RU856REW)CqIg##J_GLf)uPpoVkxqx!1!4HN5z;zV87V;> z0znf=XT15~n2vQ_Cup$ZvI+g_*p#w)r)|-jiTP~G{^L>4`81+9yv$T|BYf4qY!I(I z3ilFs^iXTcF`Sp-NjkJitx8jTe`MoI1bRQ(+lG40y>5ID`$?g_*Wn-%`e;){_RW+^ zz)8j?EA673>i4JjN2*!)v+#IVN^JoZKqe<&+RYL=IPVQeLBaZ5YS8yeUygdD894@o zfJbrsYy6kBnqdc%*3kf1E!V|23f^3lC|w(E!FYBl?B7Mf&$vP z;JU%Tl3lMye=SZa>A8&vOEv|@7GFtD&&n>vBn&lQ_sK<}ct2H)8N`O21Du3*vI0jqsDrZ^;oJx(M*Jmd?a~5uOkx8`` z>HV|(dO|+Ba4WiteMx=SODLwV<{Zmk$CY{_N3r7gGy^nf)Q^SuTRZmETFv&*duasq zOBKPeiVeU ze|uL#rPY6QRdC~_{E6(=qZ#etdN~_YTGR6wJc{;;bNOG7w9Si~AX7d{I9P?O3OUQF zcJrUt!}DNH9hW`I{g#VhecW`H+@m;)BBKo3e(NG5DsvS{jA^p^{BGpaC$@yd#B)`8 zj{*opdO-8PF4x^3(5O-m_q^<$ydjgirLgPZC{UYvY78BdNhkHFq~t6N`o7a&--S5C zaNSX;CDr@>uy9K2NTcz$7Ujo{z7J93c5vOF{b2om^bS%9O)!98$l0za`P;n&-t6V~ z5P-iWHBSpOux07kX0x}FPX-Izcja?1Y@IE8N8CH2c&CCMW`lGQ#j>Us5gIXjT)@hY@`CM)JY zlEu*q$`c%~k689?bZPl9s$_I45uYBy$sB+4bzxyJ#?!b>w=Mjn(wuBukr1TAfqCx^ z7oqc>cqZtUK)$h3wz0K)W$Du|lkPb%2haEV^=?)_+MN*gvtZCLGVEti(y?e@IMu^t z!>OmQU+~XA&!rwew@$EEQ=xk?7syjmR(1zF0)~KS(e1_bUgvD;@~YSV&4Iv5>`Gl- zT}*TQv`RI_JWpp1#~9Gp6d7S=D)d02)iNnA_nKfzPmz&&5ie}~e=8*;#8BN-AaRX0ltIyK3SS9G$uE zohd0@nRHvbvS{jZrNX}O!J%^Qy5m|aC*ebY8$TNIjcT9nlbWH!bpL4EW2-wZ# zodyO5kPP0vdGn_6IO*wZB>&9{KMFfKbigtWg}1b{jQi2}_8BM5Ek~8q#gU(TEu?UH zjQk><|6#W~l8&lZpxO$vLq!Zb=GbSv*z?+0WQT9cm7$M4jP1UDQfq6|onzT+kCd_p ztTnpdy}O;AU7d#x$msp~l;g*V;HNuwmXFH`M^m1Tp6}Ci&V4X9`@>RutQ}+91KV{v zMo>a~t{=mZ5ZnGM@*FtH>{)5r;ol<0q-!m&x!a3PjrPVfm%CmrU&NTGzY!6`XWsn% z*qf*g7t^g-TvPfTCCuLEH#u~lwo6XWemdOeCe4Msil39rdg|5VSCVj)-j)CBf!uJ% zSx^41Mqj^F`u5@%4{?HYptiqdDBjsw-7|ATVkqCNk%xx|l>-}#(5tuq1~B4{vQ%0p z9tF6W=+9mp_TfL-e7z)^{~u!|(`}{s$MP9Yq<(0$JW}$x;_jlImE56T=J$KH+`PPV zd}q@3yARzR)RpE8-V+&bh5g)hpScZbjANtXU-rdp`ZPAjv5VB{aQI+^Uf0)O^WNFo z8sCED=$vN*AgA^Anoy6+K?2#b^x%C2QG=FvRX;!~L5D84@B(UW;E<>%S7j-+$=nJ&`3@ zv9ip710$oSxXkULrj*UFrDGk5>kP4R+r6d{@My5S=6g`t$Iczu#{}n0^$u{*R1V+D?nshI} zQQiwk-KWsv`{>C2XLr|n_2mS!lvOg?G0VMux#oI_=6v4~rqde>M)xY704Oti?PX*iEd*1nti(O3j-;Z5@ZvaZf zWsrmy-=^(;yFJKvLpX?sc>8|7mDFRG6dOk4Ti_l;BDia$B65Zxc#MC;Csx5>{dv?n zDtANYFj@J2V9&YaZENqN31L5veD9TFZIk093dOe0L+!?JXUDbs$M=o6s}|AbcHnVJ%T`YOvE?d>nX>#+@j+z*1)3n@-!sKfLDA|u0YkIVPw z-r@KB?pz9&1#9Hlb3$_>7CWg&6EU5nf`PphKK<&mnhZXUpw&?$2@6DsB-rMkDm1qz} zr9l*hObHPg8c;%m43VJ$Aw%YQsGdqGiX^j~QnqkXXd5fZOeO7Ps<5S;`jmYz?%MDRCL2b zMXgEiole&mNPT@A(KHh|I5O~jF`Ktm_^a-2n;krdrgsTOMAT-=H>|p|OwG55K9Ou6 zZk1{(*Pun`816febJ=iVY>!P%t@+pKjL{#b>F08voarj@YNznJbmsJn8EiGiN=Zb& z!;7Hv2MZW^$XteY^OF(%T;lT^(V9sIAEvgicdJYA*e0+4y~#%`y5>NnF%pmI>5_G2 z5Ul}(jqa*~nHt+?BCnKv4=b%(hQJ&B717bFb{vk?5z+i@IOwRY>A@q~_Qp&q`Rrd! z3{O-E2)`4pCEz{mX^2d8&RQeealp!Rfm%?M%-SDrWfN;CX0?4w_*btsbeR}Rxk;6N z8+dECZfVEWhMs+>m^~}44II7DD>o);^dBABdI28+?yC~wTVtaUITl+Egejiuyr69Q z8%=ItljI(iS$ZaRDNr<0<$l)u_uqe~*f!SYEUO>et`;HBQ^B7yyawhS-B36X02c6t z!-Q6!k4y)}KKHZjhIh3d3-I-M`TtD$EY;D`X`~zREn#n?t4bm+Zb6pS@vXLp=(iZU z;1xV;HtcaozayWV!BzH>#ZrbffBkLnO|tL%;0Sp!ryEht?g-y!26<+P=n)dF^73-Y z1&#)ur<@DNmRICX)&pdQC&sPEfg*jlfN57QX?ww2+v@cJjz2HYz*F=Bb)q1v`b>^l zCIU-DGNdVI&(ZWR3S4KpT1BFh47Q3rJG*(X`k=C(9bMVQW7h}iy=IeD{vE0rG4u%K zDg(3ssK@+?{;-MY*_dTg{FbEDbd-YcZ_;OA z9i@t6RZUt5@JoyQN4XW_mywEN-Oa)$+5#@8wuV*tDhY7o+z(Dq`Gc?qi3iet=IOZ# z!A$P-ooc?XHD5c8R0VmU^1vxg($R6l(*(e;hqkS#! ziZ&5uT9mK08AROTqawF^^DEVNMoWIE-|JWCt&+IS_DUU}uU^jWO|O(=YAzp_N}HZ2 zxDalt`c@}!)TPYaNo>7wlwza|APzzD@VaxTwRhm| zIVu#*7`tPo#RhnqdG3w)9{95^q~VM*!ZH8>&=+bi_J0&q+SvOB1z2;L7zY#SM1HV? zfZ-qqB5fQr8Z`9wzx7|fLd|wo_2w_cQ2^Eb9t@xZ=}~gf-bal`o#r1)U#TkCkjU<{ z+0KUB5=U}J6vZI@v4qsKp|AD(j{hc5)v0{lDZqe@g&pz?wp1$r7%NR4sGH5(fn62Z+F3P#uu z6G=cV*6|i}W z2jH|ngX}B^HuN+}6&T#GT3`FDeDg+>U{V<4QOZh($K$tLaE5>Yq!UZ0$0gN+CPNXq zLOB62c5;K#hg^DE1GzJZL~6RxcQ5hu;FmTe@*~qPbjq`Q6QweX>qf1j`Wqe?i47{> z7GJ)IXeuGV1)bkA8|Xn9V?;Rd5IW6HpE@-UY3D4R>R9FoKyeWKKwL;PHso8Ora+i0 z{TB@R(3-l#gWBfYi6=q>8QlH(odQ;dRaLL2vgEFA5f?Lc|4`pU8St&t>L2THYJYbt zf^t%ZmFJNgd#X|rakO6)4?|~rA`dPJ_HK;I-7j$=b?@BMY#eNxNXguX) zpGz=AZT3OT5EMUBxH}^5(G&fu;r8>tKPoJXOM3+iGjZRy*t=*OO<%4kqNhfcc}&Ig zKy;D0i{ON>?*}uZNHPCnK^Uo_KYb@ixnDw=xnnXfyUb@xs$tYYw}!EaGhcFxsRffN ziwo)g``CWER!v{>uvrXss#)2ppOc>%BZk7MW#87Me~ih6gu@)1sYWC$XpFf&2DtoL zZ2`czS|f%$?DN4b1Wzc}Cf0Dn`4!Bf7pQ+A0-Z(E*uOHD<)ZM5gN;TUlsUgVkG!ic z$GAifYjcLoQ8{VyK`=!cFlqUaYFWADRp(I3d%KtT6TD(`8T9U-Fv{L3zzxbE_^=Dc zoUW{7gP8;OC1CvbVsJbGhOOZWcih{d7{=Vbo{IzJb5NmhACX5Qw;zKnVz^N76XlTDl!uX8&odQRwxcr zG)65BmSG5L>>0iXh(!S1e|)?ku0tfg=ff3LF;AX5r)#tOk-=7kez1(N&7M*%eO`D} z(pU(@8UUZG>(SiU!s~`VVxxGRk4Y=3KU>BIq7iAThZ8|^Guf#zEmATw?f;*$Z@S49 zEwW<9Y)5)L7kf;DybL4swk6*MzA*#@D0*6X=TQ3m$MlX(i}!M0#V#zEqjZqgU>rr5 zec+NnC*eRrQ6mb7ett_VXkY?Y3N&8b*>T`<0QA=Y+!G!b;a7#U=63{z(Xy=~o^y2i z4t=ZgWLy;rdI|6d2@Fd14pUO{^nQs&+cs>SW@^mvp$8Km7dL;rF%X!M&PxF*j*NvHk-sKS3hL3fd`L z2WlHRowWDJp(GLX@tW=V)B^Z^41GJZ>PskaY_lv{V;ZlU(>;bl?6e$exOf9Z`m6v; zqnmLpZZklIh$}!G7~(=47G;4qVkITpwmMF&#jVGXoxFM{)fjb}R#_9#KJ09`4; z&;y-??}RJ>a7}Fq{v`iTy{fkyUthkwFkohClw1r$iLt?Nm#UxZJ!+I#6I0U-tet3U ztcJ)24}RdB&H+{l=p_XI9FB^~bDVj)1}r-PZceLUwr)35r$f58oc1aPYimltF-CMM#?OeO5$D+XhHzvzMLAF2+Xb8^lZM(?b5uwwE5Wxal8sGq|JpU zY&qM6k{hlfA$`1Y5Um`bnE#!VGe4t=a#1<57fN!ZIS5Isox&3RMgRi@B1Msi;%T#B z5Is70O04;(GbnXbGg=>vzSnQi=dW{|K*g6V6rv;jge(CT zALMwfiRUuki9gbN@86>6-Trh2@}GO^rR5m)y&gn20(l4g6^is@{e=UAS2ZT*Wm}bM zgU8(1n{Zs~5D!Q(8C|^guebt|>LjYfy+`~|aQW=0=vh=bBvabZ=hacAs=TR3Gos=W zLIq?{y!`8KzU60z^m^5fmcO?%Or&jo+DkRwY$mn3Ny6-+<8UjvN^t3)i%SkrKp zc&YmOaEEQ;=Q!*=#jp7_sy^ymb^OC>vrj_Eim~s#9%i|^LBB>H^FIFRp(1f#J5t!& z%L}b$gBmkT4PrByoe`iz4t4HlLxY>)wHI`5W&SA{_f=o8TC>`I0;Kpirx3Vfsj-;} z`VwIPNg{XsgxUVMv(FE?TWty6HhAJ^}!LDQ!HtsQ>{NEt}6Ane11uLZk9v z6u{KyKCc}aAZ_1=lsH;kcECXdpH|`F~h)6QPa`}1vLzrTqv%wZI zjk5u^@$6ljS>H$-jAgL9I0W=Z;088N zyO2iUya63t@&(PPV09t^gD(T4LrO-5^vXc2(e39?a_XDujFd$yj=M{xqyOKYRk#ifHd5rO8MSjwwG)PBM;xdcatUME7QK zu?^nVhI`N~i)0B_2_-$4%!@#7VLD!zXXy^S>T;29;8isFc2hn%AJkgRWLOt34tq`iV6!%czX zK-;#o1b|au`9|^S496!z0s-?SW=DjJ=!OEOg3SY`hb_f9 zPpFap>X?{3=e)`iBm_)@lUtLD}WgUk{{5* zjEsz*OxzTtR7e?QVssOOt%D9ItP6bia%QYnadD-SqvO|%?2_@+z0RL#Z%{`@ehhDp z7?2X-uPTU@K>~sT9sab7yW&02p3XYaSpPNBJ+Ah7CXFE>PV4hpfqqNwt41dn#u*P4 zjkkoNaq8ofyorrfI=nZ~T8#*WZbPMdet5LbqnHpDBzGk z?%!_(nHn@8eBmkaWpe21>QXi^EQ%m+fEOkxB;FjxY7PZ;X)iijQE9P32=9p3gPsbK zbBa?S@()}mpv$e!qphMf_as-I-BHGrsd{2Tt*C=elZsyd`>rY3hF9gLz{e;{{9IgI z9xN=tULThNm+_c8zl99{9N>VU`t;L#6vq?5k&%3V|1OMz+{Ye%-*y{IPw02jxZUNd zjwx%jI4UA-1X2TT4na1Kh(F#j&7ftYCW=yvVN1PVu{!UQ1!-39RpTUqMhIfm?N@9G zxRKJen*e_02X*VP=b+a?{PwL->yFroMZ34Sp)nZ_2q_w_rO~FI5kF9vn|vQZ-COaC zTPy0EMqAHvSJyE{aPG|rl2dn!{dLf2A*o0b$;i%!Rs*oZCm)#0pc&PQF^au@+3SIm zdjGwh`NUB5vhic4o7J}9Y!GdM{X95IMmFGT?=g>Uf(0jWI-*fqB^Zav(wWg20&wNK zbWoaCpc_jj2731f3y0Zcys%!symrB4ef&C9^Lt|IcF!@|$T_F38daH$rt1I_b95g? z>xn*{RdW3O%W7AFAk#Kzwu*fr&HlKBMfsz{$pmzUqVMg2YZaZOagHc0Yyh4IlZ8Dd zV-YSIN&s|xM9Br^slCYeFU7YHb$RbkZ=wum@Jgn+HpzN&(#xvLC5nBK zjyb}H`Gd;Jr+t0bN=ixse6^~*T`2A|a=hw{66f2Bj5TSC->I|Xl0xDX8$^hiRJvE; z+}xMa$G_OKM*U)~T;l7l!0)|2{S?95LImnqIr8JmPgAryEH?0tyuZ=foAm=4Ujapg*7Z3EI4$@#K7)oByZI7Q+U(aS69g`7xO%(hajLKoBA3 zA^KqV7wHWq#>Te}YzFF+m>g5O)( zEMeQ@o`Tt`Tjk%y83R4d9kHQ6BNlgVX=&?s{^)Y(=sF!@Nmd$IUY5p z)05%wn(}PFXpL%B&>wVq#U=YDa`l;-WZ|sluATR_`j1*pGh|PHc@;X8?Z`yiyo7Km z(o(h;e{0olZgTMadGL~{qhZzF)}j@PH5)nFHl*L%A;gd02m}-74Jfi9^B%g~O_+S$ zW?N`Hv8*koe~^TOFhrr$IOlixBsGp*nL^*r29(kY|y3603cr+bs9bpp*V-ifL&rS~*D=a+R3>2bB| zyQ`P<`|eNjPmj!ir)_B|Lt23whTstFNocfE1{E6$dPgUIvV5<*+c_%6J48!u_F21# zU2yPrppNANhz1}dK^9zw2BWo)hZ_QZb^M;(Sdm(1dkNfIfQTUtO^*R+Su_re3%<5} z2F!wJ-&(6D;XLgL^(|gqJ)xJR#H>|gicYAanuqnFDl z6&1nSn682fhI9_(lcC=N4XcV&_GYfKBks9?g3)<|4T!9IP!MdN-{KIst6|l5^o#oX zW$+27DtDgWNyGu5>ZU)wIY#jb|18A!_zEQheW%I`)-7m7Iz>S zh*V^j3dPU%QGik?+T#?v)^`I`ah8rvCsH1^lb)VN zbq{X;d7NW}-5K(~-PF`{jkc2YrDa;? zn}-VF!#_Qa(*L%}g2F{A_-~9hxlDf^EB>tdK>;O^MKD^($m(7$P&ZMnJ2UdI&K9-l zx7Za#xhMiaQH^Bmbg7A{X=-r)K-^>|Qo${E?`fPZ=_n2Jca!%OLc+ac-!C8x2+4CR zVty7rT74&q!5I5hnKAu#h`q!j^htgw1tlfZ3ENOUF}hChN!-cCG6= zzw@KsTBhxoe)iGMwXKcpk#!wntK2|!#0OF4%O1r>tjxl$uCr^e%qx=xFO4+%Wi+t` zWZ5O}Z*=a8ZAdmzyBcNQQ*?5g_2J@hnua^5vjq1j(SuZv@a-c`O|{wah7kZt#DWit zY~V&j<6xzhUGcctDe7I?)P|^VY3ipoPm@JDT~x-&jwd=X|3Rt_eRT-~=cvsdLS55Z z1-FSl9k6-?Ha|t|_f5A}x}GiJ=hV*b^IrQzL8yoR6Mj$4U;|dwNVuV>ber>c(S}~n z$T4UCsifb}(pWDxpR1Ztg4Rc19~Lh?pryH)R0NTMpg#{Xk$n<%8lXK8QB4f*g6OG3 zF1T4pIy@mb=hehj5L7w?eu3t1k3lYNO@%YO;?!DIX+3)HQ}gcaYM%;a(+zZUV(Kz& ztT%Zam8~&fD8F^NztIT64)R-!BKCi2zsc!a8-1S++MCVEY8rUgJ52N?Hj?|jP||AY z?%W$iy56yw8ksGQyoU-?Vov_rSthF;j>%4jBUNLsO%yHI9%si~-^J>%eWskV-Fmvc z=TCc}UTu@x%kDi2%Hg6mzBR1fkp>Vw3THTTaC0mG#*1Wj&6WFt5$x;_znDa>5Y6pe z2mFf0K(S`*cP}4k6|hy+)k{z>GFtxEd%GK|5S_aMFbs%1f;+UAi@7FIe@*m%FWwq& z<96t&S*Amjz*xdb!e6317sLNY&o}JBn~s@jZxk#@K&kHodd9#l=0QM?7{r3Sk7`~* zV|nuc&6rh$Dq+#rgXqXU|HA&3ivg=?mvuClInHd}Qxk$^xxjtUm-QX;WXAILHtMTo z*D1VsV#C@8o+|0}F*AFOx&#q{SXu-BqQOd;)T*U{U_LY;w~>=fKtlbtO=vabZhrkD zY9{4j{1867{8MFUO*{W+Z`y^T!b1V_VxLSijX_06p3il)v`%72hfwuE^xf`GjiqA& zR!sw==)S**Oa!Sg6+1QXgLt+8p&rR(!*3J)V&4doKZx;5G zBFzB>oDnuduFV(Y1tV(0^L3I7>xZ^Rdm0=CMWl%%#xSZ%o1q`R#roMo4~URNu@P$s$&I zRjB-o>f+a?Qv3~EQ7Pu|ifDTUM*Y6m!%(-^IAh0#yDx)SVjlTJh-mQ2ALyZ|FG~#Rw-`pao;fQt;yl0<4;&v6sUm1 zBf&7*GkvGGARNhZZZo73B{_eXJqR>MEfPFk)RB9+Ww-X87Q8ngkNyrsWXN`bZxSge zAj_al#bhcCG&&7TJeu0zR*N=HQgp4c!GYUxPCt?($?ailV0^i|U6#yMGZxyHWdld}sl5iGYECQCGKH$Z8)F6F>&Jc?kGP654G~H3wkFe%^OPZN>XDuXcF} z3mf>jPfXrZ>c|%EvtxU_@i@K%y%hj_R(SybMKt9whI;>u_O>8))hgLvQi?vQ>YAWgkQb#{dQRM~xRy z&jHa4)3}gHUr0jFAbNfPn4uOOQ6bNp5_K{V{~8fBNmPI%0sjHCfUX{b`vYv?izr6| zhyM&@zNXdZS2Mc=KUfsa)b_YBecysi0@L{w1Dl8z;H;XKM4^RQVmPNNa9Mz|?InR> zXg5MP=F4ukZ}3?tmm#Zj{8ha}@1h%$2BZjeQT}zJx7{g*H+d~$KmlOxLw%cHXFk&} z^KM1%^XLBoI%p)FkMjo2AfU~U8htZ={(6LrW+%hJP$vnd0x%7!^ZG$yX9X9B=~nOk z{o~4aCj8)8dOc1e&Xh6Ni7&}3*-w$i;B*p46ghZHD0^*=zh;N`*|Ec$$|R; zniJCOf4=zTp@NHu6dgh(nKOt^m;3=OXJwdH6E|w46yJc>(v7JVM2T5gIPSV(^ntH> ziq$+kJO~1eM6FR8L0uqhVE0D0{1!B(&Hq}{%r`vK01IR51eOb|4Nwu#JrMt*85}qR zeBjq1AACRsgz}WoFJ{lpss)D+D=EHm&}wGH%4cR;4X_T9(G-RX z3D&cfr$2WIfM`Wv5Wrnx9ZQB3kq+Q3iDiw{1`Z3b4DJE`5r0Yg_;KP;pN8RqUw}Mj zY!@9aLt<9D)GG`wx{-tubJbW~R`SZu6*Kh}X1+Q+6%M)T zUq-_kdj-<(W&n0JgO;1xb?VD!9#%a>+zJIx9C>UU8wx_kMs8e{bz=6)ZOF2U{lNf6 zZjH?X61rFsWIA$;+%0Mza4^Tg7>hs;`iY$^@IOjuk7Y_|& z5B29&UC){wQ5C~SL}YJPl=VSPg>A%Y3#X<7>A){XK|SLj83c6t>0`N#ZO%$CszYQd z8)6z{?(jfFc@Rz$5GkbM#$#biu`j4Kp}_^`8x~aLg23Xhkgk2z=Y{4^V1PGkH_PVd zJ~;ZJUyGuT)%1Xkn@0?b)d1%`GA86;jK)T zRJYuyFiS)5v){+Yjwf1yoI?`T6WGQkDJtqYK$Gx%tH$KHI>Qv740qr)5yz6GbTJ!U zVl#)DcGFzh9+U!#fpHcO^5o(f;M};zXaX|wS4I~J&KC?cTXF_E*GUDHxK%K=$l01^x5rKe&AP@@)y_I3apHKPYivO~UH~6hJ5dk|L#ApXLc}BXV-|PeQtZ49P-@ zjV?}ze!PQ~pn$(@Et&w+ z+t{m#F}4Vc5O6x_TmOYIY8hG&o`1Zf|FEKj>Jfu02p1!L+4|_Y{1&uZaN{Uz|8+md z{NcldBClf0!)h^djptM)jQ~3t+3ys%($c4mA5{TF1Ar3ca$@a&HuBOt4~1{Fk44%1 z+mH_t*j#e?s~Z&}#`yCi?VzqPD~L@*_Ib7t?9Wh`v2|ek9?xpaESX?P{zOm7@oD>tJz(SC9r<+Lv1MT*(R=2pkz=f zLBq+I-BGHGo69zubMN0!sD&ENEj>}3EI=wA7LW=;XPQZeWQQ1&(j6$e*Z_A1xfR6S zrqH5Kano;uNTf?q1;C*OMn;^ zYhaBKG*l-!=9=LELsPtN5qi8R%c_kkQmJ*ZtaCQUMz`j`0Qsn5!+<&(Nwd1lMZ@gU zMHeG}seAA|6jU@T>wCHn!`R0N-m_6h6|g|vL&AgCN)sE9OH~vAk|XO!W03F$*qX~l;YM0+UL)m~Xt}6T*>qXut#wfs>=NpEwnB|NGGt{$1uiJ*AR|}qh&ajGm^U`_4_SWiKX4=s zd@Jypow-nT&RT@3a~SIlQ!k5?w=()%{jnuKssnGC_gF&&p%c!>c%x$bjeofS#>OZn z8ZuZgYFUj41VNc-^VfNOGuFJb6e-23omS@N3CFud=e@u{8zcur{_xM(@H3Iyl?S5R z6d3Ak5~k2$)3*LW@LG-zL#+J_%qZh(*@i$QVQBk{!g(@U+T zrlucJ*aT_w_L&;S2sHM<%Y|bhsp1;0RIHYBKIy{VU8k_rhB-9xaW#$qHtnVDvv26F zbOGhJb_? z$GqE{FEO0Ihl>fRat4)JB{n1;5?gZlbtMb5Xn+O?4iGe~3XRvi=!Sp2(9DMEZBU+d z>)h)H8yU~D+$}bxUKJZK!Xbi|6^J09hU0f4rpt}pmbhTeox&3^ua?=EY>hdq=!hXp zW^b>vg{fxj);Djki+4v?pa|_$`avF!OCrJfto8#X=!J^@3Umint;wA}8bf203(bt} z_srfRA6%!=A|NHieHAAQK)XXG0zgBzM_CM!L=Cn8*E*q!lL9O1BnjCr>%udYpqfyO zq-8%&qmMw){LeYe{tE$$ieVPkuG{FipA_lGM_eD8~{;=}Bb^k!SkAU7*LSM=re z1N`BjW|J~)jn{SnxYhRsh{y}~0$*`#{?V&nyH23|v%?xu4kFRov ztfIEM`t*2ZS{AkZE{oY1d9yIXQbU1FCQdQ4R5jX^I)J2oY3Pc4*7AKqggz1Vkc`d9h<`_ZQ742DKqZ*XoDr5@V& zU9@x}wE=}8xH#hF+MJ`8<6Z(2>i_-p-Q>E4`jSeY<$B@0@wRz~Rn)U51VM|8pxDr) zS|1$Lx0HA1{irW_qBeWRCPK>$VqeCdR_NJ_DI8}-pSb6vnx$R>$sL?Nlvj7~ zggHA0B;lB7&z6uYkT9Xdh5!trRd0MVBM6x>n|91Sd&~CypST*j{Z3knu$fq072C4y za6$NCGm``B1Kp#?pO3mkY4aX>MhGAt?odg(H2yV_CPru zxJF;Td)BTPb;0gCb*b6(JF8cI@`b@5a0r;l6V^Fd=`zWzOKB3cvwK?uOBp3 zyj$|QbBMIyV9q-xLLopT>KY~cPfU&8vG~}$TYF<=iI%G1S(N1I*KNkuWL@`2uTd=9 zvdi8$^HFG0WJbF#s*~a>K^OW1v+%{z%smq7U4kx96+uXfb0H!>9QtDj8gCAtVo~nsV*R-batO{h zWZMDnfI^0)0v*v&v8D3M7)83mH&7bs1epP1?+hZSvNw6QN~^Y=KX=i_)C`MUWzk|L2(RF4BD+O0^YhsBO7qos7N|FvZ|AY#P}ew& zpgvH#VNV1N2dYX{t}6A8GRNU=mjH>{Q7HWy1)9_UQ1H*QRvV~1T zut|M8nQN8x~D>J}fmu_8$(jE%E{Vi{4 zZzFby&sGF-D3ehvHwCM=5!;lxZHygJzCl28PMz6mwmrLI;|et=u4=VSys{_h-0$o3 z3YGudxzu4|!TaDe_nN+`X~X1*9y*o1`|VjCv>C8-bkobUXzC?`ni27=V(EdiUds#C zN7cvU06>1pt1ysa>UJ@EVcT0HopLmMBKdnzqT|EG`+nca-}J?P)$N)2{y(x7e~K=;mCeiu{m*qq2e+T)EUfuovyjU9^8Z&i`aMznQP00^VZR{w z-MpI#^7BNGK2R9G(bqhb)t5PJTx?W0{bHu?Q&z^WWtLY*taTpnmM`MHw)wP(=gWyx zKilVt9~5!jd^;t{YL)HK{J)Kq7a_)!dtXMo=yAnyE$XC1EQ#QvIMaPyaP~D+Wju;uq%y-{@FZ9RM@}3st zm|90c@w{*UWofb)euE!v78ND`goaZd%wmCe2kw$84iHq9tCMn7Tl4oGXy<|=mBo1N zY4Moi*R&7cp31}J|Bu@#wh?9^FhjQWqo7`oOE^`MKH}GX9lLUG*q^tw&WJq*&b!6`pyQ z{l0Uk_Kw{~vYx4j(eLb=yTcaCTqC~ktY11R+34)|>!P=f5L-mWf(lp5oiFB|oqpbeAEOgg1#7-0 zVAcJKvHNo3N)}&|$g73^2n33RDPD+P?%M1zGV(1=6(^YVN-gToQzh&>X{M4EwD;EM4RT>Yvyoaj% zZ6`M>B1E(k#C#M7Od}06!yhehpFL2&PeEfJ@~p@n(8%pGIkp{vG>RtRnj`h=vQ@v( zJh#>!kPO>~4=_}sF^f{{Y9RS!05Y;fK=YO_f~h)d?!+&EE+a;3<~afMJ_TMp%5KOD zuABmL^Oz#BUIfFdSYoqRrOh1Vsk=`m$zWUz+FF4}K)N=@Y6uSB<=sz4urWp~M~=+y zKD6OFkj62R2l04ZZ zh9r!QExAEV{)c7WMD#0_+1+BDHC;Vt6F|u)gkk8PgR+6%D^QprpI67;1?g(E?$)Z! zdq$r-N`cIYsTcmEGT;mmIbAF#3Rh&JCvtDh1l8uCV*&WE0C`SjIZ`NkpcV2*0e4HD zjmx4H_dwx=m_zsZxg)h9UQd*CF+!Asw7>0-5Cl&je4{({S*b9g6GTf!dq zv=F~OU+d_a6c`y{;evT@ic!3NeTkE2kkq7bj7Uui^i{MH&8i1Dw$FZ}a6vfQ&#~Bp z&yuIO(By}D+1S)Ta0KKPu~miM1E?6b-y7q&ddo!U=U`+iNd)G_|ts$h4+Bq>8EQ}YR!8oMT)F=%o4Pf zqj|lrC4WwfjMRNV=|*`4Jqw!zxcAXU)~~pC=7@7R4)!Hy9Oh}Xn1g*tdKL}RPhx92 zhmN-g$ji=VG#>6d!%+#jfO*F_HQ%9KK-#{7C$|TUHzo@s`!{OJ5EPxg8Si<(07K6R z+dm@$!7E<^ab1j-dJ9%It|of-^pt0O%Trntu<~-c^|D+jMF1cNXpG_+8eMVEF%f0$ z3wH}hWrxlMSgFclimlCgH&G(NJ;Lh~VNCS>Pt^+;= zEw{DMn}^7Ff7ADgccM21F7DSVa^kd(SSUp=my#ciSc1-tm3>J1wai`gTk24zfr4gEA%Er5ZqGGbVktu_%?ZF@+^um0pq=o(9-GNVHFNzw^6Rc&;ZTbRyu+Wqk8&?Gxz7bWo-Brb6^;p zPo5Lzi>Km1f)1vj|Cq7_P-Blz=uFgMNKkwX@bl7h#BLgrRL5HSeN6EiN(_tb9)`2BuA;H5X#a-{v}pBZ~_nWq)j)%B_I5R<^( z0s}wWn6w`|1yTwCzu5vj=~JrcjAeY(C(i&?4?&m|1v0Bsb#8naLIe=>-{KsAD;%KS zVZ;+P`v^Zitkk-YDvqEA{7gXJSRzu$#uYr*51c#A{ zl-$HUgoTFm9noyZ_hTSpBEyl&k}6n!MUaL&tWuHW`v5~8`ObE7_BDgPN?HPlrV z6m)S#uyPDDEAq9u zI|nhftwbOK_9`e-fOdh&B4|R)fgW!7HBcYUTbQWZ%EeYjFs|>Ru7|^g1KC3xq3Skm zp8ahD*W&ljMz?^@hsas+9cIVe!U$;ez0C*$DzC{LNWI+L8{AO# zT6)F%t1>H>z;B#F`vPdGu)1)2a^f`Se2g;=VJT<4fZ@kzB5HQs02`8Jo7YxE60YgVjyA2GV`LNarJ={q&$s+re{lRK;T zu!(e)KYJ#!z#SopYeMW3DtS3&5PvMk=bew)O7`o$YCLzfK05AvhPxJ{^nE@cfGe98 z20!v7GZRf>me_&;8;zddr_1%Z6nHH-8{K~y#qQHodZktIo5e*nu?PzdwXDc!&o6P@ zkSrs{do2a;iGB~fCq`exp1^Noi4m#0drsRP>F4N(~4`cd`fR?}c+k zWgUkY4&dR#Eg-J0x#D_Rq$Hs?nK`LeThnh(@1@86P${&lU(sgx-6xs*xpKHj_EuPx zbN#FaBvs+Pj32BN^#(DQ4i6V9VmSneC^n#kOKMcOXehTkzj)C<;Ql$}#@vm*eyx1p z?DRB1pCm>mftXn)c(X+10r8NV_YbXz()u)hZOjyvs<~)mirarZ6;2MpM?HHVu6eT> zZHgXeXE|#|PqiqbNcO8tc>H4qWr>l|wg9P~O?3-gi!rSWP7)uOWSla_&7Gev3|#a; zv`C?poLB4KepvIh$2%w4HI?o+oLX=;!4bT2sy0FC&4k&Wgfl+QzV409ZfM)WYY_8` zL*Cz3coho;+6NnLrnT-L)a3oDI7>DUf&G6XvF>PJ**hz^PaM-M_MU9MyQp6&p;7Z> zChESf0@QmLVhU0fPK_6=9s0G*LAZ@~# z$M3>(VFW4~Mza{h+l6<<4A=(?XdSiA!vMMcAl>3FL-J^PS{(!e@Fh@lG2$8F9BNxh z4(*EftL|BLJ#$)5p;Wg$$C8lY1NdHaKfqPv_Mc~bk_OK}D4>L)Y5f{e4@T;st&eyJ zEV1n`*aEmmd_V|d!z@j_A-ac9YBvA#UZlVQaV+Y~99xkMAwK|r4F3S|03if|3C|6) z9kY;@Uk%0vF7gqhRiJaR0;Va!0;x1e!JS7xIjYWx|L>(?y4`6Rj4Hr|T5~0(h}ros z7hoPHYsHB|aR^rxzgt1SFpp*=?B^}B=Zz%dJ0Jh>8+~h96qP@H=}^>Oe!*p;B+k*z~|+EZl%i8c{NYE#Xxq>AS# zI7_XLiq%Behm;)cTUbTpu{m#S?jLzkEp+uqv{VgRnqOl}vG|}YL#Mo`9j}i}0!Iki zix4pau7cMG!3g={?xQO~7Pr038<3|7qCSM!23niA zv53(TvcsYaV7}kX^uP?v?hV^#i?OsFlXNAbUvg7Bo2%L)kMo^Y1mBGJbVrPDQ0l1u z9m>kWa@ZoQ2}#7skSFxm5k4PUlEC<#n|h+xsEML0l1r#|U~}MDfmvXtCz#(dla?=k zB`j~w-p0(LH)x%qI@3?xU|NqK=L~@bZa(w)y&cJ5j)+$>^bxYs7+NToB0$3lc#5nS z-vht`WJFLYF@I(4yChCp-;2|S)e9EC@i(o>AmuypBC6rgXt znua6{@*ncb*c4O}u~cJSQHP1tBSixJ$pO=0B|<$7HNL8XK-{oKq%j;}F?!-(p<_x= zBj^T6*d~W6148mAe6CO0pvKVjFN&|&U{UQ(M!-u0SrrZ+=Lk83U7XjctxsbXeyAUiE3tbzn5wn+XzD;fX+&63 z#8MHDL|AZKcc6jDQNC{rW-TSi1x8ema$02m0=^%@LO2%Z zcXF}0ojT`dnUKs0qzqo;Wy0~0m3zXycLdjS_H28t?EftE+|ACN2n0!vH9bx%{f<$V z$fZb@K|r`pybc=g(5rq|U3W7tNxdpMku;K#pX$jmYRQ&LY-(zEneC%PqB(up!jfG! zv<_ynNF4unGa_^arp$L{gGL2#4-9KPKhUm54)?%x{{YSxvKr7EyPlQ+!9-?G+!U0R zkFK1H`CrQ3FmX2YZJIEt|7)Nj13@fY5B|Js0|pmxQ)0ex;&Yyyv!c(8hyN4WT;Y3_ z0T+TBf#ZSwctsLGBKV6#g3%>qvRQsFh=>K?*-j)CL>~u`5963WCgkjbJn$9QjZ^&x#b#5)tUcr9nGxJCH&QVKVog92E_p32l-g3n9uC12v3L-s&3s{pLr+B+i}B@Dl6?GxhSfXEK{EMm8n>z1iL$rrH;os8%gfg+l8*Txzb%T*Pwm<`k|dI_X>gfE2W|I8FVk~RJ=Be8TEqhlc@NH)%A zkPKG9#=%L<4Y`f6iG1Y0!B15X2cS^_0Vseugh+S~Fxd3i{8U6-#yi-oXO4X>Wawj( zOq#t*v3cBB609YKlG-sqd<$qXNYSb0{RhMEGigWXqRu!`1Sw$fAl8GUL|ECu_#7Hk z>9KL`*mN=VML5)%RsDW1vI?Hs0X{mEM99*Bj}EvCVGNb}eUhqkbR~(ziY*c20tp0r z($Z(y$^nfH6}OpZRRzz;u1V>b)u>Nys4-~#od}Z*xm`O2iH2S?X1r&$&&kr zZX-7DE2ag*kh6uD%Zz?VNf0;jogTuBE|7ze&l8YK zQ<(T%bXIhAhGu2-ZhU4RH}FhtJK^1 zzR|{SIy7X`!#>*^c7v!eprHVe)o_nF+uy@Fe$FV!X-GJ=M()J;WjdS%sVHmA+40cMHUe!oml$YOGSk zm#9ztlJ`{;E0B3pbh0Sdy5KZSMC12wC(z?S8=tyU(WX1A#P?Wg`F zA2W7Zfz=($dInWCAQ%8(5Pn*U)xVz&|6yZ*&|*d5XVK3?JZyH*NJfU>cgcS#EFR@6 zwTM0okS*lkdb@`FT)MB6sq$)C5?fSDIe70alMmS>opic#G zr1B}S6gWm}xmc=CUvHV{#X2k=DHb8RhHMiXp_fD9BwOn8E;uG2qF#l|ENDOxCaWYOwcU0w0N`;Jl)vqxq|U79YkgAjZb} z;CNtt$k=P37~cwHf~FW&XPlh+kgTUISL4GN00O2sy0`#CA~!1WP|dVHKeAxmZ=0<{2 zqSa~3(3Cd}j^8rXavJLD+6ZHzz>l8?Z-n3vMIX==aleN?Er{0Cc7jmn`Phew(d@Ih=L_Uu7+i@(nw(#V6a zg1snYpqsbZ5zRFyCy8Ib2t9AynZ5UAP4-aX)yWhqGI(=z0>Sl5-~o`TtBs7GUj8r` z)&yYU_!M3m)l3Z-P=oZ3OJgrKzS(;{ivd!^y~`>44mJYVES&QDKCn!fJcJb z3eD}d_?7@Qoic7&Zvn0-a!FFqLUSeh9Vir&PkaW}-w$!h;^|OsHOstPTfi&SSh_UM zb9x819Y+&_PdWVOz|%}OjNDR}CbY{UE~{ezGHFOqVFfB3-&xDC_MUf8@!2vScHQ8~ zcRx(HKq4CA?SAu*kE$EDuWC~4O&LgPaIkm$R-TwDF@G79$msSJPJ#T(sWn$>xeQh= z%yVk6Zu3$}R0;}8&376XkPL5P&RhS~RF^$ox@PYjRxA-6X`0SI;-jF?hh7|1!Prpw zvNmmvNu=pF!tH88sny0*R-X^XJwZbQNvLT_{tp; zvm?n1akylD*}##=$e_i*+~HLp1^1dvL`6A5?!-fsnW7IC|K6UOe;7v=`41cyYyr#! zdSYyN!o!>&qdyg>3-WCnQzEv6IE{-#-sY{$mo1XxfpZE3a9v1*;7j!zZC4Lxx74Y= zuR#vqsNjm+0Uj8tM6sT62Or9CWx@Vw9Ysr(O$bo?jmZrt=oJ4J=)#({t zJWVZ)*|%F7PY*j`w!|2Ova$4rM#Zv~!|L`#ba>l>|G*;Q>dObx@x)R!alKkzZAw<= zJC6Fj04dxcy)yD4>8th+>S#n^aky?*hethffk z8SA{b7wtt(J@TqM^e@?%A`mA|j8p<(gfxY3>u!+GHFHX9efF8{H$_=Kan&IO%dCQ$|n3Jt`X`e=!ho3hnb>y|n-NAR3pVd;4Ggyy z-RQ(eLKoFm+;abaGu8W5*24UY(WNU@6B)=02DskcR#iBVJ6m3*@ryv~ja)JM@uMqa z^@Y8}TJog%S%yY|_PcBIoDe{via6xMMe`&g(D2t-IIPfu`TWw)uRjqilTD#WV9f<8 zF4)JRB|(151w~q4KyIYG10)G%1Hm21inFt8c<*GJ`S2A^0!@IdXYCRn(-dIdD7)9+ zK}zfAHXT}<>Rl`@N`C|p0F|z)lSfu|SJShPi~mN+V**)>@EWe_kcU4i#DbJ)p7Vw% z`e#*uT35IdFaw*35D#KQNHVfBCFh>-W-o&-30H{@5NDgEVMleM8sVOYgO6E(IyuRu30jZUbTo z9R{flaZuW!NGRxEO0##*rG~1TpO)8wqG@k$dVogUp+_+CXC;*M&URc+a>EVIG}d+I zS`q7p`2C^jJ7fzuC5RV*=pjdludBYfEUsXpeP7FY zp4WLr71WaSAe%b}y%o_lEI0jpH56rpSjr|QCD_(2*EiOq^~=B#LzwFo-JSNS-dVH8 z{kig$*vkd9EKkXxhc|A3&I*bed$w0sWy>SL5KZIkHNYlp_Hz!^q|eR!ar4ySuZb9h z8rUcr6@WCy4$I%mqSX5GfTV+bv@FtIHzb|*xbSq}Ae(feWAX)>(EZtGfgY z&6l;QO8fMvpQ*Wmb*D>9GLCZSi8|qO<^XC_@CR(eZHC&hpBsmG%Wty>A9zLS*;##_ z0RC!ex&Lai@$riAp6(_Z9}}~!<3TCuVY}}yF8ip`E}vfDbE*h_SjJaKf%JFpd=}+b zeIqWiA3+GDl*J|_avB&&V})Hq*Q-9A7@mm7#T@V}Y?f%ssMUoZ-@>uhb_JgvMleBX z-C(?ZenL(g8AGB(O}pJaY~hqhS1LY0Zek=*4&?^OHe`*sDM)967GJ$N>g}O_0gd<{ z$W+wU;J=-X14kLzhoGE3HXrywRku~_UKMv03MVNej=H?Zp}z(DaXxsGEeoB z^42aKV71X>R?!0Yj<)U(o#!7~Gcz<~o>X|YOz7(JWdM_0&^e=bZ5+KNe)S8|DNeC8 zWK?wM@LB%wK5UHEYeUA09IOHAD`+Iv@gWVYqpa`VO*Zxw?#yFKk~ zOFQ-kW*LqOA+WMb0Y*fCob6?=m za5YEuPeXNA%k|_kktXO@_?5tmrT5#CtuqxLpO{k6$NL`<`RavDX*zm(>Fh3rm&Kv; z&YHxSgxTw%&lx-99flgs|FR;!`tW-7UyqG4Af|?7W0^dddYk*(SrxJTmj-Ou;l@9H z_~ph8d8u}dqV?X%PM(9mPDuVf&uonJZOiT49CsQG0?^6{J<~KT_r7|li~>X;HoCe$ z+noi#5r**a!kWWi>lu}Rp^BFKt0_GE>)!P#@+OgAxNcNM>>mlnd^V7O035{q`Zz^8 zr&KqxmR>V$w*|=+SI5v(r}7O4VWrMspa}icZ0Jw4e&gR0T_WtPXb$1T9&CU6xwWsB zZ^drzEPW~)oJ;GorR%z+M}_wr+~b}Ww2#U+(<vgou8q6CUoIg z;WQ+KArrb#eys8<|3HmY~?tRfuU4vBhRa9?c}&DfBeD;TixayQ9SPQw6JFX3yFKVX^O5k-5LMX=Zxv z>~{Bqz156T{PNdl>wK*6+4RPuN4MCxvJVIp;~Zl<56|`ShU?C60v2JYW98k zWAh4~Oiko?`G92-97J|?oc2%Je)7d`3-UC%Ui35hHJPoxOp-AxUY0OwTd+;msT5)s z!3!e03t=Zi(&0 z>JAR+ceG&IF6Tg_$+LXZmF`@+Vs}2fWN( z0wce^^4%fzbIo(N+fsUW*;NyGh&mV^19uDOb0Fd+!l~z``*9ny=;M_MYyW&})zXUc z$a-MhUfls{nrx%aS))^GkQcLq5}41HekwI)2jz0tVke%BASwi!vT_3T=EuhT%2V^M zugXiPv1s2;7{a`p0D_A;EPxOQAU}wfp~N7blK>Tp_$X!BV5u3*TF)7n|Q7 zky!vrxW2lVs`>9}u>{MXu0uD?F*POsLoCH5ta-odf|1JPras{=RpE!Ie}&mMg0)Wb zkt1Mn5C-@z$$$ISKB%!c{QNWFY2@Mpvx#C=OxR~v+D5Bilh1fKbNvwMDaOLX9E1^i zgC`<7Wy}%PYxd^*q(8*tB`GN>Gc~cfLEJgM9o?SRl~pV$8s0y2w;P|(+TT=WOf#Ci@>oUXj$Y%=_%?U==r0QQKc8C_ zs23XH7G;bX0?dk_GeNWH*zT+`B*1JO#s{Wd8Is#8x-@-?Q$S7GF}2*`zJ?T@?AWA1 zMoD~hPr{_Cdl7cpBRu!lXTP=xnlz)mswE|*{-@t(JZZ8O51*F}(vzwF9d;3eqUCB$ z^P?J_cd?t1&<_qRnw+x{xaVwebu}AxKjcZ@S+i#g+hA%tqx_)Chx4SK89Q~jjDBo- z^w|^TG46;@n{=Sk!E~&r$5qS}jS&#|p&ulY-7MHTMJ$`C=vmRcq{`QI;`9*`bJXNc z)t^w$7Vi%f9qAeXwYBYUwedXiulZpeJ@TiL95$SN>pwi#bWqA{hvezfKYmFV;?XVP zmBy?Ke(x(?E@=J>iuQ!Or2Yc?DJ%?q--pve2BPDw(mJNfeg(29^j-Yzj1dhUbDm8# z)^-0nDEFe?k9P}$W>X}MrM%+x-F$ww&swSO2jbi<-ac>Iw!>^_a{HHcZK#VMabA%3 z2^9Q$`OzQ4)LUeDAaAR#T`>s0$$KEu&`SOD-5RHyix{ znM2_u4-)P90rp{BoRBj4Uo8Ohkt~Ys)N*U}8@Ts-H?@M@wADOP6oOUA-q;JTZv3*w zSttwE-M7bVGV$bVNOASKd-G~PMgS*SYsERw_Z_B`yCZ7)$TQdpp-q5$WuL0;$D;$I z3MS_EBWZ9Hz-+sSK1e^zdVfT$NPou@r_Dh=&MVU$luw2{tDLAS~~wfy_`qKo5~>5SYZn~_ZZtXS4&(Su1|d6#5&B}o@~*{DyPu;j1h z{lo_pSz8XArla59v;sM4{ZHL=8jCSa5j?CQzX<|!3Oa?RwCWH6ApwoQFw}6psC!*J`{Nr})#*|-+i*@N89OYwe z%(TN~D|^k$y$CqVs|`IcX3v*nhYYBgI`v=78x-Hm*&gRo?;eAoaV`l^JUVu%5qYyp zJjrcH7wUR@$y%v8aV>=MGF^WCrRQ6P%<62!>+kI;-&N(0cFW}Ct%uHhx(%DEkZd97 zsw)+C$de_V5UBsODfy|P!Zr3M^6H~fI`*mb-d(~^qQT-0s`uJb6q7TZaTuCYT30wa zjeRxoo*Q$2ak;RF=^RMYQ3m0^{MP%C#L27Dld?}K#FQt3487O>6f>mnp&6bz&VM$< zs}Hqw3IBd0Q@y#xU2D)dzx72iaQliGc*d~TQJ=1iH_e@;I6b z&EEHW(hjPNsm9!^PzCj9iEG+x4OeCU4)seCw7)-b#$uyhm##^=hW99MDm67lop2%U z>CsIqOypeqzrFJ<4T`&g!oj_xr3#%E9sPx+S5{7F|26s~l{Rn;WTe~3^ErgI`#mI$){*Lr4h(x!rO;)3CsZ@IDD@)UHA}i)0qr&uOPWV}$QH86UJtXvZm)Fx!Y$hk~Yz zWEm^wh>so3!<$~rOLZ`rRc|r4vTwU*%KJX_UjnAudT&BTDCo^{d)QEO61xfmvl1JfeCJ%Rc5&%&`!n582~_dtT>VyQ3Pd3?;;`{CQPYAo z?V2gRfKN$HAn&MY`%AzZ1==jb=oZoqK~hFVDhhZI?x^=wvi;uYrw-w6vbPrBKUOlU z$qBr5^vL}=gE$=GL%Cnnv!Pw(SdHao3E@o$fFZFzvesbl(g z808NJK~Z8I^PJEsG~u;SP&Jos+MWFvm%e+))ZrHKM{G5R1s%9bp4Yz4Vvlukqt>sk z%D#uUIiFTrzCpVzk}8fKMVnxVQT&lwXc_ndW5H&Fo$^9!0rZP49)X`2FQTCCeZo5O zV-K1Fx_DDD{{vVG)slTN6d|FQg0i#>F8n|zOUg6Xs6fLE&IIgy?ny^=qxq0K8^sg@ ze@_gZNVqq9(&!>IkldehFj?b3TV$T3xk4j3XU#7AB{kcFn)RYmb&QF zhst3{lP=755g=btjJkg_S+ixh&*vguCs+UvjLcB}H}UH;v5O zgk8$U9XpJJ{ZN%)oR;FOu+YRFsRS?a^%Z~pgLY>Dykd9Pq9zPUxOAX?NkUR&r$gfj z#CAaGL=jLQdB1Dt3{B)n$?hMsRK?KE=s_p1T0sj)|2)f2U^IjmBb70C*X_1oyJ*1m z3DKJkHJ7$6EooL{OS#sup?>+Bd-ZHR#^*pI!XM^$iQ*9o6a4%f!FhCD}>*`P4 zBy>{Nr)x^b#m#TKU>Qllhn9~THnP^r#%A6ZEe|u-`T~9!ncM*dIDHQ1p*VhbKjad& z%PilzXonh6dkUI|z8bk2l?ER`e_zA$XBDjSuCj2runx?GogegtH1QyVfY=2qO-+#~ z0J~9p5ptLkLF7UUKK#VtTf;yXxOD(>(rJ4qR$(so>4!2;e2O|Lt?koi-wo~mAg8_8 z{?O#}pNy<$4nO=q?aS9@1cpY8&-E(knR~HIC|)0+Hf}XWgrM>GLv=bWNMHizpHXSt z)bc9f6V!{s8MJ*wO=?b5Jkfu_C36?K53HMF7K2_{NO@Rv1=2B2PhMW0et~AedeFR} zPcr=~tO6{X6lKHQMwrbmcNsl2yXr%8<8+^%=2*aqTtPKO!2+J^Qdi7^pm^tq^K0BS zh91*eICc05X5btUoOW4)wYUxOIA!TFcVfW84Lm%d&}+<64TBUC{d2mNgZ+|+9fKUs z+iEGap*s^+B!nyS{%@g+CVibVr<~dexmAR7^2QKa9obE&v;Z>OpZ8rAZwDX(! zn4ym*AGl!RDtYtQ=;YUTpDl$x?S>Q|M_Wp0AP0 zdYwo*Nl1$9vk>F%|DKP`-zZ1|4X^W`G68dZYiA>+f;i+?Wp>gnwbNj27!?H zT^LiE;j6Oq-oB~ri`L%UJZMA&ovT*pLlPX9<(r`Kb{SSh=V4g-)raG;KcltMJNN>zHNh@AglFAP#O; z3io$HAHaf-iJRgUmw(0$l%|@{5irhd+r8m+EERnIW(5U?|D5W>H6t$pcoM#XoUu?ziBs|jjrYm`Y8=F z+V!q^+geHQ+-9;o=z8l(^QOOhcJEf)Ijb?hPKE(osV5rQtTGMB&Z=@$6e<@05z8o- zxA(#Z{G7P;mLOWg)r57M@rqF^cm@>4&o(93J@jjLYpz3Sm3iul^1`>Bo7JtyL%{iR zrU26Dg<&z+3#WGUy@@;$Bgl{S69*f5Jg@hhwW~wLu&h1&(2~ZTh~*UrkQ=+~?3l2~ znLjAhG2b8aH{w3BYPMfJQ@D7LsE-~csZFE8-5}0&cGdcE0UH_C2BaZ91>2yUdAw=r z&|~B-HsYa|A|{G~v5gz&+U~e{<0vN|afr5AWcb4mP4(-0r1ZE`7HKz}Qn{_ATV{@0 zqW~ecPjCHUwEe6(s?)P19z1*YY@o*4W83aT)=q93Iiy1GySDq5h*EDv1xVz;Luny^ zWZaT1_N?%^af5D_!|*k0ndd>>t=Q+%lXHWj z)}cvfx+^a)pt!ek(v*&koLFx_q~|zuTK$uUT25n(gqr;B@@sD0Fu68MZkx67pF#X;Jw8(aK1(?| z6rga@@fvk2q6k*-8k)IFG`GYjURpC%rk{7!65DR2yKM14$LWNq}=vtRM+4lvVI`n$vXz@gVWG?ER9 zYadrSW~IKZuNm1S<+8^{88nojLj`=hSM+ZC<=);=>Oe5V0V{Wmr#?3 zhAQMdeLAZ3+g@GIH@Y6r999ufH{4VEG|}_>t~u>LX2hN8Em2miu-4_tkuUun29`U< zY3l3yH8wUbS%2j9v)@9-4L_kg{6w`hYVzON4tx*My1)M&?G#UUC$ z-Dvvd^KgxtNh&Rqm?)){L))})d>hi$G7I0Y@KyAFY{nA77#W^hkAOxOdFX}0yUVsa zWk-vu6GZr@>tnS)?3Dc19w9Qcb8-Y3$Co`a+9kU!&|5gZ?XVcPSk-s?FM(3(22*#b z+NKN$xjfrmhfVgGWnNJQBOE?>{qNfWiIatH%~`J9`(=0phm1V4;ZntRsq9SO)Qy90 z4O6r>STa4`?m)xcpsR9TM@yX3YN>?qNoq4Q&KEbCmg>YLBIH)JP!uALsUqlmhnFG?u7PE7Cq zay&TWj?#cHzf1aA&aOZA+9R+oBK1>dSxaSXST|3dkn_`i^)g8-xCi)%3j^bBU(RJp z%ggo_9A&UMd=C0$20e4n+nrr5H}6Yk!V<&!P2#@II}&zZ%KOHQ51x9)oug`lUujNx zpfYMd{!!2o>i4x?j(Iy>cdDON?2-Jku+8IaKPjz#xA|n|VOj5B&hFujnJMdk?)~0( zGC#Cb&GsR0a7XqB_dRJv#@6-{3tCMrrdID8o*nI{Xb|poNo~pa7YKI{+7ympLYHFx zxW9k)vvt>!azX4+!mSA&r>!$DSHo#)|la>S2YqV|Z~f94Y5aKg?iCV2yI?;|+t#f6Ug=n7wjk z_dx}(TdQyQn_lc^|8!AC^$`{S^KX8Pn*HItmU%i@H`*xyVNOxsH>WQY6^d2Xc*ZG` za%r0=l)klh1l;Q_?+^19uklbDJx1#9c{PhoqJIjylX%>yS9bK90-K<;vGH3oK5rP2b96-d zF1xHi&jQVWKI)6x`go^KU@B&q6eGh$XUdc*I37$fFz|Z!aYvC@T9RZLk|de- z`nG!5X0zb7LKE)*`Ts><@^^Zno|Z6c&c2aL<4epRO;0%5ZVlB=xDock&{iU7lv$)a z4UE3sOIr`VYrjj!|w|y$i}xH~Cj<%(jzx(C~eIO#TET>CM%? zKRxYK4-2is$G@!1K-AF?*giF0x)ikpH|zC>zd zFSq5Ta{pb>fMJHzjKFVNYS(IKQ52Uqe(iM^7Ugy7v18J<=bN-KJmnfeYLF@Ue^?b6 zoO&0TPFthDmmw+W>(`mIwaB>wY<$Y9k*XnRU3~SFqUMlpDPf-9`arXvdxE8B*2Rfd zBkh-rR}B9&Ojl*u1if#Uk559BOFw&kCDMJoYMFh|(584#A863VTqT7dIBybxPS_sZ|+y%BV z&^e>sWuQeUPdaD)*AA60?Wqs$WIo2uTNFv1!puO)zr}J) z3MSfjMp@8YxD|{D2>6&b(4dSK{mg|6LTsN@ymX%zPX{Fhi7)Nt%VD;gH%I+)zsjLo zbA_Ml;9assS(?%mp+axk>LCPHzzzkq8v(nqNUxqZT-yyD&X&`(I8u zWriVi7HB7ROzH(v#QHItdxu%ClvV2uEDBqV_c3fSY-IT{wIk6BTF*6WfF zut%DCXXIU-T~l{TXL7+P|I(UgH<>lV^IxixbEkbjCWf=!P4Pp^-oXC^3G>5$54Ssu zI8R^hsAX@K56Y~wIslc)Ip9q(Ws-eM_*;wV0OnN+w{tejg-yDD zqGgb*>|DGR)i%qn3qCaeN;ldURzfmoq0rFpNSe#^a{<-2^qt+;2D2khC`lnaHaIy6 zo(MMY%LAFvxl|tD#tg7H4ow+0jDJKiN$E_8240}oRg%u}Ro5ws!9^L3^qZDXDq0nX zeSb=)&Yn4Q<{UJ(*H;2tp^eKRh8#@~S3g$U+zOh_;MfkjmtD(#95mwnf$EuJ zhgM}r$(E_sn!_c~qKa{03OhnODrC#$ORasRx_9GH0(TLq@SYuGHtAsIELjiPdFyZH zYt?F$s~7xUq2)kP&c9}z*%;M(yjM6Zsy!M-WYbW2G4iUmw)UB`XMce9s0Y~8CjuKSTb4cN zB6{Ua4V9iH&)wGLwJtL?RS42HmL*ppDV7l;0~j@h>B`O{@6ItQM|_l^B0^TfK(L=;$P8Eaqsh~iC#nIO7JY!zL<$DE`S_=wR}Z$1+o{od zchrEdzS56Sh2XHul=g-_dxlE9nP%Q2$1vh^nIqC02#Y`dI9M#ttB!wFcjxLm!#Q=J zca@GU3v6y&yhKAh8J_uXri@`IVNx&b?6JINhQZEb{yT^k9WCM6r*&oZ@Gf}kt1 zmdQ$n@o92uoQFdefcT(l6AHaq<-Fvk3qva>jtTI2pK&&~%D-KYB02bwU%HjnI@6%U z#B4K1t0JEdzu2A4DQDJ|po`EzYlCZkb6sM2mtB*^n7mSe2(x&Zz0J4ZH|~A&K;2t! z^CuuG#)i3RI%V~aaZFNB>J&%PONs1OaBTm&%gaw*^?ayal={r3{1Yxz!I6p&bC1>C zzXle7ly#4_XVASha9seq&2j*qGrb{`jybCB$Z|wCzMrD;0vo zNj;r(ou=uE{^4?W=n^$`$a`3-(uC#eP`mW9Iijst{pHNLb3bBev2mDcu=|j1Y5{k2 zUgm-ZJdVx=o}fHwPb{rZk&l&0G6LG9h2|OZ8-KpJFtF#_r*{<*C*s2c=3}&UDLU@V zO{Glc@&SL)T$3DKTJI%yx|@$MzhNxcwLj+w7zM=(iWx^`96ZrOxJBh8#lC&=^y%ZU z{)c|3ZyyyfDI~WFc!8>c3QGE|Z&=)t!>VOIpH5VdRNZH>XV%EjDVxu}4OpZ(pBbP*6TMWXuD7T*g8*1F*_P3nm2RK_8vuy;LGgoeNr<1`HUSf z11WNY{`WQ~+I+R6j@u5tVMhVNB!B_Wo%Z&1-ccj!T%1OfWP4OO#+`&_%(8ykcD7>Q z&2R0V=x0#;Q-+W1bMoHWN#c7)umA^X53x}aQ0dH>GY={<{K)}N?=6$w+~&K@4Ee^~ zDS5{l6YGu9Z(deD*zekJX$|+9Via#We=iPhN7WbBTHL+OQJVP{f{3fZEw#REe^7tk zyX_~x)z+WpD+g?nba6>vRMnr1%-9-2(}@8RLb(pyA?P$PcZTIEP+I?^rz%*O^HNTa zydwGR#trW4sL=Ms1q-BQF8LX^{RH_ zCnGG>BoWuHQLQ;#Zv8p6-A;3j?$>6Mkk-o*Bbsf(v&Z9`i-kgMUt`t+ZY? zE5zrxg^BA!$hQln3<`za?=W_q_!pR4}c;wikB=b|D|Uaf3SHklk6X{N7J*`@`CSKvE%0 zp-m%rnVQzOIqWMa_`b+Z_*TYG>9 zEDD6_5X*NoLN2TI1y*Htl$v$xy}#WGfAqfE=wm-3FKlH~(C&m+js{WBw~95$a4+LF zk_g4@Qr|B2e-UaKz&o$mrbjwH@$mfXJFTc(+*#M9A<=y9f z3l?uG9s6IMdZzd9Ok6TS$(aywPz}3xCh#xR)$b}OKBACjc%N2tLG(e^ympEK=8u&* zfY`w%&QR(O&%K})WE}Y7)2B~DrmYdAXXtEUSY~EwN-kH}4^9ia7<`ghIQ}ZYITZ#| z*upP_l5&4`lI@B3^Cp%Vwd#W{#=$)kIgh26HMb&)5Oc9#KJT-zt;C@1O6}u++W!^ocMHMqezP8L^#5uO@~KXiQD}xTreY`7(N17_-!(*n3DQ44igl79+2fX&9;H!P zw<71A+(3Ur!{+o2`vJ;2t-7pT_3sx=()OJs3@;?um{FN~?15DFpU-xveyORzFP%8S z3_)jts%$?{Jr9ekYfcHT$QF){10~i3?|xHQJ6)`j7XMWb_jzF6DWeY3o;Gip4oA

HR`p2wpNBk~&eZXY!G!qTQ| zrgHNryVrl)R}|y1?eXgWrtggL^Pqmy+zf}Us=6$JvwM z-=h*aJp$3s`!Ky()uVFFh(ld(>nuYR47}Gj+brXGGJnM1-Z@Wah?w577xh`&CkFN{5-cWU-l+qV-4f(FA9wIKh(Dwo! zP0M02(e&+hMRd7h#>N=bhW3 zw*fQZ;DqPTU(%z#`_HL` zTA2;aHkIj=j9v&h^G2;hKc~`hWa#^uGiywKAB3@Fgk5s;w^W<;)AW0G{WS~8#!D_2 z2LAKLlmaWdZ}mQW_l-p6)^P+MmZLl_Xq@_AT$DALZqtzJuxqI5-Kws4F@Maytp4|< zZmTb`&F&VP_fxQY`9BkID&f}@8?v`Qc`Y-s>!~?AqQ+lb8I z5$c!!wGz)u_uV|9@%^~yH=IQfto~6W|Ghb@x87E0anPP>7JsVqXIF2P*7tsO*huF? z#Of}SvFc5#qk2rro^|h9muzV>;JG@43WBTB&@RudYL5(4d7V19 z8#izOY`%DxN9T9b(sAUPp|896g|uS=WRrexb=ghyvi)Ce=oprkLt^f8oCR+KC@TTU0$1Yt&=0vO&q%~>s7Gh z_^yZSa^cK-wT_i?l}nr6y_Nl6mwI@}2H(Y9SMsKld{|uP`H#0TjJh1SZ@{bc5d)ON zTemm3d&)n){tvJ^BxXnH^{PpoAC&!gsZt*weU1MD+_a_d3O$N$UsHU=hzrCIVW%^A zk_{^l?2{j`MmgTVaL&_cb;1I|Txfh!4s8n&>C@287sM}lBl*N%QP7HgS)C{x#zdem zV)(AU*=fT$AN!F!YT!h(p|nEiF3~yB+Cx(_(rg^1{O>m!d_mINu_+JYr7Kw?gh2EQ z;f}f}oy}n29BYRA`Pfh5gH`MWPy#Y13^qoLNE2p=$c1QI@^5wpZ}AK_KfI*q;e1$f z2PzEVT*7Fk*j=|Lfpr^{WvMt!#-EnTen+!Pg#}6x=GCD9;zu=dHe$*@s4Cyb`3Raw zr6B<$dr5S%i<>~N#BjPGO&j-W&Ypb?k#QHO&VfW5(NgC6iZFpZe$ z)^u5zi4QP*AW=_$g7@4Qgu5tsbiFzhVa7>rR6&d+3E^;y`;6EK-o_QX;rx5LdcJse z$U1osQ>IUsq9l`+(#h&NYaG7nU_9;g*D|^(v?8yMC^Y~%_SB7sCq@`&(LES%107+v(s}{amkqe}wb;IP=I6Ba}AX804`MMfc*XGCV+m z#R1@hPeCyuJ{&E4M0evmoquz&p|gtjjNbe-v^h>rP87uOA^ZnnEk=@!@^xiSZrbxt zT;lwHNHx-l*FM}U>qe9WTyx<7*0U&PW6>Am zs9FF0lml~h;x`#ajm>cIlb+g)X~={ji5{&(mxbwz(J!`o^1wR=&_|$am;PgL-U3Qg z1Pw+;Mgn|7TgQE5uv08EL|TX%QFrx#@}GNj#wu~#&!rJ07I_znxgkh4Y~XlEsD>Du zK$}PL4+)G)b4KDLL&GbwsZ)sNnE$fYVi*A{T_xFcF!Uap;=@qMC( zbCtBMD^hv7X|6Hjf5_AM2a*yf8bmwHT`}I&YN#BaVEE!(?`Xt``Aesbvls9~JrU)N4*FTYh}`_~7Tz@qbE&959x!-ixh$u^qr4XxWc zqEDUVwS*eC)2~at6q;`()+gymKhC@GKdGQzy@FfY^}Adph&hdKpNx1J<1uy#rj8|l zO^_XIG0R~9q68kMKr>NA>?rzYJ4}~^9{W1ELm~Tka`7{>qc@nnFpiRXwQ2RVU777? zeKbboPIL~9eD^J5fAN!z-v+%}gb`lMjxIDo#4yCu-;Y3D0=5XL)z|lTa{*L9c6@y_ z4#k?`01+I`=qQm!v{r z!50raTSNS7?w$Q-BDFZQ%4rvOh91A!s3+^&_!%?cUMHU`$wQMv%)+M&~0(45V%3qFahDT z-s`ac5jI^za$s@TWxCttip^CliYYwOcK%`Vl-MT^#|CpiIh{~5OvDivva+)JjT_k6 zz6j?krYm3yaE)7hRdd?3-Iv>Qqt(f~oMc9rX&x>AZfV`5ndm{)2+jjNh>#((S(ev@ z(<4_ZW(SZaF8Qvxj>X%gzI#5K!axeu7g#!Bj{;sz?vIBL#mM9KfbO^BpY5)ArTWSa zh=feabp)oN>Xwz2JrfqTV8ZExI8W7>j#ZAA%joaOmS&VGykT(N|q<>NRa0mwRX!^EX0X8E905o?CswEZJK4$zC(<|P>yirv$7W9 zMFv9dH}w+lmd|JU^yv-x%M64&bYNDQaDaGK@9A=RG_xP+#BXl-Z4L|SYrx@Nv1Wou zJdEb%Fi}$HtHJc?hd~5PO-bEP6K}n4UB%#3Y^;y`WJ?Z&*i&b3KY&NXS=Z8ZwS(6m3-r@J*))eK+T}cpR)?t(zI#WMJRd*+v1d|6MFodRq>j3qFU*(&Pl&aN zC=ocgaiu35J35B-->H=0!13!`-!PoxwJ>`~V1Z!0Fqoh-MDg18ZgmPrgKRa^ap&&h zu?H$ZLc7?o3f2?~Sr?2GaPRh_N5~K9#VZ}W%=d>z4j@inleYDdU@P@Qbc>WvLPkgh zLgp5RKcv3H1;H8z4@ibL`#w0Ywxs6sK0saOp@hpI{ylikVcN2ZghRq`Ts0(H23*2; z{Lo`x>k{f4ju^CcgJ(u-`1Q$7Pkb2%vaHT=`BLfQcsY3qV-DDK`StFMqW0|klSqh2 zZjG>Z^Zfd{7bkm@t*sQ?f@Q9pykvr~u4%vz8uRQvkRQ--@E@dbxP;bfF{n(g>S&{{WFM>Pmreelp9h&_6)9{Nh&jv=?02qFY}VZ|jj z+j7gP(=c`-DQFLTcXaNKDRDWP(mdI=nq^+X<5?Kwabkq)HhEHOos%3x> z`PdHk_Rj^Ko@3+;N}{%o=9G+(^`5S8FBOk=P-i!BdS!vX zKm!~1p3!k%^iaDMuWT{eqwDca4z-kJ6go!Au>i@L$~W7nzT*r` z4EJJaNQ|TLNp=E0=)x=SxL$M^InM+bNa$w8Vgym66aB=;K^exF9889F_nA>n1xGfo zi(m23_m4`)%UyFsqWrqPvb#jf{?kw(Q%EXlZPJTj}g*h zoH^Mtk%Xq8<9&s)ob?pkyuIz;?onNAopvTd=b2WGwN=A+@w=bjomTuglF`X)`Bjz( ztrm13=cXNE+}n^T3Ji`KS+X$T+0ffF^I z7}0wFULEk?I7$PAbW?Z%rhrU9oR*!hF)Q;orhQFW>mytxeXpik-ulC=J_|psq7R3$ z`-!X_rlhn055XHZ#^fICIO7xg92OP&jINM|jTbw{pc&ufPW>r)8U_)(<>aPxp7bO_3a~Ze(K@{u=o5138m0wiS8y#YcIU*5Y2G0puG#XKmK^& z`pT_uDhGTS+YGXPB#ly$j+loE_;q1vRmm5lqzTF+W@BRRbVW^eUx3>sy2#F{11uG- zzg(btS-uP#**6`68a=CmbsdxNnXgBrNaY`z6{RYDA@E^E; z^)r}b5%StoDy`75qM|}%VZ34~Dli=I`%8y4O!4**K~&R-lg0y3U{a-i=a$;z%w}hD z8XU~`hJoKldpnAS3MhLj&*dhVXE?ms{rPULzPH_3D0_T@zOp1nXNBSevOC|GT7v#c z%o1`oww#^*Z1Z)y;ZuzR^%8}51-u!%dGz#H0lepO>XO0l-H`gU=>ZtXPjFeNnm@Ha zjAC#hBXYDrr)-`-U81{Mvfi(XIE}!LX%OBbD9kcCbX+O^&X_dj zc#qX}X8mCFCkMN89EZ}B%Ou(z}WAqcVGW z4m2#p-H)+nQA6b(EzX_nA3I>_(xv6pjTo*%0*C?SH*em=wbl&iwG-Th(y4(kxpC{( zz90j@0*>c@G%C@npH_%)ngIh_+xc~4c)BzyPmQgT`*x~~1bpyuZ|KE~v=(d`Hznr~tsET0~gpkAcsszS0Ja8hQHJ5t%qqy0=V~1#8c*)mSE)Fhq zr6NaU_ToiwqTPC713as2d#`5K=$g78%#1L2Y}|{&LU_!?#E6QQZvZ2G{rY$~CjK8j z6Mlz+gffYFT}Bkd;tOhx*VktZ^1d6kvo_|^%^B`vl(n|ZOWVXq$gy;$LQt{a`cC=E zoWCewD7r;T`*Z4tzYfHtO1~_+o!dXsJ2N=D(oyumOkiL&R=IMNzWd&~bB1)F*;+4u zj08GDXb(tp?ulh}vYz6AO}r&BuIfh|rI7;UxPJY5z`k>Rtja!yeQya9aU7i{KMi0m zs@K(rJ>o^h(`6J$5IJ-Hyy1k?!|fj3tC+b!>e!B=;G{QjyC9Ot01wqAG4;bs=#2)M zf%45ho9~*HC|R(a%A1zWtM$GYGtP$CqBFGr_3PKnb|0Fb^+KN~5EqQIh@!L>sxMD0 z?r{cE>1|aNQ$=0tet9bP=}88!MsbJ$O=y}sU3$r+m}a1jXRxbZfBR#L9*Y5JVeUy! z%RRsy -Gl&a&=)bG42@n+~jv?eaKe$s!{smsS?i@az{2((a)hpUOti*^z_`~5D>xxi2d=FHKk*SD;) z|E7l`)y2>5tT2V70w%x1-sbF&J8rfeITk^Z=#+}A)H*JZ9uNgBSz+;2<${gK2AJxn zi)Fl=&`5>qSD_gC+~w5Wl9jJce=7+OPQe-Ye$m%YVc(BatF}DQu$_Etc>6ynm6+1! zd6l-;xd190*k0{%Ht=B3{c&4KzGaTj-Bt&yq;{B_hh7pHu+~8Z1%P4Q? zBsr2~HzoHvogIZpN1(E|k29%cWs}8M9ZdUi{6J2Ehz#i6?8(ojJv97(EvN7nF+y7# zEMj1W1C4&;5Puqm9C55Pc-y?s523d zG#Xl-wEg{cw!izdd;ZmK5v0?GVPdzoQ^xLjwf^C1;&rkpd1`;;ZrGJreyVn~JFBw9 zpNT!os9q2#cV#AP*^fIovs3vbocEN!hBuq?zxbvxpG@R>LV)uXvm?9Dtmc4>ih!#N z`=^~OMb9WdOCQ|=r;r=MDBMP~;9^Fd!`K{dQ@A<66mb!|e*!FTHGkGlT9hbxb@qZz z7!WhyMN*Zbs^|3RH0Zy}`URmU%@qC3cW==i#79zQxL!Mp=3AU1gZ7$bYicuH7yqjTVD3>l;DH$X z6w~kYSwh;^Kbgj#)`p@SgcuK+W5tVjc2BQ2&x}HLd~I&USMQ1mA}GGv zn(~*eVav%sljkBS9Dc!KB7MW+iVJe?vHc4!m`DdZ$V-}}X*kwv1ota@_IUAt$YW}Q z=hgePCa z3e@s0+U#;0xa_1}?&k4Ne=DyqpXGi&d)NpvUe58^jHG6aI(J2;&&WUQDt9i3%ay5; z%bUODPqRl?YTgy++Ic!(`|#m|n5748NAt$HBT@(Tf4RWstO`#@;6CIa+6w}e2$uPF zI@+spWE{X^sv|nbOzx=W2HFPTl+fubBdy_49j-35O=`1t@$_)_GM2;8Y1nLSfAi)S zb(1%Zdl{R$b+iARr-pK+1|O`(NV%}l(#Gs#ZfaqicMyQk+U9<15o;UQ5 zH*dyLhSD_()cgO=?`o4Y@Ox_?D|$ox>f$(|j_pI=9Onw{O@l2<8fao9xasjEg? zta*BsTI3z2Sp~1EjA9b1b;AngML(JB7U%4so1g!i>f;SQBhCSQ6A}UKS4}^vM72Nt zT+*LLe!OsAVq4{8FA;IkF(M5+KnY$LTO6|^^hMN-8`9+hg_G$65T{e*5_{f?mcmAw zZmttAB*vezF_UUF_3h>b5PeqV?+-CKPiyxiXjSjZ`=}c-@0JVDmS`gHr?iU+PoXms z5fEr|jP{8k^8yqWgzGNEPKWCZYv$6zC$cOgJ-Ktg89EU`7TlJ0&4Qyr`t>+dGp2YtVCGXp|nU)mG(f)_8)@B>-E zcTJf#ZQR{A^K~x!>|aK2-34+1vLPh)XTFNj6N+^X74u<|2Ff!D5M8Y#vZ$O0S+<(k zm;u7_1cW2noi(hG3k!neAnp;bY z>h+!5Fbid5aLJ0|V2?*wGEsnZE+7-%2%<|clPnMP%D*W@B1+ebb}1Q7oluuz=xnxE z|MI6;v@Y>UbXx1S#kO6lm?`@GP9^)Ps}n04XIkBP8RzZj?eRAPXI?@F&Qkrvpo03) zG%681{xK?YNjP^MGw z&-_Vd=dM>^>jEJLLgLzkb=4=g4&VS$SXH{+%iekPe;ZpF4VxI&<>xOB8nk`ta6SxX z?m;@{s|4!&v}&dG@b1D(gZl^S4IcpTO*Ftb+D&0exPO@4SrB5%ErLw_={m)dx(5lP zeg-_HBR{>EqbhU@Q;iFkEwx_sWuG1&&x6iwlA0SFdiglU0KKtK6N5^OShk0%ms_%U1Gv` z;5T4J6qS;$Awy0kRBz^wfg@!5fi*)mh}A#%Q9wQcwWCpCPsh6F1G}_cLOiiaM05oh zd_o+TmGeOXA}qJY%0P>;)5Jj$>3Utak6p7#0IlFxe4u7epH4Lnbr=)8Q)I{K+-`D` z@TS;5LS^5dC@t7~!sdayj!P#P%=qeJB9mYHS!`S4u~IYMN-Dylh3Ny~)uJg)M4+ts zIqmhBqMe$5zF?`%hdpFWks$WURW9xsaQPRz;fye&tkY}Lmka75+sGIs3XTzVDBl*^ z6VC}cLY^^$LSNZr*m$nAO^VOnGv)iy`+wIw{1X2`!R^Y_b4lv3uv!aik;49I+T^16 zR~HS3;R?WK<6cPL*TnUS2IFJmz8YjlTj!>wO2@tS_&j}3?L~~~JMCe_EL*2Un&nq; zQ{012s`;OIQ8(npZ{f3fc!{o`qzu}dWRYJ8>mmFt5P679vD!~&5aU)~JZ`SKiHcU_ zJFZUZFR7{R1Yjt$ME4dMqAslne06|gVtuEVy{0k1x#~gZ&91rSReGcR+hnActS=dA z^=C@g>Idd#>YJ6aHoR|cb$oR!U6~^Qt&T7yVDVLn#9CR~VN$RI7_TYlah%F9pQO)K zGRNu8W@ss?yHgA*y0gxiD`xLtfcT2Ad%XX|nyPcX<9#0FZkWE}=JWV3pNCmqD|)0U zO>6}h2Q5L21R(V*-s=(PBgL?m0QHH8aJGzSKzwjSNc{i@t=Gg_)c&-63^A^{IWo(7 zc4V(tW&3<3Hr#xEs+9rVQ|py|Ue!3isWtR^l>PDrU9k||q+2nl`w!2Gy6%pwDW9DU8E4KWOu9;}ss#AJLGvkr1;IA8pAT5gbE z{`xg3i^xA3zA<;W^xBvuJ8s*e~8(LqQ^8yz;Jz$zYq% zyl(Zmn$pm~08tPV?CfGljZYJ90%}DBD==c-OnhY6x0W#HwxiN9**jV##TVh)MV24i#%Ri9#+=Wr$jJPkpxI1gkn==#lYj?jf#CfmVA-9 z{;y!mP>DJE>K9IkRL&HamWIrY0|q00N)CNcQ#esoVU*8M1sAJ7-8BOvS?dO5M69M8 zhu(_5W|7jl?BusqAl85?T4QQsOF!KiTtAs%;L#_tD^FFl4j*OcdXj+67S7*7Lr z4sCQcFa0B*|G^glVA$dRx&7MHJ9SKJl^L8*XQD);FN_&>YO(K0@6+NQ6VpB4GU&Eh zT}zrK$wtG4-FjzL=!TF05Z^odmwbcGnUcWv2*DO4EY2ns;Z{`IaSABXcaiQn; z-4Q=IJRj?~S@+cZ7q*Lv=N^3MC-rCR$HYIKqPh(_wQ%v+|JAtPh^B?*I2r|##rWSh0=GQLx&Dc zoV1QowXrrTrTlf;5db@}7}}-O;|N?SvzZra+8L4ubvjCm&=UeHU}0HneTuN!+H3IW zE~M-Z?wp?l&2=u07^H@V-vWA3@< zarR|@Hu2uXUuVZn284)E9r}d#wkU8_U{Lmdy}K(d=<~my2mR2y>lgVW2OX{J%z~%o z^;i?UI}OcT!P4d0 zN2<-hMheyysmEMwT%TyYO4(rv(o5I9eR{U{25jHcUhh&4+)5;dAtv{d(-M_NH>f;# zfro65ZX`;tSGP6{EE-s!9dz#VEOt&0lNK^5(K6s3EA~Qi=(`)|KW$5JS5K}N3t#S+ zdZr<0{kfuIll$lAt0{@V%k-U=+kIiBf=;kH*zbXk=vm8 z00Us=p2dDEd``xtuKf3N;1fwUlnWZ;_l3ZB zVyq7CgTbHwviAn)G$Lz%7}SH36Ei%9AiO1b&YXNkAM&(Xkd+zi~!;PDt z%R&nRGBBU1Dc`#OK!(;k&fjE{qE2f#XkNobOC(euU(l-m<`=C#CM@^IQ)BmDJTkgB z8gxd=@2#z@9BVeGzPj2cB<^@dMWSlO*S))fOYOelOD)Asq2(0?*1fez1o0XCKeE0A z9O{34dq`1)5Ry>YDj^BgAfi&yswn%C6xp&&wo)mCvSybSEg}@zm3_%nDn!UO$ugE0 zvc30L=l%cBd%frC`d#PzPR7i4KA&f~pZmU_E>-r7%_QCbTo8OQ)zSXAB(lLr4z%Wc zkZLtF^P33AE<)aSOTX>!PctyPgiTJx80Qm|Dd1}DT)NyWsE@GhgBaj`y)bHWQpVtX zs}eOV?#)&|u~NodfSOA&J!#VkYZIxDOEs>_)Dcz;(8eQ`NrNdSbk{nDvEhXNYBZ`i zI>dXr7K*2E=4Ivejfkm?>XAt**#Mrb0{605D2u;10X$OTRrYBo8Q%v4*fsP z7BR_aVS&>@7=evI_UpkgI>H?~VAQ7wT0C|_#ZBj}GR z!=|Bq=DxTN2&?bVlA_Z=Nz1h&`>=l0r|251PKpQ)DzT-AI*#Lt6t>8>jQe%0Cgelz zx+R16V>xrGx}BzqLt(%w#^w?54f_ubqX3-2eC)k^`Jy%BU2)qK=QXMA!w0}0Owa^2i(oBHLxEe=KB8_W4P&PRZ(T) zStFy7pj`z1fhi;42%K%{z*z&qj?*$TdN>>wneOL&mTHhXz@1itqaQK=L5}+eQ6{dZ zQA8GIQl#|;JL*<6Ecd&LiUhz^2xB``L2uoLzUTQkMCnA;SP=xNgOA^bc4RNR!9?s6 z!vt?mouKw2z7i}s)YFmcu+*3jM$`~kfTx&*g=XbRsmC`-2fklo0j{mGLpwnXGL!Fo ze4hO#GtgNrvHRAN2gAw_9fr0&G+3VFh}Fxi&eti@IGBV!14`4An-ORGG>Snk!*@$0 zu2V057SM|c_ftGXnYykLRhMtwp$S|X+;9yARcB=W3RDLu-q2ZL^K|{5|Nc5FQj&#Z z4t0lr0vj0S3F&p&%~po~;yS#%U+p%`k>Bwu)yV4bfa!^viIm(rq#0#RK-fIX4}_I_ zCQ}jtYu06VH$U

TKhN=pt3TWy@ptZ>5!m5g9B#9sD|B9+M{ven7B*o5i{c&>~gx zB;V59OF#U&RZxBxN|`Fe^jyo!jU2ILTGh!ooab(>h}T7xfbIoTMnqoJb>JQ)uCL09 zB~#;CP7f*|t?KK8D=ikd0a+ZCocesj=6eT79w#cnvm{pjp%jUIk}xo}iEqVS@#VzD z;%51%XrJQVWzDO7k8!KcN&#VqB)>}DGR7HY48D&=UzYvm&O#r8MF2%-C%@g2 z_K53e&z$K}efT8%`4i9h%(T_tzOhRQ4n7@~m_(zU&v|E<-vrBTM^ljt0Os`en5AQK|BcFw5%Ym`B*vW>;b zmFKu_Gn+{*2^@DKC89*{g`mkS`0U zVLE3UAgxRTyRKPl?;p5%;ySU0lCHDlI8NqJo66t?S-XX08duSf;19l39GoRr)9iXN zEg1pIjt2=ieQa>2P8gItSI6i2I9gfpg0W9Vogb%FiPbHW{NK{)h7sqA+IxBB%&Qic zbYh(W2+Cq?;6b3L#)L>sCFq8^#&&*g3yn9q6Kxg}zYYAk**7zV^G-I>=`(8dO%++) zWNJ6lJ3|S%)Q*c}6?wVdErxgPy*$DD(%SUN@ZCNQZoSoC%$dUEIVcp3Ny=bRbIB1K zDk5OiV9t^yldiaZCFe}|)`XeAP zr0wysm?USj;n%J6JH4t#$A|ZBpM27B5gQS{Uib-{W5NR;Yf2S`@SFfUqZYxb#Vn@N zGTgpZknI!>_M$P z1=Jo3ksB3Ju{X@k|JUuj+xP%kWo;wVR;)pQ)?g9jTpx&pU0yD(OiJ9O{G)6rOV?px z%GcdE+U6bR4gX9l#ltUZC7JwRE`SdHeWDKP9X%^q>>D;2(+d*^4w7sjGd3s$D*;No z7PWv15Wve6RdU+PmuRu@I!q+FDq6m*qHg!QH^`D0(-5W)eOh@RPzi|*M@$zbP&nh^BrO?APoe) z6~YVB9xM-8Bd%;yC*a73v0v<#2ic^Rhv&sn>8O$GqVcbbDeNl(Obig)t zcJ5N!+jn(mvlOsBYh*NdX)x8ByFm5-}Y*9;}wPuX>yvNlLUH{WB^s$Ex1Do99#K=>^3xuEv ze?YIkMW69vPFSVADobX3@c#H8wz$)S_sy8tbL8rJ$2_mgB5b)D6VKC(CS&It%=-Ji zyq8VJ9)*rd>NUe_GmVK-I0KB~AKels;SJH~Sd=x+B{A4yc;7I;jkgbOAHqqKD>N@t2LDfKFm=8%rhau)MY&|D zA!#$m-o&AIS$ZgKygF#sY&81~6!J&jpbC6!REc@pGgvgR$QO=XNz*%%9*%lfD{MS+ zHf8*fG)ah=meHFQO_fQ4;-T8(t4#64xH zJ%wxTiV7Jecd{@wxMO6n!=`@~BU9}(pEepY2$;7|V$ITdMVtY$^#1?KPkAa4`!xHG z3;H9kKrQKNoVcm#ivLY>kCc^MA)rIK17y%_~~}^ z7xDjo)55jr-Yv-+YZuqB<6xqBohs%sj6$CQ4120$o!r;SO_7_uOeqD8h394$jxZi> zxN+AB<;5b#1;e`Wn_P=|7WO*FkB0mYMswpX`((e?wrT-u@`;A=LiF7RGL?rkvr4~2 z-`$~PTzPNKcg6cvn(h9}`A`Y-}f-hk`=0|LZ;84cZ$6w1>@4K0DP&QRwCq zTPTEWs?mr5idqCVQlw88PNa^nzotmMqFzwsbfkDdXi}O~Dvg3q9c4YL@b3S6!AqGw z`_l)QJ;M9f>+onDi)TCh;kO)~vej`J{%(z($M}BEsNwNrKZ3R4=_1O1{k+$t^ofxA zbsF60)ag>#dx*IH#1_LXZp5rml*l%9I$>Jxc4}5HSClMlVa4~KOuTU#1w{gsYmbDH-A>awh(u)AtiQd|2#=4Rw_!TMWkmG&nQ0nLMSiPGxvQ zt*IY(;D66WwZL@(7W+pCT68%E~Yr9tBgBAK+WtVmiDl%@3BL4M0;svxQql^EUt8;;j18qFzJYNv z@zVeE+?=naoGxOzahTojs!h#JbG8m*$rKFpJobbr^x~5bBJ!YzuzZ#x?NvWP4n1|B z=id+awWradtm&exA!dh@Jnp#=a{vSN>PQ`pAfBaF$j72J(_z7)3yr+L72oNx#oP>D z+r2;@^3Z59a&D29-rpBu--?)(NJF`i*s{J(flvFvS_QmZ>UqT#I$Gdu=9ZDd*;sd>gzE!Oon;)BK5?urN4tjm_!iTHQp zu3Y1vImNRatc5YTnG=!S7C#>uET8wDNHbpnzZ3RPoE)={!_XTC2Xxm zX&lJ}KP(;i+91YAD1Y}xM2HDdQoCtL?I|A3>RXmCswz(>AD=#`$u_N!8NjM!4m=hR zG*BX@t4A#G^cQ&{%V7#H$bp-R+Rw_c^3BDjsji**`=xriFDMG3Vg|7R6TGQ$)S5WD zXl)bMl==yViNo{yTEiiQ+{&c=WNH~C1$YS{zyNI?GYm&!B0FrthoO>1(8QpRKPbUK zY^->>+b(6c>DQSnE|P%$1F=FYw{5ZIL13hYfFa0xEf0Ol0rB@4(U8lK&KrqE6ERda zI9l9njj-L(M~W6Wok>8GBABrX3P_LZk1cIDvC~;a8MAHLZ8KRuPIj|c)!~7y^=L$3 z)CmSNs9BX#D(gV*dB-Fd*MxHJ#aCd29L7yf3-g2z7<;RUX0f=8%TtU8A&>;ZM6|)W zydXoMmzXtb!wL^>7H;iTpn3q7^AjU*GT*5(QJ79Rel@*ycX>B^rs0}7kXr~b7{(K& zab_+c(LfSyE{JjvkXHp@EiXceMrd8pHxc?#NKC604!AoIJQg?*{EdRq0+i(7nBTizs4WylC8%wdLl2 zN7wR77D5Z+I<)6DSYZX?F}_j=Q^CE*mxFLjK<>Bju2IebmN`EwM>Aele0?E>i<;1!@`8%6j*J0j6I1hkzfORy$UY)utR|2m>Wd;$NaT6c`(DZPo@Xtja6|_vCcs&TC z_I&<$Z(kozU4*6b3A^4Ak48PjMw{&-cOPaVOcu-7b@>G^B+|?kg|Nz?6Z1O4T>wWJ z4S2d~Nyu8|OK_069?FXI7l?rSYd|;fpdbfk&#~Vv|nsbl+N& zsL%2oD2IVc!9;~1PZBF6d!6C0fJaU*oWH}$L|Yv^mz^nNrg^m^lvRTpZ8Uha5J@Ns zO~K)G6nZAq5yapXa>y@Lus9-aF7^RJGQ4dkuwv z(;xR=v)+|`9l4?6Q z#LMp(pEf3iP}?9<0hXT0gbt5@$d}c0(6Xe~&~}=C*! z9Dgr0Li~h=8SbMSIQBwriAn<|5p+VIJGEieq|rtl9w3X)pYy&vTAk{U7;5c2T^Y)H zj1SPG$D3PV=i}lKyN&)%47_!rah8~56<1;K0{d|^xZEsY1fYS(=hb|$Cn&n+F@aU@ z9z5se^;tzIcx+h->YaxDVd79f2b2o$<$8qzf?~<|QW}@|;m4)uBNk+j5mJhD7iI>P zMV<^tYyiTzo;L#xIjCl&b|YofaEKQeZ%Xnv76sEN079hvmfbsGCCeYUIL7jDyC)r3 znA%P`9DUlN`34XBCG_aUazv&5$Y?@see|u!F>r;6J2p@AooB@`ocR8U-!}Ap2gs@z zM&Y#HFt5J8I!t_Y`96Y{htZ$SZz-ErC1iFwsF!jRQ@kwZUGXvVcKqe2MFLZdJOVOD zWz3`QK~5Q1(RI^jxFF}2J@4A>Egq{`m_*NNA44>lO%p_6oiR`rO8ZE=ZXoMoil2}=ISmG4%X}ykl@(MajPug;&@@(?z?r}GivHm zVDg&_5?9-2c62`(yLRylOuZ*oBie?7N2wCB-O1~fiwH@bia9fQlBR>svE4OUGCtlU z$9`)Un>SuU3$xhGU6+L|%2tMbO)k4m_pvWU$!58Pbul8}S|ew?Q|;&eE#b@8_lzs4 z)|4tZ5pWs$dTeaNDn%l%U|2^Mt9lh+0$^xHEr}2EW*l-{;?&w|$UqTMz&}P}#qkQz zvXC7fHQZC~N{EBO!2y*6K@k~^%V%M`4AwXsGsp-81ls&KDR-z0%2Ng0fG@NE;P9h*`)YGZrZlXQ6OK`JapG-*RwB0BoXfg-3}_Sh|d~vYOoctfcE=R~L&- z^rql9Mb|NrM05L`WXyX{xx2fA?~X|zpm$=XjbT((b$>UEOfZrL&~^6`z6GmaJuw%n-k(JCK&EGu(frP%Bd$PSnSGQ1I9 zOm;;qot106OcL@KZ{=D>?DWK$27?LzJo9$prl9(%7EKnVVf#y$hzyR^YX7{eAkp*_ zi&rVpJ_aWkOziui0=MFDabXELnyrm4sV!&$hGATw<&hpGZBoS?Oh!)2|=$I-^=YM$NQk z!^v4V5r-M_0Oo>tvkda8aX9opb018(D@MKY+DJGOuNBMd;l#J~L%?ao*suOt* zAZ&zR2Wmh3k}iyD@r*_JgK&^wT2PYzo$-#>NmIOv}hwp-Fhm^o7 z56&`fJW|-4A-&xi$S1@YpbRk0*Y(Y)Es8V>lgu!i$B-0n2H(g0gy{k?P3Mc4c@H8J zbsTRQMF%vbw+UG&pjZIzV7#Bkpfs;U2t@<}q zhm!*F7-1lWYzlSvp(Ila#>&C=cdo{Z{`T;})vxtT4BJb%Db<-cZo8{KeQ%eu&O_7IW3zhMyDg=_XgO zp-}<-!?xB-Bsu7~Xs3F0>p)vcgE#tDeD(a!0BUr`R1<_sO;>a2gSplHz7iQ915n8q}}C=w1m zEV(_}a`~2U4==Af7zZK4gHuD45GOYm?T`j+PhD|Ym=)L&4qAd<(&o52hPOz$U2ZGd zh_4qS<(8G4?;hW8w?UPS8P6cGNKGi15K-Wlj_m1%8B2YIf++8n$6k1OL^j<<54R8c z!h7v9uc2~*EE{STLdXq*I>cq2A5@$SOV-73*|qyc^5)pDU6`s;2NVB8_EU`Cu)HB4 zP2O!D+OtpRZvec-RYb*uY6Sc-!>A6=!n08obb+Ovr!}*{J7Nx{UD{o?%#=DVd4bLJkzx3k#))R*Ce_i7^N$`&h=Y{)X4iX06k>6NGfRdwQIPn?P(y zg`pp4;_X+GK4h&W_O-SuA=?rm$WCddy;ydzB)dY4aB6`v4-|}yM`V$>FXkFq@61Y< z+(56f0?Z{*8efko>3zEf^a??8A%W0*7>ajW9MQg^XTUn)bRKMlpmS2Ao{9 zV#J6FM+Qv6)eD0Z13qmZ(E^YoF6Gu88nd^vgEA+Ht@Fc`Wn1`GppGSa83;UIp3TLu zHe+z7ec`RObrD#kiJ>4d)&h=BSzcC$sWW6lD@ZQep4o!EGilbc3OzNtnWV$ESSXWE@0)L%P_8W!=RhEi@2=FWcW{t_%A~Y@Cc@HmJGtzP}`a~ zTrUht7jhs$?Q}uAY8(57caFWh?A5NFsWVhr-<)aA$&TiAp zY^e2#dBm7n8e z1OtqRa7{^40+M0W)~W63^ch&dmBQ!{(HE=@x%t`k7AruY-@7_4O&fI=YSNkN5;e`ev&MI>^5>@gdDf&spH5NB%W+#>#kEQpa7gJP!htZ+$M z#|eFXT!-|EgrJOr@BJV5~K91=F^NpMDu>?9|{Uc8}!CURCxs*qzYO7G)(7 z4XPAiaKv=9w75|y$?!>yWR*W%|K$3EcRW~zw>3+KL@Yx-9b3)Codkz)K;E%I&*l|5n~ud2;Z9L6fV`lIl|# z`yL)^pNi5Ie^qn4F>Ui~e0KR0)+^s$-#4CmQpaap;1-;bj>LTLzzubIlk$~gt$$g< zsI2VoSKiu-`qZSWTv0c%I0*!mtAN&}1IM(#G$;C~ST7U?-sD0@mcFb`iMWH@ERcX> zhJ4?U`Ht~M3@4FHz%vHY;A;#eD+~>u2|Vw0-r0s#kDyW><5SD?YGGKWTfMnsgqw^=d8mNlV0ZUKdB>kTFs})cQK_(cNSQXLb zig6y+F9Ux;wgN*$Umx{qL|rd0Z!@@m=h@#i5HvivlnA_JZQeeCM~hC(`nN%+ZJTy` zLDo`llo)7|N$o7G?fh+p=j}X^{{SfBXwWDC<9{&&^fdF6MR**B!qU-SeU|KYi<=IVA)J2JWc12)IAdDd+GY;%aS}>UO0Jq)n6Cy<|ymjqTcfRCqk@& z%%tJ4sIyaL+&N{Tl_+LlXg3c-dL^{tpdvwFQ+{0!)_O{D-gH{Jz|a571^B&t#cxa+ zrX%i{uDveS*;IX{s3GI#zkV+EiLO;)W)sOlQp`a8Eh>0E@jc+;om?R-!h(N_kjJ()kr!I%Qc&T?>VZ+e$QoPevY^EGV?Aw7a3D> zXIuitWi~e`zsh9l6cD;$*;~eqHq3@%5LI!gyJ79Oc2UK6bK3HYMSW;N*$Rk*(_Uc`{SbpaF>i z00813uot_0r|zaYlsTw(0Pcf0p%d1?muy3$U5=@*TX7{gDacO7l7=Af72?`nyT-S+ zYF;}g!}Rvuommh>m&{LOAUX9ZdjaY6dfW~>65so3aQu98hFG|E6?8tZ{A0MnyA3_M z7lP+zXlApTio%=(#0O70EGz@XqOFj^3!bi`E>ZwNE=4~HIkSo|1OPwP=eyO>rcST? zQ)LjK;)0WPbt! zCec^cdJk7E_f(m0E0IDywW(Q^bBlG^U&S}A_IUW{Y=PWBbRy0Zz}s8a^60I#A#gsz z$_)mSSk;Cchy=1wkXHX?ie+h?iD-t>yDM)^v|DOzKb`-hB{o-E8!grR~Dqr?;7Vf6t^Tgvgx=i}e=S;#uWqn%%d*A#2X=@XW{h@gqe| zGp*^-<5#v`iOYRM%G;clVwl@{lRg6rF~k^P0VtH5oV`BrZlM_Bw?Q3k@_Y>`!*4Rj zO{zQSOXb1D)NhByv@vWt$r4gRZib4c(k4!|EI)W=r_$i*;Tyf}Z zy~4$j_-OXls?t*@{mpOVyrU5z3Q7c55QstJhSFtrAD738b^}y4~zdG1*p65133+!*x4Ah0U<<(#t$8- zKS;(X_8vceyk%YqnX=qD_kox)&8; zoKG-UP{)65}D^IAwvkf=nkQb<860!4}u1 z)3?m5fBe|y&7rBV4=XB-Ck+0|I-IRnJ5re&yj973O?hK$Z6!;Nv~H8D$u4DV>9(~E zV8~fF?ftgo{twpdQ_^YiSxA_Y;e~?|^6E@IhP}_!m*p$~Blt%`8OSGF-i_o~=UZup z1S{&Ue(G}t&Ck#IMknJP*dw@+v(r@HO4x65G`WrvcA|?N)3{9c2AVS(7X$be0Kx$dO;4HWC0RoZSG=zICUI5ZxCrP=I^I`7R*Q5UO0l_6EcE zIY!44mAU633{|@$tlgEO%bIn`#(Zk_gIy))PKhIp83bxa)FGId&0lTJbr?6x1L~_` zg+mGW(MxmGxL3aU`p{JEi5%vOvr{{Vf~ke+5f53VkSEERU!Tda@8i=x5OxEdHxPcf z+;z1F8OITfC5fg>xlDzIuX6J9Ydv4*oY{*0nM4ATxabbVkCF-?9JG57LP6Vve{!?D z^_ly7cE<6rX=q>nnQ4hDt=@H2^SXMTC9WobH96!pxYN_Te!tFDRj(wJ)%`KP!}+q9 z_MqxR|APPW;3yGu&?x|qj$uJ(2^ie#{g1^}C}^($!_L!zi=;?f03i8vK7wJI_$D|z zo}mWdd8cSNeFh+ANOvbBk~#J;Rrp7U1Z+!i?(vZ5D|@`ocQW=?6^stvpeQ7G*W^GH zG)`)j(cm6AJOsOE!ze-rjX?+KbYOI!SA?7z4o-lT7F3O!DgboQb!=iKac(ieYLFVW znU2rcx!BATqZO2%i|f{)sX%5X%#SK8?%6c>o_D;Iczw6P(1tCRy3yrU3rSnU)TwW` z&---Kx~J|dIJm?O=0ADlV%>g%N!R`9`p&AJ0}{pRk>&ZA*G}P1FA(*KinhkMf=zU zjT)>OTx6p_UExS;Scg8A`F_I61&NfRJ#0Rrzc4x`vMu2O3>2_1{s@3iuxhTKC5u$y z%aLn&_J0VIa{Te~Mo)d!iUim9vzlynqKlzL7;#|_##CasjxvC81JgnS7I1O7hR zncYy8z!)Th3&wmNTYvS(k^EoIT3SmtCxO2Rtw8obdyY`RW5ey;ACAAV^*8K59bQ$6)1ecKt#5tKitN?+uy(JXSuI$)5XeMx^ei*q%z78 zwrNKP&#AxhQvTJw7OIrWnMzzSkOn#!Hfmz58dp&RBAiK$i0gDQiW=8=47yxZX4u)e zjuQGm!c*LLwp*0sxItd|B9Fr?^_S6Gp~e{;MUhfc`=35*UUk}Ba3#Hk>QXXM%$OvH zGFmM(`YAh~JjuPjRp?HIg@dtTL97fgNb=q!MZk)M9VN^F=;Us77W&G(+#W%UNjDB%&$ z=`5%27DI>K4PgIOrI`zQjU`xTL5sM(zj;T{gvNw-l4%emW9Q)q{&T(7F)hh(%D{W> zPUnoBcA53s79F=D6ERjIpG}9R@y8_yC4t$F++k#23a3$|)LgzPd2KRqTeYRBw$fE! zU;X^C>M0+nUCEs<(LLt9ZWQ7g%;fkvI$zQqV|jXdt)8#ifVXs=LedH0s`P2QZ9%rN zg|std5n5B-=x9=;mw@)Y(-Z)e`16k%hCYSlINtYxv8-6_^1X^%Monrg?1PwW(v>lY zcU0-2F17oVfL)h-H&4Gat6~7#1?~($gUTt>ypzlFoxbUoP15^|&o@4s0B=Bjerh8z zam1aO9Vz2NAc7DBKK@d%9*A7Uf%hVFg`yn*haACVKnwxA(QX!_+x05wEWSRaIBl@!l&iov z4-*%;YRT1a=bLqWi=BQG?uY}=hf9Mkfg*0>H9`-vUymEfvg$TsvQF6yA?|y=ImaUKX#3fg!) zL$#;%@dNqb(Gx?!qyuN6+C@iw>eQ)*xnj{1n64ngSWjP7rlk$!^D>^2rrwvoe7Tiy zp++pnGv?DKrb!mtP{u<^~_zNa~9l$X$7wp2G;@+g~^D-09 z+C&qj4t*FW12_RnKN_%CCky}@Atu2~5sA3Q%DJn>&%6DybyUCD&w|a__1}GEZr7VE zq~G!DUz$38$J&NAP66e53Ba}+*4N-TMo2C(6eQNc!cyqWOS};zKfE3^2Ve*+Hf1$Z z0pQ5X)Io~{4$>5LVfF~*hly*((`VA^$mtR~`6KnlQExsk%5{N6ft3wkSEV?ZhhQ9n z(*bw^MUYfh>N$M#UO#tiIa_6-oS0DWLz7#|ejcwycmg5kjFVAhsV$78oBf$-Z_Oc? zCY?Yu2#Lns^o)!nRt6<(vJQusvI8`_q+$o_4Vf5xX-+UG7^qHx0Og@eF7m(*}ae z@*mLxVd0L+w7UfpnSCmoMtI$0oSgUUI4QoqGqKpm^p7eh!CzVUI^YU_^9c1vD0@wO zZcQ|saY@Q-x52?+V=mrc_U!}TA@_4Hg0LrI!NKm0GqzNq$%M6EhD|*H1(6#T{hu*X zgK{FOH*ZBPW@i6J9vJDMwZU&CnEWw^^}Yn!bliJ+5_U=i&Bn;6=bet*`W1d8;E>Q> zU<4}90s1F(9JMK!UQo}#wF|!mMT{@Wf@{idVTcZaVzPZJ0S9#~U~*W6Bv|rkD(sSw zy1w0xMuM4?lY@*72{Zu5dY9{wTg`U7tLs(3@K38nR%sibzr(@IkbN7A!|o?kfl~;D zd34?At#dhnXSrG0&1Q}J`p<#&=-Rw;;m`GN`WbB-3A`3SKUO?oV>y^$6f^5n9h;-P z(Z=CgqM?VdgiviIUwui&E(r{o5oqOncvzUi#0jr5)EoGEk1Wg5-el)&d+J%zs7~Ry zp6sjTa`_p%)s-?UvbKt121ukxNJj9pHW`Q2tKuh%T8d5sSVuvKW5aCRgc<|Ra#PtX zac^z5bRVf(G}j^f%nG7BCbT5Cc+Tc|M>dO_$VQi8=?jX=j$y+lKV)C%WKc<;dQma6 zYjdCBDZT9xv;dNQ}O>sVeJc z70sl%Ss*|QBs^>;8jWQ{##l1#B=MF=jLcXVOwvt{n}q}uMLWTeAxLys919Kzh|EN> z2+o1ULPCY33TE5yP3Ol1Q1b4LvPNvZWpg?^kj>_6|7FL^mv0cmYJ^2_>E3!WWD#s} z+~m|fBAfl@o%8Cm8OV~W*WQ=sK$V07gV+~=pCF89a|r!2k;)ov5lekKYKLz@sfhav z*?Wc^KMRxkk!Hc|K6gs$D1APaREbINb^+=9+v67tr4$R4ZMv7A{ZlqPFg{I2JqKj| zaAOaX-YM7X-l3Fj>mmy504JN0b9k^;n((Rhn4jH=zy_QJmT|Jtae~dwq%T8mfcrQa z6`_N;4gC6;$&I2X;D#Y!fq4NLUriupLQ*@yY{p1e(4Y8xqOZ#2%kc&Qy<^A-44!C? za6lZ2r~(hkcJ`GwxITp17C8gl1AIK6pL&wvyaF;&^RPD~432^-hzE_fAG@CC z+tWn67T1KLcLJ3?`mBZWMjS}uf>Mvx(#8v;HA^~ZTP|G}gAxYj5I?!*3A=*PuBD)P z67zlxM5$dGzYdk2$|*)9#Ei3-%N7Ao>XKv2Bh&1b9%V~>7|WZhSgu8n5esd&Suk}4 z0hL(8K#({PLJ5xLKK;}z1Xj3NhE!*C69b#5*oCr%94 zpZ7a;M(5=zDW#VB7!n(|*TS#-(_-4LA)m)z9M}1Mj-=7KTsfYXO)T=@j|0aW64R1* zoP!`9u>=A5>O*gFow2+hm!d{d+Z76|+eAnk5Ilj(jZ5cZ^fvpHZ3Y7 z6TTmrCOw6XK6m}T2Bx$%i+swGIdvZ%zxZY$L!>@Xy{A)kN%Wy~%#o%UyA%>h;LwRx z10H0MH-GwF%xC?zJLNv74qO9e;b*4AkbmWJ72b0RdCzx=88rNxXWTsn8 zw1VKf5Z^znSgLgHg3*U#43lgV(}Z53CxX3%$2nq5=E0A6@U4Dr;!)O4g>GO{07wLf z6?F}|Z^G^Orl!o<-5~a*ZR1#i0Y3g*+jEt766FFW4X9bFOp>UKYx!$lT4Gnbd+&GN zq|3hHk9P0iif6cdXWYhLE*jShZ&S0+8WBG)13av$2mh{$HtKN)Wi)CDlf9rQc0WrW zIN^KMHlVjvG2@3;)=}|ZmZTkgq=A1k8FZ(RoMDoHVVTPM{cHEE;5@qFs*=v@%oyd) zQ=3-$TCwMvz~c(HCI9YjCFy7BztA5J z3m0FWXLPA{e@KI}w`5(`+Y=#YK0XWNB}wglDx}@0h{r{cyh*zgIAMZ_P=>zPGlIy4 zVD|z-0Lcaa1f~aYvG6|XMwv~pGBpSrK`aG#BIypbBb8&R<3D9F02+6j1|N5E*Mm)M z_)3-H=Jt5TKY#a(J+zQ^ew4v4H=%@jHQ!i$s!X0)FvnCAqP%eVejA{oHHV_s_?d}Op z7l@h-^bpWFk4oRb_Hd{t7Y)Irejo6w&ytx8f5j^Oj3>z}9!&(y8+P8_zjkWet?twJ z29qfXmEG9+(Bzu_2unc(L#yJ&F1$HI8@u>q;MHPW%jzLZSZ;}2fp|w<_v&%7`qG-} z))7mE6HV8=2cxZt)q|jj;VH%M0pd=i8HoMF>J-ijA*v3mJe~>KRdk@MWU(@9H`Rd$ zK`jJX&pC0(hTP(KueMPnEH*Yn7(Bw&SMXz(_>7ow0O%kQ^DLWB*QjKA+FE7}mn(Q5VpnFLL zr?jm*Vd#SI6$02iukW%;!k&F~P1xPjc8S5Nqv**T{B7msOTXq*i{!TL)+5N$r~vRJ zVh;OfIQ(tPc7BZi#w&4>?9!Ae)eBGo2TUO4gtMQ85l!d&(N%jMeljzaOKq$fkP&(z z9Yu-a)XtXzQUhcw^!m$FLAw(!@r=EFxQ!qj>~5CMfuI2{=CCv{nB-T<|AtNtLQMcq z|GXxdNo}qKh~s@BNq}i&N^f0(_)dGd+Ycx?Aau`OS@pE^D$jL73YmFXWYTkBgJqN5 zCu!GpCqC5zTrn_2DZV(q>#aIbA6zML;W_u=jbYAyRFUs2xPIvFUMcoqesZxA>>0K5 zrN_Q@p+*MD3|@CqN#|6bA++OE6TCeug__kmVB1Go<9bCc{od9Fn_krEn9GsxbriUB zp_9V7B{m9y9sk5u^ngv9BIm6ofFFo$K$tbd_z%a&;DW!%-pB>3*dmap5s`s~BA9?? z*M6CK|5z060D-i+5$2$HX;y4xb7bt61=m>Zk$sldre>O8*wFm%7sLjjOaKtjC#VQ>4zsP-Id~@SIJX1p3cMuZ+tf_Q z0;f(G;SD`;g;<~ozPfQ!OW7@+YXsnerASbAkM`8?67EhX)-CC|_Z9AwP=J6RrE@d9 z=cgWa@es&#sk4nmgX+4;tDH3g_kI)p>cX6?8P}?-`p72t@pZtS4nc6*E3wE8xE@U4 z!fJYMs<9R=?ksc8>vYb8DyTX1ujKueQOB-zVQ<*GT2aI%5a9Nebl7km2n*ftEDMi4 zbC%!n%jzir=h8t%4wbMq`%RuY`HiHP8ZIHU5gTW}eUldn#d27}ItCl^CMC1oU2m3|qph?67a592 z+}Pa~zl@H3zJw<}>JeYH{_nbHzfqqf<-m~yzn5eOg(Ljm@W)S(}jzeJvd zacfBlM6ZO|uUmm?q;zs#(tO6iE@1ESg5*eYhkaGz-MGV&Xb91s4c6=%mDwDjLLCUL zvLy0XxDF6$7aqLdYvEcwOZV}UQz!Z!PoOGb5P<nws?A3joTKZ{XZ-5wSaVGZ9 zo&|CjvghQT@CM`(;0R&ja9~2^McD4*5F`KRr;neBvJwQZ&fPP($`XJZ9t1j?~+&2{dC`WQN|NhBYzZiL7D<;U%EETv}c96&abu)-N_%U32 z{B^L6ggKAsPaIXS1(Uq7-*NRbYeT}~ZdZc8OkLVDy_dbupF7XU*`M3=7DO$K3*vlu zbG3t-kO$o#-rQnu_f#P~*+nbC_tDH@eSz#OPJBlgtF5#v`zfy;%?~wG=$3B8YeemT zfG`Ra!>|fG8h|vUx54UIX@e8ee>qm*(VWjh<(r#JQ?G9pvNb=rtRy>#?~HJ3K@VI^ zk-R+}njZvaqgWfah*qr9*%EiB6H6U%eu14D$u_kyieJR7xVFdfO>==Seigc8pZRGU zFf7m<$nIIiCcCzzBNS+5YGVNMI(W;VjyTHS>D%skwE^p+-|VK8tpYFw(>-lOJeIX1 z#j!J3wuk{b!JJ;C?cG1VFWEw1h0MJ{o@u!yQl8t_5qmsK-=}T}bzRp?Renjsr!Jly zJA|PVF6=vOpu<*31T@-YovTX7tVd(NBF2<0RmzbE3Fki)>;iv*cR3}$L5XI|y zyWafJmFOLe9OS#dl7$(Em6$Uey29KkmW+i#twXRNhwBa&Ut!S?ZCEV-7OF`+A>T^m zizf9CJ0q?pYs~_vC76VA=c1!iytAmZf{0`piT8tv4Y|vs>ncokE-wEtgX0$Z@=Kqd zG!67pwTy*RuT2*Ibo4m*Idn2NDfR;UP)QBd&eqm;^XQ3}r-)LAvOfXj8h#Yv2x);S zo!HMw3fW~=69~P^tE}ZDpLr_<^O~K%J3}fg;@^t>njiBZ3&+L$IFZOGz|bADfue2d zPBX5(*k+9+e0f)k)awrVIQ8jObERSNIt3}lP?kr8Scmfs%4Z(1JA+;%CE<)c%C81Rpn}iEx7A(|$*6WRm9dh<|dU z&G4Hl0wSP0VgnV-p98ok8$!8gRVqP;{w(^mSCxd3V8J4Utfr z^pZCHZ8P6r&qHA%K?bOZ!H)9NkAiS<2B?Mu45tnk$|>r1D$~6PUa!fcyOktb__Q^! zC{|IZx9X$chatD@g*N2#MJh807`@9Q&wSKzySCZ)scr(l$H{v((YndB=GJ@t4p%sx z;`q0`&RolR%c!4tY=Na}$&EC-H)mu%Fbmi0Lf}+c+v-zVDV=kH3=QRrh%JS z--bi5GGflFk#Zro6(PWC{M0VVG3G}rB49#glHvZMLC(>~wk%ZlThhUl+m8+%NW4{O zcfM>}fw>q(;&;Sh%$!Rfm?f9}G|LmmBD&ho?Fql(>+GmPY>$EXE~9Cuqo#9ietNc4 zNQyMPj*V9Bf47pHa+!5}D@WSdC(VQGezwoAZ-AYVN*}o4)zCoHQ(jnFwLZp>?B*_9p za^+z@`IJCdY%^yx)xA?6GazT!NoWMFwpponj1-gTeulnzX4gfx5866ZL(2MHT;kUi z=?0gCuxu`gdJC`7H9t2qxJ|2eed%;a__sYc{s?sNx!eyhy;xl@cs|I^?uI1#?s%M7LQK@#+k0qwFhcD9N`r=i$sry;x0tF>iQk*1 z)#z_RT9>p)OkyUA+OZ|wk58#DSaeaU0F!wOdwcs@(fLO%W{f=#yHmP8?hzZ&(7fWt zk>%c7zgs<*>9|0qROf-ft&7lWCKtKlz*7U|=sG7RYi!d%ZH2xUx6L#T9~y+SPv!TZ6`ZLQ`zW@wfjG~3CE$&NN|E%9!_y3x-KvBZg1!p2LU(_Yi*w*okFTk%%$-pYJ_G zu0}L2)x_bIV@`!zn^d;#KC)Ao6JZ@;mcSNEKm83{F+N1iRHut$;(3Ecc%Nb}`S!;p zbfuF^-_+$ME=AZ!s+KFBVuqFk>;-sLHQmfzF607~5W=iFvm=tcdYs{0*3f0A6Gnzp zRlc+PCleoDGzB%&_eL2|k9I(OD9o`T7vIt13QmLtmA(V>-s0+qMZ8esF}!FuS498X zJ=MHxRq2=e+Z@O-438_JElI5zmL%b%A5bxZiX<7n9L*3=#*7oc)Fdv#LUCzsYw*d< zJ62l?2(NdWLbp(tg`5q$lem*%tp0P`^ip{NBAR4G3j{}5!C8?}KmweyA93UN2$yn`tjwi#(j?!vhbl52megl&B69c(PZ?opDiwl$k} zKdb`J_xbZI79Pjf?Ij0JN2W-}d*ztAydKV$TA3b1q3@qbK_iUw1dOkpmuY{}b+2c? zVn?UF(3sei*z1JBg9}f~po4vgaL34TIlV>L_ z@>@KAy}eOQ5kEmQKXT4C)*ng?tU(1e17z<=#aE&)Wa@Td_O3UhX&>L9D#MY{^#GEl@wtp|ZKq zX;;Ev%t5DS!$Z76E@&%M4EG(CxjU@?J7-C$S}xk4@R}P1#gMI_x$aSwc&Kx;YXfcI zt<{iSRa;?Ie6M$(YVMVwq)`F$4$+)VCnn&2#ics z@#@EvmmPGf{oZ9EN*d^S2aA4=PZOl2Z||}37TQCt%ohr-rw;wo1Ugv=&M{HRx_^}TVI{sj0F2-F7h7z2<@{adnx{fNcSFiG@ zwBj3`2RfgBIzj-IHM+Tf-9l^0+*-NkL-rl`T-N)51Pw4+dwDPMD6CGsxei-^B=%_O z*mRx`Hy5moc(OoA{?K9nt1Wp3E3~PR%Rag$P}i?nIQ2rGu(8^YNM&tE_&%eCK!J>I z|4{PXdM5kwp>-@L?_oFqV=`db)j>U&9pL0nRvyz|jq(lMIF5rCmFw1CfGr#OgMP+2 z6kluXC${;GegNe4cGuv$HQ)afVLGlpJFwv0NO1f1n6v12ts^}y95sZTW>$3MpJ zQ_vr0-se-2w503H2QB?E(d#_x(DC@2cgp!rv!n3GcZxC$>m3M<30(2MmS5(mdbzcM z_j1nZ72ygM_`$ z&cSzct=x{UGxpkqPLNHUvyXnPEdG}2;-DoLJ2Y(hqVg>6oQc!&xmHD8+2fqcuAg8Uqf{{q8!Hn+_K7Q? zOaQLIC>_<5OGZDXxc{gtmx(gp4uVrFSqdF8bAqo0@=OstztRT0XnmF@GjIc-0@u z%$(ZUE(}Wmj}uf;e8m9Pwz{1QQjonew~jH}LZ4UYxb-FRz=we3eLNU~L6~WgS^eWp z_UJeUT425diNVhUQbhZYh2YM`9Cx3noprVeXR&=s|{A6~uZ zwS3kQe7xmgY9R;U|7gq89$Em*`S{mhkoF8Sg)HKtPnH<_Rn$E?_43O%bj6iU+L5zTT zd-sn5caOh+`d>c3Ex^VS&Gn<{i+MZrT^HVfpMzOBWusON)4|$+(LIi9;$PP+2)dc( zhA00YroIH8>UI5o8!BTYLxU(Ol1#~zDN!gYQt2Qnq>zls6lI8zAw`Cg2%!v_Lqf_> zDLeBNJDJ;=$$x!0=l*~9vp?N?&(XH``+eW%eV(<}vmVo}8F@IT=(MQp>DF#RkC9H( z_hO3lPyn@)jx$L`Y#s1t^rb&uTi^4we(&a=*)C%=hpQ}bX!f6@^Cqp?G~Aftrohf% zX9m-h-b6{a3;a%BN4jI^$@uK1p73YN38n#Vz!(I_} z1H&868*-y41Re~{i75B{8Aw41ZC~-bcbKHgSXqQte?L* zEVB6guxzbOv><)cCt4e*N0G#(U)uFpH9}=ov#hKvMj91zgt<3i=Az)9gHJwvJ>T#q zTCngQsv}}HhEz0xQeflkLMFwg=V#U)XqyzV)v6w1A85`%1cF#K$jCVm1pNSR5<)9` z{oFTN(&UY}Te9$FdwWk&RQQBi7NKm1_$bQR74P+Crk5FJlyTSFBR6E66v4P1jPTt? z9H6!SSFP8>ITX0f-vx?_!*nwWjK5t=^Iiyr3OO6cvAv10We}6&cN5Wu=xxT{Dxh5u zB_{L`uqMb_R*e(m{wfPJU`qfl%QX!daXXH^K@a0C5v~20%!z(Q43HxlbQSq5wX}cV zed8)QlP zS9*gv0!&DZBj(pU@c9BgZAA(l*CIq)ID(j}q-OAqtwdrZ`_p+|_I6Io(y5;lj=}kD zHXuJCNx)3iDekH3iux6K=|$BeTEQ*vVoN`k!)pA75)@nylWX5J<(&=gr&HN^?eEmc zKZH{(JW7ZzPT4MZDCuM-XWth`g->w4#I_#xKRR}};>0{v#WM4EYk^H{|HB5it2aOt zym)H64WAXyYH@LKx4~NZkCG7-ky^jci&ktiVJ$I))*KRm}n;}ZQSL2wJBZueLde^r^0);1-blN%Z_9#KE9Gra@Mks6RVAG zg>u3p2B0d@#9OmCKaFjn67%ErNAl$u83Puj;x&ukT`n%^T7Gl?>8=+hQ(u{)GxK>U zj5l?$D0w!#q!%uoh_##=g%jUftitVFyxSu0v(?5D1A>c?T1 zCd%D`BO-?$tN5864c>mS|6r6VsC;7Lfalzx5V6NVRF=(mjGc)`YZ%x&Bs(BS!5aXk zwf*q2re65HrJ{y9$d9XyZVU+d`nYYh;VI)FGs7xhPp@Kn18%S*92o;4=7kL*J8Ch)Az zHy%Nt+E>tZ_#O!GuH|n7wBmrn4EMsIF7U)xzpe=$?Es44-M+@g7L&eF(&E4Z z&O^dkP{c@@e}95!XI_6q37hc|u?Iox?N=xlhy{_W7)fdvzjSt88(yykjW^I%=}qR1 zTxaDzSKpJ-qKd3qg;TXks~K}4qbhV#ns75OxV6&jy}GArcY5n8M*qI|`DEo|GLLkQ z?p!~rTKGX{)|xVF{pt2-20(aZR%n93%R~i)DIOwNhQFde9IP$9ya+~xnDm103v6_v zxN9*a)VtvhW`X9np{WoTH7FC1XMpgr<+~a^O>7Uoq~yBm*5(wy8=l?X;-wwgw0L=X zb(!{Ll&dkD^ZTbM9kvb}Wi8gai#pS5Q}vGvJerd2o7jLi!R=0@&O)t#7hKlWhp?4@ z_Gq=1!JyG})8mw(nn0PX`8jEw^}x#e*#4xnSZCX_Ek+;R**-BNKfHT5&OE1s;1cd@ z`%(n-b`@pbZnv&@)8TK^>69t_;cMC8?5w-zFDF31;LOLVjVeeTCffh?B`d5^t5t=$ z)rQqBuNrqN$`8i8Qt9jra|jFBA6}j;S?+1vLX&jL=zWWv+6#AT`g)ckwCQf9o)50Q=!1?T>|4Tx z;o!2y6NkX)`vI-rkl-7Wk?LG$H5>cz%t@;1`Ythy%^kb8=&9naoFJ@tOY?*AMlcXW z93WcbVBtYXp1kIo5(=0o0_!8Ye>3`5E)T{5_y~c{6Sz2E^_t%d=oCI(2+gS{;gEve zL|iJdxHv~+dvlpqgl zIW7)J5eWaRZb`7y3+(;NL-wowj)0W|Mg_XY*zK;~b>ZZ?^F9 z!L(_`E7=To9uR^6FLKCh_MSHQfM<`|5bcxKfB#_goG$LdPXrg&*j{c@`>XbY0;Ua86@|ePCexRLWkd5Y;erlNUhgEfRSHL&FwlLpeLCjoP6%v>+iKr! z$TBb~40kX-1^6}=B+x|K4%y-wgHWmf2SbClA|FI;c#eHZ1Ut9_F~WxYBu97JLl2L6 z55tU869e^|@dk(z*c`wRk>o(~akXrb8^Mxb2Psq#;UJ;;-5b!dhE`BOG-yq^BrV6c zSDfY`48c1~59YMI!-u{eYDW~))+qJuq5hHEU za5ch*ZB%pTOsu!htY7<(+ zg_a@KD~?CP-!TU2rM`>yWD+BdrTby|TwKuR3gCnAxr<3A+{}-~D;|KUYoGkC8Rz@n zK}}M{?8)6#aQUF&-4l-D1D6}lD(G&s(SonXMjeNm?7STB@73urw6V1GUwrevvGOlp z(o&|?CP46@px40sZmoY|lf->(57WCgY}F`!u|d#I$Ayd&&IzD3)RYqvwBNU%?5@NV zWp)P08KHUyc?^?xOi-%Zx1f9nD+T0n;qP=AdTYx2YC<8>LS${&@fnu|EJ6Zd9ExlY zj7Vovt5DyN!c|n8V91vi8kbnQpa0G`$#T$p(KJYmIEAj-a%$FnK`wmciLZCpSc01g zHXUembbLbufCC5G5PTX;E*1Ny51VkoM9yjM;1hO~vqGl_V+1#@o$FMY*0`fOo_dfP zgb)Yp35f@DleGL#-9D0ypJw3g_}$eE6~;ab_fMZbUAWaxa0?PO3{#GD88Z0&s=KUd z@HXxc)%eP5Kh}lorW9({^-b6bd>-Yp6vC7?*i;dB2&1*7A|LfEK>iD0iNXCNPyq@= zmlzf)`siC^R=vlf`uEPxGSa8Ojh*nh!`whfxsZawCv17-o4^K^PR-xdou7YA-YEQ3 z4;}>SdH65M@j_hctyD9pJUQ#G-**SNmqJ|iRZr?>Oj$(_b#@1>!D!=9c#~7yGrC zaadmoIg=^I>!%!)AJ7XUHlepBr-k0`qMIuD29DLJ&VGDwHa^9hiNebg-P1X7u2a)| zEUR(;(eQ`yjkOoUyM{!M_lP+8JW8(R3&|Z&os1~>PYa-wE+niJl|-wb%857wFIME1 zJ0rXsDF@-o(lvG%gk<5_=h|SuwZf6QwWQ&r6Ojnz`hlnp>Uhx;fr8`0?|$h{39tt4 z#Z;3~TV`K1{okcPC;*Ou5TAjV26FmB_M4;Ue$gk*Ku6kNAmHJAV+mve;2=^rl?!u~ zCa!-!H(I1LW4rtOyCPcTSOm^SwAf&d1GXgYT7g|It8Ek8{KEHw1n8T1xL)fb(-xN1 zE5lj+01QFNcHcSI@K{Qd{@-;c3st#XKy4tALR3bVAE*Fii*efTJF2|zgcybkMtL0- zeiUIlFeD}!MPm)BRX zDmF*7^kpyoLv|vBspxruCIQzvGAwNe%tO`PaV(RrXrRMZ79cCe82yE9g!c}P2M-=J z?(_U~Nc{vS$~WCKxMMijEL(Ab600Sn$pUs-d&?Tk4{WZ(GU1*90ygDCopmqK6xuTk zG72w@Fhc(|9ss3Xtn{+~5B24tVaV$%pb>+SPB~Xp`Fwpy+sHt@SD_L5QO~!CU0z%h zQX)cVC9O7r0EZ#|;r=|-q8&YQ>+REaCXezmiQzv)csk@O66;^N4cJcRknDhy2zx9K z6Fo6zX71C_WcG{Twvji^sr>Ud!hIG=c4M7NVu^GTvH_Y2mhtTkb8`wf_vi+N%u(gK z_*u${b;m(Ht0s2@`77SSH4lJU`1?8|Mqq&`ji6!x!H(g@BhD}j1BTTxv+wW$Wy7e# ze_{oDx2&x>*?Y5^>zCeRw?HOiK-EHDYcWxt4;cIS%)wA0@mR>nJj)vAm(Zts#b*)2 z)HVg%pAU&t(q!~4;?t}SO`6SmO1;J>>$<0Q*&G6kHnlyM{M2@B@vlZFwHh^t9`v4^ zG>H3sK8L5I^@3gw>TamqMoBNoLjXuiI%D%*zzpXZzY`RUJt(CB#waPKf zx)@!Wz935W!5IhV3Ee4As`!U6 zULmS5aP@;vG!g;$ty(|G>QZcMB5`9`p^fY+vm{Yfa4`PCM{~Rj1b^H?JC5p*F6{m%p0NZ_-N{gmC|s#@LPxCbeiZRqs;qQ3pc+&j<2B4Ohkzplsbq3q7` z`4H|(32bs8tGJs05&@nirm67-AOxdg17kN@3SVr=C&7{ChA3H)#~WR9>>T?c`dz1r zvIvzRg_7hJ;0X~Jpjlvl4bjNXbWm?~!vDDTrWzjWsM7j||E`)u&aIo+~q>Lz#bv3!9t7yxnX>92j*I3NG zW$l`eKlg}&kPm!GEj!Bm9pFBeStRtYskuKFjIjl$X^xDnQ;Fb-$>d`)j>l0nG7bG^2uB(L7 zj&Vug^1|facApb&DZ9C<^kuV4pX%DZ8#!z2PJ8^`{oGPip@LfH8TVGy-lOo?IUdx% zf3NiX&w@uYx$DPEYEJahk*+pa*=36cty%Z$MUG1WP50fhj`a=F^}F`AqU=C$w4Hn= zwpr>$lpv$;MkRNjq^p#tbw}kG zG5rlgNjaHb4GW^OMw%uOeUzPn{>(ve+t3}h=kQEP6qmd7+b!GTDKgkA)I?i8-n+ts6%oAy_}z! zT`x4G-p|m@CgRc@ZYWpVs@EIJQ4=YA%7DQ0iSgTz@-ghEy{&52BJ@6OjaIVU ze+rN;FDy_Sp|wScLA0SFy5eIA^+B7d$j63E6EEc7(p-_845PG>KN8jA4Q+!&pJL~P z9tcmmK+eu-+!%alI5_s`&ROb->9wn5I5na--;Q%(5wP1CM%uiU^6L9mk+g^m?4Viwpyuk?%|}RtmyrNO6lh zEH;N>kQHXz#vMt#fBu}~7Jp1 zn02egp**m%QWMKP=USiZ?=UGfK{vobh^gow8&6f6 z?75PqUbJU!s=R9T>6p5KRVwI&%WuQ5P7V03TE(=V&A`UHz_xbnnf+ey_g*$3=>?h` zeCSrAA?T%4J#;*y*>GqQ0u4NDOi|9aI!BggkL6bw6RK5-e;wh=F@%H_coM+QC&w9J zFw%c@r9D3Hthx9@bpe_tX^1JMb%=`wIiXba#jH)g-ZVK8wR4hyZ$R&;SZZS(!2FbGl%DCwEgG~S7rU#`}=Mi?7#us;UWhp-{Yv@8nV+V80f)C zksU>+#LEp#uB5n*9P=2t_&{T9dS2B0@caiPFz82y_f8KeZZKCM=LqSzqXNca0|7yA zHAVMV!)WnHn&Jb44WjJrr)(?TZ)~hlWkm;wY1*UfrwnP!UrDC5*(r~5Buue51>=@N z3WQP=oyRLy*AUleg)h@-Z+_m8TQ#NrwWi3}c5f~X8e)pgI!jz{5yO+g>;aVDSBHZO zRtIRwuh%MnCt!SaE5bhV9v}n2Bi5_CtYsijF4v+hj%`SB48~M9!r%l-bE4f1p74+g zz^NOY#Sk0qCLk#A6@!`S&kO09lj&8tlETrZFju={^?DD#ij;A#BT*T0Yem6&{=kzs$MM?NL%h4{H(B_n!CUVixV%G-{JOFe6 z>5}ArRU>ZT!y^3$!U3X%0Gbc)r=@>TV_+1T@NwU$om{~YT;G+_I#aebd1s*jeAl(~ z*98s8xWWOZuJnYVNJmbE7s1*Mav)dpYnMRR3~1=}Ir$+Ece3)djjCY3 z7^C)kpd@RNAmUD68ncr_FO3^V0Q2N^fXYwC?kj$JelYWFMlU5{*>&bFQ6J+;5PqDL z7N>C@KppiKDMuf!xkZDByV4v%C<%}f0j`3IwV5Phv8$_usTWKm zu7_@P2vlKAGrh)%!Q3`djc${~5tq{kT~ z+IJ$?-wAA_)7B1~@&pe|jA~3+noWQcR`l-#%tnhce^q-G5n*pzi>_R_)+Fu}gV&Lt zp3-+MH@cp`;P!=-a)EAi;5+na->WY_pT!lRl){8v`J4l@XxQqk7ED`RjoeXkf-|skh;^L8y-f zIc6WHnQUcmXC3IsIr(rBzWA6D4Kx9l6}-i-)l5OXeC}x45B`<%o24%zCSo|jg;?rF zC&4Wv78F6&zp0MZ%(hYJ3xjPqQVoGc!P{&hkez>e2v5ku?Zy}o8=(cqvV=xiE(hR< zh>hUzfH5e4^FTXu4VbBrw z07NqhCNN|2h|1Ii4g{e!d8P*!bL3$jG(gg@e>OjC)j7tc;SI%HB;9E9q)@>I=UYbI zmvYpmU!M@(eW`U-hm zb9U{euoFW*giHvVkKmLVDcHR!Ojgh7+DDwx4W$_l`6a@L%T+I?PbK(i{3uEUq1vBg z>9-fITNy)aTe*z1$Gz*fB^Mi9gbG@p-JpH z7**I5*_EKi9@K?D)Uk8h}-{CD9 z{W%L^Bot#17iJxvm)pwDFuHTfDcUM#c&`(Lut0!Un#qV6e-Uwu-3rH zHJCpq7?UkY%0>r`4&C(gIgH6I=old6HqOP^yY6CQo9APIcZ&HecU0@%Y?Y03aT*aU z*f>lkTTjF-O7<3UFl6!UCeMh>L08|7-Wxm&%Hq$@#45bg*!E#^Yq&kn3fB&x9E~ofkV&AI2+tE$ zWUZ|02dkLsfvyMFuC?8I$Il$wt}!}Se^q5#Fj$>=n}I=qbYV`d*!`YW} ztx~-7f8SLQA7-SgZk5OFy?}0128XAPUwwN z1YccZ8J~4WM%cdRiXk^-n3#>5IYhp(5M{2zwTaX{F(Ghaed#C!OR%H??ST`4oG7zA z9qJL(hf_2jAZJ1+PIe2j$8xzzZd)~0(>JkZy0L^3`)4nDW;}n!C#qeBe3S5jgMI<96y*1nAVl2;zbXEphSpFIr0_Zw zv^3^<(2c(ud%%6_&veRPo%3O`L9$Z_(X&j#BvFRT9zGK(O zbhlMfs_rj^Xctz`U(xx;&4JRjPLuF%L8(MKto-oF@t?O;`wgE=gqJv? z>&=B8>;=*ehtg3i_=|dYa%mip-ozDG>$AJ3;C3u@lZv`ALC?$0<3&q677{39!n6@S z#k3hTmJ#;+#eN#wvQ@Xrt=)Fc%_Cq-<;KU%_E z90nw&$Ca9l74o>|$ktqwtu^mZo!WZuOl*97=&Fg4u8&$=6pGGTkIT0xhK&{182cj{ zf;Fo>I%B1)>yMPg<@YXL;5cpR%8KVp{l5Q__cWgj$~H9HEhl*(Tp;T)xrT~1r5N6> za=b{H^|LN4TS1>RSX6yGCrH?9Am@ju0K68Y19M*!YO+NMa|bl&l2HGQKk8Z-csrz8 ze3HX;Xrtf6fwvPr|Y?EINade2CHK;&f}A)+|6eG>1puvdt~<%l#F1>+%I{Zd&rHzBflS&Hh`89B?1 zed_n0;P&neT{Sg?jen zqweKiU@Q1e&OrMus-Js;R+ocsoc%dVQoi^34Z~Nn*m?{3&vHX;t_Gi6_RV|5?P%G> zUe{M}JM+wL26}R%E0c6psYpS(yY=Wghj5&yb`y2I(Ywc;_Bz!XMpcC?=SB;8*lUHK znX4nrcRaUcu`r#)4hBPsQF36z@jy`CfvYF!|7Lm6Z=8?bRifbD6l`+Y^w8D<_mIqMagA!~actkngYWR%-bwHy&V#L}#5U-<^(@u^6#V_YU$S%nj zY8dDJWA`raegE9tm*qe-tKoS=SBr5fUu%vrrbFnDOOsU6P%1j>G$;DUguHL6ey7Ap zjUa9yT5o(Q{8xn&Vs4FTR_n8y{+UCB?K!j;V6t&_0!Lr7dzi)#E#_#*0Si2Fz0PwZ zr7B)@t6>tpC3rGiEod~^!AKuI!?^kuA#~jt@!&7tzi*yE8+oq{!!A(O3v<3j6UpvP z%VR1HX}sOfvsRkABE1j5`~#4-$Z^o`uwL%Muu20yP0DY`~VtXpw&6r5XOm z+dT>P#`Ls^(%{wo%v24&F>hhB`^rdhE(SG{9PaaVmrDx>CS0|k;c@s*7>ceemc%X0 zdYimKIH@cTIm5msDm2KxAP}Mz6X}Ij0oEQDF&v$M%SmrCLzfH?DH5R3)we26rnHyX z%0tCxs>p{#_LUlX2o>_Y7f(HROn3PSt$aPQ zH)8osB~$d9pc$GNum!9L`3kaFoQ-_IG z&e~WT@=p}Tg%f5WfW|<+;5?eB=(%)gZ81mZbhzNFb6c*py51jXTDD;z-R%D^4zdwM zJho|zkYQB+mFSOyv1@J}z;D+K7hL%9f1jBRp4l!GaY(>HAA#w0aDd!~9b{Cp6;bHe z=DVfo*AF@ZOh62V>`V6ptAAxG(_jug|FBdHTJy7)rNXO8QCTITv;1!5nu5Lb=Ri_Eh5@n%p$%DoZn7!i$6P_z>D*xMmSWAe__H+GML$;Qg zK*0*y1k4sba|w#;@_AB#(za$8dR~$)5V;|-;pOyZE$%q3`T0+7Ev53pKkZv+xNl?) zjn5Yh1zh|x`%vj>{xf}D_XJ{t%Z1clq{W(!O|SG#qYcS-#URY{WoM4d%lZbygEdH% za8iItV*&BaBv|l;NYvtH*rXz+pyv|8Hn<_7SXk~_T)ZT-VN0>OA)p~_ZAiHA08q8z zT`&kDG364|oFWv6fFm%mCPB}=fQj*lLL*Woq+?*ld+cUzIb}SguKGU*@Eh?k@h;f+ zh`V5d6B;YWrgMf_&1CNaIe`%iei29(0>WFKEga#fHBq6Qo@KA&>J*wHKs8lPmpk2uqjcEi6)K7Ty0s1C&lY7Y1} zBE2HSb}9=!Y+xJl^S3VYxi*H4N!FSJCPG|5u*Mcg=|QxA3@C|D<>8`V>OLcu?Ya1U zF0x@`>1)ula=dR&CMf>jC#;L4s@~g&=BqcVa}cPw1R-AmfB~T^N*a~t&dKyqkPt&i zoH;bpR=z>MSsLprb_3JPLg>daw{?2{n7fWP zeb`-?`(kn{o;^YoE)nTX2gs9!jTknWmWJ7AkbQ!Xz&{1cj-Ft`atW6&@LYaHG|l6d zS{|JW2?<`%q!S)8Lz7rC#9pAu0%7lc8@G^5_6u<^-d20E%XiJ(wAB-;?4vV}9+)~5 zciIJ&$dJWaq>>w}kXmMEq6vn*+H8u*=qqW_~ z*>8#@>on2Z{b1%`pvUJZkh+fUe>gIQR@Epf516rSI<5vE0&pwX0d`WO**|vUu_2xV zF#zTv*1zxHqdCfvJB3WO1@_Nh_90FQH$`)1;5>o70h;)|?q~T+M-c(BR;!d;R<~xCj43;l3IMA3(qxw}w?7lY0XWVsf z*9FCv8;Lz&p8zO=!vqrFUAZeduSr<14tEhQrM`*sK}S@E50e>j9?QDC%qM>|+=wFI z8M>~+g4n+Ns+(UEWG{|=jJYGkm{B=pWun^+)t$r!&d)YHTyHMj$`WQ$h--*^o+k|C zgga;(v=$My0lD{iGSaV|c&~~hnDWI*YWe-#HJ_)6T+WA?6d~w`QUHzi2cPM|&!8IW zBj}F-`6Dxg)p22Btl0*fE_fnf#MVmx5444;>~(0H1&s@S3~i=tl@hnvF{%gXB>Yq>i9?(PEUOm%8^#8ze;7ILOhi;Zxvxgwq%T4N_kOnu~^S{!oi;eaMO} zBFpQK2Wz%rAkFl|C=2?X5rN~C-2C8%21W^~4GSMt?N9*BxQM)ztm!m_X#{CIEccx8 zGC2NW+m#MQVq=&+WV-`F2ZXT@W5>qEcEjKb+Z$T}1s3!?M8`NXRzhurB>r|~kHgdc z7yJ4`LP7}hLx^c%i%7tO9CzU5hfNM10iPei5{HQxt{N_=V=jrr1IGv`Il8eScrwYF zK1%ZeiptN8c(`NJ%stm&if3T(-C71fkxOFyxH&nc$Z@=9x& z&4|99lj-M5yWeZ<+Ku^9;QB|}@==kX?+8x{cOV=R3FlN?ti&3iRReMkaV0Nwrz`TU zAz{ci*dtU%O-=gv&dcfbo4wmdGQ-o)oHjB4<-Gi0mcPBLvHPb)-i;&>e(+_-O^#21 zi=CMMK=c3vCaRv`L~f3GY=pKN}RAzMecJTSwR7B8Y8FgRh^fF}$2+R&tlW?gpM2#N(@3fK21X`PE_9qQ4x3;u#Yw=oMLR?mQI8IYfl z=Xh!23+P8(0P+m%&Sa}V{<98e$?@TWcO1iI9B{UphxWxR3nkCLVgO8uCVTB?*ay{p6IZmt8vggKB=|RpexMPUJO;~@OhVm*4xpR$ z7Pr7hBOk?T0PB|CI3APGR92{ks&b+Kl{D>NL z^ciqegnl%dBY9`U9rpAWUa~q=wO#zX^y(>8M(_F?x(a-YCByT56KB+G;VDc2x|~cE z#Hbdu%r%tC0#*dPi=-UY6F9Rl8ER4G6^z*U_uvf}-_(#-l)bc4G-B6&zJzSW_j8qP z{;_8a0@gBZ(P0iqLGP&ds!?9^-3n%0d4}W%EqO8cZk0VGi7lUBzmI9K%@;q(e;vC6 zb4qdhkcSu#OTpX*qF&Hqm@xFA57}o##Cq)7x3>A)fh%na1|x!5RjorGFpJ>dWvufj zjb%#ixqIU5R-!j`y2VlH!M>7z|K%9BV0oOclhVvivLCd7%VR%jNeIzC72QY38I~Sh z7CE5CpEeC*YeRyJV-vQs37Mm`PUfzaZL<7(9^BTDFS#VQ?O6BeZ=p~BsQ1ar#izqT{MheeKF%Ys_q#~}egl!n|Fr40m-xSxBeB_{s`;sIm7 zY3VvAAB{qXhr##U3_m2cFzo+#x45o9UhRjT;J|lOzhG93s#ffXS%kpd!VZpRgBw?{U3ERPA?3(QRVo9Sj>{ zyuPm^R>if;^hC(Y>X>z2gWjz-+CY7;4$z8&h$|z3 zA1a`~^J9cR8xC80krT`USEDc_5+)?{Fu#OP8{w#N|Be0B^}6|>ec9n({`})*=^LDk z&tGS7t$ATD^WiIdSsd2A=)1MgMnztjRy=utBI)be^nZTwuG|VmOwhrY0bJ39^-m)O z_3}}q_{DAcwKliEaXt>0V z0ys-RF~uD?76|ynBpwiX?>ehlsH?X=f$=)RbgHxwl)Lznc!@nY65v3Z)*6^QSpDZg zy$Wt2+@^Rf!w7112eFKn=2u^~jo^hLM;v`0P}#xi0$xVgTWuqrJ#x8A*W`Gw{`wuw zwjsXizaXsSd)F8^#yR(iRkocacYr0C*Ck4eAwYnb?YA!-_ZV38V_CP-4MzAyA^bqO z1K1R&8r3&0oUXANBq)Ft;c0{>E1W*expA;?>zS&QpdK6)LGJG7nQ!O0tdU!c9jZ@{1URWlH|p%oA)q5KVRvrG~uPpStJ z$u0c*@pr-gA&)`kfII;S5MWJ=2W}f75-cn`Fg0Kn$eXa!$b8GGCmzyxe%T@`up0a= zAi?++YO%JMxByxU8KkbR?kC_^B_(%Jc>HCSK;NYW z_#F~cx$?U_gD&SWHA#crCfpK0RG$%DO+v2|xC~;}8U_Zz2#Z7_S~>0-iMjMR&^3i@ z$nnz}CD$34#R(yShWr~t2mLX+6i_Wv4&qvZB@NG@2$mepWJJp+dZyZ87K#W;?0I0q z=$ZZG>OJ%=YS8e^eA}ZE<@lWFX>XM6YEN!vy_sVoNW3gEvCD}vg*eZAp(t-LmI1{H zx~h-MCGLBq(~c2eIIRh2q1`=S+4d`)QbC<8W(=|?t_QrR@oI-UPunzHr%v@qMju7^ zM@>Z(RQ_!IgqRMWhiJJ)ugP7&jd&QCN|fKmiilGwF%EwY=nDbNB1)dP5b}%mS}VU_ zG{5%t{ToMHG({ri=a2rF^IOA8P;k-jA~_BCe^X8qXCz{(JJkm{ft2yzAlECfRAxS4#f`F!TX zR_CT={w8OQ8IMepPi!#j zTxn^641bKorhVl8W>ZasGTSC4s1=F4|7IK|DaQk~(>|(|T~M|@+Qd0|o!CFb!ysBi z0KD=49be=AyH*IA8B`6V_`|x$lJV={Oa`@%-^JTxa7O}=FngKwOYl0r0hjF0ymMCnJ+de}@eKc9xGj~oL~ z8kYg;i-gSx&0*6EIDKX%!kITI|#nD5`k_QYnKtz1A@0__}`laAPX{PMAwU(3% z+>}n8Gm-ygj?=s{rzB1xm%v|ec5#^iaX}m-;Ln&%Mz}H~h{OFWS)lX%J-}F)c8}x< z_5!Ft6ymu37}E|jlzbn3fA2uT)3UPEDJ!K=s~G)MTE?_K^9`$kbOPSGQmkxXpdlC` z`VleHC}NDxcg_!kQu59cQ*7cDU43v5fXBKhwd=B?LYyw-X(!me_Sls}(!tIU@ctCk zPUuZEmAL+d+BV{h>KGHCx_O>_Z%ydp#->98Ms@;rA;%k%Ix*lB3M(agq@(2yw?>^} zkJa?SegoOsNT0&S%NhHQ;?hDuLAp&;+K>z~)DWY-)djW*s5ai{#F(z-x^0^(#`Zdl zJ-9n4?WGkCrDo@c`PhA(|4Cpk7C*d|9MIY87MzosvQG!wnMf`a;sAfOpU&J?(rcN?O17$azY>X*D>h43 z2O_cIG*1gT27MITDADGIHzPi+rWgF6L4iURxKd^;XT3%DXpS{W0|Oxd8H*f9$^_zc ziTogJ{x#4#iM0_2o`JYW#dj(lsSH2|vzcPiQ`;HG7q+0d#p_}6uJ~0!_WXR!(niSt zam&mMrX&!(+ITE*ZXsIR1|84-(Mj+5gdEuulep2!j{*zbtON|4i6Ml&fBtYnSpg^k zaRgS-;YrM6O<7XfH@^&pk4tS}K^BV5PXN@g3;g(d^E%dJy1I5ZZZMH>>du&$c#cpM z*#eWlIvL+JLuHS*LPoGGRHgSUTgot@e0;1qg5g{kb+S}``kV*h!+;#2SBeVQL&#|I|j31KOb&;mb}JY?c8Y&O7iy{d7g2*pK3gX;-Y3ZW_mXootM zC?|l{LN6%a!~h}yzYM(3C287m#iEa6)92*Q`o1fN&hU<-8WbWK0rs35K@Gw39?=4S zg1159r?I#W-|>v7bE-qwwG$^eaXgXHqX`s>T>X%c04SfEi|5wj(p)Er zynorl0$db^u%mSz;zk-`x}nCqrlH%H;~_Epoe@Zraq39!eI%SKy}(FC5R#Xsm;Ukt zy1O|P-Fo+(P+;Vh{5AyV0#_>0&zC|k-~B*Fefra~PJl>cwV$cC7E<4Bzf#l<^sVPQ z=5MzrwwqU3Ubz^vJo8DPT8Tbd)J*t7*xresp9gfi8iS+4kK@u=_iFyNz|EWIV$TRy zr3z|6iz6p5PYis=G*Kj@*g#z$=DZ8}fM?*!CR)A$9g~neqiq`L(oS_GI;#z%eEU9v zngSYzyajh&*BDQjL*%wz^F^B#y$Es%=@?bN7Rf*u_x(;7=y5@0^CtuqC+L1zIX28N7g9N>*~BqDEt(1ZEd5Xkb(U@DDS$5OcJ0&Z^tQb+ec zkTB>oiSBg}0mU7bo0xT)&^Lat&QZQpOMfZ^gEk<5}*oFPo!Ij2iBwfhEvGiz~R96=tpA&dnOB!-6)+o)$H+XP# zxfb1aX>#5Jgp>Q?BpKKG_tw_8P|Xwa%{MMv-pOn8r-B4P%!m2z*qiyaaNmJ*{5yPQ zdOG%ye2$OP3O1_E02;~s*;L8X+8*;t(U0vW0011Jxhx5uWe4azDVJAIl?Im>W z_s^f2>R}1-@x-We6meMn^YKY@nOSs!xlplVk}ci{xts*cg(rP5TZFGK#GiI@C7&dP zTT_rIqPqi%3X~pbUSA4=6Q_Z}HKZ!sKa?e&vxQyyN8~=i(W5~I&M(9*fZCRYrTIoH zN&TG>CWJ!Mf?5w51p+sLI^^aG3j9J_bx%12=CB?@^FCl!c-OlrZ#9d5H>0}s<#7dL z`}gvg5RZ;}EUld!F>xD1>ok6Su$FP_acr)8bW1~i;H>2SOaJ0FEDm|-X2OiOCwi&a z7G^V{>H^~X>@n60S^Kh5Mo{2A`apN<_nslopt^-41=2MfTxN~JdpB2;DopLnDr13e z=0t}aaO!;S`ON^9yy{y3aj#BklJC#3$!|;1`$vj@wV)oU>`xYoNw657FA2VDJ-U|E+ zlLlOi6}I_Eawps^RF;b1n2E7Rna?F>Wu9n)!ci9#e|7 ztSbl@!2iUeqDHtRePfh2DNO|X7SRu|7lKrF%W$N`l+;rhC5S)4%%FZKJudwmavJAi zHFp!~qUc|}H0*iXbei5lj=ww-!HY>=Sp>SxNP`A1GtLBHP zvq>>p0Fa|2jm1-Vb!Pa!F3l-T!gL;C&HH0N$D|~Ms~Z;3aDJhpk&@Hn84M|v*!EuK z=I(!ZrP8VWwn<*fuc4{lVwPr|dh;epqp1z2U1J|?d2qd>CSjWQRMyAz@$y)k%CC)L z`kc`ruJ>zV7Pnm6GS5U55+y-r$fuB@qH4iMB@z5PtU51AE0_m7%?p4mf|Ky9L)s6- z9~u4Id)hU5@ik1(SV2ahs-8h}gzq)Sb=zG^O?XlRf6p$cbqfo~P2QHXRN|+a9T*8p>;Ote92aaJ#3&>=*kFM&p3SXq5X1CR zG24medizRK|eD#QO42n?OI+twBV z#s3Nz3;PRn4WWoZ;gNhhqs0^cX#l3M+(-e{qXKZTG^gGUj}-n-3qYC719c989i9X5 zjtzd22B&8emzfIU0hvl5xY87NDCY}PFoR<9M%A1Y?^^V}2Tt}sdb_FW`o{Tg|J6bE zb$Pu-&tCrUzUY~$chqf5(6RIs%Fy*P@qZT1MW%E2Hz+9I+^tpRc)uoFZ5Lg^62UFh z4Vig)5tDVCi?245wv5x8D_f_$m*>D@>w{fLD#nBZA3()kxurHt?vqxBod*k5K z&%&T-;NpoLhZ+Tqb0qF5T4y<4L~jA#Xn=ajR=|t|vg>2sP^FGv-WSLL3JRqX{vp;2 z#}x$txjCSr#1j>x6(d?w5hf5mh=e12fBK#;)5~%iZ*XPkf#A65O<4^8GELJspNmu2 zIoDjI;AbYv>5+!<+(Y{?La_GYfRx0SJak1vQ(_nem=Cqmq1p6Kmso$R7~n;Oa8TT7 zU)L$lO>rt4Sdd(RVF9WRc_2V_=4W;#Yf%Is(B5ay`i zG)Jhde*5%xM$|H#xDlpl!$F5_i7|`m-(omci8DPjfd}9DrGJ)XC~!p_cAnp?)u_4J zk>k^gL8(TE_&DEY7F>Ej1ffh02&DER&c&(X8pj>CJiKUY;*V%Hw#%U)KM=yu!-|y! zc!&=VE$96ZlV;9xWQ$Se$a)dZC9C6ozxio)p*aUD$}In2ut-ecBMjox(wH-rFl6Cm z@|q&uO`E$o$@Ni7ndOPR)K1Ap)A6*Eyg5%APCF?D1*m21zw!REWAyZ#MX5mMW!2Zn zySCc5tV*5S1YqY~?_!$3d_&;7M-8AXusL#wnUg?6zHYgwzQTsOa)lJIKH!##XO(+_86jp1*+>=$lLvk$5+J1Qt z9>#NTYw9IHaWlt8?w3fSMAa|1cFLT zqC-k&6+;Zj#<&?gm)&tf&yAa?Ti0D(SIlzf40xw{3x~`x8g>D>jnf%6qP%N^Tz{4* zWSsVPYIx$7y=mso>0r83`x(9sw{>(VipKW_n-eN)a@s8;r?UWG-Pqx z{_d5SoigIN-EpVOANMsRhdgHeEO0B_iR1CxVr=paa$c+PmK>?BSvH zttB-%d>_vqb(4|064n>SBh?}9y89o8!{P!_Uv@VCvMG8_*`g#1xf}1=sw5RQNH%JN z?#V<3nIwF>GM2qsaLDO+La}JB)$2FwuV|-fgjjCyyR^62xj?fm{#nx?_%i4{UEUVo zC&(_@SDCDi#tBk5vsz~F9~$$}mc3AcXaUGltZqs`_*17AF!|Iy89bQ9oR-$RAbsu& z#e(ulc0{qyz7U_F(P*fwcF6SUs)L`Cuxl7D!Iz|6bFcR9wn0b8t5Ay)wKMuY80djc zsGsD~@H^XhxQ{e)&VtpNB6%es|8K)wbh=Dpnb=dsk%|{X9z$>LTGL`_hM60rvftQb zesqdmGooR|zvJWYNK<$2oX>j%d(ng4bLN1Py zSAn*!@Y+U_8}mZg$i-e-RC@7D>U9ylx(=yY%|Kn}2N8K=ON zpub<837!F#dYt6?Z^9$iwo_!XD4<22p|LvbT+4otq~gphp6w^yNH}lCRX+5>2yxY#`0S z&_hUvej}I+0>KToG;giy*UFAvG;mz0A2jFVN-cEWGV_#C+2ztl49OP^YGC)KCtip|hq8~yu zLhA=9RuLDXep;HqeQoqk5P-OW0e(a>^vm3J2%y6>)i-EZQhwyzoj>)W;qt|;LgI6- z&GwwTYEE3v8W3n<7TccY`M!na{fXlk1|Wf~x~lVYpp%M*05No{xWf<06o_xBXigI< zbZ{p{2xFo9+KgtpP15)^h>Rmks1jZbkb(A(s*c$>z zLYn(|WvX$#1*Y{Rzmv01d`#hD^1Yp5XR@C)im8uZ6t92KK3-&F#tf%(~3S#yjUNRC=Br@p~ylZe66pV z72{-51;%*f`AA`--d|c57RS8&0qmne9?tki(?=fBch9=H0w?EFc4w%Ru$vw=F6bEb z)hGl@38sF{QRwbuGp94kBCHyA zKMp#|2kA|a*an7YDl}Vrgh&-3Sf4;Gigx!9$DOP)IO>D=xpD~t8hfdIs(f&~cPM$gRw&G^& zD)g-(Zp;^}Zk5>auCAHBOt_gMH?K6*HBgHgOLxt`HfzmXEAF6J2z`-eMe2xPi7fM9 zzW3m$bMYr9<2rFc(WdJL&{GOCrF~1Y6Md#+CUT#{>48Z-(`a@IE*WCp}Ld3==SYLAK`Vw9L99lT&BFP??j+OOCZ9U)9ky1p+kPU3z8v8M8y#RTxG9_-Egt*tk0on<2~VZXzhr7{zSNad z@Lr)dhGy6ah_J%Gcs_pq3pO@77aM>9=?^UegF~dy3V&=B^PH>cVqzZ z%rQHi!RN~p?5?1!Tus;__kqdBQ6k*00ip2}orC6Hm~+`zlAsbmiNJ~aoT<6F`L+XA z5GCrRc4JBrfl>lv16&J$mizxvbtUjruU%KAQmG`QGN+PE37H~AQc9Gm44KK4sT^Yo z6(K~Ki%`Z;ii{y-I9G~Fp^yrhGIb2!di1^Dd%ycTzx&?bo1FjfJp0*ut+m%KK0Kzp zfDJtg{`|P|9<47QDfhn)gH@~`rqaXZ3q}vrN*-3MJa@iF1~53xKBvkTIJ0B4mCVbR<9* z*!swp+kLI={B6GwsUbuvNHm70%DW#O>wJOZUR6~Uk^^8ZIEEpwGWGrhBJjcmp4FW% z3`90)!u1KD9F7A-QPML5TA0YLA%R~dZ?&10_0RARuJ4!3pY+X)PaL%6Q;UF0K0+<7 z3Aq=#4*QS#N_!f-K7%s_&lBk!)Lvrj2GWND32KfAM(9z%CTp_ij5h)J2pHm&iLn96~078_k2UsjIB;A2i7jbpW0bYI#8W$feNql`MdA2Kf~*T!6K& zd}nKY&$+mat1p|?w>8YHoE38P%IsG1Ee~B$1Y`wU9R{Ox_$y#?h(ZP5g25vy=4>I6 z6|Ny;l$4mPgWs~AuPl+~GSZcMU9za!{k5yM;m-bj@a1)tC2C$0;kvsIJa}|0RSQ@E zfD7bRs6Rk2qdY~Ki1Hnw93(+}CScMt*~ycLBPd?EVa73nf zQs0UX8pxh1fV*Ps@gIjpf8IK%Uxb5u5e`@haT|b^$ECtQ52OqfKU{m%?ee0BaF?yq zYkkPT!_YqnG8r=wo{I&$i9mdzeE%f+=t#0q2y$KdbbEdMjmL*n_%^`8WCN$FTQ77u zNY;TPBFA*^yxsX;aLh3Ep-*_DSsR0!Ez5w#`Aw(jO_1#^HuM3RBy}a;6Z}75H6p0> z1?^_2l_7@!Ao~R;Jm%m9iJ%G_CX*bl*ae(M;j36QH4M^OH@Q42A4hC|HR<;~(!dJD(GQ;CD8~O62UuIYDR>kUp5+#5fgTgM5-_AWVkn z8vA=}EMI_fAare;)gJ2pmglOpggZB)#$}%xT>{1ja11N{eR_h$b17mE&V;RqCkSTj zAP0|`><7b#J|N1z7HOT9Qt@l$N|9iW9eU2S$*S~h zrb|rJ_)=cW1dHacZp!uH)|lS$um{f&`7iN8xcCCIcm2(nRy`m+_~Ub~S#=zJK5kEZrQs%0d9!dMSi~B*zGW zHNmk3D7K*eI`p6f-w08?fpxeQbaabK(He6NBgtR`?sv~S-oHP9*Dn=U1LEMJinJ)6 zu}?WS5G=$OfQfK0h8p0}>GQnQ2Bac@?l5P=Yx08?UE&mL#)8!F#0NVmoQSxduV5gN zkdj*gsPd_Hn}FYMXqb{kJB!_py%oB{?nI^+&P7#G$A1mSHlxMa1u1Wju%`pDLnr zgTMnIv?g^BSGtDtj&eN2Qb;9wUJRF!c(x<55Me0Yl>H@09CO=u$FoCPhX!`|?gd4}ao)+1ZQ|MAKEST;z8PB={e*C1DjIsd z*ddGGj0PT*OPZda(3&$a0k0E0@0N?Z>;>Q1q)R-8OH*y_qC25BH+B2&}sQaO!AR z0&7pU&7?KQwtgoMh2J-uo#_iZcYdC-^Z)Q--Tph!c#x6^K@`esl7xV5L8O-aMylcW@QkCxldfeBe`^ih%$6LT^X_UBGLfsap>+M}X_kjXUf z?&Dl7i7Yp7HR>^KZ3vD~`-7=he|zw++o-9$41{+Gs$lkDm$AY9tySIq?A9OG1&Z(v z&UvhhPgwsbSFaz|e>KmQyfFs}T-KLHsRosJI=T|Ez{a%OhAb+YXplq|i{^8p<|~ExCtnFknCTOW<>BrI~lkvD(Bpx z;AT6iHQhze@{5d& zwRY9(L*BGWd!)AzEzJ=sQAyB z;}QG>NJ=;Xe>6Bo-vfj9*UCo+L?WcV zf;kWDn!y@EStVpXl2?Y@lgtQ!?g3LWO>At`p2RhKi}a`eFgT_{=uJgQ-=}!Gaz7Ggo#bMD5W+ z@#h?-J~OzKk2)Zes*5e4XMsPX%@zp4Qd*drEg@0X$y~MbNU>D0ZqThL3yR>^ANB_h zhJw-U;YihcKufK=`QamlU;byYPinjJB*fkmovD4Pzb&sE=uV|kf+DtkxpH2*#&i$3 zp+^!<{B6sZF2Tt$YUunVZKm#rt10k2xwWYdh0UUEGqtkU^zP4G`<4JT0z7gK`mcVv z0WAHy#LmNV>05pv^_UzQb@#t@mu=`%ZP&%RtZbqPS|H-vNctvfT+#VmZW;QfD&rHm>{K&p9n;eVxh z$oU|T=L_(EcT#M46f`nq^*Gk|^!@NO;_Vp^#gPov3U)-AyogB8{!Oa`Mo>V1jnq)s z5XXDQU;j|`k1fqFq^iU~_K>y?XnW|W`|T~aJ#6+!>|gs5nGiI1kRG*IpJu19p~(`h zyYRt;9^?9TsgI1!!C(;+eRS)A?66HrtuRtASE62!qS6NC68o|wwqPwnUc4|Sy5NTc z2h{_XK|t$2IC!ymiLRHF#Kt313H#`iq;%2gfyo)?$Gwa^vhFAusUb}e74j1pYLh?d zs}J!3@X~UqRf28#uYSE&EI(0E!w(HZF@R&-|^%j>wm{ z^aNPIKRCI%(t{lYOa>|p1(cV9Xxl@65!8qXN$ANV0&jCms3J`FtA0I;c0oYBsai`$ zl64j4769Wxbq4T6?B&r2{;md?a`WwB7y+SGy!i(s5iBq26$AHDwulQms@bjBj zXYxkmBw_C$5<)bRq0qzz&l# z+^cKLW;MvYut!(rc>pYdgoCf|v(V|Sk>|U^+1HhPxOCJd)h6~J|7jK#<=nrcsQ^}L z3_0%V>qm)uAuvyT_iG{|Lu!n!%`4XHpZrG)(B(b#d3QiM(pI!apB9`r@oZFIDf-pY zpv%cgs0*s^QI4kT)$TlB&?YX#f1rhm?B_)1_ygvr9}?3CQw2^IqfFhWI}@2{*(L%_ zE>UvxRzF&#LNPuJCWbKO6A0q_b_@gAu&uB!kdu+-Z6L6yCl*_2&eAL1x>|~?Q%b_H zoixReW)(dlQ#3Y#jD|kx^($rDJ0E5u=1-W(r~Z@byaRa1XNRc(Nx@RN*adHeq`L** z^-CO}*?=!9;At>9@b)1{AlfLgjvcsc8?=MPt`q-@K@fX7ye?_l9sKzIyZl!H6~iiU@hz@)GcQ3)@OZ6tjnWLzG! zSZGM=9iQ2o5{aYjAvgW3xN+0r*P6m-k+fscX@xIg;;QHWCJdPScC3!7Q0VQmH=z(g z`j74rlA>R=ZrQ&odpX?Bph6_5I`|~~|0q0GU#Yb$5axG3c_tOr58BGKs2EA|EV`Ka zF59;w*D=g%`dzM%q{U!51B3YMCDecr(%13XLDSvcrzgaP znByqVRr?nUSK(ixG&lBF?OfrS^uXuQB4{2|ll*Y&;cyui-UFHpPxj>@d5DP&wel9p zjr%aSTSXxB@HXVrAevANK-~+kiM;`bJQQk7M;q&21t0KS=CKdxD=MwZb0Y>TUtih) zi7WbSkSn0kTOQLoAc{tf5*O5+%usDS_fr?|4`3M4LQQr!qP4*Phy=$vaMk{Kv07SM zs~fOg-WY!zx)^TSRJ`5=a-At!a_Af-IUf2U!Q0BKgR*378E-1{p+e(3xQ}C9xl_lo zi6Uf?z}=jSl+WlhpB0!Beixat2edM-8u=1G6(Pnv@>~5*g2QKxno2{OfWhC z(n4xhw5RBqvv{8UXPi&<8&v%o3N!QoWN*U+fl_tbt`)jO%7X-kd341$`ed4&=5_1P6>T9H37=nYOkV`t5rHLDx^Dxkht^ zT|4#qEV?0!qzqh7&B|_iptGdu&kL2XUBX-wIi^BPtEP;LOjCdvZ_*Xb_oo*dS{<=3|*l*QhmE@W1U4>#mszMz*N%I&F4$2m|cIcXi_p-`_=)j#!`%U^zhtLqUL5OS<*) z@r^=6Ci<~yFob?kb5exru9l!jln(e&;2ub%{aWVIIh z%2Rtzyl7#qb8VTMHTdl`_sH;?eF9Xc-`)G6OsRsDF*`S@@FBW65=pw6Xkkt73I2tyTH56Ow*`Xa zD=_nFb$9P%6twbvLmNM={x!8+7$iSqm3#W@zGAQ3#1(`4gKA8te`xpyVf+Z9D_3u# zUAcE#ML|Y}m(&*9VN24o+pD@V8I!M2nd4x^-=`b7*YU)pa(DFYv-EtV~4v% zqq}UvH?^tyo~KtLWTL^R8<`_fZwWXZ(#n91Sac*A1RrVYIjmv5F^>Scb7bgiMI{!6r!&&McG zvbpVz>oa~}$QLQwvEIrr#cfwBeiPIeEVX52aE<9-6;2hr8RK)+yC4(7q6!_psQw--WADq(1T9`nk+MWj_F8 z(YbGz6e5y?BmS}dR$_X0Gf%`mYy6c3|CaR4+xzNH-!&*|DD%pT{OD!~05R{%qn(DA z({|mP&5JUr-QT9Nzf(VP*lgHEtHLa{GgI_kZOO__6tBpJ$`wgr^A#MNKJ)hncJun* z8nAZ_*h6!GwHvJNyB;0h+DGBK{H$_IG@Z1XVAR(z)<9m6xp@TzSH&3j;BnN{s9aC3 z+kIY7EONO>!H2<>#;W&EVnvR~hvk;NldvDGowGh57+Fu}-qALeoH-sd@I>!N$>B|6 z-<*rgI(8;ZdD5Tdsa|?!r;J9!wrKiuN>A#C5_Z_7QSP6lqnoF$H>u2tT-u#dUaOb? z1>M?c;vk(0z-R{tg>o;NC`m<2Y^48E9kglE=b8x_Uxiu{g@v6|!*0&5S~o)98I{24Np555*7uG=6s<R@=W;8q3g}nBc^Ya;+TA}Kk(ZWgcj||(Fi+L-? z*U>b;erp)nPM@r>(k{HpJoVFe!R;RFSMm0aUoyD&%UdbF)n{Ry-?_m&KOPK8Q3cfr zEh&uq$KBq4dft6Js4W0&fz#T|Yp?Tk^s zkau*AP9-$`kiSq%5|Yj^(R05O8F#4`)Y3ZuwfETh+fa((WYA=Rvmt7G_x$znl3aOC+qm7Uu=6@JgnmgGAArVW3WsVS?~*wLr=CSJROl32U3 zImKRdUYBP9IPo1!=^W5 z_7d)+_q)~%XY$?kD(T_S4I#>Wtb01g-BB<;?o^x4`x!Fu^OSG9XIGE$#oAj`;NWI^ zO@RH9wg?IrZpZC`n~Mb|N(AWl~zt=#$L(bo&>IQRpkex&&Cu(;s z=T$5Hla$ z{o-MO$S|$ME2)0k69^OnJsuq9vf(_jw6ugHRC#tk1tY(3B+Gw^t~Y5Ga8fRDRCPV6qT*nDNG2 zfsqDAvn_+b0vROE>fy#L`d7IoSz68GC3&kE)FRRZ+4dBPrrC7ly!<}9z1yBH=IIcI z?0R)ZFCwcYbG>d0AuhmCh>UMypdBihg+I9q#E?c|-0iJ(roPTx4~5IIiqc6ZbF8jZ zbk`Jbdg*6tl@`iIX8#hZd1`!*v{{rhPUB>OvB}7aF#AqbMXRo<={qwqe|RCZ*U9{X z-wUj{00b1d83q0sIIzWQ7TvA;%x>1ev*+>MnHNs7Lq z=td0H714{X+0DgJWF2S%>rAj%41Sl<7K@qKfxNj9dl{FN4UczEQ3Q`suWEDSj|p{J z7y|eN|LvZ54cnu9VQv_20uS|uvY)O}Uax$d%{abg6|z;3Y|~S|3zud)%I7-dI`+pN zL2BD%8L7Jg!{O2Uj>o~OCfX+W4Cy2SX^ZtaNXEeYLw1XsLak2D1T+)Dkq6uTTI8jKdSZ7?XAzO%`~i@h~h!Q)li46X7#mx;JRMGQYhP^Y4*{%9+r69UWBz?YX2# z00HAs+8l<~Tx{-uJRX@IngX#3>6)K>hI$P#0ZS4^su^oXO8wGWC&VRc{|*r$p>vj& zE6_ar>4u*`(7BGuqN&>NWzoo;DuR<4h;kH@kTAq?J^lu?b6De}=Q$U7sT1*ERO%== znHSvwk+@+#^EkVBD~eOlg6{eu?nT3q2`9Ycn3wh!(ozA zvfB6%&r9=yBwHnjB$lkWQTOUb6u=k${B4_iijrDe8XQx*38BqIPbRryQq?l* zI5G14e5vaTgtpk|xYy_2G^s^YVRrA{AU

ArX;e1Ceny)s)@S%ayvXRAU*Th(n_X z#PL`KJdB4xihU(|K+^LJ=xSu{fxv2Sg~&A+Y6pS}3I1MFL?~1jur-v53xMsueA%G> z`NV#772EW@Q4Y`ysM=hwbRbcP?bdmk_XtTVviIP>+Er}p67F8yQW&vFAIGu zxli0R3Mw+589oC54=!-5EF0d&uoO5X=*Y_atVERTvb>Q9xzk(_%$zy+*xpl6=i!Efb#5C2 zdGR&<6lDJk?je}DaWoVmwMf8^hX(_d!S~^&ak-C$)+jNN?Td{RZIN*PRmZi*HQUo` z5;ZhgFys?8#dO*FhJ0ep+?jT*DM_61YVL|j?<8N#eC;=OPe<^~Mo!!_ah*_%yN}e& zow>5mJ~1BHINrg5ddgJ^|B^5ZeMB_Hg&8WT(N#3v9}C@SFtM}@OwzDg70-ETUE`WA6Q;A1F;{TsW~6 z5C>3SJ2>Flj~_QRh_*<)?ZmMXoE&x)=s&V6D@%fo`pnyNV~uv+U`dSXeXE_TUNQ4nP~> zdam|Jm#7FIdw3=~;I!a!!*)^cB}-;ze-wv;g8^kiusobn7-}JL91GyIpj!@o>-X7e zgr67R3ry>CP)_kMjJ(q|uAK5lbN?->v3WmiV(um-VL{D$?#K*GLY0q94k9U?OtXY7 zddF-)t3bB|#0D!DA(B0CwAt;I7$dO3(Tsx^?M>cYa!s7SPGTK?rY>Jz=V@jBnBDSq zi^p;%(}#>>(YMX2hJe-F3a;=Az^RJ44~p|WijZ(32t%yO9_Up_{XRssm7utNtkq&! z4nh=lJo-MSyvxq1L|cHsqU)~TqocA%hIU3nmK^x9p-S4z z@FSmiKeb%SH@Yd@Ec=T-^NDB+SZ7^K66GXFM^J5#S?1c8Vlag0F&26$_JB?tke(6~ zJ)$!{!&&PrvsW{am6Im08g2PF-vx}}L3HKzqj$EQ++jZ_zC5@_CrpA_1E;1U5$Jw}k> z@Wv}bGCmOp?~gQh1(-euhl2d}kcJ$YWl!<6-ql*Cs3h(ol+}ue~4Wq3e+JrOH+Kq5T)bsh~%%p<`~%voe~r3GqkNK z2$sF_8w53!aH0pARLR_%3!wbGMx;_`^UtNy{Jn%!G*P{U&H1ME_gME|Tfc$+3Uys# zTwK+sPaLPU_qTE!i+-&5tH3$`s0XuWA`inW=FeGAah4(2KT9|7GQkXTxZX8#Ull%2 zCst^Exyh4rZeDKYQ~R9DcfOMZ7??EE*1Vl3s+!n8`sNIZ8>=dvBY7ESZ0F8h4Sp0t zhgUX$s|%Z>>0iCUy_KH56hpwgSQX=bU=XwWg~JgK!eWDumwVTxkClyg%SY|V7aHG; zG4VHYlNARh4V3SNYA59vp3B;;%tVg_17{5$2Bf3o<0$DigUdozh^zx(DPYMLZSOKt zhQIW>^%j?x+rc%d$tKPo7?UX8$e;%vt z;7SyiCDn<@1V1Qi(zm;PDHd^@t7LZ0;#|#(Y>*QIt};KLvD!HspFv~u0|%JsQ3ya* z2wlEmJGLNQiJ?-{nXbIvYJDB8jn3N5E}@9?SbSEchLh^m^%o<`h5?nlbmExzzSGp| z0lfy9FA=+AzR!4mId%YOH>8Yo#Q6f|KEU#gXmnrueM)71&CT4-)n`#La=Y}-MNyyC z0J75b)+Hl_rrO%tBvHh~!>t5XB1meqn-Fh8z4`BFqOJeY0uYhdssV5GdXafNf|@;CkazwJ~M#RPc{U?MvpcFWE8>2gsQ zDY@@5VkfXvHLcmY4{BaBHJQRc*T~aCiccM%{<@+qt-B=q35f^|5}W^FpQB~2rbiW1 z3ukgh02er%`NG*>G&$`ZR+u^jn9>rxFoEe}Wux?-UK z2L6dLeb8|rVj|2Y3%o>-BjD%w`d6!+cuy70AjqEor-mdG1D|ZAio>EIOndbC+LUFKadK zNN(KbqJifNo4aaXq}L&E{tfExmjkkS6mkN%wHw2!E#H$rX6Epq#R_FEfPJ=L`1s*L zpp=}Mq0lSQX=~%AtEeB4oHaH!cK>DU%+@UV?lqThII+i@S!#F}42>8Mb?ThbWFZce z6&;s7;Wz`MWTA%Xbl*Qa6|5Yed>&INFPqXX32Bbv>pfOoTx~e<)Z$6xvP{8eLIdw9 z4|+|>T_vAntW9)56C2C<{PUSh>z>{J9wy`{f| z#_@hPLGg+7Q6R`&>qG=UzI#zn_ueI&ZY}=!h z!o0c^-wmbTkGOs-SnBvtFN%{zk7j_05&SqOH}?I_-jGjSPrkmh50k&uipLu>`Fr#0 z^KSx@q>dBcu^_%=0T@wN5CUP}zH2-T$O>PI^|Fc;=Y(n38T=mgPAt6Ve=D;x$P~Ls z^fqr27Z9hn8{l-M$B5wR^IK?F?YGx0=Oqs^P|EAiaUXjn6MB`0VS2Sd+unF&?!5hx z>N}QP*4@T*Fb#_q*HX)lYt0!zYb>4n)=THSer>7QTIW_7dKROYj;2m`6@&_;*^HSb zHF>3|4EZw!YB#(w&1+CCXRmwv#gq}Dl!viu_j@f|Q15u<(!_M9j}?k@&Ah!{dYO}Aj2d>-J4g6W zlnnQlYb55j_kAi6>7W=T(FWwQ>o3+v@FzS!(#!Yac*4Z|rl^s$sRZ?u0at?)KI=;z zIuz7cYVxR_lUBdWKMcL^GL|Io6l3S)sawxhexoTs{H@WL*Jg{iccOM&{(a%vjzWqS zT8agN4n~o8TF3hQ#1g#Pl$M|Og58A<<^-?joYHlZZ>1>L{k}Iodfw3c%&l9iPyoTS zE=GNFP$676K%gFFp{6l{OTb55e z=CUnCYGwScB^wW(=sDFG$)B#OCLZLgA_ zk%1|VFD>oKh5qOFo;Zvfmedm$W;}#WZgBRCj{L~r$gHrTiJIz>=1<91FA?%fQ?)d%RUxr|D?W1nQqit%AE;Yao{SDXy&4zYg<+;a0lF6l+ zx7SqNqJCqcUxFP*vQNB=Bme#Db(=6^6*$dN*hZn2hVE&YhQ0R}>`Uwq+1_Ve$idrt zY@h!2T4pD_eBee9_+VHB&K)=noE9`OFCz>U7?1XuJYj3kXqxl9%emfYIJ(6H7<%1`M`CyH~s0jQ6fxstmlttE-sssIV|wtnnI9eyeCA1Ay`Z zB~3=gTjsm}x=s!C;-71I{A1L!)FS*rk$^qM40$@BIp~ALGXPM-c<4G`U-T+obLJOm zaM|oAoN?PohnflGH9YFU`bO{k*QNHh-nUy3a|R8S62}4X16uiB94Zh*3?=QPD7&Qs zwLfkEG8^7c`P&u<^aQ9cN@!$SV50>?P$&N9`il6UNF=IbkRVKc7f<$XmoVUv5FcSr z$L-$t@lS+bBX)=mdmxGh#Gev3xR?7|=>OxEkkR|!SIkhve;T`@*}CQ|E?VTy);P*r zhi7|`G0^7qQaR!hlv!QyYf?ZR5tlo>@muk) z{h%QHQ(q^05t$%dxj<^82*0>L#dhm|pCoV6I&G{VHdGxa(n%XU2J3=w2eScW z3)kINHkO>H{K>kn&dC=`s8tZco%53TApL|-3se<=8$i$q_WkQ=@FuYlF#fK(>ncBV zIgtzy5@*cUppJuKBOY71?HK=7`^#5zsQqw<^$I1ruBjPDxucC6#7n=IaI`~a3CaSu zfchTi5$a~-CuA6tX`Y6bh5Em~in57(Qw(E9L;g*`tVp^6M&Rl}!C>`cKV2*jR==C) z|H)j-L1GWN13Ne_z(*CI_5q|nP%xswq9Grz3mbU*|63gN5~onmuyI%}VDvG-8JyRs zD{x+;@E}fus1%@C47IPknE1g_lcjQ8p6+swG|n}gZrCVFOnCN4+<}c?3Kxp}4gj&= zD(zQ0FIdAKG};2)I!L{c!c)h)rPtv=z*dC#gNclAf+2o%UuLJB^V*OdecoNQpacO{ zo)hctf08VK1}^+LVBf@*i-Y|5e~iycyK<~VD=avSvSDN-*kbq;>tIj)=WRRJD^Laa zi=t1gW?bWkTo2FyaWDre2}(6n5xWP~e|>!YgHHWtcru9paA-!jAcZZqA`GOk$!ZWoWfgj^r?Uv2%jER4N(ij=hvyK`(9Eb?eTZ+dDUARCHaDFtT0!aw% zIRMTs5HpkdL>(5w$8RJv;F6>J@_MzVV|v@yp!@~th(jxg zuY9OK;voh`5X)zxqamVR2(@Rt5}##*3H`tOHCPwq75QikoC&YIkDj=uoe^jPawL?v;M&=Bg8>6p4Le0VF1pKr<|iiHrpv+>S)oyM zMHr>pu)nf$5qf;|3y4R&JKP6e3Kl+W$$6rVYL3PI_YvGWc6ft~voL<);xP_dA1@ie zGGe9Ujp@8f*RCl9J0j%$-;)cknfL?{I2u(BhRcyQdO+^eFs!(aFMFP|Mn4*0YY;H- zH6AtWabcGdn3wXrBS&G46D3*sXN zyMZPrw;ppA3^ajkMDVGBNDq7f_Jae(>%zk$rqDh{(G3>Ff)yQ9Wc(mV17yBYIqg5> z;i9p<>D#_YY7Ni}GQ{(Zz)AKTF;~9LJ;Wl_n*@sH1=~Yp1$=!$3iin_bVX zQ2p}dOV#bUsFl!y+fy4hTB|s}fq2ma7biyaUbLZS*zaPDSD&4+0}c7CgLJZJ3bCP2 z2hJ`5D~gsYNl!}V-J?C(&h)TC|n4DE| zd|4vAHvGl@@suHI!bL>326)r?=P%PlknxVapa2Om$&|wdpoeixq)twDeXf}WIampK%7FoYICPDY2 zlNiovAR{s|j9Q}q^-#w8k{@ssVe;jdE0Z9*&N(`+&owS5G2yQzsCZ0cQG_#C7c@@v zHQXWepHZnt01DN(!Q%(&xPM1YRqouNc|~XwcF5XJmv%DQ2xlZ=Y%$ z{MSQs=0pi^uoY)Q)X~uc9_)cQ^02y*YSk9X%cFn-?fn?+A)?w}5)GhzX{0q++t41E zQ{jUQ6l5em4>SI#`OTQHqv`-VhP(n~B>uff%#uDZ(hwMyU%{xm1j{>p*=7)S?1VT0_+>%rE(f4aR))?7ckHdAM_JXTRdBS8Av7GDt-W?!SE&$ zpXtnTJodlD9tix_ee~;cd^b!eL7(AqYSaao9C=6T?*AXgA>h!94uEnEc^--z9E~{0 zv0|WNOZbzgccJ{V0||=oRD&EEEM18oz~oDok`tJb`@&FxdkB50C4YMPBAggKIdrrw z;SDZu5T0Tg88{zv2FhG$nH7RlIDl|KLW6}JNl+wbdiS{Z=JxzB_ky$`%?7C#)&!IP zMyuBeQ<7*EM5PQm7dakCJpkLF7?7v}p+W|Vq7FUhpqw$p{d;lq?!mg}4Gu%~iMxaN zd8f$HF%PUdJw ze)pWYmc156pf=nQMV}N>1%mKrKHL#$r^!O>MzEylF5^{_J%YfNFBGPo==GhVAx=?L zVgisr;N7@=j+mb^CKXooWyFB$6#ibl4Cw-Tn0tUKL#T&#-h>n5n+=7ESP7}gg&t3Y zhjkDF=(AZ`Spj2s#pU^XPR{nt<IbeR@$5b9JjCv5o2Nem{(XBh^vRxW~05`Rv2{P5AhG+ z1ahKX%e49UUZ`l`ahJx+Z^yYfI7VK7M21Veg^+{|Kj|rL&W-x-QoJ<~7~$Oe(d5c+ zbdu{w1IaV+B+z5pNE!znte?pAnCqW5|Y3*~0}8(ioQ+g~_LR zG+G&8g6r5&FhC>fA5z^~+!tzq)B{@{%!CpXT#rruY8kknuI1jw;J4fJmrd=rjUNj` z;5jA6h%*?K24a@_SF~+(6e0)2Yk({rPge9vv77T&(W>v`xNLk8{sJ;4$XFEQ94}sE zBTQENQP%&rJGssqtL3obYl-WoJhXx&@Sn+kmD!Hfi4SAd^toeFQfJZka<6L`QgNIJ z*e^&h$CQ~p8F7kXk}6Jj=$hy%z+BF7$e=T0cna~!{msp%jawI8`qFHc(t+x<@4yy8 z5|r4F-GO>G`mU*7z~+&{fb?X6QybN%yptA2<=4|;St2*+vN5cW*o9UNh?y3d{%)|q z&)o*tE{puCz2EB>Xjhr}&v=7t^$O4V# z8q^2!RQ?l80p4MlA=Yp>|0?7t=Dlw=7=2W=@g79-`9fg*kvqUd0)2>iIw@H{&x^6&EM@B` zRIFE!3klA6Vp^Y~fsEDP#C>}`XQA(VTQgySuonpc%p%ZH2QCK-GO=WMaddo1gb?E) zd?{i&(nlV);8A_ItfPfp!{f?%J<@2FBw`DQ|6?mYe%29W#ZQ7jAshstcIAW~>DfQP zRUw+I?j*$evf$fG$JP1SA&*VC0mBE(xkkJP@2j$fQu1 zLUW`SE!ifX2UjgUoA{Kax;Fu=T>5xmddlejEY?+lc>W6iV+R* zuE1a*-PC#nfe*kCqtW^Z8$<#`Obl;@XBPxeU!xq5(IH912VHEi%|lBKUZs8@cx1>J zA^RlOqoBATy<-nV;7Jz1+;TYIky+(a-k(Z6?-*B|=rF=ULV+;qLY{4_gik_apEKk= z%oqNh#_*DAR85lw{ZjvHtr zZLSaEC|tjo!1QV#Lvn}ezt*ouH|!80N60lewk2$?VHf0VBX zOGsxR5CZ;-iZ75CYC_Z}hMOYzQPy_8!pvNZH6z2tC7O1BsLgtv|L#0^5Bwb@YOb>0 z--eukWmnQcB8%J%2?Xw@dRQN6S)wvp_Q^mncyy*dQ!%H)aVPh+d3|Mz;9i^xs2~uG zJWAI=7y$u?WcDJJgp7374d)TU10v7yRwy8lD&Zg|)GW#gJY1i|;lD;{K#%c#@XExR zhqkd}Xsn-%!9clR>N!P{@P>R4THqF@Z`PnaoR01nB=c!OGCIPqGkWn9fbO|Raby~$ zt|n~ce_mzII$(}Z`Gb%ja*)#waL64|W7xR%K>A2!RGc9R`sx{9tY{O^F0 z;laUFzKV%}w&h+k%<#8a2tf9}gbuT37lHF?^u+G;9O{*ve@dfHQK@iVzD$EV&eiFvN)#)b_pnxW+zjq>(ANE zf3SYzhR_6ku?mX{dai09xe3vF1SL?dV14r$?TcL4P)HexB?Otix1a66&i`lu0Lj8y zC24NZioEsac=b$_UjTMS`V|+5Q9N;P!X!1knQE(yCAGR1TvLCDm*R`Axpf4XLH>uD zylmzh84{Q@gHaOJ;-O8xUZ^0D8bB8C&L1*|)*mBtGk`C!JpR&0DjSgUAp~FM4(ous z3x*ypn%F!+(r@2( z;MNGLJc-TbT-3Pxg2eHjz3;G?&<_Ye=HPZXC^|W9Sd^qc3?iH z-b|$u6958p;+H~fmA&pN&jBbFhUgG5fCrC1BZqX)=D;uSxiwiRlD8rJ^yaQNkD!K_ z)l0U!0jSN5HKlzTmp78!rN#`n=*GsbDS~d000Ix{|5YMkU};sq-*TmjjXHNmeQ+M( z*dWjz)VB-Qu*eDWf_fWuo7IWeaQOK0p}=ao!Fb*edjMGkzWRj@W?uhxhh=3I0qYbO z#uZWEB2R-n7R8gyxLLv-@JZUN(B(h|3P>0xI!3wUlv{|G=q(r-x%Dhsn`L><0FZMZ zb%YQ=C{<$9dD600%B_e!g1jJz~jo+7ZCAfZ@FD0Y#NBk9Kj z0rHC+4Ij`t?(*wkgD<@f>jO)H?i_v-&R`tmi%IhR4|ZevHi)<%^iqcQrORB;5m=pT zK&#jAd6~IppOh3(X~$K1pkwT$z(6^{enBAU<=K@Ec6RV9DC4%Ww8XZUu~Ps}u3n_f zSJ^dgAhMDUNCtrivUPfBVcQSuuhER|Wl`CHV+FH~fElCqK;{A%f}lqlK4Tm2rqy>? zEFEsHq21OCIL97{IthZwMb7J}L!a9wb9t?uZ_*LThm7X82T0Ro2eWN#l6I+AU(1(gt00-N>Fg=fz-Sf(VZw2*GD9(ToE?K&6iC z)F?z!ww93&DLOjnFmh~Bby~80+lUlTm%}q0emIuFA0^dFosW%lMr)01e}6w9I7BTX z53CY%_uIp;^nGDdq)oKNsEjcIICn%Yp#zB9kPi+R!Y6H290BMmhJJzKI9NI%za9Y@ z-6F$^I=glfgs`6$zr7FoI7DD#oJUgtoOofNO&CdL*+P|AFU{vk(C8`CA=x|8(Kr)9 zS3|E-JJqM()Z9GgZ-ewjm>a#ZLJd|9;l;>tp%+7%KzhfUnh4akNT3<@O+3mg@^@==rzSrgxU(p3wpB&ln;)kjP^?o{29{B#7|Vq0)moUvFnifTXdon!FI_R zz;TO26@yaHYgY}C44XT`N=PKtPsuU zSWt95+RzIfpPPW%=Rd*wmgsVl0MsyxKxphfJwoJysESDaR9Hv^bl8{y0Ree4Cbhew zj)Dj9+0*7j1vyAkPN_JwqTv04)ch@Vzgm>$Im0&ysHVMGch`u=t znS~K~2J$|fO&MO2=~H~OHxM90|M)awhaYTB=^Vo%K`HxqB`lAd&=F!I8<>m*gyc7g znq+JF1%R{9+<4~7@yydhB<-i~9!c$SO1f=Y#{Qughi9K1!V37d?+ISQSSP(jQzajLb?uXS5&jhts>$#Xp()Xq#=SX<1Qn(NVFc^q4F;E!ov99ve_EaLPtohZbyIKW8s^z`5@ zjCq2y`RCFvIFvy2f@}*HL;~#COvzx+^4Sz>dmLlk5~`w$rovt{hhG*7asN06JvcsJ z_La{x-B)k?G8UDaaaypx+eS7ax-xrTxQev8*9p@k0$5H@OT!`pOM|iMmN(bhs#{9= zKp7#yi+Ohk0w(nx>n+}*2?vQ5uzZ|6h+KSeO|LI=(#5E3BA{CI2S<$7n0$&+9SCJZ z7ilLgIq7D6p29~Ncaazf%M6${xqaT`QMtDKV#~1{oL)FR5!#XAfHcA}diU<#1AO7` zJxOae{O6xij;qeDKd!5<-?JxsKo;8;pczz?Bv5a@9haDR7)WYwZ*Q(;1LxYtwHk@* zHPV8e{rIf;3zg^{s;O%iD;LsfhAR#~a_^)7<^kx>J0PV-?t>~^{BAFW3%DQBu2-*K ziD>HtGx*aTkcx}i|3O(sZS)Mhp<*AE~>i2F5476+RTGL>~(c zg!ryVvw3Y-IgW7BTP9-=G5*ARteUp!K=i8RonA588^kz8tS~0ZXu1rp)kN=f&dbYS zh3A$2;fV_ihr22Faxt6$O-iT)?#9Lf+J`2Henj4S=h8|graEtk#_-nA>s3D%g(7&- zcH^W%?TBX~swv{}6~qT6%KuPxCD2%}-Ir2S5|W{WBtyxZkZ2$!i9#t;qEZxZ2pJlU zMG`U>AyhICnYv~|k|dcEk`OZU-_NVQwf-&ZuJ5~7@B15`=RD`^z0W=f)gY%rrbLw9 zoGIQ-e+`f?JZ#Qlb|R1~90U#++X}eI)lPEBzt_W(ug_RjAG>)8QpSy;_mcIJ$Y}v~ zA4lOD^XfqLEnGCyTarV)1YFVi$|0CvYL5w$2zwzT57Css>HaPnE^ZKbAU8;**9FKm zkSlfkayG{o^G#BZ*;J=WtvK}6)Y_HHZJ6c?8!v)^t|&0%U!_jB!(G}P$0L?4o2vib z)ii*{5SK}7Jy=3eFHkz+2fz)CvSyo+@&cDZAyLvjVmx@r!ARwSzS+r*s~>y0%wHIu z&}CF^8lI62=aQRs7IOL3p!6q?k>2@k=bp~t3FsB^^0~~qt{2a(qJ7eAS*3AUJ5=|P z#<&Zaf`*@>(cjT{*MMrSU*CYD(6^fueE`!=Z3l&d>=EkH1{rg$Linr$#-L(5RVHC) zI2ms3`EyE9o$ebsTLBPNFjk|3^Q@E8smp6_Q&j%_iMhR3KKGFdAGr5sHK-QINHA<0 zQv1TB2~+eP;9(ciuqUZgChW!fT(1l6D^aP4;}6+<+t7^=xPyRt}M;k93z$&W_fd(GL=}bFz(sF2}c2OcnUlIfD=eG=#(fQv?Gl zEBMleW>Uo?grim{)|Iev1!Lw8mH`k3Z0M*uAVWYys&Aa8Qli)ySgD}QVHC7nU3Kci z8O$?Wz6>TN)Di&9W^i@q_nI*3LQc@(e6X4S@1OO1$ui#Ywf%@TTl48(D8|Xt1;9u8 z5wNpIMr`!UhSRsq7=1SnIgC*-K^EwZ!OPIuDzBg!7I`^(C^|K$`EjPkd|1TS0Zo|fsp(2jFr<}re-dMpYA2qc*4 z5E2m(BJBX-yc($LWo?Gbwo(cDcX!TC)wu;Ji~r&EZ)H#%3X#RpjyN5e*!xAFaj2n~ zR@}dJ$3Lo$FU;zuGpz!g7!U@=B`~)N{Y(*!^}JqcC6gG2Pliaa}KbFvCp?k@CZrToF zEOf4s87z|{<-C%Tk~hgCR`~ToCE=~{%Hg|bglGPXJtoo|T$L&c84M<4h_a`E`2rII zU<__|?oUO-gqP6s??+oMksZWYm?EnMLOtPva9{m1OnY@z{z~mo9p_hPIQKQkAW;Dz zgUa-&ffzKAA+*67f|BSG<6|&uR;Cj3<~P3KYz5Q7;w%6KBOwsCOcwSMxkUjK%DG_! z8Ajkuk~XQszMh_*eTt01ipsB+ymg#oicT}NzImX=;!kB$2Ct92XlL~Ff5vv@+n3MB zM(wVdSx~66Q}mlpZ5o;9ZaVh;$gEMWLwHWiHq|D(Mf^B#qU}1iySQ03a>J{05o$;8 zML&ys{DhZrIkqw)85$SuP?@4L93d1-GPEY{T?NZrkBZwiDt$%VDdd=dqP;SF1F+^> zNdd#Y!0Mi>E>;`>Jpgy4M4)eyLC%_Asnnzw368MoQvLhKU;Qv7HjE{rL z9;gr2aKQurod&h<;jHcFhan`!xvs8P-khlUx+`>CmfHlpI?9pb$B(1XMUpz@Jg@Zx z%?Xh7;@8nzK@0}MYQRk7xOx9H)K<9W?99D(?OJh5HMp|R&gL;PVs<%fMC)kPR-dnI z93XWbmMws4&EA>AsBUpU%+A^wD(fiejNL=lR8~@QM^shB^}uYzK>;)Nc!zhxnKTdH zrb{r=cD#S~soqNhP6~te#7>UPYA0n3+B}q|zOB_ba)h*DP0wxO=XYu);)={n!NZa; z%s*pquXipfs$_Ua{$hVO^gl>xu_+rG8h#u7mf2DSHW-x~KsC6(^Gml@W1@OlS#3V| z{PVSq+wS0m!4G7P*z7O$7FF|_U+nU>m7 zMf@Tte>lvdSW(XsL(+wym zBpKKpkZVG9l@J+FOOIr`2dw~*eIOBm0F}s3I*UC{8W@xS1w`w-`BZWD<$Fdwx5l?_ zi*$YI@x`Wa$b7-|^xTK**FW_-tTXVUWN@FLSY%&mA8rC?X0GqyI^QSB>r?7FGLeut z{UM60V9GV{r>vyh8@B}|qQU~AfVe~*HwICo_~C9=7MA<`l5a=Hn~aIx02vi@^ms8C z>+ySclMUBtqVIT${j zRiR*DV2qj{Sx`78KxYy%k=JDm9uxA~vK7Pac&|X_fvJ&!Bmf9O*1R$<^mUu>byGU7 z^$h45c%;^_R8a%guxlvqc0z)PPEzA> zP!r^C7t>JktwH9Mt~ElNmQVng!Vv`=AA0b~fLLsb;o4n89Yo!O7joe%M9z?&y++wX zBna@NL3bS~{Sbcte9)sy-gj>XSO~c%BBVeZA*JE$>}<#G% zpHRP}n4)sm7r|SiWLf}qm^QMF+*j2<M=?NL zO-R~9f;uz1LPieldRVR~$00*8Y;^^bjWG>sXQ zHWGcJb0(rsVv~#LgRR+|HI?2}3SI?>jPv|dFBRUv8^s|9aP8y8j4VN6Ax1YPV66nv zQJVQlmHJW}=9a>>R?E26`07|>yPcsToSexjqW%hid6ZWuU&w7|oN|eUExd(nn8WO( z(}VL)ZFoOo6^-qIsk5e z%Z;5!mJ!1XbTSW&gY-+#o89J(#ue0NBOPy-5{c~O&$K$SuFxpIM^_g$Ac(str-hg# zV361!-5}^D{T3$I5U3s|?2s5EQ9<4)ab#4h>|@ez#WiH93>YtXlc56$vWQL|f*+2m z)3L?~|A%Q{cgTg1X952alF@)_e|juo*ua5F<@8FHM>CL@jzG8DQ7v8w^o`i0kG!Sl z1^c#7`;oC>-%M&dsk5CI<|e4ZK~z6*4C;mwNe$=YBU;xFSM_kLVsFil#VeCg1t61v zUlySZw>n;~sRI0fYpfKLU?A5xY9y=8R(Z%=@@dOuqB;>dVQ^wA8J0vCcr3nnvAf_D zP&dEM$hZqcj*vx}R%ek|qwv5`BPCstV;zoh-|@@{z782QifNoJ#K)%DGV*%eNLGaK z&^R6#nL`3n4FDBkio(N75Qw&+y#B1{%?}|hN&J(O$v7O;X{ciHzT(^3FrNoHQy^%V zqlUeOvV+W*L+~d}o9sUEZ3}%DS-490kN?6=lYN0o77vB$!9qXmm$^YmpHZwf4VVrD z!~Tlrk7#O|Ub!;#vE@bLRyaCjwFK_2LXx#gU$O3N+`CI;#0bt{k{Fn5XP&NEMz^bsb!l$ztZ_%KjLw-*Qiy>X6gj0c^y(s%GUF*lS zzNnBW?lW2XLCeZ*hRH5Ki<|Q#99L$gaj&iuZ`wEJ9?X9g=GT4EZ{fp&mnWUgJaeoF zP7GM44cDvjcHUm2vnobaDQjYAWNc;7Pm(o3dVphWRe1zZerV&79fj@`Bj z5yyjfKkL>Hx(k%nKul=HuhcXpSa3z&N=UurnBwB&C1qtvO*6!tzoU+ffY3{-^gbJ{ zxVe{`Pq`R194IR6TQe!gu9@QQFPpsPeF2W6YJ}n)3kfOU#~$la*Tohu^QlP)7t3t` zpMvkBRb0cEdbcjD^jg>}ZX0PaDYew*7nZ3>M5F(3C%11mP0^*7QPmr+@hfb%QCfSu z0N1ya0xlVZ;`hg%o(GITXW$i)%z}z4-Nz2sKQ=F;vPTR-5y2TR8LyhCmJ@Uy20T8t zYT%z1fH5BZP*_E6*N2Ec7f_8Ut`-NLV^V6O`(X|5nfq|qztr^V3^pCmSR7fHl>=G6 zzw+oMA6CD!EwWH7qTC;bu8s%00>3kqH;N+gUC0+EJYmpEqyvS*|092*ZP!&dX$2!B zDM&4qFa-j=hbY|9HIfq*ekYrQ{p>iNJ}w@q8_8NDG^?*x4XcbDD*I!4>$<7wzCXt= zW}E^VTm%^{`ljmA7wQ|q&0>W@v`mgc5VL58BZtXJ(JGU|$LAGD?<&M4sOSl|eevQ- z*hBg~GKTWZRop)xZlJt_uNimQsapZW=*-WdMk9*~j&&YS+ z*kVH^1`#2ir1Hm0^D?7!s6Bz@0;7H{df3S4aO{VKX)dE3Na1Gqs#8%S3knHI%F8<@ zYRHdpg~Y9B$sECx#u-5#C&EbVu|u_|qac39@kwGL@BpE0d^nM+QlEt%&6QK^{t&q3 za_Gi#|D>g84;{z_0uZqCQ}V9kzMg$Ause|IEmzex+w)5_sou-;Lb|8MvjELDLw3|w zKt4Hnc_B!<5oZB7`FG@O_PXL^%!J0$?QEYPY)4(Orpb;-_~qqAW~Rc=rJ`U!>;_5G z9H#o>o@>|P@ zp7Fx`&;kKgkz*k-F-@tE?6`!70mUL*aTcN3@&yaQIS=U$@MlVMW5PV3u%=K|E4td?5Ot=y1IxkX!1aS_%WVq@D9btnn53-)@u+ZqGbq| zg!kQ%`^x!OLl8Yrn>!d>1ZB_YgXcO?3@z`ey_uNkp?)0D>Ki1Ulf%Vp&^Q7D0n(Lj zIm|TFnNT)eI4r=5f)Hnr)JXxUM9nZ;zSJ%lt%2HyP89(<$KV|MmdHBsI%s*Qtwog| z`~W?uOn7vqttiIu=c;m&p=~3J9EmQXF?CEQP%LcHkphTgx zma68y@s&6E!}w(f=PhNt(aJxC^E4xE`qkGfW!oACkEe04vzvv$*YoMX^g(V<6_vsg zcxF*@_H$9rbNvL!mVbzUvl!xSrWM^@z7zA2=a!hr-9D@#h}qis7u8KZChZm*nob9y zSCpI!$W4tdEx2J!tCQ1uut^?*Jf#i;qL8*=;xj@Xq&Daov}!pYDeyt&$Sb)C7vmtC zwXyP3&5_~=`kL|h!TU#H4p6|nKd%A#4opm-^rH&T0B?6VwsWT}MA+KEu}bP}EKoLt zP0Gvo?OumYgi$*vk*44W%uuFP_1rFoThhC;EJCfh)q;w5)ma8%^+41_^Lu@zM#$(6 z?#FBQW&ccJM}es* z7j_o?7|!T^1{FI@MPZjWG$X|z^lEQ>eiYo(@FCd7IPImh?7s33CAq%bz*KSnzf0y? zzHKYJ_#f8w_B6R)d6oPl(TqQL7MTOJj~Ovl;>cBj%Y4A$W<8~jb=B#H^KFQH%^k@g zH?ZS~hP7Aa)EPA)NhABZg~KgHft}^jjNR`SJeJzV&uOev_3ue>k6K8t6H>ZTUq(YC zz1JwF+gDuD|N5uy3zH|66kgFs4vF`2DZcUUHBnGjlRjKBett6iDkPzMn#P-43)TmE z^(S>)s5@=Lq^&!CcVNq~-=**RZEho>F0ZsjfZYXr(pbX{5A<0)2Defd4tz0J|pq)oZWPF{P5;)tq>%2?+0#+&29j77<* z+SlE3Xhv(CT8Ac{4;rf!4K#~$u@!tZGLPz*x_5))b`ECOWfz9~b$0v^&WmTP!b-gV zx|7wgs9(zPb+(g|g<}rC#N#)YolvW8_FL)ur|)IaT22;2J+(Sx#V8gQL!RPQ+e#AO#ViQs zuFO^rD4M&`XX(Jm!V;kzl#C=fdaYw)>T(qonOyhVHlcAT={&e1T*zt9kiYCnapx;B*`Je*Y2p#qud23f-@ln>F1{w@044J_8<*7z4h5^nf-Qvscc)9qFaut353^3iIDHtkz zf3#NS2&cP^c9@8yup0Hd$3nsBQtcVc`m4@Fr?33?w2W%%ZPpF@MYi$_?+o7(KQJBE zR>EO0%g#)FGKMy$doHxd0_Bl$z}+Afh|u)-w@QTg%JiZ)-e9O&jfsW;5=D&;sZ$5)Y&-V!do!JTlJ_xW_6JH+f>p2J!^Kg>s+2vLB+K4 z|NfPx=2-t9#UZVuNHEZ60W-FPkCM%_2cX{YT<0>toiuwk<5oGs2MHcVZ zc3fIt?x}L{>FKH~)t0Sx;XstfQI~vUKpMNhJIw>G>LfM0tjl9ER^KuIf82O5}Q+1%D)w} zA=&~N1;z=%0rvNRsjAT5)eeghn6raqEE?Uq1*U>F+#pEah>h;;st^ijMs}Z#>#rNi{O_qUYb%vzTESzbW6DIEk?(pw&;MJSXb=K| zIzfD3z$;Ok#kT#zM;$t%)Okip*3K5Yj7Zq_JE*xStd z$k-#a5+IlP9cVf%XHalV(f#J%D8R9?2DU9)z6lx)W^s#AvcBcOqsJd*Rug`bk~^Ol zgT92+_^;M+Uxi5wo}4q0sqvWn>}gPffmA@d#~O z6ubC}M{8sC4_N3fUQ@j)D;LEy55NwxS#*H+I;Pfz_w{U->T&12<}a7K7@(bCLq)8P z!8u{lMq*;ByNy_Ai;iavGPn=OWRTgTdZ<3(``;7)#T#T}obX>;vZNfIIqf7sc|}s9%O`~I zC3F6bCr-D~4Am`%M}=pm#ikmROl$!%PT@$5-nsacTw(w4z^%`eF^d*C+V3>ZUm`-c zC^0ww9b!8*bahV#ZQ0;?5ZC{HR}@ zfZ>XHe-$DFuxFGb#O-b4ee2H`ESFKkL;YzLU0sw4pf3?3RJGB?k5}}jHE51`@#)Ci z-{&UfRFGY7XBZ+$PlEy$WG0ok!te~^VZHgK|2uLYGgO(U>7%5Q^IfW{4W%f0H3`7w zTDb7ZyfWV^Ik@l0E~k4b^h}29z6N{sHMe@7J*lhvd~4IyhQ192uHXYFMq^OoHSrl; z_3RV2u~J;CE%*6v)4Q1s?TL2F^Au*Cpv}=%!9$Sc2(eR#%Ab~a@ME)MWuX>p`SbM9A)a`d zMBG34CbaY#3u}LuV?rGXDDFmYxkt1xwrXk~7fXJG%7H0Zv#_ZWZP&OHJ{GlFq&N-Q z4jozb#Z2b%ZjM>?S7IYm=<(!WTDJEFkTuYO5S8QlaRj$ylD8ww24GN)pLwTc zdZs&dd-RcP0*~?bG+s=xgcEMM1vH3B>gbDjnnXcAB%aP0N{GPmmAM&}T~ zpk_F;LytqcbD&tJWRnJBok}`v!L)T4(^6 zn881>TqiqjuR(xy@Q9oNgX^4LJTz9)F=Z%*CHD!wSNHAE&zlm*J+X8 zQ?sgtkG2i=-cfosHm!f(S=9aIH*jEL*#Ab_S9C>m)}I`z`N zUfiqi*xI=cgfKj7;jmANrY>`Ed#K~v?6U`%f^>w3!M_QO`U!XRufl$Kvls8(ti@bT5yaKvIIf}2MA*&S2x$IiIi_lE% z-26l#Z)M;MwcE@wlU&TJk$Y-tzJyQ=Lez5LvUKeeHlXwgnpQZ|I@BTO`!mHfR1kU+ zj5hQoTqEdzxLlCS=5&8AGkEud;n;wY{XA#m=#=?dI@-TJ2Hiw>209NCw#%)##KZK{Rg+j=ideeJx;FyXd-2f661c2^%ld}7Do zga&3zU`Aq>g4;{n3OzL_tr5r=y74};uTO|UOA3zST|sPk_*drwmUC9 z(m+IYMA(IuE5%1o1N0jmpb0wae{Q1IMVW)iE5{NHhLapwgiiFOvNZ3<;7cFzf+Awl zg!hK1h;?L&GZ(xUERVeTY3l0sqj12yK5u>)sv#7^$`Yp#C!9I*I!@Tjdlk5t>K4ky z4}YcDV|8IG5wjlhD$qd&Vb|{6_wkW1``#3xi4qkJmXMWTy`rTFNOqBYKqdB@;aGLf znD4KKu35_10%JwJFpi|05tYU-`q?)OP7ZDh`<$mY!z(cR*9F7N^ z++x$QdFx8#F{rNtQ3B)yn$8+z0K;PYLGbGjUHm^ZB3BEU1the1IKX1D%@~%D<^$;C zub`B|{n6etvM)vci-0nZ{)8@g`fLVwT{v4^*pXE@w;chTQHJU0$L7rYfE$H+fs~Ur zMbi|yV<8l^>TbsK*`PSld}Jmivh_YfQ{~%0&b3YrK6iKGm|6SBG0Yz(QadCTz-9%wald8JQE=?Dn|_=#9&he zMX_1m^wLMqi4;^4Xo*Gl$K?00E5i*(l{*O+4bv&2(L0tPa5x9%6S}%R1>4#0phyPU z0}3la@Po(1mi7rC2Z-0^`W#N-JaerDL(nzR)Me@a9KAAqQotz$g*gu9H`j)!?$e}0 z4xx=GDu~MzI3Y9foEZ1pGgxy#Xs4Ww#8hIMl944tIgas9q%{==25@-?8&p#%=FHSfZU?Sv?O|G@sBw)I<(zhLkRgb3Nt3&Eofg8z>n z*|r8&J*|8j^H!zF&OAXvvU!tWjaupOAD-0}RuH-~EXh&$B2(k@MI(AYz8W(-a0vbt zUeOfcujA-K(&xJy@X~E)pU0iBNicjSlS)VNz`4Hhu%y4(5(-j9(j@RXu;v^Hy!JkO z&%)9rbR+X#3p>M(Kp?T%n;%E@)Xq7nlr_@@eps|!Bv7L%o|*|1D=p*jer|zk3#~S z7g1~k93q|#6nXgqPSuzk-NmZq$_njcmryEWJD?#FE@8ngJvhVA;*D%m^~#S;cn=`e zscWiujWW6v!5cwGYl1C=H7Vi0NieZgAR<~Ar3rFSBy^kQt(D~D9^9VCfD5d`Ghou; z?S!}jReV^DQDr4lt}}86lt?3FaE`d!)G7!H$e9YF1SI0y%)wwy2|NVQN~nJk7LkFU zdMQ|~Z0PQyNF5zYXnO=_^qwN^dfj1206c1s$DvHl@z!uJu;XsRgJ4fZIHlguz^VSEqCFHsu>R2qCctPn_giV+}c z&K(c>++a7SGBa%xFdiR)QrP&<&jDB#K%fM>Hqw)V@U}3qFwYC36zwD!cdF$prUPO6 zxYNHJqj{|(STizUCcXg@#MqmexJyVV@Lcg`AS}86Zmz?o$G`Ma_EuUzaHJ&>zd=%Z zyu>Cuyg^(t(Zcy3G#EQq@Uic{12rM`Y?GoY=1mbS(W$$zw8Jr(+h3%2X1-!s4=PLS z01S!t1b2-lG9vRimOvwqK-&wgD(OD(wjutg`5(B$l7>5& z9U);sp8@tShCc*`e0x%TKKrOXqziCc{q%_)QaBU?l1fUxAdtcL=gHhYz#V}10Gknl znQXt^QykD6Q;-b#5kv(+B#06xfRM9=XQu$1;Dlgp6+f8GCRuHZ{>cL9X;)vB#Rl z;V`5MtL|YMzjrC)l(<>LrvW>B3gRdhr+TcERPRVkZ$Zp~IfePH@U0@#dXUW@HR|11 zz89E^Rm)}4;k7)q!&H{%friO7i6?@$GOpXVzxRA9=@3wK^uX0Qf@X zDqImUX++z^xhr06Psb==QN$ji)Bd5pDNYO=eAvjo-P~X5JSLiuj1UPCHaKpNbc7*C zzL{4o7@-LPAeIvp$a}RP?e{S?f~zT%$)xx}cc);0yq#My7C7=iRDzHZ)BNTEVh6O! zv6k!C&Rtc#Y`M8u4a2Y{C02`&;ovRd%mE)y#MU3rA2W+WhZ{bE*L9K>S~k)Tqu&$r z>8yjp>1bi6OszH3p}wpV>o@~QC%D9L zevtYMLmajSt`9kO6nPRp7~wz#{|Z_LoX?`-JNcY`FsvN6VQ(Eg3o z1{rUDq}|9j{m7?w==bEKypzF3hx7o4_kqsNg8G z`K3}jeO+DP4~c^;6Eur87wng1j-mCqzP0a#P*r z;$AwIuW&KO_238UaS2Y`HNnX!IsQ3El_8m*DabHyT+T)a`cAe(~?0D7Ompft#^sMbGixBH}<*PF63TT2*A5^w=fPO@tIZBP9P zS*sGnT2IDgf-i(LZxJ!L5iN|m1o{lhvW0pV9qur!KceZTb{*rt;OV+;I%&ftbsDTQ zX*o0#%N zeSq&^jA&L15){$_*-Ys=?6k-+=dW1uj1&S}yShc9U2gn9F4+76(FUC1S zE8|anwM8hf9kMoXut2@FA;@Qbe`Ig|2byU+L}4^ky+unfVn2F=KYn~9y21Xdx{LNH zafT(ti4?k@?<%|!S>w;!g4`~JszFz=%Yf}+$04U9KtNqD+D*=R zeXs?$q;#if)Wy&&O2K!tTX6Qg=aT@Sht~yRkJ^xab>XL~+i@1Y*H)*`(R(Xd2~9XakhtJ|U9kA(Ia7(#5ql@O3q3g#(4g6cFDm_do_f?SX8I_*8In^v=g)r@>8f{m(n2B02m5*Lv)SE z5^F#RD5IK4mV~8(LpUS?Q+xsC0&zjRHG(l=E@ukD58`BjJ_~|!jhi{$%STM3?LRJ` z9J60L(_^jYZLVJ!8rV2!Ps}y?ZOsx&kpl^(tB4qmxRD;fO+b(qk!Uoxa+mv?)3~$; zm&582bVAe3?OUc@7atwV^Ikd6;f$BbT`q6-<;Xys>dCS%m6u}o!$8R{L7FGZw-9tbE3rMGDhN& zVp1*W3|ok?&~9Hu)%n6dzg}eBmwB^cGU}3z$gdfG?=M@D2QOd$aR0#3JeLo`Tkdg>(s z%#r^j=)hHz!t(H7qOZ_5!IW{?fWI{S+C2h2z#vH{gNX#OJcp7HO;;#*Iv}%_DuS(= zE~i`2g~6V2r_K?FHU=}#kacjpLP?1hesSZ15){F5+;CU9S=W2JxDy3&Il6Y=olCRa z_MMVr?d*IH`5?@ELg<*KH!rzK1Mx5tY8-v|mGFdhe&tlRD>z2o&f*O#Su=5BFwwR5 znuXVe7wOSi4R(ftr7lWQ8WQ1gz*n#nVXrDABxJ1+L0UEiSW$ukpGM7rIlkc85wvk_ zD4FqZz_#Nz0VbYH<3cP&=>_i_tOGek#XBv72ax{ZkX-~}Nrrww#wp=y!{ZWkvLQ#n zFR7pwA04xl-LUT|v+HPBJ0BZ2-e`z4_Z?7s(ZIN-Mr;Tc+@@O8PbAbJ#RPb%A=slR@Be65 zvfZ!vvS)wQ3_^lR7na_r?jb{;NLv$lElXK^(QsGkD$D}z`p6!{J6imEvW}%i?9`Wt z@!i<{Q1c45TFQnoMlX4=0E;JVJVl;0xvj5WK#t{sWe+H1v)4{-8xAIqPFn?n$n(Rf zCAhls&*SL)R*$wd0nAS(AHTX7x9PUWiT&Z1qF-(t{+6A6U%B5F`9^beGOBiMHk^zA3vdSG2WeKef!OCkd%D|u>6XxfEXA|iiH=4}a_5SY@x0c{)j82hQETciu+?AC zG4i?Z7xnS#%|m|$GAlj|+OsgwfuQUhcOqR8!feABz#y-tM)$b<#H{PRCzvz4SAvt< z;?3qI9Huz$mUuR^;Sfg6JlP)~1)`oltA$L90YM2_8+u>S^nm{A5r>Y6nKM!$WWWQ6 z0c>n235dfp5N;F|=(7R?6V$S49w+Q98H7Qat?+)qfqbu;sI-$YlE?Vbo~nLQ%;Njl zeF(z$Ai3MzPg%0OAAt-_IynCr?(QSyD9)5diQwjWy(HrHjXg-ZBL~I_o`j+j69$g8 z>^f9($t>lhNm{d+mbwm2M0hxj!$xaMnf2B=2EVVX^3Rs@x=}3(xOsSp)kxzUwhC%U z@K^XBZ%vp516E+yGJYP6Ikz9Q(BBrOYYL?9*8OsbH5G@TzG6>~h@8sOy z{s#MEk$59uOAWmFlWj#dC%vP4)7;t{dCdNnZYls`P>oO(+|AapzXDpW|Me-#8Q*jQCYQ_PQ+8-_R&~6yLlcvr<)?_-hAL-a$4*s8>fveWM03 z1$uQwqjwd%tVUKx48E8#$50b63q}RbE0P+$(G5Xt146Pm9OrHDT!=9{Rv)wuP`AT0 zYJ*p^kAjj?89XY<-|#EHy@&5cLPEmMhZ~~;MqDlwySV}z95NQqy+(MgT$ETO4$?=HH90xry1WirlKdG`rO@=w9A^6NXo#*AdP&zCNxw=b*@= z!q0+bh<`fd8*z0U(_Lr(W+Tp-j8lU#Ls0|p)$S;S=zq8!^d>eCjIEKvPL{jPs^NVW zpG!0Rkqsjdf(ye3$E-&U;RRUK@X~Kw2^} zoT2%ImY2)+FYSm?hzJA_)l0gcei?nszZa0=BX=eY8=BYTrNx*TDz4UE&g%a5O{F%I zr8nflx^1ju@LvU{h^FxFk)rMqzV-L{kvR-aR5woZdx_tgACf+sgj@#l{FNeTd9s#6 z3%&U^v82YW=QUj4-ROEoC!W0d><*voPd;28U3TnEUeIkN$u%wdjDy)TY{FkMWBzh6 z1AdcLA-1Nq2_roGxBei;j5`^M$w6j{+>jDv3UyDTM7P(CYVNgNU*YnDK07cD@2@(52U0u>mjjRH%Ni%-**r6OiczD&bctN~)SP)K=wxGY{ zFf8Bmhp%d`KZ`ys3hCoy^#btar*mh$-xckC9RH+mzG~Uo5eI{|>p$&xd6!08?&_bI z!8r~~0X%+42{mecLMSu{(SOA>jLUnL2-qaA8#EJtBT;E7TV2Q46^anW!sNlS@@~iI z)VCQ~$(eOGW`_juNfaeJq<&AeZeUuuj~8Iu3PppMLwQD$|JM5^l_E)akf582F#hOP zhYJUG$!l=FD7cf3Dj{vX4a%B$og#ut9eidHY7lFfOaw|KZ2^u#%Gn7ufgZ?gpbu1i z{P-vb105nayr@i4PIg$!5jR%YBc+_g$@O;-SyAt3MA&ndyV_`Mx>BRfZnOy>9xTLp@Ckq0Z|a;#;g{_&|?R@!sgH6-0c01pv` zL-(+QA~Jftq;&wOae3J2P%=@@J$i1scKe1^cbA}(2%ut~QL<$#@K9QlXDi>p^BAxj zjDydeJD?Zm&G-6(bTf+9!28XX1;hDj0!$@CqTda+u&zMLhO!br+`PQS*rvUsZ+j?} zDxn97=Nry*QZzf|`wMUn=Ne|lSx9jjV~Cq5J2H$Od!Lz%kr~yNU2Vtfvf$TDhT0Ho z%ntiE$Qs}=XtpD6L}5g1=7X6O=GJu}&XWF7*U3(Lf|zuVfJh>JdUzLL2*!Tkd)V;roejpYFYM-;!6^IIG2I1B@kO1wf)LCo*;x`1L2IXG&@#1 z!CJYZo?lC+%1}^y(_;-mxd=^!2z@U8CGY24-JTuOoaJI%zWHhdFvb8^j2~PTDll9C$}hm|ddGobft#$<_cS%K$T+o(X0h>> z9OzTeqc=uOhF~uv17z}8>VJ?fLFb~`_eYOUVDbuSDdU@U`BfjCA18OtMTr+^8(S4-G- zkaY*|LpfP2%&mdmU!%&Nm7Bj>)1@Tnd$pcl*T5&NM)1~Kz8gMuOJwv8c|X&PsV91@ z*kd`vnVS4(AC?W;13m9XvXS~h_uc8Gd;#@{0;pI4dFi| zTEYSxu_&8VVzj|m;1Q#EYCfi~Q{k9z6_kFx?d z1oWhnlap~Hu+8%OdBW*h_ghAPiF}9$$?qdofr=6&De3)97FT~^_Ge~}u;Jh?P%nqe zb28AGY0XNFuGJ-W_+T0UP^b#CVFC>hf!WwBM^>*rFB>nGe)#Zk_6?C&kcL*cB%+>& zm>5CPT(C8UXw7f<0Xfm7+N+K>TwiV8jmxM41uFh>f zO(Xgq{p-3_D&C{}1vpC2F7!vuui){cw|^6{B4QC>B2EG|1!-N$&`GAQTwan9CmWl{5PaT2S zLCI-*oZL6!fX01d=|ah2#0-lhn1K2!W@!kn#p}iM)6or_xpOF|sW#WxDc=Y&C~&{e!xqr3O;B+s?TvL*znaBk%@*ElGO%ozl`$ zf5&H0VIwXgIP9<#XCVdLjoC?f?-=P=*a4>wkk)Z8PGVoJ0b25*h@&PQVI8>Iihm^xN&(cu)rDp@nk(h2Azu zC6KS0WVQ5&#={I3Ng65%6kSN7ARwDv1=%kU@)wySQ_jS66@rR=Pv2KOu$MfnrqkfX z=gpu*o17#Bf=WXehb9CW)^egtS%-G@S}2$+!~=`eu|3i1ohr~ zt}%ejyCC|YD`uJchB6P}s}&=&pJm9vzE zA80ITyO`DtK_^sW2(M_$u5e{u*C!Ud3g!1xt-grb8slv?g=16B2&tfbQDUfqGTnZx zecYvFvH+u2)c&UR^1Hgar8I!15xt1DN-2jiAKOa9 zAM!#WNSqTBe_vx-ejB+b;bJjO10+QIkQyKJ9tlnr_zSjjt1L^%E=0A6g@&8~m3LLM zUy*p@0%&*OM1W%v;Qo66TPKh)oth5Kr2*YFE8rqZnU^9{v{gO z8j$yj>wS=|&AJwb*Ne2Jxow46A;Jl#K7#-&<$TS= zjgXz(grp2GEs&qKNqLm>$TP6Lv8vE}gy<}AQ{@3Pqr^252NocBoFtEE8?xlLWnXx4 zOa7Aa?0V03X|T1NjKrJ`q=c+XaO)a=eg1PcIEIm2KTlMUTKg`0_0WHhJy&}aPYzyj z+ZJ^~`;q^%Uv``R_yIC%A1Vh7yeQhc+JO2A=zvvH$x3JK{1gN>^;rHooF&_P&vCW5 zFrh`uX?L^i>!yKCz#ZXFRmGRqESluBIFpYcWi*5Pw`8hz8R@L`@vhJil$P31EzHb7 zLo?sWJ5LLn(%L%7$f4eiSIVZG%aTNzIPE!ciZBtgc?@q_nF`ze;6@J>MnzBp{|c9l_jr@}xpt%W0>T5OY73uF zDZ0n#f#uAYBOwrGd+1aDWiayvKZi8W{T0!`Bif~dslxhZi=xjI?RT0=z{!B|m`H^% zd#T_@Y%H?WffqeO;GYP;0RE}*qAHA8*A3ok=*L_^kaucuWE5cCOP!s*`DJNo#)u1~ zFJ%~j2Tn=`GK!ms|rE(($R3n#uai`Yu!{w)) zy&ZSOFI(FdyWQ`x+Bq4KG+8BfEC3S;xevelT+XM3Xb9$*wgOh`XrG~@sO~YO6M?-z ziv^>Kp;{N}!a49Fz(}EJ$dSUZ#9tg8!FwRYMY|Qao`BH=ZYB@LPkGkoCWE$&*I+Iin6sw%G#%z9u60ZYNw!zK(b}d`a`B{ZhKF%LjZOe}f&}zO>_P+X~ z$wARa<^Jc1=p`#0TnzG#`keN^!5(OHIcw&AQ(trwi%l|H(^1KRiQGba#2FAtZ%wpn z53x_kU?jBg!}HjgnKtXz+46U8jT}gy2sV|CxG?aOl4JJ4hlb`VmqgVGofq@zm6w-)i_yTOhS|K0z1wp_lif!(^VK}tJzWY7 zQLbyot9=P~)BIoY${fF}k#y9+Y>rUw0CE9yQdOH!(N{I*Mm=Qsj5I>uZdewt7)S<6 z;BSta4MP(>8ac={tmW=m%5Gcw6O$BPUHl}o8psgkyFq*dF?9ll45T7_|9NiHD~#b$ zfiGBS?Io`#8YNdhxpSd@;*uoK82gHhYSwmf*8SZJ=Vph@quS4<-GA|$mvMQNW$Z*b zf9>HntxA;poXX0jBjT)wqIqH)&N0CXO>&3C%*z+)B3co}@o4U`qL59eX5QS0Y zK(`0H-_6Zf@T}_H57u&zyj;L+ZBwc}{LFh0!-}y*A#cGU3BoYGprEWBLlv6FICC^`&?d9tIW6*ncbib5fZdpqam)AhPEhXsHAnGcd8ZOk>xnEwGpZTm*6tyf7o`9MsTte)ey+7aFH~Ik#MDgE=QQ zG*Pe|OXwiY5o2Rx8=6ecnl|@P^8IJm8BaLv4sm{cQj~H4z+_;HnFhbloXr>l-4+#8;+I@XFNGm{ONI^bZ%7RS+1YebSvOD^LUecEH(mqXw zq*_ls<$?nMtP~KNr&SHWhK3#?%X3q}59VcYgnhU4}_Yj}{fhV0XxG zk_Hwm4Cpo>enle%5}bduysiBC3|Vv?13iGgx{M52g5K+V{C42_%nZ&SI+|2ZoG~7o zL7fg^3=q*5A6WMuNSV$C@(c_Gj3u%$94|=49QC5@9xy!e%rQq&j+{g(YUutS<6av% z?$p+ZW=q$Y-7g4IokN(Q5x&b~M%8ydd_92gke(pni z?wv?C{O&X+R+ijTBz|bq*;RYG?YXk{>O@H;jMxVsG0Pk1Mgj(!0zdd4&Pq|?To;@z z;nNSsoJb1@M+{DWVq;j1EM*?x9mv+^hh$}BynHG*q_Rr`=bDLx!{t5GdEE!!JzZNK zb1+W-<)`Co?*d}^_IdY}LhpgQDK4>cyhoD+r!#W zAIH)qHx{n(RL71M-qr+O{e^F%9hq8(C9IdEoB8G)_%aBh&ZVJ_*}Ou3BpkUlU=@Ho zM$#oqwn&~n)*yoif^4u^(_YouAb!??h>T ze91pP{Epmq3BE78T~TF_LlWZKv>qZp?(grScgYl9E(@ zA8C-G*|Rkegi6p&r=-S}2!bbMP#zE)K_cab|0(+C$SASq%oUScv_C@9GcZm-XbeLF zh}&=bjXMc44Sfr~Q%~wkoqu+0x*b@U-B-Z8w*+Vp?18{xWA$3gDL$0oc@eFVtz92h*Ng1Ve0WqUP|UW#=T{xRd~x;@l=!ITfbKXh zu&nqFM)qRH)@{2=FP%WSmvPGcu(_%>rajw0i2+;+hNo{YXOYgO|7-8c!>P{O{u!B8 zC40)Wh|?lO$QCU}p_ow}vQL&MTS6(3rJk%wiqt7}GLP(&gB~26Da(mMG8jX$#37?Y z8f)=>zLt5f>;3!v^XYPRU0v1B&-pI*eSenwMizl4zpu}RVEwR)&^ZGy16Ses)Vd(@ znUG@!y)N`fs|7cfL+sk=*?<&rfq{bce5$~a9DN0x3p=^p&#%Tm%I!k&kKQKje!Wss zMuSn@$o^?)G4bZxQ8za?H^*JsCaXog;esg}9jDPAAHDIRaeodOh~OwxGBE1_LkpVd z+k_)?adO}(s@a4ndzvW6Mu~LNf9JVz<>KhpH69vw|Du4&CRoCdKdE<4-f_3z($*E} z_TFHm+&SVs3ym(vhk1ms1WJcX^)$b27ZTF-q7jVdf@8X<7aCu+--?}evcE`6CtN2n zQ6MDwPkh+|V1;;b;P=3)y6KD(F23OuR0k-OQPOEGFwFZy)JQw3JQTDzHnQ|?afzLv z0TxbU*btTxz{a&}*B~uNY3MyUG7x8e3Hg(GJ(_xBYfE30i1-aCK4fQFoK2EZPnHH} zE1Jda05sli`?_VwUsu(e8am0Ws_r=bTc2-LLPBRD;13(6ME7JmLIYM1>>3*0BJIxM zOsy`lNN>pkMX_y zY!f56V99Y8;^P9LgEh9Ge$Z?BB+f1to2d{@);q3m0+b4%5_&GOW3c$(vPc6_kj#+P zUVH6QYzhzx@Ex+B?LfBSxxQNZU3{gSg@EwYeRv~AJ2x4AeDwRfl;Zcmug(EhA@LZzj1{=U4VPg zD1r2PfV>C;kSm?^P9Dq{@2``%NK?vI}G>SCk3&~iG}a6^OJnVo}aDLqcajKhKJedM^GJN`{M2g=Z1#i z$I;gnlPdyx;Hbe72AJYrqWEXvGq*`srqMa8!!;*ZC}_!&kqY}(0rSq$ZFUj+AE963 zZU`!tS<+tUBb6UzRW!#S8O<_3NNu4V{t;^fhF8f~WK_bNBl%7akYM*T!dg-^wD_p# zOjCWFdfMqb?pEhsm;w+B#|Y{UZlZlq1dx$SU&ha_TRoqGh`F+Fly;m3%1y?HfJRI# ztvQDw<1E$!zy#14ZrM3*9KJi`@miEUP>`qwD~HAbk3p~DtV|71cX%&A6Zo#Ekv|>8 zWdal$XqRkq?LM=cJ42VtKG=|c2^R`DUSa`0y7hYs1P*5;uiri{FCb5Aid+0=)U7F$ zfE2q;H=-39%hy5bFe{)UU5Yw!XMi8MXc}J$yp2f(Q~hFS&3T3AMHc>L3*ac!-neDx8uzO%K!48BJUNY={hy9h}(Ji;=6~(}?E{DRj;f zW=wW^Ix$f2gGO~UWmW*-dU-yv)4M1e>?TNxs-@ay`HF1^lM}8Ne{8%CQ~~6EuCeLY zXTwlJnXoP;py0aon!Q@$IEhT8&M2Lhj> zm`}xs9d63(4-(%U6*&s93qKik5Cc*Pt&Lvxz@zrp;4a!>RRn9w0Q;#mKo3Z4eXpZ}o*rew!qLaxsp~ z`R8?G&Pu|AukwMuR*k#k89|(NddXgLr^nNhs!ufQk*@G1c3vs+>nTZ(#p##V#$L@* z*hK1a#bZcI&;r&bWy6h1S$~gwE5HX%zpe>(uiYwY-8l(c2R0e*7+h$FsPPPot)f17 zK4cC!yBuZIOQ@A6=Kd6>HMePca+#ERrTJkTufwz#5)!5T-EFtfay>UXOL zC%<8Ls@Cn2S#hAJ93aFjrl0ilus{0ZVnVsR z@_{So{kkP1YpjDKrXwGqTLLs&q*e@iBV}KUd4`yRiEhs^TblS+e1-Lg}&k zJL~-9YL=OO5BCsKO=L4aYuxQ#8sIqi11h3$A=Aj=Bf7~dbyR_y%zO7-hn$6It1+Dl z3QDw7gF?$)Lr3%`Ly*~!ZOE2(`5eA<2?6(bv;ez_FIW$oLFg?$y#DtPmy%=s9Km(m z>P~gf&d~R=Par08{srViNOnIO7g2u&r%HL*SjDVP`S-~;IDorFd_D%(B{cDkU+0Lj z`l3BFVs3TwKQlTtg#`sRvU19_lxi?(QYTm0($=&o&fm+GEj95i=zQ`57_pbr=?AvH zL3qNKKuGY#wT`MbHWtB6O)?;$bA(|nzMs>{i)>7+C`%qY-^rGYm5}hB)lz_bMG+Ci zALE~3$CmJS_2=>Jp`<|wfe#vLV4Q0*%@CgXWwwb_iGS{5(~A@!0XPd}kT5YpP!Ng7 z#(Bv=F$h>2(ET+<H)D3Pe#~@At}DH z@`cd~!IdS#juMQ;V=>b$V_hB^Eom&*Xb;yIl#~c+*a$dcsKZFU3biYwP)IN#eno;?LL`Mea@=>6b$ogS=3`fTWXv;`o03s?H5v@CFaLpr5NK&zbCi|0MJGb^0 zE8Vdy@NnL9wY9tozSQVnLxGh6^0qrGXljG6p74kKDPPYhMbOxBPgtd6TAL(c?^LR- zMY({K(JD(L_mA?iXBK(q#V=i2>zL79U$95F_Po9QP47J+GeRgX^gSdShWizy@x}4( z#2KHv-nxlzQ)$cO^mhJ3`)(fEK+lwu%6Ns;h@29PRV1nc5b0soP-|VOVdHT!m_mXz59n2ACA0 zIBwT-fS_=ep<$Cq%zi8}ZoZs`7lISIHa+)|?0k43jQVPhftQi_4PgyBDm0jYav6F- z^9;zUj-%5E3ZGV|j+Stz2ZmD-PQk9FT5VfL@`f+ycDx*^rg1d;?Ob&&$|h@Al6j1g zbDerRNYVFZ;~K3h7^P&TGeg&ei~o=(@s)WP9r(2LA-#c&2(GQ? z{83OWdz<`{9+NB%svW4b-8EWX$}VIYIUjndbPAd32o4Fy79;+2R?Ik}pY zjWdc9qRbOonB=M*cKa7J7RN?`T(;FBmA-^AsvdTowyp-t2Q~<>57;(LP{JRfD?Y)Z z1jAo}@}&5klJz*oc_X1KIr8QjPdQM?%k2Q)y-$y9RfqV_uv)O>=m1~uSq6GmKB0P!{^99I^MmBKe zg>vsacj7+LO^nb({SdwFzQ_QNZKsSrefXfj{lp>{M-iaf8)V7LerX`eqxn`Q$tz?77iYCzJq0EZdTpmf-&stW|DbFl$lL_Op2wL=#^-+H z_fMoq&2~7=P8;so2>KpsaVk6>E_M&SLtyaD!4K%6}iSg$!b^g&R!BrcAJrUk_sEZpnd<^mTF(pWFs-J^vLzsz}FT*{6a$v#^_Zo>Bq)<;t)3mT1Rdyd{K_qsG`jcCih z@b^-(({O`8BLzZSW(7A%p`*{FB9i84t=!c_^Hpy%PurpAHk`-TciXU_)_&rrw(euv z{VH9RAAnq1Z%H00>6#wdaPKTf&craH88TQT-=UcvwVKO{*;+79^=elq zKD1AaD!5tvF+iB_MQeIx$@?NlzXqz1VCVp)`J-JeUYv5Ynz{n=iRiy^gNs@khwD5qR2O*42)p!0Yl%`<-_6y+s`=CNvHeuq}#I zAcXimlF3NAUVQCPDeI2=EOb)mf@!J%#r_x=`LSfn?4`yzHJ=UG9_j45pWXkfRrBV# zKC(wTX*RQ|wA&|k;lbJe@WuPrIr4J0O-Oieq<5j*tmk+3_c{s3|NAfIe}dUZ(0t41 zIjWh*%gPox%)iClgKSmi1I*la$zC-djOU(V5j|&^nR^6NVcvi>_lW<##{c`GJegi( X-WzOp{z<@A3Vsecemg!!QI`1ySr;}*B}Q89^BpC-JKJHyL(sqd2f%WZ{Hu# z9~cY{MNzf)+HRKROFEpvqA6G_woFN@$HspSj-f!z1+3v$T5!~_C@7(z-^NYx|j zq{G~qTp}a)(IY%sRg#}{*BZW{{DG8Awm?2S_rG{x0L_5q81HlZ(rZ*twu|mqBIQ>Y;eza zChY(E<^X%F_NDv({Tc9+zD6|k!vE(}#%?afPyhQZcA)-$da38ahkviy7l*lW`(`?D z(mJ&k6Y&YR6Gg$^;0ovT_}1RH=>7j$i?5M0iT{Up3sIKtI1>JM)y~i`N=l&Bf304h z0kZJ~?td-j|Gi*+AF$zm|8J}Ook=&qG57y)DOc6WXhn*~fS8~~1<2f|9kx$q|5r-f z*ONHdzT@sg$Gq3Gba2Ff-))Nk3}VAZn)-3Hi8A~>oY}VTzqU!xEJXR}mu@{#YU%(( zJqo9AUO{io%Y!#whzK?UK3@3E(ClK>7gPN7cD3Lgnv%*TEaf2M0R#AMxWf3^IRm77KTT=uS1O&ZC<_Qb*C+;b7UoyBn*Y?joQPlk+=2~UE}5S<`TQG?&v zbv?Na0`F>A|y**1T}dm3`gIa#tOe%7ZQTOfL>0hFMDFLgM9BUmka(zG~?+u&}WW zZ@rz}My_h#i->+%a285#z*gk2|(nUV_sq7E`kB1W~|xo$cjim+xTH*5=;GZWBHsAu2-H%na1|%hmbA z%9P(Ao_(hXhwaX6b~(gPlD>;qF3XKp3NgR_%{xK^zgJ9eZ*N(7-QBy>HKds6L|Mw} z-RR?`jdX2$LPtfO?}nEo$H&LXC>hJCVi-c$u#QExqCXuZn-RSD;rmc2_-)o8^9afe zHK~w9LL4r9b0oQ%E2H+BxhkU=NS#{n?R7f_=jSn6bSJr#{sRH+seDzIcuxXDLqqrr zN90LTUf$0RsHZr5RbV9YzAuaee~h7_PECNX*koff_N&P~z^5~=Cy)eKTP7A!RMn(jr6X-GG!JC1GW{h!UA7JuGcYZcndbE^C zqDw`bKik-1ws_y`q^7AuHb4H=k(HH=pCs3Jq{%^{z}$06Z@WKpI^X2KYaE?Fa&3%i z!zUnsLKQ<(=MYg)K=};K53HT_ad+7M3U%S~%7%%%dzw{49zm5|q$=&e!yk<7{e&Bv z(6BH^hD;v;1=O$0`n`aP+;P2mkEo~j}Y@y|*-!Z`xoR@(f z$tQm$eGoLe{_{=$!Z{gK&c^MBw;})6uPHsV ze7U@4Mzl|VR&_oF3Hsh}JI5_GyIfP+ufO5+r4^OXT>OFvFJ{C>V(y)l$)Qx1s?_fa zJr7eYW48)h!`Fy?%-KVyizBN0BCNm@m_&22IdR|Y3Z`w*jm^_djfg-P6n~N|#*dc^ z(oWx#h--Ns_%eSi(Di(}#VoMU*V98n^nU4oH6uY()7(5xMdUv?U3h#G6P+|7+>1sz zeMg^-Y4jfPoU_IwK9n&dtm)A>uvbd6+2)y4u}6W0wSR#gBuXi#AP{!^X?Ho-NKU6@ z$@Wws0yTcz;|AsSa_3ploJmkp5n*|44ZLRmHeQ@lQCkC@z3+G%W8LluhPBtnBTF0G zK@3zzr<%qo+46a;dO7~fSGb`EWq?j0;c?z!-R}ErPmO_S!h-hblRc(kHhfdym{p+9 zy7-^uvBREcJQS*O4k=o&%ODKH1?B%9UpPSZaxM7kKVETiy|zV5S+!1CL2Rp<|y z0Qa-3 z|3Wv?;%Ktl*aa4t=uqvuy*L#l)0^J31VzY{u5imoGmrJ{!QVNNZwhaTy|hROc-aqq)h z?;Bf=r&mlPCg^B~4GJ5IUz3?xwre}|OxkX6;3jBCOcGaCwB9bl-n;+&AzvvcO_5HJ zUj~3Dm}{kY`hV8F3X*g4rrl?Jbrp9DIIM{U z?m+SDId$JMw_)?Itt`-bpTEGMA0thgguohcLH7|uEj%16kVGAr7!vl-LSGT6eI$8u z#!}C%CO~WY`aucv-6L&w26BSwyO~LNwru1rknJD;OszjYSvPW`Sa5Mmhne2p#g!~V zhfI4TXZ(H4m}`K28p!HeJVTkB!;J?s@o@A!3qHrc_MzwaT+yHq2^4kthv&WFFv_7c z%53<0SLwTR(+G`D1b`Op6J6F{}6P`uv_30^<+7|-^lp{0hA3y}*AjwUWJ2P1Hmq2ih!8?aZY=bSHNOT$M2`EhJ) z?BwhW{YRvDpSi7V%uw8ZJenjc)!cWFmi~0*y?l|gjgFxv_w(Infq%i-haza=WL3_e zEIADwcqxz5swjQf)$ACO5NEogiIAqs5@EasPZAEX))-+Dl6E0UPH)rgI3V@6Bw4Xa ziS|^5yK;fvq2Jr_wmH3XED zl)#C;Z}IUA1rEyD=4+}%98pcvlE=b;HzmuP2>h%0nn$(-N2clb?*W}}0%qo4S(%vw z!NQ*`APi*?R9bWBXru~f;Ys2hlNr)wjaINyG0Qxl=*RpfQd$0|y7fRVfY~b6FJtBu zoZ8Rv9R%^ZQ`_0AR4t*^FjPeQz9j9WnC8-AKn21i2juFTnObJ=6{^t=p$Q|B0_kAI z%+oWX<b}s53 zpKok!oxss^agVWN>7jFK-N4;9e4lP=;N=HvLaTDP{>@_p3x`fvhexUDQrn#Q!(_ws zh9925gFqOa4&T!W9agFaHZJb&>DoCn2XpDtwMx9rZJYUWH`Sh3&%SR_2Os$2?7FOY zY53!Pm-%JTYey#0)6-OC*M*Bz>+yN|kbwi-=q#&?ZKk3amt;_NguU294U(cXP^U<8 zg(&m#{RmMMC&ve6U={Mk{wUZ)x=6&SJ;Z2`PVZZAwHhE8RXe3@pbvWsBngEL9l8Hg zljAIE&8?*q;b6Wf;I(t-Ay9GMad23^nDC|H*gR`Hz2n^bxwdm3VgWR=$r#7ydRKQ< z;C*^?J{+~_e}7IO#FF{=2REnGyi1bWmSdAzD)6^5(8z;8xdwil zok54I0ja1aItf9zjWWNM*V8jl6PnO7Fpv^kY8A&`Co)QlwRZK#NlHhclg#qG{&b0_ z5YRm&EYXI%sl)VnI+Zz!qw>-4-NkvWy`{U{n0%{!s9arK0Z$6f-P?D4S@17=7kqvB z;*TeA{iT~VXDh0)Dr#@x!;<$vO^A789Fb4V$+Hcm*+(7NT7#>gI2bZIb-*+u;FyeIkT{*~Z4kN#aBM zKzBw#Og8%L?Q?y~2_&4v5i`&GJKN26*C?OPrj8}NpTikDVSP0&X0#5p!e&}n5g}#o zx*g8$*j8j(5RaV#jB;|!tgIrmDWVQ6B{cFZMxR2{q_8d6Te_;^GCA#?_C_&*0}4Rc z?q$A&Dy?hpcGd7?JYu2AvjTjet(Io0#aAt_BmLy;$Xw7b1x_4U6re`~5a~ig3IqL7 zKqpgYz0EysO7K1C_SO|QX34)XVJ8d>9HK@RWKj&#-hkH)+rLDDzncAJ^@4h#fjS+u z`QkAG*N7u}mi?(wMV9u6xY0#cFkE)PHT- z?s}aT746qWr-S(4`_QG{8(SUq-SRKUGv#(wvXK0upK5#^v*CyX1FSR5@gDoHx4}2 zB&G;FKX#St3n-9HoG`;CV7Q3Nd55CFoKqtZXmI7BM146J6MX;%j%N`MvQ*HN)%c#_Adx@d*V~Ya$J3 zjcb{f?t=XK70_xy1hC~*^ffG9-A}VD3$UuB-HFr)@Ht-K@FC~8<#k>2T8E_iCC}LX zvE?LX1sPt;KdU{|g&bHACK%_X)1IRq&KNVRHi4h1Eap$uB7IY7JJpXL&cKYnRCW$0 zSJ6RRuE{tz$?8;%2ip7bUw-nNkD`WZruyz4U|Pg!;VY-1X}Xy|@|>|BdALHB)?p5` zaIT`ysq4;6Rc(*Jhb-XY>#Xez^k08mi=f8}*@*~T`<=PZekGrcSFCS(Le{ivT|zCo ztj>39=|dFXzyzU5t#1if-o< zGIZY@xk|9EB$rqDNH~htoNd-gFg$EA7`9u(Zow(s*8Ml!!vV9@vAxVH1%^^gUKPcX~<+v z{eqr?UqSL)J^z^)f$NvFrK6K)kf{cNaW!i3d0}v!SI_m#TLbDgSM(S07C+b2=QzT; zwz~1FT~c07yTtQMVIB}pR+W3GRE53-%@0#8fHwKfE_LUz6bMK|Ie``GJT^)V%5(k&-483-d?)1Du?sgXNWVQ(eC{*GX!|CI({FZApeC zTLw5a6SA!9KO-n1s>Qcq9${6Bi62vwxE?Eln3TCF7x5WIY~Er>I@C&h1IdqF*g`5oFEO=(4GvrvvUO zy^;(k=VH*ZY7(@Is|!k^$S`gb6+>V$bOwzPu$Hx}q7U-kz^XK}%kbP~le_w`pG zr%9QQhM@2cMH>9$EH`qV&88dX(t2NG(2M+PF@fhs$9=Nx3kJVdDLe@e=DWI3`JDY! zji4!f>YQhGthD7VH0DxTp}b(7e24IiKF@D8GLVea;gs+5eH&8~H?G@W8VHQpMA}qk*&mC8i-# zq^MtOuZ;|X$_t~14@}tA(da_@C)qGT9Lw1fAn%M{TThkG?$VZ24juaTRU`+kXEep9 zbH8NN%eTqCx$&xid1i#0*uAKswj}z41|8Sc3lD1bJn|XTw9p0yJ4_q0I?<%F1;fYS zNpW^oLF!?PM{9$U8nyPtU+eZX&tk`O?%&V0Kw-*HSJG1a-I_JrR65AzNi-Hy;99j| zmA15;+VhlO>?pPD1huxsz9_uyu5q_kF#`i)>Kf{od~v0vG-gHS2ZxTUr$x_%t!u@o zyQ&1dlh;wnDm_t)0g(%KA_~4(6^M4+%Dx*Yq=l%d-HxZ9!+vvlTW~U%cZ)6YUu%t3 zr#&%?DI3!Da0_I_M)ltgMUXDH+6PdlQq__{I2#gJd?a_9&Es!W&aSoN4Mqf2b(ea@ z{emaK$1ii8_&E9wJ&WW*&bf2oq(+N5x@YXOHRP(NyVP^dr^_kBld`K!B0^3Uajzks$nojqP zjRHri$j>?NIX97&PH3Iqd9rBq2PyG>2WMJv%K9SDtf=uu+rH|9IFNYSkMhp*Nqy}T z&^1%#NRxc5+FCqxrEE54>#(tPR*ME-m3?;^sFkar1t7a~08agrFx+1 zaS)$o_tKBtMNDj^a2p47T3dYkd)im0{UmwvF?zOrjro}kpjQHxR@Q)3e#&fw_)$qb zT?V~@*;FeG7#PEF0&Pipo1wwRu6*Nr&z9}1-bPeM>ty9)CLUOa-A@fRctM(ZQ5cdR zO|?#2t;SYA4lYdcBg)@DCt26`ju8k9nXfCXKbitJO2ks8DX_2_YOSue`A8S*eS{<}J%zkY$f5_v@jK{|sBzm! zK^Lx(0=`dPhd7nHCUFb*OQ#HxE%yV@ZQJ5Lh^6%UI>{+(-j{dzif^!AWpsA8{`1Y_ zRNirJA*=Z5H*;%jEa9q@jZyRJZ5KiGdLrfRXVevV8Dg7`Nw=~P1~`vh%isP!g8SS` zg^9!ps|LDH&Qo)*vQw)$HK5XDWQcnFR7Co`y|iRBLsXI!(gaBhevyF4Th19nq@YUax(zN&*OQz<^nBP!gTKU}%AM_3-pXwx!4JwZZ zr?g1%%lbx^X~A>PVlrK%jB%*HHr{v%%BoC@eDJZUl<|p(>YAH}^TXmwmaH+vC8Yk= zs?uQ+GM1zN7_@v+*<0XN)X7^i>%KeUDFoRT4I&Qfk1YPB3|oM0h7!gL3b@)qLpj?7 zH+4Pn`}#_uJC3ZmsTSLx;s4Yp7Bze5@?An^l{0@X| z%H#Yu5()_kiFqq_+?Y`H#E|YrTjf%W6x=dzSqi=LOPMTJj31By-wtnx1`8?Q zZlPlx!&1;)W7}R{nKP;sqkI^Uy8@J+UbFl+H#hM|BSyT90!Mt-O0>Px#Vh3iQL?7R z*t@v&kqmEWf%nNPJFJi%e~APUcm9|?WP`;=8-*($b6*ifA6>GTwE)~7K^z|53r}&| zK5C)r;&f0fV*H4`M zvW8EzudjXeH?ENTkJgIPJI?|!;$ZS)Rd^F}se(ikmh>n%QP-;+YWNZf*P~mWSkN>w zbN!Cp8iMl3pOx|E$ziu#_zaQ>v!vM>AkZ3ftT{3V9r0QCwPp{IX8DMA0y#|qWcDq zx8!I9=Ij(Mo-jvHB`;N@4KWPB6okjL&K(^~kml7~W;_0be2+qtA}y+HubDF1@5GSO z?Y0|zCodvie^%KiL$FkA!)6)69vc@|s^%L+{A~wSN{VAm!oD+2m2yShjTgjHoY6+1 zXefHxEKM%LfU=6CVF^IuH6Q*OciBr5hN+H$P$i_v!IT9>GloH){^A?dr;HR@i3M0dSdwP#M~BKH2mX2Bn=GID z%ePM=-qh}rVD$-8%4TeLc`0xs^_nN; z)m z&Hl)q6L4ApEg_enK5_0&e2tq1b3#0c8VNzz_A>&BVl3WqX={$BT@W>rQYxTi6j+@; zl$|d*wQ~iO+-k~DCkGK*pofwukGiqI2OJkv+hkHBIqLZ`nCgTmiK2#Z)dohBqf5Z~ z?q0(|(Hka+##xRmYTNJA{~;1XTaZoyG>Q)Wg~=vK(iGCnv3Nj#Hmm!3u&xv!T$&WE zKto}O-T1DJOin|g{G%0w@T{6zVr&{w07QmVG0)(A)L?!ws<;<-0g#++S9rcr*2kXl zFpebq(kjC2b1ftnXxXEWNUqVz({`fx1|&)DHaAWN*`%ms@jxy}{}8X3oS8wRoxd7# zyLFb{bIq)84{7YlMt8LN@LD+UC5IXka?o05lKWitnMza?OITA>n78lL<8N$r{RSl& zLGCR&Pdr8lWP^5$Bm07XG$}I&ujzNCQbhCoCR1lQLPS^^H3)Qv zC`8lv(=RTR3B(=Se2-wKYd??eIkkO)Dp023o60XPhFfxNI5XFp`h5Rj$Tc;@2%|Jt z+*~&VdUn^+)iL~T5rAjk6?_^-FW?i+km*6%pK{PO+;eL897@Dc#Rkn@uBN4KJ2KyJB?p2-n)$T2}UEX6bsPimuSa*f)4%gTU&x_3V%9 zsiTA5mnFx`LP8f&6*~FOStXM-S0HZQdJIEDLz~%{G z^J2S08)M_!VxvrTxOzz=e=U1{-hJ*=S9RvDH0YY_A4dXtOO`DM;=(`zXbF&(K;;V9 z77Ax9>ftZ~TzDpK_O!&s#c^OI6EG z*^x8zvU^c!I5!>F48P~*9FE|4d3#&VkJ)@_TG`wx(k|WQJmzYY!?w&LfPsXDhHlo6 z1e99J!LVVIL{Y8E5XJVMZ9`^Sr`j_9};=e;ZX5!J+Naw`n6`6FQAZg~oTr8ue)h%-Li zPmwQ_EL28{kz-NE2E^A3q_xY(@Fka()pcR`p5b9?*15TY+0B6!pDa-V9cwHiLP9_+ zQbNrwFlx?STfe!XPahvh-);DP_FRT{oIJL7SGhQ1l3~I!a&Uk>msWH*k+U_EZzkEG zZ0PG6Tv5Rg$?N^l8ArsXsGKlKet3Rv?qikMdqB+}{MsDRpI)j5NcIyGa#&cY^&-@f z4to2^(3su#zVtHYLRzUm#1bXNL-G_bEQ{YC9=J5B^eEEC8ZgI_BIjm~yySQnu{4&J zx3=~V4&b-$*H+qS6sqpInc2k*42XPveVc0%JrO@Da@R zSFjK`18vtNF&k4F#PfwhMB(W;b_pm4i?MNV0Qm`Z=+O^na_a+$_M3#*yiF}2x(Xcz zNUoZ&uH4x}*o^hqAC0RX2hIQTY8c(=At@W918fFpAG95n{2*7TZ0hZuxuCWoP}UGR34w<51oxB<0{v6W zbQ!(v_^EM}%ha{B^!{D70eW3_kR}ZgV@xv!;t*(&omw_OQ&~kv3;$v=YN}T#RW-8q z4F-Vsw))p|lp^^$OiLii;LsqAtX%xq-bHO`bEm6H#e#M6olJQJqf*sE-Yl>+w4}W? zwu~K*ut`ZtqPLV~*48!^l}fZJCHQL9^qDqQsl`c2X@F#Z^G8L&R^c+31PKZmS(?7G z!pX&@zvoThR+~U@Hq>TRCLUmTX`%s%ujk^Tda|?LKyesRh%(dA#3V#GuX}YDt*xyM zP%~MWrq^0_d#$6QgAJu=D}bXjS^oA>QGmJz*cCs=uvx!|j(p=@V)FXwD=tG;7QXR= z2bkB_SJ`NcY`;z?V08#02S^vJQi=@XSR?9p&RRM=vWJ$Jnfm(rZczS~%pcjHa(!P_ zk!gF1WTLz|HrQKd%iCXw*~_YpEmL_vY}#~RzrkbLOn z*uo?_U7Vv!l_D4A;EEW3-gM%%xjsDm@!7dKk-C*XBS};z7)_1@VNMM=yhH@(2x-=w zl`~s{-AbNu0Ejj=E^=}4B;U>jOFC9cS@{_*x6G0m~NsK2R|F6`~;fgDJ2 zZET9de_jpT%E|me)=?B#-Wrluk{9oOsk5~-d?@|qncAu+YW7Ys3A9?q4{&| zS*hD1-1#-`JnZDermmxL_|ox*)dm$J*$daKoi|}Dm}OD0_hOjI*f`%K+g1FvQkAy6 zs;W+J-Q3lhPB4i zWwW`SYw1EPxXK&;;-pugu%o1+!eNL`obcl;G$;*cg4fcHzQ-;-g0R>=e!`7B&A{U{ z%kj!~Y8hYri&a)umMl$SqY>8y)TOGlQieW>!iLd-orD$4?gh!A6O?Vw-ZnON)pZRM z!1A$7L-N{G1mDFVGcz#_AN=703_r=fZ_S@!AeAs`8X5+ko_L1BBvh^0NevIRq&Xx@ z;R}%QBZe0dWDsnl;Bnykduf%WYt#LF-|g3X-L@|d;sFqLbBlWDRH(}%s4#>84u@_l z+dt3L!l>UGtRN2$sl`FJg#SAPpj={@6#+88Q7K+wvuDc%*Y}R_N0q$8Yk7*Qub`j`ci1PBv>aHe<#|ud~`3xu;ipC`JbZJ8> zm+z;ue=vUZQoCsoGa=UNYU*wMb99}ao~lIXOVa?|4DcSO96!hQvRP72zD6gP5CgWh zfWktt)HDqsI&|CIVh#^w@NI0GJ}YL(B?ST;#2&9=+id&P_mR7@ucWC>O)J!=ebbng zZ@XWj2~Ml)YJ?o!qqOCMREBB^g1mn04k7|u*M2>@#^!bN^Cy;`N@eS-YDKgR&Dsjr zgjdS!8)F=yKyJI1iJxEWZ69n$N5{Bs`piuHt1F;~X>D!I$;FTPor>x z0&okhftWoX_h0tk`9k>lL=?p1BkYOud@k_tvC@9#FTQ@1Yxn|czH_KMK<*DU zzRvcqoGC|D^CoSh{N|p%Fcg}~s+#&{NixFWLQnzc*4P5wx%2TNgYH)m+NCI>c`bdt z@1|!T&op#hsP z4+1*&jG7w!71>mW^MKhm0q=&^NmbX|`<4VpSlB1WV{aQf3-iEz&uD$mw64bq`(czf zg2zpa6z$;K2?ma|FUvp>*7HmOjFVa(=Ek3X3cuSj1ClzhtX$!6|7q>1vW*RxR_ywF zuy>B%c+T4c`}OrTKGET=D4ty^fJ4hEoX!7SZB)ugpHPP6uZqQvOkV75nq7F!cE?vs zwDamasHA*xuhHYVV10SoAsZ$3Zajvlsss-D)9jUvk!2rEbBgG#l*uqZzX6d*+Q;aaI50xo zOkhGC@;tTZlR)9DF(JQK{}4(;J}H?lP*lU?@`&tBc2G&u;NTMMZn$sl-NC&FCd|Mj z6fX_c{*ufwzh(A+DfZtr==6~+b@`@HnG@I}M)59XEq>xAORQkPqzY>R2uA=K^tGz9b~{H$DT#H0Y@! zKUFe5&0OUQ7VbybIaXF(zi0ECThn36o6^MIxSU{)=3rtYck>(xlulD$U(h4_>Abw@ z$LQGT>7^VhkW{b@Dh#B|*tLzly-Ed*xP&-1sJVA}89SE!8&4_rC~ZgOmh&-lg{6DN z-K>>EUaT-3Uh+Z$H$v>O(Z?U(Nc*_7ge=6!k#KEnY-$^8Wp5wCU%E}7oP14K_(Mp@ z@!=r3^s|o9zJh16W3j4SXKXlDXtC10Zvp^x94*wdEjiP`CcxR**m}#(@x6d-@B#rT z4>_t#Q+V1Yd1aH)N%QLQcT-xXy?-|xkx%#b4N!Ykld(rf=GR{yu_ zoOFY^z|cQG-PmOH(su_xrRB+N6OR~u{*chw1p_!1J1R)s3BLjsM-dLUNT4MQt?S^5 zm2q$w-+kg~2qlE8^m+Jbx78VgjZKv{*Yi_RsIRK7qibSxmlW_QE~q8LkP*F}pPT(oaXB~N<8(XuzBM)^ zPKc#DZal>p3n`?Q){s1TC2ICy4J=UMpAa9Q#*ipHMPK@w# zI>cwW2{h*|Q=`+;%6gHSNw_HHO4l!+W)Y$(sA5e_Raf`C*oJI_BN4*h-t(5EWL4z< zb8rxTwTb0yDB9^Ca4+aVbT1y6kkGErqWfF3O10jK#|BXyi!+IldOa({2qle*C&Z*+rmF92YH1nTn#V9jhM$lW6&K^Aw@dw||Ll+|BA5a>bzG;BjC=m~P(D~+X zHDYydXsnqIqhG`5%p3Ck`Ihe&o_)^Zsehi-dMbDI= ziJ4cZY*9nIBA?yh{nlvx=LF&#+dKC=&ztYD3%3*;9GuIm8-2C(gF}=w(U=Z1IJQcA zKY#rA5a}%;n$UlWw^z}$_3LY;{8520i7?STBL|0g&doO*!S}~0GMR`IAF2@J<*0EST07qp=VM%AuK2LkVbbOF!`@{zT&~cIirrX zsh3&B6dzvvl*oBgc-wk^ngY_dL-I5-DDMpeh?1unT!n1Do$m^~;j!p}c($$qhc#2@^rTs1>f2YrEy zZyh`n%zn1jH8m3`?{cY(K30pyvfJ!5{`W7>mftN7I4SMnw(32!ccBS~@Tf<8hg5z( zeX%)TTV6W?3Hqo3aiD_XOi=&dL`BM{3X}rAG@xBY*XM&S(Kk0Y&)kclxRaqs*7)w? z5~7o4j)RXcs>R_8En=cZkD|Tgo9baPRhY*1$}_M*>UxNy{B;SCEX8?3wuaW73A1 zmN`XH?+wBziQ^|k7S&T9A10vy>S;NTI7N1rRWnz;Vw=cLrD~!1`4$-{!eqBzbO2zJ z^RkkhdWxXB6HO(|L7!o+)h~XyzZ9iU~lQ0{`%@0D-zcE z=JzyMjDwBcUrAJ5*U{Hw7Xh@;=YLH7-ch=!-1o3LEKBC;Y%n-AHNAunw*x>e-X~|L zPWWcj(O3_C{x4fNI0Sjk9b=1&SbnB*RPk&WVQ_G48JLJan2R=FxB-VOVN#Nc2cgGh zwfR7LQT?rU)DaBS<`X9e;xdj+ciF5hx zW}T7(jPuZbdU}4s`Pn)7=MTBc52*fQ}p4-1u^ zol7z$7dRszZC6d)Y@hVm*x1D-bv}{mW)#t-W6Xn&0Q=o zbPYJ=N#I{#!@`F6Jg-6?hssXUq(wXR(TP)}@{^64+=hF4`P5a-T}+bix88Js%qHYV z9(U|ge@*?SVBIPq)AhW$7Vdttb~cqF{FEqNIJprP_i&YIPebtB{yOQc+xbfOBq3gs znyR!bS*6F&O%V7q~foNkooWSN4pH2COc+kfu8N{@7}(R#PGv=P8frT_=E(P2M<4~ z6#mi>@-HT+mF3)TddD4~clMo}n64|Df5W$}s_8N?a&$zKfk&sv?0SFR%BlSAaa!E5 z%aP;rgplKVhhwm{Qm|s<^f=-E&lNq!`k|NU`zte0+*~hc7}V6(_WrGzsPcQI+w{7J zg@#VQ8%1FF@ckdBc!)!*6&5og*ZK0q+WE2zseB~6BKkXx7`}u9m?avpFbn}K{IMt> zH5WO3uI&AdfDYoef<>-NipEsh1Ot_M$`QQG zcM9j#fQ}tSY`Xg6#n@SZfvT?aiUS%s@Q|}44NWObgh}Edo#6gy)0UZ!O;%6yhN!f1 z)dDvcHM5}LR4^P$e7awye#hXgHTdTKzNXH9xVIN7PF)?+VfHmT2GhgYBn#-GX*0us zK`da522ey~Iq}WR&2QPTb?D*%TB*S12Y7k?`N7}e{mqwS41MYvmChQNps3t@iBCun zm>oI$!y<(7BN-W4Q(tSRPC?Mr(K13>%N$ptl+PZhpvEiQ|8X%}#bnzFeD(_&U#O6v zV(Er}X(jxnm}0tPOEA@3GuP5|6GIfhH0|t=47J5^H~Z%(bUon~emCJH&p8J_t#z4| zPs;?s)H>^ZqA6Xl0$NZf;BkOag3pan&zfhH=k3?X#`d~KPqS=&ePjhH=5Qk=D<=N_ zIbSiX_zngFm;xI$kApD)aj;*fV* zx3wcBrGWuEO)b5>sIOh*7ru5Kw}ifrd@8I-qRO?N&7)UWEOCn{hsVcY^>^}rLwYwy z0i(Jn-}F|?S-7}{*CfK~Q^ivSG=HQgYocz4TZ;A4rjd&YOoCI1 zy0y*MbyEPg;{@M+0+m|i5afCLGjf{w$4j>D<+9ITS@t{$A3OP2)4r003s)WCr?ZSZbC3}-~MGZQmz>RXvtdG_+$1x=^tG0?(bW?-D82w zWzvW;H7A~?oCx&g0C_|219&+fPUfD+C#LFZe1Xk^Jbyp42l}i}wOS~Kj+AvY@KAtD z^YO0ixxAvnaL-vcBm3{Hq);4?&Vbf6X&efLkZ(Ib?rr$!h<4A6=-mH8fb8?Wc{5P$ z0y;p6yqpp(0l+N`fm^s!eFp&E?D2&QXvCs#QH+Wwr9(@!4eih0&CmIBz^$`vQ(N3i zf91|Uf1{*_Ow9dUp{l%ccz77T0*Y8xc;>Y0_gM)~Km47ID_1IwE+$0|i4OD4Je&w8_xhGOP}{s;K5_zf@&pD!MLAN{-%wDkxh#*IYSfc^d$P{NFEFT-@Awik%Y|7fb;30~{^-C5#Yi z=K%Xs<BpfeVgp3o4XrDdK7>P4|n5&l)q19mP@kl@*4mqE`!$)^Ss`7_*v5b z2ogTNbdq2xQ;@^Xzcsb6^-WH4R5iw;3dfIVP-Dt`38y8Y$kQ>kc1{9<8_;8mES;R3 z{!yR_%dG#H$t{O$$^Ovk0k%(oN(A2*Klj846j1FR%rbQ=+eaWcfTBuqYKi{rT3t}E zU{zDoIX-iVX5f2^S)-Y}_X@pgn}$O8mMGfWty)h1Q_t>Iv0+re|1E1|L@Fp$yinP2 z`zT!ax0bEXlrf;kq^IL_K3at?IahJ>3H;S~KhucT#UaCD?OgJ7T49qb)}&|Va_BvL zMEm&hy1cUT-xTnk)6MN|LhJy1P(TN}nq)?HcF}^>jJwOyEwAnBnmQP^ot&DQ>E&?iH!HD^4hK7Et8GY5 zu|j&7YYjpfU!z0mK-PysONWf3`0;)813uo>&Nfz zxKrK`-9sgC4v2EsH7IkY&>}?V)0fe_ZR;mBb#;Mp+4t_lLmCli&V%#wVnu1<5YfZ$1xzFQ3TDuQB5JkvcPYSq$8*K$(bdne%8|sp!V|oTg7CGd5!E}TWqP2zg|)*;@5R_@FODl zH;)21H>FR%<4w4t9|utKVMKnSEa?L^d+ram(5rTtz(hDe6nb9$vA(;Doxatx+iV@V z&6G+xnA~3wkY0kD>9Gg6h|we}gX@8&Isi%qn%$zZH}y$J@U!=4dDg%O7x7dECl@Cx ztojT~Ts%Cxf^T8(vI=p*)4ZyG334%%Q9}V(X``p7XR2!yqXZe&^T`$}=W&`kxcH3k zMwz$&^yw)rX6E4;?Y>=$j*i|2olKW-YG&zu_w>YhuAe+uroPpV(UhGD0P2DezaOt1 zzQ|a`YEV8+V)h1K`;0lc1O$GaKGJRywZ<48G_%Jo{a-X)b6j2j|KF;G)v}je%iU$$ z*7D7^yJ^|BZ7thguBBzWmi;@Q-{bN9yW6?S!2vs)}$u`ed(WGt{`Lsy1I%3{raZ1p-Imq@q*s75R}n_9f1-5 zXPjpF$^S@g;GTijBW~)T)%6x1K*Dh{O9a$YpK9*9YhLHPhA)Kt^fXH*{@Tr3WM`&xZ52MK=U5;Z^JT_B4Jq zGG@agOSVL~XwohuheB(k?;HV}$BS3^QwbfG4<6oiKL73Z%?ac*F*6G>Wc>*~%*-UH z@9vJ-@|v$O8zB)CArrw_bmCfH+Zx?rdc3Xiowz>0uqKiO7Sq0k3^(`78S2OzGhiic zggHGwWqmgc8w|s`L*X+nh;9%DFl7mf;=tBIl-YUOB+pP$0{ZUXC#{RT-Y+~L;`EkO zBGsuA0LlT=*El}^VW|Gw-oH>+!Fk|CVIB3__=$?jEzR>5bFpSi9NnBHB;$%Dp9LE2 zv#>LLe{SqJ24$g#kGukH$VGWF_8f>sE<%%aurZ^##ruN4l|mEtK`YVUk+?*pA7bJ2 z(O;r~$d3pepSw;pvtqG^#pfCusHsAC@;)M1^m!z>*87)P4R}2Rw_K7hCdv4nwmOhCKeG}y=ax3G-P+xG-6q%JAqjf-giwX z7ERg>I?N3A=nl3%_9^W#L`vbqq2f%<-=6}{*d^e2WO;ojeP5s72Z8eU>K~u&hDVQr zfTt*X2Bob~ryjWlLdP72&-3!T@%9zY{3}gyfs?=^+b?<188y_+Pn(}e>)hj&%LZ!R z_BWh>+WpBZ0-&k%9;iDY{6e@yJDkWO8sd+;X59vbu>fNpl2;jJgU9RJDb?s2jt%p` zF!-|hxz^=N_p-5xPx8jwJ_q1YPHE6$s9t*(Xmxl_(IGw|O_Da^WVCIcaG;_v3^}#4 zW4S*@;Gvcn+02wYwWCR9rQ_U@lc>e2J32ZcPhlQ0nf+a9?cfOcJ+bQcFj(`(w^)t* zjSl&^&=9Tb^$tb0RqlmZm^W!_B`O0j-o-f63k|Tepn7)i61-TzTnNGWm%q6@UTalh zdwKD6K)AxZ*83dD%+HDZFPvIBN4K}h%o^Fm(pZ=!^AvlUZrz%VTwAf*d|m0rl5Lde zZe!)B%Cd73YBz5ya3m4T4yY@u>}ECAcmGUe0KM4LKZIXt^hyi{wRV1`438ncOIr)` zDxix{=JL470V%PNxUHfmw?h%!+0uoyY|p`)c^EJ*tLrs?^95gSji%@vh59qW;x+E? z60|dwStJv9|~0q&?GhPKj)qCwGm52Pug=DY^cx z8NJ;Sz@gl8R&k)3!j!q7Nrlhuy}STC$QReUJ5GMSqOGPG`UFk7iK_Pdhs`%=;FlK9 z?D-4Yw!HuPorr5}FkbvCq+Z9Vt?lB3fmBjC6HA%^&GI3%hLc+O(`?oJnI$fTKDx+Vbok^*x0XbHQ07 zbVHO($j%OBW&~tb5|Wus=NVs3cOPRx`<7B$E3)R~<0D_CMX4JKYz7Wp<9TmHh}?hO z{9BwmPvF{j{)C0!yEUhIvh=R3&?!-mU5?NI8l}Pr4idQ(VL6(h4aBBB&P(@94ItsV>f_Hl>AZKndg|gf$yuPOWq9Nn-;^H4< zn~O=WBbW2#qmj{KtLsVfc40H-ci~qW5wh(3w))|coUYxBYuK<*sXSlQHK|;#@=NQN z+?xZGA0gpmk#Q5t1C=yo&OiGn#Hg|b2CpIcN+Ij(EJVC7pG5j!fvT<8L%c%G?BXGI ztpIx4A*7xyz_opCSwtN`H+x;QHIu(|34yXqL6!+qW-}A`HD<-AK8PJahCnn>?({`n zu>jk7`r^Q?P7}wsfgKiCb)2-c$KaqdLf$D#wU(hJ&7cT*6MZbASJQ}z1u-IlECUad zG;I#w-dl<8TvkwSyrnv!z$IQMt0ZHNfrqVsi9(Je#8aLgn_Bl!B$YOPe>G3Oqz+aW zdK~*EQ94|@CoVIa&}khtGCKO-T+aKKf&!!61orBaUzh)6uyLqr=>tsbqZ1@Adjmos z550Uy{Um8fSYK3Q4V)j?jj$H@nN~|)?l|6=s-9R5hGAf%@zYxGNWBI6|~ zF11RKkkqF;Vc#!NDmt=Xg71#_(Rk#@lBm{ml*UVU6lm}12W&Fg_#PuSe4hk9JY>9I z`U1qOjpH(I7jgXbS0FJ3cI*?-h3PB2;dH$@684X&oE}jo$$!(+Z{yDiL4wkX;x?lm zp6~E8?izp-c%|%qa_incR`d_W06PtV!LIl+9GFEK)jem=F%O6943-OY!16WnLPH^<+JUU!Yc>uA!PVPp#W$;nam1ijy5H59tQo{sSV zOtl28on0;wlB(2~-lxcNTC`S*0;}8IF9HdWw^ar$^=#`dxPze&L>C6eM=c}%8-{Ln zA*}}r52qKt6BpSNn~y|`=WmH##IKRhDCdfFNukn%X~fxCo2jt+{BMy#M`BRKbV(n9 zVTb^T*CauG+Ms!Xe=mpSD*>Wf_yv1D|LQnhcT!8b-{W~d9z?N6^%FK>o2KgO6+2QQ z!gmN7Jkk&;FBZ9I?b&-JtTkf2%&{30zGRB=>KniGdfW0t1nq=}9c0%75z)0io*<6% zzO7wwA=C4}JW&6I+#v83p(hs?SdidThXFIi^C zSeX0yU*_&Tf>szuH8Vr*FRo`cUWtK`rxflqaS*hi8j$?Exvo%8Z?ptx4T z%NbdP=kAlSkiZ?~3BBP`M=@~CCz=H#bQU=`r&1ol26+ydD&r`3*UK0kq#P<7$~swW z^>m4JT<`rwe@apqJ2h)dbltvTbNRGK;kk(xg(*42Pd01hU+kBdXF5FxB$XuP;c}Qa zmGgQK3}5TgubCPc1Zv-(&n+&7RD~QC3xYlsLHti8*a2n2DWlwFt2r6vz>ZSzub?zhDfUOiwR1H}&#ZI7O$uBppC>Q0^H(3#wJ5y_)K z#RL@C=uLGRc(`Eov$lF(Ga7O5=cd{(@mM==BYB|Nb)b>Hkt3=?4yL+Sx-Fh@L~>oZ zP~F_zcwYR=SJ0^_$jQkI=Pa6hn>+}cJV_oSX4pQT5dDt10~I=y z6D^;G(%m4P>L)(96csJm1e{8hRw**6S{2cn8diN`ZFiwqvA~lLH|BxVkE~U(W@_S+ zWY_hYY^IO{fY8De-cibBA$fU1(nUnFDrmwfo}RdbU-v9be*$0%yH!1Iuzzv{ngT`b zuy~zK*c=w^A_J=y{(XaozfUX;oDdH8#B_mHRsIRL2p}pVY?rpvj~fXaT3S-G-DvIq z`@;MDy-3xJ-$$07h&(F_(6fbtgrtS$)c(c;sZcuX5!Du=1xkIdu-P^4G5OH}faPP> zz-`@%!@~&z`3(RmXRaaV{HoiOO*R-&Wv*`HP+_xfHg25iL8ygn7S(Gly#*36Elvmc zLa4u9w?7qw4OiJ4JqdL+jJJX73+Ij4^GbB;9c*s;0YXq{#)pW%(e;>7;%J_N^!Vi1 z5uHJ?R@pgqe}sMj-i?Hbo48Q@^vbqLpu=nT@**H4rX}-`3bE%EW7{AD@YrRfKq_JjFI@)PfL)eLnJxW~4Q{$nU4OsIL=LK-LT7 zbz-r@{SjSv?!mw~P`lYqiSZts9^TGok$$5=7a0@7_IUiMz1^!LHIpJ|DuMpaK*Bb@ zp~s~&qH%R~aro8i{^4OKrq%P7F1pOY)m5AtHAVU#Aiyz&mM`o!Vy z3;wFQ=!tGGZs(345dp#F%~3Pt>hYkmvJ%jr_1s!5;(aA7P*mJK3%143dq3U%wi;yNAkhmqC$nP^8Jm z%NMB*5)7(gM+}9E85PbFI(p}Hw|HbvEidD-v9S?Y6VU9|H^T|O-h}oCKr1g}0%|#P z`jyx8cbYj6;RJIn65$*(gK@x2fH~oDl2w>ee7OVMMnUe};X6yXZ9csptMAU> z+o}VRfZZD|1P+w(1;~dp(io;0A`f@^6J?lga(v6Oe=zLc`D*|W2gVQ!P^FB=jbqz; z&TV+T14H>ge7&WmG;pJkBw^BEh=7dBd{*=t0=y$!Vq!BuUyQ?~Ut3$-0|Ym4*nVN* z=P%cD2vOgHJAuy=ZulB31dLJuY*l?3mfQ3-MeIXebit~rI-u42u7Xhd zT2#4aUJHnvj>qVL5f8vRs8O^t0wRmdNR*NPbz>qTf7@I#x)omC_7C+jcaW+*HnDtz@ErJW`Be+dvCD&q6m;gEMYKY(}zd22m6w{rlI5aXn{T=ypgV zRR6R?KJv0P>^M}r2qjrxbg5Lu;Ql@pfCaezfo7=V73t)ty}gKb0( zg8-Ff`GVy^njCTrvIXcEH&_USKgcY*u(cDg&Jg+oyG%CNm{p1%dyrXmwsCbPUdk+V zDBnE$*j$=&{zu!tC#;%-{iWsA9RPIJ!OJnWul*V7+Qh67My_6=*3{ITmX?;MSCeOz zj-C)9KL2Q$eh3!)Ezb(H_rM6z_j#lQ6#D>0=XkwN{;Tz(;F&4iO7y^*#bx`769BCM z%&BObm7QC>C}7gjxkx2ek|NaYL9%^pb=f*uR_m%t??Y1&deTe*9TD@F>-_$L_0s<$ z#wIeF0VTW=rVfG>ZlLJ4!+%T(AjiAAL(a(Y=>_Pe9=e44Q2L>yUru!ku3Tu?COV_os7e_aB^UU{m(&mY=9qy z$hZZ(Id+WR20y{U7*-^BEry_CAkG9(nRRvXXG+4XG1t-)Zp<-*!ocZBNJy8xStt(| z@3+mrfB#11_2|o5ZKh538l~9KSLGI{afCny##2-PkF>qLeYIBM#osMzfk%XX^4F!N zs4b3ZP?#xQVMq1JVQ@I{&J|~xb+S9}zgOfP-v(MeHIX?<7zEZdkP@{tFjQQ0kxyLI z)iwTu3jusebk=g?>;2Xhlw%`c#8P3 zV*9mbj=k7rn|ng_K!_@f>XOmcyHHj#-zeVEY3bh;y-K|9QFaMj;37%v=YpUY1<5Pg|KzMX5E=ds`uD{dK&Fo8Y2+>1)K`h%sy7oHW7s zy)Z~FcNJ}0=tI!YGw{a2h)@>beaujm%(XIgBWN6}@qXn;bNmwYd`0+3~^>H#M z@9@&@NYWxt3WJt@`wA2!%+y3U2x5?B=t=BC*Z8ua=bTc35 z*A5_VNo8R*9#0FzOIjP0Hx#brf)@6X&UHW07N)-t_Opgc5VDrR1n@b;Cx>tf77EE9aHqR?-4lI_6b%5XrMNr_jIO^shP#hLZ0fxGHH{w8ZOv07ZI8dDb`;>H6_#)OXM8YjZD(iymJk*#Ip7_Tof}x_S)>%F zhfcSgx)^E4W_gNBas5CNC2_ILup4(p$_b0p*wRJEZWwolK zCOB)Y>N~qS_7WFo@ru2Ll#l_Qd%Axu!PB_N-;S$~lr{ug7`^=zq9{Ds&+s!icV2&rlKC{LS(rFWO?{?^0>HC(=mu2ATnOvVNZ*l|uRP!bN z5LhVXkk$eOxk!uCil{jTHU;C1XLoN5f|4H1czxdzqb9yjt`JhINej~o;T}7R5RePg z#pkEe>&5>1+E(s)5-hHo!YPEfYPA^z>)$ZV@U_3`lJY^B6z2_6@)|Pniyi=cRO#gE zG%0{uWzv`?SsLjkg*L@&#wB24f?*#=udc1Dr?(fODmmt&tfxw*0T=f8cyj}jBMhE9 z^Z->j29;O>k)@9vfvwQRRkD6BVd8#zRltw1d0K86q4bj}@1(hYnzn{GEhWLqFx+q1 z$$$TrF<0UEyAtgtQxs=y+vZOW2nf#rHNfQ93R%KbNm^#6iBpaA9L|GqKMsC!Rh+cA zR66>cofE`y;BFH8;Eet3e7imJ12PZHZJW?Q)693u;<*&SrtIkIKKAFB%1Rq9+zJyD z06@FE`zIUF|a{LzF!pY~?=xK%B!+wPwklRCw zKjnD4LKvPTR14E(zcBSR9-<$;6{ja&eS^lfVofj0%ECAJyN`E4JS+KtydT-Fa=Nxq zUKVF5PXYHSQ%!-BlZQHuiJ2KBOOGZrw=u1smfR~?7E%!qkT^>fl^_SewJg{FXe&-u zGu0NRU_WjiO=Gv%#OxGcxZXD{|J6Czi_qZpc?m3d#kRrOOIX5}lwwcIWlEx8gn?`S zM$aT$SEp}sf%KOI1|j`2*1ZL$C512goEH^pA~IJS`$#rj^>6MEp3TD-Oj$yK(+Aot zi>I22KIXz1b9?vP7Gky|P?-DAd)Jt^5=C${044(^pzEH@A#QX-~&*7VH#M+@gEZS&{KtuV{i3N*SlUaTY&~ zM;c0f8E(MbOGC-Sw0o=w&MP>=hl=RoNIXmgBEew(@WhXSag{GZz0R%euKx{!6uPqv zylpbn>&Kf-{Y-Z_rN%muXCyO@j}X3|RM0cnsn~sJ8_FSum0yJ*JD%pZM2aGWBD9C67UHcmO>d%!d;*tKT5WBkk|wsKYn* zB*}+0c=+)S-KicEXF2zOT7bFV6O*+rnD+a^e&T+x5AZc&iafi{R$*tiSjh)wkEiuh zfJAD)l5iX9OtC)&`%noeUPW3d#IG~gZsnTP7rAPh7JODa15xxaP>Kw)nQk1P1K|F) zdS~hU>1#rwR~ep#$R3gQeUJ-u_r%-?^{1^Phi#P&uKQL1alXJVl7$+dM)QvW{{;VJ zgof%4DnO8hAZhe}20<1D6O@dpX$b>$qc2f)EpfRi3fP0sN&8@Jrw6`dn5$#1MVCLW zhmjV-_u7#|r>Ng%E)0G`ZemaMz5VI?aC6_0nYNRQoJYIxv?w%e(xOwCSZk5fxU8pZ z=H(qwU+#YJbm}-{sqAQPKXNxqR>HFy=r7K$Vr*a?DO@DXZE0j-fR9$FI7&ZbYlKFk zOOgl=(qZ^nQ9;(xK_KB)LkvES12s&4oa&oJHzs$OO%|zJlF3sfW7L{=Th!Fe+1WIQ z?mtG-KF~>E-F&c530NbP6^WO^3>6~~cIMUhGud)8=YUFV@*{F50m(;CEp03ro4Ewt zHrnvN_ZxM2a7~>{MKBtD&#F;jEIb-7vM(Gzn#5=OR$-l!x#f{{zaHgLSoq1^oTC2I zklhCO=84mrk8*nkK1?$(lys&6mDKyCHo*N4h9n|Hjyju(ncX!>B8JGJu^Z)^Gm9=U z+bPX-yC3Rdpr#&TzfFG7$BAn;=WkLTm-s+%Wv$?%td2ew4N%-P4~5`;J)-=YlzCS5 zZ~GC$v?Tl6xrbuPcB0@2Y8jk#gPO+KMVFPqF@lzI0i_!Kkw5obhu_oa#~L&JU!8Tr zXE!f>@ApCSBwv1Re$kVa#Qdr9G=LNkT8xH;UUioL>1XZ{nkAL^z);F20%ni~JPf*A zJx~EL+)Q7^eS}f&$3VBxl}?sHRVLMwr7#PT$Rs*-tgRodc*N>@as8PRaYDc@f^VDK z7ECE>lVvR$ALggcV`k=(mUL@VE|=0I_}IVp=;MFN!n2rTuItM%y|9qg0zNovn9|gP z6v2GBzY8snK#E?=^)*LbgW`kvW^IViBCH?hZ#MELRW+`i8gpa5bH1URc(#N9$vR1iMU1Yhsr z=>KZy7&s~$leQ!jt+2qyX+!F&nMOWxhC@S**&+PS#mO!Dd*b?d;hVTXiwM0sDI-&q zc8JL7KeujP7Lc5oxp}N`-L%jG6yc2-JvJqFRGw8^K0YV6sf$h0UJ};O@G$MrEG{<; z7;ft-3-Qptq{F7I_~$loY5|}=URqg!A~7tNYu|snZ23%j8Q|y6EAQ(oU}PkHtGf8T z;o`Eb^ZFKHq;O8Bm36GA=Yy9O9L@{#1?wEhfq+oL{UQ4aD*J?vJLu7ah`&RcE^D9; zh8s3-u~5Bc#sd+{z*A?#jb&FIi7y=H8vn-qHFZZNI+$djZ$De=b1rX~-QCgy za0QT=lz@LonlceLYRWQUJ6@+~D5=cXjiL7W1n8WYGlB4hpqu$bWgVtO2i6O5K|s^H z2`4F+uNxfw`i*Ovi7CsPaky%=xkbQ7!=b^`3wSb1m5%PtBiEw{`}_M3H@-e&FMoLf zx{?d%*lvs+MakG_4|m6WZ#w%C!IUB?m=Tm?-X^nUm~C%&2QYphB*$d}z9ORH3A`pZ zH#MuJJNDNkbpKaVTKGInX>WlR!OEQD7%Wf5iQRWkA`T9n$NU1wen=az`rZJ)4Pd7Fv-pSRK8Y5q3bDFMt8?gTG|Vziu7s0^ z$EcZT(}PjC%d9i#<5|=A>B-`My5wKKB2PR4@PKtWQkdNZ6;5}coa%cJtTbiV61y-w zI|s*)AA0+Ls;mIb$%FjTvREQK>2#5NS;>Eq=&Wg?dMhVwC}z0a%R149b)n!x0=Mq_ zCvK5|fI`FQ;}5ai567b*-I8T4_)4f|I@8NEl^Quw;jSw`yC|L zEs5Ga)&5*ZgCXEfzV515#me)X?%E!;MT6WXD(XO=Zy~9x0q-G)4inBc>7q+1zU}|E zqlw`&-FNX@i`K;K6sk|~u>*`+&ZkOadnH~_efSxa^{t=)z+_v6ncAw^Ah&1$P#{WX zdJjujxZTdhwP8W{A;|VHJiDLSevMrjzEIV28Rx)&ni~Zwrw0X9SkE3JO*>+OxhG9; zvx$*UI^)tAz^-v}4y<2VRZjtZlPMeR_%VW{yWE6Q!4`Ck#-xmQvRBBHx-+!4-)MU7 z-@B{7;@r@IOSuOzFWhAY={b-2% z`F1)uVaJFP>W?K_%%D_}7CIVA>pd@GNl%)A6L2`rR9VG!{w>mlsMWoXp$5wh^ zU<1I=W}9z^@m?#V7O#ArX1=1LgMBl?^t58HpDk*{;Nh8lN_BPhug2EVmlxi}i;V)s zk+?8g-Vx$!=r*UF{Ph*Q-Mu}NYyn^SPW9ny(s^N>ix`3KSae;Lx|%m4M>pE_?xc4I zn?Q~!K-DNIRHbKTXN$b(GDAiSY+eOPIwciiXOC^wc~<3`N6$pJ!DGHJ5veEtC2RoZ z{c#zYfgK0izpWDs6&hi~3|IkP{9sg_-*~UH?y$apU~e^^o6X;MH484F=-b}!I=Hhp zKA=rob7(H>8Cf`C{iYdw1OFQ(0320|hf}xqM#h0%I>-`m70*(6F&aEZ77o#^ttRr( z0UYr2mpmGa176jFYU_McgSRW1rZ962ac$L5hdrk{o^S|Tq?eZ_eoO&~3(JV3Kk~ZD zI$Gur+~7Dd{o4*9{8_j^{I-^WeQBt(9c5%FKX8Id{4h`IGX1+c&cTn=tP)R%d^c=vfGAxU8k!_INnw~m*6qS~(_%dq9v+FRV#j41dg&ArGbj{<20yLM z?F_U`YyW>exMezVsZ`9+f+>e#G|SeRvW()Y(xP82;hwFT80uSoP42IgHlFLzfCN=& zRKdA2VT^lU4_M|&=da+ecw*+No0pf@)${4|D6karWEi2`@F3k3-?%phlj2QLHw){L z^g1UGN8rmuVfwKS&rtDTLzxwmT(3;CgUw-RFe}lf;%RSKnITa+!U*Z`%}=hc;~3`t z11#UGk&^$@R>y_W8IY(}fe3E_WkqC+*R5S9`W*0wkG zomS9Bque_L$sIQeitgqH%bD3zlhFN>?~D&M8`^ zP1bO=X#8HO8e}{LFwIbM(7N@xUmYKhk(+Gd$BHFQR5YUtS2#^wz`<*`l*f$;&5(if zyMWmF6~^ilqeJXso3dH&hr5ImAwTSn3s4D(@{$lje;!NDOw z_%)r_Qyt4tO^n0ApD(r>Z9ntcWvApXk1l-reV^@or|QixPHi|W79Q96eSf?@>wY+6 zzPWs1ZwoXZ7>XVqO_nZXyNYiCG|OgyS--a_V!enP|dFpNePxhT|>rq$eK*M%Q{ zxHLpbQ2iBmLXp!yb_p7){7%9nY!znD%3Co1)!5a${f{nWm>TD~4GG$^gc|TGY_%~0 zrHWx$sI7&&f5>zpR4iJlU$s-ow9RyRmScx^&(Jo$#ITwyk2qrvb;g8YIw=%0um2?} zyf%z`Xv=`CwmF*6w@N4bj;Zr4MhR*){Hp4P0ZKQK;6PRifaz%(F?GDTNgSm%6s3S+ zC5i1F3^vZd4ziAxZW2}1Y?z!}($+V#G0C{DXSZqg%BIyTFHI=uwo5dugQInhzH|NY zBlX~67nN3sP3#~Jsc@dunl$L&r{h~EEx}yBwF~*`&bbZ6WxH|qP)%4E{#*3~)An0a zf#*d--9V*-&z;x@!*oIhKd_Iivn4@qZyRR>ssCopBn1)YZ?je*zPiEEuiQy;%6Rk` z3Y2AkP6&tz>swo;>X#J{bUv&FNWFHYWBAZ;^3}}U^db5lGnlD=ZhV)*(Y+>dUf?Iv zyTjWZ^uZxhWYb0`BL$IyX8Pb+YZs>A56kUbpVUyZeuh{q8MAo=2aD4rd6udFN9H@Y z+sjt$Z8tKxdccdUKn3X`03G=0ZN3GVyQ=ch{Fw`4C+(BB^__^Y*X@($Qu znrf0OVq3xsF+@1(OhQM$evq25p|vbOUpa3mXb>ixkW!?>#=|En#;p(4)fyH@b>?=S zZ@&);i;)@+Nrp4Wjx&#U)X$#z_9~js9mYFeIH-t z;Uv2k9Buf*gRRDl3%OZ;y5BVEnYe9u1C#Nv*jCW`P z^YAlFUBko&^H0&GvzG?C?3B>z)MsFSH2sS;n*qq#GjhVMdmp7bIym z91YsDq5{L+nq|()hYbta@I0SACC%bTdQ^7i*J?z~5S+JNwGRiJBf6A>ROtgue9cgj z(F+IaTpZJ2k_^K`_Z=J?rk;+`txi^*uN&;1ONii6@L)$|ctWQmZPR~#FmJ}EXPCTx zJm?1IcBrhvC!Vd%6Ew&~m-~kL^wC2CX&%x#K~FYfR|qyNuCUH;jyM>7Bs6;>@Q{J5ID4SkDqUG$tn2d%^}a%tFwawiidRfOI`3n%6*+DIvRs(8(4@WqC)lnk|X%?->G2sGsiogVy^PFpNGYvZ zqk!<&lWNAwLHFk&8EI*q!9xf24H%NR z9N(<0IO#Np92iRuD;7Oy|3r$y1eHCKUUN-2xzT4ah3~H@6|yIcZnr_he*x4D+S=N2 zS3YQ1SMm5)YymxY@?|m5JT`;gWQ* zMz-FdyTP59g)_gZ)Qi-d-?2sQYWi)KVB_&lzz zVk+vAZ{BF4SKedYvkn4KmnN(9 z9BaV%;MpmUD!Y}s`2}`9+7g8CBCB$p=t6#=*}bty;}8F(P!$sz#+b%MZPRROX>E-Z zUgS*iTMQa3>6$#;cvBo&`lp#iD|==^ab80(yk4`!S*dvaa-`z(u)gUHL9J{;S((KI zxeXOd0tQocMW0#ykIz9+Z|i7)Wef0A0VI7|21A1M7h*Pky{)l@Z-7u!01Sa-u0dxc z?O{pp{d0inKA1UjMTefm9Db&Rm*k9^%26c`z{>lFG00v1MnL}hmQP_D14S$HH+!8) zZk#Mj$_nQAUhDwv2)UlE{%@CeM!4=r~F$=rWA*7N#dn2R70H*=T${$Nl?l~fe{6>c% z72ZX~Mv5#=zaci77(?B21x~)J1e(DqmooJV9i3LmR)BTq=}^U8>5?;>1*8-ICS#nt zt2yM7ld;QGLHPXe(6Zp;Abe?K+-j7CI#sT}*he{fJosM|Dg-AGu@%T!jte0l4WSIC{c>~y;CH;d=Dtdm@{N14s< zJ+_`LQ?qE~W*4JXHKZd&zOi%NLyN6nu;zOl0Sy&c@XUq;MOE$;sW!CaQ~GGoIMFom z6yZWe%|r%0@wj^4z{Sf=ktMH!M_jTYw3@tSPL=2LyN(tO2J_!1+qJd{AaTf#s|g6o z)9LUG8XcX3Y`%6y5I+Rwi3X=Pe@_nV0*e+Gm(XHYYG4~`z6v9i?)!rUF;=_-b-vp&( zSM#;191kW5)1#<%+KC>8{g>0y25P{o+&n7JdKAjiFFg{{Qk&gmjW;|g=9`|3{~18t zFJqtYFN}%~YOQrkb^&=jCmyuGC2ii>9RSiE-=;S)y{m%e^!K_eAs5K1Vx_ufP>G2! z;F0yMk1;uCTOmKNaZ=K<(@C++Ch?(4luaF7lZ#|hXd$;>qD*rl=V@P*TNQZ>Ty7EC z+gER@b$jFoX%-f(6O?gzv3vFjQf3!?m88D%HUJ%OFU#BTn5W#*b5O2I2*?vc0Hh$zB5 z4G+#lGE?=_2v~8rpPC}~Pi0-Vn_$UKeAZYt?h8EESFvLHo^a&~)8gWh-TEbqEQA7o zNBkqBqMlmJ=;@aNY-V#8PF~)8y^0y@W||an$%}-MQm8Vr9Hr23G;(o2DDEum_IB+` z8|&Dyz1CL8IKA!?DM@jNya?VXISWY=tt0q~>~R~4gHgBSnA4cA#~Hz%oM4ODkw}Kd zd3S}acT$jD-_Qq~wz_!l68BLi9#9E5_aJJ|y5uI68_K0T94&k!I44lt;41ucce#h0 zMHpeG!1(KG8{0{x`AVesd<*T?6>jxcT6Xr(*>?oK93jq7eoWhTl(xTsIiYltiyxCcvO1t`%;R+OOkLgjQ>y~5P&H1PZ7hm(wrx%IVugIn5m z?RK4Z2wqaqTkg=^Iu&8FSXm_(6G{_@R7rh7><-MegIsbAx7!lh(--Nk@%UFvGd1pcAC@H=8`?@1hT#qv2mZu4sd}#luOE zFkpUArS!|6ToG@_8na&Pc*}{?RJxv~ zj6X`FHI=E|A+D>-Yw@~gOgcJAWTaQ5S4fz`3ncsemZOQr8jA~`$O>POq9T3tR!c9- zBSw{?@H7kxpc!;1)ijNu3>S-#g$ILR#Q@uBm8ddpbHB8pj?E`_( zrD@Ongz1oqvdG%h3-X(~hqopfZ(qqhy(3(|5(#*Xod$SVzVZ>QQZ2-k8)vlDuFtHq zdp&!%$!GgeN&HdH%~cYZl?o;`b@7r?dWGK|&BPnDpt5>G^&0aWBLA77NhS>F7)fxy7_; z47lj_4H0RtY7-C;ZjYma({4IH7GnYjJ`4#o+C6Rb^)SmJVL)L`pWVY3 z4p0-#vGEJgg+*W5WWm+^GtX+tiG(G@K@uyOD_XOdGk-`Nua{LVsU<+WlFfd&vBJG6 zSmrorq~0@TmNr@Mx+V|bK?Dw(p2ZLYs;D~p1kX-babkQ1W3Pk*rau<0>1Yx;k^~CF z9$H2k>K%C|LZ(W2qPbZsuRb~A?IKk(Q2JkoU8N5%lyNXE*kcZzGXj4_^if8>Cvbn^ z@Mz}zX`~eyqw#RG@sjxX=;5Qf2$HRjSfs@c56qid*Mr_#Ji34GShAk8Ml3{XU@Qlb zO(=^0BnC3-UEd;%^V4vHr6faHjepvsz(S`-;d{vo(k1oTSleG@Jl}j7-JPWmQpOAX z)Hme#A@52MHd(xKcp;7uP$O>rPYWPKBjU+^b6WbIB%gQT%+t_jqqC7GiZgY6IPdy! zdOh`Ef6KD2>Ca)hu{|vCMr*g`5n!zpqgCq1424#rtkdQB3H&V?TZ5C^TDeTM<(K29 z_Jf_Vk3f0;?GQNkd}(D}JQ#o3IG>pw5&T6%;(dS{Po6L=k$=rLB73!$xqKI~UJ~sr z;evMmC^8Qh)rk1W#_A7^Hs&H2aF?%7G+ggcm`Mu@AIJP%@~=$!TqTL+@#6K$VX;_0175ZH%f4E}bhSkVc7T zIlue;XA4slAc^&#Bw{8XDE9LSESw;7CSgi2di$sLZ<)YoXJ=q6ml^aWp((;}RXm!c z?K^REfU9YW^xVtkqypCsj4Rzfap*w=**=2?(1pO>z!gN%lB!U@nfq{NTKc~j#mUIsTEs&s>m6^n;J#!Z!cNUvFU(W0)&H44g- zW}9R%F%W7!y24nDTG;jlwkzhul_hXUOlEGrR90lwur3GUyx-;M%URa^!#iqdP}oq3 znSgPE{+HJo;8%Y47k>@i-w8Qg&~}EWFM^sHf<2MowUP%@2?*qj-RC~zTL2Z#J zO3oAbJGGq zI3qSTBlCYJMEQJ~A{M8HgN|A2)CKBX>DQ5&i*rXzTrCM<&Y2@-z0^OX^ z{eX7-{bb`nsz`Yk@zd|naoXx+jF7Nhpmi2dX#E~n(SQo zgQbTB{ugu6i!<$JyodC0=A>CQq<%#zEF68RtFL?4>G%880&gsRAfO;7iB$0}X$^7& zT2BWchn7gdJ@kZ#HIy#8+x_Y%5FcjmV396MRq!|jB~V87WcYe?x$H|-8jrfql31j? zVJM#&+Qt>#KQI6j5fK5HfUhUCqJX~e&!6o7Qf)u0Z~>?7z`?-*`7?iC(39#|2!@9Q z;Knu_t2_r#!b^?WNJFn+WED$oMEG-dtw&Z{80^j$4G@XN6p8wm&zhQM} z*PXfS?q%D&vzBYQW!qe~y)C!pmhG07UCZ`!?KzI$KTsWA`kdz*uS5zExElI1E58)V z==^kR&zLF?C7dz+^hy5n{j!Bi3u^O!#(8B0f~y|A`DncbP*&Rs=7!)TEYU27^5O?W zA45au4^`^K+Lj{o`E>Y~^vJzezK<`$em`m+Je2uWgMNJsOyS%#jwQhcbAi;!{(kDtM3-^hjEaD_F|K zi2!$!axR$g--*-m%8G@XTdJPSRa*858M|u%!V7C|^LJhY`W=R}J)le(dG~U$1r)VF zO7QZ^$!`l^h8a4&jP5J4EV6Yxbvy)I_!wMPqY@ov^;7szHGH5Q62a)en#SQS5-8IE zcp!0>a3b_MTSKoJlCj-}^HZaj1n;rsYRZ%FI?w1HW$qdC|K?w-3gzdzW?fu3|8>>1 zB6OuVhl`Jo2T0QW7}tWX&k{!MoQk=|3wGtS8FE7RbNzYE14~#`6!zs4RwVZ@Ga7 zfC3ep)n#?UaXw~UUix9>4iGW)4NIG-LA`+vCwu@;09rK)t)Aa(ViO}C@NKjL5qU6%_DrT50tYCm6b*FI@d0U>eEh>9h)}*CLu09 zKB?lhvV~){8XjK$s_82tfIhh{T(W*fH{bIHPEyB)C(1nLmaNKM5f83htbpUhwCKt0EDg*tQB&_bocM85P%nmmmgz1;aGO=hz| z`xPB|tK5F{xs;`{>58l8htJ-ttU0|QIqmwGeypW%FANHtLLbJHw8cRo`?j{gPRv)^ zikkNo!KckfZ(q=>COwj!Wq{GF{qsJc-)-WO0E9%*VzvF*Nnx|{X%k?VYPt+?m%IY< z=~T#U(XU0RUB*B2GIl9MHC(Y8ca8z=iccy=wVj<4$#g0}$dJX{$ja};?hL?@>twb{ zRzmsj>=c`$aGt_6hZ@@JdlqwWoNMnEm)0zePq$iG#eCBJyHtFva#uS#N?r#m`Z9%! zJGz`qf+#@4WeqxMVnaXv2&DGNm1kz>7DE|Lq+CD9cly)zjIw?vG1R6?ac)j3$4dXQ zP-TKOqN*Y9`UF(mU-c6he?@e>UBXs)%CO41x!-l}Q}!lOpt=zu>6Js%L41plK~=*SXky?{rQc{@UHll5$D?O0#N|C9f1{_?h#iZJncBm1^ z8a)&#i#1;D{#b|n#E}s|$IQ@*zlh@djuO*$ z)n#?lFN~;PZTGzfh#{X3aFYC8So$N>Baa8puKF^v3(*8;CCkv)ru3z#&GC4*Ly<+m z?BJjG{(U;M@JqgVEr(v?d z>eP|IIb||qDY3vvjbj=YL1`USt`acy797VjH_?2h}m_9?I~o=E1EdaXwO*cTt-_~q4gO=DXRotzNd95 z8427ST{!S*rBG%xG&k9VjUh-IUZ(#1YwD>ww*9mC;Z|H-PV>91CITunIr)r|B0mMJ ztq97HZgSYJl)WLz@KB?C_iq!wC(!Q}Y#RmaTO9~YkV7+pmoleih)ISH17c{m3}sm1 zv}$6bzBJwL0pVwZ@JL;YgJphk9o~3WG8`9mIzAPj`#cF!xwh?bYM_sP1{m^=Jr`q@ z#x=ghZiAe*PW*9)3lmVOMizFMKSbW0Z*EIFKj$X4d+#A<7zOBlK<2^t6e!Z{=6l*Z5P$U@VgnAVj6gbYVA;j;Ntwc)45=5HZ{ zX<^htJrv!JCr-6LRJvCCp)^Cyv)Gi8@D(3LYi7-?eA4IRSpFRyQC&xVm>r69E};TB zd=deEIMne$bee1U0<{dbh8W`y3gH1iL=VLkiZj)+ZI1;jh0S^1br@lEHN|i9GEX1{ zJ0u%9=9o?^CpuDXHKa|yk|dPx%~V08w{ z(Ih{r4{KK_D{BxDFU-A3 z;o-CvO*<>w!Tv|iWwD{-UgC~~;FqtwUcMjJD*gk;-Bo<^_QEi|*E^2jIQ#Ek)B|^Z z$TPs0J8A_rhb7P0doX7&?x#HhkOOxCyN<?`Y^Pjw=Uy1+Z9V6ot=~HXe0I?aOeN=Ag()tjPeme=`hlh_Wh1$DG3)u zsi(psu*obLl?pj0=BoiMW%A;FiSbF`_^aO)Uen2)%~*}uCTg_SP8LRFwl8>gOlPKfarizTQFU&meO<2_i3BsHmnoPa_o z!AzvZ%baJitfvkw<&w~oH8(zHg_r^^%w!<#yf&9=%ba%WM$IvDZ~Ue=GdO zk+XxW!i*$d`>;S4${X6o2V#h1A|gm2LWVyl&gDu;kXwZ1834u=Cn>58<=n#`1f&d0 z-jRSWeL-Lm>xRyz0XC7A(E+hs6ds_?&32(eQ88J$9LWN)mz^DMm4nu={a0YQF@eo* z+pqQ~TZq#G-{76&vbC3wqlBO7I){vR11g?%c~#90+x&o|$I|jznNjuUlKA`c-hUSL zVRl9wK2?nlvK4_9AjHnBz?71-9>^YBWG1j;=iuOSvTQM;))C@}6Dv0Zo+eg{U4FcZ z_Aij;U&0;jRVrCun3(Opb8~N-n; z#<hCowanmf+nekd&53zyLzg-~r>gpGE zb~rKtlpt+nBd$9g&b%jzlkORYZ_Uc5EV^#vN}SsIL-*!vs)yeLX}V%LFP8tY|1}@} zGWnV6v5APv2R7f1Hbh9Mzdwecfb{-we9_Vx}Gk_dQY|Bj)m=lK`I zt9>)=(=y;&6%NN_!;b-E!pjiy=fDxqu;AI5Gdw<3B?c!F#198R4G0`l=}I;+UA~Ic z2qHbIVz1n@pI9H3tI^G#PJCPhW~5|4u|G<1BE^>546ttT#EO2@)Q`g?vPUG@HWWza z;++EFRYExl-l`+9_^cZ_D)+P4-z_PIE7FEoWb(#`q!5jF00yUGXT0^AFD_IaAHv}W z4L_s%I;7a1q((v|r~~_k)(GJ#O~54EH&FSRPY{6NwtZD2yz~0;c0+$st<_!=|qP3JvxMy;42iwUPkFaT4MrJY^uH_PsS$q25%GQbaLf{B zkvYaT@lPdmbBX`t7F>iibK1D1lV57UYSv;hBU8M6G4pbVYHuW0ie=s-7%u)2leEZC z1wqt3If&HjK<@^qB};Z%+97YkSV>|-K&Sw4I{RMsrwe-DC+>-=R>s!xHuR1CW#;0- zW8B`}iB;~A@%{Tj8bL@pCFgybMUixslS-^gZF{>U$2T})lM1i;Cb$oh?p%Tao$(gO zUxT!xQ-J=%^KsyR>U|%%F5=(wk^Q6H5i9{KGtjpx1`u?ijm{**#cradoZ%K^{^gPT z3#mYDEWlCEAe)>$YeOaC7HDhpz1kpN&gFJ|lRcDQo&U0it^DX}1T#Bg&Z!uy;1KuQ@eps@`DT|9!t4s@OzMNzIXAj)iz_7yBM~` zKm0xHsC#?q|1O}71wLJ>X|q9Jz5OS|YM{*%bsO^KL=#&+>g*}Ba1XE1PX8FR-b=*0 z)ZCEu2`pWNf=rF9GrYM5S!Q-*{|KxACcB;^YdRmJb8}$;_w#Ocx{g76_s$rk5*m%v zrt-~bKYLP`Hrp+7%edV9I+taZM(`I>HHv>{o;3fCDJ}?WMS$)*R_|xR>*s%UBqkg z7S0~h+-b_c=z3DD8et4JSH>oMcHX7RPgtEj@x2HJ#@4YNt;MyavQ-BSX8g>oEb)x8 zSAHwQs6{$3kigmtoWF_q*A-|S@c0gx{7Xjh(zLa7^mI&t!sw{r@4M5B&7P0f42Rbt zCe5WLs5i#0Y}q8`yhyO)hOgg*K5u@Vi-#M067IKR z*gP+vG1u=hGjt2}zHtVaP8gwu85?mqOMKSoJ%7U1C#? zcZ&gagQv^fFgCR1w7;-Onm>WC>{-Lz14{%Gm4o{G-vhg@6#>MGpP_j$g$t(J4-$F8 z#`_)qk;XIa92cDg;X&NB(i$w(PQj=WarAXFsO|rW%%yHHn@CQdo%x#)3Ay%|(P-m8 z_A|%#599HM%W0sZrTs$?;GZ z*ZqIZeiJfh92~i^-C^8DMPDeEHT|N>Me{6T+in_RbNPpzKg?L2)c4wNI@MrOB%^nS z4;I9ZRI93$)~}%Pr`0c{#{cb|PsDLS!NQ`6)i%~0IqNL(^+Dq!V8uuzXWP1c3LW@w zn8G{%eDQujkc)%ygU!Yd%<(5CQF-QBn$&u^a#D6!gb-G^Nz$VKK7380{QDUDDkM#5 zvXSA%%rv;Dld3@ASZDBpc6XQGaC1%ko7}MMi^8mR>-95gI)Q`wH)R*6T5>!`zSUt4 zbp}yNB{&PD5}S|IwK(tBN;r*7Z0bbi4?xj)O4ig5M)P-A%g&W4LGP(T6a2sI8Aw>A zGCm@q^^f+^Ip-fv9+jiv-FDON;?~-QxDNEV_@d5Aadcz)Z_O?c98bzKBp=j4Y5lFI zF8o?MRWcNC_!&q9l=yR{qIeJN3wkhXj@4HLK4K#549c4~w0LH0Ar-($U*_Cgiilq4 z2tGL~Gs@6vm*g<0%j3ybOT_iq^Bh9r4~S`l{CZrYFmu!yW3|7qTk{z+06ruPM+(C0 z2+bWOjW6S+PUm&0aE0bmUmG5GY+tWLUX$1TN@oK(xfXspKhcsu-^(bfC~l zGTT5bne__YEJ)d#(+H)m9L7|k$)re8^fj5*h;C%4S4-#fti?d5|z zPrf?oXzha2#TaUT6d|Jq%G6p9W^inBQL)~18@YB*Dm>nRHE-Mzt}OFPK_a=Cr8nTA za7L9l+b@^`Yl7$n7hiPN`eSa75qWyZckl)bK6qV6Fvi|bC-+p(R{jaQ9 zGm|2FbSso>B|F=klknsmGS4v>$R448Qh{@m0Virx5-n~*FV=SUUiu?rDpjuXl{{gK zu^{cb!Y8A8a_hdhTGZul2m(HcB)^ZqVGNw}QuVq|(6;zO+SfTDnhLcV(u?s9$NPRZ z|6g>*Lcp7mJ>UjEhM?x^^@Im77(oZFmV>tDRsCguyJJ#5fblA3=@Zgm-Ee9Hed&SVlTc|Rg?)xj zkodqaU)#lwfjYj6lWrJ^d84CzoDP{|V+DeA&*R)58Tml6QzCYaDu2CWlntQ&B-8+1 ziTXZnueXkGX`_zqGH&8DOXynl?9_B?gvcf8(fx-uYn^V%Kh}SK&%H>38?#@vJlxXw zyYaTSogiZQ?e6H-Ljw4yj}oh@j3qPfzc`u%4)cd7=_JH~m#r~lH*4pt+z}LV}eg+KWVF^%S@lde%*@LoqItj{0eb&MyI!GfrZZt%P zP;yyJ7nZ5$yBzVx4Z>HUmeGJW$G5n3-)la=p}DiSzSjYumhLwuFo2JI8E0BQC(F#7 zj=p|z%D4%G#H3ZFo7c(?xE_k=ePc(CfOWGjNsm*W3<)Qxx_B|oZpd4Bwkes1h@Wz~LhD_{_Ci~v-l#-`V2q{%tgCZN85FnImz zmJW|6O=4P%n7;o0pm}MLZwq+GIqBT#7`620=C_Yt&S1wE!^JB*qO5QL(@Nxjyj9+> z9SY5nin|r~fTQ$^Kc?#i?WEZLs0|<*y+jk>yyLz_=W~63rJYW%0zGVDO+&_k1QUAM*hhSZi%@4MQN(69 zWi*wHox_5eiAKRiKt+H_vq>5^AF}+cQ#K{aPAR&C%5@L%%e9fWPj<_)7HFYnbq7FuSz7wzbA?k9^Ltjo zlGdinPie!nlwr^V5P5YC8+GXzliDL;P|Qm$X5hZfG>`tCl(`u;kPz&Rti z3)<%0xqO_`(be5NT&9$XFckmjVFkPdPN!{t=(e+u0${mPb;2KAPkJX08kW1m5<~T_ z$Z?VP@P5V`)H!@o$*%s}ejKx(Wt>O-t{kgasx4zyE7@<_z z<0kpqEO)W{`F^F+*^fagm`7a0oGf;O)APwnh8@|z0)c|q&igGC|CemzEA!((7q3`- z0=CN#V0W=Qsy(#Ng{u@LS%ivrA%{`j6e5i|T7g<0E%1KM`_(#IraECC#f#ObeF*5) zR^k5ikIQsFjN|e^67oG}n>PukO8i@g6c|V$l?AX%ues9q)XSF70!3FdHkf`P6z4dn z9g9ggvuLpnOhG9b;)s%;#{V%f8tuPKTFK?Bz0(IvUkdlG=ZaXin%ZVuO9A3;)oNDS zce}5T|3H!X{*Me@kMlM6N5^y^6QD8JaDPJJ%;{y|J!2||{SHdE%h3LZ0HZ(nqs?Eq zb+>kXhvWhzVLG}ooP1c-KjG@rUJ*@bh$6sHiBsi4h)Pn{y`(-#lWh1clmZF)@+;Tk0YIncaa`N^hX@^*K|XAZB6akTMqX(K zS}fZ=^Iqc%Ntfte$-)cQ6zu;cX}@v~%t6EIzMdZ6FFF-98J&@B@J=WsrFk2`w&tP( zAU5CEhkf72!*XDWQ-9yvwcl~KwhjDn3xEG$MX3F1ZTD5t2Vc|E!RG`z23i@gP$$>$ zUj09FZXN^neIl|OWu1ucJpj#mWI%j-tkEfp(#U(CZd{Zod>YJq<{7(Amp!V^mMJX#IfJ(E1v9l1|ngGcaT|r?~r#X+DBU(S7a{X6Z0D)Y75JP-3zL1| zuU-!jw1Hn>)3gk%|6+L7JpX-|?Qo?398L3#?^4wo4IjRFpYYjkNK?TDq`qNa3h}EZ zgJBEK4H7y|gp9s+6lAbn>?N%2pS_+2m{_(dV#=3b$$+Wj^F-P5##1@;P9!?LoD$rt z5oRR8wD*3?;}Ut_zSVd9kf0zK2F0C1kvYra3yPR~91^@=BDJce*6V<~pyUeg8B;Hp zn=;ytwm26@I3Ke4 zeO@?4pp-s{eSIcmRwKrs;}YTStya2xtskT9C#mQ4J<6j|+|fP!?hHI2>-P2%(Y0`Q zeU2(Nz%qqov#2ts)!B!<7D$w<4SE>LTErU%sGDxdZhQKC(EKhoGR*OTZ*aHNhRiqD zvQ5+K<&Zj9m0WN%y+(~RpE_BNQqk~GGfq#b1Ek~mu2-`Renow|{4gKtE? zoZ0JgAT6%(jmebnnZ*yeoCI7~&{|JejZ_HMZ~PY5S9TvB`8kiBfG6AXayOmd&pQ`A zB(FLIVGy_MXB4(0d-n+%_}cGTRehMCLODKp)N1%Yo4{8r#O#FoqdW4e>m{Mvb{qWf z278Z9*}ytkW5kLb6i}M6h+Jrh0 zaXCb*kXLKK<`${rzXf})^VZN!b{&pZ#n>UjX!V~C2{#cK0EZ7+_8xfRfoyYxDE)X< zDb>iAp#ABy7S9)$JjD23&}0U82~Bk8*|eLXfn#81GdTMz`v(E3LW6F3-DxYDC&V_v z=RL9nvOoK^zqhwWq*#r+Ci-m`=;QEQojAkx5O`co3jMok;P8b#9mo@jXsCXniOG^v zqJE9E^Mkf=pvLtja+WN(RnBKMu8;!L<@Pp?d4rW_LUMczD%Ys^6?-`UjpM<^)M5R_ z}D|m&X?Rj|7~=`6ym>AqTZTB(98XgZr_aXwsK8Y zJz|>PZczD{LTg8m)YARnc#9(93%?ZPHp$!T=#Sy{S=_mA@Y^nBq?)$YDy_DB|0p|o?B*S6lwrU>CsE{62^a?C z8m0kf6Aoj9Z{1j*YnV7g3?X0g^C`Xxxqn|xz?E(1!yeYSe^{ylkLOtj#!nZoAYZ%V zK~({~DZAe`jryVTCP-o=7UIjb$2vTJuf%wMj%BJMrY0Bk zTE{V{Z?@UYz;Cuss{a)w*oH;TENx9$k5>e*m&LL2YmBQ{>vkv_!z4H@xkAk4 zG(lT>1vKh>vLvXp(uCfz0DbPKuSJrIh{q!DpUvTyn#h>e31t;dVZPFayb@_l{Yw%k zUEn$BQOMn$?BbB_Z6e*+UuyEi+2qvzyYUlDdRmPJeL({nl3F1;)rXZ2M<>+2T{REm znvs~usKea^I^3v_!aB{rMPdu%y?hx-WzLVPZtK3I42wq!U$vH(sWZYFxDlelN*mH~ zgo-mpL4`ZS*cf^;hvPK9E2=UdwZhAx%8<%LeHyHb+Q(P~GSaUhR)5cUbTV}Rjf!;X z&b3OI@H%BYB|Z`+L#kcnUqBY7VCwxPxsO9Fc`@4yW=^r|g3?(ig!ArEkA_587~j-g z+YHG>e7ot$1BQ;2<5cyaY+q|#U${qOVxR*v=CNcO!SII>s0QgqZ81Df&deIeW$IF{ zpQhZ}LQP~(;oT;gRsxRvVk2#JXB|m@AN_fGSpY`ZSZHD2H+@G=k$|9Bv_YoF+&?v_ zJ?v{&5z4QH@M>}WXAWGF}_M%JdvMX)!qX5c>coVdJg|^kb^Yh4MHMEs_T_ar+ z)1ugIQA(yEtIHiOFBjdH)Dg=%`*D>9fYAN-FLN4NS*`1GBI<=;J`gee{rk7|kJRu~ z6j_;(B1ELICJ+8Co}#N3nah4?~LF9(tW}~uy)x7+zKrQ013}5t9%;A z>L%puY3|bppr474U(npyIjVHmV&?GLR;qB~o4Mc2=3{P|RNn0ryuT>8=kMQGWy1`h z7h>LK>-)-fg%&jk7MNH|T^PjY+R2vH`rW5JFiR#ZId|&$4Q-~#r%qfC+m<(9N2m(e z_yRO+Vt|x`7Qi6P%>@WDc{Mcv28(Dq7>;Fdtj1ktF9i-D(mm-DxZvM(&`zy7eY@>x{UQiF=oNKz%~{TZunLOmpV+HiKH zMW)yKAay1oO9+&9$v-^_URzI@y_)RVBh?j`zJqi+R4r_f|nX& zJ%QesYA5Km^uaU}U%Q4;STwNbkYO@ks938SJ7JJ6Cu#fDC)?*t;Y{?0EDbgwN_-hN z$0{rlInbztT6-)fsh<v58djG+m;66Hn5Yb>qd)337 z*Kk4es!0#HQ`IXWfZ_$1erRJ6r(38nlU`V-4_zZgiyW`xPQ(G?q24zRR|Ln^a+^-2 zPjv90LRbPu@SyqT#Y-T?J%Tk8;naycpP=9lQ1v}|C0nWvARqxM=k$%jaiu@UTHC6u zIV)(*8y^b<&;8uH1Eoc(;BnmFmxHF%ozUbxgHDkKDM`Glu4v;Bx>Q0=aeW?rrK(iM7qNT_)uApRq6#oB_V=YAN@fo3@jx}( z-o6#9JaWgD!eU_0fh?nN>BriBV zldg;Gw&Eq>B&l!{KY`O))LH-NOGbpc6=aE?%jCt#?N};_^G3FWJ2lkHJO^~023z7 zq9ZI4+P>>(Jm^lsn9A6%jY>2=^g@n~4$j|-*`73{o~r^DkBayC;jSz3meQ|CNFs*BkOU*5Dl&1bTM4O%2VhvE>c}A zB7$!Zc;dcj;mhIeE8Uu?Yji%G0bf)WzJtm9L&D?GIk`M9$mT(DZP5Q#T;7cVPK`-v zHg>KJ5Wn60Fc91YXaJuX^3TrK-_8Pdx?V)yMjjscR?Wi!616EI-|2cl{^@_}nHFW1 zWl2Q$VEFVR40R0z6>KVVSqeCaga(pEM#VIPIBw7sONl=gZgSs${@~6cTT|I}%nj{&@Eo+QfR1rx*JL zY;I-Qa`z3x>umzf^3sv$b(E|p1E`q)595nNTmQo$Qs*&eP(c)^m8KZC+M0~R*up}kTi)1$1f(&jq}rFSuiAuz>-qu1B4KO9$0EcqqI zG))y=WKZ04U}$?v;`tIv%3vuwp<=Fv;RG0-d{_4J-BJhGg$j+T5-1YS>>(g7S=6skN z5K1Xi@|DKSJcM0yW-;-|BDB!`S4*)Wd~sK^s874`!)2EZf8Q3klB9u2duYXz!dNDk|&(93d5G9lPm+1Vy^Z?K0kPM_O*4O_D z3h*4MULVllW@|Csp`WNzKABcS%QbAMzL9XoCPf~C({sOH=wL++C82|&C|%_Wm~PZ7 zeF#98F_f4c2?{)z%_*`aGGLk6$Xp4ovQ{AS;H_@q_aZ#5#|V&h`yb^qu$8f@r>k z?0d5AXh9xw%$BilBzF;WYbS{vqy4I{EsPSm2Ya64mA>-&N8c>Ue`oCGheqFF_lO=Ht!cVSF6FZ0 z!i%SVYPS7wl*-u|kPj8#goU zkuJ}z*z4V(pMtQjC^H+S_=Nz&I;A~hwmCA~EFSa`4sh)73JVkqkccsotL4j~A{Cu| zzBf(KVVnSv7sDl{(bSXa2a>*;Su~b3aykWUcC}7T{g0pG7i#R}=eZ!`X4@T4ij$LE zLaCI=a_nCy8ALM1c;1;H(@7YA`UC=-h*IV6uRX1so?H#K3-kA~3m(M*!Z4Z#{9#}J ze?J8Ya9P5X8i8aa3_<`R*1}5u?%_K8 z@iz2~M1mG~KC7RN>|Vl)dvUVHftvS{ZCXm!EUb+dn6P9s1IK@0dz2^|j_sK*gby&` z4w%W!$Z;;dV=cRB{%>urcC=WcZu9%px38n^ zE+e0dO_j-D>!aW38_|NfFF&U zFD7+7^-Bkg|11!0&!$vD+J(0hkG#2B;@LqSMLN{AA@c>KX& z?~hoE+#F0&^k{-4p)h6m8&Q-!q$Z&o{idOZkl{YusQZ4*SZTia{d$ku$xXGOB%F;f z4PL5{D2i*%#8SP=DF;m*T|5Jo{UUY6cd}qqZ%DRVm7)GSn{<4R?Z^JR1B3%?G!#RP zgAPp6LKIj-BWTt;tk&`Kq|gKUHQg1wj5=KP2=9i0=~t{&)3B8VOICV)){ez)j0&yl z#!)!e1uEgxjpc90fBPgne&k^n&f2`(H;RZainTK4*bs+egO zx)mEb{rnNHc+?M~@Sv@sFo$pb7r!VIcLw4hl{eP*=z}?L(ln=;Fj&%VCj1;sG%4I> z@%_$r^ze{z-<60A)%Og2jgk}=q7gF%F5P&ic<^e~u6`Gx=ufgRyQ8BZT9T3h9zS>=M36?vPL%y+Asa6HsaK~Rx-El5WL&Uq zBPSNbx0KUgm#~_eW2m=ito9avdZpX`aKM@j#FIV~2~~wZNQ**!pLH05lt((_xFSnE z{t}U2>+OcdJoQ4B*ysG7`V5w^Dhyi#k-@;#u*g=r(ZlESuiw3pp*eF;YtEy5>xK`% zgTS^b1vUrb63K;C-=}xN@MS<*k=N4HZ!%osuCOE?ftcts?A@_gdW5E#x~66LnrmgE zh^q%`j0B6Teo`$4qMp>)TKKWNmQ-<+h11rm%zsV5w3P;Vm39asm`UvWr3smsK;^lF|bU7Ym%eZ!m{K+2~&ci zbg0yYLRb{%NKNpZm-)24jN($pQ1CsJ$s+#t?%#nvS+{P z=xCj1b+tKTh3fzF0{k2Twiz56EE8Gol{5~|1e0(|5?m~4 z_yk^O9d|>!bAi`zn0X2t{J`DNi~NgUsX`R%+Vu3 zy;AX;R_V$iFmn!@bEbO+uZ_`z`K?$|#C>U8?ti*$;hOJ_i!uWQ|k7j+tvk)68>kd+yWxwt-K$+k!uaZPXU{l_E=ffuEhiV?gjH+TxBy{gpqCw} ze*orW`CaT)2W`V%Ro#yaVd|5qM?TWcO5e2luTnT4|NLP-lcjo9TfQEH+?}oO?~P{z zW9b%ey!Z;V+7%&vG8UV)&>TXdR$4l4d$U+rqW9rr)P*NUMmkEYQfM7UaJoXIP;moD)^MgN3HD*Oi7QVrlMTN;fT zoiM8%PN7BpDeQWI4JW_{wrmLkU1; zJW;j^{xf$ov_=%oIN05)c2;-6=y)z|<<{57cSsqHQK?a|{cHQ=%Q zZDQh-wd0hWOs}}G(3Zb=Gg4mn=BZ$vE_uBE=TBMTC+^A1bk+hrcs$aT0JbCWEci+8 z=#Fh=8RhgEMcCVLfzGzW!GIzg1_7=#(vZ<`(OZ?|Jgdx%=+kS;C|;NrQZNeH7)a@H zVsHwYxHdPxY(k{?%&RopndhN3|3~>}Y#632CA+KPyHZv^j8+(RRR(R(rY5N-JriEA z;v?WBFRdqIJRMaoC)JdBF`=vouiWbcOL287v1}2llxvjCRz{I8ow(z14g?w1$h4@J z&D(8yDJFIUPlsou<`08d*HzUBi$k2It#kF86kJ%t*+|`JQ^w@s_RajNSJj0^McXYp z@PO2A;8`p1Xyge6C|4S2P2wA!zt6^6H*c6PGOF3;ohbF62L%WI`Y2jGFTOBGlqF|X zTeJ~~^ZfdGsKW2c#_Pjrsz2mXAC!hym8)<4VmH6{# zWK8U(p3zi&PUtml-D0V|7Clx_2E|Xds(0p+Dh0oh4tYOjh33fs_5$D*cjeC=Zc@_t zHkk*gdUrO>S&hr~CAb-Hou|h;{?f&ibc2JyQ%^EzX)q+>uOSW(IP`&{{5Wa2UWkr? z!Orr0HLxf#IM0U-Z4Y|o9&lG$7~a47(-n}v^233PCq2MSFgxjmo7y|ZIY!q}Tj#m0 zxz6~A7uaz~V$ze3LGUoX7M^ST2M>4)fVEqZ4x=(-?|=_%Qv^P$j08{vx#~b5az3r9 zseIqlWOyHJ7_b0yJzTfBEh5 zB;jM}d&=N$H;^x}$6_H=*y^|+rpAbj^XMUiM)t^gtPa=BGe~d-LXOEuW&K~FG-s}b zzjLl6XM$J?)^CIE{$~9gyj#!Pzwer=kpE){khuu_O{_T)^Ulr(o~REsCewgSgmC|- zZaL>clA#ezwo>sSqkuVlJoXJ{FnjP`XzO=R)-V;!pNzBd@=l6hNK7I(;l9(%y%+a` z1!+oa2A;$E?vIoCNN{+^J`=s=$#ZnI4$$TM_5g(Sq4`oxQ*2)!(GFMT2B>H(6e$EX z)2Wv976CFPXI|doa)+Mp`?TVF4ZJ5GaEE{F?X_98AHTyy@a&Y>{F}1cLE5?sqyAr~ z#5cVXoli65p0PCGLkN?$qtn}_%%4u@+y{oHZm=0HHD;)=(Z=RGY~xTI_bi<;b8p(o z)anIbjRVI?cmu>q_FQv4^S1cIcY3ZFjGv~< zJ<2FykgC!n!$_ADkEW1ee?4TOe6S(Yl#%NMvfP@){JHam@JQZi;`EhbX?I>jz#le& z`3h=Gx%#BMo&WwqR}NPeZyMrYwc05*M-CuxWMYs=xERQzAf+W6b*kbUkYHJuTSm^t zn!OhswF-5slJ(zLI5A?aW^y(7^+EIjslb6bv@6N#M_XXKs;^rI`HNCa`vB~sp$l!>2bD9qYD=WWL`gSo|)3m@eT za;|DM(Zneo3oUNuUEs$|75!`9aM6wQWT((B=5)PP5-u)ph;AaWM5@m4e7OX21WPuo zN0ccQsq%JIhqmUfw8w>@2~n>4A9l-cj1;z$RZryJW2cVCpnvzAJqPH4VkmtdLqZRA zX8w^Qjmu@vWfpIKJXvcW97Gn}{x9{gn946nH1PpEIQ@;IAE8FuLkbKeqGsepA=WOf zN+gs?%4SI=0m}I>j042Qf>d5dEEl85i5)?O4}JmocpfxDa&sK?s_TzXK64__lh8`A zA+V@q?-@c9b4hswl#H$7Jmkqs12-Dac`2dWiP{y@{MJfT=%#I&CggJ}4tQAaD`Z3^ z)bZfd;mX7J!{VwdS#xC@-lW1_(!1o5z(C=tuN*u)bPoE~o0iaAr^~w=QoyYoYi738_U+hWb%Ey2jg5^f62XyUYxp%5YdzQUvtvx2cTAUY=-(n9+ody3 zm?DYK6En#~2-=54ylU&M$Hag?cPkJDr6!b9KlFU#PhZx@0f3g=X#pWeKRJzwi-%`L zeZRroR3}U<+_+A@PHO;}eiyr@w)vOz^5)djh43v^_E0NPFRMaG3W!5dR8l&)?H;6N zPi5L@Mr%41Tpw#k21aZ^#kbRk8J_xMir!~J^Q(f71jqH@#;e&c zAcfa2pOg^TR(j86QuT!lJ$LY&o$E=&9&(W4*hHsAvlA7XR*bg2^0ShK*(bN;FN9T4 zJdjF^GkOkq#8gJASIo`r?1E0zyM)#6-z5n*$1J0Ihqw9MELgl50DpVD#9(KQ-`T%j z33>dw&mMD#pi}M!BbY8el*tQs(F4zf9MLQnGQKKbk;rQXJr5%0_{JTN1gG!WRS)Ph z-;Wb-M56YnO6~K6O9l*vUe)LM1Fk_lZ%f*xhkEf>_nXcY2hVL@4hiR8Y^ z%k8qCruCLnt>NZPxNh+C?QA}4Id{k>H2DsTB^tLMO{gM$zKN?0UO&$FLKOMMi!2qb zsq9mdvxt4wey{Vsw#~)I7QgdV`97Z&&JI^H!0vrSgtx;F+yFaLmO3;Zb~*)Jbm=ms zO4ZT@Mq@sr;6w%I;?KlkiDwCL4zjeUxxx=g%-@;Orl=7;a04mGQj11nSQc4|i>em_mx3)P5!KJe zPi!+TDI2PCBS{{oq~zI9->dB(!O{6AmanPha$PXGh5lQ_Cj|~dwnVC&EF(uxOg$I_ zgaa4rUtbtp6x8$HLoR<7Zc*D98YYw!wXifnr2Fu#U}KRSQtAHsSOb2}l|?>QoOM8J z-iGx@AI{7kiFiCo%KHLw^0)%ZE)Twlb3~l+Gsr+DiXhknkT6SOSR$V(e2it|`rcQb zg#?2!L{LQ)VqX~U_dB!#-U#Kr(o(k6;Y6ik7N3OG0Fg4qvd>a!DD`UVeV6=EC1hDO z`iTZwhGxD{3Qf$2fu}d;D}qL_fJ&fPCbmvzm2r6qNlh&U`sdKS5ThMp?ihyNjG1N$ zN?#~UP`JqAKWo0U!}_GIe^kd%NoH}eB;{B5%y?#hbxVD@Y8vxN1Kl9Cr@1?V#KrOX z-5~Or=$US;3uo`j?PxP*PJpsYn*FyyI@}XF&-iJBYhemd2t9l(S>NsebPf6?@mypMbQLDn%_!PJ#es8X-D>9!R&^5a4II^1jeC zGtg28R8&aF$Q5&+n&WiBp+q6q`fs8BUJHg(n~g1TMrNp=2;K$U68mLc-(y%3%A*>} zLTteHUV$o~lE@SeRhTt#Z{WJk=ddU~P2Zn{d%ljDz`Q9q$%mZH|Lqj^M5050m`+nD|HqO0gF7Cb!s*W68Zxsnt-aEa_A zTEIEy2Hv|qV=Pc_uib*e8Z@@$5g|8N@ZvB)x*bW0a{2EFRTkK%CJ*Mw*1UjTG<%~xW+8e$H%t0 zV`g{%!4MnrngN0lH?LsftX1-8>HPfs`bKZK&tWfeMurA|Oc>GmwD^BR$dLSN(LvMy zGC1_i*qj03xJtELB2veO$$892{gNM9D3@ZQkYqG1k45d9G&DpvpY%-#>dzGVq^?fKX&`&qGML4+#KLphx{H zli{cX4FZ3G5oB_UlNG2*oh}#X+t>bX9JrWb&9KoRz35@agF^ivOoD>CSG?DG-<(-$ zB=Or4$TDRbm6Ip^^D9d`AdG8V=rucZnc!QC2xrL~4@ng8J_n=ztbWwi)&mqZ^~8BO z4#f^91~f9b{n+^R*foFeJ=T_5tmwMt+OD^H9-NlPU4=`>7a$A}WR=Tg&dqAUe&`^c zuxaEG5HPZ^z$S&XTWuY>?RXS8S#A;IEm62jj!{O72ykob)!v8!c7mfLf>I;2yT6jM zxMU?|GnKx1UAL$zv~@5lG`aIEtmH3r6xqfwaJ?V8N>YuXldb*@6A`HVFv0`RjhWYg*j}9sHA|`Q^e?~LhA}6 zn8bfN3m4i33vp!BEXBX7vO8JELQ7lU$j78f&5Dzo zn>&B)w7#)*ASTy{8*h@oYU;p+CN+uz{bN`TZ>(1i$draeUHAGMcpavvQ$-akiLNkx z;MRr!BiE;z1Y!ljz{TO=Vag)JA^stK4LzHHKo!b-5osXo%m&?yL8!+yYM`(z)h{JH z>}S`*|7`u9wuq-sZ#{ixcCM~M-k$Hl0WKK-`!r;#J6eDO9gGTQ!EM@?3sp(q%eS@z zfDH8X+tM-hD8f|9M&{;Wv$L2N_xz!SVt^-SZ4DA>5tH!2e!%i?hwSn3@ran<4Tbj4 zI2RmuJ3EO#@k3P=72_PE)LU1O*rMyvL$>+SlveVm50vRs_mLmy85w_q3YIt7^KoHv zVv@?u9@w{RIB}7D8!#ORjwU0#+EHWEMml|2B{}z3azA`lA0ish6DrgFw1MNOJ6ED# zgC4D2tbngD7a{TdJznm)(9!rq6j3=YX_d`j2FX0|sT{jSzjRS1$4L~L*<(4H{?rBv zr?!sn_V!)+n1ghSE^yi<0N7m2J%$P#Zv)!@lKFZcPdk-V;DD*2!8u%`a_D^MW0>W| z(~ybJxQUlll#f1-X54WBkMEPaLxxvJIB^JU0VzVmDr8?Vulw&CSz1gky^wS)RGm>o zwV@ijeV|%ueACp@(lu=Il5Ws2@%PUIcAVWL76NLS0R(QQ+{?`!Ir-l8jd0{TDbCa} ztlo_T^LmjMUD2479Gpe+P+h5hylR?XZO*zigL0a)mawRo8IKljM2>CN$U9PMy4*{_f@Z zzd;T&Try+nH1A$+JI7b}+=om-TxWW#$fs0O@I?1c4y`g*h2EHcFk5O2N?L$K)ybVg1H%F=;LI8s z9Y7HgE^-+I@Jdc(REyD(5OG@luS zC|hUZ*^6BHt^V*z&&)LjTMuxzkB!N?go%NL|F$!Fc}2-DD)S_P6H zG6$Xf9WM;ID0G~4U~t^{8e)V8UL#YQx^T|k4HSm&i}MikL)rDe2+N8rs~M|+A4!$-?q7p+ zCuzlMNzKD-qpi<4452s*^G{nzF>rzHiAsT1mw17w1(3;U=`Njtehw(+O&D~9g0!{0Z2Q88!_t?SDZ!CFG z3G{COQ|zs+;&&HJdJUj_s=M2M$2C>MqRZs|c8b&XkkS$i^gTG(3+uv%>lb3=55?ADqV>R|V}vZDCV1EG2( zS~F1~irSds8+N1f?!L1Y;Bc(Vx44md17(-vBrWBq#rupwDQ1jbqUK;5zA+Jdz?HrB z($mw#Gmsj5D7$%eW_uejzp?jkiuYS6nHCON z`!N_uO|X)|5Qwq1by(kTKU+P3S4tGnW^*lj&w5eY|?#* zy6)l)+GRR4wQWtq_1H|b*_xIMf~2J9YS{-B81)O5NnxRw!CLg8X0+7|3jXLrZXyze zA|n(;(JUNnQmiH1c*h&C2S^`#1LSqdOD)-fV9&p@Jrr$wQ>kbEU-e}>@pHuM9hZM^ z`01LF5tUqzavg02%LcI;(Xg=Z%OumnB5!|WqoI!cBo2y4qi}jWfJ=K_UAQuFPYEN4I;^hao0&7oS+eCSr*TmVT@crf9QM<*wBAWW+5ac~Atj~)HWBU_hqZ1akr z3ZjGBkucJXDzcyb7YiD_tlxrNR?h-$p5G0=-^-E$S3CQ66d9IJz9g z0fInODqL>Da*KETsqY>s*MFL|kK}L`QZ}bYXGix~{VK^D6B2U>Xm#AG3mB@-ms(+p zJrlJf+>ylZV;fB1AxIA>K#~U%ab*nk!G3VbWGGJ2ZSzZx7xd{`G|rVQ88}(R zQp&TxaB&VVnr0)gOd8%D+CyJ|)M|~iq-1doC4fVKAC*mzR!q*&p@@_H#lD0oxk8nR zqSe}$C|ISKtWz>}Gsb9OV>@|-nEpj)_hyAp_sbV@+-a(=NfxGpQaQ*C(z*i|{A@zv z%cp*L0P-0adtuo_%=4g^u=^BT^5HHpy!uOV422_|C}lWy^ofK&{=RvV3+uqu2tgnQ z!h>GeGh+dluykzK?ZGdp89c&91Do@w1v$7KXY(|TJ7l4kwfjlr*O=H)ZI#kUQpx#t z&d~u-k}UzobCUmazlF_l+Qg;|nktVC6F31PRO+NFJQO#Mvg<_ytLD7w{` z@slahA4EnR@qpa$Mw!3DS4VO)aCCEm`?}8(EzU}qy1I|U8?!7tiE-72D&ilWh&^RJ zM@vAg$}J$qB~RpYNXm7xLQce1OSQ!*+>BJR1(hq{J@)cB)q}pj0a!?#Gb)L}fw1Lk6Nd3qNb@hAF^zSuzx~pk%y8d*}t<@SvB;qKlCsj{x16lzq zrBf$t1c(j-FmorLZL;hAe~%x|JM^~ZdMtTY~`Qz( zctl8KLZRYwcYdp7@TE}29$1<`^2c|%mgnSrX40JC$ia87ssGt1&xS$DY}FOOV+AG~C9<#!wq&`aLhH38ygPLZ`ZqN0?gwDv)gMVK0ZF1)AGExuX1#Jd8s6{ zbG<>Ex{6`|{k}&Wn#hrkqEv3#DBh?>t_hu_IFLey-i&k1)tJe ze3Dij{y1)=6!n_qcKVF|SC5^oY1PcC$eT=+rhwio7Btb#PLIGw0sG-vC$Tr#B-ggX z@bH=uCVVi)CQHrk+kFLrqXJpZJIi#6dPf9fLn`L&i0(fHI`%YizlGPY@UE;lcw_Ov zu6*=+)nYwtx#N~p)9%Ms;q=!TOIN%r!aT*MG0Ot+5*^TYkGwtgkGA0ygG~<$|0{~$ zIr}1sgxn!ixMHcy&s5viHoS|i#4X5MGUu__+-q@2RrJbDcf(jkm#7>qnx3 zR`W@KWQk~tPO&nL!>{^Yd%sJ3LH`HHioq7=Qz#Je`Gn~IocF@kWLM3cHG`qD=q#g6 zTg*hGf#>4Q*uhrJ^~2fa5I5P$bc6Am}T zP5$spH{+;e4sb>}?<{ht_(e-rbiHiI>GON{G5NoISgL761VtCpm}Q*dRY%S zY96lAGID64xM4^T68grv7!v z)4OYPla=^+om#i$r(&62!K{_hZJUd?Z(KYa|AJ39li>4*S)J(6!`u7G*PFSgeJLSx zTh~|{`8L>>ucQUck<`x3Jq612lSfTJQRZ&UpR=8`ag5qbqW5KD!7VQ>2?+mvc>#k)yMUylrR4h53k#k3f2l^BU870I)`ZS=oN z?BDZ?#uhaJcIootRV=APhF|e|6XjTqlHvvnO?T%D-m?b^{B!ow*}W_7+AUH5?2R#_ z7D?ehn@?!d3-1H>Hsbi{>H3)E*;$HpXq_AV`?@zsm2w45UeD6UlqttqRX?(?N!I(a z7bO2JonU}c#cn3+!i_EGwBiP{Na{w+`z%Kd6|;B*M<5-!U7Jwv-j;Asb|SrwK~PJNe4p_{^?)6)Dc0p$fw>WBELWJ z{ugP>9W5e)q=~uFLO5q$-qA(LV)`A6yXRZfGg%5F+w7_UDFYwfT-|jUMv-ipw9DOl zK#OyxSwRxxI}S^O6UL0Q$X@v&*Ir-`8ypu!Wl9H#?-%6ZB1IUv6s9JG<0p@9%bWOq zyYG?vUG*wyjg;a3YRk40+UehA<1{!@rsu?wk>N)LcW@#o8I|b>d=`~DP&F!0Row9& z&N?r~;`t7CpKu(+FCv87A{m^X|J&h&2}PTB9`-r>vS#Gw7%NiLKQvhZN74fI$(6%z zYY3s~bb06RUpU7@l`}Rtj9VDnzq4r6d^Ph+0_*imMD^YtLFA+vvdlRKlF6jiD}xgX zQwPPOt>Ff(8Wd#GIO}bUk2Se()xF94`ucjGQ>Irow(05lUfydjL>?Qq{YLI(zKx-+ zGSK%T$Ft44<d;7J8xcyL;|5H8YB84rxZx!9nq2eEu9h-NVB7phYteBW(K!w>f;UhwWphAo8 z5^U6|Q1scv-y&;tZ`||xUqW&+t3D2OhJ2E2*+fZ~?Ob_kw3_S(*&Q~;zh+}{>Njpm z^L(E!_Akp~bi-#x$c93Oj?|F6_b1(LA#sz2c3?*ls33Bx6*^Z8+4m>@-JInx9&5x( zR`uhpy9K9V6=l^RS~c0_sQ3GGUUIT?RKGLpfu-Qne}g}M&?v{F z*0t$7{8p3=2tdJ=&jyUZ|) z^!Xy9lYFHj-QByp0Ll8eYT7o8=r8um_GRj3d)Gv5eBLRCP%e7pSu)N$yM%)G+kDHN zuIR&4tFZYUsAsQS_D`=fFF41y$oYTE2Sleo`o#M+je|>J=l#>0IOgEvC0VvYnk>!A z!688g0>Ltj*;OWA6>Fla59#gc!M}!L-hns^zv~5y-^*S@_ns`99afOcY(-;PU(%mn z=e@@nd8*0vZWs94=}uc0z1jJVq(7$+$MWdxjcFe~72Ye{PLH&hjB#61#O@0xoW*k1 z9xJ$}$v6ug{rG1!2%Ymz7M8JA&un+>n9QvY-(I;Qg;ujNW!NmFLK!c1bo7^(WpZ~n zFZnfGYe9We6Ec}-6I*jNd~5%oGgvOWRZ|}yf+coKPBEgjmk1@n`;c(=4nv^iDK~)} zlo@C#;9PDF{9e6m)l^F#ZOj>Lsl)EZ1?(fZ$bxO7y;AX7`k9053d^FgRt>Udh9SC~ z(WNb5sL4lDg$AQmwL>t~DwCk5d=A_8(kI?nFnX`GSp;Hb!gBB~IqtNTe9of*8 zG^KQZ{_&!=CJ%lgr5Hm)!vNyvqnf+f8o&NfI<9gxLV`pmr4oGM`pGv8biCjTVUu8c z|Kb3giq*E0L|C{KtP>_cHGM-z8DxnwNgTrZYFFd@c)8^BwDtXcJ_7854z|%ihZKB$GC5gm8~kqUwDUb}fWhDO z8;kFSEy~#vqVDGcd9thB%!us8qFVAJtSo{MGT}QVW`eIrc zgvd!D8s5YTb2QLa&(>v#;!V6VQ>1xA$~2mF^3jrfKx@vrja6Hos8%H60y z9R}t#>V7??12*V4Y;52d+dSwYClc`L`#Ws$RfSxYrt)9e5}%LPA(p${nwhOH;k3HX zMBwude*|@VHA-z=$Iw>j;L{6NCph&yvGN#yw*KsSIiZR(Q6<88L;o&9v{u)*bX0$D zu(#jxWbC)X9lt~g6DB)nK%8@(ROvHLk$xZ4{zsnfC)YVt-r-!0)5g;W+1a2?-(!ZP z^x9eDogtGqX3Z|PK3R$B!{X!9I*d@h?%&Ic9l3A2n3?zBj=X(ikyrQP6dD)^;rBG$ zxw$9w%Cn`q_t7V?;A8O-o&%Z!g;t}&fI770m-$~y42}2<8k(9OFQVt1R5>A_5p(U% z$R@xcG3FYvmENw{_unqo7f_C$aQ@kS!(AuJVKXYzzdy3GyIeR1ugBx7@y59c=S>qT zq}!J$_ZHp1w5B0N1(tZ!yn+tV=J+p|;f;?SB|T#`Zslry9$n#-_0ljZac~R~T%YX{rZ5ewh&V-RIHg zi~6{p_e&61wr)*tTIX-)W8idgHuY5{6*V3j7@)#$q7($sMJQe!l;0fI8D&83Cu0MtgiW; zzhX&_mMmJ(m3r$x67P3qVqRKS$3RGn{L_184jpoyfLG`i((l+jnyI6CwDb9}^Bnng z@AUr7QE2b?h!65z`~O7)l{OgNw2mSAxgE*+(3+k>$k@^{vJ8G01XtDl{?&JJ!5Te0 z3{rdQe>Hc#B{yk#g2^iJOGvPdloln-AL3WO#Z!d~Jz>AW#gI>#(85SVby4RU>Ieh3 z=C^h6!O*9*Od(C_Y#`W-Gz|DnY;6fgCvfrbHrtWySOqyHdA2GH|2|-S#+lmx3YweB zjA_j`V;ode)XWMwsi|qp4@Ybr&DSjP#6sroCWP;%>9#xt>r#$Rk4@%-uB z!84QH`9YPEr^hXkQl21Xlri{x8l_>SmVmG}ZPjqbCfJeITaP(s*F6X8Lp*(r+0()B zmHkG`K-&a--`QCXn?WNRClyC{O*W5QC2RjY?l|owjo)ci&z#_>ApZR-2qGLka=T{M zzuy0uzV7%r$X7)*tn{tr`e5*Fg;2-uEvzndpTS{Vc!rqQflqWYh81VR#?2m4Z4FZ8 z7ITo6_`dXg&T7P(uyo&dF$NY{bFa+F$|$*tf$;p?5!>z~*AJ^yvE(J_EWI%Ujtma2HHAnR9T}J$p$Y> z0DTu>2CFfr;rX;rbbGq8bvZ7m)Do|mk)6Fw-Fb)ach^hI%Fh{SD;JY{sTSKP-s`OA zvj*Z5C*Uv_U7Jw- z8YPqosFH`K;Lsa6x$5ZEHOU-WZ{{mg6Ju4|ar$f9>W81lg?4U^m-uzPfDMB8PfX!z z1o}k_!F6HuVe_vZtjtmt9`|H9Y$oIVr2Bg*pO^60H>^I8b3eUr#ZDM9Ma`5enk30O zA9@eb5+j8%z+D|_&(}h$jg@H8!L19BO(Y~4Uz zMyZIu!m|xHgKjiwaOJgKv~d*_#nU!UuQ$YRp%2>nYarU{qMPaU+a)&Rgw>C_pFewg zp2u7KUj@Mc^e%{d2FbmwJS;;nop*G8XTi6)M7ZKg@U+%w?@`I3w0$np0md$698`N? z{~KKaIVl5U$m^|)k49O~?E?G&cUW0OZ42JW`)B)?SfCtgRr|~wo;V#(L|5W7Z)`-#6VSqyc3&{%$YLD*WvbT7@dgj*vz!V{3 z7-JI>8lMJiJV9U4d8;iL!NeRhP)e5!#^2o~E8T6^e|Z-ZAGgJahpRbw>FZK4796Ym zv!!lmao|&QG(3pM6_M_~y2IGp+fB#A2V)F3H+aC*cB50UXz&G~rRcfrJBC|YoIOrN z6d*X{2MNogka>+k8qVv(`0M7)UQ>quRm2;$@o53QhnWnPA2jYVE>Hma>d1V=#ttQv zG}Sm#rz-4l5vBL$zqf#HpSz90H!J-8z4u!wR#jzN$PC`r#9^~Kcn%P)xncu8UiUEe zIN;KEKLkvL$N7HsiY%4^A#oPg;k6B71?+uSVD;Zk8aV9y!<2;i8WDPGiCnNrk8G)Q&j*^uD+6rpB9qrph zLS}egg@fM-V9DgT(P>!X4G9`ii%$dBEtS?c-h?3}QCVJ{?}m$yATE#8z|g}ZMRsz% zwK)*I+2_0;`{;<~;w&=qBcpC}SKkZ||FCE@sfaRc)Qp@f!J4^;U7U4+Q;cP~RjOk- z$?7+JWo7|-G0m)6TN5)@k9#t5)ufGr&xJIma^>v(G@~r+*937}n>o^ELKJ+9gs z7Bv_nN{T_|-7`)Q3)UxfB^S@|VhYiGT&&<;a)g<~AK!F|nCNRCrYKbL$+MU~6PL5` z35;G{A>fYpPhjHIMC)RFY!}sQSR{dr&1kXY&@CXFi+7krgz8mB>ifYou|Ph_4tLML zHI0p%!>v@*5NU@aKL1q(Ez~n4VH+NTXHC_I+3|}bTtT*-3|bK`R>tqmD5qxx^!n-F zx4pq?M{aW@vHbMIU_oE-z%ZElnBr@#)TB87?fmgWnexFQGLR5Bp_ApvA zlBjUI{vNgeHTjrcPC*KOvHtib9@VIC>Z)xf75cR5Ck}p%C*b}t%<=f}L_$J**Stc8 zcx95>83yZ$!iAV+)&93{hCB63yw(^0?iFu$oIIRfL<0UL4WA!D`Yg;J(^~^kQ*bkX z|B)_$6c+o1BC1@eNefMtY4jQ4r~EjaQz%;hUlb64YHp3sab$!($kJN9)r>@jzs}8>$vAyI zGwX8j+^St?n1kt`t%7sKMS3oZHUqP2b|YAmNGpDc(uoEWqr+zlfT7pm#ai;s6aq) zI$8F7cZ1IdO1bm>J@6>cO`yDNl;92iY_KBUP`YXXaVS3nPVBL07xC`+~hVz{i)48?0X}6Ns#%_2(Z18M6#t zZ6nM&#YX>@%BGl)Q$o4JV1>vvBOPL6bw;gWF1g8*MGDX@5cc(eRkC+Hc_QG`5ylw& zzET=^y7LEyF>|#_tMYc#Ebznv7dMYglhDM+C1fX*n&0~-<#>Se@ey(Hn5wl}wNf2~ zO=Pz@@^YR1moinC#g#Dp@-hiZ^I@0boRvss~q zy({zp9uzN5FaHQqWlN+2r(NZVv`UG-qNbkHt~x_S6{R(M?=eQx;s08)HD&8@71*uT z2T83_LkGAvONAy9%RaB+>H2Yzkt~gzAYiIKMrq7xqvr$kcTC7Zh01a&%kw1hbIQ}> z^Q$2=;NLT|wa)S>_|tbs)9-_YzE5G8i1qI9)&VXO(-emQe+%3HGR<`OgPZ0e80Pc4 zr8@0kljE@_MVk1&E0w@$~bt8}GQ=*A-tGKf`T!L&ey1O|&JOAFzn6o?;u2)7pHE{h z9wR~R7>(r#jo%;OI_2{Jd3S*1!NqZa042Q7`7OD7JOzi~Kio=$%Wh~O>$Hkztv-*N zrsuW@lQ%GJ_4TtWkX6t~_DGl9S{D^H=SH8J;o#yH=+|sK5{aH}yq|gPL4f@;i@y3? zA*W2dce2t(&oENCsY4cSgclFlMnAgv2QoZu@j$w?M#Wk*El>(`j|8$2}>~gwv#Z&3zsSzNMA0^N)b- z-_pi9>gPzxwv*>B<4XWMHX}2qMj^VWuW3@}eco%9mr($SGn<&0_0Vg-VN7GvE7qVj zDpAjG3%{j+{`G!zhUP!XuLF1)PJxpP@HlnBC`nrHzh;IJYN^S2LXHozUiDhw3QJc& zx-1X?JDL^C;Cdg$s8%tCa+H1R>{JD&&#ynC(ef^Ia)Mt9m}{dCu*>7+3l4d`3E^oe zGb>E+sP0ve)|w(+60sg9S)3^ue>)>`TV(eIGWsT-sr zA>He%G&0QKF6!gYfV_P8)!R@N^VR>;%~Dvi)T5(9zM`gB(=;d6W*{SI*(^xaWex zoRwd>XG&y36!=1SK7T^a*M`2{kO6GskHY1T(xm$o&>|?xe>|e}Dw@Z_eUd zk-}&lf(W2`P2NMPR~x*3j8yCQb-HKGtwM)~p*u^@>E3|i9s z%!wn+y!umkrgVaev1e2ebzZ$AL8vLk=chtZf^=t?mu7B4Ij97Ry8P+L3B=&;2a<*# z+viXGbn)s10_@R|70xmLN#Wq*o0;0A$d*JMlo!r@?=$_X_Wxdh3;u+3ip(%<3|h@_ zR1bvM5u;_b8J#bg6>!c6cndb)ME~nXA!HUfl24qPpjdFrs#|2{T6=}ZODrtyQJtY! z=o)lFL!(DSFdpvzvpu8{o-zE5$6uys0Ha0(lihO|$p1!B3m-D`@q4Q8>~`xKMRwki{6@0pbn=hqL`b(}a=xfxu8&#a zX9Mf>^mMk6f1*`GSj9(4#9#v|yply`rMyl^dINDHw~k z>O&&CySm=%MrFMhP0_*;i*KpiTh7dT4ZU2 zChjk{gs>|@acR;y{bByUw!PMvxHwil6qNJbBP_O;QNz6f-UnQK`l_tyFV` zhf#8$Mrx%Aj6yPn)~B7xFdD_K+Gf9J=sCvm0({;&yL(Z10CZnj0D_J0gJOQfBcc z0dNb$f=fgTEzKlm1-E~P^GylRN{CDBXLU(J;U72iFny<7GPlW1^$}C0^2?dAfpcVG zdLhFkqR6~V0xjH$wVWvO0DwHqTBRmVq5{~Z$CF2HNeShrhEHx)H6EI48v?M9jC!pj z;BnNsaZVrvub3j!QifF_nIv(xTB=mgnARP9=tFJJ1?)p38yk#i_JdzDktAe<$W>pe zRBl6TC@9fs7x8QqNz&LHhFl@8Ecs?ZuIvHg?fOPx*QkmbY0?RK6ALyevG1`N|i>- zm0~cMoa>nvvBJ||M}H3yXKjp|^n4;A&`U0HjB~_j1G#^xLiPIR(_<=0M zsYSjbKZsLQ^*K$zJq|2_ANzRk_`IM4v`Py?#7Aid*_mU}9s9L%rG_{h^wQ|S=x9er z_t7IUNWVA*{IA5p~T{3a=anbq; z2_C5TK4;x9y1sk-CR-*c!xFlKlt`FmnlKIe@}#Ju-+z3@2-i)b5Rm##cuZQHpZuvf zHgzQ>`32oCYkd0UnW&PE?w1Y&aCxciTx;ccvv&>cq&wP34(@`S6{9AGH@?jr1Ew?s zt$3EXTxJ!@3Lvl_pn+-wEDaqQELutvv0hVFEJ-{mrf;&kw>40o-ksuyedi{+w<4ZM z^~JJOU{??m`!K+y_xn%Ff;PS7lwA`l>&_l0p=s22nIs&&CI@SRK2eLrVfKTzOJ#o=4fj9od^ob9b@LeP^jV< z8f`5>*7-RLv^8cAO;2Qs-^$#~qZF&SvwUKfu>~tG7|j|3@hG)DoS$(fIbAW!Cs+^oqnH)YdzG`DTT}k7Duuw6AE4=d z_t+|^Sh+5>!U~m5&1yjdKmU=9U2RK)1e*(p@3?Cr=r}c+|MpWeIl@P99E??rgS1km zMtrSr5(z_~ci>2nb9@YJ;qD8Ii)3Jn0#PgD{x_YTx9zv(nmz<`-7K+0du2L@R)ijo z_(>s+F$-XtYiQG1@aas9cQa4h;EU>V+T)|RqgOMp*_w5O&Ry=fFm0Jix5c2go*opc z$r7hXz6V>0%XCcmN$W}dmq$p}##1&H7R3tv-GNd3BRg3P^?lFVx?+M$B=m?6D0xHalKIr|F~#|C;qAQ+p<+ z>ez(CAnd37n(gx+if95@te#uYnv4bm2`om`6fCoz0l6=lm+EOMpDktL!F_1Al!-?1 zRuP|;GH1q4YEF7HL~9_uRAjkqx;Q0Wr0b}KCyaW$uC?{=K;z~$Mp$o`aICRl+9KR{ zYlZQ>r~Av4=pta?`-x6%A+h(u3lwApbgMo2`o zr*8)1`r+OF$Im!qQ=+B?yW||P&zxG{BCB07kdNt-NrlcJ)SS@?Gzgdeo57PU>TMxMA z78Y+mRJ(2W!vpkVtS_XH@OXJE{#3wZ^Fy4H?vkocP669{@@oLLgD ztW4^iImE%G*5Hcj2cmw^!U)+%O=~8*I5;>AzC`u5C6{|vMv;E6b0vV8W=DbSVHSZ4 z#5T>y{)LN+TQ+M&n$*bKDbCwkn%u{*$z4hc##zF(sveoG$V2)j#Q#O}>f&lk$LiPy z(`Mq_Ai1NrK46}0P z!wE#r1QNlEwMQRqRiE%y$}9X2S$_c(SJSm^pacl6fuMss!5xCTyK8U=?hYZiySqCC zcMA@|9Rh>9yK{El^FLpms#8S)H8s+EdiUyHcHL^Hta7s9sqzNzMW<1->UVDS3?I`I zV^Zl2dME}~uWZj{S0%EDdXOvAsO^6hGp4-Yl@5Dy+U zbqgGGzv>spG3aTRYp1nbpvcCsIE%#PmZ2># zFXN~)N`+;>>$RH3-2J|ZFDU-^S7-RH^R4Ib-vwW5`qZh|1HW2?iAN$#WvcdaNeLdc zL1KNUEH)!m7J(qg3JJlA<}y*(QazBbFMJl9k5iT*n})0_A{CM%!AT!SlarR!Ba{J3 z6a%eF^C?Ni0g7%#95PJnWqtSVD5{^RPfMsNZj1K)iN_Chn4Yg4j4Rxz-O8e~A03Qi z-qHL7Q+|rxqpQQjVL_2#4b4i(qs{UvQe^Mn!b}~yLEMHhOgwcapvXot(Fq!vU(K2K2H02K)Opr&Y!`?lPkY(1&Y!V+K@mOLyy7s)hJabhpsX!!?DXN_a%P%6xu-D zsc(yR2B$m35!b0>}4n_PJtW z=lT%J3iomP<1uIM;?qfn0_nZR1D&+;D_5R5_%saF*K+^mgXk$2?!VJ-%GG(=qZ<7^ImT{S_Mx z;%!R0l7xyw<9lZ)ye9qd$v+Xs*9?vkhg??q`{z7oUz~___I$=$!LIKp!k@?zQu|jZ zI9%=jd;-KoULG{wBZ%Gs4N=sO&_cP2Mm@dqZlOpUGX2u;IcVs{sSE?!VUBBs97b`R zB5#j5Z)sVQv2m2un;>6JD{5wTJ*OF zF;gaV--pSV(!5g9#J<+!aTGOWVUTggCmJWDW!x)@&%YLfR3&-nrIa`&p5A3|{RG*` z*$GxBL2YL}kkhmGlKwE!d6~U!A8;Pp0*H&Mp=$y9`yT`-H_n*DJcw#vibklFq z8t102Jc%-t6r4jARlznu=JnyeQk?c+iivokHwFa~C63tir+U)Ss#1vPEIedFra_T* z)!p5A0g16k1Hslo$DGjO$O8jzHzb*5P05EP#qyp8+ha@hxZ;+1NmXzSFSh=r?BAIm1p)DL)QtsJ^CyiT-STX%xRRL0LKlKtkOq z_v{brXYF+6+>g2bR+N$MA7Q-iM&jP&!Q5e>!c9edIz@cYnNKCg09+NglVrri(S(H? zsBMGS0rp0!>1dAOGm-?G$@6;rc9dkc+(TEl)Ei_m?+1<%8pf4*#={uq(Wa#IetQBz zqYThVz4krJEohA=+MXSR4Z?tbILvC_FKS7FpV|K_t9UI4p$=1tG!Ig3ms9xkn_O&X zq#@Q`x<3K9bTCBnd# z_(4T1%5na8^WE~g_FjP(qb0|EsNzSaD9tPbq=Mj0Njj=R-SVHIamvM^`kzCiq?B#l zVw;4D)QedZDZ@||?81N=9NVR7A__?YIk+24_n&z@BkZUXOP2JsmD{Vj=UNcT*oVnj z1-LNj47FKg{B%Fq3Z6>eH(IPtA?6VZwOV1+mkSij(TmdX!_0`qW?U4q;LE+f)M#|A z&8*wy-caVsT7iqhB|%Eoe8vyR1JXH7v2EShu-Qc#UHe+o&E(wLrCWjxUB47-kk`b( zHG=Cu&Q9`z*2Cko`xyN-NlZ#g%r)IRdH`J-8X!AR;C_ttDEy&BzsG&t@YkEC=0XJ~lTQ{;`Efv9WkBhJl%IwgrJAZLoDk5d%g@vkgouqpSiRW->obF@dVFfd zXReW6XQas$JPyPIH2|@v!?9ca&%5dsAD0fF{MHa3tg2U8)7M&ENR-o!8JU?S zhlM%35Fg<7>g7Xn8G=6ve-sa@vHtae2 z;19AjF#Lnd6=_TXsRTn;v*fIb<8Tp2_Q5HaEow>xZmhVOC^C@w)`dnDSoJftYGopI ze0<@){VA%&?d16R6R~5DTt(MtwK=K!S{V_^$l0j?%?h}VZg(SoC9mnNHbvt*MF6HG zO_<71s^r{yN)c{GQ}%f^v+47rXJ~Fv5~aQGIa%zhXNb4!E%z z&albGb;B?Ca%TF;_a}4AzvTw+6S%1QO6^;<9(>IqrzHnJ2GLQu*mRa}JTE$?P!?9T-l}A`J_qojqfT z!TD37oZokL6O3NCN4=)Vzs%l2hEG{ZVa#1S~p~6B_?+Y`tb#t51(C zJt+O$wZM9)xH7uEU&-TxJn`+;PT=pW*_xP*eh&ed_+Q{XQH-gdi_0gkb zWJIEnbupJ~yMM+&?C4A5zRBS_Mbr?j6cIWiaa<>>B%Ki&b8Tpt7&dFJ?Tm_jnbWZa zde+0i1CfmzBc$_H@7>&SbmvILsAW!y!BatygDZw3*jwE z@6HLi8xjBB@^#7I=ac(C(Tl^dfOhBK*`aH={EZWEcY5aXdC3We_Wd{7HWWCAt=EZ( zNN)hMzwn%O>6Vt6SK2OyO)3-*_bHEr4QgnDg@>n4(Fbjkd+8mzYHo??iSgWt5_E$K z*Fl`i&)a{q1jne~AsE49VHm;Xs`@mum}Opb+W0VJehSMVJZph$5JJQn0V*61pB`ac>ILS|O>yL21iz7sNS7L@vB2@13W|3xm zJ9BPKjX*BH?hpiARxw z``@(KA3_%NLG7?Ovw~xpX|BlR7f&p|nqV6)EIJLLT|LzF%S86RsJ*)_=?|3=7Q&@i++-E;uMDjZoRQoQe767ZlB7n!tDaw)IEB`*FL? z>97()A8#{3>L(Y?ird_q*F3kw;C^)US?>^Pcg?h3xANUjN-6^n7UE4d?v)CO^C#0q zs<6>@?#Dw3P`%!c*WXS%S)Aa8g4mS}2q-5fAgB?CwX5zQvCboqhtVE;Q=QU-mTMI$ zCwDzD?)GT$c5cajuR4tbAs2gq$IJuILpp;NCNCk)qkI?pM-SQ*)%|gFKT5@y-?5V- z#-dSF;F3h|ndbJ55C_d%u9WQfcwS?CGmhVN&GN4AiybDFAF=QJBDY#iCW(0#QS|HP z#RF*Y-1j3li!SWb<#DuvhP>&IWe5_yWxxVcStnuBu&re9xrY)`<(sDtO261J224_Ehm zl;)4-C@!+^`|PsgM&G`rUOT0emjuRw(rmWsziUcu(BAj6F!mE@y5sEbMjfkldWCNi z;nZuB5g-lB)vsaItigcp%^PED06N*0>Ua)PvaLd0=LQ?-!h=YG26TtHLz=ohx!7|H zy7q}Ku4u1=9yqu#<~XXK7hTvEV*=%%v%GEJB;$mKrSqr7axTvvjPS3w?a3MhvkY!} z_nlv}VuOZa9(NKH07yoUOPy|uy?(M3kwR$WpHqaI!StT2)&}m=!)XN z&S9qiQ#kg`)$Um0`z~rHpIA@rf$)0^A_kuPA6>o z_hJ82AJr}B-_W9|-)^p7?h-fsJ250kl#eHKxw@9Xfj)1d+;>Mm$(3#_5)u+3qf521 zq(JwdJwF9jHPIr0B+6a;G`u@~yB-%wYGx%cha`SVgG)C?bRHRY75IbROK)Nm>|!x) zT|!0uRod`Pa3siu9TXacKJ!R_;!%R?lbyObV3OWT9Cm)65okC8pM9`Pb|(8tvshu4 zj)WZTu)V_ZRcG>X?fk80pXfbVwl*~7vJ@4?7zgrMZp(~IsOw(*JdfDcwN{lJPPY3}eYZ{!MfeD|Z4OKD`J~-1l^*m;U$Z@X=5~}?l&plxjelRn#JSp`={~xq z(7n+&d4^#{n2=J#{5&CdBoeUX8^fI&!uuJv1koj?>jT==#{L@vkG{v&kVv2(|`i}M|Yf4Jhv!qVi)dMSIpuRma! z_zCCxnBzrs+{h^AEF)Ah+j|k=J`n0z6>f?UN#1EHt!Dr9=7jc5QB~~|G>&|BZyav% z5aqAH;K+9WFzCW^nCt^Q((qU%r2MM8uSG5>O|yiDn-!hlW{MiGiO!_vD;_aPNm7)l z{9@Ghs+-J0woF?K@&?IJ2xPq}RO}Vqq^9}Qih+sH%d`N& z4v3rfk^M#xF(Hjr7QvwjN}of?2w(G2%eG}jbP%52u-oRFh}GXX?w5@ah$feE{A2hu zR7mRi#X#=u1XQZ$@fBS~>QiU}rbjX%n!h?#f1})8U#OW=j(Wv>1qZ$_U*M~bM*&5z ziz>zCns~tNEj^(?_CJaHd`)VjcK&(k51;)b(YBCfhzW$vEjcm*^Jo{)g377m z3_GGuy-OVD@Ke>9&)qtfn&dL^W|ajo{n0KB8cs!Hcm!;28X~dM^T(1Zq8>~}6C=i${dM&_bF1`?;89<61`UzGK>|?wwciuw z+;#e5(QTT~1ollf2?}41+Nwjc2`QhgCg3g+P-Y9Rf>-Sf+`2l$Tn`sV;(3ngCxo$m zGCGN$N-dxv^%iL`5qGY*zt7Vqf3!sYVglR*HzpT?1Heac~9n2vALW9auk6FdY#c&PH|FwT36Tj_W zEPyD*Nz>;5STKMH1mGK-Ktn>3{;~}kE8U);jS~CuJ6KZ%gu9%_&>BI4JIbuj3QpAB zNt`Ng)&!+~cQfou@qwjlMs_v5`V~@C2w{r&swJ~R^%%}7u~T_#(U!+7a=l+Yl-`$V z63;7~D?DA}N^2ox>y}CP%@~2Y>OHVnNYO0;*4}%jr^5r95%_NGJzB4O--3XCpHu1Z09lJ>rE;bGS9h)^ypUvkX?A1MT zalxcft#7#Xms)>)aEjEqN@M2W%WeJkI z&S@!`8rZMg2M>7_ge0`Fr46YP+_M<@@)9 zHZI=F2QEE9fnoiQyg)&KXpwSnBlIa2+)6_qO4O^*Cz@EieuvP7!zoKP3r@@|!BEj~ z!rWWgqrbr1ua7RKU~DBx-;1MRhM63;kGvLZr41Rb6DvSc)w;6J7D$)KZU@1W#Cs< zR{mf1X?lb>zX0Bf$GRqIhR-eyj4Xm>E~?CC_2J06ex> za(K7oCZMLxqOI6nG2tK*AE+*n4pf~l_n91W5t5;AmQVcVtw%=Be)AWTWP#ZRNd(Pr zGNg6>ud$PH0QMxdmY&Y;VFJ|&hwFy{=7zeu-!Ct`)n!0Q)O<(uF_(MxeY(zD2~CxH z#P~R}KCfTz!UEPcP=KkUQ?h0`3;z{pc>$Q?KJoVW;OkNiC->}##L?+Hp2uH;-0E9g z;t2l5$3Raepw^q_83e%JfVG@#X?PXPy*i6AzOg~9gcDDn?D;(C_HL1fRvH8;A|jGn zT=p=tPFT1;YK1AchywzNF0yaU#ely&-6Mkrn`~J5SObbJ8@^t=Jj$f&$owmvKUr3} z!$S@PL_Yoi#iwonsjo4O%wSlBao!$utSQv=zu6;F;2Eb}U0toLqQAZw!Pq03tgmei zGu0AR8lEkg{8xKE@&q{c{>l@Sa2*L*rGCFZNsKz^RxMDcs1hJwisSOz?N)i;=fAFj zfuGJZ@l5iqNI$z>B<|zLc)^nSosk48?}zCj-b}xVtu2JfCLZTPamsC_3bnEwD3+%F z7~`#R_I$HY<8=3~0L$zjq#mCSI=J4pS1Wk_-yGZZO0?1K0;_L$ z7JSsvq(RRsWwYRhfXSA#r5aKu?F8`#)j{Cudx`2uBXtzRuLL zq0=eu>8p+IB-OA#N*rotYmvRvYa7mg5jYCUC~#>e2D5$2uH}N zUh%O_tW}kqx`q6baB@N2GMFAAI%NN2YVl}L3e|i6=k%}PMsDnSb+QDk|27( zjsK-&UDIcl5Au6ku|=TK)=SFPmd?nFKKuZrN{;LOV## zP+8?i9(Q7DoPx2S3CSI_!n6HMGYmnupb}v>aF@$Duw;Fp0;H-W_dhoF_Ur7*ffp9E zfn4Cqq6CiE4eBi`7nkJUk*zj{?2IBs>Wyc9pa8?01l(?-+u=Q!YT~6@L0yCcx3M#V z;k_h}kBo^RWi#O&XU!qOk+SBe;tTC;m19{2&`o9Kb?h=e{S9@pEjY>k^P|x`6kJ;;VE}lmK_YkX_me&;8oFvzhb!VQO_kwfRyhR2Y~@~1gnTJF!r zzpX70))~f$FpRz_qacBhT`FTSAxxWy~?aWqTfjLNw9IjD6-c@Z_5>S z==N8fP^*+yd_b0UK8`MSWD+BGp!;q%hM?FNYfxUp$0T3ECT0gvUZV(Ru)EL?_BYfc z4#rQw$n41AbGxd1B_VFLGP>OKzrK995UaroSyx`!=hdIc5PlihK^{SMc_=u`yPnxp z$wk^$*ea`XeAtCje3C=yA?SoCN88$U*N2z~DhVbwvE$D$=JKAQ?D~>mjN=8PI?&)R z5%`X;Yxz>SOi>40&mneOu-DiV}Hi!H+nG- z0_-hzC4>=pY~dG0WvE-5+kM;l{X|=yld|*uYz>(U``|c}Tm+JSgJzK$?7gXDe^r#% zG2WU>aE6YGdMIZq-%9}~L>Q#UD2<%rMlJl(n1BR)2-^RKJV0(w3;f&B`oE!nJO}$a zTT;5TMAdnVPz9oG;gWBw!d(J7UPZzkLOTRuXj{)CTK``7zgPeJl`X=eI1qE?+^@G1 ziU2$=F>uX*o-7j*^uK33TuZ@}|KD?gSMi3Js-HEwd1n}5mTc@f40~TfCIwtLRPT)ejP`%y+Ona{ z@%sOM`2Tx=n;c3Fj>Ojy9YUO@C=X?Dl+W(sG~&xlXtb-^9WqRGA~- zye#KJk>wlRU_Xv=Xi~&>3r>#`BZHc?tY`^FU3=Ha*%_g4p747QgrWR4WLnNQbj0z~ z$h%$&bvT?{q%uMgeb;`%xY_VN;>|s>Vww8kms<>I$qUs`qF!woOfjG#GX@{Lmb-~+ z|9I7|%m0p2y`TTVO0e^4Tlzz?c9nRQI+YF`p!$5l+q?)K)&9l%jQGWhepN^3Z-;9Z zAR;vyU<`E9g(OnKcp3b$$~q_*4k(1I5?J8R|}fj#|HOcyxNf=Dv1D~2QDs%DrF4)lW|qA zc0?oAq(5~u^#krt;&0AI@j&&Eyh6uNt?>}GD&Gr)Y@1MJP{1YDvb^w#c@Ca2AaGpC zKzDJJkzO5RX;c!VW^b;|K3gb_S;%K=-2+))XCrewbL#gQl&4X!m=3uGqy7H7o$LY@2?c$A`}bso zk-0XxwYVH(0kvP5JYLabDGaxerCZN5MHfCfy}d%N$1YD}rcKRFsa<|CKvVf1AtTxV z`oPUnW{+jrSGnyHHt%QNo^Fj(B5v^_X^adqe^eIK2@wz_eHPX05o{5c3 zvi5xBQHM@?W-<`sjs&mRP2JjWWF8#$RCkYR;|6{8Y|vw#k)#^+uQ`0RfR{NK88*Xj5TafJB9R6;cM znWJiTSI?s$jGXOHv|v_=(f{)+N`2d(&T8j%zxbh( z^BV2N*=>4z3CYT;@%Tvf9sqvX*zCt_{ zeF_pLH2r356E)vDbaQ`;5sbco2bhf{>7ebQ7!?NWVbPJhUnj82AA-l!MN_OjU>NQ`6j;WfH(oUFpX}m7wW)=FiSZQE^w1Q+^yO@UX3xPWp|DB^z_JN zh}DFw$z&Gjv-n~U)5LeBgB78ZY%0?<$G=uo}8;|Q<@Lk*u(@1Wgt`Jl2(SHr9)gz z%}e%qwLyvg;?C9ME1v%r>@sTZnS@|vBeW2NAm%_I$75Cj`=BNg0tVFwTT>h5A=P`z z&`}c{T*A#hRDm4@Fh8J+83rs++x3o-+6+!y0^Fh+ZA$zOWMKPVr^J#C0XnCFHkHxa zV{mOZ{ zhP6$zWf~;U1ll&nHpU^j%9ralte0Ug{qbcSD>6EpL=^Z2VcdvmeMb>P2-uzc5g^4u7>JkU|f46SJK2-Z+)OIuuoD{77^<(O5KeuD?u8f4a5?FphxZJC?eEeiJ zo?t4gzi?w5;E0lqNQ&2p2ljExV(fbO+3t2;*S0R`;eq)f6^R4o?c|F3`IxLx;lHi5 z!$*I0U)PFW`zu#t8O7Avuoh`O~)n(h>CzBD9borUcvF zUFrGRj0IaLX{)O_-nZmrY zR%3#f@imX;)WU_h;~lt~#Ud9^qLb@mR(HVZNQ4`gX@Q z7t^Btor@S)nLpHl5l|Lf@I~}~(n@gt6k{CulZp|J|Kf#;GS$5;`E)G&@O_xJbuxme z)57n#Y7sRP>|`b5n&Z~r&F(H_^Jib+jgcUyUqywxW9@C`x-UkryP&xJMJaMdK3>_D zJ|aTq-zt|?0E^LxF&pI8t`a+f5@)_8WXM~|BK~(BMqpshVV04A;$vvSC^HO&k2}{M z-;Yt59sYEivQQ%%;s6wU{Y9g`;oua@;2 z5Cu8Y@yLGA_r%m*s#tuyJ5mFfCqdg5@|j#h1x+J>ZB_El)vx<+v6EyUejaS=5}5s? z{ZTsZOX4v0*b>(uj!2$n53=_QafV5TCG2j?0ozyUW%Hri{)mGev)O01l$zDoRrZY~ zHgx1)(FxG%ZM|h@J|zPa3zWB~Sf_jeE_%{Fud1|Sqe-L`h|6c&YCIXXZ58Y|Y9kAv=IJ(a^-0tIx^H8u&*#p(r8$n#{***Cw}M6eJ0kakMa4!~`5htPLoxT}rBW zxwM%vqbz7njpP^?T(b2afr17S(Iz7jOL?I! zC3&v{BZgq&3BGhKST@pG8|JrsWQxn3pG|I6g4Xlqn=>=kcEi-lho7%tfw9fks*H)s z_U#lPRdH-`W?AACxt4Rm#8WWp;ak3C!>7g?Z!545CO_SYVj``(>issW`=6Ewq|hoFR& zPu;JAP=t9JESEyG#q}Hcr9qytA12c^!mK{b<)tvV#s+fgu7jNT^Os2G2mKF=az6(L zaaOW;AUL9K@Q%M#4ityv|0YYgyy)1s{;{*{{M$q~qn(~73#H06v@yBodU(&#FOqoVA@6ElXr<-XszU(><@pq*jX+$vKtYskqV^=))4 zmq2b~4~T_eBm@9i9}55SfH9|Utw2xClC~y!xN&;BTxFxEE!L?8&tUA>F3o;|h1T3U zNwl$bg^)E;yL=FJ^o3pwaJ_k{0CY=*_5q+Y)z){(cj^xX;#tvGYy5W6jf6b47@bGh zHr~F`h$zLuT1-yO$)P&yXTw7dP1r4~K&swHf6xLBCdPdjNH?gta>EOE95Q35%PZ?J zHiynUqJsl2pr6)3#BkI}&$u`^-z>-h5xukh4PlUBQgLnb(C8BkAT~f(Rf#UaTqTaU z=rCel|Iju0P%8xk3rmwyZc>K}V#HN@&1578%)1s?)7;oN!K~ijO0SYV5U*hd^9tzO z7x_ATR@B+xmuv{<}RG z0AsUyN}QaaKk55>r1$FnCF$nL1~Mkab}8Kec+A-tJziGnb!Yo^<@*6=Lglx1P}epe(emCJ;MZL#2??AEdipFrsl@P>8f>QjSB5& z?$@{Hmy?2+)XdC6Rmgk6AArXPoHT&O;rmn%-oDXM7AGrw94iN>kLC0HgTYj(sj0-+ znt-1$@K>cQ1p=@cgaW>QwgN(cY=Fc0gb&a{7nea|K+VLUXN6X(woU9enHrCc$u znVyxWUr{?J%>q9pF$e_rO^zP803&m7^ZB31%icO0B4Pv`Vfa)6)(B!E7@h5TNOG)~>9n zak^jU+uPe~dA}e8@)t|wWAg%B6H`-YDnpa@XMeS5U$>$4FLqp+nRULd{uv*4cpaq2 z!ooTbWnQ+9){X6RAZP-7#I?2E-U8fzr{&AC1YngZoih5JcPWCa>VN$jK3#2I{=4+e z*dt56B*5U0N}2jW1?E3TaDu)b2*HuD09R0MJmQ|6#p4g`x?i{(T$eNr_NV^@L%9fsBT#2zKUv;ichlWrT?&loGGY?~eAvLmn=uj;`s@xWhYP#NJ; z_dS%iA*imS?vMHVEm*hPHL9pe4d^@@i(+PG?rp8#|2Ox#bWCi#sUU3h(gM%D6JPMO z#kKt&2~eA8=wKPA;CtPy>wC{KFa7b<0%#B_yrro0c(i;2Vru`-QeAR**ckqJ9i%AN zaAGRUD=91+QOM1pS@_;)(Y(r*P5>H$jhC4=-r8ZYs<3QcZD{%qAUCcaiMltR)+8F7 zd@}vcjeTF6{U(QUd`G7gKZ19|s*imaiZoph@Zm~rzW!M^*nEq^2W~PIJinA~0MUkv z&poj;4DB<2^hPl-S<0?Oy@P{u(#P?gErUJ%)~4%m5C5)mtkrGZ@MZ~~vLt$T(EuM!RLJ zeJ-y(y4^S-@nT9`LV`R^+D*Kv(V`WB;r?0-6X*WF1v=jMr<|2^HMzlz*D>!8F{S6KDV@A+S4(p6t=LbxLNHZ^YWNkgyPf>Hat;$ zK|ht3EJA=z9ozMc&5WAv&1|A`mR9$xP%{p+HyNiq^YaF zo1nnoJCXtq2{&^&w>NT0pM-#kpd~)^R64(yX0TqoY;FG{n_L@aeDj$XX!%NOza{|d zy+%Ibfdu#mCWnHlqmUE{@p7jOiZ)D{V5EJxpMt{!PiZr(>i_)tYrMB9qUsthlfuC2 z%%Wgg+j+tl7Y@Pl6u z5#O%C?^y$Ddf@}41?Ilv4fBMI1#%Z%^YwZ{7+4;i_niMA!nZ&L69wlL+1vgrS9s@blp*QNbml9N9S zI1nr>EIb^IF#>w^?w6E8Xt{b~pdmDIwsg5BEOh`bws~2A3tIe#v!o~TzW7iw(K&W8 z%+&ffFE&1E#H2ox%OhU8QJ5c2Q%_6Gpj8Zri2$l9)<-x1CY21}d|Y(${Ukon)6cqt zJ{oZ1o!nlkH3s1yl;|=zU5~l}Av#%EKn>}iE4Wn@p;}>@xbQHJ4h(52RiW-;iy9DM zvEO7PLk#L133aEorW3SHqML*urf6km)9An`(c0)hX9Y#K(fx*1-hUB zXu&jf9eM^-P!6qcZdnV>Xk47xTYbfbWg&HwWn5?~~x`W%C?#STzO% z-xLLb*Tl<92NrFrMjf{yU|ZYV``pKA;MccRr87ctS1@$Z@#4E!z3wV~25izJW=^uQ z(Ta4K%c~B6DbRhn;QhzNo9`GqIzq6DA=-N6S89;p$k;wvc6vEV={73W%&V!1tVl4^ zAuA}rMEFPi!;p+G4-SOXefyJxPK65ZS^o-!a)EC~z!O4_MRR z6O>uK@x?APWVF#n9Go1$IcERzgf2tpD;pVg5@vz1v2NE3!@rwiz$i}EOn~+2 zU;(936A57L>$W-u9UfXD1o0C2K9B%sp$A{(?BCNPOIpuYbqZC>-GSgcJZJd-+&Q}t zGJHX`rW9fd{ctF~dSMXX>Koo8}GfzdKIb4lO?BOygQ zyuDEHKke4E0u9Yc(n+%7=R>=m$YaRVivD?;<18Et3a#m65x*mhSXz1K--N`2sSb~! z@KY{8J!i0P?LeUpntMz3AR_N*5u{HMDFYUwi6b}An6ukOJMeu*l~xBw)Jo2;QFw!! zOF97_a@0oz2_;IY$|Q{Rf*>GgJPtov(_k$(b#kK=Q1c<$p+Ql_O~oldJ1}XDCscdr zirH`7aS8B$V+qpzQHS;=$J_EpUhOv0jmLw7<4AKYNstktP2CP85t#Px@XxD@pS=1_ zg5V8mk$F%wqG7WRFIIkN(tw2S>l5qSag5(LQ`0mVB7?1h7y(nGBk1fB8m@21J6W{Z zGnqUb6Z17)8@+!(t-{%?Dapg15E11aeH;m9u?(OrqQ;DPXc9JP6r{)z8Dds%SAB-XAd zTqsSEpw4@S^J4wu{JvHL{J|zHtX6x|6w;Ye0X&Q&BEn6I$P>&Jc?p>#T}1Sm0~hDO zyrJf|AhUuOm6?}Sz`*=xtOfO@^~M`>Ooner76kcvGFEmOjYUHzw&$@9Y_zINTs@ag zH^@=I#Kl0~^_8s6tu2B?eUAPv!4z3690OVRjQ6)aEA{VRGkx9xP|X2`cu254CYO>d zgvD-xZtwX|m%}U~{lV{27png+IMlDRS6ZwbrtwGHUM(Kz&9PMunQ<64VKJ%4y#kaO z!phkPwE?-!6?^jx08k5n4S|z2aG*;cKS;Y)L@0lZ8A54;z_*uAk(r2~=4;}Tw0w!OoG2ZZW>x_-@{wNkv;Dy)!e}@dErUvMnL#%E7d>br)V8c-@4R%-4UvakL3b98Kd1?IcS~7b9^&=p>KlCG z86=-{n0*vK^gV9dct)WX1Bs0ogHnL#J7DC1gDV6b9fSSzolR3yb6CEKb^9uysaf05 zGCXtjsa2PMm_4fVUsW6=TH=B_8d9kzg_Muy=MTxx(!2Xv+o1*>=JJP=fFat@T1=y;MY!JO0Yt~ZTA|k5 znfNqHj?SKDWrQt+Q4=PRbhs)^0n!TPP^Gak$cpK(4ew(#Y<7ZKWiUzusTm>PMuuIN z$x|fEm*6W>mN!Mr$1foUy<4pjW-^k<;?vbACZq@ur2B6>N~my zF5Htcp}Jrff`LP;y2f|5dpe(a-+U%BS1L%hxDOR<%nBNOTnMtw$yHJDcKG?Rr&080 zvfvE`jV|(xv5N|W_xQ1Mec~Lq=^y(a8e@rP(2`014rPx@5@<+9#*GmWsl^{L((+6_h*?k5()d^ z_1tf=@pv)S=Oz?(j2>`({hsgNhc~_jSzkn^b^c6`qm@%?yeqRxo9!0BO?3m6q-d2V zd6NFImW$W_?$t~MB{Z2ffH8JSx27Ch4Rvak0CXmu$t$STECPooU@K2o+FNF4*CYP9 zwx)-bg4O!fCnafKEwLJ?KfuAk5wyBMnG2I+33N;Kc}cbFeoY>VS+p>F4%-eD?)tIs zVR3$_MSzJPz&!F~RWfFc4=#sOY1%JnrOW`r03!_b+Ow>{$%D8tIV z)J0u%0;v0ukJv_)?{!y-=c4)8Qf9Oy~tM@2N4u|l*e^TyCd364;LTy&u9I|A5Bi`&VL5B=d>GG*` zMxW5?Ewe2@%}rZk&g4#)7Csb(OBNS~CCOA~H{yppaGaR3;d@8khDH_<44{?7iNaRS zFKHSZts!O)NKVsUM8jOL^;Is(~ z)I%>!YnN|y4p;Ba4#cBw`ij@D5e1$*+E+1<8(aYg1Q7J+(LZ&==pU_QJvi~elAv#%hC`znd_9Fy@^OUIv=iSImqR8{kjky^At zy#t!CReItQgVg&nbQ9zCU7Y~!+&DRzND0h`8W1qD6WMn)gLgf4Y=S@`{wz~@J{SMG z+tbkAzVEv`d-kqxr(aHq%s)iJ5v`@CyLF^t!0O|a92e&>I3%HjfJMYeVSp1TmB3Nb z+B+y81w<;DVHO=`Abih5G5?$BeiC4!Y4z!?kfR|dU1pzxMkuDA1X;8}0j`opVjPd# zz{#}x;lY^D$1P!bsQwQ<CbB$uc9xu5@Xu0HW8Li)R=Rc)=Q>m53$-Fl&ZjT7J}xHf5w>^cQ1)y-%h zmatH$ah^%c%*hnR{9`O4p#bDfr2D-lO#=h-{|VTtMWbS0z9Rjr!K|sl5GQ$kg-ZOQ z^*;sR3;-qpxweS&$A9@+fbsI3K49OI%Q>E>wo2}w$}0I(3Ctif)8P1wMIXl*gIrN_L-bQ@(s1_Vqk1 zK%&URCE8y#ic9hg$eLGsb3Ww(yt=-nLmJ1|4?^*b;T(8$?wY)GgJI0kMa!XDTI)uh zp1}a6Vqy(aumUUkI2Jyfx@g!uIDRa)o<|Gw6k`)O-fBK}%BqSf_st!%d(~kgiVZ4c z8=v&!vP5uNH0YdEGfu5&=jluTjIC-B!wk0O_w5I`+>P2%OE{I$Ogy9u=(Pz8nX1p+ zo@>vVLW#vt-w~swY*zwzE<#squn76K7P2glypSidQjJX91Cx@(ybF3X6Kgy1x*gKV z%UuHRjC76C+gDF)=MPUHi+;#9{Vd4RXDO>VH1%!sO+y90Qv_Wx-99m3Is!5gENr?x z*Q5t#$NZnk$}#Wtr%pMp-bPU?;x>U9?e{WMomDg;ps*XYhkhV zp;v4IpjrKC6v&qgEig(%W|lUdJ!-{M%5Th}I4eM_X^edUTSmIV4u)F`CeeJ*gH}PC6 zDE-rQ0EkdJO_oCh=M>G26v$|#Wn>rwD5dFXes>|+V)Y7#_cuOJf-ci(1$$*JbvmHP zOsVqC-ab?Yo8QpUF+R^&Et5!PR*2@p2LKsN{OemNpzm8*!E3eyn>jm2%2!BHX5@7? z%K>q9JC9^QjuiL|OOEu7M86jt0<-~U62goxU;YuF0o3!q;9;3c$&|nbB4h**O3$a3 zg^Uhq$(G^lvRSTq4Bw6|Q}<|oLe~`8X_fkR6ZDZez9*NNSqZ3KlV{xYdn-B$u?q4f zn!L5w!`6jelo^HWXSP1Iz6QXyg#6ya{r%A9lbJ<+xjAW2lRo-roR6<>jyhJJ20e+= z6hMnQ0J$_Y_sRe&%l}1bf#mhFY%;K*j&6D8yJ!Of0=TlG*aB`x99WXi$9sILWd8t8 z0ItO z4_jXu5LMT%O$ibrAktybA`Q|lNJ~mdcMsj5NS6}Qpfu9mAQD3l-93bK44vQNIp=xb z^W*y~GMm|Z?X~Xvy07S~)Ynm$;9y0H;XpB_haL6Z4X9*woNP}{%@;s6w#1IPj82Fiz0Cn%w4J#F_p$4u-78c6Og+7m@aL>m={osjaQ8 z8D>A41ym?ardM^fC1xWPMmBC^Mqv$CLDEc+U%w_KOU3Cxufo>j$D`{~$L~40lzDc1 zb?Rn}lcp%2_XIA0JpC*|kIc`2V&tS!O2~^zBaeKk=SF@ro2WP|EqgFOP$R2z=bG7V zZ=Ozv8FG2;(G^YIej-SSZW~)L>)$IjEg=tBKdCXrPxeJzwW+887)<-UJ&cCmQ3OwEw+%4D1gh-Qkb`GAor zto3$wALJ9<{T5lgG5?;BU||&u*fsWb?v7s>Jf#qs<{^N^sT-xFa42%O@eH_)8r>dJ z0La9m)PLj3g!B8)pU;Digxe+Bnfn(yA*p#N`63!qn`L!nz`yt`Br`FVUWtu^BZ6A+ zV;|y*At!@$DJ(05!~t^PAk!e2!;Q}GAN5Yf^>NLrr?8x(OjD@w3d0`=S4k|IfWDN> z6mJxJ7hcM$ubW#mI6BSDs79u&9X99QMk!EbX)=%4RO<=0nX`4}a@#+x_fGe<2CjsL z-rlK=a$d~foIYqn&15q%G(?6uF@ESZspLD3mbLf2`Qw`6b+&gnKB-?`SJmxBeVq(E zR=>i$c%vr!CN$(F^;1 z^BhmTmIOt1zVUo6->hDRJk-YDHi>UU&T0S2d()Mdor}Hj$n&J)uX8D9Lrufv;my)O z_F-+++(vA}C@0xdt(lsD6GGb2-x$o?6=pz}afoW+k_WdcS`)3wN$Fp-RUtbdP0Ann zfVPN>my&pRO92PwZXnS3#P)V~@ht1#s*An~v>L1D^a+mGU$J7_bdH_LR63&XgL)m?F&8Jv>a=gjJEx}J zFFvxYis^@mvqU?Ne%u&FrAn`|3~sUByV87m#EL_ zRX@uf2e0S%9>hk0qT)&;`v9*z$MEwWaUTsg_fg7f6QB6V(Hrb46<)EZHluh|db;z5 zue8JiWxjXTc8h#Ul^>1mAhF&py@#CpUyF}_&f$D>6cZrHac~&obvB-ChPP4Oc4p*x z3I5fibBvIMS-}Q>*UHSeQUD#DmX7AqsoSIn@1&Xg3Mz~5<*VK&Q1=(1-&YwYw4EDn z-s1y;Dm%K;D>X@#;Mx>gyOA!4Qt{$XL>Rw&6w$xi?t-145NUo?&=g)CGP|vdqveru zvAsKCr~hZ*yqNcdAa}mEk^Sl7nV*9MSqO9)s`8i$!i=K&zbDVD10}c_Z2*F8H3-=HJjSuS6guMjiMVGdq2&VO0 z7*-wQ1BiTa3jlkWC`}7AnBv0YO(;i$`LeZgNs8F3v=x#g=}O=y9HuXCkDh=;Bi<4@ z6CnM!~`}CGZT|9Hawz*uBB32`hQ#~n8vZdAd%14J1i+F_+AwRwm(3| z8-)Oe+bor05WumJh%@HeIMBPr{#tW01>ZOAgXEjD|nY0?H;@en>*) zMv8?B$H=eeWB*fAQk1^-C(HX(v?qPF5WHt+mM+Ud{JMr1iETm{|N6o=SfjXE+Pbt& zNOty$mI+jq8w1%cTMT691S=-=rD(nCDG@mI!8Uam?Usknc}eDPJEcqoR+nl z^%78WmU+X2ero+AnG1h(*9_|7MpTF#o*?`I>VXs&Sw<(-h(8lL93>_oB3xP07+o`K zO9d0oRkpsL_jSr8kW!Go?506f8)C>y#yqk}?_QPL%Kydn1E&POOdK9TDKdEQmu>p` zwJQk9bFH18pH{5kdWta;G+LyJME<09wjUv>l+*OP6(o6 zso1S4Ex-vTlzm#Yy$q#98EuOe`1^8%yBEo`HJPpJ%b3;uVAs*~QrOz-wPG^@ncDk^ zeO$c2t)G&L$@;Zy((2<);SFfeGak(qz3<$j^r3Ar7tiJL{vmn)!%pqc4}SUW)33%( z?oR=f)^R8<&)$yB(EL8$n3|co_vZ8pe+5kEk8++zDN&lcdv6u==`Heaesv+IywM`H zlocZ!w%n-z%2!Z!4M@GT#93tkLia>Lo*tm2xuC|2*qb=1vh;Up%@zN`En@&MxRnd4 zpUBoY0<*K=b3dyLr9=r4Hdmf{p?F&P+4Bpc$ud%;IUD-v1#KgwUKc#E3Vk%T#SLx< z=??WeXzAoBGhQSNoXRRDR;gZB=0e=fWEp|_&gbR^{3H|9h-vB;HQM0y81m3t(x<(X zRPGoYatFsx@{7ccP-0VXZff z$U)La&c+P(-SJJVx$8^pp(ntXCypC=0gr`ufSvUBr!@@aPC>nw-9#g6H6;R#r_ z{3IUQ>u?sAD_$^R#c5MFsX%UjDPh8{hVr=hsbTW?Lm5#GM$heG?0DBwQw^C|n)nhY z3O-G1$jA3HZHT9`&_vB)OY&5%C)!XV%7FbG7xl+u-PIG>Z&Kv@J~7Bz{h$(Yi5lHt zW#>}yd7-&o7xw$pQ0P?TDO-o8?PwH&U0DZyB@?QIDrpzA^_OP9>564?Z_cp#_N$a_ zeAf~G9@P^4Hu-k8a_m0J1i_n`oxQQdk9x+&wQa#0FkrJ;P&|0Bc6aG_;di_5cP|aN zU4BBM+m)xNTCAp3GP!w&D%?Botf~$_L@nC{D*zOr4 z-7T7VPj-a(3(VYPuB$O*9K)=xKi7hzZ|wfz>yL-=z41u?Y5el5Oik(pQ+; zn|j}M_S@Yr+XN%Ti^?tHbyaL$cUVg0WQ{vj4%JhVLO#BsjAg^5v3xiR*%a>VL|!u(U03eXCSzZ%;2TUA>TBF@hIUBCi&9!u zk8aXvRf+j$apPr;8vInjT2xxkLgsQ$m#$V>27bgK#r<(&T47~SuGz=lxuR{r3eDOn2emOSR6CSPhI8h5O^~5R+CJ;N!bM>#+;eHh*050m$ zV7|VvkgLX2&erO`-LSK_duU-B39`|g{4#Vs(k-}N$Pj2_U{uXg3c^fXUB9!*Q?F7X zxA0kwF@({#aa%v?%AJLwj%B+Bh?}kv1hqd;-&k(I1&YMLiw6t!-btK&=G_|sJprX2A5VTlC|}IVJp2PWK|}@)!g4w6^yCXWY-H<)sagJi>uL)A zQ{Skb&Y zF8`)6dT>O;%a~Lp*3HZ~qanNU%F6Z(z(*&4yvJF^*4orx3fk9;aSUpX-C)3%AKtDW zCi7OpkM``yjW!JlMfHB%^-U4s%%C}4WS5UZ9u`5xOgr|7yzEo* z7%}aP@xB*6GH5T|5^vI7sZ5JIA7E88A)F9n)}xxJX@>kM4!Tm$!^*Or2RHLzVrsNM zBO{1LKW?EHh8FTl!CN$0vX zFV9MBZ|AXAnz&dkWVNUfxZW`rF*a)mDtu0IdKfHJq|m)4K}2MxrJ0HWl7(YItqTxF zY()4MLnKRmHPJt8R726mQWKf+5?rz*&ioOc-&-{BV$dawf3tV=e7!a3U{fTCG@^jg zCp<0~f7PF09XF38#G$Da5Lt2AfU14N(rowUQNyd~XdafQl| zxO#G(oz=UX;m*$LFsJ#3gXRu!m?0m7lafe@h>4Pte?5U>Vuyz_Gxy&|7y}6#go0g2 zNI^KmwSt=@V&rtHr4w*oHFb302M1r5*s5^S{mwJ3F>V;1L6o>OIGoi_9?MzDxod z6ONs+{E1(LX1{$u=8T0c(E$1uPj`f1Gk6*=w(JHbkf-2%SK(`;Z zvkjd3_VRM7bEy4`Z(S4K7e(EFPW57ysOOf!>O1xn(w(H`pIQZZ%efp1Cys%<1?*&z zBpsB(S6jxPf44&@DDvc*(}h2KT^^52!f5)$(~{G3y&-{|)OyAr7GH!u>8&;uzFzWK z;9DzyPxhnIH!p(;a`7_0M2v!VXSce}<%&q(_mK9+E`c{}dTRFhn>Xz_DxYW+pK1Q4 zs`9wrOrVJCJC@o;vWU&q@pMHJ-7gEEMoabDeu0gRXhI;)G=-gla^j!Lb`pGg0_V4z z!H=w->HyoF`;mJZ$VfVHw~dujqiL=9y~}kQ2l%_8t6PmAU?+)2 z#{B>Yq*|{N!5n>R_Ai^he%EV$H6~i^*%a@S9%-D@?;{thDA$=v7F-id4%^KA#DHik zcdpl+mjOP$3tj)?9qRmm!XK+-#wj72C!OrOxBlS?EKYm9jm&9$ZxXC^O}84)zcZwRp7?>{-}D;WGVTsaxoQ*YJWrUa9piNe zP0JQnPa}2aZjNCTcE$MUxbv>D!DsvU=v1_46hOk7swE7AEnN9;PNUwpbt4hMq&ASV zWr_Rz;oWMUd4u%n94c8xf|T<8ko;wThV zI&&><;rALiu<4Xd=FHnU4Oyu&Y4EzOb7(Uj#gkoKHC}_%>Bm5*8NM;7>Lf0;rq#>I zsR`|*10hIMh)&{+it>vuZ3d!eWnizNW6oHXe{QA`Tvvi^6)oU)7rW4gQ*9N$w`>`w z0-~Xt#x5xysE#`2IlrL&UxoCF!jZPXH>P;mdxt;SoX;{ivag!XDP&UF{z5^ZdPCEiulbkv1f^JBHT z5PDwv+Kc?aSb_)}u%DOesW1lU(l_vv+(;v(>SUjE;(tr-I`fIk0;V1~v7}2ENLGv= zexG^&QHi{$_b^jp1uTzlV!4Pb1Up;LvAQhW1F$Uaoq`14m{?kVb1VG_24-PtI zS`z6MjRg5){Uh0fo;}Q0V`5I%Gx?0wc0RtKQnW}l;yEhzlHF+gUC)eJu z^#ndI*U!=@=6It{iz7h+^)cgX%M{GG2z;<`*Ll@H@EYVw$exucl}_f_gJ%~Ikg3b$ zw6`*QpCRtx8Yuo34kwsR6n_rOaa&l<5MT!?R~j61U`kS=#18Uq>np=kg?)mISkU5y zyFM}|fFkS9nb2TK>c-8Pj`Uw~`2Eq38j~ z7vAZ$3!c`FFU?o;X7`T&Py`x5R5*_tV zVqamJZLJ#{C%rA&E+c*b5|MSoQk>;!j48lroY z>aNB5*Dw*?tZF+4xyT#0IO9>Ch5Ik5>=)Euii&i9Xr$1FWpWMeM)c9GmQ4_>ipO*; z{nD$sW&tv=Jj~DjY}8AhdfIQsof4B%UPKvR+Xzdr4%AhnF%;)t&;mcDDBs&dAL|MO zs|oTr3HfFX`iCeWE}H~5I99%)1w&iFazL&4pfljLx9>V5+}WBX-L&W>$81<%mHfr5B!I zk8JMi{hqsiv8DoC8Q$V*+oA^uXPNkK)T8-EiI=}?(aik{gRgy-c{r^-w9JLTesH_& zrm8zP^MZ`eqWG}+EASZIQsq9<+ECW)e%jm5nht8rD816~eY6NE(5@v2i`$QinmpfE zVR{M?ij-0=QLjHZo$cr|M{@zI_u{+-WJA^AYPN?`PBbTHd5F%4eOF-iR=bAspZED` zWKbsBA-XdVvXGN9cdb&BK=J3hJ3H2P?;{10_;3H~Xc4NXgQU**5G6ymM5BWG=QiCm$)uj2Yl~5rt``y?+#n z9A7iaaOgl8q8kDyfk6%{*BA)H2*WI&&CHPOHe@#!=Y_}9WV4-Eina?yfqn}`IE&ke=vh!tOFX*!xOazaXQ<^kQEah znsPBihssS1F;U?Js(nbzk|!1F_Hum~K3=Evb7|P84iv+v7&uC2mGhSm9oljx+_IlU zLX{iPKE3$B%TJ{{nFA0J212^FR>^v;{bMa1^E`~n19_V_7xveOk?@UD9ZS^uPq5K zKJWZg5%eLvPBjtHibwaSJETiEZc}~yL9F2Xi6`1PM-Q{m;B)u&{X>H+$a~0mM=EV?T1^u@NTV$eWqtUT`tjGZru4? zY;X38g^*AtJMoN>BBkmLD&ot|Y5W7kEFFs4Abhg+{T!k3H73d%<_prufFdHkSFq*^ zg({!cM$63VdGPWs$)`6PBH<*-p zeKzq@jj#pgVQmov7TgIN_5jPa=IJ=6!1&_ttsUqC-7%9vEZC!T9hOBAtZS=cdcaNf zgf&sUUf`X*v`ER>O~EC!#PG&|p^#i39(&t2kxYL*4a~tV;)a=I+`*(if`(hz+ zB_lT6yWSJS-EaslkQFV>+-XhUoPD>{j2YdkgMjN}{D<$oeT{^g565YQD-1|{1%J%HvspDgJMS2BRuSH&ck;SZPa&yTjP7WyCeBYcjPMIg@P5S9!|7c#q zU5Y%|Gc^DN`3n!<7lF3KP$+^Rt zcJWLD^7iSWM0XZ<6ke_(K5OJy&^{4wX)z1iQEI4zP2?Rq>o=M!-F5fVAijx`%P*LW zYd;)#_4=N+jLKsCdxtu#51&M`0H>7mnkG!rs{j16j4u*tK9pmvt5hqufGkf+fn$mH zHr#zij;D{t(bvZPPE-Eof+b&Z$}~M?zpr!s)HQ&vBzg~~f@KFh$J(ymmhkL*jdA5Bl zIVPXT*--ekdzm?y$GqC~p6`3p-RcnX!4OL6<)grJ47^yIsi<{>aa=+sh@i;33~?28 z6H8699^(nZH$FG6sZHCd2e;VkZZW8~iapzv>z;~snMfSMmOc)aVbEa0oNdo7VN_}EmKbz41Q!(Q*UwE}%h z?lJwiSc?oE+I34F5ZjZidp{c86uC{AKhDl zk#B+EGv|hn{DTyr48+iz+Mj(KrV^%7-aTHp52B`fU?4NGFsu6BH|&3Y*B!y|s@V^C zj@53vKN>q+oG=KInArv6_!q8Bz&wL{BU5lgI<}6SmVH8d#WmQh@a_9_+JwH?zPwl0 zFSQ>1XN~8)yNBpnY!~rFX`?=&RY?GSM-811-_5%h(U?Sm;?Yt|9yA(;um9S-^`$BN z_t(Um;;11=z$CHym|e< z-&eee(U&G+Tm0eSIthjn@*K4xt(Ec4sGmX1WG(Z*&!fX_S330*a5=LNfs+=BI0WtN zrSQoLQ2NC()W80ZZP=ri2@T*k^pZa1rB90(Dg7#W@$W^h-`zZR{|=x3`8UO9r1{{pZp~D#{-fzhk!A7tcgMe911o6%RpH*>g^(xz z+_--~^Wk&F12RAqc(Ra?Sn@CAfB$R2Vwv&(ezW`k3_-#Caf_yDt?xn6-oy9j|NQ7$ z?f>_QVf!#M}Bg_8&T7g_Fwi3EYj4} zDTCoFl~7gP|MTU~k*|i$e^_N#W_>U$?$i#bmPt;s8vSjaJ^E41yx3Ch)KeU;xUw+q z0N*5WiCf#^#l?z@$*=kEZ8-mSiHn*KGoy19{HcsQVX%U>dugy^aCT=yVcT)w3V%McDRUDWIAM5Szg|g z@MgcPzPv4MT;Lu+Zfu2G5aOQ$Fc&e=%ePLv?r~Kt*K{(W!<^0+p5i1!7pu^oCvwrB zx%164zHYUNpwg*Q5+YfUc#qDexM|NdH_K*ugj5qv>Hz%(Lbu^mA4sKXAaD9a2XX_% zUIi*vJTf1VA9Lq2@bO85?fdlrW@|tJ|?zmrM$(|71NKDa;uDt$t1S^%%#*rkAJN$*{H*7V#)&mapx zVN`qW>GZeIyYtq3wOQ-hBeF{y|TdG0)__)73Eg-AU`s2j7#kyU5Wz zrr4fn&S=G7e>kjuRF1jD<-1k2 zzxiuf#^bt<&jVD;)Cu_mU}%@jbyl0N-p7k&lmls}aLW6@>$Zv2XFm9?@9IY|uXYw8 zVAv`OYSXEd3@i9X^{Z6sdfSw-K5Xy! zL@IAatKiChigl65d1~3U9qj|BfXOEp!Fc~%Q|3+FzPIXbDZweG?!EMzRh$DF=*^br zyC);k({9y%EmmIXRTTE!r{_JFI?;ub&bq!ziz@%Kwwk`RK<~1ihRXas^#r~yd-ILK z8y@#C$l97|SSQ}(gJPh~Bap_Ii2(*&f#RP=^OO*0tv6o6=S!$tCuDT&g4#Op%ad(D zSb*;Ap0&2Bd!W+F9Ibr%zvo8i#U96ddQu{IDn@*!2KAojA9-IR&kd@@NeuPebwWmUVIEGUJxGbDjzpH*)u*owskmW8WKcBJ)9G zcbVVdyV__6bkv)8KlO3{c!EuL#n&az^I1mUCb@5G z%0`pBpNOqrs+-8EX?cv(US#9#tQ!R`)ubI|>HC;SceR7%FMKBL(VS+`SV)(XFG~Q! zpx(UAFF38Bu+L5ZU*o~@xgQ%{vi*k-M0oV?rl*_pKk3Dhx_KiPoA^q>nG-YzEC3j`cwZ>Amia+Gu*&e7|)>Njs4(~rqW&z#>J`tmH4D_(B7k8|#q zU(6W8&9^fIuQpWMTUU0XqTOsh)2G`NEnv_B(H|h?r_1TRlD6VWd z5)QBRoNonrT0i)3EahM*; zCa$?VbQN)3;Djpvg-#;s;it5-+ZN}M&%N6I)kX3ivs3&V02P_yvAz?@GG5*YEAgZc+3=8de7(DE>?HW_UMJr4v!&7j zeu#r-ElP`pjtOy-Y#jYDZ|ML>L>I(M5D_OZ5{Ad2MsDP!k>qa`6oZcurRNQTWhBQ+Jm!FC0WnPB(6f5tKrxV<(n1eg3{Or_0>K+DLuj`2>FTd zH3kk`>7+bkD2LLW6@2hll#+BS1AHwpud7xw^^hFOS=$v`yIz0TqQ{>qF;kwb!UHCY zLBl2s@XaXB^sYWP!6!r6qob_TNi*Rb3F-P@6H*q+Hw6>w@}kf`B*)Imi3nUHl0MK! zoV}hPs+!1TUbHJUXHA2+S7l_U+kSA`+24pV;FkI3?b<@mmoDR`CU~*EaqyZlZ&CZv zGO=k~^AZAX9(>5R(~;w`7jQ6z0gav|HqC5VaudDn0o*mT ze^Ndn&mtW0_BQn9r`NRTh&6&E5XV<@?A3!yz5PoeN;zWv_Nl6k>JKNiJX^z-r}Rpv zut{r*e$OY@W4Nyvf71N3xhzO7E~4)Dq7t^~Wb5ht&|r2dJbc>N*qV3f5U(V18+TUn zu{}ap`1)W&ZF{qI2_8Wx{Yotk&&nYtL{h?rxr@xPQ?SI<0f0q&DUgAa-3lV>*In8> zlVi*`HKf+%8adOPvN`yb>^UTPR*5F>Vrc5pgH^(YyGOQ9LNdQFgo)Olw}9fIE?pXr zchMcflOD<0qOFc;L9g(shKTd?vco&SAu;O*QC-8h>HEtbRO#D4e#@Pg=Y`B%d1zb

^;R(l;ce3UAYU`c3q@%j8rlrd5RD+ekT9P4NiWko=*`~}Wu z@OpmGU#~QJKNttyr)nFRo%@oQkn-Vb2V{*5xW(ZotEX`S#S&r8=ov+VJFm%7Hk@8I zi!!osWYfZ0S<28h8UXt$WH6`D7mpXu*!|h@v>YggAfMs9mO;u!r1*x#54dGfU!zUW zpw|tmVm82YKLqiHLMT~3?ucmzOp*k^d2xoP71EF6{x%pyiZcSbAj4Ng7;+keWghfn zjOoK!6folG+~l}iD97*GEnud>#1o@^S@DB#c@0*MzxtV7=>$+lzSlO6LEH80fQYA3!oJB`Kg9Ui z?tq8?W%Eb?@`anom|3<^Cdeq&H0sqZ0X>K#HnMg7)x*&TAI3`|iQ(-!`COzmdefhO zJbu`3gE<@hK79Hg?ap2TxSK7;Z7w4?CtxWp!}~7EvT%#YtZ+%l z7iHmkJY1vjeJTZ)>tD)$Ne;4{J1Z4wt9y+JBv!z3PEk)+fJtL6X+H}CYwonm#p1QZ z_KF9ta$I)Kr@5V=*dPnu-<8TdKaYP`&fR!8Fzx zk~zCJ05Tr&nQ;@n4mFSrM2K6hy<0EpreSnbu>BzApYFr(MLIlX-K3qskm+m4sVvuV2TR9ew5A>W6^C`*a=wu z7{S5rC2}j-$W^)}qCYha%*>LXJsi?D{ZyG?r-2mI?#y?VvJIH^4!j*S=+X%XJ{bj@ zw7GeC^vK0M7ghuzWgW_rcLoW-`=I6&7i<0#?;11j8v`Mh~Ed#_--TIh4j-;6RV zGDv=0WIy9#7w{nSHUh#Y2M{AL(DP&&9hD=Lqy1Iw&k#0cFSxw4g!FWi8EF#_imA7x zT&nb~G%h4FWOsMB>Z6<6rgMYZtEb=zA`pFJztpjEuw*}#Mt3&A6YWZ+0bdq0vzJ*R zG>RmWF9EMkLl{D!S`Y_RJ>XWuuc*={kU@8B=qE}c-EqkK{<|&h?ve*pWo0E$r-pfC z(8%!G>{isa!pbIJMm<3fz)#=K{75~CIJm}xx+Es0ti!31;_dOaTH3u=BHZ z8N?n3g`I|j$uj;#-}xTqXbq77Ma81c$lPMyyj`s1G-dF!XE+i~vY#C5<@r8{S`XyU z=L}m6%~@w}Jh65ViuO&tEd^s828e5JZ+|~dNgpqmwghH00_dLRHn#F>6GeET7_x`a6Qt)6*$yo4hM65jsTz^z?vj4;sY=ze2+=Dp(m@ zUrW5BjmqD>|Md+<{Ls-uS4kw}brB&?sPbWmS!GXFur=^1uXy8}n3S~qXWTlKk5fs{ zZs8wSaOPL&wlWTz-EaT*KW7?cLyIe%(DczhAY=9m``=`x*k!x zXO8rvK8gHGQDtFc&%SdOQf~1@ zmT;PD3dZAF4~IM9LhDAFt|h}mZ|ayYVt&87X+ZFs0|0zjbekT=6V!VjctdK9{brRD znxz7Qt;q%J{4%brZIS+nCl}<+lT1Pm=f0}c_xB$0fml!_A6N9afbnFSu<7}k)#%Am zr<<&3u(Q&1G68~~Y+F0z<4QBVjjioSp1&)X>bU9F31h@!&bZC+9>vhME9MO!i*DU- z`@2K8^FXHNcAhh8!S$Zz*0R=G*pH{fcC~yv>xpS;Yk%xZAbWFsGhQHqz_z@(MhXy7 zxZTAm-n#QE0U6(h$Q{PL%iNgFqeZv(@a{PoQW34C(}^?zLEr1dv|Wh^_Af2b6S!u) z&!UVSFUUZ&PflS1pU)*%P61;W*->orr%mhDQPb@n^jdqrw?Hb=b}9^B{U|KJr*SY# z-`r#@Dbiir>3;6^OR;ovV>wHmO4wc0*zNu((GJEcrhEn+0o;T2bxmFEY{EJ~LXsNx zpI-;=au=kHk?2aU)ZhRKFYE*MRK6!|et`C}NdGXrPSNxB9k##8F8-Ot?!ME0A#43@ z#-x_dIaJ~0=*Iab{hTA-J~2@v;u@>4fsZD;!F z(ZyF4BGI>6Ax1^z6np!S{RJTnYFpsIX=%<)K8hwFS3BRK+3xKUwif?%Q2&w-8&Ubi z(6uf~wJx$JDhqd(Ji6`{DgprpQKyXn5R!f`YcIGe2u?Yw;%1&3UuqFz4nR+>)>aDO zN@D@9lsoUA6TyY6U6I3-#p@vP*V&vD<)=&R+uPe!Znc2bOTl#o7?!yk#C4Z@>O>lY zptGKyo0GTarKtJqEb!MNg!_9=r(CLlTU0HJK9DsKK_@rQq-y%CjsLnd&NX`_1V5&( z@EbC+whj-N5KAWgm~0O8T!CxHK^?5s(JVxkJg)8zXDrcsB1S+yJt!8f*9v9B0fhar zy!&l*xk83F1)F_hXxC**TYqD@9ivD{D+}yBwT((fI5U{QgdvNgvHNTJLA@&FgCQ(tnx-{f)n4*Ea1dF#zn?C{ZZ zxcr|(<14yHo_ptgRkXd3EStmyNr7dRSSJfq>Q_Sf-dB3GG~nK)Y? zA-7pvZru#fr5BN%;&ZT@YwHw&F2o6M4%BEZ=PI%%iArU zM*wk5VJ&oTfkl~9s4cDWl-&2EP=EQm;JvHF<&61`&AvepZnNEE?y#N>C-7ZsbsL;R z;}OK&Yc08ETXi)>y^cXo{&J-VdRBDKz@c-#b+Uw=G_s(x94?D4Ct`JF1Qf#5))?Pb zg~$d7h@z5(FcV8H>Nxds6^2D``efL4Yuz7wLW#`R-z``cf@G)dKN z>9}_)enp)0OX(Y8wCHb-2Vbaiui`26?u7M@^^M(vfiWJN$Q!6g)l1*)>+hVL>PH^0 z{~4}GZ1!f98q`%+t?O6ZBHodiEM!`#PammU$Hh|)6KG0K&{u*%-no41tmpC z$NOaS^?iQDx*5m4?SEF6g@4<}D1-9S9~(0}Q$!KdvEITMYd2f$7WAmi_Jh zNkSnV+h;}6Xy-XhjfM=~dYxK3$o3#mn14@L{He>r>9>wk&z2yJZqFQ*H7b2k9gB{o z@d2nISpT~G&@LXas|SRBrt!i!S!r9mxA-O79AvnbZZqCThxFI`cQ<#3XXU0_`NR={&)S{Y>;z8-Y%7%?nOUG6spykINK zyrYbMhG>M@<+a;M93Heu2sfPhkB_V4UKx~rG=>;P>F!;)luwLUao5 zWZ1%msTOGXu8#yPVxVwI0U`tr?l(%MiyOsuUKPysx>2$@9R_R$*1O2jRK<|H1zc*H6)1Kc)<>>D+-IE17sEth$Gy7q zmWduEK;-AE)v;X!3*fFG{RifQ54@f$1 zM(u#>aM6@95U(`(Y~Lv=Cc=JRstpWmsOXuo8UDL-9s08KJ|47;x77p@Z{FOmDkmFP zhk{}Cm3_WDTik@>WFWEldWs#Yfn7{yMOf|IO$umE*1t&prfX!Z?KfVXLq*KS4Cht4mH0w`p zV%2m~toZ8w#0Yii&e1V3H^;7&C-*mwI^H5VBg!gq$|aoEAFSujz)a!HHfXYyOj)K^Ff4?yPtQ!d-}gBtsx9u3nsRA-O%+TX(iEB6 z`hXI+)w5N&^(mD2rG8a|JJ>roTy;Khd}vW*xUz9@;TPZsuCPI^*vamI{oh>=PLPq8 zd+tN~&cGb)8%JeLeSKdoajS;aE8EUHj*Sho%+2BL{i{2CK%Zs6i>j z+`H|3?{uf9H3cx#3*(uEg$8G@Asf3H{L0pH8Zppr6xzuDyd1{_b&~nrqLtAXYN{|P zj+iC``L9trulf`*fFO#tkl8pmKJOnQ_Tz~*@VsT|!qA!5tzI^^a1My+(6PAF`lYg& z*6EQ%`EZTgcC}1Cw6Q&cAPe_lNof_+Y1)O{dH3ZmXQ@DUp(J>8foAMnBP@{p zvqAm+LH4AW=-Pf|=jKf34W`P>Cs2uVbO-@uZ^y7ShlDWhz+qzP@JZQ6h3tFk#<+zQ?Agov3o6cV=@l!^FU42!s25hN%Yt9X8ofpDA40s27oM=Uawe6bnB*%=1P?`q;Z-OsY5%jokqN_$e?M1zk&($LupE8|(Kd|)#%6T_8whrIkzOdTgCwq5U&7PqsW@_%? zr`zZ=bbij$m!GYIv>a?P9k*h0K&OscTs%aO>H1TbbNWccwEpDF&?D}sOw`?QOw&dC z!pOAkYEwUJ$Ebf-rCPboiLqxw$^OgPrOG^wpCi49=H|rD!k83}4>T#tJ2lL)d6JNK zywe89yCsxoJHKd)mmetJuXyG(hoKj$F^>Qc9DXkY`&y&^dh1f^pIDP@dpQ)tPvM@p z-}Q1X{>+w4W*0(z z(}cvHnDf-wgzsMT{B!UqB`lO>EIAjZlqOpUPHWcgAKD;OPK;$S-MZnjvhsAgmFL=( z`gTq?H8nLd7NgEa^s|`fq_*`kb6oT;5?l11kkZ#D=fQ(iF#)>&Nf%w63Q>zQ#Z<6w zD*}C!3pL#KXYKe?07x4YCZbIkc6{hp`mWb5#gasm)4*An%ah#W!ts`cX>XLUqeFM? z_?o1lt*>{Jrg8a(lbAy$|W260fW+9eL+e zS93C;BeD%kB8UPMZxMUh!p@Eo*b0=7ix%iCbwN)z6%ha3uZnPtBz<0j3>IFNHdQtC zYE}1cYTz2BlC`<@ug!DFDkXUFDwVnUffa_1PF%u7aLV5hu<-&sig04NhOV5ggqnQD zOoX3z_Xs7~!oWCRcyRQneZ21t@t`@(a2JoEy?vaT;#;P_ zyE&R}k6Z7_$m*fwDc6gyIG%P4nuX)?G+1y|X0^U685?JEF3@E~4<51jL5;}yJ%${DPxLntqNlFm6<%`HyX8w)cf@`rY_WDn}mv#wY9SIxAx?oc)k_DIS#4cjdw! z=b_k-8)DbDZ-W#WdPWy!nlHwLMmPv*5I;r9)JlkQTrVAo8XlI1)VUf=sFOVI<_r;- z7?N1{!8B|@IXNW+DGGS>H&=H^a@Z*yy!7ZZM&D*aRIigQ-rDdmY=RED^+zkG1a4`RGE`W;9(Bc7^OCpoRqu<0l?xPHjVXAQ{(?8c% zfZv_&mgG2G{rjLy8eT+CZtk(xjd(ivF^E|1o@%xIYPmqI?D$|`_L&+|m!M;xFy0@> zHCPT;T(w|k;~n+8YPDPB;2_8~DP|(T(=sQZ=D@VBRh;9|R_W;C!RTf(Qm@uB1(ZN1M)8pvP2i#x83ITht8KD0O2KSLpj2A^owzgyHaz8&X8EW@kz~6*^2DA9;d%?l z#uisCH@B(dKdR2*Sg44F(rri{4D(M@?#*vsK3yd00V+mHkI46e#C)|fK-Prd|~Cg7@*h&PSY`(ImhdL=s>Xp(>kFa)877} zr%Idv_fl<;E&*Sgtz3?cM!=@1HrxG>0@xdSoeq|?=w;EJ0ZI8Yot2$Ej(KN0 zS1DYk!SB}*7}sev-}yhcaOUePgIlNfI|J|II4pb?gK?u=+pD3XqRPVA;^8lfji9`| z{RfL|#QnveKLk{X$uuq$?VzPcAq@-NfeFV8nj*I*g~eJo-kUQ=#mE%75_||# zw?5_?AN7~1gcn!Yw#UCxh7bM-hjYKb-W7Ww_IZ3MwpL+9JXrXFE{-x8<$|+&1zW>O zE|#fHKV?_7teA+XO1+ptg+3R~ zzpG&G;Io$=Ii4;lDJh^0-NLox(eMzMFB*TcO{RRDifMMZ;0iLv7!HebQ17jPBy*{< zh|2{*>68^?<@%XalpW5d*_YcLL*{~*Gn>Lr{lpoC&!ocYx~dQx4vk*SGmf1(uh)Uc z7+9v@ zh;wQUDkjxeO;+*EMEa#mMWgAfFwx;*hk6q^@~ErVIT}cC^WX!TyEdvky2qAzWlLnw z+d0&j$OdSLTzPhhoeLa?wbZ`9p5&A-T#rn8LvV&@J+=PXA}i}+`@s5e{fcJ}cW0<@ z<)5mM=;miv;?5byl(ASjGx7AML4qMDstvx7`?MMIK+fcLk){8k@jOShbfy-KGB3I8 zdTn!Of9=J9_4`Z93u=fS9n7EIseL4ka&|#-$_05tA=E%8Xpo=P+~&HyHDbeLS`qT$ zU$lsXB{Wj2axe#}u5S5pdCObE$PUA%YsaPCOtuwKc4lb@F*n$X`Js2(-* zmZP2yuwmGrk!!(9IwTf;yjAaL^33^EAI8{4xfpIntB5X?-BpKyp>AkMB^72;QWc4i zjF?Pvp(4dsh~|ZLg*~@2SE6r|>s6sh6igSp%AD1%FJEy=EcvDC^C5pSHO|nCbPU=t z-Otnj(l!27yLyDDeY#|KACL4eiQzcAkM6HLxEy0y#cG7M3eW~r| z??wLaCKLO-{1xwa&u;_OTK8WNU8AXrHrU|F$s71(DA;fHV=lLxa3rWt(M`!)&j>7t z8?MsdBkR9N{2fB>Uj&1{pk$0%6_}f7JoMb<8$Lb~i;39cX+P#(F zx*gZngTtd*$12(K8Tv$|p^3o`>*2m=Aw&Pj!~+!kg|M|Ijs_c(R5)q^YCNNlcoVT6 zZE0`k5LyiQC%fgfX21I}3!BdNaM>7FBTN!Nh_vFOf-x@4)py*#b!u&$#fWX6JebV~ z5BK(2jp(&0*6=8Ie-1-MA6A_u+T_;eU}xicr70dvQLaLH)(1d>xeekvQV~IshXYtB zIeuYp6^8YUID|S6=H;;^uSc&jGm*thZUJ7|?)qx>fQc$j6I6iB0?3ku*t}%-Pr2~T zDR=ft>u^LpS_Ox6kIvJYJ6VqP>FSRGubbyk?eDm$yz1!0Oe%`83iPoG##ATp5vgew zE)Lm6H*XYGh)c8o*ziBbf2yooMmO4+bhrQp2Ju zeD}|o9&m-EwArJauf&GWyM?Drw7YGi9!9sniTTQ1Y-))K`F&Bv{%jdHO5E(W%krMX zz{uV{xM+@qV{F{p+$n7=dfEgg(`E z-5Po=2YSm_0WE3m67geqHLQeSUIX~ zXc=n1yK)1-nIC2BusJ|f^JAmvqT*th)x;oU)tfu+kO_u(}U1s)PZ1=O(MNLij+8TAz5Ui}M?77G7 zVxC&BG3O9IJ~#|HMK!ySjwQRH6GNY-dK=g=dZZOhN_FZKfBtpUYjcY$>Z5lMI5Z$( zsrbr;iPIpmsE4CgzE_xN$}Q;RWNn@4Cd^VtuzH8mLzgki#V0iU`!|5+YdRXnQVhIE zaJ$~VL+gTCx(Zrb+a3ex0$`XR?)2mo#7rj8;mZeWh9Irp8A5tZs`DmUG2j7w1jhNW zpe{f62q+GJ@dhO@Rx4Qv=Xzd+>#wm)IC-d7Y{4bwE%ikKEnq>oJ>Cm|i|U}HC*h9# z>5I#z@8A#9RmY|{-0kdKdI>UI!%Y1FX$bTI$aly9B^2zaM8$aHsga$%H=)EwM4K`i zugJiO2~QZD8XUH9Pa=I27q78Wbty_9sm4YGXjY5~gA@A~vx|$Sz-UN$fz$`HsNL`C z>PkT5S6Im8ZrvEaZJMBzn=}?G`)LF(9Dz@(Qo*m>sl&;mtGT6xje;1__4mLkN>^9c zX&&eD*H;UyRaw*X>Vl$Uwpi08sH_`gX2FSda-XudY5e-k!q;Kc)oySU%h{ z{cz%z3Hk}NKDc7kVkqb1@0ox#tgwz$YI-#kuzG>2h>dNhW9rc5BKXf=B#FNdvS;VGzsuj^maA$jtkVLPkC5P6$Ua$lm-Bi}wL9a;=zExJ7 zY@V1jG&b(Kw9Yq(@A`;@ib9qbzUak#9cZZQ)lj&J7g5Ifv|RNx`Hm1p0umg^LWd#g zFK!We^#DU%pRP&zV&CrC=O@S|A0Cczzg~q+3V;V)OoDm>(7dl#&!+9!A5Ykm_4oIK zhOtmsRJmXdgq$-9yIp?)9#FT-+V;O!FY}vc>gq&-EiJ`XjPrhi+A%Ef^=Fp_CaUC6 zM^F+oFfz(9;Adu%J%f7L!rqd#a|{@e`2-$ab}$%mKR?FZ-?sw5KUxqRH}^aT4aF2i zLyIRGn^ZY~gBY|@4XENmK{RfkMUMA#1m#1A?LtcV`|^vT#My=@OPybOW$0_}ZwX(A z*vtV^CFr9XY<}%ueB^z>lODB3@Xv71hu%hQ7o>sf{Uz?aMtr)h%{i&fd5C`-v_Zr(j&e05$iI!#Yu$W;YObDJP*tO#5}dqPCerueNAd{k_@(a=5C(C6c)`At=z3HBC;1ElshmKEF77ffTTqE>5rI_eHbjA>0U$b;ov8ko;5P#RB;nSxv$~i&cLV+g1KE&phL#6QP zO$+tIrn@EMIS5j4qUj5D=Z`rD)%B;L742x`D%hN22yGOjI!PBLG1~6uu}cXId0BKHQmNiC znFu1Sd#$50Q)A%TW~y~`SvSiunE$FEuWGp`G!WA^>YUDx$tCYHT$C4v>*S_;dHdia zEh9a*i2Pyv%91z6y`lX_&!6o~r;OAxgy1<@h=|}45=svA6xNHKG33-<$q$cc1D~61 zWK@-dkTZbcPy0Kp>C5A&gzkPkp0Ur`w2BQZnxQ5NPs?@%JamF=nDC zeAQnpf{7DS3AxY1Jns{hh%HM!kIaMb)$O&iB}c!`3igr0b?SdeR1C`tQN4p$IIQ8N zDPsL5VJWdm<`7%oZ#Ag8bRz8BSd%sDu6l)9)KaJi z?Z)!C3sf$N+P>H=FBR^FGn+vFQMFOXjW~o~J3@47st6AcRSnIJ*>i;+SJV1)qreNl zT4gcTa!DR6V+OvRknyJ8q7Xu))I3`i4LK${k?|Q9h~#R9zf+m`*VF;U{wixIUEHSn zmpXeqONTi~!WJB2W61j@Q|FB@jN^?0WewxKW8zFwlDPiI64wZRT~~QCm=b9{fqE#p zE8EA%sf;Swwe9aD5Q(apdNd&jz_f+`n8=3^jsQ(v-rtr|%97rQiW-7o^jX;*BM>)( zu+6V)+EhPR{sa5LZ$xrog)pOLcdi@z@GX*|B7&vAxSkoU)|Ga(uAhel^0*LSB_Iy- zXj&^GGJ+(hTLLw6=egHX*%RXB`dhAXrr5V4ps_T6g@Ng15X+pzJkDOFrHFI&LW7rg zPFuUkVOAQ}s8>3JC0AF($n_}Hx}*2^`s;6|NW;^PuAy`U@!~t*#ea8n*K|#EqGQ67 zpp2**ONZd8xsn!@Lyv`{u~>w?8-C2+Qe<^VFd646dgDBhEuL99udX4I6Hw&s71R+3 zC}$|Y(OOVl1|j& zw#uBxW#Eu~hHCFmCB?Xm%TdMJ{x!RQYXl$hs8va86MnkuMC-BHa=oGF2aSSzgXFx*^m z@&wu~mm)s*XHP>=@wCO-E#Njkzpou#iad-_5=ZWUbTc z-WwVfPF+hA*n8&X(%5jlQ?POdI0q-^bKKy-cI|KtF#v3rw!43=08?4xpKi=?4eZ1Y z<%VmY*|+!d8kwFZ0u$JCkr-1hdESgEg%_Lz`Ry3Yq0Jm=iVSMrs90E+0L%Qla~wJ|U4k@RRz?6(gS-bq^?aP65RUD1ir=q)uJ^)k>kiNk)_sKzi%n z?fRMuo$~71_E8|>Y}Z%8pUoZ^JmF+4DS;HrSF6$`j2fm@vMN8Ry;*h=9Cg!Xl!#Yz z@bc;&8bSj&TEPtZ2_Zs<3aeUtu#q@#hKX^0q$LD}xqiHufBy&k7A*`M*?^IUNBTme z-Sg>u8U#0JipK{mKGP5I;ZT4(Q*xemoWuEoAw=i3u&BN{aNTlai?-Zt5vpwQocbGy zLq%J=n7Yr!C#KRY>}QlZU%dV7fKj3rDlP=k^90&NQ^{)6HaUBY1O_`ZNN zb00uuGcq!yDi_3%0)%#C(k2@l8iHA7^>`yJ+bsQW_a(e~+!7{3y^M*|($nEqcdk>H zvdBOEc`<5!?hP{)nRwgkG*1qE?0=foLV|zXFx)!^(S#UaqTlS69`x`ISPcTREzRnzV;iR|yb7}cjQCZvC_=b(h z9$&0uhE|6KI zl;9>TQ=KaklBC9yp#&rFA$EUft+Y&hX@U*^tIoC(`{egP4{(x(lgN>k)~CGeUf zgY>e5FU!}%BV4y}86=Vly`|IeITscDUy6Vj%;Q)2LVbU?EepD!9T(DPN9yY}{Eq8W zikA&B%!xq1dAYFYb;rPG*AcWoFUWSFqCMbq<wAubA(eQA!ZD)Qxb5$LeyVt7bdv3U2t0Iy(*8GT0)_t7gFMk2IrKkNfoU5t3#u2T zl)~g3+leXM@E-+;NfjK16Cz2ll3*4+Zf#v>=}N&np=XAjd9A#hWVxzHOh5p910Pmt zj?2yskusZvaGH$#hS{Wtcy2It=@)5X2^QR*YQ?~n5_^x{>AzS5110C}h=GU!di);0 zNV4~dZ&g3qX%!K)`-saO*=Gqttk+d#o1|syM+^LGr@MbZI2A z%s=*q&;ghY!n{rFJY!s}P=}qW;B!7z^zWWfM>7u+*SPs$v!bnFC5jIobs;=aV_~gpwGYNi_9n1AfU{|k8fgTm{VCL(=du4G!(+EotwHj?>44pN_ zb2~L#@8!+6V%$)C$$mWL`yfzs!Zq-~A7mT{(z@7&^Y{Js0v3dyzb#=(eg5#$s@H;U z-k6G7g+YxV!lc*O#MDs$Z;&oPSk>?7?CVusKJvQWaShk8C-wIPbNe-};sn|hd81Uk zli(3wA7YFx!+DM{w{TOgXcG7LdzfiE>v`g6_yaYA#glZ_K`U6tE#xe+A$2-SDRTL$ z(rCY7^F>(-yP%lT(#Ij~fC9EJ?0f02-1>&ofCj~IYwj#1UOs4>hG`k0tUg{xwKPV_*kukuU#b)slP~$ibwKUEYxCiC~E+TJti!=?3dGg zvsz2*r$G?}8!f&$WMpa;WNZESii*l=Vxrv*Hi>B096cIs_QcAKGpjd~UtQ<4bhp<6 zWS!d$77O%&@hb_Ml)=Ga3DOWrMBBaaC{?8n5)Xu5 zsufQ7PAaLAMN|04_Oq8;*NZFmi`Do2;S;Bv>yRCKQF{T*OBI8Dj z!&-Uk6DVYpq@r0vev`5fC8RlySBJtxqv+$ZXy6#m+ZXN{YQc@&yv^7qXinZ(@3pC* z!EC9U7g1JG;TI4nUiud&$A8}X0Sg;jU{<8~-O(>=*R;q!&JBCXDd}{JBea)%g`f$; zkJ#UInnd5dOmB0m?*I2s^gTyRg7A0;Rt2|S7qteQ7y`V+s(WBN&QxOH0~Q!rMpe9X zapn8?$|G@@J96nBmbkL>O`euG$^*(d0{^--9m-U9HJd7$>+JQfn2(=ueJLmZYi97D z>A~a3^#X2b*Bgf~T_wLh)^rqcORhYvb1)7JBn~?+HhYKc+pQRWatJqF6utX*a};yD zf(Qf`K;=Y6K|Z;A4vmadzNC&?>()ZkPk==mkT}2VUFV#%%nIw+zN-6b=1b}9NHHO8 zRG|sj;y@=0W3t2i5fXeEDeS7x3RoJ0#~YMSslXMKWmMIot>c=vFFudf4D-;y5`v`s zRgQ*Y#yuIPLjt!*vadWdga-q-BA!TRip%NEcyG8$SbRy26O_kC4hWwRgL|n~iRNdf z+pFigBQdw@1Wdib-tXm0w&3qkD-VE_Kh~3$uGNPKVbcCu!oepDGLk4&Y{xv+rrOgJ z8{_*|0-^%N`ju0bccrOecgW`Ps+Kh{JZlZj%|HJA!-4^`Xr@jjt~hD5la|V*MsYbm z%d|wsq10ps3!q;Iw+oDIs=z_Vm6ZSMBd|^alvvYA%kUz+EL-CJsL7H}jKV~qi%L#T zX5v_RwmAW!g!$VSfSQ@0z*yb-0f?|FH1TJg2Z>Lw0RWOZUvidlz=!l?1EE{DU-r3U z08ktjSeC{?;Xsuoh-;5yXIItPDfSJ&YQ<|ArNJg)cuUU0m_M5PeES^k@ZG})LkJ$O zBXbI4Y{Qt`5x-r>cM|3RWc?=B{qH2znmL*@7NOy5H7gvq__Xg1(0$#_Lme@DWVnN=g(CW z$knlmRW7Mw$C2m)SjUCOhdZ*E82p$%XrFG*o9j$+$gGnnpsHs0RwN=mFml ztRpbTUR}Kt9W_q?^rG@V-B8T!f%(XEZ9d=m z1tc--OsHpj?_LR$^MA?+Xtx;NqXWVorA`p)>r%Kv9u)7GjRZm64BbV~>sUaWn7S}r zyA@h=+XrmzCixE4iM599_Mc-$JFGL<&;#yHxtmY1^R5hfPrv98-nwl1&CKV7`$@_K zGvVS4iRSGT(ELzjs(Z?memxkZZ{Ga6P;ReL=b9XaEUa4^f@dUd;P-$8M@TS<6Ua%N z6zYd@j0)UZ&P84~Zsc(hv7GNT=+_-HueZ$`*7-cg=eUiok_G|;xL@~6O z$jp(oK_3hC-|a}1vZ&)X;dTCQZGb9cmhS+nr1eE$R~Y+z5g_Z=sMV{cIhq zoh?}q5&xm0O?^$(kNDgZg>d~sgm-2J6bdB^dz=EUacMXE*Go2mVVbnT)HE78X@qe7 zq`?(j)X-r4plZ|PzI(`f3o)a+Ig#{wsfOunwesceD>$7jUgQwMdGC<05dwAY1nWav zNytYt3nz*^NxDlGWP4@b03JhDb_ah5B^YR zK|bQvWSO%5JzGYGqXMhV4ds#@^Qbcun6{>C(H@$V@J2rhHRFk8fCe z+|qY0SbzV_2(Gebgib0a$i;vJ)k3YI`1$3+mJcRdyV&mgd(>MFXqNuC*%KeF?ZGaj zSp0LVwyD~V`d8<^qoZoJQBO#&WMO!`i!*{rwXnmO;`7PzoXx4dp~~J81g(X zc?ojM-9LXGI@1ZUO8GmuiB5K8yLROXp-#=ata(R6_Eo;;2R*WaoA&D=mb{6a{Sc%{ zg;jo%dzcrD&JCKH?w6a0bJk(y?^l5R?AsMLg-L7!bq(K*hjMOTEh#w-9JM*I)#U)$ z-|04!`7zX(ihrg$@{N#-2rA~W_RCqF7Rc%5Q=C^vSzs&i4F!;JCjaqRI_o&KMK>Ve zAW6qe)f?wncgZ|HH1Yvz zjkpTQ^JVCwG$#EJ@`9>XF%8lFufyQXX%)-5eJ!rb)8FOd*VHULC~#qFpjsRTy)bA; z6?o-K3CvYX08yy^K2o9RHH}FBdiR1}dX@#v``+JEWU#;P$~OpP9I5Qo3DJco^y>K;J=|!p(B5xR6_K$h2NK9h(G(yscZ70Xb@&Bd z!BdJI#|3j9v>8EY0*i-|h#wvoZ;)rW3+)@QL>plU;MPwNdhy>*W8<>lj@=WD#-TibH{=LQ3y0Ih0Z(jZF|P*y<<;VSytw z^Nz+EhhO^`!%vNHnptR9wc10*b`qKATSUh2#l%fX>eGgwod(Y9&txH>$z48q9!>$> zHHdKo3UWNJ2ub*0#1>0s`{2H7W(aCiIiH z!kDJ&?#t`_8<)&iqe+Nvwa7XkOrF1ObO65KR32OQ|V> z)n<}(DTL$I&U`HXdhq1&_a&KE<@3a`qyNle95u<<7KjS_UdKnvm;RpM zyA*T8JYhUPvJ>O%LF1%FJO#h#&6_vhzGbS4RLF=H-2L7+nRSWxXdkmpjFr0+v8X=$ zIdwU%+d@4Svu|AevwNMUXi^Gj2tXi~vA0cD1Tn)$-V8Dd3gc9y8%aMs(Mno^&9tw8 zgm0NH*tUGNbES02%jbT2j+$&%-PH7RbCb>`(fd)XfIxKR32udvN3z17k-KHQ z!$(9ZyBm6A0>MLC`@H;T3!sf|Jw64GIaC<0>?<{RWQRXWb>C0l-2U;rmC7NMWE|td z2s7G|3#FPs{RL;B3y+a4FOi!w6HFZ!+#7p3>KF!-f0sDf70a~;Z*f5woJX{f?RlsU zW?f$XtBNF)*B#0b!asdoXL@oUD1C4~>r^;6IgJbsGFLlcSnkUZFwW_i5F$xMsFmzzxp#)JgMh6D zJ%8s#E^4&1=1JZDJ)}|?iqNn{Qxuli@}meANK=TE@OP{bwIIQ7^O?`sSRKw0ixM@X zF&8Pog)#&lkjU4!=Q9<*r4_#^I-^Uz6<~G^n-); zyH)&P*pejge`0Q3c0Omg1}k^zpTvfvNco!VZIS^&wPuTK(A3|5sSy0xHJ6FfH1cwc zYO%7&7QMxPw>NXUU+L3o=qvLTj%B^cy(9f{BO4o%r)I$&iqh$4DnNVQ1I?|h3tx(r z{%UiB_ahVo)s&wu7fHWe0s`j9{NE00AB}^0JbIzK%1Tw#y_8|Yr_yh9sSGd!Ry&X0x@t&H`@u;Ua!oaM7pmtaT`--1m%gpXd z;7`ABvvK^Md`dwN(PWr zj?p&ol&o-x0>%5~WsH2WL!?S#82$QeaW9wjZ|n}KZ%#XVCR&*Q@}E3WnG@PO{^*{T zRY1#O(A0Zj#V1$z0od=n7oEJ_*!KI;w%fwnte4I@Q(4_qCHG5n{;@YkSfz)71w&w2 zrK+}FiH)LQX%~m7Xx{=#{3iITk7b&il-NRDRJf)lQl2taX>8Aqy$MtfDZA7tr(=co z{4Vq-`fn}_wt<^|Q&SiOQI_))sXCUdk8C+BU7}aBM>isE6SNu>l1Qn=Byn}O*@a}W zMGV0_Z{cbF%nWFYAbBX3GaVRKQA0cs@>C$Hfq9iS(uy|2XYo7SG#T*1iMUjSVMYSa zc9B4fRb0ayh%=inby25aW}5{3^8Wce#Ey%uSakl$Te0;&Ghk977+hGz#Vy_DW6}4} zUR{JCvwS1_za+l66TSix&j$+7)h8vr5`DTy*VNQkWg`Opf#+hAeLz~7z46J%6|vQW zb|*-H4cJ9bH+S(t%M_G0tf*`jr%*ZTe7<}0ZqYSXAnKSn7~+utAhn>6ohytQE6dc# zfgf-X&hN4{C$-EsalbB1H(2AHNEeC|O6B*Swe7V29hR5tO#|gZo}|Re{yx-$8mIeO z8LhdKNPj(tZk5&l*g4;C>1}z~FoH zL^EkBL7kU(T3b6U*SRJL$G^JTz9tPL_}$YPD{Lnse0{3r&dBMrIAmlbc10M-J!@K8 z`bS3Ct_Pf2KbV(*qVJ5qCg6lTmE9=SX@MOaZ#REBpM*MZM~{FUYrA{YcWudad28%& zAu=k;sOX|!azqRfVtz`o+BS53gj<<(pr`$$b+PMSDGf->E*Tf~9{}fI@NW&<$V=$s zQ|;BVO8keLq?PSu3oe6O;PJ3rSHb(SiNbId3F7dRnz@2N$5LvuGJImsZnE+X63Lsl ztQEa!+HtEmU0sbwpA42WMig>x7XM&hnYlQ_L*N~%Hl?#erQa2N&2BzgzFHQ!@){?h z=x?xrTj{P-#9#(mMz31W6JKvg@sHJ291sJk7^za#(a;ZYek-f_E8Z++$EMDhBlkiz z_tR#whKGdDJen>bp-*bl# z+o2Z`6cx|{2t%k+X-(8o(pFO`L?-iALCYH1FCJl0g2L7Ftt6m6et2AWb0IIH! z*+ks;nrnI+pYp275n4p zqP>Zq^5g7bCv6K~eACL-MBCi%I<-sy(uj|hg$Q#8L07q4f8$+6SDt$Ia$IjWlVg-0z>FDyD+ypU;nbH z)hR>6Auz?jBPk-qz{u1MW_a?e1g=70;K#?jigw{H_O)pjt%X@Ym!9v=m)d;)`fBO1 zP?+hr&xh>FPMKO{`h0=w_rFEGU;hvjJKy}S#l|Jb8G!qQF?Si8CtLEC+R3>bSz6zA z9493m=D~9*h|S&_p5wadnE;YZFb;2>jtu~z z{^%qrmjp!GA{uSNk_N-|B(9S32*TRD|7Cp_I7AgUr1ld#ZkQ;nFXzk{Lk*`G2LcI> zGJ^1Akh{2Cmi~G1bLd%SuQ--Xn?Xs9JC)!hWoaPtI`epjIqwymBrO#WK_0Ba3y5aq z$*%fHmcHrzw9WL4tDD0Ub&wO*Fw$_1JO&m%C4^?V)C7@&1|xQ_Y{ryoQ%r>@bTTke z^}{yOp}N*PxTK-jG>F;#B1v%av-|LmwRsk7yFz(WN3CfX_#P92U>SSXa)i?|xLFjX z$YU$p1>2~N2X3B71qGYsQ$JgIf1Q@~yjK)3kbU!_@{;EIXziGwQo~qAN%>#JfPG8b7 zDM2X$)2{lJ@Tcz)tV+_|GJ^7_JsTJVju6Ixjl$Q4Ut(bu+~*rrncxggHV4t7rg0@v zh+bxze=^T@qbc)l$)KkbO+Kxb3?lici#>P2=KZhKXJ3Zv7bH%B)f#@zW*7%Ug6o5! zwO91qF7>~qYJu<8was1u^N;To!sv8`$kS~?*79g}4`3R~?#fgpq`MyQb37S^GvZcd z!&k+{KceWE`~Th{;NEzB${exz16Mc3?sP5+wVR5nN=@-uzgvjnP0W2@7@Sq2aA1I6 z4eiB}Wb;}a!nGpBo?D)$G1q2ma|^02lc@NDonE(K2B$Z#U(k5X&|xW&ch^Z%o4@QuAEYtW6HXW#*#oIGcEaK0$MgFX=$NA;6NWGit>2-x z6J@_IndYa#K>mVH9iSjnbW6dM<58~M{;pv16D@g!+jnszFMn&^2IW9JfauS(4HEN#h{g70W7pR1ZOXdAs8OhhfGpRm za0^Mry%aN&migOat7SI&JYv#eOW>kh-A=+-wtN7naW*di52(a2H&_8m| zHh0wS@0VdFitykAqm-Z#2dkXKe%E^%8(gMn(a$ z?<{#b@7zAjT;%gwPJG%&{TAw_-D+yPBfCZ>A=mzE0!?<9E=!qPSXhxIX)oxXoF$Z> z|L6HR20XkwM$g1!sZZ|kaZd@IDkK=f==HX}^g*YBAy188**`8W&OrN;6hX9JC+p$C zTl2lnGPno_E-PNdHML{>F@}U z!)Gt2sxlMwGglDnw!fJgiZ}N3_99ekMasrd*zf;k*VKGp&6cpXh8rJ-Af9OYFudk0 z2!!C(Ee%6^^DHnX1t!1{EAHjT(>hk3cOQG^fhQHnC#li=y3J0)@){!9zN=o|>?9cO zi-mZ}ZS%m|uzg2iRC-su-vY+e{;QTSE-zQn4$pR4I4(T0Tw0?Nk#6*&tE=CF&U~2< zW956z7&hGW%uKWE5k-_oR@@q{jO>6MUA)}X{g{`7uSuyzWyE6%)*7cGU`=#g&q4wM z0wBc_yQRGavN3=&TBreJI)qYEQgjLOrZE$bhtVNWHMwFn`R6Jg?6@jQ)J3Ltt}!4+ zHxYOe8COGqMAeDq;oHDjqeiHUAUPHmmfP{FX!eo)-x2Np0Y*7~)TN`MgEG@6E2gT> zh8U7`LRYd75ao#!5x^fURWb#v)7G1TJtB^@d+_>ZzVzn)99RUrx+v74ea4&U?!h4; zgQh%ww@NI(zkB?-g4dgQ&HO;di6Pb5uN{18d*OX74BF7FXsn?<3On8mdZGv;6r5y$ zOdC2l#K*8>ry{?_m?_Fmi4UMiK-yKOMTbMdd4k{p~cZsVId0f6da}j%BPM0;Jpf>)v9%lC&^`#6x;R_rVC4JV1 zU3YjEKeS&G#F8bqf02>WguH{p<^*s=M_rOM%lqxOz9_#RrEkfR3&%qMbA<-1%hHAy zp<3~4g92k&0>huH1hM=~Eu6^%l( zB1w33WAU}}#1|1oTHH`tjh!AE7(j2s#@I74%cE`*(97QU_>WhGLL^tuGIopr0(rp@ zH?(65kbi1aT(zr5Dw_CK{1trRto}3NgA|D$XUn;HC>K;B zB9s)%M29D$B|Yo?Q%-|PmFCt!TIIv!Eg*@k9*(yy02JkuDRyi zxxethUk%#BL}+%oIr%#9B$2668S6N=fryZinP+BA8Si|Sbg83S_>nx!;}9LNXAX{! zliAR@{jXSyifA%Qp^-z9GMIbRs7z+H<7BNc5fU#$pM(uM_nGNhmfG($GMRgjS{|>= z-$mZZ&D?9b`a0xYwxC6xMKmA8s4>Ja6?`d;!(w>D+0`?I)SF03r07)J*4N$QKCA23 zwd5Dum~3Tko~^}ve$gmZF1C!nw(Goi-*{!|_K0q{g8c{60(ri@$1N#a%suQ^nJQPd z^Vb7Mr~SVM3bGd1b1)!iqn)s^o9q!c#$-!Th(v;u6lt+$&Yg2c3{hm1Oi|+{wkH=c zwEXD%C30N8R3(SnV94YPU+Gl6SQuP46cw)I&V3*@RRL-qNpH6Gb%Gjv+qU&Fgn`vA zZ`G`b0tr;zw6 zh9Pq2YlxWoSNAwKsfcF>7BFpXSu)~&>M>R4=ePe93QcdR!X?BNjuX9F_eKjtacU3` z^jwW15@qR5_?1$BU3B%k z$h$TAtzHf95ciqlPn;ym1YE#(y_;gu){CWQFSznQyyC!`u%6OwzlY}+dlm%Q4)jd5 zfB%(XW}ND=?^)*R35UyLNxbOY4(KO%`DdAQ+5VNkRy)Ow=-}XdtG}69@ad^@T>K#5 zDUGY}ov8l7YW^&>j8bDqf=3J@1io~KPwQLCSJ&}1_9RkQQCCIR+J}#H4*{G=@0uq)NYc4?e)1uE2JUDm#X1Jv{L z&oLrPWz!D7ezo8*iIxgdVIKTG7uj?DZpoTHfQmli1P(0A?JcM5;~`Dt(f2qEI);+za}>h1l4C4Uk~p6^$r`bkk}kDh;> zc{wm@T3Qq<+ah>TV>0MjoDGohz_ zH8o!h5K11uH0Hi|ONWPt!zIHqT3kztDQQMWXAR?n{{i)XHAmh*=6#^uW@{*ur+DH{ zw*^k4fvI^zgI}b1(ZNg)zo@8aNJ%xBqnJzDWw_Nbz2ea)(|<+Q;+INLL4nCgw&-v= zaQYo^&Xg&V=T4-}doj6o!cH1-Y=t;RZr_EgB>im<1Lrz`7Q@2AW?3jE9U8DCLA8te zGEGHWY&UTJoL^Xeg5}XW@OaXp&Xu_icQ&m(xBYpD3bP`i8}O~_>u2W{3pC2}H^d!V z6qsO^LD@!a?oH{mZIUV^5|Xp@tEbIP!uJeAxAJT1zS($l;MYZ7+5$Of0%>0ha>y19Z zU$e-?<;4e9jWHYPb(Ar&N7W!`bOI#Vo^^DZA{uaOoS^^ey9B=sCjoe7nRT3+wpq^-2qwk*U*P~B<6b|s($dnVJDFJi zWv}RQ$upwYPX7fy00h$U;pPzMs|Z~n`v`vH!EyBuf5%K9zzPV70k&ds3XOQ5k5ZOQY$+)3NN3Q>k;K1q~Rg_orAruv`|8bX!5!KSK$ z@rbEJR9;$`DG?xUVoa=4%aiBvuMcA$PQe>LY#^V>^?f-`Djs$~PCqA?Z(3z_2c#&6 zp1;b-%F#V1mEl^z{}$#)fk6G3p;Ooxy?u^K^kSR+(ZBbyiI#SzQ4LE^(9s0kxCp1$7H;+RAbZ;ECD^Wm(tw%jytetfC+u~SFs)3mkZFE3_1@6qmf6gV#>-v(jhiCN zwrbwyS!%jRYx6MZzNz~jKGi^>Au1A`S{o_vYlsamK~k-4X0?e`>8(6;!N@~kxW4D@ zKcx7X2KP|T$9h83l2`{t8jS-L30_x4&V;cyv{#VbNa!t}SO9PSVO>N&A%bWam2*h! zE=sFfx{U&S;Jye;)tm8%0&GfDAH2e1X$4L1g{*|gs>PEX!jV4c&_F`KhDK23FosA5 zsU@##D5UV#`d7e}z?g4*B$95=WbnAP!dUebem<}2Fm_R9lUhSG)QUrAg3xC&aRR^k zomoazzAO1syFQ34*bynylXcUmkp^xI>-F$W6tBpt*4Wms|NtB~c#MZD! ztXn8JJfuG4D~KwBBlIJkv;EcV^=DuJ`bP+@rrekv5hGr{9+d=5J{9rQKAt3Qsl8dB zU4kl`cC+wX#!g;))6yQkIyUJtoU2x5U%(X3SFCb>HC_Ep#O2kD!^wvTiv``T7xizT zuLji3UPZsB#c$uXeQi2e^z&!$z&!q&mhcd{Qp>GImsM7r^?7PqBx(~qUP>)OY!piS z9!fbZFNCsD*0X`>dR{3|oZ5eqF!s=rNAI)PB$=PRC}&KP%=I48lCXdA!Q>dmzD<5OLi4 zqaf~`Ph2aK7q`9MyyD4j(V#pMX@+J=B>cL2#VJjHX|;1q;dXcT6>?~Z-$sZs%o`a9 z=PR^84lUJ0T`!ft@6B`#TYB-lajfw(BjPE{y)lN$MoaRp(t=ljk}8 z+`$LhBs~!aX^a|~w!YX8{GPS@>Q_9oML5}Qcl2ed!#r9_n91X_=kSqAX}g)=IQRII z6CU5_>YcuK%Kfo&M!?%G%ExkJEUp>N8P@$sc(y8j!Qt-B*H z=fL1mYI(G)78zOr;oeV8J>%-BmX-ZE-h;GBuZB2PSS{D@N9jMa&;gixA#Q)lVYK%e zRz?*Ki}^BUKra1kSJH3U)uv(LMWZ@4))m0J_A|W%=<(ztYrne@0p5{Ba*u8fl=u@V;nD6x7i(VE zPDOW;`9URbe6L*D7K<_Dc=-~I*>;bL;$*;68U(^op8G2q6XRIkvaR@IRO{)Iw|=WjVpk^=7c@+HDg{<|0tas6r%mLKB$cET z6rZ((OzrJ+z8|t>_mGBKpV7fks_|C7*3n6O|KS5-Z3S)mpl>h7WpO?oDy;d}NH(gs z#aSqY^-g(df&M53NhX9n>&{A=>GQ`&M>W5Ax~xa&@qckz#900sSZr}`^4-UuwoNVH zwK4QQe9dmyD$Q5=E+~F>x?jMLubzW=SI70S&K1s(?Vag zk?FwD5seDW;@}9R#m?-iEiPNR+Q&lTUxHg8LA6$rZn!p4GKh*{6**^+%hen=-ll*v z*E|YD0znX*k(E^_kRb~rg0j*XpE3n4F(qjFBZI!uCy#P0TGd1vmn0y8K15k~^pL5j z4M(&dRQ23ilp({$3qmD}JA86edA`dKUgcscP;nANI`Mh7VRD{oXiqG_f|=x$E1Ljf zG2DEjBgqV^0u`^nYB6W4mh_u*(fN8(b>>0=K7 z9YY{PDO1ULX$aAM|DBzb5&yCehD=|pa>fgv350tHR1%mgp2jlq4$JQ{G%(@X>%AjH z6(oSH6NnYTOgs~eq%J0F7BE3&?VR)*%iT7SX>VBMn}K{!4L!}Bk&xzPo<6^FH6R#u z@`k+f%9ew+9NK$q*Fa{X`);Xt|KLR~anWu{eD@P!Jb376BKTC`rPcCk{^P;X(b?Rg zg^rF+qyIBB4NVcP@)r-*FdN&!Y@Mc_;Cl>Ry=K|6$<>23x+RZuG<G7JY5CANG{cP?6@z1VH z)^W#^&~fWII_%rU-1doU$8VHp;|U>)^}NbT-Qq|Ekd$q#5PAp72qMW!m3Wr<^M_Tb z0SFK)fJtXJTw5zqM8!oO?*d#!G@$>7{AOVRq$a!637i`{UwaGlrx#t%(W#UWLE@q(8W=SHBhG}N>!z<~(>f|j8jHuY zcv4=Y98Tq7mmU6sUa&NilATOx5?aZ0C*7->%oJ|^!*Gg>suNBjLvvDfInDDsXpz%1 zAvS<{mIQONRIu|^B-c&nCY9?mIiGK#`CTP?7;t*X|2SBC2<D%P%QRAWABF(jHdveGF6-g>d~GE{mB?cO4cx0de8gIhTW+*J}0O4M?Rj(c5X15 zNE;WJryitpF5u2s#k_{TFEq?ClhuGya@Q^o?x$Dw?-@Eh*dyq*pug)x>saLJeeYYJ zRu)DrwivVKXKILhFE%TgNDZ3UVuPvPItpAgH+Z>qO9oQGAs{rl!~rw{v}RpX{F9Ok z6?!cpnpdQ`XzFPZ+Tr~S>#*3Q%ALG2?7p-jd)zkMH3Zk+c=U>^0Ix7?Dx|`uj3_UA zyN%+JP*q;E=`vw~-%*VbJ@B5{)HDM~a~0{BczR}HJfBL5-mei$bxXahkJiWFh73|N z$|ES~h_TQyX*ncY*MUuYH@?4u%D=oW^B~Irw9tq(wG2ETY2e~7en_ViIPu4 zZJXMBR~R-^`$Fbb>T5g3(Tj&vtb?>))HZkoDT{T)EI<1Oe`!e5ZT5}#YU?w;!tJ%o zowQ=8VT(@8bhaEF;c0IM`?8gtfFivKrk8SDr8c^oBB{3CjbX%bxK^Co?1JH@lP`*F zEX{f1yDB71p)|yNg8FloN_W_FukXM<6nWi|JR+AOPxsns)<*mJ*sAG(2|->R$fL+ZfGNtnfvDRNC6#t(6(F@N0o?DjX$ zY5P~6nWy#|7wllIKwFJCb*{96YF_Kax)Qb1Bj2!FEC6UKyu(uz@vqd!T%@DX+{XD(HgtKwr65~0a ziQ3dizxHMry+k8Zr=;ia6?^zJJsL*R&B3w#kB+txaXxt;##obG9AsgPhWFbyH$d3b zH!hx~Rv&|oR-#Oz&ZYBciQEr!WyLHL9~#Z~={tSy{*UIi(Gm4jonLQR@r&Mz=DYgH z@f;fy(syBkvef{N!e}xYGg<%5d3({YoQT{c$k`yid{=wD_;Qc zgi-3zTniUF)M5afJGt*dANxDUeh=n8-)27>Y4QJ)+Dkw*(i7=Rc3ed(NhCs{#iT6G zEhId&8J*5Zk=o>WNFOHF|L-3uLK0B&e>#4Ccs}{%e-pqN!1B&;?eN;KQ!$+*QGP)r zu(qu82b2l*Fs~Un7@w{drrwb0bu{+>UIHTfSJ0WYu~;fe3MC-V2O6+Xr|Y9Pb7D_H zo%J5)*Z%QhH^)yQndfGD@Fdz{aIZqT zOBsDy&inLuhsGTK<)&S=t+sn$z+q#O?rW;TKj3|_cqZDf6%|tgCgS??1HYK1wRJ{D zkz&8QyZh}^mpMSG=mr&-YI0NM#RHn!>*(T>lA%JpWefY4DolC)x zsz6PgMQQVQALtVSFP`eu@4Y^bPQp^0(!OWnC1IA88*v*%@&a&qRwD&B0}YQGyUPTBJ$R8dixC{G27qxcw(KBxi9v#Y;@ z5f8o{g8Kc<`0?W_f8fP11h~sTGI}mwhf~9OI7BRp5DikN4?CZ(0cIR(X5#1#Q2M{U zS&yGvUbgb#lm&hbAE!ZSSTua$BUp^>*N^U=m(!Iotl53e0c!Wym1EtgQnn>#K=^Rv za-RZdFb#E=ETjxVZfJoG%m&5MGvbxowZLcF+-L%hPi}r0ptm-y{UI9|5&Sy8d3Alf zA7Y2ceDtrq+y7zz`Oo5qycfp@!?sURJx!l$iJRje*SawpUH9o5sum&VJ_?9YAf;Kl zZ=3Nr?Im!=e$@wo0EXTa+}n2i&ty1!g%plpuEC-QK^D7(dQ9fXLqS)4{KwtwR{g9-#z=9)YX+ zaX>(n-C({u5|T-n2eq>Fc>k~2Gg)@#DR6%Vw2=Rjs+%`=1pue$T|&{i6uVLL5J|Ox z32?e$!7oz#^Z9bGQi~bFxGwc#S^}Z)9|D>Yd44Wr5cw4sA&@E<`X4h_gP77m+dJRk z=-ti1uhE;ea}HfYLlXx#$#_6oUsGwF;b=MW$+7jjenJDM&8(kC@{9X~5AYZR)Yhw; z`;KSt`awD%eskRF71*%s1DkB5da!d34R{Qv0+~Wts4$Mi zRydx|zy!HNBEToOZR0nhPwua|-2iUWi);N8VDy{1|8C(+VAiz!>IPp{sc8K(u&QZy zzvQ+W%{5`ffn57m`JcT4Z{O&DEXkd@c5aGmX&oB%fBMYl_VlC7Cc65v z>9U1=7CSpTX*?1TdK&qKX+$MTrU6CcN)CNF zu;>*WXqwnG_x|iQs67q(*={hD;jp%Hnz^!}+8H=^`#t>@dlAcxn^&uz)?H*f9jL6v z_BKh==X+800$f4{(rgQB4ZAdb!Go_F*ypd?Qceg z8!rjq!-0S#QvJJhP_!q$PCo*J+OxGqpGz>|P}lf*8=dU*9dL5{d>drK-xdzhX}}W+3wB$Pt79Bc4VVhd4>9QQ$rF;s~KQBetj3kp$AIHCbzqz&u@Sjyl=k_0Datt24rvmGe22H zqyXL=eSiKSiQW8Ge3=CCZ&XR95>Vq}&dyjkXvo4l9v3<`Pq*m(&-wkk+nNMb*jV$0 z(*Xo>wAB9g*WAiluVU-jz@Wb0EgR5-MY#Kg$=+9@-X=Va^mxM z*J8jPBk|(JbmL$1Io+m4& zurwHpGc!MQVnEk@6}bw+hw{@h+bSoOl*`fiCOOd$2U5`S)kL}5!eG6m{uK0;#s`@2 zaM>QEWHly9TxKno!Wudv8FaG(Rpv}{V*y33oIYMt!U-WN96kv`TGZdWJZ1g3=lbvU z0kM?iMJXJj$nQEQ*nIK4H|?I0o4LByI|%5u)NHVtfeape%8gdG%n4!gLi}BW%OnN5 z8LPeL1dZo6SPLH(gqxnBW?Z?HaUjkaSve+t4sny)KvZ|Lrbrk?)3tZVfam>VFD^D# zm_RwT6pd(aAeBnn3HC5$q()R!O!BZL5jmT&F4deqzEqS|a&}(#WB|E5D?SZ=G#4Of z0lpcgLvY=tE4lu20w}rJg3_i$P`aUa_+UEw>E89Sv9)zXcG2i^x+=q;(+ufQgHZOB zS=x1*5d%_VW0xq!?NM+X7yx3Er`9|mF8`~ipErD1`jRbF*V&8m?hk-UH1rNjL*h-O z2o)YU^Krs@G?>nUY2dnw1-M7{kDs7($EISUENWPz&Gk*!OcjFdn3X?Ksydh_cnq2D z4FMRPRss_fHFN;+bxs=GDw&_dus0w-*00KGU@M-R56!2 z$TOLhqqX>C9jvoM&!c$CI9Bh;15>Hf2aP{Ccd1dDGHs0*Ziu{zUGE_x%%ik~ODT~T z&dula3VWAS)OrRB%LG@2C943`Kj{*Ga-o*Peu)u~oEysa4E0aWv$VY;#UK~nh<+2# zl&ZkH|C)5cbal-WKqMCI`d)#*f1{evcYBKsQGbW|rFqV3Z%0$IF->5!8cM+ad38we zXCy84S?uAhhKs&kiPHgUxkqk6f#8zQ=j?YzGmyy?y$Y*h4FRs-YE-Iab3H1wH$u(T zEm42beqc+2?(G_TkURtThc0JQzO*D^kCTAI?;xFt|Dhay+;M-e?|Fy~YPvCb&2qxz z3>LyX`dnwgD;usTXtTyG4i8;W#zO0}Om|fLW8FqZ7N3w&>9mdi^7eI!n{14rm;S5{ zw~*T7YjRKP5w@)iAz$c*L-p$E#-|<8XNdpJ@+)Hds9tEDr*QwlJfN7*&C1~796m;O z9>w`HTP@&~Wtz)~nTL5)f4Sy?=fY54j@7pehP;T~`!DjNXEKjTHKogN+*-4eLA%C+ zMC|%Lk(y;3*vRs%mdX9agi(QOM zmDTEKF7ov@J#zF(v`O?5v(#G13qMb!B6-a=5W_r9OL)@>M$P2pwP^wMlqRwBhi_*O z@0y(E8ZB8;D9jJHFTE^|=UvmwMHVh??U$~^6mrS2A04+;DwXx=e`safnWXSUqvN0u zB!)>x@MGIsH*&^sp*W~t_F3u za2!=heGFu=srDL;Af%SkK0Utj6ezmHdU?v<@^nn-KtGPBfA$Zf(Np0FI18B^kIjhb zqdD|}g<#r@p;z!^nQAiWh-h~&a%X*j_&pqo5~gw48LMIQz{#wz6@g`w-#I<#91u42 z4Ru#Y{Vx|FqRC{;`s3#1E>HTUr+$0SpB+YOa%~9W-I5*lrMJo2EP5E5{5H3~=ObHH z>l1$NSd25JnVi%;FKh%Sbt$Bbh?I2g;`TfyE{=ojMX`Reu{n%=Ge$5-Qd+-yfz}mdleuai+dK?V z8cNmV%t*#!w|6x3f4Xlx&m&E->jiXvM2Ta;TdSWkj5LwGZ+VL&B^LFD(~Q2I0+WZ% z86qiHgL(q9m`e|g%%TCU-X^PUpB7bQ)vX22Lwbrx^1ijMjrfNys+3^HVt*)PGBa}O z5SWu!kxn`^0Vyr8OtB6a|2YXR=C7g-6+{2^ccMWP0_9;{q< zzU{0oeukeowWV;;39_e9H8=Gd%<{Umf8OZRs^d+x*fWY~Yt!$Y9YV32yl4KWoA{>) zO&+P*Eb^RnHlSjneu%E200^0N(?RxoHAy%Fd1@j#LqrVUU@j774%gsC0oD#=w*iOZ zyBqB2WyAt#?aBqe9m2B|p3b3+M@i0NBP%auQg zuw15J{-dc$UJx`^%Tv7>5-$*E-#6A1j~DlK3+zX8jkKGeTKWU9?!YjYi&MR6lsk{-jA;(jg~2dumYoUdd`JWcM}y#6S{gim3BO8 z7wjM6qIqd2_!7cGI1BD`V@&4oCnjhum3Agvci(|Vq}hT%llQenSNqssEB2jvOka5NW%ivsDnMp$r&Rcv0_G)l`yIE8SlABxf#vRnH#C3{c0b&qWpK#4v8>_5;$XSj{GGJ);pRt*37sZ|?>aP`xVu_Wfj)@J}+;%)UsqeQyMkRewoNr8@t zoE#JpOfzAa(Drg8QxYOZ%18p}gyTD}u_V1ekVxKtXxZ7;xfV3f9EXv(_~-fo4G_n> z;!Lti4gC@>97q>uaA~yWtKXd1G|X#}Sb$31sYv*mc;ZkGlqM40e zi`~nept0taWrnk6^v2{)*fgLs3I*8|sVF^hnl7y+s=7?$d#(bOGBJVz&U7hUfwZh&}>0#Rz zO>cMUNlcqft4n_x5^f?bT@!`H{6_}O6h$g|Si(h4(vjz3!1?+4VDbHDlJ{vu$ZnD2c@F#iES#RM~K0 z4`|DTdKH-@hlRb&P^f?{ACMVII%DYl-MPU{+l9&a;cI8r^74_uX>0}^At#Y8IbU;?r&0@zHnYKc!e3i7eIu_OExYK%L=(J*cGZZ&Mq%! zF{Q|`v&D<(qlTj9d^jJb*jZ;|&U(c}Xq8evaPyiOl`s((3X$a_7VGyfPBQ4cA_&@= zoS(wfwEh3NF2d($1=vuxWQz;^3TZE)I)T+yDF5YD9UYgc8P(+Y!>|4=80;lia@XB$`{77Jx_Y%h_=q4zbvas32w??7Evk z)*lV8@Zw~JZRa*n)IGXC<1Lar#S@=4?kyY}It6MyBUYlbxd@6p1>&I?ILx?|rj%Hb zv6@#<9(y;a`AtHYT&oq#K~S6*=TiEiFgxnfH0qr?knM>6?JAWx634T$P34h{)uN_=$NnQQe^3O>U}mzYwKzEW#q9+wi3okS6@{}?D`suBZZcpO_$6O!nko* zwRmHKu#}W#xf4Z?h4><^OAC|d5Ze)?E(_ik{={0oL1W-@uzzT>je?;mq`}vflP(-f zQ&>N=iQ#EbHVQ7>Utf0)IgL<)YK%f*48R}ycQ$C^Uoq;fWaC45o{^n>w)w0inJ zhLUYL%vf(gK1bCtJ@4o2wjG;HLmyQ5w1sL5^~W|Q&ZpZ`TijOTG;X(8wyi`dYR(+ z!@F%Uc_Ri_Qd&({EcWrODV0Z=n{_RP;ZD!_BEAu*cDt-n;;auWqUdjl5H;Q=eJND# z;_tid&WVRw+6Y|>l+xtH^GNAXvE&S+j%dfgAit1bT!?sm6j1Cre z1}ZFr#lb(xl?tQpZn=lP58kwI=l3hX`#}+|7k-N3?$__7%`a4uj35@Z%?w@uD9~M|39T?~vWASwtC7shT&198 z{Gh%*-3S+{a62;G2kawo#`KQ}>=GZ{u2TjI3X(uTGoL;FPzAnKFZg8wDe_wBnY(IV{lr{^t(dT zr6`iuKMHr^9<9<0Iu1g2WL6_WW5!aF46XxgnEXx0Jz2%{p1CL9*W`~`{AZADDm*nj zSB;r_A0fZ8$$G4Cw4V#|UelbdE7pbXSkX)cJQGE;<_?97$Y z>sr{fA!DD7ZTlL1ejmIZ?)EVAhLR~d|Fb}PPQUiYUCXM2vb`z>1N-!`Uv#A@Kvc6* zQhlnw3Qu&W&#^+qaIc(M-(#${9~FyV9P?%@Um~YfU!6V}Q!SqnT4|cp$O8{vU7H9) z?svlbv@vJVwX;`!u&V3#P>kz=uboGD|Mi~=-!?>@28>ul4h_)3I1)}GF`gjv4DNKiu-n!zO678SPk#{crm53Vt^WFV?Lz0tMpirCgre=!} zdQcQhrfq;?aCzrYY1W+Ds`zf>&pF+j*YmUjZkgqIngy!dGNpb7)_CSN;glDT*VvyQ zN?~!J*bg(XMKMeN`qHm_jHqVG5T=5?qa}pa?(51|T{`#a$Zk#lcW^c2=!V%D$YCpv zz&s@mC&3H%B=EJ6Du6y{R#W!aGgjv~3nX2b~1|+c^U7{4IpnGx0`dhcqUd0B#ZUcS&YSv%I~8{L~pLls3=iUA$QME=3M{ zuF9zT2_8lJT6Z9fs(OJ0nN_P)&X8f}?b(PA0-&H}IIrqqwGnn;?=xfgY_@6z3pg-r z+}dr`np*#gE*{Lc5uhgtsUHm8{L+ewWS^Q5;Rg>YBwf`&M4{E|5gQBE>7EVFkYczG z^Dvo)lp=k#S)P@H!_3u{2;hrxGvYqqE(jzkg7_kLWO>c72~hK`_dLhgkVg1xO5?Z3&3upst}*f;&Rm`J zekx%b7CrY#(-RzVal1T-N$B>>jD%Uq9O(>D`S9!53~P^&?efzzRgAApXE~64Cck8k z>v3^)4mj?&GF!c)Z!)GR+%S#dhEejppka&xZKU&6`f^8Pbs&T65ImSasF$Z6Hy&l_ zu--^*M?X8HP#!5XuW7w@Zj#?c&S4RH8gXVOodzZbW}Zf&yralmv?Qk@{yEBJd z6Y_+m!IlQETF!P|ggyAy@m7PMCgW@z5Pms&VuNjOl{FaXAo-?9j^w>FE;Ng(qT3J81m$r^OhRPg91gq8Yedg_1iF>ym?>$b8QRiWc9#uXnXW;yUsWIt|AV1KDPze&;O2%Rz@EL zmsA3|bIDnrw5tBCpPciLmE=v~iH$}ZwF#e}O^zxE!Qjblo-tNAioe;FkPVRd6}d>j zak~C|D+WAm=wf<^eU^KZt(Hyf520~sqLL@cRVsW5bh zi9~Y@y*HK4=JK9bynHqo-XueN`@XAWX;1z`D8g|jSanaFA#U9DJm!2Icpu3j`eQrm z`d9p->dQ#%I5f6FF=P4S(l=o)D!22)Kmi5we+#n#RrQ#W(kr**cfq1wkD{RbQK(?~ z&%FPdlN4Ruo^-uYKdg}P&-VEr%g+%I$fgFp1xw`1^&)nV3B~E|lB9Qs#U-HNk7y@l z&s}5m(Sj4B+P{lW^Ad)kTg--qZn% zNe7RLUK1Ph5+kD03?OBR1Tjv#Tu|N97X|q3JO@;~#ex&PkPEo{ z4q9fLLCsI?fl8w}5Ky?0d@@R-q`8CCSJsOSd!JUx4Ub7Xua&=j6Ondyg_kkcKRx|9 z%vbh_$@e2}=XI+5ejQXrX@S_Mv0auL1C(53RDHxE39=Kfu^_0@LQb6Imv48(i=Y`Bb%z7Y~~ zEZ@4maYBgm_@Ns>aBMfZ0gM6s8WA3$vvp7y7#INO@{jpXwMRHohM)|S-@FjzPd>+{ zs+o?7g^VDa{aE?&5eCN>F*fd z+3sA+)KFtd|6=NNA9MJ-G7BqBEtJ%c%RBd^J9YEr`+NRl=xZ502OQsGXGBJ^ld(g$ z69;#S>gCqH${GLet0Q(mTqGS0b)dzJ)>B(8TCk-&@nMV#GN*=SWY&5cc!tK;fo>VM z4aMBow9U-h9b`#-q9s1Vt2>S~)d`IY@5=@wVoG&F>heaJ5y{EX7gm)*?04{Yidtv{u zh6^?%8@9yDLMZ03*%2x_dD-?{u20xAcJOzy&>^2!gwSP*u!E?aPllQj>t@zQK00^>^_u>Gz(o(MquxI!vBz%yOmzR)N7 z`iOyv6Yw1hm7|4$+D0qh8Tm|#*k*_kEI|QgUZgZ>ht~s3akQcuF1&&rnWCcS1tRQ) zmoFI6RVRTF$(IP8y4fs+vhmXwGwNnOQJ-aqH81d$?6hfuS*Yi#A!96X5mF_JcP_?u z-uUz$OeBdJG-)=oqkF@{ebP4t3mdR%EBEBMZ+|Ypu~R1qQ0A`YP!wz9k?1;gUUYB=b8l?-VrN zRBt+om&xnt>Ohi4>$)sP3LQW%AcYWNY~7+mb!O`OD=kV>CyM{jmFCV2u{pbwczG)B z7_eWZ8%zOqhb>KpW`EO;xELcoInqsfL>o{HN6xDpY(l7(a&3{w6`)`geEw*pj*BY8 zhY&2fmI#OYHjr=5;4Rd&HuQ{hekC&NU=Vi1mb$xez@GQISU&J0yAd5bzATVYvaUd0 zR4ZU)hA|4Q3WV0m!sPg&Cl56}jmj8>j7I#Df!VGn5k2U>?14MzrckLn zI!Z98Pd6thK~PbprhF@$Bw>wSx@%H_S_o>pe6iQvbA$1P){{mkeTicz&|feN>W&nk zT+!9H#6gq89>Jk71ZuB=(Z`N?S#Q+_#>NoiRt`2=f0q5k$$s zgNl~a&L1is@%5WEtM_6aH^ur*ig4?I4r}@HD5a(I!lAW5_{{iAIf4B)aMzzlT<(AV zz#s8i?SIadm(SZW4o7?$i~qa|1;Ih{-(RS+f?uxi|GfVHKmC6_8qG2in3PCC3x=pK zkEAg4vTl)Qzo8B4<*6FIS-xJqZ+~bgwP|SN{m*l5;2@>d#@(C{>%d3N#|CrMKphiK z4cB6CloE-P5cTDUI2JP=f)x)b*l!*F&ox^V)p0#?!wsv$LCJ{{32Dh(5>LNZ37~9>RC-62j+g~dJek1H`azz@opo} z8m0$_5qGAvCo|*xB@&zPA65xfWHMM@KXmKEFf(PGa+;r6(_91VF_`JtKOIKO#Ic;^RuZ6 zWkr~0-50}My5W%TYUZtAT=(Fr0J@g81ypKPsMzbyv&_d`K`Jxrocx zAUfhAM0El-BcOQ^nB;;dMuQy*&Xdl1d)>6x*CIWhG(Pb+P57JHH(cy3$9Vf#key|O2*dwJqU5!S@6PEG;94%yhs3Oo|fXoXgy z@v7ha6Z45vdMJfj7Y1nJZmU$KBd67$0U!pbd}o#RUozK zXP``@#-4tfK3nvi(sD~Tpw~8}ttw)`=0nuO-KaE*j`KiKXcSB!kF^2+nCa{_> zG^`#x%;`4=QJoZF*pHL#f7SP2|AWJI;l7nm4JL#Lzzq*Ovd3zfGW-{*^m||^UQkit!-!Xc-_<@9>v^;4#7PKL27&bi zFzz=0^&UaZhN`?G%Ap}z?>oZiIT&A%3RKatSYKt}LH4QPlcdG})Z2r$DL_}E%o{CI zV?1e9$$JZzjt~qT)?JXzO&%$2Oc*9i-hRRTio)9-ckoyM9oQ*oX#?JPva6KXMeZ?5 zuAl903|OFEZEJ;}w_>_N8?V)ZvmtmZAimek4rHCcEda-I?#!6%QVj6?sL}^lfg&K# z)4ka(%R4#SR0Qb_=#m2yi^JzRptn))xZ z2;}9n8B-M^WQroBsT+RkOH%-Uh?<55uxruq?7vpNKRC}4pzrAnvtI!A8G#iQU`ZKf zK$RTd16DH-Tma1A-xy*K;KD7OgR({xT%A@Rf2bn{6-HiUvt#1ut=^gcx+%yM- zra&hNeEEY4ztn4wWp960u%;XuespwTfXNH?&q!&^Seb1HBW-Q4k0lOn=G(lC7!ZX3 z@2YNnh?Rk@HtLY6p6LccuO#Nb=Hd;nu1|$Aa<_q-I~acJtS}||U!0SR-P>0{{K@@z zsX(U^w3iS;fl0D4y4M^BY>QG;@&2{AW27p;TUlAnE-lG`z>PZb#&~#c!L8x={FFc-ekWw^ps**XVKU#=gE}qq!o0j6Lv96YjY*5|wA69w>pG94)>PbY%gJk2>|k zcJyDiJ!hAfA(ii{!IV7N=*M~Aa!}6{6$B;{=$!y$0&s_*eWLJp@Q|mice6LJ!lsR4RJOm?ECkz{Ld0@Fck>GtM&`k z81;pGMfGun_1UGhr^}7=Gho>L!Wn&eNOW_^0N_p#x1PV~Af>G>5C>~4B;fl#Uw6h8 zOt_5FNZUFusus=c|3lQ5fMdCK->W1t51EpXB$<*VA!9{|P;|^QNtw#rU?vJ7b5Tge zi6rxs&>NEKO)`^^DajcB_2@gl|98FDxz2UI;(hMtzV}{h?X~y4EPmvucRG>OV4TFz zEqT8Mrt}fh^C7toWzQ=sqs~1IQMrE&23YKEF@rP%?G8|KykX*^A#A{pA3r`)_Egui zNEX6gfKB+OQ*i`7c;*ME_u_Qptp>lmnzD|#1TyzIm2`Q{jcp^|1O{T`TnvtOpCt*fzLBJNB_`-t8GlMcA>gf$@q;b)0+!O;_u**lcFFqagnw_o*XX%<1?Jg+m6_vkkFRWjrzZ$@Pm=k(8@aM1Y$dXUTwQ!lz0?FZQlN3@xMD^|UcC2q{e^lG zO{%?|T=XVv@=*__$cB64OApDXd*>~U*<+gpaY$NQTWzjsk>_X|i-b!C%f#i|z?c(9 z5~tfDDDdFw(%kBDX-HrvgyN=58P)ZMWVY$|6qKz-WGaz84|lKRHI`lI(kEYG=B#3{ zDg_H8f`s|noH>z%R~GhTh22WO7O$j_3$$Vqi?u9IX2+j)TsCgFCFRfp?5ROV*B09_Gws!tUjNYkOVnL2E-G9T;eGIG z)2?=bBzj4cGy8NYJr#FnjTQb7?s_pplBn*xb#-Oe>d(Y#B}2p1w6y-!Kg%V&O7s^Q znO}vg30%n>-O)|A|LMxY0w-=4fr9|C=$Jfjh6wGn@Z1aVwWdp6ED?7JnP)k z%x;&#{`$~`iHlc#M}5PTte+VLU0B~wVD(xZL6)5Fq$ldUkv_4vh+l|7IK}7aDQ>ro z49h&bu>I*7+KJ2C5m1eHXRb8<@IJZX`@2d&p0vwPd@EUcb8DM#|M_2rfdy9E!ziA- zB$qPe<-K`3T>WR_;=*jk+Ra*vYYx&P=i9_j-`0ClLj9oXR$d;wm}vP_#LI`=;R+DvGPW_ReZog1-V`5PxUbUq>n_wd=Ot3dgNR zJyCXBz9*+2@x`3rV~=dE5s+l~5K>eQ5P>yCLWI#V;Lo@@oUQ!qkqFBb0e`sj^5EfY zrn=KlrC&95N-R#8p0Ch*M`O(O3R3b>(?fb2`^x}<@eYWo@`!N0zP_NDe*(<&$pYH_ zYepfKXfVk#5V7?1+=doOtcCI;gF3j&AvO%vEDKmt#ABm6FvMK{I5qza$qUYQNI^Ss z+u!_Tcie^#U9BD_2K~uS>RNfWlsd#DnVkx~NjcM907MCFzmVoHi`6H*iqKBnVskmS9yZR#KoLry}2D-YM*ZJjAlQ%iJB(rCXq&y{=5_;* zNuh*Wt)HHWD=rDC?>Ar{%d~YAeP77bEzK;=Oh5c8uqc0gTK*K393s%Qt|N-<6Y|RD zCCC}rZf72E*6zRwi_v_@E|jJ7_FraO@d9F8iWW!LUGVWf)`v;af{pb#zQbg-4#Sk5P+w8k3BG!VCWhY?o zA+iAg)Jkg;xNP2EEvx;}vUGtwpW9g*x*gl%x&==>#a2h(BPTrA3 zJ_63`KN#*g3V~&Q z>W#9|fe26){>#MhBM{N^=g)&*##D6LKW&M08M8C<@Djso>g_##U-$N*&PN8CR66yf zOFNXi#$TKusd&U;A|_I4sR?dz69>~EAO|2|l0Pt$%|u<-YwI{g!O_h|rU!=cTKcsYGf~0P z4Gggl|GnTVLLHF8c%pyHk<4-xo-6B|66g+%HGJGr1LadyszPzNzgJ0+rPe|c({fG1 zk^=U}fczk|EtOk+{*8;EGPs4*Mo&Ul$ngn+lAc)MwiqbAl4m0D+Ips{HnD*HrNRO$T}7o%99EH@1*t2>~YkCch z@P~^R8Nriu4Ywg85D6Hu!$yB&*;nlgDPV^k9r{>pTX_>-U(086Y@UU3ZIPLOR-s*k~0>19d8*{A=1AXAFI29+; zooKY$kL_#x23!`&taPPn>gwDk#pG;DgkdZKE`q7+?Bqgre1`MMXuON*_43a}Uo60!_-x6P9GHj#u@ejJAW}Uh?8a z3Q&Q3D$`S|uB33?EBUh=UM2mkI#TozG&1}YtSuA-CEmyF4s4?y+z^DiT~2A2r0sZD z)-uPpWCR}2G1`YHixqhwDY4p&?bfN`NWkL^N8z!*A#jJB(Wor2~? z)2g*l199&F#4Z5!t-&`^FFP2s56ryulXd5@7kRAqp{TNDcuyHEY6v6VNH2D$J6}Yh z0o_1kvnd&g59#vl30CFEPK!DLn`lk9hfV zJFx3yqjzmeesLkIYXTAs^a;EP_jf=^v`V>lHc1cj&&H z+WmVwsnEuB8U1aYHFKa7s6OhGgD;idl!#hSzL{Kj{hiJPR6KIz2$V2rA&tm-(C%E) zz3y^H9g{tBM75~%&cSzTWD7$>K#kiib{mvFoncir=#!URy&u|e%Kvuiyqw*=_nBqQ z{`xl5h9cXHH@UgHvo_nD4vmx5znNk#Xjv` z>9y^;(j&uYuMttW0a0C6K_Ondnyy|6vTsihFM_E>*YBtcN4s9Qg4p|1GW5>JwAekX zsJMxv64;D;qS_q{Cd~m~^T)+)ELN?<(>Ebk;dC0An&LYW@&!-^1U8OL9hn;LT;gQv z`}Ja~j52Z|eg&v7uqHwgbe6mROPi6M(4~%FmX@}I&^DhwB_1T^gqk7-ChZRVVl~$7 z{Q6P;_)fot(q=7&$pao7VHA$|ahM@CUiSI(=a-VxHpc8)9h8?8yUlHanEjus{VsN( z7Ubskr&Z2>e40%?zu>NuxTXh!1M(U+rI&m($cnQJttokU9{FHD4+A7t+u5gnij4J= z*;Fd_1&_|s--{?X9KFeu-0#f`Uo;Ur^F&a01dlgqRTH>wAhP>t$9P#nq@<>Bv`H&>nwc=nS(aVoJn!k6kxiGo0 zDmPpbB3EqDW6ovL*0f8s?it7VCf=U24CvOg7cZzfq=$W>BN1V;slJ#ctNr^|+si!1 z87_st-vNHA^u)-HLh7GKY(7d&pm482NmqQwd?4R7$PW)sdo4`! z`}Kxea$^6wiB#r~gU2qcE>A%+M=3LZ{AU{}f7B8i5QHIp#Q#!xO#4 zXQj2{x_b=`D}UFzKWoK8AQj?+)RUZq_@x5y&8{+%Pp9k-5c)bno|RvRzX!J& zZ6)tvt^EYXf*b}=h6Hx{k>JkDVTxyE-$@kStXuv$q9_}|7eZ}dHXjH@2#{<)cFK?| zuFQqbgb#jltybHt&2g8QV7cu~h*DYYCwPnSrGWze0$EhurDXSwoh{Fg{7c1={{v;K zrc}wE2pRY)4?0F4DQ#*Wp`PmKzxOaN?`+vcrseOKWbcq)wsFZb2L=n|9xmtU=Nt1( zz97WL&?FKeaNgS|{Yw95+AU%ix)Ml!#{XXYT@3!6r1fSgsomjMtWdSN3(#ATE5&8{8nzcT5aWA0KOwdXdN>K0yP9zjINw1?z-T7@#5e_SBWIuCPI-e>7r;YI*uAXKn4O~pEvKia+mM-lG^?p zArz!&<68pcfQ46h&1_80?!N-l=ljv;eqOAMXMqI3e}DYYl1W||PBgzaF1I>tzPfqQ zZ}rm)Mt@)YTv!Tldu&fn`i~+19o^*(fr}Wbi}+KZFfEa=j4$G~`19cp?ru~klCyu< zJk1lqVFW;c5`Y?2PDkmx6?)Ds@ndskKb*|O6M=$|17?IO^$RA_6bQ)&`oYb+J1BhMFpjpmY>yEdh@UCyqhs#{={pt4|ekb3GQ>F^Q{ ztM9x!NhvND9Ld*Oeq{=hf7RNm?;>AeuKvLH@j+Pz#6m>qP#i?Ob&8B$N{qxsQl+T* z`Sa(V4AQqsm-YG(2Hjgoaw_SubsEZXN4*(5!E@2dC-3_?#V#dHN=miqA^;Oifrl=V zmp1@5C8d9o^07}PFW3k+Co&885b!9}+^qH*VAFu5(LINE7)WRAS~AIu-gAwX%(LHX z8trrh<{&x(Y^la_SjYl~hI+zm@0^NZsl2%h#dvUzw_IO~`HNOq@^$HF3|jOFs)ku{0ZwS6toE{9%@9rSJx`#U0zv)F0^@?;^ZQ*ftUbUesC(LZM?Uzh)A%`Xprn| zO^@%dJjxxVvQ8jydT?|gHlfaF1*s}nWkY>G6RA&s_r2$HvC#MUe&138YW*Ip^W7mL1(>gM9`NS&ylQC=d-l zS8|;db>cqr*bwR>YR>n_)4@*YQQ-G)hNbB0 zEoy>YZaO9-X%n;;`l554fM0IR8v>NzZ-74AWd=lC;!~3E>v|CdN{a{&0NCFgw+AN5 z!Vt@a{w`1ATdoXCT!r?QNUv8{OW}Gma_}Nz2HXNB;wXfh$o27SdJsMUgwS`=^q6oO zOP>Q56&N!R*feU(sLEPPazgrSrT_MBB8k3GY-#S0Uu6-*d#njA~5#hr89i9FPpNg8D*H z!bS1)DG?o)3ZT;5%gN<86=t(pWNm+Ynoc-!*wAp>Gb1gTN=-qQ#L1IZe3}&jW)^a( z$~)4RuIkUZt7l<+O)v^{V9bP80;yrDr?MXPO}-S3ZmPfa5ZDx616&i8yHSsS0mxPE z^Q9aYTiW!Mtn*>tlaEfQl%Qq!(hbiQbgw7c9%)cVhTPDYNq2RfH1Q;MpOT<#m=$`zQeEK)^R4Yk!ij+ zo;sBb_GMAOKE%d?E& zEfI!>@dpdEJ5o~)i&QCcM-5f7QsDS^-qgHvc1HdbLyf<)|Cd|$g(Fgf32p;2MfSuY zam9hcF8sUT?1bWk@>-=C&?@*dF*v7j(7Xi;{uUtxRRfgz+Z`+rA|OHHwd-^ZjHpes zU3)9Mjf*3bb-v=>ciSZGv))E&Et$)lZ=JJlZR^=nu?P`wXE3epp| zGVim^eEz*2qXqn%3@Nv&GttRj6g@a~PWmLJTtQKDTgB;}hdi}eh7Z;Sa+r@5{qxFS z(8L<8)=B+uyX$t_?BgZuZ`B8^+N3M|1`=OVhn0{;dQ4f4v&W z3E)W^Zl0{fT0$TN$7ythbrt@Pe?dsBo_S>{8@P%#47!4L0;zRU^2I%+_|FUQFZei= z06Qu>z_p`U{B5Sb`C8ga;Vsz-rsYcOq8 z+N=5-)DdCVoKU6)orL~)Nfy;Lyc+iuowutBKl~Yach2^Cr)lr`K6#hk&CK3H$>yQI z`2Iek>;(h>WhzPI;6EpInL4|mOoMN`aYDRlvD4QX%qXeaIrcTJ#ct-$e6KaUFnAMG zZeZW#-3d`A0M(x1fA6|;tIt4~fH!2`zw@Po%*jcq0h7Jlzd;uXmH$&b)qYf&mgBq_ z^&&8cOuI#(w7Kjfeis6ST-Th>pR6rk^E)c8B?||#wti;YblXZ~E{BdS5 zcD@jZt42{7I2fKK2=pkH#cTEh!x)`Ary|M@VS+GsO-|;&brFl#B$wCPB^+gsyl*6G zh58maB7_qtC}$pbpPOd0SVz#*HL{$<2Gx8B7c6m5*2@tZi|^K+_D=V*$8ep2bq7@& za9?@aUS}*}6PFA)i z<UGd~8Br~{U7=m1 z54Oc#RRUGDzF&8hgW+B)m6=3WNTy(`h2-tV3aZMt>(XagHT6+spHGJ({O8lW|KZ^Pm@1^XmC1ASRNEwcFSZArqLYbis{%gmnjpRj$Zz5G z5dc#{+}3<}1H;@*dyggKwReVE*xGJ})(jhrl>Tq`F|Vr(>k;dFD=XvloX-w?u%97A zfBI;X@VC5wNQTD`FA+ezSjiK~mWmoHyN5q}2GD=v#|wP*SUq<;id-@*sh zolSH&7k_m(ilM&dMoJ&`OZ;Cews2-br&p3DfQR~@ai#LU@g3Qb*=yf#h`3Q;EJ7e_m-*{ z5rv$JiSpq}KjYOJEZX$A`I=8&z>qd$NW@YDuv_st?N7H&r2E|t2`qFwNDqCu^h|7g zP7YqdVH!22C8AQ3YJ+ z>Rk=Niz2wydLh_s{AZ-a`{nGwp5mL^mo9UKT*vu=eThIIYj{P0<^hYF0r;V=pmaf4 zF)+G0TPm;a>Te$fkY~i2K2Sf%`zT*w+o9MG2=O{_3nLDZu1I{o-%8kQz4Ne8;s4=jqP*@Pz0~)?zfbCV3scy6eDAE(r1vbz}HEr5yaq3J*;dTW>;SvMG0|-p8?QWX-W1!3gM}rmo7TDb| zQ{exARW1;h1l^z@HX=N|BJnq-mJvx4snpbuoyQid?nQG7(&3G4M~;918Hy`O>eVhG zQ}*IV%u!ev->`GX4(;w7iorjXNQPY#sDPPM`$fp*Aliafq8jjX5K~dpjGYQ2l>zap0PWX}+xD^gy^dQQiAZdusJu2N>Ok}r4F@P-OI&f&mEBUL08N2+a}~;Qnh*K0ZdD@?eK7|u;|M)xUTK*)Gkuy z(#x8I001EdBTrF`i`B110f!|#h;GvL%j(Dhx=knpER`)E_ z{PF75eE|#oTd*4O@$vf(y0R^6fIlD~qdJ69u`rCqC-%DsrSKff(mVVw1?(~A45DGz zy0j(Q3~@oJ%6$TBj?bY72J`2K_V3NU9C~1bx^{;#*C0o&G88nsAj!QWjET;Zwf0 zGRP&UeEaQ#105ZgOD4{A#TH-cjcE81*}HS4vLN!g;K>(f#ses_{*h50DcK&anUCWS^XR$v2I(z>S}cmmY+U(u68Egk#K zLzWN3HB*N=KRkWiF&Um6S=6=Tm9Vi_>D`kC2Eq1W~;S-ig4dx5# zX(XE7yjbVQgUy*>OW?e^MI|dOxs-9iv-oL*TS2(H&8|owWnY8mtcJ#}l}OtB~}V*hUWHrA9Kcgl~Cpu$F* zN%H3S=E`F&mlkolZP70pU=YFz0rfRDHoBq}0g@-1I&&uF?5OUD%h*sry03#!9Dz2# z*eJrzFL|rUx~cz@u>18#!*6%(i;VeY@?lIuv95Q@TAsIF^5)5X-m9F_+N{P?Y}apJ zW{cgG(rRx?KE*b)u1p~{un3qGpR07u?|K}R=R{Y}L_hJ~MR&~Ms>$WOQH!Vu<4sT) zU5d|c_xz)vm##ULxTCPqkm{}70WBfce#!hZkFDgfH<63f^n&2QfzH$EEJSINoMjp^ z9w!n8wFC~=SWNovs6}LZJ-Gi3E@rxT;@@j;Bm{qW?Z#&l+g?ue~o29|*^g`)J2FhA^OthNzf9FZw`84w$|39nH& zLPxCGwlzdhgI1;Ao8(l~`Q+u!)cK{PfC{{$!GmcHzDZOPO??ksX4w8w+a6?JdMh?^KlR4;paU6gy|@y(ETFmYrshMpAKtfl zBS~Z)CqE5=agBHGl*<23&wt<%rBe;E^ctAbA8Y|~#3sPGM4gK?jB}aYv3Kc@BJAw< zb+2U#XFg)PplklgNT?m47LK!L8gOVI%Zj=5Hk&jrv7KEPdyg}u6mG~cD6;^{P-X=* zSo&`Ymaw=l@+7ugWROqS^)seS8zxHX zdL{&OsF3Jeqf(L)|IQ;qNCwX~cA{0F>oz2^p7iI=oO-mCu)8=BA($#-89*{l8 zM2TV#7AN+-iTIX!Np_)_nwE=BHK{tBA&^IrF98UFxREUN*uuZbP{0pvVa$%hXIi0Q zF>$$^x?b0V0dVTJAWegn-phQ>kVTIZ7HL{SoVSQl3GlFso)mNL<^gMEc)7yemJpSz7a{+GH@iLgQ@TU+81qS-uugUDb z6&-ES^@1r{yF-4zM0_bvu50{-F8m(Yj!?_wc(xm$2Z}R-ADl}jta~rUn{Dp$4^;kM7D*cFujtbu& zj~-$P2!U7%bTON`6jA!N?T7};Le(ukYCJY7Hb_s*>dc4<1G4OEI+2^*ZoYPf*PV|f zg(QSOLRWtc!*k?MiAuJPeYU*Y^;Z35ZkxyxU9P;TWjJ0CS80@S@iY z_(3wl+-O6A<-ZRw0ehs0{>?XF18{I%Lqi*ijZ>O8J6ay)?xjf^$+_S&9YXES`(AH@ z;EBW0fx;0MT{0+)K#6>Ky7b$wKORl~paE*4$I2X8+>4tE=}gJ64WNSuTNQ-TguE^K zJJt;ABy%%Yeighz;NqR2DWp15A<-Lz_9Wwk;FEJtT51(-t5ipHzg*WeR0fLd82$C; zP>GR}Es|u+-gQ5|EYAGfvi04fLitsP3jLy7H1Is} z@;Wm=s&Q-i-AnUH&I^QJfo*qA4ju-P3-EDc+pLcN4_D(m&-G&4M&O~mMDug;jUOG1 zEk>eSLw~`3<0b#vOCBDzYJ)AkIY003-x+Sjb__eH_? zS2Uv}71~%cXFQJ~UkLove6*L-);MR437ZICcfj69^q}!aX?o({ed7d2UoGypO<>;| z986_3QW=oFGo{7OGFb@BM3kf=6o+bCotJ#bi1%5=^#5D2*jA-E`BP!0LrOM^aBBj@ zquWdV{uP@Fuk}YQz$c zURJi1R0y7@uINz4Hvc5*o#N%286TX7E66r)X8u{Nl#Sj{F(pcWL+Q>!0jwcXNw^sx zjDB0lB4^VDO_#D}n~lBm!KzF0QRMts#;8R!My9kdH9q@n0YyBMW^g20lqA52OTi;r zwl9^8J1c2UzKfH|OVrpo?#8;`Np|kOkh3LcMC?@xj!$YgDNRku4LmmoO6-cbU^@^c zos}tBSvf941XanmGyn~w9wL{E4kXdx^|>g2>p-0cmCHWmQA!4=Y-Fcm5HRiOo8r$0i)%wrA=B_y|%SBu~m|m-=5q zCK4y^Zt2PBc(6ualXU2{qVyr;f2%AQydncOW@zF}XH57h9tZ39l4sw>a<*aaYWaF1 zlq6GxZ-OtqcPg^56?Zj}3SIsTd=paAHV+#PUvj+Bi5vQ=)V}Ab0Sez`OvQ{yUMf8{(?_+}5Bv|v9T2sTMhHg>2-#D;=#I+$OZ>I-fB#lv?e^z%?((C~ zV<5uZr(tAV7T|F8iMoRrKcjxZ~ovR%1zeI51VH@uzWC;hy! z>0q=q(5h^7!7thW9|r|#vO69*2<+Bifu#$k2H%Y$iE{_mAKWkCwr@=8&9lbrt)gyU z?Iko=Kv}dOP}I2cpha6ygT1Pn6qIseeQn+C!v{Hm#CABpG0qhI3spG3%?1X&7u0x_ z!AH%`&G{&At9Hjrkx?q8S$7J7E%9&WN2kP>eXTac6VG_}$VoQ!X@7HB-GkbJd3lX; ze;3vAZrdi7UZFS|z}`B^SI#o3Oq3KoG?yVNAXY5;1t`~8=i|bMP3n5svBLd~Hz=y6 zA~Oo=7Ha;B0|~+r>^!-h&S$iucWa!x#7`hhkNmpp#L9PR#KL#Fi%u0qTF|O?6KOhC zXdaM(Kn?L4pFS1Sd-%#kAyzOvS4<$iuW9pt;J*m3itHKu9X;e8rqz@6WgYLX z0B`X$UY}Z)q*Ik9@i5Lx&{&g zzT!9-{Q5kb`t9Ly({tHvk&Au|*w{&0Y9rsq6&1g^#PTLSccfJ^sQh~7eH2pWKMqyi zG31mY7n`;bcdZse>nbVXX$vS>Z zt@A2RBLc1-o25(&J}{rE8h)hx!}GqbPrGWW!+xMl{084ObX1$X^B;J^Gp~ppBHs7w zd|7@Zl2*NB{?KecKsK>~uO$4MJQ#1+P02hV9@lIWbif7#5Y3;cKYetXQ=$AIjTEJE z64c6u=yTNIBwE$|oN@~4DYS5{`*h;{3E>7W-VpnL7g%m;*#prtN2lrwVrU*@5oc#{GgCEK^h2a9INQfpNoM{OQvt z@wdgkUwNXwO`q-j75U%k6#XE{Cw32`)-+;+%CD9BoS^VQn+g$Ky0&)rLZ4QnZPdE8Hc%bi{rlp-R*%z2W+pECX645z=5%a!jTO5L+_~SOKUb zs$dhBQT{}E@&33Rp8p0P(=5Z6o8CW%!td_XeMr}x1KJ9Y?!(iE=VaS7+0+MKHV*E` znZ^g!p)X&)1XsLPXK5s+HlS|~6J-d^mOn3J{#^Nrmq_Qc8LXi1pvD060K9%^1@pI!)__qLsI}!LHiVpsCdz+PHxbM1xdW`_{BG+-QYLO zV6U?8t+xyy2`axQ^5{*sQc;tqBnDMCeip&jSjiN@@b7N}zOE?83c})y7G)HGETA_- zAQ-x3FO?MRTL5XYUwFsKC2rM%lZ`Wv$Xst+UtHLZ>vwkTbGrZKKQF*4Wz1YT_pDUEz!dRg!b1aP#) zw>w}!6KDtQ)M$?`rp-0D&Kh$6^MfCk%Za)U#6PH6RZC<0q*Gd~5Aj01g+ubbZVlx2 zOP5dyh05?@d~UwS8gJ}YBVHQ*mJ%AV+0+5)F}egcr1X(+6TJ>;X37mHe27Zj>=3;u zl09onYdK5k{S`!DO)yCr@b`o4(QYl|Ul9 zM#;1Ptq#%5k4YcInMS-tG@OD2)K|uM%hH8%Xk&&>4vKjYW+(_-u3eG$uEHmd2WF5q z@UkE#xi7<*hq5a{Nw)0UiH`+o612HBO0A$0taQSir%kxLt@WD z!bis=CTFPY{R=BKQHq8i5}np6O1texr99{bEMxMFa`S}gyL(4vw?~CsI&fM>(f=Z- z06JJu%tsT~KMO13tmtF!l0i5krdB|7QjUEP+=fBEQ|0IX?^N1)Z#k;KHB2))(&(oc zI$nlHA{u`{4tD!Q4~7*g`-s*{R;!Gh!VAmkVBY-_`ARBDG>p4qvF5AoiB!Q7%$Xp1 z?;y+P<&pipK%!B4__w-R;>|mhJmA5)mnV|lF`ASP-hfC?+x`r_-|$;xw9#&m5nCE9 z6Q^wNbf%&}#Rx4=ftkYV18X9CA>vzg9HCPs6rGD61q?SRl$pZF&@rVsIOeE5pM&2>R6OiICR2v!wPa}3dcp%Z$h8;NK zo#`qt{A43j=^R)Rly&NYpg~zwI-vapX9@#kL378rp9lemZh&6C?H{Jwae~*Oo>yPp zMt(8gW0Sq;!$e8zzCa534`jO-hKThzJwoR z#Ph7F?yKkfv_j-!R7$~;q1htV!u^VAJwd7wZ5-e7W2-K{3#o&-7qJF!>Ac6`HZ=rzB)ocS|roPt)t2aCoD_A=#&zyrB zo&HKjYG&hD$aThR=H)+j<|q#XB0bI{XNZbYi;WbdHtI5P=$eAzmFF>XXmFXD2l*ii z+sH1N&_l@l{gZ{1uO#Wzv&0%*F};IuSP=lyYE>#tCs_BS%jfb-6WWdE8(c+s8qX$G zxBSNkV!?p?x1^PtIK;53Gh^exCx>?(D`=&ReoFMgm;CE6=vpgTEkKe&=MN4UX8j3j z5NDV(q-od<${`eJOHQ--`&$=y?D98#@yI&80J#%L9t{s}rGD0B*>Rr}TI{eYz;WMZ z50`aM-VnaR3oL*&#wJ2>8X7*6y{&NwdP0WX;LU@e3qd7w?`goRY{wG>EkQdRfGwPQ z_&|3xNhEAjE%TJBBAsm`vrsRwK=@#5MO&kWWoQW7y==5_1ms;ncWlAS<*3R1?Z8A# z$6+3ry(hIqs;JVH5p6MqJ_eH-W^j~rp%aPr@V{4-Wp_6g8}pkx4}Q9{$Q}J{gnl4YUo1JX z6i7;$cQR5_Vg=F8ye@>Q-(lzuZ5(_SMzfb~tAJf7&jZx_vmL8AB>H5dy`BhaEQ~!a zKpi0Hnh2?IVMo0r76RxzbHYNBX}cP1*Vw{X1d5*Fl4E*7eu}sJ6xU)hRzmsb_YN0u zhio7f5{%3Jq?;*RwM*LkJBs2j_m~ZvME$oEbfe+ljEgGocF_-)6`vDBz5`K&G=}%{ zj4#%f3pM9?|NgOiEU(L-%Ua&GMcReG4}}z@D&S%$z7QnE6IoSAHd3`VQl*zSY^!;A zDggfir)GCytj}x@beIFvK=XUY_GQ3 z9k|(O%}k?YoQ=^P1|q1}e=lROXm?Cs7uCG{;bVVf@XDHIX~!D{OsFBOK!63<(8cGr z2^12xur070ftjHvzJuh-$^!M zIn}|ub@XWCjy79A#s5%_WXA3fQCBb*=A#oos!t-@uqcGy{!SpJC4$btt$qJn)6h3+ zuwf$ip#KYAReTysvmCjB>tY3&Tg($S(}tAp6VwpMQyN;~reePZ}QWu};!1I4-4@b}P?yLVQ z-J~%OQy0EHOmuxHa6`RBBF%P0IZ^N1Q4m!9A7zaP&B;lRL*8^~(7Bs(050HNICaiz z-$+*6wskWx*+ZHeQ+C2bv;|ies~ELAu)ztz-O#XM7o&b-~6Nz$C zy90GUiX?S~qPclnvG0 zY4q4?XDfn_$?iO$GifExHR}x>;jclK=);7Y0X>=P49mc98-t5mLX59y_xHhy>DL?G zQ|_HtJiIYWm<2ZiX;DRFeqEUH(=#3JU@&ld7{{83M~uh}OKpcA2^B%8?`D>hNSiS+nj7~XK82)?B; zPyxH#b*t$v*is?{P;+xg7UDMRHrz7ic!_oIy6$%Oq@DNA($r@jXZh_X)f004smDTm zV{{K-9s&BX2yM73X=)i?3`KL%-w38`>eThqpM|)e%Vx)3?IQ~vNzyuYhd=xNW-hS` zhxT2NsnaRgO_EYJgeNL9Q6K*RpoykB5a(chIttV-u_9mfMt%&w_AF~QC`&n*Mmy?! zgvBJGpnY_l#gn%B%{W_q>T7_)spa+hF$d5>Mq5Vd-VP1#=$)GsK%vgE^TQqr`4BxT z`0*&b*hm?Ny>9p10m~_}0QO*A1A`LvV-&?)c_6XksN$bckM)mkcYAW$W@C3^y`=mZ zEo5P2CZdCJ9pf}A7Oc;^1qPsH_#Aod+a|nwU_8VuNHQ4qVD(l_j}ilt+D00_+Co#p zwzah#{_K66`;E`T&(|Z9?Yj+U68;ON(pNlLyx5y8m2WiWC`E2xS{qhxQr8;mXyFGn zM;Za%L*B&T2~iTu0Mm}+a*g}xRA)j>gh%Z*f8Zy3n)XX(T9=5TyT!6LNq|)NU1xH+ z%4k9NaJpl%XsXnq=yg6%^oayiGoqjLK!R*^Lum|qs9!I~cBaPC&Tq4&-!*kyKJroD z6aG8YDD!~K^!ZTRt{3Re1lDXT7?(qR6v9&{ZN~App32sz5^P7wf1E?)LC?Vjee1uaKsUX(zP5PTXb4gO!NblO8;3*N zGs7s48z)G3NKGv(!eG~fE3tKQ&>PGg!jH~8CQ2(;Bp7CtP{I`2>OJJp0ff2@txMeB zIi0+>)X(IhE#8#Fl?QZh06i2|{Ks?z#|BQe5aX_4e_fB43?oGCyaOl>!4&ndxJV$1=|OK2Z_j%6Yhz>GZYb~;W#8Falp6< z<+TMv3-|q8{VpGoN#rXKb`<^q!6P#NWsNUA^L}`l%~m<(Qe(@(6APIwr4T+KmvsFO z{>rHF>Vak?r2NrLuF5^y9ef%r$bU?fCt}rBQZj{g4d)aRea=xnxVwXeA-qFI69NNZ zZOVfXOHrNwTJSs0P z41(U`H)Av_UH#_k2mQb5u|fMqApw{W`CIZi<^=YoP5+uBn_DB-V-f8&s(X*4Pwsy+ zjX*wtU6`%=0f&0psy>?|q4Z{OVtHY+khBbEbEg$HO$6wi!xNQwT!_rCQ@oLbAU zL&~fgHzvJ6^ObQNozM0A^W0i-;_mkYS!-rNM#stR7z9B~4MmOuM2^UWZV=J(O*jHd z8W^Iw&XEg(JFf6N=c>SnJy?n%jRVa=M#T%KCQpn|{jZQ@BHL&ikUr9T^oqkkz?Ts> z4#l|VyD$<4f4HCh18Gj zDm`8@u+_8AnmcEqa0Dz4bD#oAOsPmD(d3MI$TgU+{^V%C{uNduCe0D)FE670&H!WbHDOKhZ68-(s)Xxp&cKw0wYlR8Nghp!xz8(t62gwINT$!!IIYO%wJC;%6X(CPQ? z*uQyCc+1W=j@5K~#u`mh9L;=Evlay`|7t{>-`47P--`sE#1_RXf=UtjTH`tAxo@JZ z4(C3=AOV^0=S%UeZW%u5q0m3k0rc}&;%2i(SEdktFcHG!+W4&L-;=81(S-Ycp?fL- zmLd@GAl71xZHyJfyq-RsBFqe9o}v6e5h%6G`{w>b@gQ_Kb%2aoLV=--J0slLyw;Xv zQ(8b1!0Q0&VPJ4^iO3EcRdt3VG_J39_~)aXjQ7=n_bFM4;?pWj5E3Gtg2 zXGaK?4K1i_TkAT%Eh`c|v+@|HgVF<59LmDkRqy>;$?$J&TDF!k9W{GAE@wjiW3wUb zm0-OjBy176?wqxW!k2q(yALtDEur}k;T-KM>mDdPG30AsLrLIKKyviNAZEOn2=+FL z6;%2DQCiz3E!I8b=jV>?^%U$O_&1C$#NT#9rRWr=H{qN?VO>+H2Ls@>Y z$JMN)UOP?RjH*x+)8zUwagfb8qoz5dGn8H1u>>9yScOVWChMY+mJz`bnGl}{IRhsT zWrfd!57pmb3IQ=6GRAmpd9|)C=*?a8N}tW}7EF}%ZcRLi(HbC)boB&W$L>J_27$FT zt}{eYfUe%iL8?KLti7a&bth(N&3F;bgHjsqrC*<=Re)3gFQB%8DUMLQIixiA{^JFX ziSkXXV81z<*ojiMEjou0+bL~Gba#e_oZPAU<@%wFHq6`o?r~~l+I6=2m;b2s zlpb4_y9won$h;}O4e>wIJWYw=DrmUk=gA&zJBsmWa%oohVkk3Vw`9H#ktrT^rBKV> zr%s8D?Rr6QMq2FUMz6LRE$=yv*s#Xz1D*u~2jr#}_TQ{0 zIl6mI*WF4h-UXkH6>O)B{P`)Dv{2X)%6=@-)aB!RS_h!vD` zM~(C6S!1QjfXSbpQIoJnT(%UAr7nK^(_YF6se|-!!yV<5^m2(Kf*<@u;O(MQ z)zel=`8s4qe?v)aYmg-jkzm1UhApQ5tC26>JaOvWt*%;a+B47ZK5T7jD4l%?`^F|nDuUvQcvERMYAAA*9x-{Wuy*i4(@|H$d-rV znh}gJb|Pjzx%Dz-k0<5oK%@i=s+X#narmT?!djcx1sVzI&~xni-q)MIN<$?f)HZKk z>{uA^QINAv4BEjGXk&bgGX%xYSB9w6Vu!=R2XhsAY)Hd|0cYKSjyhphyO9G6TFNoT zMYiPo*TtVCi47ERdBHgM(xP4xorF-oRZ@44@VYBJD6kcLuMzv4uP1G!)FHL~&vmu` zN7Q$K<=FS{H$;d+Dx^XxNu{ExMINOg72Py6?h*}YDD6-bl~SoRgch1eLqllX4TOqR zltR&>J^ttS&inhnN5^|S!*h4t*Y&+V1Pw&jzN>H-0Twof%I(kj(vK@XOO{N~_!6>U8KTn4O;)>0}(ds8Tn8`XtS6S<9qD zl9Nw{Z3V^IlpB(IG$fn5+wvcbzxsZ*O7tv8wKE$^Y0spSzD3@%cU%N!6AhX(|2P|5 z$U;m-%B7DmFx4W2^W*n|ZUV#xl^a&qW$zw8*Wuf;fGyP9R%m_`7nW!FJ~%zAOf+fm zX+uSoQ#Q5i+J39bZPk*p>yDi;NIz5UOk@o0sp(4}<%^-I!)O}y0m!Yq89!DQg7jf! zMd*i5rE5OJGZw`LCM3WU^I*P<3Ndv<*d>ErAslk_A4nJ&LZXwwc!ldl^B=1Kb6McR zYRbvn-HBX(e#|I?EF{K7lz&K{XdS=2!$t@)#X^ZAM#FT04>d8+%x7lY;u&z|F*3T; zp9K>wH&Y@tgwE;lv0sf2GoJ8&iZSw)aQDGlW5~U&N|?@p#+8n z$G+=Ivr~el7`|h`*j|6{@DDAmaOt?KXJ+l3-B!dq)5hqU=Ie?xz(VO7(;?-i8XNT;DfaT2%1*@b$j@w>Hx$P7UIDnLPJL7*n0wa8iNI^dMwyv+TuJHE$L=(EX>&rhh9E-(Z-RlOiN| z2|J%ju|b@r=}9X`w9Ftfj4I(cF~~~zC0%^{jqM$HAf4kH22a`LLo}6u;sL6`dS6fo zPcofN=~#QH_>Y}=_v+s; zs9tNjx4CnpkNKlvwVhB40zZaS9zEC1G`JFz$rPF9ExvF`G%-OcDd#88gf5-vk<2LL z4Cp(oo5BCPtM}@pJ`1RqB+N=l+6FqW)TFqxbHwGFl~!|H=r^nCPrw_|Mv#|wuxy&x zfuBBo&^pdHgZC`mQW;hZm|MYN6m>CL1u{)TB>|q^W$7Pj0thL%qoDhcF$?K0-~5t$ zmhtpp-x?P$kJ+hRL>&l~b2l+R)^6Tc(1zHDM!~XOBD`B91nxH=^8;<%=icF}uy(_; zcS{wLndq4p;G1Z>~Q0htU54FbKqD#*4E`h$3xU*m;NDc+6O;!CH z>@l9wvEgDgoJI1B$pIkD3j|u?OZw{7tGFb>AX3;kGtnJRw=jmZgpjyHHNF@7{_GWB z>JJxb)FX6>Z>)s*0;&&C@YctDKQH45d=CHF-NJ`eD~%K=x* zx+f)|`|peHkdx2+d8H#qj0Qjf7_nxsoKZETA+1lY5iRBPV0kt9{K|135|DJwlFl_N zU5GaZwuzby{2=^M)IAtezi2@VLIS?l6ih6@0vY)3HH3Y;kNm_V8b%@!pdF>*y|mS1u5EZ{fL zIe=i`l}QzOj(-{7&>Oft+@i56_*K2ewM@(;A)bay#N`$KXumdgoT&!jffL3 z;?nDN{PRalT1b~WyfAG2LT(afFPOt1KEnkW6Lc(6@P8?ClTNrALJT3l0FeQohuaJQ z0HSR@zo|8tW)K4dbQdIot@*Xw?tfeW<5=oCP;uZ}Tshw97milJscdp_`Sj;OFj2O} ze?FwVGttRV8Rj||QlT0QNY_h7Uwe7KqjzVq*?|FcL#eggR~&jGr#S?>I0QSuA^kd- zexDTx)tMjqd-*6aouM30A2_S8NWsVkfvu>Wzb?I89Qg}{FXYT1orC{DI!cDY)YD{k zK0gsYkH^TdRtpr|uU@9&2FdiH6}T3V`iy_26yNE*(bSVFV8G$2@wT{9Ad&N4LhryQDcbFiiaL_P?dXmEtZJVLyE0>q#z0&lmsLX0IVh|cUZUV zllMJ8tg85NfgOT4jVU_8BDLb_?Upd?; z7p5V5?3iK1-U|x822lwZunKAd5`}}y$&1&6M*e2(gV{0cwn)XXKvGU^zaF->tKFd2 zMwN;4+UYDgNVMT-#L;#q{bhN?^sz`mpqo0)#3uruCKq<8|Hf`npoD#lZsCrPj}hBz z=q+SBy)Pg!Eg!xSw)3;;M>=`HVxbX%Ck!-)tL3W=L*hFgoycBdWaJFd^GvhK?3%e= zr#Z5Bg~-CQvTRpt0P;fw8uXj@0u7PC1P%^~@wbZxo)(BLC-+gH}F5pqZHS}kEI z4aKAdV+H)#H#gET%x|*~@v73{&~M(b*@DMXArasj@;0C&00s2kbl<)u4jkfr4osn2 zXF&B>?3DhkrGo`KQXD?vr3Vp*#6}=I`^p&^mJctby;e#)Btpb?Kzl24jBGILdg8Ft zR$fO{$Eo+N5c96LuBJz4d~xXBGvkrqxxpk3tgp4sHUto>ZF(R zbA$85PznGAo~`9qjYC03oEnz4$0G$vg@n8eQrzlLHnYo8vMa#J)%3Y{xDvLfK>WA1 z?=Unwpj-SWP(E=}T9zm}UWTqORl(}tHP2PTtR{wvu2G%$nM^~FWcgncm21sNFIYP%kOF&qUDno+c$W9&YOOQ!SzYTG3Zpe z_QjxcyzVtcit%3Aa@awlU6mL#oZNom_p{1&#lK~L+G&WOBo&V)q&R}_3e<6ViG@E3fiIi zz$uyaWIP;RjfqpP!*OfI&Kr}_Vl=cy>Z+mL#`aQ&@9aCfHX+S3Yt7BxXn^u~HE|E< zgpTBo^}{ZZ=Izzz+|0brFpApe<{0%foHi4dr88P2s(9uS650SK>_rbl90|Z`&Ebe`LrA zYYQ!3A5l+9DKWg_xIw__}_Hz=@U z&}+%+CY0W5Cvzm$G;3e+dNtS-9Ml}=0=(hGGV+oS8$2VQ#~ zh!v73IhwQ2`Z{&h7U_fr@E~7$o{38Ff~6xTnVG>tljUA?fHhHIISqbFyg;x$B_E-n_Q$I*%(8itjabpGU{A&Xxp*Nn`LwZ zmA&F6t}|X(Zvqb3q__C*6^1(jVgN6>)O9dn3z#_V@2T0{ib8MZ^9Py#wNE>2z*ToM z1pLrY z&r-@_n*S!YYcPbl-SEk{KRPmaH(6r#qaY7JN0MX-268vH{T_}0V)6S^SNfpQM<&Ob zR2GzO=$?=X=-xOOmEfNyrAge!hvy;Vl?ZH@DRg)C|rs0d(_gZ!e zoiqqA$!Pvz*y9+*v0u08TW85BEc=BNik2Cl}h-TU#k|w_^ccM+d zyCh0^{+p2LkLr!%inPw<4Lew~I8sjDam`^qb@=_M$557J@Y4HZGSZUcMZ&^+4O!CH zP>?zN&6`R>r@~Zv<=>7MKe~MWjaAsfh^y%3L>c?1Comw8Om5QH!v0Y|&D1Vl`}JL5 z?J!dY;zM3_L_T4KF`nME;ceZ2#L5qbavz%jR}OZ6#4Dt7fdB}kz^vT&SywAuxRE6+ z3j)u=F^Stz9vj!>$mUA?0s2JTs!-Hr>#e@_$#>I?v%VNq5U)2Cn2I2D01X1il6wOm zC-Wij0=Z9 zt^zI1Ds$Z`ArC50H52kYO3|b~sJ%nO270ogd&NDqY>wENQSVk@vK2TJX_mn~I%AY+ zsC@sbeMlD=IJosBJ@{zp@CG!w=oZZ+HBRwKbQh%bD_u!nAiiQ(=W3ToJaYj4AbMe| zfap7fGtZZBec89-(8_)=+kg)|mjv07@YU{}Hv)t6e{?~Rota>N-TxO9Kfoy%`{Jds ziw7KYx!d^0PlocC$9Tt~$rNCPx``}ITJPf>w#FoAl2e4+sKpH<2ywZ`&DY#O5di(G z)9b3BCTI($Y!()A8PYzmi&E%fgs!L4CcT&1KeOV$Yi?kpfw@Bwfy!5|uwh-ZJV@3`Rr z1~Ue}m^1-op>IeiEBM{q@7n}6!MM+xcMdKt2K!v!pW1aYelaEHy5N<|Ty@Zup)Z7P zQ|G?_Iq|jy4juX$o}f*;8JAsp{@iW6xMSQ%R>W#yir@%T0*YB2V8jv(ago?$-jfSY zNb8o{9CcY}9Uw>i087Jw5pG)A8OpoLzo!cAq_x>r&+ZwWbUqx$wN#j2)U)L3Fvx0j za+5CgY@D{u?KVyZghh1E%`)hFFeF6(q?}9^$^QBN=D7>^G8Qy~sUGp}zwgSb^YqFc z7+BIl?RfLFDErb5dG(|4W){J2?1awChnu>8uf?TZzKzH*@u__Fxc6v-?AJYFv6taH zS!~STx9E<_JGS880VY&u#izeI^^hx-lm7!zk#G;l957M$KYPwYABNmQ9r>9PtI?w2 z=?^SF}&gW%pL7nEeTj%Q&n7s zmY-txQCP1=CCK&YcM$5-Z|A-?4hfT=H5UzL?r^KeMh zfv6&njGc%q+|Nrbcv=)3YZ9N~tiL*|5UZ%OJ7H~gmfiZ@va+i*-dx$7i-t5#>uwG| zHspZvdfVS3JO0t~U33)?c;{J{zV9qtb;|eTr`x| z{<_C}tI~2Zs=v8G_v6kr)jLx~0v(7N9Z3#?Cd^OK!@%+o0D8z~dUwBqs7&%WPCzLE zyIqt$@SR#h%NMV|=i0XBRY~j1zu9V*P^59Ur9fJKwdp<|v)!s_U)_@8SpEc|&sTq1 z?Rx-?G3Hn30ni_aggoy~mgia@`>qtEnQqzg$rX6*bF5c+t+wO-9{kGjW1Lo?twM|I zB%en3)^m}dn)aQ}Gk$>v_X@U_ybuj45`CL+3Jjy^R{Py+6564<{$MkHnD;+aZaDYY z3-|B{yseX;c`TbzIJynE9{e-F>B=*n`QA6h(N{Fy?0wB-^qn> z*ImTzL6}6?+^U$YFqzUB-tF zU2h>>FT9s>!*JOz&knCVnL=lmo4f6S-vUvsnJQ~sk#PUw?jTa{!nBGDOpP@6!T{H; z6=zE|*MGdJ6`EeH)*lKZEBMt_N?vU`tZn-*Oi9`mq60#qPZ;?npSjkYrR85N0gXQ- z-vV9G(s?LFu|?GgG(oXNFPQnw)nf#&oGA{g?p~fOqCfcrI_Ve%=|cY1zTXvvWOwbl zS`;SL)6KOeeb@1l8Wu_UvA0E;Hy*xP(Rq{lwmaZ#Kcuug(uKc1PTn5Y8GqT$HTx(6 zfCe(pjE_RjO^fW+XEhMM0J!2Vw|f>ko4_o4x1y#ti*I6K$CJ~Ip0@|<^TqThHQM@) zZraR$boeY^qg1$@qjzLV@r()j8k>06hdM`Dyq8s9yXVSVZ?`U!d&R$2@F1AutkIg0 zpq|r}{||0IC(>-AFP&TDMtKAAFB}7&X48XzF zlM{+4yt0)?jQ(l~zmmNIuslie7O?!iuP0J;q(1sjw993#;+$2Q_Z#?p)TR5R`{FKc z`G^xMQ_uD##r;ZL(Q&Ngl#kWCwfW>LZg+>G(t_@`#CM;6W~gd%x-eW}Gk;X>r25!0 z6k`z2EW?e0*9BPpD=L>dd#8u2?^BU*c!G!}G%YUF|7~@!e&&qB>l-WXvyy z1oyj6E9)%XbTQ(1P~ufIqnIR{Wz(5S+?wWB+86<-dBPa5g@ zR!M@gML@{%&hn9doz)uHC3QtAxW+b4X-zp0@}J%(T|`4%<4N zUbG{pTQsu&#C4{`!Z*X4F50dTDqYE`y7eN5Iv0=PwgQI@h%LZ6avc)%WcXGF&)7PU z9l1R|2e3t8UBAC=qCI!v=@uiRKe`V;fBL^mGpn`B!ON``NrGn zvT&&WK%L-I+8ACvq~!E-XOvEN&Uwo%nyZsn!V%oom#HfL&xW9#&DC2kMwIvmrkAHM zux~2(Pxh(G)8B4q-Y2EYZYt8YFL@f)l+xY5IWFM*=VZ@+U-D*F7NAPt`uHE{!zdSR z97k->5pd{KrLQvxvs_kug6MAJ9mh;%P*~TIoEwM(u=KY(UH#pR59Gpwn z(uVu5_wQ#kRA=2}9RLOm?DBBk=nGNEzz>fnsM0GwKLyqcVE&5J9#Qchx)07w4^j5PK`42dRNIk_5_z+3&r z9Cgf3c^hY0Xn8o-S$}+SKeu;>a*KjWCvG*Qms7 z6e*~@Pqb*}f1hmdU6lMKMN;g(%fhx2++-B|aFdG={86u_la+;2ifRzRYcW|f=ik4s z9iTc3(ytLid`g^11Gtkx^5 z`-ep}SMPc)yznLJSy&E{S6|-p-Z;ICg$9>ywKqTANnbWetPC)yVz?FWxjtwUP8Z!t z9JMhHFNQ22alr-^hL(xCdQ_KJ&fYe=`>7dObZ^dF} zGDhi3_IpKW&u{=FPZ5Qr$J~rOaviqiS@0+^N6*&!`eASbJ}CLhP%W}{5^)7OqoL2| zIx(dD2SQ~U{``>mj+`j`O2^plt<#3j_3$_CJ0B=*WB`*6?Gm&T9E;E*OaOZKe3AiL zIUzXf?paLROjR@d^aL~jQay=rK|O=t4^N!O#}=9jyqI-g|2>%=CJ}!vd~*HL{2sYCMzb>pn}-tcFq&WoL?gZ!~dl1w$xt+%*A zImG|s#$CJhhDM2$L4$vvQS@u!>cm)KKsHrwEwF2Hu-kZ>%7$LoWA58%r>FtOb?PM0 z;u}O1=rcs)ui~U*bj}|>^T)$~^nt&y*R2BIAQnAx9w>LlBris2?5*A^wSD0P_70@! z{mk%A{&%Cr!xrA&z!T4OB-R|#8B?c*tJRpE^DaLzAKPAi(tMNkld$n#&^_D)=7>ALM+w#_80mvl{_0;BnJiOZ2mN5e;AI z*#tK7(w|_>Lm3{nA+kdplUzhaXg;44t6DLxN!4BIH3Mk8up|d$TrvkF7!N!==yOy< z(FU*KniV0$J3xPozi1~^FF2aSaS;<5)TP6qP$3mnNuE>`Jh~8*5qY-j2$78&WJZgv z4zV=)vf{r?Le?LQGY1e=ArK`~y^<2F$RShuWhosy0fR$N=}u)z|s~bAJ__?5xuBLYFP^X6h$1b1!oHEvr$j6SJQM~?~5kPTsIa#a{9~|vqhxmI^}13TZi3b z)_);;V=Y@6@%-rZq3!a(4%O!;Ux_lrlAcy_T^j7Ei{=G!M_5j42OHS z6^V=a$1H3yC%n99Ehr)A7H_@JF86SyEEEK+MbTg7u+{?OH1IN5nTq{|d*TJ9{pe!o z%c+;AR(9y)jzA>{>I8rXI$e_?WrMNxSZjq8Y#j*yC=jd7Z>}r)?D+Gh`oF|%GPcNH zT%A>ZBki)4ngJ!oGa`6wqqoXA{rkPbx6y+UV%|gWsHc@BcIcYjY}Vl1 zR8YmlqsE~cDVQ|*lIc{Bqs0yb?1EC6OLD8Hc7iTh5o{&9{Nk#!tMDF%Y*s!L^7!}d z4RY2;7QzQUZ!0)@gX75GI6z{wKq|#mO}A#3MZjCk%fR5nj7&EjFU{?&pSNPe+GQwn z9F9*T*!5xh&kipLKunI4%~@M zSx|-HQV@b+Zv_2IwZy>EO$FaPiSI9&%L8=6dEyCDQ@g78Q>xOStIF96n(x8R^8bLo zl4;AMNBb_+)MOfudBGRWy@XI10q>I}DHjGOF0|^U+)?dJuDu5q(!^Fc(dV=bBNnK| zP+_`{0f(We2A-MCRhBwjFy>lh@i|yLz&RTl8eF_iM1J~_A7_CPIT7|kX^mx zbxp7T%P*L&8DlODXfIL_tnWRpwU_b&^yw~Z9p{cl-u0ANU$piAKOQ3^M1beP*NI_n zRP7;lL-lON?RUV!!O+XxZ+6!L5RfqwZ*l-+EuA$$V}T6ggn`!95vrlQCQ=Z70s62X)zGrLEZFi8^R(^-xXPgkuraCt1ja&FkUpd>HzDL{ov&Meo-rI*9>?f65`R>+;h!9Jv-$8ZqPD1 zMhC#TB6zj$2j*xdg^Trk%TIv7upkaV=2E|{I?XHJV)SGca8JOHKi_Z1XJfm=Cbp_| z;u|T315eiwb1}7?mJ9vGbaHCgdC92%e|w@)F{0Xms{ykbM=mN`H4cQ&j7Q-S>#X8V z(03$ugQN>oh7de3jTSeJ)Tu$iAAop@=_z!K@j}VZbsckhrZ<#r3EclbEfb4Bje{5FpslQuRJF?g;|oCcx^!@E2E2R+Rv9 z!+~Nsq$1t-*hK3|hSjKq7A7cUuSjorZ!u1{VO8Qt!H`bC;`qcmY+jvsLu)J%wl-%+(ZchF)E=JapFr8 z4|9J|agN;26+7J#`k$jbZWl-v=I1T*#dc+h6wQL_$Xku_mrR%!lz=3VWc@7V4sT!W zIz|FXgyI(Y%xLO=jQp{`JFVq8ij+~yV_hMDPUfN8|Cx<2k&_m;gI>5ISP<}(VS>ZB z!KT2)OzLb4?|i9n;RZz~SVK>|&+)y4p*$=PZl-BI8dTE(|GqQH&jQ~>la-6-^TKhp$*!0FF`roS8SjHbsW&TTV;A;{i*^-0`4zZ{~ zfjo2f>QwS-^_p*o$~EyRN27V?UNtuObRvPF3a$tqb=7GWVJ0=`rXC?!4pL%NeqLC1 zNWh<72u65@rn1rw68*B!&R8>|KCX6# z^b%7DIy79Iy*I`wJ2z?J#VvAa?a_q-MRT=8=Q&eu{I}z1m?Z~a3ND0bUHn!H9_*W7 zXx8MYbvJ3b_V2%oLgPcF%&gqa0JDeA@yRRy?q1JS-jQR%K5b7{G>v-X8b%6!FIHZw zM6F%ba_N8nCCn|Aet0=NVb64g~Gia1Mt#@Blxf-r|4UBW!)Qfk1nP^To>ajc-;$CBxdYlApGrOyPN$9*6mH& zb1KuD@t%}yRawr{;)a+AvI+!hI%_fwLyks#=A3+$bcLL8>j1h&k8aA z;&o;EPrTQ~-06W2bHdE}>YX@0sUj|ZrlDny+&+^vf8$W!#h5dToqclC3rWwkllWEu(B_>p*zH|Fg0 z)=$N-CW6sciyQtGTU81hL8C54B!C403JHcjF!!|J0ZR?XmF}?{DLCwZY~-BnkKI(! z+`o?w|vwv)U;5#;4J*gJ%Lf8a*7qBs@v*tWb4S!9;ALA-lQ#+lFQK8*Yw!>HUWJ z00f1d>9)^SmNQ44G@MC*eZdA5ezdU>-RKW5MZ5Y-9j6AZhu<#%0pZC(WX%)09AmK+MoGL z70D0SNGqmWsAW^)|8|=SyKCDd`wiHmq zfeRVjOE>3RoB%Hf?*dGA?d*QEYh!BSDbG{Ffj#03$|eH&C&uk)uxTHLVHiUDkU@!0FS={etaEmrd<1{WjGZEk=C13(=|{Qdj)E-fmXk^_Hv4HV>e z4nprVT$j|p%p-rpqR=u^q@D$)IYb9=C$@+AWwLB&7>j<}P6s(&Cg`p)rz`vLYe42t zHVK#rq?Jp-N{JVOTZ3q{I!}<2!1emOw|2#3f|9$40UZd+9mw=QxtJA#X)L!AMIXDb zbwQi7^lO_P>sr|Y8Bn`H5Q!UuCxD4WeWz_eC$w(6q&NY|5XVF%n*0l58+kd;8H5_24v`w3%J#bc;-G6>Ql1fLDcWFJ3ao6taE@Yd;W$2(^*4!x&iu zlmxf%OEtgW3;4JZf<7uZG#m{4SiU1gnl-ROa1me)riw_j=YniZx2(myS}Lyo`M|lW zeV&LA#M}%NRNP60#5w!Aq}skqpAE03vI6QM9B#lGy}iBlPL@cOD$~%(=Nb3Np5neH zz^Zh51Y2|vV3fX}rf>;r#*c6IsJ8ILXQoc?rM}wL!?tQP2`aN za))FYdM^MJwlP=g>gw>8k|jYn5Abx?Y3ACT@bzxRisgJs?^>?TCg{|~P}d;`U8wZe zJGg@@)F7as+ouv4L{Jm2+iRI?v5Z=g7t!v^tz;Oge_fEjB9g{Me1x#KG$75+VD^CC z`HfdoMSxub-%7Ldl}YLH2R@ z^@$!RI0@SZBwhep5L5K7&sgy5g7X#t4uDAB&l;>|J(Fakcf_}^Y}1J;klILjCFAt~ z69}AoR;H23nWA4pt+_(Z58^n;%`O@mJ(C!RvFJQ!o;&W}X) zTDI==lH?g0k6C>1Iz_=j(Q>aZdSm-fWh0}4$A5s!4dxSQf8C#)%84leEFtDBl&W^i!qNoW{xXO;b5k>8BKfNBxO`Ed3LuSS+8 zi$ah@cPAvaxd6+B;oT@($h~{_E^TwLF&+ZPh!7SR1k4k`eiKXh2KHQo`!nYAQ5ImaH@8cZp#4l%_FIpkg=jQ5SOn@Ia~Jy7u4}f3mIq2c z)^mfx1-c(-j8Tii!vL>JarUTyb;;-bh!GKo zOXoDrT#sdD}9RVy@Xkx z$89Z2XJMwi(v|zv@oN|c!7~A|7;|D&@b6DW7p}S4 zci!q#j>9JqB!wj5C&7&Z2Kc8kH(%hifg&x2zjr^ZvrhnQIRR)8&@bfK@2*`%P{+D5d_2BN zNAiTn^01z=-S^Fs?LA2&1g-w8(0mEl51D1D^wuQYN+0Qn== z_TvSZpqbAF!f0WJRz5@fFW^d4OdR!g42_EKe*4Wy)n*yxO(rO;ztu_IMZd_gNNJ63 zq#)EusAG`>xFyVRt1pk=p*A(-E4IpVdY({$;PEF==Lz~j*u|AphoA!n$~kbD+>#QD^FBRClowzq2>3M4dzjw)im8Ya((CEH*?PF zrfN{E5KB(VFb#bP`h<`Y^78AoaTS*TrO>dyU7t|+p>IqTQCi;Y%!@B)+M*)Q@oMpZ z*Y`1PypO=f!IU@y4=CWO@+1FrGgft1nIJmR1#E=Odlffd`MXHm z{D8Hd&0;eAB&)vhBnIj(V z7lJ4^7;1)7#5W~xalb%BN1Y^T0zNe|nrygc{b5&;X=_otwbYjY7c~x#Ci`ZYZU3;l zw7h`rU~C}g0nCA=PUqlg z{R~`Wye52I7R=V@0pS3HU8v{}(crWDU$m%UF2YCzmxonQT2Wa!Ta0Ty{6ND{(AE>x zKv_Bkt{$3wYpH-f&*kqAjz6%kO?Zc}2^AS$1UPuXfIzbU3d^4DM9>BY==BW5Y8X)$ z8mZ!UR$w!L2ZsKgBvO*r=KiFsV8Q1|GCC$uE)R(yiUKvyG{g}^0s~be3CJ8w_jS`J zeVM&aYP|jBo>yMp#5^U&qlCFG{EKzds|xO5rSh^P+e6^0T<6ugk)QP&U9>|6#grJb z|IhpHd!j_PwOMX{x8W5PuO^s3DD-fP5Drk03|X=Fuhn6VdnOl#`$rZoqpQSJ0Q=7I z1`Q{)zwabME4ghH6K^{2mhZ-qT4~CBfP_Ac!Xsg)b~_A9FXrD0vkg zJ9t6l4G@p*uHK*->9JU}zhu3}swRMPiG`&|4h+h?+^ut!TBoA+{D`djNZ%XRBfAfU zrJyDZt-eg}=vQpWuWOsG3_7`ymzRjjFl<0*e1TC5i7ELlIR~ZWXvebA5<|2#wCUV4 zGpC4 z!-j{!la{yk(9!Su>w=m<7l@T7=5x=?`kKFBvWj_|sPvv+cYjT2N^MAy=3$!Eh=}+@YwOfln!nl+O)w{iLV=jd&2R&clb+{2c`#rKrMJAb)v>y ztQoD2jPM41G0}R{=2dY_1+NS?0wElodW>U_dx%HP-1NIRD&MRV&;hbELJ$;EzMM*l zwq2nP!S?2Ic9RtUuDi3pC+71$tSmAKV~>~u5j6-R%V7r5W;XB`H4mzEzz5)* z5ce9K47vmp;1n|hzxcV0tqRb(C|!7V}+k*pXqT(`I~kL_bWkV?FE z3z!?)UsBz8aevry3~Ksp+O+r@^=zTZNw^adxuRJP+!TQjyqMm1F!DukGmF{~Ws&d? z9_KP^x7E|*i0UXS!1IF610qSd0CO&L*>FqqA7<+bNlv~P;v?!|#7AiQQTo9jfvG|` zIy@w`rXUNC4ZW{ube$o?uu^TG(l+K_6a8}i)9JJAcDl<~n%%XIUSWrgIKV6E5Jtjy zo@G@gP9gLlgBl67)*k#HCE19Ry(Ak}^e`}>g|grov6t6rP76#IYn6}FhROg4l^D(2 ze=SXQ&%z5!kY0N(&=FNJRcg2_ov)W zJ>Nr9r8;cFT`m`(?%0B2CDRZfCMbM>m78UdD~~%dG#f-+5V(0t@A+blyuNFxm)UP< zt&BP?v!00EkXt>P-VJ*wy$=FC2HgYW)3RR?ECJY_jNuS83!dz4^o?L1r^zsEdu0Ea znO%H?YrQrUTBv(>_U(zOl0;ES%&I{L`*wDwAe?Fo))GX8CcN+Yt(Newh+FSzG>oV{r+UoeORQ}79$dU`9 ze}lM>U>8I6xuIVjm<+_7EJJ#pEZdeo z^q`C~ARnccFzf-7I^rhs%Y9NZtN*73O=}$F<7a{fnVbMY9!o z{Pcxwk~^L-aC+?lW21%0X~`}Iq$)C#pJ>Wc?L8sspJs;xDam$W$q~wlh|jM9l;9Kv zSqzyytaazEy}BvJqE6~*xCH;S3YY6&`cz%QyzE8t{ERI|kI398i%@&Vb)K|X_jBM? zt=l3FijY`|^Q$Sj<1s*|Q4D(a-Y2}q*fjq&Qdcs3scu8_LOP|6_okKh0a*Jw`wFO zXxcCaS=3Bl&3`*Bz&c@-x5j#RbVBsNe*G8I^7%=b54i0(HI#T_>US%Z&6Ex5t>|Tx zH2UfHwipu~oMA*7PkYGP6 zY`&y_w(flUt^}QqCxNp{B{!dlEyq;?gFl(_L1XLqx|rcPIPC)KME?yz8?FuAO_e0# z!r*wFV{UyhTwSX>ny8&ydJH_1g566RJ`Lt34G-|@puIV~Eh(`^tEBJ8#LgX);{{=z zJ>0cTi^s-+Y@lq$5BR_R3TQrHF!a1>urcd(vG_mEUS)7p4$4^+DJS4SfE0)ONW2u@ zym@2T)s>2}rtNA{-*Cz&N8kLV=wD?Y}Z-KZ;L4S&dB@38%#(^9-zr0 zAtU;)f8kLDj`q#AQ$PQivRap#s2l9G#op_P(OMV{fQW*;zh5Zo3*|j51c^b5%r(Dn zu!~)GDbwf2qn77mp2&1fhfXMP)XZT!6!!fcd-#=ry`;`NIWl3zvzRd38*FlTRrmb5 zxtlNC^TPK$WojZzicnE$#T;QM6kKLel!gBIA(`1owZaYgKNS)Ro7@6j+abkMR*#H|3e zVP=AyfixT&X3n)?^RD&R5LgGRJY)Ko<==A6_x!v^n!>ehc~7oyqS%%lK$Al@18oA? zciAC`8HsIxn`y_OVas0^T3_3xBl+#~sWjvzOqd5B%K;CF*xBMXDX{zrTS24P(m;P( zmFY5-_sNkg$rLAwPVQ_rJ`4kU2mb&23Sj zEgvml8gB`B<_L#pR5)JC+?j(v--5GXrs1hmA_iDXPuO^+rS-H)ijb$&==4PYb+Z2$ z`&Uquj*7i&&i8EFd5#V&(mioH%ZyJ%&nA9uJrUPh&@rggR0XUOq*}aqe9GSNP_8a- z66I_?KLR$v0h>6RqdtD&gHHB`RwhU_KG}5Sm3EE8^Tq!GxI_;LgvwefF0)DIe~Za> zI4U(wi#h(jKDyQE^0^XID8WKg1k}x0N8QJE zIM9<$xgnrcU>SwynZAi`GOjAOF3od`VOzX4Ici1ZVWVi<`n5QrCu%6PhQLQLR8Sy& z8PBcpIgP;wb|Vx2R&=9aeS_BPkyO3J;gSVheLc1f53{|aSoXJ$Dv{zA?yW*=5FOAQ z!yY*f(__rWTinv~lba&#w^wS3+itw8=zF2-k@o0)g`~fGuz3RA@r%4)w;t0D*j&!< zG~%R+j`rXErhElXI(?&n5zzUyKbRE>Q7+zeoh?GhiA^x}n<#f<^{0EevnN4+#DJEt zoPd;f%auAD$S)UD{GB^cNO}B3uB;r-qNDpJwKqTDBsZn-qPH$OD&nmT%>t^o2MO1M^3!{Rbxoy@X>!ofTt#|o_lc$VGhTI5vIo4;IG9kyhdiV@&j zOg_*r?7fn0Aayf}O_#B`ZLz(=lxKJpo3*5HqW|gmAm}*Jb6@ySn=))SXg)G;EHIXa zzJ@rI1Gikl0FEclknS@*5Wu~>64(nZE7!t4u2`I{N=`P{S07stvC<53b4i}AZ?sr+ zTyzfkM77GuiQgJiA&r1YZ>_g+Km0*(h<4U1iJY$^_hAj5K4vuL!tFfM2WhcTl)Q7f zqHk2UROP$o59g#fL}??2pV)$asnpK?zN^|rm5Og0=tH3_+Yu8rXpnWUi@Be<_ zAfc7vPE#g{GZS8UOv^CQK|aI10n%wG>h@)gT+CEiahZ6m73VsAf&p&g92HP< zph6`$K0Nt?L_)a59p(+wS$u4Lb~-ENHq3=b-$c70-O8mY<*yW{O|<27l!Y1FRC8K@ z4ogz}8EVu#Ozo=K$uMg~<-+IV?O|K|RHS+<0^ z8RF^%d_Jj92U%&noX)`>gjT+DKFKMzcIjBl&J#Tzxf7{P&hP7WkDT(mK2Xz~)L+{5 z{lN$B9#*SXCU$S)0d7Fm*%TK-B}_8}&c(F4|5w zf=x{JC6gvYBxK^y4oXbZfabxP&6dd&{9Ak^0X)*1)i@TBXY=LDmv>DCs`oK4IQV(A zG;Vye&?uz5NLc8{Fq#6j0T3kYYA`l=oLpeGXn_)@zS?-P5vp{ESMhW~Kn1=C>o$q* z@&h(p+viKaa~|Px1zi>C3by81?Q^9a$$b{j1e*G{D9}!H54ZRn-reS=88abq%w4dm zZUlr~ywd%}I99lYrJaHsOmGzvqYjUckvgKHF)g4;iYK<#IxWZ*L_kOmrC9|egQ)bM;y{|F{fXmu-l*cT?;VB# z;*ud}_I9Xcy+qlA$)?1l>k6UWS26kfd*1tu1qy`=4=_}>i-(@dWBm60xQN>{>m;c3(6vz`Mcm;XNhj5-P` zNZiKebUpCUUj+ZxFzS=Gz;QmnwA`I|OC>iE);O`AfpHr=2Gi1v%wJHaw&lN?pby|y z#HZ=}6}ML2l>y2hQXg0gMSIYcci@E~ZHI_YR>ez|)>>swDXbDHcbrO%jyA^U*NGCVPC1v`_>-8{9_6TieDSMm5U zH$29Al+r|3s(gi#1_9&?<2#7auwX)NB>;fomh!o^Fis(AH3Sl$Z+%;>`C3t36RKTUpb{lccNi>!%TIp9xFpu9Qhj8wim*vQX@#P^bpUV7(p{(1{|0LN zs!&!p&dnH#Ra69N+QqwD`n$*6%N5?!n`&yDrzV4D8yE=OJ;6JN5Cc3vVVl8x1|tBD zaEt&%fli-asVc~kvHV6AykS6u^j2o4#2}ZNw0s-956^D4?mNHYqvJn4X%O}+@GTAH zRizF}muOZ5n)X@bz);5uL5e|_?QUY>{H>s8=h7b*QnH(RtUkYcWO?MdpBYDYYE?nw z^jvzG(1&C2ULnOwOaGVTxgW_y`GxwS!|cn%D2Zl0t>ct+D#y9F7M-~88NIdaH-;gL zL-y;C_Okm+Tk`3HSGo^MGXJ?Y0Y$hYglxum5zGmURk1$x-I?c}T4m$4mHx3S>^Jh8 zxwmzWgBb5)^2lo5)KzIqlFt}$U2GEl2M6Yg^}p!0<_UuE1>6H9XAI8)ZEuBHLZ=O= zWq|rVVRMuB@jpsfsX4U4nh@t6WCpkmNCQP}TI;6Mnyc>fIBqPao5vorY~4 zz8{oCpm<=!Nz_<)g$OooJE$~n_fOZvTeX-291M1>QUxSc1>Yu|c$f>Ev-E63$M|LI zP5V|xn#`WTMMDaMPm;&m3*_Xf>+H$~nJ0$MN3MCY6jBz@@6pXK(8ciio|(s1d+C7+ z0SADrEVSZaLLm5#oHsH;plkXdub_YugwP1ArAi-_VoQL`dMphTlaR2ovbwP3y!g+% z+kTCnok>0RyXyPQRc4T&cu2|?`a@pXhZ^pnd)YOH`S=6IGm4VS_ErsWRn<2@AA8*$S z^`lCdDxku|v>dVmOgfZ3LRMfU0s}g4ynUz^K^eh81Cop%l!bu;sPu;d93K1xn1zc( zl?}+Z<9kKzr&IV;OiuvIqf^Jmdl=m!u^IHoYE94)1}@g+DatS#0v?S6K?)OavM_PK zeMqiwk@BQ5By=I0lm4E2Q}KOxc^Sn_AbD*?+_zdgjzu%-yOA_0$;H~Cw^poPM7H;k z6`5)rhs0w&1z6-f+fa~`6=!j_dke1a;xf@(X$d+NXdr}Hgy4_uCj3FKq}t$Lbd9bA zLRaOHGFa+X$jX?|Opw;S`AH9BqwC!*=5^;h^+!)`tCY&ci0<(*(}~dOe#hbK-X<7T zffm!-umR92%oCx^xi#HR$Kpo^hCoOA^aNBe&;9ZJ#ojf?QQpH|9{>sz6COs;vfvZ~ z>;pazxTAdbmlJfQfFsN1rcRSr5EG3U>ae!fho4o>ya0KTQ-;RfRs|i%6WB^y_D3mv zi3dgXub9|S(|IeVu6ykd&VNyQ&?i^oS&?66WMrC=u;-k+ z6YLzNi-zf-J}{3b4x5+gI%f*|rl+cMNI`&Ve_ zR%pE>UYsy3BD*AERtl*JOlh!sq;GK?0rSi&f|I= z++|qaX#s1(L(3#j$ZI2k--U15f}-(hW-$SeNcIgTy>72%Ik@#K7kE1 zIBcn(9SL9n!%-~$Qy3qo3_WhM|B6Ws%0AFth}k8654d}cK5QS7?qVe|6gMcHNo7wI z&@lom`cAJjKW*6_H3+dBUILU5K>P@G$j97R;Ikx~+3m`)$32sXN~qqx{)z%l4_`od z27wg0=h9Zq=OAKwDDt}Vo`}m7OR07Y1h9^Si9VEW&=Y{+G~OgA=lYE=6@Jis7>#I# z_ma@Y&_rSGyrw{}?;rDOlQ8lMA`$V7)Ki%y4+-ai?B{^o623uw&2RJ|l|sNqD#jqe zb5Ja1qs#TE=LyBFR@?$&2VO`iqRIR!*GoNJo8(=4_)8R%jQxQZSO~~l?Y*o>BqZJD zXa~qs5UO`K`OQgV9M@R5N~gJTMOB^@o^l-P(5kh~~IhZlCq^ zB3g%40OOILzvLP(T~I+`f$#@jql^wQrcP+kr8Y=BFLU=^XS0VvJkGi1d??l#&kWAb zNz9F^%&&oC0P%B$6Y1;oe|`9|c7~M+N<^T*C_+0HZ=q<>_nddNxu@9?N8-}L|3}?> z|5M%n@#76qW+>T8q9ob#I1(9URQ67?_c-<_iXoKAOvmt@f6wjVB99agAPEviHU&XA;>$H@Jzv*VqcA_!|8`AiJ;F4e!_Gx zemmMUJ_Zp4qwCK}eXgJO1A65VsQ9u#OLnK-!4KfTYm3Im3I6uJ)@-KE;(+-wsswfo z$`Ig2Q;80B4S@Lh*DKoNUX#*$(PEt^QJWv|n_iHRLl{W12z+Y-+*~q zJT}E6Yo@C^WHHBTIZ3(I5}#hk+Y$wE$UVMrqxF3unh|5Tm0UA(dKcZ6>p8)I$@6Y` z5W}Nia*g=Kqsz{gX)dU&B-MmVnOia_)Fm2f#-?@sPtWDGlO<4-NQ~cBX_d{-KsmMoI zH;^=C##wxFZO0~lWCsD*2k2W?!VhW}8vTKW=2Pf=XJ=>gjb94=u_dLPa|e~)1(R#S z;Jrk5tTv4@86&;4^aZthg@7-Zr4&SV7(s6?y$6c99sShWIg-QZ@(a| zBX|Yu0_E-oU8q5wPrMv^!@_*Vccgbt}3H<{IiW-M2Yo{rA=^slHS zaToKYXFi2*NL|5I5Q-6?Qd)r2TvFbw(pX{m7^FPU9p8WB`uO{5)`^Oo+foK3yL@(~ zY!baEp?y#V+HUSE!xMvLHU_m`(P3d>e5I8-JkAR}*{8Wax){d75<@J8=Jd?(=$mlx z0gN#)I!gE-K(#|23m6!H(nR0ise{=H7m02vH7b>C<0Kb#twPvK9U zn4cEY1B?QArRr0x$cy8ZgCI=|*4+xe=0Qqx=OO`+PTwk| z0>?KrG<3Kc73xrbD8ihQwV;T!7Pzj0ZESWINi>7q5gi>ZC3~AH>i(BTfMvp=HX%;b z!)b8;fW!zkI$ZLL)o`ghb_bhSduJ&Daf35n8QFDyldZ(Bo{czTaZ>^YgUsEzB^15q z54wVVRIo6M2Sg}LbhB}bZSC!^bTj5%6Trd=eg}Q!`;VuQFx-Im3ZLSWl4@UC;)z%0 z%=?uqFlTOI0k)ZP?2&v$nSB#!Q{G}{J?Sl~!sXaGqLeZ+Gj;X!K0VF`O>XpwHIt9# zwqwJ?aG3z+0D23s)lPt_3TOwxd#|!SNPP74T-fzN8dLy;3l1YB(G#_D3@Nr2?(VTLl84^{C^DQGmxCQA zsOwYqGJ~&N&*H2|NJ^T{uNZ~ymD5Zc!W^H(rzy9`S+O^wE<`zaK(3{lED)+^IL0ajF+dncsQi!(2@;<0HB_) zML2~(Vx=mLqEg?#dn${t1xl~_9?YwRM4rYQ_AteqP zWfc{;aAfnfhD4S3ieSCG1$bd6i2Lpd6)e(({%Cw+2^)`{&1g$rR)RTBSA z7Fbd1-&V!o5E7zYgNB)C&Xpfg212brcc3TjkE zjz$RWjd$`AREA6PWL*%s%f&@S-*5Y^g%*|O8}Ld$>Z{0^N5IhoE%Tp{9HLc~g|7@p zDRR)j?e!O%dh`LHEgKs`@cC%&_@066uY~9QFAmrNL*?;FN#v4T%2=?^dBq}6GGYVn zvD>~7Wn#?uB_#JEknnFLf*U?avIcz;I%Czp62G=8K-b45#TvtE@aQj9GBTA&fAxl;Sp6P3a z*FT=Z)^*(5u!43#3(!6~pos@1rHRN`Wpk+)^r+-vN?`%<&8>!|gYtko44IeZ6_V32 zCR31cKiE$a|<>WQt70VIRPJGMI#QsO~g0k_FBgs z#<5 zB=W$okc~joUnJ2tTz_eQ-8a<-i2*v+JeWwt+~cA1{xqd#C=0Iz4_8@Djjht3wc@P@ z0@SXPr2YLccuM`+el`irIy@Q^k|hYX^m1Wx?EU2|qoqAB)kq!*E2(E|h$#)pm|fAB zeO0}cdnYsJ5LDLC-|h0XATd3CdFeaUDJIQTzm1kRy-YIle(N}hB!X-U^lx-^bO6Qh zbVvlQMjf8=%>0e4<53MXP8)7Anj1N`?Xcx_babA0dF?&qWK=w%NeDiZ)IG8b^Vm!@ zP%gcap=fdbJFNs!F2*d3A}FU#TKKI$#A&$Z&CSgz+a29DpQI{tw)7=1`Ca$W7}r*L z2~iC_@I^CBP#CeI@+GJ;yp?|4*MF&8Q&(i+iePN(G6T05B0JXW5N=@b7X<_hO$g`{ zNwj^M-`=0Ft$>sYETkPX_7ph)IYBBo2hz9Y%Z}_RP;ub*O)`d8IoJyK8XR}lDbN6~ z5)c%mr5v*d+GH&b&A}K(6C&5R^rT-pw9IRjm8H5cD=!yvIe4~WR*?zCdz$#fSQTH3 z%|p$@#;!5x0yzjEBT-V);iSabB`46d6)MWg=fmXDx-+=GzQ#NR#0b@BxSL~a2}xtE z(QTgfj|)mU;}N7;xg|`&F9wNoN)_^_utcl3T0yHnXkVc>$oDZflQA=84niK#LxSa+ zoO70U*3u3U2Ao!6=>5xRhR?9Gtjq3m(17IR%Vf2jKp4}#hY%EgQJ6Y92R$_J`dW^C zVp=0*_SLK#z+(8w;akN;=hOu=X0LxP^B)tkHqQAg42>VqGf#!F6Lda+&NB=78O(EK z8Ej)tA=q#r zU@QVJ49}61iwk|Z9KY|8a)~lfW1s>2B*`Z?_YWO)in(diVsbe_#GP?Mws>$`t%Svh zuEhy>)69}?Jb8FYQBfeBBVA8w^pAA zgmtBDLuFcPl>L-K`>A{hUJb2_AgGx!v<%4B#&L@CsyPO*!dPJ=y9ecNUb&f3-?b2Ax*IEbOPG7J9h`pUqPCERzl@ zod}MIP+&>rzAnAzfX1_A2vqhG#VLd{gHaM~=nnun7!@i2nbHaT-<@?Vz!(CufP@Sf zlEEO8gXSD3Cuie{9MZfkuiBzMm=&U(k{l~{Wv-rX@~gwE=k`{5Z}S3wySdzsj*d>u z{|EtjegyN#CO1o+@IUlOv6nr0qtF|p6nsqx*&(!CDPm+Od;4%m3=ME*W@fME=jUS& zZn3IR;Es^CLlEkMy=ql!jkw^dk)0lWxrA-8tM{uv2H7VCDO(CEZ>FP)E*v?D!O*fu z$}GRQI77$AaOB{;9 zA@yUekc;=5QuvLF zc>oC;Do68l!KgpH?qTxz46C5cqF&EziA$OaCOmXkvhZ2L^VWj9y&4wm#lN$ zL9HX?g@wx8s3Isf%~SiVh2daXbR?O;KgNawXjiJJ{|*){1Z-thRpnW1dWyG`_t>Y zEy%g!^I3b=5zd`!{I&)$_&Ge=y;@Gzh+x@Dn9-^tQ>lVSxMsQF zlIj2tHky*>aPcj|(zRo5?1isgh60N$2@8I~1+tqVeNc_#g?rItHZ9|fup6X{j0J^y zDGN(bWL%*MDJm+u1IeG47bEyQ8pi%p*CK$?()KV6)?QipWmH$T9o%wgy}#+#+(w&} z4lnc;*OZn0$k(qR#A=xS_31mTje6txrKRf~T>DI_gO&cfC>mSwW4S~n599s1cj3>RJbUVaMIclpZTg3kkc=!ra-A4my$9n_~JKJ zJmc?zQ^6z*v1?nEZ<8#j!{noRkYErpQAFF7u8eQ`*-e6&*L`9zi(^7X^fO=PCm+@%F`o%emH+ zxJ4Am55t{LsE{C)ds^I|YgBv5T1d8??=9cMV}U}^beWIO1b!8pwm{heBL#tYLFDC0V^94`-L>B~I4t~q!5YMim5KwoAIE2*qhEX2W_0+yk1(T6+wDRd8@*svQiaU&##T!$Ll z%9`e<&vI3B$F^D~z=-_{YXmLqTkqWJ)U1R*=-IRam}G6KSW< zSH&eKQ%*3AX9AGY-Yg86{mm-^`gIt(=D%uE2V@rCEM*}$SDP@Y5)RYv; zhzcMQ`liOmGeGjRb>?FaktsJ?RYfDclwS&Np-~wqLkl;z7^A{VqgSaIbw70&myv5q zGmp0AS(oM4jPOFUiV&6w%*s}1ztQsW@BsRi%2)rl5-Y_7_eJuhu-0e*IDsQkjDC0t#X9CZb~3&%@3`8v&>dkn97t!Q%7b6=}XK z;`#!@X9Gj0l)O@>LjM?yrylonaofs_C$KXF&4Z0a8F#eD)TD?S&~GJS?Mcuk+z(ci z%=!Eyd(Pt_-Z;{^o;`K+>zOl#ZytB!b=FlIvZ6}SK7J`&sulEi>D9uCDWxE{sBQy#)BNTDfPw*-zR*x;AZ z=D*5M-&A{K3uZlG`Je-V*3|(^@vDozIZr+%G;u*u>|eJM#k1)p#nZ~3iV+kSS3*1QmjjRO$`!6S&+vQ>*^p>n0kD zb;w(spr-<5DWt}NZu$s^^e@ys{%`@tkHWvGzM=*5BK#_5cgI3^QqVGAAyhy=!{bn# zhbrmz8&bC{r9E==6KCi5P*cPF>E^Gej1-^9*|bS|n^{NHNP6X&4Lf97AI=uedV-S{ z-)RRE6PbXl$uGY^zK`?_j9ZI!Y;CPRUGhXwTKG}=cg#txq^;jS%pA0gs16V3b=Wx2 zdZ7zxHH}z~>O!I$GN+>sx<&=Y^&Mq4IOrxq>aw;{gC~?7kX7}EdoXNcEUq#9+Zs{( z#YZ6hudlB|D1&nkwW{~_3g9k&Jrjt?n|hXDuHz0od5zLa?%?c(9Wm?LUR!JeT~E)! zM~js3#{)mC1K6WScB=R47LkI2Kz61G^uBGQpM_y)fAVN^Eht!(Dt6c-xkT-vAui;{ zQ@~smHT{%fSe4e`Cb(l0Dk_=~=0V$|*kUyet8f^kbaWp;60gbtD z$MreYE0}e`R5#spyGjp42L$6rw}wP#L*#3_A?WD>U=n~D9LcIKTT8l--(HN-e~up8 zivBOXQ1AYlM%UNWL=-+nxZ&c97B{`(LOeU)wxjXdGB9nRLbYddeJR9ldLF3RDHFiMf{3)*?BB%uDy+b) zgFtvyA>47H=LSTvMz^WOBHz(G$-I9G4wnzgobhpSc88|WCD2!sTj|SV$gX5BiqYJCE!w`=e`S-sPZvVoYwh-OJJ6knF5n%$21DN1A$8{&RHgj? zk^}FDQI+IWX#e5q)Bm*N?tTN$8_;YdQrT70mCqYI#`C49zJA|9+>=h%h zoocMF@3?01nJ?;}<=A$8Sf0J{i0q&Ki)eBV$>KgTWwv&*%+u}ti+{AR|NWaUJyudg zmOOM6T$PCbsoef~J^0t#De#s?|IX>La#IbdpE*gl13zC)Glax@N%5~ryua&8(nFB_wRT9 z-_QPk|H=MqNc{ij1E77(QkNoj-RFH-95S})lO)B zV~z+BXT6Z50YH#~V40pv7ps{m-sE8Vgj7yt{@)KU`Nezlq{bNAv)h!X%#PA>zF(Hl z2)>a`Qg28xM`=TinAvn7FJqB^Bee93{o2`w)dis$RvTWms}?v4x+inQK~_)V>4LI9 zT1ixAOwcZsJ1Q-Jmf%FPG#kM;FRGsEwmx2{Su@Kcxr6G zi4?3L6nJrwLXLdyu}>u5633P`G(D=O3^Pw&ZYPB@ zer9h|GOsYdtBeH_`yl%?E5>=nw;t@_k*`aO9N(sgT3nCiy79SG&Xg{kjz~5A`E1|> zjqP5`578F0oITz-M(g9Hi+(wFIvo#oNmhcwyX}+I`gyhP8iRK2f3cTnBZXFL)apS-#0OLKvA&8$x?(d zFTYTIU=%!RnQK(*$4I2*x#QW5li&(ckNf#vk(1w*uS~mcFE}M4A}C1Ol3_k@0@1s` z>%KBNv95;v)7+kDtDVE~UMa(3mX9Jz2N5(?>6(LbD|hezR?|x0ym}oYXFHPrG{%?0 zaKf}XpHC}C>a91jos^Q%IZ!?ssgp4E9N%SK0RySYMTfeNxJ7K3Pv=BFp-f zjGEY?!hZJ=!&p@-9i)s-O3r1sUc3(m9HT zB5-F<_oWs%Zw_km9?!Th4I7$Q^i4ux#Vqdi_SrKq33fx&OYfwH;l1X}FJum703E5U z_Qy!gcBRj5gpb!QB^epnoP1IWS(ogXEbl+3xG8kjleccRR6oC@x}R{>kg{4 zW?FqXm`HqRy#_gw@&|yE+U9G*XC5EAsSUJ@54^FvYZ3l!Ez+W5zBA+lZ^N&|XPH_B z#zfANM9FH3a1&;-{zq)bKmSM@$w#QJf|J~z+ecdt;(iBWYcF3|F*Z#BhYd+RW>Hby z8ZzJs{jHs2zunsedVQX|WMz1$xejzWXhIO?_nHgR=n5<_C<>|poDeg{3}yBTgFH^M&Tk%?x#wJkI8ke#8n%f- z?oQK!DMpA~f==G^8xao)JAu~wMMZlpLt6NhX#(STSH*shs8;2{JQoO;_^F~QerK=a zR~tz1g8YV;StBMS?GU*1vf7T#MbVw!+sE;0$laGU?gd3|+nnxNg9jDITif5<=V_{J z-@U4iMQ$8BypvILuH5wf4ihA{OwHMbk=%pD+sACk-NOo@L|?zX3bwV_<4ynBok>pt zhOoG!Ro`qKqOst=Fy4%NW#-jiMgdUNqmV-0G$Li3i_%FjR~l@e7rXWL0q|c&@0R>w>=)qR_b_wn0H-hANnsMlanbVTUjQk z3ejDSR>dx&b_vW$-o$HuA}eKnN z>>!4YsKzde+OGoqSc^ubnzy3mD&?39-H5U}#mu%wY6{OSj8N?Ze z#{8RM&2+4e)E;MZeDMjX7)^-HYdXENBV+dnb0)I|aA>=@v4bGvqrb5|Hk5=;bb17G z5{xS5iqpsgtQ8ncOt9lrg7Ht>+=3oRS2qF2zw&S5nDg#mv&n2wR7yzoU8S8ow>Unz zZmk>m#C~=Aa?{XaS(U$k$nE-_gxmE8Z@?11q4taSQlGY!yTqJ4^rg-r_jaxlro1At zKKxAoup;ih#=R3!G>YSi6?b1@7A+VX*9KrUG*z3XoCqLL|eg9S2Z$rQBV1Gb+R9siX2nz0&)csn#%XOW1;rUciY1QuvW8)dB)PwM{ zFtfHf{iLeZO9np2(93_Vu5oD@> zS&vFRcv*j#SZ}`Odj2}TikE+iA|wu(lwvTYKq6#&TvCr-U13DFK-2fKd?{grApE!Nt^A?N4MpZ#_}q9y_< zdf1S9wG2J|4fGY)XukFQ7Of)*U+%OotAfTh^lOq?Vxc-)TTVTagFdf$-AnuDUp(i$ zIu*Mn&zW18dn`8UGfN-cuvxrlP=h(#e?W*opd$Y++xg*?f_>7-WEtJlxF0LqRepyO z{@;A!7jXr^(cw~Mo`u16TYEGKRymB`?)46m_5V9yk?Mp$l1L#5iu`228+Ge548z7V*$#!- zd{$5559e6Ae~nAt8~tC1IU;N*}NPI)K0is;s_N zvgRe3PfnRO-I@t!GjZm;FF;W{LfjF94*PO5504oC7Q@!_c)P2UgO-X9@fy|L*1# zEinAEcoQe)x5pErUb;iqG*3RtVDfNPXb0n3?Dmmas4kC|5LB1@$vA zuac&0^-SGwt8^9^UgwD&t31V*8gFFMM8(n$8Y-{0Q)xduG{bm#GcS3vu^s&syT0s% zdcr1ZYyJ6UW94>@Y`uRESI*|T&{h%~Yv5z5)72I3HxdS)bDQU9U;RNohWR=6#ACm1 zSkP(dh_$pOs^yBm~fH1_-)(VmDX|k(tC%I!t}AlyPxaX zHhT)UDZzx)5P22P7WL~>L4?-MzLNs8cdTehQp>l+Vxz;uWv8(x)0Nu_gQp80u=Pi! z98Vct8chvY%#fW|g>*oJ5O3feK@#!WmI=Jeiq4o>VZYzE>d7`%_g5A*`}KS}n#pV<8}Drz1) zKSBI>+tdZ~H^{hZ8+CPdxe_m|bJAhjjLKc1h}ogdBpokVFOT%EG#Cio@WtS3+joMO z8Mo|Hi)VW)-TLC(rs}4~L$o!Zdd`eRZhtCZqR)_NB2Cz1u>}-?vCrC>$k+GklBRBV zAHQ-&l+F5T3EhX3+xYsExU}4$>G2xd z@vq~}h~u9GB_^|rp7`2rvE!`>&&9?wyhn2!>R5H1H4ZR8HQ)%!pg84Q67g?RFZOP+ zvdNSm@)81$H}& zRyf21rB`Z@D zJSpiD{)YvqDQ>OR$5pug>@|60p4{;vJ zNh`s-seN@65((6cXRJ5|r^@r($5U6*f;;Gc*)r%SPu~cWv`FDxYv1no=dCAA!PKQx z^0`>9(x49gX4-NyYf_NQz1vmB`MqmwD`_pyQjpi8sk6%WO4(v_&Q*#+Gu3;i?zp?~ zR$z_{rq^;&CQN6q?ptK!Nu+5xZoG2!Ywh+ceCoUWf{H0cU7FtVEgU6&-TO8eK}+^lpD_V#S^ z8%SNt6Hx2j-L6!lH{Ga9V_S8=V8=`9rLPP!`5d4({mX5RN2h*z9O&G>^3o&!+wh4T zsyXPB1w|l_UWbLD7F0reqcsouiMZ5Krgzq+K6lTb9(>N5g2&Di$)c}Do`tFc@RpoW zp4rAzWl!Si%h3QFhAPtB(=*M9S;z&PIL0Gs+Z)c7EvVr(}KI=OFkINF3> z)wavDp!hZ;Wi_RW@Ju#Wjoi+voXQ$Y?I@M9rWCa|zV~j5kF2~U*C=$hh3#N$Vn_A1 zP2MN54@PS%{-ZdFi{H=1O439~92j+y*1HW&?0Xx1Oy)Zwb>X$2Zk{6J7#>tBL*!23 zw&za{*oN!y5Ht6T_Io3hUO#wyEzA!px0OF<9-<4}k~NSmT|e~?mi;4~mxk3U{gx%d zS!V&nJJa}z?6bO8ze~s%dfmXW+I0cNy(qlI>v~A^XP8G}HvBzin%@VtkNeCnae_79 zE!n8Hq4{vXr`$%2vfqj9={cJD3v)7@LHzyC)UZ!}XVP-86W?e}pV@Y-zU3x@>$i5b z+(0~5l3MC@U1se~Hnbn9{N?uLN7au92Y-(LiheunkmSHJFfMcJ;hNSFpB|B^;9lS3 z0Vy*VA=fdosn1^h3uH%7xl)I8n|OD(7(auzHf z-z_p(9DjOb+Wp*xhsdn*{V=f z9`vZ^DMMV@_r9;+!L3OcGT+n*pMuIOt{jT?mbdbbwoLk>uN5e~0`m;V$bww%2?K}3 z(W#h+cBq1n;xvw8@NR=hpydUFmKF^e@%&J{_nb~;cp#pEJ|o2Zxg8+pUM#wN-(FvJ za@vy!qAZ`n;)ovx^18WIT#po$ z>#Ecx|Drn9bFQWbU4qPV-~@od&bdiG56ACr?6L4*8{H%UKM7df7%TpyD`_N5&x+1R zd**3z&k?O56nw$fLhzfoupzpuVIAOMu7+GIdr#t?EIipRMv-A$*z9dIhcKI~zPE=X zs&^fYJ)TeX_m`iRVuDWarWeQibL4_bLOIe&w{Ew+eRQu+$cJs%Puf5Mql#8t|#Ce((B@YTyg7dGT>qEIf36FD{7Me38Vs z11+cwE?$CSY`rT2CXOpra&6rsB3}=~QsCQ%%L4LXY<)@Qy5{FDs_k^RN<;RLadhF-4Q@WKiFfZ&GaF!J z)&oOSy$qSK0#0^;mTmpp)C+^tdxztd>o7^ayMAlccd0J$B1Oh~H(y5|Hg&dC^*OPQ z@#^JQi%Vi6@&_BmFH2!gW2l3wVK2lj;AhW->|bNI;Z?8M!d3_n8Ce{&A=W6k=6(4 ztHVQstn@bc`GR@rB*s0cxTc3icfM~Dm}1ogeW|TISXBfKpoCn7lm+Q8I(M#6`EMYE zWJxeB#mhyntNF#o}hp3Vq{a z>m+}cyBF0+386m^Eq9lalwqW9$LU;8xl9+a#L$~Qzh62qGJZ91>c?i|rHzeoz5E)+ z@?|2sb3+Y6zOJ$@+vMz#6Z_fW_!rXs0t;e3){GNGpA!lKacn+zmsD_brd{|U%b;)* z#tGj6`r$10x}e2MdijSEZIo*7$9IG_(^NKho}D_Kiu7%P_cxIYd`US5kd?GViE8L8 z5|2;J>+uB#l76Q2PsLxWJf2GkpOOIC1yjjNqt)7|VxlgO01w0)GX_gWIiF67Wbl4V zh?uf=9YR58vTowNGRIo`*&vforsEVUx4-0)Pe(GQ2ia2vG$F%h$M?>GIC41_L>Z#q zGza>w?0yD?c}D)u#XX??N@B5&@`F#ym(#l%zp~4+Rc}l5*tySr9dh-yqY2@NRQzBm z^d^Xb8ABpmE5$ZE?jzz#RzGy_0oV6+$Bp2YT02R4`66TjQlcVgPz340n!;vl+Jfp5 zVv%JspbF-7Nm(4PVl6i{ji;;rNP;^zhIVgHtDW?eWB2mll{g${yoAsvYN!N80GJgGq^IKbmQGRAq0$bPmRmc!aJZ&(iPb7rc zZ5Jg{5IQq||B|*7bEw~-!=(JF-5v$*!=$Efm@Lm(o#mAkV6DlP_M)$NyzBVm3{&t= zedyEUR*7Z14FcqbElz1;_N8@o1k^3`NLnyvja{YrZi!)=1a5{3d;B=O-YndJu9Ai; zBmO{bF|ojxjTh0tz|4ABuT%bC^vhuz$=fCZ-S$3zX0fY<^FQMD4Laa}FAa za32vwkr%6Vr~?f|6pBB<_X_tZP(;+#bK3R?F2d=Gn5_a)z~b84z(onMm>08EpEu72 z*^j&J>%=H|pHFl4r5IX?D*DuLTDb>Q0QSP6yZ7X&^BdTQBNbL6iLTcAutRehWz7l1 z&NiWxS(*qgz~9S^Hw#&o!zUIVY?mmQ`m{KcMr{Ag(e(^PwFW0p$$iAM9xStC8rG!- z?Sl2n^LOYBV_S}sx_`Z!{ZUEB-^ z_PzMd5w!LUFchcyLUcO$aEF)2yGy%Jr^ke>Dh4_J-XneGv-2E|8PH-Vw?AfB!F_HH ztyrhYUqovj0r|_f`n0qMj7C7Q#593eT^;*@E8^jGx*OBZb9+xx!-(k&BY;)|%#>x- zU^zrMLm242Bz)|M9dBSyRRV)FN9JH45DvD}xz)2F6unrhsau>34iwM7Ec(O<39T~X zg~QHYidc7^HUu0T#VMX} zR@dQd#W+0i3oW#E6rGo4LO9^YL?bO7lN8vW3Fxx-FKM~Y3o$k9zF;BxTK4SWwqXQy z=#QTX1T)pEPXovRdAAux;dLm#DqiF+C3G`O+~_B5n0&Eata+|d1>rddjNQdiNJufY z3e4|20a;IIZ+iVq_xyzzla+IIyLHlMV^aem!<%Yha&#R$ zi2YBXUYgHUma7y>_SYqgfXxei!>(z9d4Cl{h#cgg4(Gr3Q6$&l0d^MB60pt}sTl5a zf?iAL>;)PFcMxVK5Dx34=MbbZ7WCey*8sq5aH+TWAbl^bIWNAYvDoR~Bu3=Mv@s(<9ktuKx zi;L@29Z(^F)_bZ1W`a#^k!JvZ+4YQB-Fri-QCsO|!mH)-EL*Mq{)LD6D~|{C(n0me z1K3yB|B!v)YQ1pZtb2ZZI9x7(@-C`FCvds0q(nZS(LezuHTS}eIZ?6clSbUtSWIcD zIsll9%IUr~ut?77Bqt_XxS7ZJ)LkHYOl55`&!{?P21IA{mZL%N=(x|MX}RX{5T2vlBfm(XVi>3%(X5BwS+f7s@xrMvq2Cd;Q6 zXQm6C{mvLCPlR)ES~7PucHsf=R|;KHMfJt64$cFvoh#TdsboUcG}UP-q@$~A=4%%Q zkoa^h1yR>GKM?SiaA{?5a)mStl9YEr(+0Kq`&3pM?Sq*RjzD5njPPp(s_N?MIk&_= ze^AKdC9=L1{QEce;>C_B@%}r4qrTgA-Bz!&X@m+}m+EdOm`=G#E-l$(t$Uq@W!^O@ zpI4bt;tXhBx3tRb+G?)6pPsNn8=(*~&&to@Ro8A`)~IE62}dDt{gZqVQT>oP+&FGE zKbjhclzDiZr;cdGd=4p~R{!jSPf66oCQ!V&&?VWLsMYO>zV z5cL?CAF}yx@W=8RetMVsgOOKbma^N%$I;8TVB}i|bgU(jyAjfzmLgV+09#5y&;cK~ z5ZFbgEkQa6d1*SL5Wxb~nvsuV_D_c><6+b@<{4anJa!}rLlg}r6to+5(e(%QFy=0; zEx%uDWZ~|(aEY`i5bDC9BMHiNusiU|twh zdc3a~t5dweM)sJtcD$iPZ^+EuJy_b$Ga6r4^INq{9`LcI7)wx~x0#L$0o1 z^8JcIbZ*Gkxg5sbcLf8Q_t)H|XYfZWcrnxwbHQot`kes({fe=Nx`r8Dww)_gJxr*Q zA4+g443RtIhC(W{3uU=^`2{6gzx-snocC$Jxvy|Y?{!7QrN%dH`_|PQTm+!0@V5QPS&6%Diyj5R zF)ESwzr40+WfpgS>3-X2GC$Pe(QhV%P92&}djBmDkxEL+e|N0#`f8H}s0M*dxv9NZ zO6aArnL@uU7j~_aJX>30 zw@^*}qQUKZd?Kfi>dRd(kf~V@mOgmlP`CRYxC<7(3@uLCdS{femvxia=|W95Yy!XE zr!~Ze$ai0OaL?~(&Dh1Ei^W<^G!CAnmc>9RPhT| z?!GRN{QS2jQrEVIF2yIt4_ExouzH-YwF_c%yUX>*fT;*GcQsC zqDf2KmL=dyv(LoXm))-!;-U+qQJMKUOO_=65K%Ox z*<8;~a{&eXoBz=Twxb@2Q_(~~+q;=ks9+VFl*zF~Qa{a8xG%=1rp^YY`ur?TR|p{w*dqzM z00N_u?hfnonW=wA$`=0x!2*Gk-aN^c_W?WQi0Y;KKaUG>RvY^=8Lqsl645iu6{9nO z+tTA`D!&8A-8w3JF3YSr-@Osav7_up!pM0qu@!{MC8Q*?c3%fBdTrQp&51321!R2F zloBN708`xE_4NC@rUsPt$h*jUJR+|L_bHz|2Xn)GK)H?Sf+WHx={+j-oz5YzVTt`V@W=|}+)_5nMDMgbup^-*7PRY}KD z$rlO@>wwFHnc_8dIWIf7BqQz5pI3Cp@TkG$dFk&Fz#v!tDGsH&#`w_9ag+; zTb}{-2z)t`NT}Ezi;tSQxCG{0d$*x=S(S%X7I*4t67vgk&Dv$poTRqzTd0h0#exmf?AlLS`4;(jL%Jg=nx1pSr{h{` zz>XnG#iG*Zj{wUhlKG{%yKDU+i^K6?2y&32Y2suONtihQ(KdE=bp;MECo|>xQUm#` zzEg!1O`SsiQfZs>lnM&^!&QRlg9F4$2l1E3A?Z_6&vY}&9G>TD7j*jDzxLn>f4O?; z&6L--B-PrSYbWEbE&V>Jo~w`WJBaf(y_v+!X1a6luDVnK%tjyH=k)dU{kGLNw6VRN z&WL>0?tK$~?eyYe;=$k8`e*jm7j$C=M)gfV`bIXSv#YBCe>kl+zp(Js1QpQL^$1FO zF{F5gs~2eg2u&x<%~{TtF*$Vgj@;v*@r!5swO=hEioWf?`9RA;OH5qc#LgiYTPm3M z@LflT>QTh;;qj-63W|}N9yVt$yZOHO@FuSfvleRX`x6*R12>c`tHW8}P?HAD*{Ha{ zq>Z@RTO5Iy?id*YDyoaC>thl_otQB8P!~1sE@dIcFJS8?I_49)ereCiF!t3CPXgua zaDNOevd;X*3PcKlYRPq>nJUMLQx0|Orx_D{c7)D6j7wmhXdN;Z5Ja}m&!78oC~G@( zg@%H9b-(Lbm*>M9%Pv~nkDMm665i0ML@9I5>^PnINmh5B9k88)jjRtOdn;q+A z9PFkZ!!N4Z#_h6cuaU=(^EdC%X)@pZlrUE6d5r<+-uSYwY8cSQYE{pSh=!Mj6xeX4>R9%P(we|Ae9P(?kc2YzH3cn!A74Ua=@}&y*e{q^fH7k6 zBf3kMvLxt=J>EVM3;Tr3eCNR*xu8e4?=7jUsp`33?$)$Kxk1br`4R zrbp@UG!O2Pf?klBwKXX=k0wI+d`MGPpsDS`IgL_16EE`^kf3qKuSjWWYrXSfymhO6 zql~n-r*?#e73tM9x54wJl;+in8?SMlvII@$W8PA+`tNORLN{%g5u?VFsPeSCi$?KPH^YiiDvG8WP)qab`waS=dDa-X%TwDggmH{C*aocLtUHt&n$H zc8IdK(%7`-zJ2?)=-by982nW_mGh)$jI6)x*AXrJ`gJZnJ)OYsu7{j^{l~0vk?}AW zJt==D3_83e?O4t*mgu(K4~hO`1~gW{C^pVg0Yi`;;r^n*CGc6d(hKsaRPBZ1(XE-i zLd3J=oF`f;&+dIyzSVx44=K{I)AZ?KJw=nHmDQE5xCV=#S)=-jqyBwsSG>Xd$;UB* z3c2$!_!Jyj<$);*=;>N9FRgWU7KXE9wG(a(*)Mz{0w2uMkxw^mp~JeaXLB>-cb|8*nVH6ip;(A>&GW9I(A z(Ad;kdgg^tZ;juS4NqPwi)?kz#$G#vL-F~1idJ?u)Y8UzY^+|%waI<*k`UPzqHnsQ zP+j2C!bi`M7J53}ggz-LiBI6*LvgW_mfIVmQzxr26nuPQcNFTB(kcV|lv|rJEl=?W zqK}SP)zwSihDAI=h;t^ZVnsb(fbL&HV#2Ryj1Mn{T9*azD1mQ?lS9OxFJ|&BTspdj zS5lN6Hy1RkC2HnZRx+xtgklO!;y>A8QRhkag12|UvF-W_OIP=BV;HW$X}CFiN-Y}nQczA?vO3E`IpQInlvZr30 z2)xAGH85a8{0jd+EP#x@OL$C-`CXYnELJ@u!@~1mho3ebo0J@V_Fgy90l&uKJ!@)` zmh4Z~+&Mf>MfI4ao>Ra^VrEV4?(bK`@5&I9V7d}+cXoFN%*cGRaktRbs-*xw#7|u( z6lrpEnP7|aN$`hRC45D>Ij>FL3-Thdw6VDw{g#IGm5!0F3Bc@jU)@t~{ajWX3ZhHh zSgfpq1xeRC89w^P%wIY&zy3BN)T5&EmXgk&C7NTPC+L7|puR@7o5A$9va+%~%%0vd z2mNd9oIBfysjF?0wdSYX($H489*2e==<7>D$^U#s7AH)u`F5mxk|*8N^lf|o<(s6- zQnPdO^Cm^N)f5y+s%+_g3|Shq-1~c3_hzWNkIObw-ND|KQkPPpXJo_Qb|plOYZ~qx zpRh}|gJz~kpH_TA!sOg^91tP}B0ZE0vW}i#qi)sBnESxz;|EFrpveO&XHo6K-PoZA zHa4_xa{doZXB|}4+kfq^fP^5O(%l^*-5k185b2igQaTUa(hbrL0@7U~jdV(P!+Z10 z@10?WznJ0dz3+WKvDUS!ntzVX-QYV#$PI+cYx$u8DOO=#`HK@!02k|4^#rqsN1JA! z@Wu3={``>j9M|!nrz@6AZueh>o?>ctigjbi1FoxPYm&v9wCIC!dxV zpa5bGho*|A0CYxd5^hra-6-v*7MD&}we9xK_RT}>c$3eLHPCw^-uQ5FbFWds=K-b_e+5#6|DNJ`5J%y*`y+D<C@Kn=nbAB8f|s8%vGES9u4Z>_9PYt# z7&E{(I$0tkCiVdO+gJ<4VaM2KMa0AF^gzhSK)4vF;_8P$Ykqn$&$+#`!=8)e>FL?z zc>9i+IHPVW9oT5=aNhuDSj!@y6k*_QE13g3g#89)7_{GJ=Tt0|72X;idL>_xZ7GZW{Bi?WYhah+F$sB%?DOQ+7n3VBN)K|Q+pUNlY zqk>bLH}=VUdzMe@cpaO7+SRK910EBH^C-rP>&q!M3rf&0W+u7coRG&)O%;dBHk7

+h zXPtMv0I!tx9t92Uyv)FF;IG#;ero2<&2A_}VDL8QTn~S@FfT8_nPSgjLigsumr4f6 zO@d-$F+d3c2EXf@Hz~`@qM!U8UHJI;cK`jOz&=}>L#?njOr_DN?E*X^@RkDKyv8@M zb#8S|YfG>B`1l8TxG_)>dha9!lw{%%;r&yRdgtbN0nNCszMjMVOU$=r5^o_=Cnq)V zPjL!%e6!Tt+a>;NVG)`MrAbRqAI#?*r~N_YrEkG@XOjSEh6&L^_kkN5#x1R$P0o9m z??1JQYH8sa_L_QoM^?5s4+CvkyG5D_MUXK5iA4YZO2Ntlzc2Ts(ISt_uG&~s1huNB zprV}OjGwAilV99myI|~uQgI2M`|3I_bk?hxsoxW2 z6Nb&3DioS+Fj1|SuZxIC57g*0ss-3z09l-bt$IJ*W8Z&y64I*Bqb7)n!3mNrfaEm4 zF3{Zq9p-v%wD8WsO=L|+EHa+wFWBY1uN7t33yTnL6FTCC$ryzi-qV!cGZN{Zxutni z8}H=8LK;v0%E|d@8J>bXYrftJ#8?TO{aHzX`MTN6vutFX+0c|5lrLlCCk zJE{e^u-Po{vDV}E;jSSLOk7E|0;A#{^+ZsLT*(L4*K;47VdiaQcG}JB#U>#&&S9D-p-^`rDW2o zdP-D~`*u4s%~wcmEu#;c_y*5s#z5a}ocSXyP>j5VReya($+v6khzES1tCWPB>N4?2 z8Ttj(2NAHDz|6D2+dZMauY8FiM7o6=|IGZB=`jEjYghl74|CpzGRimOJFA3)?YEM_ zqPa1G-Zr&DGt#OC59rmDFXq|~bA6h0+71I|?>g$RQ|%9C7Uhx@x^O7Y&;*BbK+Z=y z`y)L(6}wNe6-X5_&AKLr9?-S^Xadk{7D7e8{Ct8xB-YPYH54V@5!tItRcGj&*@gjF zh{7g>xKd-dg+V|D$|H!kx>hUjc8zrQ4qmCV97H?YAbk$anf(&Ox~URv-h7V3G!h9` z&l1{4M#axA%O3m0HS_ReJaGQEcD_%uk&yyZ9G`H;6YJab&h^4BKrXTdG6~^vcZWjJ~zDTRuxYMlWQh3#a1Kt>E3A6Wqfq zF+<`1T0)5G{|atzlboMC>7!mWseNtPNFh1GsD4RmeNr2vosgdEU3Zfg^?{rV2wmYf1|(% z@7?Qgj|H>aI3+Q#q;OP-(Zg0#Mps@i11=xd_Lk8iFjl@Y3_afoxmiQ=6EQcZJxvK_ z8V>v}&QR{KuyOLDV5E&UL%gkpb8HI~i}7ZEMHNO10LyzeY06v@207nah5E%6G(0xS zNgdW6UgsJn&(G`p$g|zvR&Z4^jVr;{*SE9Zs3)C>(G-MA*_BRTty-mSbc=uKxMzGi zV||VrC#@ux;m`-qBI$GK)yMAKCnU$tPFT6LRje{epr)+7CaQYcuh_HJ^ym|QEFWOqG@^|WGbw{K0 zmPN=QZMQljb#5%W7HdZz!0-iRslDVY{XSz{zMp=z--?Ui<;4q?nocOW>sh*5Th|lYwd9i1$lf6P2G;d$y1|2+P%D4ub{|bx5;@B!zx0*o4AS2z*7@W(*aETAW%I#&} ziuP4%CXWHp&Ci|vyH-%;>>x4oNM&Uj<>(MAl6r|-=d7?;!YMV&usiQ8K}eJk@pZ zabcQcZRb3dk59+(2>~wu#W2Z$aY@`Mp~-ItXN{buwg?c5eYqjL6(W2`uJ0i#Oid_6 zwAh4VJ$j+13qo)seTY7EM*CeD`<}%&4sN|Wef`!% zCx^SlL08b!*fg~jtRvbFyI+5L-cv#%z^q>@4$4^JUom(YB~~7+6hD^LpR`s)7;AWd z_Qb&F5+^z{(jJtYxddD!g6qr+KiAC4>K(WVv>$;V^oG+R zA$hfkrO4cziG`Vik58-sGry!Htglb%!FR$$*?HmwV`vi*5Jv&N0xg&ppOlnVSU^5U zIlGNLAm1NP3yFoVzte>XC|;|pW5GD==k4~#iwoy?RR)_cwrl6NKy|O61~8izT(W$` z1@m|`xDgndZ?CSes*k;8rZ>tGOk?vkxRn#oCoK|8C`378zi#QPD^O#G#XxAnq!Rd! zi&1VKUO6jtUw#0*MlKC;4O1pbQ4MZDe0lp8Gi~#JMxu&WK)}S(BNeQ4OemEY67#Fs zrKxf^YMIm(Bpn|CMBdZWYj1b=^Oqx3N=nK)A>zW0j;ws#wc}h_Ck*DcqI(2+Mfo$p z+6NXf6*1yEH)nfL%~t3&i#aic77pEvVw%+tebI(nNGr@`V3@R!SElMQmdF!w{;61# z78?0}d!6*T%6QU+SbtX3Nrsg8MQ_t9=M*TTKPw|}XtX)jf7=R-gU>&s1e zA09}F0noW7@ecXol%4gA4aANG3Xx*Z%aka4J@2ExzQwDY-rYJ zWwHc~!3~o?ojbR=5GwzJu+-wT;O$fPkZz7}@;px&a*c1wyddNiXuzHIv33Q!$-vVS zHKQ!TiU!>msn#|Dv-8wc0)Ozq3i$ZN_)iT8zNV~s+qr^)@gN=%TLJ#N6 z+7nCVFDhJfP#F=ZlN5I7@GnT*FfNR$B%xm`G9r=*lFS^@_E%p@TI%NP3Y0$E7B*Pr z2W@RWRPl!DlA$d*5^U;0;QD|*+Ti)&m2Xz`4Dx$?e47V;G6~fTMpkr0vJd`F%{e)* z0Ee0x+rrAKOIhxq&vpMkH8=E;EKwHl!3OewHf?_Fl&RM<0~dR-vBSj1CUOHy1>gHT z>V32KEytmPXlD*X#0QT9A`BGxeG4%e%mjRZQC3!_){fS)F_D4q&vo=10`sgFupj7P zq*T;{$IxD8uZ~ejTso2MrcEfQsK6AAgBvWJVxXPvxnsSsun@MHHfZMKzqsVWuF%;E z@)?@|8}Nk)+gKFQROw$~-DB*)rQh$Or!GkiqWYA21_R@`xVT~xhQDWIp5DI40xCkL zMX9OGbIsOi4r>s93R)`=)>PYGJ$Pw7`@wHbKtF?+s{8W?+?pS&=eLMkH5cj(fycrd zlieYKq)SEh<%!xLVNg3Xr>6l9XENaa`u1i+Ddp1#3XEt@Hnv}21I{b#ocS5jSs7sa zt+WSHs0yBCE{&dVLRrxg0-{XsXowzA74Gaz;wWx3VQWbJ@}kfBzNb95oJ0YAC16U4 z^C&RA2~L1RFW>nuyg%q2CNYmtmMj@QI0*nTJ|JZRBYIijK`YZWF64O;8ZC79;^=v! z{~l0oVcL9rsMbZP)NcGbgn?;jqwkV%>GmJ$^UApJdUv!CRm>4B_Jc8Bt7^yNsMj_x zR77?W7w~VFKu-v$X+ZVm=O^6O)|NlTQGtAFgZi}u!ScmwANwC1s5OD0psukVOza_S z6taB^3Q^8(Zl@WO5KqTW%^KFIn{>c0Tzp0^N?Deu&bhPkOs4@LZz1p2v zre$GuhLw}Fisv6>Yis-S>26RNc5);=*nUZadZu!X?FRt`vTIK z2-&asZTgSt>2P>>;M~;J>(gY7$4A{sFi~04h0AVT&g$suZBA91ztH4Q#e~71s7v2% zxvRz@^ek>EA* z>k7TLp^GV%q~xTR9m2dGR9hNhmi6sZgWPIjTaDJ$(9`H#hi-3LbfF;F>hPg|Y2?2}}##;eO?+r{Vp6vl6dhcgwh5LAs;fBpIxeKtni0!;Ox{sX+m z)OTJ(dG`kFe0-A4Cb70G6P#=kYEEgMepp{Ajo~QrIaOM`79li`Osop0TZffk6@-M~ zHU8|D^P4##o?Az(8=ze~>0LehmOC+8#Pudi zQfuy*yYq4M9X!Ak#39tg1-5WD1ZgnW8Y~&wdV~RTV_$!NDn9Af=So$rmGVid0?Jn$ z)##I7BlY=K=l`IT@k{b35y`rX{jF#+=uZ-z3G3x_c6N5W{jH&UKH8J_E}VfopgFUeRDNwFFt6bdvqBTLPJYSiI2U?)zrE{zvXb4mYMK%sqwd31eO#4o1Oyi1dLJu*` z^VFzlHbceQ9CYELOtJYT+r+$k#o8f%Ern1ZRz(>Pd$o z;qnx?6!GD)qdkbPxuN0rOk*Rp4ei~QG9y?;u7+CKZ=q<>yd_EOVy}M3;**d5s+PB@ z^{0`W3l*7n6rFx(!bz7c8Ch104N8?sGAq*7)BX%h0R@TTij3=r8ahO4Fh4#?$ZL^Wq1u&~C54ctX^yFLm9G4%|#@a(%sswWZ<%-|+C0Sl!V;Dz-JHDFqSE3&#YH09jHK z?!8e#IK!ZY5UeDuWU_FMfuz{;3L+HQrP{93`%mb- z=#pxakK?gn4Yo+%2<)<;Hz>}tuNS3UbJlsaA&@j^kal_!Yzj}DsoDqb)D*qXuD+=O zd7OIEqDju!G9z*Y%l;^sEOQ!TP&94bl#TStj`h<39Vi+#W;2a0Un^43Te}-3uGhty zbce(>H8p*|*Ey`M%RqQn@?MDwG&*zq`cD14f@+wdG&k?Y8xBTPEC+qwCdq_PnhaZF zmdaUy!sJFGIrhgJ(f2x@GEKYu8|IqxditiWzQNt`48dPwWQ&@;_h4?fsic00VXg7rA3gC&BIM}qJ7T18y@#>t7o$bl)vS6l=_peEZ)V@L(Fk6$@V7EbH zSk`|RCn2z1)XJ!2%o3$h&L+$`9v>2&nzhpB(&^kO(N!lp4U5fpkB{FX@6?WDpe#_A zy%KVmH1QTsLlaJknF9)tT9`=iU-0zo- zC@F#0OO$bIV3$hT0)MY}7=wGWT(sh#q>6}|28y_N5)OG>o|X2PFtf*9mjt|q)o&pFmu7lan!vcFDp$Z`gA@yqx4+CP_{sV=74j!aQR zs9~Y3JO`%^zBZ9AqF<5wxPx9TfvyvjZ6K;88i$SI_q8xd;QfkwydGcY!6yFk^FRGe zA&;K5)D$e}#doyJ_uQghpH-LUKf60-hU9S`5GS$uvoCx^?#UJ9(mG%bFN~9+**!Y? zkI3rMQ(@9#h&$f;ko^1afA_L%Bt@;|QQkBXxAqxD(WLC`JY6%2v8uO0JFFDwsNv}- z!gpzvM?ywUJE0aF(jT>iZ~L(M$TqUqLn}G*)fDO)FMm(ZNN@F+9Sl+)`P_ae`{*_9 zMQZhzwF0?DiVIyr1II*1ED=XBI=Wgu0t!XtTX;idQNS5%a7DRqzUrS5X?Td#{!#|ZV%l=sh@LnJdaCW}1Z}$r= zbVilr1B=Gg%P80eS0+V0iK=yqaP^viT;8THI8zLh6B8nSex!#Pwh~>-ixCPx+h5D5 z+LEMO#2xhB!@n(o&ke&*Lycp~DWzb+F!JPyV~U%7`j774`OP1fUnQCLj!oSQ>r}?w zZmFr6-N$IZf6vPl76gevsFawGhh(WBIf6K0pRJJo=&~)Q470l*5rL9?-OcaX?RI|rDMk1;UU)6~`NsUAlk|yP z_L!Q}5<&09!~G)O5`=xji|d916e z1EHx7MCqre4#6QII(oWWn{e_wAMbxuc$MyC$~J)kB+~MrjX3NlEiy#~968)K(^1V* z3yX`0A>AO14RlT^nW;J20$m=bPJ`<{%yLu~v$L}v&yUx)w+7GL-AW-JJ~&B;;jv*L zWJ`!4h62Ts%F^F*Mu!C+Rv-7pR%M`~Yc5F@6FqUc#g4&wJ=_p0xT!a)H(MTER4NPl zM?QZ3z5RX4RLGmUti#uIEUWa>v&HySGGa-ApQ8#(E{V{;>c^wTQ!CZ0T?T6xBQ8Q| zmz0Nb7uPAE)DvSGwc)0z|1e3prKF@}tC!C7e@w!G`IfCrJIkf0G!9bj zO#SRCD>U`w7}`=q>X}{)DQlhN{hzoBEz?3sBHEp+#FKuBAIip2#mqNEeY6%$>LJH# zPn0N^%Zsmy(1jKHxR_Q721_b5~1p12+ zNynLldr5)`5wD=&Bmc3eiUy3YFzD~26r!lqWSo^&`%mFso9LX*1nFpTA^Z|&aC3Ea zIPH==eK19u9j3c~NSG>grrBOPA7^a$4e3+D z-Itr?wi&K472}*Kt&oG8_%2hKsng zv_+9n=-Kl9@%g?%$M~NMY=jV;AC3eg44MW0rNuCp%t)fu`r~-1g<-ZjD-?M*Z+|qj zo3KBTO{m0Csc{F9+Uw&fOtY0u+Z_s{Vq#Wnf4Ap7$f4JeAGhOWxc2w>!LL8IfVv@3wt7Jq|X$f78|Bs)m6hG~l zhI`mh)Fpds028}7to&`2?LQ$Lxc;*JCSE9D2#B085C)6ZzeyZA&(dPR>Ga~`0m+4~ zZQVUC`sx~zr$XL`4(ZS^>olT+#xPF8WikR0KM4Rf1m@M999H zcIaJrsfgYm97O(IXX)?n&va}71lr8?5104l`UYoBLDm!|;b%*Gf3^qD9pXO-3Va6o z7jVSE+zmt&0nG{!DM+7kE{E6udG-)EFV((hHx^}RPeyxrYOAg_ZK8%1RR8C6%~ z^g-^>bGcj7pFDUh3-B+EblBM0J7*32Mu&#dAQDeW+bt9j2Zi{*>t8a4|GMadvC*wq zrn2%60-dh&ZcK3In)~zwRd7&)WQwMC(>^I#A%6~%12Zz*h#Wc&FeQ{99|=9`aNO!e zK>IZ`#DR7s{Mpx+WVm5qtA>l42jk5Q7fwE&yV|RcEIs3~k4ogY$+*pK*DOjQAs={y zYnZQk?^1JB#Z=U}6#5+`8bd1fOloz2?n8#gI1^GH>c_25i~c6o*5YNepw1x)UL3x@ z-^|CQv_IET|7?F$K`Xq15wsxTOqgV86p#!Ku4n2XLaChgm%)$Zhrbd@iOOJhc>dGU{(3+0V{lQ2MfEY{}P91mtn}#LVGjW~Ijf|?E6w$4 zL}1vZ;hh;G2R9az>6C7UO2JR-A(&cC(U5cp_Z}DKb&6SV~O0o;VRd_9#}s zIx1F7aTXAlV~I2Q^Qg=S)s7Wn>Ke<6$ z_I?wKnA_5?MDl;vv2Ub93EnK4OaEZ4tm0_=bu_`=+!y+b;t%@UU`5_IibP8@SKsf= zB@S$u&W(7$ttG%GWYaGZ&rQ_Zt3hp38)>XnyNq%#ff-2#89lNP zu_zr;lG36D^4%gXfT|froGk30a2$0^&!>GT$^bbr^FLYxi+okVa#}JXJch($mJ3}* z&ehoIS@i$7tJ$O{kei^%M6aedbihmYqw4+^-bWa!*IJqr_5bauQx4j|8}Le4Iwsv# zb6&WI=PdIKWq3UJ68EB4F_(A;e@Rtl7Oj^kU_xZFRWoVOyESE}9b`Wl%-+SoHo%s~ z7{D495RFwpkrZnvi5Lt+X^S$Rj;n4QLs%SshmMOd{(Yr``F=@uX~>tP%fLj z+Y?8yDH7Q|MbX=h6W%IkQ_iF+v}E2v)P0OV`Z;Ng4)=O)Ww9Sm216~N=X#8HVNtTH zt9n>_n&S-yRsV)%JvIt+C^>;i{D<{5bYBfak1y}^9e4LhH}9q+if?*`xRoZH-x46t zmiVi`Dv_0GpfgmB?L9y9qLUy*lAJn~i~~Zvcj>w(2#>o>6zuDv+$!RRGcnu^$y6}J zAKjG2MHlWpJsPz0J-s2nE)zmK5;uR0O6t$P46)Z8Q1q#qHj_Cjgq#&<2(Bg*pcvPK zBjuHxja9#Pdw36bf|WVZyT!%+K%trh3N^9%ZwFQWAt`i=7Y+UAn)vu=3j~r~BWE-k zgCXxwDDTlG>;G$Fs3~jz)Aq3bs@+V}r=pEb_-7V9gG)Ihb!Y4`P&EttOHC6tQwO~lG3=dR9Zu43w+D2p9W{3ays=vMYIjdx{h-pNz#;Q!Gc^`igb zK0AlY+;91WvE58xFWRVYU;6!kw1$^|{NaMD-wOeMR6znW>L%b7b~K-)?>E{%np79c zUxW3qyN|nM!VV85w`Vo!GR64dHDtPCeW*B@>l$zaWq*EJq#atP))KvA>5U8Mm)|_l;X2lQ;8@|I$H2b1}2d0^$ z<+kCWGrk7q3HP}CKfk8FzO7N%`Vk%&Tya~hDG4*~z(h6Jnezrtg1nsPASrAMC(5K! z`J$iqx^L z?bpK}Y{qk^xtc7IAK$dqUVS(ynpstT-nhHw8R*Vj`UYz@SWV(j1d}|4DhPAIStwldZ6>D1>dM6#ita?N=L|ql2t_?crgvPE1 z)>m1H7!%+kBPZIuSx1Xe_^B%%PpouL<0(y#p+J^ zv$ZKcRisAHwUg7+$(9doe71NK6$JP9ky+wcXl->h=5~-8w@3?r*Unc6jrtpJg?jV{ zfh7>+5Rfe_E~kbkoT3cR{jP#33rOu7OZzE zhb;4ILg~J+RI~9&I=Ez%Vo?$)#X`(OCEn_>o|efa^|P)^Pf}9-cA%(ctEn5GAAGfF zU1n2RuF66+VK7(uoVwKYztw-@W5j%i0D{%u?uhsVR`l$)@{Ce9i$C7*uew7_lT~o^ zv-`b$J-s|X2!1d$vkC_qWFWWvZ{kr#tdmTO*Qf+Za`4zZBqnk#~rH!~{Pd+hvE6lPbAOpzeN7Ds_m z?9RR|=)83FeMIwwKAT-#ew+Y1sAg3je;A~R-GqbaFsoLUk^~@3w`b#d2iiQ4r*`UN z-`}%k+rI{M@h`@A^cY2gpAeRsd+&HB};b> z=clY!=D0BVwkmY^fv5eXe)6*FyJ&X+=dD7AJ+R|sqKZxpZOSnod3X-Q zX67iY>LB+FMI6{+Tlrf@M#o~u-Y_9MRgsGSCo2bi^3^L^PrSZmF>5*&n;NGr0_hJw z$`8Ru1J~gNjv|MLJ4j<05b*TXtX2kFT3$YZK9LK7kTCD<5u*bB*eGy}o|YLrzA5Kc zYCr{(1rQ9oW-72Qq5Z4oEnf2PcWyt`vfPj2I~J2hH%)>Zo5f1hIiO|*2G8=8oWyh+ z9$!K2_H8vFtrRd+xcUbTwU=?j5ae8_iOIZ@=HZ7y-vVVDpxJDdQ!0zHd zn@&<#fyudh2@u{Ufa;J)<_IPfyu8JHTcl7*0pDg0?WO~-1QjQ1O z^rIz@=tBcL;9i;wbNViveA4Tg`6^gfWMSezIvnE+F&iHP-2ON81s_u0mT+Lgn5D0Hu6nbbP~0=}_e)kok|9>9A@s2m8@Es419mLW| z?Z5MT;yBhmj(%QzrEA$mM-@)_2l9*K!wJneRRPzzRk4x@>d*2hO8SuK9fUrlhMzy> z(cypqX42{pNO^gWUNM^zP6-|&e*RXj=rnCd_*oYa;$l*hI;oq36(Si!R;yZqUBOsv z!uVp$Pn{?D31@rLGW)LLzB{`xk<81*v5FK3FO>4p<;Ufno|Zn$%zQ$3efXOO$^8xqh9EOi5FMC=>1#a9 z0lg$c+O7I``n?qQRM(Z|C;V3jrc^K z8Y4=R_Mg&AkebyH)w)IZblPB=FI^A*gojz%m&wx3Zmq(eq7dMa0s8G3L zeAtT&I0dq?6NmC@B?$&?v(r{RE_&sf4w(m&#j`JI6d*t4T z@*Tzw?+osovEo%mK{5|{-9OQQ9azUsEzsd!je%%Ci4vo9bAjnqur{7=)>%#_aXWqr zdo=4gI${~m6x`SzWt2}CG5ml+>FVZM{Qe(ksLYU&|Lk9K8r+VQLilz-ZuD%W!Oqfc zrRCrIU_e?S5%e4xfBwhgGeA4uB>{~UJU^(ZsRO~l>%9>J*i)Un1ig-BGTR}=rb;!|sn^m~@m)uUqw z0(t$;`0XQU$7f%|uwF*IR3`rZJp|!R{zrU;)VfH$YLRyBU$H#oYjpK|n*VHlP|VYg zrQyEGC~>K~HHf0_^nWkn!$=Gl%bm+lSWJ==(t3e@%2E@k6(P@l9f?y+q}x1$z;#7^ zu=#R+Fwa?=lks6oB#~Y09SJEpm+GARo(%=M;|w8UzVnGdv{(*gQf>ojP_$C1b>76w zEi0njdeaH%PVQ^EqQlm|5D_Ty&X?+r4kg1U9=9(mG|A_oM!OB^L_y?#7fAN&;UAyA z@^aAS8K$K`99Ou=f+YD84Lw~^7Zu`}l~UhU3*sgc%L(!@b;3Ok26zVZ!!M(Da?Zkyy$N#8}$V_lS zehmGKX#X8suJ-)llJz+)Vu$6_y%uCsZ!ClyZxpBn8d|8Hp}K+qTN^u@-(MQ~?o_(; z`6ZxK0h4m3ASkd`W^2%lL!IW$9Av7C=Nl7A@B9mwJq|wfkC?)F&EG_;y}kH*b0R*> zm1*Z0spUib>9@1f$&fiob}IGO{caYYVHo|F}*2@c>^mM1zYvk*~T;R<}EL_~HYH8`aTYwJ=yrBMDseN ztrqQ<3^m-^*MK{rdfli`H{ehAQ&;tG#10p}W2QA3x`^i;hMA z7#rUb@@UyrYtySS6+kC14E;rj$I2I)(z(z0oqXHJ5@IY2hh~u))X{coIf+avYis)y zIu=cVJhHr;c;OH+G1=r?QWlHAhE_*OG5BgAU^46gZ~82TI|$$e4O`M=IpvE^Rp^RV~Yv_q=Z$?e2t zMd`4TldB*F0^e=mIzG_utQtVay ztyAmSO3wGf<65jFll#{?Ue$eC27bNw!jE5nkAER5I(nM@g0w)EJQ_E;CH2$7!N#2l zFzMPeDeueo89J>c;RSich9F_dgUSrJAU`m#jyYba2-BRHGPCl1uHrgAIb5?IRGuaP z3f*<}0D-61yk4eUG7CGm&eloQx2&{2 z2YnJDZwOKUA3}ZE^^A?iUYtu zG)TNV+_acT@o9Rg~H9;Zb~m`26wX zM@?Hj7~yYij}ii#=mw4Kj-ATfb^FQpd_5V?4?5&t{G%YrHHyu;Poq|*W435z>AhYL zg4d7owpCEGHZF?qAA1Z6PvgM{yaI3JBBe~Sz|ZLoNG-Q6nZkZ!dBTsJD)^_MB|NXq zdXBsX`8~I{1l`~0A>VVQJiM2>*d(YS$K~bB?GI;iuC8YE;fR(x5X0a7A&@N~m;_;# zr+R!dZewW%i9*|=iK-l&f*Hk(r09&@g7a4O^dUGzvlI3xcd*ba*Ew*lrz`;*J1j%1 z=&oZd7;$q`C2S+*FoPI`Iad>h6!c00&`J6*HH_BdY|`wW^WL==3cQau=}#my)_8(2 zo9z$5r*|wpu_z!#TU8O zCStDy7#cRQJllk_9xcF~f(#*bIX_Uy|{2y2Mf-_wEmOEfN&_3)=(*TcN({jEy}D z?K&t}7QMLY)fsxFNkf8_PS0rNmGBH&8Z%_s!TKqK-);B$ZmtpHu~=;fjSPC>MXl)<@AfAyrS zu`!%rV`C!#$c>C`4fyuwpa;Hs^o>?5lB-;iw#L$H#L9GbpU0ryto}-qQoYN2m5L}u zj$%auRf#afpFDI8H4;A&6&szjAo?v=9K*aQ8#7w;Z!OY;b;AP*ttfYKpPcY5Dq#^X z`*_MnMW@c_iCRjaNj{Lr@mJ?dv>yOmV?n+=H{RAWm$+2Uj;dc7i;W&v>#s9h(?KkI zO+!1-79BoZL)OR_JH+ zjY>m?`>uUjGEl*=VNf;*kp6zwIlV)3XrT6ChZ^T&gXId*H8c4MZTKX3=K)TSWV}5r_8SH@geCBWrB8jyvC;3tJQ%4d-n(XO-qfb$I z`{@Tqq?6Y<3E?3V?e7IvJ8UDZ=oWV=H!*1TuU#F7aTUDpQSd`hwZie>>(GqW;>$}f^dy^MKG z2WL#Ae49|JcGBPbw*zpKC0Oj3fjr&@sws7HVsJv$xU|Crl%LfZXhOZ~3S^LnPwN>Bn9gb*V=ApPAzZA;4#prT}uXj6wBW?m?Ut=;#0#eqe=5fVk8 zIHjC<-!RW>v3f7`SkdQTL$t_?5(FLCdO17GjKI>UIDuz71kZ`;Zf?08YqORhsXA0j>Pg4d9%ZOAg2h-EC9aqtfdB*jEyK~l(L<8&AB2BTM<^g$5tK8e>C3c!KR|hNBynUgHfkT4B$I+i+@esKL1;s z{;U&ML%88#$+Sq3KA5ZFl>k#-M{)`3*XTKVH2$qv9C5`z@s<;ge^?V$%m_=H_Ki)X z{bQ*!m>pPzJ!<@;`Hzse%9yF?`xO>~$!|JO)L<~!tIO>L@!lV<%7!t*X=3}6uJ*UD zL5Og&vJ)_s6FMc?F@?{5PgqGFlvkYIoT??nn;mtTnvIWEHFtcQV24s8V&$jE_vLaw z!LW(Vrv6iSllSeSq-6Ik*C7bQcVP`**>Y*p~rfRQO->Mw7%$%ii1GS9sJ)mO(bl zj__?i=>}fFdV~{0C>4i9BsKlmm&$W)>cNA&4V%Qzyq)u&cPrUZIoc)vg66^xJ9#;WA4{~AYmy1U1 z>MGAKz7>6f?iR_kcpJgUCrK)PtN05+G;Z$Bt&JA^rsA9GKe7Ab>YDTRZY8T|nm`Nu zt^D&Hkho{W_a<97fF*_4ScC!Fep1A^>c%{K_l?)x`U|Qbm_0YO{?G)4&>boX$1jTC z%UP5%HigvUY|%woAwe4a(#)2r;zFxFJ|*lP3^f`uJ<% z_L=ppJ0khGu-8ZRWu^yt z&9KQ3dZ^N-$_f)m#gV;Q_;DA5uD_nMLHPTCL!cF>e=;nwdnkp#5pL@HdDu@osf~{( zzkY*9lI8KqLxn!bP$^d`nFcw~_jhF1TOAnGT}&Z9w=-L3d^FcupjKG<>qnCyr3~*$ zG=A!F!5}t>h@RB#K$HMWB~|3cq33JN%K9}jD~XBiZ{*ZxQ-|D(N7xdJE0x&puV)N8 z^beZ9&*VigGi=^!(33 zOFq?CcEqJr!bi2Y1|z$Z0~-7=OsyOWvi3ZD~5!|p^CS|i04duc`Y@+LIdQAIMDqZYC0A8 zpSYiItDoZ|Bk?6HhYkh2B+vI59n|MMTZIgpV!7lJY&eS&jrstDt5@03jH5y^k|8jRj(YYu-vMgV%m$xtp zt3?j1bY^d%*t_kzmqbvK97o-)@qiono&||DC+}l&+>PnGFON;V<-hQ1JmSn0%2cEe zaFX(H8{9&SPn1HG-;kRPYJ^2!-{mL{y}sF+MCP}lNfmoL)qMSgOqgEDb64k$?*kfu znU9*kNsxT$0!dY++%-yfKScd&%Uk-O<{KOer-CCo&h{Al1;xw05FB$~>uv!pV41XL zQxg3`9~Bh^a-)&%B97pn4mg9c$Qg@NOSD=&Q$ZwJtxN6h*Cs+DV(MLleww9i8Wm|B zOYxZODVr%NG=kc1lYg+n!^Q2j@JOgdNQLPVJFBd#?eRsPJn5%+*17MZRt2j2p}552 zvC-23(;;IlFjm%Rm6sZkxR#bnkIN_OIywb5TL3((mX=E&7#!3u0Acx7of;YGbz*K}MlQ@*I zWpBX$mx!TDa$hvb`400J&~oZc=1i1MEgql(zM4b_?8T~60@f|**EH+PRfn?@{SsL5 z%B0%Ksrgg|1qFZ0cw0{#|Ln!d7Xot;$dHYzI~UatK(ciG?m`{Cb*(RLnOk)T0UtO# zb-tFy#5_FyGqi-#MeCN*MRk2q|rWg;kk z<=P1P=ciI0!XwC&l;CZ?rurvxTo2dId2e#{Koe5l2RTiv z3acd9cr`qyO-B$Qg97&pP!efp6@3%50-=!KZ(gD7f92%lbiF%#H@e9y{N1pYO`HYu zoX!{z%t8W%eU)jI;-ishj5jal7ncn$d)Sn)PWvu!A3z$>T9=L)VKLRBk&;>F``=lm zxMi#`!FYvF!-ot4_j?MJj2+3kUQ%tSU@L*Om+d|8Zz7ww3Tig2H*uUN1PD%FXfu)D zZNhvlpK#QNbUtDM@6O{1c+n6WVU4FTCm&ZN&Whu@x!Q-zQqEZk_gH7V;e zd;m0t{tF&w`!9l4)nUhyoW&~k~H z8~8cQl-AkMP-cx$FdiKBGQ*i?Nav5c3pQx!o12FMi(CKSqQ2gBh|`Zoe@J#AJo0Ld zVLXUmp`rd|^|r4nWUEqbB;@CAguBP|!|hKx9E?s<`N78|$0)cxh!J7+01DtF2|_rx zq-pzLcA2Ie9nH7|E#=B=ii9R_3BgU5CKJOFGx{8{<5pjOUcvNA_Pe)Unf%4Od{a(wttD=pkt+ngv2PLcB z^xi3?g9O>)hvNaU6p% zcDj9bf)H%-kg?5jV^1Uj*ZX|A-r(+GOyRV~s6fgva^+a-nUaB=b+f11=+pmi`3SJx zKM@w?BB#jOUgfC}oah{0POYhNozb8k(4rnYs@05Qj&SXg4vhN<=#* zY8hZ@H}FI|Yr6L~6c*72I5>uSr6!NqQ-{ssnF55|$r9EDP0eKP2eQLn{qs_XDZk( zyNK@$11VpxqWF7%7zCTOLv9QF#eX1RaKs83_00(zkfAF#|Ln)aDGreg=GaG8Rs8}n z<48%(+*-IymsR-**+WU9N2e1PdkNhgpFMig-5&Q$Y&psvYK~xWau*Cxe1(4yeS4nu zG%WCN6%FcaG}E1W_e(mNFDuZ0@u1(Cz`8Ne%Lmrgl<#TXLwR-@WWuxoEDKv*S3Ji( zJ`I?zc)ODt@u$3iN73MQ&k3@+^r?w3#L&NUK19Y(#e3fE74mvJWj(eqsuD0hE&4p4 z3^e@QCCczZK5&`2~gDAMh}Mx_b#-LS04QeufIFG z+V$(uY@ApnFI|RTWbit}-XKZ@LU;s+UQmf7*opI6WAx{pYc<Jp8s6w3;0v=L27# z-SW2BB)`83g}$`az+|9}!3kOs^dmAaWs$E}4MsMN@q8g{8W$0Yw{C`JGD^5H2u;qA z?`yL}ZV}3A6%c$2a?9a|*!usf>1T?izM$Y=ncyjl<3);H>&-`w64y`^SlT#^XXf6g zE7!S?0bXAa7GIsc8fH_{6`qKF6~%)^SFHtdKmev` z&}cO8bJG)X6!h=rllBZJr#Sv$*Km-JIl&GC@f88z)%C3RwVaR&S!JNyf)upg1_SqF z&u0(S216N<2y&GlUn7q4;$Q%pJF_F@mrct?8E1poSjv%ox`~0)`l`}&8x)DCzK*oAqJh26xP8Y zc++vG_bc&on?%{?oe3@Zn0 z3GxlJV##+=;Q0t>pNz#cSO0%v-WKqm4Ca&DM>}YkVa8Z5-n5Boq&j1igBjDv!B{N??X&Y`VGP-f%`vx z##TfCozguwlgB9a_esDG`(<*!Zl-mub>>>U0}(Eblz-i8Bp#ygyxzk;PlxQs-lwmb zQQ&~oDIGlEDc~>?((qGgetU{ZiO`^g^W7K$_iG7H58F}XXCYbm+~M!cSSjGh>|Ji9 z+?W?iS!n&bR%8Wvesz)LR0>(=^AA`e%Pykp3xt1kgtq6$lh7lnv<_(|D=$O3xH|iadxs&6oYipVJyNK4yWd5CE2-@qD`7;q`dRu&ndx4GhQqmw#Z+ zEVqIX5%a|--Jmk$03y0dp=@WSj}^JF^eU3PfaU-*}NKrj4Uj?m9CVlTg>L+ z5-L_9&ve!mF3l`HB!(?|Z{+A0|1Y#%;rTRcZe_vXv_;`x2vL^j8A5(2f`c>Sn4^8- zYo&lkqH#OhDnr(kFBeCwV)Mxi2y{rOzuAN$Gj0Hze7J)Va(NO3)uV9?@am=i?#!2L`_0W@8mX zi%DipbO_Xq5%_fVG;yjw88d0~nvVKcq@p0f)JXp4D~NI!gw*H#WaDFx zJc0nv=jqMChkv7&6k5g93?rMW5JQ55%5_vEg#>z0KjGJe`e>j4!OY`xbUnUc0Lrx_ zLqn@~9rRI=%TeK15-n)5L#fxjS?bf42O^uXP2x>XE{aBtbq->lAoaB- zm<-j3lZ`WGOe96GSwBaMEzq$YM-#tY`bvbkI#s~LaC!9@YYj;m zAiQGa3w>gcqHV2L9aUbs}?s51O0NclLTdSX|WrB9EJa4<(oXco-I{f zT;{BaPYs%S7o0~zzQLL@+jpr%=u||95n}$qLKKtbG>yj(a;74zpzN~Du-{rOM@F?0 zo`*L!{P_CkG?k4`Q&*4Umy}-&q9r3&cWiGw2qa%>M^W7N-)xG(cEe)}Ed}cd8d}De z9l?3rGxD?!9ZoWquQrbah9?&rS)Xz4k_ysE^^UTuXP7iX+c z25GpVSly!W_z9*ME5K)fX{u7OY!sOyrnrySq1LPhrUZE3sOj28Q2I{g@?T4{!+W}W zlaPp+`S$1i9TY?CLAv#uFWME7bu>-3Jzm*5ULI4|aT6BFS9d>2UaEimWU@VkHKl&G zhoy1*1167Xe2)kXHaq*3A_^Vn8*T|dlHQwarLsr);K2a;6Q`PNLd8*5V)F|U8ae4) zExu-DTFHDoqQRhUvw17HzRFm`(HF6#m(e0*>zTbJ*(R zx(lz4dUnN!>HKr8=f5<=pK;$CpJDOMTA6>22orhmCcwT$qZP#x!J;4!43XtvM9F3S zT})gF_g&|`{3r9~iO@SbWkh4qqoCkpHCj)fZzuQNtfFVXEr)*Se#Oi7j}and3&F1Z zW>qX$!y(km&0iu}_Um`2GKQumJT_TPCu$>@&;IRcvE2;_>yi1Q-s~BuOdI|ii^fLLUZiqJoIuo70%H*SMUbhhN6tG#>F0ivd%Zy9J%6h%xOZyde6^@jI$ ze~h>OUII9r`gf>7QN)RB+dQ^0Nv^{ah!*#ro37X1NSUeYujpU8)5kSSKH<_m$ji5n zZ_}+82ZV;;qtx~Y^Y;QIQj&|zd30krj! z^C%xo9`mMR)< z((i+`QkDH>l|_-sCxsW7=CuMRyOFBTKWVi(d_Uj8P0{6O!pD)=^LZ00FH$SK>VAi=mV3 z4K&R(_o$Z!=1f*-OiA>^Y1HJc5BqP4`CX-I!Un0Oi|FZQEb9#Q`R4g-B>oNL_T8xVV~p%Xmz2My zloi=8A_8-%KmD=yX&>iNK7IOxh3JM^^wmmZgz3l`S68!ONS_H8a3do-m06Xg<0lXM z0Qc~GWci-sI_jGd3Pyxq(Q2>!a1wMnu}B1AJ=6HK{h;6u_+=|QZE9u;v<4u}@FFPsy*ov(6 zwf(qA3$T(G@4sqG?_Rl6Ot2!WDV^am{xXa_xxcvM8|T!a13XhOgGB2S z5w-AD){kvypk-4J`5f%)BU)WEMyjsiq#SIjo;$I&yBp$7h)LfAGz#of?0L#vj|cNd zxhk?)5|ZZV)KGOq+3i$DRj44w>8i|nXucS6q0)&1XRr+s`uranA_kry>wltN ziwaM~5;w-|Y#(VdFfeXR{R zHNOaLJBHZ-)*1V{>({B*Nu%29S0h74ypSTVuDd(!?`0JZa|gd_SASqz3|2d06&Bcu zb}DoITuEU$2BgDcE_@EuF6b$vcH?7=vx{JM48>7Cyr1nsYaKcS;+$4o%pKOlrY!CL zD3Vs@)!1^CnjjU$u{6TNnbB^6HeN*ZcN*7cV}J`454NBfQFd%Ab+D(8xMQc0jNI2p~*j_kS47 zOWlX$EZR&!AV1t(Me7zH?ZpS5g*`$kRHVU94qzl}SDA~$qazqS=*+5L+|F)TBS+Uh zdnDVh8 zDE5TlK=&Re9Msu|n-_%QWI%dSgKG(}oPMM^TE)M9u_)v#Q$ziJ2?pXP68;1XSPu^m zr>4yKihPdPAnyu=^CGFHK5~-_Qv;|PQ9|;a~xp=vHWFKdd?Mb*Pc@7qbaqi8vvO%!NvqOyt#yHE0j*quo%VIvxU|N*;~m_R63_%0 zKl%z3r^91o?0kHY2hv?4rKAxrXw8T}%r7tJ&s(*s=->(G@&jb7qpRx$E@@9|e&!nC zOlUkaDH2SMS&C?q=O12LCA{&IYoN$=YSPYuAH?obSEEZD^}IN3;14qdHVaf@-X9Z1 z;2(_igyYiU$f&^+x<#nnx5lQ~T2dq^U^&<+ngV5cCehRm%^)TNBmU2zbzh?t3%=d- zSB@pq(h2F8Db)lpWqZzzhbOZD&Kgv{8SJ`cq+M&B4!ZY*eL}Qib@u&tclZPZ1iv-5 zMTx&x3TA{3^6MLP2lBG>iBLnyV1aoz&8=a!>-v#0<{WAUwLftBx3~RE>ij3qt+D)1 zz^}eq>;;AZQ=dKo2SV)F&V{KyGROH8b-BjxKvfL_to zCHVgO`1~L1&$hKep{!PcP{OPX4++k!Ou~$tHi+EN_WExcWwjZ{%?CR8f3&J&To<>e z2W}-oc5ZGdreun(u-VD=j9RnqY(ppnCDkeklm$R?{yu8=+5<4(fWqg=y#8avO;j#K zusFoIaAal%Pzz}y6gVsQIv@|El4 z#t-lIWL@t8kjpP*$FP};IYH~YRy4mkKV`bq}`0Z zHJhyl$Sxnm__N5O41mzeUnpyLE95&Z*9=+^aDM25+cC`y}1=#=hVl;fGw!DER%S+ z0YZNjx_7!!BA{a1TfztrcZAf6w5@W8?uCj2iXPSW_l=kLN@djkTdy^7yc^nm@rQ%A20A$H)){NyCYiO=7c4OUTI z;vkmq_#eg(u%Fc>->U@mpHz>=+BSJ%)QSK>8fXM2NV|cnRUj7FUTFa`ZCj;^*#TE# zFsE@%HQcX$uf9uxJzm7+3Kk%rr}M0TKjcSE#;(6uuueQgcP-v9agwoe^R}7JJWq(= zy0rKesN*}T7*$GlorbwbuP^CGV=(mJ{gB2A0z#C3PhXiTL9g%slsIKxwkRs$@I&1q zFS{D3e}yE3CSCFH*9HhT3?^FE-3*NJZ{%iq^kI_3^p<_E5m0#I)%a zs#CD`$y!)J@z)8F94}X<(&RN)6o2$y&0DES6BYiHS;{6LFqYpSb~Yg26^hN*)@Ng# z?EmyK`z_l}T>AI+ON@Yw_P?3t1U_ih4VQk2(}f(-3&3JPZyAtr=2bL{s;;|g$Ok%eT6U9X?CxVwQ!tinfy2Bm+b{MW0 z2JVRSs0;4V1XokjDBPH>hpl!e>gLGGJYzD`COGo)>%SM|;tqgPHG9NIc%!nt$4wr4 zm}(ZtMkunjl?brk@t5heb=@scmkcqR)rb(*g=A*F;Nnu1%T5i)niF|qMD~N13J}c< zopbk)**E>e*>|3)j7I;)C{3lj{m&se?&ui1((vR*tIDej?)wpHc_xVhWib=JT$`-l zMa1NS9}09Gk^oaCo6hbXC49n+bPc#opg>i;uV=3a0fD!6H-lr=;C7IxZ zuR2f+ek6@}tX|}H6Yy8X&At1LX6itI?;-d4t$sIhfDo~Z2OoYg z@_x-Lo)H}HjF9?Rh~CCGy>Zj=14lL6@uTZspA<9F44yj0e&(~4lbSEiU6>=Ag`#op zrr7-`n%+M=n(0prTXH?&jIaJ2K!KLRV_MoKiIlwji~l$N?epj%vM{?cG*VQSVNBx_ zrG}Ia#x%M`_ny^V?5*jLgWQL!MGniwKl{NKa4$8uA*xBjOfs}t6pR<}>v=UfQwNnU ziiXCHnG5Cp2D zgD_mnYD$!YFZ8jp;)sl;6HBGvGru+n3Hc146R@nD+QHPv z>y;~uQcXN(9=&0d2%`G8w|6Q#(WXzp?lh875}9_p>p|ZBqmjJx*U*zrW(}^U6Ur;X z*xcE}zgr|HXV=g7&%MugZ4YA)kI$#q4|@teIn#%%yAlBRnlQ78NaYZSktBPQ6L-ol ziiOJku13WyTOy+wCS1+jz3fF>p&$Y!&PeX7?|feEN{bSqSgBelvoa(Uf$zveu$4=uQN&?nH2nXNDF-8vYmwy( zqyO?5*K_oEBOF=A$dD~DTB_70V5wyuIs+3K>7HpRlGPZq*+RI(q`SFMQ?hx^O*orB zdol^()kC`x9fZ%Oo*6|+PY2sGf4Q-*pLdDKb&*&=Gzl`q?2qf&5`0Rr z+PYDAh)R^o;0F%e7)V3BJ8^l%0_kILDjk%!>6y(+W8k64Fd!;cnha_b#a-M zosF3v`}e1eRz>^a0|qYB_goI8AAg^#ufHoeWT-c;GJ7RmPcKieyjn(0A}xsWA02-QE6B0np}DNRvgmXdGd=x6a&&kU|43_H zRD}btba423WH#eEV~01oe4GKhzTd>0;LEB{SER9}jfKktq-dnvC)l3+2@87% zhfV-13iKo%KfP`Khb43}9N6M|2u++jKBX%!D?0@WfL5-MJ5FN7UC(*Y9RbPCWemw zswD&C_jp-W@~ zA&lh{VxK>Qgf&1FB*VD6T+~ki`tu=1?YiX`yHtoG^`bLtsx2RDMO%)^DW>)~%A5&G zzT|k`$}?c%NAd&;zZ_s=m3?(I_Ko_1Ol+j}g!byPpVBe zUzsdx>{H3?i?US|^`|bcb144}`{F~d*h*``H)~yG;t*14Q9~ta8tQ-VNQIqznW|wY zRNu}nMiLUiG$EnisX#$+N7s;3Vq>A9>Z7IYt&0ZBf!wJ53keruqf+wNpjhVY))ez= z0{Hyuil;3B#*7>X1T{a`XXgCP?O%^piSW3h$&A;%FM~pu#AI@%@|CkEtPJqTjP}A7 z?y53zad&{gTXu*X4c-o*P=R|(lG@n2+~BI}MD{K0A~k+E;4Xq!zZ?3sZ;#6FLAUdX zuoXIso@1MW#5ZsFV*iq-V2^Z{X;L|Xl2ToC?@>iG&(e8d=NzyU6^m8OYCb*^wJqCB zeL%8U#s``qlYD7_Tg2EN`TCG$Zac}_ysSnr#eyd4TRzdlAtyQ#U0}`>owsp3V3Yo2 zu7oRvC4^>Pvj_;yvO}V1?a)&UBslHYmja3!UIV^&aQSyxdOnD0mkR2(NK>Ly9Msg* zshOEngje;InKB=01WsF%kI-O!0bR7a8x|7^Jv^NMLl8-fUnf+-g0<_V8~bCi#~uk3 zkHHcWVEJmE;(zca*)zmFy#RWHXmAN=1o&haku3Z?avV7M%NEeND*||x>}x?=fdd7; zCiBz;8Dln6V_ss^_%12jo`iv`2Lbor$aiC5aau=~0u(ByO*u{>80(55>-x!s*PELG zj~e0OvO!u*5{2vt-&d-xdGbCfqqCTErsa7cg#6ikk*&B(b8;QT?Spr_6rK1)NWNr` zW|V5i(YtiW(ACvNn3bVeF>Utez-z&8XiMWz`=Bb9E)eF;n>TpNOxbqu6<>0vDgl}| zd+~^kgZmrh(B>JpLjz%fG5G*S+@^z_w8Kdnr-1hD<+E?gXpb9h+J$NA`)>^B;k_wY z>87BX$vx!UatI9%e~XWd+4&_r4>=?X%4FB#IM~zk+7BK|Vd(j=YY5)8i4WIAMLt1o z_Im<9)5IZ!purMmM%iiJvD_5igF%F;@@}EPpE@*!FP5OCms1?`Rwy$ImiSQ|(4&KhF2&?qPV_Ol4+!tIXkzV^xw8nL|_WgZ1R*54J; znC_ZWvaqu8%9Z`MJ=`@KL>0{4W$9e0DR>gI&#q#r){CG=!q&pQ2N5~Wz4jy#vb#Pu zD3-iqSgv@Gro;fi1-1m~?BbSyUXnc@@L}|6B`ZLfar5(R z70EZlNMPQy;g5P8iw}Ph^`Zr9aZ5-UtY>UiCswD|&$B+;d$S5R+azu40?)_TKG$0^ z-VpPc$|p_4?>bvOuZ3PHwa3C{NT#fHLr?|oVxFIDpVBO$FRLV&-R^W-jqh)9YLv@d zYG=@_Gf^-`X3w{d@cZ6;d=O}R4yswE7|dq*l=bX~>SOO8o65rPGmETsA@l4`qAr0# z^G-cwXTje?izJe)77;7Td4R;qkl=z#o6FGh72{we&*HsDX?_XXI}?jN9$>YK8A$0= zWWGA@O=9kcuQ$NXAtepNNHUHa+u`Qpqspqdn7|0q_npgMdGFxl)cf?r8tI-=S&6FN zd=;}7`Z2&CrxCtZQ4GZip|54v`{f8s^ecnhkk6yz7H2_s#mic@lNS5yS`K=Z)J-~bXQ7~RO7YD)BKwql&_^XUMOPDK!OE+kB@ z&xYKky|`uo`z)cvxU^iTMr(cZFeCng`^YBPy;`blB2U%UmlPd7Ff{0m_WtXg{dLOE z;?tsBER^cUZ<1e?FWVS!`;hXUbDkAON9>aQW}4;d1M<77aCeJ$$W|SKcA}J&XuLNf z{z%cNED#Kup9I|;{vAdc#i6W0yfFNW!Z<=$gbR$wizwuC~8wj zFS(0H$?C~rV%_d<6l{Mq$b^Zqelg-Z@@6xM=m;DGL?kb2QyQhOij*udtg(W>l(5j% zS$-{iuoC6vkL}2&aJ!#a#zG zr*3^#g@HrzaB?mjoYe2A=pkBCGu%GyPge@=}wINIcWCH zNc(goH0rT;>`qcw>4j9z@&^w`sqSBbGe*>=)c;Zz2aMo9AMr)cImZ^%`p;1iHOs0Y z#08KeypP+3<8j}W7lOqi@l}lYv&3j};@N$n0u#y;82>q}uVgcfk>O3F@*9!8NueKQ z?|&nMTyYHgiWx zuX2gbch-CB>e1N8cUKb9BAu^V=T9nC^z)EB+e6p#D>4J@_=n~$1QT4GdLZ!Zr5669 zxua$EENF9reRPbiqXuh7*cb_y=pI=(*1EGMEPRG~+95%5dJ0v5zr(Im7IBs7-aUGF z9wSvE%KnmU;D(2SRQU_i^>9fGoZ`;<>GA%I8ed(;u#I2xXlq1L2YUo>vbn)7@inLe zuYS#l{as3}Z_|^S;NsfFW=6Gays88RFXz}{OEfq#cS65j(|yUC6-F$ge6!>B_piWT z*Hbu=D(O9=_=_vhpL-|!gHiZ;5}~HUjDwqxfBpW5!Pqar)W+^0*=OX>91HPlzuNl- zi`tvszAB;5PG>zBiyrqG-*GA#S*)seROfy#%8jzUc1Bg0<1fh(7f;pHTI=sdc&t{(ijI}+BNMA4y4mWJ@A^my7wmd+g$&phKVgeS6N+!$$V1g^$13kKcB5Vt z7*bq}@A>`Rw)FD&ldk2ECk_8o9P-Z4!X|02C~CzfW|3d-xra`hO5Di`A?w6?TgwHj zX0)2HRcrm?BEgDtOua?Cby4hn9ZS_`lxqjrX8!;~AztsJn%nV@6g=d$>lY>5jd&bH zyLLT%C-BENQIv1$y`j`HE-QwVRuco(l?}HEkB`QqL)H<*yfbo6n(%)9#$Ra@&q{5q zs$5*VhyPeBzjAancJXRjGOU1@ucn~_qsc!V$;qFm5wxNgA>@ta?7SHpGb2EfKkO_Z zLw%7EP;Q>1P98@&Y4MNG0@7%sp6_&hhCse^l5wS-qc3yo4K_G*m{@t@soN33Mq4+? z3yo2*Atj&hfQve>c06k!B+8YU{cGpyD$3&}UennAYe!cn^KLaBA#7-mgPr|VyIXaq z%c@u3)fEaz^!5oZbym~Tq9leVm0}B8BWFCaNi``EJGM(_ZhMHs9n;mD=lo}WNNxj< zpUo0^tmKaeth60Ayh1u8B&5G<9k1x}*+8k@wzct)0jTrTCWYV(DX!`fLQ*YMreo;k zl{r2A*=o_md=Jj&lFO$H^!1;Q!~38HpIX zaKIQZ$uZc{?=b(=uWsBqmzFzUBVN@9ZD{)9>a^s%3{)ZSBnW3Q~%c8_pmj znX`%U$VI>;J!StSxaV}D5#&pWs8%3WJ)JjAzs#XSaqgbmJqk;RNeI>evk#hJT*gC$ z`RM>*Dup_d2bL;EMeuT?TGp@QMR7J+^NO`}KKafLE*KNAdoxCsD@?`i|FQhQgmT5r z%}s`f-hDj+)X>;9Ow zp(ujk&uZ(Fn=UMR^<&ZMvVJInzjLB$m|wcD*4;R2gGBJ*0li7o$GM3KRl7l@K!xd& zJ6-ORU=meO5FEgvo0x48N&&xVijkh)uQ60Byn>RX1rgP`+M}lxwqnh-lWbP8&s2m6 z>?{yL1{~-ngG9da`ps@iJQP#7mG!|p(;#1xGv4@MONLTu^XllI)l!;(K&@5|%)sIY z#!IodKYkS9N@F6|Ray~LmaK5A3DTdmsxhlA?^P_E+CzBZtV9`!B2d}TOqSFB`Ne3f z@aTSHlmOZf)e?JGr*5P1S4hxsGbRZW{=TOUe$8}EPy@ydy!;mf32L-##kEMy`Zw~) zT=!>ul6(`mZDV5qtrseK$o3HW?HlF5T!HVj&Gk6c+-hlpvv!>704k=zTb+ie6b%C5;mOR@GOt7BzRx0WdA63@^eu$H&ns zT_YqR!YvE=%Eflz+pSNa_mcrKURoR2EK@)k=cH}wd}%xAiuUoU4@uitu(3K`&^+!;6~hoV3CB{0`8?Bdf0j}0 z)N@tSj20D*)grbmtG0Nkjvrofg(?>7M@HGHAImnEt|A?)P!c4G&XAlEYfsweEj1iJ zC}QhxjS+s^Y~!o$PFer*>_&?L7(AT!aghvC0Ow^7D0_o@Y-8WO6qu3?Z>Q;lOt;}- zd5eCAeG}}ne=Jbu90NTv&{6^c4IpM)%P4_i!m1WbW?lJ+3#e&QGt)&re+KXqGfqta zTE{>Pj4^)Q=PN%ey63d?p}ud*jaz)-h_lS(EjxZYMZKl(wB|jizH?uF0F|!k9!G(( zfjW7I9$MB|G-cDuHw4K7U&F5~tK>g97?b@cIOJq88^ad4_P2UzhVqm}z|=I_ic_YH z7W7fnw6*oUy`#*QWEk)rKf4%VFCI?rIYA^({&nrb@-e!)Xq821n~C$IAHK%Fg%NJU z$gOj|nD|RwV4U-lF3EV(+{NB%_bP6Xcx7qm&e@Gf&d}4-PlALG%rUE~n!83vQF1LU z!5zZ5gwtxB9g;qB4zG4fD^zIft7QPffxG63g1yn#v$~C>v65gVqfH!At37f+3>JZf z@wOWBVp7N1b3!s2>fWXGUQ8NdU$$x>+zEP_bjFSlDljdI@XLcgG}1U#Eh*AieU6Vz zc(K;3mbAzYJD{?|tX9$J)f7ji)mtg2>8+;a@}ZYYpkL%!6h{9ZPEVg~2odx>cs*}o zrab*^+a8mutJy*5g;XT97L)9j1-n^IT4`zX#Gwz?#yF7pB$oa;DZe%>n-Oe*2%ym-|Ny%`^6^*dY7xKD?q2D>_UuV z`ZT%&pao>{18e8^YRzup1Tr#6D81vGS%2$Zy#!)uk!;mmor>xV>#Ew?-pHOszx*Dj|TFJ?gMm>y;b@HC!RktAgGEVKTb1r#td@v z%4UX^INI^??yI;&-}||*plydoLeK}zMs4Dh#RT=xo3oMcddd1~zeNY+)}C2IYDv-H zVL>{kv9nE5csLBOY&kUi5$Nj|a40B;onXQo`$@;n!!!7|AN~bEK!V|%axod;U51fQ z9yJPU#hKS1lV}@Pe&ibJ3Pv56ecS9_GYqb}LZ`*R z)8eIEy7(2MBu0o|fcZNk_II@|&T8Hb5e4Q;&0oV^d2htO-7;sfocwpVc$T>T^7cpT zNUpOwXZBrlUav%h$1~ZbNpg#9BF(C|T(K$*PB<$TYW7b$w&M==WXIR+IuB1Ws=7zH z_ZV8b?pd0;z-rJJotlx}eqt{~mh)n6;q!dIB|(*5x|;Z#DLYgGziFX(LKx!L6?83E z)85uSn$M582Fc3N$=*cen9OA4@9|gQWyj=Bxq1tcT)(#7>&JIR+TJQWTSjIQEXv9) zQKBoXMK}8CZXF_kX?x<9F;Kkh_4C{KxCTe_`wT}C$aG_b@^5S-2Xq@h>%vKKkaV%n zy56KpNyNC`8YJgNlDMAbqn@v5`>|fHt=e#c4&D0wG&Two6J9#;&iR-CwE7K6Tv*RU zgFAcY0rJslA2-uS;xJIK%ChisgjhSfU3SUrC(`A%+OWBre$Cb>8GX|Z!JG<@7%PpT$2G?pQ+v9SfqBB)I$zw?i3P34tqsDV zsuW%KZ+%d*FBW!o8QYW|gQ#E}L^lTu^Ac9V$lE4L@O_l^TQ6U??Ad7TT#*{BY$bj} zOP}DDT*tv0l&HONi{B9bIfKV(xXL2g_gDsWv7Ux_7ZPRWlNNtyx8;HU#PoSL!+i** zE9aAC96zs-m)pCZda#Rx?*9qROt(D`#)bSLJ*KAIb&{;lc+L|oTq?}B9U(8R%v zjL+|J5RJy1Z=On?33@QhInpI-M|N*u$)bbwTu;L|ckUP8A7R|TxzC$=Cn`in7{S8I z(;+w<%uo+s;a(=z014&eG03&Bq^G|vCnlG{!2CojtdA<5 zFJGy$X=Ru`)nZuHgG7`YI`cE`qkxHv$EyzD0mBnlYCnL550*$xZy2eW=aV08&0VxU z+uxdf@KPi5a7{O2lPdnc!{HT1`R5%mfg$PsdYhnz2J9p7Tf}u^LKo#l;oU2hAf#@fCbg zVhvD*{X1-j#{~m8B^*thMA?I4)FV&5Z2SX$E6wr#$3!JPFyOA|J~CdRTn*sJI1&9& zH+|Mi&OE|iC}R_9i!x6u0;@?H#5o;Im)1MpZ5g~8s`6!;_Ds82$5G1Ckpw$IYIL32U5eI9thpe~I6q z_5H8|g{IFOG4TJGdke3sqIO?c1PP^)lolzG?(S|0K}x#2TUxriWJ|k2xdu`U5@yusF^B0s+rZ}bPG${fmUOrJ8P}z4sjR+7o9McyT zNRrzglRF_!Aw4u&+S;otOoArP?*3J$)wiwVTInQ6y|0Tj(}@ple-XYij6%mkMnDc0 zC@MKg7kYaUQUQBp>isIYfV(?TST{4vy-byoKf7a~{bAsmALvbQ$M;DONCErxx23ks z(?9JUd=)pHW9}hNFKak$-WU(&Y12D3w?!=2P(;?Rek1A3)hQmO2TmU?{rw&xo=swN zzxwe`_N))FBB;%yP-Eq(k3A%)P#Qy`){wVc|($)?+`wQ9%q7aQyKJNQgVio1%Yy(NY^Ys2|A5a_~+sm@gUoHA@R6 zD%=sP<n%p`SGPE>*aV!PxMV+n~a>H|D{y}P4GcS>0= z%O2cVEO`dlDtpUF&n9BGYRbwA4<5c9R!ndl0**v2^7ZS+VOBn-seUQSpHN#BdbJ$_ z-Boy*kkEv_5`TuqGiHS<1;Ly`ht|P|6}Symc*rY~Qqc#>;e!kMX`8MJgk$%;9G6vQ zUXqs{bwOTM9!XUZ47C=9uJZK3#8O^O?&{yZeua~6bpB#F_2bjfSn)cyLQZ9qn{3VE z3-OW(`p~A&dp{8Edw{b9E~=9L|#(2v7W8ShJ%*o+5iLC&9F8UF=k4;E9{6iom8#KmNQU&f*ZM}2BO?TgJckqVn&U(5 z!U+>&MC&;y*{R{*{G2W(CzEn0>y-?1e_~xMzmye*p}_aiCsDU zFrM%)648feI0*^BmHh(Uwc+Baab8Qu=C?CIXoSKtm zN=5jUah=`_=yFsv){2yU`lrrT)1O6F(ei+;ZW}L8O{mmU|HB(GCE?1&$+PAz+aR%j z#{=YqR~K`K=F=qJA{Y>(x8e}q^s_aJJZbD!VgL2zXWw(lL@*mT1r%BTZJpg^)5Rs6(l0=vT&23ro3tvB>pa2tYd2D6}q80=-gy~cpzYENQ1{poJ@en?CF=YAr3dh3rE|4!yo#F>ep~*jPl}tkz~W`%Aa3o&1*n^tco`+_ zLv1wlTh>F#e1SxCu-QoV$3G^>!JXbW+bnGl=j9$pgS`N{y^3}f8}uGY%=12%(Vw>d zUs{dq%*JEs-?(>iT~`bki+(wwWPSrj_5q~BBUaG^b2JZa%w8?c*zLH~4$oA?)n8-B zVFGiXARIad7*)c1CvWGWgFh?)32VL22#}NmHvd@zJr6S zv{+X2Ws}{)?Ri<%&{I6}Upg#d925k~E@o+{!G>S07tzdZewJ5MRJ6}{q<>-Vo0Os1+2IE!4tNDgYtZ(9(h7XxQOE3DuO-oB_ zWh252?4wtv@c`Gt`)-HnYIy~TDim=nv2?mTXpn111>v=pB({!Vfh&zA5zF6qN(vI;9M0NFQu+V9U97{54bk=K*Q3tsvV^N`i~D} z?#HVykec{@jQn9%7iyI1t>wTU(=PqKOX^J@7X7<>hjAoFV6A&_>xsEHh|i1)KZn!1 zdu;IOdA=b^zcn^$FmkXDIkI^`6vO+X^iaS{ftsk6PVkJZ%YfkRS*=v@iE8iC8K&?lCot^?7tjOmnO70hXgmZ6p8S#x5?61XKgu?Tb`?f6VK3_HS3hF;=38mbH z4}R#{ndF!yCM7;vNbpO1)^s1fuA&R z-^=7N0g&^WEv1>6neWO&U$pS@NIQ4WRV_Fx7+&eBqaeQBuJkXUWt`Q(W+935gor>< zYAloFZvyV`;n9^YfD7dAR8ND;EhnTHA%I*N%Gtl+__v9^+c3_uMTQFdi?3-qa2^W~ z#DDSrY+x33&|c-c*Brp69G_g{0EK~K+;r*qAmT_08SHM)B+r%*+BESzmFEMW^UYdT zpJ|SYKZ6Xa4(=Ts817(!jlNdntso!(j(5`mvrlUqXcZW(11GOsYwJ*Md>9xQ zj<%2O0M=h{Q5iYLv4-(e7UQ+|*BVWsBjC0cCpi5D$kaN*Brmt6TWJfvj9S+*D?~^O z_pBYq3Mifqb1nTku}n*vP-pz3XwFWA+$h(ra`427rh-T^69~g3P&=2;j>4X_4DUMG zG%adV+pFbA^W6DK|4r}$D>i^PssJum2N#@8O-*3$P`|q{##mTb$lk;lpFfLRtX}#P z%2ZX`7&39NdX5N;)y(6Aq}-HW2N$fIeBXSJ2->X^B7hzfZO(2<8{RQ~M=U4D6&B3T zo~}S-lOfrn_^v%z}IO<|N*=4kBi%eslj;j^CfcocoaA;9YY)LJ4P#KcJ=p z2~@}y^7_+LngS?Ls*Gy72n{rmc}44T9AI~kG`CAkO#G{*Wngj>Z$Tr=p1bRo%(nwy z-@E-UfJjG3Bm=px*?&a0O}mK$?_MuN6j-DN#5m^5*TnYjo@1jUc?R&<-N0+^2Jq#^ zpkL0+EUttudiwY}ik8lZkLH=D%YKg4Z5?a$S}>K8PWA|-sRzONotWpH>i3CUA ziw$!MRpU+AB`EgJab6-~@x#gHV|x`^06n1{*jscAjG5_AMov}Fttm z+Lv|*WIm$0U!Q!c7fDB2AJbEq^edaH6ex0}CJnFdeC1^dlxYER1|?Xut5fi`!U58r zU-jE>{#2{ywBm2ED930+zQmThE(F!m6~qNR57bjOy}mX$zu%7h-kF7NoYFZtvjO_e9{iUcUqk6enL_ z$)*L-msGJP!LoxAeS?ZA%FN=zha!HDbW|JuCe8rS*X(%>uhe=Q-MQf6>gu}fOzf;H zcJyw?nKv<9Vm6Bt-a$yO9KR4|0_vu%j~ACe%YIf?Rf&B6Bz)YBjPTr9xrmFX+S25; z1O>rSULR`-Rlu_V^UI5Q%J=8=<=yL0n9 zC%ZoCjFCpeXgKy*8ORALeRivKJs@P^Q?JK#Ov}@C2X}W&3y9UHc@dwiYiD(Ibbi%c z1XNWyQHL6CH}+^MrN9xi9mncFoTLzB;ibukbacag&dA`u{bz-a5ko*owuuj>W1y1n z@b)%&-cZcF*RweUt)6l#afnK_`SZ?g)joV4J|Re=1CQ&a+ob!+868q6pLu#u3liCB zH@?2T&oSQEuQxJHRP7!RePVd)jFgeq)tM@@*MoSSop0x_@22LPJrZoII{J@Un)phs zt!;lj{KFm5=Py)c1d7P+k1K`V7c*w^!$T|T(IKaoa<6@H@A0OYDq3-!WvX6A7 zI7CZg$~k}(qfyE&un65Jx66d>c1?s?1>vjrX23r5gFIc=-X1YXv!^1XyN?H(-_;wx zmBnr}S1nefRVOMc31H$6qWzXhmNUNZoZ7}Y_>`(k1|SPpX|Dky761K>+f&V6aK2ED z!SJSok^Xahu|^3&6xh`J=N^b8OCYE?2M6K4$SK1@Lz1Cd2&U|0 zg(hq1`S!QB!^KlM93H^iF0^)!DkpLozrWc^60#v`{%=NK8~$zGhZQ7DofE%zEWh)( ze*fM^1)#RQgG1o5L$}WQ_cIzocNe?7v%ml}luP&)pD52qMy$7;AV*y|ZO= z_j_@r6--RrBx%ZiXwxBVPdhSN0cKf8NSw6>Sbg%S?~Y1^7-_B|6vEDVZ0ChEH&z^T zUg0Q+;0DSMqbCp$nS8MgAe(fn?=!Ba#kFu_d#$u>TOZ+%@_5 zm}S#U5)^*j89g>XTNuxR$SLbyVaFmK#zLKUHQwiLYE;g(b8o4CJ$hSg-*} zs-tP{&f+h4DP=gQh-2raw$2%O}_#O zqTW7qCXJBt8$meb(F!Eg{hpLslZ#ufFpeq;zW9HORmnfc!hv$0@n{=}EOTm@ zvSQSDg#j(mR-jbd538Fv6O_U7mD3yPpiX!F@0D$+r?|s#P&y>SsHzX9)0wSHfX{gxDK~ zkjG-mHOb8z$3vg`e`f+I_>hxQ>ruu|dj_y#FjqslnwG zFOVYTQZZisPB2Z}GV!Y=ygqsR_MMHO^{domnZY&E#Jm+uQeBF@%iKZH^6E*E#0PQB zGW`&AJv;xE?;gBzFRNg6&y**qNEgJ|UrUGvS;|Fj~n+csfbJ1 z%Nx(mId&)5!&pmXGC{gho}7pp>l{Z7!FM5`GF!ts=VRr#TpyYIijNG z@57HW3i8Z69Nitm4y*lCq|o8p9N!QI1VWNDAk69Z!ROg*!23V==PxiNP^!4b zlJu^#jAm?kJSp3!xB1cL(5OhV$(*XsN%Tobmd6CtDkTsk~2P;;Q$?5yH#l>WPP(+w>cV~S@j9wf4E#X-5KpsF&`HeMz zd>I7|M+prFO#&?LL%N?Sv7-#q5PgSp=iSIlKX~t;XLm4uFeB@k5@l*U>Z4GBGs_QG|554O$^+ixk-Muipqn?Q~m! zBj6l0{?X5RsF;oBUui4;atCZ~wJ+1w%`(Q=%AFX^p|iB*NcY7=hee0cg)=f$poP6e zW*zIIof04gA&$$Aq^hMwp-Qg+M*FKx-z~UQ9F&|RBbN+~jf4I!Q~BmZ|4sOMm@?J6 zfk{^qRPg>Od=+jH638AuNaYydh-$okofF9*6J#i;HYPlUpc%Gd25)mvpQy%Ee2mh} zLiUyoVr^^e$vyb_nxne(b+Dw4nwUHg1q496rPut#fM4!LZk^3|2Q|hq?d30>~iW!lcR{ zuGd`J(o4eykK;dfjg29OHtbIvg}#;N_iNX>i5wCk`Z6jyX>j~Nf`_B`)nly-`aJl{*T-`-$>2U2P!fpDkYQtlzLc==lcg?GB^o=o@MdlGW2` z=BkgdJ)55m-kD~F)5&PE`0AJT_ZM%$Y2@Z+S<~;R+^r~Ay`;lSPhsc7InAH=?Y~7& zx8?uHvk~KQ^YU&QK&0vPe@s-$peZ`+-m?H=%BkEdd3nX%;XmkEIt!PoX41WN(;dxzg=mv}3OlH_M+; zFbm0(4FRM`ry**AZsoLxActV9<=Z~50<36UWwwXV9f`W>}c2;&Tc|h||8<9wY*l=kYDAl-3e&;D%aYV}@t`Byi5^opB^xj2E-5j8rCj&rMUlq`;ghOK{CWE;znP|lX=#82IDEXkubmET zf0t^dKB?yB;)2f(6Ce`FW?@(`l_ZTq8{|Zt!=D=+Mj}H)4q*YpW#dPZ=m-3lc%OUyK|I=VnrkyoeQCr}b1qI`njwc&?@%6`%4^vE8323{lhK&xA7$Wja&b=2*9RY34QSQ=%*rqK63hh33}KXV zkTC;@O9B+CFu#Y=#@F_!^@^9`8)d<9;+VZSUFi&A1_S2DbLmwCDXoDd)?r8A@5wg$ z`7pm8mKEzBbMH*oU`-2U-C`n#Ow74zua?~cm-c@f*5AzpR#(D`Wp@#XrVOJ@q)Dtk z@VbH%n!i^0?5WF^&!xUs7>HV1Z(jmO8y~<^U+@isa75i`^Rl}dMaecq<_yFfctP$?a^UFh9##_`b&giTC1hme6xEn>u&SM3 zv*FBHy;z}T!Ml=)*{nCvLXY^9@a|!V zkzY6o$9IS;rgmUF|CZ-0wb`tlB3zpkinAx*pq>$kbu3@UDsIJgeib9`PRvlB{QSXo z-g=GCpfWrH-9jd@ug8Kd*S*i2-;|#SPI(NigW33>u@bkMw%0G!GUf6q*d_v#r57Ln zD$~P8+q&Pq)MJ>?FPYUMz20?6RN<-7G?7woWoL7%4NG7oj4F(ipLDWZlU&cq_wj7ywD5O- zzDOSa_sLY@OMzmnsb@W-b|6G7lEXZo=YTYj1b$f?$%^6Qf-qguV%YPlz1y^uKOeR3 zry7mII%4(Aw~1Y@F8akDW`u6ru7w_BgtnHK{o78Z9tnYf=%bJC71ty0hA`I9IAdqa zPU?z%A0*4xbMbG8Y=dZ3iwI;US65eV1kN;^B+A{FUw*e`81b1k7brE_Vyk6bwFYi6 zpy&T(Ldg{k>&A{U%;WB$vcNZ+l4;XtQx~z4kVmm%Kxdi!^fPUxkP zZ0Cm(K|xgUIMc6)N%UC-t4B#0&rJ0BpV=GZynT;W3cmHVs3Gus?$3pP_R{9dR6hI1 zEWg#H9cl@c>}JBE!Bm9pa}r^|c#jacwT*0Mf7`|TukrpZ;TTB5erE)1bGqbVYvR~( zT_|d>vcD)Vtr#|Bg)!Z`J!nl7qs_hraGRzRP$#f>-Rq?{5Gj012$Eg?Se7_rX`>8P zr{f{b-pWEiC`925C&cLCU!3`ZNn;Kzwl3gc9o)dK(N2ml4MQv97Kjl~5YZ;%DpwK0 z7miv>72i4Y-lR_)udgEQ9E+72iP?6IAv|BLj(#Ytv#6PLm>eG}!TzFc<(lbBR_z+z zcoRKIEU|Oh{DZxRD)HXm5*j%yJ2jifsO-9@%qAa?smQQkoARffttSBmRxH7nZpuc& zJ8unLxfR?{{jj^}ql(dV#ho;XKU7sO38A1S1VU;C_Bw^rN`|;5XziA2B_<`=@51w{ zXU00R>liu=!(`b0Np<>ncEU$DAq9O2+S)n$x}fc;TQfQR+reeFT6ybhi*ust^r6$y z9#4}}4r@4N0J*_EL^JccA>i|ZuZt#%y_8LE^esg zTc5t~)vfj=X$powSL0=Gq?FNf{d|s)wP(=R3FCM&*bwAmHTrtqf^{!*gTH)~+0mo2 z`DwYbSjA_atd8_Mg4p?Ak5!?`ocx2WQxXfRIAYj1Au`%DwJ;NIB36Q^Uz5rw@W4Pc z1BwQ!v}23&&$YuUA3Z&uO&HHP(J{BO`j@2mk_IoEV`rd~EFGc)fp??73Q~>i+E}}9 z-XPrTJ{$`@uszH@KKM~p9&tTxx2=-h1^WHI6ndnBLYI7otT`d_+W6l0lM4T8cn2nS zu5HwxaR?2kpYgss76@6RTVZY)A#xihTC|-}hN^vVeB`&R^mwLpeI7{IcAeoD^|;)2eR$O=bSHMe2|L~Y+0+XhRe4$5v!#5R6oA>* zc+to6`PDefv2k)mAt6`85C}!TxqN0nn|>#AHy)e?`ClsHbzWUnEoNslJC`<2_vlto4((k99Vsl6 zcfxnHrqlaaG&>%ZiMm^8ExfXH1wA5sMEi|u116T&-#&d<80$r}!d@L^CXJb%6EbAY zX8u;Vb6&iwQkG~@!wHJ{e|I-v7)DBr6N(phL71za?ly!)o)aisO8YQU*+Q;`m(#5c z-4_}OUT%k&G${G9H$m7RWtfhLGZrbfG#h*9Lc|#m8pB`3GmM*ZL{F;iqRA!!n@$@D z2@;f^J6owpe=&Ajo44ixUr9m6Rbpk@`AYlk5rB5i8+RCRcX7WXp8B3Qz->zu>6F#* z`|4yXhsHhq(FT_ay+-oQ2H_-^=z+Im3Y8493t_JVZ7D-#Y77PCE9;GX!epUoTItub zROUDkVhk%{ey&`Nb8~in`U$>z(+FDGH<|9)a_d)GY~P%x#%G2T$0gv$thE{IZF!v= zjV;(~8+bCXf-Ovgw%!o;3hZ3Pwy3;dgkJDYxCR3OSOgOv9j=xNPlu{J+Oh{I*BO7VyHMYk7M2gP#yG%p0>e`uqloQ<*y)-lQRo4hzBdd!!h3l}!E3aWpC`_^<7z7q z4ep0U!F@;F_3 zi#9527f+(#0(JO@k}I<}uTUY-aQsm*PNimXwyTKcWiY2+<2>h^#n^qt?y1U8Dvejt z`XU@bp3rWZr3R7yh>9w#$Ge=z)3bMztba)Hk^_jIDYOk~gRF$l=FeIejGg==ec@N} z1cko{MXJA}VmRv^c8vr-TEOT@l3;5Aj>;$mCZn>$98DfGld^odwG*t9W?HQhG~ zw1vI+85|3;46=nBIW@d99La4a-N!0l9f@V5@ba}G+_oEsMGzTz(bN|jHSp1Fnr!iB zS7lZE8|J|{SBV2NSh+=XX$s0FQ5%sWR(19Bv}I<=lc57j*sXbs*24L5t<=ac{JCn< zp#lXdVQTuWQ~p&dv|4pbv;mV&?b2WlM0=KR{m+XSVN@M1S?qIK@G`*EdXK$`H=SH| zi_hUC<70wNToq^LpGmoku0Q8Ta5TtQwxzPKO*a(c38s!=*hiB^(DLK%O&iHhinz+F zRwcSoi{;ko0aEjrU+XY>=t_=wtF$!jO57*8;gar*D}F4!t*V@d-|rN3PpvG-O`3Gy zv4mja=d-`l?KtJf9!H7@zw;(FdwYRD0;4}nUu{&&u!>cqP#jNQ)((`7$#K9NRbHR! zu^_K>r%m+9cA`%h8!QXtXnob5E(OsJRBq(_ zNL(^f&l*JOBt`2XTN(-W41!c))0{Iu^*2f)m*Gv%h~L$fo6s2-16Pmj&8XhL ztIg}M5sVkw4bywmdZmf>@81Cy{l!`}1zC2^3whX~*qD_oW4o9L=Sld-P-t^!OS@rB zPk-gb`**Z9cB@ODXwb!0X}ylrC+q2L8tdv8Pj^f-PbbZwmFU0)&~QL<+hRP#KpQuu z&^KSOPS@~PvZ+;8fy{WCqG1QE7v^dTUL9=7TYv?&1nV;H9)uB&} zCtXXD?o6m`Q_*k8WaPCXMA1CKSuIkx6QlCI7JS(_l?-aMlnqqk=EvUv8L}&j-L*zW znLK+Ef|7*6Q*j4JfmA2hdlRMi>1A?Nlh2G&jR!Y8EvS7+iMgoTI0i_iAzB{k*rH)p z%flEeLc5Y(>1WToU1%qG8q&T7f5Bg;nQpeZcrj~Y)hlDAy8(Tna4a7mmVk4BL}RzQ z`UAgJJ3s9?1>;MFSl#ET4XCTZs4bdtXd^1ue)R8%OC44T^5LsB3K_|nW{X+AB~&9) z%-o|a^W_p_^u8MRk>+#H)EUQP3EVTa^sRe;P|kzG=$;+3Rq)DC-W{8N7^Vql`tis| zCm&{tJLBZfU-ZlK!HAe^Bov_oli%*(CHn@g;=T3ti-BVUA`r*zuHZ7yNVycpxjF1D zTdcB{ux|a^yA>cP+WBJXRp(wMk&+iYdKP6f zUGy5DOqU(z8D;3DTKW64nNeeq^Pr>P(a8LE7@d8qXj)$udJ&K?wM7=U^e$&c)YHP_ zT<16XcrG(dhA>_5Zd3i~R`j)&Zn}xpho1CR>W%f4*?C+qqSArZ`AmNM&kxP~t$2mE zAuJ&0ZG2To#(^KJ5H>dB%0-X1Oqbw$tVA!CS)o_yqG31rd*W`=?g6ifZ^Zp>Tjc^% zY^8oCO7}unb%ymiJ7$t-%2GLf)S+T>h@`8siTQ=Qg=xjw^XxYJ=JBm*J+pB|UbKAS z>z$}g;v|7H)tSqicQG_!Fl?`xjMM8W8lzht9x3kn#B(`Dlgd!TA*f$Q7tM^l<#8sY zlEy{i*Zzy%f8Y#7W{r(E>wU5F(Jlx5~>M{ES(6{!?_Dod!#cF@t|NNdQq z7YHthWaOzI5Sj^ET;0myXXf{~Dy^oZO&VW=cf!Ej;jVI4HW?fRX#5qUXF>B$V=F5E9ckK8z2wUyhQuX<*Og9)x9RT+h8 zY3bb2Fhr+&K`vYGp{V`+YC(tE13hPiBUzJiDP$+QtZNR@3#AbU3fbRIFq51$ZpE8r zPr9SqaY(f4j*pDzu>xJw?+T|S$aPyV=Bm%}&GVu`q@j&RCjPPX+e@vqsB5%vN`W^+ z0viJTY@fW_5*`auZAoMMKQh7EG>{aTa4EfSlk4`zqney1U#W8%U;eCP5>jcge<^Zt zIbg*`rt`R7YiuDp2op&2uHAr&7}-a)SA?XQh4DwIr1LAfoG5CoQIQKiJ*TzmE@H#vb9&A-&6()|TBG~py!^0VGTUPvy|41nLh*YJ zv}i$;pa0GT1b=XuNMKqDz8&*8CVA+1K0I8Ipu1;^zau-6ul2zi((OMtZ)kkmKYqxt zzSMVTG=J@1w`*ASQ@W=5j?p6R`9`sI*so9sEE?feCu)kBGaaml)Lmz8qdbjcMD3l{&wpx4y?~IWVQy zRVrjjKj6v}Ui=A=mV_L$%*2}G{~E}q(kb3k=sqzKD@;YV5iD6DSVB;jS zPF3U<;v`fIDB8MWcNSd2Q>N+N?Jhc>Kgo3B+$y$~!0SbPF}PF}Y|+wJyq_{W@9h@3 zYU=3QHr%O6v#U2UQH|c5e?RV%Z^u@ZBsW?pNDRCm_`^* zV}-8zO*M8BW_(tBV}%UH4FsBE(OP0D2JW=z?mb%DhV{wJ+27GhCTDJ#;CDw>RX&MRgcYf|0ucyc!=65y!bXndQ58}i6&E{0PF9dOsN7%mZ zkH=o$8Erj46M20+O4M&!bdI~E_?)H~PER@P9&TvKJSq)Iem1rWo;%jmXVnPO@rEGU z5PN&CDzotr73Q;JN2Eo4D}1X1-6VyJl_dE}?v+~g&)daQP|sIK$i*j(Z%vQ9^mo*_ z1*=n>$Xb9pqK(T#c(sb`_p#HvJd@y zRmezjdOMweT_M+-rG6We0zF$LUSqry^y}f*Z*X~Vcl(EfU%S#j5lGpD_xaD2erpa6 zNJ0J$o8`9Bdjy?p=>D?!H>k@XWBM+^C$<(Z;XbWgBpF@Gr$1Duz3hB(FI%%(mvOl4 z%I)FtRoP)ehJDT1X~duD-9o)sy7Pe?x1ObxtKWlv#=B`Q={zhkK3sD+A?78VXx4rv^6cC}r0tq~VQ;t6%f7t*b!5oYh(0iRJ9doKG~xQEJ$N}mj ztTC{C@UZP@YuMz+@aLOH@2GV)r#!|c;?mmv@Dbe-=>|_4p%Fo+?5Spk&hzF$eJWwy z^XPg*KAm&p*IL(1fhyD_D&^82=k|2ctNSezaS122Qetqqo)>1ZT^^StAF2z{_~q1K zy8o=JySjoc<>v5rIK1Yqb#EsmYrVVYeGh-YdRjAnJvsM2hb@_vL-X-rLPrZ@Sbb|k zQ(z>^i+8nqu|lUXWq)MrW`Fk=f46348&{2AD|y11M0r!mPRq$|*}v=A@DV!6GXgpO zS029X>%W9nZiTMka(oWG?jo3SMtSDjsS$Asaancn6&s5-7$*r==`S>!UcVX|2ryLB z?w_U&WbGGih;&N1@a(=FaMIT-3VzJ`g^1mGWS^pc*WI?<`#AF0UAJ_AS(jaPpXGIT zbooF%G0ZEI=K*SiZJu}jlTaJM`zR$?LAZ=y&{8H(i)O2B+-Vr5sLsH7zcB7$(#D&q zS3>!<7r4Cvag+6G_)WsE?Is7GzrkP9*YFwb7L*L*qU5W6A@Y0pbKN$osO7mf@85@? zQ-i(?i+S&b^x8~QS|2c1N#c+cQcA}odvSQBNWkW>?Y7A;V(DfDP50X*V(GREgf-{cy&jy#I37!v4doBjO^mc{LZZoYU?Qa z=)aqC82k~)T??P`ACK+AgQ|_iFw2mu3if<}|Cs3b3&tpqMDUR=U4cGdk0)hy0QWuf zXCdn68KXrA*6dbl@Hr)H)IUX?TI3@QAKg3j@6NXF_6Ey~%F9m!mu@~-a@c)I8-F?T zkMfQASK>oZ`LmRHrmkZJT7Sh}nwxiZ2c{d+N#THv^NuC3-5;QZ$1R~B?HbuLtbS?+ zzV|A5A5@?FQ}P&`+pv;4c~|!We(KKU?T{&(sM!DjVvXb+R@BxazWD$Es^s3C zyJ)6nSqB$R zoQ_7K=jkI;qnSR6>0Z{T-K8&<7G_ABrQ>HZ1fwxpo@$4|H5_2`Y^{asB>$^!yhk zFLc{`5_q4Y@Mw}#u79d`=bidLSb*E5b0M>D@dTwG9I%ges*3Ika2$9yZN?tvJb|M>E5; zm=u{|B^zR|KWP#(DypNO(jr*D$7OtdqqZSb^`uNrOnQ`%bN7zz`NQ(ky^-LUo{JPsvq!AIUYTjT+f&MddC2O`UIBqkeK6lZFf~#?%O@$dtC0} zYwut2?oL&B<}?+xk=PZTSw>WuUZq&gA%*g{-9EA!gYN}K{xgMQ?YBjL9Z2M?8Q4k* zFm9YNLiP^$^HtEU-uT^@pV~csZj&pW={lc3+m{=JEKaYSyBxNQB0IFmAePi86Qi+N zr70E?6O)ee{L3X7j`2NvE7;(MA(2*7gV(D64pr>tUJ$aPY?~PDF1JcWVb5IFd>MY~GfmPB2`qgE2)j zS^Z@D$WUFp1b}mu-Q#}SFd&nJs4mlfvZ}etYCGnm>Yt*brjSAHY4+s%T55>RJQ?4G z0z%t@SyO74{p|&i{lJc1>~J8a-{9z3`>#1sQIUmcdj_`3rh!EV%?U1jD) zx#I8CpkAdMgUByVPFa>U;k&I_I_Vp8Bh08CH)nJ**l@Ylo|RSC*NX+GJMN50mPtU0 z{!l+K8sB;D5Q%WLg9f!Z8Pri-c!c(8y23jHtrPS6s7;m zo&LE`h9(u0ucLltGB8KV+V!4N#}`%m{H=7ey$0|8r|Ani+?os`>siGqbFh6hZE8(jaty zEM-jpA@-xbdE+~5HGHSp?nw2s*7ZN|cTA=v401JD6A?A;(z)DS`aQy6j-~VEodLM` zfovU!1-CB8*@9k~TB|jgv=1ftycqC#&7o9b^j+unBvn#DAR^XT`HFW8R^pW#?eD3- zW!&>}SvV5>5P>P`P4M(|SIg2NJ9E?++)wWvyd5nw7OdU?Pjk!|R~3sX3fc-&v{|b9 z?=T+txh%2reZ_f~%TNi_Z|XsG{s_aPQa_S3Cv$~`ao@5}%D{S^TS z%3$F9&&8OAFHmxy-uFMRbiD*w!vFm8bThdB)%gE;F(W-BhTbn#T}mp+j!pR6J- zM;-+pZ|*=5ZR8aAt8`l>soELrfDu0&_n($HCe8S-zz7yr9zj7<&WtS2sG4MQWaggN zkNPHp{w0Zqz(-I?@H$8m@>R9Oy&OR`81wMe|I-(CpR$XusaZKgJ1L&bvVpV+SmY4{ z+tW_-5)E0?44~ZvfxC?eBKH4IH>;GAROg%AVgU59WWq9Tfxi7CMc1wPe;U5)%>^}l zBsgXTVz-{R|UB92AeQ$mv87>#Pyg0BeARFJ`!)j`tN2N$%>kr;*2~=1fxFLEbSZ-bMu1rI1o7x)k)!BiOJ({b08(>o zkZ;lR`|^zAGOyqx2tf-V75u+*q|58WNWC*~nn6_AD%|Hr?;!x|4sd~4E)=?_17sL8 zE2{`?XwSt3Cy+ZKN0yE^UU5oh`#8e4(5xe@SLiHQl=(RCvOxKkBWGk|XJjz2=up;P z8<|26=|5w|{SfphaW7bhB_UYgW1g0d+gE7`n(w05C`VJxoc>@qze@d1?wrX3~r2N)}GlPXR1|$aD(PAxnOqJrNJ1u>VuG@EapR7hQ+pcq_k9# zq3hv65Rl`7zI<}7%)yW)G6JGaU_%T^%mcd1mNlRc-@oA>&KO~B>0`|bMNkUHBzyj! zC#=6XS=s3!=wMz4ep6C0q+|Q8-|~=i`(CzKR?S2Lk}V*q;5@UPuNP@rD9|YVUL(k{ zE_kT71ae@ox&o3Ql}yLxCbiqaJQ(RPbN29fc#q#M{G@C|Q3NsIrlgw|BWv{g9n^n_&$-_-1Av~XT|oA!>cbSUdh83mJKWnF-FF4^1*lZ}u5tMWHn|Le5>tJQoI87fU( zbmrXvU2%JP_+J!HU?FeJ`h5u;Bg*Rmn{=~sQet9EiKm90Z*++OB4l9nx4xW=tX zbd)h7=mQwMAeMtY9*6;;(P+a#Tjak6a4b(h;@($;McG@&{WLlDTYpG^WqH}IVI&;w zLr8UjC2-@}9L}gO8?@&(wzG>_SkMJiZh)ZK_Gv`5(%3pv2xd%=!(i$SC$YUHgS4&{G=QK;)P~yCevr6%Ewm}f>i&z zOoVABBE?gso?VcTkUULHkh%j6D-iNiJX>0o<<2U9)^s@xx${3wjdp`)zk7NRx$G7h zEaNt3a!W5F0KgS6vDS`LRg1d-&kIZ)SCw05-2B(AhhTV3Pfr^d8NvI5bqfTurB6?h zR*?Pw`QNb^GudQ@9~OV|3tL2f;U)&!%Hppjfb4c{ZS9fI<3Obnw1!GXl(;ui@-5iZ zfMXO8=J)4-63J|bi*7vC7j_~L3%{*h4e=IDYG(`j$QHst(q4s}TUK74Q{v}|H{}9W zp(iFCcl49<*zN7DT{AjJg1l)L@7EUcWLl( z+{A{FUyO_b0n8Js(Nt-%4)=G=ylZ8J0(2c%OaL=Whw&*YYLtSVR|hjy^jZ>!%Thg;lQ4u;Ju_zP}wGni2z9IzZx3;Pjlbm4t4s!Kcur#yObo#s)P_zNe-2! zqLj19A?1)`gcu#`TYmKyu)!2(@QWc$*-`fD6WtKYZd48sP4h9_273srj(y^#s0ZgE}Z zbIj%iPaVJjP9~Fa8!U9)x^1F@zousY@W3PK3xF`^6CuM*KIYI!RkaDJyM7m4T7M4D#-H=HLPoZwco-kY*s@9N6KgE`dgE`TeWxC5K%b z#%-sl4qmWF;@)^147E)E>y~sr0~QZT(YaE~Xd$$$L(~cRn-;e$$=ZGs9fLFb;k=fM zPanB8x-&WCj*uCVJT(oAIH=t2zo~hH0c#4<2YD*SxR8vxT6^!%J zkBKzf=c0!tS>_8PTEveKz4B`5a_x69hB#(s$tKpYqQZ*2=)SKr_Hiy^ z(v!vi&F*jNzp5r4CEBzhjvxXPma~1T?B$gMNgNTMt>N1cJ&=c!IJP%7M0AzxipLq8 zi0l4E)RY2Cg)VA)#)pm1DT%v>+>4i$kL`I-g!y2z=FfUGeyd6Td9FEa&!_SJth~<6P_Im}lCG>JE;q zyXBa&Nykt?I>s>W^&Y**tG(X3XujDx@DpVv5;2@ic2f8#f0{+V)FgRUrQ=Ag4|?P& zgwEJ(`eirExu{v;h+~ld>JO^Ri!7FPj9vdcH12;N6#QP~zr3#V?eKc$8FDAPa%Q{M z%+Ss8J{tXv1(MRYDISY%<&`l`vecB{&6x~ZNh(+1`!Hr8@kCN^0p@Fcoj`?aiY9CQ zy6^+)7rp}akkT*Js5caLa97ilk7mIqq^_&zM0QRC6JewdDP;QUfHl;Crm4bV)`ggE zR)@;0hKAgVt&>lnWkFl{o@V~cSaVu5`~Xn{1;{XHq9K`xII#UIPgAlDsBINO%t6L3 zxu3rk@M&fUqj=iq78PXfUFz!cnN8fW#g|Wh7=>3SCIY-0C9S;cD4V=FWjADuwS2mZ z!w}@0f0)mc$nICAL6t0e(5T$Zu;OeaQRfnzG`9#r0x8}=*ovl>-Pu;;{MV$(_Tjs8 z{1O-zlC*=+LIWnBDQ#4T~F1iy_ zi&d|%&T4XoavHXfL<{ZmRa*zYXmc<5i<>56v5Om5lu#r)nF-&J_@9?kCkazlCLX;; z&{P7;Mz?8~o{^O7M>Ooo83A`)sq_&)YQkQ^BQd-U2V)i~a2FgOi#}6*Zn% zM%A55O+0kF1Q7!?G0+|A%}Op{Fs!}H#{y@K^}W?4jA%{3hVNm^lpd5U*cc||P}uUv zgIQX(oHGc$5qX!9u1E*nqyciokn;~*=eKcu@B;wC^yC;29HEe6`doL&sr}CKm99Lr ztiI5BA0y&@_qm=VfkSjj7H?Hq{XB}tkos`X;W;*podwDcr@|Jy3m4YHvfqC!u%;gr z4So06^9pWh!62@aoF(1_t{d(S?-FJ41_LFlJxO87H8b*8SHykS3IVnM3^8{BRIIW4 zBWm-pzgk*83=XbfqcpcJr8sk&!@X5@;?-GVgYp*@^aW1vg8P}uT8e7kyM*$c@;t>4 zUOvJ$mRMh>q8PpnY&BAPwfi>_%1XQnfE)qCE(f14uLJ6Yd+#o~`HJg>yoehf=EIy!Ew6lAK*V=ak%y|6~%r~P&Fvh({>iyXYXc<;C(TnVHq@vQ993-!VN z;?>7p;3-5MoehH8?(I)%07ns=v#cw{6JDvDv?9Vhj(ioi z>=$8D)DmCcNqnVvit0~IaYBpL^X1`&Vg3~g+DY`^0wfJ}zb6XRrI~X;{|X6QJeG?; z)XilTCgc@8XG|d=Xij|ky(?0pWkW{}zwd3^ zZu2wSQyf4JMNljCk;$~>)Fa~#D8L!3#<$@iECb@^^pkqtJX9W zwg{dU1bVTiy4g12s2)&6RrD!DiNa_Vv%>gmukH{h?)c*i?X^u3+&w{ zaDqRJt<4C3)GV-9-|pU5|B8?U4&yGo7rNiE6Y&2qL6pv)_xm=ufzW!$V)d>&#y_Rq zgxrD82wt;URlN2oZYMu)y~UokJaGcA*f4M&)3h|PmsR`c zjnrR37>HZ5jPahe#2W!bK>2_tgUx`FVmKCgrYrX(cHA6Z-~|5*m>y*lJba;$gp&Aj zm=(+otoK;5W;fhv%h#Osi+=6$v)BJB1q)J31R5>%;w%SEg!vK5^`9b9KeFz-RGH3k z9*2-wKt2f$*xud)YXAHJ2P#UQDd51VL!BbH50b zC7>eL+Cd^uFPe{~q&bVX67gzno zWGfr$CeI=Zdq{*B9cB;nIx-oE9Uz^bF0ViChog3gB7?;CEOyXEM)jklw>bZ{Lu?t! zdv~K>q0?H3xHrLWeJdxeQ2PBaK^zLDg4YW0;fwvUA0+fn-Op^;f~*H|3+v$KmiLmR zMYFp^Y(o5CH=P;agr~DG^$q=_VV^5TM@OZUK2qEF>D3|)*(no*Iq|3tWSv*F3wAng zAGk%z^1Ef2vmduYFI$6-D(Z0kA|{j+Ull(hO3W7$NPB!C&q8{!0}A;e%s;0sT2FG!(j z2X6ueq@b%7D$}C^8gid*27mhAobfMTNps>23)mo{%WI)uk4;vzEWd@>G>0-O#hDjL z{oRcre5p%STE~z)f624orFE=*Ol*!z91m*Seu||csW>s)-=n&mkg*`QU_T)AnQgsr zt;Ge0c`7b$EB7aM2=&ZM<=m=tXEgVnN3&ODNcV(6C+Z1n&QK;!4uXg%|3Gup1tS^N1(-Mt=8 zj1W^Y`~Wh~D)TH@l6%QgsA~sS3csFb@m)s|ro3YWP8Kz#zhcUMOzf)e8j3mAWg?S- zS9sJqz*b8Pu({hY+o_7%f5^$nK@(eus+BMdBKktEf`2edEQ>!_E!tCgNaBr_n%XHK z8YBm7@xM;ojfcmBzU0LoLL%=^e|HKWzK4ZHfpK?fS_v+8QaW65qu~Ae$@*#ay zmSNbO=(UAEx7h+p$y_|4>n%v>FN2d4=7^h`YKd?AN56fHI)<{{inSs#8E8lZfds6Q z1mW28*2-^X{N_Y8rhx%Z4-bgSC%(0v`+dTP&U?a~LcfI8*4E=LPDK{aCmuUH&Ii9< z86OWx_4>9@h`LBHz~rhC&;I_~VyJ9&lUpGB^37%oNF#V6bf$m~N2qlW$saHnM+s$k zx_rm%|FhBKSsT#SfDrjJWhG<>oJB-*PvU?}dPLUkOb!$i;A3!C>%|VMWm9`T2DCN$ z-s&#{MfTp&(cbQ$miQCJp#Fgq!y7wq7?WX`v`$#nwkJs^O(l+8Kr$!9A_)nFsTsYU zKDv=tbu)-swMs8=zF2FfUC^O>SBZ zkXE#bATET2J>+*pG&GvoEp>rs6|ET6d>8vZ!?Pv6j+Jg*W^1b@-KC2DjxCWt-(0+> zo`3$ttj(%3ic7o~916bU*;74!JFX@v z*$8Bl@L&E*o~5m5-|(;@sx_89dC~~*`lz@#Gb3YTY5s`?zePn#Z;6XoEs`B-8!Zw~ zneV*vRNJ-Ront%y3X^Sc+R*fIPip_+T8_q{8lL)AdHFM^PiyD*I|l|%*G~JcOIbA_ z4!~w=X2$ud`kZ=X>AuZ0rR!gpn#a}@yIEOUMpxgqH>XY??Pu=JyLjdyx5n0qM;^bo zf9PAL)BP4#XDZb^&-8GfDNT^pOI?n~=nG0>aj zQDI@)l`B`yttszP?ekVMx}b2tK)SV9B=p|v*RQ=jJ@0++$d+f;T1Qi68&ai2Y*@u^ zrTKEL#av3TZ10t-@#6bZo5xm%-V1$p52udslPXhIFkqK4U^jR-(`ijl;MwVYJaxrZ zim;^SU&U6Y8Asj6@`Ud>txQnZx|Mim0Fb`p{|+tK@NQ<`sQ=&X?akcE*?ctbXzKR? zEHjNmskE(%e#U+KV1y zI7g2iWA=TrrtjRYnlPopEU4`tI1zforfb5*RN!gD7bb5iKCxqTNh1+fCt5pZGI5xG zeg1CJ`aSb8NBX9}E?+Qjoup*{=g+wbx$S4j09r8_W9%z;Q%mgbX4+>sJq$G1wVHA( zRYq1jWs>}Wv!kthJiv+GL|x$8z+d5XG3aN?ipq+LJW$BQq@<%7n=uAwN6c;YPzsW655DH3OG)gNDg4AJV(ph)-h%>uYO1RGcQgNf z_pY(CGxcs=&%&st%~uj@TAv$sA!=G#TRZ#un(bY2$iJi7Q%N*hLw;y9j8rCSXL8lq zQ=IA3mcuvpkxdmpHq@jfxKT%{wfL;YgY9}o2EsLDJ@Z(?l4Qa%LrY@qYYKADWwY7h z%v^m{W`W$j_g2ed$~0d&zx{Z!#GF3+M9*|0h`#k_(n&FXW5Slgx{k-i#i?m(nwGSh zq}9JcI7`yFkm+BMubXkse)8u0Z1dP0xXszK$6(#+5^^jrxV=o6aP?4PB3s+!YBZZh zMYCb;rgG&L&6MnK2?w{Q+lu1!goU+rGiC%HLl$4Ir5fr~y>s9z`>}-ZF^N|7K&SN+ zdHjukpk!r}>kjV7HolWdi}d+0Aelw{i3tgCL>b&=37jy7Y{~BF$&`($U3mDpmdPbG ziJK;LHUe5{g1@?ii>kP@xuwd!7nWGHnDw}5;V$uS?zRp2PM%@ zpFSM|;u2eatvi@b*6UV%{fgvc9g~9<^Re2hpy2G|W1?uoXq=dsSUq-TT6iwBNb3xo zKk;@_|C<8;tJJcxZyJ1ku9Or_s`}Mz62oyWxK;_>&lZOE34?kwGsC#N{tfg~Avbq- zglVQ{Le4Ya{A(MYbFWvuBI|9B*K2a#p5Y|>Oi5Va(u=N#Z^>KbOt>!D%G{)TI}sw_ z?(G#9vcOqjf!3w7`LTn?tk)L1xdaAUAsDu~_xCnr`3_IDQEuuzQ^GR(AfU)I8{JF5 z@M|aCc@_#^H|t>2=#IM2S{`U7;PjSR47Vjg~+MFsD zYP)OQIvW<5c%Qudd|76JP-sA#y^wKlphu5t1d;)sAkX4`ar-v4y;Ic`)mmP^j=PQ z6D0*nPc0)714jD$rbYY%U%vDW4(cOU0(q9lkX)4qJ;k$fy$8l7!@3^l?n|`nxz1vr zA&+2}7Qx+`p5e2dhF9+%C3E!++$^ z+exp=0cU4s0j9X9qy(QO(!=@kJn)WP5xjb#^_{BCuW%xFL=+(u|SQWspAjkEo5Vo~p(bU8gv`&&%d>(JXcssa$@T^gn$g+z=FN%nd6jb1BuHC8oTL64 z_<=?BPG5LmOz%QCKbn<>J!it)@pb-RKi^SjH4G1*8~2MkJiJ*>Kd?jb=C(!rnh!|9 zYo+EQv%~{+Rt#r6X5$NL5_2qY;PdBCIDLxTr7Wloj6{TuKmPb*;RkkZ@Tb!oaO$Y( z?b}G#R)u-lLw*Q64$Hfqa=gS3Ms&U8JyCu&X&Sboo?KU8!_oG$x;}O!)?POTC$3IHon7j&&MmVE@;~#naPZ)PFka@YO7h*Q5F`V5bir%F}Yyw)*exp=uV1 zMeG^niEOY~nI6Q?8^q@qp@=1<`z~Gw|a|ui6jotA`PAQ&O*};ZW1dVsEp5|!H*0+|N-7YFP zg8pOE-HI2tJUd$LX`Exs_%yUBD|C{9-kmDg`pCgU7xCMhXR1cvkG)~$uty-cmj&|X zu^U-D_sK2aHoS{V>ANSisq8vW&Oa_GIk_LE1Dq%(xIkIY2+^Z!$j_M;%332h`o>0* zbbIGqo_fy*>v;A-|H{)n0B3)kXv%LqwnL#{YbIJZkQ&Fb{ZQ{ z$cdHHU*kb@bI}{JB4FIG$U?8qfftdq;a#ZI?fB*>-iB?0+D>=dv-w9(hs{)>i54J_ zsdzO4o-GV_Lna8{UjK zJ~7QeFpiYQ*(JGo51_PQG^!Cr4<4k1goG3<@i|9o^6?ah6Ao~>{WxRM-8cvw1^ZTK z6%`bu!eJZ8COO6g;JY~8Z)0I@UXPtngozE$ltv~-T})^uEjwoQ#;sq8ow~|0dsKn# zW3}D8jga&KTnK!QiCn)(m~yLAHAyVV&RlU|NiT*H=UiGtF?~inKtzG-M6mrCt?=?Q~(65=`Lik9K#&T#Vz91 z^?(-$*7#283T~5baM=+&V59xga8uewX2A#!Wt?-G#d<|b8^sOHBlTQQ zK7yeHFT8I`bMo>sLTZ`dCC^%#(na|C9&MZmKcEdvAJHYlRKAm$BhPd&U0s$hhX?gF z$st|zaWBr6Uta&aw0Z34-uK>tVt#q1Q5-5BhQ+#?_03k88&cCf9@*%+1JHumyc$Sc zB)gE`^8Q7>lZ`)N zgF2A`2JwQVsyh8kM9L*qt&@?}x&G7utP45InvP_cV@it=$;x!9MH|A!IOEk#!ZK&S zTM)c^{~VwoRV8#&^W~Y*nK}4QH1}&9I1gZ1R9cEp!lY_7@nP5^t;xAYNe~DYc>(5L zy=`6w>HYHWV4dW#us}YFJ6ZPl@tf}M3~7ShFqzB(`NOuNvVz)xoC<7TD>IlG%L|@| z_t(Y3wWTbaBV{D7*vy;-S1v)DYwGnzOa|ycCh<^h^iY*A(Tm)Vkj~&T$sKGBEF<6~ zlKdPYmV2gD%eX(XZb9()hPfm}I{A87-K?aw>C2aV=-gznaA_Sxw(fy_D+G`xfz_Lz zKMzSC!j6al~6s#kiocWHrNw={Ll@1_?C=NV8pa+ou7T5cRIRPuKt_su#rfYI9L8bUi zSZ+0tWK8_kIxfTif+ z!+QvIEMDyfgQ&M;6XYn&FI200NNrdW)d;3+V`^V?pAqS{)^~nGatAj@YQQ%BXUD#> z{JV9T=qK4-mVcxnK`eNaPU?ZQd%#*)2@_cnAhL0WV#k;WrQUI2mOw7a!|VIIk=_-k zMqr!Y$J;v{8+R?mc}NUfw{ETROeh%kwy5e=#pD|lS(s!P-a_$PO8=-PcvIj5_2lje zl1I6wr5s_`Bof1-p6-3|q7Apbq5&%es03;g8ylNHqLKLd*j@BQ7}HM2GTNI%n2EK= z4htw3KvH;hmoiR9Keu@2@K~7iPC|B$Dv=)FlL?EqH*cvN0XD6inV?}qsaC1bFz3iR zF%yC8SoPbCdV?(Ko%nZIfwWV9WcR?R zKVf6QEU>Xy!2iicWo2Zs9PeGq(o1Byw5UH^E%`vv0F z29+IVD+Iz)vp?JXeTED)53B8_5}8#rRAnqGGY4i9DUC0PggiWgv*S8oVbT3%AgtX# z&0>lqZfa2jjMMq6>b@f)lE*yk%YV0Hd@c6fOz`}!n$;vXnZw`UAM$& zu{qy+hs@c$rE@0w<(1nqe<}I-!L0}XS+;{mmhR+w>6<@V`IuF`h}7|O>>$B6NP{cK zcI}xq?(TClDdw-bd1J|(pg?kvxop^495V+Z3D?V=@(*l1yDncW)Ih>!=mM#V{XNx@ z-v4r>A=<4KlL`*vnV_7IApXhQ=5p<+@t`D*vS3%c_-axB{rDe3#FteaREV5tHV%4{+gd5(GXBsAP&WUr{LnCz}WpsYhiR>>M`>M+K{=&Zwuvu99 zj`X(Mi%CIBJy|;mwS)A&+QiuFZswK-a%KErwEq^;<8{V!NF2Yb68xWk{&K!zY7AiYIL1K){(MS*`HtixnwHC?q8 zWe^a^QZ3BP#6}QMRB^Gf8@*o2OCPCY5`5Ca#8#r^Zfpod6g0CmyZVSD$HQebi^C4x z>MtaDzQ&GZfLOIQ}p(&F&ne0tFDd?F#@_YzPKQ$k0^3T4^=39PzDX{K^HCD zKApfk@4oZFidghbl7SWy;pvozC9@K{3d(EV5F|d1xS;hZ7c{gLB{Z~kENpC}34D)0 zL|bmId}VAVFf5l}0-=R8iZx)c7*0z1t_TRY-A`YL{f=dp2ne(Y%5u_gJhT7!*?24} zrTx=EsFB+;Q?9rBu?(xX89N%ieSGZB&i&}oZ2!ZGO;7(b(hJRF$O*}-=rFlpEzi+U zuwbczjqTNE?AnM>V@pe279}LEmh>Jv#>nsst}h1|B^>{re*>5DxUr!r~+HqQrx2GahVX<_ZG>k!q!Vc}M=Fm$+U96@HmFkBT%zP*S z6S(XktAa69J-xu6*1g?e<#n#Af>FBJ;oN-CSxv`Yu%PPhF;B)Yzsf!#IcAiR{ZU)o z(7tjct;%etrS5fY#*GU4`l;FE;d3I^*XW4Qs4EM)!r9kL7r%GxpM`ql>=I8{E!enx z#Qiw>ZzgY5o>enOZT){Xkuv(>4T3D$>%&je2+;xma*PLL;zfSpCLL)8X zrz|BDdLv!Oe@z%{VKc-X3iVAmXBW%{Ecx$P>^D_7a=Jp5^TwKF7|=`H@gd5K4&IPe zv5O{T%|m|f%a9gF%`NThYDgI8^ojG-eCkp z3i>4_I-MpnG4WJ(jce7Pqq|+q1S95ga^02oQ`XE}C$l2u$WiDDWpQ`p ze*W-2xp5omeCO6 z(#LgEu*{CtC02dT&E3NAb4p?8rIB&u+PL71GX`czTf`+I0b7%Ue%cMSKzPv%SrK)& zo=9*!#8$J6TNm!N?i~1uJmBpq6<3G|S zzn8{{PN?l{bVe9Z#I9$F6{wZUwT^mD91vU-5ke;;L@W~a3`O;nwGIn7m68&)ijr*K zk_6uf9a``%^bgNx+)%bPk=A=tecYzuq<+0#%k0oV`+1YzZzeRFt!@$+f!cPS_fXyZ zFFvSi4e`}-2;xISv}y4TG8&Z*aYFfR%r`|$+iBF0}QP+)K7T( zOf{*jm^YsJhh6T7>neIOGUymXBvr1TZG^{ZwuxqX-ryCU795oDgwL#<{)N$LBuOP~ zEJGPDi^j>5i)MqgxRan~3QQtRTT&T<^S>f6&`Pe;-XKfjhI_+3#)bM3xwn%8VZOqQ zdz?vVZZ&S>j?P2e%aW{EN=8${ELQJFQ85S^gvL$rRlEFC#wm5*Y15uos~MP^%vQOH zk<0Q4C%Q}dy))3RkgL9H=M5^G+Uuyc{}qqizZck4KJ^37gCl&IbD}QyzRPlMB!uOm z1@j!v&tjF%eaIc?O|{vPN|-pw+&I2Ohe|m@Lx{WEWWU(v5e|#zprf^TpQo0{BhhY2 zGU#(bH-v>SW-y(D)pQ|}KICQp2UDm=k8KM9fs`cl1nY&i((3(bVujNhW;?2^%JYhp zebH79^;I@or4D!UH9`{ktiaqv)-?})EJC4;%hLATicRk$`*`Gcp24}BZe|!0$GXId zK7E4UX+MwF`72}C8c>MkoE*$1mZ|aMkj80o>-%=|!FbW36cVlMgoKQpWQ;j4jCe^I z5O!4^mQJZGSK_+T^-PnyxtO)=UkQF=>X>yo-%Miqm3k&Y*#dXzNDzx8wA!Vi%FGoS4|mSG`US6) z))?5wC~xsF+I3y7+?77|>$)g^_b!~}D(MU@&4xLsN-QSm9nRb>kwg0yE$0Uv@>f*@6KHxFwmDKk*zu^5KYfc~EL0)P%$|o9M zxTJahJz-J_X^$Hj*8J?Hmttala<`@Mc->esF}@JTMBPDlQ1(Z`IH5Jy)XE9k?N30eQU=)-}phPKbtOLjK6{Vx|`vu zD@H*VVn85o9yB=U!8h?XaZfxXFsl;rRixHfq;%U}k-Y#N$B3aeHt)6DNFzq}muQ~e zGnY+tM6G)sOdRsDs{&g+VIp$Ab;JOlt21xW z=-8{X(mNupg8TQJL9ZFX5}GHs$@6qF;eEQA)snZIET`es@_UE9v%{rkx}P zZNQQmbR-j2o@#FG)w9}|kIO0&X9&Po!wv>{N(sN4V;;?N2)c&wP1{t9*5$0Zz zoj)|>ubGBabb3!5YZJFPMCeb2j7wB|?z!oaURfC8b{T28sfs+8CFFIX#1=saNg<#0 zkRC>4Qin2ntM_bPqUp`eK|$uxM(QnQcL+pgFp4Vh|EkX{E!MSN0DwG9i7-V2_wGIlWJQ#@H%} zizz`}BAm}l(ZoM)Nsi^_iaDS})@OO0yMvie%n{G{I$-wp=u<{n-STkm zjqN3>qEClaabfuzVSgQ-ddAQA(9cW>XIO7ww7(gt#fV2L!BH@+J@>(v6~q3HyB|>| z&o$7e(7DjG7&ZE5Z$XIyPlAzw7Czj4=O?0yB_2onmx020xjgclyzkk}z29bw<7B9< z=I(`nzKTLC?i3o$W&_DU5&CCH$Z!Y)pWIy8RO!k#gY@qZdRShjKoVCB2}Xd{F?SQudd$Wo8>Gd^lL!|N55^K`6R6hEUb;+XU1Hcma*@->O1{FlAqaA|4#E%NAH zZ-aAXJazTVPs~+DBBEW20aw(=?;6O@wqCzt>`bjlZ>$k3G53ScsNl@x-XuEZ%z7 zrF}}`HsapOEAD1cXo#s=xOsE)3pD{lsp~b9^rT%lqjdTU(r2~gJ1>*v;D$JO1~{%P zJgMZ6+8_v>O5kUaeXBmNN~Yx+d7CP-o{7JER=Cm3X#D#RMOM3+S+s|8zwY4fJ%^S0 z1lSE$F{aPMX3Uc2;TtRIeGT&Va!ZP0n9wt}GGmzaK(tFtcA9t;*FhRmJgu#Trhrs( zGt)8+sS(S##=#xAi`zK{1B+Gbdc{}iv`>+Q<}DUtn27tb6$CrMDfaqKMq{bN`%TME zLx^K&&vYM^u}~;2srGhAmmD%pUOnTa(zMY}6G^E_M|(>|LZ6Z_naaA|`88x7i%DM; zZSlZ;Md=-dEAvp$k>o%n(ThyEMe#Und&u?ivHKxV`3akC$3)Y0y zrBosDS`-Ffi|)JU>?utT8#X0I5pEjBVawkP2+B-NKmbPU)Tw@=d7WIV!oq zh7OLVf_fK?!L=U;NYPP>b;%vn! z!mp{fmU1szUC6L`7^QC`@$ER#mSi2Dqv{Mk`_QEQK{pgBc&{a>O5)(6xfm(wC(Wwc zDqnD@TIJDla%A!0b4mXjZaVfrpA<1u;-sc``z8!x)G8hrltQU2T+7L{xM&kCCC=?X zci_1Rnar?DFQY!;-BvBc3TOT;^2tuF*qwwYGN>X3`@S>zmyr4RkTcehQN#J#KZ&r#d%FA)H@uur`ESL)#B5e^Lqd8Cdy8`@ zWicRU=%e`w(JX5QRnYq{7t=XQ2Kf-xjKn?#k^e?KDKe^+X$M}~&feI-n-aQTzED0n zXfeZQqYr{SFv(MTq~z~xdx?GKV%OnkA0bQiF~$GgEOf}*v$x{G}4r`*0$=#nQYP=$Nhz!S}a{g)Z=<>J0R z4C}ZOWs_hNzR4yjv09xBL-zMey60s@!9XZE(9$nEzhy*p$PvImmkJ=Ep*H2^ICCN% z-*aK4R7F&kky*M8T2b!MdblSqfj|vPXf@3mC1#sYS#gj^(#LHE@WX^2e$`NZ%j(4b z3oX%W)Tc2u4l128NH#oS=S}hUQH4dRo%)|KP>v7%h&8nqhRYN|bF+KHvgjz|H_xfK zQ1Uf=Qi-Po-Z6fUieJ>`vG>bGan>>Df2$4WtI|5o{5Z_p4HcmziX2rH{A!^*&QU9! zhV0^#W%U(N(P(H@j8?@7e!-37V$0w7bL-rwx5-QC5(AePZ*&NSoN43gU{sqVRV&1H zefM!XLc{(cgU^!^MoDbNYLPqlShmRZM~q5haqv8Tf^UukV_S=s4DGB|17`zQgoA8O z2&MN^Tn#Sy#*6pVlEOpMzqUobjKIbNwRldgu^j3@G33~3I>Aj$el-Vrx^hODCDKWp ztC}-z+!4>7f8&0q1$QquH-P_e#zBs|{+7_3yP11NDvciFH^jBf)!Vq0-q4hiMOxCUc6u3;mh7WOs=7ngA!s1m7|s_<|C7L3$Q6oaj=#TziN?~ z7aQclZV=sX;U+tq&Bb1id@;~J(K5#Bd0W#cc{{Ka-Gq)=LLckH&17sOW*B&sn+Ww! zWN6^sRSo1jeWRVFP~NX>|8J_oJ+`X_<=iS#l5~d--HU8kh0^E^)#mtJ{+p6{gFJ^& ztC-gel=#rRFbtZ|vT3iHDz6OOZu-2=(*5?k6z>1>UriZhZq?C*>yHPjvDt4Np(%gU zJzM*lLno`3$TMbCv?^*lzNhg~;HMoO>x%B<{ZD<|5*+E$72IIv$7Ov+Rq_ps6mt5K zROuY~VI9d)9YY@Yr|WE{?v{&p<9p*GSDo+A{!`85-;E8R{HkldZZ=CkR_3kVMCFXD zmBAF84E>j=U&qEEqdOs zn%+kTBWFae@S^Eqcooa}+V$7`};lkf(lt{iLFNVWPzPV|!Z)4C~$-y(T zTyDJ0JUR*Mck&N%(iwQnzaj$m(Qi3q!=+DB;EmW5oe|DMO5OnqKPIK?O?ZniDtg9K zDbKj(crvGC&4)T`)dKUCGx|yd><4&y#f-5<@)WsEtA(%5@#THc;_@(R)UD1rsfG<_ zvtg(hbv1v|?2t%NlCrVYN@1?53uhhE;rgOKy^Y|{f6z-{bgQ3aT?Fl@(%(z6hDg+|hMM2Ic2`+3F&U-wbYtZ*rovlh!%X(LBr z*PlP?SM68S4Eb(Cd6@Ok!wc5=9hrMGk13DDV$7M>ZPXx zJ1_p3kUeE~pS(NMa)KWsl_$EX8(9452AZjLt>?{23jZ}MgtzZZU@l@_!`j~8%GUyqmyObL zH-cKT>tz>A0K-~0WI}qbW0d0iD3>wUL}J-_obTw~7*kX-2AhpGx`?TQWQ?@|JRbI^ z8+$5>K_&=hnvs5e@g~)tmxXd%K@R(sn(AN2-%1pIlCQ=!%tlW5xJG2m!m&lpYm1?0 zf>Um#5wQx`cysPAG*taD?a6GU3e5Q4do;p!U)b#*YUL+>F zfL{WpYDrxBd+Bin%9PIcN#h4UYtD9Mx)=_IQ7fQB18a(jUyW1VLer!jT$)ZQY&K)E zQp_eJYdA~7wqvneStvuHx!PJ`jks!OGQ){<-FEZ?YEwNZ*#AYM7ky5LyZv(A2TCtd7k_v$2mNuz1~17vvg%JKxC zkI9Y3r{9@TpaRzF0jHIFVCS}PI{sVRPZ5i?nQyDS!X39skWP|?>zc(_q)ZepPRkeN zRCz|XlqAG)o330&6CvZ9A_H4E6E3wCIVWNdAsdB4gnxeD?4~?PMdQw4;{B0JC5K1P z5WDC?>=D~&)}x___IYQzs~iW!ZGyokzd(O>Tj$^S?#sQxYwv45DBZ z6yqQ(Z%*~=&AzhoBxyea8xy9(y!`8L-6?}`!vHVg@9dD9u7ntAS5rlfYzC@yh2Orv zwlilLXo>>7*0ON_<5g48IZ4FWOv;CZ!H6&qn)4e)mD(KX|41K4g;b#qk=nj#O+pE` zj_j+-+-tDy>*4ArudJQ5qF5+yIAdd%MtYnQ9Ig^kdu~tRA@#Q^HWrr4R7~nkHDDFZ z`yhkAT7oYqfCSKUeth$d%s6;GP-o{{sGxb-`BkQRCJxnK^>DRa(FJ%Ky3GY}`?f zZqK^y)v@w72InCvh^MF*8aD#isQNFyM0}cWmJw=^gs$DydyzRoP~?WQ5w&6PVPiOq z0kcQQHnGyM&?%qjqKIq`C4pYV+3$B!n=5go?6*cY#aMceatvOt{mh)QOGc_oXVkBI z!_{rlz02pp42OI#J%~0?2=^K%ridMOa0=_);+n=9tz1c<20!i$vQN1uD)^SZ@LYZB zGgv=Y`q7qz#F{>Iy_C{8Bk|=K6rbTOEyMgl61uQ&UatJ^!DKai;g-}6)2Cp#(Uf+G!I9kG#aQxHN5KfKGX z-^Gf}>K)+wEfkOG6iYwAh&55$uX*x~hLL7K8UE?qX`Tr0CR= zoWyc*PX(I*xZ&RNK(Z9Zj{~il?@s46SLiJS)W8J2ZY}OA2 z`GzEPh}Lrs4&8m4Wxn@3dszZ|4E9t^(t>e}GQGbnbd7=)EGZ;Mm>=qSDr!rWH z>;8kbxT zrBcA!9G9#ThhEUoOqrM)H2bovMZ>jV=|FvK7`WKXxOAYlRSB0HDt^9-ghh&vAE=|d zyquIwW-aWxGNgV^(L>a4jOD!i$9>#;+voE^Q#VyGNiAK!eylP@CB%+UDY`fa*BdWy zty_H%HXj=jSyACt+_p{_kjN&hX=XQh6HAy~#c+zK77Qb|pt7vagE)<-YT}nhos!us zZ>$XM#BjQ0hK`i|M!&l(L)cK}rG_~eDGz3l;QaoE#(-y#7PY-lA7{i_x)jCSTl-U- z=bNgyREmYcd!D-2j#=?VX`huVEpR3Jii~RQ4Nsqmv=^+75zI=4(AtZbH;ty2NneJa zCWTbEic7HVi!)+RN){>2Ub6PWdfZ=7e9Yg3FLzUr6&0X+tJADOm!lQJ8WyxBmrgz= z7VR`u@H{%(Kl2bqy2Fw(J(-d#QeC4Esz%F5oMtuOf7HUith$g54df$=-^>S_q%$87 zPke4?r-V*~LT9BlY4*}2R!m0U>`C0h=z(mAJMJKkNKed-XZ})lL zPDbTRJK_FB%?AY)bNUOF_Q6vx$= zw9WvBl$5k=?%+R~zt>{mrBY0omX-$gT>UIW%h=@g#3P`V8xGkc){8aloB#hT0MX&c zMGuY->wo=U?3Spf2=mCWb0b~P*)O-fIN|gLl>%2 zGvn}MhGwoKNx_0F_CM342J zy!_tM66-6Gpi+#4@BuN|jdHfWK{pPHAF)gDiC#^F;H3l0`dS9Y`%}n_;J3$!iL`@p zGM3>3rKr7kKtEMHxtI z&u_U@lUMyH@moLFVV%DhC^v31390>7v!*L|t5!4rMt4ye!xwt$6Oz{7A5q(vqppYn zDU#n=ct^Bx{!& zuP2$Lv7=b-6iLG)$kW#Sb5rzv?|0%e1o3V1Z~&jlAo^ZRSDE2f-bUj&6WIQDxpe1 zO(ijMKfvS_A!ek6j!#G^vOfvZqxk#P*M%$2d0W_RN6z{EScu~OA#*uXW%>1RPr}x` z&*oTY`dA?+P@|5mi{bhDIP>pitg>-}utQbCu?7rk<$9x<0dn*I@YHresTgra$P{oT zzM+|I<6ETuVKQCe-5Ot0!%Im?xps9$hKGm8oIV6zKxBYc2Ct`ST=-g7$N#H7PWwxd zk!#u5xqMcw{bm)V5{sWM2ces)Tts?5zi3E{AnpdIWkuWs2L#0+U@1%;y}+nwwN*~vyk&-o44jODfTo<_eBP3TGYM$luouxo`uPD_FJ6X7NUgY-|U}rs0+h_+GCp42qe|+!mRWMPJ4|w z)8F>G>zEmpACz^fi8LuBQOG}B)UKxuS^Yk{?&%7GIc=D{j&8X4z()Z(R!CU5vp<36 zbZdxo`oP`J!C|8}hE&hkSia7n7nG&ChKBRUkKxJ5M8ue=E&lfuhK7b8T9kR!{CJgU z)h2HsbVoi{WQ4rEq!nL=Y1e5-MU%PiosTzrf__9~3BtM$-9#kNGM2g~rN-gK;%UmE z$n@ilM!46$5+pTZ@BR8mm|xEas0dH63{`8gle}0IWiKaJ%u?GNG2T=X0tcTINLiEr z+0KR{e|}y=Pl?jm!=Cvt2IC@iLo5@{nV#h1?C~Qlf7kCkovVDyv>I{V_(9tVzI{~` z8dBxqDiPCm;FckweA=1epng2*M5Goow?TqXv0HLl=$EnmJxaZI9W})0M;UcVQ&_4b zwp&}sQtvKr`p}owfqMI~@_`v>)-d1i|z> zW%y=91cZ`?X7g-ktf8%~Z*UNErOhwq@uu>zYhi&REj`^iARwXAw0&dFG@!Tr@!s*N zl-Je}UYWLq9yEdaVLd>|nDa#NFuFwRY6wMcN*@c!m;Vn7B5jdZZ|Xqi7W3SyIWkz? zbE-@+QP)E6bBdhM8bxpZcWlF&?&0Q!=jXJ19Vp_qF-O+jj~gukiOv+Mv+*{Xyp5ms z>WwqZM2k-6qjLQ!R-nO7!*I@DQN>i=xV!Q& z2RW)hx#>P$PDSQYO&8x4OvXmN8*XGgR56!u+~eWF}ne*!2)qf#@crXj5d;6;3CIggE+XBiLpT6hWV<5?*j+s@sRX0 zN@f1QX%piH93tHForun|Sr^K~_PqyMYhK(EI_AacBUv zeF*b5W016G2G;@RBlX*V%-760Q3>9!O6QA|A$Lq^t`wvF*%B-|q^Y0Ac96q*s=Oi4 z@y20tb52?mFItotCeGD-0PLO$3^eQa znwk*Zuf-s+uN0g6yQ?qI#rV5{9U`JbUId#Vhb6zHQ_ktGWxu;*?t$WaHaGM+PX_Om zb&_Ld1{f)E#m!fpajN=|Lb@CF5~VCBS5WBGwmH>`=!|-e)1|x48SsBobc7-0K&jC} z&Rr9&{P5wmJz}2%S4=9(v27n4P{k|IF({t5j%uah`M0arl;j9bEEXg{j$bT`Fod35 zS4`1ZxqTL%_)u3>U7b{B;w=6uoQ3zxLDi@syl>`r7q#_U44-$IXW{)J@D%MOinM5_ zd_jjdF|;`4=|=*|Z)AVId^P3hT=DV?TYA^=@v*a;Tc}2!#QMet64-W&iHUj7Mwr2YG~N4p~SX`%Xrx{u@=TL z%YY@uvXR0)sKz&X3xSnJA!4(N#(wOX>3I2NtSZu+ENMw24@wSYEa$+p_`ES3$9EYH zt9_4_v0E70RvN54X4*j$qHv4ou~@YF(>QXc7jhTjxNm`do`+cQV&E;4@~YE1kyM^S z?gl4Iq7XV`=JOK#-_ifkqH?U|D7pb409_9hthv2C&MT9aj{Eyo6*;ay{(^sucqa5Y z#}#MTqTfw=g*Qac)|&0-Rf!y=C4bVx?Xzmy&iX(+)j4wAgfe_~*#}MBRE;g-n#tRE z$v&iQqh(}ly#DtuVVc$Ye2tBszJ6zSH}b>7ts`HS8apSaxsT8D+uPf+H>`RF23;#F z)Bw~^Pd62pmX-jg>gkj1F8hAR`>d7|7We9?3$MHC5F1O=8AGo`!h*7 z^55sT%D4ZWQQqZ3Z#$hSQ`@qU?hvnVF=oOd8aP?`OpMSsGOheZT-OJxTX;FCsi{+5 zpY89nhT{KPT6%+c;EakA&$n0f)9=@B>fX+S=N@`T5SDKciG^ zyj@)j)#;5*Omr*cZES3w3TkhU1s4zRblGDS>h3<{flk0gD-t9)^GY_HLCO5{T;+uK z(Dth3r|{PriGjHos5Mcvon{mT{SFx^K}JGroH}mf&l?9p*A{-%BgLK!PtoRHbvt3P z>KWWQ17~FQjEvsIijA?S^t_gdsIZ*bBy2=b3Le_reDTDw-rkr9S99$T#oE^DNLrhp z(%ijuJ9m6=cS$j=VaV}p5Yj~;!)w|x5Ujzny2M*GELairDzDjuyyv*Ev)hP%syHzDA$4>4pBg@0C2@$GDn_^^0)J~jA21eH?BG9CHKt;21?)2&^ z@_%QOizf?_#jy~JgRrr#if$v^`5Du5hK64I-u}Z86cl9DtuiQ=K@Tb~=XgRfFE9Px za@2_hA@_~%S`7HRchMD^MZ8f4bqb=&P>l;NJc1cz< zFqO8)L#)*1@w>=A(&N@PfNgrhWp`tsRuH7>4Bl>6=8KHe)1A>{AQSIC-`*!iS>Dl7 zi2||_IFsoQ{9r-;{P|-R5J2<9xb}-zch@y82^(@=Fc?OfRe|nTsI4tlv9@xtwBPmL zmw=OiBTV(uMlXmgJ`#Mry{^myo8DoJN!)uxSsvp9vqV1Fz{z-bS*qS*Lr~)WbMH$V zbbNj z%0k@@p748hb?|Z9t$MGh|J@bAmJ2x+7S^B(7miH;2=!A48}%7_vafdK21D~x4CU}6 zfALe`JhcMAP;k(gjg1ZTffl0(5%XGvIVtd)GH4oDR{#0DX)ZGXkNHG1dwXA^ub4&< za&(oBq?h`WAAnu{Z-#YtcDCyK_n@&nNw&++hlloCcuElH?rg}g3zyjWuyV<2cw7Wr zzDK|PZbelJPL0h=0v+p%lKsi$A6hJh-Sn>Akz)7x2%C1v9=5o~!|Bno=#w1!_m8Ap z_qF~)dV2ay+ZRJkh55eQTc10C>zPVF&w7MN7Zq6hW%$`_-k zA^F)gY8E$>o}O}$!~o+t*!(oc($dl=UAw+c2Oe2KP%yQM=R?!za&h62&*0jD*)}^Q z1ef}{a}Nor)4lb@aYB6j2~cNG1afe20D2WPi+ow2R7o2!=s=RViBP>^efI6!H!~=d z&UsOZXKc%bbvBUX+04$&*LS~u{nGRgfAax2(ZEL!X&6Ltd6oWb`y{{5?;6!E>k zdFHs%A`Oc5_yrpO(Yg^iPf?sS(hScqbv1Xc-*_9v`66F%sUd1U&p(395ysK6u_t`~ zpG%aK1gG(VO8m9B*bShOH8t8rgTiB(r9=}wXlu&~JQolNpvH|%O*c@$wS|C=7fvJAAXG8T z5WB8XR8(weX?X)vOHWHnDeeMy5Uxu2Y2V;|o9s8n`A^;?S0F&~hP)<0|dZiUy8~5FE|b zRNE;|KJlmaWymxt3acV|P-^P4Zxt1vfk*+f0!-`^7Xt(aW`Q*o6X8)GqiFf@-{JFz zt4+MSe~XUbKE>5my6MT^t=FdFSEU#&MlOn#E0aXlM4l%S!CViau?JZzopPV4*$?WXxeDJIn2Xj~bk`F=4%f?{fUv@lYk{O~D z!-MP>2fZiu+^TA8Lsg1{K_h}2EKgzr5G`1hot+&ZRlyJt*07ocI&1q>IW3#A-0+X( z&n$uN0S5A^gnD~XIbXdxy*{%6tFgY57oh9gFLV`Gb*=2My`*Mmu>FUD%hf@?JJ7b) zQL{Mb`=GLY1Jq%2b2FG? zWDPgWZ`+-tTQ2(7C-#nx7FJfMVKQ`@LbNQoSrT7oSSkBEfe!&-jM*R}DXCbjt%Zbg zO!Hq1bw?^)H*Mr!QC!Rq!zZ<$FfgV1@6&39UA7#?&9R|XUs}yC!tB`*= zrAh(0DNYFISm?ibl|$Ov+T!#4Z!j*rjjyR6PWx&0he1#g_@NH7!*#h4O!dxK-sz%a z`}%SFqi$!{SgyDZmH@_iz9q2SU*1$THiG*#Pg&jf@8G=axShal?v6yz8)^)8Pyb``wfgl2S>WmIcPQD3Y{5N=OFphY>&Z)MH4dcMQc`3!Xd^J{f3bBWroY2|LGsYwujnph0PrjrN0HJ4dM{{PGg=Pu9p3Y9H0lFPdUSkz zdUIh5WX)F;Hh}v;;(|Lj&lO?f@Y)k@*vV1F_>!aD8ysa#U}N-T6KM??UZPeli? zyP>_ke`ExwqM`x}l@tgl=Fa??59@%BOxt|5GSU93`2tb!G&|MRpH;n`4^Xf0-ms9w zusTH`#gLGat_!J~&5NZ4V@Wf7d%}jNI=%$>S8Pm+VIuf_q`SceuDJWEyjgPTK%Np_l|H6JTnCxW6CiEmJ$as*nnLZqDbqej zRqB0wKB}4&0Zr3g=MJX)(Ui(>-`2pe2O+1+%^seYM^q}sNrT!TGI#R!js`HkS=s*3 zb$H0}6k!BBoaJ4Ca{AuTP{xDF0E5iLkSK#m2P_0SMntH{i1QZLq2LOkRZ=N>N3nIW zJ`U7wi{C9t#lgnLNJILLYV~$cG@x?y{9qPY$Lrjv(MSP2CE%Pv6E|n)pyp;#z}#Tx;41W+ z{)Y4pf$PufUgK{Jhc4;llU0?KmB@iSK~z*!>pxcgLsGO+ub<=^$Y(q}Jb<3~wYu6r zCi(bn$seRxcV_$|Oa(@q;06@)&+JipuvUZKQj^Uqcw*0yQSU)Q20(tJpC%x5Q|Q;q zO5fyU((y3F1PunU7S5WfrBxq)hlZ!&Y~dO0N*DE7HvA_YjuKTWbB*}#*`PM%F#@G!b_25t@NnV9qe+yTFGbaZqAe1dh&7vkI-QQ_Q;+(bqp z$@eZS8~5VrUDAZR{m}xM;zs`*BxjDJG1Opy5zP>dzGFk)50%(M*^$41ip;zt9 zod}TuCup6&apm%dmG~Mg`Dr&S4Pw1#=|7kPA_5^ckX;mU;UdL}rLSg;lRO*Ad18^E zkX|b|RZ%axTIQ$WwTL!GwOiuOBCp)qcEQ82Y+-L--=6t-a!|ldfQNw@Xz{+l1i6`R zl;{9n?$Y~)>bM6+)P85+Kb)PN3)Ed70AYgE95}_U&W(-F-U>U)Nfo@&^J_?m7eSop z&d1m;^D9eBav;du+Ik6+cV`a|2>5+Kirv z>MHO}n*acgSKIr6IRg|?V>5OFOyPREsedP!#SRi=9Y1&TlG{g}9ARWf)`-E4v?M@+ z?g+{fd4g!HI4+;f!?B$|ZDr;<%1uZohw%XFEnpkEvH`pSdJDLMafct@0cy9livKw} z0#L87KP;M~zM3n9lQCmZP69w-baeCt6g&tP!Q)u`UrX?7VSy+2Cl_z!XO?0E%+bmY<5qmGqHUGCaXBBy-zPo8|;J&63J zYd;kOeY3lvoL6}@onU{xLr~lgd4?kl3J>RpWs1Uv6JYNR!{_CQ#W&|~scC4O0a15% z%Nm)O(C3X=0FwpK4VYkH!3Jl-#%x{2_#4gPiT`}Joe>W|K7aS_-BWGHCnNwb91Vcv z?xY)!6)uuD)?Xp}>t^OQ5Ct_kUG~@Q?A+XU&?O)*1k?cL$08u0{ngOtl@+bcFFp#p z_hMk^geYnTxhojEjlbAmz1jpM_7rrBhy))r?b`*(6y)PFfM?O!a$UC|8$lu^L2nUr z7~E`(xF*)OUu#x>kDx)9F&HiLU z+@wH0EHLONZvw&?P_5v*hG3cb?oqS6A?0u28gI2y7seU%AJ=*-HP6g%XZGR~)Gw zE)u^fx*NNJ^E5%}uS}n+Ut0wz#`P!GI$1{sp8!nBEu+VRf9;LT@1#8C#g5~sXu-%X zKKs8Sbd!}F8CkDa|T)jyFepy(~A~Jwn_W#cUoRsK( zT?dNZt)2XZjDKzkO~ee#hH=N9(GbtuemR zT|Rf0Y*-uecP*jGAB7r0gWMlv16q}H-y0jhgd$@~Y>%C};+*F%Y0c$Tzx>2?_LO3P z`F_$!UuU`i;`={b+oWccX)>Re3ogq?3<{iLFbZ1gw7M;f^a~GrOO2(ey!^kx1|mr8 zfE#~megNJxLH06{ZL$-SQhM4HAMh^7HT-U;Pb}FX(&M~X!to&h7(mcR31kzH^iO^c zh_{EGfCmUbx?b?PV6~r=IS?F=cRP|#>;JUd)7TgVOmr%J3JJC3N3gFzrX+3>h|cf9 zfyv{Ti7!Xo<=e^BJN(k`m_3U~4N01k4mwMIs9*Ug)+HA)$tfxu4uz^TA$s1P+~12k zI68uf(Be$@`@+w%3qQ`uo{jg3Pr{nMz7~FRf=^Si_`?w(|5N>tARpoO9+dI^fKBIJ zVm$^>9w6R>y=Y)xRf<2~WUG5^XC$1vzEp_vB1JgIexr4NZ5fK(Dtjfj7V86!5D?w~ zt_%Wf29)WO!W9)21s=RnMEjoR@d^So0#Ghbey}Crk@_iR2BL(e$MX(pRgT6Ck^s9I84S*Eyq6Qm2@KvV)&V<^@=dAmOwq0mXa7gFe|<&*~7!5XR*cz$`3#_I>_n4 zJmcf-R@^NxGXSmu%;11?0dS~1Yt_^?H;03f98{e|A90-mHwmhj82_0UH#FG!a&0D4 zK%l3{A`F_o2E+}tAJ5O$Gg*x1U!J}l3a4cy#`*}XG8??HC%FGKzlTD(0Hc?C{Iw3x zU;tu)0t0L^U>5)1j18Uh+>l40EDNalQ06T~c5wFtZE>1p6{szzr&vbaeuL1~S6ywa zg9qNsQ!pa~3VnQh{P)*h-1srLvP8u5u_$9U1-A?y`I&7-WFNoGoiFNR5@Gvd#F^Nx zlO3o{APYp(?R}08&If=zP(27hawv1ixbW6^mBb9!ZEkeu_-At0<=L6a(P8pL@`N?u z|Ads_S$>{Ab?dX3nVEh3_CC*>7vh&jW|*Ux>qhuaviph?4cMl@)LRFLA8;hUe`nO! z*QX=zX@ZtffJBUlrNhoPN3Qu1OmOL?tIqp zvw-9Ym0F<0iCE!u4^V}n>=iiXpf8)4nDn~O-9xnzMa9#HlkTmjhyO8_;JYBk;KjA# zpM{Q!_C>P{x+GZvQdrK4uvD01jK-Cs>cQGF`b(INczi&7E0#F{Uh?a3MyfsaO(>B- z^V+8RuCuK%@B(=Y&sMG&`+sH|3P5dx0|Lelv_-cwK-jyr=)|Lu7IRX&V=p(&Z&rh5 zarH5q8-M=GlMi*$A!@fK6CTigQ}X&oL5HfqP`y?(nP4NnJj1}2HCXh`?1OpVsd7+% zcxdMX$aw&c7@<|0B@Oz}frmm@W+`@y4i+$IbEclaC}r5?6A8454S@J^g?#&-pN0JH zw()aEPQVadEcrC){~Qb|k9KM(@W=dLHw=_)1N{}q$qSK=x44pv(6m4A6w2&ZvLapjMB!*aV;mXdBRh7+iU9 z`kcXp3&?#i@oj;39#ArXmSt-w31e_a~0;!21I~~tkm+p(}YAk7>PovONgYuT# zl&i-r9VqR_ii-+p15m1Er8jhfJVZF$NedeCX~?CG{Pp)ZP|S$iAfh>#t_!_@W&ym% zUte%CYdbnVjkTnk_;}R!!6^I%C<9zQsHy;DP4JI_>fPtQ?pq3csRZ#TaWl!Vrzl9E z3qjRhKpsLz$4_ zHZ#oE{?qd%aF8OS53Il4$zk4}{>nS_bz9h*;|h`94}$6DaTF7q;@#g$yu54K(9AY} zb>A&nHIcV&Q-sqkj7ss?$zMiVF6_}gMBo2rjnm;q=TFtgQoUH}cF(i&?ob3RS$G}Z z#OihVy>z4azlxLBQW*$`SNx!mL+cA5+v;)mH$=M%GiggMkS-?yFKRWJQ_ee}&$8>n zyweAxL`#S_*|VCoPw3vrLrzyhM)$p7pRk!>=fV@JHM+UIfjD`3MgpV{P5@wKC_oPc z{>BOCUu=ZwJ2y#hFp*Vd0sUW|w*lzUt)u1U+mn}PN8o>|_$YC3)kc9k(4r=p52_r1 z2%sqacX9$wiJ{=f680G(S$g zCO9KAx?<}Udx?S10D(sjIz#&O(_%aeDb?G1Js%cXcKT| zgkSEIpuGNuS&tIIt#~Pdr4AmrPGO+Q1J^K+MdgZdC>8vKa)-fLd_Ifw2cohW5ST$H z1YI3acgi-uz%=fQRPGE`Z3y3I;=q&0r27{}PURIV!WX7~Xc5x?X!|EX90wauU%I6a z));06whVp${VQqmjW2Iy&|r%e<(E~-R^yWkHBT%|2#x+!k*rd-pOl_B|G}Q6vHan; zTP+=njwc?BrS;WJN7Lv*#|ImZe&)eAFLqFC|b?=5PStEZ1TyFZ+SHiwj-d~*==VD9iB zj%qwzWNo6B$ZA*d+$oGtHknh3<|aQ9yrxItYTL=aHs*hG_di_|4TwrmfTiiZni3Ib zm(C+-v-&rvyIr3J4gyY^(pSZsf&5{vQaTIzfG#ExfiCumj(3{VNFq9pnwpwU9v%@u zD**hq8JIvp=hRw9-x>tj#Jw&@@F0L*32F&c8UfD;u&4QWMJy?BolKByWB_kLJ%WxW z2bDqKRHUE;G~lRTgQg1ExV*##qvimp4uBbi(yOhNNR&wI*Hr(Mb1Y|2(d+-j-~?mi zuli*6eq3>m?SBj-r+Z^VEUhK>yJ(&QW+9Z3npo<+i`=?Ptv>Z0f+&0Bz^R=B>zss% z%}vNuw@Zmh$Buoc13AZ@_B0VKQtGx9gNJ|dv|eGJkthqk^MUD1045{ZUgj}+aPY}2 zpnp_l-MT~zr$qoo2JRJLrLob^?|euV(g(ER@2=xeJACTJ(}N0v;~V0vPx8K~u1Eny zS*+R%1_6MTfNBA>u(;AOws+d8+xVE0`+DycHdH-X(x93tco)?PRj|?NAQ>SrrVFjc zB8z3&U=9IlAxr=^_H8@(2B4J9>mWhF%Jb8S|JK2Q?CT03=xTm{qw5Z9KhzLGS1UcK zU6hN>$7Uw<-@W{;uEEDc?EpJ8X_MYYW>+SW-w^s;gK0Aej1m#Oh1OU2$uu2IQKxeJ z6xIe)o{y?Mt?W8f`(GH9o~K|1pE%e{#;PQBkqgpP+k_++5_1qfT3^ZkZ9mTP9&4;> z&Jb_rq(umUqakqR9%3<;BYOoSCHuR;j?bxG^VPmcOYay>Oj&)swb0?5Z9N@tVhvIi z6S+^O4~*RSN59n@!Gcxwlk8dVHH077uD++mdFwwfH?^=__w1T|Wz3i_31CV;SPG&A zA+>uOb$S1>%vF{Bw|7^KAUg}Fi^X=EyK1L_u|J*ow zsmQ39wUkosaIe>#1BdStt^jB;BoU~{#g?CR+N1B^>PizzV*8Qw&RearpDx208w)iQ zDtaYfLGjAs;vxe*nvN_DJ%N+fcbT|Z-m`y9NFqv%BEg2>{dY9CEsTbkzabNrkTQ$1 zW>*Qofw(onEJ{vMIX3D>5uD+qWR25+hXvJYDr;)`or%E=(cyE)@!3Rq^%g#sf0yj1 zlv8HoS@mJh#Ge2ihfL21798|}MXJn@#5GPrxc_vQ8u#vA;|tk4|0Kq4c-^mAt^Mzd z1^d_dX2Zt?KJxU3bi?W$uVN`0r_E``xVo6OrinC)mUce6_i6Ip#UIFj{ly|a;Aoga`@GGF`JOwAE+=|!R6Sq4ph-jGGssz8_Mz!ZfkOZ{&l zBSh5Z?pR?dws&bfeLP~9nZA?1ZH@K`EU769IQp`iaY>-x`DZOX%am9j6079QKxT}< zW@SPYxwa@+Vt7Ak#FoN{j#SrTfH<3C8%<8N(Prr1>ah4@^{P>!1`cbb+c(p^M0An# zb^ZGjrhOFTA{1K6cn9_=W_{6%fqYcy?&1KeIqd3XTDa+lrC$Y*7c2NOAwC7txqU=- z<<32!ImGg{6cS#63z__;vG?yZ$0OuObOYfzOQ*0oVD8FNf0Wl(W^?8O_5oH88oe%JTYwk zi$01)>pY!diJlb8TPY7=;2Q-_LI6vk6C^r~(hdj9*u8dfD6I|O#uEBBzLE_S(+wGI zvXXafSwvg%bUEC{&d#raJ!bgjY5m0nKwKb)gUMNWxM&u}##Q+%{a08do%gril3U-G zm&Zya4&O)9IBLF5?oyW>uMe$;SbJu=fAhqHc_x*Cm%NH*As`4neI`@Zja#u5aq!Vc zL+-(sai}a*x@Yeug?wt>?V@PhbDg&E*mw6=*|ONMjwvq|ppIgSLV7zS!HzuR_K@z` z$7?1e{b$CuQT>BkFByvTDOt0UV6z_$anUQ;@IMaCP&*fxeNEBjk)nrI-(T?#=!g)* zxa!;g1p;6`=o|pvgNp`cy^Wn6L|`83YGj`vh%QC0?(j(LgPC&lq8g7+Rwa4YrzB3( zOw#7AOrKe_0iekExB~dA;DTZU1{X}u{mqW+Q0fsnzJS&U>>E&d4QMycCN86aYc#Q3 zicAQPgev5fLTS--zb7_eL${<`NP65L!w9ID$_FBM!dV3Gqxj;h!J0vG_Ok14J;SLV z<%2u4bW3m*G-(o#qV89x}z?rtsLvL%Ttc>puORQ`@dwolTbrGAqnb=d_ zWk&YXa>V3K4XN!2_*X(bUEk&aNk!AfzB@jFci2ZZ#r0)T1W5ajUr|Xo-rhHU1^4MA z&!e@LIz)c+_ZvN6lbwfIic3d2^76smqneA1^oR{>^as;U>c6ac8;RR_L}ZMQb%d_R z5bl8^U4i7&xvCmPolEVwcltF8)gcIEr6Hu}%jaEc-XjImtVM&tg|9?{cK?@s;sLxC z7B&C_;W^*9r6FTsjXSLA8e>6r0Y!3+R{5(&r>0rnC=UHVWW_g#u23+{9& zc%81>lM-}e6W@%{ZKyc|q#~G@m^Q%R1LgJrS@ad36}FRyBN|UhlNT~ZBRr$4Iz-x5 z@<53Ri!C7iGfx7ltN^qN)f0ee6j1RVK0ZLqJRsvX_*^m`!rDcaE0gqcU&+FiWGNMP z8!bmKs6Zw7K5HD{y%WYFJ|S`h1rKGP5Up2AbQ;HGE56ey?+WP=h!-{dRb`Q-dfbRx zQW1L9X6w(Z2&P*^1M5s2l7kR-+r+-+*ABf8>`0G3Q~WJj6(|5Pl>L*Xg;H@M%cs(h z+>DH4?+b=HFv>uSB$%Y3CQTQYS*c-|0cQe{%9_*75RUTrsLX+?mEJ3H;r zoFWC8JeGP=dU=IC^RscN;pNZ}ek-bm;Ee~-72FbX|2bCnezQFhpum7u3LK(fKm`9| zbomh>hrPo~`W{;1)4+6%9d=ZeqrWUtkIxMaC<2_YBt|)-+W>QbA5d3Yo5w+SDvze# zT-Jmz9~XDX`$YDD>%PRVR4|Bd;BUFC#^9~HP`xwr-jQUe40#ek%yoU+*6+sIsK)14 z=$)5!6UBs{WQVdWe)W-98#c*usF|m~7_zn}5k^A-{>+piok-6jh96`g0_zUYRUDk-v`*)J zyULq<{-1Vo?RwK+nmNXgO9}grxqi8IX#dMTj7f?&_<(-mUY$%f8h9#C`$g*%C+8jF zmmAUM69L4N*}4v*VjjnR_smtVN?3BtLxs0n#KQpH^#EENN*e=eU;~ICfV2m|^{mwg zdkb=Ttx~YwWDk^eAW8tz3@XBdZvng;nCAh3%XZ&wMevRa7w9(NPo?Rh$ue-fEgClo zQqMSBkDdIVWG*yHZVv5teoZwT+L>05jDJ_A%Tp~EiCBZFf1KM<(OU>2c#(=1Wo#xJMyQ2K%&SBkm2pbWuoTJIL#%XCU(44r3hx1%1)Fm3?rraAajO4_Km+LNMGG9-|l+S^y(}XbUoMO3F zNBr+wHZ5^0%cl>EPe1ZY&5s%*8-=BL^O~|8FSM0Eh?25xS|$>hPk6hC_#3*2enpYH zRAekA^Po3~J8AaLD08I|sx?$Io|&zxS{ZKu($v3y@PPFJ<@5k+6wL9^0ss@3Lyf^X z{e_+yYN)zd_um48-76^P28dWdN&uvb`*kpX;?;<;B*^{&kPQg!QZ#&21Pj4D`Ox6w z6O%(HT-_a$n>Nw?g|a;ol*!9xU^@l&pzrfgGr%XF`TNX)K;+)cjn zrl|X2Ez>s}44=7&Z>z^*J#}qXB+`?nQ%$})xG9TBjs<2gSS+8u8}$?aR*{MkNc--SB+_yX?- z;a<6Ll1Q`i^vl(Fyb-<_Wah(Ss@=A|M(TTa=17PC;2*YJBrj(~PcIX9oD(z1E---$km4=}o-8L~Bw!YTnstPH z@8QYeff;Q32Z4E?=snq}DA2+JH;PqJz$gg50ROg%|4%51+#vlLrM{Y(nK@sX4tmvq zm5d=Gdm@04Hs@a8XGkzgu$=+Pa4+E9fY*TkTP**Fd~GJg`vOGf^qd#-M=oy_w&Drm z_sK(<23xxFC-1sby$JITW_FwZEXM~`BEXk=PXt^1sU?G$c>67h-4Zwuy70g6HK)L& z@ODNv?Y=5yOt(&jQ}foBMsBvE2}9FP#aMLn!sA**m6l|E1g=KFUoa@7 zjmqO7tB;MWSLE*4U1=bP`gHbdb5j#=IRldQjf=mhXSw3iwmc>Zax!16YA&{m7f?k2 zBMNE(keST@!)Q|t-p4SF-T;4f17B%;v%xmJHEbX;1FKUY*c3KHI_k41)Ngz!Iv#c{@SU) z0Fho65m((>=1FF$Htf6VWyDqs?Y^VdX=zrp`1l-z5yYF$6L-=JtZB^6Z)tfwzfdsG zEnZG`hIaY3Lr;0EzSEn$r{<4^8-@K9|Ff0l^A{z$JQdXo)%JI;iFJkaIsKuC(-#?9 zsj%~G>leRd@s)FycNHZgZv?+ru5?`j^u{(JGU z;YsU7{I*P4QkLf(gPxi@*qC~PYZvd*4l`)*F%TX?Z=w_6WFWm+Wkj#fI}?iAbYn2? zy?yd;zW>~CZeRD?9=sJP3C`1#Vy8lV%i4p<*}t}S8XHFD(LO*$cD+1%L(wAOcmXg3 z>bQEi22Yj()Y1!_`T`#m!2<)@B!B=;PEG-U0EO!JfVcCwJ1>@)H4b=B?ZGqwIc1C6X~kF&sgs^Kf68;hc;64}$0>Bt}3IP$@; zfBJT+YvGG#9b-0xj_yy(&r=Q)H;goxQ#RyC5jPjqSy7`k+|*0+oJW8DXRq%2RMNKk z@NS;MEj9GeissUTtlqj+l|hRM=kF99>!`N>ThYaEg!#zaJh}3!mmB{V{6SL{6l72} z7$l=}fU_m!@x(kgeL9%t@x8XJ}&%(+RsEHG>DPx zxk*x;{6kCVyi#1_TdM^oFQYnb^;9^}6KWPy(AEXcIcH-t@G)E92?s=!P>VWiKsc?f zO%;Y`-u4Zc5CAI;wXQ;?SWHalxDLQ7)Efu9htMDjh^^;3SmDR^PF$CfMVO*|xRMzZ!hqM(DfWQp-=W?PWgux_fL}w(W_YN`XbEi>oMe!vY0n zl!uc?sBbO#=pg?8djY=1^&M0Uw{KarW1f%GWLx4CY>8<1+1V|Je)|FA!Eo?0xt8WO z97CyN1fgcrjn``jZpsBH@H39xq5CVgkV9r1-*R*2b z9GgspVk*V-o+8Ax?)GCcwQci@JZ9~@fNO09*UTj=C4CVm777gG!IQbfyxYPWx*i3F zuf?>JpaDa09|7)+2qPTArjJ61bGrWtV?I4B=6uXAgEuS$qQ_J^=%4f{=4<@AImUBS z+#KGa*dtXMt-jmJPGVlsCRj%rdO#tNCLsUV-cTtV;p0swtD2u(D|+!BV_8|yA8nrX zc2WERTju~o<71=x$m#xN%lGxIzdXA`MJ`}ysa>ERozn$&EK-@oJSI6{o7n&+Qy>rv zLmLdA*Utv#8K9=G_nMFeVPqoW;bfbNx3WjZ&-ctDp4F%r;~PStYXF z)nxORj}SK@kev&@l35$9Y^`zTd7dz07LQqe?@t~77V{hpd0UjcB1YnO`2g^721Q|O zYYXz0Vl#jPCUM&s%rBs?it>VKiP6Ty@uOG-YxZvA^QRj^e^TGrjL#5RnZ3#1=HE0n zm*;M=-`bfx(%7I3k&9y?GOToWuYLT3PMj>wc8ip1%<3bt*J5u78`cOYVvaWLPt>A8ls7V zS#d25v{;EET=_D;m2g4jZL1AWLfB~LyvPobVJ64|i)DcOO+;}Nyq!IPR@<}OrJDW3 zj6f{IuTDb2qV~QXh=xHxM;){ZD(+hvz+Utw<*Gd3yMmO9y!Przp<7Rm-?E#7B zPXE;In8Nzh2mfXtNi^NBfS1ASG1~&xD9KS~)~%WRfc5+Yrh+OY^PBrC!RDo<^Aj?< z0T;4O4>uv1cXo3jS$HJW?1fs-LM*#-%9$m5U)ra1w`NO~czJny%sE6;E%vUw(&cGD zv4Dku?!DE1($x1G;Tj=x;bdC8~5*Uk!eR#SzgnAOZ!M25Z z^tBb`gQ3Ab&dcqy9Lh4IM$B7e6-CpCuEmIJ?i4&>0WXYyIqsf(lEb>`>SBMeeHr7; z>B-(`@am)4L5@BS-rv5=EHo~^`JuhYg?QxzF;bgLLyWPLHXGIWrJhzMun05n?t@N@ zh^&mthG?Th@vRlhM+~`)O7!opGqKS+3DtsBYdOy+l&IRR3^6;~y0u`M`F;Oh&3`CtPlVWS zaJWZ{|I*hwlUW%J1I++u58EhbwuhDDPOMnN6I*04rm*CZTmmJ9u54zTQ`!YhI@Hjl zjvdCyhCO$D>NR!zw#a)oU%t<=t+bSrWVAx-^3q&8%!n#AE^>%dSo7t!Wki^nvlx0t zIA&Au&ESbcz0XR*Pq-6luSkWAIWjh&CK;%b5A^S`T^DGJ2g)Us`vLZhvsoo!6L49@ zA|zM5XMY4fe2B|BzZ!^EBBC652&awS>T+7iB;g!;uS0PB^{lY2K-iRHw0^*e*Y$Ai z0&z~Wjn?4h) z%~N_}rm&7g+U~UW(E9BZ?KW6*@Y||p!Trk6saKOin1%miTS@&%Rw5M&`K&a>#D#!( zJ0!rcCLBa(fOH5gio(yglt7Q`yUOO0D2P0$YDCd?U6cB{6!Qz+pNPKoej?)rIhaJO z2o))uJZ#DXLoie%dKCTF9`c$PcO>`n5-tqKmQxWz;(&rTlh289CF^q=xG|bpziJ0lf#tE?IHGWk$uMAzN z*GxU{6GbSF+G$;2TN07p-K)V)`#{b*im*whpjO5ecy7gEn}|vQ zcj}y{G}11-uXfQ!aGPY#CeJB|L;tX5`Dl{ZF9*3nZCl8TFF|pY^mt~OVfwh{{7_Ut z<-fKhwIW!xb}H*e$@`j{&yeU+MLj<)EB&uU?!MR3Xk=?qH#Fb?l&_=_c%VeE4Q+s= zV5cGLe}}-$wJ8+zb;v2wVrIU52EKlunY`E6KjnA;gp^6_BENUE_VZp*~^sNZ`-s3{1PHuve6uQ@k zt-1Q3!5WYo1L!yu8E=&BOCRh|GMfd89wD+lS)*IJgBiIj1}CfXgbOk~EG|_Y;@A7N ztVk{w8T!1+K&B4ynnP+0Ldneoil_8mc9sDuJvCvE2pgIhqgFOdL^edtG|>u$;>@5*Q01 zMZt>EgIan}S6_gzHuI%N^&LFp-|?zrc@#2i)Put{aRwcF`)#QWav`xc0$yyx2n!2! zCrsL(%0aw#%*kKQ&KsNJ7bsG=FrNd?#+o^J#C<>arC9W3im!5E=?tgM-qT~FP?gUX zF{0498eO&qMsQu_Y7Aaq9-nHw?sO8^_P!lWXpkU}dWD*kDDRAu2M?3309zt9@?>6@ z)w&_84CCuM4O8W3gAOZ|Th&t6pTT`eHNq8?ad`6!S_)S3mCBokH8zfh2=A4ilpds2 za4{WpaMKk_p%Gxf{7WAXeQW?eY%?~35Vlq-2z{~bZ(j%U4YzGPav`LMI8Eb;?7d+fQCeO*wj$yn|ararBO}6}V_Buc*~|RP+Qr|EfL@ zBdF0+V=w)tGw_)W`h+_;3uOt<{!JH;sol4vOm@V= zX=GrN8USY;c+!7YN*2CuTV;XTO2KT-G}>nxQO;A>kyODVn`{A7=r;IU`>TL^2KjK3 zjFT*vVvgGixy%{MCy0Nxcn8;}B`mBpW zriv&vEW6fRxnW&V#CFs;n3auzI=Elzgc^AeEOUS9>{X;59`?ZshCkzqGuMA@#G_HL z&1$~BXuyT_byT2;c~r=XF)L%Fn=f#hs#GbrU(zRvY2#LjH{&{bQ(uvWRS>iU5utK* zIt;7B)6pcGR7=+VC=*GgVCUiK6^jKMfuH%)ja2rg;mV()2k!+WnqKI5zh>BU$cjaDN5^{E(RV|&5OOl5eMg|kvznocZ zcd>$oB=NVgr9Aiz{bprCxqRnN264xqe3^xgpC<)gtfJ)BBd~84sKv4R^mX3xn4`B) zJ28~`#Rk&ju)6+ZDKDJqdZBfZXJuh~@rc+;GP_)FgP>aqh$hk{GdQ!4-7K&Z$HZH_ z1dbQjz96uOT9 zc&d?*kf4TmW40ckQ9%8eM@L5+!jgD!4kK-m$9lXGjbY*gNKQ0?cE8QW>DhJRm-BgE z`(>w1-l6b+k6cwfguyP!LXB~|5-m>?yG?$^9B?)gYZ!EvP7yy;|dXb z96sj?dz{3UtR}i@fK#AZ%K6{tPhLvfi2*WC@{`ZRJDCRxE@V6Sd+!O?wdp?LFmzED zJR>lS;yw+!@a9Xrc0}#@E2hs=W)zi*g2YwcQUbAl-^uj-#y7*YTqzU_Pz@iu*I&Vc z*QApgiXfk5nKdMV94Jqxt!G25kZ-a>Ex(^9gbF)_$j_=gwv(<@(%2@gmbOO{yz)ut zA6f$Ds^TV@e1+?2Oi%LbS*h59U!As*vU+IY{>?*Ta4?!`a>kNGcERyj2bN4+#MQ*1 z=)}R?DLV4nbJ+PP9s(UH8onL1s%9YwQw>k>b}9B@OM_XO+I$7ieHiXHySa5n! z>W!-;C_dxX2Pn_jpK}DN%548cW8#H@J9WN5K$o~kU}4uMCqbvnYfDBxawrH5{=C^X z*xH`~X_$MZZEpItKs;fW&Mi`$n$EYcnIM%iq%wqa#`TUpWr_X1+)ch3<*oNO?mpWi z_(N+yA_Fme)Xa>!Z&<1*_NS@nFo6T%8|51U>cWtB!%A#?elbaDMOha^J=hI&2>}e` z{0Nm;Q$n!#e8$V-eGCZ9+1&ZKzc{Jgn2k=)$m9@vN5u3ZTPc4QRA15Fxy4%y>Aqfg z1Nqm-{#WbQ)wJ92Jmm?WsGZAyAvK#TcNS&ti_&qiWG^Rz^-8(5cfwM&R#rXcQB(@~ zzQ`Xx<(lylQpnw7c~jp&iGbm45`cx(NQQLz2MJ)Z#u|L;ULz!JAzHdRQ z9)>k>WF#3gC?-%tKB!tz1wFuc1AYZueO#|`fJ5`wdM0OJSz|)gS2mPfnrc?s7OtzowRX9C^Y$1dq&W^ShN*Y;!YH-(^W16CYDLNy&cO z2f4Y#Noc#I$(dp(`M}@JEfmCdfpBZg{|zxefP!?$6>#?8M}nC9<2I{5h_g8mNSQ+J zeh)nTI-Ce4|6A@sl&mUB@Nb(LhbNVuiVse)hU?4h{^gG!R4$u&Bc{Vuo&$x{GT2>q zVv7QJx{ZoH`Y<7E-T#IYcRjJ*C3=|56BOf(bR4<83A?8$O~{H6=3Fy2Q)6C7&g434 ze*3RoF~VchX_z9f4`rrtlqMI8qQ_po+@!O=YT;{(fZeyII6Oz~bWt-!?Z|U+kW+!iK$4F{irfS1?dC=Ubh|9zgX${_qmMUD3 ziuvfOJqRfWw`OrRqcWl*rM}SE2_a?wB{*Hs{YizQI6_i$SFcK@pi1?RN>29grSLuogTfIEJvL{^Y5_& z5A!X9f%bNY`g(83PuWgfoDf!QScNx7C}r@{X6RA%0W92H{Wpy27sXAflyYjz0@S>^ zg8}r&ao(&^*r}Ds3D>#D@54ASW`i+4*(8MTGG|uV!3O2lX#8POXQ2LANT=-McmB*> z;+JQ~+JLx8rll-fz~->RkE3;R7)ew!ZiXLO^>_-Ci-}35WT0BxFsJMpD<&&nfH)~b*~f>Nz@q1_Gg`7R z4~yEwLg1a5P%fnm_T_`GYx`>6r>G}LnK0k%8qYg@^GB2=#`oxHV`{AtY6kgCA!2v)HT0#1}@fkPkh3+nCCiwSs#T z3OM@6_{T2)6%x>IG6hgQgmsV_))rnHwP^ zw$6BY$*gq33h1EDNT32mOH!gE20IB4+q>wNP@>Q3TRBQp_WCz)w~{;9?LT0h_;H2jGNfnZ1P zUJ%tutcx8}SHm_2#5#<5;G#I9Gn7$g1|(7#)ZT{R*;UF7S$@k!QI;Ul3mBPC+}1ek z8X@=0z>fYPIg^pT%LmP$gFtNoT`dTHPg}#26Az?3VgW)3nv7u@Wh2I%KKk-ey)eD^ zZw*8@MbEvsgh$s7&c~ep8^z=W;X+pWu|qSFSP<}N?{I81RF6*R`}yJcvSgAuyb8d| z0H0e4Ka?s2Jcs5wc#jXZhe_)L_4W+LzoELj2>Mly3JBxiE?w$>|$uvj~<#~YWGiIgOq$e;Dwat=OT1QqBxZ#(Gyaj%*+ zu=(Sodrbrc5h>*A!BETMWE@QC^O%eU8c}k;pJo&+t>8}AGxdubY8-Ufz`SR_QXmY{ zJ`_J`B^^*eg7weEa#0*CuI>;+M%J*g2QJZqseQL;QjBRL3^*Kz=yHqO6y|tbML?%Y z3QxL-@Jor4!{=fDWD1q#{7D5mg}U~aqZ^r^n&fG&zhW@7GsqXCfJ9vRFo~Q&rh+oV zRAtfr4{eoQl8GyuJ0PXelY|%#CQjuh7p(QI9a+hD{_|MsHMb$b?V&1Yi?UIPs5`n* ztD6ctYZG{xSToJ zZMqc#Pl~F-Uut80Wa2vRB!fz8>qbyT(r4 z{P=?oERp27feVCS6-__L;;?;FJcDMu05$h=A^Z~T*9}lH);T5Cg-WVL3|P|pd5R{_ zMH;J@gV#k;nbu1o5#BRr3y^mpat?L1fl!#`51oh}@t`1f#Nb{CSpIW++a6eG_27Ufp%;%;K#K-B9%c(-)(z`owF^uTr+SBig6^fp9(WNT zux00D`vO*rJ{MFQHNJ ze-s>ER`Zr2iwX_qk%K=W5>gbe>#s@XK>o~?yL8a`Yqupv!R%u#jlTm?%Z z)WxzhxY9VtlfP-q+4Hn)zsz-BI>S7hppzZ0GZwSuDA?Va?Nfp+B4MoeY@5Cz6bNq^ zaPN6dW6SJZN5u-QzK8jJtxN0ZIPgA*Pw(B;?JK%Ar0=ZFIJ;g=KGTtYokL4*Oh)cd z3yF7Xj83&M)9?xoiOp-uVvrD%U6VWQWFH#c!EyfS4;kG#W` zxymVtTPFT3J~BkNQ1%!8YkK;QNk|+b9wKDCFHko5P}(eDr3W{qkRIhFRG6zVAegTg zjoz#$nc7?jp3%qpWHFv;4Mt?F&niQX1H&(h#~%ja^`l-#Q}zBC4`%=oe6Z zLnHiGv`pCuHlPx{6Ys#zj-_#;49*Dm!Pzrj?geqoRFQn)WJfl^+H1~Qg@GqH57jxE z!LrW&>tlanIPOBI2iJ_gx7e{B@rGemT2t zs-j)9>d5I>2{%6$4%ghyH|tFSK_ovc%!t2X&4Te+qFU_s0t=W=*y4R69lV*u5qI9? z6m<9sx2JzPt$FQ49k@{mtWgN|3$h64O{nr>5b{hy5kg`xr^2KqU~G;W^9xK1TnNS# zA`32w6bkZ;6Hs|S@nxLQs|#yrOXgCdqJ%`+T!Fy$t z7diQwQ$d0mZZayatfr`D!w|4L9(h%a5O&yXmG@}3u6~Zn^jwt#C&i2t>gL0e`qz2w zNL*CJkSlp()UR9$WYLFkpGoXZZ6Q|dSWdp1A@p!n7-Sj>g&kt^c2DB=vE@7KeHf&{ zr2pRwkm}pvi4?=^lgJr=QCay!Pe(;zu#%-68uMx#tz~nEEdv29E(J=ExB;)QI8Ge^$ z_2g3NAew7P{&trR6+f`zaQ9mZUhzVDNwvX`D;EC4k42Nsflu~j zFN0UH>;s^F4V0Ff(kMQVUDD=V9Fk02`&5O2M z{=6WD20+0MD6sP>Kp7U63zyNI%h3vbDu9R2xNkG83oiVW^bYYLQak;Pgefo;U@Iz02ML>ye3mF!qDB% zLpc<53Z-gL`4PN40Ca%v!%fV0hEoS*EXRv$=U$o#m!5AmTlKti))~~X2mJ{0sDg5R z6V_Z}HH&CtcIY2a)`miLscXeoa}6^+r;*OuSXTO$ZxpFvi`(6>;81gMW$8 zWeX2ka7`?Yuy`RBr z$uvmj|Z1;_`a?}?;itmv_z38&ebErXagO$LaaMk4ToMo zyRr54Ri4%K30_JMVOH`;;=oDda)*Z#kv3x=F{*#b#N!I>&XSJuqj&9<%%)T*@i60sZhzmk723!Rx}q66I(o=!wkbAA z=m&fV?@{U*4TA$!+l9$T0158;9^#}K{L4QqL@GG+7~`-P%|B{M>6CvxT<#yeseI~_ zr0z{&Atl&103Y@r^r1r`1z`+0l%IH@@B;h>Y&DB|@3_{Z`*qqHx1kGHf#%(hrb+gB zrm;0_7zlt7yn=#*V4pNFmqEeE^V5Sdv$CuSM+W@5z>Ihekka6F`!}%wJYIFc2MBu32O)qQzfut|QK4`2_XEwjQ19 zsq4RYhNQn#EUGT-^CIL{W^mHpbUXg&8BB#~3%lrCx#Mj@b+)|cnG2!YaBE{`R{P7E zWu~C{?!J&SbuvYhOo-L`&p_mdrpVM)f70~+^pTR70-5`Eo4~a+b193nuKW@IG^+$Y zVi62KljHye+)o7ob6YPT-pZ%;P+^C{qGIfcdxRA_j97pE3CFM#?n4rh!PWPoQ}cxR z^q+cOQ3Ch#Tg=_K9@QZ_WG?|F?4buK$r41Q=2|Qz%k&F28*Vg zT_N>UV)?oteWs>5Kf*u%zMB9!p4lGrW7Y4;{}RfxZMkJF6;y(-2oC640kAC#OpQ>K3SGnjY!{L$xWMlOK1oC(XE$Ee6j(37 zP6_CG(;OjRLZ_`2m z&MwkYCL`ft4<3MDb_2ifP|RY-GZr8aXxZ(4x+W3=`=Y?LC-u990YDvUO%kHB7n8?H zE-6vQe)C`U+J#WWXcPjo;Q|MNeSWLvYLw)s1Ix^q zk*YIDq}qP{<;~Y|0}JsS5BtscTJ*0XZ5NwP6_O|>;*I|tUL7nFs{PQ_HnYtLM<7-Y z$x)itFN@7>_qw}fvNj7X)1myiB#Q5{(!CsDz^lKt%QXI@gc!xWy2dGUH!DbyyUL85 z3SIv2b}eiry@{RsIe;5mj6Xd)^zaZ%aT+a5_rf>N;d7~0?+|%)`?ehZyD%Cm^}huc zDY|9?8OA=JW6#W^{|Ng_>EKFN8$3QgH_l#`kD{Cl4DbDtP^({@NSvBRRF!dUH$c+M zpw~7_sNzFZ>;5g~Xour*2mgB&;menRUaZq!vG6n0HSzU{PIWC4Vn$KFDHwyOsq{yg zT(gwyySuwlk!~cVyCkH$yF;X;q(P-Sm6mR#K}sa0JKwd> z|9!^bi^Fq&N#iT{d9R$6eebtb%@^I7HFt`~ev7q%(I7+PQRZrZ4uMn~ z|L6hNMR6)vqLuS}p|@1K2ckhJ_5eDQ1E?C%wgI0s6zWWD5Q@Lr$1iAmz88=Z`Qu-E zMixTbjSwjvE+!20?tw}UgL9{}uHWvKUJdkMUfu_Y(7nbN&`SwkX6ebE)0}S5)eddt z=gBe=S8Iv6?clQGQ)!%KN=z8V%XYCllZbvjLtDAZR(252^QQY920D9#0CR$@Erz0f z4p%`cx1#q*D1yc|zGJQ}qjh(e=$BXRUj;lc3U}J(rx+%u;A)Jg=fv151(T8~+B&Lg zt!SI@!;c-#uTOXS)L7t#QR%g*Bb+g_H%Q)C7^85^KB`O^rOyvoWCX0q@4xQGwRQPd z|E$knOrCyZ4Y8*-gBQ!ww=^>|{n%wh7cXIsQ9Q+*fE0d__#3^DE|@fq3%&O)#VPD` zi_GXC&Mua{0rlrKN~3U;R+vEO87%P{(TXUYKeOR?kJe1HG9SYE zne!NOR)S_0n@yHtmNc{SZ(6FZIwtL?vr7cjXo(jcRD|4f+j}*HC9~N5U6K-daH9~X z%(}@!)OI`d2tone;3|aIYd+&TU0KvC$i(=ws&gEX-8~^#b-gNs;<_1hn|Z zQ7j)y__2&r;<~B+PyBkb!i_9v=CXNwI|9VTMmd@}Q-3&`yr`YD8v&rR0f{gRx!by{uEbK;;zgKo6NA3(-4egT!yNc^(*xh8dq@mE$ z1J~)ORbPtjB*JF4y@Aiju#rd2o=+{Fe*N6ci&j)L?`O^4{u{3!@Xw|ntE70VS&u|viinnaV+L%z zoOWB|^=xdM5L058d?p%{CxMWdcxw*Z>qA&ct6J3sYdLp=|0{%z{P7BBJ!NU!Z)iMmfl(SVTwnY-y zHw885dyHTJm`h2F8|auFSUTdY>BKrbG{1O{rn?p_zn{*rk@8tW?Rn)pWhK`9fmo8Y zffPhsDkRe85rxC}+9*Bd8g6#xE}~vy#h(Sh3~K3l4ztxwrnP zkc6oXt*CL7Buy&k=4ohR`Cc?n7;hHA$kDt@$44xIVw*F%lA9h|O`6IA9TW}!arY@G z3fPsP462~}y!EP?RiI^_^LLkJ?}t36+?*&E}EXLP(++@VP%}J zhRs2bRvA9$eoXLOV)L{%3?Z`~MCssdL8`c-JPvP{w#!)&A_HRNfjwvkgIOr(3d{;v zdOp5esMKsgVt1D5I0AW|v9ot0+@-RpuxuFd+#VUx(aHWHm=Jh6W`24*ZHyj6yb0yk zwy6ROiIWY2SqDtCrH&Hf!G4@rpNF}4dghh1o+#1--l6clQ7_mO>}k1Hmo>9Xis8R? zSd24J@TYv=rTC6#vIL1`FycbbwVbpN4?$mQf@04Fh*b@}GqBl(U5FcZ0R3i3}($7f}V{v`cdTewR=- zq$W*1Yh8QMv5c#)`P7ztmPn78UQ~%#5qXs^nBx6zt*1*Kc1B@xZ87*Gl2uvF3*X3> z6Q_Ti6Zb4*e}7R@=HlX+nBbWroynEW)HVEf2`ot%x2ufPQ;>HQ35D#=r$=y)pH!)X zR%D)U-4;Ii$wx`;hA54@t2jsex>X)Z@{{iMRYH|!fMG%kc0Onz#UhzA8=JOBz-#lL z{{D76SvVF==H?bmi3TGd0H73*AzuoIP6vpmq39{b5OEoY=Rly$5a_uBbUWbc7GV5h zS7@b-9>7&14cheoi5p;kZT@uYU(+{;dkKt2ffz<|mw%Qv-}0Y-4^%mJQgZM|ZhUR+ zy4BQY^-3_I2K$oU(ok*eoE&&npiY7RfQq0tWsZe|PH~slo+81YEY3oZZz*nM8l#R= zJ9FW1Tb^Zpgp~X7T)~CEc=0`9p^bVfyK8)>>c)}Mq($iRT3T}H(8&Tq=z#KRkN4NQ z5YMrfB4@Aw*(fMp?Am4zyL$**dp(yEvc>l@tz+E|BW!jV@E}qSe(Z zeotjE1*2nk7g)QNnO0hvh*MOJvSXEw~;=|2X$55_Qvnhf;V@X z1cKLvZM$?Q@a^D>3M{$?08c3{E~8!N^~1W##>T5A^1u>3u59g*cgxcfob zX7K9$doP}aC8Vo)m^9OT>kQ@os`x3$;$Oz(DF`uourU^^60AKAxWNXC?BZU+HEku*v1-H>nJtMmC8I#<0I2W zyz6w)|M`igqiGMz#ya>I?lGTtkX5q4Caw^wwL0y5zVC!GAz}W1EK48M0RZ}i2%muw zx(Bhmdw&x&YFHB6VAJ_<*moa=OY${zhE@^gEJDT2< zmtZ`lP-3(?7hrm`g*)e|pcsu7#EGQV>{_GLNlTz~>0{Un6niqZ(q}!-hGGYMX8KhM zaC*Ufk6fkw`W0=jj(l0#$HDK_!WZ+i$90Sm)uJF+WnS=%R5X-18x+p~|Al+vcp(=X{t9MgQ|z4qdN0JbV5R zUn(&c(Z#3sr($|jM*3IT@?E7!n%f9x8oKfY8|P#emKdh|ktK1SrNj6}GZOlw@8bm+ z>v=eE&4d6{ zelSb{3~OlQ6&RQ#xWp7N1!ot5yAZmkfzHpt{N`cp`H>2`4+UIvu>Pr;6_Rt@?&-_v~pe_SY_WS-YPMER}TkP`)ve}&Y5Wp-yNVnN} z>KTYp__TS#Tc(=3Lku2x@Z3O=v4CQ=4k*k({0)uK0I*`{)Y5~iZ@^-nzJ))5ME#|l zdF+TSpaRuz-I9MIF~9`wKRc93O8wpV`!C;KKdpvG(O*qIc^w2h#boiPid%^=kKihP z!;X9dnTl<$CWMc=g1BBrP^&cwbvDbie*Yk2|BA1d1KHv7Z{G&|#*5J9jHsBbQxVGq z2TR?UTRnw*^KZVLO$6&r^cm~SeYBj{j#iw%pBoYKmXnt4ov3`IC`Ud&O zXO-v#3{xkJUH9NoxEYezX%SM~QqjFj8b2f5p{;u*`dS&+2m~ju0_eBKZ(k`j4s#{| zO&9_Hayb95|Iis^Kh*tx0yu6cR}aeR16Brju>RV;zBfDWC_moz&9~L&v4InT=nnzv z_|VQVy%Z!Q9^ZiAAN6zwFtdmS%t)4aYn_EhjdSxFF-L_5jOI$rOG(=&|_kGS> z?5GP&*ZuiKopE9X(J+kczMhLrm$T6$>mI|Jl+WIG!eCe_KSzy5! zt3H9o_mPr&A67$hhmYJMQNh^Mk=?R0&YxeLR~O!wZg-T0$L1aG+(2QJOv+Xb!x|jc zu{{L!@1FH`7b_eb#Rb@={x2h~8^;H%Isv_=S_J=GL@{0{$_2;?%ojUBhXLMVsC^Xr zdV(t%xO&1NQeFi^mbl546y)U4UwfVjKdYdl1C_g32pSf?P!Ir?k&%&sfmGukg%1g- z8r}1uY*XeALIHv7lJE&65hbs{_7qz3UUxoIK<8QDJ_4p7=w$%DcN9?db~_6j27j1Z zI9)+=>3QWU{$;00!ptX6;8N2dW6ImHZw}Sh1Rrs@4*dxUOlBpcxs6}OtV&H<1R{Be2SRaY}K7*!MAnG5M<-N?I35JvWjekb&@!tbXCI? zL%#ln$%Lh)M<*p{F+)kgm_kyTVp~b!Fh6&vctE;!BY*`%8S!(rL{G`hns7^S=znjyf<*Hav z1j$vu)cMKk(ZQkK{?+O0TH{=|zD#CI`|44s%swEU(W5^NU2CWEXKgS>UWrZ%I zTi|yj>(W4by*hT?=&*^^&D@`*FnjiB6w@rtUGb_S4#vu~*Q*RM`B%S|*qjG@M6g~m zdsV`bh5u`>mGH0WNCAZJhn(x0?mkzsM2zepc5PGRjIV`0V8y2WO+=Rf@2D$fKbD?b_#J*l_J9v$Is&v_~)+tX6h_qlau;o2JM&KJ|e8Rc;U zd++&0_>UIxYyZFbXnu51P0o`Y+UgXfZRA z@i@OqnDgnwts8W33{~Dkg>C?L4+Sa+lmA}y1#=GQZv&lvXJ;oiVe&rWug3nIAqF$1 z{sZ`~yF1^(3Ft_c`c{_%`H!>;Nua_D6>b7dGC%b6FkNneexNws1iF5Zq9Lb_4}=ol zXMa$xuCBoUqlS;jn4(a$-jnHMEZCUsYSa)hvF(=8$>}EMRL9+yRHD248O(7$41o2Z zZI-z(dExv4qCL^RP5$$ zU!Or5PYNYbrDzAE==;6)Qoa-_8(#9c6%x%zXSlaVC}DNKGTtx(PrKC99t_yi@8*6s z7yN7{)ncg+f>8#Wnv)!y;BAp2aIKDTIfkTRe;0~mj~OCav3$Jb1tXg3gmnDEQ52@Y z&grjI#t8wJFv>RB!CfRA=F!ZX*bKP#<*K9NE^?aty&%*N6NJSR|dt#ZZu5erHkR@p=$#Yi3uvxPMHV2&r=FNqdd7*+Fec zwuVG?zeJ<*Emw5Np#l{iVNYIhGZ$IJQ zR4N)`uLlsL(bo3oRkOHWbb^9H9iN7l@V0*CI>$~kdX0WzjbDhe-PGCOPE+{C`fFB$ zfLp0MqmP9m#Oz}!s%;r{c!VJ0x@D4dEn1DIMal`C{&mqOdJ>XyxhM(d zj9uSF64vzG|6H(*p&3gaQFL;dPj6X%$J3{L^vV3;c*MM>!ru;cTg3Ten&b7Mwfs=C z!s&uw)RCcs#nZ}ZN)H!BoqI@v4!>8WX=mL2i?VMxNg`46*tb~Td2ORq6XI@u^1NDkJ=DB)CgSHd_2cZIYl`8B$WZw%D2^2 zAX1ek4e7Hwkxo!RjJrMFP#lG+1IE@Ex+EmW9S<@kWgcSx?W5tCOR9-^kJj+i|7ihy zeIi{lE#O%qAI1I>ax294p?*4QaZj(-5`kTVh>J%;42IdbI>I|@6vWpP)4}ZsF1#oW zR(dXj^u?AKo9FR5bg-2MPyX|5-gC@{f<6h*5H=^LdE6rPN5?{dru?4}b&j}s zK|Qsz|9Cl?S1cvJ`=Bo(dO#N3g`oITWJxUvYx{UCfga_{j!qecY^4_oCYjJB&++dc zy-;NR?L-hXE-fbN?`prRl%yHxt2#KrNMC>>fXt;_!ocKIq??0JC5hVJ0a&Bp=7B`9 z-w_w{2+!^w`$G5cSb#;BXxUavAB@=6&%;K1$EQSA>n<@VB3E6ueTgd*oC|e_A!VN~e`BT6gt+BP> zuYPd`6ve-W9y9e%v;5oLb#7e0sh&wMbo;$Y=;j?mO6@N~jaIbhP!Ko}vtc!s4_S*?ofRBXh*RN(Xc~I#VYs@G+X&L+DY$Yq-VAn>BEM zG;C!ckHV))poYAe1AV1bU2S5wGYpNC;0vZf?E0ysQ$1Am2<9JdT$Qr5!Q$u)>t{$% zqMgPtw(mt#mWp{rss=G;6qLXUa)O|mWa#b#=nc+}NN0W|s2ehpSLky}xO-<_e6k|3 zu3v;xjxo6wuz?!tYGu@+#tev3`rDmZsevhzw3Zs$5@2oa12|{E7>EQzXeq5S!RQIz zax&XPz=s3j8kFJTQMiZT(tJUeVg=Np=As@?;eX=E<(_@fYq;J#?|*;SgT*K|M45s= z7Ng4Rn=ljqT%O#B8N^u<%A&O6%x*}2KTZ0@LkXXn;jFH!V$e(x!-BuzN-s(2oi;~f zBHoq$$O}8e#HIK|g_G&I_${F>X}Hjd zeps?7$UPzM+--2}jp=KO{F^Pv>VbVmVCd8p;oY^`T3@J2VCQitfa^$+{7wp0A1b7EglLMAV?txC0e*bc zx_-S+sm9#+4PHmilDyeOt4A68NRo~zK^w;L&qu@HGAadOa2aNdSz}B7>d6pHJ56#= z!8)?|6GIi-A)-4~_w$-Kvnz`F$ZK5?X~#x}lm%u|Oasz@hfwj3H(ZdL((79SYy;N^ zJ72v+*AhD=uTIxkM%t&N0J|%T76GPNH&v|T7D3i4vnDUlK z{F${+yKmC>YIRYwnCaW%#z&pMHn3w@I7Exj>3bFjlzf$XxR&{{JB&QI#M%{HOW30i zUg#mq_|pv0UlbYGP!F*(7uBgheVW7c7{FX!D zCYHB#w~so%W#7d|9u`OywlTRaa1vkZO0o4h$>Hmc1@-tYPT1U@J6Hwx2zXf`#N2{` zkZsoHPznWsZi#MuBXMfD^ zL7aR0CYmdu{L_g#bZ`RG49^4PaQ~JIHtME7zC-m((T{Vair?0WJHb%QxVl+`BHNXu zg7#r5D^{J=C)a{E6Er*G5FCGf@7-0#kR4TDUDO^}s$T=cmgi%_SCP-3N|HD}6&8Cl zneR2+BBK8-?ORD@j)+k4|L%T3x7sDyEAG=I1xG_y8P|N&QG#6<%P&@8fM}30e8UXU z3#C^{VtgZ<^ljPSgWq8g4yWd~QY0H8rnzEjkJf?AznjkdqRQGaHM~R$poR*Ma;mMC zyqPDfpdLY>W$Q?+FODuNmL_mi>yg9YOMs@OL4{4!A~Ux1?k)>2!%7zLLl-xBUcZqfG~Q`c9rJlS*~arLT^1$HIe`BIm} zdn6^a<`fNXr4dGOUvA-C?tIE=tqtzVG^HfUol${<|LLNdOmF&|g?!(XJ2;hBSafY* z(NhO9;#b_i1nRDl%%?`v3iKQxk-^Fl5?Pmx!ilQsJXrc{miwUN|&%Sv8RcH z6KmXEdyH8}g7)Wd8p@Kgy(}wy12o4iRowY0Vxrl2QyD|ujdyeNO^UB%i;vn&T>p4bHZHf) zY_A$Qqi(|FU8<&!#^34eqVDJ=CAP(&&Yw3-0k1YmTE~*5w0q>(+6rFcYFJco#D^r= zZ3NblGask0@k@k!hp$BlPYrgF>~JPgH7#zJW#ZibFyn0B8OWiv!GUbG!OqqyD@I9o z>mK@Wh`zY2c`-gw(2rNaz|y8l9zXcdg8beA&M!cm#qcs`rfyWg@*7S3Ii5nXv>hV8 zA*1!;P?E=sL8Mpch};?^cfGL5j`LRU-VmUo6!%1kMJ7APP^=dfu8s%AUzOYsdO2^^ z)5^j#Nr&=|u_d^{1BWn0e=qJ3+KLNa_*@8!*mM z6_!i2S4oAG?-ai$a(8FJ!rV>mK0Gz<)`}3mmTfDqj)=#6dBpy-!0bqeg7p%OZ@m!b z?Yyw;Z(X#{J79W;0MKl%{_($W8A@kR?&g4e*|eb+_V7^L(E;vJBAQYf^N$uGmT8y3 zui=3=$JfYetler>yVt(*miys^ET7fX>{El2M$F7Ug!KHrA(7x_j<+N?E4}!*wwpC! z7TU5+-9(Hezf6&b^NHZ~dF?5N|0v{48nebH7dh_5>}TRX1HTemoGi*Qu0^67Z*y81 z*U%hSi>~>(bTXHD-XC!G@Hpw{rDK*6Ph!38cX5QhTdJuJ61tmlJy~Fx&R05Uv`QVU z;OCAA;ebVq&??z2r6^WFR@gkvLvf>3lIGwPwOwH+A8&n&C+AG_9ERF~aKwj#CNbG~Q zXy{T-LvMTJsr?9i_qwfnM2#cn-pS&tI((t7kirQwOPPQ&CVg5Q+-X)_duo3Q)Z?3S z@$*F=8lWy^i)c_&luRS!W~f`CxYOI?cP1w4@=rYQLe2<$2tS+uz}#=q)p8QOctqMp zMwZdBAlbAiSDQ=y4`$|9FBXkygtWfhS6uN8@=ZLNkK!=DNj2nQFZ>qnjzknIKiV-( z?t(Jj{Vz3(n24*;>0+VJPLe9Y0aMpbC}gW~cLXQs3HKMm{Kl`oLFX?s8L+haKf9X~ zXtpF_JDY|`E}BH}Dal-{iFL!@OyAHe;JUtsqWwTy0(>Ii(5~XiQ%TR~;-FU}+dPdU zadGt7j2Usv;!`>QD=30{6y`0gNvS_aj7LkL{*=UJ)C|yKu7QlT(*4MrJPNk{@BtN= zGbXX2eU(b7GJAzwshlrtzsyKf&&z$;3Wx9Nh7}g6;10N{^VsodIY#aCo|(YlxMq6A ze){8`Rlz(20TH2ta_gd3E+;29 z|3x+!L>O0p09_XPb<;;j2iO<(;+;_qGq9=d+p=oN7U-sxc;cIfDr{-vWw02` z+x(5+bNhAWZBHR>m)iLDsv*`p6E~!RrGjGv_g{6$RtEwH8xLypP~|8v5TpzhpZz$;+{{7V3?)2%Q8kqKEEctd0CR@Zf+g#`NS+{Y zfu+#!;m_}6pTI!ON&zT20}OM4+XmE|R(j4YPbnSj0rlx&K_;*gJ)}5K!_z1bSu)5T z?i7##7Aw*e^hWWRASHgxFX>#TbC&v){0} zej49rKO;QcKgAkpNmqG*Po8R1I3!#{+>U|2n^=PgQKh%bMo)yD4OTvE?-p?yzyXBg zO3QX$DaAK44wZEVmwhO%#yXrgu~$Loi9%aN?!C!ip0g7&yYQj^%=I=;xGc3%kb9=@ zB-y#9u`y!HV#goqss~<(LTQ&4qIt(4+e%R!BHC<(ymoA&{T4}KLfq)1qJQ7xiQjx_ z#Pnfe&N;rjyQ@5pdQiAlRd~s=#2xV=WHao6X9KPi-(^hJd7nqrKaY_`OG8zJGvfS; z@5(GW{uSZoxOZ*u+^4LO5#q7ejZ|xlt?-uzw*guUF7Q|cn9gE1f5Q8Tbt}!3cAS#% zNw>X2_l5|dlBLrp7*HY++X9qG%`0*o6PPeaSYCrl_=*)`q7`!AFU?z=djA}vQIh~f}(EyDQUec zL7W%pnq0}*N2X4hUXiad=e`QxJ6CNZ6OhwP;wCj7`ATgC$DpZ8XUcEZR_qv=_q&%G=0AQIaFA<1 zYdrO!?nHgg6+7Q?Ch229Z%T4T{xfQ@j#{lb-vmRSG-fpF&%^m~bf;?r|2gl+v7ICN zP@R}94C&->`rh8+!eUgy!sGL^C6!>u=iH;JWUaG8reOXxDcS-{R2T`mn72kuk~A^l z3V!EzaZ$*9pRnYQkErt)A)Cajs#H^Ujo1OAG6xs?;)bc`WHjo+nsH&Q5ILE!irH-R}W#hdGIr6M(K?u7EV$lc?Akzo&4 z%T54a-Ukx#a&vQ?Jw4OEjJw092Jw$zsVT#ooZCqI`rFq&RasfA)RWdz+ANNmUNG!RY$-;siT* z%;ytN#`Y;M4cv|Gbg>BTEuDuA&kUQ>c(1zydNEoUfBQDLaD(Y&EGtl`}o zzaPJ2uca4r2yb_edsRCJNw)*9^fU8+>%+L-14bJtt-%PI@cEZy{o*KxaFzJ_RnQuo z_cp*nPXExz+MU&lMl^_e~W<6NuWVS*?riHrh8C zEYwNd-##3sTI+jnb1#Hjev1&uw@nXCVhx5Po0qq-_tl}|4;Ycw25M@pcyG_)xJE^ZMbMiUQtG=u< zH^!L9ymwncPjoX;vgKN@`tfg$=#*jXwf~U(BSHa zRpv79AC8z0@k(zOblyvW@YvGATmM9ANc>sh)L=c{;#FH(j>!z#x?jGCQ_OJ{ME|4h zw_$gmua66afe~-WCR0jnkA(KJUm7j&hh`(|+}XxHOxT-O0$Y{DqdVdqTfyMwss7gk zF)eu_hePqJ*JCT2l?-EIF_zl!BQKWQsL}trRl(2(^Rs!2r`6JfbMpg^jfzA=rLmo1 zpcZWxiBz^V3n3BQ%pQK+h+q8iG4@kn!p}$e9+}wa2pi0{U91n2PtI@`zVeBr5I^z} zLD-_MP}|_lB_?Wwf;tOJ_GD2Nnqn9zUFY`VNN2hFYEGN``kv4mVW4Ub@wcA-b`_7q@v`eJ9QaEE#tGCC3@Gd1ivVE3n~OcXH>#>`pH%vFjb(1s z*4xqVzr8uInT>-R`d9z{PUrr)!_K^=#$PF!9?UYpid8-tp?AYsM)+g`b*<;VXbT$^ zzls18v-W18pkeFP`knex20V@QH^Pgb&+VlaD}M1^*A(23I<&g?p{j>v2oWmRclKjZ z2h&B3zD@X+q)saFP1#{D`JNTSS|WxTkY-^bZWC>T6*OT+jjifvZRwXF_ZWWVTKVDm z0-x5&P3wux#gN;-!?t$xpKT&pipsDGWc`Cwyq31$hIu8%TP6L25*tA+48p%I-$GJO zosC*L`fO-ZOdAo67cCqzjhe$3okVLBsjGn~%d+`?85OBZA!(HFDSV&(AW3izZ^U|$ zUFEiA|5I8$NlCu`PH3zV{2T|$LPb-b*aokem5alZucz(#hg|Z4QHSjx=DGezjsHtJ zfQkaow$Z?8T~)1;Ws+^*S+RL$&=p`ZwRY89j^4njdGGC|*WVe?ow(=P+6QnQuB`|b z=wSN+T;K_^bVSrEffR)dI|43{$FYd8IvKso#p_5Z0U}4Z`J8`_w5D&dolAm*1eR4F z`U>)GR2&bC8=USgviyElTOLZWBAN=|B_^XPDyS*e=(1yf$U8Z?iN2Y(G%Bbkfza$q zQ@Gz7kz=S%?p}vTP}vBMVOcN|6K`)Z6c2JBX|N!S-aH_^9HFj@a&gIg%6ZeeWnn~7 z@yA2_Yx}r=`KN>Hr(8Hmb^o!d^a-ggjIs)37!YmZ3=k1pTNZsT@xUSjvd%#e3oW`7 zf&|QZripOsliq88v}Pm8=rm>PgIo)m z-Cgv`HLycX3CCmg->>i2@V*ZoYKC{7QB&Zqgfei>W$vA8*{`)aBYej{`z-~6yOw_z zkkd!Sie`qyB7}QLINnRoiq#3A*8EM=(lIG)6ikm`7nHz*lO644DS^Z%QWVT3DTZ(+ zJij(zl$WEUCz_8wJ`TCZ5tXDZ#sFmgnj+4nAC!bl@HKCc%hqIxBh_G$OVI0Ks4w)$ z){;xBLlDBXl9W(t@h87YL*Rxyz=@m*^E&K}K`D1`?vx|I`S{N*hqVQ!^T* zJdVEg|E_~08dpd?uQJC|a4NfPA_^p80tFe=lmd$1md?%r5QUXK!eCub z;``&bVnD1^?bS_u+riX^{(@kL37yU7zGom_#y{$q<*@#*?g?KHt6>fj3IpWi;95)70M8Ie0s~Qyw6V0{ zl!BhCe3u+)Vlwy+pQ^)#>00u#>LHa=3Z=8oT7U0kkFFM?kpnJ%gVM>|#H+W7 zyz?;E_YP`lg6?=Wa9u+)6AeQ(y@#%+aDgTD%I_0kwxlhm3DudgRm2IXBVep~k{9uZ zRw3t$ z%U2`a<7(gp5^d{-J04L%JTw)1%rpU{_IWVBgUcpAm@E#xrFA#fr1q_H^i$0Yjp zEmWJ|K`9N`-|o^rL_+3FJA;7Jbb7 zl3wrC$&0|zpVv9x zOOCQuwAR1jV;bg79#d3h4i9{o?4-R>PMf=1_xBe1Sw&ELFz%r)ZnXI0o}OJMor=hj zJ?6Rfe))OXV`p&(wiyHV`rZo5SV-yPbN$5iaVYIrqbF^wmqQ{2JnNW*;udlJBKfIh z=#XXX7v<$SJaK%PU0EY{-&grFa7!GMo@h3LIM#Zvg)Q>qw`<{i%HA%FGT~~l3~EuU z$W*;W6NcZ?3v>~}*0sk&Qwug?M{!iEr?9AWe=?SQf2j52onsqQvk3e{zy;~fVCl}R zAFoJtT8P~;Upwm+HM*3vdN?q2Jsg-kgX(?&SX54&p0AvMAn@4n6?0f6R*fMi&HxY0 zABpek_-_;A99~pzhN+o!!Ox50I3U;Kpd$3x zO2A*m%tJ<4IQ(ZPW)yWRe73E%F-os87}d*;@9L>s@cP7=PLmEom(SO1dKlBJI- z_nOkea}*Ua5${&kvR{t;80UJgndVZaEPQgBE_Qbu6EGFlHc^n77dJk~F%%nl`X0qO z`;zJY^^h_uZ#gnG%2-|sZ$!8vgtfaQhGTaO-!dyS;jgJJoUcI7);8j=JfZwJ#KaGx z)coSrYtZtTq^Y0?5@&`7!mRi5!?@9FOwn_A`bN2DHLTXT{!a^Ft8Eivb4605^8}?c zH2D}X;R|Q>#1&C;36TAj%DYA9leS&|3zzEhjEpP z=!YM=D@DCFh5Nzz335-s7X>;?s9|%%Ib(nnW}?dGVEuTg&WBb1leYX4Yod}X-Avi&?2q(RAwy>x2kfSyx&x@kle^sFUPu5zqd42^)m4~V#3X9cIqy@>_JVJu0 z)3W1z!cXNhLhW-fAW>=q1T{eeWPn4I8+@6a8T#hKLqPv{Q4lV@Ta2oCf6vr>_k$SnXWIYkK1YZ55cNIQ_SzWyk&OQpz|fxA;%y?41StF z5hPQA*K-R|nJ1OR;k2~sS4T!MVJ4}iUpca`mbnD4mKHpz(hJ57+a=Yf+zzi;g4Qj~B99@D#Je9>#8B(oR)T#EbBereSgM)92oCbJARKfV=&UWS%gmtDdJRZtaY z{BKe-mOK^JM}I#U1NKQ6x%ycIC(p?kQAi{Xwv0S72f{zXO*1?*V)I2*aY#fB;s_*P z0CuFs&0>n$bl8$9G*wMfTv19YnqagC2IBo&LNeC^Ek5o;Ek5_ipbf^hrA-~3?M`$E z^;^;kkdN@Un?4^M|<;=0#clYVcnlH!xTX}5(I(ovFzlSwUlc64)u}X!K zt(<>Q?(odjQ|hK8&HT`7HxuTi1J_=zYQKoY)oz_~?0?vks$ zm0fK*x+8BkIePXG7|3SDFvO^Q_TQeRtzpajVX7kpPlFK83jz(sVf!VbgXiEBnd6W0 zPWVYB4A(4p^62p4B(SMb4WdOUlHQP2Iuh9k1ZqdhX&hr+3JX-TlRR;E}f@ZB*K2 zR9_Y0M0LE{^x9*0iE-{%;o1}B6-lLN6|u}ie`)vZfqAN-@3dpHP6aY}k)bpv5L*pK z5bdku%jRs5PQt_be_1XQ?%6+}9BeS#sm2k9NPzi$`zj|nQ_veO5P9|*P@_O2bu;k# z$&9x^L_2pLJ9K4K3iR|K&JXlf;JgEhJ)7obpCBQJ0nRF~W#*Ormf!!fg*e<`^`m%& zcH6vZ$o;XB)jm?0tv2X*G{1lEq zOzAS8=*6zPAki?S2mO|OwIVf2Xu}(6%_mJ9ZsXZ*Ba36n1S5(u7zkf6D?RN*jSfFb zh8~Vhzfyr^t3r~_qC-lJ8Zyiai-P!BF`7OC7gwLnxj&yc;HOSaJ0$a7#uvbuRQW<2IXyQ)$^^s()6x##OYHLf%Sx;5_^=lv`Mg zz+xe@n18+6(F}SuAoK)>-MBrap|SDbT4V|A@A(Q&5ruqyFDZ8rmxXt303cBX)nXiN znFs;kVq=gfsi<6BTta}6z@nryIAB05wMyn?>`f|X5PJaJ^7W+`GV2UgJShJcaO&u-?WTlGB^oAfJxUD-lnkZ-A(XM z|Bzq}P!D~zsw)k5*pltjN3h}V($|ZZAcm_D7L;N`3{`H=yOh%d+8^mG$#=DiIT|O_ zFcYD82{JI5uW=$iGd{<2;)MnjjJ?Ay4}a~~=_F8Qf4QU`y%2fIW*~I*yHek){d;f3 z$VuDRX-daU-`#eH7Qb;Xfp2lFXdmxaMI$RQV{;u|+c)Y%T`mB3jQ$JdoiW~P64ivng^^wjhg`!XP_H1)K3A|e)MRu?y|J0%a zA_~wOLG|Oew?2*}u=_0If~$0%SYS#6W;H9Tt7f&j)DSpyBzR!r4>xPU^=e?*uOOh} zk?YGHyl*e?eNZBT(8_^+rKwhnGh-=l9{M#Paj!QACR%fvz}l5B6EC+`10QW`8(%y&7Ffu zl*8YSUCMc1XE(Pnkda9Nt>h9D6HiXJv6N&O=Cs@YRUM$JsvuHRajH6x~7DUf-M3@L{}3w&1@9^{3F{Bc{EVl)m2Rv|YQW zWTvh2%&nMP|_dYcL(#&1WF*Z1ZeFyR@> zGLFMSL{%l?ENSf{6>eM$i0w!4*#$jM4nf1pw=(wLL}>kYuS}QCp7;ooJSdY42}4cm zZ-^5f_g@sJf^$1=8_r#9z+E(gGJ!jL%z1R#MpmUr{lF9U4NN8McrZ1BeS~;D3TY$^ z4Obo9_Qgv-og?0)$I6CCd^lbenlv*w;T^WLB$gtzJ}r7Antonb4B(*qI5$y?ZuQpH z;k84%zf#78r9P~SrcA#ddErr}-k&+^=QR%}t<}ykA7p%cJrmuWIp&U78)K5stM25n z?6hycvQgh;nVYy+SYQEwW-wrZ0%;p`&I$62L8cAc(rvReHo~MnoyyF2XJQ%zFv5Zs z+F;GFXQh6BD5jK*0H%cCC!Pmt5lp9OvUj>{@iqNG&KZ8oayV0*5@8;07F zu#`uz?T7ss=Y21DAT`x~ouD!>_9`cUD4z8?oK|I5u-jc`qMG(Z;p&GezP>GY@)&`# zdQB=xh`2^Db|m%8LKGZzY5_}~htS(D_r%*-is^EvmX7bez^X}?W+P+Np!#N#0v#KZ zHkfZPI9MRND<8udi6Vm93^H4@7#$x&`wK*{mcGQwAKynq;4LEi7Oae5nFIGNoEByW zoqnAf78%akP9Q~@nwC_W4Px^(*1!0uwf0+2zZ}KgY3{p8Mnd~fwrmzPb3gSDN8~z{ zMKnIql;iTR@O_ESG@QQhqp~iuYtb(5d%=2m8cT+xCP(EldozPiwXDsBDm7?EWplJ5Yb>7)Fwa!c z{7+O2-gCf;m!ig5p)(NZ7yUn)zB;JNF4~*sP!iH04bsvr4FZDF-Q6iA-JQ}QjdXW| z2uR0EcXzkcx4HLzGdSbu@W(m(dDdQQ{fbxeE2ZoyDcJgun>t~LVmfP@H)`%wor*WF zVp(zdxe!6bd=+EKSUTB9IvQ-{f)dl^ykN>Xjc07*^3|E~?Twho0tL+2 zYwD;xlBni0#)osW^)wTg+t=6!I$BFemGp&NeYB2R3J;H}<>ZZd?N<$OES+bK;})~N z#&Ed@uaex(+li2RLU#`L)LCd9jBh#5s+&%~L}K{cHl{zEFJ{k2L_{oAcFrE=Oqlw^`$2P^o1Tp06b_Mzd*bT*Y! z-j{?)9Fc*M|F(Xc-5tHSJSE%@**V%bpYT8Pm(HYhHqK~ZFNrlsK`|Ouc^c%Lw7m;I z>-=gnQ+Ucm$N&$OAZ$GPNMYuWW|YG&=Rt`g2*E3~h0>$lqe38%!~Bq(qAg^|M?y&w zC0wY)Av_VSiQhmet^ntSEn*+70adra6q*N#s)C;1$W9K^R#JX^R9GHfQr0?1XS>2zn~St#yMh_E zy>$GL9mS4XSnaqu*Xzj2D?+fcw7eoWqO!i<%at|j276+L&umh#KF7qLULWR%5Gaaz z-uLj}^}4$p6FR>>R3m=(4iEs+p6-K1p`pMr0AT3#B*5qeUKdc~K#2qrL3d;$J$%@o z@Hte{uf^WO0|g5FE9m_?8wZ~1AkMSVoi3eVvjLhKU`DsK&3ss{1Q|w`!sU+SwF;=LaF?) z)!M86c*k|byvsY_(<7HnIDOPh%R^adSo>-e_u1k#q4;gA=l&9!?RqQEUC$(Qx|>qp zV$i8=)ti%?t=j@AS~?0x6?L;GH@lo1nTOV6zKUI^c0#Av)rj~T^UbNR1^(DD#&P<5 z>EzALzrXSQ-W$RJi!V2dE?zP0`U%@ld*YZqrf=y{MIaLVmSpCQ-RWrK;8wHum8bp3 z_7F8QHXnLqCIc%YO;(F9Y(abP`N0Gg8stc5Y4lMdh$0=y$1hQki!6T>J?1M;p6=BN zQTaBitHzc5V2aV`r}M#CN${Up!XdK)%{XW|(sQHrm1jBscFx@NUwgTxp0rd5E;d-; zHOXL$LICbNIyQDr`f^U{&bj(p%3efvA47aE0%>TwBHt{d-1zwPTW4vaj@bPJ4 zHY}5ta@0FxlzHrfGLHdJ{Rc2(@m8J@8kN=e8CG|JAHYNa8>v7H@PBuA=afJqe8Dy6 zz3B3viMi`^+pN1}oT1$m?+3!oTgObs!><#q7uFITrqk{7UTe&$A~$HRtoQLw6FJ76 zP72gvxYy<8l*fDs>5La_D1JUr(-wJ^BJ&7~ON~Qr^Z!{xsRjzE5$&@-NqAS8BvsdR z<$<-}HsXG(a>DZIKmC6~D(5&ina-RQ&a>|Cj>Oa%*>LP0M)7Vfhn{TTD{r0fa}2>r z3_qh$L7~+p_2uG3Di!^Kxw3_E@QIn9W}?|-W%)^&fMrk_;N2KQW(ll$$ihTpB}3Ci zd`)YS7C28}q$4OG&Bf}d!gQPe#H4T13DCT0vler5y7Lh@U=sq9`r1Q{5rTbL&HFHd z$=xWSY~X}r-%U2##~!2C(?ir{B-n=h#-NfkvLl{vQny@ms?$umNmP<M_`8lL~rvg`kr<+PLbdWyr0Mc0N6T`|TAiX>Nvw0`C&&<-hS|;*e)3rp@w$ z=1T#4n@*mTgoGtk7Hp`%ep@xX@IIXU_NaO_`KhTVo2hW80=}FRT!r?455*Jxiu%5$ z{QPX3fz=C+HTK-ja*1gAw3dMson`iPb=WivWE#*p4-$~W`Ph0FQsuQr5 zoG0w<8er)H-Kr=2mIL13K)XV>iVDZc%l~0iK|+@}y*f{t!JhrpI&eE-D=&m#pyT|B z+pQjX5$i|?-2jSr{hZVBFlCJ*LNqR{_25!&QOkty7Cx4OVkd3)=|P9d3iz1b)DL=U zB0Mb9Jgo*JU|Vz!1^#n((h5q}q@5MoA*e6sPGup3e(NI~S?^1G*_j}mR+xQBKtf6q zr@gZNeDWru^*tzD#37jeE=0?llt9pzZLk!CZ?;D8d&Qh1oBfAR+|4vHLZKYaWj@DR zWU=Cyt(yo&%8_^i^qZ$-O(Jf}InH`MZ%x?1_{aA2l>3Mdr_)unrQrs%$reueI@5@g z`nvfFqt6jK43!dbSUCe&>eINL=qhIab!>2Je?Q0)5ge)Ux-FMJ{I_A0)#LA>`_E|> zaAgc$4OxK1_m%Af#%bU}3_R9$p$A{lpEl9P$LEA7X^nJG>Rn(h^t#fuwPnc~dqsHm zzkx$VUe?gj9ewUpXLS7eKRf*d_MK;eqn6C#^vMk zNwZ2YTY=8}_XhtkpYo@+BhM=zyv$5zCL+6TJX?=14_8BiLn;^cttm@kj_-$_Dr9Or ztnS1IkJZx(o9+0>;Y3vDenpDFHBhEi^4^;mSr=GyI~D z=BwHY&$Z_Z58bB>9V?}UJwns1^j4jV1OEpE23Z113aPxTKy;g_R$>%NsaeA+%Jilc zlSnclG?Kjjn~rf^1|k_DhNeFx%B0~7)t_*CeeVbV|1MK3%-j=Lgc;M0zP^Q5Dm?33sLEln?qAgcF!~=fw zmKOq(Z_$!#ZXc9HVbGH+vp3(+&K$m?LKW!Rs`EN>0CV2gO&c)XotVG`JOwpH@0P0+ zr_F-n*82t5av~2sI*Nkf)u*#FC>c700BA6W0{v4?{pgrCl)I;&%GJE!e973*HdbV{ zz*F%I%}9|=BSTxu;n-;DacPjp%AAXq37ZkB1Mw!% zl-`snbxk*~^wirn&1#X*MFYUQ%`wKqOD9cXI+%~c^j$+v$j!1#2V;4S5e%6g`cg-) zZbqTSvmwJ%xe?h4tcw^-sjWDaDukV!>h#A4Vs&eJmZL!_yd389+TM9&cvTEXpQGIh zq!FB-ga6dI-Uv4E+TWhHe+*0-GI>t@_$`9U0HyU!_?um4)4>|f+KP7P{u<<@xdl=g zK-jUI?eq|S=n;vN$SpZuG#An}kDg`RY(wMn#f3{?Eh>}$8c!n?k4?cTn|GB$wMbYJ z)u;O~cD=Y;-;ehuRJY#$-|_os`zi|ih2M!OsFGA8Z~7DwfPf%pyedZ=T zeqV+cL@eLv86L7@z>K=Nu%JjulZi)4;HsCaf8@)ry?5$wv_{ul?;>eASvO8i&`Dj_ zNqqO*dWa0^zhCY0H)`lj+|W|&Mv8SSd{*NEu3toWNel- zP81ytDGjZE90n88$?0h|fB=KZ!Ho|nK_YZI?v*ADn>?fLjpuHW``^MrlMb3dL$DvT zW$&mmT|MnsjCM~e4Bh0xJMxNZY0XMx6B!2&nX9YoD+Q!)la*(;`A-0bp*huV=;oaS z{XvP-U`1oiwPJnDvctuyNf6j_gXRhx@Cs?`^Gs%z^FkGa%Y3Jx5JbQu4MKiVZ_pp2 z6>Pzlx@G!{qOZ`Lt$`Ko1SM&14y~wul=}woM+~do3^p#_yQvLz^BpP0I&y~`*G?1O ziT_z`>$;>kLz<(xRIMLyJ^x+W>aJ}{z!D)KH&x%`rvsagjA*iI(=vY;?Jyy7Vr7)5 z$WY-e7{@>uBG>1-Y^G{tm)i5#euJ}ed+)4@UlsF1BZ(AEzCd=ZOgT2pHwnvML8=^@ zx|?<+RX>+8Ajbw)b>&4AzpB#d`TIO6nlYifoZ77%nT2<Sia^kbaXsW zIc%lyRAee7Y;kOdZ*m;Tky%(_0x|Njr9yqbD1~ys8$y@k{97i$hXl%x)BoYn^=A)G zFvEq`X^)}yJYXA3^80pMQ4e)e)A-1hP;c_CMT;u5wIZ{lz%_F_J5trU z^;e_28m1?;|KW|fMf}t7&9q66*ZP>+o(dA8L{_Cym*-GP&>_XK&??Szq9W)sUw6dk zS62yU5BIx5FF<$~qUZC(iT9?CtGiP)R9i!(4sXy6{l(6s;^W7U>xZ+Ifaei1LkQqw zJk$imwT#{HuMDwV>2hE_Mi_N=yvY4J!)LubzW07O27W&mhIYQ|Kgw{#56y15eLA$i zqRvJw;(S{c%(n>tV;DCZ2DSlUQ}g@Po(eb-twIfxxO;jIN3=CR6j|#0J1;madjhFC z)A0{=L(zWkK0gRpt!o3p{$#A&&5707^z&WwiYRks?{XK)EKY$r*#QfBC0dr0M(`NU zo3hxvgh4N>WjTEX>LulCt8~Frt?%E$YNm%-`Tz;#8Mci>X>mktt5SVN^#y9F?!#fl zs(5Hk+NTHYWDysF?V8`Yp_p`2>SeX`Z3s)Q3$R^JqbEv@r|B&i)@&;u`dEn&4v-L^ z(;D{J6IQ9xJ_tz69vz2QMwk~d5I6Z(6eyT@)Qn!1RxI|pwnXEO+k})( z+}tL7jPY#IelKXb>}E|tYi-0^^!*Fwu|4PL4WY-%zwS<4m5S`cfATl;6@xshK`s9a zMZ7x60Q4HD#ZfWMQ} z>1?DtYj~#HiYvYOwD$BNz0(5G>ZI_&St~*UQ^_b*tTlvpmzQ$?{WGmm%1{|rI?H?9 z2f`_~PCM>^4Ck*^-z=w`@};V4Ez%o;OsbAkE$h#Klo%|PK*Nh4CE4sTF+Z;{s@J4y zap+}WBo~g&V-hZ1AS+zwys5#Q47{;jHwmL8f#j_h;QPbe>CZ0E|^*WE9Qt)y)l+8BT9w zW!aBaQvV)ftg}m0Bu#6Ai0}KY7FEnOn@yyfd(R=37EK0M_Ew@1Il5ibdpfq=4r&(- zBh`}IdWXZhXHe1jRSy^LXEwK5dgnA83KrFU_JTMyX@w1Hwpt<2^Zus0KT@;)-IVkUYS``CKrN?F3?Pq-;|NN zMx3W=PL+o?cglCZUt(rSOvp!jXStwTLbSnIp~}m-zEEZCwm}FsGjryxcT|2Y^F&c}XG`HT6MndTp*oNvt{@oVlZ1Q8Q^o($~arwv1ylPdWOr$nXm8RLtPNVe^S5-vg!28AH zCM(J6)rYZS(P{Og-1<3*fK6LkqI0{4W30m5-?KKYL@Af;UGHh0bCj+c`I%2#pSwGX zd0*=F>(wgcH(R3TZ{|-2ckD_1&EJ{Z&|g$|n|t5$i>q_z9W`zQ^WqRnaf;Bh&*FX| z&{eQzV2KbC%Uq&=%Al3_=M?g_ME=vtUp5FlrW@JZvxc%OogQ5NX02bd?X^@(>V|8nS&j?b$YZIHDk>_- z0$(Fd?0Eo6)Qqrt&FZ>%h(OgkgT08S=CFOCE<^W!ym=t#Gr->fb2pfrniE$$`kz<+ zv(#TyHe>W^f{F22Y3(ULWHyIK)C+aYQVZ6(FP&W*TgtX|jcIX+48-_RJ}@oY@yW>5 zkF@;+(Ngp1bJ}9C`Z539ya}W{cyO9SYkvhWPa;&_HEk-GN;aG><4VZhbr77gA($*) zKj?~9e->X3n8QOIgv-!b(82Mk`;c-|ETBIhR5G}(ci)!GLid7Wz2F@j$>P}HXy+|N zo%&~kHzGX&Yu_yrm{w1Wz|#DnlcL8sgIfL)xPAr+sC}!?0>vlyHbn%ROfesD%oWWo zAJyH5nve=6;gDNTX##x}e$XdEH~Wf*Vq!qZ?LGD@ps5qxV_1?fzl1tWhTt2$q6wwbJ;rR%F`Kt$Zm1J{6elb>E>P-18qhd2VMi#NIi{&m_^`aWeYu za@xTY+&cNJwmdO88kN)*qD!Qn^q>j4FnyYVu?hOp@vIOMLqFu_F(UXTa#rA*?{&mq zGmK+1!3mfCZnPSDk6A4X>%rlG^z6`mCd1t2F>ZKAUKTeYEXY>*a8iMiq8!*0fSj8;cHp0Vv4XLN?1RntdizRq_3F=Ss}fz_Op? zj)2xDk{>7Rr|x#15pSro1O3(I1gTYQj}ytZ=8FfiriYPEHA2aus;zt#@94hNPX{yM zx)DajE(wCHB{8puB&+x1K?w||&ugzeRG=~=<&exqqCBPnA}P?S>g(;bu8!Nq$ivu~`V)m5|1OfDfI(5vhe;O)20lHP~n$?L;c}e|Fk_NLETFHyfGP}XM z!9G_!JT%vnK{#O#x%pNd!rIaT3({e&xSJ)m!`JXjW@cu`tmjuB)0gMdm!DSKUN2X2 zI2AX^3AQJcTu0 zBGxpKv~Sf)-M!u1Ug3^-Dxh`xKRZ-F3BNQ;rbD;ZTXIdTH^VdWA3~gSV7%a=)0ztL zZImb5vXwBQ=@TPBMd3bJGK2C{f$k2fG++{&7G1(~iBdTuFv_}jRS++F+PydBxb0vo zTs-ZskoD1N3bBpTih61-u?ZpjZzgEwJ2ljD40?(G*c_gh-tJG)ArY>8cU-~;_nSqt z$)O~~>C#AnX~}G|dGtgLT#_-0t~LsNReS@NkJzl;gX**p&I89$4czx-q-xhklt>ayZNIRVHl)7BAP8?4W zM0K>8tMV3;!IB!YR_`uclTD3ao6$qLRJGd9i;)-})@HZ54a>0QDEa*s350o>8^) zjChQoFZ3rPpm+O`yOMg)*8P%ARF@+- z{m#DS_P=9bp2UAdRF1=;OCns+RpOWfH7+Bj^^*N~VJ(Pah&N+l^}ju5kG+g{c|v>Xb1)>r3c$faF;fGXEy7Q=kFcxuNYiu_ zpW`o^vARJF1}$}_OX@S-Cwz)Pe`7^-`0QmtTFL~0N;`y6ji1zrgk<8s`?xVgFZi%K z>h+1r`0*NYN?TLTxtD7f$A8#Z(kZtu#$+)VNqQ3Qc%QIqNepJsVpPnGD8;4A=~!>x z)O@%btCc+k-zt#qfM|nPjv&w(0PPB%GrH$tcVW-h%x1@|zs-MTOiNW3>Hi+Pc>KBk z%k^N%OSg3Dm7%8sl2Y|44gqECnALtB*_;NS=*Kj?#t1dCQV_JS&rmSpc`-@Y|6ILBf8vW5)8mfxX&)AFW*- zBtcT6%;S)ze@YZouJ2z`qhl0p7?V_nK?@n!s{*$Kz@SGZ-}EHIjKKQA790b^JXESnplp z(kgMx)DROB4&n=Bt51HbTn>tTC@iYEy|V|@w{u78mE5eFh6GHDY`!NooEy4?L>qe7 zH@DSc4d;knD0j9>Ga=Ka#k89J&V4UTLH7a=a?d_DM3&nxkDBk<<0}6aZt0)TUdH0(SdE`XCqei$WKkJoxi6VwPelM>+<{bS-Jpy86iM0t-qhl z0IE*ZSJ9Uq-^UYP3W;2Hf(rVrvQ$9Vf4C4a`N3=mvVosoHBC`-Co`%FUGgHC2qW(o$2&yhuWK$G9klz z$x-VSYZa?&9?huJLHq^{?HyyD6W6UhI`|=`Ob!a$f(m#N%wO%g@9g}YHB;%8?J)87taG56|MM42L$?<6%7upUmsens#(=;SLz-yJk^Q@V zeJ+W_H%pZjUM`+-Tq&?yq^^uU{UFG;hsQ4e=W=GOryx z9sN(FNy}+rK!hA^)rUd8BJ4WV*vY$#K5}RzBq{N1F&Iq3WD4mbBy3ge;vI})U;i+4 zvOqFn)8i^SwJ?5CWbvt`5gC9fYCu%zWlbzZyJ#f_c<~K1M&L{qWMzwdC;v&{7CXC< zShZhRy>pp>*lohpmXCd3cJ4&Ew5&e8^P@<+QN{hHc4(eD;_P65(?PL*QBP2!v)@$C z3V@Z@YX{6M!0QYs_tHl|cp-?}gerqT7B5syP6SMPvfllsl0Lh-3ItP&cJ{tYtGj;H zkl1{4DFgA`MjjMcS^}u%^;&!#dlUzkEP)$;0t%w=`J5W-s`JiZr|G-)^LvShPMpaI zLlzzAj)9tsyp7gc;-(!&y}WBpRBfEg>lK(pE98rSRTp<%f;SpG3vdtAAdHDL zqXdjJwC}GI_u3=2LM38d%Km43LZ-IkBvs$o@nT`(_ptlPmFj3^IDYb$We5qW6jlQ;cY6nOBnNRjm8Ukd+k%`s50aQV#g6^O z0baDh4Sxzx&zX=VaD!Wtem0tp9`OH(IkG8`2h8j9O~>K1hqm%dom9E39_ww-h&{hIE(KWrw0BW zVgCNQ^;9fmo{`;wP?;Cb#A-{FI#>a=tj`MsY%Re;`O2Z=Kx!KiX*y(`<5JzXBS*9;NoXc zh4uJ4-`w)KY_q{0=dT2_xN{YX)5}K)LYFHIag!fMh`$_%@@X~g8;i0xI34alclqSA z+FK$c)==DeZKaPCA^kuB&JFVY^q`Vy?s#*-Zg9djM^y1J5B60GV6QE6Svq z!@_i8|AhgH%mgqtF%2Pt@^$hhOeRZwS9UWmlV)h(mJ=G*CSonO{yO`^UpuJc~P4v+d89*xx-6>nv@f`+78Sq%WU@ikfn2$pnx1`uF6 zlQxRYPv>_R)uBdtTb7w=3jnQ-VNgWR+zCF2p0*&CN}6O5EUs2BK`8+le2U%r>9t!J z^+(R}Ic2{+814_&TCv{v8g^PexF;t4F(eXF^;o6JBBgNQ40#80s`vCYk;z!Cx6!JI#y4^77zez|Gt7CC{-lO$pS?Z-zup1@uk6B z1DLJ?)$wp>{WHpz<+3mLW`RA0$|JLB?G=4~r)D?jr~Jx0?Wii=6VIs^xK9io-YWch z>AGz=gZP^zRyXHg`tSoWn)}+o9zgJpYbMvc?8&J>%0M9rHD9WzXu(O91+ef(;FEkF zH%Y;f+_yXT_1XO*;@5v5goA!vHIYGp>j0o$ypofDIb8?!0**!WI?dw%IRvY_iMl`(@zu~@(aECW4yf)Ld=#!UD>gwwP+f3ocC1hT(B&9Y25%`FMUq%4-)@Zrb8qb)2C zupp^8RhyC+MagD=Nk0&MEEA^XoK9!QLx?NvYY#(M(=eqfC9k_xkU{6_>oQzrb~=^V z^o%+pH(VvWt}$EOeMq%9(4V^ZVEArU4lkJLocP0=#RXHTQ`+OO>HZGj#o!fpuQ~^C zEvCc-@-d^wr#Ok6J?(1IIA^_hMGA=jlFT|XtP1KrIm@-sIo4{qebpDNTZMkT%A@;` zHhZ1V;hk|_#EwD-8{_CJvy!Qc13AC{H7p_z2=7?_s4hn))Q9sYWNKXV-5Owofg3!S zEaqLJq8=xVl@(F)Wk*I57K2d^K!GWNO3GIU@vgT}u3xbi(DbJ&p%Z~{=< z)+!hSeFfHno=u4b^{NcHsI2iCH>0=3beC^K5I;ve%Ca_3pGP@0cEWoabiLRTk?WK0 z)=odWwCZ4p+IxDU_Teua-^eV^&?P^fO0d^nVK6m8H zU=)Y1yMH#(%1p^?>~duweiUb23UVG;*vy)HV<%U%LMUTZmG&R4*hBKJ#*P85nl z4)8z#tRH$m@(wUTC-+gI#-m^T2r{yKgkSC;=JmDM1sp?FRB9_mLAG(oO4{Zffk2%5 zmSwaHpF1gnks&r=(n7EKL7srteZP>R9*MC57dEk-62jxZ)?DFd#X7e==e!&q-s#is ze3#eM`M7r$)Z1Cor7oVl`u(Xz)(yaK^|uwVJ1yNR8qMKYDNW2DWQvy*v+Iiiw#^Y+ zXt9zcw-ma9RiWQFT5`Q&txzgXgyo+1%|MHWL(NdLQQ%)}-o zFfr+|4Lmu=SkI0CKspbh;gYp)BP`1(IRO+CSskx1Y$6U;dcQhj@)`;>R(j+o@=d5s zNVf>A(w3~eFfCd+^dKFKNy4!oOoI0HA(f(taWxJdWwxvz3;dw0w%K};p+>ZCn_Pw5F@P|K(t08XYkG*=Z?jK*-@vR9d!MM}v)AhJc;@BV_;BbZL#lbH( z{BlfeP>DDbkxeUx6%8!9ZQ*J(`-j68*pGehZj?|$pJZ5CiEh={Z|G5BAjF9nc z-x)Y^T5Mnj7a-~v&lsv{A#WaAw!yAnh8PGFPtTI6Ark7Z4;M*?flzjKGidBz^GPLx zYFx(8&yPKr1vL`nuFy#8kA*Do&E`^AGh`6wqV#;lzdMI@j`wYfbWH4Ol$Hw=^B+6I zt-mb~usMW4Tq0-dhfcFvE0<4~M9o~#7kNKi+RxoCDUMmTe1@J9({r^8%IfKM4K2~n zEs;C>Xk2q2c1o&Qnmq4NXTT953}GFvs}+>S9VpGTy4aP65rv|IPhbm)mjVt>?2LA# z4lnjGTNaVqj(%Jy6ZPfTmJ6zqBb}EVU4j7hUcN5huy^GO%&f?s91g7 zJGR%SnatbPKg*5NEznKkuMLm$?`O>wK7N!UglrrLC$u#mKNa@KWF(A~5_As~2HF>c zy0dU!XtF9oI!a^lV&Qlx@wZ5sRAh}#XAk^qiy!AUDPeLaY#K0bfSQh1;=v8_=yS7D z%Nwmda`^-;r4(G2OQ)#2Ts}4BwX-sxa{5BfbrKaepE*DED>FYyd@PslM=I0}JeCAY z;`W}n%;P8Jgdy>s&GRvt^u+gvy$YyZ<{UlPK||4W{Nn#L3FD<5Sxa#ZN`>%J z1klQubESu>BFBmTVwIBqxhqv(UHxP2Gr2GlRl&jXHourwRNWWC59L#Yz9Q<*aqpTv z@qAfumCU~-~;a_G5sqZA6@gYJ z<>72(h_RAS;#t-7Z0@Z<#Z*Ylr=2y1o;>LZN?W4KHz|k2$Zr$tP=D7E);SFOWfV1h z5T!Yo2n7s~;Sfp}R~OoU%!CR?1rn9Bc@vA-%Q^urC>tpPAD9;|Qk^mjIb!;@c-?>g z$oiQR;g63u)jGe=gKk7k?id!s5}a!KCjR~aplQ!@yVhE67?t;Cpma&MPtaHWZFr0|$tKO1yz%Na>e@KtC?jD#pIGC4CA zfqjlfm1q*y-sFnP-Qj1$Rhd9}5W^fPZV?cVfueo&bJBvGIU-sT!Rz~%4v*o#Au*9F z$83ZdZ-)XwqPpr&ApEOcQ*o>bTF2MT0ID<=y6e5?`YG zKness;Rr%6rj(y57PW!8v9U&USiux+C?qmyPWmf~^uC$_3qD?fG=8K#<0zFv6nygBT0gf>%Y_grE z{Ud{rvr29{{^QG4lMd^$xszDGKpl9F1u5PajSs{}r+Hrx^DSiTjDEcT`V-6MHhV&x z`aFm(AzoZTTwx3Nl7j^hFOWe({sR5gv1+<%e9Ioado;7ay3J=&$o@tNvL7paKsRsJ zzfT#NS#d6^a|;U;DmMAb`p5rORo2RXG>&Q}i%$#>O-Sp-oi$NcUVoQ;8n!Y1%Uwvj3R2*hDM3(32kTC6^jaI+9D08h{w{aP)W;`heJ3pk@|6#x6hd ziKS71G>Z0)>CxK%!8Y5)gbZr!s-&UiLn z^hq$la0H7=Vdxh`C~sYo@Sypl3SId3{)5Mx+z**EwX(4Z%sJq6-Z32d zMtF=XD(;}w4oQZJpqjl+@qEr}`q``N+)xy$#n8o}U zqinf7lV6Ag=Jy=xn*aRm#sm>N#3v`15}55#at&Em=B^9oRs)Hby?wIuR-XD^ zfXm3%ZDVI1YMb|S!mkfMYa6QlaIzf;q9h0s|897qmVGzy6-$f6}bnHn_UOj&m4}u3} z;_!OB&oHQcE1?~de>VT~n#wMK3&9FbAwR?P5C50XVN6aU0@y+U7^@595Aybl=NpG7 zUKuE^G3ok{lf$MFiVZySvt0|!6RTD`*qGsNN|JLl{annmHx=8rxv0`7)DO?-mE@tH zkzDi6w#5N2D-D)1V_wcUC2$=MuN+ZZQs4JSFdSSpLNOz~tE{k50y`m^1`*_&!Z@+T zxY(6KR`)aX{5ttL&+oEcfmJure{KCYH(VFOx7unyH$k_5W&C60pKFJLPRHG) za#oHRMvM=oOQaQA-+brDvYyb?6Z=I+AFUum zKIohs)u-9WtUd4*Rvo!q+P=4mt0hv4tENzIc7*m2&jv-xwCOH zj)05p^StYG@$%IFGPbfp0$v>N=ZCS!r`^uWt9hlg;>*wSaTJOZ1;!3D3&qi0ic+Wu zjM$n+k&<|6xwJA_h+zrH#s7&L>6ibRxoT?rW%W>k__(R-+^9~a`zWCON1A)M;eit~ zKu^nq1g0Y0W4&X`wh8_93FXoPq^_-Ws^Kg*kd?NHhQ7IPp zzaG6`^iv-`43Q?^qU1pzyQ9k5va|?C>Dc<^`i$OPC9JPtf-ptnU9m{k-ts_65BU4< z=`%C9*3RS7!$qkW65$jM3gT>5)vB=w8o}k&`WfiH%wcxPlwB%@&d!OzUd{3Q_rZ0G ztbhR6l}?|rsb60$Dx9UJ5h7_m0072v5o|ew@dl3y*tGd+ln4242Lneo#Ko?I}J( zy$q}GSrye*qj)Mf8z5s0IlP*t@cMX|W^wsXYdPliUWEuL6eMK5>rgosX~O9EgY+Ew z0Iwt&j*%vvq_3q82-bNMq2R3t68d%@sW?0;#ff7VjpaQMEr>bxy?2?JMuqvey@iJ! zVipx6m1-oHvAZPbWVeTX5*)`sHZT8*_amqp)cTG|e*hew#+NL6qjdu^jiy8?9bSr!$=F5?(i{-rtlb?`Z@f z2N$Q_ruTc2y79-9ebsmoYCrK>o1*jW1sz@KUHk z(t>_9Ao}eO^REa7+RMmR}y~(t`%b;RbSAz?Op}hNz2J-AmgqD?pm(pL;dYKAmMJvf@|aGF%tBY zAerE<@#WmE&HL%yvTZVl-22I|I>9`Vh32d!UTYV`kZ{o-CIqVdSt!!QBcVosawBOn z$u3ho-@7^Y*6{Pqi(|4LK?GKzO74VD=Ld&%Qi`1*tF~f6w4vtk{Xb=_KMPfw#ui}Q zI4J*Vo;96<1_=Gl^vJIRCyWlE4NMicw1pp&()(g$gQU3^FS#2LEN^lNk?4em^1;Po zvXh6eax-IkpK+rh!oa`D4P{9oAp&o1I)?{S#aCQ8GtWLbCT>YgHv$!l;L~DoO)}*Y1ZbmU(jU-}rB2<*gu zK})te(zs*F7Q;7%!fbVZ&SOm!K#{bv&KVkz;w?0Oql^upG>r->IVFHnfNEAGRo&T% zu6XwE+V$~j2kn)oH9DgjV_F)NfNX<0n63opP85h8?Ox-u@tF)ZS69kUJmF9!uYI}n zmQy}_;M03Wlfv!4>o>>5+A$$l8|q12El3E^%x?|Ja~g|i$Ric6>RCecKJ%X-&+6

Bm(JYspPQI{!U%1Pack}{Z#UpFzi}oV@dYth zCzfqT*^=x%=NHZ#2RL_YoE|Ado9Y=dln6#IG$_Y%)*1P1)*i)PWCBj#elmTFvAHpJGvk0mU}BGj?GDWcx4EJpS0C9BA~s7ro+(g!+RTDz z^qtcVHD=X@vP{3I&UQTT2o+B6PyDxsiH->xCpAbV%+>#+=`7r$`o1o%fRso#2-4jl zjWk0_cMjblB_-Y6Ej4s^OGt-+4Bau(-Syt@?|I%o02uC^d(J+4f7V)A$G$7Fj*y@%Sa1=o!`2eU@)0%H`?=?o3 znGDhokts0mNXc2Z;^?Vx@s_aG2?fu^;#FtD!RkfX!aeH$3V!~%H_OS+$>ktLo0g^u z`N9aQScNPf&)D%s6fVrkFea-`u75d1;R5D~^a>l~8?J2H(Qa-$ga0LH{~MUnofnpCs#J*nt?xgCM%rad-1q3+61g_<~%7S!8~9DH{a+a*~*ZC$(AdCL+-!LO+q41QZT##NE*Z z7H!E!pan^|+vn@^regaoY(m7fKJJ?&tR^)QCwWbxtM3M(}vW3;nKr+z( zBlC6%dz4ri7`x9FBD|d+ed-%qdHK~@WAM|H p)rZ})Id-I3N;01b?MC0tJ^$5+ z*QVQjDvN&B1d2snd6gaD{;onw3ZMxbkB?SW)=j#Vm6f&UW=VS%7|nHQqA43UA%V;s^{$w`Al&cUi!gIO}xF|4RXs?^Tn4uePJq-Q{q zr@P28&{d@Kdlz)+I=FpK9Tz}#lSBJOC6v60;7|ed4sXbWB6w`P@>1T{tTF%3JL}{` z2t&eWHPmV>ai|qqwdbd$FtKpqtxeNZ9hKF4s$!p7&?K4jqjsuSCyK@8^wt)WDS9`a z`dqS`{-s31fDejKg^gjJ$FE21JEAS$4`xzHUxmx*MBb+;46ogOYK{$8tXB0%v4=3^ir!rx-CQ+0m~hA<{}u@6as!T}6BzhEM&UdP{OX zHahPqZJ(zDWI^l=1|f#b@Em%w(x9ANax35`J8q82ppZ`#BW+Iwyn`%xk!0SL@^xa@i_?7m6i*3_zZpR+F@{kH!)fyvR!R0!` zJTj_Tf?D6M&cCY*|7M#R*`C>Z*$~|{o-|u4F#R1IQzlR`k+F3UyUa06rAq` zG^RhP)6Bj2lN$`ZuG3eU59~8cTS=auhx<6j^`5-7;47fPv(0fMUMAtz(qdQs{Bw1y zXnSziO6rqFU++D?Dx>Lc(Mws1F~tm`HC|O;Ou}em6uWwC6A!*E8k8^@S9>=*)}D$Y z5{ybiyM)Pt2Es0k3cQ@95bWr9x*0m{^nHy5eoa;v*?&f|vWda#{*N)rf`X)^r1c#g zW0wSMUsWl6hi6<^Q4YD^05@{dkK_%H=Tv}0$WE&nYf+%AlwEbr?EW{;mn)(QrixNo z%pq2Rgux8;h%etQ211MyIQCBJ(}lOzJ|#m!2d*`0hhK;a^4C_CZfn=tAa#}jYSRMbznAvK}f#gvJ zr)!bFF5PvK)IJ8D)}GN~ARt?#rH9EHkvsd|Aq&BYjM2Se7vMKxCru>(=u+D=^{-SJ zmjOC2SXomu>(d@#*YvXk4kVsQ+&KM%W{OjNrxw(t-ywmroWm9gj zCuZqm3NSv)b@9}Rm-hnYb(Rd%+HXFk_ZVH{+1bFdOheICOy*=`y-}ovrr)%o-+F;B zOAoUSWlK&*PfZq?!%XPM+g~kLz2{4%->dSKs8b=7N5`^Z4Il@%AzPO*X*8vru6_QM z>V7_GDZu)unzrsMy<;V(;F|~HXC2}pvn~-H4qy%na=jWsAQ2iD!_f5t4kJ;c)Ig3woiINP&=0Ta7#xXQ=a8)@d2`p%| zY^?I`lp&EclnJsZn%cjTWB&r1!0}|I2{&1tzvr(!$MuV$Hmog6 z%7I#zi{~6%zq|CUy^#68oYPI^ih4wc@rHBO^aC;t|L5y~b-%|PfSq`CYHf*AkCZMH z>zJbjsR1rVgcwMG<&;Zpx3r~iOtZdNDPd3%F#5=3*k{KGX_};1HXO%2CG->ZNdL`Y zpVu0f*#6jA5|-{JtvHLIUtDPuKEr+*HpD&q)24LoOqZO}b6UW#_+-p@DV?p{tH!4A zFxc@k$9#==#YEO@zO>a^M%(bc4|z!C{BK`-wU3IQPamAn$dh$c+%O$Xw@V!yFibA< z!rEhQZF?&vxwN`&MZk$4=Z8MDK>2$SF;(? zE0XE+ZR5R_=pFLnOP}kBpN)yDJMOBpCZFZ66vO|lxvuq9f!n(gY4<65Z=0{MO~9jH zb7Cnl1Cpwn+zSNGEJSb`a2;>J%*YYUL({gH(N2N?%1_0rpDy1ryA)I*`-f>F?qt$) z0srmMNP6!%R!QCspMcPo5Bo1-32>o=rFWK@ZH|>2UdLK!3a{6}-X1#(ipMY0h!2GD z6%sPzEk)EyXdlc~RO7d3)KXV)aiQ@fiVh!;G3CCH0)6cA&1@h9Pw|rq^al?xBgutj zXJ?Gr1 z!p_ierF!o-vV;2JL;K$XM_pA6ndtKq&U(*x#gbWogiaTl(lIh}-2=g?lNMs9p%BqT z6f8(FVfQhhR(74y+@qqeV~%0t+?psq%uHc8&ox+Qa@S=F4uNw&HovZi-OTAKKY@Mj zbFE85`Q~)zAE<-R+}h+yXR|1^OE?ZF5_;%`>!Wo>D`iD)Sw^Qg(Wcz2D|A|`Tq^b) zwcJvB4uy9Frm{v!6%Kc#$lbMqzGao#9(KY)6~+xfNPW@CWV5{0hv}$_{RI|sp99z^ zoZGCgPZf|*zvx$6=IfsV(3x>l(l7%@$=fqDb zaJHbtzZ`-yrt*P(c-}g?4vg1V^SfS3spdO6q0q?}BPpyH&IXrk>R68S6|G6(O>S(& ziN!?*An<;B-pJqF!y`?z;`i;k@07CW^FR~<8-QN1syXBSSHLti{SJ#G2hxg693A6; z5-U=O&W4*-(z{6iqu<>o(hT?R;tfu&u>a)ulBg!uz-d6lSr#r`xL?mQR5#`tPcWbo zZ9@-}d5i0(D4%u|qdNLMuL*H;4RDR`3Gd%A*p?T4h169xyIs(X`29Z%02Lv8KvSRk z=M)omJAY54(67;m_W3ud0+&fH6e%Cvd4JmxlJ&yAc(mo*;EKLWM$Y~3nO?P#qjqqEyQzpd-2G`nx}vfG~60j0D1wOw6`jtw%YyTDSaAK#6^7yrQ_sajvx zJa`LuAHdjh_%$qJ+&e<%LbnU4V$sq0R+eE`Pto)-ItB(biUv1kYMJ_k_VM+NEve1l zcrcTi)41&Oz1(NhVOovh$MrHF>-dhB&K_j+jdj)o%xj1FRq&)kGT|LN^gomLJ$51R z|8-bzlzie1uV=*h`mA5f*4JRY7t#MrLEW9)i7Fa1F5ERs<<&49nxNlYy^ z)wi;brLOpCX}o^6*8|O>W1RKO{krnFW$$CeaE+p4SciT0%=YWwlYoaCqJinfe`GV4ddIoD=y-G*Bv#Uj}Mh;-l%dCPHdtpjp+1lDl zswhy!Vu@p?xLP}XA3Q(j5X^I55M3A|lyIyXnx#|L(E+}pS$PpeoKPd=A{5RqkIoJ5 zrgAsMb!bUl!(#3K{EbkJ4t~~nJEX9Obu4CCSX1}?xjv4InrhIYJCfnj=J!5=e-)BPLDh;XnZ zRRA?D(}bhs;x9PZw<81^@==SC-kg!Zv!)2>B3$h$IT09$@9t&zR@pLGiXJZ zO-y%a8N*ojS8iFRi3JeZ6YBiTc!y^ika$U<6)nS{RsD4O<)!P*&5h^li?Fx1cXTKf zZ=T)7!?kU0kLRSE_q(zclMFn5IT_p%y2)?lKHZV}-J$fLaLV26%86AjDS!Xr zRusCWLsPP2*I9g|+jnkIZmx_Vb7GIVn0;DW@f4g71SgHRjOQ#E1xan1VGF70w4ptJdRG<;U`=8!Jkj)`h3`W zz{Qq*lA-1Ar_!0q;V{LD$pMtGwpL zs;G$V2&W>B@!ZPW80?=L(q?n3AWh!m-2A6tpPedp7$|Pr$ogmZneh)XU}mJh=I?Vw z#jyI^-y84OGbQ?ZuMEt9lyhO7KKF@$ckzEc^h`*S$h;KR(fi00_bdW7Lm(7}5G|~- zvXRbLttlAV{)MF9Q)q8zXJGg1!s6!9Q#QBazWvR@+8SipKtfh5fdIGtCwpio{TEbp zU>hVs!&I%EacHP(19|wya4RxgaPDW_SXkvf;V03xZjS+x*Z(2&UvQ= zs76wLS~(yj6IHehNqE2B{}8gfdJ|Y}L3VS7{UqMb4HxpQ4H%3kgs2~-R=&ebEHy)X z>Ofs8bFx9SWi43K-gMP&J)KCjlp8zI6>IjrUqM6*LO(_MW|+Fmp>v6f8*idd3%F_e&hZ|!n^qQZH-aV zAc|#4NvW)}pZnnSIR~9(32?eedrdmAZ}@PV`P#=^$9z~~VtTXV6$(vhV_<{2~C zps$r*OG6(AjA4$M$n~^)`z-~sR^fkHys%TRz~fdIfB`fOE)wX z9{6&us+)OrqlR1!gQ*559Nqk7aW`dDqd8%y5!dmH&ipEFHPu2@;pH>a#f%p4lBsHa zzOa!J&k{7+XbjU*i7~Eat}gGcb~*9Gso@m394~q5irQ!=@iZ8q>RFtupGC_%UOzp| z|2~^utWx%P)U!@kmgXT+C;mWm2!N`;bsducV#CfcsC15&H3MJR-1i{yZK+6)Lk{tFU#yMKKaHq_ z$>1S#k=O)lIb5kUQsOGNe3dTv`nps~a#vs=9oF-*6({=obNv(`i9H{#h?Mh$3upJ^ zsR^cIvL()bZ!&B&_=0Cr`e;g%#NhBCs=9TEc+U z=ze?4N1pBK=`_4KO<@@%Rz~D}%oe@rbp7LX+C>+&n6i;oaiW!PyyCi?b5LXD%A_;= zhgGJ5S5x(E!dGp4jR|}DfifFclV+$}LT!YHeT9hp8E?h$>?ktKY1PXM=eUDGx_lIw z<#Hvfw7;^6x8}(jqLlx$Q9$%?kn5>SH@E3V>Dj0{P{+Outa=y@vrcdEUq#GuUiH#| z37z^!MRVXvad?F!##s;SF;4{eZ!qhZ1VQXSiflwJ|^|&A1?R5 zg$a89GZHa<@FgxGLc#3OEo^B9Jr#v5>036gNe^JbB073_+)?_|=rNaQ6{`u4XKtOLklaq7j6tG;VRm+m8TtDfd%91~ zT26VMZ}GF`_I!$^RIo-=M{>;7c+A7JqQDF(!gLTZ*BJ!dKm-BE1?UDT`}>+@$0Vo# z5gS$p#*HTIWB?D#FCb#dM`-%4l)hlmA-^E}I>wC`IuF#FZuxqb(ZDh5nG8=b9+i?- z+;Dl5*AZ{1Q9QaK{^vo9-Z{s8y)o#j8~dZl>ZMQ#J?e!}UN)CkE{@bRb-D6&H|89d zHNNZDuw|j3zWV-4BU|CcsNUmQ5xsiH2GJn#JxijId$%Pw9EH&NHzBTzwpe|Z>X)CH zXS&rpExZ6(97Ck3IAJJ`h?$T0q*sEaK9bvW%Mrr4{nhc+_8vi99K0x59GyafLnQJKs9`(JoU|*H6Kcs4;PQJ9$y8V+{QvnTf-nRX?Rj%6}fLvodZR zG#;_vqvU*Ne-jH;O~bxBU$7P$1V;*$Ok14+eM8`us8rJeG^S0ti4|!i5664@+=Kvi zWUxCoq06^#11Be}GBRP&(h`8s@OSm*6ToK4(!=x}8pS}rfnG{g3h#tw)Xf1Z(C?XZ z2#ZlH$pOtK_Zo*o5e*P@X&5%p#cx=G4%Ub{kpLg+diUphY_ihXo1L&9jIJBYB&gII zeOja2?U_M3tug#xQ$iox@uWB?J_$c#X8MbFffVPCELF%;524SDXX9e2=D@;3as3Iv zv566H%59p}%-snn2-8HgAj$lS=?6pfW{5sSxxa2<#RR!`O?q=b$4$|N@IRP2E!(cu z^jMF-WhYwy07Z9hE344{QC~_{*#r{*j!kfGvd~I7g=e_5^ zn*mY%o6l!i?!%|lr`^8KtFxQK+*7U|{M9A<558E(QUgMdOgraX+nh`~P(vuqcBoc{`%5R$1O?uo}ic3|z6}AR|H#^fR`mtt| zMMq3z_9g$X(r?IGob9*|$#O;Y?a#JQAi4*q$m`KvL-8Z_xYr4{yb5lDxlWa&L+0K_ zb5s4iSkB(3vHFqweVTEPeExLJHT!~cq6=^P;WwHvHMg|~;fLl!zd26fFswyg|)AbmptisUIpt@YNrUi69L05{C|T(qPixDE+Evr zYBe=JX(@joBI@SIwmI3Wx|R*(vHZ;4P%J9Aa89%56s3D3;`rJq(MKp@xU=IGFc6a!nZq9o z!YQC194u#snSyb}6~e7dzJNeSJ$k*mEm2}pyiO*?kOlR65%rb@M1nOng>e4R#)7w~fgW{QP5!12gJe;>xRN;J z0lHK!2eEjbzdq((K|IHj+wE&26xe@QFTXq&n0ihuZSFtiaZ?3`P>M{K?axkXfi*hU zr88KYL`S!q?af+;yvL9BCK)0qFo&F-&hPkPyIMJyC7S|`bDZPfWJwe{MD6riH7mAB zaw{&KG(uk?dt&smH@l&MgYnH5uTwbP!MhU@27=Wy&DPwjl+QY=6+Rupu7_A5Yqx%I zT6Ev7mislwT^1Da>|$$YqP1$=aycr7Y`82 zn;6t{nNuk^^xBvbO*^z&eR1YF_`%jbtE-(fv?pJACzZjkQp zPrkjOR;P+w4%I|HMOj^?c84t1$sTts8Efq*5H>UBZ>-3f=zMm122iEwFd8VacE;|~ z%$tOP3TTQD5u_c5NCYP~Sf`UFFNNs;ZvD_a=K!uM#$tkg=A}36beTyH&8h(PbG!vL z1SF##PrR6hsA3hEA=>fB$}y@J>36@kt&>(z80?H2EqMIf$!mb*_Gqyxa3wqY%`_5g z4L0_DI1}DK^gDw&=}-~Po6b$1uLV4sS1N!HJ|4mfiU-$Orx zuoJ2870%Qx1KrdlPlPC4HH|_LoaQR&pH6rT*3?I519F>@GYZVdT^zrQutgpaU*}bf z2dr0AHsR=wo~m6Qc;oH8GHPo|kiL-5@OMwFaZjF(sNnKXh^Y(g-YZ2F{G0iqMfb+qOBaJaR+{vcxt~;DtIIn_$ zfcX&{2NGHSl$Zu5W_dtG*eT_BZ@n&e9B@QMh@Pg~jy}Qj7qhfq$ND-<>$(*1u~Oa8 z7eqE>G4mh~%4Z5Y1NGh-#bRsrTY|$PCkvr|3gF)J)vAEcw73J91=BJq^X4yyqzeAi zs6hFWnL6OSg&I4`)%$stOISL2>7Aa#=u04IK{W>2`$Cz+(v>)aMHqt!WT8#F+@sZB z6|6_i+H*K$SUm=hxh&6(t3X9OW2cN5?}TzO9aO!V_eO@qQ*_~A?{$eTZ*&*6jvbf8_@Bj0d>@)_zJJ9Q%ZgL%;3TE z8d`r=F#`CJ^K?Gnim$6r4U||5r17W!#vCU(&4Pqj8sG?k!~7Ht7mk+qt;dk{OQ=Dd z=jyq03vBz;nsdhLG+6mKHmhSs?`P-I+!)hrK9kSF5;2zlF zJCbP3vd6XG>wfHT@(Z5-n^HHvz`6{ms{fp8hbLmOeT+vuUfFIoy~}%F8%!_6lpus{AgVf>z|MDcjrwe_PZSs z%%HKK<=1BlfpnjIy*Dq`+A1YnK2|SlMPYtxxZ$svzOMNyi)RxJ$!VMQ>u@}?2P)$H z#taW-5|7?hb$4swVfIZ(73*3|gdMUKAxUI%WcR;3npxfucwZKHVdx@wjL0(2=v-3w zyS+}?p4xvj-3&%K#FUmio5JYNd{7+AA`MAE+5?H73LX%)2_T}kHvZ`v%pau=C23`I zTmkY;+bohd18<#4 z<)($fUtt)|IlkBBVdrkQE_xXfmuK-rAsvHmj|b%gx*A__QBV8MtEpo9O9NikYz%}> znEEkq+?v+=-N*F=TZ%b4qTF1KhAi+cP; zmfx}Q;G%och8CC$ot*})_)Ht;=|uT9eAp#_V^vL{AG=3QYQ|ZC;duo=A#5*5y(1px zY&4*p7LQLR4tmd(p9t#ZK*UiDV7VvR2Bybs`rLWW0e>@FuejJ%RSAO_CmhYVq|6id9`inFI;8#@ z?E`p2I|&Qle=w)DDKy}w?-9wW!h=HR@ApgcJnOp@SN0Wh*@~DSmHAkt_OE=&p+^Oe zy0ZT?M70!~`d&@CzdeXh2`QH&xZIV?ksU5+$f&bLbsW@KBe?LL%pI0+;BESnd4Pmi z0>1*RTE?1=Zli#{6gH`;?jmrOwxUmD z1c~3@Hdwlsr-}hRV;vm(HEFly90ED-L+YLvVd_{3$~GGj^<7B0!azOAEHlq}rR@Rx zm_uFp;LTJinNkZ;`L%q$k{sTRa|&PC@{#yo^MdZG@5_nh*F3ew_c5#)@FqTXyY;z- zTwg~r?V`E7DBRsIoC{JYjZcF&&; zwRpdU94&?{fjd-G25xUspIq~uS#{&B+T*GU_u%GT;xk2m2CF7< zR`Ps036-sdwhN&`6Jv&e`ExBW!v|VSI;m8w)<HFA=nx9Z2okQ6VRR#q{2tlKO@>CFK`$y z;Xs;uG``W+H_v{eib9@3T;%w8%799LyfoPyjpB?uDTAf{rheN}F#ftBmYtBug zXlCt7o`&A?!60%0>D|6@^y)sg<8EdWB4Oxf@oT zy4Da9x~+(eW)#ZGkIQYe%4zvO->IY2m&TWmDH;puzyw?GX?36BUWJQ}N$i2Em$ zMzd5?<`mNfCZTCOitk924%mdTYT&{85b-ZkEYO@1IDfivST{EMlDU? zQuH2k#_Lj!ziypFS`9b%zytf)DRXY|QEz*RqB5#_TO$2{bFpCL;(b`RECuC$!Od6W zE11$P`d)(&*5{b7zkw{K#kDosNYjhy^i3yOUKh|({bqRQuz`1@MRpuvtaS9Pi~2Wd zGZvt&0z6~GPETNRlAVV^81oV&G)1SI04hRGQRo|KTW4$3dC@(^IRC>Qb(iiop)vZb zT58i+(p8TAoaH229rT%~)%tHthUJUTx2&&VBahe0FIczM3Ww|y)U?U0^BEg=>nGli z4#XG%45j^?aKE#D8BD%f`x@ATSzMirXgH(LzUKH^e&jGnqwIV7e-@x+IIV7^2Q=am z?v(`Ad@hHmZ@%@(N^-z|>pS-dGtinJY(MOOEivf$=crZYikEQCA!YC?SxmpMxTnX? zx>ACt^iY+OUmM!VO~Q$aZ9V-X2DdDEKs=vQD|ZwPmw1x%=>CUY&D8xG8O;BfT||(| zp4W<3OMCHTdU4^?Ni#)E-3LopE)O-RfRK994#I{SU5{oOAQ`XyCg?M%>}H~sV7B>8 z;P?l^V{VE;V_@ksVQS)xgwrRoj+fUVgX!6=)V>mmnS3Z?om+vc#m_sj*If&@iLE_& zUAry(E}E>+m^i5~+xmoJOG5GuIuEKe<|a+h?gb`OSFKZkH(sIX!1N{-QW(;Xr9^DQ zTe|!BR;;AT<^|95Xu9g^ueWJLLCf5$tqgxBrfj?`mTKZ5 z>CI#g^$rveVWviUQGp!T%qPEJY63~bB)0{nHeEbA%uaV!4TGxJT z*V;SA?Y3|o=$k(@PiwHp=N{I0Z@u%rYMg3n{BmRLGf(gD2#&I5t4qSYCRTf3EOi-g za$l;4Gy{;z$t^!0-_N^m13;@7_Z^57AT0p~g?k=q)u@NbfC-278?&v}Ug^<2;k_7$ zpl`NKqGO$P4;m7Ouu(ub;!-s-Gt%!A{|Z>Z>Lj$husWQwQ4eZSms#O zeNr$>ISsuFr+;FV;1*&fgwsn)P%+KEmb$1u311M17aNfUXkdv~rw?7BaBVdpB z1`MtNRqD~{sUS-PX5%tbqpm$*T2G_KZ;z(h8eft|h>%BmLS~JTYQQv$92541P^53O z9L%k_tP4xys)_E9ZZe$pLAU7Ax~;Ol8q`pP?HPQwU!CAgE2E-*ZYbZ?XrjCvw5h*k za>NsxLSX*`S6#Alb?+epJ#C#Cf9)MzxrhG@^&kl&;3#~!Gm z96QzQt7C-<{xUNiIR?W8sS2S& zK0`3Yd}goJPQmLy@sxsyG-$+z!YT`H(OLCUKP40%|IMJ8k z1}!$CSZN<25lEh8Ha-}dC%U_MnASKiAPI_`cwdS2uP?;9X=b-EUe37aONxcQ`0$8O zW(h-ETzW~{`$C)Gp0L&$%UOdAyw#GMFcBIG6RexJ3UryzZc*tgE06|nyx9YlVwwYF zCZl-)%AV>KukE&IX=D6q7c#c;lo}jKR@FFj=jH9!uR&zY4Rp*BmLl{MZ}oE*#uQR( z;cIYIkD6)!6<|*bXt-)yIu0FwUhRl)Uipj2i?If2D!<8w()mf`LZBZ$X7&8d7mTPN z|CWY;fX#p=KFVnmFQ3dy!7jP)aB6pw4is1WPxJ)I>jGhc@ln_YD-8w4zXX^lxoIow|XX(!#)a?4}4{`xhnUY>15a@;XbL#B&55{V_4Oe4cXJsh2-SNB4gOcBU~7vHy4o&ahTvj$I6z3Iix~>Rr?0TW#jXr^LH7OwyQ)#X^6=mn z6)l;z`}41AdTrs0@V;0}kiPYg$Xb;VW>7#ps}8aF&tO;D*s;i_(Ic?6^)8j1iks<2 z(R?&nCY8Q>cu8F~T;e1_F1n-+v|%r1cE&zxL|Z(<<7`OG?+wz_1|d4KHZyx^okpwy zIf_iUMfZ%Lrg`*~)z-=#pVUFIhc-wfuW5wdvn*(-y|q+rjep*z&Rw!-#k+8yv+R z-4Y^D;0wKXp1W*dEX}-mZ9E;@;)Q-qLD3E-(uaJwwI&m#9yBJh+%tayIT*OIPeB}X zf(JGf<%srJ2G5AJK4@rm-7>zfXzvY%C(WEx=Rq>Ex$JtcK6f&AUiKnt&oNgfa~dd% z;tJPV**|;9nyyO87W_@AJcCw{oPA{(Z0V6U`|Jxa>M<5UYZh@;TFQW%XDuM4aL$Bd zz(ftMOPjFGwmds(|Bljq<2rSl<$@L2NHN8AMPK-b*o3v-ovE0%Sbnr}M7OX>EZOVh zLqEqx(LX6eAOkvLJ6-&Jk;eR5olvjt-#c!;n9v;{iOYw53&6qv;DqEA*7KUB-1FKC z%&jPy;^n3tnmhqkxNDxx^K$P4+gfKqSG{;ei!?S}c6QFid|1yNtmbtG5OSF5NOv5c zcma31ia9{pcjdJQwPzx&ZL__X1G^YBrkPr@9v+gu&CJR@`LHsMg+ zj}>;XZ=Tx`t+-Oj>3}n~a1_#fy*57B!qM0e7FNTM9;$PbHHlEtX!}8YKe)Ntqv@Pj zWl1?rdaIqX{>J^WH1SidamW{3rNel_pueHi9PZYo1E|@ ztb+9n$K2?QpLf79WCY;_UxrYNsoiPB206U+mr8Ym5DHd)Hsy1;Uux&ZOt*g`PGL)- zl9a{<6$LhE7K^AsJXUSmcY?4>2Vve9I*RM}TufG9VygRoz za<8UkPS5kPW2fg!HlQ7^zdzuwa*s-xU`^gEA^6)ZRc5cClr+1Z5g4pSvXUHGrN5cl7V&9&caEyH)A7M7ci zZu6^J%;0_l4|@YqgAiz|GQtgKRP(-4+-o;*5DoaG*k8<%4y0th9*ADWn#r30F5yD@(B*EzsCN+h` z{pmlBR$avP)E1Z&Hx50_H(oHHP65zJp(*G82;+Dp3az0f6JsIC~6+F*p@O zMvZdsgPPp=vJ;Rt({zwAOMizTAdhbSh<^XH$>sliXmo!j>JNK*%8u#>cV61L3s$<# z-}SsazwWHR9X!@&B>i^t1j&ui3Fp6GVAxvlLl zAeZ1jHN@fJp~?7YJ@Hs$tcnU&JY0Y{bRj<&mm+RE1qEttFCv5fH*U?}i0c5hq~?@& zrXreTxcsEQd3Z+1h9xwi*U)#F&wmoN){g;_eKAD&c|B216eq$a^4x}=yJsv~Qfr8j zRdzW8Szo1N?s5N{0ja#@!vYWO_RIY^>&1S(`cbRHOuQ<^MgMzPZ_~*XYQRd90{aw4 z25&E>9Z!>7EThc4=C!nqQh1);QOE_z2U>+Yev-ynA7*#08a$kO5TP}*>lyrOW)L8n z3_~3fM|R-C;Bz53Af}%B5tEYHQCIy#2|fi<9|c0J3sZeJ*6f^5n8hHTFJ=jcSN0W~ zhNnA+N{^p%DqhZc`GYSyUh2n>7}avT_}{W6)4nF-+sO{63j7776PPBEazqIsbInUf z`CT;g!w3!6o!tEEkv=2 z{rs~aFl6+!*c3g`w;1W5MXu9_q4c!l+Vg0yr=%8dqTe-JV_iTWDOF9@&usY6HH2UE z+R-0hXzy8=*ZF(MZe68a*Ldf%iJjCsQ~p7>_Ajq_C=pNYMYg%$Z4-%!@O;6fS|>f| zpXE|8He+gDDBassdBQvxjkY@(MEkML6QN?VQ$ht0LsOVXs^$28wMGLnP4vmgUBA1B zc4Pr|qKk`*d#jJPjcdnWQj=hC$>P>AwfGc$7RL5R4YG!icVt0-#nzwflw6A37V3+* zX);iBLy|fFDgf<4%~nDga))gvR@8sf;LUQ_JLSNJe5=eeKjQCPm(^+tes>}t9(}`N zFah-npicna?HhhYFNIQQhC9v(nE^~*(?=-rsnOJ8p>go&h8#$##yPXIajN6BV;4j`l-_fhZQaAqWjxR5j|YXg(siIm${Ve?;0IfmF7$L zT^M0|+M-fA7ohNzg2?{d_pn|;9~z-h!tZQC?jymqSALzhMw(kG=qFy?;dt>qR^Wp| z_819MqU`6JjDP$IeOy?ns^8Ti z6l$B~#D8QD!GSJ6ol@lTO@+GMC{&h-B|x&llb1r(c>MrX47i488|gbe-Nyslkn7!4 z+{(%tdpB8qU0p9=eB+3YewXIaQ^&tx=4$_uh6aBM*tnp*WL~-Xf-GMJIt4RS780W_ zxd1oYf&a`~T%G+uC3Cw#OPJvGq2?9I(F5ITBzh(6)~4#yrI9KeWOTt(S=gXcw}XT%_sW zWbu%h2e3j9S!@?eE78`OM%q&KqP(3OzElP@F#IUyfc2LgW* zHoiTzPRWlI3n*lk@BAP&@rSc$N-koU(?^gVQ3>f`#+mfrS5dJyb-W>G9IJH20X{e5pq^n%`cKLf(9sMMtBA;2*f&QZ7&Qz_Zf%U%_= z6CbzPOcj3x%@GiIS5{R`udi+WS3lf8KeO3=Yj2FVq9zN&=JeD`?qmCzpWPGJswHjr zT{JUr&UU@<1~5b6*bL+Z|DNWFN7sE*heTaEv;u|QQr6W!I;Q+;$CJK zgz9Y+v!riNg;f<(Fz6%#y;Qd#_Gqq z@4f?M%Ks%Yk^r%I*#<_4GysMfr0D)~LGYh35)kbqm=&aW1AX{557l;b6GvWHMew%G zP@v_r9I5|5n!YkDsF)0CZs|r!7&@h;Yv}Iol9nzB1qKipN*H{PO*S^+T=Q_hji`0ZYfnU0XRaK2||Aeh<@$Bk+Q?woO+qd(dMB$|0o2Nkk zxp4e|1>COv-M3|Y^!KLTb$zCV-e3PH0&`J=9o{CaYcYCn`0pZl;RjyJ3i2TOTom0h>d@oLY{Du~;}z*rMueRw8}JF>e5ojUQH4A($f`G=3Vr0ZS_{j1o&2>%OY`eQ zwksSZ(}MKwL%aMqdHAS+9>HZ?Mv`3_dn?YJcHD3O`nl{2_4{yA(DuT62B}1(u|m0Gjmnj!o7}RlrZ(! z;UR7uF>cx)UMgW=N-LUc)V=N2H!Bs4Z7Ft128uMjsLMD?2;F#&bPAtv({%h?cD@XB zAKV^9mo4J`L$1Um4gfVGkgS#uvMmq*WDn#|SA_)y6e0ePnX|L6diy2(3)^UT%k_2; zvJl2LKU?z*5z(74Lu?fUopsv@?2aNlY004|U~s*6uZx@8Rny-W_xhUvr3N4r;8%fw z$V??jfFY9gEur=-BjbN0A-DG|LDf!mYQoH{eV7}H_$|ZpG4k?4Ck`8Z$dOr=_a;_R zA_mh#@3`Hq=554`lxl=w*LJJk2f_9UC2TX(!J-o(pRWu`o>L zTd>nd4n1BU4#21D3au5r!MphPHyMqH_Y2W_?ljsU)S-mt6SFHyY*y(QSY%KnE{$yc zU>iL!XEZ9xa?Ei8;e!XKrwN14*Hl4I7hyAb&aG@^>u10U{OjXZsLfx?qL(>=ekdtf zzc-LXdBPKk;)GYr)2nQ}Pn&HNBjv1K2>Z;2=FHxyjQmQc7+ksOw8*;Rc-QmGf9?z0 zU1uQ+p)A8NP3y5x5%o;Wrh{Asn&PsHx9|7Oi7B?Q%5=~ zgk2){4g;U1#n!Ej_2)U2U$u!1O)p z3fx6YC=8~D_ca|qNy)758{|zQIxwly9c8WCzC?W{Y^?lvcRo>y#GJKD4XNw&yEubCP!{0UXQpZl~((8=MYmsTLTn_1enB2t_v z-}`Y=4c^f%acPatm!-iBr@zL_>!zq$6{yc#qK0yO(?!n`kNB(RaD&Q8#=5`F+FYM@ zf$u&PTn!^HCy(@+x1SsO(Dny+R;$id+*c{`z6Z5km*X!THJx+N{ZucmGX|7!$KeF< zGu_zS^au=8^#P6i8AC9d0hr2xWa}<2E(>lQ(hf8)&_`_CUlCtx%H`t9$;CF{OEAvy zchT0}laY32lbp?S;i~m2Rv}YrC18aQQF^G{j6fKR+FHjzewnUMjnGQN$bHIx!9E^i zk}sFOn3~~L+rOX0=!i8L{;L+-n;m*J_`Hwx|5ek##>BRtsMHp>d%1 zJDb)IGvC{>!Kq8OF-))aik7}9hMRCtjXb%S+<(|~`dVxND_OA9+6%Xl8+sw1h(d!< znlm*p96$b{$m|ti(aqiRlJVG4(*zB3aO&wo(A=XO8EV{W-r z|9Zz<%9iFeW4H`svm+H1Uk+DK_rcXKpq)ejv>(~A?<{7H+|dC=mW+!JvkAM=$&U~F zpT+17lh&>HP;stET-YC&%XLTn)4*wQrTkB9Wm2`({>P4{rv6O061Ra*Ed+SKU`$;Z4wO7bsZ|b*E+o#!9B4AuQIIvfGnj?VpOh+u1 zlU4G4Q4TA>UmgDRUjfXvAG2q9m?aDkS2VK|Q2qBr5_^>9qNO;Gh-G4hRY zd8P$j=!gqcG1#1ct?BfQULR|H^KkNzP2U@=ed>RIj;jd!*(u3{)Pl6CxEo;PbXYQ* zGMA(o;Z?;fAqN!qMsL*-AHL&bF5ze9Wqhu{7BW^1`R8G0?=gvxB29ct+x*>#OdNN7 z*fyLj8HnRPxOxJ&U4y3cXdt8k{TP66!W#5v$=24Wx3}f#6uyiigpxnFYAUYZg{Chu z2b;B0n6T1|=4dtd3VfYoEr<6<>HQwNzPH6^GgEmjO^E{z6 zD#CPJ>92!_+`M(N^JrOyGHz&KTCF0n%Uk2CR80dit}knn^a=PADG2=w+3+kz*$moTa?%$u(W-1U>P2)&uL~u4bGLADFTa$% zumtWQF}Y5_LNUJ(fsv=q?#B;By4v?@G^|buY1^b3v2@|nKPJBVQW1Yfjp3yq2m8Ua zY3VjtXtZb;}aXmS;%s*Y@M-9D?;HU3w2b$>U zE?JFvjXyxR4G_}~#5nRWGpLONmn-|;C9yO6<_+ch_4VIKc`oj*ZGp}6fPoP3gf*&U zt5)PyqLyk+-Z9r0Gsn^#m{X_7q@r+jqU#qjBFyzj@&26CJ|i!3jwH-JwDRQ9nnk6T z{oW%qzG$Vv(dZOb{t!?P{*etzIq;~aJk!23m*63q0y|PUxmOq&lNjD#?<%s+-!=5x zrN_dw!y221RYz2W?^VRStt307)Cx4en2!f*Cq{^v%X-hihc37~4tqE!KM!zhSw6TW zFv|?PqW5}?>*S`LUO7jRhy5QHAPwNHH_mHxf9bOg5-eSGTUp%jx=3&$(wClRTc?Ytf#A+g|hS1Cb ziT18%<5Hp!MBoTBZg&&<5L7)O)0c33f~2RO?9er(%D|;}It?_X?y=uBncf@f3li#y zy=2Iny?v!sbEr=3_7NIWosHP?xh9b5X-QODZm}=18=2+hbWc1(P>Ff!m7$299;O@$ z7%?i#x*aYPnq+DH@tv}7U`?Q{aT^t zSV5@sNreDRXcz0EhG~N_><|9Yenkj$6&X>}i~1bV7;JJ_%Opz6GIvoDf_=L!}O+9(`U^1z>d&iXz^g?vlJNU?g%m#RM|X+E)N8 zv1koofXJ%54+_gj6Sc&hyup!K(0?6Y#DH;cOt1Cs_+gX}MjcWagbcqN-$@)IWWfK+ zLzs`iXW~4G-=m?gQvJm=4m-&%*iTa*!FKpluW~aqtI}48+!GWs1-Q@>RXL!;qsd>+ zit-BzK*1pON`Ant0WfiBri@R+KqPB7VHZHm3k$2>Vw~ZLOk--m$)P3qpbC;#B9IBS zb(~MQC!eU9CP}4^lo5{40e|`amYW;7xM06s#8s~s;S4>p7%ZL2C%4}s!M|woOO=pK znDNw6`#JUT_O*rXfq}s>fq|dDlEY;m|4+RG81vms3SK>a6v2QXaAbM6nPu*HAYn1X zNk5{&%|E3Ea5MNr{&={sm;nXChHd}jJtYRDtcLHZPK{<3Sur8uvkdlYgd|yGCe!M*ivHCvJwEM3wJuQV0kl)i-!gffBL9 zeY$22EYmwW_}d|?Mnh|HoGW?w$*hD2{bhU%&6J4>z?8tV_nn1DmKkX5N98P!EZvuO1v`qM3m%36+y-)-Nb~FodeN(r`zCHOL(}bBfd`yYv1=oNb3Egs}my+U<9% zAw8u$&p&M^;ZZTDqZQnmb_gt)wA|0=l}}eZ0hr*=lo5W3jsD0w;%{(558|5&+2}JB zu)=T%lVRm}04UwJF+)t^Cg2sDx=xB8`CiChc|F(PPC zKNo9O0@NCQo|#ntq)FLj3*;R^hFoZ33U4OQiL6Fg4!)_W=|>Nbp?~-$fIRzqOpUDx z+ZpdBSOGWnkVdg11v;XO+wB?@(@J*J4XJ_;5-n$4!N;<=s<%purV^1gRKXcM8_yuW z&)$3~4++IeS#S0UKvZ-3*yWp@_df3OE&JPkX-mA9ccR+aHrD6)ri3>>^(==# z4KKnVH>aRhw>Q9WOE{N^A;{pN2$j z`44Wa29zY$TV!L!h5APxNlqJD@9v82MKRUHEnT$G&)s5d4Gr^)EVN9`wDA#CoO%Fh z9~l|24AhE{03x*o0#aZLQ(uMK5#i)AYG^-a1F|R^Ew+QI8BU|50HLlfV!2NHXDr+H14`AQ~4j=%IkyBD4e)w@hM%_0E055={ zFyKhHwuxFI6EVc#U$_{*BWJ>z?`^teI-k54L#%=C6que~b-;~Wm>|cTC*z|xruD0T zBIm1y9y**jQ4tvy@uL!7N7=Dsx4-h*^|m3@>1@zleVHW2z)BiuJYiqEgd7axMo_o# zfB`eAj9E(!O&|kbK8%|NB0keP2~7*mX`enpQ8%KKLQwtKa8Gm<+F6k0Dmi8mpb%FjU)n4kvs;E<#9r)>Hqdo4jfgg~VoLm? zMuy9tBhZYSf7TLTTN`!R|2VFw8gkl=FU>Va$JG3DFG{+2c=gpd87hn^4wrR=c&+po zV3z?jWBCxwSKwr86jfbJGlPI59qHXj$H>Tt5O9Y;~m{RGjlW zgtQSkKLHs@=7>IYtkM(tujy^8&A4?%i^pX$hLc8Jj4}l%Z7>qpeEec4lC;NDj}kKV zKC8)AGM6D$WgkadWgpBw=vNO z6G*EPq`fH$DYkTprdnptY7);MGKql1wPG(meIo5EVmJ8L6)6wp5;0;K@2kk zd;P;F|K`WHKf*)5Y%e{>md{9>lms$L+?11-=fBBjP@vy0fktNyO!4jHqshtCbcP|} z%BB_tAbsMj5x2X*L`IN7MEnugQtflbS49(my1Vi%tgSG!!^rt?GdlZz?n^m&B6iVy zii5*Oftj_!JM4v{F71d{&Utx<1+GT#BRVv&CA<-(NNn!oM{oCJ*6C5jCRcG-=?s*> zSnYYP8DVH=xv_Rlo;7yGx{0v_0?deZBpHlhnW#B9xXIUC?)g`KUzK9+2w7dUK3||K z`femD0@j=!4}T(o9l^@+m+)FEu$e0HOFMn~>st&e=4Bd{FN&FHhD|*vjft65PCq7e77~(%eU(qn zbGA#VsV$89?rCqje-bzTeg~n%93S3s*3XF{`QzQ4ICOKKOVR*!cs>jO^9nAqsNemTJSE368%l*&3A7 zQ_jD1fQNTW>dPbI)NKZQ{4Po@pCM$@gl_7ODc8I-e?zWC)Gp35{>$NAJXhx2sAZxw zr5P7#B5^^Hj;Yy>!aE)*mv*4=Qm=1SF2ijQgMnH!$-nKv&_Mz(CRvmXv zV>Iv+``760IWu9wO1kfH_q43pYjtx3_S#QebGwxL^_JF+n}nVkQgDqH?}l0_c7HZ_ z8#%N5T&>%)0niv?i+o^BYP2YFaS6v*UEPR0lW1T6)c{GjCMHNa)1G&D&{j=T~6UQ2y;9_VDtWj7fhd5&ZCNXN{z+S2V6vPD#3} zcMjl=AmxevV^wu^X*P8!H1$SfhpVxGAx!#rghmND(O<_9gTFJ{eEas$4Zp~NA-w0C zC}_)&DR>d^|5^TH!AKOSr1GLo0 zCrgUop;hLteqVdM!dk&%#m$TqJNKL3MuGccG14ftTCiqpWkuF$*ZU>_8Ji^T?UzYY zTm52$j!WU62;as0Y4k|O`SK!R9Wm05god);{Jz~&O_ZQElPu2ih^z-c1olo^bXo=% z^d^$i6O1Ipogq=T{YROoh)QJi`FPqi&h<{$fF9YOV0w&Yf#(;gtwu`rbCjl)91UY> zGa<|#7WD(Q0bc}-AiCl$t9TUqm$vBMos{>yJEy1lBk{Wb3aMn01SmLo)=c|{*~yO+ z_vTK>K4%~t;1YhaxZ7%jzOPz&AK&s)B59(Q!+VOo5H%F&>9oP#;lx+;` zt@I7jaX~xhL2@jHY%PvV6_N~Ufy&bHcvB~gXrd1=;n=G%*ZvgtGP%!GaFoVjwskF? zY%tYwGM~$-XML)ZFqiycY~pa*4gdKVVjj8TSZ&1oT9OKQ+P+^wCTAyB@(fXl z&8q?5pPm#0u`@UCbNT8^Z-a5!`a^>x%k{n=pOW3xelIamG)CN4-AL%}>lr35@C1P( z2FPeDtQ%HrY-|dkFN8q*br{owkYfM>q11*vQWK+Xpud~rA**Y{+$`iUW`0{({GW)f zwVE;@#iVy6VBuA5V5!x#AqT=!>MkiJ6cvL+BpbvK4Gj%9@NEDyh<`IJfXRkBWZm3& zhiv{t8~fH1+#YBCnmM%vGa})PFLk*FiU_ATpw!fw?#QnBj&qG^3R24wis`$Z!I_D}_ph=Rkx7<)>i@ZD&IvpYLu+H_ zuOQU@sdo!%N`;1E#~mXpk-kLfANqaugL=lw7O#{U&3{+np0jyx zgTv20Y=|?+z);9?xi)|IYtn>6+OfCp?M0;CZ?<1V;8i6vR{d%>(^-rPrYIz5rhYq~ zoR7K*|1hkJUw-4dA9)AL7SEdp+sKAvpaV94w?CcDz5-^{-?q_uacUpRQ7L1833H9> zJ%5<%`z3#&Z}k1?u0RZdwtq(N?rT*ks6_LKH0QynteHTD+EuIgd32j$$T@|^W3!sr z%cK~eA)Zaiv(uPBRr?|Lr{Lh)KLi_9Qv7_z?1|)8B0(jvQMZVNn_Vz5znTL5r+czT zBzA)_p0Hw_;n>-AF8r}ZQgiil_yj!@NhxmrJ3Bku9IkOLW(NE&U%)uuEidkL#lt^xWXj3LHWISs_!%wg8*O-!8_V=VgZZ3M*2_jODHa3?+ zBfZl5OBz{qW3<#iYb1{)=n!!13le+!2C#Ut%4h%r=-HW+e9zx=_D7^g^v;Nh;`A;p zmG4)$75x4iB9-5a1sGx!E8ku(F~cN9_?jFW+%PvyY!$awM2 z?TcqJl=yFcEgmfBwy-310?jGgu(NOxp276j`NV%MLUY23CCN^S#&fn9l8#C{Bx84B z!}j>@`Ibihct_P9J8JcQQMN(VhLTlg%%(~XUu;BNht(EL(1Br-wT3NH8Yx#g zsfJyIUnZ1woZu~!RwGlW~2;XsEr ziaJZ=M@pwPlQw!jW2C^24DD6v8=8GJFs=%LCt)N6Tq&m#)k#RJUekfNM?SZ%wMLAs zQ(8WXN}Pqq(bV*%uVqQi^-Qx1UY1G&8g8d<(b@PFSNY*qqb2cr9Qt{H6= zFP;_aAIDQGA|{6!5|E``51>vLG2R0aoCx&N4-*tjv1?3G1XJZMG%RCi)^pKV-OLb? z(NW8BJYsch=)_G)F&X|FB5}k@ZZq%vPc0j-jHiW_PUS;xVgGAw?V5Qo>o0PffgXY+ z?V_k!Z82I`Gp%;7!}#5SKZfN8;ajNq8t6YYafDQzMdJxu9_fuFX7#C?9UKRk!$zT* z4k3bnW($DW&b|Bt!anpT&hZBQ8YdclSMlF#-@&Zb59=<&*2UX$qg>?dC`nX;HaLUu z;EF(@*XlJbnLlzl5AiC{3+ZgoyUIiXxal4?ffXbHAZ?c@014ML5e{tNPF>&^x0|BS zfzXMU7YVZU_rC+255)lB2W&9JN>89Oh24lpl5bxm{GJb;s*jO5SqeG!f}>SRJVe6) z{v1EAEB3v;FpQgi%<*5CU>jkBOn+3I^f8i2Us@J?=JiKb*FFByQd4u3;84`1`OTQy z7Rd;nW;UA*)Fv#}ed^suV+auI)!M48zc!kTD{?^c8P*}MP$X@A0+2USXfN$t|2DXf z0eYE$2h+Ej(pSWv{jk*UC6WaR$HPIZc{sqD_Fy;q((&8amIN0mCeC3gL3j%OAkRI@LOg&mMo)_OUO=*^0Hb# zI%!ygx-wiG8j#Qi?i^h+LLnm}4o3c6UY5rSa3b!5yhZ^qqbNC9NK~|}JqbY3w|sp8 zPgB_T*?TBCd2>gP`hl zD)9?v=B3vRywWi+2!M;3tM|*6xXG0>B(ljjav|gOA=>jv3r)7=lOr&VI}hnwlAao% zOxYBZ!a{OmB*RafD}?GVcz5_=w~`UbFWr{RrH^8S7@916`q(g zB4Q3F2-UwlN`#(2AU1y~_2A92#Mzqyv0eD{)o2cZ+U1vqRSyUgQ!OY(N;5}A8!Wa{ z8H}h=~ey3nm3ME|BJug%p*c6D@-@-GQ!*ZuDk zl3|u%LL7>n_QKZBoqy7)+pMiWH(3^|Yg-P%{fc$Vx&E2yC4>eIcnqJ(wwL)1sM^ENy}>ksgha7*+eQap_0VO z#+psDCwKUL0@0avY(v+PTiwyAiY6I<6T-DX3FgDZYR9vLsQcN&~0xvp@R%e}q~`wmJOn~&1mVb(?_Y@w7! z<%}D)uaVI>qVshWSLZNe9SJtE%f>R^846(1so3Tjok&(aon=p71f6SWl9-u;0!7PL z&VYQDsV;%lRnin!?~?VfSPHY2|USTG1BccGYD7 z`pH(ZOz;5Y(HQHdp=>JGcZ=U5UOsR{76*qn@GjgrcPIIdn^2r^_U=zgA5W9n-R511 zILO$)&fkneQYP$y5-~$X*by~jW?PZmV9N$n&5G&NI;VQ|tgQr}CdMN+K6q->@c8*8 zTr$@M)#Ex4$NRdIn&hsGBpDI6si+}nPj81li*s*M@QEb8KUsNBK+BJZdt1DM0u&e+ zPnHBMoOW}8IgKk5fVM6FhfRqDzPBYiX@*7t$P)EFKG&>SHm!E@^t5P7-)k;1-2MJ! zQUv6hh_6ROCcI~8-aU&o7aT6l$AK<>cMuN`|Dl~R=D$x9l7Aj0UxsFH_`Alkfl%X+ zzt4gpfA)!>e{Wd=IljX!>)eFN+FcOZ*6&oL=2gQYAHM!pYmm^3J=Aap0)B0 znA@Gsm#;5VJW7pIK0-?#H<0F6OnzpL1>XgpJBZ&&`&&E`CR(&%#Lq4Ee7Yx=J}mKh zW?D)Fg%a{*MEo{>zSAU0o`{|bI)h%U`oCnumj8x8C05A0Drt6@_XL|Hu{Nt-9(!6k z8W;F8dh9Uzx6rm~#YHG|W~EgKeBfl@n2R!`hNLRTL#NlAd0Zx;5Z$JlE{{Tadi8BE zD20W(YL*2#&5HX&Ll$O<31s=6>(R!s=|kh<_~N+B$ZSW;1^}6QhK-Uv}k9%;LmoQLmh*G~BT#CN|8>cn45i#Lw7d-MxSzzqq%5 zYTWX_b+QFFSwCok!**xQG^WT#qvA?8XTvnWPlL|#8mq;B2qsZ+iD!*?;^3G_!jvS}rz)b^J`+KIyr)oMTqO@ApnZ9az zlNGirji|mPxht>&=W_4@cZn$~0%W*+lmOwtjr@FT+Q~s!Vjf2Yg8S1Z*{nJjLyoY* zFO&%DZrYCPx}s+NoBX1FKl1h4$q)G!#nkusOLxs)g?uBlgM{-04?^%lbv9kA0q9^r z=;9XCVLw=DAtYADZ!_Mt0Yf3iPe7cGsfyd!-ZYf&fEfh&`2njQ4XiGf6|=2aay08Q z9Y1^5=TaK->`qyx$8J*}d@52)W>4rZYC$^y7+CfNYF(4_$$|)tWhfU@s2Z&FOSn4Z zMduOxir$0P1SAz?`{4sv>JL3Wc>{(jCE#4)NGh3t7amC6+M~Yp&yCw)RluLwPHk9U z*;3!JBqclrOs1`EVG_ZPy5uKgOgavv#!ygr1pA{z+Aah9nei9Q=Z06md!(5sul>rX z%L7RYo6-!o!)VO=j`*QfcgQdYJq)-wrsSS5)6fs{5C6vnh{IOk>!$GZCP+(^1cqk- z;p!&C?9M?4vUi_u-(TSOJ>C#O2mD8Fp_B~<8g+{~IUs4`WEydP2Tv=!Bf@Ze zl+2Zy*r6C}sQN1-ug?O1H&s76N8Ei&__uIUGcE-yILl|kgeX=^Dv^u=G$tdeWqfq* zfi`=%0H4!l6L2Rm!1`CyA6n^c)A{Gnb6haamelJ4fZz_;;X@Wi9SUq0V2*^SfceE&u0! z=*rf_DaFi28#5U{d|B@}&$Xs$Ghpy!{{8Q#1Mm2U%YN>8%gc@E1gh9d!Cy3o4`bJS zgAeK_WY8{TxW5ZJ!^U6^C*JxWu<0xK^Tx}TZ_~kU@G-5Dd7)Rb-X36nIIXKC`3c4> z%e>^cc7Z@MO4N5=vznDefFRdL9jsoq_$x}2@=eyy_5}S`n$kRjz3NcV^d4jKM}yw7N$oPjG8TCR=wwO{SQkXOAjyrFLd)V|7#)Xk@As9CdD&mnICEnY>gf;o3LFV{ zjl7@3Ozswvgk4;4>G2J8TQ#igoxS{%8DT;HUXuO0X#+!(|8XyPT~KYXajEbUgM(tA zJuIP8pPd?|HgSbO$U>D{^U}E>12x!?T0%%)7JcIz!H!B1m^B8P33~S)jFJd3JqxZ1;g*BmO<~e^?c#>7R)i zzmLz4=R}jOa^1-kyP z_O1#*&@(6OGTtC}BOdkM|5`|sq&LeM+&-4WW*E%?dD72VfkiWe_bwgj)u^w}s>d9)jdQ`Ue2ALa0r|zt!9A zUCMKa7P<-hq&-Zz_6u2MFM@?A@;+Zxv2cim->1I30tX(Q(s6Q>-4)AjAAI?#*;x!m z#fg7ZUQH@VPhjizM9}y9KnU*s-PZqX_1(7?%udq0*O%M$^++qQ z!NYs?V?3NlV{?9Q({9|&Lk~*aa8P8H?U=4)4E{N7o0#(5f5wi9cgJ ze%(a-U0|u3nK^!yxVvPv4N1vC#c!f>L7usDe+vbcC7?KW%-8&$jgRwl$)0aj-aUHg z(J?a&$o#Wz_i?M^dfab~f2j{%T2$1k*(b+$SJ_iv-_bbm+7XDDC7y0ItxdNmLk~KW z2~}cN8uo~r(umE_i4NCFb_xs*4?VLD{V)g$+i^YOY zS9^_qIx_&fd*TTq5e+cJyD$&;s}c;WjcZf%*Ri#>O-t9xT9*1~_b~B|*$2Y0Wqz<= z{50-qvHsYFKJS!lhRoYd0`d)v7w@n9()a89D)+lzg2nuymw+SOr4%s_U2xq?B(2nL zl2f z`re4Fo4fmWnIA`9t6S@Pv+IK7fWl|J%Y7{rBn8-3%z&nUXhG2<#@;PDR$TCqA4H)z z5ASNLlvE6hCSL-w; ziX)sF84p@R=%l6SC+;S2tVo5>prYYLuu)8-dFF8EsHGSa8a31p;nNItCvaAOp6#ef z0!l<%8=N+{a5zE7EOOL@uOK;Dbo=-b+(A>bL3|yBYhZYO67yRXH}jxGg7e^PDt*e< zUeXNT8QR`F7dYPPY8(l}*Es&jaVouc>BsP!mtyqBcfEa1;&*-4)yv7*E!6N?9kILe ziVN%d;Y#O05X*^z)Q06xX^5M^%29CU?Sc0-+uC*GV3S!qdck$0x)HuP_?7}jZf6{w zGRvgPb`>(Gi+S$+cV$}jZi?s5D%CUPA@c!EH$ubgk3R=|a%Go$zJc|!Ci&>`fdp)4 zOu!9L?D%uk4ov<#;NKM%+^m9@bqn)jm40`O}pW*o_k%mh?NTH95;0=hD2|*#|B;At> z)qfzvQ>vVrU*MTI15sEXb9k2@JTB!l=@rdK$V^cBk=TaBJQ-Lx4;Py8_Vvqesc}I{ z#l3ndexBtIK?u(pB!Fz>R2^`I70)YYwoU@POXbZx3ZljBBh5YD`83le-34-xm5z+7 z(&?x{4)oQ66^B8oFY(Oo2>z0X91APqtDRn8vHNhP&2QVT&7Fy{Hd{Fex#!?$a)-VteoZ zmy&wob&4Tx{BaLs&Nx!1ecg3h83!D}!>|)5U$;z~XQWVuvw!xvl}QUy_KWXwRV0R4 z6%}dRB<`>!choR#cqt8c0j#aBr=rWb_@sGVV*bnBct$(-&D6}wM)qQ^#+_QARxs5= zZvU@9m_utJ7SFZf-5hRF=x!uhQ z4+xAeMLua^s|%S^)dTq4aj(=K-)GYAU4FsMO!x54A&>WWa05|_Dd4!i1e=At@5H!1 zFnr!*uo9lqG0vQwB=VN0-7IsW%Z-Wo`KN*HDn7yY8VndmO(v5363Nct~(Q}u05`-ui{6aJUc;~3YGMIOO_v@UJdMR{=v>! zA4&fPY#rYfWU;1=mvu8`YTN&~6F`M2yz=v!rr=U$Mt0S<7nzem?!&yi5iogL3{GPl zR{OhYU+x>Dbv90kB3eom(SG2jTcDE5LvF6%&0Y0eh#(E6S6-N}`FDinb5v`FGUeSC&s~BZk2?z7TyY zCYJI0I)zt_bdjPqU-~ILeL}_oqo$Z52@fCyVgC<;?+A?yG6bNQ++qQ2w7$UW%z^?} zpH5%L4wLpCGcami^N(1rvDR#mvi-e)cO`P}q@46ILF&7#$=~s+cMmRaar~{ilzxV#`U1ltfYZ zFTRR+i<_CBBb}*&Ra1I-Lm??XQAJfBrTah_`I^U!GTj+&JPouh5BFOR+Rxt`kEMqUier8H5%QcX7!?q7UgLfv+DN-ub#eNmnPX0 zJ8yDjpc^J@W18!19nj29Jv^$z8*?rsG&z(d1)CmC_AOIkmZ|vynh+<6u150m7Y5B( z?AHx9-=0rAmCT?<3Q3Ppl^veI>Arz)fywa?cMaFrlzWNJFO4KUCV%q+Lb4lsadX4% zhjljv?{tqE<$aOO~WkZ;Jy>>0+`K083K&Tgff2SFal&^r-bPTdxd8hF>{WN}(F z9HnYTo?WlBM1$oK6{(&X;`o_|6x zEGmhMoe?CCxj8^X;z$#r!$sUY82WWR;tfCqSdU=uDZnhyU`(>`gY{&jJ0#|qLI8O_oM+!=BWgc&h*dP%#>$%XK2 zDD^M6n3M})?OsXP;hC7zIUHgJMxP12DH^~zOG@;qCQ%WrSl5dm2*5?vdB)-e^Z$vc zJS+Rw{an$)Oyha)Z%*j)X2xjx$Nf_JW;yz^y1~lB&ci!4UCoYDjw^CLtQntP_1?|G zOY(2MhS$C`2@_p}0>?3I->{aIVA0&s04u9&{g?ksl&2QaX(kTlU>CJF)iGRzqydb2 z(zNLY2z|~w$_E7yX!;kulI)EQ3c9Cyf~eN@f|DcvC_lzaSa`Y0ERrqc=gk(um~pSq zv!G7LzG+11RXqct{OiW<^Qxm_!jq5N2&&VXnRe|a4byFC77Wf?G436{oN0>(&-p38 z9(!Qa!I^VK0WYqfG))ze=5tW&rr=bXON|#kcA3^Pt~B!G*b->FpeZsi%@ey} zT}E-A7Sp*#2dD9fxBt$OMoZ;K+z%t?(~rhUoDBF;)*qceHp&$yea|9FNak?FaOdtU z=X_iCLi!v+b>kNQa`0dOdW;6rieZ=sL%fweZoapzgujTrpc7o|M+}`2TpX9c`pS9O zhaj11Vn!t9^XpG@ttrT+nL^Ga&`s{X{{5%l;PmwLdpJ0LkvG~`mZxHEea+5S@MZm{ zJr^+maLV3mL22iC<>IunN|^E!%-_%;phkU0nc&^w1RWH+sJlMOARpJ!M&UWZ>vEBq zQF?O)JBEpznyOjrm|d9r-Wx^{=+A4?gJb-Y8E0sjBxF*U z6GzkSNDJ(2MbO~-$P1`2it6g^(I(^HT*M>?RY^N?v8A0gUa8hDAMCoSz@yLBTHS_N}2r*X2Q*oG?@P+QZAlB z`{CvQ1Hvmmf&Fdfl_1V?@7y$j6U0fj8GGBPgCI9j#A62#i1J&T7H6A6?&CNuAKZAN z&xcT6d;eQ`aP6j>;aScUcb<*q3j}^4Y&ToE(%MMQH*p|b_{{Hj+EY6d1{CgWZVjh~ z2+x$4=9e^h-WN@Z1Q_Y)L2z27giS+&Oe+Yp!Mg%uHn8NH?dV3<)qJZ+1?!ePzP#~r z#Rj|x*RjYutKt0ha1*cPUI%L2_3>&k$w?4KGKs=aa7vjg>%%IMwq|irrNL%N3^p-c z6o~YxaH!}uYAf$vvWUkY;tBEfoj5#Cn!9Ar$|72gU;k>pG<^ujoYyIUt_0)p&1e7? zM6{USpHh&U08C6H!*50rWyAUbuK7CAkp1}-$$RYUA7iUIk(dIt=5emR-aUW;RHa!2 zh?chx7ij=Oq^X%%l#p4)>e|}QEL6fBIbnH(W6t2tKX1p zLm(*_D~@K!JUAO%)-Cz%y@gYMr8po%a!M443iC%=!eV4QidfljCb zx2V#UeD3NO zwJ+x1RCuj#(S^l~v?W_(#^-SEtlKYnw8@}ppF6S~)F^q_@^c}1DHrz~Evg3|1<441 ze7IbdfOdTsUwN&}**;~WXEA!@e`JyS-%Mx2K$tQ!>ke~r*PYC6-^ihs(exv>u+CWh z@;a-WK6m$7nUx@YbTo3R({qI5b7sqHw<_eRp3OX52aK!QujUvep(6W$uqrQ}rtFfK z8KIzq!_z(8XAsLB>2nCxU@s+VZU+HV;tV-NByd#F_HecAyPF1 zZi(Z6=b?E%f=Ok63MK!e>+nzSi+RUe*IWDet>59`F*L@X-{CwzM^t}v(KrXk$8XEl znda3uG;BV(bd$a)iju+PWV$%oSFimpPUolN)?L(!JCL;IR4%^i{j!1OuTZ21-`w+bZ1GTv&QYf4*T&PLg%9Z<7{HHt9FYFt!aW4oqtaMgS{zk%U zamv?^x{wwN)sVzYn^Jo-5a~{nY!0QmO?1&NuGY(d#v%z1rd_VS_pGO69q~h!Whs%J z8jOIU(&?itO{k$FN+;Yqzo4$pe>>{DH)_6~k zA5Vup*qdfjCXh48_Ce5h$Bu>s(ADYHUF}HGLPa72zDZY%7OGr|T4B<~NXEW1qY1zU} z26eOGs24NIChMTdqR^M2%PyM-U*GZ(9BuONPQI(_?;Qd`0LQ}BM?moCHlV(-{)Y>{ z**D<5c$jd#=uDT|OVb=qi&cdd*6O{-MwL4suV|5{_8<|NVPJW~89p4YC1MUs{$rWR zZ%G+q@dRwvmhsO}VZ9I%0T4ke+KCc4@4A9*+^O=#YWF7EuJ^bGb5={+u%9Cm= z`VzCwffZ%HvkbxU;kmPWvu4Gh_gSqqZDoJcF^IzEk2wFV{vF3-e9Sk!oSgjVTDZj` z>N9>Hec2r}vcj`9jRi5F&>8%EUH3N9SVoq>#-hXgAQ6FSkn>trU&V+MIr6pia_~We zhWScom35}UU~PlljFKY>>U>V6(p~1(g*j*9LK>d+_s0wb)6j1wkE@>8Jn>D+kuwF^ z4!RA_nmKD2NSzx^tt4GZsW}ixt zQrfl#7vNjmJ%jS2uX_hnoP!JeoZob`%r$qZq|u=p+K^&IIl-i@{{&}%yVv7}iO+(z zUAJ*&pk_R@hyZHWfYFEI%8~f*tG9|w|3SA?_ z4uenhZgov)GD{^UXqA4zGStdtxYLI^lDi)qG>uF(7O8i2H{A8aq`^ zp8NmNbXGxewA~i|LINR3a3{DkxDzC}YjB6b-Q6{~y9IZL;2tEn4(>8YaQD-xQ-4)* z!3|Z@({%55?X{kjk4C-?PqtMF`%pCc?l&(9lWHA~2_LGP-)}^4tF{nwXU4!(64wh| zh^z;=tlqZ^3%gUF`!?^)r?m7F!!OboP&bnmsoC^mNgF&rv~3Ui{Zt?8eUqfusPEqm z0y~B@{*XFvhNMp9kz>c4CiIwAv;9%8U(L+&?RvUD9kN&Kc_KCLSIVuzd@6S6g)7W- z4X*gyk2>^%Ipub`M*^p1Xa`F^k=BHWF<|SRU%hSF6qWN-R}lS2we$`v69p!zaw*gDY%51!&i1M9Iu zDhM>UnaYjrVywzJhBs%4Lp-h+JIVnz(e|vRt;5*f_s^zp&%*<(MMi>TFJQ(83fG*y z_z$Xt+Qn95dbI{w?7grN5NK~|}(4-BjVwKXy-n<2Ir@A|f z{1(jE@f{q!`+&eXO4k=+Cnu+V1A7I+0WtrJjn`W)H8u66rPciACBRXJDpp?GNJE5S zkg5LO)y6w*LrjUo3YF)gdC+JYerrN5TBfzU&^t-qMej$(rgrHatY0P1#73GWoD`YT z)?g$?Um-!Ilud(k$W%VJxTumXUWT0UF*J^%W}*4BH!u9nkolR z1{GTEVwUf|44uJeZ{NY6Ok2BToyN-vb}WFWVbw;Xm0A-x{4Da4FLcPMOxi=E=v%4d zQW>wBOLTo7+q3S2hWkcsm5Vc9hiiH)e9d7u{awRR#-4xxtb&r3dvKHS$O(14aYa=r=<%P9BOq&tH4I~WjQS`Aiiq{c3b!tAkF-u zb%IT;M5F_Ty+cRep`#hfX>VxtTxz>D%-$oRw$Y#vm;8_*OW>T67fq`Z9wkRAGp+US zI|PDi`d!{zKv(MS!NCu^jV`QY#r8GET)&r)&Q5-y;&gFw(Ew}%bU%xfthuJZ)Zl&3 zX@MJ~l)pN8)4j?r>wzDtwX{O34%Unh63n7rXC?bBLMVAq|2^w?t&M2hf5zM0d6Q*F zrO2bM#Uo#a>s+q3^@fVGn&ndhjT57`MTVf0RW16qh9P>0944pQ@B!&)5>tcH-1KnB zqT^a1-R&yAHlbgwv7GDb5K(ab9$7>HZ?M*W{pEI573`!C76$9FxBJg#)oSc3bIQ?4 zbG6C=C-X~o(n9aGIB~&bp8EsgUKuzazezKt7#w*0ilhpYXwQ3|XP@EN;EnNy)6V+5 z2?X`7)S*UKRgc)#Q4%=vj$t<218 zPA--;EMrWzAK+Bi#=_P*2<){b0^?j0E%Y=PQ!+68Q$6U4{lT_ko0zR z+Bw1i5>0IpgVfXmY#YnV`70;&z+~3j`z{msm7e_g(Qs9TTdXJ#I@Py%+2@O4-OnA_ zaoag1s1pN&8rn(sSjw2Mm%hkh1U)jgbE>5`@z@hC@Hc6jz&vFbVl?c*`!&i z9-1Fiedik3<7b&|2y^JB7}|V`Tf@elkf1KQy4ybB440qQ(@eRxp5`5nGf4%qXzxU+y^F)cIpYzmU_Pt_~g=9Dcuk^$msx5Eu zm+E8hH1?ZftAr&bqk8shX8cD$=I`Iju@m+TeA#(r=%1w*PsO>eu|rL#vgFjUy;k7o zrT=j*GN)eMlw06Iu}dpiI+!4VcJnax|XRX`=jXoQ7X$o@uYBT6K(sUIyQ z9*@X+{-M)|+yO61_B8P91mS_hj2X9P!gu#hUD+_o1usYTbf$Nj4OE^2R#Nk~cA?%rdNr4rY)~q)4+|&u)p(`$$9Y{HR)@)6cCDM`z7wZ2 z9E2U!YE{y50fo$1Vj=TqJVhwcM-;FMWH&W^iq%fZSHI*hsF|G4E(Mimlv3Op&Z%;XCrj(!bkGsO~w|cVhKB`=i}BzAMxNF{(%6!F6t%PJ)&INzBs-uTfc`c*Dg%rp zJiNSg!7#D5%`!k;2Y^NevS_5sr{}*Cb9r zQ!fD9J1gYZJ!zdCZA7H<%__BsZd4f)rs$I8&VfSOsHjJ)!TxQ(ZVq(8J~ksi4ghM* zA7o}~pv)`{tGc6J=E)T$3yLXz?WOc5+S{G9qw5&ufq^ejIz$!C{%6dE=2ZOHlIjpm zm@}E3BFJ0iE~g1qTV;5SR@g>nv!{h@G*77$xhEn_9Xwj)S2wMx;YzEJ4}5bU#;w%6 z81Bc(8wu~9Qi{CZh}#lDbR_PGy`Y>GE9>Y|r8Ve~`jD+&*_;WJ(N9C>Z+`9Duh&Tz z&4}VJ+B570MVJn5$v~kmZ!V`rWtGGHuc+Y7VvCk&*3dd(x&dY`-O8gsf3ar-VpOZh zgI`-c)l-0Wa)2|=!^gLq`}SNy-@%FdUmV3B@;n0|FAmt8paOH2a#2zTN3eMxx^b0U z55dt+vuL}tvbwNlO6ctHF%Kn|ox`cQ66MT>=IUViHd$5Spjwzlf-o8%~9M9<1^?JLXtT%9P}I z*3jvniPil`@1k=hYy2w@p6Y5=tPK`XB?NoqGFa{8_OT1xam@yg(IJW?pKt3b^;-#; zb#`#|)?1&@YGasMENu;8zzfSZ5vn|R2}q1&hkAr>@KHhO{?y3#We+uK?|E_AW$NMk z>O6Nh-6}Or^KR6iA*Z?w0j+0_mDA!F8%Vmzn+1StXaZIJ?my>c8weM~!tPGv&?T=K zW}K>yux+G&nWRYQ=|A!3{2ZnmU2b)6q#*-l10W{u>N>czga^ni&lf|)I{Nxfo}SXc zzTCtlM6YIOe}6w%v9_yA9;hamHpg73>MOWu=wX(k{No%NG534Ydmfx?@+XbQyP^w2 zSokmO>QwhlugyfZU6o66R6$sI#k%FW>DFYV8zQI<`ansQ3syV1QZIJs3@8`gd`!pL_uI9B48bAD098!Alz(W^QiHs0eLq zj1Gi2!o485=}I0jQ6GFnl;|s#jw@L-gZKXEYmM@cGt)0=AN%0a{VOR)1SRlA;Y_V@ zW4~AadPu`J!iuN(B$a@v8plSdD2;tpV?SEYI3cW3SdOgp3pb6&;&zDiq$Wtyxtq}c z>v$8@^J!7g9?n{DZvL0bg62H2eCz4#W(L)#Jui6KYDcbH+3%(NHQ0FY#Gz01CF;XD;PY#X? z;%K{d5(k?qRnO&F`7*~zyM^+rc8@kvpH7GJh(w6=5r~9|C(j3x)nPK=0F{^!C1vsR ztKtCSGp_@b09?Zur+IZ_BQc5%Ktg1$F#=o-_Fn)*C14&-6ro7R2lfjph!#s6zA$cJ zE%J4t)?q8_eC(~+Zlk58g{B_m=Eco{A1e?cM%J|=N z5vQotl{+%egSRfE=uXDFq_-^}ci@U3Ru^lRJ0jjeW$sE6A6dCVx3c%+$#HBbooU^( zz#K`6h2yY4=xF~+x$!TpS7H+*W7+eQBk23X#_SoZ@YVtU5Ftn@TeTvp?d+)XzgZpk z{Vg`(;;ylpkZ0NX&ZA|v6QZl1bl?ezp~=QqQwUF~W4njcr}I_}>N)w~8k0c2(cFfJ z^a{3;&VdhQ?O|sMxQJWy6saLWsiKA~@a7Q-F!UjaKPV@A;&@IhS%1OPYRuMseb~?0 zvdJ}0>z&6soW=u)KjJ~8($e|SSP)LGa6y~?N8V;DWU`_bJKREab4q4xxH2*DCAJh~ z&=x`F7}q#n9Y>ARFLW^4B%e(E5BC=*K+?76-USvwzzBW;&Goz78^xLFSiTq;K>#KL zV9>vMxH9{fOJ80llS1dsmPh-(nd>zCeD83zL?aBN&+)Ne<1>c&{ITM1{PUOXkJv)3 zf?}Xx7w|s&J$5`kZqZ^d?pMMBhH9hkKymL9WCaQ9K=qPW($zhZE`LgbGL3vL8iIFLKS*6&exRUPhf+um8VLV2)4n7aX zd68&ilGkS9I|Gf$oNfu@M6b5gbKs2)(pp40E9sc!)%WF!=s|PngkjX53KA$4?;!|& zXZi<74vovl*6wNbOOE48cah&|L?41R+Q`Q-)7%HeIt6ysFl{Jx=ic0P+-MHGF3 z3H7BN)>RFxX=9(1JhNDESDk!4#usYzBa;gM9eYksW1gP8{Qq;>u!7h|waIfnEDMc%a93CdV-EF9*UqE%K6*Lu?4u=G5U`*9kr>$rmQ{2DY zbC;DbipOe#1rSxeoQLh^Jw7Bsm9v?ZP4*D{zVEs6M-x|Eb!cqABfjyFWeDW7+0<|7 z-!p#`Q`Bqmb)sYXdEVyoYN}vZtbf!x2Ie{M3vg?v(f+IljuJgxejkIU-lCFZld0Z9 zi9#gT8znw`{Oa$X9`PtHj9 zuF>a9GlupabVKSRzeEID;M4* zH!9bjW*0KRpM6aTuA<{EK`HDx=s!be>U&4+I@3DqmtWcO*b5zT+JFAOJqm3J>mp~6cOHh??mGR z`KA{4=bcD#;{FD`bwdAJbD+jGZXMX%UWLUYCjP>MlV2n859CGod1-aE5O5_Q&Qb%% za|9V7;J6NgHA&5zk{jM&6P-6jiy3m-&tQSW5<`KVr5oHS`2FQRN?IUBw1n0o1ACG~ z8O;F`V+buCr&>UABP9)msZ286rRuKF+RII~biH8&+V^DfgdRIvpg#S0dG9Hs`M~R< z37eZAjlxeNy!FK6bU+zAmx}G&jdRsm+H*_38E=ou%F2Yr^Olg7`lo8D+C9=!(XwaxAcl2tG()>LJ=m&{<#H}y~(TC(zw?g7auov%k;lg z^=pL*JRSY^$N$7HwDM2EyjNHRRcamKb=oHF;N1WGipOKe&dVAg zv7{&!hf&TWu;t6wK{Iju9?*<=U-DAQC~@e3licwVKpb1#O`r6gEL7Mfd33i??v~u9 zXD^K_ma^cj?jmD{i^+~75=zr9J?V79yO7Vizt+8erNwY}(Hbzj*VUE0{oKUDM$$Tl znHuD`fia@&^W)N(2Ktcwi)Nk9++;mO{qsJ(8C~F#?SGRNxylm*{mqoKct(}eydvvJ zEuEbv&cJzTfbtHSYMfp?Z9a8ad49O*uT~*9ryJnBmkN`W?=LwT>H}#aOG;dyY=Vj zfmb^_e)Ok1VkXSst9)C%WDapsJ@Vq>a{G*I>Wqi%Z&pDRla@uj=0Oweyf`W3SZLHN zQs@{1EI)=0HHjfrg4;Vx`)p4FNPAB%e>`K;B!V7RttMwm75cqaQ-$5TUEX|`J!Crc zRa&#>@+LA+eHp#J^5M9olxiODP$uQngwqv4@A~qIIEaziLV|cZRKA_c7(o107&aX< zI;U2@p1mA>C~8h4fpxgV3rc!g=rb`^YQ<<8UbFL#^p@!~bD{VPBUUULuem?78p2zUW@yh4L>MAn;nfrtk3E_yYU2s z@af}Q?)w`YfaMQZ(kY~r=V;C?>|X}Iz^P0Kukom+Q~6SCqvclzy0$`s{nY53t|To{ z)8B#7291)wQJ}Ggqe=Ze!awgBlX$p_lCG}o6Eq{1QM8PGdoInESu$W8W3xNvJYsFo zpV-}-{A6*#cYI2TMLa2%{WQqewRToUEHf5M7hqHUMZI$?{>hn>?4yd8BGQd_ZG9O#oTL3_>oxQq*ZTLT*N`RCi>ts z!c@L}(TIqn0%DrMZSn$ZeqXv`i{<_Cy;qg7o$3c{D=S*uXF~UA-0pHOP#v!D=o;C* z((Rhqx$$VP1i7_K{Jlaph{y%jMtBUolm>f7f0BMZW*+q)Ijksp0fqQ}%KM8=Lv(^+ zE7^B(d*!Q+Bz4#iO(tof%s707!cof3#TFLfNrA@Jc(KUSx^OR~j|UlcVPUI2U#Y*u zhO2*d=4zJoT`F#^>uqTkp0RLhKr1K|9pMw`Yw?7~{)T|h8H+g0xKGjq6WwDR>db7! zjqm8Cde={#z?%Q%;0FeXxv02 z@>*d-^nh?!UYN)Rq@@ltIa^I(;X0#n+L$!a2S(Vg%Or4e}<12{7^r#9T z0XE(W2wEMX*bTQ6=R7aGK{GZE5Y?JD=11_-}vJ(Xspd+)+yf z;5!;xTQ%WfR{$WXtt~dKROj&&Ux229Ps*|rG;vg6#{_( zYi*N)1>j%MJa%Q})IkeviV2=Fyfu+QdD7R@i1MPvF@vgJ0-i>eYdgEvmac6;Nbr7n zOfIn7;O^!Fv*X8`;$>!5Gxh?a`qw8fXRfwuPA~RMIjwPQI%Ua2_NiBo-ks~Z4aBYe zb2b(@2(D~Oe9Vx?fKosdc$S0921|-y(dV)|oyd@D4NkjN@7Sn<+;+3+_85qK-0D znh&_S50upGRMaYZ4M+O?7(_bi|0%bFM5T^ahI1uLdI(I>!>(JCp&6@@xy0l<#_ zlX2uB>LGx(CKRW|KT?aZq#o&-IEgwJ9_;e9_>jl~9r_3^ec zxmIh&S$19fHB{TUqteuaz*&}XcXtPwUZMS@7YhKti>dX?I(aaKh1-EkJ4GfxXB$Bj z1iw_0Wf1Ftj&5CCo(*LGPP^-6^Kv~A51`<*baiYWFVuMR^X=ZRa7{2W;YN!iHAT`c zF0UB5nT2J)@ZY-L0XC_;HH$=wKgNPEfk!#SP7r@(&{aq|P~XC^U3-`w{`ozqjfkbI zRU}s@|Lk;%+9xy>XPCms$_lY{1#IOOyVc9vHoe(KJ|*syS|~{x`Bws2IjQOIDm^|t za|B3+r6B3`h9f9bJ0_;j;fH==O?k+P_I7)1ih< zTpXPAjRTx7-%x61Fkpj*Pse>~Z6(m~25UySSzQ z@)k>Uz&p9>J@Sm)fC>1BJeTho7fyx-$+v!Hv08;rq$O#+Ta9Bu-NPPbrS8bFyT2cr znMn}S<+F3YsEPPlK>^^UBF(TI6C7LH5TB=ZKM$^VKT9_+fIHi_KD?^{?r=#*H@dCh z|42W?N7T;9V-8)uTO|%z6iDrDcJ;*CUw~|5)xim+kb#jnYku&Jur{)!Y2+sam&TKT zLwb#DnwTn3fp8ckO(G=(hB7j+R}M<7Zua10K>Gu9k%P*5Mw*RWMUgkyUEP)N_DpV! z@i<*2B1oU|?hi5ipw{3{l0(KvC)4x_F9T;iu|>?N`r$8MgW>6Z#$&KhhIgX)?pL!B z@}_2)!Ub)`{e28=XkZf$GXqr4*(kDq!q4)up_LUu8~v0uIv4(w`A?PIdSuN=74(`) zE-G>vjwPCYa}iJX-mNXPjjk`P+a^QQt0-`*CFK8f0yV40%!iz|Ew z9!*S=mU(GWRfZGgx4Kk1bYQXxI+!2%lj%+tf%VnF!ahJVc+sr&+@1cNAd>eT%JO`} zRN7p0_XKNYQ>i4H1aLTq-Y^_-{VB1|5*D!eDR@csqEu%LO1YOzQ|qwL$uGZBfkjCc z-Fxy0zNyDN5)?@3>-`#r)|wgXb~BA%flS6G2@Y?J{Hh4CuqbkQqbi{;b6%che*^hT zN|BMXKxSlxqLD6iBmS@>4H`E$>*QuR_K-jzWBf7XbCe@FPZE5d@auU=5D!rXw*uToM zY~VlO@PfEDo3rCDit7|oxS;F?oJ*6UDYy8MzT3f71S8~q`!U2wM~sz6TqEH@PG?o2 zr^S4ZUD?+xxca`*r_^e*h}nkOB9tgrYIaQ3Jq11ecPWLqC?g~6$>6Lmm3=0CYm`MA z>iP3xO7m!2RT(2yuL$U_G2I<;AwRa~KZZKdY}P#4pyP^f0lwnE+`E}PFZCi(HqMv0T30zDz!Et`*vnn=r1eZafyWZXs# z0xDTJ#9ktoG^c$(PQ2 z#v;8^mq)$o0I|>Z;NeCp8Mbq)6DGs4B?}G5G~{Dm&(DkbwyVHz8d=8F5!-N~N4#)bd73GK zz*M-G1H4uviVQk_$3_)tPk)urZCXTK|I*(%xn|u_^i4Xr37+LJ%m!$V;kY=7Te)(2 z)c=be4XXLf*Iw+Dgz(~vR~Di z!13|UVoxg9!;Yw0h`YhH26Zq=Ne6gO7YUj^`WjS?8b;v{SnM*#J?Lk!Xdt}{+wkTP zRG8>dE%w1U1Q}Bo-!Y>~hYPsD{K^W)zkC0s(=R2vsK#r%;U}65SCAgsI-Q)9 zQm9evC;bPD;yteI7OGhhIkWjzA_s{ed|&=8js+%aY8B}tplXCRy8+*={p$?@P^E^{ zo$a*HEfUSDwv%?M77KV*e4mc>fy#CRAHOgPDcnYKMf6P314+ZA9)wRk-=(U<)!>8e zVZ!Izlu>I31?o}x3N0BdZ@MncJCV!Ftsf^h*w&1NB%)vi)wBdvj#AL$+cC#6*t7O6 z6jU+gbl&0zNV*}hENy})DPvRU#CQ#uoagMln*|>!m@Y1qEO=-d-PoRYQ+R6hQTCGR z&g3GuJ^5RIc{IFxRqqZBJ;AmjB)eDed*YYGAu=wgap;iAWqug}~Da zXbj71PB-Nvilao&wy1tuAoP10yiiXKIM^QL0tQ6$SkSm>qAjqm zHI1G0$^|wD#ZC2t5C~Ot6NgH|&kPtagUHsSQKQfg65RQrrlYX43Y|^soV?*7B3U)D z&o8qlVe*}vv8Zp$R`oh=@OWCV1Yd9On6chedKKl2E@4nAzL^lzc^$ZBe9gKfN`Ulei|9~y^l zZ#j>@Y6D>NjN^)^fINEzas;{~Eh}@a1+%>-s@vY|0j<$&=HBn@B+uPF{1l0TN?XKf zUbH!yzTN7MZtgO93GXiKU{dUM(Sr`f#NuG4#{q4_5N1iYQQ#{d~NG zz#aA$-Ft1fck$hW(s`7|ef7MxjVj{g=!{JVKGM9nak@9H?cHY2H^B`B4 z2PbE<_l>Xw1ZjHj(}F?nMC79dxN_TAt4LrlT_9%=6aRBm)r35I1Bzjf09ynK!=KGa!`6~#cbZw8nihC{6I-n7Tt8!w z7SsSg#pQE~yMl%(y=U0DXp^jap>w)kgpo!_sOny~$N9-Gh$d3#i*|KQwN%X`(&-sq zZf;^R-f?vBzjdR!n)%sjF9ad31a6#FoP2!kg?~f5`JdI`d1xy)3t*>q5i#R_X4Y!> z^=nYgD*5Hb$9HY*UnO(rxDCLitdb}2m-R4xG0BO%9r z8X#oU-@E@AFcVt4c{OS?Xr255&kPv%curTLpPYAIP)u8un}plNxy~#rQwa-7+jrFX z^QpFf`EakMiK`d%?TKV_YJu1>fCJ}qBV>54heiyFFN#K+U>ZatrRX3_7lK$hv=m3d z2>QJ&WffO5k9N#=O3|&KGL&YOTh4_8GjdhbB0pb;LKyc7wB{|Ci2p%+m4P!3zw_VY z^9I#|-dFk&f_>=1o7b`rXIO*}ok}%~r^KMnQRaO7(LDLf~6_cuIt`M~xQE%YSXp zE)Hh}y8r$KUiSmH%W%=lDC(t%duwmk^QkM-0E0tH9kBXevB~KEGPSfssvG@|8|yAhKl=hY@G0oY z>-8B*tfQyr=-@r#&Sy$)Szotq;%1k63bdAaeFUmE>)Zd;(&r~O_TbAZ)*cDj$N!Kt zXc}Zrf1T-8zsdy|_2G6?cI>@6!lcFiIiPv^6k_qO6t=sP%7J zWG5$xoi0Gr%rJ_gQbNZQgD)DU(5S$JipA*aU$zACfo-_E$YLep1O^KySZn zDf=6Ldky5fd@ff`hl%Zg5fIJ}@~HfR3z$LIqnf~*EKKt|6HJi3hqQgfz(FotS7+JHdVz=iVVn3+g zGj!wMC>Z-40KOyR)pd4`_x_9f=PoX-85TA44T{IgmyBC_4ZxPc1-^6V!U%{P`BJlU zl;dAwIkJ(cxT&i}WXZz!s^iLVIjKe)0(oq&o-7}q!i}C#PDQ34*Nt%ORo&x2o*`4s z@W#0|In=stqZxVwUh1)dbm|#xXZsI6SOSjUE@L;>+dqizv4__ZuBHYT@^ZD2%@N&W z^269vHQ=`Ve$%i)>r22*rBX&#-c}kM%r5q_(@4Y{zxuj6jg^5-|JFg#-ezRYAuP%1 z=X5GKJpt#;dwmjDFF^<;b&TX_10T42Hr~Dwx`o; z2Q%8b)KW$&tKY=pXaC|)xw6_X(*(S0bcqUpL&Z!tqfjZvzr^2^I3?roF5G=G59`Ls zbF~F~_8HZ}C+cb#Nef;~t|?5{Q&NwSQ8I|F!54~3hR=n9#-|FBoU_OrLlp^^^2iM+KHaD7^tvSl;tyS^>WtEkF8c_b~X!?SQ^RP(9#xQf<8%Ae8 z7NQH**K{dc-b%@IdkK3Si)Ss5$<=O_A47H(2uGbL**n z-KHy0V^a@cK}&9V0q>anUn2x-qW^5{Rm0?_Ke8D5c*b9gQu4LT4u{iqpiDX>z^kjK zhu)b;X*i4A=Mem8Z?51caSY7WjGSNvZYbW-AO74C=IlC0%4h?kbYxhbWm0-ZhDjLA zpbHkwu$?8Ka=aoN4Oh+Mac%k-HCDl$sNPDM zD+q&w65qUmCtQpg(OR5E3+(LF@JR$dlq*+c-5D>u@dN$w_e4fZ z7WnR(^3=LY|L2!lV~Uh}^R%$ff+13`2Z6ol#KbbAyZHp0{awFW7KG=j{tlkw}^Z6vi9|u z@t;M^cNuVgy6H^Ui?@d)Bos#|#Rtw|VM|i_i;ANc{vOdm{S)i8xIN#({T>My)#JbN zxO`MHz0C9{C3tJy|88ZjC!a1W@I;higlZxARrJoq4VY~B;33*U4V9cWc4Y*-vX93J z5RK#2II^crbTt*peZyDWEn`Url2H@c^tUkhh3W6(3&N76_6piY;gsb{rncoe;m<67 z&l2eC$H9SgIOV)rUrbNSE+O!_>hmw_4FEmQKCclNSb8S67XPJAJ^y zRnXimGGmU~oGD2vw5J&=t4GOWwp}zr`O4=~G`?c~K`g{{_`Ot>*zVZI`v5k7Z*_rh zd(MtBAUG>f`o!=|6lm518BQo^2*+Ip8^TN4huio|^1(B}LJ%M0GQN&5lF}DD3HHA; zGA#kP`6u~#4;S}X;a+q>MnHkX>S4rjlxX}GiSFXKX0a!J$^Vvk%$yi$1 zoYLhV)qjp*ktDWwkIw98%n$qIY7s3{V5-48+EG{59gv~op`s5IQvGh1oPUT}ARVjT zX;_o(kG1{feVPR_qo#$1(6_ZXG-YYqvqyN+hwD)@;N-CQwDAh`kV$}n*m6aP zMVlj!@7nNp=s6JPq@pMGXHeZLt0Fj=zTINjE%8ce$duy=?tXT1);OD1F&w!C z*Wsx0T==Nby*gDgBBwz> zBbxTkQg1F>TP&z=GV+q$#X_?}F?qcS!LAiDs@Yhs^XtfbksUM>W)V2!m47kKm(kE{ zd(MZ#3nJWOKXDcQR;uN`Cxj-9nMlg#mIKRXQy;c*_$;8xUk~|tC6OtgA}fv?i6w8c ztfLzM?tQy^I(r^Ed+U9B$@POiU&8n|gch&hP6Kl($MWB|W`*a+OZc6cX3Y{P|eUJ$3wqL4tT`)IQV3Bw=sryl2C=1Y={ z&b>BnX(FiLwdS2NlM!la;Wbf(^9pMOw-7(?+WdhI2BmCZ*NA@=sv{{>{%K^!a-Lil zZ_v~Lf_EGZ`sGgGFQY?2*8uyce1WIlP37?}CA8U^2W_mF>#EviBed3BH+^4Jz~dVu zh5MuS5c`u8U$$3`>=kHyvr_Js@{xl&x`n!&Bbd>#ymK8ITpI! zE?@DAzHqyc%h5&rZi~0-fmr}FusDX2a- zQSI02M!o%cLYkYP=5CjQs*zd|$!%IK;T+akT5}##kx6zc56X(R3QDyB`shi;-?-7nF8 zPtAU2UT$GP3I$vwmq3x0rDVsMX{?o4ga^^er^zsoeDU$|oEhqc;SiMx_PHsZAM!Xk zuD=-@WlstTyj%=hC^t%i_F8Enj$DngVUPwL(36IxplzIiMiGkHI@T*&Pr#y;sQ&4w ztE)aQE>sJ#$W+x$*okc!a0M4XK%;TO*mx0H$$vBx8|%!$%o;eO+fP+8V0LINpNs4W zY+J5la>*`j?rkd{&M1s+sl(M(0pHLD`3TZ@qjQHz2j-QATejvTllT6ln5G34e<{ehMzE&A zXOrZ*U-Pq8)RtTKRyWpJh++jl20a|o=^N!W$58M)PeJ}i;Zg?OI=O8vLB%+d)%9!uC2@LguK3 zxu$9SD1AR=19GnEXdK2pkthE5eE53{6ZJ-*&hy8=&{JgcNfnrJ5rX1RUA|dWO~Car zV5Vub_m!tFS`S`I|LAhuF(ZvJg)ZpVIcU#EgtKFH67+W6c+-5aoF&0sdOX>I5ZvZ) z@f4{)+EwFA6oxG^$*iuaExi9q`?T%UlodJAZeKaH!=jmj)mT1e&ae!*%b&V{zavp7 z5gy1Ow(1t_425{8t>hrS5%M#cN0k+RSHhI5OmDl{oAjVRdCI@~InwaEX6-yn(`)Mc z{t;h9fmjU>vg%U;H18SzOYG3V4VgmEEPM8SzVtVZLQ=bEMQ(tNgcR2QsY(oY@jF+% zz7wee{6~+t;<}VN$K~a@vo9WB!CQ1_vESt~>?$Ez2^4wL=BO2bQQQS^%-*kTybktu zKLh-D;a~V!Sy}duK0@7o605fL&CNrnx12z&C`%x_TzDl-TJ5LNfKQEj7|W=3+i@Wf zo=QXJ_}1Y!D#aD-`P>0;bD9NqIV*UNi&QYZ)Bk-e2kI>I8KE!oxE@XRliXaa$U%fr zlp!}EwiA!sti)tx2cwzh=P3JJmuit%*{-fm>v9{kIL2f|P%|XZK@BkxL!Q`xNG1S+ zXOUqS=Rv?)p?iXOXasl7ZC4Phj)r1$wZldD882EqGIKt?!Lu$9}~2>`&l;bX)4gof1_9oH;qw9`j6 z7(6@6kH>^P)*fFVcWhGVP_+Vy%4)^r{*xin{}0!yCs47k)mTHu6qznuV`b66rDACLmP#1@vw+_|)7#6&(%Ztu z;^GEr1i1S2iBLE9iumn08(8)(M^gb5rN+GGR;jPeBtJ2Q5$c5|ny_eP-v4n8Y`r^U z5Z=wG@}Fg8V_zJ|Lf?;n+v*V3N*6+q^HbfJjBL)C+~NaG$|(F0J{E3S3Qj?H2N9~+ z{8pAmny@qoa%!5!m9Z`JTwIK5`VGmpazad*hifElSpI~q3_L5iW0dIVvL>Hh*ob3| zgbawfm^f-QvD5UU#96)CS7!>U-1=S~C}9nR%(69sa0dj8@WwLp<-< zR;o&6qx2mOy~Zn9^8DX>$=nD7=7&y;LNJ{}GPQZKp zeAE5L1cX`0FUv>h&fGGJb+U4u*m1G@dZAl!K`#o7-*{pamC?#RWSii|1K=0W-P^kP zpSN@M{am~6{wRo)T3jQE6Al5sS;D7v**Q>fCavOc9QXWV2GT9KBnqjE3<@cssdC)O z^Z5-FnpO7>KC>z@P1wPK526rQY%l7UVgDqAaKCDUWKl~oLhU3+%^LjZ(Ai+9PIF!8 zus97Bhq$kvHr`gf{+>;;k#OlUMxsVgoCP#y* z*3Uzv)mQ0~Lh^R>_TrK|f)r-sZquA^aTh8@nXIn(;BtJZ!h`FRbK8kCckwQV(Q8yF zfns2>3sz5=yHtB8FJ);`om5olqaVc#h z2+}AWN4iD81Ja;KBYEg8tKlrx%WN>KN$@8&i>Y3YtFgmOd2T>FZ|>{ zg?w7wa{FT2BAt$xqL4eI^U`g)r+3ytXWrZSwM53(%sdGV}h9tqC6^=QP7 zeTFGWJ}>XvtQp87QHHHfW1CQPUd2{~bUM=eOZ~4VtZ)5i3_h-zTzTWpMSYF^zCiH z{s)a=M%UMWcypN$B6jhRC=-+YxVS%hezO}gA)Ki8%U*7=E*R+SXeRG|m4dM)pYDR& zXco_b+N$nLa1vZna!*@@hcEFc*Xhb*(BvScMeL46x4zJMCzeoeUGCbOtCStjKCK(i z@EgB?(hYZGCK{f0BS=GQLBKQjCr(Q5sNWQ(64>7J@lnT`$Z@kufqUM#ro;laO}cx? zdBYDo%58{cdiwd)O@x`PJGp!6-=Rk;bV#+zEic7wwEIm1$|QfxMzz|~zYkQ5t_7r`)W87+KMw%j+;Yb*yi_{(_J#qP_upuA!MBK!%n)Q@W;XW zHt3dY-&qnzhSX}${di|UuNw!-sgX6ASoz6a{9}?GRL%xQj!nSC!bbO;lArPQlRnr0cWP+_Y71}3cc6+)8}40e(N z-naUdPho4RpukQQqshMMRVmYljY5C)1Nkq|0&^Dps`r12IRyKRHg%dz!#!yTE3&)8 zjbS=;Xo4J3x(-@ODzSn z4idW+ioi00P?X6I<(I#yP=gDNd&c;5Z^m|SXBt=?*$-t@0et4#t~oPNLz&Y1lU5um z8+?0q{Fh_aZL?lIf7-YVv6oZ`-h|k?WtSN1Zrw_F=TNW;$P7yNm2p}u`8p2e1Z5-F zmX#e(OFWV_5)ZpAZdD)FaG+AWnJ;KZh<){T1Mr+HK7joPg4UhlF)UPBi&aK%ZMjf} zg32m&hpc&%jiVzr(>AL2Q1|}FTfYa>yHhZ%ZJGM5KIO$k7v@D-$Cg1bM$Cl|~c^B2SPiz^`o4HO}D$1e2{4RK; z#~}Qu!72okALV@k6*uE^Y=-f`n%_5&$c{WxQ8gfy(I*S0i86lijtv=&5ebJatu#g> zza6qnOk0`U`Ax9=al?A`_Jm6F`JSe`35l#mln7dMg3rjZ?DCc-3dhffE#1+fD(S>6 zA4y5usAb)hgI$`5W#*8R35^Cf7Q(#Gh}ip^xcb z4q6yC^TWf_5YL;niNgH<>XRkeZn0~vSeOjSj+V@A8uNGh|9w9%i^58Xwkx=r^e6uQ z1r~Jl@R#dp{gQNj|17FdbV;E@d54X*%|1W`Ntue2#_Vdqmef0U$H)rPRu`y8hfei6 z;s0v^fRN+R?-BKh-nvOz25SW?E2q{^qTut@2kZ10cd{mr8@|jaP?8Sdj~=rAqul(5&Q*(~$%SIpC#VU`fGiYB*8Xik(V*%-|(YefmbBQOuQfQyKe*M4 zgiISglJP!QH#T&JSHCU1zU_jjxm~r|v0GpJjGP)1b%_iUITLSciX{kqv+ODP`ND2$UH#U( zTW+YoIxF`e!U@?wQ@I_mhpF3*fBO*MD=5eBTu7YoE+f3rduW_{Qu)(Hz_a{W4N*>l zeV0?Ouku{wkRu;1xd(lu<)&2ePh1=XJeYKkaWej&hx`Q&(%zzzK^yy~^uF|X{8Fjv zHYgo_?HQN54guPrC(*#sLxvw4^`Us`d|!j(D)NUMC00e&S*nrK zA=vHo{iEdH3|NYdp@^1945j}iB3Zc_8f7xWz;ua)rkzJFo{&e6Xrh7rTcfw_oojRj zb^`Sos7w=HJF?a9dd_(A6i^o7dN2q&|00XMfoa!fL9^cE%C?mn#G7DRI@aPXi&>Ry zQ;MIOAQ<5Le3N}quf&vdVy_+0|J7f05{7DoV1~8ezH-HxJQ%Am6~vdAeparPkDxVz zaQ#8tXST|N$j7FDXU@8!1|BE3V`^mYVmq>T$jhQtapZQ!#%JEFTVF2*4WZjY$adW4A0hKG`iGM0PS0<&j=jA}n6t6J<7-*4qGjY6{T_AT zUu~cQk8YCu*$@}!+SwW5#KeTKh)B3)J~T`2dWSvsZwVXp*(9) zssEvRWdY~&PMvRNO=RIU%LIQkS*}f+n$m>*)Fk)wa>e;H!P^{F7he{6r6rTI6nEa` zH{Et(HZ40;avdUXoaA+DWZMUMwJf=X-olMA{uD%qr3j_gcAXXS!-iIO=Wd6!$%f## z8ACUXeMFQ+d9>6xU*VL#&*nchLSTolhigW{trR8bc#hR-wv(-kmy9_{`TSD}HVloQ z{8?Ff*-F=i;P3gZKjNam$x8%GunF?3Ye0l{%QZc1@aNCl-mQ_$%I0P)CN=fm_7&ll z;D!B6O}t-Xosid7ac^OMI9<3ADKy(Bs%mWI^h6|byGyx3CUbR+>+yx%RoxiJlfvyC zv@i{8$GlTjMg-+Fnkc$ZZt=c8bF-DAYpVKO?9Mo?qGUoi)y%l;c|~?~I05$a&3)^L z^ZGx3Z8y$pk{Cw?&vK`JI@St0dM|l1uiuk7*4bh?wr!PHmQ>B z?3YDrE)D?EA&d?vL*$B~MM5QeP?DwS}V&)sS>7l)GYx zF2djxG9A>YVyiGq2xr5{GjGH`zNF!(!PPM%gux&~>4F01N=N$*$rYbIj9$tfpAr>& z%Y{;igFf<&S!1TaqDI>obbiA3;*K3p-1p4c*t4;qI1&0yvzldhS#5Q?7JcHU38s~y zYCwF!-~7vz56Mv#oV_1jG2?$K&9ZTdAA?_vDmi4!VG6Q|7~FjmOh+cjd|zUKv$%EU z!agBWJQj^$OCkjG9X<3uxx!GX0~yZvBsGSyj?_NIwatu-#gY7sve%18zRU z`2_O|XhV^rhsa$zdbwqY1F9O9bB)c`MGF1IG=c}iFy$Ax!XpUZrjqF4S#%;0;&xs5 z3l?MY7GKDAxf(IVgvudo3cH9shu1MVMrB;-##?})kX7;hxjBLdjq2Ma!!D5gdvSSr zd~_A+_i&B+FC3GCieja$?Jh9#oP(W%!@}2>_9gHs=fReVSiYx?X2wxz{*10cfuw^= zLg;mn(i#dSuE_C(@o>Cm?Rk$byb{`=aUl0y)KlsQKfkn!QW3jS1dnive*8G=zSSXQ z1o5Yu7sLSbtj6~~h6ykJcXA-!GgZM}k8gy0@lKn_!EgwF_wHq_EQni|IASCym>?aVvRwXg2}}gb;uR|x8^D7-7nN=*^l2*vCXWkPzLQw zfm*!>Z2r!=vN<-TY_!D{qKQIp zpNwIdY6b`QeK^lJFY;h!e{n=XG&HzKpva1}RuDFxEkf&fI78s}mR3569nQZVxnGWF z9Gkwa^!deIMF}GwpAvbq?xRIBmDSb3({rrY2B=_prvqe7&J|j3ONgyqxlq3WbHK?U7pM!} zXJ`CkUrNgal<(eIZBl53@<+GoxZ*Vn9oA5_mB?7ne#*}xo6D*EhuUSwlhF<1%j0>W zmF`HJXE7@8rB<{8$JqbaKG8sO<7doeZp$>I9GcgUoWmg^X+X* zc&RLM!(nlKkk0mmud*ny!iW;Akq8|kF*xZH^(r-7`})h800*k>wv^_*1Cq!ggm)<} zXDWx^aE8MaNx7e-d3~3KrY^y=L8uqnoW~xWSTx|3SDK9+F6}4VNvOK z^SOs+kL3OrC2RQ$VI{R(&aIL5cQ>p3LtA0Hi#W!$39vVr&F+u*KWbth5*15LFm-J# z^zTo4>bLn~Tt-Kv5_P>O$TO7F&IL}5p@+W-sCtlD2Y27VC*McOMqR0VMyym)G^(}gOnfQf~)^q8I=Hr zuXs$ZwLQi&0b31b-v>G}kx8lwx8Mc71T5HIFJ@t=k;u(Ma^ngHe>2$EGpp9oYggGd zlEP2jI>fXJEdEi@J+RMmkfcn!E*n51(!X}J{0pW3yPAIy=({Y-!5hRKxlVXA)Oc3f zQehymK~P$c*rNC2TKFiX^Lo4atrw2>;QWNqsf@`@C2LBObj;;KmVt5V<7AdQ`HO%7 zl>yttY&72oF>j2ENluCgo?ogo<{HpV%Fm&Q-v@*-dM3Rgiub?kB@_dsg?H%G>A3q> zTwgGNHnvCDsHdZ2g`GXiqyl2=3tv7ZFli?M9R1cVE{Of^&z}R2ou`O^r&D@$wnJc7 zV<5O{6O$=nVS<@-lbT{P7>s0Gz|xm84L>m#S9jc2ASlH*!7AX8a>MzCfdYrb+KeYY zL<#{{OaGZgLeHeDaQ$BAxev`VIg~#P&L>f7MFS+VAX2Enr|}pa)<$I+~aQpj_y<-76z>0D`65#hz|4>kLW+dR~#5xN^*38^ujbEB-Te_TurEO&G|3* z3k&g7r;fnJ_}%^G^3}yZav;ri@$rcS6>?#5(cI0ApsK3MMlf~uopxdvNye6?D@{B+ zFZwovA^zub?J;87kYJsT7qTt67qJ%RjrX_O4T^ zD@y!h3tjrF0@dimsb7R$Y-rjahYwIQsfHLO+zPZ_9{XVy$zYy~izgzg^r?+jt5k^e zZ3goP2^DiSk&3o~`u?K|L_o_j69zJG7#GMX8FO zBv%wpU2Z;gm_knix$^MoDtFYc$W>8niBDJl-QDu+TwD-aTlD2tpRUD4%7?pC^$$(P zA4Dn3%F4_f)$~aNX_OEX7uhi}#m^Hl5TK+63Op#zU%8**FU%{h?g&k?^dKm$kJ_a?F|-s0@i8)Oc5(BG-0I zM_8WD%A+qB`Lj5HW6t}`d*J4uz$GJJ5#J%AP8G!&M49yLy7_0x&`4b^Rd0awLYM-4 zDngabP>+b7rOJ$_s^G~sD@0WvyNDcnsy#K%Wi%_6)u6g$RN9O1Lu`r@Q>{Pu>8$Sq zHSvfF#EwSnfOV72F^CwKMEg4_9dx%;4$Y-Q7d#rIw$?=YxnX|ukG0oE)0w(AO`#|{m7+TT?B{(p>o zmXtLk{>&-)4U!KY!t2hn=6|AcgA<$a+L0cf_<+RgJFPb@jHW9H z92IbtcTMPZ2S&K{W_=g?19IrymFgM+c1}(Ub94Ae1;nWJcGt833%F`zrAKir6Z^_n6FV7g zLj`^CQkW>gv!3`;a+UbF_?9j2jh-*KuYROruvRgfl0n;x42RB8CqgtngQ(e1a}tz_ zmWyyg5-#H;<)Fwh_7jDV_9V!;!t8NIJ^P4LRqR?Jn2zKv3L%-wW(UhWWXXDnZVtxt z^6gHIyGbk+qOm88O{7;=OG15e`HtKI!Vn^ivqy#=slupzkEVedSu~2w^rvF%k8zTk znc6#a#!q(c<-uC{EP}5uPcmnUk7x8%b;gZ}e?a~h!^()tnTQ9Vf-fj6tgNpO^=gR% z7GJBoyBJTk9C#`MooOWSrE_p_c!wZ^i-Z=K)$21*kie@MGTI`)cCKnRI@9}0_}u|S zBgKyv>6@?uNl4KQxeFz#nE3HzSx3F#2RGvs=p0i&V2~i&DAHqO`6|y1DP%GP>Pqn6 zjW%d7dVHtaIv`oKwpC zj@qSR5|d}88(Cpic^I0dD($7*KDv*BYF8ilcn9+W-ZPF3TXxTG+WSyH-0WN-ZS zWBJOYwR;fEUm+gHI`twL4t3?$msqhq8NxaF!e^r_|yhfcYNwu{pv9n#cC>ZBH6-*3u!Y8{&LlfNei#gsaT#|3ow2X)P z#SUspCNu2yaUZ6=({YveW=V=qvZ_Y)S#FZzCgtw!5aiTbZT~EtoaCBkJ2=npz=%&4 zqng=&Do_g6L*RguyKvaipB=Cjg70^C&E$Lg&+m99({FWuUo~BvTeo)|f~uwR5pWEr zxJd9iyRWoHCWVYLUE3Q5ICJMlI$%oL*>=w|e2hrE!vv=wTR#e0-Rd!8qXHzp&R+T` z6=RE+PT3XB>?NnB%I1!>gTVmESf3t!P!QtX;o}3>Q}8g@#sSV1;KhS@d6Cr^Jk!?J z)^BfO*(X$PnER%g7`sbpcSlNKmyt&tR6j3I60!n^AnD)L5GG`X)yxny9gXFd@vIY$ zaEyDmM?`tHsf1Z^yQNyZT8$A5#+vN-C}F$0??irP(^|c}ziIY?e?BZ6tl)eEn%Kf}K2fCTZUH+8e8l zd{uoy#d`1h-koD~V3Kn@@7%WfREUAKn5(xp=JMt0E!D2Jwv8Zpa%!e*i$q95f^O$u z0{dTl;a|rj(0}MG4z{r_mMrLv*n>-9CX z?E7~MN5^M=cgLttz32P)fk0DeWNef(Wzc0}Pg#EM{&Z!U`%<|T%XeJ*5W%S#M}1&Z zHf2-}WDfHRth*(xo1rrqF=6wrDPbQ$M(nvu%7cqfuIXG+{96UHkKDWvN@i<%E+1Jx=Hu6y7lUrlZiG} zK@?$E4KMqbd&u|5WzeOIz@~;A%dufM^?16Zd7kmJHszAA1@f)8#lhk9V|d;4>&TzA zjRS=CjeZR<|EBP2^TEU;mX^irgBrPi^1yI?` z^)Lba(t4OX>tF%S;Xe;@9M8+E3rEzZmrZZ&Ykt(!giOzoO&f5q4~2*vd|&T$O`lA6_=$EC?O#H6 zaEQL{U>&Re$jmMY-AGJGsIz_+H(gy8QNrvs!^}ZaTM1<&+p5#(bzM`;R$HcbIAe@f z_fT^T*YD#-GS`VK>U+#gu~8$aWo#|*#xon)OM9Ly&4Yn(eRUKH+)u!YPv#ZqF%^2XcjKX#uHH2_<}4q#B|`srgZa4rTL8+Yeh znZn{C(%=mTW{`63ZaG+hhYu#86>Ll}V;EP3xigN6owKp*~?V-a_bY7W({K9cWZyrcefS zfa89M0eK@DbNS^xRjrTUn_o$JID*Iu38$hpf>+ zDhNzBgkBn7Z(g7@d3)PT?!7fO?D8rN0S3fF!y{mze8$JemzqS6G&`Ns0Pj7Ufb!Px0x3l9|E0V$(6EKpvx?J`H6o%jW!3A+RCMNhRx(5bW zt*oq8pq!%7l;97Aj{x41RMF8m_05gH&Zh*NoSigYX$Poe|5hBuVywvN`1W!0(ouE^ zy-r=|kARKng%nRV4@GalUZXx^?#_;FSy{OS#2ynB6&3JP?OdC_Qz@NuZbo2LGNYlzHX-843V{fER z@&cykhYB5U7a6Zc_erhH!v6W_?!9{`e{5@Q?fHGc`C9!o9uhp*Ha*@c%pA2VH}~+s zKKG(be~k+GhwTrd!Y{?2Os)V0@uRNp$xMJ~(doHs9(EbN3^P0}p8qRE_+$1^T?PaS zc_u+@K^%t;y}6B06eiIqQK@phQga_4@j-)Xa1Dlpgs^YyOidAiYtaP?{d#|YKeS;3 z9vZczZ}8zQKo~x6K||NC|DE=jD+o*fr^(JPT24)hcar5c1O&KjY@@;|BMkvCbeFw3T5`px#_nBux zgHer|u5CsCpA>LNVbxX8vM;2=tvjB z^iDC1XJL#q%HP1c2a5OYZM{uh2PJCwtMIEk(0VyJSh(7Qz}cc&eOVf@$7 z_4+N7kyCCyzPNlYWAK`P!J&Ra$-#*?_w;-YzJ$fZey!(*4Fevj1K?+giHS!urhevj zb{LtGeqb?L&!awtSZ;`iuf~?E*b;9ChTEv1)t5mKf_y!^4~1`THvP{KiA;fAo;O$jZjMP22hxa@ z>g&sgzAd^>Tz#($(f*<)kFOXS?!Q^8(p_@2Ao1eJr)cV#SokvJ5zY3yeEGep?5W`k z?xyAUs%PGzd-X>xy}Hfx)axmp>u;-^O@y>eM-C*IuR;o}RMZ<=Snc*1QlF)>|?>$c!{ z;_$d+m!PQf1mzK-Z(wh!03;yvk%;^n)Rq6ObrF}L_@!S2fCrkFdVJY;~{8+?)*9NkNZ``i;D{vsC&rX9ve_wn%Ud4 z5g-*Y{2TQMn)gE`8C))fEpcmDYn4U(m{eg6Hbx6$Uzf;MKAY&h^tX z5Ekx33I?_!pKi|30iyHP$mmyRCjwcd!UguC??2zO@#~Y}EXcFppVg_$s&HVIF6i~D z`FWNyaOM$L+2MaTsbm;jn*Z+gpZ~uW08aZm4%Nq)2wGPXqa^yjcCB)Aa^Dw&$IXRG zZF>s-@}D>L7Ofc#)!b4cUPRS}YW|wT3M=8XxtBVus%8Ho_?PEVGDX8Ek@J)?E?kI< zA~}^&TKB%j z>;<<17>g>Kno`r7fgPd?cv|2?0hXt^Q+BKCz0u%I4h{|;`AAlgj0TOL8I8(x8Zy3> zcQZ)0D(?};qL(uVW;JGM#e6K|e9wfnne4*D_SQKrXBgV6sbIbX@*m63Q<>753D@6j zh~JM2X@`qByc{SK6U=ltZGje=UuxHx&sa@)>MC+FoMrb_O8X5tQ0UC5Tp>$;bB%BysuBEAzTT1%drDmtNsDq2)I%UJ2D?+Lth6o zgyA9ya4#dC8vVjXgh;Iw95_d@j*F#5h8JEEx$Cy^l`_-V;cxBz=&Ldyu9@w5+sZ$% zZA~>Su$S(suHRHr`!YJ?`PjAZK*fo#Vbe*^4;7y;<%Ux&&M_d7VP2YKdnS2Zcd6Lc znTRUr!4b2bsyZ_FAeC2Co8P%~G+#Myu~u+=Ic)o|Xi3}-OP2c7=Kc2#Gr|td+&)C- zMC+Hs&ov@*P~VTi3ExO^bWM&&(3hv+B18j+o|cwwaIV%E6mXGPnLSHTey3-2C7~G@`Xo>4nI_ z%(LoTFQ)GLPuHi(6+au>{x;xM1Sp>?eC$2ytD%Spq=^g~6MRnQ^96QjLSJDVFOoNY z&rI6Q9CmbcVYbk4cmZ6I^S;3g8;*>O!QVrUvGJ;j<0;{JZX@Z#>x~LKg}x$9!N~d(0YD!TKTDuC zE6u&UF{W*oBtbKuIY>?f%nY02O@QNoDIW|&yGqp<<4cHR#^%_k(+15c_-%K|F%@!h zh-E9EbNVyG3eALjPnc%SG+c?TP`D ze*2za&71B>@(-V?*3_QRZ_e#E?W(J)f`BPqT6#K& z{>fc*S_j}F$X@vPN$38LgJ6}E<-H@ui>|sUl6ClzZ@@q0l8TG+y(06gK&C8;M*?)> zZrpTSWg!Ft{D4xUu!VNTU!?fhzjivHJmYTIeU+A47{W3&v{fX1XOCyK`|qc9>9!g> zqg|Gyr;PR(9ll4$1?QBx6ug9FsQ-kjN%DnV5TgSu0&4rB*uw1XXstcU*IMuK5s{CM zSagIo$*m7vldI+Xg>j*pNY`!a?#G)5-_-s(N)$MIXx{P%iH1r1a16!4!2tyU+(4~c z*|QEbMacSzIa`PST6(nq)hZ!@Geq(^G^dCOF`9+KHhFGy#!9SuI;2O*<_*h4OXw1g zSdAL0MXA{Rz;F=&3|uuWJyJ3=6{}`{0l_Qil7It6TsjP-Cq;pF2)w4&#p)E%@ zUXus4@_UI7;@I7bSsR4IfG!)g*PRjaxBg&1Ze|Y|bB^Qo&1Icwt_YcJlKIrOX*7WWs&dI! zUV)>ydR)g*)HwKUo_@Q6{tw99o8HIRJhd`f^Q;~UqlJ_;D7Y!JZ|9)C8z=_nece6CiebwJ}@ zi%zIJ&&^zMs&}y_9XlgI?x%P5_|k;9woD(2Fq-Pd#v5D6!8%VEM$)Ef#f8y@3+6_1 zl0cr%HKc54X-&t(oaxTwGlPxJ+Bk0HG*(f2O^em^Y#~niyJwAVW0c zuj&61J4g6)Dn>#{MIS{HFYxN^LNm{M9cra&+4jC=a{QMvb)hS2Cw!iUt;!1|l8IC7 z*(QE3evY2YOHN9xrk;Uh^ZVPEOULxttP{ze%gnH2a~olzkKu&h4nOwhtq)_O>PkN^ zwN)4xe9s@Ic5{4;mz0!L-`0k@MUinB(Dc zzXpOxdU~U&D}Vg(2aqYiku98@IQN`q+-ZZQ2<1oSG#Ii(6ovvwgR&Oip9vxIeqIR1 zew(NjsGL23ui+$DsnaQ)hJm(-cEW=cPLOav;JV^3``#qdZ%bW249?$n&wyQ8KER=0 z_~v2q#ipZb_g5*{&KzNZDS`a-8x=?~(I}RxU`31EkhJE#B5XjXjs8$}?}*BcTTEfY zN?WdL*@C!zru~vTbLkkV)I~#q+0+gHFVB%6LG83k5^@VH)d{a-#}l!krA5AJQK4== zWUn!~WNHN}(a9}`iePXjLEs+6~0p7FP zM%R|=Ssg}%z)Jq^V*mK!9#g|-KnzAgci<6=HkAqMhEu9C6$$-@qzcFLYO#UjOMJ0H zDi7VSygfEbdQ=Lmef_X0YRdbYPn+mS&i&#?l!qk9pPx~nC_GOxFAZmDpIzuK3RLMW zpS}2^JR7up49Q{N^j{lBW_WHpGjzy$AYZ#2h9<914LfXZZPojd+06sY2ko>4u3B+2 zdhq|;O0=N7!%Dpg1(xf{BdMI2wGzqIt_1--I&ZTKlthUQgw0g*6RObB4B%GNo@Af^ zj?*8Q7#<#GPaOl^$N!_*8I)Zk-Q4(I1(b{^C#R$Y9^M}LP&<(=7!opA z^Nv}~NJ5dssrlUYb=rR*GW^8~L}Hbo(F_a#v&bT@d0|jJd55boMS(;NekgvQoK#Qo zM_LN*-Vhf8C3FTOqCprEghDkh&@BfGn@)KT@>Xn(og>!6ef*!_G_AtzDdMt&Jp^BH zMw3Ocn#M@ZkLjz#)>QOE`3#2|MCq}Rx^Y@@tTyK+VeG@e!ZS=mLtDSU$PS7B9D^+4 zH|E%<8RLu+HO$jU3S+YTxN^1S9l_!}_KlHAOZZiBvtV?|_jLt4$#|i}T)V;gn>@P) zK@*{hVdvlF%T+?0GQ*R+C*Sy@zPNr69)`@ zpcMD9V?2>JiMg}0vt@U_v{ltoQTT)t4Q8941qC2y5>wRbI{|WZF~4{k&Cy^CE)+|A zZ}M<)ze)EXD5s?IE3-%;ZJfj_6%jV|oMmc=KCW#G#U%_!YWhX9!JShcUTN}_WNkKk ztEwYwx6ia~j3KyjdE0L%369;eaC}04zD?&Tx{CrsO}&(FJN%g-!upqGSRudJ%dJzI z%7M&^+`%^k`k0EixG1Yyy!-J=b8iVd<&HqDy`=oAL zR%0Kl7B1W7JxHELvuQqwX~ZQnyzs`Sjmn#YW_wFb(v)(jxv?<}lqW!80lRw(KR;@q z7ka`&!O44?>s(y?B_FO3pEM2Q<60N9OG`a~6$E_!6VD1X4***k`q#(ctIUeD{U-=X zJr#2uO9K@ZlxaCHj<4$xLY|}`-U9yfmnLt1JM9?!6@(Sm5oS0Pv;Z*Vc{E8;1Su0a z42dY>%WwZ;rI8@O7%!&0<`f%2-rYsA8@XKNP0VnH6aFTPup+j2H2xMTR)RWiGGD(8 zzTr?&cqR)i$f3abg~vAKVP98$8A@0D#7GtNQY&f6oa8P^#X@>nk#vpD)4{9lp{cJV z1~oPDCcU@P|3 z-(P?GW%r#fV9U#ejtFzTNx}%#Y)F$j`!4M4Zqh{VhN6ufu0c`zS0?fsb$m)vrmzKk z6hPu?=+sFgd~=5#!uMaF*VSa3sv1b0Fk4`KhSr7+sxNtOx8}B;5^C!5|K^J1RcnBn zCbSNuD#lHFOpte2wNOr$7wsC5<3~C9h-`p>iEx&6b5?RX0Bdoe&UyPcM8zlgFUV%#Ov;k;?0{kPfSCV zHq5Y`7~thkGdD2V92yycI5}ar9$s?G>63qBdL!8XAr%Q<%k#@66~^;k9F<}zS}UKdfG%}8MvFXl7s znk2$JF9y-ty4?-_%9}On0yAz>&qpjbl#t;8XAJoDAD?uI=oB_~H06uN;LmOhPsykK z|N0XDwx_A^g(AMn%${?*As7GZIoh8Eb2TVnp%DI_nxSuoqoAYe5cp$WzHq)9pkg`P zNPps%o2ON@g~(DLb49KP(F}=(m6z`CFD9~nAPc0-(%Q`1nvOXr&Hc>EL=S`mBNpJr zyFjukFkl#Ss{0?92r_K4z<~8%%;E_}1DE;h*RP*W`RuGdkSUmXdy|8D3&MhW@9sWX zDHmN!ko>8tj1eoZ@EB3vqs&J@BH~;V{;XY!2=}q%Thdo4{LkASUHk-iE5e+gW&-x0 zFgwY2w$p5kyrm(8_}QU&J**1S4CwgM{6$#FCy4Hn5P!xVEi073ee!p|m127+B}Z)E z@d@l5=Ft3Z-eN-5-IL#9POtkwnaaI&#z3py1)5x%lm_43Z*TrHWz^Ny&Rx*WXC%B? z`f*OG*wBcTIvvaE*u&#_E($~=<`n!_X*|YdvN1o$oEeU*M zWlHLID-5+{Cqa3G&62DkQ()SkgJw=*^()AJm^OEZ&YLzwCm=r^TQpq}sLEUpvH*5h zTwI*d)#eaBZZ$rHSzACbwE0@kGC)&jr_NlhD=1}u=c>om!Cf395Ds+P9v;L`0=*go z{h0Sni#^MJkCC7QrJw|y>IE?XmxN!w97IC<=X56dcq;h>l>^idkPtx|8(ePItRTJhdh#TF`mqhO>9uv_DN z3>(>4(rW6>8}C4tKU?<_%AFmClgX`+JMOIz(S-Yqi^qPn17;m8B_OTL zEs2LRIEOD~5`O-LG~6kBn;Mn9Q)@nv2e*xW5VbDM#)O6d1Z=A6>QAlQQ*{AP5-5E^t&Gy^f zjnGD9@tFq1S)xEH)URAA3dN$YWS=be5%MH5WiSms-Vht<^HV(M{b!g;&33d9rh9Rn zVK&a19xya*(vxA4Jn+*#f;>*{a+&8kD`Kl?!Bs*0vD8M7W!pFTa{m?Yl;yD~g6;cV2;$^*~+A;Gy_RVeCxL_tT_q@`9igj;-MaP6(}jIJxJ z!nnb;i+?`K8~6O(PuSmTv-V_BrLZl$qz`l+O^fU_v#!QqDl!MNp)~GaJ2EvC*|yXi zrQ2~FL|*19?ud42wP`4@7zr)lF4~L5RY+`PwaqC+>=z&DIyrg2>G1)GOrBcj`7scu z0>mvq&=Q`WP6-sMmQIS^-ri4YUW0Ptr?cHwU4-DT{oS~ULVS;$mN1tR^H*iNYS$ZA zl-S_?p3p{PxpdrEB{*taY;Bo<7QVSz|W`zvQ^M9h8xb;W!S!FEI#viDt zW5o&%6f_YO;i%l&8Ae{8dBhS=LQZgFv>Xv+O4!8dX<{4Hi6Z|+ht)N9G_O-KGP~Kb zmJHp8{Pv#r)%mQ6fn{m$5);3Z`?;Y_7oC@L9`iI9HDy1D)k4kkr)@g-o?UAdh5}`1 z#a0gSLN$$9V;F4O=w20X3FDt&h+(-|!Ir!&)o8wuUO$<%F&`sdJZa%fk>K<@?DM*R z4boDBqpb%mSPlM2L48YHItrw4Lp}x{T+>#Lr5lLUZY5HNQLL=3`BvL6BqngUxmerK zpayUR@*OaFW&$TZ4(Yb6%J@hD7Fj_wQY|LELqn<_MFyax zfVGn+4H@XI0nqkzzg}IDfa&?EhXD=a@#%=pQ^3S#iROEjA z%@%ip1D$(hH9@UCu6}{nf4eM4T3&trrGK5M#SK4RH}``*n_a2esN1lzg^JdF^NN5O z-|ei#jgVKSniTh=pk{5mEp z^+!z$VAJJ6kmLS4lM?9}%k8(QtpA8>+KPsQP@K-geK%tq#njfY#CT?P49)3X7~!*s zpSNffMHf668WMvK5NH8^02Bdq^T7V$sev?Y^XIV02w_=|uZ2vt$>?d_KHo!{f- zZx!5KCaPnp5& zXKFW#C(PPP*{qWa3(d=)cXCZf5`wdVlA|6QV*lb6?u1{N1L{^12~fl5W4I+vNX!igS`Lm<_Yz z9ZE*@!SVUcfFLNtzi*~(rPHRq)i!gU?dx!pA^h3})SI{oZD7^BR(TpqbHpRr=3ibu zoZ;qRBmzcbY$*5#Oa5d zJ+A)|Kc1eR!eXMoHl|+uo&&~&tMtD2=WjmUouQX=%eh&RMH*1HZgqMZ6TM@{M`OXe zY907Mgs4e~VE$awtKI|`$%PEwu0}MiT^;r?X`0Sf4tk?_W%X3jFF!d$k;F8qWH_w> zfWZ42_BMFnJznp256_*!n-|M!5S*BNQQg^?U^qI`fO8vUWD;_*2HV+!lW8e|(n&ef zzX(zgI}=Q>)7}Q*|M3q7{efN^pGb_3o~y9bG*qKIE#Wpi=R32kQz;i)msolh)DV4c zpIwI0c&Y1HrB6M7UwhM<7XlmCu3D;aY@tjrGh>w=6cbm??q{VSAdKTlE%G&xqsH`a~~%8 zc!OyTR^gn>A(l|mswy|}VpT^h_J7oKOF2BlRb+(bB%KP&FH5daZeA{aaIkt+(!${; zl+&5k`i0ltD{ews3|#mB27I|mT&EQ@B58$elQfyj3zle$N(;>-_f4_Hz4xAFgGjqedxhU6PJ%!6s|LF;kp+MeA^%lqlFMn*WAF zedqhHn%lTnw`m;0?o zHc*ur6@FdN!zdso)X}%g4B!-LkqpGiN#iQAk%Km`-{tg)q#%Vx3Qk`VrHYrJ znUbVd{d)UDRou)*D7L+TC<-((G6sMT#^8rL;;nR^^80I9a5^z zZ9`qz-CZyyxx&%eH@Iv__3mS(J97lR?U#zi7*RC zD^jA)Ey{dwC6onlF{pC^s@4ErUybh6j(+ET+Oo?pi0)sai3>ZK_>C$0K4F!I747Y!O18v6 z!M$Q_^OjGIis&T|K_0iVs;VmJX+e~Q&o2hRIbL76dIHs;fYv`$m2&b4`~3cyuH{oJ z6XUhFw@78R#5&tg+bM8#JwxNrSuBPdP(Ta$hRu*48((g30=HF>7LFf=j3#xk1PTVjBf2jI z^+*aFG;K79rkBFgK5F8gJ{n)|s;)Whu?Cv8$`VOkBz!(zw2&oawG!Lj+mGY+=cX8# zl2PS}yB^>?ezgoAL$G;k4?he4k;ssnwO+dXN;Z|twnff}%_yhQR*OK{XL9#`tLcnq zDW`B-Kw_q%K_21Pp{O=e0or1~+3T}!b&)|H8K-h-U%AY(!wIQsM18J1mCm z3Iw;o#e~H_;oBFtd<#U_5uK3$)wRq9b9G%yUwLZNF+!osRbL{Haq zeMm77Lxe-?;fyTY7<7ILX7q!uEi9}(lXCQmab>Ss@OC*T5U==_H=WET2L&>e4NN71 zkhCNaVlWgccKa1g%`jalF1n?HguC_f??CTRQwgN#3Poy>REbS-k*KLid1XiEch9t2 zF=;%vxcn$L3G@1D7G=|hO*!_Qi>UJ+iTnI6F#h_I(8A>trh?z1vj$N>#2EE?b}zl` z1k7b5%j=UJMRKA^UZyK9*Vh38Z)=l@+M;j!s+S@*o1<*wX}XhaH3@w4EPw8^m#=?E zkBA0sQ80bYs(5Fra7N_qW=1ZXtI6oL%^=>8aEVg|)H$;mnNTCr; zuFZ?)8gpg)sNR8?r{&q%Pk_Uekw$p_oZJf0-+p*<#!XKp%~8T^(6!0`NL>ISGl5SE z9l%#Ou&Ntg6X5Jrcj{pmN9n&5l>Ov~o$r`s9oJ{BTQnk&SmtVy?W5MCULhO*b6k+1 zhqHs@&-Y-b^1sLvuzb@~%A2$>)2+XSfyi3Sg)`+p`39SA+Nr^OyUT2m9o~{h)U4!= z!&4Z2erd-Br|fSMOy-?mt1+Cdpe{<-VT`4{$}%j5RTIKfdlL}d$k6~Y5^70FKWkQ@-Q$lnZ1YhzI}(d6Id$irl+UV0$Q? z-)4y_Js*mu;TxKQeQ#f`y_JMLcdyFsDEjY4dL$t$O()*ZI@r%fL8ZEYQvRliEo7J< z#-uTNouJPiSAZp5^5O-+>kaK%vy`(ycE(a3AZY8`W@kz5!PG@Dk#6wQG;#8G(7X7m zV{jaRV2pii_|$$a;1?cklKr@KhM9au?5Hf(3`ke})*VCXBnf{x2ECnw8+CFlatM8= zjRF%Z-#3O%cx<{*Nh#2U!5E|9#5tpg^__TUu0JJzu@(LtzaQcL+^+S26v18pr$JG7 z#6sw;>$<0Mhd*d`@L4T&V%};iBRj{&wRVAqj58vX(EZO z)U286{YFvOhQgoco|yyY`d()oOsp&fI5qeI%FK)u_CyHk_9?be519jEy9`)!YrRCviq%~Tjmw>?A$ zVf0c$ukeq(Y2Ham@XODqz%yL$ZMQmYHvm)kApdM;5}wi#J<9;oLugq~FEAaPAGE_l z&4~8IYoe6T5ew2_u;R(*5EhQi+k8}-S&>!S7}43{17=viIXYsus$&Pp%!rU4VZ%YT zl|SaBx{rJG^!3{-zJ8Q5YeYO0KDN<17L=z&DSs!G@vWVtzLRqsH8cl(0!XJB46b;% zQNvZp;&+=Nx^^7;B9qJ@o!-i~t(ljPZ5#`bu6~B32#3<(HIZ|gDU3_sqDYf9f1&)u z5E((zJt~wvbCZ2L5Ha-)F+;XPk_Zfsd5wwIc+i-^C&a5ZAlaJkOxePJ`2?VgM&7;^ zw6V?_i6XPuj5b)Aw3~Wo^0DTWAzE&B>yqga{OpF>V&Z%LJMIaAe0#>XKRBz!w68vA z-^^N-M}~3FsK$;fbNAmk^zXb3B42#0l4g~i8YUUcGUbH?xRZ? zoh|O#(s=VpS3RU3u}X>upi62s#pqB*KdI zD$u=SAm5lu^~L@C(ylo_Y3$M`alN&R@s_^3WlxySmE{wySS(fNnJCXbc4>L(>+0I5 zob~55Tggx$@b};Sr`sm4KgP=d%C+u$f(f=}p>XCnu@f4ML*L@(_?ck=4iS#l8DlI= z2SJJC+|AKTSN}0ZIi(}{)ADV9ZqyLr;{qFWBr29#J;7alim)RICI8=wydes_4|4oF zDaaGfD?3+jKP$fCCYF3ssG>sO;U77QThc(vTdgpVhxEKwYWMHo7vvGy<@X7uT-A+H zlU$1P76uL%*6Uz9lg2y?7=9)CW)`VhXW zfftTiq=IfRvL2tY$E{1^_ndHyd*gnplyEN5%*n|9v*rjY&LG0M%$H|)Z-s3OJK+1@ zE1BEfyxhb+Af%)-E7}jYx9Jo2e-V9j6E8IQ{>nDjAL1@fQ==1(NH7Kz4Sge-$)98| zMn~h(kfYMz4W0f-2*A{-epNuv1I%UxSi)@Z)>l_)UVTYR0Xhk5KywuG7@!O&=c}7p8Z0x24vi1wZ>fW+3NunA9xnmrkVn3zldyQ;1l72wGwL~4l z6F(kM)-%xSn*76)b|D1DH?iSFIk|X!ZaGAx>|{HnI&|{%2~)rP0QHPO$?B5<0u9nt zChqbW+E>IlWdSS&JtSOCf%Rj0x6s)z!w+182vXkaZ7mhU<$q5fEr~6JHok zZR2PXG-|NB#9(&$DdiPEFit2-(Oxj@ed9MkM~%5RB77k;hRe;vwtNW7>Oi#kaz0m! zftK&!*?RPh2O>Dd0^TOrl)LylOCk)&(C0YB}*|88{S(Cy;XGU!N zjn7B`rTeV8-(GfIZy;b0jlg3YdDk5|^^_Kl`N50!b5wqE|{e+#NMO^XA~@JD%A5n^^Z}IL0Y~X!`idrOS_t! z>uLp1u8+lG0iDt!UrO382L3)$Q&Wd<^kulOt|~7O0aic88v~%@ta$bu6*MZ|uVd8b zXeM>zKS7cb`-Ms;MI9w2P#z-J5|u^VHr0`zgQFFn`fM;hNtJ|*q&x^&E`@q-M5-kn z_8iQbh=8fc=x3v737fyL{GyU@ZGWIC!^?l(CRc6DXWDA@a|@&2f@u$VuIH^Nue7-h zUQvnJ)ZZwrbp=lJd&E;xHq1ril|I(zBX&*;)3HvETMV9L`}Y{=KN7L5w_8`arO8e1|xV|h9 zLVBDXX73`MMRJKMGqeZjEl;FgTU#@KrpQA4(H5T+W*}bk+6+?+&cS7dwzDylu1WbF z_Uu)B3f#KdI<7X+TAHXsF+g{qn;-Yy=7rp&#VMuuVQNx@a+0^A`I1klkGsr?k3_ z-ytQ`N-1C*a?oi?IZ|&)BxBIwP}igNT;iePd}(`BQp8P|5|8i$p zb%#8%o!)TyyJn0b{Ki@Dcegts7BOp2ZVyAXjX~2c|9umcr+vDz$H86*8k1LRSy}$+ z7(*}a-1gSh#rk^-Em+#8&(j&yA4kz>u*3 zH?x^H=N;^`#l(@gUVXtA=Amb5_oXA(t~&2Cf3(%;wyM;rri-3Jk+)CQ!aKV4)YsJK zMLi4t>s+DKeqV~A&fwVvgF%@a`A6Y4P{Razb50&^VIZVlSX^4I_X&0|XEj5I5hlisW3-y%5ObCwFQuf%LolRj|9Nr!%0(%FMtnvtohNACQvE8A5IZZ z=Z`S!s@X2F_YVIFg24C?9U^|}A{KKZf+YXbz3o?B@C&myytUP`Nj1r7XbImdDL%<9 zvNLsSSD2Cz3-Q5~?UPDsBRw{M9~*Ss7D{ewXO2oAmDub)D(0s8aXaW+(Vv3YlIm`I zL@HBRfMl8Yev{Mm?ZX_&rHHaN<#VO^>{P|H?;(vBX*yyq>IFxn3CqQ6#>q1wH$raQ z-){3gA`NZ^Px3BPfI=p1+B>fw=pMJt*dDHd-S%_th%J{^WJZblREe4`CItJBXhG9G7tY~y9nI)V)?I#PcHqRgYZA0ky5?fA*!zCWHeO? zRhMj!=(0QC1-ZfP0Uk^StHo}+xtW!szha8~EHo2?B)Q>X3NaEH@3EqD#JD4O}q{|}aC-MluaLUl(Q{%-Hsrv1^f6AQ3X7HX* z2^B##BQ!{mtV8Au>V6uT=_>_om{6^o&BYx@ZZc;8++FhMDBcLE~&(6 zhbA?pz9`SyhTDi*lDWF!pVd+oS^Y-eC;qx>sd>}y-6}^~tt{Rcsm+3R4|6MCdR5aJ z{YlQsB5`ZNRmuB|LVJnV%BPzTPHgFn&;RNtw({XA>_Bb-1zzCHBI6yHv3Kvz4ovbOz)>4x=|-ByFIshDuD zNbW=W9M-FAvszE|b@FideEFhX?#__Xx)56+V|&W!*%y7g>{+YV@9l{vxViQ8jk{L? z3GeE8aO zWNZIiaol&*iUgl-hk{9oZ0N8X%t3pgdFpg&KR8^9CHvS@6Cb0ag{sh&K5nyd4BHj? zLWUZlhBZDh@rTld$p`hNAOj+*t!+AW4*iKu7dL|ZGFAAUQcrxG!Sib;W~P#xq`G)- zV{%i@$DWS6kHwN>IDQ4a{VAAK?pq}aula`jmB6lBX0e6bSxq}p)z@6~YGD;t**M-> zc6XYZf7##Y~@gee;FsF>Nod z@hLj0czi@i4YIR_RG2fP;T#46p{fmj)UNk*O2W!ei?(Kr&RDZr*jI*nB0RrwRrY=9 z)@wJ|zja!U*|{zr{r$tpff;y@!tXe$pU%-rOq?f%1crf;^_bBE7Yoz{*+$pP7I*z( z{1|?k4N6>WaG#NRE&9MXB)V<9#5Xxw?^V2q^Gb#4VY+N*xrbu>V6V?t4BgEEujI@6#Ccu>Pt2J32Stxwl8AGoyo-BptX)t3=<-^auep zfaVhdlPu?k_zW|9-w$lxw^Th(ZRkV^+!YMw%(&P)+%lgOME0!WF|2l+|Exz1bIKxk z54RX!thnjDbvsg3I#N7R&Y@7u8fBHC+0VBTe<~EC0OKi2P=6}vNnTQ>sy}LKxSq1- zA@uVZno!WFtiE<4ubr5KCWG6#W_g8JC`lvz{b;QF(!K5kTV^fJjVSjQ)Z~sWVx1w4?HqK}*!skZP%2scAti zfNI_T7F_B{9{K9D1f?;ExyB`YJAjgQcZ3w2LsXzRU{0@KlJ{647ZM)XWFO$mO4`q9PJJ zRO!>XnF=usED6XVE%vxQDpI7y!PPP9X`#;>uXuu`{~=L>P(Ed{McjH8u}Pymn>#*4 zMthlO`{gsk?jR*L1+B6784f)S>`@mP58GAo3o1$FK(6zIeCn-((*R+ekM@0>jK{QO z9(5rWzm^t`?d5(-jkQ$Vg9TTlea7pj*bZrqwJ{1?gSsC%^xJJmDA1hBSlP~{@0n(> zXHxtQ>d(0`{1Y;>;8IV1+Ky_%5`7Ou$P+Y5W?t$S$_twv z9Ui=SSi$~l;B|F+{TN6LID&n66fJ&ye~Sw>3hwR*w+~z(qC?p<<=^Gz_P^7hMf&4= z{civhk#8W+A`FxeL{MT^8hpZ@UJ%5TW3rdCY^IEN4}TIIRqQ_`K^>$LeJ9bw-M~eC z&4tm;MY+6bw2uBA)>UiU?a#LSn#?6!)k<-$+>n`sBf7w*#r4{dS#RJ&)Vr}C8P!9V z#at&HE?)FPqq_X4h`5 zyH!@)Z|a$RsAp&@j=d?dMOJ9#|IwJ9PB}vPcfM4KkbDov)md)R`i*^t)N4;U$^?{n zDVea>nxtz7GOb24byF?eim9scZ}9D2<*lCHOtkQ+nrN$euslOgdul3_hxi1=B7sBZ zE6YjeSL7zR)W<8AoNv_bVVBVxrwETfKi+Zk(zE7!`AV;C?ndbE`}a|hBF8EX!iB9zwxlZTcFJ4SyR%{)i~tR;2%pW*(fl{T>3I3=IT?d{19 zU#7!L*-Y7x3be~q;L4oH<~7(2JTo&mp?gQKnD1KgBc|D7$pX@uUN?I&&fW$s!hftrEV~d$}A)3a696g>XVsoG5OSlrw>M+GlsaXh-?x3hQ1Iv7R`STjx&JXkNAm0 zrq-anLyDs0-Mim_DEc48g#kuD(gFi6NkDZ8w2(X3*E#17xB(Ak6)X;r7wf!TUNZA~ z_jvxsUMJJ%zq|Bct#ouf-zgi6_lq|?R8V2>XfVW_H%Vq&c_{5n z85tTC0*F;cLYp*Gi%W|GH#o3rz4Qo4C(@)rH zhUQ$%mv3eWt2MukEsbWAc}-BPI`^$_F6qM#A!!j-tXn{57trq~o6&sT>8Owo8i>-Q z%6SM(EiBAr@O}wF3BRuv`CdvQUkU=9uM0FZabA|ezyJ6V4*Gw#b`C3Rr}zbojF5O3 z(9$^pd|ohf4dR$?ZCPAy0@hBmkC$HnKN?gU>b?YR5-9-0v9Zia}_7tqK@@aUXttZytfI!7QCvi>zyk z)Ph5`h+he=i6{pXrAm3K@-RFyi1#bGWsmgBcRq9Ii+9_cWX8-Fn(KEEwhzcfD-!7E zn+hU2iS8RUeIXymMNoJU-@F_w&@7rOS0Q==;tCL5pP6(_=U zbdn(Tt7#y?Y`{uR-u!EJmieczXvfeHZ+V4-qx;oG!G~28llNllL+SkPw|`mr>V5!e zrhv^YF5-7?@ZzcDORdbVW4E9FQSC41=lMj(^^-;OkrSP%qmgZv*pA$YSh^2x1(13~ zn30`C&hIE2R%dFqNE6AwRfoEypqNZuxJH!6dgT>jITxQP>dt-MCj!os3g-B!Jy}oj3$;MZrE#E1K5obX}*MKD=qOi|kg|L4}FnQ09 z6I^O9i9r%hqWf>MG6LT(NGY^ZN7m=N|F1j-of-bIIdMp5{V(nk&}qYha07sJ%Erd# z_;7&62Hw*6&$gbF9@YQZWywRCP&yPo{1Y!;%?En``$ zb>{aW$sb%rOY!H;yPgv*6FU>Jf#%QP1Ob!5b|X$c(KbjcY{E#`u0{Te2lYEDR9CXp zFv+mJ-jUfFldxR7gs#M-bJ2x4+kIU%`EjWO{OqLMQoc>UpV~_q$?@(Oc=$S4W}>&^ z`}iEI23q;ai_rsxaF`y@(pX!gg}9zTkPv_*0oxBU^$<_JwY3NI*&zHRZ~*|AT{ANL zy}CLwK0bM9a_Oh21R&H-cljwJxn8E|s|*mfF~GIrIAs{2#8inxkb;@-D8hOw8}TK1QG+ z`Kc4!^N4kZIGCn`189m!<_-t;|4E z0VEXdpw+l{0ojpk3vI*}E|~*~Wx{g6$I&Q)8;jtJf8{2}$;tJK-ep2%FsH#P>^^5X zsSu%da80lr)hvS%rZRjs{L8{oanQbrFTsSi&JoU&62bNZZXp?W>7??euipC8yBnBt zc1`(+<|{|Z_?10AlyH91w+Cza+VK8OuK%)1Ac2995Hvt>kWn0f=;;7lv^a!qUs2(- zBqk3)uz+L&Y2ASWCx9IT9Htq7BLSWSQ}RHeP6>js2(c3(eYUB=?jr=x7UR_$b~}}( ze_!g3WofT8qw>8PF8waQ9~#e$;^9VH2TWAhv20MRZ(X|+qG?9uF4M$(4VZ75&aV35hl*$1v3h-Kay!%pH#B?= z@JWDF2=IV_RslYcpgG`jgEtQ3a^NN~>57@pd~9@*u`e*I0~IS0;vTt&n#4`zzBOgp z?|5zDyv#B=)hSx18B8?xAmSzVxyx%D^-^uvMc6SACZ|K!Tk4_B=ks!(;oB?3PhvG* zm4+V;Y8%^X_G8YCCzOypcFZy=vu@Od^PSaW%A<;TfAugE9D5!;$$p$j&jDx%>8hZu z1aV~n!vIJfK+GD0vWd^atr<8AV?B;*-oLFd&Hy-MHeERIn@!~8d zJi6ihh-H#u^vp^7Uge=+3P;3TeTLn8)@n*1*FmdwtD(XpHK!$*=K@*oauDW88EjX2 zx*yzWdLoJj`|KVY7{v1O{7QkyHf(poFtvBnM`(-7I!!w5 zx9D+1)#GFBoD=#t1cc|jC=XxK?IbM>al4w?PMf3Z4swr-G0=GpifzfJr17c*n2wR1 zpZg|`Ygf$VjDUj$#u-6xD7$d z;2}W+R-;TO=N@~bTiJV)b_%~+mZcQX(yNu&4_K%Hy;hv>j+{qA`t=Bq*C1_Dpl}FI{A-}Gc6oUj+W|r8<#O;2c2tmZ-nDK&QoQ-F z@bfDnT`T8(^324?YmteE=C&@&ak5qq|8s`M@iUgmjI845(r2t#Z?f;L6EmK{&DXyF zd1dsl3+Blln;Q9S)J3Tik@7gq-^7UposqkDNyjGA5UfqUGbSkQ_Am3%&Lr*E&gz47 zWPxD;a(zI&1WHH1$%HhFf#-p=r+`R3Fx>q7{8#_(g&}4UxOJd@)$DUi3~UjkD6z00 zlQRYa2J%^r$QPKt%#D9c$&?#2c2x;V_B?k6jZI+3(mWe3xZ~9!IP3+!< zh*9lPE#UM`8;va2J19)#wV_;JvJwSO2ncw<<^Y>=%7*A@xwQva11K~C`X1sBcLcyn zgC*_y9~&PWJ_ywbe)-|5N4yd&JNOr{JU}~Nri42|i9TTf)Qm0cX0-(gyc5|Q)stQK zP36fqvzhD+6P!KWcUJaj@*5`2#8wiKqr={i)A;DTcUQx25-U+j^`Zgc8&oI(QY@N9 zfneR6KR$@fF|MwyAwjtZ2#kOkg+gx-wXLt4fhr3rkT<(J2C{l!jLz23Gr;Hr9!GxQ z>#0#PJ!_FO-PMB=wF~JMsnClrQ9nN`T*|Uk_jtGQ9nsaMOt_tWiRXP+2}8>q^B^Q; zBfe4ee<7!skjsH($+rELgQVHnD0#pF4kH3znFC7iU^fBUJ+PZ&dvcIMNPRt&8$n(g zyeY_D0YSh2^72850a5coAPg+~6O?uZdn9!chIhQ`B3lGG3!$oYD2!h<8hT*ppBmJy zwnB60PK>%f2Z{`5tRF{-O!qve6mZZAnk-!Lm}ZXotQEfxu09-~PoG_q_}4h=GBaEQ09R z22{RZ5{V%(^tK*y)2q1=1od`o{nE2>OUgb24`<(%JjCvO#Wm|U zquM86AIUhzO0A7GhqSEy&MKT(9;(>*DD%=k*jYGvd`zm0{8f6(rhfk7VL*LDkSSbZ zb@kh){+nJ#I`!piWZczNVhV#`xThx5LLqPpK=K5v2H2TkA3&QEaH=683*h*HOUs_v zT><0eK(Pu6RzYL{l+gK`y)JP;qz?t9X+sRKL`+y>f9R8m{R=rA;kdrR1>TXws*;kD zSfO%o%(e{!b9hh+X>)pYL^5B+JxwX?J5#Zw+?5P&+|uS$Kvv|pr`!3v*!SF9s;XyD zqC3cCW31R77OyIhmWQGiWnA&}o?%lGb8FL?)RZJ!vk2f7Fu?r+vkKWxht~UGpb5J& zQJ@Y{V#R!bE5~C4CqQOveSKX)F^eclfi!D0AAAinz0f1AzhgiO8+ZVyutbdxEuQz) zF-GxyqJ`gvcOgF(Bsf7$ zdhxk1)H_bb@_AxYQ90Zz%(${c){kq6V+2gZ9y~7%ND~kfK6YGT24JU zr{uSu9V1NVQW%K&vm>;I$1<-$HTwXLA<@`X$P*V>B|(NE`osKNX5eF%p#?0S#7g;W;-C5F0}70*u^)8ePEH zh=_`=f_(!?!4;@~g)~WkfCH3UTUw%l9R|{CP+S2^`usxULb?dv0W`e9dIAf2e10AX zf)+>v0{jw`o`LcUI0GfVkBp^?ZEDkEaZYh2e#6JUM}^>-zcJ6GzU-z>xjL`eZ^On_ z?5Qd@&R@5l@|ioZEUDOkV2SB=Zf%a(;EjC#p?BMHM+7tek5(MEM`2>AOOj7A0_PbJm7oX@ zSROFH6AH1w(*i8zI`B+@uL;ge7tmn=&jP@fgb>?`5(q% zJc0MaKMGx4UGR>)YT>Zfeh2-*O($ zk1u%Rg0+M_$C}4yRANX^yAvT=_=LjSF`}s}NpKe5c@N_36 zZ9GZfQ(yH~f}z6v#npUQa&_*q1%qF3WKu zhdWF2kDw6oKh+hW19$x5jvoEf&h>f2&!2K2R0Y2Yz9c(x(S7zzrht-CzkR}^QNc_t z#;au~ruO7hvYF-!mZ6(3LO4>?CfGb-Rdh7XHT}Gwl|I9oJ5aT5(-R&|c%Bq*qMXUh zqu@Kby?EERUlpYt8Q?_nL+M+5ruZyhqtL#U-{bi&lvdLDHlPNWhtG@&3^(Lz|0}LB z3e-1%unDN5Kt>k4JSaf`#A|~x9YW_CV3R$Rfb9mOBXTm13w^yWvBd|F4Mn?Pc)Di@FV2iYX=nO2yf2gk_GT>|EVZ>o4})P`w&Gd8#(yZFnk{*kp3%EWwa&lpBp-1Nx9;9J-I{&5H40QuuUy!_$oA2o`#L_uUtf1=J#IDu3(+1?8FY%(|0aQW%pd~ z^$pawu|4KH176iIFUZ!OJjK>*b-!xAS6bk;rfx~-K3tsUiZ`zhj71P^~#C-R~r zfs+TqM-Y^P02!isgFprZ&S1qMOaI?8ALz+`0D{PCfcOaIYC=Nc9dW>pfHMZw+ul}( z0gw$;8Uhv+6x$$*-~WW^z)J=Ma&URwl9P?t)|49w-oAo+VXT-HdBb$SoPZR_Gu zg7`XqwyHyyXCD*qk2D#H?u++0ndjn^C4^dN)VZB}8 zF7l-M>atU()Sm9Pp(dyhpy4*s8_roAlMt$6G5996Q$+7Yyq?)OAuNuTO|+=A6ZR{t zTpPo}TD>YRv&vTfFg&RP#j?$iQ=xdL3FsTC+Nn=_8^l_H2n7UukmLSest+7F=t0Vd z4V?P`syUDs%5hk!Pl1W4Tz0zn22nE-%n0Qn(R(N|b=K6>bLgoJ@jJa$sF%Qz3h z$12URkbzCO7%YgHRLZ{0cp?u+yASq^&lVf(VYp`3G?0H|9TQ9B~6HFcUvRNqP9FAA9 znsI=FDkvpX&&sqe_W*s4n)%biLwiC^8Kn-8xPqkX-`UrP*TCNc;qfueLMZ71D57&) zCFiBZMbM^|0tM6?0o-@KO@8l7QGIJ^wmXZ5+Uu$FG%?*`!uDN9SkTivq={=7J(lY4 zlg#gWJPL;NI?m@M@z9LA6{ z$^?e0B#-tHSfj%_;26eic3ePGA~;u>A#A{W6qDH|!tZqtc0iYDFuun2iiH`D+`kZ^WzM4XCR&1`}~GLSq1 zxe>J{K5!FY1TO!nps+B|yws^#ux>Ey2IW>)Zzm^CPv0jffPkr#)<^J>Z>(nDoV|jZ zIYb0Sos1LSEVTcGb;haU);!25(+=~QR*UqnI=EXN-E>WV>iIu`OUIujaioUW*3GK) z58<67D%sR$laPs^Jnr+d0eoc9GC zjczkhSz3*4PRrwyZJ1S_(Y@@EBv1C%VTubeBOAR4u;nM>t`0)j9d?n_X4EJMVv(9M zq%qU-$k_EJHkIY#G~r#v4e9JHR&OH34UvW>xPjoHy!>asIwZUWOcEdpK;t2xVSx(^ zSs*zOAcqE(U0;(TWYfce%YFXn8`DY>k8i|7QjMz5fQ#}cY7*;t0kd8i0{tKKRP1rS z7NqsHv3qI!C1zx1pSNWZbNZ#{E;3}Kst%1rur!EJi@0nG=aQ)=4%{8qF05<)2hY|l z)|bSK>msIfPY`FmGcLdAfiG~9heUZ$^;`D3jB~@ye^IjmBL&8y6!qs_7?ywVAK2it z=d!~uT-p`tUBfb7+(_N<9gc~^TA;(sDpP4=il0dg*a^~M)(p~dG!6SV0h(@UVI1Pj z{<5Aj^{4A^XtbE`6eSM&q|Fp!Vn-5ncBs*_DUx3#D%A+#1wEmlqFM(@8kAKuG_(UP zSjeaOwnhV&4kBKt>IW)Kz&S_bCs`O-kcOkDJKKKhFCoxXWP-V3%Fl%k?B~Ts-UM_E z!e{a*f;pL2Rc82{i*$s#JCk=_ zTIcCEm2jBseQ(*woxwjA()mLJV+{S7`tvok zkURrQ>Y&UCO6s662t;7BW^JLF>7Y~;`xFv;05LImL7=!rf`tku;79>j5jcd}x@kPB zHD8j}%s*{#-ub{RQtOBcTy9h``Ux>*KvQ_h`;SXb8{OUzO`>Na^~g3wF%?Gi4?$^6 zmU81Gy0b#NOd5p-iC@u$<@w*!Yrc3OPSwuruYwxPp4RZll{~#h^gaef!F{e|aApD7 zJMYZZ=F~m*{p@TzX0zXWSvF5+Zn~|tia&3dp0mvje3CeGeYvGiQE0Nue{7($rQ|C> z{G%GQ4LQmPkDmMd_ zXYdSli~8pM+-%{CuuUR5Q&z==Ed8vc5vhw2BAKj3@q>z;XR z0i#IxZsxrHLV4y;Yc^wi2u9$YVw{c5lx!yD&(oe$4R*h=~z!0o`1!R zhvUFM!=9*q5XxY>i?{kRG9nucHx^_?1MK30fK|sUuJ^b1zC4P%NvQ+bFRN0{tvUYQ z-Yd+~Y)UXZ+h5SOcE|3PU6AZ}q)^ z=Y|p~g;q=Y3pD*1Z8Y7-w7NJu4~_y63F1WXi}#xr*34*ZmkK4^9L3~?B6c2|orS0qQxPNlUO#)+J3lp%1qhmzg;)L zmhq^P2BzN4h9Vs6JfHR1kFD1$a1{>%$0kzI`kcWWQNCnG$O=FCb z`||+-st=&_HD*PPj`Zi~2pJ(WGZO$~Du4cr0>v57P~9;=8gnvEFwR!4$~}3)xhuF* za;7o=a?$;_UY;iD_q8!@^kUBT=#u{4SiTlVL3YqzgTacK?x|xOJLqdD zbPb_7lD`?sUdg|l>z7IdeQex?`VlUpf~ut5;U=+(Jdx9uNfYx1I{$E7n5TW7W|+~i zh^YX1RAB2^7Ja@7Ryyulz0RG$!8flT2N^s}9^yp_$a}Be#DRBYP^RYQ9fdrc;Aax{ zLxeBm&A7ETwPLmY%mt)_H46?Gm>iSU^7x9IX&XcgSyMK$cVRjl2KxG-`0@Fl zmgsc|6?d(4(t7GdF4o7mYZ^L2XW1}yg-M1@=j%j-Z(2AToJOSDtrJKECK=m)J0$LS zbYZAqDo|!wl~5Bp{W=p`xeMNy=%u;yrr(ubgJIN8Mj$I}y}sXI4pnykOxsnSXYdhB z4A%(=VBI%*_H=LhYkt?lI2PfF2$aPvp-YX8HnI-o@xW8bB%L)bzo)2+?sNa9iF0+8 zcW<{dmpxc3q;Nwg8~Sr#)0xL!x-pdOv12t%1>bp8A-TzB(K)THJ~2)_qzNH@a)t*( zQ8}ed?8k=zi;i6FR36he!{hN1SP{wGYN6VofJW-vahv@AYI^IasNV1WTS8JAB%~yz zQ_4X~VrYf|97-vbZegUPmG16tlm-z5q>&U!X&B-yF^Dug_k6y;$3I+4bjjh&Iron1 zwXe;m^%es=+Kh=)!`7qPEJo#7iGrtT`*80Q9-pLc@2T(A_Jr9~|NjRlIpYw8>amgtJBM5`Y62rm{ zVny}-?6fv0MSh6Qwta-}dLE)By+S0~%f;B~qG!tU0EQ?jNMBdK0FoQ<9DJw>ncyqgO{9I>))2m6n&@s{LaohhO} z67Q5@d#w&>*HzoGHR>i8t5be-yi@W}^NZ+Rb0^!6>qJ2fLlyp19w)<3n9BHxIikwC zg*>p^G-T^vs(ZxN)NVs~zWS0^?k3#tar<-XnvidxF8=cVl}G=S^-tddQ*efXr79Ue zURnP}Vdn^5g?{sbm-yrO|1o-KB$+>*{svoOLJ(!rbTeehSjpvKF5w?tea}^(GU0XR z#R~?aNtLYEyJxXPg2$is+o3z3WL2L|QHu^<2KM2NwbGM@>#=o=Bqv~(i@&o8gfjR! z*nEmMlYZ;r$3tu6dY7fM{T_!Lv^B_|DuAspdgwXUw&c%^Yre0a7C#Md3YtCGndfkN zPBBp((eK7wg{G!`d>Xsk{k3g!ybF~#8+f)Zx=<8sVEkiKY_mu}4!2xjcb6*s4t&Hi z%NP6i*E-EfO8MaE{`x)Kh@$H}szr2+ErGu4w`CnF0j8M=w74*-6Bb|Kx!8@P!b`Y?Yl5JrFXtX~7;bd#bd!6`I@s^%$&k8K ztSBj%K!@XST3J==8d}&AmxH5!>w5SZ8W+S7t#eT=>X5c1qSZ1&wU}qOHVB*GHuh>= z!ZTR=v?G&hPgo(7zELZ+vZ;3xz-()2o)6;w{7-QUDk3S7W9Z|UWGNlBiZr!Uio*<7 zS|+Z_$OwyF>Vk%OHd1#(z$}?DxJ`A{EQ?1OY&+y)dinWWLZfegnb{Z0(Rk#@-3V}3*)~V2 zO2nx!1^sPNdzU6*eZJ9bXR4;t(>~dt-2p=q5(>IS!^s`oW1;l1>lP>q;+lieCvX)5 z^Ra@N*cDjpU$4(;l<;2=n&Tm=!V@(-v-Uh9nOBn0fr9jvqm+g91^tci{v*5Z$o;Br zgL8G>c8!eTVA#g$Dp4}|o3+9w*8~pK%Z&9<#h{i=#1Iq2;e*ee)>VZQ)0Go+&fj~} zjZJR5tX~t*Oijxram}s}Mdn0>tZ}q%-)K3(^Wqb3nyNlemN`oagH2Shbgk2%lrCX< zTkL*EL3eRSNl0}A4YomMg(B}pV_ThuOf=R08cc%JEX~|dclyOiZn&wI3}oB}o&1op zX5|Fi|KzceM^Iwc$&mSNPg(ARljq4gI;46; zdB?}3v+s}Bjp?~B(<9m+(*w4|Jtg)Yw_1LDmt!gm2sS|$!^Xzynx>AyCz_rxKcaQ% zPqsxpt8YpgJq9bpU&UGmT_jxmYEtYL7ZDWu-22d{D6(oNY&(SuX|k+-apbY0XFY;u zbf};GGJ03g(OO+xo3H9oO_1F?L*_i9<)l@=o>iA;(rZMA{NvTy@%j^r7D+f%nkgR< z+qz^=UYWg7hjnr^9CVMr{#qS)mh$L9QNO{vpsjo@*$Co=3Ycq-n7uB~8EpF};c?r# zYf_nOfs_aH4oA>eltu6R7;_E*Y87c0*Wsh$?iS(!Pn){{kFiK#4(8}SY}di7`{AJY zRL+@*Wwnxeu<2h!h$^$Zc!qT&+q8QGc8*Ejuuo!s@KrPX)5hedE3fk9Gd!xl@da zZ(jqIVDq=#wLpWoZl#=yUaO~hR2!aG?2&%bCG-^q#F7)eHB-+whdL@oOF}lpzYX`f zE5lFLHcQ>nX{sXm4`SO|Hw+X-S0wHvQVzA?zG{2XI7r~wx7OCCMe$O-K!kH_qnAyb^HSzW904vY&p{(Lft9euP+f zT+vn*<0{nC;HJJd8C^qC6#nc+Fr!qX`&a7KEZ-Ow%ScFjl~tuiUcNe#cIN8keCDh4 zIO~s3KSHOqvSy zU11j+CYAY}hGKEurZBQQgyu-oed@8lz|pyKPIXn5;JMx7jh%%1_B3@Fkahmy^!_F- zDqiIsQ0IVN>KWJ}k0B2LECuQ-uY)D5JAh`~zyh5)d>|NL@C^XK{@n8f?N^ZM0tQmS zUT+3b3GaXPl??KVR{4-LEl)JZ?a*CjxM7Dmb-da&PgJukNgTm7TIxYKyQpU3LHtEE zFbRiLzo}E3{*Y?-Klxq{t&unLIYJ9C_XNbkXzZmw6GSNhD|hijF5nL_u)+UzdBFz@ z{I!5E1oXn-t~;1Lu|RmbxjG)(&H&=l9n2~nkh&JbkS9+QS9=)csTe&y z%&9ZBGV^vuap({wX)h}ZN2K$X8Ji^Bve#RDi_~jMkYOmT^bKBw#Jj!*lxggdNCD&a z+70?=YlTAqLlpCIc>5RZ^?ewBd}H%_E7*g68Z6%g2XO|B2m`+D&GPMK04Nf>K?18+ zIStY)uy7pMJqdwKhH0-P ziu2e0Si7DI(hij5fz+h@l{yhKvtMT^L6;e7yBRE@i#E!)w4n8qWo76nwH%0!Ifad) zTMFGNQ^lptNP*z0rD2|i@Fb^V`92@7=1BvmZ+Z=VDWs`x7p$J-)9=2^@m5X)5IbwPs{SX9h2`WCGt~=0Ugyalsx=VAQ>AHU!7KZ$e_}^0CemOo zB_Yhyv%m*SOlw%`$X3Zfhlq!Ry_m75-4CuPOA73A~@r^+4 zW9aOX?Yf@K(@pe$?Meo6H98*|n@gzt#TFvU4v=Q{%Z5=p z0G$bJ&Y3V0KR7FZLUaSXX~5P-7L48j%*+7NE5b;MVa%5OFG&uTzV-m(jW;>sIApZi z5V=nrdmcDsm3y-5f;^;Fi(_?$ot*_ti{?`bO5?Zftt$%J3|;l zdnU1eW0ijBZ@f|9l%Zg%GGQN<(LE_Zy91pO^l55pYYVN{`}HEQ?WQ!IDhl zYVh8Ia_WDm00t8QClBT;=Cz$``Z?T4YC;L-gx=?#>C3N~YJ4p-uCjd?(cLp1n`xz4 zfvzH=XmA;-T^Y`HDz`}KQUSQP=6&DPdB_A`=>$YRCbPZ!eq@g4?If-$^gFc*pz{HP ziUCBMotG^ETnU7`JMaImbCM4_z`zk2qiz6!7^CZiY_1;KSc#LxB7z(dI=`h#jwSrz zxE;yXI~FFC?i6|R_GR03se(EmhBK8_7^QjC)pG7iQvONeyBmcldk|hD$XoEX)j5H# z`JNyltgf@(^vBRoT1wu&0u66bzX&1&rp;S6lX2F^!TNgDiQF5+g zAZIQy^|(T2Q!lP*P*o5Wac`y#bkEz-b2M5$o_Alw3>BUYh-J_J;~8+DJI0=SCH|JX zE6V`-4j6dcQvysJKR-VPvMHV%9vuMN@_?uVBiKE8$3f?cHP}DHiNoU^;JDk3m4FXck9$Qf%y&$4j@ z4fJ^m|5UA3+$Kc!Aet-GfviJxx@x$)_V1mkL5L#i-&!`bpFp`RHT=P=a-px>e7P%E zA$<5CR0AOo6Mqf?DTO!_#kDI z`;64Uc!@6cQuZK&30H7XG|^+HKa}q`j~ zS$uT?>zb$??#=!+nK5dkTWpW&D$hyEc=iRpLvNA^5*)t+yY( zR9Q3M3xo^ZyztD*Grn)$6k1_k^QXX`YU&z&4U>RPL&J{v_7dLdqAB|ADKMl>kGk=;S_Kjg`Ck27(Eo z$1!+iY`_&}4<5j*BK|!A@jdn7Wg85bq($d+fT05ge7Zz{1a-O29N1oa_ zif%hThB$Hm_Co5CzrHIW%>g5g5d5S@;+KP8r7EKPS47f&<-X2}&3ZkMLje?l*S77B!zufJ)4(8+ohY-MyF*x%-QP5MXJHwH0Ucl^u2CNZg@irI(%{LXa za&{&I5kd)QnT^KLLs;GyhXZ7jltZr+=m&`Wr<(uf6^bl(6bd)0=6k8(jZl>h8EkPc z&MCy>$>dVoGPzv*xI4)f)rtVCVKJif+1Q^y0;FIY4PbsiKmgaVb`Ws;1hX3uKwRKR z!q9%09Vx(~{q$hD@KX`^K>!*9UP6rE3+~MAW#?@qW>y&N^n?)r5Qxn=G`ydmy#{zW z4?Tnf3yBBf$MV1IGME{VV|( zU;;#Y3|V+{J{DjJeqeA8f&c~5EkL4AR48L6VSzl8kk$!gE1)l2ouhIwFdv3p0?w=; zEd(!Z{>&baB=q=jfSAJ(eTz7*Za+GP=9PKoG(uXDeU%~B29`lr*|cSEm71ns-_KKL zPCS|U)Az(N$^=ipE^hyc#w_JMn;^N)F#eG?_mX7*^6X65+|#L$dy-9xA@u?A7;wG! z0+aV)o}hpK4ngh=C~iR0;>Z^=8uQ zi4wb{4^Ib?*crKrA%^Mu>7}{?WKTS-W?PKoRiCa7 z#hFoN!K1pxVg_H{ucA;6adP9p+5$q@{H7EEHb%w1zC6V|AE08vYJV;Z`kF~{7+*M` zabr+Zu;-E+%ujLr1#my$OTdSMq0+!pra-;|iVFquJX66!Q$d|JdYD3rN-Em}k4b(n z&>+@&C^G7v@NbV}s~Eyd$jm(T-iVY4&$4P=^exlzdX!JrNvdbs>3Y~ZUw=g9Ji|uER^A7>3!i0>+yF_5$u&N=ga_1%{PV zbMkq~F#SJV4pY>Cys%>0u7MW&g-pinmimo&U#y*(7vTdlZi5h{&jx*kP3P2eB8ZlM z{u*kXsH^FsckX%?p3?sOOTFm!_8Vm<)8uf8G%iiNF-TK%jCn%L4wScgC<`a3+2KA8 zl98I3*;OHk{8sb-{3PfBcm_^6%rvOEIl%ir4DlGz@}>@hAcsjn!S#Zt8o;{Xdcii| z1~-acFe*XEg9jejWOYvCA@Q+sLXpWl)=+7a7h7g$r)PI(7%8$@X^mFXe_i!0XG-dH za||W+`_)7QA>K$PW2r!#sVBW={W`*8flM z4_hV|7nBzAIgR}Ecg+$mX?CiNkRmc?Xe~ngs{oS0%k`LpJG9^tuCMQ~GOm=O^lH3K zq_ba+QJJBrE>aF{9xEkmlYVSK;gG}HiVn%T_u)Po&+K|r`X-w@?IQMtl`%BGOn7=i z!s%njRNbqc`Gi>Zrw5vGuJf#tw5*m zSi=_Uw2z~EOwAt$E6LH>m|=X=EsCiCvuv~7$IzC^kGA|7^L@X(5*6p&bJa=nb;^7! zm}MY5=%URMLY_xQR+KgOagrm`ZiYDqAt6H0htEPGNn(60-velALeRDmj5L)0-R-GE|8Olw46 z^N~?l{eND-6QjEIQgcR9_9*AEq&-dB0gaj>Z~Qvk}C7A^WUGl%o?cK;Xy%Ss^~fX7a~I0y`0#s3(On9r>l zez+I&)IUP`|7QUz`M2sNnjDve_Z{5=E9ISo?VMbYCHEv5@F!nBCMkp`eC7`2-I-wN zj}tlQ6N=rjtVs$k&~E2)sn%&8jIuM*IDe7D9@sDTC>Cl7v-;^dr}cXmD)__|eaUmv zHs=5mKftWAGcvGT{)I3^9xoqADcB z1s)Dvs-(gNbF=%NCy%A2l)hVROWRVPf)KYGJ~|TtowRNAt0eb4bzxUR*>o;z*j*86 zGfJ%qihdXeeM<$CCY6X5sVi;)R6Z4=gt`;O@skRz`d#0#b$ueM9OIA~{<&drbYvhmk$I;u1XE*CC1zh+n}YB_j};C0j`6ymiB;(IR0hvTaD z5PFge!dL90c5&|E%ME|d^{B59{wRf<}YSn}19nbmoez-I?R17ZCdNj@09c`6yqBteWQc9<#uoFCfeE z(*e9=Skn`yryKe9$4HxT%5@_83RE<)bk~>|{0fT7PBQe!KBPvOC3d$YA`>oK-WSNI zf~gA@D2an9(j6f%xG|AkBA;ZPg(hc9=PSHOF{e(H9N28H(c~(FV54O#I>bY#M?_6g zFH8o=e-~aj5)BShv2DJuOCwKe ze6m`1z+S>@4?mRRB2p>KKjlvx?e;}9i@sTHX40{^gx2^(E##Gbr0edkWp&hLOv|P# zeY5RXKF5}hC4YX1BQ9H%(^;C2Y<}09q4=l8tmz_&DujiyIT5rKyu$-v zy0)tVFdV1H*6KGm1>OsA=+?KghoooI622nXH}|I9IjwKWuaE6LE6Lrq-AWzudnXqj z%5u8eRM;L1@CHL?x!Gff_?~qdc+>{lx_63i%YDqxpq3pKvTohBTEvZB))l8lSVac2 z{fiD~s_+*W;$WcVT^*o)D#B-#DYjkklZS*M>N2W*;=1T!NSt&sL;w!;6kw^(^51NR zi)$|B7j1GBiO|l6$aWAAT>nCbIpp9>eTnQQ+oF-^z4IQ{m4fOE+&QUlKJfuIfQfK$g>_PyBy z5vVlW^(}NSkyfR|KF`8m5VZc4Fgt`Oc84Jxld*?Mg_~^heD~8fcfAjqD6i z18rQ>$Li#>juOI9-*BfEXjaBB&fEuvQxi@}Gfx|bZbOMOX2 zO{a05POSL6bqT%y;j>cRk8VjpQwCV`y|gTc){@~+5)*7EQ%wz@60G6sJG_C4kLl3r z4T8Ws3jkWoJI+gZT={&C15J+=i~~0~68jYIAZbZG$C1r&Lv@2Hj|qT*mG}7d5E3R(f^rr4t!CkECZp1-% zemi+_AVh4h-Y(W1dVOzvG;nF)*B!gp#~pCr2vT&HHD*n=oDiW}_&v|tZB{%%Zz>Dd zpuxs+lJS{|%z;vtmo{a)dDekTWO9-H%;zURXx8D!g;6{x@k%NJgZn2OS4JtylKpf0 z`bSLDVjdo)buK!Yc=bU=h-48Mw?_Ir$H)JY8~&@ANjpm_qL`YUo|xjdp!C!@W|El; z@_*t(3U{%XtVwj39DLqau3pK1`|E44gn0Zw=4Mb{{o?HW_$!e|*w%7=6x61PU3KpT z-%1jPDgLs>zRuyW(zYWMK~iwt%{WX%bLe}&n0(E3RP`zlsKpF98Z$1L&~JvM`T?ao zy`Ca(cizm43$^qOd`WR${G5!h7Au5*Qa;K`uFA{{d!WVJF9bwxe$R+FMys|P=#*6Y zT@rFf*Wjp#CB-}Op)pm`H6N@*Jb6BmaFwVUKe_XN77F+9aDb2zV)3T2BFaiShs(R2 z?j+nJKz)0+!pe?o`_0R&n}+Pvd~4oH+}DN(M9%v2ts+$oub5M&>(bZ;^!B~oxvyc=MBNqd;*lO|`H6`i1*Kuwj>n@0? zhVy=;w_9|3MM6J0`G09Rs4+m_(7xcab>CFo#`j_tHcKMDwQjuY+$2zGv?dKj>`Ib6 zbNY=srCa_La_*?!UKRCbDGc?t#uTd02jO$tE6LzTIEL zvMc%YI}$HB`qY^m7ud0|AII$?5htF8VYmint#_M1U?(s(wG~g+Fm|gC*|RfTJbZ?} zYqa%oe=v>x!07ADTP2vA@u1hdm@i6z~M5^zNoz`}z%ACPoIn`pHct-ShI=}5~p zUaTyF^qD}2c?F$0YbLtiK+u3lqOqBpbO!KI|w-kZ&@*C5f@1~K6h_L^`m{dGp zzGs#%O$zrU-?mhame)kvr>$dG4Q2@CYS{cac?a~7c>Jg{hLsw|mS671f8`rA{f55+ zsrxWlbfbXLB~*%ZOncst_ag2XU(yik8yGNy-N`*#zi^ zrPS&D60hdtrWYAd0Mm+CD@KZ<&9gh*x~5<8pxn*ksiMVQL#ehf9(*WG21IKGu4Dds zG|6Cz((JtcIm{`m2}amHsWrC8nTsmq#$Rnuo<4&AQEiK_j+(q|E>S~_Va`3zNL`SyAH!vj( zX|zXOAu0y2F9a@AV%$U3PCKAhi^!MDIHy^T(Ey%C$$Cpg>{zX*g7i_}zfKm&alG8rvvAZ^P zO_SkNvVK(T9)w0}Y`%TAV$xErx;lck(_(`Knlna~(-ayqr#aLlyn7r*_qK7Ut@zvC z^WlFJQUH%*`~|kIc87$uNKF3;<9NVmDCKs24WBYVXj|hoS zc~WiqBqO-(w-bknZs$VW+kn2+yROU1%oGViYB!}9Vd6aLxZk0V2%(GCrsHe-x;5w1 zd!9e-8Y%j!d~$}LBrfLOhi{?`QLF?Qs}1972{w84SG6+SW_gM7rkf5@j4JugcT}k5 z=wJ05qI^(J*Q5s&VonTDptV1YW1T!+%SCHjPLxTsfWB6*$qDQJ}^8*{fJGgkLhSsk( z^htiwyAu>sImAh#@;-YhaX{l+ZE=mGg>u$Gf~b50^w!qlSX9R@Yj=Kv2FARWcb;~U z+EJerV1~SIv55|I%ZuNx)EcTj^T}0U)^+~}cKeceE2S7w zk8s4)7Gc}sWL2gVx`eT8bZS@sn^2_;Pcv+`=(T2#`^yJP5ZW*?L_m#$OwsZxL5)SGZ9Pf~j=lqW68 zEsnY!V(Oic7-CyDuGQdiThy)q4WY_j>7rpT=lDSh_gu3wd>n{kYLmfwHCDV~!1eA& z<|l$OR0hXb+^y$&YBSi>5Nfy;-x}L&_tozr%>$3Jx*#{Hjf=XEr8F ztW0+~CCrvduG%UptHddXHC6k<*e1>`OdFrz{Qa-T5hPR8T&>f={>Pu#a$H1Wl(}MA zxkeIay$f=)zXv|XIpHbd;Zcqhe!RnJ=}8s{CY{(vFA9tWviH?lldWsHRoVwT6*^sa6guALiW}p z|G9atC$qG*ChbCh2?ah&^g5HVa99gJr>z=_Bpuf@$&3lU9^rp{fqiu53e!~5UR_T) z=7&)#CR^s!Z9!WIxD$g$DIdpl>*J~she1j!>KoMds^Cyr>8bbSi#1Vvgnk_5UxuIM zQ~fMGXX_-tUY3)Y!fZ-`}`-TK#?eL{n6&SW|Au_3f~MVtrO3}8#}mG@y?(5 z;zi6{L#WQhuOp}FC!SYha1|sOveIK_Z>jQ$YJU-HdE3~t)5QPWQ@wWH7*94{2`pRK zS*;zcr##ju+m(xndR*h1^2%p>E2rgPpm3yIKLYNZ{X30({jK)oWvQ$hnsoY`0S^Ih zn5<)qCXDyh9$UNG_ww3u(-J~MNx}*)x*CFQu7~Fi2AQvNVBCg)3-Pe?5=eU);Ci78(F_w8>mX+gQMxB;~!z0uW zBTabD+RxMg9cItXPD-by;AJx;%w}{G9a6f~th;C1$rlqEQu}0NI6p$~*GcuRY|Sqz zeWas{UXbR{LPJ~wqjD}|U$4|P$}(S2lp}kX(CD?)%IQM8UE618kl6Jbtn%9a`zXGo zcIY-l!eWzt?mB&4vgbipn&UbTeVx^C5_`qcKTyP{x@d2TyXs0(i(sg#3(^+JHhAPt zD%I!sf&W1QmB=z8i94Xz6?LCNV#YJ;hF|f)4!_&q7hZojhEZ-=I&Qp`Y^7EP%Wd!Q zN~TNOK92Q9yjfF)itKYd+4rkJStFLK$Y+K$m$SIP@CU`uymdM0;}l8Zw8fe6SxcG; z&2{m&DX{t75~-nTYnIyQ<6t%OuiEEz!i_d|a#Y^M0U8QxYL9mOZCbTwGJ{45z}35d zHTTY6%=WOqcA*F|7_l(6@JAJZj z@gtcRIs&L~Jyzyj0&o822KZ>Q)CrYTH;hm@ZYPDbpL`5wUTmuf7Hrq^AH)9h`r(;Y zp_#+=&hC#75Bf5=Tf)kIVE^t@D68Awsmf>WmDj^BCpNW|Kwg_E8P`2ZB&=UlRsJSU zf8Fw{xuBavcgL~{*rxh+{o=0QejH`>mk0X?*P!YL_W+9dZ()ccE2C!?%E$9}l*4%N zTvB>y-d~9@>l1`R4ptS?HbsUk9fVNEYlzVz1~%k*7|V z9{O%BucFYd|K&m$uX2!}v>NTye+(#yQ##y2xuQM?BpixTP@^uZnnMoIXwpd z$M&y&xJAJ7EBw<$-I(+vDG#PfDygrp9K!bO*shv+R3@6eKEJfOLY$QP);gkd;H_Zd z($v$C>lZTA778bOyaCY-5m`xaCV#yt5WCjJhkrNB_Kq8;g?U^<&GAnaX1fhrQ9e=G zj1bmV_{&KYs=RiKsYA~E<#CWrKAn!czfheeE0Q`{a)1ACJ!z$zFks`O5fC(SqP=qt z!BZ!{MDx8mnI1^vWsxu+VI?sAAxxYN8Df`PQ4bWj@inCWAayb@DU~|iUSQm)`fk|s zYG%x{9$)ChXG9=DSX>`cZR-a2+lv`Qy-Vv#9PB${*0Ku94Ap(`8}^XU-97rZ#JO1C~)D&6CDy+32_9 zvcch++ck~J$_7G-qOfNzA>HX`dKm|qTgSk|Q&kfApO3BC&p)RY7l-y{B7d5MCrZj+ zl$z}qu?9k?n?sOk!Jd#M@tAe*2OK3f;%++{X4hv&5W;!5ATy!WgUWh|TesQWeRtAV z)V$5L{b!#Vi!1l$P6w@51f~;%qf2aIImP}1ydfe7D;CLIoVNFO8D}c0^g~P+dK%Eb zixeX`;Q5<%S@q*H%$+OA!PrWV=~_)-y+2>sTE;|H(N|^(`(>htk^0nV-7u*Sg=gx{ zL3_utugL1zq}s+ie>`yjGcTPOjxRYl2pA?QBmS9wL19R@)>( z(&9a`;hD4l8h??P3Abwi2H>I2@71;9)V+GR=vU*yT_v~|YGIb(u@EDe+NJRC)+VLp z+FxisJB(1eIK)=?+;!>vi!v%Qr~!4Kaw`}t$@#HP^GfvL+*@D6IB{gjfMfpd3;oFj z@!g6@$!PcGDz0a(ZJWo&%MC+T3|~rcJ8jvW+-$O*bxNIN8k(e?5feH$Xk52=@>8yj zD6K!DK{Vyom?_5>LK+|8x zy?9mc&2NReVB`Z-%-@7gC5%YxUe)nF9B2}WGNLnQuetkf(f)el33B#A(AKw(MQ17y zWjps!=ntZZ5kJ=9NL~6cc)9RHT2WBmyJjapFEUBBScUl%8~U5ocv9138>XUWt=9%W zDm^{TQ$?eL3!H@?JDr;y(5t5ZJ5+nXKBO>#|GEFlV$!u;!s1bk@?z{~+Om26;io4N zicP{N*`_;OqsQ%a*<{~I9%dH%NAi6uMcg9nRAaBKxTq2wp;L>50@u{U$&Wtx>Ei%m6TG)DfwJk=^lS!E*YEFS9_0& zpUvUgc%u!gwx2InJnb3fzlMb|mHxK99Z9*TBxQZY>WJn+8kr1<9|p+nCkWEiwjYyM z!xroMzp`+0rBZ~-=&gN>{$?Zq=e%*O6> zkYyPaP2kDgB)+=L_-7*gd;QY;Z5}Md`;ib+BuBvVIWzOCvZ{IBJ&DdGE9!ML{a9%P z)^BFMZ$iO|2O4B#Ptc;V8^w&wiGeSw2YPy}?1Yi^2^;drK(X8pnJs&g^F!Po`C`m- zjtSC(A}dCGXx5ONJCF!mGtqSpx-x1*#~OstY&%{fA`1%BuIez040V;XLtmRY+zanX zC($wFWOeuF9P8giLdQz)%Kh7-)g~N7hVw|jF{JDws2LPT8&6*=bMWs{-;jn?Ib-nU{e~qAQe)ESw(% z#6p5`Epb!%T)ZM91sgrIwGqU7Db^GUbylnpNgxGkdIepc?id&p?a6fzq<5fPI>a^ zxd*7VfdSLCP9@3KX8~C>VYT`Jw|*=eE7^n79sc~Bi3N+@PI05gqv0bhW><(3^Lami z*eNhwakDke&BY%J?Du6m3|=f$V|}+N5}#xH&fA*ox9KXS#9CU3?x3Ai%YCcAXxWs>>)AQDI z>=A>e$Iu=v8s{0mFJ-BfEyb?X{ZMiI;g%E<1B7Xg^q)b2$aiDJ9-gQ##OOWK1-qPu zpX^T~>;h4jv?JXddlW3lO;6=xkD`ef+ta%&%glDv&>P;rfb?Sj-k#iV1?eKl6$841xQLjADvHu7!S$43hG!_>oKbeu^MpO`k<7)iL^20Yjl@&UOaGgD zW@bChqE(NEwHZ-!70C}xxkuYc2l!X2`R0}Em;l7%;9@Tot(cHb;f+tenqO=4Bb z){;(3ZgMsF8}Za%c8gXl9%w&BiI&fh2)3WKoKG*~aGIFo~0cp6t&C1mMk?Xu{XYbWsb|E#z z$TqV+SxCeksvooh--mi|(y+$58rF4+lSg#ym=oVOuZ@sy6|-DkcT6NIGtWYhU;296 z|Nen=D{r6r&KH*H?#DT3aL}u=>S&~;Itkt^r)VZ}dEm%Z4r(Q97FLLLO8k(~S-`f` zC|F%adr^S11{SSsLKebxe&-J1U+6Oh_;Zt`1+bCb)c7Fdz5~=1Ql!3=h)@3Y?*CqG zCKO?7n=>*NQX6SBmJ$9%{_(y?zel^Rp}>IaZpzF>KaUaP5acRhitAUscPQL2i(kD;%a7Xqrk7SQ^k| zbMo4IYovsS_qPWM^|49{i3R$QZpbFQ7=yvkkU+yf>pmY?)GKen6Zk~fen7(MYeKU+^4OCS zgBPa5kN-Tqy!~S~7n7+@E^zPrdQ&w2aP?N&AVN}Z1EyXEM+w;>LGWJkz1N*j8gHa;9Y=QX>%zY%p>wJx+{6jk%O zq&ZgbDbhr7y?)}Ugy8kpP*RzTcOBu*S#eXrH|+P?U-TZ6oVeDCBoU>g1+P#^{qp?v z{D_I?^5^-tL{)x9GsTnU14)Z@C^6SFbER8q|C2|geMdCvYW$pc55tzlj(nm7c|^kw zEdB_!u#s7j^fBTwK6%p7eQ_BcaJKttgVOr&eef}47&10`w(;{wTuX@d5;xq_S4Tlk zD>1Ta?eSQ><`l=el=_Tk9se*l=g2Hui#{&z1Fwth1^kJs{vZ**6dTu_Yfm$q3@-Yx zF8axQW3%PwN~GT=A|9;wo-xPvNA}>FFFj~X+G(p1I+=N%`o@Y;H61m_-!n^fKlZ2a zJ7I~#nh#R`|77uw)xXKyHG3HQDXi4?9nsdUWcDRvh!}%&^TLm#hrc+oLXkOU<+?MT zXm3c-p3KhWjaxA{k1zgjoD0i#->(PpvESz2I2`5q%MVD!u%UnU4%ewIpT^+-eM%Yq z^N6{;Q@ZV4=W=$te?Y)3HC)5BNW+5O<4P8vC$`&sW_nI=#eO)Y6`MIW{4z@9Tg{!^ zB`3+&<%5060UL_j#pBQL=a;pEfRHu3%I ztM>$QaAQ8a!Fl|zl}6mP>J14UoeCcDYrp6NcY=;>zrjXM_b_{52?8q$S0bY)iuY@* zUk2vnae7mR{fv)qt@d?3%G+AEpxd~4s zLU>EjVlJ)8Zg(%8f~uk7$W`O#QrMI_&H9hc@cU)y(l1?bneh4%*}>ySKYbL1ADR@@ z-!;NljYSAPG_h1h=x;dx=Y6j+3x_%PEh+i?$v+oK`{@*k%W+y)bcWWwX*sXy-D3o4 zkiWf0RFa*)h5=b3M~hwuYMr~Y74_)k%?{jz&1=*?|EIJ(4fZ{oD%adrP);92_^rA_&lGg|rfxHvRb->IOrXZN9z;*Wf1AUzN>nqz#9gjXUy6H3(GGMtCwN__N`4T|A+?cTm2vvy0_~U-j&=x z?M7-hE?4@S{MMEoXTqc8&je{)=wc)G`S;B>vl(e*vU_gUIzuBLtzuZUKS5ZEfs|p! z@9Kv=8t2dP?N=6eKO6g8tIz-X(-#w}JEZg<;~T#oQJ-&YDq2qke+7M2VR|FLNiG^9 zwM&3+K^K;Kv~z6#mmSp%e;%PUb6@eCwO~Y@;M0v%O{iXDc)~5PE&+Ce}iTP$*$os4(1I?{W zdf}avuTemboQk>g=D|X+rva-C8MiHCe=54)*mH=%rwYkOe8-BuKfS{!q;{FnQCV22 zc8(!1BL&ZMTcmqM%PE)1%(OOoD9&AXvXn7tzC>o=o^&sS*^RVG1|_g*%C8Y#=ia_9 ziOYJ~5%9E{AoD{NH4)dYW@HvkIo_#Jr|;=J4rgEV^y{yI6R-|Oi@w)p%cjGhW=-PB zdRFLivvD4k6yA%h-s^eI@4}lHZ4*`;^)D@4Ku?uLeyrmlJ2)?^p?tNpW#_eH{ zd#z2DVUTK+m@-AZWoVrZf`d|mxpsx|%$xM@_cLKK7w>b%c=XCoz0tZ+?>l8OFU?1z zOD^Y$Eu75*_ctdQd`GWQ7Yq|t@qgUDxsm?$l2YPNd@fP%?(*tpZrVcLiE+4suu z_YD8w`|XY-qo1n@RxYJt#!6{ytzbTZPXk@372~1EyzD%!~TJTD%J$0&em z(>8CXdqQk8P7|+Qxoq@t$lup@01Sg@?CQu(Z-zs3ZPiK{EJSX4zIyy5IlZF=y3I#9Ob_XM z;*Iz;g4{u6k`er?-L%bLS|f5y(~IJqBhzGs0NMZUzTXPTVDUc~v2;qee%8_Fpc+$# zaQ8DvlR*uekHFy#>r;G>=xE1-&sIM+gFJ~3m{u?U`Q4&e5+3J|?Oqo@65)q?x5@bV z;eB#Dl*YfxuC1R;WsGikgCFEhtUXgJWA||gfrAZ3U1ob$jYrI&cbyR-u#5D0H<5kCd z>Cx!l_GCp(!N<>;KRZ4|qp(>0mNibnpqzoE?!tKa$DbYNeeNaV#X@!-G(uNu&og8F zCZ_pT2PVrHq^2$|!Y}RwTx^ewW+nSICH-dDr3q54e9^VRU2FGL3%gpZvOkC3W6UCB2{E#nQou(4)_IC)_zu_ I_%i7K1O4|j>Hq)$ literal 0 HcmV?d00001 diff --git a/ml_system_design/seminars/imgs/sem1/sem1_3.png b/ml_system_design/seminars/imgs/sem1/sem1_3.png new file mode 100644 index 0000000000000000000000000000000000000000..105df1073256422c4c3efd602ad2c01dbb53b63c GIT binary patch literal 6827 zcmX|GcQ_nv*Pq2k+33Bq1Swi{qGq)OA!?#T?_pV;uv%7KElQLSB}DYz%cDj@hzOz+ zHH6i>Z#?gFef!5=Gjsa6&z$={zlqe=QY9r}A^`w^R9y}66aXL`;B^_%4e&ojYd8~p z@Yq4m$iqleL)yyqrGSOCtEG*A&r2jo2Y?6iK1d5ICmRnAOB*`}7g_jrb32^F!CDq> zD5fc-iF{~d@1W-AZlmj`rDx^mWF=({mzN`X;3EwxcxmHd!Qu1L*##x-BMbjmuQYgl z{a6sr@h^*qlPp~E+8~FKrZ&ezS9co@F#&OYD`62y4hbm%VKFJm`=a+a?h6Tv3kpdI z3XAd!2}=t}Nec;c{P%;)k#Ib4x3-mjictB_7X32T^Dq z*59w6q}IcW_X7_ehJ47+p+qF1hbH2j)`q~jAF*M69Js8qM!A^w$N)G!(AjK?G0+KP(s6w;$@=-i5 zXFzy(K^WGY-A=iFye}&b+FZa%#%oUl6MyjL>{CsH;Ztg!ON&KY)*?a+eBYt4ir`v; zEV_=DB1g*WJiy_{*&r_Eg3YuBrxwEXrx3V>+r%{ReIek%v1!|CyNqHMTGafg?#6tp zH47cHw#Ui6m|M@QuSQ9!?NQH0B9!(oVjN{AZ+6rRfr)$X%Ywv5ztZu}g+8^&wICWJ zMYj$O%G}hPY%PUU%xxcJ_HlB&Z-CI*eR^V%>*7O|Sor$Zv$-dNx_gqt5lSsntMNXL z)943Qir#$(u%}de(6d(Nl|vR`m?|k^_mZ<^wP(28B?2 zzC#&BW?Bxa?-OnSl7YVzREy;&T~B*An&iNS0Wz8?EMHOf6&*`YviSaqBcUL^?Z2@s z@MF2tFhFROb(lE|FN)gFrN;Q(1c{F55p;b6kg)kX`xEH6`erZ3L6st&nmj`orj(7? zlO&i(&-!q=FVXy$&OEz4R)zo)tI%=QR|F(Pcw8wSSe8@2YBKD?eSh51OE9zRZ&t?0pP@<56UYsMcdOgGTYM{Ble9WRq>zA7-OY*mxK~dwckcNg z{q6bvIjOn*=!o~{1d*OTL`J(e-`)M^PdYDC zj*@^jOs&d}yuut(VTCz@5Tb)FV3O`(PfVnflf@=ir5vH_VNsx6wJP^pJDb&)L0(;? z9mlwh-aJ(34blVOzoF#V#nkppOq{&S0tF>;#=p$F7nwXunS-1nKGMk-Xroso;EWFR}+nj_rPrmp_Vh7{nZD9G&L6b)U!%%CJz8 zCA$ZcXuB2^bV$qr(e(K8C&U>=wikXIfpPy#R<8=V&W75%xTrjvbnNaU?^+rBu{%Nv ziZm_U0!2oj;2YdUu2F2;-?UrozL_a=B!*@2&GzQwrlCazKUYYsb($Qfy?fkiq5bx) z_tN(Qff6IdLi6-OO95>N-UO-P>QH54;95vE+#O{H0cQQ2Bv#ebwC}9q(dNkd196y{ zg=fm+d~Xuth3Fz9)z>yR5t@T!vkMeKlD|QgM5_67-+5f0ud1a_wcRQNC@!u;V#AJm zIO)Hdq6fbCH!(Tn3in*BsZK`3v14@$wdNT#ZLipvwp1F4GjJJqTZrmnon1&vz2V*&09}jZMMu+5Zmoo6w6tX719dJl z15#AfqY46PPWOzSF&bO)iqRgAX8WjG`7tumWy%L7^kQ&12_`~@Ftv1(vyX+xy@~*k z#G~0;@YvL7%=Rn8Q< z9D*}e7#mYiga}y@?5r2Ih?>0b65y|*+1_eZts%yq=}~gOE(*AlRILW zlT=e%Uw06d(@TrU0wUq#RRNvXM1E=tcHCA7^cStd`e;IkZIds?kP=CXnyD15*WQn-|n6!+Hj_B4H=>I7WR}Wx0n9Vigbhb# z;sZzf4kvYw8^E=H`AaX>*j4j4E7s9+h&*dKWRpKfJe4+tgCGFh*r&yfOz)c|2mPiL z?+rJQ5R1voybYj>iw85g5>gjW-dCGww?cocRKedD=(W)e|EhI1%`Fm;9p`QMoj#SJ1 za^HU>)--+O98Bx{Nj}x$OTDW0gHkteO_YBB74QCh=cZ#jHV8_UDJti@^~b!VY z)kJ%N#+-J%ngFS@;swe!gqbZHJKYR zeUkV-&KXA+OyOX-3<2k^@n9Saq_`M;c@aF*oJ{=2-au=pmEpcMkyfT8mYNs4N2Y2( zxrzyPSH$7{rVt=6qgIZ{Wgdi14HKD9PY5TxEXqgd-j$|0PzO`^^8QfPh|V%5!Cg@= zo|VF)e9y0Jh>Vgi#`&SXe(pqjJKroaG-f10O=@c>N50)-J#q1@@#1g_@kvZGx@NEPe&rJ-fg~^X*nim{{CoIJ3!vE;)GvGg^~T* zkT5-sgW=fZg*caS79FJ;eO4)@v(uWk4gtb#PVQup3sQN-U?y=cB)-EnPa;PL!fQRPFhQ+af}o5G`P(MMWY_?9E%w)>iw!A4a==)BFhY}`pU=Vn zlC!O4f_9{?0B4b$%$$pTlBAIc`{e)`8>^f=Z$IFl|03wC`cUt1A*BGh*+h5tqSng1 z{S(}8`xsZhwDIYTdDXHe(p^yoY=7skV|Pp|(B9nKK!Mz+rY3>;ELK+zJ4co79zye_ zF0hSN+C(eEuLMyGS%Bo z?Zq%Iihi|j%!$S0E?qnRD$>G5oMA9zGB$k1r0L{OZCjW>|9L#Ul_b&Qfi$)S%ljhQ z`PqE=b21CUK$0)~7dz)3b>(l~&=4^CZ1*^}Qgf-8V6a$0?H55pxv!kCWGGI$b67ms zLZL1&)2|+oZ7T-wfcDvzl8n6s7VC$3U7blZb!1=Mxdj5-umWd z2mFg{(KoYSvWIsCTnGd%R{1s3b_nj(cS8eQ$J?)FOyiYk;p?a02sFB*UI}uX$b=4; z&jG*X+8E513Y3JpkAMs^HtIBmrK@-JTbvKZI1D}O#cQLJage*N0)w*HiUV8EcShE+f#9d_Qd;Mta& zHzLbHq&9g;Nq1#!a)iptVolpC{nPSp?$n;dV3V}1G#DSH?T=(E+nW3K$5)32w|pjp zoCzyEUi?IA23^}yzI{B>b`@^)g2wABd(d>qWVM}Te>~?z{kFoy_R`ST#+=LJ@h>WI zwp;<@<*Rgf#Phph&t3!^)Q*n^m6^fu)-83HDG=Br7{O$ZMc%x2~5 zx}~FIRfCh^%kJ&v-tNYZRT}4>sCTritAk5aNTQ~QaA2$Ni9F3EGSmtt>%%xapnua_2$Z%3VB4N%IKBEb(p7L_ zR%7dtc|s2StbJFT!!{l#MWT@rcDcptwqK2!DSs0c#TV5e_floJ^zfp!UNqmzPwUBh z95}m8hf`+x9i$|o1Q!<<_p1ZbdQplF8}u9w%6$%_=+8;|bF#!QHFRV%Q@Z-UTtc2c zeTtm>V>mzj35CnLo0KqJ$M&rJ9Qt{V@yCVV$0CHTp404rl3PfPP&RrwvXnyETLbsx ztvfrwTEecf)mZie~Wq&fR{F9S=V&{+nW-`ZPNz zogL%{6bAA1o_+pV!zRmQRQsrQrkFoH%w5Wa1d&Vr$P@;)5Pf|I zoR#}}qV=b4k&kfe71653YDF5vgL`3XE9-WfztQ1VUpIFn1(_#_wu8gBC@gBFsOpQ+ z$6uOk*0;1pw2NuEW64ML?y6xLbGSVPl*pNT%MQ2a$gQpU+GK7;h7FR2h8yOHidtV8JU!b$@?_Nt4^*{nN})a&RMDm@rMhX5n}F zjYf($_xZ1@%XS9k=7Ra9_)W@9GSVq&j*b>`*iWxrz*AKC&fs-v5zg)zvn_?*~0lPIOF z)R5)8Mp{zVrQE|2|9~qs)qvAq_O=cAmmM4>CK%{h%hrlk#L`o4m`@vB%xsl-7h}ms z-2{r%+wk-ZY>R1G^Ejj6!!499G`VnQ{|w9!VD%UE3j1mfLwQ&qUS`~aSQnZCmV4y88&S8q$C z&M>y3>|)QE3r!-_Hm^6y$_nk*$bHUV#)SfbKVSEL84vQ?`_TaTa&rwyM`pSqE4O*n zo{C${AVzT-RMO^*o8gnO;+3_t8P#ijh>!W?u$qLv2+Uq}n$B_YO$NiWHkJx?=ez^5 zCQ|(zYp_qySYVjyJHrk%fiBLzxh=0^V#4zLIre?+f%PVHzDdNReQ1C8U1=5nZ}HR7 zI|&oNf1fj;Z!UFdWWp+=0`H}X*8ZMy&G6ExRjX|*QRDl|h{+#cH;hH3T-TFMbQx6C z4!aKKr8h0tFCus1B4@E{?ijaZ#SFgH6iNNY3jfY5n{zy7BMA&$mlr|Z=O@YxQtsSd ze@mh_2j9Siqg(zruw=@=2u7a!uirACmK^g^)!-J{D_iTsxb;KxB1-_UqTEj0!<8%# z|Jo?4QcQTZTJ_P)unJec6+uA|l5!e7CPG#HB=ID7rDr{;?@A}}>W++j>Q%|f0T}KZ zArv8JW?y!9cij%g3{}PDVhkjbd-N^ZpLWu+c5RJ)c&2%v((#&f^M!*$02lG{kc(r(D{O#XbL(ke}aSEA*h%X$bZ z0^6q1NPm7`@JSv1P)fw6?@czECbGj<!Qg4?pwFQCFC@MO@yk4 zq4wX{I+h@>$!zFnY1~&zhA#+B%*dvF@+_O-BpDKMwC_7Z5x-aetgg&Yn~2*ub>J-S zEQh=elY=&IiO+|UqYJZbXRvpioC(0Qdpgs6(i6QoS>d-ie=u%CP%e^Lyz91*KJgaw(Yb+MS?h@>y8_D>o*$5cFpBoVkjh^mhEs&-L`1S>6*f?<8gr zrr7RjK6)DZi~RjC3Mww&6kDFKcs>vb2a$0QZwGcH=i}Yp`cScfSUjLs|0+!GlPxtZ zNdK@(LC_NImg5N@Q-wyPJ>SZ!=NxfZWqs`c!p0xQR;nzQV~rC+Z95XXWmt4UXk6oN zdjZXXveodf@7G`oG%Ptc563G7tGbd4INZ literal 0 HcmV?d00001 diff --git a/ml_system_design/seminars/imgs/sem1/sem1_4.png b/ml_system_design/seminars/imgs/sem1/sem1_4.png new file mode 100644 index 0000000000000000000000000000000000000000..363d512880e63bcff7f441e931f8ae961ca993fc GIT binary patch literal 5546 zcmcIoXH-+$vp*stM5;7J1w>Ga5C}a;6%i8Yod`&;0qIBwDWW0@p%+1e5Ro7qLJ3mz z0#c;61O@56ck+VwuK!)@|31AB?|hiG&&)o**)wPFDLYn2>pmTb0|Wp7orb!q9sry{ zk;gUERAkB!)^ ziTH_&xO+N)#bjk=!J-f_1R_kHA?)?U&D+Xf*v;$4Da79xs&-yBo=%Uwo!s5HPcf~m z-F>{}d3eC5O2GeUA)5#OkA38q|9HsGjqDarvi;l|SSSFnaA>G18~A4~6GC0D%m#m3 z@6PkcG8$P@gnoK@QexjBPTA@#1on8fyui9n@k6B$8w(V#p{=O3Q_rifB$T@?OLgM- zl%A$PBn+TQB|m-Xe@hsOBOU_9Ka!v8n8{(7wfK2)THzIN+!;8i2EJ5TrKpAHe|}M5 zf`LdlJa_eK7C%w?_x%2yZZ8JyXyjNW090#KQJMn)HwQHf@FuwgqMX8brG$n>OOQL8 zD)57?$$n19v#4Q8&@EO*eRI<|fwomVgEcdMrh$@o5`^Y*w5|_)hIcvvW(!kJ;c!wz z89gDhE9hAVgEG^yf3*CR;u56jp@yYJn&rRxtIPMCIShSIP-n17<6MglF+ps!d9|Kl zQhYk8ikg`_8uA`_L_Fk`x`BF`^y87Q=o|##E+uON0QXjh_6xvI0g7|LPtgA&O1#rW z`-ozb_7n*05>=JOy2a1rOn3( z?r5{}mK8~cJ)h)D2>;sP1qu&&*U&v1Bto%Zg*BKFQHme{gI(j3}T1ODp z&S&@1E&uL(i8`@CAyKq^EhTK$C_s1#QMp0($t!g#5Ww{^9`4Yk-mS106uesBdve^H zEw5}ea0x1x7k*|w{Cotq#h4Nlb`Td2XHeib#~C!>%k%>#X;fN8*+{kZ=1|`UxY_KV z3+puD7k$N+X|6v?=9~6ZHfB;lRH_>nQ<+COP@_J-aMGqeGgf-?m^Gp_O}>?a6cli^+-W%ZV9@Sw2s)Ps67UB+x} z$rMKWuF~VA`mLYjNJztD7~tm_sPq}ahOLu~hg-{UQRb{OlJ}Rf>ekF_0KhYyj{h+J zi$p5uK#XqAT?8U&m!qXsxcHyS>q9;T_1FQ_Orvz}WO0AeKx#tfRlxav?XOrJN|-?6 zYpY9!Rkm7sA{f;VEZ!J^gK{~V#N)A;q<1rS5Obrn%v?Ezf}7V-dYt`4sSN>93zQK6 z`d|4rGv}smm}%OV$0brInLA3?F`Y1hfSl`*A(0e-BP}HeXk^U=q3R^@zq>&=7T~&a z?XS0pry@C==B5a7Bu&Mu@^j{(u-7?+HSGd5{siw3GwYLl8xu&2rZWC3)$JT*@=f2I-WF5WUx{w#JOEgZy$;dHShMq`Us}vyg z{uG#E`bb?_u-v67yXWWloX?s%Oiy=wg02xpMr8h|bXhy<7q zl@g*8TFaMWzl4F#_I!4f4q{Dbf`v%j$_o(OiJxilR04$F5!Q?y6^3QZQQwJ!rNK`+ zbh;jNaN5dphr!CPoPacQUa~ycrUz#OWCyVe zOV>}zh^ct;`lL}_%``<4J7Po0&@Rp->Bl~`yz4&)cXcN@VijT^NJia;S zu!D+nnb}oh{j^oYnj>llxA6CV->FG|mg^UzR^g3`o10%u)XLf*RU4kQ+q9qWls*=@ zg`;MtjAM+A*UUgm6W7S1{JMbTV77wdZYP$J!4Tby^aWB{j)#EoLGs%~P0ei*EyBhv zICy$>Y-48(Oa^+?DB#(5b9N&9jA92UWyXGfZ@pF8&sS>h`GP}Fa9q~NNUPV*p2zGR zH5o}*V5eZQ&oIt#VI`UV>TBO+5RRkKoE;3y*M`q_X&7V4$F(U^=?qZKBOx6_2}nr& z9cKBcj#39~XIOwC9RR>SBJaS*=feLb5r!VIbV^0kET5huN`V5_@QchZB>?|8rP$c% z8*~&@VPn*&R5K3I$P0ipBmbiuV3$$$0D$vhRIe1IlgpKzG>ltb{9LKwD79PkK30Q=vflvA6f)#6hNU+qwy8o zVz&HtbFg@6a2+5_;kO(Uf&H~|HMpq#pt5hO| z_&Vz+US!3ToA&4wmTWXOVei69^94%3hG6M|>+-&zd&9UmZej8`U{;HBLS z8%ALR*IB;mRq`S*C5BwdkUj-g1AJP!Q^x$|z?;ZX=bz9ku>vEk?)!qXDxEjT>#W1# z*VD1;2j7pm%!aG1n$_rncKQ;~;J3$BVS*QOUt7wVfQ8Z$Z3T~eIGW>kmK*xZID_@? zONdo#it9(^z-gk$A*Km*qlC+BJP_K z>fd8rNni^Z_pkX>B%(5pVlB8 z#CAnJU*(f6Y)2^)0lpeS(v-(v+t!5<^%ix$)QFlE?Glm<1)Fa8IYc+pPFr7f@U{Ek zYS7jaOt+Ej2}#N}g>Z1e1yUqzc_D_Ot^?#y7wy?D{GFYFl~??wgW=2L)hf>Zz5x8n z;lVlchMt9uGM;)8tz z7VVq&Kt+wGOtiL?+dUS(;gTT&vtMn%g<_p^UN8O7S#H@ID9&OOlX6|FMfdy_Kf_y8 zm(Hh!3&_OUuD}QKp}g}wv0R6`T?;d8+U4}D=q8Vo_B~p4O zHARYwlPw+tS4~26^Aj8eY7nSo*L3YXw*4bxB4fR7REnfGXuR?B2VJwlQgO=wa3429p_;VOBP&iJ%Shp+EyKk^9< z-Vr<5kUL|*%1H_1^SUM!%1AHXA)aH6sO^+?&7j#T5SSLO=%QOTPVQB#-|-tI)vm4= zeGqL&L}$vy4buh7G3Dqo@+(O`=}7aE?kl}TRj9B;wr_E`arMyTQcr4iu)I!Zf?WdK zy#xYwzZ~!_<|JGc^QA3O&mq72{57tW+wjrS){)yCBj;y3WuEkI=i5b6^mAKXV(rjO z10e`e7kYE=>XlSicUKMC2R#f}Q=ae5+c?h3>Sfke58T-^P=9K6uwSy{J3lNopB?CS z;zWaby|{#;qC%hNq{b!mO!9df!(VJI6bglsA-!KIWxDVuChCRa%b%2OuKq@+RD2O? zeOK;Ryg5eb)!Pv!PL6)sxM+R3*<_p%`M%kGa7)G^vUdVZ#}J5Mq8ONP*kqgF{klM?>>aWiKO^V&JTNt~ zjn+G>DZWbu;}atS^1I{KxgLaM7dKyGdCe-r9g@cOwnm1OtQAC5XRM=PgF0_eMrCc# znUJ229x)|O8uss9w9XQ-Kxw^A?#fEUK3rdKoSN!Q6YnkShFVSp7BfqDSpMSEKvcm9 zgx>J8y;yUsd=WGMVm&(kr?5L+Mn+z9Oy1M;9lo1v#>P`{>PK{+Hb8Rm0URDpaO&!( zr#UAAQ49bNm2>`2Wbd|(9gojdAoYIEp6h1^SaeeScaN}uCxnp_BtqJ)u4w&7=nz6W zWO}|t=ater!q~_L7BDe<=yNqk%fio>wz(&o?n=e`ef;*X_oSr}51m-0b*29*+n!GW z*bPkCCQT;XX;T>O)u^)3FbWtrYo%_xRipO%HGJUTq@gj`&1B4PH3o;)C2 zW)nm256Bu-C4PSqvd8i#Cw|N(}x;3BL`>v+zR#- zgsST9=cxW=pzJ)+aH4(ftkvgHb(^Dkhw|dt_yr3=T0cg91=E1I8GM6MJ6b9EJ2nv+AQTb zIMMI}b|V=ibmTd6-z#CHFr!*d=CS_Z7-qEk7hC;Q))g0puBGl!^sh4TqBzN4eH9rn z)O51HDD-J{<2?L&9mMa=xW!<43WR3bRIZK!{(z10|l9cuA$WaRxXSn1? z5ogyz-Ssi+BXL8^5Mq3ink9E@`}ZsxzG1o2EqJ!}emjK?aZFLjX)5dO06{LjeMix6 zMwhU!0Uw+2LJV-!Tt){{v&5r5yhsovK8S?O`1l{1Vr28czS?|yByT?IaD_%_BIHz9^ z50T9xTEKQ%4&0dNtG~<}7500)9w8#&sg->P)790L(_zB;9+lxRT#3YQw08t7V9GeSNZ6gF z{bwt+t04rBZ(TVqXM=Q${BU+1Z07X=1GCQWrkbKIK)vT)KW}ZFZk8DRVNd=#^w)*^ zJ}cwm%0m>CldbHQ*m)0$=n5w%ryqsI4Yvs|81;9UY(!){0?tlHVlZSYrfjL+K7FF9~0$Bz3>RTCk#So7M-h3Hq zjN^Rrdow-NKl|K$1#TU{e_^uq1vL1eocz!7g-$ym`a7zBe%tvgX8Vt WbUVA{^pS5^0gZcFs#ujr5&r{LUu`M? literal 0 HcmV?d00001 diff --git a/ml_system_design/seminars/imgs/sem1/sem1_5.png b/ml_system_design/seminars/imgs/sem1/sem1_5.png new file mode 100644 index 0000000000000000000000000000000000000000..9c14a28078d1c627c9d124ee78f5d59e25979020 GIT binary patch literal 10339 zcmZ8n1yq#X(_c!uk5{Icq?b-fSGrSLN?JgWEHZ$x|NH&F zb3V>t&pyxInYnjn?r&!9+(->I1#Ap*3=jx}{Yp_*3j{(40lsgcJp(>@SB2bw7s_@z z`X2hKD#Dg9Cl06;%)*+($H@iw9Rw1U@Nt1!I#_$qT3Elaa~1<1w|0VQ?X1MWdi<(f zsxC6twswksZr0jdkfB(SZ7__2pRyM*~vhx251D?ddwjLfX!knDm-rgMEyc{sMH=Nu;LPDHe zJe)i{?7#?icVA}@s1LidJN*-j|L~BtcDHo1bMdf)InzGzgj&EnJ;cCZ&L<(9|4IRt z$NAs&0T2JxkhL?Q7B^u1w68u3gFv*PSF%z%KG}!MK^{6Y%_o13>AqQ@H>ld~`>j_A zMC_wz&M7t7zmPJqt$d+V5TeMbc!$8NFA0{Gp_h${Xxf;ReZ?43&aFd-5RHhyc)^a0 zXdI%)8piEvXXA zdpv10y}d#a5FH%vMiC!)B1s0*!tpp>$VsBn8LLDE<4HGtAVQ#nyKu80N~5h?Xd%P# zx_gVz(CFaNul`Gb`COw5^lZGd7`OKO9*%-713*r)ryiu{mK4;>Y-kH*p!W=C;N!MJ z5~5og?6ST02S-wFmiF1gId};0y5IM4g~1w0Rqpw4-X|fhS^V?vt}vMJ(VGi>o8#T0 z_#|cGxSX)Gbdw6*dk`=#4n7qSoa~20U^wWJfX8u4z=`0p9T}KOkFQG$$WFH_O8gG% z7;VoBGV*5bMg-=&4z?sk>T;(E%KD@{W^&c17XK*%?mS6K+%0DjS^S>3j_z&C@^P?) z&}Sy*{L~m^xJ#rohF$(nvYX(EB|$8oTQ6i4V+qfB zg=#q%&N$$UDBaZMwDhd&EKvQMx=ogMN5K2*k~2hORq`I`rr@BG@w)7Gml7g`5EWGP zb)Sc@6XX{=ICA<4PXe31VSP%JCpvs68&;I0rAMHntWJ~<<5uw|?Jjr{XI_O3{p1iQ ztDpKrY@QN#Bw9H6fMlla-cP)XVJT|(7KJYth1 zFkXR$S?v)Xd}oqb9)T-s{z+X-a@=`~C2K3A&DR~)&cKgawpO!vk zt>K2bM|syoBYW={mvo>(s{7^3(lrLAVt}6tARRl867dI}R;&&)q%ns1ucSd3U(|bk zMH&6GG!;}7&20Mwy=}R&Nlb6&bEwr@Ma=-JFP;hGqg}Ldf*l*f<9TdTQh@r9zc%2s z%@mtRXrN)+a3KX7yYVZ44^-q4LmUCJaWA4^-y_MbeDJjMytwqv%x^#qG04a~1Zbsx zmSjG4!b^`fWM8awY#TFYvNss)igp33WoNSyOWxC+Y+yOLn|=N9aYP%QWD`7Ine7H@ z^Lb*g_URp>BEQyP+3NDbUDXAdRhqAe3NCv7yAUZv^z1d+A7-+j-u)w_4J{0Ze?TN? zQClZoiw6B%gu-A@Ns_!9{1$Lo zG>Y9BvhG7*>(WZohorJXzegyxeZf20grNv@BlfpJSsk{#jQzh-Q;J^Ibl9-Sc(n1L3L>M1juE@h*SdpN`-&jEMPkhi2sxu>0 zNTx|F1BU?4MOCD;SUvpA!$f33LZPGbI#r~scy!zA$4~eCM~29VKM=Sz5iK~sUo0f( zZpu~_%-Mk^aib~NLojhR5JT$5^wnh^BzIIqOr1feEv2jVijn(!4V1|Ns$;uQJf z@ZFs0yI6|#rWl_0JE=33HZQeb(Z?Fv_uT&Cp<+l#?uFOXuVZbaDfo4&YrFyWzttid z9or$Af+DRHfC%4!H&D&w56FOb$pYSm4%JL|LEjc6>EMMgChyXQ0EkjkM4%+tL*~3r z3~ZY4CuB!S)j;fOZL1I>SoZ)>t%epr4ckQEhu$lSZi($}01=#NW1)0xFD~vn;_w~* zb$kRmwqiJep>1MVO$0d^IvSKthP0bIPHqcp{lbcYHX$(>FU>8+63ZgthzlMK1d?tr zp#N|mMR$g7)hB~Tb8&HQ5T=&VChXE8JUNN7{+Js`p4Si<-~@1hqhLYN`h}>kt@BC# zg)MzRfU8jYK8{RN0uX`tlEZ4oUwwK9A7|UchDrbydSX+RyJZLB8Jwn!bYTSolA$GwF91hElmK;781|O;2HpUOAiYt*vtxY-ajHJ; zr?8XW$j;g?GIUM9FnIrzlqmmdtoVP9)iFj>(#U;x@q97`V9cYpW09JVO{Bkr~^0TRwHWe9rk}ERFqL=OYzIC*{_UIVx5Cvl0oW=99%8Mfopz z_vG|Zxg@^$!RM88gk2HaBN7tTVKg=gum4h(BX_uG{K_*Um&$t?J#@GZ>gj1XR83+2|YHrV7vV%+0ed%wMW%rNvd{*aTHF0~A zp44^{KGq4b#fzaNTlN8eU&YHE^B7H(O@9m+PMZK%`ei^0BnYnlx=iERQ5<(*P9ZV$ z*-aHtC?h*rQh@3*s({HabQ6Pe*U(23(G+&AS8Dn^@Q|0k553l{3@$2axMXAyw6U|L zdtX2Pt0k<)h+-YAgIr#|mgz9!+ujl2sX$Ep zP?bDaBk#^Iu?ft+poqj*=Fu1N3r}g)w|Duq_%B~_I1=e;5%@H{;e8fXF@|;FzJE)s zIT_ss&Do`e9-&YFL@O(E^2YE~L};!MRt)CP=jXRjg@-+1QzgV+WxSHMP(kGys~R9r2ZsM-;wJ_iDe)wG;?VZjsWS_kwZ+& zS9}gIo3yXYr*|Ht+=${g154LG=!~R2UZm-BY-)9EC@<&BzGkaE%f~GdsuCeuXTTq# z;30@;upVCf9Y1RFKxqsDV6`@8hDu52des|82nZ%>1mD`|=tFEN1jmnBIEi}=5!D7E#!T{jj)Z$^96^6^#DwYD05))l6<2dpn(ZwWN{pz=Q`uEgo6zG zIEA$B;8BMCy2IF z?0_y#@DrDuH?xU7vzj%hsnVj!+2{3S8Oi=%criIEjWvScl#U$kPBWy-S_Y^ql>(QqZ?VF*X4YBCk2NR}YZq^c1@Ma7@N4^AzW$(1 zw%7R>B4{;S>33Q{0`q6D_g zYQ0=B!DboOk_OMpdaS8+#8Za?J@ltv(S`-83xrXX^n|QtHfsU@!`eDtFb6{4(R_FB}qr8fL44&3GmBl6D;)Hp@bVY!0-ZUJerc@ zbacw?3N9)-dpK`-#qu6XY9hgfIK+Z#j>1|`DX}m#G*VWL0h8Mg_&U9Zp&%IYscoHU zN@`t8+tT#Wy$dAhD(%guS(`bdV*S#tsQD%S3kHUhW$V>VBiz8~N+bieSEdCb3{XAd z0Sagij*^{Iy`RgnGrq4cfOD)~=;x>Lh;3VOcb_Mwr@u?|za1(|SN}6I@WyA4#hp<> zRW){N0Dh=X3xgMs8-#&L;?hs$osgS5R`)}^LWl=Sp*u*?zT#7`P(dqa(l$Dz`Z3gr zqA0~}c0~&CUl9Y0IS}QjLt#WD5!z8*XS+Zc@T)&k^mE)U*q465`mK{) za48|)pr@fRZbx?#R9qaZ_3G8DmL4Kq%lx*9)~9{3F%trm)EDR&t$L>1k1^wX!5BS5 zd11eFzL*J<-AM%v(R%ubwYIuipU+foF)+QNuhMfB`ZlF5f!&qFm;(v-J}kGMtu&ax zdi7HZek!l%r!6(Y?%git|AkN960v zMX1of_xJDPsOrb|{*|}#!e?*jFJ`O=c$iTUZxA3#wjfK+iTD>RYHMw^m-E0J3%>*v6#B zwK5Fy8+eo5dAE6eZ$bW|+LAM*5%Ynl2 zGO|i6bZAIPH4(iEU|`%BvXo`L#*(?X{?OyOMkZdrPx67Mx9!YG{#gteVF+3e)6y+^ zl@;R}P|C=>HeB!$+1uE#ilY`A3J@Z+b#{Ki#1wva=bKqTXs9m;YjQaL2w)eM@B!KC z)3Jo898gwE=_wwq;x?{eycz2%)5ga>w|FT{@jc*TTq1-`uckMXi4GiThYZv7!w^` zNaJdnSU9Ds_R0>2-gAVha1w#W#l0RycAHR=6Ps_dzq%+^s|+kn51`|;`VDMKwO&WR z3dR>hUG`4pFOp>TY96F$^!}+Nk>|PV8YktKCAGB)cXxOBAQ1QMoG;r6mIll)jMTgYlE|S z{jR_Jc5xKoS=fKW_M;@@MbA^Nr9OSyiz`}0tim#UqFVNxE2D7s(QkjWd*{c-@`}Ce zhIz*UTf4_9?<1Pu=_thLhoG8@ijJqJXNwHMx_NTj`sY3u_1@kLcRvxYu2IN-+$STQ zR-3tPaXFw-}tR_?i1}QiW(BZa|MF7OrouJN}-rD>w$broqW32ieKa5=DJhAWlk?5@2>i@Kf@Pb! zr!35z+JB^SOS9w5NO1ozNznZ87P-FGjI+q^)5T!_q+~?#MmWKkOPEm;qvM~5&#Jki zkv-wK$tfw{zr90kzF2VZJ)hxk=^XtmE2?zOUko@c>l{pWH`2o$&E!}17A#;TKb2P>RHRLKkC|WG zd&fJ};_p@S=xQSI5OmseeBso7{%t>6P_$b{k7{N;O4OWoM z)%KmJy=K(d=m(+XmKI?GLc;m46=`rd@`~R+`!Y20!Z8z4#JxlYWaBNuimxW9RI9Uv z+@n@i+CtJF99ME{);zajs3!eZRh1<^*6j~|w=nh9A@82$3B2=}tv17zl#-I#-(-J} z@{=ECn-%fJUVUxFf0R&^I#A!6VWCokl?a31aJLh!VaIlNvbZWB^z6)Ic^$3TF$XZU zOYLYDv>SP64p&X^@jLT#bMHwytaZ|Ujh*WWJd=vDvh|MJqr&FqjH@f3m8GWCdQHd< z&Tx)nE~JRbrPlX!WPM50`{10qH2>|>QHMrKA%1?-{l(&w zmjcZ<{<#m>)LCekl4GlQff3=|H!WbE72jg4|@{uFvpTwWe!H}z>^X-UPx0t|#!1|}wjgHYLDqFGz9 z(SwR0^FFL$)0-;R^*hv#>`hRNjmJ+@ZQ1^-Kfi@se<7=jUo*J6y2elnCG~{IDs{ygSJB5Iw`2_g%OaA-!@2%dvaflnpr%Za%+@h0> z_L~L1G7H^EQ8%%@NkY!|IgM|e zdNGzKY#FhkZ<=rRD(1gBkg%|@9L(2+XF0bdKHgM6&QhiE3bMhPTG2+bP)pQ%ID^VW z2o3}Lk3st1!J~;0E@WKhAXdH4R(&yKAS>%w>%|5qKn5s$6Z?`lq&IqfM^jNL6m+}Y z)I0UT2-hm9Oo#wK>ZsxLlUk0FudfgYfL}H&s5`F-yUehg`O5ezDRl}GeQxx_pL^xj z23YtaEdJpSyX+XAS2$-Mgu^f>SA)aV= zmz8mC>9`$LCamj=^@Px;_tzBN^8DsC=;IoK?~5;pkxh>j+U+ob=ewW zd1!KFTFtg=y`C>lzp&gKN+lvDMm(v@Q#F6rqub+=rTjPSG+I&>+i}U3P9f)!^=3#5m1ZoLU4|mRHrvpY zOgGCd@DtL-JMO$-eTu=J*I$eF3O`nB>*~&uF9Nw5P1;YkVQb%>c{y4-DHe_LawUL8 zVj0hKjIa9avs6me(?1#9II>_Fx^wQLswk3$a?1IPx>fMPI_wmz5<4p8N+LkDAsA@pgs;V_NG}S zeE%NPcdyJ0fy;TRX;#^AAx-%->@=!tYji7I$Q+a{m@h)E%+2+mb4{u!mx#s{zi3pN zw+UNYHb6)+sI$QafT*PhBPSZ51F$ze(7k8!Gu~346G)|>OizYj*5z{9dun=G)^LG2 zj!Gn~|2MQ3lXC}$)q`V_epUUzA6-$ke{S0mpZ17m36~IEZ&BEF;_B`v+kzI;5rR)pO9RDk*8v1YSXZnu=H$ z=R&f12|jT5{F_4;0_*pUOb6+|t5XlcO=nN_^l{_y499mZ5r&g_SON31!5QBt@!{zJ70cvi(OioXq`#l#;I!F<$ z9Tgmuhue%NkNOZ+L+FP<@?#F>#QdU{OOf4q=kA+PKu9Oah%Sw_4@)~{l=gi;FIr2E zx_Z2)Y1FzzTsVi>3%KRiN2kE$-`oBmh}!zQzQTiBOEv?8#>r}!BQ8De=eE1&ralk8 zps46*GX5b85m%)ZP__p0Y<0Lvt2vNN*8+ujTHQOU?|7ECucgF$fU}_#E#}usJsX|} zjCY;2khgAVqTxUXEZ;akrCo|{;>8ySJ$7_-^tSU^BYZKNM#YsT zNe2ZmWf&HBmN2XCPTY3}bLyWU(6j!hy8v$4lXw@>;fF*GKALOTcSZKFcmXKuUyr{n zGo~mkFV}|8H}rGbwp2g1Eb)WCTjq#fXFO%#%L$M2S+{dz9cof>H+fTUq|sWu`(i2P z7Wg=rnIjflMHqbxQ`;2~qe*%u3KLS0rw;G4oEZf3>5LC~nR^zYBJXcxf+4I2{YFNp zD|?eQozDKf(ZgM>!3HGQRu{~2P2Q!LbHuLA)FSbcFT#(5Or)L!9E7SCTWPjm4DZS3}6*fmvdx3{B- zQhH`m@;FL4kB4o)EgO97>a6PL`pYj~wwxLTk;ZuvmKOCld|jqG>(T3JG4U*IfUU0B z5VkDj+!jy-Z^Q~57$Lt<<9mw2GgOg`vATr-%t_DtG*Qs9WcmL^&BzscT+&EhB8K(b zF%=_{6y#D5$8<)A5`|f!+&o&T`Fj3&oY|~)4R&m zHazADY{qV!ps9!7dinqrBr`KJ$b6;EH-<(+{0nMI+XRb0aY!hlyIuP8Icg(G1eE%5 z>8B%H^mn+NY*|bHR)KXiHD8D^C-Wed&00Z?SlW+e(+~y1nuKJtau<>sb3E zX~WP^$>CtKvH(=QTX_d>*gV%pEecV#dh!Bq>|V06M#jg#xxYEbBIAJrds24GJP9IP z;?GR*B?_b1!ot{nw=q{y1O1}YWyE)Dy7#*JRGYu*5+zIL_?w5ut+IsI?RC4jE}Awk z9rgaN$DoJp>*MHO=MF85EG%XGvy+pPCml^2vB3w8sb_27#($zcfBw`Dvt=9XGAHd3 z9DxEYM|^_39|{HrWFXV(^vOxCs_CRQ{Dk&T@3wwGBnGB+u4$1-dzrh)-k@P#vaquk zx3wV#fBC|bl$Gz z^qh@Yj(JN44Q-^1jL!gWGC$)?-_{=OJxZ+%#_X0s3fI2F$+~s=tKH0^| zMw}>9cp@ppQ(G@JxdQb%uh)S-Z~*r#FchGxd+-?V3fW2xDla!*5?r3pPYaQvbOoB{ zahKED(JXAnpsps@-=*c{dsnoB6w5Y;i9qZ*x$NxOnfNQ!P?x>=oj@6#am1PfNGsp7 zvgvc>?d>%#W?Q#aTu5+=O8%8stP~?S;XL#^79$nX0-z8$|7EYQUr7TLlcibcY%}-S zuuHW0AnS*?rL!tF{lhsYAjWDGT;t2jW`fb`-Sa!X=Zkd<{TGIJadRQDbYXSbGkCFh zen2_Wn5zzQVLZ7&!gV1bJaon9XC!!GwrAk>)q2fzNX6xC$TkQd-f z$5&SRFDyO4)(#~e4V2lutlGgqyx)2qWH_eoelTAMj2G#v6==MjOgFU8=q6IdN5>CX zy*={oK9x2Ks1o_y5_ujadAoWSPcugtjxG9KrYcCCK6dqlB6b2Tzdt5}cW`eioRzyI z^vlf|^q1Kq`QQB_8_I&^O$K8+KN8Zz&!{x;J$q8Vfwr9I5BmecawyNMf~t#kEg7VD z&Tl>U^(B0HK#<;E{Jb2h$bhF!vV-?Myis>c(X*2q@k*i1nDjD*?|Xz7#Gd;h7AzKl&?elqV|pnywmPKG!-Mhl z_OTTSpYCDJm0ghC5nol+emoafkHbuVMmHrT4cvNSR@>ZNKW8pB>}+d!H>n6*{z21w z;*6TsXo7jewANB#(j-m9$de|N(kX~qf@;luHgw@-Hi NU&*P-ewH>5{T~*eSup?r literal 0 HcmV?d00001 diff --git a/ml_system_design/seminars/imgs/sem1/sem1_6.png b/ml_system_design/seminars/imgs/sem1/sem1_6.png new file mode 100644 index 0000000000000000000000000000000000000000..5ab6d378143e7dcc88f09ff7d9b681e0846b24a2 GIT binary patch literal 60074 zcmeEuhd-C^`}W(GtV&2iktCsrvQ=bdWtP23XxUp)MnXtJQCXFhR5l?A2_acoSy|cZ zcf9*NzvrKLp4aRC`hLoL-1l{z=W!m#ah_LqHPz42(Xi1F2n0H1rL)=u0+|khKyrhc z5`V*^?S2~nH(-C}jOKOibNFx8SW7cAsYU|18a*xTdyj((@_*E6c0_5>&?<2qd;iX# zMBL2EY}u9W>~UeE?{os@!*yxQd;0}QFH%2~CFChdJZ$_#YH>)ub0?RphqC;(`>)6> z4zVx=bjq=a%rM<1KSkChBlq^S#7@a=cax$oKBl!AC3MYFmk&4Fn&mE6Ra8jr*dHev z5n%2mNfz{R->rwAPg7EE>QM$?iQi$7vQfEd%_ON4eNl^a=WMH+6`zuT3c0FC5UJ?V zh=76CYm}6|N|clX+i7WyS{U8@NiH7~exgi!2#^2uA!|MbBeKqla zBz4XiRs;eULHVq_j{D=u9=Gf4b%o;neYIVWMVL<=4?A%FM~>sEo@?)oh9hrO$A8c* zn#_MG5EZR-=aW5&W5vj0TN~RxS%aR(#(~=o_#6L{(iKo^ZjKyOl2#fY7=0&2*9C{*p#C55ZqO{Y*|rP%7V7hr+KDrd!u8i#`f_SCaQ_ zdTGpK|Imrmjm?T7)3lba()0J26i5hmh0Pr&4=M+$r>BiZg)_OPlsA+FmT)y3e{=C@ zc{9l)OK&AnpNo`hTz6CNxE4p~WmCM;+a6O-9#TE~BjstL=Y1Y!JF!GYg&m%y9Exc> z0=X*8guikrQ73CqUVO#;V@ix5$4LILaU?)FE|t%F?s>A3(ajBC&QcSGDc8Lbie#aw z6~osm9STX^H%qi!R20vrx5}5AgrxEbC#bFarhBb5e&`Y8Wayp}!wrQiayyE3Tbr_Z zFxc4-knt$*y!M`5Ok6ywVYq*3?U=auQmua|ZI)j4NePM5{IREtx@)ScNMF2o(SJkY zY3mq4lTqb0hgb`h|F!qwX=&W-92}>$nAFanH+OQPcPdJx(!Ka9?oZ5xziU29(YLIQ z-%U!|C(1^L7Yx)!?49u}#hQALh9=8i>yk*KpIec#$YWJkR~MF)46_rZ!6QW|o*`-9 zi?{#2A<;H*b8>2GKXX{+fQy;0FGoQ^fpB7DywBYsmT#?;?ICBF6w0#mi;KgWWw+R8 z-PwzrdIB4UQy;Ip>F~$qmX<~=EZk%YQ()h>Z=k4r2?z*iYD$dQ-Qu|5IHRpG5~0W)T65rSQPFIU-^O-R?^z0R z`HJcgh8;Aw1t+xY;u>v&>a9)}UaT1z;l&I4l2ekCU+j~;cJOd0sTNa|639~61#;uF5C=t9t zmHtXRqzYR25(<6GJAZC5C>35*5MZu$>+=cW^j#)d@Z8tX(C{!R={6U2ypM-v-QX;7 z52>iAPM$m|C%_!kJ*9Y(J(!C+w|mMv@XW4r*L3X)FN!9fcf4|CU~Rhm9A2WAT{G2P zbWB#3A-;hkNu5eT>+8ehC8I_i=MmW_3fx#>NKmw!OShV`=fyH#^b$)_ZIU_8d8K z}i#%}yxT0>$t+Uy#;!B%6j18Ix&o?eX0483fZ;+~lmZ?H&1N-O_w#dwx&=_n~jKJTYbXuti=wYZ6?ZQemp^wkBKp| zPD_CKX~)FCwfD;vb-efgvPWNC$g2OaI*~6hI5Wf8=RHqXwmN>~S*yK(px`eCzhyF~ zzOo22VQP8C<)zD_Y=1pV1GadD_(Dm8DhUX;nEG314^+1L;Dw=_$UrZ<_4(&nz5cr7Jd$T4#ZXIUV!!I@kpOCLWa| zk}5#XpZRI+{XgZK6Xlzu`9gt@=~P7;hOfCVPVSLen-qEZ`t|8(o}cfpQ~9lR_qnVs zUU{g-YjQWFMEY&M6CPHWRr8fzl*)6LYu5rjOX=eqZsYb-nyJ!S+`HmcKP8m@kvk<> zp2kOm_2rQEyydsG)^}1$>Q>)$9p~2axu2z8EFvNz-ovparM~41fId8Cl6r`vSQuc_ zQEYp5I}JXqsQY}T?$FP?t8!XQ&$JIqc`edzt(9+u;gP8W{k@j{3@nvzNi#Du1D`$k z`FB5a_j2T@W!65c^>J}g3||eH#ip{;Q{jquZW-*FQOnv;-_zAqnRYI6riR6oE2iGe z+?<4Xd$Wms>y#!rER;b3s|k;z5CJWAw<^v04{EfIiK;x`NtkJqO>9(@)Uk3b{X@%@ ztr*`hocrNJsNd#%$})iONsFD!(jVjX&86}>V3&E}#T5fGe~mY7mJB_dL2bRMWM0Js5XKb^#7QxwM@3aPQu| znMU!x{%+^;n#xKNBS99Km7!hhV;X){A3u`QQj$%nC++ZRLIRM>`*Ts-6?K0ao#%^h zxKlSM%t}j588qWAg?o$luDSI%!t*#!1M;J{g6chAXlG|<1&gV7fOGi<^LrzD)TCu_ zgi!1Roxo3m(4!S?;RRxP{q>Yaq$1RSDA)#IHlbq5m79^e!clBq_wr3fh32Cx&#eq8 z33%~Gi(^v0>#SQ#ep?~#QuoY+?`X(wFr|3UZ5tUGF*}~He1JC5^sCoQ4+`VMMjMkG zBS5s(4H9m=2Dn_yxB+)NHIR%>q_-Z{NOseWPAm z_pr#|-4M-oHps2gdw9#j%S?Gh)X=h;2dF$AN+UL@@E5tzp3y z0uGlOOg96m7&y3?Xeu4W7=GURJFxbrEmPA`A+uxRsdgq;f?BGP;F&nHG@uQ4x0WNfD$a5GkQa5+5!R({OFT@CohVB0 znD8*PmS_BGC+dR2h@4f9HY@7harbQhwf9^Nb4OaYs859tS~iq(?#Z|^&F=n%mDKTar6~e_2tW#m)GZ;X9pu?Q%!O#LFH0>R}cT` zE02`psz}t#N^I8Vy6K9|2;_RA!~ay1518@9BnK?H+NNMXJ3Zq0bZ>r8oe`?*4rnf80v4I3-1Dveu#sCYIKIFL)x!yq}6CHm2%jKkad=1}L4AAzZS4^vX=5GjZ{x{&H)GuL%)kH%Y)rI_T@ z4Gr-){d^Y?DZ9yv0@>xr+?*_3z0~Iyshvr#9Q}KR*D~Z)TvlK4bM0;2-Q5Pp#tH|b z2n01Xwf>RD#D~erzL^YrDYVOJw?_jbWmbQ46S4WJCN-Uw z6L|^F^U^HJJvtY(m<$A%zcefw+e1*bS>{iK%1s-Ma zP6L5oT?Mv8T9j8*RM$M~y*^uq+Y0V+D*sl@Gwt)jhcoA!ebDuSayAe3~-DjSw29s=lp{?aYZXbMgG{T)^v^8;doQxqD1o$ z<-zh$j0TY&lamJMzj%~nYqszqzad9teZ#SWwJ_F6%w8-0j`b)zZUnUmng&`%NYh-D z?cDe=#yQ@}pAUGH!7clBH-HvE{69U#Az)UK1{W`~DIL+HV$pxaShME-I6Wf8k=j_O1k~&UtO5M`x(heD(0JQ^%}1;EqF?oUC*x7UNl|09v`8*^PXZF zJ{|NX#3bh&wilua7yxyd!X4F;w${A0-n{jGGK|ZaS9vFDA|7UDs;G~+`@kl*KR+u| zw$^QbmUzVIcau^U%?mjoI(5l4R7uzuC<7c7BKNWDGf^P9Gj&2WT#%>-qNu%P`kqs~ z18>mFRzMx-?d2$2{weW1jXG$|q zImL5bge^jtEdo^#W&h-Nif zajJX$F^}rP-r=Pm+02gZ6xV|zs_0`MRhs$pg_7yYc$IRz)H?!H2kn~Dj^O+8l#L8~ zZ<50ur9>n)5DTHHT*w~>2b$K_R#5l#H6cs=q4UqH+ADWShW*V7aC~>_cg6_a<-&_7 z!&s7C$TP^KjCKbi`}pyLZEj`#lZoX1^cFi&JgNKK&^A>0z#9^D@{GCf-rd1|aj`{I zS>(;!4jjlE$?~o!P%Ig}8XPX2?kHX{v0wqdC+_sqzk3Q4th2kjdVHK8DJ7BA^eIX5 zVA}ATrC?8!yt1-;-ZBh`?v)h}qjKMUEp`v|3$0T+(%98Vg60_)eg^FJGowfgzVP7O zfB;`HuRNo$v~)zI%qlg+5M>Yv#C2j~B9<0eMy6o@>Gu;69d0$BmG#Mh!?LO7|g$K+vka^JtdD^#{jD!a8V;!w!8?kh_aLd1&n zFfkE|nsi3${0RTzX^X*pPj&Ea3?4Dt@he%}QXyoF3mFM^g0*%z{pLHj)`IHtdA6osE<)8cX z884Y<>SaT68km~a&CVV>t9AAyd+o1Z2Z;2rpi9wa`1c#*Lt=Y=yB243Oz<&>Ayi;8 zkQHmvhmg4z?=#7XXJAc}@<&90K9ZA?d}(gxW~Dul(u}yYggKFCKpUk*j_N6v7!k;S zd=3c;l9u=OZLmciby!W6>Y;N7cA##{y#J83%YDJ;B_9n)1+KGNw@`Qbrbq4Fr#+jq z%TqRr)zgC-hO64znBbPUA8bMCEG#_n<;xe)-)g5KvUd{2w}vKpl!;~r9tEXAlRx%J zyTh?lr|wR4gQqJ))RekwF>*t+zy43$mZB%UR zZbxV5H0?~V5_4NystXq`0Hers^~RDp>Cz-G9V8?(c!MUR>JzsJOb8_dnaRw;a!gXP z!7W{}(nan$9}PHWPO$zfJ&4v8N3oSZI>kLiZF2DyE(mNW6rv*VEG@}z%%EeN^aT3I zp7cdcKmF8yaL`i9^Di~N`p(${D4UVRU^kyiAySDJP6L-$lIxH^dC$L+U4 zgop$IIa~4bCp#E+QWuXiLon0G`7gG159qqq-T?Ck8= z)Y;EEjz`KmCgh`eCja~)B|xeH7U55U7k$zBey^rN{+v1w5bNSgS7c1D3|-> zGpsEj>hiNm4pyQ3lphYOVq?Pxo0(=LSZyat zBvE)@fL#A`8jbWGo%pM1_4(l_xi=&tu%D3okb9teV5$IZu>CZsm+q-l&B z25y}aZ;aA&Z6XJC>iIws-%wK=8d=p}*(8o=fV?A`vmPFM@LE_O*nJ`&gNpLVw09i| zy{}c#WcPjICRPo;+kVjczG+Z#3mY3sJPyLyTmFpCoi%=)1(-4@HGp6^6(Gj2w1Dq` z4cX0QZhCrpM*0w<9#j=i8O|;CjCWXm-)<)gjIDe>e7kXXiFzG+#XWpHznTfeXJfvDZZr)Orx0JXUm@@sbf>X@Cl! z=GhqlMr+5q@u5%rGn6{kaB*pg2=zMr6oC2*SG%OEnu6}_WY#o%rFW~^9JPX|)D=rj zMhCTE+~5OxoH*_B)dd+MK5&Op@PvefboTX~6O1P#kO#@XeEE`S^s1*{AA0+;@ZwQZ z3KgE6B_G%3=8gk*2NpbG3wa0~B9sVR&Tjuj_1^GQ2hx^0kRZ z)W5bsD7-G;-)`5h6KN22NHr*%XxD!3fb%@)jUe=9z7t;S8F z8L}fnk%+0VJ9>JI9UUFHcHX7^%gu9k!*_haQ|if&-d;t)_@Isn1)iOi=#Hee@V(TV zxJ(v)_5F0$u^_ZX9yD0P$u1+jqNh1`>zwmcB z`}K>{txx%1WbaHY5E*mxRZ0RR+57jW%Gw(OGqu?I5f6P=Lu)JmKd3Q8;)3`ODg=?R zPN32h;7fS?_*U!K+2S5WvBVmf76^!KX)Sl`L_>i<(bk^~tvL{-_&@~n>}OMiArb3E z*>1OvJu~J%zO(b47WyR!02D058+BlX?8h#0)%4a`)G9XTrkD=_MKA)v>|4i()xO6m z1ic7+*PlOv_aY;qz&upuW;T4j>u$7u+w2r<|NB=IeVt}22=^+Ylyy0Yu8E>kIM1l+ zhRGhmsph~e?aW(%I8^U>nY-X9a6*wXB}_{pPr4lE4rmc5YlRjzmOs2Gj~{zuoP_{E z(!mj!oSq(pXeXB1E=O+t!WsV5R3`3Q&-3%jc<-JiMHc`}VPMP`<@h01rN?PB`{<;t zA_PJW)B$`rFefg?{?QbK4*FmfO>D)k05q%?B;51o2fs8m9bgXIAKw5pBBm@rkUM^{ zj%b6Inh=o`;ZBUd$7J8`aRbZ}ZvxFTaEKnKI>aHdN*+5_ zIju}6IrL4JPolX9YH~2kC#_@ZNp9hu7aY@Cs9|BjSB6B5Pz+LeUd7^&{^A8=gyP|LH}bDL zzf+HcHtu$Zx)vG{K5>E~vaUL8Z}^XAMse0y{<6?tHzWpbNMOs6#W3Qyl)q)lHn?UR zI3J1&M5hQ03cVsS%`=J~V3N0q& zOzWBf*T*ukpD`-Njnh~1<{!b&f?Wpv0XB-OmYA!g#9`uZj>sjx3CoiFNE$TK)AlR^w{Vb-Cr-0>=jLI1*ZK>4~-0OB8=;rB+0 z=yHL$f;3tga;$YY{nU~>5r{t#nHZo}hab@fAPvmSJd9FSH9B8uCptH+?{~*ktkfhD zj0?B|-D6c?hzj2uG%i{lv)$Fi!NMX?yxuA#=W-#N1_S#E5FF=b4cwipE80E@rfc9w zDU;z1{^~MD;6G42vKxPQ0sgSks4p-bb4yCX>+6-vxUa|Y77nT>30h;9MA`65k1=;< z(Y$?Pg`dSBa#kWhpL}QC;b@ zhc*Rw!EcFFPKSTil@oIHVM0Pc^+5mb8+8k%ty8W54ov}OkJ>vO6T@Tg2AMTUn(OQn z8-r_V6nd+w%!Hp93C`Dk0qaOow>3+DY$WxAll>Ul*0b)9{e)Gb$^UaXaMvdM##bKj zJLSdZg8YCdS>)YOd5+E|8hYk=2JXT)E|RI%4$f9sJs&=Ilh zud!?yy8%!@fdE3DIS_@;t9;?bVSB$W$G9BIo!44W>tshCHjhTBr+c*Q#X~pq|8Nv5 za}lAjY*3Jxn$3T}ujd}(b6)yh(sP~FZZrA4Ydwzf{7f{&q_3EFxCJEv06I6kjSH1cW(J0>7XeJs8=q_}YlN}oV zq+H$3+*K8hj&9%dIW9(jfO)$@*Xy+A(ZMS>YNrayH zu-;sJ<>v6fL#u5#$}oN&MF5^RB#Ck~aDhltE%H9?^~LIRek5`&APs7h_pjYFw_zlF zISlAdoughwcGi?m+?+T+filYxcq_X95*uh zn$wn7vc$Zvr1GH^k*3Kf$E1Me1b-p~jTMg1N2KqL++z(0gPj4!3V#yS2Ji^|aZ*NR zgsv_#Z)x~x*H7xP1<#TlJ!pYswew>=i0YP^nepyfH0-Ia%?1?gbCF?y!G?!YPmgh1 zGBGhB%1Qvf$MbUWq$P`8G3x2n&7|O8XgFd#Vy!HkZSfJL1DNEpw+y&CexgnxgdtqG z6V&Di^ZBMC*G`PxkT7wOjBh}jx6&dHe$!l%3!3iDw$UtYwlCU(8+5X_R#YD^N*a(n zMuG#>VVz)-1;hw|V&>)+FZ4Mo+w$Z_o0+g&oHc^o%*!h={Yhe@4ZalqarNMAc>5nF zVfTn0r}(8;R&^u>Uc29BJbnG-B}a4Z@Vq{(Ax3i4Wn#Txr-!$Ij!y4~0r33=&y9Sc z1(met*{Y(CvmZB)UWFXj;jc;j>LJBJ{B$U)u^XiB$ttP(gm=~_%ivRl&cdF$A6=xT=f5ifvr$~n-lp|M2A`0?Mr z@5D#i%8IDuaIef{H1LmJJ4Li7j0DNteVcDrn-jlv%9Vks^!?}HY1NxSy#v!1a=cUA z7(WM!qrnZnhV;b13MOYzB}z-BA&l^z8CRw^dp8FzEIqYI;8^kI0L1{HzS0X@N`Cb4 zA(-w*)%K0rsq5gl2mo+gq&K|hoDRwR!G1s9r(WImqOJ2WS`a%?8WExS0^BLhFe8EK zl@Q7A54<-Jtul!o*!LXH4Giq+>ND7B+;$qYAhZuwB}(a5ioNEvQpB$(=tkzgd2_38 zeMEeDt#9jI`TDRBh8BKQB!qYPyn{%FfEl`W3Z)IVfp`jv(RzLI#p}PruEjfODFrlp6>HyGu2-vKdAZPB(3r?8}h3JdX($c!P7(ch4Rm8x`%kH=ogaS-(D;_dR-? zxOGS-SXmYG6h*LIpra8;9PE+5Y$;B0!V~m*xL{?m+lG#k3|&Gnav>|>d+pP}d^k-{ zH7j6iphP<#f?Kw1$tHOAnD>y`<67~8E?RJH7@WwxGO{j*LZaW&T2w#$WM7tQRTE`i zYz%53tdkoOjSXSj0|{vJYw~?;?zSuZ5w9nI?%28Bu^Uwmg>!F$e9N1gPba;t+V1bi zu$SG{)s^xQTQgj*!Pyz*UKh`5K0uWBjAcXw2C<@l~E^}2P_am6&cJG-vAN@dm?%C z%iCPTZRZuVDCsYfrk!BTZ=V{_VST&d`(y2%v=}N)!?62T@I{zP;b>^U`#uA1N8fnw z;)g)J?BTJK^Yi~aY)EVHPS`yR%wz$C>3;R41E(0qmty~o3=q4q_nSvowywW7`oA*P zXKb{y)a#=2Gp+ZdO+lU~yiOkJf3qbsH8FbgJi&cG(I7+uJ<;TQqL)o9B9fAlK+jre z5^z^a8IeyR*30ve8wEy!gJ`Hj7{NC!G5dxUE$^ty6b^dC7YgQxqC`lZ&MmMRddb>g zzr?#Xniy&s!St8*icM#^a#?Ix{Eov)5!=3DI)902pCBs z+WiOH>T6Q{$9Lu>C4!-V?E(lHbZq8Uyg?x4g;{TPIdbs*5_Sqo`9OVJVrGw^=?|Fo z@NuB^aSIRu#2##sbDyFp+dhMvY%l2B1ow7d5lL)>ivcqhT@ym`5?i)jHj(zo>PXOZ zsL42j{i8g;#LNklSCXU~%AkqFmoK#$^9}}U64k}0r`MW8PBW6@ZlDIFoJRm>K@~1pt@ZL26GKk%WRRhaGWr`0FZTFz%U@#|MDh- zwZ2{{VdUC-^gbH*9w#FXw?IQ6GhgYY@mIzBlV+)YbovWChdl{ui|IK9Ef7R#VvGvx zr>?4SYI`@dMt>pw2r28y?cXOB&?p5po0;qqArAYXbi?&nULK6hgCTh#ib>&A;pu*R zNz?ed(kAJxcU?|?X?T~_P{O-&8^4mg;H^|X2vhWg2W{#7M`K>PoTSa^@N&2*`%15& z`DoUKP~ZBq3Q08HKR6*AH4ZIsikW+I`t`Pz4Hz4LfA~e_NGJX7M1y#hgD_Nx##y6H zjNcYtc*M{K8+`@>5j_;lC_?5`vuFT@i0;#yHy<0GM|K;Xj=QscyeGvjGE$r@az&DBn;;rnm^3r+@YpDaJz8d5gkQy#Mv1w=o zd5lIyndCwim%~pdCT6P))dZF3k5u)cRhJiX{33|`E=KB^zGG4+lfR#_ z)M?V$7D#~Q0TqH`k75Rw_&@W2*y~uD?n@_oHuQ60%?B{nj{yU8eWZ2=$1W{-mgWeU z%zTKhcD^1-7isD|-YWWfsaiozQ$w=4wn-P=xH3Y=*bQ`HiMgtncI@r;t3y4xOvbLS zc@rDKwoUGXiOd%$65R8$ucDWL85ndo(9OXdCl_^<+>a8^JRA0)T7C1ST!>7bo$?5> z8jAr#K?r6fqWCU(7Hh7x;~<>Oa4N39uNOnSDrZ^BWWu7M{DZGK;k_t=RO zH^)z%I_-OS!AbK#R0a4WVP-sf*REav^~EG^o|u0#J7QeRcJsv1#lFd(lxs^J8-M26 zIxsuMAY(FoB~+W%Z_D2P_O#?fEtYL0$wJ4$aIo4CIdH#Vc7sWv&O^%CiAsF^@Js4- zn30Tvmit%Y%DEQPC8^iBojCoJang0IqEwvzI+YgFe&wA=FXttsEiM9WS-fHBME0MW zs{$&Oxf3T6)S$!BividHVqk`O%NSS@TVkRu%G&`A>tk}&H3byLwgJ@n>D{qkf8Y#2jm z3V_gK8V7R|We+B}Ic4G&cSP1fLIMrl?^_miqiwY(km{on89^H&%=3*=t+P0!EO z!gqM3H&M8qdh|rS`GyA&-1LoSeBFiw%c&C$^s;i9K^D3il5d}dz1KMRfv}PS*@XV1!F-L)GsNH=J(Gxb1ws=mPZnuXNVI=7hjny zZ+s%%CRVmtzO}UFvbE`Rt9oE*vD0+vVUuONO2mS|jp81uqmOiNhNGDr-%t|Sr(neS z-az10T}X+BVVD@O4-iZ6u+A+OZr&#@7|0{=4y@!+BQIM-t=!h(IL@`K zlzr#MCv39QiG37cAsm*gQ$J2fxz7+lW6Jp+YHx+FPM3yn3qlcTcc_6!kB*n@p}Np0 z(0PoLj#lM_8PN$m`8<3LHf+DW49K8U(cUPfq1at9MH?GAYp0k?ko>?$lbe(0V@G?J z$o(mZBg*Kxpx2G4idAsxEX>}jXIAdyT<|j$eOpjbD+V5i0t^zD*7AwrxjsfgTkWwI zYU=9nVM=r={KI7zWiadfPeDYpyeB2Vr}=MWHKtzF#n0H4x6VE|n;}l+pb3QsEDv-k zpg%W!Q}o|hZZ90Ch-N6{3+i$VhEMVQS6AL0&(fwNT;ClE;~$B~%%5qVJ2DfWA!Y^FKYVIOT%!7F9I?rCW)7oJPv2!zuo6r1rTeG2^DZ4& zj6}MCRm6FYHj`^Pt6sew(%F1881AedsF<7-(1`Tn6!bd&d9`;1wV$1yLjX-L5DkPZ zVk@4vb*LKSJ?<%Iz6XUL{gz)7gTk5Kx}x1zZIUCN?{lw5^oJQ^pP;(@WBa;t)e{*xdKj)DS!0e0f@u_y2~A}&F$^)YP|Djq1)cY zza(T#SN8k#?#ZjH5sI*nF%32_)|!qsAR#&3r*`ma?~&kFJQ2S__wUibqzQN)v3rx& z(jPtw!%CUFl zht@GnLx8$-L8K%=93hqr-!l5Mw3`}@N; z!!Bf;G}BGR%5X&7RVHQh$Wg?yVElGC?@PC$tXG0M^R3T&6m$s?=}dV!am)oK#m_RI zy~LTE;vS(5_PxS>V_a@6lDgx4%_jQAJ)7TXuD;SE8o=4@fgpw*C+UME_V_?fC#YHY z`K5@{Q6-ljI>gJXe*TqDpF!*~J;O+!sW;^ly3=b*KE}>_`O3l?j0ono{QpLmIMEgb z+N{VgIEqPrjdWl=+8ZtoR<<`zjkh{+g;eNUHGo4SM)FleC7*@-FYqFSmMN3j;9buW z&E{R5f#-2P2UkHC$^o&xQMS2y+4pha!m5#hK?OQlD6IzGi;I}k>i*+eYz}a96!Y*r z(ctZIfrM*rGkiqZ+#xR_JHe~4QfO8|7|9WohRoz$KWc_R_Z}({wPAGq>a)=pR5R!1buf3;D zYgz8iy0-)8s8$5+B$7s^z2FxeSy_sZgpO}ADaeB|Vsm%d$5}Lw>_-0c#@GvtFyNkW z3vdm96X>!|tH2~9a!t94Cb|5&23N`egEspFnGT#c?*r#?y%9{q;Vqg zJYXIQ8AS=Ln}P4IE|s(i37<^3F#RR(%|S%b56WlGgZWK5r`YLzYXgR}Rike~B4LGW z3btu6)s2uvhReJ?gStk0{%)sk3e=rin2?U&c&|(7RGGSY)Utx=soFJ=oB0%ytk81HTQDzw{N8_AZ0Llzzb{W;e zTjiBsAelwWF%k_62rWRUDU_&R82<#t8nJh(w)c4%s*6oIo^bYf0y=mYCk$yGT~pZg zuz@-Yg&WC(!D9S}sj+1?x}rPabBy}#8yd%W6379LSe$*XO)~SV^v|{T?iy@ws~T$e zEbvbW9i^H4}JRq43@XNx=xm(6WTzic^@y{;f5AmGw8Iu8(8m$nsU zL#x{3rmXAt-!FBgg+f0>zkpHSc&AG+`ooVGp6jG0oTk>;K^hvN0V=cx9`mnpUn)unuGW&a9DT?FdWFFcVa9C%>(@8 zQU0ZHxOSF377vXvO2CaoQY{WJfQMk-24`d%CEsQ@AJ5gj(67MNh5k!UhgNp`a@Mys zcCOyKm6t`8detaV7(nbx#<(O_2Rs%ssl=Ile>s!ng&kovU#DD2{5d=6$w^Cuq9iud zjqlF;>>XWNuva*2vUFnMoojDo;`23#Bd|Xi5^EAi^ieW(enlj4M}0b?S`#z-jE@E& z4~_P(12%H_`hOF5=`q==e)R=>gu99z%} zqkkqHNhT)rrh5unUh0WO7$59R&}C@NdpnK%XCFQF<3bh0vp?1Bw+(_5fz5W_bSUa3 zk>Xsgy=jd(%~_KRa4vxO=)STW>|=Yu0U9Blqgg$dY^Xo8wA+YYUqrH*fu+=>*^LX- z6WjqGi9APNNFqtGpo=}R@^QG#O?zLlyEvxxU*E`bcS+>j$Lajc)--4b1euXwN5v`r zw_aFc_Y-flQZ!?Vp#I#`-1Qz4+{=_;cA^*R;W=$D{YY`ar{eX(o4;)X{whAfH0~zu z=NQR-#S(Vsn}wA+a#TM0b#E^i+P{9+$*-G%M^izSQT*B_76SA{hrGczD?0^gkJvWM z^>*QG}0ldwkA22|O? zf^K(+jlaM-T6c<`@5u)<%vi$B298!2!6!jq48`?Q%i!vWEr}MiDH@+>b3(R(EMj2J zolq0VcXM;zrI}nWcR%_OyC2TqNNzsW;AWF^?9$P;e+T+-(5v0yCRYW|&`X0Xfy=+M zWyGPhcF>1-9DEs;<$CH~aPwv1m(-ZpEeSNh*zf2C)^XKZ2y^i zEa80ahn_!|6Hmpoq?b8kv=JPuYvI0i+Q9#Gx-!d+Eq| zE0&gc2lsxll&)-vTv^=A(hubt@X5967ud;NOF7xwVI zovEq4j4?z^XEh8jFJtZCRsg#Z6&_^mI9hQbv`Nz6oFo>_Ik=aNHe@rYX7Wf&d%y9# zhv7nR8DW`Yu*<_#wSVPoqR&X~eBGpNt+ks&XZGuO3Ju2rxI$%;eGY|`%rbxrTmU&( z5q+8aD$hKa{@+=Ej?bS3Ud338lQgF=+%yk$DpI{3dRc zbZvad%d0$;`|{{VoXA3Hob`vky>=esqt&oL zo}62f3$oB+TH(Klj!}ooNO2U!&CSF6h3G^X@ZA@Hqq+U)qXGa`sBszZ;K#{Z- zqGtra0rTOaCY~C>ITQ>%_V%`jlpF(F-aS77gjuCtFQZaU6=S!Kc;TL9CO zt3NZ}jkkq8MBk=|)m3n>yG#xa9OEfD*EaI+Qzx7lSeZ`5yn95 z<-{n%{kxM&t)K;Mikm@2a*nJBw+yc9HXCaY;Ir70k(F)UZk|_j8DPzlW2pD(q@WF1 zGiN&b4Y+V}^Qas43P2;42%a2LJ@DjB`Sr6T?G)onDdO*5>1CHxaTZs0RieO2#EBQK zvg2*U?g;VRZpd5oT~I>~i;SmO2Cu2)rW|9Xg)$-@m3i`j$9|H3Ag==h6xHY`ARk`b zdO2qaJi{izJGM3z+;d%^=5|uOrL+OIhv>qojD(!b^W-$RKLocNH{Se~l+|55grR|~ zU-~+^e1EBbTd~6E#$)`O23R_HWAHFH4Xd?xHw+*DF}0`~CSg^QJMV++#FC4LQJFk@ zSg_PHGddiy_yO-ho0@n^0nS%E{WLnYN4fn+m)@?bC2&!fl&rqX@1DZVleM$A-#!fP zpW5= z4a{3k1_iuY<_|p{XYc1Y8gWys$UuN#A}aKNXB${dtctbS!o&LoW>nE#YhkZ0N+zkF zP1Evah?p4oJ6~JNrMCk|s^&)T7P^>qp5D<-XHjf`qkZY$GxeLAw@cp7X&pmR!Quk~ zRuT^D(cRNswLHpJJM~4-A!zwGpd zV{b#a`E}}r4m!$egFZ4?BwGb;z0FtH?b&>HPzF6F2{e56@PT7PZ+G{P;vXNf)*5nN zJ4`P+I(O|nsLFJmKt?zaQc3H-CcjX~)=08Vg_`V13V*!S&S(A!$$x~(IAPb~1O@oz z-bhRxf#3tJ3^?2QYQ?`C(0*WX>+9cfm_i4F-{kIAFb`F$uoAx&R34V&C7s2NBzw3vuff2uCi6SMQXPtE~nb`B_ zjjes#S7FT$!6j$f9mPoejW5wWDD1wHk@e&H>z~4}PYEr;y1?+00Z($EArc*o7e9l| zg%7!WJH8n$6o_+5`#?A97s*mY#9$FxANJbT{PkkeS!;8SRd1egYgWo5mq(s|tdHtn z>lhn7Wh@_YkHPi@eV+V1J1=vVQx#hApPKz6L>*{9t-1h4ywowLQG7x>LccrBigzr)+$8!c;ma|{STD`^C#Fuv; z-o5*MoIRsl>!&U6LIN8F&roIcv^|eZ<(6zSpQ@w#Zrw6+>feG44gb`L;}tcFg!e=9 zKiY2Y!!VO>*~|frAx*6lA4RKe{WG%^Zf!m4x<9C_sKtba38W`qXq8Ol>j^mI_<9A9 zy_4*5&1?7X$NgX&@Cy7beB__)IoU5;4vyWb?_c}%!bRt}pLQuK<>p%jAT6}$*$ahlz zH^9KFEj$*{8D0E!<5|9JPDcaNmkh%cmD8D{im!v}gC38fS8)1i1v|fXE_ZR@A3q^2 znu2mwUWV}+6vUVD;pBHQkN~N1VeP?^=Sig_S3cbrQ4raocr_s& z(|y$B4|kpSvpK$}&7tu1-KCw%<>*hrMl~`u6$uGlbwH8Q&V1mglC151*gMM`IUpdS zkd=*n1jrU%ypnp`*JjUn!;48K*}*7%@0eJj0us}?bL~QZjbid`pKHGt*{*Z^Qy>hm5a;=q4= z!_C-h7zq1bd`Kj@HSFP28gf#%<4mGeL8RVuB?)R7vWl&ui!Wn>)Pfw=j!ws_FANPL zbY|V3Wbq$aqNo^YZV?f>5<0-8(Z1jO?Wx!XYe=-$*JQQ5xeLX!?#8K!L+>w$%>?Nt zsUJer#D7jXJIBg*t?|fRQd5V?!+Ql2)R&_DK> zuD$>8=TxR|)R}{H6ZJtnt+1q_5-%ToB1VevsOA2&Cb<_yc0?)h@Wu(Y2tj#KIlVJ< zI}wRipX9%ZM%0_n7aZU_p%HN_RpN=21mpCfeV%QiwBBPyX&%<3;$$Ah?(#wlOt;<(iuK0Lb7ILVG5nRysw$uDI-N3<6<~K-s9AeV72Q` zqYawm)l*YP;o@}Mx1sr}cwh(o-PfZ=y`y8dSx#Zn!`K>!h;|nh6&cf1T{}w4yypcw z<5KMT`)9!3(Cahgx$BU)TOj}?CIM%QANghkJYeVF{F-*{sNsZwhX52e)FuEVmJpT7 zX?>)`Bv2hu@Cn!1Kq^^zFCa}`+8XHTZLX=6H7|-Qop8Y{a-n+ zlCFMNn(;87_2|m|`?5&BO=zaxL3LIrHJQGaq4WUE{_~kDpLd80JyIB+b^q}Bs?yys z9+!jBzWt*Sn(z5$4$7Uee$kd8kihZj>CPoqU0n=8P1A-u10SSzs`~T9kTfQyB|kJ7 z4HKA7+6NQpdd5c|lU7mYvrk8jn?A4$%qj0>Y-6zuHfM!Z1Q2-{zI|DHdVGEbqurg; zQC}!|l&YM)tyry(??E#B{va5!Pr2{@qYJz-<^g-hqqKB)-#vR;<%Pz6or~(DRJv;S z^Kpvkc2<_LbZM;0?fNSX*KOy7W6BRrx_WgmI@deOH5d0y+ZJT_3;jBX7Zg7Jn!JB1 z?BB!74||cPs2}7`zxXQOj?47VIYXM&Q+P)UPnpMq?~T|<5!YwqY0pgUat02zDo=jI zzjwhAsY-TPWkBJ+ztHYo4`O-tTsfW!GYe`0Y}{zqE^6hzj)|MZ4qi&Z*S0g55__7J zRZ=8EDil9Gzw&{dKg)B6b?fjCR)KF%*@V3Z4{aoAG|bl?yW%YvpM9At%}R`myW?eT z63?P_R|+ZwPri|0K~9UjYW1_V1ZD07DZXMl@2jts6yoBWUYMCcTEpp0Yq>9Qp!S9;35)WuzXo81zO6S^nC5}K({tTu&TM`Pdm6h8B zX1Eg^-P;w*Xabv9%i3CP3XlRgd5M#Yave9jE{U7pXA(==b?amp^|D1gA)m(PM_%{T zm%Nc6KEj6)&DjaL*LN$qj9W?0Y>zMT)xMJ5?(pU5uLxG(nbgND$1d@O1}gDX@u++7 zHJGttQKQSyC>N$BgFg-1*C$CxXmQ2+1$~Lt+|%#}=B$q2_sf1sSY$2RU)o})bfRV3 z?bZI@hNB`6Qghj&QRFG;-g@dBr9OEN|p0yp*n#wOY%YKGX`e;+&aLS+Y$|yAs z7(I8jXDqlMMCmcz-WL1G)_3(OAr${2fymEzeq$wyIJXR|*sA{fWHiC#)0$dS{G2DA zO>6l``oE5*-Mkq+R`Zgn&ZZzwX{Tw{{wOK(h0A)`x`dw76jlbRFBr52BJ|g}2QTWl z@ho)Ve(18c-C8}UTu4`u^qD2|_bJJlcC80bhl28u>ajuavjz8F_`w<^A6!O7YbsHB zE;yq|Uf|V}i?*AD2VW@LG!FH;jXQHu8L>%5`#vqBVHraYU*GyY|Lbx+ z*L~lQanARA#{2zR?~pw;9H&=0>&;pSN6KJ$||*(x6B;qbNuJthjkh*D0aJ z!J-Ixd}ed{br;EDUSLy&<@gE9#hhE|yx{1Ilba!f_TL@4gYk&b=Tm#K%k@ug%37&& z7B$s-XH(SqyX1D6;Dd$S8E?&WmN=CUfnj<|O14sIUpsn3*|PT>+sx6UQgbpb<3w0? zHcDJ;ui`L$l~(KETI=i=YDEo_Ivmxd;o9sGS9a`rvgKE2I78j&{b-e8pIh14-TT** zyDcOl57*f!D;SpK-L~ac8+E92sInK_uXpmbppocQIdMi-cdOiwe!*WG5WH{Kn2 z5omp6p-3Z&LWi44eoscsTFM%m=^vSRz22Y+C6NXJBiERWP5?zgJxx+9!9nOsJqaZ zoeGlLnGvEb0816p_&RsloM{te7TOt^%;cl~#hd1#h3<}?1+DEdoI*t>!sI`4f3Pcp zEm9}qm}&mT*~x$kib>9ogGtA?In|MmTV3O|W7TiK)TnCWtU z+uF16Uo1xq%Z2y{mk{w`svb*Sz~XCnChXI$P1=Ly%eNMuxllxL#W@spqOih?8+*zZ zAAWiFD%RGx70;1FoD_b$nOsf^X#JzFcfI>lKKb0jgshP;C!E ztHej;Jkl6N2}w4~-|XV5mTz!OQu0mTd+y1@WAme)o$YPn=cRCv*(^}ybX~ozM(H}oe?TF`cqr-ed-Y&Y zfxsp~T;qRXTZfx4M>rep<^ML*Ys^2B3CeoHa%YrNsBR$1)DmU~K$GUzblTy6!?OjxwB~KJZiFkhmElbhUKgv(J-MZ8-83<6t`>}Tn zU7)^P-6K=+BiQZy0_oG@@w;ZOx8$EeumIc;QferV&YnFBHF&|KzBeStL^cbY5X55$ ztSNCQxiS7c`I`X!bFN=1JY@H$`%(pKC>Zkjc{F%yv!hkV`R1rhDZ6(IM{0u6`Xu?t zHcj{B$?2a>9E3&~kr(dnr8)DiXQ!xz*S+4mGT=gss+-&6y2UZ`SbM#*k$iabV2$*KV-Za(HHCcTd!~H@1Ghuj@~`lR zs=5eFt{+Cr_Ue0%2HLfvkUW=0d+>Z*LE`1>i2>@+&pfsi*lgvZqdP zQ*FBD;ukPtE2x$33qoXN;agWRLH#S3A9XU5%hUUbf8qJ(Xxzck84O~ z&u1MBdg*tB`Os1OfJ24x5eGLOG$CWIsoAuAq*_$?d(1UvuH;vHWfU}&Lhjnyakdw2 z__$?GxJtdQ$<0}<`t0#60jjl%Y0ClkqUSEnrd$yIKyfB>t-F_G>8+M!crQB}Zxf3N ziVEQaz}cZSy$6=R4;;h-B6|M02(fx`DAn2xbY&!u{&}x&Ub9=vUK$35$W9@{4b_akq_8fC`vgq9YLr%_W28q#hO-+21VEEp&8I0lff2*& zqr95}>^F#gHT{x%i31;XTZ!=PNQEX|#Nu3RN%qEubJ%^kP_hseHxkeak^v zO^;sRUNW0v;$B+Ev7_X$7Ci<@m7vK4F*$PjT4givbY2>|V5}~=<>Qewab=;5m$G)$bbII+BWQS9cMp*{9DmeNq|JTcI5=P&!j}=y zz1{mwz=tm{RmhJgz6)E~o%MI8dm6t&;``#GwE4Y*`4fskX{Oi5t=(_ zU7-EK-QoVIVdx&vEJGnQK)*SGt3l~xJ=rl4$u;0pWJUjM@O#0^{=M8=kR?EJz7Y3e z&|<%%a(I%bVoyxZoP8rNmAqr(XGlL{-{AV5tm*?1h{rE3iukGCeQ439gnPo60) ziW@Hy_b_Il*j^vEHDszMTt2Alsyc)VlI+B;0t8PQD=)HyQRS?;dEcKuj5y1QyjjHU z;M3Si!Or5bDPo}_O|WL_#Y>f%9Qx2F1S<&rc$&f8^oxxJ;JmdT@yBWtCeleVaNozm zvKo`&mX#|#H5Vw}E!Nenimq~Q@yL2|IHx*bYRJou?#+*d-*IVz(O`Lc)qSsngzbO;|7+sy zC>(5AdPo`Jr&-Rosk zsV$`2l@OL1?|ti*@660i=$P@{U-qi-A3{5xoy{AojV~gF2$@0$r_O8o2aLL`^^7`; z*6B=+G3?D=nG-Nl5}GS0x&PbKH}J=-uaBw3At^qVZJY3WJeFp+{^fTgZL^)s?Z*zl z^$%6j*w`513qfowX^iVkO1^1+SFTNJW1%34M1m$UH|eaTG*%R@cGI`bsnMJM_`d2a z_Y#y4Ajj496)1$1b6%-iol2(rEYWf474)ElQn8^y3mP>-3tu`}QH|sK$JJu~!jjd9 z)jE5x7ba#A_Y6-1G;3~YxpDpaonuK9Py+u$K-iyC_TmGNj*f(3>dkxppKL;~J zk$}^%0kBnp1mKInLXpVu+!$%Pe+lDidL>-A9D#v>ILaGO@bL4+>Mrl2*q8MC`r)q@ zN3M+dZ@6ao<@BLwLV<^KkJ%-##!9;&&;s-{t|HJf!B8R@=X5wLnp6q8(b(B*C@puX zD9IcgvuR_PZ1NJQbI8%?@m^OblAwIS{av58 zd&~lbS-4pfcbt+PA}pvn`u}AcB2A02W>v#o<#l& z?9~$YV4*_ji{fnVj`ih>Nq^dEk_Bt{xV{<>Bymd};AOv9W7_G zd~PtopL2h~<{(a?UyE_+j)%Gr_YSvkadA~$E#AxisBq>UXC4IY_;KWY5Qaf(YoEwp zL8Y-CJ030vnru7%@m#mfUgPO0a?}RF_b=rqt9=t{kK3z)>@6^PzqoiLC!`+Yb>=`^ z<5;WzVL^eS-I>EGV8!#aQ$l3TT%1*STyIyP+LLF``hWe}2FC>4AHgibtQfWHYZjjOiYLr+=To=q=}+?0CI*B2#y5LNg^Qk1yZ&l z1Bb2*A$EwO#kW6Nx!Fp2$@rU!Z2-lDjnzIbv(u<|->oejSX&sN2Plk)9U_Ai5eiVD z|Ms#MXCwB~#6&+LQc%%?mm#uakg!~4;k}@~`F~n~$u~-Er*HJQ?R1x1R8X70s7t-F zSXph)*Z5>uqv7S_?T(2Hlp}*C1jMV`5AJ8n#_O)GTXouDaNK~(1N=BL?SMoE*Gjx@ zsp?s_DDV8^%0n0Bl=*R_q@?7(+F&02L(#DkuMlyhNxqOY^=dLFCx=3j)G|3f-iL>N zt-I_fEdD5gSG>GQWxhxE%ct1o zcA=(1ISt1Hj$OF$9Ijrq!t;c2($;z88AQkMjxc|^C>@Vo>$1M4&|T*0gSr6NJ?vg^ zNEo~lC_3M_@@>>s?<8SudAIUs``TiQH?kIqd>RppvPx)zPkB1aJ{9&E23jq+4ced_ zFVuuCbH6%N?WZQjNJHz*+vl{axg{>gL>AAGyM*H7afpbB5Kax8-Z*mcX+q2>W}O=? zG58lM!V$*#fJoqIYH}`r9BeGhNr&ZvbxoYU z^YhHu)5t|dzGLP3+T^+wtim1b!C@9`x?aZdF|k*TcUJG*)YI#%t9_(R7kA*wHjPvC7YX{$??@McUU3k5r?p(nW!A+Js zy>H(1fpPZ>&50oyTu7*@)BPw8{)7b9MV<~f17sQoS?`kvKg)>)(L^q+levt`FP-7H zto39d-5_s&_^QE5GE`dW#@gpMJ>=p_@-%ihwhSVl;8*5l^j9|ODDX;0>xN~HD?UD+ z19vbezlGJc%?`BX+bGM3va3@6+)$*sKT2RESYk60I#{d|ob3>K5TPl2 zjC=F--b_{mjBm6`cx@wUB7X1~hc`}1=*gjX#OdGM+>9&O-Hr586k%)eDw{kkR!};C zVh6Qb?QCe_mLI0HcCzlbV-I`G;KiRrY&D6f{-G*|Xabat*t^n}#}-+J{|ycCU%hsX zKq|3v9p5h1vx1d}-IoX+hio2(A`qpA<>acNphm9ZiOrYh#S1Uc>I^)yW~BxqfN-Q6 zo}RSWsdy{c*_qjHlPzv=o#R;`q#Y7t@0IDz_%7Ur`y8cvJs&T7l79p+jA@k454>+Y zGBM{_w;E!00dgO_!=lbI>(V1nb&+h2S^>0(+YICl_-W#kLSH*yH0!S3#An|ruuDtU z5Lq<{39_U0`h2}>)Pws^gZd2RizSz^w|ozn-MDy?Qtm!Z=}n@EJiB1oBFV{Il(>+2 zk-*mRhM^`YrHjAO3QM!Qdtgq^0f-HWCKskYG`1+EJy(g$ax^LM#cTR&{bm19cLN0c zli?C`@zJr@f72_Z6F!O3;BiUc4*C4r?MN&IKmp{tCuIxa3W2(aI3&!TOFNSs+U8k` zXv!nkip1N!p3(TAE2|a-TTXl`*b?yf17bKza%T0dvd}W(M-_+wLPmc~j(N+8$6VOy zKx&!VwV`@&&zV!=XN6C4lVNv4Su!1H}Zs1uwF+KqktY){ad2{bZm;r&3m-EZEcH_?*YwrQh!+ms70R zm-alTFcN?In_(OWHcZk*c7T9#l8eWy^kuHZU2$tma^p-6mDG7u zY^N`r!z$Zz&s2kNis7xV++w5J@zW9TF*h#i@o|&Cj zZeqDMM}e}PKk3J)M^xFnH*fIh)u43xK5j?eF!FRYEgw2|2o55wuU)-*9Urwp)@Rac zu~7T<1htHlD@&O@fkw@j3R-kz4Snv?e_BBpF;UWE2k__ zWiE;FTlx*D?th4OA7BEu2=ygs&82sK`0aV>b14d9+|ghJQS;#-(KZdD(-j}2o?CQZ zPQ-VzGjMHlJ2|_g0nYfxj~~51f5Oza9c{j1 z!sO#wv2~zg3ftan+CYiTg=2?l+XH`(pN;cxyzYm(jYrRt^;=IGdOy*9^u{lEp!}hI zr^>bhJF>B3wC?cD(i1qFK$(Gd=b+Yg-{k!`w33b1aCkWz*}WgfX_tx{v-20UUoxq1 za7W#WAHOB%p=cSs)af~sl4+l9DAb470)hu7Eu?otd}r-9E=c>vi#c~!vPJktwz66B z#iVF4`O#VNkq_me-~WcX6KNDC!o0|a$1(peOpUyj+nFRJ^ziZWOzim@^6aZm7M92% z72Jk(gl6aBmv6!23x)|dphE3xO#41m<>O-BlB_Eb&HSZxJb?eBnhPe zwLf!T9TNP=)Eg*a1icXFOPu{8A~2pi(;m-AtjHro5HKY+zUK3*-=^K}rL@)LOHyHH ziC=E3FoXD63eo*qOL5hG%KwUG+&M{OvS`@|FEk>uk)2Ot^r0|;O7m=Qx!tA{6Y7oH zY`#?!S8%do$Ew`iU#-!)k0RY5bHMj@#gH4T;$JOnUoDTf&1S((=axnjl4v++{mLPV z`K!7GHi$wz%FMFe7q)3Ak0^iqv%XA{$*U_N21w+wUcF{`NY}oePcd-S?qqKI%7WC8 z=fJO%Y%*;o-11lm=(LD1HBr|0D9`X(LA#rrbjX--RWyRo8e%w{Hw_o^{{?)vUU2$- zGt*|b_pHgcB*sxAZdHaURE#(sJysSNAnj30uzctB=+&!N&srPwuUo!1JM{aS3dmr6 zX&RrVU2qKMCOrO?L|aXC^T~o}E1{r|5_4-3Hw^28OcLQQ$N~OO+`zE#XnivID76dt z^`<6T$X_sZ0?DB;J3%XzUU<7s2qoRW?g9DB~aY@UkKQ0R{nIQL^Bv z+dc0q6C#xoOeP#6bJ^D`ka+vvQTIU4I}H^hqc6P2j;^BYMNKmwh5f3Tb`K?q$HEjP zYBiWjOprDMkn=@B0R{(bG%~6+Yrg8+kwDe}KoH3mk<`z!-ImA6ytefAkrZp+rdB+<{+-p1b8a;)EC87fQy}bN<+;Gr2AlmvmP=+3_ zWm=SoXkL))1tTVfBK_h^AxBRd7Tgao$$s&VVIzXMec`o%4gViVc+AL%=zE-^0BD?k6keTJ|5^Z^X}fbLa1_B~!Y_78AiTs2Tu13$$Vg$G^#Go;*jd-Ig!5pNQ) zw10E9VeB_EI$nN!sVhG>D)t$(@Bz*91m8~@(|#5v(t^91I5M%2@c9yzZGX*nVmrVW z*Ls1b%Utdf3B)ZeMFv$j(nk1w)P$*$3=&{)CcF$eMmQD+TD;e8lEzN-M{tV4LTh9+ zirGiDU@b>y5ThMfQ|F>x*UanqNI?fb_3=C6};>8p2> z>js1TedPI5Fn}p)BYd$iXB-t| zvZ8yLoejF-AIzB>9Y9^uu17{B&^b}TD*RYOoDPBr^V9-vd zre9QXpJcp=q6#%8F=AzY{0vdoojs(qM5Zxt?3yG4CP7h$h!<#rkCNu(ZoQXOv@Y5< z*Or}41o+k8$&w9dwlDtv2--b|9Td5jK3qyZZFhph`h|9cJyD6xxrJ(`p(+3r)xvx~ zZw&AmC>>>V1`x|*4-lvjIz=1ttrX%~QfgDHJD?TK&Yn-Jw~g8`>^*!~?7%5@vU}K% zG=r>Ogt{QH4S%S_M+Dr*${*0(bmw~5HlGykp9T@+E#N2zy@Bng z!jR!|!X-H&for&2lqO~D+q}eyw=Dg`J2GyO`;$J>P5yrPm|iVl#C=uv`pV?a{Honz zJMBiu<&STAescaI`8+58{<|;byM3bTGcp(78Dxp>HxqjIdzjp+aJBe5U?TH%y45q) zx3WRQ;)I{ZlNx{=9e@5S1{S?r{r>JeVz5zs=^pv?w?1<{5plk4e%-=J_GzA{?~>)x z;!6Ka&E$dbFYe!S_OFT^qh48W?@{+h`cCb@c`E}e>ag3H$78obSpk6bwwsH(j?#Kw zta6p;q=2RI$#rVmhgpr!p0(nMY(6=tR-M5u|!Ez*W6OX#@uL}3(cfQ`w zStG(cICe&+_jSA(sbm_u1}QCWe)B;%Yaq$8lk!G>TC)KS&E_4U)f%rU|O zle4_3@^evzbpM}_q=U!8UJf#~m;X#^3arrmpg?v%>r8}`^#lo!nMcVBjpDh^elJd;IH`>k=m~II*^)h&*z67F z+-H8$dMysI;3P$qZ7(s&Yjl_>a(^*@xQ6tFD}Z|8_#==u7?|a` z5$HS`aJ@xH@+Lp*>ew+jmMVwGwBmaO=^r6D2j?I{mJ!?29;E-*zjoe~?i-V;|A1GD zOG#e!kRy_f3QcXpuA-MDPBsFnG{|~EGpQ0wx3*XY*cY%fbT~ksaQ5M9j%)h)^tYAB z$I5`?DPOz0KcF@$vzWZ2bmba;1A*{>{Y0YEz~apC!c4+?(CK^&L`lD(`X1mnEXmvK z%4T@({+E=p;DH+9%;V%U-5g^_b&b&8qfZ3<1WI5h{eY#65Kc;hcEiFbEOgB8I*qhU zp~#r9g!WOJqwEEZ`3WsV4?^eH`CF)NRw9&c*PXnNl%L8MmsY({L4%>gq$1=>_xAU1 z0B}}~ED4GCcAlqh9p=QDMnu{pffxJ4^uwLP?X$yzh|nbl1Q1RoIBTOG@eO<&bxQ8K zx)%^V02l&$D=^tORdXkK#8HWZdvyYM8{jp}Gk7F*>1K~RH^~h6B9Y->BaV6){{SHw z$`E>sYxTRsH>6beowN0hU`Ts=-XrUT2zq6XN-f<^0Ht?Y=;mG&8J<3U+GTnE;MJ>F zi3FqQcPpESkPK8U2iVfc=ZqDlb#wnHQg0REtCCx=oXLHt)FQVyyLz$A*I1>jIf9|r z)q#5940-lq(-a+JMQVPh10pF0x=L>^EKKhOq|sS%vh%6F4eLb_T|UOIKYpm@bXaZ6 zRVJHB&``0kun^yco&l&A?h)V~gaWpnWpqaIJ#o`Mkgcywu<^`qw@ z?!JhIp}eG~;87V~HEzdr2qt2}f+Z6x>$!oL5ds7&jC#RUa+$foQ)=47X%Y$rZ;V+3 zcJfTY-Ith>cvm?w$6=?a_k7K>tznsuRC#|(`P@3ZxC}amNTM5TC1Fg;uZ{ym6$UIh zQt8iR*Kao(cZDXiI{-8aVnwVpTCIbwFbef+1(aJ{i&l{ubFa$ml?Ytn6kT&=e<0SZ zq{U=7$Uo12J8A6L0FfsZ&XEziugqn;h4aTK z?t}P_AD{WLC7_TcQ0+q8d?Nb~>z>liR~{ovieI*}@}Y}H=ZdIN3PqV8f%~tzS*9FT z&HQlEam@dZfkz=8+L6wjvexYyd?&wKDCKtf6O+)f`-PW%5*AtzLHUwU=i zD6VEnd=nIN~An{P^i@A;ua`h5*hV_3*_-{GE} zX&-e04gwsj01OAd6G#_vw_<`6*iMOE3B9z6Yn^PjzWhpZB;Al{r)Os`(TKg=t2HU` zsppyYW5_9U-+#qWEtsH&92HEyT#{;>53l`kvnYYBV>o^F7|9up7D#FFALQ}vc0b6^ zmRIFHs10eNg7%)XnxE{s{l)+Cc_woEKO;Y?OG2}v%8N3vF|T)h{Ji~|;8*oqKwN&A z=3Ql_CgMF=SdzxvS~~5M72a>>XZUf~8)$wo|Lda{Y#y%6;0-H(3z!0EEQm@_GHR-?)A|=Z6Y~@Z~b(z5-@U(Bi8vTU?|=w&8oN;(KK9^E{Vt3v-KS&;a@hR*a9*7d&* zTfaCU))3UXjFd%(_6a9h^zQKI*iy|ejV2{`^Bh}wdxKN2-%!X+ zI?0zCISPUmY~GV4gH-B8X#lIB#fAum0D!QE@R^cjPbp72rk=O?WXDy{+v*1MNu!H< zP?eUgcbR-K1c?ex)KFLxofUR93TkwemYx<$(nU2Ymp?*RFfjdBRz@6&{;I;o(z%0r zt>(!5!R_RkbT=ldk^OwK1kEjP&32BVU>Bs&E)f8k5srS|neX z8Q|LC`10&Tj?xc?CNzj*j3;Gt^A_xS$cs--P8j9v--IMkK zOD+&p;l{;Ez}5$$Q~N7*XM4%SsVpYoi-5CHZjF&fScJqgfDY(~-Cq6qJKT+aZ9hIUY4{!JaTj}>6dz7ue)7f)n-qOy zX{-AH>CPL|HgBeb1GEbkruX%u?3x&@f_Etm`+fQ5*?C{TduO#Mpn?||x`ks1lrGWm z0wKViPJf(};}JEkw$K*h0~&Pk-D6e!J@(QV-uTVT6GsYpMxHhDDr9p(ayeX>s)WP1X?EECdyy;324IsTbRfowIX4sLc1PY>-@0cF&14 z(aiNpQ{8{3g$N@Qr(H+wW#96wVBhub#L>C#EE|FFI?;?q5aQgyX!dY@6uFsozgdf- zn%$#+*Dr4-GrmSGqbAqYi}P}|!+d1ts)_QH8?Feuy0%lHe! zNuVsa)K$iLJ^>cf09$ zIKyb4itV>lri^T%I}?nB`AvGBO{+V-)7wWs+Pi!ZrSX}wm1d`vw(wOIz!t;T*+ZwJ zJ>2r=_K`zJKRA}cX{U@2CtToUiA}k60jq?jTisI*UrHk*079sYu~kuTQQX1?X|#yZ4E~l<#?h0VZ@jP)|fRfE+o}326kH z93&_ZxaNx2`qMqR19o4&B<|V`?Lto9$_gg|K}yHtHXuIZWI-I(XxVT3?g*54^M5H+ zfQp7&5|DRP@Jc}Kx~~sv2uQQAV2FrH0JF&dAS*kr<>I7vS^UF~DMU7lldOrAESnY@ zf2x|L(X?w`wvH&`&Dd2Gz!lfx3YZAkA|5aXPnLEy{|)`Y*f|=Od)!p23r~rGQHy;Uxxv;3o}*oHJ%^V`l4p18zR$ zK1Fxxo zo@su`BKv!1z-j--M#3%#&2AyvZESVO0%T-lFha)u%6>qNNT9Ty$>S^X8{!l~5(kJ^ zynDcO#Ox2;U)Wv5A4eOAzkgHR#U~~r4*VhbeB$iHb42KGnb#_NOM()lct{c?)DD*p z`FwjnbXU}D7h!;hq{T)I|gx`Yy3(2Kdi`_>; zPqwnkEGw=HoD^Z8tt-Z2#@zyZcS>n}Mri@lwvnF&Iw`h<$5!^^p8k#xs(zXzXHdGZ z=U@)Yr>ef-Bocd^^yc#RMfplkRJw#%3NM@BA`vEVPeAes>31A3X#5PGCd7=WS^H`N zS#m9Pk4Nzv`w2;<&L|zm+C|F(4HYp|6mJ&{?$D6!>jXUtrvr4)MCoa0#HDp=-P4kXJjX5h(hsNzL4!gcql~~H@Ga1O zW8jJoN390mAY^F-{D&dy|9FU)o%`$pw&rj=FIU~deUr{#N#fv`ty!B+ z!-`e_+`8}i8 z55IWv;?4z0w+ueluZ`a_2eQ> z4{CT~Y!*i7V!$8%3PIw5*|E3xt4q8b=lhRllD2oit?a$nVCg1%>Q7JzOeNGWOxGg@ zr}cWIvdWRRX(!Y6>DIO|{d{kn+PF5+_}?eA`S`ejYUKLed_wk_Y+!yWWZEpx33(tm zPjMp3ZcHxdtc^F=7*`%{XeI#ljLqwG^)zz2u>oUa7*?3^xS!yejV=nMk&KmumMfE6B*$ zXUBDjtSGia@GP;k6EEIBhrjAQqk}jqjv5)c zly^TPdmHEH*of~S{Zbh-${X&+3j+q=cKPO8PGZHqecSm;U2pC#6jqQcBR_&N{x4az z#hld1$|HOExPRvWaj?Fo;E+HaA46Dz5)jSZ^3IJ{zLmvusCLzRXG|3QGN-RC;zhrZw@%@nFBmQ_% zEsz-KH&+mUjvESckEoDHE@Q*yOUcyj6+N+C>W*KrN+fP&YWsaws$*r0ogb^Ij=mT0 zIMt>wggpLz+C|>MI1PSh$fI#3LjS0(C5(J1+}x5n)~m%#nMvoK!lc)zd*=D=32$>(XPgfHN ze<8=K88iu~5ugJFYE~^;VsZIzW(As8V4gT7;e_a)Uii4nq6wZ}IB;poZ5A3+RczG; zDap=|?naIG(h3&YX&I0q3&Fujbbv_BU;ZF-;34@UJE<&^)Fx>$>Gk$xN9209@Yxj8 zg`lFxWwn;U$$}1#nT}+7Y^R)BTxvVFfSMCEDK>+iATj>o1fTf4du8+y%=C=q=b&s$ z3ey%gtXJuzyFr!0RE8Ot=szKe!ltS=?5bE_bH@cCoqO`?;p(*0`=3NU4bf1wXepg_ z*GXUf=I_pzzanTSX|vd4(>BH#Zhv&Mhhl4qm6YPO7w*(ud}pUh(!o0dwZT3HXw51kD@&k8;n7&oAp z8q^N)_QimTfS%*}Ll!}3!VSAyYVU4koZakm)Gkx_&e=vE9~q&G$2&(y_jgx#5~Dg% zpNfA=$(Xvs_Po!urjQ!eiEFA{auTIuom`xjH+o(-!z7BP@+@A0KMTwl3G z8)LaUJJ|@>47?aP4FX&RWQALvkgklJDs4$mr4i^%{lm%4NjS7#o>TJ+FtkcNH8&R% zsqiTQ5+{^!gklE~+58q)8TO^i!NOhzAc^25W!D0w(IIMDLEN2Kq8nUrCN zvyZoR*>_hkVZc{oEc@_}_ZL;OS7;wvkV08b?BFqw?M-c~uZ}T~#~8Zah80%8o4Bli z$Po(%4kXaH#9&6yHfY{&roXQ$nRWMl`*zl#BiVU3Zw1d=MLzP;4^T~aUo8gGe0nrN z{zL{$786ERx|cs(EhZb@4_|)#YKL0TLVmk~bc4aP(t*h<>#V+%I_)NQn)*zH-WM=7 zI&ZvoEOCf6&B@F6Dyr{K987pbU1I-xxg(nSA8E&36(X z`Q%-O;1a?m>@cD`Liqp)3StfXTEOf@wC}GL4*>gI^x(#vKZupn#?)71M~XiEtmRNZ zffkT#lDzMXINK*Ll_}p-xW_D``IU<{v3<9@EBDQQ!FjLN-Q3NSs`^>!3%Tt_TrF#S zUdk+Gps2_1#32MM1);OGzyYurz&buSL>Yh+5tu6=RP<9Q9D>I^XIe_P969xejYpnX zwD_y2H?DY;si%T)0Utppr8Z}T6`%I8!45=pCI8k0&KQGWC~WGjV)!_Z0&b zPr0U4P4?#f8)k_R3jhd2m1}NJ%(%UJl@48W`X#Tp^gh5Q=xhs29JD8wB&_xsJn`q> z7I%W$$0^g7MlEQxvVT40Wv*lt5B;RJ6gw?Zh@(A0Kc1wwrJyUeiSG? zxrOPjakhI~B}Q2YEIYn?3Vh^WH-s0C2STu%k(zknVt$eLUIGY!sR@zdz>qi_+?jJM zRKJH6#`9g^Wg`+m#FJS!1xJWa@JugFa}Zf`XcSS-(xU}~z#j50bY#_{a2MgUFf>y8 zYo;T(-%})x+b`2k^Ke-n5(<^SZ%Q8t6M25!?q-KQ^PhJNYEOF_wtXtxw^E)m{A7ad z=+LCcD-{v`vl=p=msLqXJbOW(0%55f4l=^Z#JxABZe&EXgs8po-|?y)Du&+dIJ1S~ zW~9=^(=^eXsSb~`nWYpyFS1~Kv+Ah(5~ZWv3TB|ZFV}9 zBEChZ9gi967WuX?*#a>vHVOL{?TG(|95HB#qHRl*Jk1=_A0h8q=Wo^{>rD~wphn4@ zIKIryQ}FZAW&Lq2PJulnU*|-3@Sorqiw+2iZt>XqY^0?5i?T=+!wsGo+Y=`Do}?#u zudxxDfmdemJ3?GTs0kZ}ela>FUXE=Tg7y=~JZ6N-L=F2sGygVOb4OwFVf%744P7km ziO=fjteWgjZ965up_P@mRv>%^4NV;Jm=z!2^rc;lU*OBAQ=x&M}GN)jaF)Ji2w5FJnLOa`jCA?yojQ?Bj-p$TV zrwcvLs^I=}&{MkhAC;ZZGJw(B3zO?>*8m7nQIuI+JDtBLpRw^fKcI}?lLPmDbKao+ z<+uff+Ii3yf!7gP|8x?;%+MA&*$dksLk}GbY@N`PN-in9rsm_Yx zUfa3n{w`7a&JRrAaV8{%rJ1i{*I>(lj)u7ftzs;#fY7jik+4eA4j{j%=uC1RU3izV z{$6^+wvxJG_o)+pmo|RlWtK4X7)@}>=Jw^C0(eEtdnfGvICfNEU1i)N_NO22pNs5Z z-{EMnsPKR|LiTa+hTaDDks(8^quc7N6MqXwrcNzyYcfb`vvQ5mRdSl#e_fqFA^H5F zwbCJ_+)WiH{4A{%f z%j1pIeDvzqgKGi|%b9{M$2`MlB^=FZsL`S@C~i2>xwG|p`bROYTZ}gwxfjq|{yZ9T zig%mb4|P#i&YazwuGE+06}j23mAlJ7E4b1aR)@ihFU0lzs$0{JA=VGg*~E$cJBi$a z_wIG}nJ!fEG%ET-C-WZE-J!9rm!3NM4GO2FBDKL)sf_kf^zj5f1zZkSnYPO@!+xnE zvA^>?c7OM--E{3#hzs@9kZbg>s%jOCsUa@L+nK7E5!Fu)1(h8qEEAkWW zwKzUpby`^p31XS%hV1B2>Al;T3qgE5sS>5Ze;h`Elf&$f@xg%8iMgHnOh04(pb{M~ zVv=O(U{RmzUbKJOqSwmZQk?jy<)f77VH@8|9|4)c5rWV#JQCY7{2Q}c2wS!v!z0Sx z25Nz*T#u$nE-9xiVW9^q=*=V;HIyFmdsnj9`)jVLo<|51+pA|5@7o-Uey0W*pX9bV zGsP?El)w1wh-q5~kKw+i)6CP>Mibk={Ar2Wl_%5XF81)hoRzxW-+DvoIzo~*e{x7Z zLcy#b!eQw>6;E}=s^^ze&~hC4R|=zyqW|EYOrGNoF%>6QRK?35rfJH1h-y+_-50zp zB(6%iX(>)#YeKy&3XpsLpTjpc*1XA=I?`cR^znnj?VE9-r=2(IG5KFQy+7dS&evIf z^$ua>sT^L_Df?PA9%w4&bO@n%=mF7aonITJO^1^ZpD8SeO?ETQWc12T=F!JHXs~^V z+b7GgM7%GeX?A%Pm7@ZcSLFhgF5^uXqmzzLGCymRoTYp8>~=4ypB>B2R&-kpKCnfz znQ_wvxK2-tJ`j(~&Mp+9&YF-l8I0uQHM~6rJFN2IwcdWe#%>mk2oJk&n&H^uxHTY< zkkl!iQFzxQ!X>29qZb=M8k3@J1W(ew&$-U&82?WcXJ*fjwZF_0=9g+ki}<$Igj2D) zvSnwoqLpLVev>o#q}VNwUhY5MaF~n9Vgx{27egAZbc=lQdZ%2=Pw8gip7p!b>|GZI zX?C335NR81+Y)5|A@3Utx%#(P&~n}DTbFNtkl_Aq-JsdXcVh5~#C6M&XDQShg<_K^ z72gR(@$GYVd!{(;4gQ)U^i`R|eFVxEjL1H&9boHPUt~OFFpzYjO>NuRdmZO2A~sb1 z*UjuE_A8^XrR6i@7UrWm9WxuKHfDJ@k`Q||?Pqy2Oi$xtc$#JV%17(?36hE0*nS=sfM zUQ}{eyXklM^xjgwm8K}C<{hS!bWMTbEcbs&CC5f;)P%gVvzu8z8jfX07oUAqC|y;G z@J!_NUQ6pf$hP@Wl*Wr4p2^|Z@gdFhv_)J!3NjO^;~%(qsEpc>!x3K zI_McWCs7>|J;Zdq==->YZJ&+ysawZb8;iG>tfLRZ%$VmD4uLiQDHtM5YWAeLc>g7f zJumC(3TZH^?c%paikihH`W-`F#apv)g17&z7fO{TLf*MWaU)`-k-YmKliP`B@4BuA zdc~f(W6^QrFlTe)ZmMoW9k94x$LDVc7;FjJ+@-X-``VJcZPu)*M%DP8dDmYFZ9U#X zk3oDNmB%C=n0fwmuR zwAoj)G-WX4QqTAd)DDQrwap}}rz&DYiE#%%2(`V={Jh<@AZL-Hex+3Hp+T;+jaHa> zlAr4S5myHC>K_K?Whv!QWu5k~S#2$-;MtS!&N3%o<9>Fs33~?QfbjqWo?v+0Mx1)@ zde*;?Fh-<)sPLg#4`b#^&iC==S-sFbX@c8=?RHd^4*7`FNylHYJ1lYJ)Qr%zdmX;_ z>{27+!z-xSdF$ftvaor>=o@)F*P`0{FeCSl6KwFSXVB-%MW4Dl_Ft6BXUT+5`! zz;AYs{$Alupt*V7pgZ|}gFAPeGO9z_A@W%8Qp9V4V z9-;KNHe_E-x~*0GeV3W8DTzl^B3xtaF>I^TVH?$0lyaKV43+0y8P7PwKLVnP zGyG*Qzd*TfXk;6IkLGfm8ozj3@#gZ~b;&f5O?K9Dh7s--io~B`p3|ee#(al0*JXKk zmdEOK3{5d8D#&zZ_Mlm^x94U&${^C#VN-GXc>a`(s=FlI&_6 z>GJh7y=V1dHaS$l*hFD`1>2(eG$0WT;qxz|>o?uuu|;yK9bui9UKNU|&CR`Rq485` z)_!PzSt=&MR_3WLl8|9O>kQqOnSVOUw&0!co~1$^%&paFUr>zG-uL@ZGB{7jA2-S*KzY6JFO*;s81{y8Eg5;WZ5$C-`N zhC#Kwes4$3SV#Q*ZPC3d2uw@A2ra?NkB|)}7)|}kY*)E!F~zE=efbov!rs({G#+UY zlS8xBo^vUTKQ{%xUoGd-n><_mz2<^szosBgC9uz{r(UiH4&z)g{wAeN{|ci(r$_m9 zS(^4T=5o&zd7p3%)LhMcryFH!+|Ae=(QBPl{sH4Yhev8O7#fY(Sjz<$oZ`uP+dk^s zoe@cR-V$;Kv$!%rG8XtCS7SX80EM$+G6A^c%mzG;>c9`p>F)$M)B63 z-3umIjW8?K4#YADu?E+UJU%fs{68(gEduVtf7DtuF#jx&y8dIuEQwqE7fVX-rt!L2 zveDga?mIGU*+{{c7<(LaK&L%XuwI8duXktCvV(NIz1h|azp1C}__oir3CqoltrX{d zU!)kmAbGFGDpvnP%IdxP$~^8FL%dQL z#tI(z6Vgi&xzaHPW#4%0g|;iDGA~{Ax55|y02fhqHbu}|e@`029$nunu-xQy#Jz3# zH@`S8eVW`Z!MCGqyJOgiTRoHHn|k-^m9XSs0mKYAG$D6}zS;i_Hk#zy;gq-JDm<$Y z#y7qDOo?&MPm@+D_D9DF-4bc6EMY_bQ!TiM^GVy)^rL>QZSPj5E=a$4mRB)W`|u8R zYer1b#m~l)?L5R;0(Y(sm65Wx#k>3jU_|DrTAlz5WvP!)61FUP}ug&wspGIhExA%z|8sP z$-{TOZb{3z7uh`&dm4AR2=NP)-YXPopr+l6m}Sre5dT5w5&hbDe~OCHF~A?d5HX%% zIoj3~ra4HYKpEE3veN!aNWR;=q<}Qe0`sGtZUx;U2}5VjYyG#fJ%Cf_Zz(QT^fr_| z-0N!PmHRgD35F0D=B-qyLNp`N^=^uGU1Lo9M3TkEI8^gCGe~2%$$do$U7@Sl*+{3F zBF`>+Vzyjbqst+o@pca5h*1&bZX?j!Q0TYzsQqf_K;l?; zpi|(83lslecVFU;b^Cq$P$Xn}B_uLblE{!DbEZT>2niWWLZl>9BxQ&cGH1#ZLWMG< zNaiF-GDI0OlbN$V^*h%&*ZC99_j2|2EqR{j{@nMy_g-u5wcokR4r+1b?gAwVM&THr zU!65JH_k8a`>OHBVLyEr+ZKyekBO-WLQ@rw^2P zpAQWugQC5cS_Q-G{+SQ0e=B@?a83SD@1`%~SHT&B%|jQ{*SnZ|ZRdgt6S?)@o_#kc zRo_C32n9dU?g2_10A;k=!3MWd47^b|wXQsr>O-G<+;HOM(f5VDlS6jV$AQa}fz4Ua zVvlS+U!Ahw!=N5u7*st2Kp-I%!3+W5kQ}T_U+1%uT2=ja5v$kLg;5nSbNhWzj3WN3|QDch|@mC($ZYiPB}fz3$V>aKhTBCbH>6inohq+mlK zZvx{3WXSMk3imfKz)*j=Pxn&*O2jP!%7{d?``kZfz2^&uP1QWx5t4(cu7%U~=J}VF zqA^AzEd$evQ8xHN%@sQI31(?;h*6kWe>8zYrwp1B+lZI~?IKOc4?(dY4+4TqlJ87T zdHUac#KgpeC3-giTao^?PBVt8bta3(XyIxC_X`aVKsoiw-atN$-=1uH7LP8(ab!=s zHwNX4yEdcsk~F@ZM`gdz-Qc`bZyn)-N9}3Tw#4!5UHKKMP1WxMy?6wxY91~R`tFwDiXD;12_6cfiD<2=9CuFbDD`1#&`HiJ< z@=Q56e{5QO2B_W8)eb8bPPc+avv8@*pI)<3^*4XyuoAxyxxf>Mf+LFIY7mru0stkD4%M0tnq?>uPJp zKCdJZcVf}AB1UU0qiiSUs@#6ZJ-LHW_~4=7DnsY(G)&j<j5SvRgG*z}JBP#(hhzf6Yk${+6pfvXCTL3@MdwFCZ zUSM_1d;?*V7~}o!htX)o2`>fJ{EoQ&8=zZJn|~A^M+r7{=3{iwsgb1hT^h3oxA@=W zUMb3IM^_Z4(^-kLH&LC$P6Pf1p_7FyCz=H>JNKQ!1%vVq+|c5WKrV=|-ZX#4=t|5i zs`t;Un>ZAsg$xd7n&^fFRR`1@j}xK;q-njcCM2s?8>Q>j9%NG5j5zt9xc-1DaFm^U z=>(!=en+sUe2dpnEc4&7yfmq;!(U`q+eVn-DFVzNKNCm%`;}KgLnrC@BKvjr+Ka}# zu`N31<&~f*x!JFm+$J-#AeJ^8j6Zf3Avh`&bmd(U&6O0jfW9SB!RH=OZLo$=knd)? z`|Q@-sO&$e(>itG${F)fVRa$+R zfeM$r%UyF!Y=Eg(sp()}knSVx3|u{ElrZ(y5{O6Qj@^Rh=>90GzG53)M0p5FiI>NH zLn09hqE5RcbSgE{9?hWLzW_mKuN?nyrW)Q6^f!dT~@WD!k;=&^er!!x|0XArrf%X^;+BZqMC1X&ui zdPm(Rlb7YaB7F8SvcHPLAYRmaK&gbHH8<<#J^EFw7fqYfUe9HuF2#%~ckM2V(rh#a$2F0sm^-l?VGLAdr&!|mBie0nqqx* zNMWGMsj_B=xhENhNAZrjb~is#_)=EqB+y-zN2B5BybN zm!5vsF?QKU)FyL&QqP1&^2Nl+MuOx_R`tm!840(=6QP`S`k#38%r8V7U&na4#@%*E1F(aYo90*gQhN6- zUZT1-&lHn?F(HsW6`iHXhVcM3*`bt-8bUF1U?n28sjzLN561}{6uLmb*UfvZ<$8x1 z_=UiGv?+!3Q>U&l1kwcbS$iEMud014ER$@kkM9GSytvZ4(cFBe(?RWpNewP15D7U- z2RDgdRgB=nHID#-OJe}x4dMi!_1Prja&4~bi93EjzPP7Bbf@E+v17s%GMNrKTzw*i zr_v=Okov&J`al_3e(DOghgtqLHAd}c-#^Ye6d3hE(B&*u67PvQ*&1@vdzO`EBt}Rlm zDlaK)HgHT%uMdYAXRe-=Px(1X`LUzmRQ^LmC6LE(16lfL;GY7i<_;V|h~xgwf_>1| zYE6|l+ECFoo`OsdqQPY4dsq3HnchiIRBrY99KC$_zq{31w}aB!PGvphZt(31T(1$< zAj&C1=L_2fB&aF+0tbKFwEJia3vaX7`$?10OwTkuUzkR`E5lUCbuSZ%-gkawEk2pN zx^K$hjkPF-v${kDC0=AI`*d1`^>X=GM|4HUw5Yco-X}WRvHB47B0(U(`9^DS-D`tO z`YBM)%@%`UYe#TYnMo<-G$c*D<*P8BI%j*_`TCuNxrn9k8dV!9WTaeI`cCkQG%*^f zX8z2@XpMIJ>A@%FQ_LF1s>e7h40D#E+z=bgCq1+dYx&`~cG=T?_0}Oo%63FQ7dDDuQU! z`@%!WUih&WX3Ov;3sRHcAJ62v4_28F|6Ms^$01NKn18!nYZEKOT7yxP4Wx5o^!x`; z96#8=dU%~-&X4w&ON>pz{?I(hhfy`|ORP`Y_DcpYc$CLP#|bt(HSgY% z{3c)_DCJNL!8pV`)$QxZl_A{6TH0I~p9A;j_lnkA&tL1NMZcxvk9;uA} zlG;NO37Vc0ntRXGRGM12Z?qeou}+aIt*>p_nsuE;tWSr#?3)@)$noKfx(P?WlNqOw zMaCy3q3`%{*Er^a;FvegF?@18dGpbAnd)J(?W0@M8MiJonPZXnRH(_V8yvl(XDT|| z8oHFpKeb%*3+;Ef#bE-~EISM-PLX7TVApFDK7^44yxstMZIa40JfQ9~=hN&XxN`gP zwY?ZgETf;DZHXzg-jzOTO~;;|sLD7b%$Rq9T|!dwvb(z!Z#P;=rFK?mxwf45L6Sp= zUU8n#hke8MGQedS;e>6gvE#=DtglfK8g5{Y} z$uaDChQ&RF{6PgDwjv9Zb*dY=0*i}8+a3pr&SaDA=QpL1T}O}DO+{Zjp%GR7x#rIq z*~{OVgYTX0J+C=xPqeCXWQio2`K2@)BQdpOJO4fyl0XQB5eG0BJjcsGU^_xG$ffkl znS%{8SRxD@!~{d2Ll-K%cRX)Fx{7KR87nD24po#?*bAe*Ky7ec!V3dmG7Wr5zY!~D zmoUPucU1X5K?VJORU03C(xONto_|3Ir~N(Y9^S#Tk{onP-!IVZFraXIeHP^xXE~xE zV0BE`gWsL8nt!1L#wg~kBGzYiQtV0wBa2kgJ$K*&H2WF|<@%Tg&K#mj)H$qDbb-+>7BDqP2 zXUffOZz;Q}mR&4p?LcA&unNcm@a2|!s9T@T*v*AbdWG{6gEk?f2TG5)X>myEKeqB? zhim!wY~>1G_vGhAN{IpLI1u&%b;;DaUG&Vh6^CcE#ZB-(rD=CcUoV`yOmv7-yeeE6m*Nwmz0bp5>;eO0|}Vsp@DE!VqAH)yQDZ zC9SC{;EQVAeSK9LGzuaLY*KJJU*FaJ&`XU>EcP%285RXb*j(p;fB%&VC-c__h`rn# z9sJc;m`yaIxA+5w9wQT|{CVex^BpM0aAUevHqdSrAvAl~!Kh8+-p|u*Z(nM$n!Byj zfA${N-FwItunBN<961b_VRyP2eS6=f?B`s!vvRG8S!3pIa1CL_>0JOXAVhdH_~CjK zO22>NE%WCw6)umPJ1x!P-#SQOb{CQ4qiG9OApFeUq16XM$fgvQ28>omeThlfu;Ck& zqa@{I={-03Et0ckzoA;HE<}YWcaia7YheZ`PAJ|GEv0|@tBqxWu(0jzAET#Q=P>~u z*c;*(nycaa^|VCGb>#1i(i->+5ce+9kjcsoPW19@_^+*D%5msUpVROSo=Ez%;BzA5 zls8=_(=lf`D&3%+zAPyz{IYkD`i0b5u{feB4Vd0X@k?13zX!hKMox+AAbJR;{)fV{ zXU`|SY+TiBbc^;~T4&w`5uoXq)`Gr)0UFJtoYVPJ@3$B{^ThW8*72Um6uf2j9_Y=v z3wK%{uUX!oE2^lpZ(VN9Zw2dtR&JtVF%m?j^u5xD3ylyka@ya~YjLQrq6Fc=bqz^T zbaa=)%LUs_n;U4dJR-O+ff1W4(ih-{hN(G0#=&}L7P*R#;3U(@#Ya|8I<*F-A2nUnq z`Wt;iTF*ywlJgv89`n?I!GQd_&kZwHEcVl&IeDjKg#7m48b4I*DE;Ml-~C`f)rPcZ z+THVslfg3&ok=Zmb{p~(zMds1{7#D`{1_1=9h45rFZChcm_$Q5dsL7{?C)$K9) zZ_Y_00@*Co`-DCkT4_Q@g=GPHAhyxrCUX#Y0)Q&j^LYF~_4zc2ODlLxXaDoQQAU}u zZA$dgm%NevME2q45s0i0ajp_Y1x<|6<$ppg) z=9a)q0z6Yot=d_JecC&zjTSclcG z%G)G8W{SanM#DJjxPgi8*NcO3PE!~xjXRiRRIh0G*H1;MsPiW`pkIKTTadvEMPIz| znS&yU&L->F6gBC<8h8TWgUVej9z)hRv@YE1iHS`TOYWr&)sJ;%ydtk}uPnT2Z8aCotV2%4 zUFan!&Cqi2^lP~NCHn7pBlp!s5zP(q{GpzNganjch*3MQhL#JTTI2T0NfUnsYY0NO zjcf$qG;06Xy_Be@A>iF{I6K&8IH;h$zNv+s1zQx<@ZgsWwU7;ZRg`QWUqwq{g zh<_pDZ|mC4U@9?Z8u0EMJU-bdWb~F(xhvJotajvv(Hl#CmVAL`Ho}ht=s0rW#Rl_r zKX{zLzNj!>%BCvhUQaUUr?T3QvI3%fvl5LVkh7sp>Dk$viGbC5(9wuf2SEMB0+;E&0!%6 z38wUGPv?yHFE6;uCf+4y>z@9&Q;6?a>v{2#kO!aP?81fH4cq9`_cK*9JPo46cj>_p|h zPb72(HBkFlo9b#vH#Nu5pGVxr1%X~VxE;boABdc@j-mN}q;=aC8Y>U@E*G>5*cQb! z`0rOdYI8aENfn5v1QB6RRqd$t-a^45TDfgBPpDJkO77vXAYDX}BW43A4q$EL(&y6~ zRCz4-!TJk%Jen7lOKPd5sq7fTwsxt$%*OH#g(d(Zw3hpa>W%%h1{C4QkZzVjpAcG* z`Ll+<>|#;UWKyiQIsKV!l>V}fr+1go;oPA}u-mMI5*x&K39rM6CKj%fAT+WV4%v!1V_7@4ZrXZi>VMj zP;jAp4hQ6QH(gO@qrkOg<2z~tqDi{JH8WAldo-9p7Rn?%R&C~poIXXt*i`T;>u~!& zwjG4~rU$9$Xgh@ez^Y!gYXy&UNH;ao4Yr4h(YIAA9%0JMD}% zw4I@ZGj+izZ~S|^*mGG~5j-EPKaTJU@J&6I>ujcBN znCF?O^4L{%ofjs~qTccD)T`qfKb%9}^cV-9pjH5|;;nEhc!zAE=iH5+x{)hAbLYIp zmoufCN^nH+!dR+RFW{I+**WN1HeU+)#=Eyf7U>jH)&n23_x@*;YuKEJC@A#b85a3H z4cLG!*Z78l2DQkz_IaeYn2dBW=j8+*_nG*-5?g0&beNSS^U+#UsBP^mg^KPAGJ@2k z^heqPtM~1~;b{6rG=SJPy1`~KdgIEGUp-m)e4Q(QHcm0@nI8Tp^!9&i0SK!mv|aYOorD$Epo;>OgevFr*na4+ zk$|US=YjVG`-$L>JQiz(90tww>HF27&U|*o36M6>mt>v3Z)?}vE3W^_n|2QY2IWys zm(w1kFF8^}XH%nwKu8B5??ezo4RphOO(_C?z;oRl4T zKFgAw!jne~*B0QIdj3d4;i}gQlV5A!EDP{7n`i~hu7rkM$v@3#Wt3X%d#y?LKHuF2 zF~T&$*_osf0h$-+d$@K+0X773TD_#_cS5S2rJyK(bK2Z1z%XatP-Vo6S`G&j>Umrp zINgK=9YFLdft#tVm}B$?T%|y??xV{5=NAYDhm*6Aogvb6{P*uEj*unpbn+?##TJx1 z@5*m1ASZ$21a05EZ!gpdU`qfyE34<$4ortMPFstfGmt6j3;{3>-ioN>kmCJwRsnU_ zcX&&q?ZpooV^@wy$;$rJO4LmrmSH2Nc%ZF6we?QXDXbyV`}_xAtsJ@1csRmp3Bz5- zV${H(@d;cAlow~GW!jKckJ@6mv}^EVdxTX$I>E#ocyqw7bb(gMV%8FIsF{1}^+KWS z2?_5SuP_X#M463?3^4@56bi>dLxJSNtpl9$cF{H`n*QvaB$H=IM^RJaS7<5_>YS8e zn!UWdtkrwJRYEDdt3y1L*L<0*c=lAo5VWCBsgAL|h8(_4HZN)4xz8v3Z%LpOrtjfZ zBD%VQ?s@!D=9LcgYgdcl1IA3)zDApS|CovXM;hqow6oQ|I~FdMZZ3?lYgu3+8Urf? z;+`uty=O3iiXG)Kv+9qeILVY_QyAw=OpN>C{Ph`Kq<0G!EhP#;Vjaz>AsYlXy0FsA z{`*4OLO`EAH#ar)7fiH3J1Y(@0s*R25c+r%X3CP=q(!Tp7`-0PUVGh*wsfR^thAIU zDhNKkvs07P(+PHrKFwg*BDK^xh(%y_z6 z5h@!rFGjITV%T-ZnCYvda#G9*?F}3|DO^&X8XiEdZEXzU4g1YN`n`0nma8jzue;>iWKjl|;D8-I2w{j(O_R7( z0fP8f^(DNhl&ngLwOdn^tJfP*C?59rz9*sgP3l5XN@z=F>yEYZ$4}fxaV=~`C56a| zI69yeZ}wDqcZm?61_o5U!{znYsSHmTF&kC}fQ35p%;x3i2a1t>SCuAp3lHFG zT<8qvaROv=d8?ZHmXU-#<-3wMR(QCxgTEX|sBHLUd6@t`(3)@mcFxBv?JM@nM`$v~ zueOd~Jtq-otkO6zZF=UJAKxT~LqJ`Eniks!sWeUjh?yKVp|?~E*V>_vqkN0p3~nlr zo@D7$W4r{BK4RoOnu8P+)Um)2A5P`xu+1nN^Oa0jOmYWW-cLQIm*G)6aB3_opK0wn z1fLU0^+U6!hA4!Ql>(8W!;x(jKC-WXg`y*%&N_DgO2g1B$rlZ`2>t-T%<^lS@95SC z*Tt~Y1(w-@3Ww5ym>ZI%k5UcI=-0m(zV04e(^L4!>MoXpS@OwxS;Wl1L##+aSncJS zX&6Imt(twgK#=QYS%|Zv2aT{Fhkp$;S5K;SY5$Zbe0+2df(SUKM0Mn2$OBqv0h;qO zFQ-YLtPBXQelb^+$j_kZnwBZ5?zE|~i*{D7YkBr05K4I3tpvq40G6qI@Ngxw?2$er zph-MYK<0!EJJn`)`q&-yrPNqc2$%>11fggs8L;x0NC1$bj@M{Vlc6TIQeI`3?@fSW z-&Kg`41NIk4+==)3dw1YS*PTc*5mT;x%=2BOpUj>qIC#bZ9v)wW>~MAJ@EbyP8)(3 zz9DB}rQNN;^bfK(6^afQVfikFl<==(6tS^^FXL3=+2ORKA@;VxDzEZISx#guR_IfM zH-}hIjJ5Od7P~WoQ8Ch@4`cAikosap{4l`8fj%Y?!5t3=sTQ70dOY*p4)vJ}=G$*H zjVtv!{l*ia3j`JjavBj5l3KtNNU|`Fu*yEXgG`z`EiGfRNFqin{vhT!J{{~rAoptV zQAn69E21bX$RYLw0;*w7&8#RosL(!jhWhnqA65HgV$4^vj&IvYHIO`5o%fFVSJ(pr z#)=Z7M=)fm??s^t3KX9E7IyZD!PY-0qQjgsX@M$3)~ybw6j@Hurve2dcebu=<1Er^IkzMtQf@{RxVu6lob5t8_KAFL+sr zzw!&wbYbspS9E%K<3NdNCbM{q7BOxMijkwqIuT zWSFklOT@*mC6~rueVt(QF)Qt4)UV>>lXK?RjwB!@JBoZ6$@)J}DV!~IpFw)5Me-vr z`&VE9=0kyjyn=vSCPSBJOYQkn-O(70j2itBvx4t_94clMJolkHD9|3(6y9J0TVVsII1T_!xEi_U8{G^|{`Ou)9c6!?d z*&Baoi7G!!zyC4wwza)Bf?iHf}2iC5f)Lh5S|Hx73ONKZ7uV91MK2Gnbh_q~*T&#KZ!p zfO6n)h($obz2D#LumZ!gW#W6O*nj80RG!?U;n!rV43Aa2Bri%5Zcf4mfz*e+0yL;> z0@?3BAD!z_XJL8fzl9!St2RY9xQ+ig4N1uHbfnScr{MketE=WnrEydnkGbx7k&lNe za&r$t7V7e4FCY%ABwFub<)mrn`#=O+1C7Z<5{DcN=jwKR!>_mKfyYIPdKU)>gQ0L4 zOGX`!fSH-2R3J4eY0LacNZSz9;q;KWRKWk5e>5IF7TcD}!DtYYI_xh_wFC%IF!X>f zFgZK>+#hx?Csmv+U^ZZK_%u++iuFsSea?{d91K+{Cr{+lKw*p1q+`(T{OjIND(#F@ z{{#qN6ANO`U`Y@zi2zt`saA5$FYcX#gt9I(CpSFejr=gmX;w=>Z^suJt+U%b&TSX4 z!g@m_P83TrGX8++h0w81*hS!SgX~4~d*Rcz@fqxmIIf=$DP&8%9g)8Bq)h|lP3CdXbFxwG0ZA;HxfAG&=n z;P^UDLde<2*U0CKj?9lmFTVshh!G^@!0h$1#0Q>|ENZK*MY*APX&|FGkjb$qn%}~6 zS)h;tL8{B^VI8kqCF%w6zi?$MDIb-9;GFW7wf42ER|z*FOsE6r3g#6v3So-&z0`FJ znt-xYw()<{Wa?j-=vGF{gT*1=cKd|RW28ohTO@bN$ZkhT)38lb%Owa)#C$3|Phg?S z+hIkB#041O8HIfkN>wKmTh3x?3&AoK7uT-E!h!@?zMzQ`IfGT-Q=Xh&Fae$_dz;{pp@7gCILfOMl8cX)_mH$v@^8C+G&HfKJ$=ErdRGOnRelup20z?yd8j4K;(NpfK(P+5En648bMLV zVBSPusIF9(nF0{_rYvi&aHD_Xcn zB>W%6EI$Wc-`-)FIC7<)x&>K_tY1L22uM#`j}hT-Nsb=9uTD?}*86_+dufsje5$Vo zxe~MmsLP=~x1&w4@#E37Zq#jhExwSE4JoKf;$M@=@&mmp09Y{SgbY11@apzOR zzplVwXriY)7-&QaR1=O zv$m&Q4*t!uz-kvQ2jb5 zHH#KxRxc=vO7QjO@l(^BZCW_b_&nNFT`~Hz`g~Ew`961X_8Leu5RIN1js-M-UajCS z@kk@awk=jldNKIb;L1XU2ND*y1W{BU-;)=IN!CbTzdtN}YN|i`wDwrKri#Mv$pt65cO7>t zrcE~{m)0zLG)yAh457z|Fme@6G;r;y0kVUDOdva$EX#q(h&tJ~$gOxZm{4RxUrJg6eV_rVroNYkDi=d?x8Q(GZ&LJ_KFiw%aHN<0N2Kw^>u{AxJ?zMd*Q1|hY#peYKStZJmJu%BO>gRN}_5`Hx|3io04Q?b!c1$kdL_unF2QsB9 z3+J}L3siGFtf#kTJtEJ)REY~R;t$bXGzyOl*y6bN^rZCimayGYwsFP^!HF0FbfUKs z&inU(2;padH$jy){rk3o@v$9|{jBM8l+}U!~Ish)ts48xvF$MjC2-S%4_bhiN2Toh9Z_poHIP<*YyGl$!k8!#}a19R( zW(binu$T{pAt<3`Jr~2bo5P8GcL^0*Q$b?@^d|x?N_n_QwEsM&Fj{`94L2{#comV(^NJU>L~wKT6fPOOR2ITAr>PG1T2W$lkpj&CvzGd+g%k* zj=5_j_@3=TJ?3HUiefT99AbTPIJxu`$R`kPxTNtLECC=u9O$QmS;99f?lqS8tx*Nq zZ$4f0o(7J<5NwVjeW13f=02DRI%3+dQ zgdA*XvDCpC$<8uSLh5O#TOUC$=VkW7zOURJgm_z_?gEIev& z6p6Upq!m}VEsv;W|2B537IM%TY%5_2CgvaG17r9H40(u7Dwp`9$=3n`Dul9q3Ul^|AuCW?V14kek!TS;F_818>!HblNqAJ# zw$(EZ`vgfhLJIUzoc)?~5L}@WD~srq@@v1P!ZIAQHQ*?b{UM+MkVEX#oGtFUH0Mzs zN&khNB6jOb3@bo!gES4sad@~yX7s~(G1q=a!Iqs{l4tBJ2FEPX?p9}g6ol8miEiku zBa3D(WjKEFw7=Qer{E4TJtmOL`wig+1_T+~1d-}Tn9nNj!r?p8hx}}m6ll;M3ML;V zBTfjOp;pEzD07jnLBr@Tr0Uz}@^SLN&;NCu7oTbnuI%I5Slj{x20|m}jdd^2*(OOs zkrA&w^g_Im#&FA;$F5MdaK5dO#Fv}V17k)qirNg2G-MxyjD&t#=(Wkeo(95Bju<|K z1w?tUh~G`W?p>pUdgBL&&`lAB*UxSmTR;71nvFqF5NPzdy_=byE0K5@oy2A**gKdXDjmPwyy_5tZ9g0J=8mRS*Yic4c;r*Vt-QpU1vk}tz7K^G zqzCcou{%yOcpkwaK)l_O01I*;@%=F5Mb<>}1;2#B#c=y11QWh@bZF%1m>wq&-Bx>s zNJsoJI0$??93&L^DCj^v^vk)`U^x*03IBf79f-T8|}|0N-R}WB?`51d<1x z(6P8O*K!_H#y}A`1E&QQlUN*eJRKz;{4`W3|3o|< z-USDC4hR~aC`+(S)}Vj4MD+#=aS3SWE;4`Ivs3F=b$(nq&T%#le08B4dDB_|L3PgxS ztUyTgTaLiR1Qh$!{b4G&_I=O-7L4+XK03Cp&XLSxLLq))O z#2X?e-Qmp>tq-F)`BP|EAtuy0NL22T*!!h&nt?5QkADcqzrOi*i@>_Ep%5-meE@$y zpy>5s&bg{)cvlDRT*8Z#ShyO?Dnb{Mp|EyAjZdYQUg`FJwz%0M?&^4|oA*B|gw4Hu ziMWNYP6woCIRDtK04C6!OsLx+Hh*iLX1EW{RxMyNEe^dwBLLy^L_mKCaztn~U!U2r zI&rO1`4DS-Y1F{9`x7Q5bnwl!gmgL#x@oryi#K)wQFJVRH@P7E{J`cU?Q*kPxV}#% zZ7d}bhLQ*>;J5DPS1ePb;No}|TiL^N;F9bugQ?(lNPx~!49J-K-JDUXCXu%8)KF2>k2x z8*aHjk~UWNfib|b`a5C^1F|8rNT^e5Sttp5ip@;d9&uDykG)=<=@k%WJPJG$tmNbZ z$}b^>)THfYNl*@hskVRd3>vt~wQ(o--uptZ)&{T&vfwlKtws|*@#ej@h><^>Vf>sMK5z4M(5!EpVx%H;KGwq0>ZTUWH2Z#y_{O0%9*XaMZo(^svH*OlM zpRO#X*_`yY?2=*&JWSr9Xo5Ea#{9xG0Xi{E=fjDQX$`xTH;GPEc!uUzt_KloP9#qt z&he4$2s842o;0m_Zx+Hub!yvt%XM(F%yi)AD|!ko6!U+tdb%4gCifbtGkQ3D6dq6QT3T7t+A!>@u%jyGD$!pLRzk17uq@c99pAFJ1$wFwqDLqzav%oiQnT_1En*RKhps zRIh%ez}#DF(YkG8ge)9f0?rPW1(NfS(IlfS!O6xqu`Q705UsXG?UO&=1rn_x5VWYU zC^}m~ums-LWxjzpuDJEUJ9|qEh5A(0=(G$tOI71lVQdLGIdbVzCk!T@Zp>febRfOG zEp~`U`9<3BwyUD<{2p2tmca)>1>tX(RdwH6ZORe3GcoMGxN?o?WGxgN7|XAGkBbz< zvecMle$dtXVtevRE!bju#Q>P`X`p$DL`W*2yO~@-t%nj?I(SbBE7R-!%+!nd8_U83 z!k?YpPxK*fEGcZ{m#>Umung@ddHSZbvB#?E+lNz(P>M{OTZU-5Dpa%z_caHcPrIOF z*?weR$kA4GH~E5T{-G(^om4qb(r25Unypq6%E%H zh9K92H~Z6tKbOmpTJ*w69SI&b)*Va;tQqUbhxmoKItJ<<>_b^MdgHErNn~Krc=FM9 zm!^z2F#gA%T8gu`=lUR7oBlK2g(r#X`uh9)muE3|kO3nR@1oK5eSJNg))o&zE5rd_ zW&ZjpMulEy&KipQ5;=by41;eVTL+1W`9SlVpMk@uAyQE%r4XtIz7I0^fk-~@D^%dlEB-abm3w9<~R0d$Z8KXh(2k%ptbh_ z>=A;yyStIE!koVdh@?0C|2SEx`<0HbQ0ebbc`+q)@L&<<6O6 z$Aswv4!B#maQBAdKdQRb2{PBvoOIBmJh9HiRKOtIej;@A>T9sOoG>_|^jGqPQ#|%A zB$vx(@t>fb8o-iF&yBPpH@l2^g@EoBFN8D1N7_&R4BaV78hu`|+zG`f7u@A^=dp4EPRU#6c-i zQ`K!py$bDThr#e7Uscsr(Pf@Lk*-){^ZBWMlWox>l;#QAV}Y~=V+C=31Cchy^b5F< z^68J~nFz!=s0^L7=WNnjlSg#IYj<~tVn@I=6>o6ti}}6N0nISktX^ODZnQ1BbB8~O z2Yv!_Z0&AE<(so<@|{6-iQ^y6OkS zou1GYGrvpvxiPtLWFppx;`G6a@xiv&eX->wYS%Vy(9qBzn;&(x;JLM5sQBHc%h~%= z>F5>8&37%IHK^pJqcm7-qg7rk*ZyX8Q0qmKoc<1@qQkkyul8k_Sd;}iPf=DiDK=|N zCSTm9D1%39K-Z(3$*?JU8aAFg4M*5=_L~j_kS%@S|wRRNTob|#${Lh PJxN1VTP5$Ph2Q@I<@Y(3 literal 0 HcmV?d00001 diff --git a/ml_system_design/seminars/imgs/sem1/sem1_7.png b/ml_system_design/seminars/imgs/sem1/sem1_7.png new file mode 100644 index 0000000000000000000000000000000000000000..867329aa79ed7f74b39b7e5485a1c61958e91783 GIT binary patch literal 178385 zcmY(q1yqxb`#x@?k^%yfN=pps6cjb;zAP5|=o@+-AM_m!OsAwvZdiFg;i?O| zc6aOM-H8nMi5n0^-{X#@TYsh+yvaxsjq4-`z)<(zbxHunq2yCe2zn{kMR-b9ahc4*FQ;M64 z-Qp+)u5Kg5NNpOe_rG&Y&?U-m(B>X1t)1LlN_dZRXxfR^bsw^zQKY)@*+xx2MOo{Q z87QeYqENX%B2DN3A-8q+sqkk|iH#0+pbi;+si3*49+L)5*c$$SKRc=^Ycu-e8%^vZ zIp-dfDF}(BazmM#ml=AI@=(+ZreY{x$j5fQ`ZW_$kd{tr^GdTmfo(oKO+MF?FP-zk zm~%PYZLD)k?!zDCEd@PF-DXCP)<~to*LNtMvw!Kav6)g^9#o@-8h(i_W ztg#O#t}pbvStq^MU1S$j`hPF;dMv}DF@JbmI@vwtJ~i(?Va{7qklNAp=euSl@vNyB zR;~a%6h)yQ16{>?Sw14bnRO&tpIkg`C#_)8m3+)j@nxEIReiN*VcUhN=2O7yFB#N# z*$SVp>`*r7(SIK6gFJYe)SHP+V?|(>EAcXc_HA!P;YihTnWGl-$LwZ)tdtXJLj$<@ zyHB4#y{XZ|zVri}@}Jn^`RjgD7S-e)4g*0X<0uzAq&ABLLex3qRdes2z-7xuBYkR= zkHkzlES>AVffXwbLw`VO!ABzI5TeB3PpckMz!fZqyE>#l25y+m75&{v6K0E{cE^5M zw$I8a)0oQ~^*9{OHiuV%2u1nei>e1IU%t2}5#3Ykx=`R8{QpmNRZD??8ZGnSo(max z!d!zbqhCStBCzG)r)Mxv@zsA5;m?N05M~pz&B7zK!Pyc1@mcp{Wp#&NWQI2g@McTd zBHMIB_5&`;3G!xrp+6}V0mkKHr{QUziqwmp9^RphNE`9Z{qSXli{fV%4`nDraXh65 z?|)K3^MQN!ZOv%26!hdvit)uds&Tm+>!v#9ch7$0IxaZ%5>KG}@K7fwr*fmH_S4g~ z`kyqjV`EcO>Um#NJa|%j7T*+5FT2M_+dm<6(i@M??<%my_1i_HraIi9&cG~%_u+X0 zCc*zpJzvssr=XvIxTnRWc!hY&WPW75Fgn7eYvylRH}^Mw_O{K#t)fsU0)e2wI_Jf3 z-;nQ{oqO-9hp~S_hrFOgx1qlAERyHUq3Pl3vly`c>wB8{(#T$Wm_zJjeDAVy&|F&x z%*;&xG`Zk^)#c5jzXTffjjm_BJHG1hBO9UcAF@a-b2WRjpXs|)0f?En9^jb52QrU_ z={*xH8g6JOi&WCgQEan(X4JEd=40~KQf%g>+O1ex&REe$7nhd4HqlEsNs?aq^TOr@ z<(XMZlr&UmKKR8i=3}Z9*)W$4S%lDUbzff%p7eWMteKd%YLi6{zj3+htaq9=%u)ob zcwz4D<+Fcx=l}dUJ$2+kpzWi!n`!r6@5^L<7ql-Vc^cW#*!`+EZqNT^QB)|0*!-df z!|W*ZB;Y#Vhs<9P+NMv5@Polv$V}b%)Sq{z2LBeABvR(m#>fG$rd@vyZOI(mssiiF z7e}HS{B$oF_tPh-L>6tlUt~l=Y%};8fNy$Q8AE|I^P5xX1otGf(WDR{UgH#Mh14XS z9sEV3Pnv3kwBAfLS)M0Tf|4eU@r$kj0T;UqT`dHRYY?HxBJgn7u)V#VmzS6QN9@Sx z@US4(NrP3|$kepadwXWoBIsf`$zr-Wz~9a;4{fZkt4kIAT7`uytmx*j0pj0;d?WZb zk$8kK{_^O)+IY*uKYeeCi8&1t!XJ8)eA!e$`%ZtKRSnbE7s+P0q?LY##T+qQvW-k~A+}Z3P0vAib$sE44|JyhE*e---zL~ye zPGO-?hJ#jqeCI-Hh&tSQVHCKSD?-pEa$!Mj&h2Dkxb|{h;BtQYxJ+q1%N&W@^b!}V zxpVVn(4p`ZJm=q@B|r=Z`|?zN^)^2W;-M<+tEEyk5^J>Kn!Cj!4Bv`39ZK!Rf9+wv zp41zX#vDte!|QglR0xTlLJ>ZGB6-gAIW4e-MeFvxa#d35l3ZDn3c5Vcjiejr9 zmbJL=N2?_XP}O;?zA?+8iFX<<{Pz;ktv@DaC@XH}{vBNiCH{1K9>ZipyuP`xU?#dS-3b?hqlkX-=$PX?y5Y_?_D_7C%R#dY%uC8;}hW2y%*O0@a`um=?4G$ zGgi+Be1-avy_vJ;|0^Rp3EAaWp_~buEOC9EvZCHkkuNE?ZIEhhFmXXowe@$&?Rx<& zD&mQqdXJ?>A~3|$=dbo@XlarX6GuizaI4NPE-t#dx>d4k`zZoR2!+Ylm1qZu21IfX zWy+a8KBe9#z(;XZ|1s0@M=mO51Kd%<1G}OO>CZV-y!UU^LaF(*rhdy-3wqZ_MYG2o z_0u|}aU>ffd#dGfQ*}RaJa#jo$>xq=ZLBL%ukB6O%B5Md=j>8i9yKUD2WfE+`Ch`E zbc-6$b9@qvx7#`CL$RmrUi3lCEdoiHnAoTo(%2kfux7qEr9++1F7(x_=t;bXWHbvR zesEoMro{N3C8VXcE*WojHU45=?W&)~-Vpjf)M6`cBk<;k1Ftt9?b7SQZqD7no-CR1 zB&qk|{gCMB=KH>#i>$Z zuzfW(wY{bOcOnyi{>&d6_KtAmy-}YNrtYq*eiV!C$7pJ4SslB5nNXKRArscDKNG*x;rv3NaPCi?6cL6OkBqRiZKnxEL`^aRd=2nas zH;MM>+z;pG=rWU7jtdj* zg{kN07V)YPTerUgg>~V{M8s>&o1jg3VtVYun!B}(1a+@opSe(#!MC5DqA5Dd^?#}_ zc24+#(&Ki>qK52RJLU}r3C^?+i3z5h3*1JbDkdtSS=M&Ff=X7G#d*Z+Q?i2b8_6qWjK7Nl}PSUQq#?ocaeKmb3o_x7vSkrA4^A@Y#gqAHv-A&To+xB!h9 zRVwgg>6B_GsmQ_@7#IXh>;4|nt9Vtikkw+rwn5jsSARem;zCJ5)Plb#nSPWWonVl8 z+c7BE@)#6ytkwLbcd3GUQQGovk!Jd*HqTS(u+$61!=44VI=}_eS5=y&EfHU3ZobMfA6~f4XrP1npwTKlAbZgl9Q7M?PJ7e1CFO}Qr$>v z`}_AXNUroJ3h`DZQTfzE>baDZYQ@>{w!KgaFf*6;k7VacFT>{ZQt16ibrpLo?>EA|5$mu_FQ(67Ib`w{h<1Y1Y-i0zYUC zlXG_3Cef3QjNvDtTeT$Y|P<1e0l*6Dn2(m_Yp-~$f%fSODOo14Skwzmd;{rWwo07|`g;uJMoBOT4u#II{g zb}q?uFFC~mnUb!;{wHVG|9Pm+my*}Qxw};b4-%ZI=ae~g*Vo-!u*)6|$|=~T=n~aR zeLGfb+`v?=hs1ppql;SZ*YHoc1zze1vZNxkv2C;UxT^CjJy4SMPsvUzL4wRwy0mv) zP+wo4oAg}9+Z3xpJ}$*{wdd#xyheB$&?|mXBm7e zWhKuNI{mB^bc6K4lU|bp*0@4Nl3NQ2{WeoBlDWdX6gIE*>Jxsd+k8q|VvK02Nc(;{ z7XL%N4L5}r$;rQ`nBR*qjg5{WnGJHgn)%4pV^d#Vx!rU;(?IBCNPdPSz-?Cz@?H*- zEHf1>?g4kP`E;$Jpx_=-d}3(oV2(U!FPmlh2lUasje{Z9V4~w~Ah<_cP6z@m=W9j5 z!NI1ov<1;airL_h`GbRl=>~5WSw9g;Nl8)3p5ZR748F4B6L|C5nxj<6u5j-&semG* zZu}k&r)vSf1cEp);v(SG!_wCmm5Z9*E;>4W-r1RB{G8|mYRWMv=&CI6u)}*cAW7Xe zYw(>$_ZYhDEL{a(c=IjNqW-tC zYMv=4W^-;Aci9@;I!;jBx_|lNi&J2k`^_(()am@+q2jbgvO_;UdFQQwIp-=o?Zt-{>jsNQL~y zSG^4byQkg8!Qd3>muozNSuRi3qSxn4EUIxj`o((^k@}sQNRE_U z`EIyZQL>hEf4~{Wu;sKPzel04?qMDDaKhqJ?$YcSWmoGjU648A`z=A;g^ZL`w*J`C z)>kqKtr-;^9UJREwN=v}6B-&ijv^3|vj9ix&9?euYD!~{Y*+}yL5kq$#jGk|u8t4n+`D_dg zXk@PrBClqz4lB>dneVg@&NLJAN9#~qVN3ej4B!FQaCq@m53YQ05GzYKz-cv~9Co!l zdXzc^)$>f3Hl!tGFy1?OJE((?;_naIKODUpy*dmEI`6fJ(d@rhoaOD^^R zxXZ6Bzwt3uT{`&g*-ko>?p@S@%u6_k1v0ZJMrqWi^tQQC7ZT26ql5ABsl|Q zhKmbn7-0VLn>yMd_M>k`866AYR z2p*tNSGcpYogF8lFI2v@w7)+ukMrs99kxV)MWA4w5}iK!XJ)$^Z#l1QZf;hvf zzP`QyY5?H;+rZ#Pmh-z?Unrmpfc?gy(xPv9WT%TwrZ|GcI=_>H!Z8aYvR;$FE;IJ8m+}`9Zna z!YNJ7e4m;n*0VhC5|&b3t#IMV$!UyC3;}zK_o6Y&#oIKG@Yy!2LyL=xABo6;Pn4Dr zsj8`W#6;MO5`p_4T>rAaEO$kayIhcqiHZWs;CWHdVOiiQ4jFhbsI=X<-yh8&o@{vY z=1uC)pR688bQ&1u=W)Ua=N-hfmmMu9w4YDtk_D0^`o=esh27_s6L}Qn;xu5PMf|nBBO6(yboJ%)zzS_#Z8q<9( zsjun5TZujumg{%He)`Z}{ZOk^3&W>@EoC36d}-kV_E&Q9xAz?AVBeF7lB0CZ7l%m{ z!LQfWPCT4d*3iUs;0Pbu=SoVqn%tTzi+s;Y=sE53Y*@n)reN^;=9cgN5(Dj7|LE}0 z60{>X7qQ#3+NE{Im7o8rTGVw4zOaO0V46+QNqBSt2sk;&*UhkY`XlA(|`y#TF zC((Hol{i>=t`S&mUD~zTa}$m4JpF-Peo6z=g+;E1dvH1#xNv$)2*p_WLHyoAojFs- z9i2AUR#qYuvdqi8gGjvyjlgU&T4u3q1O}`5vEoq9!JNLuG*V_m+D7K)fjfWan*6UUtCq?8F0{!G;Q)jq-uEn zGSp>Qlb!__p@kx=G=>+p?B`w38kS;dIYstWl(zfzgkfF{$xRH z$t!Kna{ier$#qSRH#MT}bo&Ij1mo7$R$q_Rg&esl@YoR*p7p@#M>o}$qwCv?7p1K-^0YHRZ| zn_D;imW0I@HQ5)8`tW$$vM&G9q9Rnm)A$~n%1$q9J{ksL7Eg}`Gg^@8I~S*3{NA`8 zdUN;jH3_qR05$++u)m!8?b{c7Pf^Z`XJTSvLPGMDWu_>~=+`J6-?YGd9HJNB z$Q(PlkT=<**~Gvg6H#DS*RKyx{Q|EjVjy||)w2XG@?9UXK{BAfc;i4JH8-JZosrn3S#E=idks>Q~p$h>3>i zqaro6ew5a%!^=i}ct70p#cY5HZ3|(s^Xo;l{l{XWBa$`HNMd2)eO&o4KKf*0p!@g4 z>pj!skkHPNHOubJL)u7`{F4U#Zfa;jiM(v83V&Hy83e+)N2qF#)1c`J%TG!gp=U6e zoCHtFV$r8i|#wnhjV3>*0f-f#qEW3t=q7_I*)hTiE(;^krr%QEbMa zh!@-w14Dzu(@Nkvss#0zEbF45sPf&F)+UR~y#;@7mQObhx10B~Yi9#nI2k?srxl3; zkQkabI9xn$d9JdVhA!+C!noHC4twkV1feb}E?zA%MAVq!0k`&ac*-1GR#NgjozbG# zUK*4ZTUDbM<}?M;nu+7Bj}FS~#qWnS?v_T3sYb~|J3m(b=b1-sIDe36)XW!VBpV$a z6&Dc^5fw#EL-ze^tgEZ5fes)YbP0b(mmvPLr-9(*9giieSIcemV}u(BUAPqgNNO#R zK^QB_(aGJIh1v+mSUI``1O!3?*_z|9v@*=zuU&;_ajN--R=oQ9n#FG1)M$Gl0It!^cXTL* z7D{PHKRl|7o>q3QX~*Z)@0RaMdv6VNI=0N7DCGlX&zcM7%AnMnuF1o;z4!GcP)(d% zvu!FK-`UN{;-YYd1YXWG`KN~8N`lK;aML+MAooJrnQL)A&&z&RD`w2iWo|d0L7YAK znjgi!UKcQ{H!`GEY=EMirQ&C%rqYtr(o)jrG;!nOt6v0d>6)ZX6{X|i93zAwB_W_|Xx=x$G(tV-V3PY<@8XXF0W9VkfzTqi%) zv{RDTB{I6-b5C=3c)~~)|Dw6?mMg5~`W@xvWs{Zpjtdk)Y& za7w~Enuw4IXkC=6371&>!h$YNm^eJ4oi@Dc;J~9=bo7?lOx8y^nCoEQz0R$iU8DTn z4b)_HMWf7fK?GK!M&@!NFR>Y@;L;5;5Nm0SkK|&F`?DJ9(NPBa?CQ9m?VH`UlsKvd zfT3E|8^P~YRUy}#d_<`WU}pjBAZlXsG6vnW-aOs#x?Rh+<;mW9l12w_|J@24&L zQ-wuEs~gVb!=Zl&eZLQe;xF^}iHk1RCi-he>c9OQwU&Kpg11>QFfc&MtRlaqspaoi ziymSyOrC>MHa6!+!Z=hLP<%lHX!QNA&d$zvW(j^~%&}eT?!M0tLr#=wK z#1x+2Aj}baXtuGkaqkm6Ey&{3Krl%KtEnYh=j{z}v~+iCyuH%aR}08O_YQvI(lJZ+ z_O#5;MVg-~n8}^a#RAjT6vR+AHQd$NrgA(q^t7hNzY`Q}mnBu;yD zYI<;TWMazH9KTi0;da!1EQZ7kK<2+@@c+ zB1^L8O*J+(6=^;-;|^_ao|%~$85yyBX1SsKEg74a`XpwFEk(InpS!fvV02{E-p;Nl zjjXmy*O1o5)#>=mEQZILD!xU2@PzH}@fpB#O-f5mfs)u#b3u;3ztTi1f`I-9lpOyi z^8}*SD5lr3qj--cm?TM&Go3Ir*(9m=X+Cpoeq|+4MywNPX_l0flmG;+%t#XYA(O&T z#rix=3aPX9_jB7tX=aGZjb;ce#{H}=L2%OU zP08$)4-W%G(A?ZyK|ulVq{!vYchbFQ3QD3Eql-FALK17Ls_1PwbU!PAyH`969?VWo z;yky~c3qv8J}=c&Gx{#Z03GrNS&xRwzezWG1ODn4vZL(xlaAWk=L3Fx)9p7)3AskkvsDu78&5rMI;{m5E9s8%nunJFA*VL3ZG6MZson04{-!-ep z;_MRfsgTJxtPFhulP-#4a>UV7r{4~nysM953b(QH@$rE=N~U_&O$EQJ!q0c~b{aNo z;0&v68(qH2DGd$|4NZ-X(v}pFhTH%ErJtiupdD*!)G~3*Oh%-kYoqb(!2FQ`+e1ZQ zkOY+D^7;xH;peFGnk{#f(+AJ%uHDN=JBDC}tqsTREtg1Iw`OwdENkdZQj*MmgR0scL-!nq(x;n_ADT#FCA6j1Z&nnG+>P`s$XeoWSIPczE z7C~6ScfcMHvaD{fxXZ*TSJl?GTwR_SmYH)rip$Qfhsx5e;S zk$gM#NN|X}Y$ZxiT3T9MyrsfQW@UMwKu=Gh?*=h#lFbtl+j zYC6Cs2DShs*Q%+vx3zWlTTsoz(4XhSqe_z#CSa4va>Q~`TYPq{l$63|T@$PD%A7!A$_mN~O2!GzKK0#7d%`aIcmbvkMgWr;%)$3js^;ck zz0}rq=Gy#sfdl;UJ->0C8RPo`{aY}O0b01;4iI)y6zCo?-S3{3$|tuMyLl5^D3K=j0M6B9i4 z`hM2dj*jT5Y`!};f`LMovBg|3zlB1X_OH0*KOyX)(EunYIr3{1c~r2{lofcQ^nax^ z_mglF{w;9}jP0x14bAnHm6dodtsM@4(CSCZ1??|s!0pd}+Xy{vNU8f`ZRg_O7p!d^84D8&C?(S0^j3t@``g8j z*c@bp9b}zRh}3WC{!?-kz0~4Rf^yZ|-tf8x=6X{6*G-K2N1l8(io${-R}YDEP(D|D z3@Q0@MrLL+ExeOsD##*W-&84fb|FWKpeP+`%Zmh?8)u}Xf@4Y~O#9t(YRa2yo|x!X z+rnQ@ua@2)4sFc*(q3KNNY6k=|1Bfq`0Um5=_bQ6)9nps@tU{gYWgvzvj2cLL0LpE zeG3PGMH=cSoE+stKCl@+ep#;F=i-8oHuN{??kowP4DYZoHBq(ulkC6Xy$*@pW%I6` zoEq@x!}4oj)ghAinUVg&50Mt8d=fR^Ouk8DJ#XJ2%kLd)=ckDl*wNMn{WHXYO-$yG zD9DJwliwIpSw*XZwYdi-Cg6B9VjTdDDCqFi*rr=UV7%_W@bz*EJ3FSt9?DArx8nR9r20IGgk+HF{ zKNBWqW}6=9-iK;GT}(g=ZVjUbqcpU%qa!0Sps@4HZ`tL(_ME~&zs%bbvy-t53^09t z-FRJMVq&A7`tK|ZCM+e#K24ycI7Ob{tXz~m1j23g%{_EQ*TTI!AFAV&xvASqBd8$@ z77-D>y}dapsYtK|_}H(}%xoEC(Y$zA-*Mi#5T24^{4C?cLEX*Z7iMOFy4t_IZ(G@6 zI`TG9>&H8IM>nchu6t4UQ9UO@6&+vL`r>;&+N2LA?%dCD ziTn1&Sd#9_&u&p*z_ru55M*MC|K4M5ZOyp41_;zMGZ4A2Dp=|1CCJW*0l+{eTUgpW z>UzfneFQECiSLipmHy*y5THFwNci|ZV`B~agC#3XEX&`qAVHW^Dk{Dng?bd(v7`<~ znF9J6zzv4o>puZ_<$K@P0F#SE|4EAjRncAGNoPYH!6}*s!QgRcKaQHlia2FE;u!fK zoI@kSLvL6LcTr~gw#N{shBDv=NZ0J4;CAl5Z*4s#b=|1bGdkbpl;rm4L7A4pu`!?0{vExNcBrH}Wjn=Tz59$OjH z%UmhEr&Ma*Wa!i7#qI85l?kp*H@Y zfx2emq~`z3)Jew9VTx?;yritGzmJcEBuY=R7BJQbrSuI9Xv-#XzzJ`(Bo^HbBuB)= zUMq!hadv*~(TME|*_e|2Q=pfreK*Vro4)RQHSH}uDGfR}^r)#ly_nJd6*;!2ll$qV z5YqmA+U*!23W7Xmim7QD@>|B|Tp>kdb~&mkx1?xdTXaUy#I3Hz@Nhi(Fc*c2iwi$_ zU7uH8aQ0_-Dh-7t-fR|M|t@!I@0C@JR7ZM3VcwtS*#p$}Sf z;C`PTg0MnVABN@!fk1qGj9mx!s6)1Tz=hh1_ZNStoZyZ+!8cdk`sv4$!c!ZNo&CTcf znZU-amxAo1q@*^-geYWUYOkIW)H?f%3hW~b1A|f_Mj;Dm`fVYRdUN}dDz6JVd!;Z| zfFp#D$G~v(MzE!p);7S%ehhvbeqV8)KZPou&uc$KW`u@Ba&m$-?(=RT);X~)-##yn zMXgD{yR4Y*!(2vrAvZm_yp#b(6a-<|yr1IQe#@rD@Xw-)lT-U?<*g?TlM4_Bh@fo8 z*6#dfxrA5-kD0l-1vpsbq#ux4)T% z>{nc$4fM77!mE97x1E{xKV_V$&a~7|jlc_47N4qX0)j-K9% z=|gtMX5Ft%4INd>>5jEX4k}*pL~}mZohoB1CYW^iM%}^55s*;wDNm>BzjIG^S6H-h zldK&t_E3pXgv51c2ipe|y>uXFk!7T#d+gj8dG*jwAoGom<<$JHgzW0d3OB^zF~!Q( zmTb_ajJb^jV<-Oj>|*epUIy;>oSB(fhZt0eGh6+-NaC5O?t8FU4$fg~2uhXoUOzsI z8%^f5^)!P5jFJa2fnza!ST~jqJ;e{6P3ZxLhor;9%(cC6-aJKjry|Sk#8x|afECpF zo%7?A>4~wZvyZ_sF=W=fx;69|rasmnP1vhSv`#lf>gnJ@U+%naJWNG4G7^X6VIhuv+{qoElQcX=) zxYudDVB%Y6^9=8DJ+^!dPuY`bNYlmDxfdd@QOB9&?X2$$zx*~*vvqpvN9oKdwzOmq z9shu(?Ov>?B!<;OrcXNr0(SwDmf?!>Ay}hRoR;=!U=Pa=h%-d!*sCP?mkrJg1|!b2 z!~kB0Dai4B8v*=^7O8q9^hwA_0PPRk z5BmFpUON3B#;by1>deh@XR+mMs{t@SVX#=(g2cofignU)OwNmQUe$(xvm?MR#2Tla zuRQ*cI#hprp)0oK^3*AYuRVtFf?^!N07iQOdp%qLA6tA}OdRp%gUnZ&*e+nyeWv@1 z@;Oap&v16|_^pFSxUl2kQx^!t>fs>^Gy!+bQ_&xwCF-2A?SQBPI<2#-D|v|gNtTiP z&e;W^!*>!fOibK`Ices;f#!3*ZvtFKA#bU+CepInO*u&QDZbPv=zd_f^Zc!IgEH|w zBFl>6$?Z7ZpCH~cmJzava9uv?I&p>;L^4u~Bc3^yi!xk9Ow7QL0l9U3u$~ERKUNG_ zZ(pXRrt?!X#VMPCor(x6R;V8pl6JDf%1=5lF}K2YxDTb$X2{SVd`-2$4}u3P+wcgnhN>8m(NYB|TxUQEvd61E(mlauk9 zuo_wm@_9a{D=sJ~KVqyUCBoR>Hl-E4R2s$fV>or6ZrvYzH?%24^V}wZNZk}Bax_+%q6e_;lNgH4cb_5jNYo@YwhAuAyw}8|BUZqZCYUYs~l>x4RLG1cn44&49cE0Q0E*4|?B9ie}IAdQ}Y^<=*v=jU+jw5L@iVGmj zL&G`^4aMcJD@iZ{2DdfI(Q5slHu%pvzn_^TmN@ph$lR+_eDYE2bW}_%Ah1lBVsA*n z%?2JAJE}+-R>`xuPhFb^ju(!PWqF>S3cgFU?rIuz+~hsGy|z|5AKt<9ys2!j+J)nV=Pc>0H}?q@1dL@jZo&_hzs!%sl{Ytv z9oYO#<`)sI(LZZncM1n6^#B4s32>kR0@FkmR9iYim)tl5|4Nox1u{ z@_(#24f|~TrKLef^a9`cmWaa@DvbDshKAh2J`WA$is>@+rbgm=<6fBA!|bc&l-t{@ z<6=r-{4SkN;>IsOmB04{&>&G~QmW#R7LXMXB1-KGyH(ZIb5+@kK1(ZNs@)NWfvPGuhMy~n zJYkNl6uQE=2I6LCw~x=t1A*pA$w=x^2H=OLCMI40=o1b{Ewaa8OI=3A)M&M~W_;6) z0h+CRK#T;2rZFsm;9Et8XfFrguF^e~UuMbvg5gc(3n{6*${_2jruc96_tMMQ@DpU3E- z<3#t--hIKUq{7RG5BfN%7DX?o8@_Y}xB9nsTM;>A7NO^kZ*vC(25!wvH{ASq6O_Lm zEe;7hnG2hqejfayTAz_l0A2;>%I^&^D=mtwpv!#`HW{ay$HfJpb*}p%R)BL{7Z#sm zv-==?$X%U-`0mg0^0%^Y9N-w4IQ717EK>R9o0~3xC|ubGs(UXUn5rzV(a4akRJth@T(l_*F6c@K$#>hw9eGhRrUfK&Db9 zx+AOHS6S{4)&71bCNwki&(e~e??lIntRIZGrKVoYI`Gf*rk5g$cz9G--dS|XOLEnB zKLtJCw{k*j0hg7K`1Kv|3rKG_A2X|!T7It|8fPCxqtSqQ#CvNBMSQ$P`L{;F#$fhg zE-?2ck7<&*SuBVF!li|}rY3Z|lW9J~KTRp##sMIs0PbLNVxo=~_R;@jXAYo#&V6u3 zlF4-9FFg~@4UHafP~MwzFkK-cXa4DB)#QD7?wk0g2Fgr2lq&D%=V!!OPGM`zJ9)tO z$tKQ0KokhtKC)iV^MuycR=_8yrKJUM)atXjYDsHrYsCsmH-@u*0E)G0`)T=kv*$X^ z9Wmxu1_&aHkwK4k0aa5IDi@iL-9*rB)igCVO*P=-)W41&8{7bTD8*gNhk)NS$`0z? zNe8$!vd_7f+uCmA88O>gn3>_Jn{2&)bn--0$aR?%AwG;-NlH9>_E^ro&NH%Q8xY8K zd#Xm)-5bot)gA4iRyH;({ptSR+cJ!gGJcu7ovuJ_0M2Aw{>Aau>;k$8FtEw_p4J6D zGkn$}q|5j7K4A0kJ@8pX`W321|8;aXIkGG=D_IYPLkcF&;t{TI%tc7Q_+^<3Wkwi^e`PkaY36R5Y9#cRZT<+Z|ntr_tFdTWP z>Zbf{d21N4H|+xK#rirbZC54BLy94?_}KED$|Ch1r3Ye z{(aKd-;WkM?d-hsdb=%z_=L6#&YEl`m^1h;VfYfT@C>vAu@A0=XsD}Oo*H4}f2kI7 zE48^D^eGALf(GCA6`WV4v7IhKQ9q%19~jOKlgHBtBK1wF;kmiD;BPBpV`CS}#Y9El za+O)M#1L|Cr71nmox6#GCZQq?tJ( zj4eCojSM7qJ`0amq^$;Q#qT@l0;TWm{Nc#m zqyKQHVgqX&PwMi8kG@4`lNj=^B4KU=(D2lZW?9lP7_Jbv+p z2R--;n$<-24-y!9%1E}bIBzz8B#EhQ_>N_c4QqIorxnQl5)=bfy+LBx_eohDP6 z@BR#EBt5od4;3ZpN?R`1TWUUZ**QCtAZ=`IIsUOo z&@UB~Gyz1O+U91ui_0b{X*7TcioWaJ}+inbZ_Mnti|%#?PWrauGk zFRx){VM!MX%t=?f^{O>5uah?kBrY!Md!bo2dZOq5#KO&4pJP7Nyi5S0UQ$wOTZv*2 zhj^q*(~{kq>MZLm8nG%Tbli?QB4UC6V=xjE9oB(@keT7*@W8-8!6fU}rVK3p!h9Cd zo8;$@9e(YI(gt$XP+MZJA&NK`~pzQ3yH=YfeAot*5( zV|>ofJ0e?-{acRv8@3OQ4nx|X)Z$u^yV>y7Mt-7WPz%gRYh7nAmxC^sGcz**#3XltlWX$bw_~R)4>ALR zHI?2+`4BS(6w5T4pJ$;^lD-#-1Tusl>t(%=b{eULSBv?ApUlYG`Q8PZrHLWjx#JvDy1)dGqqH3=mG&cYOli zK>-+}KI=ZD<)w00`?lr9T>*an%r!tcq-%O75^*QOMFhqzK=z1U+7f9%eS06#eCwy# zHqumRT3`1$!;Eww$2RH^Dzb>62j77$C?hUvY57BhaKk#rEE6uQ27S$s=U z@y_X&?5MN;OBqrxe!}D7CmGYo?r^+3t(Q zZV9>oHUhedlR&^GrfD-gx2&udg}N8g@%mfui~XyRsfj;(K=JSSDw5ID1jrb2mmlSF zXS6lwoD93^k;GaY3nG_oA^Sk#9O;NOe2Q5YkIIg!(@C@i^G#`z-$Z{2m49+0_{BA+ z$?>d`9=uU z%mx*IUCrt>1XWkp_~Av<^~n~vrp8UHX90;=ZbfP|WEt;aZ1IF|k$9=;x4t2u%X&B0 zmep2f6N?v183E7&nr#n8}-SF zNhilP5Bfv;AcN6IN4JaG()p<4xe^!2-+G$?4&G*SUk_cg=T9OPX!T5+YxCe)T= zK9K-o1Q>X_*VLYNcEH}(ARd~TTQ}Ko*-nZ0N-G)sGuha`=*J&w1|8dV4X?Kwpu&Ih z;*l}CLVx;&Fa3te-pfL^XvvM+{~uXz8C8WB^b1Q#h%^X-bc3`?mo!Mn0i;{$l131u z;}8B#{ zR$#6W$al)ORdB)9hu3RH=~3Jfbl}cBqDd${lFeOBd5lR{z>lobR#?o|E&IJ2-`YFH zZN@gj{knv1)qUgvSI?X3tbE7CFB!e)rC1I^I44xI3f-mbg#?PxM0>LND&C`wt9*Nn zR63T2>LknL!_$mAE-%0T=@TK2*?4>n zVnTXE`g3)2{QwbNnxZa%J$1FF{R|K2EFbjuj?kAO%&e?rsXJcyiXx{BEW<~~QY+g8 zs8`Bd8~N6>?H%s7QaCv{_2d&@Dftj>N0B;s(zrETEIN4hpTKeE*5$lSG&IGemy%d4uW zGy_H)xO*pX7hEU=YSYFfZ@6@0Z+<1xwJwvbkGZ^VZqM4TeE&8ET%4L*E}LBb>ydD@ zw-?w#aG%ZXC*Q&De;HgY=m$#u)1Ce7D=MawMTtv`(QWX~)TA)lF(V1>{X+azIC(zY@KqqKe?$KG?>%bj%dMvE?w+1qs%y=D zv?TyFI*DDY58P62x5yGHZ* zcP5aNZjxY{EODJMsLJlejutTpyHpQm{iD35*%-^v;hdC3!fQ!QS03$a7nq=eiaB3( z;dXs>1{mJS+RG$geyq?TaxW8$AkY!k7pyMgV@vAhM-k-915#!C7z0&GIF!vyHTqfQ z-1@Hf40Xy6hjS#x<5nF`@}_o>H-BYXlu{^)lCa3X*KE94WVZAu!P!}ug8pOo){3_9OQq$dz=2YQFzL5+VJ_aXa1jWSW(JK zjva{?nqsQB87;g2L;E#VwVPq5rziiem2530Q@2b#+5VSKgJ`TeD?#~lTStej4f|U^ zZ(fWU6FPqN!cRCjeDm`UxM7|x_sUmX^v{Ew#91a*Ht3OtyX2aAgQ;bG9Ty7$afrIk?+A88?!p(@ui z52(|#GuVs9>X-gqNzCo0c|e~2d+)@5CgX43aQa=`It?u$5sPbH?OS|LH;Y z^$Wq`^PB%_;ZB?BvfKX3x4El}Ir)nr%+VXmjGqa0_eqQkWnCjrb3-_-EUG8k z+i#@Dokr|)V{0E9W7;1=5(*U!%@ruoTY-;B+61TIS)W?K$n^BrXC@nCyJCv$c~scs z;z@(@o2jipCq0eUC0>9Bf+t$qu3fyGbw{j0kBH}D*eI(Xz2l3Xo?e)AF3;uBkvOwK z50e?q<^qu^d7lFIDE-;lz^z_OKF~YDiy`=du_`K|gNdQ2x9@=Nbtzkrf+BNIKT5UT zy%Bo4#P3^!O7U8jIru_bW7$RD%$`e0FTGRl>pi_qfFS8q+U1KkOU@!uw4~t3pWKE!g>#8GYQ)&K3B7JqRo=RWNwD$l!H*R$>JeNU%?z38n3zeuxIT~R+Is%*qC2ONQXIed`|I)p(kuSr$p`V2y8caGQh~Pn*2Wz=?|2^y z+{25TTR!&H$TI)@FA<$~%`W$+#Zl9b38C`5hVzIK{)>T@Hg*`9#uuL*PH8J8I@P0?B#*K6EO#w$oEV_Vf0U6G%LNyDIZ7Qhz|xd}H2~ zzN>4C>59*U^8CTmeCR3Hp^KihKgz=9BjEPf)C%9jY5$k!A$j9A6&2O_^{4e|=aGUhEZ-LMoI!7XrF)_DC2bGGN|AsTS zgZw!181QaxPEIgfnIeZG!DOmYn%O8Xn!_GUf=cAXG^Khoq~w;9G^)CxVN{I&*=C)~ z(2$?X+|EvwsG5znPEu#k8ytvk7-N!T6lT*qmUnfjOvxOQqVD%o zb|8FP!O0Qff2ID`_LF}pYybMdl>Kp$a#1&o|gs}H0jWKoB=56PwKe#{Fz zq^zrc9BT73-HJnPV<+g*tf$*2yqJd1-ZL7`jnT7@$A8QHBQO_oJDGtV9qfS}9imai zZbpAVK*!Df$z4ZiB+c*P+eZQIjsYyQel(J#?~70aiW|%fYULZ$zOjtxU4JiWH`OSw zykTf*dPx1iSjn^^g(5P(oYZ!`o0sAKC;%-~p9`&9}g_qfJ0< zp0YsxR_x1?7u~9PlBvCDTNXG8vf6iFMKb{s)d)nUu8X~Wvzq&Sa&&HX=py z(nO+-mwSzL{aS_CrTCTnr2I#=g%@?VQhQxFA|5R|3)N8>R?8Fyy7Sc{!yncf+bB+r z{N1OFGuaxct?^e0s;{vvWhb>zD~XL#>AnA0H~8%Fg3)D2d0zE2;;tJ{Dc{;m$HKzD zFJF{6sj=i5MVh7z3`Du|*Z=K%&FXNah@wAVbk%KW$Rww!`e2TS9-Mr^2H#R8u^{8j zK+PnlaX-+K&PE_--dUOpINOBTY!bJ_2>C^XPUJG7UIknmj=|XsddIc{4&S=ABa5=S ztT9aUp*jcvQ(zoIh;Dilf5ftRo9Zt7gs-_|& zHZV{ky`k3HZw~dA_tmBU)^Hq+ME5V(e0ja{^M1B=!N`XAmmy5fQwSm|@&nAtGUCJ< zt!0lzTtgjeiYql{qQ|cuBgD_sOj$uj*gw4d(D>%bzy$@rp|#yttqn<{jqDXHQNu#B zhpOg8{f^A)6Q92(7V+s*R+B?Z`j_%<>3cMCGQuIl<(>L6dAS&x?=%a#*Mth|AZ^91 zR-DTS(S*s75!dB@(vxRG9NJY0;&GswDPl%v{Si5Pd#kGEsWp-SCzQ*$`Cm7s6FO>; z(EjIv>S|tpo_&vY-SUEh0-#9d8r(3Co0^&^4sk9@uHEHpfcW%AFTKN`*RL_=aHo{`C;D)ymSw|!fFWo2bS!~_rw z8YC+~aN)F!Ve*^s;6{pZMGGd~R3$C33S>;fVpA}dGG{EH#V$*Ua7$clM8(J~Y}Wm{+>HyEaI5&Sy-!}*B#<>FK;%;Ni=Cu2+TW1>{h`VJ zV0SFCH$h-LZ?yGv!$gOaivUG>Wq$HKUeQUv;>lvttF)1Geu-9#(7LOmJkH73Hun9; zl5#KlN=ElLXh>pcs&WduMHJn<7ZY`kw`NYj9G>~sSbWZRH;l&L{lqVudzE{sj+FBS0(xud&qxvs=Mh?_4a?`}$fBXjwIOa|H3ZFFbZguW#xft7(5ew zf5C;yK3`%11xk`$7gE!Y^7D6Pxfu^t7dw!+_CU+;+zOKqi1=2({|t5D7SEQX2yMz2 zUs38w`^cWfL}p0W5f}A4YszbSlhcfvbCdPP^DN-%&Qq@TOsFLSW`>4#QUXs>uG3ne z0@#;-AbojI^OE^xM~)q+<*(WJv@|J)8&AL<^%Q|f&Cc#2jg!j{Xv=opAoBc6#2YGY z23nNad`TR-J2+H$VdW(K9O&tt!@92^Z>By!AUoi6ZE9*N^wu5m)75+qy^5Rwb3fCf z@9J1`PC%79I}7BfthMkHDpvN&MyQ?Ef z{Uy?R{AAIllNvC9(6W{E;w004+Q9EqzfWswFOdFvc;Lc6jqyWgUJUJ|^5laNIoI5e z|D_U|XYjrTaLwP}A6P^1$t7{B-DY^qNqf7LQl7Uf=hDxFSJDn1AM!9P}ch14h&zV+MR?G99VJ3dN7(F1~4g@m&n(e@g z7Tm9)dbCe5VMg^28vFAjY~rnd*QWqxg!ia>FbVam+S>inTuP9XE&I9q2ih{{HNc-@ ziAiy9HaxRZH@>faBQ|<_W@e^`Ch$DU$*z0rRD1@lJ$^-iKMc++6B~EmXQ1*u-wE-zCR_iPosUWVQ~S9M!a* zmy;-dam{67ui7=0`3rG1wgThvlO|S+4?jXuTl(iXT1kB4@R>G%`4^Vd(@QzuXZ$Wc zPDBBiEZ88_P1g+jI8X@ywoOtVc^~|ADtqLhDCmItBBEG+pNb4iLvRYN093rj!!ch}>h z$%{C0SN4m(@cencU{e+m+IJEpy0vJJ$tY|)_`sk4j)!A1G&AK$k^7ah8a-Vmc8J6G z0bS;PrK^$GhcSmGeI?uySAMq6tDig1t~H2TP18|Px42T8TDcmRS-*c*!N)&{!^Mk@ ziFNE=6mR1u$4zy5@s428=|xt|BbB_1x;r}K4Dr^##QJABeAI+gakOe}-WREkn)&&i z|C;rUqZao8j{r=kPIHYS4}{j;eBQ&5-uA-d!I5Q7pu4-Jk1NDM&93i{kIOzCX9;bU z|E@x5ZX|d?cHJ`Bkdo1JuDX1cxH{j5m((rrbK(_Dp{^IF9vRMC+wYK}0Tg8x&Ta7M zg}r|5JiEKO+qv8A;=0qxZQKl|PckfYg@*lyj9uw3*cXY`8L1fQ`@WVFK(uElqza1P zjY$_u(j(Bu!U*qKgt*7Rv%0`Dkh0QWy}ep#yGD=}LCT;Kwn{*nmFtDH~{zvp#rPXqaN6ua6we{oQhWcxcq* zdEG^{wo*TNY@-mzEAw8FCPJoTo`lQLvG$mVv}7Web! zW;41clNd5m^13Ezh?Bgw2_!G>?f2&4OT&Hfj4yKMi+Zra*C$U^zlE^ekkw$e zCb*p>&wUXf!7qL3kRXd{5SfraK^y5{!@2pG=vDQ!nWHtK1&xU?mST#l%x&AK`*Z}} zlD36ada3MX>Wa-sH_8$h8demniCI=FC1XQtktzwyl3grXa(TOZ3B3dK&PrqVs0g7D z-=U-XsvO%rFh@o-uoU*2R^Rhpk`cp;t_@WS>WE?@w!^7Vf+}PxnycoT5!apkIIoGY z4~@y#Iyzh%?K9^a`8{y~hx;a~Dz4nr+3D#@j9PSW!g6_z7h}HtTN+&&IKX{zyc@CW zM>hxcXJp2e{dm;|tWCi!c_u@7xy^6Z($b35vaa(z@u|hZE^KO-JTX&NQjV>QiCFle zr8qv$Kuny^kbmoz8^_nLoT>Gn(4%e0%7|Vzy*+gEyv|;jq@==)o!LW>Exr*AI8UV< z0@YW**K0EUjd+4$y3uxYnE}EYw5UYC14hTkUt_jbRe{<_iVlXZDH{%Af_5%_Pn`P6 z`O@;TGEgnT@fjZ*^Kf?ur&|qdZ2K*po3`F@|DXrf7%%^fUWEa@=ha{D@wzpYOWDk#y7DA%Z6|sp} zL-#a{#)}6~RoR|o-6A{GGFDV?2ns?ORip0nySg}@10BTHmSYDi_V0HQn)(zfKlSWa z{$vNnmYT9aNLcpu3B_Zs$h4O-?+1jmskJqhzR&HmNF85LLLWV8-*aJ*VvazWP5u&E*^Kw4wlWrSMocY~PraUe| zp~Kj%AQ8n|`7j1w!4X$%Yfi=9+)Bf^?(1{gh_5lVz0}i3Q4IGU%-?;QRJ{AnXN+rk z^V^#f>aRZ*vGRRcvna;Px7R!Vau0Av$Ha9ly0pzH*E|&tGpLX3mbk0e*CcyEo$~dY z50;io#o1FvO|XjAlC3a@-VfUi^}0T8LgZ!AFezNUSENU-Zf;&=Ulv`h<;RIf%H`Ko zB^K;g{H|CWansxLiJ2a6Z*RZ4Y`fZqS_THn|M2wBm^7nfW7YMO-WE)}NpA+&qG}<8 z7$Oi-u9n-*Q$UwZ*B5v^ck$wCW$yg#_wTAA-|Xh{lkeP|>awj_Y4AM;8@Jlk+SNaa zOZ=w`S35M9omV>&=lG8tXMPiPKH)P|#Es##sQ(DE!`T;eK=)pNKxgE7pu9%*xBmX6 zXL2T0fTrOn?&@D$;QpgPx3!UiKwwZV!e{~%n*X6C;UKTI)>@~{ zdf)n)hsWW1Wt*H%@Mqe!!0qFJN-&HQy|fP#gES3UF!Q~+!)*Em{O{!?%p_k43y5q; zrvVh2L67!!k%$&igc1yYf7X5!xsQH&{!`tF zlkwIKOIo?4wC^OJ>(5@z52U}=D`hQTWeSepbEjVDL$msL+`!O^UpuwYk5raZ4SDw? zUdt!Jd9D7|8Ghq8DdDS^vdN!UQzF^fBQvPtbbs?A-)M@U5WE^Ji54lcEo_#jq*HW8 z&?$>t2}rW9sddzJzfmyMUPBn#i#SWN_TY$pHQx129!z4nwNM?9FHw$2{O~E+P9QE# zhm~wbhb!#Ic~1;g%eFwr_tFs?g1X39X`~{;xoJjD+Q2*Yb!&oQ+R>S`n~+7*?D~TM zo0KPiQr!*`9v{{B*)SI!SDS|SWsAjxCk1Dc`1C)ycAOZaECrB6buFa{K3xUJ5Y6RI z*wrGOCF5o=tquRPfJ&ufoF42F{S9@*e+_teBK!?^QOx*22lAc3M;9$Vf2hc|SEX%rDQoCI0k+|edmuvpV zp71D5CE$Fw=W18tvPXq^fBSN_?F=T!x&D05rx+r%@zitHc6Mub+WvLToqwCd#lx$u zt4myl8x;6sb~fg8UOSwKz48X`BQzXHDgD>fb|#`ScYZE9zQTRA#~rxyiUz!+h&9g% zD))#~z|I2Cdjgw1)`9=~377I}58BwzAirtaj+g z^X|g;Mb@s)2d@U+%pNKCcyqdfO_`TZ-;C%WDkr7mK?;Cz_A@d-rgOp2#0rwHMz+V zdX?ygMEzmIG%94g2r^o5KL7BS8|A0K>N80R3#wH$WMaODS<}GCqMMMy9~vp`NLkhR zTzOfktuF4beu~P-pc}NwxV-HBQe^srY_ZLk^57D&?>*D470l%DNCI!E zU7d$Q(RPKOyE%(|`(TFsWQ#D;T!r5q6%j}2!=SfTuo+9=vB_BA5z57B@A86IO{MpF zD4a|ZD(J0i{;sA6m_b5Za<(#;X1YhVrNs!LKZm zJw%J|pkUFWSw+5?5_K~vvyY~pM{usx)#7v?StkGR`(D+oxF^aSSd&|lL^r$OY7kf7 z)w_Fk zu{v;meh#zE-iaS8iSUapu+|crQ8>P>ago;}j`-5wuf(1#ZHE^0lC#k20k>VRG-C?9 zNo)`XqV7u6t23dK(yIx?*eM<5CrC=XjgJrFe?qS5NX_p8ph1GM$P4qL7ppOd56ngV zXIvVL9K#O_N`r%Qaf{KFLW%MM=AG*Y`uo`Al^!D(cK=WHt5Rya@sOra`h51#x!fx+?Y?e^A<$wsOt+Wj=QjziMkAf`$w4{;%B--H z#P7mloPDad-=`Cm-FoVJckq3G<^KIu8r73TaRG*nfCs8|W$WjJE0tycZ7`X<<+lgL zwMZ?Si<|rB*ci0Ka`c4f#9a60bv55~HQ(FR>=oao>YU$!7%xce3btsgsR_0h+~2c& zI*FI!I-ZuE4ioYAn>TNq*&zq}7&CAO6QoOroob7v<&0KS!FL&nuqTZf3q+}wypMj) zX-dbO2(8&aRQA#K&$eoO0;~k!G5-Ey^yL*5;$AzZE-pxR#DW1^(g%FB$URO@&YYlV zzSGVeAVuW!HHI@pzEv zUM7I@9yG5}ZAK3TdTp+ZH2+#vk%rI^VV}KK(>vUM z#A%HrO9;^0NGfR9csW)@#7NDzBLA_vG4;7Un^_sh^PApZRD_9#ynTqu_Ghho-iLe~ z7UunATX|$~;ib(Qlu*E>T8)p3g3=v+gCKsFbU>xa)aW7MJO)RT+g*8T;j;GMO;XTO(NhAkukCB)<9myH5U64TZtSCoqP*a8rdhR#hJfo zg)I`lS??D7{Q1*GRK3dR$5&6$E=}6Lop#8{E7qvM5h0Br_ZIwESqWgr!bcdfqGKrL zEM@}3l!e@Q3RA_~YfVqNp4JZmp6U>g)}xDy>WPSd(g%RNQbB#lR^KbT@nwf0O|D#i zbRKg?xnrze9X=xNc}-RvMFT@aurDjVd~K1Y(V@a@QJ}6$l71vzq@mn@Oq671_3JiAXaxL? z+TwyF%0hm$CtPP*v7JG_nvVn^jWFq=T8|PP9w->NL5%>@FkT5-usbmv(!ppI zrbqKwOthp81_lN`K0b|2O+rwCiHV8fU4&e7Wf48)etkxZ;01_={o~pIgCs~yU%5#1 z?#Hd((6{W7nkXUt^0Mr=qKXkn)|^_uexK5GO+DcBu5QEr{92odVDdELq1!_}^6v#4|kc;z|hM#`|UY;W>Vg z&IMlC*J(sG9Q;;<;*&B~VnWRY?C|6di&Cn}becHGrw@sotbK-ios;mCN<`&;6g5mNU8tlNpK;g_cVT-+=^yHRR+IMnq^7HdYmu-x~ zusGZ7{lli!@0-%cN0#t9>i-~a*WlK|-fh8F5H)j|`WDrso}SqNs00gl41AH`$p?tl zYkS;kIl2>33E3IYV#4Z#i)Q1>XfGaenfx-D!P1bJRmv2Zl%Eps9?eOR>6`&Hw#{^ z7FIj~WYSLpfzC@UC{am&%(=}idB4~(C+h3#e^xX`Ali4^(;(?5@86(j4MRKX*Qb0G zzB*Lp6ApL?&c2S7qJiC%W@GNwA4^N`E-Yt^p9{ige>Zd2iWLI-;HB7&XFy`1&Cbpm znV5iZ;SSXr*oaZ{O$A5tNWBtVKasE#4ei)JY)V;CxT~Ret)f+gL~Niz7=DTHV+b)- zyN)Xs{n+2%hszZe6=h4*2j-v@9cFH zk6@$VeTPH&!Sz+9v@S_Oi&Xn+>ukKT-0MZUf_qdJA3Zx8p)@@=m)(p?9{m!@M@UzG ziz$VXnnxEN8oC+*DJ%27#+Z8ydKeLtw>XB8@ocmR4PT1_ewHNCPcZ|W))~nQJP6rl zR(2!0>mFZLh&=Wpg1+Sw;g*=g+0m8j9hL=HLV}_{i7h0TRV#cY#8wXd zX!wy>vEhzx-kmO&KM_!xzz9>-&uMhgYfV7S_Z_=c2{=3_CntyxYQ4=C_hEsqvcX+I z)Ej*GyYu)uoVV(s^1@p!(^2jNyFEaRpgI+062rz4C3@6UQ^Uu{S5sB>lKWna+?@BH zd$C`pPtmORW|8k-zp!F|*kIY^^f+45Pdqz?2L%)*j6oz(A$zsD8J^mBblbB{J#c&b z^)Y>GVD5VG03@S< zuy7W+V&8lFUj~@h&hO7KCWGuWlLL^iZpIq0jmQ{DPi?^UU4?5h^)h&wr)q{pFmbLq|4)hbGsshS zbX3hkleJbXq{4KYX{8Z#v!+D+v7vBRFrWNzQTw0z$_?wb>^oKH zP+nMW%wr@1@CX4laXk!T%<_?W&pww;wwHZr|M(-h>5r?VZ91tibz#Ll!ZpS9CH zm=$bqvCWU9-Wv)!GGQy&#XPxwA4rzld#93;<2@Y3r#@6w@$m9OEeQHU6(O^u*)dAoFN}v_v|WfJ+Nr=CQtQBt%~9G%=#YB)oL3m2?{t7G1i$43PV=eS9622_U>~ zVf74`e!6lF?t+GPY#tixkuh7)-zD}wybAt;M0$Xdzklb`H?KzMwlVp4!)pNXPVf{! z^i`;IE)0XYxk1%(_zjJ5^vS_^;lh)MJ1SpBA_F2bWsF*wR}L?pArC-v5^2c z#~({nLLys**}+Ozm4HG#mSwDt^(8cql+i2Vh=`+hGM!PC4y%dXchyJ*!-v?t+h2Iy zSt)B{4L2bR4y1D+cLrbmC8Q^S-RbkRQ`?gppDf2=6(QR^>8m4y+)lS0JYKnl;#7vC z0mn}FD<81v4t#FY@r}SrKOI zzaaRt2kYW=Z;^zK_vA8Pih2#y<`g<&r~4_F;V$9qe(bDRluz z%dUO+$7$u47&jj2NAtLQdiG1uKwu}a_vQHs_{y5W<#Wy12LW}%d@KE_0hSrtUysha z8!#SM;&|pW-XLnblZRb6a`T_A%MKHOD@9gx{dE>|0!QP%LG?Ajz zJ=1kR-TEm|yE^X?9bRiqjs=1Ha&&T%NZRri%>q}rB<*k423Cik+<63#n(7OPX% zCdA`2y@S?c?H%jh8o)OpCoiv6Lc+IQrds01b2l{>ehj!Qiu)ga@5M2JEg!ywnmGri zSxR^OCPuXa4cc^5!IZ&thKM`gAX!w}Twpwa-Hk>eu66RNnIuMz?e>qbfz;=tm1SJZ zmO}Lrf)2RlK4cN{(n&HMXRnBYLjY5BV-!)a%OEl(GYx@R<;CXIrxz-}0^Y#K`n1`r z8jQn45#(`XzAZ}$32aKS9Vi%D#%8!r9(+>PmZnc$q&31B_hw2fxzKRS`R_!!5Mp)< z^P1da&F1KO-$RF_;dlMAT&t;lGaRxrKtQvT3E69WX`rgE3$*gqC3B06!KLU7I|bVq zutFVs`@bHGJ%6nm(m4-F5wG7pVJ#Yn`tBsboARK|C$5Pa_omBA`BzLy(R1*xoof68 z}ImC|h7OUwCxS zUzM#uL9M^qeO}6mQqE?sZoS$4e|B^xG5^cdj!GKjqCEtlxCdR)XNSS8focq@n`A*_sDw zumK<}-@}1YAX_Iul-k6^ghd_DwZ{QF-+X?^JHuEB?HAtzzyv@v{I{;TIpgax-Y2F~ zSWK8RX%7jr9%g2ANClqoVwyz`8lL&mWOtJ)jhaMiEObH0f{=XVSxO^bbiRJ;fw={5 z6`ZO;Hhk;6k8o1aS<%y=foI!Me|y=_(QV8g(!ephLf~2Y(ee+LCz()Q!Gw396!{+RylYTihTs`IygC{n8+KXWQX-EA`Re_ zI&Tm@Kq~a=S_@}#WvchSdkW1mx*2-qd**If6>-crNdP#W1iR1cRqT+1pBjA?|;e(A%7;|4Wo1;SPBefc-0pfR&S80)O3C~!mrRQ zeBL9U7=i4)c)nE1$U1AI@m)B&5OB>LfmYlknTp_YyX0syn{Tsz*e^YD>8oXC=-!EK zOPaIe{BMJ^nsnb^@Gv#|OBY8swhaXdT#!`ocjB)P(xCtN%&Tiq@O9<_c|g=2(QyS! zc3`x@Lq~;fhB^**=(!{b^iR?|iAy!WY*JH`pjtE>3IBTl`W}lVV6)y-njRh|jF7Q( z!?F6+kfg?(IGU_fAS@!%)Yu3G&3R11SL?P-dmlBn<#5Z+8? ztI8ggd=F5t9q0z>k2H(+SknJz_p_@EqM~{n#dr2KW!%qiC0@VF9$W+j_F5JVYSE#; zlA3%@n=$wYvfl|VRVrmn43~~)p+{Jy`|5ITd=oD8o8HX9Pn&4$F-nk5AKE!zc9J*` z=e&kU0k&PifUYnE952j4Zx08%bnh$0|Jg2%x)FN;oA^Xgd|2(0mD;_Y6(B4(s;Fxy zV#ISKQpFGAQSML2&R~w@6WNk@7f7D8l;5sRYAnVG%_Y->7cKE?qaTl@XD|08oBAak zX7Wvr(7X+lT4j=7!jI-hA3C}=aks=MowmRbZZPB*bkl&2*ltg?%)Vd$_3kHWQv5tV zB$H{6!tXL?>pH%*BcrST#y{_%UZvh&-k5)0~9GR|i?UYG>oHv4KI4|57^W@uG2K(5`rrY6dhAOc*T!+!R|zI3CiGjM3TDom&@>x zMwL{)Vw%RVB|wJvZu@&YAEc^jc%=ko+x3eNr1RSf^dsNn9vCYr$W{3(pk~O?BcXqS zLYvD! zTwQuuZ)y>z);#Nie$N_phP%oqtfd2YRO4!IswWXIsTiuBQM~3Dc=b7V$}j|8z&!X zD#nAei+}sM>b;wuMVf5SBD%~;JbbcJU6pboqkn@(=~SJS1Xy=N1B1Nb;`&zIJhYc& z-lEhW7eGn{^G}AVs%rP53ywVRkiam+2xl=S_FZSnw)!>X=FY3@y!84FO@Q_n^jxw? zBLpHxWvt}`7JJxQJ3Vi3u{pp@9D;cPxT~Lgl9GxY0cZ6Ibz~%hTvrNC>fH&}1N1~e zaq-wvz#$Hfj{4sREF+ix(3_Kwq<-QNp`V^RmhW8FDHm9(dado9Ou#f5;E@gN6O#%p zHrJaoUY9-Q|WTjdKmo>G^ixW7Ebv(L>7CVk$W|F>p3HjnQo&s%ao`iN@Cz~HKVrkLY;^J1fP zE<)ubCHQ{}nyshN$9X3L2?+_n`!M&0_|`s<)J zKsz_Lwz@le(t3_p{(=t* zmBt9AZ7vY}nTp(MW4ZQlDsB1hVl6H%!dG&A|N4g0u0^KsF)*iDCSB}auswM!3y~Q@ z(kI51cV-{ES`)}~gu2QRD2EQmR};@Z4kdp&87>s=IFUoXCwlhn?IYM&5#k86b4+wJ zhz7tHW12bitU3hr;Sj_(|8c;JnqY}!w$nauLSV4R(p-S#Ff3Y|zTgUg`x*#DO ztLL;X_o2>$5f3_*-AKikakIaE#i|9wV-VYIgmG_Ra5#E05`lJB=sKD6LLNd^Dz6UP zE|0G+=dQ*~F2-oiKhrcr`U(UhY(o;dgF^vVT+d@{!9SY4#^F3v1!S%o$^v(pBvnTI zFzI{HE={#csG#8jpM$zYN3;{%$iFW_Tu5$1aP=r`dIr^Fo*h8GUwRiM#Iq|QO9-}f z^?@V^BB56w&4q>UP2e=+7uXf|F1tPu5XcG8T+4_!wT7sd-|+TqY;YEL3O=MK{!+htLnV*0MUc3WDXRMjG0 ze~uMpA-mvY1tDxtgPSW&F{B>jg5hbx;fkWP+AIojRv+}OpTh1zXgDE6>`>8DRtVMR z=Vf~Y#hq(lQN!5A`9s%_*e-Lps`V5y5NhC!|J&?>VunL8HvS}(7Io7i%l zsusrlpDF+HNg~v2=r!jPC0-D_v1orG#NPe0thdu==?a_9{QZqGPxvt{9*K#4+sPFz zrfP&6>w{iiWtL2Xdy{Pm;)rGkrBwi#6u5hsPD67C)0d`FQs4dlo#V4~<|E%>ZzV zifUtfn}&wQW<)=QRMy*JsQf?fRRiyv>XrUOcfs$2gNq}Jq#|{5XsgF(alf6W@rWjO z>!46c=c@CNFWF|}WN0G4F89Axs6eAXONJs3G(Zgn`{gW5?)E2(Q@o}tg}&a1 zyJ$ZcQ@--P03{l)|0%wZ5=L;=V@|TS85w@|^n+C&6mAgpCTSQu@c0N1^@NFk)MIF( z|M9ImvYb&H`@i->2;KL-?M~kA{PNGq5UbFTq8NUsv+2PuV;)7HX?_;k-38OHS!`WX zP1@+a3$6{ZRqX}NA!YC1FDg`@_+{z-zTW&>qAf(sgTO5iRpsyrcKe!2t$yqo%C~us zKC&j)ax~6hX~gUh9Vuggn#ACw{jn%Q)QyoX`2#9i`a`pSSQd&(b$8>iOM|WQRY>r4 z=2^R3Nq)%xt5Ra6r7NIgU(O%I!ml;w=pPnzxsYm+iiAvJfJCV8-CORBz7;CHmyV%W z0{MpjuLxru?G5aPnEg0Eue1LGglA);uph3Ulkebe!^CovQtP;M9}yW%mR5uScR-x~ zT7!a}r_cEb7$Q%SnWzbW4L3$K9+1Yi_&=7+vVqJaC>BHKNyPFu^+u>}YnyWy)Nto2 z_|mZ*%To_n7RHOe{noEnq%JYm!jFy?cFOClxbJ3$C^84HJoFfDk)#F18+^C&QN}NC(l`8Eheg(~ zLelFumKp~wb)52#Bz}~?J@L5#xOTk$ ztJM4$*!*d@jq)5zb7&;Xf*HRhx^s$Em;}g3t%8EQ8E>qzKa@yTQsi^5M`cNeh?JoJ z?E#)T^Emr=eTEl7kSA0+hB@M9^h1T1C#45;ZJyjOEO3VNgvAKilkM zgAUt!<&tQ_Vt1Jmm4(Db#$M_>S;)2+WR#sqOK4~}B88y;AJ#OdO;&;&HPGxJ<&t#& z`s?`WXym_}SY^D9XD3;%|84pkchP77GQ7WkU$Fb#_x1h4k{Jz$Q9*DV2^Km*Dgzfm z7L>T_oVnPRweRaA4X%Wn9sW-kiTW&w&TN+S!Ig`FMt?JqA)ao>M)3fE_2Cm$W$J`R(nW4`s_b?p#Aj_kAJq<$3H0ex&#LP?5~!WZgIrn zRiplI?~UEwPo!o6PbhVIe3<{vqouGnx_L?%G_duw&qla*VDQ~Y{56Qese0wzmVjiT zuoA)Megue*F@TW65Na&gm{67=bpaZ!L=2`Sl5%C{#KshNUDMPma@aW@!rLq)i-wnGf11s6Jbx09?Y&V!i+3@9yp{ z5{X1`cB@#5PnCYv!DfxryxDDHnvS3&R>fHNqvSAT%Kc$@-B~0pcCd$e;B%p8iT+k_ zj-=dvV4{mid!D07K&mDVIXfe6kv~c1F3GD;_ia$!<35}BCafr{T8XTci4x*c$6+#| z3l-e|x3rp@!A^?D(B;*)Z79^0+Pm^QZ9>r|7Hi^}svXu}`Nyg9$xe;Q#36)pwI{_#g#*Y$j!a~|h0j#=%@3~i93 zimgxg5jlmaZJEG@!avGb&c#(>RR=4hyqInLE!vj&(oFekD{V1l+-2e;+duq4;G}iQ z@D8D$M$f6znw*Pjvjrbb2O9x~I;v7&JOyS`4Q{mgg9n8iUH}xJ_iD-j5C~Rx&486yFb$wZ$V&?DS6MP$1T=M&?Mr3}%aSdg5C`ePfmYZTPm^ z^W0nrms393b?#pYN*MXk|>$q^u${z z9`=bFSz7oc{#V?2MXCODy*AghL~@KFl11Ngv#T5BX5zW0OKvetq-B#Lp3auPF#fgxUMVkK3e2Ox!=&Ix6)=@rXOJHCXMrh)dbbrrRhEx=7Ho zI20)AOU0-$)Qa?3@d#s{?5Fe4V(!;f6;@>#%PfXv2FzqpRcH~hvkk|u5-M$L5^^-Q z8c(HKu(bPbifRux&?^Y$CjL8z9t?Za69Ulxb=W}I5NSAgcy)e>h}$>3;Y={mW;sLF$}n?{1s)!V?C( zomsnhtnG7t~}>iytjN9g8~h!5HOi6%I8Vfu*I+j*h>8 z$N*E6D0SKrC6Sh|FF^{YjO~HIXvgq$div30F^s~9;c0Y{>96*2n7Cf1WH{vlh9;9X zBtFx|N_#OZ3Gt-9{G%Q2A59C)&f*W{x&GVUXGR(5Sf5$sa{K=iavm1$>wEXIGrx#i zTZf*Mi@$J^91rt$fgah*KT1WP)f`btw89>-S0*z3Wcwcciet?t82JXJt)*?uQH?!FLvK^pEv= zyAc{365b}{epWEF+Z!OW9I(6gsEiPYh&93E5i?AVDFr+UXxKgA{W23-hY7T3>n95i zslG*LzGa;bUD(U^c3T~waRA-}re)^h0bMWHd@7{G`2rMvM$4fUKF$1XvXy0I5N=0{ zV>r7g_dl!%#rXT;Grs1`c$siRvcbBqgmc?RYvW@Xp2zRGRZ)eCb&H2B)A7jQpC~Ts z&70m>M+%#=^z?bk%oB2GEJ?awivk3V@d_^jn1<(YvFyFyEVm79BHNJe_M=V0Mbx*> zq~*2XB_ufEFT{`SO263@=NSF@>8UOy;T$dvg>w}=ev&nwPkP3n_MYZ{YAt^KHp5lz_mN=Cd&(o-U0ecFJeBrEDFHju6k=0&z2#Dx` zziP*qP^iY_&XcC=$1lj^G9QnfZz153Ug)#lLNR%08jnOo3&@|yYEmkG=CMX+X%=i6 zQgEn9jItMr|Erd$I}-Pr{_(s(@)L7$VtzeIRzdp@T|VM&ldKzxsc)=*+#2fUA-X}d z;E{m4Xf5_hwLzEJo^t*rnx3iP;;#>-nPM5z99H<=Y5!m5A7jCrU>?3=rzuEj=-xAL!n9`Z+C(e4Xq<{D<@h9FMEjwI1Ly4m22Zj0 zgP8<{>?J=JUdWwjOX|qS>u{2R6G8Tf-QaJOZ|r8dUO{cLu0APbixWDUWVw4!NRPN} zW*+}|^toMkozwVxuoa6qf4Vv+>6k4=kz?U{vz(NlQuAZvsd?R+9~XbFMoH`v60j_| z2B4X?{ypS^j$`FjzWTfM5hQ~CU50?)doU&)8KFjqlaj z@3R`u%nc@ddfSR#S-UR&uSQHs3?(shN`$Cod#xhFQfLYmDPG(Bi7ZHO=3U16M zSN4=di%n;y=}+5XoaGjL6)qb&{2CMqy6?2iUy<8hB)?#06x&eMiMua*f0cuw%e00^ z;wXv&MWT(X#Ol94)JppWsh~kSH+_!0iLp)vp5d&BqG|CmyIYaA>C2>d->PeCGeR}C ze*T2bPxD>p-)ZsdJS>m8sUk{0pQmSDkW^05ysg3_0OMi0Crm z(mqx%7bYD1=*&Ya<3PUwqTxYJj!L2iLf1fSddDBt=zca7FNx|F1{6qxt&ew41~_Yg zuw}jd>8X6p3Ot_pxi+`5@Uqe(VOLw$rHp)g)J)T2+ZMgY#9Y_;kUGeZ&!>IgHmH}E zS~nEo)g2PWwHBhiENPoDV~dUOva4pRL}#z8^C+KAjrQt`dYpLYGBR-Aj@{tK*_3Uv z;jDYA65$0(Jd&nFcW2OEFxx@@JsDp4Roip~^FiQZfkTuMMc*$&UVBSIIhMki6+@Q^g=hwi z$V4ZubsF6nIk>QIZ@dWf!JTUw^Uqz!wBja?r)B7rG-8?j2@I7gvZLhOO`53At{lNb z*$iDg`2>Q5B5#xt%2aB43BG-bgN>J$-5jJ9?J;@`H8f>=T{xTScf>^dLr8;KPl zZB)fboIcu%44dvhQ$w~WR`*lW>9kD?PT}rkRwd;~*sTA?Ry{rFq>5G@DK{8Svt#?* z@DWR#Fma@_%j9jUnyepx%V#?c=4KMLPIALd8sl674QF1Q$mh7TL!OS3aqC#`M*UQ$ zf3%W*3KJ4k5ho6O#hlN*=INr~OZ1xfwleCmGo^z(TCHft?20i5qKJ^IH3?~cX9Y{m zFkw(#CJvt(-8p)_nZXh-&RV5yZW`Y#o)h!-B>oO(88$(>1KTt~u(dz8s?5##4JqWH z^0YCLD^uFLcSPHP#?>d$c0SS67&vA`&$=9=$Fo&3<^9xpGNv^}>DSkcO)WSCOIch^ zXB<5)jvidSB)yK80D3qP_gx@l@2>X9h>GT@GDGIPqvQ9Z?T^91!2@3#%lfu^XGEU} zX^?d~p*GK3+cVKVr8y4jDjy=^h(McH5-jqNi~@b`btF9~yX0Z7ElDs9;;h0)<%XOD zS^a>dJP148n4Lv#4Z!km73|qS;(2oXE*!rK&n~-HZkn!9Lyl;Sq;a5GDvFiZ(#*^j zQ!3<-2N>pr!iJ;-2jDKqOPJ=$90A@5sB3N=TSX>3O@ZmE8t?JA{Ct94EL*D4WC}X4%%>Bc8iXILDJ)@`Ev} zg^>|?MY?*N+om)jk_qF}JKb^SjZ(DM_qcAhA|=%5>gBr3%FMy9BGe7rel?PZRUY zm|Y8r_>T_-DZTGdn*EAO!c&n?AVmzkQyf(0A7$oN=xJF+CwlB_!zf^{+7vs)dmmNe zrV3U3y5>8cNy{mmzxw|%sMydR;^Ru zxSft4r!l3UEEeL$s0^uUctSu_`9Elkq-vHNgKxX-Sa{@k7k3glhThxIsS9Lu+PMmB znTcWi_(`rjuUNBkGu@K}hb23d69wn9fJhm#*4{WfC4nc>Dwbu1w?{Zcqip@VO?eot z1D)}+P9j!%7Gz)fv|1B!OXopBw)&)!u9qT8mAY^-N$$>@lK)*vcx{m=Y-=aF^j}7R zPzBx!e?TD#(U4%&X?C4*T1ZDj6AFIJ?|?D_t?!0FZC3c}%$M1U(&S!pKm3Slxj0bj z*f)-3kto_S`s&B&+AV;PLSV`7>zosyaYIJwvSCa{Btk4 z(_KVXoxS+OhRr)1g(SKMD7V!R-kww#IO?4yHBVK=mg&?wi9t3%_~rFN7n`bbJi6JZ zNZT={=EVg8FW+!x7US=eHV-(Z3a}meu!crk%w)`#Pe5zm{9NRXpW0W3Y&qVCjzS* z_#{H0>xg}DoYKynBFd;+t$+5o*RELk4rBWF_A9Y`BD=-1PDB2`Kz*2*nE_rBJU-bO zxcIIhGwPbq2@Hq*fF*>#7gCebwz@lsv|OM6nBQ8B*~6}kU1Jrvrg=Kw)0 z0R=ctZ^dt4!Qh<^x&j5^O8EZcDWt>_ctB~az(jEwgtK-yh8gv7@T7VFk!_(@D@KO; zn}GZC!?=VP7?@sVE@VG`W%Jp=!eyNQnYPz zK)flEb7)d?n3z%&Y(B)q#?nazMRhAN-fjvomVrENdDwt(X7>Ls&VWW&_bjbxPUARc zlrc@kCV92>BW2L!?{`V{jNaY@y-NoCgq1XPhfLO_);0znR%PhhgNkjo*gKv23FbTl zL^dg##$xKJ9giECzc`WZ@61E@n`HIKo+!0b>+%3vdk%jG$p9S@zbv%P z@|pElT_rr|Lb_>x_bdKN~;VqKMvLCxy8btz1Z~yRIOFS8yv< z$;(sHle@{Ucc;yi_rvnmPG5lS|VkuknbUi{sDcydRgA5(((=V@)_>t+}ZIM92Kca6`OT54VXfE`%es>ff<{RPyiHgbS6771p;!kzN!oC4XPr#o- zO5ic@ihuq3MXl3c$qLu>yQu+P#W0$ACWW3%#Qq zSfme4+zpXIj}iQvQ7qG==9F*K`x`VdWS6B86+Bzv_L8ad zVT)45DE~U()!S)^=4XZ5d^79d*JEy7dgnU1Ps;yY4jZy@W=|rsg?j!P{k*q%-?dVy zDeWlM*DC-{C+M8OKGX=(t`-)Ifd_7+72$JNr5}Jm7y+NJjX?a=yKno=5vl*Y$N0b% z^K*p*B8-4u29euA;4T98vEVxf-`{2!XC{#p^TbqJYGQno)ZKCW&IlSNd&AjAMm*j4 z`T6wHzvS{TN$>$vHzRrs*%fzK#&)vT@zG z*7eU32CE{5F_8>IdA_tHT;EsX^yw=N+al$7CA4Em41z+^D0n#f(w*Q(w17bv~?FjJpxon1=6(qivL8^f<4aI@h>@|7`|Tg*lL!%0h7CpFK?9mR^w7!4$<=jzxcYNQ%+R;}ILkEG z@OrW&B2~V{4Sl}~S>T=8-Trl#`~6DTrymSQpHNltY~s#k59QFa0!IFPvhhdC7C!d% zQZ0K9TVPO80jKdV88N)!`?2b%9Aeb?#1G&-PXR9lnN6$uq*RBsC9%#*$!)BnlY}D8 zqKUVVu!50F)tK=C?^}y=le(KMh=*h5VhhCY$i~Th9f{Goe)_Q2w%f#r&5bk%uU5GY@VZle1ikc|_U>-WW9A~& zvmizbyL?vyN&yuL33rx*^m%Z&Og5Gg@NA>W5RC8R1TS+dbl7&0laW!}QS(4&_omY! zu$L#@XuWMgT8`0Q-tq#qPsFgF`O}!lwt}AF;Pje^38hpIsba3_%9ZDsEy5&-OOy#AW2$E)Ko9_=Q%JM z0LP{umbCC>{~QJ$-rF=!XQ{AT%+%d4(+F1MOaqOdB`)ZKf8!+Fla|)By3V-tyuF-} z@8kT$D`ZQ}`;Dk$Ej0>c;zsE-!YXB-O|`n?lY-ip@-5n=a+hSU1ibM&W9}GCRKG{S zkO5NvjYIuuRWh6&cjVnA+IOW9er`Edi;#|fG2=y5om>lB!YE4e9cDDosLS>i3d6~= z-}?Otx;Sys=-W%Px|&L{1XbtEu;)BpQQ#OrgsSc&3Kp-=lJ%06wpv_6xlqd;GX#YU zYKZ=^2|B8?_0QJ+RPIBss$pW(!(V1;+wVH2mZRYIU2LP70*Cy6b zoeQKx#YIG7sAPnNuWfJMrO=ImIE(7n&Bqyxr9CJ?9i#=mK7MpfL0#Y z`h>(w*Erf;hQ(#bkhZ*8z)pqs?x?k&)hJj^iv11gh6NlYHdXbQTfeF`%ld;^#3SQ$ zhj9EH4bcFEl9-!a*kVhaV5T=8la=NhM|85WtLfX-h~T+b-i3E`$2=RAGT)Hut7;XH zYTRE6-Tjae%;B+76j^@_1P1r_$Jt63LJ;Rr#MOF+*0m28upI0JM_3jg3{28dQ4O?1 z3SDysW1tq!e7HXI=ZZR^!Dmt=c4bOzTx{i;q;akiACW}fy&JbD$_kyG`6&hqRlEdo zkMBIa%)YTKkDxc=&1Q<2^+hl1&5m2n5boPMnR#AaT`l3i8v^!hRVLhIOeh^#Wx3Cu zy$L?y-QQxU6kZ(<>i^KSmAIc{!^`%WWqQExJMpQxYrQSIzqFGd5#DIXA3UkxrOmFfhN1O*0~_@oB} zy8j)tugIEq7n9bV9kC(jGFU*RL-}zERz2?G2Yi$z3G?>EV&wxvHw3pSSsN=C`Y^19 zMekUI?1bOYMv})najW!KbvYdr^KbsNZfxct#T4(|D!QTa0Yyo)^k=<4_jT=A#F$rZ zo@)jgfPlb0bkkvwb`EX^OM9N*f`fv1^MHv4lo?i{czD{NN$b=e30u?VyZi|p9^iDB z#M6j!11Dp>+yVk@4LWE`2>QeY#AaPMG7(dSe>kNxbnm@swR;{Im>$#T|EO@p0uQE9FJT{#?)4M~AUwspbRZQ;Ro!@Osi z=!)|CI`o@=Ha@F<_3LeDKxWizYtv(v4g>(({j6nD`VGXyibGEw0>*&gFKzzH1LYR@ z=_;`Y;7eoRSUt)adW?QdO7w zqB>A6=KMk5($b*I@2tgN;6wz~6?U2_@Cx8!={)OVmPB6YBguO$#Z79yM-fD4{z5i6 zQr<;yA--EwQ}B7NWagr_1mpF^$w-woWULLH6#Pz?{pA(1Z$nKP^H?I4 z8rCZRcIN%@RYeLU_6PI_>2L*2Kl@T`4?79E(%k@>Bm0yRx`)4@2rJ?iOpb>X+U|HjG|ilvJhjaw&2qBlM#=nmvzh(6I+A45r%R$AtzMhf(|%g3ug_w6m=F97;XC&in_s_H!@mE4tN0N;p(y0~3_T?Wzv4Ll_=Ujkj^*8& z8cs&3WYEyX_&Z4Z80@?wHtHUBg$PX&+{{;FS!hYxX=HKVVKDUBTaj811Vd#I#*8fT zw8|bqrvPbUwUG+4_lF8}_B(wou{%-&3WUA!hsLs>ShzYb6?oh6pTk4p>;AL$;_~MU z<88242qXn*<;3<;Nmv!+b>H5n@V$RODG!&P%{^>~~RRUB4_RKR35q zp$h4atYs{6$^6pNmk>4g?!^#|a6@JM{gRvW{O ziAgWwsrvzr6Jb}Ep(HHRmXg%g)iD>^EZid@QfH@{(I5$mONNPq8xKP? z=Dw35tDer9FlMA29=RU@ZH`qR0wq*INlXw|_5_fg!Ci*Kh|Yd)o;X8Q`~?H2cK+aT zW^XPkq+sFvOUYN9p$y9Jm$IJB8sMDil&$UY>A1+x@Y8`l9FZ&P4T#7jz;pHw<&o zv10u8_Vg4WdkVXXQNMJ36^1DdPEDZxYiJA~-+Kq?nLl^?mQ|Xl?%dI)LRJFIR#_P< zd;mNTJUqNOku2;xz|hxth&^un%JW!ku1@q9w&6SqgIj{Kn!Fb}#~~|2q>Y$%8K{ky z&!GOT`+m7e&fB=eO`UPW{95mN!Jg>VoL3Uu+H2qgQW%TwIN7#NC4!ObY9Cf*goabckwDX8&W7x3U zWhylA=*5)Qndt~0_s=$a`44FF++=J)a(L$4nV7ONQhlu_{6@*$V|!gRmFd?$Q8%8> z@l{UVNvKrM-8)IjrGb$QKf@ppWB+`5S{GbI(mVi*CI~}4vf@`Y)(p`^b~iW6Gq29f zl&%GqXpg4K+`W7B`^OA)5$13FenA*9@EA`EQMji_2PU1Iohd0P#k@Di#WGrgH^9HP zD`1~dN9vYo{T|x-sx$WR^KY~p@XId%1_ zJq!j01_mZHJ7@}|Z4a_qoAGb`P;YF;Ct1uIE&k&$gTnYNy?t1x{@T{m+uIwv*9Lm= zCgc48>bd^iVj~y=jAit%<064m3uHp19S(n}dH&)hkpc1+7%x3qdgTe{^{4ypK`^It z8_4$9zd_piY-4CFc!>K|)ZC1fSKOTfM#e8_~ z2T!Nh#6YDARC-hT7$dXX=ZMEHKDH%)Iitb*==rNdOlayXYLGg}hFY zy(oR(kgAL;zbDHW9w}QIh?YuK=~L&D_c3XKWkQ-mshr@PBd~F+UIUKdl#`blYev*I zznGXBzt)HC!_y~yUs@a|X{Cag`B*#?B$OV9?yn8$RceAfozUxPq!MZwnOwl=IfmsI zBIlM^3wpOTZ^G&)AGfdB!04#W{`B^USidS3-<00Ft6JoE=ABh*N&Mx4wHo-5;(4Al zoWqm7fVO-Po`o9SP*a0giBVwC>4!4DXukF1huj5hAK3ohSf8AjP($JL^Y&>jb+>=Cn;r;#J{1`RM5BG2xD+3Fk^Mu)OAQiomR| zAh_l&PGcvNKNC)_?lZ7(j`)koV=`FvZ=iQ>M&{}CwdQ>_E zo+sm=7^|jAc?Wj_lnH@!_%P})PF=V%$PkHXPKLj4sol6(B-MTTlRhEVW|+q>0HHb- z>~8MvCSX(5&^TkRCXo7BZ~Be=OKrj*%i@6s)=8V#>{U^IJae6k!=`(!YdHuB!v+V( zoqPK4Ck!sz=ZeqLt}n(ZYQBJgZ#Uo^z+VKD0?sF`eDOIjDFh_&!w1Pa?GV)}HdRUogPH(d||F=B8++{x#taQDD-BMCnLv%mbtU!jIRp&^P|*UBpWQ7c^93Bzi6>2!J%S*C+Fzyfs_Rtt<)+pmeen-PlhjEa#*pc1w;bcYj0LNX zojR;v5$q(U=H})mCUUf`yWqb=t)i?9CMy6gjHLO1b?B!d5*=kfft(CuZe^vwa(s-e zqfDA|FyGqTbU5AmdvMU~dNwNDP2`S#Bq0xJ8Zcbs1H5vRBz|5LQ6j2KyXX{E;Y7B- zt|azj!>V{u!R7QHD|r&^N9Fi`S$XgsB3?RVW{lYB>CJ#eeGehcqDX^#CqVnfTc*jz!sZ;f&gb@c<$o-(qu1xap@%VXPs1L!A z!XwH%^8^A;50ZnGm!+BYg9<%VJS2oNn9EH1I$TTipM)&_waa4X%f*UoZiVx37&OwZ z^taFqKkA9}+sp5Uv=`U4<%#X)S7%}Pv3J!MPPKnD8r^nM%XOoWgNFc)Q^&7}89pOy z8ndK|t{)X~Qta1B7G_D7?`v%I@&5{oUzw}Z$*cB}2j@c%&QGuX$uAZQFJ3Gw3|$AB zf-aWreS03SdX5=u>W}-+WbP-UA!UNo2dGgG$6bjh$$iHBZYJ$i(y+K({zYhB8p{es z_R(8_206KkIuAUzV%I?D3ht353G9~igoJab8#E;i6>#t>|z zHctG2(9O$zRA~ zyJyYN$4r>2?mD7JxAa*xus1sW%LC17O&+3F`ujLUj!%3o1@+e{Qr>|i0o)kI{l9oI zeBw)sj*bS51orwL@>(Ah@c#lsk_g$ysvwzR%TU|yKaR?|X@Bm(A~ZnMC=2vh%R{WZ zvV|$z2kvsOKc-u853P=u=X6>lPeog>r$_;w zY~Fz>tL)fT@l5CJ-vaU^=^M6fCKvf4Llk*SdXFoLMW{y<^AkA&PK{Z%zF_w+DW7@==tif!GJHkV>ATh-NmqV^=Ad5K+?nMjV( z{BzIyV#KFn{!>BpS%S<`3>}$P;M;0c6Bjk$g$D-aa;(9b^Ru%0lA$Gi`X!P;n?lEw zBP9&nU*FmYR3KlMm@GC3J`2QvMF(IzU>L6Awi137dPn8yac@fa`M2v#gVx8ppfS$m zmHqLd$SS|*sH!@({g^pPeDS5Jg9bO1I59T5HnVV)pchVA8I8LJU^TKAAGkPnj)DZ9 z$x5(0!d(qKo%27QlC*S`l&^!1I1{2`)FUyTO%yjbH@|#Ix;!viENQcQjCq2Ah{K1} zKZeRBZLjc5#*wMV%u8nnVmI8~SX0fY80yD9^B-!|g|Kl{K)R5bJNs)>=>h1bPd#FB z=U+pny;KmF-u2jNVCTPiYt_fx_({(?OeK$lV7hID7j z%;T%KQ{!#^q!Wr};&9;CgPlDr4#0;Sl3MxOS337TmL_)Eg2s%qCxS_WWPT&EB5vVl zdT7~W{&iriuJuJ?U1!&U?krlKgSb|a_Q1sqd?SJ4+7X+~(iZOayh58TLfBhSMuu+{ zlL8M@=~1r0srX0WS9w*bj=SegT&W?6=QJI`gC(G%jQGN)W&V2Uiod(&Mi?}8;Pz}LMbiJ+y4;F8(_`AfU>8Pls z87PLi52|(kHQwOKeL|vF)Z1k28Wi;R_}6E#7Xf-XwN*<1(L7q|Jbku$j(Ia)uu#k! zlSh{iBOA3w{UKC7_QV|NB&N%A{?-@0!MwaoBL!_@ZjQ{4rAg z`+X6D~YZlHA%^wjj^zkL?r_4|XuE|I% zSsj4oUUOv7UQ1Q0SNJaKhvBhq0vJ;qR(E$^84n*9ha@$9NRGmXpvnxkuU1vNv|kK~ zekon3+OpeW9^;hqJ~60Tf1?rMdSJuajyG=$NuAQ|=@s|KQM1{({pVMd?H-BIZHBk^ zKm4~JLI2-eWhpN&(Z31f>T?PIhW7UCxc$WzM*uAp_Y}WS$4|l&8M#IPVank601f@z z97~^_riT;yONZxtXWC*${?V>7Q%^sht399I&*`C;GskW1WmhEPr;-jhUKK3C$HO!4 z3d&q$vRUQtu}Ev(=}EPcHi-rU67J)aR;vF6B?}t2pQd?W4&x2K-90!sC^`Km27M!x z;VA6hSsi0mTy{SI7Z3&FzCYo*g#_3yoWpa|#U|(r3Q8!8ia3<7ATJa>K{%NLiahpf z&Y|@R+q))+m`NSK+6`=t;CL!;dLFu4zoSzD$GE0eUY*KZeRq^Oqr?$LIr_$V69qwQ zTjh|q4|`ltL`VRu2}-B`-ing<`l{C&tFg?kiUwT~v^sMKm`$xr{5tDJ&QM#f8Jv(s ztM~iQzHN{S)ka}7LNSY|n&M_sQP`41mIW8(x=#Iws+`wo2K2|9;T|49(e#EjsM@zI z@?4X<$xD|oqf18SIM&hW@tk4}N8GELqUQ8^5b0})S;dx?j&SEXm@EINDn~i$f;vX2 z_YvsQG^sD=H-Gbg@UHn6?(k>tiP{2C<#;?-Xs_#p7WBQ}Cjs;Y3oaHJMvyWJ2?)qB z1NNx?6|Ngd)8EjQcfByXW_AUi)<3hjd0fIKC{17SLY<82s4`Wo~mpB+H@_H|x^uzSeD1$1byz>~TuU#O4 ztpun_5CoWMJr~{IS!}rmQQyQKkKS`G(xisCp ztc=dWEu6F|;UU3H&-V2wC^j~>=cH@TqQfVCINZf0LHj&@1Rh-cFRDGn9zggoCN;Pva6Sk2l6pbT=Jq zn=l~~xf_n;;Gp8ddGy6Ot-Tn!FK2f04V@wQqV>0c*to=0MtS?7^ai6^pTR!?l1?N) z|4ymUd1?v#guJr&ACdd}p6;^0Tfj1sbm+z?xrBC_NG@)Ls>t#%c#r|)0QVwv|2`LA z`XT}NBZfdf8YH5=yyE(Aplf={R>+k@OY0WwO3EBm7I73}uN6JsPhAePO=K+mgX(JhF_}$?`nKr8RJm?7yi(|cw`R0t$~WTUaTXOV z&CQt`zxzCV_|yQZAAvFblPiThGiSf{B=x}d&&%L&*0mS9=O2FJf1Q3tVBrF;?k_DD zP0LS710aJi2rd_hVHgJd?f~BkY?^PAJc7X0m3PR=NzRRRcDND*bgDK0@p4Wu)o_-i zUHxU8%yG6ffovCqRT?xkfq zuFlS1RX3~kDnDo}Qj#Zu?xku8_LtCIbQJ~Y%Zb6lPs(5m7@>^&e08cYV+`$)|wF93){V6Pb0M`7N2Oqw6Z&D80Onsqk6MS04T0>o- zUMe<^^y+!I5)iQR3%54U!SR1<+AKF-hN&tFf1DKM&FDvV5eVFaQVnF)p;p^Ru&Y(nv-ZSSrEQ3O-GV z^Z8IO*M^?LZaffP-gU}5T&;OWry(SI7HG~ePEdgVQbP_gr=X6o0XWI9oTg70b(00v zS?3tmd_c(cY4Ed6);!d#EcHCAOvsUCy@D9O@IRB+)iXGJZ!cdeI7?#_{6PF9NN`7Z z9!S$my0G$r^Yq@8^qsga8`0+d2?Gw+Dm^uAOeL^(mc6)U3qofNZAxm^#9d9uF$dl^ z$B56hFUV2~m`Q>e_r!m@zYE}Awn#CBmEL7#yOWg8@#1BAA(N~pduQ#zqZZs;!~1eDlS5Yy@(svV2RrCEejM)Fnwr^cA>EaQnx#uzc>@|F z3AanrXeqrGvz%l9mB8;Hj)TeGp7h!iprd2`r6Lhb-gVd+e0**uzQ<9xNwPfUE_y6} zH*408f|R5f^=v|V~el1J9v2f3s8>X=E^I<_=K{^+G!Q!Ur^8kJgRKhI2@nMaZ7Q#!Q##F>GwjJ zYyNwg_$d(QsL)i@K1f85Ozt(2BrbB?s_k!zrlS4^ZrM@G5}s@5HSAh+)6n@)Z>&z| zIM()~6wgOTaqm*`=jPqR)KTRoIF4ef%wcL$^-E5hzBB*RS3NkfqE>PwSJqzXZfxxT z3ftTlBw-lwtS9<&hZ2=!Fw(PL1xl@2F61J~|E*G!q|MgA9p1nHq^b7nO{{H7BdAnu zZA5b^iHXBEZNNpeL`2}%SQD1l&(Zl5F;=vZB_XGqw??HkL2J8u9#}jRwB2t0XitFh!)$$yl106jLBX-30I6^@UOoFR?hGqy+1Y@*@h3H?UX$g0N8d z$J&REb`SY%Z46f4>eA~)>)WtZ74$F;)so0{D5yCJLD-U;ThlI#WCvai23z(AXC>mC zvD03q#qCuYzSd`$j$gbI{xP@bbSgZUmH#>=HJjJ~xSjng>(@_#4w|#@vXjY-?Y}iDf;jFrGZItoE7a1 zNz-eXIpirQshrEx_@61yvuEN_)`Q0cq|!crg6pWB5faY$~sl z3$Mv?s{fI)RXYT$TyQ|dXRc69%f%$I0G zZ`#x?mm*h5DGIIl<1lUfDDY2`eT(e~)$s0Ao!W7`h?=t$Qh5VM*24p0a?><49xxO2 z?G}4gvDg6lX}C7J`I(llOEu=*(;`fEQuUar-rok#_U3}REE5?HvsxI>-@icSwawGYzp*+9@C zEKnW%wmzIX<|&9(MUI*Z8E*c3R*= zgYg>YeD=c%#ZLRWX1n8UyU>%iQLHCBR-7Xzd^opk+_dqK+TFir8m7w?GO5w9hn>kO zmT75ecXxM{-)wohKLd;tE|-}jz+r;n5{8N7P9Jlg8?`M(G)=`Y!p$~QXhe)Bjgd|ozVU?2%EnemQJ5*E~ z%T;g@Itjaw&LQt%5lxn2a*G!m0r&`AAFW6_0!3$|r56qvOsb;6$2(AkwzjG|*;TG} z=)4vk!uF$Ezl}<*SNve#bFTFSs z-SkFNZpd30he2Hj+ov#1Q8z{9^j$Zk0547D3OT;I>6JIV6C0T~e(sm3GzaL148L@9 z%1D}Cebt=_v$+4ek{tGS)}=l|>Gt&<(bBy7f+h3kV)g1`^hK`U3BEt?knwR_3h=JI^Y?q_Um@Wc`1V&n zuXAkw&Cb1AdvJa;`D&-|qUAb-b}ddG_ThE`dM5(EhR2bl+(=8>CBE_SokOn*7F4HkWr6t^@|bMWii@7#YKkzqo(?M1sZhS9`JF8_aDyZ8Uc z)OCkb+5i8@UfC)5l%-p9(G8JVGEi;R$QY>q8u?=2#GW>!d&NKQzRLXrJ@d!FZe zUBCaI=elz4`~H01@7L@l&;MdVH4TchCsJM*xZz-Q09f%mjG&NXs%yM7Go#9BdrVjx zC^#oq{)jXY@+|;Zwe+gEjLOK!fFlvEhTFQYVfu#zP3lWXAJxmC24t|Lh%wydhM#JZ zh}r{|;MRJ_hy@Cwgv%+P(G@9ON(WE!L5vJPF3FQkSNn@^kmnWgnu(#^*;+RGsQKDB?iA2R;rsOYstDn6ZxcUN@T9v z&rL-17LUIVQ{(lo8%w2%_Mp!KhI&T?@-3*yZPbkq8Ak&WCztS~;i82<6BHeUIM@|^ zcEZ=KA?&;iAsu6^13btEmlFY)rgrNYQ$>TbSwM=QcMJjVGghlk6nNstO=>%nU z9Q3nm^zzzEkg$NawVa_-aC~E>n*2dj6Px&6El9KBCjj>fEEru~PlEON`=_d&>4x4o z;1J{g<8VXD$#>@t=K%*gkgBHUeH@xo!&!`hdc@?FXcVsCmFm}`SZZp{f&4~W@*5Fr zKNKy@D92EU74(&jh}aA#d#4<8i`*{*moY>Co>!K<^xrb*PrEb?ig0$D)U+APQ+mzq z9UvY1k(zQ~IZ30FiV?KS8L#zo*yLyjRAk>2qV*oz(L@n9B^fp`kyk3-_(2?lDGawl zHx>jvoeov#B52l=nPwb5*@!K4`&hxG*%?*f%#?#AN?l?he-{Z?b*Y4QhRjH8b5W>w zj|i>rCHgEAWHI8hzQML=+Z3{(4&DrpXKd5d_*}~sBx2=g4vkmaYX7}*#UF&5%&<9s z(S@_-BUKKPRxkB))Xs;o3fT*Y)m=pwrNyThtm65)Rb9pkG| zwgjHc&%8mjV@75-#fiLVwU_3l#K<+3AJDuEn#e*sTA8xxTJ)nwt41r=nY9t+PPpEd zm03#jGkc&c5u-IwPQmsN5nBOIMHrjU)VHlM2VZCowR}~T@i13y ze_x<+sKCx`itw{*gc7y+8yH@XoWh({D)zWjPwE;gs(TPpR@h9O!$8shXeC{|bV9^Q z3*q0X&w-vA-KiYPsFo(SHZ6eW8f7Clemg7iRk}ZE;Vu$RQQz$8i3D!u9Grdk5%_I- z?^;Owm8%mC{bSQi5C8X1s!zK%P>)7Mv}JiJvlDr*(1@pyDw)*WRqCACi@~Tln`&@o zg=d#atm1CKB4f_ov-HN`dD>)`DtjN7xHqEyrq0T!0nd%qi)-x93WJ~P{biQ%&-HN- zwR!mz;ri^e=p(0{yJzW{yd^coc@Y*>!Ai=$msY4pNl!kWe!?4_aTz0(pcTxhoydFm z2jSB-q&iaUrZA#w$!_|jm6D;`kWh}zgX96tKSR;fCLKFbgk9^wkE5nDv{tF3@F<_b zr;*FSnpBkR&l!epB1Lr>`t7m21lnQn9I|hW03e{0v*-9d85_mif`(Z4Qt#NJX^+FU z>Ds9s<;*rc+nr&tz~>L!pGZjCtCu8%=#G=#mBbb&I`F=Dq_E=oO>j!?Rinxl!q;Sm zMorDSuhS*W^k$`)il$I`F2}$^S93#?#4t-&<~b!c<+2wqwf+}HX;2YhQ5+~(aj^0#mQyHvjmwB5MBW~&&2q^Gc3sRM?ivAuuvbG{? zc%4k{IIa=dcw=n9+%9VG07rvmOqELlKk2_)QvI3jFG<3XcQKuw5vx~Nj<$59A02~z z4umUQb+=eSVMd~*UV^x7E<-#1bAQ~1aswz9s@O;zkt32wG8>^XfCn9I$9C=niA{ z5D_G=t|PEG!sPF%(B1?;nPZnoNKLW1LS3ADD!L;#j^koZYUUAcqb7$)JHGcdR|zMA zxUn=#3+@6%S@OM8;7NUzV&FmXyPKt7ZbzQ$CJF&L|&?v z%EjPvQ)E?)z;+Vqd3l@tv2e{TU3H_pw@((WhS7tLLh<{ z?7}+wt4#M$%>uM|lN*1~`m@4zM-=5d_yz_Fgh!15*5ep~M?w)_8>KX0k}^WOI1A5i}ReqI0YWW)A!Crjbz z!y6Fio_;^eKK@;19gCqo`6%Un!oXJ=L%V3i#|S6l zqUZs;q_)?r9S&N~(HB<6E6Zw9TuiUp5?If4Pl!h73sS~gMi5zDq@Z z{IY$EA6fD$UncG0khBn6^Iej{0L5H`f`E$rKdNvG#Lc>>w=1B~Mz zJ90VHXN6c`PT4_?4!2toguFQi$AFF$4&af(*wkmGJjHp9Z_udv=kEq4zM`0OjC4iS zdBKTL#7YP*a0gYV?vpBtH*@Z%TCtV_1`3)ibWnTwt0#Vjw_r2MightWY@45EK;v%=B+xt3k9ruf(-$Kom8%v_v@#u%ygNc+mg9XHv(y zmGO!$mX~u{N=QaaYHS2?GpA(KPHLE6@xdLtE>PeKA6&g~3lVFB@fR(vAA8 zxm&aEI$;9LqsH)X2E^7s-p&_^2@^d3$AZ}&P;fwtf`J^WHmeT>NTFN35{<&7aJFm zf4}l*tkDpZ@4m5ljiJ4U-Wf~?(`eLP@1BCty;_}_nc7mnAo6LLNTEPD(;@-fneX8wPBxuuL|#G=%!g!J+|}H*5nCoa=Vy z4nog*d&y<^uHkN3;d_(wKLjrLKHKzUb8qUOX%*RX*}Z?w+g?-RviWdQF7q;r)QJqY ziYQ*UDex!?0`+T?jR1>5-R3y_poCMrk;a%*JRHb$-|OQ1Ft&oGew%)|$y1prR(y(0{GkM)}mR$UQlbR4U4bmGn zmI^d3{q%s%76dg=i3P*uoGQMMCt5H6O zkQ*HT|1b|9i7JH@6Xl_I+_MN3M0g`Lug~Uo=UtW0N2a;;jh|>04N?1`2S| zNt>0HID3rjrWT+0UPAhj7NmJUwA-CBNpgEIj+07WiNpWiD&)vjT3_|)`)WTO)@bT< zLi9rkv~U{5hXzX!MD83Z03riw4gjaYE)NM!>8ZoKc)C?zJ?Dz44wfvB5LHi;tt18J z$C05FE19ju?nVE;cqb9}*@enFS(M1+HTw5LqA(G2$*XvVnj*;)Obpt9QGj7!{t7>? z*Nk^Vohuo>?5$Wg*j4~hx4T#l+~f(XA_IGz-TvYWUG`LhDY^$E*Dxt^DS49W@>)eUFR%D?!~=lb2@oM!Ln~Wz)B6jd7U?u4_FXhhBSx%e+qdZFt?3 zpL^O2A!1E*qxR657?mrYCN?lTI=(VqkmA2-e&YTp6+uxQ@nX=IC;s*KGzc+-N{Mrq z5P1Bmfw4OL)D;9`D$p1MQ#BH{8%?pBZF|^=|Iw6azM9)5$JCz=zuZo?aky@o3 zLZ+pBcVy=yl-AP3dD-io-tVj(RZh0zMmGJLS!B481I1Zkl#$|l;|7_xX2W3+gZ81a z`MbO3Umh~JswSCclAbtD|8xBMTnOm;1eYSHq_?kZYlD+nis@yz_(|rKljmYsbkg@C zR@!u>`VzRv2~Y}EFlJnxMHRpKT3uJC{ucFcvK^c!%2zU{`OKVIMa+514!xd%WMO(F z0R!~vs42HWo`tHB{|Hf&qPcW4%W^$o&jrrKch< z#~G<}KAJ~l&!l4;;mcZOyRTWKjJDp;>Lu8}#9(UC@Vd2CM1WAocx?lFS7@IOU65zb z_CqX-5XF~9m{(QIX+tL#6S0Ewju>&)d&Gxl2%2~zwg>%UKUiFe)>Anm8h-=s1#A@D z!-uatLU+$0EfARi66S*aMwhubY|i`(FyDDtD+beAGTwX|T5AbrzOmKbd}g@P8No8s zWZCYQUriI};j4<}?=Ep|rsO0WPiWLx@{}qW$+VQEIQLz@*4X49Yw`iFYrOMA>No5h zewkTxPr`~e4MXaclMc&&r$Vx5fSk@+&-Rv1-=7`Y0y9L{X76b?1c*7Hx_K78&$o}l(E16{5d((dHqz5|G7Z{ zXZaqJZfpS?Wqa}E-sbrEu##HuDBggdS#3lT_e!-ch)z5l7vIAdRSJ8lMSSq1(yz^$N%dA!x zQOC}ta6ua3Zgpw3j*FIR1~xk(VUm9((H<|Z4I(&pdOGQM^(}d?cMYw;7pz~X<4!&T z^dFQr5Y+MX2)<88u{>Zm&P4?F;nqS~BshzvmzT;!#k+~2iXC2xA1Cu_f>L#Pjh^xDfx;F24Z2j0)mq~Q4sej4B%Nw4G$Xkm5X zxJDsvVQrn5vZ8O+?te0xe;jM^&w&W*p)L6>*^tBZ^@zFbx46ZQ@t%j=-b*)5YSIUF!?7~0$ySedE z#*!xk^aw=HbC88$KJHjBrmBu|1Tn!!k^k`Y0QlM1KF?W@2M7N7ZnAYHlI}oqHMWLh zS)%MG2uKI#YMjAmP*&g51I|L5=XeC4q_mcIus%i|g5VqiPJsXO_x48T!59U45U-yg zT0o4Jc1lO=F!pu5)j@1`Ul_Tg8hOE#sSsa5w&fJNL$&94?0rKwU?J>r#IpgSMTO47?F-T&cmeJ zd|E$pnla^$7uvXY;~7`g4EAVxUf#Rb{3A>kEYM2$0IF$cP;@FU7OcD<3MuW?BZ)h}#BC0*6>Mq77x68`{Kwf>6;Ds|bO zwl>s6-h-}BP_gkcLPda#3RcA6K=zG@$KwzD?%y9TxAzL3l4G~|Jt<%^D}tlSMM5RX zAq5J#EEdPFrcFeEumJ%8P;($|b}4uOxKssv{-g!rQziBm^sAAuY6v0TTAyC+JYKeO z?#F%6BE3a)5R<6V?{GRxrIBgV=Snt<>!Y{q*JnIJM)53;b1oPnh5av?UlcF&;%}Xt zQ^MiJwGd8nE7S~gTuWJMTiJ}mw3K{^K#{=3hlFWeC&|wq{kF}wmE{+wg%NAGf~Io9 z_j6Gk7>poi1S-Us&P6QeqbgXQL4U-=$cT9noA=k735YxRw+BSCSNVa_ebwuXg`}c>>m3yQis-SQNyvF;p1BJ8O3%dP(;Z!*Ry7N@wFaFtQ zFpLHGrL!HgDrv5wI{j6_Bqss(Y}>PqhbM0pPTs=(21@y*gC|P|j}-nsf_|S1w=E;H z9LS_YjSmtwY9maL z55d$OkG)=gT`VQSS$|h6TXC0$YvV|Wi|SRXDh1K^3A>sIBubw3DMV}^1iREAN4Qe# zr~hocbG+BsasQ{iI0Wni7aj`ISD^7&MRsm-`9BJw@itXWeBdDhiDx9cuic}#?mzx$ zC+3Hn6`H^JVRVYzRl3K>yDidB8F|BLKP9qC$Yh6qtK@>=PR2zbe80*@>6d$C`Q z+EKVAL}PN_P1$iKPpiA(W@TiaI<+(vTniLoIfGdaYBg4AUB3V5C==DwF4+`=ovNJu zr!+RcbH<-nuM`{Q=^uHz4LrC?j%OV_RpIe677 zI}zT;r-aKDfYrz3*%VcpR%}iZt9me`evesX^&Cnd_s~X}uX#RWy)8nYpk7?T-d`N5 z3KG%k$jTx``*Lz)cud!sxfZ5+7jok?Ku=lc0{;Pk$Sh{kO@pPML*cBU}XO;>CC3LvbYV+k`1krJEnS9nXs5&BSHo>*2wt`Y6ILbc8&FA^)eO!t!`!d$Q+JX;|5W z(#bJ5FEq<=8?BC$uJ9o_HIwN5Up=J|ADT*7Aa%T_J?K{I zxbEUn@qN19aX$6g^BM)Zv)i%7Qt>>V@|zC_unL zy@OjxU8b~>tX{nHCVb9LXqzO%TK2^kDJ|%3omi9@Ew4jrtL60qNdeyQ+uZONK}0P< zJo4ztiYDpa3$eA4yxu$`O@iqlvX@e}YZ-6Zy|=KcLz%tbnJ0JFt)k^Za7LKcipz+l zYl}X*T+;k^sqPEA4+I|=ieb5n-wkJp-ED16v5}?c!$^6Lc{w;-r1P=2$1dN4iJED6 zczD~&VNE!)iZ9~BOQGJyL021VYnuE@81#WJtgO_`lOIN)(Zf*h4S)$+ul;k$qH2S=iR3hkAr`S}_b)zU)bSyJh@ z>D>_UDaB?p{>Hfpkw}R{y}n5EJ4-CGTnP(2p)psxM8IuS zMj@E-gHM&+_Hh34w6{D&e+*m}X?$%vbc3t5Ot+>~EcIpIbp_TErE1XIp{uwEWej`C za;6at*Rip0(I_DlT$%l4qNMCDVFkrMpz8DBoUdX~YnSs(hR`ut?OzqP>j3{oOM z{|1Nob)U6_<7`f6A_uiQ&B=|c42f4%SfGIxBL75&W|Wy{RIZoOeIpjF=;vyuk{<2L zDTz8C2_ebPHh6o;%r?aD?DPcm-6iOL)*lyEVTGwy)GScCUg|Xc@`=Z%qjM{Qw8~O# z8!I@bq)%k*Y39{&KD$^y@5*r1yDS4;?SHi_Q|u$C%MdyF11d+W|1B>UKD>ocmedd( zE0cp|;sbnlgwb`Whs~^86yH`?osQ>+xxpKmpPP%}y_^fUz~2IlQSOam8qq3MguXeG zg_X|rKM%S#(ACpZ0Kf;p zQ!pj0vweffZA#tP%iUd9Yl}g(G!;U6d}I2l3MtA(nKqir1o~W17_4}%dhF4xK)WRU z%e1~8-^&Y#3IEzbQ&LEW*O!-Ylo=@d-emV7sz5!sq)*4Tx)e!Iw%xO!I_D``*dbQJ zC?jn&Tp8iI^rk9j|&Ub$;O&DIOY_E7A5)`OAu9)#N9a4T8$SWGAvT-F_qDU7gek=SedBiTTehJ zOR!qO|^e zK~2X~eW+02N}lDmrDqj{9&O)Ykq<^a)O`Fm>o+?~c<|0jEY4m)ua#Ixd6$$PN`n}- z5#O(pg&|&18FZxxAI=&sbn3M)lQOzc;G4`Um++-Y# zQ()F84h2njMWTt_K5{OsV%d{EgWF$ue??otz_mK1tW(rRzp_SkA1)ziz1hPV;_zN` z$(WPl#Zx~g3;<)*(W`5Xz2y3_Te2pd^mgRg3io-KOq`8*dTXT%yyYc@BTj>kG1b^wr*1<#D z=J3MzD%h6X=8#4|AIl)A_2SHin_gOoyPMnV2o%YBdT$_x=7u{M+q*vZcxSlS`jd_r z*d*=H*%D$5o^&Hewl2uG`qh$`w$?}+n-jFqQnD5X)v#_LyE~sf|5~fV2mXK&d2Tl= z@Jeh!>QZqiTxBJQ-M`yk;L%eNYi7uWMdFAv#a8NDWAof&S7$q{N8AQ{vVFLJbR(yx z>~8V1vY;em!v5F^!rG}upcyLY!od>@q_4}vH*vKoK{(RkID9;|>4?XFh@!cVSogNw zyi*RaHjbSfxqT?tG9C>tFpD<4))iQ`!EG_WlS0d>A(TPGo*{XaYRyRK2Wd487616) z*ma2s*XIrG3;3ogQMpZr{E?OD=&M!eJn#1xU8QN2s)+OAE^wAevg&f|gq4v<8>-jD zO{uEkUQPU36C6U$O?Dd;>PcTWA!xGR>P5tt_fu2henq%^_O~jvEg4I;;4%v3eqR0J z1p|JV&Wpj+mxvzdB)fKPe`S=4dA%*&?HZLzduOMtoLpk~(A>_k90ZbpgBAvE7>Hf; z@%+Ln*u=>-(@|CCM26YLtT|jo(KXz^{;=;*GyP8jqwaIzv-<^6kvh(i@@G)icTOZ} z-QUm-|M=r27MJhYkZ`CHF__ul!Z^<@aB=B1sfFATP#FFAG4y9p6fh>CkQ}z`KV5mh zUpFFqya_cDZCoCPhBCj;{7w??40jL`srkw)y!WCU8;-pf6d*!G)YLe$Z!(|auif9@ zj3W4FiYqG#YgwNwZFB1OzFk~QoGr}1=JBZ{q4T8iW(owGq>gCq2>};@%tC8YY8q?X-TYfLh;`Aa!y|`yz#IBex{=AS`$ZGGtGg=>Y&`*L5X&roo zvO-{8e+Xg5LQKh1@V>rz{d$Ldb7KPnmcE0HEC2*W0DZ$8BP*xvQl2Jpl_+Q^l{t_| zFW1&um+a}~vgzBW7g)J6M3}y5%XbNNll z+C_FluZhtkG7Et|J_E6}G3o)n+eO||Fkoi#2rst&dl5?-Be#XIvxDbp&|S%I>Ab zjX$7wA0;m-S!+|=6crJHi1FU*O>O+#LBEdJn3IbsBjtES_@*G;W`15vSt4bRFjHSa zx#rt9SM0)}A+^$g4(wWvZnUu81$N%UBCqzj$1Az-vEzs43n}MFXNBBC4;X0))%yF* zqx>%>&b)hP7Y`*$gtZ`~am{C%(X;=H@kq+Tj)={R{9R(;(GEG8s-tTABgR@NSyoqF zc^zmz?jdP?p26o%SIla>Kx5jn=um$raf~U8QY=+tOPZLP6Q;e$Dw+c@fC);F|HP*+kB04*sZRN~FW(z+c-bA-_KNEq2_8ZPvn6 zXZ>H%8n^{Vitpaj;^<9lZf|GQFkqgnx}%vAdoi{HCBtX?9D3W5 zzdg>$QM!<{l7HO~w!VZL;P?N12K!4&-d|TfjM3dmT>$A$r zSP`33Nt^JXF)mr2yjz{iP%!8gzGIsj)CMf#OxgLS--*G=3>-%G@XTu~uIDreu zBoKS0S}V(-ZROWc(mK#U9*g9ya^Wub{zFi(WZ0m^LU=S&&E%?0@oBC}j4iR}_LI9D zPMiACLl{(rY*+NxgNUPt;*8X{_8`MqaAC z>XL>xn%r|t;f>)UJV#&&5>=!8i47P+u&|4RBzWr|T4qTO;|t&s$NcGav^#9zqZg&| z;}0@q=Vj)mcB&J9s7oN3W_{rxugtpCWW3{h6m816(L9Jrvp5uhD&`+&aUAKNYPj*& zECe%4lx`&BHvRUN*rS=yXehk>5$Wue5WSc9E+0YoeN4hzg8=Q2dHq%mR<^l?%+D8d zjd9FAS_~+@;=N8zwg*BQ7E;r)D&UQP$V#YGtF!Gn1=Gj~-ILaqmM35?27ANNuYpUe zL6HrmWirm2q0_j2r3l8`wL^H`gA;(E7)EQ{HZr2}7t?V5Mt3ug23o;D8~bvswx(tQ z@&w=aoc@6a=|x$g_7iA{oj1RVw0#q~R*>*HhmtRAN3yM`bc^EFD_inLJ$b3t?K^D1 z$f;B|_K=I3s%G~@sRup6ve@Taa84yBs&jnS#|VR$@bmCz7mOGf(?EdksVPgjnnfLh zI)QkmwinHhHe&U1Y@wWVGIi;vEyQ*_emtNiG?iAI{dc$ow4Z&)zkf@d@{NR~OY77Klty@BvEs20^4= zq4I@NoL}_>q?HhK4>LG2gYy25jEM!-NPpi*Jny*KDOU3Pnpm&AlytOW2(P9XO;E)3 zjTMEu4PA<)thJ(Wl)12|;+jg;rM9Zy3Gq44pI@cE85cWu^mD5=ickXk0aXUaGd}b8 zFx2`;RhZBc(x5rAsHsg0wCe|}o-5RxK3`3D-gV-?a>o+Tn4bwGRZ^x?i?`m%7JTEQ z;MUi5Roe7nckP{J7_;j~WFe6Z9mjir`bU0Ay`29{{s}yS@xt9HJOX6&H{PFn$4`%b z?(gsK;?&pI-|tx5@wnPlP2hQxPV>Trv)`JkO2*UO6PHrToCJW5gq#A|V$n_zBFk0| z1y|PTG9;Q22S&jALw4zsPYL^0X|N>E78mwN%m+;eLf3F5O|^nl4)vGWkNj$RO$}h8 ziVut`ewyZKZ_HnHgtcgzA`0fIb?vSU3#MZS%QU>orhjnCq-F_ohi<*9 zW7$`t5r2uC;7DO_SfCy zq(;2ppS<4i-f53hiF=-4g|%?Ptvet{9gpAKLL;8U$IM((ySJ)Adm~l+GOlD!%V(sh z#g!Hb@7Q;P<7nHzIoa90EyX(b@9(>&fgHB1j9XUDH8KvH8_6{?Y##1L{*`nI{gwOD z$?MlQChCtLUn?Fci#_$^>yPh1WfXIol8qZeY5 zHNVy!NmacO8cRRjNmT9PDx$;hRCFVUUsEyk@w9nGoGUK!ucDgido+Al>1^QiHsBR$hlnuFZgesBB8LR~ z-$L2%afcc7dyvLbb>aIp1%)2CaIvqzp~Os2Um#PRSGH;JAZkdF3e@w2u?EVfRK>$% z{rC3Ub_=tZYKViPXxk9vl;JOIMINaB-I1>}u@7=__AtHAR~Trd#D%lTgY0kLJ8a-* zcKyK5#)iq+VMK*Ha%XyAUVr0M zXYUDm?X#_ppYUloJp9$oi-^}2WUe}(!R{w7(4c>Po`x&P5AgKc!w8@+@{J-jPcB$qAG?7LiGNTBo|8~f6W1e=Sd5& zx7R#oyRGxDgS8ODhb@X^)2sZuivk}ki%8^oL!y)m3l5$<^7iB5)l{oWDbtc*e3Fw> zD12Sn`v67du4VU1R2iF+8tQP#p4jOU=EHY2vScEm9LU;FA(tBBZmDw7RYP(JL8O8u z!6aD3F{3F&qw##rHRVEb!HxNn7Ux}_Dz2ucIJdFOPU(Y&g(N_13a}qk3`QRm-)<@p zXib+TPp6EKvt~C|;!6S`bSH=og43?72@~6$@P_rex!5~PdJxvgj8N(ODke!Yl_n}1 z6yCy?jwh&;=5##$fs$KBn=2#m95ETNm@l^bZ9oYq$Xj0Wfj95pP_aYs!a~E#4rzwr zlXAh4rm@uE#(btxthlKw8a+!()6-&*J&k)uqNmj|fyM-9+R$dckRo^r(5*m2hq}=c zM4ozNx|n_4e*F#g{jv+5>DR_@M)|+}58LK!D}=lj@Q&?BTP)1NdkYpiS+#BXj3Bc- z)WPyFW?Nya?ES_N%aGqo2EifQlKZQq7h|E-21fEg!8-VpTU*T{0$+KRR8$epvC_0=SVMUKr@rPh3s2&&$5$NM zhTk1-v6y1H=+CIoMtx<(`vv|RSQa~Bu!AX7gd04Z=8qwr5+=J5x|?5yH2tEQq$Q%H zv8PuW7Fcseg!gE7qrSq!iQLs>=#WL@-L?KFQUzYSzSS=WN%^`j7(RxA=~W7wHLbM8Yb#a=gn^ zXiu{ExV}GM%kEcxwpG){ibsUh#z?ZWuX3s?8;pOc&eQ4WALH}L-isI^A8bIeZ~uZo zua1Xjr=S*rTuiu9U%veTuQhaI668C0ewPKV#&A0v=`+Z zvQ&MLE(-rq3Q7_CYr6+P^m>}WBe}H*C zRvKJ_@bW|?v;rxZbU%JT*EY203_!%4C%R*VvrHW3lC$Y4&Mqzlq>fjniXd}InR@2p z5cHe~61CfPI^-Y=M4AcJ(zObRp!P5#2&<3$4=gx4N2jRkQc}pU_j7Z9$GRm}2_H}r z;6O99Xh;o5`OUTDY^s4`?V+>S)r@Ta*NRWHu-8YlGDzBEkOa|(c6k6l=??*T=_$pO zsJ?o&apRe9V@#67FhMhR7lyaIti9{^uMk0^6plw|tt6E3b3CdK)C853{xwA!C>>UC z({9qbs)o-KSoQmk#EhMTg5XDkp!yhPHXRJGEr9z3PHg+WT%=6nHR*NgzQwOQg>8yz zwxS$?UCB{P)9aw5{RQ#iw06^)<7H&7JtTNTnF;;%!yzpWyq!GR!1L^&xA%c=2k3m0 zt!O7KQ=N>QA{0PlKwYO=6rwRJ zd{RIw7|+KnP{D!$u4w4hrEvM+zvr*HXK{-^qZu-lrEKRX}A%MQ$}JXnO}jIp1R{~qV2r-Yjd;a`0)c+I?(5m zRLFnmH|e$dQh!5Nm#ym9hs+*ntt9zJi(K>LM6v3dmVaf9k5sKDKz)&CW~DwI5Cei1 zpmz*dtOanH^jX=?d>`tg%;Jm#1~}ndfRc=qs6pB`lAmk)E`K1h%~W0 zNs_F2@_^@wDko(iWIaIc$Ig!Tx3YREnaUzw6GTA;Bs@Hm6&L5w(q4q9BrtuR>}2(H zK0K2@P(3B6IYi@VWPs6(FM50N`2d;&)kxzl(ugm@ie1Tw zvqaYl;q8b|9uEUiGU>tLMm?O|v5RS!GJrMqx_ejWH1MXC>bs_hvIgmbd3*%N8`UIi zxvFC70$Do*-Z(FHO*n8}V;rWB zz3rl71J4!C5GCT8!XsruFLmkOyN&^gej+0ph-Y|62c-7nV^H!{ibg-FwYFJ^8(a ze>hgmA#H&=wO74Ciw=9SfFg-C>PMrl{B-W1EZT@^?ClO5V6fk5OheuV9DA(a-?rb^ zv7_KpLMHsV1HlXXBS6_e66-N9F4@I*(5S#Ml628I=QC4$Od?mBk@9bFjedvd^{3g{ zDb;0%mv55zvYFSqe3IIEZ+10f)0{2|N3?%7mXtz;(5NAyj1^zZY)8Ua2AzlQU3Z5V z{E3ML;OL<)A`sjWw3jaZfZ8^bf86?8KLxF*{G57~ zdZlVCbw}EDKK<1&91TgnoC@SKaN0RQCD$5qypN0N`a9u} zIe5iVow637EFw?_-v?=nT`Z*XL3q-ou_Qv?@{D=83R@BSbm8H2TL&t+ug*#{0P8Nb zd3}MKa6uA~v1QNfod+=|Ka_sl;VtI5ED^E7#K5l7{ha>;4$;bri+|L1Gk;&n2&V&S zzzwgJz+urRHypTDR94Sz9-x_+m`Hf@RR*l;aE3z~gg79pfcz8SII>W!$6IErO2l3L zafb(e;X#G5D1N1yeL1!SS$e9+n*a!$fUY%Z2>1gBCZuDGbq%gUxV@a;_nGskmAy@i zr~M(<6bWSXh`QqAqSRnv{4UmKcgHOyC1aBR-!L<=On~_e>5^Z1ykR-Q z83pi2^>y1LF7=_EH(#Pv+J7*5^AXDYcG{OA&K%Xi_;lXH9Kf~>(;Vz@3sxV-nu%PK z1XU3Nylg~RH06;DJL5OJTYR-h7N;0e+Fp74{ZP z_n<2~PYFE7(MiT)#wz=Xv;>okB}}KDCs8Y2v?~u)64F#N5h(AJP{v9Ckaix_r1`Re z{XVo0KSLBY96Nw>@mZ~oh|}?4>9v%XYuH>_t$H_k6&1G|)-gg4et2O8ror7@FIa9M zWfBG(7`4?kdJfq_)Bl&;C4o=)iR}jGDHHSZzmDFguw}L%BkKZkOs9%Ib7RPlPEKJ~ zNFH{5+x|<;3Y!ve!ru`7QhLJlbC!&mhuv!sKLnnz1S1y}oj+u#gCpn0%;rB){=s zR*$=u3rKqJIEU%}i84CI@SkEuF3xwL3F z2klZ8H9z=elu?Tr3IvMK!|8OM83-SUxU8&>3<(1VOZ?C;PKvcTxxw9X%hYE&PDsth z$bV^W-Pq5gu@_Y%7l7*kqwi1D8Dp#}P-Fy!+s7r2UKLS3ns)*$+hTDYT)karFQIYd zxmLaOY`a#6UioAoS{&m?|kMlZCl zjBot0cQ;?x=jA+eYvSkNh>BNz+<}{+o)7-EDa8*98i}JTp-SPgdyplx6!fzTB16~| zt|K}wMZAk#bH^q%1|%mU+vS^t?OC#Ub}Nyy4R_~f&a`i03dYPNcmTQHTj~yhITIMv z6tJW)(bKp2exsaKvGMpFAluBXjX-f32x{AUH*=Wn8f0xYRqD*A2H#~2T^k%6gj;vj zb~3OZ6AwrN0O=Eq@WAE-t|k7wu$UL;E70Hzot*6M(?YZ?W7-5-yU3Cvcw56*6jJVZpW# zH)QYqPN0RuMHvFMDyZT#1V3yy3Ozz9^=W|JrH-@CU3ekqvU==tlq4Ns^KxU}m#sIn(vi$8cFzM_;U8jXy{T5(Pl zzTV5cx4#b#G4eSM1w5n6FRw96ghyriCgnCBuGU|W-aGi*Yi;nb)RW++^L4SPGqN;x zexZ6^`?vQ%4STfbE^s*qK1mS4c6zT8IY%}h7mLL?zUR^*y^e>zv_fDSJI%{}*}d%5 zn@1YO209DyY0&D0&Xt3dJ6P5@ii1my{=qdT(Ulc+i4r4@BspZT!lz5G&W*JmMeF!B zQ(N)e#F&1jd!prEe(gU3u^wlMEI&E&{V|uV7duBPfzZ#D70%cBZTd{{e+{Lg0NsF* zMk=={IW{JjrOq=p^IIJ#e*ishV?oIC6GjAbewdphUrN7Vb8@jGeWnwOycLiokc4uk zjl#hebBP7wk1XEDMSOyXTx7s$-$+jpKM%~BYLvbjHM&}{(QxKcMf__B1WiazhD$nC z#VYb?acI+<(m-+6HYZ)6ne|CF3p@KFX%Qy~3Pe50`%rR+_?C1QyMaSC`q}!zLOaN9 zg%C3!vTG_Ul|Dj{xZIe$bH>58&x32Ve;O}^NY<-L)CSL5Dx}-e$t>>dRQ>>xXN*px zNJJ_CZVR|psWVkWDy(WuU+<(|^p^Z;=%r?qkbO?`Cn z3_5j;mZv-#^}HhtO^_fuNU3!sG2Xjs`^e>aZEa#P(Cb%B+})BcxHJAs>@kUqYM;O^hxfZiQ;PS>}75-I3|&r-k#U>LqgV$fr^#fOrOX}p-=6Mtb0r8e*0sR{qh z#r@cPlqFR&A|fLbFA`7@uncoudEK7Uk_gJ~@aSlY{kI4;(Rn@vL{p3I4o#TBe6d31 zm~Lxpo2<${s4&~&-qs8{a_{G@tyBmTGqam?{)*I)d?I(COM}UW>1v9CsOH^IUF_X= za2DDrqqG;|4|R2P(knu74gB>eSr53B55QS3Jw(QFi7)XMFEb@1Hxls+a;Ia%96_&H z|9(7iKzGobb92c(8Ju^DtNu}FGi2@M?R$OU+g-KU zMk2J>N6|0FcMs#S%#|kGEqTh71E5;-tox~p9^8BetvkItg+eW;uaFtz;^XI+p?F0& z7~12yw-))zrB6B2G-=c6FAh~ySAU^4VcR2!QZ_6=eN2olXm?SA#nn}U)N1>pCC{q%DE0n6|_pihj5S$ zVAR>q+XW8}0!@2Rm{mnh#oLqD45aU}Vm(uRnx}=uDrgd?NL;~$jgogzZVa89074ij zV?g?AB=)tTl4zeG>cw~Q9@!+3r#Ce*5329z#p5NQ|G1c}h!1)qhU z8nk(>%uF-gC$I)#=6SA4LpWcN@_GN@%8KLIakJEeqT@eIlkC64+nfRd=6>!MJ2&m3 zc1EH({5Vok!OI1mnaY}A@P9{KWPhQGbpRr~Nhz@*0F ztT+_p*s7`N+|)7lVRhWP4VDtvoRe}jEUrl|6lV9Q9c5gENg;$B1FR&-mGg?4M*tAz z{OK?Ys*Ag+(&PmCPy4e~;}HC{f-4^hH-yZ;YGCsT2I+cj)prxcHS`oE9 z#T*+Vr#9?&I%ixn21jtJKXe4gL6EF+Q(4300)+@S{J_nIq@3MxrO$ z*}>ku5S~&`h)ZJ_XV(J2PGAE&CoTOrw@fyPu3MpadU~-%Jv~X{*X~ZIH)**%e__p` z3G~01Ly{kCN$cZoBJ(s|jBRxw^;7=87t|-dU({VT^~ZPoZ{Ox&UWSPfLPp2N;_mVw zTBik0DXscLIA3-{FOB>;Sc>eHkOvTKsV6W@j-Fk*wwTrd709shhSg> zjgr{s&9SR@z;olh1V1mO1t7hUsstlcVWbPi_8ge*SNO>$*_B%&+BGzP^N|yfkdpq{ z+cQK@IswKN5_07vcE}#+j|y@k&1LXq9lYW-SYw~0Ga#o4SD$pB2Ujl_5g%{#S~h!b z7%g;RIM_k$0toj+Eav!|*P{-!59@I<3W$hQXg(aN6G{(xBBY?xW_{&G?p;V=(mw^@ z8x?*^^>zRE?}AYC8^%{A?F88NiCv?^=HGJfQ@HlIA$>eWMoS&uNn6@{X&+ayp#E6* z30Lhw>FFJR+{0*-KAh!ecGu45Z)iwk4wS_aWdv=hJUpW}!a7#)0dFl(tn+9KbY;(u zOk$0Wz9*N6Dd)pGZ(+F;O~?1f7a?3RYePs=Dw|1L9e;6j;ucR17Bj|;B~1t6Y$C%w zqO(P%eqxEYb(p4bK*j%`7u76ia>>V`5-94>R=S^)g4OB!$>ce-d-W9KaTS4lsb~*Fr;MmyDV8^LBN}HFtirr~5MMZv09fjErn6%;Z z)(Z56o#&39AI-_~+~lMusK7x;bM@Laqj;oxOPOrY=rX~1eL;?Zpr8TqADN)NnqWPC z>yf?cHPWzmbSFw&@ZZwu5Y#pXg_S;7wFE$#dJ~?_xwbhkkBNl^=oAea*Z(8yz2m9= z`}pAsWhEJfBztCbY)Mw;u~$|q$KIPVN@QdmoH%y&-Xl9?9D9^Kl2Hhul#zZf*Z2Bf z_wT;%fBVBjKA+F~{dzrTToDdMq4Sb};n$MPXGheL>5HSrW%C;G}*~l_V7t9q# z^1S5^Y~Ej_6lf-P04YMABReA~y+mbBrgk~P=)RNfNR1CivpWywQcu6{w3Cl;dSJF& z5XYN`pXfKS+Llh=bWK;UObrVeD#o;jf|Iz8Z7%CLRMZLQmSvyol9CGAyN4n zO#G-(K=|t-tlK|+{D7#^=M{SSocJ9<@1?7`W?sSMmXPp9^BPsGsqE?|zI4uYLtmUXUARwQ}PG15dkF)|&Q^}H0JnkZ5H-a=*3jPqpX95*!8AO4Sd}NEd`U0HVK}e`(`XE^3FhA3Z*y~B zB7%hCaU+x&e2PER_Ma>V6X@Qu@Q;lW0Y@Obv7uJh11(EgNxhfsLG~L;8nBnV0EJ7W zWMuwO8Ufpr{H2@*-F{%1zLBrUXb70)t4O3;t^rL0^rhiKS{g&(qazY_0?2+QeF-e7 z$HRwbQpJ6CS+#_wU(LluD&2aK&#C80OlNc9QD{f&@6WG5q5V1qz$LThCms;r(9mG? zt&lT~MjLS!r`KYcE)ZFn!OeTHg2((Ozj7mzEI+62Ml{1wMA(xo9849Qdw}`bLYf$d zlLfQS&Nc3I*&;@-7{Bt`A5l&gxi`MPb7Y$(#|io%O2 zN{2_LC?!`($@@>%&{6m8d^n5lu~I^4zyUL+fqVFz5{dpenPw_WusDDrz|a8-@Tui< zW15uT6Q~-l(_udw9XORGvqqI4+}ef~k_~kK)vF#&6J#ZIwoP6ge-L{s2HD%grtU>Y zco68(5{WtDE*|w6<`+b^?uX>Yi(dJM+>2cO{_YFo=sRk5I*#>sZb@lrUH90jw46A! z*cXCh>6ezG^z_7r-$ujCCnVcYRTZc6_y}gqlxr*J3p{o0_ALRd1uilCu1dW6X^1;; z6pfJqLfr&8)bL?i4}wab@GdmZ-njE5h)n71u{njf1m1fl&O&*FJU?f$FNbA7KmbS) zdy$$PM{hnbC!&o!txbreY1^4TK!LRg4QXa|z8TaY5jD9zc_WP`lyQrA$R#5bg1?&6 z(vntl-nIPc?$X^d-&=9v)(OW7-$5Qa{J@8rkPV+A1qR70Z1#3`alA@Jt9^(whxIUb z-?{|}?Ud2ox1Io4y}K>P?R#%V*_>vjW7TBzpI&4QR(`@Nbe{8V1YP_is=fPu}?4FC&;`jJ(luv|wFS(VW_v zKm=-_ChO?f%!uO&SFn`y=OZ{Sx%d)U;*lu75K=uVBq?gY7Sf*x0ZF7+5)V>iVjF{B zlROqaL}>pntWDhH`+?-@1zw^aNbdbE3(1$TXx)*toTZvo<6;TkoD>YJaJk{a3=L*B z*}#N|rJs@d+svfiIfBD+yi89`g)F8Wc)8UenV5K@2{cd{a%Dbo(gT{wK-ll^?`K{+ z)up(7A?tm|cC#<6y~Y8)o8Le{_vN} zztK*9b)*viytek_us4SZEjanXj)Os}-)qhh z9+*Ma7o6SANSBt4A8&B>7DL=?;T%g9{)2!QKSL-`(FI!TQK#lv$lP}WrZ{{xIuDI zH|6?_Q){QfV)i4^awEtnJXpLG(1TZ0QE^%BeF`){^(!H;@xhwwOm=5o51(9Kh#lb) z!ULAg4l#9v+r~q~dsu+dd8?>p6;>=AL3(Hkj z{H(8ox1&9?mlrD*Zp-|~x)-^M`9wz{0+n?Dm7RJvyG{I68$!GHRPy31-@LC2fwK|# znU&^jpd#V5bNBRwXw?+&qM+4_1Ya7*eXCCWNjF84PFHczfkV!l-iQ9J3=&kPfpzY3 zmZc*lVnyk!W-uH2(nq4x8bEjh;8KHPmT1u`4hBN5*{pbo7~oVyG|A{g3}&ST(-?g+Dz}72EuLNu0b-Qg9%( zUM?%longx>>BBpAD@#})Vs7hAG%0_|fc&~Q-Vla#mX3xj1QKIT^~$iix_EF9WL$X^ zVJ}h;O2}!}FgP3-B<=16gf}et@H|g7Gf`0#^b`u(3h^!Kdwt| z-F5%D{t@UCs717!D18*gj8>`~HlX}QDM9VQ#?%cMBA^QoT}L_~)EMmRmy%jD9rcE- zcY8$TGj*X^<$THcp!fFS`Jo&M5AX~x1z?Fq1J;PW5KB=xCT&Wd4uayXG)5+2H_JL} z+ov@9X`>8S5nHHfj3vIhXn=ZDRDzY>{F$1Q*kg-CX2OhVva~J zk%d=)Xm<{g4Ld)in^csSmp3$on{x?7A_q5g+nY0AauFSoe_->d=IA~9Z-m@x=sTva z>%L!7y+YT+N>3eX;9@zJ-6<@QWqYBRlfqrd^Gj$!qq7g#+}|7T_e?r(2SDBy=$?8x z1jgvOGdVzX1ykN%__CGmBW~Y5^>@KDVCJ_Bv<_Wf`rOwQ>qK%A@uZhrjH>4}_WsBD}A^j$ncz``L*kzbA_a5a%37 zxU1o;gje8{A}=TyNy-)9%2v~KjW_fxen9fWu{HVoy;z(sGxk%IF(lvr=EvUbAL74R z94uQQk4PTW?ECGO^fK^;-a-Du(Q?Ut29139S*JQTKoSA3x?lMG`Sa6Yh94t}K)y%{ z-C?-IuDZXL&vw6rp2_wlFA?(Ff#(M{lx5;W^<4;MdB`6&1-Z3S@Z3P(`|8)^%uHYZ zL)@)wnCv76b~wE&_oVZ}y{Vke+n$%sAz{}&3>|5GSo)9tB=j97K46mL8Ak)SgdJ>c zncFYymdG|LHgO(x$wYZ2BiTxt-fr_QGpl^klP>YR?DSzx(K8*Y!45p{T@VvW!xGI>@mm0jl4z(_Z-!y993Ou@vB7pm;Sb;!mu%*2Ueox4$`bs9N8p*NsIP}`l#e(5d{qRN zq3~8fO8`eT1H<{^;P=O@#W!a>-`S*@GL@S+Xg z2H*^LZ%arBEWxuabY7$4=9jB23&wgX;m&Q|HWX8|7++M;7aOIHD!K2;L*-XdEiT$B z*;wB4wvsl?K>qQRHS;ynyg-oLEV?wz#*0pSB*_U*m(TMGXV7M`%-pzl@vIUm!c15c zL4ffV?q|rii0nMo1R{U;0Ilc!0t08(-~1a5c{S>8$TshDVITH2bh9?g4AR%`#1$yzRe|H< zse_M&kB5UW4KcfpQbKrVCJZ?n8+LCc`ucbPnCS9S&E_7Z-O^b%ili4I#JLB{GO^0X z!T}O+>fjFeBV2kJE14#smKcggUrwn8WhK;wk`myZi3m%iiqdy0yLA5CI1WbNYzx9l zxeKgf1me8ssB`!--sfZ*WcM+=E^=;~G%-~aj31;dc@x|*@Mr-D?=?XjXV zkHMx~k2rBQwRoOv+k^eEW|sdQg`Ds5&VD|CYk|k0H05qU3#_Fy#w}@FG^9o=o?&9e zo?qCzv%(d!PVa7uZZD3+R-ksFt^=BQ$U3@5<2h8=&OMhlH(vtmBl_@ilWD8}AGmOL zyqkJL+V`_KKFm!{p61Mh?vT)o=<qPulKyag&BpeW!= z85kOZIQjq>+v&;rikvSH-pPC_#M9~=e3tc{6xbsn4N#)L>}gf64QO|o2oDmJs*OaZ z@i%tcm=_EWC~RiZcU*SmYnjt<@5iWjs?9xkn%*|lH_%41rD9sTYNOg}w@&uc+PYqs z7D~%8>j~d(t$$0gFxji{n0n2gKz7fnDGv9dYhCr^n{Ps#NA`hEa{`$gMm^6Z#G3+! zci%rhCnPaY69@NAIg!}CNw%ibo(~2zl7HnEV}Iedfr60u`&AR6J$Op8Nm+#YtR8`+v&9vcy|HsePtjV)OFyqSQ@?@(%j;Z#bN???!Wdk0(EOYcQ0$_QC59h`-nh z?8TA+Cm^uF`xAb{_fs-qQsuo5ENCSd3{D^&ozXJRuL~eT1?J0qQyw_=%LF{3+*e4? zYe}|u!bRo72AKp2oT254_w4rR;0v*Bd337 z5u6mS?nl};7;=aJXae?hFvfVpGN!6(2?aiITeFRTtV!d3zx7R=rOGs}8iQ z(+TGsHvUt^FhYO%_l;+L{QR73aX9$nellL#0nF!<+zUN-jg$0hr_@BK&;gkI2!v8o zA3o>;ka^>{?M8~YTke}TGUu2wju#J%lXhjOUa+*Cx7D}tw_5}h12Ch!e0k_Mw8duK z)tv3*C)BgP@biJ08GJp58Ucqoj}0Doo)!UtL^nt}UWW9kSz&_j=}=vh!Tmf5bQ^%- zNrX8tY5F2Z?g3@yuqo;G?$ z6efC#K|$aIO!i^2;sjZx~l#|XK*qflGVQ@d1KZc0R}2gM`Ttog_OQ|=b2P<6ps%31M@C=70!ppZT- zcq`q5v|KR2wKO%^2iy(|QrSMc{<#sn#Zl4GOKnhL;0s}Z(8AH-mk^+I0o`4g;WPHA z-q4-m<`Yn|0vzfLL$PcWK)NvBJOya~$}45upNz+NMnDYWPLqY<8UwMe20Uv}1%kq9 zUay}qR5wYKdfbRtvx_ zVdLkiF*>!WSU0mC8w57uv^$ot?)linZ_ct}A%ZZud4D;@OTQJ)`+;)c>Ev8)(Zs z4P}Hq4T2!v%0ZPVd|k)$zgoI=h~$FImiG>vBXvnwk&PCQYE7?{55GpvoMfvQv)M2A zGv$ekAITWujUpCVW8ODeYoeS?S^1+PcrFy3v@z`#1x3idG$-3?L`gO}#v4 zx=27;jw2}Vo2Yu_TU1~ zhVb*4uN;c3%eQ*NS$MV49eS6IM>Dc(z7Q@u$M>f`6cN)#QXC}m((-#>x%qBcmA{9G z)ipe7{_O@OPhrpDh3zv;7yWsqolfNrw)*J5a^w&2JIzEpktj@3Q(a z@Qdol;45=-+CKd_PmX;gnd-UEB?=2Uv3PIFx5C@(3vFm49RRw5Xcy>zVTR~eR;^af zZ*A!P{C|8M0oo|C#T4GJtZ1qA)0!>a>WuP@`f*&{Sr(Lg= z;?1HUqVPu(k)urEP=_RwehOOm_V&W^mbnZkeRZwI-1xT(J|a^SRK|~T$SzFtU-x03 zuHdsbA6jlD`1>GCXji3pls~~hymF=YjB=lE(u7&^PDEE_m^?d}FU@bU!hTs$RJ3=* zoe~x9p5!5V#m_^y-4xfnws!>4vFFZ)*d1v}QjD~I=~4T#F~ z+NAP3u4Qobss34w3D_>u!DuSkJ=7qn9P!o6-(r2!GxR(VTPFWgn;wfFj4?iUSc3dV zSXOpn*1Fw0eJCX0uAS@|>7Id+mM@&as?WLKMv#+*Kr~e=WbOi9Ge#A41knxPpLy?+ zH_%*fkt9mw{tk5GbD*ludselWM1_6qJ%{aYMfI*duPXX^?|jX@Pzu>W zk)SuPgc5OA>^?NTep3@3E@WYLBIw-0I>y5mk42YO^oPQBbgH4lhK?{?zYZO38K|%$ zcH%7o9z9b0Nf!?UnL%y)<)H>`+U|BE)FU6>*ZV?3b;3fTPYStX!@>s!!fQVMoT@*- zV8DzKsVkvv{AFXa#L2#JS6^Q&S%GT7-B_gufnk~u7@pa-7+J^ z9WfJ+nnGVrcBLOEaO5cv8HzF7fvW^b61NBHS{TUYTl{V_M4Q`!Y>6Lsuk{ZF&!3+) zxq0xB?iOM3?J90c&W)2^4Jh%q&W&1Uw*B4{=iHBJoB!q8MARzP)hGZy_Ree zW&#y?WsD9Qxty&N6iVT%zF*O~?@wFHn8lh(>W)CGJ>~H<^BglIWk!*a^FDlwj^ePUy?2E!2u z-NCnBKLwhvxw!#T7KG{|#T#Xw8iR=RKLzoNQl`%)$O7NP2Gz}bjj%ByK#k~3UQ0h)^=`!bR@T)M9)kTfadh% zcUeawvD?w;<0HIP%NFr6kkwzn_76cvtT#nP?tKgZl(1r+fNKTRIAFHC&p;YDO)bG^ zjI}1nbd{G3Qq-M#!V*%%D$NxU5vWtoy&OMvsn9RVwkz8rYPV(^6&_=Gkwj0MSjkf6 zo7eN}8@neNc36$6UU~JE%wYnekCzSH)MZ95+*4m-%sXdb!9xiZ;<@o7|K9x%uDxY> ztGU~P;-qRP=pNaIOC53fFMD?h7n$DoAxle3fFZy{^>J$I0LPII-p{Aa4`vxV?nzj4 zAl-_0lJ5q|GS)$z)ham!VuqVKGYlB@ zL-R>XK%JStu)fZX1(E-5G~dfq1fEiX9y7dnKzf62`)D=U33h3Kxo(_JfUr|dY8Sol z8xF>d(o0qTh>f9NHOn!(t;Bppm+eC@7j=l`Udu@Fco4ezoc6*Lhfvf^Ko@`VH8tPv zKC6W{x%Vij2T5VFG}O--cO z6^DbvjXK$Q=VQ&+*;h3mYrGMQF0}lXhbeppwSmC`x$nLW&q~a7wP5O7S_YSU`Vvp$ z&tcLwRAIOSs0Y|pZhNeQ^wG@J^g3l{?<(@P+f$QT=uc97FDU$BnvGR4h{Mb#N1jHqHV~XC z-tY@TP%?e1PRRDTt!1f(g!Mj74Ox6+U9m;J)pE*I8Uv3MpnX7Cek}z2g^B0#&nmtd z0puHzmmz0(>~fGFSmRI@xzJaF_Q@|>;(2Ei?soZwFpQry? z{A)4kT&~5@R4@1eCC4Dj1_)7i2>t_R7d(*oug^h0ni@)ep@U8lO%Z85`Mc6nXdRHbkoR*t27ltx~2`6v!{; z!C5dqAp&J{qII=2@e3Q4$gaD#Vp!QL!H?*+JLs%tChHK`7?Ax+d4K-|Kr&=A5&Z)T zwtDX{Yh*$^(zl4>f}1&pBeD(_kE1v{&AxbAd+)wfA06y39Yo{bqzpb}iytVcd(jCZ0LG!iBXHr30si@&gUk5}lzhtlWooEa5mW%y{h+{DHw9TWO6s^_V?wW4M?aBql&42spjlaLW0MLEPkv7>Py6Eid))fh^VQ!+m@ao7d^+q>5U5_?djj|>^am#; zn|kw+h3puI4id{b%tjHH4Q;p@yf;@?9G`sBj>@j>f8-2UK?g(uGq^7y_>jn~pW z%L>~`C@MbgPARrEfS8@{5l^Mk<=Xs(Hw&;RTKP%!06{HL> z@0edotPtv=E8;XQ@ay9R-xkt|Bz1)=2F7> zOM}EwjGxrQ>_R!}jIE-^pXI)frplXb$Q}7cx=7y;GWgElUEwor)K{kIqWx^KmHe6= z2L*R&ro2o6Zn|#N=J?kYX=@d`3#0M5?ygrv|EeeC#nKzP6PGKD3t#)svme+BH~pAn zhUy^ICK=G`&Fa5OByGT^v(m#AEZ)m*sa}>St1MPkU3Eud?~8pnOQh9x)|57Hl%?hB ztA*oLh&c2;L_xfBF9tF1S5&&&R_|sN0GGwdC&jz=*7n6EWm(K|H<`oU5jX`UST*Q= ztKv4s7NNT^n1QeZ3{bfDD+>!M3R)^US3C`N0;2P#lXnj8v`7c{8KRXk&lh~^QsAT_ za|+g3)5TO}mNb`48+`Gsf6(s$+W(ibMGwmA%Ib1pvaO8@A#}Tzb2UWV1H;GRkgL7( zP=F>{0cgk5CQU#bFgXO4S&)Wj1gkt;@33S(mwqSZjMZpMvz*&B#(wrQobY7KlWtqP z;~WD`thbQ29Q!HG#SIy`>Gtuq_h`+jnZyRiA!4~KmbC}{eyJ?4VoE!$G6*u%qAD@C zF|*p2TynWu1M-J{&5QyRyX6m6lm)S~ip+_l&X{ryEw1*N3ym7tX7UzvK(=8uplR=4 zGIsB)pOEn!9?Rg&VCQ)7dj} zLk-Ka!B*#UQix6$K$jq=khqbMlra`|b4Lqin#bR%Je^wmdCbu+XeEd3d$BLxlI5Yf z5{RRwv3GM5L~ljD9*Fp+S`+!8QhiA)&GXt(_O-CDfcjZYv>fkG*Ia!?0)qM}6nAya z3K9jrn9_*UKL8T=AMfNjlsH_x2zijP=2{PuT$_zyUkvy#MEQ#O^wZxjb_25 z8k*;;7OCd(bF)j;5u;w$*luC}NuO0m%Y25DLFPi>#v?ZpE*bPox&Svf9kodQxPiez zH85!0GjCg$&S=dmsmc=k^ebk&SZVmx*zZK2;2Cs(wqTK6AOC~?vYP{LDQRg@^6k88 zk)XeUU)rU5Urz$Qrz&)GD2%8jluIth$4D#Ku!3gnCULmSt}do~wKW>A0%kA7N%a z==?IKE~Bhc_JElXsVMGbfrv>l{2U)^8M%$p6qjwWC&(GVRf}ehiu3X$8najB=ALIV zE4*LM@n4Hk7Jdl6bK3hGHLkxdI*ca$u{IsHe$X|1SCBK7oWS70u5i+ea|Bh|{8`^9 z2#9l4<)H^s-C6b8o#cqj(nXJ@PU2Rn$}k!(Go49Y z4^EzmXR)NOtSYhh_%maYfSM-XkI7}T#P5G&nv|jCw9!d!M6g;g8)Ji3 zq%BWWsEPXbNVZYA#EwZ+Hij9mLluqo&32!%c0b`6JHCji9_zu;+&CkuB5|E8!Dj?5 z%y+x$r8n79s{CkI@NueKQKlh+WhNmItKJw_O9_LN zZve2_-;Y{cTr4G9>V`H&ze`E;SYnD;^Mu(ng=>ah;T2yQ%T|@oR?AYj&Jr1R(}x@2 z@*c8yk|VB|BO=Sl9zGe-zN{Anakofiipq~lkHiV;`B~f0)eZ8z1@|QoIipg3BcZnG zS$;e^`^Ik53kA0e(&XCu{Q{4btnDkE0^XSnC!UAZenA6sdkMTFvGRs8Ew{U7)UUSCO+DC~>8>Bd(A^fX^sjICDxlg5nS z6yGS9^Zb75v%6;A?SEscj(IJkOea>O&0jS}qe^?bee$X*B8ORV>gwz5-evA!ixV>n z)qkXDuyWB;y#2%|lYq(b;i(9DXhb`&gbK58VWZ>F{}M_NgN(}X)ayyWr-~TPdCmP- z<=e&AvYb@m3Xx`sJ4b)Uot|Y2t|8fZwoQ|aUr)LBF1lwAICQ@~7|aMPVyKcc=)w-5 zOli%NaZEFzWad@M^9$8RGyYbTv$RVD4H09LJ>+2)#)6Jp5eht!ODKB_2S2~Z=KeM= zYhnC!uR4#z+&O9N)qU2ltq*x#x^$5&wYOG#G+kgmSl+vCMs2`eWT`hS7Dw86{dd7yJ4<@O{U-%4ea2JD11x1z@hWM06QM=gv~orY{(nnSt}pz?j;(KEjN} z{g!A{`2FI?{!(J%;uQ_m$_EY82cwC?6aCG|f zD`zon1GBNI5f@kc>Z*?4mdNPt>)-u;-3wVDvf)PV;Im#@vW(8ns^>=f_y37C$<5Vh^L<^q6ytYQPjzHtd2cOq&a)^;Mw&f|2Brn0>ed&ExR!`bo1BI zN_QE0a49)E!Xn(0%eT8*(U1e=W!J4kInU~_P zhnZxLnr|&pi`OYnlM5ubVr+iK{tf$e-B~{ouZk%-S=pR3O4EXx=h~ncSVvgbzgRn3 zSn`anp%BpFC|r~FY{9^0S?}!}HDBG1-Kw!MwV>cDjr1s|l90%R!EPTG@YDDgY z?s-&>zD@3E^sRe08jBh+)C7n;2*sTJ|3G0-op|~Aw%>c1SGlBGOo{q0MX3!R z*R%vJaJlhRDgAI*P@KF(AW9c*n>6CYNgu9|xfWi$voUc(mj`1{GOHicT^<{ia-JFC=XVFva`d~!G z(b6E@k=kyBdmh(|rfsY{?QPAC2j(muZ5ToWd4~5_!FrY!awT)*YrH7MtIn@;8+%cf z$Dj?lq3>Q@E%Zy*vw=G$k}1oqY|OXlLyGn^znJHdEY-rt_!6YSc#O)b?a`NresofD zEp4flAno~>!R$(f1C)ea)81=D0B+iNbKg4_L#>{`vuWrhmNJ6U6QD`Ksw0t-X2xMm z5j>Qi3X|_fcVm^n;Ke&QR90O4p7PQt*1e{+ung9rqUQeq{iPKJ6%x|PTfHh-UX6Fq zZ3y2+iGryZouf$-M~mK^7ur33%)5*n8-r*ac}$7uZhc9_lwg360=|FqE=L^BS*#z8 zl~;nujZ%j!*ZBN)G!e3Owx^#%HCmttH~LDzRX5hz$b|cd9A$Fzg5Ec9=X%Jf+gu2@ zx7cdH`)BBdYqLr;KDpE<8U5^Hv0LQoC&mvKHc;25u5t$I4ZinI9_e=xD>1twB3M;` z;=U3I<77!mdv9-L+u^hkbkB4z9i$o=IdzVw&RTy7K>;5pfggA-df zTrO!c3xb|ZIRqpF#R=rwnX8mYl%g4rHB|j&-6zuH; z8H^$v24Cv0$`qc{iKqb;bheCLcn10*l)o)Rue3- zm4q=`S-i4~qxC>l7xlRGhwVLgrWWzIsZZO%JWZcfbLZBG%R1KFEN9nvtz3mEvF-#1 zGfKhC)VNU5>!nCGo=WAVguX}vl}qQ$?~kLZeyUqN%`(q#e1dxW2$k<*UH&Gk8SyDH@3HE& z4a#dpLt_QON$dB$4E`3xSwX^pn23mokkIgl51)QL-4yJP#j)oWn@+O^EGRNHHjm>5 zy2d&Qn&QN;V z5Ro7>V4ykRYyL|!Dn(*o;ge_M+}YMvKqxBSRJ63rZ|u?Cr59@b{<(AZaGH)6MD0j_!&aZVhq>oCvPa!Ud#6y=9>uU=D<~$xOK2Si7!mC+jvoWVJlg{=ChvJr8O;2vE2HQgR z*q^!d4)bGi)8ia^8z1E{Zw<;@p!ErfqMssGtZaWSKyA3(+aQPw?~UO3v^(B--D8m0 z%ax^8E*aG_B8*RmR+$=(P=aVt*#bOgVx2De%I}Bd0BHoL6`B0tJtji(ofas zotz}Hy*dXZk=pLBC*h3Ah2=#?Nuqew1O%F@8nUC@iWNoIm7jNk5-hOV_*>0`srFK~ zv3~@i{}Bi)RA~@Sn9)|;xU+9S2jd?Ci)8&R_@KdEg5EJYuduN91|fu}>t86m!=ND& zr<@zRDrKpx`M^`fD^OfHCl0N|Y8yL%S{vE;c{uRvmNE=u6CZS0@S0iD4j!)>-DHsK zhg1T?X{-E8M4)e>xsI4z=$?I9?X&LwKC=RldY~s3THKDdQg1`H9_eet>vVLdSvKGZ zf&`Y^(=)h&;$nAfs}D2H_M@by$RF9Yk^Nm4`HrvX8{K2@Y+fdt@^0ffR6NGxgfz{uA zT+r}OVk6{-qHxbiA3r4E-4l&WVVd0HN=&6y|H(Y zD;&L1;>}?61U*fh`=TP;y9q&AhRjP1eN@vLFiIAE@CEUg)!_u&Z2xzpV5hsuB687c z+4{Jtey7xWe9a|W2aA$~yBPv7cfXJPxhC4;=lg3W?~>zrQM2Dgqbx5iGk_Yy3K320CrF;l3zKKYs>J3O7suDJ*W2oH1~o-m zLmAF=y;7dej<+QJtrku}jCx~=LhBD^evVP{*x!C9aNY#G8zM*;tB2nn+&Qm0wW(h! z*^+F>#9UR4yt|jRv#aPoD1c_N!sEmuU$(!2Xx6oYHINf5fS$wC^GZc>vaxy!O}+2I zE?CqwB2uikt*zWK4CdAcOOtySycad|?(Hd5)~O+NtEsZf?M=9Qd~!0^{}nI%^Cpmy z5DgMMJ!3|O;$F79&O@#>AKx$0FG^gGI`;M9PKt8qsujt+_QqQ*O^vl`1jSzYw&87M z3kAt}p6gME&suZdw7^HP=AN5McEW8!eT=tT8mIe(tZV? zHi%^;f9LvZ1byAi=H3Ai@nQ|8rJv2IvLl{6)lwHcwrX@_MrU;Ea<8~c++p~&&y(I& zhjP`N;dD$$CVBqsE;*rd=ZesiggW`?GdlCzjNOwm!v1ZXaVoS6JV{7)i=k=X7l>qJ z?)dn`DPbq&>;7eM6GVKHPmVdSM1GtQg_sP`?~S0*Xb9ifkJ>+c`Q)d6QT0tC%(yif z`4nySjgP#!6rQYZd8&*J#QIxpZ$xedpQHG{kIXC|)t{L8UoVr2w z$_#^eE+~5>l2qj5cFOyf3W;?;e#EEy-8%FoJN958as25EGTWT0)%5CZSJXkDnD$z@ z{Aj9@LGI`YaymWofTvxrAd!RGlPz|T_{4;o@5I+oqhw}bwZN&Y*vU<_&L_!e?x$K* zL&MuVk=4@Z#-ve<>0lCrhF^4JX`i8K`p%OqG6OLkjB`Ct3MLBjH>J+dwC|gZcbc&q zI~-fs9;oVhAE=7aAK&9iSwwmYrWr=(inJG$B<@_y3M&_+QzHCBPV{h-o(G5tsB(=WE@jUNV zOZW@tZzLG}glHr1BBtP9HOPNG2m-|C7rUvVXO{L3{`fC7tE$r`^{wn4Ho=Jimw|u4 zyG4NqoxO?MY$_OLCEBP0cL^*qca#fvm0msMr}@g+01q|cO&DdzwBFrdTv&o*Mh9HT zgT1{fK89l(KlSJg?f`^A7nU1~b3fD+IUyfXp>C(qMPQZKKZkj9(u3l60T6-N*_41= ze}9iGL0-`usNXFu*(o9+bt^hDRpROXENI@rbV)~dEpozWZVM?ACo=y{vca@Z`!#Ye z_pzu>>}qZ7*yybXokA6Z(ZnI+rvFYVPKj$!YJw}7}MbLao(!`~NQLig8=g)TF zIK>cC;4^5=>s@oTgq=6fPNZ4p1oeaqzkoM7ud;spw{+dzO=A4QjTDdSPwI5WDW&$9 zjh-$&%x3DCTv-b_Adpekakb$AhZpsJQ_5<2IJ!SD<2|COep_9$lD6Ek7OONXjo=%@HdI&S)koH=nLCvG))D8}Tj;-yRici30&<91d+2ZU z;8{lI+}Cy6?wajI=8cA-9(&lh0Jv&y!%j~$aflEW{#feHibWot8wAP!{K@?Dw&%}J zPKyPiUlC>PkP5!eqRix$zO1}le&IR=CPR@fm%s*}aE#^%9+jf?{Kt4#G@L?ySdJ+E6aYyYp{lK<|8jG7hA z(ng(sO=45zGJp8t2RVzSRG@BRRG5m!R(8cqS8vBtAM>yDiV04Y-$tuh#ZKWz>Z4QD zFBJM3S_KPkW@gN2DU+1mtl^e4Ob|3)92AQ%7g;Lqv82goNp@$?!hJPjeoK9k*HR>~ zpy!DM1@fg1&YL^gwSKmgJ9^^xfzy(~yE*FWuRkQ_r0VK=7x(Gz>Tjv(p&l<it>)diVlClTp3R!v>C zZ-nq-)o$2sIYHxE{o`^?Wzr(dHB-NBg8e+PriF(W}BHSz{PU%-sUjIGQ z-=(d-{HuwPJKxR5D_7`s;==NVGUMWShB#zNUTKKdQji%G7eK`D_wQkGd4nl;KF*BI zeE85G8mXQF%Fqn*88b6ZBPJWX%R<71x5zB~nGko7;akd(_SFKF)u<@=S@@ z)fH69GPUzG=O&#v+3HgmZq0sD-aEQTKD4ysd=n8To)Cq6h~-(OR4U5(LUG`#rxF`e zt+Jf=k<;~MoLhrcA`%sctFA=0%*g%MIQGw;u&0k6pu1K~;8H(#?8AKr91**(geva4 z+u8N~&h3+z^|ZXsuD+*XyJGIRlf67KWL|N9^!T~c?${a>bZZRjPK)Kh=*M=0$V zi@2_P^71ZCD*YuFa zuJi$&nxf5)R?5}s>a*i>Ls?#;8`w~EL~bF(=kSgSj3o}!{`n97_hlVm(m|}An2uvh zJ`EMxwRBi-QH%W{vU2De!{l$Nk5?Of6xGM)u5>)PVjv=0q05>YVURsW-F0F%z9s~r zY$%wE*(#^1J2su4q@;yM+)fUc&?fR?UsWckLT7Vq+7W-Ko%n5Ze9KB=YcSgc8#Lz4 zCA8L~kBUr}{>8@A6lQPAZG3g;2_>zW#73{~_p8Olg!+2+dg7vYqN+QvrOr=yxc!u! z=KiKP|NS1vQorRpQrx?%x7jRuhK_;eZX)d<>79GpFI7vb5k;7-KPG!V=}&+Cxp;JS z_Q+>Dsjji1(P*s4z4PM}iXD%B5_pDN7>a3gK0LDjDZEb{-^b?1WqJ zkgU<+H}N62%T$XqqY0&hwVF_!Vlu_^X=kp-zc<sj#${nzPx&Qd^z30 z=tQ&pO^(gio)f(28+0^ZZ5-^v-?vNs`10!jZ=JKSx7>cJHumh@kI##KwD^FS0sVj2 zI;*g_x-MG>*WeNyf&>Z;Zo%E%2?QuCSa5eI1T7>$2oT(@u;A|Q7J|E5VP}7TpYGGA z|K{PM;D(32)?RDMnD3}?MQVaL{q*>C>b5)IZAJX%6`?Ws6(r>>fm#Mbgf}7%5OE6v zDv3XUVJxS6mQY~|52m>;&ejvAj6(_$mtYwlvf#W3JF(zQAKq2y&BYm> zZj+$3V)Z+~-#u{i2?`WkMH`78A1-?0I`;UckuuvNjVpiJR6PInz_k8>;Y#fBgg5{6 z20I2r5_cyk5S|7tU*f`=dueWNNpwP#gP((+e*zc=m|2MYI753p$rz1}s5I~sJ4^zI zcb*UsTHKEhtWU*)G}zvJS0IF&cI?t@GpLAeZld4n?3P4g{bFeDl9A?F;?TPZ3p(^D zPmQ-15XJN~O#iQc>pwnp@8TY?CKzR?EyriDRexe803q34M&rX93d( zFeO4z`#IG33lvElw*Md~K zU7g!3MSrt&5p}LGe&Cs)X_)Q0e@Wry)7a*PHwyGfHx)tCc$+oV^m8N%_HEFR-=I*(`x$iX z#{N2N>@}T&|F$vy^#nI~wN8uz#niHjzxK|y1N{aL1gQP4a}yBmLS|?G_B^hMgoPj&kV%_kYzGH1Ui`nkR*K)+bWOp6}?ybscgKdhkt>}j%p z*V|T~^tb&)TJDf>*#W^>cj~5DTj@5=<4(0P_ukSs(%ooniu1 zM7(oPM_>@b?}5tas2TwU3t(MnZkclIr9KTS7iDsTp0fGlado4QHsY7%*|s1kKTSfRdYV4DO#+iz zWF_Y)en`yiiH0Uh?4#fmtJ>k|#o4AVm;_V3MIjfse3>(-AY#v9E{; z3z0I{&$a`6wSIy@Wyj>ZVOYF{e%7cK3dd6h`w(WbUuO-wDVg{%Y6*@TNb|wZnUYHn z-6mjr6cS-?YJ0~$quU$ODaWm$shJl? z&+AMNOwK1nlBk(bHVnh0XjOWP<#EoC9%0mJV93S2F9~35lmyMqvzak}OvoFQN z+m^5{IU8UZS{}Ok{|`J$k-~|L;IW7I#%bZ}e(e_N{L|d_?dzPm_hMx@CDpmF+rEce z8*Bcq&Tp%(&jBV;asb_0P7}1PxS*mMTEK>@NY^!4M1tCp(}yY(fW<7e{w!-c(Po?#QA(T;Pi}=Qg(Fo@bUSb!$gVMsZK$KWUbAG46%|p z^`x8*iHN|iR{FBaI$Z9P>Mc4Qgs%mMR>pCG5-4Q%^?zM*HDs9UFV*&8jn%~i{+xAg zgf>%dg7PQ^fH?M@lq`dXlyp{o6c!Exaz{Pkg0+xhUV)jh5PN-~b)PZ1?R+3-S*-^m zoTX7N)vB54ADZ=2$x1*tf4HVmu3s#9V}H_E(tcZ?ku&ZI`jhJ0OQ;2d%VkI^zQ2z> zKaYxvI&Zqaw*cug5=eI9fE^twDl1Fpv)Qrx%+3WJ4)EQsWmrfxsopiPTn{==OPN;% zbAbq;l)}Dmi&kQi8Pw4D=Ip7nCH}WNfmr04Q_&Am!%x|;t+<@++RZVdhw*MCk%aRo zejPR9sH)xcJH4px1h4tFQ!Gg(_M8(>sBK0!Px&NlPR>4$L@_x;eRza{ujcash>mMJ zkq{lxoabE1*nWbTarYPkv9P864Dkgo*S*f(z`mP__&uwyCYx5X&fTb<+Fqt=Cvm{+ zLPMRq{@|q~;Af+NY^nbN-;u~ZptEUfb35$2gviICITymj>MadBXKwE&h2w4JdoHSq zRdRhp1L($7dugTP#Qf3^<|RJj`ER?&%+*Va&3*N-zSw41&A62X*P#y6q2}T}y#Mrv z>wP{P{XvZ{UT0^Ke+nUo?E6Bugid_#nQsL<=RS;PF;elatiSoqyZQgc1i;Z>6z+eX z|9fU~aVptEC;Fu#o&sl7s*@W5C~d1J{Ua6uic}5`(b9R|=DlpiN$CenE`yWu?SmUc zSMmyI#PDM9{T|~J^cwMo7HYrinKxDzQxfFKn1}oB7W(Q-xd4zt6EI_Zae>ZscyJ&n zDCFbQ($bv4q@JTTuzUFn9XZ6!?b`m>ezIsB^G&Ci8r7^;+yVmLUmb;$&hAS3ll6go z5&L zWItT41)NBph|evwu}@JctLkn%bhde&@s$>OO&Y~&P?o0+>iSfDwg=4Jiwvl07Bf^VfUrZ==!@4!I+Nf`g?oH#Fk4ra(mNsI2J;AGQ zGPo;@x!Hcy%djtD&`v5XRmj&}okpJu&G?>)ZJrp*D6b4yS`rcWYYYl3D})F4O0BFf z{J}tuHSIt}vY*znQog1~=WdchoGdi*w(48XJU^5B67gEr=DC>_bm;mao6@tq_rJ$< z|6JW?YJV>8yEdGFLUr%oC@CPW14MvM9OM@fX6K%aZ)#Xu^cYiE+~_p@7MBIy7eiGn z8hZU$3w*QW9!-;KyyvgS;4%*&t;`Y#BDub6D!Cz9Sp#JMf9}3Up)G<>9T%mg1QWW+%o>rU%#{^lOe6HCjdW4|EJl9x zjO6IyAvvMA-xh-+!Qm_>Gm+s(Rr(j{^xam5{!f-F*ZJgmb`O}PWg=P*cTXApTz-gu zgHUx%Q_G$29d_R%fs=-LM{3yBrgTMcn8M8N#fJH<4$9!NTSIpxtkAS`7ycfx5E-3`IC0;OmA3b=DsT{Cxm-* z5%EaC)kE~zh-X^(u4R*iJ@1q{R{P7YP`7R>Jh=^T{(Z~-*~k&g_RxAr0vr0-NwuG7 z_<4q^UE{ixBE}Yrs#chByOtb#yxNXUg5R~dqF_9IwP@Tcd0|$aIcT9fub`;7e}4Xt z_qQ?NedFrt3ib+H00srWjg9@|F1x%e#67b%WhV7jhT4?6-d-OkG&muj`pSg)$?L)ozxJ z{$ej2aN3Kej$1iH{QN6&ZJfZtGD|G)B>gMw)Qr8Rv$=+(OEcM*nv{^UZPLWKP)7n` z1WntzWwYD0*I}|XOTJCNa@OXVWO=|;YW7wKepB`hNcP#_G_a7EidR#O3>{c{?jq;j z!pV2h!%fiHy(hqS;rWmEL3h;OEd$Ut2K|z6`P2F811$TgmUV8ay`UMVhWt-ABkeG}f#-87LKyHRb33JsKIgySp107#O-x zU`PrQNst4gSOC9^iHUWxxC*q%ljW#$aN|$!L%&-0$kCa#0#gPN;ee?UU~0a)GBX2E z@Z$OhV}Bp9I-U`HI-yYTe!crVDRFAKj{NTL+Rxi3 zPA->^s8;UHT>NOV7&SHLu((9_^A+?CN#qGM5?F3@4R#qKQ+p0>w@w{i{3!@Zzch3j zNKctV{wS2HxZ~WU!B)wOuc^P6m!JnoK>W&R$k8HLlawUsd*7LrX2~j2y%=FzaxESK zd`_@iFEJx9@OL#+M{R4*7FtBZ99=i?O0MGBnwnL>K`+!g8nb_~ z)w8wdOp~QzrQkkT(>1B)=jUfwUJ2kkN;|*xZ;qn5{{76M=@f!cp^sH;zZ5(Uf~5N1 z0A^V=YE>*kIBE}7qdZ^m)xSyI-ZR8t>HMh?gd9}iU`(^^{%!>_0=`QKG=EJXN8l#z znNKGr&=!ZmZ=P8v-cb%iPIb7rJ01C5qXHMK9fpeP;d=|m#K|@!^$sLltz2EzV6^UvmLFcWNKVwSf$78xX$F&H@RYD+s*o z&P@tTUMtSbAoAV%89iGtQWAR%d}r*zj|&YPGKSQ6vs%8-8cV!hG4i*tu((^Q2f{SK z^F<;$AF$c6`DfPic(oqm-O?JRqaC-|muwO>n@$F*dV$z}%)4NkT40_lCkJ3!>90ht zbvQS8u4u(!YJ(*3+*aI%6K2O5rLvQ8t%qIzz|K*(h~&Xv9^ZPzc(M~{$%}Iv`&$9(g1?kb>wh zVSyp`n2k&=?>}6uq?za3(!umC2P>wy-CGlTeumE94K(eTVH-hX$VP}B_A84yMPbkx z_n9P54O9})VMV9~YSkpb2qQZn!<&@iAkd&1KEDs9ce?@UX|?d;8fFF8eTC1|?cO(H z<4gQu@bM!Q!QEU^(lPh7I{tn9{;h0``gLHFzqhdk8%)8I#cOUk5NoR7`Dpw5ke)LSU#j6F`H!IDRVOhvfMVqhXv9MienFDH-en3weLa0FlA zv@0f6P-Zd1;0}pH;|#uk${t!lyJh+zHWPgK?<*y|f}|}(x}pfEQd6V6RA;3*%yk&$wHFi3Gb1GYD&}(6#p5 zH(s@am+9*-t-mx0Nj3H+0_Q?%%29D~6B!=^duF>6;1dLR+3x`gW`L~Ve!AxOr{j6G z#ZX9)^x#^=Qp`SAP_4%P#M~jwr%c5`noi@-QpWOo(BFhVlzbVW4TWctUcaHrQWdz_ zbng}t?D!54zm16C)r~n12j(9bP2TLp$A3)5`^X%BUkouZAm{L*aGGg)l$NE zF=6-{rgVR}HcI1MVakSnq>L7&ze7^SO&G8-b{0)@v_KX5gGwTw)7|+=Ritcv#knky zFsH3jLZIHM`AZ*IVDP_cecvymZXc@&k{RexN7caV^oxpO9{rX}-4*dS^6y1pANa z{f319lHiL&h*?Jp(t6G2FDxOJj}Rp-V{4S)Z#0}-7pzd5P%cQUviBi`ZsDDhleEfv zb+mrM0Btr%5dpH?c_}rwc5)(l9LG8n1|KMT|2iNcEi%~}`eGKl!)q&@$VH0K zOVOP}IRRk#f3H16pkV4d*Om}RwsVr-PQRinUKs+=j&i7v%Z}h7s^hgb$o_3cinpp| z6JuOWa2c!LqgtTVV;R&tg#gHNfVJn=>a5Cnu*s zJ@j`?;&}0B!rWZ*+FDys8!)NSe3N1-?9kJ7z+uT~omTJ(_9=%ANz^u|nyt8k)9dG2 z;jkX(>^F_~k*NOKM%g6lAKTl1QGS&s=|NWFCgt#jNk@Qe2KVEzG~*44O`}hY6a0rX?I06 zQmCvjm@*0@VNDW6da}jiF!xHwqDC1?tA#1-ynSbxG-`RyObdblI8%VzJe$6AeXRwO zX3^00S3@OkR64<(=qCRot75ypU9eeNt$C+%S9Gxg$B3_8^keI$d?chOOi9SdkI zb>Kn!fVnjYNt9GPPGgG!mN63JR~T)tx(JHKueZ1#+ zV==$Od`7*82Tasx3j9SatIFCkHydXB!@xy+FxLK=7S4W!TA;&xAGoS!vYud#jTGXB zbnWC_^d=;BU-uNhZs^%;QjQ@u_E&Y4wbHnS=XeJ z#$1GQyG)o2brWshpo1NTOz# z;{OyqYDSZ!0Jb9&>2%8U9Zn`&ypqWm1V>lYwQR@llzV`iRqOL)&Krw-bs2)AP}F?4 zQW>@5qnks%8XB2Jy+zFUwFdT30-niC%L3ajNXvOSx@4iGjAH%x*(bv8tm$-!Ks`Y- znl{6TKsK_JI?PR-Ne?nYCCR(-hk-!gOZBB5%Z>xTL(EX!9THt?tl|nl(l7I~vjaMv zgfYEPDDU;Br-}*yp1R|fp^)4vMXcjOWbK}zCCe=9NjpSWMimn~Cq z$VyaApM-0^Iqv?FHyfoCb-i!TuSr{kO&>lp$ZlmQbu6}@lw?7}-j@8t)7r4hUWzlV z*1x!wX}PEtJXk!!BLo?=UHSmq}TI3Ga2vw=uO(x)Ucar6IG8XV7>>K z)PMghc?V1uj;kR5n#m3E70FVm1kdm?>*EkfSfb}$>$9Xn?1FH>d{=#D`kxhvhriV4 ziIr{QKa=87KPx0NgL2UEDy(^f_yfhhWT{bni+_c!9l|M(1lRXRZG{U|i?3y=h1MmC zjDvRl7DQrGsxj_>AGy_~DSV5?#6&r|hO7Muaa#Nfm$*rx#zK^!>9?#f%w+rJFZ3s; z!lMSa24Fy_#{!6`R{%-;#00>!dC%oF<*Oa=WWuW?pQ}{b<*A0@|1)%L!vj?;S?h!U z>>m#iNE3d95W@4f)3%^7CIY-%l6P{Fkco2tp9_ zb}|sc;*w_7c2#aW`PuzrL3tyxyxkk5c0nO1!Mq6v`$&0N#g<(^~5 z-n8oGQj!N9mK~LpI((8c{Qo4UViTxHS~hOqr0tQNB{aFq>H-&w-nCta&+HT{IrMD8 zV~KfzR(wJ~d;W4(1tW)umrxRi9a*Hm-GVNF#OTju@f;14dD|OfJqJi)daEe5Lb)p5 z*yb-deDf^nsb~(PVGDvk-$X|-nx|cCQV&)z3*^Mb<0C-V7C~-fr8`upo)o>(LSIE{ z=mN}|+#DPn?Ck6Si}F-MGk^x-ak};oDHA~R0?q{l@O+;bacn$>D6n!>ANTO(mddXn z!lx9Fux2VX#AK2??#v-69&AJ`p?^}3Ml1f+e%gqn*8g11kce@73(5goBkJcNrxjI% zcq#k(gH?P#i9@Jn`f`^JnODuQ$DvV9u!>w~aw#pZv9_LHhdSZMm&pmi2_eq|ABj6Z z+s}1J^JKpYu&RAwT9$gP!A`RZU>E)2-54p_C^h8)%&_4Dt?#g-ID7Ea_I*>+dG*%b z-oVUE-_p`|?Px%w%iF+neZ_ryHSbY}LYe_zora6?Fx%bK99Tdk@e4h^wYW`4Y6HQR zRt{0hzm+F%t#2Rk{u*txYiMa|>SAa%0iaqeR!3&s|ENC7;7j+)mYjQK+& z8_Ib+`v|j3MBl8HT)R)QUQ{!4D7H-y{x3i*TD1WnJ%Hf_Yb`%y+CyExY3HR}}x`MGk4F!qDT&MgGvH+B^h@ud_TfqQ1kwek&)u zk}@lkyVS=$!?hM2Hx7ER8@8fV;EfENi_TKCa`s}zLGL~6V$Bf~w&-USvfF(;{M?(~ z^mlLJ@7^c?ys5Wipo$62_#R^X!R@K_`D9E^HD)r6!?s&yg!^rfSx41V+i#N((jPf| z^Aq)`wHjs zYURzpeBf?r`)U^AI9RMzrb zF7lOv+I~A1bx5}kD$1V;8|j1=3}a*4ZURvY4fZ=pn(H=Q2~fISSWlPEqjpc15tIo~ zSpcwta^%H)jbaPVCMXnHT$u7*iyYlsJou@ue8xnsv^gZN)JA_bJQlvl?{ zuB*j#_$%a9?|jqrL<B=SuW^a z?OxtrZv*(zk%fiFo}R4>4P=JvdSyV**6P=4oK-(Mny_R*OY~lXM$#`$)-P`*xE^*B zzm%j+=4ETBy~Tk!ts|sXkgpZu`e7()T?g*H2Jc@O6$x;JYGGb5`G9GSI(cc2uA$NnAn_3DU^u1-wJ_9q$#! z`ORDf-4j;zMu|AXupzOlO^L3oj8RtIUtxqG!f>IS#9bXx2e9+jui)HCYoK%mq@BvK zFyDY2wT=!kIy`wgJUmAG6gswXg&lb78JU6xh}WqYaYM)IF0);m*o|BG=T> zKbnh;A2i0pt3MyecbEL@=Ej5g=^%ay_^n8;+ZQKRf8rgwj<-wu$d)m;(KQz@oX48wW6_&)_4K+B6|;A@P{53xtcMk7$$K-26Q@_U_t zbGwN)_z1ab%t#dk7gw;lsQDy`zW0}O%qbyQ0TCT+@`~gYtp)85BORL!8ZZ-brvC;@ z2mq-H0gSkApG)o6PaNsBd=Oz+;*mkTQkxYGs+ieS4X{<-1w zZjoX_F`;D_^jr`p=v6#^TMU|@0nOLERij)qn8iTM?P-;jlL*X~M%Ru*fzGr9To&a? zRwLm(>}@!-MrDdWsmOOp)142O|C~D2Xr8FUiiC&>zk01D4xf`8Dsd!n*_a>T!K}%b zXZR4ZERlv0bhq)dX)Q%c0vFB2c5Q-CgK$GK;f+t99!o#(yy|Qwrqe6`_ zt>UgL(ZtaRUUAF@Z%a(RmujU(kiErk7BzH4nlQFUp;g!aMwa($yv{($8CnH-c$sEOKhIP`uQmyz9vhyhe|g&u>m)InuPgOjS)$OkQ0Wpo1=18UF*xu&(Y6z-VF`LXG12ZS7#3?HQ zevntUr;{U?*YIDm1Q0PDJuU7!U*F9(7CHDn2NxGa!2y)ut$+1y{*@e2a?Gr#sXopa zsfXa`ZwzB9|5FVe%xd67BUT{Rc3A@1adoqlqSe82M~|t=`aLjm$ai&oOkn z(_~YPmY1`zS6V{k5`66ivzSIw=H};PmFRFVr8Xybb_$?#p5~gGeyrg0{zv-kb>i9> zrOI0DaDFY(fJe)QS6M;>fAyynp{B`;D9@n-Z9b zWk;dyG^O6dE^bw9MR($nWFu9edzSmz0%6joJ^3J!_D7*L01D;A6#^8QXY%NS(Dw}rrk zKpY>oCE*pg5**hCDTWdvT2AB&XPX*c{ORnrLYmspx)xPgu~(xwX|)HJ#7fuOTayCC zNuV;kKhnJPL95%S*eJ2haGdvwJM!Z<@^0Mgk{y8eE@Og>W<5jZs-v4!tStkcKHFFd zhs{ugBzr$Ksmc#8lpsqyJ=n`y$tuVGaHr8`@&K4}4QpPNg@wC({+oRCH~=)9dK~B` zIe;L}60h^h^8p!jK~ptTLZmF2AzH$dUM2SRzmn=~$-SmPgQp?ZkeZLM<0_UWvMbN> zsb)%S%D~UqZ_>Wgo&1IjmLI-IiJf>X4AD*Gly+n(`(=>q$gT_}o5OK(v45u@;FZv+ z*82U!$mrw8k1LD?8v7Rp9O()Mmof}V71#|Pbshj5ln)S7&LWwq(~|0#V8x-ZSe~dG z+J}cn*GTTl!Ysa;0iD%ilMI`^-O;S$;kYV@J_}iIqTs%%J`PC}IbobctkxP(OX-c> z`un%9X=8G7asla@x{93$I(*Y4$M6Po>~%dkzY{gAYvbwRmMq7bpB(+}YHa>kD5|0a z%%^EeqToy%+DNGeR&n6Rai~DjIJJFkwp_*o5Z5%s)#OdkbQ5K<7l#n#DC7FU^V$IF zcPbdv>;9w-#^r2y56{F-lZy_=EL7h(O+^ad^H;&Z=u;R~BV}MV9bLa$LpYjik{yyA z6G4SjM{PY{AQ~&Ku2h%{z^M{8AJe!3Y^BqckR=7(j~0NJb+wNfGpKvpedR@`fO!kb z?ZJ2fjn{Bw6}r&GO@6{(8mdd?PIvWovp?>O1(X%TMVQQtMCg_sb*ve-Xko!{Tw?Qq zR~JIBNmzE;J!vPd5J?%f~+X8y?-Nq-Lpr2eh z{dvqi5Gutz61PZp0?ftmXX7!)$=h9Y`dkI*x)v=QZ8ItMUX~dkI8;`4G%@k<@nPxX z|1>tP{ISLR_^1h=Pu79+jku*@bi2KDSz_;1cu-JhLR$rt*B#&u6My?KKe@Qi^`z4& z7_iX|7+a_+$4$~RB+0|-0!{pURQxs*KFxAVY*cBF^ygORv`T(eMY%byl%KhSr{7T_ zBXg;2y(!fVcm|A+$LHIgmw_^Ev7If@jk2-v2RcxnKB+Y8YxlVdW{l3v_8lA^cnbkj zD=jG5UdZ?OpX!+v)=LQBem?auy!($sv6~i&ww`56&$a3*9zkTeqW=Uv+^(vorVDj! zDN(ur|BKLox={i8-+RwRuRF}na)@q!l|uhM-!%%;QqPc0! z$@hf4<4!4H|18Sn#K+kOYwqM0PQS=*^g4NL+KW=JA0*T??pZW&b2@d+5a8#3<9!Zz zB1?+UQt!SGGPzl#`|P!*C7vUv7yk?|2|VN;S|GzUgU7_{R9KM z+DzDC3B>Tt5#La<0~%yVlc;6(IWo{`>lbck`a#MM0HH;w#Y@EmUu-1DPd@ScNIh|d z$5D7if#1K0-e?MaODFQns$tNbF--!_p9CW#cw#BEiQk^_CeeXZX7XV%Ce-Q^a%^%L zgBEgYQ&Y}I3)Sy_c;*6@VK*OWXmgy3LeEfs<+ma{0*6Xj@Bp>b6J3H9n@bhXp-^68 zh9@0jDoi3Ndqk(e)E*3Wa6bWX>A$~hBFDH(5D^gpGv_+8aCG#4p^P^p%(DfeZF-W{~%j0PxQ~$P(y0o2b3(p1~qB( z_4@MVi@klU0ODb+E2ne-9%k5{)4}0qU@&FhXC@H;g6rM)bVEG@gCS!3Y0Mt;`YROx z^QhLhSsx|!H;nirzxS54m8?sS1?z}%+}CeLt=<4>2b82ZRUW&NY4Fit%xYHEih{xz zXlel@WLvK!)Z5$Jm$`;^<&XYcYM|tbk1ei%`(4xhpamxnK=u$3fpRo-)_^`vPXK^} z>GkQkIU?N;W}fpE#y|$Y@&RDty&IAlqe4|uUVYqG^}m=8xb6-Q@3YE&dW$r;rNLZU zny>MLv&ZaHvk#pq$3R)-y?l%$rucbh*5-ONsKc$`K)orLYCO>t_Ks zd7LPz(W>XY=wg*Kq#+yrha)pD7su_nnFc&qekPDeU8;)1v-ojp-J{KB3uEuSGBQJ` zam&w^)2TZn9%FLT=xj(>TUdxR$s{2mv2TGYb*q+?mrpf?1G&2Ihws3GjZjcnj8Sof zD6NJZkcX|S7Q%n_B7eTYuPDMG2|Vyg)-P@yNlZy4GYVp8EDz1Kf@dC8CCh_<9x7Lo zKh#kMx9Y-5)ku)2&R2rg!Ym>lAr0EpI`!Gn#u8{fZu_Np&W6&j&J+_?k6sBQomAuo zGD^IflZpydq(&>{-$Zv|)PHJ%m5nG;D;YHc9x*>%<_zysyj$NWops>TSEXg8r+-@c zv>kbV1Xx!Mn>=3to{@TWZ$UPLT3fg}+H8S@HZ~%6<}4HdMM<8ne8Nr|tVJL2iTR}e zl{5lf#5>X?u}3k5Sk654>-bzLG|E*SOIts42mj};`^#Dvmqokq9ryd8?HFRVk7ShJ zr1OAe|Kaxd^yCl>7e`qP5^NLWXsmI)oGDJ~Und*0;HBZWYD}`d`cdBhEX!^Lp{F*I49&uAaI90YD#EIhXf`+z~J&4^XLWD+(V*egTl| zIo*`Pw1Tp7_Rjlk{~P3015WVG)s^o(vx(R9{Voc!xBNCA`bb6dl91AMc;>6xEG2h8 zcXzf_6TqXQ#d&+^b7iah;}ZtZxv6(6R~5ay8aDB^v9@-!SEd6>A;8!CJRjfe!|eA$ zxpn`)J%#~K^8rtrRIM^_hledu_NTVTM@PUYc8<{T$w^sdsd?S?yHV4lh>f4`#7njbMZWQcirZY|iPJugfnVN;4Y#xrp$ z%NxXwB(4%|@#K%ct3uHUVTpfr$VVi1a&)A)zjVTPUVg;OC zxF?cyfJP+kqg&YW@qjIPcL%G3KZk*J;DjAdh{AWD9w^k69a-k1GYDTa=1YV0A3UU! zu))?>I^R_DqH8ZD-nEA9-dzjVZRsA<6KlE5?q3PkZl^CofSzcF5^4G}*MW6cwVsux zra6BuG!$OsTC7>0jRPM`&#T4m@RnDAoL)Bjf$6LS+TMNghMa{=#P(7RikYDHq;8m= zQf^qMR${f|=U;1GJ%?JctFf=Ow{vkYWb4bT&q6q_#GE?nhofkQUAQy~+~JGHug~>_ zt&x?va4c_B%=`ut$-^jKz(Q#UYMh^+QGY$}y)Bxzbtsv!$8z7$($%C%r7LwO?KQr3XhK3NO+m=@eB1E*gO< z$Qn}9trG}ZVeBXmZ6y0XfzdE@)T4S1=Eu>YOCX7ycqPxQWYn>8ZiAYSs3Ajbn0_XX z_LUuQw_u?JTu4=F|Iyx=$9=fz!eHmJFV36S*~pw-k1tPhIX6*zY5Q%`gzq%VR?k-zr^jH zz&-iFCql1U6PdfzpwM1f)#$Nv{pnA?s4p`_<=uhc&->b!jrL`(x_56t91oGQxxezY zRAm0S)trPZ+T`L?UnFlMvQ=Ip09zg*$5w4df^7eT;=*h%*xqC}1w~dt@sBdJP~Z7V z?3XL6m#aBL$Jno9GxPJJ_gB@G0mp~3*i<$S4rXu?stOH@Q&XreFhi3?kUssg0vCp! z(RaV&`uXR>fagv~sV-;J8NRCUc|TB)U~jw9P*lXH)vt~Z%}9*;6xsIu!w*@XYV z8GrO|4f-TUXiix3C9wY0BU_NCSU#23>KZMyzR2f2SB<1_U<4XhyjC5SL`O)Cww^$n zasa)Rz&K+@nzmtjV+}+gLm6v0G#W~Ymd=Kl zk5SOouU%|vygT1w$y3u(Sz}wq+7>-qW5p(C>%}?j}6z>iDLSdB##XT9|WIltE--L7_tZ- zwb}f&nf%+E{~^&LbmhtMZ4h<7E?n;>G^99G|xUrf8g@XVG-c*E-&qFyxJ)8xLLeO<yVY7~mNmaME=Tq=o^zLhD{f)%{B?g1Cbqf_)@43*MwA&-1K+ zhtua(W>MIy7W@6h+tt(O+L!qkn5c)x{B-UKA=JS2ek}k(As_ZNJLzrws5N|It~H!V8Awm#L2hoOPlE+^;_`SNdO`@em-pVVv_5M8Fo-cx( zjp$BV;5b_eM5#~&7gx(xr7Hkq#+9zbLS+7Zj5LsUc6k|KnkrFKv@9q8=Dij`?0Z!N zi!*+pOSNZO1W*=9fNHtyi=HiT`*M8)0JdfkKzl$uWU(s<0Y&eDo10q>54*h&=dg>m zMcP%NCJrqU4_;Ys^`&v+=hREWSwh)&BOL5M{bpcAj29gSs+MZhmA`g|?pCy?P(0@M z7RCM2=a_U?lJG#{YJsF=QnADzOATip(D~mvLSGT132=dOgfL z>uQi)d<7YXt_@jCmEBNjueeJy9|qQ}Y1H{)!CkFvIyh_<9Wxqn6LDWNdm#ANGwEG{ zr}jG@kvVjeakk`UJfU;yLU16Sh7^H1-#Wrx zFA|_|-@R84v|H9_E~?wG52M2&4U~N(FnNSe9dRpFV}jnPGdTYhPp9z%;lDtg6Db_% zFH64udTYu^bEn>QaRH19rvmONRgI0Pm(S17Kv@fLWX^amU82fZis-U|Xx{*dl0^j# z8ht{9s5|6H1$mZ4o>Fw_+^L~AK{QZvCyO$Nl37qYFN>g@#(KQ=8SVLY&s&4|z`e6R z&Nc#gQ(d;-XRM#>?_a0GY+@1#F(dR<-=(Ik`Cd{z$Gu#>2)mdC+b-TNUu+^4XVMi$CoD^T6^H}8 z44>_;`_q7z2hqofY=8?f!+ZLCybgRn5C{1F=~xRKjQkvm(7fyhlU(iah2?LybuA1G z09IMkRL`sHSuNZSuu=BkcM#FXJW+4JpXGbTp=1NAivp(geK0I~a{V#2{b^7&;NW)l z!}XQsI_$>dpNYnk6BRB~#Ov3yLQ9 zd7`jxtM)S^T|=%JoyDK7rk|&8o0nCIgkDcm`7{G{#a+ZEqi<2q5N6#oj#dClm;zTu zbME2ldbCs@Gx$D;^P1R`b+}s=$(#-6@Ox(FzY=F9rNfCFNg%@nkjyM2HH$~UXcu6w zt7vCswVR|W5_MnAk)&M_VWK!9uf4sErZM;WyX`gAmx|%d)xH=#h2t8rBD=D1-7D^g^~Uxtrtlj}Jvp{_ zkc-r}5Mm@~ro$PpEjPGoN4kLU(fyD$*OL>7-VZ2BG=4EwE4HQHKtv~?YSj$SeWhN5 zyTede^tU)z(TSZJqrZU*lo4XOtmS|q!EsBFli_YyOg9mW7ni&=nwuCdhUQ_8r0ok* zmuK>)OU_2T%2Bdno|Y!G5~sJCSA8$75}5tBP=kqqBbCj#8OVU{?)XPgkdbeWmjTWD z%*@O$B~9III$Q=wQ6g3NK|_r14=1nJ^yGA(z!xQ25cLWwQW}@2)glF0i$r4H`-VU8 z1}HqwgC9)WqMWPufb`gi^BQL8*mij%6y&tG4H!Sc_6vdg169_cB^e`BBHW%uN%iIU z_W9}MJm4{acvo^#Zfo1och}^l{pEU7^(aQ>Yp%9NYiEJGQMs4rt9aN2H*6ws(wpc( zJ|}Isw(JQAHccHqZ7fuq13UcU&mYr?Q*$LvRoCR>-ll}?$E|lS`;{-SZtQUb%fEXG zc>!na&kv4%ml;|1qn%>Z1fd$|>V%ALdTb?rqh_FVrMAnR<$!CTMlsB%!U4KE@;bT= zc)1NYZ1Dely7mz@q~O3_TcS{OCTbSDCk3+H=(#^-@-liG087gLI{phNlm|QrwLhQ) zoS^`LX-O%Dqe>AS$)*`&hOz%`mdDF$=1N89DBV?b*|gY1!d%7sce;e!9$&5eXV zSmt%}u|OA}1@t*&5* zJ`)(S#o8~@Cn}iU2fUE|fct*o$DI6H(W_2@Lk%z=V9r zY(nNI1DA`lLu8LJ)$x$9$l(?des+mhMe)`AF{C#_-a;9h(*S(tmnA{ub!1oHfH*LCLiWy=GN< zRjka3n(HWh7`DXI(2%nRdx7$TD|W}REtOM5+flLt!Q)1KG5SQ25GJz7j6!NZI8A$f z&hndmU52FsENuQttx!VvE>R;j$5~9JawR1dgWWzNqMaul0n%7~_4-n`wT}p;%AdTW z*4RI^IIfG77A{_8fc-jZF0=|dDzB+!LgE!9SmS;_Yod^)e9Rs=)*+T_0@2XODYnMw zb4n?+Vg$vM)38BhngoIsM2nh{94^W!)q2TkT$(cNT8mq6qBTQJnO(#Q@!@&`8Hr1H zfxm&Eh8L;G!e#+FKNb#C3Z(K@!lgNn5o!3*R;lESBFC0&ke)K$uaI;<0(=1`RjZe2 zZ^k=10)JN{MrNK>4oT=~H@_MiV1ij>F4-_^I(tZh@{B|g#e^HRl|V@=B#D{i`D|yK z;woZp!~V=cQUb}3FNkVd{SLaiI_E(35r|PVN$^7U;10;(uK8xH?@LRI#X6+CLB*}* z%33)`%F{k~3qSjllu5+%U%24x5F7o^eQWNLBhU9EDw++Oy?ea#h5@P(;Ms)v3P?0y zw+qT}zW#ktK=KXDY}k$c9Jl%GXikAQ&)$qDP#UH!x>3@Upk1S*tNX@aZ)L9sV6QJN zFU=}`P^yoUe>>YheJn_POZPS>8%;_OVgLkj1sWEU3kwsIlf`YVMd4UkVZn5!EOm49 za|!Vxe%9j?mwvFvu26l?PCC3)|{LE)J8F=JyfGzU-cQCI)ioHCE zh_aY}kV`wo3HgJmdU|Tg;UxH}scTocI@?8ZiglmONluhV<%jV7^TVlC?INJ}+<^r; zPzm2Gm;*92z^+XguvPBsLtbSf0i3&&E;OT5YduR!PLzdHHH5{wn5*so*%lnZk>RuA9Xw$GETVE#*hIr$FoFtZZg^{H1Op>cO{>NP)9ms6|4Tv4<|6LW25E1 z7cU6k)yI_n1n0V=S{o1Ti^AWxZ?xiU4n-d|zdf_WiV*D=iqrp%>I;{dElMkEfbw3n z=VjTrYU=9#ZfN*qt$O_YbPdo3DETK`|A@u3En$)G@jLyz>VW zULBql%~6oc)5&Umi~Fq0@!D$DXX59c z4zb@2>{9@;WsiG#S;F34;~MjQJF->8nw|*5 zGcHnGUG?90YQl}AYDxTB^3$8%-q}!{gv~;QbQMJ>>=k@I^D=r=31Uze9KV;Hgl!X^ z!%v)7O@#d78AV(2ECskrp=dmF!{-&tHv7uKw-Pywg+;9-siUWlr*Y zmNbj4^>8PBXAz8RUPY*J-`Rf>=L6yLEJiI*%6FBX#<<6)z zDQ$xYYYaN{ze7qvD{F*|`d6oP%b^*xbK4GI&?yJ~shb>$7OhHH6^X`a{|`y$9F^ze zzwz913#(<@b}ie+vh9|QW!rq>vX-&j^77*9$*w1T@6YeopPf!8ocn(3x?UG$DX?FA zG+5JL7ZhBa{AVcUV9@E{X6JT0+L}9(r-qs}S20>J@YZ!0DJx0L&&!zRgBTIm6}%>T zf9FzE5W7T3TG$z&2(8udJQY<@)>i(a*wt$k;szo*a_eGlB*4y4I)eRj@gECn8~JuzEvcHW*VE8YoVzf$($Z4r zpH~QX|9Khi@Bj169YUC7v(CM}ys)C9y8cT;hd=~0tZg+48HCB=V&w$~R6Z|Zs6kLT zhXp<+5>XHqOeo8VyP!baYp{+=Qc_y8eME#^EW5pJi0M@6fkHV2H-F5vytJ?|RDze7 zBscJ1tCCEt@}sRyni@GIY8GR7YM5$c{WGR9vgteO#(K zf>f@4fAu6jV-{J~@&Bn~q=Jr9xTWRq^~1+O=XmvX#QGk$fM|C@w2|@s(d4`l8u-## zqT^|z<&^=F`j*}l#4h#cZjSDCPFUv7twTcGdJUPj9>&Gpe?4AFzki)+Jn%7#M(s=p5zrF|qme;LvxHcGg%J zYp^jEGGYscAq;?R`ru7}fh@;T8y=&fk2dE|ofoZyNpVNr?H68KUk?NVhsmL^q-F;^ z-wQwOr~2KW+S=QjD<+>(nQDC%;`pm>5hClE=z=YO z*BL?)w%e=saTjkx3V@B&F~KQX&u0-=H#plwkD48+?|#Ihm}F=)G3Q|u5KwQj$+I#5 zy1Jksieg{DLktREyYnyD(Y}`n4J3_r+dOH82l*O?z=@!Cbm1npupmKe=`FkvZO@SL zgTm#g8DHjo^Pi24qvthgyH?}nA1TFna!r?VrmYP0^#OLpi`B@$uN2>qwKRm2B$MA3 zRYzd(BI}_wj~HovRdrdp$_U-jv~DUB3k%>kgP~0RV4npX#d2~aLA75$PD#CLf3eh~ z`!V19?WW?5K`Oyg#m`U1JXPP#`2oTddA7R-@ny*+Bv|tUOl1i(%KPeAm>^#EtGWk=#f_$3OfCjA?Vez!NP zs+Iu|Cg@D%KB{+!-i&Za?%=z==20HPE35qhE$0Sr1Nm+6(K@*bS}kj*J(7> zsB^a`R_IVsQkgdS7=?|h+a^6=ln*cqP{lF``dg6M)2uoz`;f#JFc#ZC|5uoi={_(Y z0O|wMz(?`}APif_+M4zRy2QdXxU#b|Ta7GrJT5VDa_CSsWs_DN&s$3C>_=KjFgUYW z6Fi`Q+%u%LxGa!@Y1k5gSQ;8UGu;UacIhqr?vs`-`xS-(#@&qWR|y+!?J=Pi#WKtb=Tb6!s$Od^KD|?z$Y+L1ao+bT1qwL^jzQD}?ZzyH@|A30X}UlN z_}nuxtgKG0?Con?Tid$*Y*^hNVWZGVM}dn>eNBV8GRrlbqEBRrS^xmxZO_H~M#1?_ zkNS;XMwmkdv5fbT0lK}KM0XHLGk@-E-TJ&v68aO$158vGOuGNDF_KIgGugzBkIaX4 zMD(ccNNf)ozA^&k-)BkU{JaKajp<~nw|j$V$OWsAr zMf=nB3Q-CE6<9>J?P-Lv596;Zosf{CEVi4|Ijnl(t;|dSya-|Bbka0IUiy!_U~60q zj02d-z9m3TFR7%YU|_FgR3}PS*wr{0Y58ZwL4co|9pJ8|PUFCa$ZFUt=s2qcNThz| zQq<^Ds2>K7vwo&0m^(*e84TjHvvUnmxQKv2 zg+9*(?{!veLxyNYHl0G&{Am@%>9}~-+FGlCH#Y@^h5r6?qwagI#btK*8@)&^b?4o0 z`x2qBYiUymd46axHR58HMLxf&0*cm2VX|kDrj>&+YGg9g>yy?~fM~NWG}6QqT8%3q zA=NHT25xqCc7A?2or`c0GR+Lj8Z0Py$wF`H=c-_X1ByZ>x+^H`Vmm;O53F9sEv`2@fk(r=g{jfqhQafCT)YeyLzs$$AsR-#3-BWXaz(kq z%W-7GbisDunq>mEbg2Q;a_MUb)G4!Re$-%9@0S6>_eeF-zxIEdOjQ7 z^lv{@9(%p%^M0KC1VC{*#Hmvm4Uyd<VuOpAlF|sj76OC zCsFrd2c}&(@h+XeQmN^XTWmZrfG3d&yvB6Ut0t|hC!N(}Ecuo6SoY)12FCU|C+!(i zHQaff4pYY6Ku7j93aDQ2_w9sXXwpG-jg{luE138W-ye9E&g?K2N54ZGZ?(yTaUYgm zi}gh}NThLfwLw)FA9lyQDi-~y%POFZC`t{eO?Dr-!WD(m?-Kk(G20$sv>4EI@bQ#~ z8w9Jokx%H9YtUC>7Mcm9Rrm*I7cFJYU0i+uHcOJk$qg|qW+80L>H#KDFWpgh;^Fj9laOC)t&q!ocN1 zLDntXXhQ=n5(>OTJQ~{F9U7*VQ*H;S+Ah=}F8Gu3r$ynS^Zbq|Buh!s%{VW(2=@l1 z{z5NhSEz%MU>{BE3KdCb#~ZpTh;P+!7%@gE?AqC{RjQ|=a8>@mK?wn9W-cyS?i0|Y{XLCGlMIQ zB8i+8(z~_Q1?;RS>zWfgg;n84OuvLGe9@Hx4(`-g5J9U?&BWYT4k&gB+KLIy&F;Vn zKSRq(u|y$m#N4X?^=kK%!w@>0qU2h;^L9b8Fc1Lh0i|8gSm^Yn)n>ix)$*eauAj#2 zA%5+jUjV}74tpZ4r>`N%#wKx}+RnuP(ZZPP%YqnfI+|o|8D(k<%9V43_MxNWKlA=+ zg6CR`PzG%T!|^rkhYO&A?9CZ zxW|qTo3PqokiNl*A&lY(d`HZs|30G0;O)GlVj7}(pzQ1AL_&mLHl~J(B$^`27b5%E zf2Jqj$UNrCnAfqsbJ@kK%Jn!kJ-ylC3VX+~;*BE3NA6>Rm`!g(hm(_)6|BBhq>`o? zPxIaal??YdqD(ge{8o>Tj}a(%_=0qqs(}<@RC5esJP6#QsurmW)4&)k*`w3&H6O5F z6&A|qlWX5~fvmNza;02gzfe!-lUsDy$T8>)Lg#Vk`bDIkmWqYbGP^?|%gV&#XJxQ? zr0)UGHiwFdxH$EEqfR_4iAZ4*0=kEN2Ru@8Rrl@|eLMUixGS9m&p;wj_%B0{fht(O z$Rf{RzVq7nfpPO|hmwNaAE^VlQmPteeQpTdfma{GD^YH? zUvh)?d`Qu%di`C^9KdT!_D?C?TrJ)!=L5QY;wv@AK_I-X$c8i};~t-@_!Z+mE+Oe= zr}NK{QD^^?C%i(Ga9zucY(K#b*@_+!fL>D_K;5F+6d!M47ihY382?fGoN}chF(HA4 znKiA}jEM*Z4%V)dk8r(QNU7FNU zE))^w5y+VQmz4$wYYL&rK*&nv`T9{|mDC+)S!znhs-nUfh=0GENC#N#W+Db-mI0Qe z*)$p;b}B=|G8l4n847Sz_C#isytkEsN8!E*KQ}i}pNavT7KfnVB$J_YsBZdpWfakh z4EqwANF~m(L!eWjshNYBhl9i8#D6t?YE;_Y9Cp+^{X0r*jY2G=f`Wb9SuFU1*=b3g zB~__%9(&ImXa6uh7qlN~D;-+=V^-7HDl#v70|4d!q5Do+kdrr7mQqD-0x2f%)KVqg znJ80tcjp`i=E(EW%cb&zyBtYJI;>CV$olIn{#dp7hHXFEdPRh}IeFaNeeHZ5EO0!j z7U*eW1C66YptRU>(?!bWcju6};@I`j_AQVAlRAW#*;BetRkeZp@);lBX+6Qi0R0x| zo%Z&y0pL`Jg2H6WgbQEj{REHFG`V&V1Gy#W!zThbT|K>GhJni^#-WvZmZ{kT`mc>} zU-)p;G%Y==mDL#glGpxZ732_f^1Ho`dTPUKuSU~ifnV7z&N!R0J;+mYvva!Kjt5(7 zbSh{nPE&Bvq+p5x18k%&vL2Hq=VvA$*d>;?-ncM7UyfR1PL>G=DLK=L0B3lvJ;e|r z!}Oc3lwP1Xy*&M7Xhpa12VbY_&Ef7K41?vR)vaat!nhNxK&7-z*0BRm{`A!B3>pRo z&@^oRZ4Mr;pOLye51+`lAw34*5s+rXm``I0Xw;X(!xi@+vsRnn})k2QNXUj>rB0# zjho-=dN@X-a%O6V`4^#nfzNIX-dBj3v$Kj2QIQwEHBPZXn}Lp(`L`!npjIuQl40M3rJIya-nkRXC9Sk zkToxMoOj5^*&WzFk{;g5(x~21PIT|^rSljSH+9qD>nV>6hdTX~#Jwt)AR8*v)X(}P zuqe>E=l4Nu5TMrp*ei-zKrBs2Krq(D@$d`{U(DxYBAYBV6sI?@f+fx;Rlk_{2#>XE zWK_xPRk^Z})$M_{a0-f^JHqXVL_{*s5H5oSX5a^% zKn>tc&E<8TnVK@$vNJ7IUZ_Wsv2r@tq%m`#Dt`P!Rp1D0w4|Iap!j4dvHGZ1tJejC zsbHorc=`vfY&8M8`=5E>x|_YPN(=<3ZvjIe)2AUa#^aAz91wS_ix^3>hx1K9kou

)o&ADZJtEA8Jj~UJr1xiZsJA z*-VOux`Qic>|{C|uDT2A$cXrlmx}TDBbuq6s;cB4)t*izSuMZlP*k>1Zpiz-Xys6q zA;XDNl3a2A_6FK)Z*NW)0P@Azq3XZ0r`~m|^@pB_ALQNgos?gy{Lu*FxY`wxULcRD zt9Q&0FI^sF@S%qhHpyilF1%E3hiT_;va=;5ZhsAu&S>``ab@{*v%S5ibb8TUW>ex1 z1T?k_xt@=~5b@cIU$Ms1wzIcGzz}*na}Ij`+sm?6Gt01G&c9J5^6LZzF1@&nBjTX^ zzO{2gdgg6f#Bm0$)y7 zIcuL}WPWAoOO?z)Aktiizd>h}r>9e=S9`yL?$#Q<2HY*33=I*EQ)wh+WGsvQ9>!PY z`gEdtVkcX!u6R6?FX#=~2Q^+bc0Tdbu%}}0jfk@oC5ZXI9!>))YoLb;Tm7oy?97K# zA~-{h6 z;Y5TPQj-st6@>kWqg>*`dX|FtFz67RZeOdv7n75b@u>&n-(=as?>xS5gKB@%Cs!J3 zBpADXek78Xh-Byk)qE*SuV21k#a+4z0fy5;kpReU#7#7?Dc}~6Cznq8{w-8UAtL`b zsumVn7VOgNpZmqB+YuBzHl&=P>v`nKoMX8W(?BnD*i!HqFQ&!yPT6L4Um_Y#J))go zpx39f{qGTgEug8nk{2r`Qv6F#SmjJ-nKs^Rfw1?>V@zYC+GO1A!vm?`RW6`Nc6LNk zD|6g;4LwD5d>L(h5cPSVXth5%S|NEnr{M$9H3T3cSErw@Sn7&sM{Y8P?SOBr9RBL`U z<#}QPxv_R=99a6>*Z6gdKST~7icG%yJQt6KY z#dsD|i5=}ZH1vX0&rt@2`BD|JLg^`>BjK2E7FX9byxPDpqyaoinYsro$k%BW6^^pOT1e?u$+J%=5-TT9M74^DL_*j+un^^ zU=iH@URi1PzW8dzk?6*_z>LylySW{Tr6v&#P~U!=)8b(7*fMDv2LuR9{FuB^6NeiO z<2M~wbU2b`kAu19cGR{r4;PKr${yp{Uj$#Hi7qK6q(-P-YF}azx##1 zgUiBKL8z?D`vW!t<^04vx}1w;7MZpa^ziOSOlp!Go)PPmAsm8O0(|`6?zYyg^ue45 zJs!tc@yX*`NpEjV4Ar~GJ1a=)on|H-^}?F^X$q9&Fi0N%X@{#@49J;J(1XMNhz@hf zk?o`;iFJAki(Lg^VrsYJe!0m&g=s55I1)6_$UuEPjn*m~Z$6jkZ*q_XH#mSd$CYdf z2H(9x?&2CLCamJC$(O=WEWE0Pl%dohkJKct<6^%UI08gQW&bU1&}Xy5TGuF#Hmvq;r>K4wo>sobLZj7q#s zL!~kpGNDO3tinzo`};R8N;be>clF1`L^mj{p4Z-VikRG~Pa7MrA|ISoJ(Bci>i;EW zxm>M(5h?f^JJc2Cg@Tq$)eK%>llfHBg}g%gZ$IX2$GFMBwM-;x47~VHZ0sKzI4P90 z`Xj7LB>k7@wZXZRRku3gF?X&Zn(ECyl2}4e)MQ`NT?1hZvfZ1T@mLJ?^!3ldXT)pG z?e)dWw>2wgcF77{9Hm(~M1$Nu#Wi5eg3! z%6u|Cl1`>hMMWj6F~2A`cYz|c9-ncCPr|sCfsC_`PP=H4va13jyC7%40WxP2%2bN8 zp-y44{#T=uv*Yzvdlf3I62jla)iigj9zr-bFHbkleSR-5PqCAe_GoC+zVcw4Fpy^ z`w9>e*rC<%iSp)7TL35jEuWiwov+_4O)bki>nwjXG*q-ne~xb=v=n&qFVI_?CgiRE z+9|**2>88)1OnZCtvCprZ7xo0ogL^hPP11*fLHQ3CYNzC_BeijYJY!l(2ZONK*QKO z9)}wUq!JQ#k8^o@`IhL?Wd+(hIUVjtWh!I@9F3(e#xBs>o~Z@?yMB@f#@vmU%ggu$ zYvmxH!=adQB))6bBB{U2 z)qLmi@%X_=Ib$Yk&-2zZZ9*Y_p;-LeXvHBUQd+qG=p|%U*GM0j+F$mEA0cbDZMb;;OBpK4(A@fo%wVR4vo~R zlVc|UR9TjukdP3Upj32r-$PfsemS?>>n`?*Vh=&06~8i1?Ou+KP@&WCJ?D z*sN&N1`E`0sa$y9vqOKTWV_pGkA0eY+~-<;8#TB9M*Xmql9II?e5#w~1)T;%{nbBJ z0N&fd`6dbmVlCxU^}UqYL}=X5*UjpC%f-)!x2Z*OGEBgWWaH#iG0ws?6SwuVt+$t` z#@WHvvHX=R*e+ck&akj_X5I5?u9zUj62q9Z*Y}J7yw>W6_o~82H25>-?g<$+VbYyb zcO*vRpY^{_C7!~F_oDX^tUW)iZGP~8&Gl*3>G_FX9ehs*N5K>E_IO)<4`;cQ3#iDj z5{bgzrxAehhj)<{#x9{0-HD?AEYp`jsBo)Dt&l(P8hlVwb1ZJ|KDkAyLhAR{xwZAl z_ahahOr?}fdR8Q>>LD`yq^k2jc4R}}U67R;stnk_Q9#iKZxLzZxN~(|Be`Xp(tp_uR9uvkAa+=<&Wc`zzUbu4WD*kD!}N@eE6K6NJ3p@+Ysp3^FdQaukT~auYi)J~)3S^oWEkSh*x1)f4eahx zr2Js0cnJQ+kRA%fafjd)5G|eJJbgY8Do#G$YQJ(xB;wxz0>#+IV?U6I6_4JfR$GLa zHIl)M+XKiXtw{uygK}dmDa&R(Gw7Y*rl6m@vDK``y;CU#t-|t=v^!&A_`?%%@rp=& zvq&exp-l~Sb;Zap5)u-?qBsrmMT;d;l3k5EQYl%jHVqFWtFX}etD1%ecK0hj-X}Wl z>qc%U#79nx-#Ik2w8_wsSuA>gX^};X=xyOM*;f=8-^KT~Q z>>KvuaReI`70yiYbfQ}@gPfs6d1=KXpyet0DU(P-jE^6Ef(S*e4aMt0P1wuT1t{^& z3(uW-NCKJ3d4ls*pMepyqituomyo)Oqo8`7L9=}GyC5y1OObi<$EX5T0SU6*&Gh$I zsKcd~S6}KX*8R3u%XKzKnx2Z;Yr?7GSg>O0!9vv5?VfFgNIFQ_rs?sLFfd)&NYRX* zzu5`_O1@+Uar`vHB}lJLj9F^H0t@RCD~_HKSc2qS3Vp3q1$_)@Oi7@~Ex5ccAxu&( ziV&5C0Bk2<(1~0ZW2V%hicQlZ7B~Qfq$o|^>OH>kRn9WyJ0|_}CoOv^o6fr$PWlX0 zhNUrM>aa&=Q~Pv!4r27Q4T49IcU&snDtD>OVENVsDpraGV|jS@e=<{8LrAwe1#K7^ ziCgim>(w^*Qd^b0);9y{_qGnso)*eMDdo?(d3;L7z^BHw#Z-D!=~X!ht^f8@-g&OH zgyxT#gyzY{lJbwkhqPlUe6WyJhbMoO?Qmy^kwR?`o_E}(*wMBWDXCMWQdLTdJG;K! z2c|t84bUxcLqolp^O5X{N%&e;d#{KZRFtQX5Es{bvn&WqJTo6ayuXBRb~6hE?+um2 zi4*?hmC~2j)G3O|-R1txBJY8$<@P;}%+k_~0`_p@z$=tQ(YMF90ExP~x;fd$`SM8s zS1$39+cUn^Q>oZSmHDj|#QrTCztChUqY89`!)tk<*uk(ubvGKDfE0zP$vciBvAh$?^IL~iO1NRT#x9dgLpr=V--NlBqDads@ z#W^eVG`vSlOnm1V6JO@5o7H-Cg-u!@=skmqaDTDxee@gpP+TP4R z?|5#0-i(RhE)APy;E>g2!74sKW7 zo%>#wpGk=-A&J7zr+|~#`}Pu0%p~}5H8uTobGOiB@AQw?q9*yWjffQm5(@=X*!_GV5Jbpl|J&qIWJYVnyJtYs zr#6`D+!wGrj5N^-O}t6*YR%oTANdL23_?2=W}_!#{i=FaATlB$@dB9`z1feJ=z+8W6%Rhnf})->-#); z>ba@j9>2kz%ZS*_EG?NmBnLjCgtB>1axO3X=_mvu-0j`o+TMJcot>>`Xc&rM@SS$| zi>G8G45T7UqoGmM{g6pP#3Jm*Jdrd20;+sC({R>TbgJbfF3ig=jrX|s0{*b6C)f+51tOfBf(izX8T>_kXMnb@eMe z-lY6)`^WzZE>0a{!sALT;aYXu$H{p|Ho$A(_?<~o|I2}m7v$oz0KXqKE?gOkmKldu zhlhuFhgNetXIXpiQ^V5>fHXuTkcjwDzW2PK!1^l$TpSj)rhoF6*%x!gcFJO0^mFHu zIn(Ii%OHR&S>>$UVJj(k)N!WP$aQ(?bbTEL9Pi5cyhknD#5JUz?= z+`01~ch&57rori=lh->trY~GX;l&=+VZLHd1^n)Zp}@aZVCeH62QmGD6geOu+uPgg z%=1k|HcwS7vl+7?6c!6cFq9Li76Nj#D@8^9?wMZbHU0bqpVTMQGJ9hUtMtH`B)Lq# zn^RAVLw1usA_OzgO#%j@-v}fVN2(j7RgyWNK+GMQJ;1948&SRp_?T*|GrTME?OXFW9 z*&Z;Hh!suUL36W^1RDeB0}zf_CzRYUrT?%7d`j7Zt#5zO%depDanbjG??l3-DubKV z;TVBn2%rZqQ_o^Q-|8WpR`r?_;#%hjQVr(6rbLKONI02DXY{a#8MRy^?uXs~UB9u5 zxLdi)hN_QT^nDILz6Au5BLJmNQ$oWJs3u2!3V_@m$H$}9#vVTuOGK&C*WnKwsn9Vs zdsL`vBERyNq-eJh%b`H$i=|&`!FRfMM#JyJNZf728Qy6B~M` zDDBVa8*ARBaOoKc)lN04>0XTIc|`popQr*@g^N8R#fG~X-x1@!rx)pIr##TBX2YrY!dxI*qE0OMb9kMc=vY@X$ERSe{*jn5uh%m5zN_@FeXmf)S z_hW1X`Q6Dd4i$eC6^pDJZ`01EsQKXS4Dvqym4toeLOn5}t?kq&qlrtk5rN&sNz|qE z?Hp>~;s+%XmRVu6?+TS*F@x!WW4Wy0&dlw6!iOja1O84=iRu!`Cl(b`3a+D#1Exl? zlJmHH8%x7+HmC=|ssU-g&gL zvNGqrT`CqQk^Twrc@L3bcC@>ida?1ew0#7(0u5oQ3eng<&VRGUS-FL+u>%1Thy{Lc zPit!(=i9#pboetj_&l#4MPK%DwMRfL0sFtrjh`Xyi6ZwN3hs^+YJ{=)g!cA$ti~Pw z2ZIOp3jj{OHzZ{H@v!~=e4Q9M>NbS2oWqVKRoi)k&->niCRN%9P+2Y+ z{QHv?jj!$M>+fOl2wv-2U0B-g^<&Gaid|r(hy$P3-Ht+@-2c7pREoYoiN2OtWU=<# zjx+@A*(0KpRL3J=U*W%kA#32M%9*&04p&bPGb^Y2{$fhwSMUlVA|yFnEe^(X7avpO zA8_tTLh!ikANZ9(RHkOw>E`NY_Oq=xlZrcD$^Ay5xfxqlWO4o5JhrZtL9d~CMoU=bfcGT*dl>(59K z1=}V^`~mk|s&n7nGBD37^zW1ct;U8sAdj+W!cUaa7~cmz7Kb&B9DnUdB(}Hmh7Yv| z3VEACUR<^J8g00+X8tv$LB?qd9w##UIM{v{%9kq_h_ogfZudO&qp9vHLdfs^HV?STC>w`?_&r2K?_^3PmHWcGOi-GNQ-HSG z6TWu9ftlCAX|{&w^V52t&z%lq%_?o7!2PE4doyJeIv1CD@qN5KFHoSk-P}0{V(u`` z!KbJ7F(at!vgIj5D-9}&$~?T>r2M{B#l?YcZjn#lwivu0Zc)#-^2ZOH4g0z}eztf& z^#83NxIY}dyIbP42AInU!-VUkLD$E+C4qKUPF8jdI59$A`oouY0_=y93>6GG}=`PyO!y;1&4ZtGc`A zA6JHH=O>n6kJyN6#cD?2Yz`hgY<+tjzIs?$87ejr!f#;xR2u<`nX@0_Wi9OU-x*Aw zG|XhtG1Or(KJ2>OIR-!;2?@&6v#d`g(|(cYKQGIjQP0sBA8=q z@>5(LAFo-BUq^DCe+C{-+2;IPZ6=*(Z7g`X+=!~2_4ffUBBrLAqoK9>MS2C@@dp0O zHlMw_6JFh1(B*-BOUe;YgZ|#PzcVFAM7Nje;5nLz5cj*KOOeL?4!qR9<*VSym%+`( zcDbLbat=5Xwb-j_YQg~WQr;`zq&NL2TMd}&A<){3aY3~hVYxD?w;z1$Q{*PQk+=Q`2#ikqty36 z`#*mEWHNw4L?;qMdf@b{0p#ltVE-hRtdcJI0R9?C zhejaoavcrT174X_Smbabc56+Yi=B7_%lL0BUoBjQwa0fGf06jTT>W}}el8Cj7tU4$ z#-&9t9xOS65UK393)DsQq%%O+hht%56J1Sfykf#&87$_aGw@^Hfd^$B@Y(h~Z228w z6N@}-xL+p-DaM+q?*rMI&DOKxVaH}mO|jy zhI$PDjuX5b6|xy}C{40PT%o$)Hyh0fMVxyH(rX~R;_rLU_H$32LH2HD-4w7MJU@3@ z>-L~IGVTx-6Lvs>>)!$>O8~tEJ^G8BT6E5=jiQPQwt6`cOawyGr~Kdif9LN8lpyU# zhpwKql~Jf1fx_;Oz?As*z}uD;8cU8NIf(H4~SB z83lSniW2{#cwEN6(JG0Vgq(z;Uotlkvh0n>w!znX7A$}Kz=mOg4GpIph>IDPmz$d^ zEPvEU2jMyTWV&hnx$ z)Up>j#z!na_uo$Kln>D(k$fK)D{`CvEMNOrtZK2lf~qGsR$ej1BazfQHhs^Sh^)cs z;W@^M*YaI02*jSzfM$(l#bCD&dms)BO*y?}CkY&x`npG{EZ>XK4DMJ|d!j&wS|(DS zCrvMrT5YZ|YU$AVUNLa`+MuEJA~`2m_yNz-4FfRo-f)ATJv@Wam|5J5v^C~V-EkP-CtJ*9b$L6?r7OHMNJtZ3C+7bA%;hBCIeqProgxN3)ZBy zE_`WMgo+Hh1V;f}HS)4SM6QTVKu91yFfGHNIV45&W&p710PT^vd007Jso=s1K+JK` zy`#%*ZF#~|oe71$LL?;$I!G^U{1f)JYF)tp<6TTbqBPO6;+R)r;IQKGzRj!4+EZID zsJcm4lYicvQ$@Z~eZV9*yq3;pLNWK&)Sxv;2A!N!5>JAxSsIJNB~?UFx&ovrK%mvP ztS(lmK_20FpaI%BS=si#VP0ldT{xa3w?`(AS}lpD?-3a_LpUN z=SQcfCl>%4GSJHH$9&&~s;eSq_Bj(XD_{$I3kd-NG9-=aV(g+@9D?X@;zzRf$#Jnb zs3pR4qQO5~*{CZ*%uCG`kMz(R%~pYDRzpQpn2ik+rN(@it6o=;xP|_c0%Y+YAeD0e zKeW67pW6x42QOIn!=jG5I!C(iV%(D&vjVu;2qnn#9LJGGb#?hE%S%f`sRoBJwUfm% z7^S6@!1W|PUP(g2Z~qj^5$2UM+oCO-bw7Phd$F$CaENaIrj(L$?0u&Df`grSt^46m z^X&O*k03#84)k5(Oz5gzqwWv+PXwIP2DvCKY4G!>L*xvM$CpoL0S!=KUFL~JQt^ISJVcyN42WmvWHoU^ zMl1Tr6IvBWEz*}P(;T`;sogmE{T&}O8{V$>K^hTeJL8oF8|}`b)#jJ?&*@i1>~^2E z%}_guG!W3rzlbWN@oxL39!%d(dFbe@0BsHMs^AVEYme5<2dDYi z^gJ;4^!0|>R0e4(moJqLT*{dE!s?@43*z+80C@sc?Z4_9%)hswt@-#F5XNgtO$2VE z7u`3WWv^yy3dA#;B(=yWMl*=ff(LsSj+t;ntZ!08@ky4HddEvb^x6QOF%QpQSKSLZ zM^#IO9@C$_y&(h_Cnu%07wg^L@4c@b5`Oon=&1J(ccw--A+<*kHd@OU*VgtEk>Dz@ z;o@b{(wkaHmjy*ctBRuHk16wSiJ)c%t1o}n*C!{~ssQiaeie!xDG75~%;Jy|P zfN)W({~LQaU+QtiK9u*`NB>+0ho@bJ<@fS`O@(DwUxg9?vFbC z9dkLHh^0z1HHS<78zmZxpk$^%_&M?YF|mcd`WiT<1IQ*PXXjs8^hI$r7LsQg(x_Qf z>gg;d@X(*3KaR(AAc`s0l>NQveLk;g=!L`@zl1hI;f*<#ThsoR^CbE-u5xL@2AnrL z@Sw|>(ujB1+2r-LytRU*1xT+XUXs5*HGW~)k8Npb1(xSIVlpy_tzmvTVzWsMp#PY$ znO}Wl>aq6J8Z1QvAk}Y%d`plI$e~+oqNu0}011{doP=-VACQKT+PDrUv8(68{V!PW)e4v&?DI^p5O$xKArZ~;p? zj;}P&xEXD92zfBqr{nOEq~ZE%F`NwPG*vA1rE{Sd-!SkfLcc_{w<(KDsL%$iO~d&C z+$P#R4RsAgOPz66<)Shy@2eFVzN&)JW;4h#Rpm}u{dnEV_2Mb8K+SeZeg&c}WI%3F zNXCHJnut6v??#Uj7>SCchS1i@0wrEMFGQ4-{O3$t7RTkB8-E3ErYDjiF4Fxy_%eD_ z*$7^KZ=so!_t!7^wz;__AdF%Vd5&Hv)%^`zH+7I+o;*j$c~jGwVj5+)26?!Dcwnc+ z@>?`uvMeQ9z(U1Ur zsiyB9FyA)DfDGs8(iT(WfaDI4W)6RVw+b!et0GNj3y`K}(U_#lWrZNIA4fLTk)>m) z9&uj(w!GX{P_SjxjX>gedWqM2IoN3at7Oh+RlV?ETb-Lo`q!*n(V+X&b?2Yo0(@HP z+m0J)6Nve*J>2buEtw_EaG3?25wdo1mY4A|byggPwilO4zEkT8^TTgC1W>;UlpR)B zX_NH`3nv>SDwnGFY0({^l!t0*HtbTyZ=XXB9XeSLbZ+6Uz9|H9mzE?K8>-hqIHd z?bYh?=o%OTv1?4LWFs7(8r;2}zqT`y2=I1RF$!?BGk3H#)$Y;e$hD+SmOrFjq;))0 z;siKglNLrjbY%_zY}4}*JmS!+t8MJB_Df%{0<`_}XqvZS z`tE;FIEO^~re2X@(h-*XivZM9q7ONqo@hI&T&h>AEI!kAbjy{Ip@Oe|9;aU5qsLm2UiJ)FH#y z4jPc+oqG->r)O*WHbR`sqckl)JCE<}4e{dV-Tl}Y-{||k#w_aDI;n~z+nYMM``JW9 zbEQgrd{u*}DFP6Bm(I1pki3M0n#E3yuXf|o5umw9`JYV0q&$SCEU;G#KJE7F_2L)Im=Sq1IAx_#aY#j*YgwC zcgeY&u?ojOn=uj@3!Wu0nIGA8{k*KaTrE56i;GLUIdRY@W6TkKy{){QOwIh=Oofj* zVb6^617KmfTR#>C?n`8sfq@_>73My9rFN|e>l8x9*jR;{r%*L6Gb32U-Y3FsNVDkc z1whAn$_%;&&>O%0TVOs$1&wWsK8A_zHu$~Hk-mtmaH@!`s4k?OzEk*8Bl-q>Pnn<>*7ScWZwI8VuNXo1 zw7&{`VfF;C1_VXlqWXd^^4kF-4bJG|EA0I@`y*H6`{1;qF1OR@l>4j~@oHK)Bfw z25zn*MY3!d0yV+s&&4)zALHQDaMZ9N3dSk)L{$m@Z}b8*ljnfg#3RLKJ>RpF3=Zu- zaounX_R&v33IG1~dh76m;#1oX3i8e*!99+hXn6*RuoxEP>ti8sWLgAi`~h^tEK?Wo zV`1p@ABF3x+mL0o2GQ7|vb)3>^52=okd(BJ+u&`F8D{hN!=`jUdffp%oY}fAcLhAQ zuXCE5tcJz{7|bAG@uRH%BbJe=Z9L=yTkWu{D$Eiz+p*fK5ED606kV+p-AB~j#zyvA zV9x-bGYjm}s}8JQ$tg>UGS|w;T4)9>FXU=-T?sLE>&s zmyO|jq$jj=P>91gKd)dIRT(@TG~QKk>Hs<>(i}o{e~?ZHd06Gqbat``RuUy-;PF3H z5+KyO`VZ^0!gICMy>mjaJ^y&f+xO(x_u$ueXV52wYhII;eG0>QS9)G$X*WH7O%5e& zUh@YZjT8`>Hu#H;FO?Y>du%rODKnaAOH7uRFJzN(wknEJp0MM&4JJoY{^BVQ^$InZ z%j$aBh|n$sC_1!U1-@YVmlnZ_lfRuxM`!YgGYN-hemOw*Q5+`jFm6fP(bQ@7SH6f) z9o|ez%9|xK%9vAibA7qv{Li^a&cT#H9Sn(P$ghxYfJ zH?~YQbT$h3*PY;x9Pvf79B2r1vdjSkQu(~C>A#vf@%uMxY^rMhHY-qF*C*(%=^1A)&#Ee+^r6_wf_C|DbqF8mS;iJ%0|qJuj2?h6H|v=D}J{b9+~=}$6E;b zMygW3_Ijepx3R}@H9j6(N~&n%B*$1_M$;|?Q01kp_(#;)KPCbh#x1RF(xr`HGd&*v z^h$@gg!s49^unH-nQj!68lX88@OZ>TnKQtcK!7ofF#FP{Ad#MV{PBji#scR%n2#jT z7nw#K5Jv!Wo^2t7Td?pKY3wLWNYU21+6mnxEX~@XL=xfo-+xrEuv-OG!}ODWwN2JqkNG|nY#xQvDV%L1 z;c|nmSErDp-0tr0Mt2)26zVkBCh*EwBSf!fXJ**(!jaNyrIEpE0ZVd~DdSHO@KgX7 zxcpJ}dT~L;(}SnKv#9Auz(CSxHaT&SniLzR$s2&9t4EI_03VEhmu>jAul7HV&M7>v zZi~XP&BnITq_J%^Y8u;aY}>YNHTdGjHk&lIjkEuAm*>f4cGg^LjxpXrbl_Y~liz0J zW$KX;wW7=#{3XMN3qJ}FJS%zfe7gYdRf(D+eGJ zL|0CHk+3%WeEAPq2^^IPQ|;O3_XA)<_<)4`m6NM7vw8g4Cbp4nVJUoYdHfDCd7XZl@D=b*+7=UVacK#kvX?gqu;me;d`LSw;w8b(!6qpr>6uD z7n@-QwzkMS^?4fJyg9!e{|(=t4*Z%yVk#_Z0?_%L#`Hl(j;5~ndl5;vEI*4SkdP~4 zF!pF*BocyhCCaILNpM)3;do&=!LDn%vthwOmRPRc(-Tt$#OFlk$!(QI(i)HX z7No*_0CKp}zMMwZU0<|CU?$NDv5KK1n^z(>F^O7qVY*o zEhI#n<@ge6n-+C4fIkEpI;9ma#W7Qf$hBGL^O|zY{@Meh3Lqnsh9Xg4bcrlw0AcgN zIsORKb6J7me~y_#sbbhk<3I=Pj^q*m6XIk*5A}sK_-!H*_W7`GZ+vAE(7Qe**D4we zKBK0PY$4g*=a#VBn>vetE0ik3sI~gW3caHXF5uAQmZ2HophI~6c(&SfXW6WebM;d4 z+c4t`e7-n5GBPqNrT(bSV3Aun#2sTL_37ygfx_LP`gQ~+jqk@TAUWZJgb79%GZ1Ew z2=fCPdwO!^D~w)L_Cyx1@PPLX!eo(oW2v?1B3uwsG{hiz>xD;c9-q77!!a#;%U_dv z$l`BccH!da%3tlMXo+0IDu-M}`YIscq;~^#8UL8-LYD|4k4b3Lvd<;TKGYmlg}|Vw z3ipBqiyncoaZ&b(l~RfT{y{3;b`|egGPD0=_aqu6)fA&&^`e@;l*fTA44wd+LGtL| z+E9KFiJr=_iHKw~3N3uuZGwvbo;qzwZgw^$;tAIJu-LrtdAX<=3;DF3h#Ve1z9#^b z20gUY`TR59`Hx`>y3M(K8@7Lgkyj?#<*9ZL&2+M0|M6x})oVup9uLll*T()f>py1t zR2m3)3%__mZEWG8acp9^8yJA0o*(^(RqMXPXp!-m1@pTrA~F1ebhnb2v>F5xY5E|l z+xu3}{qoa80g6dyqC)ld2&aCxup|lU7E%kV_0L;yErF3u+zW;$W8Mes_jU{*|GPv) zFM*-jr|s4YPKm9Hi{ zZui1>XB%(4fjS0>!@{G<(LAj4JXTVYX#I}Y|G*U?kSq9pgah0TS!Fti!=GX;k)3@d zSV~mI%DUIIl_UYp!!`G=_YVvQefRe0KVGl%W-R6Fj#O{$* zAIc$atQKZX6{;dcMiZt{&s`d-WfY49olD$YR^$y+OAq3lX8VZN4C8W$&O_Oog@U3p z+Pb>-1Sf2@fHBv+%9xkLio|beHHw!ijoV z8#`H)OPkK+X=vPPG|z1+)x0c#bqJQg&R3`U!L`c#|)z`Kh_kdaemY3cP=@#m3fWj#AQ z*k*te0lX+)v4+U+^PC~A?y#|>HTB|SOMoZ`(m|5JYFoi{kEKd#kYm+FHE#&9wZ-KE ze*;t1zFIvWoOM!EZsMC*fPtozIXT9Ib_p=a8|#~^K{f;^e-NQNE%0h=H}!~!T)(!A z*kMOnk6Wu@s_iGD!w1VrAWm{Fs7-+1oiPyX8yOn9y=;Wh(9lR4HLNWyP1WXtKMrQR zbj!(@GfqtX_~!m`H=4An-c!+2(o|Gc+Zxbew919Bx-oom@-*^vm2%puA?O)wI`!WZ zPPP;JC`MAaMs4Z2W5|JZVRLnHVa6g&_Ox0jO;esDTmj|oQg+b#7;WJvf$EARb&A>BbnqKz+I#dU$ZC^#G*!(quv2&nLh`fal}m8gQE_Oi!7z^W>RAA?f?4 zpf@I0J27($usvVsULxt|tY|STEPl=3>&d;y45l~!W<^yP@sayM0gEbdz4C8b;Bg~K ziGOx=l_-h|0h*yDp|m&xcuN!r;-jQFtu6pWnTf#Zn~c6ANp|kKo^>?}K9(MXVBV~} zoZOtuA3Ay>5bIZUwl=JpjeiZ_34x1mYh%U45W|U2OjOl)!7omgZM5j2K+}5@-~6)y zp_Ehd29%MTB3zYzQ?!UgZ|}R#Rp%OijoGiKC4y`yK&45O4G2$mQqO zC85{Db!#;&1)$YEM*(t@y^#?;y8ZfBhjx&Ceru_sk1uPlQP!GppBr_` zM2V`KpytT>kSI?Q!(QZio)thxjNn5_^e(uSNEv62G`aJV_!KxS>1+}2`~o%}-?ubOQ$IW9St3OBx&d$Ge*=s6! z&A;rPRXlFL3{CsGTyKnKRJgE}RrVg+I7oy!`C#Nr#^>IjFIH)?{On3sEYrqN(*{Vz z(KASkqNU^I4|FDeu2 z6nF2#9QU7I=PhGIR6Bm=!l|+)v^R0ScSZkUX4{2607Us!5XoPF=nPPo@&DT=?)fiH zOdy#)cYW!679lxaY2a3OE52MDg1iZ1|YJ)d1& zfn)gsLf-e@7Is}9W(BKzt?4xptG@v5|(Q2?6?WFg+NCQpDMYAM^;2h|4Xlxk8Aq}Ih$2$m`zz*i`P z&CxP)5?1XF0ilfG6!wJspYPM}m z@juJ^IL<4UoC!_p!zm9f7b*zy{VyKR&mS$wmJ9SkV}+oQmH*fqxnSK%r>&G!Pt5<^ z+WLt^0CCjd%&SoioilT&<~V31RQ{p~dcm$7VA%jufGh!gQK}B~=!*rnz;iS*^kWvX zx?K4i&=pbs6I6;8P$6rRev&UT3jWIlW0g>#ulU~5o7&vb+V{NdJ=&n1N8#CviXwX+H#kO=I_4u#x zZoukVQO_81H{wWXe*=6*n2x}ZwIHAuN@E@zL&Cabc6J!PjCk|oHXw)`sHROM(xYnq z!wDHwQB@@-8n){e-bcYod`1YL5Q9UO122J=^H)^z^Rvd@=ak#;mYXfes{*}3?OHf4 zWmiN9EYAPR%aE#^vi8m%F(Cs#Va<_2(`lI!1+HveoIetY&6UaO`pP;^7SpLlg&%{q z%}(7kTzKRHAxvig{^dS|v)M~xb-G%+4%5;_#5DyHTk{Ad4Mg}C0ej#Fx({>>sb+a` zt$ztK$kZvr&EXa($=W}phxK+?xr4Rw>*d;KzsGJ~z&3bq_kiD{G?URExe$bSy7vDq zZfso3Z%nnwQFtO;LQfi{WB@mF9Uo_iiHYdx=>hsyn%i1Nv)t}r4Y9Pc>S`-7 znV-8ng(z(9`x%A1w%+His*-$XwX!=Z2+T44d5SYshX9mCc9;}-8 zye!sTY__ow$DOL#b78I=AJe_tOhpq?O8_xSY$3am91#cqd5cB#ah{#pCokg zvHlOjI6(Mlb5n03SEjM6!wp+aEU!;E{+U*7C4X9<8AtUvc}+iEpER|@gEvRTt5Hu6 zFplL;(khG-;t_Q_-Jk3lQ5(Pj2RttyFWqmMPz#Z04Q#{AlI5`*1b1ZQnC@<$D|qOP zOZ@)?xi+l@71L_xT;CVG@8U5Ed_8mFFum$tmX;pAhTyl>_G$=anWZ#kXeoj9Kh&pd zH2L%Fj*F~a(8RpL>cz~ms}W30V~GkDBO-vT^0$v9lFuiWk9qyKPQOR{&v($L^U@GN zANZlWuDnP)sSFabbF%|1Ayt@3Nl*rw)%)d+Wm$_XIo=Pe=AVmsA61|Ge$V(p1&p~|ZU>a+ymdHB2O_8tF?@G| z4^{7Kbhsy1(72PQ&XJ{_&+3%>UQxVO>iU||_X_FrEi{shN^NwwRB|w!W_KY6vj)5m zNL2&1d{v0eYr(2II_hewoxyfN8-Y%Exh^7DnDa)QWQLlW=5lgPYlV(cq_^rNdbXIm z%lcmV2nUA;^C3uhEiDyNbfbEj1LyTI6{Dl>fc2W?hn$UfHNbdcaxUzC>uz=7Sy zwPKme<~f>>>>93QC+1NsbxKmi{NNdL&fA^Igt}tVG=@)!UU1yq0ojR zGrd)!?py(EM$wZs1CV z7wB`+85N`hz|_XGR-Y#z?s3{jcwYx5tV2X*PEJYj$gp}S8ZDWFNI3tJf{rX1gbf3X zM5(w5EXj|vY)9EU$q3psu{L;hWQzhg9&TGxL{I$9Y~?8|LX-tTXK2x&6dA0vW>YuBuw;K5)ZSFO%#jPSdVDf+06ph~Lc z-rW)^JfN~f@@kwm$Ozn1@_e_$_x**4>wZJnDOn|&d>*O6YFV5CT;QcIIJ{rXBLUo% zO)DRY&GkkU9!P(aBG>}uVDrKbY;uXDO8ZOnWFtz)^)h3za(b+WzW{qpI<|70gYj4} zeg)lAZ~R7uDL$5WJ?}~2Mg#Kdq{(mj_9`p7{P6A?7Xv!{*;Gg)Jh{tG@ zRy4xGZi(^HSN^uPK{p8CR#KxYJZ*|UB&YhdN5vpLkt}jzWNoyYNM3ar z_en@sWg?dlpFgb8Z`{o256dg&X?MhpE(;fF3Ff2VQt{8AU(FVFvm6=hpT+|c-a=(MciM-*Q@P+-^IMtxonyw2Vn&m%1!Cka~Z0R zC59Qs(h3M@3JvbEm$KR#w~oHWSkli66|7dYJx3c^cMBErN)E|GD>HDwe}5I|*1Hy; zFp;<%!!(dMk`g{3Y`PsMsK`c-S#D`Xy}N&SaFOtCYlUV-V$P$>Th-3fS4WKNc=i~O z<>bCx7icbP>HhjdoAF+Ol`lyK;1-%Tdff9EBi}Pe`Ohvbjm=e+wNZRg&717|CP_2u z*R9TY=)5!;(cmul?HSnJyxoxa#a>kl^YXnc{L7Oz=8cO=d*{N9oJd2O*MA@=DG8~_ z;%1qiVhN#$=;`x;^g`kM`nn(Be(#3TS>9@Jd%XXgQBs0!75ue6(|Tzc`kpwmaZR@B z7SD_|eG=DxgV5*n9o6@vYFYnv-&*LiOw;e?pC)i&B>_E7t(RY~;P%|L*ndpz(8O}K zNKsTvo;TK|%hfUd>~Y&aIEr!U!K$-8JKYv&zErl28P|B%vcbv_tLk1Jt;mQr9JIFf zypP2;yW|pFlLV@&Fi_6d7nimOkupP!04Jx3(}`KBGUq@-;tofVifT)1%S^h>fKly8 zwWFsSbHRTD+FSs#srH=8Xl-TpaaJN{(QZHx5&ByI_K~|;d~DAepDNgx z)u6#Vh5C<_ee@4~xf*PFKRPUF^F-&{Ce2zDzh4h$uQZC@zdpQMkj; zoSf->k!4S95EbRSF7((4m6xB%HFGz+Z)Y1DCnvJ3gjVMkmnW)TEhkdER6&_#f2^%u zU|?QgUUyrr8&wGN+D8lB?c7=|prYIiT#Yx)Utryl`CrfGnV1y*{iFErw*UFywcY7( zZN%Hc6NOyPA|9ipq%bvEThiEPQRru2|M|D)_e*)Lc_ZB`c|&PxD}@sOa|ny?@%0}P zds|z?kUL+X1V|0DtlL4t*f5o+>NIMe;~xV<)x0~BQqNt zt72)-`zHMvIB`!{_X(eTWp6;w6S93j|oa0+MNO9^+T`~?n z4ncl?er`TZT7n&Mg@!$nyzI|ohR-XK&)w(*T-A$}Xf*adj0u3UU5mbRE-$F{AIyOU{=&}?iS;}!ksiOq-n@c-8o2sgcYHL&7 zkd0kuGVXluFVu4|ZNh0Cu@R^;KPURv=B&oJCVrAwWnn8zOW&8Sg3&?@L>)Ah zwZeD)^V{~k-HdNP4RiY%V{X44a1Y(?1Hn09`{?2b^!-fp-%e=u++aiOfAwG{r9KA= z3F`emeV2v{+0a`M$}{CNu-VF0OsY|s3i4_OWNlJ>I@4l7kPsV=FH8czJzon)U1ev%y2 zfM~f$^WhxWb!TD`d2(xgy)Xto-B2-^w5_Nx9KxT!6^P%)eFeDUNRVf0+3XX=qC-3M z7V5;&hK#1c;#N2$nP1AV8a3#(Nx58u`()V=4GmaUnde5$TYNof}L^QA!^| zTrz&VgRZhxK)j68jv92tCc=$q3>Kl?Q-dMIwOln=Zg3GdCk%-d!b{Ee!ZIPf{s?1cO-Aq-Ua-Ox*b1V-hGaA>*d9P5 zjtV1jZ~pU|b=|ux&`tG5b%OC`WZnf_bjq=&(!)%eF^kh_gQ@9!D9)h`H)wIO^w;Pl z#4OQ_eHF9Arm5=Dsv*8T=5fZCA>26Xr44ulG3Z=L-1hkIqo!*6WwZ(zJiq<0va8XrgD1Fty`$RD;&IjxjLD!AK*NR@C%pZ*zj}5 z^1La`C)?60N!undhU^}Ifz%*nT($2~Gp@d-rgn0A8mMopsjhCEFh03m_PbsFeAs^P z%=@@28<&%l-(S3By2HCGXp162Dpv?lZAh0b8y;SKMm#9LN4{rl#?QhR6Y%a&|H|CrfGM;^I>I7;|Ee4qkTjA0mR$+F-Eh zzAcXZ7zy)T)Awaw=<`?{3BIq%ZAxSYBO%?Pq9Bn%%AxRd0g+1r z1Q_k|rZ>}?*Tp*D{r$9`W_Vp*kHk-TLVg$LZCk&A`RhbcH+52(_M=)gwWUSly->@w zwV7I_2xXK0cqED;R;NB7GxSB6wRabBpm#V!K&^Hwm)NJC`v-mM0`c{o3w4p8*3NRB zszgJLt?gVY;>iGM#v=CpiSQDbI`b(HQguUx%+w%Yzz+s!6(Rnag)U!&pd!Y&J1V6{ zv|*;3GD{|L?r9dL_1mw;l=KZR{H}MQO$@$zov*Q0K~TQ{Z89s-m7MtWnNzs=c_nyC zO7kN+x(gLbjSk$X3@VM)Hq_(Q#D1)3;={!eOaBgYib zNWbJkV5@C`n@xmU0FNP)g}-B@&e5E{Abk3Xc@F(dJWxm(aShaPD_>U6vx?FiS$Wdu zsYyxPRZnU2?xw*`XvpHk{61Iqwu$R<>R^O`Tx%hJS^$G4r!nRjGtmn(9dpFi{wFh= zKigcXmv1|47M)Fb*Cwvw=;9)puTeAn>sv3vZ4qV=nM`uciA?1m z9S#gwT>$x7uUOsHO)=tV@z#}}p2sCD8z`bZPeUx^TtTtK3hKvbl1>#+H)^jgK8+mG zvtQO)_2i-o9aKuc5dTf3EVSZG9W|++20|AiGkP?)^loTDRCa3AR(2nZo5$e#W(1B6qJWD?ACckxM->S(g06cz@eMM2Ib z+05dQ8UbLQCw8W$A(e)-X|tGVqu9MoO!??QBLj^&6}C;AnL0Gw1~Y^Pm}uo1L@6RI zJ9FEh>Vs@%&EgBIF!Rc728X%tLsK5G#om5h2feRf`nkN>f3AN%_`U0YE;H11i82)F zfUaZgmB$xIlaQ@aoxo{8u*fzkxtJ7v)NB|o%x>hRqQ~@$(oE8D#Bi);Hqp#!lqf@` zldK2^Tvjku+mn%Oe6#Ms_0Z`8NlBkiB%c({L!X7$X@1XXpT|P)WuWq2V6pxQ9thB5 z{jPD9f0HsH2CS}Xr}M3zfzi=bO2XR(_5WrS`a#zY_C1)C=0Rqaj9U2?D83jl2Y)f= zSzP{x>Os#^i?H`;rcZXR_m-kuus%6SEI|Ao|Cpb@=q)$B`|JvB4As;E_%x7_kd(C~ zB@fmo626e&1*RMy2ZT1HXRxXOT~tVc(JbO@{$rGf9Kn?cC0Ibgy4@lov5M}P+0BFq zpr#QJ=z4m-=;&lEr2QW4V)v_3o-(Jl`=Ohu%O)#XKp4K*cD4;llR;6JAEeFDZEq~` zou?&u9@;;?S5H)3<|aHk^K2}w-CEG)icBs{oYUlLJU(3xlaiE5JT@XtHV9qbUN#Ta zio-t{$)7G{8mLX5o|0FXn5g~IBfX59`?2Yu#VSANo9KSeWB+rHe&+-n(xVha)h} z{-8LMbM5BNuPh`tU4B%lBLEw|VT-Sxw@W#4X@>{SefbHn_(|~ktAEtjn!Ds%D|0vjV2C~dKh?d5fUbgy{-Cf-#rQs4`@P%>y*?dG?#TC<@6o5q zrHE2x<@i4Rz1Uo5yqV|mllxO!8?g2FByK$BBFf`dsiun_QW6+>?J)wnM=dc5~#>F4K`JoYwGxA+{6NN44+ zizJN&5JJnh_e(lPuVHV;Zy7Swc?c1y2*kkk)r@b>o$cSoSU%^rp9nsO^q*7oc^;Rt zh&J^a#MudBq>KbXoLHy_%VVZO))^@-0IGI!V)AQC>)quVQOqz$#ogWB!rn?T=*<4| z*z)oM`A>f_T4j0~4NTyJHYVI~Qp}Jn&+JUPPA&;i$2dY##mT9?_mv#z&HB>su{)q3 zVCo@N&qvQkU{gSkXx72P%IIx(h@prAb+SLP7@_0&Ipg|xb~R))l4W6KV+&;MC*|cg z!?sfM-KBg8H*&T%#S%mXzX}O;a|-5UXiFakPS(7OL5e9L zPr8T~DVEKrd4CkLv~UVtJ6PHAXd+PJ>tyQk{pMRM!dhJ_AUrkNYi`VJAT!uj46pFp(JxUg>!JXJ=UH_=D;L~J7 zLOND=yxd8mWA~jxhL>VDy-;25UB7a1E6>}lQvE5(r~rO@vDw4H#?_iQwW7qDiQWFr zYeY(lW3+DkE@JJSV(x!ucuMU+oyC+KW!7dUwy|yD+b~{E1+JjzgafP9R(_b#*Z#*- zBf7u(0v|Z#baaj^rp~2y0+9JDJtF7Fzuo#@Hg9A8b5vAi7ozaoAaBS&0Uu2wCh2eb zMXiRjSL4QQk+tbLs%-;aJV38EN9j|vyXewB&cr-PYbj;V2XZ>~#vCEhqx)vWYAq5S z8;e5d@5i1YE;`n~X@>ZM9)^oSHsP6yh#t)$i*ug9*g%`FOU{Dv({6Izo}qxGR88-o zrgQkkAkvDA4x_jP{R>q-m3E;TA0P@h7Snt|km8?G9hXLo5Er$IEF?pf?!-s z`haU1?JlyS*W0nUy!_`M7<3|v3Z=-Pn6w?RZ~TqT1kKr@BOXvZfVXO44`>x9 zET1WJjs;}f2;MwWcXk^`J*HJ7zPc_>wHGXe#pVz>I{Ji^5YW!RkdNl`K z6M(KyK_91|(|@kxzCXi${v~<)uMXJnKl3HfRY@s5OX=9?j7}oLS?_nRhvt5{CE;|P z2Iij?Dr=>VmIet4$te-ZLRLUOSq2qEJb4BrYyc713kV%;0iasEwCP1eUyQ+~S@H*s^L;VW1lZkf_pg z!+siLvlURloc59U>q7$3(8P;xE9XZn*$2SGftN13kIuqViF8ALcF z96wN8|I&q!Hi^!VDYns=NeeSc)_@fdmcS>5M$G2|8ksQ)kb+UdF{3L}$~)+o<_P(- z3;6!NafASVE6Q7dgAG8FQXac*2RBUhX_chs510}cbh5ME*p<2i@l3yepBtbUXmqVA z>&(K|VKm$pIusa__=%Qsk(}*L^<)+Sw>?pVZ0bP_E%z)k&(G{o(|ti*IL)! zUSF({%)%Cyx@!XI?W3e*AO1CzF~3X8b%`1@eat z#=zINw%SzJ^|Dq4w21NM0yfPTCBWHG61ALDkQ6ScZ$*_gg@bsoye~RvuTO zFGGnft&nB|rIka2dQY3&C!TUYSl{Q6N zG!5~Y-&tyvXuu{*kr)nY5_gM+8l56R{sND^G~R@qxV2SX?F1u^8MJz&&D_*KitF?b z6O&@#z&-ig))uJP=65+_jWorZGC)Uo!pA4Dp!`-L(m%skAtg>BwQAZ+C3n7*isLC_ z9MphZM;=oI!w4Z-U_ffpBcMi~v6%yeO~V5c*ZFv1!eF#(fc^Vg3R~iV+Cz7MvvX5L zGD|8aOF&6+HI5daoS0~9LP7#S>S+du=b|q3K=^%6u4iay;U@C=3a6Y1f)7 zQbx1KCQz#@&Y!!|?KR`gCwGyVYeZBs_V$*ovi8P*o{G&n31)%O>PN6N7Kr*HUp5D{ zUKsx~9v{ckX=!FykZ`rFr>XnFz63y*pGQYWQ`G5ANK}}Xn6cwv<=ocWaIL_v<>SW9 zGEV-^r`zD;ET3ty&nZ;e7u~mL{u4RGifET90@7i&^#SR6Z0` z@;@GtWiUcGN&O<_>H$20s%XKBXedA8#o)d$4l8rYvwD5=R4|P=b8a78cuP)GS1J_# z(ag$txxYB**SOrLs=s0W5!8EobDC4iT!z=3Gg@pK*Uf+ zN{6UnBv$^Kk~Q3u2NIo*h-a6Smx6|uhJRoRXtw?b)Yk3@gJF=d@{)n=>{~LJk`-l| zg4cnvC{z>D5Yu|~rYKuXH9Euj{YAmx9r+&lr6uiFE#F=r&WmJ%vCS5I%kKjq5D*PO^$Th6$E4Sr0SFC41buVw<*1iWj?Yh=|ed;{EpcUdT&7-eIe)TWA~RX;?VEy9eY)Qzt>$z-!Pi!D9;WVp05Q z{*HLT4oS&+9>B8)yjg$IIv+U3ZOwEg_02=2sMajNRThypb8c`-9JYqYaU!{BNU|Ql zaMH(HNjO;sKzztYXOt!6bDDXmF{K;@ub|E6L4s%E;R`d_uU;NRm0(oM%!|0t+I29P zO}2D>yquCpB>j0&Mq5Lz(aXe)2@9N9SL)T!fR+7=gR(|*FCkWgknc^9|2o4bOc;Cl zAkrA<{LYkbHHcHALuB9NY1(KTnw8%|r4D#* z!EhTDr<)~NR;9rx$il{Nn1R-4U|u?T5*Kz{quU0=ReK|I%iuZis4e*!v^Q_%c|30R z07BF+q|w1?jRh1@d`dl8X6}OuAwiGG>B%WJq7L^<;-*vquSX!r%x7&jMHp@n3$(uI zXa8(>K7Uww`q8>)E!|2a=u%nNNwLfAcWMww2fn{8JTslhpJr^AJZ3g8ZAB$Y5NATKl8N?egM%b?8T9 zh4u7DLF|QF5ymV19l$l8)MlVbjv7kYpV+&5J3GTLwUQOrS&QD}NRUUmzvT5fTj^+1 zba(r@1^W7Stt8ardH-@IPp8TF9pd8$nGmSCJzEDp*gK}?+@}+^^EclcxG@>${IC<- z54!K{6=6jKnsuA)-26I7p#*EZuP`rJ=chY}LpKB0*92|$z%CgZVJOmrB=7koi)~=9 zDd``o{WiX~&IEOqC+s7!koVbDud))m(Sy);gp9kfxsA8Cr@<#DdLrNxy};vjIQk19 z9Ve^1&e{&u#-j!vFMf16IePk1W1XxBINUrn7{|DAW$9~oxa^#kG&f)+2c^ifr7@e7 zznE~c&ule;>>VzBZ`a1ArXDOfCkz6(~&1%J}u`{pB90)<_guSX=cvTZsUI zbYX7+Asx>S&L>|LFLV}60uBN@FyC$ar9n}2oeK1oGdoT;tAU!Em7oN`GpXbYJUH(^uY}%i{qE~N z7D?XvNH*GC`TntSmT+`*K3*uwN2`GVyp5Gzgs-uPq-{H*%Nkv#3w6s6S`D4GH6Vp` za<-1G#IArIK+?BoHLlE@nyPs~LoEdmvl}J!lM_n(9HzGb*1|7J#ySs&@)e;Br;%-y z@eilgbiMQ}pKnfn9)`1;wau+WJc+1FbzrMz}cd-)YNn~An7?Kx{yW6)V>F? z3(}?}mlzywPzdl$PTPF1Lt^*nZ8c3%$@Y>0~5 zq5JlIh-b{tLJs|vgdY+uh}ml zgLR=l*yof1o}6H)!qP|=$X*#yqF{jW3^`kqVw)aLh=oBUGUjU72FcwN?i6S-s~iJ- zUJjK)>BS;K^4$aZ`vPzsih*xIJV}~sl=nA)3z@x-iXy8Rye0lCU6Em>s?#CT24LNF z0g`2a)M92fj4bxzBAMJ_BV_#R{zPLH@6Y_+LB!DYffYJL4L^d?W38veLfXo(PrjNH+(~C z{QU^4g&yJ^Q13A=BsJ3m!J$P5J2qrcfkuB=B;ibX6Y`5WPueta{CGu`w>A5E-#)3(9f28vNB{6`v}4Jpw$XAN z5^i#A7$4>)aHeGQd#G-S`~4e^i4%M|8fO7@Ss0En`7QH1?ZMrWl&;FBTAa>>1@S=9@m71Pe^EFWdy3cL6$E|4+eK~O2b1+ zavja|_|BVTxF*t>04lF$WWhx|;2x1ztKDw*Jw0I3fzJtN#mea1iLWDD1)?dS8TJo~ zTsmjgzYHl&3=9*M09H0<)C}_)3bVRrtH-3VK)G`77TRT~*~{A~@R})YR8dj>Ct%>a zofho$K4!o8F+@2ep9{&B&f(hZ-n@<)3mglq+?hF9rmEq4C8{2#R8(ri;{7W(z>=t{ zv`spSlA@?+e*VDcd}77ZrlKRH)^6*!$K!>bYSD}lvk95RLp4E07@GuQNdH8a_Bt!B z+#rB8p-*<2oX)VdD^I4vQ*&lTXj@HUZ_~n6nNwyRAwL8{j=)<`+mQ1#%Nlm)n^bcUh(~vtJP6m_8%c3r=MKe z$b$Vy&w_uKGMzR#8LY8zeF`(q zkO_(KPaRV5e~z~@2)9NvQd0O!_!gmOBgxbOq5!8DFaICgZKL!Gpbn8$={>G3Rss10 z5U?5@0{wrTwEgk<_VC|aT4CvAQvMmM{+sjRYUgR?%MA&iTlB$sIN8hadD&7VoWo`9 z^9Q^yPzqoH<1z8mmcXjpoB~?DWFGMrF(W!6U7qqNk}Gp0qyTXy zYP71Rrln@Gs~M$3*+FDbmkN#3o;P1YB@$Ke=`ZL-*YEW6>hWXC4`>6lSIYgix-oYI zr4!cGwLSCEpXM8+^bN!Ci?~j0<>A~G`1PtNMVg(Wwc(etB-y$Gyj_R9J{NDt`K_al zY|}5rG?Cq*x)@Oly^=4(U^#Z*aj9AIl40k}9gU5J{a=+F>6b3R|7N4n`18lCsO z@#DW~=i_O@>-x86%Y;D`TB(Pj;yb%)K<60Q*~OytH=#~mrIV&ok_SPWC-&=yLSQ6U znn(i{P~L8W8h}?8-XdTK7x!iq-j^U-QeIIxhH+)W?f@L)W@aP8nZ+AROT*6EbKs4C z6JVjttonY9P%7CiMwBNE(uLr9Lp@$3Eifj|{{|aXp{U^D=2ylcUTP$Xxp9(-Er;9O zMKkI$QxaddSZkTRk=PkdW>~I<_iVq_6+t~p zWQvi%5r^N{ZZCBIv8bu<7zK+^MC%N%+7$R@zdbxI>dApxzcQ=g{cqp@ZAbuSwkmb` zBYo%F5=1zN-LAj29 zM>A3-byEzJS_jj6|3<8tE(JhZJgcKQtHAu^r~CGg=R6g`D3{JhguN2;CM8*-;YVP5 zAN*bU4Hc5KILk&2hdaDX-?4kMMkcdDlv4rb8(l2o{{DnjxS(}ef-cF=WFpw}K}P{1 zi{aQ)(?d(^M&F}H!r^$!?}iqwLE7RekjIxw6hZ-~t^%^-JG;kKrk!z)=d-(~pwW&B36AY* zN}b(JF#*^j5q-+uT~KI#^)b0&=`s3ntTN@s(ng|z;deGkD2UD-wT!nGg0^Y>4j#5n zLi-C|&y$#eFx0QyH0jNe>8S>SNkCpLi!e+6(D__IuXEj<8ycVBttT|TPMJPVNpUkT z`^Us#VV^AH9$>(e(%QfY3nP(Nl5geWc5fYgm$+R{x1rW}kYebQrrlUr27x-*wCSEj zZ#?#$Mi1*I6j)HUeH`Amj*Lpb$Tqv(o#U-Fm9>=g>C=^HriNZ_AwEOOlsd{cYR>sR znEQQvK7l?5K!E(n^0~+Tp|9`lA=r|PIgVgVWNIhPRq8a3IPUzB_*^8^AVX_nFq)AhfMb`maLFP(5^SRN5-Mcq8i!42i)h3yR z@C?*?tl2V$-YLSA7*UeD*IJDQ4YOKq@0JG$Chvefrr7Ep)l~e5y z;4S`lFW7$X^piq07r+rM3+O%DIJ+5h(ll4{`@Aq=Oqf29lp@{&af=GLxj!NUBz5=J zh8~%M)YIkLYD$8{?D3RpkSg4hT!<)dOFDy@57D_#7z!cD+GbifbkT!rVLn4Dg4jWN)6X&4Ot)DpLE^;vC9r^813s}KZZ0kn5#K|< zHxkg(S(C)8L~P@`D?fu=DiqvelWAF*>yH5NhdkF6uh7Zu^=91w!G93HWC$iibP4)f7Hin!cxp;uRbz6IVypFhD zr{}BN<6{xk1;$iYk`z?4u`zk#k=>=8^#y#B@?CxgEE$;xIM1p3l zIP&3tI5;@8+wSrC_fRu>Oa}y7G9S+HS314Qm%T0fB1oN5*q`@1LUz0cZkui=s+~F@ zwl1!l6aFxrt^=8-|NPI~+_z^2;PUzhh7-briF0chx6ThaU-W(Mv3UWwMT;LcDKC+U zr)Oq}fN>Q6J(cP5`3qd}aeK8-%xK?i5fJ_4700PAm-!!6XW3O{*R^3qNldUC$h9Xx^>P)VWK?Il`N)G?-BQ9$TKNLV}!GI;&>P91k1Sy(c;>ltDb z_CVJ3ZBV^p9(bP5GELqCMlt~0rf~k5ftV{i2aNNQOi+$762(~o1GZJ$9+nV-Y0hSJ z%WNShOPU8{%fK5~QsJ*(v3g$y3SW4eO(=o2WAf?!<#rMOwKTq~2Kl7IyivgCqiaxUzap1RIz+JyrDgKj_p`-_0X`rv+;(!A>lI7eJMH#Zrp5I}4^W-vKBjbCq{1NWL+I%Qabifh6kL-Qxja+r5LuY) znC|_^C`nPA(%>nTN25j|QtUV4_rOdr{fwvB`TA&&|9<+N4ThKw=meOcfJ!3P@8v+< zsX@OoQtb2ry?*y(jOZXGL{Z#=)U!rsi6Y$3#_|XybO|=^Qd9*1Nx=KOJe-@u^8DGN z&vEd5R1~+X?Oro)K!@g|&sKjlPzz49mg_2LoGaSlagA$3R9P+%nmck4@xJ(WDVE{b zKu%K+7*0yblE3_;ws_3mfYtwU0Q>QSrDiy0k@g4Z)_lxM>W2^L>~j|f_rPoHAJ0<) z8>eK%P{Lg)Le8#G1T=M545H-X za1urztoc-dKTIOeIm;gB#JY8&hu?hig%~m=nj4@gtTwlFqr>hsY?Hq)t^Xd{3ssyR zg*!LjF52FT345lTo>)*KhcYDq6_8;on}*$ZMz*)S&M0;l^i>IxLw7z=V#gPkZt*qJ z`iJEwNPWe-QVYmLODtD-*dR`*9N?;by)WPiL5Perom`J25CNmCK>Vdgp{*0 zhpIRcvcVa_kal!Q#y9v+@`8xyX{BwMXNX+zqv9w zo8?Gxbl(pPM}mh}u)zLQ7yx*;rdW*I`YWC8Utc_^DMWyPA=2=8UHF4)VUo>eh^FFu z@RtV%&w!F%90_P zK`_!Bi7W@fCwvO(`K*>-f=`GDoj4ru>By8x-$>IYYt9gnySp4^WTr{sR_EZ^-bunXz(_oE#cYef$`O zyuj7sk8)b6Df19JN`+;>u-z%(tq!_)3cASYddLBm*{_FP4?J&!`a*k%eQDWCW@gfC z6UKi^OTiF)^TM{(TPeSiIC(vuve}A^MI0nNB=}XM{1U}&NhZ#*)pi6qDu*B$Mvj&A zUF>#!&aN4bk7sK@GM~T+4dj3@9Vrhu1ig+&fUJ?8}f<-lX3(Zpe{Uo82dI49jwz4yrtd1&I4HPgYC15uc2k zNHRh8_N8qD{L*Lls?v+Uo17zbJ2}V(c1_!8MVI{RmC{7o!?k;Ns&{A|5JB0 zS~a>>_-JNd20!2yOhDpy|2aGrrVr~h*fsQ$4DSkiH$CI{+u8}S9kgxs+mwGec&4nr zI92RpN7mnu{`C~oNwU;#&z`hx#lDk~7nNjX4P8f!{*vWq!LK~GnZjg}?1biLYPCiB z3J)O?^iJCU6c5%OO~oBKG&f{NQQvvGJ1^GN3>ySRqDX>NPI5$l?Xj^`T%yQPr-DM| z`CK->`Qt~>aDAM02jZ>@9wD zTv*%>#-E9jNk}A1A33ZaVWJ(Tt3-V>1pn-$Txb#rJ4s%#PxW(coQJoB^t&Y+OiD4y z#z;TCoCoF0iNquAs@iczqf}@jRM%s2S$tv*noW%T=o%BUU0Mkn8#9ZQ! zFV=ck83*?YhL14s`(GN!Oa?LM?}3-hSyNCmU}=$QuB>qZnwK11uP4o|A4r}Yjt>WWVT*Td}bOJnFW zJF&WY8`>)PYIgR()x+4A_NSjUSLq+#77Gh(&Im_aH0AmxUgOdF-WdVR%aoU8GwMDh zdvk}liu(oVKFjlSUo1(G|7rb{tIA5tWIg2WqL3xEREgC$OLwYfRMKI8OKKq9r9*kA z^gF8n;;3@8w;gM~rPClZoQ4`rb)&YIGh&F!3;eDn^}RtUe5*g5PYAmE>EBM(`JdJF zrB}1PS%*0q%tjD#g2g(9=bGjtSTAg<SpdfjmWT|xjolm_v$IUiOV-Y=&q?`-pa=-nNggDM2H-~G8;69B>? zCkG%ieCN6wjiIu18)$emu)RP))0qV~4Muc)cyUCXQ5`wsAH&_p-KQxl&tSI=qFjM2 ztt>-u;v;mlXYZsVI3AwA7YcOflJUL5Ut<^AxNrILL3~YkhUrv5c9z zZTWX`NpVAc=FezM38?tN&DL!aPeW(+P_<=#b&h{}8Il&49$Q*4MG`%*Jrbc?L`D#c zAff2T(BlU7X~QlvG}OcQVfEZz{1QYvyR>?mc#kon>nCIH?ru@5X%=Xk!E@^9k?y?` zaH&0D6L_9VKpepP=u)YQbbI}YSfIiAM_04ZUTktTTOAGu_+);5eE7b9W!9~zqSo1^ zpuL>~`C@wpWz zkiDbbYKOfS?{`FLiw74vLPp&niy=$3I7Nt}wYi&{r8i|`ik&QXLjz~}Xt+VM^UK53 z>vQ7kL89=>$=pn`+FpNUJM|#VAaO$BKVYp&i7_JH`a8{7-ikf_TUSMC>#v&f(u&rk zs*B&?oV@r!^V7V%9+zPfl(3iRZz9SD4A>zM^HvO$#_5eEfs{+xR?wPCh;}kUeC_l1 z>|A+jjL1{sj89NU*y0u!Mmv+8rG=$6vq1mPKd{F1*ggp)#Z2iA>P-M;wx)(AV5Lh= z89%2S+t>Mj`F&h&TS*YVTWuMNY*-5705I2qa*=O}3F`1lhzAssJI zDnxuM3rEEW$y)3LwowB};j^<_^86-F4ySla?OioNJPHVDP!|UWs&qR?TU&c)XOL_} zuQj2AySu%`2L=Nu-<=!P`Ca<(n2qpHEwY-2(t(-F+Gy~hTkF4tRX*P7Sk@Jp&q1^+ zD~oG$b4qmbrGiJ7InDjqXV%Cuy}hZJ$R+`ad9i>r^khv zTs$?OD5WJa_Vd-LJFdU&O{!a1JF!`H?v1l9tj-YFWMNcQiw$Nt2-mmN-J9CEdOL7y z{0K82nY@iy`@T5nO)1ZYkDIr_HM}3QjhT8UJ%pp77eqS0+WxDn3vIKw!H;J6XQ(Dj zr>aDqm)G0((5Jr3e=E-?S6AlTnW*sNX*%Ls=$vJ5rqt;K_&yiAe=|iMu7DpM1};H5 zHS!Yy>X(0A&5eBKc5`)gb>7E|sE=cS@F(a+<#ojQxj5)LG4Sc_Y5DGab!zvgh!a$^ zu%bT1D!^}}JNUn6s7Ou=Qijc6IyJNP-U)^P<7Mdno>xNxrw^!eKLY8yyuZeNOv$o9 zEb0vyKS; zJ7>zU*lIDDG|GQ~4H#p&jI^|_gT@+Am{2vItZbg4sV@SCgt{GJuQQ@CH)*mqXo53c{mZoQGIe&T6i5^K%j=bYrzQz5h6VIjd`$V60B(4CQEH!eDgY@Jl?@{_r;B-BJ^e@`4x zisx{bS8Ht0!iw?*{ddz${O9H6Wi>8G(buGC-_!i| zTxSEd0l*#h^769hEC@KnCS*tgB#{f9v)!tD4B({Uh)+k>BucJ)g|&vG;NzBp?)ykm zSR`Pl-MlB+%?oJd#VFFw+kO+f6bTu8PH9VB+gw0#@>eX^M_9u664oh)o8%;m#RIn$ zvkm$7?piDY5Wx7MRe-z^MODgQSaJCSpIe?gf@j`oarDj&<97h_c)yW8?q{R8EYaoS z(o5a4o+JXZrs+l=Q7oz`w8Od9lAs02Gx?2dZ;cP~V(T8K>1k0Pi341K65;MJ@_T^B zsBlhEf+?eB{H}!RZ~w4BN;O9ZT`%{BCS9) zAfIey&1-~MV;OK)j??u=`(aD_Uf~Z{#6D$W@-Bu^~`IN@7)l7UxHBB%|qQgQVWkquD?-F2+EHR zgci59f@x%d`2vf>sq2CwXn=Ed!Bf|#!4=2BS0L*q=AvZoOL2*Q7x$<&hhiF7x1L(E zOR*7Lk+!Skci=yf#LLIW#?RlDq8~j}JQ88)uHdeqz~HU`txEdYZCL#nMuGm|;=@a1 zcGXdr;^w3qQ@XXYLy5V#nVfu9eU@6TOw8}ld0^R9;>d_!m%;Kd9{ZMoJ2Cb*rc_-* z!a_#Ih7+R{AGTLmAC4&!KZqXyflN@Vx>?3V{J`AamLwq}aB$=x5n#?rw96Wt|2N$o zB1rTxGiEZPlYMnmM_hb|tRgsgmA0g`l@B8!iIj;whl!Dy*{!*}8TVs7n6YZ%B%*#S zJrv;+z_~UUhih+Z$51Q30C{^lW%%&Txmrg8%!-wT=3k`{hsW(|^uA7KTIU6Qq`qH!Mdt21jo9^i-Haj>Q0#9V5yIJL-7 zXr(e845lhrA_wI_u*Vc~ghcz*&ySGm^zhILga|%g{I{a^708Pct4x1H@I?FVFV00N zz{WPhZgICkifroTtV9mB#8F_146#mjXwCtDSB!q{d54-9SF+2=$$fo&J8W{U!B7$I z11*gLc8P^T67#r+Q!L}f%5_eTw(09Awc3<)Y59uT>jwiCZ2noI#qSA7>Q&N6M6v2A zFA$3N$}5vNMGowf^(9gh+(t`VeW155kk(cl?W&=~djBF9j~c^46p0d*M*Yii1!ZLW z&TnxU;1(5SMGW1hxQ3hK5}rIDg6Pv3*W&COeEF78EW(BwHMHZ(SC zZ1VtnTc2&}r2>1>kLWY-M|BitGWEwR){?GVgEnY8M9HRWTvaQpu`WQpBrfd^HQ{uV>}e<{1ZPEx-c2=ny0z> z3wbMJ)JXM9;b7FJg>BSR5d>9n*N%StB+xMMwAcgU&+9(>Ul#M@9PX328po5!jUuGD z_<)y0{M9acvEc56zfI&A+M5|p$fPuRd3C>MxkDGuR`J`nQvxRnMrq@z=@_Npr3n|; zt{ErUuWRLPEIknrdgnqvr`XW;0a5cC?}KTwt*y_SQXIpI(m%3GfMYPU7vE=^1ATvQ zZGNm-lqg+ta$t!)%1Dx8$Z;0-?~DlM-hHfh{ynGj2gr zvUL6nH2A+sQ1KdNSh3EWtW-Q&JOTE@mU;jX`r-9F!2_psj(__Y8iBt!41;hO`}(mP z3d3`Q%c#N0cw!>wj_k9hocOIxy<#tQnkQ67=%bc0oDp z>*La#gnpyDnLF;wyED`RLUz>DCNVC)OGjMyZGK}Fd^>!hIq$60|Kt~(ZV_p~fj`}b zSAAG`07j1}{APOXwM3YyIlAoJrR3c<2yoBwXXS!Rx)p~{MI~7WY0YO0M}~i4j`-U1 z%IWsYT*3K@DeY9luDx@{!>0)*X8KtGr|$Wkwf54LdZAV~_bU)GQy@26i%eB69mjC6qi=3GFwkS`Qdu$G1R_H-%U}9T^c)$)$sr`b3E+OZgF&3!JOVr%93ox6c}1KP!XKzy zT?{)oub}xRkJryYa=m__&e?Qo!|yzg-*@MCjpoO+7Y49Hq3$P{ER}zia9u1LHCSV> z(G?F*eH6i<$8mcRZ8;Id0@TdQPEb@5S>a`AlvFIk* z*Kk58EW5^p*j_KT1{C(Ef`oW@f*$7?{?i?-1^5wcZN_|2r=F-WWnO9Xqxp=~@d#+= zJ6^Qu@*l2ZSpZlRkP>h319_37qumDp>0qu)?{|SN&;ttxh zF|i+bFplBfk$YSJ2ZdR%S|)O_e11;j)_ zmk8@3o+q?p|M3=gq2Z}C%4f@6k%P{ zfPkBb$bW)UJXE6iZBl4LoyVY)ZIGjq%fGfT=XA39I@-I{JA84V5_naf?|)@WOC%hy zmktmnoZr?ypbq0((n%$`k%LhpU^rh#M!>q9Z9#86-@m`Q^#72wJ_X*u+TBj+&-!FO zk^F1UXKb;VZT^5C4&-%6zTm&^%uQME;h+%-`ahnu0@@Yo>=f<#GOF^qvA@9oIbSO3weOS%71Mg{fwx)BXIcLv87weN8^$!SRF*$KM*0ViO?F zhJt#!i9S}62TM^X+iBVfhPw3oqXhh|J`3)7?Oc?8v?$cX%LjM5?2hI?Ur!M$DmIZU z-SwC}lG&#OPfB00pK}c3N+jPqu=xj3bBXW}Mu!1q2HBP8`65N0iVIahaQA{n^7@== zKJs?*S)Jw<-V3OWB4hw~DW1?J%gdFC z&s)bGz9aA((dvIQ3v9Nu$M;PDJ#qgf5Gi8X%Q2vOur>2FbIM*S4_YN5?(lyY%{0j) z$^Wiw18tAI{e*H2;PS(tUmXVj)D);QOI3ZXGZgFF6Tqvl-#ZQ5Mk6u4KRV72dVZ*C z?_)SLw9%QGp^)KUS~gFXtyNXDQ7=sLS*?4JkZlEW#mcpe28N`!^ZoM+V}C^+cAyHl z%o7tV_u}Z9bE|X9Gi%vnSjavmt+F{}-|329NVqgZdQ`{#)rENLSw#f-Q_Kf7tmz0F zajZWDm1-6)V-^}RDk-U~19RBaf7RFLP?5)rzNzP65If{?&8ze6h2;7ED}BJ7sshF7 zI_R&i{B!33xD_}-{I6%G09*0I&$gr~9&VnS<(jBzdPMQOlyL0bioThdAYhKzXK>Y zl*+pp<4?mwN`~6=J5ujvX;}4APrAF`DX3Z;G^_n2hshkL8PA`Hh>RW{B~ruxmAkB> zSg2aCF!O~j`Pt|*zV2G8b2{aXM8EZuG6u-eaaYl2hC4j{$_-bQ9ga+9z8CCPNKT6M zN)F!`Vfw?%($doR>(dc1NB_!nLMMVMP|3Rk6%(;w-QzUZaG*|1|{vO0;Aqku7Zu#nXBbWUo9 zch41JKTBW`e9z14unqc>sH_aQE}0DELk;YRs+SdBzAq!|6~K;0H5}w`DHs)G0PEiZ zSYIBip`qvt#S%9%7Th6wt#>slN|f&gdNv(zIpKNEEHEL=pxg#bQ4DE}CB9QVeXh*= z*d%f^n{1+2*mri3?@CDJW?=LZfwlSHI7%6&`-?x+IN?-TSf44MQz0Ekxx@EyXtavZZNMHVB^OZ3Z~@*V>P+{>uOqs{c2c4QJrMV#YvetdD3Mpbwx_sT zpq9wl<(PLU@`##8!0tm+NMQ|lNG?o%v1~0<#&D-f6T*T)agH>pTld?=@6*l`Ea$%BK(r3kN@S1 zS*to7LROjAjR)JRTvZ7UE{@Iq#4@%*rz$70#f&og$r;P2TKDE%BqDdFzS->b(h_>Y z=dnL67juasmTO&q{`g;P1Owmwp_;BsUoYq8m)#$1Y2f>`b*GeUqM(c2OcSH~qhIC~ z-p^?d>1ONQqP$o?(z56tajk8DNU8hVYlk@5Iz*%&<)r7}Wo8CJ$S4mGt~i~7(85FN zW_ilA^#2WqwzfhPnV0{~|6Bf>sYqE~PJLdR(&-bc?Phu|j3WzGU;yf4u~yK=Btz!6 zjm^;A0UsPZnM5KdC;Lw_eLbSA>7$BDO4jw(Xk!~2#MYa;>Qp&ggj>`9B1PWtf8lO* zUOpb5cfP#>E`ufh?2}(oN-q(3sP(O2FgW`1Qha6|po3dvY$>H;qUptFB_fAQ{0L7Y zQC3`Obr-;kIbUz?Ql+03qTK^Ze|oepJK7jY8wv(qhD9kjUK zKX$gX(B%iHR?I1#0!^mG@2Ud`lOF(-nnu*%QLL@2$vJb`KwjP#TG|}>Dn^#Xgt$0i zyeiEQ&>9J1G5#a%SWZu2aKvDXh-f?a0)1F$p?G1Ge0iA-K6RUKTg9Crvj898%^^*G zmX#$NphSa^yd1E6=M5~XFQx#MSL}w=N*8sQw zuE5auQQ^m%5|PWxOG<;-%V1mGbhld5z&uHJpf-gp$>(>Qj@J3q*HcJVA3A$BB$+ZW z0A>LeU;9C?Hud%Hk)QIP2TYy^KKLb4W@m+~a>SLOAoVEP?E-TmkMsKG;quBF`o~D; zwKl0KaT;KtIzBs+>(4XUgQpn$*4)t0^5+kG$8T{$r}HB_kS$AjCIAEuRPK6wm;!Y- z?09uPbpV>r>r;X#=_)iEw@GC%Y6Sl6s25^8u*X|9ETyhX6%)v|3aZcH6 zs?+LU^+E)(`&WaiZl_B(z;DnA5N;Nm$`867!OahH1v)Ok3#l(v)t8%DHlS36->*t% zq2un3xyqX}CgjZu*uCEHYpadBZg0YoNdoT4&T3~6WV1$chSaI>$^=;TCnqKfBQi2P z>n-ak(o1`fm~d1$DG9TB6jhW=T79kVUoU0>y!%$~?3OxpYN-GtIG}wS7@x%YMP9c> zULjK=M4dq*L`eCH?0KSV{|jPkKe%jzyiHkv4{w0ehmumaRhn@LdnYH&`iwvH?NSnj=j!Z{N>f_C*N_MUq z`zk7Gv7Pi2;LIdringrR^Cc2Wqoqtp>|cUOp__&2W$jc`m;0?8;G0bAB-tRDTJWue z(HEYXPFVi46^8V+BAzXDr8lx&F&?i2zmD0cEiA&v8r>*nd@l76^Oe)6j73n!WdjXX zG~ZA@TwY3{b5^fJUDW!=H5NR11n63hSr%p|VX{KT+8&_tmQ3A?qEnJPR1%#VtuQD2 zos0ZK3v(k|F+(38Srm;S`C&>ui@uWD?%2j67t!9u$$+S)d@6NE%8=lv`=-TWtt3g8 z?+rcE#eY?g?1R;7nFuGlB;S(srCuSm1Y@H0@^2-HU+bwZFGEXyFr!!D4!0Yt0IC3^$k2VYaj{ZhG<;ee+(F zbgJwysx?krM1A9+1kc9=*ZG)q8RhruASoySvuh=yGzUfMp<)>WWTno}tMBzyRYj$w zk%~APuUf6q-=aqGA(pDxe5K-28>sYm;NbsvdT;&aQTp=Ui%MzF7Fb;f1-%N^ zn`CqV)mF2Cp4reyY7H*a#i8QV`hF^Vxgn+L@b(h9jt^d?W7~%-#-Xw;bc@!Hx+UCF zC_FMmh==kAO1E-~U@CQPc6fTIhQ`~b$n%rPGf=>Nu*rXUE>_7x2zpr1sjODB@={UB zS=C6AF9*BSUvodD?sSC@g!_n30CmBdk_?GtSud5l?;U>2{7d9B(?EPQoguU9@$p#X zp+4W=ZFg_tsrW&()z8#R&@RZsY0XvdGJHJeaGAgDL(lZembsmekFAf7|MTYRGEa?B zhwtSvvL$_eXC7&EQ0u@hS6(vAehddb}c&@Kc=h z($e`Wq`uzq>+#U!WB^_b;%xGBIXI}bh>jQpKMWXnIjJNhmTG6<;Mi~+?uI_0{keTS z&NFh{F&NcBdcatUEr>FWTG69bR#HX*C@-$iL11tLVs*VQPkq-3+W_kIA`eYAb+;6f z(kkB#qDDuO8u#*62OBa2(yD!HYuy;&2%^D2jZ6TjFwb}v-)gm#CItO?m9a@V!i58$ z@4pL4IG$GRO)0&HdP>$SFn%81b#FdjYxmkG4uH->=72i?B=BgPii++b%L!8k=^A~CNwD-K6az-YQSE0&^-dkH&N2de;2=EQG|Peg!vMXa#3 zbp|h$ zG)0+DdKTt%%xDyr!XV!Gk2)-^&Odc64XhkOe2=Erw^cbPa)qYe=basqXkVP{or_bm zpFt9PM&P~B)i{pvz4MjU)QMezHecoMf?sdbd2}PQ_i#9!Q3^t|GjMf7QZxmV{h>Er zBHnK=!K*?|(N8d9L9Ij=IXtuxnRAel5!=xmW4*oMEC17zf9*jm$lI0@p&uDbnRR;K z*B5>Zrf?LNQ{>S5o|Dt6+|?F%x>R*MS6?6cblZ75NO&HwAKF6(O$hl7k+Lt!A*0=Z;toY`>4&MOf0`U-5A$ zKxLA7k_ABWcr!v)SAi{~%t_cwx(R)1Vbzu8>A`{J1kuOHm|ct|;0>t=@|&BFOPE`E zTe6m^@<)679SJWW!uuQ(VPjo(;ylJ+WE3?=5FZwLB@G{X7{#eW|D!a0rh&_3xTZwg zNtgCqXTPh><@FihiU8!8UqCSOx$cG3p;Z&Iu>J-lcDmm3&i(HMx?GN~!E2qu@W1LH zc}K_@;F4KS~rKPoJL zHOkm?qw74YMN7MZZSeF$!;Ablus_B_rF7CVeXpWkobWA8E`vIAK3Q7TqcExFH?;^zAx9AdcmAy{r@{8Qy5p90%2jO48` zqnuFp%-eB1A;X^qYsS*ovw!}lcQz+79QHvbilCR|N`IG1S z$%MZxFBEdJI&Tf1!}n=~yjQz;uh|p&UxXKQ1C(Qc#}w%)SkX5@%>Tk!Y2RpsGG*|h zQ$qmbZ?xM5F19P(V`h7i9g*fPu<3aT?+2o23MyjQD^@AeQ23CMsmMclj4za#xcGE6 zKfa(wVN4UhvUFas@Gh5%64%Wm){DWT@(XZLb@f_PZmvuaO?f)jEsJSU&sGn#R{7f} z$=fjQ()Hp0d99X+u=>9fx7VnUK3u7XbczEv(g#LQqwLP7HiKs5SdP@h+KXMl+`%h9 z-A$p_o9r7cNi(bgT5B^mtS}L6??FiP!%qR-iYy4{SiU$Ln0ZkCb=Z<0YTE1NYU$%a z-jZu?3vyWdOR~0ld~A0+K;G%m)Y5S6VrE^Mq%b5AZ*+>>U-qXiD;&@wPKE+Yo_ z*6Mp`yu;8l$hqH^^X`sMDxpv+qPExYW~4lBIf^ipG<9wNj%Yhna>{9yE!TyAjTA1> z?EE$s^uQwWc0E=D*n|9n@EnU4|E_y)ciaJI;maN1c3lP30OumZ4LjXE62QU1=XP?y zwm34B0Iug%hGf>{3)~GJ*L2(-+1GGTl*9c#d3zpXaW?E?BJrWuS1&b>t+W#t()QDF zEA&g`2SXgVubv*?`TX|Mbxh9BNf3vM%;)I#Vw6VUt)ZMUdmS?oWx#{^2ZY;X_R=US zU|TuM7KBCi=gZQ#fpAVXz=x6JOP{>h*@6tr#-^OD0|lNcE+P~u^N(SY85xTiYipgU zX+Vw!N4e0YK;81eXsZW38o|I|DQ>4nRX9_jbdGZx0>Q^|M&bSN@&e>0eYWHO$~8ec zv*(s?=}XCHxyfZTIKXVeyX<<|-mF;v7P^k_Yqvw&5mM!#P*JFLw>jBo&e+^eBjAe( zHmayp2hpQol{NlL!G{)gg*Z_ZZcJ}XLv&1p+^%Kq{W2fQlC-dzLxV||B-Jckx9%^C zhg5EFZU(mYreLV!xU67)(b|_lWfT`n_x3XWTOhwyuL=#0I|#>Fgn$hLu&avc4v(h^;=XF-6KcqC|N(hpg-UlSZCPQ9*>KpHd8#}8s8 zC@is2?wUNnQ6W%}XR}e4z>vpEkOD1SX8D{F@Rw-KG2ERmtDJG_N9fgEWmdi_P>n9E ztz|^fWDB%`;42(5_L@0GGDj?z^bPJ#*JZi_w{QOXJs}`UYz=L0Vg!39MMl?}+r);lmu3R|?ATa^S-A!iHh&39E3@-FQ z&MU+wlKTV=WQ0T_-zNNWE0m31 zEJprsodJGNPhMQy-0DJDw3%aGo*u)AHu$koN~i#D37`<&t+l$Jvnr>|9M0L?oRxY zLs|7Eem7qS8>$l$_A>TV8M||lnGhyOj3m>`%)%#E8CUWblB)A(^T+=@qC5NA;)J{9 zW_)Y{_ugSM*D~Jiv_#AaLaLnACqaK{3BIER)`;B$QC^rdyT{lV*}O!(O6al;g7w$AZ(jr8%TiInROI19X2% zMM;URGEd0Q6R@5Bz(rJHr0LAlZEvHJ(JyNWZodb@NwP@_#Mvbh()X+t*}1ur3!Kgo znE)~_RV`t~@h808CR~{Kh#r!{`}Y$q@)@S1?qTrKaPPGw;x|moJP=9K(OppFet&;LLRkRZY z>mW6`P+r;fTZ>UXzf^irwSlR^Zt}8V&xiCRIT5?SV&jj>m;qQpOn9tx^s02 zmytJtc~j{VmpX99^=@xxuN}?*tCs}cior5&TTpHw>O82QlyT78^B4tX$67qODy+`;>}ZF$X2!4N;n-{|L{iEekp>o{ zE9x9y&Ht4accmHWKd!DnqRd%EYv1Q4_ajEnrLw zisu;Q4^51AtkRYy_oLgJ@$cjn;E)gs0D{_Yx9iWxz)+);eb@1FEAW={cH8y5dAAn* z;D6{}tJlN`Au*f%p)jC6c($Y~SN=nsKGcnSc^!UmoaGlrT29hnf^A@vLs1Imn1pNQ z71d9XoXO7&4zKzo$zQ}I>35JeW&i2?5G29}HWmh>mgJkVMG$}1!qQqghr3`xNgAwT zUDIBNrWn--Fj#)(U)K`9zO7}I(ggJ`D6BhHA+~a2Zy!fTouQ5mPjR4XG5*JJE`7Sg zpuef5xf@K-5)}igrrdqRApGD^luvpmsP~U~i>7szxuP6? zelxn+)fxN*Jqm{zo_5w|)~*W||B%~d;H=|V|9pVmh!y`Z%WEk+0j_zqRN#Z!FD~9r z0&NoC@>aE`2xZudC#1vY-e><(%QZb5;3{ayW>bt+){pgs9Jbzw3UTERT+Ehz(Dw+Ei@Q;F@0Xm@0X9WzC>L`V^wkL%}iJXJ9bDvJIBC4X*=zfR4->VrFT|hw6qptdz7~)k0n1u zO77VpS^qHMO_@PT_$1T6(DfNFr^Z#3Ig@PhWTi4Wx7@Nd%(UMT1UfoBEww}Jdq-c4 z!DVh$J?qESlrmATy~0>uVV^BiXzF35n7hfkT0ij|^)HU$7s+fgIsU<-WWO41md>(j zQOvr(j%0Sl^0|X*S1pzS&y5vvUL3g;CH7fUm7~g$2}_3CX(f- zvQ+;$Cq#SxcpgIYIcmjvu3fQT?)C0*#I1%23edrIE!B4x-)ql@wnijF6tZbw6W-+- ziAhrBDs(z{TDjTL&rD;L^|5d1DM=TjH`C|i7oBDwxmD1hP!LxEg#5I^y~ms5OlG5^ z<=G=ZA33wWo>*Knr5mpld80^5>GU%F+cpq*s8dt~8?XHFNoCVB?vud*W{SNOv4xKv zN~Bdfn&`w$q;ILA5mWghZ$O{8$cz&GB9j=1F=gNIyzQ;8w0k-Cz8*mYqXpYpj8>vx zNwXwz55xd)Cc2(KpfIk`jN!Ds&9z#TssLiP|5<)I00qP?TiaKvsCQFR@hM!DnF5%E z5y`+AZP?+P-fh0J2@(4x4gGH_1OG?J8MpVz!uV#)GAF#Uu~3fuss&;DU9f3zJ3ujy zD-<53$Oqaq@xO%A*s&N&0G#jwoX*bu`$e_7U^V2O*H3ail?5o-U5nnCg@Ic7uC_Ydw-N7l@rRS^{PonKCQ73CYYbp9#fE ztHKc_<-&Ux92*Kj??@zqVk!bF^-uU!h7s8YclerPPcC3c?B6Zi4a zj!sUuK$&C)mqwY$?$bSzut}w*(g(&@J&1ujnwHV< z-W8Pv&ik46iVyW9sJa(fM5{%ydZ~p-Q*JYXSj)^cI~pP_Ut&?hcMzV4CqX`$Dk56i!eQEn=n_w)XR%kh>@a*4 zm)~<+_}#lbl{PBPlmE!t-VkTd8w(VN9A??Le;_?*%Xb>~JTLKMp`edNR7m!rH~)r1 znhM%QQuzyu_93j%yydHvBu)->bS)=28h?6vqb_F#0Syc61(p(N0Deg8Hf`$ILPf`&nh77_15YHbp{?npK+3Fh2P`l<8n>}z~5yq*6+?zLsP z_?O&l@s2D%72?qL7meWPI#!-Lh`uLc8NA8oKJD2}H}o z+@8dkvhb{uQE}s^N0?0+6YjIvt3yO=YE<%6=bFgl4B8^Dshb(}f`-hJ+Y0F8l~OM} zywy54q$^cB7sNt&QGR+zuB^!=idN^w!_7wEn1RR=e3p>pjMb&lX3LeJnf6c&)6fT+ zFuT=1rvc{fvU;+K1Z>ReJ|6TEA)MvTgIJigamz>A7R(scmPui5K&K8|R%! z>Y}W71qA`=+S%T|?{X@U0nk0b+GlA(I3*=sgx_penL(Ur>*m=<@XHB`aC>+NKuI4S z9tIa`j9G*Kbp>@jxG#C;us6fQRn~)Y%&o2W0H!2x5td#^_)G7BRudhyK?O*d4{5)FijvvUqB(_c%N~4cJZB0px`%I}cbl387bR#x)7v zur9G%#TPR(YHFW^Io-;tKox*_>7%ZV4bAtJs-d)2oCrD(1)IXZNsP6i4oi9Fv9U4E zJs>st`>YppSHSh?j6J4~m{TqLSA#kM_opSoQ+e(Al?Cpy6T=mF#bs^=OfD;7HrWt@ zV@@lil@tZKD8gCrrLR0S54Zhj+&@SC!tm>rdCgs$zgbvc*Y%@@%tHf0J3*Y7Oxf{Y z^y=S8aXFV$Vi4LR;UiNJ5HR!dItO9RvBj~WNA*#*9|&{UJ6fULUWNuRLesaP3}X#z z@?=w_p4ENzRzit*4w}t42jE6ZO{XdbWCj8H!<6vry#;Z?_}!ZCan&{utDa7hW7m*e zq!>pF0z40^z#(-}={lJwFx1}yi0H6XlSgWBIOPav8ddQ20vYW-TdWI1t#JDXPxHm8B^qi-y-MyY#voVWKyAB-}`M+Ffss)*l3d_ItyTKXeRk+^?G4 z_WVkRj9f(S+w#A)%}Wk?ORm`!_B^3*SrA+5R>OvfA0;Q z*U=i_({#JORNoHa)*1zK7{DA(1O~NgGG>mF*|n)&z(Q%!z%4N1Rf*Y zOfLO7kP;61_!GlR5lkxoK@B~VsH8r-CA65zkJ`5y)cmIq=a{Zn7Kd9PvMwr z39v2_r!KV5TU+ERAmR%_n1R%C()ySPc<9n+>(r}1E?g2==ezK#t(uti2$11H%5RLk zsiayeqe{1T8|)KqTYutM!^KC&1NZj#>39p?V?~L`7YK30ro4oL)|UT}AP0-l51))S z=c5&cL`{Mjvy9}F*jf({4mMD@kH+A4;fT^QBY61v<@-{pBV$yfIGZLXm1kOiAoi1? zJIW_UUQjBhO7G7z>b+|e%_PgLu4d-{r01t)6twldkmW7s8Q!}rLJ?NUH*BU0aPc{VQF5szAVdB6u+=2vC%M$z0gT3v_?fK& zym-Hs?^c`be*$I4rY1Iu_&AGOJ6?m~^hrV%RPB#8N9RZMUjU-hpHH6L-eExuR!v4` z`Q+(-6^of`QM5+=v>sNkt2ST^rPM;jFy>T=uO9Dye)??u|uF>v7!fg?=!BMEDdi!Z4LeB*V>?JIb;t zqK!s@J#h=vMt!qe#vV})Oj%4n(xC@JJ1jp_@}$E4p7{Qf`TP9$uT7@?m+aq-zjrhM zVQ^$TZgGPsHZo0D>~J#UVv)C|20a}<7r1(gdPb(#tK%wPtxZsM!a;_P66{K{zXs9wMUp>mDI*>fVrG%_N;IjtA7y zgy$<$fyUj1c4496$fJNru{ijuwCt7IE7p%iVm_V$KCG{So#UE?YRM!~^>Ey^fMqAhK-X8Gms?v@|DE zCN`V6GjjQIN(J1&vV}fWTs>x;E-N+!(*thujlEn!s8f@zPsEfKC(ny%d5ZFCI2P_E z$@om)u^zdQETwG%p@G$&k_!YpoB5NI6a=z1e;T_-*bFacev}J}-&Kfz=%p^01nZsT zR$5A0LXu5w?pcc@X_)!mGkI`g;uqp9L5Ecd8&O>2M5j_hFZnnyMo|+L`Xxz5r|}A^ z&tka0)7mAj%qt{u8pgPD5wV3C?{7{OdJl2Qv#kx`qsU%$W2SsasU*j5CWTBgg#w@Z zHmDS>!1PuS(_9Hex2No1RfXQv*w(?U#n%V9@eO2-m`v6Da&Ud?*P!O4%dnsP7M^m} zZBE8R@7?Rnv&FWB;BB`a$~(u-^H zL_Vf8F_|9rPEjMK434oVWyLV>wHY@;8ImR&lCMxRlegPB&MBx_;Rcev|Fx{9LQaU5 z8h|;=D_6Fp`lwSxZB4Ia%$<4gxj&ErD}t#W7&b1GuNH#U~S!K&Zo{jgA}Lur1o(Fx4n15wuZXA7yB2*)vwivb z`8PH;Uh@Gc7XWSJjQuX14bUr~=>>-hY?)by4 zD)wD>6JDRZ-7a3=UVYOfMRly8H}n5&`~eep6cNGxis`!#NXKYzrM(K zc5&HhXZY1*X^CmvXjK!2FFqYI{}2WJ!C?xAT-e``)uzkR+S})~MgXT_6Vliaek-<5 zpF6g=h0Hmjo?#Dp#ijGmuKQw%wUt$@jzbJ(4Q-KlR6|wuN+&-7XL{^icxXs5oS^BJ zz1u`07b!6lXZ}aVX}wdQ{CSBO7gV?bp_=)=u>DpV{;wFqCrk{G4IApTTw{gf!#Kv3 zaeZJmg#CkZEjXdVxWx@66HM7z)4Gr8F+Vv!eN;51y9mp}$UxXUUR= zleLQ^?d@ZQrPTIw;XDiH)>vANsajbriIQrduR~-mS#&3&+q@(0Re=Xh2Kt5gFsI&5 zP&o7@MI1$m1;}Dcrp|FD#HGGUWr(%+-k&80@b|Bd@q#XXX$-j5M3ZMck zOOtv}m4GnS+I^D5m{IttvqDkuHq6e_z9gAtl(eY0?jlh~XTc9w1~igNEwW8&-6h>@ zo!pV8qRrOBi9RN_Lk*twv#YX|Rm#HV#qWR##?ENWWA@{uN5+tLs55>7LBJ) zva&`yOk6X7d}U>IRa<9V<~^NJ93nNb;UoLno^}#-r9*GYrq@kNdl|cXg%TwCBV0&&Bo4k6_8I zmMhqgk$*d#XenXrr}j8db1vc7V|i!PC$M$T*UE$jrje|9L0Kiq=Jxv|ZF0uVF1 zMTPOpVgzBLX&=*9cvif;yxh(<)@$uIx26n@-Q0}Li>oW?R_69N3|M@FhNR2q0?(ul zimCN8ym(N8veVK41mH##F2}*af$PP_3m}k+h=|C>$~tTSf)l~T3SlLhy-!Pfe0&7v z?)*ryClzF6-yJRFQ>}0mC@3gw0(~fNZ*O3T!LY)CERxoRY{6L~yfsPg z=VvOM{rBGhfY0gmeEOpO8kgtkt+jo}dT040wyXa8<~UTv*Q#wkDUC zCvLCyy++6D#jrc@jVd9{&n4a>9IQia#`Y=#z6?5Lr;1!@9~Xq;b(|&X z1e|hM04|^^{<4N0O9A~vyQH>Ve)7@;l7#U@g0vy$fHm3H*X3zb7Ys~`^Lk^bZ@?^!lp|j8A9A^M~t@z zn(aBsTwHmJNng=x#vIkT50Xn)0$e`=nK26sOR6lO-2BuZh7Q2QtZi-Y&WH9+@$mwB z41sgv>-+mNLbN+?mV7b5<;j@r_cFWzFujRONI2G%*jQOflcE)z?m+n{EgQYV!abXr znE`G=+>ams45H$3I=spD=AUd^fm!AjwrkS?*vf0HqJzs4qjRi1b z`@>vuCcr;9=X4<6{RHiM^SXfcQ1=+vi!^sM`SisJG(1)(XD5_ql8YW_W1l-DUKoYl zd6<)T6bX91ulRDP#!TnGDr!G8?I4l-vCH+0V$%~-CPIXkL3Iqeg^pu(jha~x{&<2c z?igtGe6J$Q_315qcM$U9by#dQ)3J5vFXAqv9Oe6D=>r`S(|U+{T{!hI z2_9in=L$W?^iR8!V`C=u%NDFFo15RwC$k0z#UJ#z zQ0k~(%>8HzE|&zjY&!X{&#F(_+uMNw;>?$)pZSnf?3_edk?{{uHOmK{&r7vHlNkYe z$UyZ1*bH@VukLru1*E%VQ$ldyRq?6AuPK=a+ej+^SOfBUsaD&}b4yns+IGYK7o?w4 zM+<+Qn`|$yxw>_+GQGy>)5&;9h{$IH&Y+6Txy)Mm?>0Il23_~}0VwZb($muJFE)Jv zo(|yk?Hi2u>4CvbkxUOKdv+`|tEEVKe8*0m$%$&;pq!RA3q)jWP704q!hnJW|HQ_~y zDdyqGbW`f$APk%e?}*RrWSol%QRcD1c{Wjg1Fi0lp#4~U(6k}_M_RGK0m&)s%U$D& z4-nry5X`!h!+)vwCfel0BXG4d+z1&E?M;Tfw9X?M)`_TWq<^4GBhh6KF?0(^Q^-ix z5a`9FU+(*Mb^fE`*V}tW$cnF+nZL8!v_&6IwxN{S-+IcBqT%D?16u)@Cacp@9Q5e! zyuwRq&wEy+#0V2LsU~|x!*0Qh5(ic`hJerW{#5HBij5y25{XSuGn5gxcC-^_*ql92RPD8EDeXlaU^nbQMJGBZSGY8-}kSd<=kN_^{sfa zl#1T|5VkbJWHv`(&G7OqS38|Dv@brKGzyaAYAn*Oy5*{tUT^oEWi-==6LvMp+56!h z$2_97d__1hkV1roZjQ{W;Dd>OE0N7nCfn&PR(!!WUsb2;Kz;docr*KoZKiT;T;AOf zQ>-qhP&zK)Hl<045k6oPCJOm388r7qcO6BfIik!~<190QMxH8r|MMp@5)vTxBSSBm zt0y(c(L06GV4Fw;a^B4OA#pX|rOss(%3q<-xUSjl`ODP1n6U zhIo(#D?toJA1Z~N3(n0U(g{^M>nFa9{!AGib+}VL02R=U$KJv5J|yT$e<1|b5Hrp^ z(QtlyuTzURoK366j;7f8h^q_k4X1jaJ95c&Twl8U9g!7kr`md5NJ0RVGo*kgujlA^ z!!e^BBIn8jS9UQW8tcR6vY_?v6%wDRoXc^%!H>n@l>%}5!rF@12AlwbtEW|=BITj^}* zLr={5SiNAGh1!7K{aRS}{87&ooeVgVEa}XO%3$gR7O09S&EjhgShM36tTkgm!^J@fJ=E6Fm(u&O%~tU0-l^0%pVv?t*J8n|3-`Q{ z-~=J7CfzL0&D1fZ-F!8+xP4ZA=-)YQSzN8oRS}h<^nb+Wbl}X;s(XcRkLq>#|bip8M z2^6P^LGf_wBaFN=Cl3dWq)sQ7?8ojRhq*TSbS-3x=y!*M+!fg3a~FXAb^TWZhqZvv z%c@m61pXew8fiFo2{MQ% z)kcoKptkm5ho-xv+BT`FwDAI~BJxpUMW}&+Sw1#CK0ST?dsF5v@2vJboe@x0csnq^ z7U1e@TAD#I2EGRG>@+=K z*CtjZ;Vr8V^yO>gX0?Omg^p{*ZNIiJeOaCV$whr~xfcWfFTdl8$NQpX?vzCKNv7SX z_D7XQGb+zCBLh}~jMQd@V>3^FG6BG2fHh=XDzN8?Lo@L4vUvxmWslZ>k=O;%2nFI} zAOb}HEL=lni`E=KxY_DH2=(mmz_>Csx3Yn1ZBPF#Fg2`oVA^r>HrzfpHe;Net9UeS zU)Y4})2Lb=LgBC|SfB&tgVyDGO1SO^8Xu?8-vfG^hi*p6gF7S$;|Zm|oQP8BgPlzc znp9~q!V8Ufb)pL*YDd1fyVHNU`@Da$eLZL7K!iVnA>vU#n?RzVjOU9@#q zjc!Q?naq~>tl22Q-VGz!#RIEhSWf+xKl;Sd=qz z5v$)2*KD=T^ZzS6`?II&5@N|pD#Wzv#EIh^I8XI1_~7cIP336RsYeRGw#Q!wDaHg? z5IBrh(#-{s?hi=K5kvGVp*Mf12yTzxn(fA%j97D`U#PiSFAjdl7w*Q6YWl;8*3iGh z#LB|K)6B?pq@z!7QaA7=jm4Ao+L_u$p@1)lAK$4)plV8<>LI;?^@|{ zyr+vJoOd6#!I#9a^$g9ld-&hr!MC9Ydp{YSU#%|Gu*Jb)@EJ?7ZUoxkh0Wu1tJRULmK~ zNAqnEO6DAGX9T6T?i_^OOSDY&C!8*;S0e&Rpsd@lcF=(9#a0XcwXwj>Z<9b3jF}N^4$X!mhc_8o z3-k82Ul#o;a0ZCB6yPf?xn!(hQKH0K$*4m5KI2|8kuwe;NGCONw|vC%g{#qXM|6$n zOr%WKFSsZ3n_~+wrCSRZC9c(s3^iJ*VY0eedb5hO6K71GkULI7L8r3!6AQ1v4_Br3 zY|(l*VbdTXcC~50Du{NqK8UKS$wws}m|;f03BHyt^S;~o@x$T({G!T@PxCZ>SgiAr zGdVv_xiy0h`)yD6^&YJ!{UrS?CGjgjj8)g#*jQUzTLX{@*3T}k?nH=6S56!9>cc-d zwd!&vQgmFr-U1O27Q6#kxh-B-Gmb^vNe`RprNxpO2N=Bg*$0-K}TkvbT|x zO5L28K7ErXmx7o?obf12Df4BuM#Y8GP*?w<7@2YXa+QW=C#|7l_I5vqJ7_OM8o=%z z&}+v&15iz`n{f^v9v%+&+isQkO`4h?DnS^gp13f;wiglQm@Iaql%{KtqyFOr@+kQ? z$HvBPZ&}Ny0QmI6q>)yMucHPFDOF9%9_`bY-XwS4jfQYd?jXd4k#Gc07_7YjcW_!q zQR$BlTk3pB)Tj=jQV4MQ(-yQ?`S++!#&Dl-w?ug1@Q6+~V#yMY zwOp!uLdKnDCoW_1Nh)))NYSQCG4+EDyMtQ*eQKwZy{p-vKz(F}zj9$Nk(PA2y>nu? zr2WS?NT75{Qe!@RfFEvu{cOa9d1Zdv+Tya2Ot!5Sh3sb`^Js+3^t&4XV6-1Mvb?-} z+b_#+@MCGd9;irw+a-)_E4xhkHRu!34))O=!Wk@7QOi`pg=%o08!^NaR_(xF(r?X5 z%b2k_e0madTe_%bF)`mI>tI>dhn7?YIfVt4s)8H4a_7s?gyaV77_3<;A-R+=oQThQ zdwUaUG$yhIVs-&-#IJx@FVOY*p=-UbyIOyIWF0eL`(8P{D!o6>U!6K8w@kS7U*>Q0 zO3mX%{*U>+Pd=k^@biDYJDvx|Y8mM0YVq)Ka8k0fHw{Zi(x$wc511K^Hd{x+<4j5I z7Gi1LjB7Q4*}b1#Kl!i{DkeKe2@}Lv zEZAo=<@VRdK|!85YOn^2N|v-ffMTP|vDjo&o$i0l)zdhW*>rf$kPB?SlnRrh6J%y) zrP1GP2=m8NP4fYutB-5#i$8mq3`kI41}QPw(k)~L;YEo8(wR-s zcoIf(${UMh;aUlB%NO@+kLLQ-l6B*;eM0WTy4`2^^;C*fJ7G!ej!fB>(-_%^+)yXy z14*O#Ewihr>dgeoR9x6fi(&IRqlD!9x5ny?fwGarh=zfWn3vaKrrXBXr!$}cpH?Kx zV0UJNP&KyEC5tsISeP&V0hED@#b)|{2OvD}lvJ}{PF5D+aP$SxWdpXn1Tmt-8;M!% zSwjgrb>w@>>b@D!ixRPz?#!w0L;5P5ii?T>FvTG84FDn%@VcjMt(o|;m~ls&f+=`2 z7;gDfFl=(LtlYA4X{kanxLJO2r!#`oPhlZIL^hT64Mjf!3^zzGRWdZ=2mC|lK@9c6 ztj@rKpqxt4k7C`@7CUDt!*;%e=kTw^FsKQn$&Y;KarrPgI?QE9$fsWgS#nZ6KOk z!*MbOBu*Kss8sgW0>Y%nykknKCd6P48Nhqb{&I#uUo&7= z+l=QpAC_Iw7=AiXUVaGn<`z@F5~M_ndNg7(AXE&02#j+eL%j;=12}j;y~pY!|6EPQ z!d=_Y(2&t3@CuAV^@Mqss-DySD$TrIBW_IaZ1e?cVFTzd+nT)CjXX3YfC{ms7S5Z1 z`wC&86Z_+im z@koV*=b|-(8YIGqd+q)u>6pfECBtRO3{aqfOR7O(qCsI<^2hibBEww% zJY9orG9p-}?)@DrOX&-M?23y+&7uPIECJl5g}wdHG-kaIx)0^>br3oLnJGh&)kK4= zRYtuAt`+!ym7C9F6BFT7^@W9|K(7-RCow8Ktisg`q5XNRtI+mT?@&+p-RJ2ZaJa!w z0Jm-8t(KG>b&ox!MNyGMiX1z4AGNgxQAI4e%N7?dnB zY85Oe|Twp{~HdrM)83TTC)hJTbe^F}fxgp9Sc1olXOaFd4 zF1r`?LIz^`zK(<$SxIN9fAFC}d|8mbe_fE>h}fLu;9b2|xg~Ih*psO+Bf5fbpP$#N zbW^^>ldzDG^!1Dsx)W~;u*|t#PW9JfvBpNLOYbZ5V_d&`-i{ynTuC)cRr*Z$5p{iL zS#PPy7VCD*qNqLW0S-IM4<=pm2g);H`jdX53~BRw)*{q9Kh@7N6Z&4ZxsR`HWFLd4;GIXLgVoiWJPNM##g+O9udqHb@2n41 z8w=D9m6Ju68U(e*o{kYzVy=!pdv-ZS(EximJQOjefCM4N?Yp-RgYH#CO=}+yB^w7g z)wOp<$7{VsNR!{I8BH6Js=`8Z)%n9qY}iK|PUHE$O?2v&f)FdR6H9yY= z2wecDvwmjdK1S2AwkhU`~?wo_aT#+BIXQ*GP20SL_*OGcXy;~1er;B4USTMvMRj0)2sYSkb3N1k^*T0{#fNwp{rLFAB9Ru_ zjwV8usJldp+LYISHZ)43{wk8uW1NL5n9S2vq^;s^ z8LKQ_jByB%H7cpA{?LPl#p@_z2uD8trWv;!-X5#~E2U;i+Dek!K9A}%lWv9euEVaR z?MrFAhl-dcgPfrDv5%=~FypOT0cL6;MxLhsoAZPK8An*PWPSFQMl22a>h2a)k-dUq z4xj}XI3gyO`oA3=IWRErhdulLY!#?Q13rDJg6gJr@8$szj`n%jPp&YY1UR!#mzbDL zxtkN)J_}7);juYkWv88ZYo!RddURz)iWXsvSxLxaHMgbcc3JtgZo5Xu?b-E?bsWU*0`*pjOO?OXA@%8Bc4sLUB!4{OnUGo%#bFGc>=`^lgca6c`bREL*`_g1q zagWb+tI{^9} z+8r@LQ+Ncac1k{JlAJa*pigI=9MB%euPIq3Qq8891{u)kzi?M9=a-jIu$c&^UVG{; z^GSn=o~%&qq~PPR`R$6VRrGbRdUTG5b`C=O#*sc7L zxMzVImK-To6X>*Dpx}-3TfqSspP;(m>G?ZcP{wt%$@_GvPC=swu^2$nyFf9_bQBM< zRa#`?v@sIH=he`U1RZ&QH&>Rp0S&o`*gr9JImYrdh21CJiAl())%Zr~9|b zk?Da@QHfs~Nzx&4M2c%$Y!P_!K1c$OS*?} zM5ggxVkObYykLnicjO;(X?lq;M0_L{(_0X4=P#5@n0B%Yf~eQxO0H4{Crj! z6&%;DfJs3S0n5OtpWY)?%fGHHaDp!%s_ovV>cTBycayFK$xHnGR`IBIUpZ`}xvqwh zo&SCdfLt$DC);giefS_HCr?LDPtU-Bjg4hCIE2tgvg>v}7WrO5K@KxXV@zMnI+tE8 zeHhikM>jq-_73vY8=a=*VCxtHfd#dPGZ4#MB*D2|Cgg}As|=VA3- zx*{+BV!md5Sm17B_>zd8Z@tR!tAp~gP(|f}XU%v|m?W;l&o39N7d=s7N z%_>9gD3Tm@0Zq~-M4uq&Lr1nWW~)+tnH0|0V|@bN`By46?h&h~E@Jpv5YJ#LR$(s5DL@tL=pr zYgts4=$1qZ(K#Lc*-8_IA|=+$iLV#@iP8+T^Z>^e11)WV3Kb+PJ8M&86B}v5wql7* z*9HlBb=`WHr2x7Kb-DO{T6>IgJz`51pa6GQLuZsvP6PpT7{21kTOcHhv#GN?!dlx) zi7s}M)QD9?p&JbmN7Vx`-llUrhKSuK%5$8?U6+FYLpc1&LCqY`AUSn^YG);T3@}-` zFKJGkiEX4D74CT019f`TX1oK$6n5yfXh*qw<0R^XyR-qP2E0&ApV|Nh?Q4l}vnLav z;TufSx}&NbGD6#HjItR0o=Y&)sWeHC?p|LAnufo^s%HHU8~pMFXACxYSX2M`~|-0!)1S2|O1RWLWS zslK;n}z`ona^oITrsTL9THXQ+4YpN ziy!~%;y~E&Zk5B#b*j|!_TLosQ3`hY;SD1puz;^ezLxMAk#IFK;r83<8jP08&ESls zL6YVAt0ZZ>Bsstrg}b_{)84kDrw11ueGdq?iWnRJ=|7|4bvZ0~=g|O3OyFy=Ax3@U z73@Tm+Hpg_l2&?Ub>sFTwOnt-oiF>G*8QD(?+*q(Hr%n5Hg&T9mcJ;GzTOyX80%0~ zUU$gCfM>fu7Ig`|r{U~y8_=UDxV&|^)-)bdr>XG4FtpErWDd?Xb|8duBMggkY&{pdR&mULCgn*t+>1#C=Ef>x7E}D4GX_vn z-tN?9^V!&O@rpB?j4Vr1sjt7-`up-gqb1Z*cB*QPSJl_VyXoWR^ydvN{%O}ue2(p3 zhV`TI3*ij{*)dGY>n)Vth>noHq`H2O<;7w-JV$Aor5!f2XT?CP9zrvFd6`RPHyfF6 z*}yVZ(`fG#se9w?j&%h&PyQs$e`DCmH;Vy^wMAptpSLS{)pTd$Pu!Vk)|a}ox-ou{ z=0tPH*aH;Io<$N5kYAlg)+PEJK#&KQ8e>Ek;~-4S*Vu!`3;6)}HRVlc#( z;iK5vtJyvM$0lZ6H)NnOh zc}|amt1xdk(4Q;JBf@zPrZdHzekow!jK=ptoUq*&6aG6-fUD);!cLMdRNk*Q)eY33 z#E3U(rZL@L-oh~?2$tSE4RXh5{%Wg+yXV9=d)N%Gs$R&pyxnp|giFNr1o<@&w>e|| zO*s|z%K;_S7!eQaK8o){mq&p~v?x(GQ9C~ws=d3^_z6kKDfm{oUc0d>F*CSH<3D98 z5PsO{&2?IK69OPg7`W}Axwcb!s%SwG;M^JhtKtrOfyIYfIunX^0s!)SS}FVQ*VZSj zm>%i5(hh9Ye}p4+73Z=iiFfM$sY%#Lg&>Tk!Fr=VgxxyE{G*48A}dN)cZL~mec zg1kF80m92N5z=aWn>-MTZb_uMH-_UI#obOi@G%7!o)AV4Zy3Hkm!UaNM@B>i!; zaG+J)73tPzw##Z;)oPt{I*WXz_rK5KinGB+aUO*jZIo9QhHiuyEGz`McjJL(U>{cA z0cA7w9%LIN-|)yilRS%bV`HNU9_>$(%U+rL>kp`neye<BvB6U+ggv_Pr=3kH*xr!8EIL*yCnnQmHHF)=#Ml-xU_5T1|96vP z!(M!>1h;fo#=kWE0&t&~mzSMhKb1>W$rAu&W?DlF3{7+tVhTjkk{XHknm76#|+ zcy@e<28AgU%zlvRnBh-cf7wE-fapWT8w(rDd5STuGgn3^g}o;(`1 z9>pSuq0g#hoyJ@UA!$G|#*&~_cO$&u9*&RRqN;LGgre5M2OBbXF~MwFG;6s=Im>3M zT$Ki?F+Xa&tR2;kd93fG{#Qc;sk>eLhMxB7oq8B;y4Df{J^h3h78VwBYNXp;OV6Xn z8QlyBla^{LX`rA(5dB^G6|jz3Sy=$S&AU6SXb$2LV--Hs^Tc4!o@Y<~ZbXZoYge3< z&D4&eN-E+jX^|}6%$qD9WMKiI$Vl3wa+=azn8wWUZ-dW)j!doD&eco2MzsF+;BPKI zjh9JHO<_pV+?!h@TcS0+!TufV_MnB`5?moe_@AAHvH#G^tZ=Dc zr;QXWT=A$W&X1*@SXYA);(l=Bv9JMoBJNG=KvQkEl|%cF5$`_hf5#tR0H(2)z_*OKa=Bx+wwG+X(I0d&4EE6C zj-2RPz%NP$4z(zuWpOJzjQOlWO0_AKrlxZ3zt)Ta#mE^0uUcDGOPn1^7kvb>%+*NS z>)^(_d{-uB9jP5I!(#HSrCBsy`7qtS$F{1752quj_xGW})V&hVF=lC$!kyO%Y>%HSh+u^esj2!E{K%f8u{I zQhwdzG{NF?L!)VK1w{{lh!a!N*IyIRI{P7@Uiz1>4E`XJ`*suiW3-c%$KCRm6VC$IhaiX&S zGe81-^D>}h{@RQkVEFkHx2^;LQ2IcHHAb9A}zcqS~Ll7afK zn?MO%c?LI8j@8v_WJFT?`Cr257E2IaK~yhwy87-V4D&$eR6n6n}pXU?`A1b`_taIj|Z3WBxyHo(Z{TKtn<} z!a9wtkJ|~VfRb@NG>UE>$A*ZM8W8^1aR93bov8z0<#NNRRSfxt@tpj!|11k&G+ZJc zcmtFsvsd>)cQ>OhMW{fbAybdf``&PZwH7)q?PtH%U;bC?4bQV7*>`GHr5;fr*>?lE zFIa{-(-EfYHObJQ5Vf{{mgG$z2Yj1%-n0cT0dh#6`bIeHR$80xCp>nmPSufm_BNfF zJCsy}U}5S+YOIJjNhl?e+$m#{Ub8|Ur&tuA_Y8E=B_rPYYM@nL4=9Q8?K_p$bHNqR zhLMooAq{UGClth1ng#T)XePv05E2cT?`rCIQ2F7aDmi1B=eKIH5I#x$*sj&lyRLI$fm16P8to<)D z_UR?V0A#3$?oCv|K_S?H-k(yxU!|AxgHHcFx+3YnT0C8Mc%@5KopiC_js{RCcJ5H!_$1wd!=5k8C3B9}ohX_I%rF7N zCWtxk6a$DtIH)+J#QZyNXimvvzciu7jmxcJy3|X;vpI&h5!p#nv_4Mh{k3@4s)hq} z?1U3L4t5oFrU<<-HtcfCa^KviEm*N4g8M%6imApsZO=5iw$51w*4)Dzd&kyCke;{z zU4VG|L1B1F%68cOa|GxeOxgh>)LIfOJY^^cHuXaIOjM0G%flwD%l}X(saF7^EmoXI@IBfy`zdWCFp|aYSN5oD|ZZ>n?Bu^7pvSm%upLU6K%Fkj6u!DNb7YBd(lbmI)NFSn-HuC_P{_-CApA`x|fZK$6Tg%8(HUDP+K6 zXNCV5bTE!jckE?te zCUPoDiC)W5W%u)(#jXnWTZ2h>#xQc#8#_n#0d%lIVySMPte|`lsLU&;lKi9kHU8X zyD5Eh<=wz*0FJ)Yak5ostkJFm9lm~o1YekpyD%~J7ShtDwAZYEqtraZ1A9QwUIe)w zQR^2nM||s2YI(l7Po}nA!&-IOQCK`hyX{2rp+BE(93sL*Vr5L+?|c1d{0NhOBuqq; z{y^{eta~TD+Fh&VU+z>fGHB^}u~Rha>?#-JwCHuu&7MQhlap0Ymh+B?Aha(%?(gh> zpNiP4QSg0mv^~C!ZBJLJrqKrsNGwv);7Zd@2sVWC1LZ_LHN%U-pni!<&$j5hcpvsq z>B1^8VWy8e4szOC@ZSky5Z~MdK~!t&W`<^j?uCQte%i^cy>Yr{&ngbrNDXKaBS2~H zyrI36hI9&J-nZCI6!A^hU|^6o8re>Pv2AOY$H_fFnrr%J0kA}q$f-*?>x!+34`ktpZJ-0FLIa}TV=C1941f7p%mzu`- z#WbYTP2a1>{=+_6ij8l4>0zRiQ|?Z2CpXT8#`&f8?X(PSmB7sq|9G-`Kj;=W#IA+Q z2D&0jy@PTw1*HY!`rW{04ym=XGXod!?zrkduJ*{1Rv3-h^gK%G%zvC^P;$bd(QU~r z`-|o>JM5NOuB*fT>bHM9)&BMeh8qJ1m6aQyVpWV)&8tAHqxNVur5lsncib3t*RB=p zUQd4B)Z9Qfod0(KuzWSAyDo4>_Pc47t!XTRB&?2PNo#@8c0>Ok?vVj`(kZnXD~X~< zN2P4UynCY2&+_K-uMFu=C}CSv)VW_Pp3*2z!)6Thf>xdjZM!4>eoi2#6}U^caBZ_a zIr@-4cdQ|RJJ%agA^x}BfM^1k4}<0W{l7}&pSOU4lYbcH&j;fB{qv9i|KhU$&p-0m czIu55-Fth@{%x0G=MC_Z5|bCL5;hF@f9y;`SpWb4 literal 0 HcmV?d00001 diff --git a/ml_system_design/seminars/imgs/sem1/sem1_8.png b/ml_system_design/seminars/imgs/sem1/sem1_8.png new file mode 100644 index 0000000000000000000000000000000000000000..97aecf09e72af7fb7af5e0eec7d0a58d40a52d8e GIT binary patch literal 75078 zcmeFZ_dC}8A3to9oseXdBuQ416%vu`oc7*C_FhQ{A;}IQgv`_4GR|ZrapKI*JhS({ zpRUh+UEllu?fwJqj-%^34u?+f_v`h1J)e*D5~ivwM?uO+ii3kg@mO9)9S4U{9|s55 zlY|Jq@_9ob3jQ}`^YEdni@KaN4h}<{nX$2W4-SDMB{_MG*LB$kE6SwjW7J5=<*)G9 zRKLKzXKZe~=T0Fbz^6Y%!EG{&%A&tK$&IH=@<|e>R9-Z(=Nq2sjR(UtSKPfGKREZc z5a0C1McTk&sf%~kXoCqv@JA%1Dy2nf#Lk7KzSI3cZa$AQvQE-8+h=85viG&QS^WIf zc)`d(6CW}Bpf=`LiQlD(h)y(#LhKXHo2DPPoLJC`X}r@_!=qUr@G$3;=T;ziawiB+ z@OEV2)POS)(YQPj(G(duxqd&j#|zwN{CpoDliz^Xavm0XmBlKDgqJ<%D6iv!gG1SR z_7@lB@Yx&(=L*hanFku4AO4Q*yIl52p5OOisa{PXSbg}*=CMUW*H}?_+R9>5*hu7Y z>FH3^aN*~Xkh90QZKVWL}!}nUDy7Ke=mokGg^MvJjx)$o2cwsVZfh7JL z`x4R9wM7xs(j-@3{ont2y{dFv>LK-izCd!-pNHL%?*I8MXc;`M|Na4dr>D~Y{|#sB z^Z#KTwpe|LVX1v3i?9;A$V^VjpK1SRsn1q*@0^RlMYNj}6YB6%vlG**j{&;hZNK{B zzw7T$vT?DRTKY&vCqCF7lb91`kI{~wJ?^cvUpu+xOG`k;gVt)cV=G);By zAwe&mCLID{gt9z+9yN1h9eo=fgcgB-k8rQ+85y}$X@7y7DC|+zE9tA5{hT2tB~?{& zHTpRtcBaW3EyizsB~Hy&8~?4&b>z+{9(vI=+$LE#DtLK#I6iWD*tn0VO1Qq}$}cwq zJItd2!LL(%{nqcLuB>&isJu>(*!MwI+B+<~%j8_Bjmoj%O?40Ol8?>LAFhln!typ0 zdb)7Lo`d&mXt-}$%CPSFL6uX6E#?X>DSxW+{zezCh=|BlY7%PF;BWJrpVV*K+`97b zy4F9KxaF9tRf(6F(+P_)v;G!V#Z|Pu>~;yW9i{~_Za15Ut$o#M@^euzA|isO`h-Lu zeXN{;@n}fhGa0U2Zm^n<{TtiK-i+qJh(!gNFu2wkkheCw8N6({(rHd(fDRaV*cEtT+qt$Fb*63kDb5>&QbN zu5SOguWi`f`*>OxG5l(DenQL`p#^u0?^X+|O%;Zm2p?X)#}am&kD08MkCU6{JUP+1 zgGP~(67C3C=ZV!D^b!55yLlR1Ti*8tm0F#cG&MCzLR+nqL+mlI94B1?`@Ci5J9PS# zeNzsh;=FYYnVjQ&n|#|FO}yiC8_R9cU2Xo9dz`$Pt+VL05y_%3J^L+Chqn1_;5E#k%OC;_fC?cw!S``0P2YL*X^dTEe^I- zo|`nIv7H31e2I!2d%lhHT-e)Prh<%KQ@6UK8ggt5 zIZ}FPNC;qu#?1^H`z%Ve-wX~S9K`m0Sp-{eiQ=uAMbO*;09;@TCqZQBeoBUHP6y5`Hb6RUCvgyMiXtQ1|bR*}Ammsis4tOZRNLAz-`ncTded*Hk@T9vcy`*+d| z)z{EEc@j2Xju{rqH5le<7Qp$}(bF??afwyrxVEhMe`@9LAifs5=CkcUJ!uitmH>Tw zrM)tq1dDhg;*)!wLB|ZgmV1%oOb5ld|DnB*kWk|`|C~u$CMP@1Ip|Fi=~S|6uTe90 z4i1|`dJbL>%ZP#kFTQ@=wA{Xf%X>KHaqfo(;&^BXM{+kGm7d?)`cPR}d2cd^YGKNS)ToOLE<=JmaGGj|UU8=(|CdwVr*`kV9-P>W5sJWunIsV?Aa z>*-x%jLgjE3TkPt+k30X@i=z?&SJ+*sa7E?WvGt6{+2{59MNU>6lj9QG!>c!rA~eF{ly$9RtYSuvH5*=h6}yyJW& zR5)qh^~wE<4W=o4W}E8WdZPVrn)M?HH6m5n@h)C%brjB=ST<42dDW^GpP)H1{M*va zf?m`2;@I;9=bG;Ib$)2b>z>_kerxOkv!_XG^n1xd@dBH9Gk>nQRsSxVa{eY-Jbfs% ziRrzS$~z%qoLxp;O|Nbqht%Vk|-5X(8 zjB07Y-CY@*S)V~c%XO|{x_tREEL}{GWkbXjNsmqc&-wYzhdcK0awB&1i3Zx=ERnyf zoq~dc32VoSv{Li)xzI%hW`_+^&$w{-RRxG0&CN0jwdCNZsq0{r!OSOPSalY#bLZOg6m6*u@syZc$+sUv(?=c zJw5iPkKqT2ra~*IpIn`nxP5$>9GV@TDSq#-)p+dYDn%bpRs3GglFoXo3llM>a{cfa z{^`!n&S%X6->rT*aqq3yu$86Se>x&B>(@B9U-rM7q{u){boR`vB}6hYW-LCMG-T@)ln_3qSu@o~)W zka5$YU!!98eErM4a&moLr0)Iou=$_`hle5L0gu)d`X_5xkMXMsU1tyY(arF9+w^*( zXH6IG&w@m)&fcoKr&`kJ0P?6AK9*>HrC1pGEeLnTj{Pere50-*~6~A#S{AJ(#=0SOH!7Dd|8!XYW3fc9xzwCsb@G%fH3o51E_jx4Dw38ol z>@S%fa3T_Lj5PvOZ!U1@T_^k6plVAO%ExeVY4>n_-?z<_zgX@stb}@ANlD3MA>P|& z#J{N1)5TE4k&sZ9@OMPGyeuxUT~`hot9DcC^PnyHfuF0=wIqdiynn(_Gka;zcK50t zGNJ_dlp>}a{}B2}jL8YzA-e@2!oMl1Z$8C-G5E)70yWMt!(3p#J!YI>`(neerPR^d z+|;7A;Pgzi`4{#&^9#O(eEZVgFC3R)ePr0Yy zaAsW?W`0~89kfjtitX*PLqkLI9I?N!ZfA2hOD=kNeB6JfbnYLp2xF_?n$`df>t?v% z?dsYFBe-gL|LvWB8<=#x$XQK=SoAcWE=32>A#l4G`t1jACUglhQrMy81d=jKN*?i4 zNpmMBkNFxB>{sY_nD_^EnzwoFzm_~%Jq>-7#jm$hbXa{{P^F`1)S7#kh7~z5&BpRx z-P`ByE_i(!?{($SwCSV*l-1{)d=}jg`99 zvhs47tp4WtO@78mVG)t3@dh7p&$Sn0V+J_`H&2iHPP-#7--B1oQRVcVnK_{`oOgKE zz<;WrZ1g6vY6Yi$hb+P2&gpNwwS8Y`<1n6tp>9P}f3EIAOEN_K757Zl`mB;WFi%99 zqka=ADXafAd^n4^=Okj*u)%vflIg;Ki_=~29E_dKu0Tq>A}zANlcs20nG6ek5G0YI zn&>Ohr|uL6ZG>;nAfe0_j#pOyrv(;^6i1u^+9h3G_mb zaj#G;x;VDe{Oq0ZaG^c)rQM%_g*k&GOe`!wC9qiR(cHbJvr+JAz{ZPts)p?T=Jk^V z;vY82A#lj>v`|g}C;>)>w)WXujk}DNadUTVaA^)K3I6;0uzI{WSlvHl34|=0RL0bUS~K zPIi_>?*VN-SJZtAfu*0HwLTB^EmDPYdcQIkQgJegFbBpa);CmoN#)?fy<38#^NBf1 zTuG)G)e25tsq%s^b8HQ>mvj}TjwS%UFU7?!yDJ7o26b~3K?kMBg`0~O zjfJG!h@QhC!|~{geal^|>HBMCUd4m^K8`1|!jvKW*ns=2&N=x@E^!5$#=heR8H2WJ zc^cLgx@RM#q~xsSeI562iq6kBJdD#Vd{l*r(`5a3_1Z4)e7_LexTNKIp6fGLvOE9B zZ2cVF!rqg~-Ow;uGxIVXAa4`L4t}vP@@GxO!ZY8=vqQVE%+qf3$UFa6oHLad=@+Mf z_FAyT?6~+b%wq!hKP74vG(6wTMorL%>r)Cf^>lI3%ww94nC2e*s9*gPLFdc+d~?Go ziCA`XX2*2zz;w0qV$ko4>9Ma$10*q(m&l1=A>m+1W$|CuwMyoga%4ce=|GQ?{^b=n z)7g1OD}8oXoM2!AJX_rM2G|V;&hv1&ssY#Sl*CZH%Fyldga_-~+-meiNe`Z?fy97E z4Szj~u$iz<_YLnVwU%)MPV-P}o8R!lYMKQEq&wHV!Pf5iO2o&<*V{i!4gsFV zL#jlU0h!mNu<+js6In()Cp+Yfh+(e-AwJaX{PaX9d zZg17rGGozT`8@}FKYE4grxA~`dv)?UN+cgVbu%p`h=_Rd$GLC!?FZsZn;#z_5R8#T zxw*M;%%K*vjg01ZmN8$xe8EA!aO7h!W*`DlU}nZFr1dJ!h+RB@>I~I2j-Vz6A(5yc zn`D4ny?uRo=-x|Pht>Iy#98KhQ>%cj0If*~c^G2yV|90AVq*7ekDU-U*m&YP@N16V z^iy+z7Cv$Z9VeiOG-1B>p9kydUf5$4bLq#P6Qh%sJ=mF~WCM5@v}g&@YL5CB5vjos zk%!8cqEWzb;c)?>0__QHg`)}#Bg`}0(VTvd0I%^0@sD-^rO{MMAP@rs7ZkE-*3I9Y zwG$b(2WNa5jWyAs2eR$m5M|#-gjC9Xs;Q-=V_;xmW3%`ScbzxmokQdA`myH&z&gal zvIcF>N3;Wi0x+|*EgCgbQd!xt>c1;C?zd|IgQmeSy5Cx+3NxD(Eit{Y@Y=9A6k7|A z^!#^q%i(HMUr)-tGc1nR!rCfnS{L1Qk;sr-k2mzori;_b=286zts<^)X+8#a#z=s5 zjs^+qo{9~Iasz@;`Tf>gHGTIml_K}=&+-QBlN}x&0>J2)S$BymhxzsbP>Q24dyKsJ zZsUIqsV!ywzJeex9u=B!*3|acT(Mx}lRD49>hAi!YuO0-lP9uJU9TVQ_tF(@UN5Q8 zja)Z>Il&4a^0x1yWnfEohTbM0zGYYb;ju(CtK=MTH6U7jYRO2DFS zdyf@-W2Ouhj%wY;q8Hukz7!V5C}cO%S0DIF0HCgNJ)8<6R_wSy-t}NmLp_h+$&)9a z1{O!2H&RoJL_Mt0pMzem$Z^5B<^%fa9qPRui7J3!Oq6+=1#kfEFgciIfmG!NuIip1 zB{*M(j3w~RTydl*RIbK%I_V4sKw?%v{!}?%sSwWod^TInQ-qxqMy^>4B>i|^b+%{= z3*RuIQ;rLS-|{rVQ&XP-JByWPPbbq?YHcpN4d}a0CC%})#V?97ngo^kAw7L_;`A_J zH%bRN@@J|w6q@9ttn)=!XLt7wN8wLn&wZXw4lI^yWgk8{rw0o(vz`b<4o)(90ExzW z&CdbF0Z9mcip%VGI?#ubbNaE{o?8p&LqkL1WKmL40gc_|5jx|~1zN>r)zy3Y6aDw5 zEbQ@kLKBN=s{IE<)~Z%cxgM;{`lYOH*2{L*8#MdYZ7!|v+t1IJ&j>?zU_!0yO2emt zM$~Q^V=l0WXaYzHy@Dp}ZKpXyGzwVaJkOVIUXmWkc@0FiHn%!AeM5`hzfJd*=bc8a z$t7V*q_oz>l7;PqU*9QuZz*wvJm#diro~yJ%Kfghoy1_vAyTH%4}0vNu(`<<`G{nS8BkSwm*pE~T8%izVRFXz5)BI$v zZ4i|^Bo_DINE{m@UDiyI5fC6tytA$|d2AhhLnZCU^Ic#&eZh_Jj<)w;z24ul&|M}9 z;h+8DtB`wda&Ip&<(>nSYM{=fZk`6{8ErF8u=T@geVBU0CfWGa{kYsHnL}8sg$4u!V!R}+_AKj zuXS^uqq`u+9&<7}nU_GAG=VyjZk|19<*lssMDRYVGtr6Pt)IJZFrTq#E?^?g^0ZL% ztci`-(L_E1HUx*Y!DknCgf>DJLPvrv7!Nct*m_HL6MQS?Yd4zXDW1W!}x&? zq#fVx;J?W;>l|#@)Q7a{10-z;bRKhWRYa6BJYRt2A8`o~KBi-S_d=x1dH~7-v-twG zx_f~%*z&3+vK@%TXU+UAIpqA#c-?B_*2$Q1LQoLC#QxR=_z{nDHxuW+5Etjz7}r%( z3g<_>bmlLYLs=+f>rMqxN&Xp7k<8F%Svxsg-BdX}dIn>qb8Z@$KeZ_Yj5u-5ugGxB z#KmR6$F8dkG#+5x1Bi? zK)H+2vnnGVHeoQ5q5Z(()Q#m#tX_zG1Zq1dr4pw{F1@|I3$CKD$m2e%_g8+`%v3N! zS1|k`I+146OEeiQebwFFeRy<~EupKSe_oX8%6$5b&Fg!cGkm+#rww07cQDq2JT zIU+Sz*LiN()UM!k?zHQtE-D-I+{M>ZX8-S-)a6_Hb?$Mx6_3OjTltxLm3#NL`qE96 zZbQ!ka4%N1Qc~F%GSkiRN(JSYCb9o6%w}z5KET3jldCG=m?dchN$_!S$`U|sI-3Nxrg z!4s@kn4)?`1$}2?(g_veFvrE+1#v293J2r7=w9o)vu~87%Mqs=YZ&k*%f>Ll00&(u zEFm%7N_Fy$IN<11SXlJy>h9E}00ULOA0sgG8OCa1?VP~Ac~tS|H#rw5cO>UyRV1gV znogTQJN^B8-^2e9d-`3^!Qg7DLI$x?^VD1+)^~r_FG^WK!`4lIep)Aqe#rcLE3uJx zam&G|XsUm7aInfg=(V5GT;c~CS0f_G2##=Jz+lksoILdBXa_|dv`<=DuWR@{{+zs&>~-BvOfXXcSt;kvRJdqsYa83!M_M8~FB+ji%rp^IGbttF$>-%HSTp+`+c(Y%Qb^T9g>^xByGb`)a zQ~tCT1X`n*`&2ZQ5_a3P^jmK)<-O;>ucY@~01d6#&Hp)ITu2ebY=!g~`YbxfNZkq67T>mFot-mMfNx zaQDlY)CaA2q0m8Z2<;JXR25gSP7q0wQ=Y)231tm&Jo}XU&PO|9c0W_4|A`J zEUj1kMjSu;bJ<O%0;%__O?F%!Ra?h?%uW=pYj4o65SO#e#9}CiFz3qd+tcab$ zH`cVV(Glu-gl{mQZJ=jXs?832MNZUIY3DP3!{x-SMtWl&L?(W~iD)d$N+wi>DyOGk z7!@Vu8PEc876b~1fI~h1-MWd=%E}NhuG+~lm7TNBXZQ{@b$|l6X9VY98w(uuEp*xD zZ3n{U2~_>hni0E=&Kv2*7@-i8`kiLFMn=atP&D-PW>&`PijVnuQqnh(eJ^zmi4D+~ zC_}B2=MWb>M49skxnN(jv#-NMZHOSg=P>?Bm6MvBsNwC_Mk$D}(6(WrK?Q<#U5qvp zW}+eO(Ifzvc!Bam)WbLbW&uJ$;Rk?xZ<%4a0rYM`cVuGXiIhBw2 zm~O;;FRj)#H_4FVrjf$6jA%eeB_RwA8U2IvjW1vDJ|5zJ`_~mG(viJg+9WRcD_5ua zU+m$>W=Fn{`b8jccFu2_|FqFZXKNOy6{C|BA0@`O{1R04V+^wkG3R;0w=wvFtG%Km z=c%0wX=RW|>dzG=Yu6C+5F*>(D#e%_O-b-kViY+>wDqnsQJP^mwwEqtZ(NF9q5|X|GQ7r!d;~_i)IPa_E8X|VYA5}^nJuTxHU9X7p~r0lpYtQkj9!gQ*wT}$a9qfb z;{r}quF)o_#4V`mrjKZK*Z)DQIFO@L{FEndctY%>uX;z>mDt|0n$n(w1NxS?lswv% zf%$9+)`#w$q^ma|KG`up3=rtRY4D(#NV#Uj;3krukv`HzHB z*1rDq;v2UXJV*49cEjsu5_y!FPuFza7>`7H@5(?fQmRtb*v2MIn>!@mRyc}cMGNsU z{_e9NSTJa4eH5GrdC9x%R8IMN6!3P5`Tm4)sA3&ckMMX4}p(8M+K?xQ2-J^a{ zHQ}GKI}wmxQE}IJi0Dk>zJLFA*BL{E77B(g$QW~ArGe>&t(mgloLw&-sBc>Ioz!8c zS?98uFrQ?k3=Jq@$*HvW=>sbYWWYkrF6^cIVz~$)B^V3_=2~RcxOar3up(Q`M<_T@ zwoA61Q^NK))ev#UeDVTGzC}H+Oc<<_A92&e{Ou;1I&Ram|5qTUmZSrxyMp^-d-;#@ zk@9^nm8U3o5|s2O^~VB9G+gV+;blunx0$!`jss>BD|^J^Nk4uL(T&!#M+_+j1uU-iX8Ya zOtkmhE~1kVR<=uD@F<$yEgy7p{~BmeD4TL@tH7dRD8u6-`zI&YUa052%FKWD@ak5C z!r=Y@QYpf)ZWhs#A1*DFV&dZ?RPud31@T_DGVyHhM_LVfVYEs*;d9NVuBjH> zGa27VVvhz@siUJ~V(%Ae#}SloW6j9Pr5z<(p+38?_qx;kYsb%yc5_$e0cY@u%f;;B z<@S98<%PSA`PwR(9TuYP^~~>h0e^=viahsMV&bp9lbybmJH~6G-)ljfT$ zSUgD)I@$P(Y_LVS;!L6RJjIoP7M~m+iu>%m0YeL%vNOjOkl*;*>e$!|ymo_bsp@%c zt)Wx^>K_gW4x!OCeuqM0Vq$>(U=*9h6-Lc`v|(IyWmFp2V?CV-=pN5?`1lopK$J%; z9sFa4f@gc=fBMYtL)ZV@z9)NGXAzA_Lyf+s-Ekj~kAZ&xnmi*dC4=Ciwoa|peAnDt zVr!j!V3ZKBx_j6l0c22D(hS$9^*(7sv~S&O<6RPS@4mQi<9*8CZE)UR$~D>7k$y}G3@lm3$NQc>4r!` zkpX7uOpk(a<4sB$8(Wrs3KU^~Jg*Rz5S@nFF!Ye;{7W5;s_~iU36@T?7AARNpYpMQ zQ+PNe%s%-|F2`rl@VQneIwsln5(7JY4KymQ@UD9$I_!qoR*F*EUy13HAl}bIOMtVV zY6Ib=BMJVcQ~!6AVc=?{ZXB_ZZ?9ay0a>JXO4ZPWsl8jMxj>}%x-Dn#Jwkotgw75F zrV{p^iHQZ2Eto9YD|U|?>~BsUI5Jb{2Y1SL1tVjhjz)4++63X3T8C(`sYq1Ga9!G9 zX}>pd$xNykV}3ef^%vA`-HNdMJnrIg6N_!03~L4@{3n6_4&U|{S*RL4r3!VeOftR| z>QxPE8xa%c%)Or~L75AfUQM_Wt8kt)G)WmF?zQ(y&Pd{so163rXU%W6_I5g&by|NT zM}Keqty0&8-9wNtpmW4q7?iV>G`bD{Qi=H|k78Sqd~fS^N>3TMrxNM8>UnD|#H?q3 zf7l5<=7{}ARG;fffx!iU4#W#+cE!#c(7QsZSj(!aLM?RcoB_$!aQS=WV zfK~<8e7`jiJCLg3gE#(;+&LpMpf&;x-QM0#2r1n3`>-46r}J8rDvp;mX#Zm&`k3k8 zSQ5#)bFs$MlIUbDWq4-hg6Qy>M~hSsd!9=s#T94iAAY|jHkl-< zUUhwA&6|3K8lN?|$cc%PC9qc2uo&DsU)3igmb7Kn)(($8soQJ>i!Zv8%A!H$6-=gp z{!&|1T3P#j^kB68)oQW#rKMWR{FgJ`@qgUK47j^w5LPB{a`e}GN-QVOL=4==OxquqDZ%E+QqKc281wB*J`b3Q zjSNEB`NoRO8cN&JYm3IhmyLJ+@DqoIzUeTNZn;vbP2SWOZPM+nwlk^(9swt-_$R|+ zBq*BDY8=MAh!F!NA4agWq`_gYu7TwU;f{6}21kIY4Th-I%?umHdqop#@dmdN!u{s$ z2?16IwY-2tkv+z6btwJG^$BtIJ4%~1C2ry@pj+2C&ys@hHs$#3i~*#dY~481mVp)k zn-ADe2r%*UV}1c`4e--H9&zxD@cS-{v&@4aBT$Ryyv6`sJ+{yedXDZW$aCV4xm6g^$ z5WOUa+ucdPx8RZTyXo{TmE=)ke_N)uzPXJ}TyXI9t%PlbJ)$U!EY34|3+(!`Jn}`E z@Q1T|rkqI~zXbX0FUtnH*D2RHOIJxs4IU3iz2v378v7ktSq4y`t+J(@T)Jgai`Tt5 z=O<^Whr3kGQwv)T`A-6hhxFUd5uJ|o%F%MSA+gi?AmT_pCbFX7;%#F39~yK@ghoH) zBobOmDW=!?0zZCr6rZj)%up}nOI7ao!`-MMbT2YAGcgJK(JmzrZHrI2teo0X62xDj zde==zrW^6VsUAtT+I_f~q_s>L`p~w55x6DnvoI6zMKG7n!is11_?ckT24Z1yPHAoJ z_p^pMI{LMY8>#@pUNFz%6B3*Z62QJ_+?yt&q@)Cu9g5_vyg(}X%N9uBGJ%;5S2sYP znf8#Wv0;QW2fP%#9-!-h>mlc0WE6B;-+C7L!_ni70YdwHe0 zTQ&c+W~7fmFLhS2vJa`=^)D5$3dvUTJme1KQirJ<(^Bnd)ZsFkq2R85fL;>BgQ zn{N8V+*ni2Xo~o|WY_W$AWldaT!quzW7+v>fOq1Oho*g9b%{JT<28neKF1XeSrrd^u(W3g+kyA#PhJ)$Dm;^03 zO_+=$in*ip{QUe~nDU(IsV0d;HM8Uq?Xv2fv^cR5*(+I{FBB@ID|9mkRz6}G;ao!R zWM}Hwl@{yt;#DW4o4vlDkdrT5^rXSiN}Lf{S^j{Jw*GVX;15~~bW*THJpT59$ngVK z#30V)R=)Lp%VWFPPFnv{?$7Ioew?Hb@U-Y7Qg;(&hQX2}Qt*+CChEOY@JR#sMi znu0X}TFn%=rLZ-#EYg$uF78Ud|DF5?h;?^N^+H&eze`q@Y2i?6^e)mfH znzEYa9nEP|ujOp1HbQh#h0t2?%TLk|nsEt7Q|aYG4Lx^0tPrytx(EP zAucCRa?w|f3`3Fma*doJ>z}rFy>`bY9OvimhSvy6Shl3+>{|`=-tqdI*HUtCdg@qC z!}hNJmDsLQmu1FwU&=AUrp!z((PQQGtYfoYg?SBIigL%7u0kp%PJvSFZ~XbCewRVt z#HD4Ddr(Kh)Ik2NU6S`u0*g?Mk9Nud#YGR(aAijmu>mo$abrKTSg9YO5h4Zd z4Z?CS5<-R~+0pxR$BxV;ym-!Nx3$BUA9gQ)NK12a*KBd48yXTC{{5Rhws&o9>e|WC z`VHTceZTxcmv>?8r&}tko5iOGMgSz=D|d;H`E^(ePA&D`)zm_l+zwTdY(V9v`+q+{ zJ`8^%>+iP9yz+aW?peWQ>tqKZsor8U9iIHmkDSG)!#xR@x*{wEzLla{o8dq@v_HE0H2M|=F8=I1fcA6$Kmu0AFTO43%rOwwcj^N7PMnXZWzF`Ec9RT2DlcFv-dka2!_Zn z=&r!NEOceEoJh&Yz7!Uh0y`!7ypS+wYG62-|Lj0^X^~iwy!GRlZT~7&iG7N-;L>f6 zkc)Wuyz)<{t`F*ir+V_S&r=-ag&1WkZ3CUFsme1cxhbU0cG~e4I_0DGz27a8BZ(xM z(xH(OG%217~C#`CWrBKtp`sL z(9xjLxejVXda|{Q9x$FR!|EyuJz-m-Aq{=Z@$ePC|9dYpgDcb|e0ME_zA&}7JK0I4 zP?!rC(8`*LUovALf~d+snWKIJgb6_?^gsRUrunN_x+#x8TVG;ka`MHatiT+bKbwTG z5SPe_`p*jA{_C7aYY=sFqriXhys8y0A8c<5b^N;eo;0S{Qd__KLZmFo=RS{q zjXBAhC0QPiQnE^g+>u=MX9J5$43weZ)I)qXPh%;?W3EQkgnltCjYW+oy??R%b$cc^ zH?1}-Q<9<)cfoGjxnZH8skgS-(u?Il_1Ti-RRMkJa;tuOWknZW6yd*EdQgaa!^-MI zT3n)biHk)9#$&<~`yKJKKjI|3@_Wf(mb%Rk8>+-)q9`nlO7=Ip4fX+>mnM82s$!B> z!q#QgCBoS3ydKIJvz~lbI@t#n^dV8Gr{ArRjkyrX{G>h-G24&S#EUn(!x9YvI&xxs zwthIoc!$x^!UgYt_=;x8CpvN~a3@KreH{5~fdP#=D$y`O~|b zk7mvA0kAtuv*y;RT%wTU z=HYhU9z_(b=v$Olzn&=0%4Zx{B!Jw6nAkP?2*@P-@RM-e-X`+kB?|O!VjU0+MBn?p zXc1U2u^RAPYK#g3z95CnZ%zSa@!sZws0#?SHToQ2eU_HC{QUV2tvLy@9<+utN6q1F zQjSdk^5=itf%=r85zVj$Eu{*RtzP)^c@>xrpB`QX6)jIAS?bD*ov+t86B54Fy_m+M zRLuQlivVN_2orM0V2Ule9HgeXX(W+ZRP?~=%$0a5$$ZXMHzVUWXbuS*P29U>0w16sX^X)g(cbrJ9K;8Yn~ZI z=Suu2{2=8}BvN30ejaLFLrHdbQHIR~juvo1iiJp&2A9QX&(fD0`6@SJxKj z2>lD)BQ{5IlXIMI*DEQaFGZpZ2eVBWt!sP1Z}4vlUsWHU;UgRJOO6h^n=xhsg{(#! zX)Yjpg`wK|*3{%EM|h@jbO<=)&|PQNJ%#k23|Ku`Iz~ns_P&SAzc7Pp!61YY0n>xG zTI0RU1?&!a{g-xt7AxB8$^nNiWi{n+WpL4<<M-Ef+jB&`>Z27F|Wbygy?G9S20oMg7a}ApL@v0e;<-d?K_L zhyu!m%7N%di#SUi{;u&0t=PWnD>0#kYApxXRfD&;P+QQwe|mbG5WVT5jdp|Hm1DC3 z^SwARXR(5APol9f8?Gdn!2sS0X>IV@Y#wxCvco`<;*5p^qd7}q+1>>s3ewQe&_zAH z>z1()Lx5~Y+Ue0dmcoDawl>M~CP~Ls-dVJUV%Mj^rKg303)@7Bxg1yT%U?~sK^H-@ zantZt;Qp~{V(=Fpq+m}{VXT7?=C7of>1j!uqn)YkeTdamdu?*SEsFi2F=r+p&6@nr z8odhQKJLv^Q~cq0Q>w2Zrk|w9lI~!5)xFd#{$Zj1>Jb{+{-Yr{1k{o3}|jO(Q&%cEsI9u*?43VB<}4ce4<^Q|FU!xtN%@tEx4CzQFII z<6w61oMW!GE-P2emM5RO?PHV*GRi9eC~o6RT5omE7Vdy2N(VbM78HunP2`v|8BOhOe(X zb##RCDa971-WRH@eB5?tYmT_o?gDBX47tRd;CWA`6}k~FJASb{t!;bkX{)6*6|#96 zY$?lTroJDh0u9-OeqmMf-V2M1v%$SOpGhCwMzpR*ceyXydHMDW>}F1Q^RAv-f}ic? zAL;Jx*_aAt-RLqpqEUCU z!!)}YyIBPH6e>Zx%F4>puXPO@A2)>Lh7xKDLN8!rAnWR$h_hJ1s#Q&Rr8J&MKtTbH zcoP*RAR_Wu%j#lHFE=rgHPqdyCituZ`@#_kAP9* z9THk2et%4tZ%NRChlRP+kI@nMJfP7LyLY{x78e&Y3JSVTby8syh z!}Gbnf9&aw(P{i(1p?<}gZK2^>3+aIYw7K?TR$MRC3NugkB!N)n?5ZLz|3kF4-O9x z8@t*irunA9_L2tU%{pH~JIUv$7jnHU(llrVVFhkB496?U$q|Fpr_D`Y^o1p&{VVT4 zxo(8leamk}i=yKCMpldCG;z*JQyFS@n6DJci6OXfg>{4>9#pxxKYs}NcEj3DbNw

9zkeE#aXd+Qg!eV~#;x5_a+DnwDhz{kE0nW!P0aj4iK$pi zK>UD0v-5q^HIQ)W(xt2D5naJW+1sD#2nwYxPC^jicT+%m>M}F5%?jN@w@VG!AJ*j@ z{$#;;a77U^{oX!4NgqCZs;Wwm5qA>3#NwzSKHV~k|9w^<&ow+F<7%yF-SGG*m|Kwb zgmeB$Wq1EkYev7dqq@Y@XHS{7C_ZuV?q9!XA3l62lXadeP842sd)p2C7ZFiWQ%m=d zqF$bNdF++;)O%JT-*}~bB>fD6T9KI5S{}F$nSL@hls0Lvf`$~0AmjBI`B$hPBb2i+ z8Z^lur_+H#?ZZ_txS4V0>49Mg+0h5t>L$jspPg+=N6V@4Gj;EPDKibyjzAh}J5brZFE}*vDQMtIh zoU41=Y*Z*l`~C8=0f{0J5wTtcLFKtUPs0o3={b5GRJP<)RL=Vww0SZwb+cct)-iCn z{!w>z^LVSw@%cR?tq_Z|tG>DQN4I@vwwSg}{h!P>-o)OE3sJE*+h5D!{q8sy=puUk z=v&+zONd-H9o8aMlM9V})imvLH5T#MaEj|)HUB9^hdl9`D~m75+eB+g@tn=B#p1H2aS8)#y-72gjIyoZB*U~oVT z%*@Qp1d{hcV`F1y(_v<2MsvA!lo9G1rs%=pA=KLpoXw+F$s>HYjCXc%W4Ka@C*d&9 z2=9Mg_xwJK3RAVvKleR7CDK-7UjqxYM>aIbmU_OnNp8DvPE=GBhSz-Ky|os5A-=+) z?2@P%ny|z~ciIxiG0{+BV!Ktlr#xCXuhW&QC7!o%quomT-nSApMhA%cYZaqMMr@SM zRf;m|RozPH(zr$E%gS9aC^Bj-41~q`?6O0Ed#Y~X_unZl^@l5UZ=$1PBq)%R-Nwc1 z%R>L)>5l~k5%o{9mkA)oS8P zUWt+afu88w+7k5h^AlY1fv%O=Z;kzV;2N^B!c!|cz(n8mcVz__8Qfdk+S)Q^>!V-w!$c_8d{uCJemSW%QdF?^!TGD*~GMXeMCiGG65 z<|f!|YQ=+Ls0*KFQq7TtVNX#ux9crF->h%{s%4^GvlA%Ykg&0`)?K9+|`760pIu ze}I~SkmLo(npq;>L`M#c4LqdYBk(Watr)R`Y?nO8JCI}^$)OhhE`3tZQ!Ug4LkM7v z`c18cr4>_O(+`b}67Rwi&`t-njs_PomHSHt#fj8nANxW#}8_#@WhH08wtrx6gOOqg{$nh2m)K;ZaMIYiOB&r zf~%Wf;vai_T4y58)tucKBb2DwxzukSax3e?QM=Iz)e$z}&><(?572>>@#XSksc7mpKY4TGV+)Akb5;97(FjS5U3)MbOjT`5vQ!K&0A7m-`%Me zQ>4FAKG#e%foi+*?wf$1KufXzX0eU;59^_t&Gz}D5n?DGH`T(buWBeKPI@XZ6Ejsg z`R?86`Td(ImAR05P>9X5F?6*?*zg7M7YWaAC;l;>;+?Z2I^N#3rEgwLx3Iw3F?Z3? zzo?LX?WF7UFnzE1bh&9#m}Rx$5VvW6bHdSLh18&iGV}aq_oT(MenHG@>S&H;Dq$Xa zn_o+UA4J9UFndK$*jP{kZ-zc@n|XMr`6VL9T$rTV%99!eJDZNjn$B)QFfp@U*u%Y% zO;fO&pt3vh!d#&JcQ?7g*w`OY(S?;|%GKUVrTjsTwE0P43jN~8+$GRRR0$3&^nCDc^#XV`3P8zg`eSvQ?SFq%X2S5aTKTHo zu=Hou591ShTU12o*?ltyleEHRKgEJL<(|I28DUBY(!p;$5TFO6(vtU#rcpb@2q?#ifmny)7+Q<1L&#JndG;Y@VtU0jerJ53qr;o^>r z={bFm_d!|rL1W&BOT1^;!`b;=bigrx)6v?)oSdB3?M=(W?V(p3`wFMiIzPeapD)oS zAn!nREMT!>%b9oids%REZ(p3kQ~NNaB90>YVo~w3%U2?L^-$wkVUZc83=;jrHvLYg z%{Vntl<=zEu9%iT*NdLdsd+&d) z`!;;sj%*6aN!F$)^#HL%60d^U0rRJY22V@rUd9*qbXkdZ^Q6N==GM>5E0!c&B>I z#3hQ`i4mi|!-$VKlVX7Ny^Frw2F$EXHx;|dS>yeM6{)EK(cPQm-g}8PbMa5H{Vcr zb5-;*9OFtHa-$Z@-O96h^OnqsmqjNzQ;^wI0%G7B`%zd{2A-1t;k*+Kp48>4MV0d{ z#@%QM_RtO8pQk6mzKugh&+(@IVXHo?fZyC}`VfX3jn{6cXVnCS?=;CjBEDha*PKOu z&%*VuSN4U5g>B+bw(&WALtEI5w`;$_zC-aIoOr+)lI!anYR&h+VmS;@!BPz~$9(QZD5%x2)dDHP}Z9{`aXPd-&OH@vciG^@N@LzFlA=)>bGY7!vVY!b}u{V*UB5K!ge z;c;|(QrWX7`csoIY;}lV(IHw`C^IyaFTGp#*9$3g*|^hpg4PJJ=8}!m-`6)CkNjaS z79St~=FOX{>HmyJJRy^}1kR3H+35;9k2}k$s5B9slfAt{NG!-tw$hybz>h5Hi0{PJ z(-+x$=3ys2yc~G=_y6; z#~)ldBmcU6gr_wro4^m!4~9|RuzBKl#O;(tj)lcnb2M|JtkBtc3ISc8uK@huldL=O zK$76)4w{&>dDoo?4(F#OHBbW$L)mZRbrzPJqi|w@?LJYjY$X@h^pD;)F+Gu^KWArWKL6SWiU3fx>(Q~W7YtN_*MiEOf@(eh*IKQBK0T_&WMVR+uh zpKVZcuB0ch)vhk=^W@O8)#dBvZ4HWs)QYXO?tHliu5J$cb$xY4^Y^-ynV=Ozp3o+% z)mf{ViPh!RgNELQ$}Sa18Ty}uL>-rnusMZiKuGZqxs~R;RQKNeJ*dya)p3P#( z`nEbQRE_loRJ^mi_fMkbJal73>-)H^>DT+$S@T++yV@FlM02~_@g2wVkB4tNNj3r? z6UZAAwDg;bic~-UyY<`g%#%-Fqm{Ge+s8s19&KU8_Xo~ROC|5NIP=DNLXJ+WH|+Ai z$1b=cTUdF#y0l8xTy9)FAkRDKU9-fG|34Qbt)a31p$C}PrCN`;KhN;}782qp)Dt;* z7o`wK(FZkRLTVL{1A0iLC9fXEH?2v-?<+24{GNR?Ft*iqGWOETMwfN&51c|dZ^r&; zMe>l8t9$RgtKU(92_2d#$5Da5*L`>}Le*UAMeP0x!w zgin5B`+_t&R3WG~>YG<9nP4l-RsTdE-#M!$9bKS1^)16O*ygr}m)Fgv)6!cNx%Qg| zy<@5Pa_nrLwd`JRL)NcVc@b@Mk;4?)brpm1TN>sOC~k`?l4(bKXDeJ^hrnQb&wL z^>A2H&82+rlRItFM-5%RCdH1HX&YQP_3(tU1Xo9AXZ@_2pSucthm1ZoXp4a!HV8oV{B&NKNx~LG2l_)aNrM^#1dd)d6Wq4f1bYsiv1& z?mwAt@S?q3>A(Jz&YyJ)tj;K_pUvJt^W2+`BK0kyA>r(D9tv2%2T%L>D5rJPOp-jF zrzGlql$9Zw`;Ai@3S4=zU%ZoS+{uG5 zkHI5{=S9xaCw;d*pv+b;V=tbi&%a>HBap$G7bbIx^lrZPiFEGg5QEres!pwR8y;)k z+YgwZl<%Of`IK-r<-|@J7J~~HlC61Y=(0OIQzAXpx!c?#+Vh$^?)9;Eo;sDQY2G-m zh9xQU_oeGBnpDp`@Xx1)b6>dP!adJ_DE|8COJE-Q+cci)df%C7{*pN;@;rWEj@tv6 z{_l1M8qcR1o2vHzz02~J&9xg(#>a>Dtf$%hba%p8g|xKmjhxq`qvM-BOC%EvMTF>G z`PY%=r|KeFhXa1~(8<6tQ%Cpu-}oio=7t^6hbBXWQ;JQdohn7K#AqjrE@E?e3WFt$ zd)iqie3~k4o+{Npcq*OH^W9P*D|?>3+BT29MUK+GcX3$MF(}tUc#=Bh{7k3Qv6E4W zwBt^l#m|_j)OzaO)y+xFN_Wkj4wniD2q0$V=jZqE^n~z3gy>f55ThBxXRJ(e^b?P( z_VD^wux-xXGFwI=NAFYgafa5H!REWpksIRwz2(KJ40ShubKTKH1GJR+qfe+_hrj)P zMWOolubrPi3w{7(V|XD$UpMi9+UM8ZOjl~fP~RQG4FHvD#u1 zZNp&!sgo`gUUMdb;hH}_O!WQ?YPse>XKkCw>|d|B@}nf3`4max$Kt_Yp{|%(`SAUA z!{;xgcQ-yemOcK>lrbb&NczBwW6)58ev|v9sKHMgQ7IW28R<>;?k0oY|4-3FS5DHZ zIaAsLPK^v3p1f}-;~O1wD0^nsJ-#i-Xm52TTj;-+67HG(Vo+Mby>419R*1g6qvNi* zq@5WT>K}+=E|ztbH7{E=bRXI<*iu3Ghae2H${=W;P zJwWTTi@MEAD#wP$CnKz187*{x3lUBHoS$#~`Ze0#45c0fyl)-QY|kYGQ^R!sf^r6y#bDBymd^;0H;vWSJHfb_{l z4-ah#F0?-1)E5o8e_{f^&ckwmafZO7uCO|im zF&uwz8^E!=h|h8-DbJrHOSJmGq%Ho>@@i{a-e}w5N()BUX~f)+y&@~Kpdq4M5(%X! zJrx>4fD92>J~RI%pmL?#Td_Th0` z8ykYGE}Vig&vb`lo0B5{#dH-46iqDB(<_%-R0l4m=UprQa_J%E1N+`fqLVH`wXwVA zXoFXm?Q(Ot|Ep!O$3;z_q=n2NTlD_=h6gPMM-BA#5u8zsOiYN;Q=zEFQzy!i7fdRW zI9ygu1Fke0bcv?muU|n90t`S+O%1Q`Ay)yr%Ea`u^^AObNB7+mj}ZB}r5u)M6`}wA zx$l$<<$`5}!JXydgNOciHa<<^gDF58MZ5N3GkCX3Oh!H6B4BTPQs;yZ10{=XoC?Tn zX)!=8>*zx~RiMZ79ZtTVe{?`4Y+*sUs;UZM3y=g##CfKsV4BLt)fS!93_JUDFZ!6$ zoA4t`_+a~FH z?ZTDgzEc8U)iXT5%vN~+?pJc-Wr_0{aJCq2Slpg{(&}0x_umKR?t98)J~-%P$nh+w zs#G;l>si22&DoXtW17`}k9#HC4w(d#o+x^;jXsh8{pBmhI}9d^PQMRXNgQ0N`TLio zn~_KU?uWLvWH{B|bzx_ZW6Y{G{@*Dc2oZPP`1hIF%Y@dO|9|;ob8p8NUzUTfaF6rb4utJb{fVmGg>imC}szr9*Z71)-_oa=k;u@O7+ zxU!hsTKv20)#%We#@CezA^Pg#SgVg36H6M^6Ly^`dU2VL=bBWiOz=ad|DCykgfE#xW4yiDdMh1mONwPi0$m?adAejw3RQ62 zNg-fDJ(+9X##|FDz2T^|h^7hdIccUqVsvlpwlmiZo}Ofea_wRGB49;Q1BKdw2l1Zj zz4)$1H~c+pz>j(E2G6fa(}Ci`(>q#g-VoOylLC|29=Bawcdl`5U%C~S09=i{Fdn=&_#$$KD zme?)R$COXiyw4v*&k^)D(9Zz$Nnf;3Vzkb%omLBygWY94oB8-E-!-za`+nNKb~$|w z#+@=ipoET)3P3mDcK9|%<}gSKyYZn$0}>N789*dM^2fx?%pSep)9K&Jk}ozn{mSq> zib_hryou&Q6!8WIL`)uAR=o=zaV}Uwqcg>1i|WKANNq^|QHtW8Ru+~zwnX!?qhZ6= zIC_A-i;KSKY5w;8yRCymsJY~P*Tm-7xm`-BhgYDHfJ5&-#_2*-40NadC6wJ1Q54Qn>>k*1%TZ1GB)v+Z$S<=q!bMl2ui`lrMK;!1Fze zM(0MVaW)S)Pm<`R&xyt#Jo%_MgTk z%;{Z!_;5Rp8P?>PCmZSmLWW8cYMO0b229L57eD6gofcWRFU z{}(s~2thaBI-tFskpDoTA?MR~bc%$(di45NYRw|P9xAQfX-~H~*|#`VK>N`e{d-1p zRSsfn$;q2EfH8x=fSa;+NwhP-f4sX1P!oW7JTKY49kliQ{Cq}VwYa41ywQEiEIs}+ z%t1@ff|{|=c;nySO=<|4N{2INM!Lo`9S`PLo%Ks~=_H3(pbUq7hpcH$O{Tu6J2`57 zMCFmw-plYIe<>bsdbZ7qU}h4eJO4%5nuP`_Vu7LBgiz_w{mizG zLw7pKM=tGvEVmnbEC18KTmT=R@P1{~%~9oRIz3$`cx=Evr)?A!n#rY})eLA4A$#{3 z&3ooE|J56?yQ&h|?|qWUv-<6=@>p_0hYx3e*mvM!nJdSU0nek4J`t4|NZt0gq^kli zn!#C`!hd9>rGxFwY8wO%*Gd$1wBP)DAmoN7MJ>Qh%gf8S0<&e2uSh8dp9trmc7Kja z)B8skIo?o%5Ud2pq&sLeU}$J)WPF^N`%U@3tVQDtS#u@`@iT^}po(*BM*{KDVA^ft zjh2;`rr2X>NV=w0@~W9&O+o*N&8cJOXjPtuuXm2WU!FnhHX*gR_wF9l3;g_2;^NqW;3~gg zE`1zz3*uaYf{rkxC{@64X9oV-{^-#ovdhPKiN{yKatCTd+#w){#HK(++6|rG?lzAm zmdI^UBNxX>7ze3ULO~dGHG=6N!lA;QuTie4>0~-1(x`>kKa z(b2Ac{_rcvgmCC%`jN(1z3Pt&|9$-DV;Ul8$wpnIjFtP+eVJ@EVvXmb89j_s)dZhrWu&WgClcD1~{=44B)5{EWEM#Hm+ThmZF5@tyA2 zUg?s&V0g#0FeK;mPTV`0hg1P% zeBvCCG?uPID{BA#{e%|eCLSKB`68E^{F;*z#bN>Tz4QjH)MXyuIDnTS>4X<)#`1%1 z*XSns(+#l=?Vo5U;|O5Q<2wq`;Rvr!Q2kk1<>Wv&eevQ@k3i2n#w=hoKv0mTVBo?Q z6#fbMW_Vjj)odtzy%TNyuLm#kl;k5V$2s1#$;Qho5fukyrEv$qif0JvEjzb6L&PI` z|2@zr2wu_@4qMy-5l|zBV}pKgg^4F;`R0t>Fu^1WvfD+t>$*BBLK_=l1R#6}9-n)V zon7DZvADHEO8AV691%rnLBjwg!EfK3K-nQQY$32h+$9MvnC*43V#X}tuSO622@vd_ zaKGGr4s3$XXBXVDx~SZ35)c^5d)94>OLtm!@x!x1{$F4=K;>urS&sv*5{%^c?>puv zdWI(_p)U9P!?VK!xf7}T^G*=G2Pjs)}SsLFrdU zkOz;niI>;b!(&4*u~VSg;r;}ls0t4e@-@cXh>0aY|Aam`jyouTs1(X7DCl^5&zE&t z-V#ieS0X}44#|T`s;Y+&)IraJkO7CB+^}ztszSbw_dmDrx>v8C7YAU7a5Rq+ zO{e&z1xPqHeR^c_ZDJzmJ8OX!TUzQZ)e4~#^1K0Afd^XU>yIeI6})u-3^{;i1MqV$ z;_Bf>t`&U_>FPEfbGi2_ii3u7-MV#z9}W857&MT2q53YW(pYrFHFejD0K}J!yFdTl zFFK8^*YYtZ4P~PJwN?J;{fMQ=Q$Npek9G93oJ`t-$rMg0|5;wda3r)850QUv^jGDW)V(K8aGw^e^r112?qiYG=G~zTe8Zptx1+B7r z;O+n)%&YyAVZF=C%QO5wxPY%EYLdJU4f#M> z08Cxu^o=_(pgS?)yys&_M-xa5Fs=YOWHmKOn215N@`K(-TDpm{ck!`@!1e&R5dz*s zyEQr+!P5pq7KqQcA3xAIL+PLWx!)~54-W*g8+4fVv(b>}c|(yGJ@eM4;mcs8CYL!ML6fJ=GdU&2FDt^VUb?Q<6Vmhqy$ecIIAQxxN5wJj zjRvXjml)1rtYlE5#;&*UEU`o(@^!9TNC*95hAS|InV*HeR95@IP_n3a`4iDtjC zd3B`Z_ksRCUF(~mr$Q_Qvrc3@$Q#s1j3#M;yyNc_(hcNrqL8xU8W0w~wDc7!-zb^F z8}8)0nR$IJovZ#meFzu#6&{4hl=_b2ju>uWc`Ek0^H>pV7pOolU%teZU7y{uGhsbO z5c~sVcy(o2`bHHzafkC!8 zekc<{a&X?TnJU;&q~l17;PP8$b647z%@P|-tO7QEb^sJEhrj|m_AVu4*IE?r3O5Q5 z1_loyuVQoY;&g-nG+VEFt|K-J(vgGzMUTjLV4_;SRtH><46i&dPbP4#X?2)MoV>kv ziNFqb-KbRh6VTEQXT`A#&g&lcWG@rkdO+PMf`c7;l`?)}2`aI7o|e8Urc(wW`{T!X zJSQwAM3<50LhAsX14>MOr0IawhuLR!GET73;0Lf9!~lpNKQMF!L3ZC)?;hrgP?Dsp zr&r{JGi~r_IT3E4_=$+9-cBxAA4j`@5U7T8w2``a>9r?9Yt+cQC+7Ljjc1s2mcZjV zqOhj6{R2xW(IE2a>~61t`{xA3kW3?0|AS#*+S16#86T>Cz5LqX&r8Jk1?Z@qCJt8e zyZqiW-ld=cA^j{fXK60NB}H0;Ua-K)6;ejmnM|Iv)YN+*R)C##WgRPG%--JKJ&p0< zza{>p_$sq!ChxkkXjws9uP75fh(9m@S z9A=Xuic>}OvnVR=$NL2Yq~k;oR^;Jvzx*X`-xH3PqD)Lo@QWchf#toOl9Ki8nUWfd zz_2qtEG;1;g`f%A6Sy|&>gtfGM?u~&1YKr-;t})+&0rP~;raj{#h$;g)L!C}iddYO zBLN0P^+YjRl!;jW?vaJ}ck~8yqz}va$Ce|G#Z3m@051zuOz@goTW4OF90IZBYlU|* zSgt4YWZ`nHtgK4knJ*3OTLT}iE)QbWAw>XKCunk|b0RL(2AySf^L4nLe2om}XweA{ z)T6Lxr||UdKM?E1@9!U`9K4V3(7>%&<1U{jt~Bx>ebJe!q^+6;mt?UeAWo4y^?5_t z=~TxbmU{&a6oXBGa1neh7>xf5ivpqi;Qi_LL;vbn{sXH$H|NDu@_Ez~W;RLE0`b`` zlwbso#pd4*^!kF{IP_=`VMkC9(Z;maz;0V;YPyc1ClvFKGLT`x{^L?@h-#Sdu+}iR zKt%0g{9GL z0R&+bL5KhY5o2iH9v@lAzoC)v!6^rCI_btwhX5!bUAi%pjF9{n`-xx=N!lP zxv=++=_5|_lVGjM$jCg0(F2mQj#Ys0{n*VKPL`+74jt2;7S<7tD_H#rLxB<-aop0= z?ZHge7M^Nn0Mm#>`#99b6(gqz!2qd{ zo>X4AD?L0WlhHir(;ux=XWTOnjUIDzP<{8s_fN;uhXex*8X6u3idZMj1d>g1dU`-# zL(vzUAOiO3=@(NPBJiofylm-xOu60%Bk`U}^frUYHUu7xx!2yORqbgVjWQN(6k6YW?2r@Ztd5IJ$RGI%2xH=t>2|73)4Z#X&#+7eax91cHqN#5Q zb|QG^51f#7@tjydSV}N@D+Sf|BR@3$lOGy9-IpRnPmG=+*Apb#V-JrbT-Vpv2S~Da zclt`pyxMsG6R}gBQRkmZSptsy30t>rB|u^tt@d=^iS8=*D7uJBCJ=Im z>uzCzB`wOeC6lNZ-mmn{iIj|%>jV62T__`nCH{krTRyC_y^nzU1diI(KSt7X|h# z9b8hxzrvIOu!RnN(`jU1WSO5M^NqGLP-GY+(UyYLd z`Jc`Lss?n9`3#M22&0Ln46_qHRQK`%a!bUHA10_(u4o2Ikys9l+_(%)QH1z-*fhoi$*c-6XOcic<169{G~f@ET_%R2uadm7RJ}}_rP3$1X!pI zB6<#h-aVgam-k7#>9WyazyoUI!?~m7U0?Y{$8H=FjW_R7U?Q4`=tFwD1s2l;oMjUS(7hBSwxGiw!{S{Qq6aoM#{31f&wkJ{eUUa3t{4A|2RKR5BO}O?^D|wvQvwVMvLu*YP)bj&jfUfF<0XGd;r`AU{c&g^x+F0xUId_JRmLtN#bWTIJu(BDLbNRzpCYID1XA8o!Uh(@*`S2A; z+(u8O97Ek)1R!a0dhige2toBTG@NlTsXgyF7iJ0N9Au12l64{Dfd$d@ZJstfbg}I8 zje|@zCJTY!2zXfN-aWEMF;z!4`KnV2ntF+^wo#ldHT8d((bX%7ZPbZ^7xH1bjEC z#)^_7-m>!aj7Zm0n&;N%D=`(LXC8$bpEuVYIGJnzx^dtIjfvjP(so|2$RPT}olWLN z=$`UEt~tVIO~MpBM7FgE4iId>>wu>nfgt8$8Hd2+QB zt`_vJxBh3OoO`Ug-&cU!!ECHCa6-Yqr`3}mku-)?l%m{q+HC**qhv{w5TWoHdWL*r zTea9Gtjm0m&|-pPCG{_SCQ=gchg&R$oY-jKZHOm9l(F8T%~xX_pNpY-4EZl2phWGf zjN8B;+Q8szteBIfnk=cfs zww|6F^G=Ma7mkY9?F(-X8}|Lp+kN)w9r5*eTg)B7hg0+(Fy_HHj^{t6SI;j*nT1cEdXBhn!NxKEcU)iG4~y6WO_?O8^XJb~ zQ6MG<&)P5|mQmJfdd z$yGIH-B(Jj+8n8N{Y*2xx3%WTB7I0Lt)#Ir8W3PXAex9)N#5v}uWqw&oQmkLqtQQc z4VwPFz5=JsLi0!AQjTmGJYvVY@ag{Rq8qI7Nv|_Tmsavc&E8DyR{NprVj8-Y9&3PY z#ifKV!D=5wyI*V=EU$!{h6(eZ4YLFD2!hxsaiYT=6I5Wg(HUYc8Rnz5Qph^>3WIXN z5CgaY00D3(RviMnCmwlWPi* zs1Z%uJ)vgV4LX-#?ER!$J{}beYYkY#N;7ZC*=2&U5>>0qx1mHPCpjvC_!vEY6Vt;08Md!D!$?tbPV!p z9?13yQKm=RHd;VXjKPFXP$RI{7#bOw#rLn-C+Xbn8!vSm^+~mx-?UHG_2e7!1R`Vh zq`{UQqAEoa_d3UIfo+@PCZ!!}nSXuxkC(hH&E5!STVa8y0nluHiA%TiIk<$B2LWUI z)May-excZgGmJR4c^V~6)>>tj=I4R385j|ww_;zz4 z+pCR&oY8ffHNBfSKR1?l#wO|4q#e|A02snTpp;2D=DhyIBjb-}pHd5Q!#|o`rh#EWm>{X{Og4oa z#oYKQc;WLj|h=XpDc4X3?ZJ zn@wE^(0zpWT4m;#R1AxXF-W+xNZ9?I1zs{V@;yL_BjtgOG*7KG(@(!DR*g1Ki}n)c zp82~uJCHTprOAU%BcM8$RP8U6VDNuD`l`0mNPtfLQW<=^hBk;KhE z|11i%5Tp_HGip#>MM>ziZ1PrxJJf+yn%IalDRj+6h?zoDZLWbmnP~sqjSr>;Y!Zun z-HzI08)z5(1pqUXV=O?fD=fSW4y3@Z*|m2caNUOgrtdq-$)$opHrxm67|qNQ4Y&2n z(^FYa^M#gOi;2p~!5PK|cKN=L6=UsV6A53ozTZFrx9lB7t!n!keVV|{%MQHGa3@q1 zVFiyHUvo-9zx>wNu;p({8kks$OM-twhL6imqorJQscSv}v#j^tMZ*@+ysmT}tA>pw zJ=qrC-&Zb)svL|5UnBHF20OYM-Ohy_`W43iaOtgKXNfN#bF;bq{KI>vn~H|-AKm_@ zr3Jwu%oIYv3^H$w49>Fh=>Pl)`Uf%Uv@uo?{7!0E3wQ~{W3U9++X(Hxs2=?xJA z=ZMXg<+=Y~E&vTBRvW4wW1?m<7F^$m*#e1zp(~Ek~=SJNW5vn{2tJ9 z;e)m`%VLetqlrJ6>f04!8Tv|b*(HaH)duuML2&X9yx0a6Vj#0i1OBD1g zJE#s-F3W2F2*6`ViB=|@>#5t5kD=7TSOeNr15RR5$5?gUMA#5j#V{TXvHn_=O$@s^ z`xNwF5xp!G9V|xhUz4X$J%L$&F!_+Q0#_Oow9~Rp%_5X(C;y>vFU!#0F1u451za7B zq{z@GUO=akd#EgbltA6BMsdd%F~4fY16=8aQBYype4-!r$sY*B;3t&Yw40mR=D~2(EqJa z?iAF-rJ1NpT=uci{F}2Ldm`?Bi({mogUj4dcpme*-Aytf{qC8bbOw!2gQORQHj<@y zLGTTfcE}&zhjS#t(IJ=7zt6xVk+DfT2~Gv&_|1UYCId26KZe;zdUcinoFw|Bfd|ls z6r8IRv>%hZ9Imj-w(o3}T}d#8aEzW?6BGC*46i&Vh5+h1AhSoF!B@e1wXCh9Dq`?MWEOpM5wf1f zix~*fvKyMh;()xs8Kl{S2qE5h30y=fPCvU9lkah-?V8VfS|mvw#bh~&=Dm3*>M_Bp zpsI=)GgvStYKEkV8vso_tyXz_l4Vh@`0GNhCpSg3+B}hQj+7%!DOLVFG5`LX2ZZtA z@BDkj#Ms@=fVc>zIcB4vWHE(g5a$5r5TPM9=WuJx&o8;#lf)yDcfn#2q_hn>&u4~7 z#NmMWUFtkO3o%}$&lDA+YQ(BUb7d67L9yr2gZ>?5+c+5T@s`sTc_*fR&7SlB^(`9# zjGeDk-^J%ID<+l@K4Hp1#lhcT6e1||un=qxwU*~4ZWEz1$`22Y{1*ssutYOZk%2}u zfI-HY2u=aDqXxr@%IDx^TjsjxvHe>jg-QD}($auZ?Yn;z)~NwYh#(LWOob$X5qt=sJ`wl%0!A#Wpe#=x@vLaQ#DY7M#gu!Nv zc_$bFVNgtV_N;K#EWW)U4G8$6q@cY!jtV?2HVZ*Kj52;F>N5!L(OEFw{+{;5;FU7H zgTTZnaJk zMKx16t`;OZ5K&YUFX^znTt-eXW&YBwHf`AEeL}*XVpB@RP>3DE(iur` zm9_8Y6LUR|0L@(`?!ca#A|Q0Bmd^WC+10FbTeOxu-{$H2(&y<(9Sm^%;qtj%hAS#M zk7J8O`JA6R_M_3wM>}-b7uOBJ7ko4dm5-@e-q81!ypy~`>R6k2VWJ2gZ+ z0uS7>>A$IR=!y{BBY|)r7KKnP`b(Kw<8Vg6=m!lL zlDp3oE*Vn$x6VbyV!coHgc61ZtV0h6#wN=N1Z=g!y@D+H`;Tg<|-)iS+YFFAX zK;1b;}8{IA^J*lC|K?@+5l&Ov9Bi{(KZ)p%x&Qq-k|7(plr08u;#8 zd7FRd=tOK^ntDqbOGt2(Hc~8HpJaVH6qSSM-o(AiI&?rtm?dgr2|xVxJb{uV+E1PH zZm0C)C}@wiSvOjs>}Oi&{jri!^+a;KBFsQrDI9~xZhwP9aUvWjezWlGHp5z$*uqdG z0Y6uo&q$hC*efMbWGPYzGw>(}NK+ZbiR%))F) zKJnqH%B-9n+~MsG%$%pTyWdpa;k@sLLJKbw9lj?g2X__zGdxRqZmOXu$=qviYeaq#9@RznS z8BLACw7ApeZTzRWz5AwXU+A>TFDg@cy*PkKrZ(s{o$9(l@xAZdUMgLZOcetUc9ia% zU)$NI1w}<6M0AEl9O864W8Q|lh&Xtlh8A}6db6Tx2p;D z88bB6;G4TgO(4vM2WaeYo|J^dJAYyNzBl_m)Bg9&Kw@w+a6cY6)p=UrSAoreq%JnV z$1ZGFhP_#6P7bhBlv&tx;*{RXGErAg9E%t1dxsDeL*+0R`TR4+MmLIJRrAa9I+<14 z@it+;3%*OsosGc>7LEOGBjqjbndqG*JjJ+IlZ*i$L!zAJqfZ!|rfqapL=OP~zPgqd z3zc)iRnM-3lCHR{cPKVPpdeX?QZG(zbuz*|x5T@unrmod5?8A{a$TnS-upi#F39pm z2aeUN$`BWAuAu&D=lG9{MsO2I`0;88$SYpz7-`G%>PT>P&iD?$f5^oZY3fWB_02lu zoDcOC8sR>1oPWe#XXJg-DBMenlqp~8wcJ}q>GA2e=Q|FztL%sjL0T|O zGYuUNl120cm!u7u%o2U65B1Em(;!#J9N6KD@u5QD&FlVp+uf%3a@T@g7A#PwB2s@f z0bSv^(swLiX%mPJc<|TkJ4-NV_}%C%Hb82fSojs>xcaTyIJZtI6t$^KX{VKDW3`ko z$acL5FMFAcHvj7un|IT=9Z@5J1qdUK_{Y4mO`aLXC^Bo$&3_nEpW*l*4!Mj~n&`kW zl0RIo$xh=YdFO%IA2+z;_PJLxYOs6`#TbExbRC&F(r*uo;n=DJhl_c%*f5i9l1}pv zY}NX04UkdqzMCS%5(!*y)(_K(y54VZmc@)2$*@r)-tjbr(8rAMsBs4nWbJ8{hk3&R z$IIh1l4e@Q%QytriMBsGxl1DyF)dI?(fCT0%@x_{!-wmP=D)x+r#dhIc{OP+f=xT4 zfyW(EsWGu%yA-Mf>bxw5yuB%(y{x^{lZ;l>hVqZM9Z7smo&Utndzu^ zToz?v-WkNA`VU;A2nR6Ft<9NIb!t#=Ezh5sVM^LlY<@aKnMsRn@-^7#lU^js7SVKVxIm5&Z9cbW3COMbu`lcHl1pvxT0Dd#|aJuzP{!*7qaER)%poaJ4Q&&g~*T1-p9wS;fwy!EbjOZP1N*G$USD zWal6(d+zjx^Jxu=9sv6ZD5aj5+4;#Kl}Dmpfg^&V_FC16;&bRK9^8CI!U2PBXSNFR z0|5-XYmMj+K#=6N5*MVyKlX6K8%^Eb^Ww$lsPA*v8~XuaN^Vc=_lv8r;uOP_-#w2$ z?L~TIE#3R$3AS~(wV>2bpy9+z*J5E9KmQPk=E{41_ulWpaL{nwgK4^^{R*G2w#)~J zN7l)in|)TEd^F;hG2AK*9cs7avX|F2*k0yu?OitchN8Vz12Wqpg)b;J z(i9*899<>_5d$5XtZi)deun%Q74AZY}A?BtIHsLo9$&#>uxRY zRZ7*&2zxX6%AC0!T@geT{@(PAq zf8T$l*rfGC4)((5d}NxraxZxvt2u*VF?O{6Jz*0|1rkws%OeH3O|Fu%!SmCnq;^Xj z6-pz2#{upUA%e%4(emGZd@4dgm{6CfNJ!8K zE2C#VFlSrp^x3uhG#d>dznFe=)5Z-l0=GA(Eh|NhSl6isTll_8(&D2k*!h@?g8OcL z)+Q~!tFA2zlOXEw#hg|4?B?-gAxFj+yn4fzKUQ4E^hq3BW+++hd4;$t^tSXRH<*X} z^=`gU)wJT$wNw_2E!(i}3Z>N>4jMbn=eMHyKlg@3^NZfPslK$9+av2NK63k7$~?yez%wZ?NuP>xEe!mKkZb*453{3=CE4=vx<)sY z!~v(sCTpYQo0CI0r)#dqwu+{hCDOLaJHh3xAL3zU+@jA{E|JQUB|g31<4v4nr=or< z@a%z$+gNmz`?hsmHE8o3DX%_fX`wv@ZY~Pc?&ck7vWGb#f__Pbs0Up83;pnunUqWZ z1%M2pbEQQF#;x4OA~qqn#Um@Oo@w)R+Hz>@kdHyZLIn9Xi)f;>ZnsXDg>HKqRc*f> zE@NA|^nl;Cxe$w3vloo+M|6eR&!l*f)BE3$k!NdKQB=yVeNM~n|Jhx7Z=X4}3nt%+ z5f2A5X|4#@O;5G&O&q)s!x2hvt7)3Hr=7ebQkXkJav$gJM6>3uQ1U9bjvGnBAcO~Q zyO5a~0E?Zu(XB&d-gUK`-^;iqyL^AwwdMbDMEw6PJ5VYi_uEwTb%u9nsso%ot%<0t zTwy|*xAyI-l)0;`sS)}4qf`r@rZx#!aVL{!?Aoro#%GcpJh+$Tw#4X`#%|D zebXs&=+WT>qj?9tn?7FS;-=b-z8V)`MHRV&9wo0GlD^=p?uY7GNKxf??1mc-)+C<` zXLw9g$X&mgEkww*Q8`r=I8)-F#g8Y=+NJFLYxcynz_DL+EX`q+Yh^|lcna=EmiX4y zuO%)Ny_?nGq8yq3yUjw~g(k;Rqa<%#P1d%u zK!$_MB5bFO1fQQ_v~CJNuzz~%A2p!woUfP z7}%PRZ!4$M0%iyD*`1v4)v^3yOBVaiShDMGNsI9Rf*-Dz_A#eF7u?9}yFHT1OyhRi zN^t424q zjN$Ru#ixOe06#}q7}Wq4O}A=BywKPHe|P()?p}t>b$N~9Rj72w^i$MU8%*`bZ1u`x zt*N%*0Y^hE%(PXWqIW*rwlLgW()-T$@GJEB_Y@84X14ol3RHi7$5*cWCpR?XqCmzp zDd8HTeBs@{e+o4pYPl}tZfY!`sN20qQ0Mk0$$Q;Vs1Ko53@E5oo^q`8wuoMch~5$7 zjX61hOMu`2PIYX(aZk1@_!>Voh2)c%9n50kMpv)>c4e8?Q#IEy8n9*Sl(rT&D(=wE zI{_AJEpq<2=D|{HkE7C4-tB`2ZQ0z8*JbtT2}cSekpe_Q0V>Vi<2#53YQZq@A{G#S zgE|4U2K3t9w|KlhF0yf%)X$5TD_QX2{5qvzF~IEB-ktsu@?$_9mNO#U8U7l$^@tWH zmdu$p87KOr9iSX4XQv^nzfY`M`BU_LcziFai4UBR0VX+}OuXh*!At>!2oxeu3qWN$ zMlU6KuF*PNT#s`ikuS@Bb$Qj5Zcy1K<|I0?c$g3a;ieJtpbLy|dC z9@rPMGxbn?*3L51hsdqP=Ne<2%d{X^Y4{SG?b^!i19&y z*9?`!g!GF&XZ8hG4S z-LCYXDWU6Uo$C(%L_xE9b>$L>e4w@qVeF)-gfQZ3uBf{}F55=QXh^~!V&Y?~SAL!A zs=DN@pIN)q_=>BvL2XLXdV|s9vN49shWY9N+dmENwG~ed^!LsNH@Lfte^|sh~whtZ_AZsfgYu8k4@R zzYMITn+5H=$5175VyPE@zRi5P5}7rM4MOxNJE0zm$~XQ8cqCv?p!T{}I`l!GCGI2| zF1ot9+FR89E5{rk(=zXJh*;+Fa{KYjSLQt2+QbshIe?msVa%UGSG8x`1C;WSHroZQ z+SCo}&x9&pz}W&)U9&uX!Dr>SO1@=PBsxY=)1KOa4BE$ir?D~8TGX-#@Er}nvFa-h z3k&U;PvYDHrvd~shP8|~AkS|u0tAHjLnlM{MD-F2coZ+NHAo6$iwIHlP&&|dUx2B@eag&E0_+oz*HCW6-6eh_W*0VK z18_HxZxxaRYcCnCC$9>$xZaL#-Icyq zu#3=JCz{fM&WB7a5#5_$b=VfR=R3n}8Q!R0BOr!vmCKi^%^tVp`QcO9Q^@y|PdARUxVXO3IGV}<2Z_JVRSdqDO#5ikuLT&kbi)*IV{~U ze{_26WS>9U&{1_p4-7g?qd_6WP*n@}E>8=M2{7I91mK&X+YC1k7rAE-pVFnbTY23b zlgOjt!@dY9K}!t!)tX7bMCbvswYR4Q-b&$>MbtXc=mek?!EpK4AC0?dLzKDxL^&@u z1&?>fa;%;<6Ma5>i)Vg#jHpSX08$^Txe|^lKRADz?U|Ea%TUS#X$K9$!GR8BS7L2Y zi|tpwA@WU|`}RGJD>Jeye2tU^#ny~OEMM&C( zHqvVOTqxe#Di54AJS*HF+dZezo!yNutmg_3`K3zPWUwN30Qo^aARBSBbOzhd&AM5wJ4wVLw0KAap=aqSLSsJy4Z;o3+w( z&kPXh_X}O2G|cRk1g<(*O8!xaD7I4mGv(3~2B`vrU(1?Lgf`D#y0+ON{o@jfcR*as zxbU2C;A`ZoVS_sbj92aZw-Ky}P}EkSfVKwumYOzaDE;Uc@T$y6x+cll+wOmnZ@5bK z1+qgOlagRYsUcFQN|1i&=GW1Y?DQ3uhW&E(HOD@OHxLPz{|@HjV5@6cldJ`udV!Yo zj9+qpC#lR{w%W{gHigcjZE<2cOye#S*QCG-K{XIYBYz{C^6Y3h5zssK;;n^6_qtD;Z6q5 zj6-5Fs9`C>F5@EZNbr-A^IuTMXUp?~?tow-)CSGtmd1O`E$dRHkjtKUM6OvGt(mN6 z)$9f59tBO*+5mA978hq1?Se$ZBGt3=U-x2QF5(I{nj^*u`Q#U6*HRpan(qsS!X)>b z3PwnTz;%Z)MLYwDp*H6KF!d$iRPJB6n`E8|$(-brL}VUPB!yBbI%Y{qw8<=l%uy7ra9OAcK-mv4p@&H2YSkLMK?MQl=b!6OZkn*<(gJm)w&YXVn?1A%5O4`AuyQ{nB&1-8|v+-)dm@%knC+uSccX)g_0b*nKi~IijnL zi4Tk>pKj*EJSZR`X=D9fU{tMcu$Zp!1WBpS09cJBCG_5wO0dC zP*3d;8Nfm~2uWyyN<|O5alM{Nw4@SJG(|y#O_fuwxor@2XwLI*f}Dtu$IbF@G~s>A z<-uw;H%{Ce2sL3N(%4g_nUPZxiyO7#3gVjZE!=-0I7>jhK#!Y65#%$)TmbSJ1A=oW z$o#%*$@RP0+db|y{bfl_9sR9OZjV(z8f0m0*!In5pzmBc^7>7J7j z1;k%(%RD^&bL1GR@bcWNN+Q!cB&Tu}dy7Rtd+N%Xt2g^3ury_Djm`HN@(~fDbZm(c z`BkB{+;u&&+IF^DMR_4O)oF9g^oP9?qm+<;U>lY7iRt5uv%?G6xg84G_Fhv{!rewV zK49irh^@%V0+nkKL$>6{Y0MW*v#>jJO7ZJ?@Qre;7(*caTwb1S+4?(xSOqYtC>p72 z{6Aff_A6e3%|*E(A3VA^n6sa6G=h*fhw7KZ^sz|q@$3-01x|d(VWw8*d(y3Ph@~<` z@G@5*9LkbR>uj4js@8JB!A@!FnYTg~ucyTgT0Ec~&Tb600!mV~v2Jto`h?iR(~~g7 zS3|4%VdQ7zYC!GA?5!`QS2#02iH%C2UxRIxxbL*r7F$qaiJXwtsc>yEaQZ6jh)*8w z9}1A@y!euCKxLDVZ7OQ_aU$Vc5HSFFrV z3~!I>kWfmLS6^yZ~LudB;XZ3AtE_06>D62e;quZeu#(NMvWJ9=l7SZ#u45jr>5bNLa{CsGTQWjehb zo9Qa(v>8cdrH;QRC+_r@4tb}|PkilG_3EIK#AhZZdscGZ3}v-g2hOLc2Tm^arXZw? zlkSSXh2_tt_PGBJedgSqu7@9JRHM3JLLnk{koUd;oaN3(W`)q=e(iX7P_ovVHoRcY zm>YhRXP+PvGyYnPR1{cqVEPbim}y^6wXGhLyFsfjKK799PDsAt+mNaNYOUR?CzP2vbN+vLd^p8OFozaD+hJclO3 z76WL_apXbB!{FOY4j9ITfDYo$Pmq-&S8JPd8s16EV`?QlCpdSBDh-XF*5WrFtb4fc z8cKs^5{(W%fg`+EPi~0`zOYMfUZynVEuom8d}jP0#|lpLtRk}xCnOq_nG##b81T#j zH%n{%uD@XerK>J(#h88IfkUur!*YVdM#v_@JS2-IhMt#BP=-FTM&81(55}Ch+u@kT zL`QV3$aB+cjmR2T8PYCq&|6BksI)Hk1vZ!_wd!ML{V+b{0 zrdtScbUdK$F_D&J4-jiC^eZN8nV0K~6b_)n*NIv$FizmIoXy?k3yH zFV8P{^$0JLCnC~Qz7{w2)RKjz^I65b=ikf+-a+#Z_yla+vLB85Dj06GF?MSli7MP5 z6{898&iD${kjseI!8QR=Lf%V=vdaDV2}vN@l^|iVt_d%I09XMu{N`A3{jn_qFin3` ze4^a(94ewjyl*~)UKB7AL3blmOQ18@8M1fCA0U^ukQH6}1q^4{r_{pVIgd{^FhSC3 zjOCW?aF_JfmM8m#^qbO!0)(YBSXnA+b6T`Ed-~VXe$Yvdicx!DGO1klrh8Ojp*@$4 z!61GLwGP;Xy^qhv>`}N2kXFM4eShi6-9`le196wFz``^|6qp*~Vye1)WnSfXxzx(B zLX8A<2WC4IV3c9(B1UYB7JtWV>`W5g8j(Qc5BO#<0MH77x&a^r>dcp0< z&U>NNgdK-7impQb9@pp|VFhh^+o^TNBd_ z_>Fxy8#cv{3DmGP)@1MY)D-p@-*h`w2x^GeXa^C%-!f}_iN`*NEdN8x^WzfF)H7+9 zyHcd7rpJAYE2;Aiv|}c5Fs|J!j~#r@!&lTIIIytXem-}|2@eaNK)hI86f~?KB?O%3 zUNRfR!)XOQaibO79-9IKr${I7B*C21 zILO3+oVbYWnTTj$l2|%|O5tQ& zYd#`-2Ur8pfa=b&LU zXK*p<6|dXrDqCue1SeS9fC=9EngcY16ACzE5* z?~0ZN3-j!DD>)NQe?ys+UH&opJ9e_EU+##LG?-km+P|YndoH+jR)c`;oH-lGo=B{1^o?0@TI9T*Ei$HVedrkRZY! zG@bp{&|8(+ITd~~6f`f@vjPJb!{lvGQ9V|dmf9;z+iZ54e3yyoDxh{kMSU9#Y<$sh zRBNt3*}Gzt(YIt|rYMxXjZwL|_^jM3*-jfa{R93HCiCJ^JVpL=6(ntvkRPdl8p281 zpBe324uK&Dl@1fI0U_ru8>xrfjx;WABp1xPu!*kh;7Wm$3HAPo7o8JYn#S zO4H$WkmPoHgBEdLAKUwXEPfDTVhd44)nvfDI<#caaRuzKu&@Y~jw0HEsfB5Km=4Tj z=>MS3=#QW@^%5%2E5!t9_IGC$1`(T6Qc`$l+750apKIh+|#^#eA&I-vkm&b3@5~?pi#tm64SB-I8k&|oqc*c|7`3ZxrJi{RGP+_wVO#e9`P|j zB89Yr++)87mb5@S{R6;&oIqd0tJgQ!Dq|gAEId3k-h6)G+|QMXU#yM69+n=X$%Z#G zmMuk@gYq60AZ_)|LY!-O6>E_I*rjgbxP|D$=k>ioJX;V~;xUA??Q7p|O-;7WLZV!X z1}<~K(}D>`M?*yWrz7{Okp4Aon^)}>RC{)2_wNs`_H;SPYq-=7B4tYE9+sfh;OZ&0V`_J76j=ijf)kC%35;gX%5w9P*=~Os#{@>0 z*!#nR-EYtwM@RGx8TXx~E98=27OYxw7~iTHyZ?4;%KvJ2`&(X@j4bu6-N47-b8X7e zKPh|n5;?wSd-E=x-dcCd#LGPY0RMYmQHiM(pI=^4E`d^v6%B7#Keg_=FK~uSx~KYI z01ofAvTKcr@ctqfH5QG=*P?)RX%F|ZTGE=$JixP$fbFk=(xusB!YlJMd4M+arH z50KD4{A@UDiB1d(aDOB?E_>iQWgo(Lb=9B!?Z=$t62jKyFT!(keT2k9T-F(}?acW} zirkS4##;F@;)2o0bfU#c0sJjqh16R;zwW?wPP%mh`#E|7*ECm`hqWPk{<8}e**?L0 zQ*#d;Fp}gZ62L(G0gy70y|buxT4=)sx_>A=avGQ+oRm1HV77t_0bMD9&k|RFW6E)F z7F0D9MT;N&6RS;p%28>(LruwmB?%(~nhXc?i$adi+3cbGu{7uCZ(ioCWj{p!cdk6w zl;T02!@o5VBH&=|FW$iSLq_|C{5hJId#9jIJ^Hn8{kRIBn{6i5Jp6JP+=56pfXMO2 z0V1RUGH)D_-;8!8O4n)q&6i|j{#mx~@gKIfuQgr2G`}nHZ2G7g@{g}puKvow@j>{~ z&~X9Qd^r01N}T3b#T|^a0%7tW^puwU1Y)lX{7N**JxZ2hJ@#~`JhKwNna9=1B?2aM z4Vc}8!=3?7WY?m+r48;ryKcnlHRm>MF^GrE5zsBVm>#(pU5Uw1hWo?M3s3Fn?pu|n zG{c-VPt!ZUM`@_T<7U&St%=9+=M*`^Hx9$^?y5ZqA04s^RqlotG`s8~-{1ce9TR-F zjnQoGx6i6!y@5&#pUNiH@96?mFJ^5w9`oR1@VM33b1#B{PTGK58HxO>ZrujAOY?c<_;c9l0hj5a z33Taa&bBJ0H{!ctxFxlW=O|C4K-6mu=GQ_AceelG5oN5ZQELxh->}~9N%bvglTJx8 z(!vE#ME?E(+0^cLtG0M)GJr_Mx(u-1Xy~R8aST)#wyd&OKHrwK%Bnhw*&~C*#_F!A z#;&7o=q;wDv!T3QnkZ^2yz;J|{()vd%u?VY*L_ch7iTs_30)aw=A`q;)JzZ~E&M9= z0X5q;=z#rMf%VT$?wtB`vWI};KnIrAooL8^n6)inb)J!N8iY_7*P^Q(@{tOx8pu<@RH(GOqYq^8Tqh0}rgO?-1bq5!=9q0*7;AOXes zg15df^~r#c){seyY7<%Uho6R z=3u-eP>imAnYsN54nU!oJ1bW=`PPk2KeT zwSxKXCi3Zymy-uw-}&(h>Ab($G{IN8`>s#Cgv*<+*Cl@HSeMNUD{`G2x>n+s;pqE? zRhU($i1yt{-}zPPd$rFc_5{mGHm>T4G4j1Tede+H==1WqqW;%+rdx0tw`N6ef{QFC- zi#<)H|E5L0%wfgmTarvb<^BMlL{2I(NFkf)q6&1@#n&kx9sXq+rnw>q5;^v?ZFxHq z`?l>-mr9#Y^6=$-bwXFh=+OX=TWE#9U>MDUT)z2|oS#s$y<8pri$;&Pp^d zafpT2ifgp&gH>EBCD1Yb#m(!@3T3PzIxts>L7xmlSL&t?R4d5rXp%`>w{!fIeWe1| zz45=}C2_;fB$9@rG~+JE6;qu3EbNMrsW?3k>*7)p)Bf-s6!JU-B#Wd5xtL?f>-c0} z5+CI8)UoL^CTMN`yisG{9((qah1Qwt%g}1C{iUMzVIbBeYzih~v^w^_M@!*DES>aW7k+of1wg32oE%$|# z@bZalUXy3004!x1YQEIdtCef*FJ#e<5NDJKmJSu$&$wCn7g>VDWs=xyBrzsnNd<+v zKb;FEB{<=tuU=g*9W^tQ$k{DpAb2A3ku>vh)B9gy<#lyJW!@g)by^Ob?w$VWBSpVV zVyxj*SMO}^>d^TfF8$4|{j1A5(q1k`T`_vj$9&eDJZ9-4kVJ%mqoxO=5>X=nI0Bmm zUyN4DGzeVVPYiPawQ-t3RFyq?Y59Tpzay5a!h>QFYd3R)Ej(Rj#^3(;{#_`4&-@~r zkl(U>0z*T73_8ft;^vl?l;4^?gWb5IXgZ)pkAMEty?I7z%AVCX@oc>iJy7>q4XyV1 z%WE$_D9j0GmhLa8mUBz&ojZbzD!(hHmFLn9jMjwF~rXc;CLtE*S9 z9=lI%muvtxW*vK}?fJ;Smhp4Y6DdD^kK87cFh=JO#oa_qEo4py@Xkd1tK4f8`eygH49h1KCqzvAqoN zSqM&A2S=zxnaJMm_7P?dA$2&P@xN#KuHA`kdJL>`;36%DsHW~?XIk$9QQ0oD8W3E$ zuc)vCUVV7-dduL}I%8AQhfdorHvY1mv_v{90vr{So9rD9#qHmW4&g~wl!dG^*WhMG zhSn_xtPwS-^HagnVv#gD^ubl(!;9@5T{?ksK{C-#Ot)7RyquQ}cOdMiHL%?R{S}Mc z)sm;}+_~wG{*R7^wRR6gb7*pZ{QCV^yV8>PA5DLRyNA2tz5lcTk_PdSE{|;mBv|*w z?B9H=VQ`;yE&U@~0XxJdLKmK$nR#K`Olod(3yiiT5)#@6-VYmbyy^bL`NI;x2X&*8SfLEd zA74GmKJVskFID!&Pp9un#eKE8U(`0Z&}LUD9RSejDu4;#p%jfc$x!XFrva-_P z^#!qwjH1%gQW$1X)0UUtK1ktf)=%dG8Yywj>8@iyc5zIsx#tRu8Vhrsu7r!Zsj2C3 zx!GjhtOk)Ln@p=E;AypoR+naP&-#AO_(yxFMu}(G?|PcueqHUz#KgK!U+HE@*4gV@ zIAX_BwFRnKB&?2#ifWXf$mt`SM=4tQc49$HZ`@5FL zzek6cKEnRuo$c|jw%SRWREI3Z9g#^ed2wP{9!4uZ^x6$8bd@j6t1TdzW3#tw}H;I;*6_RVe zBC-aNkbu@$M8i7>clE@A7E|Ljibe3S`A~{g=Ir<9#{Q?;11esf&hSM&hf{UDdcb4W5ef$)%?yk!eBjV-!opYj zs=yul;xXR>ppG4|!J`TIOhav0v5vfa;?iq}FWt`S^24t(oc>rf0)FWEZAG9NV=#~F zdCy3$^<2Sn&4)SC8N;$3nr+iDk$lttQPVOw&n{8&dfmrzGmVM|rP{0Pt%7(oUw5#f$QFEP_#P|c%nw{>AZbM+~sS~_( z6$p8tF*Wth9)Tbb0UrK67!C=!9w-wSi;o+T){Z?oT{0nb*S+%bzmmx^(ms9FFAK^7Bb z0R{66_Ffs%!EK6i`E~J(K)rg`3#XG#UwqsXsNQoG&wpO@mGk?;nwu_^KhiwmSbgbc z$R!?$IkS=*2fwKL#E-wV&_$61PfgGv^F4ua5bY7176zfh3x_Y2p5VR8l8v&C??KV< zJiebfC~fQGdq1fl^7fCE^$@QtV6DbfY0nC=kPvxNK*f+Gg)Iqw!{@gc^!L99|A}V~ z2qktdARz5^mucf;ibY^;-*xpc^9;#o&cyEd_C40|k~l|*)CcH=5K`v7ydXc(Rf-*a zz7h-Y;FR8!xStpSSj*>b^$Xkv!63u(Q)AaCPb4Ei{_uGE{tMMDRdAvCRd^ z_@I#>yZ4LR3b8fBdc@1B>_Pa4_u|Z;*#Q;K{dj$}2RLK8%mK#?SiWGnN=q_Sbg<2x zJ%EKkNhzq|(4jzE1az#}Vxo@6c}&Ca-jg^lJ^Hdq1$MtNnU=S0m7;~u+m>3*CprP6 z5zO@(sDoYt8wIcn0c@p~k>je%g$rn8v0{S&aUtLMD(v%N(12xV#C^=RbIo3)bL-VX z-qDf(sT+pFObv1j#-8O7lnt{7`bm4a#UuCDrE*9HWjm21>%uReWUzdg#u$+%K&GoB z2g^nFt{-rlf6sA6@(O8>VO>bQIg{&HN#~c>KZaz(Ld0w8!lfCHye6-|K@yEs_uuTj z-~P~90V&R_-}clQRed4n4JtHE7v^37n@=LntD;_JQYSjhuU|v$)qxPg)rlv@pQGe!7pE)H1jE@r;^`4`AD9e(`1YcdBY(dRbzHZi}UZT z^DBvg`*|q`xW$+^(**>{vG%A*naf0EWdUy4gA^3DsrJi!e`hz0eXC^rS6SQ4HS`e} zE`d?uc7cZwPaP(8uoN28{wL}gf;cWAFrvlA&@0NgN8`Sih*QZUUT<}GrOOJQAFHHS zCQTZW`8->a|EHqS6)JPz-1}4_Cp5EAYvrLRtHctqH7Iruk|>0Ef z-^DlTjE=iDB2WL-*TA+V)HvK?TL_NFgqP^mh^H1=&>wwnhF=yT@5)}Xkz=F zU~`bo1)vYyC*4%WrJr)`g|H~W@Q%)@ieLutk|9b!&x0IwEuHFvZ<5X;$~zhgXaxoL z(=x~of!f(tfRLV|ZpKUYA#0z2eemj~qX-4FHp7DrlXpo6W-CUAe}?v(*2}RHj5<`T zpc;@j*b_~<*-5CdprZ6T4L@$nNj2_Vypr0w$q~EIkxzynmU^bh0NcG zXTRJ0y0!gnlGuLEP{vxu1E(KIRB`DZ_;5u*X(LOHPSsug{r2Ib-!fC%#(Q7S@-K`~ zGQEpuXg$SZl0P&bKl1dJiQE?H_+S-e2C%pIFa)U>aEcb4p_CO;#tl&Uw>33M>=?>_#H2F5eU08vru~A-yDNs z#?q(QmqAF2Fr5LGz%+wIJe00L+0toSqaf52w8guU8g)Ilx;t1sN}eBJep;2BOg&s( z8fsT>&Jy!n@+8%Rh}(zQo50nGd$+Vy-DCoZqJ;JiaX3~PL=dvQ`lse#r{O_!SEyT$ z{B9uGCbj_{=7xOmxdwjBkIMAcuI zt`Tg_9Ud)-1@kd`)ait5Q??Nx-M1WVid+Hv%T?APKqV1V5SN&c_ zL`aqs@~<3+{V(K zJ--hw$_s6-zA~zK@Vca{oy=Z2b4A_E&g!<5l>R?r+pfvLx;KJ-%_|f$t5fy0`mf{_ z+CpXZF?5Hliu~?cRq@rmMG~jKE*4w+>Z&!#L}-t4eT_SWW1Mv0*L`@Dc^M*V$WqI5 zFD@*vmi7#Uiw0Cpu2%M>QpcyaZL(D6n9#2e-d=e}?Mn$OJe^6<^EW)h7wAjpeR1(Zprh&p!wBcQh=~8;hQ|@h}V6e$m_Mr}?dGyY8HSdc1~e9mr;$K#WV^2WoMK40Iz zt?M()t6G=(^(tKMd2%1*t*5p+OGR5qu^0&tE2?LQOU81niKIucWCx4c-7{j|!ztFl ze90(WW_!dPI{2=8 zze%i5%~LUVLRW4gCw}Dr|Ux zSUL}f7RDm7`*s6v6a##D#RK>{q1r+YZ8`jU>D}YFcP&^ULhjys@ylRISs81~tkl6v znk@s(!z!@^Yy*1^W*n{ems7-i5*{U|-+TEUA2P9Q0rsENPv>l5T0y%~&W2gZ!k757 zi_p&C@FbQ4P`7nml2aq=lBeFtCvxLk@_7op(|<9|3vnQ#nn%^uEMZwT`?kF~TYN(mDxxPfB8)wZbj57-~P>s2jLt z>Zelm3|c#E6RL^k-zgTM+St@xRU1w`gfw@ZV=e!8U5sU$u1-x+>Md>0yY)em*R&O4 zX|$)V*D-1Agzz-{Lfq8Pj-3mEt37L*x{Ud+?dKs>hpU6utAjSQCq>i`a0og#0lFn9 z(14{KCr+NQ6_g^n`o%>(#C)2oCie2LXH?;|Tn`6NO`-nTm!7$+=h*TeL9szNM9wxU zihXBjV65Wk>1uTLU)`k4y*=UJo6|-QKP5$6wVS^#b8HidKj6x=t~sJd{-ATvTut74 zC>6`_-{=&2djz2g@sfyZXHY#NvNQd`1fPGq;j$tQU!UErD$X0(-Tec;vvn-db<9eX zUto7H$|$8g_*Hn?E)K!+sB|bzIK!hoOPd(-VaLp$8znw zi;sf;AVDxSmHXtyOJeHn{@=CTrnVD2Ms1L$vNx^SYd;Tl_0_f^1Zc!rd{Lzi zY2x6Fik|H>;6^-BLp~=E>i0R(0#n)UM@w!YQ|Z>5+bh?YMFTZEvc%xtY1|d04Z+%)=>mb@@JjpZG6Xy{?UmsjL?DI7> zGizJFND7EQ8skv*Tb{!QBka?& zFn1{mmt^lVT3TMvQjB(gG=+ zUS1FjeL)zDvR^S=W{^tw9lc0)6(y}<8$QO2qWk#>T4`?MDHX- z*%)Z`T!)*&Rx4)nb@>o4S19n>y`W6bd+{GS)PPZia1jfHM!XgXK3(OdSy(hMIU?p} zTh?DQfxsiM;)8`9vfzsnpCnsdUefK00S7R59V%* z1qaKqoU{mvuIkXOo03e>UMutZ^{BA~R|)qunkIFh5RG?N*K`7>W>xM>IEpD`z+KwS z=)qim_N+{J0Vu%Si5`G@F7PnNyUf6~1v;-bn#{RKq-J`&L@kr3BR z1%iS$mq9ttj$(&dc=6lVsn>NEZTxo>5LOjzx5qZmp)Y~q{8#?Ra?f-SrXKSIWYS>J z_u5^MC?r+FGdUy?ePYBxy~*n-Bg5mxBwDuumWAZNW&Y*CU$UMjKj%`v=z6(s%Ko&D znSuI)j>IFro+f+X;mgwK7xy()tU}Mdj4hp?=o|XLCa{r>IY`>r=5HlW&q5p`z}7vc4AXc_IVs2bNV zW(_QfiA7#fInb-Bh0H8uMkb$6v&z|HN*V6w-D0azeeZ)?&+#9=4-Wo~21^RNfvuS0KWJofOBnm2Y_E@nvP8C;FzjtJz)ioFa83YY`HJbfMj>#i;B$@sppQnel*ERw<0^mDdm%!g;(OSw}lrFxMHKh@h zg*Fov4y_ZRuf@_K$C-}05u0J#glA1NLP>}2bsh6(PTegmEWhI3TFlzuEmapJxm}v? z5U@vf0JV&*gT0fzn|U#g2}qUuC~Q*!A1w+Xn^0 z@IJ9aH+-~+F@!TKBh6x4`f*tzbKZOA6Axaym0e54VTuQ5=zt8F;wctE+Ze*+VboSX z??a}Ia250pGl>vmo`!tlxN&*a;J(Ml=jgMma;zrLK0TCv_j221E%uacAHJ=ySC~w6d*^vLSQxAWsIaVNE(yl@6R^(_c_K*J+oe6UR#}9i#>6kL3?d^GqJkfCvE1Mr6|UAoN8e8uuLVs z*YXor-SodJZDg68u3*N-4w}-k0`?Zr{~A^@e3`_xV?Eq;d3hfeiH2V2B-J-1;!wAdtHQQ5UrCD%$f_#>(Xx77?ve`(B>V6HgHyzTr|? zGcsU9#9G6G&)1sYCvnA@bIZ%)K!VQ3Py=x zOnU3=L^(tA6qI5qdA0*)!L{kgR}GVXd!S!n53LC|u|DP#)f!O{K2a_?Auq;vUjv_r&{J^Bm32h$#+x>M!%)`kBY-_##p?GgZwIl7lW17iKege++6I`zXLG77b1hP~g=#>5`%%+eUf)bF!^-Fkh)ql}Z1_KxypbHW&UOA%}@ac6o0~ z>kF8!#=RVGOCcoTM1bx|z)4*p^tQGZOy{aH& zi%QdyqK$N)g_-EB3(3}NY!f}@A$C?e_aq}6!~{RNR`>D7aeBjjRwOVN5j;f$@Aq0$C&f?o)H0H(OfUKKtx;_vU7gPh*;;`1H> zJ#u-n=02l-P^T$m`G#5j9#vfI*sb-{i4 zw{m6uK#R!cn@xLoC3VRWRcuqUJGJ|N4biVI4_n*(trfPJB_nB{>-*O!fy*@6!!|B1 ze7D``Z3WV);}1JWdhZNt+m8R*9+Adc+nB2Ty?@v`pk{elChPXLAwHHjr;kfs4>Ai9 zV39t32v=dJ?ceMbODkTefdoN|umNDUwF(wM4RD?#rxwEha;uc0fLzhLHd?Cu_9Y0u zMSu|pUppC}cqztTuwAuV(FnQZL_Kg* zU7bIydL;Y!Hkc^oSnbpPRw^~#7*@TBk(-YE5}ik}8&jOfWqN~nU}4`>vvEEAN=YWX zHhbi#tN&>Me$j*7WDFr;<@)PugMOfH+<~b?&~nloSzk4~`RtFPw?j4k605&+)&%n< z(tl>A+Ao~S@;!{b#bbK;wL31wlPp@jSU{GjN>;9AT4p=> zU!G$tcIGR|Myi~x{lIQSq@tT{`t(bAeoIzHs%mE))~>EMIyOCUSeuB-z(fKn3^sBT zUhL#TW(2?@3`j^%PqoUKjY@Bbzu|x5zgjPj6o@O^c&_z_(9>OV*R{}XUu7a;xLZrv z?X}b{`&cEm_|5sdQUKi-?Zbulk%bVP5#N5$LLhNA!r>D2+-@KMpi|>AWB2I0B(-~D`thM?2{UGTa4)Txn z6YowHM4oy)tmw-9ap5;nF>X)cUAsf!A_=OWjLn&bF%OmcNdf^$#ogHL z@^fRT2*QTSO~6SN`AxE?$<>cJ^MK~iE$`kFU*{ikAfIHwCkN0~>_B3&Wu47?{o*4; z5PsI6?HSfv+t!~gfg!BVhi_N0(5^)xK7JRmz#AnlbR!C6Xt^3;PoL&F+VOtzZ zj>+9#<`g|xa@tvK-*nmBRvk_$i}sXvUG@2EiJWz-Edh=#2HGhrJ@c2{2K$m)|MiuXVQK531jU=JMJ(sW9IvTva)1>y|)~WkFteU zU{g{wT-tUjLe;ZGFtT1eL?}mI>;?wOZ5}3RVB@%duIbv9~F;tmSWgP6+gcVoeEZx5lVN@Og1dRN~1gX{y3Y?MY+YzVG-%m zEAP?f44rWW^K5^b4Jw6R23es_9TZbedfXkLknU!r9)C_E{aNZ!Q|;Cuca4?6YM`6n zbBrxUhUJ$VvG$Hra^35_G@Cp3*2~>!h~3)A=c)*7kL$HayyX7WBm3heFTNlvnmv1o ztq-AgtwE`qA|ymV+%iMZ-9%4d1l}+}GkZnBRD?;)YM{oU1OfktyzCCYKuSv6K(us} zmcfP(rvI+AD0=jsH1PA0-gzlCt6>e5&!M{Uj}%;Q)uhn{DND5e?9o`X}juQG!!Ed)*>#uW>U?q-M1i;8BxH+ z-kfy)Yj!U6&nlV{838Py{|ud$1=z;C$OYCZE^{o4E6Dhtq75SSaj!blOM;xyr#I%XkhsISV`9v zPo-gwE9YZqPyZ$CuDG(Qf4RFaPV};@arcU+>kCo8RoltgxxBz{!@?oy2}#YLMA`qb zIR+OM$|-LJ9}sH0z;x&PfP$tOKON1C&RY>3p?cdI<5N!@_a5NYvv4k#ebhDMT;A49 z-v5Qhud8@WqR*#VsI@J2T8zf}bJ}?K*I?X@fDr*Pqwa?%T`m)He+)TPo#= z^Z)E)dbez{dw8>Hkt9Qk!euk-^4jHt?w3bXaULU!a}GhJudjREK4)dh?w*@}^xLd- za%P6hNuI(<{7Kins!icvJe*?c=3G%Onm3P`9aP^ipi=RJ0C9=yN*YrJ>aJ7Y`L-Bd9_ywFWl9r2pf(2@VGbT zne0O@(J<+#_lj&`wo_6eBazk5N-7PN6YQyjPE_yT7JpJx`MLf}ZuQKK*EKcW8XEUZ zBSI6|Y1mHJLTBi8G}FzoUz(jq`NLAvrJ-*gsu}HhiaUxBOoTK*i>|ku`uz`3E(!6` zxPQ93zc72`yt~?Jm=1M&{||}a)1?j3{+2F5e?Bp>4wJf@e{0p1P%`h&j3llf`j9R_V|RDtJiGmux2l$QE!oy1 zD?jpgT`spBjvaY-^OCOqZ{L6<>V2_@XG{izNZCQSu6n=9&<$GWs%?z4I~gjBn+nW$ z_(&Q&aUMZuj9!zrc}dlmHFO~&RMBW)@x@4(GPTe9pt9m!&!TtvjiUC~Mo4Pe5$QK1 zog`urj*Cc03c0srqK?i^ZS%r5Ec}8Zyea4ZB-kP%yT;s?^Kd^WP1%l;U6dq5q@CUcj}?Up zL@(3}dMnQidG(;n!n%3BhF|5cU@5Ly0=5lE&D_Z8-yLPt z4hjxy&PdDN+jyC8DHwCsT^;^$Kk1)HeRe1_aSAr@nk#G?V&&H30)#Ms+53U=4NV`@ zhSauZ@u3BWYjtKDw{QA>GbF;B9|dZ7!jou z`}g5h(7Fm`KNv=PMLKC+6gQA)eaCzPOElz+X5Xubp=pl(*T$q{vHb&^@`ny@Qc?T6 zwDsA)_r$WDzLHnEdq)v>9oe>{vhfBbP0dUzceI2jcKR8!dy(%0isetkiw+WsAyRS? zke$@j)$u3G+>3p2Kzl9ghLtr*<42gO4e6xU-dni(9RH=`os#RiB{}~6v@z0A7dYID zL_Rfs%fI0wTa&+r&&|PnN+XVALLrHjwbuK5SKGA!#(ueW`PcUbpb9bk_CNdOEbcG} z?+0!wL9$oK5EMBeec6JGnp~T$U=WXV@WhP86K&5gZ7zA1zhe zuYJkRhk>e4)H!AJou;D48l5<<$w>AGYBxGk)6GYA& z{hyySR=MClihcpB-^uOnrq&*2H$^=sY_iFo0S4|Hl7+Y&539uoS7#qPc^?MPmny9L z0#x zhh-neWmNnxw)|8ztPz*%EwT+2u_rgd!4goc+E%&yrVVJ2pveid1(D1h7QumV84#-v zRFfo`V4Z+->8xVO&*rl9g!`)XTMWSKgbt{X>W2x^I4No4*}F=8+$maxZ*+`>AVx!1 z3?z{dIq=_Ji0mymyr_gE2Aw~{ zCCnS;R+H~`H`z%V(DuxOVu$UnS)6YxI8OloUd6_a#a6e+_uSKjM+1x%=qmBEP@utL z)HZ_LWv$lg)n*9>*>&OLONP!4CX)CE3oon_(3inaKx&TG(|w%b^qLDq_Y z6zJ6-?+LabAynYO*(%QtTiI#pC{*sST5*8WDU!DwHo|&|uM8(+#Rv8-MDTMo?SDu4 z3AwFFG7bK6=LP74@{ZN#%OLA~i*R;~94lV`WCnZ8>s_Ds#N5`l9=w1TUU*@4MH;(H zuuq`X^05J{l6-C52?}{(y7pdVhqI?d2=_xXntpF+-yu~&kZuO{#6rG(P|YIATac(E zl#43gco0$!n>k_g&+xA@d*om~M%-0Q|D8fgxir{pUZyCY(o(Bvgre1TPKVR*mUgE4 zKI_l2tt0V+k_Hgb@LR6eLF~zU#S0P#gDz|hR*`Mo(xgj8WP>3CPs|64!U1VEo-nfl zVsip;x!!l7yO5!5U*aTvFvy!fUuGZiFu#oxRWcY_TD^U@jMQzYo0|~}fg!%CRAw@c zNFytCdiYV!$R#1H5omp=e)iu3*?EtY!H3Y=-N{YB1awX6t{(WjFl2e}C21*dm+Vc+ zDFTq<9T@gEJz zx3gQYUvX;gb_&rjF!nRo=)$Q}{JwgZg;5)JU1=C&*;Y0A9@G4hQ<4XjyVaUz9<4t? z%ut0S@9^1K>u^J+p|{>gX%z$U+%c{YYig*Bs1;Cs_IEu6XTs?1&Bb zP|#*+FOj23V#7U!vMc;0DCkKfQ{0r%!x^RbGo7 z^+Um`^IX7KS+#@#F~HyKyFq*WGP3dX!v6`$Lse0E{`07*DWZ>S<&ZYoa?Rb6A90#G zXHHcm1;pKyZLpnamoz}8Q0S(KvWZV-Z>7$kt}C(4RBt>s@7TZ3hW3W70CId;B*;R@ z#*;P9Ux``?NC;;0+nK<>i6ZYx@=7qIFx8P88&YcJzI9$H9(I}u_U6~$-2^@t+UA>Y zr{1nRakW|~8xOzxoKKC`=F4RdJ~^<}CZPMk>wu{YU-gXlC zAc-UOu@29`cGy8mE#KWst>sr4@`W%I^2EV+aKB?I^Xk`2vD=qyH;564m7=c6&(AZ& zG4=oIYn9NMwkWN6Ae;1O(a8ggpgEpkm1M>GL)H+F;j+2nN-zxR8+?|<*U_I0kEvu*qHd7ib_z3ykNdo3B= zWel^p9((va(GP;r>nM_{vAlEnA@`Or)Zya8VH{{%J>jb<#JZ-xDxX&si-?Mn#Ysck zpSUbj>YA7BX13!cuctA*58NCkouB{(9iM=4AlOmy2NFy+=h7}nw-HA@(2#-OgnDzc zo$D!c$hl(b9ZnE&Fpk&fms-z*jYt@FHyX+lZom=IYVg2HVipe)`NZMD=-*G99J+n; zS+ge>2)$|FL@kz3i?@5!a?mY-=z~8=RJ4wbfnCFV4uVbwcT583%SiQX`a0RIG%uAR zz>LFA+;NQBdp)mN%SnLke_N3@eED3dNC8cDJ-*Hw^fd?wen#qQ#bfcG9@-y+tFVQL zMq!BU5~ajw=I}l>D{&i*DbQZQq%3q^pg6P1AWJ_p%u8y8)zB+-&<7+YRt~aG!X0iU z(swS$Ig2W@)?$Al=|;T+*p{*~D85&oUW)rrf4ho;xIlC`;Cym7>e%L5vjamQ=7jad zDofmk;yCH=kSB7>;}NLV4_CH&Ok21l@oxORr3ymz;HHkkHQ@Z#4Y>I{-pD1N!06?C zd{s~O=6)z!;Q02wT4L*KLv%~5_7cG#V-PNp218N;N+Lcpvpy&hVvEME#5h8ik$a)! zv$#RJ|4RpK*>$Kgcb5i5@zLk8U9ZdHg~lK0<0p>F>*bsJqp{<2N( zZ)Rs^(CzM%S4gRx(Nc|s9NFrmR-(k5?Y#e0aD1-&bXgf84*ZQwpPCNWi^K5=`ucH$ zkO`pL4JvH1@+w65`Ip7M?zm|W)?IQnIISU}1JagyB<#H^sz6~v+umbjDK;wxNn(hg zY9x%FM>vm63AHdsuL)E!z{TUN=u{d-a(Z6yCId!}(!Fv})!hJ~!GHoxoG<&_KIHyt z+v~7bor%~u0F}_^n+;#gDR+By*16g;4-x#m{VJ@_T87w(|_%f#z2cf=Y z-Ek!{QM{5fif?^1-h^172mm|sIZ7*0k^!U?&j+iE_^xuQhj2@}|4AP{-{BU{ofyO= zYfwmk=k*&J$u(r3ct2=JsQeT1D4=!O*SUlqXgJDdtaKJrPq1w0u|PzE4<%H8&Yzcy zn%;7agop>5e=w-=^;2$@u=u`xnm8H4Xjm;vV#tgzDz*NyT72?c`V;meoHkG}czk60 z0?cftMtOoW#3Wr_Rie<7(55-XnZWqX%$9bcgYe<(q0kCGCHQ=sU3}$US;k%aD(i5D-UqP67xFuOaKNrKWdEPzn z__KkN9AmSF)Z{W9z=JH%BI*XzK`?&{Jxl(wTS+F*q;AQ3t~P#=X>K_;Y-a8_UY8KM z_oI%}9SQy9OPx&-@6I}O9ETyHWGYF_qOmPGz6_(als+UTay@AF*JktEp@cw#cN0Sd zXZ2+#jl_@bC-cAKI<%Vf4%)R{Z^SrpRkvXkq@q%`&|t(sBrBGC=Mn%?bN~F$Or63f z?k2IZu}1(T;LV}9G^$o0eCyT`ey33=4dYM_djuuGU~3M1xWrk}x+c-)U8uzdj6OQr zUWkD}S_$VtKoa5+7)(s`#=nBgfxK=w&PK+|Jt9;R?((V<=0ymf5_1TMF_cET2;m28 z9su%-#}}$J@(kY`alf1UPFLSjdMS`HmiYd{BZ2zAg1NliI|JUzHYtj96dvK+LQH!g z0HFVP4I$cT@~71MCz~$?SWp=t4{0Zc@&*kwvmlxh-UDG9=m7rp)R%<6rphG)7S(xl zhAI*dph@?(6-uMOplsiJdUz*JDIfw!m_3a8`wPw5-{e_i*TwJurTG8ZYPC(0&3cuJ zfm#32D><|`;)VhSEtZc#>j+Zmrc`w1T<%Zd@)^lfJgc}v=~w71bV{n<_g!Mk;eZQW zEucM+OpJ{L>aG>NHphxs4)vp7Ge0?NiJ~0H6&H2L-1$AiMo;(#kQWLlW!$>;sI zmXIuU(ZCv^fbz;9?}C3MFCufIW|*yTCXwz{bKMxSiu-H-7vARqIutP+hXK=wNax#LEQkkLf7kmi(K1ffb;?Qy;K4ffxXR&-p zWes0UD!)kg`ui>BueSKfk!rVHOuO-T1HuYM$P%+eSQ&|4A*kda4GZedcU5A=VId>V z7Ez84piN-h(NRsYD^|}hMA+7y-9Ko*r|$?94{VG+_4J&|Y{g^>qLk(MeU#z$NAKk& z zk-k+Xdy}Y_`30h>(TjwP4!h!fGK$jT zitRuh%kq8wH9hcJr8g_pP7LC{-jY97db-PI3NNJ%@>Hm7X*xB|JHA&tm-loowOxu_ zWEcFnONnRZeg5}N-%E#+84jH>54LRPbv%)Cz-XK2@q^k=cpuolT?rm}m*h60IH7$z zdMWVA-Yx2z)6JEWO9E%d%FkuRI26PiXSGFN`DS(h;(@(g{-sa3iuE(~5+6K}TH@15 zzq`G-JUc@8$0LTTS5Sf?CMTEeDj{4g+H&!@US_LDU|?W>fBPZH5eMCcm*pZSHWd10 zYtH{OxiT@KU0|&7{(?@7P2JdRrlGKuUjK|A1EqJ|`|IpNL4N)dWGNvh_a!DJ(Y}9I ziB=n^e|WH3GLD{F?jO zcIFU#k~nun;u>zenP+M8rRmOvM@~%YlomTNx6oalrKTla&PvH=Yx*xSF|1)QP;dQe zJ&mJxg$(H{Pb^}-F-tb zwT-&%d5MY0Pk-4O%Oi&l9~Qd*KBH$W(>QDR>sLi^8kfnBKW{llFRzbQQ^G$pS{*Pe zz}U#BxxW79q%o=Z^ac}R8F+UvryB}8*xRedsVh4dof{k+#9Iy4MViExEwy(u2k6Y4 z;Q{wE`7^<)ix>A#xv`L!D#K{_dTyIQ7AfG zoSf(!Ig!1@ZgJB-{-}lycf_e?yOxrek&050(%flCMPYYvYzezrV;O({zK&F)zQB(C zt~D(_(%e|?kZKEj_p_&at_`IP09>%IxN#`^s+}d(uWMjPSinOf(S# zG8elT z)4pM^QY@03om!rakpZ9Fc@q=()ATa}0!d9LqF*j*UbjESqR?!o`_tbH?fz=K+oVpM zpbQPA3r63(9T5?&&NndmQ;Z{=Y+5o;Hj5mi9hR)i6*h_)n0l2U-K{adzau&~EG*1H zjN>Sag1fuBU~EJE;3UhV>MYk1lL_B4=8%*118&A9CM}JPw=-HZhNoX(+q60q+*RjG zn+O~$E$?1Fu}~p5B{O37Ze~A_^FOuyXJwP9SVs^8#SciGs=CFv;KdtVG_gA>PhqH! z91DzF@o`V?%Nf4ta^`m}s92V2b(v9k%B9$q&)mA4kld`2YA+RF_xqjesG`F~VBG5tu1thT^yxYK^>*Te!U^h25Hi~g>agknhm}sId8K2NC2o~}?^7RGw z^ZZizF@elxJ3CQ!0a#g_Iiim*jW2$ zT#KtjOJCn3pwa?#c=nr-XIROG!hG3OA&Qu%kB`p*-gp)Z`a{e?&Y%oQta0Uwy3;dO zF;h*q_8h6Ix(XTmJzQK|2iaL10t36#U$1q(#q~rn`Rq<+CTuu-S@<37wz$-Ev+T?L zSKiX51*7V4Uj;e0WOmSpr>3ULvWvpTA?S(gl?bqb-w&O1!P9sR_aF#H$`rvI8I7#5jhav+=pJ zv!$-?hB`%8j6*M7_v5>Fe_yNWZCd(mO>Z#kuT}1#$MlRD?7wpc|NG0A$6v;a*9Bk2 zmYJTPrU?b`XIBqS;?@gd4RC#;?2C3gBTZX<0|POJ*WI?%4fZLp!_Bv9`O~h`3uE{i zSXKl9GVB$XokFpPlsX3X*s`*@-hXM9ee4XbT_qB4O1LTAkn7yi|7mPi2nJ#zBE%h$ z*?w_&ZfsJtaR?u%zfi2%;Vs?fDxq7{;hE7zFO-Fu z-%=*hGY?HFH0I#aV(gLqBt9YD$-ljSYUG z{z4uMQ&%K5s%PvnxA)Z!%k^YPn!$eEO1t1DeaF9GUfAd8K0T@hduX1(ws2)n+{&n^ zD4v(jxjkQ9B|LUDQ=T6y z8#7aMgAHy37J@!DM(geU6Yf}U_SDPcxVdAqt%%(|AY5ua=sLl(i?%T8Z2ICd2 zjS6|NknS&Dr8}8J?$%lB3hW@>O`A@j$1er;3^mMKZN26-V1m8Jpi>&3Mj0aZZ5c1U zkO6)mxav>XBc8Z}?HBdo>70ue)(4;Np5BdZSkwWoYuG~y{fxTF&1a;!4Fsc!U70uS zBf)v}AI73aQ5LKoo-prWd}D+gzy{cMpX=P`+T68i)<+Lk^o;4cMb!2SQLoz=H5Q0- zZXs?{mH9*Kqv?wK_f?CM^CycN#a&%pA7*78;%|W`KP$~m#h-DP?zO@Ixkl1!+%i)W z6Qq$t02{Y%-7@%m&Sdsw{+RqsiS*A1`*^rgIrfL*J}V_dKV6CzP)sf*BNGi*2}2TP z$1Qo`Cgbk3rr7bRD-(${7ge`!@1rv+@aXYLNwIhvJZ4*4jMwGM;_NJBnWwb_Za_V> z%M}ZW#MmulF7^n(;mXy!Us$bI@(OD6Eh8gGHeL!mkHGNLzdL;@N1?5*X@NZJfpBCq z+7Nr#CO)-(AQx1Sp1_WTa_^{GI2t%tkyy&aL@xFOzz_Vqhrj=`3wb`tR=YzVnTtNf z^uD3ICRyw_DuN8;(E5b;ep=EGtA6BL_5e*;5R4N0yF;-g367&L#*W#GvJ;<6%m?%E_07QdgWOxdOuH?>#Mp?@ zGP%#x#E&Ga#7M>t9Ajl!Cw}Z0S`426G~ru06l8!#1Q0dL9DXx;+J0pL#oxYjKcG$) z3$i3(eSe?pcWFaNmeWT@;35u2tJ(Yb7=@gazxrqI=cF!C7KDEB`x<3|OUC^C{P3-U zyKdo${c(p%Yju@?aMz2oE9$)pz(DZcw&v&3D9zp758w`a+V}hnpvH92^|9^A5%}7` z5Kb2_K5e#>$j(!^r=0u<%2o(YGTuM_te9`jzN^B^TbKT=6o(<17xL2a-Qh6txi}iK z$F0NkfYPCrExo~?X1TJduv33zlkP&j-1k5;;u+;_hmHl~V&(G$2f_*!7gA)IvKmTJ~y1qAULGP%ZjFVlPQ z3>w`El=dS%r$`rxZL5R0EiJF>#L=iai8B7|1y=SOiq`CyVx?W$ReqG^HEntrsQuKb zQ@%^{1@Z4wA+IzAt!d0p&`NIpUQgrinKT`9F^VZ)nmM zOH5*@v}hY6csD9HJL96{2>tD}Z&&)naSfdtiYIFWh$%#bHL9<<4FM*#Q0<6Fn4 zo_zGrF>f+u=;<$%iUbpw&iMR!>R?h38RkE<@NnqG;0hu(H0Dwv^RS`=S5jiq28Ema z!opN=N_{Ie@*7A$BW5Q5!6HF|Z=H{n^c$$3Ozo+)dF=)XG}K ziRh+5ab#k+Ozbi}!JSS{PQaXh=*OhX{WaZI@K@3!Xf-MWv?d_X7G zUCp#a*CD>a3e)JoOP3z0@P5hves3gtV`pJqc{Kn$dYoV(2vIDcm+L9v<3{A7{`aiIu}eH#(k5Zr0Mz zZvMg*kEHgsz z&Ra^hv}3K8i#4LEx6$SHgg&wp%RsIJSCFoqPI!>3jwL2uUXF_`KZm?Z5zhdh6GCnj z_4I7zeDFq;Z_Bql6lKoVRX5ijUoh);;_NLhA{@)gv~)mtCM8tN8* zv9(C5_+J=?b3`dQIfLZp4@eh~93k#mv%e|uQu|cvUSfirK(5%}Q&mQ7Kgtf=N=%s7 z1#7bVCAzkVY2Xh7@i^MroYSj(ORKd`P*^3ej~u|%dmz4ZQ)%y$L|9h z!&|jM4U{nd;NZ1-nR-ge&3p7pK2wrpxFaI#zDB#g9h+^SO)q~}$V8af_M&`=)xju+ zGK2niKw}~mv9VD?1A6_BL)_-$IY_2~er>n?M3J|Lo7-a`k*w@&N9eXHpNn#uITU@=D($rwmpqD7jKJn%_(rWB&SPpat&JT6@ zcCoS{x-L%P;^%$~wSe5h!KgZj&y?ma6c&_5U+% zx-|(n7F>yTx}BAk0$dF40zx(gqZEkLR=0J1=dRd>6j(pd3BZ#8=#nmHh%jS(4qdHx zexLj;Bvy!iO@mQXTAB9MQLoZ$(>tSXC1UKD%h|JHwzAZ-k_FTU%nCLKz7W`8(2((D zu~QT^IN@RFZ4@kA*+<6Gd- znz_4l5TSy|T0VXH0No&%HLNxN#S6mMqz#~$sB!j90aGv8(ry=tR4E>7RRh|B|FIn>uTYchIKuD=M;a`iTS?;1AB8w!E|eh5~kP zaJyzYB%(^Ti7)spLJ4rAiAWmm1(B=Za0ZKmCjrkA6U%aYXUqG$zxi|yc@VA@#UI!c z_Q?G)cf1AE#r`|j!5bhQ!hb2YyRq7W1Rq7Vx3{kw>avSZg|1{e>>8Nge`WekHF z>93JZ5m6a832t^^b|KNMF`fF%kkwKi0~_n! zUN(WL<>%x)BCe&&8GPF?B>Vximzx9M{*nHe>53N9RUu-Xz#t$_euo+`P(dT_cQ|v~ zj&Duq83aQ|!$~{^?hy>f$B&W3h#s3fBgFVZn=euQ`3y3K@Go>aeFz0P zQnIr86+M^D7ILb0l{&m^w);LZqK<39SG}B3LuX<+xpy&vFNl0D<^a|hYl7bfLy8+t z{I>7Ui4<_uTYYo8clk-QJ<&`{#z+_%$Oea^4j>l-s9`~n2~SEz)cy|S?=aPD$J>xw z+(zn@Tm|-2YXepSq)s-;8HjPTVlZMd2ALz3s26)5G4C@HW=jzRv@IwpA)p(Q1lSn9 zLyGo6zNh_B)-J&!(Q5Oh6$>Z2DuT{;IU8XA${D_%`!qN z`;`Tg`TgBSyw(7iLHe)_h8*26KqMgs*=9qa4mEkG9a~I$b{0cw4Y=M89uC%2V$#@t z#M0na$JzrYCvmJmz`}ix<-f%nr^56vD4K@oCm7*wj={g0qbXFo^NUFpz1VRZ#bn|ABQPcBY+}c8*~S z1Uk{AOP-PoTJVh@>8~$X*GnE>q!A)tE1f(D1-&9U{7JLEGEOconUo;`&Ml5eP{zF% z%M?^ov%q*&myVQsBfAAO^_}>71F2?0`pCuF*J*DC-0a0T>iWmw-a%_QT)u1oq&hsi zFtM<7v$h}PCeNXZM>KTsLjnd9OOu$8V27YqvCuV^(494<@FuNZOarmfR=_l~RXa-` zQ2qmG8sLhfak_h1|M3uio$yWOl`I+F%XSW9T&G9ckE!;|Dg@0>r6VB$7&%~?^`fAn zd$enK+#4)(r{pSsX%b~04{|85qNlyEq_|jsB1RZ5kW;6z(t|Q3ikL>4XO+3=nWB!? z_VzfCX9@dwSMxT_G)TvX*c!E+a`7Q8vir?NfILTfh}%eH7vQ3BR33miMzEu{pFc~U zL>!YCge!q(Mx9$U4#NSU;7TaAZn+#==XL<+!yaLk*eW!n;yMC5K7RX_iuWZfzb!Hy zKpe6$ia{DeF!zOph!2Q<#I}WVeoN#pL&7gr=H-)&Ug?uA@7zgb#(!8FbTTrm0j~|5 z0%{R|0@35Zwj1Z-%w;*karTh-!vm={CQQxamJ#R~L(GRULdZ3>+^@_GzsUa-S($Y4 zmncguqgmfF5b;ZX7HU* zM2=QVxvQ2^9QZ@^zR5qXrCnHbWYxsSAXZ~BB{=nS46%&&hGqKb{V1!MfD=qSJgJo=+Y0F5FPk(ON9WFu+H_=0N`%z8^IvZfrZAJV6j+g02Gj_OP^#l0 zeSMdGMG*v}e2cibxrZ^5sTpPi=&?MMw5*Cco-^1zqU?8Tag-3k$76j}tLCeu{nlng z9e61s`>-*BGew#KCvQLww$EGN6o8pCNOI;gE9QlkFtC-tgrIq%m3EkOjC^oIP!wgc zee}M6oWOy(!pODp#L9wT1CW<&O{Nq-;EOk*IyuvF~h^5uC)f|e+nd-duX$jM3ZsOT&?FLhF}qx8ze;gyLo zx!Ex6*<#0$^-86~&Y%jjm=L9bdyh5y8*dE<%A(RhjT2}c`iM9V(E$}ruoW35A}2Z& z>48~)DHfj|nM9_N(QF6GF1>?}FYn;wq=5*Rp^%AB17{5uoY03;fSMCLNDKIWqQ%Yr$lzuuiyyuG`e`l+k}M-+&a04(6YkgMU)3?~)`>e8<#hT^s&NAB;pB7he3s3I-c z=fi^L)+11)k1dm~X#KK29`lK+w(fNhWk>FdqZ(lHL|LDY9@m1{x?RNFgdDu|c_$Roi(D|5!u4bzGY_MxCgoQvcX`K+qfbV1i7eAE*8f{?w5!>rE(~o6XWIDp4fc)^q<*^6C4UslRNY~-5IU;~? z-32?2)4Tn>T1_9FL1y<66i1#C<-w@>o;96%UYoAEm5faBpgA5MtI_gR{-<*}uu9xa z&u}h}KuY2W{0zvZe$|2no(!14R&56_B3AP7{1$R zObyJK2Zyy(frw^k){iI zzEj`t0AR!QYNxj(nI2i5@oQgu005duawBSYZeS!*-7~6=vl4@5OueyhajGiUb>I-v zh>9~{YC%jRkBAg&Rpa)%^^|vWm;&IXL^K(=A;v~^ekE&@txL}*5@~Ics-nEE_w|~? z7+2DQn3GL)&nTrYM^J_%F3u_+XHfvrlBjtq$MA2bra7gYvF)K2Punlzv;_&8*$RchJCX_qzXkc;?b%bIpI7ZAGh9M@uPaEgjdR3 zero1v12(ZM6}%J&pJTg=)#OZE{i)&?dWwo?NQ>K=wXq#~*{-bDU;NmJ_2x&POfp%<{@iAUw#GX#ON`p`K^oJjq*m#+w-Z1r((+iIReyf#-`J%ayl zFWKXXRU#fbCqq`35HHm?5_op?@qc=`dLTr+!(46kxPy3k<>0>6HUBR!Er>jG^`XA; d|Kr75l-rdVC_9+Pa+Ub5s!E!Q%R#DCB~$u)uwQT~Ox|HZda5Z9#g^l|;G(7s zoRp^)+oybo`v_-BR=)1Bc-lzz=myrg)Kk)p3oWl+ye79FBeGV8q>;Tb2I_uDVz-wzxo4;KGOt zKK`sKKK{Zj5)zXka`ynNS0chWY9w4>ST2(iA$iOyHDItrZ&Z!mLLj7F|Gu$MPQ}&` z2n$3_NkPveV<*#ln?WDb{*)r*(4;+S{Bc_i9S_TQo!pn$ezrv1Us+(w_fs;Xf&$p^ zcyegmCGPm=|aO0Fp?qFfrj`uM0#;{QmQy}XC5>(c)bt1mDw8{0R&Sth8 z{gJAeIZ|u2bGJpV{_@RuJNRvFHnoGsN}e27;lD46b}i3a|NEEH0V^tyg)r#fSGabM z^Z&dgWTSLp_`mNG1`)nvAo%YB@W$i+2QCjwc*DTuL_x?F%T^0VIg2J1sSXOJJNrol zu~h%4+i8ZWMt$CiUcW|!FoaS5c4O2S`DIKJwEx4I;iWj;J8hHbCHZm#92i{m+Z^`7>yOb6KgjSy6)4^OZj?qzcFPHq0$5<(EkXM z8ER8Qlva42Wt+wcjmC+{#m%Blw67%&wG1dNWj8P5UT-AL)FR;T>oun2>G^V`)?VlM zFe1&ijkB6HQuYN};_Gt(VG{Owg*pcYR3Nx939(|2(`z60vRi=-N?1Cvy{1a$p z2Prl2@{Y1e=ZCCZZXHA||E=fcLyk@5SkEYg4{slTos(Fkw)EWs{`40rX*(=?&HEe)nzO~lrM&t=f8il4@%$PixahiD6xKXt zjBF@{J9UoKg(7#9^eACiFt`ic#5q?M$|y7<9~~(ouKAW9hpNu8X-7-|%(DH&K%`a? z&dA6}iRqkk#_(;|?y4*-Bvi>rX#Kk~!MY zF`eje&ghAjAZ0`7R!pe3=#ow@{4y4=xTbGa{E+fN zkB)@Yv24rB`-YEQO~b~@o&_U}v`h3J54tlD2sUkt8rIlEUC=_vptG7xe7f z=7gdyVagM3?k7*+2^zinaLRRz*YDT$Vn!jGP|^(KwB2t93CoS)f;hhs`&tVxFS_ZS zI(UYnz1WqLg{v2E?AEqx7U& ze;o?eKX^4RUo;X}pq|{QwbJ?SRzYt_$rHq;P}#GJ=+Cd3_c0{e{N6M6wezF8Pu(uU zm!P2cI`QRmdx~Xa*qS562ba$BO>Z{}k~!_0qL*rLev}fh-PW3)yr|^Z?(r$+WPPw}!v(iUguD z@>OHk$;iM46YeO>n_K-`T=RLhokTj0Z#BQgw+}j`p7l*E--bWG4VgeVSMtA_ub(We zVmQJ^i}uDTx7G2NiJuUwO=n$gyUk)gSP!W*f2ZA}d=EdCK4EV)|2ZCqs8(Etejuc4 z;Eqso74+khd^RF(Lle6HJk;`xNj>s)z0_#s6`hnMjal&64$`P*%DUes-Y!j<6y;r9 zDw!Wn38NUVvZN{D-u+Axl*?U(?_jpnKY4;gnd6}KKgupVAV#o$!Qp&t&=iU0l*-ha zckU9_DSylHeu-sxDp#V{u##z1y#Fe4_+-4oeDLp;-~6LoSrW)=TBD>DQm>Djb85~E zm09Pak4;%6pXm+eS)h>|=N?K|$`WaX_mK@p)(I?;6S5vS#iFk8)a~B`w3-ed1j-Hg z4s$hsRV)qOTj72|`e_NO82RT-_Ac1&y1KgTPngNt%57Ta7R25o`4`-WL^SV~j`6xS z&2;kX_2Ot~9=2X|rIWT#_|nZkC(s+oBgnKq{uTFn50DmzFk_Tk;gX zpTlu`sscC74zDWjU4>;0`qZZwBn-L@8rZV9|xx~Z4#&r&XiexgBq|*EHhV`#A_Zi^C z6#h=5s0ONvp_d`M_Jv!2wKEB7-!)Tb_I|P5Tl8u3!k>f-ie@qEuKOM5F?S%6O*y4i z#F0I0Dp`Xlp~#tTNi8;^I{H>~yR_9pXtce5tj^S@W|?HsU?zXPJ!Abb))?x%J-@XJ z&q{?hb)2@ZghTF^BC^=*5%XuThg?LOBO!6@ZM*Ml9#nVyRjZLz8l(#?o(pGN$X70;u){+am7!ou-@j?EzF0n-^~l?EhBNL{xa@tENDWtO;(WB`N)PJ+B%@|5$I(r}SAu0Ewhciy2wzSZVs~@P=!Cvxjy*^*J(RhuB<$@2z&k zP9ceq_6Pd;d#sxy(AV*5M%(0%NQo4LB%%+x6=>U|1vC>+T`Y!#sc6kC7u(J*osTX+ zQb#t)B$x}N>z5gHfV8l{C3pUQ|4jF(3|?QdP}rW|X;bS&!(lJq!j}1sWb@@=UrA4~ z-L!lbzuZ~)wBT7|$B`pCx`h*o0Q>xDz1-Q%JWtiEav_4*}&&oJaX z77~|1neiB$GBZBACiO_KmfgAz@hxd08b>^u=xLOx|8n!`xD%}qDW+~tIS(rdL9_** zaGMzV8R6u+WnAenC2H8_*Rp>GvIhh*znbnu#ng5>>-}lWzT>p*=4#Y>c>&FWuUnag z-a!`csHpUxG}9Q2PX~Cl$X2cvft`FZ>dY{qRx+{mnx5tX8S}!?L8R6nNCkD5aHyza zv=kj|IFG8E+)7DDPf@;QXkNvS@R(Jh>sW zkM}ml>pcSj46b7o5Va%wr5c7JzA$-?)3dQ*_i&6a^3;d}5B;N9wD+aEU7%a^fCfAR zKfqEZJe`PRD7es$l;lL2F&req`fQp|)@CM2so5Ua=|6rKJNv)H2HP*skKUmwnsy}( zD=h-wP>4a=iMTQ#^)pC%ezRt%riiJ>#cKH)HQy59GcVd5yif$~o^yGfFt_m+ujm(Q@;bs0)_iIm# zB6@D|<$RsXQvGIR(W}zFMUSutaNSEE9Wf~@+^FVMQ~Cv ze)e{{Xa{$E+P_hASvXfs{ftaRwn`6BT2S)aWw*~!-^*wnXl%1;jIEzJKvidy&%1F1 zK)GayMRh_h;&dew z4-5G`hulniKkzDdM`q|OX_jc+GCCpPX=F?TjK=d8T;l)Y=ZM_=kFVJ`!!Ioc_6*_s zz2UmGexBY4aV5|BqMR%IziRhV?9#qi#wS+iZAk*~J(sZskDcPOuce#ZYeDC{ydt(h z%|9LaI!eH#nt)t$=htjL6?pD@zM%j)$Q}NQ$yv{W-I7d<%ketdw00G(R((e~gHD%t zKwKQDr!U~JPloZNLlUSd-rSo35i&MF)RiVk^$A_D?jM1I_07VemkmN|yo-_quI*|4 zHqJ-pL_sNp00BUwbq!n0sG61v){)^z{U;FR*VWVLWR~iXn53UMJ-Sn7l~0Q>$X&Ra z80;epAsZ2F^uV*gtv3f;{*RJ{s#4%Ujf5X@r>kZdiM;sfJ?h|Yg+&|SuIot{6bgLxx|rEnD}dT#Gg}5-h(Ea{|Bb<#rZ*aX)5VHbPs)AGEiEFC244^c#%Swp>^eU0Mjo_=V{z;$M zjClc`8ZV)ket#MIJwKc=gr%Et+uew!hmBq_z(w#x{V^2}O_VZw?W;9d(TJMcP^lyO zwWOXA;2EBgvBXB3FgD`|2S80!2|cU6L*u)@Jjpwz9TDeh9sH?5&xcdz_|zv8g{Ry6 zlI0QYayfORbi@7OU@Dch5O*wk<%wR1U=Q{r#RhleHjJQe+?m0hF0B{oO<8MJL8c`6 zK(kLX%Q-K6hm?J-(5~^2vV6c(hBUT~X?d%B=wRLKJk3P+;N~p<>h?GnSlTtB#49iFG|=?{{AX%$EYwH zhC3feM?R0)1HLrV^RCeDw|#ABVbb|P%dGS!Y)_?*8$Hh%QO*QRx#7vfG@8HlLf>?%>Q3_zi6$BhdnwcM_qnvCXJv&Vn|A!VT{>h%o}}|`(glfA zUHr6nk6sF}ON%2A5Nf*={gowyK7}d|oJ(e>r3HaBoO2|s&)zr6FdCeM__p%yCgPYy z(BNcKAwvx-wuqq0ukwSQzMJhwz+V9%^HZ#$=uj%R4cNIN#Dsos{8MR(^D9-1OItR; zvSre~*2S4ALRdYO3i`Us@5ia>B?$}angv#?2w8?&6C-VA_br)(=6(a}O%sX*LDO?o zG;1G7MaQ2T(+_nlATYeQ#FMxIOxiulN!~qmh$j}5SO#g2WG4y0lH$_MafQ4gkXp^w z3FApniV3L5U{>j9-`+Y$dtHjSvEBVSWD{eFz^vjRr1CFYI6K2-1ZDs6j&=#}b}WK+1p-D~ zGTF17edxfjczS=}-oCkX^y!b(@EpmzjDV82w)IJobJS$Pv`UIi58XD(*YYnH$ovBU zj=85X65`du#LfJaB5%Z#&1!$Qx_&%W;{LI|<9&aLX^Z9DJ&KIt-nNL;}iNU0jgjD>75CSHToV`c6 znSqj-(caAZ`c{vAK_SQ2=f2Fx(zzWO^-*4KbiKV~^DQ;EE$s}sGTvY_y!|E8iaM7( z^y6yutCicXHzrj0tgMbn0$Q-SNVWdkn(;qr9`7ErYN?zef%?WLcf8Z^Ou5KVTSojh}K!zO2J2IvCo zkC1OO5GfnCGs=enJ42MRx$99r5DtHDhuY`wK=Vhd{lS^;T3 zXtWq5>_<D_)}0fQks2kwj7pkxFwF z55&)Bj=_}daxGo3NVXKrB%Fi^+qw?*DDgHiUfX`2eFF8kOw_Qh1F2D*S+65LY9&1h zsA073Yomoz=^do^vKKzYQsxNJJ0aFis=_d*7UcDa|HoNzUNrG-lknpVkhy)J7oF&k zO4`B(3l=NT^~wn|Z?&e~;_cRp-QRnsmTgxj!7~8r=fA?6iWlD|)6tW{l@}vTUCcaD zx*z=H-7`@_-s|QOxHjt%V@P)InA?oZZj{Ag@U!bF?T*4Lhcbh5Vrf4VqkDVJpzN0S zWfh7kxM{D|xXw^8cSCRX=Q0}4{JPwr@4=mQ@lpE+r#GT(%z3{gdOD@Z+mUOUiC8q- zx=Y>Mq#1;5$gnvTJ9j&_;RmqM;O6+~n`hEI!rXG#TB9~9z7kHjGY9!#Pt`npnJDp8 z_MGF7=UQgq+8=PD15r-jR;VT$obxoQLc%vw-8s(!o=v9Fn^b=ADw%n$o{Bgfudk2E z@x>HQ_hc>jpU$`!m_6mJw!Zr)&oDavg};Z6GlQ_Zj9?0x(BBcamSY|PjF&HqW7r^^ zQZvl+p8u>z)cwLpNPsW`fAC;c8)rLZQT<}d;EM^7`Ni~vPt;+f+t&Qo5v|~(0ja@L zI@=6q{0!G&wf$jrp#nB`$!NmeM!d=v5?EaI^K$%9OA?K)r3W5eBD`X@0}SVM5Qx-H zg2~wE7A7z?5kzV9YvBs%pZtCKtoH5@^o8drGMUQEbn10Ifdr!GZfk`nCWm{InNzNf z>V@97vr*d73WXYcBgq%G*}f7PF1iHvSLl}*{5MfEv9dOC5QjcbYR-kuCzhdnj zApMCT>^wUzP10H0?GK{PBrHf1 zX56^I^e&n<>D5OhYLfJd|88G3zV+QtcF*Szgq5pw;m021UgQ^QhNgfe^64gca}4nT zh9_iPYoEAx@XXBfovANoEO4UXWct|jWuag=W%W>H%_&O zT%X0D%&9e`-K!;QbSt_u{;BLwNI-v{z4eF@AF_SeCzOoTG*WeE z5ZANaQpH?UWiJjeF5-wdLzvr|>={taaf2e59e@=Su#34kknZnAUa$8TI z5gLlm`(BhUZHYW|A|*yNC4&RS%B!#wUFFIU#=X1(Rl>>?z7{hNo$YX-^7<{c{?uhK zKUy)1!;K-ArPS6T9xU{q^qY(y&`@`sGw(izd95JOl*UCc<&<-5nt0zDf=iQc(S2Y) z=v)1g?ClUcv$KjNC{77&^io3;*Zfy2crUJQEChcAwSlg*Wxo{B9LW?!&1eg&(7xq6 zZw=%H1nxnTx^wjYn383_Uw214WRXpV5QNiu$An`<&w86u3Y=&7d8tgJpdX>)#NMp#?o zO2juO-J5-E;S*tIgW&kA(Xy1yzr`}>{q#<}VIgSC?-no6#~g9qZ)p0Y-Q68uno*Zt zr<=XEub`o+8Re^##PabUK;W5}s&h8ao>l`mP_8s(GNqjaB6oZiSqqU|;Ov`8UHm22q%v%u1&H11|@i~=-P>kF) z^jD@7_4pUl^m=Zb=KaoF{~83!tLbbmsL-|3J2hj0^ba5lC@vkX4GtP}Kqh6gl$vS~ zeN-DlgTJu9DuPBlW^+kce>m^&YAirW^~%ORy%lObd*N}^|Il)(8li}~9Dm4?Tc7cl zVhxYWIWsl}D7A04%IiCa3)HQyqFH*shsj-9dK~|7^66p1Y=6{gt}p zJpdGsg&)-BxDf7lBM``&vxTs(&9SnDNb}1V+jE5_-mCrT1Y6_f!HT4hII^@sX_2QA zL>0xhxv=^u2VH#O1U-hp6gSNV;Off4>6-2x$K?%ii^r2Sd>(S5q>R z+SH23iVZJm=2tsi#wA}umGArEm zg%qhJkE$9~)&Jaq3wP+2J4pH8YHQs8zm(dU45rSw*R9SG&AB28no)9&1(_?Sr#g}L zUp03B-7KRvL+5M>sN=>xXxgq;pJhAKv7YKBc| zgD&^{Z|LpX8=aSw0U8&6HO}vGzLI?ZNeUB)}U z(R-(s8Z`Ldwp?tu1nOJplLG8L@3{+$kwQ(+$oO!;!UHJg7-Zi6+s%KkWVZo$`d7Ft zIT(=IlW&^?Vw`fC+ay3Z0P*5L+P9}_5bu+D0kH%Z|6^57aQsQD7#R~qO)#K`J}gV~ z!DItQX^aehXA56urhHFl-X?LzNdIhHxq5wc*gawF*4Uc0PP%kDkosfZ{FFv`;#l1#N-$QM$--wBO)^KQHz<7 zc*{oi&86-9L1iOXCpc)gq(O)YTnkBjYbf(`*~$OHc>chwf;w#%14h$J`IDL#eN2O@ zX4GfaA{GteU#yz8gFq;5{&@igrH@D){xpc}XQ(iFa_oCdLMy%svr&Qa+<(xdwfm|6 zUtN5N!&s?)=h9Q@16&*kvX`gm%xo!Ek<=1^2|jeQ=i3*LKDwMt*@{6d{^N2UO)~&T zd}+G|@Cmau5n~bx{|1;3Eu<2xGTfsXy(0>-0ARj(?*XVMe1`-$f*uHnn7(eo7=Y9b z);DYaOkJo2{Kxk&KKr<(8=&DnNPx`E<^ zmG@rD!!m=i*0l`hZdww^BzufBn>3Tbso%@AZb$$qLmIZJr#CN4?lvsZK!j1wAt?W| zE@se`P)ouXx#Qo8;2tB}%*5OT^tYR9P?)V$7yPE=;R9lfLq*HU3+BJS!+a{7|85k4 zfxa65tfsD!tj32H@>#eQ=xe~4m$cJGCie>xkm35oc#8IZ{A{ z=#8?PhplXWrC>cXamL#QYXz3Teb%#bul1r6RF&VMo=R?T{Cl3|&yV-*-an!MP5>xS z)Gh4opLkq?K7;}e4ABA?EgZlisX9QtYY$zBaQWkm>Hwh{2W8*>(+Wix9P+V)9&$dvkrhb}>zwsw99G_D#{Bik1Q# zA*Ad}Bkvnivlg?kk`f+sV+M(VTX_>(_y3x5F7JFxxc%X~4TS(#cyp{sP3ZgwP^{0p zm4rnzbsDL(il>|-uGt3+7%YVD-L2t@KRLu^V7bM}aFOnpQHqaWe&P}#(pU^K*+hd_ zFqEn!6(<3!17&7%ToGJefi}BqXu?=ArRHw#PY}G}t`X9Zn8y?tSr9clp748HafE;7 zQP1XS%z!1wu!R4FxWOq1>R|~yq_FWLpcN{?6NVqE2K9Wb#!%}`mX@yah9y@gvICTI z`w{5|{2$ef?oy?kV&@2lw=*(YbC-jX0|c_}D#>on#~1O^wNg-Z4?Gvwy6|fuEER*G z*dd2JB7L9ZdjG{A9v2QC&4}5edoV1NGqDht0(rb5#f}m!j)CeBMha-NET}*lcLy{+ z=pO@rMfsTcA||@5-3{11I4l0DJy7Ry_GClb+4Ii>Z|5ca- zhY}VDNJTw&L0Ip{-{QA{Zon%k5Cw^M=uMk8a+OkwR(u?hGNM8*+Ws=^mqhdvVf%b( zvL8mFgmmr5^IjXmB_Mp2{?(trR0+yueeD|-?;lP?0KRSD zozU@5peJ{TMd+sCQiLo)OGu{mf&RCV8=F*hE0=^qJm5epk-y6#pWhmuv^0s2iu#&? z635o4?x20{BLt`A4*oZXtj_Gk5Om^ayR-uRI&L{uW`@eO+jafo!}P*BOyk#8{6JG= zPcu5EZ6=^g&;7{p5}wrhDs5HXOM$`-LEBgT3t9A%6820b(47e}Kw5Cm;tB90-Ko84 zK7LC3UwyHI(0&xZI3Sr_w#*sRs?j6l5USGLM_JK7)!_OKQW3rq)*~Ipp%C2 zhe?Ega+c0uO~O%=s3XEV37w=OY$Cv=fdvsGVLz4z){<6`A&%YQ8_1^5=$$9uk_h$n zfs=!wff=Iut;mw*SM_JT6(Qav#O*{YVDLyQT;T*PNdumsSFyKq%Pg&bHI^jznyqHW zB7GI@88;*@pu?4)#^ccF?0nh4- ztmlXQwb8pu|H^;Fp>Y%5-ng25Z^u6luJ5+)i8#Iym6jUbL8j(%e?sc(q1e)6S3L^& zk;M!T%q=?U@{2`8Ig5^Rhm$u#!gm++$#eHU=Gf5zpA6{pC#(UEdB*TF-{gBn+~wRj zW^iL|Ai#f_a%gnaPQq4EG^8S+A_!EtlI?G$uO|g6WKT4J>BtFF_w%gaofv}byD@oI zjI}Wq+V5@@r(medeR|iv;ox;~rLkZjrrETe_dEH-5G3B0<=Zsx5>6>C{e z&~+VPuzM|Y2)w#J@wa?in8x&1e2rwQ0}}s4;xkuBhCL|H4KLf*qkm*Gt+wm^1cHyT zxgEwd;LL4*zl*F3KsSQyzoSL--|s%9%<5WOX7^!wP#Bg{NNeYqx&-}ljq#h?a1H$; z^5DTrdUxQ;cB0d0HD!xuy+K)EX=!Vu*2HHc9RI%~**VY4I{_@XFO90;$3o^S<@OJY z-UTvUcBnFURwfZD?vIMfHk$LJ z$bd)D|2#=Jb5nIYyB|7*%sStx(;!~eqb<@3y|v25dR^gpbGbq&XOpK4h_?&`(?FhI zR7jwfJIZP-EybZ=_z+uARf~Q*C`0XWSzbR&+|muK9@g)d7NbBtb4giIxG@l8S~ey> z;a0EpW{CTSwgc~?q36~XXYr1Oo?Im1b6~dkQ}vjV2a`T@6ch({2%|B*jXl^W^_Y&@ zM4rENqS%*uOGax>vll30Odfye?e6x=uDr|TRz$O>6+X&G0V1w_v4m-;m*E@>CSdk? zGwP=VcayVC>ByUI7i8P4sO#@AvJ^v9(K zvk|!@8Cy}Wc_Y~P^LD)>lZV4iu6EtqtRU@vm@;#sm^L81kwCE7QvfmHPk;256W!=m zCjM~y_4XtF)OD=MEq3HDEyd%obOCW}p!0BeeO*ZMi*+^LtY)9nfiD4+y z@qjgMd1hzrJvCLyrAok-D)VAwr%(Txo(2Ikn58)J2Ec6cvw&E|x&q zjxI!<<3-ld##8^ae^9#>Rq<_=4moaQtCk$)L|y!S$n%)=%tJ9~WssS=;QqcNTX9s* zaIfeJOV3mtKi@E$VcVRFqdT_>ZP84euvH3jE^z~z<1;CAIc>!hjZ=YYLGVo1c3(_M z#Zt2N*~x1Y;9{n==jFWz3IBWonjSzAaf0rl1jW=RFyzC*sI#h zT=uN$x4g>xqMW`fBeZGT9qLwV9w1Eg(pbqiOQcGXLEJ-NWZpn2Wv0Vr(Xl!?A8DKM zFS~!-KQde*AN_sy$EhM$Ys5MH3B$Xe$rN&`(eZPS1eNTBX8&URx{=hBE1h-hjoA=*Bl$|cW`i%R^}Vo`_=`w>hVj093asr=vP|Jr5IsL01N_maN6<;u=SwU^01%i}a1G!|Xuf4x z8@-@Cnv1+#?%Dpz{89ab zx@e*Hz-Ng5sfg{^{x;1=yd;D|(@fG|>;mo`QXYLRJ4nMg>qcaRlJB$QZ`?wX^tCN7 z(~?TF4o>W@Yl``YZ1#atYN`()3#I~C$hN$esfiEsO_S_mSJP-mTNJV%Xwnz^kIr45 z=N+%*AHHb2Gb8m>iZ%V~w$`5mG9BfAHeZclDh3mB-4Z>Eh0b?XeodfK71)t=`WBQ4 zfe8~B(HO-gqjC5QJ_pc>+5EHT`n54%v{nNX@NIt6MoQkq2<_{4x2S_0(Uor&9-rQT zAZ6x%<#t*D3XecAdmGnQ45C|7>N;@F!P;#4=?v=}QoW;rFun#Pqc@~3%W*251<7V< z>ffI6-U9`-N|%*hL-g~-ATgg;d`U#|9!-kCt1_Y`bsI1wiu(SfRABt=1jb2;-voJV zn@yK%eri-u5$-meef;5WgUVN6 zw^fNEd_f6()LKRHXm9pdfcpXV8aq|-C>gUC2K5%IQ1T813{(k^D2N*$Dg~^&M^vzX zQV%e}yJZ*{NGX*(`3Tpq)gJka%Zgne(}%W$sM>Bq3H62%y5pAn#=LiM6?$?E%Ayd$ zox&u*xbSVGPxL=$5|OM^97}|1+;s6nY7dj$#0NfEq6*~Siz$C<^>7Z_^3?!9{{MZ1 ofm{Fm{|apX&wiXC;f6q_F79p4qm&%*SrLdDOiSs@V~e2w10c#BX#fBK literal 0 HcmV?d00001 diff --git a/ml_system_design/seminars/sem1.ipynb b/ml_system_design/seminars/sem1.ipynb new file mode 100644 index 0000000..f06c582 --- /dev/null +++ b/ml_system_design/seminars/sem1.ipynb @@ -0,0 +1,3249 @@ +{ + "cells": [ + { + "attachments": {}, + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Семинар №1\n", + "# Введение в линейную алгебру. Векторы. Матрицы и операции с ними. Библиотека NumPy" + ] + }, + { + "attachments": {}, + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Векторы" + ] + }, + { + "attachments": {}, + "cell_type": "markdown", + "metadata": {}, + "source": [ + "**Определение** : *Вектором* в n-мерном евклидовом пространстве $\\R^{n}$ называется упорядоченый набор чисел $x = (x_1, x_2, ..., x_n)$ - собственно, элемент пространства $\\R^{n}$.\n", + "\n", + "Часто вектор удобнее записывать в столбец: \n", + "$$x = \\begin{pmatrix}x_1\\\\x_2\\\\...\\\\x_n\\\\\\end{pmatrix}$$" + ] + }, + { + "attachments": {}, + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Помимо того, что вектор это набор чисел, вектор еще и геометрический объект, мы его можем отобразить на координатной поскости и в пространстве:\n", + "\n", + "![](imgs/sem1/sem1_1.png) | ![](imgs/sem1/sem1_2.png)" + ] + }, + { + "attachments": {}, + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Операции над векторами" + ] + }, + { + "attachments": {}, + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Векторы можно складывать и умножать на скаляр(число). Результатом будет вектор, элементами которого являются результаты поэлементного выполнения операции." + ] + }, + { + "attachments": {}, + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### Сложение векторов\n", + "\n", + "Геометрически сложение векторов выглядит так:\n", + "\n", + "| для неколлинеарных векторов | для коллинеарных векторов | сложение нескольких векторов |\n", + "| --- | --- | --- |\n", + "| ![](imgs/sem1/sem1_3.png) | ![](imgs/sem1/sem1_4.png) | ![](imgs/sem1/sem1_5.png) |\n", + "\n", + "**Пример**\n", + "\n", + "![](imgs/sem1/sem1_6.png)" + ] + }, + { + "attachments": {}, + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### Линейные подпространства\n", + "\n", + "- Векторное пространство $\\R^{n}$ **замкнуто** относительно операций сложения и умножения на скаляр.\n", + "\n", + "**Определение**: *Линейным (или векторным) подпространством* векторного пространства $L$ называется множество векторов $M$ $\\subset$ $L$, замкнутое относительно операций сложения и умножения на скаляр.\n", + "\n", + "**Определение**: *Линейной оболочкой векторов $v_1, v_2, ..., v_n$* называется множество всех линейных комбинаций этих векторов с произвольными коэффициентами: $$M = = \\{\\alpha_{1}v_{1} + \\alpha_{2}v_{2} + ... + \\alpha_{n}v_{n} \\ \\ | \\ \\ \\alpha_{i}\\in \\R\\}$$\n", + "\n", + "#### ЛНЗ\n", + "**Определение**: Векторы $v_1, v_2, ..., v_n$ называются *линейно независимыми*, если никакая линейная комбинация этих векторов не равна нуль-вектору. Иными словами, для любых $\\alpha_{i} \\in \\R$, не все из которых нулевые, выполняется $$\\alpha_{1}v_{1} + \\alpha_{2}v_{2} + ... + \\alpha_{n}v_{n} \\ne \\overline{0}$$\n", + "\n", + "![](imgs/sem1/sem1_7.png)\n", + "\n", + "#### Базис\n", + "**Определение**: Пусть $M$ - линейное подпространство. Базисом в $M$ называется минимальная система векторов $v_1, v_2, ..., v_n$, для которой $M = $\n", + "\n", + "![](imgs/sem1/sem1_8.png)\n", + "\n", + "Свойства базиса:\n", + "\n", + "- Базис является ЛНЗ\n", + "- Векторы из $M$ выражаются через базис единственным способом\n", + "- Любую ЛНЗ систему можно дополнить до базиса\n", + "- В любой системе образующих можно выбрать базис\n", + "- Любые два базиса равномощны. (Это свидетельствует о корректности определения *размерности линейного пространства* как размера базиса в этом линейном пространстве)\n", + "\n", + "\n", + "**Теорема**: $n+1$ векторов в $n-мерном$ пространстве всегда линейно зависимы.\n", + "\n", + "**Доказательство**: От противного. Пусть Они ЛНЗ => Можно дополнить до базиса => в базисе n+1 векторов и более => противоречие т.к. любые два базиса равномощны. \n", + "\n" + ] + }, + { + "attachments": {}, + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Далее поговорим как работать с векторами в `Python` с использованием библиотеки `NumPy`" + ] + }, + { + "attachments": {}, + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Отвлечемся на введение в NumPy" + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "metadata": {}, + "outputs": [], + "source": [ + "# !conda install numpy\n", + "# !pip3 install numpy" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": {}, + "outputs": [], + "source": [ + "import numpy as np" + ] + }, + { + "attachments": {}, + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Одномерные массивы" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + " \n" + ] + } + ], + "source": [ + "a = [1, 2, 3]\n", + "b = np.array(a, dtype='float64')\n", + "print(type(b), type(a))" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "***Если типы разные, то идет неявный каст к одному.***" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Для list: \n", + "Для np.array: \n" + ] + } + ], + "source": [ + "a = [1, 2, 'a']\n", + "b = np.array(a)\n", + "print(\"Для list:\", type(a[0]),\n", + " \"\\nДля np.array:\", type(b[0]))" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "array([1, 2, 3])" + ] + }, + "execution_count": 5, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "d = np.array([1, 2, 3])\n", + "d" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "numpy.ndarray" + ] + }, + "execution_count": 6, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "type(d)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "***Можем посмотреть на все методы класса ``ndarray``.***" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "{'T',\n", + " '__abs__',\n", + " '__add__',\n", + " '__and__',\n", + " '__array__',\n", + " '__array_finalize__',\n", + " '__array_function__',\n", + " '__array_interface__',\n", + " '__array_prepare__',\n", + " '__array_priority__',\n", + " '__array_struct__',\n", + " '__array_ufunc__',\n", + " '__array_wrap__',\n", + " '__bool__',\n", + " '__class_getitem__',\n", + " '__complex__',\n", + " '__contains__',\n", + " '__copy__',\n", + " '__deepcopy__',\n", + " '__delitem__',\n", + " '__divmod__',\n", + " '__dlpack__',\n", + " '__dlpack_device__',\n", + " '__float__',\n", + " '__floordiv__',\n", + " '__getitem__',\n", + " '__iadd__',\n", + " '__iand__',\n", + " '__ifloordiv__',\n", + " '__ilshift__',\n", + " '__imatmul__',\n", + " '__imod__',\n", + " '__imul__',\n", + " '__index__',\n", + " '__int__',\n", + " '__invert__',\n", + " '__ior__',\n", + " '__ipow__',\n", + " '__irshift__',\n", + " '__isub__',\n", + " '__iter__',\n", + " '__itruediv__',\n", + " '__ixor__',\n", + " '__len__',\n", + " '__lshift__',\n", + " '__matmul__',\n", + " '__mod__',\n", + " '__mul__',\n", + " '__neg__',\n", + " '__or__',\n", + " '__pos__',\n", + " '__pow__',\n", + " '__radd__',\n", + " '__rand__',\n", + " '__rdivmod__',\n", + " '__rfloordiv__',\n", + " '__rlshift__',\n", + " '__rmatmul__',\n", + " '__rmod__',\n", + " '__rmul__',\n", + " '__ror__',\n", + " '__rpow__',\n", + " '__rrshift__',\n", + " '__rshift__',\n", + " '__rsub__',\n", + " '__rtruediv__',\n", + " '__rxor__',\n", + " '__setitem__',\n", + " '__setstate__',\n", + " '__sub__',\n", + " '__truediv__',\n", + " '__xor__',\n", + " 'all',\n", + " 'any',\n", + " 'argmax',\n", + " 'argmin',\n", + " 'argpartition',\n", + " 'argsort',\n", + " 'astype',\n", + " 'base',\n", + " 'byteswap',\n", + " 'choose',\n", + " 'clip',\n", + " 'compress',\n", + " 'conj',\n", + " 'conjugate',\n", + " 'copy',\n", + " 'ctypes',\n", + " 'cumprod',\n", + " 'cumsum',\n", + " 'data',\n", + " 'diagonal',\n", + " 'dot',\n", + " 'dtype',\n", + " 'dump',\n", + " 'dumps',\n", + " 'fill',\n", + " 'flags',\n", + " 'flat',\n", + " 'flatten',\n", + " 'getfield',\n", + " 'imag',\n", + " 'item',\n", + " 'itemset',\n", + " 'itemsize',\n", + " 'max',\n", + " 'mean',\n", + " 'min',\n", + " 'nbytes',\n", + " 'ndim',\n", + " 'newbyteorder',\n", + " 'nonzero',\n", + " 'partition',\n", + " 'prod',\n", + " 'ptp',\n", + " 'put',\n", + " 'ravel',\n", + " 'real',\n", + " 'repeat',\n", + " 'reshape',\n", + " 'resize',\n", + " 'round',\n", + " 'searchsorted',\n", + " 'setfield',\n", + " 'setflags',\n", + " 'shape',\n", + " 'size',\n", + " 'sort',\n", + " 'squeeze',\n", + " 'std',\n", + " 'strides',\n", + " 'sum',\n", + " 'swapaxes',\n", + " 'take',\n", + " 'tobytes',\n", + " 'tofile',\n", + " 'tolist',\n", + " 'tostring',\n", + " 'trace',\n", + " 'transpose',\n", + " 'var',\n", + " 'view'}" + ] + }, + "execution_count": 7, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "set(dir(b)) - set(dir(object))" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "***Например узнаем размер массива.***" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "metadata": {}, + "outputs": [], + "source": [ + "arr = np.array([5, 6, 2, 1, 10], dtype='int32')" + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "20" + ] + }, + "execution_count": 9, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "arr.nbytes" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "***Если вы не знаете нужной функции, но понимаете, чего хотите, тогда можно воспользоваться поиском в документации.***" + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Search results for 'mean value of array'\n", + "----------------------------------------\n", + "numpy.ma.mean\n", + " Returns the average of the array elements along given axis.\n", + "numpy.mean\n", + " Compute the arithmetic mean along the specified axis.\n", + "numpy.nanmean\n", + " Compute the arithmetic mean along the specified axis, ignoring NaNs.\n", + "numpy.put\n", + " Replaces specified elements of an array with given values.\n", + "numpy.full\n", + " Return a new array of given shape and type, filled with `fill_value`.\n", + "numpy.digitize\n", + " Return the indices of the bins to which each value in input array belongs.\n", + "numpy.isrealobj\n", + " Return True if x is a not complex type or an array of complex numbers.\n", + "numpy.unpackbits\n", + " Unpacks elements of a uint8 array into a binary-valued output array.\n", + "numpy.nanquantile\n", + " Compute the qth quantile of the data along the specified axis,\n", + "numpy.ma.dot\n", + " Return the dot product of two arrays.\n", + "numpy.count_nonzero\n", + " Counts the number of non-zero values in the array ``a``.\n", + "numpy.ma.fix_invalid\n", + " Return input with invalid data masked and replaced by a fill value.\n", + "numpy.matrix.partition\n", + " Rearranges the elements in the array in such a way that the value of the\n", + "numpy.ma.MaskedArray.filled\n", + " Return a copy of self, with masked values filled with a given value.\n", + "numpy.ma.MaskedArray.partition\n", + " Rearranges the elements in the array in such a way that the value of the\n", + "numpy.exp\n", + " Calculate the exponential of all elements in the input array.\n", + "numpy.ptp\n", + " Range of values (maximum - minimum) along an axis.\n", + "numpy.sum\n", + " Sum of array elements over a given axis.\n", + "numpy.var\n", + " Compute the variance along the specified axis.\n", + "numpy.copy\n", + " Return an array copy of the given object.\n", + "numpy.prod\n", + " Return the product of array elements over a given axis.\n", + "numpy.block\n", + " Assemble an nd-array from nested lists of blocks.\n", + "numpy.copyto\n", + " Copies values from one array to another, broadcasting as necessary.\n", + "numpy.median\n", + " Compute the median along the specified axis.\n", + "numpy.nanmax\n", + " Return the maximum of an array or maximum along an axis, ignoring any\n", + "numpy.nanmin\n", + " Return minimum of an array or minimum along an axis, ignoring any NaNs.\n", + "numpy.nansum\n", + " Return the sum of array elements over a given axis treating Not a\n", + "numpy.nanvar\n", + " Compute the variance along the specified axis, while ignoring NaNs.\n", + "numpy.allclose\n", + " Returns True if two arrays are element-wise equal within a tolerance.\n", + "numpy.gradient\n", + " Return the gradient of an N-dimensional array.\n", + "numpy.nanmedian\n", + " Compute the median along the specified axis, while ignoring NaNs.\n", + "numpy.ones_like\n", + " Return an array of ones with the same shape and type as a given array.\n", + "numpy.lib.recfunctions.assign_fields_by_name\n", + " Assigns values from one structured array to another by field name.\n", + "numpy.percentile\n", + " Compute the q-th percentile of the data along the specified axis.\n", + "numpy.zeros_like\n", + " Return an array of zeros with the same shape and type as a given array.\n", + "numpy.ma.exp\n", + " Calculate the exponential of all elements in the input array.\n", + "numpy.chararray.copy\n", + " Return a copy of the array.\n", + "numpy.ma.var\n", + " Compute the variance along the specified axis.\n", + "numpy.chararray.view\n", + " New view of array with the same data.\n", + "numpy.nanpercentile\n", + " Compute the qth percentile of the data along the specified axis,\n", + "numpy.chararray.astype\n", + " Copy of the array, cast to a specified type.\n", + "numpy.ma.median\n", + " Compute the median along the specified axis.\n", + "numpy.linalg.svd\n", + " Singular Value Decomposition.\n", + "numpy.ma.ones_like\n", + " Return an array of ones with the same shape and type as a given array.\n", + "numpy.ma.zeros_like\n", + " Return an array of zeros with the same shape and type as a given array.\n", + "numpy.ma.MaskedArray.mean\n", + " Returns the average of the array elements along given axis.\n", + "numpy.ma.MaskedArray.dot\n", + " Masked dot product of two arrays. Note that `out` and `strict` are\n", + "numpy.ma.MaskedArray.var\n", + " Compute the variance along the specified axis.\n", + "numpy.ma.MaskedArray.copy\n", + " Return a copy of the array.\n", + "numpy.polynomial.polyutils.trimcoef\n", + " Remove \"small\" \"trailing\" coefficients from a polynomial.\n", + "numpy.pad\n", + " Pad an array.\n", + "numpy.std\n", + " Compute the standard deviation along the specified axis.\n", + "numpy.take\n", + " Take elements from an array along an axis.\n", + "numpy.isnan\n", + " Test element-wise for NaN and return result as a boolean array.\n", + "numpy.nditer\n", + " Efficient multi-dimensional iterator object to iterate over arrays.\n", + "numpy.reshape\n", + " Gives a new shape to an array without changing its data.\n", + "numpy.quantile\n", + " Compute the q-th quantile of the data along the specified axis.\n", + "numpy.full_like\n", + " Return a full array with the same shape and type as a given array.\n", + "numpy.empty_like\n", + " Return a new array with the same shape and type as a given array.\n", + "numpy.asarray_chkfinite\n", + " Convert the input to an array, checking for NaNs or Infs.\n", + "numpy.ma.ptp\n", + " Return (maximum - minimum) along the given dimension\n", + "numpy.ma.anom\n", + " Compute the anomalies (deviations from the arithmetic mean)\n", + "numpy.fft.ifft2\n", + " Compute the 2-dimensional inverse discrete Fourier Transform.\n", + "numpy.fft.ifftn\n", + " Compute the N-dimensional inverse discrete Fourier Transform.\n", + "numpy.ma.ravel\n", + " Returns a 1D version of self, as a view.\n", + "numpy.fft.irfftn\n", + " Computes the inverse of `rfftn`.\n", + "numpy.linalg.cond\n", + " Compute the condition number of a matrix.\n", + "numpy.linalg.norm\n", + " Matrix or vector norm.\n", + "numpy.histogram_bin_edges\n", + " Function to calculate only the edges of the bins used by the `histogram`\n", + "numpy.ma.MaskedArray.ptp\n", + " Return (maximum - minimum) along the given dimension\n", + "numpy.ma.MaskedArray.anom\n", + " Compute the anomalies (deviations from the arithmetic mean)\n", + "numpy.ma.MaskedArray.ravel\n", + " Returns a 1D version of self, as a view.\n", + "numpy.random.Generator.wald\n", + " Draw samples from a Wald, or inverse Gaussian, distribution.\n", + "numpy.polynomial.Hermite._fit\n", + " Least squares fit of Hermite series to data.\n", + "numpy.random.Generator.choice\n", + " Generates a random sample from a given array\n", + "numpy.random.RandomState.wald\n", + " Draw samples from a Wald, or inverse Gaussian, distribution.\n", + "numpy.polynomial.HermiteE._fit\n", + " Least squares fit of Hermite series to data.\n", + "numpy.polynomial.Laguerre._fit\n", + " Least squares fit of Laguerre series to data.\n", + "numpy.polynomial.Legendre._fit\n", + " Least squares fit of Legendre series to data.\n", + "numpy.polynomial.Chebyshev._fit\n", + " Least squares fit of Chebyshev series to data.\n", + "numpy.random.RandomState.choice\n", + " Generates a random sample from a given 1-D array\n", + "numpy.polynomial.Polynomial._fit\n", + " Least-squares fit of a polynomial to data.\n", + "numpy.random.Generator.lognormal\n", + " Draw samples from a log-normal distribution.\n", + "numpy.random.RandomState.lognormal\n", + " Draw samples from a log-normal distribution.\n", + "numpy.random.Generator.standard_normal\n", + " Draw samples from a standard Normal distribution (mean=0, stdev=1).\n", + "numpy.einsum\n", + " einsum(subscripts, *operands, out=None, dtype=None, order='K',\n", + "numpy.interp\n", + " One-dimensional linear interpolation for monotonically increasing sample points.\n", + "numpy.kaiser\n", + " Return the Kaiser window.\n", + "numpy.nanstd\n", + " Compute the standard deviation along the specified axis, while\n", + "numpy.average\n", + " Compute the weighted average along the specified axis.\n", + "numpy.hamming\n", + " Return the Hamming window.\n", + "numpy.hanning\n", + " Return the Hanning window.\n", + "numpy.loadtxt\n", + " Load data from a text file.\n", + "numpy.polyfit\n", + " Least squares polynomial fit.\n", + "numpy.bartlett\n", + " Return the Bartlett window.\n", + "numpy.blackman\n", + " Return the Blackman window.\n", + "numpy.can_cast\n", + " Returns True if cast between data types can occur according to the\n", + "numpy.random.RandomState.standard_normal\n", + " Draw samples from a standard Normal distribution (mean=0, stdev=1).\n", + "numpy.isfinite\n", + " Test element-wise for finiteness (not infinity and not Not a Number).\n", + "numpy.random.Generator.multivariate_normal\n", + " multivariate_normal(mean, cov, size=None, check_valid='warn',\n", + "numpy.nan_to_num\n", + " Replace NaN with zero and infinity with large finite numbers (default\n", + "numpy.random.RandomState.multivariate_normal\n", + " Draw random samples from a multivariate normal distribution.\n", + "numpy.fft.fft2\n", + " Compute the 2-dimensional discrete Fourier Transform.\n", + "numpy.fft.fftn\n", + " Compute the N-dimensional discrete Fourier Transform.\n", + "numpy.ma.copy\n", + " a.copy(order='C')\n", + "numpy.fft.rfft\n", + " Compute the one-dimensional discrete Fourier Transform for real input.\n", + "numpy.fft.rfftn\n", + " Compute the N-dimensional discrete Fourier Transform for real input.\n", + "numpy.ma.polyfit\n", + " Least squares polynomial fit.\n", + "numpy.random.SFC64\n", + " BitGenerator for Chris Doty-Humphrey's Small Fast Chaotic PRNG.\n", + "numpy.ma.empty_like\n", + " empty_like(prototype, dtype=None, order='K', subok=True, shape=None)\n", + "numpy.random.Generator.f\n", + " Draw samples from an F distribution.\n", + "numpy.random.RandomState.f\n", + " Draw samples from an F distribution.\n", + "numpy.random.Generator.gamma\n", + " Draw samples from a Gamma distribution.\n", + "numpy.random.Generator.gumbel\n", + " Draw samples from a Gumbel distribution.\n", + "numpy.random.Generator.normal\n", + " Draw random samples from a normal (Gaussian) distribution.\n", + "numpy.random.Generator.laplace\n", + " Draw samples from the Laplace or double exponential distribution with\n", + "numpy.random.RandomState.gamma\n", + " Draw samples from a Gamma distribution.\n", + "numpy.random.Generator.logistic\n", + " Draw samples from a logistic distribution.\n", + "numpy.random.Generator.rayleigh\n", + " Draw samples from a Rayleigh distribution.\n", + "numpy.random.Generator.vonmises\n", + " Draw samples from a von Mises distribution.\n", + "numpy.random.RandomState.gumbel\n", + " Draw samples from a Gumbel distribution.\n", + "numpy.random.RandomState.normal\n", + " Draw random samples from a normal (Gaussian) distribution.\n", + "numpy.random.Generator.chisquare\n", + " Draw samples from a chi-square distribution.\n", + "numpy.random.RandomState.laplace\n", + " Draw samples from the Laplace or double exponential distribution with\n", + "numpy.random.Generator.standard_t\n", + " Draw samples from a standard Student's t distribution with `df` degrees\n", + "numpy.random.RandomState.logistic\n", + " Draw samples from a logistic distribution.\n", + "numpy.random.RandomState.rayleigh\n", + " Draw samples from a Rayleigh distribution.\n", + "numpy.random.RandomState.vonmises\n", + " Draw samples from a von Mises distribution.\n", + "numpy.random.RandomState.chisquare\n", + " Draw samples from a chi-square distribution.\n", + "numpy.random.Generator.noncentral_f\n", + " Draw samples from the noncentral F distribution.\n", + "numpy.random.RandomState.standard_t\n", + " Draw samples from a standard Student's t distribution with `df` degrees\n", + "numpy.random.Generator.standard_gamma\n", + " Draw samples from a standard Gamma distribution.\n", + "numpy.random.RandomState.noncentral_f\n", + " Draw samples from the noncentral F distribution.\n", + "numpy.random.RandomState.standard_gamma\n", + " Draw samples from a standard Gamma distribution.\n", + "numpy.random.Generator.negative_binomial\n", + " Draw samples from a negative binomial distribution." + ] + } + ], + "source": [ + "np.lookfor('mean value of array') " + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "***Далее можно почитать документацию про контретную функцию.***" + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\u001b[0;31mSignature:\u001b[0m \u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mma\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mmean\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0ma\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mparams\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", + "\u001b[0;31mType:\u001b[0m _frommethod\n", + "\u001b[0;31mString form:\u001b[0m \n", + "\u001b[0;31mFile:\u001b[0m ~/miniconda3/envs/py38/lib/python3.8/site-packages/numpy/ma/core.py\n", + "\u001b[0;31mDocstring:\u001b[0m \n", + "mean(self, axis=None, dtype=None, out=None, keepdims=)\n", + "\n", + "Returns the average of the array elements along given axis.\n", + "\n", + "Masked entries are ignored, and result elements which are not\n", + "finite will be masked.\n", + "\n", + "Refer to `numpy.mean` for full documentation.\n", + "\n", + "See Also\n", + "--------\n", + "numpy.ndarray.mean : corresponding function for ndarrays\n", + "numpy.mean : Equivalent function\n", + "numpy.ma.average : Weighted average.\n", + "\n", + "Examples\n", + "--------\n", + ">>> a = np.ma.array([1,2,3], mask=[False, False, True])\n", + ">>> a\n", + "masked_array(data=[1, 2, --],\n", + " mask=[False, False, True],\n", + " fill_value=999999)\n", + ">>> a.mean()\n", + "1.5\n", + "\u001b[0;31mClass docstring:\u001b[0m\n", + "Define functions from existing MaskedArray methods.\n", + "\n", + "Parameters\n", + "----------\n", + "methodname : str\n", + " Name of the method to transform." + ] + } + ], + "source": [ + "?np.ma.mean" + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "np.concatenate\n", + "np.conj\n", + "np.conjugate\n", + "np.convolve" + ] + } + ], + "source": [ + "np.con*?" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "***Посмотрим на количественные характеристики ``ndarray``.***" + ] + }, + { + "cell_type": "code", + "execution_count": 13, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "[[[1 2 3 4]\n", + " [2 3 4 3]\n", + " [1 1 1 1]]\n", + "\n", + " [[1 2 3 4]\n", + " [2 3 4 3]\n", + " [1 1 1 1]]]\n" + ] + } + ], + "source": [ + "arr = np.array([[[1, 2, 3, 4],\n", + " [2, 3, 4, 3],\n", + " [1, 1, 1, 1]], \n", + " [[1, 2, 3, 4],\n", + " [2, 3, 4, 3],\n", + " [1, 1, 1, 1]]])\n", + "print(arr)" + ] + }, + { + "cell_type": "code", + "execution_count": 14, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "len: 2 -- количество элементов по первой оси. \n", + "size: 24 -- всего элементов в матрице. \n", + "ndim: 3 -- размерность матрицы. \n", + "shape: (2, 3, 4) -- количество элементов по каждой оси.\n" + ] + } + ], + "source": [ + "print(\"len:\", len(arr), \"-- количество элементов по первой оси.\",\n", + " \"\\nsize:\", arr.size, \"-- всего элементов в матрице.\",\n", + " \"\\nndim:\", arr.ndim, \"-- размерность матрицы.\",\n", + " \"\\nshape:\", arr.shape, \"-- количество элементов по каждой оси.\")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "***Индексы.***" + ] + }, + { + "cell_type": "code", + "execution_count": 15, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "(1, 2)" + ] + }, + "execution_count": 15, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "a = np.array([1, 2, 3, 4])\n", + "a[0], a[1]" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "***Последний элемент.***" + ] + }, + { + "cell_type": "code", + "execution_count": 16, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "4" + ] + }, + "execution_count": 16, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "a[-1]" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "***Можем изменять объекты массива.***" + ] + }, + { + "cell_type": "code", + "execution_count": 17, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "array([ 1, 2, -1, 4])" + ] + }, + "execution_count": 17, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "a[2] = -1\n", + "a" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "***``ndarray`` можно использовать в циклах. Но при этом теряется главное преимущество `Numpy` -- быстродействие. Всегда, когда это возможно, лучше использовать операции над массивами как едиными целыми.***" + ] + }, + { + "cell_type": "code", + "execution_count": 18, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "1\n", + "2\n", + "-1\n", + "4\n" + ] + } + ], + "source": [ + "for i in a:\n", + " print(i)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "**Задача 1:** Создать numpy-массив, состоящий из первых четырех простых чисел, выведите его тип и размер:" + ] + }, + { + "cell_type": "code", + "execution_count": 19, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "[2 3 5 7]\n", + "int64\n", + "\n", + "(4,)\n", + "32\n" + ] + } + ], + "source": [ + "# решение\n", + "\n", + "arr = np.array([2, 3, 5, 7])\n", + "print(arr)\n", + "print(arr.dtype)\n", + "print(type(arr))\n", + "print(arr.shape)\n", + "print(arr.nbytes)" + ] + }, + { + "attachments": {}, + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Создание массивов" + ] + }, + { + "cell_type": "code", + "execution_count": 20, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "[0 0 0 0 0 0 0]\n", + "[1. 1. 1. 1. 1. 1. 1.]\n" + ] + } + ], + "source": [ + "a = np.zeros(7, dtype=np.int16) # массив из нулей\n", + "b = np.ones(7, dtype=np.float64) # массив из единиц\n", + "print(a)\n", + "print(b)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "***Часто нужно создать нулевой массив такой же как другой.***" + ] + }, + { + "cell_type": "code", + "execution_count": 21, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "array([0., 0., 0., 0., 0., 0., 0.])" + ] + }, + "execution_count": 21, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "c = np.zeros(7, dtype=np.float64)\n", + "c" + ] + }, + { + "cell_type": "code", + "execution_count": 22, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "array([0, 0, 0, 0, 0, 0, 0])" + ] + }, + "execution_count": 22, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "c = np.zeros_like(b, dtype=np.int64)\n", + "c" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "***Функция `np.arange` подобна `range`. Аргументы могут быть с плавающей точкой. Следует избегать ситуаций, когда (конец-начало)/шаг -- целое число, потому что в этом случае включение последнего элемента зависит от ошибок округления. Лучше, чтобы конец диапазона был где-то посредине шага.***" + ] + }, + { + "cell_type": "code", + "execution_count": 23, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "[ 1 5 9 13]\n", + "[ 5. 5.2 5.4 5.6 5.8 6. 6.2 6.4 6.6 6.8 7. 7.2 7.4 7.6\n", + " 7.8 8. 8.2 8.4 8.6 8.8 9. 9.2 9.4 9.6 9.8 10. 10.2 10.4\n", + " 10.6 10.8 11. 11.2 11.4 11.6 11.8 12. 12.2 12.4 12.6 12.8 13. 13.2\n", + " 13.4 13.6 13.8 14. 14.2 14.4 14.6 14.8 15. 15.2 15.4 15.6 15.8 16.\n", + " 16.2 16.4 16.6 16.8 17. 17.2 17.4 17.6 17.8 18. 18.2 18.4 18.6 18.8\n", + " 19. 19.2 19.4 19.6 19.8 20. 20.2 20.4 20.6 20.8]\n", + "[1 2 3 4 5 6 7 8 9]\n", + "[0 1 2 3 4]\n" + ] + } + ], + "source": [ + "a = np.arange(1, 16, 4)\n", + "b = np.arange(5., 21, 0.2)\n", + "c = np.arange(1, 10)\n", + "d = np.arange(5)\n", + "print(a)\n", + "print(b)\n", + "print(c)\n", + "print(d)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "***Последовательности чисел с постоянным шагом можно также создавать функцией `linspace`. Начало и конец диапазона включаются; последний аргумент -- число точек.***" + ] + }, + { + "cell_type": "code", + "execution_count": 24, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "[ 1. 1.73684211 2.47368421 3.21052632 3.94736842 4.68421053\n", + " 5.42105263 6.15789474 6.89473684 7.63157895 8.36842105 9.10526316\n", + " 9.84210526 10.57894737 11.31578947 12.05263158 12.78947368 13.52631579\n", + " 14.26315789 15. ]\n", + "[ 5. 5.77777778 6.55555556 7.33333333 8.11111111 8.88888889\n", + " 9.66666667 10.44444444 11.22222222 12. ]\n" + ] + } + ], + "source": [ + "a = np.linspace(1, 15, 20)\n", + "b = np.linspace(5, 12, 10)\n", + "print(a)\n", + "print(b)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "**Задача 2:** создать и вывести последовательность чисел от 10 до 32 с постоянным шагом, длина последовательности -- 12. Чему равен шаг?" + ] + }, + { + "cell_type": "code", + "execution_count": 25, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "[10. 12. 14. 16. 18. 20. 22. 24. 26. 28. 30. 32.]\n", + "2.0\n" + ] + } + ], + "source": [ + "# решение\n", + "\n", + "a = np.linspace(10, 32, 12)\n", + "print(a)\n", + "print(a[1] - a[0])\n", + "\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "***Последовательность чисел с постоянным шагом по логарифмической шкале от $10^0$ до $10^3$.***" + ] + }, + { + "cell_type": "code", + "execution_count": 26, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "[ 1. 1.87381742 3.51119173 6.57933225 12.32846739\n", + " 23.101297 43.28761281 81.11308308 151.9911083 284.80358684\n", + " 533.66992312 1000. ]\n" + ] + } + ], + "source": [ + "b = np.logspace(0, 3, 12)\n", + "print(b)" + ] + }, + { + "attachments": {}, + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Операции над одномерными массивами." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "***Все арифметические операции производятся поэлементно.***" + ] + }, + { + "cell_type": "code", + "execution_count": 27, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "[10. 12. 14. 16. 18. 20. 22. 24. 26. 28. 30. 32.]\n", + "[ 1. 1.87381742 3.51119173 6.57933225 12.32846739\n", + " 23.101297 43.28761281 81.11308308 151.9911083 284.80358684\n", + " 533.66992312 1000. ]\n" + ] + } + ], + "source": [ + "print(a)\n", + "print(b)" + ] + }, + { + "cell_type": "code", + "execution_count": 28, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "[ 1. 2. 3. 4. 5. 6. 7. 8. 9. 10. 11.]\n", + "[ 5. 10. 15. 20. 25. 30. 35. 40. 45. 50. 55.]\n", + "[ -6. -24. -54. -96. -150. -216. -294. -384. -486. -600. -726.]\n", + "[-1.5 -1.5 -1.5 -1.5 -1.5 -1.5 -1.5 -1.5 -1.5 -1.5 -1.5]\n" + ] + } + ], + "source": [ + "a = np.linspace(3, 33, 11)\n", + "b = np.linspace(-2, -22, 11)\n", + "print(a + b)\n", + "print(a - b)\n", + "print(a * b)\n", + "print(a / b)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "***Один из операндов может быть скаляром, а не массивом.***" + ] + }, + { + "cell_type": "code", + "execution_count": 29, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "[ 15. 30. 45. 60. 75. 90. 105. 120. 135. 150. 165.]\n", + "[ 8. 6. 4. 2. 0. -2. -4. -6. -8. -10. -12.]\n" + ] + } + ], + "source": [ + "print(5*a)\n", + "print(10 + b)" + ] + }, + { + "cell_type": "code", + "execution_count": 30, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "[ 1. 4. 9. 16. 25. 36. 49. 64. 81. 100. 121.]\n", + "[2.000e+00 4.000e+00 8.000e+00 1.600e+01 3.200e+01 6.400e+01 1.280e+02\n", + " 2.560e+02 5.120e+02 1.024e+03 2.048e+03]\n" + ] + } + ], + "source": [ + "print((a + b) ** 2)\n", + "print(2 ** (a + b))" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "***Если типы элементов разные, то идет каст к большему.***" + ] + }, + { + "cell_type": "code", + "execution_count": 31, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "[ 3. 7. 11. 15. 19. 23. 27. 31. 35. 39. 43.]\n", + "\n" + ] + } + ], + "source": [ + "print(a + np.arange(11, dtype='int16'))\n", + "print(type(a[0]))" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "***В ``Numpy`` есть элементарные функции, которые тоже применяются к массивам поэлементно. Они называются универсальными функциями (``ufunc``).***" + ] + }, + { + "cell_type": "code", + "execution_count": 32, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "numpy.ufunc" + ] + }, + "execution_count": 32, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "type(np.cos)" + ] + }, + { + "cell_type": "code", + "execution_count": 33, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "array([-0.9899925 , 0.96017029, -0.91113026, 0.84385396, -0.75968791,\n", + " 0.66031671, -0.54772926, 0.42417901, -0.29213881, 0.15425145,\n", + " -0.01327675])" + ] + }, + "execution_count": 33, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "np.cos(a)" + ] + }, + { + "cell_type": "code", + "execution_count": 34, + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/tmp/ipykernel_2722/4200754868.py:1: RuntimeWarning: invalid value encountered in log\n", + " np.log(b)\n" + ] + }, + { + "data": { + "text/plain": [ + "array([nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan])" + ] + }, + "execution_count": 34, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "np.log(b)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "***Логические операции также производятся поэлементно.***" + ] + }, + { + "cell_type": "code", + "execution_count": 35, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "[ True True True True True True True True True True True]\n", + "[False False False False False False False False False False False]\n", + "[False False False True True True True True True True True]\n" + ] + } + ], + "source": [ + "print(a > b)\n", + "print(a == b)\n", + "print(a >= 10)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "***Кванторы ``всеобщности`` и ``существования``.***\n", + "$$\\forall$$\n", + "$$\\exists$$" + ] + }, + { + "cell_type": "code", + "execution_count": 36, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n" + ] + } + ], + "source": [ + "c = np.arange(0., 20)\n", + "print(type(c[0]))" + ] + }, + { + "cell_type": "code", + "execution_count": 37, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "(True, False)" + ] + }, + "execution_count": 37, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "np.any(c == 0.), np.all(c)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "***Inplace операции.***" + ] + }, + { + "cell_type": "code", + "execution_count": 38, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "[-0.7568025 0.2431975 1.2431975 2.2431975 3.2431975 4.2431975\n", + " 5.2431975 6.2431975 7.2431975 8.2431975 9.2431975 10.2431975\n", + " 11.2431975 12.2431975 13.2431975 14.2431975 15.2431975 16.2431975\n", + " 17.2431975 18.2431975]\n" + ] + } + ], + "source": [ + "c += np.sin(4)\n", + "print(c)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "***Inplace операции возможны только для операндов одинакового типа.***" + ] + }, + { + "cell_type": "code", + "execution_count": 39, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "[-1.51360499 0.48639501 2.48639501 4.48639501 6.48639501 8.48639501\n", + " 10.48639501 12.48639501 14.48639501 16.48639501 18.48639501 20.48639501\n", + " 22.48639501 24.48639501 26.48639501 28.48639501 30.48639501 32.48639501\n", + " 34.48639501 36.48639501]\n" + ] + } + ], + "source": [ + "c *= 2\n", + "print(c)" + ] + }, + { + "cell_type": "code", + "execution_count": 40, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "[-0.51360499 1.2431975 1.82879834 2.12159875 2.297279 2.41439917\n", + " 2.49805643 2.56079938 2.60959945 2.6486395 2.68058136 2.70719958\n", + " 2.72972269 2.74902821 2.76575967 2.78039969 2.79331735 2.80479972\n", + " 2.81507342 2.82431975]\n" + ] + } + ], + "source": [ + "b = np.arange(1., 21, 1)\n", + "\n", + "d = (b + c)\n", + "d /= b\n", + "print(d)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "***При делении ``ndarray`` на нули, исключения не бросается.***" + ] + }, + { + "cell_type": "code", + "execution_count": 41, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "[ 0. nan inf -inf]\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/tmp/ipykernel_2722/3088186758.py:1: RuntimeWarning: divide by zero encountered in divide\n", + " print(np.array([0.0, 0.0, 1.0, -1.0]) / np.array([1.0, 0.0, 0.0, 0.0]))\n", + "/tmp/ipykernel_2722/3088186758.py:1: RuntimeWarning: invalid value encountered in divide\n", + " print(np.array([0.0, 0.0, 1.0, -1.0]) / np.array([1.0, 0.0, 0.0, 0.0]))\n" + ] + } + ], + "source": [ + "print(np.array([0.0, 0.0, 1.0, -1.0]) / np.array([1.0, 0.0, 0.0, 0.0]))" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "***Могут понадобится константы.***" + ] + }, + { + "cell_type": "code", + "execution_count": 42, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "2.718281828459045 3.141592653589793\n" + ] + } + ], + "source": [ + "print(np.e, np.pi)" + ] + }, + { + "cell_type": "code", + "execution_count": 43, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "[ 1. 2. 3. 4. 5. 6. 7. 8. 9. 10. 11. 12. 13. 14. 15. 16. 17. 18.\n", + " 19. 20.]\n", + "[ 1. 3. 6. 10. 15. 21. 28. 36. 45. 55. 66. 78. 91. 105.\n", + " 120. 136. 153. 171. 190. 210.]\n" + ] + } + ], + "source": [ + "print(b)\n", + "print(b.cumsum())" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "***Посмотрим на сортировку numpy-массивов.***" + ] + }, + { + "cell_type": "code", + "execution_count": 44, + "metadata": {}, + "outputs": [], + "source": [ + "a = np.array([1, 5, 6, 10, -2, 0, 18])" + ] + }, + { + "cell_type": "code", + "execution_count": 45, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "[-2 0 1 5 6 10 18]\n", + "[ 1 5 6 10 -2 0 18]\n" + ] + } + ], + "source": [ + "print(np.sort(a))\n", + "print(a)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "***Теперь попробуем как метод.***" + ] + }, + { + "cell_type": "code", + "execution_count": 46, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "[-2 0 1 5 6 10 18]\n" + ] + } + ], + "source": [ + "a.sort()\n", + "print(a)" + ] + }, + { + "cell_type": "code", + "execution_count": 47, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "array([1., 1., 1., 1., 1.])" + ] + }, + "execution_count": 47, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "b = np.ones(5)\n", + "b" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "***Объединим массивы.***" + ] + }, + { + "cell_type": "code", + "execution_count": 48, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "array([-2., 0., 1., 5., 6., 10., 18., 1., 1., 1., 1., 1., 5.,\n", + " 5., 5., 5., 5.])" + ] + }, + "execution_count": 48, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "c = np.hstack((a, b, 5*b))\n", + "c" + ] + }, + { + "cell_type": "code", + "execution_count": 49, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\u001b[0;31mSignature:\u001b[0m \u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mhsplit\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mary\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mindices_or_sections\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", + "\u001b[0;31mDocstring:\u001b[0m\n", + "Split an array into multiple sub-arrays horizontally (column-wise).\n", + "\n", + "Please refer to the `split` documentation. `hsplit` is equivalent\n", + "to `split` with ``axis=1``, the array is always split along the second\n", + "axis except for 1-D arrays, where it is split at ``axis=0``.\n", + "\n", + "See Also\n", + "--------\n", + "split : Split an array into multiple sub-arrays of equal size.\n", + "\n", + "Examples\n", + "--------\n", + ">>> x = np.arange(16.0).reshape(4, 4)\n", + ">>> x\n", + "array([[ 0., 1., 2., 3.],\n", + " [ 4., 5., 6., 7.],\n", + " [ 8., 9., 10., 11.],\n", + " [12., 13., 14., 15.]])\n", + ">>> np.hsplit(x, 2)\n", + "[array([[ 0., 1.],\n", + " [ 4., 5.],\n", + " [ 8., 9.],\n", + " [12., 13.]]),\n", + " array([[ 2., 3.],\n", + " [ 6., 7.],\n", + " [10., 11.],\n", + " [14., 15.]])]\n", + ">>> np.hsplit(x, np.array([3, 6]))\n", + "[array([[ 0., 1., 2.],\n", + " [ 4., 5., 6.],\n", + " [ 8., 9., 10.],\n", + " [12., 13., 14.]]),\n", + " array([[ 3.],\n", + " [ 7.],\n", + " [11.],\n", + " [15.]]),\n", + " array([], shape=(4, 0), dtype=float64)]\n", + "\n", + "With a higher dimensional array the split is still along the second axis.\n", + "\n", + ">>> x = np.arange(8.0).reshape(2, 2, 2)\n", + ">>> x\n", + "array([[[0., 1.],\n", + " [2., 3.]],\n", + " [[4., 5.],\n", + " [6., 7.]]])\n", + ">>> np.hsplit(x, 2)\n", + "[array([[[0., 1.]],\n", + " [[4., 5.]]]),\n", + " array([[[2., 3.]],\n", + " [[6., 7.]]])]\n", + "\n", + "With a 1-D array, the split is along axis 0.\n", + "\n", + ">>> x = np.array([0, 1, 2, 3, 4, 5])\n", + ">>> np.hsplit(x, 2)\n", + "[array([0, 1, 2]), array([3, 4, 5])]\n", + "\u001b[0;31mFile:\u001b[0m ~/miniconda3/envs/py38/lib/python3.8/site-packages/numpy/lib/shape_base.py\n", + "\u001b[0;31mType:\u001b[0m function" + ] + } + ], + "source": [ + "?np.hsplit\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "***Расщепление массива.***" + ] + }, + { + "cell_type": "code", + "execution_count": 50, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "[-2 0 1 5 6 10 18]\n", + "[-2 0 1]\n", + "[5 6]\n", + "[10]\n", + "[18]\n" + ] + } + ], + "source": [ + "x1, x2, x3, x4 = np.hsplit(a, [3, 5, 6])\n", + "print(a)\n", + "print(x1)\n", + "print(x2)\n", + "print(x3)\n", + "print(x4)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "***Функции ``append`` ``delete`` ``insert`` не Inplace функции.***" + ] + }, + { + "cell_type": "code", + "execution_count": 51, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "[-2 5 10 18]\n", + "[-2 0 1 5 6 10 18]\n" + ] + } + ], + "source": [ + "print(np.delete(a, [2, 4, 1]))\n", + "print(a)" + ] + }, + { + "cell_type": "code", + "execution_count": 52, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "array([-2, 0, -1, -1, 1, 5, 6, 10, 18])" + ] + }, + "execution_count": 52, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "np.insert(a, 2, [-1, -1])" + ] + }, + { + "cell_type": "code", + "execution_count": 53, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "array([-2. , 0. , 1. , 5. , 6. , 10. , 18. , 2.2, 2.1])" + ] + }, + "execution_count": 53, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "np.append(a, [2.2, 2.1])" + ] + }, + { + "attachments": {}, + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Cрезы" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "***Массив в обратном порядоке.***" + ] + }, + { + "cell_type": "code", + "execution_count": 54, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "array([18, 10, 6, 5, 1, 0, -2])" + ] + }, + "execution_count": 54, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "a[::-1]" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "***Диапазон индексов. Создаётся новый заголовок массива, указывающий на те же данные. Изменения, сделанные через такой массив, видны и в исходном массиве.***" + ] + }, + { + "cell_type": "code", + "execution_count": 55, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "[-2 0 1 5 6 10 18]\n" + ] + } + ], + "source": [ + "print(a)" + ] + }, + { + "cell_type": "code", + "execution_count": 56, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "array([1, 5, 6])" + ] + }, + "execution_count": 56, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "a[2:5]" + ] + }, + { + "cell_type": "code", + "execution_count": 57, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "[ -2 -1000 1 5 6 10 18]\n" + ] + } + ], + "source": [ + "b = a[0:6] # копия не создается\n", + "b[1] = -1000\n", + "print(a)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "***Диапозоны с шагами.***" + ] + }, + { + "cell_type": "code", + "execution_count": 58, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "[-2 1]\n", + "[-2 0 1 5 0 10 18]\n" + ] + } + ], + "source": [ + "b = a[0:4:2]\n", + "print(b)\n", + "\n", + "# подмассиву можно присваивать скаляр\n", + "a[1:6:3] = 0\n", + "print(a)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "***Чтобы скопировать и данные массива, нужно использовать метод ``copy``.***" + ] + }, + { + "cell_type": "code", + "execution_count": 59, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "[-2 0 -4 5 0 10 18]\n", + "[-2 0 1 5 0 10 18]\n" + ] + } + ], + "source": [ + "b = a.copy()\n", + "b[2] = -4\n", + "print(b)\n", + "print(a)" + ] + }, + { + "cell_type": "code", + "execution_count": 60, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "[10 5 0]\n" + ] + } + ], + "source": [ + "print(a[[5,3,1]]) # массив индексов" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "**Задание 3:** \n", + "- Создать массив чисел от $-4\\pi$ до $4\\pi $, количество точек 100\n", + "- Посчитать сумму поэлементных квадратов синуса и косинуса для данного массива \n", + "- С помощью ``np.all`` проверить, что все элементы равны единице." + ] + }, + { + "cell_type": "code", + "execution_count": 61, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "[1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1.\n", + " 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1.\n", + " 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1.\n", + " 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1.\n", + " 1. 1. 1. 1.]\n" + ] + }, + { + "data": { + "text/plain": [ + "True" + ] + }, + "execution_count": 61, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# решение\n", + "\n", + "x = np.linspace(-4*np.pi, 4*np.pi, 100)\n", + "print(np.sin(x)**2 + np.cos(x)**2)\n", + "np.all((np.sin(x)**2 + np.cos(x)**2).round() == 1)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Матрицы" + ] + }, + { + "attachments": {}, + "cell_type": "markdown", + "metadata": {}, + "source": [ + "**Определение**: *Матрицей размера $m \\times n$* нахывается прямоугольная таблица с числами из $m$ строк и $n$ столбцов: \n", + "\n", + "$$\\begin{pmatrix}x_{11}, x_{12}, ... , x_{1n}\\\\x_{21}, x_{22}, ... , x_{2n}\\\\...\\ \\ \\ ...\\ \\ \\ ...\\\\x_{m1}, x_{m2}, ... , x_{mn}\\\\\\end{pmatrix}$$" + ] + }, + { + "attachments": {}, + "cell_type": "markdown", + "metadata": {}, + "source": [ + "**Определение**: Квадратная матрица называется *(не)вырожденой*, если ее строки линейно (не)зависимы\n", + "$$\\begin{pmatrix}1\\ \\ \\ \\ \\ \\ 3\\ \\ \\ \\ \\ \\ -1\\\\0\\ \\ \\ \\ \\ \\ -2\\ \\ \\ \\ \\ \\ 0\\\\2\\ \\ \\ \\ \\ \\ 4\\ \\ \\ \\ \\ \\ -2 \\end{pmatrix}$$" + ] + }, + { + "attachments": {}, + "cell_type": "markdown", + "metadata": {}, + "source": [ + "**Утверждение**: Строки квадратной матрицы ЛНЗ тогда и только тогда, когда её столбцы ЛНЗ" + ] + }, + { + "attachments": {}, + "cell_type": "markdown", + "metadata": {}, + "source": [ + "**Определение**: *Строчным рангом* матрицы $A$ называется размер наибольшего подмножества линейно независимых строк $A$. Аналогчно определяется *столбцовый* ранг.\n", + "\n", + "**Пример**: \n", + "$$\\begin{pmatrix}1\\ \\ \\ \\ \\ \\ 0\\ \\ \\ \\ \\ \\ 0\\ \\ \\ \\ \\ \\ 5\\ \\ \\ \\ \\ \\ -2\\\\0\\ \\ \\ \\ \\ \\ 1\\ \\ \\ \\ \\ \\ 0\\ \\ \\ \\ \\ \\ 0\\ \\ \\ \\ \\ \\ -1\\\\0\\ \\ \\ \\ \\ \\ \\ 0\\ \\ \\ \\ \\ \\ \\ 1\\ \\ \\ \\ \\ \\ \\ 2\\ \\ \\ \\ \\ \\ \\ 3\\\\ \\end{pmatrix} \\\\ Строчный\\ и\\ столбцовый\\ ранг\\ равны\\ 3$$\n", + "\n", + "**Удтверждение**: Строчный и столбцовый ранг равны." + ] + }, + { + "attachments": {}, + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### Умножение матрицы на вектор\n", + "\n", + "![](imgs/sem1/sem1_9.png)\n", + "\n", + "Пример с системой линейных уравнений, ее можно очень удобно записать в матричном виде:\n", + "\n", + "![](imgs/sem1/sem1_10.png)\n" + ] + }, + { + "attachments": {}, + "cell_type": "markdown", + "metadata": {}, + "source": [ + "##### Линейная регрессия в матричном виде\n", + "- Ищем закономерность в *линейном* виде $$y_k = w_1x_{k1} + w_2x_{k2} + ... + w_nx_{kn}$$\n", + "- В матричном виде уравнение записывается так: \n", + "$$Xw = y$$\n", + "$$X = \\begin{pmatrix}x_{11}, x_{12}, ... , x_{1n}\\\\x_{21}, x_{22}, ... , x_{2n}\\\\...\\ \\ \\ ...\\ \\ \\ ...\\\\x_{m1}, x_{m2}, ... , x_{mn}\\\\\\end{pmatrix}, w = \\begin{pmatrix}w_1\\\\w_2\\\\ ... \\\\ w_{n} \\end{pmatrix}, y = \\begin{pmatrix}w_1\\\\y_2\\\\ ... \\\\ y_{n} \\end{pmatrix}$$\n", + "\n", + "Любой алгоритм машинного обучения очень чувствителен к количеству объектов ($m$) и количеству признаков ($n$) в обучающей выборке:\n", + "\n", + "- Если $m = n$, то решение (скорее всего) единственное.\n", + "- Если $m > n$, то решение (скорее всего) нет.\n", + "- Если $m < n$, то решение (скорее всего) бесконечно много." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Матрицы в NumPy" + ] + }, + { + "cell_type": "code", + "execution_count": 62, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "[[1 2]\n", + " [3 4]]\n" + ] + } + ], + "source": [ + "a = np.array([[1, 2], [3, 4]])\n", + "print(a)" + ] + }, + { + "cell_type": "code", + "execution_count": 63, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "(2, (2, 2), 2, 4)" + ] + }, + "execution_count": 63, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "a.ndim, a.shape, len(a), a.size" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "***Обращение по индексу.***" + ] + }, + { + "cell_type": "code", + "execution_count": 64, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "(4, 4)" + ] + }, + "execution_count": 64, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "a[1][1], a[1,1]" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "***Атрибуту ``shape`` можно присвоить новое значение -- кортеж размеров по всем координатам. Получится новый заголовок массива; его данные не изменятся.***" + ] + }, + { + "cell_type": "code", + "execution_count": 65, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "[[ 0 1 2 3 4 5 6 7 8 9]\n", + " [10 11 12 13 14 15 16 17 18 19]]\n" + ] + } + ], + "source": [ + "b = np.arange(0, 20)\n", + "b.shape = (2, 10)\n", + "print(b)" + ] + }, + { + "cell_type": "code", + "execution_count": 66, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "[[ 0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19]]\n" + ] + } + ], + "source": [ + "print(b.reshape((1,20))) # то же самое, что и shape" + ] + }, + { + "cell_type": "code", + "execution_count": 67, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "[ 0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19]\n" + ] + } + ], + "source": [ + "print(b.ravel()) # стягивание в одномерный массив" + ] + }, + { + "cell_type": "code", + "execution_count": 68, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "[[1. 1. 1.]\n", + " [1. 1. 1.]\n", + " [1. 1. 1.]]\n" + ] + } + ], + "source": [ + "a = np.ones((3, 3)) # подать tuple\n", + "print(a)" + ] + }, + { + "cell_type": "code", + "execution_count": 69, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "[[0. 0. 0. 0.]\n", + " [0. 0. 0. 0.]\n", + " [0. 0. 0. 0.]]\n" + ] + } + ], + "source": [ + "b = np.zeros((3, 4))\n", + "print(b)" + ] + }, + { + "attachments": {}, + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### Операции над матрицами\n", + "\n", + "- Сложение матриц\n", + "- Умножение матриц\n", + "- Транспонирование и обратная матрица\n", + "- Определитель матрицы" + ] + }, + { + "attachments": {}, + "cell_type": "markdown", + "metadata": {}, + "source": [ + "##### Сложение матриц\n", + "- выполняется поэлементно\n", + "- Можно применять только к матрицам одинакового размера\n", + "\n", + "![](imgs/sem1/sem1_11.png)" + ] + }, + { + "cell_type": "code", + "execution_count": 70, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "array([[2., 2., 2.],\n", + " [2., 2., 2.],\n", + " [2., 2., 2.]])" + ] + }, + "execution_count": 70, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "a = np.ones((3, 3)) # подать tuple\n", + "b = np.ones((3, 3)) # подать tuple\n", + "\n", + "\n", + "a + b" + ] + }, + { + "attachments": {}, + "cell_type": "markdown", + "metadata": {}, + "source": [ + "##### Умножение матриц\n", + "\n", + "- Можно применять только к матрицам, в которых количество столбцов одной матрицы совпадает с количеством строк второй.\n", + "\n", + "![](imgs/sem1/sem1_12.png)\n", + "\n", + "Произведение матриц встречается, когда совокупность векторов умножается на матрицу (например при подаче в нейронную сеть батча данных)" + ] + }, + { + "attachments": {}, + "cell_type": "markdown", + "metadata": {}, + "source": [ + "##### Свойства произведения матриц\n", + "- Ассоциативность: $A(BC) = (AB)C$\n", + "- Дистрибутивность: $A(B + C) = AB + АC$\n", + "- Отсутствие коммутативности: не всегда $AB = BA$\n", + "- Существование нейтрального элемента $E$ (Единичная матрица): $$\\begin{pmatrix}1\\ 0\\ ...\\ 0\\\\0\\ 1\\ ...\\ 0\\\\.........\\\\0\\ 0\\ ...\\ 1\\\\\\end{pmatrix}$$ $$AE = EA = A$$\n", + "- Для квадратных матриц: если $A$ вырождена, то $AB$ также вырождена" + ] + }, + { + "cell_type": "code", + "execution_count": 71, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "array([[1., 0., 0., 0., 0.],\n", + " [0., 1., 0., 0., 0.],\n", + " [0., 0., 1., 0., 0.],\n", + " [0., 0., 0., 1., 0.],\n", + " [0., 0., 0., 0., 1.]])" + ] + }, + "execution_count": 71, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "np.eye(5)" + ] + }, + { + "cell_type": "code", + "execution_count": 72, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "[[5. 5. 5. 5. 5.]\n", + " [5. 5. 5. 5. 5.]\n", + " [5. 5. 5. 5. 5.]\n", + " [5. 5. 5. 5. 5.]\n", + " [5. 5. 5. 5. 5.]] \n", + "\n", + "[[2. 1. 1. 1. 1.]\n", + " [1. 2. 1. 1. 1.]\n", + " [1. 1. 2. 1. 1.]\n", + " [1. 1. 1. 2. 1.]\n", + " [1. 1. 1. 1. 2.]]\n" + ] + } + ], + "source": [ + "a = 5*np.ones((5, 5))\n", + "b = np.eye(5) + 1\n", + "print(a, '\\n')\n", + "print(b)" + ] + }, + { + "cell_type": "code", + "execution_count": 73, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "[[10. 5. 5. 5. 5.]\n", + " [ 5. 10. 5. 5. 5.]\n", + " [ 5. 5. 10. 5. 5.]\n", + " [ 5. 5. 5. 10. 5.]\n", + " [ 5. 5. 5. 5. 10.]] \n", + "\n", + "[[30. 30. 30. 30. 30.]\n", + " [30. 30. 30. 30. 30.]\n", + " [30. 30. 30. 30. 30.]\n", + " [30. 30. 30. 30. 30.]\n", + " [30. 30. 30. 30. 30.]] \n", + "\n", + "[[30. 30. 30. 30. 30.]\n", + " [30. 30. 30. 30. 30.]\n", + " [30. 30. 30. 30. 30.]\n", + " [30. 30. 30. 30. 30.]\n", + " [30. 30. 30. 30. 30.]]\n" + ] + } + ], + "source": [ + "print(a * b, '\\n') # поэлементное умножение\n", + "print(a @ b, '\\n') # матричное умножение\n", + "print(a.dot(b)) " + ] + }, + { + "cell_type": "code", + "execution_count": 74, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "[[1. 0. 0.]\n", + " [0. 1. 0.]\n", + " [0. 0. 1.]]\n" + ] + } + ], + "source": [ + "c = np.eye(3)\n", + "print(c)" + ] + }, + { + "cell_type": "code", + "execution_count": 75, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "[['1' '' '' '']\n", + " ['' '2' '' '']\n", + " ['' '' '3' '']\n", + " ['' '' '' 'a']]\n" + ] + } + ], + "source": [ + "d = np.diag([1, 2, 3, 'a'])\n", + "print(d)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "***Задание 4:***\n", + "Создать квадратную матрицу размера 8, на главной диаг. арифметическая прогрессия с шагом 3 (начиная с 3), а на побочной -1, остальные элементы 0." + ] + }, + { + "cell_type": "code", + "execution_count": 76, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "[[ 3. 0. 0. 0. 0. 0. 0. -1.]\n", + " [ 0. 6. 0. 0. 0. 0. -1. 0.]\n", + " [ 0. 0. 9. 0. 0. -1. 0. 0.]\n", + " [ 0. 0. 0. 12. -1. 0. 0. 0.]\n", + " [ 0. 0. 0. -1. 15. 0. 0. 0.]\n", + " [ 0. 0. -1. 0. 0. 18. 0. 0.]\n", + " [ 0. -1. 0. 0. 0. 0. 21. 0.]\n", + " [-1. 0. 0. 0. 0. 0. 0. 24.]]\n" + ] + } + ], + "source": [ + "# решение\n", + "# print(-1*np.eye(8)[::-1][::-1])\n", + "a = -1*np.eye(8)[::-1] + np.diag(np.arange(3, 27, 3))\n", + "print(a)\n" + ] + }, + { + "attachments": {}, + "cell_type": "markdown", + "metadata": {}, + "source": [ + "##### Обратная матрица\n", + "**Определение**: Пусть $A$ - квадратная матрица. Если существует такая матрица $A^{-1}$, что $AA^{-1} = A^{-1}A = E$, то матрица $A^{-1}$ называется *обратной матрицей* к $A$. Матрица $A$ в таком случае называется *обратимой*.\n", + "\n", + "**Утверждение**: Пусть $A$ - квадратная матрица. Если строки (или столбцы) $А$ линейно независимы (т.е. $A$ невырождена), то обратная матрица существует и единствена.\n", + "\n", + "##### Обратная матрица при решении СЛУ\n", + "\n", + "Есть СЛУ: $$Ax = b$$\n", + "\n", + "Если существует $A^{-1}$, то у системы есть единственное решение: $$x = A^{-1}b$$" + ] + }, + { + "attachments": {}, + "cell_type": "markdown", + "metadata": {}, + "source": [ + "##### Транспонированная матрица\n", + "\n", + "Транспонирование - операция отражения матрицы относительно главной диагонали. Обозначается как $A^{\\top}$\n", + "\n", + "Вектор-столбец при транспонировании переходит в вектор-строку. Поэтому скалярное произведение можно записать так: $$ = x^{\\top}y$$" + ] + }, + { + "cell_type": "code", + "execution_count": 77, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "[[0 1 2]\n", + " [4 5 6]]\n", + "\n", + "[[0 4]\n", + " [1 5]\n", + " [2 6]]\n" + ] + } + ], + "source": [ + "a = np.array([[0, 1, 2], [4, 5, 6]])\n", + "b = a.transpose()\n", + "print(a)\n", + "print()\n", + "print(b)" + ] + }, + { + "attachments": {}, + "cell_type": "markdown", + "metadata": {}, + "source": [ + "##### Определитель матрицы\n", + "\n", + "Определитель квадратной матрицы - это ее числовая характеристика.\n", + "\n", + "- $\\mid a\\mid = a$\n", + "- $\\mid\\begin{pmatrix}a\\ b\\\\c\\ d \\end{pmatrix}\\mid = ad - bc$\n", + "\n", + "![](imgs/sem1/sem1_13.png)" + ] + }, + { + "attachments": {}, + "cell_type": "markdown", + "metadata": {}, + "source": [ + "##### Свойства определителя\n", + "- $\\mid AB \\mid = \\mid A \\mid \\mid B \\mid$ \n", + "- $\\mid A \\mid = 0$ тогда и только тогда, когда $A$ вырожденная" + ] + }, + { + "attachments": {}, + "cell_type": "markdown", + "metadata": {}, + "source": [ + "##### Вычисление обратной матрицы с помощью определителей\n", + "\n", + "![](imgs/sem1/sem1_14.png)" + ] + }, + { + "cell_type": "code", + "execution_count": 78, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "[[2 1]\n", + " [2 3]]\n" + ] + } + ], + "source": [ + "a = np.array([[2, 1], [2, 3]])\n", + "print(a)" + ] + }, + { + "cell_type": "code", + "execution_count": 79, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "4.0" + ] + }, + "execution_count": 79, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "np.linalg.det(a)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "***Нахождениия обратной.***" + ] + }, + { + "cell_type": "code", + "execution_count": 80, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "[[ 0.75 -0.25]\n", + " [-0.5 0.5 ]]\n" + ] + } + ], + "source": [ + "b = np.linalg.inv(a)\n", + "print(b)" + ] + }, + { + "cell_type": "code", + "execution_count": 81, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "[[1. 0.]\n", + " [0. 1.]]\n", + "[[1. 0.]\n", + " [0. 1.]]\n" + ] + } + ], + "source": [ + "print(a.dot(b))\n", + "print(b.dot(a))" + ] + }, + { + "cell_type": "code", + "execution_count": 82, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "[[2 1]\n", + " [6 3]]\n", + "0.0\n" + ] + } + ], + "source": [ + "c = np.array([[2, 1], [6, 3]])\n", + "print(c)\n", + "print(np.linalg.det(c))" + ] + }, + { + "cell_type": "code", + "execution_count": 83, + "metadata": {}, + "outputs": [ + { + "ename": "LinAlgError", + "evalue": "Singular matrix", + "output_type": "error", + "traceback": [ + "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", + "\u001b[0;31mLinAlgError\u001b[0m Traceback (most recent call last)", + "Cell \u001b[0;32mIn[83], line 1\u001b[0m\n\u001b[0;32m----> 1\u001b[0m np\u001b[39m.\u001b[39;49mlinalg\u001b[39m.\u001b[39;49minv(c)\n", + "File \u001b[0;32m<__array_function__ internals>:200\u001b[0m, in \u001b[0;36minv\u001b[0;34m(*args, **kwargs)\u001b[0m\n", + "File \u001b[0;32m~/miniconda3/envs/py38/lib/python3.8/site-packages/numpy/linalg/linalg.py:538\u001b[0m, in \u001b[0;36minv\u001b[0;34m(a)\u001b[0m\n\u001b[1;32m 536\u001b[0m signature \u001b[39m=\u001b[39m \u001b[39m'\u001b[39m\u001b[39mD->D\u001b[39m\u001b[39m'\u001b[39m \u001b[39mif\u001b[39;00m isComplexType(t) \u001b[39melse\u001b[39;00m \u001b[39m'\u001b[39m\u001b[39md->d\u001b[39m\u001b[39m'\u001b[39m\n\u001b[1;32m 537\u001b[0m extobj \u001b[39m=\u001b[39m get_linalg_error_extobj(_raise_linalgerror_singular)\n\u001b[0;32m--> 538\u001b[0m ainv \u001b[39m=\u001b[39m _umath_linalg\u001b[39m.\u001b[39;49minv(a, signature\u001b[39m=\u001b[39;49msignature, extobj\u001b[39m=\u001b[39;49mextobj)\n\u001b[1;32m 539\u001b[0m \u001b[39mreturn\u001b[39;00m wrap(ainv\u001b[39m.\u001b[39mastype(result_t, copy\u001b[39m=\u001b[39m\u001b[39mFalse\u001b[39;00m))\n", + "File \u001b[0;32m~/miniconda3/envs/py38/lib/python3.8/site-packages/numpy/linalg/linalg.py:89\u001b[0m, in \u001b[0;36m_raise_linalgerror_singular\u001b[0;34m(err, flag)\u001b[0m\n\u001b[1;32m 88\u001b[0m \u001b[39mdef\u001b[39;00m \u001b[39m_raise_linalgerror_singular\u001b[39m(err, flag):\n\u001b[0;32m---> 89\u001b[0m \u001b[39mraise\u001b[39;00m LinAlgError(\u001b[39m\"\u001b[39m\u001b[39mSingular matrix\u001b[39m\u001b[39m\"\u001b[39m)\n", + "\u001b[0;31mLinAlgError\u001b[0m: Singular matrix" + ] + } + ], + "source": [ + "np.linalg.inv(c)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "***Решение НЛУ.***\n", + "$$ A \\cdot x = v $$" + ] + }, + { + "cell_type": "code", + "execution_count": 84, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "[ 6.25 -7.5 ]\n", + "[ 6.25 -7.5 ]\n" + ] + } + ], + "source": [ + "v = np.array([5, -10])\n", + "print(np.linalg.solve(a, v))\n", + "print(b.dot(v))" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "***Найдем собственные вектора матрицы A.***\n", + "$$ A \\cdot x = \\lambda \\cdot x $$" + ] + }, + { + "cell_type": "code", + "execution_count": 85, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "[1. 4.]\n", + "[[-0.70710678 -0.4472136 ]\n", + " [ 0.70710678 -0.89442719]]\n" + ] + } + ], + "source": [ + "l, u = np.linalg.eig(a)\n", + "print(l)\n", + "print(u)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "***Собственные значения матриц A и A.T совпадают.***" + ] + }, + { + "cell_type": "code", + "execution_count": 86, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "[1. 4.]\n", + "[[-0.89442719 -0.70710678]\n", + " [ 0.4472136 -0.70710678]]\n" + ] + } + ], + "source": [ + "l, u = np.linalg.eig(a.T)\n", + "print(l)\n", + "print(u)" + ] + }, + { + "cell_type": "code", + "execution_count": 87, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "[1. 1. 1.]\n", + "[[1. 0. 0.]\n", + " [0. 1. 0.]\n", + " [0. 0. 1.]]\n" + ] + } + ], + "source": [ + "l, u = np.linalg.eig(np.eye(3))\n", + "print(l)\n", + "print(u)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Еще чутка numpy" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "***Маски.***" + ] + }, + { + "cell_type": "code", + "execution_count": 88, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "[ 0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19]\n", + "[ True False False True False False True False False True False False\n", + " True False False True False False True False]\n", + "[ 0 3 6 9 12 15 18]\n" + ] + } + ], + "source": [ + "a = np.arange(20)\n", + "print(a)\n", + "print(a % 3 == 0)\n", + "print(a[a % 3 == 0])" + ] + }, + { + "cell_type": "code", + "execution_count": 89, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "[[10 0 0 0 0 0 0 0 0 0]\n", + " [ 0 11 0 0 0 0 0 0 0 0]\n", + " [ 0 0 12 0 0 0 0 0 0 0]\n", + " [ 0 0 0 13 0 0 0 0 0 0]\n", + " [ 0 0 0 0 14 0 0 0 0 0]\n", + " [ 0 0 0 0 0 15 0 0 0 0]\n", + " [ 0 0 0 0 0 0 16 0 0 0]\n", + " [ 0 0 0 0 0 0 0 17 0 0]\n", + " [ 0 0 0 0 0 0 0 0 18 0]\n", + " [ 0 0 0 0 0 0 0 0 0 19]]\n", + "145\n" + ] + } + ], + "source": [ + "b = np.diag(a[a >= 10])\n", + "print(b)\n", + "print(np.trace(b))" + ] + }, + { + "attachments": {}, + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Шлифанем тестами на производительность" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "***Производительность.***" + ] + }, + { + "attachments": {}, + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Без Numpy:" + ] + }, + { + "cell_type": "code", + "execution_count": 90, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "49999995000000\n", + "CPU times: user 469 ms, sys: 0 ns, total: 469 ms\n", + "Wall time: 503 ms\n" + ] + } + ], + "source": [ + "%%time\n", + "def summ(a):\n", + " ans = 0\n", + " for i in a:\n", + " ans += i\n", + " return ans\n", + "\n", + "arr = range(10**7)\n", + "\n", + "print(summ(arr))" + ] + }, + { + "attachments": {}, + "cell_type": "markdown", + "metadata": {}, + "source": [ + "C Numpy:" + ] + }, + { + "cell_type": "code", + "execution_count": 91, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "49999995000000\n", + "CPU times: user 15.6 ms, sys: 109 ms, total: 125 ms\n", + "Wall time: 108 ms\n" + ] + } + ], + "source": [ + "%%time\n", + "\n", + "sum_value = np.sum(np.arange(10**7))\n", + "print(sum_value)" + ] + }, + { + "attachments": {}, + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Без Numpy:" + ] + }, + { + "cell_type": "code", + "execution_count": 92, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "CPU times: user 1.12 s, sys: 578 ms, total: 1.7 s\n", + "Wall time: 1.69 s\n" + ] + } + ], + "source": [ + "%%time\n", + "arr = []\n", + "n = 10**7\n", + "for i in range(n):\n", + " arr.append(i*5)" + ] + }, + { + "attachments": {}, + "cell_type": "markdown", + "metadata": {}, + "source": [ + "С Numpy:" + ] + }, + { + "cell_type": "code", + "execution_count": 93, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "CPU times: user 93.8 ms, sys: 109 ms, total: 203 ms\n", + "Wall time: 216 ms\n" + ] + } + ], + "source": [ + "%%time\n", + "arr = 5*np.arange(10**7)" + ] + }, + { + "attachments": {}, + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Лабораторная 1\n", + "\n", + "Решить 100 numpy задач, дедлайн `01.04.2023`\n", + "\n", + "https://github.com/rougier/numpy-100/blob/master/100_Numpy_exercises.ipynb" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "py38", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.8.16" + }, + "orig_nbformat": 4, + "vscode": { + "interpreter": { + "hash": "c0330b53543e07ef1d56d28db427de4965ae0a62dafba4360eb741e652c9c2e1" + } + } + }, + "nbformat": 4, + "nbformat_minor": 2 +} From bdf980a863e21326daf7d6fd6b09a6ff5b3f2b63 Mon Sep 17 00:00:00 2001 From: PitKoro Date: Sat, 18 Mar 2023 18:49:00 +0300 Subject: [PATCH 2/6] =?UTF-8?q?=D0=BE=D0=BF=D0=B5=D1=87=D0=B0=D1=82=D0=BA?= =?UTF-8?q?=D0=B0?= MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit --- ml_system_design/seminars/sem1.ipynb | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/ml_system_design/seminars/sem1.ipynb b/ml_system_design/seminars/sem1.ipynb index f06c582..e0cb0e5 100644 --- a/ml_system_design/seminars/sem1.ipynb +++ b/ml_system_design/seminars/sem1.ipynb @@ -3073,7 +3073,7 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "## Шлифанем тестами на производительность" + "## Шлифонем тестами на производительность" ] }, { From 49493c467d585c3705bfc784e02fc22ac850ab54 Mon Sep 17 00:00:00 2001 From: PitKoro Date: Sat, 1 Apr 2023 20:09:38 +0300 Subject: [PATCH 3/6] sem3 --- ml_system_design/seminars/sem3.ipynb | 5332 ++++++++++++++++++++++++++ 1 file changed, 5332 insertions(+) create mode 100644 ml_system_design/seminars/sem3.ipynb diff --git a/ml_system_design/seminars/sem3.ipynb b/ml_system_design/seminars/sem3.ipynb new file mode 100644 index 0000000..09b280f --- /dev/null +++ b/ml_system_design/seminars/sem3.ipynb @@ -0,0 +1,5332 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": { + "id": "Faqf8pG8itot" + }, + "source": [ + "## Линейная модели, градиентный спуск и метрики" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "KS-4su3tladF" + }, + "source": [ + "**Линейная регрессия** — модель зависимости переменной от одной или нескольких других переменных (факторов, регрессоров, независимых переменных) с линейной функцией зависимости.\n", + "\n", + "Ниже на графике представлена линейная регрессия переменной $y$ от переменной $x$.\n", + "\n", + "Есть коэффициент наклона $a$ и есть коэффициент сдвига $b$.\n", + "\n", + "Эти значения могут изменяться как угодно." + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 283 + }, + "id": "monS6MN5k2I0", + "outputId": "059b12b9-1955-47cd-9422-26735ff519e1" + }, + "outputs": [ + { + "data": { + "image/png": "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", + "text/plain": [ + "

" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "import matplotlib.pyplot as plt\n", + "import numpy as np\n", + "\n", + "x = np.arange(-6, 6)\n", + "a = 2\n", + "b = 1\n", + "\n", + "y = a * x + b\n", + "plt.plot(x, y, label=f'y = {a}x + {b}')\n", + "plt.plot([0], [b], 'r*', markersize=10)\n", + "plt.ylabel('y');plt.xlabel('x')\n", + "plt.ylim(-10, 10);plt.xlim(-5, 5)\n", + "plt.grid()\n", + "plt.legend();" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": { + "id": "CrJiBTo4bN6h" + }, + "outputs": [], + "source": [ + "def draw_ax(a, b, x, ax, ylim=5):\n", + " y = a * x + b\n", + " ax.plot(x, y, label=f'y = {a}x + {b}')\n", + " ax.plot([0], [b], 'r*', markersize=10)\n", + " \n", + " ax.plot([0, 1], [b, b], 'y', linewidth=2)\n", + " ax.plot([1, 1], [b, b+a], 'y', linewidth=2)\n", + "\n", + " ax.set_ylabel('y'); ax.set_xlabel('x')\n", + " ax.set_ylim(-ylim, ylim); ax.set_xlim(-5, 5)\n", + " ax.grid()\n", + " ax.legend(prop={'size': 10})" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "XQw67GHzmD3h" + }, + "source": [ + "**Сдвиг**:\n", + "- Если у нас не будет сдвига (коэффициента $b$), то линяя будет проходить через точку (0, 0).\n", + "- Если коэффициент сдвига не равен 0, а к примеру, равен 2, то линяя будет проходить через точку (0, 2).\n", + "\n", + "**Коэффициент наклона**:\n", + "- Если у нас не будет коэффициента наклона, то линяя будет параллельна оси Ох.\n", + "- Если коэффициент наклона больше 0, то линяя идет на увеличение, при этом чем больше коэффициент, тем более наклон крутой.\n", + "- Если коэффициент наклона меньше 0, то линяя идет на уменьшение, при этом чем меньше коэффициент, тем более наклон крутой.\n", + "\n" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 606 + }, + "id": "lYWYDdbymzQ_", + "outputId": "00c3c0e7-d7a7-41d7-be62-0cb1aba977cd" + }, + "outputs": [ + { + "data": { + "image/png": "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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "import matplotlib.pyplot as plt\n", + "import numpy as np\n", + "\n", + "\n", + "fig, ax = plt.subplots(2, 3, figsize=(10, 6))\n", + "x = np.arange(-6, 6)\n", + "\n", + "# 1row, 1column\n", + "a, b = 0, 0\n", + "draw_ax(a, b, x, ax[0][0])\n", + "\n", + "# 1row, 2column\n", + "a, b = 1, 0\n", + "draw_ax(a, b, x, ax[0][1])\n", + "\n", + "# 1row, 3column\n", + "a, b = 0.5, 0\n", + "draw_ax(a, b, x, ax[0][2])\n", + "\n", + "\n", + "# 2row, 1column\n", + "a, b = 0, 2\n", + "draw_ax(a, b, x, ax[1][0])\n", + "\n", + "# 2row, 2column\n", + "a, b = -1, 2\n", + "draw_ax(a, b, x, ax[1][1])\n", + "\n", + "# 2row, 3column\n", + "a, b = -0.5, -2\n", + "draw_ax(a, b, x, ax[1][2])" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "usj8aWL8-84W" + }, + "source": [ + "С самим уравнением прямой разобрались, теперь давайте обучим линейную регрессию, ведь по факту она и есть прямая." + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "RmcjY5KeviKG" + }, + "source": [ + "## Получение данных" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "VbgNr2MC7c9H" + }, + "source": [ + "Возьмем и сами нагенирируем себе данные и обучим на них линейную модель." + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 1000 + }, + "id": "px8DWvILiEae", + "outputId": "8f12c244-f8f3-48f6-c732-d605cde16d88" + }, + "outputs": [ + { + "data": { + "text/plain": [ + "array([[ 0.63007982],\n", + " [-1.06163445],\n", + " [ 0.29634711],\n", + " [ 1.40277112],\n", + " [ 0.68968231],\n", + " [-0.53662936],\n", + " [-1.11947526],\n", + " [ 1.06755846],\n", + " [ 0.1178195 ],\n", + " [ 1.54907163],\n", + " [ 1.29561858],\n", + " [-0.03107509],\n", + " [ 0.56119218],\n", + " [ 0.42105072],\n", + " [-0.4864951 ],\n", + " [ 0.08897764],\n", + " [-0.18577532],\n", + " [-0.17809318],\n", + " [-0.23725045],\n", + " [-0.88623967],\n", + " [-0.47573349],\n", + " [ 0.21734821],\n", + " [-2.65331856],\n", + " [ 0.72575222],\n", + " [-0.38053642],\n", + " [-0.48456513],\n", + " [ 1.57463407],\n", + " [-1.30554851],\n", + " [-0.17241977],\n", + " [ 0.73683739],\n", + " [-1.23234621],\n", + " [ 0.31540267],\n", + " [ 1.74945474],\n", + " [ 0.09183837],\n", + " [-0.30957664],\n", + " [-1.18575527],\n", + " [-0.68344663],\n", + " [-0.31963136],\n", + " [-0.00828463],\n", + " [-0.64257539],\n", + " [ 1.0956297 ],\n", + " [ 0.06367166],\n", + " [-0.57395456],\n", + " [ 0.07349324],\n", + " [ 0.73227135],\n", + " [-1.06560298],\n", + " [-1.68411089],\n", + " [-1.54686257],\n", + " [-0.20437532],\n", + " [-0.286073 ]])" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "text/plain": [ + "array([ 43.6543408 , -72.68235021, 21.19644643, 107.58765071,\n", + " 69.62063217, -32.57566222, -101.61213107, 87.44514699,\n", + " 17.69898683, 131.00190463, 97.97802247, 2.70819092,\n", + " 52.42715419, 27.74476129, -31.82947365, 1.58209228,\n", + " -9.72570848, 4.57391214, -33.24586607, -74.34292886,\n", + " -22.6419015 , 15.84607909, -202.79645668, 49.05026172,\n", + " -34.9916168 , -33.95608308, 121.78273292, -123.72382672,\n", + " -1.90918067, 64.06753923, -91.73785524, 9.55252237,\n", + " 148.12427806, 22.21183346, -16.35144507, -113.95075954,\n", + " -47.70966758, -22.69082132, -1.79022499, -58.17761844,\n", + " 91.76970817, -12.7798199 , -38.1435921 , 17.48650737,\n", + " 40.52468632, -107.65815151, -134.20798669, -127.22516755,\n", + " -34.31360406, -10.90920383])" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "from sklearn.datasets import make_regression\n", + "\n", + "X, y = make_regression(n_samples=50, n_features=1, n_informative=1,\n", + " noise=10, random_state=11)\n", + "\n", + "display(X, y)" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 388 + }, + "id": "CsCmE9Clj68Z", + "outputId": "8f142659-863a-477a-fb80-05d8a7f9b393" + }, + "outputs": [ + { + "data": { + "image/png": "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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "import matplotlib.pyplot as plt\n", + "\n", + "\n", + "fig = plt.figure(figsize=(10, 6))\n", + "plt.scatter(X, y)\n", + "\n", + "plt.xlabel('feature')\n", + "plt.ylabel('target')\n", + "plt.show()" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "wuQyanTXsg4P" + }, + "source": [ + "## Одномерная линейная регрессия" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "2MnA-YZHBxzL" + }, + "source": [ + "#### Из sklearn" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "uJFYdPE1_TGm" + }, + "source": [ + "Возьмем модель `LinearRegression` из `sklearn` из модуля `linear_model`." + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "LsrXG6K5_aLV", + "outputId": "ffd3f68d-0274-4fdf-f013-b3838092ff39" + }, + "outputs": [ + { + "data": { + "text/html": [ + "
LinearRegression()
In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook.
On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.
" + ], + "text/plain": [ + "LinearRegression()" + ] + }, + "execution_count": 6, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "from sklearn.linear_model import LinearRegression\n", + "\n", + "model = LinearRegression()\n", + "model" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "s5qE3TNG_h0_" + }, + "source": [ + "И передадим в неё в метод `fit` данные, которые получили выше." + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "h8weuRBk_mHJ", + "outputId": "8017dcfd-345a-48ee-a1e4-a9a1a295d86d" + }, + "outputs": [ + { + "data": { + "text/html": [ + "
LinearRegression()
In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook.
On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.
" + ], + "text/plain": [ + "LinearRegression()" + ] + }, + "execution_count": 7, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "model.fit(X, y)" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "rS1iXWQG_nz0" + }, + "source": [ + "Всё, модель обучилась, это происходит очень быстро. Обучение линейной модели заключается в поиске коэффициентов, конкретно в нашей задаче - это коэффициент сдвига и наклона." + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "Yp23Hf8h_5ji" + }, + "source": [ + "Можем эти коэффициента отобразить, если возьмем у модели атрибут `coef_` и `intercept_`." + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "8RoiC6eN_-8Z", + "outputId": "3885a9b0-3b4e-4172-da10-bad49e8772c5" + }, + "outputs": [ + { + "data": { + "text/plain": [ + "(array([80.41862354]), 0.18171887542100862)" + ] + }, + "execution_count": 8, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "model.coef_, model.intercept_" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "pFDck5gNBHG6" + }, + "source": [ + "Вот и получили два коэффициента, осталось их подставить в уравнение прямой и будет готовая линейная модель." + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "metadata": { + "id": "UFJn6GsFBaD7" + }, + "outputs": [], + "source": [ + "model_a = model.coef_[0]\n", + "model_b = model.intercept_" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "NzfeXeeJHkMe" + }, + "source": [ + "Данная прямая наилучшим образом прошла вдоль точек из обучающей выборки." + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 388 + }, + "id": "RuEUmXzTBPDE", + "outputId": "9b8ca55c-cfae-474e-f08a-c07166020dc5" + }, + "outputs": [ + { + "data": { + "image/png": "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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "fig = plt.figure(figsize=(10, 6))\n", + "\n", + "x = np.arange(-3, 3)\n", + "model_y_sk = model_a * x + model_b\n", + "\n", + "plt.plot(x, model_y_sk, linewidth=2, c='r', label=f'linear_model = {model_a:.2f}x + {model_b:.2f}')\n", + "plt.scatter(X, y) \n", + "plt.plot([0, 1], [model_b, model_b], 'y', linewidth=3)\n", + "plt.plot([1, 1], [model_b, model_b+model_a], 'y', linewidth=3)\n", + "plt.grid()\n", + "plt.xlabel('feature')\n", + "plt.ylabel('target')\n", + "plt.legend(prop={'size': 16})\n", + "plt.show()" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "0bPamEKfa5rK" + }, + "source": [ + "Чтобы теперь сделать предсказания этой моделью достаточно вызвать метод `predict` и передать в него данные." + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "86a7D1DLbIzm", + "outputId": "225a5377-a197-42eb-8290-b8268c33d056" + }, + "outputs": [ + { + "data": { + "text/plain": [ + "(array([[0.63007982]]), array([50.85187092]))" + ] + }, + "execution_count": 11, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "pred = model.predict(X[:1])\n", + "\n", + "X[:1], pred" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "WPgV0jDvbTci" + }, + "source": [ + "Или же можем можем сделать точно такое же предсказание, если возьмем коэффициент наклона и умножим на значение признака и прибавим к этому коэффициент сдвига. " + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "ssT9MavubmJZ", + "outputId": "c2a0116a-752d-4dc9-8318-c0ba4b87213d" + }, + "outputs": [ + { + "data": { + "text/plain": [ + "array([[50.85187092]])" + ] + }, + "execution_count": 12, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "model_a * X[:1] + model_b" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "LyRlTyGBICT6" + }, + "source": [ + "А что значит это \"наилучшим образом вдоль точек из обучающей выборки\"? Как подсчитался этот наилучший образ?" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "3Fon3qOWIS7P" + }, + "source": [ + "Чем построенная линия ниже, хуже первой?" + ] + }, + { + "cell_type": "code", + "execution_count": 13, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 388 + }, + "id": "8mGFavM-IXUB", + "outputId": "e92cd78b-486c-468f-a127-421c2b2208fe" + }, + "outputs": [ + { + "data": { + "image/png": "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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "fig = plt.figure(figsize=(10, 6))\n", + "\n", + "x = np.arange(-3, 3)\n", + "a, b = 50, 0\n", + "model_y = a * x + b\n", + "\n", + "plt.plot(x, model_y, linewidth=2, c='r', label=f'linear_model = {a:.2f}x + {b:.2f}')\n", + "plt.scatter(X, y) \n", + "plt.plot([0, 1], [b, b], 'y', linewidth=3)\n", + "plt.plot([1, 1], [b, b+a], 'y', linewidth=3)\n", + "plt.grid()\n", + "plt.xlabel('feature')\n", + "plt.ylabel('target')\n", + "plt.legend(prop={'size': 16})\n", + "plt.show()" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "OKsxKEysIb8w" + }, + "source": [ + "А визуально она допускает больше отклонений от синих точек, чем первая. Давайте сравним не визуально, а с помощью цифр." + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "PK43OAJOJ9nh" + }, + "source": [ + "Для начала составим все данные в одну таблицу:\n", + "- `X` - это точки, на которых строим модель\n", + "- `y` - это настоящая целевая переменная, которую хотим предсказать\n", + "- `pred_model_good` - это значения на линии по координатам `X` первой модели, имеем предсказания модель `LinearRegression`\n", + "- и `pred_bad_model` - это значения на линии по координатам `X` второй модели, которая создана вручную, а не силами `sklearn`" + ] + }, + { + "cell_type": "code", + "execution_count": 14, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 206 + }, + "id": "596LHapcI8Ra", + "outputId": "11c37a82-ca4f-4246-add1-fdc308197537" + }, + "outputs": [ + { + "data": { + "text/html": [ + "
\n", + "\n", + "\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "
Xypred_good_modelpred_bad_model
00.63008043.65434150.85187131.503991
1-1.061634-72.682350-85.193462-53.081722
20.29634721.19644624.01354514.817355
31.402771107.587651112.99064170.138556
40.68968269.62063255.64502134.484116
\n", + "
" + ], + "text/plain": [ + " X y pred_good_model pred_bad_model\n", + "0 0.630080 43.654341 50.851871 31.503991\n", + "1 -1.061634 -72.682350 -85.193462 -53.081722\n", + "2 0.296347 21.196446 24.013545 14.817355\n", + "3 1.402771 107.587651 112.990641 70.138556\n", + "4 0.689682 69.620632 55.645021 34.484116" + ] + }, + "execution_count": 14, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "import pandas as pd\n", + "\n", + "df = pd.DataFrame({\n", + " 'X': X[:,0],\n", + " 'y': y,\n", + " 'pred_good_model': model_a * X[:,0] + model_b,\n", + " 'pred_bad_model': a * X[:,0] + b\n", + "})\n", + "\n", + "\n", + "df.head()" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "Mw9kAfmtK4Pw" + }, + "source": [ + "Посчитаем отклонения предсказаний от истины для каждой модели.\n", + "\n", + "И здесь на первых 5 объектах тоже видим, что на `sklearn` модели более маленькие отклонения, нежели на второй модели." + ] + }, + { + "cell_type": "code", + "execution_count": 15, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 206 + }, + "id": "YeSOjxBjK8I7", + "outputId": "1bfceed7-8cb4-4c42-abe3-ed9fc728c3ef" + }, + "outputs": [ + { + "data": { + "text/html": [ + "
\n", + "\n", + "\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "
Xypred_good_modelpred_bad_modelresidual_goodresidual_bad
00.63008043.65434150.85187131.5039917.197530-12.150350
1-1.061634-72.682350-85.193462-53.081722-12.51111219.600628
20.29634721.19644624.01354514.8173552.817099-6.379091
31.402771107.587651112.99064170.1385565.402991-37.449095
40.68968269.62063255.64502134.484116-13.975611-35.136517
\n", + "
" + ], + "text/plain": [ + " X y pred_good_model pred_bad_model residual_good \\\n", + "0 0.630080 43.654341 50.851871 31.503991 7.197530 \n", + "1 -1.061634 -72.682350 -85.193462 -53.081722 -12.511112 \n", + "2 0.296347 21.196446 24.013545 14.817355 2.817099 \n", + "3 1.402771 107.587651 112.990641 70.138556 5.402991 \n", + "4 0.689682 69.620632 55.645021 34.484116 -13.975611 \n", + "\n", + " residual_bad \n", + "0 -12.150350 \n", + "1 19.600628 \n", + "2 -6.379091 \n", + "3 -37.449095 \n", + "4 -35.136517 " + ] + }, + "execution_count": 15, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "df['residual_good'] = df['pred_good_model'] - df['y']\n", + "df['residual_bad'] = df['pred_bad_model'] - df['y']\n", + "df.head()" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "-l8fSDyULRLR" + }, + "source": [ + "Давайте теперь на всех объектах посчитаем метрику, которая будет позволять оценивать качество построенных линий.\n", + "\n", + "Возьмем MSE - mean squared error." + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "KB2SivGPL0pq" + }, + "source": [ + "MSE на sklearn модели равняется." + ] + }, + { + "cell_type": "code", + "execution_count": 16, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "HAZLA4slLz-b", + "outputId": "58806d83-9bf8-4cc7-bcae-f810e8120a39" + }, + "outputs": [ + { + "data": { + "text/plain": [ + "111.93097544862607" + ] + }, + "execution_count": 16, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "np.mean(df['residual_good'] ** 2)" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "o6TH3Ym2MGtv" + }, + "source": [ + "А MSE на второй модели равняется:" + ] + }, + { + "cell_type": "code", + "execution_count": 17, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "EabHNLaMMGt8", + "outputId": "65204e86-74a5-4df4-9e17-08db98f565bd" + }, + "outputs": [ + { + "data": { + "text/plain": [ + "873.1554374932329" + ] + }, + "execution_count": 17, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "np.mean(df['residual_bad'] ** 2)" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "VL93OuOCMKlR" + }, + "source": [ + "В разы больше, чем на первой модели." + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "hikZUrXxsqE4" + }, + "source": [ + "### Как обучается линейная регрессия" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "4CivR_6rCVN1" + }, + "source": [ + "*Как же модель из `sklearn` умудрилась построить такие качественные предсказания?* Ведь у неё была куча вариантов построения, можно менять коэффициенты наклона и сдвига как угодно.\n", + "\n", + "Ответ на вопрос - **методы оптимизации**." + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "xvDvoalkMmkW" + }, + "source": [ + "Ведь ошибка MSE тоже своего рода функция, которая меняется от коэффициента сдвига и наклона.\n", + "\n", + "Можем взять по 100 разных значений коэффициентов сдвига и наклона и посчитать в них MSE и отобразить на трехмерном графике.\n", + "\n", + "Так же отобразим и коэффициента, подобранные моделью из sklearn и наши коэффициенты." + ] + }, + { + "cell_type": "code", + "execution_count": 18, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 575 + }, + "id": "MYSGND_5NNii", + "outputId": "3d169bda-23db-4cc9-d98c-f4a990193aa9" + }, + "outputs": [ + { + "data": { + "image/png": "iVBORw0KGgoAAAANSUhEUgAAA7AAAAOwCAYAAAAHvZjCAAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjcuMSwgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy/bCgiHAAAACXBIWXMAAA9hAAAPYQGoP6dpAAEAAElEQVR4nOz9WWwkaXof/P5jj8idO1lVrKW36p7u0fQymu6u0mgsaz7Lsj8f+9jAgWABNizBupFgGAbsC8MSYMEXhmAYsgwc+MLwsXWOBAufJY02f5JGy6hnepvpmSarilXFIlksFovFncklM/aI91xERjCXiCRZxcyMJJ8fMJC6mMwMJpOZ8Y/nfZ+HY4wxEEIIIYQQQgghKcf3+gAIIYQQQgghhJDjoABLCCGEEEIIIaQvUIAlhBBCCCGEENIXKMASQgghhBBCCOkLFGAJIYQQQgghhPQFCrCEEEIIIYQQQvoCBVhCCCGEEEIIIX2BAiwhhBBCCCGEkL5AAZYQQgghhBBCSF+gAEsIIYQQQgghpC9QgCWEEEIIIYQQ0hcowBJCCCGEEEII6QsUYAkhhBBCCCGE9AUKsIQQQgghhBBC+gIFWEIIIYQQQgghfYECLCGEEEIIIYSQvkABlhBCCCGEEEJIX6AASwghhBBCCCGkL1CAJYQQQgghhBDSFyjAEkIIIYQQQgjpCxRgCSGEEEIIIYT0BQqwhBBCCCGEEEL6AgVYQgghhBBCCCF9gQIsIYQQQgghhJC+QAGWEEIIIYQQQkhfoABLCCGEEEIIIaQvUIAlhBBCCCGEENIXKMASQgghhBBCCOkLFGAJIYQQQgghhPQFCrCEEEIIIYQQQvoCBVhCCCGEEEIIIX2BAiwhhBBCCCGEkL5AAZYQQgghhBBCSF+gAEsIIYQQQgghpC9QgCWEEEIIIYQQ0hcowBJCCCGEEEII6QsUYAkhhBBCCCGE9AUKsIQQQgghhBBC+gIFWEIIIYQQQgghfYECLCGEEEIIIYSQvkABlhBCCCGEEEJIX6AASwghhBBCCCGkL1CAJYQQQgghhBDSFyjAEkIIIYQQQgjpCxRgCSGEEEIIIYT0BQqwhBBCCCGEEEL6AgVYQgghhBBCCCF9gQIsIYQQQgghhJC+QAGWEEIIIYQQQkhfoABLCCGEEEIIIaQvUIAlhBBCCCGEENIXKMASQgghhBBCCOkLFGAJIYQQQgghhPQFCrCEEEIIIYQQQvoCBVhCCCGEEEIIIX2BAiwhhBBCCCGEkL5AAZYQQgghhBBCSF+gAEsIIYQQQgghpC9QgCWEEEIIIYQQ0hcowBJCCCGEEEII6QsUYAkhhBBCCCGE9AUKsIQQQgghhBBC+gIFWEIIIYQQQgghfYECLCGEEEIIIYSQvkABlhBCCCGEEEJIX6AASwghhBBCCCGkL1CAJYQQQgghhBDSFyjAEkIIIYQQQgjpCxRgCSGEEEIIIYT0BQqwhBBCCCGEEEL6AgVYQgghhBBCCCF9gQIsIYQQQgghhJC+QAGWEEIIIYQQQkhfoABLCCGEEEIIIaQvUIAlhBBCCCGEENIXKMASQgghhBBCCOkLFGAJIYQQQgghhPQFCrCEEEIIIYQQQvoCBVhCCCGEEEIIIX2BAiwhhJwTvu+DMdbrwyCEEEIIeWZirw+AEEJIZzHG4HkeTNOE53mQJAmiKEIQBPA8D47jen2IhBBCCCHHQgGWEELOMMYYHMeB53nwfR+e58HzPAAAx3EQBAGiKFKgJYQQQkhf4BitJyOEkDPJ933Ytg3f98HzPBzHif5/xlj0P9/3AQA8z4PneQq0hBBCCEktCrCEEHLGhEuGHccBYww8H7Q7qA+wcd/T/D8KtIQQQghJGwqwhBByhtQvGQaCZcJh4Az/PS7Axt1PfXU2vC+e5yFJUrT0uP7+CSGEEEI6jfbAEkLIGRCGzbDq+rzBMvz+MOyGgdbzPLiuG329eQ8tBVpCCCGEdBIFWEII6XOMMbiuC9d1AaAjITIp0LquC8dxGgJtWKENlxwTQgghhJwWCrCEENLHwqprfSOmbqBASwghhJBeoABLCCF9qH7JcNiYqZdLd48KtI8ePcL4+DgKhUK03JgCLSGEEEJOigIsIYT0meZGTccNr90MuM2BdmtrC4ODg3AcB7ZtR1+v3z9LgZYQQgghR6EASwghfSSsuobdhE8SSnvZdL4+sIbHEv4sjuNEt6kPtGGXY0IIIYSQEAVYQgjpA3GzXfsp3HEc1xCgw/2xofpAG1ehre9yTAghhJDziwIsIYSk3LMuGe4nxwm0PM+3NIU6a88DIYQQQtqjAEsIISkWVl3T0KjpeTRXYI9z++ME2uY9tP36/BBCCCHkeCjAEkJICoUdfGdnZyFJEq5cuXKuw1l9oA2DsO/7sG0blmVRoCWEEELOCQqwhBCSMvXjcSzLAmOs74PYSSuwR90XAAq0hBBCyDlEAZYQQlIibrYrz/OnHvzOmnaB1rIs2LYNABRoCSGEkDOAAiwhhKRAUqOm06xc9lI3f476QCsIAhhj0f+aA23YEEoUxb7eY0wIIYScFxRgCSGkx46a7XoWAmwvhRcCALQEWtM0o9uEgTas0FKgJYQQQtKHAiwhhPRIONvVdd3ELsM8z0dV2X6WpkrycQNtWJmlQEsIIYSkBwVYQgjpgZPMdj0re2DTEmCbJQVa3/cp0BJCCCEpQwGWEEK67CSzXU+ziVMv9VPQaxdoLcuCaZpRgy0KtIQQQkh3UYAlhJAuCWe7uq4LxtixA89ZCLBA//4c9YEWQBRoPc+D53kNY3vqm0I1fx8hhBBCnh8FWEII6QLf9+G67rGWDNc7zQpsGLx64SwFuTCY8jwPoDHQuq4bfb15yTEFWkIIIeT5UYAlhJAOqp/tyhh7phDTr5XLZmfl52iWFGhd14XjOImBNrw9IYQQQo6PAiwhhHRIc6OmZwmvtAe2/5wk0IZLjinQEkIIIcdDAZYQQjrgqNmuJ3FWAuxZ+DmexXEDbX11lgItIYQQEo8CLCGEnKLjzHY9ifMc/M6qpEDrOA5s2waAlg7HFGgJIYSQAAVYQgg5JSeZ7XpcZyXAnpWfoxPiAm1YwXccJ7pNfaANuxwTQggh5w0FWEIIOQW+78O27VOputaj4Hf+hPtjQxRoCSGEkEMUYAkh5DmES4bDLsOnGV6B0w2wvQw4FMSfXbtAe//+fZRKJYyMjLQ0haJASwgh5CyiAEsIIc+oE0uGm1HwI83qA61hGMjlcg17aMPlyM17aCnQEkIIOQsowBJCyAmdxmzX4zorAfas/BxpE77+wkAbPsfhknbLsijQEkIIOVMowBJCyAmE409c1wXwbLNdT+K0gp9lWbh9+zb29/cxMDAQ/U/TtFM4SpIW4WuRAi0hhJCzigIsIYQcU1h19X0fALoy1uQ0AuzW1hZu3bqFwcFBvPrqq9jf38fq6ipmZ2ehKEpDoFUU5ZSOvBFVYHujXaC1LKvt2B4KtIQQQtKIAiwhhByhfsnwaXcZPsrzBD/f9zE/P4+lpSW8+uqruHDhAhzHwfDwMADAdV3s7e2hXC5jeXkZd+/eRSaTicJsqVSCLMun9rNQgO29+kArCEI0g5Yx1hJow4ZQoih29TVPCCGEtEMBlhBC2uhGo6Z2nvWxDMPArVu34DgO3nvvPeTz+ahyHBJFEUNDQxgaGgIAOI6D3d1dlMtlPHr0CJVKBdlstiHQSpLU1Z+DdFb9EvjmQGuaZnSbMNCGFVoKtIQQQnqFAiwhhCTwPA+maUYn8L04Yec4riV4HmVjYwO3b9/G2NgYXnvttYYRLO1IkoSRkRGMjIwAAGzbjgLtwsICdF1HPp9vCLTHvW+AKrD9gAItIYSQtKMASwghTcLZruVyGZ9++im+/vWv9+zk/CSP6/s+Zmdn8eTJE7z++uu4cOHCcz22LMsYHR3F6OgogKARVLlcRrlcxuzsLCzLQqFQiAJtoVBIDLQUbvrTcQNtuNSYAi0hhJBOowBLCCF16pcMh/tPe3kiftwKrK7rmJqaAgDcuHED2Wz21I9FURSMj49jfHwcQLBMOQy0T58+heu6KBaLUaDN5/MNja6oAtv/kgJt2BTKNE3wPN/SFIoCLSGEkNNCAZYQQmo8z2to1BSeoPfScU76V1dXMTMzg4sXL+L69etd6Y4MAJqmQdM0XLhwAYwx6LoeBdrl5WX4vo9SqYSBgQE4jnOqDaFIOjSPkQoDred58DwvcWwPBVpCCCHPigIsIeTca57tGp5chxXYXlZh21VgPc/DvXv3sL6+ji9+8YsYGxvr8tEd4jgO2WwW2WwWly5dAmMMlUolCrQ7OzvY2dlBtVqNKrTZbJZCzClI03MY/t2EF1GSAm245Dj8v52ep0wIIeTsoABLCDnXmme71p9Ih/+31wE2TqVSwdTUFERRxI0bN6BpWpePrD2O45DP55HP53H58mXcv38fvu8jk8lga2sLCwsLEAShYQatpmkUYs6YpEDrui4cx4m+3ryHlgItIYSQJBRgCSHn0nFmu9afdPdK8xxYxhhWVlZw7949XLlyBS+99FLXlgw/j3AJ6dWrV3H16lX4vo/9/X2Uy2Wsr6/jwYMHkGW5IdCqqtrrw069Xi9xP6mTBNpwDm245JgQQggBKMASQs6h4852ra/A9kp9gHVdFzMzM9je3sZbb72F4eHhnh3X8+J5HqVSCaVSCdeuXYPnedjb20O5XMbKygru378PVVUbAi3toT17jhto66uzFGgJIeR8owBLCDlXwqqr53lHNpIJv3bSOaynKQywe3t7mJ6ehqZpuHnzJhRF6dkxPYvmSnIzQRAwODiIwcFBAEFYD2fQLi0tYWZmBtlstmEGrSRJ3Tp80iVJgdZxHNi2DQAtDaEo0BJCyPlCAZYQci6EjWRc101cMtwsDUuIgaBZ03e/+1288MILeOGFF7q6N9B2XMhS9z8qRFHE8PBwVGV2HCdqCPXw4UNUq1XkcrmGQCuK9JF21hwVaKlCSwgh5w992hNCzrzjLhlu1uslxI7jRM2PvvKVr0TVyW5Z297F9+8/wt+++eZz39dx59kmkSQJo6OjGB0dBQBYloVyuYzd3V3Mzc3BNE3k8/ko0BaLRQiC8NzHTdIlLtCGqyocx4luUx9owy7HhBBCzgYKsISQM615tutJTmR7GWDL5TKmp6eRyWTAcVzXw6vjevjjj29hr6KjopvIZdLVUElRFIyPj2N8fBwAYBhGtOT43r17sG0bxWIxCrSFQoGqcmdQ2PApVB9owwptONO5ucsxIYSQ/kQBlhByJtXPdmWMnTi8hp63cnhSjDEsLi5ifn4er7zyCkZGRvCd73yna48f+nD6Acr7VQDA7YVlvP/Fl5/r/o7aA/u8NE2DpmmYmJgAYwyGYURLjp88eQLP81AqlaJAm8vlKNCeQScJtPVdjinQEkJI/6AASwg5c3zfh+u6J14yHIfn+a5VYC3Lwq1bt6DrOt59910Ui0UYhhHt++vWSfaT9W1MPXgU/fedhSd49/UXnzvwdet55DgOmUwGmUwGFy9eBGMM1Wo1CrRLS0tgjDV0OM5msxRizqDjBtrmPbT0WiCEkPSiAEsIOTPqT07DwPe8J6KdrhyGtre3cevWLQwMDODGjRtRh91un0hbjos/+eQ2FFmCaQV7Ciu6iYWVDbw8Of7M99vLQMBxHHK5HHK5HCYnJ8EYw8HBAcrlMnZ2dvDw4cNorE8YaMOl2+RsqQ+04d+17/uwbRuWZVGgJYSQPkABlhByJjQ3ajqN8BreTycDrO/7WFhYwKNHj/Dqq6/i0qVLDcddvw+3GyfRH/zgHvarBvJZNQqwAHBr7vFzBVig992cQxzHoVAooFAo4MqVK/B9HwcHB9jZ2cHm5ibm5+chimJDhVbTtF4fdiIKV88mfN6SAq1hGHj06BFefPFFyLJMgZYQQlKCAiwhpO+dZLbrSXUywJqmienpadi2jffeew/5fD728YHuhL/Fpxu4s/AEAFA1LAgCB88LHnd5fRs7+xUMFnLPdN9pPuHneR7FYhHFYhFA0Phrf38f5XIZq6urmJ2dhaIoDYE2LXN403JR4CxoDrSe52FlZQUvvPACLMtqmEMb7p8VRfHU33MIIYS0RwGWENK3nmW260l1qonTxsYGbt++jdHRUbzzzjuJM0y7FWANy8Y3P70T/bfvM+SzGg6qRu3xgVtzy/hr77z2zI/RL2FLEIQoqAKA67rY29tDuVzG8vIy7t69i0wm0zCDVpblHh816ZRwrmy4F50xBtM0ASBacixJUlShpUBLCCGdRQGWENKXnnW260mddhMn3/fx4MEDLC8v4/XXX8eFCxfa3r5bAfbPvzeDqmE1/JvtOAAYgOAY7i4+wc0vvQJJPPl81X4+oRdFEUNDQxgaGgIQzOcNR/YsLi6iWq0il8s1BNqkCxKkf4R/c+Frt35bgiAIFGgJIaRH6BOWENJ3wj1qnaq61jvNJcS6rmN6ehq+7+PGjRvIZrPHenzg9AJs+PPUP2f3Flcw93it5baW7SKrKagadvTf95ee4osvTj7TY/dLBfYokiRhZGQEIyMjAADbtlEul7G7u4v5+Xnouo58Pt8QaOs74ZL+0Bxgmx030DbPoKVASwghz4cCLCGkb4RLhsMuw904ETytJcSrq6uYmZnBhQsXcP369WMHmtMOsM3h9UA38Zffv9vuABr+89bc42cKsGf5hF2WZYyNjWFsbAxAsLc5DLT379+HbdsoFApRoC0WizSDtk+c5HWbFGh9348CLc/zLV2OKdASQsjJUIAlhPSFbi0Zbva8FVjP83D//n2srq7ii1/8YhRyTvL4QGeql4wx/MX3ZmDZDsJlws10w4IkCnDc4Hnf2NnH6tYuJoZLJ3qsbo0jSgNVVTExMYGJiYmoGhfOoH369Clc10WxWIwCbT6fp0CbQs/7em0XaC3LgmmaFGgJIeQZUIAlhKRaJ2a7nsTz7IGtVCqYmpqCIAi4efPmc41i6UT4+8HsIzxcWUdGVaCbdsLjAqoiRQEWAKYeLJ04wJ5XHMdB0zRomoYLFy6AMQZd16NA+/jxYzDGGmbQ5nI5CjApcNqjq5rfu8JA63kePM9rmENb3+W42+95hBCSdhRgCSGpxRiD67pwXRfA6c12PYlnqRwyxrCysoJ79+7h8uXLePnll5+5whb+zKcdYLd2D/Dh9IPaY7S/rWk54LggzALA3PIavma+iox6/FEy56kC2w7Hcchms8hms7h06RIYY6hUKlGgXVxcBMdxDSN7MpkMBZge6fT++jCwAo2B1nXd6OvNe2gp0BJCzjsKsISQVAqrruH+014tsTzpHljXdXH37l1sbW3hzTffjBr9PO8xnGb48zwff/LJrWg5tm5akESxocpaz3E95DIKKroVff+dhSf4yusvntoxnVccxyGfzyOfz+Py5cvwfR8HBwcol8vY3NzE/Pw8RFFsqNBqmkYBpgu6fcElKdC6rgvHcRoCbVihDZccE0LIeUIBlhCSKuGS4adPn+Lp06f40pe+1NOT9ZOEx/39fUxNTUFVVdy4cQOqqnb9GI66HwD4+PYcNnb2on9nDNCalgk38/zGx781/xhffu3asU+eqQJ7PDzPo1gsolgs4urVq/B9P5pBu76+jgcPHkCW5SjM+r5PYbZDTnsJ8UlRoCWEkHgUYAkhqVHfqMl1XRiG0fOT8+PsgWWM4fHjx3jw4AGuXbuGF1988dT3zp1W+FvZLOOz+w9b/l23bASNnOIfxzBtKIoIywqWcx9UTSysbODlyfFTOS4Sj+f5KKwCQVOwMNCurKzg4OAAc3NzKJfL0e1kWe7xUZ8NvQ6wzY4KtABaGkJRoCWEnEUUYAkhqRAuGfY8LzoJS0PF7qjw6DgO7ty5g93dXbzzzjsYHBzs+jEcl2k7+JNPboGx1p7Drushl9FQ0c3E75fFwwALBCN1jhtgqQJ7OgRBwODgYPQ6+973voeBgQEwxrC0tISZmRlks9mGGbSSJPX4qPtXmgJss6RA6zgObNuOvk6BlhBy1lCAJYT0VNJs19Oav/q82gWv3d1dTE1NIZ/P4+bNmx2rfJ1W+Pvg83vYrxqJXz/q+a4aFgSBg+cFx/J4bRs7+xUMFnLHenwKsKcv3EMbjmeybRu7u7sol8tYWFiAruvI5/MNM2hFkT76j6PfXq9xgTa8MBhWaJsDbdjlmBBC+gl9ihFCeqbdbNc0Bdjm42CMYXFxEfPz83j55Zdx9erVjncrfd6T6XuLK5hZWAHPAX7CXemmBUWRGqqs9XyfIZ9VcVA9rNJOPVjCX//y60c+Pp0kd4csyxgdHcXo6CgAwLKsqMPx7OwsLMtqCbSCIPT4qNMpbUuITyrcHxuqD7RxFdr6LseEEJJmFGAJIT0RVl19328IrqHnmb96mpqPw7Zt3Lp1C9VqFV/5yldQKpU6fgzPG2D3qwb+7Lu34XkespqKg7bLhIXEAAsAtuMi2Ccb/L7uLT7FzR96BYp89DLVNPw+zxtFUTA+Po7x8WCpt2EYUYX23r17cBwHhUIhCrSFQoGWmNb0e4BtdpxAy/N8S1Oos/QcEELOBgqwhJCuap7tGhdegXRVYMPgtb29jVu3bmFgYAA3btzo6t7CZw1/jDH80Xc+h2kHSwhdL7nTMABUDRs8z8FPKNNatouspqJqBCN1REHAvUdP8eYrV9reL50Ep4OmadA0DRMTE2CMwTCMqEL75MkT+L6PYrEYBdp8Pk+/uzPquIG2eQ8tvR4IIb1GAZYQ0jXNs13DJWxx0lKBDYP03NwcHj16hOvXr2NycrKrJ3HP81x8emceTza2o/82TAeaKsO07Njb+76PfFZrWCaceFwCD9d1MT33+MgAC1AFNm04jkMmk0Emk8HFixfBGEO1Wo0C7dLSEgA0zKDNZrPnJsCctQrsUeoDbfi36vs+bNuGZVkUaAkhqUEBlhDScfVX9pOWDDfjeT4VFVjf9/HkyRMIgoD33nsP+Xy+J8fxLOFvdauMj249aPl34YglosEy4WRV04IsClBkGQe6gZ29Ch6vbeHy+HDi99BJbuec1nPLcRxyuRxyuRwmJyfBGMPBwQHK5TK2t7fx8OHDhrE+AwMD0DTtzP5uz1uArRf+3BRoCSFpRAGWENJR7Ro1tZOGsSubm5vY2NhAJpPBe++917Purc9SgbUdF3/0nc/hxVwE0E0LgsDD8+IvEFi2g6ymoGrEV2nBgIymYPdAj/7p89mltgEWoApsv+E4DoVCAYVCAVeuXIHv+9jf30e5XMb6+jrm5uYgimJLoD0rznOAbRYXaMP/WZYF2w7eKyjQEkK6gQIsIaRjmme7nuREppcVWN/38eDBAywvL0eNbXo9euSk4e9b37+L8kE19mu+z5DLqDhoM1KnHUHgYVpOw0WGxaeb2KvoKOYysd9DJ7H9j+d5lEollEolXLt2DZ7nYW9vD7u7u1hdXcXs7CwURWkItIqi9Pqwnwu9buPVb/8QBKEl0NZXaMOGUKIonvhzgBBC4lCAJYScunC2q+u6x14y3CwMR92ugui6junpafi+j/fffx/Ly8s9X8p80grs/UdPsbCy3vY2Vq2pUxLdtCBJIhyntemTpsio6CbymcOOxowx3Jpbxlffuh57f2moqJPTJQgCBgcHMTg4CABwXRd7e3sol8tYXl7G3bt3kclkGgJtNxufPS96vR5fu0BrmmZ0mzDQhhVaCrSEkGdBAZYQcqqedclws/B7uhlg19bWcOfOHVy4cAHXr1+Plr/1OsACxz+Z3qvo+Oant2DZDiRRhOM27WflADDAclxkVQW6aSU8HqDJUkuAzWVUVGqh1WnqaHzn4TLe++JLkESaK3oeiaKIoaEhDA0NAQAcx4lG9iwuLuLOnTvI5XJRmC2VSj1f2dAOLSF+dscNtGFllgItIeQk0vvJQQjpO0fNdj2JcBZlN6ognufh/v37WF1dxRtvvBHNzATSUTk87jH4vo8/+vBwZI6mSK0Btul+29EtGxwXhFkAkEQBRl33YtMKOhobph399/2lp/jii5PP/DOQk0nzcypJEkZGRjAyMgIgmKEcdjiem5uDaZrI5/NRoC0Wiw1jXdKAwtTpSAq0vu/jww8/xMsvvxz9/inQEkKOQgGWEPLc6me7MsZO5aQjDLC+73f0pLZSqWB6eho8z+PGjRvIZBr3cKZhnM9xw9/Ht+ewsrET/bdh2eDAgSH+e6uGCVkUYLvxs2Fd16tVXIMqrSyJ0fzXUHNH4+kHj2MDLCGyLGNsbAxjY2MAANM0o0B779492LaNQqHQEGj5Izpmd1Kv/+7PsvpA67putD/W931YlgXTNMHzfEtTKAq0hBCAAiwh5Dn5vg/XdZ97yXCz+iXEnbKysoK7d+/i8uXLePnll2NPltNQOTzOMTzZ2MEnt+ca/s1xvYZ9qs0YAEWWYbvJzZzcWqfifFaNnQ1bNSyIogC3FoI3y/t4srGDS6ODJ/4ZyPmiqiomJiYwMTEBxhgMw4iWHD99+hSu66JYLEaBNp/PdzXQ0hLi7ghX7NT/bsMKred58DwvsSlUu1nihJCziwIsIeSZ1M92DU/0TvNEor4Ce9pc18Xdu3exubmJN998M1riGCcNe2CPCn+GZeOPvvMD+DG3SRqVE9JNCxzPgfnx929aNgpZDRU9fqQOYwyaIuOgLgRPPVhqCbCEtMNxHDKZDDKZDC5cuADGGHRdjyq0jx8/BmMMpVIpCrS5XK6j4YUCbHfEPc/h50n9VpL65oDh15uXHFOgJeR8oABLCDmx+iXDADpy0tCpCuz+/j6mp6ehKApu3rwJVVXb3r4flhB/89Nb2E8YiaNbNlRZivbFNnN9H4U2VVqOA3iOh8+Sg3CwL7bWHQrAwpN17FcNFLKHM0GpAktOguM4ZLNZZLNZXLp0CYwxVCqVKNAuLi6C47iGDseZTOZU34cowHZHuO2knaRA67ouHMdJDLS9XIJOCOkcCrCEkBN5ntmuJ3Was2AZY1heXsbs7CyuXbuGF1988VjHnobg1e4Yph48wurWbtvvlyUxMcACgNOmSpvTNOzpBgRBiJaJNzvcKxuEYN9nuDX3GD/yZuNInV4/j2fVeQhZHMchn88jn8/j8uXL8H0fBwcHKJfL2NzcxPz8PERRbOhwrGnauXhu+p3v+880Zu24gTZcckyBlpCzgwIsIeRYTmO260mdVnh0HAd37tzB7u4u3n777WjMRzeP4XkkHcNmeR/f+mzmyJOyqmFC4PnE5cSGZSOjyA0dhgFAVWQc6BYAhkxWxkFClRcAvKYLDXcWnuDdNw5H6lCQIKeJ53kUi0UUi0VcvXoVvu9HM2hXV1cxOzsLWZYbKrRHrbZoRhXYzgvf1543WB4VaMPHqK/OUqAlpH9RgCWEHOm0Zrue1GlUYHd3dzE9PY1sNoubN29CluUTfX9aA6zjuviDb38fjusB8JDVVFQTZrp6PkMhq2K/khxABaHxRI7judr4nOBxTbtxpE4zw7Sh1YVgw7JbRur0+nkkZxfP81FQBYLRWGGgXVlZwb1796BpWkOgPeq9gAJs54Xv753YghIXaB3HgW0H71EUaAnpXxRgCSFt+b4P27a7VnWt9zzhkTGGR48eYX5+Hi+99BKuXr36TMee1iZOf/m9GWzvHjTcph3LcRGGUQYGDo23r+iN3YRzWmPXYcdpXCYcp3mkztTsUhRgKQiQbhIEAYODgxgcDJqJua4bdTheWlrCzMwMstlsw5JjSZJa7odet50Vvq91YzVPc6ANt8OEFVqO4xoCbdjlmBCSPhRgCSGxwiXDYZfhXszfe9YKrG3buHXrFqrVKn74h38YpVLpuY6h15XD5uf93uIKpueWGv7tqJmulu0goymJy4AZDrsJZzW1drvGxz3qd1E1G0Pw1u4BHq9t4/J4sGS7188jOb9EUcTw8DCGh4cBBO8RYaBdWFiAruvI5/MNM2jp9dp5p7WE+KTC/bH1xxEGWtu2o8AbBtr6LseEkN6jAEsIadGrJcPNnqUCu7Ozg+npaZRKJdy4cSO2qtLpYzht9VXg8n4V3/zkVsttGGNQlfYzXZurrs0M04YoCrAcB83hFQB004aqSDCt+IZQSSN1Lo8P0Ylfh/T6tdmvZFnG6OgoRkdHAQCWZUUdjmdnZ2FZFhRFAc/zKJfLKBQKDYGHnI5OLSE+qeMEWp7nW5pC9fq4CTmvKMASQiJhcD04OIhGUvTyA/okFVjGGBYWFrC4uIjr169jcnLyVI49LUuIgWBf3x9++/u1gNlKNy1w4MAQH2qqZlClDZYTt3I8D6V8FnsHeuKxiIIAILmjsWHZDaH/4coG9ip6Ki4EEJJEURSMj49jfHwcAGAYBubn57G/v4+ZmRm4rotCoYCBgQEMDg4in8/TfslT0K0lxCd13EDbvIc2bT8HIWcVBVhCCIDD2a57e3v49NNP8fWvf73nH8bHDT2maeLWrVswTRPvvvsuCoVC14+hk8IQ/Vc/uIe9SnK4dF0P+YyGAyNhnyoDFEVKDLD5jAo7IRyHqqYFUeDhJnQ0bh6pwxjD9NxjfOmFibb3S0iaaJoWhdQvfOELMAwjqtA+efIEvu+jWCxGS47z+XzP3y/7UThCJ+3PXX2gDT8Pwv4QlmVRoCWkyyjAEkKiK8tho6ZnmcvXCcepwG5ubuL27dsYHh7G22+/DVE83be1tOyBXVrbxueLGyhkNRjxzYYBAB5r/3wZlg2e41pqtIokQjds+MyvLROOD7nMZ8hkVey3GanjNs2LffB4Da9fGe3580jIs+A4DplMBplMBhcvXgRjDNVqNQq0S0vBfvRSqRQF2mw2m4r30LTrx07P4fFSoCWkdyjAEnKO1S+LCsOrKIrRyIFef9i2q376vo+5uTk8fvwYX/jCF3Dx4sWuH0O3VE0L355+gFyhhIphQhR5uG58UNUNKxhnY8dXUl3PR05TUKlLwRwAXuDh1yqzoiACiA+wAGAcMVLHtBxoqgzDDG7neh7uL60d62clJC2S3gM5jkMul0Mul8Pk5CQYYzg4OEC5XMb29jYWFhYgCELDyB5N03r+fppGYYPAftYu0FqW1XZsD70mCHk2FGAJOaeSGjWFH6hpCLBJFVhd1zE9PQ3f9/H+++8jl8t17Bh6vQfW83x8MD0Py3GRA+D7DDlNxX6bZk2SKCYGWABRl+BQPqs1dCeummbbZcLHG6kTvHbyGQ37uoHbD1fwhREl8faEpM1x3wM5jkOhUEChUMCVK1fg+z729/dRLpexvr6OBw8eQJblhpE9mqZ14SdIv7Ss9jlN9YFWEITogjBjrCXQhg2hRFHsWbNEQvoRBVhCzqGw6up5XsuHZng1PKzI9lJceFxbW8OdO3cwMTGBV199teOdQXtdgf3g87vY2qs0/JvVJpwCQMUwIfA8vObgzQV9iA3bQVZVoFs2MoqMit4YhpnPoGVkHLQJqC333aRqWMhqKvZr97F7oGNNbH/c5NnQSW/nPMtzy/M8SqUSSqUSrl27Bs/zsLe3h3K5jJWVFdy/fx+KojRUaBXlfF7cOQsV2KPUXxhuDrSmaUa3CQNtWKGlQEtIMgqwhJwjx5ntWh9ge61+/6nneZidncXTp0/xxhtvRN1Cu3kM3Ta/vIbP7j5EsMj38Bgsx21bAfV9H4Vcpu0+VUHgg5DrebFLgU3HabtM2DhipA7AQRQPj5vjgPmn24nHQ0janNbfvSAIGBwcxODgIABEzfLK5TKWl5dx9+5dZDKZhkD7vOO/+sVZrMAe5biBNqzMUqAlpBUFWELOiePOdk1TgA2rn9VqFVNTU+B5Hjdu3EAmk+n6MXTb3oGO//ujz2vHALT8Oo44JMuyazeKP+Gp6BbyGRUHenzIdRwXuYzWdplwu5E6QcC2Gp6/jb0qtnYPMFzKtz94QlKgU9soRFHE0NAQhoaGAACO42B3dxflchmLi4u4c+cOcrlcw5Lj025OlxZp2KrSa0mB1vd9CrSEJDib74iEkAZh1TVcFtzuQy/8ME1DgOV5HuVyGbOzs5icnMQrr7zS9eVmvQiwnufjD7/zfbhOuFeVA9D4+6iaJhRJTByJY4UBNGGkTi6jJGXbyFGvgappQRCE6KJISFODZckMwR7YICRzYAz4fHYJ/8e7b7R/YEJSoFvhSpIkjIyMYGRkBABg23bU4Xhubg6maSKfz0eBtlgsdnzrRLekYatK2rQLtJZlwTRN8Dzf0hSKAi05TyjAEnKGhbNdXTcIOcf9gEtDgHVdF/v7+7BtG1/60pcwOjrak+PoxXPxre/P4OlmGYWshv2qEbuUlzFAkZNnuga3iT9uRRKD8MnxaF6eXE83rWOM1JEbGkBxPAfPZ9E92m799zLcW3qKH3nzFWiKnHjchKRFLwKBLMsYGxvD2NgYgGDOdRho7927B9u2G2bQFgqFvg2BVIE9WvOc3DDQep4Hz/MSx/ZQoCVnGQVYQs6o+vE4QOuHYDu9nn16cHCAqakpeJ6HycnJnoVXoPvPxezSU/zg/iIAwHIcHC4Dbj0G3bTA8xx8PzmAKrIEyz4MkRzHQeB52K4HH0E34WqbZcKSKCYGWCCYK1t/fDmtcVmyVWsY5TjBUmPP83Brfhnvvv5i4n2S4+v1iKezLC3hSlVVTExMYGJiAowxGIYRBdonT57A87yGQJvP5/sm0J6HJk6nLfwsD5+3+kDrum709eYlxyc5ByAk7SjAEnLGxM12PemHVtL4mk5jjGF5eRmzs7O4evUqDMPo+VK5bi4h3tmv4E8+mor+27JdZDUV5YRmSq7nR1XaOIwBitQYYPMZFbsVPXpN+AmjckIVo/1IHdc9HKmT0ZTEPbXh8QDA9NxjfPnVaxAEOnEl6ZXGcMVxHDKZDDKZDC5evAjGGHRdjwLt48ePwRhDqVSKAm0ul0ttcDmPTZxOW1KgdV0XjuNQoCVnEgVYQs6Q4zZqOkovAqzjOJiZmUG5XMbbb7+NoaEhzMzM9LzC1K0A67gufv+vPmtZEhwMvkle5uu4ydVRAKjqJgSOg8cYsqqC/are8PVwlE5QSW3FfAYtK+Og2makjueDF3g4TfNlo2MwLajyYVfVqmFi9vEqvnDtYttjJ6SX0lKBbYfjOGSzWWSzWVy6dAmMMVQqlSjQLi4uRmN9wkCbyWRS83P1w3Pcb5ICra7r+Oyzz/D++++D53kIghDNoQ2XHBPSLyjAEnJGtJvtelLdDrB7e3uYmppCNpvFjRs3opmIadiLGwbYTp9o/dmnt7FZ3m/596phQhYFWFb89xmWg4ymQDfib+D5PgrZDHTLbtqPeuioSqhpHzFSx7JRzGnYazO2J+iiengHn88+ogBLUq/fwhXHccjn88jn87h8+TJ838fBwQHK5TI2NzcxPz8PURQbRvZomtaz46UmTp0XBlqO4+B5HkRRjK3Q1ldnKdCStKMAS0ifq9/78qxLhpt1KzgyxvDo0SPMzc3hpZdewrVr1xqOvdd7ccNjADpbKbgz/xh3FpZjv8YQNGs6qCY/D0EzpmS240BTJBwk7HWt6BZEUYCbUEE9aqRONqPAS9iHG9JNC3zd87dR3sfj9W1cHhtq+32E9Eqv33tOA8/zKBaLKBaLuHr1KjzPw/7+PsrlMlZXVzE7OwtFURoCbXgBsRuoAts99ecHcRVax3Fg28FKnOaGUBRoSdpQgCWkj53WkuFm3QiOtm3j9u3bODg4wA//8A9jYGCg5TZpqcACnTuZXd/exQc/uIt2M1tN2wVY8u+1YhiQRDFxObEkiYmNngCAgUFTZBy4yRVUL+H3IIoCLMuB5/ttjyHYjyuCsWCuLRBUYSnAPj8KAJ1xFsOVIAhRUAWCpmrhDNrl5WXcvXsXmUymYQatLHeuY3ga9xmfVUnV7rhAG67osm2bKrQklSjAEtKnTjLb9aQ6vYR4Z2cH09PTKJVKuHnzJiRJir0dz/NR99pe6WSANW0Hv/9Xn6FqWsioCnQzfh+q5/tQleS3a8YATZFiw6OmyKgaJjKq2vZYDLOxm3Dr1y1oigzDavx9KJKIam35siInB1gAsF2vtqc3eIzFp5so71cxUMi2PTZCeuEsBthmgiBgaGgIQ0PBhSTXdaNA++jRI1QqFWSz2YZAm/R+/SyoiVP3HHe5dtjwKVQfaMPP4+ZAK4oi/R5JV1GAJaTP1M92Da9en/YHR6cCLGMMCwsLWFxcxCuvvILLly+3PfY0VWBP+zgYY/i/v/MD7FaqANCwvLblGIDELsAh3bTBcxz8uqDN8zx8Bvgs2EurSCIM34/dzOp4R4/Uad4rm8+qDc2dDNMGz/HwY+bPclwQxHMZGZVa4GWM4QcPHuHHv/x625+NENIdoihieHgYw8PDAIKVMmGgXVhYgK7ryOfzUaAtFou1/e3P5jxcJEiLZ91v3C7QhhXauKZQ9HslnUQBlpA+4vs+XNc99SXDzToRHE3TxK1bt2CaJt59910UCoUjvydte2BP06d35jD/ZC3676phQpYk2E5MBZPjYNkOMqqcWKV1PQ/5jNqwzzWnqdivjbUJ99ImdRsGjh6pUzUsiCIP1/WhyCKqemPjKM/zkc9qbUfpNHcqvvtwBTe/+DJUpXPLFAl5FhSuAFmWMTo6Gs3itiwr6nA8OzsLy7JQKBSiQFsoFE40+oyaOHXPaT3XFGhJGlCAJaQP1H9AhCdVnfwwOO3guLm5idu3b2NoaAhvv/32sa/Yp6kCe5rPx6OnG/jO1P2Gf2MAVDk+wIaLbkW+/Ylh/T7VfFaLwmuoalgwTQOO7UDLZFpONHXLhqZIMK34ZduMMWQUGQee2VLtDdmJS76D59GwHOQyKnQzCL+u5+HW/DK+8vqLbX82QrqNAmwrRVEwPj6O8fFxAIBhGFGgffr0KVzXRbFYjAJtPp9vG5roOe6eTl0sqA+04edkXKBt3kNLv3fyPCjAEpJy9UuGAXQ8vAKnt4TY933Mzc3h8ePHeO2113Dx4sUTHXsaKrCnHWD3qzr+8Nvfj72/oFNvsOS36SAAxnCgmxAFAa4X3y1YN21kVAWe50cBMcR8H2sbG/BsC7bPsLm1BVmSoGkaVE2DqqrBSYYgAEjed6xbNnIZNXEurGW7wVgfM2HuD1qbDk3NPcY7r147cpwPadXrv4+zjMLV0TRNg6ZpuHDhQjRrNAy0y8vL8H2/YQZtLpdreE6piVP3hCP2Oin83cYFWtu2YVkWBVpyKijAEpJipznb9SROI8AahoHp6Wm4rov3338fuVzuxPcRzmDttdOqBLuehz/+8PPEZbyu56GQ0bDftA81rMAyMGRVue28VUHgwcDguYfPm+M4WFtbAwdg8tJFOJ4H32cwTROGYWBnexuu60JRFOxqGeTzWQhCfFMOSRST+jwdivmdHd4VC5ZLyxJsOwjKVcPE7ONVmgtLUodOqo+P4zhks1lks1lcunQJjDFUq9Uo0C4uLoLjuIZA241QRQK9WK7dLtBaltV2bA/97ZF2KMASkkKdmO16Es8bYNfX13Hnzh2Mj4/j1VdfPdGeqNM8jtNyWpXgv/jubWzvHrS9jRO3D7VWgQUAw7Lr/7MFD8B1D++jWqlgfWMD+Xwew8PD8BlDRlVhWHZ0sgkAruPAiAKtDt10oKoqNFWFpmmQZBkCz8Pz/LYjeYCgEqwqEkw7uZKriGIUYAHg7uIKBViSKmm4eNbPOI5DLpdDLpfD5OQkfN9HpVJBuVzG9vY2FhYWAACqqkajezRNo+DSIWnYb1wfaAVBiGbQMsZaAm24f1YUxa6fA5H0owBLSMp0arbrSTxrxdHzPMzOzuLp06d4/fXXMTEx0ZPjOG2nUQm+NbeE6QdL4DhAkSRYCXtFDat1XE39cBvbbW3WFMplVOxVDeSzGdhVHdvb29jf28Po2FhUAWeMxU6bFSUJeUlCPp+HwHOwbBt6VYdhmijv7oLjOAwNFMEgQNVUlPJZVI3khlCiKAANATZcih38V9WwIAo8XM+HIkt4srGDx2tbuDw+nHifhHQTLSE+XTzPo1AooFAo4MqVK/B9H7dv34bneVhfX8eDBw8gy3JUnR0YGIB6xPgvcnxpCLDN6rdENQda0zSj24SBNqzQUqAlFGAJSZFwn0gvqq71nqXiWK1WMT09DQC4ceMGMplMT46jE543wK5tlfHnn94CEAQ4RRITAywASIIAo34falPJ1YsJ9bIkRKG3Uq1iZeUpmO9jcnISknzY4ZcBqJomFEmGnTCz1fMZBot5SKKEYrEIxhhEHtja2YNhHmBrewu7GRUcH+6hVSE0NZgKAmrcft3aEjLmI6tqqFQN8BwHxoDP7j+iAEtSgwJsZ4WhpFAo4Nq1a/A8D3t7eyiXy1hZWcH9+/ehqmpDoJVl6lb+rNIYYJtRoCXHRQGWkBQIlwzXL5/p5ZvxSZfuPn36FDMzM7h06RKuX79+ah+SZ6ECWzUs/N63vtcwx7VqWhA4Hl7MvFQAqBiNzZrqK7BAsERXU6QosHIcB1EQYTsWdF3H+vo6RoYGkM0XwcX9Llit43FCgAUA2zkMnrIswfdZcBKJAfi+D9M04bkOyrtlOBsOZEWGpgZhVlVV8D4PTZOjkTpxL2fDtJGv2/O7tLqFrd0DDJfyicdFCDk76i8SCIKAwcFBDA4OAgBc141m0C4tLWFmZgbZbBYDAwPRPlpJknp5+H2lHwJss+MG2nCpMQXa84MCLCE9Fi4ZnpqaQjabxYsvvtjzN16e56MlzO24rot79+5hY2MDX/rSl6JZgad5HGmowD7rXlzf9/GH3/4M+01Nl4J5qfHLgAHAZwz55mZNTc+DJIpRgM1nVOxXDezs7GB3dxfDw8MYHR2GnjAOBwiqsDzPJe5nNW0HWVWBblmQBBG6c9hVmOd5ZDIZ5LMq8oUiXM+FaZgwTANbW1vwPA+qouIgl4WsKpClugpw3cOJgtDSD+oHs4/wN979YuJxk1a9fr84q6gC23ntQpUoihgeHsbwcLAqw3GcKNAuLi7izp07yOVyUXW2VCode0TbeeT7/jP3o0iLpEAbXlQFgs+n5qZQFGjPHvpLJ6SHPM+LZruGYS0Nb7I8z8Nps8QVAA4ODjA1NQVZlnHz5s2O7FXq9wrsX33/Lh6vbsV+zXXbXyAwLDuqvHIc1xL0KroJQeChKTLK+wdYX9+A4zi4dOkSZFmORuroCR2PPZ+hkFFbOh7X4zggp2moJNymali10Tdi1KyFgcF1XBimAd0woFcrMCwHmha8PhzHCU4oaq9zu+l5uPdoFTd/6BVkNaXt80NIp6Xl/fgsO8lzLEkSRkZGMDIyAgCwLCsKtHNzczBNE/l8Pgq0xWKx7wPbaerHCuxRkgKt53nwPA+maVKgPaMowBLSA3GzXQVBSEVYA9oHR8YYnjx5gvv37+Pq1at48cUXO/ahmJYK7LME2HsPn2Dm4ePErxuWg4wqJwZM23WRz2iHVdqmx/cZQymjYmNrF09XV6GqKiYnJxt+F0fNVTUdF7WIHPt1z/fBEpY5A4DvMxSyakOFmQMHSZKCvW35AmRZxMFBNQi0uo719TXwPI/hgSL2IUDTNBTyWeiGVbtPH58/WMKPfOmVtsdOSKdRgO2855kDqygKxsbGMDY2BgAwTTMa2XPv3j3Yto1isRgF2kKhcOYC3En4vn/mK9RhoA1/z82BNpxDGy45Dv9vfRAm/eFsv5IJSaFwtmsYEMM32rSMjAGSg6Prurhz5w52dnbw9ttvY2hoqOPHkYbn5KSV4PXtXfzJx58H3YSRXMk+6mQqbNYUV4HlAGxubuHJ03UMDQ2hWCy2fH9FNyEJApyE5eC24yKnqagYVsvXeI6D7wOKLMKyk/fKGnb7sT627WKwVETVVFAul3Hx4kWIAo/d3X0Ypo6trS3kMxo4UQrG9mgabs8v493XX4QkUvWE9Bad1HaW7/un9hyrqoqJiQlMTEyAMQbDMKJA++TJE3ie1zCDNpfLnatAexYrsEdJCrSu68JxnOjrzXtoKdCmHwVYQrok3KcRhtfmJSzHWbbbLXHBcW9vD1NTU8hkMrh58yYUpfNLPE9jfE23j0M3Lfzet74Lx/Xg+SYEgYPnxX9v1TAhiSKchGZKumlBk2WYhtGQEH3Pw8FeGbsHFbzy4jW4LP6DljEGTZHh6Ebs1wEk7oHNagoqugnX9SAIwfzXOI7jIZdRUNFbQ3B0HFH8Dk4kFEXBwOAgBoBomZfvOiiXy9jY2ICiKPiTDz7FzbdeQ7FYPHcnXSQd0vDec9Z1qsrNcRwymQwymQwuXrwIxhiq1SrK5TJ2d3extLQExljD/tlcLnemQ8t5DLDNThJowzm04ZJjki4UYAnpguPMdk1LtRFoPBbGGJaWljA3N4cXX3wR165d69qHfFr2wB53KbPv+/iDDz7DXkWv/TdDIadhvxIfIBkDNFlKDLAAIEliQwXWMk3slrfhg8Pk5CRkVYUbU0EN6ZYFngPCnNr8m9MtG6oiwaxr+JSrhVcgGHdTULS2e2Xjxvo0PIZhQVWCbqFZVYFZ1+FYEARks1nkNAX5Ygmu68IwDNxaeIKiFCz7KhaLGBwcxMDAAPL5/Jk+yTwpClmdQ0uIO69boYrjuGif/uTkJBhjqFQqKJfL2NnZwcOHD8HzfEOFNpPJnKnfPwXYVscNtPXVWQq06UABlpAOC6uunue1bRyQpgAbBkfbtnH79m0cHBzgy1/+MgYGBrp6HP22B/YvP7uDx6ubDf8WBMPkfaa6ZQfLdRPuv6IbUXOKvd1d7O2VURoYQKFQBMBBN6xgqXLCXlrX9VDIZrDfpgorCQLM2lJnWRQawiwAGLbTdpmwYdb285rxxwAEHYdVWcRB1WiYSxuqGna0ZDifD8bojF15CReHCtjZ2UG5XMajR4/AcRwGBgaiQKtp2pk6ySTpQq+tzurVRQKO45DP55HP53H58mX4vo+DgwOUy2Vsbm5ifn4eoig2zKDVNK3rx3mawnMQkiwp0DqO0zDmkAJt71GAJaRDwsYBruvGLhlulqYAy/M8bNvGRx99hEKhgBs3bvRkgHxaKrDHOY4784/xg3sPW/7ddhzkMsmdfF3XQz6rRfNSm/mMQZMlMN/H7u4uLk9OghMa37olUUCbImzbCi8QdBMWa8uERVGA0RREHddt+zMAAMe3Pwk1LRvM9+MHwiJYZqwqEpy6rsTfv/8Ir/yN95HNZjE5Odlwkrm+vo4HDx4Ey5FrJ5iDg4M9eZ2SsykNF8/Ouudp4nSaeJ5HsVhEsVjE1atX4Xke9vf3US6Xsbq6itnZ2Yb3moGBga5sozlNVIE9uaMCLVVoe4cCLCEdcJwlw83SEmAZY9jc3MTBwQFee+01XL58uWdViH6pwD7d2ME3P5lucw/tf4Z2I3Vsy8bKxhoA4LXrL8fOdq0YJkRBgJvQrMmwbGQ0JaiQxvwufcaQU1WAscTZtEfNBa4aFmRJhO3Eh2VVlSGIYtvnUTfshtm0a9t7WF7fweTYIID4k8xwjMby8jLu3r2LbDYbVWdpLiR5HrSEuPNOs4nTaRIEIQqqQNDAcG9vr+G9JpPJNOyhTfvFMwqwzy8u0Iar7MIeJs2BNuxyTE4XfbITcsrC2a7HqbrWS0OAtSwLt27dQqVSQTabxZUrV3p6PGGA7fWJZLsgXdEN/N5ffRcCzycGyKphQpGlxG6+hmXHjtTZ39/H1uYW8vk8RI6LDa9AsNc2n1WwV9WTf4Yjnj/f86CbyU3EDMuuLVVOuA0DlIQAm8uoqOgmLMdtewLl+T7yGbUhRH///mIUYJsJgoChoaGoG7Zt29jd3cXOzk40F7JQKETV2fM+RoOcTK/fd86DtFRgjyKKYsN7jeM40cWzR48eoVKpIJfLNQTatF08owB7+sKGT6H6QGvbNjY3N6MLHc1djsnzSddfFyF9rHm260kHZfc6wG5tbeHWrVsYGhrCa6+9hvn5+Z4dSyh8/np9IplUgXU9D7/7l99FRTeRz2qwErpIMwYoUnKABdD4IegHVfBqtYrxiXHksxk8Xlpqe4x6bb5dUtCuGiYUSYJpOy1VWJHnYbseshml7TJh4YiTn6pptXQsDpY3B8GcMYaMIsFO6MoMAKbtgMNhzfrR6ia2dg8wXMq3fWwAkGUZo6OjGB0dBYBojMbOzg5WVlbg+37UpGVwcBDZbPZMnEichZ8hjXr9vnMepLUCexRJkjAyMoKRkREAhxfPyuUy5ufnoes68vl8Q6Ctf4/vBQqwndccaDc3NzE4OIhcLhctOeZ5vqXLcT/+DfQaBVhCTkHzbNdnmSHG8/yRyzQ7wfd9zM/PY2lpCa+99houXryI7e3tnleDgcM5qb3+4E0Khn/68RTWtsoAAN1sXP7arGqYEHgOXsLXw5mtumFibW0NHM9h8vIkFFkG8z0YtgNNkRIroI7rIZ9J3kvLahVS0279flWVUNEt8MIRAdUwIYsi7IQlz2El+KBaC8Fc0EVZNw836AZVWCFxUbXjelHFNjzu799/hJ9474ttjy2OpmnQNA0XLlyIxmiEDaEWFxfB83xLQyhCSPf0SwX2KM0XzyzLimbQzs7OwrKsaDXIwMAACoVC1wNtrz9HzyPP86LKK9BaoQ0DbfMeWgq0R6MAS8hzOGq260n0ogJrGAamp6fhui7ee++9qPtrmvaeAr1vphL3u/nunTnMLCxH/+15HgpZDfvV+ADp+X7QDTjh64wxuI6F5SfLKBQKGBoaAsdx0FQFu3sHAIJOvkDyMt+kJcyhqmEFIbruZvWh17QcZBQFhhXfEYoBUBQpMcCG94FaDbV5OTDAwXZcDJUyqLTpOtW8J/j+0ipu/tDLyGXUtj9fO/VjNMKuo/v7+9jZ2YmatKiq2hBoJUl65scj/Y8qsJ3XrxXYoyiKgvHxcYyPjwM4XA1SLpfx9OlTuK6LYrEYBdp8Pt/xcEkBtvt832+4UFFfoQ3Pa8KJD1ZtFRUF2uOhAEvIM3qWRk3tdDvAbmxs4Pbt2xgbG8Nrr73W8Cbb6+XM9ccBoOfH0lyBffhkHR/84G7L7Zw2wQ5AbYlx60gdxhi2trZg6BWMjY0hm80CQC3w6tHNK7oBURDbN2tSlYaKZ71wj2m4T1WVJVSNxkB91PlN1bDaVpqDCqoCx/UTQiprWGIcx7SdoOlU7ft938fnD5bw1Tevtz+4EwhnPpZKJQBBk5ZwCeDi4iLu3LmTuiWAcXp9cecsowDbeeflOW5eDaLrehRol5eXG7Y3DAwMIJfLnfrzQgG2+zzPS/zcCH+/FGifDQVYQp7BcWe7noQgCF0Jar7vY3Z2FisrK3j99dcxMTHRcpu0BNi0VGDrA+zW7j7+4IPPYo8paHSUXMG0bAdZTUW1Lti5roO1tTUwBkxcuITBUh4HuomMquCgVq3lagnWZwwFVcZeQhUXAASh/WvRclwADDxXuzjQ9GMc1U3Y9/1g7E81ea+s5/ng+dZl1+GfiWGHQTt5bmzz0Nlb88v4yusvQpE687EliiKGh4cxPDwMINjTFi43vn//PmzbRrFYjKqz3aiYkN6jE8XOOitLiE+C4zhks1lks1lcunQJjDFUKhWUy2Xs7u5icXExmncdXjw7jf36FGC77ySzd+MCbfg/y7Ia5tDW7589r4GWAiwhJ3DS2a4n0Y3QWK1WMT0djHt5//33o0pfL47lOMK9xL0+ljDAGpaNP/loCnZCsyYAEI7YR1r/cqlWq9jYWEcul8Pw8DA4jofr+pBEEY7rgYU7RWvfwxiDbtngwB1+rUlFt2rfHx9AwxAtcDwOjNYQyoC2ARYIlglzXEvGjIiikFihDb/nqL8b3bQb9vzajouZh8t4+/q1tt93WmRZjpYAMsYaGkI9fvwYjLGG+bOZTOZcnkScZb2+cHYenNUlxCfBcRzy+Tzy+Xy0vaFSqWBnZwdbW1uYn5+HKIoNFVpN0070vPm+D8ZYKleRnGXtKrBHqe+lIghCQ6A1TTO6TRhowwrtaZ6XphkFWEKO6bSXDDfrdGh8+vQp7t69i4sXL+L69ettrwqmITSGjprB2q1jcFwXv/+t72J1swxJEOAkjczRTUiikLicuGqYkEQRa+vr2Nvbw8jIaLT3GAiWzxayWsNIHK5uybHjurV9q/EVUMYYNEVKDLAAIPEC9hKaPQXHaEHgeXgJrwHHdWuNllorzRlVxkHVQFaL2696+HMEXZHlxM7NACDwh3t+M6qCz+49wpdeunLkRYLTxnEcMpkMMpkMLl68CMYYDg4OUC6XsbW1hYWFBYiiGFVnBwcHoShKV4+RnL7zsry1V8L3daoKNuJ5HoVCAYVCAQCi/frlchnr6+t48OABZFmOwuzAwABUtX1/AHque6N5D+zzoEDbiAIsIcfwrLNdT6JTAdbzPNy7dw/r6+v4oR/6oahL4lHHkob5q+Gx9DpM8zyPj2/PY6sahClNVeAkzFxlYNAUBY4b/3XHcbGzuYmqbuLSpUnIstzw9XxWhd8c2OsqsADg+e332upm8j5VRZawr+tQZTm2IzEQLFXOZdRoCXOcuH2svMDXGjBxqBoWVEWKeYzDY5IkoW2ArRomZEmA6/pwPRe24+HuoxV88cXJxO/pBo7johPMK1euwPO8qCHUysoK7t27F83+GxwcRKlUooZQfSgN739nWX3XfpKsfr/+tWvX4Hke9vb2UC6XsbKygvv370cN6ML/NX+uhM81BdjuCVfsdarqfdxAKwgCPvvsMwiCgK997WsdOZZeoABLSBv1s13DvTqd+rANg9ppnjQdHBxgenoaoijixo0bxx4TEn7IpeEELg0V2HtLq7i/tBrtjzQsq+0yXiNhJqthGFhbW0M+l8OVy5NgTc2c8lkV+1UDHLiGKi/XdDvdbL+H1PX8mA7AAM9z4Lkg2Eoij3ZbUA3LbpjHGvd1TZVhmIcBNKPIDXNkm7smN7+UqroJUQyWTcdhCObnKhKin+X79x/hjRcu9fx1WU8QhOjEEQAcx4kaQi0sLMAwjKgh1ODgYE9GaJBnk6bX2VkTvj/Sc3wygiBgcHAQg4ODABob0C0tLWFmZgbZbLZhDy0F2O7r9nOeFGh938dv/dZvQdM0CrCEnAe+78N13Y4tGW52mqGRMRZVgq5cuYKXXnrpRG+iaZm/Gh5LLyuwD5+s4bP7jxrC6FEzV+O+HnacHB4eRqFQQD6rYb+uEZKmSFFzJwbWWOVtqsACgHDE7yVuCXMuo2LvILjPim5BEng4CR2BHddDXlNRidknGwobQYX3XWkKzFXDallOXZ/pGRg0WcaBm/wYjDGYdVXa8n4V80828PLkWOL39JokSRgZGcHIyAiAYCZk2BBqZmYmGqFR3xCKTuLTp9cXzs46WtZ6Opob0DmOE33ePHz4ENVqNep3sb29jVKpFM0lJZ0Tnjv26mJlfaDVdR1DQ0M9OY5OoVcwIU3qZ7uGYbIbJ5enFRpd18XMzAy2t7fx1ltvRR9qJxH+vL1eugv0tgK7WQ47Drd+LWl/aCgcdeN5HtbX1+E4Di5evBjtjbQdD+FIHVHg4ftoWDqsm1ZQLWWspQILAJUj9tqatoNcXQDNZVTsVw4DdbBXVoHTZi+se8RS5aphQpalWoOr1v2wjDGoslR3jK0/R7vlzgLPw3LclpD72b2HqQ6wzRRFwcTEBCYmJqIRGmGgffToUdRxNAy0J23QQjojDStQzjJaQtwZkiRhdHQ02i5kWRbW1tbw8OFDzM3NwTTNhhFhxWKRVoR0QH3xo9eq1SpyuVyvD+NUUYAlpE79kmEAXQuvwOnMPN3b28P09DQ0TcPNmzefuZFMfTW413pVga0aFv7o259F3XibnwvdtKAqMkwrfh2uYdlgvofl5WWoqorJycmGDzLTtpHV1Nr9SC0zU13PQyGrYb9qxFZgg722Mhw3OYCGFEmEGbNeuD4kx/8MDrJt5soCgCKK8HwfupPQtMq0o4Aa/Ck1PlYwmza+KZWmBkuSfcYajnNtew+P17dxeaz/rijXj9CYnJyE7/tRQ6j6Bi31DaGa97PF3SfpDHpuO4eWEHeHoigYGBiAKIp4//33YRhGtOT43r170YiwMNAWCoVUhK5+FzZwSsPru1qtNjSLPAsowBJSUz/bNezk1k3PE2AZY1haWsLc3BxeeOEFvPDCC8/1ppm2Cmy3j8P1PHzjLz+JwmnScymJAuKyHWPA3t4u9Mp+1HwjSdBxOHkpciiuCtuuegkAVdOEqsjgEM5/beR6XjDTNaGjMdC6b7X161zbkTt+uB+3zVLkoNFT447brKZES5I9z0c+qzbMnv3s3mJfBthmPM+jWCyiWCzi6tWr8DwvOrlcXl7G3bt3kc1mo0BLy/+6hyqwnRWO0KHnuPPqV3ZpmgZN06IVIeGIsHK5jCdPnsDzvIaRPbTF4dmcZAZsp+m6njg2sV/RpyA59zo52/UknnXmqW3buHPnDvb39/HlL385aiTzvMfS672nobAjcjf98Yc/wNONHXAcB7F2BTXuGCq6CVEQouXCQBC2NjY2YFkmJsYnkMtlEpf5chwHI6ETMBBUcTNhBZRrrQK7nnfESJ1gb215v5r4GI7jIlzKHKdqWJAlKXb2rabIONB15DJaQ7hsZjpO7d652OXYzaOBRIFv6V7cHHKXVrewUd7H6EAh8XH7kSAIGBoaivYrhfvZdnZ2ouV/hUIhqs6m4W/0LErD6pOzji4QdE/S1qS4EWHVajUKtEtLSwDQEGiz2Sz93o6hkx2IT6pSqdASYkLOkk7Pdj2pk4bGcrmM6elpFAoF3Lhx48ilhp08lk7p9h7YD6fu4d7DJwCC10dGlaP/v1nwdQX7tWZLlmVhdXUNsixhcnISgiBATVjmq8oyDNNCVlPbVjB5Lox+8a/LIBzHB9B8RsNeRW/b6de0HWQ1BdWElsRBJ2ChJcDytTmxjNU6FvMcWEIl2HE85LLt5xTa7mGQVuTDhlYN95FRGmbPfu/eQ/ztG2+2vd9+17yfrb5acvv2bTiOg/n5eYyOjmJwcJBOLk8JLW/tvLCzP+m84/bW4DgOuVwOuVwOk5OT0czr3d1d7Ozs4OHDh+B5PloNMjAwgEwmQ38nMdISYMOLElSBJeSM8H0fT58+ja4+puENmOf5KEy3wxjDw4cP8fDhQ7z88su4cuXKqR9/L5buxulmkJ5ZeIyPpu43/JthBXs4k4TLjPf29rG1tRVdpQ5/HaZlt4RwQRDAAHg+Q9WwINTCYJxgFqrYvML28PFtu9asqTHwKZII3bTg+wx5TcNewlza46gYFgRBaHhtZtTDkTmuW1uK3KYK67pe7B7YkGU7yGoqOHCJnY8d10d9WJ9fXsfuQRWl/Nn6YG4nXP534cIFMMbwne98B/l8HuVyGYuLi9HJZX1DKHJyFGA7L1xCTDrvWZtD1s+8vnz5csOe/c3NTczPz0MUxYYZtPSeEwj3wKYB7YEl5AwIlwyHlYurV6+m5srUccKaZVm4desWDMPAV77yFRSLxY4dSxqW0XWrAru8toU/+ejzln93HBdZVYGREKpM20Z1fw875V1MTEwgk2n88HZcD7mMhkqt2y/HAZp82LTJ830UcpmoituMIajWtps724zngyXgXq2yqx8xt7Zq1BpSJSxn9hlDTpVxUNurGzcyxz5iKbJpOVCl9h85HADDTm4YFYbcsDrr+wzfv/8IP/7Dr7e937MqXOo/NjYWzXrc39/Hzs4OVldXMTs7C1VVo0BbKpVOdZXGeUABq3NoCXH3nNZIvLg9+/v7+yiXy9F7Ttg0KvzfszaT7Hdp2wObyWR6fRinigIsOVealwwLgpCKKmPoqOPZ2trCrVu3MDg4iLfeequjzVzSsoS4G8exs3eAb/zlp/ASZqImdem1bRurq6vIakGX4aTfR33lspDNYK/SuKTYsuOX74aqhgWO55KKl6gaJhRFhmUFATSnqUH34hrHretonEASeSSsIgYAGKYFjuMgiUJsV2LLdmrBNjmAJu2BDfmMQRYlGF7ygTQ3rJpZXMG7b7yInNZ+ifJ5wPN8Q9Mw13WjhlCLi4vRKIX6hlBpqRCkTRou3p11aZgzfl506rkWBCEKqkDwnrO3t9fQhC6TyUS3OU8X0dKyhBigCiwhfS2sutbPdj3ukt1uSQprvu9jfn4eS0tLePXVV3Hp0qWOX7lOS4Dt9FJm3bTw23/+cdtuu6blQBIbP4gODg6wsbGBUqmEwcFBZFQFRpuROpqiQBT5lvAKAJbtBuEvocrr+R4yipRYQWUAZFGAZTmJQTWpkVSooluQRQF2wu0cz0chq8L1/MQ9u0fNxjUdB0U5/mMnbOKU1dpfrQ8aW8nQa2nb83z84P4j/Ohbr7b9vvNIFEUMDw9Hs6Bt247mz96/f79hfMbg4CDy+TwFihpaQtx5VIHtnm4tZxVFsaUJXdxFtPpAe1a7qqclwNq2DcdxKMAS0m/azXZNWwU2LjSaponp6Wk4joP33nuva29CaQmwnVzK7HoefvcvPsHufhW5bPK+HY7jINSNFtra2kKlUsH4+Hi0/Fw84oNKFoWWfar1jvoZHddHu/JlVTehKXJiCA47GlcTvs4YgypLiQEWAASeb7vP1TBtaIoMw4pfisyBgxgTkBRZio67alhQZAlWm+7MzSe9t+aX8ZUvvABVOR9X9psdNwTIsozx8XGMj483jM/Y2dnB8vIyGGPRieXg4GBqegP00nn/+TuJmjh1T6+q3ZIkYWRkBCMjIwCCMFUul7G7u4v5+XkYhoF8Ph+F2bO0KiQtAbZSqQAAdSEmpJ+Es13DINb8Bp6WkBZqPp6NjQ3cvn0bY2NjeO2117r6ZpiWJk6dOg7GGP73t7+Ppxs7AIIAKIlCfKWS42BYNoqeh5WnTwEAk5OTkCQpuknFaB2pExIEAabjgBc4JKxSru1DlRL3obqej4ymIOmZ4DgOsiwmVoHD27RTNS0IHAcvJihnVQXlAx05TU0MwQDaNrwCgp9zoKTWug4HoZZrWlosiULbABs9V7Wg7LgepuYe4703Xmr72ORQ3PiMSqWCnZ0dbG9vY2FhAaIoRsuNBwYGoKrnZ5k2VWA7j5o4dU9almvLsoyxsTGMjY0BCC7Qh4E2XBUSjgkbGBhAsVhMxXE/i7Q0capWgzF6tAeWkD7AGGsIr0njcdK6hNj3fczOzuLJkyd4/fXXceHChZ4cSxr2gXXqOP7qszuYfbQS/TdjDJqqwKm0NlPiEMwq3Vh7ClVVMTw83PJ6Cr7/sNHR4fdyUGUJVcNEIZeB4yTvQ5VFMTHAgkMwpibhszyjKtCN9s2adNOEIkswzPgY7Hl+sAS5qUGTJPBR4PRZ+4sJVdOCLEuw7ZhlxhzAwKDIUnR/uYzaMse2qluQJAGOk/y3GVS8D5+rqQdLePv61aBjMzkxjuOQz+eRz+dx5cqVqDnLzs4OVlZWcO/evWgvW9gQqv4CzlmThve+s46WEHdPWgJsM1VVMTExgYmJCTDGokBbLpfx9OlTuK4bbXMYGBjoq20OaanAhiN0+uV5Oy76pCdnzklmu6ZxCbFpmvjkk08AADdu3OhZh+S0VKc70YX4B/cW8L2Z+ZZ/DxsV1T8eYwwHlQoc28b46CWompbYC9i0nJYAmc9qUYdh3bDA81xLI6JQUMXl4caUaTlwqJoWhkolGE1NnwrZTLTvNdhLGh+SGQtCss7MxL8J03YbJvZwAOS6uay6aUNTZZhJHZ9YMMInNsDWvl4xTAi8AEUWa8fadDEAwXLmdgG2YpiQJQF27TaG5eDOwyd4+/rVxO8hx9fcnKV+L9vCwgJ0XW+plKThZO20UAW289Iaqs6iNHXETcJxXMuYMF3Xo0D7+PFjMMai+bMDAwPI5XKp/Rv1PC8VDasqlcqZnA9OAZacKWHVNXyzPuoPNi0hLWTbNhYWFjA5OYnr16/39AMnLc/NaR/H3ONV/MV3b8V+zXG9hgDouC7W19bgui4kWYaiqshoShTmWr/fbRgxUx9egWDPbX3YbOb7DPmMir24r9dSpSQKMOqyY0ZVGvalul5CcKypmhZEgYeXEKJtxwnG/tSWCcdVSAWu/euyYpgQRQFu03JsrhZUmc+QzYVje+L/Rqu6lRjmgzsBFEmKAiwAfP/+I3zppcsQhHSfqPWj5r1slmVFDaHu3r0bVUrCJcf5fL6vT5gowHYeVWC7Jy3LWU+C4zhks1lks1lcunQp2uYQBtrFxUVwHNcwsidN+/bTUoHVdT01oyJPEwVYciaEs11d1227ZLgZz/Owjxhh0g2e5+HevXs4ODjAxMQEXnvttV4f0pncA/t0cwff/GSqdnIa//pwa49V1XWsr68jl82iVCphp1wGcPTSwnAUT0ZVWmalAoiWzibRTTt2GXD4bwe6CUkU4bguRCHYs1t/W8NykFUVVGNG3QDBiUxWVVpCaeNtglCoKXLsWJyqYUKWJNhO/HJn5jNkNAn7MfuJo2NlgOsm/159xpBV1MRqMgBUDKth37Ljerj7aAVffHEy8XvOml4tdVUUpWHpX1gp2dnZwdLSEgA0NITSNC01J5bH1W/H22+oiVP3+L7f90v+67c5XL58Gb7v4+DgAOVyGZubm5ifn4coilFDqIGBgZ6+76TlokGlUklVsD8tFGBJ3zvJkuFmaVhCXKlUMDU1BVEUMTIykporZWdtD2x5v4Lf/fNPoJsWspqW2IhIN03olQrWNjYxMjKCQj6Pqq5HHYB1w4J2xMicfFaDYdmxx21aNrKa2qaK6yGfjVkGXKvAMjBoigzX9aBIUnxQPeLlr1s2OC65qbFuBsfouH7sfloGQJHFxAALBJXeluXStePKZVTs60Y0OieJYQXzb1lCtZgxBlWR4bgGOA7gOOCze4t4/dpFOjHuouZKSf2J5cbGBubm5iDLckNDKEVpPy6p19Lw3nfWUROn7jmLy7V5nkexWESxWMTVq1fh+z729vawu7uL9fV1PHjwALIsN1Rou9mILi3LtsPRRWcNBVjS18LZrieputbr5TJZxljUHOXKlSt46aWXcPfu3Z4H6lBalhCfRgW2alj4X9/8CHot7CW9SlzPxfraOmSRx6VLl6DU9q9waDyhbTcyh+eDUTHNy2frHfUyjfve+qqsblrIZ1XsJ4y00Y8YReO6Hgo5re1IHEUWUTWriV+v6BYEQUhsguZ5fmxAFQU+Cv+HS4jjw4Lr+chn1bbHWa3tG9YUGQe6CdNy8ODxGl692v3GZyTQfGLpeR729vaicT13795FNpuNAm0aZ0HS8tbOowps95zFANuM5/koqF67di163ymXy1hZWcH9+/ehqmpDoO3kHtW0LCEO98CeNen6xCDkmJpnuz5LeAXQ9gS8k1zXxczMDLa3t/HWW29heHgYQHpCI5CeY3neTtG24+J3/vxj7B4chrGKYUKWGxsNGYaBtfU1aKqG4dExyJJ4OFKH4xoiVsUwEkfmaKqC3QM9eSQPgtAVBMz45cTBzFYZen2Vty7nqbKUlPmA2s1kKX4UTfh3kthkCcHe3fJ+FYokwnLib+czHzlVa+m6XM90GgMqBw6SKMKrva4c10U+o+IgZplydB9W+5Dr+wz5vIa9g8Pj+O7dRVy/MkEBJCUEQcDg4CAGBwcBBA2hwn1sc3NzME0zagg1ODiIQqGQipNtev10Fl0k6J7zEGCbNb/vuK4bNaJbWlrCzMwMstlsFGZPu7N6WgIs7YElJCWaZ7tyHPfMH4K9CGn7+/uYmpqCpmm4ceNGw5KWtIRGID3H8jwVWN/38fvf+i7WtsotX1MkGbbtgoFht7yLnfIOhoeGUSgWwIFrGKnDNa23ZT5DJiO3NGMqZLXo3zRFgeO2juQBgrtSpOQAC7TOLA4rsIoswbBsSKKIINS1aYIk8rH7TDmOg+W4yMY0pFJrHYeDEJwcYIHaft02S3wdxw2WQ9cqqFlVhm7aKMiHy0dt12v7cziuh1xGid2LG/4sjuOC54HwZbK9d4D5Jxt4eXIs8dhJ70iShNHRUYyOjgIILh6Fgfb27dvwfb/nnUYpXHUeLSHunvMYYJuJoojh4eGoYFB/IS3srJ7P5xs6qz/PypC07IGlJcSE9NhxZ7ueRDdDGmMMjx8/xoMHD/DCCy/ghRdeaDn+tDSVAtLTxOl59sD+6cdTWFxZj/2abppgzMfq2hps28bFixehKocXE+pH6sTV/wzTbvj3XEZrCLS6aYHnOPgJx141zGCpccJzXDXMxgooB/C114vn+/BsG1lVTW7WxBhysop9N7lC2nxoPM8DHKJjrhoWJEGAk1ABd72ga3OlTaMl23EBBHtV1y0HWqbxCrdlO8hpKioJe4IB1MbpxIfcXCbowpxv6pb8vbsPz02A7fcQ0Dw6o1qtRh2OFxcXo6WB4ZJjTdM6fky0B7bzaAlx91CAbdV8Ic2yrCjQzs7OwrIsFAqF6GLaSUeFpaUCW6lUKMAS0ivP06ipnW4tIXYcB3fu3MHu7i7eeeedaElLs7RUPYF0NXF6lufkg+/PYOnpZuLXq1Ude+VtcOAxOTkJgW/8oGkYqRPT8chxa9VF3URGVVA1GkOc69WaMSUssfV8v+1InahKWwuwHDhoSuO+1qP+BAzbbhuiddOCpsjRntSsquCgrrmVzxhyqgynzTJh23HaNoSybAe5jArH82v5s/WG3hG/X8txY0Nu0CU5OF7Ddhqqwes7e3i0uomrEyNt75ukC8dxyOVyyOVyUafR/f19lMtlrK6uYnZ2FoqiREsDS6VSR/axUQW286gC2z1pqQammaIoGB8fx/j4OIBgZUi45PjevXtwHKdh9vVRWx3S0sRJ13UKsIT0wklnu55ENwJjuVzG9PQ08vk8bt682fZkKw1dkUM8z8Np02W2W8Iq6En84N4CPr09C0kUW0bSMMawt7eH7e1tTIyPQsvmovmkzdy6Zepxx+B6HmRJhO24sQGuXSMnIGhi1C781Vdps2ow0qZQPFx+WzFMqIpc2yfaqnmubRyxNjM1DOPNdMOCwPHwWPzr0nKC2bfVNt2EJVFExUhuCBXs+VWgm8mrD5rnwfI8B8/3o+fOdVsvGHw685ACbJ/jeR6lUgmlUgnXrl1r2Mf26NGjqLpQ3xDqtE7UKVx1FlVgu4cqsCcXrgwJR4XVb3V48uQJfN9HsViMAm397OtwxWAaLhpUq9WoynyWUIAlqfWss11PopMBljGGxcVFzM/P45VXXsGVK1eOPP60LNsF0lMNPulzcn/xCf7iu7cAhE2CDoOZ7/vY2NiAYRi4cOECNE2L3QcaMkwLqiLDtu3YFkKW7SKjxc97BeqDWfz9246DXEZL/H7PZyjkNPi+j1XLQUZsbTAhCwJMJF9ocI6YO1sxTOQyyc+B6/soZDTstwnBnpf8+8lqKsr7VWiaDIBLDOtHMW2n4XcVN8u2ueHT080ynmzs4NJo/IoH0n+a97HZth3Nnw2X/YUnlYODg8jn88904p6G1SdnHVVgu4cC7PPhOA6ZTAaZTAYXL16MtjqEgTacfR0uNy4UCgCQmgBLFVhCuqRTS4abdWoJsWVZuH37NqrVKt59910Ui8VjfV9aQiOQnmM5yVLmR0838L+//VnD7cPKnWVZWFtbgyiKmJycPHZzBlEUYpcQcxwHTTl66eJRr9ujnmPPdWF7XmIj3gPdbLtPNQh+auLcW47jIfICfJZc/TRtu00f4CCoZ2OCuigKtVE5AB9VuY9azpwcxsPfa1ZrDa9AENZzGbXhgsCnMwsUYM8wWZYxNjaGsbGxhipJOLKHMRZVSAYHB5HJZI71WUJLiDuPKrDdQwH2dNVvdZicnARjLJp9vb29jYWFBQDA/fv3G/bu9+I9hQIsIV3yvLNdT6ITIW17exu3bt3CwMAAbty4caK27GlaQpyWavBxj2Ntq4zf+8tPW/ZTGpYFyzTxZOVpdCJb/5qqGla0DDhOVTcgi2JL7MplVBzoRjQaJqnSGTRjkmAlLMfWTStx+SzPc/DBoEhSy1LoEAODpshw2lRI210AyKoK9nUTAs/BS+gmbLteSzA8Ehd0MdaN4OeqGhYUqf3VaJ5rf4KlmzaympL4uwKCplH11xser21jdWsXE8Ol4x97H6FK4aG4KkmlUsHOzk50UimKYkNDqPou8PUowHae7/unOraEJKMA21kcx6FQKKBQKODKlSuoVqv49NNPkcvlsL6+jrm5OUiSFG1z6FYzOoDG6BDScfWzXcMrs50+gTjNAOv7PhYWFvDo0SO8+uqruHTp0omPPy1VTyBdTZyOOo7y/gF++88+gt0UEn2fYXNzE8x1MDExgUwmE/v9iiwnhiLGAE2VAMaiPrj5XAYH1WBEThAgpbZLdRU5OcACgCDEn1hktaDDbkbl2o1ChW6173jc3KwpVL9ntJDTsF9NDqhHrVSo1h7DrD1GPnM4PickCDy8Nr/KqmlCkeS2z5UkConLnYEgwDaH7U9mFvD//No7bY+fnD0cxyGfzyOfz+PKlSvwfR97e3vY2dnBysoK7t+/D03TokDbPAeSAmxn0UWC7qEA212MMQiCgGvXruHatWvwPC+2GV14UX1gYACKohx9x8+AKrCEdJDv+3Bdt+NLhpuFS4if94PUNE1MT0/Dtm289957yOfzz3Q/aQuwaTiWo46johv4v/70o5bb2LaNtbU18DyPifEJqKoMJ2YmKhCM1Gn3OIblRMuIc1kNlaauvFXTajsPtWKYEHg+sdtuRTchi0JtJmqgfqasbtpQJSm2AgsEzaTadTQGDps1hRRZalhWbJg2eA5I+BFgWA6yqpI4tgcAhNoJkqrIOKi23s60XGQy8RWvkCTySFpFnMso2D3QY8N4Paepedajp5tY39nD2ODxlvKTsykcxzMwMAAg6A4fNoQK50CGXUafZ/4jOR4KsN1DAba7mhs4CYLQ8N7jui729vZQLpexvLyMu3fvIpPJNATa01idEO7VpQBLyCmrn+0afph18wMtfEN/ng/SjY0N3L59G6Ojo3jnnXee68QnLaERSM+xtOtCbFg2/q8//RB7B1Xkc1q03/LgoIKNjQ0Ui0UMDQ3W9quqcFw99n48z28IjHFf12QRqiJBt+yWIOl5HvKZTGK3Xz8amRP/+IwxqKoMuxJ8f7DHs/G+BIFHQv4GgFrVMn5WKtAYknmeq/3tHf4cTtjF9yTLhJtUa12Rg0pw/HJnWWy/jLhqmJBFsSHMA4AoCFG3ZZ5v/7d6OFu2fi/sQ/w/vvrWMX8Sch5IkoSRkRGMjASdqi3LiubPrqyswHVdfP7559Fy4/ouo+T5UajqDt/3ab9xlx01A1YURQwNDWFoaAhA48W0xcVF3LlzB7lcLgqzpVLpmc8tK5XKMxdV0owCLOmZ+iXDALoeXoHDDnHPMq/L9308ePAAy8vLeP3113HhwoXnPp60hEYgPXtgk54T23HxO3/2EbbK+wCAqm6C5zisb2zg4OAA4+NjDfs+DMtK3EcKBB2FEwMgV1sl4HkNoa/heNz2I4dM224bxquGBYHnIQg8LMdp6dZrmDYybfbMHIa2+AopQ1AZtV0DGTW+c7LjtHkOEFSaVVmGacdXPxkATRZRPogP8hw46KaDwYwAz49fkswAKHJrgFVkMVo6XDWtYFm2nfycu033/3BlA5u7Bxgpnb0PcnI6FEXBxMQEJiYmsLm5ifn5eYyMjGBnZyfqMlrfEKpXTVnOCqrAdkf4+UkBtntOek7ZfDEt7K5eLpcxNzcH0zSRz+ej959isXjsDse6ridun+pnFGBJT9TPduU4rmdvrOHjnjSo6bqO6elp+L6PGzdunNoG+TQF2LTsgY0LfZ7n4/f+8hM83diJ/s2ybBzslWGaFiYnJ1uW3wQdapNH1liOg2xGi51nKvA8GIIqoOfH73W1bAdZTUvs9ms7bktVsPFn8lDMZWA7Hmwr5jE4DpLU/u8kaQ9sqGqYKGQ1lPcrsV83bSfYP9pmj6kk8kga15rPqNitGhBFIXEGrs+CmbbtxvJUDAuiIMCtbSnIZ9XG/bQMkAQeyUcZjNSp777MGMOndxbwf/7Im22+i5AAx3EQBAGXLl3CpUuX4Pt+1BBqY2MDc3NzkGU5qs52cg/bWUVVwe6gANt9R1Vgj1LfXR0ItqmFgfbevXuwbTsaF1YqlVAsFmN/v+ES4rNYgaVXM+mqsOpq23Z0haqXb6rh1d+ThMa1tTV89NFHKBaLeO+99061u1vaAmwajqW5Euz7Pv7wg+/h0cpG9G/VahXLT5bBCyIuXbqUuHckaQ9qJCb/cRyQUVXYjgcuoTIZ3TZx0Ezt2I9xQcCwkmOZbtoQ+OQPxaCjcfJoH0WSWsYBNWs30xWoLUWWWq99yqIIw7LBfIaMkrB3p/b0VU2r7TJgxoLGWMExi6jqrc9JxbRij6Ne8+t3/sk6tvfiw3s/oyrW6Wu+aMbzPAqFAq5evYq3334bP/qjP4rXXnsNkiRheXkZH374IT799FM8ePAAW1tb0coikozmwHYHBdjue94A20xVVUxMTOALX/gCbty4gXfffRfj4+PQdR0zMzP44IMP8Pnnn+OTTz7Bt771Ldi1VVKmaQZbnI4ZYD/44AP8nb/zd3DhwgVwHIdvfOMbDV9njOGXfumXMDExAU3T8PWvfx1zc3MNt9nZ2cFP//RPo1AooFQq4Wd/9mdRqTR+7t66dQtf/epXoaoqJicn8Su/8isnfk6oAku6hjGG/f19MMagKErXGjW1E15lP84sWM/zcP/+fayuruKNN97A+Pj4qR9PWkIjkJ5jqa8EM8bwF9+7jQePVqL/3t7Zxt7eHkZHRpHP54MmSwnVPcO0oClKYgOgqmFCkcXacuJAPqNhr7Y3tmIYyGhaS7fjUMUwa0tb409eD0fmtAayQjaDvYqeWCUOg3xOU7CXsJcWSB5FIwo8XM+D43ltl1Iblh0cY8JzxACoTWOHuNr9G7XKccW0IAgcvLiWw6xuz3GbKmy1dh+8wMOP6xDNAFlOHn8U/ixZTYmWHjPG8OnMAv7WjS8lfg8hwNHLWwVBwODgIAYHgxnDjuO0LPkLG0KFS/4oQDSiJcTdEV4ooOe6e5qbOJ2m+nFhFy5cAGMMuq6jXC7jd37nd/Dv//2/h+d5eOutt/DDP/zDGBoaOvYS4mq1ii996Uv4mZ/5Gfz9v//3W77+K7/yK/i1X/s1/I//8T9w7do1/OIv/iJ+4id+Anfv3o1Gkv30T/80VldX8c1vfhOO4+Cf/JN/gp/7uZ/Db/7mbwIA9vf38Tf+xt/A17/+dfyX//JfcPv2bfzMz/wMSqUSfu7nfu7YzwMFWNIVvu/Dtm3Mzs4im83ipZde6vUhRY4T1CqVCqampiAIAm7cuNGx/QQ8zx8rTHdDmgJseBzf+t5tzMwvAWBwXQ9r62vwPR+TlyYhy0HlMWmPaihpZE1Ilg4DaL7W2Cn82GcsqAgmBdjg+8XEABv8PK0nEbmMGjV4avecMxaE4HYBtGoYtbmzjQFTleXa8mWGfFZt27GYP+JEp3WJ7+E4HgDwPT8Yo9MUxOsr2Lpltd0T7Ps+BvJZlA+Sw3pVt1q6N7feT+P9r23vYWe/gsHC2evKSE7PScOVJEkYHR3F6OgoAMAwjCjQrqyswPO8hg6juVzu3AcKauLUHfQ8d9+z9FV5VhzHIZvNIpvN4p/9s3+Gn//5n8d3v/td/Nmf/Rk++OAD7O7u4tq1a/ixH/sx/NiP/Rj++l//63j11Vdj339+8id/Ej/5kz8Z+ziMMfzqr/4q/s2/+Tf4u3/37wIAfv3Xfx1jY2P4xje+gZ/6qZ/CvXv38Md//Mf43ve+hy9/+csAgP/8n/8z/tbf+lv4D//hP+DChQv4jd/4Ddi2jf/23/4bZFnG66+/jqmpKfzH//gfTxRg6RVNOipcMmxZVnRFKg2hqN5RoXFlZQUff/wxRkZG8O6773Z0M3xYbUzL3tM0/K7CkPPh53fx2cwcbNsBmI/l5WVIooRLly5F4RUAdMOEKie3n68aJqQ2nXDDZkq5jNoQylA7jvDrid+vm0d+Xa7rJqjIEsy6TaVJy4CDzxoWdAtuM4ombIJUL5/TGvbe2lGzpoRjNE0obZbn+oxFx5hVlcbnqcawbbR8PnKIgrfr+siqyXsGNUXGQa0xV5JgNUf7UQOGZUePoyoy9qomPp152PZ7CAGeb2m2pmm4cOECXn/9dfzIj/wIvvzlL2NwcBDlchk/+MEP8J3vfAd37tzB06dPYRjJF5POMqrAdkcnq4Ek3mkvIT4JQRDw/vvv4xd/8Rfxq7/6q9A0DX/wB3+At99+G9/4xjfw9ttv48KFC/jpn/5p/Nf/+l/x8OHDY51zLi4uYm1tDV//+tejfysWi3j33Xfx8ccfAwA+/vhjlEqlKLwCwNe//nXwPI9PP/00us2P/uiPNpy3/cRP/ARmZ2dRLpeP/XNSgCUdwxiD4zhwnHD0BX/s5brdlBSqXdfFrVu3MDs7izfffBPXr1/v+BW1+rE+vZaWJk48z2P28Ro+mroHhmB/xcqTJxgaGsLo2Gjs70Rq024+2F+ZvE80HHlTbWpkxNXmwPq+j6yWHCB9xpDVkoNZ2A0YCJpCAYDb9PqLrxJz0fZV54j9dRXdjOa+5jQV+5XGKqZpO21/BsZw5P5S3bCgSBLshGNxXQ+5I2a+BqN/Wk9geY6H5zG4nt/2uQRq1eAjRvMw1C6EIBjv8+DxGsr71bbfQ86303zv4zgOuVwOk5OT+NKXvoSvfvWr+OIXv4hsNovV1VV88skn+Oijj3D//n2sr69H+9fOOmri1B1Uge2+XgbYepVKBblcDu+//z7+9b/+1/jmN7+J3d1d/M//+T/x0ksv4dd//dfx6quv4l/+y3955H2tra0BQNRYKjQ2NhZ9bW1tLVqFEhJFEYODgw23ibuP+sc4DlpCTDrC87zY2a6CIKTuwzluqez+/j6mpqagqipu3LgRre3vtOcZ63Pa0rKE+M78Y9xaWMHVq1extr4O13EwPj6OQj6fOEalapgQBD6xIZFuBtXBuHNURZJiGylxOKxZmpZ95D7Stst8zaBKq8hSbNfisFFSwz5T7vCk2rAc5DJKYrdgnzHkVAWmbcN0bMSFxKOWWtfPjY3j+T7yWRW7bZb4mrbT8DxzQQk24jhe0GG4aalxMAc3+DfDchJ/VwDAfAZNk3DQZhmxbloo5bPYrQV5nzF8evch/uZ7X0z8nn6RhotMZ1Enq4M8z6NUKqFUKuHatWtwXTeaAbm0tISZmRnkcrmow3GpVErFyfBpoyZO3UEBtvt833/mua2nKW6EjqIo+NrXvoavfe1r+Lf/9t+iWq3i4OCgR0f47Hr/7JIz5ajZrmlfQswYw+PHj/HgwQNcu3YNL774Ylc/YJ91rE8npCHAziw8xl9+djv4vSwvRx3reJ6v7TWND7C+z1DIaNG+0mau5yKfyeCgqYmQJApgjMFy3GCcTH2wqktRtuO2bRZ11Mgez/MxUMgljrNhjEGVpaYGRVxiiItjWjZEUYCeMPPGsMKGUgnNmsJjSAiG+YxWC9D10b6R47R/HgC0NGHKao2B1vWCJdPNIbdetVaFTRrdoylyS6V4dmkV737hBQwUTq+LODk7urm8VRRFDA8PY3h4GMDhDMidnR3Mzs7CsqxoZMbg4CDy+fyZCCS0hLg7KMB2n+d5DUtke6VSqSCbzbb9Owv3zx4lbFy6vr6OiYmJ6N/X19fx5ptvRrfZ2Nho+D7XdbGzsxN9//j4ONbX1xtuE/73SZqj0iuanJqwUVMYXuO6DKepSVEoDNWO42BqagoPHz7EO++8g5deeqnrH67PMtanU3odYB88WsFffncae3v7AICBUgnj4+PRB3HVaL/XNKySJmlehhsucQ+DTvPP3hzT/CPGzbR7neczGqqG1fb1VTVMiHU/X7gHNlTRLWhtPiBVRTqyYdVRJzVV04IQ03Qqo8g40I0j9+MC4fMclWBbqtKWHczHBQBJEGDFrNAw7filxiHfZ4n7nnmehwcG3bQb9haHVVhC0iacAfnaa6/h/fffx7vvvouxsTFUq1VMT0/j29/+Nm7duoXl5WVUq9W+rcJTsOoOep67Ly1LiKvVKnK502lYeO3aNYyPj+PP//zPo3/b39/Hp59+ivfffx8A8P7772N3dxff//73o9v8xV/8BXzfx7vvvhvd5oMPPoi2FwLAN7/5TVy/fh0DAwPHPh56RZPnxhiD53nRbFeO4xLfLNNaga1UKvjwww/h+z5u3rwZjUbotvC5S8Nz1Msw/fDJGr7xl59gbXUN+/tBgC0Uiw0Rxvd9ZDPJ+yNtx227f9K0bWRqX+cQVOlMq76Zkt2wV7a5Y65uWm330objaJplVAUV3YTttN+H6vk+Mg0/X2sFVhTj/84K2Qz2qwZsO7lZE2OHY4MSjyFmD6rIB+N4wnu12nRkBgCreb9tzOGE3YxlSYQbM3onCMrt98LqphXt+62XUeXa89C6DHl2abXtEmhyfqWlOhiOzLh48SLeeOMNfPWrX8Xbb7+NYrGI7e1tfO9738OHH36ImZkZrK6uwjSTVyqkTVqe47MuDVuSzpt+DbDhxI2pqSkAQeOmqakpPH78GBzH4Z//83+Of/fv/h1+//d/H7dv38Y/+kf/CBcuXMDf+3t/DwDw2muv4W/+zb+Jf/pP/ym++93v4sMPP8Qv/MIv4Kd+6qdw4cIFAMA//If/ELIs42d/9mcxMzOD3/qt38J/+k//Cf/iX/yLE/1stISYPJewUVNYbTpqtmvamjgxxmCaJnZ2dvDKK6/g6tWrPf9ATUuA7VVDqaXVDfzP//0trKw8haIquHL5Mh4+fBikj6bfTRA4GZKqc/4Rxx52uM1ltJblxEBTM6WYjZhHfUA1nzOE+1rDKqR3xO/ZtOwoODdXYIH4faqaIkdLmy3HabuEN2jWlDy3FgAM0wbPAeGWWVWRGvbeWo6LvKY2dDluFv4ekvYFm5aDUiGD3YPkbqyW47bdC+v7DLmm8UAZTakdV/B7Di4qyNGy6aAKu4CfeLf/98KS05XWcMVxHPL5PPL5PK5cuQLf97G3txeN67l//z40TYuWG5dKJUhS+07dvUJNnLqDKrDdl6YAe5LJGZ999hl+7Md+LPrvMFT+43/8j/Hf//t/x7/6V/8K1WoVP/dzP4fd3V38yI/8CP74j/+4oU/Mb/zGb+AXfuEX8OM//uPgeR7/4B/8A/zar/1a9PVisYg//dM/xc///M/jnXfewfDwMH7pl37pRCN0AAqw5DmEy27Dq3vH+bBP0xJi27Zx69YtmKaJyclJXLt2rdeHBCB9AbabLfiX1zbx//ntP8ba2vrhvMTaEtW4IG0fscdSNyyoqgIzpikTAFR0A8V8DnsH8R1pq7oJSRLhOG7sTs+qbkISxcSuwBXdhCLJsBwHPM+D5zjY3uFtDTPchxp/fLbj1masBiGs+SlgLJjvartBaBMFAZ7vNwT3o5Y6h2OFnIT9o47rBcegmyjU5uI2c/32f9OGaSGrKdhB6xJiIAjFdpsQDYS/a7XtftqKaUXNu3iBr+2Jbf++dP/RKt79woso5Ts3HqvT0hi0+l1aA2wznuej98oXXngBrutG82cfPnyIarWKfD4fNYQqFoupOLEGqIlTt1CA7b60jC46aQX2r/21v9a2aMFxHH75l38Zv/zLv5x4m8HBQfzmb/5m28f5oR/6IXz7298+9nHFoQBLTixcMuy6bvTGeNwPobRUYLe3t3Hr1i2USiWMjIxAUdovT+ymNAbYbni8uoH/9//vd7F/UMHExAS0jAYAUaOmpLfUo6qscctKQ/ms1vbnYwgqmo7jRnNgG75eG8nTbqyNLAuwHQeaIsd2HD7qxCJsTBRXgQVqDYx4Hp7vQ5EkVJuWEOpHNGvyfYacJsNxk6uftuMG+16r8eHRsBxk2wRxoFY5jbkKwPEcGAC9tuQ66TgB1EJ2m4q7F3RGPqiayCoKDmKeb920kVFk6NZhFfaTmYUz0ZGYnK5+DFeiKGJkZAQjIyMAAMuysLOzg3K5jHv37sFxnJaGUL36OfvlIkG/owDbfWlZth2O0TmLKMCSEznpkuFmvd4DyxjD/Pw8Hj16hOvXr2NychJ3795NRagOpSXAdnMP7MOlFfzn/+9vwwcwOTkJoW6up+N6UGUp8aqgbphQFRmmlTBSR4+vMOYyGg6qRlAZ5bnEsTJVwwTH19pBxRxD1TDBczx8Fv88VXUT+YTKJRBUgRVZTFzGa1g2spqK3d34vzPP91HIBWE/6TGO+hOtGGbb58D1PCiyBGYlh8ukkUEh3bSgyiJ0o/E+cpoau3w7jmU7tSpsclDWTRu5jBobXiNNzwd1JCbN+rUxUjNFUTAxMYGJiQkwxqDretTh+PHjxwAQVXAHBwehaVrXQiUtIe4OCrDdl5YlxLquo1Qq9fowOoICLDm2cLbrSauu9Xq5hNg0TUxPT8O2bbz77rsoFArRMaUhMIbScjzhCKROn8hN3bmH//G7fwwlk8PAwEDr6yocHdrmOCRRTAywQZVUgeMeNuvJqGpUDfV9H/lsBgcJ4c/zfOSzwUld3BF4nh80TdLjmwFlMxqOWsZ61D5UjmucAxunbagzLKiyXOvm28r3WeLyYCB+DE3LY9Qqm0abkMtzjSdRwbzXw8fUTQuaqsBoU4V1XR/tqrAcAD6mc3LcsdZXYT+eWcDfev+H2n4fOT/OYnWQ47hoZMalS5fg+z4qlQp2dnawubmJubk5yLIchdmBgYGOrU5ijJ3J5ziNKMB2X5oC7MWLF3t9GB1BAZYcqXm267OGV6B3FdjNzU3cunULIyMjeOeddxoGTKdpXy6QngALdPZYPM/Dh59+ht/+s48wMDiEsZHhxEqc7QQVwIQCISq6AVHg4Sbs99RNKwrjqizDsu2GMBgsU04ORdGs0oQAaSZ04s1qKipVA6IoJDYwAoIqbbvl9RXdhCJLiMvIqiyhqlstYbAeQzDjNgywwd4zoP7nNSwbPMe1LMku5DJRsM1qCqpGclA+Kjgalg25Vl2XRCG2g/ERdwHziCqsqkioGsH4Hy/pBQO0/KofPA6qsEPFs7ncipzMeQhXPM+jUCigUCjg6tWr8DwPe3t72NnZwfLyMu7evYtsNtvQEKr+s/N5hO+/FKw6jwJs96VlD2w4B/YsogBL2gobNYUhJqzKPatuh0Xf9/HgwQMsLy/jC1/4QuyVKEEQYMfMnuyV8xBgq9UqvvWdj/Ctzx9gbHwCoiS1f11wgMDziQ2JGGPIaAr2K/EBznU95LIabMeB5/st3X9tx0FO0xI76Vq2g4wqJ1ZAbbvW7dc4fHxFlmBaDhjCMTDxnY6BoAJYUBXsV5NHukii0PL4gsCDAfCYn9iEKVSpLaXeKZexsbEJjuOgaSo0TYOmBUuQg4ZRdV181cZ9r0cV46uGBVWWEiu94LigszMHyJKAakyltWoE44mMhIo6kFyFzWmHTZ7CxlNJwrmw4Z5bxoBP7izgb9/8UvsfMmXOylJX0nuCIGBwcDAaI+c4TtQQam5uDqZpolAoREuOi8XiMwej+nMK0llpCVPnBWMsNc/5ac6BTRsKsCRW+Af4vEuGm4UV2G5c3dZ1HdPT0/B9H++//37iH3GaAiOQropwJ56btbU1fPjp9zD9aBPDo2PR6+CwiU9rZY3jOFQME9mMFjU1ambUjZyJw3wfHDjYbsIy2iOCAENyIymgca+wIAhgfuOYnHaNngDAsKzYCmioec4px9VG5tRCt2nbQYBLCOE+87Ff3sPW1jbGxkbBcTxM00SlUsX29jZEUUQhn4coSlA1rTaT1W+oGuum1RD6mjEAoigASQG2dh8TE5m2Yf2oSq5pO8iqKqp1rxVJEqDXdZvWzaOrsM1P9dyTNWztvoDhUr7t45Oz7zxUYI8iSRJGR0cxOjoKANHIuXBkj+d5KJVK0XLjXC537OeMKrDd4/t+akcpnUX1PWJ6Tdd1qsCS8+N5GzW1E16R6vTVqbW1Ndy5cwcXLlzA9evX2z5WGgNsWo6H47hTOxbf9zE7O4t7DxbwYL2CXKHYcpuk4MIhOA5NVXBQiQ8+TsPImeb75eExBlHgkVTY000TqqLUZsu2smwX+XzyW2awfzPYZ6pIYkvIM20HWU2N7UQMBFXadvtQAQ5S3eu4kNWw13TbpPDrui7WVtfAccCVK1eC2/oMqqqiVCrB932YpgnDMODYFjY2N1HI58AJIjRNg6Io0XsAf8R7QUU3Icti7FgcDkFl+qjXVPWIPbvBz1p3HxwgCQIc5/DiRrh3uV0V1rAa98IyBnwys4D/8+abbY+PnH0UYFupqooLFy7gwoULYIyhWq1GDaEWFxcbRvqEDaGSUAW2e2gJcXeF585Uge0sCrCkwbPMdj2J8A+6UxvcPc/D/fv3sbq6ijfeeAPj4+PHOqa0VDyBdAVYnudPZYmiYRiYmppCeb+KdQMQJDn2dklzVcPKqmm2r7LG7YHlOA5abV9kOFM2SbuROxzHtf06ELyWcprQJjS1fy4tp32V1rQdiDwPVZNbwisQVkgbq9iGbmBtfQ3ZTBYjIyMo5bMoH1Qavo/neWQyGWQyGWhKcKV+Y7sM0zCwsb8f7B2uLTV2HA35jAYz4ViD/bbxAZbnOXiuh6phQhYF2G2WPYsiD7RZ2W9YDjKaAt2wkM+osRcujlOFbTb/ZB0b5X2MDhSO/T3k7KEA2x7HccjlcsjlcpicnITv+9jf30e5XMba2hoePHgARVGi6uzAwABk+fB9P3wPp+e489KynPW8COcb9/qiQXiRKZ8/myuKKMASAM832/Ukwj/oTgTGSqWC6elp8DyPGzduIJPJHPuY0hIYgXQdz2kcS9hAS87ksLClg28TAhmAjCJjr3m5bW1+qO26KDTt06xnmBYymgq91miI44BsJmikBATjYhRJim0eBAQjceJG7tR/fUjLJH6d57i2XXgP93cmVXnDBkWtYYzjOHi+33YkT3gMAAAG7O7uYmdnB8PDw0HXbQ6o1hpaJYVpjuMBxpDP54MPPsZg2zYMw4BerWJnZwd5TQXjBWiZDDRVBd90clTVLUiSGMzPraMpMspWBYwFldh2Abaim1AkOfF3BQDMZ1AVKbGh03GqsMHS9ca9sN+//wg/SR2Jzz0KV8fH8zxKpRJKpRKuXbsG13WjhlBLS0uYmZlBLpdr6G78vD01yPFQBba70tKBGKAKLDnjOrlkuFl4Veq0A+zKygru3r2Ly5cv4+WXXz7RmzVVYJM9z7EwxjA3N4elpSWMX7yMv5p+UNunirbLQ6tm617Q+qpr0MAnWf1rN5z1enhQgCyLiaEoGLkjw3FbA2KwnDr56/mshv2KjnwuE/v1kCgKQHIj38RZrEBQwbRdt+1+3aphQhIELD9ZgWmZuHDxAlT1sPLseh5ymoq9mKXYQXh3Idd3GuU4yIoCWVFQLJXAfB+mZcK1beyWy9hwHCiyHDWDUlQV4ABVkhoCbC6jYWVvP8rNFcNqe7EgOJ7kJd8AYFoOchkFJmu/5/Yke2GzmoL7S6t465UrGB9qXeZOzgdqjvV8RFHE0NAQhoaGAAC2bUfLjWdnZ2FZFhhjWFxcxODgIPL5PIWsDglX1JHuSNPzXa1WaQ8sOZtOY7brSZ3mKB3XdXH37l1sbm7izTffxMjIyInvI02BEejdqKE4z/rcWJaF6elpWJaFl65/AX/4nR/AqC1rZQyQJTExwLpe617Q+hE0umUhq6nQE/aS6oYJSRKgKQr2Y0JaVbcgCDy8hI7GVdMCx3NgTYEnDNFxX8/Udb+t6iYEnm/pdByqVOOXSUfHH7MMOHx8SRRQNSwUcslVWMu2sVnehu8zTE5Oxl4JthwHLeN2uSDA6qYNx/WCeawxlWKO56FpGeSHBpEvmvBcF4ZhwDBNbGxugvk+VFVFJZtBJpsFzwtQZSmqioeYz6Bq8RcDoufKMCHLUuxyZADIZZTE0UmhY++FVWXYjhs91ke35/H3/9o7be87LaiKdfpoCfHpkmUZY2NjGBsbA2MM29vbuHPnDqrVKp48eQLf96OlxgMDA8hms/T8nxKqwHZXWiqw4RJiqsCSM6V+titjrGvhFTi9LrsHBweYmpqCLMu4efNmQ5XppMeTlsAIpOt4nqWJ087ODqanpzE4OIiLV17A7/z5x1F4DVV1EyLPw0247+alpc37Xts1EmKMIZdRUd6rxn7d930UMsldcD3PQz6bSVymHAWi2tcVRYJlO9Hx+b6PfC75+xkYNEVq25U4rplVLqNiZ2cHQNBQKk6lUsHG+gZKAyVcuTACJ+HvzLbd2kzXw4CazzSGYuGITsAV3Qr2sQLI5fPIhcuNHQeGYaBS1aFXDmC7PoqFHHhBhs8auxpXDROiIMBt834gi0JsgM2ocm0sERfthU1yvCosgypLqNTuZ2ltC082dnBpdLDt80DOLgpQncFxHGRZhiAIeOONN8AYQ6VSQblcxvb2NhYWFiCKYtQMamBg4Jk/3wkF2G5Ly55jXdfBatuBziIKsOeQ7/twXbcrS4bjPG+FkTGG5eVlzM7O4urVq3jppZee6/jTuIQ4LcdzkiZOjDE8fPgQDx8+xPXr1yGqWfyvb34Y29XX830UcpnEKqJp2bWAVQslXOOSvopuQpIkODFLgfO5DPYrRtsLAYZlN1R1m9m2g+Y5o/UhOgxUkijA91nL78s84v6rptX2+Cq6CUUWo6BayGpY29yOlrpathM0LqpVocOKxv7+PsbGxpDNZaGpMpw2e2XrxxHlMmrL76JqmFAlqU2zpiDwNVxsqJ2YyrKMYrEIjgME5mNzZw9GZQ+2bYPjOezs7NSWGysoagoO9OTXe1U3IYsS7LrAL/B8LZwHv5/manmz41RhBV5A877gj27P4//1419pe9/kbAov7JLOqK9wcxwX7bm/fPkyfN/H3t5eNK7n/v370DStoUJLY2GOjwJsd6WlAqvrwUV6WkJM+l79bNfww6MXV5ifJzA6joM7d+5gd3cXb7/9drS/5nmkqeIJBMcTF8x64bjPjW3buH37NiqVCr7yla+gYrr4X9/8Ttv9nKblILmVUGP1g+O4hhtGVcym5ymX1WpjdrggIB81ckdPWIbrOMhltJZmSmGAtRwH+YwGx/dhxsytPbz/+MDkeT4K2fazUGVJhmW7yKhK7Tgan62wmui5HtbW1+B5HiYvTUKSgxO7qmG1rXKbtoOcpsL1/djZu4wF3YSTAiwQ7GMVBT5xGW9WVcHzPAYRfJjv7u6iqlfh+R42tzbhez40TUMmm4GiKJAkuWVpMwMgy0JDgNXUxsZNRlMjpjhV00pc2i0KQtDhuanB2MpmGUurW7gyMZx4v+Rsoj2wndUuVNWP43nhhRfgui7K5TLK5TIWFxdx584d5PP5qDpbLBZTERjSigJsd6VlD2ylUoEgCGd29QIF2HOifskwgJ52/3vWCuPu7i6mp6eRzWZx8+bNhpb8z3s8aQuwaTme4xzL7u4upqamUCgUcOPGDaxt7+K3v/khbMdFIZeBnRCAbMdpG/Kq+mHH4LhqZkU3G44vl9FQjYIeYNo2mquo9dotWwVamyk1BGoEY14O9o9X4YxzZJVWN6DKMhzXhc+CPar159S6aYFjHpaXl6FqKiYmJho+ND3fRzGbwV6bkAwunLEbfwwVw4AsSYm/Q58x5FQF+zEjbGRRiKrvPB88Bs/zEAQBI8MjYAiaxxmGAXgOnj7dAc9zUFUtagglikLtuTAhSyJsx0vs0nxU3vDbVGEVWUTVsOB6HvKagoO65cgf31mgAHsO0R7YzjrJ8yuKIkZGRqIeF5ZlRQ2h7t27B8dxUCwWoyXH+Xyefnd1KMB2V1oqsGEDp7P6u6cAew7Uz3ZNw2yqky4hZozh0aNHmJubw8svv4yrV6+e6odTGpcQpyXAttsDyxjD0tIS5ubm8NJLL+Hq1atYWt3A7/7ZR1FnWd1ov1S2XYWWIRi1EjQcap39Gi5DPqjoyGoqqobZEGJs242tooYM0w72TsZUHwFAN02oihKFsPpjyGU1lPerbUfiGJaNbEY9XAbdxHbD40sOwZoqobwfVpnrK7AMe3t7qO7voTRQQqlYis3pcR2d6/EcD8a1786rSGJigAWAqmG37C8NAr4Ao1YRDZpymQ3HyIGDLMmQJRmCwKM4MISqbsAwDOzv72NraxOSdNjdeGSwBMa4xN/XsaqwRmsVNqepqNQ1BLNdr+GZXt3excLKBl68OJp4v71ElcLOoADbWc+zRFtRFIyPj2N8fByMMei6HlVoHz9+DABRBXdwcBCapp3r3yUF2O5KU4A9qw2cAAqwZ1q3Zrue1EkCY7g09eDgAF/5yldQKpVO/XjCfZ5pOWFJU4BN2gPrui7u3LmDcrmML3/5yxgYGMDC8ip+7y8+blhO6npe26W8VcOEpsowEmalVI2g2VPzHtiQZTtBx17Ljq3AtRs3E/x87X/fzUtKGWMNHZKP/JA6Ilu4bRo5ZTUV+1Uzql6GFVjGfGxsbMIwdIyPjaNUKtaqzTH373mJS5XzmaAamdWUtscYjuVJagjl+T4KGRX7dRcK8k2zenUz2PsKIPY5CfaoqvAZg6apAAbgeT5MMwi029tb2NhYx0A+ByZK0DQNsiyDa0rtR4U53/eDiwa1wCqJQssFCMtxW0Ltx7fn8cKFkVS8PxByFvi+fyp/TxzHIZvNIpvN4tKlS2CM4eDgADs7O9jc3MT8/DwkSWpoCKUo7d/zzhoKsN2VliZO1WoVmUym14fRMRRgz6jwTXxlZQVXr15NTXgFjr+EOOxmWyqVcPPmzY41bQjf2D3Pgyj2/k8ibQG2+Vj29/cxNTUFTdOipdz3F5fxRx98L3Y0jX3Efl5JEGEg/jaeHyz7bN4DGxK44HWd1MRHN6y2VdKKbkIWxYb9lfWquhnNKuU4Dpoi1cJg8LdUNYza0taE7zdMqEryzFvTdqLqcb2gC3IQOoMAGnTbZYzhyZMn4Hkely5NQhTFYKluctERlt26VDmjKcEeUi6oSqqylHiMvs+Q02Q4bSrFumVD4Dh4tSXFzR2YXc8L5uTuJb8H6abdUB0VBD46MWUM0GQBVV3H1s4ednd3g99Hw3JjEYblNDb/in2cw327siiiGlPRtV234Tnb3D3Ag8druH5lIvF+ydmSlguaZ1Wnnl+O41AoFFAoFHD16lV4noe9vT3s7OxgeXkZd+/eRTabjQJtqVRKxed+p4S9TyjAdk9a9sCGS4jP6vvY2f2rPcd834dt2zAMAw8fPsQLL7zQ60NqcNQSYsYYFhYWsLi4iOvXr2NycrKjf4DhlbI0h8ZeqT8WxhhWVlZw7949XLt2DS+++CI4jsPtuUf46PO7iXNVTcsJltImLOWt6EbbUSqWXdsD21RdUxUZlutAPWIvtCAKQFKeYYCiyIkBNmgWFcwqzagyqqaFbP7wtchYMEan3RJbSUyeeRsdRJ1cRovCKxCEXI4DTNMMRvzkcxgaGo7+Jg709nNlLcetVVuN2vEIcBwPDCyqYEqCADPhIgIQzGNtN9vW9XwUshoM005s+mSYdnCxIaEsfdgpuDUoZ1QZpmVDlFRMXszCclxYlgXDMHBwcICt7S1ItcqsXyoAnJB4AuH7PrKaBgCJ+69tx22pKn98ZwEvT46l4sSEdB4F2M7qVqgSBAGDg4MYHAzGYTmOEy03npubg2maLQ2hztLfePi5mYaK4HnheV4qumRXKhVaQkz6Q7hkOOwyLElSqvZ2htpVYE3TxK1bt2CaJt59910UCoWuHA+QrgCblt9bGGA9z8PMzAy2trbw1ltvYXg4aGrzg7vz+PNPpyDwAngOSNrS2u400GcMeVXGXsLIF9txkdVk2N7hnauyDM/z4Hl+sAxZkWEmLUOuq6ImfV0Q+MQAXjFMZDQZu2UvttJb1a32368bbQNmta5KrCkyqmZjqLJsB5Zewfb2Nniew/DwSMPXGWPQ5PZzZcOLAxw4SKKEit34XFeMxrE9zXz/6DE0pmVDEgQYbZYzZ1UZB5XEu4BuWtGS6RBfC87BvzAosgjb9aCqKlRVxcDAADzfg2maMAwDK6trkDgOjBei6qysKA2vQcd1j2z6ZNhOw/7h8kEVM4tP8cUXL7X/RnJmUIDtnF5dIJAkCaOjoxgdDfa0m6aJnZ0dlMtlPH36FK7rolQqRYE2l8v19esgPK85S6E87TzPS0XnX13Xz+wIHYAC7JnBGIsaNQHBm5UoiqlcPpK0B3ZzcxO3b9/G8PAw3n777a4t6wk7MqcpNKalMQvHcTBNEx9//DEkScKNGzeiN+ZPpu/jg+/fAVBbIppJHktTNcy2y1R1024bgH12eCVZkSR4vt8QSCVRTAywjDFoqgInYR+u7/soZJJH2ogCD0kQ4DMWWz30w5m2CffPWG3kT5uAKQg8JFGA6/kNIdnzPKyvr4MDw9jYGDY2NmK/v2qYEHgBnh//GjYsGzlNBcdxsSGUAZBFKTHAAoAREy7rqbIEBg5ot5y5TaUaiK/CZhS5YT9qpa4jcUjgBWQzWWQz2f8/e/8ZK0mWXoeiK7zJyMzjypxTdarLtJ3u4Uz32K4eSnickSgDgQIE3DuXoweCEkQBAiERBCRQgEhAlAhBgiAQ5A/pj4RHPXAEEO+SvLzS1VBjOLZ72sx0nfLeVx3vMsO7/X7siDgRmbF3ljkmq06uPzNdJ01kRGRkrL3Wt1b238D6Rgee72Nzc5NuX0ZmTcOAoKtQJJG7PVGcoGUa6JS25f3zN/Da8WnIQ6ZmPMs32MOKkQK7sxiW/avrOmZmZjAzMwNCCBzHKRKOb926Van0yQOhniWMCOzuY1hmYG3bHhHYEYYbZdW1XI+Tf4GGxY+fQ5KkSn9nmqa4du0a7t69i0984hM4cuTInmzTSIHth+M4WF5exvHjx/HSSy8V59F3PzyLD85drTw2SthkgBBAVdgENopjOvfJ7GWNYWoaVFkGyR5fRtf1IMsi4rj+GDqeD0Fkz8p6QVBbaSNJEgQImf2VHRLk+UFtUnIOWvnDJn+uH8DUtApR8/0ACwvz0DQNBw8ehq5KWFparH1+khK0LLUIl6qDIktY71KSXXfbSOeBJYQMpTq3CXdqCHBD19BxPaiyjN6+2uprJDA1vrXKCwKIgoiUpLUpzQToI7C9SFJg5vAB2G4AAhR2Y9u24XY7mF8gME0TraYFSVaY10c3CCvHrev5mLt2D5959Tj3M4zw7GNYFhGfVwzbwjpAF4Isy4JlWZidnUWapkUg1OLiIq5evQpN0wp1dnx8fNvq/HYK+X3NMCwW7BcMyz33KIV4hKHFoG7XcjjRMPjxc0iSBD+zSbqui7m5OSRJgrfffnvPvmzDOne6V0jTFJcvX8bKygrGx8fxyiuvAKDn3P9696eYu3Kr7zn+gAAdmigsIWaohLxEXgGAKNJzPKgJhSKEwNQ1dOx6AkeVPbMyW1pGFCV9yp8oCFAVGV4W8tMwdASMMKgoTvqSd8soVFrG301dR/n+otPpYHl5BRMT4xgbGy8lENc+HQANrJIEEQnpP3cMTcWm7XJrZggINFVhEliAKrm9tTyKJGXHRECYqZashQgA2bwwh+TGlCj7UQTXZ3cEDyKxUbaYIQDQNQ26pmH60EG4flDYjbsbG+g4HjRNK9mN1eI6SlOc9UrP7YeXbuGNU0ehKaOfz+cZw6IQPq94FvavKIpot9tot9s4ceIE4jguAqHu3LmDCxcuwLKsSiDUMChvZQxTA8V+wahGZ3cw+gV+RpF3u/LsIflFa1jUvBy5wri4uIhz585henoar7766p5+4YdJ9dxrAuu6Ls6cOQMAeOGFF+B5lIwkSYr/+cOPcPnmXeZzeT+SyQCrLa83Vc6Sgnm1OI4XcFXWIApBSVP9NpZDpAQIMHStkg6cpikzgAgAMwgqhxf0pwEDKGp5REGAKApYWFiEbTuYnj5cicD3ghCqzF7VjZOcJFf3ryxJSNIUKSEQB9zEON5WQm8dojipqLACAEWRKqQ4iCIOPRUQxSmapsadp/WCEIoiMcOxqAqrcAlsEEawTL3oARZEoeieNE0TpmlCAHBAADY63aJ/loBA1/WC0NquWAkZ84IQP71yG2+/8SLzvUd4PjC66d85PE0P7F5BlmVMTk5icnISAK35y+3GV65cQRAEaLfbBaFtNpt7/hmHUel+3jFMBDY/V59HjAjsM4Z8pjUnr4NW1mRZHhpilkMQBGxubmJ1dRVvvPEGDh8+vNebNFQW4r3clqWlJZw9exYzMzN49dVXce/ePTiOgyiO8afffg+3HiyUal364bg+NFVBwKqNCUKu1ba31xOgBEySBHh+lCUC15OavHO2y1BhwzDOLKn1xMnzQ1pp4/uwGnqfmuqHMXSO6hYMSFuOon6btGXqxb4MwhCdjTUEQYTZ2Vkofe8lDLyh9nus0AIEaIpSBEPZng9DpWnKIAToeb2UEFi6zrUie6VanmaPOgnkqcd67X6mb0coyRXYirKuqdzgLwBwXC8jsbw51q2/NXQNds/iCAFg6DoSAjSbTYAAQUjtxo7jYG11DZIsYWq8jVSQYOgGREnET6/cwadfPAZDH2774AhPjpGFeGexXT2wewlVVXHo0CEcOnQIAOB5XhEIdf/+faRpWgmE2otKkxGB3X0Mywys4zh44YUX9nozdgwjAvsMoS6oadDFcJiURYB+oW7fvo0oivDOO+8MTcnyXqueZezFtpTnkN944w1MT9O+S0EQ4PkB/ugb38eDpVUAfKWRAFwCG/ZUuvTC8TxoilKE68iSBFkSqR2UELi+X/SN1r5+GIOnsvIUXIASLJYVmFFFW8Ggm15KqOj2mSWF13VdLCwsoN1q4tjs0dr3EQRaqaMpMjN8KOyxQjdNvRJCBACyzP9hdbyAW5kTRQmshgEQUvTT9j+Gr0ZvnQf9JNfMemRlWSpmYeuwNQvLfq8gjGGZOggh2Xxx/7ba5YAxAdA0DZqmYWxsDCQl8H0fvu8hCBwsLS1B0zSMt1v4zofn8NffeXPPbw5HRGtn8CxYXJ9lPI/71zAMHDlyBEeOHKHXHNvG+vo6VldXcePGDciyXAmE2o2k2mGZx9xPGBYFdpRCPMJQIFdd84vRo174WYm/e4GHDx/iwoULmJiYQBAEQ0NegeHaT7tNYH3fx9zcHKIo6ptDdv0Q/+uD82i0xrYeH2RKJWvW1fUhSQKSpP7GmkWMgK2wpyCKM/IqwQ9DakklZCtIiKEQ+mEIy9ThuPXb5noBDF2Fx5gDFSURIYN8C4IA1w8xrSnMxGO3VIlTu32ZTTqKE4RRhDQlRSfhgQMH0Gq10GR+Pvqdl2WJm56bE7pmT4JuDtv1oCkyPMb5nqQpd5EBANI4QZgkYC0U+FGcHYdegioUqmudCivLYrFAEhd2ZfZ2UBWWPwsbx0m2cMG+ZkpS/Q2eIAowTAOGaaBlGljv2PB9H0EY4Dvvn4HorWPm0IGiZ9I0zefupny/4nkkWMOE510ZFAQBzWYTzWYTx44dQ5qm2NzcLOp6rly5AsMwCkI7Pj6+I1klz/t+HkYMy6LBaAZ2hD1F3u0ax/ETDeMPgzU2jmNcunQJS0tL+NSnPgUAuHbt2p5uUy+GTYElhOzKDdTKygrOnj2LAwcO4BOf+AQkQiD9zu9AfPddrHz28/izmRex0XEqBBbg97omKZ9kun4AU9fg+iyS6UNTZIiiUBBBQRAKVXJQFcsgQYq1Mtq0DHRsF82GwUhLppOdiiTBRz2BBSgJ5m8gVVODMMLS0hKCIMCRI0eK1Xg/iGpnZfNTwXZ9qKqMiEHagjDCWJNt8yYAJbCM/Q/QJGCWCpsfC0NTuXOsMTMMin6uOhVWU9TK3HFvCnDdK6kyn8DKMr1m8sKpnAELDwBNujYNHWMtC13Xx9j4BAKVzrutrKzgxo0bUBSlILPPQkLpCHyMCOzOYb8tEJTreE6ePIk4jrGxsVHU9Zw/fx7NZrO4drTb7W1R8UYEdvcxLAqs4zgjBXaEvcGTWIZ7IUkSN911p9HtdnHmzBmoqop33nkHuq5jZWVlaNTOHMNktc5/bHZyjoIQghs3buDWrVt47bXXcPToUQCA9Du/A+lf/2vMyxr+x9mbiH/2L4NkCcRlOJ4PTVWZVmFKMtmqF0vxAqjqZeoa1rtO6R+35maDMIJl6JXKmd5tMzSVqZI6rgdFkSs2V8vUYWeznLbrQ5HlvlnbXC20PR+KLFV6aHvfX1WVWiU3//5GUYT79+9DURTMzs5WjnMYxwwbM30uISmdBY7qCaosb4U2sdB1fSiKzAxripO0rwM1h2Vq6Do+dKKAd4z9MEKz5zj1Xr7KKmzdZ47j/nToXticczEPcaLzxOxoKQAQB1xaKWmXsWHntmkBtxfX8aVPv4Zjx44hSZK+hNLyDenY2NjoRvIZwsiavbN4FkOcthOyLGNqagpTU1MAaNVXHgh16dIlRFHUFwj1JIR/RGB3F2maghAyNAR2pMCOsOvIu12fNgJ9rxRYQgju3buHK1eu4Pjx4zh16lRxER0mu26OYVCqc+w0gQ3DEHNzc/A8D1/4whfQarW23vvdd3FTNfEn4zMQCYH08CHIyy/3vUaufDEJbBgxZxwBamNVFKlPRZRlCYos0d5WbNENocdrOujmUpElJoElBNBVtSCwpqHBCcJC8SSEQK8NixIKZdzQVUSMsCgQqnDWEVjL1PFwYQndzXVYloWJiYna73bdDGn+MEIoyZYkEUkPARUEQFUkdB0fpsFWuQkIdFXtCzUqwwv7K3Oapo5uRvT9MILV0GEz7NoAENVUJpUPXZhZjeMkLV63F64fcFVYAFBkEb3iqaLIhaJKA7TY5yMAOH6Ihq7RgKsaiIIIL4izedm4+Cw/OncNv/Czb0KSpEJ9Bej3bG1tDWtra7h48SLiOC4CXSYmJvYk0GWER8d+Uwh3G8MSdDMs0DQNhw8fxuHDh0EIgeu6xXjJ3bs0+b9sN37UcYURgd1d5PeRe31uE0LgOA4NJ3xOMSKwQ4bebten7e/aCwU2iiJcuHAB6+vreOutt/pivIeJLOYYNgsxgB3ZnvX1dZw5cwbj4+N48803IcvVS8DZN97En1+8j1QACAQYx44yq2Nc34csiogZ28nhGyAEMDQNUbRV+ZKT13w+tUw4epOLHZ9v+cwVRlaYkOP5kEQRqkpJOOn5DK7v9wUI0e8hKZ7PO2ccz69UrwCA1TBw8849dDsdHDp0CBMTYwjC+u3zwyibMy4Trvw6QJPI69Kgm2bJuj2A5G9tI7syp6yK6qrS1yEbRwm4KmwQ9SQS9z8uSdLs2DJmppN0sArr+tDLqrtA+2nd0vH3BiRgA/yQL8vQ0HF9NIyqLfjmg2U8WF7HkQPjlX9XVbVyQ+o4TqGw3Lx5E7IsF2R2YmJiZDceMowI7M5ivyuwPAiCgEajgUajgaNHj4IQgm63i7W1NSwvL+P69etQFKVQZ8fHx6FpWu1rjRYKdhdlx+ReYxTiNMKuobfbVRAG12YMwm6rnZubmzhz5gwajQZOnz5de1EdJrtujmFShfNjvp0ElhCC27dv4/r163j55Zdx7NixvnPrx3OX8YOXPgnhL29CuHcfZPYogtNvgzx4WPuaCYNE5citvCyS6XpBQQJlmQY2lcOVyupinflTliWAIf4RQmCoCpPApmmKsaYF2/Nq5zyTJP9sWwS7LAIneecqQ4VNUwLL0orOW11TcPnKNaSEZBU5ClRZYRLY/DOUUVZgAUriy3OqvXPHrh/SWeOgfycJEJCkKSyTP8caBFFxHSKkP4DLz7tWOUpu1Pe9qn4uSRIhQeDuC9fnJyMDgFS6YWiZ/fU+cZxQBZmnOgdhrT29oWuZnVqA44UwdbVC5n909hr+ty9/nvm6giDAsixYloXZ2dki0GVtbQ337t3DxYsXC0V+YmLiseffRkRrZzDarzuH56FGZ7cgCAJarRZarRaOHz9ejCusr68X149Go1EQ2rGxsWJxeqTA7i6SJIEgCEOxz0cW4hF2HI/b7fo42C21kxCCO3fu4Nq1azh16hROnDjB/Az5Ng3TCvcwKbD5xW+7CHUURTh37hw6nQ4+97nPYWxsrPJ3Qgi+/eMz+OnF64AggvzsXyr+5vseFM68qh9G3MlCHsnMe1s9P4QkifB7iG4R9hSElRnYHKxZ1eLvns+0ntLk2qjPglv9bGFPmFL1k/o+X9FzPB+CKABJgstXrsEwDBw4eBBids7brsfd/v6wqy0FFqA/lPkCgqlrtURUHDDYOYgYhnGMlmkgBWH251IFl6/CWoYGxwv6ZmDzXl5Nlbm9sI+iwuYLJgDQcRgBYUE00I4cxXHlSEui2Je4vKUY0397sLyBmw+XcXLmAPN1yygHupw6dQphGNbOv+WE1rKsoblW7heMZmB3FsP0+/+soTyucOrUKURRVNiNr1+/Ds/zivn7MAxH+3kXMSwBTmmajgjsCDuL7Qhq4mE3LMRhGOLcuXPodrv47Gc/i/Hxce7j8y/3MFlbhonAAltJxE+LXBG3LAunT5/usylGcYxv/PAnuHzzbu3zBUGoKFu9CKMITdNkEgtKMtmBR0m2Ct9LXnPkq5i9M7BAprLWzqpuvXa70aioqAAl1bQSJ+CSonw+0y5szNWb2q2wJRaxS0DiEHfuPcTExARa7XaF4tHtV5jbD6Agu9XPvfX/vSCk87ZRXHu+2G6mgof1+3erMoetwgqiAIfxGQFaDTRIhS0T5HwzNVWBmz0nCGM0GzpzXwKPpsKKgpjN3bLsyMnAWdggiqlSmz3G1FV0e+Z8vSBTnkv//qO5azh+ePKJVt5VVcWhQ4dw6NChyvzb2toabt++DVEUK3Zjll1whO3DiGDtLEYW4u2Doig4ePAgDh48CIBW462trWF9fR0rKytI0xRnzpwp7MajBbGdw7Dc1zoODcEczcCOsCN40m7Xx4EkSYgiduXH02JtbQ1zc3MYGxvDO++880g9ZvmP1rCsVAF0PwU1Vsu9wtMS6nKIFksRd/0Af/KtH+HB4gqaDbMm9RYF0ZNFCXFNIA+Aypxo3XaYmobN2O37m6pISOK4UEPr4HgeNEWBw1A6Hc+HJAhIGGTfC4OKsidJIhRJ2gr34VSrAOjpshX6FEJWlU2aplhbXUEYhZienmYW1tuuzyVltudDV1WqBgt0G8rkLEkSWJaJ9W7//s0hyxLQy19Lp4LHCUkyNRUdx0XD0LhhTTwlG8gIn6HD87ZmmgVUA6KCMB6owrYsdj0TQBVnifBvih0/gCyJzNlfgPbYChDQMPrJa44oTirbu7Jp4+Ltebxx8gj3/Qehd/4tTVN0Oh2sra3hwYMHuHz5MkzTLG5GR0rhzmF0k79zGFmIdw66rmNmZgYzMzO4fv06fN9Hu90uKnvKDpCJiQkYhrHXm/zcYFjua12X3hOMZmBH2FY8bbfr40CSJPg+W214UhBCcPPmTdy8eZM5U8nbJgBDM3MKDKcC+6T7J45jXLhwAWtra/jMZz5TpKKWsdGx8f/7Xz/A2mYXAO9YUOJoGlqfkpnD8XwYulaZX638vYYgqYoMAYAfxjBF9sWeEEBVFKZNOUlTtEwTHbd+28IsfdZ2aOiSqsiV7fSDEA2zNyxpC14QFMm05RCn4vlh9vwSyQmjEIsLi9B1BQcPHcZ4s8lUedM0m6XlkLJeAlrmK6aRWaw5Fl7bpYsAAWORIGaosLIkIoxjEALE8SCCmu9Hngqbn2MEltmvtoY9ymf9Z2GTz4ZBn2vqfHUyTVI0BqiwURSjbZmFQlyHIIzRalRnbX98/jpeOXYYirx9NzCiKGJsbAxjY2M4efJkYRdcW1vD1atXAQAXLlzA1NTUU9VtjFDFSIHdWYwU2N0BIQSapmF2draYv88DoRYXF3H16lVomlYJhBoFyj05ckFqr+E4DhRFea7dOiMCu8vYactwL3YinMj3fZw9exa+7/fVsDwK8hnPYSOMz8P25L27mqYxQ7QeLq3ij7/1I7gl0rY1b1klofmMpx8E/FlXDgmNk6QSMKSpCgghWVcsfW9D0+AxFHDX8yHLElNp8qPeWdUqkpiu9BuaWk9UBwlY2feTpQ6Wt8t2bCwtLeHwgSkYDUokooRv4Xf9gKs8Oq4HVVYQxlGFRJdDpnrtrNXtowsGfQS2dNnxg2pljgD6nPx8KMKaOKSvN8m5F14QoWFocDSFaRUOIr4Km6YpTKN/OxRZQpDZpF0/oO/DIZ+250ORpJqAqdLnIYQ7K5t/pvLiTNcN8PHVO/j8J05yn/c06LULfuc738Hk5CS63S7u3r0LQRCKm9GJiQmm+j8CHyNle2cxUmB3BzQkcetWXxRFtNtttNttnDhxAkmSYGNjo9JfbVlWcQ1pt9t9bQUjsDEsCqxt2899VdvorNxFbFe36+NguwnsysoKzp49i8nJSbz11ltPfGEbptRfYPi250kI7IMHD3Dx4kUcP34cL774Yu35dfX2ffyP731QO3dZt2qYp8+GUYym1UCXocLanseddQ3jGADtV02StO9xvLCnJE1hahqT1VDljj3L6gUh2paJTZujIHPmRJ3cxusHqGO7rhdAV1Xcn5/H5uYmTh1/ARC3vhd+EFGllEGo4jhB06q3cCN7R02TEcaUgBJCCWtZER9k4bVdH6osIWQcnyhJKiqsVaOExgPs1q4f0uofRo8qgEJ1Z2HQsQQA1wv7KopUWYZTWnwZ9N0hKYFuyIjc+s9kGTo6jpctvHCU2jjpU2E/vHQbnzx1tAiU2g0cPnwYhmFU1JX5+XlcuXIFhmEUysr4+PjoZvQRMVJgdxaj/bs7SJKEq6hKkoTJycmi7jAPlFtfX8eVK1cQBAFarVaxINZsNodCYRxWDNMM7PNsHwZGBHZXUO52zW0zu3Xh3i5ilqYprl27hrt37+K1117DkSNHnuozDFuVzrApsI+THp0kCS5duoTFxUV8+tOfxoED9UmoH5y7go8vXmeGBjmen82jbv09P8aEECScsCFCCAxdRcSolfGDCG2rAccPaomQ7XpQFIk5UxrGMVcoZZ1LAmjSLaurNodUNydaQh78VKfKJEmC+fmHcBwXn3jlZQR1n2GAmBMMSHPOw7AEQYCuyqVkYoqt+heWCksXD8KYTQxporQAy6xPNH4kFZYzDy0KApIkgabwf3b8KER9cRJFmqaZXZju57ogLar2sq3hQEbqFRlhz/FS5K0ZaZ5lOYfjhZXHhFGMH1+4if/XW69yP+dOoFddieO4sBvns3Cjm9FHw4hg7SxG9S67g8fdz+VAOQDwPK8IhLp//z6toBsbKxbFnneV73ExLApsTmCf52MzIrA7jDRNEcfxrlmGe7EdBNbzPMzNzSGOY7z99tvbEsu9W/U+j4pnlVA7joMzZ85AkiScPn26NowhTVN8672PcebyDaickK2C5DAIrOuHVElkKGyuFzADlUxdQ0oIU8WjibwaoqheJY3iBAaH+LhBWMyqFtsOoJnZbAUB0FQVQcgIixpQaeNkZKf3o/m+j4WFBeiajtdfewU2Q61z/QCGrjLnhMMw4qbj5vtHlkQkSQpR6L8hSQdYHgclQkdxjLbV4KqfPCIH8FVYy9Cxsr6BJEm45CCKkoGJxLYXQJElSKKILqMyJ2GEjuUghCq3fQRWkuDG9DilJEVD48/L0iTnqmJ97vo9vPXyMbQtk7sNOw1ZlnHgwIFiUcvzvILQ3rt3DwAqduNRmMsIu4XRAsHu4GkXCgzDwJEjR3DkyBEQQmDbNtbX17G6uoobN25AluVKINR+H1kYphnYkQI7whOh3O2aX6j34mL9tAR2cXER58+fx6FDh/Daa69t28rSsBHGYSTUg7ZnYWEB58+fx5EjR/DKK6/UXjT9IMSf/cWPcfvBAgBae0O7N+tJiuN5kEQBSTbTl5+yRYov58Icl3pJy2gYOnw/QJIRZFZljuP5lfcug86S8s+X3u9X0zLRyWzDxRwog8ASAuicSh4CkllCSfH4TmcTKyurmJiYwNGZwxAYIUo5ePsOyBOR2WFMrh9AkWmwkl5jA2XNMedIUwLLVBExVFhZFJHECXf2r6jM4aqw/c+nBI++bxgnsAYEPvlhxO3YpRZgNZvrZcxGB4MV462UZ3peUBW7+niq1Ep9RLf3dTRVRpDZo5OU4Ednr+NvnP4Z5nP2AoZhwDAMzMzMgBDSF+ai63rFbvwoqfLPK0YEa2cxCnHaHWyn0i0IAprNJprNJo4dO4Y0TbG5uYn19XU8fPgQV65c2ffXkGFRYG3bfq47YIERgd0RlC3DAPaMvAJPTmDTNMXly5fx8OFDvP7665ient727XrWCONugrc9aZriypUrePDgAd544w0cPny49nEbHRv/5zd/iNWNTvX5HIJCq0oaBfErK7BAnmgrFwFMvdgiFPR5DUOHFwTFeyqyBNaIZJKklbCnXoRRjIauw2GkajueD11R4EcRTfbtmXm1XQ+yLDITdZ0BlTaOR5OI05RgeXkZrutgenoak+NjNAmYAIosImK8PiU5CpNE+0GIRsOAwyBcuqYOvBGp642tfAbXhyyJfSqsAKpQ236AlqmjwyF96UAVNqiosJpStjzTmWq6j9lk/VFU2MHpWzRNmBcKBbK1sKDVWLPpQ0jWtcsLfKLKbYCt78X86gYWVjdxeLI9cDufFk/y+yIIAlqtFlqtFo4fP444joswl1u3buHChQtoNpuFOttqtfYV4RgR2J3FKMRpd7CTVu1yHc/Jkyf7riHnz59Hs9msBEINA7nbSQzLDKzrujDNvXUA7TRGBHabUe52zdN29xJPQmAdx8Hc3BwA4PTp0zvyJRjG0KRh2546Aut5Hs6cOQNCCPfYPFhcwZ986124NWTP9Xxu6i8lWDm5EDICsKU8qqrKJrBhhIZhwPH8or6mnA48yMbKet1CjRPYpIUAUFUFqqb0kVeAEvemrtf+DShX2tT/nRIZCffv34coCpidnUXDMBDFMUimGuuazlQ4MUAFBgDCIId0ztNDGCVcpdf2fJi6Ctevf48kJWgZel8vb7MUVjQoCdgNwoHzpfn5IgoCTTeuqOokU3INrjrqBREEUSj2bRl5yJRl6lxiGTxCKJTj+YXFnbW4Y7t+5h5gHzvbC2BqCtwggqmr6Lg+vn/mKv63L3+O+ZxhgizLmJqawtTUFABqj8/txufOnUOapn124+edgDzvn28vMVog2B3s5qxx7zUkCILiGnLp0iVEUYR2u11cR57Hyq9BoVm7hZECO8IjYze7XR8Hj0sUHz58iAsXLuDo0aNMW+p2YNgI47OgCC8vL+Ps2bMD7dznr93Gt977uHAA1IGX+huEVXLRa+V0XR+yKCFmzhiSzErsoFdhI4TA1LQ+ArX13lEt4ci3wXF96JoGn5EYLAjgpuA6nt/XSVuGl/W91llXPY8SSKvZwNTUJFRFQYrqXKjjBVwVlxJ49qyt6wcwDb2iBFqmUSQUJymBqavgfXNEUQLAJlrFNmbfP8vIVW96rIJH6GPlhTXln6Nh6JBEofI65UtilCVTs1TYOE4K4l6GosilkCWfO9sM0K5enh0ZAFRFwgYjgAzZVkqPcj0XBIglG/yDlXVcv7+IF48eGvzcIYOu65iensb09HQx+7a2tobl5WVcv34diqIUZHZiYuK5swqOCNbOYhTitDvYy/2saRoOHz6Mw4cPgxBSCYS6e/cuABQK7vj4OEzTfOa/c8M0AzsisCMMBCEEGxsbiKKoSHUcli9hTswG/RjHcYxLly5haWkJn/rUp4qOwZ3crmEisMNsIU7TFNevX8edO3fw+uuvY2ZmpvY5hBB8/6NzeP/sZQDVntBe2ANCi8o3++XuUYDaP3lKpSxJCKMILGLi+AGXRNaRv7KVWZEl1I15thq0KodXmRMnCdqNBnPboziB1ajOCBMCrK+vYX19A4SkOH7sCPwwgiyJ8HoUuULFZbw/IQS6riCy2YsL5b1m6lpF6RQEajXWdJ2pFjqex1UL89ChMIqgqyo8P0DvsaJBXrx53HBg16oqS9io2Q/5ZgdhxE1OBgAvCCAKItKMMAsC6H4vnbeKLIIxVg2AHtM6IpzD1FVs2G6mKnMWP/wgCwpjv5nrhxhrGhUy/MOz13Fi+gAkae9vap4U5dm3F154odIdefv27T67cbvdHoqbuKfBqAd2ZzFaINgdDMtCgSAIME0Tpmni6NGjlRn88qJYrs6Oj4/X9tgPO4ZlBtZ13VGI0wh8pGmKMAzx4MEDeJ6Hn/mZ4QrtyL9ISZIw+/+63S7m5uagKAozyXYntmvYCOMwEmrf93H27FkEQcBNgA6jCP/9u+/j+t2Hxb/RedR6bNXesCt1DF3LUnP71Ss/CCFAqNiDgZxEOmg2TIBBoOIkQcsy0GEoXlthRPVkgpLvqg25TNYdL+AuSHhhwLXIRqX9liQpFhcXEUUhZmam8eDBA4RRDE1RmdtHVVz269M51GqPaeXvWS9tQgjCKO7Z9wLCOMGEqbN7Y0k+a8w+/q4fQJVlpITUpkZvWW8fL6wph64q2HS8muNYvWGlwVw8FTatkE+rRp233azHd8DnlbIE5zLEov5GQBwn3CojANmiC3t7TZ1uR/l11rsOzt64jzdfPsZ55WcLvd2RQRAUysqFCxeQJElRtTExMfFMKisjgrWzGIU47Q6GhcD2oncGP0mSIhDq3r17uHjxIhqNRkWhfRY6rIeFwDqOw6xUfF4w/GfDkCK3DOcpw7IsDxUBysEjsIQQ3L9/H5cvX8bx48dx6tSpXbvQDRthfFSlercgiiIcx8G7776LyclJvPXWW8yL90bHxh9/60dYWd+s/HsQ8hOHXS+g84kMIiJn506d/TKM4qz2hb52ubIGqCeZ1W3jK3yiWP33sgJLCIGhbqXp9irNlCCzVdAwitFqmEwCGIQxrIaB1fUNzM8vQFVVHD06m9lfBcii2Ld9ZQxS/NKUwLI05vYBtJc2jRPESXWBgW4DQRDGXFus7XpZYFT9AkWuwvJss4NTketVWEkSQUBnjuu3b+vfgqJblq185uRTVxXmLCvveADsgDBT04rU4UexTnshu29XFAXESYow7n+d9y/exGvHp6Gr22uzHRaVUNO0it3YcRysra0VVRtlZWViYmIoZsQeBcPwW/C8YhTitDsYllChQZAkqbg+nDp1ClEUYX19Hevr67hx4wY8zytcHuPj40Pr8hiW/W3bNk6ePLnXm7GjGBHYJwAhpAhqAijZGFYCmycg925bHMc4f/481tbW8NZbbxUr6buFYbQQA8Ox6k4IQafTwcbGBl577TXMzs4yt+nu/BL+5w8+xGbXqf17yplVHET0chLKIkq5miUIAizTqJDILYWXRRIjboiP05PY25uG7Pq0c7bR877F6wf8GpZBlTzra+u4/+BhsfKbB1kZqoyu61OFmYMgjPgqrOdDlMTaRF/+vCbdD2EUcUkyIYCmsAls/lzeAoY/4BjR9+l/rqGpBcHL+3lzFbbuNI6ihLuvaDK2QZOeGXC8oOQYqIft+RUV1jL7K3P8MOLuEwAI4xiiAPQ64C1DK9KbvSCquAC8IMQHF2/hL336ZebrPi8QBAGWZcGyLBw7dqxQVtbW1nD37l1cvHgRlmVV7MbDcMPXi2FZHHheMQy/tfsBw6rADoKiKDh48GAxzlYOlXv48CHiOMbY2FixMGZZ1lCcT8MyAzuyEI/Qh7LqWq7HGTZClkMQhL5t29zcxNzcHAzDwDvvvLMncwaSJFWsmnuNslK9lxefMAxx9uxZdLtdHDhwAMeOsW2HP714Dd/58RlYJtvy7XoBt3s1ZKT+Alngkq4yCZXrB2iYBgCCbg2JdFwfkiDUWlTz12e/dzWxt5fAxkmCybEmVjft2ucHAwiel4UMuT1KGiEEyyvLsG0bJ47PQpS2FLO2ZeJGltDsBUFW6VOvHIYD0m8pKatfPMgTdpsNoy+cqJwIzTt2ANBlVB7RxGEXSdbby1Mc48dUYakqX329XjLYe9iDaIAKK2SLJQMIxaBbF9qDqxVBWnWW4yhOBtYIhTX26kaJvALZ4pBpoFM6/nPX7+FTL82i3dj5EY1hQllZAeg1rjeZtGw3bjQaQ3EjOiJYO4uRhXh38KwS2F70hso5jlMotLdv365U+uQp6XuBYbIQjwjsCAAGd7vKssxNfd1L5ASWEII7d+7g2rVrOHXqFE6cOLFnP9DDZiHOL/B7OZe7sbGBM2fOoNVq4dixY/A8FvlJ8K33Psbc5RsAqFLKS75VZJlJYP0gzGpv6t/L8QNIolDLHWRZgiIJTBtqkqZoWw1moJLjBVnlS/22OV7/rGhO3ij587gziwnDvsxCHMeYX5gHCDB7dBZNq1kEKJXt0fm+6J3/7Xu9hH89qEs8puFYdH/ajgdFkRFVCOjW93XLfltPtgghVMUuPd/Q1EoolB+Ej6DC8i2++fabula7LV5Fha2/3lCluP5oNk3aB1unmJZRJDjzgpi8AKosQZYlpqLr+CH3+wQAXukxkiT2HCMK26tWRsVJgh/OXcPfPL39OQnPEtFSVRWHDh3CoUOHQAiB67pYW1sruiMlSarYjfcyyOVZ2q/PEvJRkNH+3VkQQp4bAltG2eUxOzuLNE2LQKjFxUVcvXoVmqZVAqF2a2xhmAhss9nc683YUYwI7CMg73bNyU3dxWBYFViAblsQBPj444/R6XTw2c9+FuPj43u+TcMU4pT/kO7FMSwvLLz44os4fvw4bt++DcfptwXbjoc//c67eLi4UvxbmqZoNRtcKzAvNIgnXcVxAkNX+9RSVZEhCsCm7daqfDkocebNurIv9GlKYDW0gjjmx6isXDYbJlPlpH2l7KRcx/Nhahq8IITruVhcWITZMHHgwAGIgphV9qhQFKmkMG8RTtfn21a9IELD1OEwCGYUJ2haBrrZAoDVyO3QmdoMQFeUCjkqK7AA+kKJetF1PaiyhDBOIMsSkjSpkNWoUAp5KmyKQSpsq6HDCyL2YkLFyt7/qCjOZqqd6nYYuoquQ4+f4wfcuWoASAeEQhFC0DB1rHfZ88dJmvapp72I0639ZmoqujXEOiUEDUWpbO/Vewv49PIsjhzY2+vvsEAQBDQaDTQajeJGNLcb379/H5cuXUKj0SjI7NjY2K7dHI4I1s6h6Il+zojVsIF3z/o8QRRFtNtttNttnDhxokhJX19fx507d3DhwgVYllUQ2na7vWOBUMMyAztSYPc58tWrnLzy6nGGmcASQnD+/HmMj4/j9OnTQxGgMWz7K7da7zapjqII58+fx8bGRmVhoS5F98HCCv6v77xbG8rk+yFTjSSEwDR0dOz6OVnH9bg24zghUEoERNdUJEkCP6LHT1NVJoENooibZuswbK453GAraEoQBDRNvULUB6mcg25ARUnE+ga1M05NTaHdalf+rusKNjtb79dbKSQNuDGom3EtI5/VNfRcGa1ur+35UCSpNLNb/bsX5KFCbBVW11VEjg9VkWuTk70wqp3pzDFIhRUEer5GnM/qBxENjerUW77zx5RnYSVJzFR0+g8kzSqIOAR20LbqmoKNrgtdVeBzumO7Hr+vF6DHpm3p2HTY5N/2fBiqAq/0Xt8/cxVf/crnR+SoBmUbYDnIZW1tDVeuXEEQBBW78U7NveUEa3SMdgb5b9to/+4s9guB7UVvSno+trC+vl5cR1qtVqHOtlqtbdtHez2GBqCwWI8U2H2KuqAm3sVWkqShsxATQnDz5k14noeZmRl88pOfHJofjGGzEAO73wXb6XRw5swZmKaJd955p7Kw0EumP754HT8+e4mZKBxGEayGyUkc9rnhQCrHZhwnCczMxtcwdPhhgCTZeh3HrYbj9CJhMSNQaqJpbAIclxJ9G7pKFd+SpdDzMwu0X08i7ExFrftsaZri+s1bSKMIR44cga7plb83DB2drgtFlhAWowPVUUzb9aApKrOyyAtCGLrKVGnDKEbbMmF7Qa1Ne4uAeqX3rz6QNWNc3sZWw8Qms/s2fioVtmka2Oi6lEgzZoLp+5St4P2BTlFcnS01NKWPiNqeD1WWi+NRhzCKa0OhRFEokpGlAanFhBBoCp/ASqLI79xBtseE6s3MwtomLt2ZxyeO1/c5j7CFcpALIQSe5xV24/LcW05odV0f/KKPgFGA085itECwO9ivBLYX5bEFAMV1ZH19Hffv30eapsXC2Pj4+BPP4edNFsOgwI5CnPYpctU1X0l5lBO5PGc6DBflIAhw9uxZeJ6HdruNiYmJodiuHMOmwAK7R6rL9UUnTpzAqVOn+o5NTqbjOME33/0Jzl29BQBcpZRHvuMssKcusRegVlPWHLcgCEjSBGMNWsnTe2+XpCl9bYaF2fV9mLrO7E0dRICjKEarYeBWEGGs7gEC/2aTdqJW/y0MQywsLECWZbz00ot9c7iGTq3FaUqgGUqJMPXr3KoqcTt3JYF98yDLEuI0RZImYNleq7PA/Y/JA6kchgprGcbAG3IvjGp7fXOwlE3L1IuZXd7MaPk1uNuRzeQ2TK3PTgxshXsNIrB1qn9D14p/c7wApqHC9dnHzfZ8rlKrqgo6rg9DVeGF7PRjNwj6rOzvnr2Ol44egiLv/Y3OswJBEGCaJkzTxNGjR5GmKTqdDtbW1jA/P48rV67AMIyK3fhJbYIjgrWzGFmIdwcjpbsehmHgyJEjOHLkCAghsG0b6+vrRe2XLMuVQKhHXRjL7x+HgcA6jgPLsvZ6M3YUIwJbQt7tGsfxQMtwL/IfymHwv6+srODs2bOYnJzEm2++ibm5uaEji8M2AwvszjbFcYyLFy9iZWWFW18kiiI6jouv//fvYGFlrfh3RWErpa7nwzDYM5kslRPIE4c1dOz6x0iigDiOmUGwvh9wA5V4ileSshN5AUoU0pQwFWTH9anKWZMqC9C5YVWRi9Re27axtLSEdruNyclJeEFIiWSmEOqagrA0814mkHQbel7f9bmzmbbnw9BUeD3HTRQFKJIEx/VpXQ2DgNLeWJWGVtUosABbMbIMHV3XgyAI3G3sVT/r0KvCamrVkuwFYRa0xFNh8+9XvZobJwnGrAZTLQYGE0sA8MMQgiiAZOp/w9D6PhunYaoAyyJe3leP8hMRx0klLKvr+fjo8m28/capwU/mYD8rhaIoYmxsDGNjYwDotTW3G1+7dg2+7xeLtxMTE2g2m499Iz+68d8ZjIjV7uBx72P3IwRBQLPZRLPZxLFjx4o5/PX19WJhTNf1Qp0dHx+HotT3eefn9V5zgCRJ4HneiMDuFzyuZbgX5RqWvTp50zTF9evXcefOHbz22ms4cuRIbY3OMGA/Woht28aZM2egKApOnz7NXdW7t7iCb7w7h0OHp6uv4XgVstUL3kxmEIbcUCHX8/sSaQUBsLKb9fbYOIB60hBGWSARo7bGdj06K8sgHZ4X1M5hti0Tm7YL09C4FmhZkpjbRgDo2Xuvrq6i0+ng0KFDhb0mJQRNnRJEVaV9zmU1OK9f6ThuLYEkhEDXVEQxO/RHknqOi0BV3vxYUAWWDccLMrWCFaQU9FmVtWz2khC6jZbJ38bgEVRYqvQGEEUBAui+KSNO+IFPQRRBV9k/O6IoIogiiKJQsalXQGr2Zw+iOEGrYaDj0MWFuvPOC7LvAy+12A/Q0LVKXZKqyJX/dv2Q1vNwXieI4r56np9cvo03Th5Bc4AqPcKjQZZlHDhwAAcOHACAit347t27AFCxG/NqNvbzwsBuoLeGcISdwfOYQLzTKM/hA3RhbGNjo0hJP3/+PJrNZiUQqreGca/P6zwAdDQDuw+Qd7s+zWpVfpGI43hPQpI8z8Pc3BziOMYXv/jFyok7jAR2WBXYndpPDx8+xIULF3Ds2DG89NJLzB8VQgjeO3MR3/zhT2oVpi2llFFNM0ANJJx51DhJKkqoKAhoGDoWFhahqjRkSFEkRBFDxWP8O5DNupZ6XfuemyR9vak5eQWyPltVYd5Y2q5fUVl7sdm1sbC4gCiKMTs727eC6ngBNI0qvVsqYfnvfolA1qnAHmRJzAhc/fZpilJYjZuNrfRhIJvlNQ2mDThJqE27brYzh1i6bm39oG5tj+321xKVETKSgHu3AyAw9fr6Hn+ACisgI7lEqOW4pqbC9vyB2+G4GWFnuBEAekwlSYQi14dXAUAcJVznAFC1RgsAZFFC2HOuR3HCJf8AreeRRRFx9npRkuDDSzfxc5/5BOfdR3hS9NoEc7vxwsICrl69WqgqubJSthuPLMQ7izRNR/t2FzAisE8PWZYxNTWFqakpAHQ8r7fHut1uY3x8HJqmDcV5nRPYkQL7HKO32/VpVk4EQYAsy3tCFJeWlnDu3DkcOnQIr732Wp8CPKwEdti2aScU2CRJcPnyZSwsLOBTn/oUDh48yHys6wf4H9/9MW7dX4DV0LG4VH8z7Nb0h+YghMDQNEQxax414M7R0j5OOpupKkphP817+wxNRRTVq3h+ENIeTlagkudXbuB7ESUxcvWuVSKvOURRZBJYQgh0VaklsL7nYWFhAVOT4zAtRtqgQBXLDqNeJUmozZlFIFNCYOlaMQ9aB1WREUQR0y5NBpx7ju9DFNl0i/bq0tndugAkqiSrxTYKEPrE0iAYrMKONy2sd+sTrQFk7gC2ChslKRpmf/9vHtaVfxbeXDSYr76FJE0x0bawtsne1iIlm9Mv64c0Qbnr+mgywq6CMCP/nOodWs+zpcJaho6zNx7gteMzmJ4cG/BpRngaCIJQqdkoqyo3btyA53lFKmlZnR2Gm9HnEcOSFfK8Y0Rgtx+apuHw4cM4fPhwJVgur+xJkgRnz54tFsZM09z1c91xHGiatmNVQcOC5/vTcdDb7boddpbdJmVpmuLKlSt48OABXn/9dUxPT9c+bljJ4jBu03YSWNd1cebMGQiCgLfffhumaTIf+3BpFX/2nXcLYpMkKZOs0WRes9RNWoXj91uBy5BlCWC4HYMwxFjTghcEcL18xm+LLDueD0li2zt5X6E0TWE1TCbJ84MIlmnQ+d8agheEEcYU9iWrLwyKEGxubmJ1dRUTk5Noj0+AkP6wK1mSIEsSPM7CAEDDkgSw/54TzF5bbQ7b86mtlRl2FWQLAPUHJ0lSmJqKmCMXioKAZsNAx67fx47rQ5ZERHECAkJJbAmDVNhGRpB58MMIVoNRZZOdIH4YVfa1oakVRZeeKwZsjgrr+iFMQ2PuL0NTsd5xB3bHemHI/b7k22toKndG2PMDSKLIDbPquj50RUGcpggye/f3Pr6K//3Lnxvd0O8ielUV3/cLu3GeSgpQ58zk5CQMwxgdn20EIWRErHYBw5DJ8jyjN1huZWUFV65cQavVwvLyMq5fvw5FUSqBUOUWhZ2CbdtPnKT8LGHfEdjH6XZ9XOwmUXQcB3NzcwCAt99+mxuXLUkSgoA9n7UXyC3Ew7QSu53Hb3FxEefOncPMzAxeffVV7o/1h+eu4Psfna2oTUEYQeUklEac/tMkJ7gMNcjhWElNXUecJhWrb5loJEmKttXoU0fLr63rbIXX9QMmyRMAqJKMtS6jKzQLImKBKlyUIKdpiuWlpaJCSjeMQkUtE0hJFKEoMryMBLUaJnOON4oTNAwNLAWUvn6DSVBNffBowaBvQhDFkBX26wiiwLVyJylBy9SZxw9gq7CqTBXkOEkLRZKFQdbcKIrRbjZgu35B+noXBgZZ1gF28rYoikhS6hrQVH53bFHVxPs8SYoGJygsf0yzwd8vBMhszRK6mc16fvXpa3WG5Rr6rELXdczMzGBmZgaEEKytrWFubq64CdU0rWI3ZoW4jPBoGFmIdwcjBXZ3QQiBqqo4fvw4jh8/jiRJikCo+/fv49KlS2g0GgWh7R1d2C44jsMVTJ4X7DsCm6YpwqzyYLuHrVk1JNuN+fl5XLhwAUeOHMErr7wy8AI1rPOmwHCtEG6HApumKa5evYp79+7hjTfeYKriALXc/q8ffYTLN+/1/U0QerWxnuf6/ECmkFPrQgiBafTP0TazHtk8lCgnoYIgVPaLF4TcWUxlQKdsq0aFFUDDota6Nky9XlUTBAG262N8rM0kJK4fII4jPHw4D0kUcXR2tvID4QdhQcglUYSuqZW5UzqjyrG/xgl4kTuuF1TSb3MUKcQE3DlUx/OLCp86JClBS6u/eW4YGmzHR2NAKJDjBpBE9mJNWJNILAoCRElAGGTzm3HCVav9MKolheW96gcRxJpjkIOkBLqucAksVe31vgRnOktLzyHH86GpCnP+mj4m4M4w0zRn/mOAfNZZ5iZ+U7W3+m8/OnsdLx45CJXjMBhhdyAIQrEg/OabbyJN074Ql9xuPD4+jna7PSIJj4mRArs7GBHY3UUe4pRDkqRi4evUqVOIoqhvdKHZbG77tSSv0HneF4n23a9lTlp34sDutAKbJAkuXbqExcVF/MzP/Ax3nnI3t+tJkH9J9zK1uRdPS2B938eZM2cQxzFOnz7NVcXnl1bxZ3/xHpMACIIANwigqgpCxo03j+IGYUQtmMzE4QCCKBZzl1Q53JoVLJPQXqISRlFmYWYkDjseFFlGxFjM6SWJNCxKKxRjkVO5k6Ypd8Z3c7OD7uY6TMPA5NRU3/c8jKhF1nF96JrSR5yCkNqYWQsDUZKgobMtQHES9xF0VZERJ3GhOjd0lbnvAH7lEEDJoWZWj4mmKvCDCAQEtutlhLn+vKG9vTrWIrYVuDeRuLeGhs6F8mc+gzBmqrAEdF9ONBvceVrb87LvAL/3tfw+vX21hACyLILnfE7TFI2MpPaioavF56QzrGw3CyGAIklMAitLErwwgiSKlW22vQAfXrqNd37mRfZGjrBrKIc4SZKEycnJovIsCILCbvzgwQOkaYqxsbHiRnUvZt6eNYwU2N3BiMDuLgbdzyqKUklK932/CIR6+PAh4jjG2NhYYTd+UhLqOA73/vN5wb4jsMDO2a12kijmFSyyLOP06dPcCoDd3K4nRVmBHRY8zX5aWVnB3NwcDh48iE984hPci9iH567g+x/OIcl+XOrCagSBBuyoiswksLbnQ9NUBIw7c9YsJrClhHZdD5ZpVMgrQGtvFEVGFMW1ShtvPxVhTywCG0awDNp7mqugZXLkuPU9n/n31vH92n22traGjY0NHMksw6xPH8cJTENjknveDKMAAWEUQ5XYl04/DAtyIksSfU4p2XhQQFHX9Yswpr73FwREcQLL1AsSLEkS7bAubbfEqRUCkFl32c6Msgrbaujo1MyihnHEVWHpQkePClu69poaJYa8fUEIoMrSQAKbByhpigLX699vRWoxoyMZKCdFb72XJIoVBbhbnJvs7bG9AKauwq05frqqwPaCzIatVQjzT6/ewRsnZ9C2nn/r17MA1n2CpmmYnp7G9PQ0CCGwbRvr6+tYWVnBjRs3oChKxW68F60Ew46RArs7GBHY3cXjCjK6rleuJa7rFoFQt2/frlT6jI+PP/Isfq7APu/YdwR2J1f9JEnadgsxIQQPHjzApUuX8MILL+DFF1987AvSMBJYQRCGLsjpSRRYQgiuX7+O27dv47XXXsPRo0eZj3X9AP/ze+/jxr2Hxb+laf3cZH6e2o5H7Z51PaGEULsia97U82HoOjzG/HOcJDA0rTYMaitxuJ7Aun5QdILWwfF8bqBNmll4NbVfBSUAFEWuJbCEkL5Z1iRJsLi4iCiKcPToUaiqmnXX9quDgiBwu3IBGsZjmTqc2hAiSlDHx8eYlTdhRMmf44dQZKmPiNJjzg5aEsDv8wW25lQBWk/Ua7mmKizbipwSAlNXmSQeyMKYDPZMZxDGA2dhgzCq7feVRRFxkiBKUrQa7P5ggC7U6JoCP2BfW/0gpO4akRPGNKDakxBAlkSUhWtDUyqVQASDjw2A2m1oGnox9woArh9BkUREGXmPkxTfn7uGv/XOpwa+/gg7i0ftgRUEAc1mE81mE8eOHStm3tbW1nDnzh1cuHCh0hk5NjY2IhQYpRDvFkYEdnfxNCNx+ehCo9HA7Ows0jRFt9vF2toaFhcXcfXqVWiaVlxLeItj+2UGdl+e2Tt14dzuGp04jnH27FlcvXoVb775Jl5++eUnuhgNI4EFhi+J+HG3JwgCfPTRR5ifn8cXv/hFLnm9N7+EP/iTP6+Q1xxeEPSl9+bnaJIkaJhsy2re7cmCLNWfL5qqIE4Srl03T/VlQeA8N0moJZOFMIrQ4CTI5n22lfcrEel8ljUIAty7dw+iKGJ2dra4oNctJAmgs4y253P7cIF6ApJvAwj77zmiOIGuKkwC6XgBRM6+tT0Puto/65rvgzCOYTV0WCY7tZh37AC6CME6PwC6v0SRPesM0FApnpU9jOKK5Tp/pKrKCLM5ZtsN+D/6ZDBpjOIE7YYBnxOy5AUhLM53CciriOh+p+dK/751/ACNAYFcfhChaWy9lyJLfYpskqbQeo7x9ftLuLuwyn3tEXYeT0qw8pm3F198EZ///OfxpS99CbOzswjDEBcvXsT3v/99nDlzBnfv3oVt249MlJ83jCzEu4PemcwRdhbbORInimJR+/XWW2/hL/2lv4RXXnkFiqLgzp07+OEPf4gPPvgA165dw7vvvouNjY3iuU+jwCZJgt/8zd/EiRMnYBgGTp06hX/1r/5V5VpFCMFv/dZvYXp6GoZh4Ctf+QquXbtWeZ21tTV87WtfQ6vVwtjYGP7+3//7sG1GQOcTYt8psDuJ7SSKm5ubmJubg2EYeOedd54qentYCeywhUtJklQEfA1CnlI5Pj6ON998k5kkl6Yp3v34Aj6+dL1Iuu1FFMWwzOq8av7jTgjJQpPqrZppmsLqmV8to2wFztEwdXheiJSk3AQ8OitpYo3x3o7rZWFP9aTBD8LaGUhVUSCCr3KkhMDSVERxvTIXRjGSKMCDBw8Ke00ZXpCFXGUERABglXtGCwWZPSNs6nrfMcvnQj0vQMPQ4DCOqSJL3M9Xl4hcAcnqjjjBQ6IgcHtnbdeDrqp9SnaOotaoRqkWBRGiJMIL6hXUHNS+qzNrdwCq5JZrakxVpqFJ2bmXkhSWxp+ndbyAVubU2IMBOqPbcfh2ZCAPn+KTcpLmhJM960qdBeywLwDww7hI3FZlGU6NfdnxAhiqAq90jL778RV87a9+ceACBPDoSuEIj4ftUghVVa10RjqOU8y83bx5E7IsV+zGu1GxMQwYWYh3ByMFdnexkwsGvbP4YRhifX0d6+vr+Of//J/j7Nmz+MQnPoEvfelLCILgiRXYf/tv/y3+43/8j/iDP/gDvP766/joo4/wy7/8y2i32/jH//gfAwD+3b/7d/i93/s9/MEf/AFOnDiB3/zN38TP//zP4+LFi9B1Klx87Wtfw/z8PL75zW8iiiL88i//Mn7lV34FX//617dnh2BEYLcV20EUCSG4c+cOrl27hpMnT+LkyZPPXD/to2LYtutRLMSEENy6dQs3btzAyy+/jGPHjjGPz2bXwf/9F+/h4dIKAFpR4/qMucu6GViBvl8UxdzeV9f3mQS3bAUG8poYtyCVbp56yyBifhhBFFmvTWd0WQQ2jGI0rao9VFcVJCmBH8cIoohLsFw/hCQKSNKtQBVCaDXK8vIywsDH9PRhGAbjQp1tci95zTHoa1UbplRh5PUvQEOcXBgaX6VjJRbnsF0PmqJmoVf5NtN9YJk6Nm0HTZNdlwQMJsF21gvbm6xbDtVqNYzaGdgcvQS1F1Gc0NdwfViGjltBjMne7fD8voWWXjBTrxUJQRQhSdO+9ORePIrt2Q9DtBomNjmLAzTEqj70KQf93DoAAR3G+xHk5+EWGV7tODhz/R4+88oLzNceYeex3QqhIAiwLAuWZRUWwdxufO/ePVy8eBGWZRVkdmxsbGgCDrcbIwV2dzAisLuLJEl2bRFKVVUcOnQIhw4dwve+9z1cvnwZf/7nf47vfe97+OCDD+B5HlZWVvDlL38ZX/nKV/DGG2880nfu3XffxS/8wi/gb/7NvwkAOH78OP7bf/tv+OCDDwDQe8rf/d3fxb/4F/8Cv/ALvwAA+K//9b/i0KFD+NM//VN89atfxaVLl/CNb3wDH374IT772c8CAH7/938ff+Nv/A38+3//7zEz8+SVcWXsyzN7Jy3ETzMDG4YhPv74Y9y+fRuf/exncerUqW3Z1mEjijmeNQtxFEX46U9/irt37+Jzn/scXnjhBebxuXj9Dv4/f/LnBXkFwC359AKqMJVRJqWs2hVgq8eSBScLyqFExO1TRGXOD1wYRWiYBlPpoYnDnL7aUgCOqWuI47QIdyKgaccsxEmCRimsTBAExHGM+/fvIwxDHJ6ewcGpXiq0BcfzYerZPGwNGbFdHzqHZNqeB72nsqZ8TBzP7yOpOXkFMhXY4CUWJ2hybNY0xKt/3+rF3LCAIIq4Fl6qwrI7K1OSwuz5wW01qmqo54dcq3kUJdzPCVC7sq4q8KOosGFXt4POc/Pg+UHf+wgCoEhyQcBtz+f2JwOAF0YQBfY53zQNuBkp58EPIkgDHhNEMbdSBwDcIEKzJ5TvxxduMufLR9h57IaynQe0nDp1Cp/73OfwpS99CS+88AKiKMLly5fxgx/8AB9//DHu3LmDbrf7XKntoxnY3cGIwO4u9rIW8tVXX8U/+Sf/BH/8x3+MX/iFX8Df/bt/Fz/3cz+Hb37zm/jiF7+I6elp/OIv/iL+y3/5L7hz5w7zdU6fPo1vf/vbuHr1KgBgbm4OP/zhD/HX//pfBwDcunULCwsL+MpXvlI8p91u4wtf+ALee+89AMB7772HsbGxgrwCwFe+8hWIooj3339/2z7zSIHdRjwNUVxfX8fc3ByazSZOnz69rcmF+XYN24/GMFqIWduzubmJM2fOwLIs7vEJwhDffPenuHj9dt/fXC+ApqoIGDbl3hvmMlny/NxCWX9Ty7tJFgQBzYaJ9U79/EGdzbiMJCHMm6eUEDR1DZsMK6yfWXkBwPcDJD2vY3uUALN6Xf1wyz6dJAlc10Wr1cJUVpHDs4sKAqArKta67LkLRZbAEJ+z+hUJ5VSfcrUMUJ0zbWYLBGUMmpX1An6Sr+36UGUZYUb6VYX+//zhRQIvRy0cqMJ6WyqsZeh9tuQoSR5Nhc3ssnVICYGuKdjo8tVgVVG4HcZxUrUA94Z10SJ5pZivrX2NOGHano0sEZsgr8xhf+ZB+0UQ6OyuKArgjOYCAPwoqjhAwijGD85ew1/7whv8J46wI9iL38qyopInkuZ24zyRNLcbT0xMPNN24xGx2h2kaQpFYS9gjrC9GJZaSNd18elPfxq//uu/jl//9V9HGIZ4//338a1vfQv/+T//Z/zKr/wKTpw4gffffx8TExOV5/7Gb/wGOp0OXn311YI7/M7v/A6+9rWvAQAWFhYAAIcOHao879ChQ8XfFhYW+mo+83GJ/DHbgRGB3UY8CYEtW1Jfeuklrqr3NNsF7O3qUB2eBQWWEIJ79+7hypUrOHXqFE6cOME8PvcXlvH/fO995lwlQKCpCpPA2q5f6X3tJTY81SgIwmyOtko+dF1FmqSwXY/byamrCpPABmEETWVfKmzPq1h9e6EqMjY6ToX4Fe9NCLfXNU/0vfvgIVzXhWEYRYcaQJU9U+8PgxIEoGmaWO92s0UDRhWR49EkZ8Znp6RKRpj/vWcn2lmtiizLtSTSG5DWHMV8AkoIgaZS0ipJIsQa0h6GcR+xrn6GQbOw1JIchjEzdKrXzt3/OXIyV/85Ggat/ZElkVrj684FUMWZR2CDon/Wh1myOZdhD5jNBpDN4EqIS0RXFEUk6daW2X5I05I5i2y9CwxlWCWrckPXamdgc+R24zIZvnxnHj9z6ihmpsaYzxth57CXi73lRNKjR48iTVN0Op2ie/bSpUtoNBqVRNJh+m0fhGFbTH9eMVoo2F0MS2hWb4iTqqr42Z/9Wfzsz/4s/uW//JfodDp47733+rJDAOCP/uiP8Id/+If4+te/jtdffx1nzpzBr/3ar2FmZga/9Eu/tJsfYyBGBHYb8bg1OkEQ4OzZs3BdF5///OfRbrd3bLuA4VkdyjFs1uZeBTaOY5w/fx7r6+v4zGc+07dSlSNJEvzwJ+fxwdnLIISgZZnMDlR6Ey8xLMHURskisLbnZQS4/sa8lxI0G2aWuJsW/82al+R1k9LtqH0aADq/27YatSpsyzKwvtmlc7bMRF42AU6TBHfu3kXXphfkuvO390YoJ6+5GqrKMnefaarKJLCUQCoFge0jigTQNRWbXUYYE+rJWhnBI6iwmiLTkLE46XtckPetPoUK6/khZEli1h7lvcG80CjXrz+Hyl2wdCaUDdultmzWuQJQtVKVpa1FhRrUzi+XkKYEDV1Bt0RgG4ZamWlN05TOAnOsvNT6LPURWF1TYJdeK0lT5gJSjq7jVxZTCAH+4qeX8Yt/5QsDb/ZHZGB7MWwESxRFjI2NYWxsDCdPnkQURYU6e/XqVQRBgHa7XaizzWZzqLa/F6MQp93BsIkWzzuG5R7bcRw0Gg3m31utFn7+53++9m//9J/+U/zGb/wGvvrVrwIAPvnJT+LOnTv4N//m3+CXfumXcPjwYQDA4uIipqeni+ctLi7i05/+NADg8OHDWFpaqrxuHMdYW1srnr8d2JdXkGGo0VldXcW7774LRVFw+vTpHSOvAIofimEii8DwWYjLFr5ut4v33nsPYRji9OnTTPK6vLaB/+//9U28P3epMhvJmp8jJIXJmXt0vK3qmj5SQ9BXu1F5bqY8QRDQtLKwptL+jRL2DX+apjD1+u0SBAGeH3DnRWlScvXftrpOBW7VT5Kmhc24jDAIcO/+ffhhhFdfeQmyLNenIXs+jKzWRBCorbRs5bVdDypnvtJ2+XO83cxiTV+/ekxMQ0PHdou/14EmGrPtfmFMU6hZICDUPs7yOmNLhWWB9sLWHz9BoCo5bx8ANPCLV2cTJ2mlMgeg5L6sPhe1T5yq1kHX5yiO0TA07ly44wVoGPwxjK7nF98nixHIZHsB9AEWvK7nF9U7QBbKRKoLF3noEw8E/bVXHcfH2Rv3uc8bYfsx7POmiqLg4MGDePXVV3H69Gl84QtfwKFDh9DtdnHmzBn84Ac/wPnz5/Hw4UN4HnvRaa8wCnHaHYwU2N3FsCwYOI6DZrP5RM91XbfvnCnfq584cQKHDx/Gt7/97eLvnU4H77//Pt5++20AwNtvv42NjQ385Cc/KR7zne98B2ma4gtf+MITbVcdRgrsNuJRFMU0TXHjxg3cvn0br776Ko4ePbrjF3JBEIZO7QSGT4HNLcQPHjzAxYsXcfz4cbz44ou1x4cQgo/OXcH3PzrX9xmSJEWrya62cf2AW4vTshro2G7tY2yXp+BSIiJJEro1aqjvhzANg5mEzKq9ybdDkWX4DGUsjKIsKdnbUkDtKonkqce+X33vbreL5aUljI2NYWJiAoIgclVKWZIy8tqf1kwIga6qTMWOVvawbcy5ChtFccVCrGvUmpymmXLOsb4O+ooHIbsqqWma2Og4UGSJ+Zggyq217JtVVi1L06TWX1EUoEgSIsa5FafpwFlYx9+ap5UlESkhlTnglBCosshVpWnPKpuwN02aaCyJIlMxBvJkb07VDaGEMVVkpuJLkO23ATOs9CPS92oyZmepDZu/zY4fwjI02F5QbPW7527g5dlDA1OtR9g+DJsCOwimacI0TRw5cgRpmqLb7WJtbQ3z8/O4cuUKDMMorMbj4+Pc+rTdwLO2f59VjAjs7uJZUWB5+Ft/62/hd37nd3Ds2DG8/vrr+Pjjj/Ef/sN/wN/7e38PAL0f/LVf+zX863/9r/HSSy8VNTozMzP423/7bwMAXnvtNfy1v/bX8A/+wT/Af/pP/wlRFOFXf/VX8dWvfnXbEoiBEYHdVgwiZL7vY25uDlEU4Ytf/OITr5DsxLbtBYZtBlYQBARBgMuXL+PTn/50ZdayjI2Ojf/n++9js+Mwt59F9ABqpeDV4nh+AEGoUWCRzStaei051nVaucIK0gEACOy/RYztykNzBoU9xXECURTQ0LW+MCNCAE1hE9gwjml1ie1iZWUF3W4Xhw8fhpldhF0/gKGp6DBIou16aDca2OD04fKIf658s0KhHI/Ob+YWYlWlbov88bbLr4FxPB+6pnEWAOpnYZuWWSxG8NKEAdTOYZZBVdiqldsyt+ZW05TAMlREA/pYefspSQhaDR1d14PWo77m8MMY6oAbaFb4lalrsF0fhIAGMXHrcKK+fuVeuH6A5gBrdE6oWZ2/AHUgNE0dSUqYwU9xmgys3gHo91AUQAO1stf64dnr+Cuf+wT3eSNsH55lgiWKItrtNtrtNk6cOIE4jgu78Y0bN+B5HlqtVsVuvNskZ0Ssdgej/by7GIYZ2DwA7kn5xe///u/jN3/zN/GP/tE/wtLSEmZmZvAP/+E/xG/91m8Vj/ln/+yfwXEc/Mqv/Ao2NjbwpS99Cd/4xjeKDlgA+MM//EP86q/+Kr785S9DFEX8nb/zd/B7v/d7T/35ytiXBHanfph4M7BLS0s4d+4cDh48iNdee23XV0CHkcAO0zY5joMLFy4gTVN86UtfgmHUWzo/vnQd333/Y4RRzLXUhmGERsOAwyADvL5LSiQbTLWtrve12TDheB7SlKBlNZiv77o+dJ0dcFM3u1vuX9VVlfnaeSARax7UdjzIkoyYYWV2PQ8P7t8HIQSzs7N96YmsKhdBEGCZBlKOqpemKZqmzuz2TNIUbctkpimnKUHT0gCB1g4Rgkpy8qB9A/DrioDMBlw6rpZpwC5tr+36VAVnKOhbAUeDVVjHcWF3NrAEEYahwzBMyLKc1dDUhxIBdD8NUmFtz+f2qBZhTQn7eHlBiKahwy4FosmyVElgtr0Aiiwz580Buk/KqcW9sEw9U845Si0ebYY1jOKBvy2260NXFPic8ySIErR79vGFWw/xxskjmJ7cuVGTEap4VglsL2RZxoEDB4oFWc/zCkJ77949ACjCoCYmJpi/fduJZ3mB4FnCiMDuLoZJgTVN84me22w28bu/+7v43d/9XeZjBEHAb//2b+O3f/u3mY+ZmJjA17/+9SfahkfFviSwO4V8BrZ8cU7TFFeuXMH9+/fx+uuvb6t8/jgYJrKYQxTFp+rN3S4sLCzg/PnzOHjwIHzfr/0B79gOvvGDD3Dr/lYEuJ8l/zoMxSXlVLz4YZil09Y/N4pjJoGN4wQtyyxsxs2GUbHrOp4HURKZ70/nURlW3qB/u8rb4TKUSlVRIIhCJdW1F7RyR8Wm03/MPc/DwsICpsbHYbXaEGp+dD0/hCJX/10QBFiGjq7jQhQEbiWP7fncJF1nwN9zcgeQWqJKZ2nZhKrrejB1FR5j8SCIIlimToOMMgtt+fhTkiyjW/tsijDmJxJ3HQ+e08Xq6hrGx8cRRhFs28bq6ipkWYFpGjgwMQ5CBAiMBQPbDbj72dQ1EI4LQAAlcq1mEz4jkZt+llJljgBoigzH23o8SQk0nU9gozhmEm5TVws11zJ12JywpnyGlaf4qooMQRC55JQAECWBa0kWQMmwoogIsx5lQgi+85PL+MW/8vnKjf+wz2o+q3ie96thGDAMAzMzMyCEFHbjxcVFXL16FZqmFWR2fHx8R2pYRiFOu4MRgd09EEKeixnYZwkjAruN6K2rcV0XZ86cAUDLgZ/Uk75d2zZsBFaSJAQB3063k0jTFJcvX8bDhw/xyU9+Es1mE/Pz832PO3v5Br7z/se19lfejY7nBzAMDR7TeshegfYDGvyTknoSGkQRJEmEpioV8grQ2T+eRdlxfS4B6f1MZQKbFDO6W1ZdQ9cQxzHCIEEA9NlUK+/tBZAEYasPlgAbmxtYW13D5NQkDh48yHyuIAiVECGqvOqF7TYlBJauIWKoqEmSbTtjv8RJWiwMsGDoKja69TZlkhLopsIkVIIAiCJ78QCgqq6aWZHrAs7yOhsWeCpsmqZYXFyCQBIcm51FQgATBGNj40jTBJ7nw/M83Ln/AACBrKjFza6qqMXpmpIUlqrVnj+aSudJ05RU64d6QADIA5KCgyhCK7NVN02jtrfV9miNEasiCMgqcyQBcUnxlSQRUZIWymwQRhAFgdvb6wXsGdamQa3BoghuLRNAj2HT0JnpxvkMrWVoBYEFgKX1Duau38enX5plvvYI24P9ohAKgoBWq4VWq4Xjx48jjmNsbGxgbW0Nt27dwoULF9BsNgtC22q1toUQDcuN/vOOEYHdPeQutb0+r+M4RhAElRqd5xX7ksDupIUYoDaCpaUlXLhwATMzM3j11Vf3/CIyrAR2r1KIPc/DmTNnQAjB6dOnYZomfN8vLkKCIGCza+MbP/gQK+ubzNlNx/NhcGYbeamtjpf3VTIIG9ihRZIoQtEk5nwfLxRoUPeq6we09iYj3kIWq0oIJWH5jC5VrmgoVHnuVpYlgMHZ4yRBu0mtummaYmlpCb7nY+bIDHRdhxdwelMFAa4b4OBBGVGc0B7QHpuq4/qQRQlxWn+uu74PUQBYAqHnBbVERhJFaIqCdT/kRrcPen/H43eyxkkCU+//XDkIIdxEZSCzsvaosGEYYWFhHpIk4cUTxwFRgF0ihKIoFb2TAIEqS1hZ2yjshpIoUTJrGjB0WotDO1vLPaoCBGz11Goyg8Bm11/7UWZL/bC0SFFz3Sb87xhAF12osr31PkZPzU0UJ5QkMzuc6QJHnQqryBLcTFVPU0CRJC6BBWgVUGUhp7Rd+TbYXlAEOuV49zwNdDL1UaDTTmM/ENheyLKMqakpTE1NAaC5Hbnd+Ny5c0jTtM9u/CT7ab8sEOw1hmEmc78gv7/e6/1t2zYAjAjsCI+H/MS9dOkSVlZW8MlPfhKHDh3a462iGEYCu1chTvk88uHDh/Hqq68WCw/58YvjGOeu3sL3PpwrbsANVYXHsDsqCjud1xlgK+Ul+/phBMvqV+1bWbCPwatmYYQCFdvl+ZXaoF5USUF+o0G9nHnYkwBqi61LSuYlDnt+iCiMMJ8RqtnZWUiVChf2rGueCCzLci15r1OIy6D2a7YKGyVJZjnd2m+iIEDXFLhegCRNoXPIAw3Z6g+xKoPVySoKAtQBM52CIMD1QhzgpAX3JhI7jovFxQU0my2cfGEWXddDw2CfO4CAKElxYGoCQRSDpAR+4MNzPWysb2A5WoaqqTgwMYZUkKBpGgChCFjKYXsee94zO2cexapJK6nYN7uOFwysGXLcoFCEWd8Lxw+4KczAVidvUPQC055hx9/6DttegIauwuEEuRWEubS/6MIKqczrRnFSWXAJwgg/OHsNP//515mvPcLTY0SwKHRdx/T0NKanp0EIgW3bWFtbw/LyMq5fvw5FUQoyOzEx8ch245EyuDsY7efdQ34vu9cKrOPQe58RgR3hsZCfOLZt4/Tp07sShvCoGEYCu9vblKYprl+/jjt37tTOI0uShK7j4ev//dtYWFmv/E1RZCaBtR0XsizVzn8SgkztrCclvLlJURQqNktJEmFoWmFxzZN5WZZb3o14kvCJnO16UFUFYRgVN3LlmzpJFNhW2kwlZBHYtfUN2JsbaJgNTExO9N0oOl5/Wi5AyZsgUJLKt2jyybkfBNw5UT/7zPnnNQ1ta85ZEOAHEZotdjCQ7fITjW3Xg6bQxOjiswEwSu+Tz8LWISWUREec5Nz8tal6so6DBw9g5tDBLVLrBTB1lUn6CKEznUEUQxCFwkoM0AUez/Ow0XEQhQGiJMXU+Bi63S5M0yx+wAnJyHrPsSofbTcImZ9VAP3e2X5QVPOwwJu5BWgnq6rQGiKbobLSLmQNkcf+3tCu1i2F1eohoTkeJfSplww3jH51N4jiPtX34q2H+MTxacwerO+mHuHp8TzPwD4pBEFAs9lEs9nECy+8gCRJCrvxnTt3+uzG7XabSZ5GCwS7gxGB3T3kavden9eO48AwjD0n0ruBfUlgt/sEI4TgwYMHuHTpEiRJwmuvvTZU5BUYvsoaYHctxOUKo7fffrtvdSpJEnx07gq+/dFFzB471pcSbbs+ZFmuDZ3KZy/rulcBSsgkUUJSYyslhMDQNUR2ffqv4/lotsYgySJISmD3zDby7Lq091WHy7hh94KAe5OtKXItgW2YBja6TlZRUk+ibNfrI/WEEKytrWJzcxOzR49C0dgqoCyLfZ9LlkTIkgTHC9BqmOgwLNBxnNC/M8h5GMV01pVBAHOVznZ9WIZe+YyCIGQ1SAY6dv3z0zRFy2S/PwAoilQhsFaPKsiq+xEEAQTZwgdHLfSDEHZnA5ubHRw5cgQTY+1+4vbIibnVhQhZlosbWcvQ4fk+Vtc24HoOVldXoShKQXgJIWgYOtyexYjyORdHce15WFaRG436GdgcXk6EORZg1wthmhpzYQWgqjFdFOLM1GbW5yRNmSFsfpgpvZxEaAICWRIRRDRQihUQ5XhB31ztX/zkCr72V7cK4ff6pul5w4hgDYYkSZicnMTk5CQAIAiCwm584cIFxHFc2I3Hx8fRyJL1gVGI025hRGB3D8My1513wO6H69e+JLDbiTiOceHCBayuruLNN9/ExYsX92yuk4c8IXmYsFsK7OrqKubm5jA1NYXPfOYzfeT04eIKvvHDD7G6vkEJQo2ak5IUlm6gU0M0gfpqmxxJktI+T1aokuczAmSErBbHwNqmXfvatsPvZuVdw6IohtVg92Targ9ZkhCRzBojitA1DXb2OXh9s4QQmLpakLwkibGwsIgkiXH06CwUVeUTYMeDpqoF2VAkCbEsF7OjjsefNfU5M8BAVq8CNnmPopgSoh6Smz/HD/ivTxON6wN/ADorqylKFlTUT3b9IGKrsIRkgVH1KmwURZifn4euqTg2ewyGriGIor5tdT0fpq7B9esV/C0FlR86pasaxsbHMTY+jjRN4XkePM/DysoKkiTBWKsJUaakVlGUvpPSj+K+4CnL0Cv/bdfM3PYiiCLuMWkYGuJ4wLWZ8MzKW0gJVVh5wq/rB9zuYYCS4aZpIAjZjoKUEMhyda52tWPjJ1fu4K2XR4FOO4ERgX18aJqGw4cP4/DhwyCEwHEcrK2tYXV1FTdu3IAsy4U6G8eDK6dGeDrkeR4jArs7GJZ5Y9u2n7hC51nDiMA+BTqdDs6cOQNd13H69Gnouj6URBEYTgV2p7eJEIKbN2/i5s2bePXVV3H06NHKj2YQRvj+h3P4+NL14qbX0BTmDbDr+RAFsTYZOFf9WDOnfhAyCVNh5+2Z25RlEaosY6PrMJ9LABgau3/UcX1uUBSv9oYQAtPQ0bEdKDK1X3qlDlLXzwlQvQTseAEkUYDj0oqcfJ4qv8jzaoYIsjTXMIKqyBBB7ZS5b5fOurITg8Mo5qqwQRhlChnbulqLLL2KqrTs410kQbv1759bWlVV5qQi9x+bXIEFcvt5NU3adV0sLCyg2WxiamoKY00Lfhgwv2eDrJK00qfepi4KIgQBlbodURRLYVCUTLuuC5JEePBwHVJm7fY9D6qqFueCH4bFIo6myH31OrktnUdgoyibX65ZFLFK9lyePRugKcGDanVkSYYAwOMQzyRJ0Wro6Lj8a5woCMxu5BxOTaDTB5du4cUjU9znjTDCXkAQBFiWBcuycOzYMSRJgs3NzaJ7ttvtwnEcBEFQ2I2HQb16npALKaP9ujsYpg5Yy7L2xQLRviSwT3tgCSG4e/curl69ipMnT+LkyZNbs4FDOGsK0O2KOErKXmAnLcRhGOLs2bNwXRdf+MIX0Gq1Kn+/cvMuvvXeT/sUwCBKmBJMnCTcepog5ihVUcxVYSnB3ZrL1DUNShZU1GgmaDX486q8mUtFlsDKt/GDEA3TYFohHc9HwzSQppT0qWo1pIO34hgnCdIoxMOHDzAxMYGxsTGUd67r06ogl1GlZLseGoaOKIrhx5nNtES4XC/gzroGA853SvxIZZsEAM2M+Opaf1hTeRuo4seehR00i0tA04FZYKqweQBSrsLGNEgrn0c7cOAAWq0WBAhI05RL+ryi95dN1sSa66WArEfVCwDEMLX646goCtrtNgxNRWssgO/7WFpaQte2sbGxAU3TsnRjEwfG27C9AKIoIIz695mdJX6zZr4Bqmj2zsuqilyxMAchX6kF6GIJ69g2dBVd14MsSlyVHQC6rg9DVeAxjrNl6Nh0PLSy6hwegiiGJIhIsgW0KE7w3Y+vYoz7rBGeBCMFdnshSVKhvgLAT37yEzQaDURRhEuXLiGKIoyNjRWP2S8WyJ1E/rszDKrgfsAwEdi9rOzcTexLAvs0iKII58+fx8bGBj7zmc8UF+QckiTVzknuNYaRWO+UAru+vo65uTm02228/fbblWTEjY6N7/z4p7h5b772xjO3vrKchiylEwCCgKp6NmNWj5cuG0YRGoYBx/Ookut6iOKkuMn2Q7aCm9uMWWqk7Xr8Tk7Ojbypa9SyClJvYXbrq4BSQrC8tIQwCDAzfQS6ode+Pu8mRVNVyJIIx4uLx1ZmJ5MEbYtW8tShUFk5qrhlGpW5yWZJ1fWDEM0em3N5G8IwGqzCWvXvbxo6HM+HZerM45J/xjJ694HteJBEEQ8ePoTv+zhy5Ah0ne5rq0HrZ3j7gGR2ZB4cL6u7KRHUZsNAp3Ke81/DC0I0LQOCKEKSZUxOTkJRFHiuC8/zsNnpYHlpCWOtJiDJMAwDcu/NABk4toskSWE1tki/ANodXCbxNAG4PzCpjChPK+55jCyJCDPFO077k4R7QTjbLEtSYZG3PX9gf2wUJ2g29Mos8K35FRxS9q5L+3nFiMDuLARBQLvdLtKNXdfF2tpa0T8rSVKlrkfj5CWMUI+cwI7O493BiMDuPvYtgR20Al+HnBg1m0288847UNV+hWZYLcTDSGC3e5sIIbhz5w6uXr2Kl19+GS+88EJx8U6SBB+cvYz3zlxAFMdoW41a4iMIAoIggsSoA/DDkDu7yZsL9f0QDVNnqp0AgVUK/ymfo2EUwTJN5vu6fsDpfaVkkEWUXM+HkXWwlpFbdFVF5n5fequAoijC/MICREHA9MwMxsda6DICjxyv3qJq6jrCMEIYRYW6XLcN3oBZ1IhjkQZQUUfbjX4y3Hd+9shydJ9WVdwyXD/os53nvcGEENiOD1WVETKsqH4Q0TnlMgEtv38YYnN9DXEc4+jRo8V8d7kKyA9Cxox1to1B0NeT2otyYrNl5K9dUtMLJZdHChMIpefIsoxmq4Vmq0UtwqIAx3Wxur6BleVlqKpahEHpuk4rhHxOT3AGxw2gyjLCOIZlaujWfC7HD5nJ4cVjvKDvMZqqVN7bdj3oqgKfYyV2/YgS5p59o6tKYQlOCYpAJx5s18/eb+uBV+c3EMZx32z/CE+H0Y3/zqE8mykIQjF2MDs7izRNC7txHo7ZaDQKMjs2NjYURGHYkab0N3OkwO4Ohi3EaT9g9Iv3CCCE4NatW7hx4wZeeumlCjHqxTASRWA4tyu3EG/HaneujG9ubuJzn/scxsfHi7/dfrCAb737EVY3OsW/sWZSBVGEH4Y41G6xSSpn4cPxfJi6Do/TSVkHU9cRxjGU0gWwl5jxwmAGJe86Ht9mLJXrekQRhq4WSmQYxTB1lUkSywqv47pYXFxE07IwNTUFQRAycsYmeb1KW8Mw4AVBQS7p5/L6yGO+bY+ksnKIf8MwIEkiNmv2XV7z4hSKXvWcCcIQTdNkps0mcTarm722piiI4rj4bAQkS3vmqLBl5b6kwLoenS1uWk2ceOFIkUhsmXr2fnR/RwPODQCZ5ZZHxEM0TA1JQrJzu/9xPCstkCviOn1mz3E0dRrYpZsNvNBqwQ9CGgblulheXkaaptAzMrt1qtZvKyEEiixBUeRa8grQm42GrqPLIbBpmsI0VNjZY+pUW4JHs+h5QVSxG9PXqm6b44d9c679n43Wa+VQFQmbboD3zt/Clz/72sDtGOHRMFJgdxY5uaqDKIoYHx8vfsOjKCrSja9cuYIgCDA2Nobx8XFMTk7um3m/x8UogXh3MSwhTvkM7H7AiMAOQBAEOHfuHBzHwec//3m0223u44eRKALDuV35atXTrlx1Oh18/PHHaDQaOH36dKGMd20X3/3gDC7euN33nCCKaokHTSFOueo8VSw1JkmVJPZFrAhVKkJqBLQsE13boUppY0v57SWwfhDA1HVmaBJv5jNNCbWzsmzGHk3FJaAr471zl0lKQGrCqwB6s6epKuYXl7CxsYGDBw6g2WxubdcAkme7HjRVoQQnU5nLip/rB5AEgam08mzd9LPziZWmyFjr2pznl96zZhvCZLAKK4giZFFECtK3EPFIKmw2C5vP4K7n865TU2i1WtA1FZHrwdTzLtnqtrh+wJ3XzO3SXDtsQpCSFAnju+EHIa0d4qiwfhD12aBlkS6s0N1MCT1NyG7AajRAAEQhJbS51bBpGoXV2NCNvhuHMI6hSvUuihx1ambdYwxNRZoSpurr+kH2udnEk87PUwKsyhIcRvJzGMUQRYHr5HD9EM3MJi2JEgiAuev38MbJIzg00WI+b4RHx6gHdmfxOOm4iqLg4MGDOHjwIAgh8DyvsBvfuXOnILy5QpuPUOx3jAjs7mKYLMQjAvuc41EsxKurqzh79izGx8dx+vTpyiwlC8NIFIHh3K784vqkX3xCCO7fv4/Lly9XwrRyu/CPz1yAzpmdqatgyc8LZwBJ7ZvPK8F2PS4hkWUJCGm4jCzJ6HSdynNpfUy9NZa30hyEEZdAeBybMQiBoauwXR9h1H9zHcUJGobOTFG+duMmoijE0aNHodVY61l1N9lbQ1MUaIpSqxLm4Vme79e+vx9GfTUsZWzZTvv3S8tqYK1r0xlPxrF2s97P3Kbdqx4GQZjZfOv3exwnaDcbCMKwliw9igqbq6uEECRJgo2NDRyZmSlu1mzXo52sQVi7j+IkqdiK6xDG7OAiQQAgClCFAUnAWT0Gz9JtaqVFGgCa1mvL9aFrCvzMTysAUFUVqqqi3W4jJQRxGMJ2HayvrWMpXoKu6VkYlAFN1SBLElLwVWUC6jbgguSfn23BBui+G0Q8bdeHoSgQRBFhzCCwcYKWWZ+kXIbnh2iaOja6brGZ3/rJJfwfX/7c6KZ1GzBSYHcWT7p/BUGAaZowTRNHjx5FmqbodrtYXV3F/Pw8rly5AsMwKnbj/WqtHxHY3cUwEdiRhXgfI01T3LhxA7dv38Yrr7yC2dnZR77YyrKMgJGqupfYycTfJ0VZgX1cxHGMixcvYmVlBW+99VZRpn79zgP8xY8/xtomtQvzLuCeH6Ch6xXiUp4VlDlKqu14UBUFYY3qSQiBripMQuK4HlqNBhzPQxj2JsxSYssisI6XhybVq0a8m+w4Zqco03lXB6JYfwHOFwZEubpPgiDA/PwCVFXFqy+/xFSpvAGziwIIXM4AYBCEEEWRSYx49moWWlYp2fkRv9+sIK24JtE4hyiKSJMEAceuOkiFDYIIuirjzp0VEEIwOztbWUSRJJFaxDnfJWeACrsVetVPxC2DznBqisLtzw2iuC9oqBdeGMG06HnUrCHVBLSihwVREKBqGl6YGEPX8RHFUWY39rCxuYGGroGIVJ09ODUBj3NeOX6AhqFxZ2rz/cxTamltU2+wVRV0Fl3BBmcRAQC6rgddUeBzHBWSJPYdg6X1Lj6+dg+feeUF7uuP8GgYEdidA89C/DgQRRHtdrtwxsVxXNiNr127Bt/30W63C7txs9ncN8d1WCyt+wXDNAPbGy77vGJEYHvg+z7m5uYQhiG++MUvVji5TQABAABJREFUVqyQj4JhVDqB4dwuIbOFPu522baNM2fOQFGUon93daND04XvPqw81s/VMYaiUe6wzLcpJ0lckgoCXav/G0DrM+pmTiVJhGnoAAhzHtV2PChyHpzU/xgamlT/vq4fwDQ0uIwb8t7tFQQBVmMrwdgytVoVNA/QOTDVLLpgO50ulpeXi3mlIIwhiCLIYyxICAKtrdm0XbQsk5nUTAN5dGZnjZfNqrL6PR3Pr6iwFfKa/d3U1ErdSuX5OQF33dpt2Jq1rb6/IAgwNBVd16dJ0aw04AEqrOu5uHePBhulaVohr6IoQJFlbNoONEVlWslpLyl/FtYPI4gCUBYSaRov3W6W9b7yGkHEVSMJAXRVhmlolVndMhyf9p5yw5q8ALIsAlCgNBW0mi2Yuoq1jQ5cz0On08HG2iokWYVu6JUwqDJo0Fc9LW8YGrquD0WWKhU2dbBdH6oiMRVqXVPQcT00jf5ApzIIAFESAAZ/FQSqHHfdAKZedTv8+MJNvHj0INoNg/n6IwzGyEK8s3gcC/HjQJZlHDhwAAcOHACAit343r17AFCxGxvG8/s9GSmwu4skSYZC7R9ZiPcB6lbhlpaWcO7cORw8eBCf+cxnnuhkHEaiCAz3dj2OAvvw4UNcuHABx44dw0svvYQwivHt936Ca3ceYLNTP8fIqwlxXL+YvwSqBJYAMDgk1XF9yJJUq/6laYpms4FOd4ssmIaGOEnQtV1IkghREpHWkFhCCAxdw2an3oppux4UWWIm7PLUqyCMYDVM2I4LWZagKkplLtbx6rtL8/0iifR/l5dXYNs2pqcPwzRNAIN7cp087ThTvEVBQMPUC/LsMEh/jry+hNVNwrNvltFLXnOIHMUdoBSnt4u2DPr92lJhBQFomHqRIOz5IZfg16mwBASbG5tYXVvF1NQUJsfauHbzdmWjTF0vFmgUReLOQtuez70WRFFcmYU1Da2PlPshP9WY1tSwLd0AVSzVJAWzdBlAPOC6kKYpGoaBbky3j372GLqhZ7VN49TWBYLltY0iDCpPNjYMA4qi0CCwmu2VZalIF36U6p2UEJp+XENgRREAoY/xwxCSKCAZMOfKCnRqmkbRGRsnaXFeAvQ78hc/vYy//bNvsnfcCAMxshDvLLZLgR0EwzBw5MgRHDlyBISQwm68uLiIq1evQtf1gsyOj48PBQHZLowI7O5iWCzErus+tvD2rOL5+bY+BdI0xdWrV3Hv3j28/vrrmJmZeeLXGvYe2GH7YX5UYp0kCS5fvoyFhQV86lOfwtTUFM5cvo4ffnQOnh9A1/rnLnMU86w11m4CUiWwPQTDdj3IolQ7w5mkfEXL9bKZSRA0Gw10HacQ75IkLWpqarfZ9WhAS20tDiW4Eaf3VdMUBAyVNkkT6LqGNEng9ihBSZLUErycwG50OlhbWUUQRZidnYWiVC8hQRgx5ygBQJKE7H9F6JpaSQ9O0rS2yiZHFMXQNYU51cibdQXoeTDetrDecWr/ngf29Fb6FH/PziMW7fDKKqwAWGY1HTmKk8dSYVOSYnlpGa7nYmZmBoZuUIW6tHNbZvX8s10PhqYxP0OapsUsLGtWNSdYsiwhCONKoBb9HIPtsq7vQ5bELN24CjFzXvAs+gBVcpscVR3I7LaqiiCKIUsSvJ7ZUkmSIAoipqcPIYoShFkYlOM4WF1dhSxTq7FvNWAYxtauFWi4Vzlsyfa26nlYsD0flq7B7glpsoytudYoGdwfCwB+GPdZvnVVqTwvjGIYavU7eGt+FVfuLuCVY4e5rz8CG8P2O/m8YacUWB4EQUCr1UKr1cKJEycQxzE2sjC8GzduwPM8tFqtQqFttVrPNAEcEdjdxbAQWMdxClHhece+J7Cu62Jubg5pmuL06dNPPfw8zD2whJCh+2EWRXHg/nJdF2fOnIEgCDh9+jQeLq/j//4//ydW1jeLx/hB1rHKuCnkzrO6XqGk9t7UJylB29KxadeTHtpDWk/YojjGeMtCEEbo1Dzf5zw3SVJYDYOp9lGCy55nVCQZAcODKGfP8xnpvXVhT7QfN8DKygoOHZjEVONQpc4jRxhFXBXWdj00DB0pIbXHynFp4nBd0m1uNyckhSA8/g9F2zIRcwKIAP55AmQz1Rx7YX48eolljkdRYTVFge25WJhfgCAImD06WygDYZTAUGkIUu3iCalWItW+R2aJDcL6bYjiBG3LRBDFSFKWpZk/T5ukBE1TqyVpmirDDyPIng9DVeFxZkvpgggncI/QRRFLqX8vgC4EmKqBOPahaRo0TcPY2BjSNIXv+3BdF8vLK1AlAakgwTBNHJwch10sQGVvRQgUReISWIAS1LIhuaGrfaFMj9IfGycJmoaGbqbCCoJQqVHK4YUxdFWpzFh/9+OreOHQJHRtcPDgCPUYpt/J5w27pcDyIMsypqamMDU1BYCOj+V24wcPHiBN0z678V5v8+NgRGB3F8MyA2vb9kiB3Q9YWFjA+fPnMTMzg1deeWVbTr5htuoCwzfYP2h/LS4u4ty5c5iZmcH4gUP48x99hCs379U/mOMgpfNp9XbgNE1hWXQOtC5t1PUDZnBNFMe1SqogCGg1THhhhIBTmcEje0EUse2qaUqVUpaC6/XbjPN5047tomGyqwboZ9pSYQkhiKIIruviwIEDsFpjUCT2jXzIqbXRFBWyLGOzW78gECcJrfupUykFapG0DIM9q+r5MLPE4OJppTlbQQB0VSvVGFUxqFrF9QOoMvs64fkBJlpNrDHs7I+iwkZRgPv37qPRaODAgQPVmyZhax6Ypfzbbu44YKiwhKChqYXroBf5dyCK2RbeuFBy2Sqi7QZFP3COVsPA/HwMVc+d4PwbwjyVl2dHhsC349JtoUpt+biKolgkmgK0bzKNI3i+h2s3b0EQxIrdWJIkOF7ATawGaJBVvm8kSURYc32jQVX5VYW9D2wvKGazm6ZeWId7X0voeS03CPHdM1fx177wOne/jFCP0QzszmLYFtIBQNd1zMzMYGZmBoQQ2LaN1dVVLC8v49q1a9A0rWI3fpRWir3EiMDuLoZJgR2lED/nuH37Nq5cuYI33ngDhw9vn9XqWSCww3ThZc3Alm3dx0+ewtW7i/jvP5qDprK33fF86JoGv84qTAgMTWXOs7p+AFEQIApC3+wdi6Tm6CVsmqpAlqRCtbVMg6kMs0KLACCOEuicz+tyanEIQcVmTOdd5cq8KU0zric5OblJ0xSLi4uI47hIeySEQNdUJoENwrCYsy3D1HUEUYiOHfF7T/36Gcv8hoe3z4CcGOTP2SLt+X5RZAmMNQUQZCFZLHIHPulqWSY8BjnOwVJhCQE2NzexurqKmenDMMz+HyEBAiRRqHzGOtSp42XYDluFtQyaIlwOb6p9DTeAIklFxU8vCPKZUHq88kAkYIsguHkKMOuAAHB89tyuqsjwgxCyJHPTkYHB+0RRFDRaDcRJG+3xCL7vw/M8bG5uYnmZBmgZhoG03aJp3ZxjYLsBFEWCrsiFgtoL91G6d5EtOOgq93FeEKJtGei4W+916c48XnvhMF44PMn93CP0YxgJ1vOEvbAQPw4EQUCz2USz2cTx48eL6rK1tTXcunUL58+fr9iN2+320H2eEYHdXQyDOEQIgeu6oxCn5x2HDx/G5OTktnvFZVkeyhlYQRAeya6726jbJs/zMDc3B9fzESsN/Nl3PyyCkgZZhVVFAkscsV2fOc8aZ6rj6mo9IeSpin4QomEYcH0fTdOA43kVdYsXUsX7PIIoII5jVmYR4jjmJsI6rg9JFKFpCqI47ksmVmT2vgrCEIos4fqdO5BlGaZpVlYXbc9jBlgB6DumVoOS+Hzf8mqGqErZb48VMnXJCwK0mxYzodb2fBiaBj8MYJlG38KD7bpZ1249Sd2yd/b/XRAEuEEIXVfh95CuZmmRgx7T+u2L4qSPHKYpwfLyMlzXwczMDA5MjtfOfuqaiiBO4A2w1jquT9OoGcSQgNDZ0Z7PWK7RcTyfO/OZkhSWpiPikFzHo4p2SgiCsN5RkGRBRCzymaQErYaCrlM9pwRRgCgKCCOCJIlofQ+H5Lk1tVm9EAQRsgwIUVworwD9rnmeB8/zcO/+Azp3KirFY+SeWfCUpGjrOtbtAX2uQcicFc4RRhEdJ+DUAQGA44dQJRFh6bW+9ZNL+H//1S9CVfbtT/0TYURgdw7DOMo0CJIkYXJysqjrC4KgsBtfuHABSZJgbGysUGhN09zzzzcisLuLkQK7+9i3v2p5AuV2Y1jDkoDhVId7t2l5eRk//fhjrDsR5jeceuLIkVm6jgtFlmtVuiRJ0LYa2HTq7avFvF0N4dyqSam/IZUkAQ2j3tZJq210eJxKkDoIgoAwStAwqpbYMnjzeEmaYqLdxHqnWzu2abseVFVBWEPUuraNzfU1WJaFiYkJLC0tVf6epgSWpTFVaa8UqFSnXncdD7IsZf2p/fCDEAKEaoCQQEksvQFifmwAdJbVMuvt2YQAqiwzCSwBoCgMFTYbWpZ6bgyaDaOS5jwoEdkLtlTYOI4xP78AALTfVZYpie7p+1UVGWEYIU0JTQtuMKzWj4jcLh2UFNIyaU5TAs1QuOdY1/X6bMJlEICqtGnKfIwfRlSJ5FTLdJ3+EYBcKc7hBSF3LhcAoiRmzp3nNUyKIvctDsiyXCgyhBDEcYTA99HNbIayIm/ZjXUDmqZg0/HR0FQ4DJcDkM+56kyVFgAaBt0uVZa2krgzlD9GkhIYuoIw2Xq/juPjvQs38Zc//TLz9Ueox7D9fj8vyL9XzzK50jQN09PTmJ6eBiEEjuNgbW0Nq6uruHHjBhRFqdiNVZUdMrlTGBHY3cWwzMA6jjOagX3esVM/TvkJPCwncxnDSmDTNAUhBFeuXsV33/0Aa24Ms2Exb3hpqrAKr0ZdotZZFZHNCCgKQqbaQ62vBjyPMZ9YFywEAU3LhOO63B8pXrCOk88s9pDU/Bzl9aoGYQjLNPuItSgKaBhGqWez3mas9xBYQghWVlfR7XRw6NAhHDwwBcfza9U+x62v3CmDZb0mhMDUVXQYClUYxfWzsEJmk/F9ppqWbytLHQaypGZFLshb398dD7oi9wVd5XvSdmlvrFf0DFfJl+cHaHCs43Gmwi6trmNhYR6mSeddyzZXSZSQl4FKkghRFKhdl1Ba7wchBFFg1kS5XraIwFj8SAmhScMRDQHyg7iP2A3aTwQk6z7luE6ErEYm/8+aa68f8at5qLVbRH6q0oTn6r6N43SgChv21ATl0FSl2E/F4gDjdQRBgKKoGG+1YDV9GgblUbvx6uoqXShrWhBlFaTVpAsVzC2ijgE659q/YGKWrMOK1k9g+17LDyvBTwDw8bW7eGX2EA5PtrnPHWELoxnYnUP+e/G8LBAIggDLsmBZFo4dO4YkSbC5uYm1tTXcuXMHFy5cQLPZLOzGY2Nju0Ish/Ee9HnGMCiwYRgiiqIRgR3hyZCnhcZxvOcncy+GkcCKogjf9/FHf/rf8ZML19Bsj0PVVARhyLXHypIMgB3kw+oTDaOo1p6agxD2zYuTWVPzOh5NUSDLUpEwzE869jPbav021z03/4G3XR+WZfYR3By9x1RTFQgCCvWRN79bVkLjJMHCwgLSJMHR2VmoioKc+NYRWFoj1ECnRtEWRRGCIDC7avPPxet9jWosxoIgbHHxmvsfURRgaBqtM+EEVRFCoCkKh5gBiqr0JzWX5DtJkmBl9u+6c4ZH7AkI5heXsLy8jInJSbRa7T6buON5MDQVfhRDUxVay1T6exQnRSUOCynhd6narg/LpPU8dcolnRlmE1ggW4BhVPfk22f0pOH27q8oyj4Lh3zaWYBSmqZMUl4XHNW3vV4AWRYRZyFVokDN6eWFANurVzyr2+MXAUtmw4TZMDFJJmGoMhZX1+B5Hm6tr8MyVEBSC4W2t2+YgKrdvctMkri1jfl2W6Ze2w1bhhdGFVuyKsv45keX8It/5fN9zoER6jGMDqrnBfl3/3ndv5IkFeorQElFbje+ePEi4jiu2I0bjcaO7IuRArt7IIQMxQysk92LjSzEIzwRhKzjcNiIIjB8BJYQght3H+K9jy8ghoCDBw9WLgA8K6DtuMwb1SRJqVWYUX0T8eZZwxAqp8xclkQIENDK1MGgZGm0Ha/SKVsGIQSqKoHlJqR23mqwUaHA1lhWy/CCAKauw/UDWA0DrhdUyJMfsLtZqRKqYXFlDQsLCzAMAwenp4vj4Lg+DENjzlu6Qb86K0sSlMwGa5kGc7vTNO/CrSdgfhj1zYrmFmKgnDhMd6okitBVpeiBdQYkCg+yv9pOv/oo5DUm2RxXSlLmgofnB1u9sCUQQuddHcfBS6dOIuaIPbIkoSFJW6+R3+hkg9GOH0KURKSMRQDPz2asGaRHgABFEuF47OuCU9TdsOZpgTqDAQ1tosfOCyJYpgabMRcMUILKC4XK3y0hhKk6E1INjqpDXqtjx362nXrfQhlJySMpnimpEk9Tp7VArVYbrRYNPAuDAGEYYGNzA0vLS9A0DYZhwDRMqJoKQRDgRxFaZpXAmzXBTX4QDbRJx0ma2ZJ9CKDq/eqmjfcv3sLpN05xP88IW3heCdZe43mwED8OVFXF4cOHcfjw4SJkJye0N2/ehCzLFbuxpmnb8r5pmhaCygg7i/x+a69FK9um7QcjAvucY6d+nARBGDqimGNYtosQgis37+LPvvld3LpzD4qm4cj0dJ+i5vo+TEPvCx8C6A2joWnMG1UvYCf0ekFI7Z01VmFREHnhoojTBFbDwGadLRbUkssMB3J85twntfOqVQJLhz5ppL/LJsdAVtvDUFrDKGLO7xIQPJhfwMrKKsazNMXejy+JEq1WqblpjuOk0keqqSoIIYVKvTXLyVLLA0giuwalV8GlBHLrsaJAb4JkSYKqSBVljjvLCrpfdVXhzm+qalWlzfeNaehwgxANnX+zEffMw8dxjPmFeYAAR2ePgggiRIGj1goCknIVUmnbBFD1naew59vAqmtpmBo2bJcGPnkMgkoGJ/g6PQFJWrbfy1+/MEqo2sn4gqVpioauIWKQ6TwBWlNkRJw+X9vNFjZq0si3HkPVbUmU2CFoAyzYQD6/S23LkigiTtPKZxYEAZquY3K8jWa7jSRO4HkeXM/FYmcRBASGbsAwDaRJAl3Tsqqo+l7bOEno+/VeE3v2adfz0dBViKJYLH58dPk2Xjp6EAfG9oe97Gkw7Cm5zzKeNwvx40AQBDQaDTQaDczOziJN08JufO/ePVy8eBGNRqMgtGNjY09MikYK7O5hWAhsHuC0X477viWwOwlJkoYyiXivCSwhBFdv3cMPPpzD+UtXEIYhDNNE0zSYVYi82VHb9ZgJnnSG0qwE6zwKBEGA6/uY6SGLoiDAyoJ6Whb7a9N12em8KUlh6QZzm2zXq1gb8+0hhAAEUJV6AqsqClKkSGoqUXLUbk+aYml5CZ7nUSWQQSJz8uy4rBAeGrhkGhr8IELSk/LMSzumBIzdZ+sXM6YZyejxWdqeB8swECdpbeIuTxUHqIWal7Tbp8JmBMrzQxAQdF2vmIVlbX8zm5H1fA8LCwswDRMHDh6AKFD7NMvSThcGPDSMkhW6R4EFcis2+7sdBFHtTGerYWLTdrJzrPapBRwvyFKN2WSOqoLULUBdKNXzkc6f6tnmM841z4Ou1Fi3gYIoKpLEnZcFBlunAbrowSOnAP3eDKrncf0QsijC0FV0GQqz7QVoGCocP4TVtGA1LYAAQRjAcz3YXRurK6toNgwoqoYwoP9bd5PfHZAwXYCgkhOQpgT/64OL+D++8rl9c4PzpBjNwO4c8gW9/UhgeyGKIsbHxzE+Po5Tp04hiqJCnb18+TKiKEK73S4IrWVZj7zfRgR295D/9u71/nYcZygSsHcLIwK7A9hrosjCXm0XIQRXb9/Dj35yHvcfLmB+YR6aqmF2dhYbmxsIowisMiPb8ZkEJElTtC2zVg0FaI8qC47r0eCkoD84KU0JVGUrpbZh6EiStCBZXJI6IJ3X8XyIolCbUpumpK/2RRCE4mbcdry+hGXLNOAFAUIvQrNhMvtNadWPXthrwyjEwvwCREnE7OwsIIoQWHZYQqDIMvOmLoxiTLabWNvs1t7odweQSC8IqArJuGcs22N7VXVVUSBLImxGgi3BgMRhQqCp7KRdAjpTnBNYU9cQxQmSdGvepXemsRd5qMfK6gomJyczlXvrB6ZuZruZkdf879QqHVQU2BxbVmz2Yk0QxpVUZ8s0sNndsth7fgDL4M9Xsmy7OfyQ9poSQpivkycFM9+D0M5i9BDYhr6VkBwlSaXup3ZbgjzZuF5dzeezzWxemoUgrCf/ZSRpijHLxMaAypwoTqrEW6BpppqmYWx8DGmSwvM9yCTF4tIykiSBruvUbmyaldT8JEkgCgDrCicASAGYhlrZT8sbXXx4+Q6+8IkT3G3d7xjNwO4c0jQd7VsGFEXBoUOHcOjQIepk8ryC0N6+fRuiKBZkdmJigms3HhHY3UM+/7rX5/V+qtAB9jGB3ckTTZblEYEFvYBevH4bP/74AtY7XWx2OlheWi6sMRCoBdQP/Aq5KoOA8MmPHzLnO70g4FbfyLIE9DrxREqQHNeHrilQZKWvimUQSXW9AKIg1qpANKG0gU730QhumbARQmBoKqI4pkXrplFR7myHXYuTPx+gF7nFxUW0Wi1MTk5CEASaistRrL0gYIZUNRsmHD/gqlSKwiaRURRzVVjXD9AwNDh+UJmB1VUFaUqwaTt9lTNl2F5/BUvl7y5fhe1mKqwkivCCEGk2/7r1fJoi7deoeYQQ3Ln/AEkYYHp6GqbRv1TTq8JaDSM752quUWUFtuczKLLEDM0KI7rA0XU9mLoGx/X7vjdRkjC/SwAlnzREiE3mJFFAh0Ms4ySFrsrg6aO261esu0qWlFzeLMcNuJ8XALwwYC4W0cqgAIqcMh+Tw/UDblerqsjZflXg+uyu1jBK0GrozKAqURIxfWAKXhDCbLXhByG1G7su1tfXIUlSEQSVpgbGmg2sd+vn/HO12g/oLG1ZrX3/4i2cOnIAU+39UXb/JBgR2J3DaN8+GgRBgGmaME0TR48eRZqm6HQ6WFtbw4MHD3Dp0iU0Go1KunF55nUYQoX2C4YhgRigM7A7FQo2jNi3BHYnsd8V2DhOcO7qTXwwdxEbHZtaVZeWIAoE0zPTMIytYB/au8pXdqi1tn52NIzjygxmP9ivbdfYR3PCaJk6BAjYZLyu6wdMG2OcJJQsMJ7r+QGTKBRkJiNzvYqj4/nQNRUChL7PTECgZV2hdXA8H67dxcLyMg4ePIimVZ2FK3pwGXVBSs8FWhQFmLpefE7eYoHj+tzAJD9gL0TQz5ZtRzYDa2hUCY0T+nqKLDMJ7NasK+fvGl+FNXUNXcfbOt4921mnKsZxjIWFBRBCcPyFY8w5X4CSNlmUoGkKHCdAL3l1fZrAm5PH3ldKU4KGoSGK2SqsHwTQVRVBGFf7dTMEeR8rR22M4qqSW4Zl6tiw3cL6zIIXRLAa/A7uJJvbFSBClqS+GWoaxKRyCWwcp7Aael+IFu17zSpz4mRgn26SpPTcriHuAuixD6OYJglzzmGA9tnqWn2wmKbIcLwACSFoGjqSlEBRFLRaLRBC4Pu0qmdjYwPLy8tY1DS0mxkJLVnKdVWuWJnjJK3ULaVpim9+eAn/+899ZnSDy8F+uQncbYzmi58MoihibGwMY2NjOHnyJKIowvr6OtbX13H16lUEQVCxG48I7O5hWAis4ziwrP2zMLmvCSzrZv1pMawzsKIo7iiBDcIIc5eu44OzF4v+yzAMMD+/AEWRMXPkCAiqN9+F4tlTUVNGmqawTLOoq+kFi3gAlBjomga/5nWpoqlVnm/oGlRZQsd2IUsSM2CIhhfVV8jQz80mg2EUo2WZ6DIsh16JzPW+hqlrEESBqVbart9nMwboBXZhYQGKJGL26GxtZ20YRnTetO5mXhDg+gEmM+u0qshZQMzWY3nnFiWRKpPAFvVGjH3iegFMXQMEUBU6ihGXZm1tx4WuqtzEYUWRmOE/XYetYBqaiq7rQSqpcL0Ezs6UzbzqyPd9zC8swDQMHDh4EElKskWN+s+XpinabQsbtltLDoHMYs1QYPNtUBSZmbJNQ8Zk+Iw0YYCGEvHs3EEYZ3U31c9haGoRtuZyaqzodhAoCv+nxw8jtBoGCATmoojt+jA0FR5j4QKgSm15kUpV5L7ZUdvhL64AdDa37r2apRojP+xPEu4FTWzuJ0YCqBslIUn2flsVPQC9DuTqK0AXRzzPQxiGEEBw9969zGpsQGm1UL7KhnmvbencW1zbxEdX7uDzr42sxHUYzcDuHEYW4u2Boig4ePAgDh48CACFU2NtbQ13795FHMe4f/8+0jTF+Ph4RTgYYXsxLJ27+QzsfsG+JrA7hWG1EMuyjICTzPmk6DouPjp3BWcuXUMYRdCyWa1Op4OVleWi8ywloDbVknJYttrWWXpzuL7PVDyLoB8GOVBkkRkiZGfzrFR1NbC6vlkE8sRJglaTbW0NwpAZ8BLklk2GCsu7WY5KN5yiIGYr1gIapo6uTdVoFjkmJIWhGRUC6/s+FhYWoGkapg4egqnrTBLTS3xz5CnEpq4hThKEUYwwqr4GTXfWi8WLXtieD1mWmYs7VAFmK1iCQAm8F4QQ5Z5eUWQqLMc+bagaoojV/5urutW/G5qKMI6RJPSzF2FONduYr3Z3Oh0sr6xgcmIC7bGxQksNo5j5+XRNpeeiKCBisEcvCLZCkBifQVeVWgIrigIUWUbX8yGJUl/QVo6ocDTwFNSwYrtVJAlJkhbfzThNB7yGANcLcKjZYoZf5ahLIH8cEEKgyBLCzHYvCiLSns9OkD2G851E1g9d/r43dLXvMzp+AFUWEcZsk7Trh1mS8Nb3pNlDfGk3bMpUu2VZRrPZRJIk8H0PYxNT8HwfSeDj2o0VKKqaVfUY0HQdXceDoauVUKcfX7iFE9NTo1TiGoxsrjuH0b7dGeR24yNHjiBNU/z4xz+GYRiYn5/HlStXYBhGYTceHx8fVexsI4ZF7R4psCM8NfaLhXh5bQMfnL2ES9dvV9QWRZFx7/59OI6Dw4enKytCvTfXZQsxtfQqCON+EhLH/DlJVg8mkM04MmYgkzTFRKsF2/Gw2XUq3auCIMD3+SSVZ5vN7a11KNJpGfOChSosAJqSzeJm6mQcZxZllzVH6xVdkZubm1hZWSlmZARBoAsFDN4QBPWfqUyYwyiuDbAC+PZJqqQb6NhsAtvkKNOiKAKEMI/1oKohe4AKaztuRYUtk1cgn4VVKnO4ZXQdF05nEytr69m8a3XFOwjDWhVWVWUkaZrZWU1EMZs8FjZ6xo7u1u0DATD1rdnVLct9/U0kzx4PUNtty6LKoygIUGSpUApz0O+chJCxrwkIt67K0FTYXgDL1Ln2XjqX29+1Wwatw9EgiiLTHp0/htWXC2TBUFmgkyyJtR2xSZrC1HSEMZ94ewFNLo7TFKbW3/cKAH6W2syzdAOZ8mtZGB9r0e/Q+CQ8z4PneVheWaH1PIaBdtOComiQFJmGPKUpvvnhRXz1y6NU4jqMSNbOYBQutPPIA4Wmp6cxMTGBOI4Lu/GNGzfgeR5arVZhN242m6Nj8hQYWYj3BvuawO6khXgYCex2WYhv3Z/HmYvXcO32/b79F0YhLl25B1mSMDt7rG+Vz89VuuxmWhC3UnYJ8lnEegLCIiYAvek2ShbOMggBDK2fwGqqCkUW0bGdghT1ElhqbW0wZ2xZShZAA6bovF39DWjC6v7EFpG0TAN+EEJRqwQhiCKmOpOkKZqmgRu378JxHEqmSosIjutxLZNJTfiUKAgwdBUd2+HuD9fLu3sZRCGz4rLspXSb+jtLqb3YgSiKTIstAYEqS8zzJCUEDY4KmxICK1Nhe8krkPfCyn1dtACQxNSirSoSZo8erSTGlhFEVZVZUSSAbCnfg8KY/DCCqansyW5ClejyPuhVQ+l7sEO14kdI+bWzOhs9s1f3IiUEmqLUEticF7iM74csSUhSquiyLPFlBGFUmfGsgyyJ3HApIO8c5pfm5IFOuqowk5ZpB6sGh1NzEycpmqYOxw+RpKwzms6ta6qMIGR/fkIASaIOCULoTK7VaMBqNEAARCENg9rodCGRdQQxoXZk08RCmuKDS7fxxddPMl9/P2KkEu4cRvt2d1C2tcqyjAMHDuDAgQMAAM/zCrvxvXv3AKCizu4nG+p2YFgIrOu6IwI7wtNhWGdgn8baHMUxLl6/jY/OXcbK2gYAoGEYleTgrt3F0tIS2q02Thw/9kgdrLlFNofj+cyKmiAM0TTN2htmAMyUXGDLKhwnSXaDZ6BjO8iFI6ruVhXY4rNzrIWeHzITlOnrMJ8K1wvQMIxasqfIMiRZpK8v95OhMFcra4hkFEW4cu0hCCE4WkOmaDUMex7V84IKCZUkCQ1Dx+rqKibBt3MDfOUiSdNiX9chyJTpnDwJQlYpY1PF0AsiTHJi4m3Xr/a29sBx+6uIKs93XLQaBhw/qCXZdjYrW2YcgR9gfn4ehmFg6sBBWI36Ywpkxy1TYSVJhCSKFdszDZRS+SpsmrJqk+k2uvm8Zoim1W/lTQlBQ2Mr1cDglN80JRizDKx17UfYjv73yU+d3lAoQaC1RXkCcUoINJVPYKM45oZPaVmokdXQuaQ8fATFM0lSjLcaWGekiOeIs5obXj5d1/Vp/Q5HYU5JfUBYL2RJhCCIfQsGAgBVVaGqKtrtNgghIGmC9Y1NrK+tYSmOsbS0BDn28PKJWTSbzRG5wGgGdicxCnHaHfCU7nyefmZmBoQQdLtdrK2tYXFxEVevXoWmaYU6Oz4+zlyQHYFiWGZg8xTi/YIRgd0B7NSs6dPiSRTYTdvGxxeuYe7S9f4gpCJPhmBlZQXdbheHDh2C1bDgcWyITlY74gVBXwox7XZtYJNBcHiqpc3pG01SgpalAQTwwrDSgQlQcgygKFgvX5C8IEDDNJgklXe753g+UxmmqElzbRjw/AAbHRuGrjFvpupu6h3HxeLiIizLwqnjLzDJPi/ZuQxNUwFC0O1u1eTEccJVYR3XYwZy5X8XJZFpBaakia7S1/XiigKnRxQEaqm3tRcpIbB0FRHDxqxrKkRB4AQQ0VqXnHB1Oh2sLK9UqqEGIQhDSJIIVZFrZ0Bth6+Qx0mKhqFx5yzFbJGGVcdjZ9VArp/UWnlpyq/GJLCWqWG9Y2cduYy5Y7AWM7b+rQiFykhc0zT7AqK6WVhTbxJxGY4X1NrDRVEAsi5l1wsKaz3/ddhBWLqmYqPrVQKW6hBE+edik+GmqcELw4FE1/VDtEx2/Y6uyOg4PiRZ4tZBAfR46IYBSVFAUoIoC4P6zsdXYK+vQJIevWfyecZIJdw5jEKcdgePatUWBAGtVgutVgvHjx9HHMfY2NjA+vo6bt26hQsXLqDZbBbXhFarNVqA6MEwzcDmoV77AfuawO7URXRYLcSPo8Devj+Pn164itv356mdr+amL7eD3r5zDyDA7OxssVIXxwlaTZPZd5qrpXkKcRm8GTzXzztja6zCoPOidQTW1DWkhMB1/drXDcKoCE6qs5bzVuRtzy8Ur/rPyl6Zy9OX/SwYp2HqPYSN/d6+n83/eR4IQTHjcuDAAbRazcxmzA78MXSNqZK7no+xloWO4yJN0r594vo+14IvcdTwJE25AVd0PthESkjf/KMg0Bnf6TY7AMgeYJGmKmq/qmfqGsIwppZtro03hiJLWF5eht21cXj6cNWi7floGBozgCiKE26HJwEZmIrrByEkWWHuf0JIHlvMeI/Mvsxx1Tqun6UWV7dD1xR4fliQeRaBBeh32TL0GrstqTxGkiQYmtpHXnMMulKnJIUpq30E1ixVDyXZDDZvXjYlBKYiMYKwaOAcAUFC0gFmY74bgM4NR9SRMCC5GAAcv/6clESBKvICDdWSVREctzGAvC6JKs2KLENpNmGZOqTxQ/jkCwewtraG+/fv49KlS7AsCxMTE5icnES73R6Km7Tdwohk7QxGCuzu4ElnjWVZxtTUFKampgAAQRBgbW0Na2trOHfuXJFqXLYb7/fvyrBYiEczsCM8NYbVQjxIgfWDEOev3sSZS9ewur5Z/DvL8uk4DrqdDZpuOzXVp4wFnHoL26WBOHEU0xnY0r02TUJlV9QIHAXO9ryKBVlVZOiaWpBCnn01Ls3B9pFqz4dh6EwllUdSbcflhgvJsgRTpOm+vSFVNKCFbQlJSYokSbG4uIgwDHH06JFCNQmjCJZpcrpZGfOoAp05jeO4Mhtc3idxnGQWZrbCq3NUM88PKkm2ZciyBFEQal+bbgd/f5PMdsoigHUqrKnrCKKwUOcsro2XYH11BZ4f4uhs/bwrO0lZgGlQUiWIIghDDbRdj1kLRGezYxxstWpVcE2jCrQ6oKomr6FhK6gk289b+0mRJaQpKaqlbNeHaWhwmQ4Deq6U535773XiJMVY0+Tae90gRNPQa7tYi89TuB3oOdeb9Eu3l21r3npMkM2wVj9TmQwHYYzmAEtySghkSUTvWwkCvS7lr991feiKDJ+zYJGkadF9XIahqYhL3yEnSzjmkfT8PU1dheuHNDDLDfDTq/dw8sgBnDx5EidPnkQYhlhfX8fq6iouXLiAJEkwNjaGyclJTExMPNdzciMFducwUmB3HnQefnsWCjRNw/T0NKanp0EIgW3bWFtbw/LyMq5fvw5FUSp247qKvucdw0JgRzOwIzw1hrVGh6UMP1xawYWrt3D2yo1a4u0HYYXAEEKwuraGzc1NHDx4EJPj47VkIQjZVTKEUCUnnxXsvWHgqTq268JQVXg1N/dpSmBZOhzXRcM0YTsuOqXQnoDTgen5ARqGzlQWZc6PASXk9WoLTxkWBQGiICCIk9qUZEEQqA2SgfXNDtbXVgEImJ2d7VM+ebODaUrQauiVGUlJEmFoGlXOBRQktG6fBAE7oRmgZIfFa+IkqV1M0FQFBMCm7TDUMrodWwSPpcL6XDtlWYVtGDr8IKj0/XYdr/b5ge8jSRIEUYIXT56oTaIFtpTHsu1cEKjCnidPtyyTacMGslop3pyq7/dZsRVFRpoQJEkCL0lovRSHzHBOLQBZH6muwfWpQ0CWpH7lm+d/BeBH/f2o5VNJlkR4QQRZlpBwSBwNwOIH79G/0Uokm0Oq64LCyqALYFtnt9XoJ4WOG0CVJISca73jB5naubUt/3/2/jxGjiy/DoXPjX3JzNpYZBVZRTZ737fpfboF6XmsESzBsiHIn/1pPkMeQ7IECYYMA4YlWDYgGBIkwLAg/WHBMqSRDQn6w5DnjfU8kvXspzfsbvY2vXezm90km6wqshbWlhn7dr8/bkRkRkbcm9XdLLK6mQeQZoaZGXFjrXvu+f3OaQ+VA1PQ/BkXj8nxQ7QtvdxWyzQaWy28QNy/3D/GLC+ppgBh4/ifr57BT/3Nx6EpCjRNw5EjR3DkyBGW1e262NzcxMbGBj766CPoul6S2S9bLMe4B3b/MF4c2H8U9++1VroJIWi322i32zhx4gTSNC3LjS9evFgrN75ZqjbSND0QxN113XEP7M2Cm62EWJblshQ4CCO8//EFvHXmY6xvbgMADE1D0/QximO0bEYGkyTB6toasjTF4sICNE2DYfANgUQuvT3Xh5qrRCxjcbA3rjBs4pQgK0rj5J7k/6cocqPSOlgq3ARC+O7UopgW5lDL7710PK9WtmrqOigodnNnXx6BdbwAnYkpREP77fV6WN9Yx7H5I9DNVmMvYxhFYhV2QAnVdbU8zuKgShLacE6iOEG7ZXNLgXuuD13TuIsGXhBWzrVpMIWpWERpKltnzyxlKqwiceOAKKXQdT6BLYyMNE2BH0Q1JZipuGrl971uFxsbG5AkCZNTU8wIS2C2VNkmQR4J0ycubhCwslSRCpuXlw+fA0op0jSr3MuyLEEmEoKBfF5mksRfZHCDEG3b4vZ39/eZK5ANZNgLczdhwTa8MBroP+3fqKVpUx6Zw3t+ACBKUnQEzy7A3msd20QQxVwS4odRjVQOIxwwdNI1FZ5fv9EySqFqCiJf/K73w7g8dlNXG/dbKMy9Ebm3fphAkWUQsIWBJqQZhaEpIwlsFCeY6ljY7vXP567j4dRbH+NvfOXuyncJIWi1Wmi1WuXEtXAx/fjjjxEEASYmJspy41ar9YUmKWOStX8YlxDvP4q/Kft9nmVZxszMDGZmZgCwcuPivfDee+8hSRJMTU2VJce2bX8pn6uDYuI0LiEe43PjoBJYSZJw5eoW/vx/v4APL1yqGfioqsJVtNI0hef7WFtdhWVZmJ2fL1+OrhdAlgnStKFn1Q9zBac+McsyirbNsjIzmkFC9WUrNlup9zG2LANJmmHXcdBp2dzoiUSYGRvA0LTGyS+lFJogfsT1+A7KGS3KUhNmmmCzWJhiL47rQ1GUmgJekBVdU0oCW5pmOT3Mzc3BMG1GdKLmyS8vsxVASYIoGKEcNlYqckWjOG6six0m1cNQFRk8r5skScpM0pbFnH8HS2q9oHBD7h/XIOF1vUBYpjxKhQVlDtMiAqmp7Hpv5iZlc3NzuHr1KpCrwKLeZy9kpMz1ArYYM0S80iQbqcLKIyRS1w9Z7AzNcqJVvQfCMK+C4CxgABj5rvKCEFOdNra7fMfhKK66CQ8jSdPS1IjNX9j32lbfwMnxWO+wKH5mL7E6FKio6U1gpJIIv+f6ITSVxSbxHLddv7nceBBJmqJjGXCCCBkF9xx5YQRNlhAJ3k9JmqJtGawKQXCe3CBEx6yXUA/CNDRsd31YJislLvDO+RXcevQQTs4f4v5WluVKn5zv+9ja2sLm5iYuXrwISZJKMjs9PX0g1IlPiy/jRPsgYFxCvP+4XgR2GLquY25uDnNzc2XVRtE/e/78eSiKUjGJ+yK+F5pwEEycKKXjEuIxPj+aiMiNRLfn4t2PzuON9z7EO++dwclbgnojGppVQoDxlpXLV+B7LqZnZtDpdCqFbmlZDto8EZcEhj5+GEEizRmOXhDANgy4QX0SxjI7GSE0DR0yIRWVMQyjSt9dZZ95qTBPdVIUmUtqRDEsGaVoc8r6AGbuY5ls8jn8nYxmaBsmdp06gU3TtIxv8YMQq6uryGiGxYW+aZaqSODNZ4PCRblBPSNgf+S6jtesWFFAU9WSOA4XOYZRhFbLgsNRxVjmbD2Dt4AfRujkBKt5/9V/G7ymlAKqLHN9iFgkjdpIYNt5eXK7ZXKrByilUCWCC5cvI01TFkmkaSCbm2CFqqMjTtK0cNVufjYcLxDm4rp+8Qw0k/gsy9CyLWSg3GsQhJEwJzWIYmFecdsy4QcB93kCGIHt2CbXhAnIFxRUtkpNaV5KO0zqU7E50qhYnYIkt21TSNqTNM2PmU88M0phm/rIyJw4SYWxUkA/MmdboB6nWQbTNBCNUGFHmUcV8EJ+KbEk5U7bBIjjtObO/H+/9gH+f19/Eoa2t/gM0zRx7NgxHDt2DFmWYXd3t8yYfP/9979wZYXjEuL9w1iB3X+kaVomKtwoDFZtHD9+HGmaYnd3F9vb2+V7oTCJK94LB0HF/Cw4KD2wjuOMCezNgi9zCXEUx/jw/CW8e/Y8lq6s5yWHKdI040bCUMpMQQYnh2maYm1tDXEc47aTt1QMQwbBInGaJ7giQ58kSWEZBnfCQAR/6OIkYQpWU6lwHAtJtejSB1GMVrv5ZTRInJvgeD5kIiGlVUJCALQsE5SwBYXm39YV3IKsZJQiy1IsLS0xBfzwbMU0qycobwbQSF4UWYamqtjtOWU2afO4mAkV2xCtnbxEUPZJARgan8AaupZvlqNKlYsNxaS+WsosKusG2HkZVmEZefVBkffCNsSvAKwc6uInF9FqWZia6VccDN7ojh/A0nV4nMggVVVq98IgssKFVpQFuge6IjJMK58TwT7CKGksnS/KhimoMGsVyN2EJZnbNpBRCl1RARAYmtLoJB4UJf6C/RQGRP7Qio1t6PkxkpH3BVC4LDcbZQFAy9Sx3XVHqsJRkghjbti2DPhRLFSpAXY/iUqJdVWB60eQZMIciLlbApKMwtabS4lts593G6cpO8aBfbpBiP/7+x/gx55+QLCHZkiSVJYN3nbbbYiiqFRh3n333YqL6czMDEzT/NT72G+MS4j3D2MFdv/xWR2I9xOyLJdktXgvFOXGZ86cQRzHmJiYwMzMDKampr5QbQgHhcCOe2DH+NwoCOyN/CN46fIa/sdfn678W/FCE2XUun6/L8/3A6yursIwDCwsLCLJKJeIxnHCXGmdhkkyzcuTOaWWSZpxCYzr+blhU/+3qqLkETCu0Gk1FhpBBTAbJsEA4yeqwJDE9QNu+SHLsbUqipuhaSASwa7j5g67LI6j/tu6sVFBKnZ2drCTR+TYrValXxhAqZTyJuxeUHVotQwdcZLAC9j1CqOYuwBRLGyw/16/p4MwEpoFNZFIQgibRLteXqbJV/cG/5mdj/65oxTCsu5hFZYpdP1s1IxStDQdcVxd6Oj1etjY2MDU1BSOLxyrqHnDChjhlPkW94GhacLjY2WxfNMdPwgrhlaDRLMowW7bprCs1h2RgRonSa03XM+V84JwBVHEdY4G2HM8SoXt+T4sXUWcZlzFMgijkeW9w+8LVSkM1Ej++Wh1nAJcgzSWkcvuqTjJhNcPKJyE1ca+VE1R+pE5tpjoAoAfxVBkqdbqQMBKykOaIU1Yy4SIoAPsug+7EluGznqxSfV7LVOvxB19vLyO9z+5gntvmRfuYxQ0TauUFTqOg83NTayvr+Ojjz6CYRhlqfHk5OSBMIMaE9j9w/jc7j8OIoEdxrBJnOd55ULXhQsXIMtyudB10DOpD0IPbFGy3W63b+g4ridu/F+KLyGKGzlN0xv2x/i248dwaGoSV7d3yn8r/mj4YYSpiU6jsQ+bZNm4uLyCra0tzMxMY2JishTdVFXlElFRbqXjNpcns32mMA0dTVNrCgpVVeFHERRZhm0acDy/JHmizFg/jHLzqeYJNbs2TQRWguN5mJ6ervUJA3nJdJuv7hZjopQyk6OBXtckSXPVWPTbKsENggBBEGBufh6HD81ws1sd14OmqIiS5kltcf07LasyJoAp9sw0i1MK7LNSV95Cg6ivmFLKjL7y8mim/Crl/cfKT/m9oJ7fz/5tysXdqwprGEWcEql/nueuUkqxubmJbreLuTmW7+p4fjXmZEip7Jf59klCx+4vYgRRhLZtlu7Dw8gyCtvSESf8UlV2H1YLuNslGWeGWYauIuAosazMv6rCDk8iBxVURZYBUr2ucdLvY+Wh5wXCDFsCAlmShDE25X4ERM8PmbNxz2O5zYWTceV4cmMooblUQ04tIQREkpBRdgxRPFoV5pFhAna/Rwl7z4iIboEkTRtV2GH3YscLYBtaMWju9twgLBeQZElCnKaNZseMOFcrQP76jbM4dmgSE61ro5IOupjecsstSJKkVGHOnj2LMAwxOTlZTlpvpAozJln7g3EJ8f7ji0BgB0EIgW3bsG0bi4uLZRvC9vY2VlZWcObMGdi2Xb4XJicnbzhhHMRB6IENwxBpmo5LiMf4fChI640ksIQQPPHQPY0qbJZl3B7PNE1x7pMLcD0fx44dhWEYlc8dx+P2NIZhxO21pJSZhsROfWJLiIQkiSEpzf1WXhBgsm3DcYNa7ygrj+Rnuw6bElWOhZPPSghBlmWwDX4/q+fziXOcpJieaMELosZxBYL4mWTgeKIows7ODgDg+PHjkGUZnl+PTilQRBPxCGwYxZhsW9jhlDBzzY5yaKrCLX4MCpWQQxZ6eWyNJEmgoDVTryDi9ywPosiBHcReVNi2bWFzt8v93NBU+EGItbU1JEmCxcV+fzGlFIqqADkpI00DJf3/3UTGmcLNj4BxckfumEP8ChLccwP2x97QagsziiQD4BOjnucLld7C0MvJSWiT8Zrjh9AUmRsfRCmFqvAJbNsysLm5WSr6/LGOMOACa1uQJQmWqXPzWPcSvRNEVUOnlmnUFnIcn/XvRg2l5gW80t24P5b2EOGnlK/6DqLnB2gZRhkFZOpqoyFTFKcjo5CyjEJRJUQJYBkatzw5TTPYhs4WS/JtRkmCv3jlPfzkDz66LxM0RVEwOzuL2dlZAKioMJ988kml7PB6mr6MVcL9w7iEeP9xEBTBz4PBNoRbb70VcRyXC10ffvghwjAsXc+np6fRbrdv6D11EEqIXZfN68YE9ibBft3wRfP8je6Dvff2W3Dq1bcqMSfFZM4LQliGAW9ANQoCVjKsaRruufOORndVClZGzOtpFE0UXT9oLI2TJALPDzF3uF0hQLIkMXdaLwClQMLprQsFvWVeEOTHWZ+0UQoBgaVwfJ9rsFMSzSGiIkkEbcuE54dcQhXFiVCFDcIIruNibX0NRt4fXLwckzTFhKC3t+f6jU7Ihq4jo1SolAojdwgQ5M6tPIh6PRmJNLHruI3nM4rrJayDcHMVdmfn00UcETDFeavbFSqDV7d2sbGxBllRsbCwUJusu2XubNy4+OD6IWxThyzJjUpyoTLzjq8g0TwCC/TvczOPnbFb1QUfxw9gmXrNibjcR0ZhmBpiQfQPc0w2uWp4lmXQDZ1LYIttWGbdeZwRex8ghOXvCsqRKaWMMAoIbJxmmOpY2OnyjyeO0z0ZOhX9vS1Tb/wuzShUWUxgAcALIiiShCTLYOlao2rrBdGeSomjJGZklzIS2vRqjZMEhjp64uSGEabaJnYc8T5ZrFKVhF/Z3MUrZz7BU/fdOnI/nxeWZcGyLCwsLJQqzObmJi5dulSaQRXlxp1OZ99Uj7GJ0/5hrMDuP75oCuwoqKqKw4cP4/Dhw6CUlq7nW1tbpev5YLnxsPCy3zgIBNZxHBBCYFnWDR3H9cRNTWD3C4SQA2HkJMsyHnvgbvw/L71e/ttg7mTRu0cpRbfbxdWrV8tVL9Gfb8fzucfn5U67TZPoNM0w0baxO6QAFn2NxT5L4uoH5Xcd1+NG1IRRlJsQNU+6ZZlPulh8jVwpFZakfs6miGiGUVVJtU0DSZKUqq2oJDfiRPxQSnHlyiriMCh7QwoVtsBwfurw7y3TqCi/gy6/UUSE2axNJdMAyp5bQ9e48US+H3INwtq2hZ2eUzGeGsYolbIYSZN23aTCEpCcNLPrx8yk6mN3ej2sr69j4eg8NLP55U+LTNwoBk8q1hQV2z1+1MwoN2DH9aGrGsKYX6I/2W5jeSWBvpeG4Qb0PK9iXDS8iNcy+YZq5Th9X2h+BNRNw1i/c/9ZiJO09m+1/Xh56TgnosbUNXR7o82amHrKX7xg+/LRtky4DXmvBdyyJJnvIZBmGVqmgSCMEWcZt6LADSKhGg7kubeWAQoiNs8KE9iGJjSaUmQJrh+NPA8AU9l1VUU4sEj5yplPcGJuBvMzE8LfXksMqjAAK5ErJq3vvPMOsiyrqLPX2gxqrBLuD77o6uAXAV82AjuIgqQNLnT1ej1sbm7iypUr+PDDD2GaZqXceD8rISmlB+KeLjJgb6b31pjA7hNkWT4QUToP3XM7Xnz93ZKwVHM0WUnh0vIKfN/H/Px8uXoThFFuHFOfYGZZxnpAOaWoohen6we10ltCWG9lkOdN9lyvRnLTjKJj6VxlSJR16rgBDE1tnHBnlKJl6BWSOmgU5At6bMOIZWz6YQTL1GvlwnHKv/79ktD++U2TFKtrq0jTFCeOHwcFeykNE4qibJrXC+t6PmSJvUxNY/icUagqP5s1iEKhCut6PnRd58a+NJGfwUWAVkvnLgjsRYW1DB2+z4uk6ZMZKTeJGjz2njtEdvJ+191uF0fm5qCbFjQBGWK5r3ojhe7YFrZ7vYrZUu34kgQTLQvdJqMz5CRZlcBrD9U1FX4YCvsdvSCEnWfPNu+EkZkmtK2+AZMoX5dS8aIQwPrP2xbLIdVV1ptbnLPi/LleICxHBvI4CNTPt5JXRqSUwhhRQ9tXT/nPIyFS/pzzqwgA9syLjKwARpgnbBO7gl7hNM1g6rqQwAIsP3rUmIAiCodvfGVqKhw/gqnLI2N4WPksMLhYlFGK//fNj/B3f+Bh6ALjvP2EruuYn5/H/Pw8KKXo9XrY2trC6uoqzp49W05aZ2ZmPneP3LiEeP8wPrf7jy8zgR2GJEmYmJjAxARbXBvsq//oo48QBAE6nU5JaDudzjW9/wpB6CAQWMuybqpn66YmsPt5oRVFueEKLADomoaH770DL7/5HoCqAhtFETbWt8qev+FVKlGuoeeHOfGsT65YbE6zoUyTiZGiMHMmQiTEScJVAZsMjgr0nVobyv9AoXEILNA3KCpIWdEDC+SGMoIeW0WWAEq5va5cMoiqQU4QBFi9sgrDNNgEDax/zvOa81kDQc5tmmaYmmjB9UM4Xp3suW4ATdMQcVRY3n3LyuIz2JaBLicbs1TggxCSRGAZRuVaO15ziXOBKG6OcynHIAnciilzo02SFIau1dRvCgpNkRFGMYuHWl1FkiRYWFiApmmgYH2+/F5aVh0wPD5mbOQCIFxiX8D1Q0gyQZbyVNjcHXuIPGqKgiyjiJMYlq4JVdJ0BCkqlM3Bcn3b1Cv3qTTi3chKpo3GKJwCYZxAUXJDu6Gee4oi01UVEtggimuGTgTMIblQZt0gLHN9heO1dO54bVPHruvXeliHESd5ubHAGKptGQiiBBJhBJQHxwvRsnSuoqsqSt6fK40kzVGS5AsG9W21B0yq/DDeU/lyEMV5WTW7J1qmjtWtLv6fNz7Ejzxxn/C31wOEEHQ6HXQ6nYoZ1ObmJj788ENEUVRGckxPT8O27U/1935cQrx/GJcQ7z9uJgI7jOG++sFy46WlJQColBt/3sqNYr50o8/3zRahA9zkBBbAHkoWPxsOQglxgcceuBuvvfNBGW5drF6vr69jamoKJ+aONsZreH5Q65MtkIjMkyjNY3P4ZkIAI66WaWB9g0XMTKs6U/g4KliSpOi0+SW9ousodEEeKm1mxLx/7cIoqt0nhq5BliRsd528rJCTfckxywIY6bZNA5dX17B5dRPTM9OYnJgsDVSSJOPen1Gec1tTYUnea+g0E1+AETldVbgE1g/DRjOuYiyuFwjzPglhRFCSpBp5zzIqVGELVZtXDu4HEXRBz18QRrDyeJ4mOF4A0AzLS0vQdB0Li4uVPzyO63PVevZ7lmlcsOjhWB6mPPJ7LpM0xYRd750eBBkqs1ZkBUQiiPIxhVGMjsH/o1tmqYqyZWmGQl0zdBYnNXi39N15BSpilgqNt+IkwWTbxvbwYscAkXC8AKauM2WZAzcIK73zw268QF5+LijPBviLI4Ok1Q+jxj79QfR8H6auNTopG3l/ckbpSMdmNu6kMd6IANAUGW4QIUkz1pc6Yls9L6hl1mqKXIkgK74ncqzuf8+HpWuI0xR+fu99cHENx49M494Tny9a51pjcNI6HMlx/vx5qKpaKTcuDNp4GKuE+4exidP+42YmsMMwTRPHjh3DsWPHKpUba2trOHv2LAzDqJQbj3o3DOOgEFjHcT71Qt0XHTc9gd0vHCQC27JM3HfHSbz9wccghKDb7SKKIszNzeXW5aagHJj/MDAVkGOq4/rcfisKiunJDrZ3e2y/tE8+KYWwjDMMI65hk+sXWaf1yXBGKdqGht0GF2SAkfWiVLgwcSr3OUAIJCKxzFPXKxVqAUeFFwSwzea+UJpluHLlCra2dzB/dL62EuiHIWzT5BLR4V5JRZFhaGpJDkVk3/HErrdNRKC41mmaCWNvin5UHvkZrcLye2EJIdzyR5YnW82JHUbP6aG3s412p4PpqalaOS5F7iIu6KkkADLUyWuBOEm49yjAekhFJKmMDQpYZYAqSxUSkmbM8EmEIBQTOj+I8p7PEEmSNrtpDyx48fZRmB81oW2Z2HV8qLKEePhYi+cdAP+KMhTuyD0vaCSvAFMgO7lLMw9Nhk6GplUU0CTN2IKUSKHM31fDpbgSYde8OJcs2kgTEkWm6Bo1Rbc1dF4dL4Cla/B4tf85ogFCTMBUXG+oN5ZS9nzvZfE2TlNoqlpRrv/6jbM4OjOBydbBNAsZjuRI0xS7u7uls/F7771XlhTOzMyg3W7XJp9jArt/GCuw+48xgW1GU+XGzs4Otra2cO7cOfi+j3a7XSk3HnUei/7XG/2+8DzvpnIgBsYEdt+gKMqB6IEt8MSD9+D1dz9AEASQJKkSE+K6fqW0eBCO50PXNYQNEyeWj9islpXZnwMEydA1KIoM1/PgB0E5eSKkOtFuMlYqEEa5gy+HPCmCPgQRcYoHSpubJnZJkqFtW4jiuKY6e0HA+g4FalVtf3GM1StXQCQJd91xu6CMkvIJbMjOv+P6sC0DYRRXJt6iMm9KKUxN4xJYL6ibcVX6p8PmOJ+2zUqmTYMfOv55VFhCCPwwYkrTwNgMTUVGKcIoRhTHuUI2cM9SYHNrE7u7uzhy5Aimp6a4iySu5wt7QIMoxsx0p5G8Fp+LVNg0y2AJ8okBppBKhEBX1ZqbLyEEjh/gkGlzXXpZnzR/AQNgFQ2yRBBy7oFwD0quH0aNCiIr+2W/szQD8eB9NLSNpviZYfRNlvhKLYv/ETsFO35QyURtcvd1vLyMW2CKFIT1bFjb0CtEtJ/aW83vHUZfOWXHZmha7V1CKXP5HlVKHKdpWUrMzmnz+QqjpOY23ARDU2vvnyhO8d2X38ff+6FHIX8BJsmDUTy33347wjDE5uYmtra2sLy8DAAVdbZwML3RE9IvK8YK7P7jIOSSfhGgKAoOHTqEQ4cOAWCtXEXlxsrKCrIsq5UbD9+7B8GBGGAK7M3kQAyMCey+4SApsAAQhz7SwIGiKLBtu1ImkWaZsM9TU5RGAgtAaELiuCyGxsgzH13PB/L5FCufZcokkUiFPFNKYRl8giOKGhGVgLLjNIXEiZC+C3EBXVMgy0xd4bud8v8gu74P0zBKZdh1XaytraHTbmPm0CHWj8qJNgnCGJrANCVJU66hFiu55l/Xnhdwy6oB1JxeCPrnJU2q9wwhBG3bKv+35wcwTaNRDQc+hwqb16wO/qtl6IjiuFQ0Ka0uZGRphtW1VcRxXPa7qnkvLO+wFcEf/7ZlIgoTKBr/mocxP9oJyA3UVBkxh2wFYYxO26qZmZXHlFFomjgnlcVWNZ9jIrGoL1WWuQQWYEptE0EtMBhBU8A29cr/dv2iTLj/Dhk+K0WvJ28/siSBoNlMrUBWZtDy30k0o1B1Fs9j6hq3/5QplOJcYncgE7fFIeD9knIxUYyTBDKR2HmhtLF3tiSdeyglnrBNbt5r+T23Oe6ogKYocIMIWUZrCwxrW12cfu8Cnn3gNuE+DiJ0XcfRo0dx9OjRsqRw0MHUsizEcYxer4d2u30gJqdfJozV7f3HWIH9bDAMo/JucBwHW1tb2NjYwEcffQRd10syOzU1BVVVD8xiQeFCfDPhxp/1G4z9epEeFAKbZRnOnDmDd999F3/76/8HTE5Jqp9HszTB8Vj/aBOCvF9yGCR3ge20LLiez8hr7UvsP6TchXgQRWZsE5gC15zzxQyb+GH3nh9ws0zDKEbLNssyVIkQdNoW4jiF4wXCflZ3hOqoSMx9aGtzE2urq5idncWh2VmmpuXutk0ghO8EUywMDKugg/CDkEutKc2EY2YqbP9zQqqEzAsCEEmCrMi5WVOVbImMgLKMLVLwEEYxWlb9Gpc5xnnmacsyEERRrRy357JzGoURlpaXQECwuLBY3htFrisPTu54PIyObcH1A/hhhBbnHgRyR+UWv081o5S7f0IA2zLya1c/h+w5pfl9wy8lTtMMptGwDwLYeTayF4ZCFS1JU9gm/zoBgBsE5SKLoRWOw/37pKY/NtwXLFaH9wwwM7qux0yWRCiycIXj9UNMtVvCOJwgitEaYe6RZRlURYGuKvBHxO9oI2IcojiFZWpomRoCYdxPAGvomg6/twkhSLNsT+poFMeQOe9ZRZFLtdcNQuha9Ri+/+FFXFzdGrmPg4yipPDkyZP4yle+gmeffRYnT54EpRSffPIJTp06hTfffBNLS0uNjvBjfHqMydX+Y3yOPz8IIWi32zhx4gQeeeQR/MAP/ADuuusuyLKMCxcu4NSpU3j11VexsrICQOx3cj0wJrBjXDMchBgd3/fxyiuvYGtrC08//TQevv9eHDtyqPFBi5MELZufgSkiOoNyiizL6LRsKLKMnuuh5/o1U5oCjPRpjb16aZpxSWo5KA4c14PGacRP0hStBsJdfh6zvj9DU6GqCrq9fq+r54ewBGNSJP5K/W7PwdWNdfQcBwsLC2i329Xfclb5CSHwo6hGRDotG1GUwA9CrpkSwNTqlqCspFBCeRicGg8rokmSYrJlQ5YkuH59gcIVEHOg7/7MQ2G6MzyeYgS6psL1A25Jpec6WF5eRrvVxvz8PKSBfVEAqiJ+/Q3zrLJ0Pf+AnXf+fej7oXASUcT6VHfK+h8d10cUJbA5hK24DKMmKaystko8OnbfrTtNsxohatyGgIBlGYWmylBkGRko0oaSdS9kjru1A6iNtf7cti2zLK8NwljYlw8wgznRuqRlaGWclwiuH0AdERnjhSF0TW085gIZzUbeawBTfUXmUUBeSpxlwnPQtgy4QbSnuJskyWA0fK9tGZXe2eIZGzxllAJ/8er7woWALxpUVcXhw4chyzIefPBBPP7445iensbm5iZeffVVvPjiizhz5gzW19cRx2ITrDGaMVZg9x9jAnvtIcsyZmZmcMcdd+DJJ5/EV7/6VSwsLCAMQ4RhiFOnTuGtt966YYtdN6ML8fgO3yfc6BidjY0NvPjii2i1WnjqqafKG/uRe+/gPlg8V1qAqbC8UirX99G2bbRtCzSPlCnKUpMkERLGovm9KRrHzTNYm/cZcpUWpm7xlSk/iLgTXApgstOCF4SNJaaiP7uO58PU6mMKwxBLS0vQVBWLCwvQGkid43mNilzxh17KFwFURUHLMtF13PKcFdmfPBTlrE3Isgymwf+t6wcwTa0cy+C907JNOIEvLCMXTbTTTLxI0aTCFmPotCxs7/Y4Ki7F1tYmLly8hBPHFzA9M9144Xr5AgoPLCqGbX+wn7QYAzNC4i8OxGkqVGkpKFSl+ky1LavScxpGEcjQRITdErky5gewBYtLlNIKKWRl3tXFhiaSO4gi7kYEL4hgDfW8DyOKEkiEcHNIM0qhDRE9lk3bL12N0xS24H4FWHwP752j5CW/UZLAtsRKLRuPuHy0ZTCiN0rtZFE//P2piowgTkaSU4CVEvOuuWVoZR/8qH2WY/Or1QSaqsBpKCsOo6SmkvtBhL985f0vpTIpSRJs28bx48fx8MMP47nnnsPdd98NRVFw4cIFPP/88/j+97+PCxcuoNvtfinPwX5gTGD3H2MCu/8ocqmPHj2KiYkJfOUrX8HU1FRtsWt1dVU4t75WuBkV2Ju+B3Y/S4iDhviZ/QalFB9//DE++eQT3HvvvTh27Fjl81uOzWGy3bxKwwhDc5ZqllF0WsNlogQty0RGM2Q0ay4TRnMMTQEWS6LDdTk9nILeXCJYf3EE/X9REufbHciilSUWv+J4yLKUq+qJnI4ppVBVGYPVhN1uF1c3rmJqagoTU1PQVA1hw8p9kUEaDL3oivuz53qYmeyg5/nNWbeCyVMUxWi3+NE0ri/uRy3IM0i+n4GoHgDCa8RUdoMbk+J6/OsEFH3J/XuHEAJL7zstDx93lqVYW1tDFLF+1067A1fwHEqc6oBBTLQs7A7cK5Wc4DjhEjIgV5klgpRzP/U8H5ahww+ixv7sOE4bTMtIRcBkFQJ8oyDH82EYOhRZ4hquqYosJJ89z68bYw3ANnXhQgaQOwVbFrZ3+O9cJ180cP0Qpq41XjvHC6CrauNz1P+OXzN0IoT1dXr5vei4RWSXqI84rBmGFWgNuAePMqECAD+IGx2ZCWEE1gsjJCmE5kvl8fkBLFNDt9cfuyyxLOvBO80NwpHGVgBbBCtc42VJ4n7f8UK0TA3OwEtuaX0bL7//CZ6676RwH18kNL1PCwVmZmYGQN/wZXNzs8yXLJyNp6enoQuqT25mjMnV/mN8jq8fChOnVquFVquF48ePV5zPL126hPfffx+tVqvsn52YmLjmvfWe59Xm+1923PQEdr9wI3pgwzDEW2+9hTAM8dRTT9XKVItx3XvrIs6tbjdug2eiAuQ9j0SCLEuwDB1BGMLx+hNiXdMQNqw0RXGMdstGj5MZq6kKN+4jiEKumYrr+TCMZrfYLMvQEhCrQl0lpOhrDEvyEIaxMGtUpLb0XB+6qiKIIlzduArHcTA3PwfLskDByl55E++e69Um5kQikIgE29SRpFmjMzOQK3GmCY/jhBwJJvtZlsEWZK8W/b2EEEiEoGVWiVbRC0s5984oFVYUyRPFSelILEkSWpaJK1f6k3svCPNFlwBRFOHKlStQVRWLiwuQJDk/L32H19qx+T5so5qdOQhZloVlnUEU5QST7zgsOj4g78MTOAa7fgBFkpHkpeKDCizA4pZGGQUZmoLd4TzWwX14+cKMIKaFt9jHjo8d/yhnW8cPoMoSQsGCS5pm0BQFaZY1tn9TUCiyBFGMaZOhU8s0ai7B7FkWuwRHcVJz/9VUtRJp08t7U4cjawaRZCnaZtWRGajn2rLIHF0YmUMpW+AbrFCxjLopVZZRyKoEQlKhIVWWUciEoG0aI82f/CiBqsiVBYuXz1zAwuwkFg5PCX/7RcFeVMJBw5csy0ozqJWVFZw5cwa2bZdkdnJyckwocowV2P3HmMBePzS5EA86nwOsurFwN37//feRJAkmJyfL71yL/NabsYR4TGD3Cdc7RmdrawtvvfUWpqam8Oijj7IsywbIsoxjh6ex6cbY6Tm1z/0gzONT6pNQTVFhtDSW39pADDVVbSSwgNg52A8iLsmJoiTPXW0mCKosgzdddr2A62waRhGmJzrw/KCirgFFiSh3uHB9fswKpRQSIVhZXgEFxeLiIpSB8szCEKvJ+beJ4NqmAVkCeo4HVVVhaFpNpe0PnD/mMBSrsF7ej5pyyJpEJBiaCkmSagpwMhBB1ATX82GaOjeWxBmhAIdRBFVVoMgydna7NbUzzbLS2XliYgIzM9MY6t5t3G7/YwExc7xamfJwNUEQih2HHd8Xuj2ze5S/2JWmGdqtwTib+v0pcj02dQ1dx4dl6nAF5HLUn08vCFk+7cC7oW0ZlTihIIwhE4nbE5rRDLqmgLOuxI4lSjDZNrHtND/zAFMWR+W1On6AlmnA9cOKWjp8TKMUzzKrNd8XkQgjtEm9b7/Ikuahl4+pIJqmrtb2TcH6q0dF5kRxWvYvt02d24vqh1HuXiwmphnlPgoVpGkGTdeQpH1STCnw3Vfex//3a48JS9q/SPg0E0pJkjAxMYGJiQnceuutiOO4NmEt4jhmZmYa4zhuFoxzYPcfYwJ7/bCXGB1N0zA3N4e5uTlmQul55fvh/PnzUBSldDb+rNUbYwJ7E+KL7kJMKcX58+dx/vx53HXXXVhcXBQekyzLoJTi8YfuwV89/2rjd8jQ923TQBjH8IKAkQzOBM3xXO5EPQhDbnkyK+3jvwB4qiPbp88tJ0yzFBMtG7tDs2XT0CERCX4QNsbtEELgB6GgVJiVIjYRWN/38cmFTzAx0cbk1DTIEDHPKEXb0LDrNJOZguCmWVb2uiZpBppHx6iqDJ7I43o+LNOAz5nIilRYFjHEJ6FSHrsSRTGa3pFeEIIrlUNcqjsqF5YQwNJ17PTcGnmkAJYvX0HouTh8+HBjD4jri3N6mUprlGWiBEDbNkvV1AvCnHTw1W2RCptlFC1Ta3wuCnWWmV2JyoCZoVAcJzUFlo0hqSihBXRVRZykyLIMSZpyS/nL47RM7nECuUFSTpQtoyit7Y85TtJKBmwTXD+qOdoOomXr6HoBVFlGLHiHRlE8kjBGcQJDV4XKqB/GUGSCJBUQTy+Aqavww5gpuQ3EOYwTdGwD3RExN2EeGQQAaUYbFx3CONlT/E7PC2BqCvwRJcKOF8LQFQRh83uHEPZ/XY+5b4vOF1DEA+lD5JviL1/9AH/32Qe/8OTs86qEqqriyJEjOHLkCCilcF0XW1tbuHr1Ks6dOwdN00oyOzU1xV1w/jJinAO7/8iybBz/dJ3wac81IQS2bcO2bSwuLiLLsrLceHl5uazeKNTZycnJPW1/3AM7xjXD9SCwURThnXfegeM4eOKJJzAxMTHyN5IkIU1TPHjX7Xjx++80TupdP8BEy0ZKKVzPr6itURyXJZ3DKNyKYw454/UBEkmCG4Q4wlEAgzDiqrCUApqgLNfLjaCy3MzG0NRKOWdRfjo8HkopN24HAHqeV/aMFdje3sb29jYOHTqExaPz2G3o62W/9aHIUmNpakaBScuAG4T9jFWpHzPkuHzCDoija8IwRsu2KmXfg3C9ELIkV9TAwXxXRZYRcyb5e1JhBb2wvFxYlvGawAtYKfkgSc6yDGtrawijCLeeOAGRfyuvRL38vOixBdBqKPlNhs7JMAn0gwgyIUg5ZKqpPHywtNgPQ3TsqoHT8PgNVc0rGZorBNwgrFQcqApzBS7OaRjGaNsmdx8A8lxZflcvI2kmwjhBFCeNJb6Oz1yLuRm1RS91A2Hv2GZJwi1bR+wJCGySojNUfjuMNMtYuwM3v7nvTC5ScwF2243qde25ATeHukCcpnkcEGlUhQs4nj+ylLg8eyMMhCiloBm4hH+wvDqKE8iKhDQROyL3vLAsm5YlgihOcWltCy+f+QRP3fvF7oe9loZMhJBaf9zOzg42Nzdx7tw5+L6PTqdTlhu32+0vNcEblxDvP8YK7PXDXhRYESRJwtTUFKampnDbbbdVqjc++OADxHGMiYmJktC2Wq3G5+dmJLDjO3yfsN8xOjs7O3jxxRdBCMEzzzyzJ/IK9N2RFUXGVx64u/KZpipotyzoqoqMUjiu1/iHnFcKCTAFkRfL4gdBYwwNIQRpmgojaoQZrC4/pzZOErRtGxMtG0mS1IhJU4keyztlZNHUm11qKQWMvMwjyzKsrq5id3cXx44dQ6fTyUlq83nIOO67EiGYaFnY6bmVxY9BwlSUGfPgjMijTVL+tUuztDIuVVVg6FpJpP0wErrVMndn/sRE1AubZbTmKt2yTPhhhCRNEcUs5qk4F1EUYWl5GRmlWFxYQAYIHYG9vORU9HnbMlmpacPiTBDGaOfOtk0ENk4S2Dbfbbsw6SrQ1BdbGFbxUBgpNSmwAPJniJ1DWWa96sOl+34YVeKEhhFF8cis1SCKIZHmBRigH6sjQhgltetlm3qFHDICJ4746XkhNy6GIC+fdn1urNan2RczMBNPvClyNVP4LfYlkXJcbKsoJeahZRnwowTmCJdogJWZW2b9GE1drajuSZrCUBSIIqIKRHECRZHZwmV+P7z8/ie4uPbFzofdT5JVmEHdeeedeOqpp/DUU09hbm4OvV4Pb7zxBp5//nm89957uHLlCkLOgt8XGWNytf8Yn+Prh89LYIdRVG/cc889eOaZZ/DEE09gdnYWu7u7eP311/H888/jjTfewO/+7u/i/PnzAFCWJTf53uwFKysr+MY3vlG2NzzwwAN47bXXys8ppfjX//pfY35+HqZp4mtf+xo++uijyja2trbwUz/1U+h0OpicnMQ//sf/GI5Tb1O8lrjp7/D9+iO1XzE6lFJcvHgRr776ahmwrI6YnA2iUGAB4JF774Sha2jbFmzTQBTF6PVchFGEnutB40yKgjDixlQwIsKfADedbyknBJ4fcCdrnh9yYy8yisY4FUmSMNGyEcURuo7XOGH0gqAWDVGomBmlwtIux/WQpSmWlpaQZRkWFxfL3oUsy4RxH44XQBkgEpahQ1NV7ObjtM3++R0mTE6u/vIgy/x7Ogj41w4A3CBg8RGmAZrRSgk1IUQ4f2dZwoJtjyDXg5m07TyrtNJrGkTltVleXoZt2zh69Gj5xyNOE4gm3bz+XoDdK7JE0BOUvoaxmGA6rg9FMGlwcgI6WJ48vH0RyQYtSrH5PdqspF6BrqkIGlyOkiRFixM/VcALwsq9OQhCCGRJ4n7eH0fIiThCGenkh2FZSqurCoIoqZaH00GlthnM0Kl58tC2Tbh+CErpyPECyPt2m6+vRFgJveuHwkxcgJUktwXPmK6q8LywdPwVIYz5kTmGppZl7z0/HHldAfbeGfyeJLGqgeEz7AYh2qIc7hxJmsE21IpLMwXwF6+8P7L8+aBi0PH8esA0TRw7dgwPPPAAnnvuOTzwwAMwTRPLy8t44YUX8Morr+Djjz/G9va2cCH3i4KxArv/GBPY64drTWAHQQiBZVlYWFjAgw8+WL4fXNfFH//xH+PRRx/F/fffj5/7uZ+Druuf6bna3t7GV7/6Vaiqiu9+97t4//338e/+3b/D1FTfkO+3fuu38Du/8zv4vd/7Pbz88suwbRtf//rXK0krP/VTP4X33nsPf/VXf4U///M/x/e+9z387M/+bOM+f/qnfxqEEPzcz/1c7bNf+IVfACEEP/3TPw2ARYL+/M//PI4fPw5d1zE3N4evf/3reOGFF8YEdr+wHyXESZLgrbfewvnz5/HYY4/h5MmTn/qGHRyXoWt48K7b0HPcegQOpdAFxFjkVuz6Affl2URiCCGgeY+eiFyJ1lsct6/8EhB0WjZkScKu4+YmJqLtVs9hcU5prkLz+vW6vS7WV6+g3W5XiFQ5pjzKpwlpxhRnSWJj9YKwYs40qGQPE9iMsmvHg+MFws95ZkkAI3mTnRZcP6h9jxACzw+F2anBKBVW8FmWsb5KXiltFMeIQ/bCPDQ7i0MzM5UrF4QR2jZfhWW5rfX7QJYkmJqGHccVqrhR3pfI6yPNsoybTQywib2pa3AEPZJeEEISLEC4fpAb93BIHUVuwsZXbhwv4FYsAOwPsskxkbBzp2J2j4mJnGiyTfPSZtvQchJHGt+XrNdS9OwysjW8CGUbRuUe8nyxAg8wBZyXm2qbrAw5yzIoyug/m44fNCrDEiEgOWmMk3Sk6guwRQl76Jkr1vkGC0j8MKrlCjchiCIo+ffsvES/cb9BCEMXL47KsgQ3iNEaOm9+GOO7L7//hSZcN4JkSZKEyclJ3HrrrXj88cfx7LPP4vjx44iiCO+99x5OnTqFt99+G8vLy/A4rSAHHWMTp/3HmMBeP1zPfuPi/fDss8/ipZdewscff4x/+S//JcIwxKVLl/C3/tbfwg/+4A/i13/91/Hqq6/uiYP85m/+JhYXF/GHf/iHeOKJJ3Dy5En88A//MG677TYA7Hn97d/+bfyrf/Wv8OM//uN48MEH8Z//83/G5cuX8e1vfxsAcObMGfzFX/wF/tN/+k948skn8eyzz+J3f/d38ad/+qe4fPly434XFxfxp3/6p/D9/t/pIAjwJ3/yJzh+/Hj5bz/xEz+BN954A3/0R3+Es2fP4jvf+Q5+8Ad/EJubm2MCu18oiOK16qXpdrt48cUXEccxnnnmmcrqyKeBoijIsqwc1+MP3QuZM+lxBKW5hVtxE9IRRHSY1JHiRUsp/DDkThwY+W2e8KW58tuxLWiqgq7jVkqduf14YK7Cg6R6kMCy0s/qPimluHp1A+vrG5iYnsbh2dnmMaUpV4UCWF+jpqqNjs7pgJLdRJgc1+deG0ppOUFtQhBEaJn166PIMmxLR7fnNhLNYhwiQyZWsi1WYXnnRJYkZDRrjHIpyrQ3rm6ynjKO215Umhzxxld9oSuyDE1TyrzRKImF5Z9+EIJIfCMkx/W5Cl3bMrHddYT3BCN1YqKVZZTLX23TwHa3xy19L34/ipg4nl9buGnbfYMnCvF9ABRl1w3HMnCCHT+AbejCTFc/iEYqlVGcQCZ9NZdtr3olg3D0dhw/qJU/t4b6Xj1/tDqZUdq4r5ZpVJTxnheMdO2lYM/VYHVKyzIQlPm17N+TlELdw0QqSTPoigzLUIU9uKxvNhOWMJu6iiTN0PPCGsm+vLmLU++cHzmeg4Zr2f/6eVG4l95777346le/ikcffRQTExPY2NjAyy+/jNOnT+PDDz/ExsbGdU09+DwYmzjtP8YE9vohTdMbdq4PHTqEb3zjG/iDP/gDuK6L//k//yf+wT/4B3j99dfxwz/8wzh8+DD+3t/7e/j93/99XLx4sXEb3/nOd/DYY4/hJ3/yJ3H48GE88sgj+P3f//3y8wsXLmB1dRVf+9rXyn+bmJjAk08+idOnTwMATp8+jcnJSTz22GPld772ta9BkiS8/PLLjft99NFHsbi4iD/7sz8r/+3P/uzPcPz4cTzyyCMAWKvkqVOn8Ju/+Zv4oR/6IZw4cQJPPPEEfvmXfxl/+2//7TGB3c8+FwDXRIVdXl7Gyy+/jKNHj+Kxxx77XAHpxYNWjKtlmXjwrtsav0spFZZ8jpzocz7ruT50rT/ZGSzZjWMxAWpa6SIEaNsWkjiB6weNk+EgjITqWsWwKS8XLHJNXa9P5NM0weXLK/B9H4uLCzBNS6i6eX5Ym8jKklwaYfH699h+WUlqE4HNKBUSIcfzxSpsNmyWZIAQFj2UpCnshgWIYhyM8PO37QVhf1GiAU2PnKYqUBSpLLMdRBRFWF5eRpZlmJufh6Gp3ElmGInV9mBAjdcUBYosVeJ9wigW/j5OU7QbyH+BjNJGxb5tGawkGgDlRMwUcP1AqPJFcQLTqBPQjm3ByVczR3BL9PIMZR5Y2W3/WWtbdfMn1lcsfhf5QVwSy+oO2H+0TEMYIQQwUm8JxgqwhQnbZO7iEmk2g0uSbOR2ho/b0NTGDGEvjEaWJXthVCHwLbPZcGovpcTRgFrbMnV0OZE4bhDtqZQ4jGPhIlf/ewkszmJHy9Th+P1nJ4iTmpv8Gx8t4ezS+sj9HCRc7xLivYIQgna7XbYOPffcc7jjjjtACMHHH3+MU6dO4fXXX8cnn3yCXq93oIj4IMYK7P7jRpKqmw37WUK8V/g+a7e699578U/+yT/Bf/2v/xUbGxv4H//jf+CBBx7Af/kv/wW33347fumXfqn22/Pnz+M//If/gDvuuAN/+Zd/iZ//+Z/HP/2n/xR/9Ed/BABYXV0FABw5cqTyuyNHjpSfra6u4vDhw5XPi2ig4jtN+OY3v4k//MM/LP/3H/zBH+Af/aN/VP7vwvzu29/+dqMfwPgO3ycoJeH57AQ2TVO88847OHv2LB555BHcfvvtn/uPatO4nnjoXu7LTmTK5PoBl+DGSYJWi5dJRSs9nKXimRNGZmbT/EvH82EM9Oa2bBO6pqHnemUMCA/DpK1yLF5QMUIZJI2sx1ZDEPhYWlqCoqhYWFiAmiuzrqBUOEmrxkgtixkR9XK3XtcPuWW1aZbBsgxuyWrP87g9untXYQk6LQt+EFTUST9oGld/HKIJd5KkwrJP1wsqpN8ydGRZhjB3bu15Xqliuq6L5eVlWJaFo0ePQlEUhHEifA6CkH//AMyoqCDJTW6xQRhBIDrl54b/ueMFlXu0neeVFlfQG1EWm2YZt4QXAEBYBujgORg2hRL1jAMAKOA6DlbX1rC9s4MwDOt9kB7rD2+Z/LzVOE7LntYmsPt/qGUg/37bYnE77h6IsOP5I42Kel6AtmUIHYBZHI6YxBbZsJIkIaO00cE6TdPKNebBDSKoigJNVeDzMrL3WkrsB+hUlFfOPv0QhmBhDED+zgz2dAyOH9ZKqxVZgj+0UJimGWRZqq1s/tX3P8BmVxD8e0Bx0AjsMBRFwaFDh3DnnXfi6aefxlNPPYXDhw+j2+3i9ddfxwsvvID3338fq6uriHjZ4dcZlNJxD+x1wFiBvX44CATWzRMvBl2IFUXBk08+iV/91V/F9773PWxtbeGf//N/XvttlmV49NFH8eu//ut45JFH8LM/+7P4mZ/5Gfze7/3evo/7G9/4Bp5//nlcvHgRFy9exAsvvIBvfOMblWP41re+hT/6oz/C5OQkvvrVr+JXfuVX8PbbbwMYE9h9g5QrZ5+VwDqOg9OnT8N1XTzzzDM4dOjQNRlX8YdjcFyTnTbuvu1E4/ezNBOqsLLQ0TTiTm4dr+8MSvIQwsJkKYwi2Dy1lAKqpqJlmzANHY7rV/JYPT/gEkJfQHApKJSBSV8lboJSLK1cwdrqGqampnDkyGGQAVUpTcW9j14QwdA0FtXhepVSryRJxO64fpCX1jb1XFJh+aHj+dCFpaKUlQw7Xo28NKmw7N5h3+y5vrAM1QsCoQpb7LBtm/CDar8tpRSapmBrawurq6uYnZ3FoUOHynuXuTjzj7uIeuJBkiRoqswtK4+SRLgQklIqdIKmtF/KOUxe+2NMhMSPVSk074OAsFLtfIztBkdjgC0kND0KFBSbW5u4uLSM6ckO4ijC6uoqLl26hPWNDTiOU74fJELghVFjVimQG0/Z4nLanufDGOqn1zWlQorDSKxC9g2A+apS2zby3FiB8pQbQ42aPvthBNvQuP2hAFuosBtcfQeRZRk0RYYiSUhH5My2RpQS73XKX1SY8zhCy9Rzcyv2DhH1pRdw/RDGQGWBrqmNx+OHce044iTFn59+F6HgXB4kHFQFdhRM06yYvdx3333QdR1LS0t4/vnn8eqrr+LcuXM31AyqOLdjcrV/oJSOCex1xEEgsI7jQJZlGIL2o3a7jcXFxdq/z8/P495776382z333INLly4BAObm5gAAa2trle+sra2Vn83NzWF9vVppkyQJtra2yu80YXZ2Fj/6oz+Kb33rW/jDP/xD/OiP/miN6/zET/wELl++jO985zv4kR/5Efz1X/81Hn30UXzrW98aE9j9xGc1crpy5QpOnz6N2dlZPPHEE8Kb8tOCENI4rqceuZ/7B1vkDuwIJtphFDeWogLsJTtIrqQhlbGxn4cAtm0gTRNEUVxxyC0QJ6nQCVdUvskyVtkkrVA9i6zRra0t3H7bSUxMTKJpKukFYWNuLCEEtqFDV9XGHFuA9fjxJpFJvoDAKwdjKmzzy5NSyu2TtS0DYZSMLAMf/CM4qAQTUu9lrow7SYVxLF4QYmqihZ5bJ89ZluHj8xfgeQ4WFhYq1vDFPep6AWSJv38vCBvvWdvUEcWx8JwXv+flABNC4AUhVEFUTM/zMd2xG8krwIhfW0D8mLsu5/WcryOwvObmTGagMCWqPgtFL3Gv18PCsQW02m0cPnwEx48fx5HDh6EoCnZ3d3Hp0iVsrK9h5coaZFBhzKjrB0LzIEqrC12apiCO0wopjpNEuCgBsIUgnnJtGzp6rj8yLglgCr3YLI6Zbe2lADOKUmFeNABIMhEu9BUIR5QStywT3T2YZwFAECWNUV2aosALB7wB4mRkWTXASuNZ/zuBbepwA76i5zQ4Im/3PPzVax+M3M9BwEEtvf00KLIlb7vtttIManFxEUEQ4N133y3NoFZWViomKvuNL+riwBcJ40WC64vraeLEg+d5sG37M13zr371q/jwww8r/3b27FmcOMFErZMnT2Jubg7/63/9r/LzbreLl19+GU8//TQA4Omnn8bOzg6+//3vl9/53//7fyPLMjz55JPC/X/zm98sVdZvfvObjd8xDAN/82/+Tfzqr/4qXnzxRfz0T/80/s2/+TdjArufL1JFUT6VsUKWZXj//ffx3nvv4aGHHsJdd921Ly8hWZZrK7Cz05O47cSxxu+PMmUSZSyKckddLyjJF5GksoQYAIJwYCKa97gaugbX9eH5obC/MwgjLjFzfX68BwWFnm+XEII4jrG8vIwkSbC4uIiM8klbktQVS9PQYegqdh0XXshXhuMRil+U8IkmU2FFcT3VfmNCgE7bgusFiJNEqAoNu0LX43xG98I2kUhZltAyDYQNMS/FOU/TFHfefntjvzchJFeIBWZIDee0bRvwgxBpShHF4nta5IhNwM67qfGPvW1bLBaG+w1GyESExfGDRmWfgICC9ac3VLdW4A64GsdxjOWVZWRphsWFRWiahihK0LZZmbphGJiemsKxY8dw8uQt6HQ6CMIQ5z+5iOXlJayvV9XZAlmWjTSFcn3mFFzEDDW5mDPVeURETUPEj6YqCKK4PNf+HvpTXT+o9WsWsAwdjhfkCquYVMdJIiz/ZcQ6gBdE0Ea4F8dJyi2TNnW1VKwdr17S24SeWzeIUhSp9u53vHCk+gswgt3K3ZhHgVWdVK/lxysbeO3DSyN/e6PxZSRZhRnUfffdh2effRaPPvooOp0O1tbW8NJLL+Gll17C2bNncfXq1X2J/ytQ3HtfpnN70FCc4zGBvT44CP3GjuPA5hhbjsI/+2f/DC+99BJ+/dd/HR9//DH+5E/+BP/xP/5H/MIv/AIA9qz+0i/9Ev7tv/23+M53voN33nkH//Af/kMcPXoUf+fv/B0ATLH9kR/5EfzMz/wMXnnlFbzwwgv4xV/8Rfz9v//3cfToUeH+f+RHfgRRFCGOY3z961/f05jvvfdeuK6L0Uu5NwF4PYafF59GgfU8D2+++SYA4JlnnoElMBy6FuNqItZPPXI/Pv5kufE3hSlT01lyXJZL2lRuV2TGOg35mlmWoWXb6PZckMGS3eJzStFuWQijGD232kPVc32oqow4rp/fKErQbtVNZwqIXjaOy/ovKaW4urGBzsQEZmZmWDl4RtFpsZLbJrDeSAlEIrBNo+IuHMdpadzUBNZ3WT8HQE6WBESx6FNuisehlEJTZYQRoGkqZFlCt9cfgx+EsC0DLseN1MsjkZhzZF2dGNkL27LKfl+AjUEipLwfbNssI5xc18Pa2hra7TYOHZqBH0TQVbVmylU8r06+AJIkzc+Y6weQZYI0pejkSuXg8JnKKnEjoZzcRCsZ/pydCFZGram1Cf1ghm3L6jv31s5PmmKiZWGXcz8BaOy/BGEKoZsTmqZzVCBNU3RaFtY2tnBl9QrarXZZjl1cS9cPoSgSkqQ/8dE1FSASDptWXhJNsH51G7u7XWxsbEDXdZimCdO0oOtM/TQNvWKINYw4Zn2ju920sSSZgpVeh+AveiVphrZtlq7AEiGQCBANXKMkzdCyDCQeP0ooyyhUXUHU4EodxWk5uihOIBHmjs1Dzwtgm0bN7ElV5PLeSLMMhqYjSsS9iI7P+oGdgRgktv/qu9cLImiqjDgWby+M4vIeb1kGHM458cKI+w4fRJIv3DgN1S+DyCjNnZjZe7PA2+dWMDvZwokj08LfHwR8WUlWYQbVbrdxyy23IEkSbG9vY2trC2fPnkUYhpicnMT09DRmZmZg2/Y1OxdjdXD/MSaw1w9Fld6NVmBd1/3MBPbxxx/Hf/tv/w2//Mu/jF/7tV/DyZMn8du//dv4qZ/6qfI7/+Jf/Au4rouf/dmfxc7ODp599ln8xV/8RaU69I//+I/xi7/4i/gbf+NvQJIk/MRP/AR+53d+Z+T+ZVnGmTNnyv8+iM3NTfzkT/4kvvnNb+LBBx9Eu93Ga6+9ht/6rd/Cj//4j48J7H5irwR2fX0db7/9No4ePYq777573188TQosABw7Movjx+ZwaaXuGhbHCTptu5G8UQCGzs8TTAUykesGkGWpUkIsSRJatokgnww3GVBQmsE0TMRx8+Q/ystjGwm3V0y265OwLKPoubuIkwSTExO1enzXC6DIEpIGl9M4STEz1cZuz2uMxgmjqOA+9fHGMdotu5HgEkIYmTOsRoKbZhQTLQO7DfsEWDnrRNuG4/mIGtSTTHB9kjRFp2Wj67jNcT6emLh4QQBJlpClGWzLRBCGiAbOHYt0AnZ2trG1tY3Z2Vl0OqxkmIL1wvIIbJZlaFl247kGCvJmgwCNJDFJcgLJWVRIswydloWuU10IKe4ritwUbeCctm2rJK9AkblLwetgZJmsci3ep4AXhowQD2THWrqOnW633IeqysIYmuXLq9jZ2sLM9AwmJiYajjOFbVroJT4IITANvbKgwRZvgPm5wwijGEmSwvd9+L6HbncVAIFpmpienACRZchy858Vpq4SYTOn4wdomXzSD7B7ztJZHq1lao2kzPWCkYTazbNhi30RFCW2/d/ESZoTP/542PeSygIUAaDKMrwBwurm5lA9AbEGWInz4MKJZWgVQguwe1PXFGG7LwDEaYaWqSOjlEteAfYOkAkBuG9NlGMnhMDQlJFmUlHM3KM9PwQIgWVo6HoBvvvy+/j7/8dXMNkSl3HfKHwZSog/DRRFwezsLGZnZ0Ephe/72NrawubmJj755BPIslyS2enpaaiCiqtR+DKq2wcNYwJ7/VDM7w8Kgf2sz9WP/diP4cd+7Me4nxNC8Gu/9mv4tV/7Ne53pqen8Sd/8iefaf+dTqfx31utFp588kn8+3//73Hu3DnEcYzFxUX8zM/8DH7lV35lTGD3Ezyls0CWZfjoo49w6dIl3HfffSOl9ms5Lh6xfvqR+xsJLNAvzW36895z+SqgHwSwDANeUJ8EplmKTps9eBIh6LRseH6Abo+REpGTruP63H2GUczUPw45aVIO0zTF2upaXjJtQWsoX+2Tmup2DV2DLEnoOT6yBnILMDWnIINNCDnnlxBS5qvy1DqeCitLEmzLQEazxmgRYO8qLCOO9c9FL800YecLYKr58OTQdX30ejvY3e3i2LFjMIZKGZlZlFYx6gL6E6Ge60FVFcRNiyc5V+IdF8AIpCIr3FL3Ite1Yvg0qF7mZdR+ENWcgIEitsdCr6ECAWD3k21aiBO+ChtFSUnaTUNHt7dbWXRwXFZq7A0tyLC84qvo9RzcfvIEMiJ4lvLYJVWRK5mn5bbQf2YURUa73UK73QKlFGEYwfM8rG1chYwMGZFYvJRl5iXgBB3bRNf1IUmEOUwLOEIUx5Akwl1YoTSvzrCaY2mK8bKJHJ+QAcyFelChbCKXjheU15g/5qSiDBcOy8PwgohbOVIgyUmnE7C82Z7PUU2DaKR7M1C4XuvgbKaEH8Y19beApipw88gctnhUV1d5Y2xbBtwwyhf9CIIowX8//S7+Pz/0KLeM+0biZiZZhBBYlgXLsrCwsIAsy7Czs4OtrS1cvHgR7733HtrtdklmO53OpyJKRQbszXhurxcKA6fxOd5/FIsFB4XAflHwrW99S/j5t7/97fK//8Zv/AZ+4zd+o/F7YwKL/SshVhSFSxSDIMBbb72FOI7x9NNPV+yv9xsiAnvLwjzmDx/ClfWrtc+iiK8SUkphDZXNDkL0Li16QNM0rf3e8wNYpgGvgYRkWYZW2yrJbm27gnI41/Nh6DqCPFsqDEJcWb0CQzcwNz8HZ3eHe0+wbFc2eWP9nCa6rluSO1GpcBiF3PstimNGjodKn4vve3m0DV+F1SsE1zJ0pFmGruOBEAJNUxsVWGBvKiwvW9DzA5imDt+vT/AJYV67jhfWfhvHMa6srsLUNSwsLHAigepmRoN/mCmlMHWtdq0JYWXcu46bLxoIVFbLQNdtvley3GxskMCSARmdAvnCS31Ro0AYsVge3inuuR50TUPIiboI4xgTto04YeZlNEOtBHf43KZpitXVNaRpgsXFBWREYiR3kJyQwd+zsuRtzrMEsJJry6gSZdY7q+cLD1NswcD14LguVle7IASYnpyA0+vBtCwAstDwCWA932wxgG8uo0jSyHd2EMXo2CZ6Ln8BI0lStG0TlFKhMsqeDzEZ7uUZxhKRGskrkJcS66qQwAKslHjCNuE0ZN8Nwg3Ckb21tqGj54V7Uk0dj8UvuUMkVpZkRLQ/5ijvh3UEpL5Azw8x0TKx6/Svw+aui7967QP86FP3jfz99cbNTGCHIUkSpqenMT3NSr7DMMTW1ha2trbwzjvvgFKKqampUqEdZTg5jtDZf4wdiK8f0jQ9EAsyruteVw5xUDC+y/cRPKJ49epVvPjiizBNE0899dR1v/FGlTY/9cj93M9iQami6/lct03XD2rmSZZhoGWZ8H3mdstTCEVusa7nQ5GbPy/6b5tAwXrUQIHubhcrKyuYnJjE3NwcJEmCH8Zcd9HC4KdtmyCEYNdxK8pkUSrchDBKhC7JYRTXKiyL8mrmsMzvjWZqogySEyo/jMpsVUpp6bDcBD8I81zYZrg+U7t5pKEpEkZVldzEyqsZLnmeh6XlZZiGgUOzhzHZadd+3z8uv5LdObwA0HP9ipGYLMswda3ss2UqrVjJ1zhuzexzr2JSNExjZFnm9tECbLLPc+MGintxhMFPmiClGdtPA4/yBxx6oyjC8vIyJInkecVs7KyfduiH+WVr2ya2u06jc+0g2HuD/zxSAEdmZ3D48GEcP34cJ44vIs0our0uLl26hMuXV7B+dYuRLgH/7Lk+dE6poqGp8MMIbhCNVPAcL4AmuPYAW+gSLeAAeUWHPULtpOxZjQTvSICVLoscugG24BdnGWQivi/6bRfN16Rl6nD8YCB/U7g5ACwjevB5aFsm/LBOVJvyYZtgGxpcP4I+dB0+Wt7AK2cujh7QDcCNnpAeVOi6jvn5+dIM6uGHH0ar1cLq6ipOnz6Nl156CR999BE2Nzcb5xiFAjvG/mFMYK8fCgOnG31Pfx4Tpy8yxgrsPmK4hJhSinPnzuHChQu45557cOzYsRty448isHfcsoBD05O4urVT+ywII7RaVmMkDCuv5ZfIFg96y7aQJHGlpDhKEmha80RT1LOapik67RZXheWRYgDoOi52trexs7uL+fl5mEMkgzeBtgwdaZrBC8JGNTSKk5p5UeXzKOaqsGGDyk0IKR2a/SAQqLAZJtstRHHSqAb2ckdintInImEs69YQqNKFUs6ukWXqiKIEfm4y4+bkOk7TsiRt9tChsveB1wNaQBpYGBk+d5RS6JqCKI6hqSqIRCoqIaUUhqYhjptVsYxSGEMq6yAoCvfpuBhAqcAWyqspcKIFAN8PIcv8RRrHC2AbBtyGMntdUxEnrJ+Q9XaTRhOkMIrhex6urK5iYmIC09MzFcLihyHaVr2cuWX1Dc/Y+4qvNIZFqaxAHXW8ALqmlqRpYnIKE5NTSNMUvu+h1+shimNcWroE0zCZGZRlVsqwKCgURcKwUbUiy8goLe9VzdBqRkyDyPJM3oijeMqyhAwUNEu5/enlcbkhdE0tF4WGURjRmbqOmKPAFnD9kGURc8bFypkDds3La9KMOE3RMjR0hxRkVZYqBmNhnOypBzfNKDSV9cMamiL8vuMFsHS90jc8CEkiiJIUSZpBlpXau+v0+xdwaLKFW+dnhGO6nhirhHsDIQSdTgedTgcnT54szaA2Nzfx4YcfIooiTOQmiNPT07Btm0UxjcnVvmJMYK8fDkIGLMAEgbECO8Y1xWAJcRRFeO2113D58mU8+eSTWFhYuGF/JEcRWEIInhaosGnCJzouJzNWUWQQSYJl6Og5bq2fLE0zodOu6CXh+T5XLfWDsFFVKuJaaMoicobJK5EIXD+olK+qioyObcILAji+L1RSGUlt/iyMYmFWZRRVY4AGCZso57bTsuD6PlcBopQKlchRKqwf8pVltgP2H+0WU9UH+3GL0sm1tTXs7Ozg2LFjlcb9MIzQFjhvu55fRso0PTc910enZSGjGcImtcgTR7T0BHnGxe/t/P4kYOdyYqBs2A8jtAX3Q5KmtUiTYTRlFGuqkqvvSa40K438koJibWMDO1ubmJ2dzZ2z6/sIoghk4FmxDQPuAEFh2bFidZA9F4LcV1CouTHb4MKELMtotdosS1mScfstJ6CoCrrdXJ1duYzt7W2EQVjm3A4+J4SwDNlBszjHC0ZGwDhB2NwrSpiaG0UJq4wYoT5TUEiE3w5hmzr8MEbX84XROgCQ0QyKJKNpocA29bKX1gsidEZcDwDoegFaZjUuS9OU2qJUzwv21DfrhzHapgY6omyaUrb4yIstsnUNcf73IowSWIZa2R6lwF+88j6u7vJL1683bjYTp2uFwgzq7rvvxtNPP43HH38chw4dwvb2Nl577TW8+OKLOH/+PHufjahSGOOzY0xgrx8OggMx8MXrgb1WGN/l2L9yoYIobm9v44UXXoCiKHjmmWe4jlvXC3txR777thOYnGgu6/SDgFsSWRggFTANHW3bQpqk6OVOtk2QJKk0MWqCk5v5NCFJUtiC0trhCYnnulheWmK5l4dmYTeQNkII0jSFZRqQJIKObSHLsopJj5/3pDaBkVQ+oREpjqxcsX88w4rj8H5VRUbLMtB1PKbUCSbiw7mww0gygZqVZbVsx0EEUYTJjo2e49emvHEc44OzHwE0xeLiIowGg6wojhtLkQsMXsbha2pbTB3mReqwOCE+QaUUwjJiAPlkHrlTr1oz1AqjWFjuPqpUmZnt9O8ZTVFAgJKwZZS5Mg8rsBnNsL62jp2dHRw6fATTU5PcfcRJUhJUMzfHGlZzhxduhpFlVKg4ExBkYJmjjZ/nJDChwJHZWRw9dhTHjx9Hp9NBEidYXVvFxUsXsb6+jqubmyWxb1lmtYc3R5Q7AIsQhFGtvaFjGZVeT8cLYAgWMYDc6Kjh+WJuxv1tpVk2ckxeGKIz9I5QFbnm5s76V0c7vwZRXPYXty0DHtcZPBpZVg0AIGRkni7A3r9Nfc22oaM31Bfv+HVCHsUp/vuL78BvyIW+ERgrsJ8fhBDYto3FxUU89NBDeO6553DPPfeUc4/nn38er732Gi5cuIDd3d3xosE1xJjAXj8cFAXWcZyxAjvGtYUkSdjd3cVrr72GkydP4uGHH+YY1Vxf7IXASpKEpx7mG2xQQblp4bzKnDtDlr+Zf8YIVH0yRghhpI9LRClUQQ+n5wfcCaPnh2zSSSm2t7awurqKQ4cOYXZ2FiAEut48HkozFoehKui6bk3NiBNxP2sUJ1y1JggjtFt80h0OqLDDBJZFe7D9FgY0gzEfrC+4+aU6SoUNAn7fcBHn07RtTVWhKUzNGobneVhaWoKuG7j91lu5YxtF+tnCiVE7Hx3bhOcFZRYpDz1PXOrbc/1an3blOAKmCLZts5EcRLH4fsgoFaq8xTYImFMvkQjCYTLjMtfj4oFKkgQrKyuI4giLC4tQVQ2GYIECYETaNnTESYa0MXc44y4WlePw+OeqZTHTtShOuL2ZlDIirOXPtCzLaLVbmD08ixPHT2DuyBxUVcXW1g7WL1/GzuYGLi1fRthgahTFyUh1O06zyrW3Tb1mEsWICxUsoTA4fliOG2CtBsMltGGcwDZHK52OH5SLQoQwxT0eejfTPLSJcypLJGkGTZVhaIowhijLMm7FSgHL0NBzA/h5Puwo+EGE9sDxyrLELcnveSFss3p/7boB/q+X3hO2MVxPjAnstUURxXP06FEYhoGnn34aR48eheu6eOutt3Dq1Cm8++67uHy5+RkfY+8YE9jrh6IH9kZjbOI0xjVFHMdYXV2F4zh4/PHHccsttxyYP4p7zae9/85b0Wk1lyUUPY+DMHQNnZaFNGXEjRc7oXEILKVUqMI5jt/4W4BN5EXkIU1TXLlyBd1uF8cWFtAeUMEd14OmVLdrGQY0VcWu40JX+KQjCESGTSNUWIFL8qAK29QvW8Tq9Fy/lknLoln4k+eRKizn3mB5oFntutuWiTRLEUQhvJxkAowQ7Ozs4MqVK+WCgeuHUAWLOEEUCZ+TLM0q56NwbS7OjlD1otVe2sZjFH5GoCpyborT/B0vCPPS0GY4I0h0GDPnXEki3F5LgJGaIAywtLwETdVw7NixcnGsN2R6NQxZliErzfFT/XH6FeOqJjT1YXdss+yxjeKEU67aP8uOF9QVTQLoho6pqSkcPXYUJ245AbvdRpIkWF1dxcWLTJ11HKd8jzmeD2ME0WKkW4OmKgiiCE1X2w/E5f0Au68LZVKSJBBO5E/PC/ZQSkxzF0ummroNff4AU1dHGWwBrJTdUFVhLy/7Xsw1YJIlgjjNAMKOixBSKTvnoecFJYk1NLUsHR4GBcu6HS7pX97YwV+/8dHI/ew3xgrs/qE4t4Zh4OjRo7j//vvx3HPP4aGHHoJt27h8+TJefPFFvPzyy/joo4+wtbW1p7nKGH0cFFJ1M+CgKLCe541LiG9WXOs/Vru7u3jxxRcBABMTE5icnLym2/+82CuBlWUZTwhUWAJGGNotC5bJImm6DlMqA1E5sOPVDJIkSUKWZQijGDaHiFJQ6Cp/QhhwejSjKMQHZz+GoWtYXFzMsyn7yChg5P2NhqahZZvMECdfCfaCoDE3FgCipFruO4xYkAMcjOibLFTYYQLbtkzESSIkW4UjcRP2osLyyqqZ2uvl22a5va7nV8yJsjRDlmVYX1/H9vZ2pd+V5oZJPMRxIuyF9cOwJGcty6yZVbl+IJzou54vJPeu39wzTQhByzKw3XXya9bMEJIkhSXoMaSU7xgLoDS6GlbhBhFEMXRFZs7Zk5M4fOQwpEG3Wsp/p8myzGJeHFdMriggjdAimct3/1y1LbOmavJdgPvnL4r5JcC6qiLOKCY6HczOzuLEiROYm2PqbNk7e/kytra3EYZhYx/xIDKaQSYEacpneK4/2rnYy12fbVOrGCUNYy+lxH4YYaJlNebvDmKwD7vAMFFtGTp2PX67xSB6XoBWw7NgGlqlxSGMYrQE/gSDcAMW/+M2RGoNIs0oQOsRWe9cuIK3zq3saV/7hXE56/6hycSJEIKJiQmcPHkSjz32GJ599tnSFOrMmTM4deoU3nzzTSwtLcF13fH1GYGxAnv9MO6BvbG48fWsXyJQSrG0tIQPP/wQt956K0zTxKVLl270sGrYK4EFgIfuvh2nX38H7pCrpmHokCUJhq6h1+A6HMW5m26DGy4FoOkqwgEjh0GSlghIn+N50FS10aiI5ahWMz97vR42NtYxOTmFhYWjcDmldWEYYiJ3My5UQJqrKkmaYqJlY5fjrhzmxLnp72oQxmi3TPScZlfSUb2wnZbF8lezDEoeD9Pz+sZBIkdi0Zgdz4ehabkSVUeS1q9BQYrSNEO7YyFJ6rm9QOHuvIk4ZgZZw2XzjudzryHAFgx4x1XA0NQyJmcYot8Boyeow07BkiTl553tLyzLpIts0Cocz4eqytysT9cL0LLMWpmnIstQZAleEOY5qM0u1r1eD4Ef4Oj8PEyzmex7fgjbMir3uyRJ0FRlyM27+RgApiY3jXMQQRhDliTomlopYy+QZRSarlTcdof5XBQn6NgGukO/LxaN0jSDl0ZlxYGu69B1ptAyZ2Mfvu/j4tIyTE0FlRVYpgnTsmqTC0WWIRECX0A6s4xCETgX90EH7oVm9J1/+edQkWV4fgBD0+Bznke2NyBKUiiyVKu6AICWpaOb9+FmaVYuCorgRyyKqHByZrE79TH0vJD1+Y4g2ZIkIYr5YxxEGKewdBVplpXvTtvQ8P++dQ5ty7ihzsRjBXZ/sJcYHVVVcfjwYRw+fJjln3seNjc3sbm5iXPnzkFV1dLZeHp6+kC0ZR0kjAns9cNBUWDHJcRjfC4kSYK3334b586dw6OPPorbbrsNiqIIydiNwqchsIoi4/EH78n/u4JOy4Zh6AgCpraK1CSxKZNXMdUhhJSTrSCMuOXATMETqLBRQSYpNjY2cPXqBubm5jA9PQ3Xq2fRypKEiZaNOE5Bs6w0tCFSVfV0fb4KG8ZxxbhqGDwiUxyrUIWNE5CcQAG0JK/A6B7cvlJaB6UUssp//MOwriwXEw9T1+H5Yc1sBgB838fy8hI0rVrWWt03hL2giSDvVte0kXmWfhAKzwsjZnyVdvCaKLIMQ1MrRDBJUkaUODw4yyhMTdz/mKRp5dlg5FUu1bye59eMe7Isw5UrVxAEAYgk4ciseILPVPoB4yldq5BXLwjRFrhOA6ONqZKUuWIHUdwY7QOwPs9hpW/43Dl+WMkpJiAwdLXSA+wFYc0siDkbtzA7O4vjx49jamYGpq6j2+vlzsYr2N7aQhAEaJuMgPX2YNbkjcg4NTQVjh9CUZqdhAchKiUmAAxVQZRkyOhotTZOksaxq4qMYIBMh0kCa0QJOMAWB+Q8R1tVZSGxd/1gZFm5rirwI5b5uhcO6IVxeW8okpSfB4rvvnIGGzvO6A3sA8YlxPuHT3tuCzOo48eP4+GHH8Zzzz2Hu+++G7Is4/z58zh16hS+//3v48KFC+h2u2N1FmMCez1xUMq1xwT2Jsbn/WPV6/Vw+vRphGGIZ555BjMzbGL5aYji9cSnHdcj992JmalJpGmCruMiCIacOxscZQGmiPKJaJXESJJU+eMjiurpuR63jzKKYhi6ipWVFQRBgIWFRVhWv7SCkKJ3jTkLExDs9lxklMJx/XKCPFy2m6Qp13mZ7ZffCzuKpIpU2CzNMNW24QZBY8+iyAk5zeiIctpAuBjA+pH7KEiQ5weI46TSZ0kpsLOzg8uXr2B6egYTk1PC0uqe6zWaZw2Obdg11jZNJHGKOElZTBH312zRQPRcx0kqnGCHYQxNUZg6NtyXSIAgTsqJfxN6nideaBlYpOmT177yRSmt3ONl7BOlOHToEAghrEy8wf21QBSxPmkCAtvQG6sPgiiCLDxPSdnT3ARVUeDvoeSWOQUX/6u+v0L1LNCyjVoZappmI4mnomqYO3IYR4/mzsYTE0iSBN2dTbz7wYdYX19Ht9cr+/RFcIOo0V1XliRkecYtI7riRQCAZbU2Lfa1LRNO7hUQRsnISCCALQgMO/mqqlxTPHuFed0IsMgcHaqisNJeDihl7yPeQh7r4c1zn4dMnUToeSE6lsFKl/N3XJyk+D9ffLfi7Hy9MCZB+4fPS65kWcbMzAzuuOMOPPXUU3j66acxPz8Px3Hw5ptv4vnnn8e7776LK1eu3LRmUGMCe/1wEBTYokphTGDH+NRYWVnBSy+9hCNHjuCxxx6r9FcO5sAeJHxaAqupKu65/USz4kRp4ySvQJNKV8AZcMutRcWEIdcAiVLKdZv1fB8fffQxNE3FwsIxqEO9tr7vY6rThixJ6DpehRRmlMIyjHI82VA/nReEYhVW2As7SoWt/7bTskAphR/GZTlzfbuJkBw7nsclOZRSbtQJwEhcQdplWULLMuEHUXleioWELKNYX1/D9vYOjh49iomJTjk2EUdQBS/+NE3LawEAHduC5wdIM0ZMwzgRnu8w4rspF5+LzhsBYBl6Y4k1AasWsDgLNwCb7I9yeg0CVr45TF4L9DwftmHkqvYyTNPE/Pw8ZFkGpRRplgnNmgCUecW8MuA4SUeaA7GS7/qCkSxJkCSCKEmrPbgNYA7NxbMFNKmWbk64OraJnts83p4XjHT3dXPFt1BnF44dxfzRBcznvbO9bhdnPz6HrY11bG0zdbaJtGRpBq3h2TF0tfJec/1wZARTnKS16g9L19Abui5dTl/qMBw/LE2QTF2DFzQrp25QVba5IGSk+guwEuamKC1NleEOjaE7YOo0ChmltdJ/xw/xnRffLcubrxfGCuz+4Vqf28IM6oEHHsCzzz6LBx98EJZlYWVlBS+88AJeeeUVfPzxx9ja2hpZTv9lwUHpy7wZcFDO9ViBHeNTIU1TvPvuu/jggw/w8MMP484776yten1ZFFgA+Mr9d0PnTJZZX1rzZ4woNBMNSmk5qWty2hVFKvRcv1KeSgFs54637YlJ3HbyllJtZdtnhFBVWVk3r6y363hQZAUSkWrjSZJESIqiKP4cKmx/QqxramlQlKQpKxsUqJXeKBXW4BOUUdEzcZzANA3IRCoVvOK8UApIBFhZWUYUxVhcXIA5QIZGkUjHC2AKSKDrBVBlhfWDOl7/euQNx37Aj04q9i+aLPlh1KiKGbrGFHk/aHQULrbZdTxhxIib96HyQEFhWQa3DxkAtra3cPnyZczMzGB2drZ2PEzp5d8bLdNsbs7OQQgjuaLjoFl9kYoQAl1TS6fkUWXZQNXQiTckiYDrxFsgSdORZCuMY0gSyRcRmJtu0TdbqLO6aYFQirX1NVy6dAnr6+vo9XqV/m9Gqvv3aNuuZscCbBLDzIjEyp0zQL4VWUY80PtZGXuUQBmhoGQ0AwGBqYlNpDJKASKuMjI0FT0/hBdGI4k4kKurQ9dakZXagh/7bjiylFmRJYRxgiCKa+R4fcfBX7565rqqomMCu39oMnG6VpAkCRMTE7j11lvx2GOP4bnnnsOJEycQxzHef/99fO9738Nbb72FpaUleJ73pVXaxwrs9cO4hPjGYtz9jk9fQuy6Lt58801IkoRnnnkGJqePrCCKB+0P4mchsIau4dH77sLp19+pf0gpNEVBGDZPxEVOvI7PVNgmwxE/CGFbZs1Aiu0yg2WY6DoJsizD2vo6wiDAsTxnrsggJARo2SaiKCkNh4IohqnrCBrGSyllGZHdOqEGCrIoNU7WCrLecz+9YVOhwkqEoOf6lQgVQkiZD9rUZxgnSW7YxDH98TxhPza3j5kwMk0B+Pn+QVDO033fx4ULq5icnMDk1FTjPR7mZci8qYIonoMQglbLwPZQLxwBu05JkpYxOk2I4iS/Hs3nJY6TmumXZeiIohhJfi922lbN6biQlWme6yqqMhg2hCqg5Mprt+dBVeTavUEpxdWNDTiOgztvvxUp7Z+nwcUe5mrMKem0c5dmwlQ6n/N8sjxWWXgcrsfcnQtyaZt6zdAnCGOhcVBh6MQzjTJ0FU7AVNguR4EFGMHr2HXH40HEcZpnJKOxdFqWZVi2jdlD03D8EFEYwfM99Ho9XL16FZqmwbRMWKYFSSJQZBm6pnCVYS9gpcTDimp97Mz0ylCVsnS4NvYkZWZKI4h8nCTQFIJRybVBlKDDMZKSCMmfTcoicxSZa0g3iMLB2PFDtG0DPa95rBmliJMUqiJxY3UMXS2No6SMQpUlFuOT49zlTfz1Wx/jhx6+Qzyoa4iD9Pf6y4S9mDhdK6iqiiNHjuDIkSOglMJ1XWxtbeHq1av4+OOPoet6aQY1NTX1pTGDyrLsS3MsBx0HoYQ4y7JxCfEYe8Pq6ipOnz6N6elpPPnkk1zyCqB8iRw0FfazKsOPPXB3rSS3gOP6FVOmQQSCcuAsYypUkwILgFs6C7DSxjTLsLS8jCzLsLi4CCNXG8MoxvREG5qmoud4CAdULgIIy557LlPWmvY9qmT3s6qwlqGDEIJdx62RY0II4lis/rpByC1ZzTIKW6CyOl5dhVUUGbapY9dx83iivhlQRjPs7O7g8pXLmJ6exuLCMe6kRNQHDTTnCQOMOCuKhJ1dp3ZfkXLSzcgJz6gKKMq++Z+7fr/XlpkRRSV5BZD3RVcnA4UxEgWF44pzXYMwQmfo+FVFgSzJCMIIWZbBGMrkTdMUly9fZj3ci4uQZUW4yuv6QU397LQGFlIEsTrlcXqB8B4BinxggrZtNrrRJmk6svzV8YM8G7b6bKmKjCxjJKrn+jBHqHZ7MWIiANIRsTquH6JjmUydneyrsxN57+za+houXPgEO1tXsbG5KczOdYNwZC9wkmbo2AaXvBZw/LDW5zoM09DQ9UIYI/YJ5KXJDaZUtqVXjLLCKN5TCTMAeGGElqXD5ZDXAnGa5ouU9c+GXY/jJP/u0O361rnL+P7ZpT2N6/PioC04f5lwo84tWwxt4fjx43jkkUfwAz/wA7jzzjtBCMG5c+dw6tQpvP766/jkk0+Y6/8XWJ0dK7DXDweBwBbVBDcjgR0v0+wRWZbhww8/xMrKCu6//37Mzc2N/E1xY6dpeqBWxD6rMmyZBh6570688uZ7tc+YGqVxo1FE5cCu60NRZI7iGcA2Tbh+XW3pdrvo7e6gZduYnpkpM1NblokoTuAFIVcV7nk+DEND0DCRzDIKyzS4f8SEKqwgPgioq7CyLMEyjFwlDNFuWbXfFuQ+EqiZySgV1uXHDwFVJdS2DIRRXJKUIp6o53ggINjaZH2DR48ehWmYIyN5/JwAcycFQ/9uWwaCMCrVS01TquMe2FaaZui063mwBdI0V2lHfA6wcurhIWYZRctSq1UExami7FqInLgBZpRUKJOaqkAipHKuHM+DqevwwwhRGOLKlSvQdR3z8/MsliRJK7E6TecySpLy39ut+vnw/CISh69aJlkKCPTyMIoxM9nG5i7fHbbnMvdkUVlrFCWV944kESiKXCrEFMVEl68EslJE/nlvWyyWh5WkimoAAC8MKyp40TvbarVAQRFHEZI4Ruh5WFu/Ck1VYVoWLJMR3+JYsiyDTBQQpNy9WbqGnZ6XR+uIiZ/jhzBUFUHDM9s22e8pgDjJGlX8Ybh+BE1VSqXdNvTGMfS8II/MEY+PUppXZUjAiIUCP4zQMo2KKZMiSwianMyjGC1TY/sfuE+ef+c8WqaOuxYPC/f1efFFJi8HHQeFXMmyjEOHDuHQoUMAWDXR1tYWtra2cPHiRUiSVMb0zMzMQNP2loN8EHBQzvHNgINAYF2XVRbejAR2fJdjtDrh+z5efvllbG1t4emnn94TeQVYaR8h5EAqsAA+k6nBEw/dyyXjw9E4g/CDgNsPmGYZWrbFHc9w2SylFFevXsX6+jompqZx5PBhSISg3bKZSYvLFNcgZISweaNUaCLk+vz+ynhUL2y4N0fidsuCJEmVEtc4Tmr3Y0FMwihGWxBW7fp8k6lsRPyQ6/mwTR3tFiOkw2XffhAizVJkNEMUR1hcXIRp5OeAQuiGmySJ0KnVC8LSSKjTys2aBkoIHc+vjJ0MMZue4wl79xyvrqJWQRCEMZcs9Vy/du4I+iSySQEdRBSnaJnGAHmtEhLWS0zgui6Wl5fRbrcxNzdXmYQ4vg9V0KfK7g0jzxxuJutRHIMIJjbFNnho2ya2dh1h9QIj9OI/K3HaNwIihFUfDJc3B1Fc67MchhdEjeM1da10ww2iRHhMAFsE4fUAExDMTE3AarVw6PAR3HrrSUxMTCBNEqytr+PSpUtYK3tnU0bUOONWZAlxysgtI5PiiU+Wsf7V4feQoalwB85XmmX5u0xMvLJ8UYAQVhLdRB4LuH44UuFu5STa1JVRuwZQuCf31V1DU7lZsY5fr1ygAP7qtQ+xsrEzemefE2MFdn9wUNVt0zRx7NgxPPDAA3juuefwwAMPwDAMLC8v4/nnn8crr7yCc+fOYXt7+8CbQY0J7PXDQTBxcl0XqqpWDGRvFhwcWfCAYmNjA2+//TaOHDmCe+6551PfrAcxC3ZQGf60x2ObBh6+5w689s6Z2mdFRitP5csE5cC+H5a9jcN/4IoyU88PkCQp1tZWkaYpFhYWYeg6Jjot9BwPvbzHdRCiDFaRCguwaArueMPPrsKCENiW0fh5QXAH+2iL80EpZeW84Kiwab2ncxCO61aMdwah6yoURcZOr34OAaDnOOhub4EQgtlDs7VFDCc3g/I50QVuwHJ0eUp8RinaNkcppahE6rAy5v4ZoJRC19Wy77m27YyiZWr1XmyCfJ8u2i2L2wNKKWqmOqyMuRqzJFIMoyRujuTJsXz5CgLPweEjRxpXUrOMwjbVcoGj0TWXUoRhDF6PaRQnec8w575EXpKtyEiG1DzbYhmqRd+vSO0r+0EbetcL+GEMU1egqiq3l5W5+8pCF1ovqH5HVWSkWVa5P3puwL3v+/tqVh3bdv840iyDbRqgpToLRGEIz/dZ7+zmJjRVhWVZmJxoA5JSeZfpqlr2EGc0gyIpiEkq7DcNQkbki95aiUgAJbV3qReErC+4obS7sr28HzalFJ6gjJlSyiZnnGfW0rW8p5bA9SN0bB3dEYotUPTOGgBBpXS4CYWLcW9AtU2yDN999QP83WcfwEyHv5j3eXBQSdaXAftp4nStIEkSJicnMTk5idtuuw1RFJXq7HvvvYc0TTE1NVUqtJYgA/5GYExgrx8OgomT67qwLOumfGeN7/Icwxc/yzKcPXsWb775Ju6++27cf//9n2ml5SA6ERcP3Gcd1xMP3wuZo8L0HH5Gqx809zsCbPItygUlAHw/wNLSEmRZxonjJ3BoZhKSTLCz2+MSoyAM+XmkAhWWEALfDxtdaIE99MI2qLCSRNBpWXBdT+iiGg2psIMElvWUClRYL+A6mGaUTaKH0WnZSOIEOz23UT3a3d3F5cuXYbXbTAXkeT4JjilNU64CX9wvTQZVBVwvgJX35jUReMf1a72kg2C5s1UVt2X21cqeK3bzdXITo/4GqoMQ9Tfrmooso7VcW4Bd07W1Nezu7mJu/ijarTZ/DK5fi2Ip0LbMkbm+7DjEanSWZbX+U9PQ4QdRSZpF4yjghxG3GgDotxyIjJjSLIM2QgVMU1oq05JEoCpKjfBS0D1FxPhhVKkkMA2tZnzk+kGpDBMg752drPXOrq6u4dLSJaytraHX68HS1Zq7shdGaO8hp9Xx+/3JlqE1lhQDbEFulONvMXBR9m+BKEmYUj7EsCWJ5H3i/W103XBPGbas5DnZc5mu44doGfV7+tsvvNtoSnUtMC4h3j9cTxOnawVN0zA3N4d7770XX/3qV/Hoo49iYmIC6+vrePnll3H69Gl8+OGHuHr16oEQKw4CqbpZcBBKiB3HgS2YE36ZMb7LGxCGIV577TWsra3hqaeewrFjxz7ztg4igSWEfK5xtW0LD951W+NnoozW/AuN/yxJEsIoaSy9pZTi8uoaNq+uY3b2EO68/TYQiaDbc5EkKdIs4xJjQOz+2/OaDWMIkZBRCrNh8lSg6IVtQhjHlTLjtm1CkWR0HZc5c8bNxwoUpZz93w4SWAAIw0gQm5PCEpRe9lwPek4KFFnOnWpdpLmiM6jsUEqxvr6Ora0tzM/Po9Vqw9IFvcEcQ6YCLPe3er4KIuQHofCcDKJJgaSU1rZd+Rz93FlZlmHqGpxBhZBi5B8iOrBI0uQI7fsRZHmo3FNXkWUZojiplUInSYLl5WXEcYzFxUUQSUZHUO5KB///wPG3LBOuz9TRnic2lcoyWl5/HnoDBJWprXGtbC5Ni37ZZiRpys1rJYTA0BTsOp6wtBxg90xrBMlzfRbhYxsGPE7PuxfWo1+GkaZZma2qKjLiNEVTrYMXhI1l1LIkodVqYXZ2FnNHj+GOW05A0zTEoY/3PjiLlZUVbG9tI/D7ubPs/SNedKCULWpNWGalh7Tpe8wEif8cmJqKnhvCDaKRJcIAM6YaXpixdA1RQ2WLF+5tm4osI4qZM/EoULDYnuId3TZ1uEEExw/x7RfeFfZaf1aMFdj9wxf93BJC0G63ceLECTz66KN47rnncMcdzB37o48+Ks2gLl68eMPMoMYK7PXDQSCwN2uEDjAmsDVsbm7ihRdegK7rePrpp9Fu8xWRvUCW5QOxKjcMSZI+F7F+6uH7GxUlAOg5fJWHmTLVJ5KEkFz9qU56syzD+vo6nF4Xt508iXarxUjg0ITa83zui0TkggxKG3t6iz+yPdfj9neyXlhB72MUw9BVtCxm0hQl/clWEEVoC0qPwiiuOP+yobI/hlEcoyX4revxnXkpmArbskyQPLJnEF4QomWbSJIEKysrCMOQ9bvmbtthHHNJez5I7kfZ0EJDp2XBD6OytJcRd/5xeUWuKqdW1/HEyqDj+ei0bCiy1FjKW/QBc/cf9HNtm0h0nCZoGf37zNQ1JGlWLqBQipLgBkGA5eVl6LqOY8eOlfeu64fc56oYw2BvoJ2X1pdjocxTRwSmoIrJXEYzqKqMjFIkDfEnYRSj3eBqO4iu6zcuABmaijBKQClbuBC5SAPsfh9llCUTAo9Tvl7A8UNh3i3Anp2WZTSWURdIs2xk/yoBEGUUx4/N4/CReZw4fgKTE5NI0gTr6+u4dPES1tfW0e31EEXRSEVUkgjSPUyG4ySFripoIt4SIcjQX6jKMsp1Lh9Ez/NLZ+Jh1+BBsG1mwgzblqnDDSPEaQpZkvakjNP8uGxdgx/1/5Zu9Tx858X3RppXfVp80UnWQcYXoYT400BRFBw6dAh33XUXnn76aTz55JM4fPgwdnd38frrr+OFF17A+++/j9XVVUSCvO9riTGBvX44CD2wnufBtu2b8p01vssHcO7cObz++uu4/fbb8eCDD14T5+CDqMACn39cnbaN++/kqbAZDEFDeVOpaPHweX5QTlbjOMbmxjoUieDI3DzCJIXJmXgX/Wk88MxCgLwXdtikJx9PmqawBMfihVEjoZMlGYauQVWUqtI3gDDmR+40qbCDpN0XqL9pxle/JMKyXJM44ebz9noOlpeWoKoqFhYWKs9BRqkw4sTzQ7QE0VI914euKsxV16mHyftBJC5FzrKyV/rTwtA1UGTCXkjWO8nfdhTF5Tls+lrP96GqMsuTTZIaCXK9EGkc4fLly5icnMThw4crx5uMuN8AZnBEwMirH0a1c+H5ddWsdpxZNnDv1Q8kSVKYmoZ4hNGPyNAJqK8zqKqCJKMohP4krZcsDyNKEmG0S8vSsbuHsuaM0pFkGQAUiTQqjINw/XCkostUerYYIMkS7JaN2dlZHD9+HHPzc9A0DU7PwbnzF3B1YxVbW1sIAr92PSWJVYTsJVqnGFunYcHONnVEUf+4ojiBuUd3VS+MYBsa/FCseEZxCk1VyqipQeiqAi8cXMRjfdB7mXYlaQZZlmrfvbLVxXdfOSP0V/gsuBkng9cDX8QS4k8Dy7KwsLCABx98EM899xzuu+8+6LqOS5cu4fnnn8err76Kc+fOYWdnZ9/MoMYE9vqg9AkYlxDfMIzvcrAb8Y033sDy8jKeeOIJHD9+/Jq9ZBVF+VISWAB4+lG+Cuu4HhSZo8JySk0lSUIUM6ddQlOsrl4BJBnTA6ZBosvijlJheZN6SmsTcUIICGGqguP5XBU2GeqFJSDotOwy0zXKyUYTwihCS6A4hlG/j3b4fhzlhNzz/Npk3TQ0aKqCruNxHW27u11cuHARx47O48iRI42OyE5u9MMDi2NphqJIsEyDayQUJwlagjLaIIjQsk0uxfQ4jsC2ZSCKYvQcX3je/EDgXA026W9ZZmMJMcAmDy3TQBhHFSdlBorNzau4cuUK5ubmMDk52bgPx/OFZb5JmsEyVARBVClrHkQQRZVopNrnArdcSZKgKgrcIIQiKPPMsixX+/gY7A2WZQkykXJSXzVZGpVBy7Jhm9XcwoyIKfBiQtZUEjuItm1ix/Fh6KMXL90Riq5t6nC8ANbwuAnrnZ2cmsT80XkcP34cmmFDVySsr2/g0qWLWF9nvbNpmqBlGmWpbM8LaqSz6W/VcD9s29QbY3GcPRBxAKWh017YphdEaJl1125JlivmWgArD95L7mzHYmZOilJXbS+sbuGvXv/wmpVrjntg9w9fNgVWBEmSMDU1hdtuuw1PPPEEnn32WSwsLCAIArzzzjs4deoU3nnnHaysrMAXxJt9WowJ7PVBMX++0efadd2blsCOXYjBJgDHjx9Hu92GyomB+az4siqwADDRbuG+O2/F22c+rn1GKYVl6ug6PAWnPklQFRm2aWB5ZQUbm5uYPTRbK+F2/QCWYcAL6gYeaZblLrzNTrqjVFhTVysKQ1EmmmUUbcvAbtK8XTdXQ01DQ0ZpZf9hnqHKG1Nh9tQ0ZwqjpPxtU8mqF4SQJVL2rw4iyzK0WyZ2HQ8EyF2R3fKss+PtuwZTSnF14yocxyn7XaMGs5UiFsoydK7bcRBELM92iKRaJiORO11H6ArreayMtk4AGeIkEcZ2xElaEUg7uaszLT/nO/kCQBjGkAjAE3VcP2ALNw2ft0x2znVVhZ/2S8ayLMPa2iqiKMb8/DwOTU9yz1/Rz8sriLV0poRJEgFvrSCOE7Rtq1YiPggvL98dVOJZf2qfFLYtE72Ev43C3GrYoKiyHz+EpihQVQWuHzYuQiVpykpcOdekzIYdWDhQFdanXqhvlLI+VtF2AHb9VFWpqcumoaPnsuNwPdZX6/h8o6CMZlBkFU23Mfst25bjh3kGavO2CnVWkWRMTk/D9Xx4ngfH6cHr7WB1VernzhoGMpoKn4/iXMRJCkWSIEkEvqDqoCDZvP5hoJ+ra5s6XD/EKCbb88KKM3HLqroJV77rB+hYBtfFWFVkuPl7OYgSWLoKP0wqC0gfXFqHrir4wYduF45rLxiXEO8fvuwKrAiapmF+fh7z8/PMr6DXw9bWFtbW1nD27FmYplk6G09NTX1mZe8gqII3A4r5840+147jjHtgb3bMzs5ec/IKHNwe2GtFrJ9+5H7uCpTj+ntSYW3TQNuykGYZLly8hK3tbdx95x3c/uPRKmzzeILg0/XCMpLDJok9j68oy4RgstOC6wfwGybyQcA3XQrjWKjCBmEIiZBaCTHA1F9b0Avbc320TBOGrqM7QF7LcefnKU1SrKysIAgD1u9qmSwKqGHbBfHruR40jb/+xZTn/jF3WjZ8P0SSpqCgwkzTNMuEPZpxkgrLTsOw36vasS10B8grwMqzxbmt4muSphlMTasR4HZupsRWwPvHHscRlpeXQCnF4uICNE1j5JHjcA2wXsymknhL1xAlCdKMloZDPDieODs2TbOKqklAmBHSQLzKXkyG4jxCiLufLEPLKogPwzC/DCNxPzmQ943bTK0rHYeHSGgYxcI+Zjaeuvu4qhREvj+wYVfiJrD4muq49YEFgMFtaSMmOkmWQpUVaJqGyckp3HL8OI4cXcTk5CTSNMX6xgYuXbyIpeXLiHxv5N+Voh9WlWWkKZ/QU8rKtHmO7Iamoeex49lL6XSBrhvCNjSYmjoyMqfnBWibDfcZpdBUuVIi7IUxbFOt3URvnbuM0+9/sqexjcLNSrL2GzeTAisCIQSdTge33HJLaQZ12223gVKKs2fP4nvf+x7eeOMNXLp0CY7jfKqqgLECe32QpikIITf8XHueNyawY+wPvswlxAAw2Wnjvjtvbfxs2LSnsn9JgqGpMDQVru9jY2sLScImwgsLi8gyyjVuKVTYJrBeWH6JYCLoOylyYQsQIpV/OFgOZ3ViLEsyJlo2ojhG13G5sSFREgvLVsOGyJ3yt3GClm1yFUMWm1OfeBIQtG0TkkK42ayO50MCxdLSElRFxcKxBSgDhCeI6sS7VKUpbYzkKY8p7+GVJQkty8wJdH/8jusJ3apd3+f2VxJCEEQRZAEBjOIEbcvklir7YSQ0BnL9umPyILwwquQEd2yL5aTmx1jkivq+h+XlZViWhfn5o5DyMSdJCmuECVKapJVFAOb+mpSKe9f1y2ihJrCoGjHJLZR4gLkZN6mEongjgC1WiNyEO5aJra478Pw0n/eeG4x0sXW8ALqmwDZ1bo6p4/kwR2zHDfpEjEgkz+sedlrOoO/BB6Hn9cctyRIoaE0BTrNMuJhQwAtZnqsksXGBENi5s/HxxUXMz89D03WsX93C5voq1tfXkWUZgiBofD/IsiQ0BSuQJFl+v1evjVS+dwZKvr2gViLMQxAlUGR55D1UuA0PxwC1LQNeUFePHb+5BP6VDy7hjY9X9jQ27ljGCuy+YXxum6EoCmZnZytmUIcOHcL29jZee+01vPDCCzhz5gzW1tYQc2K0CowJ7PXBQVG6ixzYmxHju3yf8WUuIS7wzKMiFbbq4mvqOjq2DUqBrZ0uJFlCt9vFysoKZFnGxMQkZFnKYzj4k2JRf98oFXavubDDpLGX9/VKhGCiZYMA2O25oJQREmFvpSD6hvX98l9AfhBClqTGCWqaprVFAl3TYBoadnsunJ7HJZrdbhdLS0uYnJxk/a5D55SnRA5GgIiyV7MsgyIwsRLNY7KMH8dEQNj9wSGAiiIzcirYwajrlaaZOA4KKHuMOy0LXderTtIpsLFxFatXVjEzcwiHDs3WJm49V9zrGkRRqe4V5DUZLMEbKJ/lwXF97nkqxglQVirMuU5BGI80hep5fmM/bNsy0c23myQpqpelHoU0ypU2oxS2oaPn8kt7Ke2XG4vgBhFURUbLMOBzyPBeFMfieSCEwNSaI2bY/vamXvZcHxO2hSAaUlgJgabrmJycxPzRozg8fxQz05MAwJyNL13C+vo6er0eq84wdHQ9Hz0vGNkbDAB+GKM9tCBimzrCBiMvL4z21Cds6xqCOIG6BxKdUYooTqDnC0O6osATlT77YW28AHDq7XN475PVkfvjYdwDu3+4mUuI9wpCCCzLwuLiIh566CH8wA/8AO69916oqoqLFy/i1KlTeO2113D+/Hns7u5WKrMKY6Exgd1/HJS83XGMzhj7hoNMYK+VC95kp4177zjZ+FmWZbB0A23bZj2XQVjG4FBKsba6hqtXNzE/P5eXcA+U8AUBX4UVRKaMUmFH5cIWjsSsbLeaizrRMqEqCnZ7LpJ02GHW56qwcZKMJKm8yXucpLBMnTuxcv1+bM5Ey0YcJfDyck2mlFYnmpRSbGxsYPPqVUzPHMKxo/Pclraiz7bA4OSDUgqFEyfSbllw/VCoAHp+wGJxOOg5XiPBKxYWHK8e12ToGohE4AchvBEqqiOIG2Kf+9zxE8LU7+lOu9bLSinF+sY6VtfXceftt6LT6TRugzaYh9XG4AdomUZJXiu/B1sYEd1XAFPXRJNGRZbFqwno98vyQDNaW41umQYcr080wziulEU33c5eEAnV3JZlYKvrjMyPDfYQ85Pmhls9j0+GizHxYsH6+4sw1baFvcAAI12jVOaWxXqKR0XcECLBNFi+9PHjx5k6q2lwHAdXLq/g3IUL2Nrahu/7CKJo5L0G5KW8VtHaoaPH6UvNMoo0E2cvW4aGrh/k2bTyniJzkiwDpYAiS5AVaeQCTa+BxFICvPDeJ/hweWPk/powVgn3D+MS4k8PSZIwPT2N22+/HU888QS++tWv4tixY/A8D2+//Taef/55vPPOO7h8+XJpBjU+x/uPg5ABC7Ae2M8b9/lFxfguz7Fff7AOcg/stRzXM48+UHtpmoaBdsuGHwTw/bDSHxonCZZXVrDT7eGuO2+HZVk1wjhaheXfvqMciblOswOkQpL6CqxtGTB0DVu7DjLaTPyTNIMtmFi7guibeMjNeBhRnHLv0TRlaqKlG9jtubXxMaWUTZrTJMHllRUEvo+FxUWYlsU9HqDeZ9ukSpsDE0hJImi3TPTyiBzXC4SlvkIjGqCZOOQOTVlGYQyUHNqWiShJEOfKVZpmwmgVVuIuLsHllX0SELRMHcFQeXaapri8chlhEGJxYREZyEgSLYp/0lUViixVyOugAgvkiwyCfYQR33G402LKK8tj5W9juF+2CZ7fPxbL0OAGYa10lJUAj9hOEDT2Y1q6li/MEHhBCG0EIWOOvXyyaBs6dnruSFU0TTNoAjfmYlvbPbfuODwESqlQadZVFZ4fIYrTWjltE8I4ga6xsbHe2UkcPTqPO26/De2JSWR57+z58xewsb6KntMb+d5n96RWV4CHEMcJVEVqXPuQJQlxmqJYGfPDmJ2bPYibLDZJQzhi/wWGSWzbNOCHMf7nax/i45Wre9rGMMYEdn8wVmA/P3Rdx/z8PO6//348++yzePjhh9FqtXDlyhW89NJLAIALFy5gc3PzQIonXxYcFALr+/64hHiM/cFB7oG9ljlkUxNt3HP7LVBVBZ22DV3T4AcBeo7LFMQBNcTzPCwtLUHXNBw7dgyqkvePSX3TpAKfT4UVGAEJch57eX8mIQS6psK2TLheAD9gmZumIKfTbYivKTAcuVP7rUB1yWjWWAYoEYJOy2LnOW2e8DGlT0EYBFhaXoasKFhYWChNy/wgbDRs6h9TX6ls6sUtSjV1XYWmqOg5/VLUNM2EKmsQimNNHM+HadRjQ4oxOJ4PXVfRaVnw/ADZECFmubN8IsBTecv9u36t95kQAsvU0fP8XDFk5y4KIywvLUNWZBxbOAZFVZghleA+BFhub9OkzjJ0RFGCXcdrNK0qrkKSpkLTKyB33h0ifKz02S+3MapkutdwLYYRRTFMXUMYpY0VAxQUjCfz83bTlNauiaYoiJOsdIZOswzaCNO9srS34TNdVRBGcd57GVb6mZvg+hGX6KqKgjBmjt1xmo5UTsM4aTSakggjg2lZoh+iM0JFBgA/TCr5sG3LRBCnsG0bhwZ6Z6mkIA19LC0vYXllGVtbW/D9eu4spYAEaU9Eww9j2A39sKahIh7qKWaRPaOPR1cVdN0Qhra3jFiAkdiWocPQFPRyw6gso/juqx/iwurWHrfCMFZg9w/jc3ttUZhBnTx5El/5ylfw1FNPAWALBR988AFOnTqFN998E5cuXYLruuPy+GuIg9IDO3YhHmPfcJBLiK/1uJ5+9AGkaYpuz0UYVXvKHM+HrMjY3t7GldVVHJqZweHDhyERkjsSmxXTpAL7qsIKSi81TYFtsrged6g3sOd63PzHNBUTFtcPuGXGSZKgxSGShJBKqTDAYj80TUXX8ZBmVKiQXV5dw8b6GiYnJjB35EjtvMUCVSbN0nIxoInAur6PyU4LcZwgiOq9hI7rCUswQ0FWLoBaLyNBdQwt00A3V3yHwVRUQekrMLK0MhswsZElCZahww+jsgQ2jEL4LjNranfamDsyV6lG6DmesGy0qcfUMnSEcYwkS2uKXb+FtKqEGwLFLssy6AOft/JooUF613O9kSorzUZPQDVFQSrIAvaCuJZlOgzHY6XTADvnEikUvYHvcDJ/B+E35N3KEnP1TvMFvCzLcodg8eSO5b5WzzEhBIoslxFdcZyMPIdAYYRUHVeT6un4o42tBr9nGVq9JHqgd3ZiZhb33nUHpiankGYpNjY2cPHSRaytraHb6+aLbAZ6fgBVblZXa/v2qr29LUvnug73vOa+1f5QCSTC4pHcIGKLdnucdLtRxK7PwPezLMP/9fIZXFrf2dM2gHEP7H5iXEK8vyjezXfffTeeeeYZPP7445iZmcHW1hZeffVVvPjiizhz5gzW19dHmkGNIcZB6oEdlxDf5LgZS4ivNYGdmezgntube2HjOMH21U3sdrs4duxYrS+QgCmwTT1Po1VYgSOxYILb1AuraSraLRvdnltmTw6DUkAXTFJd1+OrsCMIOc/9lhCCJGFEUpYkTLRsBGGIYCC70XHraiKlFFevXsXVq1exuLCAyampxn7HIIrQ4ZVVgymZqqLUCKwsSei0LARRxO1Xy0aQ6yiO0Wrxz4k3FH9ECMsClRUJpmFgc6cnLAV2XF+oLjpeIFx08HPjL0WRoWsqXD9AWccMirX1Dexub+LwkcOYnp6uSX4UVNg/WhxjUQZsmYy8DpZXu36IdjHG/PoNnm0KcMvTy+PMXYtblgGX0/dJa1uuIhBEEMmyDFkm2HXdkQQ1jJOR5ySMYiiSBENXEXAmW2EYCUufAXZ9jXzBiYAZyQ1nEDODJXFfbZZRyKS6nNIyDfjh8GJdgNaIKB+gGq3TsYzGftMsAyjNII80t2K3RUZH+f0yZ+CZ6UnMHprF4nGmzuq6DsdxsLZ6GR98dA5bW1vY2tndk/kTwNT5lqlDVWVh5izAlFib8zy2Tb3ye+dTxPa0DNazOzzmNM3w3196H8sbO3vazlgl3D+MS4j3F4WBE8mj92zbxuLiIh5++GE899xzuOeee6AoCi5cuIDnn38er732Gi5cuIDd3d3xws2nxEEpIfY8D7Zt3+hh3BCMCew+4yCXEO/HuJp6YcMowvLSElzfx8lbTsBoKMF1PR+mYdRKiAFG+njKJCD2n3EEZDIIw9JlV9NUdFo24jhGz3HLlWLeS72JLBZIMyosXx5WUgeRpCmshglbGV8DpqTtOm5NmMgoKmNK0xSXL1+G53lYWFgAJRJ34gjkcT6czzKawTS0CoE1dR2qoqDrePCDEC1BKXBPcL4AwPdD4WpmnCT960wAVZYhSzK8htiXJoyaMo0qp88oI6Fe3sdNCPvN6uoadne7mDl8BFOTk9zfO75fU9wGUSxO2KaBMIwae4PDOC4XcghQU6ZcUc5xDlmW4DX0phYYVZkANJcjE4lA0xQEYQxQgBK+CzAjWtnIiJ84STHRMuEKMkTjPfTmFoSEAGjbZr4A0XRcIbQRpcSDii5zbm7elh+ONk1KswyKKuc5qXzzpzBKxEQyfzBkSRpJ5gF2L7PJlwQCAl1j6uzCsWM4ceKWPHc2w8bGBt47cxa93U10e6N7Z70whKUpwsxZgN22fhjBGnofWLpalv8OoruH2B4z/y2lFEEYwzSq206SFP/n6b2T2DHJ2h+MFdj9hciBWJZlTE9P44477sCTTz6Jp59+GkePHoXrunjrrbdw6tQpvPvuu7h8+TJCTvTeGH0cBAJLKR27EI+xf7iZSogBYHqyU3Ek7vV6WF5eRqvdxtz8PNqCB01qKE8t4Pk+98Xs+nwFLcuoUF2jlKKdE9fuECkMwqhUbuq/Y6SXhyJypwmDJblNcN06wVUVhZUHOn6jwc3gfg1dQxiGWFpagizLlX5XEcI44ZtbAeg5rJ+UuTFbCKOoUjIcxTF3MYFSjiFTDrZIwT8nYRSXixgty0KcpogGlBrPFxNob0S5aRBG3F5cQ9cRRXGF3FAK7OzsIE0TLC4uQFW10r2ahyRNhIstWZYhy7Iy53UYUZygXaj3hDRS0DDi59tauSuw6N4DRjsOZxmtlNATQmAbOvwBEhYEEVq2eD89V6xUduw8P3bEee3tQfH0wwjTHTsvm25GRrP8uEdEE3kBOrbJdegFmOrHCKx4W2GUQNcUjDDbZe7AgmNsWwYcP4TjhZV+WB6iJIWuypX70TI0JFkG27YxO3sIi4vM2TilMtKI9c8vr6xgk9M7a5s6vDARvp8KZBllYygM8whBmvHLd3teyL3GBARZhnJBJ6MUYZTW+sb3SmLHStT+YazA7i8+TYSOYRg4evQo7r//fjz33HN46KGHYNs2Ll++jBdffBEvv/wyPvroI2xtbR3IOeyNxkHpgR0T2DH2tYT4ID78+zmuZx59AIQQbFy9io2NDcwdOYKZ6WkQFLmwzUQmiGKu4c4ogiO6eo5XzaIFAE1liqvn+QBoY5sVkSTh9FOkwmYZbTRq6Y/Jr42pQJqlZUksIcxsh1KUkSQ91+f24FIK+J6LlZUVTExMYG6u2o/p+mICMxybU9k2KDRNgamr2HW8vDe0jzBqzo0tj9n1hQZYjsAAC+iTTM/3GxXTKBYTxDhOhfdJEMa13mDbNBAlMeIkYcq5IiMIfPi+D0VRcPToMcj5QoXjerAExxdEMTocktyyDHh+AGmEglb0XzcpsAAjJq2GEnXT0BFEEWhG4fphqQ42na80rU/+h+H6YXkftaxqXE75Ha+u1DL0dxrGSeP91rYMlq1LgSRLR0awsO2II112Rhh2Aez+H5V5W/S8jqjYhuePLks2dBW7jg/LGL3A5AZh44KapsgVVbnn+SPdkNn2opIUsr7VKiEnhLVKTE5O4NDhw7jjtpOYmpxElmV57+ylvHe2B0UicLyImVjJhLuIMgjWN8x6iHl5s4NwOEpsy6r/NssyRHFa6wvfC4kdlxDvH8bndn/xWTNgCSGYmJjAyZMn8dhjj+HZZ5/FyZMnkSQJzpw5g1OnTuGtt97C0tLS2Awqx0FQYIExgR1jH1GUEB+0B34/Caypq9BICt/3sbi4WKnPzzJ+tIlEiDDSRWSANFKFzftkNU1F27b6iiuAMIwb/6gWxkkWxzlzpArreFyn1DTLYAucYx3PR8syYep6aVBEC9MZShvVPtbvuoELF5dwy4lFTE1NNW5bVC6bJGklNmcQtmkijOLSsKYJfhAKJ6+iz9i90TzxliQCQ1fz3p7m0u4oioXlr2EkzkyNk6TSb9e2TXhB39k4TTNEgY/Lly9D0zTYtj2Ui4uRtcose7Z6D7ctA64fIqOM9LUFymWZ6yuYBHY9v0LUzNzNOMtLO7MsK6NseK8l5jgsVjWjJB4wg2oYa5ZxF1oYKOKk7shrGdpAnzFTKUcprHGSct8rmsocjNOMgpDR5eQs6oeX/0ugKKyUfC99ro7ncxcDOpYJ1w9BwbJ6RQQcYO8xClrph5XzqK9BBZfSIuJm9OSq6wXo2Aa8QNy3mqZMbeh0Wpg9dAiLx4/jaN4767kuPrl4CUvLS9ja3ML2bhfmkLrLQxgnsHVFWCZeHhcAx49gD7wjLF3jKuFpliFO+CR2iUNixyRr//BZCdYYe8O1Or+qquLw4cO455578Mwzz+Cxxx7D1NQUNjc38eqrr+L06dP44IMPsL6+fiA9Xq4HDoKJU5qyefa4B3aMfUGxQnPQVNj9IrBbW1s4ffo0Hn/gLhxfXGwsXWXqY32CSCQJvh/yTZnSVJizKkKcJOi0WoijBD3XqyirUdxMfMp+T8HaA3Nu5aiwlMIQmNkUxkjDkCUZLdOEJJGBnstqyehw/Eu/39XH4uKC0JXOH2FYMxwFJBGCiZYF1/eRZhQicSVOEmEprzuihLXn+vUIFVWBrqrouR4bmyKzPsuGC8MUZP7E3Q0CYWaqk0cGtVsWeq5fEjwK4OrVq/hkaRm3nDgOjXNdR6lu6dACTts24fhBhZBHcSJUHHuuD1NTuItilPZNowxdQxQnpeNueZwu3/ysAFvo4I/D1LSRZLDpeg8fWtftkzxdY5E0wyW1Pc8f2eva8/zaviRJgiT1HYz9MBben8Aggakf3aBpU68hYqm+LUb+hhdubENDb8DdPEoSmLq4Jxgo+mEHFic0FXHDglKcpsxFeMT2SP7dUb2/bN8xDFUtFwGK3Nk7br0FRxcWMDU1Vaqz73/4MXrbm+h2u0gSPjlWZAlemMDMF6dGgVIKL4hgGSokiVTykZuQps0k1tAUfOf0GXyytt34uzGB3R+MFwf2F/uxQEAIQavVwvHjx0szqLvuuguyLOP8+fM4deoUvv/97+PChQvodrsHTqzZLxwEBdZ1XQAYuxDf7NjPEmLgy09gKaX45JNP8P3vfx+33XYbnnnqSTxw9+2N3+WpsEyBHRFl4flcAuI1qLCmwVxX/SAEozw885qodg8UPbmeH/CJMwUURdQL63LLojOa1ZTUtm2BEIKu68JxPWg5wR12/6VAqe72+12lvN9VG0kUo5gfXTOYoWtoGnSNlQwX4/B8sVmQ4/FNqort80BBoQ6odrZpIMsy+LmpRCXft+FSMgWZf9xpIu4/zrIMnZaNXn68xT6vXL4MNzfDardajXFCBcIoFk4ieq4PQ9dYH2VeJlv9vXgRAACYmMp/Zzl+gIm2jSTNGg2h2HGlQpWMKdbN56ptm+i6HhzPH9n7G0Vxo7pIK6ohharIJeFr+i6lGdcYqtxX3N8XIUzNDYZccXtuIFxYAtj7oGNX31Ft20TPr6rNYRRzWwH6Y0oqJb2qIiOMktrtu1fH3V7e59q2DDgBX7n0ggitEbmr7N0Ys34uwcJPATeoOgi3TB293ICN5c4ewvHF45g/Oo8UMmgSYnl5GcvLy9ja2qz1zho5AXeDqrIqQkYp/DBBy9ARCfK8CwyTWFWR4YcJkjTFf3/pDM5f2ax8f0yy9g9jE6f9xfVQuGVZxszMDO644w489dRT/3/2/jTGkiu9DkXXjnk45+ScNWQVqzgUm2yOzZ5YFLt91daz3nuN9x7g/ikPMGz4wZAND4ChH7YBw7Jsw/CDf9gyYBiGf/jKk2Tp3mvZ98qtsVvdbDbVHFWciiyyxsysnE9E7IgdEXvv92NHRJ44JyJOkqysSpK5gIbEynMidoxnr72+by1cvHgRJ0+eRBiGePXVV/GHf/iHuHTpElZXV4+0GdR2NL3qowtHoQe2JLDHJcTHOBSUluZHrcziThLYPM/x2muv4YMPPsBXvvIV3HfffSCE4LlnHm8lmyGNa0QFKEiaEI1EtIQyQOqY3BdzI9914LmKuO73jrbnkTapsOV4AEDI9nMVUNpabinHnIHHEVFljOTaFnzHQRBR5Dwv9rlfKjw6lhJhRJGlCW7evIGZmcFEv2sXWJp1GjaFNMZM30eaZbWYkJK4dZFQznlnrE2SdJfyhhGF61gY9DzQOEE+nv8ZxQXZaTF86cjpBYrS7oZrQghBz3OxvTesznuaZbhx4wYA4MyZM7BMFaPjOXZr+W2a5Z2OwxISvmNhGFG0kdCIJp35tXnOO8mJY5lI06zzOjGWTe3RjGhcLaKU8F0bIVUZslJ2u4ADSpWvL1hNfiHNOHzXRtrRB5mwbCohU6XE6tr1PbfR3VdWC1ndAw9GlOHGfFWo3ny7s0xaocy1JSh6aFuuS0iTqf3H5X7Hn4smBFG7+ZPvWNW7Mc2UqdP0Amu1ONL3bJiGPpFbC6hNWJaFmdkZzM4vqtzZQp3d3NzAtSJ3lqcJdoNoZLsMgynXt4RrG4iStDP7eBScC2RFT6xlGrUM4P/+43fw7s3N6rOfFwXpXuDYxOlwcS9KtB3HwcrKCp544gk8//zzeOKJJ+C6Lm7evIkf/OAH+PGPf4z33nsPOzs7U93+7xZevrbb6vVxUByFEuIoimDb9oFMOj+LmP7L+zlCl6rySXAUo3TuFIGNogivvPIKLMvCc889B3vExGZ20MdjDz+A1996b+J7Qgj0fBfZyISVjMTWdF2GiNLCSKU+fgJANzTM9H3sjUyMSsiCEGYtiwlxwor+SjExnjhWkTthU7+fRGf/WhBROJZVc+wtoWka+r6Hrd1hozpcfndCgZUSm1tbSFmMEydONvZARIVyHNHmHsU4YY2qt1nknUohJ/5WjqPMRg0iiiYEkVqgyFoICSsU76bnTdM0uLaF7b2g8bsgKg6mTU2XUsK2zVYyJKWEZeg1F2ND12GaRkHM1PWklGJtbQ2DwQALi4u1qT0XvHOuH1JVHt50rw18D1t7AXquKh9uAhcCruMg61CYIprA7/kTeca2ZYILgSTnmOl5GIbtzrthnMA0jFYyJKSEaepIi+PwHBuUpbXnM06UuVaXw69a5CkrIUqojRAQeI6FvZDCsYxmUlSOlyZwLQNxx2cCmmCu72MnbL43gX1Dra4xS6jrYFsmWMbRKPlDPWddkTolaMIw8F3sde5TqrxZnbTG0ei6VvWhH2QSFiUMjmXWlGhT1wr1cv/7NEnR9xwEB4imCmmCQc/DXtT9WQmVO7s0PwvKfAASaZohTxNs7uwijhMYpgnPdeF6nnI4910MO86lrmtIc6FIaJ5PHFsbuBDwdGti8UAIgd/6o3fAucCj9y0DOC4hPiwcq9uHi3utCmqahtnZWczOzuKBBx5AmqbY2dnB1tYWLl26BM45ZmdnsbCwgPn5eXgd8YiHAS4kfvedDazMOphxPxnpOyolxJ7nfW6fqWMCexdwFJ2IdV0pWJ9kxW59fR1vvPEGzp49iwsXLjRu56eeeRKX3r3SWBYYhDEs00SaqckHGSFTcZwowkgnJ6FcCAx6LobFBFXXNPiei4SlCMJ2NRRQjsS2aYJlkxOeLM8x6PcwDMJqPKMEq0vxCGkMz7VBmxSfIkJmlMASEPQLhXFrdw92C8GVkDAMrTYWzjnW19aQ5zlOnTqFmUG/6pUdh+gYc5bliuyPTPT7nouYpQhpAgJMTA5Hx5GmWSsJFVLAsZxWAptmOQZ9r7qGJWzTBNEItvcC9Dy3IpR1ECQshefYYC1EJohiuI6FuKXEMoxieK4DmjBF1oGKXEkpcXN1DTQMsLi0hEFDf4ly++0qRZbwXXOCwA56PoZhVGwjh6YRiJYMFRV5YyNquKcIIeBCqBLMEQJrmyaEkNW/DaNY9ZW2nCchBFzH7ry3ozhBz3WRC145GTd9po2wAwCkuhfVD239+z3fqYikeoe0q6OycMqa3Mo+PMdCVLhpt0USAaXCatUqDMaR5wIzPQc74eSC2CjCOFFEt4NIeY6NNM9BQFoXXwCV99tzndZsWNc2ERamRwdRf4VQ73mjIL4Eqv2ANjwbAU0ORGLVs8lgm8ZU92AhJdIsr+5DyzIxO+jB66vS5TiOEVOKzY2NauFmaX4WQjMavRJc26qeCc4FCPIDjcM0dERMXR/PtkBHrrsQEt99+TJYlsM7LnM9FEgpj0uIDxlHzSTLsiycOHECJ06cqDJLt7a2sLGxgcuXL8O27YrMzs3NtSZU3AmkucD/eWkNlq7hi6cGn3h7R4HAhmH4uTVwAo4J7F2BrutHsoQY+HhlEEIIXL58GdeuXcMTTzyBkydPtn520Pfx5CMP4ZVL7078TUoBx3ErAquNlcl2kf6QxvAcG4ahVLPhyAQzTlhrtEfpHNxEYAHVR6tpWlXqNKpAJglT5j4tilbXKlhYlBnHiYof4ULWxmyaRiOBLY/VdWxEUYSUMayursKybZw5e7YqUW9DzFIMepNEsdp2FMMo4oJ81ynKWhUkSuI9QmCxTxxYltUI2TiUk207iYyoMlQqFzd6nos4ScEL1THLeSNRKY9XdLghQx5MRfEcVbZaEjgpJTY2NhBFEc7ddxa6YTRHLBECyhj6g5lWV+YgKhY1EgYC1d88eq7SLMPA9zDsyiYVsrMyRBkJOYgSpsirlDUSWRo6MTS/fwiIKm/13VoUyzhKZbCNbCun7w4Ci6Js3fcK8yJVfqyOf/+eUypl9zlR97SLYYP6Z5tGcT0F+r7TmdVaKqwa0Vrdzz3Xxk4QFee44/xICaD9Wjm2iShhkFK2vptGEbaougPfwXDkmGiSwmtxTB5Fmim3Z87TYv/t5yWMkwmCVzsWq3T+ldCkBlPXGo2kRpELAcI1mLoOxzYRFAS07J31fR+QUrUsUIrbWzuAyJBLDZ7nwXVdOI6DgTdJ7HMuAEJgmXpnT6xpGsiKd1GSZvAcq0bipZT4g9ev4LRP8KUHTnQezzE+Osrn4vOqFt0NHDUCO4rSDKrX6+HcuXPgnGNnZwfb29t4//33EccxZmZmMD8/j4WFBfQKr4k7gWGc4X/88RoIgP/bU8t3ZJv3Wu0G9iN0Pq/P1DGBHcFhlRAfVQUWUCTxo9TPp2mK1157DUmS4OLFiwdqHn/2S4/j9XfeB88nz4Fy07XA0rRWsgsURLRBhfVdF4SorME28tQV9aLyW9U+J76X5xUp0xruhzJrtOk2iWjSqhpKqfIaDc9D0KAqKzdjuzIrGodRLILcuHEDs3NzmJ+bq5oPVamwg6hlUszSrHXMXAgszPYxjJIakajGNaYsjz8jcZK0Kl1Syolc1dq+ucCgcPvt97yJa8nStJEgl+9qmiSYm51pLuuGcgRWZd/N5L3sz84TdV/meY61tTVIKXH27FloulGQzsnvk2L8nmt3luhCShAQ9Dyn8fyGcQKrIF1NaFuAqCnyhRLLhWwkkGGcKLfjjnLPLMtb33/l+NyOsmygVIydTiIcjkT89Fy745x0k5EgSuCYJpKRhSijuNfKZz+IEkXWWpRMQMW49FsI5cDfJ8ksS2EYOvKGd1i1rTRrJJ26rqsInOLcdinrowhpAs82QUvV0GmOjKFpfqDtRTHDXN/vLGEG1HuCZXnjfalp5aKeOpasuC94Q7vBODKu4pDiloU6EALLsor+2VlIIUAkx+bOHjY3NwEp4LoebMeB63k1P4M85zAMvfW+6Xs2gpGoHmUElcF3LERjC2yvX91ALoEHH3zwczsxPAyU9/9RJVifBRxlAjsOXdexuLiIxcVFAEAcx9je3sbW1hauXr0KTdMqMjs/P9/q/D8N68MEv//OBjIu8Y0LCwda8DsIjoICSyn9XCuwn447/VOOo9gDqwig9pHGtbu7ix/+8IcwDOPA5BVQZZNfevThxr9JKStDnXHFE9gv21W9oj4c20ZEY4QRRRDGrVmHCUtbnXKVg2/7SyyKlTLYNKFnLEWvw4CoScnRdU0RtTAG7zCDao13kcD65iY0SJw4eRLz8/MTzjmipV8OKJWvyZecrmno9zzsDLvLI0edX8fPSdaRGwsoEyCvw1k1YSl6vtu6EEHjpNEdVY1jv4y5DWmWNZoMqetBq15cxpRbqmEYWFlZqUqZojZH5WJFYBhROB3uqUmaYXbQq0WmjEJlsnb/oEZx0ul0K6WEbU2WK4+Csawzg7fNeKokCVmeF3E3UxyHs24HZiklNELgWkaroimEKJ7r9nta9dLtFxoTEFj2ZBkpS9OJ3N1xBDSpOesCZTXC/jXLcwH7AOVtE1E+RPUkp2PEN2FZq6FcCQmJnKt8WEPXkXHReEYI1PUdN9sah6FriGI2NfoHKEtzJ3tsy4qFUcQsg2ebnXFjgKqwybiAZRidUVwliKZBMyycWzmFs2fP4oHz52HaqhKldDbe2t4GjWMIKZHnHELKiUgg09QRsclnQ0oJyjL03Po9LQG8c3Mbv/3ye60VB8f46CgNfI4XBQ4PnyYCOw7XdbGysoInn3wS3/jGN/D444/DcRxcv34df/iHf4iXXnoJ77///kcyg3p/I8T/vLSOJBM4O+/i/MKdI3tHwcTp815C/Om80z9lOIoKLHDwcUkpcf36dbz00ks4d+4cnn766Y/cq/D1Lz3W+p0wUq6wZQnx6JRBComFmRkQAEEYIRnp9ZRSdPa7Znne6rMTRBSO3fxdzpXTMdG0CedfQE2K236DleqniDMhwEzPAwHBMKQQUoJ0zIdCGk84LAsucGv1FljCYJpm68sqZgz9juiVUikt4bsOdF1DEFKIQsFrQxQn1WJAE6mvsllb0PZj47mFk2/HOcl5WyyOGodyj24/7jTN0Bsh2ISQQu1Vyl+a5ZBcOQ0PBgOcOHGi9qPEhWh0VCZAlRHcltmq6xpcy0IY0SkmX915opwLeGP3ankdbEuVDZfOzG1I8xy9KREtIU1qjtmGrkPTtBphkZCdrsNZ3u1AXW6FaKTbqC3uvp8BpU6XsTM9zwaNJ5W9LO++t0uwLK+Irm2ZRV9s/UCjKRm/1bbStLoWfc9t7E/nRU/qtKl8mQ9rm8aEWdcociGg66R1kYKQMrKGI4rZgdx7WZqr3Nfiv3tue+lxGDP0/e7r7rs2WJaDshSePT1DGCiU0izHjO8hl8DszAxOnTqFc/fdh7n5eUghsLm5iWtXr2JtfR3b2ztI07TWG2zqeus7SEqJME7RH3c/JgTXN/fw3158e2Lx4RgfD8clxIePTzOBHYWmaZibm8ODDz6Ir371q3j++edx9uxZMMZw6dIlfP/738frr7+OGzduII6bF4d/cnUH3720hlxKGDrBTz24cEfHeBQU2CiKPtcE9riEeASHmQX7aSWwnHO8+eab2NjYwDPPPIOFhY/3Euh5Lp55/Av48auXJv5WTsRpXOQ3Auj5ypEyojEIac6GBIpIlJYSTJZm6Plua4lplzJTusg2lVSmRQRNWy9snnP0fRdpltcMkgA1Ce75beZEqByQASBlKVbXVmGZFhaXFrG9td1hbNRd3pzlHIOeVygwzoTiGdK4082zVMKbCCwXAjOeN3GsJeKir7FyfCUo/ltloGY87yyfDqIYlmEizetmUiXzjWgCw9CQ5833CE0KFZUo5b3MeJUAtre3Mdzbw5mV07CdZnIShBSObSEZ7QkkpOLdNE7Q97yaymoaOnRdR1SQl0HPayyXLcGFaL12gHLxHTcJs0xD9bwW977r2J0kZ1gdx8g1JvXYkJJoa5pW5GXWSWHC0sJ9usPZuOhPbyJudmE6laR5J+EGVD9sV3k1oNTT2b6L3aBrPNNLiTNemHKlWWFu12KsFSdTXW9zzuE5NmzL7HQmjlmKgedi2PI8lzjohDRmGfpuswHTaGmzlBKc86kl0QAQFc7ELMvbS38LBFEy0aNbwnfsqu8VAMKEFWOdnhNp6hooYzWHak3T4HsefM+DBJClKeI4RkQptra34VgWfN/H7EwfYZK2LjJVY6cM/SLTFlLCswwEcYogTvHrP7iE//ezjyqV+RgfG8clxIcPIcShGiHdK1iWhZMnT+LkyZNq0SkMsbW1hdu3b+Py5ctwHKcqNe4NBvjBlV1c2YhUC0bK8e0nTsGcUonzUSCEgJTySBDYz2sGLHCswN4VGIZx5EycgOkEllKKF198EVEU4bnnnvvY5LXE15/6Ymu/bRDFGPgeXMeGpmkII1rFvyQsbc0sVeS3vawxTdtV2NIcqQlCiCLrs3kimxSlp+PwXLuYLJFWR9LOc54wRVLDEDdu3EC/18epU6cq1+gyI7YJpUlOG8rS6aZy3dLspw1J0YvZxrKCiHbm3SZFqa+ua+h5ykG62owESEdNoZQC9tjksSwhBlSkjWe3q4ucC/R9FxrRKgIohMDa2hqCIMDplRUszM+17x+TMUlk7DywLKvKIu2immCU8AZR3H1+WIpBh+I4rlTbpqHchkfInVLw21UwiTrxL13Iy//LBUdIY/Q8Z0SFnERU9O127UjlbNavabkglHNR3G/dPz+ci6kkt+fZSFg+NYs2YenUCUyp+nb13kohi+zb7h1yITpLtksMaYxexzXrFaXMAU3gTynfBoAgTipVukRT72yWc5i6NvW8AfumTm2xPqMYRsnE8Zi6BtbkfxAnGExR61UZs3JPVmr65H1HUOTOzszg1MmTOHfffRjMzkInErfW1it1djgcImsx71PjYeh7NnSNIBtZwFjfCfFr339jamTQMbpxXEJ8+DgKZa2HDUII+v0+zp8/j2eeeQbf+MY38NBDD0FKiTfeehf/v1/7Pl54/TJosIutIcWXz81iuX+wfOmDoryXjwKB/TwrsJ/tO/2I4NOowG5sbOCFF17A7Owsvva1r8FxussPDwLPdfCVJx6p/RshBD3fg+faIBpBnDCkDSv9pRFRE8oS5CakWdbZs6qR9keAFjmpTciyvEYWHdtCz3dB4wQ0SVp7L4EyN7NlTFJifX0dt9dv48SJE5hfmAfIvvKpFLCuUmE20bem61qhGNPOPrlSOWtDwlLoWltsjormaEOaZZgd+MXixKTiRKmKammDijzZHxsZUw6DiE6Q3BK+5yCgcUWSszzHjZs3wTnH2TNnYFuWyhftIAijZdRA3Y25PD7fd+E6FoQQE72Y0xYIAKUUdhE2mjAMeh4c24KQaOx55bmo9Sw3baPnuYq0Fmq/pmu1BQSRC8RJUpHbcez3qLZDLabsvzN0XS/yO/eVfJp038tAkbHqN797PFfFqSRpOkHaxpFzUZDudhLW8xzshXSih3IcLM3Q63hO9MKMLohieAcgnXFLP6xlGohZhtKxmWXZ1PMOFOZPRV+2UWXGTh53zNKJ3t8m9FwHu1PK3EcRxWz/uAvX97aYpmGcTJbvju7bs6vMXy4Ecj69Z1zTNPR8DwtLy1hZOYMHzt8H13EQUYobN2/i+o0b2NraqnpnRxFQBt82JkzpdsIEv/r9N7C+E047/GO04DhC5/DxWSkh/igwDANLS0uYO30e14wVnFg5B8fzsRMmyHZXkVy/hDfffBNra2uNc8uPAz7izXIvcUxgj1HhuIRY/ci89957ePXVV/Hoo4/ii1/84h19SL/65KOwbQuOY2PQ96EXaiuliVJ2rPay3V6LWZCUsjMPsU0tBYAoVnmgbegmFDEc20K/5yJhrOZ2y9Jus6c0zSZohuAcq6ur2NzewaNfeBh+b//FNJ6/2kqsc17ro+15LjRNq8pmgyjqVM+6noA0y9H3fTUVblFhvYaFDkJI5aTblCFajZ3nHeRLjqm0dSItIWE0mD31ex4imiiXUl1HnCS4cf06HMfByunT+yuoUk59/rM83/9MgxJdxsy0lfFGNOnsoeRCwJlCeHhBKnkLuUzStJXwAQCkRBQnlTqoEQ2apkHXdRi6gb7vIkoYPMeBlGpCxDkH56LmpKsI/UF6ai0QjcCyjImyW1mMZZr5UFlKPAqVKcorAhJEydS+zihmGLSc/4HnIqAxuBDQNX2qMqmMn5qP37EtpBlX5fF5DkOfrtaqftj9z2mEQNe0QslWyLmAoZGpBkgSQJpxWKY+kRM8jrBjgQAoSn+L0uOYpQfqJ5ZSgqUZHMvEwHcmXH7HEdAE/QZy7I3k3ZbIuQDnYuoig+86SNIcXEgQTceJpcVKnZ2fn4eUstY7W6qzvmthL87hWebEeY6SDP/1B5dwZXV76jk4xiTKaLpjHB4+jwQWAN5dD/HrL99UhoyCYGF+Dl+4/wz+v/+v5/H444/Dsixcu3atMoO6cuUKdnd3D2wGNQ5eZJrf63N9XEJ8jEPHUcyBBTDhQpxlGV5++WXcunULzz77LE6fPn3H9+k6Nr7+1GNIEoZhENVX5mUROdFWtpumraWmQRTDaSklzvIc/Q6n3DZRplSJzAbCZxgGPMep9VROjJe1K7jj5b5pmuL6jRsAgLNnzgCkTuVGCSzLssooqglRrCbzfd9DSONamamQ6CRJUZzUXVTHoFRprZE8qXHW/9sw9KLnliLnHK7bvm/Guo+LjplJjV+4KI4rwydCCPq+W7s2q2vr2Nm8jfn5eSwvLU1MpuiYyjoxvnTfMKoycSrQ9z3QOO4urS220dWPF0YUfourseeoPtxp+whpS4mvlFVOrGub6vhLPg6iKgiSFJquVfE9KmdYK74uKkIrhFBqf0dZrpQSuqaca9uygIWQjc/XKMZLiU1DLzJc998dEqWhVuemEMbJxGJXz7Fr/cuUsc5noATL0okFrr7v1eJsspzDNk1Ms+iNWQp/RIn0XbtQX8c/l9VU07at5pzDs+0qgqcLQdRcxmzoGlieVw+1EBI55wdSgdVCAEHS0b88ijCuk1itJZoLUCRWiEm34RLOGPHlQiLNc3i2VfXOLi4u4uzZszi9slKps7dWb+G9K1eRpgx7EVWxZ2OLD1nO8Zs/fhuvvH/rQMd1jH0cK7CHj88bgRVS4kdXtvA7b9+GY+kIkgw9x4BOgK+cm8OMZ2Fubg4PPfQQvva1r+GnfuqncObMGcRxjDfeeAPf//738cYbb+DmzZutZlBNOAoGToBq8zsmsMc4VBzFGB2gPq7hcIgf/vCHAICLFy+i3+8f2n6fefzh1t7TNFWT6yZkWY6+11IuISWMjlV5miStL3aaJBPuvwCKibuEOfKiMnQdg54PzgWGYYSww4FXlRm3E6KEKXORKAxx4/p19Hs9nDp1Cpqug2X16Jtx8ySasEZnWwKlPjimhaDFNKjLgRno7tEVQsCxjdaJ86gy53suCEjNdCqMuklenLDOH+AsUz3N4yXE+2MXMAwdjmNVRkNSSmxsbmBzaxMnT53C7Oxs6/anxfKoMl9DXY/i31SWrerpDSLaWWqZZnmnCiuhFhnGh+A5NtI0B+dCGW7ZVusighBiUtUsyKsslGZVZqrGSaDKZ8fNwTgXMHS9KP9V/yOkjJdSZdOWoYMLDiGbFWFd16f2gkZxtzJdfcZ3oGkEhqE3GjslaTpVFRZCxfiUI3IsA3GaTdzPIY1b30Ml8lzU3g09z2k0t1LuxdMJcUhV/+jAcxrzXku0KZaj8GwLex2LIeOgbNKZWJX+1hWKnKveXn2Kqkw0glyoKJ5pvc6Auu/DmFWRNr5jd7r/tpHYNndrISSSNKtKq4Gid9Y0q97ZRx++oPJnJRBFFG9ffh+319cRR0Gtd1ZK4HtvfIg/eP2D45idj4Dy3XOMw8PnicDGGcf/dWkdf3xrCFPXELEcjqEDEjg16+LC8iSxs20bp06dwmOPPYbnn38eX/rSl9Dv97G2toYf/ehH+NGPfoR3330XW1tbU+dBR4HAft5LiD97dmWfAJ+3EuJSgb158ybefPNNPPDAA3jggQcO/UfGtiw8+6XH8HsvvDzxN1L1WDaPoSSiTaUfYUjhus1qT8554Z7aTOpG1ZxqLMV5CKIY/Z6nXGVpXDNBEkKg5/mtOaZhnMDQCfIGA5Q0y5AmMdbX17F84sTEShplSVVGqAjTPgHJ8xyDnl/FwQBKWdUIwTCKoBGt1cFVSnSWNSrTLL/xXBFCECcpLENH3qGQqLFNnhMhleLW5iyb5xyDfvv5VMTeq5k41cYHpV7tBWrsXHCsra2B5xxnz5yFppvojSmzo0izTI295T4RZaxOUULcdJzTHYVjWKaJtMVQJmbKtbnMIVXlkKxSpJShU/fEOaSxUqBpMkFeyyvPBVfKa9EjPA6WZZjpedgL4+JZINivuJaQUihXa8dGFDOlghKl5pZl40EUQ9Mm78Xxd0zMlJrZVe4aMwbfcRB0rJQHUemA3F62GheGWZSlEJXhVB1K4VVkrYuklH28Wc4bY3xKlD3WTarqKIRUjsjTECUJXMtC0pClq/peVQlzUJSthw3OxLX9irozcd9vJ9EszeE5FijP0KYB91y7+r5rW6q8fso9KyFBkxSzvou9DvfmEjkXMKDBNvfzf3uOjaDlOggpldLtWojGPtNzbYRJBs/zEQQhXNeB67qgNEYYRci2tyGIBtf14HkuHMfBq1dWEac5fvqp+ztbWI6hcFxCfPg4KsTqsHE7YPitS+sAVIVFLgSMoipL0zT8iQuLU7dBCMFgMMBgMMD58+eR5zl2dnawtbWFd955B2maYmZmpnI39n2/un+PigIbhuHnWoE9fuveBRzlEuL19XXEcYynn34aS0tLd23fzzz2CF56/a0JQx+NaAhpjKXFBdCGScw+cWsmOJ2mTLGKU2kyFElYOhFRU/Y49DwHhq637jOI2gkJ5wUhC+qESAiO9fV1cM5x3333wWgwQMpzXh1r+eIcXcUu81eFEOiXzr7FhFJIAcdyW4liSFWpcNQysWWFOjw+6SyVR8MwkDe4LNumCa3F6Gl/36rvOG6J0FBmU/XYnFGU6vP4PqqS6TyHpmtI4gSrq6swLRNnzpypVqbL0te2aCYVodROpsIohmtb4LbVeE9Mi5oRQvVstxFYQCmOhqErN+CETZCoNM8xO9utwjGWQQOQN5BXQJUzLwz62Bq2G9MMw+Z4JUIICFE/4EIChqmqOWSxr57nYHcYghT3kDLeqS9MjV6/vIix6SKwrm1P7VmSUMqx3lGCCqj7v++72OuIA2KFWh5OIVNxwlQ8Udq+PynVMY73tY7C0DVkOYeuEeiEgHc8Q0Ko7Y2rmwTqGYxGIozCOIbvOLV/a0KWc7iWBcvRW/NeS9AkVaSv4RkeJa/AvlkULSKKuqBrBBFL4drmVLIPoFKIbdOAruut5LWElEAUp/uROVBtDknNeVo9K6ZpYWbGAjCjvigybO0Osbmp1Jm5gY+XggC3tnbxneefwExX7/kxjkuI7wI+DwrsW6sBvnd5E76tYxir6hnb0GEbGkKW49tPLsMyPvo5KM2glpaWIKVEHMfY2trC9vY2rly5AtM0MT8/j/n5+SNznqMoOtRqyaOOe38FPgc4iiXESZJgY2MDSZLg4sWLd5W8AmrScPGZJyb+nRCijH465jlRnLT23kU0bjQSApSi0uWym+X7JkKGrmOm7wME2AtCDMOodbtSis6IlDCqlxmnaYobN25ASuD06dOY7yhpLUnqKIEdPZ4Z34Oh6dgLo4q8llClwu0lhF2KSJplzeXPBQkKI1pzBQaAvu8i5xw0ZohZc4lzhY59l7nAbeCcF8eltkFAMCgUYxXTwgGe48aNG/B7virLHhlLnvPOHkcxxUxJ1zTYptmpatGEdZZOqr7DbkOngeeCxpPkVYGAJqzRvbYEyzK4jlUZVI1rHwPfw3YQFT2azZCQRQlwh/lWzjHwVYWCYRiYm+kjilm12CGFQFg48rY5GwNFH2RLqW3fdxFESacR0+hxe1NKbJWqnU0tbw6KWKE2EKJKbVmWd/YDA0CW8SIGZvL4S+KZ5bkqde3oFa+2V0Xh7B9Dz5skqlIqEnkQlTDlOUyjvcd9FGEROzMK09Qb+16jhMG3ze5W4MLJvMwJnlbCXaLNZbkLwcjYHdOolUo3HjohILqFc2dO4+zZMzh7ZgWW44JSijfefg//9H/97/j+S69OLT38PONYgT18HBVidRjIhcDvvbOB339nA70R8upbOkyNYJhk+Oq5WZwcfPKFJEIIPM/D2bNn8dRTT+Gb3/wmHn30UZimiQ8//BBvvfUW4jjGlStXsLe397HNoD4pKKXwuvxdPuM4VmDvAo5aCfHW1hZee+012LaNwWBwzx6Apx55CC+99iZ2RxSgsoSYxgl8z62yYEfBOceg18MwbFOOutW/NnWNpap8VH2OKnVN7pPGrt/eMIph2xZYQ3amKjNWCmkURVhfX8fMzKDI1SUIKW0dExcCA9erjJjKsdimCdM0sDMMWkmMRHd0S1xmzjacY3UOkpEYjn2Uqlo5+dc1DZ7r1EqO85wX5afNpbg0YUoxbVHAAkrhOjbiFtUoSTN4vg9DVyplqYRKCezu7mJnZxunT5+G4zSTHRW703y9AKUCe64DOrZ/0zCg6wSbw2HnYoi6Rz0Mw3aFL8vzRpUbAPqeh90ghGdbjS6uhCil0bHNxjid8gc1jFTWcTJ2nH1/f2yGrqFL7KJJvaS5CUFE4doWDF1HQGNoxX0npaxKmEtSX1YFlGR2dFJLG0qJ+55bqNnqc2GRQ9tWXaDGoxYImhYZBr5bHUtVZt2B0gW5aX+q/Fp9X1U0dCuXKmt2sjy37zkYjowjKBTiYEr2aMwyuEWszKhj8Di4EDCk1qkAA4BrWdiL1EJCMOVY1DgVESyPx9QN0JZnKix6gdu2O3pehBBgaQ7XNhCz6dVLpmkiYRkcy0CSHqzaKaAM8z0X21GCestKcwuL6tNN0fdsEDgYxhZcvw8hBJIkwe/88U1cW93AysDE7OwsFhYWsLCw8LmeYI7iWIE9fHxWCewuzfB/vbmOIM7gmBqilEMC6NsmkjwHQHD/gocvn2vPc/8k0DStUl8feughXL16FWtra6CU4kZhvjk3N1eVG9+J2MlpkFIeK7D3egBHCZ/1HlgpJT744AO8/PLLuHDhAk6ePHmglfbDgq7reO7LT9b+TZEjNcHqWtUKY9pqnkQ7nHSFlI0GRpZpYNDzkLAUQUQr1WvUPCkqSHUTJGSnGhZEEcLhHtbX17C8vIyFhUWUkyQhJFy7/YUXUgrb3FdkZno+spwjpCrHsC0Dt/xul9qoomGa/8Y5b44KKRo8ozjB7EBFITX1y5a9nm1IO7J9i3bKTtimCaKRqgxaCInbt9exu7uL06dXsLgw3/pd1Qfc3cMyTiwd2wKIRMKU0RNLU+gN0T0lhgWpawNLs0bX44HvIaAUQkpwKVvOkVJFgyieuL41lZOQifdav1cvb1YmQ9NNlKwO91kpAcvQEdEUo1eOEAJSxPQQTYdjWdja2ipKNM2RmB7lbJxXrr0KvmsX5awjObVCwjjAJI2l6YQK3vOcGhFXRLd7slH2wo7fkYpw7RPGrszaUSgDrf1j7Ll2jbxWn4vqn2sDZRlmPAesoyQdUCXRSoVtfrL6nouwWLAJOtTwcQSUwXdtDHynlbzufzbBoCH31TaNib5ULgTStFSt21GWMudCFGXQB1uXN3Ude0U58egjIhtM1EYhhEQuZFVhomkaPM/D3Pw8NmUP1F3GzOwcNjc38eKLL+KFF17Au+++i83NzSMxD7hXODZxOnx8Fgnse7dD/OrLNxFnHALKoTznAn3bQJBkMHUNfdvANx+eTBY4LJTP/OOPP45vfOMbePrpp9Hr9bC6uooXXngBL774Ii5fvnzoFRnHJk7HOHQchR7YPM/xxhtvYG9vD1/72tcwMzODDz/88J7/oD524X786JU/xvbuEIDqYS2zQuNCpWsiR4IL+D0fQUtfapfKoAihCZZlcGwLpmEgjGilsIz22I67/4qWvkmgKKt1HMRjxipCCKyvr0PXCM6cOQPLmpzABVEE2zLBGvpKVfSNCc+x1aRr7JhVT6aNmDUrG12LFCzNMOh7Ez26++OiE+MqfyRmeh6SJG1UAAE10XPcdrMiZRJUN6IaBY0Zer6HkE7+3XccDCOK0/0+sixHnqsMXQA4e/YMjOKaeq4D2qL4RFTF7kQtqlWSMKWiRupzcZLWFlVyLuC7dqvhE6qJcLspWThm6KSUzv3ttfXTjppE5ZxXSm45PjJCXNVz5FZmZEGDKqzU0eb+cEA9T26HM6xXqH+9MSWxto08x3s3bsBzbSwtrUAr+pjH/7cXRuj7LriQiFk2URqvxquMmIYt1QOAMkPquU7Vp+k5VuO9kDAGyzCQdryjY5bW+mE9x2o0OQqjZGr/pnJwVv2ruqYhTpo/KyGrMuGs471DiLoXLUOfqJYYR5QoFXS8d9W1rQllNKSxMuiakuOqjkmA84NNHodRgoHnYFjsTytMv5oqEXIhgBytyqpp6IhH/p0LAZZjeg+tlDBNHZRlCGIG37YQZ/lIuX7zsWgaQcZVLI9lGEX1Qn1cl9f2EDCB/+dXvwjPMipjmHfffRdpmtbUWdd1Pzek7riE+PDxWSKwORf44ZVtvHFzD33HRJjk8G0dScrhWjoClqPv6BAS+PoD8+jZd4/OcM6r8zxqBnX//fcjyzLs7Oxge3sbb7/9NrIsq575+fl5eJ53x56Dz7sCe0xg7wIMw2gsl7tbCMMQr7zyChzHwXPPPQeryEs9CsRa0zR846tP4X//7vcBjLoQK6R5PkEiS4Qd5klJwtDr+QgbyIWUgOc5MDMDIY0nyitpvO90TAipkZaYtZNqABMZn2mWYXV1FYZhYHnpBHqe11gWqxSsZgJrm0Y1uU/SFPaYgrzfp9gMmiSdpkI0bicvQkpYhlEbl20aMA0De4Ei0so1uJ0Ad5FrVabcTpzSLKtdf0IIep6L1dVVuI4LzgUYY1hdXYXjuFheXq6di2kxF3nOOx2DacLQ63mIQlqjUeWYyj7j8Xto9PttpLEcX2noNE5eq23ECUxTR1YzmtnvS2WpIrl7QbRv1jSecVuS8ZZxcC6Kctp2QhjSWJXzjhFU17bA0hRCSAQ0aSQbKWNYW1uD47pYWFqGrinDovHe7vI+z3MOyCI2iZSLJvXjCmgCxzSRdCiPYaxKcVmaI814M0niAp5pYlrlaUiVw3HGOVjG0dSqICGRcwFdJ+ANzuPVPnMO37XBuQCX7aQzyzk820LORUtjhIRrmYhYClPXYBl6Z/wMUKigI6XT+2XF9T1IAHGaNpp4jUIvSB0XHI6tSnmnYUj3SWzPszHsMI0qSfnEfSVV1cu46qvKjzP4jomoZXGg5zkIR4h5xNRx8in9tL5jVUZRaa76nj3HmnC9XtsJ8J+/98f4f3zlAlYWF7G4uKhaYyjF9vY2tra28N5778G27YrMzs3NHQln08PCcQnx4eOzQmB3aIrvXd7E2l4C3zYQJDlMnah8aZ2Aphy2oSMTEg8t9fDA4t1VIbtciE3TxPLyMpaXlyee+ffff78ygyqfebOjSq0LeZ6DMXaswB5D4TBLiAEUMQV395Svra3hjTfewLlz53DhwoXaMeq6fs+az0fxhQfO4cTiJaxvbtdKiAHlhqsIQBMRFbBtt1Xhy9JsZJqv0PNcCCGwsxe0GvXkfN/9V2twu02zDASkURmK4hie64LGcdXvOpiZwcL8fOFq3JUxSouoDTUZ0jUC3/MQhhQszaHrequaGsUqyzZqiRhJCyLQ9HXldux1klDPdRAXJOgaF0gYg1uYENGYtfbWSaDzmLng6HWosGmaFSWvFKZpwNB0BCFV519KbGxtIdzbw8zMDGZn5yZK/xLWTdxU33PLsRNSGXeNn7aSwEqg26wKB3M9np/pY3svaPx7qX6OEtj9a6kI3zCIYFsGWJo3vsc8x55K5sty2rAhmqVEwlIYhoY8L0mFiYznIxE/+4ZR5d5oFGH99m3Mzs5ibnYWQkh4tlnrcy3HTAiBoesgGoGuadDyHFLKYuz7ixjVMRaxPU3P4v6YM9im0eiYW6Lq8+0g8BKKUFqGjihvVyWzPJ/eD1uEGVum0Wh6VBsbSzHw3EZl23dtbGzmWACQcQHXNqEJMf1a0xg9x0GYMLi2VZUOj6OM1+kixur76nyoEnBjQpVswpAmmOv72OnoEy+RcwFI1Mh033db+2mFlKAsQ8+1EI6VJtuGUl7HkaQq19jUSGMJ8Sh5LcG5QCyyWh9wiShJ8b+98Bae++I5fOmBkyCEwPd9+L6Ps2fPgnPeqs7eaaXmKOBYgT1cyKIC59NOYN9cHeKlD3eK9hkNEVOLhb6liCyXErauV//2Uw8t3PUxHjRGp+mZ393dxfb2Nj744ANcunQJg8GgIrT9fv/Az0hYeMAcK7DHOFSUN3qe53eNwAoh8O677+LGjRt48sknceLEicZx3esSYkA95N/42tP4tf/xu7US4hKMZUqZbZiUhRGFbSkFaBwsTTHo+6oUznXB0rSmyHb1L4aUVu6/46SRpVmnCiuEwPb2NnZ2d7G8vIz+SE6XKlt1G8tWpVSKNIEy2YkZwzDYLxdmaQbXtlun6qIhy3b/u+lEbmzteDvUbACF0mBXqmstAiXPMdPzsddyPlTvcHup7jCksG0TrEW5oXGCnuciYWy/XJAoBz7GGE6vnIbv+Y1Zvur77QS7HF+Zf1lCK4ypyggjx7KQNNxj5fe7zLBUrmazAq4UZXVu2gydgKKc03MQVZNkMkHsTMNAmk2eg9F7tb6NSaQ8h060VlUw41wRhzyBbRrgQlRktkRcZKMGUYy94RDb29tYWlqq5dUpx+FJxVfTCExTr7KcZwc9FQ8l1YLVaKkxANBYKDUxThp/+AkITEOHELK1kqNEQMtqgebrTKB65Q/ieBvF3SZMo0p2WxzNKIY0VgZII9fOsYyJGKeYZbWImDZICUSMKaO1DnMuQF1z2zQan6FxU6acC2iETM30BQBT1yon47bM2VHkQgCZMnYSAjUFtQlSSrV9d3+MBICmaxC8mWCnOUcuJLwxXwFdJ0jz5mdCSqnMrFwLUZLVnmHPtvD9P76KW1sBfubpel6srutYbFFn33//fViW9ZlSZ48V2MNFKUZ8Ws8xyzl+/91NXNmI0HcMRAmvFtwHjoG9EddhQoAkk/hTXzxxID+EOw0hRFXJ+FGg63r1TAMqDWR7exvb29u4fv06AFRmUQsLCxPVdqOIIvXu/zznwH467/RDxGGsEGqaijq4W2SRMYaXXnoJm5ubuHjxYiN5BY4OgQWAB+9bwZlTyxMlxEAR6dLi5CiLXqYmWKYJjSglJwgjpGNlcBGlrXEbQkh4jtM66U1YCtKQOSuEwAcfXkWWMpxZWamR1/0xt6veUkrMFCW52RgZIYS0kjRA9ekN/HbHS5WD2/zICynhtJhBDXo+KI1rPR/j50QZNrUvzvBcYDLIRaHTLZkod1dCSEWUhBBI4gSMMaycWYFjO51GVTnn8DsMaTgXNbOlMn+1XOyQUkLX62MfPwdploF0/JAGdNLQSdMIPNdGEMWFoVO3aU6ec9TF7JLIKUWyJE2jGF9oybK8UxFP07zzXAFKqR34XqVINiGkMfZ2d7C7s4PTp041/sjSRPWeliCEwLPtirwCQEQT2JYFTSNVTI9h6NU7FZAYRhSOaUJwASlk7br0PAc0SUFZOtWUSEp1L7Sdn77vIoqZyhGdYnoFlL3pk8+U79o14k5ZVuTkdiOKGbxie7qmQYI0Kq1B4fY7DY5lgibK9XkaWJbDMvWaMulYRqNym+a8eu92wbJMZFwgpAy9KbFHJXIhkGYCltVejTIOZUiltt9znVrPbBOEkEiyHP2RKCPPtpBN+a0M4hSWqcMqfo98e1+xfX91G//xe5dwe7fZs6FUas6ePYunn34a3/jGN/Dwww8DAN599118//vfx6uvvorr168jiqJ7ar74cXGswB4uPs0E9uZujP/yRzfx4WaEvq1jL87Vgj4hmHUMhCwvXIfVe5Iyjm9cWMC8/9FJ5J3AaA/sJ4HjODh9+jQef/xxPP/883jqqafgeR5u3bqFH/zgB3jxxRfx3nvvYXt7e6JaklIK13XvyMLWP/kn/wSEEPyNv/E3qn9LkgQ///M/j4WFBfR6PXznO9/B+vp67XvXrl3Dt7/9bXieh+XlZfztv/2372pb4rECe5dwt7Jgd3Z28Oqrr2J+fh5f/vKXOxXfo0RgAeBPfO1LeO3SW+Byckw02e9LHUdIk1rkiuc60DQNEY2xO8ww6PeQZS2ROx3zgCCKYJlm42Qhy/Oa2ROgyl3X1lah6zrO3He26Hue3C6NGfq+XyMWtmXAMi0EUQQpmydzhBDEMcPJE4NW9TdJs1YlL+dcKaVtpcJj0TWWacIy9eoYVT9qM4EVQsAxndZokyRNO8uUQ6piT6KR2BNd1+DYFvZKFdS2EIQRVldXISHRH/RhF4ZYQRTBNi2wrCUWp8GMqn7sMVzHgoQy6hrvU45oUhhK7ROPOoHNO1X5cUMnQ9dhWUZNlQ6j7p5OlmZVNJGu69jd3QVjDJ7nw/M8mKZRK1du6qlNs3xqJM4wovBsq9VRVimavPXdIQTH+vptGBrBysoKjJYeHy7UwkFaOGH3XWeihJcLAUcnGL1shJBqQUGpsWXvl+qRKp/pmd7+cRJCEERxY7/iKNRi2WTUS99zamqq6oft3pYEin7Y/fJx2zIRpxnqrsoCkFqn8g2oRaaMK1Mn2zYnSmNHEdKkU9k1dA2cS2R5DtsyoRGt1rrRhJil8F0bNE6LjF/S2jvOMpXjylLZEhPlVOROQpHzgyjRgDLQiuK08x4dR0ATzPoO9g6g9EqoeyqIGfquDQE5UTrchiTNYegaeo6FbKwHehgl+NUfvIlvPnYfnjjfvKhcYlydjeMYW1tbn2p19tiF+HDxaSSwuRB48YMdvHZjD66pqaqSIjbLMjRoAOJcQAig7xpIMg4NwCOn+vjiqZl7Nu6DlhB/FGiahpmZGczMzOCBBx5AlmWVOvvmm2/id37nd/CDH/wAP/3TP41vf/vbSNP0jrQZvPTSS/jX//pf48kn64kgf/Nv/k389//+3/Grv/qrmJmZwV/9q38Vf/pP/2n84Ac/AKDOwbe//W2cPHkSP/zhD7G6uoo/9+f+HEzTxD/6R//oE43poDgmsHcJh00WpZS4du0a3n33XVy4cAHnzp2bemMfNQJ75tQyzq+cxOUPrk/8Lc+5MgsKGlawpYSua+j3fGR5DjqW/RjRuLUHkcaTxGRks7Ats7WsU21XncMoolhfX8NgoPJdORcTBHcUJRksS1XDiFbkKm4x/imJY9rQ2zu6XUVSm/cbRHFrpqUqYVb3zKDnI4pjhHSfOSgC5beq0gHtdkNWCnC7YVMu9g2VPNeuooLU2CQSluD6jevo91TPx6iiKyVgmnprnqmUEoahtxJYFNd6GNLW/sE0Tatjb3q2Qhp3kuTS0ImxDJpGJhxxhZTQDR3oMCXaCylMw8Ds7Ax6PR+UUkRRiK2tTZimqVZCFxbQc91Wd+QgonAcG0kb+ZJqLE29pYauQ9c0hHHSSITzPMPq6hoMw8DC0rIqAe4gy6XJEgFpHW8Uqz7mthJsQgAuJQZ9H0EYQ0iBnutgGMbV6Enx2SzLp2ahliZHZc+pZ1sT/axSAlnGoWt6Z1VElufKyZcz1cMO2Xh/sSw/EIHLco75voedsDsfVkI5NTcZMBGiXM3L2BqWZvAcu+gL7Vb2okLdlSBTxxqzDJ5jIknz2jE7ljnhbLxPYid7Vkfhu/vuzzHLDkxiCSGIM46eU5T5dh3niGl4kuZwLBOa1qx2NyHnAppNYGuTfcOcC/ze6x/i2sYQf/Kp+6dGBJVj9zwPnufVeme3t7dx+fJlMMYwMzNTy509ikTxuIT4cFEq3J+Wc7wZMvz2WxvYjhh820Sc5TA09W71bR1JxmGbOqKUw7d1hEmGnm3ANg381IOL93Tsh0Fgx2GaJk6cOIETJ05ASomFhQVomobvfve7+Gf/7J9hbm4Otm3jN37jN/AzP/MzGAwGH3kfYRji537u5/Bv/s2/wT/8h/+w+ve9vT3823/7bzJrwewAAQAASURBVPEf/sN/wLe+9S0AwL/7d/8Ojz76KH70ox/h2Wefxf/8n/8Tb775Jn77t38bJ06cwNNPP41f/MVfxC/8wi/g7//9v/+xSqw/Kj4dd/pdxGEaOR2WtM45xxtvvIErV67gK1/5Cs6fP3+g4zhqBBYAvv7UF9E2iYpoDEOv/+DbloVBz0ecMHDOkTSUtHHO4XeEybcRKkCRybYSOy4EfMfGzs4O1tbWsLS0hMXFxerc04S1/pikWYb52QEIgCCcLAljhZI6ipI8qR7cdue50tm3CUIKOB0vljzPi77DqJHwR7FaDGgisFJKdFSnFrEz7aWNCUvR8z0Meh5oktT6cYfDId6/8gHuO3MaS0tLhQJUH0MYqV7nNkQ0bi3THfR87A6jzu8rlVX9vYnEK5LcPSHlXEDTtVan1ogmzeWpsjDp4AK6RkCIBsuyMDs7i9OnT+P8+fOYm5uH4AKrq6t4973LWF9fRxAEE8+4BEBa82UVEpZNlCPrugbT0CtCFEQU/kjpZ5IkuHHjJlzXxcmTJ6FpWhET1H1ONEKQtCx6lCgXBzo/E8Xo+27RP65Mz3Rdr54jISWSNINt6PWs3KZtxQy2aRbxOrxRaczy6RmlQBld48K2rMYe5dF9DqaUJnuOjZ0wPlDJrZASXIiJ/N6+505krtKE1Upm7xRoksG1zOqdqGlEVTk0nE8JZXzUaxmHoWu1PlQppVKFnenj7rk2WMYRJCkc25ha3lwujtmmgSBJlbnTASesyuxJxfN4jgXDmNzX+6vb+A9/8AZubA4PtM1RlOrsww8/jIsXL+JrX/saFhcXsb29jZdeegkvvPAC3nnnnSOXO3tcQny4+LQYOAkp8cr1Xfzay7cQJBlcS5UIe6Z61/YdHZTlcAwNLBcwdYIo5Rg4JjIu8dMPL8JqeKbu6jEIcVerHgghePTRR/H3/t7fw+/+7u/iww8/xF/6S38JQgj8nb/zd7CwsIBvfvOb+KVf+iX85Cc/ObA568///M/j29/+Nn7mZ36m9u8/+clPkGVZ7d8feeQR3HfffXjhhRcAAC+88AKeeOKJWoviz/7sz2I4HOLSpUt34Kin4+jf7Z8RHFYJcRRF+NGPfoQ4jnHx4kXMzc0d+LulC/FR6qdZXpjDfaeWG//GuYDn2SCEoN/z4HsOWJpWZKvLiTQsXGybkCQMPb954kg0VdrXBCEELl+5gigMsbKyMuEGl+c5euMTUkLQ91W5514Qtaq7aZpNjGmUNNEkaTUv4IJ3EsUgonDHJn2EADM9H3nOi/7e5u/mXDnidrkhd/Vylnm3TTB0HZASUZxUhEFKiY2NDWxtbeHUqVPw/J6aBJHmfNuc5y2dtgpZXnfpJUT1NpZKecxYp7lXEFJYHUSqNOlqgufYSNMM1pQfPpam9Qm2VAsP+/nIKQa9+j40TUO/18P95+/D0slTOHXqNCzLxN7eHq5evYqbN29ie3sbjLFq4j+tjzOIYtjmfs+lZZg1gyOJoi9XIwjDAKurq5ifn6st4gghi77r5vul77tKVZ5CcoWQxXXtngALIcBzFYVCCFG9mAWRNXTVOxsmqlxVCAHOefUOrOU9C7VQYHS4RwOlIjm9H/ag8/agyF5tgjLpUr1gQZxgUPR1dt/vHLq+/w7zHRth3KzeBjTBoOO9AahWhyhJEdIE/QP2rUZJCt8xQYjKb2YdJF5KIIrTRoLu2NaEYZiUBentILGjvaiA6jk2i8WYxjFAAkVJe1gsNCVpDglZ9SC3Qdfqmb00SQGJxl7oME7xGy+8jR+9fePA6m4TSmV2tHeWEILLly/je9/7Hl555RVcu3btnvfOHpcQHy4+DQR2K0rxf7y2ih9d2YZr6uASoIXCGmccbuE0rBXvbi4lWC7gFbmvXzk3h+XB9P7+w8bdUGC70O/3ceHCBZw5cwZvvfUW3nvvPfyZP/Nn8PLLL+Nb3/oWTp48iZ/7uZ/Dv//3/x57e3uN2/hP/+k/4eWXX8Y//sf/eOJva2tr1QL5KE6cOIG1tbXqM+P+OuV/l585bBztu/0zhMNQO2/fvo0XXngB8/Pz+OpXvwrH+WgP9mi8z1GBrut44sI56A1mQ7ZlqSgF20QQ0glX2zhm6LWYGIkiiqQNZVnuOAghCOMY3tjELssy3LhxA3nO8fCFh+C0bDuiyuEWAHzPhWtbCCKKNM2Q53kr2QEKd1x9lGyRygAqz3nnd4OIVuRjHCo3dv/8OrYJ13GwF0bgQiJhKle0DUqVbiccWdZOIoVE43c9x4amqTLSUtXjnOPWrVuI4xhnzpyB67qF+uyqiVDDXIyxrHPs5fcBZdbkOHatNFWd1/b7REoJS292py6RNRg6+Z6LOM2Qc656TDsm3Gk2sgAh97NRQTCi7qcwRog2IQS+5yCksepT1jTcd2YFZ86cwX333YfBYIA0TXHr1i1cvXoVt2/fxtrGZqupV3msmkagEU31bjaopCzNwGiIzc1NnDhxAoPBZF8SjRkGDRUQfddBWJz7KGYYtCwilYhZikHH4ohpGMhy3qh4ldE7uqbB0HWwjMN17Oo5EELUCC0gQTTSuK1xlP2wbeh5LoZRApamE2roOCTKOJf6M6LclDXlxFsgoEnnfkvELIPrmDANrVVNLjGkKkqpCere0yqyFXwEEhvGKWb9yQzhJoz2xJYYeM6Eajz6hTBOG8eiaxrShgUIFV0k4TQtnEjV503HSq9zLhCnWadS7TkWsjGSrb6Xq/GNvRillHjzxhb+yw/ewvYB4oSmYVyd/frXv35k1NlPA8H6NOMon18hJf7o6g5+9Sc3sRtncC0dIVPxa4ZGihYPCZrmICDo20b1976jI8sFzs37eOrs7L0+FAB3zsTpkyAMw8oc8dy5c/jLf/kv47/+1/+Kzc1N/Pqv/zoeeOAB/It/8S9w69atie9ev34df/2v/3X8yq/8ykfmDUcJxz2wYzjMEuI79YMhpcR7772HDz/8EI8//jhOnTr1sccE3Jt82jboug7PsfHEFx7Eq29ehqYR+J4LzgVorCaB/Z4HoHkyUypsjT2ahXo27kYMKAI76E/GzFR5nyPbo5RibW0N/X4fi4uLoAlrjVnhgmOu30ea5oga+mwjGsMwjMbycs45Br1epQ6OH1cQ0dboGyElLMsEa+mnVOW0LjRNRxhRCFn/HO3MLiUwO4gPSzPM9NvNosJi3yGNQUAKc6eo4qNBREEgcePGDViWhVOnTtV+LEIawzQM5C2GUdGUXtuQxuj5LtI0Q9zQxxdEFI5tIWnprSu/DzSrCmmW17KL+z0PYRjXKgSEHM1ynUQQUbiWhShJRrJV9/fDc46eX5zDIq92vI87pLHqgYRase33+6qXOElAKcXW1hZ2tjdBdLPqsbPM+uRcXUsPO8PJnmoplDqeJAkeeuB+tKSMqLHESs0t70ffsdWx1cabdPYQA8CQUtWvOdZDqWkaNI2ApZkyWJtiVFX2ROdcNsbzeLaFYUiLSCtVctv22yClWnQYj2ICVEZqaUzGuYDZEs01Ci7U50ZNnfqeg+GYciqh7rWDlLZGMcNs38PuAUhSFMfF9amf457rTBDQME4aPzsOy9AxpGkRBZRMlaRHSWyec0RJ+z1RQkXZ2AhG2khc20TY8t0sFxCahO9YtfFLKWEaepVtXBuXVG7DfddGmKR1x2uno39XSlVSbCvn5dK923dVhm6YpPjP338Tzz1yBk+eX75j85Dx3tnd3V1sbW3h8uXLSJKkyp29G72zxwrs4eKoEtjNMMXvvbOB2wHDjKODZqIyOCNE9bsOi4gcDUDf0bGbZICUGLgmhJBwTR3femTpnh7HKO61Aguo6ku/oZXMNE08//zzeP755/GLv/iLjd/9yU9+gtu3b+OZZ56p/o1zju9973v4l//yX+K3fuu3kKYpdnd3ayrs+vo6Tp48CQA4efIkfvzjH9e2W7oUl585bBwN1vI5wJ3qgU3TFK+//joopXj22Wc/UYjx3Y73OQg0TQPnHM99+Wlcu7mG3SCqiECJMKSwbQusgWAwltbIwyiklLBbCCyglKJxp2NCCIRQrrQ9z8H1m6tVruVo07xh6BOc2rFtGLqG3WEAs0UNFUIohSZsvjcUSVWlg+MTXyFU9E1bdmsQUXiOA5pMqh5lRFAwQhxHkee81QyqVKXPzM3VXINHUfbhtpFIzjlMQ4dtmhP7CMIIe7vb6PV6VV5a7btCwLFN0Lh5Ms65UPdAiymQ79ogIK2OyVKisQJgFF0qM6D6MW3bgm2ajUZeCWOdubxCSFU23EBeR/fhe04x2W8yIVMq4qgrDSEEruvCdV0sLCwotRgcG5sqXN3QDXi+V018HdvGXhDDNgywkfcX57wqE1pZWYGuGxAyb+xtLI9HL8bi2jYSlk18VkhZ9Pe2EzwpVVns6HNKoHJ6R+/zIIrh2GZrrzEAJEnxroji2sS65zrYiyi0YhxRzGDoevUMlv8bRZ5zeI4FnvPqeTINHTmvO5EnTMUlTTNAStIMPddRZlkN5LUE5wIgcqoxVc9zsBPGGHiTJHQcUiq1e/T8NZHX0c92mikR9fuX5plSbRtcnhvHAVVpMPAcsAO4BwPKObjn2Sqax7NbyWsJLiRokqLv7pcZu7aBOOVwnPZy4SBmcC0DuZBK9ddUv940UJZB1zX0XAs0zWs9vRkX+INL13BlfRc/89T5A6vbB8V4BmW5iLW9vY0rV65Uzsbz8/OYm5u744vaxyZOh4ujRmAzLvBHV3fx6vU9EEjMuAaGSV69Dw2NoGfr2I3VM2rqBI6hSoUJAN82lMO8BP7vjy3DaYlLvBe42z2wTaCUfuwM2D/5J/8k3njjjdq//YW/8BfwyCOP4Bd+4Rdw9uxZmKaJ3/md38F3vvMdAMA777yDa9eu4eLFiwCAixcv4pd+6Zdw+/ZtLC+rtr/vfve7GAwG+OIXv/gJjuzgOCawdwl3ogd2b28Pr7zyCmZmZvDcc8/dkR+Yo2bkVI6n73u4cP99ePHVyWZwCTU5bPN9SViq8mSbMhKjuBYVM4o8zydU2HIyLYTAtes3MAxUv+t42UVIVZkxjVVupWUaNQJlGUYrcQ4iCsuykDYouFIK2LbbSGDL77Ydjxp//b91TYfvORWp6iJRo+S5vs3inHSpSGVkTwuJ1HUd5ji5kxI7u7vY2d7G8vIyTiwvNxIzQC022B19k0FEGxyRSaHMqX2q69V83lRsjluVuI4jy3I4ttmuKkipDHf2gtYxRjSGaehjWaoqwkNKtWgy8D2ELYRD0zSVcdyg7Jcot9GmRpqmCV23cd/ZHliWIY5j0Ihic2MDpq6D6AY8z8Piwn5vfZqmWFtdg+3YWF5S2c0sU6XZo1Ez46AJw+zAR0iT1rgYmqTKAbhDPS0ji0rF2ffsiXMki4zctvdAiSCi8B0HUfH8+K6NKGFVD7KUSjfXdQ0aV0605TNY9mKTIoeXJin6viJ6hBAYhlHLtC0RUuW8PI1IhnGC2Z5XuSE3QUIiywUcywBN0sbFqJ5rV4QxoIk6xikEUgiJPMtVKTMhnU6/QihzrCbHYwAYuG7tGJTLs4PhAUhpz7WxV5LeA5LYkDLM+A4idrAFY9VTnKLv2UgzjiQTOMiycFxE5ni2CU3Tp6rQJTgXCOMUcz0Xew3X4frmEL/yB5fwU4+eweP3LR2aatmmzr733nuHos4ehUn/ZxlHicBe26b4g8tbGMYZfFtHlksk2f5inmcpv4thklf/neUSKRfQiGqX4EJC04Bn7pvFiZnpPgN3E0ehhLhNgT0I+v0+Hn/88dq/+b6PhYWF6t//4l/8i/hbf+tvYX5+HoPBAH/tr/01XLx4Ec8++ywA4E/9qT+FL37xi/izf/bP4p/+03+KtbU1/N2/+3fx8z//87DtO7v41oZjAjuGo1pCfOPGDbz11lt48MEHcf/999+xcR5FAlsqK89+6XG89tblxlLOMNonjOPIssmM1gpTVoFDGteUw1IRvnHjBjRNwxcfebg1+9HQNTWxjijYGBlVBNduJExSSkUUGwgssF/SSsik+68K+24/nihO0Pc8BJSi73tIWFo7L7SYrDepN0JKOJbVQGALB9CEdWafDiM6ManVCEHPVyXDhqHD0FRfnxQCtzc2EFOKlZUV2I4Dznm7GkfaMyjViamTd13T4TpmRV6Bwhioo4yXpVlrfAYhBAnLYJnmhMqsaaQir2WpdBO4EPAcB1le/r2MWNkPf6EJg2nqyMaMb3RDh2noGIa0U20GFBFS/aHNE3rOBVzPQpZz+J6Pnt+D61jY2RuCUoogDLC5uYnZQQ+5ACIaYWYwg/n5+VpPXxjFjeW9JWzTAC3UzLzDGCmIEri2VTOMmvwMLUrgSWO8DrDf79xFqiEBlmUwdB2GriFm9bJQpX4r1Xem72MYxZAFiZWQgNg3/SGEYBjFytSJKCLVhpAmcG0TcYdCbOg6aMJUGXLLAlWJKFEOxuNk1zaNIne2OlwVrzNFnQZU76at69A1gjTrNv8RQqhMWdMAG3lf+I7VSNSHUakstx+XcvPdJ94HJbGEACkXsEwdMkOnMj2KMGaY8V1MixIaRc7V4sFH/TnuuTZ2KINVvAPH+23TnOP33riKy7e28a0nz2PWP9w+tSZ1dnt7G1tbW5U6Oz8/X+XOfpzF8+MS4sPFUSBVNM3xwpVtvL0WQidA31GmTOX/JQTo2QZCVrjBS4m+YyBMcvi2gZwLEAAsF+jbOuZ968j0vZYoTf/u9WLMaA/sYeCf//N/Dk3T8J3vfAeMMfzsz/4s/tW/+lfV33Vdx2/+5m/ir/yVv4KLFy/C9338+T//5/EP/sE/OLQxjeOYwN4l6LreSlC6wDnHW2+9hfX1dTzzzDONJZWfdFxHicCWhBFQrpMXv/Q4fu9HL3/k7URx0trDWTrFNvWkCi7Q67uVKpllGSilmJmZweLiIrJcTJAqyzRhW8pYqud5HdOf9h/vsKPct8y5LYlj0/GU/ZBt6HluI8nJ8xwzvV5Hbuykwjt6/EnKoGukpV9MFnE+anLmWBZAUBHoPOcY+B6294ZYW10FAJw5e7aaHCVpWvTHTo6bEIKYsU6VlBamXnmeQwATKl3CmvN2S6iFkOb9ozgHhk4wKqAaRdxGua8kTTuzQgNK4bsuojiuejDLbFNA/Vh6jl0jsKapsljLHNcwilvVr9o2OloYwkJxjiiD59qIaALbsmFbNuZm54rsyW3Esbp2w2CInOdKxXE9aLoGiSImqIH0K8dXgjTL4NpWY8ZsiVI91QjpVPl1jbSS5RJBVD7r7SQ2zzl6nnLH7XKDDaIYA89FUCyaSSn3HaKlVMQWElJysFTUldrxY5QSeWE4Ne6sC6g3hWXooCxFLiQc00TSkQ8MKAOmgbevbGpFqfP4symKfauIoG6V0jR0ZFxA1wk47yZ2nKt3o2Wo/FND18Cy9sTVIW0nseOROYAisSort/ua90aIrmMZ0IiO7AC/cX3XwW6UQNcA64DlioauI8kEOM/hOyrLUkw5T7qmISleGmnOkYGj71qI4sl82htbAf7j9y7huUfO4Ilzy1VO92GjVGfPnDnTqc7Oz8/D9/0DEdOjpBB+FnEvz6+QEn98a4hLt4bYjpTqmuYSQZLDs3SESQ7L0KATogitbSBKOXq2Ira+bUBCIs0FhARmXQNCAt96pDmR4l6inJ/eawIbRZFaRL5D+P3f//3afzuOg1/+5V/GL//yL7d+59y5c/gf/+N/3LExfFQcv03uEj5OCXEcx3jxxRcRBAGee+65O05egaNHYEcVWAB45vFHMOg1l0nQOGl14lXZr+1lJ105WUGozJF2dnawt7cH0zSr7NEszysDH9sy0fc9ZFmGIKLKVCVvdjOeNl4AnXkYUVGi3NYbmOd8ok9SIwSDno+IxhOZsrXjLUqFmyCL7dSGOaJ+ZhmH3+H6GxbZqwPfQ5plE2r65vYO1ldXYVoWVlZWJlb2adyeaQuocnGto19V14Bc8NbFI0pZp9NsEKl+63GUZySk+7FBdpF3OaocTnM1BpTxGKQsFGEyoeaEVPW6AoBlmSAgNfVMlcp2v8pDGqPf4eALACnL4LnOBNmTkNjZ3UEYRjh/7j7cf/5+nDx5EoZuYHdnFx9e/RC3bt7C7s4ugiiaiIExdFXqXBo4qQif7rEkadYZx9T3XQwjCusAOawJS1sjU4By0UwcLNOV7ZeuE0JANBXTYxgGdENHv+cioAyq21e1HnDBK5I7iiznMDW9Ub3re05VtiuEgJCi8zkoUZI8QJVDJy193hkXqje1475RPbgMrHBFJgcgT3nRA2wZGuyG6oRxKBI79nxI1dvf9N0wLvNqm9+Drm3WInOStHQb7j53nmVWam+Wq2fRnxKZAyiFu1wkjZIMOiFwp9xHrm3WKhDKEmbL1JpdkUHwh2/fxH/5wdtY221eaDxMlOrsqLPx0tISdnZ28Ed/9Ef44Q9/iLfffhsbGxudPh/HCuzh4l4R2NW9BL/68i289OEOIpbDt3VEjCPjAoZGkHGBXqGuxhlX5cJc5buGLIdJAB0ATTiklJh1DQQsx5967MSR6nstUc4d7zWBpZR+7BLizwqOCewYjkoJ8ebmJn74wx9iZmYGX//61+G6h9MDcBQJbNlzCihF66e+8mTr57vG3pX9GicMvRZizIXA5sZt7O7uYn5+fuJFxXOOmb6HNN0nriVYmhUuyc3Ied56j9E4ac2jBUbiVBqgom/2v9vzXFiGgWGRNdtFBIUUrdmsQFmGvL/tcQU6LNyQm2AaBgxdRxDRCTUtCALcuHEDS4uLOLG83EjCci4mIoxGx5DleRW7U/870Pc97AUUXkc/BhccTsffpURz1Ex5DaVUmb2eizznyBoIwzBU/bjN25dIGIPnOo3ktUSW5Sp/V4hG86k4ViWkXYhZ2mpORQiBaRkY5yhCCKytrSGmMVZWViChoe+7cBwH8wvzOHNWxfT0+j0kLMHNGzdx6a13EA53QWkEQtQ9MK4OBzSG27AwUPtMFDfmGfuuU5TnEhXRMyV+pzQMa3zuiIo+iVNlMORNMc4RhcrYtCDk2hbSjEPXDWRcYOA5FemTQk7kzgLqmvTGCH/fcyZKgdMsh2XojWZeo/9UlgjP9typRkksy2GZeiMxtS2z1vcasxSeZaJzla1AluVwbRtpfrDflWFUxPEU52Tgu539pEHhTjx+CTRN5UaO9wRkuZpMtxFSjRDkcr8eQEKCSwnKsk4jpb5ng46VYWe5QJLlrd/zXavKlh1HknGwLEffsaCNnGfLNMCFxO0hxa/+4G38/h9fQ9qRpXvYKJXZp556Ct/4xjfwyCOPQNM0vP/++/j+979f5c6GYVj7nTg2cTpc3G0CG7Ecv/P2Bn7j1VVsBQkcQ1MtJmz/3nQtHZahIWDK4M/QAFMjSLnKdyVSVUkMkxxaUXI8TDI89+ACThyBvNcmlKXa93oxJoqiT2Ti+lnAcQnxXcJBiaKUEleuXMGVK1fw6KOP4syZM0diXHcL5Qt4tJ/j8YcfwEuvv4XN7d2JzycsbS0jFULC96xGUgEAKUsneiCzPMfq6io0TcPDFy5gZ3cXUdE36To2dE1DGFEMdL+1NI4mk27GJcqc0raexSzjrX2ZaUFi2hAnTBlIGcbE9nMuVDlsy37DDnOrct9lSWeTG7LtTboh930PccKwOwxVT3K5bymxtb2Nvb09nDx5EpbrVmW0TQjCCI5tI2HNZcxhFMO2rKrv2DB0VdJd7C+IaO3vzcduNRruAM2GTuVPl3K2tkDItF67yd7l/Z5XtUjg2O1j1DUNpqEhTjoWbGLW2euac46+N2keRAipyoYBVCW3eZ5jdW0Vuq7j9Mpp6EXuLE3q+zEMA4PBAIPBAFJIxEmMPE2xu72N3e1tEN2A7/vwPK9S2MtFqq4yYRUTk9XKbF27jKsajZOa3jNLW8ysxs9HluYNxlp1lItFo98zdR1CoirXJYQgSjLM9XvKyGwkz1dC7htLEWAvouh7irS5ttXaO6uOYbr5kWMZiOK0KuXtAmUZeq4NmrDqnVNl445dlyhhlSrbBc+xMaQxLMOYei5LlD2uWc4RHsAMKYwZfEcRyCr6qCPGhnOBSGQ1t+ES/vi/yfL/SARxgr5jI2JpzTXbNnTQFpMoFbWjInNSzqt7t8wf7oIEECQpbEOHrqvc4iCp9zC/fnUD76/t4qceXcEjK3e+KuujYLx3No5jbG1tVb2zpmlWfy89DY5xOLhbBDbjAq/e2MMr1/eQcQHX1GDpVuUmXKJv64gzjnwkMsezDOwV97Ola3BNDXtxDksn0HXV+/rwiQGePDN76MfxcXEUeo2BT2bi9FnBvb8KnxO0ZX2OIssyvPLKK7hx4wa+/vWvHzp5BY4egS3VzlHyp2kavvm1p1u/k6Z5a3lb2FICCqjJcd/fb4KncYzr16/DcRysnD4N09Srni7PdRDHCcIRUtSmOuZFRmcbYsZaX4AsTdFrKclVxkGsiCOpQyMEruvAGSFu4wgLItcECTmhvtXGlSniXYxkQgkue3gBdQ1LY6GyDLDMZhVCYHV1FWEY4syZM/CKFzDnvFXbkcDEMY8SWCFlVQbsOjY0QmplsK0q6ugepvi2MJZNlCoTQqrFiCCKVY9vC2KWVudPypLA7f+wS4nW/jbfdcDSFEEYw3Ha9yFEt5IOKOVzVNUcJ6+Aei6yLMWNmzfg2A5OnTxVkVdAkYG2knOiEXieh9m5OTz56CNYOnESrusiDENcu3YN169fx9bWFuI4RsLSRoV1FFnO4RbnVZWkiok+VaWYiU4zM0CZinkjC0BNTsAZV72h01D2wwIoXDMn3bqBwvXXsUE0Ak3XoBs6dF2Hpms1dVbl/hpgWQbR4tCsjkH1jbbBLAzoMs6hkcnnpglhrIhpCc+xakZM9c8mneXfuqH6TaVUCq9GujOjRxElDLZttruqNXzetQzoGlGROVN6YyFlkRVrVfvoNRBaADWlO0gYLNOAVbxjCMpWl+5xUpYBEvCLOB67iN05CFihGgNa4zWMWIb/+eqH+LUfvoPbe+0GbncbrutOqLO6ruP999/H9vY2bty4gatXr06os8f45LgbBPbd9RD/8aUb+PGHO5BSomcbICAYjiyyGBrBwDFAR8irbWoYFOoqAPiWDlMnGMY5XFP5JxiEYN638I0Li4d6DJ8URyEDFjhWYIFjAjuBe1VCHAQBXnjhBQghcPHixVrG6GFi1DTpKKAtm/bC+bM4c7K5oT/NMvS9ZtInJTr732gSQ9c07O7uYnV1FQsLC1heWgLRNGiahtlBHzFLQccMkqSUnf13YUhhtkyElWlMB8FNWGOpp+rBnew57fkuTFOVCwdR+37FlHMxXio8jpDGMKpSzGZF0XNs6BqZyOHlnMPQCG7cuAEpJc6eOQNrhPAlaTpCkFvGNqXXdmG2j4SxRiIR0aT7nDOGQUfpd5bntUk+IQSeY1WGVKo8rvvdUZZxSykgC5KiziUp/s4mFj56nosoZhCFckek7HQ8DaO48zjVsWSVwY/n2BM9rzu7e9javI3Z2VksLi02vhMj2n6vEBB4roPtIMTcoI/Z2VmcPn0a58+fx/z8PIQQuH37Nq5evYr3r3wAnrLudyNNMNv3AILWnso0bS4lnzz2HIauF0Y/zYp/lLCpPboAEMSqDNp3ndaYGVXmzmGOTHgIISoCSder/5UTTwJMlBpPRGfReKLPuPyuaRqV+VGSKlfgg/yiDQsF9CAZtUFHP7VjGjXFlWUZNJ1MWUBS8BwLe2EMz7Em879aQFkK1zI/UkltQBl814Zt6kjSye+VrtKjSNJMVfQ4JvqejTg9WERPzkWhxLs1N+ipkBKmbiBIUmgg6LWUP9/aCfGf//At/O4bVzsdre8FSnX2woULVWZ9v9/H7u7uR+qdPcbBcJgxRTd2Yvy319fw3bdvVyZMEgBNObiQ1ZqTb+sgAHIhULZ5K5KrDJ0gVZlwmgukuYBv62C5gGNqIETDNx9eqhaKjiqOEoH1Wua9nxcclxDfJXQR2Fu3buHSpUs4f/48HnroobtaZnMn8mnvNNpI9Z/4+pfwK//7bzV+p8t1WEXuuKANJapploOGAXZ2d7Fy+jQ8z0PPd5GwFGFIFdFoWSkOQ6qifBrKboWU8G2rtZwzpKo/t6m8Oc95o/ttqTqq3l4duqYmv6OlrVXZdNiy30gZAkUN8UPqfGStZZ1cCPRdtzHaRtMIDEP1LzedjziO8cEHH2BhYR79wUwzKSoIXhtJYWlaOR6PjkHTNPTckpBMkuvq2NKsiCJqVrim7b+MM8o5h2mqCJTRPdGCZLcRo5xz9Ao1dZS4jiKO02oMTaXmMUs7s3sBgBW9rk3PAqDu+YHvIReydh9IALu7u9jd2cHy8jKWFxc680KjmE3kBBOQGjlkWVaNRdM0+L5flT0xxkApxebWFuKEwbSsyv3UtvZJjK5pSBuIxjiCwjCsLTcXUAR2pudhL0rQ1c9Z9sO25QQDSjm1TWNqr2mWc7iOBS7ExHNVZsmqqKsEtmVAiH1n49FKFFIsOgDKKMyxTMQj77S+72AY1cdCE2WYNW2MgCJbByGagDrX47E9beXNLM3hFJUBbfFJo8Q5jBXBjFtybUdBAJSmv+MRPl2IYoaZnttcBtyy01wImEJAJ3rxoYP9TpuGjiFNYeo6NI0ciPz2PacqHc6EQMZUD2/OOdiYO7ME8P7qLt5b28MzD5zA0+eXDnwd7yY0TcPCwgJOnjxZORtvb2/j/fffRxzHmJmZqcqND+psfIx9HIYCuxkyvHBlB7cDBpYLeJaqPAiK56ZnqwgcQyOwTQ0R4+g55b8BtqkjYjlsUwOBhG8bCOIMnqWehTDJVe8sl3juwXks9u5OfugnwVHIM5ZSHiuwOCawdw26rk+sMgoh8M477+DmzZt4+umnsbS0dNfHddQUWGDSibjEmVPLuHD+LC5/eH3ib5w3k74SEpPby/Ica6ur0HQNFx58EK7rII4ZhsG+02Oa7U++PipKwtOUY6uIpt3anxtECSyz3ldakjZNU6WrO3tBI7dW0TftPZ1dpW8syzDw/e5eWdtGFIXVv3mODSEEhqFyMx6P1dnb28PW1hYWFxdx6uSJVvLMhSj6FJv3nWYF+QhpdS4c2wKRqL4z0/cxDNq/P+i35AND3UOKNDYTUCmVoUouOOI42a/9HUHMWCN5LJW0YUSruJomKOKq8k3b7uWQxrAsA2nLRDgrStjbYpW0Mvd3ZOhSSmxsbiKmFKdXVmBbavFF00lrLIgQAqZuVgSWoCRR+/vN8ryx7xYAbNuuAs9d28TtrR3QKMLe7q5Shz0Pvu9jfm4GlDG4jt2xPKEQJ2lnH7BlmZXC2jSmUaRpDsPQkbf0cPY8B7thBN91CqLfPuGOk7QgeJP7HPguhkVWLUtz9DwHUZJUpebj/wOKySpR5XqAioAZJ68lAppM7Z01dE25hLKsKMedTniHNK6urTeS2dqEJM1aSaxtGhPqYRQz+I6FJM07o5R6nlOVAOsagWebE8ZKTeh7DvYiBlPX4FrmWFaubDTL0giBhIYgKftbRWMEUh0SZtEvy4XyNxi4FoIkba2Utg016R9HxLLC6Maq9eSqNQ0ClnG88M4t/PG1TTz78Cl84fTckSKBoyZOo72zFy5cqPXOfvDBB7Xe2Y+bO/t5gxDijp2nvTjDjz/cxeXbIQigCKelg4785vimXhHWJOOIGIdraohYXjkNR4xXDsSGThClOQaOCS4lwiSHrWsQEriw7OORU3en6vCT4rgH9ujg+K1wlzCuwCZJgldffRWcczz33HP3rBSgiVjfa3SR6j/x9S/h/as3Gic1QRRPKEIl4pjVFK04jrG2tob5+TmcPnUKukYaiQ0hBEmatpoy0ThBr+e1ZpF2rdQFYQTHsZE0KJZSCtiWUyOwmqbBtS1ICWzvBXDtFtMlic5YlThhnYZOXUqkBKDppMrp7PdUZm55OdIsLxTCCFJKbG5uIgxDnD59Go7jjGTWNpOHIFKOvTFrmYxHMWzTRJIo454sy2pkOYjiVmUbUOfctkywlnK+IIrhuhbihr64nqfG7TsOgow2KtF5zov+X3U/jBIOoCgDz3hjVqr6O4oS+vaJsSKOFlJ05Lq2KO2apsGxLESxWiAhmoY8y7C2vg4pRC3KKM3yzpxcQBGNQc9FEMXotSw+BDTuXBgAgJhlOH1iCQHtK2fmJEFMKWgUYG19Ha7rwvM8nFha6DTC4UL15+b5ZM5s6cKd5wIhj+HZdmvpL6AWEzzHAueTETiubRUZtESdgxES2oZhlKDv14mz70wS6TBWnwspqxGQUpEtx8LSrMiL1RDEivC2EZayd7Ypc1XFzJiV829JHrucgEsENMbAdxGn+dTW1STNYFsGCLSiv7Mop9Y18HzyeVSmViayXDQapHl2vX+VC4k4zeG7FqKOfljHMhGV6iYXyIXYN3cqj6HhNPZcu9ofZRkMXZtKmJX6PRp5BQxpqiKbpKzyYEc/oOs6GG9+toVUJk+Wrin1P1Eu1uFID2IQp/jua1fx6oe38eyFUzi/PNM6vrsJIUTr/Vn2zpa5s+Wi57E6e3DcCQV2L87wR1d38e7tULkGE8B3DAzjvPY+NTQCAQnX1BAm6l7Vini9UpWVUL2uRAqknENIwDEJUi6QFHE6GiFwTR3PP3S0+15HcVRKiCmlxwrsvR7AUcNhvRgNw6gmHzs7O3j11VexuLiIxx577J4+DIZhgLWQhXuFrnLrhbkZPPHIQ3jtrcsTf5NSwratRgILoPr3vb09RGGAldMnYdkuIhoDBI1kkhBSZHm6CFqUuyzNW5WhiMaFq2sLwe34wQkiWpUx9nwPWZZiayuuzk1XzyUtej7bVLiYpdBLJW4MXHQrkUmSouc6sCwTew1qZxhR6DrBtWs3IYTA2bNnayvD2Yij8TgkutvfhJQwTQM9z8Xa2lqNvAKFkZHbrmwrQycdDG2TTgkpJsXVUXU/ZgyGrrde8yCKVW9pnNRUs/LdkmZZYxkwIar8tlSyuxx6y57gLlKY5fXzrOsaLNMETZJqHLZl4OqHN2HZNpZPnZqIhwlCOqIwNkOR2B72wrD1M3HCpjrS0pjBNgywPIfnulhenEMQJZiZy0ApBaUUb72zDd9zoBuq3NgtStpr+2JpoQzunxtN02CaRlWVIGXZD6u1lrUCRQmu79bOs2kayDmvXZsgig/k0BvSBL5tF3myJpIsa6miSDAYM5kihFS/FVJKMMawcXsDi3OzxTuBVJ/bLzce2WacNI5x4Kns2hJSqgWFac7O6sMA5xKOZRzIbZgVfbkmUfdC37M7leGYqXtU0+r3jq5ryBreXVJKRHGKvmfXjqkEIQSS1B2WpQQCmqLv7RPicQXWtyfNnnIukPMUfddGkKQTLwPH0hElze+hJM2VGutYCNi+GjvwnJohThtSLpDyFAPXQt7ifr6xF+O//dEVnJrz8ezDp3Bm4d5Odg8ao6PrOubn5zE/P19TZ7e3tyt1dn5+HgsLC5ifnz9WZwt8EgI7jDP85Noe3l4PIIoW8L6tnNWHcb1VhkAR0yDJa87cfVtHkgvV6wqVwa4RIChidTxTR84Vme3ZBjQAAhI/+9jJI1ny3oajQGDTNEWapuj1etM//BnG8ZPfgCZl5ZOivOHLiJwvfOELOHv27D1fSfw0lRCXeP6rT+HN9z5oJClB2F62m/McCQ0xHO5hcWkJlj1iQiObyWQ5oQ+jqFXZY2mKfs9vdf/tOr/TCK5lmIBHEEYUWZbX7kulZrarv1met0byZFl+gFidyTJkTSMY9DzcWh0ibVExE5Zgd3sbuq7j1KlTEz+qLM2KUt7mfau4EHci8gRQ+ZSlMtz2jAaFkdG4OVGJKhan5ZwnjFUEkxClvI6ONedKZW09uVKCjxnwTJCJiMJ1TMTFZFXXNDi2WRGltLg+bfcUUMbZtJPCNM0Lp90Yuq7D1PWaYq/6ktexvLQIr9dvLYBN87x1sQMAPNdGmqcgmFQ9S5T9v10khwsB2zZBcuUQXF5/0zQxMzODmZkZZYqUMoRRhI2NDQghKnV2NKanNDqKEgYCAte2Jkh4lnP4noM8ntIPG+331mq6Bl3TJnJtJdTChm0ZYB09jsqdV5XTStmttIdx0qrwJUmC9fV1nFxahO33VElsGBf3W93her/fmoDGCTzbqrbZc5qJnhAq39gxDSQdfaUD361U3b5rH6jXlmWKxE4jr9XnU5WBO1pd41pmTXUcR0AZep6NKGa1R7Tn2QhoMykPaArXMmAUfckldE1D2lJGD6jIHNcykQu5f38TAESDkO33u5TAME7hFG73AmjNiG2CBqg8zYyj55hgGa+U7VGs7kT4jRffw/nlAb7y4Emcmrs3ZYeyiGD7qBhVZ4UQ2N3drWJ6Ll26dKzOFvg4BHY7SvHK9T18sEVVLisk+raJJOeIUgFDq6/LeJYOUyPYi0d9DyQGbqnSKlgaYBb5rwDQtw0IKZFygZ6tco0FgG89sowZ7+O1aN0rHAUCW0Y7HhPYY9wVlBPZq1ev4qtf/SpmZ2fv7YAKHLUYHWD6mHqei68++Sh++JM3Gv9ujL1cPNeB4ByXr3wAQ9OwcuZMY+SGIpNOjfiUP4Y5F+j57cpe3JH9mrCC4Hb0Xo4vmtiWBdPQsRuElftu08JKlrWT1IooBs37DWk80WdbQsXqjKkQroM8z7EThEjzXBG7MYIVRSHW19cxOzuLMysroC39rmFHuTfQrBD3fRc0TpTqKkVnyWLG288LoM5Np8JJY9VfS0ijyhmEVJGQhu9KAHGcqMWFFpIsZdlnpxRlQ9cmCNa0cmrOOVzP7SSFytjIRZbxIj9VYTgMsLm5icXFRczPzSHNeeuCQJblrWrvoLdPNLv6lwFFyJqyWEdBY4b5mR62h833LCEElu3g3NwcAhojTVNQShEE6nisESMoQ9dg6gYc22p3HKYJ+j0XwZTyX5oocmoYRqsazYWECUz0gE9+TqBnOo3EcRRCSKQ5h2XqNZfdIAiwsbGB++87A2KoHuKQMsz2lRHUeM+sIrNFKTshYCyFY5oQEp3klAsJUkQmNT2nnmPVSpKDmLUqnxPHJtU71TanZ6MCQJpzGLoq/zV0/WBZsZTBs1UkEBcCvtNOXktQlgIE8CwTjO9nD08rp47TDLqmoecoYq3I/MHIaJKpyKOB7yCLM0zN9CrQc+3K6ClMMhAC9F0TEcsb2xM2A4Zfe/E9rMz38NUHlnF28e4qsl0lxAeFpmkT6uz29ja2trbw4YcfwjCMz606+1EI7PqQ4SfXdvHhFkXP1otFEAMZlyOkU6/+f0PT4Biq9H/0ebUNAlPTEDJR3bV9WwcIECSqMs231TWIi37YJFNmUF840cO5xU9fD+dRMHEKi2qn4x7YYxw6wjDEq6++CgD40pe+dGTIK3A0CexBVOGvP/0YXnvrcqPKFhbKpEYI0izD1vYO1tbW4HkelpeXMdPvtRv5jBHQ8gdXClFlyrImdTfPO1XFJGGtfY9J5SwbwTAMZYgSUpS7YWlWlQSOkwyWdrvSRjRu7WcVhYtqE4EFylgdDzRh8D2nOmflOKI4HjHMUaXxOzs7WF4+gV6vB1lMWJqIkcosdVoJbJZzzPR87IURNI3Ad92aGpllOTy7/fXFWIaZjvMyTYE2TRO2aWCvw+0XwARDllD3ipQSNE46nVFpwjA36COKk0qJHd90SXTblM2wIKhtPcWWYQASSIseQymB7e1tDIdDnDqlMlqnGXcBikwrFXKfnIyfvyCKp5adhjSGY5kTCuboNneGUacJGbDfexkQAtu2MTc3B855VWq8trYGADi5NI8dqcH1vFqWbW1MRck37difEBKOZbcuSJRI0mzECbl5st73HOxG9EAlx1nO4VgmdI0g50p92t3dxUP3n4Mg9fs/oGWJcLL/3pKyZgYFKFJMiLrHGctBMFluXCLnAiYhsAwD6YhXgqHrVVxPfQwMPbfbBIoQFMZGGcyil/Mg7sE5V2S+7VloAmUpbNOAZehgByhxVvuRSLIcA98BoB2ILAPqtyNMUsz4TtUXeFD4jo1dqnpbLcOYqsS6llGR1xJSAkGcqVxc1wRN9g2wfMestnlzO8TN7RCnZj18+YETOL/Uvyuq5UFLiD8KXNfFysoKVlZWPvfq7DQCK6XEh1sxXru5h5u76vfCM9Uirm3qCNn+8+FbirwSqJ5WmnJELIdjaeC5Ul17xT2lawa4ENAJ4Fo6MqGqAiyNQNNUlUkuZOFSnGPGMzHjmPjq/fOHfUoOBUfBxIlSCs/z7vk47jWOCWwD7mQJ8fr6Ot544w2cPXsWSZLc85WbcRxFAnuQMVmmiee+/CS++/0f1/7dcx3oOgHPBYI4xnA4rJSmwWAAQkgRQ9NcDpwkdbVUxVyQagJoGkYjgQWAIGxXFbN839yoCSxNMdPzEVA6QbrKnsmEscb7sitCiHNRGPE0k5MgiuF57XEhuq7B0LX6uIn6MeRCwHcdpFmK27dvI44TrKycqZxlVeRLuzN0EFJVPt2i0gZRhL7vIcvyyVJaQkCTtFPFDWi3yhtEFLZlgaX169n3PYQ0QcIYfLd9fGmmHGMFCs1kRPUihbprGu2Tc891EMVxt6suSyuTpDYwljVef9syIaRESGMMeh52gwi3b99GmjKsrKzAGnHXHkYRXMdpNgUr95PmMAwNeS4K5bV+TSRkZe7VpmyLIse26R2ryobVNgUXnWXLgFJ0Rw25dF2vsiallDB1gvWNbRCR4/btDdiOrZyNPR+mZVZ9jlKqBQ1T15G1vHf6vou9MFKLBS33QzUumrSaOo06ER9EkQYUKfYcC2vrt0ApxQPnz0HqBjC+GCZVhu3oIoJa+AKAfUIrhPIKYGkOjaC6b9p6Z7OcwzKN6vyUebNtuaNRrMp3wxYltufuOwdnXMAAJpyAm6AWAIE4TQuSfDBiydIMfd8BEbKz3xnYX49Spd4cjqV13s8TYyQENFUmbbZlgB6gn9W19gmr6m1VkTlZzpE2jFeNp52IcSERxuqd0LdMxGk+Eb0DAKu7FL/58geY8208fX4Jj5yeO9RexDuhwHbh867OthHYjAu8vRbitZtD7BVVAQQSvYJsJln93jA0ApYL+JaOnMuqp7XvGAiK2BtSKKx9x0SQ5HBNHVwIxCmHrhG4llrg4rmAa2nIBRBnHANHhxDAz3zxxKd2MYFzXs1x7hXCMPzML8gcBJ+tJ/gIQQiBy5cv49q1a3jiiSdw8uRJrK6uHkmy2NVvei/QVoo7jqcfvYA/ev3tyuyIpVmV9SqlBA0DbG9v4/Tp03Dd/X5XISV8y2otB04Yq+WFjk62w4jC81zQBiVm3zm4LYOVTkR8aISg53sVkWmLuIloXOWsjoNzXpQKt/WzUjiOhaRFSWiKRTUNA45lYTcIMej5YGNxPiXr2tkLsLW1iSzLcfbsGeh6/ZUyLVuVFxPipqPu+x64ELXS19ExCCk7CaoQAqZjt/5dSkXQR7aqiNlYv2vbghYhBCFNMDMzAEuzer9r2TtdmS3Vr03fVz26QsqqxLcNYZTANs3aNRhFxvOJXmjHtsA5r8qLt/eG2NnaghAcKysrjQtpQggQTYNsefayXPXUEkdrVWuTNGvtXy4Rs3SCuJW9uiVYlrXG7+yPVyrH7RYyHNIECwtqlf/kKQOb2zuIaISdnR3oul6VGruuiyxXvbw55xP3Ys91qgWEsFB+m+JwRtFk6uQ71kR57TCiBzhOgfc/uArb0HDffWdhmhbSFjVRCok0y1vvF0JIrdTYsSywLAPn9ZieMp+2VGfTLIdtGdChwbO7e10lVPluU0+s71oTxDMvXJ49x+wkfL6zT1pDyg6cb1tG7RCQfbfhztGrx9fQdQRxCtvQi5ia6aqqP+JUnOUcfddClGQTaw0lNEIgCJm451RkDsHAtRAmae37vmN19v+W4FwgiFMMPBuy2GbTOHYiht+7dAMvXl7DU+cW8eiZefj2ne9LPAwFtgtt6uwHH3xQqbMloe31ep96MjBOYHdoikurAd5dDxEXJFWDKulNuYCEhqQhgcK3NJURPqLIupaKduo7BkKmHMddU0OU5OjbagFGAujZOghI5UI865rKlEyq/z9gOb7zzGk45tEScj4KjkoP7Oe9fBg4JrCHAsYYXnvtNTDGcPHixarR2jCMIxdZcxRjdA6qCmuahv/l2S/hf/utP8Aw2P9Bz/Mcq6trMAwN9509C71hpTXoIHVZVldLydgKfBe5Doqc1Ka+RSElfNuqzJX6vo84Sar9BBFt7UlVPVyOmmRisjgxCGlrPIxsMagqMRqrQ0AUuYoTBFSRlIjGNeJdEoYkSbC2uoa52RksLTevqHIuOpXnqnx6hBAZug7HsrBX9O42xe6Ue1IGO2oBoAlliW2boROlCfo9VSbtWOaEWqxKtD0MW6JkOOcwdA1Ji1kToM7vqEI6rkpPKwMWRbQGWggsUC6sOKCUwXWUE3e5P8YY1tbWMDPTx9LcidZtsHSSWI6DYHp1yrAqyW0nF0G07268X3I79pmRnNE2JKlyHB5VRZUDdH3fEhIL83MYDAYQUiCJE1BKsbW5hZznlRHUycUFxCOLCa5jIRp7lgOawLdVFmcbSlMnxzKQpIr8JVneWP4axUlr6XWe51hbW4Ou65hbWMJ8v4edjlgjQD1zhJBGRdmzrYpUk4KUeY6DJM2U8ZiQlZIOoc5bSWQTlmGm53Ye9yjGe2JNQwfLRONiFRcSCWuPwGlSXAOaFEZNaWsVg2MZiAp1U0KqMbk2wpYc1vLfeq5TZV6yXC1i9RrI9yh8x54gx0GcVtnYcUO2q++alcI1DiElhnEKy9BgGTrCJINnd5tXjaNXEggoZa3nGIiSHLzh4JM0xysfbuHHVzbwwPIAT5xdwMr8nZskf1wTpzuBUXUWQE2dvXr1ai2X9tOqzgohIEHw3u0If7w6xK3dBL5lIM5Uea9nqbzWgOXwLR3hmNmcoRH0LB17Sd0ZXdMIDA0Qulbdq5pGoBEC29QQFM9X3zbApQRlqqVoxjGwF6fQNQLH0hEmOf6XLyxhqX9v1ctPiqPQA1sS2E/7ossnxafvKT3i2N3dxauvvorZ2Vk888wztRfhUS3XPWoK7EcZ08P334eVk0u4sXYbgDLPKftdl5aWMDvot5Kntp44AIhipXjmOS/yzfbf6EnC2k2ZihLJNoQ0xmy/B5qwiXFJKWFbzQRWjSmBaar8wPGdyCJepi3ftHLebSEnMUvhOQ4k5IS6xoWA5zo1AiuEwK2btzC/MI+ZmVn0+35HmXIEx7aRtJgRKZVWxZn0XAcsyxDS/W2l6WTszmiPXz7FsEktGLQTL84FLEPvKGVOGhcHym2Gkco5bSstzTmvIlH6vttYUs3S5jLgElGcTHUlzjPlqhsnrFLyo0iVDc/OzWJmdnaq0dIwovAcp5F8lt8tHY3bym3LY+4qAZZQC0U9zwVNkta+RpqwTpUd2Ce6YZwoRXFEDS+R5Rye6yDnHBq0Sn2VkMiyDDSiiMIIlza3MOh5IIaF2ZkB0lSDHJOtpJRguXLGbVNCAUXKDAlYhg6AtJ4LISXyhu2laYq1tTU4joOlpSXM9DzshJO9yE3Ic64UU7l/DQxdR9aQaUsTBt+1QZMMUpfVMUICQu67aRuGhmGUwDINCMiiHLx7AhUUSmkYJzANozMzVUgJGqcTyq1l6kjS5vMcUgbPKY2a6selCkUmn/sgZvCKfNkm117L0BCxbCKDN4xT9Bzl4DxeUmzo7T225b3bdy2lgha79BzzQL2yaS6Q5qqsWOV7H2wOYepapbwBQC4khkWP7MA2EY+5FvdcC8NiPJfX9nB5bQ+LfQePnZnHw6dm4JifbLp42CXEHwXT1NnBYFAR2k+DOrsVpXj9doaX6SagWwBUH6uKrNERp2LEkInUSso1oj5bGjiN3tqGRuAVkTmjd/zA1jEc6bH2TA1xxpFzCU0D+raJYZzBMtTCOZcCj5+ZwSOnBod7Iu4CjkIP7LECq3BMYBvwcV5WUkpcv34d77zzDh566CGcP39+YjtHlcB+2sf00899Bf/rb/yf2N3dw+bmJhYWFjAzMwNCgIjS1hLWrgib0d7RJvKTsHbXYVqWjY5slwDo+R4YS5HleStJDSLaqV7pxVia7tEwKnpKWxSrNsdiXdfg2hZASCvZV2XaDihNsLu7CwmJUydPwSsckpMxlXEUSgHu6NkqSqAhZSO5SzNlyLQ3SrxGCCxLM8z0vFbDJZaqHuImgt0vnIL7vttqPiSlgKFrYLV/U9dge3sbjDGIPIdp2x25rQwzfR+7w+as1CzPp6qfEU0mStBHYZlGZRQmIbG3u4ednR0sLS+h56sqEBXfY3f2uuacTxiOjarGnHM4noMsbn9G0yyfyE8dh2Ho0DR09hdyIWCZZqeRFbBPwliatxJF2tBzSkBgmRasWQuzs7PggiNlDFmqVOs05/BGYnrKlfc853BtGxrhreWh6jwU9+aUPteMc7i2BZ1LcCkQxzHW19cxMzODubm5WiZsFLOpJleA6ln2HAtxqhQVyzRajaqiuF6SW5YQ69CL86T6uWOWIU5TOJaBNJeV2tvUO1sioAnmBz62g+5zABTlxzHDoIjYIYTA0A3EeTvxpYkyatI01By5e+6kIlp9h2UwdR2uZSBOR1s6FOltQ5iksAwduqbVenZty0A0RRkNYvVd09KRpDly/lHsqJSKGCaZiszJObKGvtZRWKaBqCHSiQuJYela7JjIimvYpARvBgn+4K1b+ME7q7h/eYAvrszh7MJHJ3TlIsi9nvg3YVydTZIEW1tbjers3NwcTPNoxL7EaY73NyneWguxETBc3eU46UjYuirvBYCMS6Rjcx/b0BClvDJoijOOkOVwDL22CNS3DYCgWtQAAFMn8C0du6XDtgQGrgGWC+RcwjYIdE31gbumDsZVL+2sZ+HiAwuHfk7uBo5CCXHZA/t5xzGBvQPgnOPNN9/ExsYGvvzlL1cvwnEc5XLde1neMw5N05A29D224eTiPHxLw5XtLZw+farW78qFmOhrHIWKsGlW70rXYULIRF9gluWdETVpphz8QICe5yFNs0qxTbOsO2Kl5TgJIcrQxXUac27V8YjOWJ3S2bdE3/eQsBTDkBYlQe29ljzPsbp6q8p/9Tyv+luW58W2W9yQCwWx6Tq4toUsy0DQPrlRhkv7Kmh1rxYHOs2wKYxiWJZZjV2VcO+PZziF2EVVL2tcTcaWl5cRxzEopbh+8yY8xwI0oyI7WvEjZxo6jCKDtcsQZjhSVtsELgRcx2oksP2iB1ZCwnVsXLt+ExGNcOr0KTi2U31Oyn0lpE2RTrOsFpvTdN0iWlzPDmIWRGUJ8ORn3MJEiCZpEWPT1TPLOsu4AdVLSEi7ylliGFH4joOobYFI0+H5PgbLixhGCeJElRrvDYfY2NiAbdsj6i0w8FwEHaZOfd/BbkgP1DcbsxS+62B9YwsbGxtYXFxEv9+H51g1xVVKldE6LXMWUOSu59kAyFTH44AmrX2lPc9FEDPoRR9+lks4hXswF/Xe2XEy67sWdoa0MLCa3rMqAQwpw8BzQAg5WFZspgzGytzcLvJaIuMcuRAYuHYVB+Q6FvKuFQmgUMl5pRT3R+JspiHNOdKcY853q9LLg2C073U0MoeySeUZUGpv0FCyPAopgSBRvba+a8HXdERFL+M4ciGVKru6i8UZD/ct9HDh5AyWB27Dp5v21d5icdTgOM6EOru9vX0k1Nk0F/hgk+LyRohdmtXIJZESfdsAMTTlz9BQtVD2q/ZtRTpHy3/LBQzH1EEAsFyAjxhk9BwDeS6qz5maBtMgEBJgmSKqScaLyB2CkKn+WEIIvvXIMrSORexPE44CgY2i6HOfAQscE9hPDEopXnnlFei6jueeew6O47R+9qiqncDRqOsv8VFKiJMkwauvvooLZ08iiHOQhpdkEFFYltVIikcjbMYhpSIfRNMaJ/thFLe6GadZjrnZPsIwbiw1zgqC2zRZiBNWmPLUSUP5Q5llWStJTRjrjNUJi9JoQ9dgaHqtJFWUZcgNBDZLM1y7eg0zfR8Li4u4du0aJMosU4UgonBsq5Vcx6yu0hJCMCgMjoRUxKsNQkqYug6G0kVRobwuQkhYjtlu6CQFTENHmmYwDB2WadTP0QEia2jCQEZcW3VdR6/Xq35IGEsg8hxbO7u4vbEBx7YxNzcL6TjIiAYQdF4boFn9HEVIk0I13t/GaFkw5wLXrl0HyzKcWTnT2MvFWEFQafs4lOLuQte19nuJxp0LHgBAGZuIYHEsCxnnFdmktNukClA93m2xM7qmwTB0BFGs7qcpUTcsy2AaenN+LlG9jHshRc9zldGRbWN+bg75SEzP7u4uiKZKkU8szIMTbUJdGjWzGkYHMH+SwM1ba2BxiBMnTsDzPFimgSznE/ckFwK60CpX6G60q6PjCGgyQTTHSW2pzqZcwLUtpJwjz/dLjUfflZZpImGq/HAYJRh4Ti07tgu8NKVpe1GOIc8FOM8wcO1G5bEJUkoM4wR91wYIwU5hhHeQ8xXEDD3HaixD7oLv2tiJGQxNQ981VTZtx/4MjUyUqo9G5qjs1/3SZLNQ2A48HseqMmtNXYNj6ogzpRCPY+DZ2AoYtgKGVz7cwpxv48LJAR48McBCr33uU94TR1GB7cKoOvvQQw/dE3U2yTiubsX4YCvC1e0YXEhYRSYrADimBkPTlFFXJjBrk1okTglLL3LHda22uOGaOkKWwyjcg0OWA1IZN6WpUl0tXRk22aYGLiV8SwfLBTRCVO5xkX/cK6LtApbDMTXEOcf/5+mVKgv2s4CjMFemlB4rsDgmsI046I/9xsYGXn/9dZw6dQqPPPLI1JezYRhHlsAehVWlEgfJgQVUv/Err7yChYUFfPWrX4Xpv4Ifv/bmxOdU/I16GTeBJgyarkE0TETKjMi2LFPfc2sEVtM19DwPcZJgbxjVCN4oqvzWln7ENE0nVLLyvkxYioW52dZy3y7nXykl5gY9bO0OkchJwhBGcWGatE8CYhpjbW0N/UEf80vLEMVkaly1V2ZD7c9AlnHM9D3sBUpNNQy9pgYrM6l2wydV6lsQFEIm5rX7Zkjthk7zgz5CmjTGBiVJ2poNq6JWMvQ8D0FOG98Rtu3AGVjwen2lkgqOrZ0drK2tQzeU620cx5ibmWnNQS3LnbtIbpywioCNktcsy7C2tgbDMPGFhx5sVXIBIKBlr2s7oTINDWFDFEwJISQ0bfI6jIJzAccxKwJrWya44DXSxYWERUjn4oGEOjfjxFPTCBzbrI51GNHKgbgNeVGuq0ow6/sbJZ3KcXhfZTZ0HYN+H4Mipicp1Nlrt1ahSQHNMCt1dm6mP6FOB1GiTIoaynillNjc3ASNKE6dOoXF+RmEMYOuaa33SprncCwTQpOtCx6ubSJKGKSEIo9TVGCgIJoF2XYts3G8JZIsh2tZIFALEuMkVpkX7VdN7EUxBr6jjJ06fmdNQ0eaceRCRQglad56jHVIZELCs42CGB7gK1CRObquw/4ofZ4EyIVSxXqOdaC8WGVkpe7fXCiXYM9WcVdJixO5Y1mVEdU4uJAI4gyGTuC5JqI4VW0GByTwrmXUTKEyrvqCCUjlREzLEnRDm1D1diKGH7+/gR+/v4FZ38b9S308sNzHyRm3/tsg9hctP80YV2f39vYOpXd2GGe4uh3jymaEW3vJ2GK1hKlpsHQNKRdIMgEpeZXnGo9dIwLlLMwlJlR5raha6VkGaMbHInOUUkszjijl6NvKEE2puDlsQwPLRNEjm8GzdAgpEaUcPUdHkgl888ISTgzaFzY+jTgKc+VjBVbhmMB+DEgp8f777+ODDz7AY489htOnTx/oe0exhLgk3UeJWB9Eqb5+/TrefvttXLhwAefOnQMhBBefeRJvvHMFccOkPKJKUaIN5X75lIzWcVVhFMrN2Eae50WPKMMw2O9zVGZPzde8i2imWfOYSlIb0bj1u5zzRhLU77lIkhSbu0PllNxSRpnnvHKbLX+gF5dUji7nHD3frc7LxDHRGH2vXQULQorZvo+Axo2GUzRuPy5AkZiqDLehDDbLMmhEg2jIBhr0fERxUiuLGkdIY1iGiXSk507K/XsgpKrPuOk+AtTiwkzPhyTAMIxw4sQJSCGrUuPbt29jZ2sTumlP9FVW5yii8ByrtV8x5xx9x4Vjk4q8xnGC9fU19Hp9LCwsIIqTVjMmdVBFRFBLbE7f97AXRJ3uyMDIokOHMVSUMAx8F6wgIU29e1VmcMd2Ms7hO05FYAkh8BxnYsGCxqzVkXt0f6Nl0oB6PiZJZ7OjMiEEruvCdV0sQE3QkyTG3jBAMNzD2voaPNeH53lwXFUKKyGRsGzi/hJC4Pb6bWR5hpWVFRimgSCKMTvoYbclGquEyoi1i/iZcWMhFYdRPiJDmnwEEqsU4yTjUzNQVU+sCcJVJE45ae+5yoFYqypYFNHeDSj6nl2Qskl1mEAZX5WGTzRR2+dCNqvmIygjcwClLCZZe0/06P50XUecZuBcZVoeBKqEuCzrVUQ04929qaahV+7GJWiaFRE/kyXBfceqMmK7kHNlMjXj2RAgB8qu1QjAZfPik4SsSLOha3BNFY/SVZa8GzG8EjG8cX0brmVgZd7HfQs+zs770Kt4ok83gR2FpmmYm5vD3NxcTZ3d3t6u1NnR3NkudTbjAqt7Ca5tx7i2E6vr2UA2PUuDrhHsxfW/laZrhqaBFbcfAeDbOliuFhqbDMMGTt3gCVCKbJoLeKaOoFByVSasqmIKmDJVNHUCLiRoymEbGriQYLlA3zGQC4HHTg3w6GfAtGkcR8HEKQzDYwKLYwL7kZFlGV5//XVEUYRnn30W/X7/wN/Vdf0j9XbeDRBCjlxpc1cJsRACb731FtbW1vDMM89gYWHfGMCxLVx85gn87g9favxuV/yHirBp7qHMco7Z2ebyVssw4Dk2dodZYz9s2UfLGspqq/zW1hJNOqE4lQ7Aqre3uae0PB7HNpGwDK5jq+Dxkc92zSNUWbWL9z64iiiKcPr0aTju/ipqGMWwWjJpAVWi2TSBMg0dtmUh57zVbTefErszauhEGuqoWZZPGDrpug7PtqttznQonEJIWK5REQxFXsWI2kzUD1jLBLEkKmxk0kk0As/34PkeFuWiegdIjo2tnYm+SttWar8o3KybTrHqJdonJkEQYmNjozAv258wKIJKJlx0q3OZZo2kse/tux2HUxYkgMIYqiUGpgRLcximjqijnzGo+lPbCVaUJFWZcM9zGntnuRCw0JwPO76/MqanbVsSEmmWdZpnAUWp4dwc5mdnkXOBvSBQCxYbtyGEqGJ6PM9Dz9WhEw1cCvCcY3VtFZqmYeX0CrSigqHnudgLYri2WSmYbaAJK8qrE5SSIyGqfJeOXZNhWSLcoayX4EIWZk3TF12TNFMkFgQZ5+h5qpdb3av7hmtKRZUIYwbPVgZTXNR7Z5v6cJNCfS9jiZrgOfWM14ilsE0dhtbuEAzUSa8QEnHGC7fhtNWgy7XNiryWoEyV9LbF7fQ9B0FbFjckgjgtyorV901Dr0U6TYNt6giKrFcVmWO2RuYARezPAXp3cy4AS/VI2qYOy9DAMo60hajbliI5b6/u4e3VPRAA856B7a0MD2xTnJ7zCmfuzxba1NkPP/wQb775Zk2ddTwPt4MUN3cT3NpLsD5kVe+1Z+5fdwLAs9S5ohmfyGctIaWEYwBJLmAaGjzLAE05QsbhmtqEQZdj6HBNDTu0fv01okqNA8bBitvG1Ah0DaCpqCpk+raOYaz6pT3bUAt4mUDPVvOCUwMHFx9cvINn92hAvcPufQlxFEVYXl6+p2M4CjgmsA1oWyUcDod45ZVX0Ov1cPHixY/c72AYBuKWvMp7iaNGYNtKiBljePXVV5HnOS5evFgzESrxzGMP45VL72BnbzjxtzhJCkW0qURUwrasRgJLCEGcMMw4blVm7Dg2DF1DRGNs72atETVSShi6AYbmiUsQUjiWhaRhYUMICd+zkdWI5/6kPAiV+ps0qGyqbNqAZZqNZJDGSeGy3OTAzPHO5fchJXD2zFkY42V1BNC0dgKbZllhcLU/7r7vIk7SSi3rKvUNoqizvDWIaDFZbl6UUH9X59R1bAghav2ew7DbsKksow4iWimvJXkFyjLfycUDQ9dhF1mynmsDTXNDAli2BU3T8EB/AJqwqq9yb29PqYoF0Tm5tDiRgWroOizLQBCp6KG9vV1sb+/i5MmT8Ly6oUqaZVOdjfdjcxJVNug5E72xlLFOAielVP2KLcq3ZajopzybEq0jUZDFlv7UAkEUY67vY7vFQA0oFF2/O0MWKNyhp7gEq5JjAznvJsRplmPgu9gNVX+S7/uAVO0AlFIEQYDNzU1YloWF2VlwQrC1tQXXdbG0tFT97vRdlYurooY4LFOV03YhjEtiqu7pXkd+7jBKWrN3S6gSYrUt1bc6nfAmaQbHNGCZdiPpVoul+2Q2yThc20SS7pcf+45VXAsCQuq/xVnOwTUJ3zEnHH/1opxyHCzj0DUC32ku3R7PVS2f9SBmytmYECRjvwmapjX2hwKK9IdxqcaK6j52TKO1DHgUqqyYwTEVWd+j078DQGX1ahoEL8uTuyNzfNs8sPGUbRoVaWIZr0qgHdOAqRMwLqr7s+dYE8qhBLC+F+P6LsdvvnYDGiE4MXCwPOPh5IyDE/9/9v7raZY1PwsFn9emrfrc8mv79q3uVhu12S2DpKOBA+KCQETojj8A6QIuCCLQPfoXuCBmFDMBESMiBoZBRwHnACMQ6kE69G6n9m6btdbnTVX6fM1cvJlZLjPr66299/rUWj+i6daq+rKysrKy8nkfNw0wDW5Gsu87Nevs7OlVgu++eYz//oMzHP7Zj3BWUkg/QNioONrAv3ZhNPIYrAXyRr4LNFbwAcbc4wTKNsFele7CnQjQpNK7kayRHiuNWbH6fQklAyPA1RLYlRQQnHTb48R1CV9m7rs08TlgHXAOpVt8CyTDX/vQndHmgb+s094PPG0A+8wD6+YZgL3mPHr0CN/85jfxyiuv4JVXXnlbUpibBhTbuWn71bc/rd91f38fH/vYxwYvIIwx/LXPfxr/9j/+f3sfL4pykJlxwTXehj+SUNJ1Vhrj2LFsDXwNVdQATr48VNdjrQXjDAP4FvNktVZned8tXK3O+hBCunqYcCRUrChdyfiyZK0sSzx58gS+7+MD73tpsNu0rGvEYYByKPU3yRu/o4Ev5Qa7VVX1xmu3436kh4GCsRaM0obt7Hvc3dBOY5fMu86U2ub/DX1ejkGt3I+/xQp4bWee5ivMuiclANt1yWZ5OSqJNcZAeBKMMUwmE0zWfJXn5+c4OT7GdBKDCQ9RFCIMfFBKkeUlrDV49PgIFBYPHz6AlLL3dbYlG8M6CT1jDIGUvYsKWmv4gRhlIKuq3qiOAhzrDoLuPJmMACvAgZQo9EcB7CRyyb9jUnNgIYMdez0pHMM4dC62k5flqJyaEILAl7ict4nDzXFoFiykJ7G7twutNfIsx+VsDpjaLYhZt6IeBiGi0EdaLJJga63hUQHOyCBoWrxflyIMkO3APSsQB15vKFbUSH+77XbBTttBbKU0YiFc8NCIctexrc4/GvgSWhkY2KanFgDa/ybdc1v1SVrUmITSeVwBABaeJxoZ9eZoY5EWTb9sVnZfZUYJlBlW5pS164+OgyYFurnWroPevslKp0KZtMeYUhh9ffuQ4BxXWYVQCmhrO9A4NENJyH2VObUyqK7lJ27k1ZT0sq1FrdC+pGwYPY3+zC2LxWKEsRbzUuPJmxf46pvu8UAy3J36OIg93Ip93IoldgL5ly651lqLy7zGaVLhLK1wmrj/OBBKIL0DhHf2gLxAlue4vLzEyckp4tDDThwhjgKUiqNUm+879hnmhV5/QYTSLfgpsxneFHsc87J2AU2CIS2VkwdLhqoBxpxS+MJlGbSSYViL2BegxHbgNWjSiWd51ciZHciulAZnBMYAlAG/8qE7COVPJ7Ro70uftoQ4TdOfSP350zo/nWfZOzjLktVPfepTuHXr7csibqIHFriZAHZZQvzWW2/hW9/61mC/7vp86JUX8PyDe3jz8eHGY3Xndx33lq3sD2UIAx91raC17mVpF+E7/ayQ0noQOKdpdu1anfVtpHmxwv7GUYi6qrv9qOp68HXrevVYpGmCo6Mj7O7uYX9/D2leOK9suXmTSwhxAJjxXtbNWIM48HGVZr3vq1JqVALtvJXDjzuPZ3+4FqNNIqwd7hhtvZvrbLyFhTUWRVU1iwD9N+22AdGAkyAWRQW1xiym+Xhva5IXK/LcFV/lwQHquoZRNc4vrzCfXYIxDs/34Xk+Li8vABDcu3cPu9N48NwBHCikjMAMAKBaaexOI1wMdNS272UajXtd52nWBIC5YyY4AyV0JV143nTuzkckrIuKns3XWmaUo8CDGumiBdwxHlI4SOG8WqpyoDndwjKuhzotzyRcyHJnad7IYDe3x5gD9EVR4ODgAC/s7eDo9ByXF5c4Pz2F7weQno8wCiGFbMB/jdCT0KbuXXBZHm0MGN3ODlg49jkKvJXFDU8IFJXaACAu2Gk7iI18BxIFZ5BCDPZdL09Z1ZCcIZQeZnnVsLRObuwYD9O971Zm7MB6gLQoEQfBoDR3eeZ5iTCQqCoFZQwCz+sNXlr+fbHWYp6ViHyJSmkE8vrMpbEW87zEbuSj2JoUvRjH1rprRtb4uGNfolxjUdtZD2Lqm7YyBwB2Ig9Ku9TmbTD2uhVBldIQnCGtFBhxabbu3w0q3VgwmufGnthgafNK48enKX58uvj9nAYSklPsBhK7ocBOKLETCEx8jkjypwZutXH+1HmhkJQKF1mNy7zGVVajNnal2mZlrAWnDJW2mEQhbu3GIHD+6VmSoShyPD45B6W0+x0IgsAtLopV8MqoA5SlMqCUIC31hi0oki5NeuI5OfFqQNPif2eVQq0J6ub3QVICzhmMNY55t4vncUrAGQOBhdLGLfxZC2oJLAW++L5buD356QptWp72PvlpM7Bpmj5jYPEMwPZOl/ZaFHjttddgrcUXv/jFlX7RtzM3MYUYuJkAVmsNYwy+/e1v48mTJz/x4sGvvvoZ/N//X/9bf/1Nlg/KFLO8bMJdMkgh4HsS52dnmCcp/CBEHIWDVS1pU1GjerZbllUj2e0HANtqddp96gOjVaUQBL77MVy76V/IefuB9Tx1/amHR8e4vLzA3bt3EUUuHKDJSeodQgiqqsbtgwlm6eq2BefwpcTZ1cyxVgNsWtJIgYdSVtO8GDyegLtpWmegA0+6G8c0a+qChlm6NCtWzgOLRYIqIQRpVoyGKWV5gf2dKS5ms15gobVB4HujzGVWDINcIQQgBD567y4ur+ZI0hTzeYLLy0sABFEUIc8zCM5H3+d6r+vyUErhewIXV8lG+vT6JFm2PRypcCmogAsO6Q3qKsrRzl6gUUN4csXDOYmCBtS6zzzNy5XU4L4xpvEsrUmXOWMghEA1+5BmxSpzOrhf+Qbo6wO1aVFuLv5Y4PLqEhcXF7h7z9XkKGPx4nMPkBYlGCE4u7xClma4uLhw3u22c9ZYTKKg8Qf3fykFd+ErSteDlUPLY+E+i/Y4U0KbLt1+WLMNxE7DoGNua6XBmR39fi+P70mkRb30mRO094gLEGs7TzoAXCUZduMA6U+QK5EVzlu6Ewa4SvutF32TFhUC+ZPLXENP4KpJQ54Grst1LFyJOi0wrF39biSFS6Wf+GKl4oYQgs0s7eGJfdHJktvKnGIIGAt+rQApwIVNzRtvsrarAUSSUQjpGFqf02uBeU4JilpjXtQ4S/oXUEPJcCv2wJr3EQjmpNeCQrD2PwSCUTDigq1AnM+TwEl13X/cYkOtnQKgBd2Vcn7ovNLIa+2SwtMaWaV7z5PI44P1RZIRF4RkCDxGUWqDumU7CcPezhTAFJN9g6IokOc5rq6ucHJyisCTiOMQQvrYnYSglHT+1lAwJKXeaARgpDmGFiv+V7+pzGkTg+eFAoVTmRljGqCqYZQBIRa8OX7zQjl/K1ytDywQewxXRQ2PM1ACfPT+Dj5w96ebFWwDnJ52GNmzFGI3zwDswJydneGrX/0q7ty5g4985CPvyIrLTQOK7dy0/aKUQimFP/uzPxv1u47NvdsH+JkPvoJvfOcHG48ZYxAFPmrVf7NKCBBFAbI0R1U7OV/7g5WkmUsb7mFXtDajIDXLSzDOoPsAblWNBjoVZeUqS9YArCcFpBCOlRiqn0nzQZCkjcbVyRlmsxmee+45SLkaVpXlpXtP6520TUrxehfoNAqRFWXno6xrNRh4ZCwa+fSA9E8bBw7UsAw38CTaozltWOz2tZTSDdvX/zlroxEGIWqVd8xrdyNAFoC2T2pMCUEc+riaJ+BshGXtqSVa3YdxkDuJAszmKaSUIFmOosixu7uHMAy7PtLj4xPs7Uy6Ghch5MbCwzxtZeyL85Yx6oJ+GqBTlNWo/7QNlhrUXsP9wIeeRKUUyoGwHa0NPMFRq8HNwFo0K/wM2mhMwqCp9NmUcq8DyvVxcveFjJpSCiE48mI94ChfCkMa2C84dr4F4HHor4DqdoyxUEp1HbjWWpydnnWhaJ7nNdtzCzWTyPlwp9MpptOpS64uXHL16dkptNLOK3uwC0v5Rv4CIQ6Ut97TNC8Q+d5oBQ7gjnNR1QgaOfs20DsEYgNPbvytapi3bQFfUbDonc2rCpNwVcLsWFfW7O9ikYlSIC2dPJY3MteWoR0bSgmSvMIkWA19GhtCHPKZF5ULeLpGrQ+lFHXrP7TALKtcwI4Qg5U7cSAHGU9rLeYtkG0Si0NfbAT0DA1nBIVa7PNyZU7oOb9vWtbdwqUlZCvjDzTJyiMS50oblKVCod0ivqo1IslAKYG2aOqSVl/IF3y0y9dai6LSeHxVdMzh0EQe7w0+WtoaQilG34MvKEo13EbAl2TWnLre1NaWUGkDQoCrQg+GgvlNd++yCgdo7B26RJKmOL28xNHxgp2dRCEqQ7r7E0IIOCXwhUv9vloLGXP3D87runw8Qo87WbHnwrdggdCjIISiqDQypTHx3fcvKdxFexoKXOU1Qo+BguLe1MPnX9kfOcY/HXMTApystc88sM08A7A9k6YpvvzlL+PDH/4wnn/++Xdsu88kxNebLMtgjIHnefjMZz4Dzt/eafpLn/sUvvPDN1D3yNjmab7CkDBKEYU+ylphlqSYxnG3qr0OGseCXJJ0OFhpUW8z0N+aDTO4dSO5bfeFc4bQ9zBPMlctQ8kgSF0AdrWxzcMnT0AoxUc+9IHBtNO8KMEpXZHItvthrIEQ7macc74h+yzr8V7TNCtGA51maebknT2sDyEE8yzD7YNboJT0Sk5dj+94IFQU+EjSfAW8du+9rXdZCrsSnENw2r3eNpa1LCswRgeTl4eSfh2zlwIgyGaXOD46xq3bt7uVV9/3sb+/D6UUsiwFjMZbbz1y50YYIgwjx8w3N/RVVXf7IThz1SFL56m6hv+06DkeyyMawOZJMeiPBhwT5tKihxnPFiACAklWDtR9OAXCtuCnpJElJ2mBwJP9gNc6RnJ5QaZvtDYQjCEK/Ab89wOmWrm+WWKBw8OjlZqc5YnDAGkTGtQeM0IXYV6wQFW7IKij03NQq6Hs4nHf95vzZ3GOW+vO3eukGBtrITkdrX9ZnnUQyxmD1rYX7GhjUVb1YIiS4HwlJMlJdgtMgs0kYgArADVq0nqtsSDEIvRWO2uXk40X/wYArvtynleIG0ZTL4GgPgDspLRu20lRgTOGwOcbYVLL4zp4Vx+vlUGtKpfcarHy3q8brGStSyyOfAECCk7JBgDsG0+ITpq8sj1YZM2/c0YRSAZGmWOOrzF91UB9+xxy1jGzao2p5JRAcgf6GCUolct3GEpQBgBfDjOe7WwHr84bPB95Tns2rP/uc0YgKAVr/rvUBlAGtbFQZqk5ABaCsUHmPfbYxj62EmEWcFzlHBMvQrxnV9jZ+fkpDJcIgwCeYAiEWygmwIYXllKCSZMOvXyqxNL5WpXRaA+lA6ukWxjxhavjyisNTgkij+MqrzHxBCwsdgOOX/nwnafOSr4XcxMqdABXo/PMA/sMwPZOHMf4xV/8RfgjAThvZ24aUGznJu3Xo0eP8Od//ucAgI9//ON/odWuSRTi8z/7M/jj//MrPY86iWPgeRCCIcnyFWCZZlkHJtcBbF6UTZrxJhC1Fp03sm9cvY1E0VurYzAJAswHGMd5msP3JKLAhzF2BRQaYxEFchBIzdeY47wocPjkCaIowu3bt5uwon5GTCm94Uls62IIiHu/QiDJ+vc7SfOVm/P1WQZWfaObFey+ffOlBGHoTYAGHMAx1vTLs+0isIlSF0LRh0WSrOj2P2zA6nIvaJJliMNhD3Pdc/zWJy/LpfPNnbuzJIO1wNnZKZIkwQfe9zLqnkPEOcd0ugPOGfYPDjCbp8iyDCddhUvYgZ29adyE06D3HEyzopdxXx6XsrzZhSs472TDVa02JMB92xkNmELrNSajoV6uH9brGL+hmSfZ1m5VY9wNIKV0sMoLcOoBQVqP9fCNW5JluDo/g7VmpSannUm4kEAzRvql4ASQUkJKid3dXRhtwIjB8dkFjo6OEEiOYyY3eoWNsahqtVXGG/kSs6wEJdQxUdeQ/LYgdp4V8IQYlNm3+5EV1WZoFHFeuz6APc8diE2Ksvd7P4kcwCWUgLTp4LXCTuySmJeZWvdSbUVPsMK6JkUNyRikR5GXdb801JcbHlulNZTWiAOJrNxkY7d5R7PSfQcngXSvC4JqC5O4PIxRVAqotbt2TXwxKAUGGonvSIfr4n0ZaE2RFPWiMkeZwRTsSXC97TJKUNnh74kyFqoBSIQsFksZdTJgTglACAgstHXbK6qB63ozglIUfRfMpZGcIusBwYQQcOK+kx5j0NZi4lNYi45VVdpCaY2Iclzkw5+1C1IakBZTgrxJO6MECAWHgQOLRb3qUV5mZyOP4SotoasCZZ7h+PQKxlqI0xMkXgA/CBqpKxBLDqylCwMNMLUGxVLaWiibz7th6kOPNrYE20mzZw14VcYgFBy//OG7P5W1SH2jtX7qDCzwzAPbzjMAOzDvNHgFnnlgx8YYg+985zt4/PgxPvGJT+ArX/nK6M3odedzP/tRfPVb3125IaeUIgoDJ8/hrJcR1ca4m52k33daFGUDenpCmbLchTL1gIBtEeyuU3MzOIkQgjgKcCk45mmGXbGZOrsOUtfHGMcwXl1d4eT0tOkN3QFBI2EeYUod8PZQlIs0ZE8I+L7E1TyFJ/lgjYpjaf1hAKuU63YdYKYXrN8qgI7DAFlRgib5Vvn1ejesC3hyCbCVcWFW6/Ux3f4bA+F78DyJeZL23jCVVTkKwmdJ5qpLBj4bpTUmUYjUlIgCiVniVAhHR0dQSuG55x5CWXTdvr3bUK5/UxuLKAphm87ZLEsxn89wenqC8yjE3s4UhnBIT3Y3/8uTZsXWAB5V1yu+0mXwCrjzXBkzmu5r4fy5Q/7d0JcoSucZ3CZDTYvtfljnAy4gBEM9Ihcsqno01Ik3399565sdCKSq6wpPnhzC9z188H2vbIQ6hf4qoCsrhcCXMMaM+yQZBUDxyovPw1qL86s50izD1exqo1dYerL5rvYvIEnBUdTa3Zhbg7JW12JtAQdiD6Yhzueu9mZsrAWSrHTAs2Fuh1jWduZ5gciTKNQqQxp4AxJcQpAWNXaiAFlZQTf+Z/f6rqLnMsnhVPANICIEldYgWmMSeijWlBqc0VFgmeSVYy090dXkyCbMaNtYC8yzCpwSTEMPl9eUMwNAIIWTc8Jd1+f5atJwsXR+S06RXrNPtpX1AnatMoe5Y6EMqkbpIAXbyoB2+8AorsPv+5KvsMTaGKxfUjmjABasM2n+jbb+1ia1mjOKlgi1WKzQEhDYJmKedTLvhRdWN00DtXUAdlYOh6dxRlCOKBeCxqPaO9bCa1LajXVBVsnSeTMErkWzBhYHHnLOIcMY1EtwcXEBzjkuLi9RHZ9gJ/Lg+SFMFIDxxf0Cpy71WWmLvAH4FM4fnVXOX01gMfE4lLVQ2iL2OCptUNQKoeTIa9cv+4sfvIWdn7IKpLG5CQC2lRA/88A+A7CDM5Tc+heZNl133XD/tOdpA9iqqvCVr3wFVVXh1Vdf7RYPtNZvWz7cjhAcf+3zn8a//89/jDDwwShDmucdeyqlGK7VSRyYvOp5fJFm3A+66roGoQT2JwS4aH2GzRC4ZOGyqjBPUuRFid2dncH3a4zzNPUxVnlRoMhSnJ6d4f69exu+4iTLB8N1bLPqDbQLAD6KsgTj7serrFQDIgd8uFmTtDwAMGZJugKQN/9+sW+eFGCMIStKTBvPbjri8wVc+m0nDW3Aa/s9JHBS5KHAppa9sXaYB6yV3tq5WtVqtAe1KCrsRAEu5imUqnF4eAjGOB4+fND17jb33YM3VElWYNJ4bgkBPE/C8yT29vYgOcPVbIareYo8z6GtRRRGCMMQQRiAEndn5Hw+AqQeZj4rpbpgKCE4KDYDm1y1zjVqcwIfKldYBkGBJ1FWdQd+tdbgjEINLBAALv13qN90J1p0vQZcQlHT+91sxyUhbwJiSigE5x2YdonDm++xKHIcHh5hOp1gf39/AXYblr4FlOvXlbyoOr/u2K+PhWPLOGfwPJdMvb+3D6VV1yt8eXnZSZF3plNEUbhSxUMJASUUlV58btoYFwp3DRAbBx7O5zniwHltr/NzOU8LTCIfxmIUvLaTlhU8wcGo80oy5gDWqI2jKOFxDiloxzwLTlHWBrSzPtjuS9R+v+eZ618VSyy5L8XW/lalDZSuEDVJwYwxVNcAsO0EnsRlVkJyBsEp0rweXQ+Y+F6v79VaYN4wgZHnWLe0qMEYv/b+RJ7sTTQuag00YFZyBo8TUEqHE3eXJvYlji+KLUscrdx3+/Y8vgqcLbDBPMcex1U2vq2Jz3E18npd7+rIeb2+L+t/b9Z/M6xFIBg4c1Lpq3wg+8BjG6ytq66h0GZTImzhjl803cWd2weoKoXzWYIszzCbXaI2QBAEuLUTw0gfsAx583kG3MmbVcMqS0bAGIUlQF5qTHyOpFAIPQalHastGfCZl/bxcO8nyyb5yz43AcCWZQml1DMJMZ4B2Pd02hP/nQBm7+Q8TQA7m83w5S9/GTs7O/j0pz8NznkHLN6pffroB17G1779fbzRU6tTVTWmcYxZ0lchYkEoGZQTuqTifsBXVuPpv3WtBgFuljsZJ4gDBfOllN/2uAxJp4qy6hKLl0drg6OjQxit8dILL25IGQEHXLzAH05Zzgrs706RpBmyvISQqyqFJMngSYlyIBW0rurBQCcLYMxaYoyFDAQ8TyLPS5TVaj2QNsZVHQ0AWGMshM9RVvUGeG13wFhsgH/JORhjmKcZGKOjab+uczUYZO6qWg1KiX3ZpCdnOZSq8OjRY0RRhIODWyuLXXlRNWz1sAw2y8sNMB/5PvKqQhBGCMIIoe/h7OISaZbh7PwM6lgh8APH3EUhkGOrlHieZpjGEYqqHkwmnqf5VhDrKnoW4D/wBCqlVpjbqlYuYEkPp/AC7visJyVPl8Ar4Lyhk2i407WdWZKvsOYErut1XfKcrKVVp2mC4+MTHBzsYzpdLDbNMwf2iqoGRq5vSQd2h/ePN566LC9XwCZnHNPJFNPJtOsVTrMUR8fHgNHwg9DV9IQh9nemvaFN1wGxnuDIKxdwN89LxIGH7JogtqwVPCHQ3xja/3zGKCJfglAyGIC08jdKNT2sDpAJJqB0DcZYtxBlTZtsvJAal0UFYy0iX4BSdq3XaictKkxDH8aS0UWm5fHEwj9aKY1KafhNoFLWc+y3BSZ1+9IAs93Ih7YYvO4uT+Rv77dt99MTEleF6vpAKQhKpVGtAUnJKPJKNQuzw99bwegok9nt40jSb7ctSjpmcWg8Trd6Y530dxjgxj4fZlfhvK3zUkMwAp+7865QBoUy8ECHE4s5RbL0mMedjLplY/s+/4ATKKDpenWds5PJBA9u72GW1yC6Rp5leHxyAWoqWCYRhgFu706QK2DiuWqdSDIUSkM0AU6+ZJgXCtOAIysVGKHwBcX77sT4mQfDC+k/rXMTQpzS5n7wGQPrlAPP5j2a9sS/aUFOTwvAPn78GP/jf/wPPP/88/jkJz/ZgXpCHGh8p/aJEIJf+twnBx93ftf+BYUsdzfgfSv+1lp4clPK206SOXaqb8qmZ7RnZzGJQxhtkaQ5qjUw2PavTuJh/0OWFysX2aqq8dZbbwEguP/gIXamwxe+eROatD5CcJeIm6TQxjQ3I6vHxFgXVjE0ZVPnMrzfrpu1bxijrgbBogOQ68z5PM2a0J+x9+ZtgtdmimL1M4kCH9baTs6ttYHvD3/egPtuj/UTtqFRyxMHPpTSqOoal1dXOD06xu7uHm7dutV70zdLM/jesGxLGwMpFp9DHAbIynJlESYrSty5dYBbB7fwwvMv4LnnnkMQBkizFG++8SbefPNNvP7GWyBm2FcqBUc5IuttJ81dbc7YzFPHgPtSoFamV4qdNEB3bLRxvrjWh+48ppsgfN50tW6brCjhe+4zj6N+v66F85pKwbpU6Lt376yAV6BJ/C1rRL4/mNDczizNMR3YPwICT7qFM2Nto0rYPL6tZ679jO8/eA5xFKGuSpydHOJb3/0ezs5OkefZxmfcgtiw5zxjhICsScOTBkjTbQnAHdtZIPR4p+rYNlobMLY9YXh52h7WvThYCUsiTaUKYwy8qaCilILA/RYABFXT+U0xzvYuT+i5VOOkKF2oTs9nsjyENKqsdXtKpZCVNUJPwF+r7uGMDUry18eXDFd53XhxLSa+gC/6r89OHnzd7S6Am7EWaakwL2tU2kAwitjjmHgCPmfgnC0Y85HPTnK69X2tpyj3jrUQfPwYEVgH6Ec2Ewq20VW7sr+Mdt7V9df3OcWO5/yyklEo7WqF0kp3+9Wyu33T1vzEHocvXO3QvNQO2PYAZsfKapSGdKwq0PTAVgpTX4BwiWC6i5deeA73H76A23s7gFL4/uuPcPTWG/jRm49hqwzzomq+k26RJ6+0Y4NzBcEoPE5wd+rh5993MHL0fnrnJoQ4pWnqaqR+wmaOn8a5OTTgDZt3Q0JMKX1Hgdk7Ne81gDXG4Lvf/S7eeustfPKTn8Tt27d792ksROUnnYf37uAj738Z3/r+jzYeW/hdhz2aQ782Y75Tow2iIEA9wGi2ALeuFdB4XFWtuhqe6WSTGW7PyzTLBtlAbUwnb07TDEdHh5hOd3BwcABCmgRm30c+kMy7HJpEiGOw5lmOedpU5Uyiwe9HkuWOAR7wk87TFL6UKAZY2qynS7dNUJ4ljvEe85pWldr0XS4xLnnh2Mmhv5+nruvUkwLzJNvgh5I0H01NLusa0ygaDGyytgW5jtWfNjJcay0uLi9weXmJu3fu4s7tW4OvYZukyXEpcYmdOIRt9rlPCpzmRcdWSiEhdyR2d3ahjUae58jSDG8+egRtDPyWnQ3Drn7HWotK1Qg8b3RnXA+rGJS2A24phDT/Y4jhBpoFgC31MEVVYxIGTc3TcErwPMsR+t7WAKKuzmlEHl4rhfnsCvPZDA8e3Ifn9YPPwPeu1YMLNPLkHvZ62UcKuO87NWTrNoUQEELg3p3bSMsKs1mCLMtwfHzShH0tPmPOObQxKCoHpJbZwDDweuW/aVG5CqVaDQKIMPSaz8QlUXuykQePJEgDTmY7zx3DOw1dIvB1fp+jwMNF4uwDbqGhP7CHAjg+O0NZlnjw4D4IoS5tmNKG1ap7U43bYZSi1ovQnaJyvd6xL5HXuvd6sz3oyT0WegLWWDDOOt/rtqGEwC5934xF91q+4OCMIK90Z2fwxfXku6Tbbv/1s63mAVxwVF5rhJIDkqESjklcr72Z+GIULLYjOe/1hC5P7ItRVrR9zljqMCNY1B8NDOcEVWUgm55USgGtgVIZKG1gLBkJ1GKYFz2v39TTGA2UUCvsLyXuN215nyJJGym8C4JaPi85Iw3gR7cdAgvJKHwukVAKEUS4dQewVYWrNMXjk3PUVYW92IcVPoIgwJ2dCFe5QuxxWGsQ+wL/y0fuPnUQ97TmJkiI2wCnm2RDfFrzDMC+x/O0/aZ9817uU1VV+OpXv4qyLPHqq68OJqm9G0D/l7/waXzvx2/2MuCt33U9PAkAlHJ9o0NjzHBK7jxNEQZe17W58nfaIA4DeFJCKYVkTRY6T7KNMJ0WOGpjmo7TfqA0S1LkWYrDwyPcvn0b0+myX8JiTLpXlI4J1cZAa70RrjRPHAM4dANZVtWg19NYC8ZG0ii16QKbKCGYRMGKXLaT4Q6Ea1VtbU97XNYkgkovQGPfEEoQeh4u5n2S8vb91aNeVscmeitJxat/794D4NhUay1OTk5Q5AUePngI6UkUS6nEfdNX79M3rsd4GFjSHlMtowxxFCOOYlhYcEpwcnaOy8tLnJwcYxJHCMIQUnoQQiIvy84POzR5UTby6f7nuF5Y7ZjauoOzG2OtOwcEY6hHrg/aGHDGRtOLu21tqeBxrLAa/I4bY3B8fIyqqvC+l1+C0uh93WVPrUfpVk8v0CwIBUEXADUdkGPXSkNysvW9eJIjKSoIRrG7M22uv7YJ+8own89xenoKKRepxi4AyS0auPqcYe9qVlbwJQc1dqOWxwHv1b8tKwXOGQIpkA/I0F2F16KmZ5aVCD2BWmnUI4ucgnGUDeAp6xqUUgca167FxhgcHh3BGIOHDx9iZxI58NrYWfJKYxJ4yMp66Tvfgll3Te7zylqgA8FOyryQWIc+v1ZlDuCAbCAFrAVCybdW1gBAFAz3wxa1AmrH9MW+AB/xYq7PWE/t8njcSVktLPJKISkVSgUo7a4nHmdghMASF560XtO28brXAKaS9YceLc9oqFL7HMk3ALWgBKKp+eGUoFTWLbxoi2rtOhTLYV9sIOkGePUbYG8twSxXvVesQDSSY2sReRzaWKSV7qS/y7kqkWQA7MZnuhMIZJXu9pdYi0hKzLRFPN3F7u4eBDE4vkxQFjmu0iucHBHsTkKUno+7+1P86kfu/ZVJHO6bmwBgkyR5BmCbeQZg3+O5iV2w7xUrPJvN8Nprr2E6neJTn/rUqA/43QDV0zjCFz75scFaHTKwqkgIQVqUuD8QEtSXkru02d4fJJcqHDYdoay30sRaAym8DQBrmmjFIaBkrcXx8TGMqvHw4YPeRO28KHu9soCTC7ub+7qXPW5/LIcAbFUr7EyGU4Wd53GYpZwlGXYmMaqq6vV6ztMMge8Nh2+1Sc5F4UI0OsaSdI9HYbDRLduC8ovZ3NUkDexf3QUYDbCscAsMQ74z1qRXG+sWCI4Oj2CtxcOHD8GamwOltQu9UsOg0LH/HrJ8XWaOBvinCH1vlPnMy3IUCBMQaAO8+NxDzLMcjFLMZjPM5gny/LTrnM3zCAd7u6NspktiDjaSmGV7vim1Eg41NLXSCAMPSuv+m70mACo3JSLfRzqwkNBuK/AE1IAfPQp8JFkOa52EOFkDYFprHB46b/3Dhw9R63ZhaY01DVcDocq6Ruh5MLbuTTJvx1onnY58CRAy6tt1/bt8EBgzRmHhrh+lMfAEh2COOZTSg5Qednf3oLVGnrsgqMPDJwDcterOrQNcJhkoHb+BKyrlgn6Wko8DTyAZSNlVSsMYs1mzAwDWBZGtM+5ZWUOMAF8C5xddfswY09TzeEjLumHXNZ48eQLKGB7cv4+dOFypzCGEgDAnzRScI+TUdc9aC2uNOy8aUDzEzipjMM9L+MJJpotaYwvhvDKUuOCqvAFVocdBCJAOANTIHwavy2OsY74L60J7QuGkt0MAOfT4tTyybQ2VXfr9dmoed2y617BA4AvkDdij1LGZoql/sc0fGmtRawvapPX2j0sUXgeTy0MBGKz9HlsLzigYaV6fU1jj/K8WTb2Pdt2udaXhc4rU2MH9GAOvlKD73CWn8LhLdS5qg0o1x6xvmx5DVmnEHkOtTZdwHXDaAW1rLXzOEAgGSskKSBaMIGrk5O32JXNe1qvmOxlIBskIrnKL23s7MHYKQgBVlTi/nKOeXyGg5/jen1/g4OAABwcHmEwmf+VA1E3xwD6r0HHzDMAOzLv1xbyJVTrvxT49efIE3/jGN/DKK6/glVde2Xp832kJcTuf+9mP4mvf/l5vWq7zu0YroUnAIjjJ94Z7VrO8BOMUuieIIs8XvbGEUEziAEVRdknIQTAsx0nS3IGtPO/2ZQHaNmGJUgqHh4ew1uL+/fvY25kOAq28LFcWCgghmESuz3SepphE4aD8uawV9neGPRjzJEUgJfIhqXBZ9t5oEwCTOEJVlYPdle3bd4xYvzfZYhHQ4vyuq1Or1RCtOHLhS7Y557K8GE81TjOEvo9sQIY9VEskhaubSbIcBBZPHj+GEBJ37twBWfMDJmkD+AZAi7VO7r1c5+TqhfzudbPCsemjYUxJPqgSaCcrSkzCAHlZIYpjRHEMY6yTGmcZjo+PcXJ8jCCM4PkuJIj3/NDn5ap8VjaLWNXScU7SHFHgjfbDZnnpZL1rMmtPOmauZcqquobkbFSimpdOcrwOrANfIisXPaRJWqxIequqwuHhITzPw+3btztZXRdc1YDYKPB665OysnTBVFvSeBfncf8N7vKUlWp8k3YlcZgQd2yWFxhcmBKHAF2ROzLGEMcTxPEE1lqUZYG6LPHo8AgwGpZyhJFLr5ZC9HobK6XBmUXgC9cjaezgghfgpNouQXuVpXV9rwOhcMp1sU4DD7O1YzgJh/9unpeQnENbjTcePYLn+7hz+zZ8bzxxWBkDVRnsRD6KWqFWGh5nDoBZdBVipGFnQVbvI1of7m4cuFTfa/7khr5ckQ5nDWhpq23SpZoXzijKLax+N9aCM+5ClmAxb/5OMApfuO7TFmgySqDMdSK3hjpn7cZpMgnFCtAyBiiNQbkiT7YIJO8Cnpy/3SX4tl5RQlzKtDJATPnK69hGzGGsk89W2sAXdKkypwGpALgFamMGVRGk8W0MhWEJRlAMXWesxSQQMM332NURLV4nEGwluGl5m7R57WRdUmzd+/M4hc8JssqAENsxzAQWsS9QKY2kbJhd68KniAVmTWJ17AsYYzDLFSLPnc8OLFsQKnDvzgF+4QN38OKeh/Pzc5ydneHNN98EIQT7+/s4ODjA/v4+5EgeyE/L3IQA1jRNEYbhX7nFg755BmDf47mJEuJ3k4G11uK73/0u3nzzTfzsz/4s7ty581T3SQiOX/7Cp/Hv/o//1vt4WVXuRnGpz5RSBxrnaYrQD3pBi9J6tFanqmtM4gh5UWwkE+dNeNHQ3yq1SB1eZx3zomjAcYayLPHkyRP4vo87d+6AUupA6oDcVanFPsdRgKrWK/sw71J1N9kwQgjyskIYxb0/+MaiN+l48dpqA+D5UoBQ0vl+x1javCgRBcFgt2yWFY7BzPJeMapLn44wT3NEob9RaaS1RuAPL1gAgDJ6MKEacFLr5XTa0PdQK4WiVMjzDEdHR3hw7y6kHw4G6xZlNSoLLau6Y1AZpfA9uQFWW8Y6H2AiLSyU0qOyaCkFdOMHbYdSgigKVzpnoRVOLy5xenIC2fSRRmEI6XkgcIBbCueHFZy5DuK1z9B5FWsITjdkqMszS7MVQCkFhzVmxUNba43Ak6Baj7A3zg87jRfMry8FqlptsLLzLEcU+Di7uMLh4SEmE1eTs34zMW8qfZRyPtIhBjzJC0zD7YnDSlsYVS+qoEamqGr4UsBCd/2pk4HXGAKx7bRhId7ODiZV7fzbVuH47BIX5+dgjHVS4yAIVlQsShtY46qUrga6ctdnnhZdjVDoy0HWth1rnaTYsaouQTj2vUHwungdxy7fOdhDEO80wU4Uxm5nLjtZcOihVhaUEhBiu15pV9OzQHvL3tlJ4OEqc/sW+wJlrQe9ku45ctD32lbbCEYRSIa0VE3A0vUUXpPA633uso+VUQLf45B8U/raN4Hkvf5S2yLJZrxrSHkB549d3l7Hii59L33OkOV61C4QCoarAXnu8j6N+YBdKvEwQBWUrYQoMeKCtIglIBSDMu1YcszXWO9QUlAQaGsx69mnUDBUyiDyGLJSI6s0eOMvttbJkgEgKWoEkqFUBpJRcErcwo+x4IzAYwxFpQFYRJ4LrtrxBbJagxAg8Dg+cm+KD96bAgDu37+P+/fvO8A7m3Vg9pvf/Cam02kHaKfT6U8lwLoJEuI0TZ8lEDfzDMC+x3MTJcTvFqhu/a5FUeALX/jCT/Sle7cYWAD4yPtfxpe/8R28dXi88VhV1y48ackDuQwal4Ht+syTFL4nV+TAQnAEnoek6SEd8jRmRQHGGXTP42VVdYFOfbLZPC9cRP6TJ9jb28Pe3l7347EMUvvGyX1jXA14PpXRvd4/QgiUUggDv186DVe7M1bF4gCe1/gjHfO9vLqdZPlGJcryFFUFzlYvYcu1GFlewFvzEC9PWdeII78LzVqfLrBpqBanA4/DUmK34o6GkXNSVPfDf4pbt27DC0IEI0yu0hpxGIz6GudphmkUotaqd7HBWieVHKvSqOq6V/oKOBltXpYwxg7WALWds4DER27fxsVs3vWRPr66AiEEUQN0jLXYncYoynoQjNVKI/J9KF1unHvLk+UFfCkWjGvPcWprc/re2/LM0wxxEKBUqvF/96WMAienZ7g4P8Pe3h52RjqZq6p2fuYtgHOW5YP7R4kLZ8qaawoBIAXrDSNanqKqnZTaKkS+NwqQWxArG2ZofaLA61hRxz5wfOCVlzBLc+RFgSzLcHp2Bq3UahCUEIhCv+nK3fS/Dk2SFYhDbyO0ZmzmeQlfuoC3YsuxyXPX0bu7uws/miLwBIRgmGXXr8xRxoCAgjMLSiiKWoERd2PrFCAW1jT/3VyPPMGb8Cl3/UwKFww18SXSSm383gnGUGypgwEawJkbTALHhvuCbT0GgWCdFHVstHEKlousBmn+jjPaBHwZmKVPiBICYwjQE/C0LCEGAFwjJNMX/am7y0MBWDIcKgW41Ox6C3s82VKZE8pxwD3xOdJSI5QMjLoAp6I2SJsaHTOQniwZ6WpxKCGIJEWlLbLKYOK17Pj6vrrzrDYGVfOVssbAaxLDY88BcYtFPc7EdwFYSltX6yPcdz2rFSLhkqKTUiGSzpvtCQZOgef3Q3z2lc3EYUopdnd3sbu7i/e9730oy7JjZ996662fWnb2JgDY1gP7bJ4B2Pd8biID+26Axfl8ji9/+cuYTCZ49dVXf2LZxbt9nH71i5/F/+Pf/G/9SbrJar9rWxnTVqo4mfHmTbzFoirJ9yUEF0iSFLNmO/M0HQRkSunR3tgkyyA43wCw1gJHxyeoigz37t3rvbD1AWvGGELfQ5JmY+0GKMuqFwC3+zHbwu7lRTmYlmxhXYCL7+Gqp4fXWOMkcQP7Zq1dqYtZDmtq909w3gtg4yhElheNPGv4Zqqs6tHU41myJbCprLG/O8HZ5RyAxdnZOebzOe7fvw/fDxoZsAIjbrW9b5IsxyR0SdB94wkJrTXqkQCTlqkd85Y61jBcST92bFjRgchtAVXtc+IoBGMMk4mTobo+0gxn5+fA2SnOggC7O1MQKsAHKnbSotia/quNhUcJCGXIR/y38zR33aojINZat1gkpRiUL19eXeLi/AIPHtxHHMeDCwuUElDGkBcOWBVbwMLQ/kXhauJwrTUkHfa5Lk9eVphGAdJ8u2+xbGqA1uXWQ6FN86zAThyCUOrqHKxjzbMsQ5KmOD07wzQKcC78btHCBVBtB7Gk8Toq7eSjfTfxQ+8hYp4L4Rk4F5IkxfHxMW7dutUF21FKkOS1YzvzaqxmuJs48BZ+UAJMAtn5aruKrubS1AJazjiq5tq/fJ2a5e46F/sSWVk3ybKA4NcLbALcokZaaRjrPjtfMAhGkZZqY9GKEgJLKKzd/vsqGO1AtAUcw9gCLhAETaKxNgacskEQaO1CQhwHw6xyO+4Xd9i7307ki1HgCTh/5xj4lIyOdutSSpwEfu3fOSXwuWM12/7bjRApa8HXmNmlB0EJgScICCjyWncMbygpkjU5u2QUgXC+1/WvfsAX2Qotqx5JjlJpBE2XK+AALcgikXon5EgLBWUsdnyBeVEjlO7EPYg9/NIHr6eY8zxvhZ1tw+BadnYymXTe2b/M7OxNALDPGNjFPAOwA/NXyQP7TrPCh4eH+PrXv46XX34Z73vf+97WsXy3g6Xu3znAxz/0fnzt29/beMxYA0/6SwDWyXFaP+WYLBcW2NuZ4OJqjgKrN1HWus9/iFGcJyl830PRAw5cJY+/AraMMTg6Ou6qH3amk96beAuANu+h9bmmed6B8LFAJ8ABqI005OZ4OGRjBxNaldK9LCwBaQBVNtoNOxb4RJpQm9t37qwA/+XzLc3zlfdGKUUU+Iv3XlYNSNoS2DQAHgEMBjYxyhD4EueXc3iS4/U33kJdV3j48CGEWPQ7tqnEQ/sAOIa+ryYlDDyUVY2iMh3LOzQuRCnsZWn7XmcSBV2AUTvWus90DNRbWGiluue0faRBEEDcuwtV1biYzXB+cYmyrEAoRRQ6T6XXhHO1M9/iA2aMwloLRl3y5hj6mKf5aKgTowS88T2vA0QLi7OzMyTzBPebmhxGCfSS/3gxBIHnIW2Ye0rp1tTkdv+Wmcpp1C/7rep+n+v6SO6YW09yFJUdZN8X29UQnHWff+TL3rqcdlpmNclLWEIgpYSUEru7uxCMYp6kmCcJjo5cSFkYBri9vwswMRoEFS/5V7UxmARyqyQYQOMnds+bhBJJUa8sTF1dzXB+foa7d+8iaq45UjCUlXY3/nmFqPHsjn1WkvMueAgAYIF5XoFzilCKDfDsrrk+krzubn6NWXj0rbWojEGtNDiliDzuwtKuWZlDiDvHzNI+F7VGUWswSjDxBLQ2HUiL/eslCQOAEGww2dfAIm8SjSPPAUnJKSRnIHDBS2VtGhDqvptuceEaKco+38q+OlZ0fFuxN74dApeKX/V1urav0zDaoXApxBZAra1jMCvXz1oOWB3igCPpkV5L5o5FXhuUa6/Nidt+e+Z6nEFQV3tUErMBXmOP4lwZKGohmvNdMAJGgFzbbkFq6jOUSnf7uuNzXGU1CIhje2uN0ONQSmMnlPjVD9+7dk/z8lBKsbOzg52dnZ86dvYmhDhlWfaMgW3mGYB9j+emMrBa65Uo9rczb9fvOrZP7+b80uc/ie/88HWUPUFDLuE1QJbn3THpqliUwjSOO1aStonCdY00z1ArMcjqpVmOOAqQ9AANa922hsZJbn3MZjPUtcKTJ0/AGMXzzz/XXFSH/zYrCuzvTpCkRX+AVTFc22KMgfT9njRk90PYSoCHpLRJl5brboZ9T4IQ0qUUz7MMkgtUqv+mKiuKARaXwFqDPC8a/5rpPX+zhgVmjedywyO6Rao8TzNE0WZqcTtlj5TYkxKwFkmWQymFo8MngHVJtX03785v7HegZ320MfA92XwG7j1OogBplnfezkV40DBALaty1FNrjAGTErGUTZhZT0BPrdw5PALqK6UQB6vyaym4kw4Sgt2dHWBnB1JwXF7NkKQpDo9cmm8YhJ0MlTKKvCh7vZ+MEniCdx7jsZoewN1Cl3W1ko7bDiEEvic7ZjnwPWjj5J7GLmpylhcfirJGFLoU6OVvugOhi/2oawXfky51eixxGK6/NwokKGGjsl8nERawVvdukxICyiiqsoZSFUJfoqg22bj1qZWG4I5hLGo1Kt8GHBMbBx6ysurOQ0opGGMIwhBBGDpmuyydnPzoBMRoMOF1YV+tPxoA4nDVv2qsxTwvF0B5KP018Fb8svPMAXdrHTN7cXGBq6urRvnQpLITAkb5SoBYWtQO9A2AZkIIOGOoe5hRpQwSVSHw3Ou2rHvky44Fb69P7U3wMoi11qLWGkwRKKNch2ylNwDL+kwCD7MBQKqN7cCq5BSRJ7ZWzSy22xfGtDmcOtkrgI1wovZ1A0EB5hJ/fQJUtRn0pIeek+OOviZxRPDY6SkoRb5Fgj3xxcpCAWcEklEwQgECMApklXEp0D3bcr7R/n31BV0Br5wSeIJ1i3pDnlyvkXf7goETgrRUKOHCl5bBfyAZKHHds7W24M25FQjqrCrdcy2mgUBS1E1lEUEoGWaFgmDUqX+MhWQEZe1k0L/20XsI5DsD1PrY2WXv7F8mdlZr/dQ7cJMkecbANvMMwL7Hc1M9sAD+QgC2rmt89atfRZZlP7HfdWif3i0PbDtREODnf+5n8Z//5M96HzfGdCmH7v+2aBff5kmKKAzAKEGaF13wELAIbBryVlaVGgS4WV4gjsONTth2LNxF9K233kQUxbh9+1b3mS0HOi1P6PsALGZJ1lsVgmabkyjCXA1LmB0TtpSGvPR4OpLaay1gtG2AfoB5kq38rTEW0ueDAFZp3VutQohbTCirGrvTeBC4aa2xvzPBxTzpff+OwRv/Uaqr+tqBTXEYIC9KaGNQliUODw8RBiHe/9ILo4nCda3AKYMy/TdEaV50gU1ObpphHWDmRQUh+GB6dK22e2o5o805P3wtSFrJ6wiITbpUbyfJB9BJKNupaoV7t29hFkWABYrSeSovLy9xfHIM3/MRRiH2d3eacDX3d5QS+EssJ7DwsA55lgEXLMSZ86m1wI8QB5yWP5u8KBGHAa7m6aIm58HDjdX3NCtXgqSGFhCKskIU+MiKarybFhbEAnrEa9/tY1kj9CRKpdZYYIvQX003zorrg1itDaxow6O270fSBC6VtYY2GqEvVyTYhAC+78H3Pezv70EpjboqkOc5Hj9+3MmQ93amSChB33k3zwqEnnTdr2v7JAXrlWiXlevuzWYXmM9nePjwwQrbMw18zHpAqjYW88yxsUoblEvfFdcjO85etnU3ceD2t9Rm8BNfrt6x1lXFgFBoo3GVlmCUIJQcpXLBReu/z6Enrs2mGgMkpYYyBr7kLjW36g+R8sR2ENmOL/lo+FGlDNJKIxBspd6HUeKYQrr4jbWm8X1z6lKCdb+71ZN8sK7GbcglE2dNQBGlFJygSS9u/kMBbYHI411djjJo+mhN082KwUWnMV9sW/kjqPObauvkxapUYAQgQ5U5ksECCNp062Yij3XgVXLXG5s2ntVaOzZfUIqJ57qL20RjySkkc+ytsW6ftbbIaw1fMlS1QSApauPOU8Eo/peP3sNu+O6wosvs7CuvvIKqqnB2drbBzrYM7U1jZ2+KhHgse+Gv0jwDsAPzbkqIy/J6YRbv1bRfSKXU27pgzOdzvPbaa4iiCK+++uqKPPLtznvVTfuZj30IX/3W93B2cbnxWFGWDdO6HJ5EEIW+8zg1oLBvknSY1XNBUcN+17KsBsHS5dUMnFLEu7u9F7E8LzrppicFhOArCbtD/l3AeXTDwEc2FFpUqy6heR2Aa60RRt5gai+lBDtxiItBj28+KmNeJCIXcH5kwPN8zOdzvP766zgJAhzs74EwvuK3ZpQi8D2cX816u1/bybbIqKtajXbDWrgbluVtpKnz2+3t7WF3ZxdpVoz6R1vZ7phcOU0L7E5CXM43wSvQhD5JfxDAAu5YD/lhnVzbfUaOER7xuubFhrd687VcUnBR9XcKA6tpwr7vw/d97O/vQymFLHVBUD84v8AkCgAmEMcR9nd3Nthqa9GwtZsM6/IUVd3ITQsAZBB0XlzNcHV+BsaZS/Um/Ysc89QdT9v876FJ86JhrocBticE8lqBgMAXYmsAVFZWiHwPebWQyzq/dE9tz7VAbAN+C2eTCD3ZBUiN7kdRwZcCk9DbmjjMOQPnEaI4xnMPH+Ls4gp5nuHs/AxZUSFomNkwDCGWfo+ysoJgFKHPkTU385QQMMp6rzvGGhwfHaOuanzglZegQTrZexx4veB1edKiBm1ClpKiQuhvB6/Lk+Ql4tCHJARpUW9dOGgrsJJiVWqclAoEjpl0gNpdfxmlqM0wOF4fz+NdBU9RK7S4N5DOx1opg7LWHag2I8FI7Ux8ca3U44BTFMpi2Syijd0Ah3GPp7UDnE0tkS8c4xsItrbQ5n4XLACPUWS1aXyhbpG0AhxibbYpxiqHrIVkw/JpRtEbeAYAkhOEnKHUBrmxqNdArt9TmcOISyPOle5lsIvaQDICjzMklUKlmoTmogYs4DPr1C1AA6otJr5AVikY6uwQE9/5XT1OIRltwp3c9YZTl0j88++/jfs7Qf8xeRdGStmxs9baLtn4rbfewre+9a0bx87eBACbZRkePnz4VPfhpswzAPsez02VEAN4W4xn63d96aWX8P73v/8du8C8V0w1pRS/9vOfxf/z3//vvY+neQ7OODhjiKMAIBTpEsAYAj3WGnDORvyu2SDArWu1AXCttTg9O8N8NgPjDLcODnpv2pTW2JlEztOV5hvyaFepIgcDb1rWuY8lquq6C3TqY5Db8J5leTRjznM6SzIUZTVaA1KUJTilzQr45ijtEpFN0ykZBD6ef/551LVClqW4vLrEPMkhpUAYhri1vwfKCJIs647rGIua5gWEEKgH9q9l3ftkvpRScOYkyrBN2M/FBe7cudP5VSzsoF92cQxzTMIA8x6fagvGy0p1tUp9k3RM7bCnNknzFfBJ4Njx2VIHclVvekGXxxqXtDr2fgTn0MZsvbak+aZMmHOO6c4U050pbNM5S6Axu7zAkyeHHcgJw7C7hmljwLHKsPa+/7zAThTAEtLPmBYlDg+fYBJP8P4XHiLZEj5kjOntQ9143TQf9FRzxhqFRdN5SdDre16ftCgbEOtY3j7w2k4HYut11taN69Z1f++SZh1AHvINr+4/7YDsUIfz8hhjkRUl7hzsAmQP87xC3QRBZU3gl+C8+4z9IECtXU+nY0KrpmO3j0XVODp03tsHDx6gUhacE0S+RK11Ux2yfZyEuUIcSFwr3WlpJoHfsaOcUcTSAY6hwLhJILtgqD6pcV47pi2Q3O0JIUgbNc8ykzu47QGg2fpYAUByhsgXHUgeSwqWnCId8Y6244Ci3fr9iLz+CiBjF/5tyVwy8VDgHQD4nGJeqtHarMhjw5U42A7MPb4AtwStnJegVBqCUlwO+HzjtbRjr2FTy9qg6AGvhDi/LGEUaaVQNX8bSuoqcgQFQPC4tog5Rd54cjklmBc1Jj5HWRv4Dfs98RiMBeaFQihdh3AgGRgh+NkX9vD+u5Phg/YuDyGkl509Pz/HV7/6VQDomNmnxc7eBA9s2wP7bJ4B2Pd8bqKEmBACSulPtF/WWnzve9/D66+/jk984hO4e/fuO7pP7yXQf+m5+/jgyy/guz96Y+MxT0r4ksOCYDZPFv6pZsqq3uiNbSfNckRhuAJ427HWQogtgU4NwNDG4PDwEFop3L5zB2enp/DkZkcpYwyR72Oe5PA8MSBVHA+6KQZSh9uZNfs1VINQ1XUHaOIoQFnWHZAyxjoFwkh1ythrl2XlQFbzeHvDJqWAlLsAgBdf8HF8egYYhR/+6McAcV2lYRjCBCF2JvEgi2qMQejLQQALNCB4DbB50ikOkiwHrMVsdomrqxkePHgAz/NW30NVjfbbAkBeVRueX8ldRUgr4R7zHAObAHXjvTaeO0IpYO1KsFX3XpVCFAZQYxUsVT3IGntSwhiDLC9H/b1ACwDtYDgaoQRRFCEKfBzs38LlPEGappjNZjg5OYHXdM62P+yu/ijH2Llugd5zuGXO9/f3sbOzgzQvEXgS+cCxDH0P6VLX71hCM7CoPVqWX1NCIARfWViqlYYUZCMZuG/SosReHGI2wpi3kxUVfE+gVmblWMfhJvg11oFM5zEd3rYnXNKyNm6b1wW9bhxzRAEIIbqbWGMM8jxHlmU4Pjlx388gQBCGqFWI27uTXkZUaZcPwDnHvbv3OuZcKQOta+zEwVb2dWXvGjloXlWIfQ95XXf9ukMTeKsASGmDWV5BcuY6UJs6nXYkZ8hGwOAyQK20RexLFLXGNJDIq7qTAZPmuVh6vsfZuNx2aTinuMzq7vMIJG9qYSzKeuHZJHAhdduCyQBAcAZlzEbl2cpzGEEx0vnc7R+joxVSLbAfY7u3gVdf0PE+WMmgYRF7DNq4wKwWzLr30b9tyZsFh2YfjAGyWqNUrhpnvgZ6OSOImgThZTDOmWOV4wbwW8Bpr4EGlGpUyiJuvNjaWFTa1fKUDUie+i7hOmoqed53J8YnntsdfM9PY24aO9vV0T1lD+yzFOLFPAOwA/NufRluIgML/GSe07qu8bWvfQ1pmuLVV199V75M75WEuJ1f/eLP4QdvPILWGpwxhIGPsq6R5Tmy3DEhQ4BtDHQpNcyWJWneyFr7A50Yo6iqCk+ePIGQEs899xyq2q3gJ2mGMHQhU4zSbjuL/Rg+f/OiGJUSt5U9w3LgxiPZdzwaP6pSpjeoKs3yLd2w6Urg0+KAuB+Q2TxF4HsoeoK3AMe83L93G/MkQzSZuvqWNMPZ2RmUOkYYBNjf3wVlorfaKcmKLVLiekVKHIcBsrxo/FoaR0dH4JTg+eeeAx1YqZ2vhVqtj1J6JSQp8DworVcA1DxJEfr+IFhqb+DG2NF2scIYMxjIlHbS7vH6nXUQ60sJbTTq5qY0vQYrXJb1YO8uAWkAVg7BGOIohJQSe3t70Fp3rN3l5SVo46m8e2sfGtSB9LVxHuIclJKVGqirqyucn5+vMufWolYKUnJUa15LTwqUSnXXhrKqRwPB2lmWTQNoPKvDicPbanOk4EjyEoEnnM92i660KGv4ngBRaDyRzd/1PNfCus8v7E9FZoSCUNJ1WDtmtbhW92voe0gKF84UeALKmO6coZS6RYsocjU9VYU0yzCfz3F1eY7jI4nJJMZ0EsNSDgKCqnbXy8APcPv27Y3f8Tj0cJkWkJyBS9ZJkcdmspSKnBROJeJCnkr0XWcZJdAW/SoWpVEpDU9wCE6R5JWTA3M2WoO1PL7gyJRxkvnawIJiGjAYaxdS5eYEYIQAhMNe47edUbqSfmuwWgtDCUUgGBh1z71OwFMcCBdkZMfuqWxTGTT+/ifXqMyJfbEBBJeHU4pyoJMVQKegWX4GpwQep6CudNZ19vZuwoJTirzefJCg9a66KqJl72wsV8GrYBQed9esq3wtbA4WsXTdrsXS+/C5e2/tdvwmyKnt+p14i8Tmic9QKA1fMFhjcX83wKvvuzV4TG7C3AR2tr0/vgkM7DMA6+YZgH2P5ybW6ADXB9ZJkuDLX/4ywjB8x/yuf5H9eadmZxLji5/+OL7yze8iSbMNQEopGQ7wSTdrZtopq2olsXh9lFIglPSGCx2fnmF2eYk4jrF/cNCtrrc3y9YaTOMIWVZu+GnzvBgFillejKQOW4SBGASwWV5gEkZI5qvdrW1Fzzx1AHhoXDds/w25bV5/BfQ3SbBtyFjbs7h+gzgJQ6SFSyQmIABBV99i7UEjT0xxdXWFJMkgpOxYO89b1LdkI4FUQAtAfXBKOya1rms8efIEUkrcunMHu9N4EKy5mhk9KnNtQ5IM3Ge5LpmzcJLqsW0UTQ/oUDKv4C7sifPxH+QkG2dzAQdQfSlRVBUCz3mh1z/feZKNeqzb15qs+XMJ0IFXwHWhBpx14Lztm13unM2yDD9+8xEkp7DUyVCjMAQXApNwcUyMsajrGoIzHB4dL3X0riotlNbwGF2pDxKCNwsXi/epjQE1ZPT8WbzXAnHgg1Iy2k/rEodlw6ZsfmcYpU6+aDRUXiIKvGuDWE9ycMZhLEZZKwtgluWYhpu9sEEgN7zS1gLztGj8xUXveprgDJXW3X7mZe0WDz2BbK0DE4RAeh6k5+Fgfx+UElxezZFlGc4vLhFIDkMYiqJ0zMytgyanYDFxsEg4boHkJPC6/ta+iXyxkUasjME8LxE0yov1ntrAX0iBh6asFcraMdehL3CZXo8RJgAIo7BLYI8Q0iXk+p6AxxmKWqGsFEJfdH2i7vrmApP6wKQLYxr+zTXWIqvUSn2N1/TNElgo445rez3yBOu2Z60dXFOdBHIUdAKNb/Q6tTpbtuMJOspGT33HqkqfQxuLWruEX1XpDoQOSZNdSvDqtgUl8AQFATDrrdOhXUesbLypaaVBtNn4rseek/peLqkOKBwT+1ZlEGkLj7vaH2UsCu3UQrHn5M6cEjCKLolYaYP92MOvfuTeU/eW/qTzNNjZ9n70aQPYZzU6i3kGYN/juckM7Lb9Ojo6wte//nW88MIL+MAHPvCuXvTeixTi9fnsJz6Kr337+70sRK00otDvecQBSSn8XgALjDOaroJllcG1sLi8vMT5+Tke3L+PKI5hOl+cO+bTOEJWOKCl9BDQLMFZ/+PaaEyCEHPVD26SNF9JHV6foqpWpDRh4MNY270PTw7LbIa6YbttL8mYlysmWuCaF+XK3zPGEHhy0e2qS+chTpa7Ydelxh6OTs6QZRmePHkCQpxENQxDBEGAaRwNAhDBGDgl3bHJ8xxHR0eYTCbY398HIQSzJBuVk5Z1vZXZpIyiKMpBv1dV102H7Uj3a5q7YJg15qyVPedlCa7YKOByx9+AMgIzIJ00xsLCOuVCWfcCLQt0YHEsBTlJ806CSuCSfdffY15WA8nUi87Zg4MDKKWg6grnl1c4OzvDNApxLiXCYLFoUdUKVxfnyLJ8o6N3ecqqdp+prsG5C9Hp83rWSsOXAmaoK7o9Hk3q7DY5avt+A1+iqleDbwgcaMmWpMfpTwBiy0pjGgUotvhs25llBSaB37Gm0yUWuW/maY4o8JCXayFGxHXu5mtAVWnddL96gx20YQMQ48kEcbNoMbu6Qjq7BGcUs9kMZVV23cJCCnhCIOt5j/O8hOQMQnCka/siGOvqYfombz772JcolVMbTELv2qnAgJPEXqZOWiw4RVbUo6FMcTC+faVtd70/2IlcPYq2TVItALT/3fpmm/AoX2wFiIALG1J2sbxY1gblWr0MbxhExhzjqK0Fo/0Vcb6kW1/XLdr2M9rdaxKytVZn4nPMSw1GXEIxa4KhAEAZC0ZJL8hsZ6wyJ5CLyhzeJg8bx7YKi17QTAFQ4npoKafIaifvJcSpGlpFeSBcx7WxdkXaHDbe1XnDulNCMPEYtAUqbToWt1IGkjufbcgZ8lpDMorQo/hfP/YAnD1dSexfdPrY2bZ39mtf+xqste8IO9vejz5NCbG1FmmaYjJ5el7lmzTPAOzAvJsS4pvmgQXGAay1Ft///vfx4x//GB//+Mdx7969d31/3msJMeAYlV959TP4t//xjzYeI4QgLyrEUdR7U5pkbYDRJihz3i1/lNFrZYfGGpwcnyDLXdKc7/mIA+f7pJRiJ47wiABXSQICB6KGgl60boHiGJMYXCt1uO89Cc7AOUfgy43qniwvmiCqsVRhbzDldp6kkEKgaABgC17bSTPHekvufMTrcuV5Mt7tmhcVbt+6haIsO9YuTdNGaqwQBAHu3DqAJXRFahwGLuF3lmSYxBEeHx7h5OQEt27dwnQ6XXkNZcx49U7aVhOtHn9CHGi7nLlU6LEQn06KOsbglaug0UmSVfd/K60RSgGl9OBNYlnVLlxqBKxwSkEpGQVttdKIAg+qqX7oG2ud305wDl+KQYDeeUlHADznHGHgYzKdgFGK84sr1zl75apxgiBA1SzGvP99L6Oqho8B4NKq4zCANnbQEws41jT0JfJyOLSn9ZwSQrsKprHJmwCmstJdzc5kAECmeYnQ95CX4yB2EnmYZbk71tdIPQZc+nQceCCUjp4Py/viSwFjbeflHZMXW2u7ftm8rFcWcKbhZvVNmiQ4Pz/H7du3sbu7AwaLk4urhp11QVA70ymY9BAEwcYN6IKNlcgrBaVtF6CVXSOMKikqUEKwG3nXSuRtRzCKUrkU4VJpFwDEKIJG2ry+cBX516/M4Q2Tp7QFAcUklCCwyCuNWuluUcpa55FNCgXbXF/H7n1CbzvQVdrCFxSzJflrrS0EIR1wZJSAEQJCAcEcK2ithbZtSB9g4ELihsKdWlkyJS6huVQurKhLLAbQSnkoJSiVAaOANoBeA92i8fkOvu+RyhzWHK7Y41DGoKhN57GVQ55YazENOcradgxsO7F0QU8+p66mr9KQlLjjAddv63Hq/Kzu7UEQC8kZLAiySiH2XbVRKCgooZgXNaaBY84jwSE58Dc/8fAd63q9SSOlxL1793Dv3r0VdvbRo0cdO9sC2ul0em1A2vpfnzZb/awHdjHPAOx7PH/ZJMRKKXzta1/DfD7HF77whfds5edpMLAA8KFXXsTLzz/Aj958vPLvhBDUdY0oDAblwHVdDyY3Jmk2WONirQs3SrMcTw6fAACef+75DjileY79nSlmaYp5ljnJYpPFZLSB9IdBTifbLPpvNLXWgx7dMX8vIS78xxdisO82zQoIwVD3hW5YF2zS69G0FtpaSNK+Vv8PRhj4uJolvcfbWgNGJbp4zY2Xt4A1XejHQmpsuyTUk9MzlGUJxjmiKMKd2wcd2LcAXn/jTZRFjnv37vWmAlZV7RYQxnyfa4FNnDNIwTFLHCjL8gI7kxBXI9vIi2qU1VRaIw78pgPWbzpqV49ZlheYjtQEAcB8xA8bhc6Pa43dkACvT5qXW/2wSivsToZl2N0+NQzfWN1PVSvsTEIH6qIIYSO/SpIEp6enANx394c/eh13DvZhKYMQciA01UnXBaMYfodusqLtfi03ztEWsLogKYOyqhFI0bF6o9tsanP6QpdWn+v6WYuyvzYnjhYgslYKljEEUiIf8Jcvj1IGjJONsLGhKSonDw48Ac74Vm8s4GpoPMkh4IJyQk9uyHlbz/Ld5juotIEC8PDubaRlBa0NGAxOLmZIT0+htUYQBIuaniW2fZ5XLi04kKCUbrzWtslqDTRsZjqycAEAsIAUovMotlNrgzp318U4kKhqB645oyuex20jpegqcyzQ/W8C12XLGEFRKVS1BqUMpl4s3PQFQQGOwbwOS9sLOJsKulqZ7ooc+wJJPr69yHOyZtKB68XmLHHvLZAcsxHpMCVOrl4OhURZ24Ro9e8Lb8Dv+t94kkEy6jIpSo31xloCF0pXLXliOXGJxYQAl9nmPkee28/Yc/LrNnKRMYK6dkFMWaWRVgq+oCgrg4kv8GNtITlFXilEkiEpakw8Z3GYFzVin2Ge1Yh9AcEJfvWj9zANblbH6rsxY+zs17/+9RV2dn9/fyN0cXluQoUO4CTEzwCsm2cAdmS2xci/nfnLJCFOkgSvvfYafN/Hq6+++p7Glj/N4/RrP/85/F9//9+tMEntueDSgT0UPV2+fXLg5VFKD55TZ+cXuDw/hxQSt+/cBiW0qaBxDGlRVd3fA84Tyoi7mCbpMPsLtNU4/SDVeXSHE2373m/o+9BK4fS0QK3V4HvSWsP3Q9R1/61+WdYbUl8sSYbzosTOZHPfwsCH0hqXV/NRgJjlRRO41P95FD3vnRACKaU713d3EXgSZxeXUHWF737/hyCEIAwC1HUNpTVeeOF5MMaHWdYkG03gdaAyQJJp+J6EbVJ7l2eW5Ag8D/lAf7TSGlHH8PeD/SQvcLAzwfkA4AeaHuCRYCig9cMKFEtyyzjyXc1Ms92+56zPLMlcUFUf0CXAJAxwOUsGK2fasbAuOGmk+zXwJJKmg7cFfGVZ4vT0tPNKKaWQZRlOLy4Bo1Br24CcCEHgd9+7ZTn2EPu5PGnehhktnudJgUqZFVCpjXE+25GaqW6bRYm9SdQbqLQ+K92vS4sWUU+qsNIaxtqt3a+MUlgQpEWbqjveu7u8fdl1d15vyspVX00DH9lSCi6sxfn5OWbzOR7cvw9vzbM8z0t4nGMa+rjKKty6dQsHBwfd4lSrthBCLGp6fL9TBmhjrpX+3E4YeJ3vNdHuuEjOXMVPz/udRj5mI2xqW9/T9r8KznCVXY99dTLmATsAgGwJNO/GAawFpDDIKgXdvP/lIChCCDzBkNXbF5Q5I73dqq0FpJ3oGkyuA46reoj1S9eyh3RoQm+8Eme0MmcJ3PLGzwoLFMpJpwWlg4nGrjKnUb0IV22TVhqlMr25BR6noCCotcYylo595qTgfBHSFPsMWlsITjDLK3DiPMa0qVaKJEdZa1TGYidwbGwgGTgj+IUP3MbtSb8d6qd91tnZ+XyO09PTjp2N43jFO7vMzt4EANtKiJ95YN08A7Dv8bTMomnkhTdl1gHj8fExvva1r+H555/HBz/4wfdcNvE0JMTt7O9O8XOf+Cj+x1e+0f1bC9IsnBxpaOZpDk/Kjf5VYDjQaT6f4/j4GHfv3MFkOgVjru8zy4ruuSp3cuCrJjhpHYRU9XCdT1FWm0BxbZ+HQqgsFt4lwTk8TyJJsy4NeRtoTwdkst1rL9UFoblxWvhdHfASgjcdrgRxGKwAzm2BS2leQHCBWg338Y5VpIAQ3L93G1ezFJPpDtI0xenZWQdYDw+POtauL9UYaPpUKYMy/edzkuXY35ngKslWAoHaaQH9tg7bMTntNApwfjWHPwKErXVM3Ni+LvaFwBiLSZuWvHQ6WmObvjza+37ayXq6X1vw2jK48zRzbO0Io6ua5PC+Ch5fCtSNr3Ke5ZhGIZ4cn67U5ABN5+x0iul06t4fDE7OLnBycgxjDIIgxJ1be7ic664OZJ7liIP+1OTlmWdFF6YlOIO16GUta60hCSAFG60KCTyJqzRH4Ankldq6yJoVFQJPuoAdbeBJ93d9K1rGGFcXM1Cb4zy3sqsNqpowsutIoDljqLVFVhZLyb7bF4iNtVDGIJAcaVFBG4vTkxPkeY6HDx5ADCysUkowz+omJKgCwWJxand3F1rrrqbn6Mh1xk4nMfIohvR9cM67btaxYzwJ/Q1pbytL9iV3fvmlxZzQu74U2MItGFxlNaRgrn+0VIMyfV8OyG17JvL5KogmFHEgwKhTx+SV+97AOnFxXWuALOwcm/cEFh7fZJXb99E+u5VOb3vnTsI9XpnDKEE1AqyjpfTdvgkEG6zM4ZQgkhzaopHsGqglsOoL2vteAReklFUaE4+j1roD2c73SlEtfX6SU/icolJmI2V5IplL9V563Ynnrj9Fs6BDKcCpRV4ZUAZMPY6kUqAgiCVrGHwCwRg+9/ItPLf/DPwA7jxur/nXYWdvQgdsnucwxjzzwDbzDMC+x9Pe5N6EPqnlaQGstRY/+MEP8KMf/Qgf+9jHcP/+/ae2P09DQtzOFz/zcXzr+z/qgNkyeMjytoZmE7RZayA47wWwgGNLW8+qbViEq6sr3Lt3D3u7u4gjHxdXSS+rmOclBBfN66wB2KphM+f9QDJJc3hCoqw398vtMxsMocrLEge7U1zM5h3Lu8y6ztN0NKW2UqoDPJuv7QDyZliTG6M1Ql9CcAatzQYbq7VBEHmDANZojcCTgwDWNsK5PoZ6EodIEgfWBedIsgxn5+cIgwC3bt92rF2a4vDkFMRqGNDeVONaqcanOgAu4whX8xSseY9944KtwtH+2HmabwAJF4K0+DtjzWhycVUrxGEANSLta7tfCaHN92NzQaeqFKKwZZ77F3yMMQBnoIy6kDLikqTXZczufY33qxZV1bCji+d4UnTMZjtvPT5ElsxXanLWhxACQjleeO4hsqJyHlmr8eToFGVZQnqySTWOHEPme1vB2yzNMYkDKGVGvbOV0o23u5/9k4J1TGm6JCfeBmLzsnJ1PNTVa4xdW62xSPICkyDAfA2cT6JgI4VYG4usrHrDwrohxMlam+M0zwqEvgPVY/VAAFaSjyVnOD0+RFmWePDw4eCiEWOuysZYg3lWwpMChADFEuBgjCGOY8RxDNvU9BhV4eziAlVVdd3Cu9OJSzjvURQ4wDgMRttwLL/pUy1rDWWuA9vdhJJj3oQDVbVGVesudMlYrKQ1U0pgcT3VmGAUm9jLIl9i0gmlmHgCnqAoawMLjbJu/LOt3LhNhScEsS9HmUzSLYQOy3XbcZU548+JfT6aXuyA8vCxoKTpw4a7QnmCNt8P15vqQp1U72dFm27g9csogUUoOShxvy3ztXMj8hb77HEKwRwr66314HLiFhiSQq1cqyPReF+b74zHCWAtcuXuUSYeR14bSEZhjAt2KrRBJDk+8fwu3n/3GfAZmj52tvXOfvvb34bneTDG4PLy8ifyzr6Tkzb3nM8kxG6eAdiRebckxIADsO9WBc3bGcYY6rrGa6+99p77XYf252lKraUQ+JVXP4P/9//+XwFsngt5WfYyPkAT6BSGSLKeQCdrIKWPoqxwdHSEqqrw/ve9gjAMkaYZLq5qSNEfPqSaUCaQTQALODbT8yTKnhtkYwy44L0AFnA+2ziKNmTIkyhEWVW4nCVglMIsMXOLOh+3qjw0C3C9eTwsLNKsQBwFmCfpCngF3I8yJRQgFlnVD2CSNB/tbk2z3Emsez4PACjKcsXbyRhF4HndIoLWBtrUePzoEXZ3d7G7t+cqFYSA3N3F7u4uKCVIkwyzZN6bajxP841zglKKyPcXyc1MosJIYFPSsNl5PxC2cNLHFqBSShD6/grobcHnmEc1yXJMowjzgeMFACC0+fyH1QhpVmxlT8vKdb+meYEo8Hs/Q2stqibUaayaJskK7MQhrpIcnhDOz9xeQywayekMD+7fx+7OdBR0GuO6Xz0pMG08vQ8fhl3nbJqluLq8AqEEkzjGznQKysXITY1LcBaMbfXOVnUNTwgIxhb7D3deUkpXQFhalNcGsWXtgrjWa196xzae59Dv0oD7KnS6p9uWafZ7A8X6QpuyooLg3DHJAyBwsvSaxhj86PVHoJTgg6+85DynAzsTSA/pErtYVi6fYBJ4SPJqI6iLEILbezuYFRWem+xAq0W38I/feBOUUuzvTBAEEZiQzXWJwFwTMLaf2TT0YEDc4sQWVROjBA5/rW7fWtvJlQWn8AVHpTSE4FtrZJotNOzmtudaEOq6SFt3rBQcnmAgTa1ZbSzK2oVPzYoKaNyq6+ysq9G5XtrxO1WZIzjtBcqCOhDtMQKlAXCX1FvUBmh8rE51NBznFkq2ArAD4eS5RaOImPe8btvf6jddulmpUapWarzwKMceR6EUimpRSSQYQSAo8tqgbsCrY2ItLtIKnAAeo1DGuqqgosZOIDArakx8jg/eneITz++NHq9ns5hldvbll19GVVX44Q9/iJOTkxV2tmVox7yz7+SkqQvzXK95+6s6zwDsezyEkKcqjx0aYwwODw8xnU7fc79r39BmJfRpSq0//L6X8JVvfg+vP3qyAWCVUqPS2TFJ78XlFS7Pz+FLgZdefL5J0W22Y12Qz1B67jzJ4Es5EFzk0ieHeKo0zbbU1yxAeeB7ICArzw38AHVTu7N+PLKiGPWj9tXK2CZl0oFYJ2NeBihh4DesawrGODinUAPSs7wsRwNl8qIcBUBJmiPwpAvrUrqryGlTDM/Pz/Hyiy8AtF9CZIzF7TsHCKJwMNV4OpkgjiPY5kaQUbYCErO8wE4cjQY2FWU1+j7LylXr5EUFLlhvDdI8zZtE4WEoNc+Ga4AmUdgtNvi+RDECBOdphtAPBkPEALfAsLczwfnVfPA5SikEvgete0K/luYqccnERVWjaj5rayyOT45RliUePngIIQXqWg2md7dTK42dyap/ua9zNs1SHB8fQ2kNIb2egCC7qKQiGJXUt1PWNXwpYOE+awICX4pOurty/BoQW1T16LGJgwCz1Enyr+tbnWdO7muMvVao0Sxt0oOLqtuXSTScOOz6gklvbU7giQ6EKqXw5MkTCCFw9+5dl7QaeCgqBbW2iDiNgt59bdONPcHBKF1JGI4CrwFgbhhnmEwnmEyXuoXTDJdHxxAwoNLH/u4URe1d+7dyEnqYNR2enmg8smU9mBIdSDEob22nVga1qhAHEkpbTHyXxluNSHSnIx7Z5WGUblTTGGDFc2qt+63mnIFZdLU0LTtpjKvRsXDAdKyDFXCY3tpxhpo3icFDzyGEYOox1GYht3WLexaVMlAG8EFwmY9Ji4f7YpdTggWjqLRxx6R2Xa19LLSkDtiHcpV99gR1CwnWIvZ5IyOuEUnnWyWwiH0n+VdNL61s0oktgKRUCAWDsYAQruc3KWpMfY55WSP2OF46iPG5V26NHNFns22klIjjGGVZ4hOf+ETHzj5+/Bjf/va3R72z7+S0/tebpN58mvMMwD6FuWlVOicnJ3jy5AnCMMRnPvOZG/HlaJnqp+0V/r/8wufwf/vX/59eNn6epIPhOkMJvkVR4PLiHHEUI57u9ALVNGvZun6AQQgZ/IVPsya4aABY50U1yBwrpbAziaC17X3tJFt0w/Z5orNs4VfdmMbfurhBWZUMG2shpQOwlNLG67p4D1orx6IO9NYqpYdDgdAESnnBcNcpAM+TuJon3Q2ltRanp6dI0xT3798H5aJTKvRNkmRdAm9fqvHl1RWuLi8gpQfpSXhe4FZulw7lLB335CrtkoTH0jurWiEMvFEgnBXlaM2QtXbDD0tAXHJts6hhrYXR415Xa4GyHk5JJoQgCnycX81Hw64AtwixLdSpXaRgjAFKwWi3MGetxcOHD7vrSq01/AHfbDtx6GOWZAh8icIq2DW94HLnrDs+LuRt1tzctAFBd28dYJ5m3fc2zQrEvo9kBNQDLrnXlwKA88SPBUalTeJwWateqf40DLrQp7pW4JzBl6K3x3ZjP+oagZQglMJew9aRtLU5xoBzhiQff42uNif0kDWdlpy5Hk3TSHsPDw8RBAFu3brVXXuSvITgDLEnOhlv5G8mFa9PC9wnoYesrMHYKqu9PivdwnBBUFQrpHmO86sTMM67RYsgCHqvjYFcZUfLWjfMJYPvMRegtPS5Tbf0vS6PFAxZrWEM0P6JC+yhLshnCcyu78fY+A2IGhtCSBca5X4fbVOD4/434K4bnDg5a8BpU0XSbsGpONprLmdArZ1fevGo+/9aZrdNrvebflRr3XmijYUyFpITzJsE397jxegwew/HgPbX9lgEgoLQth7HrMh+Q0E3jhclBJFkqPWmv5U6nTECwaCt7V5z0siMA8ncd6NhUeeFq8fJmi7bXGlEkuNiniHgBEo7NnwncIA34BQPdwP8/AduD77XZ3P9aT2wfezs+fk5zs/P33V29lmFzuo8A7Aj8252wd4EBtZaix/+8If44Q9/iDt37nTs8E2YZan1kM/pvZiDvR189mc/ij/4P042fGMWGJWBuX5XF+jEOUeZ5zh6cojd/X3E02lTJTKckDt0c10rjSj0oQZ66/KiAGe8K7RfHqVULwvLKEMUOhmv7w9fcKu6BqVkEWq15G3SxiAIvH4AC8fwTuMIV0nqmNf2b5tDmGQ59qYTpHnee1xc2nKAZKDzNElzRFGAdADEplne+94F55Cc43I2b7zNGbTWjlVTCg8fPoQQ7oY88OQggAUcMFwGa8upxru7u4h8D0mS4PjsHOfnly7VOHI3v2EQAtTdjA15ht1xKgZDuaLAR1nUnfx1CKBqYyAJ768xambZD0sIQeT7G9LjqqmWSrMcQ3JirTSkL6G0XmGbHHj1usWSoqogpUA1AqpcqFPU6wUWnIFSgrysIBgDtRZvPn7kWLs7d0HWwteKqkLk+02dzeoxiBpfq7VAljt/p6vDGdw1WBDcurWP6c4Uda2Q5zmIUfj+j34MAAgakBOGIdKyQOR7SEd8vW4fa+zFEeZbngcswppqpVeuG3HgY7a2MKCaLtDRADM4awCjHLOsAaUEnf9u2367yhwGa68HmJLM1eYAxNWKFRWKouiUQfv7+xt/UyvXazoNPecN3BoOtJh55r6roS9xmW4/vu1MoxClstgJYuwd3IZVFY4vZjg5OWkCvxY1PZw7tldb0qvGqbVGnWtQSjDxXTo1XQt9Gpv2N9usBRnllQbgrkGeoJDMhQEpXM9/O/HlBuDqm9gTHZvrfgcI2pybtms2SRLAGlQGMEUNQhbPXb7Hin2Oyy21OhOf42oE2FNCoLd4jCkFzMBvp+RkJZiJEcCXDLBAqQwMSG9lj2OFTfe6HqMQnDafAxp58upMPY5S6ZXXCyVDUWvEPkPa+G9jj6NSFqFwsmlBXY8u1cSlCwuCY+OCt6aBk5MTENyeBvilD9196r2lPy0zlFsz5J19/PgxvvOd7yCKoneMnX2WQLw6zwDsU5ib0AWrlMLXv/51XF1d4fOf/zwuLi5wdnb2VPdpeTpQdAOA/hc//Qn8t//f/4mzi8uNx/JiLNDJIvA9CM7wozfexHw2x/379zrGZp5m8DwPZQ+Du5Aob96oE0KQpDmmk7hXAqmUbuS8A92wS6E4hFBMIudBnDUJx0YP1+5UtQPAQyAjSfNRJnSWpPC4QF6WK+CVM+ZYpjQFwfAFvmwYkyHGryqr0QWiLF+VEsdhiKIskTZsWJJm4JTizTffBOccDx8+XPnBSfNitMNUKw0/DDbYRkod0zhPcjAh8cLzz6Moy06aeHZ2hmN13N383r1za9AXCDi2dx18TBsfcAuypCCDnyPggn2m0XgwVJLl2J3EqJUa9N6mWd4ck2FmNC+qFfaUEJdcuyyl1dpAMD6atgwAszTdkOGKhk1tGcUky3B2eoI4jLC3vz9o1U2LopFTL7YVtnLcpQWEJCswjYNef+fyFEWFMPBgrcX9O7cxz3OE012UReFY+MtLnBwfw/N9TOIY+7s7UAaD+zeNAlwkWcPEYmvgURvWRAAoYxB6EmlZ9Z4EWhuUth7t0Q0D6apg4ECpuCZzS4lj3pK8dAzdNXpfAVebM41DGGu7dOD2xm9skmaRQWm79Rgtj+cJXCYlIk9AWYNySyULoxRmCYwaC4BJPLx3F5IznM8SpFnW1XNIKXGwtwMq/FEmppVoU0IQhx44c/LibWgzDuRWOXBZu8qXSSChKo3I441VwqDsUUX4wnWQbhvBKPKRBQNCCObzFOnVBXYO7sLzfNjGO+u+W4sgKMkbKe2Ip34s9bedUI7ve+wPM9AEAIXzmjICVNqiVKbbXuyx3mApAldxU1QGsXTdq1lTlxN7m0FToWSQjOByTSkgKHHVQdZ2++gLx1ZXzeIfgXW9t3kNwGLHlzi+KiAYhccZtDHQ2uLWxMOvffQ+OLsZhMRPw1ynRmedna3rGmdnZyvs7N7eXgdof1J2NssyhGH4bFGimWcA9inM02Zg0zTFa6+9BiklvvjFL0JKidlsdiPAYjuEkKeeRNyOEBy/8HOfwL/9j3/U+/g640kIbW6mNM4uLnF1eYE8y/D888+tBHdZa8HpsGfVyRc95GvsCyEEWmvHVg2wnfMkQxj4yPokmdbCWCCOQlRlvZFc7Gp3Nut+1rfdvof1i2kfyLTd67pgkGXwOolC5EXZMaOT0EPSU/IOtIm+4WAIUa30aCeu1hqBH0AbgygINhYe8iLH2ekpojDE3v5+7w9FkuWj7GYH6Jp9lEKAUYJ5A/DadEi6LE20TpqYZimSJMHp2Sl2JjEIE4iisPmhW+yLqxtqvOLGYBo5Znv1vZRbQ5RmaTYa6iQER1VXICMhXQAwT/JRIAS4BZtJHCLNS+fv63nuIk14BChatxDRAinOWCMDdZ9HlmY4Oj7C3t4eHt67h6zcZFhX9qup15mlrr+2qlVvSvMsybtAp7HJ8hJ70wiXiasXIgB834fv+9jf30fddM6mWYbz83MEngSTHqIwgr/cORss+mMXcuLtINY9l0MIiVLpDenz8hhjXKp6uNlpO43Cja7ZuqniiX2JZMT7HAV+V8MzzwrEgY90C4MNNLLtrMB8Pkd6eYF7d+8iCMPxP0JzrBpAMAmboKYtL+YCqtw5k5aOGZwEEmkx7CUOPLkSDtVOW5sziQLs78RI8graGFCrcHw+Q55fAEDHzIZh2MvERIHX7RNnFIHkKGrdK7+P/O3gtXuvwSIheFnmyhmFzxlAgFob1MrAgsBg/BwjcFkN+Yin9fLyEsnsAnfvPYTsbtQbabBdlRpT4oD2EDvbumZGTmVMPD4a/tQX/MQo4HPmFlwIcVLonr+NZD94BYCpLxp7DFmRHvsdKG+24TG42xmLqzX/bSQYtDWdRxpwKcUAus9YMgLJKGZ5DU4JfMGR1QoBJ7iEY5ZrZbA/kfhfP/4Akj8Dr+/kvJ0eWCHEtdjZttJtGzv7TEK8Os8A7Mi8mxLip+WBPTk5wVe/+lU8fPgQH/rQh7ovzNMG1X1zk/bplecf4Lm7t9C3N0prTKIQVU3hSYE0zzFPM5RliSdPniAKA7z4wgu9P4wu/XcIcNmug2/5b1v57ja2UxvTeU6XJ44CKKVBCRmszpkn2ShIa7OC+24Sa6VWfLgWi/5QwAGrnUncgA+6WZuSuVThIRA6TzNEQTDol0zSbFRKrLXBNApx2TDO3XbnCU5PT7C/f4DnHtwbBCrG2MbHNZxAmmQFPCHABUNRVKiqNYlfBy6b90gAIQV25W7XUVmXJdIs61KNwzBqbn5dhU1brWOt3QCv7cySrElgHgZdWd7vh/WkAKzt0mKHfKyA8zSXVQ0hmOuLHJi8KDEJfFyNsr7FVmbYGANjLTwhQAhB0dRWzWYznJ2d4fbt24jjGGnRbGvk/QMOyO/EEbKiGgWIsyTbysSGvsRVA+j7gLjgHDvTKXamU+fxLEtUZYHjpnM2DELs7+1gbizI0g3NTwJia2UQ+gKM2tEuXqBJEG5qftr3NWmAZN8Ya5Hmpeu27XnOtCe0KckLBJ6E0mbwHPKlQFbUuLy8xOXFJe7eu4vd6QSUUOQjjO80DDBfAtPzrIQn2Ojf+d5mQJJLUq4gOEUkhAPES7cA0yXAOzStt9UTDL50/bB37zovfFmWHQt/fHwM3/c7MCuldAnJS+BYadOB8sDjYIQgKxWMtRCcXaNL1Y03Ep6ktEGydH5MfAltgYnv+oqrpcqW5Rlnfi3Ozy8wn13h5Reeh2WbbQtugbpZqPFcHy2lpJedJYQ0tTrD90weHw+IEpRAaePYVeo8t7V2dTlppRF5TrLbN5K59N+V7TECjzMwYjHL643fdtqELFlrEXsc2gBpqSHYarpxwBkIddLntFgkIEceg9LWhUNZizgQINZ23lhtnNLA4xSniYJoFox3Aom//tEHCOSzW/t3ehxp8PbDTfvY2bZ39hvf+AaMMZ1vdoidTdP0GYBdmmdLNE9hnoaEuPW7fuUrX8FHP/pRfOQjH1lZ7blJYLGdm5TWTCnFpz/6fnC+ugJHiGMQjTFg1PViam2QpikePXqEyWSCW7fvII6HWYSiLAdX9vKydNU5K6+5AE5lWYMOyITKskK85JeIwgBh4CNJcxRl5TppZX+Vk7UunGdwn6sKodefhgy4gKswDNyPuDFd+Ivr2GzZVzsIQosmVXhoaqUG3zewkBKvDCGYxhGKssQszZY6dYHz8wucnp7i7t272NmZOpl1MBxV7wKFgsHHjbWIAoksKwYBxCzNEAb9EiLGGPwwxIsvPI8XX3wRd+7cBaUEZ2dn+PGPf4zDwyfI0gR5UWxltYrSef2GRhvTMcLtBL4HbWwXeFMrV2NDRiR+SmtwygZt4YwzcM6QlSXEFnZglrrKoLHR2sCT3AFvC5yfuSCN+/fur/zIz9IM05HPCgCk4CjKEp7YfuM3T3NEYf+54UnRBCmZpt5pvO6AEgLf97G3v48Pvf/9ePjgIeIowNnFBX704x/j0aO3cHFx3tgMrFv0oRRi5LtBQOB7AklewBjTgd6t7yvJMQk8Jzve0mtr0XTbBt7KGREHw3LhvKxgrUHkb94EckahLXByeoqryyvcf3AfQRCgrBWKusY07P+eBEsBTstT1hp5VWMSehusBmO0Scnt/+LUygHHwOMIPXfswiWv53WmBR+cM0wCCdZUX+zv7+O5557DCy+8gDiOURQFHj16hMNHb+L1R4dIs6x3v/JSISlqWALEgUDoCehr1PdQQpo0/O3PjX2JeamRVRrzQiEpFSptwShBIDgmvsDEF5j6AqUyA1cCi9PTM8znc3zwlZd6wevyhJIjqZy3kDHW/addIAQsQkFwmZUw2nTZCSvvEU2Pd/PPjDrpbewxTDyOUFJITlFpi7w2SEqNtFoAc8ko8qr/Gu2Co1ySMqeO5Q2FA5fOv9ofFhUKBskpBHeVQHmtQYgDvrW2kIw2dTkukKkN7Iok6xKG89oB3lBywCzCnIpKOXaWAEWlwUlzH+IJ/NrP3MckuDn1jD9N04Y4vVPTJqp/9KMfxS/8wi/gU5/6FOI4xpMnT/Anf/In+NM//VP8y3/5L/GHf/iHnc3s7Xpgf/d3fxef/exnMZlMcOfOHfydv/N38J3vfGflOUVR4Ld+67dwcHCAOI7xG7/xGzg6Olp5zhtvvIFf//VfRxiGuHPnDv7xP/7HTzWQ9tkyzVOY9xosKqXwjW98A5eXl/jc5z6HnZ2dp75P15mbtE+MMYS+h1c/9XH8tz/7Cjwp4QmOrCi76pg2/Oji4gLn5+e4c+dO16U7TzIEvo+8J3nUeVaHk4OTLF/xbS4D2HpLnU/ShN4orZugncUYYyE4H/SzZl1YUD8Tlpc1pOCDsi5Vq4ZEbsOanA9UK42rWYIoDFCi/7VrpUdZ2Kqum2PW//i6lFgKAcFZd5ysNvA9F7B1cnKCoijw8OGDpRVWC6XUaMjRPMngex6KNQ8za3yC51fJuDfUAqrWK0m/65NkOXbiGIRQJzU+cKnGuq4xm89weHSEwPcxiScQnte7aquURhj4Lrinf0+Ql1UnJY7DAHlRbMhonf83GqxhctspeyXegjcy3wYYBYGHWjkf19BkRQnfkyh6QoYYY83nmSMOffzwx2+gKIuuJmd95l2H7ub3TzbAvKwVlHbMZTYSFGObJOEwkMiWfGxCuKCc5QWLeZJfiwHW2oBAY29nAmUiRJOdrnM2y1JcXl65Xt+Ghd/bmUJy5vpE18bV1rj36YKzLAJfIt8CSgHnQfV9CSi9Kd3omXlWIPQlqlpvTfN1+2OgtGPh29ocQtwCwo/ffIyiLPDg4YMNq8UsKxB6EsoYVA3DzxlzgT0j++mCmihCuZA8+wMy4PXJy7a7VUJbCtjrBSu511gwvFWtwRlFJIVLG9YGnPOOjSEEUHWNy1mC09NTZ3NYCoISSyGG1lgQUFzlNTgjCCSHMRZZOSB99UV/ou7aSM5W6nGWRxsgN64mhlMCQilqbZx3lRFw2rKaBkeHRzBVhg+98iIMKCiGFwoYo6h7kr1JGzsMx5yW2oA2IJzCglMCSpwfmVLSVNlYeJyi1i6NeCXEzBvulSVwfu2hfZz4HNpahNTJpRd9rRacbHqABaOImiqd9ZDF2GPOJ+u5dOdKG4SCIim1A7vMpRhPfI55XrvE4UqDEZeuHAjnp90N3GKKYASSAzVxvuVf+ehd7IZPt/7wp3mGQpzeiRliZ3//938fv//7v4+qqvDZz34Wd+7ceVsg+o/+6I/wW7/1W/jsZz8LpRT+6T/9p/jrf/2v45vf/GYHiP/RP/pH+IM/+AP863/9r7Gzs4Pf/u3fxt/9u38X//2//3cA7v3/+q//Ou7du4c/+ZM/wZMnT/D3//7fhxAC/+yf/bN39Hhcd4i9ztLcX9Exxowmjr7d+cY3vgHP8/CBD3zgHd/2+mRZhi9/+csQQuCTn/zkoGn88vISr732Gn7lV37lXd+n686f/Mmf4H3vex/u3r37tHcFR0dH+MEPfoDPf/7z+Ff/7j/g8eHpxnOMNUiTOc7OL3Dv3r2NsunA81AM+vFIE6zUz0g6MOZugh8/fowoilYWIvq8skHggRIK2GGmE8AoeG5j4/t6R7///e/jwx/6YK+csZUMT5pgISk4PE9uAM6hRNnufXcJt/3jEnCH31sUBaCEIMs3mVCtNWaXF8jKEnfv3ttg1wGMgmTAMW5VrbtQlyDwoLRGXbWLDUDg+8hGvKFOujn8Hgkh8OUisCmOAuR5Dm1cT3KWZdB1jYurWSM1Xvjslm0Q2441AOzvTHAxS0ZBwSTa3mO6DGKl4CDEgaON52wBdpK7UJTl868Fr0VZQxuNo8MjBJ7AdHcfbIRppoTAW6u94g3jsyyf5ow2/zZ+488ogec5YCg4A6F0cDFomxcZcCqP0Hcy23Ug2HWRZinSNIPWCpMoRhhHkNIDbwDfNNr0srr3ThH4ctSjTCl1THRVw/ckjDbXShwGnPxXcDbqi12fyPdQKAVfCHz/x29Aa4379+6PfoaMUoSeRFKUbkHwmmm9gJO9MspwdY0+23YIcaFeeaUQ+dL1zm45JpNoWGpMKUHkCVRKd4FRceh3gNoCqKuqWbjIUBSFq2OK3MLFwc4EeW02fkE4c55IWCBvPNwTXyDZ0rvaju8JFAMs5GIsIl/2VutYa3F0dASlFJ5/+ABciBUAxxlp2GA0So9FVdLqdpZfzWUFlMrZBYyxC0VP8+RILqTDrdx4+ZoXiE357/L0Veb4goIzAgaCqwFP7MRjK4x8IFwCujUWudIbaz+x55Qpaam6BV/BCIgFpKCNNNzVH2ntQHpWKbdIIRiy0iWLd3U6HgNpvMppMsNHpga/+sWfG3yfz+YvPl/+8pdx//593L9//z19Xa01vvSlL+EP/uAP8B/+w3/AD37wA3zsYx/D3/ybfxN/62/9LXzxi19cWfC7zpycnODOnTv4oz/6I/zSL/0Srq6ucPv2bfyrf/Wv8Pf+3t8DAHz729/GRz7yEXzpS1/CF77wBfzhH/4h/vbf/tt4/Phxd0/+z//5P8c/+Sf/BCcnJ38hefXbnWcS4qcw75UH9vT0FF/60pdwcHCAz372s6OJZzeJ7WznpkmIWxP/X/vcpzceV0rh0aNHmM0TvPLyyxvgFXDsVDwoZbQwdkiWtaiQAVYZ2O6vm45VwIHZOAyQ5yXSLEeaFxsy5JX9Kope8AY0/ak9kr92P1xg0ULS0gJXY1zaZFuNo43pBYJpXqwwDOtTVTUYHZES18NSYsYZGCjyvNwAr1VV4a1Hb6E2Bi+9+OLg+58nKaJgWH5aVjXiRiY6iUMURdmBV8DdkCmlR+XO88R5fofGNuFXjBJMowBJmnXsKKUUcRxjZ28PH/+Zj+DOnTuglC5JjQ8xm82glMI8TRGPyKKnUYireQq5RUabFSXklh/MJMsR+BKeFLDYBK9AE+o0IMVtp1LK7U9zcjPGIJgDr7Wq8fjRYxBKsLt/gN3puLTKWAtV1/Ca8403QHjd+6scrbdV5qyNRVUpF8LE2Gj9zywZlzETQhD4EkleQmkN3xObjwcBDg5u4YUXXsBzzz0H4UkkyRxPHj/Cm2++iTJLcHx22bv4YKxBlpeIB463W2iRXRBWu9B2Hfkxgftc0rJCPCCJ75u0KOFzhtffeBOwwIMHD0bBK+Dk7vO8wF4c9rLPY2MtQVJWmARyrP1sZeLAd+ymdUnHxhpMAg9sgIkJfYkkH/5tb9OGy1oj8gR2Y3+FDSZAV7n14MEDvPjSS9jb24NWCmfHR/j293+Iw6MjzOfzld9GpS2SokZSugCqqS9AGYUn2NYk40ngXQO8NgFXPeDVGNe1rLXGg/v3EfreBvuotEWlXBpyXmlwSjEvnFx5+T95vfgPZxRZpaGNC6xrO2AZYy613hOozCKrpP3d0Vo7uSdxPtehaZlSRp10N/YYBKMoaoNKWcyHWG3pwCulBBOPwxcUea1R1ga1MSvglVOCnYAhrxTmxQK80mY7FkDS/LtkBKKp48kqBdowq/NCAQTYjQSSwjGzlBAooyEYxRde2MGt+Bnz+m7P2wlxeieGMYZf+IVfwO/+7u/iF3/xF/EP/sE/wO/8zu/g6OgIv/mbv4lbt27hN37jN/Av/sW/wFtvvXWtbV5dXQFAV032P//n/0Rd1/i1X/u17jkf/vCH8cILL+BLX/oSAOBLX/oSPv7xj68QSn/jb/wNzGYz/Pmf//k79XZ/onkGYJ/CvNseWGstfvSjH+G1117Dhz/84Q2/a9/cRAB7U1KIgdV9eeHhPfzMh17pHiuKAm++9SakkHhw/8GodzIbAWxFWWEyYtAvy2owPKgoK+xNJ46dK8qN0J4sHwapSmn43vAPYJJkvcC7vXFIsxxSiBXwCjh21JMSeVkOfo5aaxcWNDCVUoM+UaDpIe1ZLIijANQCszTZAOBZnuHR40eYxBPcvn3bsWcj3s6qrkbrCLKswN5OjHmSbay8L/Zx/MY+bZKNh6ZWCjtbGNQky3H7wEX0tyDH930kSYI33ngDb775Fh4/eQKj1ArVQeBY1VmaddI7RoePh9bOM0tGriltwjYhGOwFdvtcINxybLKixCT0wZsgqaKqUZYlHj16BD/wce/uPRBCkeQlgi21BLXWAHHMruB8kMEra+XY2ZHjADhgyBmF6en4XJ9ZkmEy4OuNQ79jR5U2UEohGDkfhJDY2dnFnTv38OKLL+LFB/cxSwscHR3i9ddfx9HRMebzZC0J3C0o9S0axGGwwc7WSqOqa8TB+M3xJAqQFqUDZ1mBSeiNfJsWwynBd37wIxgLfPCVFwdB4cbrBT4ukgLE2l4/bd9IzlAqB4bmeQnJGSJvfKEmDuRKqBIAGING+mwbRnexz5wx1NqOJl6vbMu6ACDBKSaBhOi5xrBmgerO3Tv4wAc+gLv37kNKiaurK7z++ut49OgRLi4uVqrYCAhqA8xyhbI24Jwi8gRiX0Cu3YBH1/T2BrJfhmuMwZMnTwAA9+/fx04cINvC+voD21rdr83E4JUhAKcUFmTFO9uBWWPAKDo/erugCrhu11hSMBD43LHAaaWRlNpJo5uX6MO+klEAFhOPgwCYlwpF7f5G8gVg5oxg4rl047wyK9sKBEXkM1xmNerm++lxBkqIC4SyFoGg7pgXyv1vQd3/9jgIrFMuSIZf+OAd3I7FuyZtfTaLeac9sG9nsizDrVu38Ju/+Zv4vd/7PTx+/Bj/5b/8F3z605/G7/3e7+Gll17CL//yL49uwxiDf/gP/yF+/ud/Hh/72McAAIeHh93C2fLcvXsXh4eH3XPW1ZDt/90+572eZx7YpzDvJljUWuMb3/gGLi4uBv2ufUMp7cDHTbkY3iRQvd5N+SuvfgY/eP0tnJyc4fjkGPv7+9jd3XXdh206cI88UhuDIPA7P+v6OLmt6E0Hdn7XeAPAhoEPQoDLeTIYfKS1cZJN1Q+AkjRv5Lj9MseiqMAoXfEWtfuhjWm8ig7ASiHge3LFK7kziXA1H/bpjiXltoE+Q/s2T9MulZgxitD3Vl47SbNO0no1u+pSaiex8ydnednIa/v3r1Zqo3e0ncCT0E0iNGesV2rt9jEbrf9x1Tq0t7dVCg5GGc5n89HKG2stiqrq/NJCCOzuulTjVmqcpinSJEGlNMIwdF2k+3sr2yyrevR1gIXXdegzc3L4Cp5s2NMBSbK1FlVdQwg+CnSLssIk8nFxlXX9oHt7e9jZ3VkkYhuLWil4nKMcUbgopRGH2+XLeVkh8j3kZdXr8yaN7H+e5ZCCg3MGtYUVdFVCqynGkx7Zr9IG1ioEntgqk5VSwFiBF59/iKyoUJZLnbMnx/A8H1HU+CmFdCnfS7U5fa/fjjHGeZ/DALOecKZJtJlWPEsLRL6HUqnBADOjFF4/PIQfhLh16xbmeYnQk6jNcEox4EKbWhZQaQOVl4gDibzWg69FKQFlbKVyrKwVSgCx73UVOMvjCYZiRHqqjUWSV2CUuNcva0jJkV3Dbwq4BSIDd/2slUGtKgBOXgwAabXaATsJXKKx1/jc9/b2ljzS7rOmlLpwlf0dZE3FljtOtqt4A5xE3mMUjBFoS0Fpm/w7tK8OzK0vmmqt8eTJE3DOcffuXYTecMdqty3ijt0YxBeUjB57wAUWzZeZ6yWFhnuc4Sp31UgeaxKPLaCM85PWmqDU/fsa9fS9SkrgCef9TcueVOZG2iubdOK0YVxDj3WAXlICKZgDvo3EnBCCSFLAAkmpQUAQNwnQjm11cvOuXsi6btrI43j1ldt48SDGj2YnN+ae7ad53k0P7HVnPcSJUopPf/rT+PSnP43f+Z3fwcXFBb797W+PbuO3fuu38I1vfAN//Md//G7v7rs+zwDsyPxlq9HJsgyvvfYaOOd49dVXf6KSZN6wgjfhS9rOTZIQr4Pp0Pfx8r1b+OY3v4179+4hClfli1XlJEZ9NwZJmiEOQyTZJpi01kAIb7DeZpak8Js6iih09QxZvrgRF7433A3byJCHandqpTaAejuqrcZZAoYtgLXWNgDN1bskab4R9DNLMgRr/sPlKYoSgjHHkPVMVVVglEEPhB1VSiGOfBRl1RsylOUFLi8vcHl5hfv37yPwV5mweZoOBgYBjt1cB6AOwGXdjZ07tsPna1YUo0DNVeusAunQ91HXNfK6SSHMi9F6I6U0okB04T3ttFLjOI4dO8oIzs4vcXV1iSeHh4vQmCgC59zVqmwBsfM06+1FjQLfda8ag6zQ40FWaBUADHogMEtwJ5m7uEqh6wqHR4crCxDr22Ke2FhsaYcRCk9KXCVpx3qO3UynRdnbYUoIEIeLmpyqVk5uO7KI0c7i2I5X/GhjUNXjIFZyFxxVKe2AeeCBECw6Z2vVgZzz8wtwzhCGIbIwwq39XVBKe4OtlsfaJnE49F2/avPvke91Xa99x01w1rvvZVHi7PQEYTzB3t5e9+9Z6ZQOse8hKTa3OxTalOTu7ya+XKnTaSf0vUFvblKUoIQ08ti662gmlEGP1EG10wLZaeQDcIFC9TV8w4HnId34TG33b4xRBMLVpVBKVsBaO4wxTCYTTCaTziONusQbT06glNqo6WnHAVqNQArktXI9xU2IEJpAo0qZjk0M5GYQlFIKjx8/hu/7nYrlOq0+vseRjrKvdmu/bCR57/HgTaCTZATGuuToSplmUQQdCxs30mFg0zcbSd4FNTHimGdjLfJKQ1raC6xjj6PSBpF0YLXSbt8mgQO1lBDn1S0VuLHIGztFJN3fERDMyxq+oGgLhIpaI/Q48lq5mqG8RuwzVJWBzxl+7uUDvHzHXf9uEunw0zxPS0K8PNtqdPb29vDqq68OPv7bv/3b+Pf//t/jv/7X/4rnnnuu+/d79+6hqipcXl6usLBHR0e4d+9e95w//dM/Xdlem1LcPue9nmdn/VOYd0NCfHZ2hi996UvY29vb6nftm/YCeFMAI3CzJMTLwE4phS9/+cvYCQU+95lPboBXwMlG43DY11jV9eCPTprlmIxEpXtSwBMcaZatgNfF3w6/7ljtTlXVo9Ulzg+6kB8uA9go9FErjawoeuWU1lqADC8K9fn+VvZN6eHKGcrgewIEpJcBM8bgrUePUVcVHj58uAFe3f4t3tPQpHkJIbiT9YUB5mm6cjPtalOGj73WBoLTUf/dLF0c42kUIi+KFVDf3mDT0f0sRit+CCHgXOKF5x/g7r37TmocBJ3U+K233nL9dOeXCLbIe+dpviIBnjRSVLv0vZ0l2ej+AI5hdZ/vKjiR3AVAFVWF8/NzvPXoMV5+4YVe8NpOWdbwpNg4RpQS+L5E1oCjJCu2enAB12HqvKPtvllMehj5ttd4qxS26V3dn0ZbWWAHYmuEPVJZxhpmsTnn237WZR+qEBw7O1Pcv38PL730Eg4ObsFai5OTY3zve9/Do0ePkCbpta7786xA4HFwSuEJjqJWo0HFtdLIyxqTpf1J0xQXZyeId3ZXwGs7ShskeYFJG0DXDCEEUnDUAyjJdaaWCD0BuWSVmIT+1mApY52smBFgGkhEgewClq4zcSAxy2vM8soF7QTSeU8HZhr2gdfV0dogKWooY6EtwSQQ8Ee2SQjBzjTGZN95pJ9//nlEUYQ8z/HWW2/hjTfewOnpKbJmwW0SiC512AIolMG8dKxhWmrUTX3Obii7HtbY4wglg9UKjx896qo0CCHwmmqZsZn428ArEHtiFLxKTmFhEXlNrY/HEUoOziiUcRLetNaNtFfDNAn4pPHO7kY+coUVK07rm+XUIq9qRIIilAzGAkmpkFUaUU/YE9D4WK1FWWuklerk47F0bHQnNS4UOKOolOmSitNKQXKCpEkeLpUBIy4IysIiqxRCyTDLakwDlzbNOcHPvXyAD95bKOueAdj3Zm4CgM2y7G31wFpr8du//dv4N//m3+A//+f/jJdffnnl8c985jMQQuA//af/1P3bd/7/7P1ZjGX5nd+Jff7/sy/3xpaRS2QtrOJSZJFFNllsdhVbUmskdUuyYT9YgA0bFjTAPNiCoBc9z9M8zss8aebJEAYGZHswY48wbY09bUMYGRi3PM1KbsWdxdoyM/bl3nv25e+H/zkn7nLOjaglK4Nk/oBCNzNunHu2e+N8/9/t5z/n/fff7wDxm2++yY9+9CMODw+71/zFX/wF4/GYV1999WMe0SebZwzsU5hPUxqrlOK9997jl7/8JV/5ylcWVlU+yrRf6DcNwN6U/WkBbBRFPHjwAMdx+O53v8ur0xn/+X/5f++Vr01n0SDrmBfF2vqbJEsxDbOTfgkhGQUeaZZzHCf4Pb7P7nfTdFDOqmXI/mA1ziSK8D2XeICV0Syt0LUfQmBKHT4TRQkIsVam3DGMA8c8jZKPLCUOfY80L7rj0cDi8udlWfL48T6GYbC9c4vNcTjIKqZpvlZKXNcV48AjTrNe9hwgSpK1DGmc6F7f6ZpqnaIoGY98JtOh+qK82cawH3YaxYS+2yt7DjyXLMuJqlL7boVgs/G/1I0sMYpjHj16xNHhIZ7v4zRszjLAbyXApmngO86gR1eHOq0mZS+8Jkr0cc2lFyv0wsrR0RFJknBvbw/bsUHItZLjOMkaaazelpRCs15LjOEkSq6VEDyNEsaBx6RhTYeAZ5rneI7Tee+GJnBdzqb6Gg3Jd9upakWa5QSeTdQk6Ap0MvVyX6v2uaa9smApBUHgEwR+E16VczGZMptOODk5QhpWx9gNLYDGaY5r21iWSbYm0bjbn6YCJ/RcDo5OmE3PGW3tXNllOI1TLcs2bOIsb2p3rk4PjtNcAy7PpmqCja47RVXjCt3T6TvXkwPblrGQclsrmCY5AghdS1fczAWY+Y7NNL3m3zRBx0amDdh0LIltarCYzG1XCoEhLtlfy7LY2NhgY2ODuq5JkoQ4jnVaqFQcOpdJ5YbR/xhomZLJUgpvlmU8fvyY8XjM7q0dLEPgmQaFUoSu2flHFQqltCFCKV1VU9Xgzi0uCKkQXNbmGI2UeeTq/VFKg+u6kf2WtQZ48WDglMI2jYXzPT+OqUOh5hlXpRSW1AFKZV2TVTV5tZhq7FvGwr3QMqplVVPWug928X30MZlSdP5ifX3AsaXu1810QFNdKWxTNPJjiSE12LWkwLP1tfcdQ99YKL794g5fub+58H43wZv5uz7tQsfTPs8ftwf2n/yTf8K//Jf/kn/1r/4Vo9Go86xubGzgeR4bGxv8B//Bf8A/+2f/jO3tbcbjMf/0n/5T3nzzTd544w0A/uzP/oxXX32Vf/gP/yH/8X/8H7O/v89/+B/+h/yTf/JPPjJh9mnNMwC7Zp6khPjTAGat3/X09JQ//MM/XDFgf5z9uimMJ9w8CTHAX/7lX7K3t8crr7yClJLd7S2+/fVX+XcPfrzyOx2rR38Q5GQWDT7Ut92wSaLwPY84TTrgJ4QkTlI2t7Z6pcZlVTEKhvs6J7N4GKQqzfqIphJgefJcA+84Tdne3OD9Dz7Etm2CpuYBpfR7DwC8aRTjODbZgFQ3baSHQz64PM+7a+EteV0BkjzvwHuaZuzv7xME2mcnhOhY1CHwM4kiAtclWqkzEk1K74ww8Acl3no1XKyzfTKL2vO/et0t08QwDeor9HjT2Xo5eOeHXTpWLfm9DJuyLbPpQdT/WxoG4WhEOCdLrMuC05MTDg8PF/opW9tBUVZsjUdcDCxMtPtTFiWWZVCsYbfa4yrKirpWpFnGwcEBdV1z//59TNOkaCXHAxL9bluNVDdK017w2k6bELzs5Vx5XZSwszni9GK29nVJluG7DmneD2L9xlerpffJtd67Voo40Sm/LSM8HTge1ezrUN2RlLomSBrmHAuqKPOck7OLBT/lch2TQCANSdQEa10Fvtv54NFjiiTi7r37MACYlicvSoqiZHsccB5dv/pGKUWWl5i2iW+YV7Kd7fiOTtltVRWebYG47INdHrFGaqzTZfX7urZOt9Upta1I9OoZec5KwFJW6CRf0F5W19IMoCHlim+zHSklQRAQBAGWIUizgsksYjKZcnR0jG3b+L5e2NAPogJDXgLIdtI05fHjx2xtbbG5uUlVK2xDcrYmdRm0FNcwjA4Y9o1lCJ3YvubzPHKsTt7bN2PXYjKwYNGqMeomrd81DQxDUJQ1WVnjGAZZoZCG7FRFKBBCA9SyUjimgWMKkrJmmhUEtrmwiCDQ/tm8VEvXTTF2TaImZRm0z9eSdIsZTpN4Pk1LfNvEEJBXNVIoDAG1UHz7hR2+9vz2yrHVdf2Ra1SezUeb9nv8aQJYpRRRFDEaDauPhuY/+8/+M4CVgKd/8S/+Bf/+v//vA/Cf/Cf/CVJK/sE/+AdkWcbf/bt/l//0P/1Pu9cahsGf//mf84//8T/mzTffJAgC/tE/+kf8R//Rf/Sxj+mTzjMA+xTGNM1P7IFNkoQHDx4gpeS73/3up7IC8lnV+1x3bsr+KKV4+PAhAF/4whd48cUXF37+x69/nZ//+l3OJ6sPtn2+xsWN64fC5VgLx7YRaJA2mS1uV7QR+pY5CKSmUUTg+4PBR1U1DFKzLB9kSrWvt9Y1BkHI5z7nd4zd+ePHSCkZj0b4gY9tO72MndGsbPfVfZRVRej6FGX/fudlxc7miItp3AvQy7Ji5HvsHx5zdHTI1tY2GxsbnWy3rrVUeZC9U5plnvfbWqaJbZnd+ZhF8Vo28ZJp7gfxulqnXPFp+p5DUZQkSRuusz5oKEkybNskH2AcyrLCd61GVq0YB8EKyEzSfDDEp61uAY97d+9ycn5BFEXMZjOOj/WDb+D73Nm9xdlkupaZBCiqEs90KGXde9/N77dlSM7jqAuJ2dvbW5DJaUbSI07TtTLWWZywOQ45mwyDa4BpnAwGdbUzDjxOzqe9vt/lidOs6Q8tFo7VsS2yslzw+k6uCWIVus95exxyOsDOLxxTlDD2PWZJ2n27CMB1LmXUlyOwbJcvvLTBJEqbztmYk2bhovVT3tm9Rdywvi34nsbZ2uTd4+Nj8jThzt09hGES+g7TnkCovvFcm7MoxTZNDCkGweTCkUhdoRU3YCb0HLKiGvTXA9p/X6uF76SkUVH4jq6CWn7v0LOvleCb5hWZqAhdB6SkmOuAHZrgGtsuq5pZVev9yCo8W6dml5XqGNvlMQ0TwxJsbdldEFSSxERR3KQJ6x7p3a0xhel0D+xtcNrOzg7j8RjQQVTrAGc7rm12Xa39ozCNK3yvjrkWvPq27PXFtjNyTWqlvcVpCySbl7ddsEIIHQjX/K1QqsZrgGUldbVN+xZj12SWFc3v6G1kZUVdQz63+OiaEseSnM/1Anu2Jgr09VWEjqmvWVkxci2U0lU8eVHh29py8vXntnvBKzyTEH8W0xIpT/s8X+WBHZp1ve7tuK7LP//n/5x//s//+eBrXnzxRf71v/7XH/n9n9Q8A7BXzNCD9ieZT8rAnpyc8P3vf5+7d+9eqyLno+zXMwZ2ceq65qc//WknuViOEQftM/uzv/EG/8Wf/796tzFLdEppX7hSki3KagPfQwjBLIrJ8hzHtlbuwfZ/R12wUP/DbFEUg0FSWT4MUqEJNXId0uZBVwhBGHjEccLFVFfTGFJSC7HI2CUJURwzOT/XslHP69jZ7mEozRiPAiZrUolHPSDBtjSQPDmbDIM7pfjg4SOyJOH27du9cps4SdcCzLwouxTp0PdI05xoDlwodVkl0xc6BFew3PPv0aTRjkKfWRQtgLFZnKwNvqrqGltaCDHsRYxTfaxKqUGGtAsVWgOgJlHM9sa4SzZuZYl1mfOLX7+jpXa+z51bO1RI5ED9TJJljHy/YehXX+O5NnlREsU5B/sHuK7bsefLEyWJDkEaAJNC6EWBs8mskVMPgyal9H0ReP1MrZYP6/fR/bXrzxfo+/wy0Eph2ya1Ur12g8k1rgHoxOCzadSwn1eDwEmcEHgOaVZSq5pwzXsodA3OOPDmemd3KIpCeyergp/8/JdYltV9ppVSBJ6jk3SXvquVUhweHlIWBc8//wKqSd2dRvo850W5NvDIMk1dS6N0arBAs5KzLGdda1HgLvpeZ0mmOzs9hyjJqZfAthACy1pk0+Ynzloga4PSkuCRv8qOrps2Rbgdz9Ze+igrVp4vHMtckCWvG9cyu5TbeQAohcC1ZbNIpkiLksBZBcWGYRCGI8JQf39nWQplxsPDE4pCpx6bpkkURTo4rWV/lMKxTOIrPK0j1xxkhi9fs77KxzbkICAHzd7m5eVVFULgWhJTSmqlkAIukn5w69tGr7fVtSSOqX2sldL3oGEYKFXjWy1YFgSOJKsUkyRn5FpdPZBl6H1QSnXvbQqBY2k9VlLUWIbQVUAKKqXwLL190CFOni0xhOS157b4xgv94BWeSYg/i7kpAPbjemB/V+cZgH0K83EB7Lzf9ctf/jLPP//8jdivJzVPe3/yPOfBgweUZcl3v/td/u2//beDAP+l5/d49Usv8ZNf/GblZ3VVY69JB07SjI1xqH2JS6Asy1e9svOANk6zQUZf+2zDYZA6i3Bdm7Qn5KTJXNIx/75HURScT6bd++v9Clf2y/N9vEZKvGcaHB6fMJlMODo6wnGc7sF3OmNt6m+cZF0djEADvChKmDWsSJKtphYrpTg+OiJOEp67v4fnemvrijzHJhl8/5Tt8YjTi2nvz/O8aED2EBOmKMoCw5CD9R6zKOnA5bTnGimldHDEQKIu6HtnXVqwZRoN27v+ASeK07XnA9BSXNcmSbU0OQx2iJOUcLzZMXbvffAQ0xCIxk8ZBJdS43amcdwLPH3PIc1zZrOIg4MD7uzuEo7Gg4sEoIF137Za8NqC0SjJ8F27Yw/7pq4VaaqDgOI52ek8eAX92ZjGCaHnXZngGyUpgec2QUti8H6EhtEMtc+2b1rJrlLoBOM1r13chwzPsXFsl4srmGPQxxZ4rq4PqhWWZXH/3h2iNGe8dYskSYiiqFvU832f8Shkc2PcAY26rjvp9+df/lwne53fJ9M0CNxVHy9opYf2f16eLwVMkgzHNDAtY+EaLZyjnu21HbWOZWCb1sJrNLi8hr+2+Wxs+A7FR1jT1kzt4r62x2VIie+YlE06rZQSpKS+RoiUlAKkQPWEJ9VKNeBSbydwLIrq0l9a1Yq0rBcWN4UQbI1DsjLg+fE2RVFwdnbGdDpFCMHp6Slpmurwpq3xlYFMriU/cd+rRDO9+cAJFyh8WwccKaETlrOiasB8hWcZxAN2DMsQ5HPfzZYpcU2pw5SkWAC92g8LtmGhUGwGDlFWMssrUArfMbiIda1S4JgkRaWrekr989C19PUVkmmqk4XjvNQ+2Fp7f2XDnqMUrqWB0mvPb64Fr/CMgf0spg1welK2wutMm8HycTywv6vzDMA+hWmZzo/yxVNVFW+//TYnJyd8+9vf7k1w/DT26yZIdtt5mozwdDrlrbfeYjwe8/rrr2Oa5mDFTDt/67vf5jfvP+qVll4G1FwCHse2sC2bOEkoi2oQzE1nEa7jkGaXbGgLYKuqaoKTBjydswjfdYlXPJ36gVCoYb+mlILNccjp+WTOE3QpsZrOoqbvs4cJ0sV13Lp1i6qqqKqKKIqI45izszMMw2BnaxPDsnGcValxVVV4joNpGqBYYWvLsiIMfIoG8FdVxcH+PrVS3L9/H6OR/Q4BBqV00mOfwsJzbepaM5br/LKdD3VANlsUw/2xoPs7i7JcK6fNi2JtsBU04Vf+6n54jk1Z18RphiGNteFStVLUdbUWcNe1oq505y/QscstW9cydnVdkSYJ5xdTTk4uPXa+HzTXWgPP+VoizVRmXFxccHx8zO7uLn4Q4jVy13V4YRLFDaurtyWkwHftBSZVJ4UWuLZFOnAOQD/Yt5U4aV4w9r1epl+pJp3Yc68EsWmWEy6B4MFjmfXLiXVlTb7wOb18bUIfmz0/piFJMt3Lmw0wjfMTJSmOZaEMLbuMs0KH8cz5KYFu4eL45JTH+/tsjUfUUjN2pmnypc+/NBiGVDbVPyPfJWrA8uXxDoPKrKzIyoqR55Dkl12zvmczu8LvmhVavus7FiAwDHkt8NqOaUjiQlFUGhwZRsui9r/eWQp5Wp6qrpmm+ue2aRB6dseoXjWBY10JEPV2JWlZUynFPFYUaNBmGRKjMcKLJsyorFXXG723t4fjON21np6dcLDkh7fMRQ+mbLtj1+2XoWtp1r0mcHXfq2lILKn3Uwj9XZRXNa5lDLKrZiNx7lXQCTClpKxrRu0CQlFRlDWOKUh7gqJsQ+JaGoBqmbDANAwcU5IXFRueRZyXTJK88TwXmFJiWJJpkjPyLNKi0qA9Kxq5sCLKSjzbIC0rXEuzrnWteO35Tf7gxZ01Z0fPMwD75OcmsNxdgvjH8MD+rs4zAHvFPAkJ8UftXJ33u7755pu4axJoP8ncNAnx02JgDw4O+OEPf8hLL73E5z//+Q5cXSVpDjyPf+/N1/nX/+Z/6P150vQcuq5DXSuiOCVtHrhmcdIF7CyPosGDjVd2+Z6crumVBQ1Mhu7jJFuV83quo/sh4xiB7i7N8nwBvOotq0ai1S+lLcqqkzgbhsF4PGY8Huvu2qabUsURcZItBMZIqaVvUgqEEoPs4iyKGYcBJ2e6y9Sxbe7dvo1oPlOzJGUcBEwH/Mdplutu21ZKLGheP+seSB17jV8WzYC2THHvPjaVSMvXNfQ9kjQjr4tGJi47+Vj/ca5Pyo3TbAGghoFHEqdU7UJHXWELY1BSDpDlpZbaJilDgEhIiWMZa5k/KQ12trcZjca6WimOiRs/ays1Dpq6Idd1MA3JLEo5PTvl4uKCe3fvNd7bthJovbcWtEzfd22SvNBhPD0yYC37rrHNy+qZvimrGilrNpqO1qGvf6UUs4ZhjQZArBQS27a4mMYEntMAwfV/T5Y9sZ6jj6vv9xb7Wfu3GzTSWYXSANR1iNakQbejAb+t/YkDINR13a5ztixLptMpWTSlKit82+T9h/v4QYDnuoPshWZGTaQlSbJCH/s1EoenSYYpJaFnk1eVlpFe8291nBU6pAl55f3QjRDYlnUp2S0qKCoc08C2jAaEX75cShDSuBabCuDYJqdNWFWXNlwqkp7vHy2lvoYfWAik7D8+hU5eLqoalAaLcVqhFFxcnBNNLvj8i8/hex6iqRhSWyMEglmaMZ3OmEUzTo5PsOzLBGvXcfBsey1DKwSYhmaPLctACB32JJrvHSW05DYptFWj7a6dn9Ax1kuPm9ThlX83dE1OWuq6oKKaTxjW/7f93kShU4CV3ufJElg2pcA1dVdsXCqQBpaQOokYHeSlGka1rirKqiYvKwLXIi/0QszY04nXnmViSP398wcvbF8LvMIzAPtZzHWf1Z/kRM1zzDMJ8eU8A7BPYdqVnKqqrkyPOz095fvf/z63b9/m1VdffaIfopvGwH7WHlilFO+88w7vvPMOr7322ko583UA/mtf/gI//sU7vP9wf+HfLcvEczXT2Bf2BK0c2OjtMtXBQLr+RohVEJLl+aDUNM3zK6TEcee1tUyDaZToGoRaUVPjSAspZO8DcpplbIQBFwN+0mkUr0hchRAdiyMEqLrm9Oyc8/NzDg8P2d7axLKdxgNsr2UOj09OOTw6JAwCtre3WS5ZnSUxjm2T5UPsdkzguRRVhWnIlcCsq/yyVVXjOPZaaegsTnBtmzTPEQJCf3GhIsuLtTJg0JLRdZLruq6RQvvqAt/rvdZJlq/1jOp9TfWCRs/xBp5DlhWkWcY4CAYrc9r3Cn2POkkZjUJGo7BLNY4iHQ50cHDIra0NDNtmNotJs5T7e/ex7cXO0+k1AHxb5zPy3IaR7J+i1NVBpikp1yQ925ZJmusFp6FE7PZ94zQdUCI0FVPzMmbPIbkmiB35HnlZUFbV+rTlOCVwHdKioFp6nWdbpHP9lHVdEyep9vBewRy3fbazxhd7lVy5qiomkwlhGPLF5+4xjRJOzi44Ojykrmu8uUUqc4nNyIoSUQq2Rj7n1wx4ArTCIMsJPZesrCnXeGrnxzQkFULbJwSErk12hSd32cfa7XvDCBtS97XmTUhT4Dpr5bHz49mLPaPzacO2KXFMnTYc5xWOrTtErzOhuz659/LYbGaZ9tKfnJwwm83Y27tHLa2F/fJtk6ioQJqMNjYZbbR++Jg0Tjg5OsAWcOYFBL6PHwSYhqHrcBovqVKL3tO+cC3XlMSVYui2d0zRJfr2Hk/D3AIg0Gy50JJh21gMVepGKTzbJMpLpNDHWpQ1cVYxclfBcuAYVLXiYv6eUIoN3+46fKVhYEsNjKdpqX/umUxivfgS2Dq12DINXTVU1XzjI4BXeAZgP4u5KR2wpmk+tcqamzjPAOxTGL0qKteCRaUU77//Pr/4xS945ZVXeOGFF574ft1EBvaz2p+qqvjRj37E+fk5f/RHf9QlLc7PVRLidv7en7zB/+G/+G+oq4rA10zSLE7IpyVCMMjYlGWlWdiBHtVZlGBbVsNkLP5lL8qyYRPX+F0HAJBtmfiew/lkRpq1HX76PyHEStDU8kxm6/2kaZYP9tIqBZ7rsbMjuXf3LqA4PDpmMpmQJAm2bbOztYU0DRxnUXkwnU45Pj7m+efuY9r9X+p1rRBSDFYZ6QRMLfeKh0BuFC8EWi1PHKdr/bCaWVXYloVpyN7X6SCl9duo63qtHzYrCjZHI04vJr0/h1a6O1yxBFquvSxbHgW6f7dl2idRtCAB7ptZnCwkE89LjZXawbVN9g8OmZ6dkRclpmkxm83wmzoPMccCT64ITpJSs2Nplq9lxEEvGHiOjarVCuADuiAnpRSubaMMeu/ddupaddU5LYgVCIKeHt64AbFpVqz19oJeBAg8h0l0NaCL0gzXtpCi7kCYbeoH7OXvLIX2uY59j8kgiBW4zqU/dRIlhA2D3AemkyTh4OCAzc1N7t7ZJckrDNPi5RefZxIl5EVBHEVMJxOOj46wHUcz8b6P7TgIdHjRRaw9rkKIwVClxYNReK7LpOleHXkOUVas/Z4WQuDY9qW0WcEsafpjXZukKFeAcDgAXuenqlXXAbsVuuQ93tS+MQxJybDSKy/rLtnWMqWW05qyA8pDaonrgle/Ac9K0fUt37+/t7K4PhrYnpaVhwRBiGvd5WKmPdJHp+fk+0c4jt0tXNiOw8gx17LHUooG8A78vEkLHvoe9G3NOIeuSV3rQKQ24Mo1h8H/yLUo6prQMbW/tQGswRLTGzgmRVUjEAvhUp5lYBmC87jtaoZxIxvOyhrPsXBM0UiGTb0wpSqyXKsVqgq+/sIW3/wI4BWeAdjPYm6ChHg2mzWL/k/Ph3vT5hmAvWKeRhdsVVX85Cc/4ejo6In5XT/qPj2N+awY2DRNeeuttzqJ9tAK13XPz9bGmL/xnW/y//kfH6w84KumZ3VIyjmZxU1/4yowqJXCssxBOfB0FhF4HlHS49lD6VAmLoGcbVs4ltUlHo9DXbOi6kvw2j4bzaJLFrFv22pp2/MzLyXumywvuLW5wcn5hFopNjY22NjY0GxRHGsJcF2RZHn3IJSmKdPplLt372JadsdO902SapZ4GYCbpoljW5zPNBibrfPLquFrps/P+modwzBwpORiDXCMkmQt25znReO5XQUdlmliGJLT80nDVq55nzi+MtAoSdJuXzRru7p4EcXJsAe6mTZkaVkCvBH6nF5MmDVeyVde+hwnpxfEcbwoNfYDPN9DIpkl/UynIbVMt/1317ap1wB9aMBh08c6f0lD3yFO8+7zlea5Bruw9rO/DGL7wGs7cdL0xBb9YLA9JtM0uJjpFOEkH35tO2leYFsGbsMcScMYvJdgLqE4L1fO1ainZ3aWZLiWiTLFgo82iiIODw+5desW21ub1IoOnE/jhMC1sS0Tx76sbmmrty7OzxFSMh6FhKMRlu2Q1rqr8zI1eHjGgd95WBWNrNiQuoZmQIasj231vCilAaiULABZ19bexuuO51icx7oixTIlrmWSFtUgk+9Y19++bVoNsNLbMg0D1zIQQFHXpC1QswziHh/n8liGbJKedWJ0nufcv7+3ErwW2OtrbEB7SqsaHMfBcRwtK6+qzi5yfn6ObUpcL8BtFrL6gJdvGWu9vb4tFwBw2+tqGhKBZqnLWpEtKSykFB0LPD+GFAS2oZn0Jb+ybeoEZIEgcE3yoiLKyq56B3SHq9EskrZ+XK8JYSoqvfgwcnXNTlEpQtdmlhSMPYdJkhM6OoTq689t8LW9DfI8R0rZ/XfV3AR56+/63AQGtgWwz+ZyngHYpzSmafY+EKVpyoMHDwD47ne/+8T8rn1z0wDsZ8HAnp+f89Zbb7G7u8tXv/rVtX8IrsvAAnz7tS/z9i9+zcHx2crP0ixnYxRwMVAjk+XlIFiKGq/s0VH/w2xRlYP7qZnUkDTPO+CazwHSaRRjmSZZtup3rdX6Vd7rSYlXQewo8MjzkuOzC7wlcCKlJAxDwjBEIKirgpMzLTNWSuG6LkVRYNs2s1myNqBnOcwqDHySNOsWClqZ6nRg/7Nlv+zS6GqdaqE/FjQTNwr9DjyvYy3rWvuJ1/nuL8PALrfhu7o/Np3r51zHiNdKUZbVWhltVddYqGZhoP8+1dspsUxjrcx2FicL4H4ceBydnrG/v4/neezu7pIXFffv3mYSJZdS4zji5OSE8rDEcz38wKcejXAdl6zpPzYMiW2aCwsHaZ7rDtaiWBuQFaUZoe82bOsleF3+3CWZ3l6m1FpQXNeKNM/ZGvmcTa/uidWVQdXKNjVDaHULDFHSvLasBgO22smLCsuEke+tXSxpJ0q0d1qrEPQD+TjwBztp00L3F4eewyzJmEwmnJyccOfOHcIwwDTNlc9gnOUYhvaqzpIcwzAYjUaMRm11S0ZZ5uwfHFFVJa6rw4GKwif0PaSQxD2f69BzegOYyqpmmuR4tl7sW0iUHgCv81PXdEB27DuUNdTX8ciipckaA+l7qChrilLvo+9YSAFxXnb32Eep4xl7zgqIbLtg2zGkwLMMpGFgmIq8rAdl0QLdfxtlJQcH+1RVxd7e3sqDumVI0mtIs/s8p6ZhMB6NGI9GCKAsUs4nEaenpxRFiee5l0FQlnVlpU7oSPJKs6RCNGnKRU1SVIiyxjHlYDetO7d/As2s1kpRVjpIaVmNISVIoXBsg7SomTUMfMtYm1JX5czSEtc2yPIKQ4DvmEyTgtA1u9CmKCsJbIOqYftHnq4/GnsWqq75g89t8/Xnt6mqqlM/tQq9VrHX/t/lecbAPvm5CYsEzyp0VucZgH1K0wcWz87OePDgAbu7u7z66quf+YqPYRgUxfo/7p/lPGlA/fDhQ37yk5/wxS9+kRdffPFKtv2jAFgpJX/vT97k//h/+28HmNbhBN+8KBiPhuXgepdBAAEAAElEQVTASZZ1/rSV3+2p3WnHsW0UNUKIhWAjjZV0P6XdMLx9qrS2U3SdlHgdI5fMeXxdR/fIzgOxqq6RQlL3hBkpFLatk5jb1f2WhT0+PsZxHLY2NzBtB9uyVrywCi0DtW0L27J62eAoTrEti3zgMzCdNdLaAQCa52UjvdXbtkwTyzIXzleUZNi2ST7AuCRZvpatBg1i2zqbUeAzi+IFVkEpLY1dlyiclyWB51JVWW9IkSF1wmZ9xecvL0t816Gq6kFJbK2UXmiwLBzLZP/opJObbm5udp+7Set1nSWXUuMd/btxFDOb6cCYwPcaP2WAa/u9QD2eB6dr9n8Wp4wCj7pWveB1fnuB55Lkw68BnZ57PkvwPZv4iiCiJM1xHQtZiTkfoMB3V7to29caQlwROKRwbIuLWTwXyLV+srzQ3mnXQUg5CF7bqeqaWZyQJRFnZ2fcu3cP13Wb2qL+Y66qmlmir0mS5VSNvFYIwe7OFtMkJxxvURR5l357cnLSdM763N7eopZGx5Z7trXQe9o3rQQ5dG2KskIa8lrBR+0oBaUSJEVJ6FgU9SpDNz9CaDvG0H61QFpIQeDqRYPJNcHrdeXAVa1QYnG7ppQ4pkQ2n7OqrsmqGt+2uIjzrg7p3r09DGPx74pAYBiC4ope2tCxrjy3gWMyw2Vnx13oF47jmJOTUwLX5MRpgqBcF9s0sI3LTmkpYNYAzbxn4S0Y6HQFOtly4Gi2OskrZpn2ulqGXAGvphT4jkGUlqRzIVq2qT3xoWsSpSXTtMRuUojb+pxpWnR1RSiIs1L7YpNCy9Q9i6KsdU9sXfOHn7/Nq/e1yq595qvrugOz7f/fzjI7+wzAPvm5CQxsFEX4vv9MQjw3zwDsFfNZSIiVUnzwwQf8/Oc/50tf+hIvvPDCU7lJDcMg7albeVrzpACsUoqf//znfPjhh3zzm9/k1q1bT2R/7t2+xR9+/VX+3fff7tmHy3Thvuf+6Zr6m7pWmObwHywNJC9/13Xspow+IctznQjaJBrrUI2624ckzddKUOcDiVaOCdWECfWnEpdVxTjwwRVMZ9EKsNBhRv3gLcsy3tvfZ3dnGy8IdVm967K5udlJEifTGagJeVHiBzpExPW87rNkWSaGEIPMVFXXOLaNKMtBBjRJs7XVOrMoZhT6mkXM8q5qpp26rjDEsAwchtnqdnQ/bM1o5DOd9L8mzwtdrdMjJ28nalKal6+1ZZmY0ugksDrUqX/RAjS4uyocqq4Vvmvx+OCQg8NDdnd3e+sAprOYwHeJ4ksvqW3Z2Ju2vta1vtaqLDk4eMyHZd2kGgcrksQWnA75Ztup6hpLGldKdNtO1yEQ24Jv0J+jq+TVAGlWYNsmljAoyvW1S2lWYFkmrm2SDiyAzLOn0zhp7AhXf6dXtU7wNa7xd0cpxfHxMUkc8/KLL2BYNq5tXxkKBfqaWKaJ4xrEaa6vzxzotSybjQ2bjY1N6rpqOmdjfvXu+xhNrZfpeJjGeNAjufKeaY5tSu2NrssOPF81oecybVPiM+1tDR0Lpej1UY6867GpqtasX9oEP7mWCUoR5WXv3wLHNEjL6+3zyLOZLslvy1pRLoHq0DWZZTknh48JHJP7e3sIKbQvXGkgXFQ1oXu1dDiw13taAUauuXBuhBAErsM48DDEDihFHEdcTCPOjo8oqhp3rqbHsayOcb3O9uffJ3QkKIUUEM3vpwLPNhbSkj1bIoVEolYShwUCz9SLIC2LLyU4hqQUortXxq5JVWtWVwrByDU5j3ICx8BsZMxSgFA13/nCbb6yt2oRWwaobSd4W7/YLqS3i+rPQM2TnZsAYGez2TMGdmmeAdinNKZpUpYldV3zk5/8hMPDQ15//XWdpPqU5qZJiKWU3Qrkp7XCWBQFP/jBD0iShDfffPMjeQo+CgPbzl/79jf4xW/e5+xiuvIznSzcDw5ar2wf0BFC6CTRwYoYLXV0XQdDGERJDHMsVZLqpNmLSbQQ1tTSrrM4wbGsTqY5P+21aAHw8qSNl3bZjyqAUegTpyme4w6yYrMoXgm5aj12W1tbBOEIz3MXgOGyJBFVc3x6xuHREaquGY1Ctje3mExnSGk0tSz9wC9O016/bDtVVeHYFmXZ/7DZktd5UQ4G/yRZdmUAUpQkOI5N1sMuWqap2dErZI1tNdM6P+xyGJPujq1IskvgNZlGDRAc3l/tde1PJjZNA8swePeDD8mSmHv3LmtylkfRsI2u3Umi58eQBttbWwghuCvvcnI+IYpmWmpcll03ZRBoOes0StYmGPueQ5rmxEpdCcKhAbG+S5LmC4s04+ASvMKlJ7YNhFo3eV5iWWYjwV///kVRUhuy18M8DleTgqdNzU405+vtG9fWYTN1VhA2SclVz+tVXXNweEhRFOzdv0+FILRNqrU899IxlCVFWbI1DrhYkzgspdGFA4EiTTOSJKZqqlukZRMEQSc/HXqI1yyixSTJNaDwbOJsNbF5fka+0wGS7tih65l1LRPLNIhS7aG+LnjVxyVAXOY7zKpGDi8lnqMfktNCV65IIZCGQX6NKh7fMa/VC+uYupf04cNH2LbNxs5toh7WOHR1ZZBlSAyh+1elkCAu8xSEFKA0IL6U7eift4u0lpRkla4bqht1SK3QIVdVhUDh2ga16THa8hht7ZBlmomfTKYcHR0z8m0M2yMIgpWMCs9eDFmyDIFj6QWpulZEWdW7oNqCXiG0nLisNDMbumIRbCMIXYOyqhfSi02pA6OmaUEjYmLkmszSklopPFszyJO4ZWT1sSulMAzFm5+/w5fubV55vdpnn2V2tq5rzs/Pu2eSoijWSo2fzcefmxDiFEXRMw/s0jwDsE9pWrbz3/27f4dSijfffHPwoe6z3KebBGDnv7A/jS/kKIp466238DyPN95448oKo+X5OKFSlmXy9//kTf5P/81f9D5AzuJ4EKi0XtllMNWC2jhNu4WQ+Ql8D6UUpmlqVrJnptNYV4Vk2QJ4hZbhNXoBLFwtJZ7OYgLXJWoY4MBzqVXdvT6uU2zLJO9hMRX6Id00pH5gOD/n7OyM27dvEwRBF6YzlMYrhMC2Xe7duaMlw6bB8ckpDx/vk+c5rus2tS6jFZlxO5NZjO95xAPspa7WWa0lsm0LKSWTmb6m67pdL7t7h/2wKLXihfY8h7KsOgC/ruIHNLAMfJ0gPDRRkuI5NoYhSdKs98Fev8ZZALarxxQx8hcDm2zLAhTvf/iQONbg9db25lrwXtc1VVH23iOObaKUZphz4PbOJlPPQalLSeJspgGtbdtd4Net7a0VOW0LXi+TlePrgdh4EcQO/U5dK6Ik6/yi68ZzrOYcWyTZFWm3Va2l5r7bscvram6mcYrv2BRV3buoYpnam9c+CM+SDMcyMYXofLEAdVWxv7+PAu7v7SENQ6ckx1qmHXpuE4p1NZj1HJuLKMMyDAxLLvhU+0crLnZ3trSHsCoRZcHR+YTT01NM0+zYOm9OdQEQ+E7nX6yVYtL0x448e6W7FcB1rF5ANz9poaWlppRs+DbJNRnS9tijHsayqmtmad0crfZaOra5VrbcjmVK8mooaf1ypBCUZckHHz7E9zxu7e72Zhi7lmYm9fetoui2fLkvArAtuXb/TCnJRU2xhvUOV3yvoguC2trawjfh8HxKFOkuaaC71uMwoKoUvm0ghaCoarKypqhKDKEXBfrux8AxtZfWNUjymlmqr7dnSaK09cmK5udVV2EE+s9G6JgIYNLcV45p4FqSaaIrskauCUoQZ9ofC5oJtwwd9vTXvnSPl2+vNh1cZ1p29vT0lLfffptXXnkFz/MGpcbt//9sPv7cBAb2GYBdnWcA9op5UtKMqqr4zW9+w507d/jqV7/61D8ccHMBbFVVK6mIH3WOj4/5/ve/z3PPPccrr7zysa7rxw2VeuH+Xb7xlS/w/Z/8cuVnda0wpWTo8XYyi/Bcl2ROStz+MSqb6pxZWeo/toFPURYdWBHQK0NWSlGrGkuYTb/r6kRxykboD8ptp1G8VkpcViWubWFZ5ooUtqq1B7YPwIL2VYa+x2/ee584jtnb21tYdc/W+HxBe4g3RyFlVekOy41NxhublGULcCImFxcowPcD/MDHddwOwysURVmu/TxMomiBKR6FPlGSUjdhMzr0ab2XNUmzQSDfHmfYeFzb91j2u2qA6hINyUSVDthal25c1zWObRHF6SArVddawmYZRm9vY/NWmmFv/Lme45AXBR8+fEhVVdy/f18vqsxixuH6Kp+iqnAM2S1kgJbCV9Vimus8w2rbNrZtL8jK21Tj/f19bu9soQwLz/MYNSFeyw+38z7cdRPFKaHn6gWLK/ppW+/nkJR3niWvlbqWf1bVqqvCqVFXyqTjLMc2DTzbJpn7zBpSYhirwUtZocPgQtdmluaUZcn+48eYpsmdO3cQUuLaFklWdt8fsyTFsXQXcbIm/dgyDcqGHcvqElHosKQo7Wd9u/MUuJ3c2DRMMEyevx9iG5LDM51gfXR0pDtnGyb+zq3tDrzOT1nXDZAVjFyLqAlWMk1JpUSvD79vhIAo10FJvtOGRg1bEK4b2qTQf290mrG+Tq6lfax5VTf1Oc0+IPSC+DWArikqfvneh4RhyM7OTi941Wm9/Qqb+QmuqMMBsExBsiYNOXRW+1WXfz7Lyi7MTylFkeeUWUw8PWdycoiw3K5juGPilQ5fWg6VMoT2thalarp25/bVEBSVlvf6jvYyT9OSkWswSRo/dQN8QYNXidBguNTpxJYhkaJln3UugCH1godvGwil+Otfvsvndj8eeG3n5OSEH/zgB3z5y19mb2+v+/d5dvajBEE9m/VTVdVKP/lnPVEUPZMQL80zAPsU5oMPPuD09JTt7W1ee+21G+NfuGkAtj0vn2SflFK89957/PKXv+TVV1/l/v37H3tbH0dC3M7ffON1fvXeh70BQFGSDtbAKKUTgOcratrzopQiSVK2N8bM2rqZ+d9lUYbchjW1jF7apBIPMqnr/K5quF/VNAzdn4kalK9GSTKYWlxXFb/81a9xLKsDPcszmUW9PlEhBGHgMY01i7qwX6bFeLzBeKwrekwJ+0fHXYhJ4Gs5oud75IX2kA4yl0qRt8FEttmbTjydxWtBbFVXXWjWcOqwBntKqf4E5MZra9sW+QBoqMpa1+wIsQIQBIJR4HM+nRF4LmVa9UqjQcuifdehqocDm6q61v7cwOViGvPo0SMMw2Bvb2/hwWkaRWsZaGj6Wl2Hqs5xGzDcF0o16c7z5baWZeVpmhJHEXWVcHF2ypFh4fkefiM1Xt7edZhYIXXgmBTDnZXQgNg46e2xHQXewmekrmuStAW8V/S/KijrSoO5a0xealnqJXPb9qEOpFXXOnjJMSUfvP8Q120YOyGwjYa1XboPskJLMke+yzTKVsLgpBQNYL4ELQqYxJkG2JbFrEc6HnhOb3pwUeoFjVtbG7C1QZwV5LmWnxZpwtu/+DVOw8T7QYBj2wvKi7JWTNIcU0pCzwJhdDLhq0YIgWVdhkm1vbKGFPiuRVnVC4FOgWt13aJXje+YCyxw1Uhh25FC4tpa2msYktkVjDGARcUvf/MhGxsbbG5tDbTHann0MvBbnrCRyq4bLdEd3o7bk1q8uB+SpCibihz9tyavaoRwsB2H27u7nM2SbqHq9PQUwzDwfZ/bWyNmqd0FQPm2ZkzbqpzlECgpBJYhMIQgboCr/j2DaaLBJ9AEQZnM0oLANinrmqTQYVCuJYnTEtcytKc5K9lwLeKiwLNMDKn4k1f2eG7nk4GQ4+NjfvjDH/KVr3yFe/fuLR7Hknd2/r91QVDPZv08Y2Bv5jwDsJ/h1HXNT3/6U80G3L6N67o3BrzCzQSwn6RKZ95f/Gn06X6SXlrXsfm7f+MN/qv/9t/0/jxKhhNw03RRSiyETogNfY80L5j0BCK1k+U541HIxWS2ENakbzvBLIoH62fqWq31uy57eKWQhIFHHCdcNGBag5QhFjfBsW2yOYBcFAX7+/tYpsmt27sYhkkx0M+aZIuBSq5jI6XsgF4812W6PFJKlIDPvfACUZKQpRlRrOsdysMS13MJ/IA7t2+RDjzgW5b2oq6T8EZxgmPZZEX/NrQfdpiNtC2Lsiyo1fD3RFVVuh+4CWHpmzTNFxKSQS80OLbVdbxGSXqlNzdOs7UeYmh6LeOYhx9+iOt53Lp1a+V7TqlL6fJQ3Q/oe2xrHHIxi9eGLE1nTfBVDyAWQnSpxoHnUpQFh8enzGYzjuekxoHva6ZfCF2rtEaWq8ORYhRaIp9k63talboMVZpGbZVTv6/4EvD6a5lV17HIipI4LQh8h/QKXyc0EtooYRx42tN5BdObpinvNuFpm1tbFJW2c0jTGAySUgomUYrv2lRV3aUmC8B3nF5GFDTAzsuKke+QFiVF2TLvuo5k3bSy65Fnk1sm4zCgrGGzLDXAiSLOLy6QQuA3vlnf8xCtmqWuEdIgzkpGrk1aXr7/0Az5XqtadWDbNiWOZVBViqy8itPUYxmSoh5e1AJ9HeOsYuRZnDfsoGkIHNPoGMCy6SCtAcqMX374mO3tbTY2NoaPybVWQqCWx7MWg4/6JnTWg1dDaDHy/O1qSqHTkqX+i1M2Xtm0rGDpNLc9rJZlLfSGJ0mCKhLee7iPoSrCIMBwfQrXwzJN/B5W1jYFrimZpuXC9XFMqcPnnMvj9SyDsqq64CopNJgvqlrX6ZhaspyWNZueTV5V2IbENOBvfeU57m35a8/bVXN4eMiPf/xjvvrVr3Lnzp21r+0LgmrB7DN29qPNTfHA7u7uPtV9uGnzDMB+RpNlGQ8ePKCua7773e/y4Ycfkq3xkz2NuWkAFj4+aMyyjO9///tUVfWp9el+0pqhL37ueb76pZd4+xe/WflZVdWd5LJvprOoq54BKMuKySzCMEwqtAxxCARNpjNcxyJO0lW/q1JIQw6mIWu/Z9CBnNX9ivFcB6vp4lxmc/M8xzSMXu9drWoMScfipknC/sEBo0beVitdlzEEYMuyIvA9qrIiDHymUbRwDFVVdemVfcfWdreahoHwXFyvqXfIC6I46ryUYeBj2jaB72M7jg6CCS77Udf5UOtaISRrWdbpUpBSO2Hjs8yLCtuyBs8jQJpmayt+oGVztVzVsW3NEiTLoT/xWkDdvmZIXj4OfA6OTzjYP+De3ds4nk9vJxP6oaAoy7XVRaHvcj6NCH1vLbAG7Sdfl+Lb1uoA7N29rWtzGqlx1EiNhRAdW1ermo0wXGFi22Tf9mpGSUrguqT5egCpFExmWvKsajUs+25mGrdM8OrrbMts2G79flGc4doWhlRX1OxcTl0rTENQDvgT4zjm4OBAh6eNxtS1IvRsFJKohyVd+f00R0qhZbNx1gD2q39vGuuasJFnk+YltRLXXsScJTmmKbUHPysHmfiTkxMOy7JLur17a7sDndMk1yyya1FUaqFGpZ2R515LCpyXNWWtq41MKfANoaWrA9dICBZY3XXj2eYC81pW2rqxsD2gzlMODvZ5ce8O440N/b2nasqqXrj2gWNeCV5NKSibIKKhcQaYVaOprJFSe3brWmkmv4a8qubSkhW+PcwCawC9eu6llNzeGiHFmI2tWyRp1gVBpYdHbPg2J7a+3q7jELo6UdqQrNQZ2YbAkoLZnETeNgS2CdOkJmtUUWPPYhI3XnjXIs5LLFPiWZJK1YgmzOlPv/Yct8af7Bnk4OCAt99+m6997Wvcvn37I/1uXxDUM3b2+nMTemCjKOKll156qvtw0+YZgL1iPg2G9Pz8nAcPHrCzs9P5XQ3DWAnfedpzEwHsx2FgJ5MJb731Fpubm7z22muf2srZJ2Fg2/k7f/wd3v1wv1eaOosTXVkyXQSBUgpC3wehgaxSeiV//iGilVHOlkBFmzLcfgH3g9SM8chnMh1iSqNBKXEYeKA0OOqTleZlubYSJk4zNsKQh4/3OTo6Ymdnh/EcQxC1SbpDLKdSjEcB55P+sKrLwKn+38/yYkXma9kWm/Zm56WsioKLyYRHjx7hODajMCSKoi4sZjqL8TyHZCCoJ02v9sNGc2xxm9g8vxiQFwWB71HGw/dfKze+KtRpcxwymyWUdf+2ZnGM7znEa4KHJlELFi/91qPQ59HjQ46Ojri1ewvH8weTidspygrHlr3gvGXvVfN+G1fIepXS3tS+fR81HtT2Dk3TvEvyDUcjwj6Ac6BTjXdvbaOEiWmac+B18V6P0hTfdcjy8koWtKoqTGlc6TGE+fOc04r1TUuv+ix3c6Z5gWUaVwZBjeekzLZl4tqrbOp0OuXo6Gih7qiqawTah9lnHeibulZMopStkU+8hpFbnqquiZKcUeCSV4qCa/4NEOBYOiBKysXE4XkmfgcoGqlxlaf89Fe/wTR156zv+ziO2wFa3zGRUhA1abO+bfXW6AyN79odg9deFtcydadoVXd9tQCh61wrSdg0BGVnCxme6WzKxekJm7duI52gR/YrOua2RhHYBkLqf2+vcZsoDApDCqpag9Tm15vXqsv/icAy9bWvlWZRy7rW9TxVzcg0uYiHz9/ItZgOsPTap1p3954UOoUYpbuU80p1Hvl5T7xvCQ7PpmRpzOT4gJNKYbketzZHYDrdM4JtSBxT+4zn64NCx6Ao626/XcvANuA8zrGkTuJOigrXNinKCtOUTZiTwd957Tm2gk8GXh8/fsxPf/pTXnvttU+FhRuq6WmfF56xs4tzEyTEcRw/kxAvzTMA+4Tnww8/5Kc//Slf/OIXefHFFztAfFPB4m/7Pu3v7/OjH/2Il19+mZdffvlTlWh/EjlzO57r8Kd/7Tv81//df9/78yhOOlmsY1vao5YmHZjZGAVcTKNeRi9NM0zToCwv+4Xb//KiXAtuprN4sLpEKf2HbP6hNfC8BV/mOoC2ttdUwbvvv0+aJNy9exfPX5VYzaIE13FI5xQLlmniujbTWYxMxcrPl99/uZpn+diHamLaxaYv3NpBqZrDkzOi2Yzo7IK6rvA8nyDwEXKMYchej2b3HmvOUXtfOZaJYRq9vmQN5oc9y+2xrgOfo8BnOot0evUAw6GUIs91P2m+RiYaN8nEWVHguzbvf/CI8/Pz5jpq//Ekiq4E1VleaG+tqjsJtJbaxgsg6aJJCV7HxGoQmi9Ik0eBxyxKFrZVK0WaFQuv6wM4URxzcHiMQOHYNmdndgNwnBViOU4zraIoq8H7wHdt0iapNwx8oh4wvDyzOCFwXZK80PYBuRq61E5RVlR1reWtPffAyHcX+lrzokQIoRnF5vVt8vfdu3fx5z6P8526rm2hYCFMaGhCz+F8lmpW1bevxcKCIvBdJs1rR75DnJeD5/Xy+C57ZetaMY0zTEMy9nQY1fzagmXb7PoeCslWWXads/v7BwD4vmbr6tpHSkMza7ZJoQTqmiz32Hd6Wc2srDoW1jQknqWrpqbXYF51+u/VPtXJ5ILk4pzt23fxvWHpal1DqdSVaccj1+QiuaIT1jHXgvvANtZ6ZwPHGASvAs0Ag8CztXIgKao5ea/sZa4D20ABd3Y2iLOQWimyLKPKE/aPTsnynMB12BgHZKaLOfK7QCzXkkj0vZSVNVIKDfKVDnFqA5zKSmEbgqLU3a8SLS3+s9eeI/Q+WfjPo0eP+NnPfsY3vvENdnZ2PtG2+maopqeVHD9jZ28GgH3WA7s6zwDsE5q6rvnZz37G48eP+da3vrXyxWOa5o0Ei+1q3E3x5l4XwCql+PWvf81vfvMbvv71r1/pD/k480lCnObny59/kS9//kV+9uv3Vn5W1zUbo4C8KJlFyYp/czqL8Fy7F8AWZaXZyiLqtgWXKoJpFPemEkMDUmFQbpukGRujoJHzihUGWfe3ekSD1TMZlrnoZ1W14vDokCzLePH555GG2VuNo5ldhRR6nX8U+MRx0oHny5/L3vRQpXSq8Hyi7fJoBnTRj9uObVqUTXWP57p4rsvODk1YTMTFxYSjoyO2NsYIw8QPAp2GuYRwojWhWKDvddOQawHqZBatrcVRTbjUvDcYmnAr32XabNs0GawiAiirCtc01oJy7flUBK7NO+990CVG287iA9t0FhN6HrOBewM0+At9jzhJCYPh7tpptF4mrPerJi+KuRTsoaoi/brlZN52LNtms2VwHIs4STg4PuXi8cWc1NjH93zdh4n2NHuOTSlY8VD6jk1WlJ1Soe08jrP1Ha3QMLyOg2GaVyYOa+CWag9vnNAibd1J2x/INo0TQt/l/Yf7TCYT7t27t2C7mK/sAc32yiXg2zeuY3UMY1XXTKOUwHUo6nptt+k48DsgCq2sWDS+06yX/h3NpRTPT1nVTBogG7oWUcPI6o5Vk6yoejtn4zjm/Pycw8MjXNchDAI2tzZRwiRwdQVbNAC2APxr+En1eVFUCqLGg+lYEtuQ1OgFgmWJd+hdvd3z8zPS2QVbt/eutM94znU9revB69i1mKxJJXZMSVrUg8s1bh8AFVoybEiBFBClFZWqYem0jxxzBfjqhQFJUdbaR9tuUujFGOW7bG9vI1TN4dmE81kM+RmP9wXjMGBrHBJXJmPfYZbq8KaW3Y2LktAxiXP9f5O8xDJ0J65lSDZ8gz/96nP47icDrx9++CG/+MUv+IM/+AO2t7c/0bauO0Ps7LzkuH2dEOL3gp29CQA2juNODfNs9DwDsFfMxwFyrf+yLEvefPPNhVXsdm4q2wmfTm3NpzXXke2WZcmPfvQjLi4ueOONN57Yh/zTkBC386d//Tu8/3CfONUPf7Zl4rkOcZpxej5hPLDSphkEoQN7eh56JzPtF5wPfGpHKUVVV0gheuW+SZoPynU919Z/uBSDALgoC0wpKXtAka7O8ToAW5UV+wc6/ff+/fsoIfA9ZxBspFnO1saINMt7AV6aDe87aAnuuoCiulYIWDk3o8AnSVKipMCQ4lLmK8BxbBzHZmtri7IJi0FVfPjhh5im2aUat2Ftda1QqJ6FEKHZrWuw2e3D9brU4bKs8FyTstT3iGWamKZc2GaW5RoIDrDS7TldBxYd26IoSh4+PCTP88HEaIUiya7ukY2SlO2NkJOLfjm43lbjOfWczs/aN1VVE/om6TU6VXOKtbJbnUadIqTJF1/6HBdR3EiN48ZLediFfvmBT4I+N7ZpdH5Uz7HIm4fA5WP2XIe8qKgGJN3QKCCkIM3ya3XFAkyipAt3si2LNCsH2V6lFO+8+z5VWfC5F1+g0ZECGvj29djWLfD1XNK8XPnc26ZBXbcLHXPHnGZIKRl5DrM0W1kw0yzxKhCtGmDuWCamIYnmzkHg2Vem4s4D2bFro6Qc+B3dOeu6GuC09VuqKvn1u+9jGma3eDEOAzzbJC8Xq20cy+AaZCrQyIHrS0yelbrL9HJbEsuQeoFRyiuApOL09JQsmnLr7nNXVn+MvPVhS6Dlslexvdo/O7xfsm0UGJDXm1I0lTMCrwmiqmrNsCZ5xcg1uyqb5QnnWFvR1OQopcjyirxkxW8shWZry1p1XtrRaMTe7hZRWiCqnKPzKR88PsSk5MRy2RyHnBcOW6HX3c9RWjD2LC7inLFvUVU1liHYCW3+9Gt7OPZH65lfng8++IBf/epXfPOb3/zEAZQfd4bY2flQKPjdlxrflBCnPizx+zw3A6Xc8FkXwLI8FxcXPHjwgK2tLb72ta8N3vQ31QMLNwvAXiXbTZKEt956C9M0+e53v/tEu7o+DQlxO4Hn8Xf+2h/yb/6/b6FQzKJ4IcgmSpLBjtAkzQg9tyf1VFfkREmKaRq9zFmW5+ulxFGM3wBp0Mm+lnnZ5eo61iAAzvNyLfiKkoRxGHB8esb+431c12V3d7djr6ZRzMgPVhJuTcPAdx3OLyYr1TjzM5kt+jL7jm3dsWsQrAOrLNPEta2FY6lqhSUEQkjUEtNrmibj8RghBLdv3+Hk9Jwojjg4OEChOnBT1z7jUdilAVumiW0t1vDM1jDlej8q3eErxWDybdKkBVd1TVGUJOkqAJnFSXO8w/Le7jVL5yzwHKI44cOHjzAMyRdeeok0LwYBUlXXlFU5eE9LIfA9l5Pz6UolzvLUSpFkedc32zfj0OdiGmNZJrZpkq/5rtUgtuxN427Ba3tcrRe3lRvvsBj6dXxyjG3Z+IHP5niM5+vXllU9uPiVpDqASUpjoeO2HYEg8C57ZKu6JhwAlcsTxRkj36Vu6rj6pq4VBwcHlGXJvXt3kVLiu3r7WmJdrBU5z5IUyzAIPbtLNTakQBoG2YCctK5rpnGC51ggBEkDJLRfdD04z4qSrACvCeFRoJm9a/6NLqsapCTOSx0UVaxPHDZNi+fu3WGalox3dNJtHMUcHR5xsH+g65h8n+2NEZ5ta8AkDaorUozbsUyTZI18twW0rm2QZ5W+9yyJIS9BYV7pUKbj42OyJOb+/ecRxnoA5TvGleDVaL5jhmqz4GpmlSbIaJ7lFUL/niVFZ7JNSh0qVSz93fLt4a5Y3zaI0wrfMRBAktdEacllENTi7wVNHc7FUiWTbxugdN5Eic32zg6f27tLnuccnU84Pp9S54ec2xbC9tgY+WyGIbOsZORa+iZUcHfL5U+/9hzmJwQ77733Hu+88w7f+ta31qZGf9azjp39XZYaP+0QJ6UUURQ9Y2CX5maglN+RefjwIT/5yU/4whe+wOc+97m17O1NlBC3cpCbtF/rWM+zszMePHjAnTt3+MpXvvLEv2A+LQlxO69+8WV+9s57/OKd91d+VlVVk0o8UCGT5tzanX9A0eBV/0GpewOd2lnnCW17Zz3HwTQNplG8UCOTZsVCdc7Ktrs6k/73Pjw65vTkmNFopFeVlz4iSZZ2UuM2GChK0i4FOS+KBZ/v8qRZjmUZFAPyRO2ntQercSaziJ2tERfTuNcTm2Y54yBY6dxtR7Pcio2NEUEQoHZVV9FzdnrGYaHZutu3buH7HrWqV9JolYKiKDENcyVV9HI/rk4dRoBhSOI1vjXdp7u6aLD8mvkwpnHocXI64fH+I1zXY3d3l6RlwNcGNpU4tr0iSzalgeNY3cLDdNamJQ8fW1XX5EWJ61grLOs8E18UJbZtYpn94LDbXlXDEogNfbfXozppvLiTZn/nQ7/qqm5SjSPe/+BDHNtiPBohTAvP87teyuXRAUxm7/EEjYe3Hd3PmgwmFM+PbRlkhQ6WCj13hXGvqqrpQRbs7e1hGPo7bhYnbIY+cV6uBS/tFFVFEVcdG+s4OiDrqunqb3yPqq7Iq7rXwtD7u2mBYQhCzyMta6r6egvCo8DtGLtpkml5vWtR1XWvhzL07A5ASSEJ/IDAD1AobSOIYqaTKcdHx9i2za3tDYJgROi7ZEW9AsgW9sWzryUzNqWgrkW3cLa8n0opTk+OqfOUL778OUzT0t9FCopKNQFpl/eeZQiy8qoTrXDMVRA4P4bQAHS5XxqlMA2JaeiKmqpWjFxtESlrpWW9RUVK0xc7cA5sUy6w0e1IIfAtbSkREuKl329rdqDp5bV1FZMQLIBhIWDk6PTgeQXByDFIi5K8htF4zN7tbaKkYBpF1HnC6eEhh/uP8VyPrY0xvufxwu6Yv/3V+xjGJ3sO+c1vfsN7773H66+/zng8/kTbepJzFTv7uxIE1TLNN4GBfeaBXZxnAPZTmLqu+fnPf86jR4/45je/ya1bt678nZsoIW6/YG7Sfg2xnm041pe+9CVeeOGFz8Sz+yTOzd/9G2/w4aPDXrZtFsdNKnEPKGgkqabRfsGqzrsshGbxRqG/wOy1o5SiqPo9oZ5r61oZIdawtOt9mEm26ncFrU44PTnluft7OK7b+2BcVhWBZ3c1IctAuShLzbIOgJGyqggcj6IYkAqrmtZPu/z+pmngOTbnk2htZc0kipowof7zUxQFgedRliliTo64s71DURTEcUQ0nbK/v48SomNnHcfpfLNFWeJ7rg7TGOA2hlKHhRAEvpYkC1h7rUCzrG0i79BMY73oYRqS/aMTDg72GY832N7eon0wnsya+3WNhzfLc3zXJa11kJFlNnLQvjofv7/XtZ2y0g+kl6yu6j0fea4D0SxTrmXaqqpGUGnvrGn2pg23o0FswGQJ+EtDEo5CwlGIZRokScp0OqOIYw4ODvE8F98PCAJ/ReVSlCVVLRcWl0b+AButmuTxQCcU9+2naUikkF0NzDTW/a9tGnNZljx+/BjLsrl9+/YCuDYNgzgvkELiWhbpNevDZknKZuhzJTZamiTLdB+vbTSe2qu/z4UQOLbDRZIhEE3tTkWx5jt6PhG4HaUUs+be92wTU0pmWYFS4NomycDBCPT7O7bD1taWZmlUyeHZlP3jc/05DAK2xyHjcUhZLcpZrwte29CmoWqdWikODw4oypIvvvQiSQUsL3wJgSklphQYkuZa63M8nzTchA2jhMIxDLKyxrdNhFCgNFidX4MxhKBSYBmqWfxUVDUUlQaqrim5iIcZ/HXeWqMhZ1vZsdWmJauavKgpqlV5cLvNWVbgWgamFER5yTQtCF2zS5YWQr+uqtUCeHVNA8cSTOOSGl3z41iCJK8QhuT29gZSjFEKzmcRVZpwdnpCKXNCb4ff/CZjd3e3U+N8lFFK8c477/DBBx/w+uuv/9axbcvs7O9KTU+7708bwD5LIV6dZwD2GrNOQpznOQ8ePKAoikG/a9/cRAkx3DxmeBnozy8W9IVjPel9+TQZWNBS4j/963/Ev/qL4VTivp5MIQRJmrK3ucH5ZLYAXtuJ42ywYzPPC0Zh0AFc33ORQnTSVoEgaBio5dEMYTEY8NP2s7YAVinFyckJs9mMe3v3sBxHB/X0AB3b0hJlIeVgINQsTtgIAy4GAHbUyV77gVQnFZ77/VGova4tKHUsa21VSJQknR926Od9fapB4DEehSRpyvatW8yiiMlk2nWQtmDW83Sg0WgUdMFLfTOZxQsAVftdjY59V+h06nX7qlRNUVRrpbZCCAwpODo5ZX//Mbdu3WI0WmUHprOIkecxXRvYlBL6PkVZohRdCvDiPjXpu1d4XYuywrEkpmXi2dbauiTHsZre0+Hvt7Kq8D2HohheOGinXciY9IBs29J/Wk3LZmt7GykEliE5PDlt+oWPsSy7qW0J9OKF0N9vcZwyClyEkGtZaIBplOC7DkVVLzDMhhRYlrnilZ1ECYHrMI1jHj58iO/73Lq1u/C9IaXENNukY71IMPY9JleER4EOX7qI9fXSPcbFYFBYO0IIbFvX3UCB71goxEq1z/IEvtsBT4Xq2NSxZ5Pk5QrzaVsmRc1aqXEbOGUZEs+2qIQguwbIBNgIfaK84u7doKtkiqKIhwdHvPfwMZ7nMR4FbG+McG2H6Jom2XCNv7eua/YPDlB1zZdeepFkoNMXdJVNWUPgWFcGMoWuyVmyftFi5Bicr9mOa0riohr8FHmWsSaxWOFaJgoIXYOiVGRl1V1T3zZWWFcA35IIFK5pkM6pcALbYJYUSASBa5AVNXFWYhk6s8GUTeBTpZgl2is+ciyyUsvVHVOSFTVSQlU1dUCeh+V7/NFXPsd3Xt7h5OSE4+Nj3nrrLYQQ3Lp1i1u3brGzs4NlrZdztyGUDx8+5Nvf/vZvPdPWJzVuwexvGzvbPvM9TQBbNX3lv+33xac9zwDsJ5jW77q5ucnrr7/+kXyjhmF0H+qb9MG9aQzs/P4URcH3v/990jT9SIsFn+a+fNoAFuArX/gcP3/nPX7263dXflZVNZ5j9gLYulZcTGZ4rkOSZiwv+OrgJLsXwIIGG5vjMUWRr4BFhaLIh0FqXqzvd237W8+ns85f99z95zCbB/vpEvAypCTwvc4LLIRYG/ozjZKFCpS+Y1vnI22ls1meNx7URZAYp+laL+Y6P+zlPi5KtcPAI0ky8rpotlGzs73VVHUokjQhjmOOj4+pqgrf85lMfe7s3ho8TlCkmQ51Mg2DIi9W/K5VXWNiYEhjMCioKEs818GoxIocsGVJ3/vwIbPplPt7e9hOf7KpUpeJufGawKa6rnEti4s1kuO+Spz+fS8Yh35vwu78ZFmB69igVG/QGOh00kkUY0hjMJ14fpblxAC2aQJiQf5fK0VeVjy/d49pnFA1UuM4jrrFC9/XlUye53MdBrKdOM2wLLMLd5JCe9ejAUb95PyCo8MDbt/awfVHS98bAndJ/qvUJfDNitWwpnbGgbeQSDyLU538O+eN7ZtwKbQpzpouZF9XBy2n8LbvNemt/FJMkqxJSLbJioq8rJCtJ/catT8AZa2oEMRZReDo76woLwdXtDxnsdZmvpIJ2sTymIvJjJPjY0zbxfV10u0o8PVCTlGtqEJGrjUIXqu6Zv/xY4SUvPzi81ynZld3rK4Hr67VDw7nJ7SNtexxG8o05NG3DEFRL8rFpRC4jbdXomtq+n47dMylc6L9rpYhmKUFy7eLa0rSsiJ0dSL2NC0RSqcvp3nFqLl2LeC1TYkC4rzANiWGEMyykg3PJMoqPFMiDUFV13zp3hZvvnIXgL29Pfb29qjrmouLC46Pj/nNb37Dj3/8YzY3NztAGwTBSrjiL3/5S/b39/n2t7/9O8ey/bbX9FRV1dnrntZEjWXpt42Vf9LzDMB+zHn06BFvv/02n//853nppZc+8s3dgt2nbQ5fnpsmbW73Zzab8dZbbxEEAW+++eZTCZl6kuD+z/76H/H+o33iHl/qLI7ZCEMuOpCl2dYommGaBrZtIWU/kGqDk+bZyFZiWlVVw7gOqAvKcm1y73SNTBng7GLKyfERAPf37iPnvEEtQLYME89zSJJsYR+VUk0qZX/VS610aMhQoJQCyqocvJ/1iq/2iA6GPnV+3uFk5HVSYlCkDfNnzwVhzU+cpB0T7nu6kmVn5zIYaDqZcnJ8TBgGmJZDEPhYlr0AOqqq6rpOh2qCsrxoGPXh+zdpqmzmfZKe41CUBe9/+Jgoirh37x4bG2OyrBj0Rta1Ii9LHMsi61k8CX1dgxQrdWWIVFXXjXe2n0E2hMBzbc6nMZ5jdx7woUmzXIPYkhUg1nm3lT6nOQrfsYnXgGfQIHbke8ySDMvU8vusx7uuuAS80zhhNAoZjcI5tk6nGtvmGQcHJn7gc+fWDll12Y87NEVRUlYVI89FCTEY8BRHEQeHh+xsb+P54UovbOi7g78bpRmmYfQGSGl/bX+FzSzJCDydtLzsQx4HHpO+ah9gEqdNWrHNLMtpv95C3+kFr/NTq3lG1kIa5kpwz7oJfbcDSdG8l9I1KaqadA6sWoakVGItY2/bNrZts721hWFIzicRURzx3oePAPRnP/DZHo9wmt5SlBqstymrSsu/TZPn9u5Ssf79AfxrVOGYUvtV1y3Ttszq0IhGejvknRVCYDTf26Fjag9tVZMUNXFeEToGFwOJxvPg1bEEtqGZ1qysKStWwKtlCCxDkJdc1uwoReCaCLSqZJqWWrFgSF3Vk5WdNzbKC5JKMW7kx4FjIoT+fvjmizu8/vJqXZ+Ukq2tLba2tvjiF79IkiQcHx9zfHzMr3/9a2zbZnd3l1u3brG5ucmvfvUrjo6O+Pa3v/17kTI7FAQ1z9K2r7sJNT3tM/rTBLBxo4x7xsAuzjMA+xFnXsL6B3/wB+zu7n6s7cwn/l4lL/ks5yYC2Nlsxl/+5V/y/PPP86UvfempfZE8CQlxO77n8vf+5E3+r/+Pf9P781kSNw/xOXWt2NraYjqdcHBwwMGBZlOUkL1BMbM4wbHtzj+aZvlCwJP2lK4BqT1S2HbiJO1Nls2ylP39fba3NhlvbPY+WlkNa3gx7a9NuUxMHpICZ2tThYdYYse2MQ3JxTTCc51BEAya3RoCYtAAknBY5mtbusJmNrCP0Mhu5xYKBKJ74N3a3KKsSoo8Zzab8eGHZ5im0fkofd8n8FzOL2aEwfp+1ChuwfKwJHkWJ42P9TLR+fHjx5Rlyf3797Es6xLornmvsqwwbIFlGAuexBbwt6d7MouuTB0uS70C7ljmAjA0DIljWR2YSrIcz3FQFNRrpJR9IFZ3psYLazlVVZOqYiGVe2imccJG4JOV1UqS8fJoEOsxizNUsxjVsnUj/3lOLi6Io1hLjY9PCAMP1/OxXa/xSfePqi/7u0UPoJlOJhyfnHB7d5cgDBuQqJnVvNCe66tkwmVVMYsrQt8hyUqqusZ3tGR3HXyKkgwpBSNfg1+lGlnyFax5XddM4hTLNHA9i1qpa0tvoZELS4OLpCBoqsCSK6TJY9/rBXpVrToPpWMa2E3HqDDMBbnqmp3B87QHNwgDgjAABWmW6s7Zs3MODw9xHZeNUYAfjjAtC9c0MA2BUnpBIE5zHj18iOu63L59G9MySdekGLf7m13xGgRYpiRZcywds7rmYi/7Wg0hNJMptcVFAFFWUSlFvuTV9Sw5CNp92yAvakau2YVAZUWtcwbsxQ5Z15JYUpKXFZP5a6kUG54+X204lGzkwrOsoKwVhgDfNrlIchxLEtq6Rsi1DITQC2Tf/eIdvnL/evYlz/N4/vnnef7556mqitPTU46Pj/npT39KlulFlo9DgvwuzG9DTc9N6ICNogjbtm8UVrgJ8wzAXmPaL5Y8z/n+979Pnue88cYbn0jq0X4Qb5oP9iYBWKUUFxcXnJyc8Nprr7G3t/dU9+dJSYjb+dJLL/DVL73M2794Z+VnVVXj2LLxbylc18Xz3Ia9yYijiKosmqAYjyDQHaSmaVzWYkSroUgwX5XSD2ziJO0NZWr3S8uUL382m804Ojpie3ub0XjMaGnbruNgSEnUgJZ1wT+TDtz1A8DJEvhbHl2do1NphRAdgMpy/QSWpNna/tiqqnAsc60fdhbFK3JngWAUap+vQifjDjHVoPtAXdsm7ZGsmoaJ6Zns7GwTxYlmL+OIs9NTTo61l9IPAqq6ZmtjNHgsMA+W16UOx+xsjjk4PmV/fx8pZZNQe/lHfBYna887QJaXXX9wpZQOPeq7/6KY0PcHmXDQDKMOYtJpwpZpYEi5AiyTLMN3HTJVDMoXoQGxrg2FXjwaYoHruibN8itBrG2apEWBYRiDqoH5mUTa35vmZfedMgo8pnGCbdnYmzabm5tUtfY+5WnKdDqlqGq9aOH7eL7f9WsCC8nNvuPoZOCqBqU4Pz/n/Pycu3fvdrLWdqI0Y2PkD7L3fTOLNdsc+G6v9LVv6qbH1bUtHMu6kkWdn6KRARumReBoqeh1ZjQHRqO5oCYpBXET1DQ/oeus7TNtJysrsqJkFHiUtWLkWuRl1ZuYe7kv7uq2BYuds0VJksRkecbh+x9gmk3nrK998UVR8PjxYzZGIffu7OJYJqWCoGFOy0rp67gUtKQY7mDtjt0xma2RBQs0o7lc+SNQWFJiGhLLgFoJQtfs9qeo6g4UjxyTycD5tQxBUamVhRfHlDimJC9r8qoin38+UeC7RsOSaymxUro/VlqQzl2PwDGxJJzHl/eObxtIoThvFlJC2wAB0yRn5FlN4KECVSOFpFbwJ1++y+fvfrxeVsMwOvb17bff5vT0lHv37nFycsI777xDEASd1HhjY+NGqfM+i/koNT2fFTt7EwDsbDZbkZ4/m2cA9tozmUx466232NjY4Fvf+tanImG9aYFJcHMAbF3XvP3225yfn7O1tfXUwSvQyHSfrG/57/zxd3j/0cEKS6aU7optQUP7PabZGw1mLdMkTTMm0ynT6YSLizPCwEdIk9j3ubW9RTHrf3iYxckg09h6afsALGhmb9yAtbMz/aB8585tfF8v8ExnOrW4BSHLQG4WxYPgDSBOMmzTIi/7H1rjZD1LOosSNsYhWZrPybAvZ9JKhYdY5jRbK3XVyZt199mxLQvDlAvvNZnFa+XGdV2jUIOeY4A4ThmPQoQQ3L19i1mckCQpcRRxfn7O0eEhB66r2XhpDK7WzuLhGiUpJb7nsn94zNnpCZZlcfv27d4/nJcS8mHgmaQ5YeA1qdZD1UMQx8ngPrWT5drDajoGVV0PMp1xmhG4Lkmer5XepmnO1jhgGq9nV+u6Jsmywf2zTRMhRdN5qqt9jFqQr6ntAc1Keo5NWWnf6SxOV1ZJDGkwCkcQjhr2SnF4csbJ6Snl4SGe6+IHAXd3dxYCn+IswzIMPMfiw4ePmwC1PRzHWdmPceAxaa6hZqKv7peFxqNc6AUepYorAVI7usokI3Qd0h5Zcd+YhkRIowul8mwLKcXaztjQdboqlfnpgppMA9cySPKSsqpxbWswcbhvxsElIL30Txo4ptQy4zkmM3TtawFj0zS5d/sWUV6xdavpnI1jjo6Our/LnucRjjcwDYPzvoosoWWzhhBIoSXONRoIttMC93bhwTIkRa0IGvmypEknFm0uusKUgrKG0BG6V7hW2itcQV4pDEMxSYf7eAPHGDwHUmg7SNYcowaWgrzphlVNxdLyhJ5mv0eOSVpWndx75F6ywIFtUNUKgeIi1v9mGxLb0BU807TCbip/EIKsqPAdk7pZByjLGss0QFX82dde4P7OJ5Nx1nXNT37yEyaTCd/5zndwXZ0nUBRFFwT1gx/8AKUUOzs77O7usrOz80Q77m/i3BR29iZU6LQA9tkszjMAe4159OgRP/zhD3n55Zd5+eWXP7VVkJsCFufnScpkrztZlvHgwQPquubll1/m9PT0qe5PO/NfpE8KwHquw//kb36X/8uf/0X3b0qp7qFgFid4rkPaE5BTlCVbm2M2xiG1gsl0qjspo4jT01MO9g/Y2trEMC1c113wUdZ1jWHbiKLs9VJFSTJc6YNmQi/OL5hOJ9y/v4dtXz4oG9LAlJJC0ctCtkzZkJS3qisc29KptT37pn9uIopVltQ0JJ7rkibZ2vTZOEnXJvVeJXXNioJR4Gt5XJKQFav7OYsTXKf/2oGWTF8lzZ3NYrY2xpydTwDNZruOw/b2NkVZEkcRx6dnlEWB6oKB2pTb5lFUQZrnK8drWyaGNDg6OeVgf5/NzU3u3Lk9eE5AX891+2waBnVVX9mNWCsdRnUV0ykQmFJQXCGHjNKUwHNJsmEQOw49ziYRrqM9xeuAlKqV9iv7LtO5Y10Er3rSrMC2zBXJc98kWc5G4FFU1ZX9p7rmRPDyC88xiRLyoiCOIlSZ8/bPfoFt2fiBT9CkGudlycNHj5GiZm9vD6vnAXjkLR7PJEpwbRsFa/ddJxXrzty8KDUb6w4HR7VjmyZlDbXSHaxSCkaewyzNB4GPFALHcZqU4ua8Nfek71g6D2AJyK6rv2mnKDV41inLNkiT7Jo+WQ1IV++XvKy6hQtDClzLwJDX6VzVM/LtjgWVUhIEAUEQkCQJ+/v7uK5LXdccPPqAfcPpPt+2bS88lxSVokB3r15c4XsNHJNJul4CPnJMzuLh7dimBptD19A1DZJ86DOrvbBK6b8VaV53IVICLQdekTUrGHsNy1vWTOaY1lHjVw1dk7JWRFmJZ5vEWYmQMLJNZmmh7QdpwajpjHVME4WiVjVSaKWHbQpME6qq5H/x7ZfYGX8yj2pd1/z4xz8miiJef/31hQUly7K4e/cud+/e1WFkkwlHR0e89957vP3224zHY27dusXu7i5hGP7esXEfhZ39NIOgbkJOTVuh8/t2za+aZwD2GmOa5ifyuw7NTQWwT1PW3CY7b21t8bWvfY39/f0bc47mvzyf5Lz0/B7f+uorvPX2zxfe6/LLS61IWg3DIPBc0kyn6sZJgmmajMdjxuOxZpGShCLPOTk9RSnV+Shb32ycpozDkMms35M6ncW9ibBVVXFwsI9pGDz/3HMIqYG+lIJR4BPFCRfTiDDwBhORu2qbaEDGnKZNdc7Qz1f9sKPAJ8myjvUcBd6aVOEaW1jr/bBximPbZD1MsWUaqLpGGHKQidJMbbW2Y3YWJwv1RvPjOBYSwfnFpAu+WtwHk42NjU56FicxFxeTpZTboJOQmobRMb6+55IXBacnJxwdHbGzs8N4PEbV9aB8vJ0oSXuBp2NbgOqSoMfBFYFNVU3esKxpT3BSe38nWY3vOqi8WElMXt6vfhCrGsWAvhfSLMdxLCzLoFjj/1NK6fClpp/WNk0d2NTjqcwL3bN8VYJy2PhOpRBXVga1o6uTXKQU7GyOmcYp480dvVg1l2oM+jvj3t49tsYhaVEuyIQDz+kFnGmeI4VcAevz4y2lHLdgMPQcsrLqlSObUiIMST53jutaMU1SHMvANEyiHmmwrssZYNtbRtbRn90oK7AMgxqj6Xy+zgiKWur7wDSwTYOkKHvTj0FLkONrANKqVuRlDdKgqFTHzioFabm6/dC1eyW8cRxzcHDQfSYdU5IWNbMoIooiLi4uus7ZVmospVzbsdqObUqysl4LXgPb6GWy25FSIBCDsnlTSqomlA8ApZrrre9RQwgmAwsHQdPp2o5jSixDIkCD7pXUZhOU9vy2YU+uKXW1m2OS5CWTtGDk6p5x25RM04KxZ2l/cV4yci3itMB3Tb2wqxT/y+9+kdD9ZAxoXdf86Ec/IkkSXn/99bWMqhCi+y7/whe+QJqmHTv77rvvYppmJzXe3t5+KqGWT3Pm2dn2GWmenf00a3pugoQ4iqJnDGzP/H7d9R9z7ty580RA3dMGi33zNBnYx48f8+Mf/3gh2fkmMMLttF+AnwWg/ptvvM6v33/I6fnFSoT7ZY9phOs4ugYmijuJZl3XnVdwft+DIIAg4Ln79zk6OSWOI05OTijLS99sVVWEfkCS9fW/an/SPMgripzHj/dxHJvd27cZj0KiKCFselUvppeAcxYlbIT+YH9r52eNh36+WL2zPC0bWFYVhpArct1plKz1uyZdKFQ/SK7qGguFIcUCSB35HkmaMYsT/SC5RgqbFyWB51Il9WBq6HQWEQY+szmwHQYecZpRN8CgLEosy6IYWBCo65qtjQ08z9c+zqaTUl/vsrvet3a28T2X6Szm/Pycs7Mzbt+5Q9CkYeaFrtepqmFgr5QiKzQIzJqHTd9zyPNiAahPomhtfy/okCAhxQo7PGoCqtoH1jjN8D2HNF/vdV0GsULoFOTJkuw5ywps28KyTIp1rKnS0umNUUia5WtZyrKqqVXem9oLGrxGaaaZBKV0d7DvD4LGheOKUzZCn6z5+yENSTgKCUchZVny+NFjfa4EfPjBhxx7LpvjMRsbY/JK4Tk2adavaACd8j2NYt0xO9e/CUr7lQeA9izJMNrqnDiDDkjrJN54gM3PiqqRblqA6IDpOPCZXsMr20qLfdfCtpwrw6G6UUqnczcALSsrsrIChE6cRRDll0DJMiQlw73w8yOEwDAvA5bystaAthnbNLrkW4C4R1Uwm804PDxkd3eX0WiEIQUKqZnE0YjRaLTQOdt+vjdDj6kb4Pn+ILgxhAaeQ7VIoKW2WVGvUQcoPHO4z1UAjqkP0LW0lDdrOl2zUvtuh8CrlgEXGE1Ha1WrjoktqkW21xCCwDFI8mqh/9c2BI6lA57aFOKRq/tl4+aajx29cCIEbPgWSVbiWgZlUeLaBv+rP/oClvXJHpWrquKHP/wheZ7z+uuvf+QwHtd1uX//Pvfv36eua87Ozjg+PuaXv/wlSZKwtbXVeWt/H5KM56d9LpsnGOb/+6Ts7E0AsLPZ7FkCcc88A7BPcZ55YPUopfjVr37Fu+++yze+8Q1u3779VPdnaNpVvCcNqJVSSCn4+3/yJv/nP/+LlYclKfQD1HgcMpnMViSpZVUReB7FQLLwLI7Z3dli5rlsb+/oHtgoZjqdcHx8RBgEuI6L5/v6D+2cauUSPMckiWYGxuMNtre3Or/UKPAWgOv8XNXfmmTZIOOnUGRN9U5Rrf5cSoFpSPK8IC37tz+Lkq43t2+uCo1Ks7yTEpuGgefYC69VSpFl+VpPbpRo8LEubClO0q7D1/fcBTALmvHyXEMDywEAl6SZBsJx0qXcKqUoikKzdbMZ0fQCx3WJ05yiKLi3t4e75JNsU4ejJB1kaqqqxpCarXUdi1mc9D7kX8yitcnRoAObbOsSTLYBWMtbi5OM0HOJs2xtKmoLYtMsx/ecQRY+zwssS9dS5Wtk07ZlkqT6+uhrPCzrqmtFlKS6K3bufQNX9+TOnyPdt9rW7GQMx4bpmpdJpBdMRp7T9a+WRcnjx4+xHbvzLreVTGfnF+wfHLI5DphYLo7nN9LywbchTjMMKTsQPur2bXiqqmYapfiujVKCrCgIvGEWdeH9GiA68hykaTCJrx/0BCClyUWcabbTNojSYu29MQ68AV+m6mTJUoomHAiUlFcm/upfVwSeszYUqQW0liERUlIrhW1KbEOCgPOzc86OD7lz507HvtiWuZC2C6uds6oqieKYyXTG0fEJlmV27GxrJRCAaxtrE50bSyjlmhM47zUVQuAYEsPQ3tuyrjGFGPS9+rYx2HEbugYohd8kAM8yfc5NQzRKFr1PtilxDM3wztJyYZEtdEzKuurqk0Tzb1HzOtuU+JbBeZIT2CZSQlXWoGriLOfOhs8/+M7nP7F8tKoqfvCDH1CWJd/61rc+cZKslJKdnR12dnZ45ZVXiKKI4+Njjo6O+MUvfoHneR07u7W19dTlr5/1rKvp+Tjs7E3wwLYS4mezOM8A7DXmSenObxI4a8cwDLIBj96TmLIs+dGPfsRkMuGNN95YKWp+kt2rH2eeNICdXzl87t5t/ugPvspfPvgxAK5jY1sWUZwwjWJsyxrcn77+1/lJ8wLTMCmr8rKyZWuTsqyI4whUzYcffqhTMBtfneu6IDTIq4qc/f19bt3aZTweEQY+eVYwmUWYhtFbrQNaRrvM4s5PWVYEvktZVr3sUFmWGpwvhZeMAp80yzmfzvBdt/HL9pzfJvzBlHKQeYiS9V7V6SxmZ2uDyTTqBbplVWFZ5lo58sU1Qp1MQydIL4PXdnRAkr9QibQ8syjuanFAf5fZtk0Q+JiGZDKNODjYxzIkuVIcHBwQ+D5+EOC5bvfd10mb17xXXpRsjUKmUT94bWc6i3Xlz5oE47wocGxrrWwcYJakhL6r2ek1QCW9IoirnaKosEwGQ8Ucy6JGURRlV9Oka2GG31ypJsQr9HT6cBMwNbTwMInirje1T4qrpcY5ilbWnDIKPE7OJzx+9JggDLi1c6vD1ZZtsWlvsrm5iSkFszghT1OODvepahppud9bwQVaeTCLE7Y3wq5G5joTpzlCwPZ4xMU1pNHzo4BZXDDy7GsHPWm2Vu9f60U1pGDkWWT5aghQbypwz9S1IkoLQt8lLRWhY6FQxNmwb1Rv++p9lgJM0+gCn1pQe3FxwenpKXfv3sP3fWxT4pkGFRo01kpRVYqibnqPm8+pFALbdRCGSTi6tI5EUcT+/j6gr/fuZsi0dpEDD+YChWOaxHMAV6IBpNEERJlSUCmFZxuUVU1RKdKyguaUjtaENjmmXKn1sQyBaxpICZOkWDm3UmimNS0qfFsnw8d5CU2tT/tdGzgmStU66K15D982MSTMGtlxy8JepDmha1LXClXrbSRJwct3x/zPv/3yldfvqqmqigcPHqCU+tTCP5en9Um/+OKLlGXZ1fS8/fbblGXJzs5OB2j7Qtx+l2coCKoNgboOO3sTPLDPJMT98wzAPsX5fZcQx3HMgwcPsCyLN998s9cTcpMkxPDkFh3a1cF22+3K4B+//nUeHR5xdHJOkmYL3sC8CQ4aAhXTKMZxbLIetrMsy6a6ZPH+M02D8XgMwL279zg8PtYPPweXDz91XVPkOS88/xybG5vkeb6QmlxWFX4butTzhJdmRZNe27/fOtV42A87D849R/e4zp8D7eUdZvmyvFjrh61rhVJ1byKwaRp4rsPp+UUHlPsmSbMr+01nSbJSv9POKAiYxTGeqxmyIXw0a3pohxYqACbTaAF8tvLeWZpycHCAEJJbt+/y+VHA4fEpURxzdHhIXdddhYfv+0xb9rTnfmul02eTGb7nUmf1sOQYRXRF6rAQYFsWeZ6vTWYGmMUpoe8Rp2kviDWlxLYtzqYzXbFTrO+JLUpdC+O5NsmcfNWxdSXQ/DWfRtqPmmZXp/BOZglbo4Bpkq6VPYNOKLYtzWancx5A33NIsmJlcefw+JSz02N2trcIR+NeUtgyTYSQuJ7A9Xy2tndQdcXR6TknJ6eNlcDtvPHzD9ujwOd0kmAYi4zvVTPyfU6nMaZhMHJt/XtXLAj7rk2S6/tnGmcNy2yT5uWCPHR+xoHXgdf5qWrFtGFxA9fS0tG0wHftQdnr4PYbMNaygVIIAlsH/2jfr74mQwFPq6PwXaeTL+t/gbOzMyYXF50aQimFbUjOh/ysQmhgKXRoVFnrUKT2NG/4NuxsAookSUmiGaenp2R5gee5hEFAEIY4tg1Nf7AhBGWltHS3YTzrWpFXCirNnl6k/d/vAOEa8GpKSV3rhRHPbgL+ypq0rDRzW/QtXSo8Sx9TraQGruheWtBy/cAxqZQizouOuXVNiZSCWtUkad0kMWs/rqr17+u/uzWWZXB8HvHNF3b4O9948erLd8WUZcmDBw8QQvCtb33rM2HxTNPk9u3b3L59Wy9uTaccHx/z8OFDfvrTnxKGYSc1Ho/Hv3ehQOuCoNr/2te11q1nEuKbO88A7FOcm8rAfhb7dHp6yoMHD7h37x5f/vKXB1e4bto5ehIM7LzEpX2PdgzD4O/88Xf4z/+rf937u9OoTYFdBUpKKbTLqV+MOIvjJlhniKXN2dzY0Ct/CuIk5vj4mKqs8FybaBZpIOIHGObiF3ycpGv9jtNZvBbETqOIwPOIkiH2MWVrNOJ8OutlaiezaG067jRKGlnnOpC7uDgwCjzSLO/2OS+KtcE/01msz8Egy6ool0KdDGngeU53TaIkbSS0w0D4KtkzaKAbeC6GIZnOYrI8Z3//MY7jdFLTKEnZ3dnG933UrVvkWUYUx1xcXHB0dITjupz7Pndv75LOseuWaWKaRseoxklKGHhEcTrosVRKdZ2ty+FPhiHx7EtptufaFIq1Xr1ZnPQysZZpYBqXfbFxmuE7Drkq13a1VlVNlmn5divnLqu6d8ExilM816Eo67VJ14HncBHFOLZOzr2KVcyLElnVhL7DLNbnKs2KlYWBKIo4PDxkZ2eHzc1NHNsiWjqnOrDLWKgeqpQCIXnphftESU6aZcRxRBTNODnR/cJB4HN7Z6fx5QqqSjO+gad7ZvM1oVejwLuUNlcV07jCdbRqJB6ov3Esk6Ji4RhVA2TlAJANPffKsCKgC4gKPRvDtMjr9SFg3XF4/UxtrdRCwJBrmbiWpKi1hPaqVqGRZy+EIyng5OSE2WzG3t5et6AbOOaVgLisFN41Eod9x6WSFjujLcrGSnAyifnw4BTDNPB9n9ubIyppDYIb22iCnwaOz7ONBVA+P5YUBLau7Klr0cih2+8+HXC1sBCkFL5jYkqxwsrKti5I6vPdSr7DplIndE1mSYljaXl24BjM0kKDe2CaF4xciyQv8WyDh6cX/OlrL/Dtl++sPYfXmaIoePDgAaZp8o1vfOOpACAhRBfi+PLLL5PnOcfHxxwfH/P+++8jhOiY2Z2dnU8sbf5tm+vW9BRFgWVZT7R94qp5xsD2zzMAe415UqtUv68e2A8++ICf/exnvPLKK7zwwgtrX3vTJMSf9vlpvyjbL8e+e+3W1iZ/84++xf/7f/gfe7eRZPlKaFM76RXBRLM4HkzWraoKx9MsY1mVnJ+dsRGGbN/aIUkzoihGlAXvvf8ejuMQ+AF+4GNbdiM1Hu4bhfXVNUpBnucrfleB0IFGSUqUJBhNwmTveUlzbNMkH2BJp41Ucyj5ddqwm0ma4S55XaH1oTpa7jzwMDe54j3mQ508z6Eoy5X30fLT/mTidrTPdTWZuB0hJYYhyfKcJNWVHKPRmO3t7Y6pUUo1ibyatXccB8dx2N7aoiy1ry6OIn7ys18wCj2kabO9tQnKWHnfWZQwCodZbtAPDFleLHiiLcvElJLZ3D2TpLlOHS7Ws5yaib0EsY6lZYLJEliKM92/Kkp6Jbrz+5ekKZuhZk3Xfe6TNMO2LRzZX50TeA5JpmXDSaqTwq9KKG73YRolbI9DJnG6Al4nkwknJyfcvn2bIAgoq4oyqRj5npY2N6FjlmWunIduG1GCY5lsjAJs22Jzc5OqqonjmLos+PW774OgYeIDfN8jaipwxr5mY5fv/9B3+xOOm30IPIeyqpvAJD2WaYA0KAdA8TIjmxWaGekLPxoa09AAM0q0vDlwLJRSHaO3PIFjMxusf1ndv1mhKOsagcC3ZRf4lpYV8+sloWst+GMVcHR0RJok3N/b68CEYxmk10g8Dh3zSjm0bUjy6jJx2LQsxhsbjDc2UI3UuM5j3v3wMdWS+qJ9yDekQAioBhQMlil1nU5zUI5lYBma5cxKnfp73iNDFwhsQ3QhTW3acFpoVvZi6XckirFrEuflQj3P2NVe5apSzMoC25A4pmZsZ2lBYBvkZU1VKzY8u5GZwwfHU/43b36Bz9/buvJcXzVFUfC9730Px3H4+te//tTZu3Zs22Zvb4+9vT3qWsvUj4+Peeedd/jxj3/M5uZmB2h/Hytb+tjZ9hx9/vOf7xYv51/3WQHaOI65e/fuZ/Jev03zDMA+xfms/abXmScJYOu65mc/+xmPHz/mW9/6Fjs7O9fanxbkPW0fAny6DOy8fGUIvLbzra+9wjsfPOQ3Hzxa+VlVVbhrQpsms6hjkpanVjqZdYiljZMU2zQ5OjjA9TzGW1tUtep8s0IIbt++w/HpKXEUc3Z2tuCbNU1jUAJaVXXDRvVLZIuyIvAv/a6h71GUVQfkqqom8D3KgeNu+2GLsv/YFI3n0TAoBu95hetYg+xmJxUeAJdKKbK8HPQEgz7H25tjTs8ng5666SzSaakDATo6nKnsTdF1HBuUlhLnecrhwQFbW9tsbIxXtlPVFUYtV4K0TNNkYzxmo6lkStMUVMX773+AUg24CXx8z+8+p9NZslYK3r5fUep9FDSgsQfU6dRhlyzL17JmLYjVUt968LomWY53DSbUc2ymcayTmteAcdAhUIZh4Ls28Rx4mwev3WuLEllVhL67tvdX74PDJEpxHIu8qcJRSnF2dsbFxQV3797tAnzamcYJvmtTKc2+XtXPmhUloigZ+S6zVMu27+xukxY1460d0jQljiNOT084PCxxXY8g8MnzgDDwkEISZ61U19H+0LXBWg0Q9R2SvEApml7Zq5nUlpF1LFPLnGVNuiaMqB0hBJZtd35TpS5ZO9OQuJZJWVXdzx3LJK1hXZhWO1IKhGFQNinDCkU8B8R1n6nRSX2LWoM2hbaOHB4ekuc5e3t7nXTblJJaDdfTtONZ68OYYC6QaQB4CinZGoeUdUC4eYs8z4miqOkiPcZxbHzfY2dzTFobja5nfhS2KfBMQaUEdS1Ii7pJl9avGLlGP1OuwHcleVEzckyKuibJ9XWYD4nS50TgWVoGfD4X8CURjFxd99MucjmWgQFMkhxDCDZ9m4s4x7d1nZGWFedM04z/3b/3Kre3PjnDlec53/ve9/B9n9dee+1GPLP0jZSSra0ttra2+OIXv0iSJB07++tf/xrbtjup8dbW1o0B4Z/VSCmZTqf88Ic/5HOf+xzPPffcE6vpuc48Y2D75xmAfYpzUz2wTwLA5nnOD37wA7Is480337x21Pu8vOMm/DH4tADsvOfiKvAK+ovy7//Jm/yL//LPexN0oyRhFARMB8BCUZSDQHK+lmd+TMOgrkp+9etfcXv3No7nr9jXlFL64Wdzs+kNVcRJTBzF7B/ss38AuzvbIE1830OIxWsYJ+sBYBQnbG2MyIuyN/QniptqnAGA2dcPOz95qRnQMqkWHlMdy8S0LCazGNM01nahTmfx2nqestRVNH2hTo5lYUjJydnFQtjS8iiavtIBxlq/T4Xr2FSG7Op2wsAnThLqWnF+cc7Z6RkvvvAchuWgBnoy86LAdRzq2qCqV78LpJTcuX2LOMnY3NpmMpnplNvTMw4LXcnULmBMOt/sMIgtqwrf1YxcuqaWJm4rcdYEIIGWZhvSIFXrv1uTvMCxhqtzfN8hTfV7dZL3K0BsVVUkWc3I9xqprUuSZb37W9faC7ycUDw/rmNRVBVVXREnFZZp4jkW73/4iDiOuX///mCfZJLlhJ6HuiaTooBJnOLaFo5lETUVRfMptzs7dKnls1nE8fEJtm0R+AG3b21jOU4jL70a9CmlmEYphiH1PXLd6hu0zBzDYNYAc9+xELSAtO94la7gGmApy6pmVult2aaBaxkoYZBfI+QJdMBevAZEKiAtK2wktbrsirYMyeHBYyxV8sJLL4AQZGWNUALLNDpGcmhsQ8tx151tgQZz6/bPlALFpXz3Mthvi6qqiOMYlcf86t33sQ3JxihkHIZ4vo9CkFc6cfgi6T9fupN29XvLMrSPOC1riqpeCNrSkt/LLlfTELr6RkimzftIBIFrIITgogG0hpT4tkFWlMRVTehYIBTTJCdwTFCKqq45myZUdck//XtfZ+R98nCjLMv43ve+RxiGfO1rX7sRzyvXHc/zeP7553n++eepqqoLgvrpT39Knudsb2937OzyYtnv4sxmM773ve/xwgsv8NJLLwFPrqbnOhNF0TMPbM88A7BPcX5fJMSz2Yy33nqLMAx54403PlIS33z36k0o6/6k52corOk6EwY+f/dvvMF//d/9970/XycHLspybWLtpGP4UmzLwrEtPnz0mNPTU27fvs14Y4OyWYFcnizPOw+mkKJLRdxVu7qfMI6gTjk8PNDgpgmJMQx9Pdv+1mUfrw6xcTifTPE9d/C8aCnwsF9We0SHE3SjJO1AtADC0CeKkk4KWpYaYFVVNRhMdFU9T1+o0zjwieKErDmn0ysk12VVYxhqbahRmuUEvkeSpgRN8JJCdd66e3v3kIalKz4G+nT1djJ8zyNJF+XRUgp81+0WHKQUjEYhruuys71DXuRNRU/EyfEJtm3jBz53b98i17TTyug05gTLMtd6imGu13UAxI58lyjJtOetqc5Zx9hmRYFlGisLA11t0NzvXhfEqloxjWN2xmPOZtFasN0mFI8CjzjNF9g217apqsUHpCwv+PDhQwwBe3NS0+URQOC5TJt7aeS7xOn1PJ+1UiR5oeWz6ep5tiybzU2bzc1N6lqDmyiKefe99xCGyebGGAwbz/eR1/hu812X81mCaUgCT7/nut2UUmDbzsJiR+updSwDyzSIs3Jhv8eBf63EYdAsZWFK0rzSdTymBolpVvbevyPPZXoN5tgQAimMzr9b1TUPH+lwvHt399C7pwBB6JlUNV0YU60axU4jjy3rWve4CrFWBg/aQztbc+wSDSSTvMKUAtmEQrXXTgiTncAhKTcY71Qkie6cfe/xYdcpfWsjILVcLHP1fgwcY8Er7JgGtikoKoUp4aynKsmzdTJz4BjUtdLgu2hqe5KikX+bZIX+fpomOQgYOZZmfcuKGghsk1rVCEXToasoyorjScSGZ/O//9NvYX4K7GKaprz11luMx2NeffXV3yrwujyGYbC7u8vu7i5Kqa6mZ39/n5///Oe6P7wBsxsbG7/Vx9o3LXh9/vnnefnl1STqPqlxC2afFDv7LMSpf54+IvgtmN+3Gp1Pc5+Ojo74wQ9+wAsvvMAXv/jFj3wu578obsJ8EgZ2OaypTbn7KPOll17gtVc+z49+/uve7Quht9vHgFyVWCuFXlm/mEz58OFDkiRhb28Px3GulXi8wgALcD0X13MRwO07gpPTsy4Z0XEcgkD76hJD6tTZosCQogPb7b4WRTno81VKg+h1LGmcJLi2tRBis7D/s5itcUiaF71ssGZyh1nWurm2VzK1o4A4zfBsa+U6XHbI9vsoQQOYwHNJ0lXfYTtFWbI5Cjm9mFKrupMn3r9/v3vA1B7V9cFPcZI010EDNtu2kIgF4FsUJY5tYZqSsqyxLRt7w2ZzY1MzNw0b//Nf/JrQ9xCGie/7eJ6HkGLhfsyLlhG9DojV0tMVkBJd9sVGSYrvOlp2u+YzW5QVyqBLHdYBVEnv+dUVQK6usVmDskLP5XQy1QszV9TsQNuPbFErSVaUuI5FVS2GQlVVzcHBPkopbt29y8YoJM5WAaZAe1Dnk4KncYpjmdjGsBcWNPsohCAvKooywbZMTNtakETPj5QGYThia2MDYRhMpjOiOKbMI85Pj5Gmjd90kFo9C5Dj0NfgA71AM41TnfTtWMySYuW8CQG+5w4yqVqyWnWBT3lZ4dj2tcEr6KTnKFustQGdQO5aUvuqcw2QR75zrW3r3lWrY0GrquLx48cYpsmdO3cWgP7Is64MpRICXFuSV+BaEoEONdKbaYAnCtOQlJVi5BioOaOIampnlNLgtd0v3fmqmCdrgwV/7WLnbJ7nyDLl8GxKmh5hWZa2Evi+7hJvgWjTs5oV2veclRqc93UDu6bAkoICFnpiR65JlJWMHJOsrJmlZcfSBq5FUVZEeYlt6D7wJCuppcI0BLMkZ+RZxGnB+0cTXn1um//tX3/lU3m2S9OUv/qrv2Jra4tXX331d8o7KoQgDEPCMORzn/scRVFwcnLC8fExP/jBD1BKsbOzw+7uLjs7O4NqkN+WiaKI733ve9y/f78XvC5PXxDUk2Bn4zh+BmB75hmAveYMgYJPMr/LEmKlFO+++y6/+tWv+NrXvsa9e/c+1naEEDcK6H/cUKnrhDVdd/72H/8hHzw+5HwyXfmZlgMPA61Zk4K6zDbVSjGNdG3Lo0ePqJXi/v37C6x3L0idmyhJBvszFdrjppNSt6iqkiiKieOo883ubG+xMR6jhOBiuvgeeVES+O6gX/Gq/tiqrjHR8rJlT5khpU4yjeK1q/GTK6TCeVHiew5lNez/U0rhO87gIkJZVdo33LOf7URJyniAUQ4DjyTJOL2Y4Hsuv/zVOyDg/t79FR/TtAmHWifvnUUxozCgrmrSPF/p0gSaICaHWpWddBn0d8koHDEKRzogKk1Rdcnx8TF1XbOzvcl0Ol0IicmKAse2sUwxuBCgz0FG4OlU3kqpwesSpxmea6NK1lbxlM3C0tY44GwarbU9zqJUB24VVW/qcNiwwKr5TPme23lX102SFZiGZCPwSPJiYdtlWbG//xjDMLh79x6y6XO1LRPDNEm6z7MiDPwmNXhxWp/rOPCYxtnKIZqGgTQMsjk2Me/6bl3itD+52TAEpqUXhxbATVFAVXAxmXFyfIxl213HsOM4bMyB1/kpy4ppqT8Hnm0TZZcLFWHg94Ke5amVYppkhJ5DWevQpDgrBxUU7Yx8byFcaWG/qprZ3DXcDFwqBL4tSPOKes1NE7p2t92yLHn8+DG2bXcJ4Jevuxq8QhvapLc3tNYTOAbn8Wqf6vyMnAFfajOerdnsodkeecxSiz1vRFVXJElCEsecHR1gCoXj+hiuR+75C98/vq39qu2+SSHwbQNQZEXFZCm0KXQMUAop6MC0bxuoWuHZkigtkJJLcJsUeLZBVWu58IavK5x+/eicv/W1+/xPX//kHa8ASZLwV3/1V9y6dYsvf/nLv1PgtW8sy+Lu3bvcvXt3IeTovffe4+2332Y8HnPr1i12d3cJw/C36nxEUcRf/dVfsbe3x+c///mPte9DNT2t8u7jsrPPPLD98wzAPsW5ScCsnXafNJv38b58qqri7bff5uTkhO985ztsbGx8on26SUnEH6eX9qOENV1nbMvif/a3/xr/8l/9P3sfKCcDklxoQ5skpmHgNxLL9nVFUfD++++zNR4TjMe9X6xRkgzKlOu6BiF6vZ7QSo018DIMs4v4V0ohUZydn/Pw4SPiNOuY2XnfbBSnbIyCFXDb7dsV/bGXUufL8zIKfNIs46L5HdNYDx6nUdxb/dJOnPQztTrcx2E6izEMY7CfF/QiROj7zAYk0QCTKFpIJhZCH8slY13ws/ffZ2tzTDDaQIr+P5KTKGLk+0zjYSYWtV62DOhKHM8lSfuZydZHKQTcvn1bh4acnhFPJ7qiZy7FGqU9haphdYcmSjIC38U0jMG6JtApxp5jUyLWhHVp4Hk+beqXrpAJJ4lOHbYNi3xuMWgU6Oqm+XMQJ6lOKDaG/cvtmKZBnGW4jtMt1hRFwePHj3Fdl93d3YXvj7woEVS6sqb1064JhVLo1GHPsVBKdDLcNqk4HWBnp5FmRke+w3QuSExKgWvbvbU4tmWBZXHb83nRtjg5nzCZznj8+DGBa3Niex0b3/dd0wJZw5CMPRsM2eujHBrfsYkLhVJteqggcC2dBN0jBx757iB4Xd22yUV2Ka8XAnzLxJD6uy8r6o71H3tOBxKLouDR48f4nset3d2FXfCdq8OYoJHRXrGfniVJ8moteB3ypbZjG3OJwj3jN3U5phQ4lkQKk9CxSIMQIRRlnnM+nTE5O+fwsPmMBwFb44A0R1fk2AaGFERZSZKXWIakmAuaaj2y0yX5e8s6t+fLlODZJhexBrKbvs0kyfBt3X38+HTGo5Mp/+u/9iX+6IsfbzF9eeI45nvf+x67u7u88sqnw+b+No0Qgs3NTTY3N/nCF75AmqYdO/vuu+9immYnNd7e3r4RFrChaZnXvb09vvCFL3wq13KopqclM67LzrYy7tFo9In36Xdtbu4d9XswN9UDC/rD9nGS59I05cGDBwC8+eabuO6wd/Gj7NNvq4T4o4Y1XXfu3b7FH3/7G/zb/9+D3p+nA9U6jm1jWSa2aSywgEmScHBwwGg0YrS5OejnrOt6rUz5qtqeaQO8WoAX+p5euU8zNrd0hYEhBIfHJ13i6bxvVnt1tcSzb7pO1AFApqtxQrI8xzSNFTY57QG586OUfrBeJ3Nta2/acxB4mjluGdOqqjANudD/ujyzuGVHh4HZdBYRBr5OtBWie780TZuanBHj8RbeQAK1PiD9XoHrEqWLrxFCah9lc61G4bCPGJr+18472v8ax7YpyhLXcbsUzKqsiOKoS7E2TIPAD9je2sC0nUHgLJtKj6IoMaVcKxNOmgCsoZqbceh3IFizzuu7d6FJHTaN7rPS3Xc9x57nBf9/9v47WJLzvg6Gz5M6zszN927AAou0ABZYbAIIQqQYIdIMRVOmLct+LdPWZ8uv3k92qUpytktylcuSig6yJMuSLaskyq8sW9JHSmImCCaQYtAGhEVahMVid29OM9M5PN8fT3ffSd1zF3vv7gV5TxVY0s7cnu6enpk+zzm/cyillZVKhi6QpCmiOEEUu6iZJtZabVy+fBn1eh3j4xN9IWqASrxtOS7GGvVi5nUYvCACAUHDVpU4ZSS0E4pQqnTjVCpF1zKMvs7Zvv2TakbVtlXQUyKBlXUHjutgeWUlCzkzSq3GSZJCEgrHT1A3NARxXNh6y2AIjjDt/o5KUwknU/YEV6nDYazmJWumgXawue91XdC+bUuJvpAkwSksTSCVipy2HB+Xr1xBvVbD+MREF3nVBUMQy6Eur5rOh5Jsjan50qoeWkuUd7UCakEDA+pyBCUQnEIwgiRJwSlBlKSIO84dAYHOGVKpYXx8HOPj44iyztnQd3H5tRUVjGdYcA0LhmmAEQpd0OIcmoKqhcQk7SKvOqMwNIa2F8LPdq2ucyRpipYXwdI5KFSYl6mpvteXZpcRxin+n/cewaF945XnbrPICc+ePXte12jU9yIMw8D+/fuxf/9+pGmK1dVVLC0t4fz58/A8rysIarMhntcD+ULEnj17toy8DkKZOtt5j5g/Lx8vy5/vuu6uAjsAuwR2k9guC/FOJbBJklw1gV1fX8fp06cxMTGBe++9d8ui13fSedqsGnwtYU2bxUPH7sWFS7O4mAWBdCJOEthZtQ4BYFumWslzfaX8ERQqbavVwuLiIiYnJ9FoqGqVNJWglAwMoClLLc5RkMgSstN2PIzUbcRxjPYgokko9u3dgyAMEYYRXNfpmpttNOqo1exMme0/p67vl1qZKaXZbBgrJcGtYla4PLnYMnQkcVpqSWw5KhSLUTbwPOWzrIlf0SFbJPiWJxMTqCTTPPjJcRwsLCxgfHwcIyMjkJAIwur0YikBLwxhajq8UJERTQhQSrtCv9pOubJfPMftnpvthG1libxJiohSWKYO1wvAONtQ4ztSrC++dhmcU5imCU03YVkmKFXfKZwxaIKj7ajj3oxNOAj7A5sIcuV64zglskWIioWMHEmcwE9TjI/UsNJsV1qP0zSF43WmDm9cu53kNcfi8gqWlxYxMzUJ066ef6rbFlabbQjOYBva0NocdZwyWyipbaq+Jofrqw7V0Ua9Uu3tRZqqmd5EUkyPj6BtGZByAmEUwXUcOO3BVmNl61V206YXgACwDJFdt/37LRhDSjmSCpIbxSmiWJ2jEdtASigMQYoKnTJwRiEpQ7yJblZOGVq+UkF938fs7CwmxkYxMzUBzigggUSmSBOJFKjsOAaU4ukOUVVZtrhYZVc3OEUQJ6X3MxQSluBIpYSmKxIYp2oWOErSLAgJRRhVFyRgGb3kWGLENjBeNxFEo/DDGJ7nwXEdrC8uQCYpxhs2qG5gcrQBQhi8KIbO1PlJpITGCDTOEMUp2n6ERKrzkaQSiUwRJClqBle/V0SFNcVxjOcvr6JmcPzMB45henRrCEAe8rN///7XbTX9XgelFBMTE5iYmMBdd91VBEEtLi7ihRdeUMFfGZkdGxu7YUFQOXmdmZm5rgsRZepsZygUsJGTsptCPBi7BPYGYifOwHam/l4Nrly5gnPnzuGOO+7AwYMHt/SLYKdZiIfty1aENW0GhBB88F1vKa3WCaMIE6MNtNpuP6GQyvbZXF/D8soq9uzZ07UqGoRhZb1Ns+0om2uJ0ul6PnQhEETdpMkyDBBC4Adh6bWfpAl0KkAJLeoc8rlZlXjqoLW+jlhK2Jkyq5R+dY6TJIXg/fOuddtCEIRYbzngnBWhUYMwLBF4WKiTZRiQqUQQlxMJx/MrFwIARaYHkUZGCWzTLGp+NCGwuLRUpEZ3rtYmSQJGaWkIFgCkSYqYqBAqTQj4QdhljQVQLIBUnRdgsILZyAKj8s+E6pIN++zYnSnWkIAf+EiiCM1mEwsLCzBNA6MjI6jVbLj+xrFs1iYcxQm4VPZSPwxhmWbpAkHTcbN5Ub+SmNYtEyvrLTTsasUcgOrjbbtqVtkPkaQySxtOuva73W5jcWERk1OTsGwbNUtZhAehs4ZHqbcJ6rYFx/crVTgCAss0sNZ2QUBQtw203BCb6T2t2RZWWw4EZ7BMHe0SVbkTuqYhSgjiNEEYJxCcwdQ4WoRAEwKjo6NI0hSu68J1HMzOzsLUBJZNG6ZlF1ZjCcDJrK+m4BCcwQlCpFnSLBMCQbRZNZXDiSSSVH0XCU5hcIokBdyeAmlKCbjg8DexbV0wRKn6c8/zMDc3h/HxcTRGRuBFKZBtQ4U7MURRCsEoOKMFCUU2zS/T7P8iat4Y6ca4RifK6nJUwFOWgkwBztR3IyUEkIAk6jUSKRElKQzOSq3FlAKcEPgl3yN1U/W2MqJ6bwkBvDCBFybQOUEQJyCUqKCnfFyAJmi22lhZbymrsaajUbeR2hZM3YBR1OmoFHjOCHRO4YUxTMGK40hTCUIBP4wQRhFeml3HTZMW/p8fuh+1LajJAYBWq1XUq2wm5GcXCvl3+i233II4jrGysoLFxUU8/fTTSJIEExMTBaHV9a15r4bB87zCAn7o0KEbuhBRpc4+99xzqs2hIqvi+xVEbrWs+D2KKIq23MYaBAG+/OUv4z3vec+OiiL/4he/iIcffnhTKz5SSpw/fx4XL17E0aNHMTU1teX7861vfQu33HLL6w6C2kqcP38eQRDgvvvuG/h45xfPdpdbF/v0ykV8oqNax85mDXOF0dD77cB5Oi2FxOh4eXpglZLKKAVjHGE0mKSZuo4gDJFKCcswiuCZHFW1PgAqVVApUzACzC8uwcm2YVkWbHtjbja3dBqaBjZAcbUMHX62f4Og5mFZHwnvxEjNxnoHAWWUwLbMYr8NXVlmq5TBqrleQC2aqPlEdZ5NQ4dM0o5EZYnW+jrWmk1MT8/AMAbfAJiGjiDqDlrqxVjDRsvxS63NgPqx1XUBb4jKV6/ZSrW1VZ1P2bHpGi9Nuc2haxqCwEcQBFhfb6LtuNA0UVjL1U0Pga4JpFKWEvUcgjPUTAOrFbOzOWp5bU/Sf530LmLUbQvtnvqdMhi6Bs45/J7ApvX1daysrGBmZqZrUalumfCCqGtRZliHLKF0YOqwSio2u5KKAZWUyyittBM3av1ztqaugRAKr2SuWxMcsqNCpusxzmBovK8+R9c4Wm6IVrsN13URxzEM01TqrGWBd1QIMUJQMzSA8U2FIAEA5xSUia6Zy04wSmBoDBQEfhxDE9qmZlQ5I2CUI0zSwhExOTnZP8MmJWxDDN0mpwSMqn7YHASKvxJkycOkY+5VFpsHsGElpoRA46SSgNer5mKJsh67JWp9w+CQEojSFH6HUkyQzfd2qLIUBJbOwCjQ7AiaSpIEoe8h9l20HAdhSmBbFhr1GkZqFjhjaPuqRqdhCIRxCi+KYWscQZxAUGBh3cWri2t48LY9+LvvvGfLnGDNZhOnTp3CwYMHi27QXVwbpJSFu2ppaQnNZhO1Wg1TU1OFI2w7iGUevrXT55dfeeUVvPe978WDDz6I//W//teWjOR9L2FXgd0ktuMC77Tr7iQCu1nLbhzHePLJJ9Fut/HmN7952ywObxQL8VaHNW0Wd956M47fexdefPUSfD+A06OKJmkCymhBXOIkxtzcHAghmJrZg7GRRmnwkeP5pfbTJE2haQQkGqzZeEGA8ZEGwigaaDttZ/OozXZ74GtX9bcSQpECuO3Wg2i1nWyF0u2am3XqdezdM41W2x1o03WLbtbBJCbOuoerQp2abadQJG3TQBTFXYRGBTJV226rQreADgWVMZiGgZbjdNykbtTk3HbrQSRp+Ryd5wewTROu3z+jyhmDrgusNtswDQMpZCnRTdMUYRSX2rSL1/M8jDZqWF3vT8vuPLYgUgSojPwAyhEwUm/A0wLohoXJNIHrenBdpdQRQgoyO9JoVFqmBWfgjGGt7aJuGQMTezvR9nyYmkBC1bxtjkEKfJ7kHSdpZYpyjjCKFHnzEkACK6sraDab2Lt3b9/NSsv1oAsBTahzVUVeAcAPIhBCVDiRF3akc0vUbHvgcfthVJBbL4z6Fl4atjnQNpy/dzXLQBSr6yMHZxyEcYQl9twwzhRZRmFm9mfOGVLCoRkUE4aBCQBRGMJxXbQdB0vLy9CEgGXbsC21gJFSDsePYWhc2eqDaOAIBJCHVmmVZC5JJZyMDNctA0GSom4I5Em54QDiSwmgcQ4vSovRjJmZmYHza3VTDA1jolCqsNdDciU2CCok0NAZml759Uaggo961dmu/RkS6tRbe6MLBsFUaBUjBE1vcOJxzdjYrsEZBFPqKQGw7vakDZsaqKnBCWzUxibh+z4Cz0VrdRGzVyLoholazcL0aAPrnqpcU4FNIQQjeHluDetugPcduwUfftOdpcdytVhbW8OZM2dw22234ZZbbtmy7X6/gxBSjI/cdtttCMOwILMXL14srMhTU1MYHx8v7b2+Gvi+j1OnTmFycnJHk9eLFy/iAx/4AD70oQ/h137t13YUR9gp2FVgN4k4jrecREkp8fnPfx7veMc7dtTKyle/+lUcOXIE4+PlgQeu6+L06dPQdR1Hjx7d1v6v/MtmJ/xwXLhwAaurqzh+/HjXv98o8pojjhP83ic+g8Xl1YGP17PE2iAMMDc3B9Mwi0RTQlAZ9GPoGoKwv5MxxyAbrGUYAMmCfSqUVkIITF0rTfVllIILUZrYSymBpgn4HQpeFEVAmmB5dRWe66HRqIFxpdZpmkDv3GyjXm3jrQp1AlQ/qmVoWGuWb2OYVZhR1YPrBYPPg+ActmWg2XILxThJEszNqfnnPXv2qNoau5osA+ibUTUNXSkfHUqzZZpZ12z5z4PgHISSLrJSbFPTkMgUYRRVKuk5GGfQePnx5+dPEwIgQNihAuUVPa7rwHFcJEmMWq2GkUYDjAuwjlAgXXCAkP4Z2CHnTB0vA2ccfhCULqwUzxUcnLFSUm7qqqM0SVKAKHX15Vdfg+d52Lt3L4RWfqNGQDA+UsdKxfXWC8NQ389+GGVJxcMtv4JRGLqW2YNV4FNzE1ZhAnU8bnaOhdCLtONN7avGYZgmXD9GVLJwlCaJshpn/9mmAaJbsDusxoQAti6U5TjoWGUjKljNDTfnqKpbOtoDnisYhcbV6yRJCi9MYBkCbpiWqujFNjeRJAwpUTOGhzYNq8JRz+GVfbW2zgqyPggNgyNJJShVXbFBtDH/b2VVO4O+KeqGWrjQuEo0zlXkegepVQotzxxupJhrphSo6aLo9g3DEIgDtNstrLU91E0NtZoN07QQpgSXlpqQEvi/3noID25R0jAArK6u4uzZs7jjjjtw4MCBLdvuLqqRpmlR07O4uAjXdTE6OlpYjW3bvur7rLyzd3x8HPfcc8+OJa9XrlzBX/pLfwnvfOc78Zu/+Zu75LUEuwR2k9gOAgtcnV33euHxxx/HXXfdVWoHXl5extmzZ7F3717cfffd2/7hOnv2LEZGRnaEbefixYtYXFzEyZMnAfSHNW3XvOtmsLS6ho//8WdKZ0spJF565VUVfT82CtJB5DQhkMik1OpaZSUGNuzAtmlCAnA7KmAopRCcD6zeyV87TSXiZPB+Kxtu+b7pmkCSJIiTFLZlIE3SgjgkSYwwCNTcbLsNxng2j2NB142CwFtD5jpHarUuq3Bx3JaJIAzBGEMUDe7JzFE1M5ufBwnZpfIBqprF80PESYKaZcHxPIRhhLm5zi7Jjc9go0JVLraZLWj0zqZ2H5uVJQqX/0SofUaX2lizlMrbOXKxKRI7wE5MQFCrmV2z2JpQ1RiDFVZZBH/5nockiQHKYVkWJsZGwRgfOCPbqFnZ/Gr1Z5cSgpF6DWvNwa6BThBKlE23Z/HDNHQEYVycn1SmWJhfACPA9J49kEO+P9R15GXp1ptTegH1ORxt2Fht+QO7kstgGRo0IdAc0B1bBcGZUnq9cGhAUbGPhMAwDHhZsrZlaIiTtDJcqWGZWMxSjXOrsWmYas7SsiC4AKMEpqaCiRjnm04crpsa2tHm9r2mC0SpRHNtFaurK9i3bz+YpvdZlGs6hzMkjAnIiOkQ8mprytJbdRc3jLyagsKPlPU4txkzqn7H0lSCUZQSZEMwBFHSN4ahcQpbMHhR0vfe1XQOJ4jAKIWpKXU5SSUMwZQySwhqukqIVmp3ClNjkFBKb5ikYJBwXBduu4WXrqygGcQYq5n4u++4Gw/cfeuWKHUAsLKygrNnz+LQoUO46aabtmSbu3h98DyvUGdXVlagaVphNR4bGxtqFc+V17GxsR1NXufm5vC+970Pb37zm/Hbv/3bW2aB/17ELoHdJLaLwD722GM4efLkNXelbiX+/M//HLfeeiv27NnT99jFixfx/PPP4+67775uq5FPPvkkLMvCHXfccV1erwqXLl3ClStX8KY3valIi+tNjLuRePK5F/G5r3yz698kgOb6OlbXVrFv7z5o2uAZydoQ9a6KxDZqtppnKXlcqbgxpBx842hbVp/1uf+1y/dttFHLUo0Hk1AVFqRsxrlSB2zMzY406iCUlpIBAoJaR1+qxjl0TXTV9dQsE47rl96YEqKU6SqibBo6wlDNOTJKs3nabuKscYoXXnwF9XoNExMTGES6hpFYSilG6zWsrDdLnwNgqNIIALoukCYSUZJ0kPRB86KbJLFCwA0CdYNr6AOvScEZGNuYCy6DlCnSOEbbaWNtrQlCaWE7NUyz6/Nat0w1v1qyLUoJLF2H4/mbqtnJ0ahZaGXdsKp7eSNPIUm7VXRD06BporSaJievOThj2Tkangas7PIeDF0rnY0dhJplwPXDwlZclXCbgxACyzThBiEYpbAMHY4flc6bA2ou0rSMgTOWtq6pNM6g26basM0+MhpGIVxHKbO+70NoougYnhofgxenMDUOKSXcoDyN1zIEKly5XaibGpq+CqdptVrYu3cv9MyVRImqnuGUQKMEcapSh+NUZnU3/a+/GfJqCKVqltmkgbx2p+MgpMz2hYJRtW9SSpWAnci+GWVb56V1OzqniBNZ1FcZQtmDoziBYGQg6bU1hhRQBDTMzj0BbI3DDRPUdAYvTJBCQqMUiUyhMzU/a+kqsKkdRGq/vACvLjTR9HyMWwwfuX8KkdOC4zgYGRkpyM3rUeoAtVD/xBNP4O6778a+ffuu+u93sX1IkgQrKysFoQ3DsKjpmZqa6nM0BkGAv/iLv8Do6CgOHz58w+/TyrC4uIj3v//9uP/++/F7v/d7O7o7dydgl8BuEkmSbEti8Gbsutcb3/72t3HTTTdh//79xb+laYpnn30Wc3NzOH78+HXd33PnzoFzjrvuuuu6vWYZZmdn8eqrr+Khhx7qShreSRaPP/vS1/Hs+VcAKIV4aXkZjuNgz549GG00Ku3AqutzsDWRZkm2hf2SKJUujGMEQQgjC20q33Z5qjEw3GY7KOWVM5WC2mq7aAwJQ+p+fanmhR0HrusgjmOMjoyAaxos0wLj/aueuc2Xcw7X8waqrcMIn+p/5ZWzozXbgswqR3pVxjwU5uYDN4Hxcts+IeULDoauI5UpgsxOulm1tgqmoUMTfKBK3YnNkFjKKCzDQJwklQSVMabmDYeQ2IZtAiBYbztZfYdKuZVSwrSsIhSIZtdSEPWHbjFKoGsa3A4LbcO20PK8zYT2wjINUErh+kFBOOI4xuzcLAQXmJ6ZBs1VdJJt2+1WSqtSjuu2laUalzgoaj0LQASoWxZcPyw6NgchJ6/5UxijsA0dra6Z2m4QALZt9VX5cMZgGRraXj+RLf5mCKkWjMHUBbwwgqFrcIa0BSXphtUYSQQ/ZdmilQXTNMEYg1UENSWIMourqXGEklYS7hw1XaAVJlhaWoLruti7d6+yuvdA4xSpVMS1E4wScEpACVEjERSIUxTrUvn3qSz+JwtwIlnybv5E0rmURUCJqr9JUxXklKSyqyuZ5z3KJXVDpsa6wpg6IbK/FZyCSKhzl31mbJ3D9bstxYwq8hrG/Wq6misG3DBCkiqV1RAMlCBLHwYaJocfqc7eui5wZaWJ+TUHYZzgnn1j+IlHjsDQ1XZ6lTpd1wtis9nKlsXFRTz11FO45557dkR45C7KIaXsqulZX1+HbduF1dg0TZw+fRqNRgP33nvvjiWvy8vL+MAHPoA777wTf/AHf7BlLoLvZewS2E1iuwjs448/jkOHDmF6enrLt/16kUeL33zzzQCAMAxx9uxZRFGEEydOwDTN67o/zz33HKSUuOeee67r6w7C/Pw8XnzxRTz00EM3bN51GIIwxO/+8aexvLqOhfl5xEmCPXv2QGSreVWEhBBVSO755fOwcZzAMg14ftCnWA6zGlc/TrIwo5J9A4FlGXBcH5QSVRXiuMVNGQGBbZsDO0hzlNl4oyiE47iQaYzV9SZ0XS9UG01oKuVT1yEYhRuGiCtSbofVqSjLczow6ZcQ9ffpADW72WxieXkZ09PTqNVqqNlmpQpIslm/TsU3TwbOb8xVYE95HVLxdxXEXNfU+aEE2VxntXI0jMSaho40TcEYK52NzkGpmtN0S2YzRzre73qtI/RISgSBCjxzXRdhGMIwDNiWhbGxUQihFenTgjNwOnie1TJ1RHFSeT0ASp0PoxiMM3h+iDAKVU2MaWJqcmrgd4hl6oiyMKgR28L6kE5aTXAILvrOWaNWHvakCVWb1B6QAm2bOrwgGmhP1TUBwTnaA96fes1C26taeKCwdB1eEKvPrpSo1+2ugKBhsA0dlKvrrmp2s9gny0ArTIqwt8JqbBqwLFtZjQWHxilMwSGpUv2GOZ8tTdmBFxYXEQSBml8eoJoIpn4nwiHqtSkYgjipfF1OCRhRlTRlKNTZkts7Rgg4IwhKrNmGoIhi2bW4oYKgGDgjSJI066Tt3r6Vkd40Y9uWxkGJIs9hnHRZyRklasbXi4rXoYSgbqjrMUkleEZmW0EEQ2PQKcX52VUst1xQArzzvgP4yEN3lP4G50rd4uIilpaWEMfx0MqWhYUFPPXUU7jvvvswMzNTeo53sTMRRRGWl5eLRYwoimAYBm6//XZMTk5ua17L68Xa2ho++MEP4qabbsIf/dEf7ch93InYJbCbxHYR2D//8z/HwYMHd9Qq35kzZzA6Oopbb70VrVarWL06cuTIDbE0DKuuuZ7IV2YPHz6M8fHxHTuf8MrFS/jl//57oJRiZmamb9W5ipBoQiCV/QRLEwKGrkFCVipyVSSVEAJD1wb21gJKVeOUISip5uGMwbJNeK4/0O7LKIPgrKNipvf1h9t4LVPH/MISHNeB53oQQmByYgwgLCO2JvwgKL05JIQoy2RF4I1l6vD97gofQ9dAQIogo05Fenl5Ga1WS9lMM3uUep3qY2GUQM9CuCzTGPi+UEJgmTqcITZUlRjdrbDWbFOpitmNuWnoCKMYSfr6SGzNtuB6HtJUglICc8jxAeo82KbRZaNVCrTZ9xqNmj1w1jWOItUx7LrwPA+GrmN0pAHDNGEY5sCgqhya4GCMlarFNTuzlksJkhGH8y9dQKPRwNj4WNcsei8Yoxip2VipcBb0olGz4Hgh0jStJK9d+2gZCOM0mztUBNGP4kp7KqDCqEAIvOzz1qhZaFWQ105QqtRcxgXWN/k3+WtGkhYkjzOqFNM4HUjIbFODG6te1U7ks9KF1VhoaNRraDRGQIT6LBpCdbNKKRFGCaKO82EIhiBOMTs3jyiOsXfvXvABvwcssxD7JUpnDp1RJFL2KbSdyNOEe1OJOyE4hexRW7u3QWAKUppILDgFpISUSjWmhCBOlXJKAOicwRsU3CYYgiiFxgg0ruZqwyTNrMZpQV51QSGy36N2h+Je1zmiVMLPLOR1Q/0OuUEC21BOi+cvLSOIVSjU33zL3Xjzoc3fN0kpVb9yRmabzSbq9XqhztbrdSwsLODpp5/GkSNHdpSosIurRxiG+O53vwvDMDAyMoKlpSW02200Go3iPa/VajdcgGg2m/jQhz6EiYkJfPKTn7xuPbjfC9glsJtEmqYq3XSL8d3vfhd79+7dUQEB+cxpo9HAE088gYMHD+KOO8pXObcbL730EtrtNo4ePXpDXh/YCGsKggDnz5/H0tIS0jTF1NQUpqamMDExsWPmFdbW1nD27Fkst328+Nr8wPeNMgrBeGnVSOc8rGXooIwphTL7tqiqn6GEQBOi1CarCYEkTUuVOkNXc6Bpz7xs3VZ2ZUooggoCOSz0iXM1S1VGSggB7KzWxjYNrK2tYW292TU3OzM1gQTlPb+cqaqWMiKdH0/LcVXViW111eMAma3SNPDShVc31J0eW9FmbLQ1Wzkmquabc4ts2cJCjk7i2ahn6mbP26D6Zof3ZveS2EHq+MZ7MYTEQpHfluOpWdWS2Vkg62qtmFVO0xSe6yKOIoSBB8ePYNsWrI6E217QrP+3V/2v2yZa7obN2HVdzM/P46b9+6Bb1sBu2U7k4VKNmoW2FwwllDk0wdGwLCxfBfGllKJmGUgl4IXDyWsnaqYOoWlY20S6cScaNRvtIIala0ilLBJoy6BrApLwUpKncwZNMPiRsgObukAg6dBe3iRJEXgewjjCWsvJapm6U40BQGTkjFGCOJF45eIlxKnEnr17wQZcFwTKjuxWhFDl2yWoVmhJpmiWzaQC6nPMKBCWkWWZJQ73bINTAk0wMKKu/yDun4clWQ+sM+A90hmFniUN+x3KsMaIsk0nKSydA9m1ZRsblTy2zhHHCSihqtNV54iTFFQV3SJOErScAC9cWQGnSqH98Xfdi7tvmig9D5tBXtmyuLiI5eVlEEKQJEnR87pTF6d3MRxhGOLUqVOwbRv33Xdf8fn1fb/LXs45L9T48fHx634P12638cM//MMwTRN/9md/dt3djW907BLYTWK7COzp06cxPj6OgwcPbvm2Xy+efvppOI6DZrOJI0eODAxzup4oq665XhgU1gQA6+vrWFxcxOLiIjzPw/j4OKanpzE1NXXDLCCzs7N45plncOedd+Lmm2/GJ7/wVbzw8qsDn6tmVgfPwxICjI004PnBQFJDCIGui1LVSdc0RHFcSmJsy4RTFRhVs9HK1D7bNJHKtGs/lIL8+qtvTEPLwpIGf/2Zhg5NE1jvSpvtnpvVOIMkDJatbnR752Z1TSu1CucYG6khCKKBVtk0STA/Pw9NCIxWqP2Cc1A6OJU3D3MSnPelBfeCZ4saVfO5AEG9ZiFN00rF1jJ1+OHmSGzL8WGb5YSzIKebqLoZqdcQRYPPZyds04A/YNa12H9DzXPHSQKNM8wtLHXYTk1YliK0vTc8jZqNtutBStk3891qtbC4tITpbOVf11SY0uDPkOxTT3VNgFEGd8jMLwCMZEqzqrPZfAChaegAIWCMwbkKVbRRs9Hygg4ld7hbqWHbaPXMvFq6BkoJHL+/U1RwDsq1PmJVun1TBxGaUgKHWLwJITANHV6YFrVMZanGZvbd/urlK6CEYP++vTAEB6MqEEnNuSo1u2Zo3SFKA8CIshgPU2iH1eXkCcKlic1SzZJGSQqekUNl7U0RpxKUAIIT+APrhSRquuhSTA3OIDiBTCWCOO17XwSjxbEFcVKQ6rxCp2YIhJFKj9cFyxKPCfwohqVxSEistQMsrrex0PSgUWB61MI/fO8xTI70VxNdCy5duoTnn38ek5OTaLVaCIIAY2NjhVK3SyzeOMjJq2VZOHLkSOkic5qmWF1dLRYxfN8vgqAmJycH1l9tJVzXxUc+8hEAwGc+85mBXdG7qMYugd0ktovAPvHEE6jVarj99tu3fNuvB0mS4Jvf/CZ838eb3vSmHZGOfPHiRSwsLOCBBx647q+dV+QMC2vKw3UWFxfRbDaLFMTp6elt/yLM9/Pll1/GxYsXceTIEUxOTgIA/CDEx/9/n8baemvg3/XOw3LOYZk6XM9HkiQwjfJ+WF0TiJOkPDhmyDzssNCm8ZEG/CCEWzKPO+zvy6pvNvavv+KEZtUnbceDELySgOZ9swvLywiCYODcrGUafVZhIOvKzOZRa7bRlSwLKEvr7OwshKZhX9YLWtaFC2RkOU2LWUzOGQxN65ptNXRVSVJFqAVnYJSVVh4Zuo40TaBr+tDwJ8s04IdhJYkVnKNmmVjdRC1NXxBRD3RNQEoJQ9MqZ5A3nq+ISNBbW2Qp23Lne1azLHh+AD8IiiRr3w+gaVqhzuq6BkDZmTnnXdfm2toaVldXMbNnD6yOm+GNqp3Oa1yWHivJFhDyVONBGKl1z2BzxmDoOtpDrNimoSGM0+LzbBsGEolSl0aOeo9tWM1VGwiTBGEJmarbViWxE5zB1AS8MFaEizEITS96RIdBCAYwvUhM1jjLrKyJst92mlKk2l+npBe2M9U4CHxYhg4vSqBpGvbu2QNS8ptQ1xn8WEIwFdCUv6SUQCpV+nCSphCcldp5O7dVRl4pJWBQoUtxmoIgT8QH0lS9VpxIGJyU1unk1uSy/ahnacSmpizFOSHl2bH1Kr6mUEnH7Z4Z6rrBAchCIWeUwBAchEi0vQiUICP9EVqOj4tL61lQVYrDN03i77/7viKsaatw6dIlvPDCCzh27FgRTtkZCrS2tgbLsopU45GRkR0V2riLDURRhFOnTsE0zUryOgj5e760tITV1VWYplksYIyOjm7pe+55Hn7kR34EQRDgs5/9LOr1+pZt+/sJuwR2k5BSqiLtLca5c+cghMChQ4e2fNtXC9/3cfr0aYRhiNHRURw7duxG7xIA4PLly7h06RIeeuih6/q6OXm92rCmIAiwuLiIhYUFrKyswLKsQpltNBpbbsVOkgTPPPMM1tbWcPz48b5O4bnFZfy/f/I5JCUqRL1mI0kSUErhuG7XDUeuMJWF1Ayr3hlGMmtW/98bug7OKBzPqyTQBCTrby1T7ogKOqogPSM1uyC5NdtEGEZd1mLbUq9f9i3JKIGma2i33WyGUs3NMs5gW7ZKQ5wY67LA6poGRklBzDeOQ/3/ge9jbm4Odq2GyYkJgBBogmcdseU3uko1jGBkVTyD1FbLyFJ2K0ilJpTVry+gqyMAKldFqwKzAMA01T4NIrGmYSCOIkRx0jGbWo1GffDsrG0oxTdXGpVN2BsaEMwZg6aJons2T2UelLBr6rpSrLLrI0nyhFsHruupcCLTwsz0BCjjEEKD6wdYyeaX9+7dWzrflFfUpGmqCO0Qtdk0dEiJPov6SMXMq20aiJJ0oHXe0JWy2XtdEGQJx2GEeAB5bNgWmgMCoPK/rVmqB7VzkaBmmXCGELaNbRDUTA1MGGh6obKGDAFnFEwzSm20jFKYggFEIggTGIa2qV5YQgAiE7x88TIoIUjTtLAaW3a31VipjNXKK4EKPXKCGASKiNKMeBICQBIQIqELhiBLA5bYSCVOU6jwIylVGFLF66k6nbKFCAlb4wMWFCQMzqALNcPqhd19r5wRcEIK5ZhTFbiUpir8qXMemZKMBEdxcR1RItEwdax7IdJUwhAUFATtMMJqy8WlxXVwzsEIwbvvP4C/8qatH2O6ePEiXnrpJRw/fhyjo6MDnxNFUVcQFICuIKjdtNidgZy8GoaB+++//5oIZxzHXe95kiRDw782iyAI8Df+xt/A6uoqvvCFL+wIkeiNil0Cu0lsF4HdKQm7a2trOHPmTBE73m63dwyBnZubwyuvvIKHH374ur2mlLII7bqWftc4jrG8vIyFhQUsLS2BMVYos5uN9K9CnhANAEePHi39Yj1z7nl88evf7vo3xhhs01B2PylL7cCWacLzy0mcIhWDSSoBYJrlJJRRCs45glDV8HBO0Wq7xfnmnIOg3PrKOc/mWQffnDFKITQBv+wmm+S207g0LGhY/Y8ilxv7KFMJ13PhOorQAsD0xAQkZZiZmoDrBX3zvYxSaJqG5eUVzC8sYHxsTP2wdVx3KgE6Lu3hpIRgtFHDarNdOfNnmwa8oF8V7oQulJoZxTEIUbORvQRz0yR2QLBTPfu7zv0cprBuPK87gKyeJU/3HrMK2yqvlslBiZpfBSFDVWVFePtTj5Xt1INMYiytrCJJEliWBV1wNB2vtFqlE5rgsCwTa5ucW6WEZNZqpcY2NnH+1Ly1iZYbFOerjLx2v5a6Bhw/LGZj6zUTba/f6tv3mlCJxomUYEzAiwZXswzeXwrLsuCGCQSjMDSOsMOO2reflEDXzaF23BwNS0eQAhqjkIAKaho4lyyhUeD8q5eyRakJSKhFX9fJrcYJDNPA1Ggd4Aa4qJ6nq+vKSluFqh7WHDWdVZLXQTOvG4elyG/LVyRaFxSCqRAoP0phanTgPjJKIBhFGCUwdXWcbhCDEHUucxuzoASGxkAAtQCB7Bo01Pvohaoz1tQ4/DCBE0R4dWEVQRgjThPUdA0/+tZDePjQ/r59uFZcuHABr7zyCk6cOLFpEiGlxPr6eqHOblXn7C6uDVEU4fTp09A0DUePHt1StVRKiVarVaiz6+vrqNfrxXt+NaJEGIb4sR/7MVy+fBmPPvrojqrPfCNil8BuEttFYM+fPw/f93HkyJEt3/ZmceXKFZw7dw533nknbrnlFly8eBFLS0s4efLkDdunTiwuLuL555/HW9/61uvyevm861bX5OQzF7nVOEmSwqIyOTl51QEC7XYbZ8+eLfrNhoVO/OmjX8NzL16AaRjgnGU3/epGT9c0RElcpMn2olGvoVlyY02IUtPKrL6cMxCQUhLaqKnamHYJeTANHX5Q3i9rGkZlqJMmBNJU9llnOWNq9tLzoInqAKNhSrIKjhqgbErAD3wkcYTQ97HWbKsZygFzs67jYGV5GWPjE7B7VPQcdlZf1Hushq7ssH4QbqiPVf2etgVniEJpaGoeMa1Y3Nio4dkEiY3V9aXqf0r6TGsZsa3c2obCWrWt/HXjrIqmDLlST0DQHFIntPH8brJIM7t4fh5838fCwgKSJIGucVCmZe+7PZDIErLhJmjYdl8HbBVMQ4ehaVgb0q/bCU0IZZFP5VDy2gnBWDYnS9EeMKdaBds0AMrAqPrbivBlBUJQs+yBaq2lc3CqQn/iZKOCxTBNeNHmjqVu6WgPsA0LRlX6LlRnaxClYIhx/tXLaDQaGB8bG6gER2GINPKxst6G5wfQhCg+57qudx1vQ2doDlFoLY3BK+lhLY5hCAlW24gHboMzAjtTTGMps/qbjgUlY/C2CQEaukAilSqb5whQSqAzCi9KYHBVt+MEkSLInnq/a5pQs7JEKbR1QyCM1YjOleUWLi+3ISgQpSkm6yb+7x86goMzo5Xn6fXglVdewauvvooTJ06g0Wi87u1sRefsLq4NcRzj9OnTEEJsOXkdhDz8a2lpCcvLy6CUYmJiAlNTUxgfHy9V5KMowo//+I/j/PnzeOyxx4oxr128fuwS2KtAEFxdwuJm8Morr2B9ff2GqJ1SSrzwwgt47bXXcPToUUxNTQFQMyGzs7N48MEHr/s+DcLy8jKefvppvP3tb9/W1xkU1rRdq6lSSjSbzSIEynGcrhCoYRaV5eVlPPnkkzhw4ABuv/32Te1nEIb433/2RcwtLg98vFaz0K64Ca5S2zhnIKScpFqGDs8Pum6kTENXN7Oud439scMft00TbqYiq4oVG67nI07U/mqcQxKUWnQJQTarWE7UbMtUduOOo6SEZiRLkV/OGBaXl+E4jpqb1XRYtoU4iuE4Dg7ecjOYEKXhQupYVSpv/tU9kimSnTefZX23XdsZQhbrtok0SRFEceXcbH5u2k71jKVtmSAUaFd01wKbI9eEEIw1alhtOsUiTBk0wUFLam4YITA7iKeq8elXyAehYatkYAoCTReFyyBJEszNzYEQgj179iBNU8RRBM/zsNZsgXMOO7OdGroOxhhMXetyABiGmqetSrHu3I+W66vrwgsqFy46YWgahOAIs/d4s6hbpqpH0TS0vXBTJNY2dfgximtU5wy6LuD48cBzTQDUajW0gyHhSwBMTYAxAsK1oc8vjsHU0I42d55YGuHipVlMTY5jYmIcMpUIE4koSSA7WKmlUfhRilQCaZJm9nL134bV2MLUaH3ozKuZVfRUuSSGKa+GxrIZZAmNU3BCASKRZqFLlmBolpDfmsG7grQIUftEiVrQcnsUXUoJdE7BKJAksqj5qZvK2lzTOfxIEWQV7kQQJaqWx49iPHNxsfj9ZYzh4FQD/9/33o+6tbWVInlWxGuvvYaTJ09u6ezh6+mc3cW1ISevnHMcPXr0uidHp2napcirOfkA3/nOd/CX//JfxrFjx0ApRRzH+Imf+Ak8+eST+MpXvrJb0bRF2CWwV4HtILAXL17E4uLidVc74zjGE088AcdxcOLEia65ydnZWVy4cOG6WnarkNub3/nOd27ba2w2rGm74Lpuocyur6+j0WgUVuPedLrXXnsNL7zwAg4fPnzV/cGLK6v4n5/4XGkgWWf6by8YY2CMISy5qbbMfpLate2MZFqmAUJIXwrxMJWzat828/eNmo00lYiSeGAgkmUY8CustYwSaEOqZvLaE0CR5jiOu0Jw8plZzw+QJAkcx8Hq6iqSOFHEJpuZBWGVxKBRs5XKo4nSNOdh5wPI0px7VEdlTTULxT1XT6tItSKx5QscpqESmSklSBJZqYgCavbYGxB+BSgVkAsGzw9gGjqiKKkk2ICqjTINo+tcqe3wvvdzM6ptjpppghBSzKzGcYzZ2Vlomobp6emuhaW8r3ZhaQWO68J1HFBCMTpaB+UarJ6KHkIJ6paFZtWMud0982poAoyzYqa3DIamIU5VoJcKhjLheBGSIcS9bpldxEYTHLomKomsZWgIEjLwvWSUwDY0RImEn5NoCdTrw8lrDkIU2XXDFKbGwChBEJXbjGuGBife3G0PTUK89NocJibG+5Q6ApX6yykFp2o+NZESSaK6XONEAiS3l/twXRcycNDyYxiGAdu2YVlWn/NG5xRxKksT0oHMFpyRV0YAxgg4VbVeBBIUQJikRcJwL6qUW1vncIMYlCrSCqCYfzU11kdeBSNZvU9UqOEA0DDVGEKuknNGYHKGOJVwwxg6J5hdbuGV+XXYBkeUJOCU4sHbp/HRdwx3FF0tpJR48cUXceXKFZw8ebIvK2KrX2tY5+yu1fjaEMcxzpw5A8bYDSGvg+B5Hr7xjW/gF3/xF3Hq1CmMj4/jbW97GzzPw7PPPouvfe1rV33Ptoty7BLYq0AYltsYXy9uRECR4zg4ffo0DMPAsWPH+iwPCwsLOH/+PN7ylrdct32qQqvVwre//W088sgj27L9fOU3DzK60T8sYRh2hUAZhoHp6WlMTk5ifn4ec3NzOHr0KMbGxl7X9p85/wo+9aWvD3xM1UkYcEuCkfKAoLKOyKp+WNsyITjHWnNwIjIwONSp2DdUz9MCBDXLGPj3SulSK/9VYUHD6nc0wQGUz9wCwEhDEeUya6vgDIRQ+EGAufk5yFRiZmYGQRgUc7OmroMyrmo7LKtvQaVuW6CEVKYsA5sjsZ3BSIYmQAgZSOqGklgMVunrtgXH8wpngyaEmhkeovqZZj85NTUNiUy7zr+uaZASle8JoK7tWraIov5Glv6N4Fylw1YmPwsgIyyGoWFlrYnZ2Vm1CFFhD6tZJoIohpRAEkdYXl2D6ziIoqiwGVuWBZERG8s0EKf9+1p2rSqHgQXXHzz/20leO8EZg2UamX25H73ktetcCK4WU/wQnV8Npq4hkqSSjOWwdFUTRIWG1ibJK6Cu33bYv32dU2iMIoVSBNNUwtYFvKSvungwIh+vXJrH1PRUJdnROUWSyj6iSJD3spLMXgvEKRAGIRynDafdhuf7WZK1jVq9BlMzCtu+JABR/1PkD0ipwpKCWKW/x0m/0TzvXi2rG6rpvPR9tDUGSoE4UZbiQoEFYHXM0hIQWAYDkUCSpoXiiuyYa4Z6bk5otcyW3fIjUEJAkeCpV5cQxTEEY0ilBKfAX37wDrzn2MHSc/16kbvN5ufncfLkyeteWdLbOZtnYUxOTmJiYmJHkK83EnLySinFsWPHduT5a7Va+PSnP43f/u3fxunTpyGlxCOPPIIPfOAD+MAHPoADBw7c6F18w2OXwF4FtoPAzs3N4eWXX8YP/MAPbOl2y7C8vIyzZ89i//79OHTo0EClcXl5GefOncPb3va267JPw+A4Dh5//HG8973v3fJtv96k4euFzhCo+fl5AMD09DT27t2L8Ypu0GH4wte/jbPnnh/4mBAcUqIIsepFb/VOLzpVyJzURHEM3w9ACIGR2YkHoTPUaRA4Y6CUIYwGP84oheAbXaYa5zB0rWt/rCEdtMPqd4p51wE3iI2aBTdTBqvOEaMEr126BMoYZqZnQGjHdZfNzRKZYmFxeaN71LYxUq+j1rFI0KjVhhPUTdiJG/UapJRwXa90HtLKwpjiinlJlVqrLM2EZP2tA/ZPE4ooD6to0XVNVZjFCWqWsoEPSjQWnIMxDn8TLpmxRh0t1xuq2qrAI2vggoep64jjGFGWzu37HpprqxCGtamFJcvQIYToel+iKCqUWd/3ITStsBqbhqH2xfXUDK6liGYVOKMFIc2hApuSyoUIPZuPbXeouOrcD595FTyzQ/sRhMaRSDpQASzDSL0GN5KwdIEoTeEPsdqO1GtoldTfdIIAGLE0pGCIZQo/TCtnjGPfxWtzi5iZnoFll9egaVRFBodDumlNoYKNem8fkjQpbMaB54IRAmGYsK3uVONiO9rg7eTgjIBIlO5PTWNwgqQ4dkMwCKYIM5GAG8bofbsIANvgaPsRTMHAGc2eJ2GKDUVWcAIjm+nP1V3BVOKzF8YIkxSmoLi4uI7XFpswNA5ICYkUDV3D333kCA7fNFF5Hl8PpJR4/vnnC6fb9ai1q0KaplhbWyvGh3Y7Z68OSZLgzJkzIITsWPIKqPf5H//jf4zPfe5zeOyxx+A4Dj796U/j05/+NL75zW/i8OHDBZl985vffNUZKLvYJbBXhe0gsEtLS3j22Wfxgz/4g1u63V5IKXHx4kW88MILuOeee3DTTTeVPvd6WHavBr7v4ytf+Qre+973binB3K6wpq2G7/s4c+YMhBC4+eabizmbKIq6QqCuJs4/SRL8/p9+AbPziwMf30geHny9VxEnQghMU823+kHYZ1cWWTpnVFLNo+saoigu7Q81dA1hxeN5SI6ha6oSpecYeknuIKjk4ap51+56HV3XVPVPRhZodg6cASQjCALMzc1icnwctcZI5Y10o2ZjeWVN1RslMVqtFhgXyn6Y9c0Oq6Ah2fGUkVhGKSzLUCFGQ8iwZRgIo2goiW3UbcRxUqGW56Rz8GxqJzRNwDJ1rDUdVGlnlPbbhHuRhz/ZpgE3qO6oLf4mqw/KX9k2ldU8J4F5B/TExAT2zEwjipNK+7HqqwWiOELdtrvmmXMkaTZD6ThwPU/Zjy0Lk+PjGBltoDlk3rgTlqFDQiX6hoOCxkpgGjoooSCUwQ2uLrDJ1DXohgE3TEqVwF40arW+UCVdMOhCzU+GPd8XI3UbrQHK68D90RhiyQsyTYlK3OWUIE0lwjhFlJ2XwGlhbmkV0zMzlWSCZ+rqsG5ag5MsKKv8OZQCggBrrY06rjiOYRoq8M2yLNRNHVGcquqcAWCUgBOUJjDbGgVAQAmQpqqLNd+WKVjWAdy7bamIfyqz7lf1HhBCCvJqCqXaOkGMuiHQ8iNwSmBqDGGcIEmVzbnpBbgwvwY/ipEkSVbXJXHLZAP/93vux3h964mblBLPPvssVlZWcPLkyR1JDnc7ZzePnLwCwPHjx3c0ef0X/+Jf4BOf+AS+8pWv4Pbbb+96fHV1FZ///Ofx6U9/Gp/97GeRpikeffRRnDhx4gbt8RsTuwT2KhBFg/sMrwWrq6t44okn8I53vGNLt9uJNE3xzDPPYGFhAcePHx+qEGy3ZfdqEYYhHnvsMTzyyCNbskolpSyUV2B7w5quFevr6zh79iympqZw9913Fz9m+YzNwsICFhYW4DgOxsbGihAowzCGbrvZcvC7f/xpeCXpwYqktgc+RrLwG7eHoAnB1VxiGCFK4tL+WCtL0y378qmyElc9TghF3TbV+ekgHb3QhEAq09L9I4TAMozSah1AKZuO68G2LLQcp08VYSwjyh0EzXVdzM/PY2xsDKOjI1lgUXXibKNmqyh/x1Xdo05/3+z01ATiFKXXca6E99q7TV1ZafOwoM5e3DJYho6gwk5sGwaiOIKh60NraRhj0DWtvOuXEDUPG4TQBsyr9j0fpJSs99qpdU1k1SnDZ10tw0CUJDA00UU4W60mlpaWsll1ZTPlnMHQNLQHHJOp632fC0PXMkv5YCIvpYTn+/BdF3EcwvF8TI6PAdncLGPDvxNtUwdjXPW5DlGeO1GzTBVURCjc0h7RbuiaQEoY4iQFIVmFTgJ4Fed5pF5Da0gXq6lxcM7gRzEsw0RrkyFMGmeQlHfNZw6CoARuawWrq+vYu38/mNDVHO2AjxQjgMbp0MRjnSv7dNVrEwIYnHbZcAEgjEK4jiK0ceiBcw2Gpcisrutdn3VGlPrqRykgJTSulFVKAZmq13CCeOAcsiGYIsYd5NUQNFNzJVo9M6+EEFiCgRAgTlJVmSMl6qYGL4phcA4njKAzWvzWPvvaEvwwyhLNY+g6A6cUD9ym5l0533oiIqUs+tFPnjy5qd/EG43dztlyJEmCs2fPIk1TnDhxYkeT15//+Z/H7//+7+PLX/4y7rrrrsrnJ0mC7373u7j//vtvuDvgjYZdAnsV2A4C22w28d3vfhfvfve7t3S7OcIwxJkzZxDHMU6cOLGpFUjXdfH1r38d73nPe3YEsUuSBF/84hfxrne9C5qmXdO2bnRY09Vgfn4e586dw+23346bb7658r3wPK+Ym11bW0OtVsP09HQRAlX2txcuzeKPPvOl0uta2UEHExrOuUoejmKYpg5OGdruBpFTKq5Xanfr7fPsRaNul1b3FH+fEZK82iQIw2JWcFgysQptKq/fYVSF/AwKfFLbN0EZw3pzMMkHAE3jkKnqiG02m1heXsLU1HTXTJ1SUAcfp22aCMMIhqH1HUtn36zrutA1AcoF7JK5WUViN5KUG1kKce/xj9RtrA/pIjUNHVHcH57UyM55kZDcMV9bBqWc9qvVQnBwRgvSqqpmTDgl89mdUKqpqhLKA6YGXQuMURiaDqdkEacTI/Ua4jiG4wUAJNbW1rC2toaZmT1936t50nXLdTs+DzqCIBqogBJCijThQT/JlFKYuiLFYRgqUhMFKlCNcti2BcuyoWkCvYzLMnUEoVJeGc06fV11DFWomSacICr2xzYNgJBKIqtrApKwgaqrqWtgjML1I3Q+OlK30QquxmZsIwaHYARhnMIvWYQC1OwlYQLhEPIKSLTWV7C23sbM3r3F7wwlebpuplxKIElSMErghHFXCnEvRDYWUKlAE8ASFG7FzK9gBEmSYL3VLuzGlBDU6xZGajXU7Ro4B5JUEcowTrvnkAUtTTQ2uFpoSFIJQ1PVN0GUIIwS1AyBds97LRiFJRjaQbRBeKVEw1YqreNHSKVSdBkBLiyu4bXFJnROESYpKKGgREIXAh9+8Fa8+/6D5efmGpCmKc6dO4dWq4WTJ0++IROAdztnN5AkCZ544gkkSYLjx4/vWLutlBL/7t/9O/zWb/0WvvzlL+Pw4cM3epe+p7FLYK8C20Fgc7K4HfOdrVYLp0+fxsjICO67775Nf+iDIMCXv/xlvOc979kRBE9Kic9//vN4+9vffk0WoJ0W1lQGKWVRsn7fffdddeR6Z2DE0tISdF0vEo1HR0f7jvtbZ57G1759euC2KKXQNAF/gPJFCMFoo4YgiuGWJeFuhoReQz1OzbZAoAJ8Bs1TDk82tipnVTWhkjQ77c550mtuVR1mNzZ0DfPz81hdW8OePXtgGP3XsApS2thPxhisjjnaosanTJWW6n1PoghLq6uIOwKBbMsCyz77OZlLpRwyBzxciTV0DXGiwoB4RkIHvVebIbEb3arqeZaZzdv2WnEzoldlmc5RsywEUQTBeaWVWVmsq23YdVtdJ3lA0suvXoTjONi7dy80rfzm2DQ0pBLFPlTVoqjnq235HcSBMQpNCHgD5nuTJAGRKZZW1+E4DhjbILOGYaBmGfDDqM8aqgsOoYmMjPejl7x2oozIVpHXTnCmyHgYp9ANY2AAUxkaNQvtuPs3SeMUOqdI0xRetEHUBKOgXCAcmjgssbayBMf1ML1nb6XKRQhgCRVQRAjAqSK3nBIQopYECFTYEiWqR1ZK5a+QUipiSUg2+ylhCo4gipULKPtbEEWW1b8pipxKCZmqlOM4SdByPDUv7TpgaQwijOx9t8D5xv4bgpbajjVGYQgVQOVHSRchrZlCdfVCfTZtnal9l2lXBZDGKHShwpmkzMYHTIGldQcvz6/B9UMYmup6BZHQKEPd1PATP3QEd+x9fSGEw5CmKZ5++umiYeGNSF4H4fu1czYnr7kIs5PJ67//9/8ev/qrv4rHHnsM999//43epe957BLYq0Acx4XtdKuwXWRxfn4eTz75JG699dZN94TmiOMYjz76KN797nfvGMvKF7/4RTz88MOvO/p+p4c15UjTFM8++yyWl5dx7NixaypZB9SX//LychEYAQBTU1OYmprqSj/8ky9+Dc+/dGHgNjRNIElTJBmJ45wVFuAoiq+p3qawIldYSA1NG3jzXrNMZWMlpNQGDSjVsazeZdj+ARv1OoSqGpTeuVqlDBoD512llOq8ywTj45PgFZ+nkZqNdcdB3cqV5J6uxexcVSmQjDLomsB6s7URCBQE0LOk06nJCRBCIDStksAW+7MJEssYQxwnpcFbgDrHw+zEAEGjZkECyv5d8dM0bO4XUIRKEwJ+EG2uEse24HhBV69sXn2TXz9SSiwsLIAixeT0DAgdbmMbqasFg9Ym51ZVeJSJthuAM6rmhCvOLQBwxqHrAotLqwWx0QUHEzpMy1IVPQMsd5ahg1DSVbtTy9OGh9waWIYOwihcP4QuNEg6nLx2olGvIaUcjNKsD7b69Rq2BSehQ/qBlfonGAUoQysoDzwC1Pu5srSAMAoxNbO38uaYQKX0toPqa0kRWiCotBdL1Axe2eNKiVJxy2ZaCQBLY1hru3AyF4bv+2pm3LIx1qiBC60gr4wQGBpTJFkq0toXsNVBXvOAJzeIIaWELlhhczY1BprthDoGiZqhATLFc5eWsNb2kaQpCAgYp4ijBEJQ3DZdxz/4oWMYsbfHzpumKZ588kn4vo8TJ05cs2Nrp+L7pXM2TVM88cQTiKJox5PXX/mVX8HHPvYxfPGLX7zutZjfr9glsFeB7SCwW00W86Lul19+GUeOHMGePXte1zY+//nP4x3veMeOmRv50pe+hAceeAAjIyNX/bdvlLCmMAwLm8yxY8e2/NxLqWyPed9sEATFCm5jZAT/59NfwtLK2sC/tS0TSZpCMI6250L23PgMJamGXkpSi37ZikoTYEMFtU1TqS2ZKsw5A6GktJaFEgJdr+5vrbJKA8D4aAOttltKhBilfR2xaZpgbm4eaZpi7949GG3U0XbLLdWCM9RrNlbWmqX7wag6FrfiWBhl0DRR7EuSJPA8FQK1tLwKxhlqdg2T42NIUD3/PSzBuF6zssCt8kqajW1ltt4SCkIIQc2yQIgcqtgCgwlnDtsw4IcqaIlzBk2IynOWw9Q1JFklD2cMmuBwM5UyTVPMzc+p93PPXpiGDs555XZHahbWs2Op2Sb8MCqdu+5F3bYAQipnwfv239ABEDDGsLLWRNtx4DoOwiiCaRgbFT09vzU100Ai1WfJ8cNK0te3n5YJrutouuVdsL1o1Gpod8ywUkpg6Srcyg3jvtev1yy4cTV5zcGpWqDx4xSUEBhC1WdtBBep50mZYmlhHqlMMTm9Z+hMXU1nlYRTHQegs/551oHbqiDClCh104tKZvShumB79yfNUo0j34PnOqrSxrahWxYo15WjhlMVIta72CAlGpZynARRWgQ2UUIy8hrD1nmm2MawdA7Hz/4tSdF0fTz32hI4o4iSNFOTAQrl5PnBu/fjR996aNtmF5MkwZNPPokwDHHixIkds/i+3fhe7ZzNyetOfz+llPiN3/gN/Nt/+2/xuc997rpWYn6/Y5fAXgW2g8BulT0WUF/gTz31FNbW1nDixIlrUu++8IUv4C1vect170srw1e+8pWr7j59I4U1OY6DM2fOoF6v47777tv2gAIpZZGeurCwgHa7Dco1fP3MM+BCKzooGaOwTFP19XFeqrRuqJCDb7bVvGx596eua4jipNSibxoqtERKCW+A7dHQNYRxeTJxfjxlBJQQwDL65yutzNLp+sFQpVZkM8HK+hphdnYOQnBMT88U7ooyQlivWfA8pVrYZnWAFWdqNreqMoYzBs4Y/DDqCGoKIVMJz/PgOA4834PGeeXcrNrn/uNmNKtoyVRQTXBQQocqhTXbgjuAdAquAnpyMli3LZW8POTXyTQNxFGCKNl4X+u2lSm4G88rKn0qlPji2BiFZZsI/Q1bepIkmJ2dBWUUe2b2FOeJkMx+PDA0qv+95pzB0DW0h1Tg6JpWhIzVbQttP4Dc5PhK3TJBiKo6yWeU446KHs/3oQkBK6voMXQdyFRfgCBOJfxwuGKt9lMo5TVOIBiDaejwogRxhRJbr9lwKtY6OKMwNF6QWdsy4SVsU+SVZgs8fon6SaAsxwwSly9fAiEEY9N7K2dZAaCuM7SGkFdCCAxOhpLXusGLmpmBx0AIdE66rLpdkDLbRr4/EnoW2kSIsvx6YYwwTuD7fpZq7CKOY9QtA7Zdg9CNghDonEIwCgLZt1+MqDRhQgAvS5SmhMAUaiE4TlKstn2sNNtYWHfAKFPKfXY6BVP27r/2A3fhrfeUNx9cK/KAn3xGcqeSneuB74XO2U4l/eTJkzv2/ZRS4rd/+7fxr/7Vv8JnPvMZvOUtb7nRu/R9hV0CexVIkqS0G/NacK32WEDNR5w5cwaMMRw7duyaLSRf+tKX8OCDD16zhXWr8LWvfQ2HDx/G5OTkpp7fG9a0k8nrysoKnnjiCdx000244447bsh++r6PxcVFnH7yHD77tW+jbltoNBqgjIMLUdzeVc2MFkpqCYkxDBViM0gxAwDbtgYS4Fqm/nJGq6ttbBOu65Xe6A6r3+ms1xGcq+CknmMdNjOr6xpcx8Oly5dg2zYmJib73s/OkCRdE0r16jhuSmhpBU8OztW5rrLtCs5hWybWW+3BdtB8bjaOsLSyiiibm7Uzla7TrtVJYk1dQypl38yx4GxTXay2aSAIN8KMLMNAGEd9tUrF84YsGmqaACUUQRipjtyqmeu6jXa7PJ0ayAOq4qzL10MURZidnYVu6Jiemh74+bQsA1Gk6nNolpxcRZYbNQuOHwxMcrYMHWHcnVSsa6qbtWxmtTi+DpJOKVVp3Z7fFeqTJglcz1MVPa4LQgimxscALmCaFiglqFnmUCLbSV47QQgpFF2v5++HkddeNOo2iNCRpmpbVZWylBIYujY0GThJEizMzUITHOOTqoOZMwKNMTAKQAKJVIFIUZKiplcTTkCpjIZWHcYEAHWjmggTqNAlZwB5ZUTN2loaLWZr41QijJPivGiMQkIiGmQ7ThM47TbWWm3IKIBpaNAMC0I3MDla61ZzJWDqHBRShVVl26dEomFq8MMEThDh8lITbhCg7UXK8swpgiiFpXEwBoxYBn7ikSM4ODNaeV6uBXEc4+zZs5BS7uiAnxuBN2LnbJqmeOqpp+B53o4nr7/3e7+Hf/JP/gn+9E//dFubRHYxGLsE9iqwXQT2sccew8mTJ1+XPRZQVTxnzpzB9PQ0Dh8+vCWztK9H8dxOfOMb38Cdd965qUCjN8q8KwBcvnwZzz33HO6++27s37//Ru8OAODr3z2DL3z1z4sbXMoYbMsqiI1RYcc1DB1hWB52VqtZaFcQwEZH4E/NspAkcddrqXnbqr+vtrzapgnXKye5uqbB1DQ0Had0Jq8qWMp1XaytrKBWb6Ax0sDADg4oAgMQtF134LlilCoyXEFYNKEU32CAddfQNFBKEccxKK1WRlXCrY619Wbf3Kxl27BtG5oQGGnUAAm0HLf03PAscGiYXdfQNaRpCtPQ0awglIauIU3SoTOsSlHUsbZePbMLKLIZhtFAlbBmmXA8v1hk4ZTiwqsXYVgWJiYmQCqUOsYZTF2HlOlQoqn2mUPLanlyFB2zpangFoIoHng+6iUKsyY4dE1Da8A+SSkhKMHC0krWPZpkixjq8z5SryOREl5fYJMGSSiiIYsLhqZB0zjcIIZlWVdFXm3bgg/RQZ6U8kcpgR/GiDqShSkBTFOHG1aT1ziOMT97BbZpYGRiauhvQ8NQc5+cElCi/gPJ3T1ACnXDrTGqiF5anu3cRV6lBCGqR5YRApKlHGtUBT8REKRQr5ET6bRPee2GIq+D+7UFJTA1rmpsojgL3lMJ5jL04CeAZVlo1GyMj9aQShVE5Ue5jRio6RxRksINYiw3XcyuNMGoUmYFUzbtMEmgCw4CiXtvnsb/5133wNzGecw4jnHmzBlQSnHs2LE3hLp4I7HTO2dz8uq6Lk6ePLljZ5illPiDP/gD/PRP/zQ++clPbluLyC6qsUtgrwLbRWC/+tWv4r777sPExMRV/+3ly5fxzDPP4NChQ0OrVq4GX//613HPPfdsWvHcbnzrW9/CLbfcgr1791Y+741CXqWUePHFF3Hp0iUcPXoU4+PjN3qXCkgp8Sdf/BpeePlVpFJZTl3HgeO6ak6q0YBlWdB0Xd3Q9aCWWTjL0KjX0GyV98uOjTTgeD6CEiVvOIkdkjw8gIDmPaleEIIzWqnUEoKBoU3NZgtLS0uYmprCnpkpuJ4/0AJrGQZSmUIIVnkchVXYLyefmsYBSRDGG8xgxLbQcj2kmSyjCT6cxJKsyiabU1Z9s+o991wXmhAYHx9FvVZXwS8VnytGCQxdr+zQZZSiXrPgB2HRQVsGwRkEL++ALfpcw2hoInSxTcHBe7a5kW6szpvneZibm8fk5ASmp6aHdtAKoVTxPHG4N/m3DI2aBdcLYZp6NiNd/XeMUtU97G5cO2XktROmoYNSBqfjOEZqFppFQBUQRWERCBQEPjRNh21bmJwYh66bcIMQhi6Qgg0lr50YqddBuI4EEm4Qo2xhJ0fNMuERrdJCrnM1z5mmEpQxtIeQ1yiKMDd3BSO2jcbY5LBdyGzDwxm3ralU4k5Q0rHUQQA7q55RacTomwPPA5l6t9O1PxXkNbcAh5nySilgCA4lKCtFNuxTZaWqyvEiIA3hOg6W1lpIkwg1y4IwTNRtGw3bRBynkJBYarqYW2li3Q1gCF50/VIKpIk6EI1TfOjB2/CXjt8+9NxdC6IowpkzZ8A5x9GjR3fJ61Vip3XOdqZH72TyCgB//Md/jJ/8yZ/EH/7hH+J973vfjd6d71vsEtirwHYR2McffxyHDh26qroUKSWef/55XL58GUePHt1yovnNb34Tt99+O2ZmZrZ0u68X3/nOd7Bv3z7cdFP5HM0bJawpSRI8/fTTaLVaOH78+I6ZM+5EGEX4fz/5OSwurxb/JgEEvg/HdZFkM3VGZjm1LavrBqJyXjQL62l3hCZRSmFbBvwgQhzHMA0DbknargqFKk8uRlYV03YrSG7H/tUsE3GSwO/oex2m1FJKoWdBSVICq6srWF9vYs+emcKWpYKhNvaBMQrbNDKFWGbVMWZlOq/gKqW1iuTpmkAq1faEYAOtx5slsZap981nmrqGZquFtbV1uK4LUxcgXMAyy+dmq7pXDU0AhMDPFgt0TesiVYPAqHrPey3mtmXC84MuxbJhW2iX9Kn27aOtunDVfOzG9dhuq/nwyclJNBp1EBDUaxaaJeTY0DUkaVLMeGuaAGfVAU+dGK3ZSKVE8yoCmwxdA8+uj+YmZntzWKYBCUBwUV7LhGwRw1UJt57nglKGmalx1EfGEINVLmJ0olGrw4k3KJtgDKau6m2CAWphzTLhE31oKjGQqbKGBi9MoQs1yymlRBCnXSFFYRhibvYKxkcaqI2Mbw15lcPDmIA8/Ckq/S5RgUy8qK0ZuD+GKN0fQQkEIwAhYASIE6WySgkITgBJ+gKbpJQYszXEaQo3SIrPj8YpojCE4zoIPRdrbReGpkHoBlbcGEGsFFjLEIiTFGkqYeocYZSAM4IRy8Dff/cR3Ll/exdkoyjCqVOnoOs6jh49esOVwzc6bnTnbN7b2263dzx5/dM//VP8vb/39/D7v//7+NCHPnSjd+f7GrsE9iqQpimiIUmbrwebVRdzRFGEJ554Ap7n4cSJE9tCgL797W/jwIED2Ldv35Zv+/Xg1KlTmJycxC233NL32BsprMn3fZw9exaMMRw9enRHf1Gvt9r4+B9/prSixtAEZhcW4ToOgiCAbhiF1VgIUTkvmhPAKI5hmSZc10fcEcTDGAPnDEEwmHBxzkApQ1hC7Cgl0Evqd3KMNmqIoqS0lmZYBy3nDJRQXLp8GZ7nY+/ePX3vp6qP8VCzFdHqXQAjhMIy9MpqHGUVpgN7bnOMjdTgemHlTGyV5XhjfwhqplL3CDaCiAr6IQE/8IEkwfLqKoKwfG626FftWMio2xZc3++a/VQzoxUdtx3b61x4yOubBv2AWYaOOE4G2ik7wRjFSM3GasspbMPr602srCxjenoGtm11b9c0EUVx13ZtU1ddq0n/LGi9ppTRql/Zegd5Vt21gy3CvSCEoG4ZSAGlsJUEpA1Co2arTmyJoQo4AKSphExjNNsOmq02KCEYHxsF00zohgnKBhOIXvLaC11waIIjzBTCqyWvlqHBKVFeBSPQOEXg+7j42muYHBuDVhu9vuRV42gH5enMmyGvDUOg2fG4oOq4KAGQhcYNquwZlDZsaQyUKhW49zU1TmEIhjiVRccvSVNcmF3ExYVVBEEEQqSabyYq6dzUBNI0BaMEt06P4id+6H6M1bd3rjIMQ5w6dQqWZeHIkSO75HUbcD07Z6WUxYL+Aw88sKPviT7zmc/gox/9KD7+8Y/jIx/5yI3ene977BLYq8B2Edjvfve72Lt3b6W6mMNxHJw+fRqWZeH+++/fNpvHX/zFX2BmZgYHDhzYlu1fLc6ePYuRkRHceuutXf8upSxUV2Bnk9dms4mzZ89iYmIC99xzzxvih/filTn8n089WmqnbdRtNFsO4jjO1BoHnudBaBpqto3xsTGk6L9n1DUBXdfhB0EpCdU1gSSRXcS2+3ENSZIWSau9UCSX9tW76JoGTXC0HU/NzVVYXauU5CRJsLK8jCROMDk9Dc77LWyGrsM0NKyut0pfg1IKQ9PgVnTZappQoUs9JEUTXIX7uJ5SAZO0krRtRoklWR9rnKSVxNo2TTTbbbRa7f65WctSNyJEbavluKjb5fPJhKhzvb6J+pyRuo00lUN7ZTXOwHi5BVvX1HdnEIYwdEWa5uYXsL6+jr1795TWWDHGYOoG2q6Hes0a2lmbV9t4AxZj1AJBf8KzbZmVqirNUr/ztGrVHWvB9cvnZxVkdk1n544ANdNEnKJyYcPUdYRJqrYtgSAM4ObKLCSEYYHrBmzLhsjOa6NehxOVk9dejDYaILqFKE5L62NyEALYhl5KXnN4nof5+VnsnZyAbtfBMrWSUwoQIE3VzGmUpJDYPHkdFsYE5NbicuVVbWdwQBQFAedq5jc75UhSVW2TW9OVbRgDrMGAJihkCiQyhZnNpHpRohTTHqsyZxS2zuEHMYLsuzSVqsrq8mITLT+ExhkEJ/D9EK4fIk1CEJlCCA22oeOd9x/E33rn/dv+2xsEAU6dOoVarYb77rvvDfEb+kbHdnbOSilx7tw5NJtNnDx5ckf31z766KP4m3/zb+K3fuu38KM/+qM3end2gV0Ce1XYLgJ7+vRpjI+P4+DBg5XPW1pawtmzZ3HgwAEcOnRoW38szpw5g7GxsaH7dL3w5JNPwrIs3HHHHcW/dc67EkJ29I/ZwsICnn76adx66604ePDgjiXZg3D66efw6OPfGfjYoPqcNE2L6oYw8EEIVeqsbWNifBSMsmJGdljok2Wa8PxyBcsyTXhB0NdLm0PXNcRJjCRJITjPkmWdYnuMUQjBu+zDvRhEYuM4xuzsLDjnuOXmA0hS2aXA5QmwuUI4rIKHUZbtR7linFerRNnNfaNuKzt3h8KyGRIrOAOtSDCu2xY8L4BlGmhWdOMCeWJvgjhOOiynDjzXBeMctmVhdGQEExOjWG85lUokkCvW5aFOuhAgRJG8IIpLFy9yEJJZuXuIsW3mPbHq76UEVpaXkCQxRscnNqUCjI80sNZqF7PG1fvRrcZuENByEm7qOgilfTZkRglMXUd7wMILZ0zVGw1MsJaVs7I1y0Q6gGhbpo4gSkuJcRzHcB0XcRQiDAOkVGBqahLQamoRYBNfdbVaDQE1i+uDMwI9UxC9rGc4x2bJq+M4WFycx/6ZKQhjuEupYVCEsQSnahFUAoCUSGT2WyMVibTEcOU1n4vtpO8EyhnCiKq7MQVFlMis+kZdg3GaIk4k4iStrNupIq86p9AFRZzILLlZ7QMlKsjJCSI1c6srt4RMJcIkUcnGqarburS4jqYbIEwS1A0dqUwQJapjF1KCcYowjKATibfdXseMFsO27a5AoK3+jfN9H6dOncLIyAjuvffeN9Rv6PcKtrJzVkqJZ555Bmtra3jggQd2NHn96le/ir/21/4afv3Xfx0/9mM/tnvt7RDsEtirgJSytCLkWvDkk0/Ctm3cfvvg0AMpJV599VWcP38ehw8fvi5ptcP26Xrj3Llz4JzjrrvuAvDGCmu6ePEiXnrpJdx77707Zqb4avGFr30LZ595YeBjLOscHUSIlL07he+5WF9vwg8jWHmisWkqojck9Cm3ipY/Xh3cU69ZAAHa7cEBOUJwoKKjFui2EwdBgLm5OViWhclJVZNjGQa8IISUKeq2DS/otwsPI7GcZ92tFWTa0DUwygAiS2t2NkNiOWcQnHWRlaLbtYPsDQvEAhSxhuxW8GSawvM8yDTBytoqkjjF5MQYGNegG0blYlPNMuENSOK1TQNBEBakVdOEUpMrzldxHB1zsfUsCTsnF2kqsbCwgCgKsWfPXoyPjVSGKeWBX622C00TYIwNDXjKYeg6GFPErEpx33itTFkNQ8RxAs45BKfwKoK9gI3kYUVyCQBlNx5m0wbUjGwe9mSZBvwOEjQMaZqCMQbHj9B2PQSxhGVbsC0bpmUOfN9rdg0BMysXN0yNgRGlBlHO4YTV+9Nut7C0uICb9+0F1QYr6Z3YbGBTTVPzrCQjoZQQUEqAjlwznRMEUYIUWWCTlEglNs6hzMKTKmdeq8gr6wps4ozAEAzIXiuIk76ZV0rUc1IpITjNUpxV3Y0fJUhlijCM8fLscvGYLhgICOI0BSDBGUP+JhEAN0+N4CffexQTDQtRFBWW06WlJVBKC1IzPj5+zdU2nufh1KlTGBsbw+HDh3fs7/33G15v52wneT158mSp22Un4PHHH8df/at/Ff/pP/0n/PiP//jutbeDsEtgrwLbRWDPnTsHIQQOHTrU91iapnjmmWewuLiI48ePY3R0dMtffxCefvppaJo2cJ9uBJ577jlIKXHPPfe8YchrmqZ47rnnsLi4iGPHjr3umqSdgDRN8Yef+RJevTQ78HFdE4jTFEkHaRJCKZ6u58PQdbQcF0EQqHRbx+nqHVWpveUkoCq5WD1uo9nqJsGMU9imibbrwjaNSpKr65oKaStR9AhUYNDC0jLm5+cxOjqK0dHRrmtvtF5HGIWVwT3DyDrnHLzE4kuy8KsojhEn3T2hvdgUiWVK9fWCALZhIEqSgXbukbqN9SEkVuMctIN8q/nXzB6bzc26jos4DuH5QZFua1n2wJtbI+uazS3TjXzetefnqiDdmyFmhg5dCKx1XEdJkmJ+fg5SSuzZs6e44TJ0DQDpex8YZdB1Abdj8aBQeV13qMIsBAclaj7bcT0km/z5ZZShZpkIomhTM6s5TF0D4xwgpLJXeBBG6jZAKNp+tGkCO9JooBVsqNpJHCGJQqy1XbhBqD7vlg3LVvPS9VoNPq0mrzly1dCLUhiCglOqKn7iFJ1rHc3mOlZWlnFw/16AD1d18pClzTyv5VU/r6bzIbbhayWvFBojGVlWRDknq7pgiJO0rx5KUALL4AjCBH7n7LYuEMUJgjDCS7PLWGq6MDU1b08gEUQpBFc5xolUym6cSBiC4aE79+Fv/uDdA8cm8u7RnNh4nofx8fGC2Fxt96jnefiLv/gLTE5O4u67796xv/ff79hs56yUEs8++yxWVlbwwAMP7Gjy+u1vfxsf/vCH8Qu/8Av4yZ/8yd1rb4dhl8BeBbaLwHaSs04EQYAzZ84gTVOcOHHiun7Qn332WQDo26cbhRdeeAFhGOLw4cNviKThKIrw5JNPIgxDHD9+fEd/SW8Wnh/gf37ys1hdaw583LJMeJ4HyzRBCIHTo2L1KqlhFBVVLYHvY2x0BKCs6B3txVAltl5Dq+WotF/L7LPXDiK5Xftv6pmKOvgrsd1uY319FY3GKOr1evHvgjPouo52NudZNZuZE+F2BeESnINS0hXapOYoUSh9hq5vksTKykAgzijqNRtrzVYlidiMEssog6FpiJMEjJFSIk8g4bgu1tbW4fsBdF2DZdmw7WxuNvOcCs4gNA2MoLIihmRztlXzs5wx6BqHH0SwDFVXE8dJYQOfmZlRSlrXdinqtplV66iuWUJIabCYaWiQsp/0Fo9n71n+fgjBYWhaid23G4amUo4ppRBC9CVFlx43pdA0AQkJxvim+mkBoG5ZaHs+ZKa82aYJL44RDbCs5hhpjKBVYa2lAMLQx1rLRcv1MDYyCj4yBcuyoelaZccuAWAZHE7Qf70TqAAiwRlWVpaxuLSMAzfdhJQOz4fYceTVFGh52fUjUSQrZ3wVftivrgJKoQ7jDfJaVAxJiSQF/B53Sd0QaLZ9vHBlCW0vRBQnsHUBEMD1I1Cm7MZekEATquNVAmiYOj7y8J34gbuH53Xk6O0evRqrseM4OHXqFGZmZrZ9bGoXW4tBnbOTk5NwXRetVgsPPvjgjr4vOnXqFD70oQ/h53/+5/GP/tE/2rJr72tf+xo+9rGP4dSpU5idncUnPvEJfPjDHy4el1Li537u5/Df//t/x9raGt7ylrfgv/7X/4o777yzcrv/5b/8F3zsYx/D3Nwcjh49il/91V/Fm970pi3Z552KXQJ7lSjrprwWnD9/Hr7v48iRI8W/NZtNnD59GqOjozhy5Mh17zg7f/48giDAfffdd11ftwwvvfQS2u02Dh8+DGBnhzW5rouzZ8/CNE0cOXLkmu1TOwkra0383ic+03cTzxiDbRqglGCtWaGUltho4ySB57pIkhgrq2sQnBdhQLphKBPkgHnb3n0YqdtYbzl9ibDF69draLbKiZhtm3Dd7vocKSVWV1exvr6O/fv2odGoww/CIjjHcb0uu+tQEksILFOvVMRyAh/HSWZXdfq6IzdDYnVNQA4IfwKUOiehFluG9bYCyAKLvL796MSIbUMSDCW7nCkls9lyO8K/XDDGC2V2dKQBQgiE4JV9uTlqlgUvDLoWLQBF5GWadi0IGJrACy+9DNMwMDU1VfldUrNUErEXhl0Og0Eoqnnc7rlt21QW8zQdUBtjWwjDGGHJQoNl6Ah6Uo4ty4RM1T6VQWSW9M4kbtPQwZkYOD+bY+N97jm2rPYpBSlSanMMI6+9aDQacBKKZsvBSssBo6ywGhumAUo2rMa58uqE1ed+ZWUF7VYTtx7YhxhMkVpGwSiFhLKKR4kKbAIhO4a8qmApCoMrRVlKNQMbRGnxWdM5QyLVfGwvTI0hiJLieMM4gR8lEIyBEhRVRYwQdR69AM+9toiVlgfBGQiRMARHy1PfabpgoIQgjBOYGgdAkCYpDkzX8fcfuR97xup9+7BZlFmNc8tp529lu93GqVOnsG/fPtxxxx079vd+F8MRRRGWl5fx0ksvwXVdcM6L9/1GdM4OwxNPPIEPfOAD+Of//J/jZ3/2Z7f02vvsZz+Lb3zjGzh58iT+yl/5K30E9pd+6ZfwC7/wC/jd3/1d3HrrrfjX//pf46mnnsIzzzxTSvj/9//+3/jbf/tv4zd+4zfw0EMP4Zd/+Zfxh3/4h3j++eevqp7zjYZdAnuV2A4C+8orr2B9fR3Hjh0DAMzNzeGpp57Cbbfdhttuu+2GfHHnhPHo0aPX/bV7kc+Rvvjii9izZw9mZmb67Js7Baurq3jiiSewd+/e79kV4wuXZvFHn/kS0jSFZRpgjKkb3qyGpEopHUZCKaPgjGFlda1QZwkhRT2PXbOhaxr8DnWPMw7L0jPFV9mBvcob9P5Qn07UaibameInpSxscHv37oWmaUUYlB+EfQnHOYbNu1JCYBhDEpDrNpIkqSS6myGxmiZAsKHoEoKi4ij/+qdE9fBWKZ2Amk91/QCp7J2xo6hZquNWbX+4YptbonOyL6Wam3UcF0hiuEFQWE6npyc3ZYHVs7nYfLa3bplwPb9rgSEIAszOzmJ6chKjY2NDK2vqtgU/DKEJsWkbrmmoBGbPD1HPbONVxD8P/Wq7fpddt579m5QDlM9sASUII4Q977+ezXWXVS+Zug4uOByv23HQyIOmhhyfLrIU8SiGaTfQDjZva26MjMGjRvEajBDIOETbaWNl3UGcpDAt9b7btoWaqcMdQl6XlpbguQ4O3rQPMarD/CghaOgMUZJmqjsp9kWFNUmkKZBIwNLo6yKvnYFNjAI6I4gVb1ZzsZDKHZGkSFJZaRvWBUOS9pNXRglMrtKU/ahbmdW4mlcN4xSWzkEJsLDmYGmtjcV1B1GSwjY0EEgkMkUQJtBFtkhOCDgliFMJwRlkmuIH7r4Jf+tt92zpQnqV1diyLDz99NPYv38/br/99u/J39HvJ0gp8fzzz2NxcREnT57smp293p2zw3Du3Dm8733vw0//9E/jX/7Lf7mt+0II6SKwUkrs27cPP/MzP4Of/dmfBQCsr69jZmYGv/M7v1OafvzQQw/hwQcfxK/92q8BUJ+tAwcO4B/+w3+If/bP/tm27f+Nxi6BvUqEYbnF8PXi4sWLWFxcxIkTJ/Diiy/iwoULuP/++29o4M+FCxewsrKCEydO3LB9ADbCmjqj3BcXFwEAU1NTmJ6exvj4+HVXqAdhdnYWzzzzDA4dOrRj6oe2C088ex5f/dapwQE6hKib8RIVkjEKwcuTf4VQq/5RFEFKCd/34WRkNk0SNBqNrHfURM2y0PY8pB03b6pDlpcuNhFCstnYcrLWqNlYa7UwNzeHNE2xZ88ecM5hmQYgJeIkUR2LFeRnKImlVHXV9oT5CM6h6xrajpspsbKy41PP5l17Q6M6oQleqFqU0YHEmRBShBNVwTINhFFUzAsbug5I2fd+bsZ2DKCr1oUSpfI1W04xN+u4al56YnQUTNNhGEalq4FSRcYJiEo07vi+9jwPc3NzGB8fx8jICBhjsEy9WLDo2ze7e/63XrPgev6mkodBgImROlaazmACOgCaJqBxpZAWxHfI701RoROEiJMUhiaQpOmmumQ1IWAYOhwvQG1Idc8gjNTrSCkDpQJOGA8lviOjY3CJUaFUAkTGCDwPzVYLYRSBCLMIfutVavIFpjD0cfCmfYjS4Teb9Ux5HbavdV2RSkJIFtaUOX86nmNwCi+KM1Ka9eVmBDVHTedol1buVNuKDc4Rpao6hxLAEAyUEkRxCk4J3AEBW4ZQhJVTAj9KsLTexuxyU9XfQJFrTpVSyzPVlxOKKE1gCoEwSUAJUeMFhsCPvuUePHDn5jrqrwW55XRubg7NZhOapmH//v3blmq8i+sDKSVeeOEFLCws4IEHHuibgb6enbPD8Nxzz+F973sf/sE/+Af4N//m32z7NddLYF9++WXcfvvtOHPmTCFoAcDb3/52HDt2DP/5P//nvm2EYQjLsvBHf/RHXUruRz/6UaytreFP/uRPtvUYbiS+d7yNb2AwxhBFEc6ePYtms4k3v/nNXTN2N2qfymyY1ws5eZVSgjGG6elpTE9Pd4UFPPfcc4iiqPjCuxF2FCklXnrpJbz22ms4duwYJiYmruvr3wgcvedOLK+u4y+efKb/QakSck3TGKiEJkkKRiWE4AOTf6Mohq5rYIwiSVKYpgnTNDEBIAwCRFEI321jfm4eQteUMmtZBalJkgSM0tLtSynh+uX7BwAra+tYX18FIQT79u2DaRgQgqHdQe50XQPnrFT9bLadShKbpinCMISpG/ACP7NoWnA9ryD/YRRBEwKa4KUkNghC6LoGAl5KWsIoxki9hihK4JZ0u0op0XZcjAyZJ1WhXAKMUuiaQNvxBob8NFsO6rYJx+tXbLue13ZQty2EcQxGiDpfBDAMA4ZhYHxiHFEYwXEdeK6DtZUVEMYKUtNbeUOJqgYhNKsnyf49r3+YmppCrVYDoK6VVttF3bbh+l5BTClVydK94VWttgtdE+A6qwwdU64BA0trTehCQAhtqEUbAMIwQhhGGG804Pj+phZLpZRoth1QSjFes9EOgk2RV0BdX2EUoVGzIdMUGuelVuau4wNBvV5Ds+Mc5EFTKaFwg6SvPmdkbAIOhtQTEUASDqPewOjoKFpegCT04bkuFteWIZmAYdmwLAu6rmNhYQFJHOHWm/ZjSKsOAEVK235YTV57+llzVbZ39LRmcKx7EVCxtWshr6Zg4AwQkiFMUgRRAidTuuuGQNvvrukRjMLSOMLseeuOj/nVFlabLnSNgTOKWEogVUnFhsYQRak655AwNQHPj2BoHJQCB6dG8Q9+6AjGGlbV2doy2LaNOI7x8ssv47bbboNlWVhaWsKZM2cqrca72LmQUuL8+fOYn58fSF4BwDRNHDhwAAcOHOgSKs6dO7elnbPDcP78eXzwgx/E3/k7fwc///M/f0MWTObm5gCgT7yamZkpHuvF0tISkiQZ+DfPPffc9uzoDsHut8AOQJIkaDabGBkZwcMPP7ypDsLtxo0ksDKbAyoLa6KUYnx8HOPj4zh06BBarRYWFhZw4cIFnDt3DuPj45iensbU1NS2d4slSVIUcT/44IPFjfH3A9758Emst9o4/8rFvsekVJ3JQoiB3clhFMEwdCRp2qWe5giCsAiFyu/hLUNHI5/PkxJTM3uwtLwMp93G8tISNF0vrMYSasYzzUhwL3LyaBga/J5KkiAMMDc7B9MycdcdtwNEEZcg6L5RDYIQhqFDprK0I3MYiU3SFGEUolGzEEYJWgN6VzdLYtVzRJ+t2dA0UEqx3mqrWUPDKK1wkVJive1gpF7DesWssFpY0BENqVhpOR4s00AUxYiSCmIkAZGpQoMgNIFRbbTYR5kkWFxewdrampq/thWpGRlpgABFv2quDs8vqtX9mZkZWFb/DXnLcWDoGiihiNMUjNJSO3UQKtKnFhv61VhGKXRdbNQuRRGCKFJW5CBCXHEeVEWPiZX19aw71obnBUP7bgE137rWbgMgaNgmXC9EPGDmtvv1iAqqyq5PAgK7pA82ByUEdq2GVg+BT9IEzbaafxecwzQNpFBkdmRiAo7c3O8aowQapXDDBIxxMLMGzayhMZ7CdT2Evou1hTVF7gnBzNQU/DgdqtQoRXVICKMEaiYfbhs2+ND5WbuSvGKDvEpAy8KaKIA4laAUcIMY6YC6oHr2dxKqUsfgFHGSglGg6QZYajq4srSOMEohZQKdU0Cq6hzGKNI0hSGU7VkXHAZj8OIYTEoYGgPnwLuO3IK/8tBd11X9Wltbw5kzZ3DbbbfhlltuAQDs3bu3y2r84osv4qmnnrqmVONdXB9IKfHiiy9ibm4ODzzwwMDv3V7kNTxTU1NdnbOXL1/Gs88++7o7Z4fhlVdewQc/+EH89b/+1/ELv/AL1/W638Xrxy6BvUoQQrbUQry6uooXXngBlFI8+OCDO+aDwxhDWnJTvp3oJK4AhiYNE0LQaDTQaDRwxx13wHVdLCws4MqVK3juuefQaDQK5XYzX6BXgyAI8MQTTwAA3vSmN+2IhYfrCUIIPviut+IP/uwLmF1Y6ns8jhPoOiuU1F74fgA7m1Ec9JlyXQ/1mq0ILkHf3GwUxdi/by/abRdJkhRhQKtrayo51bYxOTEOQujA7SdJAkoJNE0U9TGe52Fufg7jY2M4sH8fHE8RsLLPvF90ZYalttJm2ymdC+acwdR1uF4AUaEq5CS2bEGg8zm6JhCEUWEtbXXMJydpAj+UWUpzuV10vdVW1TVOfzVMzTbhByGaReKzUTkb6no+NI3D4Dr8Hlt3Z6dqfj5sw4BT0ZFKCAHhHHfefhuarTZcz4PjOGitr2JxYR6GoaqZTMuE7wdYW1tFEPjYt29f5YKWH4SwTQOWrmGtgrwDao5xQ43lhSVbhWbJgWpry3HBKEUjf096lDtKKUxdK86FlBKtVhuMMTRsEy23XJHdmLMFAIlmSymy9axTdxABptk8erPD6i8hC/Jv6jo0ocHp6ORllME0TbSHpBlHcYwoqysam5wBwGBxwI+Bql8VTik4BbxoQNowoep9NQ1c8QIwpmaEr8wvIonVAsFIo45arQbKBBKpZkxTeRXk1eBoXyN5JSCwNAanh7xSqsKYKAE4IYilhM7Vok0Qqf8AwNYY3CAZuDBUNwTiJEXdEAjjFH4UI4qVWvvS7BquLK8jjGPUDA1pGkPjHFGcIEpDNEwdYRyDMgoniFA3BeI4RZQm0DkDo0DdMvC33nYY999yfceXVldXcebMGdx555194ze9C9a51Xh+fh7PP//8VaUa7+L6IHelXblyZdPktReEENTrddTrddx2221dc7Ovvvrqpjtnh+HixYt4//vfjw996EP4D//hP9zQe/A9e/YAAObn57F374Ztf35+vstS3InJyUkwxjA/P9/17/Pz88X2vlexOwN7lYiiaMuI3aVLl/Dss8/iwIEDmJubwzve8Y4t2e5WYGlpCc888wze9ra3XbfX7LQMA7jmL5IgCLC4uIiFhQWsrKzAtu1Cmb3W1bt2u40zZ85gdHQUhw8f3hEzuDcKbdfD//zEZ0t7WnuV1F4MIncqzdVGFMfQBK9MDlb1OBuPp1LCc104rgvXcWAYOhhTqcamaYL2vO+6JpCkEqurapX/4C0HIDStsAYrO2j5TC+gqnFcP6hc3GrUrGLWkwCo1Wy4rl84HRhj0DReVOUMgpqJJQijqvRZDkNXdTZl2yLZnHJVWjKALGVZBQsxqs5Dr5qcE+XmkNlZxijMrMIGUEFAjNE+K26e5Dts3wDANk0EcQRLN9BstfvmZhljkKnEzJ4ZzExNltqdgTzASBF2y9SRJCmCcHNW3EbdQppK+EHQ18M5CEZmkc+PXXAOzujQ917TNTg9RLbzuhoEStXMdxjHCDIFn1MGTReVIWLF32chalISSCYqk497MToxjXa88T1OiVKKGeeIJEXQEUzEGQEnBH5Ufv6SJMbs7CwMTcO+vTMIs1qfKIqKBSzf9yGEKFT5qVEbUaKuXzXDmoU2SYkUam41zSpr2n5UWSdVMwScjLxSoq5VRikIkaBZMj6nyOzGEmkqkaQbYU2UAKbG4ZQkNqvtd1ucOaUwBIOgBC0/Qtxx/xFGMZptD68trUPKFEqUVjO7ufWZQNVlJanMHlP7naYSlBIQSkGlxNGD0/g777oflnF9R3BWVlZw9uxZ3HXXXdi/f/9V/e3VpBrv4vrhxRdfxOXLl/HAAw/Atu0t3/5mO2eH4cqVK3jve9+Ld7/73fiN3/iN605ey0KcfvZnfxY/8zM/A0A1kkxPTw8NcXrTm96EX/3VXwWgzs/NN9+Mn/qpn9oNcdrFBraCwKZpiueffx5XrlzBsWPHIITAd77zHTzyyCNbtJfXjtXVVZw9exbvfOc7r8vr5cqrUsW2vt81/6FbXFzE0tIShBCFMnu1icZLS0t46qmncPPNN9+wlOidhsWVVfz+n3y+tCMzT70tQ26zzROFPT/oml/tJal9f1/yuJSKVCBNsLi0jDhJ1OykZcGy7aJiw3MdtFpNjI9PQIh+JV0FPxmVwU+2bcL1qkls3baRpAnSNO2zLgObI7GqJ5YiGEAklO3UgucF4JxVbodAPXdY0JJlquh+RejKycsw27HaP7VgIVM1hzxImc9REMqK7QnGYepqAaJTtZWpxOzcLKIwUoFeYQBd0zE2OoJard6lAm4Q8O59Z5TAMs2hycz5vvpBAE3bfEcroBYIpFQhXWXKei90XYPGBVquN7SyqRe17PVSKUstwoOgcQHKOaQEDENHGKdFPUsf0hTsm9/E6NIqWrffg/QHfkDJjwMgOFPWbcqRElpZlRPHEa5cmUXNMrBnehphybWTJEmWZu0AkQcvlsXnfdACFghgDyCV6nkdYUyGQMuPINPBedKUEBiCliYmU6pCmdySRZG6LtAOQjBKYHA1hxpECcI4Qc3QuhTktudjbqWNtuuBUQovjJGkKTTBwQiQpBKCKdswzxKJDY0jjFMQohY1/DCGbWgQlOAvv/kQHrn/4MD92k4sLS3hySefxN133419+/Zd07aqUo13rcbXDy+99BIuXbqEkydPXreRqkGds52q/CBiOjc3h/e97314+OGH8T/+x/+4biJEu93Giy++CAA4fvw4/uN//I945zvfifHxcdx88834pV/6JfziL/5iV43Ok08+2VWj8+53vxs//MM/jJ/6qZ8CoGp0PvrRj+I3f/M38aY3vQm//Mu/jP/zf/4PnnvuuRsaBrvd2CWwV4k4jq9pNjQPawqCACdOnIBlWXBdF1//+tfx3ve+dwv39NrQbDbx3e9+F+9+97u3/bVy5XXQvOt2IA8KWFhYuOpE44sXL+L8+fM4fPhwl8VjF8Crl1W9ThkpqarXMQwdtmlgZa1VXhliWQNnRHMMS/2t12wsrayqeh7HUTOwppFZeL3MukZKF6gopTCy55ahlvXCDvpS1QSHrikFtYrMM0ahCdHV39kLzhk4412WXMs0kSRJQTLVLKY2VGWrWhwgBKjbNYSRskiX1QblqNcsOI5fqnLSLNyIgKDl9tuTe2GZhrKjDggWsgwDcRwjjOKu6p40STE7NwsCgj179oAyWljMXceF57mo2RYI5WiMNDBSr8OtIPq2ZSKK4tL540bPIkDNMhFG5b2uXds2DYRhpJKQXW9z6cZQdtq6bSrivgkVNUduceaMgXGmKnqG/o0GgPYdj2Ho0DQNfhh3Vbjwxx/H6LdPwTHqAAGSH3wb0re+tXz7gkNSVWsjGIUu1AJNLIEgUippGIaYnZ3FaMPG5MQEogF9qL2o6xxNL1Ap5pkbI04SWKYJK1NnBecwBSsllTlqRvVcLKMEGqfwSsgrowQ67ye3hKgkY4Or4w2jpGthgEJ1t7aDCKmUWGk6WGs6aPthphxLxImEJhiU4puCc4ooSpGkWfo4JAglCKMYhq4syIIpUjs9ZuMnfuh+3Dw1OvR8bjUWFxfx1FNP4fDhw9tidewlNbtW4+3Hyy+/jIsXL+KBBx64YXkgURQVQVBLS0s4e/YsvvrVr+J973sfPvzhD2N6ehoLCwt4//vfj2PHjuHjH//4dVXqv/KVrwwUhj760Y/id37ndyClxM/93M/hv/23/4a1tTW89a1vxa//+q/j0KFDxXMPHjxYhE3l+LVf+zV87GMfw9zcHI4dO4Zf+ZVfwUMPPXQ9DumGYZfAXiWuhcC2222cPn0atm3j6NGjxYcmCAJ8+ctfxnve854dMwPrOA4ef/zxbSfV+bzr9SKvvZBSYm1tDQsLC1hYWChNNE7TFC+88ELx5TA6Onpd9/ONgmfOv4JPP/Z4qQrZSZZya26aSrieN9SqO6xDFlmvaLuK5Ha8viY4Ll58DV6W9qppGibGx8E1Ac5Fb4gqgIxcatU9s70kNlf42o6ryHE+mzqMxGqiUkFljKlgpzCCZRoDlbjNKMfAYPJv6BoIIcU+cM4hhqi6ADZCm3oUOkNXynZOui3DQJTEiAbMO3aCcwZd0+B0LBw0ssWQ3stM1wReufAqKKOYmZ4Bof3vokwlPM8DhcTa+jr8ICwSjU3LGvgdzCjN7NMb55gxNbM66NxSutFxW/YD2+jp4s37hduuV6nic84gOC/sx4auuonbQ4isaWiIoqSrbkkIDtMw4AUhogG/a6ahI04wNETKMlW1UZQC1h/9KbzXZovH5K23Iv4bf2Pg3xkaRwKKuIS4EwAyjTE/O4uxsVFMTEzCi9O+ROAuSImaKdD2uhVmCSAKw4LMhmGAhmWCakZR0TPo92cYeRWMgFEKv+Q65lRV0kRJCp1TsOyajDMV29bFwPlcpegyrLQ8rLc9LKw1EcWpChkjBCnUdRknMWq6hiCOESdKHZYSsHU1BxzHKTgnoCCQFOCEQkLi4UP78X+9/V4Ifv3HXxYWFvDUU0/hvvvuuy4K0a7VePvxyiuv4NVXX8XJkydveItGDiklzpw5g49//ON47LHH8Morr+Dee+9FFEU4cOAAPvWpT33fZZd8L2GXwF4lXi+BXVxcxBNPPIEDBw7g0KFDXT+UcRzj0Ucfxbve9a4d82HyfR9f+cpXto1U94Y1kWx+6EYiT73LyazjOBgfH8fExETWNRji+PHju1akIfjWmafxtW+fHvwgIWjUbEBKeEHQV3OjVM5y1ZAyCl30d6dubL6a5BJCMDZaR7PZxoVXL4IyVtxA5TN0MokRJglsS9XzGIbRdW3mxLGKyNVqFhzHQ81W1tK+Op9NkFjKKAxNK00MBpTiKSUq53OLoKQhVtOabcHNiHdv+FOxT5TC6phjLYOmCVBKC5t0o2aj7bp96rbGOTjnlQookCvBamFAkfX+1w/DEHNzs2jU69i3b1/1ecu2RUBACLCwuATHdRFFEUzTLCynvTe2tmkgSVLIbJ/KuoxzGJoA47xPJW3UrFLV28jqmQYFY2nZgloQ9hMqQ9egCaEIcM9jtmnA84NSd0G+eARCCiJsmyb8KNn0yAzjHGZjDOQrX4P52GOIuQ5fM5G8bbACa+oCkSRIKlRnP+vtnZmaQH1ktJgv5oxC4yyba6VIZZYsnkiYmqrKqdxXSkAhsbzWVMq866qeZ8uCZVkwTVN9bjLbcBk0TkEIEERpsV3BCBghoJQUqcJhnHSp1ABUXY+pDSSvjKj3eHa5ibnVlkoZZjR7DfWaaZLbgSnSzBZOoMiyYCocKrdLSwAaZ0hlirqpgpqO335jAl7m5ubwzDPP4MiRI5iamrrur79rNd56XLhwARcuXNhR5HUQzpw5g3/6T/8pLly4gOXlZezduxcf/OAH8cEPfhBvf/vbt721Yhdbi10Ce5VIku4V7GGQUuLChQt48cUXce+99w6c85BS4vOf/zze/va375gvzyiK8KUvfQmPPPLIlq9ObnVY03bBdV1cvnwZFy9eRJqmaDQamJmZ2ZZE4+81fOHr38bZc893/ZtlGaCZoqdr5SSVMQbBWSk54JyBMY6gxGJLGYXeo5LmN+hRFKPlOFhfXYEEwdTUVN/CSSolGJFYWFqG47iAlN0KHSFDSaxlmhCCo9lql6tphKAxJPyIUgJzACHXhIDgHI7rFZ2l7QprMzDcYg0AI3UbcZJWJhQDpM82OwiMUli2CaSykjxTQmHbZiWZB5QaqAlle+21qfu+j7m5OTQaDYyPjxUVNK2200XmirnfHvJYt014YQTP8+E4DlzHgR+o69TKeoY1TctUfgOMMjR7tl2Fmm0WqrRt6puaq7VMtXCSf05MQ0cYxaW9wzm0TFVtez7SNN1IKN7kT70QHLVaDV4YDyTKg/9Gg6iNqCTdNAX95jdBXnsN7OYD0B95BIQLBCkprL+2ocFPUFnB5Lou5ufncdPMFIxao7SqKgclBKbO4QUxOKPgjIASqkgcQaHWS6lqkqJsISKVgExTeJ4Lp+2oMYU0xdhIDcKwUKvVwBkDgVoMyr8vVBBSijhVNt4kTbuOR+dMzTcPkIsJUQppnmZMABiCI4ojzK+2cGlxHWGskojTVIIzqr5zGIGEhB8qtVUXHKlMwbOwsjhNYWoCcRxDZovCjBFEcQpDMNxzYBJ/911H0LCMTb2vW43Z2Vk8++yzuP/++zE5OXlD9qEXu1bja8Mbhbw2m0186EMfwsTEBD75yU8iSRJ8+ctfxqc+9Sl86lOfwurqKt7znvfgAx/4AN7//vfvjoi9AbBLYK8SV0Ng847Q5eVlnDhxAiMjI6XP/eIXv4iHH354x/SIpmmKL3zhC3jnO9+5patS2x3WtJVYX1/H2bNnMT09jYMHD2J5ebkr0Tifm93KPrLvFUgp8ckvfAWvvDarbrzDsOtmmDEKIQT8EgIoBAcBSucOheAASGnwDWMMQnAEYYi6ZcEPQ4RhlNXkzGN0ZAQHbtpfqVzmM5WBr0iN47qI47iYoRtp1KHretcx6FqunimCkiux1SS2mlgSAtiWhbbrKmJmmZk6KrufkxGVKlSR2LqtVFJNE0hTOTRUqF6zK4mRbRkIwximoQ8lu/m+laUEN7LXSmUKTQhwxguF1XUdzM8vYHx8vO871jR0FeQVhhCMQ9N4aeVPXmnUyt67jblZB67rgjGG6ckJgFLouqHSdCmrrPzphMa5siE7TmV4VS9sy4TgKom7ivD1gjGK0XoNLdfrdwBUoFGvqcoeqO5lwTlcP+xKv+2ErusgZh1RPPyYdE3Asm2khCNMJMKSWVYnc8LcctNecMMaOhtMqbLbuiXpvjkEUxbeMrsvoMgklQkWV9fhOi7CKIRhGLAtG5ZtQQgBUzBEiSw9J4bgiJN04OOUEFiaIp6KSMe4stTC0nobTcdDItVzcvKqcwYh1IIepRRRnEISwBQcEhKMMvhhDMFVjywIgRdGqJs64jRVr6cLvP+B2/HeY7dVnp/txOXLl/H888/j6NGjmJiYuGH7UYUoirC8vFzMT+5ajavx6quv4uWXX8bJkyfRaDRu9O6Uot1u48Mf/jAsy8Kf/dmf9QlFUko8+eSTBZn9zne+g29961t48MEHb9Ae72Iz2CWwV4nNEtggCHD6tLJRHj9+vEgPK8Njjz2GkydPVpLc643Pf/7z+MEf/MEtUxuvd1jTtSC3Od1xxx04cOBAn+V7aWkJCwsLXYnGU1NTGBsb29HHdT0RRTH+8DOP4tLswsDHh5FQXdeQxCniZPDnTdc0JGkyUJGilKJuW4jjpJidbLXbWFpcxMTkJBrZosOwepzO4KnOGTrHcRAGASzbwkijAcMwMdKodc005lAzsdWBRYpYVtiACTBar8P1/XJVjGBTqbT1mo122y16SHVdAyOky8a72XlX27IQBH5XdQwlBLWeVN+6bcH1/KEqmmUYiJOkWLjgjMI0jL7wLqWk2rg8O4ulpSVMTU2XLv5RSjDaqKHteKULIp2o2SaCIOqaCZUyBQMwv7gE13UgO1T56akpBGFUOSdq6hqS7Lg457AyFXYzP7/5vK9lGYAkmwptyi3XTcdVVljLhAQq+3oBoNGoozlAHc5nqQkh8IONKhfTNJFqFuJNhCoBihy3w47AJ8ZgZHbzREoEMbC2vo6l5WXcemAfiNCHBn0xSrOApOr3VucMEkBYoWDnc6ed24qjWI0XuA48z0PN0KCZFgzTgqEb6B2WtzSOIE42rNES0AUDZwSMAHEKOH6Alhdiaa2N5Wa7sPpGiSK1+XZSSERxgjBWwUthHKt0bEOD60fKViwBwSnijNhCArYpEEWKvO6fqOPH330/bpq6cQTjtddew/nz53Hs2DGMj4/fsP24Guxajatx8eJFvPTSS0PFmRsNx3HwkY98BJRSfPrTn95Urc/i4iLGxsZ2Fyx2OHYJ7FUiTdOhysT6+jrOnDmDsbEx3HfffZuK5/7qV7+K++67b0etTD766KN46KGHtsQWcqPDmjYLKSVeeeUVXLhwYVMzOnmicd43C2w+0fj7AX4Q4vf/5HNYWlkb+HgVCQUA0zQQhCHSEsXKMHREUVQoWpwzWKYJx/WQJAk0ISAhsbC4hLW1NczMzMDquPEghMIy9UrLbJlqGccxfN9DGsdotdtIofoybduGputd97WbIbGKLPeTT03wrJ7FG2o5BrKgqiGKp22Z8P0AdtYFO+hnQM27Gmi71a+n6xoggSAMYeg6ADnQ/m3o2qaSjFVokw6ZOTWCkuevra3Bd11MTE5BVGQHNGwLLddVnbFRODQ0ClDEysxIpsjU/A3Lu0QQBEqVd9TcbK1mY2J8DJKwvpueum2qLt0e8q5rGjTBC8W3Fyr8q7931zZNAOVElhICyxoc3GVoGnRNwPGCriwHZbmuoTlkthlQfM02DWiGiYDqyja8CfSS10FYW1tHq7mO2w/eDMOyISVBlKYIk3TgZ4dzmnXHVu+DITiSNO2fQ+3cFlWzo17FIocpKFbWWmhlqjyAQpm1TAt1U2SdyYpYxmmKIFJWYMEovDDClaU1rDs+2lkIl8YZCCGqBocxUELgBKGyDVMKP4qhc4ZUqt5WThm8KCpsw4bO4foRDE1ApikoU2ZnTgl+8PBN+OtvvbE95TnROX78+Bs6/HDXaryB1157DS+++OKOJ6+e5+FHfuRHEAQBPvvZz+5oi/Murh67BPYqMYzAzs7O4umnn8btt9+OW2+9ddNfat/4xjdw5513Ynp6eqt29Zrx5S9/+Zp/dKSUhfIK7IywpjKkaYpnnnkGq6urOHbs2FV/2Q1KNJ6YmMD09HRXovH3G9qOi//5yc+h2WoPfNw0dQRBeb+yZZkqKbjERmiZJtI0hRBqJrRzO1JKrK///9n78yA5zvs8HH/eo++e2RsLEATBEyAA4iIk6rAsUrKigxJlybJ+shLZlh07sVVlRbGdVDmOS5QTS3bkuJy4fKTKqpLjipSvZTmiTlOkBJq6LQJYgABBggRIAgT2Pmem737f3x9vd2Nmd44FsItdAPNUQWLtzvS80zM7Pc/7eY451Ko1DG7YkFWCNGI59TiLq2YIVbU+XhAgSVJQShCGIWZmZuF5HiilcBxHdU9mIVDLmsSWHCxUvIuPUZ9enN/GdTsS1HKpPdG1TANaJsNtNzkkANw29Uc5GGMouzbmFiptnx9nSxOFm6HHdSABNdFe7DSVwPT0NKrVKjZu2gjHcWA26V9VPa6N6cycsWyiu7zu1N6yizhO2k494ziG56lqJpGmME0TTNPh2DYG+ntR7TBpzb2u9Y/BmapAarexYpsmGGNZarH6mcYzst1hck7rEsD9IITjOB1TjOtRLvfAS9Tfl2no0HUNiQCCOGn6+pfLZVTbyHulVN3jCwvzuOu2WxGTxk0AAkX0OKegJNsAlQAoUQFJQraUGVtZ/2m76f9ypMWuoalgJKpCmghRIVMLlYryuosERDNh26qiJ9/I8MMIVS/A+GwFFS+ArmmIkgQEgG3qRapwKgTCKAFAoGVpxXEq4Ji6IruEwNY1xEJAZxRemMAyNIShqscpAtcIwUDZxC/cvxs7b7n6IUn1eOmll/Diiy+ue6JzqVgsNSaEFGT2epca5+R1vW9IhGGID37wg5ibm8Ojjz56Xb3/ulDoEthLRCsCK6XECy+8gJdffhl79uy5ZCL6wx/+EFu3bl1XxvEnn3wSu3btuuyp8OKwpvVMXqMowtGjRyGEwL59+67Y99sq0Tifzt5oaXczcwv43CP/2DK4ybEteG1IalFNs+jjyrLMQnJX8/2G+wshMD4xgSRJcOvWW0AIaTnpLRJ/23yJL7sOKjUPJcdGEEZLJomcq+mb7wfwfb+QGtfLTYcG+hGEcfOu2+JxXAgpEMUxohZy4Xqi2wquu/Sc5ZU+eQWNrmsglCDskKZbzqbDS8gkkHlSGfwwgOt0JruASk9uNm0uKnMy4naxkkeRHyklJicmEYQBNm3a1LApVO+hVZU9zSfBgJLX+mHUNhCp7Nqo1nyAKAmumlS3f165bxYyhe95CKKkeO0Xp1kvhmNbkBJIRZpJdduT0ByGpsMwdMRJilSky5JJ5+CMw7YtEEIRRhHCZdy3p6cX1VigWYIVYwyWoYMyiiiVCBOBcqnUkbyqDYkKtt9xO0LZOdTP1BhSgQaPKSUqgTdP5qWEQGNKmiwzX6mELJQRUqr/yjMEc3/xxbAnCSGBVKgApIofN33/AypNeHq+ViSZV2oegkQiEgSEUqSSQEoJ19IRJyJLDAYMnaPmR2CMIsmmzLpGlaQ6y4kIogScMZQsHUEcQ0qCMEnRY+kIYpVErDOKKBUwdY79tw3h5x/YA8tY2w3TvBP03nvvXdf+yCtFK6lxXsd3PUmNX3nlFZw6dQr33nvvuiavURTh53/+53HhwgU8/vjj6OvrW+sldbEK6BLYS0QzApskCY4dO4ZKpYJ77733smQKP/7xj7Fp0ybcfPPNK7XUK8aVTIWvpbCmWq2GI0eOoFQqLVvyfanwPK+QGc/Pz6NcLhe+2eV4Mq4HjE1O4fNf/mbLQBk3k1q2+kjK/aiEEDiOBZGKBsJZP+FM0hRjo6NFTQ6jVMmNkwRpC9LSqeO15KqO0PmF5pNkdYzGdGIJFHJTr1ZDHMfo7+0F13WYlgW+6L1mGAY4U19gOyXHXgxRankT2LaJMIyQCgHHNhHH6RJSzJiqLurkrXQsC0EUNUhPy46Dqt849S45dscuU0CRwiAICxLi2CbCKFmSMcAZg2noWKjWMD4+jjRNsWnjJrAm/ZWmYcDUNSzUllb2LAbnDKZhKJJah8I/2qQXl1HW8TzpGgdjFH4QggKYnJ5BtVotNjLyf83S1x3LLMhMuxqgJfezLcRxCtPUEYZxS9l1w/PRdYCyhttahgEte/8u7vEFJHr7BlAJlkeQKaVwS2WAUlDKkAiJIE4bKKCUEpOTUwgDH3fefhtC0fk6YRv5RLXD+8vUUO2wVlNvHbaUo2Q2r7pRT0CR1wU/Qi0IMVvxMLPgqQToNAGSBEEUAiAqB4MyWKb6G0+SBHEqoXGGNJXgjEDXGMIoQSwkdM6QCAHX0BBGKeLCfgMYnCJKVFq6rmsQqUCvbeJ9P7Edr9m+tt8hpJQ4c+YMzp07t+6TaVcD16vUOA/h2r9//7omhHEc45d+6Zdw+vRpfOtb31o3adddrDy6BPYSIaVEFF28mHmeh8OHD0PXdezbt++ye1xzz+ytt966Qiu9clzuVPhaCmuanp7GsWPHsGXLFtxxxx1XZa1hGBZkdmZmBrZtY8OGDTdEovFLr4zii//47ZYkstVkDlDTonJZkbZWk8mSa2Nmdh4XRkdhmeaSmhzLMhFFUcsU2GYk1nVs5XfNpnmL5cRLj9G6YieKY3i1GpIkRrVag6ZpcBwHpXIJfeVygx/VdewlU+XFUKS9NekHFLnhjGK+zZoJgZqe1tpPT3MJtpQCGtdaSoHtbLNgKQlqhJreUjDO205u0zTFwtwsgijG8PBwU/JHCYVrm6h4HkpO64nxYri2rabdcQItC69q373rZN2+S5+bY5kI47iBhHPOYBsmJmdmM99s7WLfrHNRblpy7YYNCSUtRkfCrJQBFzcM8nAyKWXL+zqWhSgVLeXjBIBlmuCMIsgCrcq9/UXtSydwzqBbzhJJLiEEpq4m9hLAK+dHUQtC3H7bbehgjwWgSKkXNZcp16Nk6aj47ddqGxqCOGmb7OyaestOWSmAOIlxYXoBcxUPlBLESaqmvBJgnMHPrBGMSERRjFoQQKMEjHEwTUOPqwKw4jSFZWgIogRCKmKtMQqZkX4B1TlLCVUpyZQgSQU0SgEK7Lp5EL/4lj3oWaN6nOKcZEq0Cxcu4MCBA+umVWGtcL1IjS9cuIBnn3123ZPXJEnwq7/6qzh+/DgOHjy4rix5Xaw8ugT2ElFPYGdmZnDkyBFs2rQJd9999xX1mR47dgyO4+COO+5YqaVeMS5nKnythDUBSg7z3HPPYceOHU37ea8GmiUa5zLj3t7edduReyV4/sWzeOSxf2rpWVscmmSZBjjnxUSyHYH0gwALczPQdBN9/f2LA0IBKGIQtAmGUgRUyyZhadOqn06dqizrom0nSdY4x/z8PJIkxuzcHChlqnPUcVQ1CRT59MOw5Vrz5xNFSVMyUnYd1HzVFctZ697a4vbLCIAquw6kbN/tCqgkY70h/GgpDEMHo6rjshWBjeMYo6OjMAwDW2/ZAkggiBpJhanrAEGDZNg2DQghl9y2GRilKJccVKp+y9Trxbd3LAuVOk9z2bVbBmIBWWiTrqFS9TLfrJKbBkGAvp4yQBkcJ+ubrXvntiOyPa6L+Tavl6FrqhM5jAoZdtl1UAuiZVfycMbglnoBSpEICT9sTwwNQwPRrLZpv0JIjI+PAZC4beutAGXgnIFSAkiCREjEqepYzVGy9GUR6OWQV8fU4EVxSyJMsqqb2qLnKoRAtRag4vuYmquqNabKf59LtymhMDSOOPPLAkCUnXvb0BBGEYIwQhzHCKMYnCsiGwvAtQxojKEShHBMA7UggmVoSFKhlBmZ91dV8DA4Jsd7X7cNb9x1a8fzstqQUuLUqVMYHx/HgQMHbhhl0XJxrUqNc/K63hOk0zTFRz7yEfzzP/8znnjiiXVlx+tiddAlsJeInMCeO3cOzz77LO6++25s2bLlio974sQJaJqGbdu2rcAqVwaHDx/GwMAAtm7d2vG211JYk5QSzz//PC5cuIA9e/asmw9lIQRmZmYwMTGByclJSCkxNDSEoaEhDAwMXFeJxsdPncY3Dn6/5Zf9csmFlALpIplwjlKTYKFqtYqJyUkMDgzg5s2b2k5J25FY13EghJI6tpITt1pDPSilsMzWCceuo9Jko+wLref78LK+WSWTduDYNvr7+pAkaVtiZWQJv7k82zR1UEIazh1lWapwk5qUxevygmDJBoOucWhcBWWBkI4kHlA0rNTidiXXQa3mFUTKdWz4i6p2wjDE2NgYHMfBwMAACCFLanry/tqmScpNKn2aIa+qMU3lTe9E9HMYug7OGBijy+q6BZTMmWfhS4xR6JxjYmoanleDl/3Mtp0lvlnT0Oqm3qRIkF4uXNuGbhiYry6/T1bXNHDTQVBXK6NxmqVNEwRR3FChZFkmUqK1leSmqcDY2Bg0zrB5y5a2FTyUEmiMwTQ0pJLU+VcBAeVRFVmAUyoFXMtAtQN5LVmNkmBGSfYv889CbUAJoa5p87UAo9MLmF6oKkKpa0gSJTuWkDA1VZujjkVhGxq8MEacJLANHamQSvarcVSDEFKqzas4SZS/lgGeHyKKFaGmXEN/j4tUqEl2EKfosQ14QQhKGXSNQUqJHZsH8eG37EGfu/bER0qJZ599FlNTUzhw4MCKVe9dz7gWpMajo6M4efLkuu7uBdR3p49+9KN48skncfDgwRX5Tt7F+keXwF4ihBA4evQoRkdHsX///hUjP88++yyEENi5c+eKHG8lcPToUZRKJdx+e/vy82sprClJEhw/fhy1Wg379u1bt7vE9YnGk5OTiKLouks0Pnz8WTz+3X9u+JlhqJoPzw9gW2ZbEqrSeKuQAObn5jA7O6tqcrIvT52kvrZlIQhDCCFUSrBtI0riItCokycWqPehNv8YJYTCsc2Grtk8lThPPdY0DkoZwiy0R0oJPwgUma3VIIRAb08PbMcG1/QitGoxtIxgMsZarokQolKF25wXQBFgIUQxVSpncubF0uuSa6PmLyW7i+FmPbBCCGicQdebJ+zqmgZGKfwwhO/7GBsbQ29vL3p7e5d8ppRcG5RQzLdIt66HbZpIpVgSVsUzb3R95UxOumue37G3VkmOOQghEFIsm/gCijRTSjFX56mWUqgAsJrX0DebJ9uqTREdpm6i6vvFVLUTCCEol0pY8PxsemwiFaKtPNkyTQimQofawdC5et24jkCQtlU1aZpidHQUtmlgw6bNHX2shChZdqfJKyVq4u6Fsbr+IL8Oqd+rPwUJS+fwoqyjVS4VmOeVNjkhrXoBnnl5tHjf24amanGEhISEoWfT0Yz8Esjsd4DGGLwwhgRQtg0seKEKXNI4kiQFZxSmzuEFCSRU0FQcR2BEYK7qQ8uC5SzTRCwJHEMH5yqY6qdfsx0/tfe2tufkakFKiZMnT2JmZgYHDhxYt5PE9Yz1KDW+lsjrb//2b+PRRx/FwYMH15UNr4vVRZfAXiLiOMahQ4ewffv2Fd1lfP755xEEAXbv3r1ix7xSHD9+HIZh4K677mp5m3q/KyFkXUtegyDAyMgIOOfYu3fvNUMCmyUa9/X1FSFQprm2vqcrwfcPHcP3Dh1Vnr38C3XdJ1KnKV/ZtXH6pbOo1WrYtHHjknTnTvd3bAuMEoSRkvMthkonbl+xs9i7uBh56FQUxTAyCelicM6gcQ3+Iu+lBBBlIVBhECBNUzDOldS4rqojX0cUq8TSdvUrgCL/lVqtfeUN5wWR9docz8ykup36XQ1dV2FRnt++uocARAq8cOYlDA4ONk0wtUwjC4kTMA29aedps+OWspRkmR0jTUXLdWucwzT0llNO1Stb73clKLkWwjDueC4cy0IYxUjSFJapZ5sOi59D3jfrFb7Z3p4yTNuGSnTyLQAA/ARJREFUrhvQNA1uFmbWjogyRmHZTtPbaJzDMvQloVGu6yJISceNiRw9pRKqkVIt6JxB1zVQQhCnEmGiNjeTJMGFC6PoLTnoHRru6GMlhMI29SUy3iXPjxLlOY9ak3lCCFxDQ6UNEbZ03iBbrvkhnnlptHivOqYOP1RTUkYJtCxELE1TaJwhSUVBfHXOCwm1Iq8BGKVwTR1+FINTBsIAL0iyvtdsukwI/ChGOeu/DsIQSRxBJimYxnH3zYP4tQdfg83D60M1JKXEiRMnMD8/jwMHDlzT16L1gvUgNR4bG8MzzzyDPXv2rOsQJCEEfud3fgdf+tKX8MQTT6wrC14Xq48ugb0MRFHUMWHzUvHiiy9ifn4e+/btW9HjXglOnjwJQgjuvvvupr+/lsKaFhYWMDIygoGBAezYsWNdE+1OuN4Sjf/pR4fxoyPHW/6+FQkVUmB8fBwG5yj19kFrsUudT2rrQRmFa1vwgxAa1xBErX2mlFKYpg7Pa00S2gUucc7hWCaklG3JNGUUltFacgyoSo80jjA5PYMwCKAbBvr7elEulRCnopg8NUvRXQzHseEHzZ83Idl01fPhWFbHYzGm0lVbEUlGKWxLyZdLjoWFNtLXhYUFTE9P49ZbtsAwzSW1MCq0qJF8l7Ln0o4Y5zANHZahY65SW9bnuG2akJAN09V2tUKEUJSyiXMz2XeP62ChyYaHaejQNN5yM8TQOaamZ1GpVhEEAXRdVzJzx0G55ELXNNT8oCEl2tA1UK4vywfMGYNtGmCajmqQoI2ytw4SPeUeVNrU5JDsduOjo+jpKWNgwybEQrYN+GKUQte1tqQUWDoxbQZKCCxda0uEHVODH6WFtLqevJLs9xIEDES9poTAjxK1EZIT3+zvKCevhEBJf8MYUkpwxiAyqw2jFHEqYBkcfpioJGIhYWoMSSpBqJoaExDonIASgTfetQF39DLMzc3Bdd3CXrJWwX9CCJw4cQKVSgUHDhy44arhrhauttR4fHwcx48fx969e9c9eX344Yfxuc99Dk888cS6st91cXXQJbCXgdUgsGfPnsXk5CQOHDiwose9Epw6dQpxHGPXrl1LfnctkdeJiQkcP34ct99+O7Zu3bqu13qpiKKokBlPT09fs4nG3/rej3Ho6ZPNf5nVmtRPLpM0wdjYGCih2LhxI3rKpbZ+1JzEcs5hWwZqXuMXfTubeLRKJ+7kZwWywKUgLOpbGFNBP1XfL0hiJ1lzPq2ttkhizm/j2jYqtRpEmmBiagqe5yuinHWOGqaJng6PBSjJtpRoSHW2TAMSsiG8SgUctZ/YKl+snRG7+vNiIorihvok17Xh+2GDRDeXzc/NzWHjxo2wLAuMUliWkmBzxmAYzaXHQDYx1rW201iayWcrNa8IuGr1mi+G6v+NYRrasrynlFK4tg3P95CkApQQOE7rlO0cusZhGmrqn5OinpKbkV51ZvO+2fwfYwy2bcN1XQz2912UpAosi9Tn6Cn3oBLEivSZBiglCOPm3bKUELgdOl4B5WMeHR3Fpg1DMEq9hayXEAKdc2g8v34QCAm1KUAo/FARxVZQxFG29dtqjIFRsiQNuR4lU0MQp2CMglMC348wcuY8kiyESecMc7WLm1eGrhV9ubauIUwS5XOF6lWWQnWzaoyhFsbQOQUkECYpLJ2DUoo4SUEIRZQkcEwdjBJEiUCcpHBM1fdqaBwAwa6bB/ELb96DvpKavMVxXBCa6elpMMaK6Vx/f/9VyUoQQuDpp5+G53k4cODAZbcvdHFpWG2p8cTEBJ5++mns2bMHQ0NDK7TqlYeUEp/85Cfx13/91zh48OC6st51cfXQJbCXgdUgsOfPn8crr7yC17zmNSt63CvB6dOnUavVsGfPnuJn11pY08svv4wzZ87gnnvuue4j1a/1RONvPvlDjDxzqunvCFETw2rVQxRHGBsdg2Ea2DC0Qb3/iOokbTUpNAwDjmVidr4CKZt/4bUsE1Ect6z4yetJqm3Ii22ZiOMEpmnAWzQNy9FsIrz4yarn0vo2JccGYwxzCxUAgJASfpZq63keQAgc28aGoQFItH/dOWfQNR1BGBbPr9nnm22ZqhqnRY9vDsdW0lohJFzbwkILj6qua2CUwQ9DSCkxPT2NarWKTZs2LZnm9PeUUPWDlvVJ9XBtG0EUIln0Ouay43qpOOcMlmEsi5Aa2Xo5Z1ltzfKIL6MMJddCkohlSZ2L+zFFgCklmG3TPSylzHyz6rWXUmJocAC6ZaO/rxdJIjpOYCmlcNwyai0qY3SNw9A11Wuc1diYtgOvw4Q0CAKMjY1i86ZN0OxSOz4KQE2hEyHVFBIAZxScql5kQknmM1XkWYAUGyr1XteCIENNw/P3cj4tl1L9vaRpqjqD/ag4jhdEeOH8BKIkRZIkcEwD1TAqbA26lkmDpYRj6gjjFJQQUKr++WGs1swZhJBIUgHG1DjVyAKZ4lTA0DiSNM1kyQkoVdNWxihAMk+voeM9r92ON+2+teX5EkJgdnYWk5OTDVkJ+XR2NYilEALHjh1DEAS49957u+R1jbDSUuOcvO7evXtdf1eSUuKP//iP8Wd/9mf49re/3fD9tIsbC10CexmI47iYsqwUxsfHcfr0abz+9a9f0eNeCV566SXMzs5i//79ANQHR16RA6xv8iqEwMmTJzE1NYX9+/c39dFdz7gWE42llPjGE9/H8edON/09IQAjBC+ceRGlcgn9/f0gdXUjzUisY1sAIWpq16GCB1B+ziRunfhbP/1c8juqpLtpqkhS0iZgp1P4E9BcOm1bFoQUxXRUHadxMiqlRBAEqGWEljMVVKQbFmzbbhoC5ToWGGOYb0OUgItBUe2qcQA1MZSQHSfAhBC4joUXTr+IMIqwadOmBm96IYeu1KBpHLqudfT3AkrSbJtm8Top2XHr822bpjqvYXMCVx9CBWTeUdNYVtesbWUyaKmm0VVveVNfXedgjCMIo4u9rst47oauY2xquqFvtq+3B/29vUiFRLjIn6tpGjTDakgabn98DVw3wTgHAUGcpuq+i64FnudhcmICN22+CbrV2drgWia8rAe1HcqW0dbLClwMW0pbvN4UBLapNYRDeUGEky+PIkpSUKLItB9ExatraBxpKsCY8r9GSYo4VtNWxpQkuGTpiJIUYZzC1BgYZQjjRIU1RYkivoYOQglSIVELIpRtHUkiQBiFRgmElNh58xA+9MBuDPYu3xKSZyXkZLZSqaBcLhef+47jXPH1Ok1THD16FHEc4957771mciRuBDSTGudktpPUeHJyEseOHbsmyOv//J//E5/+9Kfx2GOPrSvFYhdXH10CexlYDQI7NTWFkydP4id/8idX9LhXgrNnz2JiYgKvetWrliQNr+dpXhzHOHr0KJIkwb59+274YAkpJebn54sQqDAMMTg4uC4TjaWU+Oq3v4uTz7+45HfVWhWTE5O4ZcvNYLzFmokidUQCURw3JSSdgp3MLNSmJQElBOU6jykhqnonCMNiOqnran3tJoZOVhvT7rOk5Nqoej40rpJem01/HVulKTcjRRJKMRKGAcIgQKVShWlZcGwbtuPAtkwYul4ct+RmlTRtWATJqmmaSbZzb/FC1btYodMmJVgIgbHxceicYcPwxoZ6FysLh1qcHHwx+bjzZ3DJUYR9bhlJxYB6byjvaqYwQesaIKC+Eqf5BPcicb74M0bVdNwPgpY+UDXFXtrta2gaDEOldC+eMDPKYDs2qnWbC3Ecw6t5qHm1wjfb19OD/r5eEEpBKIOgWls/aj1s20IkyJL3GqNUEVvGIAHMzS9gdGwMwzdthmF1Djss2RaqQdx+K0BKlB2zY8dr3hfb6licEmi8MfSpFoQ4+fIY4iRVv9dUzQ0lFASAxlmWcKymsEGo/K+qX1lJoFmWepyk4iJxlur2YZJAYxSM0kzFRCAh1fSWAIRSpGmK/pKF97xmO96wq3N1XSeEYViQ2ZmZGRiGUZDZy1HkpGmKkZERpGmKe++9d02ScbtYHi5FapyT13vuuQfDw8NruOr2kFLiL//yL/EHf/AHePTRR3Hfffet9ZK6WGN0CexlYDUI7OzsLEZGRvCmN71pRY97Jbhw4QLOnTuH++6775rxu3qehyNHjsBxHNxzzz3di+wi1CcaT05OolqtrrtEYyEEvvKt7+C50y8DUNK/+bl5zM7OYsPwBrhZAu9iMqdrHKZpwvN9WKbZ1hPbqcPV0DUIqf7WWx6j5ChvWxg1TZ3lnKsexzbVKpZlIo7jJWQkB+MMZUeFFrW6DVDvZW0tFSWEwtA5xicmlcxYCggAlqV8s7qug2RrSpYhFXYdW/XFZmTGsS3ESbKEtNu2hTiOlxwvSVOMjY6CMYbh4WFoGodtKQlz/vq0ujrpmgZNZ6i1CdayLRNJkiJOEpRsCxXPX5b1g1EK2zYRhhE44w3pvK1gmSYoJcWEVHlt2/e00mz6HMVJw0ZLT8nBQs1vS+YIAVwrm8r6AXSNdwxrKnyzNQ+e78F1HJhOCf29vTAsK0vYba8IqEai4zmsVCpYmJvF0KabUC6XoXEGmnlcE6HCjpJUQEJtELi21XGiqiS1RvtEYilRsttPZw3OIEGyhGAJjTGEUYwTL13IfKkqSd8PL05edY0jilNFQBlDECegQJFCLKXIQpmUH5dTCgmAUcDSNfhRDEoooljANBgooQhjlT5saAxBlMDUNey9dQP+1ZvuQdle+ZTZNE0xMzNTEFohRDGdGxgY6LiJmSQJRkZGAAD79u3rXlevIbSTGnPOcfLkyWuCvH7mM5/B7/3e7+HrX/86fuInfmLVHuvWW2/Fyy+/vOTnH/nIR/Dnf/7nS37+2c9+Fr/0S7/U8DPDMBAs47rRxZWhS2AvA0mSNPW2XQkqlQp+9KMf4S1vecuKHvdKMDY2hjNnzuC+++67Jsjr7Owsjh49iptuugl33XXXul7reoHv+8VkNk80zn2za5lonJPYZ0+/hOmpaVRrVWzcuBGmoQg2IShIrGObIISitoigdJq0dpLxahpXXzYXkQJCCVzHQRiEMAy9LRFmjMHQ9bY1PIauAYQ0TBpV5YubhQylMHQdErLtRDf3srZ7LADo7ynDC0LUfF91zWZBQJyxop7Hdd2O687XzigD46zteeCcwTSMYtMhjmOMjo7CME1sGBoq/lYNQ4ep66h5wbLCh1xXTTLrJ4L51DSvy6lfK+d8WTJc17aQCglKKWotpqvNYJsmGKdIkhR+Cz/pYhCipOGAer+0S2huhh7XAeEMYZgsK20YAEpuCZMz86h5NdSyvwHXcdDf34ueUgmpBIKCxEn0lMpY6EAyAZUg7VUr6NuwEabZmogRQmDqHJqm0n3Vy5/lFUsVQJVK1alKKMAZRxAlEAAgLhpdCQCZeUYdQ4cfp2CUgGaPQbL/BiQYoUiERCpkQaRVz+sYklTA0BgAgjBTXhCoTag4SdXvpJqUaowiFQJRIgApwBhDkqZIJaAzBhAJSvJHBZB5pTnjCOIUGqcwNY4wTqBpDD2Wgf/fG3bg1dtuXtZrd6WQUmJhYaEgs3ktWz6dXeydjOMYR44cAWMM+/btW5f2ky6Wj1xqfOHCBVSrVZimiY0bNy5LarwWkFLib//2b/Ef/+N/xFe+8hXcf//9q/p4k5OTDd/vjx8/jn/xL/4FDh48iAceeGDJ7T/72c/i3/27f4fnnnuu+BkhZF1vCFwv6BLYy8BqEFjP8/Cd73wHb33rW9fFB4iUElNTUzh69Chuv/12DA8Pr4vpXCtcuHABJ0+exPbt23HzzVfni8D1hiiKinqe+kTjoaEhlMvlq/6+jOMYf/HZz+GFl17Bxk0bodXJhhmncE0LhNIiyKgZyiW3rYS1Xf0NoIgX5xxBEBapsn4YNEwTOxFhQqjyPrYhJpwzaJoG3w9Qcm2EUbyErCqCqrX1n7ar0OGFb7OmZMdRVARWifogoMw36jgOhjcMIhHIJmhL4ToOoijqSORzlFwH07NzuHDhAlzXxcDAQOFiLpVc1GoehJAZ4dWbdKMuBeeqwqdS85RHt8PU1HVshFHUdMJ8UTLsIU/usU0TIOjo+wUA13bghwEMXQelZFnrB9REmWscQkjoGld1OMtQ+ZRdF1U/LHy4pq4ryWrcoteYUrhuCVX/oipASiU39TIyG8cRTNNCueRisL8flusiSiSCLJirFebm5hAFPnoHh2F0uFZonINxhrBNOrB6PhpSiSUSZ4KLddEaZ9A4a1u5U8p9s3V/o1VfyYZTIaDzjLzGCUBUoBJnFCILa4IEvDBEItTUNklFNoFVHmDOKEydww9jaJwjFSmSRMC1DERxChCCRKSwDQ1pKrIEZoZX3bUJH/zJ3bDMtbNx+L5fkNnZ2dmipiUns0eOHIGmadi7d2+XvF4nmJ6extGjR7Ft2zZwzlct1fhKIaXE5z//eXzsYx/DI488gp/6qZ+66mv42Mc+hq9+9at4/vnnm34H+uxnP4uPfexjmJubu+pru9HRJbCXgdUgsGEY4uDBg3jrW9+65v7SPKwpTVOcO3cOk5OTDX2jGzZsgG139jVdDUgpcfr0aZw7dw579uzBwMDAWi/pukCSJJieni4SjRljxWt/NRKNoyjCyMgIpJQ4P1vDCy+9AkBJNTlnqGYpsHkIULtqkk4kdnH9zWLoupZ1mHotZbydiHBeMdMu1MjNkoXn2xByQpTHtFmIVD3Krpt1pcqM1Dqo+V7DpFLXNVBKl0icJYAwCFCr1VCr1aBxBsY1WFYWAsUYNI03eGfz9fth2DLFGVBflmemp9E/0A/TspW/UNNUh2mTyajrWNkxO5O5vt4y/CBoGcRUj5zoVz2vIGWdyK9rW0iStOmUk4Cg1CQgzDINlVrc5v3p2CaipNFzTSlVkuw4gR8ulaBTQlByXSy0mSZbhg5N0xBFMYIogs41aKYJv50MF2rjqFbzEEUBokSAMK2o6Cm7NnSuQUIiTgWiWIUuzc7OII5i9A0Nd0yltUwdiUBREdTyvJhqotqONJu6BiHVWpqBAHAtAxW/8TVb8AI8e3a86GSlhCBJVJUOpQQap0iFVH8vhCCI4oJ0Roma1mqcIU5SxIkA5xScUIACqeoCAiEEUkronEESQKYCjDOIFLip38bP3b8HO29ZX1Uli72TaZrCMAxs27YNg4ODXQJ7HWBmZgYjIyPYsWMHNm3aVPxcCIH5+fliM+NKU41XAn//93+Pj3zkI/jCF76Ad7zjHVf98aMowk033YTf/M3fxH/6T/+p6W0++9nP4ld+5VewefNmCCFw77334pOf/GTT+skuVhZdAnsZSNO0bcLo5SBJEjz++ON485vfvKax9K3CmvJAiImJCczMzBRF6sPDwyuSbng5SNMUx48fR6VSwf79+9dU8no9o1micR4CtRqJxrVaDUeOHEG5XMauXbtACME3n/whnnvx7JJAHwBLQpWaoROJbdYDq2lqYln1fEACdv7fLY9hIYyahyldXEczklPnn8yIVadpZqfnA+QJzBJJIpqfN6i/b9s2W/bOSgBxFCGOI3ieh4VKFQN9PWBcg2laS7xzuqaBcQa/ybSyWqthYmICQ4ODKJfLKLk2IKGmjW3OGeccpqG1nGYyRmFn3a40T4n22ic859A4h2noEEIiCCOkotPGJEEp863m0nKNKylsuwmtoeswdI5qzW8IqiqXXLUZ08bxapkGNMZRDVR6scY5dN2A14TYtkK55IJSDYkQyt/Z4dzYloVYEoRRDC9Ls/Z9XxFrx4Ft27AsC4QA1fl5BHGEm7fcAk1TAWhR0px4lmwTXpQ2nINmcC0TtboE4Oa30eG3ORajBIauwauXPhMCPwgL8gopoWkcVT+EzhkIIYgTAaqab8AYzfyyys+qpqdKSpynNps6VzU6mbc2FRIaV9U/jBKEifLPEhCYGsMb79mKn3ndDnC+fslgFEV46qmnoGkaSqUSpqamEIYh+vv7i+ns4qqrLtY/cvJ6991346abbmp72ytJNV4JPPLII/iVX/kVfP7zn8e73/3uVX2sVvi7v/s7/Mt/+S9x9uzZlufrBz/4AZ5//nns2bMH8/Pz+OM//mM8+eSTOHHiRFcNuMroEtjLwGoQWCklHn30Udx///1rstOVr2E5YU15kXo+nTNNs5jOXS2paRiGGBkZAaUUe/fu7XbRXSW0SjTOv9RcaaLx3NwcRkZGsHnzZtx5553Fe0kIgW888X2cOHWm+R0JUb7HNqRuWRU6SQLOlJx3sSx4OdNe0zSQpmnbAKSSq7y7hq6Dc95UWlwuOaqipc3H8+Jql3rougaNcyRJAkJIx6mkSsttHZoEZNNvBrwyOgEvIzSarsOxsxAow8i6NwlKrt2w/vmFBcxMT2PD8DAc21YhTJoGKUVRO9QJrmMhjBrDoGxbBTUtDtFS55a2DXkC8lokq5B11jp4fov7gcB1bUAS+MHy/LoAwBmDkwVEabp+Sb2whFD0ll2kEqqHtkN9T45yqaRSeYtNSQLbNEApRRSnSzzeZddFNet6rYcQQtUzZZN5KaUi/4RiaMPGJYRGy/pQGaVZ1QxHmnewZh2p+YTz4pMESpa5ZGK65DktDmuSEoxRMELURDXzzYqstzzvZB2fq+DUuQlwRpGkAjrnEJCI4wQ0C1/ilCAW2eQ0Owkao0VVEGMMIhXgnIARVZ9DCEGUJNA5B6dEqRjiBCXLQJSk0DWO24Z78aEHdmPzYM+yXre1QhiGOHToEEqlEnbt2qXOi5So1WrFdG5hYQGlUqn43Hddd11Yn7pojdnZWRw5cgTbt2/H5s2bL+m+l5JqvBL42te+hg9/+MP43//7f+N973vfih77UvC2t70Nuq7jK1/5yrLvE8cxduzYgQ9+8IP4L//lv6zi6rroEtjLwGoQWAB47LHH8LrXvQ6u6674sTtBSlk8p0vpd03TtJCaTk5OXhWpaaVSwcjICPr6+rBz5841l1zfqMi/1ORk9koTjcfHx3HixAncdddd2LJlS9PH++aTP8TRk8+3PEYnktrOr2rbJjjjqt6kFQFdhhRY0zgYpS1Jo65rsE0DFc9vK7dtV4+TQxFmUaQlqxobO0vwlcXPbKv1lDWHbZmIm6QP53LWahH2o6TCUTad82o1eJ4HylhRz2OZJpxMcjs2Po6FhQUVwmWaKLtOw9T1IuFtT6CBfNpqoVqrta23yeE6NqIobpoSbRpq06v+dXJsGyJNm8p265FLhis1D24m9V2OdBlQrxkhBJyrXlF/mWmVJddFNQghpSLCtmkgSdOWkmcCglK5jEoHEq9xFTRGCMA1HQte+8knkMkNZ6ZRDRMQShHHMUzThOM4cByn4QttnphcbRFqRTMJr64xMMaUBJfUNzxfdLwSqKCrKBVIhYAaoipCnL/fHVNDmAikaeOzmJqv4qWxGVgGhx/FIEUaMcAZBWT2/1Ak38smrHo2KY3TFIamg5HsMaVEmirinIoUtq5DQGYS4ouE2jE1/PRrd+D+3bd2OKtrjyAIcOjQIfT09BTql2bI8xKmpqYwNTUFXdcLMtvX19e9Jq8zzM3N4fDhw9i2bdsVTwVXW2r82GOP4V/9q3+Fv/7rv8bP/dzPXdGxrgQvv/wybr/9dvzDP/wDfvqnf/qS7vv+978fnHN8/vOfX6XVdQF0CexlQQjRtl7jcvHtb38bBw4cQE/P1d2hFUIU/64kabiZ1DRPtB0YGFiRi9rk5CSefvpp3Hrrrbjtttu6u77rCHmicS45KpVKxWZGJ3n32bNn8cILL+Cee+7pWKT++Pf+GYeffrbl7y8luEl1uNqIk7TwgnYioMt5DMaY6uusIw+apmXBRIoILidZWNXjLC99mDGqpoEtSPFypro8IzO5H7VV4JGuaeCcFbJZWRcCVcvku7ZtQwoBRoCevn64riI2XgtCZVvGsqaxpqkX/sNL87v6xbS67DqZnHfp5oCarloIw+bEt5AM1xFHAgLHtiBECs9vTX5LJTebml98DUxDBS/Vr69hPZSi5DhYaHHeNM5gGQZSIeD5ASQkOOMwLAveMpOQGWOwbBteEIESAsPQVYiRgKpGqntPCZGiMj+PWpRi48aNYIxlvlm1kZH3zdq2jd6eMhzbQdChksnUNYg6Qtn0eTIGrjH4YetjlW0TlSDCYgY+MbuAsxMzhURY1xiEUD2snDP1/iYESfazRKipKmdUPS4lSFIBP1J9rqq3VcAyNPhhDMvgSIUEYwRxLGDoHEgF7tuxBT/3xl1wzPUvt/V9H4cOHUJ/fz927NhxSZvYMzMzhdw0TVMMDAwUE7r11DN+I2Jubg5HjhzBXXfdtSqS1pWUGj/xxBP4wAc+gD//8z/Hz//8z6/p97uHH34Y/+t//S+cO3fukibMaZpi165dePDBB/Enf/Inq7jCLroE9jKwWgT2ySefxK5du65aEFEe1pR/abqUyetyjj03N1dM5+I4xuDgIIaHhy9LciKlxLlz5/DCCy9g586d2Lhx44qss4vVQX2i8czMDCzLKjYz6mXmUkqcOnUKY2Nj2Ldv37I3b5744SH888iJlr/vXKFjgxAKzw+aqilU/U17X6Oa5raeGpLMjxlEUQNxXfo47etqmpHheriOjSRNoGl6Wwk1oKa6YRS3V5AQgp6SizhJ4LWRuDaTCgOKOwRZsmn+OMNDg6CcQ9MNcNb6bz8nm83kzKpayEHFU7/LE4Nrnr+sxF7OOWxTRyrksqp0CJRkPIji4vM+fz3bSYYdywQBaZAHU0LglhxUqq0fl1IC2zIhhUAte9/pug7KOIJlSKwB1WHb4zqQlKEaBEiWEX5lGgZAWVvyyCiFoWsgUuLC2DhSQrFhwzDSJu/9vG82jSNUah4koQ2T+cXXGNc2lC+2TViTZWhIUjUFbQZKCBxLR9VvPE+EABOzFZwbnwHJpL0UBH52PnWNIRuYghDV0COkhMYobENDkgqEcQIhCYTIqnYymXKUVfZwTiGzpGTb1CCFxMa+Ev7lm3bj7pvXV0hTK3ieh0OHDmFwcBB33333ZX8PkFKiUqkU07lqtYre3t5iOrtewh9vFMzPz+Pw4cO48847m6qaVhpXIjX+7ne/i/e973340z/9U/zyL//ympJXIQRuu+02fPCDH8Qf/uEfNvzuF37hF7B582Z86lOfAgD8/u//Pl772tfizjvvxNzcHD796U/jS1/6Eg4dOoSdO3euxfJvGHQJ7GVgtQjs9773Pdx1110dJ1ArgVZhTav1WJVKpSCzueRkeHgYg4ODHf2rQgg899xzmJiYuCSS08X6QKtE44GBAZw/fx61Wg379++/5C833z90DN/98UjL3zeTC19MMfZgmgbiOG45saSUFunDrdBO5qvrGkxdBwjaSo6Vt7Z9cFNOFuuPY2U+xnoyVnIdVD2vdRoysrRdrjUlzYRQlFwLlaoPyzSQJDGiDtMz2zKRpGkxJU6FwNjYGCAlbrv1VqQixfT0LHzfB2QKIYkKAnJs6Frzv33T0EEoLcKgDEPV0vjB0ukm5xyWoaPSoT/VtRUZ5Uz5Mpftd81INaMMs5XWCdGLYeg6TE1DmCQgpP1EfzF0ncOxLARxCv8S7tdTclENYggpQaDOo8ZV7YsfREt8syXXaRuEVA9KgdGxCYByDG/YAJIROV3jYJSAZNcQkUlxg0QgihJ4TSbzThYC1VtyUe0Q1lSyTdSy51QPQhSx5pTCNDjSVGYkVMmJwzjFixemMDFfVbJeKKKbpx8bGs/SlJXfFUSC00wunCRIBMCp8rhySmCbGmpBrFRK2f2jJAag0odNzqFzhp/adxve9ept10xib61Ww6FDhzA8PIxt27atKHHwfb+Yzs3MzMC27YLMrsfO0esJOXm94447cMstt1z1x28lNf7e976Ht73tbdi2bVtx2x/+8Id473vfi0996lP49V//9TV/X3zzm9/E2972Njz33HMN6wSABx54ALfeeis++9nPAgD+/b//9/iHf/gHjI2Noa+vDwcOHMB//a//Ffv371+Dld9Y6BLYy4CS9C3/S8Vy8cMf/hBbt25tiDZfDdTX5FyJZPhyUe+brFQqbX2TcRzj6aefRhiG2Ldv35oFXHWxMshl5mNjY4rkANiwYQM2btx4WYnGh48/i29978ctZbF50JFjW0jSdEk6rpLxouXfc7te1RwqxVYURM/Qdeh6YwjUcojlcpKFS67qXdX1jLA1ed6LfbGtntdi0uw4NuJFflHGVMdqOxIPKJ+tY1mYnZvH6NgYdE3DHbffhprnNywxSRMQKTAzO4dKpQpN02A7KgTKMAzUOx9BgJJjg5AstKhTcq5pQEos8a9efA0bn4Njq2mn14QU18PU9Sx1NoJr24jjuGmVTjOUXQdBlMA0dERxjLBDhY1ar/K7VrwAElkdDmcIwhhRi8k5JYDrllBpI19mlMA0dNBM/qrpRtvb10PnDC+eOw9dNzA0NNT6mkEIyo6FhboQJkqpkt0Sgqjom60hTRKYlg3HceGW3EapaTZhp5QikRKAhJSAEDILgRKFhDdK5ZJKHiklXhydwnwtQJhNSkFIsdFkGZqa+BLlq04SoSTAVKmQwjiBY+qqfzaTEodxCp1TCCFh6hxeGMPUNTBKkKQSO7YM4OffvBcbekvLOqfrAdVqFYcOHcJNN93UEJq3Gsg3MvPpHICCzPb396955+j1hIWFBRw6dGjNyGsz1Go1nDt3Dr/yK7+CY8eO4ZZbbsGb3vQm7N+/H7/7u7+L3//938dHP/rRNSevXVw76BLYy8BqEdgf//jH2Lhx46pKPZabNHy1kJeoj4+PL+maJYTgyJEjME0Te/bs6V7grhN4nocjR47AdV1s2bKlSLQOwxADAwPFZsZyvVPHT53GPz7xgyX+QVVdokNAeQNFizAkzjk4Z0v6UOvRSZKsaRxGFgpUrflNiaVtWYiiGEnaeqLpOja8oPlaOWewTRNJKhAncduk48Ve1lZwHOVTJYS0vW05k7/KJp7RHFEcY25mGo5to7d/oO36GGMwDQ3jk1OFd5ISqsis7cCyLJjZhDlOkmVNWHO4jvKvxkkCyzQghGzrqy05VuanXfr6l10XNd8vJniAIlaObSFtE/ZECYHrLpUM25YJRpW8uNmVV+McumGg1uS9SHCxVzYMI4TFhokGxvVCFtsJnDGYlgUviGEaqvtWQiKK02yi2HhNMHQNz595GY7jYGBwEK2uGJwx6IYOrwNJNzQNhFJUPR+1mgfPq9X5Zh04jg3bsmDqGmptjlWyTTW9bZKWfGZ0Cgs11WOrcQpKKHISrDGGWAhAShBK4QWhUlsYuvJEZ5NlL4ihaww65yAEyp+dJHAMHamUEFKAE4oNPS7e94aduPfO9rUk6w2VSgWHDh3Cli1bcPvtt1/V7wK5xSifzgVBgL6+voLQXmoAYBcXkZPX22+/HVu3bl3r5TTF5OQkvvSlL+GRRx7BD37wAzDG8P73vx/vete78Na3vhWl0rWzCdTF2qFLYC8Dq0Vgjxw5gr6+Ptx6660rfmxg5cKaVguLu2allCiXy9ixYwdKpdK6W28Xl475+XkcOXIEN910E+66664GL2yzROPcN9vpC80LL53DI48/CSEEHMuCkLLBv6kqclqnhzNGYehGWy9qK8+rbSvPox+GsM32kmNd10AIRdgm5TYnwnl3a54sXPW8gthyzqFrzWXABTp0ynLGYdsmgiBU4UodJLWGoYOS5jLeIAwxNTmBwYEBOG4J1jJSjwFFBOMkQRhG8AMfXk11jlqGDjAG27Zh23Yh5xapWNb0k1KC3lIZC9UKkmZGzUUgRK0liRMEUQROGSzbRKVF92yxfkt1+NaTa0PXQSlrKxlmnME2dKUKyIKWHNtCmIhlV/KYug7bMhAJwPPDZZXq2KaBFLSl35USAkPn0JjyeiZpilOnX4ZbLqOvr68lebVMA4lo7VHN4VoGglgs8SynqSj6ZtM4BKEMumnBcRyYi3yzlBDYloHqoqodSggIETh1bgoVL4CQEpRShFECicwzbRsIkxRhrCpv4iSFxiksXcukzASUEMRpipKp/g6jRCAWApbOs+5YQOMUOmd4y/478dB9145cOMfCwgIOHz6MW265BbfffvtaL6ehomd+fr7omR8aGupe+y8B+abErbfeumrfI1cKx48fx4MPPoiPfvSjuP/++/G1r30NX/nKV3DmzBk88MADeOihh/DQQw+tWxLexdqjS2AvA6tFYI8dOwbHcXDHHXes6HFl5glKsy8XKxnWtBoYHR3FiRMnsHHjRqRpiqmpKRiGgeHh4a535hrGxMQEjh8/jjvvvLOjrCmfzE9MTCw70fjl82P48uNPLpEJ59B1NdFtlei7nJ7X3PMqhIRj20jFIlnyMmp2GGOwjPYdoPltKFNdpmkTYrAceXO+5vrgJhWG5GZVNtlxSdYFW1e/0wzNgps830dlfg62W2rYOXdtG0EYtvQY56CUFDU9pmFAQmJ+oZL1jXqI4wiWZcG2HbiOg77ectvgJiX5JfCDKJtaGypMaBlXOkKAvnIJUZJeUkeroWswNZWk6wVB20CiJes1dNi2hYofII6XR14JVKJx1Ve1OoypCSIhBEGcNPUtl10HtTBZlt/V0DUEYYyz515BX18f+vv7VMcpY8iGmUWfq6bxzseVatLdqkonh2ubqPkRap5XEFopJSzLQrnkoqdUgmXqxWPlNTpxKlD1Arw4NoO5qkpi1jlHnKbQOAMhACUUfhhD5wy6xhDGCVIpYRsaqn4MzhkoCEyNAiBY8EPlk5WAZXCEsToWJQTbburHL75lHwZ7rn7l3ZUi90bedttt65LkRFFU+Ganp6fBOW+QGncrepojJ69bt27FbbfdttbLaYuTJ0/iwQcfxL/9t/8Wn/jEJxq+z73wwgv4yle+gq9+9at48sknsWPHDvzar/0aPvKRj6zhirtYj+gS2MtEuwnK5eLEiRPgnGP79u0rdsyrGdZ0pZBS4syZMzh79ix2796NwcFBAGvTNdvFyuJSanIWY3GisWmaxetfn2gMAONTM/j7r3+rpRyWcwaN86ZTRAAdp5aUUpRLDuI4aS+5dZ2MNLWMKG75OIRm6cVhBMswsFDt7IuttahgycE5h65zUFDESYqwxQbcxS7Y9jLQPLjJ82oYH5tAube3qeyLZx7adpsCgHrOvSUHUZwUCbw58oqWWs1DGAbQdQM95TIGB/oKGS1wMZW4GVk1dA26prWVISvvqVNMXR3LhJCibS1O8TwphWWr2hrHMpbdC6txDt00UPNDJRG2DHDK4AVhy0mskhmbbb27usZh6BoAgiiKYVoWFpbpdy05FqZmFzA6No6BgQGUy+Wmt6P5hk82CeWMglFa+EgJCAglijxSiqWcPv+7VR5Xyrgi/hKQUF5XkSVGL1QqSOMQNT+EbpgqCKqubzYIY5wenUbFD1U1DmMQQiARApauqfqcVGSbuSpozNDVfeNUwDV1pKlAkgpVkSMEDI0jSVLoOldrkhLDPQ7e94Zd2H+NyYVz5JUq68kb2Q5CCMzOzhbT2TiOGyp6OgVA3iioVqt46qmn1s1EvR2ef/55vP3tb8cv/uIv4pOf/GTb729zc3N49NFHQSnF+9///qu4yi6uBXQJ7GUiiqKOoSKXimeffRZCiBWL3l7rsKZLQZqmeOaZZzA3N4f9+/fDdZvvbOcXtFxqWt8129/ff81Jua53SCnx/PPP48KFC9i3bx96e3uv6HitEo2HhobQ19cHSinmK1X83dcex+zcQtNjUEZhGcYlEVBN47BMU03+0lQRYU1rOe0FsmltFCFtM4EsZX2kUoo64ho2+Efb+WJzGIYOArQkTXliMWUM1Wrr6h8gm+ZZZttUZM44gqCGqekZlHt6OyZIO7by/zbzxdq2mfW/qrWXXAeBHyJu4hVO07TwTXqeD9syUS6XsgmtXchxWyFP5V0sDTYNA4SQpp5Wx1IS1lYTWdsykQq5pDPWNg0wxpTftclE1nEshHFzyTDJjsuYSi/OJ6qObSFOpQoXWgZ0XQPnqg5G1zVFJIWqhlkcfESgPL8XJqcxOTGBoQ0bWn4O6xoD4xqCqHO/qwRB2Ka2KZ/s+i2ORYiazFa9EFEcF5PZ3DfLNB3jCyEEFFnmdaFMKmwpyapxGBKhSKxrG0hSoeTCsUCUJCjbBqJUgBIlsS5ZGvwstbpsGXjj7q1492t2gLFrc8N0dna26AO9GpUqKw0pJarVakFmK5UKenp6iulsp67x6xU5ed2yZcuKq/dWGmfOnME73vEO/OzP/iz++3//793hQxdXhC6BvUysBoF94YUX4Ps+du/efcXHWm9hTe0QRRFGRkYAAHv37oVhLK/0XUqJ+fl5jI+PN3TNbtiwAYODg93QpzVGmqY4ceIEFhYWcO+99654B+DizQwhRLGZYTkOvvToP2F0YqrpfZcjv3UcGyJLJlVpuotrPChcx2w7XTR0DejgebUsEwbnqAVBy+AjQ9dBSGuCCqgpl2NbDcRT0zSYiwKQbNNAnKRtQ5YA1ZXrBUEDAc/Ti185fx6zc/PYuHEj+vt6kSTpEgK3ZH11ycdSAoxT2KaSDi+ud2GUFRVGi3+XQwgJ3/dBZIqFShVSSnBNh+OoihZCWn85skwdPCOXahMhUAE+bWAaOnRNpUvnNTXlkqsSkts4UCllcCyVDO35wZKU4eXAMnRYlokwq9VZzqWn5Njw47Sl1FrjDLqmgVEKCQnGdVwYm8TE1BSGN2yA3YIQuJaBIJEdu3dLtolauLT+ph6OaSBKxBIynYNzqv42FoU56ZyBEuCV8SmcfHlMbVwQonzajINzjrJjYqGm/u5MXYOQAgQElsnhBQmkFIXn1dQ1VLwQps5haBRxqqbCOuPYfdtGfPCBPegvXbsJ+NPT0zh69Ci2b9+OzZs3r/VyVgRBEDRU9Jim2VDRcyOQo1qthqeeego333zzuievL7/8Mt7+9rfjXe96F/7sz/7shnh9ulhddAnsZWI1COyLL76I+fl57Nu374qOs97DmupRrVYxMjKCcrmMXbt2XfYENd+dzcls3jmWT+e6UqOriyiKcPToUUgpsW/fvlU///lmRi41DoIA5Z4ejDz3Eqbmq2AtLpblkrPEr0qImnommeRQpKItOetUf8MYhdUk3IkQqkhGGEJKCV3XGoKnFoNmk9FO4Ugl10EQhrBMs6WMmVG6rKAlTePQNY6a58POknfPvXIevu9j06ZNxeuak9NOflxATYMNTUPVD1qGauUwDQOM0abTclNXHmHPV+cvDANIkWJ2dg5hFMO2VQiQCoFa+rli6Bp0QwcEUPGWl3AMKFm0Y1uQwLKTkXM4lqWSeoOobTJyPTTOYZgGanVSXcvUARCE0dKeXkKISs1epmTYsUxEKTA1PYPZ2Vls2XwTyqUSGKMgRAl801QFL5mGkR239TWFgKDkmO0fXwLlOvnx4vVzRqBzVlwPZCYnToRAHKve2pmKh+demQQAMEIRJzHCMESaJNAYASiHaRgqqCxKYRkaUpEiiFLYhqZCnghBIiWkkLB0DVJKJFLA4Bwbex184I17sGPr6veyryampqZw7Ngx7NixY9Ur+tYKuc0on84CwODgIIaGhjAwMHBdbmbn5HXz5s2444471vX3vAsXLuCtb30r3vKWt+Cv/uqvuuS1ixVBl8BeJuI4bus5uxycPXsWExMTeNWrXnVZ97/Wwpqmp6dx7NixQvqykmtt1jW73ETbLq4M9TU599xzz1WXddcnGo+NjeG7Tx3Dhel5OIt8cznydGGNazBNA54fNAQPcc6haaytXLjk2qjWgtY1M3XhTnmq8OLHyT2Ynbpgy6VM3txElkoIRcm1kaQJhJAdfZglx0atTcUQoGSojm1ibqGCCxdGkSQJNm3a1PRLoZ3V1rRKCjZ0LUs8DlBybHh+2LZWKIdr2yqtOIoKqetCC3JOiJKlTk1PY36hgiiKYFlmVtGiXv9yyVHhWNlnuKpcujhdbYey66AWRBBCwLVNSAnUOiQ4A+q19YK4eEzL0KFrHEFdJc5iLEcynPtdCRTRlJQtu1Knx3Ww4EeYmZnBwsICNm3a1FQBwxmDkVXkEKjNFE6V55XQzO9K8owF0nayTEEARpBm6dCiuG6ptF8pJMq2hUrQfJOYM4YXx6bxytQ8AFWLI6SS/moaBSRB1Q8QRzGkiJHEKSxLB+UadE1N0b0wRskysqRigiCK1TQ4TtFbMvDOA9vx5v3rmxQsB5OTk3j66aexc+dObNy4ca2Xc1VQv5k5OTkJz/MaKnquhy75nLxejf7eK8XY2Bje/va34/Wvfz0+85nPdG1eXawYugT2MrEaBPbChQs4d+4cXvOa11zyfReHNa138nru3DmcOnUKO3bswE03rW4gxuJE2/qu2ZWWtd7omJ+fx8jICDZu3Iht27ati/eg7/t47Mnv4+D3n4Lv+9ANA47jwLFt6LoOx7agcY6q57dMzCWEwHHaTyxNU8lEWwUgcc4yb63fVr6rPK9+W1JpmkY2dYyy9QGu68IPwrq0YeWprdTaT0V1TQPnDJ7XSNDzxOFqzUecxJiamoLGKHr62nvNizCkuqTiZunFgCIjtmUua3JLQNDbU1JBT8tICKaEwnUtLFSqmJ9fQK1WQxJHcBwHlGtwHGeJMkDXOEzTaJr8zBmDZVlNvbC6xmEaOvxF/mVAkTrXsbFQa70BYhk6NI0jjhP4YaSmmCWnSBleDpRk+GI3LKeK2EVxsqQ2h3MGw1CJv9PTqou3fqJeD9s0kAiV9NsOrmXCj5OGztwlxzJ0dawWf2eMMRi6Bq+Fl9nQOI6eGUXFD5T0V+PwI7UpYBs6vCABYxSMEcRxDMooTM4wW/EQRhFEmoIxhl7XggAF1zTlaZWApWt47d0342d/8h5V43SNY3x8HMePH8fu3bsvOTjveoLneZicnMTU1BRmZ2fhOE5BZheHAF4L8DwPTz31FDZu3NhQRbceMTExgQcffBD79+/H3/zN31yXk/Au1g5dAnuZWA0COz4+jtOnT+P1r3/9Jd3vWvK7Silx6tQpjI6OYu/evejr67uqj1+faDs9PQ3HcQoy67ruuj536x35bv8dd9yxLrvbnj39Er782JOoVCoIwwBJEkNKUhDacsnNOlpbTy2V5Lj1hJRzBl3TGzpV9Wy6V/N8CClh6I09r81g6DoIBYI2SbO5NJmQXEranDirGp2oY51NEVwlZCFDjpMESZJgdHQUmqar5OeSA98POnog1bSVgYBk09PWU0HbNEEIliQQF8+VUjiOIsCUEjiWhZrvI+1AqoCLRJaAYL5aQ6VSgZfVtDDG1GZG1jda3IdSJa3NOmpdx1bBR8sIT7ItE4xS1DwPhmFASLScsDaDa1nQdB2JEPCDsC0hVM8PcB0HlTaSXdVzrIERCsYYBKHwgwgXRscQhAE2bboJmrb0y2XZtVH1o/ZeXQKU7OZy4AJSouxaqPpxS+uNbeqIElmEWhFCoHMKzikoKPwoxsjpCxmpl9A1jihOlJeXcySpgJASlqEhjOJiMhzECQyNgxAKjQGz8zVApojjCIxS2IaBXbdvwoffdh82DfS0e6bXDMbGxnDixAns2bMHQ0NDa72cdYM4jhsqeiilDRU96306eC2R1+npabzzne/E9u3b8bnPfQ6apq31krq4ztAlsJeJ1SCwU1NTeOaZZ/DGN75x2fe5lshrkiR4+umn4fs+9u3bt+bTz/xilpNZXdcLMtvtmr00nDt3Ds8//zx27dqF4eHhtV5OS5wfm8A3v/MjTM3MIRWiIDK1Wk1NylwXg/39EBItX3/XseH5rWtr8mljHCfgnGdBRI2gtLOfVQVNWS2nkyVX1c5oGs/W0/qjnHMOU9c6dpv2lEqAlJjPqnuiKMLo6Chs28bg4BDyU6JxDkPXUW3jHdU1LatyUeFTcQe/K6CmiFGUIIwvkiHXthElyRKCzpnqeK3W/LayX13ToBkafD+Em6UhB1mGge/7WUWPOse5Z9a2bRBCQClFT7mEJE1Rq/nLDl0iAHrKJaRZ/UrND5aVmVB2HXjhRZmxCh1SgVNJmsIPGwmgaeggjCEIlycZLpdcVPwQUkiMT4wjiRPcessWdRxCAEKU31Sq94wXRmqToMXfgs45GG+dIAwoia++aKpKiPo5pRSMqo0fIVFU3KSJKKbGpqFhtuLj+EtjSNI0q8lRtTyuqUEA8PwQJLv+JULA1DjiVIISNbUVQkBABX9pnCEfaw+5Bt6wbRAlEiFN04aKlmv1C/eFCxfw7LPPYs+ePUUVXRdLIYTA3NxcITUOw7Dh9V9umOTVgu/7eOqpp7Bhw4Z1o25qhdnZWTz00EPYsmULvvCFL3QzSLpYFXQJ7GUiSZIlErMrxezsLEZGRvCmN71pWbe/lsKagiDAkSNHoOs69uzZs+6+HCzumqWUFmQ2r2fpYimklHjhhRdw/vz5FanJuRqYr1TxxW8cxNTsXPGzejLj1TyYhgZCOWxHkRm6KNHWNHQlz2wyVXQdG0IKVaFSW5peXI+y6xSpvK1Qcm3U/IuSYtd1kCSNPaOGroNS0rrfNj+Wkx1rEdk1DB0a46h6HghUINPUzAwuXBhFT08P+vr6mnIY17YQRnEDOVUE3kG1etFPyihRCcm19s8VUBNTx7EQxRE03pl063nScpPzWC45qPlhw7SYZOtOUgE/uDjxDYKgILNpmqK/rxem5UDTdTCm+oMtU4cftCfjhq6DMQ6vLnmaMQrHzHtlgyWdqJxS2I6NitdaZqzODYGVBVtRxuCF8bIqdbjGYegGvCCCkAJjY2MQQmDTxk1Lpk4q1EkgziehUBsunDFQQkCoWoemcSSpRJH0RNRtpcz+hygSHCdp1ukKCKnqbfL3n64xcMbhNyXgEq5l4vToNF4cm4GUEobOYHKOVErEaQohVI8rozRbJ4GlawjiBMi2GziloJQiTNIsqElgoGTjXa/djp/YeRtIRtoXFhYKMlOr1a5J3+T58+fx3HPPYd++fejv71/r5VwzyHMT8td/YWEB5XK5oaJnLb9f5eR1aGgI27dvX9ff9ebn5/HTP/3TGBwcxP/7f/9v3W0EdHH9oEtgLxOrQWArlQp+9KMf4S1veUvb211rYU25L3JoaAh33333uieD7epZBgYG1r3M6GpBCIHjx49jYWEB+/fvv6Z6+MIowle/9V2cPnt+ye+UtzQEkQLjU9NI4hi2ZRfTufz1Z4zBNJQ0mGYS1yiKiz5TICO6TTpC62GbRlZD044UaTANA2EctQxmIgQoOU5HP2nhefUDaJzBNJbW1Xg1DzMz09iwYQi60T70jFIKN6vvyc9Bq+di6Bo0jS9JZF6MHteFH0UwDQPVWq1jsFL+vHIia2g6mMZQ65DE61gmaNbxml8KKSXQuYaxySnUvBqiMIJpmrAd9R7QNa2uG7Zxslp2HVSzgKdWUAnQBggI/KzLNBVAtIwJtbo/g21bqGbPTc+SoikhSLLE7PpaGte2EGZVNalIMTY6BkIINm7c2PBZrN4/tprQtnl8Soh6vdudW0JQsk1Uvdby45JlwI+ae2Y5IzB1HademcR0xQOBRJIKBFECTWMgBIhjCQGpEoWz8y2hyChnVFVgMYogSlCyDQghYOs63nrvHXj7q7e3/RxfnJvgum5BZkql0rq83uYqmP379191a871hjAMG6TGhmEUr39vb+9V/Q7j+z4OHTqEwcHBdU9eK5UK3vve98JxHHz5y1++ZjZ+urg20SWwl4nVILCe5+E73/kO3vrWt7b8kFJ1AqK4YK938jo+Po4TJ07gjjvuwC233LKu19oMeaJhTmajKOp2zULJr0dGRq5aTc5qQEqJJ354GD8+9kzL21imgVrNx/z8PGq1GqJIkZncN+nYFhzbwnyl2tITyiiFZRptJ4mMMViGjmqt8TaEAE6WwBvFccfuWkBVtcRJaxKZr6mn5GKhUkMiGj/HKpUKpqamsGFI+cJLro2q11oyDQCWYUDTOZJYwAvaTxEB5ctN02QJGc89vV7dJFnnvOiybde3CmRBT2UXQgJVz+voHy0eQ1OSaCFTJCkaJutJkqDmqcls4AfQdC177R1YlgXHNtVEkZCOhLlhrVndTZoRrSTJJMJtnqNjmYgFOk5dOWMwdQ4tI8epEAjCEGfPvQLGOYaHhxtUBbqmulO9DunFlq5BEtrW02toHJQx+GHz2zBKlPTbj7LNAgbGCCihkFKCMYLZSoBnz01gwfMhpXoPxEkCjXNQSotOV8Yo4iQFoxQCEjR7LnGcglAKRghAJDSu4b5tN+H9P7kHrn1pE6F63+TU1BQ0TSvIzHpR57z88ss4c+YM9u/ff02oYK4lpGmKmZmZYjorhMDg4GDxbzXVZEEQ4KmnnsLAwADuvvvudf39qVar4X3vex8opfja1752TW1od3FtoktgLxNpmnbsMLxUhGGIgwcP4q1vfWvTi2K93zX3Z61XSCnx0ksv4cUXX8Q999xzXaQg5l2zOZmt1WoYGBi44bpmfd/HkSNHYNs2du/efc1PpI89+wIe++4/t9yQ0jgH5wy+rxJ+a7UqRJrCD3wIqXyTw0ODSIRo60Mt5MJt1pLfBlDT1DBe2hfqWFkoU5sNNEYpbMtakkCsfLVZhU+awtCVv7Lm+4AE5ubmMDc3h+GNww275zrn0A19yeRUYxymZagJrrzYoRsEYUeSlU/8/CCEkFJJjKtL/cI5zGx626p71TZNSAB+mPelMjiWiVr2XNtBSZwd1PwAjmUiESn8JmQ0FanyTdeUd5oyig2Dg2C6Ace2YZkGkjSF14HImoYO2qTuhjEVKkQIECcCQRhBQmapxG5HiXEOw9BBiZo+AoqEXxi9ANMwsfmmTeCcgVG1+UkZy7ynQCrVezjNNknrX4uyY6PaotomR8k24YcJCCVgVJFSSlTFjrpmESRp1ueapA0bDJQQ2KaO8dkKTp2bQJgov6vGKSSy44GgFsZglCpvrUYBQhShlup15IxmZwxghGDn1mG87yd3YvPglU8lhRANZGY9+Gbz6+y9996Lnp7rI4RqvaKd1HxwcHBFcz1y8trf348dO3asa/Lq+z7e//73I4oifOMb30CpVFrrJXVxA6BLYC8Tq0Fg0zTFY489hje/+c1LyNC1FNYkhMDJkycxPT2Nffv2oVwur/WSVgW5Z2ZiYgILCwvo7e0tfLPXa9fswsICjhw5guHh4XUvZ7oUXBifxCOPPdmSHJFMNqkSfxWpTNO0CIDyPA+WZaLklsA1DYZhND03tmkiTpKWHkpKCXpLJYRxhFobssIZg2noHf2hrm0hiCKkSQrXdRCG4ZLJbP7cXj73CioLFWzctLGlb8m1LUSxWn/JsVHz/KZTTsZoRkg7e157Sg5ACKq15U1MTUMH5ywLVZJ1CcV+0+llLnGOoghBkwmjY1lI0qUSbtM0oHOGmh80TTvmjCIVAhNTM/A8VQ9kZ13DvT3lgsz6i0if8uVGy5JFM0pRciwQyhAnAuEiefBSyEzGHBc9wVGcBXFZNgaHBjNqlyUt22ZbKTCB8roamoYwSdR9CYBss0J1uAKUKu9v1Y+avt6kSCpuLk+2DQ2JkDg/MYezk3OgRJ3fRAglCxYSafZYhkbVxDqVgASiJC1kxIRS0Izs3nnzEN73hl24Y9NAx/N8OVgPvtkzZ87g7NmzOHDgQJc0rAFyqfnk5CRmZ2dh23bx+l9JEGQQBDh06BB6e3uxc+fOdX2dDYIAH/zgBzE/P49HH320u4nSxVVDl8BeJlaDwEop8eijj+L+++9vuPhdS+Q1iiIcPXoUaZpi37591y2RW4wgCIrJ7NzcHEqlUkFmrxcpTV6Tc/vtt2Pr1q3r+n14Oah6Ph557EmcH5to+LltmWCMoloL4NoWqp63ZAolskRj3/chkhhBFBdkxrKshnPVjHxyzmCbFjzfR5KmLSeoi1F2HVQ9r+XklwAolVxQAHMt6n+klGqalCS4desWhHH7aWVPyQElFHOVakdyahoaOONNibZtGQAuyoU5Z3BMA5UOqcI5DF2DbRnw/AhhG49xDgICxzYhJVDzfUWyLbvlpkVxP0KztQKeryStJdeBH8UNtTpBEBQbGkmSKHmx46BcKsF1bFACCEJRXeYUFZDocV1UgqggowAKQkkpQSoEkiRFGMfgXHmAa3V1NmEUYnR0FKVSCf39/QV5tU0dCUhbmTmQBTolou0E2zYNJKlc0jWbw9A4GKfwFkmKGSUwOIWuaUhSgedemcArk/Oqaiqr9EmEAGdqWswpg4REGCVFKrKhcbBsqgsIaFzDpn4XP/P6Xdhzx+r2iy/GYjKTV7Sthm9WSonTp0/j/PnzOHDgAFzXXbFjd3F5SJIE09PTxXuAUorBwUEMDQ1dUnZGGIZ46qmnrgnyGkURPvShD2F0dBSPP/5413vdxVVFl8BeJoQQiJfxpelS8dhjj+F1r3sdXNctwpqEEJBSrnu/a61Ww5EjR1AqlXDPPfdc89LSy8XirlnbtrFhwwYMDw9fs12zr7zyCk6dOrXua3KuFGma4lvf+zGOnzoD2zIRxvGSvlbTUIEwrYKZpJTglGBiagrVak2Fx9gXQ6By6X/ZdRBGMQxdy6pglk7Wcrlvu87V+lCmHASqGzSK40KCXHLUNDauIy1CCIyPjyNNU2zapBJpXcdCGMZLpsSOZUFICT9L1zV0HRpnS3y7zZD7RP0gVMFRmSe42dVH1zLPaxs5saFp0LNaoDy86VI8r73lkpr6Vn2kYvlZBoahwzJNREnacL4XI47jItE4DEMM9PWC6gYsy0bZdaBxlqUgR02nxiqYyUCtQ6p0jpJjI5ESnDIQSrJUVQ/nXnkFbqmn8EXmEu/qSgU1WWbbntiSbSCV6ng0+9hLU1WRk0t9570Az78yqUKpCFGdrVBTfAIgiNTkl2sMSZLCNHQEYQTL0ICs8ocSgoEeF+98zTa8Yddta/4Zu9g3yzkvggCv1DcrpcTzzz+P0dFRvOpVr7puNkivJwghMD8/X3wPCMMQ/f39hdS41cZ+GIY4dOgQyuUydu3atebv43aI4xgf/vCHcebMGXz729/GwMDqKB266KIVugT2MrFaBPbgwYPYv38/enp6rqmwppmZGRw9ehQ333wz7rzzznW91quJJEmKrtmpqalrrmu2viZn7969N8wO69PPvoDHvvfjliqLInSpjYRXVe1ILCxUCjKTJAls28bAQB/KbgmEUohUIIiaJwsDzQnqUhCUXRtVz4Nj2Q3EtXHdFI5lYqGqqmLGxsZAKVWhPnVfqhmjcC0L89UaTMMA4wy1Fs/VtW3ELR6vYYUE6CuX4YVhyyTlehg6h6Hl4U35s1Rdps1CpdgyPK+qm/bi60apOh+pEB3ObzZ1DePi2JrGYRk6krT1fXVNA2UUk9OzqNVq8H0fmqYVmxmWZcI2DDDGkAqBMIxgWaZK520rFc6fczapX0Q0fc/D2Pg4Bvr7Ue7pURN9UwehalOx/nNHShQkWslwGRhVvbNL+osLPysF5xSpQDbTVfJeIWW22aIClmrB0teZEIKSZaDix5ipejh9fkpV5GgMGmeI0ywxOVWVO7apQ0olD47jVPW4ZhpmSgj6HQtv3n8n3nrgrnW5abqSvlkpJU6dOoXx8XEcOHCgS16vAUgp4Xle8frPz8+jVCoVUuN8UzuKIjz11FPFAGA9fzdIkgS/+qu/iuPHj+PgwYPXRcZJF9ceugT2MrFaBPbJJ5/Ezp070dvbW1frsH7DmgDVPffss8/i7rvvxubNm9d6OesWeZph3jVLCFnXXbNCCJw4cQLz8/PXXE3OSmB8agaPPPYk5hYqLW6hSGO7YCZK8v5TDwRqsjY/v4Dp2VmEYQjTNJXcfHCgCB9q/kgErmu17JUlUARLkR7ZMQ3X0BhePnsOJOs7bvZlydB1WKaBKI4bUoGbP08K17HgeX5Tj6ZrW4hTgTCKiiApPwiW1WGq6xymrheEJmxD9gFFrlzbRJykxbSYgGR9umHLNGXD0GFoGvwwbJhSa3pOett4kjmDbRhqQh0ESIXyotbCuOHxcql57pumlBbT+XLJhWmaSFOpSBohSNIUQRg3fc3trMt2sXS3VqthYnxcfTkulQBIlJ2la1l63tTr1G6iCgBlJ79N81u5lokgSpA0eSxT16AxBgGJs+OzeGl8VvW3EgJQIAhTGBpHKkSWTkxR9SMYOoNGlffVCxOYOodraHj97lvx7tfuhLHOesVbQUqJSqVSXAMuxTcrpcSzzz6LqakpvOpVr+pWlFyjiKKooaJH0zT09/djZmYG5XIZu3fvXnffBeqRpil+/dd/HT/+8Y/xT//0T9i4ceOqPt7DDz+MT3ziEw0/2759O5599tmW9/nCF76A3/u938NLL72Eu+66C3/0R3+EBx98cFXX2cXVR5fAXiaklIg6fJG6HHzve9/D7bffjsHBwXU/dc2nc6+88gr27t3bLU6/BAghMDc3V/hm0zRdV12zcRw3eJlv1DLyIIzwjSe+j+dfOtfyNrZpZiFAzTe0NM5Qsh3UgqCBfKlE41rhne0tl8B1HaZpQdf0fLTVAFNX6bJeqMgUIQSuYyOM4qL6Je+DrbWQ1EaZL9J1S7ht6xZUao1BSxrnsEwDlawblUDJSYMwQpy290zmyb8LWXiTZRqglKLWZEKpwpXMliFJOXRdVdyEUQJT11Hz21f61MO2THDOFJltMg1sCqJqXhiloIyi6oVtJdxL1qtpsCwlEYziJJuuL30xpZTwfR+1Wg1SJAijFIZpwXHsBqk5AYGuc2icgRJVF8MZx4IXLvEKVyoVTE1OYsPwsOqs5QyarsPr8NwNXWuaitxwG42DcQY/bH4bxhgsXUM1iAAJaBqDzpjKbYAEoQRVP4YfRnhxbAZVP0IqUtiGjiBOoHEGRii8KIZlaIhjAUNjSKUAJQRSqmAnU+N47a6t+OnX7oJjXdvJ78v1zUop8cwzz2B2dhYHDhzoktfrBGmaYnJyEidPnizaJep9s+ut2UAIgd/4jd/Ad7/7XRw8eBA333zzqj/mww8/jL//+7/H448/XvyMc47BwcGmt//+97+PN77xjfjUpz6Fd73rXfjc5z6HP/qjP8Lhw4dxzz33rPp6u7h66BLYy8RqEFgpJQ4dOgTP87Bx48bCM7kekaYpjh8/jkqlckNO51YSi7tmwzBs6Jq92tUMeU2OZVnYs2fPmpPp9YB/PvoMnvzRkaY+VSCXFBuoehcDgWzLVOQt77LUNWisOZnLE43DIEAcRUhSoWSmjg3TMBv4DwFBueRCSgG/TV2NIj1ag/TX932Mj4+ht7c380USmIYGxhjCMIZlGaj5QVOCyCiDbZuodqgCApRfVtd1zM0vdLxtLv31Fkl/KSUoOU4mF5Z1t6ewTRNBGLZMcy7W66iQJs4YbNMoEqQ7wTQMUMYQhBFsK++mbU+0AaDsuqgFUcP507gK7QJR/bL1HaoaZzAME1UvQBiG8LK+2TiOixAo23bAufobtE0TqURxDI0r2S1jDPNzc5icnMTGTZtgWhY0zhHEaccpd8mxUAvitsFZZcdSz0vKwp/KKQWlVNXbMAopgFRItZmTpMXU2DZ1CKm8rHNVH6cvTCEVAqauPteiJAWlqs+VUoJyJumO0xRxImCbGuIkRck28eq7bsZ737AbpUvscr0W0Mo3Ozg4iLGxMSwsLODAgQM3TDDijYA4jnHo0CFYloV77rmnaDaYnJxEtVpFT09PMZ1f6+9YQgj81m/9Fr75zW/iiSeewNatW6/K4z788MP40pe+hJGRkWXd/gMf+ABqtRq++tWvFj977Wtfi3379uGv/uqvVmmVXawFugT2MrGSBLY+rCmKIkxPTxcBQJZlFTLTlU4yvFwEQYCRkREwxrB37951t0t4LaNZ12x/f3/xHljtc53X5GzYsGHdF6dfbVwYn8SXv/VdLLRI8wUUgZGQiOOkqc+TEBXepCaUzT96pZSgkJiamkalWgMhBI7jwHEclFwXjmOh6gVqIkdJx87Rkqt6WefmFzAxMYGBgYGGaivOmApZklJNWTukqxfhTU08sYxSuLaNSuZRLSawHep+8vvatgk/CGEZBqI4aZuSSwjg2BbSNIW/SOJcch0EYdyUvNmWCUZJNsltfA0IpSg5TjF9rgclRG1KEKJkxnXHNnQdnPOOk05AkT/L0KFxjkQCXrBU1nwxBMpDGAYwDRNDQwMA05tuaOX9vRs3bkTJdWEYeiEjJ4SAZfJbkvlGCVGve5aw0HCs+ufNGIWQQJyqPIZ0UdcxZxSmbqDaRGLOqDpfNT8CZwyvTM3hlck5FWpmGljwAhBKoDMGEMDSNVQ8H5SqZGFdYxBSwtI13Hf3Fvz063ahx7kxJo+5b3ZiYgKjo6MQQmBoaAjDw8Nr1jfbxcqinrw2kw0HQVCQ2ZmZGViWVZDZ3t7eq3ptFkLgd37nd/DII4/g4MGDuOOOO67aYz/88MP49Kc/jZ6eHpimide97nX41Kc+hVtuuaXp7W+55Rb85m/+Jj72sY8VP/v4xz+OL33pSzh69OhVWnUXVwNdAnsFCMPlJUS2Q05cm4U15bHs4+Pj6yYAaGFhASMjIxgYGMCOHTvWtVfjeoDneQWZre+aXY2ewampKRw7duy6rclZCQRhhH/8px/i1IsvN/zcMg1onKPm+cWXy3ZBRbZlLDu8aXp6FnEcIQwCeH4Iy7aKeh7GmPJ2duhQrdWqqC7Mwy33wHGUqkMRVwtVzy8ksowSOJaqCupUZWNbJiAlvCBU4Uqug1oQNlTL5HAySW2z6XM9LFMFGlFKG2TRnWBlkt80SUE460jqATXhtS0TUkh4QQDbNpEkcnmVPAQwdQOaxkAZw0K1eaJyM2gah2EYDedC1zXonIMSVY2Td+0CKnF5vlrD3PwCfN8H57wIgTIMAzMzM6hWq9i0aRMG+nrhR3HHSXHJtbNJcetFl2wLXti6r7Zkq7Cpes8zZxQ65+CMAIQiTgTmqh5OnZ9EzY9g6hwgRE1WDa46XrMN3CSV0HUGIaTyv1KGV999M97z2p3oK994Ch8hBJ5++mnUajVs374ds7Oza9Y328XKIievpmliz549Hb9H5d8F8wk9gAapMed81dYqhMDHP/5xfP7zn8cTTzyBbdu2rdpjNcM3vvENVKtVbN++HaOjo/jEJz6B8+fP4/jx4027j3Vdx9/8zd/ggx/8YPGzv/iLv8AnPvEJjI+PX82ld7HK6BLYK8CVEti833U5YU15AND4+DgmJyfBGGsIALoaZCPvAb3ttttw6623dgnOVUa+IzsxMYHZ2dkV7Zo9f/48nnvuOezcuXPVQxmuBxw58Rye/NER6IaOJEmW+CspIXAdCwvV1h2jlBKUbBvz1dYTXcc2oWt6lhosEIZhQ6JxLjPt7emBaRpNJp0Ss7OzmJ+fx8aNG9Hf1wuAZBPU1vU8hsah61rnjlQQ9PWWEcXx8up0LCWHri0KRNI0Dss01eNlV6R8wpqkAkGHECnGKGzbgh9GsE0TYbg8qXD+2KZpgIAgWUYacQ7bMiEAhGEMlnX7UkIQxjGiqNnkWCqJcRgty8ObbzAIFbgLKYE4SbGwsID5hQpqdX3EG4eVd76VPzWHoWtgHSbFhq6BMwav2QaMBHRNybETIUFJdh2TElGsUotzH6yUElPzNbw4NgMpJXpsC36SgEBV6aSpkgX7cQJGKCgj4Fndzn1334L3vGE3+ks3HnEFFGk4evQowjDEvffe26C8aeabzfMT1otKq4vWiOMYhw8fhq7r2Lt37yUPAXLLUf4e8DyvqOgZGhpaUYm5lBJ/8Ad/gM985jM4ePAgdu7cuWLHvlzMzc1h69at+JM/+RP863/9r5f8vktgbxx0CewVIIqiljLATsjJqxBChVxcwkVHCIHZ2VmMj49jYmIChJBCXrQaabZSSpw9exanT5++7ntArxXkSYbj4+OFvOhypOZSSpw+fRrnzp3Dvn37bpianJXA5Mwcvvbt72FierblbRzbRBTHiOPWPkTHNpEkaRHwpLo6HSR10lida9B13kASF3eNmqaJDUOD4JoGJQuVmJqaQq3mYdOmTbAtC45toeYHsJt4TpuuzTIhhCjSfOvhOrYil2GkUpAdG0EYtQyzWnxcEAI/CFFy7CU+18VQfmJS+InrUXLthnqb4j6moVJsvQCyybEJUVLjmt8Y0qRCrFTqsReESz7jOWWwHQsVz0crg6/GOQxdA6EEcax6TAnjTc9jMzi2hThJlyQMF5ASU5NTiKIAA319qNSyvmHHhesq3yxjVFXkZBNOQ9cRJinyU0Hyf4QARG26aIyieESpbiWkkg0nqepgVQR80ROXQMnJJrKJAKUEL43NYGqhVvS9pmmatd+ox0myqatrqkmsxhjecM9teOd9O1B2b1yfZ5qmOHr0KJIkwf79+9vKhVv5ZoeGhtDf399VSK0zJEmCw4cPQ9O0yyKvzVCr1Yr3wNzcHFzXLd4DV7KhIaXEpz/9afz5n/85vv3tb2P37t1XvNaVwqtf/Wq85S1vwac+9aklv+tKiG8cdAnsFeByCeyVkNdmx5qdnV2SZjs8PIz+/v4rDuARQuDZZ5/F5OQk9u3bh56enis6Xhcrj8Vds5qmFWS2nVdGCFEkW+7fv3/dBoatZ6Rpiid/PIKnjj3b8rOAMwbLMlBpM41lVCX9SgB+Gx+qqp9ZKtNNkqSoZwnDAD2lEmqeDyEFtt6yBaVSWcmC68OQqOp6Xai1D2UiAFzHQhjGiJIYdiZZbFatoxKQbfh++4AlQJFHmU0XlzO9BVQNkWno8PwQuq4V56sdKKVwTFVxk09+bduEEGgr4QbUObJMA4QAfhjDMgyEdfLeTsgrg2pBDEPn4IxBSqh6nCjGYgbMGVMJ0G0k0FJIjI+PgxCJrbdsRZikkFKlS9dqXhYCFcE01XR+oK8Hmm60TRi2dB0gJFvTUpi6DsLIkgmvqobSCsKfpgKjMwt44cIUOKXgjKLmR0WAWRjH4JxDAtAYgZQEpsHxmh234D2v2Y2Se/2FM10K0jTFyMgIhBDYv3//JUlD843tvKInSZJCZtr1za49cvLKOcfevXtXJRyx3YZGX1/fsh9TSon/8T/+B/74j/8Yjz/+OO69994VX+vlolqt4pZbbsHDDz+Mj370o0t+/4EPfACe5+ErX/lK8bPXv/712LNnTzfE6TpDl8BeAS6HwOZ+15Ugr4uxOM02iiIMDg4WwQ+X+oEZxzGOHTuGKIqwb9++rtfmGkCzrtlm0/n8tY3jGPv3779ha3JWCmcvjOPrB7+PhWqt5W1KjpK4LiaftmVmk0Iftmkg7eCN5VnicSt5bxzHGBsbg84ZCCEIkwS2rUKgTMNQjLEOpqGDMdYxaMm1LGiZrLjT5DaXUDcjsq5tIxWiwSOs6xpMXUOlRc9tPTRdg5kRrjRNO3bU1sM2Tei6hihOLul+hq5B13WkQoIzilRI+GEE2UYK7FgWEikRNpUTq5fB0Dg0zkEZBQVFLATCOGl5foUQGBsbh2Pq6OkfBGkxwYnjBL7nQaQxZheq0HUDtqP6ZusrmhihcGwDVT9s6uEllMC1LPhRBI2pXlYCJWdORAqN8UIunKQpzk3OYa7iqx5bEPhRDEZVBY5paCAA4jQtHvc127fgXa/ZiZ6S3ebs3xhIkgRHjhwBIQT79u27Il9j3jebW066vtm1Rf7aUkqxb9++q5Lsn29o5FLjOI4xMDBQbGi0CoSUUuIv/uIv8MlPfhKPPvoo7rvvvlVfazv89m//Nh566CFs3boVFy5cwMc//nGMjIzgmWeewdDQEH7hF34BmzdvLqax3//+93H//ffjD//wD/HOd74T//f//l988pOf7NboXIfoEtgrQBy3L4avR7uwptVAfWH6xMQEfN/HwMDAslMMPc/DyMhIkZC3miEBXawOWnXN9vb24uzZs93XdoURhBG+9f0f48SpF1vehnMGy9Dh+UEhE10sK6WEwrUt1c/a5vFsy4QQssEfmqYpZqanoesayr1KDk4hMTM7g/n5CgghsLNEY8uyGj6DHNuCSNMlE81c7puHDqlpsY2a5yPp8PlHCFCylbSYcwZCaFuPKePKXxmEEeJFCcSUZefFCxpIrqFrMHRNkeUWxI+AqOCiOrmwrnGYut7W90opRcmxlzwmoMidnZH/VAgEQYRUCLXBYFuo1JbnpTXyuqM6XyqlBLrGwRkFJRQgQBInOH/hPJimYWhoGBJAkoqmhN+xTMRCIoqToqKpVqvB93wwzpRvsr8PpmVDSAlKCChV770cjFGEcaqOsUgy7FgGokRV9DBK4UcxXjg/iTBO1KZJlIAzlnXXEsRpCgICTWPocSy8btdWvPO+HbCMboI9oL5LHDlyBIyxVSE4Xd/s2iFNUxw+fPiqktfFyNsN8vdApVIBpRTf/va38TM/8zPYt28fKKWQUuIzn/kMfu/3fg/f+MY38PrXv/6qr3Uxfu7nfg5PPvkkpqenMTQ0hDe84Q34gz/4gyIJ+YEHHsCtt96Kz372s8V9vvCFL+A//+f/jJdeegl33XUX/tt/+2948MEH1+gZdLFa6BLYK8ByCeylhDWtFvJqlvHx8aKaZXh4GENDQ0t24mZnZ3H06FFs2rQJ27Zt617crgNIKbGwsIBXXnkFo6OjANAwne/Ky1YOp19+BY9+50dNZbGmqUPjGoDOlTWmYYBm/aOtkBOzatZ1+sor5wFKMTQ01PB3yyiFbZmYnJpGNfPNSiFg22oqZ9m2UoRAeVvDKILGeQNxXQxGKRzbRK1NGJR6HnpxrDReStibPi9C4NgXE4Jd18nOV+vJL4GSBlNCUPOCIj3XtS3EqWgb6sQ5g20YkJDw/QiJSOE6tvKhtqnyWYzeckl5TAmQJEom3MrbSzMPbtUP2/p/AUDKFLPT00gkwYYNww2vLSHqfFFKwBmHpnFESdIwUSUEEAJIRYowCBAGAabmFgAAjm3DdhzYlgVCKXTOoOsaav5SFYDOGWxLTaKllPDDBM+fn8BMxQenVG24SKh0ZgKkkkCKFBrX0Gvp+Mm9d+Dtr94BTet2S+eoD/W5Gr3b9TLT6elpMMa6vtlVQpqmOHLkCABg//7966ZTPQxDHD58GB//+Mfx4x//GAMDA3jzm9+MTZs24a/+6q/w1a9+Fffff/9aL7OLLtqiS2CvAMshsPnkNU3TFZcMXy7yapbx8XFUKhX09fUVnsmZmRk888wz2LZtG7Zs2bLWS+1iBTE9PY1jx45h69atGBoaato1OzQ01JUTrwD8IMS3vv8Unnn+RVVNY9tIFsldGaVwLBMLtday44uTw0CF4CwCJRSuY6FW83DhwgVw3cDAwEDL4xm6Bl3jqFS9i4nGnockjotE46FBVctAGVtW0BNnFLZpohYEDfUthqbB0HUVdlQ3S7YtE0R2rtQB1FQYWY+pF4Qd15KDUoKS44BRirlKrWMlUD1MU4ehKYmyCtLqbBWxTAMgdMn0mhACQ9OgZXJukUltGWFI0Vpe3LAeneHMy+eg6QaGBodaXEMkSq4NL2xdoUOIkm97YVQQ0DAIiveASBMM9vWCagZs24Fl6tkEWK2bUqom0dnx5qo+Xh6fhYRKJvaDGIQQcEbBKEGUCBicY6jXwU/tvwsP7Ltz3XyBXy+IogiHDx9edp3KSqPrm1095H5mKeW6Iq+LUalU8OUvfxmf//zn8YMf/ACapuHd7343HnroIbz97W/v5p50sW7RJbBXgCRJmn6pzLGSYU2rhSAICjI7NzcHANi8eTNuu+22rk/mOsL58+fx7LPPYufOndi0aVPD7xZ3zfb09BQbGt33wJXh9Muv4PHvP4X5SmuS6ljL8L1yDsvQC9+rrmkwDQOeH6BSrWJsbAx9fX24aeNGgMiOPai2aYDUTVfjKIIUKSqVKhaqVRiGCcdxUCqV0FsuoRb4HbtF8ylvHCfQdQ3Vmg/ZRgRt6jo0TXXnLp5AWmaWOlxH+AkhWZ+sRNVrTX45Y7Ats5D95uuSWWdtq01HThkcZ6lEmVIKy9TBKF1CaBljsG1TJR0v40qqcw7DNFD1A3DGoHOufKWEABIq7VekEKnICK/A6RfPqjCmwQEQLL2GmKYO0oQ818MyDUgAQZSAEQLGKBgjoESRTUopfD/E3MI85ucr8AOVau04NjYM9kOAKu8qo+CU4szoDCbnKkilBAUBZwyEqJofSgk0zrFlsIy3vOpuvHbH1u5UrwnyKZht29i9e/ean6Oub3blcCVhXGuBv//7v8dHPvIR/N3f/R0GBgbw5S9/GV/+8pfx3HPP4f777y8I7a233rrWS+2iiwJdAnsFaEdgVzOsaaWRpilOnDiBubk5bN68GXNzcyveM9rF2kBKiTNnzuDs2bPYu3cv+vv7295+cdes67qF1LybUnx5CKMYT/7zEYycfKHlJI9mabWVWvtpYW9PCQQEcwvVBl/T4OBgUepOiEo0DqKlPtLFcB0LjFJEcVKEKiVJUqTZBoEPXddRcksYGhxAIkRLubCmcViGAc8PYdsmoiguqoHagVFFOPP6HU3TUO0QKKVzDtPUEddJki8m/oYtyXY9GQ3CuHjMsuvAj+IlAVvNQAiBaegwDSWljZIEQYf+1WKSHnTugM2Dk2bmF3BhdBT9vX0YHBwAY4rg0Cw/gRACymgT+TFR1TlQsmpKGaIkQZoKJEI0EG1dY9C1pXLhOImRxhGi0EfN86FrOnTTRCUUmK6F2XWNIE4EdM6RSgmNKb/rnTcP4h2vuhv33H5Tx3N5oyIMQxw6dAilUgm7du1ac/LaDK18s0NDQyiXy+v6O81aIq9BStP0miCvjzzyCH71V38Vn//85/HQQw81/O7FF1/EV77yFXz5y1/GP/3TP2HHjh34D//hP+Dnf/7n12i1XXRxEV0CewVoRmDzzr3856sd1nSlCMOw6Mbat29f4YeN4xiTk5MYHx/H9PQ0HMfBhg0bMDw8DMdx1vVz6kJBCIGTJ09iZmbmsmpy8vfAxMQEpqenL7trtguF82OTePQ7P8LU7HzL2xia8jDWEzjlNbURxbHqXM2I2ujYOCYmJzE8PAzbXpriSilFybZQ9ZdOTxmlcIve1gSuYyGOkyWEM00FPK+GWs2D53nQdQ0D/X3gmq6+mBGSEVcTVW9pgrBrW0iFgN/GxwsAGtdgmQbSbMNPVQAt79Jk6hps00SUpKguQ5Zcj3LJyUKGJIIogugwZQaUBFrV91wkrYwSGLqS3AJAKgSiOEGcpHBtC4mQbT24OVzbQpSkmK9UMZ5N1Xt6exfdSqLs2KpztWVolfIye2GENF16HiklKFkmKln6sK4z6IwVnbAAKZKJU5FienYeFybnMF+tQRICCQbXsWBoOiij0DWOvbdvwjtfuwtbNnS7pNshCAIcOnQIvb292Llz5zXxOdr1zS4PQgiMjIwgSRLce++96568fu1rX8OHP/xh/O3f/i1+5md+pu1t5+bm8I//+I8YHBzEW97ylqu0wi66aI0ugb0CLCawi8Oa1jt5rVarOHLkSHEhbeXRSJKkIDJTU1MwTbMgMt2d2PWJJElw9OhRxHGMffv2wTTNKz7e9PR04ZVabtdsF41I0xQ/PnYSPzhyvG0gkWtbkEKCcdbU/zozM4NqtYI7b7sN8aKp2mJwzpTXtloDZxy2Zag03kXHJFDVN2F0cTJZDyEkfD9Ls/V9OKYBp1QCCIdpmW3fA5ahCK8iuRdJosY5LFOR33rCyiiDYxsNE9ZmcGwLQqC4jWno0DWOOEngB62nv0YWLFXz6yXKFwOn8pqfeuKvaRymbqCyTJJsaBy6riMREpxS1V5ECIRQuQhJKrL3gISp6+q1DiJ4tRrGJyYwODCAUrm85DwS2l4u7JgGUqnkwgDACAHXKBiloCBgnAESSIR6/DhJIbPbObaJWiaRNnUOQgguTM3j9OhU1tmr0qDiKEIYBrAMjldvuxnvfsM+3LZl87r1+a0X+L6PQ4cOob+/Hzt27LgmPzfr61kmJia6vtkMQggcPXoUURTh3nvvXffn4Zvf/CY+9KEP4TOf+Qw+8IEPrPVyuujiktElsFeANE2RZCmi6zGsqR2mpqbw9NNP45ZbbsHtt9++7PWmaYqpqaklRGZ4eBg9PT3r/nnfCAiCAEeOHIFhGNizZ8+K7wILIRrIbN41u2HDhu5u/DIxX6ni2z84hOdfeqXh55QQONnUMggjlGwbVc8rZLtSSkxNTcH3fWzatAlaFpSka7xlLywAWIYBXdchIVGtem29qTmRjZpMZAHAyLyrk9OzqNVqEEmsgom4BsdxYGeJxs3AKIVtm4jjGLqmo+oHHSW1pq7D0DUEYYQwI9aWaYBR1nbiqnzCGoQQ8DLpLudKrlz1ws4d3iQPvdLAMhluEEboEBgMlgVrqelm+xszRuFaFsI4BqEMtWoF4xPjuGnjpkISDqJeE5r1zyofrDpuLhOWyCtwJGIhIYREKgTSVBQbA7ahgqm8RXJnAqDsqKokKdXU3Y8iLHghzk7MIk5UNVCcxDB1HakQ2NBbwgP77sSB2zZgdlb1TgdB0BAG16pn8kaF53k4dOgQhoaGsH379uviWlnvm52cnES1WkVvb2/xHrhRfLPXGnk9ePAgPvCBD+Av//Iv8aEPfei6eC92ceOhS2CvADmBvRbCmupx9uxZPP/8800DfS4FzYhMTmZ7e3u7RGYNUKlUcOTIEQwODuLuu+9e9degWdfs4OAgNmzYgMHBwe5EpgPOnD2Pb33/EPwwgq5xNW1dROh0zmAYBhYqVYyPjyOOY2zatGnJxoRtGiBUVcjkcB0bUqKh59QwdOico9Im/VhBEdk066q1DANM40vqgaSUCMMQMk1Qq9VQqdZgZonGjuM0vAfygKWaH8A0dCUXXgaJzVF2HDDOUPWCthVEi8EYQ49rIxXIiGgINAlEaoREyXERxHExLScgMA0OzjgIUVLhOE4QZWspOTbCOG07XQcuSnz9KC5kwAsLC5ienm6QhBMQlBwLtTCCaCIFBpRU3LUNVLyw6baErjEYmo5qEIISAkPjYNlEWHW4Jghzr7SUSKXAuckFzFRqymdLSFGRc9fNQ/gX927Hvdu2NFznpJSo1WoFkcnD4PKNrWYS9xsJtVoNhw4dwvDw8HVdTXcj+maFEDh27BiCIMCBAwfWPXn9zne+g5/92Z/Fn/7pn+KXf/mXr8vXpIsbA10CewVI0xRRFF0zYU1CCJw6dQpjY2PYt28fepd4q67s2Hkc/8TEBKSUGBoawvDwcHcqd5VQX5Nz2223XfX3Yt41m78HgiAopGVDQ0Pr/sK+VkiSBE89/Rx+dPSZYsK4GCJNMTszDQKgt3+g7d9TybHBGYMXRoja+C5NQwfnDNU2k1tAyXQ5Y0hSsazqG0glMZ2emYXn+zAMA+VyGRsGBxAl6RKCziiFbRpIs87XZtA1DYahZ2m/6pJlGQY0jSGM4pb+0twvHERxA6lkjMLUFYFOkgR+FKNeh62kyVL9fBlwLROEEkioPlYCglSILFVYII6VVBhQHlohJYK6Y8/NzWFudg4bN26EaSm5f8m2ECWiZQ+tqsWx1HNLBQAJThk4p+CUqXUQglRIJVdO00ISrVKolY/X1DkYY0hTgZcnZvHS+AwsXQMnBCkkLF3DfTu24q2v2omN/aVlnY88DG5ychIzMzM3BJFphWq1ikOHDuGmm27CnXfeecM89xvBNyuEwNNPPw3f968J8vqDH/wA733ve/FHf/RH+LVf+7Ub5r3YxfWJLoG9Anzxi1/Epk2bcM8994DlARjrFEmSFLuE+/fvX1Vpj5SyYSqXJEmxEz8wMNCdyq0CLly4gJMnT2LHjh246aa1T//MJzL5e6BarXa7Zjug6vn47lPH8PSpMw3S0yRJMDY6Cs45hoeHUXJsJKnIpogXYRkGNJ2j5gUQUqrwoDhtW88D5H5QhlrNKyZ4uYw4SUWD37LwstbdthUIUfLfwPcwOjmdhUDpcGw1mdV1fckQVHXHaghjRUo1TYU7VWpLA6Ia7qdrMDQNQgp4fnjx+QuxrK5VmiULGzoHpQzVIMrk0+0/0w1dU17aoH1tEaBeH8YZhJDFZBNQG09zc3O4+eabYWb1QciIZy4XJiCQBCASkATglGaJ0FL9SwVSkRY+Vdc2m3bCGjqHYxpIUuXBDeIYBATz1QDnp+cghEQYpzB0jl7XxE/uvRNvO3A3DP3yv5jHcVwodXIik38O9PX1XRdEphUqlQoOHTqELVu2XJJV53pDvW92cnIScRxf875ZIQSOHz+OWq2GAwcOrHvJ/FNPPYV3v/vd+P3f/338xm/8xg37Xuzi+kGXwF4B/s2/+Tf4P//n/2DTpk1497vfjfe+973Yv3//ursg+76PkZERGIaB3bt3X9WLRf1Ubnx8HFEUNUhM13tK33qHlBIvvvgiXn75ZezZswcDAwNrvaSm8H2/ILPz8/Pdrtk2GJ+excEfHsbZC+OIowijo6OwLAuDQ0PFlw4lQVXJwSp0SMJvRqKyKV0cpx0rbXSNwzR0AARBFLWc/AF5+JIO3w8RN0nCVb83UM3ItKFr0BjF1Mws5ucX4HkeGGeFzNg0zAauqHEOxzYhJUGYpS8vF65jKz9o1ku6HLnwxQmvXxBzRil0XYOWbbhdlAun0DjLnl/Y1k8MABpjsEwTFX9xV6zE1NQ0arUaNm3ahJJjQ9f1tr7ePMTJaxFQVbIthEkCSAKNq+AmCYBICUmyZGEAjBGYuobZBQ+nR2cQRjE4o9AYwx2bB/Hm/dtwYPstK/4lt16pMzk52WA5GBgYuK6uBwsLCzh8+HChiOlC4XrwzV5r5HVkZATvfOc78bu/+7v4rd/6rS557eK6QJfAXiGq1Sq+/vWv44tf/CK+/vWvY2BgAA899BDe+9734tWvfvWaTxvn5+cxMjKCDRs2YPv27WtKrvPeypzM+r6PgYGB4sJ1Le7CriXympzp6Wns37//YujLOkcYhsUX2JmZGbiu29A33L24Khx95jn8f1/+RwjC0N/Xp0aaGUxdh65r8IMQlmkgCKL2nlACuLaNNEmaJtjqugqDqgUBCCGwDQNB2OGYUEvK5baeH2SBUhqqvt80GZkQwLFMQEpMTs+iWq2i5tVAQGA7Nnp7yujv61e+2LoDGNn6kiSB12LaaWdJyIvJHWMUlq6DUqImylEEmSUxKSJqouIFHYkokAc02YiSBDxT3SgvrKpPS9IUSSIgpAAlFK5joupHS7papZSYnJxEEAS4+ebN6CuX2oY+6RqHpmmoBSEICDTOwCgFowQ0+/8klYiTFFGSFEFTusbURDuKoWta9hYimKv6eOH8BJJEqImtY2L/HZvx9vt2Yri/3HQNK418czNPs/U877pRaczPz+Pw4cO47bbbcOutt671ctY1rjXfrJQSx48fR6VSwate9ap1T16PHz+OBx98EL/5m7+J3/md31l357OLLi4XXQK7gvA8D48++ii++MUv4mtf+xps28a73/1uvOc978HrXve6q767PDY2hmeeeQZ33nkntmzZsu4+uHKJ6fj4eIPEdMOGDev+orDWyCXhYRhi//79V1yTs1ZY3DXbrWhSmJqawrFjx3DHHXegGgl896ljqHo+HMtCkqZLpq2KhFidiSzURFZIRTgd2wQhVE39Fl0KlBTVUh7RDhNQ2zLAmKpd8YOwY4gRoLpIHcuEEAILlRriKMDk9BySNIFt2y0TjTlT008ACMIQmqZlYVCdZbyAmsValqEkx0CWthy37Z3Nw5T8sHX3av2ty66FKE4BotKBCVWSYQJASIHR0TFEUYStW28B45qS/tYlC0MCAhIUBITSwjucZJU3ORxTh0RjsrDGqApq4gxpKhEmqo9W5xypTPHCK1OoeAE0xrF5qBc/sWsrHti/HZq2ttPP+hCo+fl5lMvlwnriOM6aru1SMDc3hyNHjuCOO+7ALbfcstbLuaaw3n2zUkqcOHECCwsLOHDgwLrfZDl58iTe8Y534Nd//dfx8MMP37DX0y6uT3QJ7CohCAI8/vjj+Id/+Ac88sgj4JwXk9k3vOENqzptzGWlL730Enbv3o2hoaFVe6yVgu/7GB8fx8TEBBYWFgpJ0YYNG65ZcrZaCIIAIyMj0DQNe/fuvW5kd/UVTVNTU+CcF19g+/r6bpiLb+5n3rVrFzZu3AhAnZunT53BD48807Yuh1JVwxOEEeIWEmDKqOqZheKsNa+9vxRQ001GadbjevG2rmOr6WvQ2Kfq2CaIJC0nsTkMXYOu6/CDCJapQwqJ2YUFVCoV1Go1xHEM27YLQluvaLEtE5RSpKmErjFVPRQt9X0uPj+upVJ9629HsnReTVMkPJ+mRlEC2zYRxWlbSbWCRMmxESWiZRiXFBJjY2MwdY6BDcOQsvl7mjGapTWHTYm1nQVwpUJmoVFAkgpISHDGUfXDzNerJNDzno8XXplAEKVwLB07bt2It71qB26/aX1eG8IwbAiBsiyrIDLrua5tZmYGIyMj2LZtG26++ea1Xs41jWa+2VyxtRa+2WuNvJ46dQrveMc78OEPfxif/OQn1+3fTBddXC66BPYqII5jHDx4EF/84hfxpS99CWma4p3vfCfe85734IEHHljRD0IhBJ555hnMzs5i375914ystB5BEBR+ybm5OZVgmtXzXAv+mNVEtVrFkSNH0N/fjx07dqz5jvRqQQiBmZmZ4n0AoNjQWA878auFl156CWfOnMHevXub+pmTJMWx507jn4+ebEtkCVHkMooudrmahg5N15Q8t4686boGU9dR9f2GnzcD4wyOaYIACKL4Yv1KC3DOYJsGkjSFVzchtQxDhUE1IbiUENiWAUoJFqo1zM3No1arIQxDmKaJwf4+lMplREnztZq6Dq4xQErl/Y0jUMLgOha8IFrGBFXBdawiwZcz2iAZFjLrWs2SfU1DRyIak4UXQ6Qp5udmkApgYHBD0/cwpRSOZSBOBQjUZgPNZL+A8uUmaQpvUR+tzjls04AQKUCUVJpAIohSnJ2YRcUPsLG/jNfvuh0P7N8G8wpCma42kiTB9PR0QWQo/f+3d+dhTZ3p38C/CWHflxAEFUFRcGVTq7XWHZUlobVTu7fTaTvtaEdrO3XmV2unm7Xt2+kytbadabXTsYskLO5VQW3VamUTVFBcUFmSQAiQhKznvH9gzgCCggJJ4P5cV69ehiwPcEjO9zzPc998u+w7XV9fj+LiYowZMwZhYWG2Hs6A0tW+Wetx0NfnBSzL4vTp01Cr1UhMTLT78HrhwgUsXLgQv/vd7/D+++/bzd8IIb2JAmw/M5vN+Pnnn5GRkYGsrCxotVokJydDLBZj7ty5t/VGbDQaUVxcDIZhEBsba/dvst1hNBq5ENN2v6RIJHKoZWW9QaVSobi4GMOHDx9UFS07q2o90Aq/sCyLc+fOobq6GvHx8fDxufE+RC7InjyDZs2NZ2R9vb3AAlA3aW74nHwnPjzd3WHqomiSs4sz3F1doG0xwGJh4O52rXrxtX/fjIuzAF7u7mB41rF07/h1dRHA1cUFJpMJGo0WinoV9Ho9nJ2duSJQLi4unf498Hl8eHu5X5up5AMsC6PZAoPR1OWss5eHOywsC73h5i10PN1dwaC1HQ2Px4Pg2p5UPp8HwFppmAWfx8fFy5UQODkjNDSUGyvDtu6dBQu4ujpDqzd2+rN0d2sN9Fq9ETyAK4rF5/PAsECLwQizhYWLgA9XFxc06/SorFUBfCA2chjmJI5GuMg+C7z1hL1Ws1UqlSgpKUFMTMxt9VYn3dOf+2ZZlsWZM2egUqmQmJho9yvCKisrsXDhQqSmpuLjjz+m8EoGLAqwNmSxWHDkyBFIpVJkZmaioaEBSUlJkEgkWLBgQY8CmlarRWFhIby9vbm2PgNNx/2S7u7uXJj18vIa0IGupqYGZ86cQXR0tF20ybEV65V4695pvV7v8IXArKsm1Go14uLievR3b7FYcOZ8JY6fPIP6hibudnc3VwicBdC1GGC5NuPo7u4KAd/pumXAnWm7ZNjD3RU8Hh/a66rotmrdy+oOhmGg019/H+tssNn8v5Y8AoETPFxdr7W90YPpcjity3ItDIsWw7Xw5uoCJz4PTU3NqFepodFqIBAIuGXGbm5ucHLiw8vDo8sZV+uSYYFA0BoCGRZ8Hg8sj9fl99n+5+MKgAfdDfcGs/Byd4feaMTFyitwdXWFsE0laaC1KJSnhxtaDKZ24+TxAIFAABeBE5ydBdeKRLW2vjGYzHBzdoaTkxOMZjNcnQUAywOPD6iadKhRNSHE3xt3TojEtHGRA/KzAGj/XqBUKqHVauHv78+9F/RX0FAoFCgpKWm35J/0n87aNPXWvllHC69VVVVISkrC/Pnz8dlnn1F4JQMaBVg7wTAMfvvtN2RkZCAzMxM1NTWYP38+JBIJFi5ceMMZmW3btsFoNGLixImDplG62Wxut1/SxcUFIpFowBX/YVkWly5dwqVLl+y6TY6tWKtaW3vNWk9gg4ODHWIFgsViQXFxMQwGA+Lj4295zCzLoqKyCsVlFaita+i8pc41zs6tLW50LXqYuyi2xHfiw+tawSiBkxOMZku3WtkIBE5wd3WBhWFgMJjg6dm6H9do6nrZrhOfD3c3F/D5fBgMRhhMZq6AlMnSOlt6Iy4CJ5hMRmg1GjQ0NsLZ2RkCZze4ubvD3d39pu8F3p7uMFtYrl8uDzwInJ3g7OQEPp/fumwY14or8XgQ8J1gMJmuLSNmrv2/tQgTi9alxd7Xlh83a1tQU1sDTw8PiIKDrz1f61JhAd+J22/LXnt+i8UCs4WBq7MADIt2AdmJz4ebqwDOAmeYLRboDSbw+Ty4OrtArlKjxWjGuIgwzEscg2D//qkkbE90Oh13gbOxsRHe3t7tikD1xWeCXC5HaWkpJkyYgODg4F5/ftIzvblvlmVZlJWVob6+3iHCa21tLRYuXIg777wT//rXvwbshStCrCjA2iGGYVBUVMSF2YsXL2LevHkQi8VITk5uV8Tigw8+wJtvvomPPvoIjzzyiI1HbhsWi4W7AqtUKiEQCLgQ4+fn57BhlmEYlJWVoa6uzqHa5NhKx16z1r3TwcHB8PDwsPXwrmM0GlFUVAQ+n49Jkyb12uxxjbIe+aVncfbSlRsu7eXxWgs+sQwD7bXeo+5urnAWCKBt0cPSYVrU/VrhoBaDscvgC/yv5Y3BZIKrizMsFhYt+hvNsv6PkxMPPp6eYAAwDAuD0XTTqsrA//rONuta0NLSApPRCH2LDozFAncPD7i5uUPg4srNSHAVli2WG+5btWptFYSbVGNm4cWFYROMBgNqamrg7e2NgICA1vDr5AQPVxfoDCZYmPa/Gx4ALw83WBgWDMvCWdB6AsoyDJwETjAYLTAzDNxcnOEs4KOlxYimFj2CA3wxecxwjI8Mc9j3ut5mNBq5C5z19fVwdXXlZmZ76zPBuirGUQolDja3s2+WZVmUl5dDqVQiMTHR7mtvKBQKLFq0CPHx8di8efOA2FZDyM1QgLVz1sp3GRkZkMlkKCsrw+zZs5GWlobDhw9j+/bt+Pe//43k5GRbD9UudCz+w+PxuBDj7+/vMEtqzGYzSkpKoNfrHbpNjq1Yq5h23DttL71mW1paUFhYCE9Pzz5b8q/RtqDwTAVOlp+H7lpA7Qyfz4OPlxd4PECjM3Sv96ubG/hOvHb7X7083QF0vgTXic+Hu7sr+GgNgR3b7LhcmxXWthiuC3YuzgK4Xis6ZDKbYTCYuFYyri7O1/rOdtZDlYXBYIRWq4VWq4XZbIKPjzeC/APg6eUFJyd+a1hkWJjNFpgtlut6wXp5eMDMMDfZD8uDt6cbTGYL9NeKWun1etTW1MDPzw9+/v5wETjBzbX1+wMPEPCdIBC07mHl81r3zRrNFpiu/QcAAj4fXh5uYNnW1jsWhoHAyQk6gxG+nm6ICgvB5JjhDrlsvj9ZL3BagwyAdkWgbuVvr7q6GmVlZV0WWyP2p7v7ZlmWxdmzZ6FQKBwivNbV1SE5ORnR0dHYsmULvR+QQYMCrAOxvrFu2bIFH3/8MQBg8uTJSE5ORlpaGoKDg21+Ym5PGIbhiv/I5XKwLMuduAQGBtptmDUYDCgsLISzszMmTpxIH0i3ydpb0Lrc3Na9ZjUaDQoKCiAUChEdHd3nr2+xWHCusgrFZedxpUbBBT13d1c4O7WfbeXxAA83t9aCQTr9DXujAoCzgA8vT0+wADS6rpckd+Tm2ho8rc+u1enR3Q8iPo8Hby/P1mrADAuTpTXU3miG14nPg7PACfUNjWhsaoLBYICrqxs8PVv3zVr/xpycnCDg8+FxrRIwri0fbl1Y3CbesgD4AA/81srADMOVpNLpdKiqqkKQMAiBAQFgWcBobg2gZouF+/m39rN1ubb/lYGriwDOTk4Q8J3AgIVWb4DAyQluLgJYLAxcXZ0xPDgQsaPC4O/j1c2fFmnL+plgDTIGg4ErAtXdPfRXr17F2bNnERsb2zqzThyOdd+sUqlEXV0dnJycuOPAentCQoJdrtxpq6GhASkpKQgPD8ePP/4IFxeXPn29devWcRMp7u7umD59OtavX48xY8Z0+ZhNmzbhiSeeaHebq6sr9PquL6oS0h0UYB3M1atXkZqaCn9/f7z33nvYv38/ZDIZTpw4genTpyMtLQ1isbhdpUvSGv4bGxu5XrMmk4kLs0FBQXazX8TaJsff3x9jx46125DtqDouN3dycmq33Lyvf94NDQ0oKiqyWSVpVWMzSs9exLnKq1A13rgqsZNTa0sXhmGvm1X1cHeDwMkJmjYhl8drbY/jLHCCvk37no5aly67gbEWZuLxWlv8OFl7uXZeiZeH/7W20emNHZ4TcHV2hrPg2r5SAGaLBRaGgatza0i0MP8L12azBVqtFjqdFi0tLXB2doG3txeCgwLB8pxuOAstcHKCh7srdHrTdQWitFotFAoFhg0Ng7+fLzQt7fciOwucIHByal1azTCts78WBiaTBR7uLnBy4rf+PNlre15dBAgL8kfUcBFCg/y6HBPpOZZluT30bZeYWpcadzbzduXKFVRUVCA2Nhb+/v42GDXpbdZ9swqFAjU1NbBYLAgMDERISIhdFwZsbGzkJi5kMlm/1HxYuHAhli5dismTJ8NsNuNvf/sbSktLcfr06S6LD27atAl//vOfUV5ezt3G4/EgEon6fLxkYKMA60AKCgqQmpqKRYsWYcOGDdzVNpZlcfnyZchkMshkMhw9ehSJiYkQi8WQSCQYPnw4hdk2rHtjrGFWr9dzbVmEQqHN9o/YOtwMNm2XmyuVyj6foVcoFCgtLcXo0aMxdOjQXn3unmIYBpeq5DhVcQkVldU37Y3a2s/VDTweD0aTCS3daDHj4iyAm4sLWLDQ6Q3g83hwd3eDvpMlxB25Ogvg4uzc2tPUbIKzwAV6kwnGm/SdtXJ3dYHAWQCtrnXJrrOgdWaV7+R0ra8qwLKt7wVGkwlmkwn1ajWamrXg8fntKhpb/wpdXQRwcXaBVm8EwzDggQe+Ew9O11rnaJqaUFdfj2FhYXD39LpW+IltnXk1W+Di7AyG/V/4dnUWwNlZAD6PBwvLwGRunYV1d3FGmNAPo8JEGBrsT+8D/aTjElMvLy/u/cDLywuXL1/GhQsXEBcXBz8/P1sPl/QilmVRUVGB6upqxMTEcHtn2+6bFQqFdjMj29zcDIlEAm9vb+Tk5Nhsi5FSqURwcDAOHjyImTNndnqfTZs2YcWKFVCr1f07ODLgUYB1EFlZWXjkkUewZs0avPTSS12e1LAsi+rqamRmZkImk+Hnn3/GxIkTIZFIIBaLMXLkSDohaoNlWWi1Wi7MarXadm1Z+npJjlVtbS1Onz6NMWPGICwsrF9ek/xPx16z1v6SIpGoV3rNVlVVoaysDOPHj7e7K896gxHnKqtQfvEKKqvlYDqsxfVwb21Lo20xgGFYOF1bYgsAOv3Ne8B6urvCyckJZgsDgcAJLNNa8dd8k8e5uTjD2dkZOr0BDMvCxVkAF4EAPB6/dU+ohYHRZG6zZ5aF57Wqw9obVGFu+/wCZ2dodQZucTDDMGhpaYFOq4VOpwOPx0OAvy+8vb3Bc2oN1CzLtpuNdnLiw6TXobpWgUBhMDzcPdp8jQdPdzcArY8Dr3UVgJNT60wxw7b2iPV0c8EwUQBGhAQhNMiXVl7YWNttB/X19a2/K4bBmDFjEBoaSr+fAYRlWZw/fx5VVVVITExsN5Oo1+u5Wgp93W+2u7RaLe699144OTlh+/btPWq71tsqKioQFRWFkpISjB8/vtP7bNq0CX/4wx8QFhYGhmEQHx+Pt99+G+PGjevn0ZKBhgKsA/jwww+xZs0afPPNN0hPT+/241iWhVKp5MJsXl4eoqOjuTDbH/vvHI11CaBCoUBzc3Oft2Wxtsm5ePEiJk6ciKCgoF5/DdIzbftLKhQKtLS0tGvF0JOLGm3bIE2aNMnu98y1GAw4d6kKZy9egbpZC53BCIOx61lPHq+1crETn3+ttYsRLAsIBHx4uLvBaDJ32QbH1cUFLs4C8IBrbXpM4IFtrfjLtobj7nB1EcDDrXVJMnitP3MLy4KxtPZNbQ3K1o85Fl7c83ddUZjHAzzd3dCs0UKlboRWq4XFwnAzsx4eHvBwc4GrswtqlQo0qtUYOnQYPDxal5068VuXAmuuhX4+jwdXVwFcBK2zsKyFhYeHC8JDgjAyLAhDAh23WvpAZp2Zu3LlCgIDA6FWq8GyLLdiJzAw0G62n5Bbc/78eVy9evW68NpRV/tmrcXA+uOiRktLC+677z4YjUbs2rXLpp0JGIZBWloa1Go1fvnlly7vd/ToUZw7dw4TJ05EY2Mj3n//fRw6dAinTp2y+Uok4tgowDoAmUyG8PBwJCQk3PJzsCyLhoYGZGdnQyqVYt++fYiMjOSWGY8bN46uKnfQsS2Lr68v12u2N5bsMAyD8vJyKBQKxMXF3bDXL7Gdri5qCIXCGx4H1lYMcrkc8fHxDtcGSW8w4mKVHOcvV+NilbwbfWBZeHt6XHsf4cFoNsNgMF5X2bczzgIB3N1dYTb9b3bSwrQu7+1qubG1EFRr5eIbv4aLs6C1MNO152rt7cpDx8xoHTuso27djgqWZdHSokdTczOMhhbo9AY4ObuAZVuLroWGhsLd1Q0e7q7XZlYZ8Pl8sGzrjDVYgOUBAb5eGCYMQHhIIIL9Het4GGzazswlJCTAy8uLq6VgfT8wGAwIDAzkZuX6a8UO6R3W8Gr9/XZXV/1me1IMrKf0ej0eeOABNDY2Ys+ePfD19e311+iJZ599Frt27cIvv/zSoyBqMpkQExODBx54AG+88UYfjpAMdBRgB6nGxkZs27YNUqkUe/bsQVhYGBdmY2NjKcx2YDAYuJOWhoYGeHt7c2H2VvbFWCwWnDx5Ei0tLYiLi7P7Uv2klXWfnFwuv2GvWYZhUFpaiqamJiQkJDj875dhGFQp6lFZJcelajlq6xq4ZbQuzgK4u3a+R5XP58PdtbU4kcXCwGBsX/jI08MNPB6/3TLejpz4fLg4CyBwcgKfxwOutZ1pMZpgMplvGF49rHthW25cUdnDzQ1OfF5rO55Ovu4icIabmwv0BhMsLAPWYkGdUgGjsXWW2M3NHa5u7nBxc4eLi3Pr/laBM1xdBAj298HQYH+EhwTBy73vC62Q28eyLM6dO4eampouZ+as20+sS0ybm5vh6+vLXdyyl/2SpHMXLlzA5cuXkZiY2KPw2lFXxcB6c9+s0WjEww8/jNraWuzdu9fmBcSWLVuG7OxsHDp0CBERET1+/H333QeBQIDvvvuuD0ZHBgsKsATNzc3YuXMnpFIpdu3ahaCgIKSmpiI9PR2TJ0+mMNuB0WjkTlrq6+vh6enJhdnufBAaDAYUFRXByckJkyZNstsqh+TGOvaa9fT05JYVVlRUwGw2Iz4+fkDOyrQYjLhco0BtnQqV1UooGxo76cPaOQ83V+5n0lqB1wyD6cZFoVydneHq4twaWjvMyDrx+RAInODEb9tX1YmbCf3fTCrLzahaW+PwrxVgat1He61lTpsiTwKBU2uPWKZ1z63JzIBhGdTX1YHHmBE2dBjAa22do9NoYDIZEOTrjcjhoZg4ZiTCQ0W0NNjBWFdO9LSVinW/pFKp5N4PrEWgvL296TiwIxcvXkRlZSUSEhJ6fWVMV8fBre6bNZlMeOyxx3Dx4kXk5ubatO8wy7JYvnw5MjMzceDAAURFRfX4OSwWC8aNG4fFixfjgw8+6INRksGCAixpR6fTYffu3ZBKpdixYwe8vLyQlpYGiUSCadOm0X6fDjr2GHV3d+dm5Do7adFqtSgoKKA2OQOM9Tiora1FXV0d+Hw+hg4dipCQEJsV++hPeqMR1QoVquR1qKlrgEKlbrfkmMfjwcvDHSyL1pY8HR7P5/Hg6uIMp2uzrCwAi9kCJ4HTtcq9Btzsk8rd1QXOzgLo9J234bHycHcFn8+HTm/sdFbW1VkAVxdrf1YLnAVOcBY4gc/jg88HrlytgsFohCgkFEF+3ggJ9MVQoT9Cgvzh5eaMuro6bp+cs7NzuzZNA/04cHQsy+LMmTOor69HYmLiLa+csO6XtH4uODs7cyHG39+f3vdtyFqToC/Ca0cd983y+XzuOAgICLjp+ZTZbMYf/vAHnD59Grm5uQgODu7T8d7Mc889hy1btiA7O7td71dfX1/ub+XRRx9FWFgY1q1bBwB4/fXXcccdd2DUqFFQq9V47733kJWVhfz8fIwdO9Ym3wcZGCjAki7p9Xrs3bsXMpkM2dnZcHFx4WZm77zzTpo57MBisaCurg5yuRx1dXVwcXHhTl59fX2hVqtRXFyMoUOHUjXoAUin06GgoAA+Pj4QiUTcVfj+7jVrL9TNWsjrGlCnbkKduhlKVSMatbqbztS2BlFn6PRGmC0W8HiAE781RDo58QFe60wpzzqPyueBx+PBbLaAZVkw19rjWP+6WADgsXBzcYOFZWAxM+Dzwf398a7NxPKu7Vk1WxgwLNs682qxwM3VBR5urnBzEUClqIW/lztmTE3EEKE/BDc4AbVYLO3aNAHgZuS6c/JK+hfLsjh9+jQaGhp6ddm/tV2XdbUGwzDtikDZqm3bYFRZWYkLFy4gISGh32tO3GjfrKen53V7Wi0WC5599lnk5+cjLy8PISEh/TreznR1zvL111/j8ccfBwDMmjULI0aMwKZNmwAAK1euhEwmQ21tLfz9/ZGQkIA333wTcXFx/TRqMlBRgCXdYjQakZeXB6lUiqysLDAMg5SUFEgkEsyaNWtALpO8HdaTV7lczp28WiwWDBs2DKNHj6bwOsA0NzejoKAAIpEIY8aM4X6/1pMW6/7ptr1mB2OIMZrMqFc3o6FJg0aNFurm1v+atDrweXwYTGbou6ha3JbXtb2zOr3hurY/Vq2VhN3BAp3O4PJ4rS2C+HwetC1GOAuc4OflAR8vd/h6usPXywMBvl4I8PaEi4CPwsJCODs7Y9KkST3+vXXVpkkoFCIoKIguBtoYwzA4deoUt2e9r/pqsiyLpqYm7qJGS0sLAgICuFm5vqh0T1rZMrx21HbfbG1tLZYsWYLIyEgkJSVhyZIlGDNmDJYvX47Dhw8jLy+PqvUS0gkKsKTHzGYzfv75Z2zduhVZWVloaWlBcnIy0tLSMG/ePJs11bZH1jYqFy5cgL+/P5qbm8GyLDcj11/l90nfUalUKC4uxogRIzBixIgb9mhubGzkeg5bQ4y1Pc9gn4lhGAbaFgM0LXpodHro9AboDUa0GEzQG43QG4zg8fkwmsytxZQsrXtTrR9h/ysq5QR3V1eYLQx4PB4ETny4OLcWVHJ1doabqzPcXVzg6eEKDzdXeLq7wtPNFS7Onf/89Xo9CgoK4OnpiQkTJtz232vbk1dr7+mAgACu+A+FmP5lLbim0WiQkJDQrz//tkWgmpqauKJw1lk50jsuX76M8+fPIz4+3ubVeztz/vx5bN26Fbt370ZhYSFEIhE0Gg2++eYbpKSk0DkCIZ2gAEtui8ViwZEjR5CRkYGsrCyo1WokJSVBIpFgwYIFg7oSY9s2KtY2OdZ2RtaTV4vFws3IUU9BxyOXy1FaWoro6GiEhYV1+3Gd9ZptG2JoRYN9sC4L9/f3R0xMTJ+cSOp0Om5G7kaVrUnvYxgGJSUl0Ol0SEhIsOnfnbUonFKpRH19PTw8PLjPhsGwj76vXLlyBRUVFXYbXttiGAYvvfQSVyDp0KFD8PT0RGpqKsRiMebMmUMXuAi5hgIs6TUMw+D48eNcmK2trcX8+fMhkUiwcOFCh+uDeTssFgt3YtRVmxzrcjLrjJzRaERQUBBEIhHtjXIAV65cwblz5zBhwgQIhcLbeq5b7TVL+o5Go0F+fj5CQkL6bdl/Z5WtqZJt37C2MjMYDEhISLCrZdxms7ldESg+n8+9H9Cqne67evUqzp07h7i4OPj5+dl6ODfEMAxeffVV/PDDD8jLy8Po0aNhNBpx6NAh5OTkIDs7GyqVCklJSRCLxUhOTkZAQICth02IzVCAJX2CYRgUFhZCKpVCKpXi8uXLmDdvHsRiMRYvXgxfX98BezJmNBpRWFjYozY51mWF1jDb0tKCwMBAiEQi2iNnZ1iW5XoI9sWJkbXXrEKhgFqtphk5G2hsbERhYSGGDRuGyMhIm7xXmc3mdhXOrRWNrZVsB+r7Z3+wWCwoLi6G2WxGXFycXb+/ti3+o1AoYDab2xWBsuex29LVq1dx9uxZxMfH2314ZVkWb775Jr7++mvk5eUhJiam0/sUFxcjOzsbOTk5UCqVqKyspPcBMmhRgCV9jmVZlJaWIiMjAzKZDGfPnsXs2bMhkUi4q4gD5U1Yq9WisLAQPj4+GD9+/C1fKW+7R06j0SAgIAAikYiWl9qYtc1GXV0d4uPju9X393Z01nPYGma9vLwGzN+NPbHuaY6MjER4eLithwOAKhr3JovFgsLCQrAsi7i4OIda6dJ264FSqeT2T1uLQNFqjVZVVVUoLy9HXFwc/P39bT2cG2JZFu+++y42bNiA3NxcTJgwoVuPs+6ZJmSwogBL+pV1X6h1ZrakpAQzZ86EWCxGamoqgoODHfakXK1Wo6ioCGFhYRg1alSvfR/WPXLWQh9+fn5cmKUTlv5jsVhQWloKrVbb5bLwvtS253B9ff11bZoc9e/GniiVSpSUlGDMmDE92tPcnzqraBwYGMgVA6MZua6ZzWYUFhaCx+MhLi7O4YO/TqfjLnA1NjbC29u7XRGowfieUF1djbKyMsTGxtr9EluWZfHRRx/h/fffx759+xAfH2/rIRHiMCjAEpthWRbnz5+HVCqFTCZDfn4+pk+fDrFYjLS0NISGhjrMB7BcLsepU6cQFRWFYcOG9dnr6PV6KBQKyOVyruCLSCRCcHBwvweqwcRkMqG4uBgMwyA2Ntbms+AWi4XbI6dUKrk9csHBwfD396c9cregtrYWp06dwvjx4yESiWw9nG6hisbdZzKZUFhYCIFAcEutkOyddbWGtQiUm5sbN0s/WC5wOVp43bBhA95++23s2bMHU6ZMsfWQCHEoFGCJXWBZFpcvX4ZUKkVmZiaOHj2KyZMnQywWQywWY/jw4Xb7AVxZWYnz58/3SjGfnrAWfJHL5WhoaICXlxcXZqkFQ+8xGAwoKCiAm5sbJk6caHcnvl31mhUKhVTZupus++UmTpyIoKAgWw/nllFF486ZTCbk5+fD1dXVLv+Ge1vbC1x1dXXg8Xjce8JAXXJeU1ODM2fOYNKkSQgMDLT1cG6IZVn861//wquvvopdu3Zh+vTpth4SIQ6HAiyxOyzLorq6GjKZDDKZDL/88gsmTZoEiUQCsVhss6IqnY3z7NmzqK2tRWxsrE1L9JtMJi7M0l7J3qPVark2KmPHjrX7mU1rr1lrmLVWtqZes127dOkSLl68iNjYWLvfL9cTHSsae3h4cO8Jg6misdFoRH5+Ptzd3TFx4kS7/xvubQzDQK1Wc8fCQFxyXltbi9OnTztMeN28eTNWr16N7du3Y+bMmbYeEiEOiQIssWssy0KhUCArKwtSqRQHDhxATEwMF2bHjBljkxMx635IjUaD+Ph4u1q+a61eKpfLUVdXBzc3N+7ElfoJdp+1Em1v72nuL50tL7WeuFIxsP9tYbh69Sri4+MHdEGUzioaW5eX+vn5DdhQZzAYkJ+fDy8vr9sqqjdQtH1PUCqV0Gg08Pf352Zn7elzrLus23ccYfUEy7LYsmULXnjhBWRnZ2POnDm2HhIhDosCLHEYLMtCpVIhOzsbUqkU+/btw6hRoyAWiyGRSPpthsxoNKKoqAg8Hg+xsbF2fQXbupTMGmYFAgGCg4MhEokGzb6oW1FfX4/i4mKMHDnSbirR3i6tVsvNwliLgVkvbAy2YmDWYnIKhaJfqknbE4ZhUF9fzx0LwMCsaKzX61FQUAAfHx+HWD1hCx1bdnl5eXHHgiOs3JHL5SgtLcXEiRP7dfvOrdq6dSv+9Kc/ISMjAwsXLrT1cAhxaBRgiUOyLpXctm0bpFIpfvrpJwwdOpQLs5MmTeqTExadTsedFI0bN86hTvasJ67Wq+88Ho8LswN5FqanampqcPr0aYwdOxZDhgyx9XD6hLUYmPXE1Vq9dDDsn2YYBqdPn4ZarUZCQoJDzjr1lrYVjZVKJYxG44BYXqrX63HixAlu6b+9BzF7YN2GolQqUVdXB1dXV25m1h4/HxQKBUpKShwmvGZlZeHpp5/G999/j5SUFFsPhxCHRwHWBl577TX8/e9/b3fbmDFjUFZWBqD1w3fVqlX4/vvvYTAYkJSUhA0bNjhMZUxbaG5uxo4dOyCVSrF7924EBQUhLS0N6enpSExM7JUPX2ubnNDQUERFRTn0SVFXhX+Cg4MRGBhodycr/eXy5cuoqKhwiOVovaVjr1nrXkmRSOQQszA9wTAMTp48iZaWFsTHx1OV3ja6qmhsfV9wlJ9VS0sLTpw4gcDAQMTExAyo47e/dNV32F4Kw1nD64QJExAcHGzTsXTH9u3b8cQTT+Dbb79Fenq6rYdDyIBAAdYGXnvtNWRkZGDfvn3cbQKBgDthfvbZZ7Fjxw5s2rQJvr6+WLZsGfh8Pg4fPmyrITsUrVaL3bt3QyaTYceOHfD29kZaWhokEgnuuOOOW/rwVSgUKC0t7fM2ObbQsa+k2WxGUFAQRCKRXZys9AeWZVFRUYGqqirExcXZtCCXLXXcKzmQes2azWYUFxfDbDYjPj7eYWcX+0vHHqPWisbWHqP2SKfTIT8/H0Kh0Gb1EQYa6+eD9VgwGAztZun7ey+9UqnEyZMnHSa87tmzB4888gj+/e9/4/7777f1cAgZMCjA2sBrr72GrKwsFBUVXfe1xsZGCIVCbNmyBUuWLAEAlJWVISYmBkePHsUdd9zRz6N1bC0tLdi7dy9kMhlycnLg6uqK1NRUSCQS3Hnnnd06ib1w4QIuXbqE8ePHO8QH5u1gWRZNTU1cr1mDwcCF2YFaxZZhGJw5cwYqlQrx8fF2e3Le3zrOwliXnDtir1lrD1A+n4/Y2NgBeRz3JaPRyB0HbWfp7amisVarxYkTJzBkyBCHXyFjr1iWhVar5Y6F5uZm+Pn5cbOzfd2qyRpeHaVXc15eHu6//35s3LgRDz30EB2ThPQiCrA28Nprr+G9996Dr68v3NzcMG3aNKxbtw7Dhw9Hbm4u5s6di4aGBvj5+XGPCQ8Px4oVK7By5UrbDdzBGY1G5OXlISMjA9nZ2WBZFikpKZBIJLj77ruvu5JssVjw7LPPQqFQYPPmzYNuVq7tkkK5XI6WlhYEBARAJBJBKBQOiBksi8WCkydPQq/XIy4ubtAVM+ouaysO6yy9xWJpt+Tcnmfp7b2Pr6Oxx4rGGo0G+fn5CAsLw8iRIyko9BO9Xs/NzDY0NMDT05M7Fnr7wkZdXR1OnjyJsWPHIiQkpNeet6/8/PPPWLJkCT766CM88cQTdEwS0ssowNrArl27oNFoMGbMGNTU1ODvf/87qqqqUFpaim3btuGJJ56AwWBo95gpU6Zg9uzZWL9+vY1GPbCYzWYcOnQIW7duRXZ2NlpaWpCSkgKxWIw5c+bAbDbj/vvvx7lz55CZmYlx48bZesg2Z73yLpfLodFoEBAQwM3COGJLFpPJxK2CsPdq0vakY69Z6yy9PRb+aWlpaVd0zZFmjR0BwzDtZumte+n7c69kc3Mz8vPzMWzYMLvpET4YmUwm1NXVcUWgevPChrUqvKOE16NHjyI9PR3r16/HH//4RzomCekDFGDtgFqtRnh4OD744AO4u7tTgO1nFosFhw8fRkZGBrKystDQ0ICgoCC4uroiIyMDkZGRth6i3WlpaeHCrCO2ZLG22PDw8MCECRNoVu4WdVX4xx4ubGi1WhQUFCAoKAjR0dF0EtnHbHFho7GxEQUFBRgxYgQiIiJ6/fnJrel4YYNhmHYXNnqyhN8aXmNiYhyiKvyJEyeQlpaG119/HcuXL6f3HUL6CAVYOzF58mTMmzcP8+fPpyXENnTu3DnMnTsXHh4eYBgG1dXVWLBgASQSCZKSkuDt7W3rIdod6zIyuVwOtVrNFXsRiUR22aJEo9GgsLAQgYGBiI6Oplm5XqTT6bgAY8sLG9ZZubCwMIwaNYpOIvtZ2wsbSqUSGo0G/v7+XBGo3jgW1Go1CgsLERkZOWB6NQ9E1gsb1hY91q0o1kB7o+rWKpUKRUVFiI6ORmhoaD+O+tYUFRUhOTkZ//d//4dVq1bR+w4hfYgCrB3QaDQYPnw4XnvtNTz22GMQCoX47rvvcO+99wIAysvLER0dTUWc+tjx48eRkpKChx9+GO+//z4AoKCgAFKpFDKZDJcvX8a8efMgkUiwePFi+Pj40AdUB9ZiLwqFAiqVCl5eXlyA8fLysvXwuFZIQ4cOpb1yfazj/rj+6jVrDTY0K2c/rCs22lY0ti4vvZVjoaGhAUVFRRg1atSAqwo/0LUtAtXU1NRldWtHC6+lpaVYtGgRVq1ahb/+9a/02UJIH6MAawMvvvgiUlNTER4ejurqaqxduxZFRUU4ffo0hEIhnn32WezcuRObNm2Cj48Pli9fDgA4cuSIjUc+cOXk5OChhx7Cm2++iT//+c/XfZ1lWZSWlmLr1q2QyWQ4d+4c5syZA7FYjOTkZAQEBNAHVgcmk6ldf1F3d3eb9hdVKpUoKSnBqFGjMHz48H597cHOaDSirq4OcrkcKpWKOxZ6u9iLdbnhQGx3NVB01XdYKBR266KgNdiMHj0aQ4cO7adRk75gMBi4Y0GlUnHHgqurK86ePYvo6GiEhYXZepg3debMGSxatAjPPfcc1q5d22+fbZ9++inee+891NbWYtKkSfjkk08wZcqULu+/detWrFmzBpcuXUJUVBTWr1+PxYsX98tYCeltFGBtYOnSpTh06BDq6+shFAoxY8YMvPXWWxg5ciSA1pmLVatW4bvvvoPBYEBSUhI2bNjgEMULHNGnn36Kl19+Gd988w3uueeem96fZVmUlZUhIyMDmZmZKC0txcyZMyEWi5GamgqhUEhhtoOu+ouKRKJ+mcmurq7GmTNnMG7cOPo7srHOqthaw6yfn98tHwtyuRylpaUYO3asQ+yVIz2vaGy9QOEos3Kk+6zHQlVVFVQqFQQCAUJCQuy+bdfZs2exaNEiPP7443j77bf77bP/hx9+wKOPPoqNGzdi6tSp+PDDD7F161aUl5d32u7vyJEjmDlzJtatW4eUlBRs2bIF69evR0FBAcaPH98vYyakN1GAJYPa/v37sXTpUuTk5GDatGk9fjzLsjh//jwXZgsKCjB9+nSIxWKkpaVhyJAhFGY7sFgsqK+v55aROTk5cWH2dgJMVy5duoSLFy9i4sSJCAwM7NXnJrens16zQqEQIpGoRyet1dXVKCsrGxS9mgeqzioaW4tABQYGQqVSoaSkxGGK+ZCesy7/HzlyJDw9PbljwWKxIDAwkCsIZi99nC9cuICFCxfi/vvvx3vvvdevIXvq1KmYPHky/vnPfwJo/fsZNmwYli9fjtWrV193//vvvx9arRbbt2/nbrvjjjsQGxuLjRs39tu4CektFGDJoMayLORyea/MyrEsi8rKSkilUmRmZuLXX3/FlClTIBaLIRaLMWzYMAqzHbQ9aVUoFLccYDrDsizOnj2L2tpaxMXFwcfHpxdHTnrbrfaavXz5MioqKhAbG4uAgIB+HjXpC51VNLaeoI8cOdKuWjWR3mGtKN1xXzPLsmhqauKKQFkrnVuLQNmq6n1lZSUWLlyI1NRUfPzxx/0aXo1GIzw8PJCRkQGJRMLd/thjj0GtViM7O/u6xwwfPhwvvPACVqxYwd22du1aZGVlobi4uB9GTUjvogBLSB9gWRZVVVWQyWSQyWQ4fPgwYmNjIZFIIBaLERERQWG2g44Bxtp6wRpgenKCwDAMTp06hcbGRsTHx8PDw6MPR056m/Wk1dqqqbOWLCzL4uLFi6isrER8fDx8fX1tPWzSB2pra1FaWorg4GDodLo+qWhMbMsaXkeOHHnT+gTWSudKpbJdQTBrEaj++FytqqrCggULsGDBAnz22Wf9vry5uroaYWFhOHLkSLuVY3/5y19w8OBBHDt27LrHuLi4YPPmzXjggQe42zZs2IC///3vkMvl/TJuQnqTfazDIGSA4fF4GDp0KJ5//nksX74ccrkcWVlZkEql+Pvf/46xY8dCLBZDIpFg9OjRFGYB8Pl8BAQEICAgAGPGjOFmYMrKymAymbgwGxQUdMO+rWazGSdPnoTRaMTkyZNv2KaB2CcejwdfX1/4+vpi1KhR0Gq1kMvluHTpEk6dOoWAgACwLIvm5mYkJiZSe6sBqqamBmfOnMGkSZMgFAoB/K+icW1tLcrLy/utujXpG01NTSgoKEBkZGS3iut5eHhgxIgRGDFiRLuCYBcuXICbmxt3YcPX17dPPldra2uxePFizJ49Gxs2bLDbvbmEDHQUYAnpYzweDyEhIfjjH/+IZ555BiqViguz77zzDqKiopCWlob09HTExMTQByJaf2Z+fn7w8/NDVFQUmpuboVAoUFFRgdLSUm42TigUttsPZTQaUVhYCIFAgMTERLvZK0VuHY/Hg5eXF7y8vDBy5EhotVqUlpaiubmZK6hmDTD22HeY3JqqqiqUl5dj0qRJ7fauu7u7Izw8HOHh4dcFGGt16+5WNCa21dTUhPz8fERERNxSL18XFxeEhYUhLCwMZrMZ9fX1UCqVKCwsBJ/P52ZmAwICbnjRs7sUCgWSk5MxZcoUfPnll73ynLfCehG348zpjbZDhYSE9Oj+hNg7WkJMiI1Y93nl5ORAJpNhz549GD58OBdmJ06cSGG2A5Zludk4hUIBrVbLFffw9vZGSUkJvLy8MGHCBPrZDUAMw6C0tBQajQbx8fEA0K7XrJeXF0QiEc3GObirV6/i7NmzPdrXbA0w1orG1uJwXVU0JrbV3NyM/Px8bja1N7XdjqJUKmEymdoVgbqVPdR1dXVITk5GTEwMtmzZYvOLo1OnTsWUKVPwySefAGj9nocPH45ly5Z1WcRJp9Nh27Zt3G3Tp0/HxIkTqYgTcUgUYAmxE01NTdixYwdkMhl27dqF4OBgLswmJCTQCVgndDod5HI5ampqoNVq4erqioiICK6XIBk4LBYLiouLYTQaER8fDxcXl3Zf76rvcG/3miV96/Llyzh//jzi4uLg5+d3S89xs4rGtpo5I62s4TU8PBwRERF9+losy0Kj0XC1FbRaLfz9/bktKd3ZQ93Q0ICUlBSEh4fjxx9/vO69xxZ++OEHPPbYY/j8888xZcoUfPjhh/jxxx9RVlYGkUiERx99FGFhYVi3bh2A1jY6d999N9555x0kJyfj+++/x9tvv01tdIjDogBLiB3SarXYvXs3pFIpduzYAV9fX6SlpUEikWDq1Kl0AtZGQ0MDioqKEBoaCjc3NygUCjQ2NsLX15eWlg4QZrMZhYWFAIDY2NibzqC0nY1TKpW91muW9C1ry6veLMrVWUXjoKAgbnkpVTTuXxqNBidOnMDw4cMRGRnZ76/f0tLCXehSq9Xw8vLiLmx4e3tfd6G4sbERqampEIlEkMlkdnVh9J///Cfee+891NbWIjY2Fh9//DGmTp0KAJg1axZGjBiBTZs2cfffunUrXnnlFVy6dAlRUVF49913sXjxYhuNnpDbQwGWEDvX0tKCvXv3QiqVYtu2bXBzc0NqaiokEgnuvPNOmy9lsiWFQoHS0lKMHj0aQ4cO5W43GAzcCWtDQwNX6EUkElFFYgdjNBpRUFAAFxcXTJo0qccXbxiGaRdmra2agoODERAQQCsb7ETbitJ91fLKugXB+t5AFY37l0ajQX5+PoYOHYqRI0faejgwGo2oq6uDUqnETz/9hM8//xxz5sxBeno65s+fj5aWFkgkEnh7eyMnJ4eOD0LsCAVYQhyI0WhEbm4uMjIykJ2dDR6Ph+TkZKSnp2PmzJl2sbSpv1j3yY0fPx7BwcFd3q9toZf6+np4enpyYba/2i6QW6PX61FQUABPT89e2dfcWa/Ztu15aGVD/2NZFhcuXMCVK1eQkJDQrxWlO87GUUXjvqPVanHixAmEhYVh1KhRth7OdTQaDXJycpCTk4ODBw8CAMLCwuDk5ITc3FyuCvZgxrIsfV4Su0EBlhAHZTabcfDgQWzduhXZ2dkwGAxITk6GRCLBnDlz7GqpU29q2/8zNjYW/v7+3X6syWRCXV0dV+jFzc2NK/pD+yTti06nQ0FBAfz9/TF27Nhe/9207TWrUCig1+uv6zVL+hbLsqioqEB1dTUSEhLg5eVls7G0vdClUqng7u7OzdRTRePb0za8jhw50u5/ls3NzXj44YdRWVkJs9mM2tpaJCUlQSKRICUlpV1V7MHCbDZzq71aWlpoWw6xOQqwhAwAFosFv/zyC6RSKTIzM9Hc3IyFCxdCIpFg3rx5A2bZLMuyKC8vh1wuR3x8/G3N1lgsFi7MWvdJWsNsX/UQJN1jXWoYEhLSL32SO1taGhAQwC0tHagXg2yJZVmcPXsWcrkcCQkJdjXj2VVFY6FQCH9/f1p23gNarRb5+fkYMmQIRo0aZffvq3q9HkuXLkVzczN2794NHx8fnDp1CllZWcjKykJRURFmzJgBiUQCsVjc50Wo7M1TTz0FjUaDxMRELFy4EOPGjbP1kMggRQGWkAGGYRj8+uuvXJhVKpVYsGABJBIJkpKSbDrLcTusLVSam5sRHx/fq1eALRYLV7VUoVBQCw4bamxsRGFhIYYNG4bIyEibnPC2tLRwxwIVBOt91gtRSqUSCQkJdn2BjWEYNDQ0cMcDwzDczCxVNL4xnU6HEydOICQkBFFRUXYfXg0GAx5++GHI5XLs3bu309U9V65cQU5ODrKysnDgwAH88Y9/5FrZDESHDh3CzJkzAQDLly+HUqnEq6++in/+85/w8/NDfHw8lixZYuNRksGIAiwhAxjDMCgoKEBGRgZkMhmuXr2KefPmQSwWY/HixQ6zNM5sNqO4uBhmsxlxcXF9utfXesIql8u5FhzW8EJFf/qWSqVCUVERRo4cifDwcFsPB8D1BcGsVUut+yQd4e/HnrAsizNnzkClUiEhIcGhLgh0VtHY2l+UKhq3p9PpkJ+fj+Dg4H5ZRXG7TCYTHn30UVRWVmL//v3dWiasVqtRX19vFwWpehvLsjh16hQmTpyIw4cPY9q0aVi5ciVSU1MxZ84cKJVKZGZm4vz587j77rupmjHpdxRgCRkkGIZBSUkJF2YrKiowd+5cpKWlISUlBf7+/nZ5kmEwGFBYWAgXFxdMnDixX6susywLtVoNuVzOFf2h2Ze+oVQqUVJSgjFjxiAsLMzWw+lUx16zbm5uXJh1lItBtmQ9KW5sbERCQoJDV3XtqqJxT/qLDlQtLS04ceKEw4RXs9mMJ598EmfOnEFubu4NiwIONo899hgSEhLw/PPP48svv8TWrVuxYcMGjBo1Co2Njdi0aROqq6uxfv16Ww+VDDIUYAkZhKyzIBkZGcjMzMSpU6cwc+ZMSCQSpKamIigoyC5OOqyFfHx9fTFu3Dibzn5ai/5Yw6zRaGxX9GcwtzO6XTU1NTh9+jTGjx8PkUhk6+F0S9s91HV1dRAIBFx4sdeLQbbEMAxOnTrFbQEYaAGvq4rGQqHQYbdt3ApreBUKhRgzZozd/x1YLBb88Y9/REFBAfLy8hASEmLrIdnc/v37MXfuXADAunXr8O9//xunTp2Cq6sr3nnnHSiVSjz77LMYNWoUdDod7rrrLmzYsIHrQUtIf6AAS8ggZ60Eag2zhYWFmD59OiQSCdLS0hASEmKTk5CmpiYUFhb2WyGfnmBZFhqNhguzLS0ttJTwFlnbIU2cOBFBQUG2Hs4tYRim3R5qALTsvA3r/nWtVov4+PgBXxTL2l90sM3U6/V6nDhxAoGBgYiOjrb779NiseD555/HL7/8ggMHDtjtyo/+tG/fPixYsADPPPMM/vGPf8DNzQ0LFy7EkiVL8Ic//AGFhYXYtWsXLl26hKeeegqTJ0/G3XffjfXr1+OOO+6w9fDJIEIBlhDCYVkWlZWVkEqlkMlkOHbsGKZOnQqxWAyxWIyhQ4f2y0mJSqVCcXExIiIiEB4ebvcnQhqNptMKtsHBwYOqN29PXbp0CRcvXuxxOyR7Zl12bj0ezGbzoO41yzAMTp48Cb1ej/j4+EH399BZReO2M/UD5eKGNbwGBAQgJibG7t+zGYbBCy+8gH379iEvL89u9tzbWkFBAWbOnAkej4e0tDT89a9/xcaNG6HT6fDVV18BAC5cuIB9+/bh//2//4eRI0dCJBLh66+/tvHIyWBDAZYQ0imWZVFVVQWZTAapVIojR44gLi6Oax8wYsSIPjlJkcvlKC0tRUxMDEJDQ3v9+fuaTqfjwktTUxP8/PwgEokgFAoH3LLJW8WyLM6fP4+rV68iPj4ePj4+th5Sn+is1+xgmqm3WCw4efIkjEYj4uPjB/z3ezNdVTQWCoUOfXFDr9cjPz8f/v7+DhNeV69ejZycHBw4cACRkZG2HpLNXblyBSEhIXB2dsY777wDDw8PHD9+HE5OTrjrrrvwt7/9Df/9738xf/587jE1NTVgGIabuWYYZsBckCH2jwIsGXCqqqrw8ssvY9euXdDpdBg1ahS+/vprJCYmAmg9qVy7di2+/PJLqNVq3Hnnnfjss88QFRVl45HbL5ZlIZfLkZmZCalUioMHD2L8+PEQi8WQSCS91iLhypUrOHfuHCZMmAChUNgLI7ctvV7Pnayq1Wr4+PhwvWYdqfpqb7K2UFEoFHbX/7OvdZyp9/f352bqB9qyWovFgqKiIlgsFsTFxQ368NqRtaKxdd9s24sbQUFBDjNTbTAYcOLECfj5+WHs2LEOEV7XrFmDH3/8EQcOHKDPfQAHDhzA2rVr8eSTT+LBBx/Epk2b8M033yAvLw+rV6/GuXPnUFBQgOXLl+Oll17q9DkovJL+RgGWDCgNDQ2Ii4vD7Nmz8eyzz0IoFOLcuXMYOXIkV+p+/fr1WLduHTZv3oyIiAisWbMGJSUlOH36NM2QdQPLsqivr0d2djakUin279+P0aNHIy0tDenp6bd0Bb7tjFxsbCz8/Pz6ZvA2ZDQaufCiUqng5eXFhdnBEuIYhsHp06ehVqsdroVKb+vYa9bHx4cLs/bcF7U7zGYzioqKwLIs4uLiqMDZTXRW0djPz487Huz1c8lgMCA/Px8+Pj4YN26c3YdXlmXxxhtvYNOmTcjLy0NMTIyth2QXmpqa8Nxzz0GtViMlJQVPPfUUHnzwQSxZsgT33XcfPvroI7zyyiuYOnUq9u3bZ+vhEgKAAiwZYFavXo3Dhw/j559/7vTrLMsiNDQUq1atwosvvggAaGxshEgkwqZNm7B06dL+HK7Ds+73y8nJgUwmw08//YTw8HAuzE6YMOGmV2VNJhPKy8uhUqkQHx8/KCp2WtuxyOVyqFQquLu7c2HWy8vL7k8Eb4XFYkFJSQlaWloGRSGfnjAYDNxMnPXihqP2mjWbzSgsLASfz0dsbKzDLou1pRtVNLaX48FoNOLEiRMOFV7Xr1+Pzz77DLm5uZgwYYKth2QXrDOner0eb731FvLz8zFhwgQ0NjbC29sb7733HgBg7969mDlzJr1vE7tBAZYMKGPHjkVSUhKuXr2KgwcPIiwsDM899xyeeuopAK3FB0aOHInCwkLExsZyj7v77rsRGxuLjz76yEYjHxiampqwfft2yGQy7N69GyKRiAuz8fHx14VZrVaLJUuWICoqCu+++67dzjT0JbPZjLq6OsjlctTV1cHV1ZULswOlYqnZbEZxcTEtJ+0Gk8nUrj2PI1WwNZlMKCwshEAgwKRJkyi89gJ7rGhsDa/e3t4YP368XR+TQGt4/fDDD/HBBx9g3759iIuLs/WQ7ArLsuDxeDCZTPjiiy+Qn5+PS5cuIT8/H7t378a0adO4+1osFvq7JnaBAiwZUKwB6IUXXsB9992H3377DX/+85+xceNGPPbYYzhy5AjuvPNOVFdXY8iQIdzjfve734HH4+GHH36w1dAHHK1Wi127dkEqlWLHjh3w9/dHWloaxGIxpk6dCpVKBbFYDLPZjO3bt1PzeLSeHNTX13NhViAQcCerfn5+dn+i2BlrqHFycsKkSZNoOWkPWI8HhUIBpVIJJyendseDPe05MxqNKCgogKurKyZOnEgnuX3A2ntYqVRyx0N/VzQ2Go3Iz8+Hp6cnxo8fb1fHYGdYlsWnn36KdevW4aeffsLkyZNtPSS7ZA2xDMNAKpVi27Zt+Pbbb7Fx40Y8/fTTth4eIdehAEsGFBcXFyQmJuLIkSPcbc8//zx+++03HD16lAKsjbS0tOCnn36CVCrF9u3b4ezsDGdnZwwZMgRZWVkDpoVKb2IYpl144fF4XHhxlPYbBoMBBQUFcHd3x4QJEyjU3Ia2vWaVSiVYluXCS2BgoE2PB2uo8fDw6Na2AXL72lY0ViqVsFgsfV7R2BHD65dffom1a9di165dmD59uq2HZNesIRYAfv31V/znP//B66+/jsDAQBuPjJDrUYAlA0p4eDjmz5+Pf/3rX9xtn332Gd58801UVVXREmI7UFpairlz5yIwMBD19fVgWRYpKSlIT0/HzJkzaXlpJxiGgVqthlwuh0KhsKvw0pWWlhbk5+fD19cX48aNs8sxOqqOvWZNJhOCgoIgEokQGBjYr7Pc1kI+Xl5eDhFqBqIbtWvqrYrGJpMJ+fn53MUoe/89syyLzZs3Y/Xq1di+fTtmzpxp6yE5hLYh1ro/1mw208oZYnfoiCQDyp133ony8vJ2t509e5ZrUh4REYGQkBDs37+fC7BNTU04duwYnn322f4e7qDz22+/YfHixfjDH/6At99+G2azGQcPHkRGRgaeeuopmEwmJCcnQyKRYPbs2VQw4ho+n4+AgAAEBAQgOjoajY2NkMvlKCsrg9lsbhde7GGWU6vVIj8/H0KhENHR0Q659Nme8Xg8+Pv7w9/fH6NHj0ZzczMUCgXOnz+P0tLSfus1a+3/6evri7Fjx9p9qBmoeDwefH194evri6ioKGg0GiiVSly5cgWnT5/mKhoLhcJbqvxtDa9ubm4OE163bNmCl19+GTk5ORRee6Dte7X190zhldgjmoElA8pvv/2G6dOn4+9//zt+97vf4fjx43jqqafwxRdf4KGHHgLQ2kbnnXfeaddG5+TJk9RGp4/99NNPWLJkCV5//XWsWLHiuq9bLBb88ssvyMjIQGZmJjQaDRYtWgSJRIJ58+YN6pYrXels5sUaZoOCgmxy4tHU1ISCggKEhYVh1KhRFF77Wdt2LM3NzVyvWaFQ2Kvvb9YZdn9/f4fo/zlYWXtRK5VKNDQ09LjCtclkare32RHC69atW7Fs2TJIpVIkJSXZekiEkD5AAZYMONu3b8df//pXnDt3DhEREXjhhRe4KsRA6wfc2rVr8cUXX0CtVmPGjBnYsGEDRo8ebcNRD2xbtmzBU089hS+//BIPPvjgTe9vsVjw66+/QiqVIjMzE3V1dUhKSoJEIsGCBQsGRaudnmJZFhqNhgsvOp0OAQEBEIlEfT4TZ6VWq1FYWIgRI0YgIiKiz1+P3Ji1HYtcLu/VXrM6nQ75+fkICgqiGXYH0lVFY6FQCF9f3+t+j9bw6uLigkmTJtl9eAWArKwsPP300/j++++RkpJi6+EQQvoIBVhCSJ+yzrxmZGRgwYIFPX48wzDIz8/nZmavXr2K+fPnQywWY/HixfDx8emDUTs+60ycXC6HRqOBv78/F2b7Yml2fX09iouLERUVhWHDhvX685Pb07HXrKenJxdme9J72Lo8XCQSYfTo0RReHVTHCtd8Pr9dkTiGYVBQUACBQIDY2FiHCK/bt2/HE088gW+//Rbp6ek2GcOlS5fwxhtvIDc3F7W1tQgNDcXDDz+M//u//7vhXuRZs2bh4MGD7W575plnsHHjxr4eMiEOiQIsIaRPmUwmnDt3DmPHjr3t52IYBidPnuTC7Pnz5zF37lykpaUhJSXFYVvN9LWWlhYuzDY1NcHX15frNdsby0rlcjlKS0sxduzYdtW9iX3q2Gu2u72HNRoN8vPzMWTIEERFRdHf2gBhrWhsvcBhsVjA4/Hg6uqKhISEXikC1df27NmDRx55BF999RV+97vf2Wwcu3fvxg8//IAHHngAo0aNQmlpKZ566ik88sgjeP/997t83KxZszB69Gi8/vrr3G0eHh50gZaQLlCAJYQ4JJZlcebMGWRkZEAmk+H06dO4++67IZFIkJKSgqCgIDrB7oRer+eWlarV6tteVlpdXY2ysjJMmDABQqGwD0ZM+lJ3e81aw2tYWBhGjhxJf1sDlMlkwokTJ2A2m8Hn86HX6xEQEMAtNbbHMJubm4ulS5di48aNeOihh+zu2Hzvvffw2Wef4cKFC13eZ9asWYiNjcWHH37YfwMjxIFRgCWEODyWZVFRUcGF2aKiItx5552QSCRIS0uDSCSyu5Mae2A0Grkwq1Kp2hV46c4+48uXL6OiogKxsbEICAjohxGTvtS2t2jbdk1eXl64cOECwsPDERkZaethkj5isVhQUFAAPp+P2NhYODk5XVcU7HYrGve2Q4cO4b777sPHH3+Mxx9/3C7f51955RXs3r0bJ06c6PI+s2bNwqlTp8CyLEJCQpCamoo1a9bc1l51QgYyCrCEkAGFZVlcunQJUqkUMpkMx48fxx133AGxWAyxWIywsDC7PMmxNeuyUrlcjvr6eri7uyM4OBgikei6PZIsy+LChQu4fPky4uPj4evra8ORk77AsiwaGxtx5coV1NbWgsfjcRc3bFXhmvQdi8WCwsJCAEBcXFyn7bhut6Jxbzt69CjS09Px7rvv4plnnrHL9/WKigokJCTg/fffb1dMsqMvvvgC4eHhCA0NxcmTJ/Hyyy9jypQpkMlk/ThaQhwHBVhCyIDFsiyuXr0KmUwGmUyGw4cPIyEhARKJBGKxGOHh4XZ50mNrZrMZ9fX1kMvlqKurg4uLCxdmvb29ce7cOdTW1iIhIYEqQg9g1qrSERERCAgI4GbiWlpa7H5ZKek+i8WCoqIiMAyD+Pj4bvWSNplM3J7Z+vp6uLq6cmG2s4rGve23336DWCzGG2+8gWXLlvX5661evRrr16+/4X3OnDmD6Oho7t9VVVW4++67MWvWLPzrX//q0evl5uZi7ty5qKiowMiRI29pzIQMZBRgCSGDAsuyqK2tRWZmJqRSKQ4dOoQJEyZALBZDIpFQz9IudNwjyTAMeDweYmJiEBISQj+zAaqhoQGFhYWdVpXur16zpO+1Da9xcXG3NLPeVUVjoVCIgICAXq9gXFhYiJSUFLzyyit44YUX+uU9SKlUor6+/ob3iYyM5C7mVFdXY9asWbjjjjuwadOmHv8MtFotvLy8sHv3buplS0gnKMASQgYdlmVRV1eH7OxsSKVS5ObmYsyYMUhLS4NEIkFMTAwFsw4YhkFJSQkaGxvh7++P+vp68Hg8CIVCiEQi+Pv7O0SrDXJz1pZIo0ePxtChQ294X2uvWYVC0StFwUj/sVgsKC4uhtlsRnx8fK8sC++sonFQUBCCg4MRGBh4269RUlKCxYsX48UXX8Tq1avt8n26qqoKs2fPRkJCAr799ttuzWh3dPjwYcyYMQPFxcWYOHFiH4ySEMdGAZYQMqixLAu1Wo2cnBxIpVLs3bsX4eHhEIvFSE9Px/jx4wd9MLOe6BqNRsTHx8PFxQUsy7Yr+GOxWLgwGxAQcEsnbcT26urqcPLkSURHRyM0NLRHj7UWBbMuK73VXrOk7zEMg+LiYphMpl4Lrx2xLIumpiZuZvZ2l56fPn0aixYtwp/+9CesXbvWLo+nqqoqzJo1C+Hh4di8eXO798GQkBDuPnPnzsU333yDKVOm4Pz589iyZQsWL16MwMBAnDx5EitXrsTQoUOv6w1LCGlFAZYQQtpoamrC9u3bIZVKsXv3boSEhHDLjOPj4wddmDWZTCgqKgIAxMbGwtnZ+br7WAv+WHvNmkwmBAUFQSQSISgoiMKsg1AqlTh58mSv9PPtrNdsf+6RJF2zhlfrBanO/qb7wu1UND579iwWLVqEJ554Am+99ZbdHj+bNm3CE0880enXrKfbly5dQkREBPLy8jBr1ixcuXIFDz/8MEpLS6HVajFs2DCkp6fjlVdeoT6whHSBAiwhhHRBo9Fg165dkEql2LlzJ/z9/bllxlOmTBnwwcxoNKKgoAAuLi6YNGlSt75flmXR3NzMhVm9Xs8tIQwKCuq3k2XSMwqFAiUlJRg/fjxEIlGvPndXeySDg4Np6Xk/YxgGJ0+ehMFg6Nfw2pG1H7VCoeAqGnt7e0On0yEhIaHdMXH+/HksWrQI999/P9577z06XgghFGAJIaQ7WlpasGfPHkilUmzfvh0eHh5IS0uDWCzG9OnTB1xbEb1ej4KCAnh5ed3WMmqNRsOFWa1Wi8DAQKpea2dqa2tx6tQpTJgwAcHBwX36Wl31mg0ODqal533Muo+9paUFCQkJdnMxyVrRODc3F8uXL0dQUBDmzZuHe+65B5GRkUhJSUFaWho++ugjCq+EEAAUYAkhpMf0ej32798PmUyG7OxsODk5ISUlBenp6bjrrrvs5sTwVul0OuTn5yMgIABjx47tteV6Op2OC7Ntq9cGBwfD1dW1V16D9Ex1dTXKysowceJEBAUF9etrt116rlAoYDQa283WD7SLQrZkr+G1o+bmZmRnZyMnJweHDh0CwzAYPnw43n//fcybN48uehFCAFCAJYSQ22IymXDgwAFIpVJkZWXBZDIhJSUFEokEs2bNcrhgptFokJ+fj5CQEIwePbrP9prp9XouzDY2NsLX15cLszfbD0d6R1VVFcrLyzFp0iQEBgbadCwsy3Kz9QqFgmbrexHDMNz+yoSEBIf4WdbU1GDRokUYM2YMRowYgaysLDQ1NSE5ORnp6elYtGgR9aAmZBCjAEsIIb3EbDbjl19+QUZGBrKysqDRaLB48WJIJBLMnTvX7oNZY2MjCgsLMWzYMERGRvZboRSDwcAFl4aGBnh7e3Nh1tPTs1/GMNhcuXIF586dQ2xsLAICAmw9nOt0VfAnODiYes32gCOGV7lcjsWLFyMhIYGr5MuyLPLz85GZmYnMzExcuHAB8+bNQ3p6OtLS0iAUCm09bEJIP6IASwghfcBiseDXX3/lwmxdXR0WLlwIiUSCBQsW2F0wU6lUKCoqwqhRozB8+HCbjaOrViwikQienp52W33UkVy+fBnnz59HXFwc/Pz8bD2cm7LO1lt7zdIFju5hWRalpaVobm5GYmKiQ4TXuro6LF68GOPGjcN///vfLpeRl5eXIysrC5mZmXB2dsbPP//czyMlhNgSBVhCCOljDMPgxIkTyMjIQGZmJqqrqzF//nyIxWIsWrTI5q0SlEolSkpKMGbMGISFhdl0LG2ZzWYuzNbV1cHNzY0Ls97e3hRmb8GlS5dw8eJFxMfHw9fX19bD6THqNds9LMvi1KlTaGpqQkJCgkNsZVCpVEhJSUFERAR+/PHHbu/TNRgMDvH9EUJ6DwVYQgjpR9YejFKpFDKZjFsKl5aWhuTkZPj5+fXrSXhNTQ1Onz7dJ+1TepPFYuH6iiqVSjg7O3NhlvqKds+FCxdw+fJlxMfH2/yiSW8wm83tes26uLhQr1m0htfTp09DrVYjMTHRIcJdY2MjUlNTIRKJIJPJHGLMhBDboQBLCCE2Yj3RzMjIgEwmw5kzZzBr1ixIJBKkpKQgMDCwT0/CrfsgbVGB9nYwDNNlX1E/Pz9qtdEBy7I4f/48rl69ioSEBHh7e9t6SL3OYrFApVJxxwSPxxuUvWbbhteEhASH2C/c3NwMsVgMHx8f5OTkOMSYCSG2RQGWENIjI0aMQGVl5XW3P/fcc/j000+h1+uxatUqfP/99zAYDEhKSsKGDRvsenbPHrAsi3PnznFhtri4GDNmzIBEIuFmJnozzF68eBGXLl1CbGws/P39e+15+1tnfUWtwSUgIGDQBJeusCyLiooKVFdXIyEhYVBUbmUYBmq1mjsmLBYL12s2MDBwwPaaZVkWZ86cgUqlQmJiokMEQa1Wi3vuuQfOzs7Ytm0b7WkmhHQLBVhCSI8olUpYLBbu36WlpZg/fz7y8vIwa9YsPPvss9ixYwc2bdoEX19fLFu2DHw+H4cPH7bhqB0Ly7K4ePEit8z4t99+w7Rp0yAWi5GWloawsLBbDrPWQFNVVTVglpJasSzbLriYzeZBEVy6wrIszp49C7lcjoSEhEEZDjr2mjUYDFyvWaFQOGB6zbIsi7KyMtTX1ztMeG1pacGSJUtgsViwc+fOQXFxhRDSOyjAEkJuy4oVK7B9+3acO3cOTU1NEAqF2LJlC5YsWQIAKCsrQ0xMDI4ePYo77rjDxqN1PCzL4sqVK5DJZMjMzMThw4eRmJgIsVgMsViM8PDwbodZ60muUqkc8IGGZVk0NTVxvWaNRiMXXIKCggZMcOmK9XddV1eHhIQEeHh42HpINtdVr1nrRQ5HqNLbmbbhNSEhwe7bdQGtlaWXLl2K5uZm7NmzZ0BdSCOE9D0KsISQW2Y0GhEaGooXXngBf/vb35Cbm4u5c+eioaGhXXuO8PBwrFixAitXrrTdYAcAlmVRU1ODzMxMyGQyHDp0CBMnTuTC7KhRo7oMs0ajER999BHuuOMOJCYmOsRJbm+xBhe5XA6FQoGWlhYEBgZys3DdrXbqKNouJXWUQGMLOp2OC7NNTU0O2WuWZVmUl5dDqVQ6zN+1wWDAww8/DIVCgZ9++smhtzAQQmyDAiwh5Jb9+OOPePDBB3H58mWEhoZiy5YteOKJJ2AwGNrdb8qUKZg9ezbWr19vo5EOPCzLoq6uDllZWZBKpcjNzUV0dDTEYjEkEgmio6O5MKvVanHvvffi6tWrOHDggEMVbOoLWq2WC7MajQYBAQFccHHUWTgra/uUxsZGhyniYw/0ej3XnqehocEhes1al4grFAqHCa8mkwmPPvooKisrsX//fgQGBtp6SIQQB0QBlhByy5KSkuDi4oJt27YBAAVYG2FZFg0NDcjJyYFUKsXevXsREREBsViM+fPn4y9/+Qv0ej127twJoVBo6+HalYEwC2fFMAxOnTqF5uZmh+n9aY+MRiPq6uogl8uhUqng7u7OHRP20n/YWvSttrYWiYmJDrFE3Gw24/e//z3KysqQl5dH70WEkFs2sDcBEUL6TGVlJfbt2weZTMbdFhISAqPRCLVa3W4JsVwuR0hIiA1GOTjweDwEBATg8ccfx+OPP47GxkZs374d3333HT766COEhoYiJSUFly9fRmBg4KCvzNuWh4cHRowYgREjRkCv13Nh9uzZs/Dx8eF6zdr77BbDMCgpKYFOp0NiYqLDzyTbkouLC0JDQxEaGtqu1+yJEyfsov+wtRCbI4VXi8WCP/7xjzh9+jRyc3MpvBJCbgvNwBJCbslrr72Gzz//HFeuXOEK4jQ2NkIoFOK7777DvffeCwAoLy9HdHQ0FXHqZ7W1tViwYAGGDx+OpUuXYtu2bdi1axcCAgKQlpYGiUSCyZMnD7rKvN1lNBq5MKtSqeDl5QWRSGSXS0oZhkFxcTEMBgPi4+MpvPaRznrNCoVCiESifus127YtUmJiot0di52xWCxYvnw5Dh8+jAMHDiAsLMzWQyKEODgKsISQHmMYBhEREXjggQfwzjvvtPvas88+i507d2LTpk3w8fHB8uXLAQBHjhyxxVAHpcrKSsybNw933HEHvvrqK65IkU6nw549eyCTybB9+3Z4enoiNTUVEokE06ZNG/CVeW+VyWTi9kfW19fD3d2dC7NeXl42XVJqsVhQXFwMk8mE+Pj4AVeQyl7Zotcsy7I4f/48qqqqHCa8MgyDlStXYv/+/cjLy0N4eLith0QIGQAowBJCeuynn35CUlISysvLMXr06HZf0+v1WLVqFb777jsYDAYkJSVhw4YNtIS4n5SVlWH+/PlIS0vDJ5980uWskF6v55aAZ2dnw9nZGSkpKUhPT8eMGTMoCHWh7ZLSuro6uLi4cGHWx8enX8OsxWJBUVERLBYL4uLi6HdmIx1bNrXtNRsUFNRrv5fz58/j6tWrSEhIcIieqQzD4OWXX8a2bdtw4MABREZG2npIhJABggIsIYQMEIWFhUhKSsJTTz2FN998s9thymQy4cCBA8jIyEBWVhYsFguSk5ORnp6OWbNm0ZLULlgsFtTX13NLSgUCAVfsx8/Pr0/DrNlsRlFREViWRVxcHM2e2wmWZdtVudZqtb1S5frChQu4fPkyEhMTHSa8vvLKK9i6dSsOHDiAqKgoWw+JEDKAUIAlhJAB4rHHHsO4cePwl7/85Zafw2w245dffsHWrVuRlZUFrVaL5ORkiMVizJs3z+Eq8/YXhmGgUqkgl8u5/ZHW0NLb+yNNJhMKCwvh5OSE2NhY2sdsxzpWufb19eWOi+4WBrt48SIqKysdJryyLIvXX38dmzdvxoEDBxAdHW3rIRFCBhgKsIQQMkAwDNOrQcliseDo0aPczKxKpcLChQshFouxYMECh9iDZwvW/ZHWWTiWZdvtj7yd35HJZEJBQQGcnZ0xadIkCq8OpGOv2e4UBrOG14SEBHh7e/fziHuOZVmsX78en332GfLy8jB+/HhbD4kQMgBRgCWEEHJTDMPgt99+Q0ZGBjIzM1FTU4MFCxZALBZj0aJFDnFybQssy6KxsZELs2azud3+yJ4EUKPRiIKCAri6umLSpEnUDsmBdVYYrGOv2UuXLuHSpUsOFV7/8Y9/4B//+Af279+P2NhYm41lxIgRqKysbHfbunXrsHr16i4fY63f8P3337er3yASifp6uISQHqIASwghpEcYhkFRURGkUilkMhkuXbqEefPmIS0tDcnJyTbrj2nv2hb7USgU0Ov1XJgVCoU33MdqNBqRn58PDw8PTJgwgcLrAGI2m9vtpXZ2doabmxuam5uRkJAAX19fWw/xpliWxT//+U+sX78ee/bsweTJk206nhEjRuDJJ5/EU089xd3m7e19w1Ujzz77LHbs2IFNmzbB19cXy5YtA5/Px+HDh/tjyISQHqAASwgh5JaxLItTp04hIyMDMpkM5eXlmDVrFiQSCVJSUhAQEEBhthMdi/3odDoEBARAJBJBKBS2q1xrMBiQn58PLy8vjB8/nsLrAMYwDMrKylBdXQ0nJyfw+Xxu+XlAQIBd/u5ZlsUXX3yBv//979i1axemTZtm6yFhxIgRWLFiBVasWNGt+1t7mG/ZsgVLliwB0FrRPSYmhnqYE2KHKMASQgjpFSzL4uzZs9zMbHFxMe666y5IJBKkpqYiODiYwmwXtFotNzPb3NwMf39/iEQi+Pj4oKSkBL6+vhg3bhz9/Aa4y5cv4/z584iPj4e3t/d1vWZvdfl5X2FZFps2bcJf//pXbN++HTNnzrT1kAC0Bli9Xg+TyYThw4fjwQcfxMqVK7tc5ZCbm4u5c+eioaEBfn5+3O3h4eFYsWIFVq5c2U8jJ4R0BwVYQgghvY5lWVy4cAFSqRSZmZn47bffMH36dIjFYqSlpSE0NJTCWBdaWlqgUChQW1uLpqYmuLi4YMSIERCJRFQFegC7cuUKKioqEB8ff92y4RstP+/NXrM9wbIs/vvf/2LVqlXIycnB7Nmz+30MXfnggw8QHx+PgIAAHDlyBH/961/xxBNP4IMPPuj0/lu2bMETTzwBg8HQ7vYpU6Zg9uzZWL9+fX8MmxDSTRRgCSGE9CmWZXHlyhUuzB45cgSTJ09GWloaJBIJhg8fTmG2A51Oh/z8fPj7+8PHxwdKpRINDQ3w9vbmKtd6eHjYepikl1y9ehVnz55FfHx8uxnAzliXn1vDrEaj4XrNCoVCuLq69vl4WZbF1q1bsWzZMkilUiQlJfX5a65evfqmQfLMmTOdtu356quv8Mwzz0Cj0XT686EAS4hjoQBLCCGk37Asi+rqamRmZkImk+Hnn3/GpEmTIBaLIRaLMXLkyEEfZrVaLfLz8yESiTB69Gju52E0GqFUKiGXy6FSqeDp6cmFWUfoD0o6Zw2vcXFx8Pf37/HjrTP2CoUCjY2Nt9RrtqcyMzPxzDPP4Pvvv0dKSkqfvEZHSqUS9fX1N7xPZGQkXFxcrrv91KlTGD9+PMrKyjBmzJjrvk5LiAlxLBRgCSGE2ATLslAqlcjKyoJUKkVeXh5iYmIgFoshkUgwZsyYQRdmNRoN8vPzERoailGjRnX5/ZtMJtTV1UEul3fZhoXYv6qqKpSXl99yeO3IYDBwYdbaa9Z6XPTWRY7t27fjiSeewLfffov09PReec6+9t///hePPvoo6urqOv05W4s4fffdd7j33nsBAOXl5YiOjqYiToTYIQqwhBBCbI5lWTQ0NCA7OxtSqRT79u1DZGQkxGIx0tPTMXbsWLuswNqbmpubkZ+fj6FDh/ZoJtrahkUul6Ourg4uLi5caKGWRvaruroaZWVliI2NRUBAQK8/f8des25ubtxx4ePjc0vHxe7du/Hoo4/iq6++wu9+97teH3NvOHr0KI4dO4bZs2fD29sbR48excqVK7Fo0SJs3rwZQOuFg7lz5+Kbb77BlClTALS20dm5cyc2bdoEHx8fLF++HABw5MgRm30vhJDOUYAlhBBidxobG7Ft2zbIZDLs3r0bYWFhkEgkEIvFiI2NHXBhtqmpCQUFBRg+fDgiIyNv+XksFgtUKhXkcjmUSiWcnJy40OLv709h1k7U1NTgzJkzfRZeO7JYLKirq4NCoUBdXR0EAgF3XPj5+XXruMjNzcXSpUvx+eef48EHH7TbY6mgoADPPfccysrKYDAYEBERgUceeQQvvPACt//10qVLiIiIQF5eHmbNmgUA0Ov1WLVqFb777jsYDAYkJSVhw4YNCAkJseF3QwjpDAVYQgghdq25uRk7d+6ETCbDzp07ERQUxBWAmjx5ssOH2cbGRhQUFCAiIgIjRozotedlGAYNDQ1cr1kejwehUAiRSAR/f3+H/7k5Kmt4nTRpEgIDA/v99RmGgUql4pYaA2h3kaOz9jyHDh3Cfffdh48//hiPP/643YZXQsjgQAGWEEKIw9DpdNizZw+kUim2b98Ob29vpKamQiKRYNq0aXbRG7Mn1Go1CgsLMXLkSAwfPrzPXse6RLttT1FrmA0ICHC4n5ujqq2txenTp20WXjtiWbZdr9nVq1cjODgYaWlpuOeee+Dr64sjR47gnnvuwXvvvYenn36awishxOYowBJCCHFIer0e+/btg1QqRU5ODlxcXJCSkoL09HTceeedNumN2RMqlQpFRUWIiorCsGHD+u11WZZFY2MjF1qMRiOCgoIgEokQFBREYbaPyOVylJaWYtKkSQgKCrL1cK7Dsix++eUXbN26FXv27IFCoUB8fDzKy8uxevVqvPTSSxReCSF2gQIsIYQQh2cymZCXl4eMjAxkZ2eDYRgkJycjPT0dd999d6etNWypvr4excXFGDNmDMLCwmw2DpZl0dzczIXZlpYWBAYGcmHW3i8COApreJ04cSKEQqGth9MtGRkZ+H//7/9BpVKhtrYWs2fPxj333AOxWIwhQ4bYeniEkEGMAiwhhJABxWw24+eff8bWrVuRnZ0NnU6H5ORkSCQSzJkzB25ubjYdX11dHU6ePIno6GiEhobadCwdaTQaKBQKyOVyaLVaBAQEQCQSQSgU2t1FAEehUChQUlLiUOG1pKQEixcvxksvvYSXX34ZlZWVyMrKgkwmw5EjR3DHHXfgnnvuQXp6OiIiImw9XELIIEMBlhBCyIBlsVhw5MgRSKVSZGZmQq1WY+HChRCLxViwYAE8PDz6dTxKpRInT57EuHHj7L66qU6n48Jsc3Mz/P39uWI/1mqu5Masv+8JEyYgODjY1sPpltOnT2PRokVYtmwZXn311euWDcvlcmRnZ0MmkyE3Nxfjxo3DF198gcmTJ9toxISQwYYCLCGEkEGBYRgcP36cC7O1tbWYP38+JBIJFi5cCG9v7z59fesy0vHjx0MkEvXpa/U2vV7PhdnGxkb4+vpyYdbd3d3Ww7NL1vDqSL/v8vJyLFq0CL///e/x1ltv3XTPq1qtxo4dOzBnzhxaVkwI6TcUYAkhxE5ZLBa89tpr+Pbbb1FbW4vQ0FA8/vjjeOWVV7gTS5ZlsXbtWnz55ZdQq9W488478dlnnyEqKsrGo7dvDMOgqKgIGRkZkMlkqKysxLx58yAWi7F48WL4+vr2asGampoanD592qGWkXbFYDBAqVRCLpejoaEB3t7eXJj19PS09fDsQl1dHYqLix0qvFZUVGDRokV44IEH8O6771KbJUKI3aIASwghdurtt9/GBx98gM2bN2PcuHE4ceIEnnjiCbz11lt4/vnnAQDr16/HunXrsHnzZkRERGDNmjUoKSnB6dOnbb7X01GwLIvS0lIuzJ49exazZ8+GRCJBcnIyAgICbivMVldXo6ysDBMnTrTL6rO3w2QycWG2vr4enp6eCA4Ohkgkgqen56CsWmvd4zx27Fi7XyZudenSJW5p/UcffUThlRBi1yjAEkIcEsuy4PF4+Oc//4kFCxZg9OjR3G0DRUpKCkQiEf79739zt917771wd3fHt99+C5ZlERoailWrVuHFF18EADQ2NkIkEmHTpk1YunSprYbusFiWRXl5OaRSKWQyGUpKSnDXXXdBIpEgNTUVQqGwR8fY1atXcfbsWbvp+9mXzGYzlEolFAoF6urq4Obmxs3M+vj4DKi/za5Yq0vHxMQ4zJLaq1evIikpCUlJSdiwYQOFV0KI3aN3KUKIQ+LxeKitrcUnn3yCXbt2cbedP3/exiPrPdOnT8f+/ftx9uxZAEBxcTF++eUXLFq0CABw8eJF1NbWYt68edxjfH19MXXqVBw9etQmY3Z0PB4P0dHR+L//+z+cOHECZ86cwYIFC/Df//4XUVFRWLRoETZu3Ijq6mrc7Prvd999hzNnziAuLm7Ah1cAEAgEGDJkCCZNmoRZs2Zh1KhR0Ov1yM/Pxy+//ILy8nKo1eqb/twclUqlcrjwWlNTg8WLF2P27Nn49NNPKbwSQhwCvVMRQhyO9QT42LFjiIyMxNixYwEAR44cwbhx4xAdHW3L4fWa1atXY+nSpYiOjoazszPi4uKwYsUKPPTQQwCA2tpaALhuj51IJOK+Rm4dj8fDyJEj8fLLL+Po0aOoqKhAeno6MjMzER0djfnz5+Pjjz/G5cuXrwtlb7zxBp5//nn4+/vD39/fRt+B7Tg5OUEkEmHChAmYNWsWoqOjYTabUVRUhEOHDuHMmTNQqVRgGMbWQ+0VKpUKRUVFiI6OdpjwKpfLkZycjGnTpuHLL7+Ek5OTrYdECCHdIrD1AAghpKesSxFLSkrg6emJxMRE/Pzzz/jPf/4Do9GI1NRUAK1B96OPPsKpU6ewceNGhztB+/HHH/Hf//4XW7Zswbhx41BUVIQVK1YgNDQUjz32mK2HN6jweDyEh4dj5cqVWLFiBaqrq5GZmQmpVIo1a9Zg0qRJkEgkEIvF+Prrr/HFF18gIyMDiYmJth66zfH5fAiFQgiFQjAMg4aGBq43KsuyEAqFEIlECAgIcMgZwIaGBhQVFWHMmDF219e3K3V1dUhNTcWkSZPw9ddfO9x7IyFkcKMASwhxSBqNBjU1NQgJCYGTkxMef/xxvPHGGzhw4ADuuusuAMCHH36IjIwMpKencydoZrMZAoFjvPW99NJL3CwsAEyYMAGVlZVYt24dHnvsMa5AjFwubzfrI5fLERsba4shDwo8Hg9hYWFYtmwZ/vSnP0GhUCArKwtSqRRvvvkmBAIBfv/73yMkJGTA7cu+XXw+H4GBgQgMDER0dDTUajUUCgXOnDkDs9kMoVCI4OBgBAYGOkSoamhoQGFhIcaMGYOwsDBbD6dbVCoV0tLSEBUVhW+//dZh3g8JIcTK8S51EkIGNetSzYqKCrAsC51Oh3Xr1mHGjBmYOXMmfH19ERUVhaKiIrz++utYtmwZXnzxRVRWVgKAQ52s6XS662aknJycuGWXERERCAkJwf79+7mvNzU14dixY5g2bVq/jnWw4vF4EIlEePrppzFlyhT4+PjgL3/5Cy5cuIBp06ZhypQpePPNN3Hq1KkBs1y2t/B4PPj7+2PMmDGYMWMG4uPj4erqirNnz+LgwYM4efIkamtrYTabbT3UTqnVahQWFmL06NEOE17VajXEYjGGDh2KH374Ac7OzrYeEiGE9JjjnMkRQgj+t3xYqVSioqICx48fx3PPPYdPP/0UGRkZmDp1KgoKCiCTyXD//ffjgQceQE1NDR5++GFUVlbirbfewiOPPGLj76J7UlNT8dZbb2H48OEYN24cCgsL8cEHH+D3v/89gNafxYoVK/Dmm28iKiqKa6MTGhoKiURi28EPIizL4uWXX8Z//vMfHDx4EDExMQBaw8K2bdsgk8nw4YcfYujQoRCLxZBIJJg0aZJDLpftKzweD76+vvD19cWoUaOg0WigUChw4cIFnDp1CoGBgQgODoZQKLSL0GUNr1FRURg6dKith9MtTU1NuOeeexAUFISMjAy4uLjYekiEEHJLqI0OIcThsCyLTz/9FM8//zzuvvtubNu2DV5eXli2bBmcnZ2RlZWFP/zhD3j66achFAqh0Whw+fJl/POf/0R4eDhefvllMAxj9wGiubkZa9asQWZmJhQKBUJDQ/HAAw/g1Vdf5U4+WZbF2rVr8cUXX0CtVmPGjBnYsGEDRo8ebePRDw4sy2LlypXIyMhAbm5ulz/35uZm7NixAzKZDLt27UJQUBDS0tKQnp6OxMREuz8WbUmr1UKhUEAul0Oj0SAgIIBrz2OLENbY2IiCggKMGjUKw4YN6/fXvxUajQb33HMPXFxcsGPHDri7u9t6SIQQcssowBJCHJJCoYBMJsOcOXMwevRoKBQKLFmyBKdOncLMmTORmZnZ7v7l5eV488038fzzz2Py5MkOEWCJfWMYBsuWLcPOnTuRm5uLyMjIbj1Op9Nh9+7dkEql2LFjB3x8fJCamgqJRII77rjDIfZ+2kpLSwvkcjkUCgWamprg5+fHhVk3N7c+f31reB05ciSGDx/e56/XG3Q6HZYsWQKGYbBz5054eXnZekiEEHJbKMASQgaE6upqPPHEE2AYBj/88AMCAgK4kGqxWLBz505s2bIF3333na2HSgaIb7/9FmvXrkVubi7Cw8Nv6Tn0ej327t0LqVSKnJwcuLq6IjU1Fenp6bjzzjsdas92f9Pr9VAoFFAoFFCr1fDx8UFwcDBEIlGfzDA2NTUhPz8fkZGRt/z77m96vR73338/tFotdu/eDR8fH1sPiRBCbhsFWEKIQ7K+dXWs8Go0Gq9bVtjQ0ICPP/4Yzs7O+Nvf/kazr6RXMAwDlUqFoKCgXnk+o9GIvLw8ZGRkIDs7GyzLIiUlBenp6Zg5cybtWbwBo9HIhVmVSgUvLy8uzHp6et728zc3NyM/Px8jRozAiBEjbn/A/cBgMOChhx6CUqnE3r174efnZ+shEUJIr6AASwgZEKwVXjsLpnK5HC+88AL++Mc/4q677qIAS+ye2WzGoUOHkJGRgaysLOj1eiQnJ0MikWD27Nn9slzWUZlMJiiVSigUCtTX18Pd3Z0Ls15eXj1ua2QNr+Hh4YiIiOijUfcuo9GIRx99FFeuXMG+ffsQGBho6yERQkivoQBLCBmw5HI5NmzYAB8fHxQXF+Obb76x9ZAI6TGLxYLDhw9DKpUiMzMTjY2NWLRoEcRiMebPnw8PDw9bD9Fumc1m1NXVQaFQoK6uDi4uLhCJRAgODoaPj89Nw6xGo8GJEyccKryazWb8/ve/R3l5OXJzcyEUCm09JEII6VUUYAkhA5ZKpcK6deuwY8cOlJWV4R//+AeeeeYZmr0iDothGBw/fhwZGRnIzMyEXC7HggULIJFIsHDhQirQcwMWiwX19fVQKBRQKpUQCARcASg/P7/rwqw1vA4fPrzbBbpszWKx4JlnnkFRURFyc3MREhJi6yERQkivowBLCBkUTp48icuXL2P+/PlwdXW19XAIuW0Mw6CwsBAZGRmQyWS4cuUK5s6dC4lEgsWLF3drhnGwsu5ftu6b5fF4XJj19/eHTqdDfn4+hg4dipEjR9p6uN1isViwfPlyHDlyBAcOHEBoaKhNxnHgwAHMnj27068dP34ckydP7vRrs2bNwsGDB9vd9swzz2Djxo29PkZCiGOjAEsIGbBYlgXDMNSWhAx4LMuitLQUW7duRWZmJs6ePYs5c+ZALBYjJSUF/v7+FGa7wDAM1Go112uWYRgwDAOhUIhx48Y5xPsHwzBYsWIF8vLykJeXZ9MWP0ajESqVqt1ta9aswf79+3H+/Pkuj8NZs2Zh9OjReP3117nbPDw8qHIyIeQ6FGAJIYMCy7J0Ak8GBZZlUVZWxi0zLi0txcyZMyGRSJCSkgKhUEh/C13QaDT47bff4OHhAaPRCJPJBKFQiODgYAQFBdllmGUYBn/5y1+wY8cOHDhwwO726ppMJoSFhWH58uVYs2ZNl/ebNWsWYmNj8eGHH/bf4AghDokCLCGEEDJAsSyL8+fPQyqVQiaToaCgANOnT4dYLEZaWhqGDBlCYfYanU6HEydOYMiQIRg1ahSA1grEcrkcCoUCer0eQUFBCA4OhlAotIsevQzD4JVXXkFGRgby8vIQFRVl6yFdRyqV4ne/+x0qKysxdOjQLu83a9YsnDp1CizLIiQkBKmpqVizZg0VKSOEXIcCLCGEEDIIsCyLy5cvc2H22LFjmDJlCtLS0iAWizFs2LBBG2at4TUkJARRUVHX/RxYloVWq+XCrFarRWBgIBdmbdGjl2VZvP766/jmm2+Ql5eH6Ojofh9DdyxevBgAsHPnzhve74svvkB4eDhCQ0Nx8uRJvPzyy5gyZQpkMll/DJMQ4kAowBJCCCGDDMuyqK6uhkwmg1QqxeHDhxEbGwuJRAKxWIyIiIhBE2ZbWlpw4sQJBAcHY/To0d36vrVaLVcAqrm5Gf7+/lwRqP4oEseyLN555x18/vnnyM3Nxfjx4/v8NVevXo3169ff8D5nzpxpF6SvXr2K8PBw/Pjjj7j33nt79Hq5ubmYO3cuKioqHKaQFiGkf1CAJYQQQgYxlmUhl8uRlZUFqVSKgwcPYuzYsVyY7W6oc0S3El47ew5rmG1sbISvry/Xa7YvWnaxLIsPPvgAH374Ifbv34/Y2Nhef43OKJVK1NfX3/A+kZGR7Waj33jjDXzyySeoqqqCs7Nzj15Pq9XCy8sLu3fvRlJS0i2NmRAyMFGAJYQQQgiA1nCkUqmQlZUFmUyGffv2ISoqCmKxGOnp6YiJiRkwYdYaXoVCIcaMGdMr35fBYODCbENDA7y9vbkw2xt7OVmWxSeffIJ3330Xe/bs6bIljT1gWRYjR47EPffcg/fff7/Hjz98+DBmzJiB4uJiTJw4sQ9GSAhxVBRgCSGEEHIdlmXR2NiInJwcyGQy/PTTTxg2bBjEYjEkEgkmTpwIPp9v62HeEr1ejxMnTiAwMBDR0dF9EsqNRiOUSiUUCgXq6+vh6enJhVkvL68ePx/Lsvj888/x+uuvY9euXZg2bVqvj7k37d+/H/PmzbtuWTEAVFVVYe7cufjmm28wZcoUnD9/Hlu2bMHixYsRGBiIkydPYuXKlRg6dOh1vWEJIYQCLCGEEEJuqrm5GTt27IBUKsWuXbsQHByMtLQ0pKenIyEhwWHCrDW8BgQE9NuMsslkQl1dHRQKBerq6uDu7s7tmfX29r7pGFiWxddff42//e1v2LFjB+66664+H/PtevDBB1FZWYnDhw9f97VLly4hIiICeXl5mDVrFq5cuYKHH34YpaWl0Gq1GDZsGNLT0/HKK69QH1hCyHUowBJCCCGkR7RaLXbv3g2pVIodO3bA19cXaWlpkEgkmDp1ql32SwVaw2t+fj78/f1tthzaYrGgrq4OcrkcdXV1cHFx4cKsj4/PdRcCWJbFt99+ixdffBE5OTmYPXt2v4+ZEELsCQVYQgghhNyylpYW7N27FzKZDDk5OXBzc0NqairS09Mxffp0u+iXCrTuTz1x4gT8/PwwduxYu9jLa7FYoFKpIJfLceXKFfz5z3/GjBkzcO+992LBggUQCAT48ccfsXz5cshkMixYsMDWQyaEEJujAEsIIYSQXmE0GpGbmwupVIqsrCzweDykpKRAIpFg5syZNumXCrSG1/z8fPj4+GDcuHF2EV47MhqN3BLt3NxcAEBCQgKOHDmC7777DhKJxLYDJIQQO0EBlhBCCCG9zmw24+DBg8jIyEBWVhYMBgNSUlIgFosxZ86cfumXCrQGwxMnTth1eO3IbDbjH//4B/7zn/9ArVbDYrEgNTWVm5l1d3e39RAJIcRmHKPiAiGEENIPmpubsWLFCoSHh8Pd3R3Tp0/Hb7/9xn2dZVm8+uqrGDJkCNzd3TFv3jycO3fOhiO2XwKBAHPnzsVnn32Gq1evIisrC35+flixYgUiIiLw5JNPIicnBzqdrs/GYDQakZ+fD29vb4cJrwCwb98+vPvuu1i3bh0UCgV27doFkUiElStXQigU4v7778ePP/4IjUZj66ESQki/oxlYQggh5Jr7778fpaWl+OyzzxAaGopvv/0W//jHP3D69GmEhYVh/fr1WLduHTZv3oyIiAisWbMGJSUlOH36NNzc3Gw9fIfAMAyOHTuGjIwMZGZmQqlUYsGCBZBIJEhKSrqlFjOdsYZXT09PjB8/3mGqJO/fvx8PPPAAPv/8czz44IPtQjfLsigqKoJUKoVUKsWlS5eQlJSEjz/+GMOHD7fhqAkhpP9QgCWEEELQWozI29sb2dnZSE5O5m5PSEjAokWL8MYbbyA0NBSrVq3Ciy++CABobGyESCTCpk2bsHTpUlsN3WExDIOCggJkZGRAJpPh6tWrmDdvHiQSCRYtWgQfH59bmjU1mUzIz8+Hu7s7JkyY4DDh9dChQ7jvvvvwySef4LHHHrvp93769GnIZDKsWLGi14I/IYTYOwqwhBBCCFqXD/v4+GDfvn2YO3cud/uMGTMgEAjw1VdfYeTIkSgsLERsbCz39bvvvhuxsbH46KOPbDDqgYNhGJSWlnJhtqKiAnPmzIFYLEZycjL8/f27FWYdNbwePnwY9957L9577z08/fTTDrPcmRBC+ptjvKsTQgghfczb2xvTpk3DG2+8gerqalgsFnz77bc4evQoampqUFtbCwAQiUTtHicSibivkVvH5/MxceJEvP766ygpKUFhYSGmTZuGjRs3IjIyEhKJBF999RWUSiW6uvauVCpx7733oqWlxaHC6/Hjx7FkyRK8/fbbFF4JIeQmHOOdnRBCCOkH//nPf8CyLMLCwuDq6oqPP/4YDzzwgMMEoYGCx+MhJiYGa9asQUFBAU6dOoW5c+fim2++wahRo5CcnIzPP/8cNTU1XJhVKpVISkpCS0sLpk2b5jC/s4KCAqSnp2Pt2rX405/+ROGVEEJuwjHe3QkhhJB+MHLkSBw8eBAajQZXrlzB8ePHYTKZEBkZiZCQEACAXC5v9xi5XM59jfQ+Ho+HqKgorF69GseOHcO5c+eQmpqKjIwMREdHIykpCR988AEWLlyIgIAA5OTkOExBrZMnTyItLQ0vv/wyVq5cSeGVEEK6gQIsIYQQ0oGnpyeGDBmChoYG7NmzB2KxGBEREQgJCcH+/fu5+zU1NeHYsWOYNm2aDUc7ePB4PIwYMQKrVq3CL7/8gosXLyItLQ3vvfceGhsbYbFY8Pnnn+PixYtdLjO2F6dPn0ZqaipWrFiBl19+mcIrIYR0ExVxIoQQQq7Zs2cPWJbFmDFjUFFRgZdeeglubm74+eef4ezsjPXr1+Odd95p10bn5MmT1EbHRpqbm7Fo0SJ4eHjgiy++wK5duyCTyXDw4EGMGzcOEokEYrEYUVFRdhUQy8vLsWjRIjz55JN488037WpshBBi7yjAEkIIIdf8+OOP+Otf/4qrV68iICAA9957L9566y34+voCaO3DuXbtWnzxxRdQq9WYMWMGNmzYgNGjR9t45IOPRqPB4sWL4erqipycHLi7uwNo/R3V19cjOzsbUqkU+/fvx+jRoyEWiyGRSBATE2PTwFhRUYFFixbhwQcfxPr16x1mry4hhNgLCrCEEEIIcSharRbJyclwcnLCtm3b4OHh0en9WJZFY2MjcnJyIJVK8dNPPyE8PJwLs/1dqfjSpUtYuHAhJBIJPvzwQwqvhBByCyjAEkIIIcRh6HQ6pKSkgGVZbN++HZ6ent1+bFNTE3bs2AGpVIrdu3dDJBIhLS0N6enpiI+P79NAefXqVSxYsAALFy7Ehg0bKLwSQsgtogBLCCGEEIexd+9erFu3Dtu2betReO1Iq9Vi165dkEql2LlzJ/z8/JCWlgaJRIIpU6bAycmp18ZcU1ODpKQk3H333fjiiy969bkJIWSwoQBLCCGEEIfCMEyvzmC2tLTgp59+gkwmw7Zt2+Du7o7U1FRIJBJMnz4dAoHglp9bLpdj0aJFmDx5MjZt2kThlRBCbhMFWEIIIYSQa4xGI/bt2weZTIbs7GzweDwuzM6cORPOzs7dfi6lUonk5GSMHz8e33777W0FYUIIIa0owBJCCCGEdMJkMuHQoUPYunUrsrOzYTQakZKSArFYjNmzZ8PV1bXLx6pUKiQnJ2PkyJH44YcfehR8CSGEdI0CLCGEEELITVgsFvzyyy/IyMhAVlYW14NWIpFg3rx5XBsfAFCr1UhNTUVoaCikUilcXFxsOHJCCBlYKMASQgghhPQAwzD49ddfuTCrVCqRlJTE7Zl94IEH4O/vj6ysLLi5udl6uIQQMqBQgCWEEEIIuUUMwyA/Px9SqRQymQznz5/H+PHj8euvv7ablSWEENI7qAkZIYQQQsgt4vP5mDx5Mt555x2UlZVhy5YtyMrK6tfw+tZbb2H69Onw8PCAn59fp/e5fPkykpOT4eHhgeDgYLz00kswm803fF6VSoWHHnoIPj4+8PPzw5NPPgmNRtMH3wEhhHQfBVhCCCGEkF7A5/Nx//33IyIiol9f12g04r777sOzzz7b6dctFguSk5NhNBpx5MgRbN68GZs2bcKrr756w+d96KGHcOrUKezduxfbt2/HoUOH8PTTT/fFt0AIId1GS4gJIYQQQgaATZs2YcWKFVCr1e1u37VrF1JSUlBdXQ2RSAQA2LhxI15++WUolcpOi0ydOXMGY8eOxW+//YbExEQAwO7du7F48WJcvXoVoaGhff79EEJIZ2gGlhBCCCFkADt69CgmTJjAhVcASEpKQlNTE06dOtXlY/z8/LjwCgDz5s0Dn8/HsWPH+nzMhBDSFQqwhBBCCCEDWG1tbbvwCoD7d21tbZePCQ4ObnebQCBAQEBAl48hhJD+QAGWEEIIIcTOrF69Gjwe74b/lZWV2XqYhBDS7wS2HgAhhBBCCGlv1apVePzxx294n8jIyG49V0hICI4fP97uNrlczn2tq8coFIp2t5nNZqhUqi4fQwgh/YECLCGEEEKInREKhRAKhb3yXNOmTcNbb70FhULBLQveu3cvfHx8MHbs2C4fo1arkZ+fj4SEBABAbm4uGIbB1KlTe2VchBByK2gJMSGEEEKIA7t8+TKKiopw+fJlWCwWFBUVoaioiOvZumDBAowdOxaPPPIIiouLsWfPHrzyyiv405/+BFdXVwDA8ePHER0djaqqKgBATEwMFi5ciKeeegrHjx/H4cOHsWzZMixdupQqEBNCbIra6BBCCCGEOLDHH38cmzdvvu72vLw8zJo1CwBQWVmJZ599FgcOHICnpycee+wxvPPOOxAIWhfjHThwALNnz8bFixcxYsQIAIBKpcKyZcuwbds28Pl83Hvvvfj444/h5eXVX98aIYRchwIsIYQQQgghhBCHQEuICSGEEEIIIYQ4BAqwhBBCCOk3hw4dQmpqKkJDQ8Hj8ZCVldXu6yzL4tVXX8WQIUPg7u6OefPm4dy5c+3uo1Kp8NBDD8HHxwd+fn548sknuf2ehBBCBjYKsIQQQgjpN1qtFpMmTcKnn37a6dffffddfPzxx9i4cSOOHTsGT09PJCUlQa/Xc/d56KGHcOrUKezduxfbt2/HoUOH8PTTT/fXt0AIIcSGaA8sIYQQQmyCx+MhMzMTEokEQOvsa2hoKFatWoUXX3wRANDY2AiRSIRNmzZh6dKlOHPmDMaOHYvffvsNiYmJAIDdu3dj8eLFuHr1KlXIJYSQAY5mYAkhhBBiFy5evIja2lrMmzePu83X1xdTp07F0aNHAQBHjx6Fn58fF14BYN68eeDz+Th27Fi/j5kQQkj/ogBLCCGEELtQW1sLABCJRO1uF4lE3Ndqa2sRHBzc7usCgQABAQHcfQghhAxcFGAJIYQQQgghhDgECrCEEEIIsQshISEAALlc3u52uVzOfS0kJAQKhaLd181mM1QqFXcfQgghAxcFWEIIIYTYhYiICISEhGD//v3cbU1NTTh27BimTZsGAJg2bRrUajXy8/O5++Tm5oJhGEydOrXfx0wIIaR/CWw9AEIIIYQMHhqNBhUVFdy/L168iKKiIgQEBGD48OFYsWIF3nzzTURFRSEiIgJr1qxBaGgoV6k4JiYGCxcuxFNPPYWNGzfCZDJh2bJlWLp0KVUgJoSQQYDa6BBCCCGk3xw4cACzZ8++7vbHHnsMmzZtAsuyWLt2Lb744guo1WrMmDEDGzZswOjRo7n7qlQqLFu2DNu2bQOfz8e9996Ljz/+GF5eXv35rRBCCLEBCrCEEEIIIYQQQhwC7YElhBBCCCGEEOIQKMASQgghhBBCCHEIFGAJIYQQQgghhDgECrCEEEIIIYQQQhwCBVhCCCGEEEIIIQ6BAiwhhBBCCCGEEIdAAZYQQgghhBBCiEOgAEsIIYQQQgghxCFQgCWEEEIIIYQQ4hAowBJCCCGEEEIIcQgUYAkhhBBCCCGEOAQKsIQQQgghhBBCHAIFWEIIIYQQQgghDoECLCGEEEIIIYQQh0ABlhBCCCGEEEKIQ6AASwghhBBCCCHEIVCAJYQQQgghhBDiECjAEkIIIYQQQghxCBRgCSGEEEIIIYQ4BAqwhBBCCCGEEEIcAgVYQgghhBBCCCEOgQIsIYQQQgghhBCHQAGWEEIIIYQQQohDoABLCCGEEEIIIcQhUIAlhBBCCCGEEOIQKMASQgghhBBCCHEIFGAJIYQQQgghhDgECrCEEEIIIYQQQhwCBVhCCCGEEEIIIQ6BAiwhhBBCCCGEEIdAAZYQQgghhBBCiEOgAEsIIYQQQgghxCFQgCWEEEIIIYQQ4hAowBJCCCGEEEIIcQgUYAkhhBBCCCGEOAQKsIQQQgghhBBCHAIFWEIIIYQQQgghDoECLCGEEEIIIYQQh0ABlhBCCCGEEEKIQ6AASwghhBBCCCHEIVCAJYQQQgghhBDiECjAEkIIIYQQQghxCBRgCSGEEEIIIYQ4BAqwhBBCCCGEEEIcAgVYQgghhBBCCCEO4f8DQBZPDQONuWgAAAAASUVORK5CYII=", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "from mpl_toolkits.mplot3d.axes3d import Axes3D\n", + "\n", + "\n", + "def mse(w1, w0):\n", + " y_pred = w1 * X[:, 0] + w0\n", + " return np.mean((y - y_pred) ** 2)\n", + "\n", + "\n", + "coefs_a = np.linspace(50, 100, num=100)\n", + "coefs_b = np.linspace(-10, 10, num=100)\n", + "w1, w0 = np.meshgrid(coefs_a, coefs_b)\n", + "\n", + "\n", + "fig = plt.figure(figsize=(15, 12))\n", + "ax = fig.add_subplot(111, projection='3d')\n", + "\n", + "zs = np.array([mse(i, j) for i, j in zip(np.ravel(w1), np.ravel(w0))])\n", + "Z = zs.reshape(w1.shape)\n", + "\n", + "ax.plot_surface(w1, w0, Z, alpha=.5)\n", + "ax.scatter(model_a, model_b, mse(model_a, model_b), c='r', s=5)\n", + "ax.scatter(a, b, mse(a, b), c='r', s=5)\n", + "\n", + "ax.set_xlabel(r'$w_1$')\n", + "ax.set_ylabel(r'$w_0$')\n", + "ax.set_zlabel('MSE')\n", + "\n", + "plt.show()" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "Z6h9XzopP8Ou" + }, + "source": [ + "И видим, что дейстивительно, модель с коэффициентом `a` равным где-то 80, и с небольшим коэффициентом `b` лучше, чем модель с коэффициентом `a=50`, ведь ошибка у второй модели выше, чем у первой.\n", + "\n" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "V4LqAG5jsyQj" + }, + "source": [ + "#### Градиентный спуск" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "PmbkaRRNQ5SX" + }, + "source": [ + "*Как модель дошла до самой лучшей точки?*\n", + "\n", + "А она обучалась с помощью градиентного спуска - это метод оптимизации." + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "dA1lWisVRnq7" + }, + "source": [ + "**Обсудим, что такое градиент и зачем надо спускаться.**\n", + "\n", + "_Градиентом_ функции $f$ называется $n$-мерный вектор из частных производных. \n", + "\n", + "$$ \\nabla f(x_{1},...,x_{d}) = \\left(\\frac{\\partial f}{\\partial x_{i}}\\right)^{d}_{i=1}.$$\n", + "\n", + "К примеру, если функция зависит от трех переменных: $F(x, y, z)$, то её градиент будет равен \n", + "\n", + "$$\\nabla f(x, y, z) = (\\frac{\\partial f}{\\partial x}, \\frac{\\partial f}{\\partial y}, \\frac{\\partial f}{\\partial z}) $$\n", + "\n", + "При этом, __градиент задает направление наискорейшего роста функции__. Значит, антиградиент будет показывать направление ее скорейшего убывания, что будет полезно нам в нашей задаче минимизации функционала ошибки. \n", + "\n", + "**Градиентный спуск** — метод нахождения локального минимума с помощью движения вдоль антиградиента." + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "K88irooYxpS9" + }, + "source": [ + "Давайте попробуем реализовать программно градиентный спуск, чтобы лучше понять как он работает." + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "zxKpYu5bWRH6" + }, + "source": [ + "Зададим две функции:\n", + "1. func - функция параболы $f(x) = x^2$\n", + "2. gr_func - производная функции параболы $\\nabla f(x) = 2x$" + ] + }, + { + "cell_type": "code", + "execution_count": 19, + "metadata": { + "id": "WYxZMCWzWQlS" + }, + "outputs": [], + "source": [ + "def func(x):\n", + " return x ** 2\n", + "\n", + "# функция градиента\n", + "def gr_func(x):\n", + " return 2 * x" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "pJdDL_TvWufg" + }, + "source": [ + "Можем отрисовать эту функцию на графике.\n", + "\n", + "Действительно видим параболу." + ] + }, + { + "cell_type": "code", + "execution_count": 20, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 374 + }, + "id": "TFnN5zo6Wmqw", + "outputId": "c5d9f425-9a76-4a58-c1a4-1b4265ac2765" + }, + "outputs": [ + { + "data": { + "image/png": "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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# для картинки\n", + "D = 5\n", + "\n", + "X = np.linspace(-D, +D, 20)\n", + "Y = func(X)\n", + "\n", + "plt.figure(figsize=(16, 6))\n", + "plt.plot(X, Y, 'r', label='Y(X)');" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "gpghPbl5W3nY" + }, + "source": [ + "Чтобы найти минимум этой функции мы можем воспользоваться методом оптимизации - градиентный спуск, для этого нужно задать начальную точку, откуда будем считать градиенты и скатываться в минимум.\n", + "\n", + "Зеленая звездочка - это и есть точка старта." + ] + }, + { + "cell_type": "code", + "execution_count": 21, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 374 + }, + "id": "a4CEhDEWXJMW", + "outputId": "a1e964b2-a644-45b6-9e0b-0bf862b56b57" + }, + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 21, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": "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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# первоначальное точка\n", + "start_point = 5\n", + "\n", + "# для картинки\n", + "D = 10\n", + "\n", + "X = np.linspace(-D, +D, 20)\n", + "Y = func(X)\n", + "\n", + "plt.figure(figsize=(16, 6))\n", + "plt.plot(X, Y, 'r', label='Y(X)')\n", + "plt.plot(start_point, func(start_point), '-*g', label = 'GD');\n", + "plt.legend()" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "OZY4SxJ8XuaB" + }, + "source": [ + "Теперь в этой точке можем посчитать градиент.\n", + "\n", + "Он равняется 10, т.к. начальная точка равна 5, а производная будет равняться $\\nabla f(x) = 2\\cdot x = 2 \\cdot 5 = 10$ " + ] + }, + { + "cell_type": "code", + "execution_count": 22, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "bgQK7dm4ZOVw", + "outputId": "dc512a3c-90fb-4584-db79-7b0cc6fd7916" + }, + "outputs": [ + { + "data": { + "text/plain": [ + "10" + ] + }, + "execution_count": 22, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "grad = gr_func(start_point)\n", + "grad" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "wxB4rqsKZO3K" + }, + "source": [ + "Можем отрисовать направление градиента, он показывает наискорейший рост функции и действительно видим, зеленый вектор идет вверх. " + ] + }, + { + "cell_type": "code", + "execution_count": 23, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 374 + }, + "id": "3G3-ggQ6X45x", + "outputId": "79574137-244f-4060-bccc-498d7f0d3cbe" + }, + "outputs": [ + { + "data": { + "image/png": "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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "plt.figure(figsize=(16, 6))\n", + "plt.plot(X, Y, 'r', label='Y(X)')\n", + "plt.plot(start_point, func(start_point), '*g', label='start_point')\n", + "\n", + "next_point_1 = start_point + grad\n", + "plt.plot([start_point, next_point_1], func(np.array([start_point, next_point_1])), '--g', label='grad')\n", + "plt.legend();" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "xMI7xm24Zo0h" + }, + "source": [ + "Но если будем двигаться по этому вектору, то к минимуму функции не придем, поэтому нужно идти в противоположгном направлении, а значит брать **антиградиент**.\n", + "\n", + "Но если мы пойдем от текущей точке $5$ в сторону антиградиента $-10$, то окажемся в точке $-5$, а это так же удалено от минимума, как и наша стартовая точка." + ] + }, + { + "cell_type": "code", + "execution_count": 24, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 374 + }, + "id": "AHzUE456ZyQS", + "outputId": "c22df950-a4b3-415e-f410-f2d98b415614" + }, + "outputs": [ + { + "data": { + "image/png": "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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "plt.figure(figsize=(16, 6))\n", + "plt.plot(X, Y, 'r', label='Y(X)')\n", + "plt.plot(start_point, func(start_point), '*g', label='start_point')\n", + "\n", + "next_point_1 = start_point - grad\n", + "plt.plot([start_point, next_point_1], func(np.array([start_point, next_point_1])), '--g', label='antigrad')\n", + "plt.legend();" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "cJ9vM4-Jayqf" + }, + "source": [ + "Поэтому чтобы не перескакивать минимальное состояние функции мы можем делать шаг в сторону антиградиента не полностью, а только на какую-то долю, для этого нужно ввести значения **шага обучения** (скорость обучения, learning rate) - это значения, замедляющее шаги градиентного спуска, чтобы не пропустить локальный минимум. " + ] + }, + { + "cell_type": "code", + "execution_count": 25, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 374 + }, + "id": "cLP5hwpIbSE7", + "outputId": "a51ce935-f8b2-44e6-aab8-58aca85e0cf1" + }, + "outputs": [ + { + "data": { + "image/png": "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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# размер шага (learning rate)\n", + "learning_rate = 0.1\n", + "\n", + "plt.figure(figsize=(16, 6))\n", + "plt.plot(X, Y, 'r', label='Y(X)')\n", + "plt.plot(start_point, func(start_point), '*g', label='start_point')\n", + "\n", + "next_point_1 = start_point - grad * learning_rate\n", + "plt.plot([start_point, next_point_1], func(np.array([start_point, next_point_1])), '--*g', label='antigrad')\n", + "plt.legend();" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "M5G3YV2Ydjnz" + }, + "source": [ + "Вот мы и получили новую точку с координатой $x=4$. \n", + "\n", + "Теперь в этой точке можем снова рассчитать значение градиента." + ] + }, + { + "cell_type": "code", + "execution_count": 26, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "HqqfwmXcd4x3", + "outputId": "03ffbec2-eb25-4291-cdaa-a157ccedf213" + }, + "outputs": [ + { + "data": { + "text/plain": [ + "4.0" + ] + }, + "execution_count": 26, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "curr_point = next_point_1\n", + "curr_point" + ] + }, + { + "cell_type": "code", + "execution_count": 27, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "hqaso7MFdxU3", + "outputId": "34dc4db0-d0c0-416f-f34c-53322b3ef533" + }, + "outputs": [ + { + "data": { + "text/plain": [ + "8.0" + ] + }, + "execution_count": 27, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "grad = gr_func(curr_point)\n", + "grad" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "-AImpol6dxU5" + }, + "source": [ + "Отрисуем направление градиента, который показывает наискорейший рост функции.\n", + "\n", + "А синим пометим уже пройденный шаг." + ] + }, + { + "cell_type": "code", + "execution_count": 28, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 374 + }, + "id": "3pn7Qfh0dxU5", + "outputId": "b6afae12-cb1a-4025-dabf-864a3a6d8f33" + }, + "outputs": [ + { + "data": { + "image/png": "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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "plt.figure(figsize=(16, 6))\n", + "plt.plot(X, Y, 'r', label='Y(X)')\n", + "plt.plot(start_point, func(start_point), '*g', label='start_point')\n", + "plt.plot([start_point, next_point_1], func(np.array([start_point, next_point_1])), '--*b', label='prev step')\n", + "\n", + "next_point_2 = curr_point + grad\n", + "\n", + "plt.plot([curr_point, next_point_2], func(np.array([curr_point, next_point_2])), '--g', label='grad')\n", + "plt.legend();" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "Nq_9K48ldxU6" + }, + "source": [ + "Но если будем двигаться по этому вектору, то к минимуму функции не придем, поэтому нужно идти в противоположном направлении, а значит брать **антиградиент**.\n", + "\n", + "Но при этом помним, что если сходить на полный антиградиент, то можем перелететь минимум, поэтому домножим на скорость обучения." + ] + }, + { + "cell_type": "code", + "execution_count": 29, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 374 + }, + "id": "HiHId2wFdxU6", + "outputId": "4d367980-22ef-4f26-9810-eae64c919b01" + }, + "outputs": [ + { + "data": { + "image/png": "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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# размер шага (learning rate)\n", + "learning_rate = 0.1\n", + "\n", + "plt.figure(figsize=(16, 6))\n", + "plt.plot(X, Y, 'r', label='Y(X)')\n", + "plt.plot(start_point, func(start_point), '*g', label='start_point')\n", + "plt.plot([start_point, next_point_1], func(np.array([start_point, next_point_1])), '--*b', label='prev step')\n", + "\n", + "next_point_2 = curr_point - learning_rate * grad\n", + "plt.plot([curr_point, next_point_2], func(np.array([curr_point, next_point_2])), '--*g', label='antigrad')\n", + "plt.legend();" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "gS0Z1swagJ7M" + }, + "source": [ + "И получаем еще одну точку, которая уже ближе к минимуму функции.\n", + "\n", + "Оформим небольшой цикл для градиентного спуска." + ] + }, + { + "cell_type": "code", + "execution_count": 30, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 926 + }, + "id": "qZc1XOjuqyvz", + "outputId": "2c50fd60-910a-4cb6-c1ff-3d697a5cb265" + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Итерация: 0\n", + "Текущая точка 5| Следующая точка 4.0\n", + "--------------------------------------------------------\n", + "Итерация: 1\n", + "Текущая точка 4.0| Следующая точка 3.2\n", + "--------------------------------------------------------\n", + "Итерация: 2\n", + "Текущая точка 3.2| Следующая точка 2.56\n", + "--------------------------------------------------------\n", + "Итерация: 3\n", + "Текущая точка 2.56| Следующая точка 2.048\n", + "--------------------------------------------------------\n", + "Итерация: 4\n", + "Текущая точка 2.048| Следующая точка 1.6384\n", + "--------------------------------------------------------\n", + "Итерация: 5\n", + "Текущая точка 1.6384| Следующая точка 1.31072\n", + "--------------------------------------------------------\n", + "Итерация: 6\n", + "Текущая точка 1.31072| Следующая точка 1.0485760000000002\n", + "--------------------------------------------------------\n", + "Итерация: 7\n", + "Текущая точка 1.0485760000000002| Следующая точка 0.8388608000000002\n", + "--------------------------------------------------------\n", + "Итерация: 8\n", + "Текущая точка 0.8388608000000002| Следующая точка 0.6710886400000001\n", + "--------------------------------------------------------\n", + "Итерация: 9\n", + "Текущая точка 0.6710886400000001| Следующая точка 0.5368709120000001\n", + "--------------------------------------------------------\n", + "минимум 0.5368709120000001, количество затраченных итераций: 9\n" + ] + }, + { + "data": { + "image/png": "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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# первоначальное точка\n", + "start_point = 5\n", + "\n", + "# размер шага (learning rate)\n", + "learning_rate = 0.1\n", + "\n", + "# начальная точка\n", + "next_point = start_point\n", + "\n", + "x = []\n", + "x.append(next_point)\n", + "\n", + "plt.figure(figsize=(16, 6))\n", + "plt.plot(X, Y, 'r', label='Y(X)')\n", + "\n", + "# количество итерация \n", + "n = 10\n", + "for i in range(n):\n", + " current_point = next_point\n", + "\n", + " # движение в негативную сторону вычисляемого градиента\n", + " next_point = current_point - learning_rate * gr_func(current_point)\n", + " x.append(next_point)\n", + " # print(next_point) \n", + "\n", + " # остановка когда достигнута необходимая степень точности\n", + " print(f\"Итерация: {i}\")\n", + " print(f\"Текущая точка {current_point}| Следующая точка {next_point}\")\n", + " print(\"--------------------------------------------------------\")\n", + " \n", + " \n", + "\n", + "print(f\"минимум {next_point}, количество затраченных итераций: {i}\") \n", + "X_grad = np.array(x)\n", + "plt.plot(X_grad, func(X_grad), '-*g', label = 'GD')\n", + "plt.legend()\n", + "plt.xlabel('X')\n", + "plt.ylabel('Y')\n", + "plt.show()" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "f7Nfx3xqqtLp" + }, + "source": [ + "Прошли 10 шагов и практически находимся в минимуме функции." + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "R_FZFLAjrN6v" + }, + "source": [ + "А если мы сделаем больше итераций, то наверняка алгоритм сойдется к 0.\n" + ] + }, + { + "cell_type": "code", + "execution_count": 31, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 1000 + }, + "id": "9mIcLlcSrKiG", + "outputId": "61132eea-bdc0-45e1-a9cb-a1f941c7a128" + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Итерация: 0\n", + "Текущая точка 5| Следующая точка 4.0\n", + "--------------------------------------------------------\n", + "Итерация: 1\n", + "Текущая точка 4.0| Следующая точка 3.2\n", + "--------------------------------------------------------\n", + "Итерация: 2\n", + "Текущая точка 3.2| Следующая точка 2.56\n", + "--------------------------------------------------------\n", + "Итерация: 3\n", + "Текущая точка 2.56| Следующая точка 2.048\n", + "--------------------------------------------------------\n", + "Итерация: 4\n", + "Текущая точка 2.048| Следующая точка 1.6384\n", + "--------------------------------------------------------\n", + "Итерация: 5\n", + "Текущая точка 1.6384| Следующая точка 1.31072\n", + "--------------------------------------------------------\n", + "Итерация: 6\n", + "Текущая точка 1.31072| Следующая точка 1.0485760000000002\n", + "--------------------------------------------------------\n", + "Итерация: 7\n", + "Текущая точка 1.0485760000000002| Следующая точка 0.8388608000000002\n", + "--------------------------------------------------------\n", + "Итерация: 8\n", + "Текущая точка 0.8388608000000002| Следующая точка 0.6710886400000001\n", + "--------------------------------------------------------\n", + "Итерация: 9\n", + "Текущая точка 0.6710886400000001| Следующая точка 0.5368709120000001\n", + "--------------------------------------------------------\n", + "Итерация: 10\n", + "Текущая точка 0.5368709120000001| Следующая точка 0.4294967296000001\n", + "--------------------------------------------------------\n", + "Итерация: 11\n", + "Текущая точка 0.4294967296000001| Следующая точка 0.3435973836800001\n", + "--------------------------------------------------------\n", + "Итерация: 12\n", + "Текущая точка 0.3435973836800001| Следующая точка 0.27487790694400005\n", + "--------------------------------------------------------\n", + "Итерация: 13\n", + "Текущая точка 0.27487790694400005| Следующая точка 0.21990232555520003\n", + "--------------------------------------------------------\n", + "Итерация: 14\n", + "Текущая точка 0.21990232555520003| Следующая точка 0.17592186044416003\n", + "--------------------------------------------------------\n", + "Итерация: 15\n", + "Текущая точка 0.17592186044416003| Следующая точка 0.140737488355328\n", + "--------------------------------------------------------\n", + "Итерация: 16\n", + "Текущая точка 0.140737488355328| Следующая точка 0.11258999068426241\n", + "--------------------------------------------------------\n", + "Итерация: 17\n", + "Текущая точка 0.11258999068426241| Следующая точка 0.09007199254740993\n", + "--------------------------------------------------------\n", + "Итерация: 18\n", + "Текущая точка 0.09007199254740993| Следующая точка 0.07205759403792794\n", + "--------------------------------------------------------\n", + "Итерация: 19\n", + "Текущая точка 0.07205759403792794| Следующая точка 0.057646075230342354\n", + "--------------------------------------------------------\n", + "Итерация: 20\n", + "Текущая точка 0.057646075230342354| Следующая точка 0.04611686018427388\n", + "--------------------------------------------------------\n", + "Итерация: 21\n", + "Текущая точка 0.04611686018427388| Следующая точка 0.03689348814741911\n", + "--------------------------------------------------------\n", + "Итерация: 22\n", + "Текущая точка 0.03689348814741911| Следующая точка 0.029514790517935284\n", + "--------------------------------------------------------\n", + "Итерация: 23\n", + "Текущая точка 0.029514790517935284| Следующая точка 0.02361183241434823\n", + "--------------------------------------------------------\n", + "Итерация: 24\n", + "Текущая точка 0.02361183241434823| Следующая точка 0.018889465931478583\n", + "--------------------------------------------------------\n", + "Итерация: 25\n", + "Текущая точка 0.018889465931478583| Следующая точка 0.015111572745182867\n", + "--------------------------------------------------------\n", + "Итерация: 26\n", + "Текущая точка 0.015111572745182867| Следующая точка 0.012089258196146294\n", + "--------------------------------------------------------\n", + "Итерация: 27\n", + "Текущая точка 0.012089258196146294| Следующая точка 0.009671406556917036\n", + "--------------------------------------------------------\n", + "Итерация: 28\n", + "Текущая точка 0.009671406556917036| Следующая точка 0.007737125245533628\n", + "--------------------------------------------------------\n", + "Итерация: 29\n", + "Текущая точка 0.007737125245533628| Следующая точка 0.006189700196426903\n", + "--------------------------------------------------------\n", + "Итерация: 30\n", + "Текущая точка 0.006189700196426903| Следующая точка 0.004951760157141522\n", + "--------------------------------------------------------\n", + "Итерация: 31\n", + "Текущая точка 0.004951760157141522| Следующая точка 0.003961408125713218\n", + "--------------------------------------------------------\n", + "Итерация: 32\n", + "Текущая точка 0.003961408125713218| Следующая точка 0.0031691265005705745\n", + "--------------------------------------------------------\n", + "Итерация: 33\n", + "Текущая точка 0.0031691265005705745| Следующая точка 0.00253530120045646\n", + "--------------------------------------------------------\n", + "Итерация: 34\n", + "Текущая точка 0.00253530120045646| Следующая точка 0.0020282409603651678\n", + "--------------------------------------------------------\n", + "Итерация: 35\n", + "Текущая точка 0.0020282409603651678| Следующая точка 0.0016225927682921343\n", + "--------------------------------------------------------\n", + "Итерация: 36\n", + "Текущая точка 0.0016225927682921343| Следующая точка 0.0012980742146337075\n", + "--------------------------------------------------------\n", + "Итерация: 37\n", + "Текущая точка 0.0012980742146337075| Следующая точка 0.001038459371706966\n", + "--------------------------------------------------------\n", + "Итерация: 38\n", + "Текущая точка 0.001038459371706966| Следующая точка 0.0008307674973655728\n", + "--------------------------------------------------------\n", + "Итерация: 39\n", + "Текущая точка 0.0008307674973655728| Следующая точка 0.0006646139978924582\n", + "--------------------------------------------------------\n", + "Итерация: 40\n", + "Текущая точка 0.0006646139978924582| Следующая точка 0.0005316911983139665\n", + "--------------------------------------------------------\n", + "Итерация: 41\n", + "Текущая точка 0.0005316911983139665| Следующая точка 0.00042535295865117324\n", + "--------------------------------------------------------\n", + "Итерация: 42\n", + "Текущая точка 0.00042535295865117324| Следующая точка 0.0003402823669209386\n", + "--------------------------------------------------------\n", + "Итерация: 43\n", + "Текущая точка 0.0003402823669209386| Следующая точка 0.00027222589353675085\n", + "--------------------------------------------------------\n", + "Итерация: 44\n", + "Текущая точка 0.00027222589353675085| Следующая точка 0.0002177807148294007\n", + "--------------------------------------------------------\n", + "Итерация: 45\n", + "Текущая точка 0.0002177807148294007| Следующая точка 0.00017422457186352054\n", + "--------------------------------------------------------\n", + "Итерация: 46\n", + "Текущая точка 0.00017422457186352054| Следующая точка 0.00013937965749081642\n", + "--------------------------------------------------------\n", + "Итерация: 47\n", + "Текущая точка 0.00013937965749081642| Следующая точка 0.00011150372599265314\n", + "--------------------------------------------------------\n", + "Итерация: 48\n", + "Текущая точка 0.00011150372599265314| Следующая точка 8.920298079412252e-05\n", + "--------------------------------------------------------\n", + "Итерация: 49\n", + "Текущая точка 8.920298079412252e-05| Следующая точка 7.136238463529802e-05\n", + "--------------------------------------------------------\n", + "Итерация: 50\n", + "Текущая точка 7.136238463529802e-05| Следующая точка 5.7089907708238416e-05\n", + "--------------------------------------------------------\n", + "Итерация: 51\n", + "Текущая точка 5.7089907708238416e-05| Следующая точка 4.567192616659073e-05\n", + "--------------------------------------------------------\n", + "Итерация: 52\n", + "Текущая точка 4.567192616659073e-05| Следующая точка 3.653754093327259e-05\n", + "--------------------------------------------------------\n", + "Итерация: 53\n", + "Текущая точка 3.653754093327259e-05| Следующая точка 2.923003274661807e-05\n", + "--------------------------------------------------------\n", + "Итерация: 54\n", + "Текущая точка 2.923003274661807e-05| Следующая точка 2.3384026197294454e-05\n", + "--------------------------------------------------------\n", + "Итерация: 55\n", + "Текущая точка 2.3384026197294454e-05| Следующая точка 1.8707220957835564e-05\n", + "--------------------------------------------------------\n", + "Итерация: 56\n", + "Текущая точка 1.8707220957835564e-05| Следующая точка 1.4965776766268452e-05\n", + "--------------------------------------------------------\n", + "Итерация: 57\n", + "Текущая точка 1.4965776766268452e-05| Следующая точка 1.1972621413014761e-05\n", + "--------------------------------------------------------\n", + "Итерация: 58\n", + "Текущая точка 1.1972621413014761e-05| Следующая точка 9.578097130411809e-06\n", + "--------------------------------------------------------\n", + "Итерация: 59\n", + "Текущая точка 9.578097130411809e-06| Следующая точка 7.662477704329448e-06\n", + "--------------------------------------------------------\n", + "Итерация: 60\n", + "Текущая точка 7.662477704329448e-06| Следующая точка 6.129982163463559e-06\n", + "--------------------------------------------------------\n", + "Итерация: 61\n", + "Текущая точка 6.129982163463559e-06| Следующая точка 4.903985730770847e-06\n", + "--------------------------------------------------------\n", + "Итерация: 62\n", + "Текущая точка 4.903985730770847e-06| Следующая точка 3.923188584616677e-06\n", + "--------------------------------------------------------\n", + "Итерация: 63\n", + "Текущая точка 3.923188584616677e-06| Следующая точка 3.138550867693342e-06\n", + "--------------------------------------------------------\n", + "Итерация: 64\n", + "Текущая точка 3.138550867693342e-06| Следующая точка 2.5108406941546735e-06\n", + "--------------------------------------------------------\n", + "Итерация: 65\n", + "Текущая точка 2.5108406941546735e-06| Следующая точка 2.008672555323739e-06\n", + "--------------------------------------------------------\n", + "Итерация: 66\n", + "Текущая точка 2.008672555323739e-06| Следующая точка 1.606938044258991e-06\n", + "--------------------------------------------------------\n", + "Итерация: 67\n", + "Текущая точка 1.606938044258991e-06| Следующая точка 1.2855504354071928e-06\n", + "--------------------------------------------------------\n", + "Итерация: 68\n", + "Текущая точка 1.2855504354071928e-06| Следующая точка 1.0284403483257543e-06\n", + "--------------------------------------------------------\n", + "Итерация: 69\n", + "Текущая точка 1.0284403483257543e-06| Следующая точка 8.227522786606034e-07\n", + "--------------------------------------------------------\n", + "Итерация: 70\n", + "Текущая точка 8.227522786606034e-07| Следующая точка 6.582018229284827e-07\n", + "--------------------------------------------------------\n", + "Итерация: 71\n", + "Текущая точка 6.582018229284827e-07| Следующая точка 5.265614583427862e-07\n", + "--------------------------------------------------------\n", + "Итерация: 72\n", + "Текущая точка 5.265614583427862e-07| Следующая точка 4.2124916667422894e-07\n", + "--------------------------------------------------------\n", + "Итерация: 73\n", + "Текущая точка 4.2124916667422894e-07| Следующая точка 3.3699933333938316e-07\n", + "--------------------------------------------------------\n", + "Итерация: 74\n", + "Текущая точка 3.3699933333938316e-07| Следующая точка 2.6959946667150655e-07\n", + "--------------------------------------------------------\n", + "Итерация: 75\n", + "Текущая точка 2.6959946667150655e-07| Следующая точка 2.1567957333720524e-07\n", + "--------------------------------------------------------\n", + "Итерация: 76\n", + "Текущая точка 2.1567957333720524e-07| Следующая точка 1.725436586697642e-07\n", + "--------------------------------------------------------\n", + "Итерация: 77\n", + "Текущая точка 1.725436586697642e-07| Следующая точка 1.3803492693581135e-07\n", + "--------------------------------------------------------\n", + "Итерация: 78\n", + "Текущая точка 1.3803492693581135e-07| Следующая точка 1.1042794154864907e-07\n", + "--------------------------------------------------------\n", + "Итерация: 79\n", + "Текущая точка 1.1042794154864907e-07| Следующая точка 8.834235323891926e-08\n", + "--------------------------------------------------------\n", + "Итерация: 80\n", + "Текущая точка 8.834235323891926e-08| Следующая точка 7.067388259113541e-08\n", + "--------------------------------------------------------\n", + "Итерация: 81\n", + "Текущая точка 7.067388259113541e-08| Следующая точка 5.653910607290833e-08\n", + "--------------------------------------------------------\n", + "Итерация: 82\n", + "Текущая точка 5.653910607290833e-08| Следующая точка 4.5231284858326664e-08\n", + "--------------------------------------------------------\n", + "Итерация: 83\n", + "Текущая точка 4.5231284858326664e-08| Следующая точка 3.618502788666133e-08\n", + "--------------------------------------------------------\n", + "Итерация: 84\n", + "Текущая точка 3.618502788666133e-08| Следующая точка 2.8948022309329066e-08\n", + "--------------------------------------------------------\n", + "Итерация: 85\n", + "Текущая точка 2.8948022309329066e-08| Следующая точка 2.3158417847463252e-08\n", + "--------------------------------------------------------\n", + "Итерация: 86\n", + "Текущая точка 2.3158417847463252e-08| Следующая точка 1.8526734277970603e-08\n", + "--------------------------------------------------------\n", + "Итерация: 87\n", + "Текущая точка 1.8526734277970603e-08| Следующая точка 1.4821387422376482e-08\n", + "--------------------------------------------------------\n", + "Итерация: 88\n", + "Текущая точка 1.4821387422376482e-08| Следующая точка 1.1857109937901186e-08\n", + "--------------------------------------------------------\n", + "Итерация: 89\n", + "Текущая точка 1.1857109937901186e-08| Следующая точка 9.485687950320948e-09\n", + "--------------------------------------------------------\n", + "Итерация: 90\n", + "Текущая точка 9.485687950320948e-09| Следующая точка 7.588550360256759e-09\n", + "--------------------------------------------------------\n", + "Итерация: 91\n", + "Текущая точка 7.588550360256759e-09| Следующая точка 6.070840288205408e-09\n", + "--------------------------------------------------------\n", + "Итерация: 92\n", + "Текущая точка 6.070840288205408e-09| Следующая точка 4.856672230564326e-09\n", + "--------------------------------------------------------\n", + "Итерация: 93\n", + "Текущая точка 4.856672230564326e-09| Следующая точка 3.885337784451461e-09\n", + "--------------------------------------------------------\n", + "Итерация: 94\n", + "Текущая точка 3.885337784451461e-09| Следующая точка 3.108270227561169e-09\n", + "--------------------------------------------------------\n", + "Итерация: 95\n", + "Текущая точка 3.108270227561169e-09| Следующая точка 2.4866161820489353e-09\n", + "--------------------------------------------------------\n", + "Итерация: 96\n", + "Текущая точка 2.4866161820489353e-09| Следующая точка 1.989292945639148e-09\n", + "--------------------------------------------------------\n", + "Итерация: 97\n", + "Текущая точка 1.989292945639148e-09| Следующая точка 1.5914343565113183e-09\n", + "--------------------------------------------------------\n", + "Итерация: 98\n", + "Текущая точка 1.5914343565113183e-09| Следующая точка 1.2731474852090548e-09\n", + "--------------------------------------------------------\n", + "Итерация: 99\n", + "Текущая точка 1.2731474852090548e-09| Следующая точка 1.0185179881672439e-09\n", + "--------------------------------------------------------\n", + "минимум 1.0185179881672439e-09, количество затраченных итераций: 99\n" + ] + }, + { + "data": { + "image/png": "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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# первоначальное точка\n", + "start_point = 5\n", + "\n", + "# размер шага (learning rate)\n", + "learning_rate = 0.1\n", + "\n", + "# начальная точка\n", + "next_point = start_point\n", + "\n", + "x = []\n", + "x.append(next_point)\n", + "\n", + "plt.figure(figsize=(16, 6))\n", + "plt.plot(X, Y, 'r', label='Y(X)')\n", + "\n", + "# количество итерация \n", + "n = 100\n", + "for i in range(n):\n", + " current_point = next_point\n", + "\n", + " # движение в негативную сторону вычисляемого градиента\n", + " next_point = current_point - learning_rate * gr_func(current_point)\n", + " x.append(next_point) \n", + "\n", + " # остановка когда достигнута необходимая степень точности\n", + " print(f\"Итерация: {i}\")\n", + " print(f\"Текущая точка {current_point}| Следующая точка {next_point}\")\n", + " print(\"--------------------------------------------------------\")\n", + " \n", + " \n", + "\n", + "print(f\"минимум {next_point}, количество затраченных итераций: {i}\") \n", + "X_grad = np.array(x)\n", + "plt.plot(X_grad, func(X_grad), '-*g', label = 'GD')\n", + "plt.legend()\n", + "plt.xlabel('X')\n", + "plt.ylabel('Y')\n", + "plt.show()" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "BWV6HvSRrTO0" + }, + "source": [ + "Но здесь значения самой лучшей минимальной точки на последних шагах очень похожи и на самом деле мы могли не ждать столько итераций и выйти из цикла раньше.\n", + "\n", + "Для этого введем значение eps, с помощью которого будем проверять разницу между текущей точкой и следующей точкой и если она меньше eps (а значит точки очень близки), то можем выйти из алгоритма." + ] + }, + { + "cell_type": "code", + "execution_count": 32, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 1000 + }, + "id": "nrM5GLhBxpS9", + "outputId": "f3c037e1-83fe-480c-aa05-272bf7e647dd" + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Итерация: 0\n", + "Текущая точка 5| Следующая точка 4.0\n", + "--------------------------------------------------------\n", + "Итерация: 1\n", + "Текущая точка 4.0| Следующая точка 3.2\n", + "--------------------------------------------------------\n", + "Итерация: 2\n", + "Текущая точка 3.2| Следующая точка 2.56\n", + "--------------------------------------------------------\n", + "Итерация: 3\n", + "Текущая точка 2.56| Следующая точка 2.048\n", + "--------------------------------------------------------\n", + "Итерация: 4\n", + "Текущая точка 2.048| Следующая точка 1.6384\n", + "--------------------------------------------------------\n", + "Итерация: 5\n", + "Текущая точка 1.6384| Следующая точка 1.31072\n", + "--------------------------------------------------------\n", + "Итерация: 6\n", + "Текущая точка 1.31072| Следующая точка 1.0485760000000002\n", + "--------------------------------------------------------\n", + "Итерация: 7\n", + "Текущая точка 1.0485760000000002| Следующая точка 0.8388608000000002\n", + "--------------------------------------------------------\n", + "Итерация: 8\n", + "Текущая точка 0.8388608000000002| Следующая точка 0.6710886400000001\n", + "--------------------------------------------------------\n", + "Итерация: 9\n", + "Текущая точка 0.6710886400000001| Следующая точка 0.5368709120000001\n", + "--------------------------------------------------------\n", + "Итерация: 10\n", + "Текущая точка 0.5368709120000001| Следующая точка 0.4294967296000001\n", + "--------------------------------------------------------\n", + "Итерация: 11\n", + "Текущая точка 0.4294967296000001| Следующая точка 0.3435973836800001\n", + "--------------------------------------------------------\n", + "Итерация: 12\n", + "Текущая точка 0.3435973836800001| Следующая точка 0.27487790694400005\n", + "--------------------------------------------------------\n", + "Итерация: 13\n", + "Текущая точка 0.27487790694400005| Следующая точка 0.21990232555520003\n", + "--------------------------------------------------------\n", + "Итерация: 14\n", + "Текущая точка 0.21990232555520003| Следующая точка 0.17592186044416003\n", + "--------------------------------------------------------\n", + "Итерация: 15\n", + "Текущая точка 0.17592186044416003| Следующая точка 0.140737488355328\n", + "--------------------------------------------------------\n", + "Итерация: 16\n", + "Текущая точка 0.140737488355328| Следующая точка 0.11258999068426241\n", + "--------------------------------------------------------\n", + "Итерация: 17\n", + "Текущая точка 0.11258999068426241| Следующая точка 0.09007199254740993\n", + "--------------------------------------------------------\n", + "Итерация: 18\n", + "Текущая точка 0.09007199254740993| Следующая точка 0.07205759403792794\n", + "--------------------------------------------------------\n", + "Итерация: 19\n", + "Текущая точка 0.07205759403792794| Следующая точка 0.057646075230342354\n", + "--------------------------------------------------------\n", + "Итерация: 20\n", + "Текущая точка 0.057646075230342354| Следующая точка 0.04611686018427388\n", + "--------------------------------------------------------\n", + "Итерация: 21\n", + "Текущая точка 0.04611686018427388| Следующая точка 0.03689348814741911\n", + "--------------------------------------------------------\n", + "Итерация: 22\n", + "Текущая точка 0.03689348814741911| Следующая точка 0.029514790517935284\n", + "--------------------------------------------------------\n", + "Итерация: 23\n", + "Текущая точка 0.029514790517935284| Следующая точка 0.02361183241434823\n", + "--------------------------------------------------------\n", + "Итерация: 24\n", + "Текущая точка 0.02361183241434823| Следующая точка 0.018889465931478583\n", + "--------------------------------------------------------\n", + "Итерация: 25\n", + "Текущая точка 0.018889465931478583| Следующая точка 0.015111572745182867\n", + "--------------------------------------------------------\n", + "Итерация: 26\n", + "Текущая точка 0.015111572745182867| Следующая точка 0.012089258196146294\n", + "--------------------------------------------------------\n", + "Итерация: 27\n", + "Текущая точка 0.012089258196146294| Следующая точка 0.009671406556917036\n", + "--------------------------------------------------------\n", + "Итерация: 28\n", + "Текущая точка 0.009671406556917036| Следующая точка 0.007737125245533628\n", + "--------------------------------------------------------\n", + "Итерация: 29\n", + "Текущая точка 0.007737125245533628| Следующая точка 0.006189700196426903\n", + "--------------------------------------------------------\n", + "Итерация: 30\n", + "Текущая точка 0.006189700196426903| Следующая точка 0.004951760157141522\n", + "--------------------------------------------------------\n", + "Итерация: 31\n", + "Текущая точка 0.004951760157141522| Следующая точка 0.003961408125713218\n", + "--------------------------------------------------------\n", + "Итерация: 32\n", + "Текущая точка 0.003961408125713218| Следующая точка 0.0031691265005705745\n", + "--------------------------------------------------------\n", + "Итерация: 33\n", + "Текущая точка 0.0031691265005705745| Следующая точка 0.00253530120045646\n", + "--------------------------------------------------------\n", + "Итерация: 34\n", + "Текущая точка 0.00253530120045646| Следующая точка 0.0020282409603651678\n", + "--------------------------------------------------------\n", + "Итерация: 35\n", + "Текущая точка 0.0020282409603651678| Следующая точка 0.0016225927682921343\n", + "--------------------------------------------------------\n", + "Итерация: 36\n", + "Текущая точка 0.0016225927682921343| Следующая точка 0.0012980742146337075\n", + "--------------------------------------------------------\n", + "Итерация: 37\n", + "Текущая точка 0.0012980742146337075| Следующая точка 0.001038459371706966\n", + "--------------------------------------------------------\n", + "Итерация: 38\n", + "Текущая точка 0.001038459371706966| Следующая точка 0.0008307674973655728\n", + "--------------------------------------------------------\n", + "Итерация: 39\n", + "Текущая точка 0.0008307674973655728| Следующая точка 0.0006646139978924582\n", + "--------------------------------------------------------\n", + "Итерация: 40\n", + "Текущая точка 0.0006646139978924582| Следующая точка 0.0005316911983139665\n", + "--------------------------------------------------------\n", + "Итерация: 41\n", + "Текущая точка 0.0005316911983139665| Следующая точка 0.00042535295865117324\n", + "--------------------------------------------------------\n", + "Итерация: 42\n", + "Текущая точка 0.00042535295865117324| Следующая точка 0.0003402823669209386\n", + "--------------------------------------------------------\n", + "минимум 0.0003402823669209386, количество затраченных итераций: 42\n" + ] + }, + { + "data": { + "image/png": "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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# установка минимального значения, на которое должны изменяться веса\n", + "eps = 0.0001\n", + "\n", + "# первоначальное точка\n", + "start_point = 5\n", + "\n", + "# размер шага (learning rate)\n", + "learning_rate = 0.1\n", + "\n", + "# начальная точка\n", + "next_point = start_point\n", + "\n", + "x = []\n", + "x.append(next_point)\n", + "\n", + "plt.figure(figsize=(16, 6))\n", + "plt.plot(X, Y, 'r', label='Y(X)')\n", + "\n", + "# количество итерация \n", + "n = 100\n", + "for i in range(n):\n", + " current_point = next_point\n", + "\n", + " # движение в негативную сторону вычисляемого градиента\n", + " next_point = current_point - learning_rate * gr_func(current_point)\n", + " x.append(next_point)\n", + "\n", + " # остановка когда достигнута необходимая степень точности\n", + " print(f\"Итерация: {i}\")\n", + " print(f\"Текущая точка {current_point}| Следующая точка {next_point}\")\n", + " print(\"--------------------------------------------------------\")\n", + " \n", + " if(abs(current_point - next_point) <= eps):\n", + " break\n", + "\n", + "print(f\"минимум {next_point}, количество затраченных итераций: {i}\") \n", + "X_grad = np.array(x)\n", + "plt.plot(X_grad, func(X_grad), '-*g', label = 'GD')\n", + "plt.legend()\n", + "plt.xlabel('X')\n", + "plt.ylabel('Y')\n", + "plt.show()" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "BJWalgdYr2sU" + }, + "source": [ + "И да, алгоритму понадобилось всего лишь 42 итерации, разница между двумя точками оказалась меньше `eps`, а значит можем выйти из цикла схождения алгоритма - это называется критерий останова." + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "iSHnElKexpS8" + }, + "source": [ + "#### Алгоритм градиентного спуска\n", + "\n", + "1. Инициализация начальной точки\n", + "2. Цикл по k = 1,2,3,...:\n", + "\n", + "- $ w_{k} = w_{k-1} - \\eta\\nabla f(w_{k-1}) $\n", + "\n", + "- Если $||w_{k} - w_{k-1}|| < \\epsilon$, то завершить.\n" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "AGx1IbTptNvK" + }, + "source": [ + "### Своя реализация линейной регрессии\n" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "8w_t8Y7Ns9WV" + }, + "source": [ + "Теперь зная, как работает метод оптимизации градиентный спуск, можем вернуться к задаче обучения линейной регрессии, но уже не с помощью `sklearn`, а вручную." + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "dhbHnbLHu14I" + }, + "source": [ + "Берем те же самые данные, но вдобавок еще возвращем коэффициент наклона (коэффициент сдвига по умолчанию в такой генерации равен 0)." + ] + }, + { + "cell_type": "code", + "execution_count": 33, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 1000 + }, + "id": "8ZD69Hs6tSAX", + "outputId": "980d2644-0aa8-4a7c-f93c-58a69a000105", + "tags": [] + }, + "outputs": [ + { + "data": { + "text/plain": [ + "array([[ 0.63007982],\n", + " [-1.06163445],\n", + " [ 0.29634711],\n", + " [ 1.40277112],\n", + " [ 0.68968231],\n", + " [-0.53662936],\n", + " [-1.11947526],\n", + " [ 1.06755846],\n", + " [ 0.1178195 ],\n", + " [ 1.54907163],\n", + " [ 1.29561858],\n", + " [-0.03107509],\n", + " [ 0.56119218],\n", + " [ 0.42105072],\n", + " [-0.4864951 ],\n", + " [ 0.08897764],\n", + " [-0.18577532],\n", + " [-0.17809318],\n", + " [-0.23725045],\n", + " [-0.88623967],\n", + " [-0.47573349],\n", + " [ 0.21734821],\n", + " [-2.65331856],\n", + " [ 0.72575222],\n", + " [-0.38053642],\n", + " [-0.48456513],\n", + " [ 1.57463407],\n", + " [-1.30554851],\n", + " [-0.17241977],\n", + " [ 0.73683739],\n", + " [-1.23234621],\n", + " [ 0.31540267],\n", + " [ 1.74945474],\n", + " [ 0.09183837],\n", + " [-0.30957664],\n", + " [-1.18575527],\n", + " [-0.68344663],\n", + " [-0.31963136],\n", + " [-0.00828463],\n", + " [-0.64257539],\n", + " [ 1.0956297 ],\n", + " [ 0.06367166],\n", + " [-0.57395456],\n", + " [ 0.07349324],\n", + " [ 0.73227135],\n", + " [-1.06560298],\n", + " [-1.68411089],\n", + " [-1.54686257],\n", + " [-0.20437532],\n", + " [-0.286073 ]])" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "text/plain": [ + "array([ 43.6543408 , -72.68235021, 21.19644643, 107.58765071,\n", + " 69.62063217, -32.57566222, -101.61213107, 87.44514699,\n", + " 17.69898683, 131.00190463, 97.97802247, 2.70819092,\n", + " 52.42715419, 27.74476129, -31.82947365, 1.58209228,\n", + " -9.72570848, 4.57391214, -33.24586607, -74.34292886,\n", + " -22.6419015 , 15.84607909, -202.79645668, 49.05026172,\n", + " -34.9916168 , -33.95608308, 121.78273292, -123.72382672,\n", + " -1.90918067, 64.06753923, -91.73785524, 9.55252237,\n", + " 148.12427806, 22.21183346, -16.35144507, -113.95075954,\n", + " -47.70966758, -22.69082132, -1.79022499, -58.17761844,\n", + " 91.76970817, -12.7798199 , -38.1435921 , 17.48650737,\n", + " 40.52468632, -107.65815151, -134.20798669, -127.22516755,\n", + " -34.31360406, -10.90920383])" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "X, y, coeffs = make_regression(n_samples=50, n_features=1, n_informative=1,\n", + " noise=10, coef=True, random_state=11)\n", + "\n", + "display(X, y)" + ] + }, + { + "cell_type": "code", + "execution_count": 34, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "dZy0pvfbu7dF", + "outputId": "edebe80a-da61-42ab-a5ac-65ce9e92d99e" + }, + "outputs": [ + { + "data": { + "text/plain": [ + "array(80.65667909)" + ] + }, + "execution_count": 34, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "coeffs" + ] + }, + { + "cell_type": "code", + "execution_count": 35, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 388 + }, + "id": "jp5KReZ1tyRz", + "outputId": "afba6c24-e150-4293-b12c-46c755135ed7" + }, + "outputs": [ + { + "data": { + "image/png": "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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "import matplotlib.pyplot as plt\n", + "\n", + "\n", + "fig = plt.figure(figsize=(10, 6))\n", + "plt.scatter(X, y)\n", + "\n", + "plt.xlabel('feature')\n", + "plt.ylabel('target')\n", + "plt.show()" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "1uqNje3QuFjX" + }, + "source": [ + "Функция, которую здесь оптимизируем - это MSE, её график для конкретно нашей задачи рисовали выше." + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "qPe_CIdRvPcI" + }, + "source": [ + "Реализуем две функции:\n", + "1. mserror - функция среднеквадратичной ошибки $MSE = \\frac{1}{n}\\sum_{i=0}^n{(\\text{y}_i-\\text{y_pred}_i})^2 = \\frac{1}{n}\\sum_{i=0}^n{(\\text{y}_i-(w_1\\cdot X_i + w_0)})^2 = \\frac{1}{n}\\sum_{i=0}^n{(\\text{y}_i-w_1\\cdot X_i - w_0})^2$\n", + "\n", + "\n", + "2. gr_mserror - градиент функции MSE. Распишем его отдельно для коэффициента сдвига и коэффициента наклона:\n", + "\n", + "Сдвиг:\n", + "$\\frac{∂ MSE}{∂ w_0} = \\frac{1 \\cdot 2}{n}\\sum({y_i -\\text{y_pred}_i})\\cdot -1$\n", + "\n", + "Наклон:\n", + "$\\frac{∂ MSE}{∂ w_1} = \\frac{1 \\cdot 2}{n}\\sum({y_i -\\text{y_pred}_i})\\cdot -X$" + ] + }, + { + "cell_type": "code", + "execution_count": 36, + "metadata": { + "id": "fXl31ElsvPcI" + }, + "outputs": [], + "source": [ + "# функция, определяющая среднеквадратичную ошибку\n", + "def mserror(X, w1, w0, y):\n", + " y_pred = w1 * X[:, 0] + w0\n", + " return np.sum((y - y_pred) ** 2) / len(y_pred)\n", + "\n", + "# функция градиента\n", + "def gr_mserror(X, w1, w0, y):\n", + " y_pred = w1 * X[:, 0] + w0\n", + " return np.array([2/len(X)*np.sum((y - y_pred)) * (-1),\n", + " 2/len(X)*np.sum((y - y_pred) * (-X[:, 0]))])" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "c375lB7cubo1" + }, + "source": [ + "И остается запустить цикл градиентного спуска.\n", + "\n", + "В начале инициализировали коэффициенты, затем на каждом шаге считаем градиент, умножаем его на шаг обучения и вычитаем его из предыдущих значений коэффициентов и так далее пока не поймем, что точки коэффициентов очень похожи друг на друга на соседних итерациях." + ] + }, + { + "cell_type": "code", + "execution_count": 37, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "qyOwUsWZyCrz", + "outputId": "7521fd22-ab6a-40f6-a36e-ee928a84a52f" + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Итерация: 0\n", + "Текущая точка (0, 0)| Следующая точка (13.245106098282543, -1.3921748530551812)\n", + "MSE 5436.432058517568\n", + "--------------------------------------------------------\n", + "Итерация: 1\n", + "Текущая точка (13.245106098282543, -1.3921748530551812)| Следующая точка (24.283455474773014, -2.270634896573517)\n", + "MSE 3812.4417335187304\n", + "--------------------------------------------------------\n", + "Итерация: 2\n", + "Текущая точка (24.283455474773014, -2.270634896573517)| Следующая точка (33.487719285860635, -2.777322881591963)\n", + "MSE 2689.1325642433894\n", + "--------------------------------------------------------\n", + "Итерация: 3\n", + "Текущая точка (33.487719285860635, -2.777322881591963)| Следующая точка (41.166652649401456, -3.0191730536904307)\n", + "MSE 1910.2491839412482\n", + "--------------------------------------------------------\n", + "Итерация: 4\n", + "Текущая точка (41.166652649401456, -3.0191730536904307)| Следующая точка (47.57624618267611, -3.0762482285482444)\n", + "MSE 1368.9634120527255\n", + "--------------------------------------------------------\n", + "Итерация: 5\n", + "Текущая точка (47.57624618267611, -3.0762482285482444)| Следующая точка (52.92889586922186, -3.008051361710758)\n", + "MSE 992.010595522475\n", + "--------------------------------------------------------\n", + "Итерация: 6\n", + "Текущая точка (52.92889586922186, -3.008051361710758)| Следующая точка (57.40095014665679, -2.8584119151984995)\n", + "MSE 728.9953237861758\n", + "--------------------------------------------------------\n", + "Итерация: 7\n", + "Текущая точка (57.40095014665679, -2.8584119151984995)| Следующая точка (61.138926872179695, -2.659260887873855)\n", + "MSE 545.1558383956526\n", + "--------------------------------------------------------\n", + "Итерация: 8\n", + "Текущая точка (61.138926872179695, -2.659260887873855)| Следующая точка (64.2646390341644, -2.433540404481531)\n", + "MSE 416.45122384278307\n", + "--------------------------------------------------------\n", + "Итерация: 9\n", + "Текущая точка (64.2646390341644, -2.433540404481531)| Следующая точка (66.87942435861689, -2.19744033614948)\n", + "MSE 326.2142980059075\n", + "--------------------------------------------------------\n", + "Итерация: 10\n", + "Текущая точка (66.87942435861689, -2.19744033614948)| Следующая точка (69.06763839104693, -1.9621124640486494)\n", + "MSE 262.86371600250254\n", + "--------------------------------------------------------\n", + "Итерация: 11\n", + "Текущая точка (69.06763839104693, -1.9621124640486494)| Следующая точка (70.8995416710495, -1.7349797608563151)\n", + "MSE 218.33518111153128\n", + "--------------------------------------------------------\n", + "Итерация: 12\n", + "Текущая точка (70.8995416710495, -1.7349797608563151)| Следующая точка (72.4336880101809, -1.520732529514591)\n", + "MSE 187.00253533348857\n", + "--------------------------------------------------------\n", + "Итерация: 13\n", + "Текущая точка (72.4336880101809, -1.520732529514591)| Следующая точка (73.71890162494748, -1.322082889991646)\n", + "MSE 164.93367151659535\n", + "--------------------------------------------------------\n", + "Итерация: 14\n", + "Текущая точка (73.71890162494748, -1.322082889991646)| Следующая точка (74.79591515037744, -1.1403332478296038)\n", + "MSE 149.37600820692927\n", + "--------------------------------------------------------\n", + "Итерация: 15\n", + "Текущая точка (74.79591515037744, -1.1403332478296038)| Следующая точка (75.69872770576566, -0.9758019720797207)\n", + "MSE 138.39982870713823\n", + "--------------------------------------------------------\n", + "Итерация: 16\n", + "Текущая точка (75.69872770576566, -0.9758019720797207)| Следующая точка (76.45573166825358, -0.8281398136089831)\n", + "MSE 130.65048482717629\n", + "--------------------------------------------------------\n", + "Итерация: 17\n", + "Текущая точка (76.45573166825358, -0.8281398136089831)| Следующая точка (77.09064819864122, -0.6965630242691534)\n", + "MSE 125.17587305548757\n", + "--------------------------------------------------------\n", + "Итерация: 18\n", + "Текущая точка (77.09064819864122, -0.6965630242691534)| Следующая точка (77.62330450567968, -0.5800232339912165)\n", + "MSE 121.30608466627997\n", + "--------------------------------------------------------\n", + "Итерация: 19\n", + "Текущая точка (77.62330450567968, -0.5800232339912165)| Следующая точка (78.07028004458385, -0.47732954550339834)\n", + "MSE 118.5693021415083\n", + "--------------------------------------------------------\n", + "Итерация: 20\n", + "Текущая точка (78.07028004458385, -0.47732954550339834)| Следующая точка (78.44544409060278, -0.3872347313389438)\n", + "MSE 116.63292990920401\n", + "--------------------------------------------------------\n", + "Итерация: 21\n", + "Текущая точка (78.44544409060278, -0.3872347313389438)| Следующая точка (78.76040322042036, -0.3084946422381159)\n", + "MSE 115.26232714854191\n", + "--------------------------------------------------------\n", + "Итерация: 22\n", + "Текущая точка (78.76040322042036, -0.3084946422381159)| Следующая точка (79.024874019221, -0.23990778502053262)\n", + "MSE 114.29184073360233\n", + "--------------------------------------------------------\n", + "Итерация: 23\n", + "Текущая точка (79.024874019221, -0.23990778502053262)| Следующая точка (79.24699368416267, -0.18034036428991163)\n", + "MSE 113.60444739461153\n", + "--------------------------------------------------------\n", + "Итерация: 24\n", + "Текущая точка (79.24699368416267, -0.18034036428991163)| Следующая точка (79.43357901354796, -0.12874079838376576)\n", + "MSE 113.11743065929035\n", + "--------------------------------------------------------\n", + "Итерация: 25\n", + "Текущая точка (79.43357901354796, -0.12874079838376576)| Следующая точка (79.590342471717, -0.08414673157126629)\n", + "MSE 112.77229367773498\n", + "--------------------------------------------------------\n", + "Итерация: 26\n", + "Текущая точка (79.590342471717, -0.08414673157126629)| Следующая точка (79.72207253439184, -0.045686805736328245)\n", + "MSE 112.5276488913149\n", + "--------------------------------------------------------\n", + "Итерация: 27\n", + "Текущая точка (79.72207253439184, -0.045686805736328245)| Следующая точка (79.83278429207604, -0.012578874154160535)\n", + "MSE 112.35420203791824\n", + "--------------------------------------------------------\n", + "Итерация: 28\n", + "Текущая точка (79.83278429207604, -0.012578874154160535)| Следующая точка (79.92584527446147, 0.01587410273549933)\n", + "MSE 112.23121107508297\n", + "--------------------------------------------------------\n", + "Итерация: 29\n", + "Текущая точка (79.92584527446147, 0.01587410273549933)| Следующая точка (80.00408061916187, 0.0402895757954151)\n", + "MSE 112.14398472877181\n", + "--------------------------------------------------------\n", + "Итерация: 30\n", + "Текущая точка (80.00408061916187, 0.0402895757954151)| Следующая точка (80.06986101275434, 0.061211690132424876)\n", + "MSE 112.08211443091523\n", + "--------------------------------------------------------\n", + "Итерация: 31\n", + "Текущая точка (80.06986101275434, 0.061211690132424876)| Следующая точка (80.12517625583499, 0.07911787359402343)\n", + "MSE 112.03822398727151\n", + "--------------------------------------------------------\n", + "Итерация: 32\n", + "Текущая точка (80.12517625583499, 0.07911787359402343)| Следующая точка (80.1716968258466, 0.09442541434069902)\n", + "MSE 112.0070849682984\n", + "--------------------------------------------------------\n", + "Итерация: 33\n", + "Текущая точка (80.1716968258466, 0.09442541434069902)| Следующая точка (80.21082541475698, 0.10749781647693545)\n", + "MSE 111.98499059416774\n", + "--------------------------------------------------------\n", + "Итерация: 34\n", + "Текущая точка (80.21082541475698, 0.10749781647693545)| Следующая точка (80.24374008920961, 0.11865080004710715)\n", + "MSE 111.96931241806891\n", + "--------------------------------------------------------\n", + "Итерация: 35\n", + "Текущая точка (80.24374008920961, 0.11865080004710715)| Следующая точка (80.2714304469608, 0.1281578677088226)\n", + "MSE 111.95818633748331\n", + "--------------------------------------------------------\n", + "Итерация: 36\n", + "Текущая точка (80.2714304469608, 0.1281578677088226)| Следующая точка (80.29472791571294, 0.13625540033755565)\n", + "MSE 111.95029014106852\n", + "--------------------------------------------------------\n", + "Итерация: 37\n", + "Текущая точка (80.29472791571294, 0.13625540033755565)| Следующая точка (80.3143311509696, 0.14314727172458203)\n", + "MSE 111.94468586607258\n", + "--------------------------------------------------------\n", + "Итерация: 38\n", + "Текущая точка (80.3143311509696, 0.14314727172458203)| Следующая точка (80.33082733176776, 0.14900899149088742)\n", + "MSE 111.9407080587951\n", + "--------------------------------------------------------\n", + "Итерация: 39\n", + "Текущая точка (80.33082733176776, 0.14900899149088742)| Следующая точка (80.34471002169886, 0.15399139770567302)\n", + "MSE 111.93788455593705\n", + "--------------------------------------------------------\n", + "Итерация: 40\n", + "Текущая точка (80.34471002169886, 0.15399139770567302)| Следующая точка (80.35639415306127, 0.15822392825594017)\n", + "MSE 111.93588031188328\n", + "--------------------------------------------------------\n", + "Итерация: 41\n", + "Текущая точка (80.35639415306127, 0.15822392825594017)| Следующая точка (80.3662286006031, 0.16181750411360082)\n", + "MSE 111.93445756119772\n", + "--------------------------------------------------------\n", + "Итерация: 42\n", + "Текущая точка (80.3662286006031, 0.16181750411360082)| Следующая точка (80.37450673505678, 0.16486705930322332)\n", + "MSE 111.93344756203358\n", + "--------------------------------------------------------\n", + "Итерация: 43\n", + "Текущая точка (80.37450673505678, 0.16486705930322332)| Следующая точка (80.3814752830035, 0.16745375234425586)\n", + "MSE 111.93273055134894\n", + "--------------------------------------------------------\n", + "Итерация: 44\n", + "Текущая точка (80.3814752830035, 0.16745375234425586)| Следующая точка (80.38734176642754, 0.16964689278727177)\n", + "MSE 111.93222152388138\n", + "--------------------------------------------------------\n", + "Итерация: 45\n", + "Текущая точка (80.38734176642754, 0.16964689278727177)| Следующая точка (80.39228075088505, 0.1715056145957146)\n", + "MSE 111.93186014187545\n", + "--------------------------------------------------------\n", + "Итерация: 46\n", + "Текущая точка (80.39228075088505, 0.1715056145957146)| Следующая точка (80.39643909406131, 0.17308032584082383)\n", + "MSE 111.93160357509205\n", + "--------------------------------------------------------\n", + "Итерация: 47\n", + "Текущая точка (80.39643909406131, 0.17308032584082383)| Следующая точка (80.39994035542131, 0.17441396169048307)\n", + "MSE 111.9314214197364\n", + "--------------------------------------------------------\n", + "Итерация: 48\n", + "Текущая точка (80.39994035542131, 0.17441396169048307)| Следующая точка (80.40288850166267, 0.17554306513125753)\n", + "MSE 111.93129209244117\n", + "--------------------------------------------------------\n", + "Итерация: 49\n", + "Текущая точка (80.40288850166267, 0.17554306513125753)| Следующая точка (80.40537102092192, 0.17649871736794565)\n", + "MSE 111.93120027093673\n", + "--------------------------------------------------------\n", + "Итерация: 50\n", + "Текущая точка (80.40537102092192, 0.17649871736794565)| Следующая точка (80.40746154046687, 0.1773073374625193)\n", + "MSE 111.93113507749824\n", + "--------------------------------------------------------\n", + "Итерация: 51\n", + "Текущая точка (80.40746154046687, 0.1773073374625193)| Следующая точка (80.40922202734937, 0.17799136854473532)\n", + "MSE 111.93108878953852\n", + "--------------------------------------------------------\n", + "Итерация: 52\n", + "Текущая точка (80.40922202734937, 0.17799136854473532)| Следующая точка (80.41070463870736, 0.17856986587197987)\n", + "MSE 111.9310559243356\n", + "--------------------------------------------------------\n", + "Итерация: 53\n", + "Текущая точка (80.41070463870736, 0.17856986587197987)| Следующая точка (80.41195327769022, 0.1790590001450439)\n", + "MSE 111.93103258931154\n", + "--------------------------------------------------------\n", + "Итерация: 54\n", + "Текущая точка (80.41195327769022, 0.1790590001450439)| Следующая точка (80.41300490199842, 0.1794724877994691)\n", + "MSE 111.93101602080289\n", + "--------------------------------------------------------\n", + "Итерация: 55\n", + "Текущая точка (80.41300490199842, 0.1794724877994691)| Следующая точка (80.41389062449502, 0.1798219584829086)\n", + "MSE 111.93100425662863\n", + "--------------------------------------------------------\n", + "Итерация: 56\n", + "Текущая точка (80.41389062449502, 0.1798219584829086)| Следующая точка (80.41463663902786, 0.18011726858776034)\n", + "MSE 111.93099590363774\n", + "--------------------------------------------------------\n", + "Итерация: 57\n", + "Текущая точка (80.41463663902786, 0.18011726858776034)| Следующая точка (80.41526499929918, 0.18036676852314196)\n", + "MSE 111.93098997268032\n", + "--------------------------------------------------------\n", + "Итерация: 58\n", + "Текущая точка (80.41526499929918, 0.18036676852314196)| Следующая точка (80.41579427417048, 0.18057753036794924)\n", + "MSE 111.93098576144426\n", + "--------------------------------------------------------\n", + "Итерация: 59\n", + "Текущая точка (80.41579427417048, 0.18057753036794924)| Следующая точка (80.4162400990548, 0.18075554163382535)\n", + "MSE 111.93098277127257\n", + "--------------------------------------------------------\n", + "Итерация: 60\n", + "Текущая точка (80.4162400990548, 0.18075554163382535)| Следующая точка (80.41661563991356, 0.18090587007021305)\n", + "MSE 111.9309806481053\n", + "--------------------------------------------------------\n", + "Итерация: 61\n", + "Текущая точка (80.41661563991356, 0.18090587007021305)| Следующая точка (80.41693198374094, 0.18103280375061692)\n", + "MSE 111.93097914054856\n", + "--------------------------------------------------------\n", + "Итерация: 62\n", + "Текущая точка (80.41693198374094, 0.18103280375061692)| Следующая точка (80.4171984672076, 0.18113997007799426)\n", + "MSE 111.93097807010358\n", + "--------------------------------------------------------\n", + "Итерация: 63\n", + "Текущая точка (80.4171984672076, 0.18113997007799426)| Следующая точка (80.41742295327681, 0.18123043682693823)\n", + "MSE 111.93097731002919\n", + "--------------------------------------------------------\n", + "Итерация: 64\n", + "Текущая точка (80.41742295327681, 0.18123043682693823)| Следующая точка (80.41761206404517, 0.1813067978911081)\n", + "MSE 111.93097677033369\n", + "--------------------------------------------------------\n", + "Итерация: 65\n", + "Текущая точка (80.41761206404517, 0.1813067978911081)| Следующая точка (80.41777137674777, 0.18137124601724236)\n", + "MSE 111.93097638711887\n", + "--------------------------------------------------------\n", + "Итерация: 66\n", + "Текущая точка (80.41777137674777, 0.18137124601724236)| Следующая точка (80.41790558876512, 0.1814256344741146)\n", + "MSE 111.93097611501378\n", + "--------------------------------------------------------\n", + "Итерация: 67\n", + "Текущая точка (80.41790558876512, 0.1814256344741146)| Следующая точка (80.41801865654197, 0.18147152931880287)\n", + "MSE 111.9309759218029\n", + "--------------------------------------------------------\n", + "Итерация: 68\n", + "Текущая точка (80.41801865654197, 0.18147152931880287)| Следующая точка (80.41811391254866, 0.18151025367739462)\n", + "MSE 111.93097578461146\n", + "--------------------------------------------------------\n" + ] + } + ], + "source": [ + "# установка минимального значения, на которое должны изменяться веса\n", + "eps = 0.0001\n", + "\n", + "# первоначальное точка\n", + "w1 = 0\n", + "w0 = 0\n", + "\n", + "# размер шага (learning rate)\n", + "learning_rate = 0.1\n", + "\n", + "next_w1 = w1\n", + "next_w0 = w0\n", + "# количество итерация \n", + "n = 100\n", + "for i in range(n):\n", + " cur_w1 = next_w1\n", + " cur_w0 = next_w0\n", + "\n", + " # движение в негативную сторону вычисляемого градиента\n", + " next_w0 = cur_w0 - learning_rate * gr_mserror(X, cur_w1, cur_w0, y)[0]\n", + " next_w1 = cur_w1 - learning_rate * gr_mserror(X, cur_w1, cur_w0, y)[1]\n", + "\n", + " # остановка когда достигнута необходимая степень точности\n", + " print(f\"Итерация: {i}\")\n", + " print(f\"Текущая точка {cur_w1, cur_w0}| Следующая точка {next_w1, next_w0}\")\n", + " print(f\"MSE {mserror(X, cur_w1, cur_w0, y)}\")\n", + " print(\"--------------------------------------------------------\")\n", + " \n", + " if (abs(cur_w1 - next_w1) <= eps) and (abs(cur_w0 - next_w0) <= eps):\n", + " break" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "yNm--2XU9mNg" + }, + "source": [ + "А мы получили точно такую же метрику, которая получалась у `LinearRegression` из `sklearn`.\n", + "\n", + "Сравним полученные коэффициенты с теми, которые были сгенерированы вместе с данными." + ] + }, + { + "cell_type": "code", + "execution_count": 38, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "yh6UMSWP9z5C", + "outputId": "d1b132a5-b54d-492e-ff59-84a165996c2f" + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Коэффициенты наклона True 80.65667909277211, trained 80.41811391254866\n", + "Коэффициенты сдвига True 0, trained 0.18151025367739462\n" + ] + } + ], + "source": [ + "print('Коэффициенты наклона', end=' ')\n", + "print(f'True {coeffs}, trained {next_w1}')\n", + "\n", + "print('Коэффициенты сдвига', end=' ')\n", + "print(f'True 0, trained {next_w0}')" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "ALbIVv6J9-tX" + }, + "source": [ + "А они очень похожи.\n", + "\n", + "А визуализированные кривые наслаиваются друг на друга" + ] + }, + { + "cell_type": "code", + "execution_count": 39, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 388 + }, + "id": "Vux7A3Da-WW6", + "outputId": "cfa10d85-94f5-40d3-997a-dd7a8619c437" + }, + "outputs": [ + { + "data": { + "image/png": "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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "fig = plt.figure(figsize=(10, 6))\n", + "\n", + "x = np.arange(-3, 3)\n", + "our_model_y = next_w1 * x + next_w0\n", + "\n", + "plt.plot(x, model_y_sk, linewidth=4, alpha=0.5, c='r', label=f'sklearn linear_model = {model_a:.2f}x + {model_b:.2f}')\n", + "plt.plot(x, our_model_y, '--g', linewidth=2, label=f'our linear_model = {next_w1:.2f}x + {next_w0:.2f}')\n", + "plt.scatter(X, y) \n", + "plt.grid()\n", + "plt.xlabel('feature')\n", + "plt.ylabel('target')\n", + "plt.legend(prop={'size': 16})\n", + "plt.show()" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "rEmVJTfv_EZ-" + }, + "source": [ + "## Многомерная линейная регрессия\n", + "\n", + "Сейчас мы посмотрели на то, как обучается линейная регрессия для задач с одним признаком.\n", + "\n", + "Построим себе данные поинтересней, состоящие из 4 признаков, это уже отрисовать не сможем.\n", + "\n" + ] + }, + { + "cell_type": "code", + "execution_count": 40, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 1000 + }, + "id": "96dLeOqm_kKq", + "outputId": "0b18b8ec-205e-4af6-af82-b5dd9fcb7d92", + "tags": [] + }, + "outputs": [ + { + "data": { + "text/plain": [ + "array([[ 0.85866717, -1.26407368, 1.11487028, 0.43477699],\n", + " [ 1.29127473, -0.96420485, 0.07175977, 0.2716063 ],\n", + " [ 1.06755846, -1.06163445, 0.21734821, 0.1178195 ],\n", + " [ 0.07101978, 0.92157523, -0.37682984, 0.91998254],\n", + " [ 0.27540666, 0.18632534, -1.13980565, 0.14180489],\n", + " [ 0.29634711, 1.40277112, -1.54686257, 1.29561858],\n", + " [-1.68728061, -1.69734212, -0.41145394, -0.04527514],\n", + " [ 0.5936862 , 0.37050633, 1.34537807, 1.01594215],\n", + " [-0.86335252, -0.13054147, -0.52308763, -0.25127692],\n", + " [ 0.65402488, 1.79948007, 1.5466061 , 1.60987398],\n", + " [ 1.0956297 , -0.30957664, 0.72575222, 1.54907163],\n", + " [-0.39117313, 1.53422235, -0.16419295, 0.36036665],\n", + " [ 0.68731235, -1.82300958, 0.8791138 , 1.84636487],\n", + " [-1.0616544 , -0.68448467, -0.47621448, 0.83031043],\n", + " [-1.1288944 , 0.01699688, -0.42442882, -0.1329099 ],\n", + " [ 0.51002802, 0.33871394, -1.17212003, -1.04596765],\n", + " [ 1.08771086, 0.53382172, 0.39521201, 0.12286753],\n", + " [-1.55107946, 0.94303598, 0.34115344, 0.13827322],\n", + " [-1.57749431, 1.31094364, -0.79286501, -0.07174941],\n", + " [-1.53214252, 0.21886858, 0.18566325, 1.83277654],\n", + " [ 1.20910164, -0.8430661 , -0.14189358, 0.38535414],\n", + " [ 1.65920462, 0.37864068, -0.46456606, 0.15384125],\n", + " [-1.44071935, 1.47651282, -0.13155348, 0.1944292 ],\n", + " [-0.00828463, -0.31963136, -0.53662936, 0.31540267],\n", + " [-0.37842255, -0.48897544, -0.64439382, 0.69914084],\n", + " [-0.23725045, -1.23234621, -0.17241977, 0.09183837],\n", + " [-0.18577532, -0.38053642, 0.08897764, 0.06367166],\n", + " [ 1.4924715 , -1.11523722, -0.70541403, -0.04723257],\n", + " [ 0.51655239, -0.08940364, 0.68212971, 0.15072201],\n", + " [ 1.74945474, -0.286073 , -0.48456513, -2.65331856],\n", + " [ 2.15667443, -0.82943725, -0.52937203, 1.56170369],\n", + " [-1.08019383, -0.43205762, 0.51608404, 0.45539286],\n", + " [-0.17809318, -0.57395456, -0.20437532, -0.4864951 ],\n", + " [-0.53085824, -0.86986194, -1.15526422, 0.79667185],\n", + " [ 0.76616062, -0.99402769, -0.26434233, 1.54220922],\n", + " [ 0.85615205, -0.04480262, -0.47748923, -0.15406552],\n", + " [ 0.68968231, 0.56119218, -1.30554851, -1.11947526],\n", + " [ 0.87427277, 0.0716521 , -1.63905163, -0.64730263],\n", + " [-1.29742262, -0.71496244, 0.51447963, 0.25771638],\n", + " [-1.68411089, -1.18575527, 0.60010201, 0.69556726],\n", + " [ 0.63007982, 0.07349324, 0.73227135, -0.64257539],\n", + " [-0.10514925, -1.58396258, -1.37177369, -0.02831834],\n", + " [-2.04905726, 0.86705521, -0.26196107, 0.57897111],\n", + " [ 0.42105072, -1.06560298, -0.88623967, -0.47573349],\n", + " [-0.49673048, 0.50502192, 0.93878313, -0.67502027],\n", + " [-0.06766856, -0.41320975, 0.1200828 , -0.69897169],\n", + " [ 0.73683739, 1.57463407, -0.03107509, -0.68344663],\n", + " [ 0.59527845, -0.68280162, -0.71355993, -1.90828954],\n", + " [ 0.81776957, 0.03679475, -0.04870254, 1.7915311 ],\n", + " [ 2.20185631, -0.0370669 , 1.93290543, -1.99357153]])" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "text/plain": [ + "array([ 43.59907368, 33.3226129 , 12.92842886, 56.76209111,\n", + " -28.24075472, 64.36182392, -220.93063391, 134.81614163,\n", + " -111.85450024, 244.9327123 , 106.23869476, 83.15972598,\n", + " 22.1607008 , -87.67552386, -94.67026039, -29.62752165,\n", + " 119.90179833, -16.36526242, -71.2734975 , -33.77825083,\n", + " 24.31113443, 102.14682115, 1.12585934, -48.81175726,\n", + " -58.59186113, -111.47215424, -12.5784088 , -14.21337533,\n", + " 64.61172215, 10.81251385, 99.11401244, -75.98950916,\n", + " -52.77978396, -112.95415032, 7.45744433, 33.69756994,\n", + " -24.66640928, -35.64805852, -76.68888106, -129.08694753,\n", + " 59.65011241, -158.52958483, -61.09970272, -97.83194751,\n", + " 36.42924987, -49.96145024, 104.10943674, -80.90767725,\n", + " 99.76081282, 152.70106779])" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "from sklearn.datasets import make_regression\n", + "\n", + "X, y = make_regression(n_samples=50, n_features=4, n_informative=4,\n", + " noise=10, random_state=11)\n", + "\n", + "display(X, y)" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "L389l2QmCBeI" + }, + "source": [ + "### Из sklearn" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "SQDLd3sv_ubX" + }, + "source": [ + "Обучим для начала модель из `sklearn`" + ] + }, + { + "cell_type": "code", + "execution_count": 41, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "3ArQO5TI_qOq", + "outputId": "4602856f-72a2-41bd-b945-efed584e99a1", + "tags": [] + }, + "outputs": [ + { + "data": { + "text/html": [ + "
LinearRegression()
In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook.
On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.
" + ], + "text/plain": [ + "LinearRegression()" + ] + }, + "execution_count": 41, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "model = LinearRegression()\n", + "model.fit(X, y)" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "L0SIDCaB_qOs" + }, + "source": [ + "Посмотрим обученные коэффициенты и теперь давайте их называть весами.\n", + "\n", + "Есть веса при признаках - это и есть коэффициенты наклона но по каждой оси.\n", + "\n", + "И есть один свободный вес - коэффициент сдвига." + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "l0TPVblH_-2H" + }, + "source": [ + "Получаем 4 веса при признаках - значения для каждого признака, которые сообщают, насколько нужно наклонить прямую относительно каждой оси.\n", + "\n", + "И один сдвиг - свободный вес." + ] + }, + { + "cell_type": "code", + "execution_count": 42, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "qsgZhUMk_qOs", + "outputId": "aeeb1dbd-d8e5-45b8-804b-3920395e6a1f", + "tags": [] + }, + "outputs": [ + { + "data": { + "text/plain": [ + "(array([59.51225616, 57.72556421, 44.70715115, 24.87193091]),\n", + " -1.6392969526147305)" + ] + }, + "execution_count": 42, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "model.coef_, model.intercept_" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "Kl5cJtLhcmn5" + }, + "source": [ + "Можем сделать предсказания этой моделью, сначала через метод `predict`." + ] + }, + { + "cell_type": "code", + "execution_count": 43, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "zUGT6x6ucsLj", + "outputId": "f20ad239-b090-477a-9534-75752a910003", + "tags": [] + }, + "outputs": [ + { + "data": { + "text/plain": [ + "array([37.14897504])" + ] + }, + "execution_count": 43, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "model.predict(X[:1])" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "QcgBXyOAcx1-" + }, + "source": [ + "А теперь с помощью перемножения весов на признаки, суммирования их и добавления свободного веса." + ] + }, + { + "cell_type": "code", + "execution_count": 44, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "h99wvhpTc4Qk", + "outputId": "58291f95-4cd6-49da-b6dd-6e13b984fab2", + "tags": [] + }, + "outputs": [ + { + "data": { + "text/plain": [ + "37.148975042692896" + ] + }, + "execution_count": 44, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "np.sum(model.coef_ * X[0]) + model.intercept_" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "Lq2hstvtBDTo" + }, + "source": [ + "Давайте посчитаем ошибку на предсказаниях модели, при этом получим предсказания не одним способом (через `model.predict`), а еще и вторым, сами перемножим веса (`model.coef_`) на значения признаков (`X`) и добавим значение сдвига (`model.intercept_`)\n", + "\n", + "Выходит, что неважно, как мы получаем предсказания они всё равно одинаковые." + ] + }, + { + "cell_type": "code", + "execution_count": 45, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 206 + }, + "id": "lEyoJKCWAUvz", + "outputId": "b0e0419a-b0bc-45b6-b62d-48d1c0b6b685", + "tags": [] + }, + "outputs": [ + { + "data": { + "text/html": [ + "
\n", + "\n", + "\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "
0123ypred_fitpred_dot
00.858667-1.2640741.1148700.43477743.59907437.14897537.148975
11.291275-0.9642050.0717600.27160633.32261329.51165429.511654
21.067558-1.0616340.2173480.11782012.92842913.25748613.257486
30.0710200.921575-0.3768300.91998356.76209161.82045461.820454
40.2754070.186325-1.1398060.141805-28.240755-21.923992-21.923992
\n", + "
" + ], + "text/plain": [ + " 0 1 2 3 y pred_fit pred_dot\n", + "0 0.858667 -1.264074 1.114870 0.434777 43.599074 37.148975 37.148975\n", + "1 1.291275 -0.964205 0.071760 0.271606 33.322613 29.511654 29.511654\n", + "2 1.067558 -1.061634 0.217348 0.117820 12.928429 13.257486 13.257486\n", + "3 0.071020 0.921575 -0.376830 0.919983 56.762091 61.820454 61.820454\n", + "4 0.275407 0.186325 -1.139806 0.141805 -28.240755 -21.923992 -21.923992" + ] + }, + "execution_count": 45, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "df = pd.DataFrame(X)\n", + "df['y'] = y\n", + "df['pred_fit'] = model.predict(X)\n", + "df['pred_dot'] = X.dot(model.coef_) + model.intercept_\n", + "\n", + "df.head()" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "vmltua2HAR7I" + }, + "source": [ + "Посчитаем отклонения предсказаний от истины." + ] + }, + { + "cell_type": "code", + "execution_count": 46, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 206 + }, + "id": "yXq4rvEuAR7I", + "outputId": "d966ee93-19ca-4920-a5ec-0d7d0dcdb240", + "tags": [] + }, + "outputs": [ + { + "data": { + "text/html": [ + "
\n", + "\n", + "\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "
0123ypred_fitpred_dotresidual
00.858667-1.2640741.1148700.43477743.59907437.14897537.148975-6.450099
11.291275-0.9642050.0717600.27160633.32261329.51165429.511654-3.810959
21.067558-1.0616340.2173480.11782012.92842913.25748613.2574860.329057
30.0710200.921575-0.3768300.91998356.76209161.82045461.8204545.058363
40.2754070.186325-1.1398060.141805-28.240755-21.923992-21.9239926.316763
\n", + "
" + ], + "text/plain": [ + " 0 1 2 3 y pred_fit pred_dot \\\n", + "0 0.858667 -1.264074 1.114870 0.434777 43.599074 37.148975 37.148975 \n", + "1 1.291275 -0.964205 0.071760 0.271606 33.322613 29.511654 29.511654 \n", + "2 1.067558 -1.061634 0.217348 0.117820 12.928429 13.257486 13.257486 \n", + "3 0.071020 0.921575 -0.376830 0.919983 56.762091 61.820454 61.820454 \n", + "4 0.275407 0.186325 -1.139806 0.141805 -28.240755 -21.923992 -21.923992 \n", + "\n", + " residual \n", + "0 -6.450099 \n", + "1 -3.810959 \n", + "2 0.329057 \n", + "3 5.058363 \n", + "4 6.316763 " + ] + }, + "execution_count": 46, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "df['residual'] = df['pred_fit'] - df['y']\n", + "df.head()" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "jGVAfwy7AR7I" + }, + "source": [ + "И на всех объектах считаем метрику MSE - mean squared error, напомню, что более подробно про неё рассказываю в этом [видео](https://youtu.be/vh2smjQyhp8) и в этом [ноутбуке](https://colab.research.google.com/drive/14Oxi6sI25mP4JbovLiJ57e7H5sbN2I3p)." + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "USkbwCNVAR7I" + }, + "source": [ + "MSE равняется." + ] + }, + { + "cell_type": "code", + "execution_count": 47, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "oVK_d05wAR7J", + "outputId": "a9087eb5-0879-4b4b-9562-ad8681386a65", + "tags": [] + }, + "outputs": [ + { + "data": { + "text/plain": [ + "92.64429127220508" + ] + }, + "execution_count": 47, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "np.mean(df['residual'] ** 2)" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "Tfem6RubAMjf" + }, + "source": [ + "### Своя реализация линейной регрессии" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "zL6O2KJQCPk_" + }, + "source": [ + "Берем те же самые данные, где брали 4 признака, но еще возвращаем веса при признаках, а свободный вес по умолчанию в такой генерации равен 0." + ] + }, + { + "cell_type": "code", + "execution_count": 48, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 1000 + }, + "id": "FIviYjjJCPlA", + "outputId": "8245f99b-6432-4137-94d3-c57e67121077", + "tags": [] + }, + "outputs": [ + { + "data": { + "text/plain": [ + "array([[ 0.85866717, -1.26407368, 1.11487028, 0.43477699],\n", + " [ 1.29127473, -0.96420485, 0.07175977, 0.2716063 ],\n", + " [ 1.06755846, -1.06163445, 0.21734821, 0.1178195 ],\n", + " [ 0.07101978, 0.92157523, -0.37682984, 0.91998254],\n", + " [ 0.27540666, 0.18632534, -1.13980565, 0.14180489],\n", + " [ 0.29634711, 1.40277112, -1.54686257, 1.29561858],\n", + " [-1.68728061, -1.69734212, -0.41145394, -0.04527514],\n", + " [ 0.5936862 , 0.37050633, 1.34537807, 1.01594215],\n", + " [-0.86335252, -0.13054147, -0.52308763, -0.25127692],\n", + " [ 0.65402488, 1.79948007, 1.5466061 , 1.60987398],\n", + " [ 1.0956297 , -0.30957664, 0.72575222, 1.54907163],\n", + " [-0.39117313, 1.53422235, -0.16419295, 0.36036665],\n", + " [ 0.68731235, -1.82300958, 0.8791138 , 1.84636487],\n", + " [-1.0616544 , -0.68448467, -0.47621448, 0.83031043],\n", + " [-1.1288944 , 0.01699688, -0.42442882, -0.1329099 ],\n", + " [ 0.51002802, 0.33871394, -1.17212003, -1.04596765],\n", + " [ 1.08771086, 0.53382172, 0.39521201, 0.12286753],\n", + " [-1.55107946, 0.94303598, 0.34115344, 0.13827322],\n", + " [-1.57749431, 1.31094364, -0.79286501, -0.07174941],\n", + " [-1.53214252, 0.21886858, 0.18566325, 1.83277654],\n", + " [ 1.20910164, -0.8430661 , -0.14189358, 0.38535414],\n", + " [ 1.65920462, 0.37864068, -0.46456606, 0.15384125],\n", + " [-1.44071935, 1.47651282, -0.13155348, 0.1944292 ],\n", + " [-0.00828463, -0.31963136, -0.53662936, 0.31540267],\n", + " [-0.37842255, -0.48897544, -0.64439382, 0.69914084],\n", + " [-0.23725045, -1.23234621, -0.17241977, 0.09183837],\n", + " [-0.18577532, -0.38053642, 0.08897764, 0.06367166],\n", + " [ 1.4924715 , -1.11523722, -0.70541403, -0.04723257],\n", + " [ 0.51655239, -0.08940364, 0.68212971, 0.15072201],\n", + " [ 1.74945474, -0.286073 , -0.48456513, -2.65331856],\n", + " [ 2.15667443, -0.82943725, -0.52937203, 1.56170369],\n", + " [-1.08019383, -0.43205762, 0.51608404, 0.45539286],\n", + " [-0.17809318, -0.57395456, -0.20437532, -0.4864951 ],\n", + " [-0.53085824, -0.86986194, -1.15526422, 0.79667185],\n", + " [ 0.76616062, -0.99402769, -0.26434233, 1.54220922],\n", + " [ 0.85615205, -0.04480262, -0.47748923, -0.15406552],\n", + " [ 0.68968231, 0.56119218, -1.30554851, -1.11947526],\n", + " [ 0.87427277, 0.0716521 , -1.63905163, -0.64730263],\n", + " [-1.29742262, -0.71496244, 0.51447963, 0.25771638],\n", + " [-1.68411089, -1.18575527, 0.60010201, 0.69556726],\n", + " [ 0.63007982, 0.07349324, 0.73227135, -0.64257539],\n", + " [-0.10514925, -1.58396258, -1.37177369, -0.02831834],\n", + " [-2.04905726, 0.86705521, -0.26196107, 0.57897111],\n", + " [ 0.42105072, -1.06560298, -0.88623967, -0.47573349],\n", + " [-0.49673048, 0.50502192, 0.93878313, -0.67502027],\n", + " [-0.06766856, -0.41320975, 0.1200828 , -0.69897169],\n", + " [ 0.73683739, 1.57463407, -0.03107509, -0.68344663],\n", + " [ 0.59527845, -0.68280162, -0.71355993, -1.90828954],\n", + " [ 0.81776957, 0.03679475, -0.04870254, 1.7915311 ],\n", + " [ 2.20185631, -0.0370669 , 1.93290543, -1.99357153]])" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "text/plain": [ + "array([ 43.59907368, 33.3226129 , 12.92842886, 56.76209111,\n", + " -28.24075472, 64.36182392, -220.93063391, 134.81614163,\n", + " -111.85450024, 244.9327123 , 106.23869476, 83.15972598,\n", + " 22.1607008 , -87.67552386, -94.67026039, -29.62752165,\n", + " 119.90179833, -16.36526242, -71.2734975 , -33.77825083,\n", + " 24.31113443, 102.14682115, 1.12585934, -48.81175726,\n", + " -58.59186113, -111.47215424, -12.5784088 , -14.21337533,\n", + " 64.61172215, 10.81251385, 99.11401244, -75.98950916,\n", + " -52.77978396, -112.95415032, 7.45744433, 33.69756994,\n", + " -24.66640928, -35.64805852, -76.68888106, -129.08694753,\n", + " 59.65011241, -158.52958483, -61.09970272, -97.83194751,\n", + " 36.42924987, -49.96145024, 104.10943674, -80.90767725,\n", + " 99.76081282, 152.70106779])" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "X, y, coeffs = make_regression(n_samples=50, n_features=4, n_informative=4,\n", + " noise=10, coef=True, random_state=11)\n", + "\n", + "display(X, y)" + ] + }, + { + "cell_type": "code", + "execution_count": 49, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "tFAeNpTVCPlB", + "outputId": "cf0a71d1-f68b-492e-88df-04474120e694", + "tags": [] + }, + "outputs": [ + { + "data": { + "text/plain": [ + "array([59.32158596, 58.74342238, 44.07539836, 25.03682142])" + ] + }, + "execution_count": 49, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "coeffs" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "-jcBg1trGk5C" + }, + "source": [ + "Для удобства реализации градиентного спуска от записи поэлементной через сумму ($MSE = \\frac{1}{n}\\sum_{i=0}^n{(\\text{y}_i-\\text{y_pred}_i})^2$) перейдем к матричной форме записи.\n", + "\n", + "Предсказания линейной модели - это перемножение весов на признаки плюс свободный вес.\n", + "\n", + "$$y_{pred} = X\\cdot w + w_0$$\n", + "\n", + "При этом очень важно, чтобы соблюдались размерности матрицы $X$ и вектора $w$.\n", + "У нас размерности равны" + ] + }, + { + "cell_type": "code", + "execution_count": 50, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "EQUqgDEnHRiP", + "outputId": "0d4bdb56-6ed3-4663-b396-1c9b9aa2eeb5", + "tags": [] + }, + "outputs": [ + { + "data": { + "text/plain": [ + "((50, 4), (4,))" + ] + }, + "execution_count": 50, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "X.shape, coeffs.shape" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "d8T_yqOeHX8G" + }, + "source": [ + "А значит, чтобы объеты могли матрично перемножиться, нужно чтобы количество столбцов первой матрицы было равно количеству строк второй (но у нас не матрица, а вектор).\n", + "\n", + "У нас совпадают, так что можем их перемножать и получаем ничто иное, как *скалярное произведение* - все значения в признаках перемножаются на соответсвующие веса и складываются.\n", + "\n", + "\n", + "А значит можем переписать формулу:\n", + "$$y_{pred} = \\langle X, w\\rangle + w_0$$\n", + "\n", + "\n", + "Но вот только мешается свободный вес. Можно пойти на одну хитрость и добавить фиктивный признак в данные, который для каждого объекта равен 1.\n", + "\n" + ] + }, + { + "cell_type": "code", + "execution_count": 51, + "metadata": { + "id": "qRd4q1PYIG-_", + "tags": [] + }, + "outputs": [ + { + "data": { + "text/plain": [ + "array([[ 0.85866717, -1.26407368, 1.11487028, 0.43477699, 1. ],\n", + " [ 1.29127473, -0.96420485, 0.07175977, 0.2716063 , 1. ],\n", + " [ 1.06755846, -1.06163445, 0.21734821, 0.1178195 , 1. ],\n", + " [ 0.07101978, 0.92157523, -0.37682984, 0.91998254, 1. ],\n", + " [ 0.27540666, 0.18632534, -1.13980565, 0.14180489, 1. ],\n", + " [ 0.29634711, 1.40277112, -1.54686257, 1.29561858, 1. ],\n", + " [-1.68728061, -1.69734212, -0.41145394, -0.04527514, 1. ],\n", + " [ 0.5936862 , 0.37050633, 1.34537807, 1.01594215, 1. ],\n", + " [-0.86335252, -0.13054147, -0.52308763, -0.25127692, 1. ],\n", + " [ 0.65402488, 1.79948007, 1.5466061 , 1.60987398, 1. ],\n", + " [ 1.0956297 , -0.30957664, 0.72575222, 1.54907163, 1. ],\n", + " [-0.39117313, 1.53422235, -0.16419295, 0.36036665, 1. ],\n", + " [ 0.68731235, -1.82300958, 0.8791138 , 1.84636487, 1. ],\n", + " [-1.0616544 , -0.68448467, -0.47621448, 0.83031043, 1. ],\n", + " [-1.1288944 , 0.01699688, -0.42442882, -0.1329099 , 1. ],\n", + " [ 0.51002802, 0.33871394, -1.17212003, -1.04596765, 1. ],\n", + " [ 1.08771086, 0.53382172, 0.39521201, 0.12286753, 1. ],\n", + " [-1.55107946, 0.94303598, 0.34115344, 0.13827322, 1. ],\n", + " [-1.57749431, 1.31094364, -0.79286501, -0.07174941, 1. ],\n", + " [-1.53214252, 0.21886858, 0.18566325, 1.83277654, 1. ],\n", + " [ 1.20910164, -0.8430661 , -0.14189358, 0.38535414, 1. ],\n", + " [ 1.65920462, 0.37864068, -0.46456606, 0.15384125, 1. ],\n", + " [-1.44071935, 1.47651282, -0.13155348, 0.1944292 , 1. ],\n", + " [-0.00828463, -0.31963136, -0.53662936, 0.31540267, 1. ],\n", + " [-0.37842255, -0.48897544, -0.64439382, 0.69914084, 1. ],\n", + " [-0.23725045, -1.23234621, -0.17241977, 0.09183837, 1. ],\n", + " [-0.18577532, -0.38053642, 0.08897764, 0.06367166, 1. ],\n", + " [ 1.4924715 , -1.11523722, -0.70541403, -0.04723257, 1. ],\n", + " [ 0.51655239, -0.08940364, 0.68212971, 0.15072201, 1. ],\n", + " [ 1.74945474, -0.286073 , -0.48456513, -2.65331856, 1. ],\n", + " [ 2.15667443, -0.82943725, -0.52937203, 1.56170369, 1. ],\n", + " [-1.08019383, -0.43205762, 0.51608404, 0.45539286, 1. ],\n", + " [-0.17809318, -0.57395456, -0.20437532, -0.4864951 , 1. ],\n", + " [-0.53085824, -0.86986194, -1.15526422, 0.79667185, 1. ],\n", + " [ 0.76616062, -0.99402769, -0.26434233, 1.54220922, 1. ],\n", + " [ 0.85615205, -0.04480262, -0.47748923, -0.15406552, 1. ],\n", + " [ 0.68968231, 0.56119218, -1.30554851, -1.11947526, 1. ],\n", + " [ 0.87427277, 0.0716521 , -1.63905163, -0.64730263, 1. ],\n", + " [-1.29742262, -0.71496244, 0.51447963, 0.25771638, 1. ],\n", + " [-1.68411089, -1.18575527, 0.60010201, 0.69556726, 1. ],\n", + " [ 0.63007982, 0.07349324, 0.73227135, -0.64257539, 1. ],\n", + " [-0.10514925, -1.58396258, -1.37177369, -0.02831834, 1. ],\n", + " [-2.04905726, 0.86705521, -0.26196107, 0.57897111, 1. ],\n", + " [ 0.42105072, -1.06560298, -0.88623967, -0.47573349, 1. ],\n", + " [-0.49673048, 0.50502192, 0.93878313, -0.67502027, 1. ],\n", + " [-0.06766856, -0.41320975, 0.1200828 , -0.69897169, 1. ],\n", + " [ 0.73683739, 1.57463407, -0.03107509, -0.68344663, 1. ],\n", + " [ 0.59527845, -0.68280162, -0.71355993, -1.90828954, 1. ],\n", + " [ 0.81776957, 0.03679475, -0.04870254, 1.7915311 , 1. ],\n", + " [ 2.20185631, -0.0370669 , 1.93290543, -1.99357153, 1. ]])" + ] + }, + "execution_count": 51, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "X = np.column_stack([X, np.ones((50))])\n", + "X" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "HmRcuBRDIS0y" + }, + "source": [ + "И теперь всё предсказание линейной модели будет равняться:\n", + "\n", + "$$y_{pred} = \\langle X, w\\rangle$$\n", + "\n", + "\n", + "А наша ошибка MSE преобразиться и будет выглядить следующим образом:\n", + "\n", + "$$MSE = \\frac{1}{n}\\sum^{n}_{i=1}(y_{i} - \\text{y_pred}_i)^{2} = \\frac{1}{n}\\sum^{n}_{i=1}(y_{i} - \\langle X_i, w\\rangle)^{2} = \\frac{1}{n}||Y - X w||^{2}$$\n", + "\n", + "\n", + "где используется $L_{2}$ норма:\n", + "\n", + "$$||Y - X w|| = \\sqrt{\\sum_{i=1}^n{(y_i-X_iw)^2}} $$\n", + "\n", + "$$MSE = \\frac{1}{n}\\sqrt{\\sum_{i=1}^n{(y_i-X_iw)^2}} ^{2} = \\frac{1}{n}\\sum_{i=1}^n{(y_i-X_iw)^2}$$\n", + "\n" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "yjbmJkF4CPlC" + }, + "source": [ + "Реализуем две функции только уже с матричными операциями:\n", + "1. mserror_mat - функция среднеквадратичной ошибки для матриц\n", + "\n", + "\n", + "2. gr_mserror_mat - градиент функции MSE для матрицы:\n", + "\n", + "$\\frac{∂ MSE}{∂ w} = \\frac{1 \\cdot 2}{n}({Y - Xw}) \\cdot-X$\n", + "\n" + ] + }, + { + "cell_type": "code", + "execution_count": 52, + "metadata": { + "id": "_GlN3Fb4CPlC", + "tags": [] + }, + "outputs": [], + "source": [ + "# функция, определяющая среднеквадратичную ошибку\n", + "def mserror_mat(X, w, y):\n", + " y_pred = X @ w\n", + " return np.sum((y - y_pred) ** 2) / len(y_pred)\n", + "\n", + "# функция градиента\n", + "def gr_mserror_mat(X, w, y):\n", + " y_pred = X @ w\n", + " return 2/len(X)*(y - y_pred) @ (-X)\n" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "siwPK0oQCPlC" + }, + "source": [ + "И остается запустить цикл градиентного спуска.\n", + "\n", + "В начале инициализировали коэффициенты. Т.к. у нас 5 признаков (4 настоящих плюс один фиктивный), то будет 5 весов." + ] + }, + { + "cell_type": "code", + "execution_count": 53, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "PBs-gz9sK9Eo", + "outputId": "4eefc925-6d0d-48be-80fb-76411c4d673f", + "tags": [] + }, + "outputs": [ + { + "data": { + "text/plain": [ + "array([0., 0., 0., 0., 0.])" + ] + }, + "execution_count": 53, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# первоначальное точка\n", + "weights = np.zeros(X.shape[1])\n", + "weights" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "vfDPdqojLykB" + }, + "source": [ + "Затем запускаем цикл по обучению и меняем веса, при этом не каждый вес отдельно, а все веса сразу вместе.\n", + "\n", + "И если веса начнут плохо изменяться, то можем выйти по критерию останова:\n", + "\n", + "$$||w_{new} - w_{old}|| ≤ eps$$" + ] + }, + { + "cell_type": "code", + "execution_count": 54, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "hsA08x8UCPlC", + "outputId": "65060637-1a17-45cc-f4fd-12ee2543280e", + "tags": [] + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Итерация: 0\n", + "Текущая точка [0. 0. 0. 0. 0.]| Следующая точка [11.76469989 8.02019663 7.1529662 3.12227706 -0.71246521]\n", + "MSE 7901.284047919272\n", + "--------------------------------------------------------\n", + "Итерация: 1\n", + "Текущая точка [11.76469989 8.02019663 7.1529662 3.12227706 -0.71246521]| Следующая точка [21.07996752 15.00192791 13.15169841 5.97919733 -1.2826889 ]\n", + "MSE 5491.413352110748\n", + "--------------------------------------------------------\n", + "Итерация: 2\n", + "Текущая точка [21.07996752 15.00192791 13.15169841 5.97919733 -1.2826889 ]| Следующая точка [28.48213061 21.0576616 18.18254548 8.55326802 -1.72604536]\n", + "MSE 3846.369420368778\n", + "--------------------------------------------------------\n", + "Итерация: 3\n", + "Текущая точка [28.48213061 21.0576616 18.18254548 8.55326802 -1.72604536]| Следующая точка [34.38479911 26.29452131 22.40202941 10.84471509 -2.06001325]\n", + "MSE 2714.9929661406427\n", + "--------------------------------------------------------\n", + "Итерация: 4\n", + "Текущая точка [34.38479911 26.29452131 22.40202941 10.84471509 -2.06001325]| Следующая точка [39.10795216 30.8120046 25.94151547 12.86498442 -2.30214304]\n", + "MSE 1931.9222400828064\n", + "--------------------------------------------------------\n", + "Итерация: 5\n", + "Текущая точка [39.10795216 30.8120046 25.94151547 12.86498442 -2.30214304]| Следующая точка [42.8999458 34.70086936 28.91118413 14.6321765 -2.46889448]\n", + "MSE 1387.0040664906423\n", + "--------------------------------------------------------\n", + "Итерация: 6\n", + "Текущая точка [42.8999458 34.70086936 28.91118413 14.6321765 -2.46889448]| Следующая точка [45.95419736 38.04279216 31.40339917 16.16788817 -2.57504099]\n", + "MSE 1006.0899187520283\n", + "--------------------------------------------------------\n", + "Итерация: 7\n", + "Текущая точка [45.95419736 38.04279216 31.40339917 16.16788817 -2.57504099]| Следующая точка [48.42186138 40.91052058 33.49555668 17.49507148 -2.63343403]\n", + "MSE 738.8060039075306\n", + "--------------------------------------------------------\n", + "Итерация: 8\n", + "Текущая точка [48.42186138 40.91052058 33.49555668 17.49507148 -2.63343403]| Следующая точка [50.42148205 43.36832699 35.25248994 18.63662244 -2.65498752]\n", + "MSE 550.6561006176497\n", + "--------------------------------------------------------\n", + "Итерация: 9\n", + "Текущая точка [50.42148205 43.36832699 35.25248994 18.63662244 -2.65498752]| Следующая точка [52.04636149 45.47263149 36.7284959 19.61448849 -2.64878917]\n", + "MSE 417.85498691229355\n", + "--------------------------------------------------------\n", + "Итерация: 10\n", + "Текущая точка [52.04636149 45.47263149 36.7284959 19.61448849 -2.64878917]| Следующая точка [53.37019893 47.27270499 37.96904019 20.44914032 -2.62227769]\n", + "MSE 323.90720485464516\n", + "--------------------------------------------------------\n", + "Итерация: 11\n", + "Текущая точка [53.37019893 47.27270499 37.96904019 20.44914032 -2.62227769]| Следующая точка [54.45141845 48.81139396 39.01219008 21.1592957 -2.58144728]\n", + "MSE 257.3166668961423\n", + "--------------------------------------------------------\n", + "Итерация: 12\n", + "Текущая точка [54.45141845 48.81139396 39.01219008 21.1592957 -2.58144728]| Следующая точка [55.33650004 50.12582934 39.88981729 21.76181406 -2.53105516]\n", + "MSE 210.038369394656\n", + "--------------------------------------------------------\n", + "Итерация: 13\n", + "Текущая точка [55.33650004 50.12582934 39.88981729 21.76181406 -2.53105516]| Следующая точка [56.06255115 51.24809716 40.62860694 22.27170356 -2.47481859]\n", + "MSE 176.4228514139174\n", + "--------------------------------------------------------\n", + "Итерация: 14\n", + "Текущая точка [56.06255115 51.24809716 40.62860694 22.27170356 -2.47481859]| Следующая точка [56.65929839 52.20585828 41.25090327 22.70219922 -2.41559373]\n", + "MSE 152.4912980600221\n", + "--------------------------------------------------------\n", + "Итерация: 15\n", + "Текущая точка [56.65929839 52.20585828 41.25090327 22.70219922 -2.41559373]| Следующая точка [57.15063499 53.02291165 41.77541792 23.06488303 -2.35553336]\n", + "MSE 135.4345952200435\n", + "--------------------------------------------------------\n", + "Итерация: 16\n", + "Текущая точка [57.15063499 53.02291165 41.77541792 23.06488303 -2.35553336]| Следующая точка [57.55582741 53.71969964 42.21782313 23.3698259 -2.29622284]\n", + "MSE 123.26530927825932\n", + "--------------------------------------------------------\n", + "Итерация: 17\n", + "Текущая точка [57.55582741 53.71969964 42.21782313 23.3698259 -2.29622284]| Следующая точка [57.89045924 54.31375717 42.59124804 23.62573786 -2.2387952 ]\n", + "MSE 114.57482521743945\n", + "--------------------------------------------------------\n", + "Итерация: 18\n", + "Текущая точка [57.89045924 54.31375717 42.59124804 23.62573786 -2.2387952 ]| Следующая точка [58.16717236 54.82010769 42.90669406 23.84011751 -2.18402727]\n", + "MSE 108.36322966694733\n", + "--------------------------------------------------------\n", + "Итерация: 19\n", + "Текущая точка [58.16717236 54.82010769 42.90669406 23.84011751 -2.18402727]| Следующая точка [58.39625082 55.25161031 43.17338224 24.01939505 -2.13241891]\n", + "MSE 103.91977162382656\n", + "--------------------------------------------------------\n", + "Итерация: 20\n", + "Текущая точка [58.39625082 55.25161031 43.17338224 24.01939505 -2.13241891]| Следующая точка [58.58608266 55.61926249 43.39904384 24.16906562 -2.08425763]\n", + "MSE 100.73863892376886\n", + "--------------------------------------------------------\n", + "Итерация: 21\n", + "Текущая точка [58.58608266 55.61926249 43.39904384 24.16906562 -2.08425763]| Следующая точка [58.74352627 55.93246299 43.59016329 24.29381107 -2.03967075]\n", + "MSE 98.45948265070291\n", + "--------------------------------------------------------\n", + "Итерация: 22\n", + "Текущая точка [58.74352627 55.93246299 43.59016329 24.29381107 -2.03967075]| Следующая точка [58.87420226 56.1992396 43.75218136 24.39760973 -1.99866711]\n", + "MSE 96.82533656557291\n", + "--------------------------------------------------------\n", + "Итерация: 23\n", + "Текущая точка [58.87420226 56.1992396 43.75218136 24.39760973 -1.99866711]| Следующая точка [58.98272662 56.42644562 43.88966508 24.48383401 -1.96117004]\n", + "MSE 95.65279460128872\n", + "--------------------------------------------------------\n", + "Итерация: 24\n", + "Текущая точка [58.98272662 56.42644562 43.88966508 24.48383401 -1.96117004]| Следующая точка [59.07289774 56.61992931 44.00644977 24.55533657 -1.92704328]\n", + "MSE 94.81084525169739\n", + "--------------------------------------------------------\n", + "Итерация: 25\n", + "Текущая точка [59.07289774 56.61992931 44.00644977 24.55533657 -1.92704328]| Следующая точка [59.14784688 56.78467946 44.10575783 24.61452568 -1.89611097]\n", + "MSE 94.20583098575997\n", + "--------------------------------------------------------\n", + "Итерация: 26\n", + "Текущая точка [59.14784688 56.78467946 44.10575783 24.61452568 -1.89611097]| Следующая точка [59.21015955 56.9249504 44.19029817 24.66343085 -1.86817307]\n", + "MSE 93.77074823912531\n", + "--------------------------------------------------------\n", + "Итерация: 27\n", + "Текущая точка [59.21015955 56.9249504 44.19029817 24.66343085 -1.86817307]| Следующая точка [59.26197383 57.04436924 44.26234925 24.70375952 -1.8430169 ]\n", + "MSE 93.45762768740872\n", + "--------------------------------------------------------\n", + "Итерация: 28\n", + "Текущая точка [59.26197383 57.04436924 44.26234925 24.70375952 -1.8430169 ]| Следующая точка [59.3050602 57.14602762 44.32382866 24.73694599 -1.82042578]\n", + "MSE 93.23210310542908\n", + "--------------------------------------------------------\n", + "Итерация: 29\n", + "Текущая точка [59.3050602 57.14602762 44.32382866 24.73694599 -1.82042578]| Следующая точка [59.34088647 57.23256032 44.37635126 24.76419334 -1.8001853 ]\n", + "MSE 93.06953693827623\n", + "--------------------------------------------------------\n", + "Итерация: 30\n", + "Текущая точка [59.34088647 57.23256032 44.37635126 24.76419334 -1.8001853 ]| Следующая точка [59.3706709 57.30621236 44.42127792 24.78650929 -1.78208767]\n", + "MSE 92.95225422283875\n", + "--------------------------------------------------------\n", + "Итерация: 31\n", + "Текущая точка [59.3706709 57.30621236 44.42127792 24.78650929 -1.78208767]| Следующая точка [59.39542553 57.36889645 44.45975624 24.80473686 -1.76593481]\n", + "MSE 92.86756633726901\n", + "--------------------------------------------------------\n", + "Итерация: 32\n", + "Текущая точка [59.39542553 57.36889645 44.45975624 24.80473686 -1.76593481]| Следующая точка [59.41599184 57.42224197 44.49275474 24.8195803 -1.75154016]\n", + "MSE 92.80635805630789\n", + "--------------------------------------------------------\n", + "Итерация: 33\n", + "Текущая точка [59.41599184 57.42224197 44.49275474 24.8195803 -1.75154016]| Следующая точка [59.43306995 57.46763676 44.5210914 24.83162725 -1.73872982]\n", + "MSE 92.76207666444269\n", + "--------------------------------------------------------\n", + "Итерация: 34\n", + "Текущая точка [59.43306995 57.46763676 44.5210914 24.83162725 -1.73872982]| Следующая точка [59.44724279 57.50626287 44.54545765 24.84136737 -1.727343 ]\n", + "MSE 92.73000824392085\n", + "--------------------------------------------------------\n", + "Итерация: 35\n", + "Текущая точка [59.44724279 57.50626287 44.54545765 24.84136737 -1.727343 ]| Следующая точка [59.45899592 57.53912698 44.56643846 24.84920822 -1.71723203]\n", + "MSE 92.70675922222419\n", + "--------------------------------------------------------\n", + "Итерация: 36\n", + "Текущая точка [59.45899592 57.53912698 44.56643846 24.84920822 -1.71723203]| Следующая точка [59.46873398 57.56708635 44.58452915 24.85548855 -1.70826207]\n", + "MSE 92.68988472679756\n", + "--------------------------------------------------------\n", + "Итерация: 37\n", + "Текущая точка [59.46873398 57.56708635 44.58452915 24.85548855 -1.70826207]| Следующая точка [59.47679431 57.59087096 44.60014958 24.86048955 -1.70031058]\n", + "MSE 92.67762200788442\n", + "--------------------------------------------------------\n", + "Итерация: 38\n", + "Текущая точка [59.47679431 57.59087096 44.60014958 24.86048955 -1.70031058]| Следующая точка [59.48345818 57.61110234 44.61365591 24.86444433 -1.69326667]\n", + "MSE 92.66869910455937\n", + "--------------------------------------------------------\n", + "Итерация: 39\n", + "Текущая точка [59.48345818 57.61110234 44.61365591 24.86444433 -1.69326667]| Следующая точка [59.48896018 57.62830972 44.62535067 24.86754585 -1.68703034]\n", + "MSE 92.66219742859573\n", + "--------------------------------------------------------\n", + "Итерация: 40\n", + "Текущая точка [59.48896018 57.62830972 44.62535067 24.86754585 -1.68703034]| Следующая точка [59.49349594 57.64294363 44.63549103 24.8699536 -1.68151169]\n", + "MSE 92.65745300854614\n", + "--------------------------------------------------------\n", + "Итерация: 41\n", + "Текущая точка [59.49349594 57.64294363 44.63549103 24.8699536 -1.68151169]| Следующая точка [59.49722865 57.65538766 44.64429587 24.87179919 -1.67663011]\n", + "MSE 92.65398547098278\n", + "--------------------------------------------------------\n", + "Итерация: 42\n", + "Текущая точка [59.49722865 57.65538766 44.64429587 24.87179919 -1.67663011]| Следующая точка [59.50029441 57.66596832 44.65195173 24.87319104 -1.67231349]\n", + "MSE 92.65144693426757\n", + "--------------------------------------------------------\n", + "Итерация: 43\n", + "Текущая точка [59.50029441 57.66596832 44.65195173 24.87319104 -1.67231349]| Следующая точка [59.5028067 57.67496359 44.65861769 24.8742183 -1.66849746]\n", + "MSE 92.64958520640201\n", + "--------------------------------------------------------\n", + "Итерация: 44\n", + "Текущая точка [59.5028067 57.67496359 44.65861769 24.8742183 -1.66849746]| Следующая точка [59.50486012 57.68261005 44.66442966 24.87495413 -1.66512467]\n", + "MSE 92.64821726459807\n", + "--------------------------------------------------------\n", + "Итерация: 45\n", + "Текущая точка [59.50486012 57.68261005 44.66442966 24.87495413 -1.66512467]| Следующая точка [59.50653352 57.68910909 44.66950384 24.87545843 -1.66214407]\n", + "MSE 92.64721013003997\n", + "--------------------------------------------------------\n", + "Итерация: 46\n", + "Текущая точка [59.50653352 57.68910909 44.66950384 24.87545843 -1.66214407]| Следующая точка [59.50789259 57.6946321 44.67393975 24.87578011 -1.65951032]\n", + "MSE 92.64646706515495\n", + "--------------------------------------------------------\n", + "Итерация: 47\n", + "Текущая точка [59.50789259 57.6946321 44.67393975 24.87578011 -1.65951032]| Следующая точка [59.50899202 57.69932497 44.6778227 24.87595901 -1.65718319]\n", + "MSE 92.64591760420645\n", + "--------------------------------------------------------\n", + "Итерация: 48\n", + "Текущая точка [59.50899202 57.69932497 44.6778227 24.87595901 -1.65718319]| Следующая точка [59.50987733 57.70331183 44.68122591 24.87602749 -1.65512699]\n", + "MSE 92.64551034659182\n", + "--------------------------------------------------------\n", + "Итерация: 49\n", + "Текущая точка [59.50987733 57.70331183 44.68122591 24.87602749 -1.65512699]| Следующая точка [59.51058637 57.70669831 44.68421236 24.87601171 -1.65331012]\n", + "MSE 92.645207742673\n", + "--------------------------------------------------------\n", + "Итерация: 50\n", + "Текущая точка [59.51058637 57.70669831 44.68421236 24.87601171 -1.65331012]| Следующая точка [59.5111506 57.70957431 44.68683624 24.87593277 -1.65170463]\n", + "MSE 92.64498231774262\n", + "--------------------------------------------------------\n", + "Итерация: 51\n", + "Текущая точка [59.5111506 57.70957431 44.68683624 24.87593277 -1.65170463]| Следующая точка [59.51159614 57.7120163 44.68914426 24.87580759 -1.65028579]\n", + "MSE 92.6448139347927\n", + "--------------------------------------------------------\n", + "Итерация: 52\n", + "Текущая точка [59.51159614 57.7120163 44.68914426 24.87580759 -1.65028579]| Следующая точка [59.51194465 57.71408934 44.69117675 24.87564965 -1.64903173]\n", + "MSE 92.6446878082462\n", + "--------------------------------------------------------\n", + "Итерация: 53\n", + "Текущая точка [59.51194465 57.71408934 44.69117675 24.87564965 -1.64903173]| Следующая точка [59.51221409 57.71584881 44.69296856 24.87546966 -1.64792314]\n", + "MSE 92.64459306103737\n", + "--------------------------------------------------------\n", + "Итерация: 54\n", + "Текущая точка [59.51221409 57.71584881 44.69296856 24.87546966 -1.64792314]| Следующая точка [59.5124193 57.71734177 44.69454986 24.87527603 -1.64694298]\n", + "MSE 92.64452167517669\n", + "--------------------------------------------------------\n", + "Итерация: 55\n", + "Текущая точка [59.5124193 57.71734177 44.69454986 24.87527603 -1.64694298]| Следующая точка [59.51257255 57.71860828 44.6959468 24.87507531 -1.64607619]\n", + "MSE 92.64446772754471\n", + "--------------------------------------------------------\n", + "Итерация: 56\n", + "Текущая точка [59.51257255 57.71860828 44.6959468 24.87507531 -1.64607619]| Следующая точка [59.51268398 57.7196824 44.69718209 24.87487257 -1.64530948]\n", + "MSE 92.64442683265162\n", + "--------------------------------------------------------\n", + "Итерация: 57\n", + "Текущая точка [59.51268398 57.7196824 44.69718209 24.87487257 -1.64530948]| Следующая точка [59.5127619 57.72059311 44.69827546 24.87467163 -1.64463114]\n", + "MSE 92.64439573573438\n", + "--------------------------------------------------------\n", + "Итерация: 58\n", + "Текущая точка [59.5127619 57.72059311 44.69827546 24.87467163 -1.64463114]| Следующая точка [59.51281319 57.72136501 44.69924408 24.87447536 -1.64403083]\n", + "MSE 92.64437201518268\n", + "--------------------------------------------------------\n", + "Итерация: 59\n", + "Текущая точка [59.51281319 57.72136501 44.69924408 24.87447536 -1.64403083]| Следующая точка [59.51284344 57.72201907 44.70010292 24.87428582 -1.64349942]\n", + "MSE 92.64435386456748\n", + "--------------------------------------------------------\n", + "Итерация: 60\n", + "Текущая точка [59.51284344 57.72201907 44.70010292 24.87428582 -1.64349942]| Следующая точка [59.51285724 57.72257307 44.70086505 24.87410447 -1.64302888]\n", + "MSE 92.64433993270397\n", + "--------------------------------------------------------\n", + "Итерация: 61\n", + "Текущая точка [59.51285724 57.72257307 44.70086505 24.87410447 -1.64302888]| Следующая точка [59.51285835 57.72304216 44.70154189 24.87393226 -1.6426121 ]\n", + "MSE 92.64432920608364\n", + "--------------------------------------------------------\n", + "Итерация: 62\n", + "Текущая точка [59.51285835 57.72304216 44.70154189 24.87393226 -1.6426121 ]| Следующая точка [59.51284979 57.72343918 44.70214342 24.87376976 -1.64224283]\n", + "MSE 92.644320922282\n", + "--------------------------------------------------------\n", + "Итерация: 63\n", + "Текущая точка [59.51284979 57.72343918 44.70214342 24.87376976 -1.64224283]| Следующая точка [59.51283403 57.72377508 44.7026784 24.87361724 -1.64191556]\n", + "MSE 92.64431450605201\n", + "--------------------------------------------------------\n", + "Итерация: 64\n", + "Текущая точка [59.51283403 57.72377508 44.7026784 24.87361724 -1.64191556]| Следующая точка [59.51281304 57.72405912 44.70315452 24.87347473 -1.6416254 ]\n", + "MSE 92.64430952205504\n", + "--------------------------------------------------------\n", + "Итерация: 65\n", + "Текущая точка [59.51281304 57.72405912 44.70315452 24.87347473 -1.6416254 ]| Следующая точка [59.51278841 57.7242992 44.70357851 24.87334211 -1.64136808]\n", + "MSE 92.64430563981963\n", + "--------------------------------------------------------\n", + "Итерация: 66\n", + "Текущая точка [59.51278841 57.7242992 44.70357851 24.87334211 -1.64136808]| Следующая точка [59.5127614 57.72450202 44.70395632 24.87321911 -1.64113979]\n", + "MSE 92.64430260770115\n", + "--------------------------------------------------------\n", + "Итерация: 67\n", + "Текущая точка [59.5127614 57.72450202 44.70395632 24.87321911 -1.64113979]| Следующая точка [59.51273299 57.72467326 44.70429315 24.87310537 -1.64093719]\n", + "MSE 92.64430023348214\n", + "--------------------------------------------------------\n", + "Итерация: 68\n", + "Текущая точка [59.51273299 57.72467326 44.70429315 24.87310537 -1.64093719]| Следующая точка [59.51270398 57.72481775 44.70459363 24.87300046 -1.64075733]\n", + "MSE 92.64429836988138\n", + "--------------------------------------------------------\n", + "Итерация: 69\n", + "Текущая точка [59.51270398 57.72481775 44.70459363 24.87300046 -1.64075733]| Следующая точка [59.51267495 57.7249396 44.70486179 24.87290393 -1.64059761]\n", + "MSE 92.64429690370082\n", + "--------------------------------------------------------\n", + "Итерация: 70\n", + "Текущая точка [59.51267495 57.7249396 44.70486179 24.87290393 -1.64059761]| Следующая точка [59.51264637 57.72504227 44.70510124 24.87281529 -1.64045571]\n", + "MSE 92.6442957476727\n", + "--------------------------------------------------------\n", + "Итерация: 71\n", + "Текущая точка [59.51264637 57.72504227 44.70510124 24.87281529 -1.64045571]| Следующая точка [59.51261856 57.72512872 44.70531515 24.87273404 -1.64032962]\n", + "MSE 92.64429483431937\n", + "--------------------------------------------------------\n", + "Итерация: 72\n", + "Текущая точка [59.51261856 57.72512872 44.70531515 24.87273404 -1.64032962]| Следующая точка [59.51259179 57.72520146 44.70550631 24.8726597 -1.64021752]\n", + "MSE 92.64429411131276\n", + "--------------------------------------------------------\n", + "Итерация: 73\n", + "Текущая точка [59.51259179 57.72520146 44.70550631 24.8726597 -1.64021752]| Следующая точка [59.51256621 57.7252626 44.70567721 24.87259177 -1.64011783]\n", + "MSE 92.64429353795867\n", + "--------------------------------------------------------\n", + "Итерация: 74\n", + "Текущая точка [59.51256621 57.7252626 44.70567721 24.87259177 -1.64011783]| Следующая точка [59.51254194 57.72531395 44.70583007 24.87252978 -1.64002916]\n", + "MSE 92.64429308252382\n", + "--------------------------------------------------------\n", + "Итерация: 75\n", + "Текущая точка [59.51254194 57.72531395 44.70583007 24.87252978 -1.64002916]| Следующая точка [59.51251904 57.72535702 44.70596682 24.87247329 -1.63995024]\n", + "MSE 92.64429272019784\n", + "--------------------------------------------------------\n", + "Итерация: 76\n", + "Текущая точка [59.51251904 57.72535702 44.70596682 24.87247329 -1.63995024]| Следующая точка [59.51249755 57.72539312 44.70608921 24.87242186 -1.63988 ]\n", + "MSE 92.64429243153427\n", + "--------------------------------------------------------\n", + "Итерация: 77\n", + "Текущая точка [59.51249755 57.72539312 44.70608921 24.87242186 -1.63988 ]| Следующая точка [59.51247745 57.72542332 44.70619878 24.87237508 -1.63981745]\n", + "MSE 92.64429220125481\n", + "--------------------------------------------------------\n", + "Итерация: 78\n", + "Текущая точка [59.51247745 57.72542332 44.70619878 24.87237508 -1.63981745]| Следующая точка [59.51245872 57.72544857 44.7062969 24.87233258 -1.63976173]\n", + "MSE 92.6442920173288\n", + "--------------------------------------------------------\n", + "Итерация: 79\n", + "Текущая точка [59.51245872 57.72544857 44.7062969 24.87233258 -1.63976173]| Следующая точка [59.51244134 57.72546964 44.70638478 24.87229399 -1.63971209]\n", + "MSE 92.64429187026263\n", + "--------------------------------------------------------\n", + "Итерация: 80\n", + "Текущая точка [59.51244134 57.72546964 44.70638478 24.87229399 -1.63971209]| Следующая точка [59.51242524 57.72548719 44.70646353 24.87225898 -1.63966784]\n", + "MSE 92.64429175254996\n", + "--------------------------------------------------------\n" + ] + } + ], + "source": [ + "# установка минимального значения, на которое должны изменяться веса\n", + "eps = 0.0001\n", + "\n", + "# размер шага (learning rate)\n", + "learning_rate = 0.1\n", + "\n", + "next_weights = weights\n", + "# количество итерация \n", + "n = 100\n", + "for i in range(n):\n", + " cur_weights = next_weights\n", + "\n", + " # движение в негативную сторону вычисляемого градиента\n", + " next_weights = cur_weights - learning_rate * gr_mserror_mat(X, cur_weights, y)\n", + "\n", + " # остановка когда достигнута необходимая степень точности\n", + " print(f\"Итерация: {i}\")\n", + " print(f\"Текущая точка {cur_weights}| Следующая точка {next_weights}\")\n", + " print(f\"MSE {mserror_mat(X, cur_weights, y)}\")\n", + " print(\"--------------------------------------------------------\")\n", + " \n", + " if np.linalg.norm(cur_weights - next_weights, ord=2) <= eps:\n", + " break" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "r2s0LuwhMWAg" + }, + "source": [ + "Вышли раньше из обучения, т.к. веса перестали сильно изменяться и мы стали топтаться на одном месте." + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "3IfMej7yCPlC" + }, + "source": [ + "И получили точно такую же метрику, которая получалась у `LinearRegression` из `sklearn`.\n", + "\n", + "И давайте сравним полученные коэффициенты с теми, которые были сгенерированы вместе с данными." + ] + }, + { + "cell_type": "code", + "execution_count": 55, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "iaxxRtstCPlD", + "outputId": "1837b72c-7dbd-4acc-dfd5-32b48d685a9d", + "tags": [] + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Веса при признаках\n", + "True [59.32158596 58.74342238 44.07539836 25.03682142],\n", + "Trained [59.51242524 57.72548719 44.70646353 24.87225898]\n", + "\n", + "Вес свободный True 0, trained -1.6396678387172885\n" + ] + } + ], + "source": [ + "print('Веса при признаках')\n", + "print(f'True {coeffs},\\nTrained {next_weights[:-1]}')\n", + "\n", + "print('\\nВес свободный', end=' ')\n", + "print(f'True 0, trained {next_weights[-1]}')" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Метрики качества линейной регрессии\n", + "\n", + "В задачах машинного обучения мы хотим сравнивать несколько моделей машинного обучения и выбирать наилучшую из них. Решение о том, какая модель хорошая, а какая плохая, принимается на основе одной или нескольких *метрик* моделей машинного обучения.\n", + "\n", + "Без метрик обучение моделей вообще теряет всякий смысл – как же определить, какая из зоопарка обученных моделек хорошая, а какая плохая? Давайте разберёмся, как определить лучшую модель с помощью математики" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Интуиция за метриками стоит очень простая – давайте как-нибудь усредним отклонения по всем точкам и получим одно число – метрику качества линейной регрессии, т.е. насколько модель отклоняется от реальных данных." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Метрики принимают на вход два вектора, предсказания модели и истинные значения, после чего вычисляют по этим векторам качество модели.\n", + "\n", + "Сначала загрузим данные эксперимента" + ] + }, + { + "cell_type": "code", + "execution_count": 56, + "metadata": {}, + "outputs": [], + "source": [ + "from sklearn.datasets import make_regression\n", + "from sklearn.linear_model import LinearRegression\n", + "\n", + "features, y = make_regression()\n", + "\n", + "\n", + "reg = LinearRegression().fit(features, y)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Теперь получим два вектора – предказанное значение $\\hat{y}$ и истинное значение $y$:" + ] + }, + { + "cell_type": "code", + "execution_count": 57, + "metadata": {}, + "outputs": [], + "source": [ + "y_pred = reg.predict(features) # предсказанное значение\n", + "y_true = y # истинное значение" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Теперь посмотрим, какие функции можно применять к этим двум наборам точек" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Mean absolute error\n", + "\n", + "Для оценки качества регрессии можно использовать среднюю абсолютную ошибку" + ] + }, + { + "cell_type": "code", + "execution_count": 58, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "MAE = 2.843325574986011e-13\n" + ] + } + ], + "source": [ + "from sklearn.metrics import mean_absolute_error\n", + "\n", + "print(\"MAE = %s\" % mean_absolute_error(\n", + " reg.predict(features), y)\n", + ")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "*Mean Absolute Error* - это просто сумма отклонений истинных значений $y$ от предсказаний нашей модели:\n", + "\n", + "$$\n", + "\\text{absolute error} = |y_1 - \\hat{y}_1| + |y_2 - \\hat{y}_2| + \\ldots\n", + "$$\n", + "\n", + "А потом мы эту сумму делим на количество точек - получаем среднюю ошибку\n", + "\n", + "Метрика принимает только положительные значения! Чем ближе к нулю, тем лучше модель." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## MSE\n", + "\n", + "Mean Squared Error (MSE) - это базовая метрика для определения качества линейной регрессии\n", + "\n", + "Для каждого предсказанного значения $\\hat{y}_i$ мы считаем квадрат отклонения от фактического значения и считаем среднее по полученным величинам" + ] + }, + { + "cell_type": "code", + "execution_count": 59, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "MSE = 1.2174295501632286e-25\n" + ] + } + ], + "source": [ + "from sklearn.metrics import mean_squared_error\n", + "\n", + "mse = mean_squared_error(y_true, y_pred)\n", + "\n", + "print('MSE = %s' % mse)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "В целом логика та же, что в *MAE*, только усреднять мы будем квадраты ошибок \n", + "$$\n", + "\\text{absolute error} = (y_1 - \\hat{y}_1)^2 + (y_2 - \\hat{y}_2)^2 + \\ldots\n", + "$$\n", + "\n", + "Эта метрика тоже принимает только положительные значения! Чем ближе к нулю, тем лучше модель." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Эта ошибка визуально похожа на *MSE*, но на графике видно, что *MAE*(красная линия) почти всегда меньше по значению, чем MSE (синяя линия). Это значит, что *MSE* более \"пессимистична\" и сильнее штрафует за большие ошибки - т.е. MSE лучше применять, когда вы уверены что в выборке нет \"выборосов\" (англ. outliers) - значений, который очень сильно отличаются от остальных точек. В этом случае MSE может быть очень плохой, а на деле ситуация приемлема. Если выбросы есть, лучше применять MAE.\n", + "\n", + "![rmse_vs_mae](https://248006.selcdn.ru/public/Data-science-4/img/rmse_vs_mae.png)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## $R^2$ (коэффициент детерминации)\n", + "\n", + "Название - *coefficient of determination*. Наилучшее возможное значение 1.0, чем меньше тем хуже. Если этот коэффициент близок к 1, то условная дисперсия модели (то есть разброс предсказаний модели $\\hat{y}$ относительно разброса самой целевой переменной $y$ ) достаточно мала - то есть модель неплохо описывает данные. Коэффициент может быть даже отрицательным - то это значит, что модель совсем уж плохая.\n", + "\n", + "Эта метрика хороша тем, что она *нормализована*, то есть не превышает единицу - удобно сравнивать разные модели. Например, метрика $MSE$ может принимать ничем не ограниченные значения больше нуля - это не всегда удобно." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "В библиотеке *sklearn* есть готовая реализация этой метрики." + ] + }, + { + "cell_type": "code", + "execution_count": 60, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "r2_score = 1.0\n" + ] + } + ], + "source": [ + "from sklearn.metrics import r2_score\n", + "\n", + "print(\"r2_score = %s\" % r2_score(y_true, y_pred))" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Про другие ошибки можно почитать в [официальной документации](https://scikit-learn.org/stable/modules/model_evaluation.html#regression-metrics) в разделе про метрики регрессии." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Логистическая регрессия\n", + "\n", + "`Логистическая регрессия = sigmoid(linear_regression) = вероятности предсказания по классам`" + ] + }, + { + "cell_type": "code", + "execution_count": 61, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "(array([[-0.86305361],\n", + " [-1.4372011 ],\n", + " [ 0.19592225],\n", + " [-0.87164985],\n", + " [ 0.00982831],\n", + " [ 1.30282593],\n", + " [ 0.16134434],\n", + " [-0.9223264 ],\n", + " [-0.10173176],\n", + " [ 0.41006497],\n", + " [ 0.27129997],\n", + " [-0.71111212],\n", + " [-2.98259876],\n", + " [-0.09300387],\n", + " [ 0.82285659],\n", + " [ 0.16493473],\n", + " [-0.40806382],\n", + " [ 0.62136283],\n", + " [ 0.76258897],\n", + " [-0.11001122],\n", + " [-1.26261842],\n", + " [ 0.04513441],\n", + " [ 0.50026937],\n", + " [-0.6784482 ],\n", + " [ 0.2182344 ]]),\n", + " array([0, 0, 1, 0, 0, 0, 1, 0, 1, 1, 0, 0, 0, 0, 1, 1, 0, 1, 1, 1, 0, 1,\n", + " 1, 0, 1]))" + ] + }, + "execution_count": 61, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "from sklearn.datasets import make_classification\n", + "from sklearn.linear_model import LinearRegression\n", + "\n", + "X, y = make_classification(n_samples=25, n_features=1, n_informative=1,\n", + " n_redundant=0, random_state=11, n_clusters_per_class=1,\n", + " class_sep=0.4)\n", + "\n", + "X, y" + ] + }, + { + "cell_type": "code", + "execution_count": 62, + "metadata": {}, + "outputs": [], + "source": [ + "model_lr = LinearRegression().fit(X, y)" + ] + }, + { + "cell_type": "code", + "execution_count": 63, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": "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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "def sigmoid(x):\n", + " return 1 / (1 + np.exp(-x))\n", + "\n", + "x = np.linspace(-3, 3, num=100)\n", + "model_lr_a, model_lr_b = model_lr.coef_, model_lr.intercept_\n", + "model_lr_y = model_lr_a * x + model_lr_b\n", + "\n", + "plt.figure(figsize=(10, 2))\n", + "plt.plot(x, sigmoid(model_lr_y), linewidth=2, label='sklearn')\n", + "plt.scatter(X, y, c=y, s=100, edgecolors='black')\n", + "plt.ylabel('pred');plt.xlabel('x')\n", + "plt.yticks(np.arange(0, 2), ['0 class', '1 class'])\n", + "plt.ylim(-0.1, 1.1);plt.xlim(-3.5, 2)\n", + "plt.title('LogisticRegression')\n", + "plt.legend();" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "tags": [] + }, + "outputs": [], + "source": [] + }, + { + "cell_type": "code", + "execution_count": 64, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "(array([[5.1, 3.5, 1.4, 0.2],\n", + " [4.9, 3. , 1.4, 0.2],\n", + " [4.7, 3.2, 1.3, 0.2],\n", + " [4.6, 3.1, 1.5, 0.2],\n", + " [5. , 3.6, 1.4, 0.2],\n", + " [5.4, 3.9, 1.7, 0.4],\n", + " [4.6, 3.4, 1.4, 0.3],\n", + " [5. , 3.4, 1.5, 0.2],\n", + " [4.4, 2.9, 1.4, 0.2],\n", + " [4.9, 3.1, 1.5, 0.1],\n", + " [5.4, 3.7, 1.5, 0.2],\n", + " [4.8, 3.4, 1.6, 0.2],\n", + " [4.8, 3. , 1.4, 0.1],\n", + " [4.3, 3. , 1.1, 0.1],\n", + " [5.8, 4. , 1.2, 0.2],\n", + " [5.7, 4.4, 1.5, 0.4],\n", + " [5.4, 3.9, 1.3, 0.4],\n", + " [5.1, 3.5, 1.4, 0.3],\n", + " [5.7, 3.8, 1.7, 0.3],\n", + " [5.1, 3.8, 1.5, 0.3],\n", + " [5.4, 3.4, 1.7, 0.2],\n", + " [5.1, 3.7, 1.5, 0.4],\n", + " [4.6, 3.6, 1. , 0.2],\n", + " [5.1, 3.3, 1.7, 0.5],\n", + " [4.8, 3.4, 1.9, 0.2],\n", + " [5. , 3. , 1.6, 0.2],\n", + " [5. , 3.4, 1.6, 0.4],\n", + " [5.2, 3.5, 1.5, 0.2],\n", + " [5.2, 3.4, 1.4, 0.2],\n", + " [4.7, 3.2, 1.6, 0.2],\n", + " [4.8, 3.1, 1.6, 0.2],\n", + " [5.4, 3.4, 1.5, 0.4],\n", + " [5.2, 4.1, 1.5, 0.1],\n", + " [5.5, 4.2, 1.4, 0.2],\n", + " [4.9, 3.1, 1.5, 0.2],\n", + " [5. , 3.2, 1.2, 0.2],\n", + " [5.5, 3.5, 1.3, 0.2],\n", + " [4.9, 3.6, 1.4, 0.1],\n", + " [4.4, 3. , 1.3, 0.2],\n", + " [5.1, 3.4, 1.5, 0.2],\n", + " [5. , 3.5, 1.3, 0.3],\n", + " [4.5, 2.3, 1.3, 0.3],\n", + " [4.4, 3.2, 1.3, 0.2],\n", + " [5. , 3.5, 1.6, 0.6],\n", + " [5.1, 3.8, 1.9, 0.4],\n", + " [4.8, 3. , 1.4, 0.3],\n", + " [5.1, 3.8, 1.6, 0.2],\n", + " [4.6, 3.2, 1.4, 0.2],\n", + " [5.3, 3.7, 1.5, 0.2],\n", + " [5. , 3.3, 1.4, 0.2],\n", + " [7. , 3.2, 4.7, 1.4],\n", + " [6.4, 3.2, 4.5, 1.5],\n", + " [6.9, 3.1, 4.9, 1.5],\n", + " [5.5, 2.3, 4. , 1.3],\n", + " [6.5, 2.8, 4.6, 1.5],\n", + " [5.7, 2.8, 4.5, 1.3],\n", + " [6.3, 3.3, 4.7, 1.6],\n", + " [4.9, 2.4, 3.3, 1. ],\n", + " [6.6, 2.9, 4.6, 1.3],\n", + " [5.2, 2.7, 3.9, 1.4],\n", + " [5. , 2. , 3.5, 1. ],\n", + " [5.9, 3. , 4.2, 1.5],\n", + " [6. , 2.2, 4. , 1. ],\n", + " [6.1, 2.9, 4.7, 1.4],\n", + " [5.6, 2.9, 3.6, 1.3],\n", + " [6.7, 3.1, 4.4, 1.4],\n", + " [5.6, 3. , 4.5, 1.5],\n", + " [5.8, 2.7, 4.1, 1. ],\n", + " [6.2, 2.2, 4.5, 1.5],\n", + " [5.6, 2.5, 3.9, 1.1],\n", + " [5.9, 3.2, 4.8, 1.8],\n", + " [6.1, 2.8, 4. , 1.3],\n", + " [6.3, 2.5, 4.9, 1.5],\n", + " [6.1, 2.8, 4.7, 1.2],\n", + " [6.4, 2.9, 4.3, 1.3],\n", + " [6.6, 3. , 4.4, 1.4],\n", + " [6.8, 2.8, 4.8, 1.4],\n", + " [6.7, 3. , 5. , 1.7],\n", + " [6. , 2.9, 4.5, 1.5],\n", + " [5.7, 2.6, 3.5, 1. ],\n", + " [5.5, 2.4, 3.8, 1.1],\n", + " [5.5, 2.4, 3.7, 1. ],\n", + " [5.8, 2.7, 3.9, 1.2],\n", + " [6. , 2.7, 5.1, 1.6],\n", + " [5.4, 3. , 4.5, 1.5],\n", + " [6. , 3.4, 4.5, 1.6],\n", + " [6.7, 3.1, 4.7, 1.5],\n", + " [6.3, 2.3, 4.4, 1.3],\n", + " [5.6, 3. , 4.1, 1.3],\n", + " [5.5, 2.5, 4. , 1.3],\n", + " [5.5, 2.6, 4.4, 1.2],\n", + " [6.1, 3. , 4.6, 1.4],\n", + " [5.8, 2.6, 4. , 1.2],\n", + " [5. , 2.3, 3.3, 1. ],\n", + " [5.6, 2.7, 4.2, 1.3],\n", + " [5.7, 3. , 4.2, 1.2],\n", + " [5.7, 2.9, 4.2, 1.3],\n", + " [6.2, 2.9, 4.3, 1.3],\n", + " [5.1, 2.5, 3. , 1.1],\n", + " [5.7, 2.8, 4.1, 1.3],\n", + " [6.3, 3.3, 6. , 2.5],\n", + " [5.8, 2.7, 5.1, 1.9],\n", + " [7.1, 3. , 5.9, 2.1],\n", + " [6.3, 2.9, 5.6, 1.8],\n", + " [6.5, 3. , 5.8, 2.2],\n", + " [7.6, 3. , 6.6, 2.1],\n", + " [4.9, 2.5, 4.5, 1.7],\n", + " [7.3, 2.9, 6.3, 1.8],\n", + " [6.7, 2.5, 5.8, 1.8],\n", + " [7.2, 3.6, 6.1, 2.5],\n", + " [6.5, 3.2, 5.1, 2. ],\n", + " [6.4, 2.7, 5.3, 1.9],\n", + " [6.8, 3. , 5.5, 2.1],\n", + " [5.7, 2.5, 5. , 2. ],\n", + " [5.8, 2.8, 5.1, 2.4],\n", + " [6.4, 3.2, 5.3, 2.3],\n", + " [6.5, 3. , 5.5, 1.8],\n", + " [7.7, 3.8, 6.7, 2.2],\n", + " [7.7, 2.6, 6.9, 2.3],\n", + " [6. , 2.2, 5. , 1.5],\n", + " [6.9, 3.2, 5.7, 2.3],\n", + " [5.6, 2.8, 4.9, 2. ],\n", + " [7.7, 2.8, 6.7, 2. ],\n", + " [6.3, 2.7, 4.9, 1.8],\n", + " [6.7, 3.3, 5.7, 2.1],\n", + " [7.2, 3.2, 6. , 1.8],\n", + " [6.2, 2.8, 4.8, 1.8],\n", + " [6.1, 3. , 4.9, 1.8],\n", + " [6.4, 2.8, 5.6, 2.1],\n", + " [7.2, 3. , 5.8, 1.6],\n", + " [7.4, 2.8, 6.1, 1.9],\n", + " [7.9, 3.8, 6.4, 2. ],\n", + " [6.4, 2.8, 5.6, 2.2],\n", + " [6.3, 2.8, 5.1, 1.5],\n", + " [6.1, 2.6, 5.6, 1.4],\n", + " [7.7, 3. , 6.1, 2.3],\n", + " [6.3, 3.4, 5.6, 2.4],\n", + " [6.4, 3.1, 5.5, 1.8],\n", + " [6. , 3. , 4.8, 1.8],\n", + " [6.9, 3.1, 5.4, 2.1],\n", + " [6.7, 3.1, 5.6, 2.4],\n", + " [6.9, 3.1, 5.1, 2.3],\n", + " [5.8, 2.7, 5.1, 1.9],\n", + " [6.8, 3.2, 5.9, 2.3],\n", + " [6.7, 3.3, 5.7, 2.5],\n", + " [6.7, 3. , 5.2, 2.3],\n", + " [6.3, 2.5, 5. , 1.9],\n", + " [6.5, 3. , 5.2, 2. ],\n", + " [6.2, 3.4, 5.4, 2.3],\n", + " [5.9, 3. , 5.1, 1.8]]),\n", + " array([0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n", + " 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n", + " 0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,\n", + " 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,\n", + " 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2,\n", + " 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2,\n", + " 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2]))" + ] + }, + "execution_count": 64, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "from sklearn.datasets import load_iris\n", + "from sklearn.linear_model import LogisticRegression\n", + "X, y = load_iris(return_X_y=True)\n", + "X, y" + ] + }, + { + "cell_type": "code", + "execution_count": 65, + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/Users/pakorolev/miniconda3/envs/py38/lib/python3.8/site-packages/sklearn/linear_model/_logistic.py:458: ConvergenceWarning: lbfgs failed to converge (status=1):\n", + "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", + "\n", + "Increase the number of iterations (max_iter) or scale the data as shown in:\n", + " https://scikit-learn.org/stable/modules/preprocessing.html\n", + "Please also refer to the documentation for alternative solver options:\n", + " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", + " n_iter_i = _check_optimize_result(\n" + ] + }, + { + "data": { + "text/plain": [ + "array([0, 0])" + ] + }, + "execution_count": 65, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "model = LogisticRegression().fit(X, y)\n", + "model.predict(X[:2])\n", + "\n" + ] + }, + { + "cell_type": "code", + "execution_count": 66, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "array([[9.81813537e-01, 1.81864490e-02, 1.43884301e-08],\n", + " [9.71751811e-01, 2.82481591e-02, 3.00932288e-08]])" + ] + }, + "execution_count": 66, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "model.predict_proba(X[:2, :])\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Метрики задачи классификации" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "\n", + "\n", + "После отбра признаков, выбора и, конечно, реализации модели и получения некоего результата в виде класса или вероятности принадлежности к классу, следующим шагом будет выяснение того, насколько эффективна модель." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Матрица ошибок (Confusion Matrix)\n", + "\n", + "Матрица ошибок - одна из интуитивно понятных метрик, используемых для определения точности модели. Она используется для задачи классификации, где выходные данные могут быть двух или более классов. " + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Возьмем два примера с, абстрактно, двумя классами - \"плохой\" и \"хороший\" - и подумаем, что в каждом конкретном случае для нас будет важнее предсказать.\n", + "\n", + "1. Предположим, что мы решаем задачу классификации, где предсказываем, болен человек или нет: *1*, если болен, и *0*, если здоров. Скажем, из 100 человек только 5 больны. Так как больных всего 5% от общего числа людей, то даже очень плохая модель (прогнозирование всех как здоровых) даст нам точность в 95% - это частая проблема для данных с несбалансированными классами. Поэтому в этом случае мы хотим правильно классифицировать всех больных людей - если здоровые будут отнесены к больным, то в данном случае это повлечет за собой явно меньше неприятностей.\n", + "\n", + "2. Теперь давайте представим, что нам надо классифицировать, является ли электронное письмо спамом или нет. Присвоим метку *1*, если это спам, и *0*, если не является спамом. Предположим, что модель классифицировала важное письмо, которого вы отчаянно ждете, как *спам*. В этой ситуации это может быть довольно трагично, ведь в письме может находиться важная информация. Соответственно, в задаче классификации электронных писем более важно минимизировать количество объектов, отнесенных к классу \"плохой\".\n", + "\n", + "Оба этих случая могут быть исследованы с помощью матрицы ошибок, элементы которой как раз таки указывают на ошибочную классификацию в первом и во втором примере. " + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Матрица ошибок представляет собой таблицу с двумя измерениями - {\"Actual\", \"Predicted\"}, каждое из которых представлено множеством прогнозируемых классов. Фактические результаты - это столбцы, а прогнозируемые - строки.\n", + "\n", + "![](https://248006.selcdn.ru/public/DS_Block2_M6_final/conf_mtrx.png)\n", + "\n", + "Сама по себе матрица ошибок не является показателем производительности как таковым, однако почти все метрики (Recall, Precision, Accuracy, AUC-ROC Curve) основаны на значениях внутри нее." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Теперь давайте разберемся в терминах матрицы ошибок: что означают все ее атрибуты TP, TN, FP и FN?\n", + "\n", + "![](https://248006.selcdn.ru/public/DS_Block2_M6_final/terms.png)\n", + "\n", + "Итак,\n", + "- *True Positives (TP)*: Фактический класс объекта был *1 (True)* и прогнозируемый также *1 (True)*\n", + "- *True Negatives (TN)*: Фактический класс объекта был *0 (False)* и прогнозируемый также *0 (False)*\n", + "- *False Positives (FP)*: Фактический класс объекта был *0 (False)*, а прогнозируемый - *1 (True)*. False - потому что модель предсказала неверно, positives - потому что предсказанный класс был положительным\n", + "- *False Negatives (FN)*: Фактический класс объекта был *1 (True)*, а прогнозируемый - *0 (False)*. False - потому что модель предсказала неверно, negatives - потому что предсказанный класс был отрицательным\n", + "\n", + "Логично, что идеальным сценарием является получение такой матрицы, в которой модель дает FP == 0 и FN == 0, однако в реальной жизни любая модель в большинстве случаев не будет давать 100% точности." + ] + }, + { + "cell_type": "code", + "execution_count": 72, + "metadata": {}, + "outputs": [], + "source": [ + "from sklearn.datasets import make_classification, load_iris\n", + "from sklearn.model_selection import train_test_split\n", + "from sklearn.linear_model import LogisticRegression\n", + "from sklearn.metrics import confusion_matrix, ConfusionMatrixDisplay\n", + "\n", + "\n", + "X, y = make_classification(n_samples=1800, n_features=2, n_informative=1,\n", + " n_redundant=0, random_state=11, n_clusters_per_class=1,\n", + " class_sep=0.4)\n", + "\n", + "\n", + "X_train, X_test, y_train, y_test = train_test_split(\n", + " X, y, test_size=0.2, random_state=42)\n", + "\n", + "model = LogisticRegression().fit(X_train, y_train)" + ] + }, + { + "cell_type": "code", + "execution_count": 68, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": "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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "cm = confusion_matrix(y_test, model.predict(X_test))\n", + "\n", + "disp = ConfusionMatrixDisplay(confusion_matrix=cm,\n", + " display_labels=model.classes_)\n", + "disp.plot()\n", + "\n", + "plt.show()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Мы знаем, что будет какая-то ошибка в предсказаниях модели и что это будет либо FP, либо FN, но что именно следует минимизировать, зависит исключительно от потребностей бизнеса и контекста проблемы, которую требуется решить. Например, в нашем примере с классификацией больных людей более важно минимизировать FN - нам важнее правильно распознать больных людей, чем ошибочно отнести здоровых к больным. Напротив, в примере со спамом менее важно пропустить надоедливую рекламу, чем важное письмо - здесь нам актуальнее минимизировать FP." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Теперь рассмотрим метрики классификации, основанные на терминах матрицы ошибок." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Accuracy (доля правильных ответов)\n", + "\n", + "Теперь давайте разберемся с метриками классификации, основанными на матрице ошибок.\n", + "\n", + "Наиболее очевидной мерой качества модели классификации является доля правильных ответов (иногда *accuracy* переводят как *точность*, но этот термин отведен для другой метрики - *precision*) - эту меру мы встречали в предыдущих уроках по классификации, и означает она не что иное, как отношение числа верных прогнозов к общему количеству прогнозов:\n", + "\n", + "\n", + "\n", + "![](https://248006.selcdn.ru/public/DS_Block2_M6_final/accuracy.png)\n", + "\n", + "В терминах матрицы ошибок accuracy приобретает вид:\n", + "\n", + "$$\n", + "Accuracy = \\frac{TP + TN}{TP + FP + FN + TN}\n", + "$$" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Доля правильных ответов является хорошей мерой, когда классы сбалансированы или почти сбалансированы - их соотношение в конечной выборке должно быть примерно одинаковым. Например, как в датасете Iris - там данные идеально сбалансированы, поэтому метрика дает точный ответ.\n", + "\n", + "Эта метрика совершенно не подходит в качестве меры, если один из классов явно преобладает над другим. Допустим, мы хотим оценить работу спам-фильтра почты. У нас есть 100 не-спам писем, 90 из которых наш классификатор определил верно (True Negative = 90, False Positive = 10), и 10 спам-писем, 5 из которых классификатор также определил верно (True Positive = 5, False Negative = 5).\n", + "\n", + "Тогда accuracy:\n", + "\n", + "$$\n", + "Accuracy = \\frac{5 + 90}{5 + 90 + 10 + 5} = 86,4\n", + "$$\n", + "\n", + "Однако если мы просто будем предсказывать все письма как не-спам, то получим более высокую accuracy:\n", + "\n", + "$$\n", + "Accuracy = \\frac{0 + 100}{0 + 100 + 0 + 10} = 90,9\n", + "$$\n" + ] + }, + { + "cell_type": "code", + "execution_count": 69, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "0.8472222222222222" + ] + }, + "execution_count": 69, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "from sklearn.metrics import accuracy_score\n", + "\n", + "accuracy_score(y_test, model.predict(X_test))" + ] + }, + { + "attachments": {}, + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Precision, recall\n", + "\n", + "Для оценки качества работы алгоритма на каждом из классов по отдельности введем метрики precision (точность) и recall (полнота).\n", + "\n", + "\n", + "\n", + "$$\n", + "Precision = \\frac{TP}{TP + FP}\n", + "$$\n", + "\n", + "\n", + "$$\n", + "Recall = \\frac{TP}{TP + FN}\n", + "$$\n", + "\n", + "`Precision` можно интерпретировать как долю объектов, названных классификатором положительными и при этом действительно являющимися положительными, \n", + "\n", + "а `Recall` показывает, какую долю объектов положительного класса из всех объектов положительного класса нашел алгоритм." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Precision и recall не зависят, в отличие от accuracy, от соотношения классов и потому применимы в условиях несбалансированных выборок." + ] + }, + { + "cell_type": "code", + "execution_count": 70, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "0.7888888888888889" + ] + }, + "execution_count": 70, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "from sklearn.metrics import recall_score\n", + "\n", + "recall_score(y_test, model.predict(X_test))" + ] + }, + { + "cell_type": "code", + "execution_count": 71, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "0.8930817610062893" + ] + }, + "execution_count": 71, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "from sklearn.metrics import precision_score\n", + "\n", + "precision_score(y_test, model.predict(X_test))" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Когда стоит использовать *точность*, а когда *полноту*?" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Если мы хотим сосредоточиться на минимизации ложных позитивов (вспомним пример со спамом, где нам накладнее пропустить одно важное письмо, чем получить несколько писем со спамом), мы применяем *точность*, а если нам более важно минимизировать риск пропустить хоть один позитивный результат (а здесь подойдет пример с больными, где нам опаснее получить ложноотрицательный ответ и отнести больного к здоровым), то следует использовать *полноту*.\n", + "\n", + "Также следует отметить, что точность и полнота не зависят от соотношения размеров классов. Даже если объектов одного из классов на порядки больше, данные показатели будут корректно отражать качество работы алгоритма." + ] + }, + { + "attachments": {}, + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Лаба 3\n", + "\n", + "\n", + "Найти датасет ( можно посмотреть на [`kaggle`](https://www.kaggle.com/datasets), [PapersWithCode](https://paperswithcode.com/datasets) или [sklearn](https://scikit-learn.org/stable/datasets.html)) и обучить на нем линейную или логистическую регрессию.\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [] + } + ], + "metadata": { + "colab": { + "provenance": [], + "toc_visible": true + }, + "kernelspec": { + "display_name": "Python 3 (ipykernel)", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.8.16" + } + }, + "nbformat": 4, + "nbformat_minor": 4 +} From 5506241fb073ba2bd9f5e0883220ad54b6a13a52 Mon Sep 17 00:00:00 2001 From: PitKoro Date: Sat, 1 Apr 2023 20:12:03 +0300 Subject: [PATCH 4/6] sem3.1 --- ml_system_design/seminars/sem3.ipynb | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/ml_system_design/seminars/sem3.ipynb b/ml_system_design/seminars/sem3.ipynb index 09b280f..1780768 100644 --- a/ml_system_design/seminars/sem3.ipynb +++ b/ml_system_design/seminars/sem3.ipynb @@ -5295,7 +5295,7 @@ "## Лаба 3\n", "\n", "\n", - "Найти датасет ( можно посмотреть на [`kaggle`](https://www.kaggle.com/datasets), [PapersWithCode](https://paperswithcode.com/datasets) или [sklearn](https://scikit-learn.org/stable/datasets.html)) и обучить на нем линейную или логистическую регрессию.\n" + "Найти датасет ( можно посмотреть на [kaggle](https://www.kaggle.com/datasets), [PapersWithCode](https://paperswithcode.com/datasets) или [sklearn](https://scikit-learn.org/stable/datasets.html)), обучить на нем линейную или логистическую регрессию. Так же необходимо посчитать метрики у обученной модели.\n" ] }, { From 8011b94c4d79539f0acc70cbaf647c0526603505 Mon Sep 17 00:00:00 2001 From: PitKoro Date: Sun, 2 Apr 2023 14:03:58 +0300 Subject: [PATCH 5/6] sem3.2 --- ml_system_design/seminars/sem3.ipynb | 81 ++++++++++++++++------------ 1 file changed, 46 insertions(+), 35 deletions(-) diff --git a/ml_system_design/seminars/sem3.ipynb b/ml_system_design/seminars/sem3.ipynb index 1780768..4015fec 100644 --- a/ml_system_design/seminars/sem3.ipynb +++ b/ml_system_design/seminars/sem3.ipynb @@ -1,12 +1,21 @@ { "cells": [ { + "attachments": {}, "cell_type": "markdown", "metadata": { "id": "Faqf8pG8itot" }, "source": [ - "## Линейная модели, градиентный спуск и метрики" + "## Линейные модели, градиентный спуск и метрики" + ] + }, + { + "attachments": {}, + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Линейная регрессия" ] }, { @@ -170,12 +179,13 @@ ] }, { + "attachments": {}, "cell_type": "markdown", "metadata": { "id": "RmcjY5KeviKG" }, "source": [ - "## Получение данных" + "#### Получение данных" ] }, { @@ -324,21 +334,23 @@ ] }, { + "attachments": {}, "cell_type": "markdown", "metadata": { "id": "wuQyanTXsg4P" }, "source": [ - "## Одномерная линейная регрессия" + "#### Одномерная линейная регрессия" ] }, { + "attachments": {}, "cell_type": "markdown", "metadata": { "id": "2MnA-YZHBxzL" }, "source": [ - "#### Из sklearn" + "##### Из sklearn" ] }, { @@ -1014,12 +1026,13 @@ ] }, { + "attachments": {}, "cell_type": "markdown", "metadata": { "id": "hikZUrXxsqE4" }, "source": [ - "### Как обучается линейная регрессия" + "##### Как обучается линейная регрессия" ] }, { @@ -1111,12 +1124,13 @@ ] }, { + "attachments": {}, "cell_type": "markdown", "metadata": { "id": "V4LqAG5jsyQj" }, "source": [ - "#### Градиентный спуск" + "##### Градиентный спуск" ] }, { @@ -1258,7 +1272,7 @@ { "data": { "text/plain": [ - "" + "" ] }, "execution_count": 21, @@ -2347,12 +2361,13 @@ ] }, { + "attachments": {}, "cell_type": "markdown", "metadata": { "id": "iSHnElKexpS8" }, "source": [ - "#### Алгоритм градиентного спуска\n", + "##### Алгоритм градиентного спуска\n", "\n", "1. Инициализация начальной точки\n", "2. Цикл по k = 1,2,3,...:\n", @@ -2363,12 +2378,13 @@ ] }, { + "attachments": {}, "cell_type": "markdown", "metadata": { "id": "AGx1IbTptNvK" }, "source": [ - "### Своя реализация линейной регрессии\n" + "#### Своя реализация линейной регрессии\n" ] }, { @@ -3026,12 +3042,13 @@ ] }, { + "attachments": {}, "cell_type": "markdown", "metadata": { "id": "rEmVJTfv_EZ-" }, "source": [ - "## Многомерная линейная регрессия\n", + "### Многомерная линейная регрессия\n", "\n", "Сейчас мы посмотрели на то, как обучается линейная регрессия для задач с одним признаком.\n", "\n", @@ -3142,12 +3159,13 @@ ] }, { + "attachments": {}, "cell_type": "markdown", "metadata": { "id": "L389l2QmCBeI" }, "source": [ - "### Из sklearn" + "#### Из sklearn" ] }, { @@ -3631,12 +3649,13 @@ ] }, { + "attachments": {}, "cell_type": "markdown", "metadata": { "id": "Tfem6RubAMjf" }, "source": [ - "### Своя реализация линейной регрессии" + "##### Своя реализация линейной регрессии" ] }, { @@ -4511,10 +4530,11 @@ ] }, { + "attachments": {}, "cell_type": "markdown", "metadata": {}, "source": [ - "## Mean absolute error\n", + "### Mean absolute error\n", "\n", "Для оценки качества регрессии можно использовать среднюю абсолютную ошибку" ] @@ -4528,7 +4548,7 @@ "name": "stdout", "output_type": "stream", "text": [ - "MAE = 2.843325574986011e-13\n" + "MAE = 2.604094717639782e-13\n" ] } ], @@ -4556,10 +4576,11 @@ ] }, { + "attachments": {}, "cell_type": "markdown", "metadata": {}, "source": [ - "## MSE\n", + "### MSE\n", "\n", "Mean Squared Error (MSE) - это базовая метрика для определения качества линейной регрессии\n", "\n", @@ -4575,7 +4596,7 @@ "name": "stdout", "output_type": "stream", "text": [ - "MSE = 1.2174295501632286e-25\n" + "MSE = 1.0322079481242069e-25\n" ] } ], @@ -4609,10 +4630,11 @@ ] }, { + "attachments": {}, "cell_type": "markdown", "metadata": {}, "source": [ - "## $R^2$ (коэффициент детерминации)\n", + "### $R^2$ (коэффициент детерминации)\n", "\n", "Название - *coefficient of determination*. Наилучшее возможное значение 1.0, чем меньше тем хуже. Если этот коэффициент близок к 1, то условная дисперсия модели (то есть разброс предсказаний модели $\\hat{y}$ относительно разброса самой целевой переменной $y$ ) достаточно мала - то есть модель неплохо описывает данные. Коэффициент может быть даже отрицательным - то это значит, что модель совсем уж плохая.\n", "\n", @@ -4757,15 +4779,6 @@ "plt.legend();" ] }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "tags": [] - }, - "outputs": [], - "source": [] - }, { "cell_type": "code", "execution_count": 64, @@ -5003,6 +5016,7 @@ ] }, { + "attachments": {}, "cell_type": "markdown", "metadata": {}, "source": [ @@ -5019,10 +5033,11 @@ ] }, { + "attachments": {}, "cell_type": "markdown", "metadata": {}, "source": [ - "### Матрица ошибок (Confusion Matrix)\n", + "#### Матрица ошибок (Confusion Matrix)\n", "\n", "Матрица ошибок - одна из интуитивно понятных метрик, используемых для определения точности модели. Она используется для задачи классификации, где выходные данные могут быть двух или более классов. " ] @@ -5070,7 +5085,7 @@ }, { "cell_type": "code", - "execution_count": 72, + "execution_count": 67, "metadata": {}, "outputs": [], "source": [ @@ -5132,10 +5147,11 @@ ] }, { + "attachments": {}, "cell_type": "markdown", "metadata": {}, "source": [ - "### Accuracy (доля правильных ответов)\n", + "#### Accuracy (доля правильных ответов)\n", "\n", "Теперь давайте разберемся с метриками классификации, основанными на матрице ошибок.\n", "\n", @@ -5200,7 +5216,7 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "### Precision, recall\n", + "#### Precision, recall\n", "\n", "Для оценки качества работы алгоритма на каждом из классов по отдельности введем метрики precision (точность) и recall (полнота).\n", "\n", @@ -5297,11 +5313,6 @@ "\n", "Найти датасет ( можно посмотреть на [kaggle](https://www.kaggle.com/datasets), [PapersWithCode](https://paperswithcode.com/datasets) или [sklearn](https://scikit-learn.org/stable/datasets.html)), обучить на нем линейную или логистическую регрессию. Так же необходимо посчитать метрики у обученной модели.\n" ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [] } ], "metadata": { From 1cce94891963a6df133919b03c81f32d0ca6fe9d Mon Sep 17 00:00:00 2001 From: PitKoro Date: Sat, 8 Apr 2023 15:54:16 +0300 Subject: [PATCH 6/6] sem4 --- .../seminars/imgs/sem4/bagging.png | Bin 0 -> 123500 bytes .../seminars/imgs/sem4/bootstrap.jpg | Bin 0 -> 181660 bytes ml_system_design/seminars/sem_4.ipynb | 2384 +++++++++++++++++ 3 files changed, 2384 insertions(+) create mode 100644 ml_system_design/seminars/imgs/sem4/bagging.png create mode 100644 ml_system_design/seminars/imgs/sem4/bootstrap.jpg create mode 100644 ml_system_design/seminars/sem_4.ipynb diff --git a/ml_system_design/seminars/imgs/sem4/bagging.png b/ml_system_design/seminars/imgs/sem4/bagging.png new file mode 100644 index 0000000000000000000000000000000000000000..3622eefa4735323469a97fb248d51d121f9d158d GIT binary patch literal 123500 zcmdSAV}oQ(vo&0;Y1_8VY1_7K+qP}Hr)^K$wmEIv?sx8UuIoO}UwA*%hpN4IX6%f$ zB34FZMaavF!9rm{0RRA432|XX004Lf000Vs0RH<+3|E930D!J!AtWR(AtXc~?`UUg zVQm5cxC1R|n5m;oHC$y(P0(?SkRE3{O%o^-COVmp34nrOSf`5K=JLNe|_q-oJ-Ai9{sVptADsAzoGz08uyu?I8`W^tmVuOm8 zGO!eMHMS_5B>6zjfH3XM?QTYex0V*wot~CjWWc&9Z}Rr?qu$ULb5P$5-!M906wToM zW#}c8_=O5k7hs3NiUwHZ6vUSaj}c^eV++hE4-bIw?T!KQ+fT8%xQ8~H?J>r=gazuY z1Mw&K@KOQ+B7i1zmh7}6C=9a(1*9_e)3XBu$k0L0Uw(MJ1NADzj*5qbiP`RFL7?5g zqVoiN2X=M&d@Y||;=cU+`lJ~ZH#RKVNiSo#F4p_`ou=cij&s-T%PLw-E)smX`UFeu zNc-Y}twGeYIr_*ku&7_FbI-?2WkA)^q&9 zgAJB(A2+SP6Sl+WY-b#6v)$stnA?}}J^3nTsP%{5>HzBi?U3zzH5g5RIzMIZhq+A? zoF=aPY?ZkzL5!+kWx?azZ*!U8QaI^eD`BS{eC*c8u%F|lhOmuAne17NZ} z#X;ZQ-#?-GZ$=$A?(m@j;g52{Pe6ds1O(_5T17m-<0Z4{A3m1aXFyX3!A9~eex^7!z!?evgZmg$H2l3B zw*A!yAaX=v0)gv=1`WWt%SH8vng#Xj#ZU)e??7k=fH(LC#f!sS@ILE=s*bu=1GvK z-iA7?DtN8_*c)4RgxJueU79uox-hF9XnSgJT)fbcUCI~Q&PZSiEEMWUe9{<2A#R

)B}ez z^&8n6p(j>X;8z5pQ5$0-25dCPFc@;UMDKzAID;7@Z932-yD8i~YSd7Jp-6p&I&&2@ z8)#M_k3UbW&ES&(zP?#~B|AOA%k>{O}oBJ17U>G`3*?ymX9D|@yeiTwmWZVeCK^Q}h zTOAEX_)Xp(7SYC381t#>u0G*velHC>-VIQKihIpaBxIh#1Eom?%@ zE+C(XoWP!Toi3bEpK_hxFVr$IFpe^>G6XZ@=(Eo-4A%9A8=)Amm_8Uy8djNf_OVX{ z8zLDq8Q+<#k2q&q_*=wcPSA{7Pvi7!_j)II1bc*pB#1-|MUWD74GnAS+xrfUUm-&Pjpsw zdUg_ahIx3oqraxVIKKqG!redK(JH7@yd^V8avYMGgyd!YiRO#_M)`*L4)rhVl?{*% z5Dh>N6c%9Z1N039eg;wtdJ8TKmJ7lPCiR0FubH$-Jd zkwvkE^P&?+okog8Ers(SjYL*Pghk?`J0Jz!`G?~Vr1N3?bV5m()B-% z`i?iw?AJF-bG`MWw`;QXISyR!IVah!97|l)9M~Lb>~ig+zb_mxT@RgMU2WV3+Bw>D zT(n$%y7KMuwn}-ZP1i(h6?P!I!F$+y275Sr&3kxy>E8Ccb9m)EuRpZh*4*koGCr5z zY230t@SMFb%gtQZ{VD8e@r(HO_IKr%=oQR&)wkF&jJK87n02f(uot-*L1>IWi*J_C zn*BXn&2U^dZ13Hs<}L0`OR;X5%y4c7lKY5k@q;5*W z-ib|)mWKNiw-#9svnO{a*`73kkdf%tHc?nnciy`};w}TlInqwjkIK(d^y~relc#h( zpCzy9t1Hl}@oBmw>sQ_cfoZVm=0x%Y&Dcl7jE9~Xnw+ZVvUQ7vs}P(G9P9C|@6_i~ z`^o0cW&-z6r;!KW{pZ~^$|!0wsx{sXkNt7}N8eqLRtQiCu?Uq2Ih+B!2u>!&GA*xK zzkH%Lla{5 zXGQbA@5qSkNlu&cl#-78=cn<_7_+Xzk6w??(NWNTqsgF&rER7iZHsnosZV>1+|-{Kk#nqIA~&ba6$cR$pw*K~hieYqIX`sEmXZvJexYwhOp%ljj~I%t#njqpA9x^Q=VUwuH6DE;p~-FU2vAGI3ij>Tg=z%{tFnPXrDrj*ri-`-s)ablSA&MEEod1sP#CoQLI% zXy^X!cFc0oTYa>B4jgfsL9Eri7R^KM2LDX_1OdJPW(^7sv7nr242BFI5uz!w8zzeqm;Av_<1*Mq^KEYRsthJm;+S%L+lf52G$UJJRcw_bd(Go? zbrJOY(R19RGISAo3Z0M5k_A=g%YNwd>{B(XMGRLnr+($EBlmgsf$E7vC+=#dTfw{0 zI^VkPMfK{^N9g?;2Zz`F=ezDj_`OfYJ9d!|yyCF(a5t~_N10Klq2Y|t4EYba5OT<* z>s%i9?YqLg!(268CDDaF<_G3Krhhtf)3H6Be7@ENM+`^iGg?C?Zm+qwvHjw;3-mNT z*8H*8W&yu(W| zUP54XbD@T{k>Q{PvO&Y`oOauC^IUk_|KSGX6wQ}3n!=XmzO-O#K?DY`K%s=cxhJC1AllgDx1&1=!~$Vy6?y(cA2U^CJov z7`QnQBXAp351JD05q<%p6gn@)JZdt=F(y@bc}RS6Ox-fufLrRrTTpB-fF#bi9fkeS z!E!%0u<53YYaxb-g=y*8!Ysq2<3RZ0Po5Xqwdj{~-%QYE$eD<2m|-M*iZ%r<=f^J? z-f^88$H^L^&W8N6{@XcZJvpfChUzu74TraVjf~$XPHN{XSc~+3E?9aK7iU*r^VIXl_BzBcAED1K61T_J{`Ty1nYf?f&3r2oOgh^ZtxrJjTz=JIz+mCa*c+g z3e39XVhIN}`%-&rbBmj&v&)+&+3JH97&dSu1S)u6*fruT0x}%r)(pue5lm5b9_5rY z18vQjL&`(-)gt7_5Id=kN#LDO%6^4_rKIKg84uQ4W~yeiX3{z!VCgE-epx zX8+|-JauU!$CsUuj9F)=mTJ}F75n|wwt3On@X-iX4gLc@7JVAkhf*|!Aq5I2m8(#} z)P1pV^D5mcM9#!`X?wqfoK6`&??awgJImSgp~_rmy*=W79##WAS0~YK(#CVG%CThU ztWtHT<@0ie3$^ypZR)}NG4t01*Zzd3AmMOM%P0+hNsfi5o%eMQ z%$@Y7&NHVOuV48k&(_GM%F|=2%(2b)cMYwLA95WPAUgxB_Y?)-dIeaN04C6mNS2BB z45P&ah`9i;QGiZP837r(Z}0B{KMKWD@c~c78T$zC&~t)!v$7PJoNz7zGx;(kJqsZ| zJ2)@g-uPdEdxED$f%0MUxMi3uu!?*)f=msr4^}Y5qUgv093hw^NcC^4!q&{LnB<|* zgG;+;w@feW-t+;XJECp;!Qpg+rdXPp5SfgLSf?bIa=#Mw<~3(sXCvlRW|K~tPgXF9 z61<1fZc&rq7r33+@6}RaR5h3PtsAU=u5PWVaYS+)a^O3X+TYv9-CZ2B?tpl&P_BI} z1LFWg2d)H1h6o8u3x^De8&>WyBN-+BEa5Dv9c39~8)uzprGB7L^TdD1!+gM6#yG?h%WO->N*~L-*PPWTq*BF2*$b-T!cudXf3tNh;fU+)dbU)#Yih&K4xMM7js zm{-`F16E$f5S)Bo9de%rJs+$p-ag1HANG=5*#4zuroB;`yp&+n?Z;;42lO2v72+wP zG&EE~C)N~~i7!=#!`|nT*z%+1)AdMc>;3I8^e_q^OO5`!Ez<7LGVPM(%<&TPD(YIU zBZIf?*!3f)u0xj?tmx86YX2k7jj!7G>8RvGd3f?A`@s8Z@1x=L{T~06ue7RL3;u=o zoA_+yRRG-BA6GG^0?@U?DjcK?vRObwvX39iOR(1U;{xosU*<*$1!`&T?$1KO)^DXZ8ST@CZU7& zThTx!LPSGoNUd4ETI)0DS6ynUYsoY-kFF12kmvxckx>%xf`L^T>{{6Px>>tv63VVhVv2aCm`_+gW z@C5s0!6x)(h9k`jL2jKu%R)6p0Y|U#54;wZ`!28fEaucFNYl&dno*C$iPYmvzrvkI zfz$rM!al>N#L@WJQ?pT z?SC7zn8$Kn*NrwSchS6E-GS$%B*^9F@m2k*ovX%wpYBlj47xX)qyKIM@<9kuj=c(^ z9ef6wWi$u8!9e#uiNlt{>WW&j(vAA!3l_y=88ZEb?TSAvpooOU6pTPRaxRA5!uTJSw8KU_A@HtL+c77#M&_&2*v zMdl>LBIt63FYmA5LqmT-}U zvz73a6Y0sY(_K3$Dnei_0-B%9k*}$CH-VR!k81eO;x>Ly_BA$S{l39 z$w})#wtLabk^1I;pM!2r|E6YByy4QMvwt~$>ig$>)2&PIZa5)J zD~PZa5B7A+qHV&H-t=IaVkIcQ9vVJv7>qm{-;9^z`CeJ<(<48yF^Z0cdU$%zBr})R}BjpbB=kXJLhiaeYlv+?N zvz6CM`wCb0%oPw(H6HU>0`PPKw3-|e69lfV4XU_If8|5n){?Zq{m z003y@|9pS|nb{Zs00BTkSU}kw=t2kFTX|@?r`QbwS1<)FIiE;HJ}5k2QA9*oMEEv> z08JSMm4@PajbDUU;o%k(3{@VXgtDGUBz`-JpbJzSm@@0)na}AeC&$a}r?oiY&Ln*X z53|#B2J?3Ymy_$&W{1h9ZEHc0C_iyN;w*$YC<`!FfT0jU*(!_G5Ef93|9>w)0A1Rg zIQ;h}pcwvJqWrFyy&*NLmH&P2fA03e_JsMLQU3Q?`A!IPyCV17rFB-|{~K;Q6c6P8 zzCobS1FvsT=z2H1{y+0IwHA#0-|Q^FLs9-uY?0`C)jI!Y>3>XO{Qsdz1j0i9uhjyG z_8nVbs2tZX6UmfH%4}A4MP>1eo_}5OpGDlk58&6;-5%~Dzd2^3GY%_?MqF&rea%EQ ziX1$i-5%!KTc3?t{Ks`f0st*6x-_fXx6d^W6Qpr5bWw6`RBPP2f&SoUSwRBsFKdcu z8fV)zzy6f-GZi`_5|ZK@xUeU-50|9r|MMd*|5* zh^eWeH?T!>cc+O(V$YP{1e} z@6>ELzV6b9XsEz&;2|nRN!VH>+o9VV2=3?N|AkKgA%A79dVq~oho8n68RU28Yl^r7 zf-&$2(aJ8Jyn;S=nHOH43gv<37@StBEt#82& z@1;d=hXsPP^DSra>n|lR8z+(|tN6HOzbgrsBW-w~fw;8E1~b^t zUKTQFKtUwRNBy&Zc?09Asrt{TbjF}NmInt1AMN*}5lZc8B!l-qeX9l^D^vt8AShCz zSdh25E5iAF{a!1oB*FXh_~c?Yc)W(DpJc3d%28P|k{5(%EJ;I3np+jrL7O}O;l6em z#0@eYZRb@omh&W4T6?~?v9QB zjo~hO33T{xi0HMS*Zzm?H#0(jO8O@@gq zbzKQr_fX9l7PDO1X}gZS2%_Bc=mAqGL@0&!6t zHH06F6*d8guP6?QB;0))z7{{!U{#eG#mefEpV*+H@>BmIMF2E55Y1X=RhAg0@(KVf zyEr8$aI74G45Wh)swYzp_2!)MLh|E|f%!Z*Tx9j3jNA8&)T!FDJz;c5zj;j({YuFT zJ7hpk%Ibe1)C(5o-+2lUoX6uU%)dMY+iZ;1y8F7Tc6@`0AGveqCIT8y8YvO%Mve*E5Ne-l_j**>@a-1WBgh#sM$P|ypHQ(cpXwecdl9U z39eSWH0u%GW)M_B{PD>}DFX3Jwd9{nbj%2R!+i5zvni11z#4PmLNs+ z%_ij_!O$Zu*{>B>C5W`m{pDD!DZxNqNpvk)%<)uOTz2=kEvi6R{`gNa>V1s?*xBt+ z^X9sji19C(anc+IX^o!$H1{-C=8dRm+3u#?Jt{5<9@0QyJVpGeH5sC(Y;(m(!B&^7 z(VP>O9)g#oPbPw)$lcDF`@c`9ffy9&x=^*)&##8l|NWng#-6hwF?pcttjK z&E46Zf=v-a@tV}PEs!7-$z;C#z!QLvicw4{YdXDHb%#!w-4-p_S?K?VjrKr9{vj(Z zPnsRDon!t)2yj30doy&o2PvKjy~iRP1r;iiK}}w(qE?}ia9RrJ%v&mM*-5o=QM5$B z%8B`OeI!}|JCT?MMF@%Z z-VUKigx$J7^F$sLLc0RpBY08m40)b*MGPkv8vo$D4S<;6tjZE)6yNl4HwPu=v~-U~ z75D9}IQ5RNN(=XSwg#P~vDzmlLrDf?G9$FriV(tt$umcorFj>QL$S*Fzf@SCKi-t!KT;(#qUOlBA!SPV~{gp-$6q? zPNqg4@-KOycxkKtfj&AJuzK`R;8a{hna!Y}^mITbsd@d${hdrDd-c8{t(jna1@n$K zK&9p(azKAy;oQWO84We#Erln=`{KDFhbcdx@(7grVax+9V|{}idbTP2-(Wrpp$Fzczu9krpo}rk4 z@jc@#a@H3G8!x#o28~QUTKH#JKL}vQfTt~86fZdT7gL7=Pi~My`5#QL2xUQQdWt-Z zXKJc**K+V4+!RJVVg`2@luWo1A-C4zfAcA#cOnx>NzEn zrvr=onLn-=K#>YczJvhb$oPf< zb$=A0fwXPao+aJw9Z1RTtrexT~!S#b5e z4ag2f^r+E&o%Jk~N`D+>;k)KJJIqDoNcp9I4{SS6OpI}Kl9^1;H+N`&>^=0TeR2DJ zJrR@jD4!7M1ESbDhf6;vt1KB>EphtIvKF_8*N93F{|#w@qWn53_09ey*NplSzlvW# z0FEMRuL<7|gOs>8jE60DKTA05fyuv?<-F=#CeYw{Saw26@w^T7N2X&2%&KL;j&tqu zJj(n=>M1ug4BmzT$T$v#1h_>FZZa+))hs9CRjoFTrbE z@Avt;E3tpEu=qFPARz8EKSfj57$*gT68}<2>ofXI%(2)0or`cRnGq%zZff&>u{>hm zLVS8UvqHboU^X8G647{n?gd+PhMFVb$84vOYLDAOuSMe=tJMhGK@!U4bRNf?z*x66 zbW8LHO@f)kfLaxKj{%}l0)9xEiY|&u0YmezBoL6B_%{=Sg##|N!cklK8U5ocSx+7o z3Si$lXTcWj;-qylErWw|8}flSeuLo?xvY#dUR!5j;OcZO`MZg_2@Y?{c!jGTj(%twt&Ewax=aIA(|J+Muv z7}n935l*_h;A>jvZcJSKd0{6}(~tZHx!P}3#%c}<72>b8{Yaaf39Zovvu&wN6?E0X z|GQ+-RgZUg0oaDe){@t!qI-6tkv%J}IFmayyj+9C>e2nJyAPRkg*C$g>T-ri_DUC;N=?2vlHBm-VnH@B~6)UoOG zhHK%(dIn|aT-OxolKBxrBRImaym5BPnmkk`J0w{?Mp@)}f@} z8u$`cGvd@Ud4&WS=e|MaoDpLXnwJs{1cV+OwMYmhLwTyhH2Sq}Xyl#i!wyS^p=ic7W zV_-i!TGji@`P^ha9=zDSf{&$h`aiZ{+xG5v&f7IYBW84el5&z(e*1<@r;#Sd!HSLK zBy|V5Z2D+D*~ZSa%_-g%Kgi@jE^=yj{v zxO3hgoL<0s&3|>ci+MYnZwy9WW+}%(!1Aj39GmQ{nUztudUyb%Au5=B@jv~4_9Lo;hz3y)h+9Xqt zEXJ}GYUx>FDDh!(;80y%EW%+ESnBXvqNcn?r)NwuOfg~;DuT@)D0N*+*HRDu%$nxu zLF~vAfDpHYl*WM~LHz#-l*}XNBGB7T1n)pJl(N?Q<8D(@d}UwPf-MvI`Ynm7N~rXzg2$@2+e%B6Dxq9+SLvy& z5jD|QQ>TtIdj#R@%z4I>>gu_c3NjObBI^lNCQ7$i(gPfw3e*65Me_$bOONxPWet%^wZ zrS4Su8@b3nl$M6OPxt$(C#Ph<@dpM;IS=(~x)2ecZ@p>}^$X@*Y}0TuB{S-)%dCRk z!1N&79>v$A{2=se5J=gTK0n6b=2i?EpPcq@?f^zB*Y*BnoGO&g<4#?hcdQvy4brUD z6wrSf84-521~VT|13%< z@T5C!{nwt=`|Gw_U61P_`^P0uBJbnM6c^mDKfWegJ3ES(_U>*qoNe}~@@`Jgm0(Yk z48+2B!As}wx6FJGekK(Pq&Vkvha=5i@5k-?d7T#HiRIV3M-U;!TzWRRQ$3oB!)0r~ z#f0{QXhR)rIzp8(6yG!Z#+0^Y7&&>;l>@v9CVej;=U9ui(|1ySNd&T}_ccjoZ|rbe zl>Rh;M14xKML|^!OQnB=&@bfnxI0Lb`i_jD^s`yWJaL@SEX1lsdC~g2b*d+(DExKT ze_y6j_E_Ok&Rr3f9QVmrwdJ%oSiiBp7#kH8G+?kGZw=XoHNwm!=GVM?w~orCtC@8M zSu7E7Q^xkb|D0Akj+P83Y(~Is+4c33;ytPUr}owAu!fk}Q(K#kv(Oj^PFvvI75}~G zy7k#&qfX(`eap?VG^1%=>@4M^7fMWjq0Q?7FNpFX)bI{63vgA%kf}zBkI}3 ze7#JP0|%9<6}C4dU_>GjqPRZN6uToWw@S&PeGmr=57%H?>HU>Yyt`NmVgM;7NRVlth^7rC8vGd@r?Dn%Ls8d99>eT&19#h(adc_0iAh z*KO}-gP}_nQrI#_ZNVFKmDBJ-Kol4_+)NUK?Tvg)EXCi=X*d#2Q|iB=TivAc-$C)f zVlnt9(+Lp%TFiGj1-VlaIz+OlsvW8CL0N&p?N%-sIOR|I=A})jo zT>=j5Fd&)qtY|$kdtV;ibP2QPIu;7P<7$3=o=Ph!xOp@oW4l^ai)2k*c$g|~-8x0@ zP~pA=DPs)FSP^Oab%-#l=My(`IT8XIq++Pcuy4s#=g(81=6}%t#KVO5uw`Nf7>O&`$!&hN$qxX>aFhzpd4ZHRvcK z$?>&FB*OsW=uiZA;9D!W&9E?|JPiJ5>eKO zK4s+LAt0;mPX>hu8PzXwE7HCYCQ3+=vSNw9r6-oX<2K%len@EcACyT1i+ma+kaQ_O zAxH}(8EHjG68ySbLqA?iG$q`v;zbhm@z}64NRb(mjt&kcwYq9uKF8f<6RAol)j&6u zMEGfP8;>zQoP)#$A09dh#Dhy{G#L1m8Pf4EH$$hHDB`XTL#e9OcHlO^U!_3 zm9ST6u3f>aw_RYqv>9FSK^#--pOq=&AG_h))!G$Kd@+C$OLT|%ZFDOFi&l|%6Jgoc~foLq^6$@V_srhKFV~SMG4Y7$O>T8k1_mX^hDwg+9vT9HEeSL=vhb zO2~RoTEl@dGWR#{o=P7g4og@pkVzGYRnPwzn+ztmPzb5VF9X&h*la`N$yPk5weKy6 zf9#0UOZ2zfmHn9U*f0PHQ0NDHEQ@$=Kj6~XsndSFH+wZSH1zlPOCGHendl!$0c+ig z0VV>dNUBgkmi-x8S#dgS?B4fI z%qB*p1(q&%FNQ=DD)ho65|GF=Nd7#QT>(vLs7{m3Yb@cQl7EFaSU)WCIQr{;GCN#g z?bf@jG3hda-2iA`N8Gf|hKjm3oK@VKKCO>Yt+q3}?rUylE+#H+OjOBej>t6-vO63n z&PjyramGw-+4{GBB5q$WG2{;u*CrisOEAl>77GnZd2yA+Ukmu~(7kL6T{leFF!PRC zPl6LpROcq~KImlq&QhC#A(IB=m;Za=AAQFKEC=Vz01T znjo-Ghf76r@_3)A5NeSa0!&&VLoX4KD0;DRCdO$oEPhx5BK*zcEK*qHkbKMKp5a4# zY6;3B3ezH^${rtw?~P*Bt;wr;Y)lvGqCf11pN*QwLQ2;**ge)mR>B3eD6KnYczNR9 z;GWW=;AEyoYjmmnOhRj^&e!j_@);QH6%;NYzG_*}fV_Ac@bDPH=cU5|IpfJEI^?G) z(SIW>(S}&@I)4+4d2(@7;9p^>knjF=(tC0nO$(z9YJ95#v_5m#XCxcTE%CCicF(h< z(HtSCrNf5zh>ejo7}zg+Zz%5}-0h(OYTi={^ow@rq0b9DW363*(LFvlE8vek(=iAc zg{Zt{K|EDF74)Ik5l+bxRU#ZuVjk@-Mx=VMGy-sRos8;DI+yNCi`w`FJT&B(_z0RJ zVjhd|AHv>NZ9R zv+CcY5e9X_l~P(8>HJtNoA+;Pk4V_gn#1Tj)kd7gcNDbG|AVI(rW0=~b{HHVlH0`m>~<%te**$`U95kGZ%?xnpE z+SV^9*#-mk$xxn#Q1erAym_FZZ`jDfvEqUFys8zn17?9D&>8{2aEu&qmEjLLD$kd{ zZj31s`?;o%ERPg0`ol1&a9;EWQC1x6?9kso9HA)yXNLGTt~meUu%|WtBcR1-QBVai_y0%u*&DTFVgBrQD<^Hk=&S0CL8%6FFUx!gj;r4A|qo zG<+y-q4fJ;vo$kP#S{|p6xv;(R?fYMt-TTQvwB4+LMUana%IzT9P++YE+;7p8MB51 zcuGiM2~gW2Hz^QCslUC;(CLo$FKdWmI==?Xt_F?(isZ?PAHP7F{s7bWu21JyqqOA6 zK-Eca${6U)b1^R#q?xHCt&i|sa($7LS<^sbL%J#7ckNs%clwQ!34 zB)#QQUA|G4>Q;yZ=ha$A#uzj30cO|1AJ_$bqW0ljEF=^)md>A8CQ)Y=^p`fHS8rkn z&doQYjiJPTj#avXqq`)#g-jgwdqiTtdWs9flHWNzDkP;IWgDx3MM^9fScnP!PBnOWWYSztvRH-$~wjZYj@o(%<&q0C)K607(*9^z&Uy_C@!*4%1uO zk=Y;vFIjESi-pMi88{Bg8tEn`EWFc7wQE2y+$easXDBEn6lLrs)UzdZ8sGZ__%LxGB^4-B19od72bI(SxeN{_$!*_a?%T>x+{IuiGDF>F3P8x4GZ_OBQ~bs z92?acg9xm%rv~?CFje!(=W|SfqQ6e01NG*gOk4KORvs^-mTO*}%yPMo2Zx&< z-+NdMu8uhPs@L{Q?(N_jMUDYYgzZSLjS9GCvB z^ky&R^!YB9Va;sGH(M^PH(TjB&xyF4FIWHVSU#RDO{7qVm3BoAZp?aN!`$(ap$RE{ z{d{E>TO*<;wCW0)SLBTpX9Ru|FDw>+Jv1ayYb*gvEO;bxr{ z7m;^-@Px+KR1aI`Uu#tpwG{Y-t`0@CiEtD33**J)RWgMtBrL4aD*zV`Zhhk>ta$o4 z4j|fvkv5;SBQRV~HN1kf_aTV6e{)w)A1!`6a4%RoP)$XV24oo^SQ7=Q)T796d^Y~9 z_eOBETzYM;6U71z#D>9WLXnGLwVys-1rY6yuZnj17(3QoQjHf#-8x%S-$NYH$w8x* z0XuCEq3O05Lq1Y0mC;UUUaBN0KT^65e?`fV-3SgZ??hR((^SmH_wNTSNdDWRO)8?F zF6u6tSi=$=S_?2M!kz|_Ay~GCL_KQ~T8K)Yik|^BltV}(LDi@>a;p<0dSd%!%VQ6` zW)c|VbBMl#*O+DP7woqnO@i8I84G+UvRL3 z=XF)C^CfJ65Uc;!-Y-a3rp2jfqFd%w@vc^b$7R0#q$3$dBA$UoxdAYAcpt7XMSs*9 z*;YBUW?l=7g53f!-j3t(XEu_!Pv4J{`p^lJU}05Cc!i7leYcSpP30PeQp_q<n-zH+-e>bt`}&l9x(?A1@n&dx=B02SWA^W7MeGi*rpKKwXC&?eNd&s4CX4-3}WLE-8tD#2WkpU9R8 zdN?-6XVO~J1Ko{lwSt8Em*OBaWfCbsmF^Bp-={!!6U9K*aclO2QqBlttbAmKk}RFE)cs7W1- z>zBqo`Gc~OVt>5u0(rTwouQ{x%U+luoh`CO!T0mcrj7L-pjiX{}#8)F_ z;G<9`^VeI|#b$HPdU~8hgYuoYphzjodTpi~o%!vA<1#C?fD<4k&LA~>V*LPMcctf) zmm@kNSg8^_xz4E5hUKHD$I_y5=}zo)$t;of9X;LjLJL;twI%u#n}PSGc`KSaL@#{D z7#wN++j{J=13nZKchd222R67y8y&~64a;m+HQexT8y2|pUKM2^Zbh$&KWC&|V0rdo zMCf&yw193elxt*=c*H)8`Yo;&XRdC@%rxVr&1Amf`6_}r`hu1jtF>!N$?(>y*^xIv+wqBrQX4m@+*>c4=>6dlUJmBJ)#<8jf)R4dw({*_LMT$Z1F@CR@s`c(Ae7s$9v#d)b6>o!3eB;vEnY&pz?X6AG%@zzb7*< z?z3UUh78RDSO;DIxtt9=u$0f2l`pTw3Dw++zy%CALeq|b^Ygay^>qV3T;>rfbF>)3@XP|o&Ii|yUlaCP3yX_!OK)fg_X!k2yL#bqdD+A-VEnYiz9xbMe@jg z2Dwt&DE|Q`?Ta1y2m5!y9dn#zsbyBCokfwHBaNdR?glP?u2ze*ZV3W05mB*cHIdh< zO10$t`bNC`2QkDX!HWt*fdi+ZbQc^10NJ1@;Du9wmHo#;gAgP&ox!^Elu zeAD310MWhd97V()v6g?{)!3v;tl!HAZ1c_ zp5Z4^zx|%pNC{aJPh>OHAcV=Kg^D5veQV!lOhh4DKc)AJd*zcmKf&1Qi$U#o(^q0$ zOv+jBPY$7xtuJ&uFgAxNNmAjkoL7z=hUZkIs>yYlj6 zVE4Db{1X|Ki^^zOB6SxS-vj&}JGRp;H=E8*D?z`Tw&_!RDjfzx9rkfPNNjXu(Fmtf zgLn4C)$tbdI~90-ON)!M6H@>#ZrByRm|GYAy@ip~AcK!6z?}yTj%8goI-73+X zFCt=Nq6}9kov24(@wV+6>|MI>&&~Udm}-|@eZxf+iI*9U|Gn~$WowRSheubbQlV^^ zkz+(ATyjZ`cp+`_I2y%8fy8Gm7>IJyLXY=+W+7=06Edkv%FKLtRDOSSQRz|j2ck0& z9#yA5TI%t~aBlNq9{$*|fF(@u3`EloO05P+!ZJ9N9r5i;Qz)lVQoGBpZP&5yHCHu* z7lGLZmB_HUpqcVpa>gN{@YN;3}7Ghmw+ZOItXsnspR zjTvLc_Nn__=ix&J-P@rhEsc=M%*iI+EbG>c5Hytwx{&sp>zhz@K}9E4KHsRtG_^^+ zCM_R-54;MTv`A{L+_VDE#bbW?;fcz`oVMtNjxYb}5$pEe`ozY+-`Zz@CreO`9{zUL zGo8>!SzEvE+I>Ky=YE^>5IjVuxZq8iv|`0oHE4R-{mqE$?i=G&_44vL_nF>?qev&u z1_6Jo%Gv1BGK2uOd1$9cJ&rAa;fctGkpjL+h9y0^$@Q4>1xZe=ykQKsn(d!WSvTx7 z9y@V|M#e-K&-^<#Cj{Fb?6^eK?e*5C^iwBK8{x505h1Xo*0s;3tRHqdGsh?s8W|N~ zJpJ!nS4gn*gOxc^)&(Q;w?8^6x?;tO@G!8S0S^Ol@wpl>rnvS@Y@MMKr3wLY6=B8Y z%Snx4CX(KP^AOXY7~d32!t}>Ia;yY#P_X$+>!}C!9yqY~;DNnqMsn-6N!@#066rQ7 zw|`;IL;b+NH7PjyK1btQ}-{e`pNV{1uKj2N{8#*4s2{`p+$ zh)r=*jj7P>&Ybg%a5Z|3owx4D?>~RryRl)+81Uf_yCI%)68D9ZM||+(!sUAw&i%Xt zWH$`|ZcQ3%jy8MA==Q{<3Ga=)CLzvc2#1fV7&u|>pOeQ91-1F@r+)g|1raW-5f+L} zB&GQ6FMl;>!NPuRk-c`n#P9JKpZoOMx5?E=8u96$%hzprNMQC1quj2>grK9TE`{6&n#9A#{cWi63B7^h89) z#YcvlJBXAI{%FBZ;)0*`AQon@ZKT{Xf(g!7W12z@gZDGArGbDuDM+&0L1o1d^xj4> zfLgj$@;J0cqs`Vc7J=KrBlS24PP$y;;8WWPU(Oil{agtym`0tR_~gKl9luN)dCP#M zGX~6gp!?lTg7UU+PRCMv#*N8zcES=`UtI zruc~!pU(g}^oMtcB;d0`r{^b5n10oRDZg)gxP7gtqzBjUAA)aA2xV&YdVFl(?;pV? z6OPP#Hd$jXTI>i||8OZAO)E(`UFxPbM)9vxMzwUR>jWyyTkS+Ci z+AAhabC6lEfK#tKU)ZeA`P zxuL6%G#20Uu3j}8+S~BPGP-VaypnhJukX!WbL-7b&7Jd%Bm1&qDwmHi zcCT3z(Xd(Nh=_~Y-81aDPi}oFB}*Q{#s;y$r`$uB>z-}3iK+;_|=1=a2?OxtbtAi;W3nOcF_Q)GgTpKapUzpR6cldAf{*sm%t zB{Yzb&$wo1=i--LvvTwB3ov*iDejsCmoZ|($`;cR4a+9mr}j2%aQqVzp3(2s zTHNHLD@2TaCYae@zC(e$5QxA(^h7kf{ki1XFQp8>zT$5KUuaa>*t25sw0S8H&pq`;Ai z*Ive)87auSs%2ef;F#^_Uri1h+3&e=vnP&ZrbeBIx2hhCE3w6Yh8j?k)J%`i;10TM8Jmd z`n|AgKlJUC*`G|sJ^pY}qu;uengg}pY>6US{3Op;sK-NC3ldeXpi0(p(qfh(#A$U9 zGSOZH?lSYH)s+*be=(5XfuT%@Y4b;ut8wj!g`)>vT{a|S^Cx%r98TLCj2P>e3Q4IF zWGUxy5;UVIu>@gqTC&JFy>-DmNH*@?rPhhf8;-W@H1L)Op1!Gx=Uxm7*?z$wz}`lP5oZUz@3pYMo3s}c&HF?8%^(ebJOjV zpPOq$#l?nLs@z6wr>DPP{V?52i7FS%r=++Rj*Gy4wz;HYm@VxGDolgm&1XhS#wrnr zfvhd6PV+8?@y&1VeY^J79!Yf53i%mF_hm)XO)Eb0+2O`(<3dpB;SKBPjs|L%u^>H> zw3Z1JFaZ$|EXcBECks&rGxA)g>EKSoD?0bRvpMF#i1HOGmg8pwg@7XEEz3FH`3xG{ zAi?ygJ1a`A3jStZn1Mf8U7`EVwPWdbYxYH7-Syh?I2&l$>>wOTa5>t}NlV)NWGRjh zt)2hdpMPZ=VTsrFN=k?=I{SUpg)E1`jwBeo?Kc&bz7OGT*vvw6Q~)_?<0yE-pH6@I z8Clu84;|fe`0&y6%&L)j<%2WYm3Ng5%f(&=JPf!Fcxwh|ociRXa5&88O)$1)fWrX> zyj??k8wKw)EfF9B*0(gz#GnW|&YH=0hMLT{0{jQM4wY|IoHW@X7!nZ`LI(p8_#1-B z9}*EA5@B9icKu;dvGg6?`j+ps!OkocJI~7bv)`8}D>%y-Yt>(W{WWFElsoRYqfVVV zd`UZW$x0=mG9XhBrawF3ZtYq5-H$*1zW2Z}BR-*S(v``{*H*@ zgaF|nkYS)z#E8dYF{dR)L*bYCp)s^xA#B-by`#`LjU;YbO$r}5;Q4Xid@%AG!O*bN z@RkWU&k@po*!sb{FL`(V)Hmq{O82{N-to!Iwb|AoalZ3n>wdS$L+z>ocn6|e%os{3 z%FVqhW)}6z=-Br4Taaix^qtYY%epd7q;8u(_Q7vvJ~Hvz8%Or6XR;|((4(7J+O3g* z5^o-fgH=|jPq-TBzNu5Ee)rvXbLPzH-Mja{|NZZpHEa4%s@f@vrFoLD*_N|!%40Rh z%(Q8G(MbNkmGj$GF=FVLOX>6%Oz=(tdiMT#dWl{O#^m zp_!3Yx4k(A$t{1MB7{LHL(aGTq|;(z!s-{u9@{kMKOK7TFiW`kp;px+3T3~rr&L9_ zW<9XvYnh6TikU-5YGXns9c+%|krV0L_aE4GFtu`2ZbEo^%PMd(;95jRMODCiGT>*x z$-rg?0(%(Jy}+Tje zt#H^jJT{zt11OA6d|*;;|BN&ob?6yTG-m~uSdrZTG z0S)i`-?}^hM>%-GhFYqM3jud{voU|?zA89(tz3-HyGA{i+%jI+d2O8o^S&AR^*beU#uveFzWD|_9EhKI;tA|?AjhMT zZROaL>!cL>GgF>rS0m}MkKgao>>?v=N6NH+zxeLgYxkzLt8BU%kS|o*1u~`)?$|4z z`0@Yl4Q*JL&kIMEt%&tU{)n=;>;)3B&FfBTnCADW9|5m zeXC}_Ir3}R)A`EA+*q>BVZSt5A-J(-lew{sD#YFH(-~ zjmi44#~y3nzWw{}zrSV6mQkZdee}^sFTVI9a-bhoQI8a)?OD5K4Vgj)ytL@&9*MDZ zfmNt<>r*p2^xl+Pt(@B}-a|vVq5)-c4{uwue)HzzscCrcd&3s(E>7^gXw`Le`|rR1 zyf4*=j<43Z)zx(?MhZFCU#q@fxfN$JVyic3+2Zng)+Fn(&EFBm`OAoADy!m9M zmd&fKT{L%hYIvjeU7A*pQODk~-5ao9HTjcKu|EyI`7t_fZ0jJTm0&AE-J)ug^BE3g zpyLoDC_?9+gn9DJGGAJ_TF7=32(N;Zpe;BlUv#K)ZCY)zxod!5NRfatsu+N@Q(0U0 z?ccWdP?e~>#OU-IQ7++RRN_OOeA7gp$v}T0kQxz_!crUxd~ufIwrJDNi~t5d{*)2! zNwTnYb(j~7OrBYF5pnpljV6(v@9kN>IQT31Op{W2i-9JtH1o*nCdD zR;CSseE6vvU%{syYhaK7GlvTAT0Y~Cw?jWvEIp#ok5_$#pu?1?zyLJ+qMmE9OS;Vkp!H_m0x_Uw*2+;=Tv3i z>0KJd@R(td_&SNK$~1@?lDS}1&aA_~001BWNklQjc==_QKJdT;xQzuTxb!TXu#~&o>~!n0 z;I_$3BvsmH%heml#BT}6afQqS$4+e9yZ^xPleMEVud9+79~Fd|1Wrb5Y^=O#0&kKb zA}`~7@Gw*~w6~$6@z0tx8jy3LdL8%!rl9-*=;6!Iycqr176^~crSeyA zLGm%5!o!#`W5%LIi*UyTu10pYJRj*-(XnI4PNkpB%XP`^3bScq#-XoDBCWOvnLIZ^ zDM}>|6NnLPlb3>$+G+k|)qJ2t1_+%@P>c+b&vmJ`p@@@2D7L9QkdpQXm2da_g3O2h z-gfZN2^XG7il|t%R_*f9AzT;OC@3}*)P6d3kC(BrwToV?sz?!JKA?mD#h zBy4%8Ynvc(xv6+EIE*tC=DBl@Z&=&#zPFy~ePfN-oYh}E^Y|OH$B+BwuC@d5cK>zX zOn{zSUQHR?wb6;qi@x|F3{$o{eZw?(Cyj@GJaKs4$fLi1_JKQuZp}pFw8_Re9HR-V z(Rs>@S@WLlIc1~q)JG{v6>)SYbn}P*dTHibl81w{^)umWBt7-nXFV>9TQ~is`$v87 z+>LLv-1Bl(H=ijXwwoTGbbalUr>ez)=I#a|?5AJupiGJ=fGB9+0T`DncmMYF>1ii_ z7U7jwUYRy++9j7vsuPjk3^z@pBVuCW7x-p2XSPeo~^9P8?Z z!L%$weaPV?XeFN~9lmnZIn>5qP;(XiHa)6^h2c?TVL59J+(z-u&Aom5_M2|HY3tUl zQV_RJ>(;GXwQAK$l`54lUmjmUu+NFp8;ch&h9iQhp<~C6Lx&DseK-i;;jmuO@o)=Q zC?PW}V{G}tK1NvOF`2nkmF4wJ`|nayI}77C^L#@ubE{`_+7IwcC}EdK4_><7f+q0yA@| zOqxV&&ncO=2zLR>I}Cm0rZ;BK6@3S<;J}U)a8*vIRW7blxfXqfwIEfl;ZY~ zOVw@KX2eUc?cBKPtFOM=uwer>lkjX8E;L?QE$t=!3ES`<3#{+s?IM~+tuKV3I>f7R$R)vH#Ii>rp2g#8SD%tUTx6ttew#;?7N($*t| zvImnUjt*dS;-bY`$Xjjdh8xB?CJsyr9HTPiP?D?V4-wmVED>AO$%#(pv z9hn4y95@ZQi@$2sswGR7;QZk6X&mo59_;Ni@y<0)`83~|uY?T%>PbGR{U%zJlVwD9`j4-D+zzGXf5 zQ^5(BUTqj#=MTT(qNxK$J=^2v?lsHNdsNKYOwWej=$eKT8;0;U_?MNz<2KUvtVls6 zQwH3d*)xG2XEM^}EtFbRW5L1V-hou) zldzf;;6x^8Hzs7#rrXkK>t-8=Zzzc+5Ls1~#b1K<)5;JU79Jg2u1b|E_&SJAQ7sCD zMwelC1kVs*M+*GcTyxF6_udOX5^g2B61^W1b^;y^2C{F2xejOBV-v6JW-OTf+nTMn zG->8-hEyepv~P#UKl7=R3q{ac@tRSee^~Cd`-U%HFlN|-F-FfxKaA}cZ`2yRW>xH% zN5_7eGHF!rNu!JrU+ur;a`7!(^qbTVrBK32Gm~ZjBeWtOSAjot^sfVEy)CX!KE6Al zK^5G+VK>FVkLGY!IxVvll5%di(CK8eF!4L~(0jolq2ZBb%SMzh9|geiJlF8y!GH8p84HphKA_jvR>C{8wm(%cvL|6(19g zJxq8W(GlShVPTk{#2m$58MPHjZaNb=uH3ZKr%z;@I+k|w(DAfvm#a#YyFyUNT7}7>MXnsTs3es;T?jw<(6Bp9R()?-3M*xU#vI5!5KXD2Li2Z zdvy6K;9vao)IE1J^Y!iykFu=oGli?scl=LdudE$!WUYO);pHiw@{k4{hfVxz@Xp_+ zzuITO@>v7s-Pf&8lX#oHTp%@M+i2zsDnVCMx3yZM{4>e@UN2 z(??MxOm71@au6z|MMk3U;7q_-jEM`cTsa>2Tj3c@@%Ynr>aPSLn z8JmR5785BhaP)rp)2D0T8qUU+#EDZm?$qqilW8G`j)(5b3r)`{lj+LCQ|9!_i_j39 zs=xtGwgJzt;)l>qXXm76=HQu|_{iXx;GDRiQ&+^fDulVfjt~`979$M}gb1b%#BgoV zSYU!U@adxPtu8SQ+S@2GsEfTKI2@?s`RAXiCXZty(p(Az_Mh1{Ri~;4z|zz#p8VvkCbh;uXozEP z0}m?0m4w%bl?L6%U5q3;l!gkAZ82QE+o;+j?W}X%J;}xgDaN?RpKdyKRKM!s)UC(2 z&!05tri3>R_i18IyvWQwEl#=K(G9K!@oZhW7j~zy?XErR)`T}`RyjPP&XxTJKmAF+ zS5xph|J)PXw;YLUT(44ig~V<{hrrt~npG5cOMJH&YV9HR(&WCzf~o(RJnP0+`;_H& zxzY|Fb;VbqSDP|FVkxM$QB}pw2o1rGh;0n3JhBLx4n62T1Ky&z^l(9eKn@-}?%%&Z zZePCe!V7p03c8ipN-Cf(n1SmTegLKd@-(uudG8ZmX|?QZIyjt*9g0(M4zgW&8Mz@@ zxn;8Rf?T;qUN+7Iqm4MBu6Zzi(6mauDqBF!MyI5mC=`XsAc^1 z`>)IPr>2#w)BMV|&CFM0Qf+2nZ^Ot<-LUkRExY#TgvC~`-?G)^^?1Ch^A`kC_t4Y- zAN`M>{=fC{sVJouiB@q;a~!n-F`X-PxpJO*<9}B-DvxU(S3A#T5-5lw2LX7}{StA< zguaY~hx2z4fJ`f#m;n((5X%zg3;se30&IvO2(+1vS|#jJj1tol(qbaDq)yjeJ~3&`BC#t>yKa_OtV96*3FgWimv!di2BLDff-p z`ssk1J{{2f`o6~QsVg=S%TS@pe4NQ`gq5pCxu5nP5&zaD;Tv9i^8-ra4INZx+V?lL z9A_l=eXMibiQgtnNg^~sv z{_1zRzxgv$SA2R`^2&J+_DnKPZeKqBgB8Y@l)r}I`4i9PhEQ!+K-%{I^!msNzr|EB z(l^nhF?wj1Rke&$O@_YuaMy5wi6tjKc|ZyqyvH8*8j!~&gBVv6_7U-ByfI_O;OTMP zl;lTL%#N_>EtUi-f*Cw2FcNTS<0>Y{LiiaVFe*66958?lmLsrDvQbcl=>h?>2V#sg zk)jAi2rPy120^K(SQ=IelxlCI6m&vSn7|hau2|eR!6ktsirCJOig6p|!3Q5)u@9~W z?IK{m53xCs3c~GT z*4A(Dx_f}kXX4IRKB<^;*H}7tCPLzHjabo4m zO7dV^lYSiA53?aGSMA5s|Ev1bNZw{n;l!)h@0n~V6L&R&gK@5qT?btMVCLBY zQw#@QcrX`$kU@Mq@jUEKb%+32@IV`85T?NiJ)kiMF%Kpoa~?8<#0O{jC{URc+O_t- z_YL}%GB#YcerBTOqXCU*I#d`(KWpddHARp~S&^e&fB7Vpb7E@>K8|A(*?_mrk_bFl^}>EO8=_}nzojPdifx?eCUhM73vN1MbR~}gxpJVH zsKQwbr=7Vr%{8ujdETtcC;sQBLSU|79YS5-{-+w)QW0QEi-HV1D0>0ZPbe;L( zsGIK__3ija(YeV3hVGp-ZfhDrf!DlVK6Qv`SNuutFbkPwj@(fW=y#;0G6 zTk#cTH^1Yp=SKDlGc295{1vUKP5Hq&r5pjuOnK zZFW7(#BpZo_7zLW&oINLlyBikRUClkz-%8uW~!!u&&o$H0Cp_N*jU0^j;}`Z=FR)_ z&p)4i_F0_HMm`vPs4ccERENtSCXtOIV0hqi=70&7T@b_w$YdU7W>dLEJfh?r!a9+O zQVtLY0S3r|F17}h3dPh&9WD$6FlsTHvFV57^eb1c#0bXl#bwZ?OP9fe2V*%54Lc16 zu3C1D0~d`P`l5jxQTO=%yL722AeRe#kYo zyzDoJvDqSPmXr;Y{YzK4I9C`IQx@k6`5P_rz(7S}%EMfsP73POz&ui$B*23eiyar{ zmqjLF&8jeo7gq;^ZN;TE$ef3nAo~pCBDU3|Dev49TEC8XM#6CITrg_p!frP|+^?#6huU0S@NJl@gFUJq)qY{D zkBANCR>01J+V4YSL9)2h(S5j5Ktq&cZyhS0YH2>$R=m8|h|hWr&q~Y4G{Pfre_zhG zv0W#+x(d*ss#b?*S8N@I+x%|qzQ;ru&;8q#L+g**9n$2EH~#27IwO;QCpo%oEUkWq z5mcewW1qPO<302)SDA>|sBnIYB(}>$7k&pA9YDj)9oyx7S65~EuXo4fKl)BaEAWR9 zS*z1ypSm8(NXsLh(1@4_bg3vY_2kocb-GXZqdUC_M8$+~u%2mh=8IuZ{K^<({bY&= zd~w0Cz#btcF`VAG@4ox+a0<>}<0coETD&Fn=@<_fA*h%g3pHkIxKvsuW!Z8WgMo`w zPE0sRYHvegqKVRu0ERm@_wa)qa5b>Wfh!g{ojZ5NE17YIk+$;o)ANFku`XK`Rj<#2 zFVY^mb=Dcp`D>uG5+gkQ4>BQkPXTMw*w@aFg z?fd3ze2=^L!_7lF)I3y1Jbmkj4d}KR7Rz>sYZjf@QyD>l?J9*7Zm)kloLzLe*%Y9bJHR7xyo4Vd5YZ9)kVhhabj`8`q{y8{ba6Z<~rM*D+2U z?nOSe<}_f508S+G5XevjJbzzX%ZhCVqn== zzWk2^cppG&3b`8mUDq-#`R?B2YEX!8c)ejP=-c^fay1aDC$%<=56oN2A}9IodxWbY zN}Gw&18LYHQODw`{Tr53`M8mf9H}IvP91;`V{H9?OU9F(P&u289Myioc;<*eP;Qd2 zz!_(RGwaQl1DI3b4>MsU^T`)N6rEW8-Dmjdb>CyR7#~fYx$u+q$NDudFN!T~G<`#5 z=BD==H~Ft9QKR=(xtfQYEg#mJObN-4LWjmEW!r578q*muMiCwLp*e*5jtojY-ugXb`smXX zDgdVtA#Dg*xUX+&5E*GXE|Ve0EpD~gna#Rzf+Qq{)~nPk5*M1-q%t9R}D^F=W1Zt;){Vi{fX67w1BDt;T?F%G5J{v z=kk_+{v+N7@qEV$QHfXIn}icKvwy)6V4+i}C2d08&N;9C7p_2$x27&TJaguxYeWT) zP21cg&dA(0nO%*0{(ki_`@nlN)(-vndzY{c-<&!0e0lEV z>7S4>o5qo=f%|T=m%aF4GBW=A{?{k1h9JqUkc6j=77r?=Oo5(c27mDdTwi95R=uc8 zZM&?R?IC9SAB{R|C^F$-K$3J?N@dc?W^X%zV6O;CzjZ0HrN@2=oh0B@V6wuL)x3Fg zyhaaeFn%fzG4;U(8vY@7Qna^m-j82ggxI&h3#stnD;k4$2H>CuUW|wr7V%59rK=&E ziuniuu_U;NFK3#+!s^}m)FjIMboG(#Ge^*?hx)$rpu@q}h^SF=>M&Om*k{ zL!!Y1VzEeYOn@tYFqZ9CtWI6>1#L)lNWK&zO>Vju^t}K3CRO2J5`r#qRJPl|`|DST zPq?&ihmI)edqvZF;f8DFtg%RRetFr0z1r8PTr07|kT=HDn3?v`yi+Vsaq`%oCUxo1 zt7pGH@^A(!4W^f2cWgk;N8dK>+BIq3E+LM8u%2XYe{0pKeqHNb(tgM*pHO#-d^6@V7lP6DNmXkae5`p&qL++$#Z{xflx9~P_4`tAxL3po5ojP^!>oHftcI zsrQ=9;T==82R~$F2BuoRQe==4P?G zKb^WlwD{J}mxhNNBJ$JT&y}P10lfa!2b2`=bsz$|M36c;12Jnhi!B~bzI{)l-+BzK zba32vOOTO#dneo)umyK`vzvz}e>W!Og|3yB-8-s&Wn=FjDKi(Q{AE~{$N)BmF%?iyj!DiUm;cYZPFm^cf& z=^&m69$VBVaoYqBlRJL-bQ|%H=~8bD3v&GR;?*7dwUSt{wn}v_#zD@B{V)2Xdtc-0 zsegdAW9ORD)KQ@%mRzHv^;2O;1&cg)I4!6`J{s?G6~<6fvD37lG>|I(nkghiPLAQm z!5yUCbWaTgkyIiA?njH)TbU0f(Zd&37YZgMnOSK%<#5u{;_H*ee3cYo9+`(fVNF5% zjv=(QMNKFTR>*+c&N3F%CK&9vdsSRY?f2DBa(e^s*Xr4`CvFf=oH+6R`|rn|2ZAq& z^Egl4+dwle;2TET+c=MhDGyB?2*s}%fxiCw>+yDN{1_=tbn__%zb14n)L-lN}PAL8Djdp{btEuAJr zu}EN%F=sm@<@!W`e-Jnnq@|2s4RPybssu(%mxF1DphBEe9gCJ?kh zcv--i<^rMc2*!OWA5Dd9v+Md+vfi;_WbB>4DCLir=Irm+3O^Cf`k*$?own=?@<^I> z@9W;Y=44`WkIn<`zQ0cl@wl?-_EgHI-KkfUj|YX2bzl>@Bk3pM56D@Zw3!;MPUNIz zIVzG(PziOY5s$?W_r-S4iwzb!t$M7A$SAXNl@Qs!BWZx|;-bq%MFsCWKW(w_lsLmo z2?d)hS@LUvPbNi?zj3AN^v9#9A$D6;GNCXjq!+(hpGr$TYNnv&_6BaI{O^DN!;1>> z>&jPMbrtfx=QJv2D(Ih8|T@$RL#ZE#O~tPUw@6?F~T+mexU;(~OmX`P3IhpAW@n zp86Eulcvwlr=Tc^1yDK!LP8ZtLVyhEZ89y>>%Eit|JGjTth>&+_uRQ>Io*e)^fCM zY#`^b-|`-e0?O`R&vt3sz1rx7*Ss25^bXrpk7(N*G~>a3(-wpjn1iuKi#=i7F1l&c zCJc0V6c)%6HQGSCPwZE2B=6hMr|EG9Big`AHSk#h*p1Ih;Vb8O6JLZ-gPVuJg(n6c z;^|?1TzVd*AFA*We_kOzbC~6z!CZnj3+Lo#(x>X<6Ah`S@jErys@0y3Ea|VQ7pNzQTr(u}@@q z&A-#_x@TYY?ouXTIlkKSzP1YZ2mi7O%Mu&}P{WVrmrupE)Ye-*v+CpTJ^kaW7hblk z{M>mb-@9=iYUjW2`WfJP;`5ih=lys5?3-&ZDgWN@aVN*6pZ(BTK#OCH9OLSvwq$dV z;2lsow7BxXwV%2ArjMrGbkA1U98@j#gM^!-YwZS_IwRbs2ZqBJt=Ft|@rsvaiB)Tk zQmd9^1vlut1op@LO2%cnn=)`^q zHW_WW5AoxrjI$1hChpe88Y50fymt?)8n_gQ9Q)I#M1OW}z+fg-0&txa1&E9fN_0X2 zy5aPoS*vhVh*$vn2O_Q#fNQ1bkYW>u(y)+Fyo-G_i`H}n8)anIZPlYZkM&mGxqI7s zywvNWbLer?ATK_3v6{AucD_~Djh9igu>qZpTF|is}Owr z^y=^L!L1b~=ic_npD$hJ^KF0NuAi>ku-!M~g0;6k_q%xow9_#}cV1Akv;d(mphQ~ zwtU*4!EZE^=&}H#kkwkbvd3x#Fs>gP%)DOo(qsWxhzBpPCrJ#=#h`OSb!jC%qK%%Op8xod z|A3*`+QipN@xU04+F_O%%}8;q+Lk@@ieKM*ASzYerfLD;^-O4Dn?I!sN`UK zu_BM=g>I{87ij*W{L5|&T*my!MG)QsS##xv?N>7GyKfD=8xoK@_rLlC{;mD&$JXQ< z#3{>WZMoyOkNFz#@@(AOn=^gQSAP@uQcr6?-Kl|vEc22|Lzy$@>J4>Q8_*~slkzRe zw%(j+m;bb`tEau!mq{K>_$S)>}xmFT}zcLFho9i2BISS z2fVD#=(SC=RF2}mXl&E!wVl9=p$u>?egi=?^0iFO>!nxIL?hoz&gj{%H9BFw=*RK~ zzQ&Aivu)kF^??T-Sh#Ru8RVdIYKeK%H5_&bL$Oa`6 zF3TVXIT8cNk%Wv2DtXFRG!ACPz#28-*ue0Wl?1R6^q-LtszPam*vZ>P;{JC3pO&4m za8_FH-VJxJeGG{6FPnnLQf!v=9P>JJsCDuZt0bG)ZQD>*c0ur-9*aC4IkqMjujsYw z>Ax3ESYpjM5-_|pyraTeBFM=0yU1KNN6D+XTmoH1O$pc~U)Q=AUBysdAqW*BRiS_; zcET9Va0P|$FkmwU(FQhCzWUX#%1VHf_INF!jkxQMtxv9P9Iw520^5yf16!EbMnN0# zB^10cG^&LS;}IG<8L%BBr$2`dJkwM3fMjy8a20b1Jps0~6ayef^1O&LOT;W+M;IhV z31zWHHc68jDwxC=!3z+r{pR5UTi96qhlOIK=mUnMAYd3^mkU9ZfJ091;%jcY=JVhF z#V^19KDyn5)3Pt!cJozu2?_cIDG^;Pk8_7!)4&n1-$PCWU8ov^l9zunrDD4m78w&*c$2H88D@^OO_oA|f87-l~P-ZA4ZT^RE ze{#)t4j$a!)r*@i(+kUMs;bNR0V1Yzymvv%(OuPd+!+gsh)O?TD59c%{kYy1j&Fr{w#wu!uu_B-XUaMYI)&fUb3rP&s^I2 z3MB_)UTK3mL1O=OI-v`Kd3L2$c#bZei8#t79c;FsO6(kC4h~tEfA;V5S3zCh@G#_uCMWO4NIs z=)8&H#&_my^M7%bAYcaGfL}$Ed_uR~4345<4?P!)M-hYciztCNVZkmxqwQoa%1A%< zVT5@=FGRO++ZYU|GD2x=iHW~x6$eCPxP5p0u6v8dXjE;?I z%e0?b^JZI5y=n|WB({3gt4K7ADw&`=j(1u2tetW;cBa|v^#nc;ZNcqH$z-{>&_!6A zEszUrUTy(>*s%fn%+iVdLPE9Au`VF(rnl%fLqNjmjV1i9wKK?7a%7id^fIaS9XJ+{ zwlMlEfG_xS&?MHX|E4~dh~Lxr18D*JSDC+giSh2bbRtPY12xS4zp(6-D0o!sY*kFD zOa@6Z*f!B60CH|Clgwjd;bsf=41nD=-0KAI!irnI*vx4X0Ur;|0lpELoUY=ITU#Zy#Y`-rf)B;cu7M zNr@fpdV+Q&0_Q15wb#bN-OLoGPbTI=3h^WHpodz#-guX!U-MElLoT_-0-)L}i?y1tsbF;9&{349VBlgE#^pAk zX=-7;{-T>g^Q_&4;E}!cynVEdl>Xzgb2s`P+0fHqr27pSb(+<_2iJ@j!-}&|ZA^A= zKzG|9y%3Zu+|Qx(bq-FIf%ILyy&r9BhC^#V!yJT>$q3nTN40+HMs$=4eZAj*eO`af zE=_nM( z%Xj0J1FA+W;WN2UXL6&kzm7=0cy{-0Be2PU{RVL>6NA!HFjQx>;Av{`a0`?;Ol;y0 zN}MJpCT0SCbCWnj;3hP4uL)Eldg@qu43tg4r-+{t$#g~acELQ%OnBn_E{t zKqEU5Ml9Okv#5A5q42yjvAVfDEPYh#dUx~!ESZWFGJ%v#E;a3Hmf|0=z?{I=%P6e_ zJ#U`XlU{4zydEb@pV5#C3Q_5eUeD<*Vvd-ePw&6{>p_iOKdB2(L8X=^YSomX@TVue z$8f9-gFgNpeRPvB4%#A(I&YtT9iRl^CJgq$xway7RMr{x;%ittK^A(y*a{q_@zWi3 zd2P3oZT25LgA8QFrW2`D-eb_r_7XwLs=!dZeq;vNe%{0>Euke9i&zOX$;IR8G1GqA z^ki7*vZ``;Ht>XsHRt3tTI{!l{({ett3ELQDF@Phk{g&;2(Vv4s9Qr|61HIv65sul zzDjDEqFWZ2{S~yxr1athhnO8*2Y6MAZ=mYCKG#-jy}l{9k%-8ZpoDiZaMQ>C*;!vm%Zrl+cK@ zhEQPBFRc^29@7ZNP$fi0LUSNV?|7^kMB#9eZ{psHZ?3GF87W8+m|fpLbDDk|>6Ikl zvJZ|1IiE&cf@nvH@O^J+RG?5K^W3lqs)k?`8*XjmmyXhpppi01yHZQXZnh;VH$pb%k!t9X-FSEPG>}*SD?9y@57wAY-tJD zd_c6Bb0*_AyFH-YH53hV{G_niqUX9&%8ARCS7VUTQA#@5*Yu(S4;~E#@N$K&N^I~V zPM%>Nbi8}x<+;;iwE4Y_g<{sb9yzXoyoZk)azomigJ)xHnFIK3R@ls4&m z*$1S?b0z-#GMoQCg`TCar`KV>0p5;9uQ}``JI0Q8mRx#<9>rO;Z#(L$!lHV-xo)$4 zU&t7BD&Pn7T({f`VO7sK%;T+{)-e z>sNVqzFS<>{+7z-vDDK6typVQ=eGbo`MpO7q!t=2>bLJC^1jbSN0aW{L?SY1Nxee9 zzDW2z8|v_e!5O=q*5^qec}!f~XirZoDk`{lz0EdTZR*ZIq)%{bl+oJ1KDza4$LT$YjK)I>lpnH9|}WIU{YKys$fR@l{PkpoFp_ivu(3*UT}(5 z(ee1YK>qcCl4dE$IXc4S1k{0U67!^{Mhkq_t)aB%I^MVja*6FoOLTZKv1ny}QtfFv+h0b}!1(fK&Jl(>m1ftkKWDiTvh#RPyLt_D zr4f*E19B-jKP=_tgZj9Z_T$>-pV+3H!!qWxm=03pphfm`bj}M>d`^Pv%lXpc*$Yrj>!wpMIffzF3EvWLCpOPkD1b$D~4%XgJm_ar+ zP)b^*%Nac1Q8Nzzv{clceKDAWhd`~ApO&5bBQ)wXejCtFLC3NMSi5RsuNq9Th=Fmj zMz%E$pg{wRG3rEATpcivI4#qSX$NUz%F|O*(Scx(2GV+~BmximR!`L}R>mq)>9$IL z=7WJXtBiw-Y4_7?F&YSCQu*3Eu9QFJw~jVi63ASEQI{tBZ zAeU7<%iXbmyK9lZV~e|EqrpBov#2~fD?76=AtDzfTJePEKQ##=q!2eUBGC;uK{6wZ zAUi!TQQJaHD>F#=LHqa!)BOr=&B3@dZXmNa`Ip?~ZKO%CsRHVmRn-qA0NWb!55u9D*WF-$yT12*9o z7#OcL?q6=_HzLNys#0^Yi=U}=Wbx8jb!aA}U=56w5^6$IP^&dpVnJ@8(ST3&O@`Wqj)b6l?wP(RsG0;yg)v3dVgdqUIdlq4#YZB)FqHPp#91%oRql>9W~Sc-TA8tPnr8ew z6*SDw#AJ1KKYvE7f4n`pc^MlMCZy$`;U9+#6z?P$ToYccZ!NbqbX69Y2AQgy9;X5) z2QSK(?xql0JH<#d&J#OVzJJa%zy1TycCdE7%1r3W|@8n*vLP=9I#-b!w-+WZi8VEYF}b0oHQ=qnMMElSjnG zeAJc^S?&EHq;{(u#W7*6@l(y!(NEb%tYk8=Qdsl9L;~4n3kCt8YcCJ zdQS&>GaQF3UU&`P!KC7I3th~E#^z@uLuUA&uum+oPEIY%%an?kT&Nqkd=EIEv=^;= z@gJK95J(2&sMxo&=XbWEwz}Qe{a3OLDmCAxViD%OwCM+9RUT5v1(L$utU!eCeAt*O z&*nTIMilFBQWFR-Y4Nkae!5=D$qQDL zaH%6!BNM$JT`U!)u?UFPa*-+onUPdw3NmyPbAwKgl$&=)%e#WgL1hF8>;!v`um6_# z-CP%~G>;i;=ycWf;v-O>vCZ?g2r0h4)+GqeRUBh~Zx?^|t3aC0fAgUU{G zfY-@01Vl*jZpgq@h=|s&UmahxQeO6z2U=PA$X+<7mY8Q{Kd{Uf`m{tfEpt3Zracoq zxe>RToH1wXFSk0v(9Z~rw#Tfn{2Ne=U|adZ61m`srbEseqf@qR91TrY2}SPUic`M7 zFt|B8TN^Ex<-fz*|B`94#A<8_lYp z59sQ0u*lDs_vOoZQ{DRbDcDQ*(YKJ#Io3KrQOU64)k^7Ltvg_>vr3v6_5Pk(QbK<5 zbaWHVyq`G4;ZEX2Ar0}8-b-&2I9JHkI&$(k#a>7^#vEA0LN=n&8F#H_*-p{-t}&050KPuBK`1$ zUA|V{)g)5;Fg4lRCB6g+nOULo_0N{?W5)wW>pw!i#kKhby}jL};3AGBC#MO9*_j;8 zl!V_TQz$+gcwMKn_%@XVBkuk*kN}{y{d8cy#Zfc)`8P;M4H~Zjs39S)aog4-rhst zC_j=C%uD=Te6TE{lX11{+W@x9(*QIwwoAI}Q^wQ3gW+K&n&W%gA4Me?kvr}rY=n<^ zJQp(?idtyrLYNDcO-ynwIK~E$CBDAC&ZV?_Ln+Z?)9D^Fkfn#uKg0 z^s_k;im?*Ii9x%uB@FMGbHy~>PuyOP8$0htL4E{EJq(39p0mKocYEDS5&VtF_!6Zq zij=jEf2%8A7d98$TibCw6v8wPaKN~J)X@pF2)UsVbF7gc+>&h}@Q3tDUlA&PiUAW1m`aOYWZZ}kTq2nkTO zRuis*kU4oS`p4teu#TWv3+lZ`@eTfs6}x*Q!odh&V>ug>!XUyVeyD&Y)Zswy(~ca( zUWu2D??kab@H||d=O89EEjjbK?G?ZA%mRcocZ?4xio3h<*9^R2u{#}s+UN3^j_R7X zygQbukuE`TBUk&+)nmnG-Pj63PzOSViA?ahfo*_$D#d-3fCPuqw-2GZ<%H~_J zBQZt!B$SMb_JewqE>lS+9q&^K8X0WxO|B-Mzm|*9mvMmI(?I4`2a3DE%_NvW7)L3a z)93Nvh*~U~&prp>8rCyjTPzdg!!yb=rk%}Vg*eIw^$p@jUZj40T;?{Ns_w4qWT7E& zfku~5?iVd0(rZk*D5-`kWTluw^Q3OGP>wNSFu8QF-qqMc#U;#2UGZS$QgmgzyNhM9 zrMQKvC61Sx$hO~QonBxnV1Dr>qCx2Xac^e5(y4*ub#Q!02pAfh*X086CTbA2p7UJs z6Tc=Fn;faNbs_+UV)E9KgN@5eO28MS0s?yLU`Lldg?0=#H(d7+-YPOq0`9iE(xzOg zG$BzfeMeA7-Jm&GjT5pwS@{GItPFJH#VZ`+S8H!$H^`RE=(kmX1YPvx( zV#Ds}tzm6EAIBp?Ah8%+tS6htuioCc~{;<6eI`&r{>2`qP~`VxIHdWWMUU zvYO7Sl1|jMn7jOxeOavh5YeZau&nPieg=@W)BhM9-g5B&>haawgCpGg(pbK+Dx95S zA2JE+@0l53A{+^T-Fsu^pC0AH3l8G=Y{wt#V-@qC*rStz-{PtdAht_L9hg7)bmbF8 z=z;-hO9$LqL(m|G*A)(UnDM2G!Q{OPzi^a#mlpWFXI|WBGEQAHIE~$^OrSa0BXyQw zY<*Naj^}ZeCPdAyqDuJ%QkIRYVr1fCE$vw|o$29SezV8l5f zZr9LsTzozD<~A5|I=DDYM8{9ZND=VNBqO)FH|(uG-_@TkzSotj$^Uw|!Ur6dW1x$` zwt4H)1(G}*ge?c?JrRqRA?~;Swy4EcS70JK*m7`AyzM z!QW8hXR4{Er1X~Z`S3cM*spaihNxXW2dS3`nw3ecl&qRkcw{IZuR4-- z(a4bz3SxNM2Og>E~JGmI37!|&^@*Oh4|5})`uX-%dy@j9OnFHZ`%## z0`I?q@~>Kqd0F25XhE$Zq!9TXmm*txhJwrM3to@Fng&B(^j(J%f-fa-idNmWM*Lsx z^`BavoFB*6PFk4ey;e5=LMJ3+o>2YA>rc5I_FdfiD#WFNUtLRN&<5+m#FuR%f)cS=Tk`{%ll;)v7ro~!l}I;!dHL(vjv)Z zjz8#*EV^Pgl&-1yOLk@BFckQImP|NZICMR|1n}a))brWl7Zi)3P%X;daCy?6gkWZu zd4!P<%_B1@+(+{^wu+cY1qtsOMzLd8H#az)_5;XKwjVqxlNWZET+1jv`jsu;0EYSe z=|5t$jxCqgWEK~1U15Fq*Yk9xqo85V+rA)rAwm)h3A-UK zV{y{k7;~0PLupH1l-(=?pgE-*$yI0o6KzW}3+s2=Ul>g!wa+B{R;$+qepXk*{)qcA zh?}g^1m|3kF+l-7do5eJdqJDaR7+EYjPh50*{I$N%Zrj~az=YQ?`csT4S8yTzil{U zBqDBjul$^BY(i>pk%fBquAKvi!4uJ0$Zuz2V2wyIyp}00`pwV?{YgLfX7a+ z_5B?`fC5nN`^ZLqB#)C3G-XUomcgVF^P~s{7mG_1;1Z-n5-EvNAr>4!{Ss)%#bjfq(3S(e8c7OeGqXwYmI^1Va3;L4G}_sgueC$ z)-$uTJ~mT%({5l@+a$Gg@OrgQhQ|){C5xndIDF#}^jWM>p|m{twq29{2rR`}F%ZCS z_$|HqnO2c8Q^VB#w$Z&svh|sbV0kpqYcdD#fH>sNmJQngB6o1>6i7{@#gVh-IRAQ4M9pQZNpYmqXDlMPSeT*+favA;s`Pq@fm0w>S(bNbaYzAkyu%$# zp`CE+TzXb&Or-=)oR6Y4_TC}5ND*(Sc|&Tl1M+xl>xVy?L$E8*n%`>5sU6>LOCIc) z^1fglx<;(BUXHso7Vo~Iel7$u7J&@f2H;3163VBdHF^MduV%-DR$1CzSg!B5ZfJA?ZSg{Yag z>%oDXqPlNEGd3e4Qc7^d3ugdkil}mR0H?!*2}no4bDn>1+-U25A)V(^^%Dj8IEM7< zRv^+Ef+(^r{fB4U=hjK(KC2SvH6+1bs3sCdr_EWYCWxTWE5C!ru0Iz#dtF~7R&#W< zKWT>*E>5VJ1?C<)VF)0-si9graV!YEh1`?fj=|uRadJ2c+HSJ1Gr~ZOk_(%X@Be-u z{Rtq21=^79%qVILM!kC)!DT%^`aeVP&YPbSJL8Z31>=X(IP z=V*A=y^ve*S@c|ndYNHsQg=amz}v`6jFOH@Z~vGJuek;Ox)2nW2>PA1 zM?xLGHU@Tz)0f35+E%&MZx64K3-`tQBfp1ReyqFfx8$Ev;zy{aKMtAQ5CEg}{9j^) zHTCv`1Z2ROF3RF}8D>)@h0?n(+)ct$C-fz8oSL&9ME*cGY=jQ`0R;62w9V$! zhAr-sUqT;`V{-+9Q+3Y45vK?(b>A-p_9q%?g0X+n;G#OS${A8cW!$0uZ4{l-dRVQq zRJc2~2ph2nYb9hPfkeB!Ccy>%-MB1&&Xd6l&?Tdevdc%5_RE;2S8>SiO%YZZ>Jz{E z%qEsxI=8wiGzO1h0g$>}HKV7?D9iyctOb0g30c8edS^MA- z3~wgqG>0nH<$b*+@Dux5p!Cg+3|X%aEGzI^4i08D1kx&Jh_V*h;hnxJgU|)Gq*_My zU#Q08s+SaZ9c_8KAK?*}#s1|^1BJ-1iHeGH`q zDT1s^ZxWJ*rbC-7M7F+pUT~^AWZi4`u=FbEJ+33dVd#DAR6;#!grq(nV@m9M3Ib(P zBIv0uFe(ZP2Lm%>VRieRwb)$J{UM$~@kUaqgSG^;Jz2Cu%KvQ>8DHZ(KLA+Nu58SZ zuD`{7cifMMw;qjyRmhMteXde5*K}7G1%|l z7@ExazOQvP|F#~5fj)b&8yS@|bqoV<4L(u`+>u*L(f?EvSPFe_vXZhEOtm;TD#r<7 z5-MP~oH@UfZW+&g>raDJi};dJM^ud2NE#{NM%j1k{C6q%d5^uqP{T+OwH_H^8;6A< z-Wn1p$>+B_{KJ#W%;yj|lIDXk!B_F6;NUvHe3}-4kQ($}aoOpV^^~_W(N`!_$eS$(w?*K|)^&u7tmA4LiDZ>zCxydd0$&@!*o#$>}1k!~R)Vl^M7Vln6W4_?_LfqvZ3Z{aQz!5=;A$ z=Fxzf@S75+XDGMk`_wWAegg$DcfwEkBQoNV`Qzm3n$nJ6Z7tV*XSWH@>2}Q84`K!Z z(haHT2+_Duh300+eNGhESCkI_OCEyGn1|CrZp$+lYT)hoLz zf3u+b(%g>y%WMQ1oY?|xwsJP%9e?ggyXmv)9o;Bp_TGZ9ahYix%$S!~nC6&WEO27owFCIkCNSexuT7L+u6JenqeMLMkH@&gp#Z+0 zyOOl{^?dE-`ofXX*Jua`MjLfjJs#s>iBiN1Hd&%O>_=dG(?ee^4b$P&CQ}Cdzv%+~ z0OBOqWyU{o8c5iRI?fFroXNeXnyPafnWQ@t5>~>`8_CJSN4BolyX^mYP=_w z;Tdks7is7&vQxJ#8d8+m%^Z?o0t6PIW8`j4&<&LJUf;K(0-yWs?rE^{Su1VjB*Ap- zZBta#hIFQx(}c;MdgqUO6^Z7Ygx^mG+D}?)`ury*@Vq^U-UfK66Q}ctS2v+UePb`v zilqnx6`Cpk1DChgVDW*34n=g{uw19mCN~@{=tc^m`7}BOoh-o+Ro^B@uIqf#ZZ0cd zSDE}b-$sZO-;6eQxKXPZXL8k3`I~q;fGo9jq&=it{;h1b6wYT94~quX^LBt^H1rL# zxnjidMZbl8>iE;yzSGBo$i&^SnVq*p_k-;5KP7o|Coc#U=?y{_x`CRJjy+gvxLekf zsR4kkU|3Hn9W1R@6U0z(|5Ce?!5sF}@4L+nzx&R-o?am;_bD#aCQ+eFGIdS)k`xwZ zN-DH(Q%g0*F8gqFU5dKw{`Q>)T{upBSK@cjT=|n`b(o^HzX36hAq&g%ELjrG)!3L4 zT}A2i{}wpUH<-EAz4f3=^H7T5Is-*3^i53k1c=|u=dHwV75oT^e7qX^ zDcD26XSBXBzn*ww)>y+r&`?S{GeBVSHwMN?~qpwtrHz3XYIoW|%ZsYj|Ih zIdx!t=lc3jxsjdMSISji`&D1&Y@mNeZ!e9qN|u36a22i9>~V8OCmEG60!D+D*wCd- zwoGUs>UbQxt-t<4q#9qvidv=Ce$-R0uKicK2TMIYM1 zB@0I5V`pNEOzzIU^)YJGB^Q)b`P5WFqi@gadk=Dllkj}^rLvx;@9QC5xmH8oT&+W0 zVNa$*k>+sj4P~4JL2EM^j13Sx%^w63{udqRpy*gv9<|D9ObGn}K1}G4#l?RL#ol|P zO|(Jwj5yL7EA}2<*AdkC69&US$j>WC+;49yYP07RhR+X1w+H;UP2b=}{C3?#UcCCN zg{66A6Y>~a?iacl!e?&;`2;>)V?N1(fZ`g(Spya!dfl|;H z@bO6;1d!`_gV^~9BMN{p2ng%Su71hJg94GUyx9?5*=h0!ILqc>{mcqLiVA{YC(#TX zkpafMhj)u3sr{gf1!zsuVcJTt&JnMZ& z0~sATQ&FSvVWHaqRF_13RAiXIw35;U)Kp6H|BH5?D{lY>_T^>inVG9=Yb1Q`(NWUI zB@X@)V(j*RH_ULWb+W59vb!`!+}COfipi|{h1Yk^u8ClcCw?^qHO`TolfXV85)$=C zb-*MYN|O}h-yxaS7bPhlB*<~t%oF+0^{Kz-CWkf@lA%uY(^RR_?ElS#Rda1_mWNlp zEt1mo#oq-n+$jaQ+0gi01YDgL(k|O- ziC!t&FebCr{n6BMS7+~Ync$yvw;%m|i=Dh1P3Ea-3P|!&OUX4yhp97+8W}T7vh=&%$UF>1~&Z?|7@_M>W4%+a~&!c5gzZ8{bIbdnVGT z6+t2xuj^gyJ>&3O6be$5P&Q0>z#HeqN?xP)4~6ZfC?gl`#qgAQss(_tLrbLD;!yk` zlZb*CImQ^F!#t%eVv5bwl-%f#Q>&v6cPXliensvCB0- z;hP)ZG}6O%8W8P$Pb@9Wqu%fl=33ac-~ncU7Fk5+j9C)^jsfxSQquIyUz^>(qkcAI zCib1yf1b{MmgGIehF;u-+5=xKCgN*i2lf3qc1M^bTeT7aq+KtNxH7R~B!agz< z>=t9%FK3=wtfrF>SL&4&U*JCGa9|Ndg@ZY0nVo@1_RcS4=jOLzVMJppVS0+6AUeiZ zGEmkKn$J<<6?s8QQE`U80od%cC**rprXb1m?;w_E;N~bKM=7wOOZ+Gwk?WV5P@gx`vukOpJKtCKIqznw^5=f}BuEfA=k% zTl*yBziYc4l3e;7C=Vv$Fe+uY(djng3-Ilay<#~8A4H-v*m{^rBD@(wgc$@t5cPry zaE2mr378oK2Q=i>EdsZHYm7+pcAn7Ci`fq-;)VqRM+A;hv|^#%hOW=YXc3 zOu=R8dmLoXF6N@hIVdTyWk)#5vy1M}=#-}TtI9DvBUwqv%-?$hTCu>oNB)`UHV$xz zJk9F(HEShYugQI0@^=w18Ww(I!20r^3xa-wUk~>v)JByV$L!s^pkM_2z_yJ2-_w-*;4z9BL>R-iki>9kn9YoGp3q_SW-k{ z^OOsiDP?I8aPZ?tN55Jr^GK*hjzAB8o;MzpuEr)n6Olrx&q* zHJQKBm4)r4q@Q7(3yB^BmJ%N*U1@QPL&cj?tK&6`Ds7NiyNj{|`Y4EcnS0%ybA#4_ zwt=8_aJEa%bf|AJMRoBo!x;}L^`BpQzwSlsu3r#nj663L0tr-=Gn;inV%i01gUU!R ztr*qteu69=K}u$0qaDbC@2|r3`m7ck(|4Ul_~GBjY~tFGw0ncT0u#BMbtOGmvLbx& zuV~LJxASo=T#2z#deCn-Spi^=x~veZf68*lZ`R`(Qe}prrLyzPb&t_@n1Gu?&g9wt z;g@T=hY@CpT>A*0qNOg#HrV}8Y6i+txyj~Qcfrgw zJ}M8cIbHZ(iQ`rGHr4)@sX)vk8fsq26(Fi$)qmG)R_vjjA}^w>R3#?-VTna-&hk{=0zK#rRF}JHOJx!9`UkS z(95fxnO@eW!Nz@t&+-H;fc@UVkho7e!Nnc6M~7Hgkhtta<7>V;JrggDxMpt&39MGI zn9fozt*imB?tRq#bU10G$U4w3c2T1`H^7Y6>)_^Fg?=JPO01bAu+hK%!`4M(?E`Go z63p3niIKYIN7=!Q?i-fLm_q}M!)(-We;Uoa?~U3O8))C3YkYh*9{+lYx3#E@=L@r~ z3fZ~Gg&rjnu6`f&KJNdI318J-)Ciy3Ei9% z>GZ$)WzzWGBEg7>Lz*H&0Wn>{l&`4xQ{dQ;wz=Ha2^fd331T1tlOv)8wat+!D6M~KbgtC{Z|LaJVO z>CN{_Ii|1uS%Zv9^Y;}>;uf0@2@k@WbK(e1`${|-{9^M3H4(W0lx)Lko)%^v8+g4^ zi{byT1rUG>8mu*&&xp3U5!oS9x_n>fb+gjKAsK47E3_t&@{sa=)|q1MEzSrJA$lNY z8*1F3wKoa6dxuga`g}9m#*pqGSb%;^zYQjqxY}%^Z;!@Um3}I<%vnX_Wi-nsgds_$ zrb#bm{epGEasWRBFE`=v_>NygN`1&4tYNG^*cDABOJVVAC7$PN1O!)16fYZHJ9Llq zW8v~rVBB{%RFPTMK3Mjoc69VjQ&g=hH+EVMzy9O5@S33;dUX0V|d zHU{4z)G(xWNOW|1r@_aT8Mspr;w1wEwB-rdQG4e}9m5jl`U_3yoS0CTks(nmdDcfE ziV>US-xSz>#WDfc57UA(^+aZdFf-Vk`u1t3rYT^Twf$ zJrj*k1NyHsxX}Hni2!(q|J86&gQN%UGl;5|#oo~2yXrjSBfSmfVxh&gz@9DgUjrfj zJFGx|m5rT35i1AFxy$iIgqKx4NqNac&ZZ$6yfe2fh0nSHJiR3a#v)zsQnBVC7$0v9FU&k3WpUx^4Qc zqXaP(YJlwx4wWp76Eq>3vJwe3{k$z1QCqJW=WVl4*qqf3?N*4Zo*HgSLHbukB>#4{ zmWhm{xDGs%Q8)l+7`}s+8U$6H2T$bOBo)?iYzCwUn-FTDD~3xTwxdELidp%?f;>-l zFMjspQ1kl5jg1)ni|fi6X}o3@K~)i~CWkrQ>S{j?;iDD|`;QeV#}(_wc#D&HaDvZ6 z%_VGbf0Lv(;(no19bJ@YSlXNNRyUudos7myTyiHw3k_ujtGS%b<{KYaLs;oPTok8Y zpyxF#^F^E5v?~w;#bC(iT{GlK;aERunj%AeWQAr?C_XVW63d@p?4L`OY%#7ky47C&5YcC8pG^i-F=R2WO*5dFgJQ+=HyfCk$4D$% z3lwYyOXwqWuExt?B1IE)9HB#=`=V^)WdirEu_y0^I8tK#!7V0lAP*i7Ot7VH7vND= z;w)Yk5j8ulAl&%9r94J~1+IZA^aPi|rWMS9058(HOxb|KF36mqq%PqFPCHY+ei+hT ze+b~JB}V0k=dGK^0_r}_$$OB1db;w^iUvWx8*KvU8eq+)qM;^GP$FO*jm+}K=qQ{{ zV$9YFZ-4i?+=QCNW4FBFk}Cm&AzHdA$yfX3ti;znWK#Plx5}H%M67i+t>8(=jUgjzXju@hF*MN(eUPIC=|j? z{n*vjRmWbS-$t{A=zU~Z9mRGSgDYcJmdH5d{2hAn?V-m(8y`|s8%;WBBsxQCNg8ep z*ba6yE`+E`{bK=Myz_6AFWwrSF}Mj@{~*1ux{BEB5Z>7Z?yO?3xhh0(nTum%9e#RF zr25ufOn-credO`*pa56DhJr%m21Q#$$He%zWml$00*N}j3VH#x#3YmuG^@4oi?U9A z6P)7Et*M!XE_JkE;WPwe)bEBA@Q}|j5U`C9is<#$+C@}j;aUD+SO>g^$9()aE8yC| ziaJKCtYa2BfZjOn`SVd}d~Z-PieRiuqUkZY6OH-z{&1KEN`g1W-&~E*G3qj@ATt zK+)}#1JwUF=B+MkN*1#e-G3gERf@!ynkSro4#{o}fn1r7YfpxV8$JsZ7}Uc3zo|ex zQ4pI@{7sw@q(s+wxM-ORg+?II(9nq4C7(3p{;`M}BW>0L~%W1PPUvMFUuqRS8xWs5y?(t)Og9X(1^j3XYOKn!rrtx^aQ< zUm_JF@aPFw(47gcz#C}SE5Ni;-AHK_tl9_7v)#tG9;qBp=Pk~4Nv{WH-!^Io4S~?X zm=_YqnAHE{-&$!Br;R{=A-;K;OmI}(I$WL|zG-7xBSJr?s=4Qc$`q)8Mh0dKkto4g z`v7)=L3(8PU9D&*!7@DXB%a2YM+;-lm+-79tav1l`0nJ$wv z!XnISx$z5nV^F3eSJT$l^!AyQ#BPT@kSXqN6%@Tkbn$;ASjA<_3&9mH^f|@5$@BeF z&C}D^p0dvsTW9= zCib@_Di|v$0m2Xeqvpxl=sxj5xN{W51#V?~n)urGG`DyY?Ec=e0J~g4$ICed#UUCx zQ{63*A13TjTeNZ5jNm|@drJn<5zL111^VDM6I48AoL;~@RNmAr-Xy&SI7Ge=(ZLlxP&NcI0WI6GsBDGXqKMg7o24N~;bcS+FIuF3-k2IVv~Ha|wpg~? zpgZ(1j{TIs(XGar9v(adFRR*cIEGy>Jmin{B1G{Dv_A3w3vy+Fn$G}|-nc+Q*}MCD zGRlECR*pEmvK7fl2c^z2TxMReIlvh*x9@t94NxJJgSi(?yHjo|Nj&j~e?pR*gSgl? zFzqT#sQJm!NGWxQd133bZ(yC8vEbo{DpWBM!wO-u@l*%X1AtimdIAE1rZSh)Y^%Ej z^_ar%JgMW}v%Ln#HVoX5U4#XcQ_riJ)RIr6=VZ%M<*4!I!~FNdmrsr6!wcuqlxTnd z4*<|WFTd3yZYGmy8jTkrE`X<5)97oMt| zP8P|D1IfON>_h1?0hr#^M_fczSbQAahhY$-;+#2i@WMk}l3}|UabaXr#W1l!Fl?9$ zAB{9kBkTg`+Q_i52oOpyRRS=L6a@(7jRqs^0z|_V6HUy~227#BbIkXRXo7V9&`ue~ z8DIj9rx_8+OpQci%oZRF-a{r!4kO=)=ZwS}b^$crI?ostMUFENtGG`;{WNIj&!1mZ zRD@VQ%GfZ3%;RUWf@u*$GgG4fBz@v=cn5c7nXECGcviT)XN zoEvn|%~B+RN-x8Pbxbod!zkpOZRVV-!sZynfkz(U2O&Jx6JVqx7XV1Y+qnP+;lRT) zs3rdxA)JHElqeT~d8NozVRI4~(RjB#Mrf?|vpu7ilTVCv&H+jhfY)zq+72zG=%1|b zINHDk7%s!m9S=YJaI{-#(uJdUxSWWR`8-XqWidw^tWU-x0phbm#zX)(3^?cGA?#5h zRRUP{n14XJ@WHaPLd+ImZ0wI-3r8CpHf#XP>8GEbnVC5vH*K(s^f(9%7X?632$+5} za$Jc(EOG&&*COT#z`RlvAgoNpoY8m0h){|da-7qV&oO}k=r}afy!(Iz9?8^NfI(O- zU@k)thQ#nfpi3sHHs_kl1`57W#i2PUjGI`jnV>(+1ByvNYwT9$kPt0GP~0<7{WkTMs_Cp+3WW$UaC&H2uEP(?76gS%y9& zv|p4B>>SHQP8#b_fM_S4W>9GHP7&<46nXQGMh#E-kSu`Kq8n$8ea1@1i)zqf6O@C| z6VDxIM0?59Hn1x=*Lt;WE_fW;@3L@YixgypL~L4xm!CcR$aErVk%(9NG}(9Btr204 zHZUrlbIv))@!d*12fJ$3Dh!*biohyeX&vom6yY9pae*@Y0O+C%S9IKD<|W5Dkj6c6 zYBAcDk+ok-jvJ0A+Aybybe4OJ;@UV23M)2BInnTX(RksbR4rs9B?kg=&R}8i@y8$U z>gvL$4o^Adlo7dUgG|M-X{LEOkz8g=b3ll37I~zU;%j>OmYZ%|??H3Z>ZWC-&ZvUV z4bU@S$%hek1%a)v-uc~My97)l5*{0=t(Ts}y+UC)K$8+12ZH}g&xP6Ch zWsHg?B_$XhaVz~p4?T3&S!czu0L-IY=qd|5H~awT@O7}j=ewNs0t6|%p=f$ibB{Os zwQVjq=AD$79OqJ&EJo=8-bVS|?|ujR^UpsY@39$?n>HW~-N!vRY(L680l@JymtvNj zqrRz^-u1-BZBJ~h+wjt+&(DX3Rku93?dg}cJ-%t%)31G@1PJpLMB1Cd&lJtSpcy2J z4Ks>R>u-8x-EV&W(3WDeLzkQ>i)PWlZI%V!}hlO!-d%aWXtB+H<}(g?9rJ~46sf?)LQSK3Df9M5f!Y#Vo0 zWE$l%eQXfZ==U8Ps7#0!#2N-h#jLEXl`B_*=g~(W#qFXPIiuwdN5>I0atYGN3CR85Kv&?x_ZC*GYGuc1sjAAkQqU zo?b|we)gqInmWbjd$Vx*?7362g3P2X(7JWgb8qjeAIQv~H0$&gr_P|m&gcp>?cDhM zhJ#0Zc_kAUoUw97S=zz3w(NN6ZPdT;+WMC__xSu}i%*&1+NcRi2NZ|>4*oGJ@Wqoh zR$Gp-Mpm0ZgYW=3CB;DF6fakp8geWHhK5%QfDpV@%`_?pg{+0JB8~%CdG4M@W*Qu{fKA-qawyeT#v0=x2~?P4qvat$5XK8 z5n(e0JxJ$~PHG9zaf&ItCU?*%X)IM_3t*ke)Kt?}h-Go=G<3)}1&c=6;L;mp@uE@q zATb-vRjkN1l>gf$+epwD%e92K;dr7AtG8^sU<|-elRzc|Xsq^mEt@;!XvJ)SZIXk` zt6&prF`9PN?bpf_%nKX$+;a~?^(j-P;JscrqY{6MsM28d7dt@jE)$LI7W`+=0H#iU26FZXMHA=HK$#`j7r? zX`VJ+SZfg(`dj%F8z5i^6{E04C}&N^CHcY0E8Bjltwed*8~7hHN#& z-S0g5tGix4<=U^DIhp*zjNuz)=!H?7BY^HfhhE~bQYcN|p8G$w>_fiK{N+B%54KTo8&u-YzH(zNm$GAxcc}RxeJ4ovpcDlN#Y<;#6?Gi_`u_3_6KZ_m zmPfZWzqIl1S75j0kDvPaQ~N-$`GMPD)fGQ{=B4e2AAR8Nf4Q(abtwPc>tFuW55Gj# ze&CkhKfdYrk3TRclMVy@gJfI2ImTkNA9js>Vt+udXs0HiXxakD0zgh4Rws_8VPfP5 zp1<>!JHK6bq?hTLk>v=D0Fm*yGA4F&iOfJ4ddMlW_`3+w46?roSJt#h8rP8xpW#tAd zO%@gbEMA+;g^#s4!VVy4hLK`45Tt3ONB~tDE<tz8trFnQv+cEs}$h&HrM z9B3n#AdVXE{DI!Io%p2j0YEFOY0L~diK`qXOtBe z&shEMKffL2r#8J6@bw+qu^w>M0pv$REpugA+ruYjmcz*at0)L6H+5;Z!AE!;O{V5Pc*pEU%skK*L1= zEE|+3p_TNqTNyxZGcvu-p%lfOX_wt|&*S%P_)H0#WLRRc-zW^n3*oF?03+LK2wMUY z0G7#YKNF2wPSF??**)m$7pOg!ZMPf|G(ud*?vi|*+MhtLA=0jp5e@y#=~z;1>KOmO?ES=p;wec)iZW zrHft*dZ1ySlYamkQQh@#;v(4R`{~y{-&{$6ulw+Ck#B#ZuKUK-%RaEycmKLuuAKJp z#cQws{3p*po$lv={67Bxk@nkLGgPpJ8#qDx?SsR}8r#frk*$=AD7o-r_3ib(yopQBdDrwZYit2c`+-;g@zR#Ify~^>%F@CN?6qZ2TCiYp zaTxn;lceF&SOJ(z^J>iK!EAt-;R2+n4G<&)t|BO7#fJ(PnW0r06TL3YCBkF1(~CZ= zaE*)!mS_V{rr^R1!{+U`-+t9qS4FzZ4P%EH@vJ<`0Z@%N1g2QrXeQT?*{aQv21zD! zwo?zhn%6<4Y#=;}CV?J_b1jE!tz7XE7`y!rY4H|fjy9-~W*TOvK<`-Y8!4wz=LT;e zuuURAs6&9f4}|Gp-HWW0|$5@z7!@@>OOUwQD-;cUAYt~>Jg`a&5LmD>5wJracT8kOWF%A;>4B<}dt{yO@?XAq>nCuzEXg6?=tXBCjez~4<3s}5U;Vc^r~O{H=<;8_ zeCsC=RrK$+7?4_^7HAj&{s-c3UH3ov8!LbP zhhI1k6VZ?_ZO-bi-gUV9kw5+tvzG7vPd&J$RU{K*6ndGAb5_a{bRH^4bBzJa#~zts zOr)dK23Wfkxk_?j5r8SF$ecl$eGh)agc_IM^6(qY&u=id$lU*_Up~DLgt*fJ4<~=~ zf1i2cmHm(Z?cNK3dUNf=Tc~#+Fwcq!eV2aim$%<`%gX8k|BzW;A{E!(@%Pt%eGA=5 zv;DvS^_l$v09;#>na;)t`-i{#mj@qv{OT1T+4`xUKk0{weOv#VLXAb=yyx|o->Cbq zFJ6W^--=sq{owpyLu^+7qkkY28sf4OBbgGourxO9NdEWItMP0V=h`++|XM z=Taqru4UM+3haFG!9V}`sTMl1DA{;RGm89^DglPUN47uE^vYxRKlkcRTP>-Uq-_9_ zi&7gm5PP&C%54+84q}H*Ugmq?y=3x67jgj{T0#mAIXuKeqbN1>4jNQ)txJ@!2*3nV z6d-z;E){r(_uycCef^zx-iew|fBMtdi5&T-QP|E9)uV*SopvhSzP0hTzinw172VB; zj`ZOm5A56WYGWVJS<}z@#8^RNdz zj@94~x5&_n*q5IApX;ZW7MILi{jEFc7MZ7?e+#pI_QKC?tAFyN?>=jCb#?91%WwF} zRYq69I`~at*%$x*{r~rg^WJy;CCkhs%|Omu`{0h-SDn0Q`G`f zlKqDL>RVs`&ZpPRUUc49e{?q?w$*i${eI-W%m4L?Rf`HrXRN-G4u`8h@$Q;}EC4~{ z?S?QCxd2Q9Edav!ik-y_qn3|s-?86Ds_iUC+~x9t95wVv#uf?V{#{0+}2*y2&By#xIgfsJRmeNV4Df5D{lAKTLxjeO0k z>9uN$rhnJZ{^dj0e(mq3L8MF5OHl(vlvtvr&X^M~P;rhGCIfJe0}%ipjt;SERfoXX z_l-oR09wA*%LcGLn#NiRv(2`a$i~Wkt1}%2ISRb6@%`_Azq`A8`t<2|&Bmx~qrmxm zSUHqi1?@U7mukFAhU0ie0j|zkdd0U^J@DQ2YcDF>bnQ)ZDt!B2dHVN{tpDQQn!j+0 z@6n4+`=M{u)nB`~u$=?wMcNbJg};i~jeIyxbwc=AMwlqUIrivaQC4%=`HuZpML%NX0t!y>!~-l z$UI)xy>@lJFKwu8=kw2P*j9I-!`Hj{hg2mJiuB_m001BWNklOgc?}bz}snl{p()?|C!Hxrl6nz%Y!4n zjiQx?!k7clXlo|m0nYRjW%hVy0S2#UcZW3YU6U{U++Tj3@$IX>_0tD_{%xk2bMe|` z)%o!AX+QXq?`uE(^$*q?{Hh!8`}yk0S<{er4wI!i2FKpa=^Ue5@ z;lhOrFTC(VoH;muVmehq#fDV!3mLO;1bt!l%LgHGJlK`+V@lgKzel_?$Q=klg zmYn=$pZ@XNSN}&hc1BWsX&L#M>4ss*xA)HsB#n_YNlx^X*BY6 z?q##5A&NQiwu=XXfSxMYY@<=Bb+qEx+VAP5SSm11;%<5@24i^xcWZwIQZ zP@}uy?blv;?a-0VjEvISlg?N=r`loo`8lW0#rlM43JIj=zh2(b)HRS>GIQ}+=hV=miJiLly|{kk z-Xop4m8YJ)EWH$HGiGo#H{3dkY(G$yafl_KN#o6xDN&1H#iBlJk&Tqxk{xJz?*88& z8VK_qYCQ9}=#r10RGH-xD-r~6iK2gfPpx~;b^qi0$;Q83G}UYa+jXJDxF`R!cx~l1 zf4Sq|*RXn_VZc>{Bp2S6P5*`1g$y1%iY9^_W=E)jT}FKF6%UI8^ZCzzK0iMnXU?cB zY?yJA^oDDL4zN(q#UODw7OFLg+HTL63|I#)YQdP6m6K*of{eH5)3S2YsMEP<$uj>k za`I_I#UAA?gT)2`6i<})hhtSzMh6t70%$#q3|pni7#3oo2ar4U)JuH6hc?~z@YWAr zhUwh6w5#RNKyF1zmX8-E&U^QH`Dt)u)3!HgM+00U0Y%JwE{sc^E;8)OMH&Oh{k=4v z%|7Mgk9_phG+%mFNoiS;e(J~)AdIfr7kC^Pk(2O7CW~B)bC3!2%oJp?55lmz7#X%I zD8UT{cD?xUmP5JctU0g59)sg(yGVt0I)h9Z0EZ<{K_k@;Cq(%Fzh9lT=L8_&yIAN}#8Hub!l9)IMU6A>m@$Me zCl{yr`geqpJo~!8-?w%(Bq6=|htGZJhMxzeAX<93`MqW|7(DpsHOt=plCA@ckVgE^ z2N2r>Bo~=pXk6?u7VRTFMmBAXHgXs-f&IU{;mhs@+dOMmT)C*ynvlF?I`#V7>^-d^ zUzRv0EN`Im5b6EJ=57;hIOUMT!4*f+Cm7fvhf`P^>`M24jC7ryo!4G_En*IQ#_)_Y z&Oo#=g6|J?>w}%LJdTE&FqWf#wqIHyK&XUJjOL}Go1_c*K#pFyEFBe@0)%PzhyMNvyQ?<1Yqtip*`_PT6 z9{S$;8!s+<{+gR+n_Fb=dvyJE4>o^xX?|HP5#IRekGEWX+WudE`93qQVU3Bc>SqOZ z(Jop4O=nt^fceLDnqA|iHn(=cY@e_0?$2F$cTno=_uq2sPp+?~xF<{!Bm+_mLpnkz zoOgJ5@(+Lb!&koY6`X*f-3J@uH6yV9)rX&X=7mFtj|>%0n!f1FRjbY`&&A2nyYWZw zUAJk~?=Cv8SdGD=g#jr>)6gLw?kIfE_2%m9LUZ+X^Qov}x0@pMo!^0JDAj_RBB79QTbP7SVD|6D(_W(=60bpz%OE=&xII2G8P}7jXZM z|Km{Onjb&@{*^O3xBT^+|MKO!Uwr8HGk5>%g|z{Bo@?Hh?)dRXd!D@ch9CX)uC=td z_^}^9zGmvaAHDxmFTM5cC*Stezc6_bl(4OCe&EH?ss2b)A7R3KfBh1N@{JxZacM={9_x&{vm9_ z$rTJ^-kUzQB+!dZD__xMT0279ko3`D z5W&pq8=}cS)85n4M@(4WoavYS=C=#dCNH9|l6t4Gyx!zgBlZkyLs&VOsPMejH@@)= z+((UvP`>{4uaD{-8?cr_0Xuh@O5;36EZs21XMh@(2#{*we3-?EHIRC&?N5PrF>Ut&VC#(fXmZbBOV;I#LqAO)wqoh? zb06w7A*Q-iXJu6L$4Nd=AHh*zGYqy$a1+8%1I3q+kbq~>k3as{vuDqD-g&2D#fr9K zq%lTT*b;D9k|Bs~hGQyDvD;?&@wT5@mE}{%!L&*!lVFoB4%(uuy3LDhZ(hhJIu2v?B(Q(WK}W%&5*yeVJe z4f~Z2wDpJc$B7)%5e{RUQ5KL7l4v{SMj zl^Op%JlJ=l(Q@t51w@rBVax^z7QLVH$lvnabi_V7Wy^C_Rck{OhG| z^zu;ijfLA6HCUnjaqNOtwv?VfFrDou;dSDc{xG>_K!Miq-4C|U7_%t#)Wu9KJal)` zr-=tb3%B)zxn+#YREvZ0kbAcCyfK>_74LqS{TSEh=XiNf$OyCx!whu5kYUR|ex zG)wwUy1}qz>8o3Q-F(RVp2pGbqXIF~O#6HHzWtXoGI32|ttPGN@-2j*ceth{Za=iH zX>t4X2`cWdSv>XI56|HC!_2UjEo+`mN!odUZa=*5fk&#A@{;ynIYqZ0Ue5ImxaXeI zCFu4;zY6!(4W|ieXAUK$vx7p-2NiE-MloY^DBg+r)GJ=CaCzt*pV+Xqfx=|GC`!&P zzyug8BNXSB6VmjW0Y(nGCPCzu0}eI}zKzo@cgKEr%(2ntAc#W5co8=kVl8ajwrwAN z_#w9XzWnmb&pr1XUbdKljU zU#riTyZU^|8Ut(h-*vJ-nL{r?L0#wPi)%FWTIAsy_4dhpda`NSvF+>O_l+OlrYX0b zhpL=ivtq)sV;9bA1$x7m_JdMg3nPBgL9P%QYu+-cJ3h(|E+;2v(xgeav3JFa6`0|G z|Kf`;qA>zv62`^|U$sZ2QOYiR{WuZ#x}#5%XXd4l!qD=kwrBZgiJDxA!unM?buwU>A#er}tlb)PbSc=xKI zd7I`uJMPou12f;Cq}Zs&HAxhlhex?q-J5DNQi4Rk zFSs6hx@Q#(H7Gi?RtHV{DYnytsPKd+U%xq~*)=(%NHkp^eT0S@a0Nb<}3OU#K7-pvWTfg>#q-9Oag8^hRzmvGSXm z00LO+giN1qLboUhu;O)awcQ}e1@%4$J_hU?y3Ynzr1Ylg+JpC`7PvPsngIP&8^x3&_uAD&qG+9N}_x3tpKdSlDd zL4}Pr^u%%j*`!^j(JBwcO4~Is5`XsDX90KX*6oo;9tjBvxl=nfglZTfO)K0|HrbKk zN*e9}$W_MPHq{tyyuAVRciieW%BZ=yGP788>~qjn8CvE;=T8Bfe=BP&P}=L33S+&|zO7(@c8L-8o) z>B)+8Q9_f1k5?!0E{YKi! zN=gMewjaJ=Qu0R-U(d?Ka|Nx*X2M*s-o(Rj|N8ao*k(9p%$PQ9+Mp0I%NQRY|Ji4s zz4_*wz#x}k1Dj!$1tb>-P)fOQmcq|Dx|FvWPE7ihwi$w^Wa%g3mOk8TUw+w;0`000 zNStP<2JbE=N9vt8+wn>pz z505J+w{72&nx2+NDz>?jn*c;G7f{Z~piZCp0>=T*lq`qXaoX#EN$^eRCXhs1n$ zDYgWCtOx1p8aH|Qr}w95$y?JdMn;tswo!IT`wJ;M$h?tnxtf`YZ5Cv{mlsBO_FLn* z+)15qHbLM>#f=*%N*qP`<(FTufdijsLzMuY?U^}qW@u<=LG?%QSk@DQJQUT*8Z>P7 z(1IdbcF}%X{`<~@PeexPTDh)*rl0^zkhm+aQt9H9i(EH6`AMq3T~K^3&U1-E*6U7Y z9*%1HKwCk+7-(?Qu+o5BaAQSC3=>oH*gTRYoMC{LlS`Jf{j@4T>4)cC8sim|_=V$V z2?yOxJ49bP9$qudui!e>4lPi8uvw_|u<%wc$_=)X5+agAyqqqnk(h6TE7E8~q{Ld8 zj3Ak#X0HsoXlDTq=W`Z-A$HP&uDQo^D+cBr+ml-Bnd#$tb*d1Q_t&?tVhwEOj1_(F zAA;c>uW9kgJ?mz1*%k3U5$d|gm3{f-Ynl5Vn)~Lt(;~^ zk;B5+cV?R_G-;16WEJ0efsn^PY#W2`6%<+s*vrO0h=IoBh+1TQ8HkaV?!*0RA@$*q3U9;!g*DC zf*r|smB!$$-R*_Pmn{EoY5d5g@shXC)Zb$wa0FT}WL;5_>_;oINCcW{X$})OY`hZBUpN6%3XK(bPed}tU zSlzS7aORpaFq#4OM=wtw(2(Am_I^FI=L_@HsEMo6X@7CSm7gcIdq;U0dvxaR$nYSx zpXvQe6xR!n66-Bqn4@dav39xUW);j?$39_|d_y02`0vf{KK-1P+6-avf!2i4101%p zp>M?X%9ymkSLU%+f(w@si`5fE;YLxg9zA*#GBMO(F;=CThE>i-hIl+&A4Q2s{aJ5A zMp2?aEFO9fqJDWL;Y6P(n?*G|rgAe8nY{aWM$=NEoDVr?_N4%qc?C~@ex@>>b)L9hPTCw>+Yf-+0p|ZxvKAK6 z&R#K;Xa$yQ{}7S>#Fl_GNdkiE_IPfOHjrjY8;==n247~AU@kwf2rXF1hvRV>L2v?P zi;w>J&(H1;$B9UC-52LP>7}oLv>P?~(f60Fpt=GZY;%gJc})w|Us=;CP*y-h!RR^3 zOJ&gV!n=zWw(6tp!R?0~N@#ghRlC#ehsd1{y}Dqu(YN0t6@vZrvX$dXkdYbuxHi%y z@`Mx4_H#avZH9v!48t*Jiwi@uv$N~ese_4Jd{_vhja&Q%8zkf7v6uwzj+vZ6gYcHX z4=O{m_?5A>a!P?IpiGP9A6%k_^JR~=C?+@s7Z;^c0YutyaOcwQ z)LMh4z0$K&MZ8db_v+ZG%V*74(Wm8*08QJqVh-dy{&w<+$7)~RzwX;#un49VT-~z- zqmw$L7R-&S>VM&nujXmq)RT+1uM+4heJgfc^j+eb@iB|`YA?@Ej>O>xFYo>HhE7aO z1wV|*N&89+H6mYL@)dRzrhYwf@Pv8eJ56nJd{TK2El^BbKw#$==69%a$Hl;U4xM1sJ6}%uLt|#+p_QOnT6C zmuF?MrUfzVKum3cWD2END(hGeT429t*?T){+R7jwbPYG?LL-d)51qru&Hg0hZ1;Vl4 z4U_pl2{&)vycsiQVAe#PnMBgaOv8r(wv~)gB9EAo5C#ii6YmWwUz(hx2O>XadsBvO5|*HshVFbS|KUza1KnP|-R>ttY79CA+y=^&7B z>FMdXpb)nce)*-oaRJfT%!}0%6kMrNr7%g0AdeKWW{lM_+?#@I$Gs^i=$+}sQ0TZx z8Dl>%NvTpW4L??8F2~4cAuw_i3XS_nsvG%?*IVv97DbobAD>i_a=mi6FIpOsR zEgum|FX~ozTkJ>C=QL@TylUEkzk!Qt60#pR4o$Qh z^;YMp%U2QyT=v<+$@nP~UMaLhSZI^pFE)W7E&tLv>W?axD;FLX>J|3LsHsQ;o=jXH z5OX7-Qe@LIhw)avH@3-xt!j#S^y01RYl-yw3%4t2KKwrOm-TL02|$wpAHDJWya{Vp z?mrb@Hk=6HnmoI5;wb%SC4vs^_t4T7^q{?{ZQ}d07UDEJW|Zs_K#Rz~kzc+>fxN3IJ|fK6&yaiU~t`#vlqb@Zq>X4FT3)6lic* zc?f`BJX6RF6d|WeDOsH%pxFzb#jPJWs(_^pF!b^<|lxr)(ib3Fxd3a za##C;3%>iK4H$uxam(YUsSMf}iKi{m8c8q! zdVs)U_K*~VLJ}9JuRTfL%vEur;vyp3R2qCjf+40LCs#hAJj9xo?Qn?=;#Fj_9d7))kucGKDAt zbjpWQl9JaapUgO4fb)2*d(wG4A(IDuHugi{)yo;DPkWy~e;&DwehPgSe4InZS}#^Z zfX4^}=k+l$i%tzoB!)H(`!MhX!j(ao&Xq_2Q1SQQe@~q{6)XtWtXUKHrbI?YVw%@8 zBbyRP6-VWewUCg)SV5(yNRPFvVMdmr9I3()F9YOLKoGDZCJ=a0fZXOxJ&8GBD5X3n zWBBQ%h!H9bkCo`EM4v^eyo>fzexiDL77HjI6dJ0}3mVqB@rx%MO8ldbKEg;HY$!5J z4L+9!26)$#|jKwLT?f+dt{q zIT^_`)WAU^BJ*n;6Q?Cknf-LNr1o(`1`l|sX?1+ss#y8^9>A;W+PIF@5(dSO>(-+y zE^(3ua;f19H3E~Fi>+~DidrEy001BWNkl5+1Fy$~qp@R# zV%1t8Xh&h|a2D0&xo0~-ms9&mdUn#AkJGS4iyQNFGos|W0)$d&famIMMvKZ;;DOH+ zgoKt13oDML5FT)lJj0<`u>-;Ytr&xfeo=^6zQp(gEf=MUfg`qyqCaGsh=>S`9d~9K zC&f6kgilr@PXwJ}wN|n1q;<<0emrH;cbikUtQ8}G7O{PXz4B;1tV6Z=;+Mp!uf=?~ zdHMXk%fVW!ecUUP$6=GKaAI(0WF5(5>D;V@Ph`HLl`)4FJ^JnFm9NAv61mxC#KPA{ zwLuIRX?@6$fSp$&gfL_E5Kf8_BDmFuzpb40Qs+gR6K0En5ynM*-fkX9abEYox@`RQ zt`iedzoRv~$njr(l)EVUolNG`<;Wi@GDE2ZWNeWE_eUGL5nMicE~5O|0{WF>C3QHa z7|5hLgOP8Dx502Q=g?9y)QFw)+w}XZglajdqiQrT#Z+&R;?;ks-P1D862XR0011fRqVIIhSSE4 z8)I(c*I$3d(XStV_yK1!acIJtOrg>?2^Tg#*C2Ym)tY&;CPy#XyRHtd2f%}erZFf^ zO+KY)u<1UaX`B{CR390}q4=78ao ze9==7Lg_L%2%iAOHwQ$7;ju|J^#3jUG1TxV=Iw*cuOVn1Xd@^Q!+ckNQDk@_#u*~a zNP(wy>(&DY4#WvqOje;N(Q>_fE_+}FUDQ0vc00k2$}XrDwi&KnzVWYvJ?@PHhkeyrU3XP{mssW_*PK%xZ5xwlD1X_^Gk; z@#u>FBqhFrIKBhDoIeamGpv9R$2AbSQq0TC$Im}Fq@pA8adAri|aYKyNP}8!c?GZnh|2-Mj*=bi*Fw zmh^ZfCo?MtJ+r^BAJr~Uf*_#AW8=TJym&PuHy=ks{7VG-={=Q)<{!~-=kb2Bs^HL8 zw@)p6XbbGb8!_as<3m^u#+6+^&F*ZWn%l4ESezz<0DrO6DC|e9rHhf003q23(QnPz zFLuU$Au;}yJG}U{Wu!QIrWJVm;)YV_Lmh)M&q_Vs-tp+TO!TtFe1j+{t+JmK$I|rD zVT0fsX%rwC%z)HVWWepl$+o~g?)}L!<+Vn)OIYWOM#Y1jg9MOh-2us^YPl{3-A0O+=+%XY(d2y`;L8| zuhqD{o)v}!Y4*X@AFKzmef*Ni&sO%kp0?%tHwSK|QZPo1HjJPJFI8nB%BDZkV~t{E z8i`~8vUOfL-WQ@MRz#3z$kzcWNK)cF-gZrknKE}+OtsLw-#;EPVSe&~S?}$^c|5FM zikd*uj8O~Ez2n1?=%4VgLJ!3wHu#uc!V?P)PZ}(az@dl2PBd&u!-EH%6legz0gyut z2tlt0N-$zw4BxoH@(MQZdwso%QQ<^m2Hi8eOYlvcZD4(wgnY&OJ8b$fmE~ z?mcKr;-EFpbm@)P>)^2F^u!m807>i@H7!XLsht_8^4j{ee zPKSmS!lAI>Fv2hp^ot<~7Fk|;=_RatN_F+?*T?oa+$?}H-Fhw0%hwnEICltosw61@ zqpu^J=sMtg`URE1oPyC?sZ)u*fuX(zUKOKuIKa!C*M$hl3XPY(vZG$O%;I>UAox|i zB+-#xAR$&idd3ojE=lhjfa6hQ5Q)SIAlbVzvEeJW5z1=6K~S_`w?`XLY&FF;*UUC3 zC1+&;iGt@`pjhE_071@556!n^If_Z>`D)0tdVYJ}nL3Yx#=ebQ3Xqc$C?BOXj)MqFEu0R0tLZ9BVXaZeVc|yP@(e z9iJTNlYJgD?KonG4i1vAsR9ot^ltoq3=A(r^4vaNxzK*#0H76N%zos^5lj+d6$TjT zm zgMaTiKv$j=1eUAaq*a8m@wUK{p1P5)?yux^@V8$!XK6tdBU?vBV3j`e;O}eq9KGOE zrfzfoC;&uR(sym#9@y~VO4_kMeor}*kr`5@am)Lou&LL^=~jt=Ezg3%RgEPG*8c{}=j^{FhZ7@%-64K%YGwYS@`tZ$ygb&TuwW1zC_*XVLqiIx{Ml$ z(S{aP8XDM4_!59XhRwDI-G?28YZKo6BC+$^z0qeC5Fy20xWu#8rnW;uE0TOc(l=Kh{Kf=g-(Xk*FP7z9=@gR4J#^5F~ zihNxx{g~`gRGIO5FK0`y5~=cCvdu`Wu;P|HYo&Kj43Y~cd{fRUxu#chrZk8pmmaI) zDNbLPlBHg({9`}NkMA}pe&wvuSk7!SXw<3sGY(wQSCp-5l~mq>mkXXN2MNRpXR%Y7 zSG){kycvKm>I7qRsu9t68JqO} z%x1G_YqWs}307xYyhu}~RyX{s<3n1~SBP>BuIl^rAZvtx`ngRA=35o_(It)Dj%9j%cF1Gv0>QvIg%oovKh7Tl)gDIsYg*1D$L_j8U1H955S~qvip5vuzc7L){l)SgGpy2xE4`SZi z9R2yjZ92B$ofZ#jjBSR;6SnRnVWUK~agDo;y1!27 z&ct{i(a%AYgGm6zKsvt(EaMujK#)=Ig}>^Z2RD8-Ve8_)t+%fk786-;Ic@9gd0Vw9 z$w$ZHOCQoUhm}yv-9Gy{VWgJ2bZX3k;}?YF)Zm8`KYZ~q`h)~)H0nTMWhaRBl5@HE z^g`kEkS=H-#{fE22C7!AifeCb)Tn{o+!#L6xF2U5xhUB}=2CnTB7zbyM4N~Vg$A`C zf+t59lAQpK)dd+WCV93rR{4UAJsUZah6US;H{neIT^)&4@Bj<3UooWUU{OaKibhdk zs4s&fi;X0h0*n`av_b3@{M}yxaBQj`LP}Af#~!4 zxYkWVg&l1won5mse)+MB=L<9%ZK$@9BBN2M5h@LyT6jtYg_bT?xq722G>w4)0X6|d zM@QqjYYfA2tPNd~JV2J<2b2?_Y_1I8Q>h>fBH z1`NPNBKlCz3)e-9*0(EK>nn;49Z${m;Ht*1cEun_85Y#b#YPZuBGfa1Ye>E~w*a3JHH z5%DzCkO0FRlr*YXAW^0mVW5-&<3WX|7jvPcn9@j;o;FS)qUm*jlhhMkw9^OObLve)SMw+(GZA3aVia=4Iw67dd1GWd@va#m#$&UAx0aP z)NiINKT>hvn0^>L5-aY!p+Q{YkD+YD>Ah>ackN~e2q zYBG}sxx(Rv>ibXQdpr}rYSw5JZnSuzI><*8wV188EI{E!(I~~OvePZIiYN+%5)^u=3&i#FxX%~u z5f|%W4F&seu&jwCQ4}cN>!f)_QR9e*Gepl8qk=dh{Ey=9vi)i=;cAYMiTE#DmCDDD zUMhV-Abo}2wEpCzgs95Or#NUl{Q?5Ss}%$m&?*=Gsx*pTpea`P_+fQ~5eAb_0q6FM z_&>jywZXenrIOyKcPyJ8PfH9PTGnNuCOJ}(;F6xnHd-L_4hRbI)+*GgQxk4{wQJYL zdZ^pW)HSE|D+vuA(*(D2{>+&jE= zB{9}CksE=uFP?}49PRu(9`b=#`mxvnho_6dO9KwT_P#_)N*e$o@xhE2n3eT2XjwHn|UE8JmsD5RYB^&VOiD6aY_Op5L$tj6fKvVv> zXuqCEEY^J=g~i2(cBB@)y(E-^%xcwUNBKtFpfBB^$196_yo7Cr*_yAPKbGKFY(YWL zqjN2f8Um}d9k=Zey-;~*-u`%@8vjmy9=%ZADDb-Xsj1s~PR`2C!!l`La1gEug)3IF z)tJSW5!W)YLhIolg!@4$ue6{?KOvcLq97>vn9_?@kNsj>>=z)!AMD4D!WX`>jL6Eg zP<_De34GO@;&38Y%7Iv+kYq|g^NoDlh;=_XGq)C$? z3RAgw%fh#c(8r>Uqiy0n&0Q6oOPn0FxSiph$#+KY46n*@g+`@UVQ0L~@G7qgtMn?q z2(Y!Qb&FZGz*G{bxTu$iHdjQob3rWkaYUnZc4}=U`9?)|iN<{yBnj|V@5U<828o7lI3 zZxv#01BXY34I75LsYFxtR;#zPN1gas5#oB+Ul20c-OgE7QG zl$UoVEP;H=UXdGMSF5y*@#H6hG3L^W+?`s4LNF+@4DYyFljxblQf z=+QHepFW2A=%;(qP=j1(t@fI>Hnt;u6o6d$$maA>0G^x`nCPebi=l>yjS{hEGciM= z*2QaQ_H3c}855pAA11i2oy8bNJNWZ~td}}Kxg-0t*S-1pXG9z~rf)OBAKfe? zDx|0#^o#*OaDK+Vl#K^ZUG(-TS*lzpy{dWy*J>QegV}5U?Ay702R6-k`;@9!uSJ81 z^30B}6s7*Y-7lhLsjFKz{&5grDSGhXr~)raAG zRclsmy&6!adZPzg zh52gN&ZchuZTsb1pK8t8HmDXx$rowS0nP`;a2g@a=nx3xC{BCf*~WGh>T}r@Qi9Ni z;)7Ca$3^BC#dWi?!w~>v5Sh(p@Q?5*A)NMNe*So&;^NbA>{g_aX*@-gs4hUrwBrqi zN>-^_EMB*ad4V$W&7u9?{z2Yc5v6&$6z4xeGAG7rKgc;D?vx$rU}?^^xe|k35ieTH zmMu$5OGBrJ&2U&f!Oa5bYRyYze3`U6G^ebrDv3c$ZjEy|}uw4<1-4eS3goZHOeVil@{X4Z|cyh_GcLojNxG zW*RH8AY;!N4O_@QD2tV9HSVYEXHOkEd+svE!(RTS%T}x$8b}!>#Y(_+`@xRx3{!k~ z$iat0GXRWJ46ncbI_CXw*%!v+XeU70cYO93Iqcxn!N+I=LwHPP;A5m1=3|6`F%1?h z(fq&;daUWd3VO!{v1o6^B>?zv)S3E(HR(F;>yEuYA5r_mwc6x_ja}c0PI@);HMxF6X<5=c^Jo<+OiLr`9USZbknCMz-7Ao?axui`Z^hRDbP7ux`SiuKZm zGndYuGKwa;af=IOqU-sKFq;ke4BWtZyicAIn@hxbJTD=HyFaqRwMuZj7kR-17BoG?J}bbb04$DuNz^Mn7KgSwdpc!rkZlH7;c`7aSx!XW#5P^*%Sn zP+;~BOXFbe(DE0adVYo)S!3YWpN#8)?9Mr~YC!)%YSio<8EGT$pSmylv($Ap&#W2n z+MUezMc835iwIY^X;Wo)MFklk|uY$KpI|m(zw-I6l|AY2B zFy4I>Iydxq_!tUUAA9UEETCXwLk=};x=JNGg(ifJd^F8l<6-0kFX>H|a&H3`Zice+ zaTl)&Re1&8C`r9i2xWK0e$}d?!Y=IrE~@aG^T~pjz|DE?28CB>cto9s%_18%s}oV3 zhZ+vmO0y;4kW5A535R!U3^lNBf(1nkz;WXw?%)DVTwL7Vy?ZfeuxTDDyM&ErrS%tL zF;an8$i!+Twqf(=9=k*_xB;Ey0WN$yt<2@tJ`rQ1VX>%g37SIQcTFj)CUw*xi zjM+DXh8hv0W-m{9b67MGpMSFK3g?FN{DCO>htdbkwHE*d?yxfzmiuML21+v_4bED2 zw~%(u3LE)qX2>W2)>N=YL$9oI5+Gvp9~o@Oy}kAuQj7kJ-VzfV*c=Lnjt*CA;4KT? zpu<@WR-+~X7!FbtCz2D-Lk;*6u*ZW1v35=s1DO=G<0YkHFd>c6#jrru3zS3oLID_U zc?I5H7hxa=z61)FFHB)_umHF%?hcMN*hJl8qa0zhGrY6(IHPxlSLL`uqtdIeGhSzS zl~;vzdj2sY@a*T20p02l8aimuAnY5(F;{%Y3%fd88EqJnOuuMgn9P7cj5E;WFslJA z7=@@q4ciLJ0xW4?z52q>yN>7U^3vDNpAN8bT)X1e#N-4{=vAUCPt+57RNVzVe1eTX z-~h8@KoS7C_3it=NKO+iEq&dmSRKHXN-YA6R{<8{U#0OQh}b;mr_>DM6#}y^oW7Wi z4~~#vDIf5mWyAWvhIi}8$!mUnSf0l-$XKA&CuWjxR~;q5&?~hwP&l4r3ovGVM4iWmu0k z74E4N@$ks0vv5K}Tk_eOYohFgyy)q_&hPwikC=YF@4$()(_#?(WBsYDo8^z54~9&0l1b*BbX3jtj4;&5paeKZ1_3C4$Uu@|WHJTq>49;G zSL6_rBN|mcYtePFMwZ}&*O|<_W4}xdnNcK%-V%K#Pi#E!zymdF)1-dbiodb zfFI{}PhT!0Ju{K(;1zkV0|hwy{p0dw>o1D0m{SxJ2o*eW3!eZy=!I`X1CzkQj(>^3 zEQe%fpZ@)aFTPtecX`sESwc8-NnU+8;?bi*xwI95+6vZJ^kUGvIm7kID z3vnDPX(XV^1d@Y=$hnm$RhKHq6}@b*G+P3yOqb*+0}iwntf%zt+ZV%g+-r_olhITh zv=-G?JD6F}%rL@0BnR!N2%(y)*WK*P$%|)fT|7gJ=+ZqZ==kDDrQPc|ZiYxMHu=K9^$ z6qa}a=$Bz*%bocSHLaQPQ6zbH*#Yzh$zYUQ_LW(ZR#%2x{LEP<<S2(2}D;u7*ode=Z zgp><_d^ilmoU$S$0000W07*naRNy7OE|h=x?N;zo@{E^V9dI453RPtayfj+^)XzDw z^LE;=T4lf(5MX40!;_eez!4H$X!z~7-@f_gn*dyIEru}tw~#>wQ*gqq$!Ba})N2W| z=T02Hdf)P;T)CSMo;u;J2kDlD$G)8#|HP2^)w4&h#-n23s8gTKICPb=4aJ0;>qvkQ z$EC*d2**ecd+R}jYCJXN5pB&!7}=2PGI9AQ*nfaMlDa5enAYHbiL-sNrv0|~ z5J6KOtv*F%UbcQ`yME0iG5C<@VO8p4Bj?-!)wXqw)s`h}14G-66$2^Co0y}+OHm$j z>EP3#LZC0}1RUr~7#kCaUeK{;6C`@;_=86zB%fFW8}7o!r#jG@EMgN+K`wpyP;XpJ zz{E^%PCIMV)99oV5NMt7PWMksnApH(wK;R<;Drqz)5gAgUPD1QSU97MRZ~pw^vGbrimV23F6E;~o*XTS z33yv;JUVIisK$Ku8?A*3MQ||-Uw$Lv?Nt1wyxFMr^k4BW7b zFA3qMQ5;Ue;Z?_zfhx26FcOYG)yYa$q2-V_Rtp1ATP<-pkM zwBN!TJLf&dzhl7(Jy9;WA|>T;KcdvLPC0jng1|w$+C#P+p^H_d+VFcjJpI(zCtu0L zho5}{g0K&RdHrjdMQK5e`%O)Wos4e>K#r$x5T75kwcj@~?y%(*p$k!*98wHtD3xg> zoU}ub+#L@}-P3CrDJ(y|VL<}yjA|PjM#qMQ%R2Sby5wyWe>~H#Sy>~QLPciEw=^PY z)HSwX|KC?4qPuk*^z`7~#fip}OPgVm_npXSR4SCkUz0xc$SjP-4y=jfPUIrdzS!ZTn0NnApJn(4|Y4 z0*fhO-V|@McC0eMQrn_bY9>W-Q1Doee$PXVn%$@g>k5S(dr-ap7&9F!!C%nDF=`uT|~Pk=9wC-j&AK8aB1 zg*08J6mIP9!~quUN5eRSxrkPkLs-Kh&dbl=lg2%ID=J(US`nX70ljLE2nwSFp2Yn- z4tZjF>PRcp957p+duYQvdcTREFlSt8CIgqp@sJwTzSgGvy8GoPFqa`5gk$bkxX1?{ z85}hrK=TU-4!x%YU%g!rTBCIr?T_WNKK}NRkB885l$Nn>0qx|7YTXbkW7h;H&=iCJ zQWrvmBF6`rF?}I1j3+K;;jjqmNMlDQWU_gnBzX$Gq$IqE5x;_rAt(7mM&2z4v)wH_ zB!E+-0~~OYmZGE`JoY)jNFmN?N2+F8A@> zzjND>(-*JhYNe}0HfR}D!idX1pO%uc@l1vm7+SV=)7Di>@r#`0)Q+FGA3Bz&(PtSN zHmFWmJO=_ae4`C3qG$H~ePH_^z-{+`$x1aiPP+V9`q^G_<^=jfd!V6f00Iu7F4!I(`N>p@S_qvj{KIzw8nva-r7woyYT15M&8-|Ogh@C(n zq%@A?pd&&DI1pPq1*IV=pyV>v-X7N1-iX)GF721pt^;n_k0AxtO|YXA1CFPkej4L+ z+&hlB!Leh<-uCGYDjA!aq^8Oyii7qnH^r+mRoFrsPEU!s3Ke~GlR)jvHdT-_8fSQw z_fEplZ;S(!=QOZfDOoieX#*E%dsfc}?t!N|VZq4S5N=d@=%whd-%b88aZ^m)mOKc6 z0DN#VtV$$Kg-jnB&60#4IqJ)0Pqe61xBJNI?<`MSH~i7gYx;Jqdvwi|-}WQE%m1Ee z1rB#g1W16$fJRWK7S z2V+37*$f)Ob*!W|)S0Poa|k&h$4OQLTqrvtR!r4-c|Z&w4njDn$2t0Oo_3;^vC z?_0H1q9UgKvU+3*E%WDz55|3#D#=IwnErIV(40fddp$X*M%?dT4XL~7jiztYU;|sU zMi2f$65_>U+Rr3G(=Km+yFpW$)se2-{GUJQhr8z;oM!(kT0wfq`jM*w)a3*K})?Ela}-YyQvH7GA^<6eWBN{Wt`mn-TNOW z%y|0kCVS>o4sA1gP2#KxG0Xp4zHmSF(be0=jfo#uH<+^Bv~R|+N0AJ2zm~(kj#}1p z`yUBwHLdU9V3=ejK)jHGUkO@cg+M(Cm81&{N}1;QeeP?{VTpGXmj zWq^HC*@=24$y5s@Qsq?%T!B?xD!q#`9onGE1c9^cb;zsQ;0o+axGTJZ2BQruqpVr8 z1|i&Mh!-~WqJX&r7)-12ph50tWb%B1bF$Gr(wbiv*KIb zS4~UbJUyo8hJA5Ar~a>Jy`a4-=YXL5)bgXtUSE zi=jrhY2Uuqr^Ka{k4JV+{9(|-u5F%;u4FpRKCesOiQJ0Xo;O(I$8nW?tr{W7-l4+t z;|x&}uqSgDqxS}GvDx+u%9x0vI4C4Cr6ubhga*t`dGfx}ITmqX$U_UfhmKRiNol5_ zB2z7C!C|c}928Q>;|OOoHh96HYd6_oXFpLYHH?TI(cHv-*5|}W3&HFk2IiRFz+wu% za+02&j*q7d9Xhm-HApAI3PZzoVZQEzR0eI?)CeW{3P`zUt&2*80-ELfhSh37SA$B~ zpoHT&Fn)c6Kn|*8g39VSm2V|cbO5O^utt^eF2G3dO`OaT&@nap9zk7aS-RRAjjwrC z>iygP-Xyj_>om*Ki4|m>*uD->+|Z{VqrIRWTBGJQ|Na<*gMV;&wpLsVsP@>43oWCv zGIGS#bwOAigK+Vl9ZoVCOVpY6A=-y2yMO5ND1nX%L z(gDM<51Sp%Td4GcT=sx&HIrFS;R;0muQN2Lc0$Nt^1U$w*?n3quw9z3{z|9hNHTS3NWFlw&mE%;n-$C8#$G}QBz_fET^M5g>kZp{c%=Sb zSiZ-I_hL7^n4o!BJUp_`9!UmWSoumJCBj0Q^d8-WxbiQZyM&*L<;sSKg?fc`7&(>o z>PqOxt87G6X|LwEh`Dv+=mzy7iNF$odjVD>5`jxwm(hgBs2@KVSr)-k9bW!uc4E8X z8&mfF4_}f(47unTzs+wU4uIvK`cY~O@eJ$u+*CyC=@q1U0tF}c>hN_M0Wj6A_(d6Q zkOfAj1LlDtmll~~BvjI1b{FWfRcnToCqUvaANzCX!As>LTi2vdoCBY^f77Om-j$m+ ziJ--&tke5;Zabkxpf7vgTJpApE$4Ax*={~)-uyJt=)U{f_Is^%^qkK zY8;io-N{F;muuRj##w#Is#M(;?Q4ezAp@ZVe{7NtS;Upk;uGgs*=p9HIxhesb(RZh zTg2L%e`wh{OnH{R10IIZE7W>3JoWGs+8&gOT@qClf5He079DG|~vAZQIx> zWRPcrm#nJ%!t57)ij;PD0BZuB>66J&32c&TAEjad7id=rv-11zo3*G{)9d=N-xkGT z9zuJ#Yh`gPg(9*18z8PT=bV&!ZFD)qYkvMEM1z$CEYv2bA`6LPGU+p=q_+WNP}1KO zfSOFxN z++#6a>Gm4*v4&P5Q_IeyrDsi^;j-ulzOe7@>e)Rs7}93MkJi8KaQz=rdT9NDYcF=- zaZjVT$vo76G^@s%hQ?qoP6%sNJ5R``te~7C$Hp*xD}GT%8=NDie36eL_3>*|Kf^x# zGw)=*K%M*joiVRrIyZjR;c*{j_`c?x?7!n>Hc06cs{DEe$PE6py(itrbpf zr$np|Nc1x0l2v_G4%b?H-&D~>!)6=wyoCs%MPzm%=8K-f1^WvVacAOSIp@ip5wA!M ztUu(9!NVrZoBv7-ZTDc;Y}A}du~>U9+y>5Grcq1Hkn$h>Ej&@T%jmnKrO4L)A1%8g zrx%PEZQ#@^KKcrnmr-tgmz&x(`B$jh8ap(Tem{0~NJMeb0g8@@S`jx7wq69UrG3?= zU0;XE32PmnFgN6bA#vN1ro<&p!JgAKQ~QN#m4ViWoIxdCHX0 zT@d-zYc$m8{a(_-Z{{~9;`rqK8T_mgAL9l}dnE4b#Yriz^o|BR^~o7$rNs!7{n=~Y zFqXl#rmtPMvU>!!=L}l7flSrc-XX5VF99wbs33r~7^=yPTeBYm@4n+y=*banNG4VS_NmsnL=LFDI;8_SUJ*U# z{Jj0lmp`V={^s}D-=rk0KlAgKQzJrYsh9H2$ZAyr!^jf=>=@8#lu{rJ2VE;Q_wCm$ z)Kv?U086taAPEeU@8Iv1lb7KgkAnZLYCLyguiC#~C`{b!mbVN$GBc7a!ohsVxNyZZH_ z`Hg`6VbI#$;u7HC$i9=-{hG3C`~Z4{O?hoeCYPC&(+rt2OaHb*^Vy#zteb89+wsN_ z`nH3uW8M;uG=fy>HLK8t2&BUpTP-&WyFc1si9E`Kj}}Zxg4ss2BC{$mwFpqV=D^sqCZ3$$z<{Gm7pj8aefQn<>(_7RXv5A_N(CoF zL{U%y*kZnw2tNHGne2;_rJ(pT$q@qeC?Vg0OqIqELM#fsHRv2~(ksk$pi5L&X}`7H zIeB;9XOpZ+b2-PXj51r)ymBI^SU_0Cno)J?H>*=Ws(iUH{!BSClN|$;w>E!Lg#?&D z1|{G`uP#uM0Z{+I#ZhLxu!>ERfURM|a;G$UvhLuIo(jc&X{rSNowUoG z+zv0zmcYMdKMW|E!?#gVQc~b!D}Ak6wOm<7Aw^u5gi0<}11C_b=M~ZIb?StdJo|X! z7JBC-my^Ert6{NYzTo5HjDs9Lne{-2HpRU#tWV#!`)`iIi}T6d+tX=1#IH(=e#2jm z284TyESJ;vrKN+>w^YQVBS#W(!_wZmb!;QyoH;NCRB7B1(SOWI+D+efV0Z1pX|W;8 z2rIw15fvJNu+az%*lqq8sc_#Ou<%!O2RdLDt>&on=?EIhrNtgG6K>DhdwO!Wy1}^f zSi{XwAyskR5M&g;Z`kx<%buT;d0Zl|qI(lV}J zhZbhR>+;nz)@86V;^D-fIOkka%3}W;;?fc z0}gD|z-Xgm$Bys6|33DL;=Zq*J$v4z#S|ySD$-yW%?V+j2AE=A(TH^LtI7mD2SLY- z_HtBuCS#%Q>QZ@CSai@V^6vPuA18IrQmx<>kj6zEEQHk=kx7miAfir^9MM9lPNL`( zyxORVm+Q6-jY+6u<6IyynQRIu%nE3-)82kg3#I2yIOGGqpbW_-gN&dV?*fS_xu$@^ ztbjL3JD2JI-LgZjBC+l|yntbxfwg8Z)~#C?`$Jv1W&^~~#pcZ#$gap8P{n@{sS-2a0byfLLYBcA`;AcGlIXN^cQ%d$duwK5q6lji1v~ zxz>GEYS1oG$^%XrU=OdV^=jgNL&CSK8D`vXP0L{I9V~;9WYJ^N_z;q0R%w|ujuDP; zN0zZVi4luB5L_`57ZT)JF~nB*GFWkC?d=uo+MAIOqZ5a1(j*C(Iuv1HAs$8>*b|Dz z6A}`F%W81<#=?aQyLaz?(?jHi$h41wZTy(Ak8)72kxJ1cz}KAtkQ`Gf=q!@*Z{o9H}^ytv6P)n?gOb2lVd z6;Md1#zI=J$9g(|-gDo`qWcXkPM%H5n;M?E}NdZTJUgcFQE{h_G&?TtZ2R&Ot zc!OO~Ird!PRc%mZvXShwI>$Qh02V!93EyI;GqUM z+!!cu;o=HWY!UtYZ(6Wu(4YY(He5Wohm4`T;h~0IPls0lbVlUE2{O&Ie4D{@+6?~a zS{5D<{=R{JJTxjO@asIs(wPZC84xk>V@vGS%zT_T!8aB>wGr=HJUzXj>)zf|H$Oi4 zS}ulFT0n^)PocV4$yTEhEJHyLEDn?Vhyy7F8ou7J`A|Pn4w>S__~t`HsBdMAZ`6cC zmI(;x_6IfYKXo&{?SOZ0oH~JCjDWY26Mdj z1~O>|tCqfulA>G(czOdj)ii9_Ff1$#jcWb+^_Vu4J)P^KoKa#l00&|8rK$kC*EP)~^mSQDfFUBVA9JD8Mh>>C?pp!|nCE$?%W=JVk0`?p> z_FuV{?der4kV?;u;zLh}(Z;c3$FR_h0^-pIiEvqnv=9Iqp6*M)u6g1TyMVw@|3E)_ zUgD`NdQU~mOaU{F8+4K)y?p@zVdaD09tAgDNo8bSqU3-G>yLH;E|{e1)_TVj?^ zl#Vh6yl^Dh5bhrsgb@n`4LlPNoiKxavM?mnA2;PdtdS756PzSf1Q=jE%Ookc?A)H) zfJQRK@Fl=V5#!kVU`))05F_f+g?b~u>Q!pRiwiK#F!+DHT2=9_J)-%PnSacCZ%*v& zk3bUkb4uSUn6?lobk&k~Epj^I|6Is^%aki@7H5lC=in3e@uk1X_<*e-D_@JFS zqa)IQ#;iLVfMZ3C;utlDf#D!{RXl=HAOpH@kTE^#0JtQvWDz)9&qkM1Ubd2i3JJou z;pHHc+!p((if*f{hBHcEHPQqM#)x&&#*mE(edSw{=pGv!0zD`o0_efkI|3Ujt zpTATwh}sB^6kHxFAh8&2Y}v8}K;_DnaX}Flnq8mz<=kN3S-t_Ul2KMG^8~WJn2JD| z8<6Za^3nNFDF`eB@NJw)rfr-x{IA$=RbM2iD5DK1LXMzr=$VcT6NLf10$R=S3={`A z_9bt#;)%`ugvOLs%`Fz zJvRn2a`5Xtw!ay_WC77WIBa3uh&BLVKi>fY0F?I)Nu22$KjKXX%U})7Yj0!3+Ml`z z3;+Ni07*naR2%D+zO$MH$*|KchlYptgWVg=n>WX3;G>u0PzvC}^{7k`aKa*I>ZlAtUQrMrKyV)oT|s zv+(H2%+B-kE*9)t4EFMirv7s$-F#^p<|1Mj5mcIJbvY zfKs;`U)RrF$}AH? zE8==8{sU=;G13)M7XR6@gYhW7`zz3`hHf=Ryiv$a8`vURgwl7GT{1rjP&*u>+)V_c zxe*~KL7-L%g;xQE6b=)c5hQM_Gs&@{O-%SN zr`RXOBFL@K;fr$R$L4H)Y>opHds(^(EQ!K4mg>DXAL>nIKq`!7Fic7L8OvZeT!e4( z@`@Buu?<0;Kelw1CSh%@)(~3jGVBzFRc#$RZs1d6#rl?iK%l-zrg;Q){zT^$WI>Jk zP2K#ISO$Y$A7dG;K&#tJti9!kwYOl?gqScycuJmFvj{PASY{=?351@Gr#D))Y6a4? zw6x>LkJqVF2h9voH(|b6G;E`KE_~1`rDx_8g~G*35ge3&45~~LX~XM?hO&suNW@B3 zr?wd~vvc#(FJ+#&eC6!Lj7wQLq5hshK9;~6*Q$6~0!rKn49@ZPrZ-)F-4V|WT4@CU z2LMrlfdQpU1l*RnB4N8z4;r}~8v00ZNqx1K!+L_b!Y! zcJAB>A@%FmcWbbM3Ql>V^wp-e1c}mdTM#~R@InJ(<%$D=dMN;{HbbbO(lK}i6UkL+ z)T&}CN+-6NiqZ)U>5xunCllGu+hM)FTh)TUq{pf79}{$_G69B zi!Nq1=|WKAUAAA(74gG4rmJ(|2h9(S5Sv4B(-%e?zx?t`?b@|n;JXP4%)pR^4PzgT z94q+T)Oj?c$1C|rS)penv4hxz-&J}z%jm{``zgU7Y;!KO0ogD>e>z+j+a<6g$7u$)&q5^TchNWjb}!Zxn~90Z9v zA;;|On@U`m#9~$azi$7Q18EVV1!aPf!G3pWw1Mh`(MJ8U-n%bk??3rZ&G38lY*sSj z|1UeL3BtKv_p}BFHiO;Mk}Dg ztbljIentFEKs>0>)1e(=>jp*}zyA8`GtWGO$0YDK;dC<^O&1*OtAO4pIabJ&IVOos zs+4?Jvrd)iRF_GRHE`ob!9SO-9Q!Bz=(&r5-dbtTYxM(jLqjmapyT!UXs)ksDZt1U zIAj+dIdBp{UZER;0|3Wz83q!R5@}U=yCEoog(XUqXdZrfSz6}bM^8jnsfgDaIO+n# zh?#YA{sE?!9=S52&@eHRF#)HEG13Q;VNzhU5Ytdz8K$dp*bMfaH8f|oflF6HdGUhLa*Y9md$WZO4`b41DWODqWw0q zZyH47K`X-hcZ(J+@G;np8#msrts6F~on?io0dP6mWxL92qE~e|;nnMg6PXt>uN^&` ze)w#9fY*(Cysx${og3n-AvgT|@Fr9m4*?u_K3V>by9L_2AX3P=MjSl500L#2K}eDrYufTn&w3$2 z5d*bW8W-2SLMumwRwA=>X)NwvvR}=f!eR8D=;`pF!e|4J((~uf@7c4bY15`?W4F?= zA`|$zAg5o2SQaY-tV<^-z*Rr_N6wr-c=~Lv zB{$sX+Wn<+O8OPx&DGz(Bzh${!02M&kSB1+A~<9SJE;>v3IDqQEJ1IG+4zzrOXlY0 zK62y2hEr!YW@a_6Ro&Oy+nO7qLfTu6?B!t`Qo#VzxQ!Q=+v?pSWEmx*%2YE-m9OH} zQX?9pp12}MrBUU(gg5NR4E(kOX}Q^#AE}sAA*>W~6?yB{^F~e1O<@!tsu^bcOO+~B zJ2%(k#?{~UAFNrvTw!9vosy8(kpUI zRYE9rpi4>?jf=R23Pp@UVF+O>Of+ghwa3IQ_-!xotCX<4 zm9VwYf>T*ZxDe-6?!V4`@g#=VNs(e+iZaGU(zB-`Q`+;Md+x#aP0+Y8m~*T6@VbR|gv&ON$_f=K@GFN+&J+?v z9^g+M;No2ieHZy0C44==C<;idi5FFxMOg`i996!F24Vz(kZARY#)x77zKvr(_Eq_J z$9@}QiWV%&XagCe=AfE#$}>3Ot2E3a@p{l)>MFgOvydkXNY$m%t9WSuF)Fn|rE!L5 z3#FaR!UDk}@kOy;rh&w{-Pv<^T$L&j6yWQJDH^U|Cd-}Vq=|;JnzO_i{q3SLDOLii zd<8AKb_X>(i9qI<2Rk@b@N&&g;%3d!M>|45YSv;$-V*}#=*7a;0A`A4<%KKhzRL&AsG6-dd%tQk(!bp)v8@xp{JUkq&sR$K7 z9jH-iV7YKejl;zdQ_9S9^WbG}H79K3)3d{n&!P}wMeyNNy-aKG7b5?z*)NPOy65(2 z1G&I?Cv(sysM_L;#)=nN{{Mw_um6X=>j03V$lAR*=Uu{*MUt?nNEAhkU_?aEL(HgW z=1eDMJQH^&Jb&|Xr>9^R#R#Y@N)iE)oI%nqu*>G%*`3*)|Eqf4HLrW7XJUC9hH}xKFoIqe)OSm9$66(9}zT2{8OH;j36v|NFg%18Yv7w^47^L?N zgdH#1RESdbY@PFaT~E~ZPi$L_?c~JhiEXoSV_S{Q6Wg|JvvJZGP2D||Juy%M6LuzxjE@&Y@DAmM)- zaV^YnSl6WUMO|Xrg7*4;OyxLiyo`H{LkC$CwxB}%LM4a>{3eyHUR}*n9m!B5 zcwCOel^6dJZ1a^t-v)2*bagZb^I+6K+}Rm+9hDj0bP$ZO>@d9RO-+ zXAnml9LDEKL^hU0uf5TqOU+W>-|nY8KsM-Lly|BjqwFHc!=<63p$@^uG9D5+G2WXQ z)^$~^EMoO*rTa|?E}DOIH9J`+gP_b9Use{gY9GLsY|2rZ7y(;^o31F`U=gc=1SH;y zHq9J1i~NO1-cyNq^kYwIa=}kXceC2M#bi&iYshx+NOCM-9(_(`w~foT*4ojn;yE8` z6E!)mS9T`t&~Z)!VPanw9%cw~$#=ni{`ld>P_$a7@Hs&|i+g&R%)#`ti<}NFx;G)1 z0zJm$>{$Y<0wdDX5>Qln-~v6SR$EApkP7DQkwB&EfTj-DX6j(DN~~Lg{c@9n>OV=% zl?>9T-9MCgN*wwmBEVhPfoKa-8#00#^(=Oz4JHtGmtj@4H8Ya{!>MGP_nHn~@64Yq zn8j7_MS7oEHz(MD*L( z+3#ie(wh59 z1e2CO5?&1}DRr3+zD7>rh;7VEk)q@`He#NIYlqzZ+~>qQ$8QXGkBqJmYVR=`L-Tpq zce5hlSllQnX_b*1*b4MFPG6rWqhef8W9>Zs4MPKKXqp36#qJe4(uMkEoRMip(e+-g z+M4tz#33;uA;`~Ty1o5mV1`10Tp6=ccZ#=hVW|`dZMqPY6bO;LPgzx{phk4siH+2E z@c5U!7+@lweLQT*Yu%O5w%62?(g8r<97oFTMhPDZ+^?q>((b_s|?${;fp1)ofk=jDGUx>E&Gr)3INo~%gd#Mok9dY z;bw3WuMZbQWUREGzSnURkqCb48qM*{wbe{~>Le*znwQ({%o8z-CK8q*jvj3a9_L-O*F3+YfHVOtb@ftEAK)@zxZi7g%ycex81{7I%2s1hf*1=n1vuY(R>irIgkt17+lf-b@3rJZH z==hAh5{l2rvk$&+D_3{GOrv9=trl~?SjX8OEmg>}$Owu?$>%j&yo%BrgRnyBjQBR-53{aU>!|G+IlDPGjj)6@t> zD<-L=W2IvdX;jd+%Q}uS-O!*Njlgn8K(K1ZQ;#Mn$9^VAz_WE@Vj%97(Qtwx?sa>H zvaZ!tfcOETZ1zoQn3M}O5mZ6_wrgDuK{_M=S^8a<8!)|!#%q|j=L*ZcCxd_uGdx8@ zF2cG~Y%(7h2}CS()`3-!XRhdDpXQ_vf(w4&E{A8(v&s(uVY6LRh@>WE%f=lphwE2| zhc&VBeVQRfe($9^;?gHDj3Q0Po?A9L43dz7Oh~!ua5$1`{K=%R=!oI?;(fAE;yKxE z9A9Dt9A1i}Cl!Xf;2E0lw>C>bwj+rvva#OvG9r3OVC*bzyi&GW`r;ukqo5r-m4lC! zh)+t}D~U-EI>yEI^z{a_#$|QCCE%&nMrqB9UvtIAu%KX0b3^yR9Er zMnBv0J8YCB2#m|AOj#bX+cZgTHV#RS6vDK-2&uh6<^W|151SN*Lg_mOVVWR4IcRAN z`t~Ki23;svfvSJ59u1fKV{!MAGxW{)`eWrPw!7xwA+!{PKr$H}GqYWK8-(HbzURST12c0w=>y~$y6wCk4UaYql^ zbOGu@7Mk@Obge9W9VDT>ncXH!UI>J-piLwIe1DO?ry@KG5!v}U)?hdjdSgH7Fr*_F z`lq*~^0ycU6nq&r)T~l7c#|)Hyj4pR3KL^Pt{^NU83xG6qgz6B^&qK;fvONJQK>A< zpo#Cw-ONRON+dxNu$Oibe^N`1TiG)Qq`j-r16XKeN<%LFC^-?%032dCHGo>k|8F2Q zNGu+P5D+3^k`1Cp@Posc^tiK-O#+7?0)G>L2ywscdXe=X1^5&|g0Xi!$kHZ5jf$Qn zPKv)kixOrdY+92gm7n{`zE`5sD*jA2?W566|tSc4cLmw8l+Mk+yp2E z;A^pVU@ttPGm`%Pu8avixkpxER0g=;RqEeU4%|fAMZrA7F)7l5Cus!zH{7Qt04#;= zzo5cE8>0Q>a<_-aQIIgXpT)0}vIwI7DRTzCZRxK%+4zo7uF8W%{WkPl-#IKDf{U+6 zu%@Xf=sC+t?pKg6d}OlbdUL*$jD8n*k}z4`f%Tf%vBhE7BX2P{RXAj+gtYg5kA zVasUchA0J?VVcvS>X3a(c40DyA@M^k?VvUWfbKBVn$rrY8|JfhjRC{$(lUy3@{VMO z+&BuH8svZmlxuEz7MRK1nn5N=jzlPA7$hT^pfENL8p+|1Qp`K(0`Wo0?1s$W(hNI^ zQtaTr8rn_AmwI?Vv@@qt+UBWjh};k7m#b=x_1AD^kt9_xGpMchP^iUl;r|=RsCIz8 z=Tk>)eu;555AM8`=fTj|ys6olzRKo?a)~xJ>i@kh=C1N|DuSR7tG_J~lMvoAz9CEa(7+^e zYHk`1+4;(ep4%d;&or6j<+gbN{%K@noxL?acFQ1W1AIUB#7aB?F+ZKqm*enuxg z;rzeXfY-@a!40Si>{p89md*ZV!5(ng*i(A4o=Ln@O0x5+3G>3JI}7oW4J+ zr{eB0^&AW9Zapl)!$-2R9Y>Y znX8GGiC=5t4mG=b3dCLU_8+EOkoGRDacCa>TbG`fV2ggEp}5jg-wJPVm10 zLd=e?+lGNx!Dnp^WFD9$#MfD_kngO(H4Hg5HFgt()D*y7$8&64q{X|#>KCG0g?xZ=uAAS z!v__fT(sHV)pjA>C=QIFZ2xTmS8xpgKLB?*tr2^a3H(KNwcS@oG6Bw+pCN$=S&$G6 z6LwVz;#DrY;eP-!rv-_>7*6!!GDgLwX>bCm;2*V6` zTrgI@YPX9VaYHhS+u?mKf)b%l`qT}a?BtEbb)<>}T|cdkvNW|U#P+#DNne{;T!9=6 z>pL{KJN6R>dVv-6U&QQ2v;km+5Sx>Ny6XCAzbfyOgpIYvGgZ-Tv^spXuxCFQKT>n; zPj?E+^cZJ`*GJ7`oinUt66jV8wC$XrZIN+_(vU#5lC>avCr5v;s?3q&wkOz@@g>Io zqPNTas7dzJg3_+FZQ^TuzQu~w0+tMr&5VGQrW=)$2^`SzHm4V|1Xb`iqcC zHRIfISho8fyTl}DT`zMpv42t<1>mXyq%fE`eussAIRTbF2RX4jPe?IhfmhLsrWVU& zWk5TTIMA>{;+POg%%C5K+=(Bx{EwpsKCMR=qzNkXWp+%$NKqqHo<#RA?zkFW9PkcM ze~lkw%BlNsh<((L#>XAa{gQqala~Xup#x0}sieU4Zo&KEx-zPaqXECur&PN;LBIT< z!Cs=DnO9Y-{pv>$1{9XDSTsGPd{AO7i@sI+HstRc8XM%gw&o$c#dMoeE1Ma;kRb)V zNn?gs9;|RZB{xw8TxmU^#>T+9Crp2@->b5vySw}D5IuIkR`yj3z2F=id07~qEx-Ex z(E4fhQt~#5Kr!`575Oscu}tQNIb>pHDWJU0-BBRD_k3W5ipeE|aJ&LrC>2H-w@>V0 zsOl~dl}a4Kn*;xyUC(Yxpx)^**Zq2yb6u-dPRaVy>CD=iVY8(lW%jeR1Jr}kjQf^Z zJzW0RY8{NC7yfvGAf|0&0fcgfM6SvWint%t~Sqh07EEJW*kq7-gvDB9oLZF)`&&G#Pb@IRoZ zntb1jTJRPOjzkI;D~X;0K~&~Xjk9h4eA~-{ z$pwbbA3$#iWD~%?#-xa{32Yi~os^u5&n(#>`OWD`n#zmFpwo5WH(ov{ArSwT+Cl+v zgR~r)vyW!S%>1pfmWqYDRj9c62$H?4fGm;w$0ALxwSGeu$mt>c=q znyE^@TssGff;r4|B!f6T9mOjaUHHIhzEd_s}mWAc1aYA z3mlvr97aGU+kOM2xpkUDZ3<-vDCQ6`3OZ-chXRLIynpQ$LvyCy;!?W5nuKMf{!a_g z@vzD3E-ASq(D>M6tzTPm)+WA^f^vxsdp0jZWbqFq0_5}}YHkf2U{O|-f%EpAbG!cY zu6Ii!v`)RjrJ&65jt)oh6`in)f^Y~aBkNXHzopixq9L}={~UN1ETy+={`}Q`^WRLR z9~X;9pW?~ZMp?<|n2|0ui=hB}!rVykH$LAv$JmE5DsA5F+D*{0j*54Nv-mzk`ca=~vcmTN$-D%4Lq1Zr^!& zd05@((38)@xf6d*dOZK9es@igz(LT1ch_z=F?D!&|MxIB_YlU1fx_d0@mm8|p@FsP zSoL)4NdB4u5{6v|${~gRahV-mZ z_U|uLxc{%#BA-H2BRCI9Ngr)AZrcDK=~aZD?(Xt(_%z~GK6HF0E-tR+xXhvrn!L=# z$fm1~s4Vq6X%3z;RPno_diFb_LHmiVYvf81kjRM*6T0NG#E$GE;)W5Gc_APNs6xh7>gsB z!?SsH#0%>2SD|Pi3mXdTv6?RBMMzWCTxlun?w;&ut2A59-OpC zN60PDVh^&wb8s67pO1o6k5%e{*{i_0hAb4tA!Do_r|ZA~-QMa+6=nU-zIMxR##SaV zsW=8)Ov{P5df|He!^7O^HdkS^YjCt;C#W3|u_GzuYrYkpN1VvI@Rh}7Wv0wF(rW_` z4b)X0c3bVV59zZx{AS~{>D26mVUQ5s;L;x%AaNX6f&fZ~9V7#5rM-gtt_SfI{;=;U z9dw-mv3g!u@yXaOOp}llISD28mGE~Tsnf^VC2!xft zw{pSaegA4#^x{Ui(j}xbQXk8KS=X#XKijVWSj?C5qRbRvs@`jt=^%&$Y24x{~${Dfe zXe2+{993sPVf2f)sc~VCPD*_@=GQmv?!QL91w%QQq&XSc;N0}|Z2UY-QaqMAAffy8 zD4V28b0Sb5%BuWp{vNt^Q>NMaoTH!oU8bgvfttRGj=H8(d3AGsJ8>5U!TjuWd8O&P zI_*e^F;z^o+`v1VATo4%=nCifxW29FE?GB9Gvg+)rx5h;Ft)`A?j-~3PV?gR)XMnZ zUv^!s6H(ETG*}2Z=@Q~I%M#|{JgZ_{DHGOj`^L=1CWkjND3h&4--98g(dq01J~WoO z1ki|wZ#QLBTKq64`Ds^-*%4n2dFM9LLs9UFYy6i~q(s^-Hrh-V21CQqpHg5=9G6!H zTavUOIWaEhs&7`1B-UffIhy9vxTL}&msS}MumcjzNK%62ER`YQnY zl0L+IS{$-)|4p_L(Z-(sq7*1GCq|uixx$|JO+)#-0v5}3IMYV?-H<(DA4VX-?ZAqz zvM}^LG$?h5z*9$aKr(ZYi1$r*E*bk-pQir)WQUycx^;f6x5wy!@s@kbQ!+k(<;V%6 z0OwP^6KV2^8G$zh_RNt#fhZZ)$e|Y*XtbD|cT0>tTX7+Br7Scw+N?Jdu{HU!C2G?L zZwCEJu89m1sh8a<5ZEiy6HHB#4h}F6_A&QOtgvGBf^3Fs2CaB=7eB9wRaBtHwa0-w zVaERb?DaPcQdZ^~p-@id#yT)8E6u#jeAS%>W^Rgl-&K=gzax=zb0aKE_3L6lRtM_j z+7dmz4FYfBVE61m$j`x!um$Mdnm69bj?7ceO@6#^&nUvF49g{+?rBYo_56w#bfEp1qbOQLE z91}W~iFrH56m;p^Q1MO}(SvV!xVN0;laBX@vJBs{+sxmta@T~eYeUHv8!RX>R7WG0 za0?7B#`PmG+V9&(#J@!1C{scrs1#!NOK=bdD3ttI<)*KvI)AFv2M@#Y-zvhJ5VrF5FeR#GxDP`HysF?xiiXm&NwCu8HqE}}HBH#G2KP|#d|8ljdS zA4?P4g(wwkMwff_9Dt^0eIWl58nm={R*AMOB5X0_u#{^bR@U}M|0L!*TyAMl*`sII z)z?-K)plDg@BGMM*#wxbq(bmV(Ias3LT(~K7qG{DnE-L`jd0-*XSW5avR{ZWN6ibS z%U<`=pZm4cffVF!s!VSXC)>`qszO8Cl$30eGQlZeY$6i#)4S1o`cnWWQ3F2Ri9LcP z!W>6$@m!IS6)$Vr#GxZ0YSwWNZ1O4L(ZE7uF!jhQV$_~XTddPVOpR@tK1!Am54uHQ zTi`u1kb$r<5f3(#6tmtFe(zW@i^pMf@tgiKnGpxC>8=5(r7Q!88YjGj-p^1vir2Bf zi_C!}sF2*m0oOr(SC%^lvxsW6q7C;Pf4j*q{=9jVF5fe>4I8)gRld7yy_t-3mMjFk za(Y!Y$h0W?Yt9wx6ise0xBm3v`{S@U?D7zt7L?13&5pfLlLKu7-C?voIfAl` z1X4^RA0SUFHZ%+@8>IC4+yo3&a~(j+Z%7<4AVnS64=ngt6YX#|%fst^E75xW{b9ff zWhLzD23>8J&^G^xYL!H-th5Gph{BDBI|NkV4KX1G9R*Hlz7SAIlF>o~8quw7Fu zE4d2?aa1($40XP!9LBNCQ;__P$keTwEaA)E7imf#htHOd|LpLLi$F#j3X+AM4HCD+ zFve()7-G{Z6kD>g?@$g7|31vDS<{8OZETQ(L~B=o$Y0RZT044DS>eW?Z7*0Hg|*0z zVSL44GHAfAYgeq9gZbAUPlJe5jR@~gOeE$auo8-~KoO8^3eoKtUhGpKh`bCv<3Tcj zD&j-58z@>1)f`@%uE1Wiuc`OfSEE=<-LNR83P?)>~B7 z+Qiq{S1_k1T%_j{3YQPgRwcKyvc>gOxZ?(H!&K~7=Co*o2fLP0Q_{XLu}ExuAGSW( zI}}s3StztIMq#R?rfMGe0ez>{h-QEpYb(mZTODg-doUV%#Vr|wA81oVZgOH6#pmlw zytiC70K*Ph)-c3_FYvJD9@GB z6Lid%Tgo$xos3!9xE4bJD+pZ#pTk^Fnj~KupR$2Id;k0X&@=BPZ?o*6Y2#t2mCv9l z)B0}%9fBX*HIsH8=@$a_C0=XO0tB`STNd4;_zU_$Y=j0CJk-;V0j0kCS;bLk(s55(W;Qe(C=R4b#3|U^WY;iLg7$U)q(?E1Ij(+|2XrR^p;}XY1c$@Ml1m5<#A7P-! zFP*3?n4-CJJfi+8qO-ibv)qxHznYtWI`othq!9%H7}mk&#f6{y)`1;DPIVPy_;jpp z9@W8>31uJA1|d`orU*JM$Dz^MoEf?wD#f$!#o(x{zM4+KNU#eq4vgZa06n6fX6?ERF3Ywn>! z=we8a=z5Ska)kJNg(5Myi&zW_sVXu6L3pI-r1eH71W%li38)}F`5$Yy$)@~+tvvz} zX>`h@NYWz#kw!W(Mmx0vj(fXb%*_8)mws8x&c$6~OBsQcaiS;3&l&0)Ow~`CwpjU> zK1*<`dHLaXRDcy{AMA|5G^q_vPg$+|7EcdrYJ0ie!+yUsWs-<)1Rkc4EL&h@ej5jn zTrs{u5sW{RX_F_adjk5^QdvYl=oCJQf2d{D4D!`MchWUPONvA1feRQBF@WKvi3tOc zygf8T1O^BMFiDZq7Yx&;*0)-K1VQznn8VP;26PTs4eCetK*iLIwrX)*_?Yt&=Zu_U zJ)}=2YUmOf>@!u;MUJz^R=G;x3T&qHC7BjsnV4STk>Fs3|66;Eii;ZE$J&o5-FX~5 zj2NLi4^!_NUP&uj*P$j}K6RFID=F$+QVEXBCM3coJ&E$rYKg7B$k>;S>xUxQhmO4? z#DXa5wf2f?9G?JnaeKL_@)QOa>b2QR&`R>yf&Kx|KIY7fq?o8cr~vv{vzO#^A}gNJ z`I@X`R(yK;WesO(pB_|oyr`L$8<`#Tm#k>(RBYJHsLE3}pQ%yglZz^U$pTBnc{Yy7 zmHxVUUVtwLL-8+gE)BU?vK+HKN-f@J6;@j{=!+C)LxtLLuW*HAvapGnn%B6E$os3u z?3>9msy2QawSkAQs|_8Z9~$S_!<*_R*xe?JI3gYQwmE;=_<|n%A?Dp|(AiS|BMmrB z_Tp$}YTgXPQi8*%N!IuMXD+_anR!DZHgRUNYNuBiCFl>}{?isUz`h6|>a1%Ck^ztn z85ydoidq||0pN_-Yx3PgMCP#*p!2g~0oWj`fX;XmM^FD0I!OPzc9m_|I;drWMdrUx z^El%X%`4(l3K1!IkgpU=E1G1mu|p*qos^6@7%4b=8g3Ye#`B8IlPW}-7p__z^^?T| zd;J2&CPfqtSfM`ndo9L4k^UJO{(`oIpS}VXPac}g4=)ue!k3Ro;%(m_x$_yI5`P}O zR+d)w%mtki(TQ-0_TpBuRqScG`xhuq_miv-k_;LW2}fB~;v6wYZ1Q)Nl$9CP0TGg1 z+{i(=_HnHt+Q*>n(N?>SxvUCz6gt!b{ShPZ9uL`qy9My7 zg%3G1XrddukgQd+5QiF1n8--ll{Ila&4oPOT?Xh7nOnH}nhmV=ZJY6h(<%Ol%FyOz zK8risBh`sGw59{|6REcd$OpwKNj+CcPeR5(U3lI3D`j7+*M+H3afO8obV^B)m2Eum z9xoDDUOAdzm}FWxi7DbdxyE{q4zfxQtr0p8)fnUVKk>4j!YsrU(@73R!gksUp1OIZAnu zIxZVnnJKq`C7nHRpdn;>TK;^bYP}+e2|$gZ2hc#@(`e)ZDD2vmTPxoi(VX%eqhHM@ zM~C+Y0d^oE#MmGrCh$aX6U6nkjshq}WkhuJj$EZyEHP%++0t-fkBVAps#juR;@RHz zyoW-A@kyhLIwChwF%C8r(s9d-&#G7;iH!Di)w2aSzVu4isiz~A$w+GqLmRcBW*&08~TiZOQWzx9*RiF3>J1!P-<_iJFqqK20G-~PAL zzUPE!K9l82jdJCGAMfmhuaAnJJy|$^4C039u=!tE38CT3;s2UK1mJVhFlU%XHAvs$ zYOn_KOkGA>$vTKFhg5^u?-8stggEpO8-hX_(oGKa0Qn%r$X`hFX^hI~Ehum?3qnJ& z*2pDjfMc=1ZbWBF^r`^;pouDwtz-I1g>p970edT^j}A$9k6hcc7X}9A>&MeU-p5@T zY;yRQiwwbWbM_5XmcB1s7 z5>2C&&-6~!^^a;eCp%5tqzH3JYzZXv@qH6lgTs3}v54`K^!VM{^pKo%;Xcsa(}0Yf z?YVnXv+#$iyn00uqPwPPP}zEH@c0H@jWhL_DaRa9+`1eU3R(={#)a1q+a7;zR#TSA zvV++EAIS2v&4oKCQp>%2rSyljsJe=TLhB9>ZlU=+?wWmnBa5~Brqftr+dP=E-gutO z|4ai7x}5M29-y2|e9<+y;LA9fEWf;T*2u=L8*MaWR}}_CfQND%JOsW7wj_pFI}A;X zK>=MRBs}umZx_1nK>&#%2uxID))H!C?AW*n8{6Ifz#ZRP1$+Cy?M??q;joqB!TIdi zmF(=tguf;lhHfDti!lzG2P`0%WP&U2-wFfg!gFRj+wre&Ofl-Qzb{4PC84{id!kt$ z2_tlWFAB!Tm^y*>N;frAku8qQ1P9qm>Rhobv0M?pb(llwjH>3 zjGSe~yrXW}n^_xM^!RvRVOmAfX_XDc&#we+?E>f1pHVPfbo5crpt@NRp2O&(eY>1- zdIo6y6}qK?IUnp8i5gt{bS3}hk)e5;12sn{U~TD4scd)W#%z`|jnvUkKlM8bvRn-; zGk@*0?=%d=A&$+W!i;bj0nky?XsQz`g9y9qmY$gP)F;g1#{N60d1_((^f3C~Xo&erb9r&wYGY5>%y5B?K|$vy1Ckz=ty@t`F*vN)a1GK& z<<2=C&^xu$l(-2#wZLQ)yea-|su%qC6$`TE?fSqn5qVFwAoeVflADNPEVXjr6$jzK z#^hbvdZf=cC6p}qL1oHp{9kS3u)Q#xuker()&njU!K1Fr_*1mm%Hb&98xM`iCC7f_ zH=@6;b+592ChS1J99%OB0wygW3{eM`(5KQ^rEbzq;|9L)*!Ng&KF|ouvy(d%Q4_{_ z5TX{@{xe`hhl~bYzAVY@&fNGI*WU$Aofju520?nlyMLxVyjdt_${PK2^?W!+K9fQi zTdrnHZ4VZLmk0<~c-r5L+|&>OpEZl?|LW|kqzk8Z!stecR-f`6ve}7=0k#Oey8Kb0 zT9>nsy;ac$$~wc%^%`#%`1gq)^|nEtwC_!nX&Z%HL&_=LD1xNRpFQH`FIyfvDsU*W zc21@YV@h063zkvZ5LC+45~`>Ynu%27%ryj}V1l~TjS0VH*ih8(90 z+8v+7?54vXC-bnwh-4(v)C;)j`M}@ZR0Dm)pGq#swqO&L{>$1Q2`h-*wO?eHzpHnx z{mR}TYGfW`Qisc@viHw z8OJ8Wc~lPp`pRC9ab6iW+R1J%$RvVAgNA@;>z<9m>igWsR9qVA&+HWgN1@`@Ny$bfHOD95i~` z@GDs&aGRq53ep4uzagO-m!jNdN*f#@|6)_}LZsR7I)Wa)QTd-)@yW<%rt;;-+qL#a z{)V(b>JWxfTU>jIAaM$s@=>aA8%Dk^IfnxhorCFGy)mip)j**{^sZe$C2DQ(q_K(u z=XMDlvLru0LjfCq{wP=RmhN8I&#(X3S#|x#x5el!D_dd)@bbPwlfuxTCRT+>^pC1b zS>otf-W3b6I{U(YeG!XJMH5D(PL3JL#4;qIG=|u%La5I~7RH}h=VV_P9M=#;G-7hS z5@kiYAfr|JjTCT3mK5_7$qYu%OHcU+YfC0RIgQmbTnC^V%CYnd%P}+#0WLc^-3c1& zp<2_%1a@ID2wY*(GZM?R>jyhB<#I7;P4M19BW%1b`Fu0Qni$Q>y<5MY*wz|YIln%d z=QF|#WLPecm^T9!vHpj@H`VN%~h}P_VSR;x|+w}M~H!Ip`MiyF8h!4z? z{6W-{MSS6sjCap5P40{I$qpg2nc-?E*(){U;HxuC0JbTI5lX0*J41qhhBh|ZdJ*(6 zD^T8R;TV~7A&s9gA}Lcd_M3etA~c3;ZjHH@Onaje#~Z07+*WG$rQeR|m$U$)ztqLr zDVP(a6&o4fxdUek`y}jAhk-Hijx%{*N!u3{1yHs}F}SH0Uw~dj16%wYj3l_biQV>} ztOk&>?81EP-JL5hu~9a=DJ-gz%6AlHR13n)XWyScTidfoOPtZfl#6norSqc6hD+NB(0QUKxRRwrIA3cJ$&E>cblWW%fbU*GeD-tQLIMv!MITlII& z?ImMmMYhRDip@wOZ#)){;63fm-vKBS<{z2yr@JhAr!**@=HFd~^Qs(W2^N@$j{v%e)A-BX1$Y^2bduV05yV&uos`}6jAk*CzY_ZLe5LCnQ_fGu_uMPw(X z&TfJ+0n*0%<_vmnuO#=Y0#!0LN|1|e_T{}#`$+j=Xryskaf2K1_U*UuHa!WQrpR7I zHEoF>0hz`)7~8O#*bReDpE-a|lZAob{qg|+Ybm?*cFWw~#`-eh%8W63NlMQX$uXc= zAjV}}+CW8z>D?aMN&S8b+8yy-Z7%H(mfVl2DXoK^Gp?+!e>t;zFz(Z>`hFXJ9MT7u zVqQ!A=((c=@ilqWoQ`ecV+{^eGsCrZrR; zeqJZS@5{1231c~MtID6*9Hcjlj#$zAl2DZXfw!ru20?< zmfJnov)y2(aQ*JTpSSt~ukSBC0zX!Nu?D~WRASX`rT;N|?orFfDKMDhs`&TkKR<79 z`M}8Hc2?U{k5`k=2z<^nX%q;ID0)(ipk+wjV+CE8uw4kT&cFzf--*5(d&JdJAQSQ+ zGEtopN-;`mrdl0xi`nBwi9%m0de9Z?^|<9l`tg+J`t5#yK^+6}DpiT&{owr=Z4?(? zJ%S+$@qUFMGrts@7lln$i2`KXfKb+OaOl}U2}kan7z`MW0@f~6#`$_%wjkzak>`!d79n#}&P8zIn!tXzh;|0gEKE{v;xc`~X@Z^9Y zwHjJohx``A2`kpqXyVDSa`DMPW)E`nMSqZo} zE1y$z=#Hmd?`5^`UeMjto4$Bo1HVw!Gtm{N#-P&?i@T;UiT{pR&^BPR8SMJ^#_vqC zX05lZzEIM}Mg1R^B4L6_^Ue11jsw#SAxHhzsYBn}g|d9!;;cYxj_Gndqv>n*f{^nO z+E*qa<=U1LF4y6NZJ=ShX%glEmzFvePLEO}+SiJ)?YuuV>&a!*tzYeje@U62nj%@s zK2+;B*t$6>$ct{Y63{>GH{z}qIPAn(3E%Oki>6i>N`kf3<$(;pevLhtBF*YSa$;5Fo*UKQ}e@cn9-Y-328GGvy;j>Fz;0HmJ88dlq5Z%CWt zQMWm+v_f&SIbhz0tdIi$iKsor`=VtvA#@KFb?oyq4_cqcPyuLnKC}*D1oMH<+=qdf zb?_ux0d4bTN~FGCUR#}R4t?(rMe)LfyL@&rWKRbNNHaUwrqu;9Icb)Gk^Qti#6^N0 zTKN^@daS9NEqEX9E+TsL8LS{k@03EcKD?TbGKp{2~|be8T! zxr5rzH>7_0`X+{71f{Ls|82ShUt4PqSLr;)ixO!#JfFX@WBCX*Ki%r>lI#AW zwwt{>oKpHr7nWx5P0(@FO|7Qz%fIK_GgpTp@UnGMH=L-8vEy2V=At6z%y@jFCFck^N!!%lHkPwNZC4 zVLYIpa^4gMAL`S?mONsJ5)7}C+{g3v$K}UckNNs|!H~;n%o)+q%6j(8@sWeVh;PeD zRz;iPT+>fxhcDop6k;VO(7atLNQSy6Su2#m9VP%lr1A8nsyY>7>Ak$uLmfb$4+cEt zDQ7kF0F)M#bJ*~0)(9r-;E z5P)j(-tJF1zV$G-rVk zah3U~_k}Uxci+F=RbjzqGsCNWE}|}DnLiM;D31m_wBEbjZ=2fUMRK`)*X7cP4=N(6 z$-t>!(HRH_%%Mdw*&T(gkk@HmxH9u2heL-wnF8D>^B`QwSVLKV$P->2H3M}4a$nowC_a^}^V>URud`at)SLT-SR#xg{zCp(#`QoA7_%+e%< z5;}Rsa}Z#lWPU$ZLF;)yT)0iM?XM-FYxHKxDA?hlpHVNSnQN%~KB84|3i6FZSQMAVnF5nkZMrt zbaxVka=2T+KJ|CXsO2jKw?Y{;Fg!Fh^K#1^JO?ISV3gn44@_j%O^=O~$4!P0`#fK) zg{(i3I-~-Ff5CQgeZIFlKfiu#x}XVqhp+tM zf|Z7oP^~g71}G4@4E*AwVrVq@I)5y~zP}9N$tpDXVh3kN-{p@%>-_LL?f8)6ojS+R z2|Q%_o8lx`(l~bW&(rR%ve@lSYc0Kp@GXB^T!towjK#2LtyBH|envVGvtQOT$ya^6>uCrgf;xPOpTwpH(drW2S@cI z5MH~}V+WMLbhHG?zffX;R}yq>DkYHT*aWt+kuFw}eh`#XEyuaO!S>^r6j?PYk z^Pq&>|E(e3L16`!Q|~mP5b1!8y|zi<8e)x_voptLX;y}D>6%!e4_}?CFGM>`CWfT@ z(=D1g|AQAL&AgzD3WJRcW-#dvgh`(iQj|VrYya+Luxo8pI5G(Pr5(zDFa$w+jdw6t zQUvDu7hZRq-`-&8}@Xca8h<|=}N&<3Q zqO_kZwiS|goEG3&^nJk3-w_IVNxNf?&WQZWE`GlVZTrYzyrv|5y1G{(F;(%NomiHRR8XX1dR^q_WHLWRI2fzQsGPCYjMDSe zDEc1upgBaqWM!k?H*T7{Nn2Ee4ydv-a#7X;c9L)BA-M#W?VmcC%(jRdX@E5aph#eioRj$vtE+qypn$^HZo z;L&n_@x2n69vl1ObDr@%H!?7;ci4hPP+zT_Ow`SdOVYNR2B$6hsh(Nm&+BDhcohWN zL8eWu$UPBfdYkB3NO!E}8r^*rv;&k7*swd$oQD{DtHXuXH}1##*|(209yb3-a^!!@ z{S$Jg4k5oSv5+O936NBb65(fZfDe5wrsksJPfE$U3kher8=lKaIBNt5ixfpkCWTbN zdS^dIT!{WLUA466`Z}u?lTX%A$97(JeRII`p}($>xyzbYFqk%QU0B}M{AM;=ND!lq$PkpQD_$|tp1NBX_zK*6q!Mj?MnL5w? z!79?k=U{i8%R~waB8E(@Bmp$3+Av9tObwwXL|0m@Xyr&zRjT>~5>mqV`*jFK*;2lC z7c;%&Do0=Z)dYGW93FC5uJ=!@GV3)h(sf)%F2u=z_!SqF0AHs@kj4q2N?&n+9-1gsriFscWU;B@zsaMZFx7_K8p8MONELbXYH{1|3 zeUHO}D%cC}vpQ0HbeYcO0MyfGtiTkJlOiBFU>Ja|1_;olb4Oyg#N>axsIbb~>9Bee zfJMuWk1p-Gd$apBnuF@(b-TV-p}ZhMut?b>g5E-+?Z-`eYO4~4w@(|LlwrL3kk@P2 z!)iSITvkHlepQQSjb{{qI*ARC#`b)y%V_dkcHq)=y<8lF z89eHuP5|BOV`@{a2I3D@jD$5S)szm2gRcup<(cI_V`AbnIYjAVKn)i|$#J({^!@Bm z60aB~fmxHe8fC-JqnjdZ-mpggY1)P=BnRA#50{8XRlj)Sk!PQH z1g=KUQIiLYIVN)CJ?m~A1+p2>PKK*7=8B7in^J?LqxjJ@W3frLn$**+2~Q7!Yl397 z&Z6f^5`&Zx6CS*U+tzJHqR*rkKUw_059Yo2`YY%4(Kdhb*VjMa#w9Blw16oPpt2O; zO=EC0@E0>2ULiGb;K29af8S^GsiK1iT)S3&{`T_^{Nvu6rcQnQgD<~7P(c-}TJ+3? z58V31?rM*Pz3UZtagx_Df9mP|I-EXtr#O0zaOkI}PaS>E1Cwuhbn?XUpRM1O`#|3g z5z`m#3S6wBgfzeuBv7_#(Z|!L-Z|yoo2E>8VAkA~RHT{4Q#B>W8!)7bUo-^1zBYdR zczlzgYuB!S{p(-x%pGp1j=Xsnqc{8s><}G&^wC(!&yBrcIwp0{3>JCW^ z6`q8Ju)ZYQkr4$^(u+yy;727dCLN)8Ie|%KQ-V{mfzym`SPH%cwwY(op8c&1&pr3t z)TvYPh5+2YD1jbrnN|Q1DCU4}b{LQz)U=)ob@$E#w=* zdtULswO9O)Xm~ERy~a$hm9Nn-B*YGwx~F!k)c?1Y^C9DkDG!}GI8D~;w66KzeDUyS zza1*jwC+Maf>DG3WgY38=9D7{5EzZgK9t*tn3^v)Hy76q=DAm2ef5PGUdYLzMI$Cx zDIn{`GR&YN1KPFkojW^C{lrA@s5bJ|6?0B%r^%O;qzHEpe-w=N6)7%9&z3*_Xq+XR zb3s((z?X_*-C<=5UYm^M70)fca&VL*uHDw>#XHtE@2Ny=8*IuowTs&^=l0Y)S-n1d zW9t8I{p!Q}#4}Bzbr5XinA%R#AfflDp z1QN5|^y$;F2#IkM7qItzHZE`ehouE1X^nx$gyfV#z-(3WW4eqlgV({G=JI39b48c{NVAs9{vot(I*~btG|?>vy%`hMQAGn0YsqdFIa*Ix**AgHo~GH z=C*6Ey%u+q3l=QEBF7zf+)-3ige*lV08#+4VKj@9N-@dhnExQT8aa3Vck%bX=6}C% z@x!;mE6ZKGuS^$Z5-G(}DXJhTNTmcoCY3sfOv=-LlTN&6!K?2t9@$mwn7G|>DV^Xm zO&I&9A?>>k=+-uS#69b#zq8<$QC$PmUzV*RDo8Kad0>M!_`2yI{FMLGruh4lHlr zMiDC+cvC8nz+gECwZ>7EBX3~?6;$^=WPwn8m|1{)WGrvM9lq$Ii&m{#g(J=AvKT-= z|NL{jgyWuj?tw?5YDL$)pBp4GBFI31N*d^9|FzSN>tja)<&?slzR8uw_^4!JphUnB ziDTqLhYrPy0EiK*A_x$< z2901bvO!AxI+=mzzb0$Q!bH*1r9&|^sRpE*ELYbo0R4Mic;_YWefHrekKFdjBTAnu zo?UcNuSBXWkd4$I=3V~7+^GC48l$nK6i9%1>E00lo({mBB|dYAuK~RC&O2}uaoQT2 zVt6!h+_-Vj6P6+asw(IPmo~JtBehZW&QET8N02`@?}rO}CkdML%u6PJa{8Y)*0xK< zBgs;rh=lmC+JjqHuiLb-xTsu9XxDSlNqst`QFDNx?$GunOMlEO(h}0z_Zc#xTV|Zp z$hCXb_bWE7ErUe_nI2q9!5j=$w;Abiz`LP$mo?zS&foIe1{ZHQ#-;>riyBQ_t() zFXYuoKYvj%AzBm&%E|jpZ8~yFDV5tybNcJWAn!C-_Cj) zcSrd88t$E7_K_n;;v3g=V#E7F5al*B=tb+=HdIOz@u6y{FRFRfRaarSz?UfC=^_av zvY@KcwuuT1T8uKj0)iI77((6#GsfWwVr-@54R{#$-FM%?g9n!{Uks0{&T$B7v(0ka@HKYZxn%!{u%_sV;`Dj^0uK`&$ELby&N z0p9}+u^6pXKRs87!#?F0VwF}tbPI%r=^vgx{GqvyTlQ|IPr&0x zdS84%BZ%0v> z#Q(PNLjg~C|NK755~q8UX!F9*2?BRI)by@@zud=aw6y4bmBiYjC^O~x4m|Med)NHiDl}wBXwT+bI0t1 z*BmdNK;H2FjhEi_2G4rztAk_kG?TmZyT{M`$6`;;dH=UAr$Z9We}oN>#$)L+;7QKB z-2`DSQGJ*)4H7}?F`!{Lv7k{`w{hv*XJ31SC~=ohs~T`MaQlQqX&Bs^WS%4WIF$3= zqMe)bODXxg$FPaJuC~TeRZ()N-&vQZwdwfw(*5zt)sfNr!oxkch`+m@=N#W*Q1?u< zMI$u{c^i#1;P20ls}Xl;SaQS?96XH29(xR1Dw{WN#@#ktjfWq87}wan_ulKd+SN!; z0|t*(7ODk2B>^)$#tOQp6==`RxtTs#t1$Vdas$yV5E>>Q`V&XsI+E!yGXCHH{U09u zS-pBSU^{kPlV}duoM?s&8FK8JVE+ilM=S#K$yi0jtmm_( zIG*zYuP4nd%XLF(loSAy&uK_8#0E`dT6XWWY*TM(68*(kEpLuHcI%lCjq5+Rf(;3zxk|a!JUrt&~F2 zoW<+b^qlzoeV3loDapC&qxn zsh6*R58g?iN&kI)a<{m`rSHC>MTwC-u``^AWSj+w=`{MCk7j-I(Aal2X!lOf&B+qW z9-H2{@v&KJzz=1rx4kP}jhuVmdw1+{$!p$!?D|LEc<7vG2km-1J4{PRB<-|w@0vcU zYiVVB@pc^1;Ii)02(J81tBb3g`CI0_bk`K*o%__iz2k*sGLN9fe;LSVtUpk#g*cbf zm6YB#CN{QP*RJ^L6W(WqO<}kiX=!OqzK4RA#qhW(uXI???6#@#ywwa%t-q)Ok%=H4cv2-XF)1!S5eKT^WRU^6o=gSa0S(b!zns3o z`v0_b|MvXSZqe|D~8afO&c6%!}y3p8`$i}(+w+DtN>YNX6DT|-;6gCFWVzrjoLc5xKA?- z0syGkK(0s{W+YouEQ~L*T$ROzAj?Rhd0G^xY;Lh_9bQy3!*Q{GVpltJ z(v#;tJNrw)f#AsBo{OJ09Xh8aXQd4qf6pM%NVUZWN`URuzI}(Rw8*TpCq0YO-EPk( zZ0h1W=L~MMy%U^|34=zQ)I)B1B%ah?o29RyteqzogD#)`+4vLTp=g7ydGXOjZ#**h z%T0TywCezb2!n3<^07&y)GfA2|B4l(&Kyo;6rVftSf1oCF)DTr)Vb?2I&^#a&G)-x zCgAeHCIW7mu&MzV0~%fUTvUT$T+3+Us;VkiZC%@x_zoF{KSdH@KGB?B;t7e^95~v< z$`hQ(%&t-(5gp6zuBl0YClrClV&#Lc;bGxH=_53!;*@9tY;@pg6Dv;`M5+6Vsw&H{ z+lY&ct}ja=fhR-WM&Pz;)s(oU#_kF33~*5);IRn!95`uDO5-V&f`Wpav#$}uro7zI zuP6{eaahH+`LkyIymlM51ln{RdgAB{`gcnQnP8DOj+{k@)!;#C8KV%46yid{RlrUm znDDsLjCqR&@`@dwUw7-xJ?>v5ybUFXE=ZYFIwPfE36yPKG-EkSmo_NvO zXAZ#5r_fvXR80Cqjq;MBefy$d6F3vNkPxsBf-RDgk`f$t!GSewj^NpMyfbaru3b1P zPx|0u!vXR$&N!oO+qN_mB1VAmVRM|=c!nL_ zM3fW&pcdC(nA9DH8^M^ESoD0FqDVFB}I(y9(_`SIPGKU$*QJZ#ttKP)+=YpmWUSbZUZOoRd$ z8VR8B+KFhqyNk9?;B8<@gSb0H^eO( zMo$bMF?k2^00^{@y4aUT7LnR|YRX1x3#XDsfJ&Ok44Ec#n<$Ky0FsC;EZ%qvyGk02 zrj}}E{V_a8MaRS^rC@~#*7kYtWf>(cNn9Q9CU3dr7PuN{Q#cv;_7FXLiNn{(ii|GC zR;B1e(n8d$Riy<|R@`j=q@E{Upv|4VaMhM^eFuQX`@M4{0bH2rzn+|S|4c4Yhmo6k z%9IcOo%-~3lYgE&^`5y?wXxH`pLSK6*7?@eza&k)W7^Ez=~FJ9K1F-r)4Yq1*WaE8 zX0tMS!}0>6GV!L1&FD-qoQ;7LK4AzBQBMrGISK!KZc=e5Z~WGq%_D z?Vp&t=AEy99dY9jvJVshpw#dHf<@^Q245p8CN3p43e5-KPPpcBNu$YK4N<6E-hd|X z#?WHCMqDS6WoSqM)kx6z3MP6(vQ8*FhA$>74xZ)YOX^Ri zvnm~YI^=BxZ?o3TfzPwyB}LdqQKKrZVJr*d>9W`^Ct#-qb2;v`RV7pp-tzTx^MO5U z%6m`T(j~TP+s_~W{o3WCchh`t%=(HEfVdpU$1wMPfj3<;`sJgL1bD6-;e`km6=-BKCB zrBw`S%5{JPf%_`#Mj>E17xN85yLRoev$GLn8pDWyV{4d!5NKG$$Ot3?=A_}nho5@t zsd)7vRyF7h4IOh6OC6EL7)eysd;(HI!%AdQqo!)4_Q_~LayevPX#f+I9Sz5G20J;N z#>Cr#`U~qvL`6r(pw%#4p+&e>1gi_dI|~>rZefA_+H0>xL*qS5ojZ3XQSe>he3X`! zMnsgOw}29LR51r6+8QOMw*@Nq{u#R`48VqlAKvP1pGa3@!b?9)JF!ceR=wtqp2z1J zEI4q{E&;8mW=Q1@lgzN-PtO?rO842=}1iTb3!6z)5 zFET1NE)F)w(iE<;=I}GDMbnrdu}WJ`qmY(bD;nVGq5uW}TM!f^ii^__i<00CpyI@6 z$x_k?uV^fI&Q&iB2)J(k^#`&4D~K2EANFx^`ez8`wpt6IPi)<~6<;$V4ZC&ghG*9C zxn{ho2p2Pk&YyNwV-*3pyth#jpVU~tY&PHr@4mZtTMR&H{m!`UgIz)rW;^UdAK0>b z{pL+2MP=@U_Pqw3)VqT?)-G~g2iGiI8QbTS&XHSw{C-J=meeWdlmWeQoW5+!qPgpL z7Dl%@X6T5M(&f%Eh}SM$nQ;8+oweVVE?SmXRF>Lx;EBTr=&!~ki6mC2iK(Dy{Z9)w z@62;XCuJW!c*ybHslS@_Px{mB^6@eHpgH`~3EGr5zzVhXs?w#U-)!Ohgm{YK-e>1#!q4 zyJLd}4FU~ZNi-i~v^bVMDl036t08)lZ7}JEW|fWH?To8OYH#FfFWhxs{}-lQ)n4}h zqHS}g-+XR|XAh3==NWiwtK<&HWfu&CtAQ7QZdob5;j6!r!?kewCwvSMPpop{x4 z_q~1PQ@Pj~ag}V_T#$TB_pIoQ-WN=|4c>+}AQL5!nurwuNd%j|cOp=9MppX?ZNk=VWwgcWN({kQm7Yq{j2;#l)atPvrXvVZ^} ztS?VhlQ)l)LD-DH`pD3@5|mX%zf!x*&|fA=wueThy)DD7VN!>LAVx6DG?6tT;n6Js z{Toq^iPh8TP3TbKaI*k9J?_$QJC0{kaIc1&Y;1+15*UOr?P2Hy9DSe|12au(d~TEd zIjLLkk+>Oq{F(n7^3vU%V)UyeHefePfNI`tL7qNEM^Yh=h4FfG*Twu_Fl9iA5AN-LnLjH`M zzF^u_c%TppbNwS?Vu=NKhyFaCCSGf3h@wjiG03n@ipeQH5%0MY2h-q$fegbO0yz+f z5nwlpF%Hs11{)Fp46%p;giJT^@In9}kW&I;kfB8&R?x^lNi8BZ1E?xXtR8`wkI0lH zrfuK8{`LA@89grg)7b-r637#wYVmVppII{U%`;9-lNYpZ4djdZS_9>Jiq?hbg%Z7B z6eFkOhku@T$IIG37kqJg7wOLtvBBbYS1pl6zjfxRsaQ#p0hxCCE4;3`M~=ZOF{GOy z4nTiHhAzSGhZ@2=7p;uOqGf!*=n`l_G$`n?WTkV#b5*sH1~Tj;5bHZ1~xeWvy zu=wuW!7pedC)_zYsbul1({q8$&WfwvJ!`_blLuZnX?UNsUuR7L5*If_`B;>|8%<>? zl14;O4S(Iwy?)^RGnUW1;*^!&+%z`FU9xTY%Wo{#rsnRvI~m^-medf4KkrbPxBQ7I zH)~V0fn%@lnXdgj{f#YxDCg>P`tz$&w}UKBwY#^iyyZ7LM%2q!#k>ke>w}W|00F)k z4QW(O*+E1R#15$(PYVfh(^!!(O;=(WeYl8 zzaty8$PmNtX90APOD?$tulefQwJVM&cIeOndni~O#wszK4?Jmx0|c@Q3Kh}EsGCvT z{Y>dJ%$4sJTq_RhtqM0UfVb&cViKLYZb9J;pq>5nfdO^J%F8i$J-0`p-WS* zCIBxX`05eqYK(pUla&X*|NhHyy)|vt#J3jg0*6xD09<(+S8n*_r`!)7zv8qjKyc2J z_u{w-JFbm?DQw5;G`$)1IthhKt5ukN61Bh_m+{7f#*#y8_}u;%|j5wWsQB>~VAwTSTtwJ8>WY$L=1aIX39bvOU_xv9_G^!R3cZUY!Y zLV}o_lHoUT`GQ4mQ2`J|P@WI#iChFaX)ieHsvmdeoNcBIz9n zhZujI&UpNrS?`Y1y;iYtpL=ei+t9xVpkhdO?!M9H}fpW~gK)X+bT@I zDh?K4(8Rt8-LcW_IEGF-C<<2tcsteOyN{pyVc9L0+`L6w^UCcbU%5T!fe$~scyMP7 zd1K%4JfU~Sq$kgLZuVCeDZM9uGW+pvSj_D=ZNhW2XK32B|K4=lncYEk-!n@uy8De~ zzwQv5%@lf1n)%7I@CI-{^p9tLLGFKMEx6&7@p>-(lq{b$3(%ymrrjoP{W3<~_wvgh zpLX+)KW(`Al+NO|o`;GR=ZqT85q_S>g9XAPqGRIXaIOLYj)Gz&|G+y20U;Ho1%OBp zz|KVJO)Lf)ImYnehQn}PFtT}E7)t>%S^CSIuXX(F?|$Cb`;=@Ny~IQ>Wr?v7GrGJn z(g~>yuVKzW8(JA@V|Wzugb)@lhUNq9{tLU{T~cg}R(T3LqoRfNSQ;r*bp;gZgKO~^!$ z%82CoY)h;Agtt~qcnbokB5_?t-Sw{P)`~KhSjUQrj*k`HS9pXTIS?xg6fzZ{g{2L< z`q^RE{G+m>O4=bVAwl{XMs);e*X%mwofvGq+EH&+)`1 zM`>RkytrQ)o~X!MJOAgOSLDkl^n|M+S9+BJMqYWTa5Y3o>v!`@k?HDSyXgJr+&kZ!yy|vqTp1`xX$ZAPW5(-dC5pj5IT)cRERLYOUnSk1Nl}#0OG9RTffR5@#>L|~T_H>Nv@CraV!U(^IAT|Tk*u%O zrpy;*;=fUGlW5Y%TPqN=UgzOeri2vUOQ{GF%`9Vj=G;u9tK=oeV=-Bl{;*$5>90zs zsx&6%BYb1zgm`~{3t-Sx?geS%J&dC3&^@TSaQFox2BYY#L1P|#fAsh_Z|(K3x!Mym z7LIvxd6<3?R|sic|4z& z3JHX9C{VG@9qYBAA3Y(5JSx}@uhc5}_3L+$7t~jn+e$F}~jn2~d>t!jC@>>wvMPEceMXpIypw z0QR2r*o|oq{dJ1MUj5(gSg)df1Egv)y*l;UN(pLCz@iLqE0ywZU};1dMw1EH2qyV0slTB>BY$nz)f*`{#QwWjz?eYmnMY~XSJS`l1jz0Swb7^D z(cLe&_TzJ^X!>nsz{u$SL@_uJPzf4@BTms7Fc>sSA*!DmhYYhpCkcywv4;YV0i-eu~s z)XArUq_$2^Ty8Dz=MNTW+9?m8nGxT6#I-rt%b&e)qlbp1NpU+qf9g%R0vA3%W7)w^ zKAC>X|F?G~U~(1J`p&*@$xQYHgoGrJLI#y*; zed^TN>aV)DPTzaGTXlWskLxFuJ8c{8=Tzg%KY#Agl^gHB_l~iSv*Er=7C*lQrox;T zlKk8s-TUzTA3rd%*eM>3W;rh4=Zu`Sa^sl=TL zb|mdjX$WC~ZN|n=QTB6DanmSx2zodig=!IQ zhKP;Mbdi`Dtm2YcRXj2R2XtzYs0(nUooLg`hXRLf2+~sKxHiVgf(S#A(aLm@SWods z)sm)cwvjZ1nP$MZI^FQYAN=9^@Ba1eEuC(=?%5@Gf}A#gW@(GMIDt>-jj4jJ{DdB_ zREW8&RxN#>eYeTWS}pU{x1Dl-?FV>+5Q)@1|6AN0a85t{B>ZlHQ3O$R__TA-vGVR` z*6b(aO1*jS_I+*mN;`D8!bH`-{@Cvy-CB<)2UNl5@s`m0Z1?kcIwWYhf9O*dSiGNV zanE1q$y;xMsj&J)!R#xq7(2LX z38lWch2+$k3Mnxv}2~8{jEFZp!v$*w&9UuRS-W41Hy!2 zdnpbm zVN?vEbkT}y)76TxPvY@nNW@O8jIl&r{Z)9}@oP2JE{1BPu9)QOsU$HWgYePh!VtN3 z0_3CWUORz&Wj}t{rpuTz1b3Mx2>FLz?ox5biC>Q>`B!@=<)NjkRqsW!K3SyN9czR*&O&8Rx`pSZ_L!51| z)jsxI?N!U_zWNDzq~FBfynXepH(l-A^j%VHY{REW3R-t;ppP+seCjwOKlG$I$2p5$bML>S(>d<@KgCTD6PuA# zL6RP7G|Gw_GFgK9yVzfGNG~pypI7#F=wB1#su!I95n^{r=5QxZnF?4 z_wZw8I?E_RmL#IL1$G$E-YO<84;LJN;wnDLKF+((iUz#b>nFxeZyD)}zqgU3N}iWZ z?6d}rRPuvj;M===l@{{cgs}7_?IgmNzJhcUk&(JmUeZDFOPOS7n{6b6UPkKj0p>r< zN~ZpB>0S5UeD%}omOr*mjcfW9x8Hc{X=4f<=YmJ>U2xHr3!c8~+xSex>0h~a+kJ~R zG|?fXlH%y|Z%6^50AwOwZZjrz=^OWBbI{ zmfcmb;Od(eJ$>H-=8v5@|2X^{rD|{uvUtKpzr1n%FK=43`f+@UkILAY*M8@R3y#;H z$5T8}xlfh|PQQ>Y8tR~TpYxJ?7(>US5+?>GH@@_cN}(2>f8;_L>=S=`bLPAgP3Fbg zHHROK2RZM(?1CTK?K^i=hK{5?gK@_sX$X6^U}E&1@y zO<4zpgo!d41`Z-Tv_XdwCRq{u3`fw8F8Oe2RtU5ZFzfna;3h}NYlH)neD3wtEEZb_ z#Bc1yYV?j{vyI;2mFd!Sq?hj9R<`$LVlX&__JACF1tKPe= zm7cfA!;|w_Kl)i1pU_h~MA7(pt2WLfNj9rKKIeY!4vW`uipF30>W5d7UusbqdCb{g z`uuel|Ddre%gHY;!_|7$Ejr3;$|;*R@Ag%5f7;ScZ>e(%%J}nxG4WSTpLhF7U#8FF zp(`s3-}gzYn%rrZW{kwsOwj#Sj8lp*UJT4sfKh}-Mg_aQ_CC5dH1$(+Yv^Ocsx)tV z=K0#!ZvOL*c_$9j*g~-@(zxmo%1EYOI5)EXt%eCRK0WL5FI{nIDanLGbk(DF-Io2w z4XR=@?K{^|I?~jDd_deb(a=FlMZh%#P1DEWE{>lqLjbBQhI1~TcDY6eS!K&g+DIxM z_fQ8nl}KP^A_inRBCqW=KWA1Zot)_vDmXmPC}6yHJHq91Z}P4+a604ShJl70h3a#=ydw>5{QF{DZ5qGe#(Qq4W);%iQaSF3hnDVo z9m9qRA8mZ{Y3E)s^|E58sJNnXXgPoNcBE?T>9d_zmjC*mN6xwDO1d58?0^1u^d!gB z(~drRU))H!B56t)}wEipZ zLRBd9YV`RQ;3*{0t%T*TpplH8*+4!6uB->yB-_fkcA^gmX6i0wBTi*>WnYl5z&O=F z(2GL4W?UkEX+Ig-W*f<%mwCFhsMNyJ>)Ja6Ul%uHReI!mtwvj6df;@Mx>gZog7W#5 zeZBJKv(qbG=`__e&I@r)4KiBaj$$yu1D8+Hhl4&j_uRpJB5(&a=0CnS^@$7}7C7fbIXa05SNe9NS3ww> z311T;0b>aqL69BE2W{dJ_;A1`Y(o&-MjnpY6h~laCVb6Q$8XFB4nO4`aH&?H0l+g* zbm{~(H4=LVS>=+T>?#CkutK26uaFku*?d%jdRDu$4~iPJ_Mg#3dA?&`*r)RpoXbO{}_}N2BYWmfglo+x`1~LNpCDQu(`bS>b+?iW_+_;hEfI`feew_+?M<6?d zf*|S8;`Es`MTZ~*?R#W|9bZ1qV&2H4t3dmn4&e;sYa6y^h4x;2(qX=DF86s!zG-0Z z$QgX>k)z0&-_}y!>J-oyW=EA|3{kFk zyi%q+`$|h10UfOS#s^#TI`>{QJ;k$$6Jsb8+OucRq8GNGH1VjK3dYVCmTJne$bK2K zC8WfHL?WfIZtZ9Kh|Pb3B^ClJ_chm^{4iSCHZ+eQE&0qd)`vud+4<8gf zn*jj?0URJ6xL||i)6MU5{4cHjsJSsUXG?LuNcV89{egi1rP3-Au0a|??)2^sghdJg zkqtbKWuR&TWfC6eDtvsPnt9?w{`Q256*uUx@TT^nE08xykhlr=4xiil`%yP zT@2G}--#3!l}Dc-cBOqX+6KJ_+R;KjT_dmiqee_uEFpzcP17M`JjOL1x0jjSboosA zy=5v#1V))lSI6?A~uTN=l*3u3eiLK>P9fs4RS{UpQxJxUCLI)Nj(MWF29r-IEMsa(_|7>-9x`!DtZNpNe0b-B z6kUnL(4XsvnXu$&K!%Sa$;H9tFq)P{eYRDrq z$WC`M>c=zTiwx>o5;BEhr!1L*_{m}a6^swS#ume4!Zc$gyXP21Ji;;YlDnTj|0mnF zebn5FhcELgYDNzoQG-vnng9ZQ$!UfVH@kH#{F0n$cB>Sp(_ztH-;naQJcYvZKc&Kjcg`3WUu0i`8@;wWAEVP49V1yYgok+J-hB*H=_o ziR)(6uZP`j{84}XK_NmnSiBhYBr<|8NLMx%45=C`lwDjidR$G^4!nz<48g?Ty~JfJ z($z70cEPAoJ&>5-5)ko(>}d#PL3(!L*!a^ug@^=y?i3P5CA#Vq$29d!vy`q`OY3Sb zy&6R(OtW_~azVfgH`#=7>W#rT^7}RNo(NPUURR(Up`M705YH$Qm>9I>EM&?c`Z0c+ zZDh)#=$AOfenbA`$;Hr2aGl7}C2c~Un`}JpQ7YwS488RwbtR2W)QkoE^cQUOxUQhj z>Dc$(N3)qg?S>$tG9(keCgeSQPeb=9qftdgnSuKV(~QnI9;?$S36c{;Pc?0g11^5G zUd%vUEK~VHxtc-5Ww)ms}W;TVqC$|=S zIsnu}5RVmkYK4qsd*r1|z-$VUz}(v7x4+_7OJ7FUciLtfea9|yHG_@>8WA0c{vw|M zr9Le~cF@vIqvkp4aBS)mil1*Ru?1tt5XPeS08ci4VYVO0{M_t%`mSzwR6fFO%9C7o z!%IcH3Hs6jn?#9y548Bho#FhvbbXcqL(a=_I`C6~lrIjrObg+eSu#hZ3e?>g2Y9B4 z_&YFt=38ueg{8SxQ>jlqd{BxL6MjMgCpD^lg_)2R7A1cFhU~hmQq}L>v^J)Sp+l zS?6-hhADOB!3bd^!^arckdGUKG%o2xbY%>1%1skMGz@7s&2&~)R%KDnhYjj?bOD8U zha@-s=&T?f2~97<3$!on;7SX*2KsQN2-FFdK>KD@kx?ySjGTOT)*9z zO-C2LLSR&7{*L_(*pAiy?$#8ZiVrpYsV3EiP<*JMmvxz7`5?YAYlv8Rba5m;8J3K# zrLRC;6Sg6A}l_<=1DPRCa@Ak~1`!3ECVre+^jrU}ADlLu*<)X}>FpimfpCSvtD zM?`IiSeYPBpnYBq2P6K5mbRSS?2~Sx)z)mc zmtQ1!5FoSN${SOLaDAzEYz)#F2gWi7D}EVIZ)tmNqqm4C7DHgrIe{=z?6 zt3NS)>XF!h*_X8Ai@}(cGKT3Re7YuP3Q3EC=tcZSO{@3$)n*%ghAtx&v2tkf$mPb1 ziEHlSk@0#&%}lvGSzY(2S9NsZvOL?7y4>vwMVC3wEfD524BK-<@HB+7w5er~JnV07 z+27c_zo}(kV>7OJH?)O{@^UH)vh%Yed0FA?uJ#UJZ~U2G)Cui+;d z|ArmAP8pR2ko)0al&~Ov`~{rSsH!zt}$hZxGJpPO_#Kv2yL^CL{K`UWaw;yg{x`|42#Jeb)IqdVR@Q` z>C=^VwG09v@206`0%OxQ+d>_62O4+OH|*TE|3Gtl6|NlQhfBIz4$lo2mv@y^wCCmJ z;@uLqADqI}ohfZo1$6{HbFnNs|Ul$&`3XzQoOkC#yiX54v(L;>P2f zcNtP#`98$Z=lHkP?aPXEj2&D6koi!R;iZqpzUBRBo{UT+LDe)0Fm z&l>B0!{C)ktd)~D1cGAXfA%UdBFwwfd;~FJ-l$)hoi2%?#sh@)9$Q) zb1kIbYmFB_Mu7t3JRH?J_?68z4*neUX9RSl=|{!*7{e(QcY%4|MGpwKU%b`woGYb_ z3_SsPHXg^9up^Ir7%-RTkH1WE=hH1R)y0zsVH1o(pFfQkGui7MNSUA z!9_t+b8>JN<9!HDP2nx1pr9b#@2KFBvyH0q;uA)+tX%uvS;tPv&(FaL8bts`O&&N1 zI(^IURWo^^B6ySV=t5JUR0u7YGPX1!@Yf9t*9o$cdwJ1upsY1kvm<4TM4ZU#~&NR z3j@&YpZME4I#zG^@buB0rKP2qzH}dn8XNJ-Q=<@;(C|!kH0^@u6~FtxFHWi0v;EDd zZ~OG(mutUs`oib7{dBmnL1ciPK)zCMQ8oK2{@8{8^WZVLqbFCfDb*EIg*QHP<0H>p zc+t0Xwo&}?`HwxfKX1fgd_6?#%9Z9WIkie-3~EMC@sq(66`O6O2*`{z!?}fy z&^}BO$P`WXCb5`Yqv>1+ctdk;e&>jRSg|8?sqSL zF#hBhoVB}~oM9!-&NVNrYp6M4+OW5ue{%Ewf(d7wKY1iw^Jv`q`fIPgRo4_MsTw=w z)YC>+=Y_Q@0S29q@Yt4IpUN{gLvjuKWe?A`FM*WO-xAmr4H zoO0ZWQ;VCoyu0mPH`xd(2*-z}f= z2cY?mnv=)Ehx(U2yTWyc_Cuo#;#LE%dR3_q6)7JV4R~9PpC6wwI|{cVS23>6h%q0hHw?msn3-Bu?O}aS)Om zy8}DhSy&BZZKP>+p(4*irRYs4$3t3Mj^JQ-R_$m)G}yqig@=GlGgGfPY>iy7H8t5O z3#-a=TE=H^K!Z#mj*9)_@0nN{WA|TKiL5cbhR;k&Ph z`cDdP>NBT3SH8L}i<#a2LC3{49yua^ZJ`i@WcBi>f`W{sPGM zpq$a@8<&Y-h_z+b;&M$_qfax^(Frzh^gHn-e83o1FF|jS@5A$baX*j9@RCLMxHm>> z)&sGj$O9JoeZHCY&?IIz^S-p+{_ZTO_WtHvsjXRLz$G&yr==#-xd?aE>F zBgC}5$I7eW@l5IcZKbfyvS-m+N1+zZp_tS5Z!oiC43*@EAH^D>-}tSDKBK<-B?1W9 z_Qf&&w5@t<+TOVV8IeNLM>MtsEMj0A#8(#3{saadMWTH~$6=jjl(Qb8qNd4oT4@ws zy8bXK`3=7H{KwhMSDRc+TOID;@CV&GDnS$ZFVvOmFW9l3&x&}QU7!4%Il!J{$ue?tX;K`<_EP2erPtIfTs3 zeIp{KDa;Pji!B@^;WAGT*$${Qi^9mLGHKxRd-UG1ym=*y`Is;ut&GRgbC~k@(C$Oe zS+c^#C>3c*MMY2N;N+Im;^=TL>BiP-5y6AZO~FCm!ih*CZ2fRw_qCQop0{U%Vybi+ zOBcTxv`d~{-T1lRB)b^+dQFn6-;Oud+p}KvDEO6WM%PeCBH{CH%dX{OO(4U(f~m=I zd6OOiD3Ew~Q}EKEeqW)Z& zOSb71u`M7oP1r$rGrInSVq=w*&s{Ra6e$<&_rK!S%2i6*icSa;4 zz%0zWQU)hO&F`owL*h`*WJmZ4c`5X~t=+CS8}nQXM^LS7gj&>@t&kYkt*J;YCJg*U zO57rl3cbgDhbI~M!p6iG;v%=xc6sl4NKBD%u#rlqBCKbKSZhCCAD)@0MMpcIlQ@eU zG|@Zh{#q0zbBY}FJx@%fyho<7&e)sqwVW)bX3KBw;Zhn2H+DPpEVaF}m@4Dt{usn= zNv4B5S2*k$^r&x_IMjM9J=Anjt<|QUD%DU}W!2X+9#i9bz+RLksw+_(PtGGroEDbB z5@d2WnN{FEUt(|kJo%~5wqWGJtn-LYB)wtFDj=unk&*V(k^4!gK@Veyx!;9JC2tdC zC*AIbhMBo!HTxAG2OO(T^9?E^LX=CO`_VKB!`o}5V+LN1R_M=t0LRnPqhBPK|^VfW@DIRs+Y725auB#RVVrI(O+hJZsZ8=cR=e=S$hrjU*Ke7q%D6BUSO95L|I z=i5@#-$xFnE_mKy&+e{&OR_RKjjq`-qwyU2di-Uomc;w$q!Knv9Znp+k{89ABh$Ls zl@&d?yL=1itFfZf*gIJ6Qf)jEf8UwNVbf{HFN|YdcYyp5y{0UA39Gh{ONPn_x__sm z*nm}Iw*M-FM&o5Ks$=iQmrd^<%8kFcXmEH^hU`*Hb84yCp5nJOsUAVDvY6`-#48?{ zX>Exrr9y26TK)YaaJ$T&TFc7YQw&%@QETu7dA`Tz)fY&l?9qv|(za2I?(t6g)D8w* z$@pREcUq&~xXyZeyM=0i2gp)py${~=Oowruf`uipnk7RVla+aIn9cjOs2=N6x0n5` zreENJPpRnYI1AMKiU(+DB&R1PmX?+l=)uwJK}}F7SjaY<$={3rDmXqLI+B3l`ICYZ zYcz;yUOIX|$7!tCd5(=se2AvSX&P^ag#{OcIv2JUc1XA(2>Vg%V(B8H=y{f}X7D7< zlB$%t-Bf^!RUE8XkPrMMXksi$CEU&zh*q;)IEqWNpDV6m)#R&nn6NIU_n+|#q2m& z(*lvLIv5OASI30d-rioh;Yt%Kg!K*r#8a>+LIxDT!D!NnfrSzn8)%Uv^oaCC`7)g+ z05(B8yM`{fQ*UIu-zqiI7+Iy$u;Ek~CKxOHsCXRf^XJ(>{)p}(Fx;U?EnEGwV%)IT z_5}Pg`>h8!%6qFqreWpi_ee+;|4Y`skAo09?JQoGt_PtsGQ2)pUH91{GE#tdqRq^D z)vo&~cdP`3TbdekZWUhSywU5zX?Bth4N3C{B^L1Qk&1nQKi;~PScdCfk6a>iNhwD} zWSm|uP$!o_E_m?aAQ!J3R&+4=ERjV)i+IFm$RVEnQtkV-7$d~Um|3t&&Bw_Ck#-pD z5i5i&GZ5KNAW4XL68vag*y)Oj3@0ppHJa{1^$B&JI-UzFg#8tg-!14{OeQI&VIQBm zc@3^VOyt_fql38_M4%{E_2Grex^0)L$=*=uTc$BShdw^OK(-G|qucj8k%W~Bs%%ls zOu^pQ@P53Pd+e{C4N6LySDfxmHhQnpzPua;1(O#6`B|8}&9bJ|04j zVHd;)_tON)xZT&4qN@eeBLj0eOZTa11FG-u(s3yz98NCnsotyvC5aSqJxkYFdq3o= z%hP^ZpW*rS<01fD6D{eV;=v(O=MbbO zRaTsfT3u%o-&eb2BxVUfJ;$m_1{ih7MLZ^p_SDxWlx&k)lwax6#=z`%QHzh|dh^2o zZkJ}aJv%B5nr!Vk(UxM&M)_B-uq#3{GM=_XS`2{Mp4qM{9kWvjsy2&Uy<3KZEH6ut zrtCb-JvM`P#eUf=IP+; zjTh0vzRnuFkYI}!=A+H;=FXMgNS%GBuB=2}XZEXh;a2|kJhpyswqN*b;FLAn>1w?w zrL6IIZ*r-{`#ObXrBrisjMebWBY{gkll|VK{4`QuVK&VxqQ<#jQ@rQd)f|B7%H0Vg ze#4~6d#Q0`B426d{%Aq`l-vx@e8SII;Gk~fP|EAvFSB)(bBn(9_*-!8FQC*-Mrw9R zW@GYA3@CQIdsJkMCs&pUdpuud!oHKQ~KBle0d|~Knxp^bRT#?dm{gZ+UR(2@- zVRp$*e%?`Bcx4X3S4kg9Tmc<JtLHAwXJe3}*VQ%D*VWbz$N*ejJs>J(UYPwk7r~%7KeGUP?FbYGxw9v+W>EX8hH`&ezoay=}DC5N%D|Ew<2qEZW-T9=T5`p zBB|OUC2DS3$hXj}g@g$H>v&cpCRGC)ADe=zvIb!4yADh_Ri3s$GT#?Z@Iulr>w~k( z7V=LXHQ`zyQPEib$F-G;9?Yk^HwMi6Uc8b!eBLi2@MnvDp#Gy+H~z4S{|(vv3kv)? z4@1Pt&2XQ<+}vE8UoL^24FG^&ThGuj8F2%@I_LJf6kNB9Ci@Wzl|?9mWmLfqq<1v0 zcqG53t*vB74SZ^5%0lW(-&T0puq}*D&>Tdqbd@GI=AD$K#Ok=}Zn&&8VM8PGnXX3& z8bC6MyjClO60)-WhKhFU6zGxCg-p4X)~0@6nQ~m;5TsTvBRGv=tHy6|cv}QYf8VY` zw{Ha8N#8ek|JLMl%}UJZY6XGj@+|TqhORNf$S8D*m%XMyNNujQQQmK>XyP&;^Vqp4 zK>J{ji6C3k_}Y|8p=C|9#n|8zBisF-YZ^!06xrk*=HU%<0b7sbU4g&0)-kVerOab1 zKBC^{?C6Y&+Q1}eX!w+`Udm{f8#Qugyxz zt4!(@bYVkx>CFH*LyUAtC$ds71O01F8z;E=``gV2^U+qr!#?Sj5s}mN>r~q#k?2yIe;^oks}z z6bm#SSTmtgBIih(AZQ%f!1xa2*SjZwQX|ogdrD3!Vrjs*P~3H_Vh8L`TKLIcW68uL z0u2w)6F(j`)W5gGl%yOKrZ*;#W@XG=E}naZJ0+Mq+$EinKP|TS7?5l(=jxH zz6hiE#c4-XoL)CY|5lz;38}VR2zO0YIX4E1NFAutEHj)F03XI`1DvCH=-NCjAM1VH z9nK#Xny8lLpL)85`&3DK1OW(-HGlGUXy$BZu28_grM;;*u7aTsWlh=y4e7g;b(_g+m;AdQ;ug%LsHFrFb32wLD94nKor@ zU_nc7fNLjwkH}d(-R`z&X)PXQCZ19vr>g;!Gz)1n=DpM8<~W@ zT!5$ik=x~KOHzCjDg%RoEHEj)?TM7)n;xKfV$(!n%8ruNQ*#nj-?6At{?oJ^O~7_7 z;gb3@z1Er@YPUG$*jtf0vcWCRz8G!dga@mvUuM>@`UM_zg;A7#;Wq^Imiv^3NnbaXGmQ} z=THdaWLdzrii=9ZrJJF2z|_n}o)~{|uzVEGO-fx^+E#B{H}1Aq_Am6PK~i9hi?K`e zl($)CCZHAIaR74X48qLXc%s7H`O(8)mT`YKdsqTWj;okSn13yQ`|L~jFLuVlVWCH# z`tjfXK?ub>Mt`)r(F&}3bqWD^$WgxOPe}*%So^@m z=4=Pn!*W(<7geS8gAK2z8#oM2D2Q}E3mDbucFn;Mpgdz z1W|{eU6{CKyzvswFTxe8uF1ck*lXUL3)w@^<$U*PmK1&^veL{WezN-s-!ONKzOwR* zyM=b@FC__B=Pa7Y?I5%u5+u4-gw)hP?R?;ql@tCC^{WgfbMTXG?7IYTmgXe5mJdee zTlmR7OeWGa^rPD*w8V5%1_G$AXQ7Y`_sZFubjdYa0OEbkMZt;<)PMXRSmQ`L)S@lk zzh{tQ@^V_NQ+N7l7)#anjN$C3BeX_=j7*tU^SjiK1PgJLR6LR{h5RKoCLQ`->eba% zq5@eohqaCUp)B<_DX~A&(GjZj`oyLr^7P~+K}}?VAsv9W24(z5eE$#$$c+bV{a|lz zZ*6T22Jh_cMV*Sh2RmfUf?mjTva_@E@PH~(EZ}A}(L=_BDE|H{V%wzX1(b>+a72}g zplA1M{FMtW2n0e@;b`$*$(I4;PS4Pvm%jUNdqZ<1N*ap1$pu9z(Y!LAS~;?-b5JiD z7yWArD2ZuD$LKc(TnYR45sjln@?WW-2q++_`s0e;TK;YK&wbCA9aaDdSqtpHNd6I3 zMSOIW3&+mUsr%Qs{){YF0Sl#t%rABSJn;V&{vYNr;2t-n_Dkku$C4l#>Ut%w`m#dS H^wWOAlwkP>MJ?HtJ`_FgRlk7dSW>%TC*PeN1&Dxi9mtO&QReWLg z000QY54ZvN54hY0&?zHroc#bKfNNLi7XZNJS296IFE2M4VPS-)kd>{gwVjZSD_q#u z%1u~A=!r1k=`&w9D;sA!uLst44lslq$8K{w#{-zH9EXtv_zBoe$<7g`?(boz@2?HD z@prb7w&i#x|KO>wj4#{`Zs%q7z!&a<@RaeDn|oiHJ)8fg+C|h&~Y!6@GH{0}6_W%SZraL?s{m8*p4n z^RTs-fheo~o2;vv9LK*6%E!k?$VW`b)x$wpL|R(%8RJ^c_~R=$D=PtJem zpls)9;{kK?g1I6d{LRtI+7;;~$8n|U|1$)*+dtXwu5J&M z^nnj}!B#df#NPRaRD15>b_YqIhL?5fw=( zAW%XY2$WJ#78jS4R($gBw92kFNVpxs>)&Z@|ASWHzoq@#8Q^YLl9lZ|VBU7NsvfTJ z2mctf4D7$xMe@Jp_iwbe|Gh3!|1GWXl`_JAKiYqJ)c=mU@}Iwp|8(4|%0FG-4sqr0 z9#@WiNd(aT!(9Qk9ak?9$t4~T0wDP(kY1HYNw2P?q-0n3tLrtgf55dXAiw%k`~z$WNZctKOC#NDIyGBa=H}?9!G+hmbl#HC>`i*Opw*UZgGSX|LWH+d9P*KxRT>rc2 z+Ep9fjfeENDMb|YIPO@vi%v15FjKuzgjjQm8Q>VbUnJyua53GLVtK2ikL5-JeNv^X zQF%2s@5HC;B#l2!c-Brry^ztZ?`$)r>iH^{DEBOxQd zl14}N@EZA*9C`|?+wN}?syRdIlQME3 z(!UzHu0%U-cCCOoB)k53XCbjWK7iLHD{}TGi3SF?!NUQ0>`lnk@Yws9bm{fL72+a+pNjexYixO zj;-3G@9WbvYnB-<3B8NcPv$NFCO@80O^wg#$W1DsW<5ikD_;T@gd^H~K3@Xb0d_Oj zeTcJ-kh9wA6shGf(N+&y1&mU@F)&bOBZ4OoQmW^b7R4wF(n}}!b<^vj_QW`Z9?&E- zXRpfMoAYuKYi=YASB4asX_bImU8c|#D&DyaRKRHt2_E|UnFkoeYSSL0?GjO{Crzd&{(XID%!@|r&lvpzsR?#X*P4|GkY3%2LD9z!mV=Gv zo!gfH$2RWMW$Hh_Nglxi54ZP^qv|m;v1@_?kv!Wu<=uF*56RxyDmxr_y?Q7D1$(x7 zoz52KllmoxYz2_qabu_c0>6`pj9J#%sh2S|Zj83m;|E{x;7i1yPRS5G-igjF+hHy= z{T;hrrLjYRQ_P|d?_6wW5Xiy%JO}p^XVEr_9T$|I$V_{gdOJ=1I0g2EH9^|tpkQ)} zr%^&sVm6p;XW@}MJZ^V{#@=GwN7&`t1`w|vv|7(slgiSl9lg?c>vxIPl!$anQti5C zlr74kBTknhyYeJ!N6l%rGzHgB_={1OC~NOhg8ZbCi0pJymL4&*+Ezyb zb1(*FCT|-#jch~{r~6_{-?9l^Ujqzqy9fMHY4X-!KJjhg!`n~W8-T8Ga(K}ESm+0ts{**>NY zg`JU`QXuUJu`7%7bOnTcIwfjrgolY{G4W`0*5BFEQ-s2UiL{`6~SV-KUZOC6gx4ZMxxgSq!u*VSt^5=>Z3BDgX8 zXtgh@mNcJ~%OMC2L5`J=P?OTJ(mk8uyy*|dQeDNUj7P?PI3{v#M?L2IX?Mv&`j{?~ zBVC-^dDb%W7!u>ks9XlRw|t>5wH)s>naiP{@j%0k@^tawf+_TUQVxI-vro&;EnBt2 zs1?MsOTg?uUS9Ys&c*xn7Hj+FWHevozO8OICkqv5c@U}`CGR+eYCG}m5mLDptZ*#J z6ISSkN=1mn8US@V_{@r?)~2M*j3D%){%4z5E??cIPisFk?#gZ_IGzuhHFr>^6={xm z*tD}eC8s>#23>H#((y;aAGM;Drvh!%GHZR)m>c1)CX>lPfeMpRyGx zcPk8vgKj!|{IlV!fA1I9O#FhT6DvH@bt79Wa|3 zSuMBO6vo&`RbMeHtl_*>V(s*)GrI(LZ?tf)Su=5T*2aQrnj(i2fDAnw$uPoG2<{kp zncp3RAJfV<>czWv-e_<%4CG0ae*DZxkl@V~(P)JCApm6fo%9%ZTB-Z3ekDNQHD- z+wwp`&<7+0#;pa~N#Vh~%7A57i(aFw4L6zat=T}6{(PuYVh)@VPOF~GA-aeJDbk}P zc!%D)-S69)uFC?qyc~Zrxv(CrqCsy( zMac!L>F%VrUw{)6lji{>X06meOeSvpio*UGL68+7&R^4L#`rAjoeul9(~68t_apoC zRaGZr#gn^3OX8iqW+(MzLg?NYw**J9XVStzTdNAap|zYKK3h ziW)@3spbZNq;xs*Zo?~+Q99(@<>0z>jpvE@wVh`I0>nZ4^Zj|a_AEYZ^LWosAwY1^ zDeMBsqtLV8e*HzBfxM>6H1~9E39~-tnSnD1!`%lXH9$DnHA`z#6I8}b!98{!eK=vN z=&0~R+K8PI=>0d`-?=cC0J}}rQlu*G@#u#sdwc(2{?g6u9VRDcv093L*PC&5_x$38 z7A}N#WRAv7Yl@Fdq9+lXrK0Z=c%Tk!KFL^a@}c}A^{9^Ed#_4Vc$Uv*eB0hApnLOl zi%of@yks5&=~%d=+$&DOHJvx>KuYksw_IpG4i1j7sOJ=)iC}M|^WKC1BB+b7H-bVbdTMU*4bpVh7}`gfRcS@eomvq^T9isEkEIo*THjI%0o4GoWtR z=GVFg!RUTL$d%X$;`rb_u?sIX;oO=My#16M99K{Jo)$^9{?f?Q{3Dm3DLgmcuG7UV ztD~04(U~Oc0q)g9aK}Y1H1XZa>#Q(82(+IYBWy}ASX37@RU}CEr3SZEEXY;5u)WiD z)r#=75ItdTZTbBb>Z94baORIYPtV&xcXx&%SdytSKTn|SMQ=ta7tCVNT=4?!o7)YO zOb!9F+3t^PW)5mLvyiGF4M?hqn3&iXWo1AiVsCo0$UCO$+d)6tA+ZAj3afp`lD4@x{9fQt6s z>&+aZ${$+~hi{aYH*gDN4y;P8vc}%0O_ekytIj3gE3IYO4ZK$|FQ$wqzYPR^7$Ep7 zhv#8>8F9?%N%>bAiOD^`wd&DeN^2EY<~eVj749xr9r4Am%xr(ho}u>nI|V137*l-^ zPX2?SxHb!=+JMt6SV!kJz}@!}a2p@EB)ja&ze>1xSeAyZnAoa!$!fEts(d_X5u{wH zW|rRVjn+11rO%9QAOb;1eo#?9=DyBz@)=jxx{9%rt@^D}*V9_sG4mqm#sudJpB4>e zLxGe)b-Z|Y@%TD0M4GK{Yu$(+s_xDOZ_t2ifZp6_tJMBc^XKa^gMKgmedE2BTH-(r zIX+Z0dHrE8d#X{QZNq4dX8cxD`D|dU61cpZ8Pok>rdEQtgQ@VpEI$wBNtS1>6H>mN zAh9hT=?flh1NNprYx$;NmI`Er!j~7RW*8;8)JiE1-6!yRe%!bqLz_9 zc{XS^)jMvP-np;>XA%P>R&Fxa5X_TLoXrOq>@AHdS%W?m>y?+vWa4s#rb|7y`h&r$ zFe(WQ810a!^}aENQkSXcU*-RQC<_=hyhMU)kH<3J@|gzbS)>ZOx=%Vox6Pl|l%olg z>w~!*{AVzNN^yEgCX6Ps-J z<0bb$w})qNUs3lmry1Lk*7Ju{*O1S{4rgi2PRt<8+`s`;qUhL^ZO#+&vHwX$QYoUR z58i4%G&nQ2DS91YIr#7-nI+Ara*B>&AR7CTAT`FxBpwk*e`9*WirON?p)kaf%Dzb+ z`dn@O$Dkjwrzk}Ld43u;R_oI+g7eO{0M<7*ORq{)~ zi&=8*?V57xLYx!0T7XGVh+MJM7Me7$DGs}RS7}5csd&ph(OV`7-q?qG^dT-;)#NT2 z-~Xiie{N%`l(E{9b!#vZvUCZ(^KGs%d!c+`_LXe{%QU!B$>YvDH4$d|2->uQ-*HL+ zWcKz}+l{OSi{yFP#;mW@H|%#BwX*KRPAgXY!~-PxSJNhz{F9D^Fudbz!lF+4;F^_w z)ofKwqXi@-S#=?7F=+mHBgX0UowP%g{#_drE+4MJ=91}#>Q$+*38#%7Ay2bZqrw>M z0CZ(KPdAyjFPgS>F%MJU>)y7WQMLOTIftZvgut2F%irHQqN-Zc<@bqK%Q2e zT0KwI(;sahAd9nj^2?nxbR8kVL=nYGU1Fq)%-6b75g~qZ^MZCzg+e@`!6WVEC->BJ ziHERfoSdQnWZzkB`w=Ul`4W)nGIqsS`GpVmJgXi>GvWB{)AvP6 zBYqycST3kxqKOU9TK*K~+g^f`!{nBy+aZ9&XXIBYqU896sY%l|g{nOW(y@%sa($PHZH+@m|Hj*3GlhyEe*VubUVB;v0l z_U+%Z4@mT%8Ae^G;b|`c3>Nnf+o}M?Z9g)m3SES^ZaxBvAo|-yTU{M)F337`>v716 zY<2RO&6YJ_sh$b&XlSs%I?dE$F9<13HDFQi*k)LGZ7tCnyVGWDPaRSN{CUD`lo0Qe zlbY!+`m*9>^5;!Z?BrU~7JX*ov2Q6Ert7*5cWt?U@1|=GZRQPms^hjT8L*n^})N;8zqM2b_|d z9+i(=b)$)!PxL*_V6}Z3F(38%s3yR#SvGj0YD6Ek(x2HGnQ;V5{(xx9H;WLtyRNim z?<~$(`VEQUiRZoUoaVr9ADytMgO#7E;G3!VTHoK&GAQ=cwxLXruUCpThc5*ovts-E zFIcvJNA&z}Zb6lyP(B<$7T`1c$+y>~-N@OiIj9qe_l_0pVzi4>tZ}3{>lxyelw+gR zFWp1V>KHVwwAz(xm_$$6TT<67%pTg}b2iy>PfMgo`F)};0nkf;hwuSs;6QPe=@p6UzXr3q^ZWTQw zI34@j@Wx^IdTa#E9S$da1L%kYZ<;fpJI2Y>qYbG{a>b87pGH@ zcQ6Ku7~ogV8yFoyiUs=d1)lq?#hX$%Zc2$DD+sMheKW%c9%IGd%tmxDChoV{%?v-NRh%|#+y-$Vw+?w0`f zXqBkafYt@vyk10tvZn;E8zc zdAzx4c!_1Sf`twIu6D~&wH+copFa^n}>)Pmo~qM2 zzYgs9DU5g}d|mk4K`BA3VxdLAnQleEA{!aicHHEoYR8zU8mysa{9Rm341z*&jE#Pn zNG35ddgLqgO=@!n^G&O0j+Gl9;FH^Ps@>=_v7c4#W)c6RPe*SAc}=ho2U*Y$ zfsLKUaH-mow|r^cf!Dg7tJeGU$9ag`?$k9Sw3~RAk@UxRAbeP_CZu9?QzAdYn zwMY6~7^<-$tQcutS=*m_?bmNaf6tG?hIcPg=VwnlKTejP3HE((!iN%$1Xnt87VK3xG1YS!}-^@^Q~VEDYykowvOGqCBxtv6WUP-pY{y9%qK1 zLG$nRJu_WDO!Ufz+4|E%EN^uC`HYSDhT%)r%+}|1JpbMiEtEBip0(vp@J_GhPudjB za@dUL4=W@-eh`BY)X;#S#Kbr_0Xk*jgmYJ@{KD)@FD>mx<3SSL9W_J=myA`ie6F7T zQ&iY*6&|Zc^UKx>eD#o;X)47iCDD~@L!bN%)3T~k*oF^HOb=z`ZE^6el0@aC0wJi> z>`e*>Z0CT>Fh$+i9=B@ZB;;!c2@b%(@J6Biq2nZ&;8OSJKmcQ>}yt&=sM^z2`g=lh= zgK9Z0*6F0c)`0Ig1fNo|&oQ2;9lR=%Kq?l+q0i4v59U*R-m-%_d53S?J>c%e$tK== z8Sr#TFo)8BC2YE87*HOC3KYa1lpxta$hm_#F8}pOtEVGw`6tHiEnf2fMJNh!q1K=jc%Rln zo{xH&9*=_zn(8K;AiAotmSWoyOX*+o5w{t4UN!=IDrpzeeB{-SZ&G>~5?SnyVk?^3 zMQ^2R1ysbRq*qOHpVZ$U>qzn&m06*wU2$HOaVUES?ruoEr)aLY-?~}p zm8Tq=$W`zjrQtryW`e?lfyq)UQ%Mi`O(J-%Nz6ZTzl(3fk33&opA8l=FM&tC?`j$m z>t`l~N{JGqWHJXQYza<;L)#9J;FydOUtv!NZEJLhqyZYFe3J~2|*K<2~kyo2VQp+JX4pQEo(L^#a&&ir&# zA$IS_kPK2Jv>$ag+AoL=<-Pa}&=r#2P0JCJ7w$PNz3J^T%521F*W`?gqTkEctY1@0 z1Z&OTN;eJhc)wtpJ%aGKPp^o21yZ)Wd7u5llF*#IFAwW_v057z&{AW#Xg|3z-!%4) zXW84JUZ!keDQu{H)vTZ>1E!(FmtCN0NiElt)#bPir{>?i?=L0#W9PfI)hUjmVlaT~ z9uhi|LD$gJbMe!`el0bRo?W=#?5r2Ww-rh3UUXnueYk?x%~*U7tnQ8t|} zJu_p}=)s1ILugFjPt9sP95nRj(oDhs72= z0i)e_S_bEPsGrsJ8@vh0i)w#VcPplPO!p=FYU$H5#)-zG+A#v;O3*U`SA1-Ad#z)f$QeVFjs_A;o4s;7`&5Pz@D! z=JJ{#&8~&$2u%(8B#bizk|;`AZcaQUWG~lS44v#+x>P^MD@(2G6))bc3>RKawXcn5q;f3s59yIN-B1acUP;Na~ z1@Q%ir%9jaN^Z!532gGd$B7plP7OZ4ZWT=Kb0SS-g&hyzy=ZzY?$*_Ru$&fZB1dc2 zz)x1yid$vA=wqzZp=j<}4j^{YTx+QH=QZZkC0Spu;{uJ0xm1sEMrJ;b*5sy}v!`@F zo3v^lPi?kRY8M7Qr(DF>H+nD1rmhAo{Q5LbWR=5h+pDY8hfVr6m7j%3}NRbr)!4nZjqN10rdKI8`JpT$>*at50d%p9;+t6nxB#7UF(jDkQj@ANu38lpWsbXR0}H zf!lrB$o=F=>!&2aA647VDF3M>j%ykh(kCnRoCXCkLiDUJL18KC!h zp4zQBxGm4-l~UA4{(Qg11L+xpHwXl;!@~;7XS4mL(RC(pe$!Y!UPZ75#}hu%g-P zj!%gy_1UYxR(4vR3^;qkk0MJVKRRTR`%D@rwLO-7LxIg?>6X@bsWk-`E?j{Y@Qz_O zwSgfw@aUS*v$&ZnueP+d$3t7hY%J*Ex2r(-oDWc z!53g4YiSP)+>eqs0TSh-O9!3-X68JnMAHR z*ZUmaSMvJ+9T%c{s_E_Jc^Z`A zrWL_>`zN|=I28C*ODUvPE9Rc21%MJ1x6j6L(Osu)r0L)~=Bhys{K``(6LRlv^4<4o z`9!*XCJ^;eaz*CN-vEZ`+NF&d#Kj3+iaT-It-LegxH&W@5!6(_)F@&(ClQb>dJ7FH z?~-!huN-hXT842=K$P2Tl%n~@fV~K|KgO-SRT6{QF3zMyv3!ivTVC_7V@?xy6S@0= zof~!@kcL0G4_AztA9XdP-oJgd{kPbCS4W&;;-T-IXB;lK`;E!CC;g;)z3I>%t+%t* zr^Qh^>Y))Kh}}ke84EMrN&XR(8!VLhUsuupj8S77+PUt0K3JXJb6)g`aRgbLC$&12 zZr7>l$>0)k^+lVi+r zGZJF0hz-pL-`FrHNoo)Pr8&V&HH51GO`CS28Onu|Kc<`l?nKHx49lB;ELZ1D!7MiJruH3pTuYBI9P-?oXkMpc{q0_H6vx*NWc5%(-$452C-DdOXdaNct#XK=QE!AtK z8xpDPU#=)p=Q5hbcO+z%20u*b=bnY``|0NmDvnW_zIUe;@9-TM=zQTZ5+1h~Bpp00 z+F$#C8x*1acBCZ+s3{?!C?+;FG&FQ1HbwH@5?o_xUI`H%pe5-M-+rmyKPzW=(Ef2U zV#x$OZnlxM`L1VkCQvP+fbNGaiYtMR4j@9)<&xnfi)|DkUu=BLq&Vb5ajm%3uuH#G zOGrV_q0d9MqG{3=Ti1r{ww+3)FUWc5^qf)yFfJA6@`07i{Iuet>+VR7wFPTbW!89x zK-KW1|JTZNn(LwpQn-Q?+x#zYK~DDmPWF~JW?R~(T3Z9&K>3h63p+vYl3b|-1bfkI zJlR>UX2Yts!{8^#$j9&XQzo7CFf@Xb3e0IJ-|UZ`=TCMrs1ap2g!3@K%&*%dUw5t!!^FtG2+*>NcD*=CyJ=h}PKMOGN*q=~ zEphR!H+;>AYrz#y<%>x$2TR~KYS;>G%?E8RR$jhfe}g1CGjynkvD;HtU%6=_Wqy2N z{3pIZ4&XH3q+$$3LHf|d)zOeHAYw+l`#3q)dm|9e)BkBIDgYtDludpphe~zI%*5P+EjvY zN=;-sPGd5(Jtey?UcNXY`||0WYrz{YYVR=AAbd?%qykEiQ&=c;Z0FmXGT{IHeXUt! zx@{}cxFGf6Q9}V|GEe^0AVd%_FTF};%{H>4IeE>Yl#xfX%a1P1#=`sQy3G69`Pg%h zbd9Occ=xAq;-Lv>Wm_2M6iIdY84dgN$8o^`@9agM<+`suZF zwX@}OcP2OdL?qmixt0~0sYx`!{h}GRO9P|Tl|KyXAvxtk%9T^oA%-Zi@vGpyKoQBs z%Ri&%u)f2x+Q?%aMevDh+f;^qsk@;-ed!&{P2u9fs$7ED_a3Vrvm?Aa4Qi~MhM$vT zj5_*1&z^tRt{YZN8AO6xm(I7?X19*i4>cHQF%gA^rI00M$M_Z1LW6sLC)sZOG`U!= zDKu~2`}ae?0Adx(cVtGr%TE`xzSM;BYX*SvwaG()3xypR)ivt?UY~iy(~v?lLD2jt zwvdyzFNTj(BPuK8aYs);$t9q2ck|@+26N(pvKxg$tl&b>sytTc8{%>HxY6CKM6I{m zXF*GjqVbwBT9}t1QwyQiq6`@BKS{l-8KJr9hRUzWWk}{#D?#Yl4G9?rkL+QSD3jPj zjaGgonE%rcHM;Po$@1bDevPqwZn~eMPp-+%6FZJKY6NRR3$*NcI%Sr8q7{!udYgEX zKYvngvT)xxytuExt!IcRYtrPLN~*BYOG|q*?)uD7*gR=hK;Xh`o4VypUVi7I#q`^} ze|aMJfIrxkQm^tVr>fDK!zp?Jjug+mp@a}n;htiRRA}dZPW$zI`XinV*DQ)~np?1I z%0w#QRaqLpSk37Un>*Qt=r6)b-e!SNg!t4&Y3-K$Z>BASS3=X?>~1>*yRbERr_W`f zEuTlRWz4eSjanC>(IKc3AM#!DlBrW+DKm2;hz^)vFAa4oBqahwM?&3(HGGCH#jOW; zdHLNmo$s`jY<&~&#&-It@vv#$>143Te+@VX(lvk9R>#WLUc7JIru{665r0suA)9ICZ?V zL`v2q$L~D6W6urpX4NbzD^iCq)$-X17)Gp*!&WOk(u%eFxCQnSpIMgMyvkZweF9gU zOGLR(rFtxWJ7V?%vc*8YD_ol#Da;mzxDS{=6yQ1dG%Fq5Va{TDWMeXaSr9-VABT$~7oomoyGV7Mku|q}S5l3-yo?%>T|a zOyQ8rd-&wKJK(zhpWU>`jgG97W{GuKVRCa*RKw2TBMA=Ldo}pTon9#4RJe6RF(&=c z`(}Rbq~D0c?ahZK?qk8Mlb#prCuz?+(o~UI^Gx#k0*LXVUZ<62Sd~Rx@lL-TSO~3e z9lT@s@wX#PujRm{S3;0d{NRy=HR;3Qt`}rC0`<(gt>Crt`X|z{COtkhs)i1QY6%o< zkj57k=SXmYVoKP$GB3(p|JvYA?iz}^IHYXqIn7aR#n-IG9h#4D2%)x_dR9~-4q>Co z$cBkuCDISRHCWKn;4;jc(j%GM^ow({0KFH5VHdF6ljKe23*-I12^oBpw6}_)0 zuRzqg)LJVLXlE8oc7+_QIsK#o^#&oUtgPn zKepyIdo4X-E;Vq1Sgh2iZ0)9TYwD!|lQ=D)1@Eaw&IY}4j~K0EP$!f%HhJL1 zX$)0XnX!dlTR~rv>iE6XpejRK^w5;4!l5B7cS^dT$r~bC_W@CJOKo|uf2R&8bz(YkxCS4oXB^wV_B&ys@W2?erm#3WM+q`9!lg~M=D!9pvohjG|O6#_2d{< z1~f-hNGsOC>XWljqDS!JIB+1g*+qY|Hl|^3tmvLe3Ys30v?nG^y7C<69~9Ue5OZQY ze;9P|g8c`+uCF#m9^Ai>z?09_kQg}v*oMP_D8jU;|)!h z1U_dw{;B9GlVFZs*MLia=luOGenjm4qUsU`4{awdmO^YleZD1BoSOOm&ii@^j;Cr) zIHv%3SCDCxW{Id{j9{IkOHzft-Wz=I-%IoV4A=E@%1pxq*}7cotGsWu+ZjAGZWQBv znN%8U&RB57UP3UM|9EiExH?9ts_h=*J5J#T*UJBt^&+m0X5M6KwG5tGSG-;Z2xPnp z2pGO^WQ|~5o*6l@LhU1lOj?Q!XMO_RQ9|JrXC^<8-@%nhKjPFC_&LkdL0oyDChhc_ zGj-q|Ey~5^wgi1VVe!2Ws{7~tVk00~;N<+U#b5uO`aOV;qcUL<$^TM zGTbTqN0-*BlKF{-r3Axc(LdsBoV%tqCYZdFZDk6ngdUGdi2aXt;rH*`!_`O`{f za#iJRmVxsK2S4AQ4E=hK8KRA@@i2A@3iUox1UJp&7(KoIKr|tDk_C!ujLMuKSZ+a8 zOa0xMTbF?Hk?u>t@6}b|->_g=-e8%^3%$%|w#IVx9@@}+$ehYShG^^B(XItoDo&1l z+5OJhE0xOL!q1@ITREiqrO)b~kXN1*eX5iHDhu81>1uzVl1z~hBh;L|oWX}J!yFAN zlHt-_uzY5^g=^iag{o=;_7)VDH+HMc$ithXw{O=TZ%R7A)ca3gBkGcCk*hK-O=UBj z5qP!YkV&5*P1r^+hsH`wo) zlbwtZVYfu{4r|y%1Mw<^SjM;MW%SZo8k(}gt_Z39O>goOpp{b+{-+=YcL|8A5x#ho z4frMd`?WfI=d<5mS1W&e0N79fHdq_Z>tw?myLSYU5IG~HBV4;DILGr)+FH0;9*v1X z<$SJ#2D^bQ9k3Qj`xe_5AAKrr@TQMV%!p1R9c>9KtzsQsb3oxfAD`CreLd%s2;;LG z9V}#`SJ_eNaqtrhu~<&X>ucIzYJy zSY%fXYT&~lWG9T_)Fv5R9zPO=f`XuT>3VJ3FF@v7?Ze-$0)hEs9W2i_GvLHAY@JVo z+@MZyWEgD3&*HGqT=H>9Vq&fy9Frw=SRkk}g0~Zy7looG(-50>7{*{zSATe&P1aQ! z_EN>uzQYf-Y4wk((}^`D`^K74`GmSq=`%FYlUnZ1Lj2afD2P@x-ek;S08RI1mRfY} zyy*40^I7TL>(53K+~-Vd8}p2OSXr!!=bLl7p?$;R-J1I2Yf~|^&=rGEsuk9P>R7Vo zbFg?P_(>JhdYHsYtFx3$01okjP>pZ1DM+tC6MmVNzpH!>$caQF8#){{ir}9DGsNH*&Z6;n$W_MeFaqjT%vqj}|Gv zQzo`VHJWh(LguQZ6` zl;)rGZMnaQCd#oW>Z?3NCQ^Wn>1U!o$|tHWt7sy##)Fo3n+`VP^3=2yOfZGNO7vLX zkIW<($Mjf(cx)IC{f5uQi!vO~2EMuKyN!K%vy=l^eZ}{_szRh!)~)VGN;YL3?^t@b zm#I%-3BTj+gKVoG72-~s{jXA|B^#3v7}b>|H1CupGHzR?>8V;x+aSJbk=C=4XQ{{c zwTRtW7II(wT)DorNmlQHiehv`|81`Fo1!tAY25`l;alV`Cye+Mm;=_rj|ggU0zpnP z2rmV!7N!yXUL#jgB(XP!j|4U4>Do{p($#FXhcz6EDVr$m0)pd|rO)C2yjJ}mx85H; zJLza1A-wj}CI(#ZzPqyC^Fg-3Zp)H={lvNi+81B^A^UTY%!Lz;kZDYzY~@UfSCKA~*__6U@Ia&UZEyOPVxWOVqvQO9=_{Yku`BKa>xw)#T9bb8p6rchN)>xGGJM2F*>j zAmuq1)i@ThLN#uemai!F!>?PnSQg|?T4G1o(C!wTL8)H5x0@NaOASKjk5_3%S{^*_ zV@fJ$emI|~Q_B4Q4&zHZ{%`JrPxk}$c~a_cQ5;--?PGL8dU3bW!498M)|hngZQD-z z6*1h48l+X(n!@d=ZeLBRc_!tEmfwv3423-vc`>TW7t^h;eo^ z2@N+N!41o#8?y#%abx>ELM5H2qHLWyrShY-a)QPCrjIB!H%p;1d6F6mz-!2PCU@$0 zH3%N`F_E}sZC*!?u2)I~;SJ9mfuRj z2PNMB%&pbt&e%+M1v*Z1p@7j1zv3B_MrW?fsSXRP+iIBG{`>2Klp76@5UvI~I_|b> zDn9@Q%huXoemUQF)d|S(?VF)c>oFE+R9&qxR9z@zP46rovzadU;gHjl%<;B*kY);3 z(-XX=v94DT{p%-bcUyUPs^8qX*-`=Z!dS_CZAo*%kOY+(KTbwPVndhfRXP}CN3R+ zmA5Aq0Wbt{vrkrrSc^Ytx)=;b$aU>-($3eb$_?@3_^28Lq?U3`&5~<9@7&><0yj*x zp%ytgxata6BAj6YWUqC0yTc`2Nqmj4i>bcE@1D|tHuYR{+85L3HtaSI-%_=CVl+z4sh%%D0e;5z=?!@I z4q86M_)i|WVy>xS{+m9&l8}^vOF*oQtu|Lufq+W+{*x#3$K0DQP3+$#(AgPivOjVt zMYA2J+r}km6dJv&IDa_83<)Al^4ExlN*sosfeeGQ|TTWqK~Yp)`* z*BnqY?Lhi!Hzi$)wP!bC^{voapWtTD-yu$_wzgS1eIz`H`(@yI{d_20l($ji@9uar z7!14DEq22&Z=gh5k6Coo1jJ3ltvQy|dIu(`6`wDpPg)0uCm?bV%Z53P5~Z0U8OI~u zi*ni*{XdVtt2k~>?C_p=HTkLL<$i#<4PTvOFX=nZ7HY2Ral?#n(XYZTegT_9foXNo zARl|%vchpP>aIKfR|Lpf~f zByYD9n?hp8rM&Sw-eKAkt9O&CgRS=TbyQ}e~uz7FU2d^ms@NAyk1 z>-(embae4vGYij;)-x^v4=$_(N8ys}Us|Mn=K?Wxl>w8YL7oKMhC-rqb+Qm^z=t1) ztcV;r!T!WMr!x>X6KX}1q=LG_`9~dzY(qbR-+(#oWsjELIehR(H~czAe>@ghmY*-5 z%1AZuj9Mg#XY;IyI}cbf`>>dsN>rB^5!kcnJBEV9hu@6gxju@Yv~4_r%hC}!8y3Gw z((@ck{)yV0pEju3}DnNRj37b+zsy#I)+ zp{`ArJM6T+NN;!5YAnFRyQ5}THiyLCWRY%X&9$i-HnpS4FoI#MY)5-#)(6i!#X znW>DA$t}cN#-$D4-rQOPV3)pUwYaBxTwu)41KYU%L@ve8L=vwLR!`(%jE!*8{06{M zhMr*|I&M{XOGt4HobM`v)(DN4`SM*eE~s@}Rv2+!A7t4jvFYQvm@-;tGB8ut^eTmq zIWp0~kOehu67|2>d&{V{zOP-B8ZD*JLXqMv?v&u(;t+ysuokxf!KF~cwFLJf0TSF2 zoYLa1K|*j1gtmB*{toxvcZ~n}@Sc0`IUnwL&mI47Ypk8@wf0mvk)q$2aHxdpv%-{PuRHGy5Sa!l7HcR82kuGUTN;K$!13arM2F z8l;?X*{_&8(ZHzPzU`1A(ZeHn{S41-@|FGE)u0no8GA8%UO03P) z*Wp%!xFx^MQnY4~6<}RrB9?#c`|0jhIL{+q9U&@I{VS${SO3qBjPKiv@BScHN)Mgd z(RCeat3V|d$PwGJ?^VV~s@rfgUU!#JuQYx^p>L;Y&D%`kq9eiE@ayH>u)lTMQ=PW$ zlj*HjQQGI@w>!>K(x1fN%`q!yN z=(3A>j0qr+QU9V{zR=8>5B!1Po$G0=uqb$Uu+^)ds#vS{kKY_}A)P}KrYUNGtfi&U zbj`LhYDhg2ucj)C8j2>8et*i*$B?$D{io8^z^@@iqcFs}9I(}uaad7Gt|S)wx6?RX z2W0jcoRX3f?C?~t8JN)@4L_n4esZUk`%X2N3j^m7TBi2;jX7+!fj5Q0@32#t4;2y$ zkvp$(sq8H@jNViVndU$b)Pn=#iGy6g=U?uGp4HBw+;B?|e$3lC9kD7dp?a&4+6GvD zv(bVmqrrH_{>|}XX*?G4Aa6n6ezdqiTRcoa9ij<-m0;Y5v6#DVbW|>M|G~HkK^~0e zKg(?K3kUZ~FkU9%*VQNH8lr{Y@=}%L`otBr>{T2U4*-EdnNJw*G}tG%#VzOihcEv0 zKW!iTuq#tyMES9isla4g-NiCDfza)RmPTlTTcD1Hm=wtd1P_##>+vT>4WgIi*C>^} zK>N^vvH4S9@V?Cqu2%|a`FM(_&gDVLl&z#okRkkKc(TCrCWPw*J2e(`Yqo)+hm z;ZKOu8Hc=Uz}N4V&5i=Z8-d(P(H!jkL;7nLuNeJ9f_aZy>-%go(^B(|S_;ADsJ;_E zq!A#SHP~m+$6vqH9%%5u3^4g1Zn|*?c9WsGLbR_;w3m7EwGxY3B@Ec`j@7yPAMXW= ztZO!^hI>rfVUQfR^I4UuTQVk|pdZ0hN?M!|Nv_==7$|PQIiPyfnF)- z9elqaVSsRxJ;ORNLDmrp7zpkVS{{+Yy^r zh>airMd&j5co+iy3XE$QP~y?Zf+bJ40bkv%;QyE&Ja(o}*v^JBPaUc*qnxoY2-eR^ zCSm|nk8-<2tyT9p$hwF3b)o#*Ir!kGoI&rWMffw^9#$cWValy=0h|Ktdo-oL06 zA8z4iWE`pY?=R`MN!9n`AZGI@gsP~Of-+@JAn0z02=g^__VWEj~V6< zVij7q=$nJzokDA>_Gb(i*IA0_h60RSH5;95-8}53b#8Jt3>Uhc5<7Wz+6XVgB#3!R9WcAiF5H8r3XkONM+ zdI)F5JWbuk0#g0S`#)T+?JHx)1xyVEFs4!_&X?t3T_U~&*7i6-voo$C0ru#*$tI7mwnzya%K zIrK{x#pdjaPI(M+48kH(EEcOO5{<`)sy-TE`Nm`-<|TYWPlFp#zwiBMCi^-g(ylm) za~G>cRAZ`J;Hn7u)C(pXonb~}{wOa3Fz_og>a#rIFZNbc5=BF@hH%;F=BNFRAi-T1{)?3-NbDXeQy5 z0ZF$)0+QX&^Ev!3yIbuC=34TFC@h<*uqY3H{o~u=BvfZR!+>LJ2Sr`z^!#s)Vps|0 zCdkVj)&J0q@Y>6r7Uj3&o;Sph_7S>~Lq>r>vp_26TS4i7Z0Yd;9Bi}5v`l=yFP=9q zZE*+g0Gr`e^npMrzw+L%vl{a!Yv(Hr{~$pZ;hJ$1U7;NIUovgmnc1mB5H_!hEX$Ix zFLT2MBBJN0)b|CIidBmR+&M@!U{n3~HbNZBIgdIoEr-$jGOCDRlbK{__CUcT&(G-+ z1_7g~(K-hJO}(bCfDY)vtvS#C(Q;1w6y~K88CiS1o$3`n`>>}^@*opX8@wb&8zW0^ z56GL$OM0>WxQkm+Yi#(6)PP7gm!WmpvvO)`eYw3!MhZul?S4`Z-Vf>7xQbNio)DB& z4ieFYkK-Ts`9ygo7sV+ITl51&GDHZf(!oL+vW1=?`3V|&u2 zRi=DcYuv>>w2`wZg|U-L&So!AFo$|;%GVO-x&jveqt4&dzdPLQ`ADO+J82$H{M0qb zt}hMLFa%0MxPqtcG&I#4Mda_kBJb319X+u2I%&=An98ja3^`ikGcFa3`@2^~x{wr8 zi?edOPc@t_Fi-6xd14=trZ~uLZ|0+*ihNJf+ z9Bz+_S2pWdf>)a>F0=Aq!^NGs*hjLpL(=FqDkfoKmC7oJBq))|dmGuu>8E`Q9*zM* za;C|`Y34P~CZrPa@p4*K7WX+VjCkTWe!O*;K+Xk{j;91sHpo5JCo=!=ujc+=lx{Z$ ze>$iSZuL!c(LJ@rj#jp}7G9UkOwq5#2k2ME2-KRt)k5DAvooZ^CcHc|(x%UbGBSu} zC6A1Lk(3{Aeb(y7(x8qBwXN)FlTgVKH2=)$zHk{;=o?e@s?9D~YaOzr%n%hfFW$um zqH+>Tq80x@s`?+@y0fAhDBOMW0~vbl?mqzPllO*pU@_{Njg&6L zjPbEaW88Y#rtf`ZhzHTVo&Wr?9tsioQ158tSND) zr<_kJ?(d$8)nl90`N5vgBs!h=O6b*pey@Mk`nFvR2L3}tZ)@2>xKHXZ*oOBbUQ~5L zO*P@=08dltQ~TbS4l6*%(5pSp>IR83TRoA~g<_O}Y0A}x!~KsY!!<9u1A8YUQ}yPJ>sH!T5%1VBaYc(jmep5L^Nc3RHD)4{yLp+zolg76`@O2~mYN`TjHuGB8^l>+I zLzBI!-(Mp@B@|K=Mc75W z?pEX>(Ec7w>28XobAH#)UfHhOfSBpKE$0rH`8kEPeQbHzAGyf7a1dy_TISj$f|{*z zlT+C@bjT9_EM@h@5)NM8eUD!=1e#sMNGF zNV=zcqa3U{T{17ai*RSxm5CIfSW;@k1%p&4fhiVWyZ!o86T@`%0WddARc-GHgKA{F z%!bG4N9{#9mXHSK8Oh#j*G&sPRE#!Tk=-6juJvU!-XivUsU<9JzD#-m?x7N8DL$Ya z&LVB7OsrRnyY!%?{~1%cP})Mp=hB{6PBV3uHg#s{*yWe(VIS*$s~S6(3^MalMEp>4 zm)wJRp$EfvV47gRSM)p7@*{BflI`t5h(>-Ztkh-JEAEw6wG-yXgU@GTWjUHUHhrO+`QGP7ojqX%Pr@3ws`8E>w;4w+mPR{ zaPfDkh)>X-Ov)#dKKhAD8ksjhzkOz+JJf?81wsHb5e}& z?3=3Ns`-(P~3J#Rub))}9Y zm=Ih2cvyp^u{1WSC($rcS+Lu=p}`7_kjlAYZD21RhOH)v*GNQqlkd`5Gx}957+;?z zy@+KlZH~<*m!qNf$`~<&La^6$>AX+lWt9^OUMSBu!45@Urxn}XfAj6`J6x4=^KarA z39&S{^!fO}wX2Z8MdP^4M=4Q+>7A575mp2yu0KaZdJ?3eK)FJ+XWuHgLTAf%Z3?^( z&5Y$%sRJAw?xe*he>vRH4EzxbiVU{kDV}vKp`v7CRiOrGjbkFKyM&$-Uty8K9S2Vz ztRs7WJhu^C$fiV<`QSsxYJ2{;8VWI{dYVn2>dy$4TgEeOJ=f1hr~*Au-=!13riG~R zAA>(EV`&b|n>)8ZTM?phcPOwK>49Xr%PlXpO=_*uPNj$Zy5--yBxxug8J5)*6~G00 z(;SD@eMa~XDUI)wwlA$*Hutw;!xyR>8z~oDp@w{+a`P=ClUC5YGH_D@E>B(34SlQd zuZ%%jq&c)CefSf{*Z7B;xn7d=-hwtiD@beh9(FdD>%8}?_E{j8h}v}IgUD}pi^e(X zBPZSzFO}ui8={$fr!vwsH0?`63^q@6@gw{dB(%-U_y?VrboTB$tvr=zr7D@XxuM^A z8zfJ-%BXv}8g=q-`xw#BL3i{?Fez$CGL0=kyG)n6(5_6b)>*${BL_!Z%RyZ@*2MY5 zgWcVFU2~CGhRMR(lbJ%Kv&pPthk)Mz@f4w{k>VGsK(cC4N{6(0qf@oWd}4VQtKAa zk;Ab_d(zU8eJ~SxPuCQC;L?pAnSZZqQBt@6hq{IDX1S^a>G^^+Nj?cG=kwr$l75Xp zE^YSrX_m2Qu4vDPJY{+>owvpC!5*~A)T-#2Kz*C7*v?;I{so`n8v~mphRD$dhT>#a z14weBII&pM_3D8_!>lWEY2_?8Wb!PwH4OMs3tfgHP-JU=G-#_Dc#sGx_~smz920qW zMAIVjL0%wPrA3)OY~j^}k3eR~+jp7nN`q#1+CmD@jBvArXe8Db{!w5rYoI{gC*Bcc z`;NG5f^%B2ZY?)QuA)M>@j+Gv_$YzTMDD?ytuv_}dWm>;< zexzq>{K8bspC_G~y@fs{vgpOmPg?Oyg?_p^NP z?X#X5Cj0@S3$DCLyh_ORLa_5g{lxKCr8}bF;8Z3eNdL>7`7d3P9`iqIgfw4P?DGWJ zshU{{6&&~_YBnvUDSK*`nsSd#!v2)cuo{dT{c3Vjt0sHJl<|tmE_ovb9scT!iOpz(=d&m$TB0rH< z-qV@em}b}6oc@MO`DmR?!M%K$^tp_ZUVCj#FEx3@F1)yGwwg?A`c^q=KBb1!rR)5X zixAqZ*8EanmU>&b$K@mKT-i^R#20f1mm+tumZI=eOtPTjnkrF&)x5&q-;3`GUEdUr zomcKtoYdH1Juf|02mI4@j^zb|d$lb?^x0fDra8YNWH;>W>?b*HTy+^SBN%0#ivxK+TmBCBuW!HJ$Z{>wq}HP@TdXDlrORhVDOvTxrB@lF)XNwA5L_*lc( zgMm*hf=ob^7CPljHxwE^lR?jJccPzO=v`Y?mycXnPxGP=*U@8k_$wjUg>Qbc&*ies zuGeK2k<1*C^u#IpA0lfV4AVbElQk^YOc_M`O+@&|Y5cgYrA{+%pJ@WjVMv15&J^q1 zz}$QwgP9IJC><)IwxKtoi<;QzG0@J&LoAi_(s{LRc6jQK&YF5Bf;VpX13O72-IEy( z5+Rrz?`n4?7h~#&+RvK-M%jvg6?DJ$wlKNZZRof_a;efLCz^82wAQwuPDb`)N<7%kGet?R6Iv@ z)?#jR?>#>5^Z>aC;Te8!w&iHgT{DJBBu5$@zrZ(tF z28fOyU>Mf3acUEzE|{)Xiudwz9vb?gLv-J~>M**bw54-qH)w`)NI1`=UAL>Y8l9Wv zbFLuheHetJbUHusJh!s=_IS+xs6-g|bnl^3OMKlhe%QEvisV|Z+1UI`Dbt{)S* zWI53jpy7r3t$967O-gvBa@g}YOugKLs5Y(PTjQ~}cp^J0Jc4;peLSo9gttRuZ`eF^ z9A#b`N%N>mUUgLKRA}#(lKB$({UUhi)+0k2{q6z2nUgk9NBpkiUK)h~oq?hc;+sCR zvQB6~d2i6QM-+SAw+!;ZVxFnBxO$~h`uC}3m+{>rE62|#4ahh5v_u&pA?_ZI`16o2 zcVGSF3U)sn`q_v5-5$Lz?XHQV-uvv4?M*M(%s1~OK9!nTZ<-7>oOS}6FsJm%;N6C1 zMPce`MVy%X)OA>g>wO&@O;Mc0a^S16Q-9wG#80!_A$-G+Xe(E4Wk|zFgR23Pc}cgF z*P{epseyatTqR*YUy))DIy)DLf4oEivbDRk-D5pvKDU>lI5Fa<3-fW=pH7k7qrA)M zFUVQ>gpa2e*)xx_C{avJhNE(bv%u6kOYxkE zoojO@t98_RFS0A7a9{9zTJDsSI_Qy3<=KlE}On@*^;c|Av4El%l&wE zJJJC=Ov@h>!khZ4v^SQn@DK=k!NYodgNAj}eIU`Lno+y27ks&1)Xh)IbV37u0dJ+*| zX-G)Z*=y1O@ogXGIVt+Z8uWYmun|Ol4g8nB!5jNMZJw{|!n&apJm<+a@CJA~49u5Q zDAu5^*K`-UNS(c0)a)ROATT_C!|#vfx@Ffpn%w+o_dXjNJ8mWztQwVndof)5ZNz#G zCLScIZwN-gY4v0^Uisa|o|5*-*mj*4c}T{5LDGo)5T&&;}=WLHs61Ybd^c5R^*T4WxhCGzOx5} z)!v-HXd6}Q9}~Su$H39t5{VuaBu;yxo8L#$Ox@Pp*`457cf01BU}W@NFN_(p((Pn(-&QNB3p-8A7rZmsQOVXcv;O3T%^S`aKGNw%q*wAmluz z1T}FSoKfg|ouy;Xh;Rp~udt{iVR$;$(Z&le z`sXsL4r0Dj#|iBB+)XS28k(LwgU*$Hi#UqXXsN$qHB8A%D9xKg2LTTwK3Q9+S&ZXu+NM2SFOCL7Um~3SmB^D zUZ!n}d=*RAcmA1u!OY;P`P}LSr%DU&wEvuuysbg8e1vnYaZqy7cuHnP|D@v&-TSS8 zc?B~T-;9Od*iDDr#%s8lltV7y^OqwKd95!_LIn_M=(av0&99cJ>;*#^7sIu8OG=#0 zUD+$22c2Q(tJ#P5k5CDV4D|VGO;w;qoS%w&x>>smx!#)5=x1Id#nKy}v;H~OqPfoS zCy1j^LshxzUKs;@Wm0H8ndYM=DzYXB>l_lKfxf+x9k@jOU_7Pc?q2N;9e9)a@A1Ta zmJfGw7g(IzN>Pv~k24lK#vW1ykMt%xi;q^L^H7Lj>Z{}c%arJs1>Ym`l%Qq8Od(+Y z1-6rEyM?FylSJ57HA2O3=#(m144P-GVfqJLKn>oN<7HJaioo>%BX$N5;a-2iRYZ+ z`Dw}=UFb|?zaBX63SjVSTUp5oD8$}i!lvx%%x$ep#*#&M(Pe&C<7#e~iN3PM-e( zH0<(0^JL6EwiTV)CGXd_$kv4s^kt(PNn#cCQ~R@d`dEA>VLH-dsXW?YubkSytmW;6 zbow=Zpr5M}tCv`=mo2qjF!4=9XZN0Z^>+S-J8Z+%%4QG^_+yma>wFE1+1!wnHsvFS zU6fU!R2uQ0o?w#Yj`K6bXNfKxMBJUdk3ki7ZS8FSs1{khnDho$y*mW8q=v&$_JdJQ zibe2GqAn?PK2f>A6;1xJ+N=4HGx&VRH8-$`hNLS4c~W1U(kTbQ{57qr("%CWS z4(ICt;=fNRt*(|)qMO8fUXDR=h>do{)_IIdYj90EA+|XJwy8SbQ~*oWu6TVzH@ENj zIL4P1T5ql?uTmEGP_Dnd<`RC8kH+AjO94ZE@u`c!Xkx`;%WUPvZr--69^$KbNCW7b zWpB8$R#dg@GB1l>!|x^(tnuh+Rq=x+*0uYJ~G@cNFQT zfB7u`8t@4%Bfm$X7);IBk2T-8=a3A5VC@uC?6ns=}Z7TT?5U9w5w3hmUDvPvm*Kb*iSN zWwX{ol|1}A5Y)*yUK)Ksx&;uBeQZT!R`#FUC)~2=y!F|$7&$4ZAsIS~f;dY&bz?N% zZuXcm{ea-H!>ppTvt_%J^&4&|vb%3Jz536a{&(%aE%0v({M!Qmw!ptF@NWzJ+XDZW z1#X=~G4l}jwl%QxJ>;SuOkZVJD5kbJ{b^sR!g} zh+CTF?ky~HC!TPc=u*zwSBp#hE8s%0`R;)?+f)Bgb7{K~zD1RWBB7=%ciICuCu8DA za=Rdd&fM(sAD)=q;6s}HQ(Z>bid52Y*V57io==n2op}bFx}u3}n7uqw_v*MCm{hYX z6aKAnQe)!C2%)#NlG5ner62bX(dU`zn|y>K2LrxoX>15s%z?wQQVg&1Wif1bccxzW zHW%;zk4Bd4eu}>uy6vT_BfEWzSi9b9gu?SnJs=`0c|&*Im*pQKPu9YD;>qV6XG?b; z8(3Q^(5?d`*|bk!i_tvp(R7->*w~vfutJ3^5|SBoTY4_VY4zg$ka=op+cqVQzJ{|$ z7e-vpKS#V%uEc`A?vedNbgn2{Yu@iIVrIlA_pN%HF^I6}6KfO=KKVMejV7tnI8TxQ zzO5Gu&f%kOijdVJ*2Wx|M^A-bjW%Y-Z%eih?>C1z{zJ5F$KM$sGy0uMzA5`<_i(15 zuWG5jXH(X3EJNZcS?1q!|F}e608h5aH=ddPtUe1hgvoGhv-^k@=UXc zz4OvWc3+R>>E)a6)k*0)S6jZ9G38fs2N0#)7&eC7`h8&$t863n(hWy}W_p$@S5>BU z#f!N>5zY0oa~R2!fByE<<6&6Kk4kC)aKC5034(q;48!Z`cse$y^%cCb`JYF zEGMeWWAA5DM&#FlayAGycTq^je){{U3^WPooe{kd&up9Y2RMA4SGYj={_PPt!QC$K zk|a0&GMT`_#w%(9V68pT*Pq0SVzXFx+2Q5=aK1dA>qzA)tn#3G_HoPz^Ga!7xvtAZ zxevmAgr=12SR-LKNO#?#xP1TFmyC@M|GR$|w-RvvA>}sXr8ru6AMrGrCwjZggqJcq znmXnBC?QK9{D-C`{|kq%k(KSiM4~h063Xw)Zg`lMp#<{1iTwrM_u1?Z6~HoUiKfsB zDl8w?qv8?GmVcYt5}j~yM(^>SdtNx8WGqLM1MD*+7&lQ@zqMiLo96@Js~b@fXluM1aXTZHd$bSDfP!Bmc3M3;G}k3 zHhAFFl@2+bW$Reznsy*puKuA=2&G)@sJN)p0*;NZq}6XH53sKqlr`kg5ngF6w-W%S z1;#?t?@P{(s>_-*4YTCJ2TE=B3v^lzOr0cnY0OIq@+%4R0f<4jX)*TxwhS@&k2$#ZywI$N;uTR#r0>y1- z%wfwie>(+Kn`c!4bmB4SGH1?Pz4qrzpF5OoBH{kpFl|?mfxhRB>$SOhYw4n1@>W>88T3YJGMy~Zh3mX0R`02VPV?cn@iLl_WR=(; zM~hEC4*7>{Y^Gx@Su>d|CH7KNCH7Jr((&SxS}8|)2s-u@+p(Ncvq3ZeYU$T+Bj)8M z5?v2DVuf-grZTUCbtBX`s17uY23AQuts1t@R6I=wCi!R#D`!;7Hy>s$N(~+&9EOs9 zh!~_sPS4N?)9p=tRDG)+NmqQ%-b_^~Yv>@h<&X59#U5-f{_sjzW<+%sFxgLaa<+JY z(6kx#ztLx-XXVz@4f*^g50B*eSD3n++8q|=YY@}T{F#hF@lr)4T6ROBL2bl;6L4beZji<=wmD+cMl@@EciMml;$@|6# zku1oLP*S0VlyLF$9B0h%pDzuP;gqmYJ6lfCXoR2zS=2#Ypmznv*=vbyyKIUE8Qkl* zWw*RCwj>&zlJ!DKf9fTTUUxmNiZCl?STUbZx>4<7DVi(iyJ@YK#RlKSH+F+Y^QL(~ zVPEQk(+<=gX~e27V)#@D%CqUmgEx=@uaXJ^De4>;qxacH&(aW|(EcCL zRFialv_pK&l*{_|(RvzF<#AlmnK^1c2GOj7d~TS;4ob-e?JT==%B26TXY6vzu`{t+ zXt<(-OjpWQJ&hhPCgvEEg65{0w+mtbLgP(@H}Ye(n8V&tHDo&Rs^a`$QmLgzy_tPb zqn)$SokRXzix~XmK4v;BN3=YVk3z4)lw%XYp?zWKz_|r{D=6g5DnQCSR$~*)l!Io~G5e-QM+6Jb|i)zVkV|Cyr&c%}nOUJ`;I{3p74RxK_=Cyht%1>_k}M%*wSr zGfJme_kNTMoN(L?RY4|xB&o#a4)i_&4T9LyFiRvtq#QZx51KXL&q}btl%}Yw2MGTi zw6GftsG48A+mI$r`h>c zLxUk*pN_=zsN1@1UaJ^mJ7JTy)W2PLzP~||13s~{`bcPHWu(yZHUf*4H35w6GfYy! zDxH2B3LG%RjZ5<99f8Tv8jmil!PFY0k+s_z@g1 zoC)|;=e~t<@+cWMODoRmQE~cA5_-WL!YAYtq2NyL9Y-@SnjXGlz2I@LaivY@uAv;< zwYv`Da<7O)(>Lpp9pDO{S2d7QZKV}yv<7cGOljmk&&*zSI2h zmUTDaz1=TkyXpHHwu76@11d%e6D#&;(}4<%CIO8~0M_aCC`_yi zxzbrq)HNo?d);NHG6q;OCz=42l{K6Pi^y8=Hwu+s)>2dso@_SOObh#J{N_%m^zTgd zN^l&W!npT$2ug>`<2aZ*`XJ_y1+zg>OC1jcVK~5%{%~(Aet)sbkvi)_ys+ll*_La| z>P~g2%8v12uI$lBC|1e~)P9Xatv)#Cg>x;81@wT%iI~X*DtRGWuuF2-mrGJseMZG0 z6%OThkKz)QD0*yipScK#vBPOU>)7fL=i8bBeq07v8n{RbuJjtw@nsw=ADCfgD1k9z z{FQ$|g+&@p!{_<0(^Dqs{;;>-fo_O+h^01$5ONIO2fn$gH2iZpbHV5X@s{}#dDSWb zj$BOTxVIJu`~9>gD9(pSL9n2iVkw#Zu;ETUW)aJSPt@ia_G`--c7eDc5P{Q5Ye)SzU-x$)tMO|U2u z#FK6}!|TNa6khLp%XtH3?#bjJQB)ruzQH-(yLDh5*v#iIA!a}dsZ!3iCGX{s@LkRs zRZKFEE7~-uY5_~p)1Si_v=ch4WD)XuW-4kl23}P^5__85hr4UcRUY!_G*QhTkHJxo3Z7m+3&v-e;P~) zuRe}i-Jh^%td#$}Ch{{aliNWI>+XVFv{#@nw98CbfUh;er#|Y+m+69xL$TO3HO;Tg|pilzgrkk z+)5@T1o<8C25@|~0xM9|{x#X!_AqL8=!nNc8nMJ#p{f(IXFYC)2N1X;(w9}bWX8#- z#1;A+m8`#f&73jvdYV19d!)qJHXm>nTt42sKcK*-wQR8%51e=Y!=yQzs2gn(OIfQE z2yi}$0B7D-d~Dd%UHr66k0lSGM~;r4!)b0<+KLvpkr@S!ELQQrCD)3PZI7QWuqqP? zyW-oKF}2m^2_|dY4VgdbWkIex2(ki>Emx?925n}>Zx^#N`#S%hpvjkL&Z~GKK4)e( z!ui~gWwSt1SpG!hq@_GgKq>(HaD{=sNp<>?cNYD^IQ!CJ*E`*d2zg-sKV?wq%k!;cp9X)J%I*1~;+i9hhVlFH> z$D+NUXDQAbB5s{S@}=$MzR8<9KHCpE*;3z?SUUauOflo?Gi1&+?uzwpcJ*Ktdwl#| zhUcp`Y;`BXtXc^=$G$dvtZ8ZB5UM0FE?uNL%es`E)0hG6=>;6GuZAHMnCOK^tY1wZ zm!$a%$ndoAh?z=sfsDoWAppyn-6u#-M_u=a=0?(X(}#vUfE!VF-y2d}jmt;`chjGw z*fsm&nD<3e)-DpCo68}o+f%?du=19{q%nc?x8gzB2G7{o(ivkdX~42$F^0Sdo;Uq& zb5_PvXC8@XX7tWy$Mp*hW@xRXTDH$jTZjQ=&HI6RY$Kpcyr3xJggV z#tXYq!TtYsM(jI~LCw^!IxOF1i%ZYGe~9!)Lvi_La(**u z^sOa*dhmXlD;}|M7wcaY;%@&CwLUFVPtTN$-;0_*?y6nU z_l(qf#f1$AVBEoYU)DGb4$fRWZY(}dJx|UQe+P&Dnk^!0_`4c)<{C}z2Nm?ETV_AR zWTP(lMOnJ^zZ&KOF8NaCLw|YoMQu5bn*T$ze4&$T${A67-8UejaG*Uc^|sqv1h3-- z+M9x@8DmwLH4T2nQ}D*o{X^7vlZSYE2PUf~BZc?(e-y7KCxok#T`)NB=|T)9*q?GJ zivB~?LQa4(ZY?whe}6-t1kdj6YN;3?w$t25q^Vd4`G?5yefd8`t5N=LiP9Y5v7G|N z8(cQsMGk`?Kq-IJghmC=Amb;IO9P&Md(P0`8L3@KTgsI;a>k8OZqv@x#JnkgI7XO# z9Q7{+Y2kdYvvl^KrDR-w_+K91TZ#-vi$)_&izQo=H3S3r8>|;dO21=1zkb?LNsnhM zS3i`>^x;&lJb8Oz08A1SvuR{6CA|_@S5Y3!jI9-jbr7z<_ZhF1(N3C?wN#+KDGF9h3X;fwZFU=XX*lg+MmSy<9*bf#z5MIZD-(&a4Ht;>{9sGH^9;)!`j? z0Nw;H)9)}DdV`&jMTbO0@-m81Tdx=MaL|46Q6=LM%Ru5kQ!zFZ5C=>~$?7#?Anyo* zu8vOHT@iacGNS)vz-*w}YNL6X`q}>B05X8CA=KD;D$nl}Qmwcs^>m$QqH!&z?|W-KmBI;89~18Edu7 z+Il>5n7+n}#cReY{;2DW3~*dQzj1tJ`x6IEb?h}MCJkbipe7|~#3(Sb51dP0OrK91 z8S@sA&zU96e;RV&P_Ne4v9TI%GT3j=&fHN1^_qVfNdBG2YCxF{fR<8*5~r3LmbFg` zH&G%%HhI0ZI^Qsl#+;3eV%A#+gB%o?b031VOF5AC0D7v8!JKphW)W@;7Z>v?Iu3S~ zU19!(FvhKE8x(3MJ-qFKMp@6Gc9QuL>EQ5@yQF*Qmf)V%4JpPXb0mu4_SYU`YDHVV zH-}=OIUU8EZ$(!*T;M+fqGzoEtbxv&6i8p;;-I~X%I%_GW@_d<81K`Cxpcfh9bUEoRa;r5%OhxG0DCcXV51S2bN$$sUgMS?M8WANOeL;T=!4T<=ix2?d!4 zwG`n1XTs5t0b1|+=Y5g+wT*HemD=yXE`c^CyK(8X8!C>!yv&5mdZT%@k=l{Yn1(1k z4V8gj>%%6A+F3Vi+l9O>H-)t8VEK%(Q4`h!yPp%5F}-@OGp+YJ^h`AsTqGgtpD`z) z;eJ!}mEajazT;A@{t{Ia1=i$WU)CfA8jH_)_*en*#7f3nUT0osOs(UdS&RP=scyCE zEd*&Jl4k%D!y6u|*O_i9Oz%LD*tOzhHPN(O>$~D`=k)@*7;+5{L18pkt@t~d_|_oD z;1+Gpt^-b^Z=Zqwe?jagWyWH1qvV5W7a?l)hj_iU;t?0Ch(h+HjKK!M+~$lN4+qxu zx3+4Z^KHxx1BN0VOfCaDTska(e+p30G}KHufi5XD z{FeLZuue)txdNtPl8f;rDb>kSif=?zp3R;fbgZSU#~kU$xdm#V4Zg6^^9G)F%r2aE zOp%Yx$1vaTK4Vt1WI4`j8>-ngy%^r!&Gp)$!9~0-*&z(; z3C!!w;&I>yce)r?b!jioaL}=@%@XH58aRc&#|Yd1Lv+X0Uk(iNX2v~nnD!g9&bcTa zhV!Pt{EA#UU;Gej!)oPQJxp^bd@azf1ye$Y8eV-%H*~H~aEVn&-86A3t5UUsZ=k(J z*F|d#j7&vRLf;&FjeAe{@%>826J}<<12L#246K)Iedu!~?~TNofy<4;f#y^Xuh5J7 z;RjJ?o0JFGyjMjBws)JdM3D>^I6u7lAW0*K3P!226l4QbFq-~{$ki%VJBudOX7BTh z`lmG=w?|NUtF@NZsr*Lu{wH*&slV+Nz?r|hqnJQX7r3*S7Fdd)c!%M?>oxjV#JyqBDg2;8Y>f8R{mT&oP> zrb5bbG&E`yeutwH9%wnm)t1gHs&_xgK@7qQH>F5Lug8RUoOp|o<7IVaw5K}<#Ayyy9K7awW@ zki`#R?Du_EyFIljA^oVEqd%DrZ0U@_fZ^(3tpR2G6+3oL`7i&ougCxSdu0N_k%u7! zn|l;I$-*hP5CbkU9c6A|tuzvGE)96^sUy-kNAeXpV7C)vjir%uAL)y;5Ya6alsQiZ z_>_h?PTkKKm2uJgJ@Z4vH#C(`PZVdy%Kqj{)XmNB-|_?dwO_kfU^sp%yKL4ANdkD# zppL)Ai%t5;gALxaRAo&(4+VMjC}X3Ew91BlGTAd#A597MQ(KgW^IS+c9uQ}e4Cb7_ z1b%*bP(~50Q+LEOAwRc3HC1dexM^JE?-MzU^=y2EDqh&^vV^;!z2WgD4ds?q36;A` z$Uoj*Cia-Q_zO>%H&$sTC&3z4nSVF^9srx{b3UcX>1K+#O@4#41 zSm1zDv*`bOl0o1v0Sl_d; zjnpW{&!DwuBf;gCP!38F_1}6NK+mC-;$fSXvtbC|3wO=onM-WZeIsj3T6Y_DNQ6e= zF?o4jmj9c>Q}m$gWGViKYbZTOTitadVYlt+>n5Z>M_~4z$OhVWaB%kW5mHv{@&%nT zTz|p3YWM+r5$9yJnTrTCwD#9F$i!&aMGCRyOW2BafRrIQd!Xwszx2n3{e^j47GLJd_Qp$DWTRO!7)2{kn7T|s)gciwk@d++<% z&;H)y+dsbbYi1=!)|FW^*UVgVUgz0RMmund*+2zI^F6?s$>4sjAa>&Yh~D_Kd#u2& zhQn@4(e8Nr(Hs%&JVL4tjg)ngHYa}`T??e44~>?L{{sr`zD8c$*mTsSgJKQ9I~lm} zRU|kBlBc;rvPYvpi?9?%>U;hNxuh;1V6 z4+`v!x8%Wp(X((1lB@b4j52x(*wjs zCpM8<&0qf=a)X_Nb4zk?>EmIoizH+Zy4V$;V?!0d547DSqjly;>HUGctTj%)$Juc1 zuk?MVb~=lry9WFj@+$v;nNw@(YR2`@Rd2#$37Iobt!Wf>ihu-8m4LK;dOYvPhWr?D zVo});TB&_96T`oVc}AsHmTzIemDX>)dtB>KS?M;7=XulgkWX)qF!o#8V~0eEz0&N3 zIj6j>vd`pnkbWs^b1YE#BsVSWm9W@-+6o^B4cs;{Vlk)4RHRY6)E0Q`6Qv;J`jlx( zr!!t}FP>_&3d?t!m_LOhFV-mExv$a0Nqn3V)ectcI3}#U3_a zgtT=*p=<5;LT@zk2x#;&zQHjsf#8KcGkn%pWR)ADc}pFHhJlKNYPzo{QRaV&CENsu zedaXJDl6gUo?djqdl&_|Q}g6tQXZ*WQGxRE#9X9ZLtbVnuEopVuUx6;#H3 zf4OAJhqXIV#r-=yug4qFwIP*tmoPq1ME{rh(}!ub4kav3a@g(0lO7js?R19)hllq{ z0-bGsy5)08;AV+XGdcl-6-YAE%Y?2pp`d34788#?A5-PBGUd2#{8o*ka34z)hJvIwwZ+_zJ%(oc`n3dhaO;@ed+bkal=;h_><>)}a^fb{Bw$?c5 zd8+Lf^Ws-|!A}TE!$VCis{b!Y<^SCoU;DhwpAqH4QFl0>SV8mf$+Y(Pm#jJghAJcA zONy7jwF*U^+UJPbH%FPgW;mwW&3B~Vcb|scp9u&~h3gUuJ??v8yrE7KlkyLhcse)s zpFve){zApx2>9Qh^UwN?QC3CoD4tWz91$L24w=QUdu-b4MyPcY^#g$4$A5nF-^)+N z=6$B$UdOZiF%i7M#jIn0KK7^xyT|UfAir^<1BCT18Dy>rW93&0 zsiL~a&)7NDIyjgm*c6fKlEGaqUk(LDDBN*WBlT)G?5z5pwc4dsAyNp*2dth2_FIGR z`vzQ}j`^N(wy$Q}JMAttux=~}~@%L9=-KG*`Gf+)!;dn)EcpoUD&J+vQGjN*XRBC~X z=DO<31>8Ka)ma5&8cM?>yK9PsiEpz~1?@T<`Bj`72|^ir0%BR21zkbTpN!sme%nl{ zcR9W7Uk5zCC(tS)aw1Hn_BS9%h!5Y4g3w}Ubq$BD27))y+LK0lIx`ThtDqq? z3K>QsrcMsc)9`El$DjEBSbUs4M_Y0E8n+b9;G-I2S~SL`VI8AbLIgiD5)o`X!i3|F z6INZ!`U#Km*Mg8w^0O%?-@Io<1jXA6ep7qgvH$@1u!;z?vWbWc^9XRl0u=!vhGjnE za)Py+HG^%{D{Bwx6-4t^afPm_pB3>>4dDs;#T3X?o7smt(IfgBbYbjf?g2Msi%j14 z8N239$*?g$$RumQe>+^RppRUh;a5Om%d8E88|XeeTRgFLe&|KbtXn{MV1eio855Cojd&-Rw1(Ru5l;82ct9t7kyar58!NT#oN`Q@F{ESO?^xf{@{S^%EeTQHJ zuB&xS8my6>ZR_gdmadB}t9}aojV*2iQ9HpT+9q5VOsR#i8D9PceL6e_D&3-WpE$b$ zNL=1x>?m!iMD!s?TxRa06-^hoj(ZDgMnAXt)0jXSWw;*it0xC3juKRCFf)6b1Wd*G zO;%9gf4)W)?BJTg``bd04tb1_t9AXLpZL+UY7Y7Y!|Dw?=-GbN()N%cDC>g;X96;C z(oggvczYK%XVzP?E1EohPCGFfj^j6J@)XAw4LU_!UPd@<^TI-$>LJDYPUqqEtmnN3 z@Uw|k-GcQx4D`(j=FU$Bkq@_0cGiQq>*wqBSo0ZId%rnZT(c={b(8NXrE2Yhe>Q`3s4q&0f>&nK zKktYBa%;j=eEA&01X@K;|734dD$w|q#Z-Tb#>40_A{`FsVkVX8zX~0M6Y;(>!)FF? zKb_ySj>#K1YiD7bfVgvX5wEbiptdp7V6^TKle4e&da_QlOk8=J7fF{q?nuYe~0MFe2 z>;vQx2gLZPt|Z#`=r-4{SH$AYQq%Wu9cKcF%4JVC#_*4ji)A+Uc5%HX>S?cDclwUz zn>Bu2bDtC~HN&5aAMt&IdtOwSd>6hPIU5-f^T&CbVQQftT)|0!il+_ASuvdalf(J1 z6K2+_QxRwWNBSDV=MUMFh*5=7NMsd?p8jEGsYpbEEggMFO|Re`dZiI<2e$}_NEM5u z2Jd^gHWDIMo*(W|o;!21`C6#fdQtCrOzTMfn-l<+e3GZq|vq zV;qmHcRRFV*ykehSSH_1>Z~s8D0Gms;qNB|#tRYhUuDJK)E=M_n>0K}(97Rh$cJu; zw##jJm*fx(fe%Ma>Bh6XJ5()GpC4tUcd-8Z1V^b5=(aY36KmMS@VXt;VI-)R4IfXZ z+mZr@J`ae0Xo*;!h^&v$_C$rCv&5 zT41{iPOOHgbVuaAlgi`5b5J>A!YWzRkS|8lBZ=dAI_oACQe%H>)T0{3|FNz9t(Q_= zL7H5Td*E*JC7ZM1FW4!q6H6}&`P-x3=r5li;lZ`wLPeC2(9-v99KCx2f|5Fe%^R6e zqxf2*EYGUy$7L=F{l%&sC=4J@zN+bL^%4veiohKU>y)5r%c^h0RAnm(PI{VGH7i-t7n!mSSQ^Bg>RYf1#P zl1`Xf>OBHGK@nyY2Vkq(-7_t;kB5!IWjo#z7=mA}S^WsweUmG`B`BOLKU7uphMqxJ zS^4$D*z^9Q6g7sJnk%}YnoJZ`xb1oH^J_xDsICRR3C?1&Y5Hhlt?M)*#UQ7=ouR>A zENhrIoah9!ntD{}Hf3g9z%HH7lNzD0_?kS}Z|=xbw6ZiZBz8A7AjV?q5yzM_V=oq1 z@VM7hoDA-(SO_(P8r$R2)C$4cag?-qfcD?tUE0zjyAm9$|8n(s_+p7H%r!0Be0)VM zyqeQE*388I9$ei6Sk0&@j^++M;f{<61F-C_*5i*RrUo`w#b&vhcFX=u4oVr~J3R-f z+lIQ~qQ2H2;3*z-$fiYjbc+X*!c398cR}b!%AorGBa|?kz|x7i!of0#=fl~@JA4Ye zr_-IQ*kNqFSB=>->7OhA3jkcTKL+Y|!WyKk71 zSPfzP9mhwi`AcV41{qc(Fv6l0Sf5X%6topPEkS)hjp;bWe(E8;D=twIDI&-Dnj$>w ztw>Gu-vBS3&qCtDA8@Onsm`IqIL9d;O#y56c38eyL8WN28s@R2KtsR24vVY}ecfVG zmAKLLgM zBQp%L*{!o>-c9i(-cP|AyaE{QMk7Zr)O2s2I$C?dT{hwJFm7}{lPH6Ryum4X* zGMqoRBrTXt}A^j7SS`Ilw8uKz_VB69F_jb&A zMd3E?XDNw;e3SQqCn2yr-hRz9b&a`Gq`xPt^-b9C z#_hS>c}T-jSHfy=!{we@svyMsLxOiV6 zJ$-w=F7FVAPgCzcD2H_Xvx^D7S}1n*(XbudDu`V~If1DY0t-K}w$$)nOIIb*j0R-J-==HhZ9xcLTG$QlWemhR#D z(nX&We_J^HYIUmQG{b!EYeLz3$dg`;FuhL#wKI1d*cThamuri^L4ExjHOh;T8@+;c z;H$w*>vMGcGcal!&DA5*QPB6f_WtP)(aKuyI_YxFUxFobHDyvi(-#P1mg*?bcg~T~ zA8+xwbp?l^Jbzw3-r#}jSewBR>t=CVEB-~smPcaYfw#ZeW{ZyL6m5$aB!uauO%}e= zompkY&&%46%YHU29$m_6K2@&QRp)xfdXDaT7gzSR-?1ri%KHbj(Pam3KY9V-I-plk zHG=-o&~yaWeF(#>ioTtLbpZf3Slh+!tV@?p&zez1)zaP4Z-NsJu80uqNT!dS0UCK= z&EI%i!Qf)FuhZ~p<9sz^v>X@1Xm624qpiKPxPY^c%bu9IltA$Dl46vA<3{9Y;lYt< zJhV{FgI_s=$dc8X)+fF)99$~3Vqs;`w5|cIC_!%&P|utS+1_jN zw#Oh_i1H3v?O0SO-}bWYkUGOEzE}C#fWWIulb(}x$94atVO<{nPK&;%ci*Fp=!0*# zoQJn{LS1#e&$BxQIMix5o6+TJP4?f$m6J`mBkiwOOg;Zm@Pp5Zv=;Y?JYeL?z4Ej7 z#hlo9@V&Yb=oFWlEE}e_tpVXNa&|+yls@flkxE+;_@G(McgmMV1DCAX-=77CgNHLD zdBLA-ufG*{(EaN+?=mNr)=W-z|3Ec@!Z-39uL-8#~ zl*5RUy>wnEP}90D{Gu{RfIlzicY3-H)-12flaEH~U8w?XVLzq{?{3gvG9G4cQ`)jB z{J};)9^dh%;pqpcZtmoGn>lCzx5KpZx}M)}wGS%}OD>?{qpjZIF$^etYWGX@yPUmY za-ArJ?eYi1>6)dd*y3XM>_7@RW^iKJE%LtP)3Q+sivhkrxDyaz>wvQ%vUiU=1A(X!P>au za^W2X1+A2=LgBO1bK`TsF`S*uIQ$BB@oPS<)^n)@eS}|Cb5whmba(c~h!)^(a@KQc zZC1TKRwfG zj(ot=?Z)!zCM)+(;`{;=w3&+w((xG>R>8_N;avAVwNf{S(gqpP#XANuThU$~7x7_% zHiLfyGG0IYzo|Z<)jOMRBvjH0^mu>SLtD00Ra7?`;Y!^i>@+N)_J`|EEwOn|rBcHv zsGU$>`y)dS2$3}asrHXA??cRrUl-{(B=Dw(1D&GJp@lS|F#@6mBn%obALUUA`X5^> zdiZOKF($kmF6jant^2^M4%d`2rj1`i`EehggUOQ52l1QVMjiy@Zgtma8e-SNY+sFdTQhE zqwo9dBWUO!%?SW_M4pW^ci{x99rR1g{{}oP3+T0+Hq%*s)xsOJMWbS((6Rd2ql$jh zrako9R10osOxg8I)_a}7^r@2GCZOZh+Dil<5fB( z-WTyc)A^JNt69D;#@d!UjBbm2nZc+q!n0=_>q6LPlrRQWX>&CnJiqm7+u13g)=z4o z=IH7YPyg9$UJjgPSnDNH!#KE*E95piR=2#C7kF)YqguyRTD>L+Qy~w<6jNmCU=zGDP3n3M2Ba+J-O*i<}8SmV)0^yuV{!VGx2iXlQ2j-3WHiQoSy zmNE%Lqx{=nn`NHk$jYUj^?7A^X?yC0@z9yRda~?`CQB8U#HSR9=s>6(kx4T_3S3ls zvL_nZ%n}mV(uUWM{*>c7a4HFYN{(i~d(vKZ_|WduIl9-n&C<;Hy(uQZH3X~^3SH5C zUzW-k;+qHHvZ~iweug8Yq7E^57S-I*NetB9_$4kEy5W)vCMoz@ZsxJzQV}=*Lt>N_ zq#7DZV*mH8PnbD;wq9T3T;(ZJ`Xi!xwrMnb*=%!)B7{H1r5l2)_3V^|zoFc3(na>N zgII;3hH4CY{lw50yvLTtKN^mh4;8+y_3KwF^!*xWL`1#;CV0@@|5;yFSXJEwHaJ_d2jm8KGqK<=eU8$QL77vFB;J3jF9Ou+fD2JJS0LL#14Ia2~D4w$%wbU_rQ4=m;2G=rZ}y_-e4; zcadWD+^F=O>DN)i+_^(lk=1cGnoe^b{oTH4bq^$8^3y7GdP23THjgSRdp1LM6-MgO z^(s#Zywi+q#tRu~t3c_9mT2b~w!spUG}MoY#1Qfm$X!E{>N|%VIBM-*~T&?;iF%^F$gW`Lt<6_62@Os8np?HhY1(- zV~5xI#x6~g&En1 zlDt&La1hF$Nt3e^@K0S}YVuH|h=|Bg=ryJKA`HgP>iw^F{?FQ-4PNL&-OMl6)`8a6 z|B=Md9@GDk(vJnPH-c5{K2a{%mFan`5^ef2MVNvNj5L9`Jkz{L9P?)QK-+YS_6x7F zAL>5!mhEL{S_6FVMd|9aQ)eH)RH$fL<2Z<1s*-O369&l)3*q?yHxGBex)z6eF0VC* zqgFNn!yorF|4%di4+U9Gxqeu}K?t&qdo*LLfP{-a0IHvOe|%BYsnwaZ!DDb+Ffjw zWLtk@B;tRHmt;EutphhH#Xy$}cf|gZMsAC^pR)8T;ASTbeJ0q-A<1u(9i|gKx1zf+ zEiQSP$o)mPsu8NC9dr~N2qCGFeB@mCr{O^XtuGR0e&WEri3(rbcKbLGCZ1KIaUSfi zz#NQwBY{$l^cv4eEUVp`ca*^#x_Y#;!Z+xygmV9MZ|P$d#WYGNFT`*`TalwTW#RpHy-m>p&Im2rTmp#tN90pI3A%6)ay0PnRIL(Z{ z^#uxdSFqKNCcmH26FVq87}q<>Fp#Wix+$K>eR2D*TG!bc&`J-t@h8`W%iXUYG7&lKari>SXfU=-dawSVZo zS!dIaaT#(8(eoI0Gf2lyC6p|0t+z;l9Hwi=fsGB{OFXq3AO^)-$A2o|z}CU_62_h6 zv2sRgA_pq&TqiYu;cTsKZt;cO>U>2*@aM$$FQn_5@q%iio9!cU&T|Pg@#9Gvxiaos zNfA6U&;zxB_3w8Ur%z*Z7SO0CP3xvg1$M`}`^h}a)lPbzt2H2!R(KRUj!{h-_;0L133ve zotB5Rn9YI^bvvUubA#?S2@7pdk+ydY#CO==yW>}c`t%tr*I7E*z~aj)KEE+Q(^u5& zY9|?Z-gV2?N(G1;v0Xa9^7{P#e%(Gy8zWQ3&T9p?{?Qy|Khf?upL|v#Y*eM!rD~=@ zz6&|S9=WkMzzjxxCRZx=*4(4o7v^tYn$x`Bi$keFAESYsiC_DZ;`i7DshB86H6o`G zS{EMy8mS0Y z#PE>R%}wq$gxtnJcJL91G29-aai_u9&y33)jm*&nTN=|@%7lLIp^uA0pX!>Z_WczX z3gXF2Ewa`OoG6BO7=Nj8d0^u%H20I|P`Y#sO}XB|dzkG$Seml&Tb@^&HaX$bf)fto zUC?FiAGt|9!$r8Uq0%xgK1W({Zt=X$@T+UwC2Gu?0l5h)B)BEhOJ|KSmek5;#)j3L zBa@|_>4fu*LT~tS()%%JUhBO)UcrhfZ}E_%gsY!VW;sjc446Dt(qbg@O$=I%A_*is z?zJrd;2AhMtY*=Wv_JP)$qbdJGAabUVpXHf-vx+ycI|NCnHzZrtxfb~W8SCT+<7KH z;vbQ+5Ep*WUpBu8J$ZY5fgXkC@6Pq@bfmv|#v7##`zAy;!Zy zs#N|$ZRQQxx5gUonF!t$Bi;B>CCVf;;0pSjqL~SIh5g|&xcDmfxUS^YY8O7WN$uB4 z-9_ZZjjVfvdLDKJ1hrK5;B;-8TVl=Ds7CFR8%=A7reYsr4BmPDayyOew11Z&Zp82O zeG{9I%))?kqN$M>v(lUVeaTGN%!;eL*l4LWpsD#Etwbu)#Lv-L{P23dvzK|)B%dG1x7Jj6qG1&x&0J)FhN`%|#W#Yk19o*&x>m z^*ts6Z#cu|?&(nT>pjzt{Rin0T+e#!h$!bN1mk?}H!}{50DBKrrprBT%6MMqlrYjp z!YX6wZ@@U_(CgJ8PUT`$9bnYtwjH1H?bvBx%Ldq#_XcK6#xPs0{y~^g`J!7VXLQya zr{Y+>>dJdh^Ldr5?x%&o+qb};;yb6)r6-dbvv{!6qA{cUX%oj#wgLWD&DLBN)p7l+ z1Vcy6c`!=2_}S!&>}16_ZU8rMr&qC>fl%r4PN4zcAX~d4Sduo~8~3GHG^O5`*nvm! zqhYFw{bbhz4&{ichswdqgeibiO z$*FSgudC1_dMG%>Jxx1=I(CUCv#JP>7TbxUPZM5KG|x78KF#>Nowr^o$d_m+;#%t? zcK*u?g;S2!tgire_AV2afxEh?Q_6Odr+K9&L9eT`PL*7Q70x@FSu z`ZODEP^BeBZn(@Oqg6PEwNYQMR@a9&>NS4jkb5L8w0|^7?IjRPk;PKTu4b%(eBJfE z0)H0kwQ|K(^*3Mx@%);DpV?L9x!0vq)dw*ZfP0Ru`&0lNghg5g5tgOf*=HPZxFi5qRz`8~U4R;M)TSylm_!HQwZJKxNOG z|2AG+37J>?exKbeZMUzO&|Nn|@onj@dcIy6FLGogQ~%NA4Yf(r84-~-;j7G5Kc;ou-d3%qV7m}I*T3{<&2F57B3ynFx8xfWnqzJQq9V@kGd)c)@es21Q zpA3M0o92R_moMUtO%z{IkHs3tL)GZr3XR-^9-5MUnpsp-D?!Etp^31;3Vvf-CapMU zqi-7gI>M&PeqX@JFZTsIyWMtThRknqG>7USDmaav|1YB+m>oB6_uJb|IA4UhD^zT! zS>$-uyAHk|YGbclLEK}H!*)G2C?eXi8I85Dzk5@aXlUvse&L!pwEs6Cjvsh^ygKQ_ z!ub-5YV4S@!w2}eD1*=86ZyWd$02HF39&!i%aV=qT<@<88b9)Txz+zSptamgikSTmNhgo?gtop|clgnWwDV z8_inAlZ)bH{1Ay!OX*4XjlXwpBWhh0P)FA8Fslg~*f_3FAHd(+E$ytyFE1rny-de$ zR}bzUJmqNlWuh}V@iG3>j27uIpobC_M+vuGczriH#mm`oX{$6p!lffy@a3u$58FZJ zWL_`)7R97a;i_YCb+Q^n#YL%WDh)I@8irwQA!O*G-LH&ZMuA_Hbef^9YCKTF&^S(#+cfD2L^|yr0IXKB44W^gTWf z45tsmqZ+}BokldZPZgeJ*-B(<_9s0K6HE0Fd*(dGU4hh@vTM*?y1ntd24yD_op6dD z^}SqQ*_lZ)7Lj3tpJ}i9v$#k&K`)K+j@ce>GAB#T#USR=*#g@=2gl5Lm$*gI$hiq& z<7uuj)-shEyMu`6nUeOJo%$~#I*6_Cn?az@a!)_4B2IpZ!xFQV7HoboKf7U5HELfZ z`mQjq2*>PAN?j)ydOa{Y^-ahtQd1IMjU2|4_(s0FvHJ+|+&WoRzxYn&4-J&J>kVQM zhYd*#ds;|G-=2{$7i-sMW7IBjEEHPu(PA{*f6;%+d*QG2n-fF7Ip5+HQi8%T6y*pd zzOdP3vGcg=QXL;vqnJ0NwwcI&5*L>oofD^Z`M*ZPzm>@K(tqQmXFM5upHKsb;$4?y zTI36jCmJbLy7M>2^dGl1imk(lVH|Ga6^n_K+%<84S7CS;Q{-~=9Ano2QKKi!(^P>` z&*buPo&Izhd;!lzR|PGKB=x0)ls>hR_(GOJPZF8SySpI3`G#&0#1N)`Oab;lGt_mg3_?@#{zRLuG= zq{T-AI&7RPYEq&Dr>1r4&M)X2LkQFs4?p&$6ht-Ej#v2iru9N~VZC1s*716@87{>5 zQQaK9xF5GIy_qTQ%a1Mwe?8&hYLC=`2Wh;0{m6|p%j4fK)vE|iKTa;jE6;KD6^-qj z6i9ID?l7d?V!-v?)ugFqs>%U9GSM5=rlSnlXtcT%(XK|p%QcWDR{R_Xmi5{@qi#RA zQt^R_+JVR=y3c19ijVRM_SNAYQk6;tV){AbPn`6gm9&OA_C)Tvv9xnHsP-Mp+|e|y zciLuiok_^%cw1ebUtV_9W>Lc;IryKDuObI46OzV?x(`SfLj@%vRlAW$pnCsBC{=+t zZrCw@Kkugw(%2arbu@z7%he}~^iBL5U|=;hDC3cQ>mrdHq?2b9C;5}hRJAkqG_kZz zDCG-?P|OIdt**@YsrkEv*}*MuG9)b%EGK>5{mVd2xasRd3`f#^spzKCYVX;)* z`4^SAfLitoGTQH-jibH9NEs%~z-(>LiJ7CZ(gKk9vcZ;z18a@V<@b2k(OLI219Vy< z4F%VbmE%S3pvKGoIiv4EJB6c%*}Mi_^jiJC!smt&I6R2wWHcX@l<#Gvk9%FUYfN$a zwcOp%D}k$U0#eUrmQdqLro@%fXwAc^hz80)b2#Xz7#k)LoCUqX-F`j;=K(IO^k2vD zcfPPtUd31&iQJO9Al1QQ{iCb8RifW;!Cf=-oz>sH_B2r$7&`CkI^{=r7H9~ zuBqp`M@cd|(=a1Vy?QPEO0^#T$KwL&?D)~}u5eNM)pVn&!de5{o{`~}UXwcalS-|! zT3s);_@FYkH{aOC8J^}2>?MuttZMcsaK>oaM;R=#EfocV7&Y1|L_FPmQc@h4`*Ay` zAohVlgT$9-vW*&!SJdAYzN?~id^kGDv+uphYBhY+;qe1y2!8Rto6GYTo-^+I}~9*7;pFKgi$ zYC*+0>pPfaJ_%ahurs;b-`dcvvOG(j-MEIStnUh$e*(w67ni0$?Y&{`@8vXsS8T|%7i{7$`Irs6YKyTu zasa@!<MRI*2u?osZ6s1zYp zS?nAfO9e=1F)xRr>K@OdfB32YUKCwjbg_=yz;AB^<>(JP_V(+0{D61X%tX5q^K0La zntw#`zDt^_gD}o?elqf@8ih`1-Fk5}dWO%aJNGDeoGY6wnAu(C?zY&VA3q80Z*MKY zsX;TdhcyfDr>eQg3<#=NKabJ0(*{TbKK|RouV4SgRo&%$wIJ8QeuLxrVc;&DIqkj@>XOFD#K+JScc}| zkn8Jpo7H}^?i-XpP$4-gWndJ2h@mhWf*=l7i=*d`3~i=k2Mp0$IlR5|lB6@9y#HN1 z%x5{OT+ep=w-Vue-^h2iNRM98N3-G!69P}(Nqoa#3K~&H3bP8c0i4IODOeyiNMi>! zj<0=CQZlcyG9Y*6ufzBC;>!+a3*CCAS>;ZH^~-*cc?V-JL?4{!!R2!nq@le*!#$Mt zhWl`%YMOEvz^URQT#dH>?(EqY53M zq$I@`w=+w(CF~58w}Bb^Gv2&0#u@(DwIcQr1z)=%jQ2pF`3`$%uvJF%gRK?yuOVj!lqtm3<;6f|0Pb0&aq5a zvagpt_w5Nf2%}t*)2XUu> z+IsQZ!x>NfHdZmgz+8{MNxE#r1@Ev{3T) zl<%SS7q+fO$1qLR$oAD7HMkYKQ@cxYEaH1FZM=MU_lU`BM)6tv`gmfKd^Mu`F2;Dg zU$W|q9M}k{8OjzPFcG-yo2puT{Ve{ue?sf zsRYF^tyC;wlTFtcEChA(DY10BoS@U)DTf57=o@MEzyQT zBl_e4fz6cXu4!+Z8JoLYgyfdXt4#7^-s#&L+yui+!nBQ)%tC(|J&yKv7XF#$aIRy( z8`__stZYpJS^k^%`G0Om`hEGCJ?+lh_`^-_Z8ni`_XilO&t3yjiJ}imopI^Ny3UTS z26*)UK+T=cUxB2J#(W{!xN|Ddvv=ulvfcFK<9V>5yKqQz=MZ!iT}g4ha->hv`IuY ztb;K7^9J=Fe^CN>BHq8eHFKOPaED!XCnesQ#!!^sTz(>zNqH+2sbsm}!+dM(+s|`R zGs_El4v+vZ0WqI6EA3>zDUoYt=t|VIF~GjMKlO~F%vD4#ek9kWVYWk$PL->o*s;Nw z4q+J11A<0@M4$=2h04At)S2>6r#x?OfK{>*?y!$GWksO$5bJ9+l2*9nu*tKp@pXJ@ z&r*kMZ$-?v^y{VG*8yHOHrfnlrShT`j-SvW{=4&+NH)vlhKim(jPU#OF%Ms9Qz3Qu{*!!~`RE*HC9cP-nu zO*4wa<3HP%64nRaaD%8^km#Ighw66&)7-IuyB+oFc(=s{8+D8o%07Q(yGI{J=s48- zngQWC1p?24O@@4t-!+1Q>yUD{e||Np-f;ltZlE$$UKBeBA_g!xh{Y3uDYb%=O%u^5 zfg?<2d3pJ~uujf}n=Be7^R8oYY>nk;AY>v@md#L|odBau_U1ZURovWF+Ch`a*wKX<-v&!#^hkhi!*pb5ZGVXMMf_Ir*<6MfOGa94 zS`YV#t4CrVRjYue|4xsq1P+|opkaQB8PTsQIS?_lD_Hph9OttZC@})N!y4IfoEb0N z`DdKs4M^*fGGR|4yl{105seQAgSDkm0o`?Dj>cOnNg>TwjNsocl?ygM+adH?qBhdc z=g52hDqWY>p9xl&+HuDgq;y^&H?Tz(4gTdhvMe7csMoSp+<*KAT`*jll2e1k^yG!} z9yIR#7ukfq);%#4()%mlL(5sSH9!fAVdS`JG871!m`zE-tHS`BakFe&@3xy@^XMm) zb_HRLvaJeSR`t4=!HuQVn-##jtNMnkQpi{sXuV}sz6wgif=T-LaLwR5e_J(>2Xri~Hcq*U@$NyV0*CzrGHmxm{uTvTk!ggQx33wN8Zg=DWKu0KaY> zsz}Rx;HxDsUHAKlld9>X(tQ?Eeg84~ilWk6-mqGrA&+1LOa)atz1(9}U4U!%VY#RiuTVxV6VLcXuC%7DPsIDq34xE8qKE zdi9=oWHiY^)T+bks&x|)HP-BNse7sE`G2x_=?i%{av!pp>9535JzdbC-neF4I7TN+(A;Q^fo*y<3)n^aziz6*3boxgVqV6^VOr zD5twAJeaZd^eVQN1)OAD9E3B7H0l(Ldf_+hIda%As4$FIGj?h~PW{w$H2+M|D$x{z zx4pI^kE#wkq_>wdcgC0|s3nXsV+8BA_*jpJl*P%LQc+yYntU!K)zOuVr6=WCPV8LGx<+~4rU#ZapWCuY3 z69L&P)cfF8=tyFn**-zJY#tD39($22Rbgfl;yzkX&5)Sw1Aq5W^*ZbB-vDY;FKjt% zMaDu!uOsP&Xl)_`Bxe|DUc1v9p*RMQ97${|SyBZLhn_HE;{v{Q)#39TJt$&QMxDWN zrfRKze)Z-4<7)1)c)b9`X-!IcH}t7o@s#lY)GoxYg#Z8pJPOfRd;2`7L%?A{EYVJr8cM+4 zV>g4@e!b2!I~Bd%BcU!MgbPVNfp|3B2OP1O4`B5S<-;v<|S;b_QDBFM1T*A!AP#!x9hmim&VgZ;on zHJUbxtz|91i3g-)SE{G46JL5;$8K7ek#;tJdYqsW1fyI;`|G;R z6{+b)J9`zD=H^KWDcSjGm|Ng~u6l@){%ic*m$_k5LD8CH-VmPAF4Rm2V(EURbc1@SRo!kg3ztCy27Df#$=Vcl;m;YqQ8 zGRQoSlM^$%e3|*=W%zA{ctFg;MgGN`T;cP@X!top)SEwxUTnj7A5WQ1Q*a9j)(atz z-$%X(0F$<+m7bZ*NAjU8(kv}(=A&^Z*-OK%N%)ND3 zTU)p9PmNMqXmN)E!Ge|GUYwvMxbNZ#1b6Ljp}0$MHX1BY+@ZzYf+x`8PLblJ`)0rA z-1nUKp7Y-4+~?l&dw%&N%(YfnbIm!&nk!>wjL#SN!XYWBRW!gDA$VQ74R-Dw^v)2c zV=&|pA3ay*5yCNQ!pVc%PB46Sh`WX=*(V%hGz0$ZF-=s&T*>eL{V4std{0Gs;@2m> z&uwp&P{x<3N5(zwZ?a5&J8T?J8eM8PmTLW@B3W1uCNC_M8M!VxM#$FK6N>lC{jYyw z{}V4D4_vu;i8}ll0dKXA=9F}X0Pga!d~Dzgj)^4ysg6nkIysd ze-FC7(`-wwqYz)thUX%u6UNRx(b+Z%(lwcPFS*ddtnYPhBq$_P8XGNZWuJH9qwob6jK%vi1oGC5}lTVA(e$NY(EzX8jRY(5JI{@Jq@ zAN1l)i)9qydIb`QMd?6cp`T=z&lN5ohHC$%Zg=nFhFO5UZ3Xc`zcGG_R|VfiM5)&T zr|V)6+o(EB(Ox}Td%%m!4f!<6AW1;T23?LzJbrGyLYUX#;3o^ODv!>7tJPlfLy63w zY8$F|$*zl3;v-^NkEY@F&Jf_@rT0i#a2?42@_N#$m&+Kf7}};!7a*Ors>Yz4(svkN zH7S;*6mP3i$3x3vXJxG|eGj?2*nM15XqF+gUvYS$Bv7#Q%di2q^r1<1@7!tSvkrE z!k%<}>9@CdRGzxe+5acrkWKA1PeXg71$`R4T*(g;u*ngVd*M`BWw2r~12@%+F<>%X zTe)yK*_HmA@jOlv1Q&yKioqbE(s%_{{s*jhiU+9rr=+-{-CN}AZ3X($@yy3$w;b*J zl>l`49=|bR8y=$s2LO;l-&~N1%Tjt)12fVBMf;1s3p2G-UDP+ISuG~_4JhXo8S<_7 zRKuYPxcz9OdU&RIpZ61DEq#N>5`!wf4luH_ys8&#y z-4RQJd{B!gos1c!L!-2eHE;`lTJWj(7KSk-2b#u_YDaSD)JRz7|KQ8f+-=c!-biIw z6m>r{lN_=_*!by$?k^iwIO-W=j$DmOm2eKa$EK+Xc4JwT%xm5(sIMJ|F*K7ls5_D6 zU*W%(N_~ATPr24&Du=R=Dl*9p#hcEcl3h1*#+GpCqg@THsjB6W&EZ51egTRfWTM%= zXXG_=ym9(qPZ4`@*qU=FN$h1Iuxdue40SDs6t*sE+FKAt6OF5EbB=r&XrbZfbGaw1 z7s$+xlWbU7s9{_@48vT_eL$D$J$dZDqDv0p7Pfz5M!~Kewud}4m1BEytA`-bf;i!p z(LVftpgeHDxDDZ4Rc1f^_ArZV*zIRtG7X%{rdcb5wnS%(rZHkH)3-qJY?ey~|6`>3 z88|8-?gA{@97P>Msq4n}M<2ep$ep{J$RS$Ssb0PIMGonR{RaBc-XHB(6{GTy3O3ax zE67WnaWbmLj7CK3%HGGZK=5Lz(gK=cRI@%rhXYK48K>Eh#^`pHyKyYEQJbLp>B=pI<-7AjvZKA)P>trxF!ctG(uQl&ng06q5>SZSY=ta~ zdXc4PAvk^8?E9smM|;SVf`PW>ua*eeSkrLeX&ryVB&tt*i%HZ*UnXr_rcl<8I`(OW z!LIkO?>r8+V}`MC-4OXxdZXQ$xdXL;cu9@2GJr`I*i_x{y_X;h4FZR0aqo~Wvxtq= z>iw}eG-c*)=zbnNGp*a$WuoJ@HU*!NkQGHlrQihgmgW0=BwQRv4)#-;PF|Wqnb49<-;&HP zB{iDA&GnE8{*LSNesi~Qlb~XEiQ%M1#12FoBD;(SQP%sE`^E3{vj5aNTb&=MRk|X^ zUSwNHno(MfGAS$$e$4W)fooiw3~E!r$|BI(X}td_ClY$ez^Bw=dS`QzVhHms{?du> zH7yZi#t6|$(i~mf4<7YFGKpK3+9p`1n)p~XlO_huJ>~Ww`#>JS~O;x90lyOs9s+5t4~lhf)+XJrAjB@IbaY#MRMVzq_2nPYn)sez(XT-=SKNyae??#6n#q*nbfBHNqj59y31 zUe)Su@L<2GuD@REvYSjj^qMrEMkil4-SUe`S)#eyF0JEmLq5koOrf$<-qY(KPVJ&5 z<3r3@;+s0k(%I1Koc>(yj#P2qZm#d*JzJN$m~lJQZX?^+vo`l;bWaB?is_r?nQL;% z%!oYQPxK#uJ%AoMVcGdgv2VU@jL>ZF`T@H|Z2NMan2RS-zptur7AjbukFp(KyhiHf z5j{LkeT9`PSQC+MEu|~$9i1tU6Qtra&Y>1(PnaToQAVg$IsTSWV?>S?eFwqUivN!q@+W%-kNUpBjOYEr2c5To^?>k*Qax@7DXHMSd z#jo8E#csSM1aUAZx4x6jW%3L`{ocW_$#|TR5O&|PhCPY0A*e)ixrJ=hmsS?=D7SYgn*bW~KFMc!y$lZhd*?kqBx>G0 zZCuJW#1_R&T)v~u845BM39#El`{?2XYDkuoQf2IS*iE-QEI+X%{}wNNxQGFkvVZNL zi&cMV2GK~y7nSRe>?{4jr&?J&q(#{onvi1}FsQd=5I5~J+rpRh-L~P~$o9t|jP&C4gU8OPTC7 zyOJu=eE7kgs?Og=D&DA7kSG%x`uXqI|KC=iRdF-!$6^{;+T3#naBFh4nr}FZ3e;02 z>hmD<%IBc9{f{PjOGtg+7cu(A_&af3zJ$vy`~h)0*fg!=(~j>U8VHs|F53dtuQDo| zBFR?-fJ8z1=x>d=_ls6Gbj`pfTz}!l@$iK3H>+>E2l|g47P=uXw|IukiWjb;3hI+1 zk@^+GI?{SVOpho;=v-7Me=TxRF1r|yINUfp>Wqa94lm^_@&UOCsutA}Pg314ID!v& z73Q+Q-iD4RJeoC8Zu+UA0-H@yCmU>v4Jf-gaN6nEVuScZDHZ|AKJBpI81udb7f&i>%tK0|HG& z@al4V{>lwV+_CMAX;&S*_wF_RDBd9%~8!Vk%--=7Vfyt%MDEt823hKINls zM$}@L&DC~K&Cq@QDY1c? z0FhwCnSwBkkY)sUE;D1vlNU~GaO;o-8qG7@;w`2-HViSAG4{QbCC2C0{*39W>P&YP zxyGM{G>aRxxBvrQW%3Jq?rt()3jF{?jGB8e2|IEA8sO&OpG!jiO4Y^qpjfpRHaY5!q=`d4TBKvl+8}NRm!6G z%7rHT9V)rARa{#fI|H*(-2%saQl=Gw8oF<0>ckhzR#KrgJgiLj*ZbPR1w1OaIpI0R zSqvTFRh*?&Z-a}s+rjzUWi&Lt4!;rbCyNBUiFY?VWnbbO4i;?Y%ix>nIAO5jPU3+b zDjo5S#icLFf>TaT+?sEXSSSopyyt4jbC8L)uSAPk{O7BnsQgAOEtIF33tl|%6!+ss#8|88 zhH`j`MVz&)xAAR_mrjZv4|`J^w8gAsQ`~*tLqIG4x~Y8oaCjW@l*`#joMX(Zicn9D zk-FqcNE<`kU0VasaIvoRfUDH=1n8s+$~6^w(9*J1V!h3Xt@K;Q*J|(cRvjqtc}*IXkL z_mKOwlgdz0PCbsu!Q*LvSa=Cp1w_B^qk-aY#AN}&OGwwaBh#>EvMAKkE-&%S?Iau{ zA{BU+o8HS<<6ffg-Uac^5G_9HDIzh2}$pqPt=g$0*@I{a@P@*hgilROE8#oW2hIpKQ0se=p5 zs4(iIt+;3OWG!ylVbTUK)K8&_5e@R+)bOBB4KqrwGd+)fd#;nxz?50os6t_BTS!(` zueO#!;H)*hNIVtf@kwND`9ndh#&m^1PAHTgaH}M3E_gXEmesBeW7`m#b;Q49>jy(h zjB4C-gbO#hUNMCkcy(*%ag_s-=CAM>KQM!7yODXPhf%w|!&&Z?7;WJ{@i;wK^z z-P*SRx#26q+@9g9%4ppoAv2^ zs%yQuR~HQenc^_bD7)emAx>TXYOmz*Q=d?pLzZqX^ce8@!ZYten-?H2jiFFx3F!P_ z=(AYqWNg?9sQBO@KdyzuDKUp)Mo5lsl{B{EVlOwsKv~b>Nr!_TAse&CKh~p$NbYLd z47}d6T!QiGYfSsumkPX3SB}&H++14{b~44y1suCPn!yDpFMs+OzfT`MzBGTe6}1p? zTC24lH`Ny0m}?iT)Uv;5QV^E(_3h##Wn|lB1&lJDvn%u@LNX?vg@>PRHAXXhcT>He z`Y6#_adXpichcnTwz_!;;R%(_M39;QD?AyHSf@zv?mrmQf5VQ)7O1n0p@7omM|Ag0 z1?PWezD4F~P6-#zHo?lk6l%)t!WZ77g`(^GWj)L$GErq}T2&uMtfY)|3gv!hc=hcy zwDF~aoWn;WTRSKtSb`e$EsjA4?c$Vb@K8k1!cqtX!Y9gjELG*i?45!FG^K6hNm;2&>0I;>h5>H03ZJqwKu?xf2-0lbQe z>*H(%tX|t15m>(--8k+!laxY1Jm-IC>VLk$_$*QpssmH`Joxk3JKCeJ@DtwonLCcY zi}P;h{TaO_9&t74pm{o-h}nb!p)hMJNtr6K^}35|XXbfr{VE}a_jVz_j5Q<9I$2Yx ziBuXtj&YpP8I=tibn0-YDyw#^T_iIzo+^ryM0PD99iEqti5&V1FfBE?Pq5rwK9lSh zCpjx@pqxzd&tY7%Z1xfQdK*Q25~t|#sm;P^kGTk21Jk_@gxvK(beOt^Zxq_C6KOhR zn}SE7{i&=E(CoajE_@n`AA_%DZbmLc#W#E13*n|&W*D!66pFU@a0yrzx@7mw+vYXT zAH4lD)n8w11*%TbWoD=p=@?y@2m^)Lxu8W3qs$+#eEmyVnD27{{_|e?@7Nm?@MRoz z_!ZBRhFCI!J42H_!%Erm!)$9DO6e2?ays`ixhsFBNns$b6;XJHT5!e~uwKR*&9iI_ zMvH4}21M_@OD)qZvkUagbDi1iKr`bM2Q*2|R2Bc_T7~S^1#%`*1ftf~2NuH+HRlEh z-?zrK`sg?x>DJv2M%MjXWu%a^vg^;tvB24IPGdrYc$(m9(^t8m-_TA15- zpQH_bxP;3Kmzn)_7fYvM9rpQ-`KcQeUZ08myVx|z6I(WNjHkNeZ($UB*r0(UzL|P> zSP3euG0{ZgLcfg;O@25ZmdVhMqHQly+sf9Y!B&sdF-K8;gEP~%C*k9A zJaey9ns(V0+xfG5V&1}-5WAojfkkUn@mrvsxSfY4%TXs~a-JtvOSm_ue0IY_Epb1i zj29P~k{OdJPxkqq_&;s%{|^q3(_u3&LPVV{sSnGeIcMcQy@_mkpLneMrmShkbH*kx zDEEHwmsViUBS`kL`Lh}#mA8$By*7otQK7=b;;!tAP(Uc4>wOXn`In9V*&334=EL*o zkC&+#0-ATS_qY9O7NgC(%G@$bAypAI(Ww!ayp(b&{!ym<`p;qEletekv>q_-#U)O) zCIf0St4u@>m6{$K(xqWA+=i^XaveWB1JKgOQb*QY9_=-vG%hQ7VJ^pqrWrj=Zr@v@ zRPZf?dKY;$kCo8)yA?}iA=*vud3=C67fXnY7edf}t(b(Wzpl+xiQ%JM3Af>gUc(ZC#GzaF5xGH`XV5N^A0k;ORSY%)o=s}wbr2O4bipfo1^-)NH$Vyg^xyc>v-p62=s1`RbzDv%m?y<%bf zhkE|zL7Qw(<_8;Qenxz$<gdtjU zlm_^V?7t6W&RJ}%_sMJeK^1yJbcc)(yqJqsK4LfRAzgc`yD9j0K?5=;YiuE*?N+U@ z=x=(N!C{sr>b;oX-ze}&eit3J9tiHk2yV1Jkv9Wm2CFFh!_Aw#rRFw}i~`VVU|AVE+31%;37-+OpAWzs@6Z2VEHkB|i1M|>YT@-HI5!m+72P{Jdas0^Xy6;&BjWOt(TlOpMqv(XS(7O3 zM8r>01Lxr5sAwBt#G#uH-(tr-LJ?5rkr&e3qzD2e3zYep4hU%_bH@qqpuK{>x7mDD z2P!pr&ogL$;ZJ5&knbGLP}K=aiMVtMmk{09ST+~(fS?C|yloBUFx@)JB5SF40F z%MmCw!%tjFNR2)9i*VAUQ}%TBO!SDHi7y~ZJ(5p}<>h~VMOO1;-(l9=>_-kdRyY`65Bu!C7o2~|&HJgBx&Nc& zM-#?zl^#Fohm)oXiol+!!#IL_Qqub59ogwkKV(MGc!6t+Eh;8ezmwVpL3ts?s;0l= z{As`9dZcc|rsQz5V97m>w0REeUFxb%19x%DB@Z9slDB}oh$DcrL)FwLaikNtUaaE~ zLw#=_|5K5fLGra?i+Xlv>R8U~k7um~W}?Y`&lOWXa1=IwSW9gz00}tH{K(GDw_O}v ziXD{UrdZ0Ixz*LUgx-8~WcD&2#(I}DI|ao_0@`TV@Mr28R%yR0V?5Xf?>3DS?`x_& zu#N1u&TPZA*FS*FjsfZD-k=0^Lu2gfP`C&dH3Ar*DFx z$vWcvG`D4N7NPgdqhqp$U&ONj5*dC7H~r`w*iR=}c-9**)gKABr~&kf%j<1*D`sVP zPt8A8_rOV;CB?=8ea`eBzvZhO6-Ye2OT;+=x;E%z1G*CTEb6_7smL$8=Emxm=WceE z?aZTBQ8bm@@)IRVx>|L+Z<6xft0GlaFEsJ3C~UO+N~{~YrP!%|kU5FOUzRcB1wA|~ z`=%}g-7v#r)Kb`9JB>ehC+%qXCMH{+f%Sxq8OZW$+bu1*w;>0A z^vXrd2q^QGp9L1im7eNVQ=|yhRxdlIsAQawzXFylxHcF+m4{~t6&kdXbA1sh@#sjTw&HZbv7kTcg3r5nzus`JPdh%Au4Y;V=BYXK zUAr~YU>HI#X<45=^a_w!{&MB`7NN>b)f~ru*w|LOP4B-ibob@(gml3v2~km>Dh&=& zw&30w-tRYT*Dew#TNpg6^;wHsmxK%-DZX2I%@bmgUZ6nEol~7ezBbxUmsF=`D0a+e zsG;_43Y28C8p|#L#sow%UV&lmS*m4q^ssxhFY z!X)TJ&~9O|p$o7}c1f zP6H?R2<~VTdWxwi*8ylFp=0Q=qgxt#rlth4rc(D%+warEqSya8LIiYOyW<)SB6qP^ zog6y04T5oO&Ceo*`fnYE3WPs!x($mNzD%6MtsJcMD=n!_Y(`}s!UL(|e$xU~HRBW9 z1zS~e(`MOa$6GMrQxTOgloqcR&vjazAf$smP{S*66sq$##1=fcd>T&e&8}X;xmdLp z&^ccglK9aH1)q>jQ%T6e6at~88_>*{tx~xa3oDNP+5lED!l*SH;X0iJMXR_5Ae~dx=gIqXqo82 z>By=`My6+Kb)qP9J*Qsd$;Xi-!U%yNo_GUlNka|L%-w zEVAio*m-fdFv$Dl#d>~fP->z_#X&|ST^U*WUmmNp#!rpjd*5(PA(54K4x^slPzLnP zRfua#fg?uuV(%*!%QvAUoKXH`RWvBlY`JztyHSIP70+l(eHmO-eqs5K&YYn@i+s0! zq6Pvb53@G53-~~g%uEg|MYQey>)nH24VhO?+2Tc|n;fApbFbUIkD@AbH`Qu6#}Lh! z7PYKk=aNn2V91<#hmCT$gN~8lt#U$Ik(x2pve<6MRr)rO;cdk1ky*&S#2TxWZLi^j zr8}6IoNSx^k_(aKMpX_0f6SP9oAl3=Ci2@?DeaXP=>4qhnz_hE#?5j>qCoEfHj+?y z0Gy(9SG}|@O6%$DLR>&us2$H0uN-^VyZ53w4Vlp7y>e~jyxjLK6r0cgAjPpI#mh1& z11#765$3(!VqUa%OQ#01pbvbOzVO&R#c7tk$<|C{B)v&&G0`-`*J=o)X3dYzHq;b` z@k|3!c7T@UuuTqH!}2}Vt30rS#IGEb&D$LGckV{Ot_egZ3G+13x7*P_z<{eEvQQY# zDqg%*oH~Z_J{n*I>MV1r+Z0?KYU3w=PO>^M4UfOS_7~w~z;CNn!u&#DFiGqaKgtwPK1G-$A)6O))MjH|p^8oQ>y2W`lI4qrT zy`od~icsXt60?G($wlyv_c~xt(rskAM2A+Vc{A=N`QRv#G834^IZx6fd&LU3_(0~N zbf`e_DJ$K&^^UhSS;S)3iRvV$dnno6h1SVur6h4rX#z+%1Sv#hlriW96v-R9{0_<^ z#lR)IXS+Db_sfTySIxa=jUR4`v}q?{o2vo8Hv31X{rwP;Ob^JHI6$xd>5TRN=D;J` zOZ%!0A6tgTRqD=K8=$_4KfzD^H$lxr9#qXs79a}vw=AyFOB9h%5arV}$%mQ)+WCzm zsgEuQB6J^b+`CcJYrrwq%U~ctQFBJd6$u{D(5>ktv>$7a4M#feF(A7`)KrW(&_q>f-xbHD9UcO)e(Eq$dvURc3W zFxFlFKHKE}#HO}(vrE``2p-DV$4H7`nCi0{e^n0-Z>3{wkK)yTRA2^~R5_T)@TlMVsFI&SjtVP@zo=iKqCVSty-HZ^ZW#^8d&3P zTv|MHBzR`gw>{g|rex02GnszWQwQkBInUcfd zEcIf2fa_@-HdLaz1Du!8<=Kg3kTn?D0_+k@Ob4<8af+Z`CzM)6XxqH$GjU)4vk#lM zBhwZJW$UaGkpTbv-qxjv6!efZq@J?@rwVIRAgwxJ(XXafHY=EB2rE4E_Km;)67N+& z?urki%I2F@gdGJJ!I=W;^RJt!oj?AOG z*vPvKc-6Fb)Vrth0-MnG>9ezY{xrTVqBqlKH5LrjpQGEeSeLfcQotKre7xN%g*nRp zLBEfM%jNxpm3OWu0quiiN>d*-_|QIkRj8_*Y8DlU zg-EsD?%1bbUinODsS3Yulzc?TDqS?`D_=_Yf0iopzjXFroA{kW*O1RWB5(*U$lXUM z05VsUNOt}N-ww%O(IH|42RQo8KdV=8V1=|tD6tQU%13z+Ux*U5WugoQFh99`<4ImM z2pF}@T-MH(fU1}V*y~{z5J({FfS~93l=|ckV0*bFm_BR4s(Id7?2M5`YEg&G7mLc% z(T_xt^xLkl6{|$~<6^xI=SK@q1ZUK_8s%6+P>!zGX@$m#4$Agbp%xG2?^LoS_n=y=DQVS^Gi%Rdz=9P7I&d1;om zr_reHG0<8|)9v^gEPnk7wk8-Wt;KzHZ`Ntz2Hd2{n?l zxm8$Y>GhW4AM<`8Y;rwfS!|obDe|TL1LVdH24(iEe7anlkxbe$m0`^IxQVbE2m!b9 zet*C6Q=QaUBpIrIWM;9s&dp7^BrIt#FK(!}j@rmtQ$XOGw3?mgm795iIM~FB8Igl? zMQHG`f`gj+=fK2lfb~{Xsv!rCQ*Y0UWAo6Z3O4 zp_cVh#eJ9$1U)pO&!XW1%=JU>hBw-h$=;gQ?$l%{v;gbxK#RRQZc>LOyREh?Ia*%O zGYzj+7n-1NXImcLpftWMmsaDtCuVuep=Nop#Y#&vk3< zjJe;UJRf=$EcxxjE+1C>iRXly*eoo`zr7V!qNlj`w2xhB6p%v8 z0V$LH?~ZX~@bzKV=e}(*cO&pw`fc6BlX?^pDRL;2>uzANy;?%p_7W0Y)2IY`d2csW ze92q}We}IFmnp@Cwkm4ZTyUE^m0bqBBR7AywAsC|WQCYTSRivuDgYC@{$A$W>|i&Q zEtnTtP++cu3K5(0oQwM42z@d#9ilECQo%ob(U6_ecp2+ksEA0UbG6x$!ec6#E5Ry6 zUa5kFZZ~@g!1_Yqs;|?ObCg|&xHZdz5h}n+{mnf;QDTMB3HIb2HTr~FmQ(r3}jPzP~sa3%shmFVC-M=&Z9nGOB zUQ|L{T{}^)*#sBXClrpgd~Li>XnU@o2&oo}XOq+{-02Mp^dmn_u?SS|oWTXnWcXsZJHC9YO;B6K<#M<1HKAxv_*vo zw#wb_R#0E7UPXULEiEUAB~5)RH`*U2n(xDJ*#JN?(S79do=UR4>ERFs_Z!@r)1ILB z*e$4VVi+QRSf7E!F9QQ8m}R5{X3O8n#UPFMOq8%$i|&b)N>Nu`-#9a}@6GYrMU zImd0_6*6*dcQhiJP|(I>5C2wlSK5)7ypl__t~chPKI$7)|7EbmmiEDp%6g`IX|G|X ztWfi4n8@0*_8~AUT^9F@iG>xR_nASq<$j-^u9pRl#3dxH)_cvj>D+hM|0^Z)M!T23 zSfLHLTYvHhY<#PR8iSp-IMJE6YpvqEnf)*$&eP$`mZh0 zxnx#1s!yrMTK*0?FZv-xAnOz1e zIX}|hgQaXz&i+x~S>Xwk$Fk@~ic0%|o<*z}Q--R7Uue+;)pl>Gi)q(f_9W zK*PTM`&&4gUcXlBb!JJ~@feTJ&VfVAtVwiH5Vf1BAfG)wNY)L20~-KhdLb1rmx(*h zjTcP=sfIyYj-`?c{q+MDO3~ImUf`G!+MSZOFr~5#%J>n`mhnv~j$^BDs-veQT~u76 z%atPGB*9CXJ;oTTr41l7^CWYs&vT9Lofw_al@%l(%yW2$_e|@q^N`-udbtJot0FMm z)@aha6!)xG(eJID3r$;eYHr>i!O2lR>QmQwktyKr`MQjIyevUfkXe-7pG%JpUv21? z)$zoRH&dFA`NS3z=0r=Oms{`P%w|93Fp+a!wTAlWkIOz1YoyvKB!94|If-dZK&Qh(13blH$4bM>jXhwm+ zKirFJnQ@6Wf}(cY?&m|`?;csf9;7+9wO%P3U&HR~I4j>O7aHmYtPQ8c&?K+Zl;g`c zB&Bnk{7iiFE{QW0Umq7bF1Kg0V-u*Xo$obQT>@tF`dZhy+`o}|XDl)N{DWo{7s6cD za@-bBCMxC~Rm&aqkPyA z@BQNvxzx4dCix@g_V)D{h(@ltk0g^#tLpt35?%4)Yif0~X3^+fH{<@?ru*1}!dz-w zq6O6xwjZ^tc5Y$&x`+yzFJ1SOTy})iqZOicQKe~^f${0Vb2hd>)se5POrX;#*9Pj5 zCrUaO!XGTyOMN^uoh!&sPIGI_Qt$-Yrt|R=ZCAWZ3X-C1L5X^aIwqY@UX2=(cLa?Q z1PEcM`0Y>Aa7YZFgJR7FA1mK;?oSQLUE()2O(H)D`HD7{&iM)zFvM8S*^e``23gh^ zhurDGAt+=uzc(@QI*D9i*TVjn=dZJ~Y+mbMW`P7>E@A7QdyFf5#>0R&yiOx=Hdsg5 zcM+#HT|~vzSdDPZ`sU}imak5mJfztw7ware{UnElFrlztp(7=!!t@sw+7GK#Cgg3T z!s^dZEm!?BMb%e|{?ylA&T2XkQ`&OT$UU-mcrjUXd4xH2%cCt~k?_&Fy#%Y;5R9E( z3yTt5Hn3?~j;dAgYpQra(+x%`tjl!mkDr7(y|#`KdA#1i@iJ*3|g$m0uHF zroUp~XXZJ|(%)F}<9iyBj9=S5OVUznBx5(Tw=|{Ep3JZFW2ZotK|LZ@5c%$d;_5=~ zE$|X$p3`!~I}7pPWmAiGTqaFQ-?kLnomm@ZnIgyxr4pbGGDPolD3K&0iT2+L*|wyO z{~x$Z|C`C^pB=h47d4fg)#Qt*YEKGMMjKWNlk`Y?sti9O0jT^W*Z_{qD)Am9-_Lk~ zcTrmc*$XPJYS-V(a0>B++79Dr-Zse~Je|7?_*h?R8!ma*3oT^$saW%Z^JgEjX-VJS zkT3j=y=Caqf~D$cJLOXHnuOtIT9b=t(E#7qL-$|Nrr6+-W8LS3_R@j9(;O+Y#Xs^X&jrF-1aDyrACI1G%u|Oa8V5!cw_JfaKd_rf~3?I?8qv(VQ5&p ze_xxjrxIkWYU_ScsW96_L9wP+9S>z2i23osU69jXagi(RKC83ro9wQ4z22nG5_=uJ z%<~w5P`Mc)oG@E2>Jd9bn<}sD7onY)&(V8VGz|=#avA=fB>CL-(|_6b0}Wg0FIJV3 zhD9bm`WCh>05iDPVT>#sZP|RZ_u=v9JS7kfSAT}!>Z~tDU~$HnMOgHU z^D48XP^zn<>1wNr1NJ6oANG8L7VrUB|C%JUP;#qwJGVVS{Cn8A_IZHIdEwJ4#?!E;z5X70DSoNp+Rq#}%EpYcvil8l)*n+F ztQo!$$pq+^ya2L%v&{r~~9m=i}Rofs8 z=sdMZ3F%FQbs^fU0ugUH!AG#vW9j(bAp#qyATCFzC?hG!Ha`&&N%J&A_4r?I_4!-A z(yF;5X@4CPL74QJV&&N%?G#sH$;s92@X|h&J!U4O141r}|8!osSLRsK9`qj$J=o6R zBA^|2iOa)3=d(aDh<4lQkA5UbCxfesZ5g?H` z3NPNN;+FT2|E4_?OTTXzMGZirQ7-!4DOnYO9yq9nM>(dOMG+SyM#>5X2#shWfGByQQCbksNLJM{S?INMUWINF%$$j33pl>^8|k4{?r z9&vtDD3$hmMotqLyIG&P)HVPX#tdhkq%jtcsrBcHQmFt{Mw)iYkSWEyY9=!Db!u$M(c0EP_NjPREA{-b z+Kp?N`K67r;DD%P-{9DZ{inc$@`SlS`k=H(UJZv1Z!9t& zm6e&5>}VTZ>y^!M(CuD{8d{Bxh8A1oUR%$3H}GVGsJ1mWBc=?0RkGIVtOGt}V)V^p zmU=Yl-u!Ep@D+)h;0rjL!A^p($3@R=>L6Q_q z@@FyF!&XfEl}*g%t8^@&s|4_kkZS2z=UB%ED#ALa(a1D1N=cg(<%mTG4DZWGPWYpZ z6hK5YmU%W@I`fRjS~5g%o$fo?GN~DI%4Nx8(ya0mPJ3@?CMQxnULnzjnHagOm22ey z=7`dC(YDsc&$HIo{`5irm3gaORNpl%D$*UTX_{5o`%Hk2&v@ySy_$gy9qTcL6Prmv z?}1rd(_;O#XtOM$w3)JHMSdA?`Ah02hnm&5_hLqtmuXussTc>bO|@L;T>2_uFXffp zWp|o=LXc<2qh0D{f3WtJL2Y(j+b}g+ptOY|1q!sdQ``!)cz{3& zPLLJ}nxMg@E$$__7YQy!B6tON2^y?OaVTzue!1@FooBB5{rNL+Qxp;oEioP$X$e4l~Gm_a6~Yvnkje=4EJxihw^*YFwhtvylh1#(q@VUgb8ZxjYvR|Vbfr4Bm|1X^cq zV&6K0T#k4c_TXhwn%e@nXL7jmzRfi3{p6l%hLl0-mSo2{Lv`{K)OEu!R3rIU{=#M{ zzz39Iy5qu^t){?6Uah<+K8*>==c63mE=P)d2FsF1w@ijUf=~rI*0>`^ zRjt272Nmiw-W-$pe~H@5n)OS)$KWmUZ?u0pe_sZJysPhVhzt;l#Ca$LhDrzjinApx*X3yI%p~^KrfF>D@^-^{o1=gHk-ah zYtE3;#b+bXCek^&T}N^h&r(3!s=_z0Xn$Cr{YFwMd8^&{ZPstxDbw^Z(Tf!+Ik%Ob z|C5nE{`kF`=Ks&5{=d7yXzp3Rfvbu+cHujp1XVUeexBvl1Cv?*64mp}&VrP_QFRki zKk2>b&@(U(%Y}xW%lLDN0W_I5D*nJ&<)nL10a4|(K^;+OFXh5)>`t%N0~MTfI>yDV z6*`<(iikYzH1V%9SHW~@_y#FmM`mLqN8PwQ*X)7%xzIWnfXS_WMArSn^=6#Awb z9KthR0DrvJ<*>gJN98s7%+z^YPcv!WF4JHOd>LPx%=z`0VG?j&U1a{z7~4BAa3-bt zJ)^6Aw3x;|KzlU~34yNKGMt?1muPq9<}Ww|vVJIB;jC)lF1G473|NU8D=whQfabl4 zN!0mEXcho z|6ovSCdkG$Bw@q{h1gZaOO?031tfRu`2GG`bEgu< z50k8O?YfU!j*`{;XQYW7iU@_K`H&Q5 zvT(PZGAq^`XSxMnueXx#87UW`pw)KCWMMhIYxAuKk6zu9eBDMCRXys=K;HDm*_r2% z#r$6KtN-eFF@X5DeG_pGSopRZl>7*gBCUkM|b(40ulxCUIX8%cFQ~}769rRQi zU-Bs)O%s*gS|1D`eQU9EyRD-5)m!=me=10pSL z&NPUP)9AxN;V`vC5A##2*6Cz-exshtoZsbqZDhiETvsq%Szk@I9&|$f8w3lpnDlZL zJg^eS zG-)}lo(5#_m4(UlrquiO4wYmW9{By~4t$z2WPtpJ2o;7yWU6Fm3D> z`u;c97T*P<9!^4PqP9MbE#iil?$~0RwXsMRWWf_((P(Lmj33e-`Nf)ucp3>+Hw%r=bmV ziOyS=)8P*3wM`*z_f5Ub9F)ytc~fK+gbWaCwYYcET1r{pD%lDkiZF4A5+qygJ>5g- zLY1TaBx!K0Ox9BMaBspi=YeV%B1B+o zt-z4b5em{Q=YQDwNss2Q#5Tp1X6ChA>ht|< zx~vRVbBUL_8>}!q85rtCgr}0Sxax+mpir3qkM1F{vGdS7#wIRJTHW#*>+t<2r+PMX zb8;SdV&oDyFwB9Kl~q`HxUW52G^64-3(>6)(Hd_1gPG=CI``%h#rGjwm1xAc&bp^A zZzhXE3yDO^n026oL9m^FQTO1l;vg|?g@I!FYg8w~oSx$kvGK^ckA@U)QyxvT+uVx2 z0K0=&rG`9pdiqrYL8sHvv@(3Z$_15CSDRcFjYuGUt@&9Cy7p|GFgc|}rfcSWL!QM` z1k_TuW4U6mx07t>2wJzodPyc)!rBf9IM7p6MVN3?8QUvRS~Lc923qDiJpVJ7^=sx< zQK#hWAL(QAm(RbpRT4mg_gQ=H>J+xnX&4$wSX6YA06bfF#Roy(rh! z+m&6v3WuWA%Ykls=ty(=+NAPfYguW*p16cDSm)mNJ`p#8(qA0A%CZCC@6=|cNn#f1 zbILivn1#-Eruog6)eF!b?~+S^pY4of{B09lv!ku*XUir?3g&6FQx%f_@oukQbggxn zZsDKsIXPbV7zh^_sA$&<-1+Uvh9>tIh$*$0?g3^E?uUwJ!l$()Ph@(jxOa76P0-Uo`qFU@`FkOJ z1fKlf9-!dPC(AHr-qeBBU7I@bJl~EE06t4EkNvxjw_FzrW|#3eiLIDKvd77Oq?)eE zs74~wDfJ7UA;12BoW0tB4Zr)%#L@cWqM5xOx6X8cg+G*SsNY^z`_gqiX1q(rppMSu zD$_8RWjhAOeNffjAJ=8*;5q&@Prl`8U|8y~zu0}U3^#YJF|WG~8z>E7fZMoGpj07s zIIy(VpQ?Ye+CW*q6K6^lHtU>*rHctJl2iA9h5AZG({Tm~-8C8#lJHC351}_$y7U37 zCd%MU!W;xd8v1A($x=56>uR>xa)+4-_8Ns+Cr1bJbco3|l_LySq^ha*$cS|^xhU~h z!4i_GOlMyweV@U=Jt;FO8$54J3D9}Eo)lSrXm0X(u|nT_)h~=JIc=hepC#6*whBO9 z)_?k#J{xHLCz(fKG}}M1zxugslY4{YchcHK0DDU_<$kU4;EU*_Y*s3~vzrlSN%_s2 zbqT($l;RZOaedkp&!$JpZgWo?I}23gxbjCBWYyWYkeh4?d01;lG-%Afs~?k6eeZVrofPKveu~$Wuh<=;IR8g7P)<>z0=OJ zxC>OQ>!{a*Q=gNTb%@l10`<9Sr!^xGCdPJ!(xcs@`NBt^kbxadC#$d(LOZKulsTe= z{R}xBR$$=7kmV?Zhy95tG@!tGvs0+8p_;G{5RIe+p7_SlW7#{1_^kgg z6K+f{W7}MIrpy@{&`laA6~!Zc-ujy?0?*$)h|MNHjOP@S&FRoSMIp(Z`F*T!|Xx-f4{+i`C3_zf9IC!gMRNaJPbQP52^k6K->?dr6S9+{% zhZNx`p1iO5!3edv&dR17k>M1v;VoivGJ)68a|O562jHD zz(NG~n;vlo)fN>0&Z_w0aI~*Mpdj!tXuuXwT1F{-B|Ojci%l)2wa|8wacDO}C(oks z)kDX_wH{S1&B;QEs{t6d{nd{bce@EsK8!=EBMnR8j)6_gGwX&_QXZQtW?~Z0we2@ca^W%|S?h|m%& zJFb+o-95e^c+=OjTxLK1TF$=V+peznAbxSIBK1jQWz4u4ps0^GGimfHyG!rVtE(?+ zDZNJJp4H-w4=d7hfz$%&_C}wsjrxoYUgM{t=CrAM$~9jj-BOf0gcd~|0#092CY^0s z3kSw;Y;4hIH|br)qKiGj1-G}fTf`eIF-s>bTLt#DgTDv>$$RxHX97&LH8gWELOaY9 zRxf>`_|KW9RXUICM7`bn>!s8q=Hu1m|A=&AimaZO(Pb3<#K1U;$4K`#x~FUU#45_vlm=q*a56WWZM}zP_dYklRCVjZrdQy}aV7guz-V#5C=q9&5BOq?g2a>S<$UC3gtd5Fqki;RO3@Y(tO zF3hjpYIXRA+cK$XhAdJzTOm|I@eQ*3B$aa6B@@akVv(NGNW^<~gG0QgIeXM8kyW+# z5;SN?{HT>-gKFc*J}e@c!&s;EzTRhPEybK3uH*F{`*z2!)HipJgtncJ(=ZR){B~#X z20zi-?$+RQ?N_1tvsIk=veMH?4F7kV$GbCrTO1qrX~K%xC7!E+;*%*xzP@Md`}(h{ zyu)FC+Ca4EH+Z!f^vJ?o})Z>^e;3^hj$-L{e1l|>-b5SRA+lry-b)XxZC z3evYngk(#RXVx+B$jVUyz1JzA%X1V5?JOVv#lmAPZ85Wq_HDdqJ+3eL8hfw1YB3YM zCK7MjxcP!GCvA;5N#k*cf{mQMw+plz0ko3qjWKzHwUega2O69F2jjIuxvc#rpr1p` z6;nLb&_3U@sy|%@zZ8mBcr6>K`Da}>gn`i?Kd>h!j&K&SZoo8cm*kSLGq`CAIy>9E zG)(QAv(IKw_uF1Q!%i2f64O`H? z&f*eRVT!^-zg>z->bKgxN3C0c#D(ipm*sM1kXi}Ow6VoK8>W{vovYR3u6QoNI<9eK zNXns!qpX;ppo9#Cd;NZXr_OfK@waNaFB$Kq$&u^bCq* zisc5U>bJ!|@sFvYoT?w*o1QaGqBdRl3$a%VK@&J2{z=vQ$zBY*{;`XO(+}%@yjB}Y zm(Lr-Qo*pbG_A&_Z%fBhRB=x$VK8F}Y?scKvEV7wT6zOVDISyHqVtz5&3=E#DBX5! zohJn(JlFct11KI!Tc?dIc7ImzMviUwIS5~jtm|0Q-p$qxA5XjR<{zNU)n~5;*wHhA zRl9$1ziVJSFO~>ykU#@r;QZEpmnA4r5Qs_mQmM$ev*#R-!?2h~e{&&v2p;N)WYU{H zq$?UL-ELw*>VEnu!o+8{F2Uq8N&SrrNW;gcH1sQ`F2I0;ZM(U2yK@dUD zQ_O7}6Heeu9QF1O*ZM1?3U>C6wmK$;1xwZ@Nn1*Z!Qz^6;qvEmTFNT4g?y6_04=H6o0GC3dBzq?VvJF+52Nm%0%qlyer+h)1euVJRNrB~ z^(}kDKlZG3SGNCDa(lnaYF{TM6V2GiD)Sl()#wam>KZg!E!kZ_hZySZ2r1GMFT(hH~ zN0{Tm9xvI|>y#L@YKFOF2kL0alF=_MIs2~gGFn||UmYO-dAOCe>;d#95s_FcutW{B zI7OQ+t1|BJy``eJ@_~Xk|3hm1v?_MazeM-?wq9STb>2M`Kf@9Wuq8F54R4d&HfP$ttoT0JaKgEkmH7;&SS7w}dc zD;;xmC6z&bvv^;se=8hOQhbvToe(h`&?=e(*(n_A=;D~2|;X+0WstY zLy-*Jq;lF&>>Wjd(Z>Om=ShulaV49>&QCKfh7W-rviD;m?yX)q2y`){{_Gt&`v&zd z-aRg9dKx%+*f!F#@s~)1;LG#3T`w&NPS9@y_K8#83vg?w)Cd*rduW1QOb0H><}2i`6bPU}_n*oo1+Ct}&?Bv*tgR1+_(A|WR6Px4nXa~ z_=+@e+h!)7((Bev{0A=}7B~RAX<*b{C&(dS?BPFPUi3}Z0~{70%q_uCQeUjX%4*J8 zx*6z8#LwvimYw_MF_6i+gNziM%?@pXST&Js#%*{>DPS+WvS+WTIkgH+;%5c0rR^9* zuUSg%>%<<9bChVpBZ@YE+29lAdd9+CGI*v?^IprRT}5Z{O3(AU!#Q4V)3{50li9G9 zi30u1a_yua6$4=F-X0S5_^kTLD5~42A5C7hehb@Vn9fZJiG%}jsqQg8B_LKlQDX@n zPKq=xMW!7CD6Pi&Kh;+$vAQPZF7-eVix8rsQ$KUdmD7z_{D@5IP}{)B044|MM)Her zRMNW!4@}@m#&Nfp!%|;G|MqWmcb(KCA{m%netU8kDlj-V;B3|e*~aKMR$oGfVEUeW zJ=EY7vlpeQ_pRAB;^pca9?dIZDEB{Kd915`eNUSkFM^8vDC57(-@Yc(BYqVcTO-!s zw&FMF*SQtS#7-Xh5R2p+aDKUoea;5KJk?=Cfg)lal;B1Bz#{{)r=B;O%-XHV;yP+C zKNy|E_S$7+eN9Vncvkv18;L9?(7~ASIKD{T}GWoPbc{3Wu_Tx#DpYv7|E6`a`#O#f5_O57pKGvr2X)UoseU*8iS zPGrO2K)#V+|Lzy;irLu8oOQ`%=S<8yw7I=BjKwizZ2AH!>S2P<;P)0u#F!IKUglzUB5nIGLQ-`zly%0mZ2cT}J5Op01>ym78vSc(ema5p`@0=1dJ z8j(T|11N6!ho(|Hv<-K}aEZKWDDN?G-R$V)&*W(gNvt%>2UpM~`*Ddp6qFU%mO^z@?;r`nkwt-^BzK_#)`17}mz z&YBOncdlykL@b_nvK8;=sJo*cJJ8s_^$wSjr(Y#iLxc4w7mx+=O6g>ZiYK?KIgKvE z=y-Ks+uasjkbeh^blS7Shs?$G?#XOf%OLQmaEqqb8Pj+ zmPpdHaIFHUKrDe)$RYHGcR-BKz%inMxWeQKi*8DA(h6T_j? z5wX)lFVUm<_R`|PY@Cq^6zQ9HZk^7~S7MsWReo-Vf9XJBch+~!3)G4rGtqr;(&zT<^@ zX47W^>X`>|-Pvf+s^wSJVm|Bd>u|90yCT;E*9u3hjRcM;z!sSYEY z9`glc*0?#=Xvz5V>wP~oqur3OJfWr~9E&2>!eE9Nh9Np_Ge9*_8&j>zT+ zF`u-LZ4m!SC(dO8f#63`#)3(pU)mDcrdj;259Rl@@BA!a>pL>tx&-%TvD6&2m83s; z=tQ$iQ-a&;`?wm!yT*Xv{4!aaHPs!4qjyLiEVFrVlU%L1U5uuTMZ|TUcq#rX6e_6E zv(aFa(hD}&;WQV}p2^A)PIa8FVT&1JPpC=4%+18}1*V)~S`c=ALq`8p>y2&uVhdMt z;h7xxj}i2zL{L)TN0z0x6AcyrX7XySlFb81C=Fy0 zus?v5C_p+_e0$9MwEw_0-90ggDwR}tT=%7Zn zxu&_?BpM)`>fm$`t6Q?5FUnMreD2q&sSXw6`7QBojHKs5afhsuV|$lFkdrL07kA;B zSoz56*&2hVW9vZCY?U{s>!_f05>Kqvgl~v5BmR$PU)tg&Zxw~lV~^=U=1aE!2`E_Q{dYj&j_%5CS9WooxN4j5uFfh+$P!FE&9_!g&(0fnu?k;ns>|J1QoqO-I!b}1I{`XFIzk!b5% zeQX?TETDm+*{r^q!eYSCL~pWp3)#U2+8~#MsQH%aek#g)Wv`;5v=aU%QSLUx_tC8u zLFhthZp`-B=atr9#v12FScVO?Yv_qm1J#y&!I>F7$}2hjP>gj1+M(&D9vwVbc=lmV zhGa2@AHD6P+&*=Sdice>C??_>Pz!A|^##hJN+MD=GvY!h=iC(}o*I-J{}a+Dg1-Mx zll;%e4Itys9iNi=7Y1)apG?eUnx)<>c-E|Y5Y3hFG=9O8lbeu)kDrg^czbk_<*+KA zZW{Jeh7v;;nUE#GW{>#jeu^4advy_Vqu}&m=HlD?PRGj!oErhl=9x_4QBIUz`EWeK zhj)4k9NK}VQ`b8RCtE}(9ttyM<$!h_{_vT^i}lnab^>i$T6!xEe;s<(qB#SP8lZSB zkU!;>3}XH_&n|i1n=;cq>pv)SzRC;-n`_F}U-haq8KJHx^i*OFb8@T}cSxKf)meU- z#HvUKA$>X73ua=n^!vMJuoGS1#g0MS9eYk|OodzbcdK2iY@G#9pv9V!1Jx!374;g{ zw|;urYFBv!8eh-*Az`d@T_0b_+9lO&#mtfhov8 zoo^#g^~p4L5q`4w#sjTrRKFZZqLmXfK^gyH&n&L=x8Mb{j_pxqnU7DaHVTUV$^Jbv zmxM=bM!N+KN%gv8V~BW`Tw5L% z9_S%t?}rO)`N~-V28Z~)Cc?NY5!%+N;bCj~*bPQn!|4qw#cjR? z^MOU%inev^0=h;Vcc=xJMd~liv`RHT*2yzksicI}%fDueF#c5_VXPqRZ)&-aT$V~= zg5flzT5!;u_wFLMtWac97JRTYBQDF6?lz>)l#}wSk41p(26%~C)>D^?`+Q~><*%1D;a}mnmwixBB{<-W6t|68=uhSBq6fCF^5u-h8x(Zsg zm)*OwYTdkF$s3tSIEV{#zg4Z$KJ7N!^Vo`3G|G&nqCLG^5YR5hp*p?tC_D2LpqHPs z%`5dSNZU%>F#MtLK2>A{?<~u5(mX?54WATgRQ5O1eJ)zwfs#=a*kdADx2&erbyR^) zQT=k9&L?_LbE5_)rrBz4UWnWX7>_48)4GRxb6BI%qleWv)U($>f5t$JD~;gk?^4Bwvt$R;>5qg5PP?hBmD3oD2&N zxYQ;-`h$_w7Vc1`L=NHpSe-1egN`@d=Og-ilPZ~gQm6Od))u3ZYpG<+Ym8Mv!M5md z2eFGNxlJ(vdd8$$d3JgdxphFvx7kFU5YJv90WQrkY7P?8(tQ) z=)ftX)bYTty%ctTiBePJIT>!}Hi{3UJ}7QO~y6^-8KSX zFZP%eU*vT)rDh~wkdQ?kYOxJcHHLyIt?A*v$DedpkvhREGYF8(-Nt{p37FP$^sD?jvvg`e-hlF`b?du@qB8;L9Zc9} z=!`k98c;US_MN5(UFg5G){W*fJb_y^GwM3%GhZ?K^d#q9;XP?9b?SAAcfx@E{o~$Y;yP9`+9HW?5$t z>2GtV5T!|Gj`Pm7o0Gp?=5%165^+rvHTJa=d0tbgFrh-n)iz`10CWj4bN$^L>CJo# z^5@H_t)J}6EsMH3TB@@_4XV~{*FY-MH;%d((D$s1R|oSlWsk?>X@y}v}iTy9-t%HrEj3m*C(l>R09Com)KhY8wKVLD&c z{Y#Yo_0ePO>G=7Ap<@h*(kx_^*Yl3Tx9Zv7{E|AA(I26pLVf8ZfV(khIzc z6<{1DiR4x-E$Jt)0R9&<`hzmFW>~MGQ`J$x_e+0>6#4X%L#*=1AMAG6Rtid`aFPer zAr0@P@-jVs05Y`zqK{dHV{F=I?UL>~4Bs`K|IN_RKlxx^#?!NTn%Jy#S|+CdoHWrJ z25NGWv*0qBihCcmKyw|i8oY7>Qxxl**?3>!B>_ZI3TpaHepx^QsZEPD_uOnl=<+R0X_ ziPYm4HqnSZQPW*r^Jq2ql~WEOO@f;7y#0NoYfS7orvkg3^_}1=i=kst&+{uK$IA<~ zn=~y1bJGd=8Hi;9YeN)~`ooHV>%zG|fmOpn8;mb*SO_nUH=%!-6`hO}i(}B7U&;k( z=CixYUWS#>w*1!fc86wpr{d*QO&qlyN}k2^jjxD6ClX2+7Cd#9e_Zs*<}WF|qp(?N=nLk6hxANqlS0niM@RHQtLG%>fam`X?0Wik3^?`D$MJP z8faj}V$8!JzKv17D_`glRSb8dt8786T{Ey1Wib^(;jV zQwLMH?6CE$C{nAnFcHdWMquS&Z|}Ylvi-wt#48@<&(LTRC|kDFs|Ekui7hb@gE}?n zMr!3a=qWdr{3^nMo~-x~tNx}jS@$QR3Z3b?^7~6<6#bV-`>Vx~ulKF2ivd0$ZUgr@s^cz_67LTM0p0!?uqEwkFK{g}^cdvvqEim^P0?D{(cGx_T!;7jL%#syg! z_O%jXpgydyBxWZ0!My0vXRK+DZjlzcg=&+CsS+D>{Q8vYFVX5GV^F}@dul0sIckR% zdvfOYHAwnpo-P(G7=bZMh6qBA&ILh>W(}0qkwRl8mB5H1YX;FXN7XvvaLGnUfhZe6 zth}~ryrGNBb7;;?qsIJDkd9WYl9*wu)+ zic71N3K|al>YfzIKcC(c4Fh32&Gb1HSckjGsJBV3i(2RZ5|vN{wXP5e+`cg(eAUPB zWiLIm4mOL9Oy9he-}!(k|7eql9hd=URW6)6tQXT!8_d^RSHy~*QHm>@ZvMwp4 z-}=uE^nVR}XvHsiSy?;cF1dktqhhUGdZBqn+e%d2yo2p4aMf=L8|a+W+v0?#{3#V} zjO|hTgcYlZmZl#lb$~hg%1|baMT!pt=>%RUl;G5c!+O_SBjWPzWDblQRToePXNx!P ze)QJ2;{2H3-gCs1YuvG5URGyH`E4-Mb&k63zWFw}pz{n(Eb`IknF*xyLWC(0`vtF% z66gR9Q$eMlXCy(a;5Nc^rWI6t#-Z5CUfvfPw$kzJKl^jb=0AsA1KNinORx*pw8!Au+hHV(D;d;1si=W$o* zf#Gf6jl=iubJhM+@h*nIN+%10mL9A5qkEHP>E7Q1GvRpQu_1a7I>4$ca-+S4TgT2` zhccqOJ7hR9CMkb?;yXDtOSyt{w)E1da`7+C{mg>w zhp;E3rQy)t1bR-GI}4~)Q}f>DVzC{~kKU7+5QJbynYxJ$XMS?Oa^8+s#Pzgb({MkW z{ft)!e&$H|%S%RJ9>DeOssvw=GiaS2znN`^q`hBgW`RfKo8Q|BloovO!^_fN-}Wb% zCO3XNNh2_q@mzNcL#_V0ztV6EPJL{vUxtV$EcvCj_|0#du(JUGkG2TgQmo1xR)yv# z^^;DXgc!@xCmnoLpl_@w>r*H_JVq0rpt*zp>*66{(! zyG|)bkK=Ri9fd{FR_-_vZ~6`=Qk{h@{gudhv}khfVw{715k z91|aMU+HHf&xnWuyfgBzDw_z5D$mOkr05yPZDxVAHxuYw;wtY?mfDU$5%CSb90%4U( zAHe_$4 zn3Jb-)bTE-a>~&jkZ)W6^4P#j)mFmMPtTh@8ef~7>M0=Dan>5w(H859YXoupk=}NZ<_5O^Z1OFg7zN9#68tmx~Q-z#h)Pb6eV9_;I& zU_u%n)!Nbad|2U)mG=qh+(^*a`o1!k0lU zH2OGmxA;K6C4m&=IgTZ;Eo30g^@wB7XB5o=lM-nK;?ps`qEC z_c%voO$=rArVqI=1H@vRS$)U6o7}wB1sUAFjf)N#R@tSBZj~Bl;988GFKc%ePpfbP zz*&KeEt>0D=T(Yirry@9;>ne(LJzZ~$!99Asw?KhAq5t#&)Y6o#qPIaQj+FaIhM_z z_8^byb%&i*CJHP;-=y3w3FS{$0MU}s{8f-<&wu3AIL66qy@`mPz!#L0y#Sq42Wi2+ z7=*J1C=n2+T$TjHF=Vm_9_lPtLIRZ(@1K9jh|PYm=?7C6GQ*CaQwq(OHRlx#$kruU zHlqTSqTkL{`BYo3>yfEai_w@n4^Xakb&+LF%M%A{hA1`0{*X!(-jpCAQ#Wxsle|DA z{wyKn&1~}J1S1!4?r_gacC~7gmDLdbF0^yts=>()GO&&;0ifpb&FLT}r~Y;n2d7_+gc zC8Oa;MqrCg`N*5r)I3ja_m16a6q8U2xl=&_XU67xiPzAwQA| z=dwILJo2)C>g9D!d&!slfLQOsm|=gAOtsoLL0>?j(~ncmzvYbkTT7{POv=VT>@PY! z^l{l~V~1XD4QU5+C;?;sAz#FmF^|~P8G?>fl-FFZA|{p>YR{f$uj5wAqfS`!D?wCL zzxI8I{;gN>#C9dZsR0EJU)jypJ+;hIzdOImE6-F zOJq|zbUyuQ5rh{M``ksKk$uE!f}pJ@N`U{zr zQRnO%4a(fsJF@x-JtKRDpI?CfxdAhnf?Wg_e0QeV$ytvZvnjeAboI>ee26AuLT z1l3gfE!}LGnZ@&uwM^sm2H{mNb?B1vg|&hA2L@i=KJ?Ig1n%pmihBRw6X8E4#&@k~ zn)$=H^-HgJd_ou}9kc#vm9;_f4wc|wo|-Q3khXEj0wTC*Ne!q@+{ z_xXcAe%?@U12L=Bqur?Q`W?jK&*As|0>-S%QaxtiHf#vp^<$3ii65c8i6A|7`$4_L z*ougV8wM4g@Lg?t0;A$e?4ACS>qS6S0E!kQuN*F9k_gds_U|sACN=N# zKK1nMHokd=KDg_Cbv1X@H~H4j3cL1j9BLvi+kx9m!VW3{&Gk!_H&sjYSn9VzX%u&v zl_rqvPydvuQM#j5Vd63nOUHscCYKp)A}5@BN3xMnjd>+w?pkg&34=1YlxqfoS=Dvf zaI&Bx)cTmFyF8>YLqN8EE7$L0OJ`2VO}q7Y zrw6}B>NzxXT#n{rE7gyJi<{209DZ^DIIotMEtQre^Ax`UTLW~X)Zfh9!?M>uzf&zx zbeAE?^N#H*)6YzcUD`L@V(MngXfmyB6qn>iTE9T59%RdA!ZhLeaUdP3jUZPK_aa33 zXWk}p^X{FQ^83!6s~zdctPgN#qL=rhY^{fLXt@$<@QOUZI2Jz@9c&uXk$#I~vYoXHGg=$W;#-(K*@$8Un!FAHlDAx&pJsw zY#5;qLa?y#lfA!rdk2H*NJh`CIoJ78@ev#ctmUM;cA=SXI$;^5l@}QL+V{T457&H7 z(=GO8G51_@GHtb6RJ$m=bmwjzx+w^Xxz?>*B2{kk*4cYv2yJ6}8F@Xe8Y|4}w80k3 zt)3{P{)=$9CCIOKLqNO@y{%hM-nwK~_miFk?A=7JQ=n66;#pXnrD#%l4x&*qQ(cuq zH6I*}!}!(ZIEdm6h`%~*-Ng?2?g~A8rngC3<@2SIpurZ$81voUucEc>fV%%nD;WJKGcCQ zvW|L_rm7Tq+M_{HS}M6ZQGJ6Pe5n%wk4=)2+z%?B0d}Wl8`5{OBdRnhpfK2-X^ zc9LghT*bFG)-muF={!2Q+l2%1^!dS;JfIH4O_r3yz$oiiL`0!}X|n-icC$6V4aoK6 zSHt{?9a0CbWItwVe6e%CKSp=lDZiBB-q*yuAhINKqFuMYXm)N@`p z=gNF6e?GS408kr`*bw;*O&Lu(qj(TB-nb0Lv7H}MqVCqxWQG_fjwbFTmW!d?&4wUg zH|$bN+G6TPAk9z}h9HdBKBHsQ=^-((@F&NL>g03ZJLSb9Yh{icsPc-v4jmaxy9DJ_ zc)o2b&~bIj_J>DbyW59vQ>sb0LJ%lV7Nl4v?kP4j**e1k+L-3pDOuU6>GO$jhnf5+ zD=~YrxL_>aU(AsX=Ml{}MwoHJ;UDGPKnN8@k0T2UeuJf+HP$DPg;K7cWc7IZfXt*0 z_VqqE6R}SooU;R9+@|ch?RZC^7nVAFO4DBN#;fIlO=Qt^7S9JeDn#p5k_wQj6T&Nf zA5tl5`s@GvyZ!Fx~-Hav|3Mw@iXOXrfXxjF_DpFScrbzJ!SQvdO zChq}9Rx}HrEJH{?p_4${>bZLoHcdjPd-$lrw+Qn)A5KX_zTWt_a-kR1DceL>-0144 zS5j<%=D8EK_DqKuwwVc zFVIX{#c^NV*<8Ntkz!z4o{doB7!P`5cU(MddM~F`33{p)-Q5^v5S%<#Ci=$=6f1G>n z`Nx0m9e0h4Fjo@RSZluTT650#ectEE?3HT*iWy{&_@}pgiYWlU)V#JOj{ToROa8qz z{@G^SpcuLkct+n59R7HrF#Xdb&C4+~$xWECplE-EFPA#oSWT zSAYWFd2An&j(*wZ^Mxzxe( zP>-`ns;;u^76qwRHX-BMd4hNUP|RTqi^_Wwjw%Yqx-S&3Ey&9CY=LLY%>zqTYdR6C zV+OT*3r*58$Gy!8!8rh%Hx;C>IX}nh9nYVs{#MZmVxwNdZTE{toF556FSxn&r?hDD z$HYP#jF`)kO^6u^rfd><(Fy}ziloJ!oUmIiHCa=|{C?tT@s$M@Bn(kASUxX>(jtUi z(_+nDLKZ1lC3^{1=EV-abkkE_Hd)`rZv~K<&zFaVgWsS%pFMI*6c{)#`>qgga!U2~ z1q2^=4!5emOP?+{DF@d3&9a;{RUg!OOS}I!h2RJE2Y2jSq6=2Hi zX(0-Zy8}fY!Gm?&q;G^eexc<8e2Nv#j|t1k^*nO(>zz$QYzzhQ&4~6xdklMK<1dC; z64<>HxXTJ*x^J|{075jE9A})$4oC4yIHC6~kVc6%#+iw|^4!~>i5{Z}FneD`J6|{x zmo2=k>*U@fq>l7l@mFI94<@Qj2l1}tcFoqjBNv5aaZT> z2S`h&Lt&%3_nGh9gp!ED@H3-O`R20ScC(V+fgkT?+~{y2Ai1%7bEQ+cJ-f;GjZ#FZ z)LG0!Db90RXUz_izEJFCfK5Zxn|L;(J&Wz{G({&bDU zDDMK-N~u)uyS_{eJr~TXKB}h|X$ub!OI{kF)=S%(&QwgCDE? zbgGLY+okvHuQ~#*{4iDUxRI$-VqTt*w*b(0iN&+3T9gFHxS(9D_s5i<9wziG8OVnQ zJT-K$p47$dP(CcUO?#QAN9?&io_a@T=&9&9K zqzqDI;Hfvyb`ck=+b$kmTNeL)F>hP(DKQ-ZGaNvM1|Rh(;TP>LK&!`~voLu9dFOro z5cfuWBZGlg&)Sl_L~qF%mGWzqyh(umTsFgV!+SN2rf~u!92r@74l8g{ zf5qlcqp~!zE}+J)cMK!HiuWq7HaTmrp}E?B9!rlLh5cBPpC7SIbQ_+1bhT5+L`MQJ zB1At`I*i^zuSY#OO!N5A^BC=Jw8PDHSF^%?_+!vo`uxKAOEmK@wz?TN)9;~zbs!5T z%TyhU2NzTJbcX5PE@)Q74aBr-u9ko2!sVn?vNMl$$lBB;ZzWO|>lFe>VCKjo z>6vBcCe#{gaB>`+T7%rqw{EIKiu?~AZ2xT^J>{bUu?0G_4%!H(ziwYqD;_pd-jX&{ zlFKP=(298VHn_m-aXWk&+t2(KN*w|j0J9e0 zQkDZO$81_+uhQ#6PcUF;!xSv+7CegeTF25>w*h%M+Y)Yfo!{hKFAiQfLd}8*x8mu) z*ds;3IKmA;jZOA42p6?sy1}&mwp^y^TsTl$RKC+J%%sLXpg1FslGHrBPf|>)%>?Bf z62Eo=l82209P1v?Aqhd`xK~=LK=J6Z!++QRmuzQnS>_+{=nPl01Je<)8@KKeIOsDc z*|K3jYemapnLw6UbcuPvjZ^oZeH%f_v4X*Dukz!vBH`ncuBRNJuYZ{T;cdJ&PAjD5 zG*o?U)1UQXt?F(3Bm+;t(jbWJ5)!>?j$Q8R*g6 z6Emo2RPPK*=t{hwS=?n{3_TGPH5Wpg{{q}rxYlH{*k2qIRciZHo4;g5y!*!om`$}V zLj0!3j$1tf**!x%)3h@2;NOJS{b0D>hy?jULf~q_Sxc7{ji`NxAt^wf4AgE!N?xxXcWE zy7%v$1LkdGFWgTPmZi8PS0ds0Q?8>jIGNg- z-*`a44j@${YSVSgur{UVv}BpKzUTC&Hx{`bI4s1sHQhb9w%v`dpt2U>s>?_GiOYNz zrSWJVWx#{VsMQeK{h=1Ay>X~kf8kkU#qE#~`Yr!My9ag6GPN$hicY;UYJJW9muD0|`p!nOOn0a~w>I=-yXl0__GojPShPrAsvarOX82r4(Q#KXp-+<<~WY{o6IxkZf z^t8VDIDdK9kib*+5#l;2?8yogY2O+-WLOmR+R>`o{NjpSwG%tMzd2H86dbBZUAsx@ zMmjWn9Km~gU~^jL8klD{wYOYkN^6AiE13=b#@*mdJ^&@)TZ)eLTNfPPGp9@XT8_zD zWNvW=sr%ov`)&JXbIyiV>gCMNUa48pPo!V@(c>ht?4j2FzKqIT4yLr|&diMMK5G8f ziq4&`c5g_e4PZ-WtF&U-6Wrn*JBY!D>t_sGK>_WcBQ^i7=Fcvp(xYWdQWkt`yUd=J zyZyI?*IbS)nkmy!v-U>F)X|@paPj6S%AfowQ`}P_bqE18dJIPXx4_z%gM_tZ?y=P&=(f$*~&U)r9v~tY}K)@gy}qENs|rxDmsQ2RZeplHIj)) zQ~Fx~OKR*zV7Vk$@76Lw{pF~q$@j+FHxj-s2`PVP3_FF`hnV6ABrA2fAxwoG2X!xl~z%`{B)8?!Gv zH;@{JW3^QSu)L(M&bLz8Eg49e4Olj@oA6EPm|qdLlAVoe$IlJ{W&X~URiw%wMP+1E zYC}1Givw+PP`b-|w6$g_Ikg79vzZ|W{VN!H;{J>MmjN8FH@pBUd{kl>`9EpSq6VrV z(v`WTP*W#<0qaddNH%6iI2G7T>7u;ilZkmojT*ByglU+}lNAHXcZdx&4POVA_KSX} zxnyAHhDFUZrjb~M6Vfw}+T>?n;l?6# zlU5#)+r@xk1^&d`3UPGN!uz(W8wI@0fRii9ZGE=rkyaJ@>#~8@zC3f_e6D*NkyMMK zm9~n=D4z`gt@q}Mwu)8Od*xN`_h-1F9uPG&VBA9bcoWdO;k(>_e?tHF?p^*p=uK_0 ze*4QBA>%A@XeZ&fFYtBa4S2|~qkcxkK!%9SksehtjyF?=quC0wLZ~`}N!CvixwO&| zESb3;iLZ#wI-sumA6_fTUv8)j#zvdz%UCAz-Au|@)zKS+e=hq<`8J}3z05fWOS{8J zmc?ln#oKgjn~3V>-XI}5gv>Texf-JQ+i)N-<^UGcDG@LOvarqX*R14M*;B5~)K?WX zj~P)ojCOz8lzZIUMBheMXAu3XfaAJV3<$3CByaw{$|X3G!6+|3J~q~IO25+X>^UoM z;N{ET9$hv#55`G%I5vxsOCmv{3h-FTwA&2r+e#E$-{V^e0sV=vbY1-(C)9=|?ph12 z(0FVEdy(FUO(zj@r~_k$%;mPD*Z=D7tF@(Oq?iCUwv-TU7_*UA^1X~htj0L#K-_`nJK_HE+>eq3v#vDLh_cgN$oL4*s38YXpHe7xaIa%$eD&nxL%BKh?gR_JGo z|7SO}`}iCMJ5Dl#e1cPoSzkSeI8p;v_on4N`m);?Hz|o-%}JuFa%-}*Sz}b@M#{*- zaY@DwWjqlD(b8+IUW!&%FYRWNs)jA=c%QS|OND(^!acYEqK7~PzTxhdfNlrJ*Akf8 z7Z;Mo&aahb_CF(OXC8Ka4r~h3b>E|}aBuW&{2n?s`ZXhc4B;@DF5DvUd=qHmky&Zt zMtV|E5l6Upc;xsY>OpP0c};J)_O*nVckedDK7?RLvjjUuubSjelBSS1!ryOC?aJUA ztD4S7o@aY}9;H&eO(Ac$H@dfB8UB*!ql(IZjeY+){`%a%Ebv$BN-71~5$}H6reee2 zu7-h;xIV7Vo!)F?^y)HJ*hq<)m$P-^ENl_C7+$=fL%(oe2scPaeuEi`*9_Q5Dz}so z_dyh>KG&5qGi3Th`cm_1-*l z-c5rFl~wdAsTO8bbOlqxQR3LWTMpaJ$@7}tW7L9=Ry7z@E7x$sha3v$H`XLofj4|{ zA0vAf7j%nD3+P3(*gQog;D6G*o$pj*i;2uTW+roc#Gmj!{QaCp6)26Acs#@_yEkW`rpuVV>wmGa^Msn;h7mD2Azj&fE+HX`d;wAnwv*Dn zaulav>B^(?(yR}^=t{yW5PQmE{p#;@{?PG#0;)88`RnFgbZP@^S)phSE<2g=zTx0I za3kdBp@VL>O|Sm<1WP=U)dnHjAsp(vEA=&J1T(2qZ%Ztif7xik*ejUK|@mEdlC zZUhMr?{!MrjVlI{wXT%|1gpbJC5m2<@XJnDiY0{ei!|@5O%=2fFQ+i|0+pc9}bgP_bME{nne02oJ?3x#HP4-el z=kJLhch{NaU%hN18~BbVBjs*m!H;BGWcx5H!lky9s&3toJYcD|>br6d6|hyBDYA6o zr^bi0K@e5uEA&;NiaKO_=ACnypj(^EFE&YVy&qsIn zl+qFj%oEj!hWO?kVPUNECGlLs^`COoW4)!PpbCkGQNUv32^1Jz#T6GI zDFw7w3bYb4ktMrugXm$R*J7|-df8{c|K*DR`!?;Zk%cdP>@P{rmf>vc0X<65qWiqC zbkoO{pm(mv`bR@{j#^>~Z%sAnpbDFc(M$+tt*Q}`ZubOY&k3|0S+QA6`p)PMJS*lW z)(RHt#a?@T+1E4ZrcFA>p|7VL6PfyOyLLk(4-Kj5Rj9YlK@EHXgc{mq&}36)1HI&*GO7`Ovwh8aD$$klK8PFpIa zVGFVa>T7Rs%hkr{;$#yY-q!}UQN-F?b*al~H&wcnrlCd2r3}qY9}6Dip&3c1VqRhf z+I>)v`yeCvzB4OOO9h_093OFs$}_&cS_`@9YQ;1?IpVZPF_k%a8CvG!2bp(BEzE6K zZPjF(2|Ez1Tjn<#*$Bb6(t>2zuw29+?D^sy=TbisKk9r&Tj9hl!B+e};qN=$;yVlw zKpvU3U)jWMtzY&^wMi23Q!^`LOL!FSW9yQjbev4HP7yhMS+Ja`-cuvdkl^CMB7ApF z4Jz`X=jK2pZ@irDuET^0xioko+?tz=eznA96SQI3ImJfV=k)8evY!6+p4+u)wpR@$gyIY;u1-e3 z|0dBvIXa8sFR4vC@6Gv%CoOO69m2&j3MP zaJFS4l`gOf5<->Jq`qS4R3HBB6lrL#rRAMk>!6Z3x0CfthXi{W-NHrR6+KDFdqWoCO^HM{!MGXYO8-AfgGZhY z6Sk-7>gvi5byW{I_j1sUAy(y@vc~=`UszxQA?nfz6DJ!0@Fp+ql z%ean@U(H5iQBjXyE9;rh$T$Uj zwWvEjjjX1xLF~Sf^_qzw)P|bSh3a5iW z$63fHVn|^?rOmso4WW5(u(^N7Ys1k#l{a|;GqAi(v){K#NEkNx@|!&&@qB^u@mE8h z3{pl^I5g>myBAyBHGSBcFWKGi>J!mY;$~8gRTn{#NSASEFLH*%bHtKJ8usUQ#%%r zPk2bjXelboKS|$U$!Bb z27Mu(_{|^tuM!EiWsK!8<@WX%Lxs1;j4%m>$jquoi$sLJylrQ8*uGK>O76o@uBMa8??E6*BZU3TN@j$%`m2WF z%Eu+SP`!racT+J#h2zQe5?m4+e>3yd+LD~`a9JxkmV!JXUE^h-oAY$D?nCoA#TgibgQd>e~WGA&B|Jr_AO2qDq9$!yq1Bj`An|n84O)@%*1+u<= zv`xmpoviSm)b)`O=xBV!i6N$A;Et|(>*f0C^e|OUICJmGOZH+R!)wJn@#u)c)TjS; z#Q*yH#X0m4I)ErXEG#{77~)a5oMs5`;eE6aego?0s*3sjT0p8|2=oC@wIiCCDT+4B z4Et&(I-XOY*tfRU4t}PJnT&-`0hTr78bXyUcV{C41^P!xnzQ$)Gi}!FH>yv%V#+*! zri~cR_-M$}Y8y{*{t|)wN%JSojb7={ZQ+oE7u6-_rWq`Mu=^D?C8?hLz7kJSN4&pm z$QkqiGumzOxvViuJ05$4-mh=L#0mv0G&=Z& zA}zhH8Ya&|mwb`KcEdg8)zQq&s^Q*`bZh_#DvReUCa&qD6!ht@VCEmH%a4aXv^EX- zT8ba{r38f+y9NIW4lHkBNfz2r)y7HuN#nycU2ELA-wb)OXY51k<`8#sou6@~{KL8%^-)7xFb6_H%AInJzYG7+*oD-nVjkymoJ!XUVc*8GOqrKY1udpJaDOtKb>8 zbZV{3&r(08k?-c>+a8Iy-9S+>-i9~HL)QD8t}S&#XTQw=YHw=+<1b>j$DlIWseT0O zZa;SMMcR>dXL^RXgKW1D2VN4z=skks7)+-ivCY)>GE>-lw_3JvQWT%2ZI)vIQCtU} z>^H#=S!!kl+N$t+Fj?V7%1hvuccLWFH|*B=>9R? ziLr7RcaN@TXsk?J`iwMh=8Sg5h26vykH(Ol9Xqn6 zB-Qk~b*s8wh?8(gINFzRpFhyuCFYKMxvo109sTA`Wvfb z;&FPIzL!dsO@Q+Xs;u|C*WF{xPvRfZ<*cojFVou>v~68}aAp-xn)%AR2R>|mUwrNx z0MBD4d;D-8qei=0eY5-q3Mh{_PJAF*o@(KHGR$bnJxNJPdZ&FB4qhA=z9?|YAZl!F-BPbeOD(=E&*qDyBv<^#Pj!*v89Q-G8}SVb!zDTtd_&j#BiSXPZ}pD*_@GFIWZZns_|>>Mql0Cq^r?IMlN^? z9~!^R%yA+2vrDglQC7l~?u+DkgX8id#V@FAO>qL~hFoYRel^W^5 zbRbTpmkA873X&ZT;<%y=oLC36mhBzzYkCvYQkQW(XTpZCltM`+OvZV?ne~__=uYk2 zx!~M*3}GW&5w*c=Q~8SufNb6t1O|4v!Q$tJ`L7Jg$p?dLbc4KD@rx1rrPGw%4+hV3 z3r&qC2g!1(+7$-rk!KaId}T1hF*`wgvuQytX)xfu8}l?caW$md!+k2*nW^!U&-dPB zqyG|Z7*(chT>NB3E!9V0SV0hW=36iBYfw}1-ft`B{{Y3y7)f#2)gdf! zo>bOJK55i==YRHDWZFW>lukVI?}5daBRKBy*ps^#yZ+@1|I3z&_VeGvPIDtkJ*rHC z*s?!q!d_DYfr0|cM!HF92Wi*D&tBiDr!Bzl;om?3bMW-2vrl&>{+CDn@BG2-D{J^N zLJrOj>-iXnek%6T_qkcV2u^p<2@iez_IRj>CZM$Az-@-&*RMssyz*m&2`K%m{SD7?bZQ#SH0F6+6KX*o`jf}|H3 z<*<7(W6VHKmWD0#1VI+B1ED~;)N>+h4aW^?;}NAT-1*7S>HRG22pW#=yYYdG2Y@4DX!`YBBC; z2tZ}I*tDvl0>GEy-5x>vUYC?P zeFCDS%>xVzaXW=GM?qnxd$MERPQ%{aCL4*;8bj5B8_K5KPQM)smivG09eckfb*$&v zEj=qaF%)5HHx#yvKwD;40Y}O3{8<*($K#!9I@fjnWiLAM@E{xT(9}C)1zyyridmuJ zsYm6sWdoc3(+pOTXE8=TJOb<9xMK}KfMsNLD4p*f-&Zqr-u?PUR@3NkU*>aTSo{xm zp}`mlL7zo1dXo>J0w4Bq-F<4wRl-x81-&lL&6`Tgw+({!8(`8ZcA6{>>{&yDUuyN0 zm67pq?)3J1<-ASTIR`I1r}-@DE;;&wf?#v_M&IZznYmT_^vUXy8|u1~f-stTBD#qz z(Wx0Xt79{R!+vH|W%r2Vbgz6&yM6k$h&-y`rHX{LTOztXGGOZ$#^1nqWCK}o)h4$1 zoPrR1Qid%&#Po%JsteG@fXCMaCe96b?5GayQL3uGdWoDTZ9**W_Xhn1G5GC=TJQ*P zB&{Yc;Q+`&UBA+?#9UO{YWV!?rDA*hGsOLGFDUE-IfA-cwM%C_9p2BBb%vb9ENPcn zj%9xin*Na3dRXi1F!*`KruvVS$F3^Iw1^ON){(gIhSRuNwx%Ql#VSLL`LCmyc+)riD zW7g~^Fb*f=XPK!tmB#80)k4srG^^3n@sEe+;gv7{a*hA{T2x9A|HOKOW)Vx3`O3%D ze80Zvd?PZk^7-kpJ`Zbs&0Kb< zy#M>?TC;){L^5YWzSYQTSo8Yg4-(>xdjbt~HFDF5p@23orzdHmjj1T5 z-XzSPu_3-VgI2NJ36M@5E{=i2I3?rVlJLuGv~Dw11xt15)21XNLyWg|TKpE+@ZuxR zi##;9#RxW~QzM}EucrR*ORQ?`vgC*NJSAyESc-c|YT{B7X8pE7+gc+`PO^nf&S|RX zXTumm9deoE*#_{{-=Qitts>pU7+eCG>{z!#uUZ^4I;quX7ILHfo|s>jP-)ml@LNjm zNryh+B@L+ubn~v_g~YiF_H(8Ylb|I8Kpv#;?dz%klDeD|{Fet@5cr<{wZgX1xTo@| zy;zYArUw}~gs$Qp*+f|mvuE$?aPLYbA#cS~?jmr$lK5W~>o9cDFs#2R*R9`4s8No~ zj{-~k!5~x(&|-qzSLl;egBS~o;KZ^-ua(C#d&|tr{1LZ#B{B{iG*x%CbgDWo5QRKRUqG+8Qn6LKYDIK zQeX=q=xM_N4T#tjv+0V=%|}*@@I3n@(VtgX_ZckSA%`u_+j4lf6uqsT1AH~bg(EKY ze=+OB_aWUa1FZwnsx07{He6z<>$(coW{Yew=Ur5g1r)8DL1>%ndZHyH>|4-x9r&8s zH#~vM7>#()hfKC(_CoWn z3~aeY2DTVds~PaX?3p-#$r2YYRj5l;pI8O6)aH21ifok-S*GR@HFO6BAN|V!)Yc{E zX-MBu%g0+bsi zyI7Sw5c)dk>K0e#_MIiQGvbc^v}PFttNU-16gz#kA|XN8{dgOK&~-K7Qhp+bx*D&v zR=C_<>OcOtdPMj!sVFmZ-D;xdW$%|s&p8N#1i5B}k8QUq#3GeQ z-n6L~C0pKDN6LgSHR^lqB3Sn_V_N+Wi0d%e&w)$Y&5co87dlN!0FY;L05z@*d41}z z4*epz2hf0av;;WvJ#}pAiaXYRnIKNvE@8j0k$d1x1bCeR05??Us>W@G&+dHSqXy*s z-7J2N5`Ob0Iwc(uzj)N_3g_?@$lEz)By#Zon$T{v*75szgaVmy;imON6=`8A6>j$RP4UuR{MjekqfNR8j7wW;;BQ@%WT@qQL8NW^?C>+-)YKXn^~wtbFXynIJuOkgdq;8r^?l-v8FG4t0{+@SO32a9|?f zhNngl6(-s-W+^BHT-ValE=YZ?F!4>vPVogf$$zdhpzq^yQNa3jln|Kf1H!NI7H)h8 zyDXgXQ33sSxSdZpgoG^~W#wV_L1scUoCk$O;9L_hvbmHecAh>3E6@Z4D(kd!^RNEn z0)AUc82XF26G<;Q=lJ{vX4-b#OW6LPet(P>|DYLKF*;lZX_7a1B2>j(4&-gNE2u>? zCN+Sy#f1Eg${G^aA;s}xghKVFZ{U`RtL1!0z);C~{jY(Yf7E@Xav(Pk%b_3#PC;CO z*24+m?tT;7R)$wiH@@6<D=MnWR*(yOFPpd z7zO0vs@e~Ua2FP{8bh(nmG8>D-OC9mJd{nXm@@a3Z!|AF76bq$1wgR)O_yWyZ8CT? zdCOe1??O!Xj46>JY?vd-EBVnEQZ9&)z6#I4HY^eSK=WVR|pq8#6xZ2rPXSZ*1_-96!@8`*q6 zR_{eC|8=oc(L*{G!H3B<(fO-4dCuIcKxZaE`}|x=Q1@o}XVGZEu&<}6^G_cyK19(f zOD_wGS=hmSOp_+2;@dSB`L%grIz)nv&#`szs9(cgqJhZyNYO~IqgM9c*3)En*)d&i z5O!A$rmGfnLZ!Hwb9DODb*}sR0%Ct`ayt<>x?!%OjCZ!Rawu0hSSc8q@GW@O=>@bP zo|_lSfHb4iqsYHx#K{0_s$y4NC>1eJ62(fkRXo*Fwz}oAo|+E$p^) zUEtQRV=%l_tgBTfEkc}C8IQz~+RbzBcxQ~w_mc%nmLa8_$cM^J%{b!_j7{TKoL^MX(}IWfeWvl@ zIHlbJ>_#8AP_73o%__2+mG?LL#uvvwX1}|i!b8EIKenVp>ot8;1-7bp!`BXE?}2fC zORl^%uEp|&aMxAgLU)xQ3xCq@R}Vb4!Ve<@FH_bsIS}qMc3rZ|r3F|LIcgE`9>EGr z1oUT8xx36nZImb!w97CLf)Dp$l1BW0u0Z?SyIBkRFCEf?EGp_vf_T#l60G&6=w44A zK3CCTuqUgZu;HarV^wZCP$A06DQSxsF_W^zaLWRx_r=NxP3PvOwt9dNH#F5pkBtj1 zjBno?I8QLBYF+Mu4rw(O2qSFgYT@#f2S!mxpqfxYZZRjR8 zy(%**)C3u1XzKN7IkW2YS+MnYVkt+ao6*TH&U}KI-9+)*rnxBEQVlYmwSXa9Q9=gE zK8#q;j)vr-R)A8Vo5z=`J&f2bGt~13t#nP?F5S>|Il0WnE|T%e5uW<%x?!#n?D5&W~DlU?~~9aZe&fF{C`RToH*xRyD&BRi{RYmmFw)%9DA`!ghq zwe@7|>|OYXZ`|60V)*BLz2cKNHptx66_N7#EfWizu)Xi&54(?J*VLNoL)vr= zC!S?kI|}#)F6pn&bH1s)5{C3K%3t@{iXCPWQWgAJgE0jj{rcj`Pr2>VTxK83068`$ zO>CRE@h$Iwa-m8wf!_*YYp`3m!|cbH5n{jho<`)^=dRoi?VRr-6*s-KJz3YfuxIQ% zTFL%EAgcwFy@I}jDQ9+BG7qxiJC-U4qO(9>|!|APwbzjXlpj}CSvkz#uZE5{SJ zG10Nj6PH23KuLdKVZ}pkwO#$v9Ra)20tetQ#x$_uA2IENDQ=~9mFBjMA9BvD$hX~| zmyKU0@E5rCTP9B?%t~45V;4R$2D=_ER&@0aggm%_2AG-{TVIF#Q}I4G4oFS)F?Qwt z+lg?3Q2-#=gqjmd6rwV>kv?^OVyokNUde$3FrK5MMJwNexrdiSasWtv*6@Rp?mHEm zm2JEAyk)tUl1A89T+NgIq!H|x$nqtB6e}Y<>%7M|Qs*uj!G~i`8ct(Z)%EJ4BBPGM zj<=UGQQlnB>RFlZcW0O76qAVi@d4i$H>x?S6^dj8wb!W7WnQ5Nuc?WsnBK0KM37M` zfY*{5L%4f^ce#1SG955#-f?eiFe_*8kY*^poPq+mUA{3D;JTt7&@kLq^30!l&wIJ{_x8XIp@polVcT*x-EDKJvhIvCv`6!N+4omI3XGoM8FiV* zp{6*tKWUMmAN(*xXHJP7{~37a&N1VHsv56ADr+OJy^09@2GP zvGM4;&E+a7jplQ7lv=!_{N^ufUz`~3|FB^DVpMWn8 zjqIs?l~$AvFz=2_x(2jeJC3e*9(j?;ys)C_-B_S5wo1{r4KE<;RHOrW>tRL58l_hB zA=j*^BMGKLJPpf zJhKA5jWZba0a=3Nb^TMK*Sydm-ZbBoS;<2__&m#_3*C*@Kiv~IJ?hQ2#im_*W^$+V z4~k^B?1K!s2JGq9`mPYyM82UgQYw)PmWaZq(UVVTR10r^YAo@Hua8QqXmMlBi*;iIe^>O+UIrqxVC#t^`RUba;YB`CWeF+kSej=~FBIaNXAIpN%eHwMBc{#t z_1~Yecyz~^EsvDH^=xe}R?{|EPBwS~;4?4+phX&zoY)R5hpZFJJ~6NV%x~67OD=C zHHp1`(x~*iVJuwB04$Yn@w%r0^W3&IEP)5tL{ClI+P57n`O0T++p~I9p>3O0&F-3} zEu7KM@@o(Z{Pex)U)5bt zhUn`3aA4uJPO1u6-X24l7a81ZM_SH06YW_-Mt$}zoiy{4?exA?_atR}ul~&%6561B z9ys@*TpVGkphP+4L$^NRZyOqJE862>+RHI2okfadSIKRmNZ&{R^%K{~sM4!WaPYwem=d*p~&tG~lyBVx-OZsaLB4TPghi*%iL2l=$TVVkbB z4=z&|Uh&gnJP%!Kj((WGKnLM!OSLkK^r8An##Y1atOKvPk3y&M(w}fzm8JQH-SrV3 z{-EfX)hpxp_L%Pr%cf3EPMq02zec8|EJ<_u=b9oN_WicA!69yU7mhz(hkAXok%lrn z9o^RzPN@|>Mw=C3gr~A|c&;6Fmv5tQma{KT%Yf91!4gLbnE40Hu9C&38<^m**h$*# zz|&@>*Z~Km!WRIG4!IuCIhZ`K-q#F2{jHjUM}^9z;O-==YEbDZvt z6BqlRz`ZoIv^%&(*r<0RuCnw+{o|@_vngYsmn$fX#Esw9_1z~3m?vg=t2c_?EK~rH zgEW;7y5A~Dp5J$EJ6R38m8?!3WO+m{ua)F*-Wp4V!SoeM{D2@T1)HFZpq2(a)eYkO zDJCZJT59z+#aZT?ZTmg;`~4IHGu(Y$mXVOKF<03{6qi$Px;x?08)I74Qiixzo0swJ zg7*k!haaPcSzpeM)`5ER(3i5eDJ3)8BjJ3+?jhmW&91e{>?MkadS;ewSIy1EiSn~M z$rC{v(n+qptz$ii4ZiVJYn;9Ij8L#>qTzSgSnk|VP)mbtc4Cj!uwPmHp$R8ST_S3SzZ~UBb?*KP1ka2 zIKXH(cx-GaV$xC8OEZkT7J+WSoZcr9vO+~{lY@Ls$kPk+fq7`Yvzub50%u>ro=t`~ zXGQ_G%86oQ=qX*kvEkm%G>)U?w)ewcpEF6Z!LhfLU{;>kg%nV{B}soG zEJ--J(9jP5q?pu2Me^Oak4hwluR-s>ceBx{tL81;L-mAfj+cf?`>$%~>q(ldhVT*@ zA~&2?x#Wt(I)nzHWVcz!Eegios0`s`k-^$=I@@s=h3_4g*GY7XT-Td?|KS* z;Lgq;RDd~t{Xa8xysH|{E?43i*9ED8qT&pkF&6%tnTydwo_<%tHHQI2YhbB~CBB6@ zdrkD_@Ihcerb~GhsqkM2;5lSh1Exy&zHvBflqd1 zT4S^a$D>VM4sMEr53ks%4f!dgzwlFQ6Dr#3Yf!uSy&E-$4j4EHovJQy05}bqJ8AuV zU+FBq)2UO2?vr=67wK7=@Y zo2je(hD$i4{0jLNoYA$xyV)e^ewTnXsWNXq0uP&QG7i zP;h(lRCPS4AsJZjE-&VBq~9TxeuiKElSXXgr^kK1<8Jfdo>!6~pqbb*t>Bk?%YHIG zRs}K}pChV;jI0o5ZSlP9&&QwttM{oXHbxx%UF#n6KWMl$XDyov;wfmd$0t36$$I~T zwSa!D!Z$t>77X8&)*qKJI;dKc;z@(o$H8NjrV@^_b4i~ReaNH9_ylL&PBSZK(Y6QJ zX*u;S5X8(ai8pr5Yi~!?pIhzPftn7?`WhHs-V^(DmiyA*tpBfbSu8(@)Bg-a54evI z4;wf<6$gc-EfTo(fIhtWk@WG5u6&(W;YPP44XZ5jKy5;4`SZUgx6 zEWn{-eS)>qxKE-R2xrc1{{hie$d*7Fi$k)4bTvA7q@G^P?_ZRSr|@Gv=b#HDGw3vq z(&$0*H4`IYExu~R%j+>m3q^SwTd#h!ju-QFo$;ryw+5L&Qm#JNV?At>t8w3wlX#)D z>twu_9-imXhnlk)8itLsmFidY7(S^F=yBK41G*d$bO<%llsN3O3hkx`4!j-#~=}E`WijUsocagm)?#n0T>8(+o)(yBH7;pk8sQNM&na9Mq`_>VI zxB*2vLBa>7B`~?)w!2+p+evQSj?HP44)t@CXAS7PR?;VBr+F4W;K*&@^H_;cpeoc# zI@B#alL@5;iPwqC4QP#<74kQXPZp<6_7NgqXBo+829C5ei7=t$u6;>V6@677Y0rTm zDsyK)g!am*K!R>BG0pem6|b(M7qL2$A@>W%M8-x89<7B~`S7_Kg%(4&K@l|fR1CZD zM-Mv)N9GL!rCXE$5W*C#=5Y{c^dW~eVRlw0SjPjqZ&v8iQN?Wmio@2}Pn`g!j<{o< zD+XF4NNAoJW~83 zjqm#wejRe4O|cVETuk|oU{dK|5S?eyFGE=>q&1lytAD~+?mz-H4I(gar!3G)1cSKYQH?fLX zlj3b|nypR$c?6HqBXO8B4 zl4q9tzV6>OMQCp8F()GSK0Gq;Mi`x>tktc^8MKI*My*3d@R67M{&b7JN-s6PoqLf# zQizwAEgCXb@vX9f__&pGIBuC*2R?ATrkvF6xt)C@#mG2qHg~4MV@prJbsd)w%OHNv zRa~_PT6jMxlnt7DiRHpQr$R%=@fZC~wK@rWSL}XED;@@0(;BIFjH=EbTd|2R3Z1)xgKr>dA5$spgCOP zyU?t*U&znGAQ>~HMAU2r|5(a^m(hm!7b2g}-0+8`S&_&MC;aZ*l5q8XsP=FndLECM z@xLtkwStiBw%!qWEV~+T_1o*hm1}nmA#A^6YNoxF0BDJ+%D%)rp5RPyz>G535#e5TPJZUaM%eiT z9y@2>mGn|435iF0+^~s*oA%F|O4eE6o0M;+-J9k-mktr1UtW2@u!U%vM0d~hOvjcP z6q=m)>P*3I8GLncT(tqc??vV7ICcoC_}139ZptTW$I8!iQ4cbL`Dhy(8y+1)-BR_U z41_E`D#@%}OVLnQ@{plNSgQz|e(jMukAiDI+PnK4Qt4I{5xK`>yIL@T_d;8^H?-tzIUq=u^1?_J&a@ zlQAqo2Mh8Nqn`ej(Pa1H+jhwqEvl+Q=|(7T-qC+9k^g`6aa#v27F=Inq&(~3WcN^p z=@8s)8BUXVtj zJSXinImbSE;+HjPu$tebWeey(V{PwTLxvUn8b;nys}kIJ(B>Q9o+>j0dwQI(8$=B zm)cXbrCA|8w+g%8_oX>e?vi&B;(TRyN10WTR~om95Xv1hxDY6tAOLk(fhl&~*r8Iy z#tI2$0K#Y|hBI3y!y#S;sB>UYGLVIV!p8sq){@Dq3^IqHu9E|c|ioMTj zeP?poU^XST_gqxy!;ul4k+b-OL_LXiO<#dzLQpj{k@w-{9m(yx+4+x}I9s1g1+wL- z%>=T2O}uamZ%`&@W}fy?>w$u(VsURp$%v&2B}T6lc?bLy=ww_(6ZVAe^J5dAhJFov z$Im)zFJs#`a8z+jqa&Kf<}W0foQ zgN+LZ>`Jw<;$u#7Ie%w;L;^;IKl?is8HTxqL{PBe%rd&N&69P+bkNmCfwiB$l6 za~qWM1J=_J<>h6*XPoRZU2QZ@B#f(;G|)xr#J!Fdo-OG?te4m5FZm|v6dr(Y+nB{t z5iTOT6I-Ce!iL^>uI=WSpaUiQr4@<>evPZ(S7KTN%D8rtGV|OgsT@OQq;eGH<&^5~ zE^@wH4v~6P4ZGYwPb%NFXn5I!fyMoi<>aQOh-NJc$W>&s-bC-Mq;MO6z3jJ`@ z(x6yH_`K7z9}h$x`OwZ(tStGxgX-yZ7{dJmLoL9svV3bN87pn1j_>TmUVpkXtY&tD zyQKN~+H?cbSGJ5LrN<8)z~S@C8tmqOqebOcbO|-olg%RoS0gs{eUTV?Vi{Z|BFx z(g5zYr=!E~lHWZ3XEu!w4z479x^SrnsNsKiISljNz~m|RASvjgRt%p0G?9~3|gSpN02sB`9@5icaG(Fb5IFM zM44-UX`<~NdninIn^X{6#uTL$hyfj|^m`f_`p2jUM0swN)=pRRbLR&f&x_0%-2}-8 zjQaXNb!!vK=6FVOXDGeVrBZh_#!~c0es*m7DyOV1JrT5cP4O|djr}68S;Bn%wqv~2C_Ror4#}+OGrpq2dP9t{|{U0g)d23e2^G) z(vB4Od0rad|6E%CKlabj_~ce8Xm}(Yzz7R`#GSv%Q^7pr5z(RkFsN_0hc8q5QlE*-n_M6!0g^I@b}t$okPa((Qx6OqxM zDDt?lSZF6)Ajvt3zmmbkO_GX^2NEI4)$35O%258eFgO}w!1{{S?Y~l9|I9@taC;gb zjDPUp$Qfc$VNBSPcOln@GS&Fms&tlD8*Dhftle>U>%Q3oz8)2cr+2?ggzeX_1FuUJ z6&0WsA+>Yuez!6x{_OjfHdW$u$&;^#@y@IhnIY^kBQsmQR}Ea9uZmr|DF()lY}D2x zP~ir4Rcbg5CpcFnr_Hjjfu$CZ2s3q}#{osAmEF5pUZWO{@&8d1-$wvU(#DQ_iR zoFAs&k{xu89m{)UCi-aIm!Z0|MpV%&Azf@a4Qx=Kzo1vIxx>flzspzf!!g5l%TVI- z_30HRq!?}aQ$pdyH*eQ;U}KcjjE6@500y5sNbZ_Sm1-WuK%z>7Qfj+`Wg{K1$e&3FRp9T&(BW8FoD>mM4WBp`8Fh0_MY3{Svn|9e>fhtDe- zvnEJVi}#6AUxV`}xoWOCF~wp1U$j-pE0FoJhVyUb%#%U+)op25?dk%Lhk72rBh6x> zGn2aLVx=leFOvvzEgC+9Rz^C_mkDI1=WHjZf;Qi2#-!#;pU#5$kQH;Yb8I*v?kGF8 zjks&6CrLgLEuQBJ%N`s0G=z#}P9Zr|-FN>mg!@8aQvP$3nlH{#YCd+bY@6bNWg-qu zty}VHX>||s+Q>O(1+&A>bMwyfdi*6d>`#kSoiJ<+DI5@A=$D~s$KiuFP5++5Iu~ry z7m8er34FDjS*>49CKxH}s+6o2f+2h##&ioTnPhPc=sgLl0-=ySZ{p;R4C-5Bfgdtj zm~UqNz%MvAzqWmyz+2yqB7{YnozdkCC+enV{7PLE?jJT_oRCFNQC<1^4yegH%}BG% z0i1%DeE?6ixhV;;6a`ehXT0|9n7Dy1e_4F+wP%sOMU~4u4!e#_UAh9Z0hTSP2U7rivdG$@VvyT7C|G z9#h+0to8V*44}}18|_5}xR`Jl|E0O``IMHtN`)PA3NmN?-@rp%0Ru{nS;+*CPORHV z7ST1@%5W?z)=|;CT8QbpV7IVBq_R9)llS;Sp^&gBtrTrp(I8)Xa=KrG)oDpn{J!!{ z&ZFTb)6{h(;R(M{FakvhJMn?+=by0e=O4<}4kui9PkM8?RsSQFb?}}UyYGiRVco+e zLzLn^i}-zl+GTWoQfFWDty%`K0)K!p{^z7BtEJZ3NCUN^UTk`L>Ua=kakDavLl?M5 zEJHZ{maOeX{iW#{z)BLn^asi{bL=mNvvrr#yJQZuF_pIl3qO2BxnH=<@lyHHXq1DH zIA7A~O<~m}0@Zh?Hw06i3nppWA8o3fmQLrEp+v>18@MBfv&stAiM36tVmZ}i9%3j7 z?O5r~KL+Iiwi;w26Yo~uXa)Fs$Ek36uz8SY2B)o~gm$@N@WJ~f5k8fX07$<0qhb$J z7|D60#tY)^`PyvL-%$CjBU^{QQMcK^ir}EtzHk1qd-T`)^&Q$y4y)l=bvYNGW+P6x zn=*Y^ZOMI|a>mlvGwFWz9&k#RT86*jF6ny>Dd*FmtUoL-|;N_o;IKBRt})#x)z)4Qep3X3vJ3eJZnxofuxL zcE%8=y+Ei_Hg=~Vmp*q7LYTAu!x1HW7F{nw(Jph5qsl%|{c*Yy+wZcX8b5P3ljQ%ro%jMrwL3%p?@ z_5?_DxfwPve1>1}v8Kwb89Rb@DUOxlwt8vF;HQWX0DzI~sm(mMpn46ch5NFN&&-%L zHF6gnW-T8UO1)qQj)e8dT+oU&P&1KQac2bkvg^re^yteMZeVqMEDu+PreoRFo3Qr4 z2H#kXL^Kd$(5q5Gr?r?pO_a=L3kMntl&(`Ca^+(Pm{%xvd?%5-U5UZh#rY-%`T#$h zm6>Lms7ovh^Zih%^I+Z*N{c~y?;X2(qQ1tJsI0( z_^Ht^04}&Ta zV)Ur4gAiK)$2gv8>r34$tD{%k5pDIU%Qy0nCY~;R#elywpaT)nF1Qx|u?JT9K0W}L zQO~{apnGg=8=?b%6%<6iM!atjP)|ct{Vw}2g4QO+BLlB+evwZd7Uu! zrw(C;i!^6%IEl?4U6FdrD^F`5nCJb8+CE>dTkG%JJkifa6gI2#Tw@@OUn}j)H^`%> z$dI(tU5E=bjsL#K{WHeP{o*$Ao^nRlLW&kXy_=cKmRrFTnc5a+q~LKNEvgd*6;n18 zac+`U-xvP%M?Ao?Zm&A7uT&wleX71_i|t_E`x#CNVU=1&U)RJO$t9HUWlA-cC@Zf6 z1GPYtT8R3Ovf3v>O(%x{WMALO(v-3wg=-d=B#Ja5d2d;kOL@+C8V}~EKlRLvM3R)~ zVdL5Y=p;16$$er7RDJ%CP*7ja*%WX&UIs|i?l0S5w5bah2sH-lrqj6a zix>px*3Z;o)PewcX$<%N^U?oT{~XIvVx!`%kNx$_N<&EHbp?~VOC{gUt6x?pLa=)8 z^N|4g?7L%ejp=&nJhdM5EKER66=>~S9jYjZ^Xu-V-0L!nai3C|EYFYM6^?B-`ZMkd zBOXwL><9Gsaj9RiFuKxap*Tmg@zzv`_`^#nhYQ>K!40Wa?76&FSfCHbyexqzFfsU? zYb$iucG?Dh8!z}_jL#{5s9$)k&holCKrQ^)lEeAdC;v6z|IxVkYSwt_5$GFGg@^)h zj6$g?Y`m*OK>7IhA9dy?oJ)M;!YAG-pwQGWTHis2Al=)J;t{)ava@=v!nuMLwN(F9 zvGdJ;Y4l=!+rgP3!?HnGU9c23ze=u-!EZwViq%A{-huCkHo57PKZ7GxTN&qRqc2sLXFg_|KHOIBB+NQ1chwdo z!BFIgL;-7jX(?Fhem_!nv2S82T7v3DfMHFKofndmZ#2agFeWp%3Nj@8ufNGHs?3Yt zUE=3G+Poe)st?i5Jd*ej>%(^2*={^$QENXHU04@X=;@DL3{n$_f!&*4|C0G+5H^Gz z5&P3GY%KIS7AQ6>1SFJex&++Mh>15IvTZ9yXxI(qPY2%ZWS+W< z?8i*Wj!efonbo2t;`QPZ$2(1m6$WJsr0O@q%y8}Lr5cM?iC)40)yMy@*`AlQj{LhM zzP~czUIJT3sr~{rC+27wq(T5pm+oa6_+n7T1DNe=DJlGkQw}f{aBe>?rn{}37WT`~-IPVim!z)XI?4yKZ*#S&z#vKg&NtcN zjIgbr5zQ2E9ghZ(HPZ48DDiztbg(O-uyNXr5Ay1kv8sj5RalwT?9(qRl-inl&+-24@vgd-^V$53 zm0wVY6Qf6nfG(tBnR4V?hl-p;(agA#e!rrgTWw3jYhgC{wXfs8LX(|uDwDo%7@&QA zcvRY5#U9)2?DA4@RB4H6bjqw`U%VI9796slG_z&=HTbn3bkjCv0p4W2)c}){2WFr) zh@2xEkZJ*)G9kK2q)<$8GG5E<%KOm=3F#7?)dEz5W{I&5IC2*FEYi zU)6Vs_(51to$zXSG|H&wYj-owEX1~}W;@-_0X1)@9RDP(8c4-viCacTNGvL{#igbU zJ_YRt3?JBhL~kxkTJJpTt=FjV3d(E}S}w{itsm%H-|FAV@3?dNipnTe^_{xxBWZdq z^ch*L?Cm~RD1<2Lrwh*dHV<$magHVYW_~M6G!KERe1_bR^BbcOab$O|%@Y3wDopRAn0UknR`Y^hMBOmC(a7QJ zJi4vpzYFI}!2?8&IA;QrNY+b1H9uciooeiD81$QY*Y;KS2!{JgSHLa30NpHK^wTRY zeOPqSi2sO))$e@X6Xpp+-Kprw5!J))dUavsPz}}4^}3QZ_pMvwX8IA+r}U@w-`Rl2b_eX0kiMs-An~X$3x?;V zK3=H0(RLs7iawXIsgyFVe>MLr!snsC!-n9}RHIPo=}XX65+FvD@vWmx8^(2I*<@@?pL!oAAC~R&^tjfE@bYK5kIO zY{AhVunF-D6!KA%yem9_c{BWOG~F*gxN)4Ni>vZd0WcHfN1bXVOa29mG+DZX6OSn*2m zWAgZpApN#0m&tRq`S?;RBZOKcCyoDx@!U4yC}j|>BBnX^PT@$iU-ar(l3VIX7TQ16h9 zgdsJLcIBI1Mdj5WWtW!g^caP0PediJXYC&tkLy|jw53?mOSK&b1LdR2?$)YWE8LF) zJo~pJ)%vA_!V~y$3qs<4;kzJ| z$@jhRR~vaW^0|L$%#NCfTYOLc(r9=Ow$a?$us9-|7CLZ$-=02xbMDl6?r^>~_QEMA z`SER<$2k8v@?pY<*98ssi!?VR_)hJ&cc!g5=clTVXPM|qZI|nT#`6tpB_9!8y$RYl zbreU@$i_4Dl3h`j0R!5`cG3I^XW%C;NP!j_ZAXo5f}`IiyuVO=3-P#oX*S+!1Q+As zGb`$< z+~m@RgneIVSc5ry>Vy$GuHNPUxnav(;c1!l;*3|3*kvx%hmcZQkh;JPje?T1mDatX zbFP^hWC5w^qM47)Q;(QghA``RvEeVv#CzDbou8Um$uq@mSXG5%SL!aL-3 z*CT<+Em#myekcAV+2oQy3!4roa@FQw{W^@l^}cG9&!EbJMn&PJK*Xq<(szaCX~QEp zwc;vX0qH_mEt}v$kTSBvOhtGpV^RE*FGO)BPK8Skd5Ao##=3dgjM+9 z@8CUgD(HjYxMs>%j_HTUK<-wDWUXy^MAv-P<-}Q1vNPdg;a3ra*O& zS{`7uXAJJd2-O9}iR+-s-k)7f6{$FvL*+?cb`ufvPS>bi{VjBOBXDVU>Ja~G zQ``G+|LU;IgRt4>gA8|!3xS|LhCIcQEUH^sQU;s5kQTk<#saA6m;LKIw=q1EPe4-Qufd+KsTnq}JVHmwS+*}1b(Q8fT zT9^%+G>H=f;p$hq)naYo4~&{sN)j0`4|~RTX9Q2PoxnFvEfBH^?)UfwT@gR+D1ATT z8@%Us&F;6yADRYCPn_K~lFzA29LP0)E3KC6>Z>oSUzsEC3)Rxg`p2GeDEq+w<4X2h zYcTTRbbIP$VOQxj;>P5k8738mb;(Q#rs)SH;U*8G49C@*_mxv?IsicarLZQsqf zR#$8P6V%qhTTw(i(&<>YzUTP@DQC-YQ*X(Cm{Wb>Z{>%vq zS}G{gC<_TayGmnHF}wG}Px*(pV$&4DT5+kF?Ek19i+`1g4267*2H0p<$E#;-UK%e0 zj%7JrRMYfL-X9u>0^X>FXx);hJ(>|cARfI_h&1qjar7wHc9m`0xduzD7&(GljZlcN zI#c3U4Hn|;>BES41So>R3SFoLS7<&N`Kxa#E| zW?iFTeWXM!xjN4k-6P3{wc+vmXtOwyuNUSBiSCPeGu`lZ{;-o!%!}8Z4cgk7yIY4i z)*W*)whh`Y`dp_JyI-nPH_oH)??Hj6)Z383Qr`m#UtDwFL^x1Lue~UfCs~Fr@)M$D zi&N1J$-@?P+$cQ1m-L-EP+6M}ZhP*`X(bc3lxq_myGgPhc9*6ei=s8{UgTuC$Q!4;#%M=i?bbDX;Z>s0##Qd$O2V0 z>|Y`-%Z@*4KhAL6yPn<^(0N*FL(qpyKD`#Tx~lp0Q}Bat{zr_nE!_jFI|nKQq@|zg zFY@o`HP7hl5!Gp3dNgizbeN{5zmE>}W|+v-l5jPLUMFN^wByWT-3%GcZQI?lmLID| zE)WK14Q?l3>E+)3=jf?ueNI0cb}`NlA(qfUtYI0@8e(|t7kswlo`4;kSGrE2*ZSxH zL$1y0t&1=Km1?FSj!Cq$-zQv;Qgmt<<2z@BL7+B|QOnQOdh$VU4U>U|kNmGIlUWcq z;NkqAns&=m_5=)VZ2=_6%;WcK)s4Q3ZJuMUvWY(f_aKcUnhZTlHa(0Fx(xphPMZI7 ztpEL+`%4XI3q8>T9TQ^HR&HCFl;LlVQ6}+($F) zAN_g9<4_u2%tgpl`9)Z*g14ovXUZ6=k?x@*Q#qfyoBLjYq@&**>z4fK3NHg>H6``I zTI|e;z}nF*bL0I(sV3=oX%8WLDJyR=jSNcL5@3oNI@&*yXL_sMD9}z?v3`*IRM8~k zd-q=&I_vZSv>_1CN_MC=D)733{39^y(AO2Oo^U@V{MkOy%m$xn00y(h;}MO{9m-@tNOuMPH7d^GB2^S5m`zR!ZOS%Uh;+JSEVh7wD$_u`YLOhvYRZyBVeRr+$>|6I|q z9qputA)gHjueVf4B}#igD3IEkWU>R-0qS<>vG22k?o^-%?-nuiH(pz=2ZSZq4nC6V z*9To^)&5lHT@Pr=Kg#z!^Ea-~O9?M%cDK6+S1jyy16eo(zl7hRnIu$?hiQFR016!Y3-%Nz<#9FIK9MF~(h$v`0;Z zqin%^heOH6Eb$3%TTx;TSEOMY2WwCO+?0sFjIon@mNz2nRypE zfiGXEG>eR^5?zsmo@5onRfDzn>?W7A0=i;Op$y}n%<%m_y2B_IiQjQnasl{8j+LgS z_c2Dca=n{hgoq1mxYS-sAKVzH-s4Iwo~aU@cv)@=n{G7NIubxsKa|k_UgXjM88KQB z;NhN8dXAGH;WqHibOFd8)45DMu8#?AsE|h{qxPA*=j(7D-eqo7JP3o=VoWb^=m^6$ z*PMS0lKKr->|E7tXd+*|WRO39sQgW+Dmp_q@p)bu*JL4oBN$+Oq59r|pAuz;S)<)?nb>bOsUka)Grtw> z+S}v^Lw&=@=VKXx;NF(1lg+lCH1CDDG!v=HN4u+z;?r8CbRT~)mxFD=wj+zl&YwJs zvP*2GZeYsi^!CM8Dn%X4gI7aRtrrQ@+>RJ+Lf9-(!NV=f+@7;^%W~U4fN67iTp#{T5(Wh=7v^PrZahrUJCn<;_TZPv6p`4M$cqEwQ7(gi?)dCo3U@mli)6 zQHXv{Ab;ksLM)^VpUrQ>i{gSIbH=8sUNFB|hHUD+%E@LlU67KI%=ZJ$9Bp9n z{kv_b#}pfQTa6zOxKk5UZ6WgBAn02DSD!qa0+?@9Z%9FSvcz^(&5Dr;h5P!qS^q!b zcFqZ6obOVKlP&t2dW3!FM7*oIrm?Dy2g%n|ZvSE~LYumC-WjHN5Os!Y5UQQv+VS4^W_#c6YJ+47MDA#hS7?);SuG{iw67>UR%m_ zU%9)TN-aDqUgLFsC3WQ3TY5Rz+F1Oo_Ikik+fX~ZF#lGYTdw9zXFjyzW6$1cgHIlJ z(|sc`PF6ZlxdN1GB`U1OY82ga4OTmru^~gNQaxSB^Z3TBReq!%>+4HJDfZhpQVT!K z6=qD|)olSNZjR+DihY^ag2xbMo9rI_EcJNItFtMkG^QkP-5&&;G&g+x84}awk{BER z{@kr2HEsC(8@9;Ou~hT;&9>a|yuk>q(UV8UbGydgp^V^9|5|#_-SmNx$+nrxsn&Xw z3JUJemKYoHnNwR3fjq$ZsJ^PY)X~0h$+n=Fvl-!a%uf^Xm*xq#UCW8mCikhCWe3fz z54UP8Pje1$o#DimXf6+3o~}F_^uJ8SA*AlSx$ynM=Hy=*`=;Q(G-cYypUz9UKCUWu z#-?_Q zK;eT~WIC(Rvus0$SY18yKX4UWp-^nzbJUUPl0>!+f6`+RY?^RSSdISl?c4==@8A(_Y_xo$V6; zt0;}8X{+6i<0WzSdfHx?)BCikL`IlV#vuq&kTihd%4BlYa(p!Yd4heQr=ZcAj-!Wnu?$VWG$XXaU<&z5e%o_~J#zVD=Js={$38zt9g(ncs_%q1GDj8 z%B9YkWOeOWA2{FtfLqS0R(Za}mv3X~R4_|m&Jz3VK-;{TAV4!#s+6wE%n zsdpk--=;x=r~RrKNbN?iYN4{8D1HAOM<#mup;9Bfc&TgiN{2X&6(0Q$m-U_i;0*AjHdR;E+yv(e<6i>~JdHpIPF z;%h`|yP*EUfJo#FG%e*+3)udaRxwgv*|l*g3Z=TuHrbQ(6U?A?+5OE$*RD(wxkGT4 znRaGCwX2^jNA`#@XLtyY+miRkSgKcYieqhNC#);jMP4Q|khr|ASfYzvBL`s5@LuT5vWa2PwxaR1}hx5q|@S#fN z9A)Xqbw+up$AnpHI;7kP+~@CDT_K$G4@^6GEPrHqD8C<#sf#FshGyI@pw6l+I?Ah) zW=r6|uV??G_X(x^pi|CoHfW@qV5@vcEOeLmJYIX1X?X-n{W)Xm3#0{$@zLw%--MPb zR?S3}x8$o1TN_|)?If1($c5*Ww2DmtQlO3`o>^FcTt{%qFX-P zyI<;`(CTqV#}O`R;ZmDY(A2wFs2@M}?ngYCjcNNfs<;ZhNXTRundRbZG~~R!U27oF zHCn8P6`V@KaHBaTq)mRH|FOC#@RWud8v;TF)NKD;)xDqXXt6tDmv}lXbZ7V9Cbd5X zP%G?!&NNOq-X1dVNfufLxvK1~x{YWY($Js%q;T=plHKyhvl>?K{ep}u@kex2p68=# z<3(&chd_eQTtW?YO~>Ht+YV$9Uq{@Mzp&%lZ-i{BPW*_(id53H!-hg@TY-3GfUYL4 zfLX7PZ{{@{3~fp}y5OnV&!SzVr~RRj68cn7c3m$42PtxRO)=-s&A3>xG^>->hL^v2 zKGYdB*}!PB_e=T&5}i-b|NB>tu^R9BysPyaTZ|EJAM20oshcaSP&Hmf_0tlh$Q9S zK^oeQ2434|GuQgbfCoM4p^7D6OtY{o>3n|TG|_W-Nrd+q-~HLQ=gSy#p&I%pHYUBv z-`DrX@r7-M1=yKzdj`oe;^B;Zh6eJe*s$eZN0(ICSNgynNHJq=@hl*^Rz4USBggEt z9#7{RNCmKpW((~#;)_KU6Mn}YMSV18|GcSdPrqb_NEWX^t2;HeIwzZ!u;lBifU}d< zxHEJEioY|L4j3jTWhUiJP+&iak*FA`RRJR!#sJlB`zX0;LFp%1!IND~V;)S)jfxG) z-SHY)=tE{BXSQW?Y@f2i&AP^PWpOG7`d8h}C8qtVai8 zZ<RFyGd8W-`a1s)-{(37VL~?Je;>}F)$!;a}4x$9XTJ+i- z^zOJxmrWA<`+N#3#y_Dg;du>gbmd0(ixy$rwX}Gd^&~m1!_^EiCfP69{LDqR9|rsk zSHJ0QOctbscCUCCev$rx;j;h2a>si`lpgEqEg&@ZSX8Qsm)s-^I*nNT=2V&Np2W7Q zqzoP0~|nfAxtfdywJ z88dm_9EMK7EZURH1bQ>k;-&(ax`1DslU#138+e$$mP^)Kl^FZ>I%veY_KjML%11(W z<~@_j-pKJjlrVdDh=#}gVfo*Bcnr(7+ZaN*eVHS};z zCO?mr{ItZIi`ua`0gX>1epKl54;?S%ywMC?wjqzOW%VJFba`VYWBz*dHKjQ8^Mclr zwZj?$z-|#61U-`aTwu0U!w>frWVyGip;>(7?F?sMk@Dt7ZA5{xlH@ z);_!4IR+Pl_B#1;G`|+!u%^;xUmn?&X^>f-H;*~8PuSZnZ|P42J04TTv|Pia7{H$e zx0TQFYX{5@CWSJK#yeg;e`yvSkED1&n^KClZR!3-;)l5oHII3=vUpGoA>%LkN8$0O z*ak4^!PcJ~y9s+SDqGQ>_4CgpxRF0s=hUG&y4A>Y zVh!<=iY`bL;s_EVkn^<=h(-n&nLnjt$9yxFT9%(#_5~&VptEl<{2k-&O}x3+beHU? zb00=cW3!5N)lMRp%#0C|FO29IT-)L`WoJkp*PawI+HAMVKY_LX{LJCF=Uj>Kq6?a{ z6clxdGio+PHn@Kki9XU-yk*o>u;d{s&F~4sU*3oQa` zzh|#={P6@DB8j<|B^|kKrB#9-)zv&_nqAkx&&;s4*~H1@ryg-!GGFq#Z_*gKHg__C96Mou+$5?^p5^Q2RZEKjI2v*4~cwn>%@H z^CZU|&X8-^si4$S$##g!6)ox$dgz#(5g@=P#~QBWD1hlSWix1t*L}sBe(ps>2ib_! z7+8k9?95{8q+auM&=_Mko$8_Pl9Ql=D(2L!c|@R6WLo3aFaxUM0Mu@J!rk*B(lv=^ zADP=9#wkw64$rKQ&0mmJ$-Fk&4l5%{gkI~;A4AluI)>}QA?cRpN^ZVPJ#q30ifn}E`gj&iR?RID+v9WUOGuW^gC@2x*@3Ytk^M=b+IsaWsY52A zaTOsC?sUMORQ*Yt140uNb)njL#aOuc? zhxLjX(C!t23Pdlpo#Rl@+!__y;v;MiOV4LI8kG3L7|dcv#r)Lda8PTF^er((2elL( zvWHq7m9*Xq_mtS7ffT#N^*40R4f^z?fZw{Qbe=ZWbST!i#FEX2oORXf%Lx6IhVsh| zD0bj$c3UYS_X5eKQpw^XCkZLXt`7>TO|~Nruli?wtcH1E`|?Uxp)h%J)Qr{BUTWT!@JYwSYMO__kUahrV0;o8Ao{h5R7}n`e~mr8hf?wpwI@7iL9XPozoc zuoez0HbiQgT+o-?OUU4_4egX-VAt}HUCpAI%jBG@*sIR@y%9LxkSj1#y{CI^DyU~h zZ)A%tGr31plqi@HmnV$S*>{5Ly##j5!W8>6j0;wW$c}FSu6_ud9q0OC8wEM##w5V$ zBf4USxD+j(pzIH=t8CD#99FT-w%X_*!Q#xTCqX~hU8!Xo zemOdQb-Uk)E@lBF&0yB`YzW)*OFB)DF$5JFh&4ICM*|i8d18y~6!X^DI<4xZBNvXz zTp|`fd}Na$fv?+f-05ix8*}*}b+N)t4*0`0=eadE7{( zwYGTyw~^rgq`#+9LUgCyLzH)&^dwRHePNThf_Sz}rWsUAO4`YURYc&;AB2a{=?mT1 zmx{hM+LieB;-)WOpi{;&QDN%fqeYv7DO2@6l%6gt4#u(TTZ6 z_itLJP~WtPhFv$MZP2r{ZUV#s(Uf<>>T${I^Hax~{-P+MY1_>nG}4 zm-?@_F6^=kL3h-)AW%qqHR%8yETJW)#}Bdr*_NCyP^dfH?qQ#;{wV;RSv3j`k!h&z zt=uO&`?6tsa`Qz!N>mNkn4o#YF$2N8qTgY2j4H*b!-`XT6Rx-2>#S}7=eBdr3dY{E0}ZI|%=mfbKd2@_ z8J`PtYzsB-u_=3E5>BSlI5GW~1P3@uHBNkJ-2 zjeHjMM`#C@c@3h=kgph0HyUAb0ZetV5r6#j4G*)?)*${y$uenR>B~pwM5uER$wkrA z3uk8`688>DmBrW$M~=+^+|};Zo#?Mb3#fcsP-@R~&==1roM83K`0BWj(|E@{+5a0y z2xZaVK#XtJ5j()M{WwPZ&!ZUD?)}5QF3e~gC%ls(mwG$p(&zSSQXR4p;xiqSVMn`k zCf^a*TE)9`+3W`l@E`MoEEH`!m#Mrvj%W|2+eZaa7AAUmTBRe|KsoZaX#UYdhDH6O z;0Eh-Rw!cXrRhbGmvDjnFHWHL>HdQ@xw}~9QHlT~@8n8-?xu?JHSoiaM>jyuIG3Q!ythL6RYs`1N@2A|jG|iH>qLjWCxvMuH z>1QdFc{qHR1l!lmSM z--kNN2encPy=DED<=IDT^6PPfgZyzy(eMJL4X&hnsz!dBP^JZEMW07^J7+m&*{NJb zw&f`PNOD#J0mZCMl8j2+SWnp4&V!dwLBE7l;|6Oyo`vbL{SQf*|JU9ASAMWW52iR- z*#kDJDtg&;P?ZmbpyTz*O4~h&AK?ClR=%8}C&10Gy^-XfB`2q<9c3zU5R8Kl=oOl- zFac%SxsPBlsQVeg#w~ky4mYY;+2F&TK#^Zk5)Pp!bil)PboBlr7)$(%uXw(jux>=C!_&FHc(o&`99Bnv>eX*tfOoaz{^Ggv%Fi>mh9|b+vDCq#p`J={@Rbb9 zEZZ4v3bH7Tb>nOgo8yL>Y+=X|TXUu9`YJCTPF=yCCwR1CJaIHL6u1{8fJJ8 zZZk(U{gIMXt^EL=m6Z>r))3$zF4U(>{#>=Za1Pv0%;rloL{X6+Cvg#hzUP6)y0`_% z$c9KWO{_}Np3Qiu6gi-YenvY-ePW?j?vQ$Wu9+9Zsf18LRn|YwY#3oG`c{A9-B|?8 zzd-mPsY%_apY)w`Op2Yf0QM9l#N5Q zY-&cdF*rz>ey^!vUGm1DFVI0l^3M%bjpMjm0#UkrWL%a+8f9$?Wi}%la~r|hQNX^I znUkA#gD|%fUjXFfvb4F)uPI43E4b>D#ENO=Nag$D^rLf4hg#*#;N`(QfhP)b23l^f z4#oJ)a1^sj=8pw0G_L$D>j{YUuU%7zWP770&OwpmO=e(|$)7TbpQRm-?AQ7#@@5N4 zV_x_31U3yAt^GW!DjM~vjh5*=N4Yls;)Ka$Fn^wyKFyt1(;FwXHVz!Y{>atta+twQ z?mw3MGr)c*Xt?1lC-Xb1MfDf4fF<20WClHwGKrGMpvjAgv4gvWU*cwb_-yV*?oa>a z^TP`MwsDEGCQUiFh7dj}Kt@CXUC?Nm?#I$4oCn)?Y_L3DmqO)h_so&a)m%tXpKToH z!&fQaBqKbVnRAMo2JCQ*Dy^zK5ue=ygRT+v*F&fNt=u%W#pgle0*5;#hleHc{&41g zmAN$(u~NMl*SOzkhR}ytPIW&~WO6ZCt9t#C$^qxLe|+KEOO>A73(6ej_mJcz&nNS} zh;O?Z;Gl-nGA%QO*MqCVqyuXLnu8lVnY59JZ+rGNz>0SI!QF%8H>wc*(+z|Nky+dO zIMW(JFqzh3331`H(PeW!ukI#XNV9}I#xk1`WHXHM|G72$dMyb_mw7G5d0 z*GPG-6dzfF-IA`s!`mP0nizWGs7vmB)<Euj+n5XlgLavZl<7T&7 zYB0QY+4{411Ss1>c{RlXRz;;BGTp2`1oqPC1jJPKV2Vp0#J;SLgBl6NXp_qDCLrqD z6db53YJdJv!m&rfdI4%flNqk1Z!U=%viCPC@?PNb0c%_hoH}eu=<~r->=KUxXkD_R zF%*$Q|-NeK}=R2AsFS4E!mU! zJdxNIjBpQ)S3fyEp?w`?_@$ym>h&y}k0C@8a@bcDDrH(+`A~nmVsZsUCODK2F0DVy)63qG;!)TU*RvY49K z%-_8fN{|=v|2;%AhDt+*UWAof5p(z7Hd1-`j>GAP#?r?LMfF_|c&9f!F!#4z*fq*h zwk5Pu?f9)#{Cn$DD93ZhIwO;$t^eXN1g!A3IUAo54>09e$k|%?fmgpe2B;``(?Y5R3xv3jH463ZycK^KWb+jNT&5JwovivEdkNt{B*fr)XbO= z$rZyOIyR>$|AwUB zXEvj9o07Vn%RB5>0s{M{5m)M#(?RYWg#Mc~i14bLRtL^w65^k*-YuC@*-$)pOYp*4 zbbL@{+(+$df#U(5K{~>sRMw@qHA`a`UqIBpl{2D)b=qd0>q`coMDcfHcMD?i?;oFk z+|Mzx&!cH#q-TTF@nd`WAvTbVkBQ0HEjQvMT@wv463Tm>G_&46F{Mzsk-vkXpv9bG7uiT4!Nbh`WT5)$ z=tkS^qh2YGo}sMM_hwrxbLlucLN46X&B}!d;LFujhE;^VO5I@G-Cn!iwS}O|7C(tx zeLT-&uWCtE`;KOW0cO|hCkTtXo5$52Z|zzF9Hi?QcOAJU15)>$I;w*j;%h{_^>9po z9 zcjhXnMSr6>63oAeH69iZEY%!OgZ#-Zml5mey*FXacM6@Lh#+oEf|RVL`p0q}zs$O9HJI9DnJcRX8;-7);wu6mk53uUcvFFRRI@} zjTMNed(5vVB_(20=>50Fk^i#R%(B}6r${jaCz0{a=~=OB>H;)$HPqpY8*_om;LhRG za&m6Tq%IBKUoIjv;7$!{5zvZGoTHNMb*h1-q0i~G=X%e6!}q$%sR-QzGly0p3w7rR zUFMaY8VM7DI$=KIEp0UmcFs6J(u%!#Vn^u=1*+6}qzDO!(NVV!!0hlSp{)0kvp?XA z9Z_*YuLu-n*58yyZ}xb$YE%{P%}G3PqOhLBVl0Jx3iLO$dh|di67LTnrNI@YTt^Tq zGj{ooaZ?$4vymfXc%SRp&wQ(!uFmuF;V#zFscEmxYa3h~V(sX=#+go8{4A#^u%~@_ zUP_0>1L-<@mer!v1eYuYV{BHU!`vmCxN-1PI{pWnY^=YyIKC7pT3IP8tfTx`(t2@t zfffDvvaU37lMl1h0@7wv7^~bd6+5e1lyI7#Ph>N8*94>;KQ(sE^9G{(EpzIKA)${n zEshk1XG^Y6OZ@<4UmzrvC4T&epYR?i&AnZ#&holMN^s_rbA30$3Ksb$iiX%D8R5LC zZQ?RHfMl(&V_71N-8PS%r}fk7T^K)l?c9(R`NoD%yOh*_ZdcmCPrx9OIr}Ih(D6j3 zdCzen%hF`xaIk*`LO)TQH`_;Z_NbrU|69{KY+yWAlm`8 z%1(j#<1|{}We8;wlAovB{8Jbc4>VAc^nih0SFI1`Bb6q75^5tf3FdHI#zof4a;w1- zjpO~Obq2SSD|nJuS46AJ+xE45y@l^Z$@!ncbhQHGBGKf8@7FB1&XohD%OrDHk{(WH zJ)9d`qE!Q_Up)+w+?JcceF)snR(L&NjR&r6_6xRA{K#yu)$CsUh;f~VXS@gP)(>Tj zxj3~KPsWE-K>U8R#h1936w1xsqrN7B2Zpbfeez(d{bkm*8>LuSh0A{A!fzJ@do%tS zUrS;tme^PUqb@yPZQC3Lz}s4S=ll2tall;lMWc1cu5K)gW7Do6hz` zP#6E8C0x1MQ;j=XsW%A`UScw-p_hQ!O)}aebwvYtpQ!4NIvUIxulU zKus{oO9Y>*=rdePN)tn#3AaCPHfITUa!Ig~td&a+|A_G9nQ1pWoLvF$N~kzy9qEt0 zM*Iq)Tw!b2s@N)wYS_L8D{8Z$Fu(Q2a0U4gzm&#=(t-JmQ?oq;dLuwGgsf%> z1jfSp_ z7OB-nigqKh$tyi@*1yysa&;1uKPoC3lRnMYlllB#7S#W(Cw_8&9a-Kq@ks}{cR?(b zJy!;B7`Jm<$<;G@oHK(bn-~qMCn)B{i)62Em7d|o1d7Utj_8JjE%XYSosoQ6MMR&o zy{1@xD9(0n$-S2^Y3PjWct^-1;RW~sYSJ%UuNK*oYR0F65h!hXp$4s#G{ocd&-i_{ z2xGm#cMOWBLdM9t1y3{M9(3b0xYX;V2ditj2!iO8<^znMHGsoMec1eot{_WbL$*o?~tERXd*`V0OdFn|oZr~vfqG#y z7qlwV=+VQreIGt^RNyR*qV| zC#PLaF864X(tj|Ex;+lGMZJYrC#i5}x#7|X4_{$*R^+Ki@w5)`gUnvGzObQ%2_n=- ziL?bd3S)i~2P^2>*qEut?LUnXF&m{TrvX%E3sdsPrc#eNoj{DRfq=Z7cxbV9zzE-} z#52q<{tddY9V_mElEryVno1@j&r1IHNPm`;r*jr^2M2?lq2hvi@P64>J!f}VH&O=K z3xlVGbf#^gVv6}yduE+1(>kGfN4Pkr`8 zTk_-e8AJYd5Rs@%k*!4KDdF1n0fVx9F;*A-4z8Z;-t=2n$pTVWR^O0$1f=~dh%z!| zQErah%Rz4{83))SPanJoTkhz@QcD+t)6*fj>Bi>oM;o$?!k2o+t+?)TPP6eo8L80c z5H_2$amw);#)+!tz7BIhRlcG-Z!u{0_l2XLtlw2q2(y_Afg1|kRoPsggKMB{%B5>6 zezRL0w{re|QJW*$Yu0LIKQ{MlE`tNe;`5&;e)*SQ|Dz5P_0WU0svW30v%2jl)K@Bu z?F&?nOh-#ch(LvQ-4i z&xNNT6k3Xtuth&^34&*30y}={-t_9zJ>ZQNIc=I-`yf(y?};|m`~GrunV?yb1fs8> zSiNr?vWAj6R*hK#_xtPcKxYefhPU}KU$XMOUS@5zX@407n(>_5(D(#h7MBuzjp2Vv4wvi5MU?8`?TpDEfJt^(6T-_eDyqqJ*F^P3__~*ul`~FDi zo(E1O;gajgfiqo9SRG%*kT;Ac|7165FQUcVVa~n1Ab5`d#o255Q*!FZ` zdhK)avog*3d47rbILQ`Su#s^}0XlQzmZ1Kro0X3?e>vnyPj&7kMUu^E`SW1Kgedi* z>U;NHP4>`juTS$vA} z+B%>^ZaCWTmY{(AE90y#UJrBJ6L3rLRQr~og_gkf>)-2~-$eEmF6j34qj)sc97cg1eX)3J>P{&$Axt+8e0`UI}f z@ziJqmu`*znvtQ8^)}g7X)qJjI}-{}zvxEPhlWVT#z!rIg&Ke z_)|9}XLUeQQBu8md?ZOw{9e11%L-$Kz&w?xl|2c`@_hEvb`G00cbDX16&&~HTtx$* z+Ax6BQV7W1@3HRD8FwJy-~0#TNN&Hzas*&ZS?oIkCX8)Pa+i_y1*;)xXQH|89*YjRFP=hq(Gke&9BSadyX&d8s9QowGyV z&FUf1NZpy@Lg4|KojqBgT5t$iE-)=_>kpE3DUSfRinV06F;^q1 zeVt#!WO2nZ+OY$cK91rVfE8S#8yDz%gs$*1!LFZEfP2Lc%K0{Vw8gI9lNPdfM7*5C zQj{xVl%Y{<`3MuFE1!A-V&e~zjmNV^JdP1kJCpZu^UFq_dw?M$*TjtI%IMwDG0U-y z#FCnE<^&4ta01^g0hE&)s`GHdqyhn4<9~g3`tNh+ox`CIL`6iVfCAEzPm-UaP*nq+;EPTXo8N1b5y&Th8YA-`==-5;Wn#hUG9B)$L%b)XefOrj- z?FbmN!56Y5&Da!6o`DO+%6m_L%Xv#b8Cb*!A6^S`Zg@~%2tr>MG47&E9sgX3?cMT##PbAJB%j`v73Uo+@g3h=0s}wP!5PDzSX6mwd$5N_ z+Ye!z%i(aB8WSV;EwznPB8ALu#mYpy{+;o|^Qi2ah#${?zkEuHKPW$%-6iAfKsVI_KdU&W5yvoe0|I{q0af!0%YqQqd-!Gj# zanl-6v%}WEXrcOC!dzvh%229Ly<=9>ll;(b*HQ|$3xxKiVh^1Tp>aB7Rk@M%ExE(r zG>rdDV~V!<91F^I*Qj7y)3R||i-^XhqO0-{eyZNO{DnkIiDzkaIP2aJIu?r+mMgVr zs?^_q<;^FT_gI=5@Wpv+=b#<80bc+~?z4w+V+p_IY5Hid_V_&b`9Gov$+q>0^`#@D z)6E>})3E>RR<*e&yDia|n7c<-VeS_W!9lkKjQIl51v^!lTjfN|It&5WoOc9L$b6}~*%2o+mM0aO7$fhBHfA#UWqN1zb{k=;G^`kKcEkP=_?yWpT;Qh)KeB$ko zCpdg+Q&H&))o;v)|L#=#|M6q~#~J>058Qc8d%mFWGmC?wcKpq?^Ayoro^!sLg(c4t zGS4xtCc%1EOR@`Lt8i(NgCmByXb=vz!#fir+Sh7WMqy6ud|2C;HDugBowZ$~jlMU? zzWshBn3d~7<1FOda_YP>h?ZLG_|p6;^LitYh5E+-dyoVP>n*{kz|i4 z^k#gc$f^p9!yA4`w_GnQMd6H|c0+Mypu8hvlph@xmr z+tg93ddouqiVl00uc(l9^%)+oe@5bOa?2gfmZi2{I+6>_>#LcKY+7obnQ&F6nbT|4 z>v5b4qt;}~LP}P);_BzWsaL!)rQD3)h5EuGqE=2^fi7(#r$>0PH6@My)yOTu*F(96 zn5?W&Wp1l~>i5p1UlYl3T1}9y#f@{SP({RzMBMeU|3G(R$1euIGohp0`Uggtt@v*5 zth{p({vOf!m0aP@jxEnRj|DYNo>9@Vx_({8hyeGD9=b?bt36OeN7qXIg5IN(tXT)d z&+>4Kp`{n@BS*@m+-(laJneIdbnZCG zAN4D#7W)v;;85CBsn*fdffAVQ7Sbz#R0vZ;&6!{oFWIHH9=2{5mdpIseq)VuJ*S|!_>cD|f-c=caxvd~@>Ag#pOgRag^0(vx?d>ti3@Hfmwp!Aqur$G-J1%}>E6Y%6VmiO!|5-TZU$}Aq$3KS;)>Qfv45B|(yk|l1nAWiX{@rrSbuFO?LT%Z_V347yP<1x z9(b~T`lG^EF_^g_TP}Oy1O4*$9liK?Z?Jw+iAEa9NSf=-n5O`~wsF5( zCFdgV%`L(Cc?|azN8SZI=;%~B=75yN*_$D7p`!nBfp#001mVny_?E`_#)b|{RlJY- zu?d^_(h+r0Mo&kt4ZQ^R>4eZx3$1X?7k*UZp6B2} z-!=hUVubRfvN1Jz+$KPRuu!kC;ak3t(<=+42g#43;sk^DTDnXzHjN)jYxJ$t z2A~sJsoZ^d-2zJ#8rZkgGBV~9fUyhU%WR@+iIU%H;!tf+8A+H2edG1wF4d*ndZI%P84bkW)Caupc}BW=P31m;<<&sdr*&Zt(&t-m^MxHqX$Ms0oYKyJ&+ zbS?Jf7%snBzUCq3>H>xdM(LFEtce8v@zqZO<$!5L+Biux;SN7{Ca&G@=ZvETgZu-4 zf*v3rqz)z}gfwuhF{IcD2baHz-!;xK87s@-HJ@2}-&tdO%y)mtY;|WVe0Qn{L!>eq ze<^E1EV(=jm;6viOY5%MwANR$AgYY0Gy+BTTd1hjEJoU^b^QWUlS>{fb^5u^Ye}^z z`2qKG^BkX;ocIsLMItnEU?B;DV^!$SGuX?CHCK>yqMlhIo=Io!&86vVL6BRTcp1!L zl9O_cfxF0D%n#1CQlz9`Z1K1tFgH7(eS}c%ZhWHk7ZMfl311co%K;h4i}l=sXe@2t zF}1i0?r#j~rxzZZ^vGOyp8D65`6^)*_?u{5*BorC{K1ngt{J zNyTbhgiVQ&#&FizBc;$@ z#G~e5|5E4J;PcSY1pV}EuB+`*m{*00Vjm9*-YAvwQYRogQKtfnr@9x@2-djDE7+VU z#~2Pt88~v6I(HS!ZuB)exocMtHEAf-o0jMZW`)?>(uu`q4AhhD#$)9h&S@35OhV=X zv0BGroG!K6G{omm^99Crytf4CCC1Uh4NYmpd*(~Es4@Tg!#*|x&~lbwfw4=MOjnWl zrk{^=dGo2}EkUQ%o=8Jg8N?l2_}GAGMC7O4Y%*#T;O1*0@(47&D1Z@D8oFTqzM=ld zNAzmHu|@zpCJ`w^<}dTjcJL%dd#!BfWS%j3j>4LQOPim!$=X-WB*NuT7J$HBagA(#}NrhMa=1CbBm2ADE)hZh_xUhq}`f!XFM-cs)Q0&kH z^&UH3WjxM$qo{m26vbM)4rl*56#7vWNr#qvjw)z?l3)XE{E&&hF^HgQYdAe^70BN9z ztj#BBE^6u`AmichW(iCQFr#g1V&vT5xz2q4?D83FlhsCwz2LkYho4j`GNqPgu71>n z@^sM(3}MSY@_eT@V=YWST)22t+2fS5r4Dkm0x;zYj!h{v4<4%zoDcfi+4xeWc1m)T z?RnhY@78C58f(L%8jk?gl|+t0t<{^HcFxtD3rUk6eU|Y>+*(8~yMlDrs1h6UF5198 zXD8|u*2u-jWQ8NE%GSZq!urP=Yu4}!46~R-fD<#;PtvP|1F?QIZDbrtsjsI2m}?QE z!?VGfkg&Im#g5e&Ne?(SH20Tku;y(bEHs*+VST%oP@$TZ7&n{DFMgEdU-buUvy|my z`{@D;w?S*d!DFo@Y4R;*g~5&jiU5#|I>Lj>u!kuf9oKL4t88b+bjDjr4q91vI`#y- z2`qT0`)bo58)GtrbG!m*61E>B&4c zge|>&m3H%7mMhg>uj}`eMs{TAH$A6wKZ?>c}y8Bs`!_#*^>r%N12N+Rxs&E*l zl=6{t$B(f`mkN9#(hX1~57p)(5-I0qVP?Tbyx7!4Aj{X*`FCeppoc?`C!!{zr0MBt z^1vFp^aU}9Mn_ysAgLF{tO13E2*OwgQJw_WEWMxQJf*Cim^v-$If%3;5r0FMmw5QZ zG%1{K4kG-;Fndn*khze?GFL600kTlz<8!>!A^%)V@5IqbZP>ad&7ow#q>ZLPKg%am zNAD+fipFs(M5hlYY!r#9e)J@2L{t;ZKJU-&M(g1BB8~eQXVWSC+=D7DityvNkb&(( zN{zaY7p>gJp%IG=geO{h%KTbNP}bSwg7={EUvaxJ8po^oxyX;RP+pb@$1v~dFK8(G zB5+zLlGshXk=<-yD6g}GLRnpZD&Da_07v*yq~{(CP9>0*wjsn2v&FTYMr~v52uRO} zB8O|UZq5E&Y5J{a+?UwhVGGl}QlI&(WK^>pshIMCgpm1*L)+tJ9>7$SSW4z%x5wpR zt!G#yXF1#E?u*bwsEaASq7K!kgcSiKzPL8C_RbcSMPre~gBVG64f9WEplL?F`G@EE z*;{>mWpFJABg--XjsnIrGg8Dy7*_PThkgZ{?KT8E25VaYihUthLPh@gZMs-W0 z6a9*OKt6?8>VPYYPiKZI6a>)m9}-ys8nH~U$lci<^_>*H`9d$J-+#q#>Ao886tA&k zh(=o`T8%rqOV1RkTHo9^x)u;V05F+(BR9MFD(|A;E-sp>B|9%9s5!%cLU?)l0!81r zxQqMkFLjS!;FsV(E!DE~Tepvd?0gR{M~-rLFQsn@VkK5il5@_HU%JSrqQ60mT;%_= zvi@9?&0^=jzq=(cf-z>8l6Sme^V0_2l`>mIFg7;#u54Z$UXC8s_GTvj)2GH?f3GBd zQtdwvpLw;zm*J*g;JUkeS@X-CRx@CpnWJ}D?n#bjVE&56RSc7i64OWC(Jb|dRE&n! ztD*~tn7zRGOkIU(RNeWwH5<*%s5lsDeg`R;!sB{Npaz9euDMjGsCM<|)Fz7n&q2*8 zVU@mqkCNn`&OKcbQFiS0oXY;l8qqnZ!YEH`M$@hD7&i!Udf!CSX)m#96k3Aea}k)w z{7jwaELQIgcB2!2MxHooKf26x|JZw8UOnXLC%XiQD)tNKNOQB6E$|v z?D4)z_N04y#nTd;Jq(N&^q%XpXyU_@l0_tUeNt?)T10ui%5TvAVyeS}qzCzY%FnV@ z4p5@f=$=S87*W}H9-?xhHnFpigaM7u3a=&VI^D1GUO|dn-3O1G0_yxG-aLxlEv>>u zrmHhF|aZoHDQ%2@4@z6{81IC@;fY*6N@^!gZ zu!kN_U@a9Nd(U#7$yzd)AiO}G!g+-~B{4?r80%pknl)Q7oFyp zL95(UJ^$O!Ua59Xfd*K)?6q-kAS<{<11`Xu#-Rblv2n$N#y5nQzUqv741K|@bJ+3~ zc%3ef>T2pG6V>f#BD{&xZDPKd_sgHY(cR+*0)Ala=}dWML1Ig+%JNgeQwOhRKgFRF zn1`iWg>Voyp$x({c=#fRaMXqhf=XMiQLsg-@?-$dUH@}Nr(wPk6rYlN@a>#B7Di8! zXXPWyqU(9tWjG<&Wf6XuOQ~PgBDpklyu*iv^hs2(`F|;#YaWR0^rCag^Mow|bf5~a zdm|@uS!Bdrgqf+kqFg=Q5w=JkbK)g4JGecK68Em#T-&SQg^?vM-DowfrUP#Gwd}5% znsNZ=eo%h$cuS>&7ZrA{M$SwMM9&_Uw}7HXOo9`oV4l6BLG9}s{fu{}pOTnMRM z=~U9t$Z_n-%aRSAvdi^T^M%{c_X2&v4?67$r$n+V7Vk1<-2p$_~m3ze8 zx@l_4r=`}C+HXBqt8Z6efi|-_lctRT`;lvi{A%Ja8pUX09oCKvu&$G^6(2TDJ#?84 zbi_$RWbz)1U|}b!D|Op#cfh>4XdkxAFM!tLiiK8XdZcD4BFD9b><{uVm2`*k!V{iwt z(e~H{wasxU^?umiieyt}pX=;<1^Z}vyWWom%gwH?MtB~RXz6>`fW`C#^puFA?xDRXp~%I6 z1V430(KBXT9N#*GMf7FxKyngm*J03ov)@<;iU>)r?nPpD6)c7$r ztztqNZ0#<+d#!RU`?Ami*i}>`GRrKYI8Va!eCU=1(;i7;2Dj~34WP3 zmzFxeS&@b@^)cx#5D3+b%1>nBCmD%1BLtQDJoA!4^YqwH`soo};VLpMg3i*tJ@F`4u{riTI7w7~4F~Kw?FYnHM&~CWDwY<=sO%c%@9X2j< z=@MA5xzimL)KrgWhMQwgdKN#E-W_iU2A7CTTGbRgcsZdQ<+{)lka>qp8y}q%vH%+5 zMt>owlMDqyOQ=6G601R8(D`fQ=Il0k~p_f&Lfb7 zSsdKr9}c2m&3uFG0v7y0^uGy+gu4k$t{P7b`&2HZ+*K{j;EubWMd-xLVy^~s@9m9{ z*BLdp(!pMwnU0l}Hk7j&fAB~2>lE0{ONS-7?}d%Hotc)QiuW|QkwS z_l(i%ph2t-$I!=+$U5Dy5GDt*frxiBVKixv5itMLB{`U2pk9_VShT$NaLDD1{ho1JZdlVfEob4CjR9?FW{gjT`d$Vq9NgAHU`A;o8 z7WO)>*&TAQQ$d;R#HG2n1kqMO0gS?e6;(G6vabi4H!AMn*<;`0twHmV1{RQ}hIU4gu(>Z%|Cnb00&U$z=EToROonKOaA|CFrYTN3&sOg|k=TY5t z1hNIQ4^|6if8EIpiD7MMu#XTn5!@pZe7(CR%9{F)Id3;A(X4j2XN^5z2=YAG(+-esX$$OH(Laf2aTL$+yq5k>8d|Msraoi z`gt{Vl?7GZP3b{u;gef~EjJghPyF$UzuVuhcZYvzi4Ekej9z2qmH}eW%8Ofqz92-` zd0(8vdD|6-z~%lgR+Cmd)8MnV%bZ^$hKIKVr&0LqxMt4v&uS#omcm}oK$N|}f7`D! zetbk0pn_C>P%!$Zlzthw;=&&!%JeaPF@;~l%-*49My*Q?|CND<<%w#_JBOI9+e&b@ z}q=S4w0;h8rYA3wm6llQ=Wj z*{}1sn;6^w%)f-6tuig5WtWQ0OU6k?JZN>+q&h{|&=P?$R^G0*~CMhao5~{lZ!z{eDTvy^hwN5y0g8UEQW=|8?pH7ts`Nf#hByP z!JrEzjw36J<#Rl;deSwU@RlH3rM4m{#->^DmVgz=RdvEfYrYsir_J;*ueqRXSRMol z;~W^c{ub%~6+G!~-r9CyClQf#8 zuRvgJm&`bF^2P=xjUqSf7O50AzgVy7jusH}!()2RLwc)RObnd-aj8okeAWGuuT#pvUPVF;-Ac4;>xF#LW7c!!Ioo z5QpAber~}`{|a>BFpKMT@_H8hY&2UK>|xS|;H!XGb0Zv7J6f$wuq;`hA`mL<@q2pC zA98y#PF5NmBJEaM;)bEglwQ5(+LwL=W-)K@o*yND+~$|ny-xYELoXKvUP z%W=C0+3xleH!Z{*>{{z=J%9`HL?-GE-6gSAn@PJ^T@(-_U*?tLY1)q-_rqlkc^~Q8 ziplhk)gZt4pU2JnGo;P4mmgx1n|ypMK&N9+T^6VGvf6;Mpg-V8LHC_-$;xG0!#C4; zw*(=#1aTQ0g>o0&^kpB`599u|dsLJh9ZB&(a=n*HX6CZBM*XZb4z z$6a!Se&-Ml-WwREdv2G5tpUj`kgMTM^wm@9>6#vsl8ogaN&M2n0#71H_)xb5bNRvJ zk(qhaGUe=7eC#&~M)k#LifTa0J3!nye(Cam{$};k@Y^=;PW@353(sjrnKk-02Q51< zO72pHeDL;&9rhw`>;?1ZPhIDH>kT@e5UdI7aE{6;8Xon{o#@JOza*bS3feEWj4RXPm^F`$ zSi|{=C1PvSMK7aQ9o)oa>EgBBD|-7x&OO$GOU;UG&7*R#Zp1?DF}dq`IZL2XAlvMllJhRTiG}*`QzHnx&lI z7?kYs5bV6}J$AEd)z8)_Ig!E-uPthi2$B32bfdy?L+We38Nl?&Pr$ao zpeJtmmf)DRtrI+MeoJ7j7xWW9Q?&>p@gI%@F`sDq&#oH0sbSYcfehof1YnM!3nqlD ze$Wl(48O#PoRJf{hRdDl+!DN9xinM=HgXd(DUA#R`{{po*nsNzBz{QYQa&ge8T>A< zrNyL+8(6!^NkkeeukEXK(a8-=Yps1`IXEv(Ec;^Z`fGI?r&<8JOyOl*2kt)R19696d8zdSO(v zH`kRQu+9FhC*_k5I766 zOm&@ai|QGecd0BjR-toNt;Zq#arOeX0>1teWfu4PlU^2;gPO%rTAE`)C-^mVkZ#*` z-@D2{r&|I-%+2KuA@yQ^+j7b+0Y384@GzL)jFjP7yG}^i^>Iy@Q(C`oB?6v7n;T1q2Vh8Iaxtj#Lu}NFX2y zmT|ytafm6WYzin%2T!Q2(kz)Va!}P*}kO{LXtHupP98qFy&c& zsX-ozed;T2&0%{UIb%#w_Lff0!{zLIfsoz->DuPA(ev2t(jhJV1q?b*trgz8-kFVk z^>daA*ExmUYh20T+|RdtQaU(sd)CDF&3M7n>Iw_K!w02zI7j{?c_XCQNnU)Q-b~IY zX20t>v}>!PsZB&VdNgM>PuaV?COB`r7amp7khyw&^lH1f^;Dx8BRFjLSkub`WZ@U= zEJM>=#cBy=Pv#;+7W8f#5Y!7{gIwZOq{BU<`J!AfPe+PuDtReka3Sp%gS`8zrZ zO5lNsyq%bD3rl@J#N{WkOqOUvLr;Heb0jB;J9fgvvK#F8arCKqwnM#EsH9>qneX;O zC*%VLva62uwJA<*b^O<^`aMnc>H$iYw8vyB$rv8v*U#tpnNDwxAw-mVcEma<&cyhO zFs!dLHq_lzzf3l6YTAE(Ff`}=V3wCH-TRkGp?X_tpjpd5Cl1u5JzIZ@5EBOfIe|U} zm?%2}d6KrEU42WQ2}y%b7zs8Po{1?)!Z zlv9-4BBWPEF`CzA_&RUrE~a9^CTY6XEdjY&IC+v7OSzs6&y8^CVBIJC@l(G{^w*PP z+BdB~(Zvuo>n}Ai0HEsm$*3op<&9whKHRt}-N{{#G&5TU zdKYiF;My}mxy;mM^bDbj=4eAr>h_@h2M~H_pGS8m6_ zq+bt<1$tK|zJD3g`es&;)sh}c8qcVGtfNyV_=Lot{r63Qt3sN%-C$dQn3_VU$D;qv zA_G9FRcV2N8$L~7t2$DHDMp!V;;$NQuo?)+WTff1ZjBxWh_+4fm!qA_`cxMMc&1LN zs*{*%DCgOsR*v}_WyvxTw~2bT5zJ*=Ce;#FNQ^UTQ;X9s_Fo@=oZs_KLAZjunDQ?8 zowR*c-vnz-98UKwLTjOVn&cV2HE}if(#G7ge#|l42P0tkS7RbHkBa&|iDrIIo2V1; z*?a$nJbSXEAjXe;|Fe`FzBOwoG%NUK`}`m56lcT^aF+g8m-8aZUpIb)3vr^WloIou z+-vTbe8bR@^eUO|?{|Bf^?Rb`;~m_aS1)oZzqf~em=s4Qbo$9CmN`GeHF*7%1P1s9 z-ptkzG|`l%cCQ)&!T{8Tpq35*)B1jzJ6|TO(vg+y$$_KZ0Gyf*jN`yLslCnIZXW19{|1XQ+sKE3AU%k%BC`XER(6 z|IoLj{j$$~5?6xc`9CMx#`)1%)}i?mX)}+~GV~Jj|d9`V8RMRLZtk z{H9$z#+{BQxr#VJwu{Ovch6vl*G$0#H#$! zos%9FN=lZWw8Fa*&i=#@t0Ejbu4)I{y=utvJZt_5hABQcp8=5+<%uvpam~3b+=__y z%FhPUH+nbLM1v?&_$4pmF$vmG6II{P^lt2ed+pPnD}ye-{(Kfn(fpUh;pvBW(}=I0ll>ltsQ_is4GH11v~8Lss(zV|jyDREGz1Wh3n=DIhc z_Zl|sNp~i)H(VAbrSkDwRv~oNk+|LrSS$K8o}J$As7q?3Iy@4;OT<7t`OI&j@}>*X zotaT8JX35*p(nBpZC0ci9Dy=BUa7@cnH~Z7*9q;CVzu)Mm@A6M0?F_0-@-Ki-S7Nh zsm!kbW8y-YXD0Q`XkQb+Q`alhVWjAKY9Pc*RYYtXut$i_08_R@LQ9s2KItmkiRy`b|2 zdlRbW)A+@;C8wUfsAdvGiUSJdBi!6HN-?0+BycqjhZilQX4{O=JacP)7Up$N_Q4+~ zOtGK{X47yz5>J84SFo+-plqG#qSmc(yNzZs#q1dLCch5ZgFO1mq$V7!?RD=;+Vigs z+@bpOVsUb+&`o5LbC{Mf|rnBb~(sIl5jGEIf4Ga>klQ;e!V>eap6xNPKKLS8dl zGyHDSaaS6ymmibGugESb1~*LDL!wn>KT%i3`OSE0nYX&9zAPAqc{a9skn0OnsYDET zo9VsUUDpu%Ly$A+DZto-RmO@m|JisM3#ax*Tw^9lU2DRIjENLOFuDRrpKGgmt&^Q; zc{HSYloG0(soJpm#x_o~Ag>MGNyRlsg04U9;6}@rZw2*6WkzuI2bMyaT>r7+$$h5Z z5t0mDc2Kl*`<-)2!mVL80>p5^%CY;NXBXFIZ#PN9`o9^tC*yq-tSN@N(9aPj9$vY1 zIiq1>LH+iVc?kK{d>P^8W92N5rdrPnwUwxLuK3%%60UIEw3XpW8;YG>Fz$1y5VZ$% z$sZJ=RIQq1v{qH%bMX*QTFuEDwca%ETT%nvdFPtt-c!*!7+d|M_pRqD3=Ie5)AF0o zSkI*khA(EQIkeNwWX{rU@>U5cy<#F?%h$?aUoU&8iF2(Ba{er~(N^3GYEk=1{ch?l3N?UrZeZ_`n1V=jMee+Pb2_|g*NRI?K5BnZUH)S zrx)$Ja(??|h|7t-n&_v)bva%9BVpB|Sx_5ajJtMP*_MMI* z!1hTqw$1A2M6;f0sLBHYJ(+fHLm$)Yfk8B(j9 z^UL`i9j^{nF(S#hq#-=q6q&F3NLeY96wdQNRB z&~tK1>~Z3sea3xnZlbk!BAMet`Ui=ekhx9PXtOB+5EbN1A|1b1O;32?LJ1szIF zA1kN|3VZxXani@RKt-?7r)@B-B)lb?l*`#*X-H+#Z9)UXA>bj}V}zLiot}|+7uAVc z67andoVmOoUkQ}3MZUupm{%zXb8M0)MaOMhy4^zqom^5t7+B_EioO-%s35J?Gn`W;aF-H_TjyYsXV*-{6r#KT-l)y0TPNTu)1&n7VaI zP&rg?%Nsz`eCmUsp!ZRu0A>t3AUl5@(mO<(XIW@hcz^=!%EogXF?l2HI!DBK>!wu? zZT$!2@D?Tof~PnyVZfcHghcGeH(f$ER({y1-7DKu6I1WhN4sJG!;oFUcfnof&nN}G z?XxW&$Wa;25astzKkhkaa*2J-4$&oAvMo1n{!#VYGohed$p={rl65TE?$r->?1MfQ9l|8Udh9Mj|pn; zTdJ-Iv(9_t9R6_vq>ILf;+plKzX62K*Y`RH^5jGn%J(4k0A1}J?H$QjdupAFm7{!| zCGpSyUtn;qrGBVYSM-g&lsqBbKEKpTJN{CiPxo$K3)cy9^XR&dfUMRCs7uyKX7!25 zR%vhCat!ZRoXI&{^?XPIbh!^VJvJk-R@iOlhB5XSP&yI>q&D-nhAdAMJk9L=G}zv; z-U+B+HkCKlv&ZX)D^XtFm!>QU&lCDal>2g|%=7tIgJLe~c=7Rt)Hl`di19=6ZCNkg z+EQXGKv}!@#5Yl3;XDn6aMaWqx=MwTV*?HDxrpw5@~rFzMGxEpOv{gV5@KA zP79m<+=1zCPbiH*SEfy1cbGlVnQ2ic+g1+rVgse%feUrlPbw-@Tk`zNUzaMJiM*S0 zmH)OS8}?%+!+}LLiqt8~Y5wPgzP^5>LoL;YwPM>D({x}aIZ^hW=fH@^KWb>vvG;Va zHkCcY;lep9{W@59y4EcGW{9>nFjIj4A%djyN$6A);R)vRou&IXax%0)4U5q~_ewpz zO7tch>0&*#-0XF&E6RCPGq(1;5cP&4%slzFomsuP6+tB9B!6L`;KYtZb2t% z?hr0kM_elnnSU$2jt+$*-Kj=qHmAWErveg)OS>C%9mtp7RQ*+#1eh8pWxG)P#Nb8`OhoBbO47@=z2`g>G8IwFVwO*(I5kWU_(81?R1`;|jPxJ+tbpTw z$lXpmRNMd>^aEO|B}lEz3U+M8#I5g-{k}=;Hm-_I8%$dj1>aRmw%t2j+?Rl%I?naL z@?*74T)BYT{=T2w_#n3C5NE$GaaP1zN><&KxA#D;H|7P?An{m)BXC#){nLd#kxS0> zH!`;C^S~cBl=+Lh(XYXl9-*nKJwa1_SU9_ga)8aRS-cf>+54LOQ^TikEQOw;U9ukA z^wxoiHu{7`c`~?RSVtj8%m_yk<3GRID)RcCmaS&q;Df^$vWw%skIV7_lnzV4VYobR z#Wwfed(vJin14=eh1L+g>X-4Z-nQs{?zn~K2JJuD&BlMrZX9VpFef2pDgu{dUFlj& zz;pMz)7+D^*&l9rY1g+bJ^uE?{*;RlwI@D{)+aS!UNMK`QKFf5 zD>6`5qyp~2*!%eG#75_GeJ(%dr+H;Xc+YjkMx=t^rEIqk96wfgb3CMHU zk_O_ds|L({^Ww&P%-IlkxWh^M*^2tiCauDio#kUv-tJVhYl4XGzEZ!ExGA45{oWN# z-3!;arQC+WXh>M;UsHaJmxz7IGSd#4vWHx3#7#a&n;5$~McYB@so0_hR(7EUZy`Rp zP@kGOOi6_}_ykKp6#JJQt zh=g5xG5Cxj`k+MqC~}&R-OP#|B#A4|lETDvzNtV-9=Mb^)+t*GWQ=DBkP=ZBHH3pf zLY|!kgoG5M34!u-$yj=AYlG*Q{P+F$(;hVR-MD8KdtxgNW|vlX8+-xw&7#KDJ;5)r zWQ7Z+4jK%s`XXNTr9&v%B#`hUc(Q3oD({CD&y9&r*OGerp!^`Cx!R- zFy?~(s6t1YlEsdkwDhxo2jWgp#{Ue&mk95^yeo~1WjXcw*KcS|5o+=_%(1``=C+m3 zd*Pvs$c64vX^p`ser9IXQ9|a;>>%6c5;eRuKO|cvOwJ%Hsp~wp4`H5~;w7_um|g0> zGXZXP3xqW5M(7n2!WS@9(bf9fhjHZ#p|HG7fA!8usZxp}rdr2C^ zw>?&;snu;cgScm#+NwFyP+yp%qG;_ole*uI?63cdyyQ?*P8>^m1zybOrUiu&JWngK zw@T7Q&i?h^Ym7X`sn3tHHe&zA5kY(BiDpke;yBFY`$|kEN9yYFMY-><4#z(}@h%1Z z=Y;<0j6tAh zsL9WpGp}^Kg)FK6-fFP&<%M(Z^W{IKbu|8bapT2roliVZzJ6iiaJ}OR=BnSt*Nqra zjAI}VNsNU{oiQk*Isee(qW&Nt>KmRM3(F_Td;Rr9G8iF@`55tgb!G$ubWhe2>OoXAaVw#ShZoN zLh@0?fSObzzTr%0xz1A%0))uw;Zw5BL>sk1YqcD-GSVx?Li4SvJ>1SQpjkZ>8h+wv zD;)kOcdpo3 z)y?I+FL$AY`huwrjyZ%KMT4>eFKN?Sj=x0!mwxVk>L27Yq{~%&stWVgjd@FkpfDcC z4z-0EX;axrN^+1mapPa65go~M#G^Sqddj=Gn-VDwQDDp7P!Rrad^=z?x$J|2;*9{C zS=fum-TzYRwCHQ>q;tp4%ostZr2QfFYnlq~E}`&nqE$L~CyPY3eiYP=*}nKFqa$!i z-*OPPdb}7Lnags2>{uX-e)%31Wx`EeGpqF746kxkkW5+x^cz}8klgm2Vn4N1?-zI8 z1;I7Beu3WtL41mdhc7>-hsL=*EVl}>;SIyouj9pk2Fm*PK>s=M-v%14a6cuYn7o?= z%=pjjKXYt*`*i02TsU|~|5Z@F|HnoC{oyp#zTvTaE)AApspnaXyEhM1S4{kxZ1*@| zKV(M^ct~VfXvAkE{eOtX-?)rfB5O1q_vK2rB<{v9Hzy97&0mQA;aBz(6%b;%FZae} z)cy9$SO2d||CfAmHsTBa&p$su7==Aw9JBoTvB#4Gxp`rXzG|~stz~eO*z$I_Wqn)e zzcHKtH-_+E(=+-X12D|Bstcb4zHCNwDki(1A?!xjh*~7yU0VRe{z~NNEnR{8a%@NQ zeIzqCihlR(`XbK9>m;oD1vL;5yAjtMman(X>@AQ`au7eTtI^^&GFsq-^mqL#XGN)< zrmynXKhn?z*kuaDmC8RW!V-vKqJ+;?H`{iE1{HE2RaUy&raW0$)O5*8;zQUe7ut4O z_Pjo1^xG5(hHq(`O2pbwD^Tsvb98uM{VdAuwb5godE>^>Q@SBXDD6-3&BTJE3GEMA zS@SYybyi_WtJ31MJSJsmk!CPptR}9a`n3Y(`i2tO&$Z8%Q8pD!Nhy=i$-i(Ns=}ir zsXffK&_6f2cawky^)%A8KEF*4tjfs+p2-SObf(^7nY%P)9-UNpRl8}7ocCa+WN0ng zrlcXWQD>`|BPT=JK^J`)UTVCef!ds+|EKHIqQ%~@%~gFnvQtQq#KWPGPJ9kY3;H zn5=*ILH)y=N%yHSVwtwm3iq8?p%No5RQF02)V$6euNdJU`vOx&VujeZ%n%!ua*)~| z8pZxK-as`UY2-d4{(*yh#uR&6c)Bx;vAD%G6+P}Omy+nDSNHYjU)|v&E|upULZ<7T z%ZH&A5ef%0m8`DP0~MLU_48eijj4!n!)Z5640@osZ!T%MUVMVTUVhVyh|}S zlHgIodOdPko_yr|t*RY2;Q}LaSUsR&kh%gnLx%mql&P`q(nxjld-aal7fgT6Kc4nv zig4dNjuR$brZ@jSQDtzq?bkTNzyrz@Ja2`brGbGFFr!=vvlaac^yYrXFu$c`-=DcQ z6)@x)Iu-4|LPw2aY-1VfNeYeqf3Q67Ip5L_s^4)FxI|l^O=)XJHPsPl#s$53$m7d% zv*ggefQht}goR1!M~jrl#b0Nhr$4JPAD)HOD`!2;ln}Kci(h@1nf;=PWZ`3+jnqHY4IchhVCIJS=L7+E#nI%&x(F>>SYGjeIp@Fd z@Ynpf_YYngT;0nmI;0KV(9?3I3BRiP4;iFv4HwPG>_ zw{i54dnWvq$A{ZjjqaKv<%K*)Z!PcS_F--C+j<#*I=>zwzFDjm%e*IwD0u^Bw z2yzlEsS9iVVyG}Rnm1XU)vVGFa%qIns!oMDx93znN2`9N&QMA-5k<8ztYoXR@DQ$_ z71?01e91g~u8){0mXmRk8fDiU$DkAH)M#a@dGP9!X_D)92OdK-$;jrF1$5_>THlPS zQ%XBIvOZ8pMO{G+WjQNq831#DIR5)#q!+J_oI^(FDHOiY`e|7v<#Tm5E`6^=AQx0; zPkei9F%%<^GCzi^A5Ge2`**rPQY)YBXjEFU?nrcb5k#k(D~?2K()}v zV{6S2t{6DS1^Vj_ZPg_rLU}bl1Nvx}?`-h<{4+5sx@BHEs}>_!{U7_dU#_{Q@fIIk z_9JqWX=hBBc;I+k{?uA1-Bi$CV}(0w5b2ia{OV0nXJq){rot=XZyV~CNf$LfH?YB& zEZWsf`BWwXXsr%GSgAHl0M&W(dBabhz$2kX%j4nkf_Lbj^>hcHhJiD!JXkR^Yq=b{ zq^#4(3J1Y&g;4rj<^t3TXdT&p*{~4NudN%^RFg{!U8)l={q!(+%em1%Ji!7G+|ruW zd?wDGUtuP$CHy+fL$Ss)>y_va2|uPIOn6m=vz$JFDSZhewdYj?fubE6?{#C~iUA%JnJiPQh~kH>W&2HYJM+ z)!`;_G*O(lcDRt$U`Dmv?McN$UjR%xDTs5g=;(Kr_TAnPSDF5kZt4M}D!~}4ce4kd zM@p|ImTv0SWVPLkiI-hjF=aYkYrg*TuQ%n(jn$Rlknx7?etGC>nyh7XlJ2o|Noj!# z?fXw&O>PYqiC&7x?weqQji<5`7223iW4Xg&hwy}j<5t%FeUfN)+UC}Wu}Aap{-*)n z=i6Sc1XkU-Trk%f5a38$3!kvvmMLe?f?IoUg*mini>`0hmHBQ)#iaIAoc8^63e!-Y zMq=knv4#Eb3pKJCp;vrF$CB0)RbQEypR~?&Bv@0O6dcubMnatf*gl3A+#{|&G~lhz z?CLC1S9jdTNZN(20zU4KRm~ZNPXE#hn7Cx=509TL=P?;h6Mi_!9TL0_WWFWy5O3pytf_xa(9O!bkm1Sw7rek@#~wdp7Tjy~qH#qlff@<#Kz0Hk$sCgzBwiEUh+1Jad*sVL!mR=(f$jl{`<}m`)5UrtbzbyY%Xp13g`USM*5Ra9_eUYFln3 zZ+M4si|ns;0I>RkgL!%s0$;Gn5y-w#G7e6I>UE|G+9oZ# zpX-QIpQa;k94K723dRCsJOt+H$S4KY83u4O8FeV)wu}{Z7T1z-c(Km+VYGDd@0Y`` z5}rH0yA?vqR5`1mr6nh$qaMO+49x5tj5Mnh zsa~#@E_gdmq4&_s8?WMZ+2hn_^vkJ^jpp0Pi3DG^nAYpJxjyMRxv_|>fMqhTl%=BP zVf&|BiuzJ`l}*c??HzSw2yYYR{<%JN`^y!D?yrjr@wAzKkZWxjQ&hfZdzQWOp^(hE z*O>L%$w!N!5n`rF`77HiST3rpFB7C z5ii0!J>*Pu34jdbdliG}6+rVVP^}_?Wi24x^w<8f;iuaa`_sH=Sb-Kh@^J^AcTZXq zY5UH3LXm-7V1!OSDN+{%o5_`Eozs@`b$@yM@#E6f(dsdBi>jAAyWrJiL;|ad*c9M8 zol847U1&n~-3H$1{|1AM56Tn10;=DGM7CoSgY_m@%%y(qq zQ!5pgpW`9FgO{b8T)s#>IZITSFUwb0L~x$ER|RY2(x>kEk;7a-K*w>DDAI;t4@s^MnrBOZPimQo5if=xpZ!DgCrtdRp zJ>MO@k8ASt|C;7p&W1HAJFOGh-iE)7-OdS&bhGvu?`cQ_EiK8)Y5exp_!)gz>Z*@Y zV*H1OIdOwteMX^{173N@Hn%Dn*Hwp@89Jg=eDSY*pA{uck`Cw{R9!%YxwzLX6jP}8 zOMg78`9w078a{Yzn0nt4hU(hr3V!htcv2)K$;m8LH0_8S&Q;{BMDIwKb@X{8dbtm{ zuW)vB$3t%R(jpJ*EiD)+jNz0{i{Q4cy!`<*G2Bx&-9~E?v+iO7Wdxo$@_N z_13g4l;^jiziEUdEy^$F$yp@frAa+JC*pK}cd^;U-!5mcq#oFHuS8K7>#o!EBkD;o zT91gkP?xAydnoX7Vr z`@hI&Ep64gb(31+S-`i*IevjkGjn6_?76vjs5e&7#FE`%;o%!3exHKjx^dNbaMA?i zo#8df87S1Vms7|PA1Fk`>(rU@PYw;PE0PU5o7p0XfBEy&@cudR?eo3dlrjsy2hnYV z+EJbdt-l3wH78J?PJMhFz7VM*Fz4+Im0aU-Oan6$8cJOBQY_$9S|4t?*u=F+To6x9 zk$c%(6`@|{@2U`dZE$AhLJEFj+4k?_yhDUF3X9l;oyVujy@Oj5%Cb^;lddfG6s1Em`RSla)mz+e(m5l`)PT-`AR1dGg1H#{O-mG7D{k-%5$US zu{X>fYL7Vu)tkz;4$$5n+57g(x=uvoM3{Y{5NL$VuesqQ{=I2E2+LtF~T>Nm|kE1Q=aj=vIhHO8TK7BWsfl{H}w z!M#rLub zuBvLV{h3=44Ry4V$Jiojs*DMTUxTWCbkj-Fvf4k#rz4n?UtcLNI6h+E!p%t7ewKCi zQ_F7!t3+JsVLue91K@wUt_B(g3OzWPZ##sozD5Y~6cdMJ1g4bCx@(IUE}G`IzG@ZO z%F+8FPwvA&5>G0RQoJ!0+9>+Gk)G;C9lR&PI2JH&)Yn+C6V$6SWv{jNNmVF(Nz`hd z^OQ8`a6v-)*$oqi(#gR+qE=1g2Bo1$ym4cGzH%&GrCQoRdODv2n2$Ag%ee{WGW37n z58D~O$W$y$NfO->yFb;%%`u;(%)`IzZue9<7hAVbvsbo!YWslcBz)`dA5rK0?80_1 zV32}9Ky1#cn`hnjC+pTs#(7%7>aOf04}HT0)#t;(ARJW)TJhOO!l94fb0AWv&+`ik z1hiMz1S+oF8B)xpM%E9P%m&5JJXbdGXe)ctG@vtwhMSk&BdF4&g(D0rgfh1| zbgCn(V=@=xE&@d~mAWi@F(@bA=6lZP~! z#vZ2^7U`I&1fZ-oNN?0(DRllio^t z9Ooh8Am+KeU;hJ2QL&y{-XS(9xezgvgib<5x1Dy$`Tu1f{y(?4!k{vIB}|n!YVP#t z8b>OOIM!|&b+FXa^qo>G{yEXxY7~|tJ68e`%{B;``MY~plh{N6GYZ#k`P~iSrg3jO zg>a`1B67Ewx^rE>-QV3T3YHrd?CU)nN)JWztuK91IX#PYV0NT@eB9_iN1ZG|jfCnR z9>w^MRXEB{yRdhcGspeq#~{7ak3zVpAaZ(QD3EOF3zII5&2p_gZXBxiMOy&wHMO3h z^QizEhN%iJYzRpegJ`NcrA^%XS$)qK?UpjNK?bTYJ5}VT)Fk9b)L?pS9$IoI_}&Gd z7L8_@lOQ?e;R_9R=_G9}Ga;x0Q6d<` zptz6DshCvbjYF}_8JK>`uA>qx;1F!=ksMMA`vT~(HqO>nSFY4!pL{mzclaR2@Vlnx zr5Tiw?)beKLgE#J$TZQ1C<$MqIdG;?*(gg34uZtWlzylMDj4M;<8Q?3Tdo*Iy0Pv= zqWzZ?m(;SaL!%w_#atV}6%G3Zt?)`+u*LN(Tot#sQ{|@lw0ep}J+ZvON(m$v=daB0 zlG~ppPaEgaJWCfQYSI|L9F%j2Y2p6`hW*K!$}|3Zf_NQ0_gnr z^}+k8Tjo6+H%9vL4`b%uiqch?+rGoa*pSyfa-dI7CS5SI;1Vso+9KypWM~FS9}q;l1E!Vhh07yQ zRL6L;!8OmSSy%ZywQCErP5FXtT0=LPv4Y-o}^(Ou{F`N>WTXq*wNTr z8ZLhW;@{u@v#LsFDJX-7Zc)S1pWpK{Mh&s2b?pJ8+ z>%MR+#Mp>nC>ZG120h91^%C_=XV)HHsN#fkH;`IJk!h%k#dW_CJd@YBFJ1Iuw(1Q;FA1d55KE7s$;6Y)YA4U`-tg?q>Lt!R-NTjN} zEWp;u<5cRuv`#r%p{Z0&L;Kp?I3-ytl3J@d^Ac5_pEgvE z(v&Vo8|Is?^7OcVOl>xx!Hs}prJId4&s5(iHUn!{!YPI$-vf1MSHdvc)6ZtXbm7M< zdXGSSZ@q*}tfcdor+ziO>^^AEV|Ff+`+K){d6|!ME>mem-(|t$sYe_of^knNSm9h& z=st1oq@;t$71g!Vj{}E5DOm)&I&F1Y6F8%HFh+|7EzJKaa`XNgG+QNj*qt)`a?NrJ zt5Kx7a)Y%Jj;+8vzg^`dt1UA(Btkt`AzFxUo%XE@sc`R&$CQ++v^b@t-A4ml{Tw{s zYzBJh?Hm$3ri0Z~^?T>~B`Q7-zay(S2@~_rpQZ(Qmd>G%Ft}UZDfjH%@r9WtMyj?y zR#Gzh$!u$y)OOp|Vyzoj)#f}r=jqn0BR`=RZ{nVy-_G~CSe^3w0gjw(tg49T6A%jS zFvr{FKx}V3nGw?zucaEc#4}Mg3j+`D$;HkG0o|YGfLiBU{Z6epT$oL_sorCab8knL z^cdHq8Op6PhVfs64F(o2EpiUh^sr!AD5%bg_7_{Qc*K;gJC+j&*$u0ti6q{!zrMmW zjIGHZH~SqDyliFm>I3fRVY_L45lZ76N`r)X5JLC(=R}>cnk0&j98Iw1H}}wA2*Tq( z!v~?H-j_7V8&ZlMffMoucwSne{NWJ@VrdQYZq>#1MkwCXB(IKn&ysr3oGNAchV_d0 z8n8UxkjRbbWavBk*Lj#_*OVb5iG&PYR!ze^cf27j3^>xTW@VYx8!zeFlLHTX8jnhe z*w2Tp*Kamm%V{9(IMby|Z6E4Me87U{vFGbJ4$gUZeJyJtc8%Fj?Wx1t5?8}2R>bb2 zHy9)X>sbuWBhn_<{#9s?cP`@dK6@p>)}->F$)AFb0!%&*$)h$l(!j@LR|)3l)q;`Q zrms?GQRgBWd0y&Ek71g6GtZH)lCU8~#15|sUBarmyy|?y2^_q#79A%odu-U2i zy2x@ybaj@%+^>5+{p?RhsA9){PG&WEgHx4~6&`aSb@SCqGPk-a=h`T-HUs*5Z1g4S zkVHgkAru)C)Z&h6SJwoOrW6P>H(~1a;8+YU*O@YpzYjmpV>yE&ZR%6+ zXC49DcH)vth4&IamFcKmRPkdO6H0KLD~C`+y3!LT$K+@i>X?w= z#K6LVTDYI8wCto5&AsOv;aY{2Y*X$Aan0(Nm1|URW7&{MR?|71ZGd`aW|%-`X4dod z)q(7xAYY|Cb``uPhcz5B-&CjthC24V^@H)S_9g4vyIqh#vAQ?CNYPDC)~Cv=CZ!Cx zeR>Nyw8^oolf2cXHB{VHq7<)Ufs2_@+XE zmDL0nShnT0$=101Pr7BAJ*&@>OjNAU$som`+DeBtz_-S;?{ja z_90*+@7${MZ}v{5luwR?VV}9Im#Ebt%7LX6nvr*sMfS27KXsz*(g$>|FxS8N*QIAp zBFEVH^y?2*IvIx~XU3cFOA#%~<&Ts`mppDq3Fbp`_$*u-MC!-q%jD)m15PQ3Np zNKaKzFV@|tA8EhwpJxlcKlpOvXUdGx>@E+3d)$0&tMs5 zLd}_o(b%75i7->gA^4N3MTI6+fzaED=T+(bOtf#ThRQwrIw`nL#iqSprA=HICMEal zoK2IeL(KM<0%kVufM&yd>dk{W7H>6a{GtJ$_sd@;!~`@M*U%-tf_tkwuv-L;NO8*bzt)oR4gKYA(D;b- znpRT*C>p6-yL*5X@@sGiAIFq`(Jj8@i)Iv2(aJo5-O;gO3F-U_w=6Pc`svyMN!2<^ z*x5T@b*`^K<4m=SCMe~Kx(T#$RRXoV(gY=s*Z8Z}KBX%ujsz5{-O+HHog`4+Uu_n0 z*;d@UZ;g1*O{-s$`jP&b4r7WNp?+DtQPA{qMYkf04VI)f4Ji!d2moly#I=rgt0|GpabMy<;l0;<}SyRq9_Go5qM5Eg3}4HGE#P0h3ir z%U2pSL^^Z5TgU<>Z|lRewgpM_xn6%*PQt@xB?(~3ZF6XHyu)h{&kyIn8sGIxP(DLV zBrfVyu*+xqe3ew}a|eGvK{yIKpsTK{=#xqX(TU=balDFv8G%c6_S`75qG5pof@$VZCx{uj@$R)I{VE z(l%_`Ggqs0{KZ6fp6MwB#Ev!w5qAlEeFU}kSSW`-qWV_RdK*XjEA?jcqc0D@sZBYJ z+wS>2O%V0r_`#>bPF?Nfj>-J4N`m>vzK?o~#M`nHuG6>D1WRy$WnWx9xqmPf2Xd1e z(HEvQKGk5xHx-n^Qj}OX_Pu`+@@R(5SwVD3NL);e!g9qX(2BGlXjR`5-_hf@zAA3} z@)13D8^RE&Z5i4vM8Uyd%I?ydv-~PQ-Mwe4nwb{L&qjP8O;CIC?TZZ+1g_x1h<{AF zX+2dc%#`;uTDAn3sqH3c=#!=kkW32JHP;@gTPE)z;@O++_3EgKgy(En@E}-?#!#dP zDTb6%%(cLA!CZXkKKlSH6fUSLCVibW>=ioLym_lj(EAfoZqO8LG%MlMle#SqM~fqE zO(BjeMUJbf1s3*WyJIBf_nInI(eMc8P#{!sIMg0w8y|uQrm$mL_8(YqL`xTLwET6b z6yi9Br&|wKq&OnB-sv~nd;PwfJ`{a}l$FsMVzH0W`p|{reAoA^Y(QQiI5#ZNp)^^l} zQR=zJfs0?|G;RN61cO$mEYuRpuZ@vVEy5J_@QQC`@@;NT2q*U^d+J%;{y}9L&vwxY zDb5*k;qDUPpOmV0A=G0jS8c^8=01~pa+MgM+k~=Tsj#`pkS5rXx-H8J5{CB#s_kAr z(w2kZEMci@6*OZQ8vlyNc?EEB-UZ_zAXC1fl@^Bq*PUOXnl2WR7dBw_Kzl7LT<$VW zk^8a1F(mj`)~kX2+Gc8-!74y;5~{qlt=bu@7ZLskF?1(7Ae82tvDwJ)rC!)}{G_D! z`@n-2WvecQ3%f0+y3-jxhdc$i(9kH6A4u2Z>5L~;Qp4WxvU5u}GX88_uALbU)Xl|1 z`l`rJ^I-XUWip{~xH>WIER6rX`3IXmyqMqzmPgKTEHll)mNiM!S-5Piod)$$CW%2C z((w*H4()+&%Z960Ch*^*{^0l-f&@#5C5jUz<4~9`P4~0nsQByA{Vnc62;p zQ0#uWui8@7XH=B0NLEljEprB*zs1viJ^tpXVk zIgI&=H{r!;E>{MgAxb_nNZ}1pnu+RyeMi?*-=J&e(y1^hX90EyL>$_QNjitmV@3o_kER#)9;w8m+~PphM=z8*}Mgj*2cly6dLI{!Y!I0?r|8U2s2n6c@4(iA#-MmYw_=6 zYDw??b}O8dTDs-#0`x$f?4sOyplGS3UV2T%!tQ$7Wd91&{Ca$2S>Hr_g~1Ute7sre zS;KHW_wab3rL`Q`3xU3?a?V{0eLqxOHT4a3?R|tRReg2Si4NjdneVpOE1#(EC6qe? zR0!&!rGOHxF)JAwog5R`f&7W-o~#*)`Hpq|QW9yqS*~368D%6XSE2o;ZRAkxe#EBV z|JB}iMm4py>+WwCdqo970hJ;h=^c+qF@b;yodnoQO)x=1kNk|Y# z2myjyr3fMPA|yejN>`9B?wfP&8RwjDj63eH^W8hn82jIvYhZSH?XI8?;{>KDBPVePzsA6QkqI9lkUuNih|n5N zHaDB@59+c8J*tav^`W}s5t?{BR*9$?cz=S_n)YS0qCWMmQ+-7KoEr40@8A^o4LseU}-ZDYqYCx)ti$*G^na%s9YM$rLEUMMoG_ox_20lyrV zPT`xK2dL}#-v%wYQHyTURy^2pmr#5eMUyOhCdc_b-f>yCBdgM*FT|1ObbmGoR#=ud zIvp&u`HF=JCnwns=P=K%hD1Y#zn1q1*-;-(4Zbb3c{8ES+iw^=w|uQV;aFpuh!~Ky z^m{Sk)WJY(R|PeV`7swsN-zS(X0685omxQuXGvQS_9THdt0Qk?dmFf z7Vpe5Y->0^YGB?I2XCex05%gVZK3nJe+ate87Il&D1E=^K(UYCivc67!JE! zbT^^jY$C7w1N@D4BSOlUqW6asn{8*dtZ3Y*N zsJ5n3fg~%!9g4XEDW)D6Q^-2k1NscB@FAUDRM`bA)X>md-*#TRK7pB>cI<+d=txhr z4>CdnP8@IWHH`n<)3&jWZ#D>yjIB#$Nx9Y3Cu)}wg4%|+VoXbM&A;>WOo}dq;yFk7 zNq5N;U+)Im#$M9OcmYIKWRWZz`$&LmQ{}g^5YfL)8=)oqV9-}F>l0kJSxC5r-YFZ8 zMWCYT1<*&gy1eK`mPB0W=_8V&ay{;baOvzPq78lQAfia}_4C zIwPJLovzwIFLOjJbcW_2J804UMCILZz3lV8y(AY#g}0h7ZD_zF4N5dFen(wPMbI66 zPnW`-bzqw<4Dz1cAjWYfW7mWeZx`@KFcr*SDu zgZ|dwj;ywCx_AheeP;(}6R_IQH`x~w)52Xs4Ab~bH3wc<^mkg#WhYZDgU{d7tFexK zs)%XUrH6$PIABfx+<4;Y(`P-zoqsf-$Cli?cH6(!X=i@g_2dhAw6dT$lCXN)c*!LH z&!3D`jvM{?6Xm+U^t95W?dq?zy|_PrYF;b}^PFq&Ynm5+_)zS(IF6P{SANsNyP}${ zX=l2^y4tZky>y%EVTG>}_Uo|9Zzbu99k^Q(d|k+bJ{%yvUh(ZJYLF2iZp!L=^KDor zqWdV*g6q<)$c(2dIj`hzsCv{foh0%74p-L{5{kS8)8Uc-H$BR%!StPvIO5LjP?HZknf znRG|**~H^W5lWz6dijaZ0^O}g&YbNal;|AM06d^DojN5Dfkr>s=q%`cAJig- zVJLX)0gTPLaQ)qW(DwwBQssO2WfPh?$n zqJRm8=7W1H1m?BPGr1|E_5vb6BH$Vb{v2A?A9dp7*CidHRMVFhi1# z4uKwPq6Ji``D^yfZvaKtB9(?17Yr)Dw1Ydyb>of~GKK`TKpj7^dohCfMK04Ze)*V) z|7ebVB|tIT!`Go7UveMK3dO=d>YAJ$`IEAtFhb=#-!v{!?<8(x-0s2UCPAhw9e)z3u(Wm2z_nvgsD_7ZA7msg|tjp|f z!)5KfiUJ@7*8rlpf|8#v9f$V%%s%H1uCyRwW%EfdzfAm&Gj;kN%yOGxJ>`W)W47{) zj>hp3AP8>~>Rbz+BNrh5sP~hvx;wdL1tWHanH88PSl!`k-pZ2At!cc`m-qbXDn87h zoNM5bu0&>)<-*;DBdflQV)wuqV45TksC`FHAss(9H~^--f)4v~_{d6uI>m&lpqQX4}IP(tytb zz7%NkRhUD1)g&%gzr|hYGll>ik5@S`st&cJPJ(<#D54|oU2duA;VKvWYf*G-lu8Qz zUm8{m>sw&KXblNataeleAq2+6T+wekjC&SnNRqE0Rt&W64RlX7WmTox{-U~MJd%Es zs6K(b)eL4_48ATa5L26Ibs;*p=4!-daVGl$w$xag)602 z%~k1T;8|iW8|%bvNM?~H1O-pXqBXW*z?*!)(}9ERtNQaF=fl4)fR`Xs``Qj2Zlj!Z!;158o2tTD zRe-XbG}$drwIXCI`+H~cyT#|<2-lTWyIvc1b?1Vvb45w+i>c@2MnVO4yK!!1nB`@(0QAR|1aHSue+VgT&lP?tLqaXWCo1{GtG`_Qng9a9L zoM)i1xU`y(p-Me zetn;eQvGA*I7cz8vz;jv6m>I7T%oz2IYo8Rb;}66bU{3={L2Hv5!EyCYQN2>6d4hH z>P!L)IJ2L+5V`~lrv;Z=E$$wpoK|*8k6)yodYpz@6sB3CNlut(|6bJ~bN^G}`MIg6 zXkqQ+1JL=X2dm{IPaQd2u4nyavvYY~-Ok!M-gmBQFXxwI)U-@@%*^ii27>{Tkt z4vDs5Rhw_O<%As>xDYfwEK~RG+ly~Q)FD}&w+R3{!Np!})8b-msqcK;<;nXaU4lR) zOmNt||CpxdI_5tp(^ zsX0yAp2lc85(#2T#sMjmc4OU(#cCYhQrAv`yf41S_;$NXLucYfnzK>^Ozc#XKhkpX zDlscpf8JZL(o6uOd<-jw)Ac7`uT=Z0U8zm_UUJ~r}Xr)rG^ zuF^>KyvY0YM{k!%F39kDI;3&~YD{&{fpUlbw4aae&A;jnFO9=X ztJ_kggkj*8CXISRd``o7vTuDs8h1N(vlG1pXPNd0$$R|Vvp0TViay4zAzMA>KTq6T zpFaL0KVbj$lJP~WB-Y3su+ojf_J$t1ZTS%R*DMneXhi3`G4&Eh zEOWddV6bUFY zATDlApAfYANjm}7dv!kUglUfiO@PV+b`O+hh`B5dcgcd>;V{*>Raw09n6U5h0Ln~h@Jl#I3GRD7Ytr{}m@>vz z`3=U=J$+!!=9TY{-xs&3Mq&HS$Dr*+G@72nlVqMM~yyD|0_Q&FwiOm79fQH8G-p0+(wRzl zmzg3XD!zD{A$!iJ3^X=vJYZb5Pq?1{eb}!p;QOCHaa7M~h4SNB_c#cqZ21(#HnrC9 z;hkQzr-n`;n}D(v!@Qpt<4(iLt)rCoES?VPv>Hm3VMq0`E+kV2BA*6AYcDD|kmH== zhkHVBX?*ZQJyC+P?XTXjLx9q6b0rmSIhuL95p1{~*TCM#^Hdv#rcKIRGW-$e$P%BU z1*7+Bfm!L{Y9>@>^F5QG_UX{+)v@&&s8s7geJBsxT>++O^rV9t9Xkf$5K+sdGxP$2 zKUl&cTT@eF-@eOdqX=E?8DB)2{XFzxUs#F^K80)7G90SMuCp($x!|9^^CSz2iETsg zhhCRM9GeSPxw4rY%Zz;_IVc zrTmg+?E1iglxq_hrn)&=O1eqMhKzHyn|N{SNxMqzh%VKxJyyM(avK16@pt?l-qRXxl`>1 zfifTW31Y=6EJ1^Ek-8r)!OZ(@k(kH*`>;nkx2pifq%jNG9bqZK{H?`3Q)`D>zV8a@ z4Fg)}d6AJ4{m5jY7^LpVU107nQ#+3iW<~})$$m|{G&+|Y@o-y{zk*1Y$J$ub`#Si^ zU_wW1DjLc0jqslMoPF+9j|6RHyQ_(_tI&RfO$7>EG^{U}%As#KYI z>O~97v*0ucK+YWV9^)e0cu{3uv8x-@4GBdyHB639ZuR|3{(q?}_|yMXrGlSFYujhT zye7wbCmVEP_I;O_>V!MxPBH}&_=l8tKnz_a{nS;s7O=lenwZ~Qu=Nlu@8w3&Q&?hx^zi2C@_N$A=I{QCtBC2f#L9Z&(JP)0P1a!2>k=J_C z5n(JDftlTd^26Lu9iZz|-Xgv%k91GOa;7J4gx@no)(bnITXcu)JrA8Yc+oSA+BES3 z@9cj0sI}{}V&u+w5=E5q07e?qiK@Dg{JXD5mdI<7M70e(9@mQ13E+C@bW|+vSwB*n zy>=e(5Gj$~WrqZ{z49zobUeQyS~xS|X?@hWV7&qp7d&%AdLi~1O}A(kxza%Vyc-cg z`h?sn=-iZc2^&NV;l}k&TX%7RI~R;jx_bo%qestkCPM-~rM_N%dYy-|Tvd>B@F!%$ zeU#IlP;*|!BaZ^KF$-B|{e^prtE?YS2DF0e#e8^9&MHwXbdytScKKW=a^t48B%xkz z6xZS8>`kL&ELAtR_fUYplUa@;Fp3cWO#Eu;-IZ^;hTiQE)1>Y#)2SE2M91$Chh2kw zKQWY)=+?<{3fx0(iicv!sToYFr+F8mATEguosS<+mlFdzX-*XBuGmHTlv|H%;E+Oa zfO0S<5Vg}KtVhju_U&M51D2SL9W!cflE7qhP;(leN&ry#VVkNC96je{fO$zDA_n1+ z;-BjPRV@(jXiMenU3j^W2ZS}^d~dBr0rY7d!q}HvK3Ra7`*9DkP3=Cjy1aqRxi(yq z&a~nIsa#Ff`!@B_A9(|X3F&j~PeZLz)gD&UUJ_lO&L7>tuItv>=R~Ed>Juc4m1;vf1NfcJiUog$_P3;B`SUeRW)Z1f=Fyhc&v*UGd}@^IoYnQ0 zg|JXZT}N3K)+=PHAag$U-jYG8Egz;rWV}%o>b@ffwg?_4&zKyMPcHMZ_OVe^yRm*5 zB8ePU$is?!5?9H~71L12bPr#udVg{ZrL%7D^Y06p1UXQ?J;$9vn;4T=p~>#l?`NY> zqor-7Ba<(@J+q%bv8UvA2c%R98NCjqvHLCE?%QjxrJ$Vll~irF=BRKklH?Prf%t)o*w!MaO|_I}#Mvn(aK zHtbK%OS=$*=-$bEm6(|?D zc&kHmB*~oL3;|#j`?tqFTAZ#RhD+}Y0>ATwH7ry#h1}hLRWV$F34ygg%0+*a2mJZd zexC3~O*P->G5^=)PrSX?RUfcL5>%|b;A;_$*YD5+K(;3TeL-QwA5{j#{SM^k6Lg zXzn#3PPr8RTFo0Ud*$_d*N~qkoTSr{yP^?ujS&g!qhJDH+38Pb>QA;rdRM&o@jlJIzeyS}D5M{k zD`WGz_|e?3c3Dge9f|TUaa&TgvuE%HIrn^4o?JM$oQ-T~R>Gd|)w_t=*ZWlH?9%2k zNwj@&cS&T<=HQw>FtYya_>8#_5@}2{yrVVbP{q>%>_otw9X|`yA$^?&xHO`E1uKGd zsJy-aQo{(A?RN8XLb$~OU2W*)M*vJNkZXV}r;7QWdg28H$W@ZT1qDKyF%<#Hw1?zG z;?nw(3%+ViX;$_il`bI`dd07O>kvJ3snPFEob=SDE=BX)jYusCT^#i2(z|(6+Or^w zFV=%9yP(5zd3~O}ytLf?AI)fTDEuQSQ8CbGOo!^bA#P+~l%d8g0n1rd;o8S8U*cyx zI=t$vi5v|zvs7n6lYvxyY(PWqtxQw}CsY=&bjkRgy)Ev}v(X8RuemujMAdk2+j^m? z?S6gOk^D7xPIX%Hw(d!gxRj9As?NqI;FG$*Y#$D1O$Gjyl~foNVtJ;3s>qYdOI z@cy-TE2a^Wt_+5o5$ar)9{|~xh{@TYQ8AwJ>2*N*{yp!(AF$6X<;9J(HCwgXwSV;} zk&3blO$_&^(~zYVPXnKE$kTl5!-C3+KYwcDA(|JgCM=enF|=jnNbtI-L6#^!q+dK+ zD(8C@L6{2U57aFvWlm1m+6*{5VqxyqWlam zwPDmPZ8EuPMCu|+->7cBdUD&H8wJbMyEff&{SL`_Mswf>2Q#Z&(5^c!juG+u=vExx zYNs5ijAh;*L^F+j0b)8eDh-p>1e*^0L~{B0X>fammtyUldA8&;S%Jf} z!)5zAsA5~|{p%+YG6RO|1$nJroT!R#%@dem6cpVfkE>=&w9AQcK*M&Z5&MAv(x$Ro z)H}1O^hDen(_C3z0Iqp)`@tUr!@xpLSncf^$jK89R94V+@Ni`pQcwPPazr*nUj$Ukd-&!q!uqK_`R!AuxB8q2#BT4S)a@-` z?y2(N?j)&S)@~=>EmuM;od}=?WjKswg%}JN2F;0OfGsrfd5x8&Luk3zQaU71gr?$_j=aWeLiXpKQ!PS8+t!L0 zq9IrDt8CU+se@lCLb#GFpBI90B?ImT5I^`@f!LZ-6ID7jW=I5&U^4E*p|<3+uv*&O zR}=_EDdUr^z?l3m!Xy8GHZbqMpbCVShd2QmGnE?VX%Zvv?zC&p6B|C=kKO zj@(k}Z~YB_|BsQnr1mPm&T%M1rN_dPbu8APGxc^Jvgz(LJxFvybi(EX@@RxWWGZzG zyp!tTY0>43J8zq zGuXdPTBS%}D-7iRc$77jNLV-pcqlm&UP_9*je#Bh>qq|`Ls0&@AkrLrPi@sD20Q0{ z4{iLlJ#t{9Y}Ls4&!3(fOmF6u>@rG)TV@$;P}d+cv)}(=2K>_+ieoK$yT3gC^Cwq? zG~WH(TQ9jN@SPc-1<87Z$m(>`xQBNTf7PnkrN+%8EMk~_Ef(R)r^|DCziof`Tu z=(}|QD%gEkcY^FZX7c{v z_J4Mj)Zwu;zs6aTJ{j;nsad)z=G1cRKwawtov-~noJsz{Zj9F_YSfu+ug%k?g3JkB zXKrGvq4dAMUbIfleW~Tx(r-q!`Ppd}-Z_jRKCNh?93o?gIlfrKLk9+D$ z?0k;)SwqEsZ>|07lm7abgme6_iTB@UOUVQE0s@mhadP)|;1z?o+%-rh*ud5v4AN&G zd6zjPTRP9$oE>ww?pLsdwflEGX%jj3Pm5>&@Zi6l+I~uJ*!H>g@-xc&u*%PMN)RPE z#U_N3m2!=E(4KE@OP_q<#1d)m*DqK_m2$}Oga2?p1D^lxN^)ZV@^pVW@4w>FUm^DY zx)1`(Z{c4$HLx%ggR|m3OAC&X%EJleEK5{z-rk^i*63p8?aIgy`s`3&8@Oo9q_I_X jWY^W{26#OF<4cQYDc&Fd!Eyc7_-h3IzmI_QpX2`v2Kb2? literal 0 HcmV?d00001 diff --git a/ml_system_design/seminars/sem_4.ipynb b/ml_system_design/seminars/sem_4.ipynb new file mode 100644 index 0000000..750dc58 --- /dev/null +++ b/ml_system_design/seminars/sem_4.ipynb @@ -0,0 +1,2384 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "id": "45e744e4-abbb-4572-a696-53d83e53ac36", + "metadata": { + "tags": [] + }, + "source": [ + "# Семинар 4\n", + "## Дерево решений, композиции алгоритмов\n", + "\n" + ] + }, + { + "cell_type": "markdown", + "id": "94996e48-9d13-4d79-b912-1d03a98cb8c3", + "metadata": {}, + "source": [ + "## Дерево решений" + ] + }, + { + "cell_type": "markdown", + "id": "64918135-d4bf-47b2-98be-f2c3418a4a76", + "metadata": {}, + "source": [ + "### Классификация" + ] + }, + { + "cell_type": "markdown", + "id": "767f1de4-1278-4b0e-890c-eecc16eb2477", + "metadata": {}, + "source": [ + "Чтобы понять деревья принятия решений, давайте просто построим одно такое дерево и посмотрим, как оно вырабатывает прогнозы. \n", + "\n", + "Следующий код обучает классификатор `DecisionTreeClassifier` на наборе данных `iris`" + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "id": "895555e9-deb5-4746-b09d-4bd12d845136", + "metadata": {}, + "outputs": [], + "source": [ + "from sklearn.datasets import load_iris\n", + "from sklearn.tree import DecisionTreeClassifier, plot_tree" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "id": "b9e0d816-b995-44b6-9093-02913f17dfcf", + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "

" + ], + "text/plain": [ + "DecisionTreeClassifier(max_depth=2)" + ] + }, + "execution_count": 2, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "iris = load_iris()\n", + "X = iris.data[:, 2:] #длина и ширина лепестка \n", + "y = iris.target\n", + "tree_clf = DecisionTreeClassifier(max_depth=2)\n", + "tree_clf.fit(X, y)" + ] + }, + { + "cell_type": "markdown", + "id": "af429158-8a9f-4cdd-a2d0-38a8bde3f1f2", + "metadata": {}, + "source": [ + "Вы можете визуализировать обученное дерево принятия решений" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "id": "5451ccb8-91d9-4d1e-9531-39899d8ebb94", + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": "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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "plot_tree(tree_clf);" + ] + }, + { + "cell_type": "markdown", + "id": "d88c4816-ef84-4d4f-ab9c-12ab40215e87", + "metadata": {}, + "source": [ + "Атрибут `samples` узла подсчитывает, к скольким обучающим образцам он применяется. Например, `100` обучающих образцов имеют длину лепестка больше 2.45 см (глубина 1, справа), среди которых `54` образца имеют ширину лепестка меньше 1.75 см (глубина 2, слева). \n", + "\n", + "Атрибут `value` узла сообщает, к скольким обучающим образцам каждого класса применяется этот узел: например, правый нижний узел применяется к `О` образцов ириса щетинистого, `1` образцу ириса разноцветного и `45` образцам ириса виргинского. \n", + "\n", + "Атрибут `gini` (`показатель Джини (Gini)`) узла измеряет его загрязненность (`inpurity`): узел \"чист\" (`gini=O`), если все обучающие образцы, к которым он применяется, принадлежат одному и тому же классу. Скажем, поскольку узел на глубине 1 слева применяется только к обучающим образцам ириса щетинистого, он чистый и его показатель Джини равен `О`." + ] + }, + { + "cell_type": "markdown", + "id": "5b57fd4f-4de2-4e88-9050-bdfe00ca50da", + "metadata": {}, + "source": [ + "*Одним из многих качеств деревьев принятия решений является то, что они требуют совсем небольшой подготовки данных. В частности, для них вообще не нужно масштабирование признаков.*" + ] + }, + { + "cell_type": "markdown", + "id": "c76867e8-21ab-499d-812a-c64969c198cb", + "metadata": {}, + "source": [ + "Дерево принятия решений также в состоянии оценивать вероятность принадлежности образца определенному классу `k`: сначала происходит обход дерева, чтобы найти листовой узел для данного образца, и затем возвращается пропорция обучающих образцов класса `k` в найденном узле." + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "id": "b9ad8671-d9c4-4c00-8a86-58a123e3e06e", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "array([[0. , 0.02173913, 0.97826087]])" + ] + }, + "execution_count": 6, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "tree_clf.predict_proba([X[132]])" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "id": "eede6519-b26b-4e8e-b1f6-f1c8b45a1048", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "array([2])" + ] + }, + "execution_count": 7, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "tree_clf.predict([X[132]])" + ] + }, + { + "cell_type": "markdown", + "id": "c7165ddd-a8a4-4c66-8fef-aba3241030be", + "metadata": {}, + "source": [ + "### Гиперпараметры реrуляризации" + ] + }, + { + "cell_type": "markdown", + "id": "f6982729-b85d-4693-aec3-e19a565088a1", + "metadata": {}, + "source": [ + "Деревья принятия решений выдвигают очень мало предположений об обучающих данных (в противоположность линейным моделям, которые очевидным образом предполагают, что данные линейны, к примеру).\n", + "\n", + "Так как нет никакх ограничений, дерево будет адаптировать себя к обучающим данным, очень близко подгоняясь к ним и, скорее всего, допуская переобучение.\n", + "\n", + "Во избежание переобучения обучающими данными вы должны ограничивать свободу дерева принятия решений во время обучения. Такой прием называется регуляризацией\n", + "\n", + "Гиперпараметры регуляризации зависят от используемого алгоритма, но обычно вы можете, по крайней мере, ограничить максимальную глубину дерева принятия решений. В `Scikit-Learn` максимальная глубина управляется гиперпараметром `max_depth`\n", + "\n", + "Класс `DecisionTreeClassifier` имеет несколько других параметров, которые похожим образом ограничивают форму дерева принятия решений: \n", + "- `min_samplеs_sрlit` (минимальное число образцов, которые должны присутствовать в узле, прежде чем его можно будет расщепить).\n", + "- `min_samples_leaf` (минимальное количество образцов, которое должен иметь листовой узел).\n", + "- `min_weight_fraction_leaf` (то же, что и `min_samplеs_lеаf`, но выраженное в виде доли от общего числа взвешенных образцов).\n", + "- `max_leaf_nodes` (максимальное количество листовых узлов).\n", + "- и `max_features` (максимальное число признаков, которые оцениваются при расщеплении каждого узла). \n", + "\n", + "Увеличение гиперпараметров `min_*` или уменьшение гиперпараметров `max_*` будет регуляризировать модель." + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "id": "4b2e3de6-4ebd-4644-8b56-df2b7a1ff7e6", + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
DecisionTreeClassifier()
In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook.
On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.
" + ], + "text/plain": [ + "DecisionTreeClassifier()" + ] + }, + "execution_count": 8, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "iris = load_iris()\n", + "X = iris.data[:, 2:] #длина и ширина лепестка \n", + "y = iris.target\n", + "tree_clf = DecisionTreeClassifier()\n", + "tree_clf.fit(X, y)" + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "id": "91eb9c03-a51e-4d6a-9eb9-cbc8eb4db3f0", + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": "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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "plot_tree(tree_clf);" + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "id": "76196f06-3718-42e5-bdcd-651ef7995a8d", + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
DecisionTreeClassifier(max_depth=2)
In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook.
On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.
" + ], + "text/plain": [ + "DecisionTreeClassifier(max_depth=2)" + ] + }, + "execution_count": 10, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "tree_clf = DecisionTreeClassifier(max_depth=2)\n", + "tree_clf.fit(X, y)" + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "id": "5d55685a-4bcb-4b08-b8d8-dfcf08327c23", + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": "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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "plot_tree(tree_clf);" + ] + }, + { + "cell_type": "markdown", + "id": "9d4fb6e8-ac23-4f03-b3d5-4601b90b8696", + "metadata": {}, + "source": [ + "### Регрессия\n", + "\n", + "Деревья принятия решений также способны иметь дело с задачами регрессии. Давайте построим дерево регрессии с применением класса `DecisionTreeRegressor` из `Scikit-Learn`, обучив его на зашумленномнаборе данных с `max_depth=2`:" + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "id": "018ec935-f891-4d85-a0a0-dcce08c57726", + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
DecisionTreeRegressor(max_depth=2)
In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook.
On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.
" + ], + "text/plain": [ + "DecisionTreeRegressor(max_depth=2)" + ] + }, + "execution_count": 12, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "from sklearn.tree import DecisionTreeRegressor, plot_tree\n", + "from sklearn.datasets import make_regression\n", + "\n", + "X, y = make_regression(n_samples=150, n_features=2, noise=10)\n", + "\n", + "tree_reg = DecisionTreeRegressor(max_depth=2 )\n", + "tree_reg.fit(X, y)" + ] + }, + { + "cell_type": "code", + "execution_count": 13, + "id": "ad8891eb-037e-4ec0-bcdb-aec87c2a4f22", + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": "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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "plot_tree(tree_reg);" + ] + }, + { + "cell_type": "markdown", + "id": "b6d9ea82-92d2-463d-8a00-def5658832ea", + "metadata": {}, + "source": [ + "Это дерево выглядит очень похожим на дерево классификации, которое мы строили ранее.\n", + "Главное отличие в том, что вместо прогнозирования класса в каждом узле оно прогнозирует значение.\n", + "\n", + "Обратите внимание, что спрогнозированное значение для каждой области всегда будет средним целевым значением образцов в этой области. Алгоритм расщепляет каждую область так, чтобы расположить большинство обучающих образцов как можно ближе к спрогнозированному значению." + ] + }, + { + "cell_type": "code", + "execution_count": 14, + "id": "4a9cd783-9748-4b55-877a-e8d45b6ac079", + "metadata": { + "tags": [] + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "12.938154389121735\n" + ] + } + ], + "source": [ + "from sklearn.tree import DecisionTreeRegressor, plot_tree\n", + "from sklearn.datasets import make_regression\n", + "from sklearn.model_selection import train_test_split\n", + "from sklearn.metrics import mean_absolute_error\n", + "\n", + "X, y = make_regression(n_samples=150, n_features=1, noise=10)\n", + "\n", + "X_train, X_test, y_train, y_test = train_test_split(\n", + " X,\n", + " y,\n", + " test_size=0.2,\n", + " random_state=42\n", + ")\n", + "\n", + "\n", + "tree_reg = DecisionTreeRegressor()\n", + "tree_reg.fit(X_train, y_train)\n", + "\n", + "y_pred = tree_reg.predict(X_test)\n", + "print(mean_absolute_error(y_test, y_pred))\n" + ] + }, + { + "cell_type": "code", + "execution_count": 15, + "id": "81e06757-4fe7-4f48-83a5-0c5b7bbe6ff5", + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": "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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "plot_tree(tree_reg);" + ] + }, + { + "cell_type": "code", + "execution_count": 16, + "id": "dd645280-1bda-4c1d-95a5-9829f696ffd8", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 16, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": "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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "import matplotlib.pyplot as plt\n", + "\n", + "plt.scatter(X_test.ravel(), y_test)\n", + "plt.scatter(X_test.ravel(), y_pred)" + ] + }, + { + "cell_type": "markdown", + "id": "ec794d2a-6c68-4333-bc1b-06a5a139a9ab", + "metadata": {}, + "source": [ + "### Композиции алгоритмов" + ] + }, + { + "cell_type": "markdown", + "id": "00b241e5-1e39-467c-b3b3-3f634cad6df6", + "metadata": {}, + "source": [ + "Предположим, вы задаете сложный вопрос тысячам случайных людей и затем агрегируете их ответы. Во многих случаях вы обнаружите, что такой агрегированный ответ оказывается лучше, чем ответ эксперта. Это называется `коллективным разумом`.\n", + "\n", + "Аналогично если вы агрегируете прогнозы группы прогнозаторов (таких как классификаторы или регрессоры), то часто будете получать лучшие прогнозы, чем прогноз от наилучшего индивидуального прогнозатора. Группа прогнозаторов называется `ансамблем`. \n", + "\n", + "Cоответственно, прием носит название `ансамблевое обучение`, а алгоритм ансамблевого обучения именуется `ансамблевым методом`.\n", + "\n", + "Например, вы можете обучать группу классификаторов на основе деревьев принятия решений, задействовав для каждого отличающийся случайный поднабор обучающего набора. Для вырабатывания прогнозов вы лишь получаете прогнозы всех индивидуальных деревьев и прогнозируете класс, который стал обладателем большинства голосов. \n" + ] + }, + { + "cell_type": "markdown", + "id": "864fadb1-cae9-4c54-b840-e8a05bf8f775", + "metadata": {}, + "source": [ + "#### Bootstrap" + ] + }, + { + "cell_type": "markdown", + "id": "6dd32d51-0e93-486e-95c9-151c65e22842", + "metadata": {}, + "source": [ + "Можно представить себе мешок, из которого достают шарики: выбранный на каком-то шаге шарик возвращается обратно в мешок, и следующий выбор опять делается равновероятно из того же числа шариков. Отметим, что из-за возвращения среди них окажутся повторы." + ] + }, + { + "cell_type": "markdown", + "id": "ece89bd2-7d39-4799-991a-28dabb988616", + "metadata": {}, + "source": [ + "![](./imgs/sem4/bootstrap.jpg)" + ] + }, + { + "cell_type": "markdown", + "id": "061a316a-0359-47d4-bbe6-7b1093eb6469", + "metadata": {}, + "source": [ + "#### Bagging (от Bootstrap aggregation)\n", + "\n", + "Bagging - это обучение каждого алгоритма МО на одной из выборок (у каждого алгоритма своя выборка), полученной методом bootstrap, с последующим усреднением ответа от каждого предиктора - в случае регрессии, в случае классификации - ответ выбирается посредством голосования (какой класс предсказался больше всего раз, тот и выберем)." + ] + }, + { + "cell_type": "markdown", + "id": "8cf961d8-26cf-4ca1-9c83-c44fb5ae00f4", + "metadata": { + "tags": [] + }, + "source": [ + "![](./imgs/sem4/bagging.png)\n" + ] + }, + { + "cell_type": "markdown", + "id": "054a8a82-7d71-4b3e-a32d-25dc0281c959", + "metadata": {}, + "source": [ + "Эффективность бэггинга достигается благодаря тому, что базовые алгоритмы, обученные по различным подвыборкам, получаются достаточно различными, и их ошибки взаимно компенсируются при голосовании, а также за счёт того, что объекты-выбросы могут не попадать в некоторые обучающие подвыборки." + ] + }, + { + "cell_type": "markdown", + "id": "b34215e6-e821-4bae-84d3-d090386c9393", + "metadata": {}, + "source": [ + "В библиотеке `scikit-learn` есть реализации `BaggingRegressor` и `BaggingClassifier`, которые позволяют использовать большинство других алгоритмов \"внутри\"." + ] + }, + { + "cell_type": "markdown", + "id": "567199e8-ce12-4958-b2c9-f78a76996523", + "metadata": {}, + "source": [ + "Показанный ниже код обучает ансамбль из `500` классификаторов на основе деревьев принятия решений, каждый из которых обучается на `100` обучающих экземплярах, случайно выбранных из обучающего набора. \n", + "Параметр `n_jobs` сообщает `ScikitLearn` количество процессорных ядер для использования при обучении и прогнозировании (`-1`указывает на необходимость участия всех доступных ядер):" + ] + }, + { + "cell_type": "code", + "execution_count": 18, + "id": "a5b7a1a3-23df-4a5a-a842-87e4d130a033", + "metadata": { + "tags": [] + }, + "outputs": [ + { + "data": { + "text/plain": [ + "0.9333333333333333" + ] + }, + "execution_count": 18, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "from sklearn.ensemble import BaggingClassifier\n", + "from sklearn.tree import DecisionTreeClassifier\n", + "from sklearn.datasets import load_digits\n", + "from sklearn.metrics import accuracy_score\n", + "from sklearn.model_selection import train_test_split\n", + "\n", + "iris = load_digits()\n", + "X = iris.data\n", + "y = iris.target\n", + "\n", + "X_train, X_test, y_train, y_test = train_test_split(\n", + " X,\n", + " y,\n", + " test_size=0.2,\n", + " random_state=42\n", + ")\n", + "\n", + "bag_clf = BaggingClassifier(\n", + " DecisionTreeClassifier(),\n", + " n_estimators=500,\n", + " max_samples=100,\n", + " bootstrap=True,\n", + " n_jobs=-1)\n", + "\n", + "bag_clf.fit(X_train, y_train)\n", + "y_pred = bag_clf.predict(X_test)\n", + "\n", + "accuracy_score(y_test, y_pred)" + ] + }, + { + "cell_type": "markdown", + "id": "58547b38-4209-450c-bfac-7f9a19657e1d", + "metadata": {}, + "source": [ + "#### Случайный лес\n", + "\n", + "Такой ансамбль деревьев принятия решений называется `случайным лесом (random forest)` и, несмотря на свою простоту, является довольно мощным алгоритмов.\n", + "\n", + "Что случайного в случайном лесе:\n", + "\n", + " 1. Обучающие выборки, сформированные методом бутстрап\n", + " 2. Признаки для условий в каждом узле дерева (вместо поиска лучшего из лучших признаков при расщеплении узла он ищет наилучший признак в случайном поднаборе признаков)" + ] + }, + { + "cell_type": "markdown", + "id": "79b7b3e9-3a46-497a-b19b-b383848f87f9", + "metadata": {}, + "source": [ + "Вместо построения экземпляра `BaggingClassifier` и его передачи экземпляру `DecisionTreeClassifier` вы можете применить класс `RandomForestClassifier`, который является более удобным и оптимизированным для деревьев принятия решений (аналогичным образом имеется класс `RandomForestRegressor` для задач регрессии). \n", + "\n", + "Показанный ниже код обучает классификатор на основе случайного леса с `500` деревьями (каждое ограничено максимум `16` узлами), используя все доступные процессорные ядра:" + ] + }, + { + "cell_type": "code", + "execution_count": 24, + "id": "03d6f940-3325-4bb8-afa5-e8472f74a48e", + "metadata": { + "tags": [] + }, + "outputs": [ + { + "data": { + "text/plain": [ + "0.975" + ] + }, + "execution_count": 24, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "from sklearn.ensemble import RandomForestClassifier\n", + "from sklearn.datasets import load_digits\n", + "from sklearn.model_selection import train_test_split\n", + "from sklearn.metrics import accuracy_score\n", + "\n", + "\n", + "iris = load_digits()\n", + "X = iris.data\n", + "y = iris.target\n", + "\n", + "X_train, X_test, y_train, y_test = train_test_split(\n", + " X,\n", + " y,\n", + " test_size=0.2,\n", + " random_state=42\n", + ")\n", + "\n", + "rnd_clf = RandomForestClassifier(n_jobs=-1)\n", + "rnd_clf.fit(X_train, y_train)\n", + "\n", + "y_pred_rf = rnd_clf.predict(X_test)\n", + "\n", + "accuracy_score(y_test, y_pred_rf)" + ] + }, + { + "cell_type": "markdown", + "id": "62646b21-ee00-4836-893b-ddf57a9ab76b", + "metadata": {}, + "source": [ + "#### Градиентный бустинг" + ] + }, + { + "cell_type": "markdown", + "id": "4f157161-1635-47df-8dd1-e906e28ce095", + "metadata": {}, + "source": [ + "**Бустинг** — это техника построения ансамблей, в которой модели построены не независимо, а последовательно.\n", + "\n", + "**Градиентный бустинг** — это техника машинного обучения для задач классификации и регрессии, которая строит модель предсказания в форме ансамбля слабых предсказывающих моделей, обычно деревьев решений." + ] + }, + { + "cell_type": "code", + "execution_count": 25, + "id": "15fd3ead-d29b-4fb4-b386-bcb9db0b4acc", + "metadata": { + "tags": [] + }, + "outputs": [], + "source": [ + "from sklearn.datasets import make_regression\n", + "import pandas as pd" + ] + }, + { + "cell_type": "code", + "execution_count": 26, + "id": "5b369f91-871a-483b-aa0e-2b5f66ec05bc", + "metadata": { + "tags": [] + }, + "outputs": [], + "source": [ + "X, y = make_regression(\n", + " n_samples=100,\n", + " n_features=3,\n", + " n_informative=2,\n", + " noise=10\n", + ")" + ] + }, + { + "cell_type": "code", + "execution_count": 27, + "id": "91fa6840-4fff-498d-9fc7-7a722af03411", + "metadata": { + "tags": [] + }, + "outputs": [], + "source": [ + "df = pd.DataFrame(X)\n", + "df['y_true'] = y" + ] + }, + { + "cell_type": "code", + "execution_count": 28, + "id": "4d707117-4a54-4a8a-8a31-405ac2ed7435", + "metadata": { + "tags": [] + }, + "outputs": [ + { + "data": { + "text/html": [ + "
\n", + "\n", + "\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "
012y_true
0-0.135456-1.013084-1.430305-37.673161
11.8837660.5686110.391718184.487726
20.4280910.781200-0.43844940.129650
31.613016-0.7462721.112609178.316824
4-0.2858300.8999550.390032-18.258000
...............
95-1.1840450.025689-0.321175-118.370507
96-0.5155050.315502-1.474370-64.266387
97-0.382320-1.6356770.070134-32.789144
980.891264-0.674273-1.34981070.299484
990.9948680.685481-0.52653775.109633
\n", + "

100 rows × 4 columns

\n", + "
" + ], + "text/plain": [ + " 0 1 2 y_true\n", + "0 -0.135456 -1.013084 -1.430305 -37.673161\n", + "1 1.883766 0.568611 0.391718 184.487726\n", + "2 0.428091 0.781200 -0.438449 40.129650\n", + "3 1.613016 -0.746272 1.112609 178.316824\n", + "4 -0.285830 0.899955 0.390032 -18.258000\n", + ".. ... ... ... ...\n", + "95 -1.184045 0.025689 -0.321175 -118.370507\n", + "96 -0.515505 0.315502 -1.474370 -64.266387\n", + "97 -0.382320 -1.635677 0.070134 -32.789144\n", + "98 0.891264 -0.674273 -1.349810 70.299484\n", + "99 0.994868 0.685481 -0.526537 75.109633\n", + "\n", + "[100 rows x 4 columns]" + ] + }, + "execution_count": 28, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "df" + ] + }, + { + "cell_type": "markdown", + "id": "466ffe90-3f98-4d8d-83aa-a1d04609f4d7", + "metadata": { + "tags": [] + }, + "source": [ + "Смысл бустинга заключается в том, что мы будем бустить какое-то константное предсказание.\n", + "\n", + "Добавим его в наш датасет:" + ] + }, + { + "cell_type": "code", + "execution_count": 29, + "id": "2e261a53-a4ea-4642-a747-5edc0b0879c7", + "metadata": { + "tags": [] + }, + "outputs": [ + { + "data": { + "text/html": [ + "
\n", + "\n", + "\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "
012y_truey_pred_0
0-0.135456-1.013084-1.430305-37.67316131.267689
11.8837660.5686110.391718184.48772631.267689
20.4280910.781200-0.43844940.12965031.267689
31.613016-0.7462721.112609178.31682431.267689
4-0.2858300.8999550.390032-18.25800031.267689
..................
95-1.1840450.025689-0.321175-118.37050731.267689
96-0.5155050.315502-1.474370-64.26638731.267689
97-0.382320-1.6356770.070134-32.78914431.267689
980.891264-0.674273-1.34981070.29948431.267689
990.9948680.685481-0.52653775.10963331.267689
\n", + "

100 rows × 5 columns

\n", + "
" + ], + "text/plain": [ + " 0 1 2 y_true y_pred_0\n", + "0 -0.135456 -1.013084 -1.430305 -37.673161 31.267689\n", + "1 1.883766 0.568611 0.391718 184.487726 31.267689\n", + "2 0.428091 0.781200 -0.438449 40.129650 31.267689\n", + "3 1.613016 -0.746272 1.112609 178.316824 31.267689\n", + "4 -0.285830 0.899955 0.390032 -18.258000 31.267689\n", + ".. ... ... ... ... ...\n", + "95 -1.184045 0.025689 -0.321175 -118.370507 31.267689\n", + "96 -0.515505 0.315502 -1.474370 -64.266387 31.267689\n", + "97 -0.382320 -1.635677 0.070134 -32.789144 31.267689\n", + "98 0.891264 -0.674273 -1.349810 70.299484 31.267689\n", + "99 0.994868 0.685481 -0.526537 75.109633 31.267689\n", + "\n", + "[100 rows x 5 columns]" + ] + }, + "execution_count": 29, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "df['y_pred_0'] = df['y_true'].mean()\n", + "df" + ] + }, + { + "cell_type": "code", + "execution_count": 30, + "id": "10da4ca2-3805-41b9-a7d2-6db0ae76f6b5", + "metadata": { + "tags": [] + }, + "outputs": [], + "source": [ + "from sklearn.metrics import mean_absolute_error" + ] + }, + { + "cell_type": "code", + "execution_count": 31, + "id": "81a82086-3e53-44d0-8a53-68ede019b2f2", + "metadata": { + "tags": [] + }, + "outputs": [ + { + "data": { + "text/plain": [ + "83.64294087330029" + ] + }, + "execution_count": 31, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "mean_absolute_error(df['y_true'], df['y_pred_0'])" + ] + }, + { + "cell_type": "markdown", + "id": "78decd4b-b981-4eb4-9952-5d1f9fef7669", + "metadata": {}, + "source": [ + "Чтобы начать градиентный бустинг, нам нужно посчитать разницу, чтобы наш алгоритм обучался на ошибках предыдущего." + ] + }, + { + "cell_type": "code", + "execution_count": 32, + "id": "984accca-7899-4733-b533-3ddd0d48563c", + "metadata": { + "tags": [] + }, + "outputs": [], + "source": [ + "df['residual_0'] = df['y_true'] - df['y_pred_0']" + ] + }, + { + "cell_type": "code", + "execution_count": 33, + "id": "78f4f69e-f370-4a96-8e38-23887c32980f", + "metadata": { + "tags": [] + }, + "outputs": [ + { + "data": { + "text/html": [ + "
\n", + "\n", + "\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "
012y_truey_pred_0residual_0
0-0.135456-1.013084-1.430305-37.67316131.267689-68.940850
11.8837660.5686110.391718184.48772631.267689153.220037
20.4280910.781200-0.43844940.12965031.2676898.861961
31.613016-0.7462721.112609178.31682431.267689147.049135
4-0.2858300.8999550.390032-18.25800031.267689-49.525689
.....................
95-1.1840450.025689-0.321175-118.37050731.267689-149.638196
96-0.5155050.315502-1.474370-64.26638731.267689-95.534076
97-0.382320-1.6356770.070134-32.78914431.267689-64.056833
980.891264-0.674273-1.34981070.29948431.26768939.031795
990.9948680.685481-0.52653775.10963331.26768943.841944
\n", + "

100 rows × 6 columns

\n", + "
" + ], + "text/plain": [ + " 0 1 2 y_true y_pred_0 residual_0\n", + "0 -0.135456 -1.013084 -1.430305 -37.673161 31.267689 -68.940850\n", + "1 1.883766 0.568611 0.391718 184.487726 31.267689 153.220037\n", + "2 0.428091 0.781200 -0.438449 40.129650 31.267689 8.861961\n", + "3 1.613016 -0.746272 1.112609 178.316824 31.267689 147.049135\n", + "4 -0.285830 0.899955 0.390032 -18.258000 31.267689 -49.525689\n", + ".. ... ... ... ... ... ...\n", + "95 -1.184045 0.025689 -0.321175 -118.370507 31.267689 -149.638196\n", + "96 -0.515505 0.315502 -1.474370 -64.266387 31.267689 -95.534076\n", + "97 -0.382320 -1.635677 0.070134 -32.789144 31.267689 -64.056833\n", + "98 0.891264 -0.674273 -1.349810 70.299484 31.267689 39.031795\n", + "99 0.994868 0.685481 -0.526537 75.109633 31.267689 43.841944\n", + "\n", + "[100 rows x 6 columns]" + ] + }, + "execution_count": 33, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "df" + ] + }, + { + "cell_type": "markdown", + "id": "7aea6601-8dc0-4030-8826-993f20e21ef0", + "metadata": { + "tags": [] + }, + "source": [ + "Обучим дерево решений на этой ошибке:" + ] + }, + { + "cell_type": "code", + "execution_count": 35, + "id": "1738278c-6740-440e-8174-d84df07364ff", + "metadata": { + "tags": [] + }, + "outputs": [ + { + "data": { + "text/html": [ + "
DecisionTreeRegressor(max_depth=1)
In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook.
On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.
" + ], + "text/plain": [ + "DecisionTreeRegressor(max_depth=1)" + ] + }, + "execution_count": 35, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "from sklearn.tree import DecisionTreeRegressor\n", + "\n", + "tree_1 = DecisionTreeRegressor(max_depth=1)\n", + "tree_1.fit(df[[0,1,2]], df[['residual_0']])" + ] + }, + { + "cell_type": "code", + "execution_count": 36, + "id": "a133c7a3-44ca-4371-b163-90395ed05e2f", + "metadata": { + "tags": [] + }, + "outputs": [], + "source": [ + "df['tree_pred_1'] = tree_1.predict(df[[0,1,2]])" + ] + }, + { + "cell_type": "code", + "execution_count": 37, + "id": "e8c8c5d9-fb94-46cf-b939-49e33a9e2369", + "metadata": { + "tags": [] + }, + "outputs": [ + { + "data": { + "text/html": [ + "
\n", + "\n", + "\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "
012y_truey_pred_0residual_0tree_pred_1
0-0.135456-1.013084-1.430305-37.67316131.267689-68.940850-84.433122
11.8837660.5686110.391718184.48772631.267689153.22003781.122019
20.4280910.781200-0.43844940.12965031.2676898.86196181.122019
31.613016-0.7462721.112609178.31682431.267689147.04913581.122019
4-0.2858300.8999550.390032-18.25800031.267689-49.525689-84.433122
........................
95-1.1840450.025689-0.321175-118.37050731.267689-149.638196-84.433122
96-0.5155050.315502-1.474370-64.26638731.267689-95.534076-84.433122
97-0.382320-1.6356770.070134-32.78914431.267689-64.056833-84.433122
980.891264-0.674273-1.34981070.29948431.26768939.03179581.122019
990.9948680.685481-0.52653775.10963331.26768943.84194481.122019
\n", + "

100 rows × 7 columns

\n", + "
" + ], + "text/plain": [ + " 0 1 2 y_true y_pred_0 residual_0 \\\n", + "0 -0.135456 -1.013084 -1.430305 -37.673161 31.267689 -68.940850 \n", + "1 1.883766 0.568611 0.391718 184.487726 31.267689 153.220037 \n", + "2 0.428091 0.781200 -0.438449 40.129650 31.267689 8.861961 \n", + "3 1.613016 -0.746272 1.112609 178.316824 31.267689 147.049135 \n", + "4 -0.285830 0.899955 0.390032 -18.258000 31.267689 -49.525689 \n", + ".. ... ... ... ... ... ... \n", + "95 -1.184045 0.025689 -0.321175 -118.370507 31.267689 -149.638196 \n", + "96 -0.515505 0.315502 -1.474370 -64.266387 31.267689 -95.534076 \n", + "97 -0.382320 -1.635677 0.070134 -32.789144 31.267689 -64.056833 \n", + "98 0.891264 -0.674273 -1.349810 70.299484 31.267689 39.031795 \n", + "99 0.994868 0.685481 -0.526537 75.109633 31.267689 43.841944 \n", + "\n", + " tree_pred_1 \n", + "0 -84.433122 \n", + "1 81.122019 \n", + "2 81.122019 \n", + "3 81.122019 \n", + "4 -84.433122 \n", + ".. ... \n", + "95 -84.433122 \n", + "96 -84.433122 \n", + "97 -84.433122 \n", + "98 81.122019 \n", + "99 81.122019 \n", + "\n", + "[100 rows x 7 columns]" + ] + }, + "execution_count": 37, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "df" + ] + }, + { + "cell_type": "markdown", + "id": "b8abd1de-75ea-4b29-b872-f6eb0a2c76e7", + "metadata": {}, + "source": [ + "Теперь забустим наше первоначальное предсказание. Для этого полученные предсказания первого дерева нужно умножить на learning rate и прибавить к константному предсказанию. Таким образом мы приблизимся на шаг и истинным значениям." + ] + }, + { + "cell_type": "code", + "execution_count": 38, + "id": "669f9a1b-10bd-4c4e-9af6-0310813bcede", + "metadata": { + "tags": [] + }, + "outputs": [], + "source": [ + "lr = 0.1\n", + "df['y_pred_1'] = df['y_pred_0'] + lr * df['tree_pred_1']" + ] + }, + { + "cell_type": "code", + "execution_count": 39, + "id": "dc0ceec8-9334-4198-97d7-c1d8e88aaa5c", + "metadata": { + "tags": [] + }, + "outputs": [ + { + "data": { + "text/html": [ + "
\n", + "\n", + "\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "
012y_truey_pred_0residual_0tree_pred_1y_pred_1
0-0.135456-1.013084-1.430305-37.67316131.267689-68.940850-84.43312222.824377
11.8837660.5686110.391718184.48772631.267689153.22003781.12201939.379891
20.4280910.781200-0.43844940.12965031.2676898.86196181.12201939.379891
31.613016-0.7462721.112609178.31682431.267689147.04913581.12201939.379891
4-0.2858300.8999550.390032-18.25800031.267689-49.525689-84.43312222.824377
...........................
95-1.1840450.025689-0.321175-118.37050731.267689-149.638196-84.43312222.824377
96-0.5155050.315502-1.474370-64.26638731.267689-95.534076-84.43312222.824377
97-0.382320-1.6356770.070134-32.78914431.267689-64.056833-84.43312222.824377
980.891264-0.674273-1.34981070.29948431.26768939.03179581.12201939.379891
990.9948680.685481-0.52653775.10963331.26768943.84194481.12201939.379891
\n", + "

100 rows × 8 columns

\n", + "
" + ], + "text/plain": [ + " 0 1 2 y_true y_pred_0 residual_0 \\\n", + "0 -0.135456 -1.013084 -1.430305 -37.673161 31.267689 -68.940850 \n", + "1 1.883766 0.568611 0.391718 184.487726 31.267689 153.220037 \n", + "2 0.428091 0.781200 -0.438449 40.129650 31.267689 8.861961 \n", + "3 1.613016 -0.746272 1.112609 178.316824 31.267689 147.049135 \n", + "4 -0.285830 0.899955 0.390032 -18.258000 31.267689 -49.525689 \n", + ".. ... ... ... ... ... ... \n", + "95 -1.184045 0.025689 -0.321175 -118.370507 31.267689 -149.638196 \n", + "96 -0.515505 0.315502 -1.474370 -64.266387 31.267689 -95.534076 \n", + "97 -0.382320 -1.635677 0.070134 -32.789144 31.267689 -64.056833 \n", + "98 0.891264 -0.674273 -1.349810 70.299484 31.267689 39.031795 \n", + "99 0.994868 0.685481 -0.526537 75.109633 31.267689 43.841944 \n", + "\n", + " tree_pred_1 y_pred_1 \n", + "0 -84.433122 22.824377 \n", + "1 81.122019 39.379891 \n", + "2 81.122019 39.379891 \n", + "3 81.122019 39.379891 \n", + "4 -84.433122 22.824377 \n", + ".. ... ... \n", + "95 -84.433122 22.824377 \n", + "96 -84.433122 22.824377 \n", + "97 -84.433122 22.824377 \n", + "98 81.122019 39.379891 \n", + "99 81.122019 39.379891 \n", + "\n", + "[100 rows x 8 columns]" + ] + }, + "execution_count": 39, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "df" + ] + }, + { + "cell_type": "markdown", + "id": "be1eda59-4685-42d2-bed3-0d2c1f25bb53", + "metadata": { + "tags": [] + }, + "source": [ + "Посчитаем ошибку и убедимся что она уменьшилась:" + ] + }, + { + "cell_type": "code", + "execution_count": 40, + "id": "8dc0f80c-e85a-4dfd-817a-fa1ff7e8a8e8", + "metadata": { + "tags": [] + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Было: 83.64294087330029\n", + "Стало: 76.18633734863174\n" + ] + } + ], + "source": [ + "print(f\"Было: {mean_absolute_error(df['y_true'], df['y_pred_0'])}\")\n", + "print(f\"Стало: {mean_absolute_error(df['y_true'], df['y_pred_1'])}\")" + ] + }, + { + "cell_type": "markdown", + "id": "7ba9c503-88ff-4d72-8204-6cc6239f7524", + "metadata": {}, + "source": [ + "Таким образом, мы забустили константное предсказание.\n", + "\n", + "Далее алгоритм для следующего дерева такой же:\n", + "- Посчитать разницу между последним предсказанием и истинным значением\n", + "- Обучить новое дерево на этой разнице\n", + "- Предсказываем значения на новом дереве\n", + "- Делаем шаг от прошлого предсказания в сторону истинных значений\n", + " \n", + "Обернем все в цикл:" + ] + }, + { + "cell_type": "code", + "execution_count": 46, + "id": "97a1cbbf-56da-4e23-b828-cc87d5552515", + "metadata": { + "tags": [] + }, + "outputs": [ + { + "data": { + "text/html": [ + "
\n", + "\n", + "\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "
012y_truey_pred
0-0.135456-1.013084-1.430305-37.67316131.267689
11.8837660.5686110.391718184.48772631.267689
20.4280910.781200-0.43844940.12965031.267689
31.613016-0.7462721.112609178.31682431.267689
4-0.2858300.8999550.390032-18.25800031.267689
..................
95-1.1840450.025689-0.321175-118.37050731.267689
96-0.5155050.315502-1.474370-64.26638731.267689
97-0.382320-1.6356770.070134-32.78914431.267689
980.891264-0.674273-1.34981070.29948431.267689
990.9948680.685481-0.52653775.10963331.267689
\n", + "

100 rows × 5 columns

\n", + "
" + ], + "text/plain": [ + " 0 1 2 y_true y_pred\n", + "0 -0.135456 -1.013084 -1.430305 -37.673161 31.267689\n", + "1 1.883766 0.568611 0.391718 184.487726 31.267689\n", + "2 0.428091 0.781200 -0.438449 40.129650 31.267689\n", + "3 1.613016 -0.746272 1.112609 178.316824 31.267689\n", + "4 -0.285830 0.899955 0.390032 -18.258000 31.267689\n", + ".. ... ... ... ... ...\n", + "95 -1.184045 0.025689 -0.321175 -118.370507 31.267689\n", + "96 -0.515505 0.315502 -1.474370 -64.266387 31.267689\n", + "97 -0.382320 -1.635677 0.070134 -32.789144 31.267689\n", + "98 0.891264 -0.674273 -1.349810 70.299484 31.267689\n", + "99 0.994868 0.685481 -0.526537 75.109633 31.267689\n", + "\n", + "[100 rows x 5 columns]" + ] + }, + "execution_count": 46, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "train = df[[0,1,2,'y_true']].copy()\n", + "train['y_pred'] = train['y_true'].mean()\n", + "train" + ] + }, + { + "cell_type": "code", + "execution_count": 47, + "id": "d79bc2fe-bd41-4336-b2fa-ea464c0c5561", + "metadata": { + "tags": [] + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Дерево 1: MAE=76.18633734863174\n", + "Дерево 2: MAE=69.78839469511819\n", + "Дерево 3: MAE=65.23121406231614\n", + "Дерево 4: MAE=59.80214563570313\n", + "Дерево 5: MAE=55.92610812568657\n", + "Дерево 6: MAE=52.97726287762965\n", + "Дерево 7: MAE=49.89017878618041\n", + "Дерево 8: MAE=47.449637517710016\n", + "Дерево 9: MAE=44.10842265940846\n", + "Дерево 10: MAE=41.8402034112005\n" + ] + } + ], + "source": [ + "n_trees = 10\n", + "lr = 0.1\n", + "\n", + "trees = []\n", + "for i in range(n_trees):\n", + " train['residual'] = train['y_true'] - train['y_pred']\n", + " tree = DecisionTreeRegressor(max_depth=1)\n", + " tree.fit(train[[0,1,2]], train[['residual']])\n", + " trees.append(tree)\n", + " train['y_pred'] += lr * tree.predict(train[[0,1,2]])\n", + " print(f\"Дерево {i + 1}: MAE={mean_absolute_error(train['y_true'], train['y_pred'])}\")\n", + " \n", + " " + ] + }, + { + "cell_type": "markdown", + "id": "809d87c5-c9b8-46b9-9ebd-f995f6ca8960", + "metadata": {}, + "source": [ + "Таким образом мы обучили наши деревья, давайте напишем инференс(сделаем предсказание) по ним.\n", + "\n", + "Чтобы сделать предсказание, нужно так же сначала сделать констатное предсказание." + ] + }, + { + "cell_type": "code", + "execution_count": 48, + "id": "ad38fdcb-72f0-45f1-abcf-5aef77ed52eb", + "metadata": { + "tags": [] + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "MAE: 41.8402034112005\n" + ] + } + ], + "source": [ + "test = df[[0,1,2,'y_true']].copy()\n", + "test['y_pred'] = test['y_true'].mean()\n", + "\n", + "for tree in trees:\n", + " test['y_pred'] += lr * tree.predict(df[[0,1,2]])\n", + " \n", + "print(f\"MAE: {mean_absolute_error(test['y_true'], test['y_pred'])}\")" + ] + }, + { + "cell_type": "markdown", + "id": "a643006a-2854-469b-9292-4e52e32d2f50", + "metadata": {}, + "source": [ + "#### Популярные библиотеки для градиентного бустинга\n", + "\n" + ] + }, + { + "cell_type": "markdown", + "id": "ae6631b3-d2f2-4cc4-a7f0-f28d3dbbee76", + "metadata": { + "tags": [] + }, + "source": [ + "- [CatBoost](https://catboost.ai/en/docs/)\n", + "- [LightGBM](https://lightgbm.readthedocs.io/en/v3.3.2/#)\n", + "- [XGBoost](https://xgboost.readthedocs.io/en/stable/#)\n", + "\n", + "CatBoost, LightGBM и XGBoost - это три популярные библиотеки градиентного бустинга, которые широко используются для решения задач машинного обучения. Вот несколько основных различий между ними:\n", + "\n", + "1. Обработка категориальных признаков: CatBoost и LightGBM предоставляют встроенные механизмы для автоматической обработки категориальных признаков, в то время как в XGBoost необходимо предварительно выполнять преобразования для кодирования категориальных признаков в числовые значения.\n", + "\n", + "2. Оптимизация памяти и скорость обучения: LightGBM и CatBoost изначально были разработаны с акцентом на оптимизацию памяти и производительность. Они используют различные алгоритмы для сжатия данных и оптимизации работы с памятью, что делает их более эффективными на больших наборах данных и быстрее в обучении моделей, по сравнению с XGBoost.\n", + "\n", + "3. Поддержка работы с категориальными признаками не только в деревьях, но и в линейных моделях: CatBoost позволяет использовать категориальные признаки не только в деревьях, но и в линейных моделях, что может быть полезно в некоторых задачах. LightGBM также поддерживает использование категориальных признаков в линейных моделях, но XGBoost поддерживает только использование категориальных признаков в деревьях.\n", + "\n", + "4. Автоматическая обработка пропущенных значений: CatBoost автоматически обрабатывает пропущенные значения в данных без необходимости предварительной обработки, в то время как LightGBM и XGBoost требуют явного заполнения пропущенных значений перед обучением моделей.\n", + "\n", + "5. Работа с несбалансированными данными: CatBoost и XGBoost предлагают встроенные механизмы для работы с несбалансированными данными, такими как автоматическое балансирование классов, в то время как LightGBM требует явного указания параметров модели для работы с несбалансированными данными." + ] + }, + { + "cell_type": "markdown", + "id": "b9f76cfa-75f2-4133-b521-a0686050b3b2", + "metadata": {}, + "source": [ + "### CatBoost\n", + "\n", + "Библиотека CatBoost – это градиентный бустинговый фреймворк с открытым исходным кодом, разработанный компанией Yandex." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "46c1ea73-eee7-4bb1-8127-31dc96facf4c", + "metadata": { + "tags": [] + }, + "outputs": [], + "source": [ + "# !pip install catboost" + ] + }, + { + "cell_type": "markdown", + "id": "bf28abb0-b5ef-449b-876d-5d46805bd148", + "metadata": {}, + "source": [ + "В этом примере мы использовали функцию `make_regression` из библиотеки `scikit-learn` для генерации синтетических данных для задачи регрессии. Затем мы создали и обучили модель `CatBoostRegressor`, указав индексы категориальных признаков в параметре `cat_features`. Наконец, мы оценили качество модели на тестовом наборе данных с помощью среднеквадратичной ошибки (`MSE`)." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "e4f2aa8e-fdfc-4759-81e3-cf7bcde33999", + "metadata": { + "tags": [] + }, + "outputs": [], + "source": [ + "import numpy as np\n", + "from sklearn.datasets import make_regression\n", + "from sklearn.model_selection import train_test_split\n", + "from catboost import CatBoostRegressor\n", + "\n", + "# Генерация синтетических данных\n", + "X, y = make_regression(n_samples=1000, n_features=5, n_informative=3, random_state=42)\n", + "X = np.round(X) # Округление признаков до целых чисел\n", + "X = X.astype(int) # Преобразование признаков в целочисленный тип данных\n", + "cat_features = [0, 2, 4] # Индексы категориальных признаков\n", + "\n", + "# Разделение данных на обучающий и тестовый наборы\n", + "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)\n", + "\n", + "# Создание и обучение модели CatBoostRegressor\n", + "model = CatBoostRegressor(iterations=1000, # Количество итераций\n", + " learning_rate=0.1, # Скорость обучения\n", + " depth=6, # Глубина дерева\n", + " cat_features=cat_features, # Индексы категориальных признаков\n", + " random_state=42) # Задаем случайное начальное состояние для воспроизводимости\n", + "\n", + "model.fit(X_train, y_train, verbose=100) # Обучение модели с выводом прогресса на каждой 100-й итерации\n", + "\n", + "# Прогнозирование на тестовом наборе данных\n", + "y_pred = model.predict(X_test)\n", + "\n", + "# Оценка качества модели\n", + "mse = np.mean((y_test - y_pred) ** 2) # Среднеквадратичная ошибка\n", + "print(f\"Mean Squared Error: {mse:.4f}\")\n", + "\n" + ] + }, + { + "cell_type": "markdown", + "id": "9d40af0a-89a9-467e-98e6-e7853f12e311", + "metadata": {}, + "source": [ + "#### LightGBM\n", + "\n", + "LightGBM - это эффективная библиотека градиентного бустинга, разработанная Microsoft, которая обладает высокой производительностью благодаря оптимизации памяти и быстрым алгоритмам обучения." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "5de56b14-4056-4e13-a12f-b4578597862d", + "metadata": { + "tags": [] + }, + "outputs": [], + "source": [ + "# !pip install lightgbm" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "86316bc6-4315-4cec-8386-59e758d407a3", + "metadata": {}, + "outputs": [], + "source": [ + "import numpy as np\n", + "import pandas as pd\n", + "from sklearn.datasets import make_regression\n", + "from sklearn.model_selection import train_test_split\n", + "import lightgbm as lgb\n", + "\n", + "# Генерация синтетических данных\n", + "X, y = make_regression(n_samples=1000, n_features=5, n_informative=3, random_state=42)\n", + "X = np.round(X) # Округление признаков до целых чисел\n", + "\n", + "# Создание DataFrame из массивов NumPy\n", + "df = pd.DataFrame(X, columns=[f\"feature_{i}\" for i in range(X.shape[1])])\n", + "df[\"cat_feature\"] = np.random.choice([\"A\", \"B\", \"C\"], size=X.shape[0]) # Добавление категориального признака\n", + "df['cat_feature'] = df['cat_feature'].astype('category')\n", + "cat_features = [\"cat_feature\"] # Список категориальных признаков\n", + "\n", + "# Разделение данных на обучающий и тестовый наборы\n", + "X_train, X_test, y_train, y_test = train_test_split(df, y, test_size=0.2, random_state=42)\n", + "\n", + "# Создание и обучение модели LGBMRegressor\n", + "model = lgb.LGBMRegressor(num_leaves=31, # Количество листьев в дереве\n", + " learning_rate=0.1, # Скорость обучения\n", + " n_estimators=100, # Количество деревьев\n", + " categorical_feature=cat_features, # Список категориальных признаков\n", + " random_state=42) # Задаем случайное начальное состояние для воспроизводимости\n", + "model.fit(X_train, y_train, verbose=10) # Обучение модели с выводом прогресса на каждой 10-й итерации\n", + "\n", + "# Прогнозирование на тестовом наборе данных\n", + "y_pred = model.predict(X_test)\n", + "\n", + "# Оценка качества модели\n", + "mse = np.mean((y_test - y_pred) ** 2) # Среднеквадратичная ошибка\n", + "print(f\"Mean Squared Error: {mse:.4f}\")\n" + ] + }, + { + "cell_type": "markdown", + "id": "35896ec9-b40e-4885-bc3b-e5fcc5d97f6b", + "metadata": {}, + "source": [ + "В этом примере мы использовали функцию `make_regression` из библиотеки `scikit-learn` для генерации синтетических данных для задачи регрессии. Затем мы создали `DataFrame` из массивов `NumPy`, добавили категориальный признак в `DataFrame` с помощью библиотеки `pandas`, и указали его в параметре `categorical_feature` при создании и обучении модели `LGBMRegressor`. Наконец, мы оценили качество модели на тестовом наборе данных с помощью среднеквадратичной ошибки (`MSE`)." + ] + }, + { + "cell_type": "markdown", + "id": "c6d4294c-d241-46e3-8391-87d321752ff9", + "metadata": {}, + "source": [ + "#### XGBoost\n", + "\n", + "Библиотека XGBoost была разработана и представлена в 2014 году Даниэлем Ченом (Daniel Chen) - исследователем в области машинного обучения и анализа данных. Он разработал XGBoost в рамках своей докторской диссертации на университете Вашингтона (University of Washington), и с тех пор библиотека стала одним из наиболее популярных инструментов машинного обучения, широко применяемых в индустрии и академическом сообществе. В настоящее время XGBoost поддерживается и развивается открытым сообществом разработчиков." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "9fbb2cb4-79b8-442b-a48c-6981567bd05a", + "metadata": { + "tags": [] + }, + "outputs": [], + "source": [ + "# !pip install xgboost" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "de909c76-3e75-4502-99ae-c35854cad3b3", + "metadata": { + "tags": [] + }, + "outputs": [], + "source": [ + "import numpy as np\n", + "import xgboost as xgb\n", + "from sklearn.datasets import make_regression\n", + "from sklearn.model_selection import train_test_split\n", + "\n", + "# Создание синтетических данных с категориальными признаками\n", + "X, y = make_regression(n_samples=1000, n_features=5, n_informative=3, random_state=42)\n", + "X = np.round(X) # Округление признаков до целых чисел\n", + "X = X.astype(int) # Приведение типа данных к целочисленному\n", + "\n", + "# Разделение данных на обучающую и тестовую выборки\n", + "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)\n", + "\n", + "# Создание объекта DMatrix для обучающей и тестовой выборок\n", + "dtrain = xgb.DMatrix(X_train, label=y_train)\n", + "dtest = xgb.DMatrix(X_test, label=y_test)\n", + "\n", + "# Определение параметров модели\n", + "params = {\n", + " 'booster': 'gbtree',\n", + " 'objective': 'reg:squarederror',\n", + " 'eval_metric': 'rmse',\n", + " 'max_depth': 3,\n", + " 'eta': 0.1,\n", + " 'subsample': 0.8,\n", + " 'colsample_bytree': 0.8,\n", + " 'alpha': 0.1,\n", + " 'lambda': 0.1,\n", + " 'min_child_weight': 1,\n", + " 'seed': 42\n", + "}\n", + "\n", + "# Обучение модели XGBoost\n", + "num_rounds = 100\n", + "model = xgb.train(params, dtrain, num_rounds)\n", + "\n", + "# Прогнозирование на тестовой выборке\n", + "y_pred = model.predict(dtest)\n", + "\n", + "# Оценка качества модели\n", + "rmse = np.sqrt(np.mean((y_pred - y_test) ** 2))\n", + "print(f'RMSE на тестовой выборке: {rmse:.4f}')\n" + ] + }, + { + "cell_type": "markdown", + "id": "0f713d2b-391e-42b3-a39b-2d6ba9d5a66b", + "metadata": {}, + "source": [ + "В данном примере мы создаем синтетические данные с категориальными признаками, разделяем их на обучающую и тестовую выборки, создаем объекты DMatrix для этих выборок, определяем параметры модели XGBoost, обучаем модель и оцениваем ее качество на тестовой выборке с помощью метрики RMSE (корень из среднеквадратической ошибки)." + ] + }, + { + "cell_type": "markdown", + "id": "f1f22246-ab30-4417-919a-06f7c01ff754", + "metadata": {}, + "source": [ + "### Как подобрать наилучшие гиперпараметры для модели" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "2bc29d71-8ce6-4f21-98c2-e1ce75da126b", + "metadata": {}, + "outputs": [], + "source": [ + "import numpy as np\n", + "from sklearn.datasets import make_regression\n", + "from sklearn.model_selection import train_test_split, GridSearchCV\n", + "from catboost import CatBoostRegressor\n", + "\n", + "# Генерация данных\n", + "X, y = make_regression(n_samples=1000, n_features=10, noise=0.1, random_state=42)\n", + "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)\n", + "\n", + "# Создание модели CatBoostRegressor\n", + "model = CatBoostRegressor()\n", + "\n", + "# Определение гиперпараметров и их значений для подбора\n", + "param_grid = {\n", + " 'learning_rate': [0.01, 0.1, 0.2],\n", + " 'depth': [4, 6, 8],\n", + " 'iterations': [100, 200, 300]\n", + "}\n", + "\n", + "# Подбор гиперпараметров с использованием GridSearchCV\n", + "grid_search = GridSearchCV(model, param_grid, cv=3)\n", + "grid_search.fit(X_train, y_train)\n", + "\n", + "# Вывод наилучших параметров и значения метрик\n", + "print(\"Наилучшие параметры: \", grid_search.best_params_)\n", + "print(\"Наилучшее значение RMSE на тестовом наборе: \", np.sqrt(-grid_search.score(X_test, y_test)))\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "2cdd7b0b-c60e-4086-9728-af6439587676", + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "markdown", + "id": "a6a79e09-5af5-40e9-afb0-3596ea6e1fe0", + "metadata": {}, + "source": [ + "## Лаб 4\n", + "\n", + "Узнать какой бустинг и с какими гиперпараметрами лучше работает на вашем наборе данных.\n", + "\n", + "Обучить простую модель и бустинг, сравнить модели по какой-нибудь метрике." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "ed0f5b5a-ad97-4681-8459-d03df4f4820c", + "metadata": {}, + "outputs": [], + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3 (ipykernel)", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.8.16" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +}