contents, DecodeJpeg.Options... op
* This op also supports decoding JPEGs and non-animated GIFs since the interface
* is the same, though it is cleaner to use {@code tf.io.decode_image}.
*
- * @param data type for {@code image} output
* @param contents 0-D. The PNG-encoded image.
* @param options carries optional attribute values
* @return a new instance of DecodePng, with default output types
@@ -463,7 +456,6 @@ public DecodePng decodePng(Operand contents, DecodePng.Options[
* This op also supports decoding JPEGs and non-animated GIFs since the interface
* is the same, though it is cleaner to use {@code tf.io.decode_image}.
*
- * @param data type for {@code image} output
* @param contents 0-D. The PNG-encoded image.
* @param dtype The value of the dtype attribute
* @param options carries optional attribute values
@@ -487,7 +479,6 @@ public DecodePng decodePng(Operand contents, Cla
* the bounding box will be {@code (40, 10)} to {@code (100, 50)} (in (x,y) coordinates).
* Parts of the bounding box may fall outside the image.
*
- * @param data type for {@code output} output
* @param images 4-D with shape {@code [batch, height, width, depth]}. A batch of images.
* @param boxes 3-D with shape {@code [batch, num_bounding_boxes, 4]} containing bounding
* boxes.
@@ -602,7 +593,6 @@ public ExtractGlimpse extractGlimpse(Operand input, Operand si
/**
* Extract {@code patches} from {@code images} and put them in the "depth" output dimension.
*
- * @param data type for {@code patches} output
* @param images 4-D Tensor with shape {@code [batch, in_rows, in_cols, depth]}.
* @param ksizes The size of the sliding window for each dimension of {@code images}.
* @param strides How far the centers of two consecutive patches are in
@@ -626,7 +616,6 @@ public ExtractImagePatches extractImagePatches(Operand i
* Extract the shape information of a JPEG-encoded image.
* This op only parses the image header, so it is much faster than DecodeJpeg.
*
- * @param data type for {@code image_shape} output
* @param contents 0-D. The JPEG-encoded image.
* @return a new instance of ExtractJpegShape, with default output types
*/
@@ -638,7 +627,6 @@ public ExtractJpegShape extractJpegShape(Operand contents) {
* Extract the shape information of a JPEG-encoded image.
* This op only parses the image header, so it is much faster than DecodeJpeg.
*
- * @param data type for {@code image_shape} output
* @param contents 0-D. The JPEG-encoded image.
* @param outputType (Optional) The output type of the operation (int32 or int64).
* Defaults to int32.
@@ -691,7 +679,6 @@ public GenerateBoundingBoxProposals generateBoundingBoxProposals(OperandSee {@code rgb_to_hsv} for a description of the HSV encoding.
*
- * @param data type for {@code output} output
* @param images 1-D or higher rank. HSV data to convert. Last dimension must be size 3.
* @param data type for {@code HSVToRGB} output and operands
* @return a new instance of HsvToRgb
@@ -708,7 +695,6 @@ public HsvToRgb hsvToRgb(Operand images) {
* {@code k = c0 x + c1 y + 1}. If the transformed point lays outside of the input
* image, the output pixel is set to 0.
*
- * @param data type for {@code transformed_images} output
* @param images 4-D with shape {@code [batch, height, width, channels]}.
* @param transforms 2-D Tensor, {@code [batch, 8]} or {@code [1, 8]} matrix, where each row corresponds to a 3 x 3
* projective transformation matrix, with the last entry assumed to be 1. If there
@@ -733,7 +719,6 @@ public ImageProjectiveTransformV2 imageProjectiveTransfor
* {@code k = c0 x + c1 y + 1}. If the transformed point lays outside of the input
* image, the output pixel is set to fill_value.
*
- * @param data type for {@code transformed_images} output
* @param images 4-D with shape {@code [batch, height, width, channels]}.
* @param transforms 2-D Tensor, {@code [batch, 8]} or {@code [1, 8]} matrix, where each row corresponds to a 3 x 3
* projective transformation matrix, with the last entry assumed to be 1. If there
@@ -794,7 +779,6 @@ public NearestNeighbors nearestNeighbors(Operand points, Operand data type for {@code selected_scores} output
* @param boxes A 2-D float tensor of shape {@code [num_boxes, 4]}.
* @param scores A 1-D float tensor of shape {@code [num_boxes]} representing a single
* score corresponding to each box (each row of boxes).
@@ -854,7 +838,6 @@ public NonMaxSuppressionWithOverlaps nonMaxSuppressionWithOverlaps(Operand data type for {@code resized_images} output
* @param images 4-D with shape {@code [batch, height, width, channels]}.
* @param sizeOutput = A 1-D int32 Tensor of 2 elements: {@code new_height, new_width}. The
* new size for the images.
@@ -878,7 +861,6 @@ public QuantizedResizeBilinear quantizedResizeBilinear(Op
* rectangle from that location. The random location is picked so the cropped
* area will fit inside the original image.
*
- * @param data type for {@code output} output
* @param image 3-D of shape {@code [height, width, channels]}.
* @param sizeOutput 1-D of length 2 containing: {@code crop_height}, {@code crop_width}..
* @param options carries optional attribute values
@@ -931,7 +913,6 @@ public ResizeBicubic resizeBicubic(Operand images, Operand data type for {@code output} output
* @param grads 4-D with shape {@code [batch, height, width, channels]}.
* @param originalImage 4-D with shape {@code [batch, orig_height, orig_width, channels]},
* The image tensor that was resized.
@@ -962,7 +943,6 @@ public ResizeBilinear resizeBilinear(Operand images,
/**
* Computes the gradient of bilinear interpolation.
*
- * @param data type for {@code output} output
* @param grads 4-D with shape {@code [batch, height, width, channels]}.
* @param originalImage 4-D with shape {@code [batch, orig_height, orig_width, channels]},
* The image tensor that was resized.
@@ -978,7 +958,6 @@ public ResizeBilinearGrad resizeBilinearGrad(Operand data type for {@code resized_images} output
* @param images 4-D with shape {@code [batch, height, width, channels]}.
* @param sizeOutput = A 1-D int32 Tensor of 2 elements: {@code new_height, new_width}. The
* new size for the images.
@@ -994,7 +973,6 @@ public ResizeNearestNeighbor resizeNearestNeighbor(Operan
/**
* Computes the gradient of nearest neighbor interpolation.
*
- * @param data type for {@code output} output
* @param grads 4-D with shape {@code [batch, height, width, channels]}.
* @param sizeOutput = A 1-D int32 Tensor of 2 elements: {@code orig_height, orig_width}. The
* original input size.
@@ -1031,7 +1009,6 @@ public ResizeNearestNeighborGrad resizeNearestNeighborGra
*
*
*
- * @param data type for {@code output} output
* @param images 1-D or higher rank. RGB data to convert. Last dimension must be size 3.
* @param data type for {@code RGBToHSV} output and operands
* @return a new instance of RgbToHsv
@@ -1076,7 +1053,6 @@ public RgbToHsv rgbToHsv(Operand images) {
* bounding box covering the whole image. If {@code use_image_if_no_bounding_boxes} is
* false and no bounding boxes are supplied, an error is raised.
*
- * @param data type for {@code begin} output
* @param imageSize 1-D, containing {@code [height, width, channels]}.
* @param boundingBoxes 3-D with shape {@code [batch, N, 4]} describing the N bounding boxes
* associated with the image.
@@ -1113,7 +1089,6 @@ public ScaleAndTranslate scaleAndTranslate(Operand images,
/**
* The ScaleAndTranslateGrad operation
*
- * @param data type for {@code output} output
* @param grads The grads value
* @param originalImage The originalImage value
* @param scale The scale value
@@ -1189,7 +1164,6 @@ public ScaleAndTranslateGrad scaleAndTranslateGrad(Operan
* bounding box covering the whole image. If {@code use_image_if_no_bounding_boxes} is
* false and no bounding boxes are supplied, an error is raised.
*
- * @param data type for {@code begin} output
* @param imageSize 1-D, containing {@code [height, width, channels]}.
* @param boundingBoxes 3-D with shape {@code [batch, N, 4]} describing the N bounding boxes
* associated with the image.
diff --git a/tensorflow-core/tensorflow-core-api/src/gen/annotations/org/tensorflow/op/IoOps.java b/tensorflow-core/tensorflow-core-api/src/gen/annotations/org/tensorflow/op/IoOps.java
index e038446af4a..5c33c56e962 100644
--- a/tensorflow-core/tensorflow-core-api/src/gen/annotations/org/tensorflow/op/IoOps.java
+++ b/tensorflow-core/tensorflow-core-api/src/gen/annotations/org/tensorflow/op/IoOps.java
@@ -160,7 +160,6 @@ public DecodeJsonExample decodeJsonExample(Operand jsonExamples) {
/**
* Reinterpret the bytes of a string as a vector of numbers.
*
- * @param data type for {@code output} output
* @param inputBytes Tensor of string to be decoded.
* @param fixedLength Length in bytes for each element of the decoded output. Must be a multiple
* of the size of the output type.
@@ -177,7 +176,6 @@ public DecodePaddedRaw decodePaddedRaw(Operand i
/**
* Reinterpret the bytes of a string as a vector of numbers.
*
- * @param data type for {@code output} output
* @param bytes All the elements must have the same length.
* @param outType The value of the outType attribute
* @param options carries optional attribute values
@@ -231,7 +229,6 @@ public DecodeRaw decodeRaw(Operand bytes, Class
* shape = [2 50]
*
*
- * @param data type for {@code sparse_values} output
* @param serializedSparse 2-D, The {@code N} serialized {@code SparseTensor} objects.
* Must have 3 columns.
* @param dtype The {@code dtype} of the serialized {@code SparseTensor} objects.
@@ -581,7 +578,6 @@ public ParseSingleSequenceExample parseSingleSequenceExample(Operand se
/**
* Transforms a serialized tensorflow.TensorProto proto into a Tensor.
*
- * @param data type for {@code output} output
* @param serialized A scalar string containing a serialized TensorProto proto.
* @param outType The type of the serialized tensor. The provided type must match the
* type of the serialized tensor and no implicit conversion will take place.
@@ -883,7 +879,6 @@ public ReaderSerializeState readerSerializeState(Operand reader
* rank {@code R-1}.
* The minibatch size {@code N} is extracted from {@code sparse_shape[0]}.
*
- * @param data type for {@code serialized_sparse} output
* @param sparseIndices 2-D. The {@code indices} of the minibatch {@code SparseTensor}.
* @param sparseValues 1-D. The {@code values} of the minibatch {@code SparseTensor}.
* @param sparseShape 1-D. The {@code shape} of the minibatch {@code SparseTensor}.
@@ -903,7 +898,6 @@ public SerializeManySparse serializeManySparse(Operand sparseIn
* rank {@code R-1}.
* The minibatch size {@code N} is extracted from {@code sparse_shape[0]}.
*
- * @param data type for {@code serialized_sparse} output
* @param sparseIndices 2-D. The {@code indices} of the minibatch {@code SparseTensor}.
* @param sparseValues 1-D. The {@code values} of the minibatch {@code SparseTensor}.
* @param sparseShape 1-D. The {@code shape} of the minibatch {@code SparseTensor}.
@@ -920,7 +914,6 @@ public SerializeManySparse serializeManySparse(Operand data type for {@code serialized_sparse} output
* @param sparseIndices 2-D. The {@code indices} of the {@code SparseTensor}.
* @param sparseValues 1-D. The {@code values} of the {@code SparseTensor}.
* @param sparseShape 1-D. The {@code shape} of the {@code SparseTensor}.
@@ -934,7 +927,6 @@ public SerializeSparse serializeSparse(Operand sparseIndices,
/**
* Serialize a {@code SparseTensor} into a {@code [3]} {@code Tensor} object.
*
- * @param data type for {@code serialized_sparse} output
* @param sparseIndices 2-D. The {@code indices} of the {@code SparseTensor}.
* @param sparseValues 1-D. The {@code values} of the {@code SparseTensor}.
* @param sparseShape 1-D. The {@code shape} of the {@code SparseTensor}.
diff --git a/tensorflow-core/tensorflow-core-api/src/gen/annotations/org/tensorflow/op/LinalgOps.java b/tensorflow-core/tensorflow-core-api/src/gen/annotations/org/tensorflow/op/LinalgOps.java
index 87d87f85dcf..7cb8027ca3a 100644
--- a/tensorflow-core/tensorflow-core-api/src/gen/annotations/org/tensorflow/op/LinalgOps.java
+++ b/tensorflow-core/tensorflow-core-api/src/gen/annotations/org/tensorflow/op/LinalgOps.java
@@ -127,7 +127,6 @@ public final class LinalgOps {
* tf.linalg.band_part(input, 0, 0) ==> Diagonal.
*
*
- * @param data type for {@code band} output
* @param input Rank {@code k} tensor.
* @param numLower 0-D tensor. Number of subdiagonals to keep. If negative, keep entire
* lower triangle.
@@ -145,7 +144,6 @@ public BandPart bandPart(Operand inpu
/**
* The BandedTriangularSolve operation
*
- * @param data type for {@code output} output
* @param matrix The matrix value
* @param rhs The rhs value
* @param options carries optional attribute values
@@ -160,7 +158,6 @@ public BandedTriangularSolve bandedTriangularSolve(Operand<
/**
* The BatchCholesky operation
*
- * @param data type for {@code output} output
* @param input The input value
* @param data type for {@code BatchCholesky} output and operands
* @return a new instance of BatchCholesky
@@ -172,7 +169,6 @@ public BatchCholesky batchCholesky(Operand input) {
/**
* The BatchCholeskyGrad operation
*
- * @param data type for {@code output} output
* @param l The l value
* @param grad The grad value
* @param data type for {@code BatchCholeskyGrad} output and operands
@@ -185,7 +181,6 @@ public BatchCholeskyGrad batchCholeskyGrad(Operand l,
/**
* The BatchMatrixBandPart operation
*
- * @param data type for {@code band} output
* @param input The input value
* @param numLower The numLower value
* @param numUpper The numUpper value
@@ -200,7 +195,6 @@ public BatchMatrixBandPart batchMatrixBandPart(Operand i
/**
* The BatchMatrixDeterminant operation
*
- * @param data type for {@code output} output
* @param input The input value
* @param data type for {@code BatchMatrixDeterminant} output and operands
* @return a new instance of BatchMatrixDeterminant
@@ -212,7 +206,6 @@ public BatchMatrixDeterminant batchMatrixDeterminant(Operan
/**
* The BatchMatrixDiag operation
*
- * @param data type for {@code output} output
* @param diagonal The diagonal value
* @param data type for {@code BatchMatrixDiag} output and operands
* @return a new instance of BatchMatrixDiag
@@ -224,7 +217,6 @@ public BatchMatrixDiag batchMatrixDiag(Operand diagonal)
/**
* The BatchMatrixDiagPart operation
*
- * @param data type for {@code diagonal} output
* @param input The input value
* @param data type for {@code BatchMatrixDiagPart} output and operands
* @return a new instance of BatchMatrixDiagPart
@@ -235,8 +227,11 @@ public BatchMatrixDiagPart batchMatrixDiagPart(Operand i
/**
* The BatchMatrixInverse operation
+ * DEPRECATED: This operation is deprecated and will be removed in a future version.
+ * Use tf.linalg.inv instead.
+ * Computes the inverse of one or more square invertible matrices or their
+ * adjoints (conjugate transposes).
*
- * @param data type for {@code output} output
* @param input The input value
* @param options carries optional attribute values
* @param data type for {@code BatchMatrixInverse} output and operands
@@ -250,7 +245,6 @@ public BatchMatrixInverse batchMatrixInverse(Operand i
/**
* The BatchMatrixSetDiag operation
*
- * @param data type for {@code output} output
* @param input The input value
* @param diagonal The diagonal value
* @param data type for {@code BatchMatrixSetDiag} output and operands
@@ -264,7 +258,6 @@ public BatchMatrixSetDiag batchMatrixSetDiag(Operand inp
/**
* The BatchMatrixSolve operation
*
- * @param data type for {@code output} output
* @param matrix The matrix value
* @param rhs The rhs value
* @param options carries optional attribute values
@@ -279,7 +272,6 @@ public BatchMatrixSolve batchMatrixSolve(Operand matri
/**
* The BatchMatrixSolveLs operation
*
- * @param data type for {@code output} output
* @param matrix The matrix value
* @param rhs The rhs value
* @param l2Regularizer The l2Regularizer value
@@ -295,7 +287,6 @@ public BatchMatrixSolveLs batchMatrixSolveLs(Operand m
/**
* The BatchMatrixTriangularSolve operation
*
- * @param data type for {@code output} output
* @param matrix The matrix value
* @param rhs The rhs value
* @param options carries optional attribute values
@@ -310,7 +301,6 @@ public BatchMatrixTriangularSolve batchMatrixTriangularSo
/**
* The BatchSelfAdjointEigV2 operation
*
- * @param data type for {@code e} output
* @param input The input value
* @param options carries optional attribute values
* @param data type for {@code BatchSelfAdjointEigV2} output and operands
@@ -324,7 +314,6 @@ public BatchSelfAdjointEig batchSelfAdjointEig(Operand
/**
* The BatchSvd operation
*
- * @param data type for {@code s} output
* @param input The input value
* @param options carries optional attribute values
* @param data type for {@code BatchSvd} output and operands
@@ -347,7 +336,6 @@ public BatchSvd batchSvd(Operand input, BatchSvd.Options
* not for large batch dimensions when the submatrices are small. In this
* case it might be faster to use the CPU.
*
- * @param data type for {@code output} output
* @param input Shape is {@code [..., M, M]}.
* @param data type for {@code Cholesky} output and operands
* @return a new instance of Cholesky
@@ -361,7 +349,6 @@ public Cholesky cholesky(Operand input) {
* For an explanation see "Differentiation of the Cholesky algorithm" by
* Iain Murray http://arxiv.org/abs/1602.07527.
*
- * @param data type for {@code output} output
* @param l Output of batch Cholesky algorithm l = cholesky(A). Shape is {@code [..., M, M]}.
* Algorithm depends only on lower triangular part of the innermost matrices of
* this tensor.
@@ -381,7 +368,6 @@ public CholeskyGrad choleskyGrad(Operand l, Operand
* {@code y.shape[i] == x.shape[perm[i]] for i in [0, 1, ..., rank(x) - 1]}
* {@code y[i,j,k,...,s,t,u] == conj(x[perm[i], perm[j], perm[k],...,perm[s], perm[t], perm[u]])}
*
- * @param