reformat readme

Author: mawright

README.md

@@ -1,17 +1,19 @@
 # pytorch-sparse-utils
 
 [![Tests](https://github.com/mawright/pytorch-sparse-utils/actions/workflows/tests.yml/badge.svg)](https://github.com/mawright/pytorch-sparse-utils/actions/workflows/tests.yml)
-[![Documentation Status](https://github.com/mawright/pytorch-sparse-utils/actions/workflows/docs.yml/badge.svg)](https://mawright.github.io/pytorch-sparse-utils/)
-[![License](https://img.shields.io/github/license/mawright/pytorch-sparse-utils)](https://github.com/mawright/pytorch-sparse-utils/blob/main/LICENSE)
 [![codecov](https://codecov.io/gh/mawright/pytorch-sparse-utils/branch/main/graph/badge.svg)](https://codecov.io/gh/mawright/pytorch-sparse-utils)
+[![Documentation Status](https://github.com/mawright/pytorch-sparse-utils/actions/workflows/docs.yml/badge.svg)](https://mawright.github.io/pytorch-sparse-utils/)
 ![Python](https://img.shields.io/badge/python-3.9%20%7C%203.10%20%7C%203.11%20%7C%203.12-blue)
+[![License](https://img.shields.io/github/license/mawright/pytorch-sparse-utils)](https://github.com/mawright/pytorch-sparse-utils/blob/main/LICENSE)
 
 Low-level utilities for PyTorch sparse tensors and operations.
 
 ## Introduction
+
 PyTorch's implementation of sparse tensors is lacking full support for many common operations. This repository contains a set of utilities for making PyTorch sparse tensors into more usable general-purpose sparse data structures, particularly in the context of modern neural network architectures like Transformer-based models.
 
 For example, while the basic operation `index_select` has a sparse forward implementation, using it as part of an autograd graph alongside direct manipulation of the sparse tensor's values is not supported:
+
 ```python
 # Latest PyTorch version (2.7.1) as of this writing
 X = torch.sparse_coo_tensor(
@@ -56,6 +58,7 @@ print(X.grad)
 ```
 
 Output:
+
 ```
 tensor(indices=tensor([[0, 1, 2, 3],
                        [0, 1, 2, 3]]),
@@ -64,34 +67,43 @@ tensor(indices=tensor([[0, 1, 2, 3],
 ```
 
 ## Feature Overview
+
 - Autograd-compatible implementations of bulk indexing, sparse tensor shape manipulations, and quick conversions between sparse tensor format and concatenated-batch format for use with position-invariant layers (Linear, BatchNorm, etc.).
 - Interoperability with [Pydata sparse](https://sparse.pydata.org/), a numpy-like sparse array implementation, as well as [MinkowskiEngine](https://github.com/NVIDIA/MinkowskiEngine) and [spconv](https://github.com/traveller59/spconv), two popular PyTorch libraries for convolutions on sparse images and volumes.
 - Full TorchScript compatibility for performance.
 - Extensive unit and property-based tests to ensure correctness and reliability.
 
 ## Installation
+
 pytorch-sparse-utils has minimal requirements beyond PyTorch itself. The simplest way to install is to clone this repository and use `pip install`:
+
 ```bash
 git clone https://github.com/mawright/pytorch-sparse-utils
 cd pytorch-sparse-utils
 pip install -e .  # editable installation
 ```
+
 To run the test suite, you'll need to install the optional dependencies:
+
 ```bash
 pip install -e ".[tests]"
 ```
 
 Due to incompatibilities with newer CUDA versions, MinkowskiEngine and spconv are not installed as part of the base install. For more information on installing those libraries, see their own repositories.
 
 ## Documentation
+
 Full documentation is available on [GitHub Pages](https://mawright.github.io/pytorch-sparse-utils/).
 
 ## See Also
+
 pytorch-sparse-utils represents a base set of tools for more complex neural-net operations on sparse tensors. For more sparse tensor applications, see the following repositories:
+
 - [nd-rotary-encodings](https://github.com/mawright/nd-rotary-encodings): Fast and memory-efficient rotary positional encodings (RoPE) in PyTorch, with novel algorithm updates for multi-level feature pyramids for object detection and other applications.
 - [sparse-transformer-layers](https://github.com/mawright/sparse-transformer-layers): Implementations of Transformer layers tailored to sparse tensors, including variants like Multi-scale Deformable Attention. Features custom gradient checkpointing logic to effectively handle sparse tensors with potentially many nonzero entries.
 
 ## Future Plans
+
 - Custom C++/CUDA extensions for the most performance-critical operations
 - Performance benchmarks
 - Expanded documentation

tests/test_misc.py

@@ -1,7 +1,6 @@
 import pytest
 import torch
 from torch import Tensor
-from contextlib import ExitStack
 
 from pytorch_sparse_utils.misc import prod, unpack_sparse_tensors, _pytorch_atleast_2_5
 from pytorch_sparse_utils.validation import (