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Summary: https://github.com/pytorch/pytorch/issues/38349 mruberry Not entirely sure if all the changes are necessary in how functions are added to Pytorch. Should it throw an error when called with a non-complex tensor? Numpy allows non-complex arrays in its imag() function which is used in its isreal() function but Pytorch's imag() throws an error for non-complex arrays. Where does assertONNX() get its expected output to compare to? Pull Request resolved: https://github.com/pytorch/pytorch/pull/41298 Reviewed By: ngimel Differential Revision: D22610500 Pulled By: mruberry fbshipit-source-id: 817d61f8b1c3670788b81690636bd41335788439 |
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| __config__.rst | ||
| amp.rst | ||
| autograd.rst | ||
| bottleneck.rst | ||
| checkpoint.rst | ||
| complex_numbers.rst | ||
| conf.py | ||
| cpp_extension.rst | ||
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| cuda.rst | ||
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| data.rst | ||
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| docutils.conf | ||
| futures.rst | ||
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| jit.rst | ||
| jit_builtin_functions.rst | ||
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| math-quantizer-equation.png | ||
| mobile_optimizer.rst | ||
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| multiprocessing.rst | ||
| name_inference.rst | ||
| named_tensor.rst | ||
| nn.functional.rst | ||
| nn.init.rst | ||
| nn.rst | ||
| onnx.rst | ||
| optim.rst | ||
| quantization.rst | ||
| random.rst | ||
| rpc.rst | ||
| sparse.rst | ||
| storage.rst | ||
| tensor_attributes.rst | ||
| tensor_view.rst | ||
| tensorboard.rst | ||
| tensors.rst | ||
| torch.rst | ||
| type_info.rst | ||
