java:constructor
	org.pytorch.Tensor.Tensor_float32.Tensor_float32(FloatBuffer, long[]) org/pytorch/Tensor-Tensor_float32.html#org.pytorch.Tensor.Tensor_float32.Tensor_float32(FloatBuffer, long[])
java:field
	org.pytorch.DType.FLOAT32                org/pytorch/DType.html#org.pytorch.DType.FLOAT32
	org.pytorch.DType.FLOAT64                org/pytorch/DType.html#org.pytorch.DType.FLOAT64
	org.pytorch.DType.INT32                  org/pytorch/DType.html#org.pytorch.DType.INT32
	org.pytorch.DType.INT64                  org/pytorch/DType.html#org.pytorch.DType.INT64
	org.pytorch.DType.INT8                   org/pytorch/DType.html#org.pytorch.DType.INT8
	org.pytorch.DType.UINT8                  org/pytorch/DType.html#org.pytorch.DType.UINT8
	org.pytorch.DType.jniCode                org/pytorch/DType.html#org.pytorch.DType.jniCode
	org.pytorch.torchvision.TensorImageUtils.TORCHVISION_NORM_MEAN_RGB org/pytorch/TensorImageUtils.html#org.pytorch.torchvision.TensorImageUtils.TORCHVISION_NORM_MEAN_RGB
	org.pytorch.torchvision.TensorImageUtils.TORCHVISION_NORM_STD_RGB org/pytorch/TensorImageUtils.html#org.pytorch.torchvision.TensorImageUtils.TORCHVISION_NORM_STD_RGB
java:method
	org.pytorch.IValue.dictLongKeyFrom(Map)  org/pytorch/IValue.html#org.pytorch.IValue.dictLongKeyFrom(Map)
	org.pytorch.IValue.dictStringKeyFrom(Map) org/pytorch/IValue.html#org.pytorch.IValue.dictStringKeyFrom(Map)
	org.pytorch.IValue.from(String)          org/pytorch/IValue.html#org.pytorch.IValue.from(String)
	org.pytorch.IValue.from(Tensor)          org/pytorch/IValue.html#org.pytorch.IValue.from(Tensor)
	org.pytorch.IValue.from(boolean)         org/pytorch/IValue.html#org.pytorch.IValue.from(boolean)
	org.pytorch.IValue.from(double)          org/pytorch/IValue.html#org.pytorch.IValue.from(double)
	org.pytorch.IValue.from(long)            org/pytorch/IValue.html#org.pytorch.IValue.from(long)
	org.pytorch.IValue.isBool()              org/pytorch/IValue.html#org.pytorch.IValue.isBool()
	org.pytorch.IValue.isBoolList()          org/pytorch/IValue.html#org.pytorch.IValue.isBoolList()
	org.pytorch.IValue.isDictLongKey()       org/pytorch/IValue.html#org.pytorch.IValue.isDictLongKey()
	org.pytorch.IValue.isDictStringKey()     org/pytorch/IValue.html#org.pytorch.IValue.isDictStringKey()
	org.pytorch.IValue.isDouble()            org/pytorch/IValue.html#org.pytorch.IValue.isDouble()
	org.pytorch.IValue.isDoubleList()        org/pytorch/IValue.html#org.pytorch.IValue.isDoubleList()
	org.pytorch.IValue.isList()              org/pytorch/IValue.html#org.pytorch.IValue.isList()
	org.pytorch.IValue.isLong()              org/pytorch/IValue.html#org.pytorch.IValue.isLong()
	org.pytorch.IValue.isLongList()          org/pytorch/IValue.html#org.pytorch.IValue.isLongList()
	org.pytorch.IValue.isNull()              org/pytorch/IValue.html#org.pytorch.IValue.isNull()
	org.pytorch.IValue.isString()            org/pytorch/IValue.html#org.pytorch.IValue.isString()
	org.pytorch.IValue.isTensor()            org/pytorch/IValue.html#org.pytorch.IValue.isTensor()
	org.pytorch.IValue.isTensorList()        org/pytorch/IValue.html#org.pytorch.IValue.isTensorList()
	org.pytorch.IValue.isTuple()             org/pytorch/IValue.html#org.pytorch.IValue.isTuple()
	org.pytorch.IValue.listFrom(IValue)      org/pytorch/IValue.html#org.pytorch.IValue.listFrom(IValue)
	org.pytorch.IValue.listFrom(Tensor)      org/pytorch/IValue.html#org.pytorch.IValue.listFrom(Tensor)
	org.pytorch.IValue.listFrom(boolean)     org/pytorch/IValue.html#org.pytorch.IValue.listFrom(boolean)
	org.pytorch.IValue.listFrom(double)      org/pytorch/IValue.html#org.pytorch.IValue.listFrom(double)
	org.pytorch.IValue.listFrom(long)        org/pytorch/IValue.html#org.pytorch.IValue.listFrom(long)
	org.pytorch.IValue.optionalNull()        org/pytorch/IValue.html#org.pytorch.IValue.optionalNull()
	org.pytorch.IValue.toBool()              org/pytorch/IValue.html#org.pytorch.IValue.toBool()
	org.pytorch.IValue.toBoolList()          org/pytorch/IValue.html#org.pytorch.IValue.toBoolList()
	org.pytorch.IValue.toDictLongKey()       org/pytorch/IValue.html#org.pytorch.IValue.toDictLongKey()
	org.pytorch.IValue.toDictStringKey()     org/pytorch/IValue.html#org.pytorch.IValue.toDictStringKey()
	org.pytorch.IValue.toDouble()            org/pytorch/IValue.html#org.pytorch.IValue.toDouble()
	org.pytorch.IValue.toDoubleList()        org/pytorch/IValue.html#org.pytorch.IValue.toDoubleList()
	org.pytorch.IValue.toList()              org/pytorch/IValue.html#org.pytorch.IValue.toList()
	org.pytorch.IValue.toLong()              org/pytorch/IValue.html#org.pytorch.IValue.toLong()
	org.pytorch.IValue.toLongList()          org/pytorch/IValue.html#org.pytorch.IValue.toLongList()
	org.pytorch.IValue.toStr()               org/pytorch/IValue.html#org.pytorch.IValue.toStr()
	org.pytorch.IValue.toTensor()            org/pytorch/IValue.html#org.pytorch.IValue.toTensor()
	org.pytorch.IValue.toTensorList()        org/pytorch/IValue.html#org.pytorch.IValue.toTensorList()
	org.pytorch.IValue.toTuple()             org/pytorch/IValue.html#org.pytorch.IValue.toTuple()
	org.pytorch.IValue.tupleFrom(IValue)     org/pytorch/IValue.html#org.pytorch.IValue.tupleFrom(IValue)
	org.pytorch.Module.destroy()             org/pytorch/Module.html#org.pytorch.Module.destroy()
	org.pytorch.Module.forward(IValue)       org/pytorch/Module.html#org.pytorch.Module.forward(IValue)
	org.pytorch.Module.load(String)          org/pytorch/Module.html#org.pytorch.Module.load(String)
	org.pytorch.Module.runMethod(String, IValue) org/pytorch/Module.html#org.pytorch.Module.runMethod(String, IValue)
	org.pytorch.Tensor.Tensor_float32.dtype() org/pytorch/Tensor-Tensor_float32.html#org.pytorch.Tensor.Tensor_float32.dtype()
	org.pytorch.Tensor.Tensor_float32.getDataAsFloatArray() org/pytorch/Tensor-Tensor_float32.html#org.pytorch.Tensor.Tensor_float32.getDataAsFloatArray()
	org.pytorch.Tensor.Tensor_float32.getRawDataBuffer() org/pytorch/Tensor-Tensor_float32.html#org.pytorch.Tensor.Tensor_float32.getRawDataBuffer()
	org.pytorch.Tensor.Tensor_float32.toString() org/pytorch/Tensor-Tensor_float32.html#org.pytorch.Tensor.Tensor_float32.toString()
	org.pytorch.Tensor.Tensor_float64.dtype() org/pytorch/Tensor-Tensor_float64.html#org.pytorch.Tensor.Tensor_float64.dtype()
	org.pytorch.Tensor.Tensor_float64.getDataAsDoubleArray() org/pytorch/Tensor-Tensor_float64.html#org.pytorch.Tensor.Tensor_float64.getDataAsDoubleArray()
	org.pytorch.Tensor.Tensor_float64.getRawDataBuffer() org/pytorch/Tensor-Tensor_float64.html#org.pytorch.Tensor.Tensor_float64.getRawDataBuffer()
	org.pytorch.Tensor.Tensor_float64.toString() org/pytorch/Tensor-Tensor_float64.html#org.pytorch.Tensor.Tensor_float64.toString()
	org.pytorch.Tensor.Tensor_int32.dtype()  org/pytorch/Tensor-Tensor_int32.html#org.pytorch.Tensor.Tensor_int32.dtype()
	org.pytorch.Tensor.Tensor_int32.getDataAsIntArray() org/pytorch/Tensor-Tensor_int32.html#org.pytorch.Tensor.Tensor_int32.getDataAsIntArray()
	org.pytorch.Tensor.Tensor_int32.getRawDataBuffer() org/pytorch/Tensor-Tensor_int32.html#org.pytorch.Tensor.Tensor_int32.getRawDataBuffer()
	org.pytorch.Tensor.Tensor_int32.toString() org/pytorch/Tensor-Tensor_int32.html#org.pytorch.Tensor.Tensor_int32.toString()
	org.pytorch.Tensor.Tensor_int64.dtype()  org/pytorch/Tensor-Tensor_int64.html#org.pytorch.Tensor.Tensor_int64.dtype()
	org.pytorch.Tensor.Tensor_int64.getDataAsLongArray() org/pytorch/Tensor-Tensor_int64.html#org.pytorch.Tensor.Tensor_int64.getDataAsLongArray()
	org.pytorch.Tensor.Tensor_int64.getRawDataBuffer() org/pytorch/Tensor-Tensor_int64.html#org.pytorch.Tensor.Tensor_int64.getRawDataBuffer()
	org.pytorch.Tensor.Tensor_int64.toString() org/pytorch/Tensor-Tensor_int64.html#org.pytorch.Tensor.Tensor_int64.toString()
	org.pytorch.Tensor.Tensor_int8.dtype()   org/pytorch/Tensor-Tensor_int8.html#org.pytorch.Tensor.Tensor_int8.dtype()
	org.pytorch.Tensor.Tensor_int8.getDataAsByteArray() org/pytorch/Tensor-Tensor_int8.html#org.pytorch.Tensor.Tensor_int8.getDataAsByteArray()
	org.pytorch.Tensor.Tensor_int8.getRawDataBuffer() org/pytorch/Tensor-Tensor_int8.html#org.pytorch.Tensor.Tensor_int8.getRawDataBuffer()
	org.pytorch.Tensor.Tensor_int8.toString() org/pytorch/Tensor-Tensor_int8.html#org.pytorch.Tensor.Tensor_int8.toString()
	org.pytorch.Tensor.Tensor_uint8.dtype()  org/pytorch/Tensor-Tensor_uint8.html#org.pytorch.Tensor.Tensor_uint8.dtype()
	org.pytorch.Tensor.Tensor_uint8.getDataAsUnsignedByteArray() org/pytorch/Tensor-Tensor_uint8.html#org.pytorch.Tensor.Tensor_uint8.getDataAsUnsignedByteArray()
	org.pytorch.Tensor.Tensor_uint8.getRawDataBuffer() org/pytorch/Tensor-Tensor_uint8.html#org.pytorch.Tensor.Tensor_uint8.getRawDataBuffer()
	org.pytorch.Tensor.Tensor_uint8.toString() org/pytorch/Tensor-Tensor_uint8.html#org.pytorch.Tensor.Tensor_uint8.toString()
	org.pytorch.Tensor.allocateByteBuffer(int) org/pytorch/Tensor.html#org.pytorch.Tensor.allocateByteBuffer(int)
	org.pytorch.Tensor.allocateDoubleBuffer(int) org/pytorch/Tensor.html#org.pytorch.Tensor.allocateDoubleBuffer(int)
	org.pytorch.Tensor.allocateFloatBuffer(int) org/pytorch/Tensor.html#org.pytorch.Tensor.allocateFloatBuffer(int)
	org.pytorch.Tensor.allocateIntBuffer(int) org/pytorch/Tensor.html#org.pytorch.Tensor.allocateIntBuffer(int)
	org.pytorch.Tensor.allocateLongBuffer(int) org/pytorch/Tensor.html#org.pytorch.Tensor.allocateLongBuffer(int)
	org.pytorch.Tensor.dtype()               org/pytorch/Tensor.html#org.pytorch.Tensor.dtype()
	org.pytorch.Tensor.dtypeJniCode()        org/pytorch/Tensor.html#org.pytorch.Tensor.dtypeJniCode()
	org.pytorch.Tensor.fromBlob(ByteBuffer, long[]) org/pytorch/Tensor.html#org.pytorch.Tensor.fromBlob(ByteBuffer, long[])
	org.pytorch.Tensor.fromBlob(DoubleBuffer, long[]) org/pytorch/Tensor.html#org.pytorch.Tensor.fromBlob(DoubleBuffer, long[])
	org.pytorch.Tensor.fromBlob(FloatBuffer, long[]) org/pytorch/Tensor.html#org.pytorch.Tensor.fromBlob(FloatBuffer, long[])
	org.pytorch.Tensor.fromBlob(IntBuffer, long[]) org/pytorch/Tensor.html#org.pytorch.Tensor.fromBlob(IntBuffer, long[])
	org.pytorch.Tensor.fromBlob(LongBuffer, long[]) org/pytorch/Tensor.html#org.pytorch.Tensor.fromBlob(LongBuffer, long[])
	org.pytorch.Tensor.fromBlob(byte[], long[]) org/pytorch/Tensor.html#org.pytorch.Tensor.fromBlob(byte[], long[])
	org.pytorch.Tensor.fromBlob(float[], long[]) org/pytorch/Tensor.html#org.pytorch.Tensor.fromBlob(float[], long[])
	org.pytorch.Tensor.fromBlob(int[], long[]) org/pytorch/Tensor.html#org.pytorch.Tensor.fromBlob(int[], long[])
	org.pytorch.Tensor.fromBlob(long[], double[]) org/pytorch/Tensor.html#org.pytorch.Tensor.fromBlob(long[], double[])
	org.pytorch.Tensor.fromBlob(long[], long[]) org/pytorch/Tensor.html#org.pytorch.Tensor.fromBlob(long[], long[])
	org.pytorch.Tensor.fromBlobUnsigned(ByteBuffer, long[]) org/pytorch/Tensor.html#org.pytorch.Tensor.fromBlobUnsigned(ByteBuffer, long[])
	org.pytorch.Tensor.fromBlobUnsigned(byte[], long[]) org/pytorch/Tensor.html#org.pytorch.Tensor.fromBlobUnsigned(byte[], long[])
	org.pytorch.Tensor.getDataAsByteArray()  org/pytorch/Tensor.html#org.pytorch.Tensor.getDataAsByteArray()
	org.pytorch.Tensor.getDataAsDoubleArray() org/pytorch/Tensor.html#org.pytorch.Tensor.getDataAsDoubleArray()
	org.pytorch.Tensor.getDataAsFloatArray() org/pytorch/Tensor.html#org.pytorch.Tensor.getDataAsFloatArray()
	org.pytorch.Tensor.getDataAsIntArray()   org/pytorch/Tensor.html#org.pytorch.Tensor.getDataAsIntArray()
	org.pytorch.Tensor.getDataAsLongArray()  org/pytorch/Tensor.html#org.pytorch.Tensor.getDataAsLongArray()
	org.pytorch.Tensor.getDataAsUnsignedByteArray() org/pytorch/Tensor.html#org.pytorch.Tensor.getDataAsUnsignedByteArray()
	org.pytorch.Tensor.getRawDataBuffer()    org/pytorch/Tensor.html#org.pytorch.Tensor.getRawDataBuffer()
	org.pytorch.Tensor.numel()               org/pytorch/Tensor.html#org.pytorch.Tensor.numel()
	org.pytorch.Tensor.numel(long[])         org/pytorch/Tensor.html#org.pytorch.Tensor.numel(long[])
	org.pytorch.Tensor.shape()               org/pytorch/Tensor.html#org.pytorch.Tensor.shape()
	org.pytorch.torchvision.TensorImageUtils.bitmapToFloat32Tensor(Bitmap, float[], float[]) org/pytorch/TensorImageUtils.html#org.pytorch.torchvision.TensorImageUtils.bitmapToFloat32Tensor(Bitmap, float[], float[])
	org.pytorch.torchvision.TensorImageUtils.bitmapToFloat32Tensor(Bitmap, int, int, int, int, float[], float[]) org/pytorch/TensorImageUtils.html#org.pytorch.torchvision.TensorImageUtils.bitmapToFloat32Tensor(Bitmap, int, int, int, int, float[], float[])
	org.pytorch.torchvision.TensorImageUtils.bitmapToFloatBuffer(Bitmap, int, int, int, int, float[], float[], FloatBuffer, int) org/pytorch/TensorImageUtils.html#org.pytorch.torchvision.TensorImageUtils.bitmapToFloatBuffer(Bitmap, int, int, int, int, float[], float[], FloatBuffer, int)
	org.pytorch.torchvision.TensorImageUtils.imageYUV420CenterCropToFloat32Tensor(Image, int, int, int, float[], float[]) org/pytorch/TensorImageUtils.html#org.pytorch.torchvision.TensorImageUtils.imageYUV420CenterCropToFloat32Tensor(Image, int, int, int, float[], float[])
	org.pytorch.torchvision.TensorImageUtils.imageYUV420CenterCropToFloatBuffer(Image, int, int, int, float[], float[], FloatBuffer, int) org/pytorch/TensorImageUtils.html#org.pytorch.torchvision.TensorImageUtils.imageYUV420CenterCropToFloatBuffer(Image, int, int, int, float[], float[], FloatBuffer, int)
java:package
	org.pytorch                              org/pytorch/package-index.html#org.pytorch
	org.pytorch.torchvision                  org/pytorch/torchvision/package-index.html#org.pytorch.torchvision
java:type
	org.pytorch.DType                        org/pytorch/DType.html#org.pytorch.DType
	org.pytorch.IValue                       org/pytorch/IValue.html#org.pytorch.IValue
	org.pytorch.Module                       org/pytorch/Module.html#org.pytorch.Module
	org.pytorch.Tensor                       org/pytorch/Tensor.html#org.pytorch.Tensor
	org.pytorch.Tensor.Tensor_float32        org/pytorch/Tensor-Tensor_float32.html#org.pytorch.Tensor.Tensor_float32
	org.pytorch.Tensor.Tensor_float64        org/pytorch/Tensor-Tensor_float64.html#org.pytorch.Tensor.Tensor_float64
	org.pytorch.Tensor.Tensor_int32          org/pytorch/Tensor-Tensor_int32.html#org.pytorch.Tensor.Tensor_int32
	org.pytorch.Tensor.Tensor_int64          org/pytorch/Tensor-Tensor_int64.html#org.pytorch.Tensor.Tensor_int64
	org.pytorch.Tensor.Tensor_int8           org/pytorch/Tensor-Tensor_int8.html#org.pytorch.Tensor.Tensor_int8
	org.pytorch.Tensor.Tensor_uint8          org/pytorch/Tensor-Tensor_uint8.html#org.pytorch.Tensor.Tensor_uint8
	org.pytorch.torchvision.TensorImageUtils org/pytorch/TensorImageUtils.html#org.pytorch.torchvision.TensorImageUtils
py:attribute
	torch.FloatStorage.device                storage.html#torch.FloatStorage.device
	torch.FloatStorage.dtype                 storage.html#torch.FloatStorage.dtype
	torch.FloatStorage.is_cuda               storage.html#torch.FloatStorage.is_cuda
	torch.FloatStorage.is_sparse             storage.html#torch.FloatStorage.is_sparse
	torch.Tensor.T                           tensors.html#torch.Tensor.T
	torch.Tensor.device                      tensors.html#torch.Tensor.device
	torch.Tensor.grad                        autograd.html#torch.Tensor.grad
	torch.Tensor.is_cuda                     tensors.html#torch.Tensor.is_cuda
	torch.Tensor.is_leaf                     autograd.html#torch.Tensor.is_leaf
	torch.Tensor.is_quantized                tensors.html#torch.Tensor.is_quantized
	torch.Tensor.is_sparse                   tensors.html#torch.Tensor.is_sparse
	torch.Tensor.names                       named_tensor.html#torch.Tensor.names
	torch.Tensor.ndim                        tensors.html#torch.Tensor.ndim
	torch.Tensor.requires_grad               autograd.html#torch.Tensor.requires_grad
	torch._C.Generator.device                torch.html#torch._C.Generator.device
	torch.distributions.bernoulli.Bernoulli.arg_constraints distributions.html#torch.distributions.bernoulli.Bernoulli.arg_constraints
	torch.distributions.bernoulli.Bernoulli.has_enumerate_support distributions.html#torch.distributions.bernoulli.Bernoulli.has_enumerate_support
	torch.distributions.bernoulli.Bernoulli.logits distributions.html#torch.distributions.bernoulli.Bernoulli.logits
	torch.distributions.bernoulli.Bernoulli.probs distributions.html#torch.distributions.bernoulli.Bernoulli.probs
	torch.distributions.bernoulli.Bernoulli.support distributions.html#torch.distributions.bernoulli.Bernoulli.support
	torch.distributions.beta.Beta.arg_constraints distributions.html#torch.distributions.beta.Beta.arg_constraints
	torch.distributions.beta.Beta.has_rsample distributions.html#torch.distributions.beta.Beta.has_rsample
	torch.distributions.beta.Beta.support    distributions.html#torch.distributions.beta.Beta.support
	torch.distributions.binomial.Binomial.arg_constraints distributions.html#torch.distributions.binomial.Binomial.arg_constraints
	torch.distributions.binomial.Binomial.has_enumerate_support distributions.html#torch.distributions.binomial.Binomial.has_enumerate_support
	torch.distributions.binomial.Binomial.logits distributions.html#torch.distributions.binomial.Binomial.logits
	torch.distributions.binomial.Binomial.probs distributions.html#torch.distributions.binomial.Binomial.probs
	torch.distributions.categorical.Categorical.arg_constraints distributions.html#torch.distributions.categorical.Categorical.arg_constraints
	torch.distributions.categorical.Categorical.has_enumerate_support distributions.html#torch.distributions.categorical.Categorical.has_enumerate_support
	torch.distributions.categorical.Categorical.logits distributions.html#torch.distributions.categorical.Categorical.logits
	torch.distributions.categorical.Categorical.probs distributions.html#torch.distributions.categorical.Categorical.probs
	torch.distributions.cauchy.Cauchy.arg_constraints distributions.html#torch.distributions.cauchy.Cauchy.arg_constraints
	torch.distributions.cauchy.Cauchy.has_rsample distributions.html#torch.distributions.cauchy.Cauchy.has_rsample
	torch.distributions.cauchy.Cauchy.support distributions.html#torch.distributions.cauchy.Cauchy.support
	torch.distributions.chi2.Chi2.arg_constraints distributions.html#torch.distributions.chi2.Chi2.arg_constraints
	torch.distributions.constraints.cat      distributions.html#torch.distributions.constraints.cat
	torch.distributions.constraints.dependent_property distributions.html#torch.distributions.constraints.dependent_property
	torch.distributions.constraints.greater_than distributions.html#torch.distributions.constraints.greater_than
	torch.distributions.constraints.greater_than_eq distributions.html#torch.distributions.constraints.greater_than_eq
	torch.distributions.constraints.half_open_interval distributions.html#torch.distributions.constraints.half_open_interval
	torch.distributions.constraints.integer_interval distributions.html#torch.distributions.constraints.integer_interval
	torch.distributions.constraints.interval distributions.html#torch.distributions.constraints.interval
	torch.distributions.constraints.less_than distributions.html#torch.distributions.constraints.less_than
	torch.distributions.constraints.stack    distributions.html#torch.distributions.constraints.stack
	torch.distributions.continuous_bernoulli.ContinuousBernoulli.arg_constraints distributions.html#torch.distributions.continuous_bernoulli.ContinuousBernoulli.arg_constraints
	torch.distributions.continuous_bernoulli.ContinuousBernoulli.has_rsample distributions.html#torch.distributions.continuous_bernoulli.ContinuousBernoulli.has_rsample
	torch.distributions.continuous_bernoulli.ContinuousBernoulli.logits distributions.html#torch.distributions.continuous_bernoulli.ContinuousBernoulli.logits
	torch.distributions.continuous_bernoulli.ContinuousBernoulli.probs distributions.html#torch.distributions.continuous_bernoulli.ContinuousBernoulli.probs
	torch.distributions.continuous_bernoulli.ContinuousBernoulli.support distributions.html#torch.distributions.continuous_bernoulli.ContinuousBernoulli.support
	torch.distributions.dirichlet.Dirichlet.arg_constraints distributions.html#torch.distributions.dirichlet.Dirichlet.arg_constraints
	torch.distributions.dirichlet.Dirichlet.has_rsample distributions.html#torch.distributions.dirichlet.Dirichlet.has_rsample
	torch.distributions.dirichlet.Dirichlet.support distributions.html#torch.distributions.dirichlet.Dirichlet.support
	torch.distributions.exponential.Exponential.arg_constraints distributions.html#torch.distributions.exponential.Exponential.arg_constraints
	torch.distributions.exponential.Exponential.has_rsample distributions.html#torch.distributions.exponential.Exponential.has_rsample
	torch.distributions.exponential.Exponential.support distributions.html#torch.distributions.exponential.Exponential.support
	torch.distributions.fishersnedecor.FisherSnedecor.arg_constraints distributions.html#torch.distributions.fishersnedecor.FisherSnedecor.arg_constraints
	torch.distributions.fishersnedecor.FisherSnedecor.has_rsample distributions.html#torch.distributions.fishersnedecor.FisherSnedecor.has_rsample
	torch.distributions.fishersnedecor.FisherSnedecor.support distributions.html#torch.distributions.fishersnedecor.FisherSnedecor.support
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py:function
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	torch.nn.utils.rnn.pack_padded_sequence  nn.html#torch.nn.utils.rnn.pack_padded_sequence
	torch.nn.utils.rnn.pack_sequence         nn.html#torch.nn.utils.rnn.pack_sequence
	torch.nn.utils.rnn.pad_packed_sequence   nn.html#torch.nn.utils.rnn.pad_packed_sequence
	torch.nn.utils.rnn.pad_sequence          nn.html#torch.nn.utils.rnn.pad_sequence
	torch.nn.utils.spectral_norm             nn.html#torch.nn.utils.spectral_norm
	torch.nn.utils.vector_to_parameters      nn.html#torch.nn.utils.vector_to_parameters
	torch.nn.utils.weight_norm               nn.html#torch.nn.utils.weight_norm
	torch.no_grad                            torch.html#torch.no_grad
	torch.nonzero                            torch.html#torch.nonzero
	torch.norm                               torch.html#torch.norm
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	torch.numel                              torch.html#torch.numel
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	torch.onnx.operators.shape_as_tensor     onnx.html#torch.onnx.operators.shape_as_tensor
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	torch.orgqr                              torch.html#torch.orgqr
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	torchvision.models.shufflenet_v2_x1_0    torchvision/models.html#torchvision.models.shufflenet_v2_x1_0
	torchvision.models.shufflenet_v2_x1_5    torchvision/models.html#torchvision.models.shufflenet_v2_x1_5
	torchvision.models.shufflenet_v2_x2_0    torchvision/models.html#torchvision.models.shufflenet_v2_x2_0
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	torch.FloatStorage.is_pinned             storage.html#torch.FloatStorage.is_pinned
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std:label
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	community/persons_of_interest:onnx <-> pytorch ONNX <-> PyTorch                        : community/persons_of_interest.html#onnx-pytorch
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	cpp_index:authoring models in c++        Authoring Models in C++                 : cpp_index.html#authoring-models-in-c
	cpp_index:c++                            C++                                     : cpp_index.html#c
	cpp_index:extending pytorch and torchscript with c++ extensions Extending PyTorch and TorchScript with C++ Extensions: cpp_index.html#extending-pytorch-and-torchscript-with-c-extensions
	cpp_index:packaging for c++              Packaging for C++                       : cpp_index.html#packaging-for-c
	cpp_index:tensor and autograd in c++     Tensor and Autograd in C++              : cpp_index.html#tensor-and-autograd-in-c
	cpp_index:torchscript c++ api            TorchScript C++ API                     : cpp_index.html#torchscript-c-api
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	cuda-memory-management                   Memory management                       : notes/cuda.html#cuda-memory-management
	cuda-memory-pinning                      Use pinned memory buffers               : notes/cuda.html#cuda-memory-pinning
	cuda-nn-ddp-instead                      Use nn.parallel.DistributedDataParallel instead of multiprocessing or nn.DataParallel: notes/cuda.html#cuda-nn-ddp-instead
	cuda-semantics                           CUDA semantics                          : notes/cuda.html#cuda-semantics
	cuda:communication collectives           Communication collectives               : cuda.html#communication-collectives
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	cuda:nvidia tools extension (nvtx)       NVIDIA Tools Extension (NVTX)           : cuda.html#nvidia-tools-extension-nvtx
	cuda:random number generator             Random Number Generator                 : cuda.html#random-number-generator
	cuda:streams and events                  Streams and events                      : cuda.html#streams-and-events
	cuda:torch.cuda                          torch.cuda                              : cuda.html#module-torch.cuda
	cufft-plan-cache                         cuFFT plan cache                        : notes/cuda.html#cufft-plan-cache
	data:automatic batching (default)        Automatic batching (default)            : data.html#automatic-batching-default
	data:data loading order and :class:`~torch.utils.data.sampler` Data Loading Order and Sampler          : data.html#data-loading-order-and-sampler
	data:dataset types                       Dataset Types                           : data.html#dataset-types
	data:disable automatic batching          Disable automatic batching              : data.html#disable-automatic-batching
	data:iterable-style datasets             Iterable-style datasets                 : data.html#iterable-style-datasets
	data:loading batched and non-batched data Loading Batched and Non-Batched Data    : data.html#loading-batched-and-non-batched-data
	data:map-style datasets                  Map-style datasets                      : data.html#map-style-datasets
	data:memory pinning                      Memory Pinning                          : data.html#memory-pinning
	data:multi-process data loading          Multi-process data loading              : data.html#multi-process-data-loading
	data:platform-specific behaviors         Platform-specific behaviors             : data.html#platform-specific-behaviors
	data:randomness in multi-process data loading Randomness in multi-process data loading: data.html#randomness-in-multi-process-data-loading
	data:single- and multi-process data loading Single- and Multi-process Data Loading  : data.html#single-and-multi-process-data-loading
	data:single-process data loading (default) Single-process data loading (default)   : data.html#single-process-data-loading-default
	data:torch.utils.data                    torch.utils.data                        : data.html#module-torch.utils.data
	data:working with :attr:`collate_fn`     Working with collate_fn                 : data.html#working-with-collate-fn
	dataloader-collate_fn                    Working with collate_fn                 : data.html#dataloader-collate-fn
	dataloader-workers-random-seed           My data loader workers return identical random numbers: notes/faq.html#dataloader-workers-random-seed
	ddp                                      Distributed Data Parallel               : notes/ddp.html#ddp
	device-doc                               torch.device                            : tensor_attributes.html#device-doc
	disable torchscript                      Disable JIT for Debugging               : jit.html#disable-torchscript
	distributed-autograd-design              Distributed Autograd Design             : rpc/distributed_autograd.html#distributed-autograd-design
	distributed-basics                       Basics                                  : distributed.html#distributed-basics
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	distributed-rpc-framework                Distributed RPC Framework               : rpc/rpc.html#distributed-rpc-framework
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	distributed:backends that come with pytorch Backends that come with PyTorch         : distributed.html#backends-that-come-with-pytorch
	distributed:basics                       Basics                                  : distributed.html#basics
	distributed:choosing the network interface to use Choosing the network interface to use   : distributed.html#choosing-the-network-interface-to-use
	distributed:collective functions         Collective functions                    : distributed.html#collective-functions
	distributed:common environment variables Common environment variables            : distributed.html#common-environment-variables
	distributed:distributed communication package - torch.distributed Distributed communication package - torch.distributed: distributed.html#module-torch.distributed
	distributed:environment variable initialization Environment variable initialization     : distributed.html#environment-variable-initialization
	distributed:groups                       Groups                                  : distributed.html#groups
	distributed:initialization               Initialization                          : distributed.html#initialization
	distributed:launch utility               Launch utility                          : distributed.html#launch-utility
	distributed:multi-gpu collective functions Multi-GPU collective functions          : distributed.html#multi-gpu-collective-functions
	distributed:other nccl environment variables Other NCCL environment variables        : distributed.html#other-nccl-environment-variables
	distributed:point-to-point communication Point-to-point communication            : distributed.html#point-to-point-communication
	distributed:shared file-system initialization Shared file-system initialization       : distributed.html#shared-file-system-initialization
	distributed:spawn utility                Spawn utility                           : distributed.html#spawn-utility
	distributed:synchronous and asynchronous collective operations Synchronous and asynchronous collective operations: distributed.html#synchronous-and-asynchronous-collective-operations
	distributed:tcp initialization           TCP initialization                      : distributed.html#tcp-initialization
	distributed:which backend to use?        Which backend to use?                   : distributed.html#which-backend-to-use
	distributions::hidden:`bernoulli`        Bernoulli                               : distributions.html#bernoulli
	distributions::hidden:`beta`             Beta                                    : distributions.html#beta
	distributions::hidden:`binomial`         Binomial                                : distributions.html#binomial
	distributions::hidden:`categorical`      Categorical                             : distributions.html#categorical
	distributions::hidden:`cauchy`           Cauchy                                  : distributions.html#cauchy
	distributions::hidden:`chi2`             Chi2                                    : distributions.html#chi2
	distributions::hidden:`continuousbernoulli` ContinuousBernoulli                     : distributions.html#continuousbernoulli
	distributions::hidden:`dirichlet`        Dirichlet                               : distributions.html#dirichlet
	distributions::hidden:`distribution`     Distribution                            : distributions.html#distribution
	distributions::hidden:`exponential`      Exponential                             : distributions.html#exponential
	distributions::hidden:`exponentialfamily` ExponentialFamily                       : distributions.html#exponentialfamily
	distributions::hidden:`fishersnedecor`   FisherSnedecor                          : distributions.html#fishersnedecor
	distributions::hidden:`gamma`            Gamma                                   : distributions.html#gamma
	distributions::hidden:`geometric`        Geometric                               : distributions.html#geometric
	distributions::hidden:`gumbel`           Gumbel                                  : distributions.html#gumbel
	distributions::hidden:`halfcauchy`       HalfCauchy                              : distributions.html#halfcauchy
	distributions::hidden:`halfnormal`       HalfNormal                              : distributions.html#halfnormal
	distributions::hidden:`independent`      Independent                             : distributions.html#independent
	distributions::hidden:`laplace`          Laplace                                 : distributions.html#laplace
	distributions::hidden:`logitrelaxedbernoulli` LogitRelaxedBernoulli                   : distributions.html#logitrelaxedbernoulli
	distributions::hidden:`lognormal`        LogNormal                               : distributions.html#lognormal
	distributions::hidden:`lowrankmultivariatenormal` LowRankMultivariateNormal               : distributions.html#lowrankmultivariatenormal
	distributions::hidden:`mixturesamefamily` MixtureSameFamily                       : distributions.html#mixturesamefamily
	distributions::hidden:`multinomial`      Multinomial                             : distributions.html#multinomial
	distributions::hidden:`multivariatenormal` MultivariateNormal                      : distributions.html#multivariatenormal
	distributions::hidden:`negativebinomial` NegativeBinomial                        : distributions.html#negativebinomial
	distributions::hidden:`normal`           Normal                                  : distributions.html#normal
	distributions::hidden:`onehotcategorical` OneHotCategorical                       : distributions.html#onehotcategorical
	distributions::hidden:`pareto`           Pareto                                  : distributions.html#pareto
	distributions::hidden:`poisson`          Poisson                                 : distributions.html#poisson
	distributions::hidden:`relaxedbernoulli` RelaxedBernoulli                        : distributions.html#relaxedbernoulli
	distributions::hidden:`relaxedonehotcategorical` RelaxedOneHotCategorical                : distributions.html#relaxedonehotcategorical
	distributions::hidden:`studentt`         StudentT                                : distributions.html#studentt
	distributions::hidden:`transformeddistribution` TransformedDistribution                 : distributions.html#transformeddistribution
	distributions::hidden:`uniform`          Uniform                                 : distributions.html#uniform
	distributions::hidden:`vonmises`         VonMises                                : distributions.html#vonmises
	distributions::hidden:`weibull`          Weibull                                 : distributions.html#weibull
	distributions:`constraint registry`      Constraint Registry                     : distributions.html#module-torch.distributions.constraint_registry
	distributions:`constraints`              Constraints                             : distributions.html#module-torch.distributions.constraints
	distributions:`kl divergence`            KL Divergence                           : distributions.html#module-torch.distributions.kl
	distributions:`transforms`               Transforms                              : distributions.html#module-torch.distributions.transforms
	distributions:pathwise derivative        Pathwise derivative                     : distributions.html#pathwise-derivative
	distributions:probability distributions - torch.distributions Probability distributions - torch.distributions: distributions.html#module-torch.distributions
	distributions:score function             Score function                          : distributions.html#score-function
	dlpack:torch.utils.dlpack                torch.utils.dlpack                      : dlpack.html#torch-utils-dlpack
	dtype-doc                                torch.dtype                             : tensor_attributes.html#dtype-doc
	excluding-requires_grad                  requires_grad                           : notes/autograd.html#excluding-requires-grad
	excluding-subgraphs                      Excluding subgraphs from backward       : notes/autograd.html#excluding-subgraphs
	factory-doc                              Factory functions                       : name_inference.html#factory-doc
	fast-mode-algorithm                      FAST mode algorithm                     : rpc/distributed_autograd.html#fast-mode-algorithm
	finfo-doc                                torch.finfo                             : type_info.html#finfo-doc
	functional-api                           Functional higher level API             : autograd.html#functional-api
	generators                               Generators                              : torch.html#generators
	genindex                                 Index                                   : genindex.html
	grad-check                               Numerical gradient checking             : autograd.html#grad-check
	gradient-scaling                         Gradient Scaling                        : amp.html#gradient-scaling
	gradient-scaling-examples                Gradient Scaling                        : notes/amp_examples.html#gradient-scaling-examples
	how-autograd-encodes-history             How autograd encodes the history        : notes/autograd.html#how-autograd-encodes-history
	hub:caching logic                        Caching logic                           : hub.html#caching-logic
	hub:how to implement an entrypoint?      How to implement an entrypoint?         : hub.html#how-to-implement-an-entrypoint
	hub:important notice                     Important Notice                        : hub.html#important-notice
	hub:known limitations:                   Known limitations:                      : hub.html#known-limitations
	hub:loading models from hub              Loading models from Hub                 : hub.html#loading-models-from-hub
	hub:publishing models                    Publishing models                       : hub.html#publishing-models
	hub:running a loaded model:              Running a loaded model:                 : hub.html#running-a-loaded-model
	hub:torch.hub                            torch.hub                               : hub.html#torch-hub
	hub:where are my downloaded models saved? Where are my downloaded models saved?   : hub.html#where-are-my-downloaded-models-saved
	iinfo-doc                                torch.iinfo                             : type_info.html#iinfo-doc
	index:indices and tables                 Indices and tables                      : index.html#indices-and-tables
	index:pytorch documentation              PyTorch documentation                   : index.html#pytorch-documentation
	inplace-random-sampling                  In-place random sampling                : torch.html#inplace-random-sampling
	jit:appendix                             Appendix                                : jit.html#appendix
	jit:attributes                           Attributes                              : jit.html#attributes
	jit:automatic trace checking             Automatic Trace Checking                : jit.html#automatic-trace-checking
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	jit:creating torchscript code            Creating TorchScript Code               : jit.html#creating-torchscript-code
	jit:debugging                            Debugging                               : jit.html#debugging
	jit:disable jit for debugging            Disable JIT for Debugging               : jit.html#disable-jit-for-debugging
	jit:frequently asked questions           Frequently Asked Questions              : jit.html#frequently-asked-questions
	jit:functions                            Functions                               : jit.html#functions
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	jit:interpreting graphs                  Interpreting Graphs                     : jit.html#interpreting-graphs
	jit:migrating to pytorch 1.2 recursive scripting api Migrating to PyTorch 1.2 Recursive Scripting API: jit.html#migrating-to-pytorch-1-2-recursive-scripting-api
	jit:mixing tracing and scripting         Mixing Tracing and Scripting            : jit.html#mixing-tracing-and-scripting
	jit:modules                              Modules                                 : jit.html#modules
	jit:python functions and modules         Python Functions and Modules            : jit.html#python-functions-and-modules
	jit:python language reference comparison Python Language Reference Comparison    : jit.html#python-language-reference-comparison
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	jit:torchscript                          TorchScript                             : jit.html#torchscript
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	jit:torchscript language                 TorchScript Language                    : jit.html#torchscript-language
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	jit_builtin_functions:torchscript builtins TorchScript Builtins                    : jit_builtin_functions.html#torchscript-builtins
	jit_iterables                            Iterables                               : jit_language_reference.html#jit-iterables
	jit_language_reference:accessing module parameters Accessing Module Parameters             : jit_language_reference.html#accessing-module-parameters
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	jit_language_reference:attribute lookup on python modules Attribute Lookup On Python Modules      : jit_language_reference.html#attribute-lookup-on-python-modules
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	jit_language_reference:comparison operators Comparison Operators                    : jit_language_reference.html#comparison-operators
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	jit_language_reference:dict construction Dict Construction                       : jit_language_reference.html#dict-construction
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	jit_language_reference:list construction List Construction                       : jit_language_reference.html#list-construction
	jit_language_reference:literals          Literals                                : jit_language_reference.html#literals
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	jit_language_reference:torchscript classes TorchScript Classes                     : jit_language_reference.html#id2
	jit_language_reference:torchscript language reference TorchScript Language Reference          : jit_language_reference.html#torchscript-language-reference
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	jit_python_reference:python language reference coverage Python Language Reference Coverage      : jit_python_reference.html#python-language-reference-coverage
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	jit_unsupported:torch and tensor unsupported attributes Torch and Tensor Unsupported Attributes : jit_unsupported.html#torch-and-tensor-unsupported-attributes
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	multiprocessing:file system - ``file_system`` File system - file_system               : multiprocessing.html#file-system-file-system
	multiprocessing:multiprocessing package - torch.multiprocessing Multiprocessing package - torch.multiprocessing: multiprocessing.html#module-torch.multiprocessing
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	named_tensor:explicit alignment by names Explicit alignment by names             : named_tensor.html#explicit-alignment-by-names
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	nn.functional::hidden:`celu`             celu                                    : nn.functional.html#celu
	nn.functional::hidden:`conv1d`           conv1d                                  : nn.functional.html#conv1d
	nn.functional::hidden:`conv2d`           conv2d                                  : nn.functional.html#conv2d
	nn.functional::hidden:`conv3d`           conv3d                                  : nn.functional.html#conv3d
	nn.functional::hidden:`conv_transpose1d` conv_transpose1d                        : nn.functional.html#conv-transpose1d
	nn.functional::hidden:`conv_transpose2d` conv_transpose2d                        : nn.functional.html#conv-transpose2d
	nn.functional::hidden:`conv_transpose3d` conv_transpose3d                        : nn.functional.html#conv-transpose3d
	nn.functional::hidden:`cosine_embedding_loss` cosine_embedding_loss                   : nn.functional.html#cosine-embedding-loss
	nn.functional::hidden:`cosine_similarity` cosine_similarity                       : nn.functional.html#cosine-similarity
	nn.functional::hidden:`cross_entropy`    cross_entropy                           : nn.functional.html#cross-entropy
	nn.functional::hidden:`ctc_loss`         ctc_loss                                : nn.functional.html#ctc-loss
	nn.functional::hidden:`data_parallel`    data_parallel                           : nn.functional.html#data-parallel
	nn.functional::hidden:`dropout2d`        dropout2d                               : nn.functional.html#dropout2d
	nn.functional::hidden:`dropout3d`        dropout3d                               : nn.functional.html#dropout3d
	nn.functional::hidden:`dropout`          dropout                                 : nn.functional.html#dropout
	nn.functional::hidden:`elu`              elu                                     : nn.functional.html#elu
	nn.functional::hidden:`embedding_bag`    embedding_bag                           : nn.functional.html#embedding-bag
	nn.functional::hidden:`embedding`        embedding                               : nn.functional.html#embedding
	nn.functional::hidden:`fold`             fold                                    : nn.functional.html#fold
	nn.functional::hidden:`gelu`             gelu                                    : nn.functional.html#gelu
	nn.functional::hidden:`glu`              glu                                     : nn.functional.html#glu
	nn.functional::hidden:`grid_sample`      grid_sample                             : nn.functional.html#grid-sample
	nn.functional::hidden:`gumbel_softmax`   gumbel_softmax                          : nn.functional.html#gumbel-softmax
	nn.functional::hidden:`hardshrink`       hardshrink                              : nn.functional.html#hardshrink
	nn.functional::hidden:`hardtanh`         hardtanh                                : nn.functional.html#hardtanh
	nn.functional::hidden:`hinge_embedding_loss` hinge_embedding_loss                    : nn.functional.html#hinge-embedding-loss
	nn.functional::hidden:`instance_norm`    instance_norm                           : nn.functional.html#instance-norm
	nn.functional::hidden:`interpolate`      interpolate                             : nn.functional.html#interpolate
	nn.functional::hidden:`kl_div`           kl_div                                  : nn.functional.html#kl-div
	nn.functional::hidden:`l1_loss`          l1_loss                                 : nn.functional.html#l1-loss
	nn.functional::hidden:`layer_norm`       layer_norm                              : nn.functional.html#layer-norm
	nn.functional::hidden:`leaky_relu`       leaky_relu                              : nn.functional.html#leaky-relu
	nn.functional::hidden:`linear`           linear                                  : nn.functional.html#linear
	nn.functional::hidden:`local_response_norm` local_response_norm                     : nn.functional.html#local-response-norm
	nn.functional::hidden:`log_softmax`      log_softmax                             : nn.functional.html#log-softmax
	nn.functional::hidden:`logsigmoid`       logsigmoid                              : nn.functional.html#logsigmoid
	nn.functional::hidden:`lp_pool1d`        lp_pool1d                               : nn.functional.html#lp-pool1d
	nn.functional::hidden:`lp_pool2d`        lp_pool2d                               : nn.functional.html#lp-pool2d
	nn.functional::hidden:`margin_ranking_loss` margin_ranking_loss                     : nn.functional.html#margin-ranking-loss
	nn.functional::hidden:`max_pool1d`       max_pool1d                              : nn.functional.html#max-pool1d
	nn.functional::hidden:`max_pool2d`       max_pool2d                              : nn.functional.html#max-pool2d
	nn.functional::hidden:`max_pool3d`       max_pool3d                              : nn.functional.html#max-pool3d
	nn.functional::hidden:`max_unpool1d`     max_unpool1d                            : nn.functional.html#max-unpool1d
	nn.functional::hidden:`max_unpool2d`     max_unpool2d                            : nn.functional.html#max-unpool2d
	nn.functional::hidden:`max_unpool3d`     max_unpool3d                            : nn.functional.html#max-unpool3d
	nn.functional::hidden:`mse_loss`         mse_loss                                : nn.functional.html#mse-loss
	nn.functional::hidden:`multi_margin_loss` multi_margin_loss                       : nn.functional.html#multi-margin-loss
	nn.functional::hidden:`multilabel_margin_loss` multilabel_margin_loss                  : nn.functional.html#multilabel-margin-loss
	nn.functional::hidden:`multilabel_soft_margin_loss` multilabel_soft_margin_loss             : nn.functional.html#multilabel-soft-margin-loss
	nn.functional::hidden:`nll_loss`         nll_loss                                : nn.functional.html#nll-loss
	nn.functional::hidden:`normalize`        normalize                               : nn.functional.html#normalize
	nn.functional::hidden:`one_hot`          one_hot                                 : nn.functional.html#one-hot
	nn.functional::hidden:`pad`              pad                                     : nn.functional.html#pad
	nn.functional::hidden:`pairwise_distance` pairwise_distance                       : nn.functional.html#pairwise-distance
	nn.functional::hidden:`pdist`            pdist                                   : nn.functional.html#pdist
	nn.functional::hidden:`pixel_shuffle`    pixel_shuffle                           : nn.functional.html#pixel-shuffle
	nn.functional::hidden:`poisson_nll_loss` poisson_nll_loss                        : nn.functional.html#poisson-nll-loss
	nn.functional::hidden:`prelu`            prelu                                   : nn.functional.html#prelu
	nn.functional::hidden:`relu6`            relu6                                   : nn.functional.html#relu6
	nn.functional::hidden:`relu`             relu                                    : nn.functional.html#relu
	nn.functional::hidden:`rrelu`            rrelu                                   : nn.functional.html#rrelu
	nn.functional::hidden:`selu`             selu                                    : nn.functional.html#selu
	nn.functional::hidden:`sigmoid`          sigmoid                                 : nn.functional.html#sigmoid
	nn.functional::hidden:`smooth_l1_loss`   smooth_l1_loss                          : nn.functional.html#smooth-l1-loss
	nn.functional::hidden:`soft_margin_loss` soft_margin_loss                        : nn.functional.html#soft-margin-loss
	nn.functional::hidden:`softmax`          softmax                                 : nn.functional.html#softmax
	nn.functional::hidden:`softmin`          softmin                                 : nn.functional.html#softmin
	nn.functional::hidden:`softplus`         softplus                                : nn.functional.html#softplus
	nn.functional::hidden:`softshrink`       softshrink                              : nn.functional.html#softshrink
	nn.functional::hidden:`softsign`         softsign                                : nn.functional.html#softsign
	nn.functional::hidden:`tanh`             tanh                                    : nn.functional.html#tanh
	nn.functional::hidden:`tanhshrink`       tanhshrink                              : nn.functional.html#tanhshrink
	nn.functional::hidden:`threshold`        threshold                               : nn.functional.html#threshold
	nn.functional::hidden:`triplet_margin_loss` triplet_margin_loss                     : nn.functional.html#triplet-margin-loss
	nn.functional::hidden:`unfold`           unfold                                  : nn.functional.html#unfold
	nn.functional::hidden:`upsample_bilinear` upsample_bilinear                       : nn.functional.html#upsample-bilinear
	nn.functional::hidden:`upsample_nearest` upsample_nearest                        : nn.functional.html#upsample-nearest
	nn.functional::hidden:`upsample`         upsample                                : nn.functional.html#upsample
	nn.functional:convolution functions      Convolution functions                   : nn.functional.html#convolution-functions
	nn.functional:dataparallel functions (multi-gpu, distributed) DataParallel functions (multi-GPU, distributed): nn.functional.html#dataparallel-functions-multi-gpu-distributed
	nn.functional:distance functions         Distance functions                      : nn.functional.html#distance-functions
	nn.functional:dropout functions          Dropout functions                       : nn.functional.html#dropout-functions
	nn.functional:linear functions           Linear functions                        : nn.functional.html#linear-functions
	nn.functional:loss functions             Loss functions                          : nn.functional.html#loss-functions
	nn.functional:non-linear activation functions Non-linear activation functions         : nn.functional.html#non-linear-activation-functions
	nn.functional:normalization functions    Normalization functions                 : nn.functional.html#normalization-functions
	nn.functional:pooling functions          Pooling functions                       : nn.functional.html#pooling-functions
	nn.functional:sparse functions           Sparse functions                        : nn.functional.html#sparse-functions
	nn.functional:torch.nn.functional        torch.nn.functional                     : nn.functional.html#torch-nn-functional
	nn.functional:vision functions           Vision functions                        : nn.functional.html#vision-functions
	nn.init:torch.nn.init                    torch.nn.init                           : nn.init.html#torch-nn-init
	nn::hidden:`adaptiveavgpool1d`           AdaptiveAvgPool1d                       : nn.html#adaptiveavgpool1d
	nn::hidden:`adaptiveavgpool2d`           AdaptiveAvgPool2d                       : nn.html#adaptiveavgpool2d
	nn::hidden:`adaptiveavgpool3d`           AdaptiveAvgPool3d                       : nn.html#adaptiveavgpool3d
	nn::hidden:`adaptivelogsoftmaxwithloss`  AdaptiveLogSoftmaxWithLoss              : nn.html#adaptivelogsoftmaxwithloss
	nn::hidden:`adaptivemaxpool1d`           AdaptiveMaxPool1d                       : nn.html#adaptivemaxpool1d
	nn::hidden:`adaptivemaxpool2d`           AdaptiveMaxPool2d                       : nn.html#adaptivemaxpool2d
	nn::hidden:`adaptivemaxpool3d`           AdaptiveMaxPool3d                       : nn.html#adaptivemaxpool3d
	nn::hidden:`alphadropout`                AlphaDropout                            : nn.html#alphadropout
	nn::hidden:`avgpool1d`                   AvgPool1d                               : nn.html#avgpool1d
	nn::hidden:`avgpool2d`                   AvgPool2d                               : nn.html#avgpool2d
	nn::hidden:`avgpool3d`                   AvgPool3d                               : nn.html#avgpool3d
	nn::hidden:`basepruningmethod`           BasePruningMethod                       : nn.html#basepruningmethod
	nn::hidden:`batchnorm1d`                 BatchNorm1d                             : nn.html#batchnorm1d
	nn::hidden:`batchnorm2d`                 BatchNorm2d                             : nn.html#batchnorm2d
	nn::hidden:`batchnorm3d`                 BatchNorm3d                             : nn.html#batchnorm3d
	nn::hidden:`bceloss`                     BCELoss                                 : nn.html#bceloss
	nn::hidden:`bcewithlogitsloss`           BCEWithLogitsLoss                       : nn.html#bcewithlogitsloss
	nn::hidden:`bilinear`                    Bilinear                                : nn.html#bilinear
	nn::hidden:`celu`                        CELU                                    : nn.html#celu
	nn::hidden:`clip_grad_norm_`             clip_grad_norm_                         : nn.html#clip-grad-norm
	nn::hidden:`clip_grad_value_`            clip_grad_value_                        : nn.html#clip-grad-value
	nn::hidden:`constantpad1d`               ConstantPad1d                           : nn.html#constantpad1d
	nn::hidden:`constantpad2d`               ConstantPad2d                           : nn.html#constantpad2d
	nn::hidden:`constantpad3d`               ConstantPad3d                           : nn.html#constantpad3d
	nn::hidden:`conv1d`                      Conv1d                                  : nn.html#conv1d
	nn::hidden:`conv2d`                      Conv2d                                  : nn.html#conv2d
	nn::hidden:`conv3d`                      Conv3d                                  : nn.html#conv3d
	nn::hidden:`convtranspose1d`             ConvTranspose1d                         : nn.html#convtranspose1d
	nn::hidden:`convtranspose2d`             ConvTranspose2d                         : nn.html#convtranspose2d
	nn::hidden:`convtranspose3d`             ConvTranspose3d                         : nn.html#convtranspose3d
	nn::hidden:`cosineembeddingloss`         CosineEmbeddingLoss                     : nn.html#cosineembeddingloss
	nn::hidden:`cosinesimilarity`            CosineSimilarity                        : nn.html#cosinesimilarity
	nn::hidden:`crossentropyloss`            CrossEntropyLoss                        : nn.html#crossentropyloss
	nn::hidden:`ctcloss`                     CTCLoss                                 : nn.html#ctcloss
	nn::hidden:`custom_from_mask`            custom_from_mask                        : nn.html#custom-from-mask
	nn::hidden:`customfrommask`              CustomFromMask                          : nn.html#customfrommask
	nn::hidden:`dataparallel`                DataParallel                            : nn.html#dataparallel
	nn::hidden:`distributeddataparallel`     DistributedDataParallel                 : nn.html#distributeddataparallel
	nn::hidden:`dropout2d`                   Dropout2d                               : nn.html#dropout2d
	nn::hidden:`dropout3d`                   Dropout3d                               : nn.html#dropout3d
	nn::hidden:`dropout`                     Dropout                                 : nn.html#dropout
	nn::hidden:`elu`                         ELU                                     : nn.html#elu
	nn::hidden:`embedding`                   Embedding                               : nn.html#embedding
	nn::hidden:`embeddingbag`                EmbeddingBag                            : nn.html#embeddingbag
	nn::hidden:`flatten`                     Flatten                                 : nn.html#flatten
	nn::hidden:`fold`                        Fold                                    : nn.html#fold
	nn::hidden:`fractionalmaxpool2d`         FractionalMaxPool2d                     : nn.html#fractionalmaxpool2d
	nn::hidden:`gelu`                        GELU                                    : nn.html#gelu
	nn::hidden:`global_unstructured`         global_unstructured                     : nn.html#global-unstructured
	nn::hidden:`groupnorm`                   GroupNorm                               : nn.html#groupnorm
	nn::hidden:`gru`                         GRU                                     : nn.html#gru
	nn::hidden:`grucell`                     GRUCell                                 : nn.html#grucell
	nn::hidden:`hardshrink`                  Hardshrink                              : nn.html#hardshrink
	nn::hidden:`hardtanh`                    Hardtanh                                : nn.html#hardtanh
	nn::hidden:`hingeembeddingloss`          HingeEmbeddingLoss                      : nn.html#hingeembeddingloss
	nn::hidden:`identity`                    identity                                : nn.html#id22
	nn::hidden:`instancenorm1d`              InstanceNorm1d                          : nn.html#instancenorm1d
	nn::hidden:`instancenorm2d`              InstanceNorm2d                          : nn.html#instancenorm2d
	nn::hidden:`instancenorm3d`              InstanceNorm3d                          : nn.html#instancenorm3d
	nn::hidden:`is_pruned`                   is_pruned                               : nn.html#is-pruned
	nn::hidden:`kldivloss`                   KLDivLoss                               : nn.html#kldivloss
	nn::hidden:`l1_unstructured`             l1_unstructured                         : nn.html#l1-unstructured
	nn::hidden:`l1loss`                      L1Loss                                  : nn.html#l1loss
	nn::hidden:`l1unstructured`              L1Unstructured                          : nn.html#l1unstructured
	nn::hidden:`layernorm`                   LayerNorm                               : nn.html#layernorm
	nn::hidden:`leakyrelu`                   LeakyReLU                               : nn.html#leakyrelu
	nn::hidden:`linear`                      Linear                                  : nn.html#linear
	nn::hidden:`ln_structured`               ln_structured                           : nn.html#ln-structured
	nn::hidden:`lnstructured`                LnStructured                            : nn.html#lnstructured
	nn::hidden:`localresponsenorm`           LocalResponseNorm                       : nn.html#localresponsenorm
	nn::hidden:`logsigmoid`                  LogSigmoid                              : nn.html#logsigmoid
	nn::hidden:`logsoftmax`                  LogSoftmax                              : nn.html#logsoftmax
	nn::hidden:`lppool1d`                    LPPool1d                                : nn.html#lppool1d
	nn::hidden:`lppool2d`                    LPPool2d                                : nn.html#lppool2d
	nn::hidden:`lstm`                        LSTM                                    : nn.html#lstm
	nn::hidden:`lstmcell`                    LSTMCell                                : nn.html#lstmcell
	nn::hidden:`marginrankingloss`           MarginRankingLoss                       : nn.html#marginrankingloss
	nn::hidden:`maxpool1d`                   MaxPool1d                               : nn.html#maxpool1d
	nn::hidden:`maxpool2d`                   MaxPool2d                               : nn.html#maxpool2d
	nn::hidden:`maxpool3d`                   MaxPool3d                               : nn.html#maxpool3d
	nn::hidden:`maxunpool1d`                 MaxUnpool1d                             : nn.html#maxunpool1d
	nn::hidden:`maxunpool2d`                 MaxUnpool2d                             : nn.html#maxunpool2d
	nn::hidden:`maxunpool3d`                 MaxUnpool3d                             : nn.html#maxunpool3d
	nn::hidden:`module`                      Module                                  : nn.html#module
	nn::hidden:`moduledict`                  ModuleDict                              : nn.html#moduledict
	nn::hidden:`modulelist`                  ModuleList                              : nn.html#modulelist
	nn::hidden:`mseloss`                     MSELoss                                 : nn.html#mseloss
	nn::hidden:`multiheadattention`          MultiheadAttention                      : nn.html#multiheadattention
	nn::hidden:`multilabelmarginloss`        MultiLabelMarginLoss                    : nn.html#multilabelmarginloss
	nn::hidden:`multilabelsoftmarginloss`    MultiLabelSoftMarginLoss                : nn.html#multilabelsoftmarginloss
	nn::hidden:`multimarginloss`             MultiMarginLoss                         : nn.html#multimarginloss
	nn::hidden:`nllloss`                     NLLLoss                                 : nn.html#nllloss
	nn::hidden:`pack_padded_sequence`        pack_padded_sequence                    : nn.html#pack-padded-sequence
	nn::hidden:`pack_sequence`               pack_sequence                           : nn.html#pack-sequence
	nn::hidden:`packedsequence`              PackedSequence                          : nn.html#packedsequence
	nn::hidden:`pad_packed_sequence`         pad_packed_sequence                     : nn.html#pad-packed-sequence
	nn::hidden:`pad_sequence`                pad_sequence                            : nn.html#pad-sequence
	nn::hidden:`pairwisedistance`            PairwiseDistance                        : nn.html#pairwisedistance
	nn::hidden:`parameterdict`               ParameterDict                           : nn.html#parameterdict
	nn::hidden:`parameterlist`               ParameterList                           : nn.html#parameterlist
	nn::hidden:`parameters_to_vector`        parameters_to_vector                    : nn.html#parameters-to-vector
	nn::hidden:`pixelshuffle`                PixelShuffle                            : nn.html#pixelshuffle
	nn::hidden:`poissonnllloss`              PoissonNLLLoss                          : nn.html#poissonnllloss
	nn::hidden:`prelu`                       PReLU                                   : nn.html#prelu
	nn::hidden:`pruningcontainer`            PruningContainer                        : nn.html#pruningcontainer
	nn::hidden:`random_structured`           random_structured                       : nn.html#random-structured
	nn::hidden:`random_unstructured`         random_unstructured                     : nn.html#random-unstructured
	nn::hidden:`randomstructured`            RandomStructured                        : nn.html#randomstructured
	nn::hidden:`randomunstructured`          RandomUnstructured                      : nn.html#randomunstructured
	nn::hidden:`reflectionpad1d`             ReflectionPad1d                         : nn.html#reflectionpad1d
	nn::hidden:`reflectionpad2d`             ReflectionPad2d                         : nn.html#reflectionpad2d
	nn::hidden:`relu6`                       ReLU6                                   : nn.html#relu6
	nn::hidden:`relu`                        ReLU                                    : nn.html#relu
	nn::hidden:`remove_spectral_norm`        remove_spectral_norm                    : nn.html#remove-spectral-norm
	nn::hidden:`remove_weight_norm`          remove_weight_norm                      : nn.html#remove-weight-norm
	nn::hidden:`remove`                      remove                                  : nn.html#remove
	nn::hidden:`replicationpad1d`            ReplicationPad1d                        : nn.html#replicationpad1d
	nn::hidden:`replicationpad2d`            ReplicationPad2d                        : nn.html#replicationpad2d
	nn::hidden:`replicationpad3d`            ReplicationPad3d                        : nn.html#replicationpad3d
	nn::hidden:`rnn`                         RNN                                     : nn.html#rnn
	nn::hidden:`rnnbase`                     RNNBase                                 : nn.html#rnnbase
	nn::hidden:`rnncell`                     RNNCell                                 : nn.html#rnncell
	nn::hidden:`rrelu`                       RReLU                                   : nn.html#rrelu
	nn::hidden:`selu`                        SELU                                    : nn.html#selu
	nn::hidden:`sequential`                  Sequential                              : nn.html#sequential
	nn::hidden:`sigmoid`                     Sigmoid                                 : nn.html#sigmoid
	nn::hidden:`smoothl1loss`                SmoothL1Loss                            : nn.html#smoothl1loss
	nn::hidden:`softmarginloss`              SoftMarginLoss                          : nn.html#softmarginloss
	nn::hidden:`softmax2d`                   Softmax2d                               : nn.html#softmax2d
	nn::hidden:`softmax`                     Softmax                                 : nn.html#softmax
	nn::hidden:`softmin`                     Softmin                                 : nn.html#softmin
	nn::hidden:`softplus`                    Softplus                                : nn.html#softplus
	nn::hidden:`softshrink`                  Softshrink                              : nn.html#softshrink
	nn::hidden:`softsign`                    Softsign                                : nn.html#softsign
	nn::hidden:`spectral_norm`               spectral_norm                           : nn.html#spectral-norm
	nn::hidden:`syncbatchnorm`               SyncBatchNorm                           : nn.html#syncbatchnorm
	nn::hidden:`tanh`                        Tanh                                    : nn.html#tanh
	nn::hidden:`tanhshrink`                  Tanhshrink                              : nn.html#tanhshrink
	nn::hidden:`threshold`                   Threshold                               : nn.html#threshold
	nn::hidden:`transformer`                 Transformer                             : nn.html#transformer
	nn::hidden:`transformerdecoder`          TransformerDecoder                      : nn.html#transformerdecoder
	nn::hidden:`transformerdecoderlayer`     TransformerDecoderLayer                 : nn.html#transformerdecoderlayer
	nn::hidden:`transformerencoder`          TransformerEncoder                      : nn.html#transformerencoder
	nn::hidden:`transformerencoderlayer`     TransformerEncoderLayer                 : nn.html#transformerencoderlayer
	nn::hidden:`tripletmarginloss`           TripletMarginLoss                       : nn.html#tripletmarginloss
	nn::hidden:`unfold`                      Unfold                                  : nn.html#unfold
	nn::hidden:`upsample`                    Upsample                                : nn.html#upsample
	nn::hidden:`upsamplingbilinear2d`        UpsamplingBilinear2d                    : nn.html#upsamplingbilinear2d
	nn::hidden:`upsamplingnearest2d`         UpsamplingNearest2d                     : nn.html#upsamplingnearest2d
	nn::hidden:`vector_to_parameters`        vector_to_parameters                    : nn.html#vector-to-parameters
	nn::hidden:`weight_norm`                 weight_norm                             : nn.html#weight-norm
	nn::hidden:`zeropad2d`                   ZeroPad2d                               : nn.html#zeropad2d
	nn:containers                            Containers                              : nn.html#containers
	nn:convolution layers                    Convolution layers                      : nn.html#convolution-layers
	nn:dataparallel layers (multi-gpu, distributed) DataParallel layers (multi-GPU, distributed): nn.html#dataparallel-layers-multi-gpu-distributed
	nn:distance functions                    Distance functions                      : nn.html#distance-functions
	nn:dropout layers                        Dropout layers                          : nn.html#dropout-layers
	nn:linear layers                         Linear layers                           : nn.html#linear-layers
	nn:loss functions                        Loss functions                          : nn.html#loss-functions
	nn:non-linear activations (other)        Non-linear activations (other)          : nn.html#non-linear-activations-other
	nn:non-linear activations (weighted sum, nonlinearity) Non-linear activations (weighted sum, nonlinearity): nn.html#non-linear-activations-weighted-sum-nonlinearity
	nn:normalization layers                  Normalization layers                    : nn.html#normalization-layers
	nn:padding layers                        Padding layers                          : nn.html#padding-layers
	nn:parameters                            Parameters                              : nn.html#parameters
	nn:pooling layers                        Pooling layers                          : nn.html#pooling-layers
	nn:quantized functions                   Quantized Functions                     : nn.html#quantized-functions
	nn:recurrent layers                      Recurrent layers                        : nn.html#recurrent-layers
	nn:sparse layers                         Sparse layers                           : nn.html#sparse-layers
	nn:torch.nn                              torch.nn                                : nn.html#module-torch.nn
	nn:transformer layers                    Transformer layers                      : nn.html#transformer-layers
	nn:utilities                             Utilities                               : nn.html#utilities
	nn:vision layers                         Vision layers                           : nn.html#vision-layers
	notes/amp_examples:automatic mixed precision examples Automatic Mixed Precision examples      : notes/amp_examples.html#automatic-mixed-precision-examples
	notes/amp_examples:gradient clipping     Gradient clipping                       : notes/amp_examples.html#gradient-clipping
	notes/amp_examples:gradient penalty      Gradient penalty                        : notes/amp_examples.html#gradient-penalty
	notes/amp_examples:gradient scaling      Gradient Scaling                        : notes/amp_examples.html#gradient-scaling
	notes/amp_examples:typical use           Typical Use                             : notes/amp_examples.html#typical-use
	notes/amp_examples:working with multiple losses and optimizers Working with Multiple Losses and Optimizers: notes/amp_examples.html#working-with-multiple-losses-and-optimizers
	notes/amp_examples:working with scaled gradients Working with Scaled Gradients           : notes/amp_examples.html#working-with-scaled-gradients
	notes/amp_examples:working with unscaled gradients Working with Unscaled Gradients         : notes/amp_examples.html#working-with-unscaled-gradients
	notes/autograd:``requires_grad``         requires_grad                           : notes/autograd.html#requires-grad
	notes/autograd:autograd mechanics        Autograd mechanics                      : notes/autograd.html#autograd-mechanics
	notes/autograd:excluding subgraphs from backward Excluding subgraphs from backward       : notes/autograd.html#excluding-subgraphs-from-backward
	notes/autograd:how autograd encodes the history How autograd encodes the history        : notes/autograd.html#how-autograd-encodes-the-history
	notes/autograd:in-place correctness checks In-place correctness checks             : notes/autograd.html#in-place-correctness-checks
	notes/autograd:in-place operations with autograd In-place operations with autograd       : notes/autograd.html#in-place-operations-with-autograd
	notes/broadcasting:backwards compatibility Backwards compatibility                 : notes/broadcasting.html#backwards-compatibility
	notes/broadcasting:broadcasting semantics Broadcasting semantics                  : notes/broadcasting.html#broadcasting-semantics
	notes/broadcasting:general semantics     General semantics                       : notes/broadcasting.html#general-semantics
	notes/broadcasting:in-place semantics    In-place semantics                      : notes/broadcasting.html#in-place-semantics
	notes/cpu_threading_torchscript_inference:build options Build options                           : notes/cpu_threading_torchscript_inference.html#build-options
	notes/cpu_threading_torchscript_inference:cpu threading and torchscript inference CPU threading and TorchScript inference : notes/cpu_threading_torchscript_inference.html#cpu-threading-and-torchscript-inference
	notes/cpu_threading_torchscript_inference:runtime api Runtime API                             : notes/cpu_threading_torchscript_inference.html#runtime-api
	notes/cpu_threading_torchscript_inference:tuning the number of threads Tuning the number of threads            : notes/cpu_threading_torchscript_inference.html#tuning-the-number-of-threads
	notes/cuda:asynchronous execution        Asynchronous execution                  : notes/cuda.html#asynchronous-execution
	notes/cuda:best practices                Best practices                          : notes/cuda.html#best-practices
	notes/cuda:cuda semantics                CUDA semantics                          : notes/cuda.html#cuda-semantics
	notes/cuda:cuda streams                  CUDA streams                            : notes/cuda.html#cuda-streams
	notes/cuda:cufft plan cache              cuFFT plan cache                        : notes/cuda.html#cufft-plan-cache
	notes/cuda:device-agnostic code          Device-agnostic code                    : notes/cuda.html#device-agnostic-code
	notes/cuda:memory management             Memory management                       : notes/cuda.html#memory-management
	notes/cuda:use nn.parallel.distributeddataparallel instead of multiprocessing or nn.dataparallel Use nn.parallel.DistributedDataParallel instead of multiprocessing or nn.DataParallel: notes/cuda.html#use-nn-parallel-distributeddataparallel-instead-of-multiprocessing-or-nn-dataparallel
	notes/cuda:use pinned memory buffers     Use pinned memory buffers               : notes/cuda.html#use-pinned-memory-buffers
	notes/ddp:distributed data parallel      Distributed Data Parallel               : notes/ddp.html#distributed-data-parallel
	notes/ddp:distributeddataparallel        DistributedDataParallel                 : notes/ddp.html#distributeddataparallel
	notes/ddp:example                        Example                                 : notes/ddp.html#example
	notes/ddp:implementation                 Implementation                          : notes/ddp.html#implementation
	notes/ddp:internal design                Internal Design                         : notes/ddp.html#internal-design
	notes/ddp:processgroup                   ProcessGroup                            : notes/ddp.html#processgroup
	notes/extending:adding a :class:`module` Adding a Module                         : notes/extending.html#adding-a-module
	notes/extending:extending :mod:`torch.autograd` Extending torch.autograd                : notes/extending.html#extending-torch-autograd
	notes/extending:extending :mod:`torch.nn` Extending torch.nn                      : notes/extending.html#extending-torch-nn
	notes/extending:extending :mod:`torch`   Extending torch                         : notes/extending.html#extending-torch
	notes/extending:extending :mod:`torch` with a :class:`tensor` wrapper type Extending torch with a Tensor wrapper type: notes/extending.html#extending-torch-with-a-tensor-wrapper-type
	notes/extending:extending :mod:`torch` with a :class:`tensor`-like type Extending torch with a Tensor-like type : notes/extending.html#extending-torch-with-a-tensor-like-type
	notes/extending:extending pytorch        Extending PyTorch                       : notes/extending.html#extending-pytorch
	notes/extending:operations on multiple types that define ``__torch_function__`` Operations on multiple types that define __torch_function__: notes/extending.html#operations-on-multiple-types-that-define-torch-function
	notes/extending:testing coverage of overrides for the pytorch api Testing Coverage of Overrides for the PyTorch API: notes/extending.html#testing-coverage-of-overrides-for-the-pytorch-api
	notes/extending:writing custom c extensions Writing custom C extensions             : notes/extending.html#id1
	notes/extending:writing custom c++ extensions Writing custom C++ extensions           : notes/extending.html#writing-custom-c-extensions
	notes/faq:frequently asked questions     Frequently Asked Questions              : notes/faq.html#frequently-asked-questions
	notes/faq:my data loader workers return identical random numbers My data loader workers return identical random numbers: notes/faq.html#my-data-loader-workers-return-identical-random-numbers
	notes/faq:my gpu memory isn't freed properly My GPU memory isn’t freed properly      : notes/faq.html#my-gpu-memory-isn-t-freed-properly
	notes/faq:my model reports "cuda runtime error(2): out of memory" My model reports “cuda runtime error(2): out of memory”: notes/faq.html#my-model-reports-cuda-runtime-error-2-out-of-memory
	notes/faq:my recurrent network doesn't work with data parallelism My recurrent network doesn’t work with data parallelism: notes/faq.html#my-recurrent-network-doesn-t-work-with-data-parallelism
	notes/large_scale_deployments:api usage logging API usage logging                       : notes/large_scale_deployments.html#api-usage-logging
	notes/large_scale_deployments:attaching metadata to saved torchscript models Attaching metadata to saved TorchScript models: notes/large_scale_deployments.html#attaching-metadata-to-saved-torchscript-models
	notes/large_scale_deployments:build environment considerations Build environment considerations        : notes/large_scale_deployments.html#build-environment-considerations
	notes/large_scale_deployments:common extension points Common extension points                 : notes/large_scale_deployments.html#common-extension-points
	notes/large_scale_deployments:features for large-scale deployments Features for large-scale deployments    : notes/large_scale_deployments.html#features-for-large-scale-deployments
	notes/large_scale_deployments:fleet-wide operator profiling Fleet-wide operator profiling           : notes/large_scale_deployments.html#fleet-wide-operator-profiling
	notes/multiprocessing:asynchronous multiprocess training (e.g. hogwild) Asynchronous multiprocess training (e.g. Hogwild): notes/multiprocessing.html#asynchronous-multiprocess-training-e-g-hogwild
	notes/multiprocessing:avoiding and fighting deadlocks Avoiding and fighting deadlocks         : notes/multiprocessing.html#avoiding-and-fighting-deadlocks
	notes/multiprocessing:best practices and tips Best practices and tips                 : notes/multiprocessing.html#best-practices-and-tips
	notes/multiprocessing:cuda in multiprocessing CUDA in multiprocessing                 : notes/multiprocessing.html#cuda-in-multiprocessing
	notes/multiprocessing:hogwild            Hogwild                                 : notes/multiprocessing.html#hogwild
	notes/multiprocessing:multiprocessing best practices Multiprocessing best practices          : notes/multiprocessing.html#multiprocessing-best-practices
	notes/multiprocessing:reuse buffers passed through a queue Reuse buffers passed through a Queue    : notes/multiprocessing.html#reuse-buffers-passed-through-a-queue
	notes/randomness:cudnn                   CuDNN                                   : notes/randomness.html#cudnn
	notes/randomness:numpy                   Numpy                                   : notes/randomness.html#numpy
	notes/randomness:pytorch                 PyTorch                                 : notes/randomness.html#pytorch
	notes/randomness:reproducibility         Reproducibility                         : notes/randomness.html#reproducibility
	notes/serialization:best practices       Best practices                          : notes/serialization.html#best-practices
	notes/serialization:recommended approach for saving a model Recommended approach for saving a model : notes/serialization.html#recommended-approach-for-saving-a-model
	notes/serialization:serialization semantics Serialization semantics                 : notes/serialization.html#serialization-semantics
	notes/windows:building from source       Building from source                    : notes/windows.html#building-from-source
	notes/windows:cffi extension             CFFI Extension                          : notes/windows.html#cffi-extension
	notes/windows:cpp extension              Cpp Extension                           : notes/windows.html#cpp-extension
	notes/windows:cuda ipc operations        CUDA IPC operations                     : notes/windows.html#cuda-ipc-operations
	notes/windows:extension                  Extension                               : notes/windows.html#extension
	notes/windows:import error               Import error                            : notes/windows.html#import-error
	notes/windows:include optional components Include optional components             : notes/windows.html#include-optional-components
	notes/windows:installation               Installation                            : notes/windows.html#installation
	notes/windows:multiprocessing error "broken pipe" Multiprocessing error “Broken pipe”     : notes/windows.html#multiprocessing-error-broken-pipe
	notes/windows:multiprocessing error "driver shut down" Multiprocessing error “driver shut down”: notes/windows.html#multiprocessing-error-driver-shut-down
	notes/windows:multiprocessing error without if-clause protection Multiprocessing error without if-clause protection: notes/windows.html#multiprocessing-error-without-if-clause-protection
	notes/windows:one key install script     One key install script                  : notes/windows.html#one-key-install-script
	notes/windows:package not found in win-32 channel. Package not found in win-32 channel.    : notes/windows.html#package-not-found-in-win-32-channel
	notes/windows:speeding cuda build for windows Speeding CUDA build for Windows         : notes/windows.html#speeding-cuda-build-for-windows
	notes/windows:usage (multiprocessing)    Usage (multiprocessing)                 : notes/windows.html#usage-multiprocessing
	notes/windows:windows faq                Windows FAQ                             : notes/windows.html#windows-faq
	onnx:adding support for operators        Adding support for operators            : onnx.html#adding-support-for-operators
	onnx:aten operators                      ATen operators                          : onnx.html#aten-operators
	onnx:custom operators                    Custom operators                        : onnx.html#custom-operators
	onnx:example: end-to-end alexnet from pytorch to onnx Example: End-to-end AlexNet from PyTorch to ONNX: onnx.html#example-end-to-end-alexnet-from-pytorch-to-onnx
	onnx:frequently asked questions          Frequently Asked Questions              : onnx.html#frequently-asked-questions
	onnx:functions                           Functions                               : onnx.html#functions
	onnx:limitations                         Limitations                             : onnx.html#limitations
	onnx:non-aten operators                  Non-ATen operators                      : onnx.html#non-aten-operators
	onnx:supported operators                 Supported operators                     : onnx.html#supported-operators
	onnx:torch.onnx                          torch.onnx                              : onnx.html#torch-onnx
	onnx:torchvision support                 TorchVision support                     : onnx.html#torchvision-support
	onnx:tracing vs scripting                Tracing vs Scripting                    : onnx.html#tracing-vs-scripting
	optim:``optimizer.step()``               optimizer.step()                        : optim.html#optimizer-step
	optim:``optimizer.step(closure)``        optimizer.step(closure)                 : optim.html#optimizer-step-closure
	optim:algorithms                         Algorithms                              : optim.html#algorithms
	optim:constructing it                    Constructing it                         : optim.html#constructing-it
	optim:how to adjust learning rate        How to adjust learning rate             : optim.html#how-to-adjust-learning-rate
	optim:how to use an optimizer            How to use an optimizer                 : optim.html#how-to-use-an-optimizer
	optim:per-parameter options              Per-parameter options                   : optim.html#per-parameter-options
	optim:taking an optimization step        Taking an optimization step             : optim.html#taking-an-optimization-step
	optim:torch.optim                        torch.optim                             : optim.html#module-torch.optim
	optimizer-algorithms                     Algorithms                              : optim.html#optimizer-algorithms
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	org/pytorch/tensor-tensor_float32:tensor_float32 Tensor_float32                          : org/pytorch/Tensor-Tensor_float32.html#tensor-float32
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	org/pytorch/tensorimageutils:fields      Fields                                  : org/pytorch/TensorImageUtils.html#fields
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	org/pytorch/tensorimageutils:torchvision_norm_mean_rgb TORCHVISION_NORM_MEAN_RGB               : org/pytorch/TensorImageUtils.html#torchvision-norm-mean-rgb
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	org/pytorch/torchvision/package-index:rg.pytorch.torchvision rg.pytorch.torchvision                  : org/pytorch/torchvision/package-index.html#package-org.pytorch.torchvision
	out_function_semantics-doc               out function and in-place variants      : name_inference.html#out-function-semantics-doc
	pack-rnn-unpack-with-data-parallelism    My recurrent network doesn’t work with data parallelism: notes/faq.html#pack-rnn-unpack-with-data-parallelism
	packages:javadoc                         Javadoc                                 : packages.html#javadoc
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	python 3 type hints                      Variables                               : jit.html#python-3-type-hints
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	quantization:``torch.nn.qat``            torch.nn.qat                            : quantization.html#torch-nn-qat
	quantization:``torch.nn.quantized.dynamic`` torch.nn.quantized.dynamic              : quantization.html#torch-nn-quantized-dynamic
	quantization:``torch.nn.quantized.functional`` torch.nn.quantized.functional           : quantization.html#torch-nn-quantized-functional
	quantization:``torch.nn.quantized``      torch.nn.quantized                      : quantization.html#torch-nn-quantized
	quantization:``torch.quantization``      torch.quantization                      : quantization.html#torch-quantization
	quantization:conv2d                      Conv2d                                  : quantization.html#id12
	quantization:conv3d                      Conv3d                                  : quantization.html#conv3d
	quantization:convbn2d                    ConvBn2d                                : quantization.html#id3
	quantization:convbnrelu2d                ConvBnReLU2d                            : quantization.html#id4
	quantization:convrelu2d                  ConvReLU2d                              : quantization.html#id7
	quantization:convrelu3d                  ConvReLU3d                              : quantization.html#id8
	quantization:debugging utilities         Debugging utilities                     : quantization.html#debugging-utilities
	quantization:dequantize                  DeQuantize                              : quantization.html#dequantize
	quantization:floatfunctional             FloatFunctional                         : quantization.html#floatfunctional
	quantization:functional interface        Functional interface                    : quantization.html#module-torch.nn.quantized.functional
	quantization:introduction to quantization Introduction to Quantization            : quantization.html#introduction-to-quantization
	quantization:linear                      Linear                                  : quantization.html#id15
	quantization:linearrelu                  LinearReLU                              : quantization.html#id9
	quantization:lstm                        LSTM                                    : quantization.html#lstm
	quantization:model preparation for quantization Model Preparation for Quantization      : quantization.html#model-preparation-for-quantization
	quantization:observers                   Observers                               : quantization.html#observers
	quantization:operation coverage          Operation coverage                      : quantization.html#operation-coverage
	quantization:preparing model for quantization Preparing model for quantization        : quantization.html#preparing-model-for-quantization
	quantization:qfunctional                 QFunctional                             : quantization.html#qfunctional
	quantization:quantization                Quantization                            : quantization.html#quantization
	quantization:quantization workflows      Quantization Workflows                  : quantization.html#quantization-workflows
	quantization:quantize                    Quantize                                : quantization.html#quantize
	quantization:quantized ``torch.tensor`` operations Quantized torch.Tensor operations       : quantization.html#quantized-torch-tensor-operations
	quantization:quantized dtypes and quantization schemes Quantized dtypes and quantization schemes: quantization.html#quantized-dtypes-and-quantization-schemes
	quantization:quantized tensors           Quantized Tensors                       : quantization.html#quantized-tensors
	quantization:relu                        ReLU                                    : quantization.html#relu
	quantization:relu6                       ReLU6                                   : quantization.html#relu6
	quantization:top-level quantization apis Top-level quantization APIs             : quantization.html#top-level-quantization-apis
	quantization:torch.nn.instrinsic.qat     torch.nn.instrinsic.qat                 : quantization.html#torch-nn-instrinsic-qat
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	quantization:torch.nn.intrinsic.quantized torch.nn.intrinsic.quantized            : quantization.html#torch-nn-intrinsic-quantized
	quantization:torch.nn.qat                torch.nn.qat                            : quantization.html#id10
	quantization:torch.nn.quantized          torch.nn.quantized                      : quantization.html#id11
	quantization:torch.nn.quantized.dynamic  torch.nn.quantized.dynamic              : quantization.html#id14
	quantization:torch.quantization          torch.quantization                      : quantization.html#id1
	quantization:utility functions           Utility functions                       : quantization.html#utility-functions
	random-sampling                          Random sampling                         : torch.html#random-sampling
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	random:torch.random                      torch.random                            : random.html#module-torch.random
	recommend-saving-models                  Recommended approach for saving a model : notes/serialization.html#recommend-saving-models
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	rpc-index                                Distributed RPC Framework               : rpc/index.html#rpc-index
	rpc/distributed_autograd:autograd recording during the forward pass Autograd recording during the forward pass: rpc/distributed_autograd.html#autograd-recording-during-the-forward-pass
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	rpc/distributed_autograd:computing dependencies Computing dependencies                  : rpc/distributed_autograd.html#computing-dependencies
	rpc/distributed_autograd:distributed autograd context Distributed Autograd Context            : rpc/distributed_autograd.html#distributed-autograd-context
	rpc/distributed_autograd:distributed autograd design Distributed Autograd Design             : rpc/distributed_autograd.html#distributed-autograd-design
	rpc/distributed_autograd:distributed backward pass Distributed Backward Pass               : rpc/distributed_autograd.html#distributed-backward-pass
	rpc/distributed_autograd:distributed optimizer Distributed Optimizer                   : rpc/distributed_autograd.html#distributed-optimizer
	rpc/distributed_autograd:fast mode algorithm FAST mode algorithm                     : rpc/distributed_autograd.html#fast-mode-algorithm
	rpc/distributed_autograd:simple end to end example Simple end to end example               : rpc/distributed_autograd.html#simple-end-to-end-example
	rpc/distributed_autograd:smart mode algorithm SMART mode algorithm                    : rpc/distributed_autograd.html#smart-mode-algorithm
	rpc/index:design notes                   Design Notes                            : rpc/index.html#design-notes
	rpc/index:distributed rpc framework      Distributed RPC Framework               : rpc/index.html#distributed-rpc-framework
	rpc/index:tutorials                      Tutorials                               : rpc/index.html#tutorials
	rpc/rpc:basics                           Basics                                  : rpc/rpc.html#basics
	rpc/rpc:distributed autograd framework   Distributed Autograd Framework          : rpc/rpc.html#distributed-autograd-framework
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	rpc/rpc:rpc                              RPC                                     : rpc/rpc.html#rpc
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	rpc/rref:assumptions                     Assumptions                             : rpc/rref.html#assumptions
	rpc/rref:background                      Background                              : rpc/rref.html#background
	rpc/rref:design reasoning                Design Reasoning                        : rpc/rref.html#design-reasoning
	rpc/rref:implementation                  Implementation                          : rpc/rref.html#implementation
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	rpc/rref:user share rref with owner as argument User Share RRef with Owner as Argument  : rpc/rref.html#user-share-rref-with-owner-as-argument
	rpc/rref:user share rref with owner as return value User Share RRef with Owner as Return Value: rpc/rref.html#user-share-rref-with-owner-as-return-value
	rpc/rref:user share rref with user       User Share RRef with User               : rpc/rref.html#user-share-rref-with-user
	rref                                     RRef                                    : rpc/rpc.html#rref
	search                                   Search Page                             : search.html
	sparse-docs                              torch.sparse                            : sparse.html#sparse-docs
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	torch:generators                         Generators                              : torch.html#generators
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	torch:indexing, slicing, joining, mutating ops Indexing, Slicing, Joining, Mutating Ops: torch.html#indexing-slicing-joining-mutating-ops
	torch:locally disabling gradient computation Locally disabling gradient computation  : torch.html#locally-disabling-gradient-computation
	torch:math operations                    Math operations                         : torch.html#math-operations
	torch:other operations                   Other Operations                        : torch.html#other-operations
	torch:parallelism                        Parallelism                             : torch.html#parallelism
	torch:pointwise ops                      Pointwise Ops                           : torch.html#pointwise-ops
	torch:quasi-random sampling              Quasi-random sampling                   : torch.html#quasi-random-sampling
	torch:random sampling                    Random sampling                         : torch.html#random-sampling
	torch:reduction ops                      Reduction Ops                           : torch.html#reduction-ops
	torch:serialization                      Serialization                           : torch.html#serialization
	torch:spectral ops                       Spectral Ops                            : torch.html#spectral-ops
	torch:tensors                            Tensors                                 : torch.html#tensors
	torch:torch                              torch                                   : torch.html#module-torch
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	torchvision/datasets:cifar               CIFAR                                   : torchvision/datasets.html#cifar
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	torchvision/datasets:flickr              Flickr                                  : torchvision/datasets.html#flickr
	torchvision/datasets:hmdb51              HMDB51                                  : torchvision/datasets.html#hmdb51
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	torchvision/datasets:kmnist              KMNIST                                  : torchvision/datasets.html#kmnist
	torchvision/datasets:lsun                LSUN                                    : torchvision/datasets.html#lsun
	torchvision/datasets:mnist               MNIST                                   : torchvision/datasets.html#mnist
	torchvision/datasets:phototour           PhotoTour                               : torchvision/datasets.html#phototour
	torchvision/datasets:qmnist              QMNIST                                  : torchvision/datasets.html#qmnist
	torchvision/datasets:sbd                 SBD                                     : torchvision/datasets.html#sbd
	torchvision/datasets:sbu                 SBU                                     : torchvision/datasets.html#sbu
	torchvision/datasets:stl10               STL10                                   : torchvision/datasets.html#stl10
	torchvision/datasets:svhn                SVHN                                    : torchvision/datasets.html#svhn
	torchvision/datasets:torchvision.datasets torchvision.datasets                    : torchvision/datasets.html#torchvision-datasets
	torchvision/datasets:ucf101              UCF101                                  : torchvision/datasets.html#ucf101
	torchvision/datasets:usps                USPS                                    : torchvision/datasets.html#usps
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	torchvision/models:deeplabv3             DeepLabV3                               : torchvision/models.html#deeplabv3
	torchvision/models:densenet              DenseNet                                : torchvision/models.html#id16
	torchvision/models:faster r-cnn          Faster R-CNN                            : torchvision/models.html#faster-r-cnn
	torchvision/models:fully convolutional networks Fully Convolutional Networks            : torchvision/models.html#fully-convolutional-networks
	torchvision/models:googlenet             GoogLeNet                               : torchvision/models.html#id23
	torchvision/models:inception v3          Inception v3                            : torchvision/models.html#inception-v3
	torchvision/models:keypoint r-cnn        Keypoint R-CNN                          : torchvision/models.html#keypoint-r-cnn
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	torchvision/models:mnasnet               MNASNet                                 : torchvision/models.html#id30
	torchvision/models:mobilenet v2          MobileNet v2                            : torchvision/models.html#mobilenet-v2
	torchvision/models:object detection, instance segmentation and person keypoint detection Object Detection, Instance Segmentation and Person Keypoint Detection: torchvision/models.html#object-detection-instance-segmentation-and-person-keypoint-detection
	torchvision/models:resnet                ResNet                                  : torchvision/models.html#id10
	torchvision/models:resnet (2+1)d         ResNet (2+1)D                           : torchvision/models.html#resnet-2-1-d
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	torchvision/models:resnet mixed convolution ResNet Mixed Convolution                : torchvision/models.html#resnet-mixed-convolution
	torchvision/models:resnext               ResNext                                 : torchvision/models.html#id27
	torchvision/models:runtime characteristics Runtime characteristics                 : torchvision/models.html#runtime-characteristics
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	torchvision/models:shufflenet v2         ShuffleNet v2                           : torchvision/models.html#shufflenet-v2
	torchvision/models:squeezenet            SqueezeNet                              : torchvision/models.html#id15
	torchvision/models:torchvision.models    torchvision.models                      : torchvision/models.html#torchvision-models
	torchvision/models:vgg                   VGG                                     : torchvision/models.html#id2
	torchvision/models:video classification  Video classification                    : torchvision/models.html#video-classification
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	torchvision/transforms:conversion transforms Conversion Transforms                   : torchvision/transforms.html#conversion-transforms
	torchvision/transforms:functional transforms Functional Transforms                   : torchvision/transforms.html#functional-transforms
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	torchvision/transforms:torchvision.transforms torchvision.transforms                  : torchvision/transforms.html#torchvision-transforms
	torchvision/transforms:transforms on pil image Transforms on PIL Image                 : torchvision/transforms.html#transforms-on-pil-image
	torchvision/transforms:transforms on torch.\*tensor Transforms on torch.*Tensor             : torchvision/transforms.html#transforms-on-torch-tensor
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	type-info-doc                            Type Info                               : type_info.html#type-info-doc
	type-promotion-doc                       tensor_attributes.html#type-promotion-doc
	type_info:torch.finfo                    torch.finfo                             : type_info.html#torch-finfo
	type_info:torch.iinfo                    torch.iinfo                             : type_info.html#torch-iinfo
	type_info:type info                      Type Info                               : type_info.html#type-info
	types                                    Types                                   : jit_language_reference.html#types
	unifies_names_from_inputs-doc            Unifies names from inputs               : name_inference.html#unifies-names-from-inputs-doc
	working-with-unscaled-gradients          Working with Unscaled Gradients         : notes/amp_examples.html#working-with-unscaled-gradients
