Name	Size	Mode	Actions
_autograd.pyi	2744	0644	editdlrm
_cudnn.pyi	378	0644	editdlrm
_distributed_autograd.pyi	908	0644	editdlrm
_distributed_c10d.pyi	9376	0644	editdlrm
_distributed_rpc.pyi	5630	0644	editdlrm
_distributed_rpc_testing.pyi	1136	0644	editdlrm
_functions.pyi	345	0644	editdlrm
_nn.pyi	2050	0644	editdlrm
_nvtx.pyi	150	0644	editdlrm
_onnx.pyi	646	0644	editdlrm
_VariableFunctions.pyi	141026	0644	editdlrm
__init__.pyi	103906	0644	editdlrm

from torch import Tensor, memory_format from typing import Callable, Optional, List, overload, Tuple from torch.types import _bool, _dtype, _device # Defined in tools/autograd/templates/python_nn_functions.cpp fractional_max_pool2d: Callable fractional_max_pool3d: Callable max_pool1d: Callable max_pool2d: Callable max_pool3d: Callable adaptive_max_pool1d: Callable adaptive_max_pool2d: Callable adaptive_max_pool3d: Callable avg_pool2d: Callable avg_pool3d: Callable hardtanh_: Callable elu_: Callable leaky_relu_: Callable logsigmoid: Callable softplus: Callable softshrink: Callable one_hot: Callable hardtanh: Callable leaky_relu: Callable hardsigmoid: Callable # Defined in aten/src/ATen/native/mkldnn/Linear.cpp def mkldnn_linear(input: Tensor, weight: Tensor, bias: Optional[Tensor]) -> Tensor: ... # Defined at aten/src/ATen/native/mkldnn/MKLDNNConversions.cpp def mkldnn_reorder_conv2d_weight(self: Tensor, padding: List, stride: List, dilatation: List, groups: int) -> Tensor: ... def mkldnn_reorder_conv3d_weight(self: Tensor, padding: List, stride: List, dilatation: List, groups: int) -> Tensor: ... # Defined at tools/autograd/templates/python_nn_functions.cpp @overload def _parse_to(device: _device, dtype: _dtype, non_blocking: _bool, copy: _bool, *, memory_format: memory_format) -> Tuple[_device, _dtype, _bool, memory_format]: ... @overload def _parse_to(dtype: _dtype, non_blocking: _bool, copy: _bool, *, memory_format: memory_format) -> Tuple[_device, _dtype, _bool, memory_format]: ... @overload def _parse_to(tensor: Tensor, non_blocking: _bool, copy: _bool, *, memory_format: memory_format) -> Tuple[_device, _dtype, _bool, memory_format]: ... # Defined in aten/src/ATen/naitve/PadSequence.cpp def pad_sequence(sequences: List[Tensor], batch_first: bool = False, padding_value: float = ...) -> Tensor: ... def flatten_dense_tensors(tensors: List[Tensor]) -> Tensor: ... def unflatten_dense_tensors(flat: Tensor, tensors: List[Tensor]) -> List[Tensor]: ...