torch/_C/_nn.pyi.in

from typing import List, Optional, overload, Sequence, Tuple, Union

from torch import memory_format, Tensor
from torch.types import _bool, _device, _dtype, _int, _size

# Defined in tools/autograd/templates/python_nn_functions.cpp

${c_nn_function_hints}

# 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 in aten/src/ATen/native/mkldnn/Prelu.cpp
def mkldnn_prelu(input: Tensor, weight: Tensor) -> 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/native/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]: ...