torch/_C/_nn.pyi

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 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/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]: ...