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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
${dispatched_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/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]: ...
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