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usr
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local
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lib64
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python3.6
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site-packages
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torch
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/usr/local/lib64/python3.6/site-packages/torch
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autocast_mode.py
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functional.py
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hub.py
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overrides.py
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types.py
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__init__.py
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Edit:
/usr/local/lib64/python3.6/site-packages/torch/types.py
(1552B)
import torch from typing import Any, List, Sequence, Tuple, Union import builtins # Convenience aliases for common composite types that we need # to talk about in PyTorch _TensorOrTensors = Union[torch.Tensor, Sequence[torch.Tensor]] # In some cases, these basic types are shadowed by corresponding # top-level values. The underscore variants let us refer to these # types. See https://github.com/python/mypy/issues/4146 for why these # workarounds is necessary _int = builtins.int _float = builtins.float _bool = builtins.bool _dtype = torch.dtype _device = torch.device _qscheme = torch.qscheme _size = Union[torch.Size, List[_int], Tuple[_int, ...]] _layout = torch.layout # Meta-type for "numeric" things; matches our docs Number = Union[builtins.int, builtins.float, builtins.bool] # Meta-type for "device-like" things. Not to be confused with 'device' (a # literal device object). This nomenclature is consistent with PythonArgParser. # None means use the default device (typically CPU) Device = Union[_device, str, None] # Storage protocol implemented by ${Type}StorageBase classes class Storage(object): _cdata: int def __deepcopy__(self, memo) -> 'Storage': ... def _new_shared(self, int) -> 'Storage': ... def _write_file(self, f: Any, is_real_file: _bool, save_size: _bool) -> None: ... def element_size(self) -> int: ... def is_shared(self) -> bool: ... def share_memory_(self) -> 'Storage': ... def size(self) -> int: ... ...
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