1. 找到torch安装目录

  • D:\ProgramData\Anaconda3\envs\study-ml\Lib\site-packages\torch
  • 我的路径在，conda的虚拟环境下的study-ml下
  • 找到这个路径：Lib\site-packages\torch
  • 版本大于1.4以后似乎就没有initpyi这个文件了
  • 下载init.pyi附件，放在上面的路径下就可以了

    2. init.pyi下载地址

    网盘地址：https://pan.baidu.com/s/1DUcJq-fj0VR3xUiYkXHBA?_at=1622265991308
    提取码：6pwe

原因是什么没有摸清，但是基本上可以确定是由于init.pyi缺失或者存在问题导致的。我的pytorch版本是0.4.1，但是在网上没有找到0.4.1的相关init.pyi的版本，所以我使用的是1.0.1的。

3. 解决方法：

进入到这个链接中，在pytorch文件夹下，找到一个版本的init.pyi放到你的虚拟环境所在目录下的torch中。如我的torch包所在位置为D:\Program Files (x86)\Anaconda\envs\pytorch\Lib\site-packages\torch，然后将init.pyi复制进来
到这就结束了吗？并没有，当你重启运行pycharm时，可以自动补全torch.sum、torch.mean等函数，但是却无法引用torch.nn等模块，此时只需要在init.pyi文件中加入下面两行即可。

  1. from torch import nn, cuda, ops, functional, optim, autograd, onnx, utils
  2. from torch import contrib, distributions, for_onnx, jit, multiprocessing

加入前：
pytorch在pycharm中没有代码自动提示 - 图1
加入后：
pytorch在pycharm中没有代码自动提示 - 图2
然后，再重启pycharm即可。
pytorch在pycharm中没有代码自动提示 - 图3
可以看到，代码自动补全功能已经恢复了。
pytorch在pycharm中没有代码自动提示 - 图4

版权声明：本文为qq_35531985原创文章，遵循CC 4.0 BY-SA版权协议，转载请附上原文出处链接和本声明。
本文链接：https://blog.csdn.net/qq_35531985/article/details/107980736

init.pyi文件可直接复制

  2. from typing import List, Tuple, Optional, Union, Any, ContextManager, Callable, overload
  4. import builtins
  5. import math
  6. import pickle
  7. from torch import nn, cuda, ops, functional, optim, autograd, onnx, utils
  8. from torch import contrib, distributions, for_onnx, jit, multiprocessing
  11. class dtype: ...
  12. _dtype = dtype
  14. class layout: ...
  16. strided : layout = ...
  18. class device:
  19.    def __init__(self, device: Union[device, str, None]=None) -> None: ...
  21. class Generator: ...
  23. class Size(tuple): ...
  25. class Storage: ...
  27. class enable_grad():
  28.     def __enter__(self) -> None: ...
  29.     def __exit__(self, *args) -> None: ...
  30.     def __call__(self, func : Callable) -> Callable: ...
  32. class no_grad():
  33.     def __enter__(self) -> None: ...
  34.     def __exit__(self, *args) -> None: ...
  35.     def __call__(self, func : Callable) -> Callable: ...
  37. class set_grad_enabled():
  38.     def __init__(self, mode: bool) -> None: ...
  39.     def __enter__(self) -> None: ...
  40.     def __exit__(self, *args) -> None: ...
  44. class Tensor:
  45.     dtype: _dtype = ...
  46.     shape: Size = ...
  47.     requires_grad: bool = ...
  48.     grad: Optional['Tensor'] = ...
  50.     def __abs__(self) -> 'Tensor': ...
  52.     def __add__(self, other: Any) -> 'Tensor': ...
  54.     def __and__(self, other: Any) -> 'Tensor': ...
  56.     def __array__(self, dtype=None): ...
  58.     def __array_wrap__(self, array): ...
  60.     def __bool__(self) -> bool: ...
  62.     def __deepcopy__(self, memo): ...
  64.     def __dir__(self): ...
  66.     def __div__(self, other: Any) -> 'Tensor': ...
  68.     def __eq__(self, other: Any) -> 'Tensor': ...  # type: ignore
  70.     def __float__(self) -> builtins.float: ...
  72.     def __floordiv__(self, other): ...
  74.     def __format__(self, format_spec): ...
  76.     def __ge__(self, other: Any) -> 'Tensor': ...  # type: ignore
  78.     def __getitem__(self, indices: Union[None, builtins.int, slice, 'Tensor', List, Tuple]) -> 'Tensor': ...
  80.     def __gt__(self, other: Any) -> 'Tensor': ...  # type: ignore
  82.     def __hash__(self): ...
  84.     def __iadd__(self, other: Any) -> 'Tensor': ...
  86.     def __iand__(self, other: Any) -> 'Tensor': ...
  88.     def __idiv__(self, other: Any) -> 'Tensor': ...
  90.     def __ilshift__(self, other: Any) -> 'Tensor': ...
  92.     def __imul__(self, other: Any) -> 'Tensor': ...
  94.     def __index__(self) -> builtins.int: ...
  96.     def __int__(self) -> builtins.int: ...
  98.     def __invert__(self) -> 'Tensor': ...
  100.     def __ior__(self, other: Any) -> 'Tensor': ...
  102.     def __ipow__(self, other): ...
  104.     def __irshift__(self, other: Any) -> 'Tensor': ...
  106.     def __isub__(self, other: Any) -> 'Tensor': ...
  108.     def __iter__(self): ...
  110.     def __itruediv__(self, other: Any) -> 'Tensor': ...
  112.     def __ixor__(self, other: Any) -> 'Tensor': ...
  114.     def __le__(self, other: Any) -> 'Tensor': ...  # type: ignore
  116.     def __len__(self): ...
  118.     def __long__(self) -> builtins.int: ...
  120.     def __lshift__(self, other: Any) -> 'Tensor': ...
  122.     def __lt__(self, other: Any) -> 'Tensor': ...  # type: ignore
  124.     def __matmul__(self, other: Any) -> 'Tensor': ...
  126.     def __mod__(self, other: Any) -> 'Tensor': ...
  128.     def __mul__(self, other: Any) -> 'Tensor': ...
  130.     def __ne__(self, other: Any) -> 'Tensor': ...  # type: ignore
  132.     def __neg__(self) -> 'Tensor': ...
  134.     def __nonzero__(self) -> bool: ...
  136.     def __or__(self, other: Any) -> 'Tensor': ...
  138.     def __pow__(self, other: Any) -> 'Tensor': ...
  140.     def __radd__(self, other: Any) -> 'Tensor': ...
  142.     def __rdiv__(self, other): ...
  144.     def __reduce_ex__(self, proto): ...
  146.     def __repr__(self): ...
  148.     def __reversed__(self): ...
  150.     def __rfloordiv__(self, other): ...
  152.     def __rmul__(self, other: Any) -> 'Tensor': ...
  154.     def __rpow__(self, other): ...
  156.     def __rshift__(self, other: Any) -> 'Tensor': ...
  158.     def __rsub__(self, other): ...
  160.     def __rtruediv__(self, other): ...
  162.     def __setitem__(self, indices: Union[None, builtins.int, slice, 'Tensor', List, Tuple], val: Union['Tensor', builtins.float, builtins.int]) -> None: ...
  164.     def __setstate__(self, state): ...
  166.     def __sub__(self, other: Any) -> 'Tensor': ...
  168.     def __truediv__(self, other: Any) -> 'Tensor': ...
  170.     def __xor__(self, other: Any) -> 'Tensor': ...
  172.     def abs(self, *, out: Optional['Tensor']=None) -> 'Tensor': ...
  174.     def abs_(self) -> 'Tensor': ...
  176.     def acos(self, *, out: Optional['Tensor']=None) -> 'Tensor': ...
  178.     def acos_(self) -> 'Tensor': ...
  180.     @overload
  181.     def add(self, other: 'Tensor', *, alpha: Union[builtins.float, builtins.int]=1, out: Optional['Tensor']=None) -> 'Tensor': ...
  183.     @overload
  184.     def add(self, other: Union[builtins.float, builtins.int], alpha: Union[builtins.float, builtins.int]=1, *, out: Optional['Tensor']=None) -> 'Tensor': ...
  186.     @overload
  187.     def add_(self, other: 'Tensor', *, alpha: Union[builtins.float, builtins.int]=1) -> 'Tensor': ...
  189.     @overload
  190.     def add_(self, other: Union[builtins.float, builtins.int], alpha: Union[builtins.float, builtins.int]=1) -> 'Tensor': ...
  192.     def addbmm(self, batch1: 'Tensor', batch2: 'Tensor', *, beta: Union[builtins.float, builtins.int]=1, alpha: Union[builtins.float, builtins.int]=1, out: Optional['Tensor']=None) -> 'Tensor': ...
  194.     def addbmm_(self, batch1: 'Tensor', batch2: 'Tensor', *, beta: Union[builtins.float, builtins.int]=1, alpha: Union[builtins.float, builtins.int]=1) -> 'Tensor': ...
  196.     def addcdiv(self, tensor1: 'Tensor', tensor2: 'Tensor', *, value: Union[builtins.float, builtins.int]=1, out: Optional['Tensor']=None) -> 'Tensor': ...
  198.     def addcdiv_(self, tensor1: 'Tensor', tensor2: 'Tensor', *, value: Union[builtins.float, builtins.int]=1) -> 'Tensor': ...
  200.     def addcmul(self, tensor1: 'Tensor', tensor2: 'Tensor', *, value: Union[builtins.float, builtins.int]=1, out: Optional['Tensor']=None) -> 'Tensor': ...
  202.     def addcmul_(self, tensor1: 'Tensor', tensor2: 'Tensor', *, value: Union[builtins.float, builtins.int]=1) -> 'Tensor': ...
  204.     def addmm(self, mat1: 'Tensor', mat2: 'Tensor', *, beta: Union[builtins.float, builtins.int]=1, alpha: Union[builtins.float, builtins.int]=1, out: Optional['Tensor']=None) -> 'Tensor': ...
  206.     def addmm_(self, mat1: 'Tensor', mat2: 'Tensor', *, beta: Union[builtins.float, builtins.int]=1, alpha: Union[builtins.float, builtins.int]=1) -> 'Tensor': ...
  208.     def addmv(self, mat: 'Tensor', vec: 'Tensor', *, beta: Union[builtins.float, builtins.int]=1, alpha: Union[builtins.float, builtins.int]=1, out: Optional['Tensor']=None) -> 'Tensor': ...
  210.     def addmv_(self, mat: 'Tensor', vec: 'Tensor', *, beta: Union[builtins.float, builtins.int]=1, alpha: Union[builtins.float, builtins.int]=1) -> 'Tensor': ...
  212.     def addr(self, vec1: 'Tensor', vec2: 'Tensor', *, beta: Union[builtins.float, builtins.int]=1, alpha: Union[builtins.float, builtins.int]=1, out: Optional['Tensor']=None) -> 'Tensor': ...
  214.     def addr_(self, vec1: 'Tensor', vec2: 'Tensor', *, beta: Union[builtins.float, builtins.int]=1, alpha: Union[builtins.float, builtins.int]=1) -> 'Tensor': ...
  216.     @overload
  217.     def all(self, *, out: Optional['Tensor']=None) -> Union[builtins.float, builtins.int]: ...
  219.     @overload
  220.     def all(self, dim: builtins.int, keepdim: bool=False, *, out: Optional['Tensor']=None) -> 'Tensor': ...
  222.     def allclose(self, other: 'Tensor', rtol: builtins.float=1e-05, atol: builtins.float=1e-08, equal_nan: bool=False) -> bool: ...
  224.     @overload
  225.     def any(self, *, out: Optional['Tensor']=None) -> Union[builtins.float, builtins.int]: ...
  227.     @overload
  228.     def any(self, dim: builtins.int, keepdim: bool=False, *, out: Optional['Tensor']=None) -> 'Tensor': ...
  230.     def apply_(self, callable: Callable) -> 'Tensor': ...
  232.     def argmax(self, dim=None, keepdim=False): ...
  234.     def argmin(self, dim=None, keepdim=False): ...
  236.     def argsort(self, dim=None, descending=False): ...
  238.     def asin(self, *, out: Optional['Tensor']=None) -> 'Tensor': ...
  240.     def asin_(self) -> 'Tensor': ...
  242.     def atan(self, *, out: Optional['Tensor']=None) -> 'Tensor': ...
  244.     def atan2(self, other: 'Tensor', *, out: Optional['Tensor']=None) -> 'Tensor': ...
  246.     def atan2_(self, other: 'Tensor') -> 'Tensor': ...
  248.     def atan_(self) -> 'Tensor': ...
  250.     def backward(self, gradient=None, retain_graph=None, create_graph=False): ...
  252.     def baddbmm(self, batch1: 'Tensor', batch2: 'Tensor', *, beta: Union[builtins.float, builtins.int]=1, alpha: Union[builtins.float, builtins.int]=1, out: Optional['Tensor']=None) -> 'Tensor': ...
  254.     def baddbmm_(self, batch1: 'Tensor', batch2: 'Tensor', *, beta: Union[builtins.float, builtins.int]=1, alpha: Union[builtins.float, builtins.int]=1) -> 'Tensor': ...
  256.     @overload
  257.     def bernoulli(self, *, generator: Generator=None, out: Optional['Tensor']=None) -> 'Tensor': ...
  259.     @overload
  260.     def bernoulli(self, p: builtins.float, *, generator: Generator=None, out: Optional['Tensor']=None) -> 'Tensor': ...
  262.     @overload
  263.     def bernoulli_(self, p: 'Tensor', *, generator: Generator=None) -> 'Tensor': ...
  265.     @overload
  266.     def bernoulli_(self, p: builtins.float=0.5, *, generator: Generator=None) -> 'Tensor': ...
  268.     def bincount(self, weights: Optional['Tensor']=None, minlength: builtins.int=0) -> 'Tensor': ...
  270.     def bmm(self, mat2: 'Tensor', *, out: Optional['Tensor']=None) -> 'Tensor': ...
  272.     def btrifact(self, info=None, pivot=True): ...
  274.     def btrifact_with_info(self, *, pivot: bool=True, out: Optional['Tensor']=None) -> Tuple['Tensor', 'Tensor', 'Tensor']: ...
  276.     def btrisolve(self, LU_data: 'Tensor', LU_pivots: 'Tensor', *, out: Optional['Tensor']=None) -> 'Tensor': ...
  278.     def byte(self) -> 'Tensor': ...
  280.     def cauchy_(self, median: builtins.float=0, sigma: builtins.float=1, *, generator: Generator=None) -> 'Tensor': ...
  282.     def ceil(self, *, out: Optional['Tensor']=None) -> 'Tensor': ...
  284.     def ceil_(self) -> 'Tensor': ...
  286.     def char(self) -> 'Tensor': ...
  288.     def chunk(self, chunks: builtins.int, dim: builtins.int=0) -> Union[Tuple['Tensor', ...],List['Tensor']]: ...
  290.     def clamp(self, min: builtins.float=-math.inf, max: builtins.float =math.inf, *, out: Optional['Tensor']=None) -> 'Tensor': ...
  292.     def clamp_(self, min: builtins.float=-math.inf, max: builtins.float =math.inf) -> 'Tensor': ...
  294.     def clone(self) -> 'Tensor': ...
  296.     def contiguous(self) -> 'Tensor': ...
  298.     def copy_(self, src: 'Tensor', non_blocking: bool=False) -> 'Tensor': ...
  300.     def cos(self, *, out: Optional['Tensor']=None) -> 'Tensor': ...
  302.     def cos_(self) -> 'Tensor': ...
  304.     def cosh(self, *, out: Optional['Tensor']=None) -> 'Tensor': ...
  306.     def cosh_(self) -> 'Tensor': ...
  308.     def cpu(self) -> 'Tensor': ...
  310.     def cross(self, other: 'Tensor', dim: builtins.int=-1, *, out: Optional['Tensor']=None) -> 'Tensor': ...
  312.     def cuda(self, device: Optional[device]=None, non_blocking: bool=False) -> 'Tensor': ...
  314.     @overload
  315.     def cumprod(self, dim: builtins.int, *, dtype: _dtype, out: Optional['Tensor']=None) -> 'Tensor': ...
  317.     @overload
  318.     def cumprod(self, dim: builtins.int, *, out: Optional['Tensor']=None) -> 'Tensor': ...
  320.     @overload
  321.     def cumsum(self, dim: builtins.int, *, dtype: _dtype, out: Optional['Tensor']=None) -> 'Tensor': ...
  323.     @overload
  324.     def cumsum(self, dim: builtins.int, *, out: Optional['Tensor']=None) -> 'Tensor': ...
  326.     def data_ptr(self) -> builtins.int: ...
  328.     def det(self) -> 'Tensor': ...
  330.     def detach(self) -> 'Tensor': ...
  332.     def detach_(self) -> 'Tensor': ...
  334.     def diag(self, diagonal: builtins.int=0, *, out: Optional['Tensor']=None) -> 'Tensor': ...
  336.     def diagflat(self, offset: builtins.int=0) -> 'Tensor': ...
  338.     def diagonal(self, offset: builtins.int=0, dim1: builtins.int=0, dim2: builtins.int=1) -> 'Tensor': ...
  340.     def digamma(self, *, out: Optional['Tensor']=None) -> 'Tensor': ...
  342.     def digamma_(self) -> 'Tensor': ...
  344.     def dim(self) -> builtins.int: ...
  346.     def dist(self, other: 'Tensor', p: Union[builtins.float, builtins.int]=2) -> Union[builtins.float, builtins.int]: ...
  348.     @overload
  349.     def div(self, other: 'Tensor', *, out: Optional['Tensor']=None) -> 'Tensor': ...
  351.     @overload
  352.     def div(self, other: Union[builtins.float, builtins.int], *, out: Optional['Tensor']=None) -> 'Tensor': ...
  354.     @overload
  355.     def div_(self, other: 'Tensor') -> 'Tensor': ...
  357.     @overload
  358.     def div_(self, other: Union[builtins.float, builtins.int]) -> 'Tensor': ...
  360.     def dot(self, tensor: 'Tensor', *, out: Optional['Tensor']=None) -> 'Tensor': ...
  362.     def double(self) -> 'Tensor': ...
  364.     def eig(self, eigenvectors: bool=False, *, out: Optional['Tensor']=None) -> Tuple['Tensor', 'Tensor']: ...
  366.     def element_size(self) -> builtins.int: ...
  368.     @overload
  369.     def eq(self, other: Union[builtins.float, builtins.int], *, out: Optional['Tensor']=None) -> 'Tensor': ...
  371.     @overload
  372.     def eq(self, other: 'Tensor', *, out: Optional['Tensor']=None) -> 'Tensor': ...
  374.     @overload
  375.     def eq_(self, other: Union[builtins.float, builtins.int]) -> 'Tensor': ...
  377.     @overload
  378.     def eq_(self, other: 'Tensor') -> 'Tensor': ...
  380.     def equal(self, other: 'Tensor') -> bool: ...
  382.     def erf(self, *, out: Optional['Tensor']=None) -> 'Tensor': ...
  384.     def erf_(self) -> 'Tensor': ...
  386.     def erfc(self, *, out: Optional['Tensor']=None) -> 'Tensor': ...
  388.     def erfc_(self) -> 'Tensor': ...
  390.     def erfinv(self, *, out: Optional['Tensor']=None) -> 'Tensor': ...
  392.     def erfinv_(self) -> 'Tensor': ...
  394.     def exp(self, *, out: Optional['Tensor']=None) -> 'Tensor': ...
  396.     def exp_(self) -> 'Tensor': ...
  398.     @overload
  399.     def expand(self, size: Union[Tuple[builtins.int, ...], List[builtins.int], Size], *, implicit: bool=False) -> 'Tensor': ...
  401.     @overload
  402.     def expand(self, *size: builtins.int, implicit: bool=False) -> 'Tensor': ...
  404.     def expand_as(self, other: 'Tensor') -> 'Tensor': ...
  406.     def expm1(self, *, out: Optional['Tensor']=None) -> 'Tensor': ...
  408.     def expm1_(self) -> 'Tensor': ...
  410.     def exponential_(self, lambd: builtins.float=1, *, generator: Generator=None) -> 'Tensor': ...
  412.     def fft(self, signal_ndim: builtins.int, normalized: bool=False) -> 'Tensor': ...
  414.     @overload
  415.     def fill_(self, value: Union[builtins.float, builtins.int]) -> 'Tensor': ...
  417.     @overload
  418.     def fill_(self, value: 'Tensor') -> 'Tensor': ...
  420.     def flatten(self, start_dim: builtins.int=0, end_dim: builtins.int=-1) -> 'Tensor': ...
  422.     @overload
  423.     def flip(self, dims: Union[Tuple[builtins.int, ...], List[builtins.int], Size]) -> 'Tensor': ...
  425.     @overload
  426.     def flip(self, *dims: builtins.int) -> 'Tensor': ...
  428.     def float(self) -> 'Tensor': ...
  430.     def floor(self, *, out: Optional['Tensor']=None) -> 'Tensor': ...
  432.     def floor_(self) -> 'Tensor': ...
  434.     @overload
  435.     def fmod(self, other: Union[builtins.float, builtins.int], *, out: Optional['Tensor']=None) -> 'Tensor': ...
  437.     @overload
  438.     def fmod(self, other: 'Tensor', *, out: Optional['Tensor']=None) -> 'Tensor': ...
  440.     @overload
  441.     def fmod_(self, other: Union[builtins.float, builtins.int]) -> 'Tensor': ...
  443.     @overload
  444.     def fmod_(self, other: 'Tensor') -> 'Tensor': ...
  446.     def frac(self, *, out: Optional['Tensor']=None) -> 'Tensor': ...
  448.     def frac_(self) -> 'Tensor': ...
  450.     def gather(self, dim: builtins.int, index: 'Tensor', *, out: Optional['Tensor']=None) -> 'Tensor': ...
  452.     @overload
  453.     def ge(self, other: Union[builtins.float, builtins.int], *, out: Optional['Tensor']=None) -> 'Tensor': ...
  455.     @overload
  456.     def ge(self, other: 'Tensor', *, out: Optional['Tensor']=None) -> 'Tensor': ...
  458.     @overload
  459.     def ge_(self, other: Union[builtins.float, builtins.int]) -> 'Tensor': ...
  461.     @overload
  462.     def ge_(self, other: 'Tensor') -> 'Tensor': ...
  464.     def gels(self, A: 'Tensor', *, out: Optional['Tensor']=None) -> Tuple['Tensor', 'Tensor']: ...
  466.     def geometric_(self, p: builtins.float, *, generator: Generator=None) -> 'Tensor': ...
  468.     def geqrf(self, *, out: Optional['Tensor']=None) -> Tuple['Tensor', 'Tensor']: ...
  470.     def ger(self, vec2: 'Tensor', *, out: Optional['Tensor']=None) -> 'Tensor': ...
  472.     def gesv(self, A: 'Tensor', *, out: Optional['Tensor']=None) -> Tuple['Tensor', 'Tensor']: ...
  474.     def get_device(self) -> builtins.int: ...
  476.     @overload
  477.     def gt(self, other: Union[builtins.float, builtins.int], *, out: Optional['Tensor']=None) -> 'Tensor': ...
  479.     @overload
  480.     def gt(self, other: 'Tensor', *, out: Optional['Tensor']=None) -> 'Tensor': ...
  482.     @overload
  483.     def gt_(self, other: Union[builtins.float, builtins.int]) -> 'Tensor': ...
  485.     @overload
  486.     def gt_(self, other: 'Tensor') -> 'Tensor': ...
  488.     def half(self) -> 'Tensor': ...
  490.     def hardshrink(self, lambd: Union[builtins.float, builtins.int]=0.5) -> 'Tensor': ...
  492.     def histc(self, bins: builtins.int=100, min: Union[builtins.float, builtins.int]=0, max: Union[builtins.float, builtins.int]=0, *, out: Optional['Tensor']=None) -> 'Tensor': ...
  494.     def ifft(self, signal_ndim: builtins.int, normalized: bool=False) -> 'Tensor': ...
  496.     def index_add(self, dim, index, tensor): ...
  498.     def index_add_(self, dim: builtins.int, index: 'Tensor', source: 'Tensor') -> 'Tensor': ...
  500.     def index_copy(self, dim, index, tensor): ...
  502.     def index_copy_(self, dim: builtins.int, index: 'Tensor', source: 'Tensor') -> 'Tensor': ...
  504.     def index_fill(self, dim, index, value): ...
  506.     @overload
  507.     def index_fill_(self, dim: builtins.int, index: 'Tensor', value: Union[builtins.float, builtins.int]) -> 'Tensor': ...
  509.     @overload
  510.     def index_fill_(self, dim: builtins.int, index: 'Tensor', value: 'Tensor') -> 'Tensor': ...
  512.     def index_put_(self, indices: Union[Tuple['Tensor', ...],List['Tensor']], values: 'Tensor') -> 'Tensor': ...
  514.     def index_select(self, dim: builtins.int, index: 'Tensor', *, out: Optional['Tensor']=None) -> 'Tensor': ...
  516.     def int(self) -> 'Tensor': ...
  518.     def inverse(self, *, out: Optional['Tensor']=None) -> 'Tensor': ...
  520.     def irfft(self, signal_ndim: builtins.int, normalized: bool=False, onesided: bool=True, signal_sizes: Union[Tuple[builtins.int, ...], List[builtins.int], Size]=()) -> 'Tensor': ...
  522.     def is_contiguous(self) -> bool: ...
  524.     def is_pinned(self): ...
  526.     def is_set_to(self, tensor: 'Tensor') -> bool: ...
  528.     def is_shared(self): ...
  530.     def item(self) -> Union[builtins.float, builtins.int]: ...
  532.     def kthvalue(self, k: builtins.int, dim: builtins.int=-1, keepdim: bool=False, *, out: Optional['Tensor']=None) -> Tuple['Tensor', 'Tensor']: ...
  534.     @overload
  535.     def le(self, other: Union[builtins.float, builtins.int], *, out: Optional['Tensor']=None) -> 'Tensor': ...
  537.     @overload
  538.     def le(self, other: 'Tensor', *, out: Optional['Tensor']=None) -> 'Tensor': ...
  540.     @overload
  541.     def le_(self, other: Union[builtins.float, builtins.int]) -> 'Tensor': ...
  543.     @overload
  544.     def le_(self, other: 'Tensor') -> 'Tensor': ...
  546.     def lerp(self, end: 'Tensor', weight: Union[builtins.float, builtins.int], *, out: Optional['Tensor']=None) -> 'Tensor': ...
  548.     def lerp_(self, end: 'Tensor', weight: Union[builtins.float, builtins.int]) -> 'Tensor': ...
  550.     def log(self, *, out: Optional['Tensor']=None) -> 'Tensor': ...
  552.     def log10(self, *, out: Optional['Tensor']=None) -> 'Tensor': ...
  554.     def log10_(self) -> 'Tensor': ...
  556.     def log1p(self, *, out: Optional['Tensor']=None) -> 'Tensor': ...
  558.     def log1p_(self) -> 'Tensor': ...
  560.     def log2(self, *, out: Optional['Tensor']=None) -> 'Tensor': ...
  562.     def log2_(self) -> 'Tensor': ...
  564.     def log_(self) -> 'Tensor': ...
  566.     def log_normal_(self, mean: builtins.float=1, std: builtins.float=2, *, generator: Generator=None) -> 'Tensor': ...
  568.     def logdet(self) -> 'Tensor': ...
  570.     def logsumexp(self, dim: builtins.int, keepdim: bool=False, *, out: Optional['Tensor']=None) -> 'Tensor': ...
  572.     def long(self) -> 'Tensor': ...
  574.     @overload
  575.     def lt(self, other: Union[builtins.float, builtins.int], *, out: Optional['Tensor']=None) -> 'Tensor': ...
  577.     @overload
  578.     def lt(self, other: 'Tensor', *, out: Optional['Tensor']=None) -> 'Tensor': ...
  580.     @overload
  581.     def lt_(self, other: Union[builtins.float, builtins.int]) -> 'Tensor': ...
  583.     @overload
  584.     def lt_(self, other: 'Tensor') -> 'Tensor': ...
  586.     def map_(tensor: 'Tensor', callable: Callable) -> 'Tensor': ...
  588.     def masked_fill(self, mask, value): ...
  590.     @overload
  591.     def masked_fill_(self, mask: 'Tensor', value: Union[builtins.float, builtins.int]) -> 'Tensor': ...
  593.     @overload
  594.     def masked_fill_(self, mask: 'Tensor', value: 'Tensor') -> 'Tensor': ...
  596.     def masked_scatter(self, mask, tensor): ...
  598.     def masked_scatter_(self, mask: 'Tensor', source: 'Tensor') -> 'Tensor': ...
  600.     def masked_select(self, mask: 'Tensor', *, out: Optional['Tensor']=None) -> 'Tensor': ...
  602.     def matmul(self, other: 'Tensor', *, out: Optional['Tensor']=None) -> 'Tensor': ...
  604.     def matrix_power(self, n: builtins.int) -> 'Tensor': ...
  606.     @overload
  607.     def max(self, other: 'Tensor', *, out: Optional['Tensor']=None) -> 'Tensor': ...
  609.     @overload
  610.     def max(self, *, out: Optional['Tensor']=None) -> Union[builtins.float, builtins.int]: ...
  612.     @overload
  613.     def max(self, dim: builtins.int, keepdim: bool=False, *, out: Optional['Tensor']=None) -> Tuple['Tensor', 'Tensor']: ...
  615.     @overload
  616.     def mean(self, *, dtype: _dtype, out: Optional['Tensor']=None) -> 'Tensor': ...
  618.     @overload
  619.     def mean(self, *, out: Optional['Tensor']=None) -> 'Tensor': ...
  621.     @overload
  622.     def mean(self, dim: builtins.int, keepdim: bool, *, dtype: _dtype, out: Optional['Tensor']=None) -> 'Tensor': ...
  624.     @overload
  625.     def mean(self, dim: builtins.int, keepdim: bool=False, *, out: Optional['Tensor']=None) -> 'Tensor': ...
  627.     @overload
  628.     def mean(self, dim: builtins.int, *, dtype: _dtype, out: Optional['Tensor']=None) -> 'Tensor': ...
  630.     @overload
  631.     def median(self, *, out: Optional['Tensor']=None) -> Union[builtins.float, builtins.int]: ...
  633.     @overload
  634.     def median(self, dim: builtins.int, keepdim: bool=False, *, out: Optional['Tensor']=None) -> Tuple['Tensor', 'Tensor']: ...
  636.     @overload
  637.     def min(self, other: 'Tensor', *, out: Optional['Tensor']=None) -> 'Tensor': ...
  639.     @overload
  640.     def min(self, *, out: Optional['Tensor']=None) -> Union[builtins.float, builtins.int]: ...
  642.     @overload
  643.     def min(self, dim: builtins.int, keepdim: bool=False, *, out: Optional['Tensor']=None) -> Tuple['Tensor', 'Tensor']: ...
  645.     def mm(self, mat2: 'Tensor', *, out: Optional['Tensor']=None) -> 'Tensor': ...
  647.     def mode(self, dim: builtins.int=-1, keepdim: bool=False, *, out: Optional['Tensor']=None) -> Tuple['Tensor', 'Tensor']: ...
  649.     @overload
  650.     def mul(self, other: 'Tensor', *, out: Optional['Tensor']=None) -> 'Tensor': ...
  652.     @overload
  653.     def mul(self, other: Union[builtins.float, builtins.int], *, out: Optional['Tensor']=None) -> 'Tensor': ...
  655.     @overload
  656.     def mul_(self, other: 'Tensor') -> 'Tensor': ...
  658.     @overload
  659.     def mul_(self, other: Union[builtins.float, builtins.int]) -> 'Tensor': ...
  661.     def multinomial(self, num_samples: builtins.int, replacement: bool=False, *, generator: Generator=None, out: Optional['Tensor']=None) -> 'Tensor': ...
  663.     def mv(self, vec: 'Tensor', *, out: Optional['Tensor']=None) -> 'Tensor': ...
  665.     def mvlgamma(self, p: builtins.int) -> 'Tensor': ...
  667.     def mvlgamma_(self, p: builtins.int) -> 'Tensor': ...
  669.     def narrow(self, dim: builtins.int, start: builtins.int, length: builtins.int) -> 'Tensor': ...
  671.     def narrow_copy(self, dim: builtins.int, start: builtins.int, length: builtins.int) -> 'Tensor': ...
  673.     def ndimension(self) -> builtins.int: ...
  675.     @overload
  676.     def ne(self, other: Union[builtins.float, builtins.int], *, out: Optional['Tensor']=None) -> 'Tensor': ...
  678.     @overload
  679.     def ne(self, other: 'Tensor', *, out: Optional['Tensor']=None) -> 'Tensor': ...
  681.     @overload
  682.     def ne_(self, other: Union[builtins.float, builtins.int]) -> 'Tensor': ...
  684.     @overload
  685.     def ne_(self, other: 'Tensor') -> 'Tensor': ...
  687.     def neg(self, *, out: Optional['Tensor']=None) -> 'Tensor': ...
  689.     def neg_(self) -> 'Tensor': ...
  691.     def nelement(self) -> builtins.int: ...
  693.     def new_empty(self, size: Union[Tuple[builtins.int, ...], List[builtins.int], Size], dtype: Optional[_dtype]=None, device: Union[device, str, None]=None, requires_grad: bool=False) -> 'Tensor': ...
  695.     def new_full(self, size: Union[Tuple[builtins.int, ...], List[builtins.int], Size], value: Union[builtins.float, builtins.int], dtype: Optional[_dtype]=None, device: Union[device, str, None]=None, requires_grad: bool=False) -> 'Tensor': ...
  697.     def new_ones(self, size: Union[Tuple[builtins.int, ...], List[builtins.int], Size], dtype: Optional[_dtype]=None, device: Union[device, str, None]=None, requires_grad: bool=False) -> 'Tensor': ...
  699.     def new_tensor(self, data: Any, dtype: Optional[_dtype]=None, device: Union[device, str, None]=None, requires_grad: bool=False) -> 'Tensor': ...
  701.     def new_zeros(self, size: Union[Tuple[builtins.int, ...], List[builtins.int], Size], dtype: Optional[_dtype]=None, device: Union[device, str, None]=None, requires_grad: bool=False) -> 'Tensor': ...
  703.     def nonzero(self, *, out: Optional['Tensor']=None) -> 'Tensor': ...
  705.     def norm(self, p='fro', dim=None, keepdim=False): ...
  707.     def normal_(self, mean: builtins.float=0, std: builtins.float=1, *, generator: Generator=None) -> 'Tensor': ...
  709.     def numel(self) -> builtins.int: ...
  711.     def numpy(self) -> Any: ...
  713.     def orgqr(self, input2: 'Tensor', *, out: Optional['Tensor']=None) -> 'Tensor': ...
  715.     def ormqr(self, input2: 'Tensor', input3: 'Tensor', left: bool=True, transpose: bool=False, *, out: Optional['Tensor']=None) -> 'Tensor': ...
  717.     @overload
  718.     def permute(self, dims: Union[Tuple[builtins.int, ...], List[builtins.int], Size]) -> 'Tensor': ...
  720.     @overload
  721.     def permute(self, *dims: builtins.int) -> 'Tensor': ...
  723.     def pinverse(self, rcond: builtins.float=1e-15) -> 'Tensor': ...
  725.     def potrf(self, upper: bool=True, *, out: Optional['Tensor']=None) -> 'Tensor': ...
  727.     def potri(self, upper: bool=True, *, out: Optional['Tensor']=None) -> 'Tensor': ...
  729.     def potrs(self, input2: 'Tensor', upper: bool=True, *, out: Optional['Tensor']=None) -> 'Tensor': ...
  731.     @overload
  732.     def pow(self, exponent: 'Tensor', *, out: Optional['Tensor']=None) -> 'Tensor': ...
  734.     @overload
  735.     def pow(self, exponent: Union[builtins.float, builtins.int], *, out: Optional['Tensor']=None) -> 'Tensor': ...
  737.     @overload
  738.     def pow_(self, exponent: Union[builtins.float, builtins.int]) -> 'Tensor': ...
  740.     @overload
  741.     def pow_(self, exponent: 'Tensor') -> 'Tensor': ...
  743.     @overload
  744.     def prod(self, *, dtype: _dtype, out: Optional['Tensor']=None) -> 'Tensor': ...
  746.     @overload
  747.     def prod(self, *, out: Optional['Tensor']=None) -> 'Tensor': ...
  749.     @overload
  750.     def prod(self, dim: builtins.int, keepdim: bool, *, dtype: _dtype, out: Optional['Tensor']=None) -> 'Tensor': ...
  752.     @overload
  753.     def prod(self, dim: builtins.int, keepdim: bool=False, *, out: Optional['Tensor']=None) -> 'Tensor': ...
  755.     @overload
  756.     def prod(self, dim: builtins.int, *, dtype: _dtype, out: Optional['Tensor']=None) -> 'Tensor': ...
  758.     def pstrf(self, upper: bool=True, tol: Union[builtins.float, builtins.int]=-1, *, out: Optional['Tensor']=None) -> Tuple['Tensor', 'Tensor']: ...
  760.     def put_(self, index: 'Tensor', source: 'Tensor', accumulate: bool=False) -> 'Tensor': ...
  762.     def qr(self, *, out: Optional['Tensor']=None) -> Tuple['Tensor', 'Tensor']: ...
  764.     @overload
  765.     def random_(self, from_: builtins.int, to: builtins.int, *, generator: Generator=None) -> 'Tensor': ...
  767.     @overload
  768.     def random_(self, to: builtins.int, *, generator: Generator=None) -> 'Tensor': ...
  770.     @overload
  771.     def random_(self, *, generator: Generator=None) -> 'Tensor': ...
  773.     def reciprocal(self, *, out: Optional['Tensor']=None) -> 'Tensor': ...
  775.     def reciprocal_(self) -> 'Tensor': ...
  777.     def register_hook(self, hook): ...
  779.     @overload
  780.     def remainder(self, other: Union[builtins.float, builtins.int], *, out: Optional['Tensor']=None) -> 'Tensor': ...
  782.     @overload
  783.     def remainder(self, other: 'Tensor', *, out: Optional['Tensor']=None) -> 'Tensor': ...
  785.     @overload
  786.     def remainder_(self, other: Union[builtins.float, builtins.int]) -> 'Tensor': ...
  788.     @overload
  789.     def remainder_(self, other: 'Tensor') -> 'Tensor': ...
  791.     def renorm(self, p: Union[builtins.float, builtins.int], dim: builtins.int, maxnorm: Union[builtins.float, builtins.int], *, out: Optional['Tensor']=None) -> 'Tensor': ...
  793.     def renorm_(self, p: Union[builtins.float, builtins.int], dim: builtins.int, maxnorm: Union[builtins.float, builtins.int]) -> 'Tensor': ...
  795.     @overload
  796.     def repeat(self, repeats: Union[Tuple[builtins.int, ...], List[builtins.int], Size]) -> 'Tensor': ...
  798.     @overload
  799.     def repeat(self, *repeats: builtins.int) -> 'Tensor': ...
  801.     def requires_grad_(self, mode: bool=True) -> 'Tensor': ...
  803.     @overload
  804.     def reshape(self, shape: Union[Tuple[builtins.int, ...], List[builtins.int], Size]) -> 'Tensor': ...
  806.     @overload
  807.     def reshape(self, *shape: builtins.int) -> 'Tensor': ...
  809.     def reshape_as(self, other: 'Tensor') -> 'Tensor': ...
  811.     @overload
  812.     def resize_(self, size: Union[Tuple[builtins.int, ...], List[builtins.int], Size]) -> 'Tensor': ...
  814.     @overload
  815.     def resize_(self, *size: builtins.int) -> 'Tensor': ...
  817.     def resize_as_(self, the_template: 'Tensor') -> 'Tensor': ...
  819.     def retain_grad(self): ...
  821.     def rfft(self, signal_ndim: builtins.int, normalized: bool=False, onesided: bool=True) -> 'Tensor': ...
  823.     def rot90(self, k: builtins.int=1, dims: Union[Tuple[builtins.int, ...], List[builtins.int], Size]=(0,1)) -> 'Tensor': ...
  825.     def round(self, *, out: Optional['Tensor']=None) -> 'Tensor': ...
  827.     def round_(self) -> 'Tensor': ...
  829.     def rsqrt(self, *, out: Optional['Tensor']=None) -> 'Tensor': ...
  831.     def rsqrt_(self) -> 'Tensor': ...
  833.     def scatter(self, dim, index, source): ...
  835.     @overload
  836.     def scatter_(self, dim: builtins.int, index: 'Tensor', src: 'Tensor') -> 'Tensor': ...
  838.     @overload
  839.     def scatter_(self, dim: builtins.int, index: 'Tensor', value: Union[builtins.float, builtins.int]) -> 'Tensor': ...
  841.     def scatter_add(self, dim, index, source): ...
  843.     def scatter_add_(self, dim: builtins.int, index: 'Tensor', src: 'Tensor') -> 'Tensor': ...
  845.     def select(self, dim: builtins.int, index: builtins.int) -> 'Tensor': ...
  847.     @overload
  848.     def set_(self, source: Storage) -> 'Tensor': ...
  850.     @overload
  851.     def set_(self, source: Storage, storage_offset: builtins.int, size: Union[Tuple[builtins.int, ...], List[builtins.int], Size], stride: Union[Tuple[builtins.int, ...], List[builtins.int], Size]=()) -> 'Tensor': ...
  853.     @overload
  854.     def set_(self, source: 'Tensor') -> 'Tensor': ...
  856.     @overload
  857.     def set_(self) -> 'Tensor': ...
  859.     def share_memory_(self): ...
  861.     def short(self) -> 'Tensor': ...
  863.     def sigmoid(self, *, out: Optional['Tensor']=None) -> 'Tensor': ...
  865.     def sigmoid_(self) -> 'Tensor': ...
  867.     def sign(self, *, out: Optional['Tensor']=None) -> 'Tensor': ...
  869.     def sign_(self) -> 'Tensor': ...
  871.     def sin(self, *, out: Optional['Tensor']=None) -> 'Tensor': ...
  873.     def sin_(self) -> 'Tensor': ...
  875.     def sinh(self, *, out: Optional['Tensor']=None) -> 'Tensor': ...
  877.     def sinh_(self) -> 'Tensor': ...
  879.     @overload
  880.     def size(self) -> Size: ...
  882.     @overload
  883.     def size(self, dim: builtins.int) -> builtins.int: ...
  885.     def slogdet(self) -> Tuple['Tensor', 'Tensor']: ...
  887.     def sort(self, dim: builtins.int=-1, descending: bool=False, *, out: Optional['Tensor']=None) -> Tuple['Tensor', 'Tensor']: ...
  889.     def split(self, split_size, dim=0): ...
  891.     def sqrt(self, *, out: Optional['Tensor']=None) -> 'Tensor': ...
  893.     def sqrt_(self) -> 'Tensor': ...
  895.     @overload
  896.     def squeeze(self) -> 'Tensor': ...
  898.     @overload
  899.     def squeeze(self, dim: builtins.int) -> 'Tensor': ...
  901.     @overload
  902.     def squeeze_(self) -> 'Tensor': ...
  904.     @overload
  905.     def squeeze_(self, dim: builtins.int) -> 'Tensor': ...
  907.     @overload
  908.     def std(self, unbiased: bool=True, *, out: Optional['Tensor']=None) -> 'Tensor': ...
  910.     @overload
  911.     def std(self, dim: builtins.int, unbiased: bool=True, keepdim: bool=False, *, out: Optional['Tensor']=None) -> 'Tensor': ...
  913.     def stft(self, n_fft, hop_length=None, win_length=None, window=None, center=True, pad_mode='reflect', normalized=False, onesided=True): ...
  915.     def storage(self) -> Storage: ...
  917.     def storage_offset(self) -> builtins.int: ...
  919.     @overload
  920.     def stride(self) -> Tuple[builtins.int]: ...
  922.     @overload
  923.     def stride(self, dim: builtins.int) -> builtins.int: ...
  925.     @overload
  926.     def sub(self, other: 'Tensor', *, alpha: Union[builtins.float, builtins.int]=1, out: Optional['Tensor']=None) -> 'Tensor': ...
  928.     @overload
  929.     def sub(self, other: Union[builtins.float, builtins.int], alpha: Union[builtins.float, builtins.int]=1, *, out: Optional['Tensor']=None) -> 'Tensor': ...
  931.     @overload
  932.     def sub_(self, other: 'Tensor', *, alpha: Union[builtins.float, builtins.int]=1) -> 'Tensor': ...
  934.     @overload
  935.     def sub_(self, other: Union[builtins.float, builtins.int], alpha: Union[builtins.float, builtins.int]=1) -> 'Tensor': ...
  937.     @overload
  938.     def sum(self, *, dtype: _dtype, out: Optional['Tensor']=None) -> 'Tensor': ...
  940.     @overload
  941.     def sum(self, *, out: Optional['Tensor']=None) -> 'Tensor': ...
  943.     @overload
  944.     def sum(self, dim: Union[builtins.int, Tuple[builtins.int, ...], List[builtins.int], Size], keepdim: bool, *, dtype: _dtype, out: Optional['Tensor']=None) -> 'Tensor': ...
  946.     @overload
  947.     def sum(self, dim: Union[builtins.int, Tuple[builtins.int, ...], List[builtins.int], Size], keepdim: bool=False, *, out: Optional['Tensor']=None) -> 'Tensor': ...
  949.     @overload
  950.     def sum(self, dim: Union[builtins.int, Tuple[builtins.int, ...], List[builtins.int], Size], *, dtype: _dtype, out: Optional['Tensor']=None) -> 'Tensor': ...
  952.     @overload
  953.     def sum(self, *dim: builtins.int, dtype: _dtype, out: Optional['Tensor']=None) -> 'Tensor': ...
  955.     def svd(self, some: bool=True, compute_uv: bool=True, *, out: Optional['Tensor']=None) -> Tuple['Tensor', 'Tensor', 'Tensor']: ...
  957.     def symeig(self, eigenvectors: bool=False, upper: bool=True, *, out: Optional['Tensor']=None) -> Tuple['Tensor', 'Tensor']: ...
  959.     def t(self) -> 'Tensor': ...
  961.     def t_(self) -> 'Tensor': ...
  963.     def take(self, index: 'Tensor', *, out: Optional['Tensor']=None) -> 'Tensor': ...
  965.     def tan_(self) -> 'Tensor': ...
  967.     def tanh(self, *, out: Optional['Tensor']=None) -> 'Tensor': ...
  969.     def tanh_(self) -> 'Tensor': ...
  971.     @overload
  972.     def to(self, device: Union[device, str, None], dtype: _dtype, non_blocking: bool=False, copy: bool=False) -> 'Tensor': ...
  974.     @overload
  975.     def to(self, dtype: _dtype, non_blocking: bool=False, copy: bool=False) -> 'Tensor': ...
  977.     @overload
  978.     def to(self, device: Union[device, str, None], non_blocking: bool=False, copy: bool=False) -> 'Tensor': ...
  980.     @overload
  981.     def to(self, other: 'Tensor', non_blocking: bool=False, copy: bool=False) -> 'Tensor': ...
  983.     def tolist(self) -> List: ...
  985.     def topk(self, k: builtins.int, dim: builtins.int=-1, largest: bool=True, sorted: bool=True, *, out: Optional['Tensor']=None) -> Tuple['Tensor', 'Tensor']: ...
  987.     def trace(self) -> Union[builtins.float, builtins.int]: ...
  989.     def transpose(self, dim0: builtins.int, dim1: builtins.int) -> 'Tensor': ...
  991.     def transpose_(self, dim0: builtins.int, dim1: builtins.int) -> 'Tensor': ...
  993.     def tril(self, diagonal: builtins.int=0, *, out: Optional['Tensor']=None) -> 'Tensor': ...
  995.     def tril_(self, diagonal: builtins.int=0) -> 'Tensor': ...
  997.     def triu(self, diagonal: builtins.int=0, *, out: Optional['Tensor']=None) -> 'Tensor': ...
  999.     def triu_(self, diagonal: builtins.int=0) -> 'Tensor': ...
  1001.     def trtrs(self, A: 'Tensor', upper: bool=True, transpose: bool=False, unitriangular: bool=False, *, out: Optional['Tensor']=None) -> Tuple['Tensor', 'Tensor']: ...
  1003.     def trunc(self, *, out: Optional['Tensor']=None) -> 'Tensor': ...
  1005.     def trunc_(self) -> 'Tensor': ...
  1007.     def type(self, dtype: Union[None, str, _dtype]=None, non_blocking: bool=False) -> Union[str, 'Tensor']: ...
  1009.     def type_as(self, other: 'Tensor') -> 'Tensor': ...
  1011.     def unbind(self, dim: builtins.int=0) -> Union[Tuple['Tensor', ...],List['Tensor']]: ...
  1013.     def unfold(self, dimension: builtins.int, size: builtins.int, step: builtins.int) -> 'Tensor': ...
  1015.     def uniform_(self, from_: builtins.float=0, to: builtins.float=1, *, generator: Generator=None) -> 'Tensor': ...
  1017.     def unique(self, sorted=False, return_inverse=False, dim=None): ...
  1019.     def unsqueeze(self, dim: builtins.int) -> 'Tensor': ...
  1021.     def unsqueeze_(self, dim: builtins.int) -> 'Tensor': ...
  1023.     @overload
  1024.     def var(self, unbiased: bool=True, *, out: Optional['Tensor']=None) -> 'Tensor': ...
  1026.     @overload
  1027.     def var(self, dim: builtins.int, unbiased: bool=True, keepdim: bool=False, *, out: Optional['Tensor']=None) -> 'Tensor': ...
  1029.     @overload
  1030.     def view(self, size: Union[Tuple[builtins.int, ...], List[builtins.int], Size]) -> 'Tensor': ...
  1032.     @overload
  1033.     def view(self, *size: builtins.int) -> 'Tensor': ...
  1035.     def view_as(self, other: 'Tensor') -> 'Tensor': ...
  1037.     def where(self, condition: 'Tensor', other: 'Tensor') -> 'Tensor': ...
  1039.     def zero_(self) -> 'Tensor': ...
  1042. def abs(self: Tensor, *, out: Optional[Tensor]=None) -> Tensor: ...
  1044. def acos(self: Tensor, *, out: Optional[Tensor]=None) -> Tensor: ...
  1046. def adaptive_avg_pool1d(self: Tensor, output_size: Union[builtins.int, Tuple[builtins.int, ...], List[builtins.int], Size]) -> Tensor: ...
  1048. @overload
  1049. def add(self: Tensor, other: Tensor, *, alpha: Union[builtins.float, builtins.int]=1, out: Optional[Tensor]=None) -> Tensor: ...
  1051. @overload
  1052. def add(self: Tensor, other: Union[builtins.float, builtins.int], alpha: Union[builtins.float, builtins.int]=1, *, out: Optional[Tensor]=None) -> Tensor: ...
  1054. @overload
  1055. def add(self: Tensor, alpha: Union[builtins.float, builtins.int], other: Tensor) -> Tensor: ...
  1057. @overload
  1058. def add(self: Tensor, alpha: Union[builtins.float, builtins.int], other: Tensor, *, out: Tensor) -> Tensor: ...
  1060. @overload
  1061. def addbmm(self: Tensor, batch1: Tensor, batch2: Tensor, *, beta: Union[builtins.float, builtins.int]=1, alpha: Union[builtins.float, builtins.int]=1, out: Optional[Tensor]=None) -> Tensor: ...
  1063. @overload
  1064. def addbmm(beta: Union[builtins.float, builtins.int], self: Tensor, alpha: Union[builtins.float, builtins.int], batch1: Tensor, batch2: Tensor) -> Tensor: ...
  1066. @overload
  1067. def addbmm(beta: Union[builtins.float, builtins.int], self: Tensor, alpha: Union[builtins.float, builtins.int], batch1: Tensor, batch2: Tensor, *, out: Tensor) -> Tensor: ...
  1069. @overload
  1070. def addbmm(beta: Union[builtins.float, builtins.int], self: Tensor, batch1: Tensor, batch2: Tensor) -> Tensor: ...
  1072. @overload
  1073. def addbmm(beta: Union[builtins.float, builtins.int], self: Tensor, batch1: Tensor, batch2: Tensor, *, out: Tensor) -> Tensor: ...
  1075. @overload
  1076. def addcdiv(self: Tensor, tensor1: Tensor, tensor2: Tensor, *, value: Union[builtins.float, builtins.int]=1, out: Optional[Tensor]=None) -> Tensor: ...
  1078. @overload
  1079. def addcdiv(self: Tensor, value: Union[builtins.float, builtins.int], tensor1: Tensor, tensor2: Tensor) -> Tensor: ...
  1081. @overload
  1082. def addcdiv(self: Tensor, value: Union[builtins.float, builtins.int], tensor1: Tensor, tensor2: Tensor, *, out: Tensor) -> Tensor: ...
  1084. @overload
  1085. def addcmul(self: Tensor, tensor1: Tensor, tensor2: Tensor, *, value: Union[builtins.float, builtins.int]=1, out: Optional[Tensor]=None) -> Tensor: ...
  1087. @overload
  1088. def addcmul(self: Tensor, value: Union[builtins.float, builtins.int], tensor1: Tensor, tensor2: Tensor) -> Tensor: ...
  1090. @overload
  1091. def addcmul(self: Tensor, value: Union[builtins.float, builtins.int], tensor1: Tensor, tensor2: Tensor, *, out: Tensor) -> Tensor: ...
  1093. @overload
  1094. def addmm(self: Tensor, mat1: Tensor, mat2: Tensor, *, beta: Union[builtins.float, builtins.int]=1, alpha: Union[builtins.float, builtins.int]=1, out: Optional[Tensor]=None) -> Tensor: ...
  1096. @overload
  1097. def addmm(beta: Union[builtins.float, builtins.int], self: Tensor, alpha: Union[builtins.float, builtins.int], mat1: Tensor, mat2: Tensor) -> Tensor: ...
  1099. @overload
  1100. def addmm(beta: Union[builtins.float, builtins.int], self: Tensor, alpha: Union[builtins.float, builtins.int], mat1: Tensor, mat2: Tensor, *, out: Tensor) -> Tensor: ...
  1102. @overload
  1103. def addmm(beta: Union[builtins.float, builtins.int], self: Tensor, mat1: Tensor, mat2: Tensor) -> Tensor: ...
  1105. @overload
  1106. def addmm(beta: Union[builtins.float, builtins.int], self: Tensor, mat1: Tensor, mat2: Tensor, *, out: Tensor) -> Tensor: ...
  1108. @overload
  1109. def addmv(self: Tensor, mat: Tensor, vec: Tensor, *, beta: Union[builtins.float, builtins.int]=1, alpha: Union[builtins.float, builtins.int]=1, out: Optional[Tensor]=None) -> Tensor: ...
  1111. @overload
  1112. def addmv(beta: Union[builtins.float, builtins.int], self: Tensor, alpha: Union[builtins.float, builtins.int], mat: Tensor, vec: Tensor) -> Tensor: ...
  1114. @overload
  1115. def addmv(beta: Union[builtins.float, builtins.int], self: Tensor, alpha: Union[builtins.float, builtins.int], mat: Tensor, vec: Tensor, *, out: Tensor) -> Tensor: ...
  1117. @overload
  1118. def addmv(beta: Union[builtins.float, builtins.int], self: Tensor, mat: Tensor, vec: Tensor) -> Tensor: ...
  1120. @overload
  1121. def addmv(beta: Union[builtins.float, builtins.int], self: Tensor, mat: Tensor, vec: Tensor, *, out: Tensor) -> Tensor: ...
  1123. @overload
  1124. def addr(self: Tensor, vec1: Tensor, vec2: Tensor, *, beta: Union[builtins.float, builtins.int]=1, alpha: Union[builtins.float, builtins.int]=1, out: Optional[Tensor]=None) -> Tensor: ...
  1126. @overload
  1127. def addr(beta: Union[builtins.float, builtins.int], self: Tensor, alpha: Union[builtins.float, builtins.int], vec1: Tensor, vec2: Tensor) -> Tensor: ...
  1129. @overload
  1130. def addr(beta: Union[builtins.float, builtins.int], self: Tensor, alpha: Union[builtins.float, builtins.int], vec1: Tensor, vec2: Tensor, *, out: Tensor) -> Tensor: ...
  1132. @overload
  1133. def addr(beta: Union[builtins.float, builtins.int], self: Tensor, vec1: Tensor, vec2: Tensor) -> Tensor: ...
  1135. @overload
  1136. def addr(beta: Union[builtins.float, builtins.int], self: Tensor, vec1: Tensor, vec2: Tensor, *, out: Tensor) -> Tensor: ...
  1138. def allclose(self: Tensor, other: Tensor, rtol: builtins.float=1e-05, atol: builtins.float=1e-08, equal_nan: bool=False) -> bool: ...
  1140. @overload
  1141. def arange(start: Union[builtins.float, builtins.int], end: Union[builtins.float, builtins.int], *, out: Optional[Tensor]=None, dtype: _dtype=None, layout: layout=strided, device: Optional[device]=None, requires_grad:bool=False) -> Tensor: ...
  1143. @overload
  1144. def arange(start: Union[builtins.float, builtins.int], end: Union[builtins.float, builtins.int], step: Union[builtins.float, builtins.int], *, out: Optional[Tensor]=None, dtype: _dtype=None, layout: layout=strided, device: Optional[device]=None, requires_grad:bool=False) -> Tensor: ...
  1146. @overload
  1147. def arange(end: Union[builtins.float, builtins.int], *, out: Optional[Tensor]=None, dtype: _dtype=None, layout: layout=strided, device: Optional[device]=None, requires_grad:bool=False) -> Tensor: ...
  1149. def argmax(input, dim=None, keepdim=False): ...
  1151. def argmin(input, dim=None, keepdim=False): ...
  1153. def argsort(input, dim=None, descending=False): ...
  1155. def as_tensor(data: Any, dtype: _dtype=None, device: Optional[device]=None) -> Tensor: ...
  1157. def asin(self: Tensor, *, out: Optional[Tensor]=None) -> Tensor: ...
  1159. def atan(self: Tensor, *, out: Optional[Tensor]=None) -> Tensor: ...
  1161. def atan2(self: Tensor, other: Tensor, *, out: Optional[Tensor]=None) -> Tensor: ...
  1163. def avg_pool1d(self: Tensor, kernel_size: Union[builtins.int, Tuple[builtins.int, ...], List[builtins.int], Size], stride: Union[builtins.int, Tuple[builtins.int, ...], List[builtins.int], Size]=(), padding: Union[builtins.int, Tuple[builtins.int, ...], List[builtins.int], Size]=0, ceil_mode: bool=False, count_include_pad: bool=True) -> Tensor: ...
  1165. @overload
  1166. def baddbmm(self: Tensor, batch1: Tensor, batch2: Tensor, *, beta: Union[builtins.float, builtins.int]=1, alpha: Union[builtins.float, builtins.int]=1, out: Optional[Tensor]=None) -> Tensor: ...
  1168. @overload
  1169. def baddbmm(beta: Union[builtins.float, builtins.int], self: Tensor, alpha: Union[builtins.float, builtins.int], batch1: Tensor, batch2: Tensor) -> Tensor: ...
  1171. @overload
  1172. def baddbmm(beta: Union[builtins.float, builtins.int], self: Tensor, alpha: Union[builtins.float, builtins.int], batch1: Tensor, batch2: Tensor, *, out: Tensor) -> Tensor: ...
  1174. @overload
  1175. def baddbmm(beta: Union[builtins.float, builtins.int], self: Tensor, batch1: Tensor, batch2: Tensor) -> Tensor: ...
  1177. @overload
  1178. def baddbmm(beta: Union[builtins.float, builtins.int], self: Tensor, batch1: Tensor, batch2: Tensor, *, out: Tensor) -> Tensor: ...
  1180. @overload
  1181. def bartlett_window(window_length: builtins.int, *, dtype: _dtype=None, layout: layout=strided, device: Optional[device]=None, requires_grad:bool=False) -> Tensor: ...
  1183. @overload
  1184. def bartlett_window(window_length: builtins.int, periodic: bool, *, dtype: _dtype=None, layout: layout=strided, device: Optional[device]=None, requires_grad:bool=False) -> Tensor: ...
  1186. @overload
  1187. def bernoulli(self: Tensor, *, generator: Generator=None, out: Optional[Tensor]=None) -> Tensor: ...
  1189. @overload
  1190. def bernoulli(self: Tensor, p: builtins.float, *, generator: Generator=None, out: Optional[Tensor]=None) -> Tensor: ...
  1192. def bincount(self: Tensor, weights: Optional[Tensor]=None, minlength: builtins.int=0) -> Tensor: ...
  1194. @overload
  1195. def blackman_window(window_length: builtins.int, *, dtype: _dtype=None, layout: layout=strided, device: Optional[device]=None, requires_grad:bool=False) -> Tensor: ...
  1197. @overload
  1198. def blackman_window(window_length: builtins.int, periodic: bool, *, dtype: _dtype=None, layout: layout=strided, device: Optional[device]=None, requires_grad:bool=False) -> Tensor: ...
  1200. def bmm(self: Tensor, mat2: Tensor, *, out: Optional[Tensor]=None) -> Tensor: ...
  1202. def broadcast_tensors(*tensors:Tensor) -> List[Tensor]: ...
  1204. def btrifact(A:Tensor, info:Union[Tensor, None]=None, pivot:bool=True) -> Tuple[Tensor, Tensor]: ...
  1206. def btrifact_with_info(self: Tensor, *, pivot: bool=True, out: Optional[Tensor]=None) -> Tuple[Tensor, Tensor, Tensor]: ...
  1208. def btrisolve(self: Tensor, LU_data: Tensor, LU_pivots: Tensor, *, out: Optional[Tensor]=None) -> Tensor: ...
  1210. def btriunpack(LU_data:Tensor, LU_pivots:Tensor, unpack_data:bool=True, unpack_pivots:bool=True) -> Tuple[Tensor, Tensor, Tensor]: ...
  1212. def cat(tensors: Union[Tuple[Tensor, ...],List[Tensor]], dim: builtins.int=0, *, out: Optional[Tensor]=None) -> Tensor: ...
  1214. def ceil(self: Tensor, *, out: Optional[Tensor]=None) -> Tensor: ...
  1216. def celu_(self: Tensor, alpha: Union[builtins.float, builtins.int]=1.0) -> Tensor: ...
  1218. def chain_matmul(*matrices): ...
  1220. def chunk(self: Tensor, chunks: builtins.int, dim: builtins.int=0) -> Union[Tuple[Tensor, ...],List[Tensor]]: ...
  1222. def clamp(self, min: builtins.float=-math.inf, max: builtins.float=math.inf, *, out: Optional[Tensor]=None) -> Tensor: ...
  1224. def compiled_with_cxx11_abi(): ...
  1226. def conv1d(input: Tensor, weight: Tensor, bias: Tensor=None, stride: Union[builtins.int, Tuple[builtins.int, ...], List[builtins.int], Size]=1, padding: Union[builtins.int, Tuple[builtins.int, ...], List[builtins.int], Size]=0, dilation: Union[builtins.int, Tuple[builtins.int, ...], List[builtins.int], Size]=1, groups: builtins.int=1) -> Tensor: ...
  1228. def conv2d(input: Tensor, weight: Tensor, bias: Tensor=None, stride: Union[builtins.int, Tuple[builtins.int, ...], List[builtins.int], Size]=1, padding: Union[builtins.int, Tuple[builtins.int, ...], List[builtins.int], Size]=0, dilation: Union[builtins.int, Tuple[builtins.int, ...], List[builtins.int], Size]=1, groups: builtins.int=1) -> Tensor: ...
  1230. def conv3d(input: Tensor, weight: Tensor, bias: Tensor=None, stride: Union[builtins.int, Tuple[builtins.int, ...], List[builtins.int], Size]=1, padding: Union[builtins.int, Tuple[builtins.int, ...], List[builtins.int], Size]=0, dilation: Union[builtins.int, Tuple[builtins.int, ...], List[builtins.int], Size]=1, groups: builtins.int=1) -> Tensor: ...
  1232. def conv_tbc(self: Tensor, weight: Tensor, bias: Tensor, pad: builtins.int=0) -> Tensor: ...
  1234. def conv_transpose1d(input: Tensor, weight: Tensor, bias: Tensor=None, stride: Union[builtins.int, Tuple[builtins.int, ...], List[builtins.int], Size]=1, padding: Union[builtins.int, Tuple[builtins.int, ...], List[builtins.int], Size]=0, output_padding: Union[builtins.int, Tuple[builtins.int, ...], List[builtins.int], Size]=0, groups: builtins.int=1, dilation: Union[builtins.int, Tuple[builtins.int, ...], List[builtins.int], Size]=1) -> Tensor: ...
  1236. def conv_transpose2d(input: Tensor, weight: Tensor, bias: Tensor=None, stride: Union[builtins.int, Tuple[builtins.int, ...], List[builtins.int], Size]=1, padding: Union[builtins.int, Tuple[builtins.int, ...], List[builtins.int], Size]=0, output_padding: Union[builtins.int, Tuple[builtins.int, ...], List[builtins.int], Size]=0, groups: builtins.int=1, dilation: Union[builtins.int, Tuple[builtins.int, ...], List[builtins.int], Size]=1) -> Tensor: ...
  1238. def conv_transpose3d(input: Tensor, weight: Tensor, bias: Tensor=None, stride: Union[builtins.int, Tuple[builtins.int, ...], List[builtins.int], Size]=1, padding: Union[builtins.int, Tuple[builtins.int, ...], List[builtins.int], Size]=0, output_padding: Union[builtins.int, Tuple[builtins.int, ...], List[builtins.int], Size]=0, groups: builtins.int=1, dilation: Union[builtins.int, Tuple[builtins.int, ...], List[builtins.int], Size]=1) -> Tensor: ...
  1240. def cos(self: Tensor, *, out: Optional[Tensor]=None) -> Tensor: ...
  1242. def cosh(self: Tensor, *, out: Optional[Tensor]=None) -> Tensor: ...
  1244. def cross(self: Tensor, other: Tensor, dim: builtins.int=-1, *, out: Optional[Tensor]=None) -> Tensor: ...
  1246. @overload
  1247. def cumprod(self: Tensor, dim: builtins.int, *, dtype: _dtype, out: Optional[Tensor]=None) -> Tensor: ...
  1249. @overload
  1250. def cumprod(self: Tensor, dim: builtins.int, *, out: Optional[Tensor]=None) -> Tensor: ...
  1252. @overload
  1253. def cumsum(self: Tensor, dim: builtins.int, *, dtype: _dtype, out: Optional[Tensor]=None) -> Tensor: ...
  1255. @overload
  1256. def cumsum(self: Tensor, dim: builtins.int, *, out: Optional[Tensor]=None) -> Tensor: ...
  1258. def det(self: Tensor) -> Tensor: ...
  1260. def diag(self: Tensor, diagonal: builtins.int=0, *, out: Optional[Tensor]=None) -> Tensor: ...
  1262. def diagflat(self: Tensor, offset: builtins.int=0) -> Tensor: ...
  1264. def diagonal(self: Tensor, offset: builtins.int=0, dim1: builtins.int=0, dim2: builtins.int=1) -> Tensor: ...
  1266. def digamma(self: Tensor, *, out: Optional[Tensor]=None) -> Tensor: ...
  1268. def dist(self: Tensor, other: Tensor, p: Union[builtins.float, builtins.int]=2) -> Union[builtins.float, builtins.int]: ...
  1270. @overload
  1271. def div(self: Tensor, other: Tensor, *, out: Optional[Tensor]=None) -> Tensor: ...
  1273. @overload
  1274. def div(self: Tensor, other: Union[builtins.float, builtins.int], *, out: Optional[Tensor]=None) -> Tensor: ...
  1276. def dot(self: Tensor, tensor: Tensor, *, out: Optional[Tensor]=None) -> Tensor: ...
  1278. def eig(self: Tensor, eigenvectors: bool=False, *, out: Optional[Tensor]=None) -> Tuple[Tensor, Tensor]: ...
  1280. def einsum(equation:str, *operands:Tensor) -> Tensor: ...
  1282. @overload
  1283. def empty(size: Union[Tuple[builtins.int, ...], List[builtins.int], Size], *, out: Optional[Tensor]=None, dtype: _dtype=None, layout: layout=strided, device: Optional[device]=None, requires_grad:bool=False) -> Tensor: ...
  1285. @overload
  1286. def empty(*size: builtins.int, out: Optional[Tensor]=None, dtype: _dtype=None, layout: layout=strided, device: Optional[device]=None, requires_grad:bool=False) -> Tensor: ...
  1288. @overload
  1289. def empty_like(self: Tensor) -> Tensor: ...
  1291. @overload
  1292. def empty_like(self: Tensor, *, dtype: _dtype=None, layout: layout=strided, device: Optional[device]=None, requires_grad:bool=False) -> Tensor: ...
  1294. @overload
  1295. def eq(self: Tensor, other: Union[builtins.float, builtins.int], *, out: Optional[Tensor]=None) -> Tensor: ...
  1297. @overload
  1298. def eq(self: Tensor, other: Tensor, *, out: Optional[Tensor]=None) -> Tensor: ...
  1300. def equal(self: Tensor, other: Tensor) -> bool: ...
  1302. def erf(self: Tensor, *, out: Optional[Tensor]=None) -> Tensor: ...
  1304. def erfc(self: Tensor, *, out: Optional[Tensor]=None) -> Tensor: ...
  1306. def erfinv(self: Tensor, *, out: Optional[Tensor]=None) -> Tensor: ...
  1308. def exp(self: Tensor, *, out: Optional[Tensor]=None) -> Tensor: ...
  1310. def expm1(self: Tensor, *, out: Optional[Tensor]=None) -> Tensor: ...
  1312. @overload
  1313. def eye(n: builtins.int, *, out: Optional[Tensor]=None, dtype: _dtype=None, layout: layout=strided, device: Optional[device]=None, requires_grad:bool=False) -> Tensor: ...
  1315. @overload
  1316. def eye(n: builtins.int, m: builtins.int, *, out: Optional[Tensor]=None, dtype: _dtype=None, layout: layout=strided, device: Optional[device]=None, requires_grad:bool=False) -> Tensor: ...
  1318. def fft(self: Tensor, signal_ndim: builtins.int, normalized: bool=False) -> Tensor: ...
  1320. def flatten(self: Tensor, start_dim: builtins.int=0, end_dim: builtins.int=-1) -> Tensor: ...
  1322. def flip(self: Tensor, dims: Union[Tuple[builtins.int, ...], List[builtins.int], Size]) -> Tensor: ...
  1324. def floor(self: Tensor, *, out: Optional[Tensor]=None) -> Tensor: ...
  1326. @overload
  1327. def fmod(self: Tensor, other: Union[builtins.float, builtins.int], *, out: Optional[Tensor]=None) -> Tensor: ...
  1329. @overload
  1330. def fmod(self: Tensor, other: Tensor, *, out: Optional[Tensor]=None) -> Tensor: ...
  1332. def frac(self: Tensor, *, out: Optional[Tensor]=None) -> Tensor: ...
  1334. def from_numpy(ndarray) -> Tensor: ...
  1336. def full(size: Union[Tuple[builtins.int, ...], List[builtins.int], Size], fill_value: Union[builtins.float, builtins.int], *, out: Optional[Tensor]=None, dtype: _dtype=None, layout: layout=strided, device: Optional[device]=None, requires_grad:bool=False) -> Tensor: ...
  1338. @overload
  1339. def full_like(self: Tensor, fill_value: Union[builtins.float, builtins.int]) -> Tensor: ...
  1341. @overload
  1342. def full_like(self: Tensor, fill_value: Union[builtins.float, builtins.int], *, dtype: _dtype=None, layout: layout=strided, device: Optional[device]=None, requires_grad:bool=False) -> Tensor: ...
  1344. def gather(self: Tensor, dim: builtins.int, index: Tensor, *, out: Optional[Tensor]=None) -> Tensor: ...
  1346. @overload
  1347. def ge(self: Tensor, other: Union[builtins.float, builtins.int], *, out: Optional[Tensor]=None) -> Tensor: ...
  1349. @overload
  1350. def ge(self: Tensor, other: Tensor, *, out: Optional[Tensor]=None) -> Tensor: ...
  1352. def gels(self: Tensor, A: Tensor, *, out: Optional[Tensor]=None) -> Tuple[Tensor, Tensor]: ...
  1354. def geqrf(self: Tensor, *, out: Optional[Tensor]=None) -> Tuple[Tensor, Tensor]: ...
  1356. def ger(self: Tensor, vec2: Tensor, *, out: Optional[Tensor]=None) -> Tensor: ...
  1358. def gesv(self: Tensor, A: Tensor, *, out: Optional[Tensor]=None) -> Tuple[Tensor, Tensor]: ...
  1360. def get_default_dtype() -> _dtype: ...
  1362. def get_num_threads() -> builtins.int: ...
  1364. def get_rng_state(): ...
  1366. @overload
  1367. def gt(self: Tensor, other: Union[builtins.float, builtins.int], *, out: Optional[Tensor]=None) -> Tensor: ...
  1369. @overload
  1370. def gt(self: Tensor, other: Tensor, *, out: Optional[Tensor]=None) -> Tensor: ...
  1372. @overload
  1373. def hamming_window(window_length: builtins.int, *, dtype: _dtype=None, layout: layout=strided, device: Optional[device]=None, requires_grad:bool=False) -> Tensor: ...
  1375. @overload
  1376. def hamming_window(window_length: builtins.int, periodic: bool, *, dtype: _dtype=None, layout: layout=strided, device: Optional[device]=None, requires_grad:bool=False) -> Tensor: ...
  1378. @overload
  1379. def hamming_window(window_length: builtins.int, periodic: bool, alpha: builtins.float, *, dtype: _dtype=None, layout: layout=strided, device: Optional[device]=None, requires_grad:bool=False) -> Tensor: ...
  1381. @overload
  1382. def hamming_window(window_length: builtins.int, periodic: bool, alpha: builtins.float, beta: builtins.float, *, dtype: _dtype=None, layout: layout=strided, device: Optional[device]=None, requires_grad:bool=False) -> Tensor: ...
  1384. @overload
  1385. def hann_window(window_length: builtins.int, *, dtype: _dtype=None, layout: layout=strided, device: Optional[device]=None, requires_grad:bool=False) -> Tensor: ...
  1387. @overload
  1388. def hann_window(window_length: builtins.int, periodic: bool, *, dtype: _dtype=None, layout: layout=strided, device: Optional[device]=None, requires_grad:bool=False) -> Tensor: ...
  1390. def histc(self: Tensor, bins: builtins.int=100, min: Union[builtins.float, builtins.int]=0, max: Union[builtins.float, builtins.int]=0, *, out: Optional[Tensor]=None) -> Tensor: ...
  1392. def ifft(self: Tensor, signal_ndim: builtins.int, normalized: bool=False) -> Tensor: ...
  1394. def index_select(self: Tensor, dim: builtins.int, index: Tensor, *, out: Optional[Tensor]=None) -> Tensor: ...
  1396. def initial_seed(): ...
  1398. def inverse(self: Tensor, *, out: Optional[Tensor]=None) -> Tensor: ...
  1400. def irfft(self: Tensor, signal_ndim: builtins.int, normalized: bool=False, onesided: bool=True, signal_sizes: Union[Tuple[builtins.int, ...], List[builtins.int], Size]=()) -> Tensor: ...
  1402. def is_storage(obj): ...
  1404. def is_tensor(obj): ...
  1406. def isfinite(tensor:Tensor) -> Tensor: ...
  1408. def isinf(tensor:Tensor) -> Tensor: ...
  1410. def isnan(tensor:Tensor) -> Tensor: ...
  1412. def kthvalue(self: Tensor, k: builtins.int, dim: builtins.int=-1, keepdim: bool=False, *, out: Optional[Tensor]=None) -> Tuple[Tensor, Tensor]: ...
  1414. @overload
  1415. def le(self: Tensor, other: Union[builtins.float, builtins.int], *, out: Optional[Tensor]=None) -> Tensor: ...
  1417. @overload
  1418. def le(self: Tensor, other: Tensor, *, out: Optional[Tensor]=None) -> Tensor: ...
  1420. def lerp(self: Tensor, end: Tensor, weight: Union[builtins.float, builtins.int], *, out: Optional[Tensor]=None) -> Tensor: ...
  1422. @overload
  1423. def linspace(start: Union[builtins.float, builtins.int], end: Union[builtins.float, builtins.int], *, out: Optional[Tensor]=None, dtype: _dtype=None, layout: layout=strided, device: Optional[device]=None, requires_grad:bool=False) -> Tensor: ...
  1425. @overload
  1426. def linspace(start: Union[builtins.float, builtins.int], end: Union[builtins.float, builtins.int], steps: builtins.int, *, out: Optional[Tensor]=None, dtype: _dtype=None, layout: layout=strided, device: Optional[device]=None, requires_grad:bool=False) -> Tensor: ...
  1428. def load(f, map_location=None, pickle_module=pickle): ...
  1430. def log(self: Tensor, *, out: Optional[Tensor]=None) -> Tensor: ...
  1432. def log10(self: Tensor, *, out: Optional[Tensor]=None) -> Tensor: ...
  1434. def log1p(self: Tensor, *, out: Optional[Tensor]=None) -> Tensor: ...
  1436. def log2(self: Tensor, *, out: Optional[Tensor]=None) -> Tensor: ...
  1438. def logdet(self: Tensor) -> Tensor: ...
  1440. @overload
  1441. def logspace(start: Union[builtins.float, builtins.int], end: Union[builtins.float, builtins.int], *, out: Optional[Tensor]=None, dtype: _dtype=None, layout: layout=strided, device: Optional[device]=None, requires_grad:bool=False) -> Tensor: ...
  1443. @overload
  1444. def logspace(start: Union[builtins.float, builtins.int], end: Union[builtins.float, builtins.int], steps: builtins.int, *, out: Optional[Tensor]=None, dtype: _dtype=None, layout: layout=strided, device: Optional[device]=None, requires_grad:bool=False) -> Tensor: ...
  1446. def logsumexp(self: Tensor, dim: builtins.int, keepdim: bool=False, *, out: Optional[Tensor]=None) -> Tensor: ...
  1448. @overload
  1449. def lt(self: Tensor, other: Union[builtins.float, builtins.int], *, out: Optional[Tensor]=None) -> Tensor: ...
  1451. @overload
  1452. def lt(self: Tensor, other: Tensor, *, out: Optional[Tensor]=None) -> Tensor: ...
  1454. def manual_seed(seed): ...
  1456. def masked_select(self: Tensor, mask: Tensor, *, out: Optional[Tensor]=None) -> Tensor: ...
  1458. def matmul(self: Tensor, other: Tensor, *, out: Optional[Tensor]=None) -> Tensor: ...
  1460. def matrix_power(self: Tensor, n: builtins.int) -> Tensor: ...
  1462. @overload
  1463. def matrix_rank(self: Tensor, tol: builtins.float, symmetric: bool=False) -> Tensor: ...
  1465. @overload
  1466. def matrix_rank(self: Tensor, symmetric: bool=False) -> Tensor: ...
  1468. @overload
  1469. def max(self: Tensor, other: Tensor, *, out: Optional[Tensor]=None) -> Tensor: ...
  1471. @overload
  1472. def max(self: Tensor, *, out: Optional[Tensor]=None) -> Union[builtins.float, builtins.int]: ...
  1474. @overload
  1475. def max(self: Tensor, dim: builtins.int, keepdim: bool=False, *, out: Optional[Tensor]=None) -> Tuple[Tensor, Tensor]: ...
  1477. @overload
  1478. def mean(self: Tensor, *, dtype: _dtype, out: Optional[Tensor]=None) -> Tensor: ...
  1480. @overload
  1481. def mean(self: Tensor, *, out: Optional[Tensor]=None) -> Tensor: ...
  1483. @overload
  1484. def mean(self: Tensor, dim: builtins.int, keepdim: bool, *, dtype: _dtype, out: Optional[Tensor]=None) -> Tensor: ...
  1486. @overload
  1487. def mean(self: Tensor, dim: builtins.int, keepdim: bool=False, *, out: Optional[Tensor]=None) -> Tensor: ...
  1489. @overload
  1490. def mean(self: Tensor, dim: builtins.int, *, dtype: _dtype, out: Optional[Tensor]=None) -> Tensor: ...
  1492. @overload
  1493. def median(self: Tensor, *, out: Optional[Tensor]=None) -> Union[builtins.float, builtins.int]: ...
  1495. @overload
  1496. def median(self: Tensor, dim: builtins.int, keepdim: bool=False, *, out: Optional[Tensor]=None) -> Tuple[Tensor, Tensor]: ...
  1498. def meshgrid(*tensors, **kwargs): ...
  1500. @overload
  1501. def min(self: Tensor, other: Tensor, *, out: Optional[Tensor]=None) -> Tensor: ...
  1503. @overload
  1504. def min(self: Tensor, *, out: Optional[Tensor]=None) -> Union[builtins.float, builtins.int]: ...
  1506. @overload
  1507. def min(self: Tensor, dim: builtins.int, keepdim: bool=False, *, out: Optional[Tensor]=None) -> Tuple[Tensor, Tensor]: ...
  1509. def mm(self: Tensor, mat2: Tensor, *, out: Optional[Tensor]=None) -> Tensor: ...
  1511. def mode(self: Tensor, dim: builtins.int=-1, keepdim: bool=False, *, out: Optional[Tensor]=None) -> Tuple[Tensor, Tensor]: ...
  1513. @overload
  1514. def mul(self: Tensor, other: Tensor, *, out: Optional[Tensor]=None) -> Tensor: ...
  1516. @overload
  1517. def mul(self: Tensor, other: Union[builtins.float, builtins.int], *, out: Optional[Tensor]=None) -> Tensor: ...
  1519. def multinomial(self: Tensor, num_samples: builtins.int, replacement: bool=False, *, generator: Generator=None, out: Optional[Tensor]=None) -> Tensor: ...
  1521. def mv(self: Tensor, vec: Tensor, *, out: Optional[Tensor]=None) -> Tensor: ...
  1523. def mvlgamma(self: Tensor, p: builtins.int) -> Tensor: ...
  1525. def narrow(self: Tensor, dim: builtins.int, start: builtins.int, length: builtins.int) -> Tensor: ...
  1527. @overload
  1528. def ne(self: Tensor, other: Union[builtins.float, builtins.int], *, out: Optional[Tensor]=None) -> Tensor: ...
  1530. @overload
  1531. def ne(self: Tensor, other: Tensor, *, out: Optional[Tensor]=None) -> Tensor: ...
  1533. def neg(self: Tensor, *, out: Optional[Tensor]=None) -> Tensor: ...
  1535. def nonzero(self: Tensor, *, out: Optional[Tensor]=None) -> Tensor: ...
  1537. def norm(input, p='fro', dim=None, keepdim=False, out=None): ...
  1539. @overload
  1540. def normal(mean: Tensor, std: builtins.float=1, *, generator: Generator=None, out: Optional[Tensor]=None) -> Tensor: ...
  1542. @overload
  1543. def normal(mean: builtins.float, std: Tensor, *, generator: Generator=None, out: Optional[Tensor]=None) -> Tensor: ...
  1545. @overload
  1546. def normal(mean: Tensor, std: Tensor, *, generator: Generator=None, out: Optional[Tensor]=None) -> Tensor: ...
  1548. def numel(self: Tensor) -> builtins.int: ...
  1550. @overload
  1551. def ones(size: Union[Tuple[builtins.int, ...], List[builtins.int], Size], *, out: Optional[Tensor]=None, dtype: _dtype=None, layout: layout=strided, device: Optional[device]=None, requires_grad:bool=False) -> Tensor: ...
  1553. @overload
  1554. def ones(*size: builtins.int, out: Optional[Tensor]=None, dtype: _dtype=None, layout: layout=strided, device: Optional[device]=None, requires_grad:bool=False) -> Tensor: ...
  1556. @overload
  1557. def ones_like(self: Tensor) -> Tensor: ...
  1559. @overload
  1560. def ones_like(self: Tensor, *, dtype: _dtype=None, layout: layout=strided, device: Optional[device]=None, requires_grad:bool=False) -> Tensor: ...
  1562. def orgqr(self: Tensor, input2: Tensor, *, out: Optional[Tensor]=None) -> Tensor: ...
  1564. def ormqr(self: Tensor, input2: Tensor, input3: Tensor, left: bool=True, transpose: bool=False, *, out: Optional[Tensor]=None) -> Tensor: ...
  1566. def pdist(self: Tensor, p: builtins.float=2) -> Tensor: ...
  1568. def pinverse(self: Tensor, rcond: builtins.float=1e-15) -> Tensor: ...
  1570. def pixel_shuffle(self: Tensor, upscale_factor: builtins.int) -> Tensor: ...
  1572. def potrf(self: Tensor, upper: bool=True, *, out: Optional[Tensor]=None) -> Tensor: ...
  1574. def potri(self: Tensor, upper: bool=True, *, out: Optional[Tensor]=None) -> Tensor: ...
  1576. def potrs(self: Tensor, input2: Tensor, upper: bool=True, *, out: Optional[Tensor]=None) -> Tensor: ...
  1578. @overload
  1579. def pow(self: Tensor, exponent: Tensor, *, out: Optional[Tensor]=None) -> Tensor: ...
  1581. @overload
  1582. def pow(self: Union[builtins.float, builtins.int], exponent: Tensor, *, out: Optional[Tensor]=None) -> Tensor: ...
  1584. @overload
  1585. def pow(self: Tensor, exponent: Union[builtins.float, builtins.int], *, out: Optional[Tensor]=None) -> Tensor: ...
  1587. @overload
  1588. def prod(self: Tensor, *, dtype: _dtype, out: Optional[Tensor]=None) -> Tensor: ...
  1590. @overload
  1591. def prod(self: Tensor, *, out: Optional[Tensor]=None) -> Tensor: ...
  1593. @overload
  1594. def prod(self: Tensor, dim: builtins.int, keepdim: bool, *, dtype: _dtype, out: Optional[Tensor]=None) -> Tensor: ...
  1596. @overload
  1597. def prod(self: Tensor, dim: builtins.int, keepdim: bool=False, *, out: Optional[Tensor]=None) -> Tensor: ...
  1599. @overload
  1600. def prod(self: Tensor, dim: builtins.int, *, dtype: _dtype, out: Optional[Tensor]=None) -> Tensor: ...
  1602. def pstrf(self: Tensor, upper: bool=True, tol: Union[builtins.float, builtins.int]=-1, *, out: Optional[Tensor]=None) -> Tuple[Tensor, Tensor]: ...
  1604. def qr(self: Tensor, *, out: Optional[Tensor]=None) -> Tuple[Tensor, Tensor]: ...
  1606. @overload
  1607. def rand(size: Union[Tuple[builtins.int, ...], List[builtins.int], Size], *, out: Optional[Tensor]=None, dtype: _dtype=None, layout: layout=strided, device: Optional[device]=None, requires_grad:bool=False) -> Tensor: ...
  1609. @overload
  1610. def rand(*size: builtins.int, out: Optional[Tensor]=None, dtype: _dtype=None, layout: layout=strided, device: Optional[device]=None, requires_grad:bool=False) -> Tensor: ...
  1612. @overload
  1613. def rand(size: Union[Tuple[builtins.int, ...], List[builtins.int], Size], *, generator: Generator, out: Optional[Tensor]=None, dtype: _dtype=None, layout: layout=strided, device: Optional[device]=None, requires_grad:bool=False) -> Tensor: ...
  1615. @overload
  1616. def rand(*size: builtins.int, generator: Generator, out: Optional[Tensor]=None, dtype: _dtype=None, layout: layout=strided, device: Optional[device]=None, requires_grad:bool=False) -> Tensor: ...
  1618. @overload
  1619. def rand_like(self: Tensor) -> Tensor: ...
  1621. @overload
  1622. def rand_like(self: Tensor, *, dtype: _dtype=None, layout: layout=strided, device: Optional[device]=None, requires_grad:bool=False) -> Tensor: ...
  1624. @overload
  1625. def randint(high: builtins.int, size: Union[Tuple[builtins.int, ...], List[builtins.int], Size], *, out: Optional[Tensor]=None, dtype: _dtype=None, layout: layout=strided, device: Optional[device]=None, requires_grad:bool=False) -> Tensor: ...
  1627. @overload
  1628. def randint(high: builtins.int, size: Union[Tuple[builtins.int, ...], List[builtins.int], Size], *, generator: Generator, out: Optional[Tensor]=None, dtype: _dtype=None, layout: layout=strided, device: Optional[device]=None, requires_grad:bool=False) -> Tensor: ...
  1630. @overload
  1631. def randint(low: builtins.int, high: builtins.int, size: Union[Tuple[builtins.int, ...], List[builtins.int], Size], *, out: Optional[Tensor]=None, dtype: _dtype=None, layout: layout=strided, device: Optional[device]=None, requires_grad:bool=False) -> Tensor: ...
  1633. @overload
  1634. def randint(low: builtins.int, high: builtins.int, size: Union[Tuple[builtins.int, ...], List[builtins.int], Size], *, generator: Generator, out: Optional[Tensor]=None, dtype: _dtype=None, layout: layout=strided, device: Optional[device]=None, requires_grad:bool=False) -> Tensor: ...
  1636. @overload
  1637. def randint_like(self: Tensor, high: builtins.int) -> Tensor: ...
  1639. @overload
  1640. def randint_like(self: Tensor, low: builtins.int, high: builtins.int) -> Tensor: ...
  1642. @overload
  1643. def randint_like(self: Tensor, high: builtins.int, *, dtype: _dtype=None, layout: layout=strided, device: Optional[device]=None, requires_grad:bool=False) -> Tensor: ...
  1645. @overload
  1646. def randint_like(self: Tensor, low: builtins.int, high: builtins.int, *, dtype: _dtype=None, layout: layout=strided, device: Optional[device]=None, requires_grad:bool=False) -> Tensor: ...
  1648. @overload
  1649. def randn(size: Union[Tuple[builtins.int, ...], List[builtins.int], Size], *, out: Optional[Tensor]=None, dtype: _dtype=None, layout: layout=strided, device: Optional[device]=None, requires_grad:bool=False) -> Tensor: ...
  1651. @overload
  1652. def randn(*size: builtins.int, out: Optional[Tensor]=None, dtype: _dtype=None, layout: layout=strided, device: Optional[device]=None, requires_grad:bool=False) -> Tensor: ...
  1654. @overload
  1655. def randn(size: Union[Tuple[builtins.int, ...], List[builtins.int], Size], *, generator: Generator, out: Optional[Tensor]=None, dtype: _dtype=None, layout: layout=strided, device: Optional[device]=None, requires_grad:bool=False) -> Tensor: ...
  1657. @overload
  1658. def randn(*size: builtins.int, generator: Generator, out: Optional[Tensor]=None, dtype: _dtype=None, layout: layout=strided, device: Optional[device]=None, requires_grad:bool=False) -> Tensor: ...
  1660. @overload
  1661. def randn_like(self: Tensor) -> Tensor: ...
  1663. @overload
  1664. def randn_like(self: Tensor, *, dtype: _dtype=None, layout: layout=strided, device: Optional[device]=None, requires_grad:bool=False) -> Tensor: ...
  1666. @overload
  1667. def randperm(n: builtins.int, *, out: Optional[Tensor]=None, dtype: _dtype=None, layout: layout=strided, device: Optional[device]=None, requires_grad:bool=False) -> Tensor: ...
  1669. @overload
  1670. def randperm(n: builtins.int, *, generator: Generator, out: Optional[Tensor]=None, dtype: _dtype=None, layout: layout=strided, device: Optional[device]=None, requires_grad:bool=False) -> Tensor: ...
  1672. @overload
  1673. def range(start: Union[builtins.float, builtins.int], end: Union[builtins.float, builtins.int], *, out: Optional[Tensor]=None, dtype: _dtype=None, layout: layout=strided, device: Optional[device]=None, requires_grad:bool=False) -> Tensor: ...
  1675. @overload
  1676. def range(start: Union[builtins.float, builtins.int], end: Union[builtins.float, builtins.int], step: Union[builtins.float, builtins.int], *, out: Optional[Tensor]=None, dtype: _dtype=None, layout: layout=strided, device: Optional[device]=None, requires_grad:bool=False) -> Tensor: ...
  1678. def reciprocal(self: Tensor, *, out: Optional[Tensor]=None) -> Tensor: ...
  1680. def relu_(self: Tensor) -> Tensor: ...
  1682. @overload
  1683. def remainder(self: Tensor, other: Union[builtins.float, builtins.int], *, out: Optional[Tensor]=None) -> Tensor: ...
  1685. @overload
  1686. def remainder(self: Tensor, other: Tensor, *, out: Optional[Tensor]=None) -> Tensor: ...
  1688. def renorm(self: Tensor, p: Union[builtins.float, builtins.int], dim: builtins.int, maxnorm: Union[builtins.float, builtins.int], *, out: Optional[Tensor]=None) -> Tensor: ...
  1690. def reshape(self: Tensor, shape: Union[Tuple[builtins.int, ...], List[builtins.int], Size]) -> Tensor: ...
  1692. def rfft(self: Tensor, signal_ndim: builtins.int, normalized: bool=False, onesided: bool=True) -> Tensor: ...
  1694. def rot90(self: Tensor, k: builtins.int=1, dims: Union[Tuple[builtins.int, ...], List[builtins.int], Size]=(0,1)) -> Tensor: ...
  1696. def round(self: Tensor, *, out: Optional[Tensor]=None) -> Tensor: ...
  1698. def rrelu_(self: Tensor, lower: Union[builtins.float, builtins.int]=0.125, upper: Union[builtins.float, builtins.int]=0.3333333333333333, training: bool=False, generator: Generator=None) -> Tensor: ...
  1700. def rsqrt(self: Tensor, *, out: Optional[Tensor]=None) -> Tensor: ...
  1702. def save(obj, f, pickle_module=pickle, pickle_protocol=2): ...
  1704. def selu_(self: Tensor) -> Tensor: ...
  1706. def set_default_dtype(d): ...
  1708. def set_default_tensor_type(t): ...
  1710. def set_flush_denormal(mode: bool) -> bool: ...
  1712. def set_num_threads(num: builtins.int) -> None: ...
  1714. def set_printoptions(precision=None, threshold=None, edgeitems=None, linewidth=None, profile=None): ...
  1716. def set_rng_state(new_state): ...
  1718. def sigmoid(self: Tensor, *, out: Optional[Tensor]=None) -> Tensor: ...
  1720. def sign(self: Tensor, *, out: Optional[Tensor]=None) -> Tensor: ...
  1722. def sin(self: Tensor, *, out: Optional[Tensor]=None) -> Tensor: ...
  1724. def sinh(self: Tensor, *, out: Optional[Tensor]=None) -> Tensor: ...
  1726. def slogdet(self: Tensor) -> Tuple[Tensor, Tensor]: ...
  1728. def sort(self: Tensor, dim: builtins.int=-1, descending: bool=False, *, out: Optional[Tensor]=None) -> Tuple[Tensor, Tensor]: ...
  1730. @overload
  1731. def sparse_coo_tensor(indices: Tensor, values: Tensor) -> Tensor: ...
  1733. @overload
  1734. def sparse_coo_tensor(indices: Tensor, values: Tensor, size: Union[Tuple[builtins.int, ...], List[builtins.int], Size]) -> Tensor: ...
  1736. @overload
  1737. def sparse_coo_tensor(size: Union[Tuple[builtins.int, ...], List[builtins.int], Size], *, dtype: _dtype=None, layout: layout=strided, device: Optional[device]=None, requires_grad:bool=False) -> Tensor: ...
  1739. @overload
  1740. def sparse_coo_tensor(*size: builtins.int, dtype: _dtype=None, layout: layout=strided, device: Optional[device]=None, requires_grad:bool=False) -> Tensor: ...
  1742. @overload
  1743. def sparse_coo_tensor(indices: Tensor, values: Tensor, *, dtype: _dtype=None, layout: layout=strided, device: Optional[device]=None, requires_grad:bool=False) -> Tensor: ...
  1745. @overload
  1746. def sparse_coo_tensor(indices: Tensor, values: Tensor, size: Union[Tuple[builtins.int, ...], List[builtins.int], Size], *, dtype: _dtype=None, layout: layout=strided, device: Optional[device]=None, requires_grad:bool=False) -> Tensor: ...
  1748. def split(tensor:Tensor, split_size_or_sections:Union[List[builtins.int], builtins.int], dim:builtins.int=0) -> List[Tensor]: ...
  1750. def sqrt(self: Tensor, *, out: Optional[Tensor]=None) -> Tensor: ...
  1752. @overload
  1753. def squeeze(self: Tensor) -> Tensor: ...
  1755. @overload
  1756. def squeeze(self: Tensor, dim: builtins.int) -> Tensor: ...
  1758. @overload
  1759. def sspaddmm(beta: Union[builtins.float, builtins.int], self: Tensor, alpha: Union[builtins.float, builtins.int], mat1: Tensor, mat2: Tensor) -> Tensor: ...
  1761. @overload
  1762. def sspaddmm(beta: Union[builtins.float, builtins.int], self: Tensor, mat1: Tensor, mat2: Tensor) -> Tensor: ...
  1764. def stack(tensors: Union[Tuple[Tensor, ...],List[Tensor]], dim: builtins.int=0, *, out: Optional[Tensor]=None) -> Tensor: ...
  1766. @overload
  1767. def std(self: Tensor, unbiased: bool=True, *, out: Optional[Tensor]=None) -> Tensor: ...
  1769. @overload
  1770. def std(self: Tensor, dim: builtins.int, unbiased: bool=True, keepdim: bool=False, *, out: Optional[Tensor]=None) -> Tensor: ...
  1772. def stft(input, n_fft, hop_length=None, win_length=None, window=None, center=True, pad_mode='reflect', normalized=False, onesided=True): ...
  1774. @overload
  1775. def sub(self: Tensor, alpha: Union[builtins.float, builtins.int], other: Tensor) -> Tensor: ...
  1777. @overload
  1778. def sub(self: Tensor, alpha: Union[builtins.float, builtins.int], other: Tensor, *, out: Tensor) -> Tensor: ...
  1780. @overload
  1781. def sum(self: Tensor, *, dtype: _dtype, out: Optional[Tensor]=None) -> Tensor: ...
  1783. @overload
  1784. def sum(self: Tensor, *, out: Optional[Tensor]=None) -> Tensor: ...
  1786. @overload
  1787. def sum(self: Tensor, dim: Union[builtins.int, Tuple[builtins.int, ...], List[builtins.int], Size], keepdim: bool, *, dtype: _dtype, out: Optional[Tensor]=None) -> Tensor: ...
  1789. @overload
  1790. def sum(self: Tensor, dim: Union[builtins.int, Tuple[builtins.int, ...], List[builtins.int], Size], keepdim: bool=False, *, out: Optional[Tensor]=None) -> Tensor: ...
  1792. @overload
  1793. def sum(self: Tensor, dim: Union[builtins.int, Tuple[builtins.int, ...], List[builtins.int], Size], *, dtype: _dtype, out: Optional[Tensor]=None) -> Tensor: ...
  1795. def svd(self: Tensor, some: bool=True, compute_uv: bool=True, *, out: Optional[Tensor]=None) -> Tuple[Tensor, Tensor, Tensor]: ...
  1797. def symeig(self: Tensor, eigenvectors: bool=False, upper: bool=True, *, out: Optional[Tensor]=None) -> Tuple[Tensor, Tensor]: ...
  1799. def t(self: Tensor) -> Tensor: ...
  1801. def take(self: Tensor, index: Tensor, *, out: Optional[Tensor]=None) -> Tensor: ...
  1803. def tan(self: Tensor, *, out: Optional[Tensor]=None) -> Tensor: ...
  1805. def tanh(self: Tensor, *, out: Optional[Tensor]=None) -> Tensor: ...
  1807. @overload
  1808. def tensor(data: Any, dtype: Optional[_dtype]=None, device: Union[device, str, None]=None, requires_grad: bool=False) -> Tensor: ...
  1810. @overload
  1811. def tensor(storage: Storage, storageOffset: builtins.int, size: Union[Tuple[builtins.int, ...], List[builtins.int], Size], stride: Union[Tuple[builtins.int, ...], List[builtins.int], Size]=()) -> Tensor: ...
  1813. @overload
  1814. def tensor(size: Union[Tuple[builtins.int, ...], List[builtins.int], Size], stride: Union[Tuple[builtins.int, ...], List[builtins.int], Size]) -> Tensor: ...
  1816. def tensordot(a:Tensor, b:Tensor, dims=2) -> Tensor: ...
  1818. def topk(self: Tensor, k: builtins.int, dim: builtins.int=-1, largest: bool=True, sorted: bool=True, *, out: Optional[Tensor]=None) -> Tuple[Tensor, Tensor]: ...
  1820. def trace(self: Tensor) -> Union[builtins.float, builtins.int]: ...
  1822. def transpose(self: Tensor, dim0: builtins.int, dim1: builtins.int) -> Tensor: ...
  1824. def tril(self: Tensor, diagonal: builtins.int=0, *, out: Optional[Tensor]=None) -> Tensor: ...
  1826. def triu(self: Tensor, diagonal: builtins.int=0, *, out: Optional[Tensor]=None) -> Tensor: ...
  1828. def trtrs(self: Tensor, A: Tensor, upper: bool=True, transpose: bool=False, unitriangular: bool=False, *, out: Optional[Tensor]=None) -> Tuple[Tensor, Tensor]: ...
  1830. def trunc(self: Tensor, *, out: Optional[Tensor]=None) -> Tensor: ...
  1832. def unbind(self: Tensor, dim: builtins.int=0) -> Union[Tuple[Tensor, ...],List[Tensor]]: ...
  1834. def unique(input, sorted=False, return_inverse=False, dim=None): ...
  1836. def unsqueeze(self: Tensor, dim: builtins.int) -> Tensor: ...
  1838. @overload
  1839. def var(self: Tensor, unbiased: bool=True, *, out: Optional[Tensor]=None) -> Tensor: ...
  1841. @overload
  1842. def var(self: Tensor, dim: builtins.int, unbiased: bool=True, keepdim: bool=False, *, out: Optional[Tensor]=None) -> Tensor: ...
  1844. def where(condition: Tensor, self: Tensor, other: Tensor) -> Tensor: ...
  1846. @overload
  1847. def zeros(size: Union[Tuple[builtins.int, ...], List[builtins.int], Size], *, out: Optional[Tensor]=None, dtype: _dtype=None, layout: layout=strided, device: Optional[device]=None, requires_grad:bool=False) -> Tensor: ...
  1849. @overload
  1850. def zeros(*size: builtins.int, out: Optional[Tensor]=None, dtype: _dtype=None, layout: layout=strided, device: Optional[device]=None, requires_grad:bool=False) -> Tensor: ...
  1852. @overload
  1853. def zeros_like(self: Tensor) -> Tensor: ...
  1855. @overload
  1856. def zeros_like(self: Tensor, *, dtype: _dtype=None, layout: layout=strided, device: Optional[device]=None, requires_grad:bool=False) -> Tensor: ...
  1860. class DoubleStorage(Storage): ...
  1862. class FloatStorage(Storage): ...
  1864. class LongStorage(Storage): ...
  1866. class IntStorage(Storage): ...
  1868. class ShortStorage(Storage): ...
  1870. class CharStorage(Storage): ...
  1872. class ByteStorage(Storage): ...
  1874. class DoubleTensor(Tensor): ...
  1876. class FloatTensor(Tensor): ...
  1878. class LongTensor(Tensor): ...
  1880. class IntTensor(Tensor): ...
  1882. class ShortTensor(Tensor): ...
  1884. class CharTensor(Tensor): ...
  1886. class ByteTensor(Tensor): ...
  1888. complex128: dtype = ...
  1889. complex32: dtype = ...
  1890. complex64: dtype = ...
  1891. double: dtype = ...
  1892. float: dtype = ...
  1893. float16: dtype = ...
  1894. float32: dtype = ...
  1895. float64: dtype = ...
  1896. half: dtype = ...
  1897. int: dtype = ...
  1898. int16: dtype = ...
  1899. int32: dtype = ...
  1900. int64: dtype = ...
  1901. int8: dtype = ...
  1902. long: dtype = ...
  1903. short: dtype = ...
  1904. uint8: dtype = ...