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t-vi/__init__.pyi

Last active July 13, 2023 09:11
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PyTorch Type Hints work in progress (put into python3.x/dist-packages/torch/ directory to try)
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__init__.pyi
from typing import List, Tuple, Optional, Union, Any, ContextManager, Callable, overload
import builtins
import math
import pickle
class dtype: ...
_dtype = dtype
class layout: ...
strided : layout = ...
class device:
def __init__(self, device: Union[device, str, None]=None) -> None: ...
class Generator: ...
class Size(tuple): ...
class Storage: ...
class enable_grad():
def __enter__(self) -> None: ...
def __exit__(self, *args) -> None: ...
def __call__(self, func : Callable) -> Callable: ...
class no_grad():
def __enter__(self) -> None: ...
def __exit__(self, *args) -> None: ...
def __call__(self, func : Callable) -> Callable: ...
class set_grad_enabled():
def __init__(self, mode: bool) -> None: ...
def __enter__(self) -> None: ...
def __exit__(self, *args) -> None: ...
class Tensor:
dtype: _dtype = ...
shape: Size = ...
requires_grad: bool = ...
grad: Optional['Tensor'] = ...
def __abs__(self) -> 'Tensor': ...
def __add__(self, other: Any) -> 'Tensor': ...
def __and__(self, other: Any) -> 'Tensor': ...
def __array__(self, dtype=None): ...
def __array_wrap__(self, array): ...
def __bool__(self) -> bool: ...
def __deepcopy__(self, memo): ...
def __dir__(self): ...
def __div__(self, other: Any) -> 'Tensor': ...
def __eq__(self, other: Any) -> 'Tensor': ... # type: ignore
def __float__(self) -> builtins.float: ...
def __floordiv__(self, other): ...
def __format__(self, format_spec): ...
def __ge__(self, other: Any) -> 'Tensor': ... # type: ignore
def __getitem__(self, indices: Union[None, builtins.int, slice, 'Tensor', List, Tuple]) -> 'Tensor': ...
def __gt__(self, other: Any) -> 'Tensor': ... # type: ignore
def __hash__(self): ...
def __iadd__(self, other: Any) -> 'Tensor': ...
def __iand__(self, other: Any) -> 'Tensor': ...
def __idiv__(self, other: Any) -> 'Tensor': ...
def __ilshift__(self, other: Any) -> 'Tensor': ...
def __imul__(self, other: Any) -> 'Tensor': ...
def __index__(self) -> builtins.int: ...
def __int__(self) -> builtins.int: ...
def __invert__(self) -> 'Tensor': ...
def __ior__(self, other: Any) -> 'Tensor': ...
def __ipow__(self, other): ...
def __irshift__(self, other: Any) -> 'Tensor': ...
def __isub__(self, other: Any) -> 'Tensor': ...
def __iter__(self): ...
def __itruediv__(self, other: Any) -> 'Tensor': ...
def __ixor__(self, other: Any) -> 'Tensor': ...
def __le__(self, other: Any) -> 'Tensor': ... # type: ignore
def __len__(self): ...
def __long__(self) -> builtins.int: ...
def __lshift__(self, other: Any) -> 'Tensor': ...
def __lt__(self, other: Any) -> 'Tensor': ... # type: ignore
def __matmul__(self, other: Any) -> 'Tensor': ...
def __mod__(self, other: Any) -> 'Tensor': ...
def __mul__(self, other: Any) -> 'Tensor': ...
def __ne__(self, other: Any) -> 'Tensor': ... # type: ignore
def __neg__(self) -> 'Tensor': ...
def __nonzero__(self) -> bool: ...
def __or__(self, other: Any) -> 'Tensor': ...
def __pow__(self, other: Any) -> 'Tensor': ...
def __radd__(self, other: Any) -> 'Tensor': ...
def __rdiv__(self, other): ...
def __reduce_ex__(self, proto): ...
def __repr__(self): ...
def __reversed__(self): ...
def __rfloordiv__(self, other): ...
def __rmul__(self, other: Any) -> 'Tensor': ...
def __rpow__(self, other): ...
def __rshift__(self, other: Any) -> 'Tensor': ...
def __rsub__(self, other): ...
def __rtruediv__(self, other): ...
def __setitem__(self, indices: Union[None, builtins.int, slice, 'Tensor', List, Tuple], val: Union['Tensor', builtins.float, builtins.int]) -> None: ...
def __setstate__(self, state): ...
def __sub__(self, other: Any) -> 'Tensor': ...
def __truediv__(self, other: Any) -> 'Tensor': ...
def __xor__(self, other: Any) -> 'Tensor': ...
def abs(self, *, out: Optional['Tensor']=None) -> 'Tensor': ...
def abs_(self) -> 'Tensor': ...
def acos(self, *, out: Optional['Tensor']=None) -> 'Tensor': ...
def acos_(self) -> 'Tensor': ...
@overload
def add(self, other: 'Tensor', *, alpha: Union[builtins.float, builtins.int]=1, out: Optional['Tensor']=None) -> 'Tensor': ...
@overload
def add(self, other: Union[builtins.float, builtins.int], alpha: Union[builtins.float, builtins.int]=1, *, out: Optional['Tensor']=None) -> 'Tensor': ...
@overload
def add_(self, other: 'Tensor', *, alpha: Union[builtins.float, builtins.int]=1) -> 'Tensor': ...
@overload
def add_(self, other: Union[builtins.float, builtins.int], alpha: Union[builtins.float, builtins.int]=1) -> 'Tensor': ...
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': ...
def addbmm_(self, batch1: 'Tensor', batch2: 'Tensor', *, beta: Union[builtins.float, builtins.int]=1, alpha: Union[builtins.float, builtins.int]=1) -> 'Tensor': ...
def addcdiv(self, tensor1: 'Tensor', tensor2: 'Tensor', *, value: Union[builtins.float, builtins.int]=1, out: Optional['Tensor']=None) -> 'Tensor': ...
def addcdiv_(self, tensor1: 'Tensor', tensor2: 'Tensor', *, value: Union[builtins.float, builtins.int]=1) -> 'Tensor': ...
def addcmul(self, tensor1: 'Tensor', tensor2: 'Tensor', *, value: Union[builtins.float, builtins.int]=1, out: Optional['Tensor']=None) -> 'Tensor': ...
def addcmul_(self, tensor1: 'Tensor', tensor2: 'Tensor', *, value: Union[builtins.float, builtins.int]=1) -> 'Tensor': ...
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': ...
def addmm_(self, mat1: 'Tensor', mat2: 'Tensor', *, beta: Union[builtins.float, builtins.int]=1, alpha: Union[builtins.float, builtins.int]=1) -> 'Tensor': ...
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': ...
def addmv_(self, mat: 'Tensor', vec: 'Tensor', *, beta: Union[builtins.float, builtins.int]=1, alpha: Union[builtins.float, builtins.int]=1) -> 'Tensor': ...
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': ...
def addr_(self, vec1: 'Tensor', vec2: 'Tensor', *, beta: Union[builtins.float, builtins.int]=1, alpha: Union[builtins.float, builtins.int]=1) -> 'Tensor': ...
@overload
def all(self, *, out: Optional['Tensor']=None) -> Union[builtins.float, builtins.int]: ...
@overload
def all(self, dim: builtins.int, keepdim: bool=False, *, out: Optional['Tensor']=None) -> 'Tensor': ...
def allclose(self, other: 'Tensor', rtol: builtins.float=1e-05, atol: builtins.float=1e-08, equal_nan: bool=False) -> bool: ...
@overload
def any(self, *, out: Optional['Tensor']=None) -> Union[builtins.float, builtins.int]: ...
@overload
def any(self, dim: builtins.int, keepdim: bool=False, *, out: Optional['Tensor']=None) -> 'Tensor': ...
def apply_(self, callable: Callable) -> 'Tensor': ...
def argmax(self, dim=None, keepdim=False): ...
def argmin(self, dim=None, keepdim=False): ...
def argsort(self, dim=None, descending=False): ...
def asin(self, *, out: Optional['Tensor']=None) -> 'Tensor': ...
def asin_(self) -> 'Tensor': ...
def atan(self, *, out: Optional['Tensor']=None) -> 'Tensor': ...
def atan2(self, other: 'Tensor', *, out: Optional['Tensor']=None) -> 'Tensor': ...
def atan2_(self, other: 'Tensor') -> 'Tensor': ...
def atan_(self) -> 'Tensor': ...
def backward(self, gradient=None, retain_graph=None, create_graph=False): ...
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': ...
def baddbmm_(self, batch1: 'Tensor', batch2: 'Tensor', *, beta: Union[builtins.float, builtins.int]=1, alpha: Union[builtins.float, builtins.int]=1) -> 'Tensor': ...
@overload
def bernoulli(self, *, generator: Generator=None, out: Optional['Tensor']=None) -> 'Tensor': ...
@overload
def bernoulli(self, p: builtins.float, *, generator: Generator=None, out: Optional['Tensor']=None) -> 'Tensor': ...
@overload
def bernoulli_(self, p: 'Tensor', *, generator: Generator=None) -> 'Tensor': ...
@overload
def bernoulli_(self, p: builtins.float=0.5, *, generator: Generator=None) -> 'Tensor': ...
def bincount(self, weights: Optional['Tensor']=None, minlength: builtins.int=0) -> 'Tensor': ...
def bmm(self, mat2: 'Tensor', *, out: Optional['Tensor']=None) -> 'Tensor': ...
def btrifact(self, info=None, pivot=True): ...
def btrifact_with_info(self, *, pivot: bool=True, out: Optional['Tensor']=None) -> Tuple['Tensor', 'Tensor', 'Tensor']: ...
def btrisolve(self, LU_data: 'Tensor', LU_pivots: 'Tensor', *, out: Optional['Tensor']=None) -> 'Tensor': ...
def byte(self) -> 'Tensor': ...
def cauchy_(self, median: builtins.float=0, sigma: builtins.float=1, *, generator: Generator=None) -> 'Tensor': ...
def ceil(self, *, out: Optional['Tensor']=None) -> 'Tensor': ...
def ceil_(self) -> 'Tensor': ...
def char(self) -> 'Tensor': ...
def chunk(self, chunks: builtins.int, dim: builtins.int=0) -> Union[Tuple['Tensor', ...],List['Tensor']]: ...
def clamp(self, min: builtins.float=-math.inf, max: builtins.float =math.inf, *, out: Optional['Tensor']=None) -> 'Tensor': ...
def clamp_(self, min: builtins.float=-math.inf, max: builtins.float =math.inf) -> 'Tensor': ...
def clone(self) -> 'Tensor': ...
def contiguous(self) -> 'Tensor': ...
def copy_(self, src: 'Tensor', non_blocking: bool=False) -> 'Tensor': ...
def cos(self, *, out: Optional['Tensor']=None) -> 'Tensor': ...
def cos_(self) -> 'Tensor': ...
def cosh(self, *, out: Optional['Tensor']=None) -> 'Tensor': ...
def cosh_(self) -> 'Tensor': ...
def cpu(self) -> 'Tensor': ...
def cross(self, other: 'Tensor', dim: builtins.int=-1, *, out: Optional['Tensor']=None) -> 'Tensor': ...
def cuda(self, device: Optional[device]=None, non_blocking: bool=False) -> 'Tensor': ...
@overload
def cumprod(self, dim: builtins.int, *, dtype: _dtype, out: Optional['Tensor']=None) -> 'Tensor': ...
@overload
def cumprod(self, dim: builtins.int, *, out: Optional['Tensor']=None) -> 'Tensor': ...
@overload
def cumsum(self, dim: builtins.int, *, dtype: _dtype, out: Optional['Tensor']=None) -> 'Tensor': ...
@overload
def cumsum(self, dim: builtins.int, *, out: Optional['Tensor']=None) -> 'Tensor': ...
def data_ptr(self) -> builtins.int: ...
def det(self) -> 'Tensor': ...
def detach(self) -> 'Tensor': ...
def detach_(self) -> 'Tensor': ...
def diag(self, diagonal: builtins.int=0, *, out: Optional['Tensor']=None) -> 'Tensor': ...
def diagflat(self, offset: builtins.int=0) -> 'Tensor': ...
def diagonal(self, offset: builtins.int=0, dim1: builtins.int=0, dim2: builtins.int=1) -> 'Tensor': ...
def digamma(self, *, out: Optional['Tensor']=None) -> 'Tensor': ...
def digamma_(self) -> 'Tensor': ...
def dim(self) -> builtins.int: ...
def dist(self, other: 'Tensor', p: Union[builtins.float, builtins.int]=2) -> Union[builtins.float, builtins.int]: ...
@overload
def div(self, other: 'Tensor', *, out: Optional['Tensor']=None) -> 'Tensor': ...
@overload
def div(self, other: Union[builtins.float, builtins.int], *, out: Optional['Tensor']=None) -> 'Tensor': ...
@overload
def div_(self, other: 'Tensor') -> 'Tensor': ...
@overload
def div_(self, other: Union[builtins.float, builtins.int]) -> 'Tensor': ...
def dot(self, tensor: 'Tensor', *, out: Optional['Tensor']=None) -> 'Tensor': ...
def double(self) -> 'Tensor': ...
def eig(self, eigenvectors: bool=False, *, out: Optional['Tensor']=None) -> Tuple['Tensor', 'Tensor']: ...
def element_size(self) -> builtins.int: ...
@overload
def eq(self, other: Union[builtins.float, builtins.int], *, out: Optional['Tensor']=None) -> 'Tensor': ...
@overload
def eq(self, other: 'Tensor', *, out: Optional['Tensor']=None) -> 'Tensor': ...
@overload
def eq_(self, other: Union[builtins.float, builtins.int]) -> 'Tensor': ...
@overload
def eq_(self, other: 'Tensor') -> 'Tensor': ...
def equal(self, other: 'Tensor') -> bool: ...
def erf(self, *, out: Optional['Tensor']=None) -> 'Tensor': ...
def erf_(self) -> 'Tensor': ...
def erfc(self, *, out: Optional['Tensor']=None) -> 'Tensor': ...
def erfc_(self) -> 'Tensor': ...
def erfinv(self, *, out: Optional['Tensor']=None) -> 'Tensor': ...
def erfinv_(self) -> 'Tensor': ...
def exp(self, *, out: Optional['Tensor']=None) -> 'Tensor': ...
def exp_(self) -> 'Tensor': ...
@overload
def expand(self, size: Union[Tuple[builtins.int, ...], List[builtins.int], Size], *, implicit: bool=False) -> 'Tensor': ...
@overload
def expand(self, *size: builtins.int, implicit: bool=False) -> 'Tensor': ...
def expand_as(self, other: 'Tensor') -> 'Tensor': ...
def expm1(self, *, out: Optional['Tensor']=None) -> 'Tensor': ...
def expm1_(self) -> 'Tensor': ...
def exponential_(self, lambd: builtins.float=1, *, generator: Generator=None) -> 'Tensor': ...
def fft(self, signal_ndim: builtins.int, normalized: bool=False) -> 'Tensor': ...
@overload
def fill_(self, value: Union[builtins.float, builtins.int]) -> 'Tensor': ...
@overload
def fill_(self, value: 'Tensor') -> 'Tensor': ...
def flatten(self, start_dim: builtins.int=0, end_dim: builtins.int=-1) -> 'Tensor': ...
@overload
def flip(self, dims: Union[Tuple[builtins.int, ...], List[builtins.int], Size]) -> 'Tensor': ...
@overload
def flip(self, *dims: builtins.int) -> 'Tensor': ...
def float(self) -> 'Tensor': ...
def floor(self, *, out: Optional['Tensor']=None) -> 'Tensor': ...
def floor_(self) -> 'Tensor': ...
@overload
def fmod(self, other: Union[builtins.float, builtins.int], *, out: Optional['Tensor']=None) -> 'Tensor': ...
@overload
def fmod(self, other: 'Tensor', *, out: Optional['Tensor']=None) -> 'Tensor': ...
@overload
def fmod_(self, other: Union[builtins.float, builtins.int]) -> 'Tensor': ...
@overload
def fmod_(self, other: 'Tensor') -> 'Tensor': ...
def frac(self, *, out: Optional['Tensor']=None) -> 'Tensor': ...
def frac_(self) -> 'Tensor': ...
def gather(self, dim: builtins.int, index: 'Tensor', *, out: Optional['Tensor']=None) -> 'Tensor': ...
@overload
def ge(self, other: Union[builtins.float, builtins.int], *, out: Optional['Tensor']=None) -> 'Tensor': ...
@overload
def ge(self, other: 'Tensor', *, out: Optional['Tensor']=None) -> 'Tensor': ...
@overload
def ge_(self, other: Union[builtins.float, builtins.int]) -> 'Tensor': ...
@overload
def ge_(self, other: 'Tensor') -> 'Tensor': ...
def gels(self, A: 'Tensor', *, out: Optional['Tensor']=None) -> Tuple['Tensor', 'Tensor']: ...
def geometric_(self, p: builtins.float, *, generator: Generator=None) -> 'Tensor': ...
def geqrf(self, *, out: Optional['Tensor']=None) -> Tuple['Tensor', 'Tensor']: ...
def ger(self, vec2: 'Tensor', *, out: Optional['Tensor']=None) -> 'Tensor': ...
def gesv(self, A: 'Tensor', *, out: Optional['Tensor']=None) -> Tuple['Tensor', 'Tensor']: ...
def get_device(self) -> builtins.int: ...
@overload
def gt(self, other: Union[builtins.float, builtins.int], *, out: Optional['Tensor']=None) -> 'Tensor': ...
@overload
def gt(self, other: 'Tensor', *, out: Optional['Tensor']=None) -> 'Tensor': ...
@overload
def gt_(self, other: Union[builtins.float, builtins.int]) -> 'Tensor': ...
@overload
def gt_(self, other: 'Tensor') -> 'Tensor': ...
def half(self) -> 'Tensor': ...
def hardshrink(self, lambd: Union[builtins.float, builtins.int]=0.5) -> 'Tensor': ...
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': ...
def ifft(self, signal_ndim: builtins.int, normalized: bool=False) -> 'Tensor': ...
def index_add(self, dim, index, tensor): ...
def index_add_(self, dim: builtins.int, index: 'Tensor', source: 'Tensor') -> 'Tensor': ...
def index_copy(self, dim, index, tensor): ...
def index_copy_(self, dim: builtins.int, index: 'Tensor', source: 'Tensor') -> 'Tensor': ...
def index_fill(self, dim, index, value): ...
@overload
def index_fill_(self, dim: builtins.int, index: 'Tensor', value: Union[builtins.float, builtins.int]) -> 'Tensor': ...
@overload
def index_fill_(self, dim: builtins.int, index: 'Tensor', value: 'Tensor') -> 'Tensor': ...
def index_put_(self, indices: Union[Tuple['Tensor', ...],List['Tensor']], values: 'Tensor') -> 'Tensor': ...
def index_select(self, dim: builtins.int, index: 'Tensor', *, out: Optional['Tensor']=None) -> 'Tensor': ...
def int(self) -> 'Tensor': ...
def inverse(self, *, out: Optional['Tensor']=None) -> 'Tensor': ...
def irfft(self, signal_ndim: builtins.int, normalized: bool=False, onesided: bool=True, signal_sizes: Union[Tuple[builtins.int, ...], List[builtins.int], Size]=()) -> 'Tensor': ...
def is_contiguous(self) -> bool: ...
def is_pinned(self): ...
def is_set_to(self, tensor: 'Tensor') -> bool: ...
def is_shared(self): ...
def item(self) -> Union[builtins.float, builtins.int]: ...
def kthvalue(self, k: builtins.int, dim: builtins.int=-1, keepdim: bool=False, *, out: Optional['Tensor']=None) -> Tuple['Tensor', 'Tensor']: ...
@overload
def le(self, other: Union[builtins.float, builtins.int], *, out: Optional['Tensor']=None) -> 'Tensor': ...
@overload
def le(self, other: 'Tensor', *, out: Optional['Tensor']=None) -> 'Tensor': ...
@overload
def le_(self, other: Union[builtins.float, builtins.int]) -> 'Tensor': ...
@overload
def le_(self, other: 'Tensor') -> 'Tensor': ...
def lerp(self, end: 'Tensor', weight: Union[builtins.float, builtins.int], *, out: Optional['Tensor']=None) -> 'Tensor': ...
def lerp_(self, end: 'Tensor', weight: Union[builtins.float, builtins.int]) -> 'Tensor': ...
def log(self, *, out: Optional['Tensor']=None) -> 'Tensor': ...
def log10(self, *, out: Optional['Tensor']=None) -> 'Tensor': ...
def log10_(self) -> 'Tensor': ...
def log1p(self, *, out: Optional['Tensor']=None) -> 'Tensor': ...
def log1p_(self) -> 'Tensor': ...
def log2(self, *, out: Optional['Tensor']=None) -> 'Tensor': ...
def log2_(self) -> 'Tensor': ...
def log_(self) -> 'Tensor': ...
def log_normal_(self, mean: builtins.float=1, std: builtins.float=2, *, generator: Generator=None) -> 'Tensor': ...
def logdet(self) -> 'Tensor': ...
def logsumexp(self, dim: builtins.int, keepdim: bool=False, *, out: Optional['Tensor']=None) -> 'Tensor': ...
def long(self) -> 'Tensor': ...
@overload
def lt(self, other: Union[builtins.float, builtins.int], *, out: Optional['Tensor']=None) -> 'Tensor': ...
@overload
def lt(self, other: 'Tensor', *, out: Optional['Tensor']=None) -> 'Tensor': ...
@overload
def lt_(self, other: Union[builtins.float, builtins.int]) -> 'Tensor': ...
@overload
def lt_(self, other: 'Tensor') -> 'Tensor': ...
def map_(tensor: 'Tensor', callable: Callable) -> 'Tensor': ...
def masked_fill(self, mask, value): ...
@overload
def masked_fill_(self, mask: 'Tensor', value: Union[builtins.float, builtins.int]) -> 'Tensor': ...
@overload
def masked_fill_(self, mask: 'Tensor', value: 'Tensor') -> 'Tensor': ...
def masked_scatter(self, mask, tensor): ...
def masked_scatter_(self, mask: 'Tensor', source: 'Tensor') -> 'Tensor': ...
def masked_select(self, mask: 'Tensor', *, out: Optional['Tensor']=None) -> 'Tensor': ...
def matmul(self, other: 'Tensor', *, out: Optional['Tensor']=None) -> 'Tensor': ...
def matrix_power(self, n: builtins.int) -> 'Tensor': ...
@overload
def max(self, other: 'Tensor', *, out: Optional['Tensor']=None) -> 'Tensor': ...
@overload
def max(self, *, out: Optional['Tensor']=None) -> Union[builtins.float, builtins.int]: ...
@overload
def max(self, dim: builtins.int, keepdim: bool=False, *, out: Optional['Tensor']=None) -> Tuple['Tensor', 'Tensor']: ...
@overload
def mean(self, *, dtype: _dtype, out: Optional['Tensor']=None) -> 'Tensor': ...
@overload
def mean(self, *, out: Optional['Tensor']=None) -> 'Tensor': ...
@overload
def mean(self, dim: builtins.int, keepdim: bool, *, dtype: _dtype, out: Optional['Tensor']=None) -> 'Tensor': ...
@overload
def mean(self, dim: builtins.int, keepdim: bool=False, *, out: Optional['Tensor']=None) -> 'Tensor': ...
@overload
def mean(self, dim: builtins.int, *, dtype: _dtype, out: Optional['Tensor']=None) -> 'Tensor': ...
@overload
def median(self, *, out: Optional['Tensor']=None) -> Union[builtins.float, builtins.int]: ...
@overload
def median(self, dim: builtins.int, keepdim: bool=False, *, out: Optional['Tensor']=None) -> Tuple['Tensor', 'Tensor']: ...
@overload
def min(self, other: 'Tensor', *, out: Optional['Tensor']=None) -> 'Tensor': ...
@overload
def min(self, *, out: Optional['Tensor']=None) -> Union[builtins.float, builtins.int]: ...
@overload
def min(self, dim: builtins.int, keepdim: bool=False, *, out: Optional['Tensor']=None) -> Tuple['Tensor', 'Tensor']: ...
def mm(self, mat2: 'Tensor', *, out: Optional['Tensor']=None) -> 'Tensor': ...
def mode(self, dim: builtins.int=-1, keepdim: bool=False, *, out: Optional['Tensor']=None) -> Tuple['Tensor', 'Tensor']: ...
@overload
def mul(self, other: 'Tensor', *, out: Optional['Tensor']=None) -> 'Tensor': ...
@overload
def mul(self, other: Union[builtins.float, builtins.int], *, out: Optional['Tensor']=None) -> 'Tensor': ...
@overload
def mul_(self, other: 'Tensor') -> 'Tensor': ...
@overload
def mul_(self, other: Union[builtins.float, builtins.int]) -> 'Tensor': ...
def multinomial(self, num_samples: builtins.int, replacement: bool=False, *, generator: Generator=None, out: Optional['Tensor']=None) -> 'Tensor': ...
def mv(self, vec: 'Tensor', *, out: Optional['Tensor']=None) -> 'Tensor': ...
def mvlgamma(self, p: builtins.int) -> 'Tensor': ...
def mvlgamma_(self, p: builtins.int) -> 'Tensor': ...
def narrow(self, dim: builtins.int, start: builtins.int, length: builtins.int) -> 'Tensor': ...
def narrow_copy(self, dim: builtins.int, start: builtins.int, length: builtins.int) -> 'Tensor': ...
def ndimension(self) -> builtins.int: ...
@overload
def ne(self, other: Union[builtins.float, builtins.int], *, out: Optional['Tensor']=None) -> 'Tensor': ...
@overload
def ne(self, other: 'Tensor', *, out: Optional['Tensor']=None) -> 'Tensor': ...
@overload
def ne_(self, other: Union[builtins.float, builtins.int]) -> 'Tensor': ...
@overload
def ne_(self, other: 'Tensor') -> 'Tensor': ...
def neg(self, *, out: Optional['Tensor']=None) -> 'Tensor': ...
def neg_(self) -> 'Tensor': ...
def nelement(self) -> builtins.int: ...
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': ...
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': ...
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': ...
def new_tensor(self, data: Any, dtype: Optional[_dtype]=None, device: Union[device, str, None]=None, requires_grad: bool=False) -> 'Tensor': ...
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': ...
def nonzero(self, *, out: Optional['Tensor']=None) -> 'Tensor': ...
def norm(self, p='fro', dim=None, keepdim=False): ...
def normal_(self, mean: builtins.float=0, std: builtins.float=1, *, generator: Generator=None) -> 'Tensor': ...
def numel(self) -> builtins.int: ...
def numpy(self) -> Any: ...
def orgqr(self, input2: 'Tensor', *, out: Optional['Tensor']=None) -> 'Tensor': ...
def ormqr(self, input2: 'Tensor', input3: 'Tensor', left: bool=True, transpose: bool=False, *, out: Optional['Tensor']=None) -> 'Tensor': ...
@overload
def permute(self, dims: Union[Tuple[builtins.int, ...], List[builtins.int], Size]) -> 'Tensor': ...
@overload
def permute(self, *dims: builtins.int) -> 'Tensor': ...
def pinverse(self, rcond: builtins.float=1e-15) -> 'Tensor': ...
def potrf(self, upper: bool=True, *, out: Optional['Tensor']=None) -> 'Tensor': ...
def potri(self, upper: bool=True, *, out: Optional['Tensor']=None) -> 'Tensor': ...
def potrs(self, input2: 'Tensor', upper: bool=True, *, out: Optional['Tensor']=None) -> 'Tensor': ...
@overload
def pow(self, exponent: 'Tensor', *, out: Optional['Tensor']=None) -> 'Tensor': ...
@overload
def pow(self, exponent: Union[builtins.float, builtins.int], *, out: Optional['Tensor']=None) -> 'Tensor': ...
@overload
def pow_(self, exponent: Union[builtins.float, builtins.int]) -> 'Tensor': ...
@overload
def pow_(self, exponent: 'Tensor') -> 'Tensor': ...
@overload
def prod(self, *, dtype: _dtype, out: Optional['Tensor']=None) -> 'Tensor': ...
@overload
def prod(self, *, out: Optional['Tensor']=None) -> 'Tensor': ...
@overload
def prod(self, dim: builtins.int, keepdim: bool, *, dtype: _dtype, out: Optional['Tensor']=None) -> 'Tensor': ...
@overload
def prod(self, dim: builtins.int, keepdim: bool=False, *, out: Optional['Tensor']=None) -> 'Tensor': ...
@overload
def prod(self, dim: builtins.int, *, dtype: _dtype, out: Optional['Tensor']=None) -> 'Tensor': ...
def pstrf(self, upper: bool=True, tol: Union[builtins.float, builtins.int]=-1, *, out: Optional['Tensor']=None) -> Tuple['Tensor', 'Tensor']: ...
def put_(self, index: 'Tensor', source: 'Tensor', accumulate: bool=False) -> 'Tensor': ...
def qr(self, *, out: Optional['Tensor']=None) -> Tuple['Tensor', 'Tensor']: ...
@overload
def random_(self, from_: builtins.int, to: builtins.int, *, generator: Generator=None) -> 'Tensor': ...
@overload
def random_(self, to: builtins.int, *, generator: Generator=None) -> 'Tensor': ...
@overload
def random_(self, *, generator: Generator=None) -> 'Tensor': ...
def reciprocal(self, *, out: Optional['Tensor']=None) -> 'Tensor': ...
def reciprocal_(self) -> 'Tensor': ...
def register_hook(self, hook): ...
@overload
def remainder(self, other: Union[builtins.float, builtins.int], *, out: Optional['Tensor']=None) -> 'Tensor': ...
@overload
def remainder(self, other: 'Tensor', *, out: Optional['Tensor']=None) -> 'Tensor': ...
@overload
def remainder_(self, other: Union[builtins.float, builtins.int]) -> 'Tensor': ...
@overload
def remainder_(self, other: 'Tensor') -> 'Tensor': ...
def renorm(self, p: Union[builtins.float, builtins.int], dim: builtins.int, maxnorm: Union[builtins.float, builtins.int], *, out: Optional['Tensor']=None) -> 'Tensor': ...
def renorm_(self, p: Union[builtins.float, builtins.int], dim: builtins.int, maxnorm: Union[builtins.float, builtins.int]) -> 'Tensor': ...
@overload
def repeat(self, repeats: Union[Tuple[builtins.int, ...], List[builtins.int], Size]) -> 'Tensor': ...
@overload
def repeat(self, *repeats: builtins.int) -> 'Tensor': ...
def requires_grad_(self, mode: bool=True) -> 'Tensor': ...
@overload
def reshape(self, shape: Union[Tuple[builtins.int, ...], List[builtins.int], Size]) -> 'Tensor': ...
@overload
def reshape(self, *shape: builtins.int) -> 'Tensor': ...
def reshape_as(self, other: 'Tensor') -> 'Tensor': ...
@overload
def resize_(self, size: Union[Tuple[builtins.int, ...], List[builtins.int], Size]) -> 'Tensor': ...
@overload
def resize_(self, *size: builtins.int) -> 'Tensor': ...
def resize_as_(self, the_template: 'Tensor') -> 'Tensor': ...
def retain_grad(self): ...
def rfft(self, signal_ndim: builtins.int, normalized: bool=False, onesided: bool=True) -> 'Tensor': ...
def rot90(self, k: builtins.int=1, dims: Union[Tuple[builtins.int, ...], List[builtins.int], Size]=(0,1)) -> 'Tensor': ...
def round(self, *, out: Optional['Tensor']=None) -> 'Tensor': ...
def round_(self) -> 'Tensor': ...
def rsqrt(self, *, out: Optional['Tensor']=None) -> 'Tensor': ...
def rsqrt_(self) -> 'Tensor': ...
def scatter(self, dim, index, source): ...
@overload
def scatter_(self, dim: builtins.int, index: 'Tensor', src: 'Tensor') -> 'Tensor': ...
@overload
def scatter_(self, dim: builtins.int, index: 'Tensor', value: Union[builtins.float, builtins.int]) -> 'Tensor': ...
def scatter_add(self, dim, index, source): ...
def scatter_add_(self, dim: builtins.int, index: 'Tensor', src: 'Tensor') -> 'Tensor': ...
def select(self, dim: builtins.int, index: builtins.int) -> 'Tensor': ...
@overload
def set_(self, source: Storage) -> 'Tensor': ...
@overload
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': ...
@overload
def set_(self, source: 'Tensor') -> 'Tensor': ...
@overload
def set_(self) -> 'Tensor': ...
def share_memory_(self): ...
def short(self) -> 'Tensor': ...
def sigmoid(self, *, out: Optional['Tensor']=None) -> 'Tensor': ...
def sigmoid_(self) -> 'Tensor': ...
def sign(self, *, out: Optional['Tensor']=None) -> 'Tensor': ...
def sign_(self) -> 'Tensor': ...
def sin(self, *, out: Optional['Tensor']=None) -> 'Tensor': ...
def sin_(self) -> 'Tensor': ...
def sinh(self, *, out: Optional['Tensor']=None) -> 'Tensor': ...
def sinh_(self) -> 'Tensor': ...
@overload
def size(self) -> Size: ...
@overload
def size(self, dim: builtins.int) -> builtins.int: ...
def slogdet(self) -> Tuple['Tensor', 'Tensor']: ...
def sort(self, dim: builtins.int=-1, descending: bool=False, *, out: Optional['Tensor']=None) -> Tuple['Tensor', 'Tensor']: ...
def split(self, split_size, dim=0): ...
def sqrt(self, *, out: Optional['Tensor']=None) -> 'Tensor': ...
def sqrt_(self) -> 'Tensor': ...
@overload
def squeeze(self) -> 'Tensor': ...
@overload
def squeeze(self, dim: builtins.int) -> 'Tensor': ...
@overload
def squeeze_(self) -> 'Tensor': ...
@overload
def squeeze_(self, dim: builtins.int) -> 'Tensor': ...
@overload
def std(self, unbiased: bool=True, *, out: Optional['Tensor']=None) -> 'Tensor': ...
@overload
def std(self, dim: builtins.int, unbiased: bool=True, keepdim: bool=False, *, out: Optional['Tensor']=None) -> 'Tensor': ...
def stft(self, n_fft, hop_length=None, win_length=None, window=None, center=True, pad_mode='reflect', normalized=False, onesided=True): ...
def storage(self) -> Storage: ...
def storage_offset(self) -> builtins.int: ...
@overload
def stride(self) -> Tuple[builtins.int]: ...
@overload
def stride(self, dim: builtins.int) -> builtins.int: ...
@overload
def sub(self, other: 'Tensor', *, alpha: Union[builtins.float, builtins.int]=1, out: Optional['Tensor']=None) -> 'Tensor': ...
@overload
def sub(self, other: Union[builtins.float, builtins.int], alpha: Union[builtins.float, builtins.int]=1, *, out: Optional['Tensor']=None) -> 'Tensor': ...
@overload
def sub_(self, other: 'Tensor', *, alpha: Union[builtins.float, builtins.int]=1) -> 'Tensor': ...
@overload
def sub_(self, other: Union[builtins.float, builtins.int], alpha: Union[builtins.float, builtins.int]=1) -> 'Tensor': ...
@overload
def sum(self, *, dtype: _dtype, out: Optional['Tensor']=None) -> 'Tensor': ...
@overload
def sum(self, *, out: Optional['Tensor']=None) -> 'Tensor': ...
@overload
def sum(self, dim: Union[builtins.int, Tuple[builtins.int, ...], List[builtins.int], Size], keepdim: bool, *, dtype: _dtype, out: Optional['Tensor']=None) -> 'Tensor': ...
@overload
def sum(self, dim: Union[builtins.int, Tuple[builtins.int, ...], List[builtins.int], Size], keepdim: bool=False, *, out: Optional['Tensor']=None) -> 'Tensor': ...
@overload
def sum(self, dim: Union[builtins.int, Tuple[builtins.int, ...], List[builtins.int], Size], *, dtype: _dtype, out: Optional['Tensor']=None) -> 'Tensor': ...
@overload
def sum(self, *dim: builtins.int, dtype: _dtype, out: Optional['Tensor']=None) -> 'Tensor': ...
def svd(self, some: bool=True, compute_uv: bool=True, *, out: Optional['Tensor']=None) -> Tuple['Tensor', 'Tensor', 'Tensor']: ...
def symeig(self, eigenvectors: bool=False, upper: bool=True, *, out: Optional['Tensor']=None) -> Tuple['Tensor', 'Tensor']: ...
def t(self) -> 'Tensor': ...
def t_(self) -> 'Tensor': ...
def take(self, index: 'Tensor', *, out: Optional['Tensor']=None) -> 'Tensor': ...
def tan_(self) -> 'Tensor': ...
def tanh(self, *, out: Optional['Tensor']=None) -> 'Tensor': ...
def tanh_(self) -> 'Tensor': ...
@overload
def to(self, device: Union[device, str, None], dtype: _dtype, non_blocking: bool=False, copy: bool=False) -> 'Tensor': ...
@overload
def to(self, dtype: _dtype, non_blocking: bool=False, copy: bool=False) -> 'Tensor': ...
@overload
def to(self, device: Union[device, str, None], non_blocking: bool=False, copy: bool=False) -> 'Tensor': ...
@overload
def to(self, other: 'Tensor', non_blocking: bool=False, copy: bool=False) -> 'Tensor': ...
def tolist(self) -> List: ...
def topk(self, k: builtins.int, dim: builtins.int=-1, largest: bool=True, sorted: bool=True, *, out: Optional['Tensor']=None) -> Tuple['Tensor', 'Tensor']: ...
def trace(self) -> Union[builtins.float, builtins.int]: ...
def transpose(self, dim0: builtins.int, dim1: builtins.int) -> 'Tensor': ...
def transpose_(self, dim0: builtins.int, dim1: builtins.int) -> 'Tensor': ...
def tril(self, diagonal: builtins.int=0, *, out: Optional['Tensor']=None) -> 'Tensor': ...
def tril_(self, diagonal: builtins.int=0) -> 'Tensor': ...
def triu(self, diagonal: builtins.int=0, *, out: Optional['Tensor']=None) -> 'Tensor': ...
def triu_(self, diagonal: builtins.int=0) -> 'Tensor': ...
def trtrs(self, A: 'Tensor', upper: bool=True, transpose: bool=False, unitriangular: bool=False, *, out: Optional['Tensor']=None) -> Tuple['Tensor', 'Tensor']: ...
def trunc(self, *, out: Optional['Tensor']=None) -> 'Tensor': ...
def trunc_(self) -> 'Tensor': ...
def type(self, dtype: Union[None, str, _dtype]=None, non_blocking: bool=False) -> Union[str, 'Tensor']: ...
def type_as(self, other: 'Tensor') -> 'Tensor': ...
def unbind(self, dim: builtins.int=0) -> Union[Tuple['Tensor', ...],List['Tensor']]: ...
def unfold(self, dimension: builtins.int, size: builtins.int, step: builtins.int) -> 'Tensor': ...
def uniform_(self, from_: builtins.float=0, to: builtins.float=1, *, generator: Generator=None) -> 'Tensor': ...
def unique(self, sorted=False, return_inverse=False, dim=None): ...
def unsqueeze(self, dim: builtins.int) -> 'Tensor': ...
def unsqueeze_(self, dim: builtins.int) -> 'Tensor': ...
@overload
def var(self, unbiased: bool=True, *, out: Optional['Tensor']=None) -> 'Tensor': ...
@overload
def var(self, dim: builtins.int, unbiased: bool=True, keepdim: bool=False, *, out: Optional['Tensor']=None) -> 'Tensor': ...
@overload
def view(self, size: Union[Tuple[builtins.int, ...], List[builtins.int], Size]) -> 'Tensor': ...
@overload
def view(self, *size: builtins.int) -> 'Tensor': ...
def view_as(self, other: 'Tensor') -> 'Tensor': ...
def where(self, condition: 'Tensor', other: 'Tensor') -> 'Tensor': ...
def zero_(self) -> 'Tensor': ...
def abs(self: Tensor, *, out: Optional[Tensor]=None) -> Tensor: ...
def acos(self: Tensor, *, out: Optional[Tensor]=None) -> Tensor: ...
def adaptive_avg_pool1d(self: Tensor, output_size: Union[builtins.int, Tuple[builtins.int, ...], List[builtins.int], Size]) -> Tensor: ...
@overload
def add(self: Tensor, other: Tensor, *, alpha: Union[builtins.float, builtins.int]=1, out: Optional[Tensor]=None) -> Tensor: ...
@overload
def add(self: Tensor, other: Union[builtins.float, builtins.int], alpha: Union[builtins.float, builtins.int]=1, *, out: Optional[Tensor]=None) -> Tensor: ...
@overload
def add(self: Tensor, alpha: Union[builtins.float, builtins.int], other: Tensor) -> Tensor: ...
@overload
def add(self: Tensor, alpha: Union[builtins.float, builtins.int], other: Tensor, *, out: Tensor) -> Tensor: ...
@overload
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: ...
@overload
def addbmm(beta: Union[builtins.float, builtins.int], self: Tensor, alpha: Union[builtins.float, builtins.int], batch1: Tensor, batch2: Tensor) -> Tensor: ...
@overload
def addbmm(beta: Union[builtins.float, builtins.int], self: Tensor, alpha: Union[builtins.float, builtins.int], batch1: Tensor, batch2: Tensor, *, out: Tensor) -> Tensor: ...
@overload
def addbmm(beta: Union[builtins.float, builtins.int], self: Tensor, batch1: Tensor, batch2: Tensor) -> Tensor: ...
@overload
def addbmm(beta: Union[builtins.float, builtins.int], self: Tensor, batch1: Tensor, batch2: Tensor, *, out: Tensor) -> Tensor: ...
@overload
def addcdiv(self: Tensor, tensor1: Tensor, tensor2: Tensor, *, value: Union[builtins.float, builtins.int]=1, out: Optional[Tensor]=None) -> Tensor: ...
@overload
def addcdiv(self: Tensor, value: Union[builtins.float, builtins.int], tensor1: Tensor, tensor2: Tensor) -> Tensor: ...
@overload
def addcdiv(self: Tensor, value: Union[builtins.float, builtins.int], tensor1: Tensor, tensor2: Tensor, *, out: Tensor) -> Tensor: ...
@overload
def addcmul(self: Tensor, tensor1: Tensor, tensor2: Tensor, *, value: Union[builtins.float, builtins.int]=1, out: Optional[Tensor]=None) -> Tensor: ...
@overload
def addcmul(self: Tensor, value: Union[builtins.float, builtins.int], tensor1: Tensor, tensor2: Tensor) -> Tensor: ...
@overload
def addcmul(self: Tensor, value: Union[builtins.float, builtins.int], tensor1: Tensor, tensor2: Tensor, *, out: Tensor) -> Tensor: ...
@overload
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: ...
@overload
def addmm(beta: Union[builtins.float, builtins.int], self: Tensor, alpha: Union[builtins.float, builtins.int], mat1: Tensor, mat2: Tensor) -> Tensor: ...
@overload
def addmm(beta: Union[builtins.float, builtins.int], self: Tensor, alpha: Union[builtins.float, builtins.int], mat1: Tensor, mat2: Tensor, *, out: Tensor) -> Tensor: ...
@overload
def addmm(beta: Union[builtins.float, builtins.int], self: Tensor, mat1: Tensor, mat2: Tensor) -> Tensor: ...
@overload
def addmm(beta: Union[builtins.float, builtins.int], self: Tensor, mat1: Tensor, mat2: Tensor, *, out: Tensor) -> Tensor: ...
@overload
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: ...
@overload
def addmv(beta: Union[builtins.float, builtins.int], self: Tensor, alpha: Union[builtins.float, builtins.int], mat: Tensor, vec: Tensor) -> Tensor: ...
@overload
def addmv(beta: Union[builtins.float, builtins.int], self: Tensor, alpha: Union[builtins.float, builtins.int], mat: Tensor, vec: Tensor, *, out: Tensor) -> Tensor: ...
@overload
def addmv(beta: Union[builtins.float, builtins.int], self: Tensor, mat: Tensor, vec: Tensor) -> Tensor: ...
@overload
def addmv(beta: Union[builtins.float, builtins.int], self: Tensor, mat: Tensor, vec: Tensor, *, out: Tensor) -> Tensor: ...
@overload
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: ...
@overload
def addr(beta: Union[builtins.float, builtins.int], self: Tensor, alpha: Union[builtins.float, builtins.int], vec1: Tensor, vec2: Tensor) -> Tensor: ...
@overload
def addr(beta: Union[builtins.float, builtins.int], self: Tensor, alpha: Union[builtins.float, builtins.int], vec1: Tensor, vec2: Tensor, *, out: Tensor) -> Tensor: ...
@overload
def addr(beta: Union[builtins.float, builtins.int], self: Tensor, vec1: Tensor, vec2: Tensor) -> Tensor: ...
@overload
def addr(beta: Union[builtins.float, builtins.int], self: Tensor, vec1: Tensor, vec2: Tensor, *, out: Tensor) -> Tensor: ...
def allclose(self: Tensor, other: Tensor, rtol: builtins.float=1e-05, atol: builtins.float=1e-08, equal_nan: bool=False) -> bool: ...
@overload
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: ...
@overload
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: ...
@overload
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: ...
def argmax(input, dim=None, keepdim=False): ...
def argmin(input, dim=None, keepdim=False): ...
def argsort(input, dim=None, descending=False): ...
def as_tensor(data: Any, dtype: _dtype=None, device: Optional[device]=None) -> Tensor: ...
def asin(self: Tensor, *, out: Optional[Tensor]=None) -> Tensor: ...
def atan(self: Tensor, *, out: Optional[Tensor]=None) -> Tensor: ...
def atan2(self: Tensor, other: Tensor, *, out: Optional[Tensor]=None) -> Tensor: ...
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: ...
@overload
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: ...
@overload
def baddbmm(beta: Union[builtins.float, builtins.int], self: Tensor, alpha: Union[builtins.float, builtins.int], batch1: Tensor, batch2: Tensor) -> Tensor: ...
@overload
def baddbmm(beta: Union[builtins.float, builtins.int], self: Tensor, alpha: Union[builtins.float, builtins.int], batch1: Tensor, batch2: Tensor, *, out: Tensor) -> Tensor: ...
@overload
def baddbmm(beta: Union[builtins.float, builtins.int], self: Tensor, batch1: Tensor, batch2: Tensor) -> Tensor: ...
@overload
def baddbmm(beta: Union[builtins.float, builtins.int], self: Tensor, batch1: Tensor, batch2: Tensor, *, out: Tensor) -> Tensor: ...
@overload
def bartlett_window(window_length: builtins.int, *, dtype: _dtype=None, layout: layout=strided, device: Optional[device]=None, requires_grad:bool=False) -> Tensor: ...
@overload
def bartlett_window(window_length: builtins.int, periodic: bool, *, dtype: _dtype=None, layout: layout=strided, device: Optional[device]=None, requires_grad:bool=False) -> Tensor: ...
@overload
def bernoulli(self: Tensor, *, generator: Generator=None, out: Optional[Tensor]=None) -> Tensor: ...
@overload
def bernoulli(self: Tensor, p: builtins.float, *, generator: Generator=None, out: Optional[Tensor]=None) -> Tensor: ...
def bincount(self: Tensor, weights: Optional[Tensor]=None, minlength: builtins.int=0) -> Tensor: ...
@overload
def blackman_window(window_length: builtins.int, *, dtype: _dtype=None, layout: layout=strided, device: Optional[device]=None, requires_grad:bool=False) -> Tensor: ...
@overload
def blackman_window(window_length: builtins.int, periodic: bool, *, dtype: _dtype=None, layout: layout=strided, device: Optional[device]=None, requires_grad:bool=False) -> Tensor: ...
def bmm(self: Tensor, mat2: Tensor, *, out: Optional[Tensor]=None) -> Tensor: ...
def broadcast_tensors(*tensors:Tensor) -> List[Tensor]: ...
def btrifact(A:Tensor, info:Union[Tensor, None]=None, pivot:bool=True) -> Tuple[Tensor, Tensor]: ...
def btrifact_with_info(self: Tensor, *, pivot: bool=True, out: Optional[Tensor]=None) -> Tuple[Tensor, Tensor, Tensor]: ...
def btrisolve(self: Tensor, LU_data: Tensor, LU_pivots: Tensor, *, out: Optional[Tensor]=None) -> Tensor: ...
def btriunpack(LU_data:Tensor, LU_pivots:Tensor, unpack_data:bool=True, unpack_pivots:bool=True) -> Tuple[Tensor, Tensor, Tensor]: ...
def cat(tensors: Union[Tuple[Tensor, ...],List[Tensor]], dim: builtins.int=0, *, out: Optional[Tensor]=None) -> Tensor: ...
def ceil(self: Tensor, *, out: Optional[Tensor]=None) -> Tensor: ...
def celu_(self: Tensor, alpha: Union[builtins.float, builtins.int]=1.0) -> Tensor: ...
def chain_matmul(*matrices): ...
def chunk(self: Tensor, chunks: builtins.int, dim: builtins.int=0) -> Union[Tuple[Tensor, ...],List[Tensor]]: ...
def clamp(self, min: builtins.float=-math.inf, max: builtins.float=math.inf, *, out: Optional[Tensor]=None) -> Tensor: ...
def compiled_with_cxx11_abi(): ...
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: ...
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: ...
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: ...
def conv_tbc(self: Tensor, weight: Tensor, bias: Tensor, pad: builtins.int=0) -> Tensor: ...
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: ...
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: ...
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: ...
def cos(self: Tensor, *, out: Optional[Tensor]=None) -> Tensor: ...
def cosh(self: Tensor, *, out: Optional[Tensor]=None) -> Tensor: ...
def cross(self: Tensor, other: Tensor, dim: builtins.int=-1, *, out: Optional[Tensor]=None) -> Tensor: ...
@overload
def cumprod(self: Tensor, dim: builtins.int, *, dtype: _dtype, out: Optional[Tensor]=None) -> Tensor: ...
@overload
def cumprod(self: Tensor, dim: builtins.int, *, out: Optional[Tensor]=None) -> Tensor: ...
@overload
def cumsum(self: Tensor, dim: builtins.int, *, dtype: _dtype, out: Optional[Tensor]=None) -> Tensor: ...
@overload
def cumsum(self: Tensor, dim: builtins.int, *, out: Optional[Tensor]=None) -> Tensor: ...
def det(self: Tensor) -> Tensor: ...
def diag(self: Tensor, diagonal: builtins.int=0, *, out: Optional[Tensor]=None) -> Tensor: ...
def diagflat(self: Tensor, offset: builtins.int=0) -> Tensor: ...
def diagonal(self: Tensor, offset: builtins.int=0, dim1: builtins.int=0, dim2: builtins.int=1) -> Tensor: ...
def digamma(self: Tensor, *, out: Optional[Tensor]=None) -> Tensor: ...
def dist(self: Tensor, other: Tensor, p: Union[builtins.float, builtins.int]=2) -> Union[builtins.float, builtins.int]: ...
@overload
def div(self: Tensor, other: Tensor, *, out: Optional[Tensor]=None) -> Tensor: ...
@overload
def div(self: Tensor, other: Union[builtins.float, builtins.int], *, out: Optional[Tensor]=None) -> Tensor: ...
def dot(self: Tensor, tensor: Tensor, *, out: Optional[Tensor]=None) -> Tensor: ...
def eig(self: Tensor, eigenvectors: bool=False, *, out: Optional[Tensor]=None) -> Tuple[Tensor, Tensor]: ...
def einsum(equation:str, *operands:Tensor) -> Tensor: ...
@overload
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: ...
@overload
def empty(*size: builtins.int, out: Optional[Tensor]=None, dtype: _dtype=None, layout: layout=strided, device: Optional[device]=None, requires_grad:bool=False) -> Tensor: ...
@overload
def empty_like(self: Tensor) -> Tensor: ...
@overload
def empty_like(self: Tensor, *, dtype: _dtype=None, layout: layout=strided, device: Optional[device]=None, requires_grad:bool=False) -> Tensor: ...
@overload
def eq(self: Tensor, other: Union[builtins.float, builtins.int], *, out: Optional[Tensor]=None) -> Tensor: ...
@overload
def eq(self: Tensor, other: Tensor, *, out: Optional[Tensor]=None) -> Tensor: ...
def equal(self: Tensor, other: Tensor) -> bool: ...
def erf(self: Tensor, *, out: Optional[Tensor]=None) -> Tensor: ...
def erfc(self: Tensor, *, out: Optional[Tensor]=None) -> Tensor: ...
def erfinv(self: Tensor, *, out: Optional[Tensor]=None) -> Tensor: ...
def exp(self: Tensor, *, out: Optional[Tensor]=None) -> Tensor: ...
def expm1(self: Tensor, *, out: Optional[Tensor]=None) -> Tensor: ...
@overload
def eye(n: builtins.int, *, out: Optional[Tensor]=None, dtype: _dtype=None, layout: layout=strided, device: Optional[device]=None, requires_grad:bool=False) -> Tensor: ...
@overload
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: ...
def fft(self: Tensor, signal_ndim: builtins.int, normalized: bool=False) -> Tensor: ...
def flatten(self: Tensor, start_dim: builtins.int=0, end_dim: builtins.int=-1) -> Tensor: ...
def flip(self: Tensor, dims: Union[Tuple[builtins.int, ...], List[builtins.int], Size]) -> Tensor: ...
def floor(self: Tensor, *, out: Optional[Tensor]=None) -> Tensor: ...
@overload
def fmod(self: Tensor, other: Union[builtins.float, builtins.int], *, out: Optional[Tensor]=None) -> Tensor: ...
@overload
def fmod(self: Tensor, other: Tensor, *, out: Optional[Tensor]=None) -> Tensor: ...
def frac(self: Tensor, *, out: Optional[Tensor]=None) -> Tensor: ...
def from_numpy(ndarray) -> Tensor: ...
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: ...
@overload
def full_like(self: Tensor, fill_value: Union[builtins.float, builtins.int]) -> Tensor: ...
@overload
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: ...
def gather(self: Tensor, dim: builtins.int, index: Tensor, *, out: Optional[Tensor]=None) -> Tensor: ...
@overload
def ge(self: Tensor, other: Union[builtins.float, builtins.int], *, out: Optional[Tensor]=None) -> Tensor: ...
@overload
def ge(self: Tensor, other: Tensor, *, out: Optional[Tensor]=None) -> Tensor: ...
def gels(self: Tensor, A: Tensor, *, out: Optional[Tensor]=None) -> Tuple[Tensor, Tensor]: ...
def geqrf(self: Tensor, *, out: Optional[Tensor]=None) -> Tuple[Tensor, Tensor]: ...
def ger(self: Tensor, vec2: Tensor, *, out: Optional[Tensor]=None) -> Tensor: ...
def gesv(self: Tensor, A: Tensor, *, out: Optional[Tensor]=None) -> Tuple[Tensor, Tensor]: ...
def get_default_dtype() -> _dtype: ...
def get_num_threads() -> builtins.int: ...
def get_rng_state(): ...
@overload
def gt(self: Tensor, other: Union[builtins.float, builtins.int], *, out: Optional[Tensor]=None) -> Tensor: ...
@overload
def gt(self: Tensor, other: Tensor, *, out: Optional[Tensor]=None) -> Tensor: ...
@overload
def hamming_window(window_length: builtins.int, *, dtype: _dtype=None, layout: layout=strided, device: Optional[device]=None, requires_grad:bool=False) -> Tensor: ...
@overload
def hamming_window(window_length: builtins.int, periodic: bool, *, dtype: _dtype=None, layout: layout=strided, device: Optional[device]=None, requires_grad:bool=False) -> Tensor: ...
@overload
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: ...
@overload
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: ...
@overload
def hann_window(window_length: builtins.int, *, dtype: _dtype=None, layout: layout=strided, device: Optional[device]=None, requires_grad:bool=False) -> Tensor: ...
@overload
def hann_window(window_length: builtins.int, periodic: bool, *, dtype: _dtype=None, layout: layout=strided, device: Optional[device]=None, requires_grad:bool=False) -> Tensor: ...
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: ...
def ifft(self: Tensor, signal_ndim: builtins.int, normalized: bool=False) -> Tensor: ...
def index_select(self: Tensor, dim: builtins.int, index: Tensor, *, out: Optional[Tensor]=None) -> Tensor: ...
def initial_seed(): ...
def inverse(self: Tensor, *, out: Optional[Tensor]=None) -> Tensor: ...
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: ...
def is_storage(obj): ...
def is_tensor(obj): ...
def isfinite(tensor:Tensor) -> Tensor: ...
def isinf(tensor:Tensor) -> Tensor: ...
def isnan(tensor:Tensor) -> Tensor: ...
def kthvalue(self: Tensor, k: builtins.int, dim: builtins.int=-1, keepdim: bool=False, *, out: Optional[Tensor]=None) -> Tuple[Tensor, Tensor]: ...
@overload
def le(self: Tensor, other: Union[builtins.float, builtins.int], *, out: Optional[Tensor]=None) -> Tensor: ...
@overload
def le(self: Tensor, other: Tensor, *, out: Optional[Tensor]=None) -> Tensor: ...
def lerp(self: Tensor, end: Tensor, weight: Union[builtins.float, builtins.int], *, out: Optional[Tensor]=None) -> Tensor: ...
@overload
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: ...
@overload
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: ...
def load(f, map_location=None, pickle_module=pickle): ...
def log(self: Tensor, *, out: Optional[Tensor]=None) -> Tensor: ...
def log10(self: Tensor, *, out: Optional[Tensor]=None) -> Tensor: ...
def log1p(self: Tensor, *, out: Optional[Tensor]=None) -> Tensor: ...
def log2(self: Tensor, *, out: Optional[Tensor]=None) -> Tensor: ...
def logdet(self: Tensor) -> Tensor: ...
@overload
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: ...
@overload
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: ...
def logsumexp(self: Tensor, dim: builtins.int, keepdim: bool=False, *, out: Optional[Tensor]=None) -> Tensor: ...
@overload
def lt(self: Tensor, other: Union[builtins.float, builtins.int], *, out: Optional[Tensor]=None) -> Tensor: ...
@overload
def lt(self: Tensor, other: Tensor, *, out: Optional[Tensor]=None) -> Tensor: ...
def manual_seed(seed): ...
def masked_select(self: Tensor, mask: Tensor, *, out: Optional[Tensor]=None) -> Tensor: ...
def matmul(self: Tensor, other: Tensor, *, out: Optional[Tensor]=None) -> Tensor: ...
def matrix_power(self: Tensor, n: builtins.int) -> Tensor: ...
@overload
def matrix_rank(self: Tensor, tol: builtins.float, symmetric: bool=False) -> Tensor: ...
@overload
def matrix_rank(self: Tensor, symmetric: bool=False) -> Tensor: ...
@overload
def max(self: Tensor, other: Tensor, *, out: Optional[Tensor]=None) -> Tensor: ...
@overload
def max(self: Tensor, *, out: Optional[Tensor]=None) -> Union[builtins.float, builtins.int]: ...
@overload
def max(self: Tensor, dim: builtins.int, keepdim: bool=False, *, out: Optional[Tensor]=None) -> Tuple[Tensor, Tensor]: ...
@overload
def mean(self: Tensor, *, dtype: _dtype, out: Optional[Tensor]=None) -> Tensor: ...
@overload
def mean(self: Tensor, *, out: Optional[Tensor]=None) -> Tensor: ...
@overload
def mean(self: Tensor, dim: builtins.int, keepdim: bool, *, dtype: _dtype, out: Optional[Tensor]=None) -> Tensor: ...
@overload
def mean(self: Tensor, dim: builtins.int, keepdim: bool=False, *, out: Optional[Tensor]=None) -> Tensor: ...
@overload
def mean(self: Tensor, dim: builtins.int, *, dtype: _dtype, out: Optional[Tensor]=None) -> Tensor: ...
@overload
def median(self: Tensor, *, out: Optional[Tensor]=None) -> Union[builtins.float, builtins.int]: ...
@overload
def median(self: Tensor, dim: builtins.int, keepdim: bool=False, *, out: Optional[Tensor]=None) -> Tuple[Tensor, Tensor]: ...
def meshgrid(*tensors, **kwargs): ...
@overload
def min(self: Tensor, other: Tensor, *, out: Optional[Tensor]=None) -> Tensor: ...
@overload
def min(self: Tensor, *, out: Optional[Tensor]=None) -> Union[builtins.float, builtins.int]: ...
@overload
def min(self: Tensor, dim: builtins.int, keepdim: bool=False, *, out: Optional[Tensor]=None) -> Tuple[Tensor, Tensor]: ...
def mm(self: Tensor, mat2: Tensor, *, out: Optional[Tensor]=None) -> Tensor: ...
def mode(self: Tensor, dim: builtins.int=-1, keepdim: bool=False, *, out: Optional[Tensor]=None) -> Tuple[Tensor, Tensor]: ...
@overload
def mul(self: Tensor, other: Tensor, *, out: Optional[Tensor]=None) -> Tensor: ...
@overload
def mul(self: Tensor, other: Union[builtins.float, builtins.int], *, out: Optional[Tensor]=None) -> Tensor: ...
def multinomial(self: Tensor, num_samples: builtins.int, replacement: bool=False, *, generator: Generator=None, out: Optional[Tensor]=None) -> Tensor: ...
def mv(self: Tensor, vec: Tensor, *, out: Optional[Tensor]=None) -> Tensor: ...
def mvlgamma(self: Tensor, p: builtins.int) -> Tensor: ...
def narrow(self: Tensor, dim: builtins.int, start: builtins.int, length: builtins.int) -> Tensor: ...
@overload
def ne(self: Tensor, other: Union[builtins.float, builtins.int], *, out: Optional[Tensor]=None) -> Tensor: ...
@overload
def ne(self: Tensor, other: Tensor, *, out: Optional[Tensor]=None) -> Tensor: ...
def neg(self: Tensor, *, out: Optional[Tensor]=None) -> Tensor: ...
def nonzero(self: Tensor, *, out: Optional[Tensor]=None) -> Tensor: ...
def norm(input, p='fro', dim=None, keepdim=False, out=None): ...
@overload
def normal(mean: Tensor, std: builtins.float=1, *, generator: Generator=None, out: Optional[Tensor]=None) -> Tensor: ...
@overload
def normal(mean: builtins.float, std: Tensor, *, generator: Generator=None, out: Optional[Tensor]=None) -> Tensor: ...
@overload
def normal(mean: Tensor, std: Tensor, *, generator: Generator=None, out: Optional[Tensor]=None) -> Tensor: ...
def numel(self: Tensor) -> builtins.int: ...
@overload
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: ...
@overload
def ones(*size: builtins.int, out: Optional[Tensor]=None, dtype: _dtype=None, layout: layout=strided, device: Optional[device]=None, requires_grad:bool=False) -> Tensor: ...
@overload
def ones_like(self: Tensor) -> Tensor: ...
@overload
def ones_like(self: Tensor, *, dtype: _dtype=None, layout: layout=strided, device: Optional[device]=None, requires_grad:bool=False) -> Tensor: ...
def orgqr(self: Tensor, input2: Tensor, *, out: Optional[Tensor]=None) -> Tensor: ...
def ormqr(self: Tensor, input2: Tensor, input3: Tensor, left: bool=True, transpose: bool=False, *, out: Optional[Tensor]=None) -> Tensor: ...
def pdist(self: Tensor, p: builtins.float=2) -> Tensor: ...
def pinverse(self: Tensor, rcond: builtins.float=1e-15) -> Tensor: ...
def pixel_shuffle(self: Tensor, upscale_factor: builtins.int) -> Tensor: ...
def potrf(self: Tensor, upper: bool=True, *, out: Optional[Tensor]=None) -> Tensor: ...
def potri(self: Tensor, upper: bool=True, *, out: Optional[Tensor]=None) -> Tensor: ...
def potrs(self: Tensor, input2: Tensor, upper: bool=True, *, out: Optional[Tensor]=None) -> Tensor: ...
@overload
def pow(self: Tensor, exponent: Tensor, *, out: Optional[Tensor]=None) -> Tensor: ...
@overload
def pow(self: Union[builtins.float, builtins.int], exponent: Tensor, *, out: Optional[Tensor]=None) -> Tensor: ...
@overload
def pow(self: Tensor, exponent: Union[builtins.float, builtins.int], *, out: Optional[Tensor]=None) -> Tensor: ...
@overload
def prod(self: Tensor, *, dtype: _dtype, out: Optional[Tensor]=None) -> Tensor: ...
@overload
def prod(self: Tensor, *, out: Optional[Tensor]=None) -> Tensor: ...
@overload
def prod(self: Tensor, dim: builtins.int, keepdim: bool, *, dtype: _dtype, out: Optional[Tensor]=None) -> Tensor: ...
@overload
def prod(self: Tensor, dim: builtins.int, keepdim: bool=False, *, out: Optional[Tensor]=None) -> Tensor: ...
@overload
def prod(self: Tensor, dim: builtins.int, *, dtype: _dtype, out: Optional[Tensor]=None) -> Tensor: ...
def pstrf(self: Tensor, upper: bool=True, tol: Union[builtins.float, builtins.int]=-1, *, out: Optional[Tensor]=None) -> Tuple[Tensor, Tensor]: ...
def qr(self: Tensor, *, out: Optional[Tensor]=None) -> Tuple[Tensor, Tensor]: ...
@overload
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: ...
@overload
def rand(*size: builtins.int, out: Optional[Tensor]=None, dtype: _dtype=None, layout: layout=strided, device: Optional[device]=None, requires_grad:bool=False) -> Tensor: ...
@overload
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: ...
@overload
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: ...
@overload
def rand_like(self: Tensor) -> Tensor: ...
@overload
def rand_like(self: Tensor, *, dtype: _dtype=None, layout: layout=strided, device: Optional[device]=None, requires_grad:bool=False) -> Tensor: ...
@overload
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: ...
@overload
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: ...
@overload
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: ...
@overload
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: ...
@overload
def randint_like(self: Tensor, high: builtins.int) -> Tensor: ...
@overload
def randint_like(self: Tensor, low: builtins.int, high: builtins.int) -> Tensor: ...
@overload
def randint_like(self: Tensor, high: builtins.int, *, dtype: _dtype=None, layout: layout=strided, device: Optional[device]=None, requires_grad:bool=False) -> Tensor: ...
@overload
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: ...
@overload
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: ...
@overload
def randn(*size: builtins.int, out: Optional[Tensor]=None, dtype: _dtype=None, layout: layout=strided, device: Optional[device]=None, requires_grad:bool=False) -> Tensor: ...
@overload
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: ...
@overload
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: ...
@overload
def randn_like(self: Tensor) -> Tensor: ...
@overload
def randn_like(self: Tensor, *, dtype: _dtype=None, layout: layout=strided, device: Optional[device]=None, requires_grad:bool=False) -> Tensor: ...
@overload
def randperm(n: builtins.int, *, out: Optional[Tensor]=None, dtype: _dtype=None, layout: layout=strided, device: Optional[device]=None, requires_grad:bool=False) -> Tensor: ...
@overload
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: ...
@overload
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: ...
@overload
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: ...
def reciprocal(self: Tensor, *, out: Optional[Tensor]=None) -> Tensor: ...
def relu_(self: Tensor) -> Tensor: ...
@overload
def remainder(self: Tensor, other: Union[builtins.float, builtins.int], *, out: Optional[Tensor]=None) -> Tensor: ...
@overload
def remainder(self: Tensor, other: Tensor, *, out: Optional[Tensor]=None) -> Tensor: ...
def renorm(self: Tensor, p: Union[builtins.float, builtins.int], dim: builtins.int, maxnorm: Union[builtins.float, builtins.int], *, out: Optional[Tensor]=None) -> Tensor: ...
def reshape(self: Tensor, shape: Union[Tuple[builtins.int, ...], List[builtins.int], Size]) -> Tensor: ...
def rfft(self: Tensor, signal_ndim: builtins.int, normalized: bool=False, onesided: bool=True) -> Tensor: ...
def rot90(self: Tensor, k: builtins.int=1, dims: Union[Tuple[builtins.int, ...], List[builtins.int], Size]=(0,1)) -> Tensor: ...
def round(self: Tensor, *, out: Optional[Tensor]=None) -> Tensor: ...
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: ...
def rsqrt(self: Tensor, *, out: Optional[Tensor]=None) -> Tensor: ...
def save(obj, f, pickle_module=pickle, pickle_protocol=2): ...
def selu_(self: Tensor) -> Tensor: ...
def set_default_dtype(d): ...
def set_default_tensor_type(t): ...
def set_flush_denormal(mode: bool) -> bool: ...
def set_num_threads(num: builtins.int) -> None: ...
def set_printoptions(precision=None, threshold=None, edgeitems=None, linewidth=None, profile=None): ...
def set_rng_state(new_state): ...
def sigmoid(self: Tensor, *, out: Optional[Tensor]=None) -> Tensor: ...
def sign(self: Tensor, *, out: Optional[Tensor]=None) -> Tensor: ...
def sin(self: Tensor, *, out: Optional[Tensor]=None) -> Tensor: ...
def sinh(self: Tensor, *, out: Optional[Tensor]=None) -> Tensor: ...
def slogdet(self: Tensor) -> Tuple[Tensor, Tensor]: ...
def sort(self: Tensor, dim: builtins.int=-1, descending: bool=False, *, out: Optional[Tensor]=None) -> Tuple[Tensor, Tensor]: ...
@overload
def sparse_coo_tensor(indices: Tensor, values: Tensor) -> Tensor: ...
@overload
def sparse_coo_tensor(indices: Tensor, values: Tensor, size: Union[Tuple[builtins.int, ...], List[builtins.int], Size]) -> Tensor: ...
@overload
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: ...
@overload
def sparse_coo_tensor(*size: builtins.int, dtype: _dtype=None, layout: layout=strided, device: Optional[device]=None, requires_grad:bool=False) -> Tensor: ...
@overload
def sparse_coo_tensor(indices: Tensor, values: Tensor, *, dtype: _dtype=None, layout: layout=strided, device: Optional[device]=None, requires_grad:bool=False) -> Tensor: ...
@overload
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: ...
def split(tensor:Tensor, split_size_or_sections:Union[List[builtins.int], builtins.int], dim:builtins.int=0) -> List[Tensor]: ...
def sqrt(self: Tensor, *, out: Optional[Tensor]=None) -> Tensor: ...
@overload
def squeeze(self: Tensor) -> Tensor: ...
@overload
def squeeze(self: Tensor, dim: builtins.int) -> Tensor: ...
@overload
def sspaddmm(beta: Union[builtins.float, builtins.int], self: Tensor, alpha: Union[builtins.float, builtins.int], mat1: Tensor, mat2: Tensor) -> Tensor: ...
@overload
def sspaddmm(beta: Union[builtins.float, builtins.int], self: Tensor, mat1: Tensor, mat2: Tensor) -> Tensor: ...
def stack(tensors: Union[Tuple[Tensor, ...],List[Tensor]], dim: builtins.int=0, *, out: Optional[Tensor]=None) -> Tensor: ...
@overload
def std(self: Tensor, unbiased: bool=True, *, out: Optional[Tensor]=None) -> Tensor: ...
@overload
def std(self: Tensor, dim: builtins.int, unbiased: bool=True, keepdim: bool=False, *, out: Optional[Tensor]=None) -> Tensor: ...
def stft(input, n_fft, hop_length=None, win_length=None, window=None, center=True, pad_mode='reflect', normalized=False, onesided=True): ...
@overload
def sub(self: Tensor, alpha: Union[builtins.float, builtins.int], other: Tensor) -> Tensor: ...
@overload
def sub(self: Tensor, alpha: Union[builtins.float, builtins.int], other: Tensor, *, out: Tensor) -> Tensor: ...
@overload
def sum(self: Tensor, *, dtype: _dtype, out: Optional[Tensor]=None) -> Tensor: ...
@overload
def sum(self: Tensor, *, out: Optional[Tensor]=None) -> Tensor: ...
@overload
def sum(self: Tensor, dim: Union[builtins.int, Tuple[builtins.int, ...], List[builtins.int], Size], keepdim: bool, *, dtype: _dtype, out: Optional[Tensor]=None) -> Tensor: ...
@overload
def sum(self: Tensor, dim: Union[builtins.int, Tuple[builtins.int, ...], List[builtins.int], Size], keepdim: bool=False, *, out: Optional[Tensor]=None) -> Tensor: ...
@overload
def sum(self: Tensor, dim: Union[builtins.int, Tuple[builtins.int, ...], List[builtins.int], Size], *, dtype: _dtype, out: Optional[Tensor]=None) -> Tensor: ...
def svd(self: Tensor, some: bool=True, compute_uv: bool=True, *, out: Optional[Tensor]=None) -> Tuple[Tensor, Tensor, Tensor]: ...
def symeig(self: Tensor, eigenvectors: bool=False, upper: bool=True, *, out: Optional[Tensor]=None) -> Tuple[Tensor, Tensor]: ...
def t(self: Tensor) -> Tensor: ...
def take(self: Tensor, index: Tensor, *, out: Optional[Tensor]=None) -> Tensor: ...
def tan(self: Tensor, *, out: Optional[Tensor]=None) -> Tensor: ...
def tanh(self: Tensor, *, out: Optional[Tensor]=None) -> Tensor: ...
@overload
def tensor(data: Any, dtype: Optional[_dtype]=None, device: Union[device, str, None]=None, requires_grad: bool=False) -> Tensor: ...
@overload
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: ...
@overload
def tensor(size: Union[Tuple[builtins.int, ...], List[builtins.int], Size], stride: Union[Tuple[builtins.int, ...], List[builtins.int], Size]) -> Tensor: ...
def tensordot(a:Tensor, b:Tensor, dims=2) -> Tensor: ...
def topk(self: Tensor, k: builtins.int, dim: builtins.int=-1, largest: bool=True, sorted: bool=True, *, out: Optional[Tensor]=None) -> Tuple[Tensor, Tensor]: ...
def trace(self: Tensor) -> Union[builtins.float, builtins.int]: ...
def transpose(self: Tensor, dim0: builtins.int, dim1: builtins.int) -> Tensor: ...
def tril(self: Tensor, diagonal: builtins.int=0, *, out: Optional[Tensor]=None) -> Tensor: ...
def triu(self: Tensor, diagonal: builtins.int=0, *, out: Optional[Tensor]=None) -> Tensor: ...
def trtrs(self: Tensor, A: Tensor, upper: bool=True, transpose: bool=False, unitriangular: bool=False, *, out: Optional[Tensor]=None) -> Tuple[Tensor, Tensor]: ...
def trunc(self: Tensor, *, out: Optional[Tensor]=None) -> Tensor: ...
def unbind(self: Tensor, dim: builtins.int=0) -> Union[Tuple[Tensor, ...],List[Tensor]]: ...
def unique(input, sorted=False, return_inverse=False, dim=None): ...
def unsqueeze(self: Tensor, dim: builtins.int) -> Tensor: ...
@overload
def var(self: Tensor, unbiased: bool=True, *, out: Optional[Tensor]=None) -> Tensor: ...
@overload
def var(self: Tensor, dim: builtins.int, unbiased: bool=True, keepdim: bool=False, *, out: Optional[Tensor]=None) -> Tensor: ...
def where(condition: Tensor, self: Tensor, other: Tensor) -> Tensor: ...
@overload
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: ...
@overload
def zeros(*size: builtins.int, out: Optional[Tensor]=None, dtype: _dtype=None, layout: layout=strided, device: Optional[device]=None, requires_grad:bool=False) -> Tensor: ...
@overload
def zeros_like(self: Tensor) -> Tensor: ...
@overload
def zeros_like(self: Tensor, *, dtype: _dtype=None, layout: layout=strided, device: Optional[device]=None, requires_grad:bool=False) -> Tensor: ...
class DoubleStorage(Storage): ...
class FloatStorage(Storage): ...
class LongStorage(Storage): ...
class IntStorage(Storage): ...
class ShortStorage(Storage): ...
class CharStorage(Storage): ...
class ByteStorage(Storage): ...
class DoubleTensor(Tensor): ...
class FloatTensor(Tensor): ...
class LongTensor(Tensor): ...
class IntTensor(Tensor): ...
class ShortTensor(Tensor): ...
class CharTensor(Tensor): ...
class ByteTensor(Tensor): ...
complex128: dtype = ...
complex32: dtype = ...
complex64: dtype = ...
double: dtype = ...
float: dtype = ...
float16: dtype = ...
float32: dtype = ...
float64: dtype = ...
half: dtype = ...
int: dtype = ...
int16: dtype = ...
int32: dtype = ...
int64: dtype = ...
int8: dtype = ...
long: dtype = ...
short: dtype = ...
uint8: dtype = ...
@cwhy
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cwhy commented Nov 14, 2018 •
edited
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I have tried this, but there is still have "Cannot find reference" warning on Pycharm for torch.__version__ and torch.cuda. Did I miss something?

@ethanabrooks
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Any objection if I add this to https://github.com/python/typeshed as a pull request? I know it's not the final version but something is better than nothing.

@ashaw596
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The stub seems cause me to now have errors which say can't find find reference to nn in init.pyi now when I reference torch.nn. Is there a way to prevent it from shadowing the normal pytorch files?

@yl-jiang
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Thanks for your good work, but there are also problems with 'torch.optim', 'torch.cuda'

@kleb0nas12
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Super guys! Thanks!

@ZDstandup
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@yl-jiang,me too, and problems with torch.nn.

@anxiang1836
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@ZDstandup,I also have the same problem.

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