1493 lines
123 KiB
Python
1493 lines
123 KiB
Python
# @generated from torch/_C/_VariableFunctions.pyi.in
|
|
|
|
from torch import Tensor, Generator, strided, memory_format, contiguous_format, strided
|
|
from typing import List, Tuple, Optional, Union, Any, ContextManager, Callable, overload, Iterator, NamedTuple, Sequence, TypeVar
|
|
from torch._six import inf
|
|
|
|
from torch.types import _int, _float, _bool, Number, _dtype, _device, _qscheme, _size, _layout
|
|
|
|
import builtins
|
|
|
|
# REDUNDANT!
|
|
namedtuple_primal_tangent = NamedTuple("namedtuple_primal_tangent", [("primal", Tensor), ("tangent", Tensor)])
|
|
namedtuple_values_indices = NamedTuple("namedtuple_values_indices", [("values", Tensor), ("indices", Tensor)])
|
|
namedtuple_eigenvalues_eigenvectors = NamedTuple("namedtuple_eigenvalues_eigenvectors", [("eigenvalues", Tensor), ("eigenvectors", Tensor)])
|
|
namedtuple_a_tau = NamedTuple("namedtuple_a_tau", [("a", Tensor), ("tau", Tensor)])
|
|
namedtuple_solution_QR = NamedTuple("namedtuple_solution_QR", [("solution", Tensor), ("QR", Tensor)])
|
|
namedtuple_Q_R = NamedTuple("namedtuple_Q_R", [("Q", Tensor), ("R", Tensor)])
|
|
namedtuple_sign_logabsdet = NamedTuple("namedtuple_sign_logabsdet", [("sign", Tensor), ("logabsdet", Tensor)])
|
|
namedtuple_solution_LU = NamedTuple("namedtuple_solution_LU", [("solution", Tensor), ("LU", Tensor)])
|
|
namedtuple_U_S_V = NamedTuple("namedtuple_U_S_V", [("U", Tensor), ("S", Tensor), ("V", Tensor)])
|
|
namedtuple_solution_cloned_coefficient = NamedTuple("namedtuple_solution_cloned_coefficient", [("solution", Tensor), ("cloned_coefficient", Tensor)])
|
|
|
|
@overload
|
|
def __and__(input: Tensor, other: Tensor) -> Tensor: ...
|
|
@overload
|
|
def __and__(input: Tensor, other: Number) -> Tensor: ...
|
|
@overload
|
|
def __lshift__(input: Tensor, other: Tensor) -> Tensor: ...
|
|
@overload
|
|
def __lshift__(input: Tensor, other: Number) -> Tensor: ...
|
|
@overload
|
|
def __or__(input: Tensor, other: Tensor) -> Tensor: ...
|
|
@overload
|
|
def __or__(input: Tensor, other: Number) -> Tensor: ...
|
|
@overload
|
|
def __rshift__(input: Tensor, other: Tensor) -> Tensor: ...
|
|
@overload
|
|
def __rshift__(input: Tensor, other: Number) -> Tensor: ...
|
|
@overload
|
|
def __xor__(input: Tensor, other: Tensor) -> Tensor: ...
|
|
@overload
|
|
def __xor__(input: Tensor, other: Number) -> Tensor: ...
|
|
def _adaptive_avg_pool2d(input: Tensor, output_size: Union[_int, _size]) -> Tensor: ...
|
|
def _add_batch_dim(input: Tensor, batch_dim: _int, level: _int) -> Tensor: ...
|
|
def _add_relu(input: Tensor, other: Tensor, *, alpha: Number=1, out: Optional[Tensor]=None) -> Tensor: ...
|
|
def _add_relu_(input: Tensor, other: Tensor, *, alpha: Number=1) -> Tensor: ...
|
|
def _addmv_impl_(input: Tensor, self2: Tensor, mat: Tensor, vec: Tensor, *, beta: Number=1, alpha: Number=1) -> Tensor: ...
|
|
@overload
|
|
def _aminmax(input: Tensor) -> Tuple[Tensor, Tensor]: ...
|
|
@overload
|
|
def _aminmax(input: Tensor, dim: _int, keepdim: _bool=False) -> Tuple[Tensor, Tensor]: ...
|
|
def _amp_foreach_non_finite_check_and_unscale_(self: Union[Tuple[Tensor, ...], List[Tensor]], found_inf: Tensor, inv_scale: Tensor) -> None: ...
|
|
def _amp_update_scale(growth_tracker: Tensor, current_scale: Tensor, found_inf: Tensor, scale_growth_factor: _float, scale_backoff_factor: _float, growth_interval: _int) -> Tensor: ...
|
|
def _baddbmm_mkl_(input: Tensor, batch1: Tensor, batch2: Tensor, *, beta: Number=1, alpha: Number=1) -> Tensor: ...
|
|
def _batch_norm_impl_index(input: Tensor, weight: Optional[Tensor], bias: Optional[Tensor], running_mean: Optional[Tensor], running_var: Optional[Tensor], training: _bool, momentum: _float, eps: _float, cudnn_enabled: _bool) -> Tuple[Tensor, Tensor, Tensor, Tensor, _int]: ...
|
|
def _bmm(input: Tensor, mat2: Tensor, *, deterministic: _bool=False, out: Optional[Tensor]=None) -> Tensor: ...
|
|
def _cast_Byte(input: Tensor, non_blocking: _bool=False) -> Tensor: ...
|
|
def _cast_Char(input: Tensor, non_blocking: _bool=False) -> Tensor: ...
|
|
def _cast_Double(input: Tensor, non_blocking: _bool=False) -> Tensor: ...
|
|
def _cast_Float(input: Tensor, non_blocking: _bool=False) -> Tensor: ...
|
|
def _cast_Half(input: Tensor, non_blocking: _bool=False) -> Tensor: ...
|
|
def _cast_Int(input: Tensor, non_blocking: _bool=False) -> Tensor: ...
|
|
def _cast_Long(input: Tensor, non_blocking: _bool=False) -> Tensor: ...
|
|
def _cast_Short(input: Tensor, non_blocking: _bool=False) -> Tensor: ...
|
|
def _cat(tensors: Union[Tuple[Tensor, ...], List[Tensor]], dim: _int=0, *, out: Optional[Tensor]=None) -> Tensor: ...
|
|
def _choose_qparams_per_tensor(input: Tensor, reduce_range: _bool=False) -> Tuple[_float, _int]: ...
|
|
def _compute_linear_combination(input: Tensor, coefficients: Tensor, *, out: Optional[Tensor]=None) -> Tensor: ...
|
|
def _conj(input: Tensor) -> Tensor: ...
|
|
@overload
|
|
def _convolution(input: Tensor, weight: Tensor, bias: Optional[Tensor], stride: _size, padding: _size, dilation: _size, transposed: _bool, output_padding: _size, groups: _int, benchmark: _bool, deterministic: _bool, cudnn_enabled: _bool) -> Tensor: ...
|
|
@overload
|
|
def _convolution(input: Tensor, weight: Tensor, bias: Optional[Tensor], stride: _size, padding: _size, dilation: _size, transposed: _bool, output_padding: _size, groups: _int, benchmark: _bool, deterministic: _bool, cudnn_enabled: _bool, allow_tf32: _bool) -> Tensor: ...
|
|
def _convolution_nogroup(input: Tensor, weight: Tensor, bias: Optional[Tensor], stride: _size, padding: _size, dilation: _size, transposed: _bool, output_padding: _size) -> Tensor: ...
|
|
def _copy_from(input: Tensor, dst: Tensor, non_blocking: _bool=False) -> Tensor: ...
|
|
def _ctc_loss(log_probs: Tensor, targets: Tensor, input_lengths: _size, target_lengths: _size, blank: _int=0, zero_infinity: _bool=False) -> Tuple[Tensor, Tensor]: ...
|
|
def _cudnn_ctc_loss(log_probs: Tensor, targets: Tensor, input_lengths: _size, target_lengths: _size, blank: _int, deterministic: _bool, zero_infinity: _bool) -> Tuple[Tensor, Tensor]: ...
|
|
def _cudnn_init_dropout_state(dropout: _float, train: _bool, dropout_seed: _int, *, dtype: _dtype=None, layout: Optional[_layout]=strided, device: Union[_device, str, None]=None, pin_memory: _bool=False, requires_grad: _bool=False) -> Tensor: ...
|
|
def _cudnn_rnn(input: Tensor, weight: Union[Tuple[Tensor, ...], List[Tensor]], weight_stride0: _int, weight_buf: Optional[Tensor], hx: Tensor, cx: Optional[Tensor], mode: _int, hidden_size: _int, proj_size: _int, num_layers: _int, batch_first: _bool, dropout: _float, train: _bool, bidirectional: _bool, batch_sizes: _size, dropout_state: Optional[Tensor]) -> Tuple[Tensor, Tensor, Tensor, Tensor, Tensor]: ...
|
|
def _cudnn_rnn_flatten_weight(weight_arr: Union[Tuple[Tensor, ...], List[Tensor]], weight_stride0: _int, input_size: _int, mode: _int, hidden_size: _int, proj_size: _int, num_layers: _int, batch_first: _bool, bidirectional: _bool) -> Tensor: ...
|
|
def _cufft_clear_plan_cache(device_index: _int) -> None: ...
|
|
def _cufft_get_plan_cache_max_size(device_index: _int) -> _int: ...
|
|
def _cufft_get_plan_cache_size(device_index: _int) -> _int: ...
|
|
def _cufft_set_plan_cache_max_size(device_index: _int, max_size: _int) -> None: ...
|
|
def _cummax_helper(input: Tensor, values: Tensor, indices: Tensor, dim: _int) -> None: ...
|
|
def _cummin_helper(input: Tensor, values: Tensor, indices: Tensor, dim: _int) -> None: ...
|
|
def _debug_has_internal_overlap(input: Tensor) -> _int: ...
|
|
def _dim_arange(like: Tensor, dim: _int) -> Tensor: ...
|
|
def _dirichlet_grad(x: Tensor, alpha: Tensor, total: Tensor) -> Tensor: ...
|
|
def _embedding_bag(weight: Tensor, indices: Tensor, offsets: Tensor, scale_grad_by_freq: _bool=False, mode: _int=0, sparse: _bool=False, per_sample_weights: Optional[Tensor]=None, include_last_offset: _bool=False) -> Tuple[Tensor, Tensor, Tensor, Tensor]: ...
|
|
def _embedding_bag_forward_only(weight: Tensor, indices: Tensor, offsets: Tensor, scale_grad_by_freq: _bool=False, mode: _int=0, sparse: _bool=False, per_sample_weights: Optional[Tensor]=None, include_last_offset: _bool=False) -> Tuple[Tensor, Tensor, Tensor, Tensor]: ...
|
|
@overload
|
|
def _empty_affine_quantized(size: _size, *, scale: _float=1, zero_point: _int=0, memory_format: Optional[memory_format]=contiguous_format, dtype: _dtype=None, layout: Optional[_layout]=strided, device: Union[_device, str, None]=None, pin_memory: _bool=False, requires_grad: _bool=False) -> Tensor: ...
|
|
@overload
|
|
def _empty_affine_quantized(*size: _int, scale: _float=1, zero_point: _int=0, memory_format: Optional[memory_format]=contiguous_format, dtype: _dtype=None, layout: Optional[_layout]=strided, device: Union[_device, str, None]=None, pin_memory: _bool=False, requires_grad: _bool=False) -> Tensor: ...
|
|
@overload
|
|
def _empty_per_channel_affine_quantized(size: _size, *, scales: Tensor, zero_points: Tensor, axis: _int, memory_format: Optional[memory_format]=contiguous_format, dtype: _dtype=None, layout: Optional[_layout]=strided, device: Union[_device, str, None]=None, pin_memory: _bool=False, requires_grad: _bool=False) -> Tensor: ...
|
|
@overload
|
|
def _empty_per_channel_affine_quantized(*size: _int, scales: Tensor, zero_points: Tensor, axis: _int, memory_format: Optional[memory_format]=contiguous_format, dtype: _dtype=None, layout: Optional[_layout]=strided, device: Union[_device, str, None]=None, pin_memory: _bool=False, requires_grad: _bool=False) -> Tensor: ...
|
|
def _euclidean_dist(x1: Tensor, x2: Tensor) -> Tensor: ...
|
|
def _fake_quantize_learnable_per_channel_affine(input: Tensor, scale: Tensor, zero_point: Tensor, axis: _int, quant_min: _int, quant_max: _int, grad_factor: _float=1.0) -> Tensor: ...
|
|
def _fake_quantize_learnable_per_tensor_affine(input: Tensor, scale: Tensor, zero_point: Tensor, quant_min: _int, quant_max: _int, grad_factor: _float=1.0) -> Tensor: ...
|
|
def _fft_c2c(input: Tensor, dim: _size, normalization: _int, forward: _bool, *, out: Optional[Tensor]=None) -> Tensor: ...
|
|
def _fft_c2r(input: Tensor, dim: _size, normalization: _int, last_dim_size: _int, *, out: Optional[Tensor]=None) -> Tensor: ...
|
|
def _fft_r2c(input: Tensor, dim: _size, normalization: _int, onesided: _bool, *, out: Optional[Tensor]=None) -> Tensor: ...
|
|
def _foreach_abs(tensors: Union[Tuple[Tensor, ...], List[Tensor]]) -> Union[Tuple[Tensor, ...], List[Tensor]]: ...
|
|
def _foreach_abs_(self: Union[Tuple[Tensor, ...], List[Tensor]]) -> None: ...
|
|
def _foreach_acos(tensors: Union[Tuple[Tensor, ...], List[Tensor]]) -> Union[Tuple[Tensor, ...], List[Tensor]]: ...
|
|
def _foreach_acos_(self: Union[Tuple[Tensor, ...], List[Tensor]]) -> None: ...
|
|
@overload
|
|
def _foreach_add(tensors: Union[Tuple[Tensor, ...], List[Tensor]], scalars: Sequence[float]) -> Union[Tuple[Tensor, ...], List[Tensor]]: ...
|
|
@overload
|
|
def _foreach_add(tensors: Union[Tuple[Tensor, ...], List[Tensor]], scalar: Number) -> Union[Tuple[Tensor, ...], List[Tensor]]: ...
|
|
@overload
|
|
def _foreach_add(tensors1: Union[Tuple[Tensor, ...], List[Tensor]], tensors2: Union[Tuple[Tensor, ...], List[Tensor]], *, alpha: Number=1) -> Union[Tuple[Tensor, ...], List[Tensor]]: ...
|
|
@overload
|
|
def _foreach_add_(self: Union[Tuple[Tensor, ...], List[Tensor]], scalars: Sequence[float]) -> None: ...
|
|
@overload
|
|
def _foreach_add_(self: Union[Tuple[Tensor, ...], List[Tensor]], scalar: Number) -> None: ...
|
|
@overload
|
|
def _foreach_add_(self: Union[Tuple[Tensor, ...], List[Tensor]], other: Union[Tuple[Tensor, ...], List[Tensor]], *, alpha: Number=1) -> None: ...
|
|
@overload
|
|
def _foreach_addcdiv(input: Union[Tuple[Tensor, ...], List[Tensor]], tensor1: Union[Tuple[Tensor, ...], List[Tensor]], tensor2: Union[Tuple[Tensor, ...], List[Tensor]], scalars: Sequence[float]) -> Union[Tuple[Tensor, ...], List[Tensor]]: ...
|
|
@overload
|
|
def _foreach_addcdiv(input: Union[Tuple[Tensor, ...], List[Tensor]], tensor1: Union[Tuple[Tensor, ...], List[Tensor]], tensor2: Union[Tuple[Tensor, ...], List[Tensor]], value: Number=1) -> Union[Tuple[Tensor, ...], List[Tensor]]: ...
|
|
@overload
|
|
def _foreach_addcdiv_(self: Union[Tuple[Tensor, ...], List[Tensor]], tensor1: Union[Tuple[Tensor, ...], List[Tensor]], tensor2: Union[Tuple[Tensor, ...], List[Tensor]], scalars: Sequence[float]) -> None: ...
|
|
@overload
|
|
def _foreach_addcdiv_(self: Union[Tuple[Tensor, ...], List[Tensor]], tensor1: Union[Tuple[Tensor, ...], List[Tensor]], tensor2: Union[Tuple[Tensor, ...], List[Tensor]], value: Number=1) -> None: ...
|
|
@overload
|
|
def _foreach_addcmul(input: Union[Tuple[Tensor, ...], List[Tensor]], tensor1: Union[Tuple[Tensor, ...], List[Tensor]], tensor2: Union[Tuple[Tensor, ...], List[Tensor]], scalars: Sequence[float]) -> Union[Tuple[Tensor, ...], List[Tensor]]: ...
|
|
@overload
|
|
def _foreach_addcmul(input: Union[Tuple[Tensor, ...], List[Tensor]], tensor1: Union[Tuple[Tensor, ...], List[Tensor]], tensor2: Union[Tuple[Tensor, ...], List[Tensor]], value: Number=1) -> Union[Tuple[Tensor, ...], List[Tensor]]: ...
|
|
@overload
|
|
def _foreach_addcmul_(self: Union[Tuple[Tensor, ...], List[Tensor]], tensor1: Union[Tuple[Tensor, ...], List[Tensor]], tensor2: Union[Tuple[Tensor, ...], List[Tensor]], scalars: Sequence[float]) -> None: ...
|
|
@overload
|
|
def _foreach_addcmul_(self: Union[Tuple[Tensor, ...], List[Tensor]], tensor1: Union[Tuple[Tensor, ...], List[Tensor]], tensor2: Union[Tuple[Tensor, ...], List[Tensor]], value: Number=1) -> None: ...
|
|
def _foreach_asin(tensors: Union[Tuple[Tensor, ...], List[Tensor]]) -> Union[Tuple[Tensor, ...], List[Tensor]]: ...
|
|
def _foreach_asin_(self: Union[Tuple[Tensor, ...], List[Tensor]]) -> None: ...
|
|
def _foreach_atan(tensors: Union[Tuple[Tensor, ...], List[Tensor]]) -> Union[Tuple[Tensor, ...], List[Tensor]]: ...
|
|
def _foreach_atan_(self: Union[Tuple[Tensor, ...], List[Tensor]]) -> None: ...
|
|
def _foreach_ceil(tensors: Union[Tuple[Tensor, ...], List[Tensor]]) -> Union[Tuple[Tensor, ...], List[Tensor]]: ...
|
|
def _foreach_ceil_(self: Union[Tuple[Tensor, ...], List[Tensor]]) -> None: ...
|
|
def _foreach_cos(tensors: Union[Tuple[Tensor, ...], List[Tensor]]) -> Union[Tuple[Tensor, ...], List[Tensor]]: ...
|
|
def _foreach_cos_(self: Union[Tuple[Tensor, ...], List[Tensor]]) -> None: ...
|
|
def _foreach_cosh(tensors: Union[Tuple[Tensor, ...], List[Tensor]]) -> Union[Tuple[Tensor, ...], List[Tensor]]: ...
|
|
def _foreach_cosh_(self: Union[Tuple[Tensor, ...], List[Tensor]]) -> None: ...
|
|
@overload
|
|
def _foreach_div(tensors: Union[Tuple[Tensor, ...], List[Tensor]], scalars: Sequence[float]) -> Union[Tuple[Tensor, ...], List[Tensor]]: ...
|
|
@overload
|
|
def _foreach_div(tensors: Union[Tuple[Tensor, ...], List[Tensor]], scalar: Number) -> Union[Tuple[Tensor, ...], List[Tensor]]: ...
|
|
@overload
|
|
def _foreach_div(tensors1: Union[Tuple[Tensor, ...], List[Tensor]], tensors2: Union[Tuple[Tensor, ...], List[Tensor]]) -> Union[Tuple[Tensor, ...], List[Tensor]]: ...
|
|
@overload
|
|
def _foreach_div_(self: Union[Tuple[Tensor, ...], List[Tensor]], scalars: Sequence[float]) -> None: ...
|
|
@overload
|
|
def _foreach_div_(self: Union[Tuple[Tensor, ...], List[Tensor]], scalar: Number) -> None: ...
|
|
@overload
|
|
def _foreach_div_(self: Union[Tuple[Tensor, ...], List[Tensor]], other: Union[Tuple[Tensor, ...], List[Tensor]]) -> None: ...
|
|
def _foreach_erf(tensors: Union[Tuple[Tensor, ...], List[Tensor]]) -> Union[Tuple[Tensor, ...], List[Tensor]]: ...
|
|
def _foreach_erf_(self: Union[Tuple[Tensor, ...], List[Tensor]]) -> None: ...
|
|
def _foreach_erfc(tensors: Union[Tuple[Tensor, ...], List[Tensor]]) -> Union[Tuple[Tensor, ...], List[Tensor]]: ...
|
|
def _foreach_erfc_(self: Union[Tuple[Tensor, ...], List[Tensor]]) -> None: ...
|
|
def _foreach_exp(tensors: Union[Tuple[Tensor, ...], List[Tensor]]) -> Union[Tuple[Tensor, ...], List[Tensor]]: ...
|
|
def _foreach_exp_(self: Union[Tuple[Tensor, ...], List[Tensor]]) -> None: ...
|
|
def _foreach_expm1(tensors: Union[Tuple[Tensor, ...], List[Tensor]]) -> Union[Tuple[Tensor, ...], List[Tensor]]: ...
|
|
def _foreach_expm1_(self: Union[Tuple[Tensor, ...], List[Tensor]]) -> None: ...
|
|
def _foreach_floor(tensors: Union[Tuple[Tensor, ...], List[Tensor]]) -> Union[Tuple[Tensor, ...], List[Tensor]]: ...
|
|
def _foreach_floor_(self: Union[Tuple[Tensor, ...], List[Tensor]]) -> None: ...
|
|
def _foreach_frac(tensors: Union[Tuple[Tensor, ...], List[Tensor]]) -> Union[Tuple[Tensor, ...], List[Tensor]]: ...
|
|
def _foreach_frac_(self: Union[Tuple[Tensor, ...], List[Tensor]]) -> None: ...
|
|
def _foreach_lgamma(tensors: Union[Tuple[Tensor, ...], List[Tensor]]) -> Union[Tuple[Tensor, ...], List[Tensor]]: ...
|
|
def _foreach_lgamma_(self: Union[Tuple[Tensor, ...], List[Tensor]]) -> None: ...
|
|
def _foreach_log(tensors: Union[Tuple[Tensor, ...], List[Tensor]]) -> Union[Tuple[Tensor, ...], List[Tensor]]: ...
|
|
def _foreach_log10(tensors: Union[Tuple[Tensor, ...], List[Tensor]]) -> Union[Tuple[Tensor, ...], List[Tensor]]: ...
|
|
def _foreach_log10_(self: Union[Tuple[Tensor, ...], List[Tensor]]) -> None: ...
|
|
def _foreach_log1p(tensors: Union[Tuple[Tensor, ...], List[Tensor]]) -> Union[Tuple[Tensor, ...], List[Tensor]]: ...
|
|
def _foreach_log1p_(self: Union[Tuple[Tensor, ...], List[Tensor]]) -> None: ...
|
|
def _foreach_log2(tensors: Union[Tuple[Tensor, ...], List[Tensor]]) -> Union[Tuple[Tensor, ...], List[Tensor]]: ...
|
|
def _foreach_log2_(self: Union[Tuple[Tensor, ...], List[Tensor]]) -> None: ...
|
|
def _foreach_log_(self: Union[Tuple[Tensor, ...], List[Tensor]]) -> None: ...
|
|
def _foreach_maximum(tensors1: Union[Tuple[Tensor, ...], List[Tensor]], tensors2: Union[Tuple[Tensor, ...], List[Tensor]]) -> Union[Tuple[Tensor, ...], List[Tensor]]: ...
|
|
def _foreach_minimum(tensors1: Union[Tuple[Tensor, ...], List[Tensor]], tensors2: Union[Tuple[Tensor, ...], List[Tensor]]) -> Union[Tuple[Tensor, ...], List[Tensor]]: ...
|
|
@overload
|
|
def _foreach_mul(tensors: Union[Tuple[Tensor, ...], List[Tensor]], scalars: Sequence[float]) -> Union[Tuple[Tensor, ...], List[Tensor]]: ...
|
|
@overload
|
|
def _foreach_mul(tensors: Union[Tuple[Tensor, ...], List[Tensor]], scalar: Number) -> Union[Tuple[Tensor, ...], List[Tensor]]: ...
|
|
@overload
|
|
def _foreach_mul(tensors1: Union[Tuple[Tensor, ...], List[Tensor]], tensors2: Union[Tuple[Tensor, ...], List[Tensor]]) -> Union[Tuple[Tensor, ...], List[Tensor]]: ...
|
|
@overload
|
|
def _foreach_mul_(self: Union[Tuple[Tensor, ...], List[Tensor]], scalars: Sequence[float]) -> None: ...
|
|
@overload
|
|
def _foreach_mul_(self: Union[Tuple[Tensor, ...], List[Tensor]], scalar: Number) -> None: ...
|
|
@overload
|
|
def _foreach_mul_(self: Union[Tuple[Tensor, ...], List[Tensor]], other: Union[Tuple[Tensor, ...], List[Tensor]]) -> None: ...
|
|
def _foreach_neg(tensors: Union[Tuple[Tensor, ...], List[Tensor]]) -> Union[Tuple[Tensor, ...], List[Tensor]]: ...
|
|
def _foreach_neg_(self: Union[Tuple[Tensor, ...], List[Tensor]]) -> None: ...
|
|
def _foreach_reciprocal(tensors: Union[Tuple[Tensor, ...], List[Tensor]]) -> Union[Tuple[Tensor, ...], List[Tensor]]: ...
|
|
def _foreach_reciprocal_(self: Union[Tuple[Tensor, ...], List[Tensor]]) -> None: ...
|
|
def _foreach_round(tensors: Union[Tuple[Tensor, ...], List[Tensor]]) -> Union[Tuple[Tensor, ...], List[Tensor]]: ...
|
|
def _foreach_round_(self: Union[Tuple[Tensor, ...], List[Tensor]]) -> None: ...
|
|
def _foreach_sigmoid(tensors: Union[Tuple[Tensor, ...], List[Tensor]]) -> Union[Tuple[Tensor, ...], List[Tensor]]: ...
|
|
def _foreach_sigmoid_(self: Union[Tuple[Tensor, ...], List[Tensor]]) -> None: ...
|
|
def _foreach_sin(tensors: Union[Tuple[Tensor, ...], List[Tensor]]) -> Union[Tuple[Tensor, ...], List[Tensor]]: ...
|
|
def _foreach_sin_(self: Union[Tuple[Tensor, ...], List[Tensor]]) -> None: ...
|
|
def _foreach_sinh(tensors: Union[Tuple[Tensor, ...], List[Tensor]]) -> Union[Tuple[Tensor, ...], List[Tensor]]: ...
|
|
def _foreach_sinh_(self: Union[Tuple[Tensor, ...], List[Tensor]]) -> None: ...
|
|
def _foreach_sqrt(tensors: Union[Tuple[Tensor, ...], List[Tensor]]) -> Union[Tuple[Tensor, ...], List[Tensor]]: ...
|
|
def _foreach_sqrt_(self: Union[Tuple[Tensor, ...], List[Tensor]]) -> None: ...
|
|
@overload
|
|
def _foreach_sub(tensors: Union[Tuple[Tensor, ...], List[Tensor]], scalars: Sequence[float]) -> Union[Tuple[Tensor, ...], List[Tensor]]: ...
|
|
@overload
|
|
def _foreach_sub(tensors: Union[Tuple[Tensor, ...], List[Tensor]], scalar: Number) -> Union[Tuple[Tensor, ...], List[Tensor]]: ...
|
|
@overload
|
|
def _foreach_sub(tensors1: Union[Tuple[Tensor, ...], List[Tensor]], tensors2: Union[Tuple[Tensor, ...], List[Tensor]], *, alpha: Number=1) -> Union[Tuple[Tensor, ...], List[Tensor]]: ...
|
|
@overload
|
|
def _foreach_sub_(self: Union[Tuple[Tensor, ...], List[Tensor]], scalars: Sequence[float]) -> None: ...
|
|
@overload
|
|
def _foreach_sub_(self: Union[Tuple[Tensor, ...], List[Tensor]], scalar: Number) -> None: ...
|
|
@overload
|
|
def _foreach_sub_(self: Union[Tuple[Tensor, ...], List[Tensor]], other: Union[Tuple[Tensor, ...], List[Tensor]], *, alpha: Number=1) -> None: ...
|
|
def _foreach_tan(tensors: Union[Tuple[Tensor, ...], List[Tensor]]) -> Union[Tuple[Tensor, ...], List[Tensor]]: ...
|
|
def _foreach_tan_(self: Union[Tuple[Tensor, ...], List[Tensor]]) -> None: ...
|
|
def _foreach_tanh(tensors: Union[Tuple[Tensor, ...], List[Tensor]]) -> Union[Tuple[Tensor, ...], List[Tensor]]: ...
|
|
def _foreach_tanh_(self: Union[Tuple[Tensor, ...], List[Tensor]]) -> None: ...
|
|
def _foreach_trunc(tensors: Union[Tuple[Tensor, ...], List[Tensor]]) -> Union[Tuple[Tensor, ...], List[Tensor]]: ...
|
|
def _foreach_trunc_(self: Union[Tuple[Tensor, ...], List[Tensor]]) -> None: ...
|
|
def _foreach_zero_(self: Union[Tuple[Tensor, ...], List[Tensor]]) -> None: ...
|
|
def _fused_dropout(input: Tensor, p: _float, generator: Optional[Generator]=None) -> Tuple[Tensor, Tensor]: ...
|
|
def _grid_sampler_2d_cpu_fallback(input: Tensor, grid: Tensor, interpolation_mode: _int, padding_mode: _int, align_corners: _bool) -> Tensor: ...
|
|
def _has_compatible_shallow_copy_type(input: Tensor, from_: Tensor) -> _bool: ...
|
|
def _index_copy_(input: Tensor, dim: _int, index: Tensor, source: Tensor) -> Tensor: ...
|
|
def _index_put_impl_(input: Tensor, indices: Optional[Union[Tuple[Tensor, ...], List[Tensor]]], values: Tensor, accumulate: _bool=False, unsafe: _bool=False) -> Tensor: ...
|
|
def _linalg_inv_out_helper_(input: Tensor, infos_lu: Tensor, infos_getri: Tensor) -> Tensor: ...
|
|
def _linalg_qr_helper(input: Tensor, mode: str) -> Tuple[Tensor, Tensor]: ...
|
|
def _linalg_solve_out_helper_(input: Tensor, other: Tensor, infos: Tensor) -> Tensor: ...
|
|
def _log_softmax(input: Tensor, dim: _int, half_to_float: _bool) -> Tensor: ...
|
|
def _log_softmax_backward_data(grad_output: Tensor, output: Tensor, dim: _int, input: Tensor) -> Tensor: ...
|
|
def _logcumsumexp(input: Tensor, dim: _int, *, out: Optional[Tensor]=None) -> Tensor: ...
|
|
def _lu_solve_helper(input: Tensor, LU_data: Tensor, LU_pivots: Tensor) -> Tensor: ...
|
|
def _lu_with_info(input: Tensor, pivot: _bool=True, check_errors: _bool=True) -> Tuple[Tensor, Tensor, Tensor]: ...
|
|
def _make_dual(primal: Tensor, tangent: Tensor, level: _int) -> Tensor: ...
|
|
def _make_per_channel_quantized_tensor(input: Tensor, scale: Tensor, zero_point: Tensor, axis: _int) -> Tensor: ...
|
|
def _make_per_tensor_quantized_tensor(input: Tensor, scale: _float, zero_point: _int) -> Tensor: ...
|
|
def _masked_scale(input: Tensor, mask: Tensor, scale: _float) -> Tensor: ...
|
|
def _mkldnn_reshape(input: Tensor, shape: _size) -> Tensor: ...
|
|
def _mkldnn_transpose(input: Tensor, dim0: _int, dim1: _int) -> Tensor: ...
|
|
def _mkldnn_transpose_(input: Tensor, dim0: _int, dim1: _int) -> Tensor: ...
|
|
def _mode(input: Tensor, dim: _int=-1, keepdim: _bool=False, *, out: Union[Tensor, Tuple[Tensor, ...], List[Tensor]]=None) -> Tuple[Tensor, Tensor]: ...
|
|
def _nnpack_available() -> _bool: ...
|
|
def _nnpack_spatial_convolution(input: Tensor, weight: Tensor, bias: Optional[Tensor], padding: Union[_int, _size], stride: Union[_int, _size]=1) -> Tensor: ...
|
|
def _pack_padded_sequence(input: Tensor, lengths: Tensor, batch_first: _bool) -> Tuple[Tensor, Tensor]: ...
|
|
def _pad_packed_sequence(data: Tensor, batch_sizes: Tensor, batch_first: _bool, padding_value: Number, total_length: _int) -> Tuple[Tensor, Tensor]: ...
|
|
def _remove_batch_dim(input: Tensor, level: _int, batch_size: _int, out_dim: _int) -> Tensor: ...
|
|
def _reshape_from_tensor(input: Tensor, shape: Tensor) -> Tensor: ...
|
|
def _rowwise_prune(weight: Tensor, mask: Tensor, compressed_indices_dtype: _dtype) -> Tuple[Tensor, Tensor]: ...
|
|
def _s_where(condition: Tensor, input: Tensor, other: Tensor) -> Tensor: ...
|
|
def _sample_dirichlet(input: Tensor, generator: Optional[Generator]=None) -> Tensor: ...
|
|
def _saturate_weight_to_fp16(weight: Tensor) -> Tensor: ...
|
|
def _shape_as_tensor(input: Tensor) -> Tensor: ...
|
|
def _sobol_engine_draw(quasi: Tensor, n: _int, sobolstate: Tensor, dimension: _int, num_generated: _int, dtype: Optional[_dtype]) -> Tuple[Tensor, Tensor]: ...
|
|
def _sobol_engine_ff_(input: Tensor, n: _int, sobolstate: Tensor, dimension: _int, num_generated: _int) -> Tensor: ...
|
|
def _sobol_engine_initialize_state_(input: Tensor, dimension: _int) -> Tensor: ...
|
|
def _sobol_engine_scramble_(input: Tensor, ltm: Tensor, dimension: _int) -> Tensor: ...
|
|
def _softmax(input: Tensor, dim: _int, half_to_float: _bool) -> Tensor: ...
|
|
def _softmax_backward_data(grad_output: Tensor, output: Tensor, dim: _int, input: Tensor) -> Tensor: ...
|
|
def _sparse_addmm(input: Tensor, sparse: Tensor, dense: Tensor, *, beta: Number=1, alpha: Number=1) -> Tensor: ...
|
|
def _sparse_coo_tensor_unsafe(indices: Tensor, values: Tensor, size: List[int], dtype: Optional[_dtype] = None, device: Optional[_device] = None, requires_grad: bool = False) -> Tensor: ...
|
|
@overload
|
|
def _sparse_log_softmax(input: Tensor, dim: Union[str, ellipsis, None], *, dtype: Optional[_dtype]=None) -> Tensor: ...
|
|
@overload
|
|
def _sparse_log_softmax(input: Tensor, dim: _int, dtype: Optional[_dtype]=None) -> Tensor: ...
|
|
@overload
|
|
def _sparse_log_softmax(input: Tensor, dim: _int, half_to_float: _bool) -> Tensor: ...
|
|
def _sparse_log_softmax_backward_data(grad_output: Tensor, output: Tensor, dim: _int, input: Tensor) -> Tensor: ...
|
|
def _sparse_matrix_mask_helper(t: Tensor, mask_indices: Tensor) -> Tensor: ...
|
|
def _sparse_mm(sparse: Tensor, dense: Tensor) -> Tensor: ...
|
|
@overload
|
|
def _sparse_softmax(input: Tensor, dim: Union[str, ellipsis, None], *, dtype: Optional[_dtype]=None) -> Tensor: ...
|
|
@overload
|
|
def _sparse_softmax(input: Tensor, dim: _int, dtype: Optional[_dtype]=None) -> Tensor: ...
|
|
@overload
|
|
def _sparse_softmax(input: Tensor, dim: _int, half_to_float: _bool) -> Tensor: ...
|
|
def _sparse_softmax_backward_data(grad_output: Tensor, output: Tensor, dim: _int, input: Tensor) -> Tensor: ...
|
|
def _sparse_sparse_matmul(input: Tensor, other: Tensor) -> Tensor: ...
|
|
@overload
|
|
def _sparse_sum(input: Tensor) -> Tensor: ...
|
|
@overload
|
|
def _sparse_sum(input: Tensor, *, dtype: _dtype) -> Tensor: ...
|
|
@overload
|
|
def _sparse_sum(input: Tensor, dim: Union[_int, _size]) -> Tensor: ...
|
|
@overload
|
|
def _sparse_sum(input: Tensor, dim: Union[_int, _size], *, dtype: _dtype) -> Tensor: ...
|
|
def _stack(tensors: Union[Tuple[Tensor, ...], List[Tensor]], dim: _int=0, *, out: Optional[Tensor]=None) -> Tensor: ...
|
|
def _standard_gamma(input: Tensor, generator: Optional[Generator]=None) -> Tensor: ...
|
|
def _standard_gamma_grad(input: Tensor, output: Tensor) -> Tensor: ...
|
|
def _std(input: Tensor, unbiased: _bool=True) -> Tensor: ...
|
|
def _syevd_helper(input: Tensor, compute_eigenvectors: _bool, uplo: str) -> Tuple[Tensor, Tensor]: ...
|
|
def _test_serialization_subcmul(input: Tensor, other: Tensor, alpha: Number=1) -> Tensor: ...
|
|
def _trilinear(i1: Tensor, i2: Tensor, i3: Tensor, expand1: _size, expand2: _size, expand3: _size, sumdim: _size, unroll_dim: _int=1) -> Tensor: ...
|
|
def _unique(input: Tensor, sorted: _bool=True, return_inverse: _bool=False) -> Tuple[Tensor, Tensor]: ...
|
|
def _unique2(input: Tensor, sorted: _bool=True, return_inverse: _bool=False, return_counts: _bool=False) -> Tuple[Tensor, Tensor, Tensor]: ...
|
|
def _unpack_dual(dual: Tensor, level: _int) -> namedtuple_primal_tangent: ...
|
|
def _use_cudnn_ctc_loss(log_probs: Tensor, targets: Tensor, input_lengths: _size, target_lengths: _size, blank: _int) -> _bool: ...
|
|
def _use_cudnn_rnn_flatten_weight() -> _bool: ...
|
|
def _validate_sparse_coo_tensor_args(indices: Tensor, values: Tensor, size: _size) -> None: ...
|
|
def _var(input: Tensor, unbiased: _bool=True) -> Tensor: ...
|
|
def _weight_norm(v: Tensor, g: Tensor, dim: _int=0) -> Tensor: ...
|
|
def _weight_norm_cuda_interface(v: Tensor, g: Tensor, dim: _int=0) -> Tuple[Tensor, Tensor]: ...
|
|
def abs(input: Tensor, *, out: Optional[Tensor]=None) -> Tensor: ...
|
|
def abs_(input: Tensor) -> Tensor: ...
|
|
def absolute(input: Tensor, *, out: Optional[Tensor]=None) -> Tensor: ...
|
|
def acos(input: Tensor, *, out: Optional[Tensor]=None) -> Tensor: ...
|
|
def acos_(input: Tensor) -> Tensor: ...
|
|
def acosh(input: Tensor, *, out: Optional[Tensor]=None) -> Tensor: ...
|
|
def acosh_(input: Tensor) -> Tensor: ...
|
|
def adaptive_avg_pool1d(input: Tensor, output_size: Union[_int, _size]) -> Tensor: ...
|
|
def adaptive_max_pool1d(input: Tensor, output_size: Union[_int, _size]) -> Tuple[Tensor, Tensor]: ...
|
|
@overload
|
|
def add(input: Union[Tensor, Number], other: Union[Tensor, Number], *, alpha: Optional[Number]=1, out: Optional[Tensor]=None) -> Tensor: ...
|
|
@overload
|
|
def add(self: Tensor, alpha: Number, other: Tensor) -> Tensor: ...
|
|
@overload
|
|
def add(self: Tensor, alpha: Number, other: Tensor, *, out: Tensor) -> Tensor: ...
|
|
@overload
|
|
def addbmm(beta: Number, self: Tensor, alpha: Number, batch1: Tensor, batch2: Tensor) -> Tensor: ...
|
|
@overload
|
|
def addbmm(beta: Number, self: Tensor, alpha: Number, batch1: Tensor, batch2: Tensor, *, out: Tensor) -> Tensor: ...
|
|
@overload
|
|
def addbmm(beta: Number, self: Tensor, batch1: Tensor, batch2: Tensor) -> Tensor: ...
|
|
@overload
|
|
def addbmm(beta: Number, self: Tensor, batch1: Tensor, batch2: Tensor, *, out: Tensor) -> Tensor: ...
|
|
@overload
|
|
def addbmm(input: Tensor, batch1: Tensor, batch2: Tensor, *, beta: Number=1, alpha: Number=1, out: Optional[Tensor]=None) -> Tensor: ...
|
|
@overload
|
|
def addcdiv(self: Tensor, value: Number, tensor1: Tensor, tensor2: Tensor) -> Tensor: ...
|
|
@overload
|
|
def addcdiv(self: Tensor, value: Number, tensor1: Tensor, tensor2: Tensor, *, out: Tensor) -> Tensor: ...
|
|
@overload
|
|
def addcdiv(input: Tensor, tensor1: Tensor, tensor2: Tensor, *, value: Number=1, out: Optional[Tensor]=None) -> Tensor: ...
|
|
@overload
|
|
def addcmul(self: Tensor, value: Number, tensor1: Tensor, tensor2: Tensor) -> Tensor: ...
|
|
@overload
|
|
def addcmul(self: Tensor, value: Number, tensor1: Tensor, tensor2: Tensor, *, out: Tensor) -> Tensor: ...
|
|
@overload
|
|
def addcmul(input: Tensor, tensor1: Tensor, tensor2: Tensor, *, value: Number=1, out: Optional[Tensor]=None) -> Tensor: ...
|
|
@overload
|
|
def addmm(beta: Number, self: Tensor, alpha: Number, mat1: Tensor, mat2: Tensor) -> Tensor: ...
|
|
@overload
|
|
def addmm(beta: Number, self: Tensor, alpha: Number, mat1: Tensor, mat2: Tensor, *, out: Tensor) -> Tensor: ...
|
|
@overload
|
|
def addmm(beta: Number, self: Tensor, mat1: Tensor, mat2: Tensor) -> Tensor: ...
|
|
@overload
|
|
def addmm(beta: Number, self: Tensor, mat1: Tensor, mat2: Tensor, *, out: Tensor) -> Tensor: ...
|
|
@overload
|
|
def addmm(input: Tensor, mat1: Tensor, mat2: Tensor, *, beta: Number=1, alpha: Number=1, out: Optional[Tensor]=None) -> Tensor: ...
|
|
@overload
|
|
def addmv(beta: Number, self: Tensor, alpha: Number, mat: Tensor, vec: Tensor) -> Tensor: ...
|
|
@overload
|
|
def addmv(beta: Number, self: Tensor, alpha: Number, mat: Tensor, vec: Tensor, *, out: Tensor) -> Tensor: ...
|
|
@overload
|
|
def addmv(beta: Number, self: Tensor, mat: Tensor, vec: Tensor) -> Tensor: ...
|
|
@overload
|
|
def addmv(beta: Number, self: Tensor, mat: Tensor, vec: Tensor, *, out: Tensor) -> Tensor: ...
|
|
@overload
|
|
def addmv(input: Tensor, mat: Tensor, vec: Tensor, *, beta: Number=1, alpha: Number=1, out: Optional[Tensor]=None) -> Tensor: ...
|
|
def addmv_(input: Tensor, mat: Tensor, vec: Tensor, *, beta: Number=1, alpha: Number=1) -> Tensor: ...
|
|
@overload
|
|
def addr(beta: Number, self: Tensor, alpha: Number, vec1: Tensor, vec2: Tensor) -> Tensor: ...
|
|
@overload
|
|
def addr(beta: Number, self: Tensor, alpha: Number, vec1: Tensor, vec2: Tensor, *, out: Tensor) -> Tensor: ...
|
|
@overload
|
|
def addr(beta: Number, self: Tensor, vec1: Tensor, vec2: Tensor) -> Tensor: ...
|
|
@overload
|
|
def addr(beta: Number, self: Tensor, vec1: Tensor, vec2: Tensor, *, out: Tensor) -> Tensor: ...
|
|
@overload
|
|
def addr(input: Tensor, vec1: Tensor, vec2: Tensor, *, beta: Number=1, alpha: Number=1, out: Optional[Tensor]=None) -> Tensor: ...
|
|
def affine_grid_generator(theta: Tensor, size: _size, align_corners: _bool) -> Tensor: ...
|
|
@overload
|
|
def all(input: Tensor) -> Tensor: ...
|
|
@overload
|
|
def all(input: Tensor, dim: Union[str, ellipsis, None], keepdim: _bool=False, *, out: Optional[Tensor]=None) -> Tensor: ...
|
|
@overload
|
|
def all(input: Tensor, dim: _int, keepdim: _bool=False, *, out: Optional[Tensor]=None) -> Tensor: ...
|
|
def allclose(input: Tensor, other: Tensor, rtol: _float=1e-05, atol: _float=1e-08, equal_nan: _bool=False) -> _bool: ...
|
|
def alpha_dropout(input: Tensor, p: _float, train: _bool) -> Tensor: ...
|
|
def alpha_dropout_(input: Tensor, p: _float, train: _bool) -> Tensor: ...
|
|
def amax(input: Tensor, dim: Union[_int, _size]=(), keepdim: _bool=False, *, out: Optional[Tensor]=None) -> Tensor: ...
|
|
def amin(input: Tensor, dim: Union[_int, _size]=(), keepdim: _bool=False, *, out: Optional[Tensor]=None) -> Tensor: ...
|
|
def angle(input: Tensor, *, out: Optional[Tensor]=None) -> Tensor: ...
|
|
@overload
|
|
def any(input: Tensor) -> Tensor: ...
|
|
@overload
|
|
def any(input: Tensor, dim: Union[str, ellipsis, None], keepdim: _bool=False, *, out: Optional[Tensor]=None) -> Tensor: ...
|
|
@overload
|
|
def any(input: Tensor, dim: _int, keepdim: _bool=False, *, out: Optional[Tensor]=None) -> Tensor: ...
|
|
@overload
|
|
def arange(start: Number, end: Number, step: Number, *, out: Optional[Tensor]=None, dtype: Optional[_dtype]=None, device: Union[_device, str, None]=None, requires_grad: _bool=False) -> Tensor: ...
|
|
@overload
|
|
def arange(start: Number, end: Number, *, out: Optional[Tensor]=None, dtype: Optional[_dtype]=None, device: Union[_device, str, None]=None, requires_grad: _bool=False) -> Tensor: ...
|
|
@overload
|
|
def arange(end: Number, *, out: Optional[Tensor]=None, dtype: Optional[_dtype]=None, device: Union[_device, str, None]=None, requires_grad: _bool=False) -> Tensor: ...
|
|
def arccos(input: Tensor, *, out: Optional[Tensor]=None) -> Tensor: ...
|
|
def arccos_(input: Tensor) -> Tensor: ...
|
|
def arccosh(input: Tensor, *, out: Optional[Tensor]=None) -> Tensor: ...
|
|
def arccosh_(input: Tensor) -> Tensor: ...
|
|
def arcsin(input: Tensor, *, out: Optional[Tensor]=None) -> Tensor: ...
|
|
def arcsin_(input: Tensor) -> Tensor: ...
|
|
def arcsinh(input: Tensor, *, out: Optional[Tensor]=None) -> Tensor: ...
|
|
def arcsinh_(input: Tensor) -> Tensor: ...
|
|
def arctan(input: Tensor, *, out: Optional[Tensor]=None) -> Tensor: ...
|
|
def arctan_(input: Tensor) -> Tensor: ...
|
|
def arctanh(input: Tensor, *, out: Optional[Tensor]=None) -> Tensor: ...
|
|
def arctanh_(input: Tensor) -> Tensor: ...
|
|
def argmax(input: Tensor, dim: Optional[_int]=None, keepdim: _bool=False, *, out: Optional[Tensor]=None) -> Tensor: ...
|
|
def argmin(input: Tensor, dim: Optional[_int]=None, keepdim: _bool=False, *, out: Optional[Tensor]=None) -> Tensor: ...
|
|
@overload
|
|
def argsort(input: Tensor, dim: Union[str, ellipsis, None], descending: _bool=False) -> Tensor: ...
|
|
@overload
|
|
def argsort(input: Tensor, dim: _int=-1, descending: _bool=False) -> Tensor: ...
|
|
def as_strided(input: Tensor, size: _size, stride: _size, storage_offset: Optional[_int]=None) -> Tensor: ...
|
|
def as_strided_(input: Tensor, size: _size, stride: _size, storage_offset: Optional[_int]=None) -> Tensor: ...
|
|
def as_tensor(data: Any, dtype: _dtype=None, device: Optional[_device]=None) -> Tensor: ...
|
|
def asin(input: Tensor, *, out: Optional[Tensor]=None) -> Tensor: ...
|
|
def asin_(input: Tensor) -> Tensor: ...
|
|
def asinh(input: Tensor, *, out: Optional[Tensor]=None) -> Tensor: ...
|
|
def asinh_(input: Tensor) -> Tensor: ...
|
|
def atan(input: Tensor, *, out: Optional[Tensor]=None) -> Tensor: ...
|
|
def atan2(input: Tensor, other: Tensor, *, out: Optional[Tensor]=None) -> Tensor: ...
|
|
def atan_(input: Tensor) -> Tensor: ...
|
|
def atanh(input: Tensor, *, out: Optional[Tensor]=None) -> Tensor: ...
|
|
def atanh_(input: Tensor) -> Tensor: ...
|
|
def avg_pool1d(input: Tensor, kernel_size: Union[_int, _size], stride: Union[_int, _size]=(), padding: Union[_int, _size]=0, ceil_mode: _bool=False, count_include_pad: _bool=True) -> Tensor: ...
|
|
@overload
|
|
def baddbmm(beta: Number, self: Tensor, alpha: Number, batch1: Tensor, batch2: Tensor) -> Tensor: ...
|
|
@overload
|
|
def baddbmm(beta: Number, self: Tensor, alpha: Number, batch1: Tensor, batch2: Tensor, *, out: Tensor) -> Tensor: ...
|
|
@overload
|
|
def baddbmm(beta: Number, self: Tensor, batch1: Tensor, batch2: Tensor) -> Tensor: ...
|
|
@overload
|
|
def baddbmm(beta: Number, self: Tensor, batch1: Tensor, batch2: Tensor, *, out: Tensor) -> Tensor: ...
|
|
@overload
|
|
def baddbmm(input: Tensor, batch1: Tensor, batch2: Tensor, *, beta: Number=1, alpha: Number=1, out: Optional[Tensor]=None) -> Tensor: ...
|
|
@overload
|
|
def bartlett_window(window_length: _int, *, dtype: _dtype=None, layout: Optional[_layout]=strided, device: Union[_device, str, None]=None, pin_memory: _bool=False, requires_grad: _bool=False) -> Tensor: ...
|
|
@overload
|
|
def bartlett_window(window_length: _int, periodic: _bool, *, dtype: _dtype=None, layout: Optional[_layout]=strided, device: Union[_device, str, None]=None, pin_memory: _bool=False, requires_grad: _bool=False) -> Tensor: ...
|
|
def batch_norm(input: Tensor, weight: Optional[Tensor], bias: Optional[Tensor], running_mean: Optional[Tensor], running_var: Optional[Tensor], training: _bool, momentum: _float, eps: _float, cudnn_enabled: _bool) -> Tensor: ...
|
|
def batch_norm_backward_elemt(grad_out: Tensor, input: Tensor, mean: Tensor, invstd: Tensor, weight: Optional[Tensor], mean_dy: Tensor, mean_dy_xmu: Tensor) -> Tensor: ...
|
|
def batch_norm_backward_reduce(grad_out: Tensor, input: Tensor, mean: Tensor, invstd: Tensor, weight: Optional[Tensor], input_g: _bool, weight_g: _bool, bias_g: _bool) -> Tuple[Tensor, Tensor, Tensor, Tensor]: ...
|
|
def batch_norm_elemt(input: Tensor, weight: Optional[Tensor], bias: Optional[Tensor], mean: Tensor, invstd: Tensor, eps: _float, *, out: Optional[Tensor]=None) -> Tensor: ...
|
|
def batch_norm_gather_stats(input: Tensor, mean: Tensor, invstd: Tensor, running_mean: Optional[Tensor], running_var: Optional[Tensor], momentum: _float, eps: _float, count: _int) -> Tuple[Tensor, Tensor]: ...
|
|
def batch_norm_gather_stats_with_counts(input: Tensor, mean: Tensor, invstd: Tensor, running_mean: Optional[Tensor], running_var: Optional[Tensor], momentum: _float, eps: _float, counts: Tensor) -> Tuple[Tensor, Tensor]: ...
|
|
def batch_norm_stats(input: Tensor, eps: _float) -> Tuple[Tensor, Tensor]: ...
|
|
def batch_norm_update_stats(input: Tensor, running_mean: Optional[Tensor], running_var: Optional[Tensor], momentum: _float) -> Tuple[Tensor, Tensor]: ...
|
|
@overload
|
|
def bernoulli(input: Tensor, *, generator: Optional[Generator]=None, out: Optional[Tensor]=None) -> Tensor: ...
|
|
@overload
|
|
def bernoulli(input: Tensor, p: _float, *, generator: Optional[Generator]=None) -> Tensor: ...
|
|
def bilinear(input1: Tensor, input2: Tensor, weight: Tensor, bias: Optional[Tensor]) -> Tensor: ...
|
|
def binary_cross_entropy_with_logits(input: Tensor, target: Tensor, weight: Optional[Tensor] = None, size_average: Optional[bool] = None, reduce: Optional[bool] = None, reduction: str = ..., pos_weight: Optional[Tensor] = None) -> Tensor: ...
|
|
def bincount(input: Tensor, weights: Optional[Tensor]=None, minlength: _int=0) -> Tensor: ...
|
|
def binomial(count: Tensor, prob: Tensor, generator: Optional[Generator]=None) -> Tensor: ...
|
|
@overload
|
|
def bitwise_and(input: Tensor, other: Tensor, *, out: Optional[Tensor]=None) -> Tensor: ...
|
|
@overload
|
|
def bitwise_and(input: Tensor, other: Number, *, out: Optional[Tensor]=None) -> Tensor: ...
|
|
def bitwise_not(input: Tensor, *, out: Optional[Tensor]=None) -> Tensor: ...
|
|
@overload
|
|
def bitwise_or(input: Tensor, other: Tensor, *, out: Optional[Tensor]=None) -> Tensor: ...
|
|
@overload
|
|
def bitwise_or(input: Tensor, other: Number, *, out: Optional[Tensor]=None) -> Tensor: ...
|
|
@overload
|
|
def bitwise_xor(input: Tensor, other: Tensor, *, out: Optional[Tensor]=None) -> Tensor: ...
|
|
@overload
|
|
def bitwise_xor(input: Tensor, other: Number, *, out: Optional[Tensor]=None) -> Tensor: ...
|
|
@overload
|
|
def blackman_window(window_length: _int, *, dtype: _dtype=None, layout: Optional[_layout]=strided, device: Union[_device, str, None]=None, pin_memory: _bool=False, requires_grad: _bool=False) -> Tensor: ...
|
|
@overload
|
|
def blackman_window(window_length: _int, periodic: _bool, *, dtype: _dtype=None, layout: Optional[_layout]=strided, device: Union[_device, str, None]=None, pin_memory: _bool=False, requires_grad: _bool=False) -> Tensor: ...
|
|
def bmm(input: Tensor, mat2: Tensor, *, out: Optional[Tensor]=None) -> Tensor: ...
|
|
def broadcast_to(input: Tensor, size: _size) -> Tensor: ...
|
|
@overload
|
|
def bucketize(input: Tensor, boundaries: Tensor, *, out_int32: _bool=False, right: _bool=False, out: Optional[Tensor]=None) -> Tensor: ...
|
|
@overload
|
|
def bucketize(self: Number, boundaries: Tensor, *, out_int32: _bool=False, right: _bool=False) -> Tensor: ...
|
|
def can_cast(from_: _dtype, to: _dtype) -> _bool: ...
|
|
@overload
|
|
def cat(tensors: Union[Tuple[Tensor, ...], List[Tensor]], dim: Union[str, ellipsis, None], *, out: Optional[Tensor]=None) -> Tensor: ...
|
|
@overload
|
|
def cat(tensors: Union[Tuple[Tensor, ...], List[Tensor]], dim: _int=0, *, out: Optional[Tensor]=None) -> Tensor: ...
|
|
def ceil(input: Tensor, *, out: Optional[Tensor]=None) -> Tensor: ...
|
|
def ceil_(input: Tensor) -> Tensor: ...
|
|
def celu(input: Tensor, alpha: Number=1.0) -> Tensor: ...
|
|
def celu_(input: Tensor, alpha: Number=1.0) -> Tensor: ...
|
|
def channel_shuffle(input: Tensor, groups: _int) -> Tensor: ...
|
|
def cholesky(input: Tensor, upper: _bool=False, *, out: Optional[Tensor]=None) -> Tensor: ...
|
|
def cholesky_inverse(input: Tensor, upper: _bool=False, *, out: Optional[Tensor]=None) -> Tensor: ...
|
|
def cholesky_solve(input: Tensor, input2: Tensor, upper: _bool=False, *, out: Optional[Tensor]=None) -> Tensor: ...
|
|
def choose_qparams_optimized(input: Tensor, numel: _int, n_bins: _int, ratio: _float, bit_width: _int) -> Tuple[Tensor, Tensor]: ...
|
|
def chunk(input: Tensor, chunks: _int, dim: _int=0) -> Union[Tuple[Tensor, ...], List[Tensor]]: ...
|
|
def clamp(self, min: _float=-inf, max: _float=inf, *, out: Optional[Tensor]=None) -> Tensor: ...
|
|
def clamp_max(input: Tensor, max: Number, *, out: Optional[Tensor]=None) -> Tensor: ...
|
|
def clamp_max_(input: Tensor, max: Number) -> Tensor: ...
|
|
def clamp_min(input: Tensor, min: Number, *, out: Optional[Tensor]=None) -> Tensor: ...
|
|
def clamp_min_(input: Tensor, min: Number) -> Tensor: ...
|
|
def clip(input: Tensor, min: Optional[Number]=None, max: Optional[Number]=None, *, out: Optional[Tensor]=None) -> Tensor: ...
|
|
def clip_(input: Tensor, min: Optional[Number]=None, max: Optional[Number]=None) -> Tensor: ...
|
|
def clone(input: Tensor, *, memory_format: Optional[memory_format]=None) -> Tensor: ...
|
|
def column_stack(tensors: Union[Tuple[Tensor, ...], List[Tensor]], *, out: Optional[Tensor]=None) -> Tensor: ...
|
|
def combinations(input: Tensor, r: _int=2, with_replacement: _bool=False) -> Tensor: ...
|
|
def complex(real: Tensor, imag: Tensor, *, out: Optional[Tensor]=None) -> Tensor: ...
|
|
def conj(input: Tensor, *, out: Optional[Tensor]=None) -> Tensor: ...
|
|
def constant_pad_nd(input: Tensor, pad: _size, value: Number=0) -> Tensor: ...
|
|
def conv1d(input: Tensor, weight: Tensor, bias: Optional[Tensor]=None, stride: Union[_int, _size]=1, padding: Union[_int, _size]=0, dilation: Union[_int, _size]=1, groups: _int=1) -> Tensor: ...
|
|
def conv2d(input: Tensor, weight: Tensor, bias: Optional[Tensor]=None, stride: Union[_int, _size]=1, padding: Union[_int, _size]=0, dilation: Union[_int, _size]=1, groups: _int=1) -> Tensor: ...
|
|
def conv3d(input: Tensor, weight: Tensor, bias: Optional[Tensor]=None, stride: Union[_int, _size]=1, padding: Union[_int, _size]=0, dilation: Union[_int, _size]=1, groups: _int=1) -> Tensor: ...
|
|
def conv_tbc(input: Tensor, weight: Tensor, bias: Tensor, pad: _int=0) -> Tensor: ...
|
|
def conv_transpose1d(input: Tensor, weight: Tensor, bias: Optional[Tensor]=None, stride: Union[_int, _size]=1, padding: Union[_int, _size]=0, output_padding: Union[_int, _size]=0, groups: _int=1, dilation: Union[_int, _size]=1) -> Tensor: ...
|
|
def conv_transpose2d(input: Tensor, weight: Tensor, bias: Optional[Tensor]=None, stride: Union[_int, _size]=1, padding: Union[_int, _size]=0, output_padding: Union[_int, _size]=0, groups: _int=1, dilation: Union[_int, _size]=1) -> Tensor: ...
|
|
def conv_transpose3d(input: Tensor, weight: Tensor, bias: Optional[Tensor]=None, stride: Union[_int, _size]=1, padding: Union[_int, _size]=0, output_padding: Union[_int, _size]=0, groups: _int=1, dilation: Union[_int, _size]=1) -> Tensor: ...
|
|
def convolution(input: Tensor, weight: Tensor, bias: Optional[Tensor], stride: _size, padding: _size, dilation: _size, transposed: _bool, output_padding: _size, groups: _int) -> Tensor: ...
|
|
@overload
|
|
def copysign(input: Tensor, other: Tensor, *, out: Optional[Tensor]=None) -> Tensor: ...
|
|
@overload
|
|
def copysign(input: Tensor, other: Number) -> Tensor: ...
|
|
def cos(input: Tensor, *, out: Optional[Tensor]=None) -> Tensor: ...
|
|
def cos_(input: Tensor) -> Tensor: ...
|
|
def cosh(input: Tensor, *, out: Optional[Tensor]=None) -> Tensor: ...
|
|
def cosh_(input: Tensor) -> Tensor: ...
|
|
def cosine_embedding_loss(input1: Tensor, input2: Tensor, target: Tensor, margin: float = ..., size_average: Optional[bool] = ..., reduce: Optional[bool] = ..., reduction: str = ...) -> Tensor: ...
|
|
def cosine_similarity(x1: Tensor, x2: Tensor, dim: _int=1, eps: _float=1e-08) -> Tensor: ...
|
|
@overload
|
|
def count_nonzero(input: Tensor, dim: _size) -> Tensor: ...
|
|
@overload
|
|
def count_nonzero(input: Tensor, dim: Optional[_int]=None) -> Tensor: ...
|
|
def cross(input: Tensor, other: Tensor, dim: Optional[_int]=None, *, out: Optional[Tensor]=None) -> Tensor: ...
|
|
def ctc_loss(log_probs: Tensor, targets: Tensor, input_lengths: Tensor, target_lengths: Tensor, blank: int = ..., reduction: str = ..., zero_infinity: bool = ...) -> Tensor: ...
|
|
def cudnn_affine_grid_generator(theta: Tensor, N: _int, C: _int, H: _int, W: _int) -> Tensor: ...
|
|
def cudnn_batch_norm(input: Tensor, weight: Tensor, bias: Optional[Tensor], running_mean: Optional[Tensor], running_var: Optional[Tensor], training: _bool, exponential_average_factor: _float, epsilon: _float) -> Tuple[Tensor, Tensor, Tensor, Tensor]: ...
|
|
@overload
|
|
def cudnn_convolution(input: Tensor, weight: Tensor, padding: _size, stride: _size, dilation: _size, groups: _int, benchmark: _bool, deterministic: _bool) -> Tensor: ...
|
|
@overload
|
|
def cudnn_convolution(input: Tensor, weight: Tensor, padding: _size, stride: _size, dilation: _size, groups: _int, benchmark: _bool, deterministic: _bool, allow_tf32: _bool) -> Tensor: ...
|
|
@overload
|
|
def cudnn_convolution(input: Tensor, weight: Tensor, bias: Optional[Tensor], padding: _size, stride: _size, dilation: _size, groups: _int, benchmark: _bool, deterministic: _bool) -> Tensor: ...
|
|
@overload
|
|
def cudnn_convolution_transpose(input: Tensor, weight: Tensor, padding: _size, output_padding: _size, stride: _size, dilation: _size, groups: _int, benchmark: _bool, deterministic: _bool) -> Tensor: ...
|
|
@overload
|
|
def cudnn_convolution_transpose(input: Tensor, weight: Tensor, padding: _size, output_padding: _size, stride: _size, dilation: _size, groups: _int, benchmark: _bool, deterministic: _bool, allow_tf32: _bool) -> Tensor: ...
|
|
@overload
|
|
def cudnn_convolution_transpose(input: Tensor, weight: Tensor, bias: Optional[Tensor], padding: _size, output_padding: _size, stride: _size, dilation: _size, groups: _int, benchmark: _bool, deterministic: _bool) -> Tensor: ...
|
|
def cudnn_grid_sampler(input: Tensor, grid: Tensor) -> Tensor: ...
|
|
def cudnn_is_acceptable(input: Tensor) -> _bool: ...
|
|
@overload
|
|
def cummax(input: Tensor, dim: Union[str, ellipsis, None], *, out: Union[Tensor, Tuple[Tensor, ...], List[Tensor]]=None) -> namedtuple_values_indices: ...
|
|
@overload
|
|
def cummax(input: Tensor, dim: _int, *, out: Union[Tensor, Tuple[Tensor, ...], List[Tensor]]=None) -> namedtuple_values_indices: ...
|
|
@overload
|
|
def cummin(input: Tensor, dim: Union[str, ellipsis, None], *, out: Union[Tensor, Tuple[Tensor, ...], List[Tensor]]=None) -> namedtuple_values_indices: ...
|
|
@overload
|
|
def cummin(input: Tensor, dim: _int, *, out: Union[Tensor, Tuple[Tensor, ...], List[Tensor]]=None) -> namedtuple_values_indices: ...
|
|
@overload
|
|
def cumprod(input: Tensor, dim: Union[str, ellipsis, None], *, dtype: Optional[_dtype]=None, out: Optional[Tensor]=None) -> Tensor: ...
|
|
@overload
|
|
def cumprod(input: Tensor, dim: _int, *, dtype: Optional[_dtype]=None, out: Optional[Tensor]=None) -> Tensor: ...
|
|
@overload
|
|
def cumsum(input: Tensor, dim: Union[str, ellipsis, None], *, dtype: Optional[_dtype]=None, out: Optional[Tensor]=None) -> Tensor: ...
|
|
@overload
|
|
def cumsum(input: Tensor, dim: _int, *, dtype: Optional[_dtype]=None, out: Optional[Tensor]=None) -> Tensor: ...
|
|
def deg2rad(input: Tensor, *, out: Optional[Tensor]=None) -> Tensor: ...
|
|
def deg2rad_(input: Tensor) -> Tensor: ...
|
|
@overload
|
|
def dequantize(input: Tensor) -> Tensor: ...
|
|
@overload
|
|
def dequantize(tensors: Union[Tuple[Tensor, ...], List[Tensor]]) -> Union[Tuple[Tensor, ...], List[Tensor]]: ...
|
|
def det(input: Tensor) -> Tensor: ...
|
|
def detach(input: Tensor) -> Tensor: ...
|
|
def detach_(input: Tensor) -> Tensor: ...
|
|
def diag(input: Tensor, diagonal: _int=0, *, out: Optional[Tensor]=None) -> Tensor: ...
|
|
def diag_embed(input: Tensor, offset: _int=0, dim1: _int=-2, dim2: _int=-1) -> Tensor: ...
|
|
def diagflat(input: Tensor, offset: _int=0) -> Tensor: ...
|
|
@overload
|
|
def diagonal(input: Tensor, *, outdim: Union[str, ellipsis, None], dim1: Union[str, ellipsis, None], dim2: Union[str, ellipsis, None], offset: _int=0) -> Tensor: ...
|
|
@overload
|
|
def diagonal(input: Tensor, offset: _int=0, dim1: _int=0, dim2: _int=1) -> Tensor: ...
|
|
def diff(input: Tensor, n: _int=1, dim: _int=-1, prepend: Optional[Tensor]=None, append: Optional[Tensor]=None, *, out: Optional[Tensor]=None) -> Tensor: ...
|
|
def digamma(input: Tensor, *, out: Optional[Tensor]=None) -> Tensor: ...
|
|
def dist(input: Tensor, other: Tensor, p: Number=2) -> Tensor: ...
|
|
def div(input: Union[Tensor, Number], other: Union[Tensor, Number], *, rounding_mode: Optional[str]=None, out: Optional[Tensor]=None) -> Tensor: ...
|
|
@overload
|
|
def divide(input: Tensor, other: Tensor, *, out: Optional[Tensor]=None) -> Tensor: ...
|
|
@overload
|
|
def divide(input: Tensor, other: Tensor, *, rounding_mode: str, out: Optional[Tensor]=None) -> Tensor: ...
|
|
@overload
|
|
def divide(input: Tensor, other: Number, *, rounding_mode: str) -> Tensor: ...
|
|
@overload
|
|
def divide(input: Tensor, other: Number) -> Tensor: ...
|
|
def dot(input: Tensor, tensor: Tensor, *, out: Optional[Tensor]=None) -> Tensor: ...
|
|
def dropout(input: Tensor, p: _float, train: _bool) -> Tensor: ...
|
|
def dropout_(input: Tensor, p: _float, train: _bool) -> Tensor: ...
|
|
def dsmm(input: Tensor, mat2: Tensor) -> Tensor: ...
|
|
def dstack(tensors: Union[Tuple[Tensor, ...], List[Tensor]], *, out: Optional[Tensor]=None) -> Tensor: ...
|
|
def eig(input: Tensor, eigenvectors: _bool=False, *, out: Union[Tensor, Tuple[Tensor, ...], List[Tensor]]=None) -> namedtuple_eigenvalues_eigenvectors: ...
|
|
def embedding(weight: Tensor, indices: Tensor, padding_idx: _int=-1, scale_grad_by_freq: _bool=False, sparse: _bool=False) -> Tensor: ...
|
|
def embedding_bag(weight: Tensor, indices: Tensor, offsets: Tensor, scale_grad_by_freq: _bool=False, mode: _int=0, sparse: _bool=False, per_sample_weights: Optional[Tensor]=None, include_last_offset: _bool=False) -> Tuple[Tensor, Tensor, Tensor, Tensor]: ...
|
|
def embedding_renorm_(input: Tensor, indices: Tensor, max_norm: _float, norm_type: _float) -> Tensor: ...
|
|
@overload
|
|
def empty(size: _size, *, names: Optional[Sequence[Union[str, ellipsis, None]]], memory_format: Optional[memory_format]=None, dtype: _dtype=None, layout: Optional[_layout]=strided, device: Union[_device, str, None]=None, pin_memory: _bool=False, requires_grad: _bool=False) -> Tensor: ...
|
|
@overload
|
|
def empty(*size: _int, names: Optional[Sequence[Union[str, ellipsis, None]]], memory_format: Optional[memory_format]=None, dtype: _dtype=None, layout: Optional[_layout]=strided, device: Union[_device, str, None]=None, pin_memory: _bool=False, requires_grad: _bool=False) -> Tensor: ...
|
|
@overload
|
|
def empty(size: _size, *, memory_format: Optional[memory_format]=None, out: Optional[Tensor]=None, dtype: _dtype=None, layout: Optional[_layout]=strided, device: Union[_device, str, None]=None, pin_memory: _bool=False, requires_grad: _bool=False) -> Tensor: ...
|
|
@overload
|
|
def empty(*size: _int, memory_format: Optional[memory_format]=None, out: Optional[Tensor]=None, dtype: _dtype=None, layout: Optional[_layout]=strided, device: Union[_device, str, None]=None, pin_memory: _bool=False, requires_grad: _bool=False) -> Tensor: ...
|
|
def empty_like(input: Tensor, *, memory_format: Optional[memory_format]=None, dtype: _dtype=None, layout: Optional[_layout]=strided, device: Union[_device, str, None]=None, pin_memory: _bool=False, requires_grad: _bool=False) -> Tensor: ...
|
|
@overload
|
|
def empty_meta(size: _size, *, memory_format: Optional[memory_format]=None, dtype: _dtype=None, layout: Optional[_layout]=strided, device: Union[_device, str, None]=None, pin_memory: _bool=False, requires_grad: _bool=False) -> Tensor: ...
|
|
@overload
|
|
def empty_meta(*size: _int, memory_format: Optional[memory_format]=None, dtype: _dtype=None, layout: Optional[_layout]=strided, device: Union[_device, str, None]=None, pin_memory: _bool=False, requires_grad: _bool=False) -> Tensor: ...
|
|
def empty_quantized(size: _size, qtensor: Tensor) -> Tensor: ...
|
|
def empty_strided(size: _size, stride: _size, *, dtype: _dtype=None, layout: Optional[_layout]=strided, device: Union[_device, str, None]=None, pin_memory: _bool=False, requires_grad: _bool=False) -> Tensor: ...
|
|
@overload
|
|
def eq(input: Tensor, other: Tensor, *, out: Optional[Tensor]=None) -> Tensor: ...
|
|
@overload
|
|
def eq(input: Tensor, other: Number, *, out: Optional[Tensor]=None) -> Tensor: ...
|
|
def equal(input: Tensor, other: Tensor) -> _bool: ...
|
|
def erf(input: Tensor, *, out: Optional[Tensor]=None) -> Tensor: ...
|
|
def erf_(input: Tensor) -> Tensor: ...
|
|
def erfc(input: Tensor, *, out: Optional[Tensor]=None) -> Tensor: ...
|
|
def erfc_(input: Tensor) -> Tensor: ...
|
|
def erfinv(input: Tensor, *, out: Optional[Tensor]=None) -> Tensor: ...
|
|
def exp(input: Tensor, *, out: Optional[Tensor]=None) -> Tensor: ...
|
|
def exp2(input: Tensor, *, out: Optional[Tensor]=None) -> Tensor: ...
|
|
def exp2_(input: Tensor) -> Tensor: ...
|
|
def exp_(input: Tensor) -> Tensor: ...
|
|
def expm1(input: Tensor, *, out: Optional[Tensor]=None) -> Tensor: ...
|
|
def expm1_(input: Tensor) -> Tensor: ...
|
|
@overload
|
|
def eye(n: _int, *, out: Optional[Tensor]=None, dtype: _dtype=None, layout: Optional[_layout]=strided, device: Union[_device, str, None]=None, pin_memory: _bool=False, requires_grad: _bool=False) -> Tensor: ...
|
|
@overload
|
|
def eye(n: _int, m: _int, *, out: Optional[Tensor]=None, dtype: _dtype=None, layout: Optional[_layout]=strided, device: Union[_device, str, None]=None, pin_memory: _bool=False, requires_grad: _bool=False) -> Tensor: ...
|
|
def fake_quantize_per_channel_affine(input: Tensor, scale: Tensor, zero_point: Tensor, axis: _int, quant_min: _int, quant_max: _int) -> Tensor: ...
|
|
def fake_quantize_per_tensor_affine(input: Tensor, scale: _float, zero_point: _int, quant_min: _int, quant_max: _int) -> Tensor: ...
|
|
def fbgemm_linear_fp16_weight(input: Tensor, packed_weight: Tensor, bias: Tensor) -> Tensor: ...
|
|
def fbgemm_linear_fp16_weight_fp32_activation(input: Tensor, packed_weight: Tensor, bias: Tensor) -> Tensor: ...
|
|
def fbgemm_linear_int8_weight(input: Tensor, weight: Tensor, packed: Tensor, col_offsets: Tensor, weight_scale: Number, weight_zero_point: Number, bias: Tensor) -> Tensor: ...
|
|
def fbgemm_linear_int8_weight_fp32_activation(input: Tensor, weight: Tensor, packed: Tensor, col_offsets: Tensor, weight_scale: Number, weight_zero_point: Number, bias: Tensor) -> Tensor: ...
|
|
def fbgemm_linear_quantize_weight(input: Tensor) -> Tuple[Tensor, Tensor, _float, _int]: ...
|
|
def fbgemm_pack_gemm_matrix_fp16(input: Tensor) -> Tensor: ...
|
|
@overload
|
|
def fbgemm_pack_quantized_matrix(input: Tensor) -> Tensor: ...
|
|
@overload
|
|
def fbgemm_pack_quantized_matrix(input: Tensor, K: _int, N: _int) -> Tensor: ...
|
|
def feature_alpha_dropout(input: Tensor, p: _float, train: _bool) -> Tensor: ...
|
|
def feature_alpha_dropout_(input: Tensor, p: _float, train: _bool) -> Tensor: ...
|
|
def feature_dropout(input: Tensor, p: _float, train: _bool) -> Tensor: ...
|
|
def feature_dropout_(input: Tensor, p: _float, train: _bool) -> Tensor: ...
|
|
@overload
|
|
def fill_(input: Tensor, value: Tensor) -> Tensor: ...
|
|
@overload
|
|
def fill_(input: Tensor, value: Number) -> Tensor: ...
|
|
def fix(input: Tensor, *, out: Optional[Tensor]=None) -> Tensor: ...
|
|
def fix_(input: Tensor) -> Tensor: ...
|
|
@overload
|
|
def flatten(input: Tensor, start_dim: Union[str, ellipsis, None], end_dim: Union[str, ellipsis, None], out_dim: Union[str, ellipsis, None]) -> Tensor: ...
|
|
@overload
|
|
def flatten(input: Tensor, dims: Sequence[Union[str, ellipsis, None]], out_dim: Union[str, ellipsis, None]) -> Tensor: ...
|
|
@overload
|
|
def flatten(input: Tensor, start_dim: _int, end_dim: _int, out_dim: Union[str, ellipsis, None]) -> Tensor: ...
|
|
@overload
|
|
def flatten(input: Tensor, start_dim: _int=0, end_dim: _int=-1) -> Tensor: ...
|
|
def flip(input: Tensor, dims: _size) -> Tensor: ...
|
|
def fliplr(input: Tensor) -> Tensor: ...
|
|
def flipud(input: Tensor) -> Tensor: ...
|
|
@overload
|
|
def float_power(input: Tensor, exponent: Tensor, *, out: Optional[Tensor]=None) -> Tensor: ...
|
|
@overload
|
|
def float_power(self: Number, exponent: Tensor, *, out: Optional[Tensor]=None) -> Tensor: ...
|
|
@overload
|
|
def float_power(input: Tensor, exponent: Number, *, out: Optional[Tensor]=None) -> Tensor: ...
|
|
def floor(input: Tensor, *, out: Optional[Tensor]=None) -> Tensor: ...
|
|
def floor_(input: Tensor) -> Tensor: ...
|
|
def floor_divide(input: Union[Tensor, Number], other: Union[Tensor, Number], *, out: Optional[Tensor]=None) -> Tensor: ...
|
|
def fmax(input: Tensor, other: Tensor, *, out: Optional[Tensor]=None) -> Tensor: ...
|
|
def fmin(input: Tensor, other: Tensor, *, out: Optional[Tensor]=None) -> Tensor: ...
|
|
@overload
|
|
def fmod(input: Tensor, other: Tensor, *, out: Optional[Tensor]=None) -> Tensor: ...
|
|
@overload
|
|
def fmod(input: Tensor, other: Number, *, out: Optional[Tensor]=None) -> Tensor: ...
|
|
def frac(input: Tensor, *, out: Optional[Tensor]=None) -> Tensor: ...
|
|
def frac_(input: Tensor) -> Tensor: ...
|
|
@overload
|
|
def frobenius_norm(input: Tensor) -> Tensor: ...
|
|
@overload
|
|
def frobenius_norm(input: Tensor, dim: Union[_int, _size], keepdim: _bool=False, *, out: Optional[Tensor]=None) -> Tensor: ...
|
|
def from_file(filename: str, shared: Optional[_bool]=None, size: Optional[_int]=0, *, dtype: _dtype=None, layout: Optional[_layout]=strided, device: Union[_device, str, None]=None, pin_memory: _bool=False, requires_grad: _bool=False) -> Tensor: ...
|
|
def from_numpy(ndarray) -> Tensor: ...
|
|
@overload
|
|
def full(size: _size, fill_value: Number, *, out: Optional[Tensor]=None, layout: _layout=strided, dtype: Optional[_dtype]=None, device: Union[_device, str, None]=None, requires_grad: _bool=False) -> Tensor: ...
|
|
@overload
|
|
def full(size: _size, fill_value: Number, *, names: List[Union[str, None]], layout: _layout=strided, dtype: Optional[_dtype]=None, device: Union[_device, str, None]=None, requires_grad: _bool=False) -> Tensor: ...
|
|
def full_like(input: Tensor, fill_value: Number, *, memory_format: Optional[memory_format]=None, dtype: _dtype=None, layout: Optional[_layout]=strided, device: Union[_device, str, None]=None, pin_memory: _bool=False, requires_grad: _bool=False) -> Tensor: ...
|
|
@overload
|
|
def gather(input: Tensor, dim: Union[str, ellipsis, None], index: Tensor, *, sparse_grad: _bool=False, out: Optional[Tensor]=None) -> Tensor: ...
|
|
@overload
|
|
def gather(input: Tensor, dim: _int, index: Tensor, *, sparse_grad: _bool=False, out: Optional[Tensor]=None) -> Tensor: ...
|
|
def gcd(input: Tensor, other: Tensor, *, out: Optional[Tensor]=None) -> Tensor: ...
|
|
def gcd_(input: Tensor, other: Tensor) -> Tensor: ...
|
|
@overload
|
|
def ge(input: Tensor, other: Tensor, *, out: Optional[Tensor]=None) -> Tensor: ...
|
|
@overload
|
|
def ge(input: Tensor, other: Number, *, out: Optional[Tensor]=None) -> Tensor: ...
|
|
def geqrf(input: Tensor, *, out: Union[Tensor, Tuple[Tensor, ...], List[Tensor]]=None) -> namedtuple_a_tau: ...
|
|
def ger(input: Tensor, vec2: Tensor, *, out: Optional[Tensor]=None) -> Tensor: ...
|
|
def get_default_dtype() -> _dtype: ...
|
|
def get_num_interop_threads() -> _int: ...
|
|
def get_num_threads() -> _int: ...
|
|
@overload
|
|
def greater(input: Tensor, other: Tensor, *, out: Optional[Tensor]=None) -> Tensor: ...
|
|
@overload
|
|
def greater(input: Tensor, other: Number, *, out: Optional[Tensor]=None) -> Tensor: ...
|
|
@overload
|
|
def greater_equal(input: Tensor, other: Tensor, *, out: Optional[Tensor]=None) -> Tensor: ...
|
|
@overload
|
|
def greater_equal(input: Tensor, other: Number, *, out: Optional[Tensor]=None) -> Tensor: ...
|
|
def grid_sampler(input: Tensor, grid: Tensor, interpolation_mode: _int, padding_mode: _int, align_corners: _bool) -> Tensor: ...
|
|
def grid_sampler_2d(input: Tensor, grid: Tensor, interpolation_mode: _int, padding_mode: _int, align_corners: _bool) -> Tensor: ...
|
|
def grid_sampler_3d(input: Tensor, grid: Tensor, interpolation_mode: _int, padding_mode: _int, align_corners: _bool) -> Tensor: ...
|
|
def group_norm(input: Tensor, num_groups: _int, weight: Optional[Tensor]=None, bias: Optional[Tensor]=None, eps: _float=1e-05, cudnn_enabled: _bool=True) -> Tensor: ...
|
|
@overload
|
|
def gru(data: Tensor, batch_sizes: Tensor, hx: Tensor, params: Union[Tuple[Tensor, ...], List[Tensor]], has_biases: _bool, num_layers: _int, dropout: _float, train: _bool, bidirectional: _bool) -> Tuple[Tensor, Tensor]: ...
|
|
@overload
|
|
def gru(input: Tensor, hx: Tensor, params: Union[Tuple[Tensor, ...], List[Tensor]], has_biases: _bool, num_layers: _int, dropout: _float, train: _bool, bidirectional: _bool, batch_first: _bool) -> Tuple[Tensor, Tensor]: ...
|
|
def gru_cell(input: Tensor, hx: Tensor, w_ih: Tensor, w_hh: Tensor, b_ih: Optional[Tensor]=None, b_hh: Optional[Tensor]=None) -> Tensor: ...
|
|
@overload
|
|
def gt(input: Tensor, other: Tensor, *, out: Optional[Tensor]=None) -> Tensor: ...
|
|
@overload
|
|
def gt(input: Tensor, other: Number, *, out: Optional[Tensor]=None) -> Tensor: ...
|
|
@overload
|
|
def hamming_window(window_length: _int, *, dtype: _dtype=None, layout: Optional[_layout]=strided, device: Union[_device, str, None]=None, pin_memory: _bool=False, requires_grad: _bool=False) -> Tensor: ...
|
|
@overload
|
|
def hamming_window(window_length: _int, periodic: _bool, *, dtype: _dtype=None, layout: Optional[_layout]=strided, device: Union[_device, str, None]=None, pin_memory: _bool=False, requires_grad: _bool=False) -> Tensor: ...
|
|
@overload
|
|
def hamming_window(window_length: _int, periodic: _bool, alpha: _float, *, dtype: _dtype=None, layout: Optional[_layout]=strided, device: Union[_device, str, None]=None, pin_memory: _bool=False, requires_grad: _bool=False) -> Tensor: ...
|
|
@overload
|
|
def hamming_window(window_length: _int, periodic: _bool, alpha: _float, beta: _float, *, dtype: _dtype=None, layout: Optional[_layout]=strided, device: Union[_device, str, None]=None, pin_memory: _bool=False, requires_grad: _bool=False) -> Tensor: ...
|
|
@overload
|
|
def hann_window(window_length: _int, *, dtype: _dtype=None, layout: Optional[_layout]=strided, device: Union[_device, str, None]=None, pin_memory: _bool=False, requires_grad: _bool=False) -> Tensor: ...
|
|
@overload
|
|
def hann_window(window_length: _int, periodic: _bool, *, dtype: _dtype=None, layout: Optional[_layout]=strided, device: Union[_device, str, None]=None, pin_memory: _bool=False, requires_grad: _bool=False) -> Tensor: ...
|
|
def hardshrink(input: Tensor, lambd: Number=0.5) -> Tensor: ...
|
|
def heaviside(input: Tensor, values: Tensor, *, out: Optional[Tensor]=None) -> Tensor: ...
|
|
def hinge_embedding_loss(input: Tensor, target: Tensor, margin: float = ..., size_average: Optional[bool] = ..., reduce: Optional[bool] = ..., reduction: str = ...) -> Tensor: ...
|
|
def histc(input: Tensor, bins: _int=100, min: Number=0, max: Number=0, *, out: Optional[Tensor]=None) -> Tensor: ...
|
|
def hsmm(input: Tensor, mat2: Tensor) -> Tensor: ...
|
|
def hspmm(mat1: Tensor, mat2: Tensor, *, out: Optional[Tensor]=None) -> Tensor: ...
|
|
def hstack(tensors: Union[Tuple[Tensor, ...], List[Tensor]], *, out: Optional[Tensor]=None) -> Tensor: ...
|
|
def hypot(input: Tensor, other: Tensor, *, out: Optional[Tensor]=None) -> Tensor: ...
|
|
def i0(input: Tensor, *, out: Optional[Tensor]=None) -> Tensor: ...
|
|
def i0_(input: Tensor) -> Tensor: ...
|
|
def igamma(input: Tensor, other: Tensor, *, out: Optional[Tensor]=None) -> Tensor: ...
|
|
def igammac(input: Tensor, other: Tensor, *, out: Optional[Tensor]=None) -> Tensor: ...
|
|
def imag(input: Tensor) -> Tensor: ...
|
|
@overload
|
|
def index_add(input: Tensor, dim: Union[str, ellipsis, None], index: Tensor, source: Tensor) -> Tensor: ...
|
|
@overload
|
|
def index_add(input: Tensor, dim: _int, index: Tensor, source: Tensor) -> Tensor: ...
|
|
@overload
|
|
def index_copy(input: Tensor, dim: Union[str, ellipsis, None], index: Tensor, source: Tensor) -> Tensor: ...
|
|
@overload
|
|
def index_copy(input: Tensor, dim: _int, index: Tensor, source: Tensor) -> Tensor: ...
|
|
@overload
|
|
def index_fill(input: Tensor, dim: Union[str, ellipsis, None], index: Tensor, value: Tensor) -> Tensor: ...
|
|
@overload
|
|
def index_fill(input: Tensor, dim: _int, index: Tensor, value: Tensor) -> Tensor: ...
|
|
@overload
|
|
def index_fill(input: Tensor, dim: Union[str, ellipsis, None], index: Tensor, value: Number) -> Tensor: ...
|
|
@overload
|
|
def index_fill(input: Tensor, dim: _int, index: Tensor, value: Number) -> Tensor: ...
|
|
def index_put(input: Tensor, indices: Optional[Union[Tuple[Tensor, ...], List[Tensor]]], values: Tensor, accumulate: _bool=False) -> Tensor: ...
|
|
def index_put_(input: Tensor, indices: Optional[Union[Tuple[Tensor, ...], List[Tensor]]], values: Tensor, accumulate: _bool=False) -> Tensor: ...
|
|
@overload
|
|
def index_select(input: Tensor, dim: Union[str, ellipsis, None], index: Tensor, *, out: Optional[Tensor]=None) -> Tensor: ...
|
|
@overload
|
|
def index_select(input: Tensor, dim: _int, index: Tensor, *, out: Optional[Tensor]=None) -> Tensor: ...
|
|
def init_num_threads() -> None: ...
|
|
def inner(input: Tensor, other: Tensor, *, out: Optional[Tensor]=None) -> Tensor: ...
|
|
def instance_norm(input: Tensor, weight: Optional[Tensor], bias: Optional[Tensor], running_mean: Optional[Tensor], running_var: Optional[Tensor], use_input_stats: _bool, momentum: _float, eps: _float, cudnn_enabled: _bool) -> Tensor: ...
|
|
def int_repr(input: Tensor) -> Tensor: ...
|
|
def inverse(input: Tensor, *, out: Optional[Tensor]=None) -> Tensor: ...
|
|
def is_complex(input: Tensor) -> _bool: ...
|
|
def is_distributed(input: Tensor) -> _bool: ...
|
|
def is_floating_point(input: Tensor) -> _bool: ...
|
|
def is_grad_enabled() -> _bool: ...
|
|
def is_nonzero(input: Tensor) -> _bool: ...
|
|
def is_same_size(input: Tensor, other: Tensor) -> _bool: ...
|
|
def is_signed(input: Tensor) -> _bool: ...
|
|
def is_vulkan_available() -> _bool: ...
|
|
def isclose(input: Tensor, other: Tensor, rtol: _float=1e-05, atol: _float=1e-08, equal_nan: _bool=False) -> Tensor: ...
|
|
def isfinite(input: Tensor) -> Tensor: ...
|
|
def isinf(input: Tensor) -> Tensor: ...
|
|
def isnan(input: Tensor) -> Tensor: ...
|
|
def isneginf(input: Tensor, *, out: Optional[Tensor]=None) -> Tensor: ...
|
|
def isposinf(input: Tensor, *, out: Optional[Tensor]=None) -> Tensor: ...
|
|
def isreal(input: Tensor) -> Tensor: ...
|
|
@overload
|
|
def kaiser_window(window_length: _int, *, dtype: _dtype=None, layout: Optional[_layout]=strided, device: Union[_device, str, None]=None, pin_memory: _bool=False, requires_grad: _bool=False) -> Tensor: ...
|
|
@overload
|
|
def kaiser_window(window_length: _int, periodic: _bool, *, dtype: _dtype=None, layout: Optional[_layout]=strided, device: Union[_device, str, None]=None, pin_memory: _bool=False, requires_grad: _bool=False) -> Tensor: ...
|
|
@overload
|
|
def kaiser_window(window_length: _int, periodic: _bool, beta: _float, *, dtype: _dtype=None, layout: Optional[_layout]=strided, device: Union[_device, str, None]=None, pin_memory: _bool=False, requires_grad: _bool=False) -> Tensor: ...
|
|
def kl_div(input: Tensor, target: Tensor, size_average: Optional[bool] = ..., reduce: Optional[bool] = ..., reduction: str = ..., log_target: bool = ...) -> Tensor: ...
|
|
def kron(input: Tensor, other: Tensor, *, out: Optional[Tensor]=None) -> Tensor: ...
|
|
@overload
|
|
def kthvalue(input: Tensor, k: _int, dim: Union[str, ellipsis, None], keepdim: _bool=False, *, out: Union[Tensor, Tuple[Tensor, ...], List[Tensor]]=None) -> namedtuple_values_indices: ...
|
|
@overload
|
|
def kthvalue(input: Tensor, k: _int, dim: _int=-1, keepdim: _bool=False, *, out: Union[Tensor, Tuple[Tensor, ...], List[Tensor]]=None) -> namedtuple_values_indices: ...
|
|
def layer_norm(input: Tensor, normalized_shape: _size, weight: Optional[Tensor]=None, bias: Optional[Tensor]=None, eps: _float=1e-05, cudnn_enable: _bool=True) -> Tensor: ...
|
|
def lcm(input: Tensor, other: Tensor, *, out: Optional[Tensor]=None) -> Tensor: ...
|
|
def lcm_(input: Tensor, other: Tensor) -> Tensor: ...
|
|
def ldexp(input: Tensor, other: Tensor, *, out: Optional[Tensor]=None) -> Tensor: ...
|
|
def ldexp_(input: Tensor, other: Tensor) -> Tensor: ...
|
|
@overload
|
|
def le(input: Tensor, other: Tensor, *, out: Optional[Tensor]=None) -> Tensor: ...
|
|
@overload
|
|
def le(input: Tensor, other: Number, *, out: Optional[Tensor]=None) -> Tensor: ...
|
|
@overload
|
|
def lerp(input: Tensor, end: Tensor, weight: Tensor, *, out: Optional[Tensor]=None) -> Tensor: ...
|
|
@overload
|
|
def lerp(input: Tensor, end: Tensor, weight: Number, *, out: Optional[Tensor]=None) -> Tensor: ...
|
|
@overload
|
|
def less(input: Tensor, other: Tensor, *, out: Optional[Tensor]=None) -> Tensor: ...
|
|
@overload
|
|
def less(input: Tensor, other: Number, *, out: Optional[Tensor]=None) -> Tensor: ...
|
|
@overload
|
|
def less_equal(input: Tensor, other: Tensor, *, out: Optional[Tensor]=None) -> Tensor: ...
|
|
@overload
|
|
def less_equal(input: Tensor, other: Number, *, out: Optional[Tensor]=None) -> Tensor: ...
|
|
def lgamma(input: Tensor, *, out: Optional[Tensor]=None) -> Tensor: ...
|
|
def linspace(start: Number, end: Number, steps: Optional[_int]=None, *, out: Optional[Tensor]=None, dtype: _dtype=None, layout: Optional[_layout]=strided, device: Union[_device, str, None]=None, pin_memory: _bool=False, requires_grad: _bool=False) -> Tensor: ...
|
|
def log(input: Tensor, *, out: Optional[Tensor]=None) -> Tensor: ...
|
|
def log10(input: Tensor, *, out: Optional[Tensor]=None) -> Tensor: ...
|
|
def log10_(input: Tensor) -> Tensor: ...
|
|
def log1p(input: Tensor, *, out: Optional[Tensor]=None) -> Tensor: ...
|
|
def log1p_(input: Tensor) -> Tensor: ...
|
|
def log2(input: Tensor, *, out: Optional[Tensor]=None) -> Tensor: ...
|
|
def log2_(input: Tensor) -> Tensor: ...
|
|
def log_(input: Tensor) -> Tensor: ...
|
|
@overload
|
|
def log_softmax(input: Tensor, dim: Union[str, ellipsis, None], *, dtype: Optional[_dtype]=None) -> Tensor: ...
|
|
@overload
|
|
def log_softmax(input: Tensor, dim: _int, dtype: Optional[_dtype]=None) -> Tensor: ...
|
|
def logaddexp(input: Tensor, other: Tensor, *, out: Optional[Tensor]=None) -> Tensor: ...
|
|
def logaddexp2(input: Tensor, other: Tensor, *, out: Optional[Tensor]=None) -> Tensor: ...
|
|
@overload
|
|
def logcumsumexp(input: Tensor, dim: Union[str, ellipsis, None], *, out: Optional[Tensor]=None) -> Tensor: ...
|
|
@overload
|
|
def logcumsumexp(input: Tensor, dim: _int, *, out: Optional[Tensor]=None) -> Tensor: ...
|
|
def logdet(input: Tensor) -> Tensor: ...
|
|
def logical_and(input: Tensor, other: Tensor, *, out: Optional[Tensor]=None) -> Tensor: ...
|
|
def logical_not(input: Tensor, *, out: Optional[Tensor]=None) -> Tensor: ...
|
|
def logical_or(input: Tensor, other: Tensor, *, out: Optional[Tensor]=None) -> Tensor: ...
|
|
def logical_xor(input: Tensor, other: Tensor, *, out: Optional[Tensor]=None) -> Tensor: ...
|
|
def logit(input: Tensor, eps: Optional[_float]=None, *, out: Optional[Tensor]=None) -> Tensor: ...
|
|
def logit_(input: Tensor, eps: Optional[_float]=None) -> Tensor: ...
|
|
def logspace(start: Number, end: Number, steps: Optional[_int]=None, base: _float=10.0, *, out: Optional[Tensor]=None, dtype: _dtype=None, layout: Optional[_layout]=strided, device: Union[_device, str, None]=None, pin_memory: _bool=False, requires_grad: _bool=False) -> Tensor: ...
|
|
@overload
|
|
def logsumexp(input: Tensor, dim: Sequence[Union[str, ellipsis, None]], keepdim: _bool=False, *, out: Optional[Tensor]=None) -> Tensor: ...
|
|
@overload
|
|
def logsumexp(input: Tensor, dim: Union[_int, _size], keepdim: _bool=False, *, out: Optional[Tensor]=None) -> Tensor: ...
|
|
@overload
|
|
def lstm(data: Tensor, batch_sizes: Tensor, hx: Union[Tuple[Tensor, ...], List[Tensor]], params: Union[Tuple[Tensor, ...], List[Tensor]], has_biases: _bool, num_layers: _int, dropout: _float, train: _bool, bidirectional: _bool) -> Tuple[Tensor, Tensor, Tensor]: ...
|
|
@overload
|
|
def lstm(input: Tensor, hx: Union[Tuple[Tensor, ...], List[Tensor]], params: Union[Tuple[Tensor, ...], List[Tensor]], has_biases: _bool, num_layers: _int, dropout: _float, train: _bool, bidirectional: _bool, batch_first: _bool) -> Tuple[Tensor, Tensor, Tensor]: ...
|
|
def lstm_cell(input: Tensor, hx: Union[Tuple[Tensor, ...], List[Tensor]], w_ih: Tensor, w_hh: Tensor, b_ih: Optional[Tensor]=None, b_hh: Optional[Tensor]=None) -> Tuple[Tensor, Tensor]: ...
|
|
def lstsq(input: Tensor, A: Tensor, *, out: Union[Tensor, Tuple[Tensor, ...], List[Tensor]]=None) -> namedtuple_solution_QR: ...
|
|
@overload
|
|
def lt(input: Tensor, other: Tensor, *, out: Optional[Tensor]=None) -> Tensor: ...
|
|
@overload
|
|
def lt(input: Tensor, other: Number, *, out: Optional[Tensor]=None) -> Tensor: ...
|
|
def lu_solve(input: Tensor, LU_data: Tensor, LU_pivots: Tensor, *, out: Optional[Tensor]=None) -> Tensor: ...
|
|
def margin_ranking_loss(input1: Tensor, input2: Tensor, target: Tensor, margin: float = ..., size_average: Optional[bool] = ..., reduce: Optional[bool] = ..., reduction: str = ...) -> Tensor: ...
|
|
@overload
|
|
def masked_fill(input: Tensor, mask: Tensor, value: Tensor) -> Tensor: ...
|
|
@overload
|
|
def masked_fill(input: Tensor, mask: Tensor, value: Number) -> Tensor: ...
|
|
def masked_scatter(input: Tensor, mask: Tensor, source: Tensor) -> Tensor: ...
|
|
def masked_select(input: Tensor, mask: Tensor, *, out: Optional[Tensor]=None) -> Tensor: ...
|
|
def matmul(input: Tensor, other: Tensor, *, out: Optional[Tensor]=None) -> Tensor: ...
|
|
def matrix_exp(input: Tensor) -> Tensor: ...
|
|
def matrix_power(input: Tensor, n: _int) -> Tensor: ...
|
|
@overload
|
|
def matrix_rank(input: Tensor, symmetric: _bool=False) -> Tensor: ...
|
|
@overload
|
|
def matrix_rank(input: Tensor, tol: _float, symmetric: _bool=False) -> Tensor: ...
|
|
@overload
|
|
def max(input: Tensor) -> Tensor: ...
|
|
@overload
|
|
def max(input: Tensor, dim: Union[str, ellipsis, None], keepdim: _bool=False, *, out: Union[Tensor, Tuple[Tensor, ...], List[Tensor]]=None) -> namedtuple_values_indices: ...
|
|
@overload
|
|
def max(input: Tensor, other: Tensor, *, out: Optional[Tensor]=None) -> Tensor: ...
|
|
@overload
|
|
def max(input: Tensor, dim: _int, keepdim: _bool=False, *, out: Union[Tensor, Tuple[Tensor, ...], List[Tensor]]=None) -> namedtuple_values_indices: ...
|
|
def max_pool1d(input: Tensor, kernel_size: Union[_int, _size], stride: Union[_int, _size]=(), padding: Union[_int, _size]=0, dilation: Union[_int, _size]=1, ceil_mode: _bool=False) -> Tensor: ...
|
|
def max_pool1d_with_indices(input: Tensor, kernel_size: Union[_int, _size], stride: Union[_int, _size]=(), padding: Union[_int, _size]=0, dilation: Union[_int, _size]=1, ceil_mode: _bool=False) -> Tuple[Tensor, Tensor]: ...
|
|
def max_pool2d(input: Tensor, kernel_size: Union[_int, _size], stride: Union[_int, _size]=(), padding: Union[_int, _size]=0, dilation: Union[_int, _size]=1, ceil_mode: _bool=False) -> Tensor: ...
|
|
def max_pool3d(input: Tensor, kernel_size: Union[_int, _size], stride: Union[_int, _size]=(), padding: Union[_int, _size]=0, dilation: Union[_int, _size]=1, ceil_mode: _bool=False) -> Tensor: ...
|
|
def maximum(input: Tensor, other: Tensor, *, out: Optional[Tensor]=None) -> Tensor: ...
|
|
@overload
|
|
def mean(input: Tensor, *, dtype: Optional[_dtype]=None) -> Tensor: ...
|
|
@overload
|
|
def mean(input: Tensor, dim: Sequence[Union[str, ellipsis, None]], keepdim: _bool=False, *, dtype: Optional[_dtype]=None, out: Optional[Tensor]=None) -> Tensor: ...
|
|
@overload
|
|
def mean(input: Tensor, dim: Union[_int, _size], keepdim: _bool=False, *, dtype: Optional[_dtype]=None, out: Optional[Tensor]=None) -> Tensor: ...
|
|
@overload
|
|
def median(input: Tensor) -> Tensor: ...
|
|
@overload
|
|
def median(input: Tensor, dim: Union[str, ellipsis, None], keepdim: _bool=False, *, out: Union[Tensor, Tuple[Tensor, ...], List[Tensor]]=None) -> namedtuple_values_indices: ...
|
|
@overload
|
|
def median(input: Tensor, dim: _int, keepdim: _bool=False, *, out: Union[Tensor, Tuple[Tensor, ...], List[Tensor]]=None) -> namedtuple_values_indices: ...
|
|
@overload
|
|
def min(input: Tensor) -> Tensor: ...
|
|
@overload
|
|
def min(input: Tensor, dim: Union[str, ellipsis, None], keepdim: _bool=False, *, out: Union[Tensor, Tuple[Tensor, ...], List[Tensor]]=None) -> namedtuple_values_indices: ...
|
|
@overload
|
|
def min(input: Tensor, other: Tensor, *, out: Optional[Tensor]=None) -> Tensor: ...
|
|
@overload
|
|
def min(input: Tensor, dim: _int, keepdim: _bool=False, *, out: Union[Tensor, Tuple[Tensor, ...], List[Tensor]]=None) -> namedtuple_values_indices: ...
|
|
def minimum(input: Tensor, other: Tensor, *, out: Optional[Tensor]=None) -> Tensor: ...
|
|
def miopen_batch_norm(input: Tensor, weight: Tensor, bias: Optional[Tensor], running_mean: Optional[Tensor], running_var: Optional[Tensor], training: _bool, exponential_average_factor: _float, epsilon: _float) -> Tuple[Tensor, Tensor, Tensor]: ...
|
|
def miopen_convolution(input: Tensor, weight: Tensor, bias: Optional[Tensor], padding: _size, stride: _size, dilation: _size, groups: _int, benchmark: _bool, deterministic: _bool) -> Tensor: ...
|
|
def miopen_convolution_transpose(input: Tensor, weight: Tensor, bias: Optional[Tensor], padding: _size, output_padding: _size, stride: _size, dilation: _size, groups: _int, benchmark: _bool, deterministic: _bool) -> Tensor: ...
|
|
def miopen_depthwise_convolution(input: Tensor, weight: Tensor, bias: Optional[Tensor], padding: _size, stride: _size, dilation: _size, groups: _int, benchmark: _bool, deterministic: _bool) -> Tensor: ...
|
|
def miopen_rnn(input: Tensor, weight: Union[Tuple[Tensor, ...], List[Tensor]], weight_stride0: _int, hx: Tensor, cx: Optional[Tensor], mode: _int, hidden_size: _int, num_layers: _int, batch_first: _bool, dropout: _float, train: _bool, bidirectional: _bool, batch_sizes: _size, dropout_state: Optional[Tensor]) -> Tuple[Tensor, Tensor, Tensor, Tensor, Tensor]: ...
|
|
def mkldnn_adaptive_avg_pool2d(input: Tensor, output_size: Union[_int, _size]) -> Tensor: ...
|
|
def mkldnn_convolution(input: Tensor, weight: Tensor, bias: Optional[Tensor], padding: _size, stride: _size, dilation: _size, groups: _int) -> Tensor: ...
|
|
def mkldnn_convolution_backward_weights(weight_size: _size, grad_output: Tensor, input: Tensor, padding: _size, stride: _size, dilation: _size, groups: _int, bias_defined: _bool) -> Tuple[Tensor, Tensor]: ...
|
|
def mkldnn_linear_backward_weights(grad_output: Tensor, input: Tensor, weight: Tensor, bias_defined: _bool) -> Tuple[Tensor, Tensor]: ...
|
|
def mkldnn_max_pool2d(input: Tensor, kernel_size: Union[_int, _size], stride: Union[_int, _size]=(), padding: Union[_int, _size]=0, dilation: Union[_int, _size]=1, ceil_mode: _bool=False) -> Tensor: ...
|
|
def mkldnn_max_pool3d(input: Tensor, kernel_size: Union[_int, _size], stride: Union[_int, _size]=(), padding: Union[_int, _size]=0, dilation: Union[_int, _size]=1, ceil_mode: _bool=False) -> Tensor: ...
|
|
def mm(input: Tensor, mat2: Tensor, *, out: Optional[Tensor]=None) -> Tensor: ...
|
|
@overload
|
|
def mode(input: Tensor, dim: Union[str, ellipsis, None], keepdim: _bool=False, *, out: Union[Tensor, Tuple[Tensor, ...], List[Tensor]]=None) -> namedtuple_values_indices: ...
|
|
@overload
|
|
def mode(input: Tensor, dim: _int=-1, keepdim: _bool=False, *, out: Union[Tensor, Tuple[Tensor, ...], List[Tensor]]=None) -> namedtuple_values_indices: ...
|
|
@overload
|
|
def moveaxis(input: Tensor, source: _size, destination: _size) -> Tensor: ...
|
|
@overload
|
|
def moveaxis(input: Tensor, source: _int, destination: _int) -> Tensor: ...
|
|
@overload
|
|
def movedim(input: Tensor, source: _size, destination: _size) -> Tensor: ...
|
|
@overload
|
|
def movedim(input: Tensor, source: _int, destination: _int) -> Tensor: ...
|
|
def msort(input: Tensor, *, out: Optional[Tensor]=None) -> Tensor: ...
|
|
def mul(input: Union[Tensor, Number], other: Union[Tensor, Number], *, out: Optional[Tensor]=None) -> Tensor: ...
|
|
def multinomial(input: Tensor, num_samples: _int, replacement: _bool=False, *, generator: Optional[Generator]=None, out: Optional[Tensor]=None) -> Tensor: ...
|
|
@overload
|
|
def multiply(input: Tensor, other: Tensor, *, out: Optional[Tensor]=None) -> Tensor: ...
|
|
@overload
|
|
def multiply(input: Tensor, other: Number) -> Tensor: ...
|
|
def mv(input: Tensor, vec: Tensor, *, out: Optional[Tensor]=None) -> Tensor: ...
|
|
def mvlgamma(input: Tensor, p: _int) -> Tensor: ...
|
|
def nan_to_num(input: Tensor, nan: Optional[_float]=None, posinf: Optional[_float]=None, neginf: Optional[_float]=None, *, out: Optional[Tensor]=None) -> Tensor: ...
|
|
def nan_to_num_(input: Tensor, nan: Optional[_float]=None, posinf: Optional[_float]=None, neginf: Optional[_float]=None) -> Tensor: ...
|
|
@overload
|
|
def nanmedian(input: Tensor) -> Tensor: ...
|
|
@overload
|
|
def nanmedian(input: Tensor, dim: Union[str, ellipsis, None], keepdim: _bool=False, *, out: Union[Tensor, Tuple[Tensor, ...], List[Tensor]]=None) -> namedtuple_values_indices: ...
|
|
@overload
|
|
def nanmedian(input: Tensor, dim: _int, keepdim: _bool=False, *, out: Union[Tensor, Tuple[Tensor, ...], List[Tensor]]=None) -> namedtuple_values_indices: ...
|
|
@overload
|
|
def nanquantile(input: Tensor, q: Tensor, dim: Optional[_int]=None, keepdim: _bool=False, *, out: Optional[Tensor]=None) -> Tensor: ...
|
|
@overload
|
|
def nanquantile(input: Tensor, q: _float, dim: Optional[_int]=None, keepdim: _bool=False, *, out: Optional[Tensor]=None) -> Tensor: ...
|
|
@overload
|
|
def nansum(input: Tensor, *, dtype: Optional[_dtype]=None) -> Tensor: ...
|
|
@overload
|
|
def nansum(input: Tensor, dim: Union[_int, _size], keepdim: _bool=False, *, dtype: Optional[_dtype]=None, out: Optional[Tensor]=None) -> Tensor: ...
|
|
@overload
|
|
def narrow(input: Tensor, dim: _int, start: Tensor, length: _int) -> Tensor: ...
|
|
@overload
|
|
def narrow(input: Tensor, dim: _int, start: _int, length: _int) -> Tensor: ...
|
|
def narrow_copy(input: Tensor, dim: _int, start: _int, length: _int, *, out: Optional[Tensor]=None) -> Tensor: ...
|
|
def native_batch_norm(input: Tensor, weight: Optional[Tensor], bias: Optional[Tensor], running_mean: Optional[Tensor], running_var: Optional[Tensor], training: _bool, momentum: _float, eps: _float, *, out: Union[Tensor, Tuple[Tensor, ...], List[Tensor]]=None) -> Tuple[Tensor, Tensor, Tensor]: ...
|
|
def native_group_norm(input: Tensor, weight: Optional[Tensor], bias: Optional[Tensor], N: _int, C: _int, HxW: _int, group: _int, eps: _float) -> Tuple[Tensor, Tensor, Tensor]: ...
|
|
def native_layer_norm(input: Tensor, normalized_shape: _size, weight: Optional[Tensor], bias: Optional[Tensor], eps: _float) -> Tuple[Tensor, Tensor, Tensor]: ...
|
|
@overload
|
|
def native_norm(input: Tensor, p: Optional[Number], dim: Union[_int, _size], keepdim: _bool, dtype: Optional[_dtype]) -> Tensor: ...
|
|
@overload
|
|
def native_norm(input: Tensor, p: Number=2) -> Tensor: ...
|
|
@overload
|
|
def ne(input: Tensor, other: Tensor, *, out: Optional[Tensor]=None) -> Tensor: ...
|
|
@overload
|
|
def ne(input: Tensor, other: Number, *, out: Optional[Tensor]=None) -> Tensor: ...
|
|
def neg(input: Tensor, *, out: Optional[Tensor]=None) -> Tensor: ...
|
|
def neg_(input: Tensor) -> Tensor: ...
|
|
def negative(input: Tensor, *, out: Optional[Tensor]=None) -> Tensor: ...
|
|
def negative_(input: Tensor) -> Tensor: ...
|
|
def nextafter(input: Tensor, other: Tensor, *, out: Optional[Tensor]=None) -> Tensor: ...
|
|
@overload
|
|
def nonzero(input: Tensor, *, out: Optional[Tensor]=None) -> Tensor: ...
|
|
@overload
|
|
def nonzero(input: Tensor, *, as_tuple: bool=...) -> Tensor: ...
|
|
def norm_except_dim(v: Tensor, pow: _int=2, dim: _int=0) -> Tensor: ...
|
|
@overload
|
|
def normal(mean: Tensor, std: Tensor, *, generator: Optional[Generator]=None, out: Optional[Tensor]=None) -> Tensor: ...
|
|
@overload
|
|
def normal(mean: Tensor, std: _float=1, *, generator: Optional[Generator]=None, out: Optional[Tensor]=None) -> Tensor: ...
|
|
@overload
|
|
def normal(mean: _float, std: Tensor, *, generator: Optional[Generator]=None, out: Optional[Tensor]=None) -> Tensor: ...
|
|
@overload
|
|
def normal(mean: _float, std: _float, size: _size, *, generator: Optional[Generator]=None, out: Optional[Tensor]=None, dtype: _dtype=None, layout: Optional[_layout]=strided, device: Union[_device, str, None]=None, pin_memory: _bool=False, requires_grad: _bool=False) -> Tensor: ...
|
|
@overload
|
|
def not_equal(input: Tensor, other: Tensor, *, out: Optional[Tensor]=None) -> Tensor: ...
|
|
@overload
|
|
def not_equal(input: Tensor, other: Number, *, out: Optional[Tensor]=None) -> Tensor: ...
|
|
@overload
|
|
def nuclear_norm(input: Tensor, dim: Union[_int, _size], keepdim: _bool=False, *, out: Optional[Tensor]=None) -> Tensor: ...
|
|
@overload
|
|
def nuclear_norm(input: Tensor, keepdim: _bool=False, *, out: Optional[Tensor]=None) -> Tensor: ...
|
|
def numel(self: Tensor) -> _int: ...
|
|
@overload
|
|
def ones(size: _size, *, names: Optional[Sequence[Union[str, ellipsis, None]]], dtype: _dtype=None, layout: Optional[_layout]=strided, device: Union[_device, str, None]=None, pin_memory: _bool=False, requires_grad: _bool=False) -> Tensor: ...
|
|
@overload
|
|
def ones(*size: _int, names: Optional[Sequence[Union[str, ellipsis, None]]], dtype: _dtype=None, layout: Optional[_layout]=strided, device: Union[_device, str, None]=None, pin_memory: _bool=False, requires_grad: _bool=False) -> Tensor: ...
|
|
@overload
|
|
def ones(size: _size, *, out: Optional[Tensor]=None, dtype: _dtype=None, layout: Optional[_layout]=strided, device: Union[_device, str, None]=None, pin_memory: _bool=False, requires_grad: _bool=False) -> Tensor: ...
|
|
@overload
|
|
def ones(*size: _int, out: Optional[Tensor]=None, dtype: _dtype=None, layout: Optional[_layout]=strided, device: Union[_device, str, None]=None, pin_memory: _bool=False, requires_grad: _bool=False) -> Tensor: ...
|
|
def ones_like(input: Tensor, *, memory_format: Optional[memory_format]=None, dtype: _dtype=None, layout: Optional[_layout]=strided, device: Union[_device, str, None]=None, pin_memory: _bool=False, requires_grad: _bool=False) -> Tensor: ...
|
|
def orgqr(input: Tensor, input2: Tensor, *, out: Optional[Tensor]=None) -> Tensor: ...
|
|
def ormqr(input: Tensor, input2: Tensor, input3: Tensor, left: _bool=True, transpose: _bool=False, *, out: Optional[Tensor]=None) -> Tensor: ...
|
|
def outer(input: Tensor, vec2: Tensor, *, out: Optional[Tensor]=None) -> Tensor: ...
|
|
def pairwise_distance(x1: Tensor, x2: Tensor, p: _float=2, eps: _float=1e-06, keepdim: _bool=False) -> Tensor: ...
|
|
def pdist(input: Tensor, p: _float=2) -> Tensor: ...
|
|
def pinverse(input: Tensor, rcond: _float=1e-15) -> Tensor: ...
|
|
def pixel_shuffle(input: Tensor, upscale_factor: _int) -> Tensor: ...
|
|
def pixel_unshuffle(input: Tensor, downscale_factor: _int) -> Tensor: ...
|
|
def poisson(input: Tensor, generator: Optional[Generator]=None) -> Tensor: ...
|
|
def poisson_nll_loss(input: Tensor, target: Tensor, log_input: _bool, full: _bool, eps: _float, reduction: _int) -> Tensor: ...
|
|
def polar(abs: Tensor, angle: Tensor, *, out: Optional[Tensor]=None) -> Tensor: ...
|
|
def polygamma(n: _int, input: Tensor, *, out: Optional[Tensor]=None) -> Tensor: ...
|
|
@overload
|
|
def pow(input: Tensor, exponent: Tensor, *, out: Optional[Tensor]=None) -> Tensor: ...
|
|
@overload
|
|
def pow(self: Number, exponent: Tensor, *, out: Optional[Tensor]=None) -> Tensor: ...
|
|
@overload
|
|
def pow(input: Tensor, exponent: Number, *, out: Optional[Tensor]=None) -> Tensor: ...
|
|
def prelu(input: Tensor, weight: Tensor) -> Tensor: ...
|
|
@overload
|
|
def prod(input: Tensor, *, dtype: Optional[_dtype]=None) -> Tensor: ...
|
|
@overload
|
|
def prod(input: Tensor, dim: Union[str, ellipsis, None], keepdim: _bool=False, *, dtype: Optional[_dtype]=None, out: Optional[Tensor]=None) -> Tensor: ...
|
|
@overload
|
|
def prod(input: Tensor, dim: _int, keepdim: _bool=False, *, dtype: Optional[_dtype]=None, out: Optional[Tensor]=None) -> Tensor: ...
|
|
def promote_types(type1: _dtype, type2: _dtype) -> _dtype: ...
|
|
def q_per_channel_axis(input: Tensor) -> _int: ...
|
|
def q_per_channel_scales(input: Tensor) -> Tensor: ...
|
|
def q_per_channel_zero_points(input: Tensor) -> Tensor: ...
|
|
def q_scale(input: Tensor) -> _float: ...
|
|
def q_zero_point(input: Tensor) -> _int: ...
|
|
def qr(input: Tensor, some: _bool=True, *, out: Union[Tensor, Tuple[Tensor, ...], List[Tensor]]=None) -> namedtuple_Q_R: ...
|
|
@overload
|
|
def quantile(input: Tensor, q: Tensor, dim: Optional[_int]=None, keepdim: _bool=False, *, out: Optional[Tensor]=None) -> Tensor: ...
|
|
@overload
|
|
def quantile(input: Tensor, q: _float, dim: Optional[_int]=None, keepdim: _bool=False, *, out: Optional[Tensor]=None) -> Tensor: ...
|
|
def quantize_per_channel(input: Tensor, scales: Tensor, zero_points: Tensor, axis: _int, dtype: _dtype) -> Tensor: ...
|
|
@overload
|
|
def quantize_per_tensor(input: Tensor, scale: _float, zero_point: _int, dtype: _dtype) -> Tensor: ...
|
|
@overload
|
|
def quantize_per_tensor(tensors: Union[Tuple[Tensor, ...], List[Tensor]], scales: Tensor, zero_points: Tensor, dtype: _dtype) -> Union[Tuple[Tensor, ...], List[Tensor]]: ...
|
|
def quantized_batch_norm(input: Tensor, weight: Optional[Tensor], bias: Optional[Tensor], mean: Tensor, var: Tensor, eps: _float, output_scale: _float, output_zero_point: _int) -> Tensor: ...
|
|
def quantized_gru_cell(input: Tensor, hx: Tensor, w_ih: Tensor, w_hh: Tensor, b_ih: Tensor, b_hh: Tensor, packed_ih: Tensor, packed_hh: Tensor, col_offsets_ih: Tensor, col_offsets_hh: Tensor, scale_ih: Number, scale_hh: Number, zero_point_ih: Number, zero_point_hh: Number) -> Tensor: ...
|
|
def quantized_lstm_cell(input: Tensor, hx: Union[Tuple[Tensor, ...], List[Tensor]], w_ih: Tensor, w_hh: Tensor, b_ih: Tensor, b_hh: Tensor, packed_ih: Tensor, packed_hh: Tensor, col_offsets_ih: Tensor, col_offsets_hh: Tensor, scale_ih: Number, scale_hh: Number, zero_point_ih: Number, zero_point_hh: Number) -> Tuple[Tensor, Tensor]: ...
|
|
def quantized_max_pool1d(input: Tensor, kernel_size: Union[_int, _size], stride: Union[_int, _size]=(), padding: Union[_int, _size]=0, dilation: Union[_int, _size]=1, ceil_mode: _bool=False) -> Tensor: ...
|
|
def quantized_max_pool2d(input: Tensor, kernel_size: Union[_int, _size], stride: Union[_int, _size]=(), padding: Union[_int, _size]=0, dilation: Union[_int, _size]=1, ceil_mode: _bool=False) -> Tensor: ...
|
|
def quantized_rnn_relu_cell(input: Tensor, hx: Tensor, w_ih: Tensor, w_hh: Tensor, b_ih: Tensor, b_hh: Tensor, packed_ih: Tensor, packed_hh: Tensor, col_offsets_ih: Tensor, col_offsets_hh: Tensor, scale_ih: Number, scale_hh: Number, zero_point_ih: Number, zero_point_hh: Number) -> Tensor: ...
|
|
def quantized_rnn_tanh_cell(input: Tensor, hx: Tensor, w_ih: Tensor, w_hh: Tensor, b_ih: Tensor, b_hh: Tensor, packed_ih: Tensor, packed_hh: Tensor, col_offsets_ih: Tensor, col_offsets_hh: Tensor, scale_ih: Number, scale_hh: Number, zero_point_ih: Number, zero_point_hh: Number) -> Tensor: ...
|
|
def rad2deg(input: Tensor, *, out: Optional[Tensor]=None) -> Tensor: ...
|
|
def rad2deg_(input: Tensor) -> Tensor: ...
|
|
@overload
|
|
def rand(size: _size, *, names: Optional[Sequence[Union[str, ellipsis, None]]], dtype: _dtype=None, layout: Optional[_layout]=strided, device: Union[_device, str, None]=None, pin_memory: _bool=False, requires_grad: _bool=False) -> Tensor: ...
|
|
@overload
|
|
def rand(*size: _int, names: Optional[Sequence[Union[str, ellipsis, None]]], dtype: _dtype=None, layout: Optional[_layout]=strided, device: Union[_device, str, None]=None, pin_memory: _bool=False, requires_grad: _bool=False) -> Tensor: ...
|
|
@overload
|
|
def rand(size: _size, *, generator: Optional[Generator], names: Optional[Sequence[Union[str, ellipsis, None]]], dtype: _dtype=None, layout: Optional[_layout]=strided, device: Union[_device, str, None]=None, pin_memory: _bool=False, requires_grad: _bool=False) -> Tensor: ...
|
|
@overload
|
|
def rand(*size: _int, generator: Optional[Generator], names: Optional[Sequence[Union[str, ellipsis, None]]], dtype: _dtype=None, layout: Optional[_layout]=strided, device: Union[_device, str, None]=None, pin_memory: _bool=False, requires_grad: _bool=False) -> Tensor: ...
|
|
@overload
|
|
def rand(size: _size, *, generator: Optional[Generator], out: Optional[Tensor]=None, dtype: _dtype=None, layout: Optional[_layout]=strided, device: Union[_device, str, None]=None, pin_memory: _bool=False, requires_grad: _bool=False) -> Tensor: ...
|
|
@overload
|
|
def rand(*size: _int, generator: Optional[Generator], out: Optional[Tensor]=None, dtype: _dtype=None, layout: Optional[_layout]=strided, device: Union[_device, str, None]=None, pin_memory: _bool=False, requires_grad: _bool=False) -> Tensor: ...
|
|
@overload
|
|
def rand(size: _size, *, out: Optional[Tensor]=None, dtype: _dtype=None, layout: Optional[_layout]=strided, device: Union[_device, str, None]=None, pin_memory: _bool=False, requires_grad: _bool=False) -> Tensor: ...
|
|
@overload
|
|
def rand(*size: _int, out: Optional[Tensor]=None, dtype: _dtype=None, layout: Optional[_layout]=strided, device: Union[_device, str, None]=None, pin_memory: _bool=False, requires_grad: _bool=False) -> Tensor: ...
|
|
def rand_like(input: Tensor, *, memory_format: Optional[memory_format]=None, dtype: _dtype=None, layout: Optional[_layout]=strided, device: Union[_device, str, None]=None, pin_memory: _bool=False, requires_grad: _bool=False) -> Tensor: ...
|
|
@overload
|
|
def randint(low: _int, high: _int, size: _size, *, generator: Optional[Generator]=None, dtype: Optional[_dtype]=None, device: Union[_device, str, None]=None, requires_grad: _bool=False) -> Tensor: ...
|
|
@overload
|
|
def randint(high: _int, size: _size, *, generator: Optional[Generator]=None, dtype: Optional[_dtype]=None, device: Union[_device, str, None]=None, requires_grad: _bool=False) -> Tensor: ...
|
|
@overload
|
|
def randint_like(input: Tensor, high: _int, *, memory_format: Optional[memory_format]=None, dtype: _dtype=None, layout: Optional[_layout]=strided, device: Union[_device, str, None]=None, pin_memory: _bool=False, requires_grad: _bool=False) -> Tensor: ...
|
|
@overload
|
|
def randint_like(input: Tensor, low: _int, high: _int, *, memory_format: Optional[memory_format]=None, dtype: _dtype=None, layout: Optional[_layout]=strided, device: Union[_device, str, None]=None, pin_memory: _bool=False, requires_grad: _bool=False) -> Tensor: ...
|
|
@overload
|
|
def randn(size: _size, *, names: Optional[Sequence[Union[str, ellipsis, None]]], dtype: _dtype=None, layout: Optional[_layout]=strided, device: Union[_device, str, None]=None, pin_memory: _bool=False, requires_grad: _bool=False) -> Tensor: ...
|
|
@overload
|
|
def randn(*size: _int, names: Optional[Sequence[Union[str, ellipsis, None]]], dtype: _dtype=None, layout: Optional[_layout]=strided, device: Union[_device, str, None]=None, pin_memory: _bool=False, requires_grad: _bool=False) -> Tensor: ...
|
|
@overload
|
|
def randn(size: _size, *, generator: Optional[Generator], names: Optional[Sequence[Union[str, ellipsis, None]]], dtype: _dtype=None, layout: Optional[_layout]=strided, device: Union[_device, str, None]=None, pin_memory: _bool=False, requires_grad: _bool=False) -> Tensor: ...
|
|
@overload
|
|
def randn(*size: _int, generator: Optional[Generator], names: Optional[Sequence[Union[str, ellipsis, None]]], dtype: _dtype=None, layout: Optional[_layout]=strided, device: Union[_device, str, None]=None, pin_memory: _bool=False, requires_grad: _bool=False) -> Tensor: ...
|
|
@overload
|
|
def randn(size: _size, *, generator: Optional[Generator], out: Optional[Tensor]=None, dtype: _dtype=None, layout: Optional[_layout]=strided, device: Union[_device, str, None]=None, pin_memory: _bool=False, requires_grad: _bool=False) -> Tensor: ...
|
|
@overload
|
|
def randn(*size: _int, generator: Optional[Generator], out: Optional[Tensor]=None, dtype: _dtype=None, layout: Optional[_layout]=strided, device: Union[_device, str, None]=None, pin_memory: _bool=False, requires_grad: _bool=False) -> Tensor: ...
|
|
@overload
|
|
def randn(size: _size, *, out: Optional[Tensor]=None, dtype: _dtype=None, layout: Optional[_layout]=strided, device: Union[_device, str, None]=None, pin_memory: _bool=False, requires_grad: _bool=False) -> Tensor: ...
|
|
@overload
|
|
def randn(*size: _int, out: Optional[Tensor]=None, dtype: _dtype=None, layout: Optional[_layout]=strided, device: Union[_device, str, None]=None, pin_memory: _bool=False, requires_grad: _bool=False) -> Tensor: ...
|
|
def randn_like(input: Tensor, *, memory_format: Optional[memory_format]=None, dtype: _dtype=None, layout: Optional[_layout]=strided, device: Union[_device, str, None]=None, pin_memory: _bool=False, requires_grad: _bool=False) -> Tensor: ...
|
|
@overload
|
|
def randperm(n: _int, *, generator: Optional[Generator], out: Optional[Tensor]=None, dtype: _dtype=None, layout: Optional[_layout]=strided, device: Union[_device, str, None]=None, pin_memory: _bool=False, requires_grad: _bool=False) -> Tensor: ...
|
|
@overload
|
|
def randperm(n: _int, *, out: Optional[Tensor]=None, dtype: _dtype=None, layout: Optional[_layout]=strided, device: Union[_device, str, None]=None, pin_memory: _bool=False, requires_grad: _bool=False) -> Tensor: ...
|
|
def range(start: Number, end: Number, step: Number=1, *, out: Optional[Tensor]=None, dtype: Optional[_dtype]=None, device: Union[_device, str, None]=None, requires_grad: _bool=False) -> Tensor: ...
|
|
def ravel(input: Tensor) -> Tensor: ...
|
|
def real(input: Tensor) -> Tensor: ...
|
|
def reciprocal(input: Tensor, *, out: Optional[Tensor]=None) -> Tensor: ...
|
|
def reciprocal_(input: Tensor) -> Tensor: ...
|
|
def relu(input: Tensor) -> Tensor: ...
|
|
def relu_(input: Tensor) -> Tensor: ...
|
|
@overload
|
|
def remainder(input: Tensor, other: Tensor, *, out: Optional[Tensor]=None) -> Tensor: ...
|
|
@overload
|
|
def remainder(input: Tensor, other: Number, *, out: Optional[Tensor]=None) -> Tensor: ...
|
|
def renorm(input: Tensor, p: Number, dim: _int, maxnorm: Number, *, out: Optional[Tensor]=None) -> Tensor: ...
|
|
@overload
|
|
def repeat_interleave(input: Tensor, repeats: Tensor, dim: Optional[_int]=None) -> Tensor: ...
|
|
@overload
|
|
def repeat_interleave(input: Tensor, repeats: _int, dim: Optional[_int]=None) -> Tensor: ...
|
|
@overload
|
|
def repeat_interleave(repeats: Tensor) -> Tensor: ...
|
|
def reshape(input: Tensor, shape: _size) -> Tensor: ...
|
|
def resize_as_(input: Tensor, the_template: Tensor, *, memory_format: Optional[memory_format]=None) -> Tensor: ...
|
|
@overload
|
|
def result_type(tensor: Tensor, other: Tensor) -> _dtype: ...
|
|
@overload
|
|
def result_type(scalar: Number, tensor: Tensor) -> _dtype: ...
|
|
@overload
|
|
def result_type(tensor: Tensor, other: Number) -> _dtype: ...
|
|
@overload
|
|
def result_type(scalar1: Number, scalar2: Number) -> _dtype: ...
|
|
@overload
|
|
def rnn_relu(data: Tensor, batch_sizes: Tensor, hx: Tensor, params: Union[Tuple[Tensor, ...], List[Tensor]], has_biases: _bool, num_layers: _int, dropout: _float, train: _bool, bidirectional: _bool) -> Tuple[Tensor, Tensor]: ...
|
|
@overload
|
|
def rnn_relu(input: Tensor, hx: Tensor, params: Union[Tuple[Tensor, ...], List[Tensor]], has_biases: _bool, num_layers: _int, dropout: _float, train: _bool, bidirectional: _bool, batch_first: _bool) -> Tuple[Tensor, Tensor]: ...
|
|
def rnn_relu_cell(input: Tensor, hx: Tensor, w_ih: Tensor, w_hh: Tensor, b_ih: Optional[Tensor]=None, b_hh: Optional[Tensor]=None) -> Tensor: ...
|
|
@overload
|
|
def rnn_tanh(data: Tensor, batch_sizes: Tensor, hx: Tensor, params: Union[Tuple[Tensor, ...], List[Tensor]], has_biases: _bool, num_layers: _int, dropout: _float, train: _bool, bidirectional: _bool) -> Tuple[Tensor, Tensor]: ...
|
|
@overload
|
|
def rnn_tanh(input: Tensor, hx: Tensor, params: Union[Tuple[Tensor, ...], List[Tensor]], has_biases: _bool, num_layers: _int, dropout: _float, train: _bool, bidirectional: _bool, batch_first: _bool) -> Tuple[Tensor, Tensor]: ...
|
|
def rnn_tanh_cell(input: Tensor, hx: Tensor, w_ih: Tensor, w_hh: Tensor, b_ih: Optional[Tensor]=None, b_hh: Optional[Tensor]=None) -> Tensor: ...
|
|
def roll(input: Tensor, shifts: Union[_int, _size], dims: Union[_int, _size]=()) -> Tensor: ...
|
|
def rot90(input: Tensor, k: _int=1, dims: _size=(0,1)) -> Tensor: ...
|
|
def round(input: Tensor, *, out: Optional[Tensor]=None) -> Tensor: ...
|
|
def round_(input: Tensor) -> Tensor: ...
|
|
def row_stack(tensors: Union[Tuple[Tensor, ...], List[Tensor]], *, out: Optional[Tensor]=None) -> Tensor: ...
|
|
def rrelu(input: Tensor, lower: Number=0.125, upper: Number=0.3333333333333333, training: _bool=False, generator: Optional[Generator]=None) -> Tensor: ...
|
|
def rrelu_(input: Tensor, lower: Number=0.125, upper: Number=0.3333333333333333, training: _bool=False, generator: Optional[Generator]=None) -> Tensor: ...
|
|
def rsqrt(input: Tensor, *, out: Optional[Tensor]=None) -> Tensor: ...
|
|
def rsqrt_(input: Tensor) -> Tensor: ...
|
|
@overload
|
|
def rsub(input: Tensor, other: Tensor, *, alpha: Number=1) -> Tensor: ...
|
|
@overload
|
|
def rsub(input: Tensor, other: Number, alpha: Number=1) -> Tensor: ...
|
|
def saddmm(input: Tensor, mat1: Tensor, mat2: Tensor, *, beta: Number=1, alpha: Number=1, out: Optional[Tensor]=None) -> Tensor: ...
|
|
def scalar_tensor(s: Number, *, dtype: _dtype=None, layout: Optional[_layout]=strided, device: Union[_device, str, None]=None, pin_memory: _bool=False, requires_grad: _bool=False) -> Tensor: ...
|
|
@overload
|
|
def scatter(input: Tensor, dim: Union[str, ellipsis, None], index: Tensor, src: Tensor) -> Tensor: ...
|
|
@overload
|
|
def scatter(input: Tensor, dim: _int, index: Tensor, src: Tensor) -> Tensor: ...
|
|
@overload
|
|
def scatter(input: Tensor, dim: Union[str, ellipsis, None], index: Tensor, value: Number) -> Tensor: ...
|
|
@overload
|
|
def scatter(input: Tensor, dim: _int, index: Tensor, value: Number) -> Tensor: ...
|
|
@overload
|
|
def scatter_add(input: Tensor, dim: Union[str, ellipsis, None], index: Tensor, src: Tensor) -> Tensor: ...
|
|
@overload
|
|
def scatter_add(input: Tensor, dim: _int, index: Tensor, src: Tensor) -> Tensor: ...
|
|
@overload
|
|
def searchsorted(sorted_sequence: Tensor, input: Tensor, *, out_int32: _bool=False, right: _bool=False, out: Optional[Tensor]=None) -> Tensor: ...
|
|
@overload
|
|
def searchsorted(sorted_sequence: Tensor, self: Number, *, out_int32: _bool=False, right: _bool=False) -> Tensor: ...
|
|
@overload
|
|
def select(input: Tensor, dim: Union[str, ellipsis, None], index: _int) -> Tensor: ...
|
|
@overload
|
|
def select(input: Tensor, dim: _int, index: _int) -> Tensor: ...
|
|
def selu(input: Tensor) -> Tensor: ...
|
|
def selu_(input: Tensor) -> Tensor: ...
|
|
def set_flush_denormal(mode: _bool) -> _bool: ...
|
|
def set_num_interop_threads(num: _int) -> None: ...
|
|
def set_num_threads(num: _int) -> None: ...
|
|
def sgn(input: Tensor, *, out: Optional[Tensor]=None) -> Tensor: ...
|
|
def sigmoid(input: Tensor, *, out: Optional[Tensor]=None) -> Tensor: ...
|
|
def sigmoid_(input: Tensor) -> Tensor: ...
|
|
def sign(input: Tensor, *, out: Optional[Tensor]=None) -> Tensor: ...
|
|
def signbit(input: Tensor, *, out: Optional[Tensor]=None) -> Tensor: ...
|
|
def sin(input: Tensor, *, out: Optional[Tensor]=None) -> Tensor: ...
|
|
def sin_(input: Tensor) -> Tensor: ...
|
|
def sinc(input: Tensor, *, out: Optional[Tensor]=None) -> Tensor: ...
|
|
def sinc_(input: Tensor) -> Tensor: ...
|
|
def sinh(input: Tensor, *, out: Optional[Tensor]=None) -> Tensor: ...
|
|
def sinh_(input: Tensor) -> Tensor: ...
|
|
def slogdet(input: Tensor) -> namedtuple_sign_logabsdet: ...
|
|
def smm(input: Tensor, mat2: Tensor) -> Tensor: ...
|
|
@overload
|
|
def softmax(input: Tensor, dim: Union[str, ellipsis, None], *, dtype: Optional[_dtype]=None) -> Tensor: ...
|
|
@overload
|
|
def softmax(input: Tensor, dim: _int, dtype: Optional[_dtype]=None) -> Tensor: ...
|
|
def solve(input: Tensor, A: Tensor, *, out: Union[Tensor, Tuple[Tensor, ...], List[Tensor]]=None) -> namedtuple_solution_LU: ...
|
|
@overload
|
|
def sort(input: Tensor, dim: Union[str, ellipsis, None], descending: _bool=False, *, out: Union[Tensor, Tuple[Tensor, ...], List[Tensor]]=None) -> namedtuple_values_indices: ...
|
|
@overload
|
|
def sort(input: Tensor, dim: _int=-1, descending: _bool=False, *, out: Union[Tensor, Tuple[Tensor, ...], List[Tensor]]=None) -> namedtuple_values_indices: ...
|
|
def sparse_coo_tensor(indices: Tensor, values: Union[Tensor,List], size: Optional[_size]=None, *, dtype: Optional[_dtype]=None, device: Union[_device, str, None]=None, requires_grad:_bool=False) -> Tensor: ...
|
|
def split_with_sizes(input: Tensor, split_sizes: _size, dim: _int=0) -> Union[Tuple[Tensor, ...], List[Tensor]]: ...
|
|
def spmm(input: Tensor, mat2: Tensor) -> Tensor: ...
|
|
def sqrt(input: Tensor, *, out: Optional[Tensor]=None) -> Tensor: ...
|
|
def sqrt_(input: Tensor) -> Tensor: ...
|
|
def square(input: Tensor) -> Tensor: ...
|
|
def square_(input: Tensor) -> Tensor: ...
|
|
@overload
|
|
def squeeze(input: Tensor) -> Tensor: ...
|
|
@overload
|
|
def squeeze(input: Tensor, dim: Union[str, ellipsis, None]) -> Tensor: ...
|
|
@overload
|
|
def squeeze(input: Tensor, dim: _int) -> Tensor: ...
|
|
@overload
|
|
def sspaddmm(beta: Number, self: Tensor, alpha: Number, mat1: Tensor, mat2: Tensor) -> Tensor: ...
|
|
@overload
|
|
def sspaddmm(beta: Number, self: Tensor, mat1: Tensor, mat2: Tensor) -> Tensor: ...
|
|
@overload
|
|
def sspaddmm(input: Tensor, mat1: Tensor, mat2: Tensor, *, beta: Number=1, alpha: Number=1, out: Optional[Tensor]=None) -> Tensor: ...
|
|
def stack(tensors: Union[Tuple[Tensor, ...], List[Tensor]], dim: _int=0, *, out: Optional[Tensor]=None) -> Tensor: ...
|
|
@overload
|
|
def std(input: Tensor, dim: Sequence[Union[str, ellipsis, None]], unbiased: _bool=True, keepdim: _bool=False, *, out: Optional[Tensor]=None) -> Tensor: ...
|
|
@overload
|
|
def std(input: Tensor, dim: Union[_int, _size], unbiased: _bool=True, keepdim: _bool=False, *, out: Optional[Tensor]=None) -> Tensor: ...
|
|
@overload
|
|
def std(input: Tensor, unbiased: _bool=True) -> Tensor: ...
|
|
@overload
|
|
def std_mean(input: Tensor, dim: Sequence[Union[str, ellipsis, None]], unbiased: _bool=True, keepdim: _bool=False) -> Tuple[Tensor, Tensor]: ...
|
|
@overload
|
|
def std_mean(input: Tensor, dim: Union[_int, _size], unbiased: _bool=True, keepdim: _bool=False) -> Tuple[Tensor, Tensor]: ...
|
|
@overload
|
|
def std_mean(input: Tensor, unbiased: _bool=True) -> Tuple[Tensor, Tensor]: ...
|
|
@overload
|
|
def sub(input: Union[Tensor, Number], other: Union[Tensor, Number], *, alpha: Optional[Number]=1, out: Optional[Tensor]=None) -> Tensor: ...
|
|
@overload
|
|
def sub(self: Tensor, alpha: Number, other: Tensor) -> Tensor: ...
|
|
@overload
|
|
def sub(self: Tensor, alpha: Number, other: Tensor, *, out: Tensor) -> Tensor: ...
|
|
@overload
|
|
def subtract(input: Tensor, other: Tensor, *, alpha: Number=1, out: Optional[Tensor]=None) -> Tensor: ...
|
|
@overload
|
|
def subtract(input: Tensor, other: Number, alpha: Number=1) -> Tensor: ...
|
|
@overload
|
|
def sum(input: Tensor, *, dtype: Optional[_dtype]=None) -> Tensor: ...
|
|
@overload
|
|
def sum(input: Tensor, dim: Sequence[Union[str, ellipsis, None]], keepdim: _bool=False, *, dtype: Optional[_dtype]=None, out: Optional[Tensor]=None) -> Tensor: ...
|
|
@overload
|
|
def sum(input: Tensor, dim: Union[_int, _size], keepdim: _bool=False, *, dtype: Optional[_dtype]=None, out: Optional[Tensor]=None) -> Tensor: ...
|
|
def svd(input: Tensor, some: _bool=True, compute_uv: _bool=True, *, out: Union[Tensor, Tuple[Tensor, ...], List[Tensor]]=None) -> namedtuple_U_S_V: ...
|
|
def swapaxes(input: Tensor, axis0: _int, axis1: _int) -> Tensor: ...
|
|
def swapdims(input: Tensor, dim0: _int, dim1: _int) -> Tensor: ...
|
|
def symeig(input: Tensor, eigenvectors: _bool=False, upper: _bool=True, *, out: Union[Tensor, Tuple[Tensor, ...], List[Tensor]]=None) -> namedtuple_eigenvalues_eigenvectors: ...
|
|
def t(input: Tensor) -> Tensor: ...
|
|
def take(input: Tensor, index: Tensor, *, out: Optional[Tensor]=None) -> Tensor: ...
|
|
def tan(input: Tensor, *, out: Optional[Tensor]=None) -> Tensor: ...
|
|
def tan_(input: Tensor) -> Tensor: ...
|
|
def tanh(input: Tensor, *, out: Optional[Tensor]=None) -> Tensor: ...
|
|
def tanh_(input: Tensor) -> Tensor: ...
|
|
def tensor(data: Any, dtype: Optional[_dtype]=None, device: Union[_device, str, None]=None, requires_grad: _bool=False) -> Tensor: ...
|
|
@overload
|
|
def tensor_split(input: Tensor, indices: _size, dim: _int=0) -> Union[Tuple[Tensor, ...], List[Tensor]]: ...
|
|
@overload
|
|
def tensor_split(input: Tensor, tensor_indices_or_sections: Tensor, dim: _int=0) -> Union[Tuple[Tensor, ...], List[Tensor]]: ...
|
|
@overload
|
|
def tensor_split(input: Tensor, sections: _int, dim: _int=0) -> Union[Tuple[Tensor, ...], List[Tensor]]: ...
|
|
def threshold(input: Tensor, threshold: Number, value: Number, *, out: Optional[Tensor]=None) -> Tensor: ...
|
|
def threshold_(input: Tensor, threshold: Number, value: Number) -> Tensor: ...
|
|
def tile(input: Tensor, dims: _size) -> Tensor: ...
|
|
def topk(input: Tensor, k: _int, dim: _int=-1, largest: _bool=True, sorted: _bool=True, *, out: Union[Tensor, Tuple[Tensor, ...], List[Tensor]]=None) -> namedtuple_values_indices: ...
|
|
def trace(input: Tensor) -> Tensor: ...
|
|
@overload
|
|
def transpose(input: Tensor, dim0: Union[str, ellipsis, None], dim1: Union[str, ellipsis, None]) -> Tensor: ...
|
|
@overload
|
|
def transpose(input: Tensor, dim0: _int, dim1: _int) -> Tensor: ...
|
|
@overload
|
|
def trapz(y: Tensor, *, dx: _float=1, dim: _int=-1) -> Tensor: ...
|
|
@overload
|
|
def trapz(y: Tensor, x: Tensor, *, dim: _int=-1) -> Tensor: ...
|
|
def triangular_solve(input: Tensor, A: Tensor, upper: _bool=True, transpose: _bool=False, unitriangular: _bool=False, *, out: Union[Tensor, Tuple[Tensor, ...], List[Tensor]]=None) -> namedtuple_solution_cloned_coefficient: ...
|
|
def tril(input: Tensor, diagonal: _int=0, *, out: Optional[Tensor]=None) -> Tensor: ...
|
|
def tril_indices(row: _int, col: _int, offset: _int=0, *, dtype: _dtype=None, layout: Optional[_layout]=strided, device: Union[_device, str, None]=None, pin_memory: _bool=False, requires_grad: _bool=False) -> Tensor: ...
|
|
def triplet_margin_loss(anchor: Tensor, positive: Tensor, negative: Tensor, margin: float = ..., p: float = ..., eps: float = ..., swap: bool = ..., size_average: Optional[bool] = ..., reduce: Optional[bool] = ..., reduction: str = ...) -> Tensor: ...
|
|
def triu(input: Tensor, diagonal: _int=0, *, out: Optional[Tensor]=None) -> Tensor: ...
|
|
def triu_indices(row: _int, col: _int, offset: _int=0, *, dtype: _dtype=None, layout: Optional[_layout]=strided, device: Union[_device, str, None]=None, pin_memory: _bool=False, requires_grad: _bool=False) -> Tensor: ...
|
|
def true_divide(input: Union[Tensor, Number], other: Union[Tensor, Number], *, out: Optional[Tensor]=None) -> Tensor: ...
|
|
def trunc(input: Tensor, *, out: Optional[Tensor]=None) -> Tensor: ...
|
|
def trunc_(input: Tensor) -> Tensor: ...
|
|
@overload
|
|
def unbind(input: Tensor, dim: Union[str, ellipsis, None]) -> Union[Tuple[Tensor, ...], List[Tensor]]: ...
|
|
@overload
|
|
def unbind(input: Tensor, dim: _int=0) -> Union[Tuple[Tensor, ...], List[Tensor]]: ...
|
|
def unique_dim(input: Tensor, dim: _int, sorted: _bool=True, return_inverse: _bool=False, return_counts: _bool=False) -> Tuple[Tensor, Tensor, Tensor]: ...
|
|
def unsafe_chunk(input: Tensor, chunks: _int, dim: _int=0) -> Union[Tuple[Tensor, ...], List[Tensor]]: ...
|
|
def unsafe_split(input: Tensor, split_size: _int, dim: _int=0) -> Union[Tuple[Tensor, ...], List[Tensor]]: ...
|
|
def unsafe_split_with_sizes(input: Tensor, split_sizes: _size, dim: _int=0) -> Union[Tuple[Tensor, ...], List[Tensor]]: ...
|
|
def unsqueeze(input: Tensor, dim: _int) -> Tensor: ...
|
|
def vander(x: Tensor, N: Optional[_int]=None, increasing: _bool=False) -> Tensor: ...
|
|
@overload
|
|
def var(input: Tensor, dim: Sequence[Union[str, ellipsis, None]], unbiased: _bool=True, keepdim: _bool=False, *, out: Optional[Tensor]=None) -> Tensor: ...
|
|
@overload
|
|
def var(input: Tensor, dim: Union[_int, _size], unbiased: _bool=True, keepdim: _bool=False, *, out: Optional[Tensor]=None) -> Tensor: ...
|
|
@overload
|
|
def var(input: Tensor, unbiased: _bool=True) -> Tensor: ...
|
|
@overload
|
|
def var_mean(input: Tensor, dim: Sequence[Union[str, ellipsis, None]], unbiased: _bool=True, keepdim: _bool=False) -> Tuple[Tensor, Tensor]: ...
|
|
@overload
|
|
def var_mean(input: Tensor, dim: Union[_int, _size], unbiased: _bool=True, keepdim: _bool=False) -> Tuple[Tensor, Tensor]: ...
|
|
@overload
|
|
def var_mean(input: Tensor, unbiased: _bool=True) -> Tuple[Tensor, Tensor]: ...
|
|
def vdot(input: Tensor, other: Tensor, *, out: Optional[Tensor]=None) -> Tensor: ...
|
|
def view_as_complex(input: Tensor) -> Tensor: ...
|
|
def view_as_real(input: Tensor) -> Tensor: ...
|
|
def vstack(tensors: Union[Tuple[Tensor, ...], List[Tensor]], *, out: Optional[Tensor]=None) -> Tensor: ...
|
|
@overload
|
|
def where(condition: Tensor) -> Union[Tuple[Tensor, ...], List[Tensor]]: ...
|
|
@overload
|
|
def where(condition: Tensor, input: Tensor, other: Tensor) -> Tensor: ...
|
|
@overload
|
|
def where(condition: Tensor, self: Number, other: Tensor) -> Tensor: ...
|
|
@overload
|
|
def where(condition: Tensor, input: Tensor, other: Number) -> Tensor: ...
|
|
@overload
|
|
def where(condition: Tensor, self: Number, other: Number) -> Tensor: ...
|
|
@overload
|
|
def xlogy(input: Tensor, other: Tensor, *, out: Optional[Tensor]=None) -> Tensor: ...
|
|
@overload
|
|
def xlogy(self: Number, other: Tensor, *, out: Optional[Tensor]=None) -> Tensor: ...
|
|
@overload
|
|
def xlogy(input: Tensor, other: Number, *, out: Optional[Tensor]=None) -> Tensor: ...
|
|
@overload
|
|
def xlogy_(input: Tensor, other: Tensor) -> Tensor: ...
|
|
@overload
|
|
def xlogy_(input: Tensor, other: Number) -> Tensor: ...
|
|
def zero_(input: Tensor) -> Tensor: ...
|
|
@overload
|
|
def zeros(size: _size, *, names: Optional[Sequence[Union[str, ellipsis, None]]], dtype: _dtype=None, layout: Optional[_layout]=strided, device: Union[_device, str, None]=None, pin_memory: _bool=False, requires_grad: _bool=False) -> Tensor: ...
|
|
@overload
|
|
def zeros(*size: _int, names: Optional[Sequence[Union[str, ellipsis, None]]], dtype: _dtype=None, layout: Optional[_layout]=strided, device: Union[_device, str, None]=None, pin_memory: _bool=False, requires_grad: _bool=False) -> Tensor: ...
|
|
@overload
|
|
def zeros(size: _size, *, out: Optional[Tensor]=None, dtype: _dtype=None, layout: Optional[_layout]=strided, device: Union[_device, str, None]=None, pin_memory: _bool=False, requires_grad: _bool=False) -> Tensor: ...
|
|
@overload
|
|
def zeros(*size: _int, out: Optional[Tensor]=None, dtype: _dtype=None, layout: Optional[_layout]=strided, device: Union[_device, str, None]=None, pin_memory: _bool=False, requires_grad: _bool=False) -> Tensor: ...
|
|
def zeros_like(input: Tensor, *, memory_format: Optional[memory_format]=None, dtype: _dtype=None, layout: Optional[_layout]=strided, device: Union[_device, str, None]=None, pin_memory: _bool=False, requires_grad: _bool=False) -> Tensor: ...
|
|
|
|
__all__ = ['__and__', '__lshift__', '__or__', '__rshift__', '__xor__', '_adaptive_avg_pool2d',
|
|
'_add_batch_dim', '_add_relu', '_add_relu_', '_addmv_impl_', '_aminmax',
|
|
'_amp_foreach_non_finite_check_and_unscale_', '_amp_update_scale', '_baddbmm_mkl_',
|
|
'_batch_norm_impl_index', '_bmm', '_cast_Byte', '_cast_Char', '_cast_Double', '_cast_Float',
|
|
'_cast_Half', '_cast_Int', '_cast_Long', '_cast_Short', '_cat', '_choose_qparams_per_tensor',
|
|
'_compute_linear_combination', '_conj', '_convolution', '_convolution_nogroup', '_copy_from',
|
|
'_ctc_loss', '_cudnn_ctc_loss', '_cudnn_init_dropout_state', '_cudnn_rnn',
|
|
'_cudnn_rnn_flatten_weight', '_cufft_clear_plan_cache', '_cufft_get_plan_cache_max_size',
|
|
'_cufft_get_plan_cache_size', '_cufft_set_plan_cache_max_size', '_cummax_helper', '_cummin_helper',
|
|
'_debug_has_internal_overlap', '_dim_arange', '_dirichlet_grad', '_embedding_bag',
|
|
'_embedding_bag_forward_only', '_empty_affine_quantized', '_empty_per_channel_affine_quantized',
|
|
'_euclidean_dist', '_fake_quantize_learnable_per_channel_affine',
|
|
'_fake_quantize_learnable_per_tensor_affine', '_fft_c2c', '_fft_c2r', '_fft_r2c', '_foreach_abs',
|
|
'_foreach_abs_', '_foreach_acos', '_foreach_acos_', '_foreach_add', '_foreach_add_',
|
|
'_foreach_addcdiv', '_foreach_addcdiv_', '_foreach_addcmul', '_foreach_addcmul_', '_foreach_asin',
|
|
'_foreach_asin_', '_foreach_atan', '_foreach_atan_', '_foreach_ceil', '_foreach_ceil_',
|
|
'_foreach_cos', '_foreach_cos_', '_foreach_cosh', '_foreach_cosh_', '_foreach_div',
|
|
'_foreach_div_', '_foreach_erf', '_foreach_erf_', '_foreach_erfc', '_foreach_erfc_',
|
|
'_foreach_exp', '_foreach_exp_', '_foreach_expm1', '_foreach_expm1_', '_foreach_floor',
|
|
'_foreach_floor_', '_foreach_frac', '_foreach_frac_', '_foreach_lgamma', '_foreach_lgamma_',
|
|
'_foreach_log', '_foreach_log10', '_foreach_log10_', '_foreach_log1p', '_foreach_log1p_',
|
|
'_foreach_log2', '_foreach_log2_', '_foreach_log_', '_foreach_maximum', '_foreach_minimum',
|
|
'_foreach_mul', '_foreach_mul_', '_foreach_neg', '_foreach_neg_', '_foreach_reciprocal',
|
|
'_foreach_reciprocal_', '_foreach_round', '_foreach_round_', '_foreach_sigmoid',
|
|
'_foreach_sigmoid_', '_foreach_sin', '_foreach_sin_', '_foreach_sinh', '_foreach_sinh_',
|
|
'_foreach_sqrt', '_foreach_sqrt_', '_foreach_sub', '_foreach_sub_', '_foreach_tan',
|
|
'_foreach_tan_', '_foreach_tanh', '_foreach_tanh_', '_foreach_trunc', '_foreach_trunc_',
|
|
'_foreach_zero_', '_fused_dropout', '_grid_sampler_2d_cpu_fallback',
|
|
'_has_compatible_shallow_copy_type', '_index_copy_', '_index_put_impl_', '_linalg_inv_out_helper_',
|
|
'_linalg_qr_helper', '_linalg_solve_out_helper_', '_log_softmax', '_log_softmax_backward_data',
|
|
'_logcumsumexp', '_lu_solve_helper', '_lu_with_info', '_make_dual',
|
|
'_make_per_channel_quantized_tensor', '_make_per_tensor_quantized_tensor', '_masked_scale',
|
|
'_mkldnn_reshape', '_mkldnn_transpose', '_mkldnn_transpose_', '_mode', '_nnpack_available',
|
|
'_nnpack_spatial_convolution', '_pack_padded_sequence', '_pad_packed_sequence',
|
|
'_remove_batch_dim', '_reshape_from_tensor', '_rowwise_prune', '_s_where', '_sample_dirichlet',
|
|
'_saturate_weight_to_fp16', '_shape_as_tensor', '_sobol_engine_draw', '_sobol_engine_ff_',
|
|
'_sobol_engine_initialize_state_', '_sobol_engine_scramble_', '_softmax', '_softmax_backward_data',
|
|
'_sparse_addmm', '_sparse_coo_tensor_unsafe', '_sparse_log_softmax',
|
|
'_sparse_log_softmax_backward_data', '_sparse_matrix_mask_helper', '_sparse_mm', '_sparse_softmax',
|
|
'_sparse_softmax_backward_data', '_sparse_sparse_matmul', '_sparse_sum', '_stack',
|
|
'_standard_gamma', '_standard_gamma_grad', '_std', '_syevd_helper', '_test_serialization_subcmul',
|
|
'_trilinear', '_unique', '_unique2', '_unpack_dual', '_use_cudnn_ctc_loss',
|
|
'_use_cudnn_rnn_flatten_weight', '_validate_sparse_coo_tensor_args', '_var', '_weight_norm',
|
|
'_weight_norm_cuda_interface', 'abs', 'abs_', 'absolute', 'acos', 'acos_', 'acosh', 'acosh_',
|
|
'adaptive_avg_pool1d', 'adaptive_max_pool1d', 'add', 'addbmm', 'addcdiv', 'addcmul', 'addmm',
|
|
'addmv', 'addmv_', 'addr', 'affine_grid_generator', 'all', 'allclose', 'alpha_dropout',
|
|
'alpha_dropout_', 'amax', 'amin', 'angle', 'any', 'arange', 'arccos', 'arccos_', 'arccosh',
|
|
'arccosh_', 'arcsin', 'arcsin_', 'arcsinh', 'arcsinh_', 'arctan', 'arctan_', 'arctanh', 'arctanh_',
|
|
'argmax', 'argmin', 'argsort', 'as_strided', 'as_strided_', 'as_tensor', 'asin', 'asin_', 'asinh',
|
|
'asinh_', 'atan', 'atan2', 'atan_', 'atanh', 'atanh_', 'avg_pool1d', 'baddbmm', 'bartlett_window',
|
|
'batch_norm', 'batch_norm_backward_elemt', 'batch_norm_backward_reduce', 'batch_norm_elemt',
|
|
'batch_norm_gather_stats', 'batch_norm_gather_stats_with_counts', 'batch_norm_stats',
|
|
'batch_norm_update_stats', 'bernoulli', 'bilinear', 'binary_cross_entropy_with_logits', 'bincount',
|
|
'binomial', 'bitwise_and', 'bitwise_not', 'bitwise_or', 'bitwise_xor', 'blackman_window', 'bmm',
|
|
'broadcast_to', 'bucketize', 'can_cast', 'cat', 'ceil', 'ceil_', 'celu', 'celu_',
|
|
'channel_shuffle', 'cholesky', 'cholesky_inverse', 'cholesky_solve', 'choose_qparams_optimized',
|
|
'chunk', 'clamp', 'clamp_max', 'clamp_max_', 'clamp_min', 'clamp_min_', 'clip', 'clip_', 'clone',
|
|
'column_stack', 'combinations', 'complex', 'conj', 'constant_pad_nd', 'conv1d', 'conv2d', 'conv3d',
|
|
'conv_tbc', 'conv_transpose1d', 'conv_transpose2d', 'conv_transpose3d', 'convolution', 'copysign',
|
|
'cos', 'cos_', 'cosh', 'cosh_', 'cosine_embedding_loss', 'cosine_similarity', 'count_nonzero',
|
|
'cross', 'ctc_loss', 'cudnn_affine_grid_generator', 'cudnn_batch_norm', 'cudnn_convolution',
|
|
'cudnn_convolution_transpose', 'cudnn_grid_sampler', 'cudnn_is_acceptable', 'cummax', 'cummin',
|
|
'cumprod', 'cumsum', 'deg2rad', 'deg2rad_', 'dequantize', 'det', 'detach', 'detach_', 'diag',
|
|
'diag_embed', 'diagflat', 'diagonal', 'diff', 'digamma', 'dist', 'div', 'divide', 'dot', 'dropout',
|
|
'dropout_', 'dsmm', 'dstack', 'eig', 'embedding', 'embedding_bag', 'embedding_renorm_', 'empty',
|
|
'empty_like', 'empty_meta', 'empty_quantized', 'empty_strided', 'eq', 'equal', 'erf', 'erf_',
|
|
'erfc', 'erfc_', 'erfinv', 'exp', 'exp2', 'exp2_', 'exp_', 'expm1', 'expm1_', 'eye',
|
|
'fake_quantize_per_channel_affine', 'fake_quantize_per_tensor_affine', 'fbgemm_linear_fp16_weight',
|
|
'fbgemm_linear_fp16_weight_fp32_activation', 'fbgemm_linear_int8_weight',
|
|
'fbgemm_linear_int8_weight_fp32_activation', 'fbgemm_linear_quantize_weight',
|
|
'fbgemm_pack_gemm_matrix_fp16', 'fbgemm_pack_quantized_matrix', 'feature_alpha_dropout',
|
|
'feature_alpha_dropout_', 'feature_dropout', 'feature_dropout_', 'fill_', 'fix', 'fix_', 'flatten',
|
|
'flip', 'fliplr', 'flipud', 'float_power', 'floor', 'floor_', 'floor_divide', 'fmax', 'fmin',
|
|
'fmod', 'frac', 'frac_', 'frobenius_norm', 'from_file', 'from_numpy', 'full', 'full_like',
|
|
'gather', 'gcd', 'gcd_', 'ge', 'geqrf', 'ger', 'get_default_dtype', 'get_num_interop_threads',
|
|
'get_num_threads', 'greater', 'greater_equal', 'grid_sampler', 'grid_sampler_2d',
|
|
'grid_sampler_3d', 'group_norm', 'gru', 'gru_cell', 'gt', 'hamming_window', 'hann_window',
|
|
'hardshrink', 'heaviside', 'hinge_embedding_loss', 'histc', 'hsmm', 'hspmm', 'hstack', 'hypot',
|
|
'i0', 'i0_', 'igamma', 'igammac', 'imag', 'index_add', 'index_copy', 'index_fill', 'index_put',
|
|
'index_put_', 'index_select', 'init_num_threads', 'inner', 'instance_norm', 'int_repr', 'inverse',
|
|
'is_complex', 'is_distributed', 'is_floating_point', 'is_grad_enabled', 'is_nonzero',
|
|
'is_same_size', 'is_signed', 'is_vulkan_available', 'isclose', 'isfinite', 'isinf', 'isnan',
|
|
'isneginf', 'isposinf', 'isreal', 'kaiser_window', 'kl_div', 'kron', 'kthvalue', 'layer_norm',
|
|
'lcm', 'lcm_', 'ldexp', 'ldexp_', 'le', 'lerp', 'less', 'less_equal', 'lgamma', 'linspace', 'log',
|
|
'log10', 'log10_', 'log1p', 'log1p_', 'log2', 'log2_', 'log_', 'log_softmax', 'logaddexp',
|
|
'logaddexp2', 'logcumsumexp', 'logdet', 'logical_and', 'logical_not', 'logical_or', 'logical_xor',
|
|
'logit', 'logit_', 'logspace', 'logsumexp', 'lstm', 'lstm_cell', 'lstsq', 'lt', 'lu_solve',
|
|
'margin_ranking_loss', 'masked_fill', 'masked_scatter', 'masked_select', 'matmul', 'matrix_exp',
|
|
'matrix_power', 'matrix_rank', 'max', 'max_pool1d', 'max_pool1d_with_indices', 'max_pool2d',
|
|
'max_pool3d', 'maximum', 'mean', 'median', 'min', 'minimum', 'miopen_batch_norm',
|
|
'miopen_convolution', 'miopen_convolution_transpose', 'miopen_depthwise_convolution', 'miopen_rnn',
|
|
'mkldnn_adaptive_avg_pool2d', 'mkldnn_convolution', 'mkldnn_convolution_backward_weights',
|
|
'mkldnn_linear_backward_weights', 'mkldnn_max_pool2d', 'mkldnn_max_pool3d', 'mm', 'mode',
|
|
'moveaxis', 'movedim', 'msort', 'mul', 'multinomial', 'multiply', 'mv', 'mvlgamma',
|
|
'namedtuple_Q_R', 'namedtuple_U_S_V', 'namedtuple_a_tau', 'namedtuple_eigenvalues_eigenvectors',
|
|
'namedtuple_primal_tangent', 'namedtuple_sign_logabsdet', 'namedtuple_solution_LU',
|
|
'namedtuple_solution_QR', 'namedtuple_solution_cloned_coefficient', 'namedtuple_values_indices',
|
|
'nan_to_num', 'nan_to_num_', 'nanmedian', 'nanquantile', 'nansum', 'narrow', 'narrow_copy',
|
|
'native_batch_norm', 'native_group_norm', 'native_layer_norm', 'native_norm', 'ne', 'neg', 'neg_',
|
|
'negative', 'negative_', 'nextafter', 'nonzero', 'norm_except_dim', 'normal', 'not_equal',
|
|
'nuclear_norm', 'numel', 'ones', 'ones_like', 'orgqr', 'ormqr', 'outer', 'pairwise_distance',
|
|
'pdist', 'pinverse', 'pixel_shuffle', 'pixel_unshuffle', 'poisson', 'poisson_nll_loss', 'polar',
|
|
'polygamma', 'pow', 'prelu', 'prod', 'promote_types', 'q_per_channel_axis', 'q_per_channel_scales',
|
|
'q_per_channel_zero_points', 'q_scale', 'q_zero_point', 'qr', 'quantile', 'quantize_per_channel',
|
|
'quantize_per_tensor', 'quantized_batch_norm', 'quantized_gru_cell', 'quantized_lstm_cell',
|
|
'quantized_max_pool1d', 'quantized_max_pool2d', 'quantized_rnn_relu_cell',
|
|
'quantized_rnn_tanh_cell', 'rad2deg', 'rad2deg_', 'rand', 'rand_like', 'randint', 'randint_like',
|
|
'randn', 'randn_like', 'randperm', 'range', 'ravel', 'real', 'reciprocal', 'reciprocal_', 'relu',
|
|
'relu_', 'remainder', 'renorm', 'repeat_interleave', 'reshape', 'resize_as_', 'result_type',
|
|
'rnn_relu', 'rnn_relu_cell', 'rnn_tanh', 'rnn_tanh_cell', 'roll', 'rot90', 'round', 'round_',
|
|
'row_stack', 'rrelu', 'rrelu_', 'rsqrt', 'rsqrt_', 'rsub', 'saddmm', 'scalar_tensor', 'scatter',
|
|
'scatter_add', 'searchsorted', 'select', 'selu', 'selu_', 'set_flush_denormal',
|
|
'set_num_interop_threads', 'set_num_threads', 'sgn', 'sigmoid', 'sigmoid_', 'sign', 'signbit',
|
|
'sin', 'sin_', 'sinc', 'sinc_', 'sinh', 'sinh_', 'slogdet', 'smm', 'softmax', 'solve', 'sort',
|
|
'sparse_coo_tensor', 'split_with_sizes', 'spmm', 'sqrt', 'sqrt_', 'square', 'square_', 'squeeze',
|
|
'sspaddmm', 'stack', 'std', 'std_mean', 'sub', 'subtract', 'sum', 'svd', 'swapaxes', 'swapdims',
|
|
'symeig', 't', 'take', 'tan', 'tan_', 'tanh', 'tanh_', 'tensor', 'tensor_split', 'threshold',
|
|
'threshold_', 'tile', 'topk', 'trace', 'transpose', 'trapz', 'triangular_solve', 'tril',
|
|
'tril_indices', 'triplet_margin_loss', 'triu', 'triu_indices', 'true_divide', 'trunc', 'trunc_',
|
|
'unbind', 'unique_dim', 'unsafe_chunk', 'unsafe_split', 'unsafe_split_with_sizes', 'unsqueeze',
|
|
'vander', 'var', 'var_mean', 'vdot', 'view_as_complex', 'view_as_real', 'vstack', 'where', 'xlogy',
|
|
'xlogy_', 'zero_', 'zeros', 'zeros_like']
|