venv/lib/python3.11/site-packages/torch/nn/functional.pyi

# @generated by tools/pyi/gen_pyi.py from torch/nn/functional.pyi.in
# mypy: allow-untyped-defs

from typing import (
    Any,
    Callable,
    Literal,
    Optional,
    overload,
    Sequence,
    Union,
)

from torch import Tensor
from torch.types import _dtype, _int, _size

from .common_types import (
    _ratio_any_t,
    _size_1_t,
    _size_2_opt_t,
    _size_2_t,
    _size_3_opt_t,
    _size_3_t,
    _size_any_t,
)

# 'TypedDict' is a new accepted type that represents a dictionary with a fixed set of allowed keys.
# It is standards-track but not in `typing` yet. We leave this hear to be uncommented once the feature
# is wide-spread.

# from mypy_extensions import TypedDict

# GRID_SAMPLE_INTERPOLATION_MODES = TypedDict('GRID_SAMPLE_INTERPOLATION_MODES', {'bilinear': int, 'nearest': int})
# GRID_SAMPLE_PADDING_MODES = TypedDict('GRID_SAMPLE_PADDING_MODES', {'zeros': int, 'border': int, 'reflection': int})

GRID_SAMPLE_INTERPOLATION_MODES = dict[str, int]
GRID_SAMPLE_PADDING_MODES = dict[str, int]

# These stubs were generated by running stubgen (`stubgen --parse-only functional.py`), followed by manual cleaning.
#
# The 'BroadcastingList{1,2,3}' types were replaced by `_size` or _output_ratio, as appropriate.
# This was necessary since the JIT uses BroadcastingList* types but static checking with mypy etc requires a `Sequence`
# type. There is no way to express the expected lengths of these lists in the current Python typing system.
#
# Functions created via `_add_docstr` in `functional.py` where merely typed as `Any` by `stubgen`, so those were
# deleted from the stub and replaced by generated declarations. See `gen_pyi` for the implementation of the code
# generation logic for those functions. In the future, it might be worth looking into using the mypy plugin system
# to encode the type semantics of `_add_docstr`, should that system ever become widespread.
def fractional_max_pool2d_with_indices(
    input: Tensor,
    kernel_size: _size,
    output_size: Optional[_size] = ...,
    output_ratio: Optional[_ratio_any_t] = ...,
    return_indices: bool = ...,
    _random_samples: Optional[Tensor] = ...,
) -> tuple[Tensor, Tensor]: ...
def fractional_max_pool3d_with_indices(
    input: Tensor,
    kernel_size: _size,
    output_size: Optional[_size] = ...,
    output_ratio: Optional[_ratio_any_t] = ...,
    return_indices: bool = ...,
    _random_samples: Optional[Tensor] = ...,
) -> tuple[Tensor, Tensor]: ...
def max_pool1d_with_indices(
    input: Tensor,
    kernel_size: _size,
    stride: Optional[_size] = ...,
    padding: _size = ...,
    dilation: _size = ...,
    ceil_mode: bool = ...,
    return_indices: bool = ...,
) -> tuple[Tensor, Tensor]: ...
def max_pool2d_with_indices(
    input: Tensor,
    kernel_size: _size,
    stride: Optional[_size] = ...,
    padding: _size = ...,
    dilation: _size = ...,
    ceil_mode: bool = ...,
    return_indices: bool = ...,
) -> tuple[Tensor, Tensor]: ...
def max_pool3d_with_indices(
    input: Tensor,
    kernel_size: _size,
    stride: Optional[_size] = ...,
    padding: _size = ...,
    dilation: _size = ...,
    ceil_mode: bool = ...,
    return_indices: bool = ...,
) -> tuple[Tensor, Tensor]: ...
def max_unpool1d(
    input: Tensor,
    indices: Tensor,
    kernel_size: _size,
    stride: Optional[_size] = ...,
    padding: _size = ...,
    output_size: Optional[_size] = ...,
) -> Tensor: ...
def max_unpool2d(
    input: Tensor,
    indices: Tensor,
    kernel_size: _size,
    stride: Optional[_size] = ...,
    padding: _size = ...,
    output_size: Optional[_size] = ...,
) -> Tensor: ...
def max_unpool3d(
    input: Tensor,
    indices: Tensor,
    kernel_size: _size,
    stride: Optional[_size] = ...,
    padding: _size = ...,
    output_size: Optional[_size] = ...,
) -> Tensor: ...
def lp_pool1d(
    input: Tensor,
    norm_type: float,
    kernel_size: _size_1_t,
    stride: Union[Optional[_size], Optional[int]] = ...,
    ceil_mode: bool = ...,
) -> Tensor: ...
def lp_pool2d(
    input: Tensor,
    norm_type: float,
    kernel_size: _size_2_t,
    stride: Union[Optional[_size], Optional[int]] = ...,
    ceil_mode: bool = ...,
) -> Tensor: ...
def lp_pool3d(
    input: Tensor,
    norm_type: float,
    kernel_size: _size_3_t,
    stride: Union[Optional[_size], Optional[int]] = ...,
    ceil_mode: bool = ...,
) -> Tensor: ...
def adaptive_max_pool1d_with_indices(
    input: Tensor,
    output_size: _size,
    return_indices: bool = ...,
) -> tuple[Tensor, Tensor]: ...
def adaptive_max_pool2d_with_indices(
    input: Tensor,
    output_size: _size_2_opt_t,
    return_indices: bool = ...,
) -> tuple[Tensor, Tensor]: ...
def adaptive_max_pool3d_with_indices(
    input: Tensor,
    output_size: _size_3_opt_t,
    return_indices: bool = ...,
) -> tuple[Tensor, Tensor]: ...
def adaptive_avg_pool2d(input: Tensor, output_size: _size_2_opt_t) -> Tensor: ...
def adaptive_avg_pool3d(input: Tensor, output_size: _size_3_opt_t) -> Tensor: ...
def dropout(
    input: Tensor,
    p: float = ...,
    training: bool = ...,
    inplace: bool = ...,
) -> Tensor: ...
def alpha_dropout(
    input: Tensor,
    p: float = ...,
    training: bool = ...,
    inplace: bool = ...,
) -> Tensor: ...
def dropout1d(
    input: Tensor,
    p: float = ...,
    training: bool = ...,
    inplace: bool = ...,
) -> Tensor: ...
def dropout2d(
    input: Tensor,
    p: float = ...,
    training: bool = ...,
    inplace: bool = ...,
) -> Tensor: ...
def dropout3d(
    input: Tensor,
    p: float = ...,
    training: bool = ...,
    inplace: bool = ...,
) -> Tensor: ...
def feature_alpha_dropout(
    input: Tensor,
    p: float = ...,
    training: bool = ...,
    inplace: bool = ...,
) -> Tensor: ...
def threshold(
    input: Tensor,
    threshold: float,
    value: float,
    inplace: bool = ...,
) -> Tensor: ...
def relu(input: Tensor, inplace: bool = ...) -> Tensor: ...
def glu(input: Tensor, dim: int = ...) -> Tensor: ...
def hardtanh(
    input: Tensor,
    min_val: float = ...,
    max_val: float = ...,
    inplace: bool = ...,
) -> Tensor: ...
def relu6(input: Tensor, inplace: bool = ...) -> Tensor: ...
def elu(input: Tensor, alpha: float = ..., inplace: bool = ...) -> Tensor: ...
def selu(input: Tensor, inplace: bool = ...) -> Tensor: ...
def celu(input: Tensor, alpha: float = ..., inplace: bool = ...) -> Tensor: ...
def leaky_relu(
    input: Tensor,
    negative_slope: float = ...,
    inplace: bool = ...,
) -> Tensor: ...
def rrelu(
    input: Tensor,
    lower: float = ...,
    upper: float = ...,
    training: bool = ...,
    inplace: bool = ...,
) -> Tensor: ...
def tanhshrink(input: Any): ...
def softsign(input: Any): ...
def softmin(
    input: Tensor,
    dim: Optional[int] = ...,
    _stacklevel: int = ...,
    dtype: Optional[_dtype] = ...,
) -> Tensor: ...
def softmax(
    input: Tensor,
    dim: Optional[int] = ...,
    _stacklevel: int = ...,
    dtype: Optional[_dtype] = ...,
) -> Tensor: ...
def gumbel_softmax(
    logits: Tensor,
    tau: float = ...,
    hard: bool = ...,
    eps: float = ...,
    dim: int = ...,
) -> Tensor: ...
def log_softmax(
    input: Tensor,
    dim: Optional[int] = ...,
    _stacklevel: int = ...,
    dtype: Optional[_dtype] = ...,
) -> Tensor: ...
def tanh(input: Any): ...
def sigmoid(input: Any) -> Tensor: ...
def hardsigmoid(input: Tensor, inplace: bool = False) -> Tensor: ...
def silu(input: Tensor, inplace: bool = False) -> Tensor: ...
def mish(input: Tensor, inplace: bool = False) -> Tensor: ...
def hardswish(input: Tensor, inplace: bool = False) -> Tensor: ...
def embedding(
    input: Tensor,
    weight: Tensor,
    padding_idx: Optional[int] = ...,
    max_norm: Optional[float] = ...,
    norm_type: float = ...,
    scale_grad_by_freq: bool = ...,
    sparse: bool = ...,
) -> Tensor: ...
def embedding_bag(
    input: Tensor,
    weight: Tensor,
    offsets: Optional[Tensor] = ...,
    max_norm: Optional[float] = ...,
    norm_type: float = ...,
    scale_grad_by_freq: bool = ...,
    mode: str = ...,
    sparse: bool = ...,
    per_sample_weights: Optional[Tensor] = ...,
    include_last_offset: bool = ...,
    padding_idx: Optional[int] = ...,
) -> Tensor: ...
def batch_norm(
    input: Tensor,
    running_mean: Optional[Tensor],
    running_var: Optional[Tensor],
    weight: Optional[Tensor] = ...,
    bias: Optional[Tensor] = ...,
    training: bool = ...,
    momentum: float = ...,
    eps: float = ...,
) -> Tensor: ...
def instance_norm(
    input: Tensor,
    running_mean: Optional[Tensor] = ...,
    running_var: Optional[Tensor] = ...,
    weight: Optional[Tensor] = ...,
    bias: Optional[Tensor] = ...,
    use_input_stats: bool = ...,
    momentum: float = ...,
    eps: float = ...,
) -> Tensor: ...
def layer_norm(
    input: Tensor,
    normalized_shape: Sequence[int],
    weight: Optional[Tensor] = ...,
    bias: Optional[Tensor] = ...,
    eps: float = ...,
) -> Tensor: ...
def rms_norm(
    input: Tensor,
    normalized_shape: Sequence[int],
    weight: Optional[Tensor] = ...,
    eps: Optional[float] = ...,
) -> Tensor: ...
def group_norm(
    input: Tensor,
    num_groups: int,
    weight: Optional[Tensor] = ...,
    bias: Optional[Tensor] = ...,
    eps: float = ...,
) -> Tensor: ...
def local_response_norm(
    input: Tensor,
    size: int,
    alpha: float = ...,
    beta: float = ...,
    k: float = ...,
) -> Tensor: ...
def ctc_loss(
    log_probs: Tensor,
    targets: Tensor,
    input_lengths: Tensor,
    target_lengths: Tensor,
    blank: int = ...,
    reduction: str = ...,
    zero_infinity: bool = ...,
) -> Tensor: ...
def nll_loss(
    input: Tensor,
    target: Tensor,
    weight: Optional[Tensor] = ...,
    size_average: Optional[bool] = ...,
    ignore_index: int = ...,
    reduce: Optional[bool] = ...,
    reduction: str = ...,
) -> Tensor: ...
def poisson_nll_loss(
    input: Tensor,
    target: Tensor,
    log_input: bool = ...,
    full: bool = ...,
    size_average: Optional[bool] = ...,
    eps: float = ...,
    reduce: Optional[bool] = ...,
    reduction: str = ...,
) -> Tensor: ...
def gaussian_nll_loss(
    input: Tensor,
    target: Tensor,
    var: Union[Tensor, float],
    full: Optional[bool] = ...,
    eps: Optional[float] = ...,
    reduction: Optional[str] = ...,
) -> Tensor: ...
def kl_div(
    input: Tensor,
    target: Tensor,
    size_average: Optional[bool] = ...,
    reduce: Optional[bool] = ...,
    reduction: str = ...,
    log_target: bool = ...,
) -> Tensor: ...
def cross_entropy(
    input: Tensor,
    target: Tensor,
    weight: Optional[Tensor] = ...,
    size_average: Optional[bool] = ...,
    ignore_index: int = ...,
    reduce: Optional[bool] = ...,
    reduction: str = ...,
    label_smoothing: float = ...,
) -> Tensor: ...
def binary_cross_entropy(
    input: Tensor,
    target: Tensor,
    weight: Optional[Tensor] = ...,
    size_average: Optional[bool] = ...,
    reduce: Optional[bool] = ...,
    reduction: str = ...,
) -> Tensor: ...
def binary_cross_entropy_with_logits(
    input: Tensor,
    target: Tensor,
    weight: Optional[Tensor] = ...,
    size_average: Optional[bool] = ...,
    reduce: Optional[bool] = ...,
    reduction: str = ...,
    pos_weight: Optional[Tensor] = ...,
) -> Tensor: ...
def smooth_l1_loss(
    input: Tensor,
    target: Tensor,
    size_average: Optional[bool] = ...,
    reduce: Optional[bool] = ...,
    reduction: str = ...,
    beta: float = ...,
) -> Tensor: ...
def huber_loss(
    input: Tensor,
    target: Tensor,
    reduction: str = ...,
    delta: float = ...,
) -> Tensor: ...
def l1_loss(
    input: Tensor,
    target: Tensor,
    size_average: Optional[bool] = ...,
    reduce: Optional[bool] = ...,
    reduction: str = ...,
) -> Tensor: ...
def mse_loss(
    input: Tensor,
    target: Tensor,
    size_average: Optional[bool] = ...,
    reduce: Optional[bool] = ...,
    reduction: str = ...,
) -> Tensor: ...
def margin_ranking_loss(
    input1: Tensor,
    input2: Tensor,
    target: Tensor,
    margin: float = ...,
    size_average: Optional[bool] = ...,
    reduce: Optional[bool] = ...,
    reduction: str = ...,
) -> Tensor: ...
def hinge_embedding_loss(
    input: Tensor,
    target: Tensor,
    margin: float = ...,
    size_average: Optional[bool] = ...,
    reduce: Optional[bool] = ...,
    reduction: str = ...,
) -> Tensor: ...
def multilabel_margin_loss(
    input: Tensor,
    target: Tensor,
    size_average: Optional[bool] = ...,
    reduce: Optional[bool] = ...,
    reduction: str = ...,
) -> Tensor: ...
def soft_margin_loss(
    input: Tensor,
    target: Tensor,
    size_average: Optional[bool] = ...,
    reduce: Optional[bool] = ...,
    reduction: str = ...,
) -> Tensor: ...
def multilabel_soft_margin_loss(
    input: Tensor,
    target: Tensor,
    weight: Optional[Tensor] = ...,
    size_average: Optional[bool] = ...,
    reduce: Optional[bool] = ...,
    reduction: str = ...,
) -> Tensor: ...
def cosine_embedding_loss(
    input1: Tensor,
    input2: Tensor,
    target: Tensor,
    margin: float = ...,
    size_average: Optional[bool] = ...,
    reduce: Optional[bool] = ...,
    reduction: str = ...,
) -> Tensor: ...
def multi_margin_loss(
    input: Tensor,
    target: Tensor,
    p: int = ...,
    margin: float = ...,
    weight: Optional[Tensor] = ...,
    size_average: Optional[bool] = ...,
    reduce: Optional[bool] = ...,
    reduction: str = ...,
) -> Tensor: ...
def upsample(
    input: Any,
    size: Optional[Any] = ...,
    scale_factor: Optional[Any] = ...,
    mode: str = ...,
    align_corners: Optional[Any] = ...,
): ...
def interpolate(
    input: Any,
    size: Optional[Any] = ...,
    scale_factor: Optional[Any] = ...,
    mode: str = ...,
    align_corners: Optional[Any] = ...,
    recompute_scale_factor: Optional[Any] = ...,
    antialias: bool = ...,
): ...
def upsample_nearest(
    input: Any,
    size: Optional[Any] = ...,
    scale_factor: Optional[Any] = ...,
): ...
def upsample_bilinear(
    input: Any,
    size: Optional[Any] = ...,
    scale_factor: Optional[Any] = ...,
): ...
def grid_sample(
    input: Tensor,
    grid: Tensor,
    mode: str = ...,
    padding_mode: str = ...,
    align_corners: Optional[Any] = ...,
) -> Tensor: ...
def affine_grid(
    theta: Tensor,
    size: list[int],
    align_corners: Optional[Any] = ...,
) -> 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 triplet_margin_with_distance_loss(
    anchor: Tensor,
    positive: Tensor,
    negative: Tensor,
    *,
    distance_function: Optional[Callable[[Tensor, Tensor], Tensor]] = ...,
    margin: float = ...,
    swap: bool = ...,
    reduction: str = ...,
) -> Tensor: ...
def normalize(
    input: Tensor,
    p: float = ...,
    dim: int = ...,
    eps: float = ...,
    out: Optional[Tensor] = ...,
) -> Tensor: ...
def assert_int_or_pair(
    arg: Any,
    arg_name: Any,
    message: Any,
) -> None: ...
def unfold(
    input: Tensor,
    kernel_size: _size_any_t,
    dilation: _size_any_t = ...,
    padding: _size_any_t = ...,
    stride: _size_any_t = ...,
) -> Tensor: ...
def fold(
    input: Tensor,
    output_size: _size_any_t,
    kernel_size: _size_any_t,
    dilation: _size_any_t = ...,
    padding: _size_any_t = ...,
    stride: _size_any_t = ...,
) -> Tensor: ...
def _canonical_mask(
    mask: Optional[Tensor],
    mask_name: str,
    other_type: Optional[_dtype],
    other_name: str,
    target_type: _dtype,
    check_other: bool = True,
) -> Optional[Tensor]: ...
def _none_or_dtype(input: Optional[Tensor]) -> Optional[_dtype]: ...
def multi_head_attention_forward(
    query: Tensor,
    key: Tensor,
    value: Tensor,
    embed_dim_to_check: int,
    num_heads: int,
    in_proj_weight: Optional[Tensor],
    in_proj_bias: Optional[Tensor],
    bias_k: Optional[Tensor],
    bias_v: Optional[Tensor],
    add_zero_attn: bool,
    dropout_p: float,
    out_proj_weight: Tensor,
    out_proj_bias: Optional[Tensor],
    training: bool = True,
    key_padding_mask: Optional[Tensor] = None,
    need_weights: bool = True,
    attn_mask: Optional[Tensor] = None,
    use_separate_proj_weight: bool = False,
    q_proj_weight: Optional[Tensor] = None,
    k_proj_weight: Optional[Tensor] = None,
    v_proj_weight: Optional[Tensor] = None,
    static_k: Optional[Tensor] = None,
    static_v: Optional[Tensor] = None,
    average_attn_weights: bool = True,
    is_causal: bool = False,
) -> tuple[Tensor, Optional[Tensor]]: ...

from torch import conv1d as conv1d
from torch import conv2d as conv2d
from torch import conv3d as conv3d
from torch import conv_transpose1d as conv_transpose1d
from torch import conv_transpose2d as conv_transpose2d
from torch import conv_transpose3d as conv_transpose3d
from torch import conv_tbc as conv_tbc
from torch import avg_pool1d as avg_pool1d
from torch import adaptive_avg_pool1d as adaptive_avg_pool1d
from torch import relu_ as relu_
from torch import selu_ as selu_
from torch import celu_ as celu_
from torch import prelu as prelu
from torch import rrelu_ as rrelu_
from torch import hardshrink as hardshrink
from torch import bilinear as bilinear
from torch import pixel_shuffle as pixel_shuffle
from torch import pixel_unshuffle as pixel_unshuffle
from torch import channel_shuffle as channel_shuffle
from torch import native_channel_shuffle as native_channel_shuffle
from torch import pairwise_distance as pairwise_distance
from torch import pdist as pdist
from torch import cosine_similarity as cosine_similarity
from torch._C._nn import avg_pool2d as avg_pool2d
from torch._C._nn import avg_pool3d as avg_pool3d
from torch._C._nn import hardtanh_ as hardtanh_
from torch._C._nn import elu_ as elu_
from torch._C._nn import leaky_relu_ as leaky_relu_
from torch._C._nn import gelu as gelu
from torch._C._nn import softplus as softplus
from torch._C._nn import softshrink as softshrink
from torch._C._nn import linear as linear
from torch._C._nn import pad as pad
from torch._C._nn import one_hot as one_hot
from torch._C._nn import scaled_dot_product_attention as scaled_dot_product_attention
from torch._C._nn import log_sigmoid
logsigmoid = log_sigmoid

@overload
def adaptive_max_pool1d(input: Tensor, output_size: Union[_int, _size], return_indices: Literal[False] = False) -> Tensor: ...
@overload
def adaptive_max_pool1d(input: Tensor, output_size: Union[_int, _size], return_indices: Literal[True], /) -> tuple[Tensor, Tensor]: ...
@overload
def adaptive_max_pool1d(input: Tensor, output_size: Union[_int, _size], *, return_indices: Literal[True]) -> tuple[Tensor, Tensor]: ...
@overload
def adaptive_max_pool2d(input: Tensor, output_size: Union[_int, _size], return_indices: Literal[False] = False) -> Tensor: ...
@overload
def adaptive_max_pool2d(input: Tensor, output_size: Union[_int, _size], return_indices: Literal[True], /) -> tuple[Tensor, Tensor]: ...
@overload
def adaptive_max_pool2d(input: Tensor, output_size: Union[_int, _size], *, return_indices: Literal[True]) -> tuple[Tensor, Tensor]: ...
@overload
def adaptive_max_pool3d(input: Tensor, output_size: Union[_int, _size], return_indices: Literal[False] = False) -> Tensor: ...
@overload
def adaptive_max_pool3d(input: Tensor, output_size: Union[_int, _size], return_indices: Literal[True], /) -> tuple[Tensor, Tensor]: ...
@overload
def adaptive_max_pool3d(input: Tensor, output_size: Union[_int, _size], *, return_indices: Literal[True]) -> tuple[Tensor, Tensor]: ...
@overload
def fractional_max_pool2d(input: Tensor, kernel_size: Union[_int, _size], output_size: Optional[Union[_int, _size]] = None, output_ratio: Optional[_ratio_any_t] = None, return_indices: Literal[False] = False, _random_samples: Optional[Tensor] = None) -> Tensor: ...
@overload
def fractional_max_pool2d(input: Tensor, kernel_size: Union[_int, _size], output_size: Optional[Union[_int, _size]], output_ratio: Optional[_ratio_any_t], return_indices: Literal[True], /, _random_samples: Optional[Tensor] = None) -> tuple[Tensor, Tensor]: ...
@overload
def fractional_max_pool2d(input: Tensor, kernel_size: Union[_int, _size], output_size: Optional[Union[_int, _size]] = None, output_ratio: Optional[_ratio_any_t] = None, *, return_indices: Literal[True], _random_samples: Optional[Tensor] = None) -> tuple[Tensor, Tensor]: ...
@overload
def fractional_max_pool3d(input: Tensor, kernel_size: Union[_int, _size], output_size: Optional[Union[_int, _size]] = None, output_ratio: Optional[_ratio_any_t] = None, return_indices: Literal[False] = False, _random_samples: Optional[Tensor] = None) -> Tensor: ...
@overload
def fractional_max_pool3d(input: Tensor, kernel_size: Union[_int, _size], output_size: Optional[Union[_int, _size]], output_ratio: Optional[_ratio_any_t], return_indices: Literal[True], /, _random_samples: Optional[Tensor] = None) -> tuple[Tensor, Tensor]: ...
@overload
def fractional_max_pool3d(input: Tensor, kernel_size: Union[_int, _size], output_size: Optional[Union[_int, _size]] = None, output_ratio: Optional[_ratio_any_t] = None, *, return_indices: Literal[True], _random_samples: Optional[Tensor] = None) -> tuple[Tensor, Tensor]: ...
@overload
def max_pool1d(input: Tensor, kernel_size: Union[_int, _size], stride: Optional[Union[_int, _size]] = None, padding: Union[_int, _size] = 0, dilation: Union[_int, _size] = 1, ceil_mode: bool = False, return_indices: Literal[False] = False) -> Tensor: ...
@overload
def max_pool1d(input: Tensor, kernel_size: Union[_int, _size], stride: Optional[Union[_int, _size]], padding: Union[_int, _size], dilation: Union[_int, _size], ceil_mode: bool, return_indices: Literal[True], /) -> tuple[Tensor, Tensor]: ...
@overload
def max_pool1d(input: Tensor, kernel_size: Union[_int, _size], stride: Optional[Union[_int, _size]] = None, padding: Union[_int, _size] = 0, dilation: Union[_int, _size] = 1, ceil_mode: bool = False, *, return_indices: Literal[True]) -> tuple[Tensor, Tensor]: ...
@overload
def max_pool2d(input: Tensor, kernel_size: Union[_int, _size], stride: Optional[Union[_int, _size]] = None, padding: Union[_int, _size] = 0, dilation: Union[_int, _size] = 1, ceil_mode: bool = False, return_indices: Literal[False] = False) -> Tensor: ...
@overload
def max_pool2d(input: Tensor, kernel_size: Union[_int, _size], stride: Optional[Union[_int, _size]], padding: Union[_int, _size], dilation: Union[_int, _size], ceil_mode: bool, return_indices: Literal[True], /) -> tuple[Tensor, Tensor]: ...
@overload
def max_pool2d(input: Tensor, kernel_size: Union[_int, _size], stride: Optional[Union[_int, _size]] = None, padding: Union[_int, _size] = 0, dilation: Union[_int, _size] = 1, ceil_mode: bool = False, *, return_indices: Literal[True]) -> tuple[Tensor, Tensor]: ...
@overload
def max_pool3d(input: Tensor, kernel_size: Union[_int, _size], stride: Optional[Union[_int, _size]] = None, padding: Union[_int, _size] = 0, dilation: Union[_int, _size] = 1, ceil_mode: bool = False, return_indices: Literal[False] = False) -> Tensor: ...
@overload
def max_pool3d(input: Tensor, kernel_size: Union[_int, _size], stride: Optional[Union[_int, _size]], padding: Union[_int, _size], dilation: Union[_int, _size], ceil_mode: bool, return_indices: Literal[True], /) -> tuple[Tensor, Tensor]: ...
@overload
def max_pool3d(input: Tensor, kernel_size: Union[_int, _size], stride: Optional[Union[_int, _size]] = None, padding: Union[_int, _size] = 0, dilation: Union[_int, _size] = 1, ceil_mode: bool = False, *, return_indices: Literal[True]) -> tuple[Tensor, Tensor]: ...