torch/nn/modules/activation.pyi.in

from ... import Tensor
from .. import Parameter
from .module import Module
from typing import Any, Optional


class Threshold(Module):
    threshold: float = ...
    value: float = ...
    inplace: bool = ...

    def __init__(self, threshold: float, value: float, inplace: bool = ...) -> None: ...

    def forward(self, input: Tensor) -> Tensor: ...


class ReLU(Threshold):
    def __init__(self, inplace: bool = ...) -> None: ...


class RReLU(Module):
    lower: float = ...
    upper: float = ...
    inplace: bool = ...

    def __init__(self, lower: float = ..., upper: float = ..., inplace: bool = ...) -> None: ...

    def forward(self, input: Tensor) -> Tensor: ...


class Hardtanh(Module):
    min_val: float = ...
    max_val: float = ...
    inplace: bool = ...

    def __init__(self, min_val: float = ..., max_val: float = ..., inplace: bool = ...) -> None: ...

    def forward(self, input: Tensor) -> Tensor: ...


class ReLU6(Hardtanh):
    def __init__(self, inplace: bool = ...) -> None: ...


class Sigmoid(Module):
    def forward(self, input: Tensor) -> Tensor: ...


class Tanh(Module):
    def forward(self, input: Tensor) -> Tensor: ...


class ELU(Module):
    alpha: float = ...
    inplace: bool = ...

    def __init__(self, alpha: float = ..., inplace: bool = ...) -> None: ...

    def forward(self, input: Tensor) -> Tensor: ...


class CELU(Module):
    alpha: float = ...
    inplace: bool = ...

    def __init__(self, alpha: float = ..., inplace: bool = ...) -> None: ...

    def forward(self, input: Tensor) -> Tensor: ...


class SELU(Module):
    inplace: bool = ...

    def __init__(self, inplace: bool = ...) -> None: ...

    def forward(self, input: Tensor) -> Tensor: ...


class GLU(Module):
    dim: int = ...

    def __init__(self, dim: int = ...) -> None: ...

    def forward(self, input: Tensor) -> Tensor: ...


class GELU(Module):
    def forward(self, input: Tensor) -> Tensor: ...


class Hardshrink(Module):
    lambd: float = ...

    def __init__(self, lambd: float = ...) -> None: ...

    def forward(self, input: Tensor) -> Tensor: ...


class LeakyReLU(Module):
    negative_slope: float = ...
    inplace: bool = ...

    def __init__(self, negative_slope: float = ..., inplace: bool = ...) -> None: ...

    def forward(self, input: Tensor) -> Tensor: ...


class LogSigmoid(Module):
    def forward(self, input: Tensor) -> Tensor: ...


class Softplus(Module):
    beta: float = ...
    threshold: float = ...

    def __init__(self, beta: float = ..., threshold: float = ...) -> None: ...

    def forward(self, input: Tensor) -> Tensor: ...


class Softshrink(Module):
    lambd: float = ...

    def __init__(self, lambd: float = ...) -> None: ...

    def forward(self, input: Tensor) -> Tensor: ...


class PReLU(Module):
    num_parameters: int = ...
    weight: Parameter = ...

    def __init__(self, num_parameters: int = ..., init: float = ...) -> None: ...

    def forward(self, input: Tensor) -> Tensor: ...


class Softsign(Module):
    def forward(self, input: Tensor) -> Tensor: ...


class Tanhshrink(Module):
    def forward(self, input: Tensor) -> Tensor: ...


class Softmin(Module):
    dim: int = ...

    def __init__(self, dim: Optional[int] = ...) -> None: ...

    def forward(self, input: Tensor) -> Tensor: ...


class Softmax(Module):
    dim: int = ...

    def __init__(self, dim: Optional[int] = ...) -> None: ...

    def forward(self, input: Tensor) -> Tensor: ...


class Softmax2d(Module):
    def forward(self, input: Tensor) -> Tensor: ...


class LogSoftmax(Module):
    dim: int = ...

    def __init__(self, dim: Optional[int] = ...) -> None: ...

    def forward(self, input: Tensor) -> Tensor: ...


class MultiheadAttention(Module):
    embed_dim: Any = ...
    kdim: Any = ...
    vdim: Any = ...
    num_heads: Any = ...
    dropout: Any = ...
    head_dim: Any = ...
    in_proj_weight: Any = ...
    q_proj_weight: Any = ...
    k_proj_weight: Any = ...
    v_proj_weight: Any = ...
    in_proj_bias: Any = ...
    out_proj: Any = ...
    bias_k: Any = ...
    bias_v: Any = ...
    add_zero_attn: Any = ...
    def __init__(self, embed_dim: Any, num_heads: Any, dropout: float = ..., bias: bool = ..., add_bias_kv: bool = ..., add_zero_attn: bool = ..., kdim: Optional[Any] = ..., vdim: Optional[Any] = ...) -> None: ...
    def forward(self, query: Any, key: Any, value: Any, key_padding_mask: Optional[Any] = ..., need_weights: bool = ..., attn_mask: Optional[Any] = ...): ...