.. math::
    \text{Hardsigmoid}(x) = \begin{cases}
        0 & \text{if~} x \le -3, \\
        1 & \text{if~} x \ge +3, \\
        x / 6 + 1 / 2 & \text{otherwise}
    \end{cases}

Args:
    inplace: can optionally do the operation in-place. Default: ``False``

Shape:
    - Input: :math:`(N, *)` where `*` means, any number of additional
      dimensions
    - Output: :math:`(N, *)`, same shape as the input

Examples::

    >>> m = nn.Hardsigmoid()
    >>> input = torch.randn(2)
    >>> output = m(input)
"""
__constants__ = ['inplace']

inplace: bool

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

def forward(self, input: Tensor) -> Tensor:
    return F.hardsigmoid(input, self.inplace)

.. math::
    \text{Tanh}(x) = \tanh(x) = \frac{\exp(x) - \exp(-x)} {\exp(x) + \exp(-x)}

Shape:
    - Input: :math:`(N, *)` where `*` means, any number of additional
      dimensions
    - Output: :math:`(N, *)`, same shape as the input

.. image:: ../scripts/activation_images/Tanh.png

Examples::

    >>> m = nn.Tanh()
    >>> input = torch.randn(2)
    >>> output = m(input)
"""

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

.. math::
    \text{silu}(x) = x * \sigma(x), \text{where } \sigma(x) \text{ is the logistic sigmoid.}

.. note::
    See `Gaussian Error Linear Units (GELUs) <https://arxiv.org/abs/1606.08415>`_ 
    where the SiLU (Sigmoid Linear Unit) was originally coined, and see 
    `Sigmoid-Weighted Linear Units for Neural Network Function Approximation 
    in Reinforcement Learning <https://arxiv.org/abs/1702.03118>`_ and `Swish: 
    a Self-Gated Activation Function <https://arxiv.org/abs/1710.05941v1>`_ 
    where the SiLU was experimented with later.

Shape:
    - Input: :math:`(N, *)` where `*` means, any number of additional
      dimensions
    - Output: :math:`(N, *)`, same shape as the input

Examples::

    >>> m = nn.SiLU()
    >>> input = torch.randn(2)
    >>> output = m(input)
"""
__constants__ = ['inplace']
inplace: bool

def __init__(self, inplace: bool = False):
    super(SiLU, self).__init__()
    self.inplace = inplace

def forward(self, input: Tensor) -> Tensor:
    return F.silu(input, inplace=self.inplace)

def extra_repr(self) -> str:
    inplace_str = 'inplace=True' if self.inplace else ''
    return inplace_str

`Searching for MobileNetV3`_.

.. math::
    \text{Hardswish}(x) = \begin{cases}
        0 & \text{if~} x \le -3, \\
        x & \text{if~} x \ge +3, \\
        x \cdot (x + 3) /6 & \text{otherwise}
    \end{cases}

Args:
    inplace: can optionally do the operation in-place. Default: ``False``

Shape:
    - Input: :math:`(N, *)` where `*` means, any number of additional
      dimensions
    - Output: :math:`(N, *)`, same shape as the input

Examples::

    >>> m = nn.Hardswish()
    >>> input = torch.randn(2)
    >>> output = m(input)

.. _`Searching for MobileNetV3`:
    https://arxiv.org/abs/1905.02244
"""
__constants__ = ['inplace']

inplace: bool

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

def forward(self, input: Tensor) -> Tensor:
    return F.hardswish(input, self.inplace)`