rate :math:`a` is used for each input channel.


    .. note::
        weight decay should not be used when learning :math:`a` for good performance.

    .. note::
        Channel dim is the 2nd dim of input. When input has dims < 2, then there is
        no channel dim and the number of channels = 1.

    Args:
        num_parameters (int): number of :math:`a` to learn.
            Although it takes an int as input, there is only two values are legitimate:
            1, or the number of channels at input. Default: 1
        init (float): the initial value of :math:`a`. Default: 0.25

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

    Attributes:
        weight (Tensor): the learnable weights of shape (:attr:`num_parameters`).

    .. image:: ../scripts/activation_images/PReLU.png

    Examples::

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