PReLU
class torch.nn.PReLU(num_parameters=1, init=0.25, device=None, dtype=None)
Applies the element-wise function:
  \text{PReLU}(x) = \max(0,x) + a * \min(0,x)

or
  \text{PReLU}(x) = \begin{cases} x, & \text{ if } x \geq 0 \\ ax,
  & \text{ otherwise } \end{cases}

Here a is a learnable parameter. When called without arguments,
   nn.PReLU() uses a single parameter a across all input channels.
   If called with nn.PReLU(nChannels), a separate a is used for each
   input channel.
Note:
 weight decay should not be used when learning 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.

Parameters:
      * num_parameters (int) -- number of 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