strongly correlated (as is normally the case in early convolution
   layers) then i.i.d. dropout will not regularize the activations and
   will otherwise just result in an effective learning rate decrease.
In this case, "nn.AlphaDropout()" will help promote independence
   between feature maps and should be used instead.
Parameters:
      * p (float, optional) -- probability of an element to
        be zeroed. Default: 0.5
  * **inplace** (*bool**, **optional*) -- If set to "True", will
    do this operation in-place

Shape:
      * Input: (N, C, D, H, W) or (C, D, H, W).
  * Output: (N, C, D, H, W) or (C, D, H, W) (same shape as input).

Examples:
  >>> m = nn.FeatureAlphaDropout(p=0.2)
  >>> input = torch.randn(20, 16, 4, 32, 32)
  >>> output = m(input)
