torch.nn.functional.feature_alpha_dropout
torch.nn.functional.feature_alpha_dropout(input, p=0.5, training=False, inplace=False)
Randomly masks out entire channels (a channel is a feature map,
   e.g. the j-th channel of the i-th sample in the batch input is a
   tensor \text{input}[i, j]) of the input tensor). Instead of setting
   activations to zero, as in regular Dropout, the activations are set
   to the negative saturation value of the SELU activation function.
Each element will be masked independently on every forward call
   with probability "p" using samples from a Bernoulli distribution.
   The elements to be masked are randomized on every forward call, and
   scaled and shifted to maintain zero mean and unit variance.
See "FeatureAlphaDropout" for details.
Parameters:
      * p (float) -- dropout probability of a channel to be
        zeroed. Default: 0.5
  * **training** (*bool*) -- apply dropout if is "True". Default:
