function gaussianDropout(args) {
	  return new GaussianDropout(args);
	}
Apply multiplicative 1-centered Gaussian noise.

As it is a regularization layer, it is only active at training time.

Arguments:
  - `rate`: float, drop probability (as with `Dropout`).
    The multiplicative noise will have
    standard deviation `sqrt(rate / (1 - rate))`.

Input shape:
  Arbitrary. Use the keyword argument `inputShape`
  (tuple of integers, does not include the samples axis)
  when using this layer as the first layer in a model.

Output shape:
  Same shape as input.

References:
  - [Dropout: A Simple Way to Prevent Neural Networks from Overfitting](
     http://www.cs.toronto.edu/~rsalakhu/papers/srivastava14a.pdf)

@doc {heading: 'Layers', subheading: 'Noise', namespace: 'layers'}