class_docstr: Apply additive zero-centered Gaussian noise.
  This is useful to mitigate overfitting (you could see it as a form of random data augmentation).
  Gaussian Noise is a natural choice as corruption process for real valued inputs.
  As it is a regularization layer, it is only active at training time.
  
  When you use this layer as the first layer of a model, you need to provide the argument
  input_shape (a shape tuple, does not include the batch dimension).
  
  # Arguments
  sigma: Float, standard deviation of the noise distribution.
  input_shape: A shape tuple, not including batch.
  name: String to set the name of the layer.
        If not specified, its name will by default to be a generated string.
  
  >>> gaussiannoise = GaussianNoise(0.45, input_shape=(3, 4, 5), name="gaussiannoise1")
  creating: createZooKerasGaussianNoise
code: def __init__(self, sigma, input_shape=None, **kwargs):
      super(GaussianNoise, self).__init__(
          None,
          float(sigma),
          list(input_shape) if input_shape else None,
          **kwargs,
      )
  
imports: import sys
  from ..engine.topology import ZooKerasLayer
label_desc: Use image node_id 1 to create a new GaussianNoise object from inherited base classes: ZooKerasLayer with example: obj = GaussianNoise(sigma, input_shape)