class_docstr: Applies Dropout to the input by randomly setting a fraction 'p' of input units to 0 at each
  update during training time in order to prevent overfitting.
  
  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
  p: Fraction of the input units to drop. Float between 0 and 1.
  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.
  
  >>> dropout = Dropout(0.25, input_shape=(2, 3))
  creating: createZooKerasDropout
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code: def __init__(self, p, input_shape=None, **kwargs):
      super(Dropout, self).__init__(
          None,
          float(p),
          list(input_shape) if input_shape else None,
          **kwargs,
      )
  
imports: import sys
  from bigdl.dllib.utils.common import INTMIN
  from ..engine.topology import ZooKerasLayer
label_desc: Use image node_id 1 to create a new Dropout object from inherited base classes: ZooKerasLayer with example: obj = Dropout(p, input_shape)