class_docstr: Applies Dropout to the input.
  
  Dropout consists in randomly setting a fraction `rate` of input units to 0
  at each update during training time, which helps prevent overfitting.
  The units that are kept are scaled by `1 / (1 - rate)`, so that their
  sum is unchanged at training time and inference time.
  
  Args:
    rate: The dropout rate, between 0 and 1. E.g. `rate=0.1` would drop out
      10% of input units.
    noise_shape: 1D tensor of type `int32` representing the shape of the
      binary dropout mask that will be multiplied with the input.
      For instance, if your inputs have shape
      `(batch_size, timesteps, features)`, and you want the dropout mask
      to be the same for all timesteps, you can use
      `noise_shape=[batch_size, 1, features]`.
    seed: A Python integer. Used to create random seeds. See
      `tf.compat.v1.set_random_seed`.
      for behavior.
    name: The name of the layer (string).
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code: def __init__(
      self, rate=0.5, noise_shape=None, seed=None, name=None, **kwargs
  ):
      super(Dropout, self).__init__(
          rate=rate,
          noise_shape=noise_shape,
          seed=seed,
          name=name,
          **kwargs,
      )
  
file_path: tensorflow/tensorflow/python/keras/legacy_tf_layers/core.py
imports: import warnings
  from tensorflow.python.keras import layers
  from tensorflow.python.keras.legacy_tf_layers import base
  from tensorflow.python.ops import init_ops
label_desc: Use image node_id 1 to create a new Dropout object from inherited base classes: keras_layers, base with example: obj = Dropout(rate, noise_shape, seed, name)