code: def dropout(
      inputs,
      rate=0.5,
      noise_shape=None,
      seed=None,
      training=False,
      name=None,
  ):
      """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:
        inputs: Tensor input.
        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.
        training: Either a Python boolean, or a TensorFlow boolean scalar tensor
          (e.g. a placeholder). Whether to return the output in training mode
          (apply dropout) or in inference mode (return the input untouched).
        name: The name of the layer (string).
  
      Returns:
        Output tensor.
  
      Raises:
        ValueError: if eager execution is enabled.
      """
      warnings.warn(
          "`tf.layers.dropout` is deprecated and "
          "will be removed in a future version. "
          "Please use `tf.keras.layers.Dropout` instead."
      )
      layer = Dropout(
          rate, noise_shape=noise_shape, seed=seed, name=name
      )
      return layer.apply(inputs, training=training)
  
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 2 for calling a global function with example usage: dropout(inputs, rate, noise_shape, seed, training, name) and returns: layer