parameters: (inputs,
              rate=0.5,
              noise_shape=None,
              seed=None,
              training=False,
              name=None)
return_statement: return layer.apply(inputs, training=training)
docstring: 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.
docstring_summary: Applies Dropout to the input.
docstring_tokens: Applies Dropout to the input .
function: 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)
function_tokens: def dropout ( inputs , rate = 0.5 , noise_shape = None , seed = None , training = False , name = None ) : 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 )