code: def apply_gating_layer(
      x,
      hidden_layer_size,
      dropout_rate=None,
      use_time_distributed=True,
      activation=None,
  ):
      """Applies a Gated Linear Unit (GLU) to an input.
  
      Args:
        x: Input to gating layer
        hidden_layer_size: Dimension of GLU
        dropout_rate: Dropout rate to apply if any
        use_time_distributed: Whether to apply across time
        activation: Activation function to apply to the linear feature transform if
          necessary
  
      Returns:
        Tuple of tensors for: (GLU output, gate)
      """
  
      if dropout_rate is not None:
          x = tf.keras.layers.Dropout(dropout_rate)(x)
  
      if use_time_distributed:
          activation_layer = tf.keras.layers.TimeDistributed(
              tf.keras.layers.Dense(
                  hidden_layer_size, activation=activation
              )
          )(x)
          gated_layer = tf.keras.layers.TimeDistributed(
              tf.keras.layers.Dense(
                  hidden_layer_size, activation="sigmoid"
              )
          )(x)
      else:
          activation_layer = tf.keras.layers.Dense(
              hidden_layer_size, activation=activation
          )(x)
          gated_layer = tf.keras.layers.Dense(
              hidden_layer_size, activation="sigmoid"
          )(x)
  
      return (
          tf.keras.layers.Multiply()([activation_layer, gated_layer]),
          gated_layer,
      )
  
imports: from __future__ import absolute_import
  from __future__ import division
  from __future__ import print_function
  import gc
  import json
  import os
  import shutil
  import data_formatters.base
  import libs.utils
  import numpy
  import pandas
  import tensorflow
label_desc: Use image node_id 3 for calling a global function with example usage: apply_gating_layer(x, hidden_layer_size, dropout_rate, use_time_distributed, activation) and returns: tf,  gated_layer