code: def gated_residual_network(
      x,
      hidden_layer_size,
      output_size=None,
      dropout_rate=None,
      use_time_distributed=True,
      additional_context=None,
      return_gate=False,
  ):
      """Applies the gated residual network (GRN) as defined in paper.
  
      Args:
        x: Network inputs
        hidden_layer_size: Internal state size
        output_size: Size of output layer
        dropout_rate: Dropout rate if dropout is applied
        use_time_distributed: Whether to apply network across time dimension
        additional_context: Additional context vector to use if relevant
        return_gate: Whether to return GLU gate for diagnostic purposes
  
      Returns:
        Tuple of tensors for: (GRN output, GLU gate)
      """
  
      # Setup skip connection
      if output_size is None:
          output_size = hidden_layer_size
          skip = x
      else:
          linear = Dense(output_size)
          if use_time_distributed:
              linear = tf.keras.layers.TimeDistributed(linear)
          skip = linear(x)
  
      # Apply feedforward network
      hidden = linear_layer(
          hidden_layer_size,
          activation=None,
          use_time_distributed=use_time_distributed,
      )(x)
      if additional_context is not None:
          hidden = hidden + linear_layer(
              hidden_layer_size,
              activation=None,
              use_time_distributed=use_time_distributed,
              use_bias=False,
          )(additional_context)
      hidden = tf.keras.layers.Activation("elu")(hidden)
      hidden = linear_layer(
          hidden_layer_size,
          activation=None,
          use_time_distributed=use_time_distributed,
      )(hidden)
  
      gating_layer, gate = apply_gating_layer(
          hidden,
          output_size,
          dropout_rate=dropout_rate,
          use_time_distributed=use_time_distributed,
          activation=None,
      )
  
      if return_gate:
          return add_and_norm([skip, gating_layer]), gate
      else:
          return add_and_norm([skip, gating_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 5 for calling a global function with example usage: gated_residual_network(x, hidden_layer_size, output_size, dropout_rate, use_time_distributed, additional_context, return_gate) and returns: add_and_norm,  gate, add_and_norm