code: def _feed_forward_network(
      x,
      hidden_layer_dims,
      output_units,
      activation_fn=tf.nn.relu,
      batch_norm=False,
      batch_norm_moment=0.999,
      dropout=None,
      is_training=None,
  ):
      """Defines feed-forward network.
  
      Args:
       x: Input tensor.
       hidden_layer_dims: Iterable of number hidden units per layer. All layers are
         fully connected. Ex. `[64, 32]` means first layer has 64 nodes and second
         one has 32.
       output_units: (int) Size of output logits from this tower.
       activation_fn: Activation function applied to each layer. If `None`, will use
         ReLU activation.
       batch_norm: Whether to use batch normalization after each hidden layer.
       batch_norm_moment: Momentum for the moving average in batch normalization.
       dropout: When not `None`, the probability we will drop out a given
         coordinate.
       is_training: Whether in the training mode.
  
      Returns:
        Output tensor.
      """
  
      for layer_width in map(int, hidden_layer_dims):
          x = tf.compat.v1.layers.dense(x, units=layer_width)
          if batch_norm:
              x = tf.compat.v1.layers.batch_normalization(
                  x, training=is_training, momentum=batch_norm_moment
              )
          x = activation_fn(x)
          if dropout:
              x = tf.compat.v1.layers.dropout(
                  inputs=x, rate=dropout, training=is_training
              )
      output = tf.compat.v1.layers.dense(x, units=output_units)
      return output
  
imports: import tensorflow
  from tensorflow import estimator
  from tensorflow.python.util import function_utils
  from tensorflow_ranking.python import feature
  from tensorflow_ranking.python import head
  from tensorflow_ranking.python import losses
  from tensorflow_ranking.python import metrics
  from tensorflow_ranking.python import model
  from tensorflow_ranking.python import utils
label_desc: Use image node_id 6 for calling a global function with example usage: _feed_forward_network(x, hidden_layer_dims, output_units, activation_fn, batch_norm, batch_norm_moment, dropout, is_training) and returns: output