code: def layer_normalize(inputs, scope=None, **kwargs):
      """Applies layer normalization. Normalizes over the last dimension.
  
      Args:
          inputs: A tensor with 2 or more dimensions, where the first
              dimension must be `batch_size`.
          scope (optional): variable scope.
  
      Returns:
          A tensor with the same shape and data dtype as `inputs`.
      """
      return tf.contrib.layers.layer_norm(
          inputs=inputs,
          begin_norm_axis=-1,
          begin_params_axis=-1,
          scope=scope,
          **kwargs,
      )
  
imports: import copy
  import tensorflow
  import tensorflow.contrib.rnn
  from texar.tf.hyperparams import HParams
  from texar.tf.utils import utils
  from texar.tf.utils.dtypes import is_str
  from texar.tf.utils.variables import add_variable
  from texar.tf.utils.mode import is_train_mode, switch_dropout
label_desc: Use image node_id 29 for calling a global function with example usage: layer_normalize(inputs, scope) and returns: tf