code: def layer_norm(
      x,
      num_units,
      scope="layer_norm",
      reuse=False,
      gamma_start=1.0,
      epsilon=1e-3,
      use_bias=True,
  ):
      """Calculate layer norm."""
      axes = [1]
      mean = tf.reduce_mean(x, axes, keep_dims=True)
      x_shifted = x - mean
      var = tf.reduce_mean(tf.square(x_shifted), axes, keep_dims=True)
      inv_std = tf.rsqrt(var + epsilon)
      with tf.variable_scope(scope):
          if reuse:
              tf.get_variable_scope().reuse_variables()
          gamma = tf.get_variable(
              "ln_gamma",
              [num_units],
              initializer=tf.constant_initializer(gamma_start),
          )
          if use_bias:
              beta = tf.get_variable(
                  "ln_beta",
                  [num_units],
                  initializer=tf.constant_initializer(0.0),
              )
      output = gamma * (x_shifted) * inv_std
      if use_bias:
          output += beta
      return output
  
imports: import numpy
  import tensorflow.compat.v1
label_desc: Use image node_id 5 for calling a global function with example usage: layer_norm(x, num_units, scope, reuse, gamma_start, epsilon, use_bias) and returns: output