code: def layer_norm_all(
      h,
      batch_size,
      base,
      num_units,
      scope="layer_norm",
      reuse=False,
      gamma_start=1.0,
      epsilon=1e-3,
      use_bias=True,
  ):
      """Layer Norm (faster version, but not using defun)."""
      # Performs layer norm on multiple base at once (ie, i, g, j, o for lstm)
      # Reshapes h in to perform layer norm in parallel
      h_reshape = tf.reshape(h, [batch_size, base, num_units])
      mean = tf.reduce_mean(h_reshape, [2], keep_dims=True)
      var = tf.reduce_mean(
          tf.square(h_reshape - mean), [2], keep_dims=True
      )
      epsilon = tf.constant(epsilon)
      rstd = tf.rsqrt(var + epsilon)
      h_reshape = (h_reshape - mean) * rstd
      # reshape back to original
      h = tf.reshape(h_reshape, [batch_size, base * num_units])
      with tf.variable_scope(scope):
          if reuse:
              tf.get_variable_scope().reuse_variables()
          gamma = tf.get_variable(
              "ln_gamma",
              [4 * num_units],
              initializer=tf.constant_initializer(gamma_start),
          )
          if use_bias:
              beta = tf.get_variable(
                  "ln_beta",
                  [4 * num_units],
                  initializer=tf.constant_initializer(0.0),
              )
      if use_bias:
          return gamma * h + beta
      return gamma * h
  
imports: import numpy
  import tensorflow.compat.v1
label_desc: Use image node_id 4 for calling a global function with example usage: layer_norm_all(h, batch_size, base, num_units, scope, reuse, gamma_start, epsilon, use_bias) and returns: unknown, unknown