code: def _standard_normalize(values, axes=(0,)):
      """Standard normalizes values `values`.
  
      Args:
        values: Tensor with values to be standardized.
        axes: Axes used to compute mean and variances.
  
      Returns:
        Standardized values (values - mean(values[axes])) / std(values[axes]).
      """
      values_mean, values_var = tf.nn.moments(
          x=values, axes=axes, keepdims=True
      )
      epsilon = np.finfo(values.dtype.as_numpy_dtype).eps
      normalized_values = (values - values_mean) / (
          tf.sqrt(values_var) + epsilon
      )
      return normalized_values
  
imports: from __future__ import absolute_import
  from __future__ import division
  from __future__ import print_function
  import collections
  from typing import Callable, Optional, Text
  import gin
  import numpy
  import tensorflow
  from tf_agents.agents import data_converter
  from tf_agents.agents import tf_agent
  from tf_agents.networks import network
  from tf_agents.policies import actor_policy
  from tf_agents.policies import greedy_policy
  from tf_agents.trajectories import time_step
  from tf_agents.trajectories import trajectory
  from tf_agents.typing import types
  from tf_agents.utils import common
  from tf_agents.utils import eager_utils
  from tf_agents.utils import nest_utils
  from tf_agents.utils import value_ops
label_desc: Use image node_id 1 for calling a global function with example usage: _standard_normalize(values, axes) and returns: normalized_values