code: def get_ema_vars():
      """Get all exponential moving average (ema) variables."""
      ema_vars = tf.trainable_variables() + tf.get_collection(
          "moving_vars"
      )
      for v in tf.global_variables():
          # We maintain mva for batch norm moving mean and variance as well.
          if "moving_mean" in v.name or "moving_variance" in v.name:
              ema_vars.append(v)
      return list(set(ema_vars))
  
imports: from __future__ import absolute_import
  from __future__ import division
  from __future__ import print_function
  import json
  import os
  import sys
  from absl import logging
  import numpy
  import tensorflow.compat.v1
  from tensorflow.python.tpu import tpu_function
label_desc: Use image node_id 5 for calling a global function with example usage: get_ema_vars() and returns: list