code: def _initialize_variables(graph, fetches, initial_variables):
      with tf.Session(graph=graph) as sess:
          if len(tf.global_variables()) == 0:
              return graph, fetches
  
          if initial_variables:
              sess.run(tf.global_variables_initializer())
          output_graph_def = (
              tf.graph_util.convert_variables_to_constants(
                  sess,
                  graph.as_graph_def(add_shapes=True),
                  [fetch.op.name for fetch in fetches],
              )
          )
  
      new_graph = tf.Graph()
      with new_graph.as_default():
          tf.import_graph_def(output_graph_def, name="")
          return new_graph, [
              new_graph.get_tensor_by_name(fetch.name)
              for fetch in fetches
          ]
  
imports: import tensorflow
  import numpy
  import logging
  import tempfile
  import pandas
  from pyspark import RDD, SparkContext
  from pyspark.sql import SQLContext, Row, DataFrame
  from pyspark.sql.types import DoubleType, IntegerType, LongType, FloatType, ArrayType
label_desc: Use image node_id 3 for calling a global function with example usage: _initialize_variables(graph, fetches, initial_variables) and returns: new_graph,  unknown, graph,  fetches