code: def _map_pd(
      fetches,
      pdframe,
      feed_dict=None,
      block=None,
      trim=None,
      initial_variables=_initial_variables_default,
  ):
      fetches = _check_fetches(fetches)
      graph = _unique_graph(fetches)
      graph, fetches = _initialize_variables(
          graph, fetches, initial_variables
      )
      if feed_dict is None:
          feed_names = _get_input(graph)
          feed_dict = dict(zip(feed_names, feed_names))
  
      with tf.Session(graph=graph) as sess:
          res = sess.run(
              fetches,
              feed_dict={
                  first_tensor_by_op_name(graph, k): pdframe[
                      feed_dict[k]
                  ]
                  for k in feed_dict
              },
          )
          for v, n in zip(fetches, res):
              pdframe[v.op.name] = n
          return pdframe
  
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 13 for calling a global function with example usage: _map_pd(fetches, pdframe, feed_dict, block, trim, initial_variables) and returns: pdframe