code: def _get_graph(fetches, initial_variables=_initial_variables_default):
      graph = tf.get_default_graph()
      graph, fetches = _initialize_variables(
          graph, fetches, initial_variables
      )
      fetch_names = [_validate_fetch(graph, fetch) for fetch in fetches]
      logger.info("Fetch names: %s", str(fetch_names))
      # String the output index
      col_names = [s.split(":")[0] for s in fetch_names]
      if len(set(col_names)) != len(col_names):
          raise ValueError(
              "Could not infer a list of unique names for the columns: %s"
              % str(fetch_names)
          )
      return graph
  
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 7 for calling a global function with example usage: _get_graph(fetches, initial_variables) and returns: graph