code: def _get_jgroup(grouped_data):
      """Get the JVM object that backs this grouped data, taking into account the different
      spark versions."""
      d = dir(grouped_data)
      if "_jdf" in d:
          return grouped_data._jdf
      if "_jgd" in d:
          return grouped_data._jgd
      raise ValueError(
          "Could not find a dataframe for {}. All methods: {}".format(
              grouped_data, d
          )
      )
  
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 25 for calling a global function with example usage: _get_jgroup(grouped_data) and returns: grouped_data, grouped_data