code: def aggregate(
      fetches,
      grouped_data,
      initial_variables=_initial_variables_default,
  ):
      """
      Performs an algebraic aggregation on the grouped data.
  
  
      :param fetches: a single graph element, or a sequence of graph elements
      :param grouped_data: a Spark groupedData object
      :param initial_variables: a boolean option default True, inital variables if it is used.
      :return: a dataframe, with all the columns corresponding to the fetches being appended to the
        key columns.
      """
      fetches = _check_fetches(fetches)
      graph = _get_graph(fetches, initial_variables)
      jdfin = _get_jgroup(grouped_data)
      builder = _java_api().aggregate_blocks(jdfin)
      _add_graph(graph, builder)
      _add_shapes(graph, builder, fetches)
      jdf = builder.buildDF()
      return DataFrame(jdf, _sql)
  
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 21 for calling a global function with example usage: aggregate(fetches, grouped_data, initial_variables) and returns: DataFrame