code: def reduce_rows(
      fetches, dframe, initial_variables=_initial_variables_default
  ):
      """Applies the fetches on pairs of rows, so that only one row of data remains in the end. The order in which
      the operations are performed on the rows is unspecified.
  
      The `fetches` argument may be a list of graph elements or a single
      graph element. A graph element can be of the following type:
  
      * If the *i*th element of `fetches` is a
        `Tensor`, the *i*th return value will be a numpy ndarray containing the value of that tensor.
  
      There is no support for sparse tensor objects yet.
  
      This transform not lazy and is performed when called.
  
      In order to perform the reduce operation, the fetches must follow some naming conventions: for each fetch called
      for example 'z', there must be two placeholders 'z_1' and 'z_2' that will be fed with the input data. The shapes
      and the dtypes of z, z_1 and z_2 must be the same.
  
      Args:
        fetches: A single graph element, or a list of graph elements
          (described above).
        dframe: A DataFrame object. The columns of the tensor frame will be fed into the fetches at execution.
        initial_variables: a boolean option default True, initial variables if it is used.
  
      Returns: a list of numpy arrays, one for each of the fetches, or a single numpy array if there is but one fetch.
  
      :param fetches: see description above
      :param dframe: a Spark DataFrame
      :param initial_variables: a boolean option default True, initial variables if it is used.
      :return: a list of numpy arrays
      """
      fetches = _check_fetches(fetches)
      graph = _get_graph(fetches, initial_variables)
      builder = _java_api().reduce_rows(dframe._jdf)
      _add_graph(graph, builder)
      _add_shapes(graph, builder, fetches)
      df = builder.buildRow()
      return _unpack_row(df, 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 14 for calling a global function with example usage: reduce_rows(fetches, dframe, initial_variables) and returns: _unpack_row