class_docstr: This class manages the *InputMode.SPARK* data feeding process from the perspective of the TensorFlow application.
  
  Args:
    :mgr: TFManager instance associated with this Python worker process.
    :train_mode: boolean indicating if the data feed is expecting an output response (e.g. inferencing).
    :qname_in: *INTERNAL_USE*
    :qname_out: *INTERNAL_USE*
    :input_mapping: *For Spark ML Pipelines only*.  Dictionary of input DataFrame columns to input TensorFlow tensors.
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docstr: Push a batch of output results to the Spark output RDD of ``TFCluster.inference()``.
  
  Note: this currently expects a one-to-one mapping of input to output data, so the length of the ``results`` array should match the length of
  the previously retrieved batch of input data.
  
  Args:
    :results: array of output data for the equivalent batch of input data.
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code: def batch_results(self, results):
      """Push a batch of output results to the Spark output RDD of ``TFCluster.inference()``.
  
      Note: this currently expects a one-to-one mapping of input to output data, so the length of the ``results`` array should match the length of
      the previously retrieved batch of input data.
  
      Args:
        :results: array of output data for the equivalent batch of input data.
      """
      queue = self.queue_out
      for item in results:
          queue.put(item, block=True)
  
imports: from __future__ import absolute_import
  from __future__ import division
  from __future__ import nested_scopes
  from __future__ import print_function
  import getpass
  import logging
  import pkg_resources
  from packaging import version
  from six.moves.queue import Empty
  from .None import compat, marker
label_desc: Use image node_id 4 for calling the DataFeed obj's underlying member method code with example usage: obj.batch_results(results) without return types