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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code: def __init__(
      self,
      mgr,
      train_mode=True,
      qname_in="input",
      qname_out="output",
      input_mapping=None,
  ):
      self.mgr = mgr
      self.train_mode = train_mode
      self.qname_in = qname_in
      self.qname_out = qname_out
      self.done_feeding = False
      self.input_tensors = (
          [tensor for col, tensor in sorted(input_mapping.items())]
          if input_mapping is not None
          else None
      )
  
      self.queue_in = mgr.get_queue(qname_in)
      self.queue_out = mgr.get_queue(qname_out)
  
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 1 to create a new DataFeed object from inherited base classes: object with example: obj = DataFeed(mgr, train_mode, qname_in, qname_out, input_mapping)