class_docstr: An input pipeline that reads a TFRecords containing both source
  and target sequences.
  
  Params:
    files: An array of file names to read from.
    source_field: The TFRecord feature field containing the source text.
    target_field: The TFRecord feature field containing the target text.
    source_delimiter: A character to split the source text on. Defaults
      to  " " (space). For character-level training this can be set to the
      empty string.
    target_delimiter: Same as `source_delimiter` but for the target text.
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code: def label_keys(self):
      return set(["target_tokens", "target_len"])
  
imports: from __future__ import absolute_import
  from __future__ import division
  from __future__ import print_function
  from __future__ import unicode_literals
  import abc
  import sys
  import six
  import tensorflow
  from tensorflow.contrib.slim.python.slim.data import tfexample_decoder
  from seq2seq.configurable import Configurable
  from seq2seq.data import split_tokens_decoder, parallel_data_provider
  from seq2seq.data.sequence_example_decoder import TFSEquenceExampleDecoder
label_desc: Use image node_id 4 for calling the TFRecordInputPipeline obj's underlying member method code with example usage: obj.label_keys() and returns: set