class_docstr: Defines inference for tasks where both the input and output sequences
  are plain text.
  
  Params:
    file: File to write beam search information to.
class_docstr_tok: ["Defines","inference","for","tasks","where","both","the","input","and","output","sequences","are","plain","text",".","Params",":","file",":","File","to","write","beam","search","information","to","."]
code: def before_run(self, _run_context):
      fetches = {}
      fetches["beam_search_output.predicted_ids"] = self._predictions[
          "beam_search_output.predicted_ids"
      ]
      fetches["beam_search_output.beam_parent_ids"] = self._predictions[
          "beam_search_output.beam_parent_ids"
      ]
      fetches["beam_search_output.scores"] = self._predictions[
          "beam_search_output.scores"
      ]
      fetches["beam_search_output.log_probs"] = self._predictions[
          "beam_search_output.log_probs"
      ]
      return tf.train.SessionRunArgs(fetches)
  
file_path: seq2seq/seq2seq/tasks/dump_beams.py
imports: from __future__ import absolute_import
  from __future__ import division
  from __future__ import print_function
  from __future__ import unicode_literals
  import numpy
  import tensorflow
  from seq2seq.tasks.inference_task import InferenceTask, unbatch_dict
label_desc: Use image node_id 3 for calling the DumpBeams obj's underlying member method code with example usage: obj.before_run(_run_context) and returns: tf