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 after_run(self, _run_context, run_values):
      fetches_batch = run_values.results
      for fetches in unbatch_dict(fetches_batch):
          self._beam_accum["predicted_ids"].append(
              fetches["beam_search_output.predicted_ids"]
          )
          self._beam_accum["beam_parent_ids"].append(
              fetches["beam_search_output.beam_parent_ids"]
          )
          self._beam_accum["scores"].append(
              fetches["beam_search_output.scores"]
          )
          self._beam_accum["log_probs"].append(
              fetches["beam_search_output.log_probs"]
          )
  
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 4 for calling the DumpBeams obj's underlying member method code with example usage: obj.after_run(_run_context, run_values) without return types