code: def _decode_filename(base_filename, problem_name, decode_hp):
      """Generates decode filename.
  
      Args:
        base_filename: A string, base of the decode filename.
        problem_name: A string, name of the problem.
        decode_hp: HParams for decoding.
  
      Returns:
        A string, produced decode filename.
      """
      if decode_hp.shards > 1:
          base_filename = _add_shard_to_filename(
              base_filename, decode_hp
          )
      if (
          "beam{beam}.alpha{alpha}.decodes".format(
              beam=str(decode_hp.beam_size), alpha=str(decode_hp.alpha)
          )
          in base_filename
      ):
          return base_filename
      else:
          return "{base}.{model}.{hp}.{problem}.beam{beam}.alpha{alpha}.decodes".format(
              base=base_filename,
              model=FLAGS.model,
              hp=FLAGS.hparams_set,
              problem=problem_name,
              beam=str(decode_hp.beam_size),
              alpha=str(decode_hp.alpha),
          )
  
imports: from __future__ import absolute_import
  from __future__ import division
  from __future__ import print_function
  import collections
  import operator
  import os
  import re
  import string
  import time
  import numpy
  import six
  from six.moves import input
  from tensor2tensor.data_generators import problem
  from tensor2tensor.data_generators import text_encoder
  from tensor2tensor.data_generators import text_problems
  from tensor2tensor.utils import contrib
  from tensor2tensor.utils import hparam
  from tensor2tensor.utils import mlperf_log
  from tensor2tensor.utils import registry
  import tensorflow.compat.v1
  from tensorflow.compat.v1 import estimator
label_desc: Use image node_id 7 for calling a global function with example usage: _decode_filename(base_filename, problem_name, decode_hp) and returns: base_filename, str