class_docstr: Custom Experiment class for running distributed experiments.
class_docstr_tok: ["Custom","Experiment","class","for","running","distributed","experiments","."]
docstr: Decodes from dataset or file.
code: def decode(
      self,
      dataset_split=None,
      decode_from_file=False,
      checkpoint_path=None,
  ):
      """Decodes from dataset or file."""
      if decode_from_file:
          decoding.decode_from_file(
              self._estimator,
              self._decode_hparams.decode_from_file,
              self._hparams,
              self._decode_hparams,
              self._decode_hparams.decode_to_file,
          )
      else:
          decoding.decode_from_dataset(
              self._estimator,
              self._hparams.problem.name,
              self._hparams,
              self._decode_hparams,
              dataset_split=dataset_split,
              checkpoint_path=checkpoint_path,
          )
  
imports: from __future__ import absolute_import
  from __future__ import division
  from __future__ import print_function
  import collections
  import contextlib
  import json
  import os
  import random
  import numpy
  from tensor2tensor.utils import contrib
  from tensor2tensor.utils import decoding
  from tensor2tensor.utils import devices
  from tensor2tensor.utils import hparams_lib
  from tensor2tensor.utils import metrics_hook
  from tensor2tensor.utils import mlperf_log
  from tensor2tensor.utils import registry
  from tensor2tensor.utils import t2t_model
  import tensorflow.compat.v1
  from tensorflow.compat.v1 import estimator
  from tensorflow.core.protobuf import rewriter_config_pb2
  from tensorflow.python import debug
label_desc: Use image node_id 16 for calling the T2TExperiment obj's underlying member method code with example usage: obj.decode(dataset_split, decode_from_file, checkpoint_path) without return types