class_docstr: Custom Experiment class for running distributed experiments.
class_docstr_tok: ["Custom","Experiment","class","for","running","distributed","experiments","."]
code: def __init__(
      self,
      estimator,
      hparams,
      train_spec,
      eval_spec,
      use_validation_monitor,
      decode_hparams=None,
  ):
      self._train_spec = train_spec
      self._eval_spec = eval_spec
      self._hparams = hparams
      self._decode_hparams = decode_hparams
      self._estimator = estimator
      self._use_validation_monitor = use_validation_monitor
  
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 1 to create a new T2TExperiment object from inherited base classes: object with example: obj = T2TExperiment(estimator, hparams, train_spec, eval_spec, use_validation_monitor, decode_hparams)