class_docstr: Custom Experiment class for running distributed experiments.
class_docstr_tok: ["Custom","Experiment","class","for","running","distributed","experiments","."]
docstr: Starts a TensorFlow server and joins the serving thread.
  
  Typically used for parameter servers.
  
  Raises:
    ValueError: if not enough information is available in the estimator's
      config to create a server.
code: def run_std_server(self):
      """Starts a TensorFlow server and joins the serving thread.
  
      Typically used for parameter servers.
  
      Raises:
        ValueError: if not enough information is available in the estimator's
          config to create a server.
      """
      config = tf_estimator.RunConfig()
      server = tf.train.Server(
          config.cluster_spec,
          job_name=config.task_type,
          task_index=config.task_id,
          protocol=config.protocol,
      )
      server.join()
  
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 15 for calling the T2TExperiment obj's underlying member method code with example usage: obj.run_std_server() without return types