code: def create_distributed_session(
      task_spec=None,
      checkpoint_dir=None,
      scaffold=None,
      hooks=None,
      chief_only_hooks=None,
      save_checkpoint_secs=600,
      save_summaries_steps=object(),
      save_summaries_secs=object(),
      config=None,
      stop_grace_period_secs=120,
      log_step_count_steps=100,
  ):
      """Creates a distributed session.
  
      It calls `MonitoredTrainingSession` to create a :class:`MonitoredSession` for distributed training.
  
      Parameters
      ----------
      task_spec : :class:`TaskSpecDef`.
          The task spec definition from create_task_spec_def()
      checkpoint_dir : str.
          Optional path to a directory where to restore variables.
      scaffold : ``Scaffold``
          A `Scaffold` used for gathering or building supportive ops.
          If not specified, a default one is created. It's used to finalize the graph.
      hooks : list of ``SessionRunHook`` objects.
          Optional
      chief_only_hooks : list of ``SessionRunHook`` objects.
          Activate these hooks if `is_chief==True`, ignore otherwise.
      save_checkpoint_secs : int
          The frequency, in seconds, that a checkpoint is saved
          using a default checkpoint saver. If `save_checkpoint_secs` is set to
          `None`, then the default checkpoint saver isn't used.
      save_summaries_steps : int
          The frequency, in number of global steps, that the
          summaries are written to disk using a default summary saver. If both
          `save_summaries_steps` and `save_summaries_secs` are set to `None`, then
          the default summary saver isn't used. Default 100.
      save_summaries_secs : int
          The frequency, in secs, that the summaries are written
          to disk using a default summary saver.  If both `save_summaries_steps` and
          `save_summaries_secs` are set to `None`, then the default summary saver
          isn't used. Default not enabled.
      config : ``tf.ConfigProto``
          an instance of `tf.ConfigProto` proto used to configure the session.
          It's the `config` argument of constructor of `tf.Session`.
      stop_grace_period_secs : int
          Number of seconds given to threads to stop after
          `close()` has been called.
      log_step_count_steps : int
          The frequency, in number of global steps, that the
          global step/sec is logged.
  
      Examples
      --------
      A simple example for distributed training where all the workers use the same dataset:
  
      >>> task_spec = TaskSpec()
      >>> with tf.device(task_spec.device_fn()):
      >>>      tensors = create_graph()
      >>> with tl.DistributedSession(task_spec=task_spec,
      ...                            checkpoint_dir='/tmp/ckpt') as session:
      >>>      while not session.should_stop():
      >>>           session.run(tensors)
  
      An example where the dataset is shared among the workers
      (see https://www.tensorflow.org/programmers_guide/datasets):
  
      >>> task_spec = TaskSpec()
      >>> # dataset is a :class:`tf.data.Dataset` with the raw data
      >>> dataset = create_dataset()
      >>> if task_spec is not None:
      >>>     dataset = dataset.shard(task_spec.num_workers, task_spec.shard_index)
      >>> # shuffle or apply a map function to the new sharded dataset, for example:
      >>> dataset = dataset.shuffle(buffer_size=10000)
      >>> dataset = dataset.batch(batch_size)
      >>> dataset = dataset.repeat(num_epochs)
      >>> # create the iterator for the dataset and the input tensor
      >>> iterator = dataset.make_one_shot_iterator()
      >>> next_element = iterator.get_next()
      >>> with tf.device(task_spec.device_fn()):
      >>>      # next_element is the input for the graph
      >>>      tensors = create_graph(next_element)
      >>> with tl.DistributedSession(task_spec=task_spec,
      ...                            checkpoint_dir='/tmp/ckpt') as session:
      >>>      while not session.should_stop():
      >>>           session.run(tensors)
  
      References
      ----------
      - `MonitoredTrainingSession <https://www.tensorflow.org/api_docs/python/tf/train/MonitoredTrainingSession>`__
  
      """
      target = task_spec.target() if task_spec is not None else None
      is_chief = (
          task_spec.is_master() if task_spec is not None else True
      )
      return tf.train.MonitoredTrainingSession(
          master=target,
          is_chief=is_chief,
          checkpoint_dir=checkpoint_dir,
          scaffold=scaffold,
          save_checkpoint_secs=save_checkpoint_secs,
          save_summaries_steps=save_summaries_steps,
          save_summaries_secs=save_summaries_secs,
          log_step_count_steps=log_step_count_steps,
          stop_grace_period_secs=stop_grace_period_secs,
          config=config,
          hooks=hooks,
          chief_only_hooks=chief_only_hooks,
      )
  
imports: import json
  import os
  import time
  import tensorflow
  from tensorflow.python.training import session_run_hook
  from tensorlayer import logging
  from tensorlayer.decorators import deprecated
  from tensorlayer.lazy_imports import LazyImport
label_desc: Use image node_id 2 for calling a global function with example usage: create_distributed_session(task_spec, checkpoint_dir, scaffold, hooks, chief_only_hooks, save_checkpoint_secs, save_summaries_steps, save_summaries_secs, config, stop_grace_period_secs, log_step_count_steps) and returns: tf