code: def create_worker_server_and_device(
      cluster_spec: tf.train.ClusterSpec,
      task_index: int,
      use_cpu: bool = True,
      config: tf.ConfigProto = None,
  ) -> Tuple[str, tf.device]:
      """
      Creates a worker server and a device setter used to assign the workers operations to
      :param cluster_spec: a ClusterSpec object representing the cluster
      :param task_index: the index of the worker task
      :param use_cpu: if use_cpu=True, all the agent operations will be assigned to a CPU instead of a GPU
      :param config: the tensorflow config to use
      :return: the target string for the tf.Session and the worker device setter object
      """
      # Create and start a worker
      server = tf.train.Server(
          cluster_spec,
          job_name="worker",
          task_index=task_index,
          config=config,
      )
  
      # Assign ops to the local worker
      worker_device = "/job:worker/task:{}".format(task_index)
      if use_cpu:
          worker_device += "/cpu:0"
      else:
          worker_device += "/device:GPU:0"
      device = tf.train.replica_device_setter(
          worker_device=worker_device, cluster=cluster_spec
      )
  
      return server.target, device
  
imports: from typing import Tuple
  import tensorflow
label_desc: Use image node_id 3 for calling a global function with example usage: create_worker_server_and_device(cluster_spec, task_index, use_cpu, config) and returns: server,  device