code: def set_session(self, sess):
      self.sess = sess
  
      task_is_distributed = isinstance(
          self.ap.task_parameters, DistributedTaskParameters
      )
      # initialize the session parameters in single threaded runs. Otherwise, this is done through the
      # MonitoredSession object in the graph manager
      if not task_is_distributed:
          self.sess.run(self.init_op)
  
      if self.ap.visualization.tensorboard:
          # Write the merged summaries to the current experiment directory
          if not task_is_distributed:
              self.train_writer = tf.summary.FileWriter(
                  self.ap.task_parameters.experiment_path
                  + "/tensorboard"
              )
              self.train_writer.add_graph(self.sess.graph)
          elif self.network_is_local:
              self.train_writer = tf.summary.FileWriter(
                  self.ap.task_parameters.experiment_path
                  + "/tensorboard/worker{}".format(
                      self.ap.task_parameters.task_index
                  )
              )
              self.train_writer.add_graph(self.sess.graph)
  
      # wait for all the workers to set their session
      if not self.network_is_local:
          self.wait_for_all_workers_barrier()
  
imports: import os
  import time
  from typing import Any, List, Tuple, Dict
  import numpy
  import tensorflow
  from rl_coach.architectures.architecture import Architecture
  from rl_coach.architectures.tensorflow_components.savers import GlobalVariableSaver
  from rl_coach.base_parameters import AgentParameters, DistributedTaskParameters
  from rl_coach.core_types import GradientClippingMethod
  from rl_coach.saver import SaverCollection
  from rl_coach.spaces import SpacesDefinition
  from rl_coach.utils import force_list, squeeze_list, start_shell_command_and_wait
label_desc: Use image node_id 9 for calling the TensorFlowArchitecture obj's underlying member method code with example usage: obj.set_session(sess) without return types