docstr: Create locks for synchronizing the different workers during training
  :return: None
code: def _create_locks_for_synchronous_training(self):
      """
      Create locks for synchronizing the different workers during training
      :return: None
      """
      self.lock_counter = tf.get_variable(
          "lock_counter",
          [],
          tf.int32,
          initializer=tf.constant_initializer(0, dtype=tf.int32),
          trainable=False,
      )
      self.lock = self.lock_counter.assign_add(1, use_locking=True)
      self.lock_init = self.lock_counter.assign(0)
  
      self.release_counter = tf.get_variable(
          "release_counter",
          [],
          tf.int32,
          initializer=tf.constant_initializer(0, dtype=tf.int32),
          trainable=False,
      )
      self.release = self.release_counter.assign_add(
          1, use_locking=True
      )
      self.release_decrement = self.release_counter.assign_add(
          -1, use_locking=True
      )
      self.release_init = self.release_counter.assign(0)
  
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 4 for calling the TensorFlowArchitecture obj's underlying member method code with example usage: obj._create_locks_for_synchronous_training() without return types