code: def _create_gradient_accumulators(self):
      if self.network_is_global:
          self.shared_accumulated_gradients = [
              tf.Variable(initial_value=tf.zeros_like(var))
              for var in self.weights
          ]
          self.accumulate_shared_gradients = [
              var.assign_add(holder, use_locking=True)
              for holder, var in zip(
                  self.weights_placeholders,
                  self.shared_accumulated_gradients,
              )
          ]
          self.init_shared_accumulated_gradients = [
              var.assign(tf.zeros_like(var))
              for var in self.shared_accumulated_gradients
          ]
      elif self.network_is_local:
          self.accumulate_shared_gradients = (
              self.global_network.accumulate_shared_gradients
          )
          self.init_shared_accumulated_gradients = (
              self.global_network.init_shared_accumulated_gradients
          )
  
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 6 for calling the TensorFlowArchitecture obj's underlying member method code with example usage: obj._create_gradient_accumulators() without return types