code: def build_apply_op(self, optimizer, grads_and_vars):
      critic_apply_op = self.optimizers["critic"].apply_gradients(
          self.gvs["critic"]
      )
  
      with tf1.control_dependencies(
          [tf1.assign_add(self.global_step, 1)]
      ):
          with tf1.control_dependencies([critic_apply_op]):
              actor_apply_op = self.optimizers["actor"].apply_gradients(
                  self.gvs["actor"]
              )
  
      return actor_apply_op
  
imports: import logging
  import numpy
  from gymnasium.spaces import Box, Discrete
  from ray.rllib.algorithms.dqn.dqn_tf_policy import minimize_and_clip
  from ray.rllib.evaluation.postprocessing import adjust_nstep
  from ray.rllib.models import ModelCatalog
  from ray.rllib.policy.policy import Policy
  from ray.rllib.policy.sample_batch import SampleBatch
  from ray.rllib.policy.tf_policy import TFPolicy
  from ray.rllib.utils.annotations import override
  from ray.rllib.utils.error import UnsupportedSpaceException
  from ray.rllib.utils.framework import try_import_tf, try_import_tfp
  from ray.rllib.utils.metrics.learner_info import LEARNER_STATS_KEY
label_desc: Use image node_id 4 for calling the MADDPGTFPolicy obj's underlying member method code with example usage: obj.build_apply_op(optimizer, grads_and_vars) and returns: actor_apply_op