code: def gradients(self, optimizer, loss):
      self.gvs = {
          k: minimize_and_clip(
              optimizer,
              self.losses[k],
              self.vars[k],
              self.config["grad_norm_clipping"],
          )
          for k, optimizer in self.optimizers.items()
      }
      return self.gvs["critic"] + self.gvs["actor"]
  
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 3 for calling the MADDPGTFPolicy obj's underlying member method code with example usage: obj.gradients(optimizer, loss) and returns: unknown