class_docstr: Assign the `update_target` method to the policy.
  
  The function is called every `target_network_update_freq` steps by the
  master learner.
code: def target_q_func_vars(self):
      if not hasattr(self, "_target_q_func_vars"):
          if self.config.get("_enable_new_api_stack", False):
              self._target_q_func_vars = self.target_model.variables
          else:
              self._target_q_func_vars = self.target_model.variables()
      return self._target_q_func_vars
  
imports: import logging
  from typing import Dict, List
  import numpy
  from ray.rllib.models.modelv2 import ModelV2
  from ray.rllib.policy.eager_tf_policy import EagerTFPolicy
  from ray.rllib.policy.eager_tf_policy_v2 import EagerTFPolicyV2
  from ray.rllib.policy.policy import Policy, PolicyState
  from ray.rllib.policy.sample_batch import SampleBatch
  from ray.rllib.policy.tf_policy import TFPolicy
  from ray.rllib.utils.annotations import DeveloperAPI, override
  from ray.rllib.utils.framework import get_variable, try_import_tf
  from ray.rllib.utils.schedules import PiecewiseSchedule
  from ray.rllib.utils.tf_utils import make_tf_callable
  from ray.rllib.utils.typing import AlgorithmConfigDict, LocalOptimizer, ModelGradients, TensorType
label_desc: Use image node_id 3 for calling the TargetNetworkMixin obj's underlying member method code with example usage: obj.target_q_func_vars() and returns: self