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 update_target(self, tau: int = None) -> None:
      self._do_update(np.float32(tau or self.config.get("tau", 1.0)))
  
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 4 for calling the TargetNetworkMixin obj's underlying member method code with example usage: obj.update_target(tau) without return types