class_docstr: Mixin for TFPolicy that adds entropy coeff decay.
code: def on_global_var_update(self, global_vars):
      super().on_global_var_update(global_vars)
      if self._entropy_coeff_schedule is not None:
          new_val = self._entropy_coeff_schedule.value(
              global_vars["timestep"]
          )
          if self.framework == "tf":
              self.get_session().run(
                  self._entropy_coeff_update,
                  feed_dict={self._entropy_coeff_placeholder: new_val},
              )
          else:
              self.entropy_coeff.assign(new_val, read_value=False)
  
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 2 for calling the EntropyCoeffSchedule obj's underlying member method code with example usage: obj.on_global_var_update(global_vars) without return types