class_docstr: Optimizer function for VTrace policies.
class_docstr_tok: ["Optimizer","function","for","VTrace","policies","."]
code: def __init__(self):
      pass
  
imports: import numpy
  import logging
  import gymnasium
  from typing import Dict, List, Optional, Type, Union
  from ray.rllib.algorithms.impala import vtrace_tf
  from ray.rllib.evaluation.episode import Episode
  from ray.rllib.evaluation.postprocessing import compute_bootstrap_value
  from ray.rllib.models.modelv2 import ModelV2
  from ray.rllib.models.tf.tf_action_dist import Categorical, TFActionDistribution
  from ray.rllib.policy.dynamic_tf_policy_v2 import DynamicTFPolicyV2
  from ray.rllib.policy.eager_tf_policy_v2 import EagerTFPolicyV2
  from ray.rllib.policy.sample_batch import SampleBatch
  from ray.rllib.policy.tf_mixins import LearningRateSchedule, EntropyCoeffSchedule
  from ray.rllib.utils import force_list
  from ray.rllib.utils.annotations import override
  from ray.rllib.utils.framework import try_import_tf
  from ray.rllib.utils.tf_utils import explained_variance
  from ray.rllib.policy.tf_mixins import GradStatsMixin, ValueNetworkMixin
  from ray.rllib.utils.typing import LocalOptimizer, ModelGradients, TensorType, TFPolicyV2Type
label_desc: Use image node_id 1 to create a new VTraceOptimizer object  with example: obj = VTraceOptimizer()