class_docstr: Optimizer function for VTrace policies.
class_docstr_tok: ["Optimizer","function","for","VTrace","policies","."]
code: def optimizer(
      self,
  ) -> Union[
      "tf.keras.optimizers.Optimizer",
      List["tf.keras.optimizers.Optimizer"],
  ]:
      config = self.config
      if config["opt_type"] == "adam":
          if config["framework"] == "tf2":
              optim = tf.keras.optimizers.Adam(self.cur_lr)
              if config["_separate_vf_optimizer"]:
                  return optim, tf.keras.optimizers.Adam(
                      config["_lr_vf"]
                  )
          else:
              optim = tf1.train.AdamOptimizer(self.cur_lr)
              if config["_separate_vf_optimizer"]:
                  return optim, tf1.train.AdamOptimizer(
                      config["_lr_vf"]
                  )
      else:
          if config["_separate_vf_optimizer"]:
              raise ValueError(
                  "RMSProp optimizer not supported for separate"
                  "vf- and policy losses yet! Set `opt_type=adam`"
              )
  
          if tfv == 2:
              optim = tf.keras.optimizers.RMSprop(
                  self.cur_lr,
                  config["decay"],
                  config["momentum"],
                  config["epsilon"],
              )
          else:
              optim = tf1.train.RMSPropOptimizer(
                  self.cur_lr,
                  config["decay"],
                  config["momentum"],
                  config["epsilon"],
              )
  
      return optim
  
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 2 for calling the VTraceOptimizer obj's underlying member method code with example usage: obj.optimizer() and returns: optim, optim,  tf, optim,  tf1