code: def rmsprop_optimizer(
      policy: Policy, config: AlgorithmConfigDict
  ) -> "tf.keras.optimizers.Optimizer":
      if policy.config["framework"] == "tf2":
          return tf.keras.optimizers.RMSprop(
              learning_rate=policy.cur_lr,
              epsilon=config["rmsprop_epsilon"],
              weight_decay=0.95,
              momentum=0.0,
              centered=True,
          )
      else:
          return tf1.train.RMSPropOptimizer(
              learning_rate=policy.cur_lr,
              epsilon=config["rmsprop_epsilon"],
              decay=0.95,
              momentum=0.0,
              centered=True,
          )
  
imports: import functools
  import logging
  from typing import Dict
  import gymnasium
  import numpy
  from rllib_slate_q.slate_q.slateq_tf_model import SlateQTFModel
  from ray.rllib.algorithms.dqn.dqn_tf_policy import clip_gradients
  from ray.rllib.models.modelv2 import ModelV2
  from ray.rllib.models.tf.tf_action_dist import SlateMultiCategorical
  from ray.rllib.policy.policy import Policy
  from ray.rllib.policy.sample_batch import SampleBatch
  from ray.rllib.policy.tf_mixins import LearningRateSchedule, TargetNetworkMixin
  from ray.rllib.policy.tf_policy_template import build_tf_policy
  from ray.rllib.utils.framework import try_import_tf
  from ray.rllib.utils.tf_utils import huber_loss
  from ray.rllib.utils.typing import AlgorithmConfigDict, TensorType
label_desc: Use image node_id 10 for calling a global function with example usage: rmsprop_optimizer(policy, config) and returns: tf, tf1