code: def stats(
      policy: Policy, train_batch: SampleBatch
  ) -> Dict[str, TensorType]:
      """Stats function for SAC. Returns a dict with important loss stats.
  
      Args:
          policy: The Policy to generate stats for.
          train_batch: The SampleBatch (already) used for training.
  
      Returns:
          Dict[str, TensorType]: The stats dict.
      """
      return {
          "mean_td_error": tf.reduce_mean(policy.td_error),
          "actor_loss": tf.reduce_mean(policy.actor_loss),
          "critic_loss": tf.reduce_mean(policy.critic_loss),
          "alpha_loss": tf.reduce_mean(policy.alpha_loss),
          "alpha_value": tf.reduce_mean(policy.alpha_value),
          "target_entropy": tf.constant(policy.target_entropy),
          "mean_q": tf.reduce_mean(policy.q_t),
          "max_q": tf.reduce_max(policy.q_t),
          "min_q": tf.reduce_min(policy.q_t),
      }
  
imports: import copy
  import gymnasium
  from gymnasium.spaces import Box, Discrete
  from functools import partial
  import logging
  from typing import Dict, List, Optional, Tuple, Type, Union
  import ray
  import ray.experimental.tf_utils
  from ray.rllib.algorithms.dqn.dqn_tf_policy import postprocess_nstep_and_prio, PRIO_WEIGHTS
  from ray.rllib.algorithms.sac.sac_tf_model import SACTFModel
  from ray.rllib.algorithms.sac.sac_torch_model import SACTorchModel
  from ray.rllib.evaluation.episode import Episode
  from ray.rllib.models import ModelCatalog
  from ray.rllib.models.modelv2 import ModelV2
  from ray.rllib.models.tf.tf_action_dist import Beta, Categorical, DiagGaussian, Dirichlet, SquashedGaussian, TFActionDistribution
  from ray.rllib.policy.policy import Policy
  from ray.rllib.policy.sample_batch import SampleBatch
  from ray.rllib.policy.tf_mixins import TargetNetworkMixin
  from ray.rllib.policy.tf_policy_template import build_tf_policy
  from ray.rllib.utils.error import UnsupportedSpaceException
  from ray.rllib.utils.framework import get_variable, try_import_tf
  from ray.rllib.utils.spaces.simplex import Simplex
  from ray.rllib.utils.tf_utils import huber_loss, make_tf_callable
  from ray.rllib.utils.typing import AgentID, LocalOptimizer, ModelGradients, TensorType, AlgorithmConfigDict
label_desc: Use image node_id 8 for calling a global function with example usage: stats(policy, train_batch) and returns: dict