code: def stats_fn(self, train_batch: SampleBatch) -> Dict[str, TensorType]:
      stats_dict = {
          k: torch.stack(self.get_tower_stats(k)).mean().item()
          for k in self.model.tower_stats
      }
      return stats_dict
  
imports: from typing import TYPE_CHECKING, Any, Dict, List, Optional, Tuple, Type, Union
  import gymnasium
  import numpy
  import tree
  from gymnasium.spaces import Box, Discrete
  from rllib_dt.dt.dt_torch_model import DTTorchModel
  from ray.rllib.models.catalog import ModelCatalog
  from ray.rllib.models.modelv2 import ModelV2
  from ray.rllib.models.torch.mingpt import configure_gpt_optimizer
  from ray.rllib.models.torch.torch_action_dist import TorchCategorical, TorchDeterministic, TorchDistributionWrapper
  from ray.rllib.policy.sample_batch import SampleBatch
  from ray.rllib.policy.torch_mixins import LearningRateSchedule
  from ray.rllib.policy.torch_policy_v2 import TorchPolicyV2
  from ray.rllib.utils.annotations import DeveloperAPI, PublicAPI, override
  from ray.rllib.utils.framework import try_import_torch
  from ray.rllib.utils.numpy import convert_to_numpy
  from ray.rllib.utils.threading import with_lock
  from ray.rllib.utils.torch_utils import apply_grad_clipping
  from ray.rllib.utils.typing import TensorShape, TensorStructType, TensorType, TrainerConfigDict
label_desc: Use image node_id 18 for calling the DTTorchPolicy obj's underlying member method code with example usage: obj.stats_fn(train_batch) and returns: stats_dict