class_docstr: PyTorch policy class used with SimpleQTrainer.
code: def stats_fn(self, train_batch: SampleBatch) -> Dict[str, TensorType]:
      return convert_to_numpy(
          {
              "loss": torch.mean(
                  torch.stack(self.get_tower_stats("loss"))
              ),
              "cur_lr": self.cur_lr,
          }
      )
  
imports: import logging
  from typing import Any, Dict, List, Tuple, Type, Union
  from rllib_simple_q.simple_q.utils import make_q_models
  from ray.rllib.models.modelv2 import ModelV2
  from ray.rllib.models.torch.torch_action_dist import TorchCategorical, TorchDistributionWrapper
  from ray.rllib.policy.sample_batch import SampleBatch
  from ray.rllib.policy.torch_mixins import LearningRateSchedule, TargetNetworkMixin
  from ray.rllib.policy.torch_policy_v2 import TorchPolicyV2
  from ray.rllib.utils.annotations import override
  from ray.rllib.utils.framework import try_import_torch
  from ray.rllib.utils.metrics.learner_info import LEARNER_STATS_KEY
  from ray.rllib.utils.numpy import convert_to_numpy
  from ray.rllib.utils.torch_utils import concat_multi_gpu_td_errors, huber_loss
  from ray.rllib.utils.typing import TensorStructType, TensorType
label_desc: Use image node_id 6 for calling the SimpleQTorchPolicy obj's underlying member method code with example usage: obj.stats_fn(train_batch) and returns: convert_to_numpy