class_docstr: PyTorch policy class used with MAML.
code: def stats_fn(self, train_batch: SampleBatch) -> Dict[str, TensorType]:
      if self.config["worker_index"]:
          return convert_to_numpy({"worker_loss": self.loss_obj.loss})
      else:
          return convert_to_numpy(
              {
                  "cur_kl_coeff": self.kl_coeff_val,
                  "cur_lr": self.cur_lr,
                  "total_loss": self.loss_obj.loss,
                  "policy_loss": self.loss_obj.mean_policy_loss,
                  "vf_loss": self.loss_obj.mean_vf_loss,
                  "kl_loss": self.loss_obj.mean_kl_loss,
                  "inner_kl": self.loss_obj.mean_inner_kl,
                  "entropy": self.loss_obj.mean_entropy,
              }
          )
  
imports: import logging
  from typing import Dict, List, Type, Union
  import ray
  from ray.rllib.algorithms.ppo.ppo_tf_policy import validate_config
  from ray.rllib.evaluation.postprocessing import Postprocessing, compute_gae_for_sample_batch
  from ray.rllib.models.modelv2 import ModelV2
  from ray.rllib.models.torch.torch_action_dist import TorchDistributionWrapper
  from ray.rllib.policy.sample_batch import SampleBatch
  from ray.rllib.policy.torch_mixins import ValueNetworkMixin
  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.numpy import convert_to_numpy
  from ray.rllib.utils.torch_utils import apply_grad_clipping
  from ray.rllib.utils.typing import TensorType
label_desc: Use image node_id 4 for calling the MAMLTorchPolicy obj's underlying member method code with example usage: obj.stats_fn(train_batch) and returns: convert_to_numpy, convert_to_numpy