class_docstr: PyTorch policy class used with SimpleQ.
code: def extra_compute_grad_fetches(self) -> Dict[str, Any]:
      fetches = convert_to_numpy(concat_multi_gpu_td_errors(self))
      # Auto-add empty learner stats dict if needed.
      return dict({LEARNER_STATS_KEY: {}}, **fetches)
  
imports: import logging
  from typing import Any, Dict, List, Tuple, Type, Union
  from ray.rllib.algorithms.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 TargetNetworkMixin, LearningRateSchedule
  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 5 for calling the SimpleQTorchPolicy obj's underlying member method code with example usage: obj.extra_compute_grad_fetches() and returns: dict