class_docstr: PyTorch policy class used with SimpleQ.
code: def _compute_q_values(
      self, model: ModelV2, obs_batch: TensorType, is_training=None
  ) -> TensorType:
      _is_training = is_training if is_training is not None else False
      input_dict = SampleBatch(obs=obs_batch, _is_training=_is_training)
      # Make sure, everything is PyTorch tensors.
      model_out, _ = model(input_dict, [], None)
      return model_out
  
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 7 for calling the SimpleQTorchPolicy obj's underlying member method code with example usage: obj._compute_q_values(model, obs_batch, is_training) and returns: model_out