code: def _get_q_value(
      self, model: ModelV2, model_out: TensorType, actions: TensorType
  ) -> TensorType:
      # helper function to compute the pessimistic q value
      q1 = model.get_q_values(model_out, actions)
      q2 = model.get_twin_q_values(model_out, actions)
      return torch.minimum(q1, q2)
  
imports: from typing import Dict, List, Tuple, Type, Union, cast
  import gymnasium
  import numpy
  from rllib_crr.crr.crr_torch_model import CRRModel
  from ray.rllib.algorithms import AlgorithmConfig
  from ray.rllib.algorithms.ddpg.noop_model import TorchNoopModel
  from ray.rllib.models.catalog import ModelCatalog
  from ray.rllib.models.modelv2 import ModelV2
  from ray.rllib.models.torch.torch_action_dist import TorchCategorical, TorchDistributionWrapper, get_torch_categorical_class_with_temperature
  from ray.rllib.policy.sample_batch import SampleBatch
  from ray.rllib.policy.torch_mixins import 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.torch_utils import huber_loss, l2_loss
  from ray.rllib.utils.typing import TensorType, TrainerConfigDict
label_desc: Use image node_id 8 for calling the CRRTorchPolicy obj's underlying member method code with example usage: obj._get_q_value(model, model_out, actions) and returns: torch