class_docstr: PyTorch policy class used with SimpleQTrainer.
docstr: Builds q_model and target_model for Simple Q learning.
docstr_tok: ["Builds","q_model","and","target_model","for","Simple","Q","learning","."]
code: def make_model(self) -> ModelV2:
      """Builds q_model and target_model for Simple Q learning."""
      model, self.target_model = make_q_models(self)
      return model
  
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 2 for calling the SimpleQTorchPolicy obj's underlying member method code with example usage: obj.make_model() and returns: model