class_docstr: PyTorch policy class used with SimpleQ.
code: def __init__(self, observation_space, action_space, config):
      TorchPolicyV2.__init__(
          self,
          observation_space,
          action_space,
          config,
          max_seq_len=config["model"]["max_seq_len"],
      )
  
      LearningRateSchedule.__init__(
          self, config["lr"], config["lr_schedule"]
      )
  
      # TODO: Don't require users to call this manually.
      self._initialize_loss_from_dummy_batch()
  
      TargetNetworkMixin.__init__(self)
  
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 1 to create a new SimpleQTorchPolicy object from inherited base classes: LearningRateSchedule, TargetNetworkMixin, TorchPolicyV2 with example: obj = SimpleQTorchPolicy(observation_space, action_space, config)