code: def __init__(
      self,
      obs_space: gym.spaces.Space,
      action_space: gym.spaces.Space,
      num_outputs: int,
      model_config: ModelConfigDict,
      name: str,
  ):
      TorchModelV2.__init__(
          self, obs_space, action_space, num_outputs, model_config, name
      )
      nn.Module.__init__(self)
  
      self.obs_dim = num_outputs
  
      if isinstance(action_space, Discrete):
          self.action_dim = action_space.n
      elif isinstance(action_space, Box):
          self.action_dim = np.product(action_space.shape)
      else:
          raise NotImplementedError
  
      # Common model parameters
      self.embed_dim = self.model_config["embed_dim"]
      self.max_seq_len = self.model_config["max_seq_len"]
      self.max_ep_len = self.model_config["max_ep_len"]
      self.block_size = self.model_config["max_seq_len"] * 3
  
      # Build all the nn modules
      self.transformer = self.build_transformer()
      self.position_encoder = self.build_position_encoder()
      self.action_encoder = self.build_action_encoder()
      self.obs_encoder = self.build_obs_encoder()
      self.return_encoder = self.build_return_encoder()
      self.action_head = self.build_action_head()
      self.obs_head = self.build_obs_head()
      self.return_head = self.build_return_head()
  
      # Update view requirement
      # NOTE: See DTTorchPolicy.action_distribution_fn for an explanation of
      # why the ViewRequirements are like this
      self.view_requirements = {
          SampleBatch.OBS: ViewRequirement(
              space=obs_space, shift=f"-{self.max_seq_len-1}:0"
          ),
          SampleBatch.ACTIONS: ViewRequirement(
              space=action_space, shift=f"-{self.max_seq_len-1}:-1"
          ),
          SampleBatch.REWARDS: ViewRequirement(shift=-1),
          SampleBatch.T: ViewRequirement(
              shift=f"-{self.max_seq_len-2}:0"
          ),
          SampleBatch.RETURNS_TO_GO: ViewRequirement(
              shift=f"-{self.max_seq_len-1}:-1"
          ),
      }
  
imports: from typing import Dict, List
  import gymnasium
  import numpy
  from gymnasium.spaces import Box, Discrete
  from ray.rllib import SampleBatch
  from ray.rllib.models import ModelV2
  from ray.rllib.models.torch.mingpt import GPT, GPTConfig
  from ray.rllib.models.torch.torch_modelv2 import TorchModelV2
  from ray.rllib.policy.view_requirement import ViewRequirement
  from ray.rllib.utils import override
  from ray.rllib.utils.framework import try_import_torch
  from ray.rllib.utils.typing import ModelConfigDict, TensorType
label_desc: Use image node_id 1 to create a new DTTorchModel object from inherited base classes: TorchModelV2, nn with example: obj = DTTorchModel(obs_space, action_space, num_outputs, model_config, name)