code: def make_model_and_action_dist(
      self,
  ) -> Tuple[ModelV2, Type[TorchDistributionWrapper]]:
      # Model
      model_config = self.config["model"]
      # TODO: make these better with better AlgorithmConfig options.
      model_config.update(
          embed_dim=self.config["embed_dim"],
          max_ep_len=self.config["horizon"],
          num_layers=self.config["num_layers"],
          num_heads=self.config["num_heads"],
          embed_pdrop=self.config["embed_pdrop"],
          resid_pdrop=self.config["resid_pdrop"],
          attn_pdrop=self.config["attn_pdrop"],
          use_obs_output=self.config.get("loss_coef_obs", 0) > 0,
          use_return_output=self.config.get(
              "loss_coef_returns_to_go", 0
          )
          > 0,
      )
  
      num_outputs = int(np.product(self.observation_space.shape))
  
      model = ModelCatalog.get_model_v2(
          obs_space=self.observation_space,
          action_space=self.action_space,
          num_outputs=num_outputs,
          model_config=model_config,
          framework=self.config["framework"],
          model_interface=None,
          default_model=DTTorchModel,
          name="model",
      )
  
      # Action Distribution
      if isinstance(self.action_space, Discrete):
          action_dist = TorchCategorical
      elif isinstance(self.action_space, Box):
          action_dist = TorchDeterministic
      else:
          raise NotImplementedError
  
      return model, action_dist
  
imports: from typing import TYPE_CHECKING, Any, Dict, List, Optional, Tuple, Type, Union
  import gymnasium
  import numpy
  import tree
  from gymnasium.spaces import Box, Discrete
  from rllib_dt.dt.dt_torch_model import DTTorchModel
  from ray.rllib.models.catalog import ModelCatalog
  from ray.rllib.models.modelv2 import ModelV2
  from ray.rllib.models.torch.mingpt import configure_gpt_optimizer
  from ray.rllib.models.torch.torch_action_dist import TorchCategorical, TorchDeterministic, TorchDistributionWrapper
  from ray.rllib.policy.sample_batch import SampleBatch
  from ray.rllib.policy.torch_mixins import LearningRateSchedule
  from ray.rllib.policy.torch_policy_v2 import TorchPolicyV2
  from ray.rllib.utils.annotations import DeveloperAPI, PublicAPI, override
  from ray.rllib.utils.framework import try_import_torch
  from ray.rllib.utils.numpy import convert_to_numpy
  from ray.rllib.utils.threading import with_lock
  from ray.rllib.utils.torch_utils import apply_grad_clipping
  from ray.rllib.utils.typing import TensorShape, TensorStructType, TensorType, TrainerConfigDict
label_desc: Use image node_id 2 for calling the DTTorchPolicy obj's underlying member method code with example usage: obj.make_model_and_action_dist() and returns: model,  action_dist