code: def make_model_and_action_dist(self):
      dist_class, logit_dim = ModelCatalog.get_action_dist(
          self.action_space, self.config["model"], framework="torch"
      )
      model_cls = DiscreteLinearModel
  
      if hasattr(self.observation_space, "original_space"):
          original_space = self.observation_space.original_space
      else:
          original_space = self.observation_space
  
      exploration_config = self.config.get("exploration_config")
      # Model is dependent on exploration strategy because of its implicitness
  
      # TODO: Have a separate model catalogue for bandits
      if exploration_config:
          if exploration_config["type"] == "ThompsonSampling":
              if isinstance(original_space, spaces.Dict):
                  assert (
                      "item" in original_space.spaces
                  ), "Cannot find 'item' key in observation space"
                  model_cls = ParametricLinearModelThompsonSampling
              else:
                  model_cls = DiscreteLinearModelThompsonSampling
          elif exploration_config["type"] == "UpperConfidenceBound":
              if isinstance(original_space, spaces.Dict):
                  assert (
                      "item" in original_space.spaces
                  ), "Cannot find 'item' key in observation space"
                  model_cls = ParametricLinearModelUCB
              else:
                  model_cls = DiscreteLinearModelUCB
  
      model = model_cls(
          self.observation_space,
          self.action_space,
          logit_dim,
          self.config["model"],
          name="LinearModel",
      )
      return model, dist_class
  
file_path: ray/rllib_contrib/bandit/src/rllib_bandit/bandit/bandit_torch_policy.py
imports: import logging
  import time
  from gymnasium import spaces
  from rllib_bandit.bandit.bandit_torch_model import DiscreteLinearModel, DiscreteLinearModelThompsonSampling, DiscreteLinearModelUCB, ParametricLinearModelThompsonSampling, ParametricLinearModelUCB
  from ray.rllib.models.catalog import ModelCatalog
  from ray.rllib.models.modelv2 import restore_original_dimensions
  from ray.rllib.policy.sample_batch import SampleBatch
  from ray.rllib.policy.torch_policy_v2 import TorchPolicyV2
  from ray.rllib.utils.annotations import override
  from ray.rllib.utils.metrics.learner_info import LEARNER_STATS_KEY
  from ray.util.debug import log_once
label_desc: Use image node_id 2 for calling the BanditTorchPolicy obj's underlying member method code with example usage: obj.make_model_and_action_dist() and returns: model,  dist_class