code: def action_distribution_fn(
      self,
      model: ModelV2,
      *,
      obs_batch: TensorType,
      state_batches: TensorType,
      **kwargs,
  ) -> Tuple[TensorType, type, List[TensorType]]:
      model_out, _ = model(obs_batch)
      dist_input = model.get_policy_output(model_out)
      dist_class = self.dist_class
  
      return dist_input, dist_class, []
  
imports: from typing import Dict, List, Tuple, Type, Union, cast
  import gymnasium
  import numpy
  from rllib_crr.crr.crr_torch_model import CRRModel
  from ray.rllib.algorithms import AlgorithmConfig
  from ray.rllib.algorithms.ddpg.noop_model import TorchNoopModel
  from ray.rllib.models.catalog import ModelCatalog
  from ray.rllib.models.modelv2 import ModelV2
  from ray.rllib.models.torch.torch_action_dist import TorchCategorical, TorchDistributionWrapper, get_torch_categorical_class_with_temperature
  from ray.rllib.policy.sample_batch import SampleBatch
  from ray.rllib.policy.torch_mixins import 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.torch_utils import huber_loss, l2_loss
  from ray.rllib.utils.typing import TensorType, TrainerConfigDict
label_desc: Use image node_id 2 for calling the CRRTorchPolicy obj's underlying member method code with example usage: obj.action_distribution_fn(model) and returns: dist_input,  dist_class,  list