code: def action_distribution_fn(
      policy: Policy,
      model: ModelV2,
      input_dict: ModelInputDict,
      *,
      state_batches: Optional[List[TensorType]] = None,
      seq_lens: Optional[TensorType] = None,
      prev_action_batch: Optional[TensorType] = None,
      prev_reward_batch=None,
      explore: Optional[bool] = None,
      timestep: Optional[int] = None,
      is_training: Optional[bool] = None,
  ) -> Tuple[
      TensorType, Type[TorchDistributionWrapper], List[TensorType]
  ]:
      """The action distribution function to be used the algorithm.
  
      An action distribution function is used to customize the choice of action
      distribution class and the resulting action distribution inputs (to
      parameterize the distribution object).
      After parameterizing the distribution, a `sample()` call
      will be made on it to generate actions.
  
      Args:
          policy: The Policy being queried for actions and calling this
              function.
          model (TorchModelV2): The SAC specific model to use to generate the
              distribution inputs (see sac_tf|torch_model.py). Must support the
              `get_action_model_outputs` method.
          input_dict: The input-dict to be used for the model
              call.
          state_batches (Optional[List[TensorType]]): The list of internal state
              tensor batches.
          seq_lens (Optional[TensorType]): The tensor of sequence lengths used
              in RNNs.
          prev_action_batch (Optional[TensorType]): Optional batch of prev
              actions used by the model.
          prev_reward_batch (Optional[TensorType]): Optional batch of prev
              rewards used by the model.
          explore (Optional[bool]): Whether to activate exploration or not. If
              None, use value of `config.explore`.
          timestep (Optional[int]): An optional timestep.
          is_training (Optional[bool]): An optional is-training flag.
  
      Returns:
          Tuple[TensorType, Type[TorchDistributionWrapper], List[TensorType]]:
              The dist inputs, dist class, and a list of internal state outputs
              (in the RNN case).
      """
      # Get base-model output (w/o the SAC specific parts of the network).
      model_out, _ = model(input_dict, [], None)
      # Use the base output to get the policy outputs from the SAC model's
      # policy components.
      action_dist_inputs, _ = model.get_action_model_outputs(model_out)
      # Get a distribution class to be used with the just calculated dist-inputs.
      action_dist_class = _get_dist_class(
          policy, policy.config, policy.action_space
      )
  
      return action_dist_inputs, action_dist_class, []
  
imports: import gymnasium
  from gymnasium.spaces import Box, Discrete
  import logging
  import tree
  from typing import Dict, List, Optional, Tuple, Type, Union
  import ray
  import ray.experimental.tf_utils
  from ray.rllib.algorithms.sac.sac_tf_policy import build_sac_model, postprocess_trajectory, validate_spaces
  from ray.rllib.algorithms.dqn.dqn_tf_policy import PRIO_WEIGHTS
  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, TorchDirichlet, TorchSquashedGaussian, TorchDiagGaussian, TorchBeta
  from ray.rllib.policy.policy import Policy
  from ray.rllib.policy.policy_template import build_policy_class
  from ray.rllib.policy.sample_batch import SampleBatch
  from ray.rllib.utils.framework import try_import_torch
  from ray.rllib.utils.spaces.simplex import Simplex
  from ray.rllib.policy.torch_mixins import TargetNetworkMixin
  from ray.rllib.utils.torch_utils import apply_grad_clipping, concat_multi_gpu_td_errors, huber_loss
  from ray.rllib.utils.typing import LocalOptimizer, ModelInputDict, TensorType, AlgorithmConfigDict
label_desc: Use image node_id 3 for calling a global function with example usage: action_distribution_fn(policy, model, input_dict) and returns: action_dist_inputs,  action_dist_class,  list