code: def action_distribution_fn(
      policy: Policy,
      model: SlateQTorchModel,
      input_dict,
      *,
      explore,
      is_training,
      **kwargs,
  ):
      """Determine which action to take."""
  
      observation = input_dict[SampleBatch.OBS]
  
      # user.shape: [B, E]
      user_obs = observation["user"]
      doc_obs = list(observation["doc"].values())
  
      # Compute scores per candidate.
      scores, score_no_click = score_documents(user_obs, doc_obs)
      # Compute Q-values per candidate.
      q_values = model.get_q_values(user_obs, doc_obs)
  
      per_slate_q_values = get_per_slate_q_values(
          policy, score_no_click, scores, q_values
      )
      if not hasattr(model, "slates"):
          model.slates = policy.slates
      return per_slate_q_values, TorchCategorical, []
  
imports: import logging
  from typing import Dict, Tuple, Type
  import gymnasium
  import numpy
  from rllib_slate_q.slate_q.slateq_torch_model import SlateQTorchModel
  from ray.rllib.models.modelv2 import ModelV2
  from ray.rllib.models.torch.torch_action_dist import TorchCategorical, TorchDistributionWrapper
  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.policy.torch_mixins import TargetNetworkMixin
  from ray.rllib.utils.framework import try_import_torch
  from ray.rllib.utils.torch_utils import apply_grad_clipping, concat_multi_gpu_td_errors, convert_to_torch_tensor, huber_loss
  from ray.rllib.utils.typing import AlgorithmConfigDict, TensorType
label_desc: Use image node_id 4 for calling a global function with example usage: action_distribution_fn(policy, model, input_dict) and returns: per_slate_q_values,  TorchCategorical,  list