new_contents: import argparse
  
  from obp.dataset import (
      logistic_reward_function,
      linear_behavior_policy_logit,
      SyntheticSlateBanditDataset,
  )
  
  
  if __name__ == "__main__":
      parser = argparse.ArgumentParser(description="run slate dataset.")
      parser.add_argument(
          "--n_unique_action", type=int, default=10, help="number of unique actions."
      )
      parser.add_argument(
          "--len_list", type=int, default=3, help="number of item positions."
      )
      parser.add_argument("--n_rounds", type=int, default=100, help="number of slates.")
      parser.add_argument(
          "--clip_logit_value",
          type=float,
          default=None,
          help="a float parameter to clip logit value.",
      )
      parser.add_argument(
          "--is_factorizable",
          type=bool,
          default=False,
          help="a boolean parameter whether to use factorizable evaluation policy.",
      )
      parser.add_argument(
          "--return_pscore_item_position",
          type=bool,
          default=True,
          help="a boolean parameter whether `pscore_item_position` is returned or not",
      )
      parser.add_argument("--random_state", type=int, default=12345)
      args = parser.parse_args()
      dataset = SyntheticSlateBanditDataset(
          n_unique_action=args.n_unique_action,
          dim_context=5,
          len_list=args.len_list,
          base_reward_function=logistic_reward_function,
          behavior_policy_function=linear_behavior_policy_logit,
          reward_type="binary",
          reward_structure="cascade_additive",
          click_model="cascade",
          random_state=12345,
          is_factorizable=args.is_factorizable,
      )
      bandit_feedback = dataset.obtain_batch_bandit_feedback(
          n_rounds=args.n_rounds,
          return_pscore_item_position=args.return_pscore_item_position,
          clip_logit_value=args.clip_logit_value,
      )
  
subject: add example of obtaining slate bandit feedback