code: def measure_pool_building_with_categorical_data(
      data: tp.Union[np.ndarray, pd.DataFrame],
      categorical_feature_names: tp.List[str],
      iteration: int = 10,
  ) -> tp.Tuple[float, float]:
      times: tp.List[float] = []
      for _ in range(iteration):
          start: float = default_timer()
          _ = Pool(data, cat_features=categorical_feature_names)
          finish: float = default_timer()
          times.append(finish - start)
      return np.mean(times), np.std(times)
  
file_path: catboost/catboost/benchmarks/pool_creation_benchmark/benchmarks.py
imports: import numpy
  import typing
  import pandas
  from catboost import Pool
  from timeit import default_timer
label_desc: Use image node_id 9 for calling a global function with example usage: measure_pool_building_with_categorical_data(data, categorical_feature_names, iteration) and returns: np,  np