code: def get_raw_data(
      rows_count: int, columns_count: int
  ) -> tp.Tuple[np.ndarray, np.ndarray, np.ndarray, np.ndarray]:
      float64_data: np.ndarray = np.float64(
          np.random.rand(columns_count // 4, rows_count)
      )
      float32_data: np.ndarray = np.float32(
          np.random.rand(columns_count // 4, rows_count)
      )
      int8_data: np.ndarray = np.random.randint(
          np.iinfo(np.int8).min,
          np.iinfo(np.int8).max,
          size=(columns_count // 4, rows_count),
          dtype=np.int8,
      )
      int32_data: np.ndarray = np.random.randint(
          np.iinfo(np.int32).min,
          np.iinfo(np.int32).max,
          size=(columns_count // 4, rows_count),
          dtype=np.int32,
      )
      return float64_data, float32_data, int8_data, int32_data
  
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 1 for calling a global function with example usage: get_raw_data(rows_count, columns_count) and returns: float64_data,  float32_data,  int8_data,  int32_data