code: def main(iterations):
      # Download train and validation datasets
      train_df, test_df = msrank()
      # Column 0 contains label values, column 1 contains group ids.
      X_train, y_train = (
          train_df.drop([0, 1], axis=1).values,
          train_df[0].values,
      )
      X_test, y_test = (
          test_df.drop([0, 1], axis=1).values,
          test_df[0].values,
      )
  
      # Split train data into two parts. First part - for baseline model,
      # second part - for major model
      splitted_data = train_test_split(X_train, y_train, test_size=0.5)
      (
          X_train_first,
          X_train_second,
          y_train_first,
          y_train_second,
      ) = splitted_data
  
      catboost_model = CatBoostRegressor(
          iterations=iterations, verbose=False
      )
  
      # Prepare simple baselines (just mean target on first part of train pool).
      baseline_value = y_train_first.mean()
      train_baseline = np.array(
          [baseline_value] * y_train_second.shape[0]
      )
      test_baseline = np.array([baseline_value] * y_test.shape[0])
  
      # Create pools
      train_pool = Pool(
          X_train_second, y_train_second, baseline=train_baseline
      )
      test_pool = Pool(X_test, y_test, baseline=test_baseline)
  
      # Train CatBoost model
      catboost_model.fit(
          train_pool,
          eval_set=test_pool,
          verbose=True,
          plot=False,
          save_snapshot=True,
      )
      catboost_model.save_model("example.cbm")
  
      catboost_model = CatBoostRegressor()
      catboost_model.load_model("example.cbm")
  
      # Apply model on pool with baseline values
      preds1 = catboost_model.predict(test_pool)
  
      # Apply model on numpy.array and then add the baseline values
      preds2 = test_baseline + catboost_model.predict(X_test)
  
      # Check that preds have small diffs
      assert (np.abs(preds1 - preds2) < 1e-6).all()
  
imports: import argparse
  from catboost import CatBoostRegressor, Pool
  from catboost.datasets import msrank
  from clearml import Task
  import numpy
  from sklearn.model_selection import train_test_split
label_desc: Use image node_id 1 for calling a global function with example usage: main(iterations) without return types