code: def weight_model(catboost_parameters):
      catboost_parameters.update(DEFAULT_PARAMETERS)
      model = CatBoost(catboost_parameters)
      model.fit(train_pool, verbose=False)
      model.save_model("model_tmp")
      model_size = Path("model_tmp").stat().st_size
      return model_size
  
file_path: catboost/catboost/pytest/smoke_tests/categorical_features_parameters_gpu.py
imports: import os
  import numpy
  import pandas
  import seaborn
  from sklearn.metrics import r2_score, mean_squared_error
  from sklearn.model_selection import KFold
  from pathlib import Path
label_desc: Use image node_id 2 for calling a global function with example usage: weight_model(catboost_parameters) and returns: model_size