code: def apply_catboost_model(
      float_features,
      cat_features=[],
      ntree_start=0,
      ntree_end=catboost_model.tree_count,
  ):
      """
      Applies the model built by CatBoost.
  
      Parameters
      ----------
  
      float_features : list of float features
  
      cat_features : list of categorical features
          You need to pass float and categorical features separately in the same order they appeared in train dataset.
          For example if you had features f1,f2,f3,f4, where f2 and f4 were considered categorical, you need to pass here float_features=f1,f3, cat_features=f2,f4
  
  
      Returns
      -------
      predictions : single (first) formula value for the model and the features
  
      """
      return apply_catboost_model_multi(
          float_features, cat_features, ntree_start, ntree_end
      )[0]
  
label_desc: Use image node_id 6 for calling a global function with example usage: apply_catboost_model(float_features, cat_features, ntree_start, ntree_end) and returns: apply_catboost_model_multi