code: def predict(self, X):
      self.reload()
      if self.params["ml_task"] == BINARY_CLASSIFICATION:
          return self.model.predict_proba(
              X, ntree_end=self.best_ntree_limit
          )[:, 1]
      elif self.params["ml_task"] == MULTICLASS_CLASSIFICATION:
          return self.model.predict_proba(
              X, ntree_end=self.best_ntree_limit
          )
  
      return self.model.predict(X, ntree_end=self.best_ntree_limit)
  
imports: import copy
  import logging
  import os
  import time
  import numpy
  import pandas
  from sklearn.base import ClassifierMixin, RegressorMixin
  from supervised.algorithms.algorithm import BaseAlgorithm
  from supervised.algorithms.registry import BINARY_CLASSIFICATION, MULTICLASS_CLASSIFICATION, REGRESSION, AlgorithmsRegistry
  from supervised.preprocessing.preprocessing_utils import PreprocessingUtils
  from supervised.utils.config import LOG_LEVEL
  from supervised.utils.metric import CatBoostEvalMetricAveragePrecision, CatBoostEvalMetricMSE, CatBoostEvalMetricPearson, CatBoostEvalMetricSpearman, CatBoostEvalMetricUserDefined
  import catboost
  from catboost import CatBoost, CatBoostClassifier, CatBoostRegressor, Pool
label_desc: Use image node_id 5 for calling the CatBoostAlgorithm obj's underlying member method code with example usage: obj.predict(X) and returns: self, self, self