code: def fit(
      self,
      X,
      y,
      sample_weight=None,
      X_validation=None,
      y_validation=None,
      sample_weight_validation=None,
      log_to_file=None,
      max_time=None,
  ):
      if self.is_fitted():
          print("CatBoost model already fitted. Skip fit().")
          return
  
      if self.cat_features is None:
          self.cat_features = []
          for i in range(X.shape[1]):
              if PreprocessingUtils.is_categorical(X.iloc[:, i]):
                  self.cat_features += [i]
                  X.iloc[:, i] = X.iloc[:, i].astype(str)
                  if X_validation is not None:
                      X_validation.iloc[:, i] = X_validation.iloc[
                          :, i
                      ].astype(str)
  
      eval_set = None
      if X_validation is not None and y_validation is not None:
          eval_set = Pool(
              data=X_validation,
              label=y_validation,
              cat_features=self.cat_features,
              weight=sample_weight_validation,
          )
  
      if self.params.get("num_boost_round") is None:
          model_init, new_iterations = self._assess_iterations(
              X, y, sample_weight, eval_set, max_time
          )
          self.model.set_params(iterations=new_iterations)
      else:
          model_init = None
          self.model.set_params(
              iterations=self.params.get("num_boost_round")
          )
          self.early_stopping_rounds = self.params.get(
              "early_stopping_rounds", 50
          )
  
      self.model.fit(
          X,
          y,
          sample_weight=sample_weight,
          cat_features=self.cat_features,
          init_model=model_init,
          eval_set=eval_set,
          early_stopping_rounds=self.early_stopping_rounds,
          verbose_eval=False,
      )
  
      if self.model.best_iteration_ is not None:
          if model_init is not None:
              self.best_ntree_limit = (
                  self.model.best_iteration_
                  + model_init.tree_count_
                  + 1
              )
          else:
              self.best_ntree_limit = self.model.best_iteration_ + 1
  
      else:
          # just take all the trees
          # the warm-up trees are already included
          # dont need to add +1
          self.best_ntree_limit = self.model.tree_count_
  
      if log_to_file is not None:
          train_scores = self.model.evals_result_["learn"].get(
              self.log_metric_name
          )
          validation_scores = self.model.evals_result_[
              "validation"
          ].get(self.log_metric_name)
          if model_init is not None:
              if train_scores is not None:
                  train_scores = (
                      model_init.evals_result_["learn"].get(
                          self.log_metric_name
                      )
                      + train_scores
                  )
              if validation_scores is not None:
                  validation_scores = (
                      model_init.evals_result_["validation"].get(
                          self.log_metric_name
                      )
                      + validation_scores
                  )
          iteration = None
          if train_scores is not None:
              iteration = range(len(validation_scores))
          elif validation_scores is not None:
              iteration = range(len(validation_scores))
  
          result = pd.DataFrame(
              {
                  "iteration": iteration,
                  "train": train_scores,
                  "validation": validation_scores,
              }
          )
          result.to_csv(log_to_file, index=False, header=False)
  
      if self.params["ml_task"] != REGRESSION:
          self.classes_ = np.unique(y)
  
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 3 for calling the CatBoostAlgorithm obj's underlying member method code with example usage: obj.fit(X, y, sample_weight, X_validation, y_validation, sample_weight_validation, log_to_file, max_time) without return types