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,
  ):
      max_steps = self.max_steps
      n_estimators = 0
  
      min_val = 10e12
      min_e = 0
  
      p_tr, p_vd = None, None
      result = {"iteration": [], "train": [], "validation": []}
  
      start_time = time.time()
      with warnings.catch_warnings():
          warnings.simplefilter(action="ignore")
  
          for i in range(max_steps):
              self.model.fit(
                  X, np.ravel(y), sample_weight=sample_weight
              )
              self.model.n_estimators += self.trees_in_step
  
              if X_validation is None or y_validation is None:
                  continue
              estimators = self.model.estimators_
  
              stop = False
              for e in range(n_estimators, len(estimators)):
                  p = self.predict_function(estimators[e], X)
                  if p_tr is None:
                      p_tr = p
                  else:
                      p_tr += p
  
                  p = self.predict_function(estimators[e], X_validation)
                  if p_vd is None:
                      p_vd = p
                  else:
                      p_vd += p
  
                  tr = self.log_metric(
                      y,
                      p_tr / float(e + 1),
                      sample_weight=sample_weight,
                  )
                  vd = self.log_metric(
                      y_validation,
                      p_vd / float(e + 1),
                      sample_weight=sample_weight_validation,
                  )
  
                  if vd < min_val:  # optimize direction
                      min_val = vd
                      min_e = e
  
                  if e - min_e >= self.early_stopping_rounds:
                      stop = True
                      break
  
                  result["iteration"] += [e]
                  result["train"] += [tr]
                  result["validation"] += [vd]
  
              # disable for now ...
              # if max_time is not None and time.time()-start_time > max_time:
              #    stop = True
  
              if stop:
                  self.model.estimators_ = estimators[: (min_e + 1)]
                  break
              n_estimators = len(estimators)
  
      if log_to_file is not None:
          df_result = pd.DataFrame(result)
          if self.log_metric.is_negative():
              df_result["train"] *= -1.0
              df_result["validation"] *= -1.0
          df_result.to_csv(log_to_file, index=False, header=False)
  
      self.classes_ = np.unique(y)
  
imports: import copy
  import logging
  import time
  import warnings
  import joblib
  import numpy
  import pandas
  from supervised.algorithms.algorithm import BaseAlgorithm
  from supervised.algorithms.registry import BINARY_CLASSIFICATION, MULTICLASS_CLASSIFICATION, REGRESSION
  from supervised.utils.config import LOG_LEVEL
  from supervised.utils.metric import Metric
label_desc: Use image node_id 2 for calling the SklearnTreesEnsembleClassifierAlgorithm 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