code: def iterative_fit(
      self, X, y, sample_weight=None, n_iter=1, refit=False
  ):
      from sklearn.ensemble import ExtraTreesClassifier as ETC
  
      if refit:
          self.estimator = None
  
      if self.estimator is None:
          max_features = int(X.shape[1] ** float(self.max_features))
          if self.criterion not in ("gini", "entropy"):
              raise ValueError(
                  "'criterion' is not in ('gini', 'entropy'): "
                  "%s" % self.criterion
              )
  
          if check_none(self.max_depth):
              self.max_depth = None
          else:
              self.max_depth = int(self.max_depth)
          if check_none(self.max_leaf_nodes):
              self.max_leaf_nodes = None
          else:
              self.max_leaf_nodes = int(self.max_leaf_nodes)
  
          self.min_samples_leaf = int(self.min_samples_leaf)
          self.min_samples_split = int(self.min_samples_split)
          self.max_features = float(self.max_features)
          self.min_impurity_decrease = float(self.min_impurity_decrease)
          self.min_weight_fraction_leaf = float(
              self.min_weight_fraction_leaf
          )
          self.oob_score = check_for_bool(self.oob_score)
          self.bootstrap = check_for_bool(self.bootstrap)
          self.n_jobs = int(self.n_jobs)
          self.verbose = int(self.verbose)
  
          self.estimator = ETC(
              n_estimators=n_iter,
              criterion=self.criterion,
              max_depth=self.max_depth,
              min_samples_split=self.min_samples_split,
              min_samples_leaf=self.min_samples_leaf,
              bootstrap=self.bootstrap,
              max_features=max_features,
              max_leaf_nodes=self.max_leaf_nodes,
              min_weight_fraction_leaf=self.min_weight_fraction_leaf,
              min_impurity_decrease=self.min_impurity_decrease,
              oob_score=self.oob_score,
              n_jobs=self.n_jobs,
              verbose=self.verbose,
              random_state=self.random_state,
              class_weight=self.class_weight,
              warm_start=True,
          )
  
      else:
          self.estimator.n_estimators += n_iter
          self.estimator.n_estimators = min(
              self.estimator.n_estimators, self.n_estimators
          )
  
      self.estimator.fit(X, y, sample_weight=sample_weight)
      return self
  
file_path: auto-sklearn/autosklearn/pipeline/components/classification/extra_trees.py
imports: from typing import Optional
  from ConfigSpace.configuration_space import ConfigurationSpace
  from ConfigSpace.hyperparameters import CategoricalHyperparameter, UniformFloatHyperparameter, UniformIntegerHyperparameter, UnParametrizedHyperparameter
  from autosklearn.askl_typing import FEAT_TYPE_TYPE
  from autosklearn.pipeline.components.base import AutoSklearnClassificationAlgorithm, IterativeComponentWithSampleWeight
  from autosklearn.pipeline.constants import DENSE, PREDICTIONS, SPARSE, UNSIGNED_DATA
  from autosklearn.pipeline.implementations.util import convert_multioutput_multiclass_to_multilabel
  from autosklearn.util.common import check_for_bool, check_none
label_desc: Use image node_id 4 for calling the ExtraTreesClassifier obj's underlying member method code with example usage: obj.iterative_fit(X, y, sample_weight, n_iter, refit) and returns: self