code: def iterative_fit(
      self, X, y, n_iter=2, refit=False, sample_weight=None
  ):
      from sklearn.linear_model import PassiveAggressiveClassifier
  
      # Need to fit at least two iterations, otherwise early stopping will not
      # work because we cannot determine whether the algorithm actually
      # converged. The only way of finding this out is if the sgd spends less
      # iterations than max_iter. If max_iter == 1, it has to spend at least
      # one iteration and will always spend at least one iteration, so we
      # cannot know about convergence.
  
      if refit:
          self.estimator = None
          self.n_iter_ = None
  
      if self.estimator is None:
          self.fully_fit_ = False
  
          self.average = check_for_bool(self.average)
          self.fit_intercept = check_for_bool(self.fit_intercept)
          self.tol = float(self.tol)
          self.C = float(self.C)
  
          call_fit = True
          self.estimator = PassiveAggressiveClassifier(
              C=self.C,
              fit_intercept=self.fit_intercept,
              max_iter=n_iter,
              tol=self.tol,
              loss=self.loss,
              shuffle=True,
              random_state=self.random_state,
              warm_start=True,
              average=self.average,
          )
          self.classes_ = np.unique(y.astype(int))
      else:
          call_fit = False
  
      # Fallback for multilabel classification
      if len(y.shape) > 1 and y.shape[1] > 1:
          import sklearn.multiclass
  
          self.estimator.max_iter = self.get_max_iter()
          self.estimator = sklearn.multiclass.OneVsRestClassifier(
              self.estimator, n_jobs=1
          )
          self.estimator.fit(X, y)
          self.fully_fit_ = True
      else:
          if call_fit:
              self.estimator.fit(X, y)
              self.n_iter_ = n_iter
          else:
              self.estimator.max_iter += n_iter
              self.estimator.max_iter = min(
                  self.estimator.max_iter, self.max_iter
              )
              self.estimator._validate_params()
              lr = "pa1" if self.estimator.loss == "hinge" else "pa2"
              self.estimator._partial_fit(
                  X,
                  y,
                  alpha=1.0,
                  C=self.estimator.C,
                  loss="hinge",
                  learning_rate=lr,
                  max_iter=n_iter,
                  classes=None,
                  sample_weight=sample_weight,
                  coef_init=None,
                  intercept_init=None,
              )
              self.n_iter_ += self.estimator.n_iter_
              if (
                  self.estimator.max_iter >= self.max_iter
                  or self.estimator.max_iter > self.n_iter_
              ):
                  self.fully_fit_ = True
  
      return self
  
imports: from typing import Optional
  import numpy
  from ConfigSpace.configuration_space import ConfigurationSpace
  from ConfigSpace.hyperparameters import CategoricalHyperparameter, UniformFloatHyperparameter, 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 softmax
  from autosklearn.util.common import check_for_bool
label_desc: Use image node_id 4 for calling the PassiveAggressive obj's underlying member method code with example usage: obj.iterative_fit(X, y, n_iter, refit, sample_weight) and returns: self