code: def _test_regressor_iterative_fit(
      Regressor, dataset="diabetes", sparse=False
  ):
      X_train, Y_train, X_test, Y_test = get_dataset(
          dataset=dataset, make_sparse=sparse
      )
      configuration_space = Regressor.get_hyperparameter_search_space(
          dataset_properties={"sparse": sparse}
      )
      default_config = configuration_space.get_default_configuration()
      regressor = Regressor(random_state=0, **default_config)
  
      regressor.iterative_fit(X_train, Y_train, n_iter=2, refit=True)
      iteration = 2
      while not regressor.configuration_fully_fitted():
          n_iter = int(2**iteration / 2)
          regressor.iterative_fit(X_train, Y_train, n_iter=n_iter)
          iteration += 1
  
      predictions = regressor.predict(X_test)
      return predictions, Y_test, regressor
  
imports: import importlib
  import inspect
  import os
  import pkgutil
  import numpy
  import scipy.sparse
  import sklearn
  import sklearn.base
  import sklearn.datasets
  import unittest
label_desc: Use image node_id 8 for calling a global function with example usage: _test_regressor_iterative_fit(Regressor, dataset, sparse) and returns: predictions,  Y_test,  regressor