code: def test_deeply_nested(print_changed_only_false):
      # Render a deeply nested estimator
      rfe = RFE(RFE(RFE(RFE(RFE(RFE(RFE(LogisticRegression())))))))
      expected = """
  RFE(estimator=RFE(estimator=RFE(estimator=RFE(estimator=RFE(estimator=RFE(estimator=RFE(estimator=LogisticRegression(C=1.0,
                                                                                                                       class_weight=None,
                                                                                                                       dual=False,
                                                                                                                       fit_intercept=True,
                                                                                                                       intercept_scaling=1,
                                                                                                                       l1_ratio=None,
                                                                                                                       max_iter=100,
                                                                                                                       multi_class='warn',
                                                                                                                       n_jobs=None,
                                                                                                                       penalty='l2',
                                                                                                                       random_state=None,
                                                                                                                       solver='warn',
                                                                                                                       tol=0.0001,
                                                                                                                       verbose=0,
                                                                                                                       warm_start=False),
                                                                                          n_features_to_select=None,
                                                                                          step=1,
                                                                                          verbose=0),
                                                                            n_features_to_select=None,
                                                                            step=1,
                                                                            verbose=0),
                                                              n_features_to_select=None,
                                                              step=1, verbose=0),
                                                n_features_to_select=None, step=1,
                                                verbose=0),
                                  n_features_to_select=None, step=1, verbose=0),
                    n_features_to_select=None, step=1, verbose=0),
      n_features_to_select=None, step=1, verbose=0)"""
  
      expected = expected[1:]  # remove first \n
      assert rfe.__repr__() == expected
  
imports: import re
  from pprint import PrettyPrinter
  import numpy
  from sklearn.utils._pprint import _EstimatorPrettyPrinter
  from sklearn.linear_model import LogisticRegressionCV
  from sklearn.pipeline import make_pipeline
  from sklearn.base import BaseEstimator, TransformerMixin
  from sklearn.feature_selection import SelectKBest, chi2
  from sklearn import config_context
label_desc: Use image node_id 4 for calling a global function with example usage: test_deeply_nested(print_changed_only_false) without return types