code: def test_gridsearch(print_changed_only_false):
      # render a gridsearch
      param_grid = [
          {
              "kernel": ["rbf"],
              "gamma": [1e-3, 1e-4],
              "C": [1, 10, 100, 1000],
          },
          {"kernel": ["linear"], "C": [1, 10, 100, 1000]},
      ]
      gs = GridSearchCV(SVC(), param_grid, cv=5)
  
      expected = """
  GridSearchCV(cv=5, error_score='raise-deprecating',
               estimator=SVC(C=1.0, cache_size=200, class_weight=None, coef0=0.0,
                             decision_function_shape='ovr', degree=3,
                             gamma='auto_deprecated', kernel='rbf', max_iter=-1,
                             probability=False, random_state=None, shrinking=True,
                             tol=0.001, verbose=False),
               iid='warn', n_jobs=None,
               param_grid=[{'C': [1, 10, 100, 1000], 'gamma': [0.001, 0.0001],
                            'kernel': ['rbf']},
                           {'C': [1, 10, 100, 1000], 'kernel': ['linear']}],
               pre_dispatch='2*n_jobs', refit=True, return_train_score=False,
               scoring=None, verbose=0)"""
  
      expected = expected[1:]  # remove first \n
      assert gs.__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 5 for calling a global function with example usage: test_gridsearch(print_changed_only_false) without return types