code: def test_calibration_display_default_labels(
      pyplot, name, expected_label
  ):
      prob_true = np.array([0, 1, 1, 0])
      prob_pred = np.array([0.2, 0.8, 0.8, 0.4])
      y_prob = np.array([])
  
      viz = CalibrationDisplay(
          prob_true, prob_pred, y_prob, estimator_name=name
      )
      viz.plot()
  
      expected_legend_labels = [] if name is None else [name]
      expected_legend_labels.append("Perfectly calibrated")
      legend_labels = viz.ax_.get_legend().get_texts()
      assert len(legend_labels) == len(expected_legend_labels)
      for labels in legend_labels:
          assert labels.get_text() in expected_legend_labels
  
imports: import numpy
  import pytest
  from numpy.testing import assert_allclose
  from sklearn.base import BaseEstimator, clone
  from sklearn.calibration import CalibratedClassifierCV, CalibrationDisplay, _CalibratedClassifier, _sigmoid_calibration, _SigmoidCalibration, calibration_curve
  from sklearn.datasets import load_iris, make_blobs, make_classification
  from sklearn.dummy import DummyClassifier
  from sklearn.ensemble import RandomForestClassifier, VotingClassifier
  from sklearn.exceptions import NotFittedError
  from sklearn.feature_extraction import DictVectorizer
  from sklearn.impute import SimpleImputer
  from sklearn.isotonic import IsotonicRegression
  from sklearn.linear_model import LogisticRegression, SGDClassifier
  from sklearn.metrics import brier_score_loss
  from sklearn.model_selection import KFold, LeaveOneOut, check_cv, cross_val_predict, cross_val_score, train_test_split
  from sklearn.naive_bayes import MultinomialNB
  from sklearn.pipeline import Pipeline, make_pipeline
  from sklearn.preprocessing import LabelEncoder, StandardScaler
  from sklearn.svm import LinearSVC
  from sklearn.tree import DecisionTreeClassifier
  from sklearn.utils._mocking import CheckingClassifier
  from sklearn.utils._testing import _convert_container, assert_almost_equal, assert_array_almost_equal, assert_array_equal
  from sklearn.utils.extmath import softmax
  from sklearn.utils.fixes import CSR_CONTAINERS
label_desc: Use image node_id 27 for calling a global function with example usage: test_calibration_display_default_labels(pyplot, name, expected_label) without return types