code: def test_roc_curve(setup, drop):
      # Test Area under Receiver Operating Characteristic (ROC) curve
      y_true, _, probas_pred = make_prediction(binary=True)
      expected_auc = _auc(y_true, probas_pred)
  
      fpr, tpr, thresholds = (
          roc_curve(y_true, probas_pred, drop_intermediate=drop)
          .execute()
          .fetch()
      )
      roc_auc = auc(fpr, tpr).to_numpy()
      np.testing.assert_array_almost_equal(
          roc_auc, expected_auc, decimal=2
      )
      np.testing.assert_almost_equal(
          roc_auc, roc_auc_score(y_true, probas_pred)
      )
      assert fpr.shape == tpr.shape
      assert fpr.shape == thresholds.shape
  
imports: import re
  import warnings
  import numpy
  import pandas
  import pytest
  from sklearn.metrics import roc_curve, auc, accuracy_score
  from sklearn.metrics.tests.test_ranking import make_prediction, _auc
  from sklearn.exceptions import UndefinedMetricWarning
  from sklearn.utils import check_random_state
  from sklearn.utils._testing import assert_almost_equal, assert_array_almost_equal
  from ....None import dataframe
  from ....None import tensor
  from ...utils.extmath import softmax
  from ..None import roc_curve, auc, accuracy_score, roc_auc_score
label_desc: Use image node_id 2 for calling a global function with example usage: test_roc_curve(setup, drop) without return types