code: def test_roc_curve_fpr_tpr_increasing(setup):
      # Ensure that fpr and tpr returned by roc_curve are increasing.
      # Construct an edge case with float y_score and sample_weight
      # when some adjacent values of fpr and tpr are actually the same.
      y_true = [0, 0, 1, 1, 1]
      y_score = [0.1, 0.7, 0.3, 0.4, 0.5]
      sample_weight = np.repeat(0.2, 5)
      fpr, tpr, _ = roc_curve(
          y_true, y_score, sample_weight=sample_weight
      )
      assert ((mt.diff(fpr) < 0).sum() == 0).to_numpy()
      assert ((mt.diff(tpr) < 0).sum() == 0).to_numpy()
  
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 11 for calling a global function with example usage: test_roc_curve_fpr_tpr_increasing(setup) without return types