code: def test_tpr_values_alpha(self):
      a = "a"
      b = "b"
      y_true = [a, b, a, b, b, b, b, b, a, a, a, a, b, b, a, b]
      y_pred = [a, b, b, a, a, b, a, a, b, b, a, b, a, b, a, a]
  
      # Use sklearn for validation
      result_a = metrics.true_positive_rate(y_true, y_pred, pos_label=a)
      result_skm_a = skm.recall_score(y_true, y_pred, pos_label=a)
      assert result_a == pytest.approx(result_skm_a)
  
      result_b = metrics.true_positive_rate(y_true, y_pred, pos_label=b)
      result_skm_b = skm.recall_score(y_true, y_pred, pos_label=b)
      assert result_b == pytest.approx(result_skm_b)
  
      # Sanity check on the in put data
      assert result_a != result_b
  
imports: import numpy
  import pytest
  import sklearn.metrics
  import fairlearn.metrics
  from fairlearn.metrics._base_metrics import _get_labels_for_confusion_matrix
label_desc: Use image node_id 6 for calling the TestTPR obj's underlying member method code with example usage: obj.test_tpr_values_alpha() without return types