code: def test_needs_X(self):
      y_true = np.array([0, 0, 1, 1])
      y_pred = np.array(
          [[1.0, 0.0], [1.0, 0.0], [0.0, 1.0], [0.0, 1.0]]
      )
  
      def dummy_metric(y_true, y_pred, X_data=None, **kwargs):
          if not np.array_equal(np.array([45]), X_data):
              raise ValueError(f"is {X_data}")
          return 1
  
      scorer = autosklearn.metrics._PredictScorer(
          "accuracy", dummy_metric, 1, 0, 1, {}, needs_X=True
      )
      scorer(y_true, y_pred, X_data=np.array([45]))
  
      scorer_nox = autosklearn.metrics._PredictScorer(
          "accuracy", dummy_metric, 1, 0, 1, {}, needs_X=False
      )
      with self.assertRaises(ValueError) as cm:
          scorer_nox(y_true, y_pred, X_data=np.array([32]))
      the_exception = cm.exception
      # X_data is not forwarded
      self.assertEqual(the_exception.args[0], "is None")
  
      scorer_nox = autosklearn.metrics._PredictScorer(
          "accuracy",
          sklearn.metrics.accuracy_score,
          1,
          0,
          1,
          {},
          needs_X=False,
      )
      scorer_nox(y_true, y_pred, X_data=np.array([32]))
  
imports: import warnings
  import numpy
  import sklearn.metrics
  import autosklearn.metrics
  from autosklearn.constants import BINARY_CLASSIFICATION, REGRESSION
  from autosklearn.metrics import calculate_losses, calculate_scores, compute_single_metric
  import pytest
  import unittest
label_desc: Use image node_id 1 for calling the TestScorer obj's underlying member method code with example usage: obj.test_needs_X() and returns: int