code: def test_regression_single_sample(metric):
      y_true = [0]
      y_pred = [1]
      warning_msg = "not well-defined with less than two samples."
  
      # Trigger the warning
      with pytest.warns(UndefinedMetricWarning, match=warning_msg):
          score = metric(y_true, y_pred)
          assert np.isnan(score)
  
imports: from itertools import product
  import numpy
  import pytest
  from numpy.testing import assert_allclose
  from scipy import optimize
  from scipy.special import factorial, xlogy
  from sklearn.dummy import DummyRegressor
  from sklearn.exceptions import UndefinedMetricWarning
  from sklearn.metrics import d2_absolute_error_score, d2_pinball_score, d2_tweedie_score, explained_variance_score, make_scorer, max_error, mean_absolute_error, mean_absolute_percentage_error, mean_pinball_loss, mean_squared_error, mean_squared_log_error, mean_tweedie_deviance, median_absolute_error, r2_score, root_mean_squared_error, root_mean_squared_log_error
  from sklearn.metrics._regression import _check_reg_targets
  from sklearn.model_selection import GridSearchCV
  from sklearn.utils._testing import assert_almost_equal, assert_array_almost_equal, assert_array_equal
label_desc: Use image node_id 9 for calling a global function with example usage: test_regression_single_sample(metric) without return types