code: def test_gmse_function():
      """Doctest from geometric_mean_squared_error."""
      from sktime.performance_metrics.forecasting import (
          geometric_mean_squared_error,
      )
  
      gmse = geometric_mean_squared_error
      y_true = np.array([3, -0.5, 2, 7, 2])
      y_pred = np.array([2.5, 0.0, 2, 8, 1.25])
      assert np.allclose(gmse(y_true, y_pred), 2.80399089461488e-07)
      assert np.allclose(
          gmse(y_true, y_pred, square_root=True), 0.000529527232030127
      )
      y_true = np.array([[0.5, 1], [-1, 1], [7, -6]])
      y_pred = np.array([[0, 2], [-1, 2], [8, -5]])
      assert np.allclose(gmse(y_true, y_pred), 0.5000000000115499)
      assert np.allclose(
          gmse(y_true, y_pred, square_root=True), 0.5000024031086919
      )
      assert np.allclose(
          gmse(y_true, y_pred, multioutput="raw_values"),
          np.array([2.30997255e-11, 1.00000000e00]),
      )
      assert np.allclose(
          gmse(
              y_true, y_pred, multioutput="raw_values", square_root=True
          ),
          np.array([4.80621738e-06, 1.00000000e00]),
      )
      assert np.allclose(
          gmse(y_true, y_pred, multioutput=[0.3, 0.7]),
          0.7000000000069299,
      )
      assert np.allclose(
          gmse(
              y_true, y_pred, multioutput=[0.3, 0.7], square_root=True
          ),
          0.7000014418652152,
      )
  
      assert np.allclose(
          gmse(
              np.array([1, 2, 3]),
              np.array([6, 5, 4]),
              horizon_weight=np.array([7, 8, 9]),
          ),
          6.185891035775025,
      )
  
imports: import numpy
  import pandas
label_desc: Use image node_id 2 for calling a global function with example usage: test_gmse_function() without return types