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