code: def test_ranksum_test():
      """Test ranksum_test."""
      evaluator, metrics_by_strategy = evaluator_setup(
          score_function=accuracy_score
      )
      expected = np.array(
          [
              [
                  0.0,
                  1.0,
                  1.54919334,
                  0.12133525,
                  1.54919334,
                  0.12133525,
              ],
              [
                  -1.54919334,
                  0.12133525,
                  0.0,
                  1.0,
                  -1.54919334,
                  0.12133525,
              ],
              [
                  -1.54919334,
                  0.12133525,
                  1.54919334,
                  0.12133525,
                  0.0,
                  1.0,
              ],
          ]
      )
      expected = np.round(expected, 3)
      results = np.round(evaluator.ranksum_test()[1].values, 3)
      assert np.array_equal(results, expected)
  
imports: import numpy
  import pandas
  import pytest
  from sklearn.metrics import accuracy_score
  from sktime.benchmarking.evaluation import Evaluator
  from sktime.benchmarking.metrics import PairwiseMetric
  from sktime.benchmarking.results import RAMResults
  from sktime.series_as_features.model_selection import PresplitFilesCV
  from sktime.utils.validation._dependencies import _check_soft_dependencies
label_desc: Use image node_id 6 for calling a global function with example usage: test_ranksum_test() without return types