code: def test_class_distinctiveness(
      get_mats,
      get_labels,
      n_classes,
      metric_mean,
      metric_dist,
      exponent,
  ):
      """Test function for class distinctiveness measure for two class problem"""
      n_matrices, n_channels = 6, 3
      covmats = get_mats(n_matrices, n_channels, "spd")
      labels = get_labels(n_matrices, n_classes)
      if n_classes == 1:
          with pytest.raises(ValueError):
              class_distinctiveness(covmats, labels)
          return
  
      class_dis, num, denom = class_distinctiveness(
          covmats,
          labels,
          exponent,
          metric={"mean": metric_mean, "distance": metric_dist},
          return_num_denom=True,
      )
      assert class_dis >= 0  # negative class_dis value
      assert num >= 0  # negative numerator value
      assert denom >= 0  # negative denominator value
      assert isinstance(
          class_dis, float
      ), "Unexpected object of class_dis"
      assert isinstance(num, float), "Unexpected object of num"
      assert isinstance(denom, float), "Unexpected object of denum"
  
imports: import pickle
  import numpy
  from numpy.testing import assert_array_equal
  import pytest
  from pytest import approx
  from scipy.stats import mode
  from sklearn.dummy import DummyClassifier
  from conftest import get_distances, get_means, get_metrics
  from pyriemann.estimation import Covariances
  from pyriemann.utils.kernel import kernel
  from pyriemann.utils.mean import mean_covariance
  from pyriemann.classification import _mode_2d, MDM, FgMDM, KNearestNeighbor, TSclassifier, SVC, MeanField, class_distinctiveness
label_desc: Use image node_id 30 for calling a global function with example usage: test_class_distinctiveness(get_mats, get_labels, n_classes, metric_mean, metric_dist, exponent) without return types