code: def test_two_clusters(self, clust, get_mats, get_labels):
      n_clusters = 2
      n_matrices, n_channels = 6, 4
      covmats = get_mats(n_matrices, n_channels, "spd")
      if clust is Kmeans:
          self.clf_predict(clust, covmats, n_clusters)
          self.clf_transform(clust, covmats, n_clusters)
          self.clf_jobs(clust, covmats, n_clusters)
          self.clf_centroids(clust, covmats, n_clusters)
          self.clf_fit_independence(clust, covmats)
      if clust is KmeansPerClassTransform:
          n_classes = 2
          labels = get_labels(n_matrices, n_classes)
          self.clf_transform_per_class(
              clust, covmats, n_clusters, labels
          )
          self.clf_jobs(clust, covmats, n_clusters, labels)
          self.clf_fit_labels_independence(clust, covmats, labels)
      if clust is Potato:
          self.clf_transform(clust, covmats)
          self.clf_predict(clust, covmats)
          self.clf_predict_proba(clust, covmats)
          self.clf_partial_fit(clust, covmats)
          self.clf_fit_independence(clust, covmats)
      if clust is PotatoField:
          n_potatoes = 3
          covmats = [
              get_mats(n_matrices, n_channels, "spd"),
              get_mats(n_matrices, n_channels + 2, "spd"),
              get_mats(n_matrices, n_channels + 1, "spd"),
          ]
          self.clf_transform(clust, covmats, n_potatoes)
          self.clf_predict(clust, covmats, n_potatoes)
          self.clf_predict_proba(clust, covmats, n_potatoes)
          self.clf_partial_fit(clust, covmats, n_potatoes)
          self.clf_fit_independence(clust, covmats, n_potatoes)
  
imports: from conftest import get_metrics
  import numpy
  from numpy.testing import assert_array_equal
  import pytest
  from pyriemann.clustering import Kmeans, KmeansPerClassTransform, Potato, PotatoField
label_desc: Use image node_id 1 for calling the ClusteringTestCase obj's underlying member method code with example usage: obj.test_two_clusters(clust, get_mats, get_labels) without return types