code: def test_potato_specific_labels(get_mats):
      n_matrices, n_channels = 10, 3
      covmats = get_mats(n_matrices, n_channels, "spd")
      covmats[-1] = 10 * np.eye(n_channels)
      pt = Potato(threshold=2.0, pos_label=2, neg_label=7)
      pt.fit(covmats)
      assert_array_equal(np.unique(pt.predict(covmats)), [2, 7])
      # fit with custom positive label
      pt.fit(covmats, y=[2] * n_matrices)
  
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 10 for calling a global function with example usage: test_potato_specific_labels(get_mats) without return types