code: def test_compute_prototype_importances(importance_data):
      """Test if the computation of the prototype importances returns the expected results for a simple
      example with three clusters and three prototypes. In this case, the importance of each prototype is
      given by the number of data instances in each cluster."""
      trainset, summary = importance_data
      X, y = trainset
  
      expected_importances = np.unique(y, return_counts=True)[1]
      importances = compute_prototype_importances(
          summary=summary, trainset=trainset
      )
      assert np.allclose(
          expected_importances, importances["prototype_importances"]
      )
  
imports: from copy import deepcopy
  import matplotlib
  import numpy
  import pytest
  from sklearn.datasets import make_classification
  from sklearn.model_selection import train_test_split
  from alibi.api.defaults import DEFAULT_DATA_PROTOSELECT, DEFAULT_META_PROTOSELECT
  from alibi.api.interfaces import Explanation
  from alibi.prototypes import ProtoSelect
  from alibi.prototypes.protoselect import _batch_preprocessing, _imscatterplot, compute_prototype_importances, cv_protoselect_euclidean, visualize_image_prototypes
  from alibi.utils.kernel import EuclideanDistance
label_desc: Use image node_id 7 for calling a global function with example usage: test_compute_prototype_importances(importance_data) without return types