code: def test_size_match():
      """
      Tests if the error is raised when the number of data instance does not match the number of labels.
      """
      X = np.random.randn(100, 5)
      y = np.random.randint(0, 10, 50)
  
      summariser = ProtoSelect(
          eps=0.5, kernel_distance=EuclideanDistance()
      )
      with pytest.raises(ValueError):
          summariser.fit(X=X, y=y)
  
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 4 for calling a global function with example usage: test_size_match() without return types