code: def test__batch_preprocessing(batch_size):
      """Test if the batch preprocessing function returns the correct shapes."""
      n_samples, n_features, n_removed = 50, 5, 2
      X = np.random.randn(n_samples, n_features)
  
      def preprocess_fn(X):
          return X[:, :-n_removed]
  
      X_ft = _batch_preprocessing(
          X=X, preprocess_fn=preprocess_fn, batch_size=batch_size
      )
      assert X_ft.shape == (n_samples, n_features - n_removed)
  
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 5 for calling a global function with example usage: test__batch_preprocessing(batch_size) and returns: X