code: def plot_data():
      n_samples = 10
      x = np.random.uniform(low=-10, high=10, size=(n_samples,))
      y = np.random.uniform(low=-10, high=10, size=(n_samples,))
  
      image_size = (5, 5)
      images = np.random.rand(n_samples, *image_size, 3)
  
      zoom_lb, zoom_ub = 3, 7
      zoom = np.random.permutation(np.linspace(1, 5, n_samples))
      return {
          "x": x,
          "y": y,
          "image_size": image_size,
          "images": images,
          "zoom_lb": zoom_lb,
          "zoom_ub": zoom_ub,
          "zoom": zoom,
      }
  
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 8 for calling a global function with example usage: plot_data() and returns: dict