code: def importance_data():
      X = np.array(
          [
              [0.548, 0.715],
              [0.602, 0.544],
              [0.423, 0.645],
              [5.437, 4.891],
              [5.963, 4.383],
              [5.791, 4.528],
              [5.568, 4.925],
              [5.071, 4.087],
              [-3.979, 4.832],
              [-3.221, 4.870],
              [-3.021, 4.799],
              [-3.538, 4.780],
              [-3.881, 4.639],
              [-3.856, 4.944],
              [-3.478, 4.414],
          ]
      )
      y = np.array([0, 0, 0, 1, 1, 1, 1, 1, 2, 2, 2, 2, 2, 2, 2])
      trainset = (X, y)
  
      meta = deepcopy(DEFAULT_META_PROTOSELECT)
      data = deepcopy(DEFAULT_DATA_PROTOSELECT)
      meta["params"] = {
          "kernel_distance": "EuclideanDistance",
          "eps": 0.5,
          "lambda_penalty": 0.066,
          "batch_size": 10000000000,
          "verbose": True,
      }
      data["prototypes"] = np.array(
          [[0.548, 0.715], [5.791, 4.528], [-3.53, 4.780]]
      )
      data["prototype_indices"] = (
          np.array([0, 5, 11], dtype=np.int32),
      )
      data["prototype_labels"] = np.array([0, 1, 2], dtype=np.int32)
      summary = Explanation(meta=meta, data=data)
      return trainset, summary
  
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 6 for calling a global function with example usage: importance_data() and returns: trainset,  summary