code: def load_artificial_attribution():
      """Build an artificial attribution map"""
      zeros = np.zeros((1, 28, 28))
      ones = np.ones((1, 28, 28))
      mosaics_list = []
      images = [zeros, ones]
      indices_list = [
          tuple([0, 0, 1, 1]),
          tuple([1, 1, 0, 0]),
          tuple([0, 1, 0, 1]),
          tuple([1, 0, 1, 0]),
      ]
      for indices in indices_list:
          first_row = np.concatenate(
              (images[indices[0]], images[indices[1]]), axis=1
          )
          second_row = np.concatenate(
              (images[indices[2]], images[indices[3]]), axis=1
          )
          mosaic = np.concatenate((first_row, second_row), axis=2)
          mosaics_list.append(mosaic)
      return np.array(mosaics_list)
  
imports: from typing import Union, Optional, Dict
  import pytest
  from pytest_lazyfixture import lazy_fixture
  import numpy
  import torch
  import pickle
  from quantus.functions.explanation_func import explain
  from quantus.functions.mosaic_func import mosaic_creation
  from quantus.helpers.model.model_interface import ModelInterface
  from quantus.helpers.model.models import LeNetAdaptivePooling
  from quantus.metrics.localisation import AttributionLocalisation, AUC, Focus, PointingGame, RelevanceMassAccuracy, RelevanceRankAccuracy, TopKIntersection
label_desc: Use image node_id 21 for calling a global function with example usage: load_artificial_attribution() and returns: np