code: def mnist_segmentation_fn(image, size=(4, 7)):
      segments = np.zeros([image.shape[0], image.shape[1]])
      row_idx, col_idx = np.where(segments == 0)
      for i, j in zip(row_idx, col_idx):
          segments[i, j] = int(
              (image.shape[1] / size[1]) * (i // size[0]) + j // size[1]
          )
      return segments
  
imports: import numbers
  import sys
  import numpy
  from numpy.testing import assert_allclose
  import pytest
  from pytest_lazyfixture import lazy_fixture
  from sklearn.linear_model import LogisticRegression
  from sklearn.ensemble import RandomForestClassifier
  import tempfile
  import tensorflow
  from typing import List, Union
  from alibi.explainers import ALE, AnchorImage, AnchorTabular, AnchorText, IntegratedGradients, KernelShap, TreeShap, CounterfactualRLTabular, GradientSimilarity
  from alibi.explainers.integrated_gradients import LayerState
  from alibi.saving import load_explainer
  from alibi_testing.data import get_adult_data, get_iris_data, get_movie_sentiment_data
  import alibi_testing
  from alibi.utils.download import spacy_model
  from alibi.explainers.tests.utils import predict_fcn
label_desc: Use image node_id 13 for calling a global function with example usage: mnist_segmentation_fn(image, size) and returns: segments