code: def cat_dog_image():
      # Note that 'jpg' images can have RGB data
      # which is fine in the case of this model (requires three channels)
      img_path = "tests/images/cat_dog.jpg"
      im = keras.preprocessing.image.load_img(
          img_path, target_size=(224, 224)
      )
      doc = keras.preprocessing.image.img_to_array(im)
      doc = np.expand_dims(doc, axis=0)  # add batch size
      mobilenet_v2.preprocess_input(
          doc
      )  # because our classifier is mobilenet_v2
      # re-load from array because we did some preprocessing
      im = keras.preprocessing.image.array_to_img(doc[0])
      return doc, im
  
file_path: eli5/tests/test_keras_integration.py
imports: from __future__ import print_function
  import pytest
  import numpy
  from PIL import Image
  from keras.applications import mobilenet_v2
  from keras.models import Sequential
  import eli5
  from eli5.base import Explanation
  from eli5 import format_as_image
  from eli5.formatters.image import expand_heatmap
  from .utils_image import assert_pixel_by_pixel_equal
label_desc: Use image node_id 2 for calling a global function with example usage: cat_dog_image() and returns: doc,  im