code: def fetch_data():
      channels_first = image_data_format() == "channels_first"
      # the data, shuffled and split between train and test sets
      (x_train, y_train), (x_test, y_test) = mnist.load_data()
  
      if channels_first:
          x_train = x_train.reshape(60000, 1, 28, 28)
          x_test = x_test.reshape(10000, 1, 28, 28)
      else:
          x_train = x_train.reshape(60000, 28, 28, 1)
          x_test = x_test.reshape(10000, 28, 28, 1)
  
      x_train = x_train.astype("float32")
      x_test = x_test.astype("float32")
  
      return x_train, y_train, x_test, y_test
  
file_path: innvestigate/examples/utils/mnist.py
imports: from __future__ import annotations
  import numpy
  import tensorflow.keras
  from tensorflow.keras.backend import image_data_format
  from tensorflow.keras.datasets import mnist
  import innvestigate.utils
  import innvestigate.utils.visualizations
label_desc: Use image node_id 1 for calling a global function with example usage: fetch_data() and returns: x_train,  y_train,  x_test,  y_test