code: def ld_mnist():
      """Load training and test data."""
  
      def convert_types(image, label):
          image = tf.cast(image, tf.float32)
          image /= 255
          return image, label
  
      dataset, info = tfds.load(
          "mnist",
          data_dir="gs://tfds-data/datasets",
          with_info=True,
          as_supervised=True,
      )
      mnist_train, mnist_test = dataset["train"], dataset["test"]
      mnist_train = (
          mnist_train.map(convert_types).shuffle(10000).batch(128)
      )
      mnist_test = mnist_test.map(convert_types).batch(128)
      return EasyDict(train=mnist_train, test=mnist_test)
  
file_path: cleverhans/tutorials/tf2/mnist_tutorial.py
imports: import numpy
  import tensorflow
  import tensorflow_datasets
  from absl import app, flags
  from easydict import EasyDict
  from tensorflow.keras import Model
  from tensorflow.keras.layers import Dense, Dropout, Flatten, Conv2D
  from cleverhans.tf2.attacks.projected_gradient_descent import projected_gradient_descent
  from cleverhans.tf2.attacks.fast_gradient_method import fast_gradient_method
label_desc: Use image node_id 1 for calling a global function with example usage: ld_mnist() and returns: EasyDict, image,  label