code: def mnist_data():
      (x_train, y_train), (
          x_test,
          y_test,
      ) = tf.keras.datasets.mnist.load_data()
      # Scale images to the [0, 1] range
      x_train = x_train.astype("float32") / 255
      x_test = x_test.astype("float32") / 255
      # Make sure images have shape (28, 28, 1)
      x_train = np.expand_dims(x_train, -1)
      x_test = np.expand_dims(x_test, -1)
      y_train = tf.keras.utils.to_categorical(y_train, 10)
      y_test = tf.keras.utils.to_categorical(y_test, 10)
      return x_train, y_train, x_test, y_test
  
file_path: hls4ml/test/pytest/test_cnn_mnist_qkeras.py
imports: from pathlib import Path
  import numpy
  import pytest
  import tensorflow
  from qkeras.utils import _add_supported_quantized_objects
  from sklearn.metrics import accuracy_score
  from tensorflow.keras.models import model_from_json
  import hls4ml
label_desc: Use image node_id 1 for calling a global function with example usage: mnist_data() and returns: x_train,  y_train,  x_test,  y_test