code: def mnist_data():
      (x_train, y_train), (x_test, y_test) = mnist.load_data()
      x_train = x_train.astype("float32") / 255.0
      x_test = x_test.astype("float32") / 255.0
      x_train = np.expand_dims(x_train, -1)
      x_test = np.expand_dims(x_test, -1)
      y_train = to_categorical(y_train, 10)
      y_test = to_categorical(y_test, 10)
      x_test, y_test = x_test[:1000], y_test[:1000]
      return x_train, y_train, x_test, y_test
  
file_path: hls4ml/test/pytest/test_cnn_mnist.py
imports: from pathlib import Path
  import numpy
  import pytest
  from sklearn.metrics import accuracy_score
  from tensorflow.keras.datasets import mnist
  from tensorflow.keras.layers import Activation, AveragePooling2D, Conv2D, Dense, Flatten, MaxPooling2D
  from tensorflow.keras.models import Sequential
  from tensorflow.keras.utils import to_categorical
  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