## Import necessary libraries
                    import tensorflow as tf
                    from tensorflow.keras import
                    layers, models
                    from tensorflow.keras.datasets import mnist
                    from tensorflow.keras.utils import to_categorical

                    # Load and preprocess data
                    def load_and_preprocess_data():
                    """Load and preprocess the MNIST dataset."""
                    (x_train, y_train), (x_test, y_test) =
                    mnist.load_data()

                    # Normalize the images to [0, 1] range
                    x_train = x_train.astype('float32') /
                    255.0
                    x_test = x_test.astype('float32') / 255.0

                    # Reshape data to include channel dimension
                    x_train = x_train.reshape(-1, 28, 28, 1)
                    x_test = x_test.reshape(-1, 28, 28, 1)

                    # One-hot encode the labels
                    y_train = to_categorical(y_train, 10)
                    y_test = to_categorical(y_test, 10)

                    return (x_train, y_train), (x_test, y_test)