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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 the MNIST dataset
        (train_images, train_labels), (test_images, test_labels) = mnist.load_data()
        
        # Normalize pixel values to be between 0 and 1
        train_images, test_images = train_images / 255.0, test_images / 255.0
        
        # One-hot encode the labels
        train_labels = to_categorical(train_labels)
        test_labels = to_categorical(test_labels)
        
        # Build a simple convolutional neural network (CNN) model
        model = models.Sequential([
            layers.Conv2D(32, (3, 3), activation='relu', input_shape=(28, 28, 1)),
            layers.MaxPooling2D((2, 2)),
            layers.Conv2D(64, (3, 3), activation='relu'),
            layers.MaxPooling2D((2, 2)),
            layers.Conv2D(64, (3, 3), activation='relu'),
            layers.Flatten(),
            layers.Dense(64, activation='relu'),
            layers.Dense(10, activation='softmax')
        ])
        
        # Compile the model
        model.compile(optimizer='adam',
                      loss='categorical_crossentropy',
                      metrics=['accuracy'])
        
        # Reshape the input data for the CNN
        train_images = train_images.reshape((60000, 28, 28, 1))
        test_images = test_images.reshape((10000, 28, 28, 1))
        
        # Train the model
        model.fit(train_images, train_labels, epochs=5, validation_data=(test_images, test_labels))
        
        # Evaluate the model
        test_loss, test_acc = model.evaluate(test_images, test_labels)
        print(f'Test accuracy: {test_acc}')