input: Write a python function to build and train a CNN model on the MNIST dataset using Keras.
  assert train_cnn_model().evaluate(test_images, test_labels)[1] > 0.9
output: def train_cnn_model():
      import tensorflow as tf
      from tensorflow.keras.datasets import mnist
      from tensorflow.keras.models import Sequential
      from tensorflow.keras.layers import Conv2D, MaxPooling2D, Flatten, Dense
      from tensorflow.keras.utils import to_categorical
      
      # Load and preprocess the MNIST dataset
      (train_images, train_labels), (test_images, test_labels) = mnist.load_data()
      train_images = train_images.reshape((60000, 28, 28, 1)).astype('float32') / 255
      test_images = test_images.reshape((10000, 28, 28, 1)).astype('float32') / 255
      train_labels = to_categorical(train_labels)
      test_labels = to_categorical(test_labels)
      
      # Build the CNN model
      model = Sequential()
      model.add(Conv2D(32, (3, 3), activation='relu', input_shape=(28, 28, 1)))
      model.add(MaxPooling2D((2, 2)))
      model.add(Conv2D(64, (3, 3), activation='relu'))
      model.add(MaxPooling2D((2, 2)))
      model.add(Conv2D(64, (3, 3), activation='relu'))
      model.add(Flatten())
      model.add(Dense(64, activation='relu'))
      model.add(Dense(10, activation='softmax'))
      
      # Compile the model
      model.compile(optimizer='adam', loss='categorical_crossentropy', metrics=['accuracy'])
      
      # Train the model
      model.fit(train_images, train_labels, epochs=5, batch_size=64, validation_split=0.1)
      
      return model