input: Write a python function to train and evaluate a CNN model on the MNIST dataset using TensorFlow and Keras.
  assert isinstance(model, keras.Sequential)
  assert isinstance(x_train, np.ndarray)
  assert isinstance(y_train, np.ndarray)
output: def train_and_evaluate_cnn():
      import tensorflow as tf
      from tensorflow import keras
      from tensorflow.keras import layers, models
      import numpy as np
      from tensorflow.keras.datasets import mnist
      
      # Load the MNIST dataset
      (x_train, y_train), (x_test, y_test) = mnist.load_data()
      
      # Normalize the data
      x_train, x_test = x_train / 255.0, x_test / 255.0
      
      # Expand dimensions to fit the model input requirements
      x_train = np.expand_dims(x_train, -1)
      x_test = np.expand_dims(x_test, -1)
      
      # Convert class vectors to binary class matrices
      y_train = keras.utils.to_categorical(y_train, 10)
      y_test = keras.utils.to_categorical(y_test, 10)
      
      # Define the CNN model
      model = models.Sequential()
      model.add(layers.Conv2D(32, (3, 3), activation='relu', input_shape=(28, 28, 1)))
      model.add(layers.MaxPooling2D((2, 2)))
      model.add(layers.Conv2D(64, (3, 3), activation='relu'))
      model.add(layers.MaxPooling2D((2, 2)))
      model.add(layers.Conv2D(64, (3, 3), activation='relu'))
      model.add(layers.Flatten())
      model.add(layers.Dense(64, activation='relu'))
      model.add(layers.Dense(10, activation='softmax'))
      
      # Compile the model
      model.compile(optimizer='adam',
                    loss='categorical_crossentropy',
                    metrics=['accuracy'])
      
      # Train the model
      model.fit(x_train, y_train, epochs=5, batch_size=64, validation_split=0.1)
      
      # Evaluate the model
      test_loss, test_acc = model.evaluate(x_test, y_test, verbose=2)
      
      return model, x_train, y_train