input: Write a python function to create and train a CNN model using TensorFlow for image classification.
  assert isinstance(create_and_train_model(np.zeros((100, 32, 32, 3)), np.zeros(100)), tf.keras.models.Sequential)
output: def create_and_train_model(X_train, y_train):
      import tensorflow as tf
      from tensorflow.keras import layers, models
      
      model = models.Sequential([
          layers.Conv2D(32, (3, 3), activation='relu', input_shape=(32, 32, 3)),
          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')
      ])
  
      model.compile(optimizer='adam',
                    loss='sparse_categorical_crossentropy',
                    metrics=['accuracy'])
      
      model.fit(X_train, y_train, epochs=10)
      
      return model