input: Write a python function to create and compile a simple Convolutional Neural Network (CNN) using Keras, and return the model.
  assert isinstance(create_cnn_model(), Sequential)
  assert len(create_cnn_model().layers) == 4
output: def create_cnn_model():
      from keras.models import Sequential
      from keras.layers import Conv2D, MaxPooling2D, Flatten, Dense
  
      model = Sequential()
      model.add(Conv2D(32, (3, 3), activation='relu', input_shape=(64, 64, 3)))
      model.add(MaxPooling2D(pool_size=(2, 2)))
      model.add(Flatten())
      model.add(Dense(128, activation='relu'))
      model.add(Dense(1, activation='sigmoid'))
      
      model.compile(optimizer='adam', loss='binary_crossentropy', metrics=['accuracy'])
      
      return model