input: Write a python function to create a sequential CNN model for image classification.
  assert create_model().layers[0].input_shape == (None, 224, 224, 3)
  assert isinstance(create_model().layers[0], Conv2D)
  assert isinstance(create_model().layers[-1], Dense)
  assert create_model().layers[-1].output_shape == (None, 2)
output: def create_model():
      from tensorflow.keras.models import Sequential
      from tensorflow.keras.layers import Conv2D, MaxPooling2D, Flatten, Dense
      
      model = Sequential()
      model.add(Conv2D(32, (3, 3), activation='relu', input_shape=(224, 224, 3)))
      model.add(MaxPooling2D((2, 2)))
      model.add(Conv2D(64, (3, 3), activation='relu'))
      model.add(MaxPooling2D((2, 2)))
      model.add(Conv2D(128, (3, 3), activation='relu'))
      model.add(MaxPooling2D((2, 2)))
      model.add(Flatten())
      model.add(Dense(128, activation='relu'))
      model.add(Dense(2, activation='softmax'))
      
      return model