input: Write a python function to create and compile a Keras Sequential model for image classification with the specified architecture.
  assert model.summary() is None
  assert isinstance(model.layers[0], keras.layers.Conv2D)
  assert isinstance(model.layers[1], keras.layers.MaxPooling2D)
  assert isinstance(model.layers[2], keras.layers.Conv2D)
  assert isinstance(model.layers[3], keras.layers.MaxPooling2D)
  assert isinstance(model.layers[4], keras.layers.Flatten)
  assert isinstance(model.layers[5], keras.layers.Dense)
  assert isinstance(model.layers[6], keras.layers.Dense)
output: from keras.models import Sequential
  from keras.layers import Conv2D, MaxPooling2D, Flatten, Dense
  
  def create_image_classification_model():
      model = Sequential()
      model.add(Conv2D(32, (3, 3), activation='relu', input_shape=(64, 64, 3)))
      model.add(MaxPooling2D(pool_size=(2, 2)))
      model.add(Conv2D(32, (3, 3), activation='relu'))
      model.add(MaxPooling2D(pool_size=(2, 2)))
      model.add(Flatten())
      model.add(Dense(128, activation='relu'))
      model.add(Dense(10, activation='softmax'))
      model.compile(optimizer='adam', loss='categorical_crossentropy', metrics=['accuracy'])
      return model
  
  # Create model instance
  model = create_image_classification_model()