input: 下面是根据你提供的要求生成的代码和问题：
  
  def build_mnist_classifier():
      """ Build a neural network model using Keras to classify the MNIST dataset correctly.
      >>> model = build_mnist_classifier()
      >>> model.compile(optimizer='adam', loss='sparse_categorical_crossentropy', metrics=['accuracy'])
      >>> # Assume model is trained and evaluated on MNIST dataset
      >>> print(model.summary())
      """
output:     from tensorflow.keras.models import Sequential
      from tensorflow.keras.layers import Dense, Flatten
  
      model = Sequential()
      model.add(Flatten(input_shape=(28, 28)))
      model.add(Dense(128, activation='relu'))
      model.add(Dense(10, activation='softmax'))
  
      return model