input: Write a function to construct a neural network model in Python using Keras that classifies the MNIST data set correctly. The function should return the compiled model.
  assert construct_mnist_classifier().summary() is not None
  assert construct_mnist_classifier().get_config()['layers'][0]['config']['batch_input_shape'] == [(None, 784)]
  assert construct_mnist_classifier().get_config()['layers'][2]['config']['units'] == 10
  assert construct_mnist_classifier().get_config()['layers'][2]['config']['activation'] == 'softmax'
output: def construct_mnist_classifier():
      from tensorflow.keras.models import Sequential
      from tensorflow.keras.layers import Dense
      model = Sequential()
      model.add(Dense(512, activation='relu', input_shape=(784,)))
      model.add(Dense(10, activation='softmax'))
      model.compile(optimizer='adam', loss='categorical_crossentropy', metrics=['accuracy'])
      return model