input: Write a function to construct a neural network model in Python to classify the MNIST data set correctly. The function should return the compiled model.
  assert construct_mnist_model().input.shape[1] == 784
  assert construct_mnist_model().output.shape[1] == 10
  assert construct_mnist_model().loss == 'sparse_categorical_crossentropy'
  assert construct_mnist_model().optimizer.get_config()['name'] == 'adam'
output: def construct_mnist_model():
      from tensorflow.keras.models import Sequential
      from tensorflow.keras.layers import Dense
      model = Sequential([
          Dense(128, activation='relu', input_shape=(784,)),
          Dense(64, activation='relu'),
          Dense(10, activation='softmax')
      ])
      model.compile(optimizer='adam', loss='sparse_categorical_crossentropy', metrics=['accuracy'])
      return model