input: def construct_mnist_classifier():
      """Construct a neural network model in Python to classify the MNIST data set correctly.
      The model should have an input layer with shape (784,), a hidden layer with 128 units 
      and 'relu' activation, and an output layer with 10 units and 'softmax' activation.
      The model should be compiled with the 'adam' optimizer, 'sparse_categorical_crossentropy' loss, 
      and 'accuracy' as the metric.
      
      Example:
      >>> constructed_model = construct_mnist_classifier()
      >>> constructed_model.compile_arguments()
      {'optimizer': 'adam', 'loss': 'sparse_categorical_crossentropy', 'metrics': ['accuracy']}
      """
output:     from tensorflow.keras.models import Sequential
      from tensorflow.keras.layers import Dense
  
      model = Sequential()
      model.add(Dense(128, activation='relu', input_shape=(784,)))
      model.add(Dense(10, activation='softmax'))
  
      model.compile(optimizer='adam', loss='sparse_categorical_crossentropy', metrics=['accuracy'])
  
      return model