input: def construct_mnist_model():
      """Constructs a neural network model using Keras to classify the MNIST dataset.
      The model consists of:
      1. An input layer accepting 784-dimensional vectors.
      2. A hidden layer with 128 neurons and ReLU activation.
      3. An output layer with 10 neurons and softmax activation.
      The model is compiled with Adam optimizer and sparse categorical crossentropy loss function.
      >>> construct_mnist_model()
      <keras.Model at [memory address]>
      """
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')
      return model