input: def construct_mnist_model():
      """ Construct a neural network model in Python to classify the MNIST data set correctly.
      The model should include an input layer, a hidden layer with 128 units and ReLU activation,
      an output layer with 10 units and softmax activation, and be compiled with the Adam optimizer
      and sparse categorical cross-entropy loss.
      >>> construct_mnist_model()
      <keras.Model>
      """
output:     from tensorflow.keras.models import Sequential
      from tensorflow.keras.layers import Dense
      from tensorflow.keras.optimizers import Adam
  
      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