input: 
  def construct_mnist_classifier_model(input_shape: Tuple[int], hidden_units: int, output_units: int) -> keras.Model:
      """
      Constructs a neural network model in Python to classify the MNIST data set correctly.
      It includes an input layer, a hidden layer with relu activation, and an output layer with softmax activation.
      The model is compiled using the Adam optimizer and sparse categorical crossentropy loss.
  
      >>> model = construct_mnist_classifier_model((784,), 128, 10)
      >>> model.compile(optimizer='adam', loss='sparse_categorical_crossentropy', metrics=['accuracy'])
      >>> model.summary()
      # Output should show a neural network model with the specified structure
      """
      input_layer = keras.layers.Input(shape=input_shape)
      hidden_layer = keras.layers.Dense(hidden_units, activation='relu')(input_layer)
      output_layer = keras.layers.Dense(output_units, activation='softmax')(hidden_layer)
      model = keras.Model(inputs=input_layer, outputs=output_layer)
      return model
  
output:     model.compile(optimizer='adam', loss='sparse_categorical_crossentropy', metrics=['accuracy'])