input: 
  def construct_mnist_model() -> keras.Model:
      """Construct a neural network model using Keras to classify the MNIST dataset.
      The model should include an input layer with 784 neurons, a hidden layer with
      128 neurons using the ReLU activation function, and an output layer with 10
      neurons using the softmax activation function. The model should be compiled
      using the Adam optimizer and sparse categorical crossentropy loss.
      >>> model = construct_mnist_model()
      >>> model.summary()
      """
      import keras
  
      # Input layer
      input_layer = keras.layers.Input(shape=(784,))
  
      # Hidden layer
      hidden_layer = keras.layers.Dense(128, activation='relu')(input_layer)
  
      # Output layer
      output_layer = keras.layers.Dense(10, activation='softmax')(hidden_layer)
  
      # Model
      model = keras.Model(inputs=input_layer, outputs=output_layer)
  
      # Compile model
      model.compile(optimizer='adam', loss='sparse_categorical_crossentropy', metrics=['accuracy'])
  
      return model
  
output:     return model