input: def Construct_MNIST_Model():
      """
      Construct a neural network model in Python to classify the MNIST data set correctly.
      The model should have an input layer with 784 nodes, a hidden layer with 128 nodes,
      and an output layer with 10 nodes. The hidden layer should use the 'relu' activation
      function, and the output layer should use the 'softmax' activation function. The model
      should be compiled with the 'adam' optimizer, 'sparse_categorical_crossentropy' loss,
      and 'accuracy' metric.
      Example:
      >>> Construct_MNIST_Model()
      Expected output:
      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'])
      """
output:     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