input: 
  from keras import layers, models
  
  def construct_mnist_classifier():
      """ Construct a neural network model using Keras to classify the MNIST data set correctly.
      This function should return a compiled keras model.
      
      Example:
      >>> model = construct_mnist_classifier()
      >>> model.summary()
      Model: "functional_1"
      _________________________________________________________________
      Layer (type)                 Output Shape              Param #   
      =================================================================
      input_1 (InputLayer)         [(None, 784)]             0         
      _________________________________________________________________
      dense (Dense)                (None, 128)               100480    
      _________________________________________________________________
      dense_1 (Dense)              (None, 10)                1290      
      =================================================================
      Total params: 101,770
      Trainable params: 101,770
      Non-trainable params: 0
      _________________________________________________________________
      """
      input_layer = layers.Input(shape=(784,))
      hidden_layer = layers.Dense(128, activation='relu')(input_layer)
      output_layer = layers.Dense(10, activation='softmax')(hidden_layer)
      model = models.Model(inputs=input_layer, outputs=output_layer)
      model.compile(optimizer='adam', loss='sparse_categorical_crossentropy', metrics=['accuracy'])
      return model
  
output:     input_layer = layers.Input(shape=(784,))
      hidden_layer = layers.Dense(128, activation='relu')(input_layer)
      output_layer = layers.Dense(10, activation='softmax')(hidden_layer)
      model = models.Model(inputs=input_layer, outputs=output_layer)
      model.compile(optimizer='adam', loss='sparse_categorical_crossentropy', metrics=['accuracy'])
      return model