input: Write a function to construct a neural network model in Python using Keras to classify the MNIST data set correctly. The function should return the compiled model.
  assert str(construct_mnist_model().summary())=="""\
  Model: "model"
  _________________________________________________________________
   Layer (type)                Output Shape              Param #   
  =================================================================
   input_1 (InputLayer)        [(None, 784)]             0         
                                                                   
   dense (Dense)               (None, 128)               100480    
                                                                   
   dense_1 (Dense)             (None, 10)                1290      
                                                                   
  =================================================================
  Total params: 101770 (397.54 KB)
  Trainable params: 101770 (397.54 KB)
  Non-trainable params: 0 (0.00 Byte)
  _________________________________________________________________
  """
output: def construct_mnist_model():
      from tensorflow.keras.models import Model
      from tensorflow.keras.layers import Input, Dense
      
      input_layer = Input(shape=(784,))
      hidden_layer = Dense(128, activation='relu')(input_layer)
      output_layer = Dense(10, activation='softmax')(hidden_layer)
      
      model = Model(inputs=input_layer, outputs=output_layer)
      model.compile(optimizer='adam', loss='sparse_categorical_crossentropy', metrics=['accuracy'])
      
      return model