input: Write a function to construct a neural network model in Python using Keras to classify the MNIST dataset correctly. The model should have an input layer with a shape of (784,), a hidden layer with 128 neurons and ReLU activation, and an output layer with 10 neurons and softmax activation. The model should be compiled using the Adam optimizer, sparse categorical crossentropy loss, and accuracy as the metric.
  assert construct_model() == '''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'])'''.strip()
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'])
  