input: Write a function to construct a neural network model in Python using the Keras library that correctly classifies the MNIST data set. The model should have an input layer with shape (784,), a hidden layer with 128 units and ReLU activation, and an output layer with 10 units and softmax activation. The model should then be compiled using the Adam optimizer, sparse categorical crossentropy loss, and accuracy as the metric.
  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'])
  