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.
  
  
  import keras
  
  def construct_mnist_model():
      # 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
  
  # Example usage:
  # model = construct_mnist_model()
  # model.fit(x_train, y_train, epochs=5)
  
  
  assert isinstance(construct_mnist_model(), keras.Model)
output: def construct_mnist_model():
      input_layer = keras.layers.Input(shape=(784,))
      hidden_layer = keras.layers.Dense(128, activation='relu')(input_layer)
      output_layer = keras.layers.Dense(10, activation='softmax')(hidden_layer)
      model = keras.Model(inputs=input_layer, outputs=output_layer)
      model.compile(optimizer='adam', loss='sparse_categorical_crossentropy', metrics=['accuracy'])
      return model