input: def construct_mnist_classifier() -> keras.Model:
      """Construct a neural network model in Python to classify the MNIST data set correctly.
      The model should consist of an input layer of shape (784,), a hidden layer with 
      128 units and ReLU activation, and an output layer with 10 units and softmax 
      activation. The model should be compiled using the Adam optimizer, 
      sparse categorical crossentropy loss, and accuracy as the metric.
      >>> model = construct_mnist_classifier()
      >>> model.input_shape
      (None, 784)
      >>> model.output_shape
      (None, 10)
      """
      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'])
      
      return model
output:     import keras