input: 
  def construct_mnist_classifier() -> keras.Model:
      """Construct a neural network model using Keras to classify 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 be compiled using the Adam optimizer and sparse categorical crossentropy
      loss.
      >>> model = construct_mnist_classifier()
      >>> model.input_shape
      (None, 784)
      >>> model.output_shape
      (None, 10)
      >>> model.loss
      'sparse_categorical_crossentropy'
      """
      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
  
output:     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