input: 
  def create_mnist_classifier_model() -> keras.Model:
      """ Construct a neural network model in Python to classify the MNIST data set correctly.
      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, sparse categorical crossentropy 
      loss, and accuracy as the metric.
  
      >>> model = create_mnist_classifier_model()
      >>> model.compile.call_count == 1
      True
      >>> model.optimizer.get_config()['name']
      'Adam'
      """
      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