input: 
  def create_mnist_model() -> keras.Model:
      """Construct a neural network model in Python to classify the MNIST data set correctly.
      The model should include an input layer, a hidden layer with 128 units and ReLU activation,
      and an output layer with 10 units and softmax activation. The model should be compiled
      with the Adam optimizer and sparse categorical cross-entropy loss.
      
      Example:
      for create_mnist_model().get_config() == {'model': {'inputs': [{'name': 'input_1'}], 'outputs': [{'name': 'output_1'}]}}
      Output: A compiled keras Model object configured for MNIST classification.
      """
      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