input: def build_mnist_model(input_shape: tuple) -> keras.Model:
      """Construct a neural network model in Python to classify the MNIST data set correctly.
      Input layer has a shape of (784,) and there is one hidden layer with 128 neurons and ReLU activation.
      Output layer has 10 neurons and softmax activation. The model is compiled with the Adam optimizer
      and sparse categorical crossentropy loss. 
      Example:
      >>> model = build_mnist_model((784,))
      >>> model.compile(optimizer='adam', loss='sparse_categorical_crossentropy', metrics=['accuracy'])
      """
output:     model = keras.Sequential([
          keras.layers.Input(shape=input_shape),
          keras.layers.Dense(128, activation='relu'),
          keras.layers.Dense(10, activation='softmax')
      ])
      model.compile(optimizer='adam', loss='sparse_categorical_crossentropy', metrics=['accuracy'])
      return model