input: 
  def build_MNIST_model() -> keras.Model:
      """
      Constructs a neural network model in Python using Keras to correctly classify the MNIST dataset.
      The model consists of an input layer with 784 units, a hidden layer with 128 units and ReLU activation,
      and an output layer with 10 units and softmax activation. The model is then compiled using the Adam optimizer
      and sparse categorical crossentropy loss.
      
      >>> model = build_MNIST_model()
      >>> print(model.summary())
      """
      # 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:     
      # 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
  