input: 
  def create_mnist_classifier_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 neurons activated by ReLU,
      and an output layer with 10 neurons activated by softmax. The model should be compiled 
      with the Adam optimizer and the sparse categorical crossentropy loss function.
      Example:
      >>> create_mnist_classifier_model()
      <keras.engine.functional.Functional object at 0x...>
      """
      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:     
      return model
  