input: def create_mnist_model():
      """Construct a neural network model using Keras to classify the MNIST dataset correctly. The model should include an input layer, a hidden layer with ReLU activation, and an output layer with softmax activation. The model should be compiled with the Adam optimizer, sparse categorical crossentropy loss, and accuracy metric. 
      
      Example:
      for create_mnist_model()
      
      >>> import keras
      >>> isinstance(create_mnist_model(), keras.Model)
      True
      """
output:     model = keras.Sequential([
          keras.layers.InputLayer(input_shape=(28, 28)),
          keras.layers.Flatten(),
          keras.layers.Dense(128, activation='relu'),
          keras.layers.Dense(10, activation='softmax')
      ])
      model.compile(optimizer='adam',
                    loss='sparse_categorical_crossentropy',
                    metrics=['accuracy'])
      return model