input: def build_mnist_model():
      """Construct a neural network model in Python to classify the MNIST data set correctly.
      The model should consist of an input layer, a hidden layer with ReLU activation, 
      and an output layer with softmax activation. The model should be compiled using 
      the Adam optimizer and sparse categorical crossentropy loss.
      >>> build_mnist_model()
      <keras.engine.functional.Functional object at 0x...>
      """
output:     
      from tensorflow.keras.models import Sequential
      from tensorflow.keras.layers import Dense
      from tensorflow.keras.optimizers import Adam
  
      model = Sequential()
      model.add(Dense(128, activation='relu', input_shape=(28*28,)))
      model.add(Dense(10, activation='softmax'))
      model.compile(optimizer=Adam(), loss='sparse_categorical_crossentropy', metrics=['accuracy'])
      return model
  