input: def construct_mnist_model():
      """ Construct a neural network model in Python using the Keras API to classify the MNIST data set correctly.
      The model should have an input layer, a hidden layer with 128 neurons and ReLU activation, 
      and an output layer with 10 neurons and softmax activation. The model should be compiled 
      with the Adam optimizer, sparse categorical crossentropy loss, and accuracy as the metric.
      >>> model = construct_mnist_model()
      >>> model.optimizer
      'adam'
      >>> model.loss
      'sparse_categorical_crossentropy'
      >>> model.metrics[0]
      'accuracy'
      >>> model.input_shape
      (None, 784)
      >>> model.output_shape
      (None, 10)
      """
      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:     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