input: 
  def mnist_neural_network(data):
      """
      Train a neural network model for classifying handwritten digits from the MNIST dataset.
      
      >>> mnist_neural_network("/tmp/data")
      Neat breakthrough
      """
  
output:     
      import tensorflow as tf
      from tensorflow.keras import layers, models
      from tensorflow.keras.datasets import mnist
      from tensorflow.keras.utils import to_categorical
  
      # Load and preprocess the data
      (train_images, train_labels), (test_images, test_labels) = mnist.load_data()
      train_images = train_images.reshape((60000, 28, 28, 1)).astype('float32') / 255
      test_images = test_images.reshape((10000, 28, 28, 1)).astype('float32') / 255
      train_labels = to_categorical(train_labels)
      test_labels = to_categorical(test_labels)
  
      # Build the model
      model = models.Sequential()
      model.add(layers.Conv2D(32, (3, 3), activation='relu', input_shape=(28, 28, 1)))
      model.add(layers.MaxPooling2D((2, 2)))
      model.add(layers.Conv2D(64, (3, 3), activation='relu'))
      model.add(layers.MaxPooling2D((2, 2)))
      model.add(layers.Conv2D(64, (3, 3), activation='relu'))
      model.add(layers.Flatten())
      model.add(layers.Dense(64, activation='relu'))
      model.add(layers.Dense(10, activation='softmax'))
  
      # Compile the model
      model.compile(optimizer='adam',
                    loss='categorical_crossentropy',
                    metrics=['accuracy'])
  
      # Train the model
      model.fit(train_images, train_labels, epochs=5, batch_size=64)
  
      # Evaluate the model
      test_loss, test_acc = model.evaluate(test_images, test_labels)
      print(f'Test accuracy: {test_acc:.4f}')
  
      return "Neat breakthrough"
  