Project: 068_Two_Effects_One_Trigger_On_the_Modality_Gap_Object | Function: generate_text (iclr)

system: You are an expert Python programmer implementing algorithms from scientific papers.
Complete the masked function based on the algorithm description.
Follow Python best practices and maintain consistent code style.
Return **only** the complete Python function code.

user: Complete this Python function:

**DESCRIPTION:** Implements information imbalance manipulation by controlling the number of attributes included in captions while keeping images unchanged, demonstrating how information imbalance affects modality gap and object bias

**KEY TERMS:** {
  "mode": "information balance setting",
  "args": "available attributes",
  "random.sample": "attribute selection for imbalance",
  "caption": "generated text with controlled information"
}

**CONTEXT CODE:**                 att_mat_bool = np.zeros((1, 26 - nr_classes))
            else:
                att_mat_bool = np.zeros((1, 16))
            att_mat_bool[:, attribute_list] = 1
        else:
            if not self.object == 'digit':
                raise NotImplementedError
            nr_classes = 10
            nr_rows = 16 + nr_classes #
            att_mat_bool = np.zeros((1, nr_rows))
            att_mat_bool[0,cls_label] = 1
            att_mat_bool[:, np.array(attribute_list)+nr_classes] = 1 

        return att_mat, att_mat_bool

    def rescale(self, image: np.ndarray) -> np.ndarray:
        new_image = np.zeros_like(image)
        image_rescaled = (transform.rescale(image.astype(np.float32)/255, 0.75, anti_aliasing=False)*255).astype(np.uint8)
        y = random.choice([0, 1])
        x = random.choice([0, 1])
        new_image[3 if y == 0 else 4:-3 if y==1 else -4, 3 if x == 0 else 4:-3 if x==1 else -4] = image_rescaled
        return new_image

    def colorize(self, image: Image, color: str) -> np.ndarray:
        if color == "gray":
            return image
        image = np.array(image)
        mask = image[...,0] > 0
        if color == "blue":
            image[mask,0] = 0
            image[mask,1] = 0
        elif color == "green":
            image[mask,0] = 0
            image[mask,2] = 0
        elif color == "red":
            image[mask,1] = 0
            image[mask,2] = 0
        elif color == "yellow":
            image[mask,0] = 255
            image[mask,1] = 255
        elif color == "magenta":
            image[mask,0] = 255
            image[mask,2] = 255
        elif color == "cyan":
            image[mask,1] = 255
            image[mask,2] = 255
        else:
            raise NotImplementedError
        return Image.fromarray(image)

    def generate_text(self, cls_label: int, *args):
        raise NotImplementedError("Function implementation has been masked for testing")
        
    def generate_text_save(self, cls_label: int, *args):
        if self.mode == "object_only":
            caption = str(cls_label)
        elif "object_attribute_" in self.mode:
            caption = str(cls_label) + '-' + '-'.join(args)
        else:
            raise NotImplementedError
        return caption


if __name__ == '__main__':
    transform = transforms.Compose([transforms.ToTensor(),
                            transforms.Normalize(mean, std)
                            ])
    dataset = MADDataset(root='./datasets/morphoMNIST_0', transform=transform, mode="object_attribute_1",
                         train=True, debug_visu=True)
    
    train_loader = DataLoader(
        dataset=dataset,
        batch_size=32,
        shuffle=False,
        num_workers=4,
    )

    for batch in tqdm.tqdm(train_loader, total=len(train_loader)):
        continue


Complete the function implementation replacing "raise NotImplementedError".