def METHOD_NAME(collate_fn):
    def collate_fn_with_label_drift(batch):
        batch_dict = collate_fn(batch)
        images = batch_dict['images']
        labels = batch_dict['labels']
        for i in range(len(images)):
            image, label = images[i], labels[i]
            if label == 0:
                if np.random.randint(5) != 0:
                    batch_dict['labels'][i] = 1
                    # In 9/10 cases, the prediction vector will change to match the label
                    if np.random.randint(10) != 0:
                        batch_dict['predictions'][i] = torch.tensor([0, 1, 0, 0, 0, 0, 0, 0, 0, 0])
        return batch_dict
    return collate_fn_with_label_drift