def METHOD_NAME(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