a573ef78d7f8b18188de9e5b6ef66cde997ba167,kornia/augmentation/random_generator.py,,random_crop_generator,#Any#Any#Any#Any#Any#Any#Any#,324

Before Change

            [0, resize_to[0] - 1],
        ]]).repeat(batch_size, 1, 1)

    return {"src": crop_src, "dst": crop_dst,  // type: ignore
            "interpolation": torch.tensor(Resample.get(interpolation).value),
            "align_corners": align_corners}


def random_crop_size_generator(size: Tuple[int, int], scale: Tuple[float, float], ratio: Tuple[float, float],
                               same_on_batch: bool = False) -> Tuple[torch.Tensor, torch.Tensor]:

After Change

            [0, resize_to[0] - 1],
        ]]).repeat(batch_size, 1, 1)

    return dict(src=crop_src,
                dst=crop_dst,
                interpolation=torch.tensor(Resample.get(interpolation).value),
                align_corners=torch.tensor(align_corners))


def random_crop_size_generator(
    size: Tuple[int, int],

Instances