854f604719b6a571e47ad3ca8b758cec4133d2f7,kornia/augmentation/augmentation.py,RandomPosterize,__init__,#RandomPosterize#Any#Any#Any#Any#Any#,1065

Before Change

    ) -> None:
        super(RandomPosterize, self).__init__(p=p, return_transform=return_transform, same_on_batch=same_on_batch,
                                              keepdim=keepdim)
        bits = cast(torch.Tensor, bits) if isinstance(bits, torch.Tensor) else torch.tensor(bits)
        if len(bits.size()) == 0:
            self.bits = torch.tensor([bits, torch.tensor(8)], dtype=torch.float32)
        elif len(bits.size()) == 1 and bits.size(0) == 2:
            self.bits = torch.tensor([bits[0], bits[1]], dtype=torch.float32)
        else:
            raise ValueError(f""bits" shall be either a scalar or a length 2 tensor. Got {bits}.")

After Change

    ) -> None:
        super(RandomPosterize, self).__init__(p=p, return_transform=return_transform, same_on_batch=same_on_batch,
                                              keepdim=keepdim)
        self._device, self._dtype = _extract_device_dtype([bits])
        self.bits = bits

    def __repr__(self) -> str:
        repr = f"(bits={self.bits}"

Instances