xhNddlmZddlmZddlZddlZddlmZGddeZ y)) annotations)AnyN)TVTensorc4eZdZdZdddd ddZy)Maska:class:`torch.Tensor` subclass for segmentation and detection masks with shape ``[..., H, W]``. Args: data (tensor-like, PIL.Image.Image): Any data that can be turned into a tensor with :func:`torch.as_tensor` as well as PIL images. dtype (torch.dtype, optional): Desired data type. If omitted, will be inferred from ``data``. device (torch.device, optional): Desired device. If omitted and ``data`` is a :class:`torch.Tensor`, the device is taken from it. Otherwise, the mask is constructed on the CPU. requires_grad (bool, optional): Whether autograd should record operations. If omitted and ``data`` is a :class:`torch.Tensor`, the value is taken from it. Otherwise, defaults to ``False``. Ndtypedevice requires_gradct|tjjrddlm}|j |}|j ||||}|j|S)Nr) functionalr ) isinstancePILImagetorchvision.transforms.v2r pil_to_tensor _to_tensor as_subclass)clsdatar r r Ftensors x/home/developers/rajanand/mypropertyqr-fmb-refixing-v2/venv/lib/python3.12/site-packages/torchvision/tv_tensors/_mask.py__new__z Mask.__new__sO dCIIOO , A??4(DE&P]^!!#&&) rrr ztorch.dtype | Noner ztorch.device | str | int | Noner z bool | Nonereturnr)__name__ __module__ __qualname____doc__rrrrr sG "%)26%) ''" ' 0 ' # ' 'rr) __future__rtypingr PIL.Imagertorch _tv_tensorrrr"rrr(s " '8'r