class_docstr: :class:`torch.Tensor` subclass for segmentation and detection masks.
  
  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``.
code: def __new__(
      cls,
      data: Any,
      *,
      dtype: Optional[torch.dtype] = None,
      device: Optional[Union[torch.device, str, int]] = None,
      requires_grad: Optional[bool] = None,
  ) -> Mask:
      if isinstance(data, PIL.Image.Image):
          from torchvision.transforms.v2 import functional as F
  
          data = F.pil_to_tensor(data)
  
      tensor = cls._to_tensor(
          data, dtype=dtype, device=device, requires_grad=requires_grad
      )
      return tensor.as_subclass(cls)
  
file_path: vision/torchvision/tv_tensors/_mask.py
imports: from __future__ import annotations
  from typing import Any, Optional, Union
  import PIL.Image
  import torch
  from ._tv_tensor import TVTensor
label_desc: Use image node_id 1 for calling the Mask obj's underlying member method code with example usage: obj.__new__(cls, data) and returns: tensor