class_docstr: Class to manage spatial ray sampling.
  
  Args:
      min_depth: sampled rays minimal depth from cameras: float
      max_depth: sampled rays maximal depth from cameras: float
      ndc: convert ray parameters to normalized device coordinates: bool
      device: device for ray tensors: Union[str, torch.device]
code: def origins(self) -> Tensor:
      return self._origins
  
imports: import math
  from typing import Dict, List, Tuple
  import torch
  from kornia.core import Device, Tensor, tensor
  from kornia.geometry.camera import PinholeCamera
  from kornia.nerf.camera_utils import cameras_for_ids
  from kornia.utils._compat import torch_meshgrid
  from kornia.utils.helpers import _torch_inverse_cast
label_desc: Use image node_id 2 for calling the RaySampler obj's underlying member method code with example usage: obj.origins() and returns: self