class_docstr: Class to represent a dataset of rays.
  
  Args:
      cameras: scene cameras: PinholeCamera
      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]
      dtype: type of ray tensors: torch.dtype
code: def _load_images(img_paths: List[str]) -> List[Tensor]:
      imgs: List[Tensor] = []
      for img_path in img_paths:
          imgs.append(load_image(img_path, ImageLoadType.UNCHANGED))
      return imgs
  
imports: from typing import TYPE_CHECKING, List, Optional, Sequence, Tuple, Union
  import torch
  from torch.utils.data import BatchSampler, DataLoader, Dataset, RandomSampler, SequentialSampler
  from typing_extensions import TypeGuard
  from kornia.core import Device, Tensor, stack
  from kornia.geometry.camera import PinholeCamera
  from kornia.io import ImageLoadType, load_image
  from kornia.nerf.core import Images, ImageTensors
  from kornia.nerf.samplers import RandomRaySampler, RaySampler, UniformRaySampler
label_desc: Use image node_id 8 for calling the RayDataset obj's underlying member method code with example usage: obj._load_images(img_paths) and returns: imgs