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
docstr: Initializes images for training. Images can be either a list of tensors, or a list of paths to image
  disk locations.
  
  Args:
      imgs: List of image tensors or image paths: Images
docstr_tok: ["Initializes","images","for","training",".","Images","can","be","either","a","list","of","tensors",",","or","a","list","of","paths","to","image","disk","locations",".","Args",":","imgs",":","List","of","image","tensors","or","image","paths",":","Images"]
code: def init_images_for_training(self, imgs: Images) -> None:
      r"""Initializes images for training. Images can be either a list of tensors, or a list of paths to image
      disk locations.
  
      Args:
          imgs: List of image tensors or image paths: Images
      """
      self._check_image_type_consistency(imgs)
  
      if _is_list_of_str(imgs):  # Load images from disk
          images = self._load_images(imgs)
      elif _is_list_of_tensors(imgs):
          images = imgs  # Take images provided on input
      else:
          raise TypeError(
              f"Expected a list of image tensors or image paths. Gotcha {type(imgs)}."
          )
  
      self._check_dimensions(images)
  
      # Move images to defined device
      self._imgs = [img.to(self._device) for img in images]
  
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 3 for calling the RayDataset obj's underlying member method code with example usage: obj.init_images_for_training(imgs) without return types