code: def chunk_generator(
      chunk_size: int,
      ray_bundle: ImplicitronRayBundle,
      chunked_inputs: Dict[str, torch.Tensor],
      tqdm_trigger_threshold: int,
      *args,
      **kwargs,
  ):
      """
      Helper function which yields chunks of rays from the
      input ray_bundle, to be used when the number of rays is
      large and will not fit in memory for rendering.
      """
      (
          batch_size,
          *spatial_dim,
          n_pts_per_ray,
      ) = ray_bundle.lengths.shape  # B x ... x n_pts_per_ray
      if n_pts_per_ray > 0 and chunk_size % n_pts_per_ray != 0:
          raise ValueError(
              f"chunk_size_grid ({chunk_size}) should be divisible "
              f"by n_pts_per_ray ({n_pts_per_ray})"
          )
  
      n_rays = prod(spatial_dim)
      # special handling for raytracing-based methods
      n_chunks = -(-n_rays * max(n_pts_per_ray, 1) // chunk_size)
      chunk_size_in_rays = -(-n_rays // n_chunks)
  
      iter = range(0, n_rays, chunk_size_in_rays)
      if len(iter) >= tqdm_trigger_threshold:
          iter = tqdm.tqdm(iter)
  
      def _safe_slice(
          tensor: Optional[torch.Tensor], start_idx: int, end_idx: int
      ) -> Any:
          return (
              tensor[start_idx:end_idx] if tensor is not None else None
          )
  
      for start_idx in iter:
          end_idx = min(start_idx + chunk_size_in_rays, n_rays)
          bins = (
              None
              if ray_bundle.bins is None
              else ray_bundle.bins.reshape(
                  batch_size, n_rays, n_pts_per_ray + 1
              )[:, start_idx:end_idx]
          )
          pixel_radii_2d = (
              None
              if ray_bundle.pixel_radii_2d is None
              else ray_bundle.pixel_radii_2d.reshape(batch_size, -1, 1)[
                  :, start_idx:end_idx
              ]
          )
          ray_bundle_chunk = ImplicitronRayBundle(
              origins=ray_bundle.origins.reshape(batch_size, -1, 3)[
                  :, start_idx:end_idx
              ],
              directions=ray_bundle.directions.reshape(
                  batch_size, -1, 3
              )[:, start_idx:end_idx],
              lengths=ray_bundle.lengths.reshape(
                  batch_size, n_rays, n_pts_per_ray
              )[:, start_idx:end_idx],
              xys=ray_bundle.xys.reshape(batch_size, -1, 2)[
                  :, start_idx:end_idx
              ],
              bins=bins,
              pixel_radii_2d=pixel_radii_2d,
              camera_ids=_safe_slice(
                  ray_bundle.camera_ids, start_idx, end_idx
              ),
              camera_counts=_safe_slice(
                  ray_bundle.camera_counts, start_idx, end_idx
              ),
          )
          extra_args = kwargs.copy()
          for k, v in chunked_inputs.items():
              extra_args[k] = v.flatten(2)[:, :, start_idx:end_idx]
          yield [ray_bundle_chunk, *args], extra_args
  
file_path: pytorch3d/pytorch3d/implicitron/models/utils.py
imports: import warnings
  from logging import Logger
  from typing import Any, Dict, Optional, Tuple
  import torch
  import tqdm
  from pytorch3d.common.compat import prod
  from pytorch3d.implicitron.models.renderer.base import ImplicitronRayBundle
  from pytorch3d.implicitron.tools import image_utils
  from pytorch3d.implicitron.tools.utils import cat_dataclass
label_desc: Use image node_id 5 for calling a global function with example usage: chunk_generator(chunk_size, ray_bundle, chunked_inputs, tqdm_trigger_threshold) and returns: unknown