code: def _sample_cameras_and_masks(
      n_samples: int,
      cameras: CamerasBase,
      mask: Optional[torch.Tensor] = None,
  ) -> Tuple[
      CamerasBase,
      Optional[torch.Tensor],
      torch.LongTensor,
      torch.LongTensor,
      torch.LongTensor,
  ]:
      """
      Samples n_rays_total cameras and masks and returns them in a form
      (camera_idx, count), where count represents number of times the same camera
      has been sampled.
  
      Args:
          n_samples: how many camera and mask pairs to sample
          cameras: A batch of `batch_size` cameras from which the rays are emitted.
          mask: Optional. Should be of size (batch_size, image_height, image_width).
      Returns:
          tuple of a form (sampled_cameras, sampled_masks, unique_sampled_camera_ids,
              number_of_times_each_sampled_camera_has_been_sampled,
              max_number_of_times_camera_has_been_sampled,
              )
      """
      sampled_ids = torch.randint(
          0,
          len(cameras),
          size=(n_samples,),
          dtype=torch.long,
      )
      unique_ids, counts = torch.unique(sampled_ids, return_counts=True)
      # pyre-ignore[7]
      return (
          cameras[unique_ids],
          mask[unique_ids] if mask is not None else None,
          unique_ids,
          counts,
          torch.max(counts),
      )
  
imports: import warnings
  from typing import Optional, Tuple, Union
  import torch
  from pytorch3d.common.compat import meshgrid_ij
  from pytorch3d.ops import padded_to_packed
  from pytorch3d.renderer.cameras import CamerasBase
  from pytorch3d.renderer.implicit.utils import HeterogeneousRayBundle, RayBundle
  from torch.nn import functional
label_desc: Use image node_id 6 for calling a global function with example usage: _sample_cameras_and_masks(n_samples, cameras, mask) and returns: cameras,  unknown,  unique_ids,  counts,  torch