code: def get_implicitron_sequence_pointcloud(
      dataset: JsonIndexDataset,
      sequence_name: Optional[str] = None,
      mask_points: bool = True,
      max_frames: int = -1,
      num_workers: int = 0,
      load_dataset_point_cloud: bool = False,
  ) -> Tuple[Pointclouds, FrameData]:
      """
      Make a point cloud by sampling random points from each frame the dataset.
      """
  
      if len(dataset) == 0:
          raise ValueError("The dataset is empty.")
  
      if not dataset.load_depths:
          raise ValueError(
              "The dataset has to load depths (dataset.load_depths=True)."
          )
  
      if mask_points and not dataset.load_masks:
          raise ValueError(
              "For mask_points=True, the dataset has to load masks"
              + " (dataset.load_masks=True)."
          )
  
      # setup the indices of frames loaded from the dataset db
      sequence_entries = list(range(len(dataset)))
      if sequence_name is not None:
          sequence_entries = [
              ei
              for ei in sequence_entries
              # pyre-ignore[16]
              if dataset.frame_annots[ei][
                  "frame_annotation"
              ].sequence_name
              == sequence_name
          ]
          if len(sequence_entries) == 0:
              raise ValueError(
                  f'There are no dataset entries for sequence name "{sequence_name}".'
              )
  
      # subsample loaded frames if needed
      if (max_frames > 0) and (len(sequence_entries) > max_frames):
          sequence_entries = [
              sequence_entries[i]
              for i in torch.randperm(len(sequence_entries))[
                  :max_frames
              ]
              .sort()
              .values
          ]
  
      # take only the part of the dataset corresponding to the sequence entries
      sequence_dataset = torch.utils.data.Subset(
          dataset, sequence_entries
      )
  
      # load the required part of the dataset
      loader = torch.utils.data.DataLoader(
          sequence_dataset,
          batch_size=len(sequence_dataset),
          shuffle=False,
          num_workers=num_workers,
          collate_fn=dataset.frame_data_type.collate,
      )
  
      frame_data = next(iter(loader))  # there's only one batch
  
      # scene point cloud
      if load_dataset_point_cloud:
          if not dataset.load_point_clouds:
              raise ValueError(
                  "For load_dataset_point_cloud=True, the dataset has to"
                  + " load point clouds (dataset.load_point_clouds=True)."
              )
          point_cloud = frame_data.sequence_point_cloud
  
      else:
          point_cloud = get_rgbd_point_cloud(
              frame_data.camera,
              frame_data.image_rgb,
              frame_data.depth_map,
              (
                  cast(torch.Tensor, frame_data.fg_probability) > 0.5
              ).float()
              if mask_points and frame_data.fg_probability is not None
              else None,
          )
  
      return point_cloud, frame_data
  
imports: from typing import cast, Optional, Tuple
  import torch
  from pytorch3d.implicitron.tools.point_cloud_utils import get_rgbd_point_cloud
  from pytorch3d.structures import Pointclouds
  from .frame_data import FrameData
  from .json_index_dataset import JsonIndexDataset
label_desc: Use image node_id 1 for calling a global function with example usage: get_implicitron_sequence_pointcloud(dataset, sequence_name, mask_points, max_frames, num_workers, load_dataset_point_cloud) and returns: point_cloud,  frame_data