code: def __init__(
      self,
      train_dataset: JsonIndexDataset,
      sequence_name: str,
      render_size: Tuple[int, int] = (400, 400),
      device=None,
      load_dataset_pointcloud: bool = False,
      max_frames: int = 30,
      num_workers: int = 10,
  ):
      super().__init__()
      self._render_size = render_size
      point_cloud, _ = get_implicitron_sequence_pointcloud(
          train_dataset,
          sequence_name=sequence_name,
          mask_points=True,
          max_frames=max_frames,
          num_workers=num_workers,
          load_dataset_point_cloud=load_dataset_pointcloud,
      )
      self._point_cloud = point_cloud.to(device)
  
imports: import contextlib
  import math
  import os
  import unittest
  from typing import Tuple
  import torch
  from pytorch3d.implicitron.dataset.json_index_dataset import JsonIndexDataset
  from pytorch3d.implicitron.dataset.visualize import get_implicitron_sequence_pointcloud
  from pytorch3d.implicitron.models.visualization.render_flyaround import render_flyaround
  from pytorch3d.implicitron.tools.config import expand_args_fields
  from pytorch3d.implicitron.tools.point_cloud_utils import render_point_cloud_pytorch3d
  from pytorch3d.renderer.cameras import CamerasBase
  from tests.common_testing import interactive_testing_requested
  from visdom import Visdom
  from .common_resources import get_skateboard_data
label_desc: Use image node_id 1 to create a new _PointcloudRenderingModel object from inherited base classes: torch.nn with example: obj = _PointcloudRenderingModel(train_dataset, sequence_name, render_size, device, load_dataset_pointcloud, max_frames, num_workers)