code: def collate_batched_R2N2(batch: List[Dict]):  # pragma: no cover
      """
      Take a list of objects in the form of dictionaries and merge them
      into a single dictionary. This function can be used with a Dataset
      object to create a torch.utils.data.Dataloader which directly
      returns Meshes objects.
      TODO: Add support for textures.
  
      Args:
          batch: List of dictionaries containing information about objects
              in the dataset.
  
      Returns:
          collated_dict: Dictionary of collated lists. If batch contains both
              verts and faces, a collated mesh batch is also returned.
      """
      collated_dict = collate_batched_meshes(batch)
  
      # If collate_batched_meshes receives R2N2 items with images and that
      # all models have the same number of views V, stack the batches of
      # views of each model into a new batch of shape (N, V, H, W, 3).
      # Otherwise leave it as a list.
      if "images" in collated_dict:
          try:
              collated_dict["images"] = torch.stack(
                  collated_dict["images"]
              )
          except RuntimeError:
              print(
                  "Models don't have the same number of views. Now returning "
                  "lists of images instead of batches."
              )
  
      # If collate_batched_meshes receives R2N2 items with camera calibration
      # matrices and that all models have the same number of views V, stack each
      # type of matrices into a new batch of shape (N, V, ...).
      # Otherwise leave them as lists.
      if all(x in collated_dict for x in ["R", "T", "K"]):
          try:
              collated_dict["R"] = torch.stack(
                  collated_dict["R"]
              )  # (N, V, 3, 3)
              collated_dict["T"] = torch.stack(
                  collated_dict["T"]
              )  # (N, V, 3)
              collated_dict["K"] = torch.stack(
                  collated_dict["K"]
              )  # (N, V, 4, 4)
          except RuntimeError:
              print(
                  "Models don't have the same number of views. Now returning "
                  "lists of calibration matrices instead of a batched tensor."
              )
  
      # If collate_batched_meshes receives voxels and all models have the same
      # number of views V, stack the batches of voxels into a new batch of shape
      # (N, V, S, S, S), where S is the voxel size.
      if "voxels" in collated_dict:
          try:
              collated_dict["voxels"] = torch.stack(
                  collated_dict["voxels"]
              )
          except RuntimeError:
              print(
                  "Models don't have the same number of views. Now returning "
                  "lists of voxels instead of a batched tensor."
              )
      return collated_dict
  
imports: import math
  from typing import Dict, List
  import numpy
  import torch
  from pytorch3d.common.datatypes import Device
  from pytorch3d.datasets.utils import collate_batched_meshes
  from pytorch3d.ops import cubify
  from pytorch3d.renderer import HardPhongShader, MeshRasterizer, MeshRenderer, PointLights, RasterizationSettings, TexturesVertex
  from pytorch3d.renderer.cameras import CamerasBase
  from pytorch3d.transforms import Transform3d
label_desc: Use image node_id 1 for calling a global function with example usage: collate_batched_R2N2(batch) and returns: collated_dict