class_docstr: Camera for rendering objects with calibration matrices from the R2N2 dataset
  (which uses Blender for rendering the views for each model).
docstr: Args:
      R: Rotation matrix of shape (N, 3, 3).
      T: Translation matrix of shape (N, 3).
      K: Intrinsic matrix of shape (N, 4, 4).
      device: Device (as str or torch.device).
docstr_tok: ["Args",":","R",":","Rotation","matrix","of","shape","(","N",",","3",",","3",")",".","T",":","Translation","matrix","of","shape","(","N",",","3",")",".","K",":","Intrinsic","matrix","of","shape","(","N",",","4",",","4",")",".","device",":","Device","(","as","str","or","torch.device",")","."]
code: def __init__(self, R=r, T=t, K=k, device: Device = "cpu") -> None:
      """
      Args:
          R: Rotation matrix of shape (N, 3, 3).
          T: Translation matrix of shape (N, 3).
          K: Intrinsic matrix of shape (N, 4, 4).
          device: Device (as str or torch.device).
      """
      # The initializer formats all inputs to torch tensors and broadcasts
      # all the inputs to have the same batch dimension where necessary.
      super().__init__(device=device, R=R, T=T, K=K)
  
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 to create a new BlenderCamera object from inherited base classes: CamerasBase with example: obj = BlenderCamera(R, T, K, device)