code: def __init__(
      self,
      bound,
      update_interval,
      grid_resolution,
      density_thresh,
      quartet_file,
  ):
      super().__init__(bound, update_interval)
  
      self.grid_resolution = grid_resolution
      self.density_thresh = density_thresh
      self.quartet_file = quartet_file
  
      self.cascade = 1 + math.ceil(math.log2(bound))
      density_grid = torch.zeros(
          [self.cascade, self.grid_resolution**3]
      )  # [CAS, H * H * H]
      density_bitfield = torch.zeros(
          self.cascade * self.grid_resolution**3 // 8,
          dtype=torch.uint8,
      )  # [CAS * H * H * H // 8]
      self.register_buffer("density_grid", density_grid)
      self.register_buffer("density_bitfield", density_bitfield)
      self.mean_density = 0
      self.iter_density = 0
  
      # load dmtet vertices
      # TODO(ahmadki): hard coded devices
      tets = np.load(quartet_file)
      self.verts = (
          -torch.tensor(
              tets["vertices"], dtype=torch.float32, device="cuda"
          )
          * 2
      )  # covers [-1, 1]
      self.indices = torch.tensor(
          tets["indices"], dtype=torch.long, device="cuda"
      )
      self.tet_scale = torch.tensor(
          [1, 1, 1], dtype=torch.float32, device="cuda"
      )
      self.dmtet = DeepMarchingTetrahedra(device="cuda")
  
      # vert sdf and deform
      sdf = torch.nn.Parameter(
          torch.zeros_like(self.verts[..., 0]), requires_grad=True
      )
      self.register_parameter("sdf", sdf)
      deform = torch.nn.Parameter(
          torch.zeros_like(self.verts), requires_grad=True
      )
      self.register_parameter("deform", deform)
  
      edges = torch.tensor(
          [0, 1, 0, 2, 0, 3, 1, 2, 1, 3, 2, 3],
          dtype=torch.long,
          device="cuda",
      )  # six edges for each tetrahedron.
      all_edges = self.indices[:, edges].reshape(-1, 2)  # [M * 6, 2]
      all_edges_sorted = torch.sort(all_edges, dim=1)[0]
      self.all_edges = torch.unique(all_edges_sorted, dim=0)
  
      self.initialized = False  # TODO(ahmadki): not a good approach
  
      self.glctx = dr.RasterizeCudaContext()
  
      # TODO(ahmadki): not a good approach
      self.nerf = None
      self.material = None
      self.background = None
  
file_path: NeMo/nemo/collections/multimodal/modules/nerf/renderers/nvdiffrast_renderer.py
imports: import math
  import numpy
  import nvdiffrast.torch
  import torch
  import torch.nn.functional
  from nemo.collections.multimodal.modules.nerf.geometry.dmtet import DeepMarchingTetrahedra
  from nemo.collections.multimodal.modules.nerf.geometry.nerf_base import DensityActivationEnum
  from nemo.collections.multimodal.modules.nerf.renderers.base_renderer import BaseRenderer
label_desc: Use image node_id 1 to create a new NVDiffRastRenderer object from inherited base classes: BaseRenderer with example: obj = NVDiffRastRenderer(bound, update_interval, grid_resolution, density_thresh, quartet_file)