code: def init_tet(self):
      # TODO(ahmadki): a better approach would be to have a global nerf representation (mesh) that
      # we can init the tets from. this would work with checkpoints.
  
      # TODO(ahmadki): a placeholder, but it works for now
      self.mean_density = 300
      density_thresh = min(self.mean_density, self.density_thresh)
  
      if self.nerf.density_activation == DensityActivationEnum.SOFTPLUS:
          density_thresh = density_thresh * 25
  
      # Get initial sigma
      sigma = self.nerf.forward_density(positions=self.verts)
      mask = sigma > density_thresh
      valid_verts = self.verts[mask]
      self.tet_scale = valid_verts.abs().amax(dim=0) + 1e-1
  
      # Scale vertices
      self.verts = self.verts * self.tet_scale
  
      # get sigma using the scaled vertices
      sigma = self.nerf.forward_density(positions=self.verts)
      self.sdf.data += (sigma - density_thresh).clamp(-1, 1)
  
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 3 for calling the NVDiffRastRenderer obj's underlying member method code with example usage: obj.init_tet() without return types