code: def _render(
      self,
      rays_o,
      rays_d,
      mvp,
      light_d=None,
      ambient_ratio=1.0,
      shading_type=None,
      return_normal_image=False,
      return_vertices=False,
      return_faces=False,
      return_faces_normals=False,
      **kwargs,
  ):
      # mvp: [B, 4, 4]
      B, H, W, _ = rays_o.shape
  
      # TODO(ahmadki): move to dataset
      # random sample light_d if not provided
      if light_d is None:
          # gaussian noise around the ray origin, so the light always face the view dir (avoid dark face)
          light_d = rays_o + torch.randn(3, device=rays_o.device)
          light_d = F.normalize(light_d)
  
      results = {}
  
      # get mesh
      deform = torch.tanh(self.deform) / self.grid_resolution
  
      verts, faces = self.dmtet(
          self.verts + deform, self.sdf, self.indices
      )
  
      # get normals
      i0, i1, i2 = faces[:, 0], faces[:, 1], faces[:, 2]
      v0, v1, v2 = verts[i0, :], verts[i1, :], verts[i2, :]
  
      faces = faces.int()
  
      face_normals = torch.cross(v1 - v0, v2 - v0)
      face_normals = F.normalize(face_normals)
  
      vn = torch.zeros_like(verts)
      vn.scatter_add_(0, i0[:, None].repeat(1, 3), face_normals)
      vn.scatter_add_(0, i1[:, None].repeat(1, 3), face_normals)
      vn.scatter_add_(0, i2[:, None].repeat(1, 3), face_normals)
  
      vn = torch.where(
          torch.sum(vn * vn, -1, keepdim=True) > 1e-20,
          vn,
          torch.tensor(
              [0.0, 0.0, 1.0], dtype=torch.float32, device=vn.device
          ),
      )
  
      # rasterization
      verts_clip = torch.bmm(
          F.pad(verts, pad=(0, 1), mode="constant", value=1.0)
          .unsqueeze(0)
          .repeat(mvp.shape[0], 1, 1),
          mvp.permute(0, 2, 1),
      ).float()  # [B, N, 4]
      rast, _ = dr.rasterize(self.glctx, verts_clip, faces, (H, W))
  
      alpha = (rast[..., 3:] > 0).float()
      xyzs, _ = dr.interpolate(
          verts.unsqueeze(0), rast, faces
      )  # [B, H, W, 3]
      normal, _ = dr.interpolate(
          vn.unsqueeze(0).contiguous(), rast, faces
      )
      normal = F.normalize(normal)
  
      xyzs = xyzs.view(-1, 3)
      mask = (rast[..., 3:] > 0).view(-1).detach()
  
      # do the lighting here since we have normal from mesh now.
      albedo = torch.zeros_like(xyzs, dtype=torch.float32)
      if mask.any():
          masked_albedo = self.nerf.forward_features(
              positions=xyzs[mask]
          )
          albedo[mask] = masked_albedo.float()
      albedo = albedo.view(B, H, W, 3)
      fg_color = self.material(
          albedo=albedo,
          normals=normal,
          light_d=light_d,
          ambient_ratio=ambient_ratio,
          shading_type=shading_type,
      )
  
      fg_color = dr.antialias(fg_color, rast, verts_clip, faces).clamp(
          0, 1
      )  # [B, H, W, 3]
      alpha = dr.antialias(alpha, rast, verts_clip, faces).clamp(
          0, 1
      )  # [B, H, W, 1]
  
      # mix background color
      bg_color = self.background(rays_d=rays_d)  # [N, 3]
  
      depth = rast[:, :, :, [2]]  # [B, H, W]
      color = fg_color + (1 - alpha) * bg_color
  
      results["depth"] = depth
      results["image"] = color
      if return_normal_image:
          results["normal_image"] = dr.antialias(
              (normal + 1) / 2, rast, verts_clip, faces
          ).clamp(
              0, 1
          )  # [B, H, W, 3]
      if return_vertices:
          results["vertices"] = verts
      if return_faces:
          results["faces"] = faces
      if return_faces_normals:
          results["face_normals"] = face_normals
      return results
  
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 5 for calling the NVDiffRastRenderer obj's underlying member method code with example usage: obj._render(rays_o, rays_d, mvp, light_d, ambient_ratio, shading_type, return_normal_image, return_vertices, return_faces, return_faces_normals) and returns: results