code: def compute_densityfeature(self, xyz_sampled):
      coordinate_plane = (
          torch.stack(
              (
                  xyz_sampled[..., self.matMode[0]],
                  xyz_sampled[..., self.matMode[1]],
                  xyz_sampled[..., self.matMode[2]],
              )
          )
          .detach()
          .view(3, -1, 1, 2)
      )
      coordinate_line = torch.stack(
          (
              xyz_sampled[..., self.vecMode[0]],
              xyz_sampled[..., self.vecMode[1]],
              xyz_sampled[..., self.vecMode[2]],
          )
      )
      coordinate_line = (
          torch.stack(
              (torch.zeros_like(coordinate_line), coordinate_line),
              dim=-1,
          )
          .detach()
          .view(3, -1, 1, 2)
      )
      sigma_feature = torch.zeros(
          (xyz_sampled.shape[0],), device=xyz_sampled.device
      )
      for idx_plane in range(len(self.density_plane)):
          plane_coef_point = F.grid_sample(
              self.density_plane[idx_plane]
              * self.density_plane_mask[idx_plane],
              coordinate_plane[[idx_plane]],
              align_corners=True,
          ).view(-1, *xyz_sampled.shape[:1])
          line_coef_point = F.grid_sample(
              self.density_line[idx_plane]
              * self.density_line_mask[idx_plane],
              coordinate_line[[idx_plane]],
              align_corners=True,
          ).view(-1, *xyz_sampled.shape[:1])
          sigma_feature = sigma_feature + torch.sum(
              plane_coef_point * line_coef_point, dim=0
          )
      return sigma_feature
  
file_path: slimmerf/models/tensoRF.py
imports: from .tensorBase import *
label_desc: Use image node_id 13 for calling the TensorVMSplit obj's underlying member method code with example usage: obj.compute_densityfeature(xyz_sampled) and returns: sigma_feature