code: def update_current_rank(self, device):
      self.current_rank[0] += self.rank_inc[0]
      self.current_rank[1] += self.rank_inc[1]
      d_line_dim = [
          self.density_line_mask[i].size()
          for i in range(len(self.vecMode))
      ]
      a_line_dim = [
          self.app_line_mask[i].size() for i in range(len(self.vecMode))
      ]
      d_plane_dim = [
          self.density_plane_mask[i].size()
          for i in range(len(self.matMode))
      ]
      a_plane_dim = [
          self.app_plane_mask[i].size()
          for i in range(len(self.matMode))
      ]
      d_line_mask = [
          torch.nn.Parameter(
              self.rank_mask(
                  self.current_rank[0],
                  self.density_n_comp[i],
                  d_line_dim[i][2],
                  1,
                  device,
              )
          )
          for i in range(len(self.vecMode))
      ]
      a_line_mask = [
          torch.nn.Parameter(
              self.rank_mask(
                  self.current_rank[1],
                  self.app_n_comp[i],
                  a_line_dim[i][2],
                  1,
                  device,
              )
          )
          for i in range(len(self.vecMode))
      ]
      d_plane_mask = [
          torch.nn.Parameter(
              self.rank_mask(
                  self.current_rank[0],
                  self.density_n_comp[i],
                  d_plane_dim[i][2],
                  d_plane_dim[i][3],
                  device,
              )
          )
          for i in range(len(self.matMode))
      ]
      a_plane_mask = [
          torch.nn.Parameter(
              self.rank_mask(
                  self.current_rank[1],
                  self.app_n_comp[i],
                  a_plane_dim[i][2],
                  a_plane_dim[i][3],
                  device,
              )
          )
          for i in range(len(self.matMode))
      ]
      self.density_line_mask = torch.nn.ParameterList(d_line_mask).to(
          device
      )
      self.app_line_mask = torch.nn.ParameterList(a_line_mask).to(
          device
      )
      self.density_plane_mask = torch.nn.ParameterList(d_plane_mask).to(
          device
      )
      self.app_plane_mask = torch.nn.ParameterList(a_plane_mask).to(
          device
      )
  
file_path: slimmerf/models/tensoRF.py
imports: from .tensorBase import *
label_desc: Use image node_id 3 for calling the TensorVMSplit obj's underlying member method code with example usage: obj.update_current_rank(device) without return types