class_docstr: Class for Deep Marching Tetrahedra (DMTet).
  
  Attributes:
      device (torch.device): Device to place the tensors.
      triangle_table (Tensor): Lookup table for the triangles.
      num_triangles_table (Tensor): Table for the number of triangles.
      base_tet_edges (Tensor): The base edges for the tetrahedrons.
docstr: Process the provided data to generate vertices and faces.
  
  Args:
      positions (Tensor): Position tensor with shape [N, 3].
      sdf_n (Tensor): SDF tensor with shape [N].
      tet_fx4 (Tensor): Tetrahedron faces tensor with shape [F, 4].
  
  Returns:
      tuple: Vertices and faces tensors.
docstr_tok: ["Process","the","provided","data","to","generate","vertices","and","faces",".","Args",":","positions","(","Tensor",")",":","Position","tensor","with","shape","[","N",",","3","]",".","sdf_n","(","Tensor",")",":","SDF","tensor","with","shape","[","N","]",".","tet_fx4","(","Tensor",")",":","Tetrahedron","faces","tensor","with","shape","[","F",",","4","]",".","Returns",":","tuple",":","Vertices","and","faces","tensors","."]
code: def __call__(
      self,
      positions: torch.Tensor,
      sdf_n: torch.Tensor,
      tet_fx4: torch.Tensor,
  ) -> tuple:
      """
      Process the provided data to generate vertices and faces.
  
      Args:
          positions (Tensor): Position tensor with shape [N, 3].
          sdf_n (Tensor): SDF tensor with shape [N].
          tet_fx4 (Tensor): Tetrahedron faces tensor with shape [F, 4].
  
      Returns:
          tuple: Vertices and faces tensors.
      """
      with torch.no_grad():
          occ_n = sdf_n > 0
          occ_fx4 = occ_n[tet_fx4.reshape(-1)].reshape(-1, 4)
          occ_sum = torch.sum(occ_fx4, -1)
          valid_tets = (occ_sum > 0) & (occ_sum < 4)
          occ_sum = occ_sum[valid_tets]
  
          # find all vertices
          all_edges = tet_fx4[valid_tets][
              :, self.base_tet_edges
          ].reshape(-1, 2)
          all_edges = self._sort_edges(all_edges)
          unique_edges, idx_map = torch.unique(
              all_edges, dim=0, return_inverse=True
          )
  
          unique_edges = unique_edges.long()
          mask_edges = (
              occ_n[unique_edges.reshape(-1)].reshape(-1, 2).sum(-1)
              == 1
          )
          mapping = (
              torch.ones(
                  (unique_edges.shape[0]),
                  dtype=torch.long,
                  device=self.device,
              )
              * -1
          )
          mapping[mask_edges] = torch.arange(
              mask_edges.sum(), dtype=torch.long, device=self.device
          )
          idx_map = mapping[idx_map]  # map edges to verts
  
          interp_v = unique_edges[mask_edges]
  
      edges_to_interp = positions[interp_v.reshape(-1)].reshape(
          -1, 2, 3
      )
      edges_to_interp_sdf = sdf_n[interp_v.reshape(-1)].reshape(
          -1, 2, 1
      )
      edges_to_interp_sdf[:, -1] *= -1
  
      denominator = edges_to_interp_sdf.sum(1, keepdim=True)
      edges_to_interp_sdf = (
          torch.flip(edges_to_interp_sdf, [1]) / denominator
      )
      verts = (edges_to_interp * edges_to_interp_sdf).sum(1)
  
      idx_map = idx_map.reshape(-1, 6)
      v_id = torch.pow(
          2, torch.arange(4, dtype=torch.long, device=self.device)
      )
      tetindex = (occ_fx4[valid_tets] * v_id.unsqueeze(0)).sum(-1)
      num_triangles = self.num_triangles_table[tetindex]
  
      # Generate triangle indices
      faces = torch.cat(
          (
              torch.gather(
                  input=idx_map[num_triangles == 1],
                  dim=1,
                  index=self.triangle_table[
                      tetindex[num_triangles == 1]
                  ][:, :3],
              ).reshape(-1, 3),
              torch.gather(
                  input=idx_map[num_triangles == 2],
                  dim=1,
                  index=self.triangle_table[
                      tetindex[num_triangles == 2]
                  ][:, :6],
              ).reshape(-1, 3),
          ),
          dim=0,
      )
  
      return verts, faces
  
imports: import torch
label_desc: Use image node_id 6 for calling the DeepMarchingTetrahedra obj's underlying member method code with example usage: obj.__call__(positions, sdf_n, tet_fx4) and returns: verts,  faces