code: def forward(self, face_normals, t_pos_idx):
      tris_per_edge = self.compute_edge_to_face_mapping(t_pos_idx)
  
      # Fetch normals for both faces sharind an edge
      n0 = face_normals[tris_per_edge[:, 0], :]
      n1 = face_normals[tris_per_edge[:, 1], :]
  
      # Compute error metric based on normal difference
      term = torch.clamp(
          torch.sum(n0 * n1, -1, keepdim=True), min=-1.0, max=1.0
      )
      term = 1.0 - term
  
      return torch.mean(torch.abs(term))
  
imports: import torch
  import torch.nn
label_desc: Use image node_id 2 for calling the NormalConsistencyLoss obj's underlying member method code with example usage: obj.forward(face_normals, t_pos_idx) and returns: torch