docstr: Naive iterative implementation of mesh loss calculation.
docstr_tok: ["Naive","iterative","implementation","of","mesh","loss","calculation","."]
code: def mesh_edge_loss_naive(meshes, target_length: float = 0.0):
      """
      Naive iterative implementation of mesh loss calculation.
      """
      edges_packed = meshes.edges_packed()
      verts_packed = meshes.verts_packed()
      edge_to_mesh = meshes.edges_packed_to_mesh_idx()
      N = len(meshes)
      device = meshes.device
      valid = meshes.valid
      predlosses = torch.zeros((N,), dtype=torch.float32, device=device)
  
      for b in range(N):
          if valid[b] == 0:
              continue
          mesh_edges = edges_packed[edge_to_mesh == b]
          verts_edges = verts_packed[mesh_edges]
          num_edges = mesh_edges.size(0)
          for e in range(num_edges):
              v0, v1 = verts_edges[e, 0], verts_edges[e, 1]
              predlosses[b] += (
                  (v0 - v1).norm(dim=0, p=2) - target_length
              ) ** 2.0
  
          if num_edges > 0:
              predlosses[b] = predlosses[b] / num_edges
  
      return predlosses.mean()
  
file_path: pytorch3d/tests/test_mesh_edge_loss.py
imports: import unittest
  import torch
  from pytorch3d.loss import mesh_edge_loss
  from pytorch3d.structures import Meshes
  from .common_testing import TestCaseMixin
  from .test_sample_points_from_meshes import init_meshes
label_desc: Use image node_id 2 for calling the TestMeshEdgeLoss obj's underlying member method code with example usage: obj.mesh_edge_loss_naive(meshes, target_length) and returns: predlosses