class_docstr: Cycle Loss for Shapes.
  Inspired by:
  "Mapping in a Cycle: Sinkhorn Regularized Unsupervised Learning for Point Cloud Shapes".
code: def fake_value(self, embedder: nn.Module):
      losses = []
      for mesh_name in embedder.mesh_names:
          losses.append(embedder(mesh_name).sum() * 0)
      return torch.mean(torch.stack(losses))
  
file_path: detectron2/projects/DensePose/densepose/modeling/losses/cycle_shape2shape.py
imports: import random
  from typing import Tuple
  import torch
  from torch import nn
  from torch.nn import functional
  from detectron2.config import CfgNode
  from densepose.structures.mesh import create_mesh
  from .utils import sample_random_indices
label_desc: Use image node_id 4 for calling the ShapeToShapeCycleLoss obj's underlying member method code with example usage: obj.fake_value(embedder) and returns: torch