code: def flow_reverse(flow_layers, latent, feat):
      for i in reversed(range(len(flow_layers))):
          s, t = flow_layers[i](feat)
          s = s.exp()
          latent = (latent / s) - t
      return latent
  
file_path: DIG/dig/ggraph3D/method/G_SphereNet/model/net_utils.py
imports: import torch
  import torch.nn
label_desc: Use image node_id 2 for calling a global function with example usage: flow_reverse(flow_layers, latent, feat) and returns: latent