code: def flow_forward(flow_layers, x, feat):
      for i in range(len(flow_layers)):
          s, t = flow_layers[i](feat)
          s = s.exp()
          x = (x + t) * s
  
          if i == 0:
              x_log_jacob = (torch.abs(s) + 1e-20).log()
          else:
              x_log_jacob += (torch.abs(s) + 1e-20).log()
      return x, x_log_jacob
  
file_path: DIG/dig/ggraph3D/method/G_SphereNet/model/net_utils.py
imports: import torch
  import torch.nn
label_desc: Use image node_id 3 for calling a global function with example usage: flow_forward(flow_layers, x, feat) and returns: x,  x_log_jacob