class_docstr: Sharpness-Aware Minimization that simultaneously minimizes loss value and loss sharpness.
  In particular, it seeks parameters that lie in neighborhoods having uniformly low loss.
code: def second_step(self, zero_grad=False):
      for group in self.param_groups:
          for p in group["params"]:
              if p.grad is None:
                  continue
              p.data = self.state[p][
                  "old_p"
              ]  # get back to "w" from "w + e(w)"
  
      self.base_optimizer.step()  # do the actual "sharpness-aware" update
  
      if zero_grad:
          self.zero_grad()
  
file_path: towhee/towhee/models/layers/sam.py
imports: import torch
label_desc: Use image node_id 3 for calling the SAM obj's underlying member method code with example usage: obj.second_step(zero_grad) without return types