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.sub_(
                  self.state[p]["e_w"]
              )  # get back to "w" from "w + e(w)"
  
      self.optim.step()  # do the actual "sharpness-aware" update
  
      if zero_grad:
          self.zero_grad()
  
file_path: bert-ultrafast/training/cramming/backend/optimizers/optimizer_modifiers.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