code: def first_step(self, zero_grad=False):
      grad_norm = self._grad_norm()
      for group in self.param_groups:
          scale = self.rho / (grad_norm + 1e-12)
  
          for p in group["params"]:
              if p.grad is None:
                  continue
              e_w = p.grad * scale.to(p)
              p.add_(e_w)  # climb to the local maximum "w + e(w)"
              self.state[p]["e_w"] = e_w
  
      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 2 for calling the SAM obj's underlying member method code with example usage: obj.first_step(zero_grad) without return types