code: def step(self, closure=None):
      assert (
          closure is not None
      ), "Sharpness Aware Minimization requires closure, but it was not provided"
      closure = torch.enable_grad()(
          closure
      )  # the closure should do a full forward-backward pass
  
      closure()
      self.first_step(zero_grad=True)
      loss = closure()
      self.second_step()
      return loss
  
file_path: bert-ultrafast/training/cramming/backend/optimizers/optimizer_modifiers.py
imports: import torch
label_desc: Use image node_id 4 for calling the SAM obj's underlying member method code with example usage: obj.step(closure) and returns: loss