class_docstr: Base class for gradient clipping schedulers. We follow the same API
  as the standard PyTorch LR schedulers, but apply them to Opacus's
  `max_grad_norm` param instead.
  
  This means it only works when you pass a opacus.DPOptimizer, since that
  will have a `max_grad_norm` attribute.
class_docstr_tok: ["Base","class","for","gradient","clipping","schedulers",".","We","follow","the","same","API","as","the","standard","PyTorch","LR","schedulers",",","but","apply","them","to","Opacus's","`","max_grad_norm","`","param","instead",".","This","means","it","only","works","when","you","pass","a","opacus.DPOptimizer",",","since","that","will","have","a","`","max_grad_norm","`","attribute","."]
code: def step(self):
      self.last_epoch += 1
      max_grad_norm = self.get_max_grad_norm()
      self.optimizer.max_grad_norm = max_grad_norm
  
file_path: opacus/opacus/schedulers/grad_clip_scheduler.py
imports: from typing import Callable, Dict
  from opacus.optimizers import DPOptimizer
label_desc: Use image node_id 5 for calling the _GradClipScheduler obj's underlying member method code with example usage: obj.step() without return types