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","."]
docstr: Implement your scheduling logic here and return the new value for `max_grad_norm`.
docstr_tok: ["Implement","your","scheduling","logic","here","and","return","the","new","value","for","`","max_grad_norm","`","."]
code: def get_max_grad_norm(self):
      """Implement your scheduling logic here and return the new value for `max_grad_norm`."""
      raise NotImplementedError
  
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 4 for calling the _GradClipScheduler obj's underlying member method code with example usage: obj.get_max_grad_norm() without return types