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.
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docstr: Returns the state of the scheduler as a :class:`dict`.
  It contains an entry for every variable in self.__dict__ which
  is not the optimizer.
docstr_tok: ["Returns","the","state","of","the","scheduler","as","a",":","class",":","`","dict","`",".","It","contains","an","entry","for","every","variable","in","self.__dict__","which","is","not","the","optimizer","."]
code: def state_dict(self) -> Dict:
      """Returns the state of the scheduler as a :class:`dict`.
      It contains an entry for every variable in self.__dict__ which
      is not the optimizer.
  
      """
      return {
          key: value
          for key, value in self.__dict__.items()
          if key != "optimizer"
      }
  
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 2 for calling the _GradClipScheduler obj's underlying member method code with example usage: obj.state_dict() and returns: unknown