class_docstr: Base class for noise multiplier schedulers. We follow the same API
  as the standard PyTorch LR schedulers, but apply them to Opacus's noise
  multiplier param instead.
  
  This means it only works when you pass a opacus.DPOptimizer, since that
  will have a `noise_multiplier` attribute.
class_docstr_tok: ["Base","class","for","noise","multiplier","schedulers",".","We","follow","the","same","API","as","the","standard","PyTorch","LR","schedulers",",","but","apply","them","to","Opacus","'s","noise","multiplier","param","instead",".","This","means","it","only","works","when","you","pass","a","opacus.DPOptimizer",",","since","that","will","have","a","`","noise_multiplier","`","attribute","."]
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/noise_scheduler.py
imports: from typing import Callable, Dict
  from opacus.optimizers import DPOptimizer
label_desc: Use image node_id 2 for calling the _NoiseScheduler obj's underlying member method code with example usage: obj.state_dict() and returns: unknown