class_docstr: Multiplies the noise_multiplier by gamma every epoch (so the gamma factors accumulate).
  This means that:
      - For gamma < 1, noise_multiplier will shrink
      - For gamma == 1, no effect
      - For gamma > 1, noise_multiplier will expand
  
  When last_epoch=-1, sets initial noise_multiplier as noise_multiplier.
class_docstr_tok: ["Multiplies","the","noise_multiplier","by","gamma","every","epoch","(","so","the","gamma","factors","accumulate",")",".","This","means","that",":","-","For","gamma","<","1",",","noise_multiplier","will","shrink","-","For","gamma","==","1",",","no","effect","-","For","gamma",">","1",",","noise_multiplier","will","expand","When","last_epoch=-1",",","sets","initial","noise_multiplier","as","noise_multiplier","."]
code: def get_noise_multiplier(self):
      if self.last_epoch == 0:
          return self.optimizer.noise_multiplier
      else:
          return self.optimizer.noise_multiplier * self.gamma
  
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 ExponentialNoise obj's underlying member method code with example usage: obj.get_noise_multiplier() and returns: self, unknown