class_docstr: Multiplies your *base* `noise_multiplier` by the output of a `scheduler_function` given
  as input.
  Note: the base noise_multiplier is recorded as the noise_multiplier your optimizer
  had set at the very beginning. This means that the factors from the `scheduler_function`
  will *not* accumulate, unlike in ExponentialGradClip.
  If you want some exponential-like behavior, accumulation logic will have to be
  added in your `scheduler_function`.
  
  When last_epoch=-1, sets initial noise_multiplier as noise_multiplier.
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code: def get_noise_multiplier(self):
      return self.base_noise_multiplier * self.noise_lambda(
          self.last_epoch
      )
  
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 LambdaNoise obj's underlying member method code with example usage: obj.get_noise_multiplier() and returns: unknown