class_docstr: Multiplies `max_grad_norm` by `gamma` every `step_size` epochs (so the `gamma` factors accumulate).
  This means that:
      - For gamma < 1, max_grad_norm will shrink and you'll clip more
      - For gamma == 1, no effect
      - For gamma > 1, max_grad_norm will expand so you'll clip less
  
  When last_epoch=-1, sets initial max_grad_norm as max_grad_norm.
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code: def get_max_grad_norm(self):
      # Only change max_grad_norm when at a 'step'
      if self.last_epoch == 0 or self.last_epoch % self.step_size != 0:
          return self.optimizer.max_grad_norm
      else:
          return self.gamma * self.optimizer.max_grad_norm
  
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 StepGradClip obj's underlying member method code with example usage: obj.get_max_grad_norm() and returns: self, unknown