class_docstr: A wrapper for Optimizer.
  Code reference: https://github.com/hpcaitech/ColossalAI/blob/main/colossalai/nn/optimizer/zero_optimizer.py
  
  Args:
      optimizer (Optimizer): An Optimizer instance.
      module (BaseOffloadModule): A ``BaseOffloadModule`` instance.
      initial_scale (float, optional): Initial scale used by DynamicGradScaler. Defaults to 2**16.
      growth_factor (float, optional): growth_factor used by DynamicGradScaler. Defaults to 2.
      backoff_factor (float, optional): backoff_factor used by DynamicGradScaler. Defaults to 0.5.
      growth_interval (float, optional): growth_interval used by DynamicGradScaler. Defaults to 1000.
      hysteresis (float, optional): hysteresis used by DynamicGradScaler. Defaults to 2.
      min_scale (float, optional): Min scale used by DynamicGradScaler. Defaults to 1.
      max_scale (int, optional): max_scale used by DynamicGradScaler. Defaults to 2**32.
      norm_type (float, optional): norm_type used for `clip_grad_norm`.
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code: def clip_grad_norm(
      self,
      model: torch.nn.Module,
      max_norm: float,
      norm_type: float = 2.0,
  ):
      raise NotImplementedError
  
imports: from enum import Enum
  from typing import Dict, Tuple
  import torch
  from torch.optim import Optimizer
  from colossalai.amp.naive_amp.grad_scaler import DynamicGradScaler
  from colossalai.interface import OptimizerWrapper
  from colossalai.logging import get_dist_logger
  from colossalai.utils import get_current_device
  from .base_offload_module import BaseOffloadModule
  from .region import Region
  from .region_manager import RegionManager
label_desc: Use image node_id 9 for calling the AMPOptimizer obj's underlying member method code with example usage: obj.clip_grad_norm(model, max_norm, norm_type) without return types