class_docstr: A wrapper class for APEX optimizer and it implements apex-specific backward and clip_grad_norm
  methods
docstr: Clip gradients by norm
  
  Args:
      model (torch.nn.Module): Your model object
      max_norm (float): The max norm value for gradient clipping
docstr_tok: ["Clip","gradients","by","norm","Args",":","model","(","torch.nn.Module",")",":","Your","model","object","max_norm","(","float",")",":","The","max","norm","value","for","gradient","clipping"]
code: def clip_grad_norm(self, model: nn.Module, max_norm: float):
      """Clip gradients by norm
  
      Args:
          model (torch.nn.Module): Your model object
          max_norm (float): The max norm value for gradient clipping
      """
      if max_norm > 0:
          clip_grad_norm_fp32(
              apex_amp.master_params(self.optim), max_norm
          )
  
file_path: colossalai/colossalai/legacy/amp/apex_amp/apex_amp.py
imports: import torch.nn
  from torch import Tensor
  from colossalai.interface import OptimizerWrapper
  from colossalai.legacy.utils import clip_grad_norm_fp32
label_desc: Use image node_id 2 for calling the ApexAMPOptimizer obj's underlying member method code with example usage: obj.clip_grad_norm(model, max_norm) without return types