code: def clip_grad_norm(self, clip_grad, check_for_nan_in_grad):
      params = self.get_parameters()
      grads_for_norm = self.get_main_grads_for_grad_norm()
      return clip_grad_norm_fp32(
          params,
          grads_for_norm,
          clip_grad,
          check_for_nan_in_grad,
          model_parallel_group=self.get_model_parallel_group(),
      )
  
imports: from abc import ABC
  from abc import abstractmethod
  from apex.multi_tensor_apply import multi_tensor_applier
  import amp_C
  import torch
  from megatron import get_timers
  from megatron import print_rank_0
  from megatron.core import mpu, tensor_parallel
  from megatron.model import Float16Module
  from megatron.model.module import param_is_not_shared
  from .clip_grads import clip_grad_norm_fp32, count_zeros_fp32
label_desc: Use image node_id 5 for calling the MegatronOptimizer obj's underlying member method code with example usage: obj.clip_grad_norm(clip_grad, check_for_nan_in_grad) and returns: clip_grad_norm_fp32