code: def get_grad_norm(params, scale=1):
      """Compute grad norm given a gradient scale."""
      total_norm = 0.0
      for p in params:
          if p.grad is not None:
              param_norm = (p.grad.detach().data / scale).norm(2)
              total_norm += param_norm.item() ** 2
      total_norm = total_norm**0.5
      return total_norm
  
imports: import argparse
  import math
  import os
  from dataclasses import dataclass
  from pathlib import Path
  from typing import Dict, List, Optional, Union
  import datasets
  import torch
  from datasets import DatasetDict, concatenate_datasets, load_dataset
  from torch.utils.data.dataloader import DataLoader
  from tqdm.auto import tqdm
  import transformers
  from accelerate import Accelerator
  from accelerate.logging import get_logger
  from huggingface_hub import Repository
  from transformers import AdamW, SchedulerType, Wav2Vec2Config, Wav2Vec2FeatureExtractor, Wav2Vec2ForPreTraining, get_scheduler, is_wandb_available, set_seed
  from transformers.models.wav2vec2.modeling_wav2vec2 import _compute_mask_indices, _sample_negative_indices
  from transformers.utils import get_full_repo_name, send_example_telemetry
label_desc: Use image node_id 3 for calling a global function with example usage: get_grad_norm(params, scale) and returns: total_norm