code: def compute_tflops(elapsed_time, accelerator, args):
      # TFLOPs formula (from Equation 3 in Section 5.1 of https://arxiv.org/pdf/2104.04473.pdf).
      config_model = accelerator.unwrap_model(model).config
      checkpoint_factor = 4 if args.gradient_checkpointing else 3
      batch_size = (
          args.train_batch_size
          * accelerator.state.num_processes
          * args.gradient_accumulation_steps
      )
      factor = (
          24
          * checkpoint_factor
          * batch_size
          * args.seq_length
          * config_model.n_layer
          * (config_model.n_embd**2)
      )
      flops_per_iteration = factor * (
          1.0
          + (args.seq_length / (6.0 * config_model.n_embd))
          + (
              tokenizer.vocab_size
              / (16.0 * config_model.n_layer * config_model.n_embd)
          )
      )
      tflops = flops_per_iteration / (
          elapsed_time * accelerator.state.num_processes * (10**12)
      )
      return tflops
  
imports: import logging
  import os
  import time
  from argparse import Namespace
  from pathlib import Path
  import datasets
  import torch
  from datasets import load_dataset
  from torch.optim import AdamW
  from torch.utils.data import IterableDataset
  from torch.utils.data.dataloader import DataLoader
  from torch.utils.data.datapipes.iter.combinatorics import ShufflerIterDataPipe
  import transformers
  from accelerate import Accelerator, DistributedType
  from arguments import TrainingArguments
  from huggingface_hub import Repository
  from transformers import AutoModelForCausalLM, AutoTokenizer, HfArgumentParser, get_scheduler, set_seed
label_desc: Use image node_id 5 for calling a global function with example usage: compute_tflops(elapsed_time, accelerator, args) and returns: tflops