code: def benchmark_training(
      model, num_epochs: int, train_dataloader, device
  ):
      optimizer = torch.optim.AdamW(model.parameters(), lr=5e-5)
      num_training_steps = num_epochs * len(train_dataloader)
      lr_scheduler = get_scheduler(
          "linear",
          optimizer=optimizer,
          num_warmup_steps=0,
          num_training_steps=num_training_steps,
      )
  
      progress_bar = tqdm(range(num_training_steps))
  
      model.train()
      # warmup
      for _ in range(5):
          batch = next(iter(train_dataloader))
          batch = {k: v.to(device) for k, v in batch.items()}
          outputs = model(**batch)
          loss = outputs.logits.sum()
          loss.backward()
  
          optimizer.step()
          lr_scheduler.step()
          optimizer.zero_grad()
  
      start_event = torch.cuda.Event(enable_timing=True)
      end_event = torch.cuda.Event(enable_timing=True)
      start_event.record()
      for _ in range(num_epochs):
          for _, batch in enumerate(train_dataloader):
              batch = {k: v.to(device) for k, v in batch.items()}
              outputs = model(**batch)
              loss = outputs.logits.sum()
              loss.backward()
  
              optimizer.step()
              lr_scheduler.step()
              optimizer.zero_grad()
              progress_bar.update(1)
      end_event.record()
      torch.cuda.synchronize()
  
      return (start_event.elapsed_time(end_event) * 1.0e-3) / num_epochs
  
file_path: optimum/tests/benchmark/benchmark_bettertransformer_training.py
imports: import argparse
  import random
  import numpy
  import torch
  from datasets import load_dataset
  from torch.utils.data import DataLoader
  from tqdm.auto import tqdm
  from transformers import AutoModelForCausalLM, AutoTokenizer, get_scheduler
  from optimum.bettertransformer import BetterTransformer
label_desc: Use image node_id 3 for calling a global function with example usage: benchmark_training(model, num_epochs, train_dataloader, device) and returns: unknown