code: def model_training_evaluation(
      backend,
      train_dataloader,
      eval_dataloader,
      model,
      optimizer,
      num_epochs,
      evaluation,
  ):
      model.to(device)
      model.train()
      loss_history = []
      if not backend:
          # Run with native Pytorch
          opt_training_iter_fn = training_iter_fn
      else:
          # Support backends: eager, aot_eager, aot_nvfuser and inductor
          opt_training_iter_fn = torch._dynamo.optimize(backend)(
              training_iter_fn
          )
      for epoch in range(num_epochs):
          running_loss = 0.0
          for i, batch in enumerate(train_dataloader, 0):
              batch = {k: v.to(device) for k, v in batch.items()}
              loss = opt_training_iter_fn(batch, model, optimizer)
              running_loss += loss.item()
              if i % 100 == 99:
                  loss_history.append(running_loss / 100)
                  running_loss = 0.0
  
      if evaluation:
          metric = load_metric("accuracy")
          model.eval()
          if not backend:
              opt_model = model
          else:
              opt_model = torch._dynamo.optimize(backend)(model)
          for batch in eval_dataloader:
              batch = {k: v.to(device) for k, v in batch.items()}
              with torch.no_grad():
                  outputs = opt_model(**batch)
  
              logits = outputs.logits
              predictions = torch.argmax(logits, dim=-1)
              metric.add_batch(
                  predictions=predictions, references=batch["labels"]
              )
  
          return loss_history, metric.compute()
      else:
          return loss_history, None
  
file_path: pytorch/benchmarks/dynamo/training_loss.py
imports: import argparse
  import inspect
  import os
  import sys
  import time
  from datetime import timedelta
  import torch
  import torch._dynamo
  from datasets import load_dataset, load_metric
  from torch.utils.data import DataLoader
  from transformers import AutoModelForSequenceClassification, AutoTokenizer
label_desc: Use image node_id 3 for calling a global function with example usage: model_training_evaluation(backend, train_dataloader, eval_dataloader, model, optimizer, num_epochs, evaluation) and returns: loss_history,  metric, loss_history,  None