code: def train(dataloader, model, optimizer, criterion, epoch):
      model.train()
      total_acc, total_count = 0, 0
      log_interval = 500
      start_time = time.time()
  
      for idx, (label, text, offsets) in enumerate(dataloader):
          optimizer.zero_grad()
          predited_label = model(text, offsets)
          loss = criterion(predited_label, label)
          loss.backward()
          torch.nn.utils.clip_grad_norm_(model.parameters(), 0.1)
          optimizer.step()
          total_acc += (predited_label.argmax(1) == label).sum().item()
          total_count += label.size(0)
          if idx % log_interval == 0 and idx > 0:
              elapsed = time.time() - start_time
              print(
                  "| epoch {:3d} | {:5d}/{:5d} batches "
                  "| accuracy {:8.3f}".format(
                      epoch,
                      idx,
                      len(dataloader),
                      total_acc / total_count,
                  )
              )
              total_acc, total_count = 0, 0
              start_time = time.time()
  
file_path: serve/examples/text_classification/train.py
imports: import os
  import logging
  import argparse
  import time
  from typing import Text
  import torch
  import sys
  from torchtext.utils import download_from_url
  from model import TextSentiment
  from torch.utils.data import DataLoader
  from torch.utils.data.dataset import random_split
  from torchtext.data.functional import to_map_style_dataset
  from torchtext.datasets import DATASETS
  from torchtext.data.utils import get_tokenizer, ngrams_iterator
  from torchtext.vocab import build_vocab_from_iterator
label_desc: Use image node_id 3 for calling a global function with example usage: train(dataloader, model, optimizer, criterion, epoch) without return types