code: def evaluate(dataloader, model):
      model.eval()
      total_acc, total_count = 0, 0
  
      with torch.no_grad():
          for idx, (label, text, offsets) in enumerate(dataloader):
              predited_label = model(text, offsets)
              total_acc += (
                  (predited_label.argmax(1) == label).sum().item()
              )
              total_count += label.size(0)
      return total_acc / total_count
  
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 4 for calling a global function with example usage: evaluate(dataloader, model) and returns: unknown