code: def ag_news(c: NLPClassificationConfigs):
      """
      ### AG News dataset
  
      This loads the AG News dataset and the set the values for
       `n_classes`, `vocab`, `train_loader`, and `valid_loader`.
      """
  
      # Get training and validation datasets
      train, valid = torchtext.datasets.AG_NEWS(
          root=str(lab.get_data_path() / "ag_news"),
          split=("train", "test"),
      )
  
      # Load data to memory
      with monit.section("Load data"):
          from labml_nn.utils import MapStyleDataset
  
          # Create [map-style datasets](../utils.html#map_style_dataset)
          train, valid = MapStyleDataset(train), MapStyleDataset(valid)
  
      # Get tokenizer
      tokenizer = c.tokenizer
  
      # Create a counter
      counter = Counter()
      # Collect tokens from training dataset
      for label, line in train:
          counter.update(tokenizer(line))
      # Collect tokens from validation dataset
      for label, line in valid:
          counter.update(tokenizer(line))
      # Create vocabulary
      vocab = torchtext.vocab.vocab(counter, min_freq=1)
  
      # Create training data loader
      train_loader = DataLoader(
          train,
          batch_size=c.batch_size,
          shuffle=True,
          collate_fn=CollateFunc(
              tokenizer, vocab, c.seq_len, len(vocab), len(vocab) + 1
          ),
      )
      # Create validation data loader
      valid_loader = DataLoader(
          valid,
          batch_size=c.batch_size,
          shuffle=True,
          collate_fn=CollateFunc(
              tokenizer, vocab, c.seq_len, len(vocab), len(vocab) + 1
          ),
      )
  
      # Return `n_classes`, `vocab`, `train_loader`, and `valid_loader`
      return 4, vocab, train_loader, valid_loader
  
imports: from collections import Counter
  from typing import Callable
  import torch
  import torchtext
  from torch import nn
  from torch.utils.data import DataLoader
  import torchtext.vocab
  from torchtext.vocab import Vocab
  from labml import lab, tracker, monit
  from labml.configs import option
  from labml_helpers.device import DeviceConfigs
  from labml_helpers.metrics.accuracy import Accuracy
  from labml_helpers.module import Module
  from labml_helpers.train_valid import TrainValidConfigs, hook_model_outputs, BatchIndex
  from labml_nn.optimizers.configs import OptimizerConfigs
label_desc: Use image node_id 6 for calling a global function with example usage: ag_news(c) and returns: int,  vocab,  train_loader,  valid_loader