code: def _create_data_from_iterator(vocab, iterator, include_unk):
      data = []
      labels = []
      with tqdm(unit_scale=0, unit="lines") as t:
          for cls, tokens in iterator:
              if include_unk:
                  tokens = torch.tensor(
                      [vocab[token] for token in tokens]
                  )
              else:
                  token_ids = list(
                      filter(
                          lambda x: x is not Vocab.UNK,
                          [vocab[token] for token in tokens],
                      )
                  )
                  tokens = torch.tensor(token_ids)
              if len(tokens) == 0:
                  logging.info("Row contains no tokens.")
              data.append((cls, tokens))
              labels.append(cls)
              t.update(1)
      return data, set(labels)
  
imports: import csv
  import io
  import logging
  import torch
  from torchtext.data.utils import get_tokenizer, ngrams_iterator
  from torchtext.utils import download_from_url, extract_archive
  from tqdm import tqdm
  from ..vocab import build_vocab_from_iterator, Vocab
label_desc: Use image node_id 2 for calling a global function with example usage: _create_data_from_iterator(vocab, iterator, include_unk) and returns: data,  set