text: import csv
  import os
  import sys
  
  import torch
  from torchtext.data import NestedField, Field, TabularDataset
  from torchtext.data.iterator import BucketIterator
  from torchtext.vocab import Vectors
  
  from datasets.robust45 import clean_string, split_sents, process_docids, process_labels
  
  csv.field_size_limit(sys.maxsize)
  
  
  class Robust04(TabularDataset):
      NAME = 'Robust04'
      NUM_CLASSES = 2
      TEXT_FIELD = Field(batch_first=True, tokenize=clean_string, include_lengths=True)
      LABEL_FIELD = Field(sequential=False, use_vocab=False, batch_first=True, preprocessing=process_labels)
      DOCID_FIELD = Field(sequential=False, use_vocab=False, batch_first=True, preprocessing=process_docids)
      TOPICS = ['307', '310', '321', '325', '330', '336', '341', '344', '345', '347', '350', '353', '354', '355', '356',
                '362', '363', '367', '372', '375', '378', '379', '389', '393', '394', '397', '399', '400', '404', '408',
                '414', '416', '419', '422', '423', '426', '427', '433', '435', '436', '439', '442', '443', '445', '614',
                '620', '626', '646', '677', '690']
  
      @staticmethod
      def sort_key(ex):
          return len(ex.text)
  
      @classmethod
      def splits(cls, path, train, validation, test, **kwargs):
          return super(Robust04, cls).splits(
              path, train=train, validation=validation, test=test,
              format='tsv', fields=[('label', cls.LABEL_FIELD), ('docid', cls.DOCID_FIELD), ('text', cls.TEXT_FIELD)]
          )
  
      @classmethod
      def iters(cls, path, vectors_name, vectors_cache, topic, batch_size=64, shuffle=True, device=0,
                vectors=None, unk_init=torch.Tensor.zero_):
          """
          :param path: directory containing train, test, dev files
          :param vectors_name: name of word vectors file
          :param vectors_cache: path to directory containing word vectors file
          :param topic: topic from which articles should be fetched
          :param batch_size: batch size
          :param device: GPU device
          :param vectors: custom vectors - either predefined torchtext vectors or your own custom Vector classes
          :param unk_init: function used to generate vector for OOV words
          :return:
          """
          if vectors is None:
              vectors = Vectors(name=vectors_name, cache=vectors_cache, unk_init=unk_init)
  
          train_path = os.path.join('TREC', 'robust04_train_%s.tsv' % topic)
          dev_path = os.path.join('TREC', 'robust04_dev_%s.tsv' % topic)
          test_path = os.path.join('TREC', 'core17_10k_%s.tsv' % topic)
          train, val, test = cls.splits(path, train=train_path, validation=dev_path, test=test_path)
          cls.TEXT_FIELD.build_vocab(train, val, test, vectors=vectors)
          return BucketIterator.splits((train, val, test), batch_size=batch_size, repeat=False, shuffle=shuffle,
                                       sort_within_batch=True, device=device)
  
  
  class Robust04Hierarchical(Robust04):
      NESTING_FIELD = Field(batch_first=True, tokenize=clean_string)
      TEXT_FIELD = NestedField(NESTING_FIELD, tokenize=split_sents)