code: def preprocess(self, data, vocab, pretrain_vocab, args):
      processed = []
      xpos_replacement = (
          [[ROOT_ID] * len(vocab["xpos"])]
          if isinstance(vocab["xpos"], CompositeVocab)
          else [ROOT_ID]
      )
      feats_replacement = [[ROOT_ID] * len(vocab["feats"])]
      for sent in data:
          processed_sent = [
              [ROOT_ID] + vocab["word"].map([w[0] for w in sent])
          ]
          processed_sent += [
              [[ROOT_ID]]
              + [vocab["char"].map([x for x in w[0]]) for w in sent]
          ]
          processed_sent += [
              [ROOT_ID] + vocab["upos"].map([w[1] for w in sent])
          ]
          processed_sent += [
              xpos_replacement + vocab["xpos"].map([w[2] for w in sent])
          ]
          processed_sent += [
              feats_replacement
              + vocab["feats"].map([w[3] for w in sent])
          ]
          if pretrain_vocab is not None:
              # always use lowercase lookup in pretrained vocab
              processed_sent += [
                  [ROOT_ID]
                  + pretrain_vocab.map([w[0].lower() for w in sent])
              ]
          else:
              processed_sent += [[ROOT_ID] + [PAD_ID] * len(sent)]
          processed_sent += [
              [ROOT_ID] + vocab["lemma"].map([w[4] for w in sent])
          ]
          processed_sent += [
              [to_int(w[5], ignore_error=self.eval) for w in sent]
          ]
          processed_sent += [vocab["deprel"].map([w[6] for w in sent])]
          processed_sent.append([w[0] for w in sent])
          processed.append(processed_sent)
      return processed
  
imports: import random
  import logging
  import torch
  from stanza.models.common.bert_embedding import filter_data
  from stanza.models.common.data import map_to_ids, get_long_tensor, get_float_tensor, sort_all
  from stanza.models.common.vocab import PAD_ID, VOCAB_PREFIX, ROOT_ID, CompositeVocab, CharVocab
  from stanza.models.pos.vocab import WordVocab, XPOSVocab, FeatureVocab, MultiVocab
  from stanza.models.pos.xpos_vocab_factory import xpos_vocab_factory
  from stanza.models.common.doc import *
label_desc: Use image node_id 3 for calling the DataLoader obj's underlying member method code with example usage: obj.preprocess(data, vocab, pretrain_vocab, args) and returns: processed