code: def _load_doc(self, doc, scheme):
      # preferentially load the MULTI_NER in case we are training /
      # testing a model with multiple layers of tags
      data = doc.get(
          [TEXT, NER, MULTI_NER], as_sentences=True, from_token=True
      )
      data = [
          [
              [token[0], token[2]]
              if token[2]
              else [token[0], (token[1],)]
              for token in sentence
          ]
          for sentence in data
      ]
      if self.preprocess_tags:  # preprocess tags
          if scheme is None:
              data = process_tags(data, self.args.get("scheme", "bio"))
          data = normalize_empty_tags(data)
      return data
  
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, sort_all
  from stanza.models.common.vocab import PAD_ID, VOCAB_PREFIX
  from stanza.models.pos.vocab import CharVocab, CompositeVocab, WordVocab
  from stanza.models.ner.vocab import MultiVocab
  from stanza.models.common.doc import *
  from stanza.models.ner.utils import process_tags, normalize_empty_tags
label_desc: Use image node_id 7 for calling the DataLoader obj's underlying member method code with example usage: obj._load_doc(doc, scheme) and returns: data