code: def _tokenize(self, text):
      offsets = []
      start = 0
      for i, char in enumerate(text):
          if char == " ":
              offsets.append((start, i))
              start = i + 1
      if start < len(text):
          offsets.append((start, len(text)))
      words = [text[start:end] for start, end in offsets]
      type_ids = [0] + [1] * len(words) + [0]
      words = [self.start_symbol] + words + [self.end_symbol]
      offsets = [None] + offsets + [None]
      mask = [1] * len(words)
      return words, offsets, mask, type_ids
  
imports: from typing import Dict, List, Union
  import numpy
  import torch
  import copy
  from transformers.file_utils import ModelOutput
  from numpy.testing import assert_array_equal
  from spacy.tokens import Doc
  from thinc.api import Model, get_current_ops
  from ..data_classes import FullTransformerBatch, HFObjects
  from ..span_getters import get_doc_spans
  from ..layers.transformer_model import forward
label_desc: Use image node_id 6 for calling the DummyTokenizer obj's underlying member method code with example usage: obj._tokenize(text) and returns: words,  offsets,  mask,  type_ids