class_docstr: A simple regex-splitting tokenizer.
class_docstr_tok: ["A","simple","regex-splitting","tokenizer","."]
code: def __init__(
      self, split_regex=r"\s+", lowercase=True, use_byte_offsets=False
  ):
      super().__init__(None)
      self.split_regex = split_regex
      self.lowercase = lowercase
      self.use_byte_offsets = use_byte_offsets
  
imports: import copy
  import json
  import re
  from collections import OrderedDict
  from typing import List, NamedTuple, Optional, Union
  import torch
  from fairseq.data.encoders.gpt2_bpe import get_encoder
  from fairseq.data.encoders.gpt2_bpe_utils import Encoder
  from pytext.config import ConfigBase
  from pytext.config.component import Component, ComponentType, create_component
  from pytext.data.pickleable_gpt2bpe_encoder import PickleableGPT2BPEEncoder
  from pytext.torchscript.tokenizer import ScriptDoNothingTokenizer, ScriptWordTokenizer
  from pytext.utils.file_io import PathManager
  from pytext.utils.usage import log_class_usage
  from transformers.tokenization_bert import BasicTokenizer, WordpieceTokenizer
label_desc: Use image node_id 2 to create a new Tokenizer object from inherited base classes: Component with example: obj = Tokenizer(split_regex, lowercase, use_byte_offsets)