docstr: Construct a Jieba-based tokenizer by loading the Jieba pipeline.
  
  Note that this tokenizer uses regex for sentence segmentation.
docstr_tok: ["Construct","a","Jieba-based","tokenizer","by","loading","the","Jieba","pipeline",".","Note","that","this","tokenizer","uses","regex","for","sentence","segmentation","."]
code: def __init__(self, config):
      """Construct a Jieba-based tokenizer by loading the Jieba pipeline.
  
      Note that this tokenizer uses regex for sentence segmentation.
      """
      if config["lang"] not in ["zh", "zh-hans", "zh-hant"]:
          raise Exception(
              "Jieba tokenizer is currently only allowed in Chinese (simplified or traditional) pipelines."
          )
  
      check_jieba()
      import jieba
  
      self.nlp = jieba
      self.no_ssplit = config.get("no_ssplit", False)
  
file_path: stanza/stanza/pipeline/external/jieba.py
imports: import re
  from stanza.models.common import doc
  from stanza.pipeline._constants import TOKENIZE
  from stanza.pipeline.processor import ProcessorVariant, register_processor_variant
label_desc: Use image node_id 1 to create a new JiebaTokenizer object from inherited base classes: ProcessorVariant with example: obj = JiebaTokenizer(config)