I am following this tutorial for the transformer model:

  
      

      TensorFlow
  

  
    

Transformer model for language understanding  |  Text  | ... 4



  

  
    
    
  

  


I want to try Chinese to English translation, so I had this configuration:
        config = tfds.translate.wmt.WmtConfig(
            description="WMT 2019 translation task dataset.",
            version="0.0.3",
            language_pair=("zh", "en"),
            subsets={
                tfds.Split.TRAIN: ["newscommentary_v13"],
                tfds.Split.VALIDATION: ["newsdev2017"],
            }
        )

builder = tfds.builder("wmt_translate", config=config)

In the pt-en translation tutorial, it seems it has already had a corresponding tokenizer model that can be downloaded:
model_name = "ted_hrlr_translate_pt_en_converter"tf.keras.utils.get_file(    f"{model_name}.zip",    f"https://storage.googleapis.com/download.tensorflow.org/models/{model_name}.zip",    cache_dir='.', cache_subdir='', extract=True)

Is there a tokenizer similar to ted_hrlr_translate_pt_en_converter that can be used directly for Chinese to English translation? Because I want to reuse the code in the tutorial like the following:
def tokenize_pairs(pt, en):
    
    pt = tokenizers.pt.tokenize(pt)
    # Convert from ragged to dense, padding with zeros.
    pt = pt.to_tensor()

    en = tokenizers.en.tokenize(en)
    # Convert from ragged to dense, padding with zeros.
    en = en.to_tensor()
    return pt, en