class_docstr: T5 translation model.
  
  TranslationWrapper class has input_specs compatible with the corresponding
  dataset, but its init args are not supported by the front-end system, thus
  we set up a layer of init args on top to work with the front-end.
code: def __init__(
      self,
      model_name_or_path=_DEFAULT_MODEL_PATH,
      model=None,
      tokenizer=None,
      **config_kw,
  ):
      model = T5HFModel(
          model_name_or_path, model, tokenizer, **config_kw
      )
      super().__init__(model)
  
imports: import re
  import attr
  from lit_nlp.api import model
  from lit_nlp.api import types
  from lit_nlp.examples.models import model_utils
  from lit_nlp.lib import file_cache
  from lit_nlp.lib import utils
  import tensorflow
  import tensorflow_text
  import transformers
  from rouge_score import rouge_scorer
label_desc: Use image node_id 1 to create a new T5Translation object from inherited base classes: TranslationWrapper with example: obj = T5Translation(model_name_or_path, model, tokenizer)