class_docstr: T5 summarization model.
  
  SummarizationWrapper 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.
class_docstr_tok: ["T5","summarization","model",".","SummarizationWrapper","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="t5-small",
      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 T5Summarization object from inherited base classes: SummarizationWrapper with example: obj = T5Summarization(model_name_or_path, model, tokenizer)