class_docstr: T5 from a TensorFlow SavedModel, for black-box access.
  
  To create a SavedModel from a regular T5 checkpoint, see
  https://github.com/google-research/text-to-text-transfer-transformer#export
code: def __init__(self, saved_model_path: str, model=None, **config_kw):
      super().__init__()
      # By default, SavedModels from the original T5 codebase have batch_size=1
      # hardcoded. Use setdefault here so that the user can still override if
      # they've fixed this upstream.
      config_kw.setdefault("inference_batch_size", 1)
      self.config = T5ModelConfig(**config_kw)
      self.model = model or tf.saved_model.load(saved_model_path)
  
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 T5SavedModel object from inherited base classes: lit_model with example: obj = T5SavedModel(saved_model_path, model)