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
docstr: Predict on a single minibatch of examples.
docstr_tok: ["Predict","on","a","single","minibatch","of","examples","."]
code: def predict_minibatch(self, inputs):
      """Predict on a single minibatch of examples."""
      model_inputs = tf.constant([ex["input_text"] for ex in inputs])
      model_outputs = self.model.signatures["serving_default"](
          model_inputs
      )
      return [
          {"output_text": m.decode("utf-8")}
          for m in model_outputs["outputs"].numpy()
      ]
  
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 3 for calling the T5SavedModel obj's underlying member method code with example usage: obj.predict_minibatch(inputs) and returns: unknown