class_docstr: T5 using HuggingFace Transformers and Keras.
  
  This version supports embeddings, attention, and force-decoding of the target
  text, as well as more options to control decoding (such as beam search).
code: def init_spec(cls) -> lit_types.Spec:
      return {
          "model_name": lit_types.String(
              default=_DEFAULT_MODEL_PATH, required=False
          ),
          **T5ModelConfig.init_spec(),
      }
  
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 8 for calling the T5HFModel obj's underlying member method code with example usage: obj.init_spec(cls) and returns: dict