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 _encode_texts(self, texts: list[str]):
      return self.tokenizer.batch_encode_plus(
          texts,
          return_tensors="tf",
          padding="longest",
          truncation="longest_first",
      )
  
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 T5HFModel obj's underlying member method code with example usage: obj._encode_texts(texts) and returns: self