docs: def encode_dense(input_sentences, nbest_size=0, alpha=1.0,
                   model_file=None, model_proto=None,
                   reverse=False, add_bos=False, add_eos=False,
                   out_type=tf.int32, name=None):
    """Encodes sentences into pieces in dense tensor format.
  
    Args:
      input_sentences: A 1D string tensor of arbitrary size holding the raw
                       text of input sentences.
      nbest_size: A scalar or 1D tensor for sampling.
                  nbest_size = {0,1}: No sampling is performed.
                  nbest_size > 1: samples from the nbest_size results.
                  nbest_size < 0: assuming that nbest_size is infinite
                  and samples from the all hypothesis (lattice) using
                  forward-filtering-and-backward-sampling algorithm.
      alpha: A scalar or 1D tensor for a smoothing parameter.
             Inverse temperature for probability rescaling.
      model_file: The sentencepiece model file path.
      model_proto: The sentencepiece model serialized proto.
                   Either `model_file` or `model_proto` must be set.
      reverse: Reverses the tokenized sequence (Default = false)
      add_bos: Add <s> to the result (Default = false)
      add_eos: Add </s> to the result (Default = false)
               <s>/</s> is added after reversing (if enabled).
      out_type: output type. tf.int32 or tf.string (Default = tf.int32)
                Setting tf.int32 directly encodes the string into an id sequence.
      name: The name argument that is passed to the op function.
    Returns:
      pieces: A dense 2D tensor representing the tokenized sentences.
      sequence_length: A 1D tensor representing the length of pieces.
    """
  
    return _gen_sentencepiece_processor_op.sentencepiece_encode_dense(
        input_sentences, nbest_size=nbest_size, alpha=alpha,
        model_file=model_file, model_proto=model_proto,
        reverse=reverse, add_bos=add_bos, add_eos=add_eos,
        out_type=out_type, name=name)