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Solve the following problem using Python, implementing the functions described below, one line at a time
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Description:
 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)