class_docstr: A model that accepts strings and returns a single representation (embedding) for each one of them.
code: def tensorize(self, datapoint: str, return_str_rep: bool = False):
      if self.splitting_kind == "token":
          token_idxs = self.vocabulary.get_id_or_unk(datapoint)
          str_repr = datapoint
      elif self.splitting_kind == "subtoken":
          subtoks = split_identifier_into_parts(datapoint)
          if len(subtoks) == 0:
              subtoks = [Vocabulary.get_unk()]
          token_idxs = self.vocabulary.get_id_or_unk_multiple(subtoks)
      elif self.splitting_kind == "bpe":
          if len(datapoint) == 0:
              datapoint = "<empty>"
          token_idxs = self.vocabulary.get_id_or_unk_for_text(datapoint)
          if return_str_rep:  # Do _not_ compute for efficiency
              str_repr = self.vocabulary.tokenize(datapoint)
      elif self.splitting_kind == "char":
          token_idxs = self.vocabulary.tensorize_str(datapoint)
          if return_str_rep:
              str_repr = datapoint[: self.vocabulary.max_char_length]
      else:
          raise ValueError(
              f'Unrecognized token splitting method "{self.splitting_kind}".'
          )
  
      if return_str_rep:
          return token_idxs, str_repr
      return token_idxs
  
imports: from typing_extensions import Final, Literal
  import logging
  import math
  import numpy
  import torch
  import torch.nn
  from dpu_utils.codeutils import split_identifier_into_parts
  from dpu_utils.mlutils import BpeVocabulary, CharTensorizer, Vocabulary
  from typing import Any, Counter, Dict, List, NamedTuple, Optional, Union
  from ptgnn.baseneuralmodel import AbstractNeuralModel
  from ptgnn.neuralmodels.gnn.structs import AbstractNodeEmbedder
label_desc: Use image node_id 9 for calling the StrElementRepresentationModel obj's underlying member method code with example usage: obj.tensorize(datapoint, return_str_rep) and returns: token_idxs, token_idxs,  str_repr