class_docstr: A model that accepts strings and returns a single representation (embedding) for each one of them.
code: def build_neural_module(
      self,
  ) -> Union[TokenUnitEmbedder, SubtokenUnitEmbedder]:
      if self.splitting_kind == "token":
          vocabulary_size = len(self.vocabulary)
          embedding_size = self.embedding_size
          return TokenUnitEmbedder(
              vocabulary_size, embedding_size, self.dropout_rate
          )
      elif self.splitting_kind in {"bpe", "subtoken"}:
          vocabulary_size = len(self.vocabulary)
          embedding_size = self.embedding_size
          return SubtokenUnitEmbedder(
              vocabulary_size,
              embedding_size,
              self.dropout_rate,
              self.subtoken_combination,
          )
      elif self.splitting_kind == "char":
          return CharUnitEmbedder(
              num_chars=self.vocabulary.num_chars_in_vocabulary(),
              embedding_size=self.embedding_size,
              config=self.cnn_config,
              dropout_rate=self.dropout_rate,
          )
      else:
          raise ValueError(
              f'Unrecognized token splitting method "{self.splitting_kind}"'
          )
  
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 7 for calling the StrElementRepresentationModel obj's underlying member method code with example usage: obj.build_neural_module() and returns: TokenUnitEmbedder, SubtokenUnitEmbedder, CharUnitEmbedder