class_docstr: A model that accepts strings and returns a single representation (embedding) for each one of them.
code: def finalize_metadata(self) -> None:
      if self.splitting_kind in {"token", "subtoken"}:
          self.__vocabulary = Vocabulary.create_vocabulary(
              self.__tok_counter,
              max_size=self.max_vocabulary_size,
              count_threshold=self.min_freq_threshold,
          )
      elif self.splitting_kind == "bpe":
          self.__vocabulary = BpeVocabulary(self.max_vocabulary_size)
          self.__vocabulary.create_vocabulary(self.__tok_counter)
      elif self.splitting_kind == "char":
          self.__vocabulary = CharTensorizer(
              max_num_chars=self.max_num_chars,
              lower_case_all=False,
              include_space=False,
          )
      else:
          raise ValueError(
              f'Unrecognized token splitting method "{self.splitting_kind}"'
          )
  
      del self.__tok_counter
  
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 6 for calling the StrElementRepresentationModel obj's underlying member method code with example usage: obj.finalize_metadata() without return types