class_docstr: A model that accepts strings and returns a single representation (embedding) for each one of them.
code: def update_metadata_from(self, datapoint: str) -> None:
      if self.splitting_kind in {"token", "bpe"}:
          self.__tok_counter[datapoint] += 1
      elif self.splitting_kind == "subtoken":
          self.__tok_counter.update(
              split_identifier_into_parts(datapoint)
          )
      elif self.splitting_kind == "char":
          pass
      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 5 for calling the StrElementRepresentationModel obj's underlying member method code with example usage: obj.update_metadata_from(datapoint) without return types