class_docstr: A model that accepts strings and returns a single representation (embedding) for each one of them.
code: def finalize_minibatch(
      self,
      accumulated_minibatch_data: Dict[str, Any],
      device: Union[str, torch.device],
  ) -> Dict[str, Any]:
      if self.splitting_kind == "token":
          return {
              "token_idxs": torch.tensor(
                  accumulated_minibatch_data["token_idxs"],
                  dtype=torch.int64,
                  device=device,
              ),
          }
      elif self.splitting_kind in {"subtoken", "bpe"}:
          max_num_subtokens = max(
              len(t) for t in accumulated_minibatch_data["token_idxs"]
          )
          if self.max_num_subtokens is not None:
              max_num_subtokens = min(
                  max_num_subtokens, self.max_num_subtokens
              )
  
          subtoken_idxs = np.zeros(
              (
                  len(accumulated_minibatch_data["token_idxs"]),
                  max_num_subtokens,
              ),
              dtype=np.int32,
          )
          lengths = np.empty(
              len(accumulated_minibatch_data["token_idxs"]),
              dtype=np.int32,
          )
          for i, subtokens in enumerate(
              accumulated_minibatch_data["token_idxs"]
          ):
              idxs = subtokens[:max_num_subtokens]
              subtoken_idxs[i, : len(idxs)] = idxs
              lengths[i] = len(idxs)
  
          return {
              "token_idxs": torch.tensor(
                  subtoken_idxs, dtype=torch.int64, device=device
              ),
              "lengths": torch.tensor(
                  lengths, dtype=torch.int64, device=device
              ),
          }
      elif self.splitting_kind == "char":
          return {
              "chars": torch.tensor(
                  np.stack(
                      accumulated_minibatch_data["token_idxs"], axis=0
                  ),
                  dtype=torch.int64,
                  device=device,
              )
          }
      else:
          raise Exception("Non-reachable state.")
  
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 12 for calling the StrElementRepresentationModel obj's underlying member method code with example usage: obj.finalize_minibatch(accumulated_minibatch_data, device) and returns: dict, dict, dict