class_docstr: A helper class for exporting transformer classifier via TorchScript.
code: def forward(self, context: str, max_len: int = 128) -> str:
      history_vecs: List[List[int]] = []
      context_lines = context.split("\n")
      for line in context_lines:
          history_vecs.append(self.parse(line))
      if self.history_size > 0:
          context_lines = context_lines[-self.history_size :]
      # Get full history vec
      text_vecs: List[List[int]] = []
      for vec in history_vecs[:-1]:
          text_vecs += [vec]
          text_vecs += [self.delimiter_tok]
      text_vecs += [history_vecs[-1]]
      if self.global_end_token is not None:
          text_vecs += [[self.global_end_token]]
      # Flatten text_vecs
      flattened_text_vec: List[int] = []
      for vec in text_vecs:
          for token in vec:
              flattened_text_vec.append(token)
  
      if self.text_truncate is not None:
          truncate_length = self.text_truncate
          if len(flattened_text_vec) > truncate_length:
              flattened_text_vec = flattened_text_vec[-truncate_length:]
      flattened_text_vec = torch.tensor(
          flattened_text_vec, dtype=torch.long
      )
  
      # Added start and end token idx
      flattened_text_vec = torch.cat(
          [
              torch.tensor([self.start_idx], dtype=torch.long).to(
                  self.get_device()
              ),
              flattened_text_vec.to(self.get_device()),
              torch.tensor([self.end_idx], dtype=torch.long).to(
                  self.get_device()
              ),
          ],
          dim=0,
      )
      flattened_text_vec = flattened_text_vec.to(self.get_device())
      batch_text_vec = torch.unsqueeze(flattened_text_vec, dim=0)
      scores = self.model(batch_text_vec)
      _, prediction_id = torch.max(scores, 1)
      preds = self.class_list[prediction_id.squeeze()]
  
      return preds
  
imports: from collections import defaultdict
  from typing import Dict, List, Optional, Tuple
  import torch.jit
  from parlai.agents.bart.bart import BartAgent
  from parlai.core.dict import DictionaryAgent
  from parlai.core.torch_agent import TorchAgent
  from parlai.utils.bpe import Gpt2BpeHelper, SubwordBPEHelper
  from parlai.torchscript.tokenizer import ScriptableDictionaryAgent
  from torch import nn
label_desc: Use image node_id 4 for calling the TorchScriptTransformerClassifier obj's underlying member method code with example usage: obj.forward(context, max_len) and returns: preds