class_docstr: A helper class for exporting transformer classifier via TorchScript.
code: def __init__(self, agent: TorchAgent):
      super().__init__()
      self.device = agent.model.transformer.embeddings.weight.device
      self.start_idx = agent.START_IDX
      self.end_idx = agent.END_IDX
  
      orig_dict: DictionaryAgent = agent.dict
      orig_bpe: SubwordBPEHelper = orig_dict.bpe
  
      assert all(len(key) == 2 for key in orig_bpe.bpe.bpe_codes.keys())
      if any(
          i
          for key in orig_bpe.bpe.bpe_codes.keys()
          for i in key
          if "\n" in i
      ):
          raise AssertionError(
              "We need to temporarily merge the bpe_codes dict's keys with a newline "
              "character in order to use it as a TorchScript arg, but at least one of the "
              "dict's keys contains a newline character already!"
          )
      fused_key_bpe_codes = {
          "\n".join(key): float(val)
          for key, val in orig_bpe.bpe.bpe_codes.items()
      }
  
      # Initialize a subword Bpe tokenizer
      self.dict = ScriptableDictionaryAgent(
          null_token=orig_dict.null_token,
          end_token=orig_dict.end_token,
          unk_token=orig_dict.unk_token,
          start_token=orig_dict.start_token,
          freq=orig_dict.freq,
          tok2ind=orig_dict.tok2ind,
          ind2tok=orig_dict.ind2tok,
          bpe_add_prefix_space=agent.opt["bpe_add_prefix_space"],
          bpe_encoder={},
          bpe_byte_encoder={},
          fused_key_bpe_ranks={},
          special_tokens=agent._get_special_tokens(),
          subword_bpe_version=orig_bpe.bpe.version,
          fused_bpe_codes=fused_key_bpe_codes,
          subword_bpe_separator=orig_bpe.bpe.separator,
      )
  
      self.delimiter_tok = agent.history.delimiter_tok
      self.history_size = agent.opt["history_size"]
      if (
          agent.opt.get("history_add_global_end_token", None)
          is not None
      ):
          self.global_end_token = agent.dict[agent.dict.end_token]
      else:
          self.global_end_token = None
      self.text_truncate = (
          agent.opt.get("text_truncate") or agent.opt["truncate"]
      )
      self.text_truncate = (
          self.text_truncate if self.text_truncate >= 0 else None
      )
      self.class_list = agent.class_list
  
      agent.model.eval()
      # Create sample inputs for tracing
      sample_tokens = torch.tensor(
          [[1, 2, 3, 4, 5]], dtype=torch.long, device=self.device
      )
      scores = agent.model(sample_tokens)
      _, prediction = torch.max(scores, 1)
  
      # Do tracing
      self.model = torch.jit.trace(agent.model, sample_tokens)
  
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 1 to create a new TorchScriptTransformerClassifier object from inherited base classes: nn with example: obj = TorchScriptTransformerClassifier(agent)