class_docstr: A helper class for exporting simple greedy-search models via TorchScript.
  
  Models with extra inputs will need to override to include more variables.
code: def forward(self, context: str, max_len: int = 128) -> str:
      # Vectorize all lines of context
      history_vecs: List[List[int]] = []
      context_lines = context.split("\n")
      context_lines = self.preprocess_context(context_lines)
      if self.history_size > 0:
          context_lines = context_lines[-self.history_size :]
      for line in context_lines:
          history_vecs.append(self.parse(line))
  
      # 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)
  
      # Format history vec given various logic
      if self.text_truncate is not None:
          if self.is_bart:
              truncate_length = (
                  self.text_truncate - 2
              )  # Start and end tokens
          else:
              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
      )
      if self.is_bart:
          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,
          )
  
      # Pass through the encoder and decoder to generate tokens
  
      flattened_text_vec = flattened_text_vec.to(self.get_device())
      batch_text_vec = torch.unsqueeze(
          flattened_text_vec, dim=0
      )  # Add batch dim
      encoder_states = self.encoder(batch_text_vec)
      generations = self._get_initial_decoder_input(batch_text_vec)
      # keep track of early stopping if all generations finish
      seen_end = torch.zeros(
          batch_text_vec.size(0),
          device=batch_text_vec.device,
          dtype=torch.bool,
      )
      incr_state: Dict[str, torch.Tensor] = {}
      for token_idx in range(max_len):
          if token_idx == 0:
              latent, incr_state = self.decoder_first_pass(
                  generations, encoder_states
              )
          else:
              latent, incr_state = self.decoder_later_pass(
                  generations, encoder_states, incr_state
              )
          logits = self.partially_traced_model.output(latent[:, -1:, :])
          _, preds = logits.max(dim=2)
          incr_state = self.partially_traced_model.reorder_decoder_incremental_state(
              incr_state,
              torch.tensor(
                  [0], dtype=torch.long, device=batch_text_vec.device
              ),
          )
          seen_end = seen_end + (preds == self.end_idx).squeeze(1)
          generations = torch.cat([generations, preds], dim=1)
          if torch.all(seen_end):
              break
  
      # Get the label from the generated tokens and update the history
      if self.is_bart:
          assert generations[0, 0].item() == self.end_idx
          generations = generations[:, 1:]
          # Hack: remove initial end token. I haven't found in the code where this is
          # done, but it seems to happen early on during generation
      generation_tokens: List[int] = generations[0].tolist()
      label = self._v2t(generation_tokens)
  
      return self.postprocess_output_generations(label=label)
  
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 6 for calling the TorchScriptGreedySearch obj's underlying member method code with example usage: obj.forward(context, max_len) and returns: self