class_docstr: Stacked model that generates first the knowledge, and then the dialogue response.
class_docstr_tok: ["Stacked","model","that","generates","first","the","knowledge",",","and","then","the","dialogue","response","."]
code: def _agent_opt(
      self,
      filename,
      specific_override_args,
      general_override_args,
      **kwargs,
  ):
      opt = load_opt_from_file(filename)
      opt["override"] = {}
      blocklist_general = ["model", "model_file", "init_model"]
      standard_override_args = {
          "skip_generation": False,
          "inference": "beam",
          "beam_block_ngram": 3,
          "beam_context_block_ngram": -1,
          "beam_size": 3,
      }
      general_override_args = {
          **general_override_args,
          **standard_override_args,
          **kwargs,
      }
  
      # Remove the prefix for the model for the specific override args.
      specific_override_args = {
          "_".join(k.split("_")[1:]): v
          for k, v in specific_override_args.items()
      }
  
      # Specific for --indexer-type.
      if "indexer_type" in specific_override_args:
          # TODO: do we also need to overwrite path_to_index?
          pass
  
      override_args = {
          **general_override_args,
          **specific_override_args,
      }
  
      for k, v in override_args.items():
          if k not in blocklist_general and k in opt:
              opt["override"][k] = v
  
      return opt
  
imports: from copy import deepcopy
  from typing import Optional, List
  from types import MethodType
  from collections import defaultdict
  from nltk.tokenize import sent_tokenize
  from nltk.corpus import stopwords
  import random
  import spacy
  import torch
  from parlai.core.agents import create_agent, create_agent_from_shared
  from parlai.core.params import ParlaiParser
  from parlai.core.opt import Opt
  from parlai.core.message import Message
  from parlai.core.agents import Agent
  from parlai.core.metrics import F1Metric
  from parlai.agents.rag.args import setup_rag_args
  from parlai.agents.bart.bart import BartAgent
  from parlai.core.metrics import normalize_answer
  from parlai.tasks.wizard_of_wikipedia.agents import TOKEN_NOCHOSEN
  from parlai.tasks.wizard_of_wikipedia.agents import TOKEN_KNOWLEDGE, TOKEN_END_KNOWLEDGE
label_desc: Use image node_id 5 for calling the StackedKnowledgeDialogueAgent obj's underlying member method code with example usage: obj._agent_opt(filename, specific_override_args, general_override_args) and returns: opt