class_docstr: Compile and save results of human+model chats.
  
  Results will be saved on the level of specific conversations, as well as aggregated
  up the level of each worker as a whole.
class_docstr_tok: ["Compile","and","save","results","of","human+model","chats",".","Results","will","be","saved","on","the","level","of","specific","conversations",",","as","well","as","aggregated","up","the","level","of","each","worker","as","a","whole","."]
code: def setup_args(cls):
      parser = super().setup_args()
      parser.add_argument(
          "--max-convos-per-worker",
          type=int,
          default=100,
          help="The most conversations to analyze from any one user. Set to -1 for no limit.",
      )
      parser.add_argument(
          "--min-word-count",
          type=int,
          default=4,
          help="The minimum acceptable mean number of words per human utterance",
      )
      parser.add_argument(
          "--worker-block-list",
          type=str,
          default="",
          help="Comma-separated list of all workers to block",
      )
      return parser
  
file_path: ParlAI/parlai/crowdsourcing/tasks/model_chat/analysis/compile_results.py
imports: import os
  from datetime import datetime
  from typing import Any, Dict
  import numpy
  import pandas
  from parlai.crowdsourcing.utils.acceptability import AcceptabilityChecker
  from parlai.crowdsourcing.utils.analysis import AbstractTurnAnnotationResultsCompiler
  from parlai.crowdsourcing.tasks.model_chat.model_chat_blueprint import BLUEPRINT_TYPE
label_desc: Use image node_id 1 for calling the ModelChatResultsCompiler obj's underlying member method code with example usage: obj.setup_args(cls) and returns: parser