code: def add_args(parser):
      """add rescorer specific arguments usable for training"""
      parser.add_argument(
          "--l2r-model-path",
          default=None,
          type=str,
          help=("Provide a path for the l2r rescoring model"),
      )
      parser.add_argument(
          "--l2r-model-weight",
          default=1.0,
          type=float,
          help=("Provide a weight for the l2r rescoring model"),
      )
      parser.add_argument(
          "--r2l-model-path",
          default=None,
          type=str,
          help=("Provide a path for the r2l rescoring model"),
      )
      parser.add_argument(
          "--r2l-model-weight",
          default=1.0,
          type=float,
          help=("Provide a weight for the r2l rescoring model"),
      )
      parser.add_argument(
          "--reverse-model-path",
          default=None,
          type=str,
          help=("Provide a path for the reverse rescoring model"),
      )
      parser.add_argument(
          "--reverse-model-weight",
          default=1.0,
          type=float,
          help=("Provide a weight for the reverse rescoring model"),
      )
      parser.add_argument(
          "--lm-model-path",
          default=None,
          type=str,
          help=(
              "Provide a path for the language model rescoring model"
          ),
      )
      parser.add_argument(
          "--lm-model-weight",
          default=1.0,
          type=float,
          help=("Provide a weight for the lm rescoring model"),
      )
      parser.add_argument(
          "--rescore-length-penalty",
          default=1.0,
          type=float,
          help=(
              "Provide a weight for length penalty used in rescoring"
          ),
      )
      parser.add_argument(
          "--cloze-transformer-path",
          default=None,
          type=str,
          help=(
              "Provide a path for the cloze transformer rescoring model"
          ),
      )
      parser.add_argument(
          "--cloze-transformer-weight",
          default=1.0,
          type=float,
          help=("Provide a weight for the cloze transformer model"),
      )
  
imports: import argparse
  import pickle
  from enum import Enum
  import torch
  from fairseq.scoring import bleu
  from pytorch_translate import hybrid_transformer_rnn, utils
  from pytorch_translate.rescoring.model_scorers import LMScorer, R2LModelScorer, ReverseModelScorer, SimpleModelScorer
  from pytorch_translate.research.rescore import cloze_transformer_model
  from pytorch_translate.tasks import pytorch_translate_task
  from tqdm import tqdm
label_desc: Use image node_id 1 for calling a global function with example usage: add_args(parser) without return types