code: def add_cmdline_args(
      cls, parser: ParlaiParser, partial_opt: Optional[Opt] = None
  ) -> ParlaiParser:
      # This method will add arguments specific to fused classifier architecture
      # like num_layers classifier, etc.
      super().add_cmdline_args(parser, partial_opt)
      group = parser.add_argument_group("DirectorFidAgent Group")
      group.add_argument(
          "--explicit-classifier-norm",
          type=bool,
          default=False,
          help="If we should explictly try to set non-target tokens to 0.5 during training.",
      )
      group.add_argument(
          "--explicit-classifier-norm-coeff",
          type=float,
          default=1,
          help="norm coefficient if we should explictly try to set non-target tokens to 0.5 during training.",
      )
      group.add_argument(
          "--freeze-decoder",
          type=bool,
          default=False,
          help="Freeze decoder for training only classifier head.",
      )
      group.add_argument(
          "--use-one-plus-gamma-variant",
          type=bool,
          default=False,
          help="Use 1+ gamma weighting variant if True (keeping generator loss weight fixed).",
      )
      group.add_argument(
          "--train-gamma",
          type=float,
          default=0.5,
          help="Keeping generator loss weight fixed (to 1) and using gamma to weight classifier during training time.",
      )
      group.add_argument(
          "--infer-gamma",
          type=float,
          default=None,
          help="Keeping generator loss weight fixed (to 1) and using gamma to weight classifier during inference time.",
      )
      group.add_argument(
          "--train-mixing-weights",
          type=float,
          default=0.5,
          help="Mixing weights between classifier loss and generator loss during training time",
      )
      group.add_argument(
          "--infer-mixing-weights",
          type=float,
          default=None,
          help="Mixing weights between classifier loss and generator loss during inference time",
      )
      parser.add_argument(
          "--pos-class-weight",
          type=float,
          default=1,
          help="weight of each of the positive class for the sigmoid",
      )
      group.add_argument(
          "--director-classifier-layers",
          type=str,
          default="linear",
          choices=["linear", "layer", "ffn", "2layers"],
      )
      group.add_argument(
          "--init-classifier-layers",
          type=str,
          default="random",
          choices=[
              "random",
              "copy_decoder",
              "copy_decoder_with_epsilon_noise",
          ],
          help="how to init weights of the classifier_layers, such as init randomly, or copy from pretrained decoder, or copy with epsilon pertubation",
      )
      group.add_argument(
          "--init-epsilon",
          type=float,
          default=0,
      )
      return parser
  
imports: import torch
  import torch.nn
  import torch.nn.functional
  from typing import Optional, Any, Dict, Tuple, Union, List
  from abc import abstractmethod
  from parlai.agents.transformer.modules import TransformerDecoderLayer
  from parlai.agents.fid.fid import FidAgent, FidModel
  from parlai.core.message import Message
  from parlai.core.metrics import AverageMetric
  from parlai.core.opt import Opt
  from parlai.core.params import ParlaiParser
  from parlai.core.torch_generator_agent import PPLMetric
  from parlai.core.torch_agent import Batch, DictionaryAgent
  import parlai.utils.logging
  from projects.blenderbot2.agents.blenderbot2 import BlenderBot2FidAgent, BlenderBot2FidModel, T5BlenderBot2FidModel
  from parlai.agents.fid.fid import WizIntGoldDocRetrieverFiDAgent
  import parlai.agents.transformer.modules.ffn
  from parlai.agents.transformer.modules import LAYER_NORM_EPS
label_desc: Use image node_id 1 for calling the DirectorFidAgent obj's underlying member method code with example usage: obj.add_cmdline_args(cls, parser, partial_opt) and returns: parser