class_docstr: Classifier based on Transformer.
class_docstr_tok: ["Classifier","based","on","Transformer","."]
code: def add_cmdline_args(
      cls, parser: ParlaiParser, partial_opt: Optional[Opt] = None
  ) -> ParlaiParser:
      TransformerRankerAgent.add_cmdline_args(
          parser, partial_opt=partial_opt
      )  # add transformer args
      super().add_cmdline_args(parser, partial_opt=partial_opt)
      parser.add_argument(
          "--load-from-pretrained-ranker",
          type="bool",
          default=False,
          help="load model from base transformer ranking model "
          "(used for pretraining)",
      )
      parser.set_defaults(reduction_type="first")
      return parser
  
imports: from typing import Optional
  from parlai.core.params import ParlaiParser
  from parlai.core.opt import Opt
  from parlai.core.agents import Agent
  from parlai.utils.torch import padded_3d
  from parlai.core.torch_classifier_agent import TorchClassifierAgent
  from parlai.core.torch_ranker_agent import TorchRankerAgent
  from parlai.core.torch_generator_agent import TorchGeneratorAgent
  from parlai.utils.misc import recursive_getattr, warn_once
  from parlai.utils.logging import logging
  from parlai.utils.fsdp import should_use_fsdp
  from .modules import TransformerMemNetModel, TransformerGeneratorModel, TransformerLinearWrapper
  import torch
label_desc: Use image node_id 1 for calling the TransformerClassifierAgent obj's underlying member method code with example usage: obj.add_cmdline_args(cls, parser, partial_opt) and returns: parser