code: def register_subcommand(parser: ArgumentParser):
      arg_list = [
          {
              "arg": "--text-column",
              "help": "Text column to use",
              "required": False,
              "type": str,
              "default": "text",
          },
          {
              "arg": "--target-column",
              "help": "Target column to use",
              "required": False,
              "type": str,
              "default": "target",
          },
          {
              "arg": "--max-seq-length",
              "help": "Maximum number of tokens in a sequence to use",
              "required": False,
              "type": int,
              "default": 128,
          },
          {
              "arg": "--max-target-length",
              "help": "Maximum number of tokens in a target sequence to use",
              "required": False,
              "type": int,
              "default": 128,
          },
          {
              "arg": "--warmup-ratio",
              "help": "Warmup proportion to use",
              "required": False,
              "type": float,
              "default": 0.1,
          },
          {
              "arg": "--optimizer",
              "help": "Optimizer to use",
              "required": False,
              "type": str,
              "default": "adamw_torch",
          },
          {
              "arg": "--scheduler",
              "help": "Scheduler to use",
              "required": False,
              "type": str,
              "default": "linear",
          },
          {
              "arg": "--weight-decay",
              "help": "Weight decay to use",
              "required": False,
              "type": float,
              "default": 0.0,
          },
          {
              "arg": "--max-grad-norm",
              "help": "Max gradient norm to use",
              "required": False,
              "type": float,
              "default": 1.0,
          },
          {
              "arg": "--logging-steps",
              "help": "Logging steps to use",
              "required": False,
              "type": int,
              "default": -1,
          },
          {
              "arg": "--evaluation-strategy",
              "help": "Evaluation strategy to use",
              "required": False,
              "type": str,
              "default": "epoch",
          },
          {
              "arg": "--save-total-limit",
              "help": "Save total limit to use",
              "required": False,
              "type": int,
              "default": 1,
          },
          {
              "arg": "--save-strategy",
              "help": "Save strategy to use",
              "required": False,
              "type": str,
              "default": "epoch",
          },
          {
              "arg": "--auto-find-batch-size",
              "help": "Auto find batch size True/False",
              "required": False,
              "action": "store_true",
          },
          {
              "arg": "--mixed-precision",
              "help": "fp16, bf16, or None",
              "required": False,
              "type": str,
              "default": None,
              "choices": ["fp16", "bf16", None],
          },
          {
              "arg": "--peft",
              "help": "Use PEFT",
              "required": False,
              "action": "store_true",
          },
          {
              "arg": "--quantization",
              "help": "int8 or None",
              "required": False,
              "type": str,
              "default": None,
              "choices": ["int8", None],
          },
          {
              "arg": "--lora-r",
              "help": "LoRA-R",
              "required": False,
              "type": int,
              "default": 16,
          },
          {
              "arg": "--lora-alpha",
              "help": "LoRA-Alpha",
              "required": False,
              "type": int,
              "default": 32,
          },
          {
              "arg": "--lora-dropout",
              "help": "LoRA-Dropout",
              "required": False,
              "type": float,
              "default": 0.05,
          },
          {
              "arg": "--target-modules",
              "help": "Target modules",
              "required": False,
              "type": str,
              "default": "",
          },
      ]
      arg_list.extend(common_args())
      run_seq2seq_parser = parser.add_parser(
          "seq2seq", description="✨ Run AutoTrain Seq2Seq"
      )
      for arg in arg_list:
          if "action" in arg:
              run_seq2seq_parser.add_argument(
                  arg["arg"],
                  help=arg["help"],
                  required=arg.get("required", False),
                  action=arg.get("action"),
                  default=arg.get("default"),
              )
          else:
              run_seq2seq_parser.add_argument(
                  arg["arg"],
                  help=arg["help"],
                  required=arg.get("required", False),
                  type=arg.get("type"),
                  default=arg.get("default"),
              )
      run_seq2seq_parser.set_defaults(func=run_seq2seq_command_factory)
  
file_path: autotrain-advanced/src/autotrain/cli/run_seq2seq.py
imports: from argparse import ArgumentParser
  from autotrain import logger
  from autotrain.cli.utils import common_args, seq2seq_munge_data
  from autotrain.project import AutoTrainProject
  from autotrain.trainers.seq2seq.params import Seq2SeqParams
  from .None import BaseAutoTrainCommand
label_desc: Use image node_id 1 for calling the RunAutoTrainSeq2SeqCommand obj's underlying member method code with example usage: obj.register_subcommand(parser) without return types