code: def parse_args():
      parser = colossalai.get_default_parser()
      parser.add_argument("-s", "--synthetic", action="store_true")
      parser.add_argument(
          "--dataset_name",
          type=str,
          default=None,
          help="The name of the dataset to use (via the datasets library).",
      )
      parser.add_argument(
          "--dataset_config_name",
          type=str,
          default=None,
          help="The configuration name of the dataset to use (via the datasets library).",
      )
      parser.add_argument(
          "--train_file",
          type=str,
          default=None,
          help="A csv or a json file containing the training data.",
      )
      parser.add_argument(
          "--validation_file",
          type=str,
          default=None,
          help="A csv or a json file containing the validation data.",
      )
      parser.add_argument(
          "--validation_split_percentage",
          default=5,
          help="The percentage of the train set used as validation set in case there's no validation split",
      )
      parser.add_argument(
          "--model_name_or_path",
          type=str,
          help="Path to pretrained model or model identifier from huggingface.co/models.",
          required=True,
      )
      parser.add_argument(
          "--config_name",
          type=str,
          default=None,
          help="Pretrained config name or path if not the same as model_name",
      )
      parser.add_argument(
          "--tokenizer_name",
          type=str,
          default=None,
          help="Pretrained tokenizer name or path if not the same as model_name",
      )
      parser.add_argument(
          "--use_slow_tokenizer",
          action="store_true",
          help="If passed, will use a slow tokenizer (not backed by the 🤗 Tokenizers library).",
      )
      parser.add_argument(
          "--per_device_train_batch_size",
          type=int,
          default=8,
          help="Batch size (per device) for the training dataloader.",
      )
      parser.add_argument(
          "--per_device_eval_batch_size",
          type=int,
          default=8,
          help="Batch size (per device) for the evaluation dataloader.",
      )
      parser.add_argument(
          "--learning_rate",
          type=float,
          default=5e-5,
          help="Initial learning rate (after the potential warmup period) to use.",
      )
      parser.add_argument(
          "--weight_decay",
          type=float,
          default=0.0,
          help="Weight decay to use.",
      )
      parser.add_argument(
          "--num_train_epochs",
          type=int,
          default=3,
          help="Total number of training epochs to perform.",
      )
      parser.add_argument(
          "--max_train_steps",
          type=int,
          default=None,
          help="Total number of training steps to perform. If provided, overrides num_train_epochs.",
      )
      parser.add_argument(
          "--gradient_accumulation_steps",
          type=int,
          default=1,
          help="Number of updates steps to accumulate before performing a backward/update pass.",
      )
      parser.add_argument(
          "--lr_scheduler_type",
          type=SchedulerType,
          default="linear",
          help="The scheduler type to use.",
          choices=[
              "linear",
              "cosine",
              "cosine_with_restarts",
              "polynomial",
              "constant",
              "constant_with_warmup",
          ],
      )
      parser.add_argument(
          "--num_warmup_steps",
          type=int,
          default=0,
          help="Number of steps for the warmup in the lr scheduler.",
      )
      parser.add_argument(
          "--output_dir",
          type=str,
          default=None,
          help="Where to store the final model.",
      )
      parser.add_argument(
          "--seed",
          type=int,
          default=None,
          help="A seed for reproducible training.",
      )
      parser.add_argument(
          "--model_type",
          type=str,
          default=None,
          help="Model type to use if training from scratch.",
          choices=MODEL_TYPES,
      )
      parser.add_argument(
          "--block_size",
          type=int,
          default=None,
          help=(
              "Optional input sequence length after tokenization. The training dataset will be truncated in block of"
              " this size for training. Default to the model max input length for single sentence inputs (take into"
              " account special tokens)."
          ),
      )
      parser.add_argument(
          "--preprocessing_num_workers",
          type=int,
          default=None,
          help="The number of processes to use for the preprocessing.",
      )
      parser.add_argument(
          "--overwrite_cache",
          type=bool,
          default=False,
          help="Overwrite the cached training and evaluation sets",
      )
      parser.add_argument(
          "--no_keep_linebreaks",
          action="store_true",
          help="Do not keep line breaks when using TXT files.",
      )
      parser.add_argument(
          "--push_to_hub",
          action="store_true",
          help="Whether or not to push the model to the Hub.",
      )
      parser.add_argument(
          "--hub_model_id",
          type=str,
          help="The name of the repository to keep in sync with the local `output_dir`.",
      )
      parser.add_argument(
          "--hub_token",
          type=str,
          help="The token to use to push to the Model Hub.",
      )
      parser.add_argument(
          "--checkpointing_steps",
          type=str,
          default=None,
          help="Whether the various states should be saved at the end of every n steps, or 'epoch' for each epoch.",
      )
      parser.add_argument(
          "--resume_from_checkpoint",
          type=str,
          default=None,
          help="If the training should continue from a checkpoint folder.",
      )
      parser.add_argument(
          "--with_tracking",
          action="store_true",
          help="Whether to enable experiment trackers for logging.",
      )
      parser.add_argument(
          "--report_to",
          type=str,
          default="all",
          help=(
              'The integration to report the results and logs to. Supported platforms are `"tensorboard"`,'
              ' `"wandb"` and `"comet_ml"`. Use `"all"` (default) to report to all integrations.'
              "Only applicable when `--with_tracking` is passed."
          ),
      )
  
      parser.add_argument(
          "--mem_cap", type=int, default=0, help="use mem cap"
      )
      parser.add_argument(
          "--init_in_cpu",
          action="store_true",
          default=False,
          help="init training model in cpu",
      )
      args = parser.parse_args()
  
      # Sanity checks
      if not args.synthetic:
          if (
              args.dataset_name is None
              and args.train_file is None
              and args.validation_file is None
          ):
              raise ValueError(
                  "Need either a dataset name or a training/validation file."
              )
          else:
              if args.train_file is not None:
                  extension = args.train_file.split(".")[-1]
                  assert extension in [
                      "csv",
                      "json",
                      "txt",
                  ], "`train_file` should be a csv, json or txt file."
              if args.validation_file is not None:
                  extension = args.validation_file.split(".")[-1]
                  assert extension in [
                      "csv",
                      "json",
                      "txt",
                  ], "`validation_file` should be a csv, json or txt file."
  
      if args.push_to_hub:
          assert (
              args.output_dir is not None
          ), "Need an `output_dir` to create a repo when `--push_to_hub` is passed."
  
      return args
  
imports: import math
  import os
  import time
  from itertools import chain
  import datasets
  import torch
  import torch.distributed
  import transformers.utils.logging
  from accelerate.utils import set_seed
  from context import barrier_context
  from datasets import load_dataset
  from packaging import version
  from torch.utils.data import DataLoader
  from tqdm.auto import tqdm
  from transformers import CONFIG_MAPPING, MODEL_MAPPING, AutoConfig, AutoTokenizer, GPT2Tokenizer, OPTForCausalLM, SchedulerType, default_data_collator, get_scheduler
  from transformers.utils.versions import require_version
  import colossalai
  from colossalai.legacy.context import ParallelMode
  from colossalai.legacy.core import global_context
  from colossalai.legacy.tensor import ProcessGroup
  from colossalai.legacy.utils import get_dataloader
  from colossalai.logging import disable_existing_loggers, get_dist_logger
  from colossalai.nn.optimizer import HybridAdam
  from colossalai.utils import get_current_device
  from colossalai.zero import GeminiOptimizer
label_desc: Use image node_id 2 for calling a global function with example usage: parse_args() and returns: args