code: def parse_args():
      parser = argparse.ArgumentParser(
          description="Simple example of training script."
      )
      parser.add_argument(
          "--mx",
          type=str,
          default="bf16",
          choices=["no", "fp16", "bf16", "fp8"],
          help="Whether to use mixed precision. Choose"
          "between fp16 and bf16 (bfloat16). Bf16 requires PyTorch >= 1.10."
          "and an Nvidia Ampere GPU.",
      )
  
      parser.add_argument(
          "--batch-size-per-device",
          "-bs",
          type=int,
          default=16,
          help="Batch size to use per device.",
      )
  
      parser.add_argument(
          "--stop-perplexity",
          default=0,
          type=float,
          help="Target perplexity to reach after which to stop training. Default is 0. "
          "If 0, training will not stop on perplexity.",
      )
  
      parser.add_argument(
          "--eval-batch-size-per-device",
          type=int,
          default=64,
          help="Batch size to use per device (For evaluation).",
      )
  
      parser.add_argument(
          "--num-devices",
          "-nd",
          type=int,
          default=4,
          help="Number of devices to use.",
      )
      parser.add_argument(
          "--grad_accum",
          type=int,
          default=1,
          help="Gradient accumulation steps.",
      )
      parser.add_argument(
          "--train_path", type=str, help="Path to training jsonl file"
      )
  
      parser.add_argument(
          "--test_path", type=str, help="Path to testing jsonl file"
      )
  
      parser.add_argument(
          "--special_token_path",
          type=str,
          help="Path to token json file",
      )
      parser.add_argument(
          "--no-grad-ckpt",
          action="store_true",
          help="If passed, will not use gradient checkpointing.",
      )
      parser.add_argument(
          "--output_dir", type=str, help="Path to output directory."
      )
  
      parser.add_argument(
          "--model_name",
          default="meta-llama/Llama-2-7b-chat-hf",
          type=str,
      )
      parser.add_argument(
          "--num-epochs",
          type=int,
          default=1,
          help="Number of epochs to train for.",
      )
      parser.add_argument(
          "--num-checkpoints-to-keep",
          type=int,
          help=(
              "Number of checkpoints to keep, if None, all checkpoints will be kept, "
              "if set to n>=1, the top n checkpoint with min. evaluation perplexity "
              "will be kept."
          ),
          default=None,
      )
      parser.add_argument(
          "--lr", type=float, default=5e-6, help="Learning rate to use."
      )
  
      parser.add_argument(
          "--ctx-len",
          type=int,
          default=512,
          help="Maximum context length for the model input sequences.",
      )
  
      parser.add_argument(
          "--as-test",
          action="store_true",
          help="If passed, will run the script in test mode.",
      )
  
      parser.add_argument(
          "--ds-config",
          type=str,
          default="./deepspeed_configs/zero_3_llama_2_7b.json",
          help="Deepspeed config json to use.",
      )
  
      parser.add_argument(
          "--lora",
          action="store_true",
          default=False,
          help="If passed, will enable parameter efficient fine-tuning with LoRA ("
          "https://arxiv.org/pdf/2106.09685.pdf).",
      )
  
      args = parser.parse_args()
  
      return args
  
imports: import argparse
  from filelock import FileLock
  import functools
  import json
  import math
  import os
  from pathlib import Path
  import re
  import tempfile
  import time
  import tree
  from typing import Tuple
  from accelerate import Accelerator, DeepSpeedPlugin
  from accelerate.utils import DummyOptim, DummyScheduler, set_seed
  import torch
  import torch.nn
  import tqdm
  from transformers import AutoModelForCausalLM, AutoTokenizer, get_linear_schedule_with_warmup
  from peft import LoraConfig, get_peft_model
  import ray
  from ray import train
  import ray.util.scheduling_strategies
  from ray.train.torch import TorchTrainer
  from ray.train import Checkpoint
  from utils import get_checkpoint_and_refs_dir, get_mirror_link, download_model, get_download_path
label_desc: Use image node_id 10 for calling a global function with example usage: parse_args() and returns: args