code: def parse_args():
      parser = argparse.ArgumentParser(
          description="Simple example of a training script."
      )
      parser.add_argument(
          "--pretrained_model_name_or_path",
          type=str,
          default=None,
          required=True,
          help="Path to pretrained model or model identifier from huggingface.co/models.",
      )
      parser.add_argument(
          "--revision",
          type=str,
          default=None,
          required=False,
          help="Revision of pretrained model identifier from huggingface.co/models.",
      )
      parser.add_argument(
          "--variant",
          type=str,
          default=None,
          help="Variant of the model files of the pretrained model identifier from huggingface.co/models, 'e.g.' fp16",
      )
      parser.add_argument(
          "--dataset_name",
          type=str,
          default=None,
          help=(
              "The name of the Dataset (from the HuggingFace hub) to train on (could be your own, possibly private,"
              " dataset). It can also be a path pointing to a local copy of a dataset in your filesystem,"
              " or to a folder containing files that 🤗 Datasets can understand."
          ),
      )
      parser.add_argument(
          "--dataset_config_name",
          type=str,
          default=None,
          help="The config of the Dataset, leave as None if there's only one config.",
      )
      parser.add_argument(
          "--train_data_dir",
          type=str,
          default=None,
          help=(
              "A folder containing the training data. Folder contents must follow the structure described in"
              " https://huggingface.co/docs/datasets/image_dataset#imagefolder. In particular, a `metadata.jsonl` file"
              " must exist to provide the captions for the images. Ignored if `dataset_name` is specified."
          ),
      )
      parser.add_argument(
          "--image_column",
          type=str,
          default="image",
          help="The column of the dataset containing an image.",
      )
      parser.add_argument(
          "--caption_column",
          type=str,
          default="text",
          help="The column of the dataset containing a caption or a list of captions.",
      )
      parser.add_argument(
          "--max_train_samples",
          type=int,
          default=None,
          help=(
              "For debugging purposes or quicker training, truncate the number of training examples to this "
              "value if set."
          ),
      )
      parser.add_argument(
          "--output_dir",
          type=str,
          default="sd-model-finetuned",
          help="The output directory where the model predictions and checkpoints will be written.",
      )
      parser.add_argument(
          "--cache_dir",
          type=str,
          default=None,
          help="The directory where the downloaded models and datasets will be stored.",
      )
      parser.add_argument(
          "--seed",
          type=int,
          default=0,
          help="A seed for reproducible training.",
      )
      parser.add_argument(
          "--resolution",
          type=int,
          default=512,
          help=(
              "The resolution for input images, all the images in the train/validation dataset will be resized to this"
              " resolution"
          ),
      )
      parser.add_argument(
          "--center_crop",
          default=False,
          action="store_true",
          help=(
              "Whether to center crop the input images to the resolution. If not set, the images will be randomly"
              " cropped. The images will be resized to the resolution first before cropping."
          ),
      )
      parser.add_argument(
          "--random_flip",
          action="store_true",
          help="whether to randomly flip images horizontally",
      )
      parser.add_argument(
          "--train_batch_size",
          type=int,
          default=16,
          help="Batch size (per device) for the training dataloader.",
      )
      parser.add_argument("--num_train_epochs", type=int, default=100)
      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(
          "--learning_rate",
          type=float,
          default=1e-4,
          help="Initial learning rate (after the potential warmup period) to use.",
      )
      parser.add_argument(
          "--scale_lr",
          action="store_true",
          default=False,
          help="Scale the learning rate by the number of GPUs, gradient accumulation steps, and batch size.",
      )
      parser.add_argument(
          "--lr_scheduler",
          type=str,
          default="constant",
          help=(
              'The scheduler type to use. Choose between ["linear", "cosine", "cosine_with_restarts", "polynomial",'
              ' "constant", "constant_with_warmup"]'
          ),
      )
      parser.add_argument(
          "--adam_beta1",
          type=float,
          default=0.9,
          help="The beta1 parameter for the Adam optimizer.",
      )
      parser.add_argument(
          "--adam_beta2",
          type=float,
          default=0.999,
          help="The beta2 parameter for the Adam optimizer.",
      )
      parser.add_argument(
          "--adam_weight_decay",
          type=float,
          default=1e-2,
          help="Weight decay to use.",
      )
      parser.add_argument(
          "--adam_epsilon",
          type=float,
          default=1e-08,
          help="Epsilon value for the Adam optimizer",
      )
      parser.add_argument(
          "--max_grad_norm",
          default=1.0,
          type=float,
          help="Max gradient norm.",
      )
      parser.add_argument(
          "--push_to_hub",
          action="store_true",
          help="Whether or not to push the model to the Hub.",
      )
      parser.add_argument(
          "--hub_token",
          type=str,
          default=None,
          help="The token to use to push to the Model Hub.",
      )
      parser.add_argument(
          "--hub_model_id",
          type=str,
          default=None,
          help="The name of the repository to keep in sync with the local `output_dir`.",
      )
      parser.add_argument(
          "--logging_dir",
          type=str,
          default="logs",
          help=(
              "[TensorBoard](https://www.tensorflow.org/tensorboard) log directory. Will default to"
              " *output_dir/runs/**CURRENT_DATETIME_HOSTNAME***."
          ),
      )
      parser.add_argument(
          "--report_to",
          type=str,
          default="tensorboard",
          help=(
              'The integration to report the results and logs to. Supported platforms are `"tensorboard"`'
              ' (default), `"wandb"` and `"comet_ml"`. Use `"all"` to report to all integrations.'
          ),
      )
      parser.add_argument(
          "--mixed_precision",
          type=str,
          default="no",
          choices=["no", "fp16", "bf16"],
          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(
          "--local_rank",
          type=int,
          default=-1,
          help="For distributed training: local_rank",
      )
      parser.add_argument(
          "--from_pt",
          action="store_true",
          default=False,
          help="Flag to indicate whether to convert models from PyTorch.",
      )
  
      args = parser.parse_args()
      env_local_rank = int(os.environ.get("LOCAL_RANK", -1))
      if env_local_rank != -1 and env_local_rank != args.local_rank:
          args.local_rank = env_local_rank
  
      # Sanity checks
      if args.dataset_name is None and args.train_data_dir is None:
          raise ValueError(
              "Need either a dataset name or a training folder."
          )
  
      return args
  
imports: import argparse
  import logging
  import math
  import os
  import random
  from pathlib import Path
  import jax
  import jax.numpy
  import numpy
  import optax
  import torch
  import torch.utils.checkpoint
  import transformers
  from datasets import load_dataset
  from flax import jax_utils
  from flax.training import train_state
  from flax.training.common_utils import shard
  from huggingface_hub import create_repo, upload_folder
  from torchvision import transforms
  from tqdm.auto import tqdm
  from transformers import CLIPImageProcessor, CLIPTokenizer, FlaxCLIPTextModel, set_seed
  from diffusers import FlaxAutoencoderKL, FlaxDDPMScheduler, FlaxPNDMScheduler, FlaxStableDiffusionPipeline, FlaxUNet2DConditionModel
  from diffusers.pipelines.stable_diffusion import FlaxStableDiffusionSafetyChecker
  from diffusers.utils import check_min_version
label_desc: Use image node_id 1 for calling a global function with example usage: parse_args() and returns: args