code: def save_model_card(
      repo_id: str,
      images=None,
      base_model=str,
      dataset_name=str,
      repo_folder=None,
  ):
      img_str = ""
      for i, image in enumerate(images):
          image.save(os.path.join(repo_folder, f"image_{i}.png"))
          img_str += f"![img_{i}](./image_{i}.png)\n"
  
      yaml = f"""
  ---
  license: creativeml-openrail-m
  base_model: {base_model}
  tags:
  - kandinsky
  - text-to-image
  - diffusers
  - lora
  inference: true
  ---
      """
      model_card = f"""
  # LoRA text2image fine-tuning - {repo_id}
  These are LoRA adaption weights for {base_model}. The weights were fine-tuned on the {dataset_name} dataset. You can find some example images in the following. \n
  {img_str}
  """
      with open(os.path.join(repo_folder, "README.md"), "w") as f:
          f.write(yaml + model_card)
  
imports: import argparse
  import logging
  import math
  import os
  import random
  import shutil
  from pathlib import Path
  import datasets
  import numpy
  import torch
  import torch.nn.functional
  import torch.utils.checkpoint
  import transformers
  from accelerate import Accelerator
  from accelerate.logging import get_logger
  from accelerate.utils import ProjectConfiguration, set_seed
  from datasets import load_dataset
  from huggingface_hub import create_repo, upload_folder
  from tqdm import tqdm
  from transformers import CLIPImageProcessor, CLIPTextModelWithProjection, CLIPTokenizer, CLIPVisionModelWithProjection
  import diffusers
  from diffusers import AutoPipelineForText2Image, DDPMScheduler, PriorTransformer
  from diffusers.loaders import AttnProcsLayers
  from diffusers.models.attention_processor import LoRAAttnProcessor
  from diffusers.optimization import get_scheduler
  from diffusers.training_utils import compute_snr
  from diffusers.utils import check_min_version, is_wandb_available
label_desc: Use image node_id 1 for calling a global function with example usage: save_model_card(repo_id, images, base_model, dataset_name, repo_folder) without return types