code: def save_model_card(
      repo_id: str, image_logs=None, base_model=str, repo_folder=None
  ):
      img_str = ""
      if image_logs is not None:
          img_str = "You can find some example images below.\n"
          for i, log in enumerate(image_logs):
              images = log["images"]
              validation_prompt = log["validation_prompt"]
              validation_image = log["validation_image"]
              validation_image.save(
                  os.path.join(repo_folder, "image_control.png")
              )
              img_str += f"prompt: {validation_prompt}\n"
              images = [validation_image] + images
              image_grid(images, 1, len(images)).save(
                  os.path.join(repo_folder, f"images_{i}.png")
              )
              img_str += f"![images_{i})](./images_{i}.png)\n"
  
      yaml = f"""
  ---
  license: creativeml-openrail-m
  base_model: {base_model}
  tags:
  - stable-diffusion
  - stable-diffusion-diffusers
  - text-to-image
  - diffusers
  - controlnet
  inference: true
  ---
      """
      model_card = f"""
  # controlnet-{repo_id}
  
  These are controlnet weights trained on {base_model} with new type of conditioning.
  {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 accelerate
  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 packaging import version
  from PIL import Image
  from torchvision import transforms
  from tqdm.auto import tqdm
  from transformers import AutoTokenizer, PretrainedConfig
  import diffusers
  from diffusers import AutoencoderKL, ControlNetModel, DDPMScheduler, StableDiffusionControlNetPipeline, UNet2DConditionModel, UniPCMultistepScheduler
  from diffusers.optimization import get_scheduler
  from diffusers.utils import check_min_version, is_wandb_available
  from diffusers.utils.import_utils import is_xformers_available
label_desc: Use image node_id 4 for calling a global function with example usage: save_model_card(repo_id, image_logs, base_model, repo_folder) without return types