code: def create_model_card(
      repo_id: str,
      base_model: str,
      train_text_encoder: bool,
      prompt: str,
      repo_folder: str,
  ):
      if train_text_encoder:
          text_encoder_text = "trained"
      else:
          text_encoder_text = "not trained"
      yaml = f"""
  ---
  base_model: {base_model}
  instance_prompt: {prompt}
  tags:
  - text-to-image
  - diffusers
  - autotrain
  inference: true
  ---
      """
      model_card = f"""
  # DreamBooth trained by AutoTrain
  
  Text encoder was {text_encoder_text}.
  
  """
      with open(os.path.join(repo_folder, "README.md"), "w") as f:
          f.write(yaml + model_card)
  
imports: import hashlib
  import itertools
  import os
  from pathlib import Path
  from typing import Dict
  import torch
  from diffusers import AutoencoderKL, DDPMScheduler, DiffusionPipeline, StableDiffusionXLPipeline, UNet2DConditionModel
  from diffusers.utils.import_utils import is_xformers_available
  from packaging import version
  from tqdm import tqdm
  from transformers import AutoTokenizer, PretrainedConfig
  from autotrain import logger
  from autotrain.trainers.dreambooth.datasets import PromptDataset
label_desc: Use image node_id 1 for calling a global function with example usage: create_model_card(repo_id, base_model, train_text_encoder, prompt, repo_folder) without return types