code: def generate_readme(model_card: dict, model_name: str):
      readme_text = "---\n"
      readme_text += "tags:\n- image-classification\n- timm\n"
      readme_text += "library_name: timm\n"
      readme_text += (
          f"license: {model_card.get('license', 'apache-2.0')}\n"
      )
      if "details" in model_card and "Dataset" in model_card["details"]:
          readme_text += "datasets:\n"
          if isinstance(
              model_card["details"]["Dataset"], (tuple, list)
          ):
              for d in model_card["details"]["Dataset"]:
                  readme_text += f"- {d.lower()}\n"
          else:
              readme_text += (
                  f"- {model_card['details']['Dataset'].lower()}\n"
              )
          if "Pretrain Dataset" in model_card["details"]:
              if isinstance(
                  model_card["details"]["Pretrain Dataset"],
                  (tuple, list),
              ):
                  for d in model_card["details"]["Pretrain Dataset"]:
                      readme_text += f"- {d.lower()}\n"
              else:
                  readme_text += f"- {model_card['details']['Pretrain Dataset'].lower()}\n"
      readme_text += "---\n"
      readme_text += f"# Model card for {model_name}\n"
      if "description" in model_card:
          readme_text += f"\n{model_card['description']}\n"
      if "details" in model_card:
          readme_text += f"\n## Model Details\n"
          for k, v in model_card["details"].items():
              if isinstance(v, (list, tuple)):
                  readme_text += f"- **{k}:**\n"
                  for vi in v:
                      readme_text += f"  - {vi}\n"
              elif isinstance(v, dict):
                  readme_text += f"- **{k}:**\n"
                  for ki, vi in v.items():
                      readme_text += f"  - {ki}: {vi}\n"
              else:
                  readme_text += f"- **{k}:** {v}\n"
      if "usage" in model_card:
          readme_text += f"\n## Model Usage\n"
          readme_text += model_card["usage"]
          readme_text += "\n"
  
      if "comparison" in model_card:
          readme_text += f"\n## Model Comparison\n"
          readme_text += model_card["comparison"]
          readme_text += "\n"
  
      if "citation" in model_card:
          readme_text += f"\n## Citation\n"
          if not isinstance(model_card["citation"], (list, tuple)):
              citations = [model_card["citation"]]
          else:
              citations = model_card["citation"]
          for c in citations:
              readme_text += f"```bibtex\n{c}\n```\n"
      return readme_text
  
imports: import hashlib
  import json
  import logging
  import os
  from functools import partial
  from pathlib import Path
  from tempfile import TemporaryDirectory
  from typing import Iterable, Optional, Union
  import torch
  from torch.hub import HASH_REGEX, download_url_to_file, urlparse
  from timm import __version__
  from timm.models._pretrained import filter_pretrained_cfg
label_desc: Use image node_id 13 for calling a global function with example usage: generate_readme(model_card, model_name) and returns: readme_text