code: def main(args):
      os.makedirs(args.o, exist_ok=True)
  
      # Instantiate diffusers pipe as reference
      pipe = get_pipeline(args)
  
      # Register the selected attention implementation globally
      unet.ATTENTION_IMPLEMENTATION_IN_EFFECT = (
          unet.AttentionImplementations[args.attention_implementation]
      )
      logger.info(
          f"Attention implementation in effect: {unet.ATTENTION_IMPLEMENTATION_IN_EFFECT}"
      )
  
      # Convert models
      if args.convert_vae_decoder:
          logger.info("Converting vae_decoder")
          convert_vae_decoder(pipe, args)
          logger.info("Converted vae_decoder")
  
      if args.convert_vae_encoder:
          logger.info("Converting vae_encoder")
          convert_vae_encoder(pipe, args)
          logger.info("Converted vae_encoder")
  
      if args.convert_controlnet:
          logger.info("Converting controlnet")
          convert_controlnet(pipe, args)
          logger.info("Converted controlnet")
  
      if args.convert_unet:
          logger.info("Converting unet")
          convert_unet(pipe, args)
          logger.info("Converted unet")
  
      if (
          args.convert_text_encoder
          and hasattr(pipe, "text_encoder")
          and pipe.text_encoder is not None
      ):
          logger.info("Converting text_encoder")
          convert_text_encoder(
              pipe.text_encoder, pipe.tokenizer, "text_encoder", args
          )
          del pipe.text_encoder
          logger.info("Converted text_encoder")
  
      if (
          args.convert_text_encoder
          and hasattr(pipe, "text_encoder_2")
          and pipe.text_encoder_2 is not None
      ):
          logger.info("Converting text_encoder_2")
          convert_text_encoder(
              pipe.text_encoder_2,
              pipe.tokenizer_2,
              "text_encoder_2",
              args,
          )
          del pipe.text_encoder_2
          logger.info("Converted text_encoder_2")
  
      if args.convert_safety_checker:
          logger.info("Converting safety_checker")
          convert_safety_checker(pipe, args)
          logger.info("Converted safety_checker")
  
      if args.convert_unet and args.refiner_version is not None:
          logger.info(f"Converting refiner")
          del pipe
          gc.collect()
          original_model_version = args.model_version
          args.model_version = args.refiner_version
          pipe = get_pipeline(args)
          args.model_version = original_model_version
          convert_unet(pipe, args, model_name="refiner")
          del pipe
          gc.collect()
          logger.info(f"Converted refiner")
  
      if args.quantize_nbits is not None:
          logger.info(
              f"Quantizing weights to {args.quantize_nbits}-bit precision"
          )
          quantize_weights(args)
          logger.info(
              f"Quantized weights to {args.quantize_nbits}-bit precision"
          )
  
      if args.bundle_resources_for_swift_cli:
          logger.info("Bundling resources for the Swift CLI")
          bundle_resources_for_swift_cli(args)
          logger.info("Bundled resources for the Swift CLI")
  
imports: from python_coreml_stable_diffusion import unet, controlnet, chunk_mlprogram
  import argparse
  from collections import OrderedDict, defaultdict
  from copy import deepcopy
  import coremltools
  from diffusers import StableDiffusionPipeline, DiffusionPipeline, ControlNetModel
  import gc
  import logging
  import numpy
  import os
  import requests
  import shutil
  import time
  import re
  import pathlib
  import torch
  import torch.nn
  import torch.nn.functional
  from types import MethodType
  from transformers.models.clip import modeling_clip
label_desc: Use image node_id 20 for calling a global function with example usage: main(args) without return types