code: def convert_safety_checker(pipe, args):
      """Converts the Safety Checker component of Stable Diffusion"""
      if pipe.safety_checker is None:
          logger.warning(
              f"diffusers pipeline for {args.model_version} does not have a `safety_checker` module! "
              "`--convert-safety-checker` will be ignored."
          )
          return
  
      out_path = _get_out_path(args, "safety_checker")
      if os.path.exists(out_path):
          logger.info(
              f"`safety_checker` already exists at {out_path}, skipping conversion."
          )
          return
  
      pipe.safety_checker = pipe.safety_checker.to(torch.float32)
  
      im_h = pipe.vae.config.sample_size
      im_w = pipe.vae.config.sample_size
  
      if args.latent_h is not None:
          im_h = args.latent_h * 8
  
      if args.latent_w is not None:
          im_w = args.latent_w * 8
  
      sample_image = np.random.randn(
          1, im_h, im_w, 3  # B  # H  # w  # C
      ).astype(np.float32)
  
      # Note that pipe.feature_extractor is not an ML model. It simply
      # preprocesses data for the pipe.safety_checker module.
      safety_checker_input = pipe.feature_extractor(
          pipe.numpy_to_pil(sample_image),
          return_tensors="pt",
      ).pixel_values.to(torch.float32)
  
      sample_safety_checker_inputs = OrderedDict(
          [
              ("clip_input", safety_checker_input),
              ("images", torch.from_numpy(sample_image)),
              ("adjustment", torch.tensor([0]).to(torch.float32)),
          ]
      )
  
      sample_safety_checker_inputs_spec = {
          k: (v.shape, v.dtype)
          for k, v in sample_safety_checker_inputs.items()
      }
      logger.info(
          f"Sample inputs spec: {sample_safety_checker_inputs_spec}"
      )
  
      # Patch safety_checker's forward pass to be vectorized and avoid conditional blocks
      # (similar to pipe.safety_checker.forward_onnx)
      from diffusers.pipelines.stable_diffusion import safety_checker
  
      def forward_coreml(self, clip_input, images, adjustment):
          """Forward pass implementation for safety_checker"""
  
          def cosine_distance(image_embeds, text_embeds):
              return F.normalize(image_embeds) @ F.normalize(
                  text_embeds
              ).transpose(0, 1)
  
          pooled_output = self.vision_model(clip_input)[
              1
          ]  # pooled_output
          image_embeds = self.visual_projection(pooled_output)
  
          special_cos_dist = cosine_distance(
              image_embeds, self.special_care_embeds
          )
          cos_dist = cosine_distance(image_embeds, self.concept_embeds)
  
          special_scores = (
              special_cos_dist
              - self.special_care_embeds_weights
              + adjustment
          )
          special_care = (
              special_scores.gt(0).float().sum(dim=1).gt(0).float()
          )
          special_adjustment = special_care * 0.01
          special_adjustment = special_adjustment.unsqueeze(1).expand(
              -1, cos_dist.shape[1]
          )
  
          concept_scores = (
              cos_dist - self.concept_embeds_weights
          ) + special_adjustment
          has_nsfw_concepts = (
              concept_scores.gt(0)
              .float()
              .sum(dim=1)
              .gt(0)[:, None, None, None]
          )
  
          has_nsfw_concepts_inds, _ = torch.broadcast_tensors(
              has_nsfw_concepts, images
          )
          images[has_nsfw_concepts_inds] = 0.0  # black image
  
          return images, has_nsfw_concepts.float(), concept_scores
  
      baseline_safety_checker = deepcopy(pipe.safety_checker.eval())
      setattr(
          baseline_safety_checker,
          "forward",
          MethodType(forward_coreml, baseline_safety_checker),
      )
  
      # In order to parity check the actual signal, we need to override the forward pass to return `concept_scores` which is the
      # output before thresholding
      # Reference: https://github.com/huggingface/diffusers/blob/main/src/diffusers/pipelines/stable_diffusion/safety_checker.py#L100
      def forward_extended_return(self, clip_input, images, adjustment):
          def cosine_distance(image_embeds, text_embeds):
              normalized_image_embeds = F.normalize(image_embeds)
              normalized_text_embeds = F.normalize(text_embeds)
              return torch.mm(
                  normalized_image_embeds, normalized_text_embeds.t()
              )
  
          pooled_output = self.vision_model(clip_input)[
              1
          ]  # pooled_output
          image_embeds = self.visual_projection(pooled_output)
  
          special_cos_dist = cosine_distance(
              image_embeds, self.special_care_embeds
          )
          cos_dist = cosine_distance(image_embeds, self.concept_embeds)
  
          adjustment = 0.0
  
          special_scores = (
              special_cos_dist
              - self.special_care_embeds_weights
              + adjustment
          )
          special_care = torch.any(special_scores > 0, dim=1)
          special_adjustment = special_care * 0.01
          special_adjustment = special_adjustment.unsqueeze(1).expand(
              -1, cos_dist.shape[1]
          )
  
          concept_scores = (
              cos_dist - self.concept_embeds_weights
          ) + special_adjustment
          has_nsfw_concepts = torch.any(concept_scores > 0, dim=1)
  
          images[has_nsfw_concepts] = 0.0
  
          return images, has_nsfw_concepts, concept_scores
  
      setattr(
          pipe.safety_checker,
          "forward",
          MethodType(forward_extended_return, pipe.safety_checker),
      )
  
      # Trace the safety_checker model
      logger.info("JIT tracing..")
      traced_safety_checker = torch.jit.trace(
          baseline_safety_checker,
          list(sample_safety_checker_inputs.values()),
      )
      logger.info("Done.")
      del baseline_safety_checker
      gc.collect()
  
      # Cast all inputs to float16
      coreml_sample_safety_checker_inputs = {
          k: v.numpy().astype(np.float16)
          for k, v in sample_safety_checker_inputs.items()
      }
  
      # Convert safety_checker model to Core ML
      coreml_safety_checker, out_path = _convert_to_coreml(
          "safety_checker",
          traced_safety_checker,
          coreml_sample_safety_checker_inputs,
          ["filtered_images", "has_nsfw_concepts", "concept_scores"],
          args,
      )
  
      # Set model metadata
      coreml_safety_checker.author = f"Please refer to the Model Card available at huggingface.co/{args.model_version}"
      coreml_safety_checker.license = "OpenRAIL (https://huggingface.co/spaces/CompVis/stable-diffusion-license)"
      coreml_safety_checker.version = args.model_version
      coreml_safety_checker.short_description = (
          "Stable Diffusion generates images conditioned on text and/or other images as input through the diffusion process. "
          "Please refer to https://arxiv.org/abs/2112.10752 for details."
      )
  
      # Set the input descriptions
      coreml_safety_checker.input_description[
          "clip_input"
      ] = "The normalized image input tensor resized to (224x224) in channels-first (BCHW) format"
      coreml_safety_checker.input_description[
          "images"
      ] = f"Output of the vae_decoder ({pipe.vae.config.sample_size}x{pipe.vae.config.sample_size}) in channels-last (BHWC) format"
      coreml_safety_checker.input_description[
          "adjustment"
      ] = "Bias added to the concept scores to trade off increased recall for reduce precision in the safety checker classifier"
  
      # Set the output descriptions
      coreml_safety_checker.output_description["filtered_images"] = (
          f"Identical to the input `images`. If safety checker detected any sensitive content, "
          "the corresponding image is replaced with a blank image (zeros)"
      )
      coreml_safety_checker.output_description[
          "has_nsfw_concepts"
      ] = "Indicates whether the safety checker model found any sensitive content in the given image"
      coreml_safety_checker.output_description["concept_scores"] = (
          "Concept scores are the scores before thresholding at zero yields the `has_nsfw_concepts` output. "
          "These scores can be used to tune the `adjustment` input"
      )
  
      coreml_safety_checker.save(out_path)
  
      if args.check_output_correctness:
          baseline_out = pipe.safety_checker(
              **sample_safety_checker_inputs
          )[2].numpy()
          coreml_out = coreml_safety_checker.predict(
              coreml_sample_safety_checker_inputs
          )["concept_scores"]
          report_correctness(
              baseline_out,
              coreml_out,
              "safety_checker baseline PyTorch to reference CoreML",
          )
  
      del (
          traced_safety_checker,
          coreml_safety_checker,
          pipe.safety_checker,
      )
      gc.collect()
  
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 16 for calling a global function with example usage: convert_safety_checker(pipe, args) and returns: images,  has_nsfw_concepts,  concept_scores, images,  has_nsfw_concepts,  concept_scores, unknown, torch