code: def run_safety_checker(self, image, device, dtype):
      if self.safety_checker is None:
          has_nsfw_concept = None
      else:
          if torch.is_tensor(image):
              feature_extractor_input = (
                  self.image_processor.postprocess(
                      image, output_type="pil"
                  )
              )
          else:
              feature_extractor_input = (
                  self.image_processor.numpy_to_pil(image)
              )
          safety_checker_input = self.feature_extractor(
              feature_extractor_input, return_tensors="pt"
          ).to(device)
          image, has_nsfw_concept = self.safety_checker(
              images=image,
              clip_input=safety_checker_input.pixel_values.to(dtype),
          )
      return image, has_nsfw_concept
  
imports: import inspect
  from typing import Any, Dict, List, Optional, Union
  import torch
  import torch.nn
  from transformers import AutoModel, AutoTokenizer, CLIPImageProcessor
  from diffusers import DiffusionPipeline
  from diffusers.image_processor import VaeImageProcessor
  from diffusers.loaders import LoraLoaderMixin
  from diffusers.models import AutoencoderKL, UNet2DConditionModel
  from diffusers.models.lora import adjust_lora_scale_text_encoder
  from diffusers.pipelines.stable_diffusion.pipeline_output import StableDiffusionPipelineOutput
  from diffusers.pipelines.stable_diffusion.safety_checker import StableDiffusionSafetyChecker
  from diffusers.schedulers import KarrasDiffusionSchedulers
  from diffusers.utils import USE_PEFT_BACKEND, logging, scale_lora_layers, unscale_lora_layers
  from diffusers.utils.torch_utils import randn_tensor
label_desc: Use image node_id 9 for calling the GlueGenStableDiffusionPipeline obj's underlying member method code with example usage: obj.run_safety_checker(image, device, dtype) and returns: image,  has_nsfw_concept