code: def infer(prompt, samples, steps, scale, seed):
      generator = torch.Generator(device=device).manual_seed(seed)
      images_list = pipe(
          [prompt] * samples,
          num_inference_steps=steps,
          guidance_scale=scale,
          generator=generator,
      )
      images = []
      safe_image = Image.open(r"unsafe.png")
      for i, image in enumerate(images_list["sample"]):
          if images_list["nsfw_content_detected"][i]:
              images.append(safe_image)
          else:
              images.append(image)
      return images
  
imports: import gradio
  import torch
  from diffusers import StableDiffusionPipeline
  from PIL import Image
  import os
label_desc: Use image node_id 1 for calling a global function with example usage: infer(prompt, samples, steps, scale, seed) and returns: images