code: def test_from_save_pretrained(self):
      pipe = VersatileDiffusionPipeline.from_pretrained(
          "shi-labs/versatile-diffusion", torch_dtype=torch.float16
      )
      pipe.to(torch_device)
      pipe.set_progress_bar_config(disable=None)
  
      prompt_image = load_image(
          "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/versatile_diffusion/benz.jpg"
      )
  
      generator = torch.manual_seed(0)
      image = pipe.dual_guided(
          prompt="first prompt",
          image=prompt_image,
          text_to_image_strength=0.75,
          generator=generator,
          guidance_scale=7.5,
          num_inference_steps=2,
          output_type="numpy",
      ).images
  
      with tempfile.TemporaryDirectory() as tmpdirname:
          pipe.save_pretrained(tmpdirname)
          pipe = VersatileDiffusionPipeline.from_pretrained(
              tmpdirname, torch_dtype=torch.float16
          )
      pipe.to(torch_device)
      pipe.set_progress_bar_config(disable=None)
  
      generator = generator.manual_seed(0)
      new_image = pipe.dual_guided(
          prompt="first prompt",
          image=prompt_image,
          text_to_image_strength=0.75,
          generator=generator,
          guidance_scale=7.5,
          num_inference_steps=2,
          output_type="numpy",
      ).images
  
      assert (
          np.abs(image - new_image).max() < 1e-5
      ), "Models don't have the same forward pass"
  
file_path: llmga/llmga/diffusers/tests/pipelines/versatile_diffusion/test_versatile_diffusion_mega.py
imports: import gc
  import tempfile
  import unittest
  import numpy
  import torch
  from diffusers import VersatileDiffusionPipeline
  from diffusers.utils.testing_utils import load_image, nightly, require_torch_gpu, torch_device
label_desc: Use image node_id 2 for calling the VersatileDiffusionMegaPipelineIntegrationTests obj's underlying member method code with example usage: obj.test_from_save_pretrained() without return types