code: def test_inference_dual_guided(self):
      pipe = VersatileDiffusionDualGuidedPipeline.from_pretrained(
          "shi-labs/versatile-diffusion"
      )
      pipe.remove_unused_weights()
      pipe.to(torch_device)
      pipe.set_progress_bar_config(disable=None)
  
      first_prompt = "cyberpunk 2077"
      second_prompt = load_image(
          "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/versatile_diffusion/benz.jpg"
      )
      generator = torch.manual_seed(0)
      image = pipe(
          prompt=first_prompt,
          image=second_prompt,
          text_to_image_strength=0.75,
          generator=generator,
          guidance_scale=7.5,
          num_inference_steps=50,
          output_type="numpy",
      ).images
  
      image_slice = image[0, 253:256, 253:256, -1]
  
      assert image.shape == (1, 512, 512, 3)
      expected_slice = np.array(
          [
              0.0787,
              0.0849,
              0.0826,
              0.0812,
              0.0807,
              0.0795,
              0.0818,
              0.0798,
              0.0779,
          ]
      )
  
      assert np.abs(image_slice.flatten() - expected_slice).max() < 1e-2
  
file_path: llmga/llmga/diffusers/tests/pipelines/versatile_diffusion/test_versatile_diffusion_dual_guided.py
imports: import gc
  import tempfile
  import unittest
  import numpy
  import torch
  from diffusers import VersatileDiffusionDualGuidedPipeline
  from diffusers.utils.testing_utils import load_image, nightly, require_torch_gpu, torch_device
label_desc: Use image node_id 3 for calling the VersatileDiffusionDualGuidedPipelineIntegrationTests obj's underlying member method code with example usage: obj.test_inference_dual_guided() without return types