code: def test_inference_text2img(self):
      pipe = VersatileDiffusionTextToImagePipeline.from_pretrained(
          "shi-labs/versatile-diffusion", torch_dtype=torch.float16
      )
      pipe.to(torch_device)
      pipe.set_progress_bar_config(disable=None)
  
      prompt = "A painting of a squirrel eating a burger "
      generator = torch.manual_seed(0)
      image = pipe(
          prompt=prompt,
          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(
          [
              0.3367,
              0.3169,
              0.2656,
              0.3870,
              0.4790,
              0.3796,
              0.4009,
              0.4878,
              0.4778,
          ]
      )
  
      assert np.abs(image_slice.flatten() - expected_slice).max() < 1e-2
  
file_path: llmga/llmga/diffusers/tests/pipelines/versatile_diffusion/test_versatile_diffusion_text_to_image.py
imports: import gc
  import tempfile
  import unittest
  import numpy
  import torch
  from diffusers import VersatileDiffusionTextToImagePipeline
  from diffusers.utils.testing_utils import nightly, require_torch_gpu, torch_device
label_desc: Use image node_id 3 for calling the VersatileDiffusionTextToImagePipelineIntegrationTests obj's underlying member method code with example usage: obj.test_inference_text2img() without return types