code: def test_stable_diffusion_inpaint_legacy_k_lms(self):
      pipe = StableDiffusionInpaintPipelineLegacy.from_pretrained(
          "CompVis/stable-diffusion-v1-4", safety_checker=None
      )
      pipe.scheduler = LMSDiscreteScheduler.from_config(
          pipe.scheduler.config
      )
      pipe.to(torch_device)
      pipe.set_progress_bar_config(disable=None)
      pipe.enable_attention_slicing()
  
      inputs = self.get_inputs()
      image = pipe(**inputs).images
      image_slice = image[0, 253:256, 253:256, -1].flatten()
  
      assert image.shape == (1, 512, 512, 3)
      expected_slice = np.array(
          [
              0.4534,
              0.4467,
              0.4329,
              0.4329,
              0.4339,
              0.4220,
              0.4244,
              0.4332,
              0.4426,
          ]
      )
  
      assert np.abs(expected_slice - image_slice).max() < 3e-3
  
imports: import gc
  import random
  import unittest
  import numpy
  import torch
  from PIL import Image
  from transformers import CLIPTextConfig, CLIPTextModel, CLIPTokenizer
  from diffusers import AutoencoderKL, DDIMScheduler, DPMSolverMultistepScheduler, LMSDiscreteScheduler, PNDMScheduler, StableDiffusionInpaintPipelineLegacy, UNet2DConditionModel, UNet2DModel, VQModel
  from diffusers.utils.testing_utils import enable_full_determinism, floats_tensor, load_image, load_numpy, nightly, preprocess_image, require_torch_gpu, slow, torch_device
label_desc: Use image node_id 5 for calling the StableDiffusionInpaintLegacyPipelineSlowTests obj's underlying member method code with example usage: obj.test_stable_diffusion_inpaint_legacy_k_lms() without return types