code: def test_kandinsky_inpaint(self):
      device = "cpu"
  
      components = self.get_dummy_components()
  
      pipe = self.pipeline_class(**components)
      pipe = pipe.to(device)
  
      pipe.set_progress_bar_config(disable=None)
  
      output = pipe(**self.get_dummy_inputs(device))
      image = output.images
  
      image_from_tuple = pipe(
          **self.get_dummy_inputs(device),
          return_dict=False,
      )[0]
  
      image_slice = image[0, -3:, -3:, -1]
      image_from_tuple_slice = image_from_tuple[0, -3:, -3:, -1]
  
      assert image.shape == (1, 64, 64, 3)
  
      expected_slice = np.array(
          [
              0.50775903,
              0.49527195,
              0.48824543,
              0.50192237,
              0.48644906,
              0.49373814,
              0.4780598,
              0.47234827,
              0.48327848,
          ]
      )
  
      assert (
          np.abs(image_slice.flatten() - expected_slice).max() < 1e-2
      ), f" expected_slice {expected_slice}, but got {image_slice.flatten()}"
      assert (
          np.abs(
              image_from_tuple_slice.flatten() - expected_slice
          ).max()
          < 1e-2
      ), f" expected_slice {expected_slice}, but got {image_from_tuple_slice.flatten()}"
  
imports: import gc
  import random
  import unittest
  import numpy
  import torch
  from PIL import Image
  from diffusers import DDIMScheduler, KandinskyV22InpaintPipeline, KandinskyV22PriorPipeline, UNet2DConditionModel, VQModel
  from diffusers.utils.testing_utils import enable_full_determinism, floats_tensor, load_image, load_numpy, require_torch_gpu, slow, torch_device
  from ..test_pipelines_common import PipelineTesterMixin, assert_mean_pixel_difference
label_desc: Use image node_id 3 for calling the KandinskyV22InpaintPipelineFastTests obj's underlying member method code with example usage: obj.test_kandinsky_inpaint() without return types