code: def test_kandinsky3(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_slice = image[0, -3:, -3:, -1]
  
      assert image.shape == (1, 16, 16, 3)
  
      expected_slice = np.array(
          [
              0.3768,
              0.4373,
              0.4865,
              0.4890,
              0.4299,
              0.5122,
              0.4921,
              0.4924,
              0.5599,
          ]
      )
  
      assert (
          np.abs(image_slice.flatten() - expected_slice).max() < 1e-2
      ), f" expected_slice {expected_slice}, but got {image_slice.flatten()}"
  
imports: import gc
  import unittest
  import numpy
  import torch
  from PIL import Image
  from transformers import AutoTokenizer, T5EncoderModel
  from diffusers import AutoPipelineForImage2Image, AutoPipelineForText2Image, Kandinsky3Pipeline, Kandinsky3UNet, VQModel
  from diffusers.image_processor import VaeImageProcessor
  from diffusers.schedulers.scheduling_ddpm import DDPMScheduler
  from diffusers.utils.testing_utils import enable_full_determinism, load_image, require_torch_gpu, slow
  from ..pipeline_params import TEXT_TO_IMAGE_BATCH_PARAMS, TEXT_TO_IMAGE_CALLBACK_CFG_PARAMS, TEXT_TO_IMAGE_IMAGE_PARAMS, TEXT_TO_IMAGE_PARAMS
  from ..test_pipelines_common import PipelineTesterMixin
label_desc: Use image node_id 5 for calling the Kandinsky3PipelineFastTests obj's underlying member method code with example usage: obj.test_kandinsky3() without return types