code: def test_kandinskyV3(self):
      pipe = AutoPipelineForText2Image.from_pretrained(
          "kandinsky-community/kandinsky-3",
          variant="fp16",
          torch_dtype=torch.float16,
      )
      pipe.enable_model_cpu_offload()
      pipe.set_progress_bar_config(disable=None)
  
      prompt = "A photograph of the inside of a subway train. There are raccoons sitting on the seats. One of them is reading a newspaper. The window shows the city in the background."
  
      generator = torch.Generator(device="cpu").manual_seed(0)
  
      image = pipe(
          prompt, num_inference_steps=25, generator=generator
      ).images[0]
  
      assert image.size == (1024, 1024)
  
      expected_image = load_image(
          "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/kandinsky3/t2i.png"
      )
  
      image_processor = VaeImageProcessor()
  
      image_np = image_processor.pil_to_numpy(image)
      expected_image_np = image_processor.pil_to_numpy(expected_image)
  
      self.assertTrue(
          np.allclose(image_np, expected_image_np, atol=5e-2)
      )
  
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 2 for calling the Kandinsky3PipelineIntegrationTests obj's underlying member method code with example usage: obj.test_kandinskyV3() without return types