code: def test_unclip_karlo_cpu_fp32(self):
      expected_image = load_numpy(
          "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"
          "/unclip/karlo_v1_alpha_horse_cpu.npy"
      )
  
      pipeline = UnCLIPPipeline.from_pretrained(
          "kakaobrain/karlo-v1-alpha"
      )
      pipeline.set_progress_bar_config(disable=None)
  
      generator = torch.manual_seed(0)
      output = pipeline(
          "horse",
          num_images_per_prompt=1,
          generator=generator,
          output_type="np",
      )
  
      image = output.images[0]
  
      assert image.shape == (256, 256, 3)
      assert np.abs(expected_image - image).max() < 1e-1
  
imports: import gc
  import unittest
  import numpy
  import torch
  from transformers import CLIPTextConfig, CLIPTextModelWithProjection, CLIPTokenizer
  from diffusers import PriorTransformer, UnCLIPPipeline, UnCLIPScheduler, UNet2DConditionModel, UNet2DModel
  from diffusers.pipelines.unclip.text_proj import UnCLIPTextProjModel
  from diffusers.utils.testing_utils import enable_full_determinism, load_numpy, nightly, require_torch_gpu, skip_mps, torch_device
  from ..pipeline_params import TEXT_TO_IMAGE_BATCH_PARAMS, TEXT_TO_IMAGE_PARAMS
  from ..test_pipelines_common import PipelineTesterMixin, assert_mean_pixel_difference
label_desc: Use image node_id 2 for calling the UnCLIPPipelineCPUIntegrationTests obj's underlying member method code with example usage: obj.test_unclip_karlo_cpu_fp32() without return types