code: def test_stable_diffusion_pix2pix_zero_euler(self):
      device = "cpu"  # ensure determinism for the device-dependent torch.Generator
      components = self.get_dummy_components()
      components["scheduler"] = EulerAncestralDiscreteScheduler(
          beta_start=0.00085,
          beta_end=0.012,
          beta_schedule="scaled_linear",
      )
      sd_pipe = StableDiffusionPix2PixZeroPipeline(**components)
      sd_pipe = sd_pipe.to(device)
      sd_pipe.set_progress_bar_config(disable=None)
  
      inputs = self.get_dummy_inputs(device)
      image = sd_pipe(**inputs).images
      image_slice = image[0, -3:, -3:, -1]
  
      assert image.shape == (1, 64, 64, 3)
      expected_slice = np.array(
          [
              0.5421,
              0.5525,
              0.6085,
              0.5279,
              0.4658,
              0.5317,
              0.4418,
              0.4815,
              0.5132,
          ]
      )
  
      assert np.abs(image_slice.flatten() - expected_slice).max() < 1e-3
  
imports: import gc
  import random
  import tempfile
  import unittest
  import numpy
  import torch
  from transformers import CLIPTextConfig, CLIPTextModel, CLIPTokenizer
  from diffusers import AutoencoderKL, DDIMInverseScheduler, DDIMScheduler, DDPMScheduler, EulerAncestralDiscreteScheduler, LMSDiscreteScheduler, StableDiffusionPix2PixZeroPipeline, UNet2DConditionModel
  from diffusers.image_processor import VaeImageProcessor
  from diffusers.utils.testing_utils import enable_full_determinism, floats_tensor, load_image, load_numpy, load_pt, nightly, require_torch_gpu, skip_mps, torch_device
  from ..pipeline_params import TEXT_GUIDED_IMAGE_VARIATION_BATCH_PARAMS, TEXT_GUIDED_IMAGE_VARIATION_PARAMS, TEXT_TO_IMAGE_IMAGE_PARAMS
  from ..test_pipelines_common import PipelineLatentTesterMixin, PipelineTesterMixin, assert_mean_pixel_difference
label_desc: Use image node_id 11 for calling the StableDiffusionPix2PixZeroPipelineFastTests obj's underlying member method code with example usage: obj.test_stable_diffusion_pix2pix_zero_euler() without return types