code: def test_stable_diffusion_panorama_views_batch_circular_padding(self):
      device = "cpu"  # ensure determinism for the device-dependent torch.Generator
      components = self.get_dummy_components()
      sd_pipe = StableDiffusionPanoramaPipeline(**components)
      sd_pipe = sd_pipe.to(device)
      sd_pipe.set_progress_bar_config(disable=None)
  
      inputs = self.get_dummy_inputs(device)
      output = sd_pipe(
          **inputs, circular_padding=True, view_batch_size=2
      )
      image = output.images
      image_slice = image[0, -3:, -3:, -1]
  
      assert image.shape == (1, 64, 64, 3)
  
      expected_slice = np.array(
          [
              0.6127,
              0.6299,
              0.4595,
              0.4051,
              0.4543,
              0.3925,
              0.5510,
              0.5693,
              0.5031,
          ]
      )
  
      assert np.abs(image_slice.flatten() - expected_slice).max() < 1e-2
  
imports: import gc
  import unittest
  import numpy
  import torch
  from transformers import CLIPTextConfig, CLIPTextModel, CLIPTokenizer
  from diffusers import AutoencoderKL, DDIMScheduler, EulerAncestralDiscreteScheduler, LMSDiscreteScheduler, PNDMScheduler, StableDiffusionPanoramaPipeline, UNet2DConditionModel
  from diffusers.utils.testing_utils import enable_full_determinism, nightly, require_torch_gpu, skip_mps, torch_device
  from ..pipeline_params import TEXT_TO_IMAGE_BATCH_PARAMS, TEXT_TO_IMAGE_IMAGE_PARAMS, TEXT_TO_IMAGE_PARAMS
  from ..test_pipelines_common import PipelineLatentTesterMixin, PipelineTesterMixin
label_desc: Use image node_id 10 for calling the StableDiffusionPanoramaPipelineFastTests obj's underlying member method code with example usage: obj.test_stable_diffusion_panorama_views_batch_circular_padding() without return types