code: def test_inference_superresolution(self):
      init_image = load_image(
          "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"
          "/vq_diffusion/teddy_bear_pool.png"
      )
      init_image = init_image.resize(
          (64, 64), resample=PIL_INTERPOLATION["lanczos"]
      )
  
      ldm = LDMSuperResolutionPipeline.from_pretrained(
          "duongna/ldm-super-resolution", device_map="auto"
      )
      ldm.set_progress_bar_config(disable=None)
  
      generator = torch.manual_seed(0)
      image = ldm(
          image=init_image,
          generator=generator,
          num_inference_steps=20,
          output_type="numpy",
      ).images
  
      image_slice = image[0, -3:, -3:, -1]
  
      assert image.shape == (1, 256, 256, 3)
      expected_slice = np.array(
          [
              0.7644,
              0.7679,
              0.7642,
              0.7633,
              0.7666,
              0.7560,
              0.7425,
              0.7257,
              0.6907,
          ]
      )
  
      assert np.abs(image_slice.flatten() - expected_slice).max() < 1e-2
  
file_path: llmga/llmga/diffusers/tests/pipelines/latent_diffusion/test_latent_diffusion_superresolution.py
imports: import random
  import unittest
  import numpy
  import torch
  from diffusers import DDIMScheduler, LDMSuperResolutionPipeline, UNet2DModel, VQModel
  from diffusers.utils import PIL_INTERPOLATION
  from diffusers.utils.testing_utils import enable_full_determinism, floats_tensor, load_image, nightly, require_torch, torch_device
label_desc: Use image node_id 1 for calling the LDMSuperResolutionPipelineIntegrationTests obj's underlying member method code with example usage: obj.test_inference_superresolution() without return types