code: def test_inference_superresolution(self):
      device = "cpu"
      unet = self.dummy_uncond_unet
      scheduler = DDIMScheduler()
      vqvae = self.dummy_vq_model
  
      ldm = LDMSuperResolutionPipeline(
          unet=unet, vqvae=vqvae, scheduler=scheduler
      )
      ldm.to(device)
      ldm.set_progress_bar_config(disable=None)
  
      init_image = self.dummy_image.to(device)
  
      generator = torch.Generator(device=device).manual_seed(0)
      image = ldm(
          image=init_image,
          generator=generator,
          num_inference_steps=2,
          output_type="numpy",
      ).images
  
      image_slice = image[0, -3:, -3:, -1]
  
      assert image.shape == (1, 64, 64, 3)
      expected_slice = np.array(
          [
              0.8678,
              0.8245,
              0.6381,
              0.6830,
              0.4385,
              0.5599,
              0.4641,
              0.6201,
              0.5150,
          ]
      )
  
      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 4 for calling the LDMSuperResolutionPipelineFastTests obj's underlying member method code with example usage: obj.test_inference_superresolution() without return types