code: def test_inference_superresolution_fp16(self):
      unet = self.dummy_uncond_unet
      scheduler = DDIMScheduler()
      vqvae = self.dummy_vq_model
  
      # put models in fp16
      unet = unet.half()
      vqvae = vqvae.half()
  
      ldm = LDMSuperResolutionPipeline(
          unet=unet, vqvae=vqvae, scheduler=scheduler
      )
      ldm.to(torch_device)
      ldm.set_progress_bar_config(disable=None)
  
      init_image = self.dummy_image.to(torch_device)
  
      image = ldm(
          init_image, num_inference_steps=2, output_type="numpy"
      ).images
  
      assert image.shape == (1, 64, 64, 3)
  
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 5 for calling the LDMSuperResolutionPipelineFastTests obj's underlying member method code with example usage: obj.test_inference_superresolution_fp16() without return types