code: def super_resolution(
      self, image, steps=100, target_scale=2, half_attention=False
  ):
      model = self.load_model_from_config(half_attention)
  
      # Run settings
      diffusion_steps = int(steps)
      eta = 1.0
  
      gc.collect()
      devices.torch_gc()
  
      im_og = image
      width_og, height_og = im_og.size
      # If we can adjust the max upscale size, then the 4 below should be our variable
      down_sample_rate = target_scale / 4
      wd = width_og * down_sample_rate
      hd = height_og * down_sample_rate
      width_downsampled_pre = int(np.ceil(wd))
      height_downsampled_pre = int(np.ceil(hd))
  
      if down_sample_rate != 1:
          print(
              f"Downsampling from [{width_og}, {height_og}] to [{width_downsampled_pre}, {height_downsampled_pre}]"
          )
          im_og = im_og.resize(
              (width_downsampled_pre, height_downsampled_pre),
              Image.LANCZOS,
          )
      else:
          print(
              f"Down sample rate is 1 from {target_scale} / 4 (Not downsampling)"
          )
  
      # pad width and height to multiples of 64, pads with the edge values of image to avoid artifacts
      pad_w, pad_h = (
          np.max(
              ((2, 2), np.ceil(np.array(im_og.size) / 64).astype(int)),
              axis=0,
          )
          * 64
          - im_og.size
      )
      im_padded = Image.fromarray(
          np.pad(
              np.array(im_og),
              ((0, pad_h), (0, pad_w), (0, 0)),
              mode="edge",
          )
      )
  
      logs = self.run(model["model"], im_padded, diffusion_steps, eta)
  
      sample = logs["sample"]
      sample = sample.detach().cpu()
      sample = torch.clamp(sample, -1.0, 1.0)
      sample = (sample + 1.0) / 2.0 * 255
      sample = sample.numpy().astype(np.uint8)
      sample = np.transpose(sample, (0, 2, 3, 1))
      a = Image.fromarray(sample[0])
  
      # remove padding
      a = a.crop((0, 0) + tuple(np.array(im_og.size) * 4))
  
      del model
      gc.collect()
      devices.torch_gc()
  
      return a
  
imports: import os
  import gc
  import time
  import numpy
  import torch
  import torchvision
  from PIL import Image
  from einops import rearrange, repeat
  from omegaconf import OmegaConf
  import safetensors.torch
  from ldm.models.diffusion.ddim import DDIMSampler
  from ldm.util import instantiate_from_config, ismap
  from modules import shared, sd_hijack, devices
label_desc: Use image node_id 4 for calling the LDSR obj's underlying member method code with example usage: obj.super_resolution(image, steps, target_scale, half_attention) and returns: a