code: def run(model, selected_path, custom_steps, eta):
      example = get_cond(selected_path)
  
      n_runs = 1
      guider = None
      ckwargs = None
      ddim_use_x0_pred = False
      temperature = 1.0
      eta = eta
      custom_shape = None
  
      height, width = example["image"].shape[1:3]
      split_input = height >= 128 and width >= 128
  
      if split_input:
          ks = 128
          stride = 64
          vqf = 4  #
          model.split_input_params = {
              "ks": (ks, ks),
              "stride": (stride, stride),
              "vqf": vqf,
              "patch_distributed_vq": True,
              "tie_braker": False,
              "clip_max_weight": 0.5,
              "clip_min_weight": 0.01,
              "clip_max_tie_weight": 0.5,
              "clip_min_tie_weight": 0.01,
          }
      else:
          if hasattr(model, "split_input_params"):
              delattr(model, "split_input_params")
  
      x_t = None
      logs = None
      for _ in range(n_runs):
          if custom_shape is not None:
              x_t = torch.randn(
                  1, custom_shape[1], custom_shape[2], custom_shape[3]
              ).to(model.device)
              x_t = repeat(x_t, "1 c h w -> b c h w", b=custom_shape[0])
  
          logs = make_convolutional_sample(
              example,
              model,
              custom_steps=custom_steps,
              eta=eta,
              quantize_x0=False,
              custom_shape=custom_shape,
              temperature=temperature,
              noise_dropout=0.0,
              corrector=guider,
              corrector_kwargs=ckwargs,
              x_T=x_t,
              ddim_use_x0_pred=ddim_use_x0_pred,
          )
      return logs
  
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 3 for calling the LDSR obj's underlying member method code with example usage: obj.run(model, selected_path, custom_steps, eta) and returns: logs