code: def decode(
      self,
      x_latent,
      cond,
      t_start,
      unconditional_guidance_scale=1.0,
      unconditional_conditioning=None,
      use_original_steps=False,
      callback=None,
  ):
      timesteps = (
          np.arange(self.ddpm_num_timesteps)
          if use_original_steps
          else self.ddim_timesteps
      )
      timesteps = timesteps[:t_start]
  
      time_range = np.flip(timesteps)
      total_steps = timesteps.shape[0]
      print(f"Running DDIM Sampling with {total_steps} timesteps")
  
      iterator = tqdm(
          time_range, desc="Decoding image", total=total_steps
      )
      x_dec = x_latent
      for i, step in enumerate(iterator):
          index = total_steps - i - 1
          ts = torch.full(
              (x_latent.shape[0],),
              step,
              device=x_latent.device,
              dtype=torch.long,
          )
          x_dec, _ = self.p_sample_ddim(
              x_dec,
              cond,
              ts,
              index=index,
              use_original_steps=use_original_steps,
              unconditional_guidance_scale=unconditional_guidance_scale,
              unconditional_conditioning=unconditional_conditioning,
          )
          if callback:
              callback(i)
      return x_dec
  
file_path: StableDiffusion/ldm/models/diffusion/ddim.py
imports: import torch
  import numpy
  from tqdm import tqdm
  from ldm.modules.diffusionmodules.util import make_ddim_sampling_parameters, make_ddim_timesteps, noise_like, extract_into_tensor
label_desc: Use image node_id 9 for calling the DDIMSampler obj's underlying member method code with example usage: obj.decode(x_latent, cond, t_start, unconditional_guidance_scale, unconditional_conditioning, use_original_steps, callback) and returns: x_dec