code: def make_convolutional_sample(
      batch,
      model,
      mode="vanilla",
      custom_steps=None,
      eta=1.0,
      swap_mode=False,
      masked=False,
      invert_mask=True,
      quantize_x0=False,
      custom_schedule=None,
      decode_interval=1000,
      resize_enabled=False,
      custom_shape=None,
      temperature=1.0,
      noise_dropout=0.0,
      corrector=None,
      corrector_kwargs=None,
      x_T=None,
      save_intermediate_vid=False,
      make_progrow=True,
      ddim_use_x0_pred=False,
  ):
      log = dict()
  
      z, c, x, xrec, xc = model.get_input(
          batch,
          model.first_stage_key,
          return_first_stage_outputs=True,
          force_c_encode=not (
              hasattr(model, "split_input_params")
              and model.cond_stage_key == "coordinates_bbox"
          ),
          return_original_cond=True,
      )
  
      log_every_t = 1 if save_intermediate_vid else None
  
      if custom_shape is not None:
          z = torch.randn(custom_shape)
          print(
              f"Generating {custom_shape[0]} samples of shape {custom_shape[1:]}"
          )
  
      z0 = None
  
      log["input"] = x
      log["reconstruction"] = xrec
  
      if ismap(xc):
          log["original_conditioning"] = model.to_rgb(xc)
          if hasattr(model, "cond_stage_key"):
              log[model.cond_stage_key] = model.to_rgb(xc)
  
      else:
          log["original_conditioning"] = (
              xc if xc is not None else torch.zeros_like(x)
          )
          if model.cond_stage_model:
              log[model.cond_stage_key] = (
                  xc if xc is not None else torch.zeros_like(x)
              )
              if model.cond_stage_key == "class_label":
                  log[model.cond_stage_key] = xc[model.cond_stage_key]
  
      with model.ema_scope("Plotting"):
          t0 = time.time()
          img_cb = None
  
          sample, intermediates = convsample_ddim(
              model,
              c,
              steps=custom_steps,
              shape=z.shape,
              eta=eta,
              quantize_x0=quantize_x0,
              img_callback=img_cb,
              mask=None,
              x0=z0,
              temperature=temperature,
              noise_dropout=noise_dropout,
              score_corrector=corrector,
              corrector_kwargs=corrector_kwargs,
              x_T=x_T,
              log_every_t=log_every_t,
          )
          t1 = time.time()
  
          if ddim_use_x0_pred:
              sample = intermediates["pred_x0"][-1]
  
      x_sample = model.decode_first_stage(sample)
  
      try:
          x_sample_noquant = model.decode_first_stage(
              sample, force_not_quantize=True
          )
          log["sample_noquant"] = x_sample_noquant
          log["sample_diff"] = torch.abs(x_sample_noquant - x_sample)
      except:
          pass
  
      log["sample"] = x_sample
      log["time"] = t1 - t0
  
      return log
  
imports: from torchvision.datasets.utils import download_url
  from ldm.util import instantiate_from_config
  import torch
  import os
  from google.colab import files
  from IPython.display import Image
  import ipywidgets
  from PIL import Image
  from numpy import asarray
  from einops import rearrange, repeat
  import torch, torchvision
  from ldm.models.diffusion.ddim import DDIMSampler
  from ldm.util import ismap
  import time
  from omegaconf import OmegaConf
label_desc: Use image node_id 11 for calling a global function with example usage: make_convolutional_sample(batch, model, mode, custom_steps, eta, swap_mode, masked, invert_mask, quantize_x0, custom_schedule, decode_interval, resize_enabled, custom_shape, temperature, noise_dropout, corrector, corrector_kwargs, x_T, save_intermediate_vid, make_progrow, ddim_use_x0_pred) and returns: log