code: def convsample(
      model,
      shape,
      return_intermediates=True,
      verbose=True,
      make_prog_row=False,
  ):
      if not make_prog_row:
          return model.p_sample_loop(
              None,
              shape,
              return_intermediates=return_intermediates,
              verbose=verbose,
          )
      else:
          return model.progressive_denoising(None, shape, verbose=True)
  
imports: import argparse
  import datetime
  import glob
  import os
  import sys
  import time
  import numpy
  import torch
  import yaml
  from ldm.models.diffusion.ddim import DDIMSampler
  from ldm.util import instantiate_from_config
  from omegaconf import OmegaConf
  from PIL import Image
  from tqdm import trange
label_desc: Use image node_id 4 for calling a global function with example usage: convsample(model, shape, return_intermediates, verbose, make_prog_row) and returns: model, model