code: def space_timesteps(
      num_timesteps: int, section_counts: Union[List[int], str]
  ) -> Set[int]:
      """
      Create a list of timesteps to use from an original diffusion process,
      given the number of timesteps we want to take from equally-sized portions
      of the original process.
  
      For example, if there's 300 timesteps and the section counts are [10,15,20]
      then the first 100 timesteps are strided to be 10 timesteps, the second 100
      are strided to be 15 timesteps, and the final 100 are strided to be 20.
  
      If the stride is a string starting with "ddim", then the fixed striding
      from the DDIM paper is used, and only one section is allowed.
  
      Adapted from `improved_diffusion.respace.space_timesteps`
      (https://github.com/openai/improved-diffusion) to add "vlbN" method.
  
      Args:
          num_timesteps: The number of diffusion steps in the original
                            process to divide up.
          section_counts: Either a list of numbers, or a string containing
                             comma-separated numbers, indicating the step count
                             per section. As a special cases, use "vlbN" or
                             "ddimN" where N is a number of steps to use a dense
                             inital striding or the striding from the DDIM paper.
      Returns:
          The subset of diffusion steps.
      """
      all_steps: List[int] = []
  
      if isinstance(section_counts, str):
          if section_counts.startswith("ddim"):
              desired_count = int(section_counts[len("ddim") :])
              for i in range(1, num_timesteps):
                  if len(range(0, num_timesteps, i)) == desired_count:
                      logger.log(
                          f"respaced to {desired_count} steps..."
                      )
                      return set(range(0, num_timesteps, i))
              raise ValueError(
                  f"cannot create exactly {num_timesteps} steps with an integer stride"
              )
          if section_counts.startswith("vlb"):
              desired_count = int(section_counts[len("vlb") :])
              if (num_timesteps // desired_count) < desired_count:
                  all_steps += list(
                      range(num_timesteps // desired_count)
                  )
                  section_counts = [
                      desired_count - num_timesteps // desired_count + 1
                  ]
              else:
                  raise ValueError("please select more steps.")
          else:
              section_counts = [
                  int(x) for x in section_counts.split(",")
              ]
  
      start_idx = 0
      size_per = num_timesteps // len(section_counts)
      extra = num_timesteps % len(section_counts)
      for i, section_count in enumerate(section_counts):
          size = size_per + (1 if i < extra else 0)
          if size < section_count:
              raise ValueError(
                  f"cannot divide section of {size} steps into {section_count}"
              )
          if section_count <= 1:
              frac_stride = 1.0
          else:
              frac_stride = (size - 1) / (section_count - 1)
          cur_idx = 0.0
          taken_steps = []
          for _ in range(section_count):
              taken_steps.append(start_idx + round(cur_idx))
              cur_idx += frac_stride
          all_steps += taken_steps
          start_idx += size
      steps = set(all_steps)
      logger.log(f"respaced to {len(steps)} steps...")
      return steps
  
imports: import copy
  import hashlib
  from pathlib import Path
  from typing import List, Set, Union
  import numpy
  import plotly.graph_objs
  import torch
  import torch.distributed
  import wandb
  from hydra.core.hydra_config import HydraConfig
  from hydra.utils import instantiate, to_absolute_path
  from improved_diffusion import dist_util
  from improved_diffusion import gaussian_diffusion
  from improved_diffusion import logger, respace, train_util
  from omegaconf import DictConfig, OmegaConf
label_desc: Use image node_id 5 for calling a global function with example usage: space_timesteps(num_timesteps, section_counts) and returns: steps, set