code: def space_timesteps(num_timesteps, section_counts):
      """
      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.
  
      :param num_timesteps: the number of diffusion steps in the original
                            process to divide up.
      :param section_counts: either a list of numbers, or a string containing
                             comma-separated numbers, indicating the step count
                             per section. As a special case, use "ddimN" where N
                             is a number of steps to use the striding from the
                             DDIM paper.
      :return: a set of diffusion steps from the original process to use.
      """
      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:
                      return set(range(0, num_timesteps, i))
              raise ValueError(
                  f"cannot create exactly {num_timesteps} steps with an integer stride"
              )
          section_counts = [int(x) for x in section_counts.split(",")]
      size_per = num_timesteps // len(section_counts)
      extra = num_timesteps % len(section_counts)
      start_idx = 0
      all_steps = []
      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
          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
      return set(all_steps)
  
imports: import enum
  import math
  import numpy
  import torch
  import torch
  from tqdm import tqdm
  from TTS.tts.layers.tortoise.dpm_solver import DPM_Solver, NoiseScheduleVP, model_wrapper
label_desc: Use image node_id 7 for calling a global function with example usage: space_timesteps(num_timesteps, section_counts) and returns: set, set