code: def get_timesteps(self, num_inference_steps, strength):
      # get the original timestep using init_timestep
      init_timestep = min(
          int(num_inference_steps * strength), num_inference_steps
      )
      t_start = max(num_inference_steps - init_timestep, 0)
      timesteps = self.scheduler.timesteps[
          t_start * self.scheduler.order :
      ].numpy()
  
      return timesteps, num_inference_steps - t_start
  
file_path: optimum/optimum/pipelines/diffusers/pipeline_stable_diffusion_xl_img2img.py
imports: import inspect
  import logging
  from typing import Any, Callable, Dict, List, Optional, Tuple, Union
  import numpy
  import PIL
  import torch
  from diffusers.pipelines.stable_diffusion_xl import StableDiffusionXLPipelineOutput
  from .pipeline_utils import DiffusionPipelineMixin, rescale_noise_cfg
label_desc: Use image node_id 3 for calling the StableDiffusionXLImg2ImgPipelineMixin obj's underlying member method code with example usage: obj.get_timesteps(num_inference_steps, strength) and returns: timesteps,  unknown