code: def retrieve_timesteps(
      scheduler,
      num_inference_steps: Optional[int] = None,
      device: Optional[Union[str, torch.device]] = None,
      timesteps: Optional[List[int]] = None,
      **kwargs,
  ):
      """
      Calls the scheduler's `set_timesteps` method and retrieves timesteps from the scheduler after the call. Handles
      custom timesteps. Any kwargs will be supplied to `scheduler.set_timesteps`.
  
      Args:
          scheduler (`SchedulerMixin`):
              The scheduler to get timesteps from.
          num_inference_steps (`int`):
              The number of diffusion steps used when generating samples with a pre-trained model. If used,
              `timesteps` must be `None`.
          device (`str` or `torch.device`, *optional*):
              The device to which the timesteps should be moved to. If `None`, the timesteps are not moved.
          timesteps (`List[int]`, *optional*):
                  Custom timesteps used to support arbitrary spacing between timesteps. If `None`, then the default
                  timestep spacing strategy of the scheduler is used. If `timesteps` is passed, `num_inference_steps`
                  must be `None`.
  
      Returns:
          `Tuple[torch.Tensor, int]`: A tuple where the first element is the timestep schedule from the scheduler and the
          second element is the number of inference steps.
      """
      if timesteps is not None:
          accepts_timesteps = "timesteps" in set(
              inspect.signature(
                  scheduler.set_timesteps
              ).parameters.keys()
          )
          if not accepts_timesteps:
              raise ValueError(
                  f"The current scheduler class {scheduler.__class__}'s `set_timesteps` does not support custom"
                  f" timestep schedules. Please check whether you are using the correct scheduler."
              )
          scheduler.set_timesteps(
              timesteps=timesteps, device=device, **kwargs
          )
          timesteps = scheduler.timesteps
          num_inference_steps = len(timesteps)
      else:
          scheduler.set_timesteps(
              num_inference_steps, device=device, **kwargs
          )
          timesteps = scheduler.timesteps
      return timesteps, num_inference_steps
  
imports: import inspect
  import os
  from typing import Any, Callable, Dict, List, Optional, Tuple, Union
  import torch
  from PIL import Image
  from transformers import CLIPImageProcessor, CLIPTextModel, CLIPTextModelWithProjection, CLIPTokenizer, CLIPVisionModelWithProjection
  from diffusers import DiffusionPipeline, StableDiffusionXLPipeline
  from diffusers.image_processor import PipelineImageInput, VaeImageProcessor
  from diffusers.loaders import FromSingleFileMixin, IPAdapterMixin, LoraLoaderMixin, TextualInversionLoaderMixin
  from diffusers.models import AutoencoderKL, ImageProjection, UNet2DConditionModel
  from diffusers.models.attention_processor import AttnProcessor2_0, FusedAttnProcessor2_0, LoRAAttnProcessor2_0, LoRAXFormersAttnProcessor, XFormersAttnProcessor
  from diffusers.pipelines.stable_diffusion_xl.pipeline_output import StableDiffusionXLPipelineOutput
  from diffusers.schedulers import KarrasDiffusionSchedulers
  from diffusers.utils import deprecate, is_accelerate_available, is_accelerate_version, is_invisible_watermark_available, logging, replace_example_docstring
  from diffusers.utils.torch_utils import randn_tensor
label_desc: Use image node_id 7 for calling a global function with example usage: retrieve_timesteps(scheduler, num_inference_steps, device, timesteps) and returns: timesteps,  num_inference_steps