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
  from typing import Any, Callable, Dict, List, Optional, Union
  import torch
  import torch.nn
  import torch.nn.functional
  from packaging import version
  from safetensors import safe_open
  from transformers import CLIPImageProcessor, CLIPTextModel, CLIPTokenizer, CLIPVisionModelWithProjection
  from diffusers.configuration_utils import FrozenDict
  from diffusers.image_processor import VaeImageProcessor
  from diffusers.loaders import FromSingleFileMixin, IPAdapterMixin, LoraLoaderMixin, TextualInversionLoaderMixin
  from diffusers.models import AutoencoderKL, UNet2DConditionModel
  from diffusers.models.attention_processor import FusedAttnProcessor2_0
  from diffusers.models.lora import LoRALinearLayer, adjust_lora_scale_text_encoder
  from diffusers.pipelines.pipeline_utils import DiffusionPipeline
  from diffusers.pipelines.stable_diffusion.pipeline_output import StableDiffusionPipelineOutput
  from diffusers.pipelines.stable_diffusion.safety_checker import StableDiffusionSafetyChecker
  from diffusers.schedulers import KarrasDiffusionSchedulers
  from diffusers.utils import USE_PEFT_BACKEND, _get_model_file, deprecate, logging, scale_lora_layers, unscale_lora_layers
  from diffusers.utils.torch_utils import randn_tensor
label_desc: Use image node_id 2 for calling a global function with example usage: retrieve_timesteps(scheduler, num_inference_steps, device, timesteps) and returns: timesteps,  num_inference_steps