code: def save_model(model: Any, path: Union[Path, str]):
      """Save the optimized model in the given path.
  
      Args:
          model (Any): Model to be saved.
          path (Union[Path, str]): Path to the directory where to
              save the model.
  
      Returns:
          InferenceLearner: Model optimized by Speedster.
      """
      if isinstance(model, StableDiffusionPipeline):
          model.unet.model.save(path)
      else:
          model.save(path)
  
imports: from pathlib import Path
  from typing import Union, Any
  from nebullvm.operations.inference_learners.base import LearnerMetadata
  from nebullvm.optional_modules.diffusers import StableDiffusionPipeline
  from nebullvm.tools.diffusers import postprocess_diffusers
label_desc: Use image node_id 2 for calling a global function with example usage: save_model(model, path) without return types