code: def load_model(
      path: Union[Path, str], pipe: StableDiffusionPipeline = None
  ):
      """Load the optimized model previously saved in the given path.
  
      Args:
          path (Union[Path, str]): Path to the directory where the model is
              saved.
          pipe (StableDiffusionPipeline): Diffusion pipeline to be used for
              loading the model. This parameter is only needed if the model
              to be loaded is a diffusion model. Default: None.
  
      Returns:
          InferenceLearner: Model optimized by Speedster.
      """
      optimized_model = LearnerMetadata.read(path).load_model(path)
      if pipe is not None:
          optimized_model = postprocess_diffusers(
              optimized_model, pipe, optimized_model.device
          )
      return optimized_model
  
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 1 for calling a global function with example usage: load_model(path, pipe) and returns: optimized_model