code: def load(
      cls,
      path: Union[Path, str],
      **kwargs,
  ):
      try:
          pipe = kwargs["pipe"]
      except KeyError:
          raise TypeError("Missing required argument 'pipe'")
      optimized_model = LearnerMetadata.read(path).load_model(path)
      return postprocess_diffusers(
          optimized_model,
          pipe,
          optimized_model.device,
      )
  
imports: from abc import ABC
  from collections import OrderedDict
  from pathlib import Path
  from typing import List, Any, Dict, Union
  from nebullvm.operations.inference_learners.base import InferenceLearnerWrapper, PytorchBaseInferenceLearner, LearnerMetadata, BaseInferenceLearner
  from nebullvm.optional_modules.diffusers import StableDiffusionPipeline
  from nebullvm.optional_modules.torch import torch
  from nebullvm.tools.diffusers import postprocess_diffusers
  from nebullvm.tools.huggingface import restructure_output
  from nebullvm.tools.pytorch import get_torch_model_size
label_desc: Use image node_id 6 for calling the DiffusionInferenceLearner obj's underlying member method code with example usage: obj.load(cls, path) and returns: postprocess_diffusers