code: def get_size(self):
      (
          self.pipeline.unet.model.get_size()
          + sum(
              [
                  get_torch_model_size(v)
                  for (k, v) in self.pipeline.__dict__.items()
                  if isinstance(v, torch.nn.Module) and k != "unet"
              ]
          )
          / 1e6
      )
  
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 7 for calling the DiffusionInferenceLearner obj's underlying member method code with example usage: obj.get_size() without return types