code: def __adapt(self):
      if not check_module_version(torch, max_version="1.13.1+cu117"):
          raise ValueError(
              "Diffusion models are only supported in PyTorch "
              "versions <= 1.13.1. Please downgrade your PyTorch "
              "version and try again."
          )
  
      model = copy.deepcopy(self.original_pipeline)
      model.get_unet_inputs = get_unet_inputs
      model.to(self.device.to_torch_format())
      self.__df_data = [
          (
              tuple(
                  d.reshape((1,)) if d.shape == torch.Size([]) else d
                  for d in model.get_unet_inputs(
                      model,
                      prompt=prompt,
                  )
                  if d is not None
              ),
              None,
          )
          for prompt in self.original_data
      ]
      self.__df_model = preprocess_diffusers(model)
      self.__adapted = True
  
imports: import abc
  import copy
  from abc import abstractmethod
  import time
  from typing import List, Any, Union
  from loguru import logger
  from nebullvm.core.models import Device, DeviceType, OptimizedModel, OriginalModel
  from nebullvm.operations.conversions.huggingface import convert_hf_model
  from nebullvm.operations.inference_learners.base import BaseInferenceLearner
  from nebullvm.operations.inference_learners.huggingface import DiffusionInferenceLearner
  from nebullvm.optional_modules.diffusers import StableDiffusionPipeline
  from nebullvm.optional_modules.torch import torch
  from nebullvm.tools.diffusers import get_unet_inputs, preprocess_diffusers, postprocess_diffusers
  from nebullvm.tools.pytorch import get_torch_model_size
  from nebullvm.tools.utils import is_huggingface_data, check_module_version, get_throughput
label_desc: Use image node_id 3 for calling the DiffusionAdapter obj's underlying member method code with example usage: obj.__adapt() without return types