code: def __init__(
      self,
      model_id: ModelArchitecture,
      model_path="checkpoints",
      config_path="configs/inference",
      device="cuda",
      use_fp16=True,
  ) -> None:
      if model_id not in model_specs:
          raise ValueError(f"Model {model_id} not supported")
      self.model_id = model_id
      self.specs = model_specs[self.model_id]
      self.config = str(pathlib.Path(config_path, self.specs.config))
      self.ckpt = str(pathlib.Path(model_path, self.specs.ckpt))
      self.device = device
      self.model = self._load_model(device=device, use_fp16=use_fp16)
  
imports: import pathlib
  from dataclasses import asdict, dataclass
  from enum import Enum
  from typing import Optional
  from omegaconf import OmegaConf
  from sgm.inference.helpers import Img2ImgDiscretizationWrapper, do_img2img, do_sample
  from sgm.modules.diffusionmodules.sampling import DPMPP2MSampler, DPMPP2SAncestralSampler, EulerAncestralSampler, EulerEDMSampler, HeunEDMSampler, LinearMultistepSampler
  from sgm.util import load_model_from_config
label_desc: Use image node_id 1 to create a new SamplingPipeline object  with example: obj = SamplingPipeline(model_id, model_path, config_path, device, use_fp16)