code: def __init__(self, name=None, directory=None, use_gpu=False):
      # 设置模型路径
      model_path = os.path.join(self.directory, "model-small")
  
      # 加载模型
      self.model = InferenceModel(
          modelpath=model_path,
          use_gpu=use_gpu,
          use_mkldnn=False,
          combined=True,
      )
      self.model.eval()
  
      # 数据预处理配置
      self.net_h, self.net_w = 256, 256
      self.transform = Compose(
          [
              Resize(
                  self.net_w,
                  self.net_h,
                  resize_target=None,
                  keep_aspect_ratio=False,
                  ensure_multiple_of=32,
                  resize_method="upper_bound",
                  image_interpolation_method=cv2.INTER_CUBIC,
              ),
              NormalizeImage(
                  mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225]
              ),
              PrepareForNet(),
          ]
      )
  
file_path: PaddleHub/modules/image/depth_estimation/MiDaS_Small/module.py
imports: import os
  import cv2
  import numpy
  from paddlehub import Module
  from paddlehub.module.module import moduleinfo
  from paddle.vision.transforms import Compose
  from MiDaS_Small.utils import write_depth
  from MiDaS_Small.inference import InferenceModel
  from MiDaS_Small.transforms import Resize, NormalizeImage, PrepareForNet
label_desc: Use image node_id 1 to create a new MiDaS_Small object from inherited base classes: Module with example: obj = MiDaS_Small(name, directory, use_gpu)