code: def __init__(self, path, vertical_screen):
      with codecs.open(path, "r", "utf-8") as file:
          self.dic = yaml.load(file, Loader=yaml.FullLoader)
  
          [width, height] = self.dic["Deploy"]["transforms"][0][
              "target_size"
          ]
          if vertical_screen and width > height:
              self.dic["Deploy"]["transforms"][0]["target_size"] = [
                  height,
                  width,
              ]
  
      self._transforms = self._load_transforms(
          self.dic["Deploy"]["transforms"]
      )
      self._dir = os.path.dirname(path)
  
imports: import codecs
  import os
  import sys
  import time
  import yaml
  import numpy
  import cv2
  import paddle
  from paddle.inference import create_predictor, PrecisionType
  from paddle.inference import Config
  import paddleseg.transforms
  from paddleseg.core.infer import reverse_transform
  from paddleseg.cvlibs import manager
  from paddleseg.utils import TimeAverager
  from optic_flow_process import optic_flow_process
label_desc: Use image node_id 1 to create a new DeployConfig object  with example: obj = DeployConfig(path, vertical_screen)