code: def run(self, img, bg):
      input_names = self.predictor.get_input_names()
      input_handle = self.predictor.get_input_handle(input_names[0])
  
      data = self.compose({"img": img})
      input_data = np.array([data["img"]])
  
      input_handle.reshape(input_data.shape)
      input_handle.copy_from_cpu(input_data)
      if self.args.test_speed:
          start = time.time()
  
      self.predictor.run()
  
      if self.args.test_speed:
          self.cost_averager.record(time.time() - start)
      output_names = self.predictor.get_output_names()
      output_handle = self.predictor.get_output_handle(output_names[0])
      output = output_handle.copy_to_cpu()
  
      return self.postprocess(output, img, data, bg)
  
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 2 for calling the Predictor obj's underlying member method code with example usage: obj.run(img, bg) and returns: self