code: def run(args):
      client = Client()
      client.load_client_config(
          os.path.join(
              args.serving_client_path, "serving_client_conf.prototxt"
          )
      )
      client.connect([args.serving_ip_port])
  
      seq = Sequential(
          [
              File2Image(),
              RGB2BGR(),
              Div(255),
              Normalize([0.5, 0.5, 0.5], [0.5, 0.5, 0.5], False),
              Transpose((2, 0, 1)),
          ]
      )
  
      img = seq(args.image_path)
      fetch_map = client.predict(
          feed={"x": img}, fetch=["save_infer_model/scale_0.tmp_1"]
      )
  
      result = fetch_map["save_infer_model/scale_0.tmp_1"]
      color_img = get_pseudo_color_map(result[0])
      color_img.save("./result.png")
      print("The segmentation image is saved in ./result.png")
  
file_path: PaddleSeg/deploy/serving/test_serving.py
imports: import os
  import argparse
  from paddle_serving_client import Client
  from paddle_serving_app.reader import Sequential, File2Image, Resize, CenterCrop
  from paddle_serving_app.reader import RGB2BGR, Transpose, Div, Normalize
  from paddleseg.utils.visualize import get_pseudo_color_map
label_desc: Use image node_id 2 for calling a global function with example usage: run(args) without return types