code: def bench_one_submit(pred_file, gt_file):
      try:
          json_pred = [
              json.loads(line) for line in open(pred_file).readlines()
          ]
      except BaseException as e:
          raise Exception("Fail to load json file of the prediction.")
      json_gt = [json.loads(line) for line in open(gt_file).readlines()]
      if len(json_gt) != len(json_pred):
          raise Exception(
              "We do not get the predictions of all the test tasks"
          )
      gts = {l["raw_file"]: l for l in json_gt}
      accuracy, fp, fn = 0.0, 0.0, 0.0
      for pred in json_pred:
          if (
              "raw_file" not in pred
              or "lanes" not in pred
              or "run_time" not in pred
          ):
              raise Exception(
                  "raw_file or lanes or run_time not in some predictions."
              )
          raw_file = pred["raw_file"]
          pred_lanes = pred["lanes"]
          run_time = pred["run_time"]
          if raw_file not in gts:
              raise Exception(
                  "Some raw_file from your predictions do not exist in the test tasks."
              )
          gt = gts[raw_file]
          gt_lanes = gt["lanes"]
          y_samples = gt["h_samples"]
          try:
              a, p, n = LaneEval.bench(
                  pred_lanes, gt_lanes, y_samples, run_time
              )
          except BaseException as e:
              raise Exception("Format of lanes error.")
          accuracy += a
          fp += p
          fn += n
      num = len(gts)
      # the first return parameter is the default ranking parameter
      return (
          json.dumps(
              [
                  {
                      "name": "Accuracy",
                      "value": accuracy / num,
                      "order": "desc",
                  },
                  {"name": "FP", "value": fp / num, "order": "asc"},
                  {"name": "FN", "value": fn / num, "order": "asc"},
              ]
          ),
          accuracy / num,
          fp / num,
          fn / num,
      )
  
file_path: PaddleSeg/contrib/LaneSeg/third_party/lane.py
imports: import json
  import numpy
  from sklearn.linear_model import LinearRegression
label_desc: Use image node_id 4 for calling the LaneEval obj's underlying member method code with example usage: obj.bench_one_submit(pred_file, gt_file) and returns: json,  unknown,  unknown,  unknown