code: def run_test(self, pipeline, name, path):
      print("download " + name)
      update_name = self.download(name, path)
      X, y = load_svmlight_file(update_name)
      X_train, X_test, y_train, y_test = train_test_split(
          X, y, test_size=self.test_size, random_state=42
      )
  
      pipeline.fit(X_train, y_train)
      print(
          "[Benchmark " + name + " Score]: ",
          pipeline.score(X_test, y_test),
      )
  
imports: import bz2
  import urllib.request
  import numpy
  import datetime
  import line_profiler
  import os
  from sklearn.datasets import load_svmlight_file
  from sklearn.model_selection import train_test_split
  from sklearn.pipeline import make_pipeline
  from sklearn.linear_model import LogisticRegression
  from sklearn.ensemble import ExtraTreesClassifier
  from sklearn.feature_selection import SelectFromModel
  from nni.algorithms.feature_engineering.gradient_selector import FeatureGradientSelector
label_desc: Use image node_id 3 for calling the Benchmark obj's underlying member method code with example usage: obj.run_test(pipeline, name, path) without return types