code: def test_memory(pipeline_name, name, path):
      if pipeline_name == "LR":
          pipeline = make_pipeline(LogisticRegression())
  
      if pipeline_name == "FGS":
          pipeline = make_pipeline(
              FeatureGradientSelector(), LogisticRegression()
          )
  
      if pipeline_name == "Tree":
          pipeline = make_pipeline(
              SelectFromModel(ExtraTreesClassifier(n_estimators=50)),
              LogisticRegression(),
          )
  
      test_benchmark = Benchmark()
      print("Dataset:\t", name)
      print("Pipeline:\t", pipeline_name)
      test_benchmark.run_test(pipeline, name, path)
      print("")
  
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 1 for calling a global function with example usage: test_memory(pipeline_name, name, path) without return types