code: def portfolio_rf():
      rf = [
          RandomForestClassifier(
              criterion="entropy",
              max_features=0.13891783454814322,
              min_samples_leaf=2,
              min_samples_split=9,
              n_estimators=300,
          ),
          RandomForestClassifier(
              max_features=0.050069426976632525,
              min_samples_split=3,
              n_estimators=500,
          ),
          RandomForestClassifier(
              criterion="entropy",
              max_features=0.38146409165028106,
              min_samples_split=12,
              n_estimators=300,
          ),
          RandomForestClassifier(
              criterion="entropy",
              max_features=0.20254434685778855,
              min_samples_split=3,
              n_estimators=500,
          ),
          RandomForestClassifier(
              max_features=0.05946371474549883, n_estimators=300
          ),
          RandomForestClassifier(
              criterion="entropy",
              max_features=0.4203242802359065,
              min_samples_split=9,
              n_estimators=100,
          ),
          RandomForestClassifier(
              criterion="entropy",
              max_features=0.8014260101515722,
              min_samples_leaf=6,
              min_samples_split=18,
              n_estimators=300,
          ),
          RandomForestClassifier(
              criterion="entropy",
              max_features=0.17540145080676417,
              n_estimators=300,
          ),
          RandomForestClassifier(
              max_features=0.12496709200924094, n_estimators=500
          ),
          RandomForestClassifier(
              criterion="entropy",
              max_features=0.02701567860915255,
              min_samples_split=7,
              n_estimators=500,
          ),
          RandomForestClassifier(
              criterion="entropy",
              max_depth=9,
              max_features=0.25,
              min_samples_split=3,
              n_estimators=500,
          ),
          RandomForestClassifier(
              max_features=0.08362628567277053,
              min_samples_leaf=2,
              min_samples_split=4,
              n_estimators=300,
          ),
          RandomForestClassifier(
              criterion="entropy",
              max_features=0.9028655839413416,
              min_samples_leaf=2,
              min_samples_split=3,
              n_estimators=500,
          ),
          RandomForestClassifier(
              criterion="entropy",
              max_features=0.014380612412532856,
              n_estimators=500,
          ),
          RandomForestClassifier(
              criterion="entropy",
              max_features=0.45369329148302284,
              min_samples_split=3,
              n_estimators=100,
          ),
          RandomForestClassifier(
              max_features=0.8965933352549531,
              min_samples_leaf=18,
              min_samples_split=4,
              n_estimators=100,
          ),
          RandomForestClassifier(
              max_features=0.477758211021571, n_estimators=500
          ),
          RandomForestClassifier(
              criterion="entropy",
              max_features=0.32405539446276466,
              min_samples_split=6,
              n_estimators=500,
          ),
          RandomForestClassifier(
              criterion="entropy",
              max_features=0.2628558322023252,
              min_samples_leaf=3,
              min_samples_split=19,
              n_estimators=500,
          ),
          RandomForestClassifier(
              criterion="entropy",
              max_features=0.05298984247042704,
              min_samples_leaf=2,
              min_samples_split=7,
              n_estimators=500,
          ),
          RandomForestClassifier(
              criterion="entropy",
              max_features=0.13728248146122612,
              n_estimators=500,
          ),
          RandomForestClassifier(
              max_features=0.4127164296489969,
              min_samples_split=6,
              n_estimators=100,
          ),
          RandomForestClassifier(
              criterion="entropy",
              max_depth=12,
              max_features=0.1,
              min_samples_split=4,
              n_estimators=512,
          ),
          RandomForestClassifier(
              criterion="entropy",
              max_features=0.6077306020179516,
              n_estimators=500,
          ),
          RandomForestClassifier(
              criterion="entropy",
              max_features=0.07198156955074353,
              min_samples_split=6,
              n_estimators=500,
          ),
          RandomForestClassifier(
              criterion="entropy",
              max_features=0.26491494231808,
              min_samples_split=3,
              n_estimators=500,
          ),
          RandomForestClassifier(
              max_features=0.003021649218545863,
              min_samples_split=10,
              n_estimators=500,
          ),
          RandomForestClassifier(
              criterion="entropy",
              max_features=0.13878410049133105,
              min_samples_leaf=2,
              n_estimators=500,
          ),
          RandomForestClassifier(
              criterion="entropy",
              max_features=0.11082304637318625,
              min_samples_leaf=19,
              min_samples_split=18,
              n_estimators=100,
          ),
          RandomForestClassifier(
              criterion="entropy",
              max_features=0.4385263141334529,
              min_samples_leaf=4,
              min_samples_split=7,
              n_estimators=300,
          ),
          RandomForestClassifier(
              criterion="entropy",
              max_features=0.15300932068501327,
              min_samples_split=3,
              n_estimators=500,
          ),
          RandomForestClassifier(
              criterion="entropy",
              max_features=0.8360934384385836,
              min_samples_leaf=7,
              min_samples_split=9,
              n_estimators=500,
          ),
      ]
      return rf
  
label_desc: Use image node_id 1 for calling a global function with example usage: portfolio_rf() and returns: rf