code: def portfolio_mixed():
      mixed = [
          HistGradientBoostingClassifier(
              l2_regularization=1e-06,
              max_bins=255,
              max_iter=200,
              max_leaf_nodes=128,
              min_samples_leaf=50,
          ),
          RandomForestClassifier(
              criterion="entropy",
              max_features=0.32778941525789984,
              min_samples_split=5,
              n_estimators=300,
          ),
          SVC(
              C=52.368035023140784,
              gamma=0.008051730038808798,
              kernel="rbf",
          ),
          RandomForestClassifier(
              criterion="entropy",
              max_features=0.07198156955074353,
              min_samples_split=6,
              n_estimators=500,
          ),
          LogisticRegression(C=0.1, max_iter=10000),
          SVC(
              C=170.5572031055097,
              coef0=0.7430320055311159,
              degree=2,
              gamma=0.049727598083142026,
              kernel="poly",
          ),
          HistGradientBoostingClassifier(
              l2_regularization=0.001,
              max_bins=255,
              max_depth=18,
              max_iter=450,
              max_leaf_nodes=64,
              min_samples_leaf=9,
          ),
          HistGradientBoostingClassifier(
              l2_regularization=100.0,
              max_bins=255,
              max_depth=17,
              max_iter=200,
              max_leaf_nodes=128,
              min_samples_leaf=37,
          ),
          RandomForestClassifier(
              criterion="entropy",
              max_features=0.27977761318292416,
              n_estimators=500,
          ),
          SVC(
              C=2854.2115055717222,
              coef0=0.9097801350305286,
              degree=2,
              gamma=0.0594124208513135,
              kernel="rbf",
          ),
          HistGradientBoostingClassifier(
              l2_regularization=0.1,
              max_bins=255,
              max_depth=11,
              max_iter=250,
              max_leaf_nodes=16,
              min_samples_leaf=13,
          ),
          SVC(
              C=2.653098517006086,
              coef0=-0.16118152154954246,
              degree=5,
              gamma=0.018933306231401107,
              kernel="rbf",
          ),
          HistGradientBoostingClassifier(
              l2_regularization=1.0,
              max_bins=32,
              max_depth=18,
              max_iter=450,
              max_leaf_nodes=128,
              min_samples_leaf=5,
          ),
          SVC(
              C=49.496117538847585,
              coef0=-0.36888378135766664,
              degree=5,
              gamma=0.0013908089928613187,
              kernel="rbf",
          ),
          HistGradientBoostingClassifier(
              l2_regularization=1e-10,
              learning_rate=0.01,
              max_bins=64,
              max_depth=6,
              max_iter=500,
              max_leaf_nodes=4,
              min_samples_leaf=19,
          ),
          RandomForestClassifier(
              criterion="entropy",
              max_features=0.05298984247042704,
              min_samples_leaf=2,
              min_samples_split=7,
              n_estimators=500,
          ),
          SVC(
              C=4297.397059178814,
              coef0=0.013499300518863278,
              degree=5,
              gamma=0.1820082271280914,
              kernel="rbf",
          ),
          SVC(
              C=0.12779439580461893,
              coef0=-0.007860547843195675,
              degree=2,
              gamma=0.011489094638370643,
              kernel="sigmoid",
          ),
          SVC(
              C=1.799125831143992,
              coef0=0.7926565732345652,
              gamma=0.01858955180141993,
              kernel="poly",
          ),
          HistGradientBoostingClassifier(
              l2_regularization=1e-10,
              max_bins=255,
              max_depth=3,
              max_iter=400,
              max_leaf_nodes=128,
          ),
          HistGradientBoostingClassifier(
              l2_regularization=1.0,
              max_bins=128,
              max_depth=16,
              max_leaf_nodes=128,
              min_samples_leaf=10,
          ),
          HistGradientBoostingClassifier(
              l2_regularization=10.0,
              max_bins=64,
              max_depth=12,
              max_iter=200,
              max_leaf_nodes=64,
          ),
          SVC(C=100000.0, gamma=0.00241115807647, kernel="rbf"),
          HistGradientBoostingClassifier(
              l2_regularization=0.001,
              max_bins=255,
              max_depth=19,
              max_iter=300,
              max_leaf_nodes=128,
              min_samples_leaf=11,
          ),
          SVC(
              C=4.523994003904736,
              coef0=0.20331467465699182,
              degree=5,
              gamma=0.033987763661532146,
              kernel="rbf",
          ),
          HistGradientBoostingClassifier(
              l2_regularization=1e-09,
              learning_rate=0.01,
              max_bins=64,
              max_iter=450,
              max_leaf_nodes=4,
              min_samples_leaf=3,
          ),
          RandomForestClassifier(
              max_features=0.04689506366408347, n_estimators=300
          ),
          SVC(
              C=0.4212172063086123,
              coef0=-0.30890982063706174,
              gamma=0.012608109887061762,
              kernel="poly",
          ),
          HistGradientBoostingClassifier(
              l2_regularization=0.1,
              max_bins=255,
              max_depth=4,
              max_iter=350,
              max_leaf_nodes=128,
              min_samples_leaf=11,
          ),
          RandomForestClassifier(
              criterion="entropy",
              max_features=0.3391406102951785,
              min_samples_split=19,
              n_estimators=100,
          ),
          HistGradientBoostingClassifier(
              l2_regularization=1e-10,
              max_bins=32,
              max_depth=9,
              max_iter=350,
              max_leaf_nodes=16,
              min_samples_leaf=18,
          ),
          SVC(
              C=3539.053405327911,
              gamma=0.2574787996118815,
              kernel="rbf",
          ),
      ]
      return mixed
  
imports: from sklearn.linear_model import LogisticRegression
  from sklearn.ensemble import RandomForestClassifier
  from sklearn.ensemble import HistGradientBoostingClassifier
  from sklearn.svm import SVC
label_desc: Use image node_id 1 for calling a global function with example usage: portfolio_mixed() and returns: mixed