code: def get_data(d):
      """Return train and test set."""
      data = np.DataSource()
      if d == "train":
          out = data.open(
              "http://archive.ics.uci.edu/ml/"
              "machine-learning-databases/"
              "poker/poker-hand-training-true.data"
          )
      elif d == "test":
          out = data.open(
              "http://archive.ics.uci.edu/ml/"
              "machine-learning-databases/"
              "poker/poker-hand-testing.data"
          )
      else:
          raise ValueError("Not valid data option.")
  
      X = np.loadtxt(out, delimiter=",")
      y = X[:, -1]
      X = X[:, :-1]
      return X, y
  
file_path: mlens/benchmarks/ensemble_comp.py
imports: import numpy
  from mlens.ensemble import BlendEnsemble, SuperLearner, Subsemble
  from sklearn.metrics import accuracy_score
  from sklearn.linear_model import LogisticRegression
  from sklearn.ensemble import RandomForestClassifier, GradientBoostingClassifier
  from sklearn.svm import SVC
  from sklearn.neural_network import MLPClassifier
  from time import perf_counter
label_desc: Use image node_id 1 for calling a global function with example usage: get_data(d) and returns: X,  y