code: def get_feature_quartiles(X):
      X_discretized = X.copy()
      for col in range(X.shape[1]):
          for q, class_label in zip(
              [1.0, 0.75, 0.5, 0.25], [3, 2, 1, 0]
          ):
              threshold = np.quantile(X[:, col], q=q)
              X_discretized[X[:, col] <= threshold, col] = class_label
      return X_discretized.astype(np.int_)
  
imports: import numpy
  from sklearn.model_selection import train_test_split
  from mlxtend.classifier import OneRClassifier
  from mlxtend.data import iris_data
label_desc: Use image node_id 1 for calling a global function with example usage: get_feature_quartiles(X) and returns: X_discretized