code: def imbalanced_dataset():
      return make_classification(
          n_samples=10000,
          n_features=3,
          n_informative=2,
          n_redundant=0,
          n_repeated=0,
          n_classes=3,
          n_clusters_per_class=1,
          weights=[0.01, 0.05, 0.94],
          class_sep=0.8,
          random_state=0,
      )
  
file_path: imbalanced-learn/imblearn/ensemble/tests/test_weight_boosting.py
imports: import numpy
  import pytest
  import sklearn
  from sklearn.datasets import load_iris, make_classification
  from sklearn.model_selection import train_test_split
  from sklearn.tree import DecisionTreeClassifier
  from sklearn.utils._testing import assert_array_equal
  from sklearn.utils.fixes import parse_version
  from imblearn.ensemble import RUSBoostClassifier
label_desc: Use image node_id 1 for calling a global function with example usage: imbalanced_dataset() and returns: make_classification