code: def _test_same(dataset):
      X, y = dataset
      if X.shape[1] == 1:
          # If we only have one column (which is also the sensitive one) we can't fit
          return True
  
      sensitive_cols = [0]
      X_without_sens = np.delete(X, sensitive_cols, axis=1)
      lr = LogisticRegression(
          penalty=None,
          solver="lbfgs",
          multi_class="ovr",
          dual=False,
          tol=1e-4,
          C=1.0,
          fit_intercept=True,
          intercept_scaling=1,
          class_weight=None,
          random_state=None,
          max_iter=100,
          verbose=0,
          warm_start=False,
          n_jobs=None,
          l1_ratio=None,
      )
      fair = EqualOpportunityClassifier(
          covariance_threshold=None,
          sensitive_cols=sensitive_cols,
          penalty="none",
          positive_target=True,
      )
  
      fair.fit(X, y)
      lr.fit(X_without_sens, y)
  
      normal_pred = lr.predict_proba(X_without_sens)
      fair_pred = fair.predict_proba(X)
      np.testing.assert_almost_equal(normal_pred, fair_pred, decimal=2)
      assert (
          np.sum(lr.predict(X_without_sens) != fair.predict(X)) / len(X)
          < 0.01
      )
  
file_path: scikit-lego/tests/test_estimators/test_equal_opportunity.py
imports: import numpy
  import pytest
  from sklearn.linear_model import LogisticRegression
  from sklego.common import flatten
  from sklego.linear_model import EqualOpportunityClassifier
  from sklego.metrics import equal_opportunity_score
  from tests.conftest import classifier_checks, general_checks, nonmeta_checks, select_tests
label_desc: Use image node_id 2 for calling a global function with example usage: _test_same(dataset) and returns: True