code: def test_when_apply_quantile_based_fwer_control_then_returns_single_adjusted_pvalue():
      p_values = np.array(
          [0.01, 0.02, 0.03, 0.04, 0.05, 0.06, 0.07, 0.08, 0.09, 0.1]
      )
      assert quantile_based_fwer(p_values, quantile=0.5) == 0.055 / 0.5
      assert (
          quantile_based_fwer(p_values, quantile=0.25) == 0.0325 / 0.25
      )
      assert (
          quantile_based_fwer(p_values, quantile=0.75) == 0.0775 / 0.75
      )
  
      assert (
          quantile_based_fwer(
              np.array(
                  [
                      0.01,
                      0.02,
                      0.03,
                      0.04,
                      0.05,
                      0.06,
                      0.07,
                      0.08,
                      0.09,
                      0.1,
                      1,
                  ]
              ),
              quantile=0.5,
          )
          == 0.06 / 0.5
      )
      assert (
          quantile_based_fwer(np.array([0.9, 0.95, 1]), quantile=0.5)
          == 1
      )
      assert quantile_based_fwer(np.array([0, 0, 0]), quantile=0.5) == 0
      assert quantile_based_fwer(np.array([0.33]), quantile=0.5) == 0.33
  
imports: import numpy
  import pytest
  from flaky import flaky
  from numpy.matlib import repmat
  from pytest import approx
  from dowhy.gcm.ml import create_hist_gradient_boost_classifier, create_hist_gradient_boost_regressor, create_linear_regressor, create_logistic_regression_classifier
  from dowhy.gcm.stats import estimate_ftest_pvalue, marginal_expectation, quantile_based_fwer
  from dowhy.gcm.util.general import geometric_median
label_desc: Use image node_id 2 for calling a global function with example usage: test_when_apply_quantile_based_fwer_control_then_returns_single_adjusted_pvalue() without return types