code: def test_MAQ():
      np.random.seed(42)
      n = 1000
      K = 5
      tau_hat = np.random.randn(n, K)
      cost = np.random.rand(n, K)
      DR_scores = np.random.randn(n, K)
  
      mq = MAQ(n_bootstrap=200)
      mq.fit(tau_hat, cost, DR_scores)
  
      #    (0.005729002695991717, 0.019814651108894354)
      assert isclose(mq.average_gain(spend=0.1)[0], 0.005729)
  
imports: import pandas
  import numpy
  from numpy import isclose
  from causalml.metrics.visualize import qini_score
  from causalml.metrics import MAQ, get_ipw_scores
label_desc: Use image node_id 2 for calling a global function with example usage: test_MAQ() without return types