code: def test_continuous_cvml_efficient(self):
      nobs = 500
      np.random.seed(12345)
      ovals = np.random.binomial(2, 0.5, size=(nobs,))
      C1 = np.random.normal(size=(nobs,))
      noise = np.random.normal(size=(nobs,))
      b0 = 3
      b1 = 1.2
      b2 = 3.7  # regression coefficients
      Y = b0 + b1 * C1 + b2 * ovals + noise
  
      dens_efficient = nparam.KDEMultivariateConditional(
          endog=[Y],
          exog=[C1],
          dep_type="c",
          indep_type="c",
          bw="cv_ml",
          defaults=nparam.EstimatorSettings(efficient=True, n_sub=50),
      )
  
      # dens = nparam.KDEMultivariateConditional(endog=[Y], exog=[C1],
      #                   dep_type='c', indep_type='c', bw='cv_ml')
      # bw = dens.bw
      bw_expected = np.array([0.73387, 0.43715])
      npt.assert_allclose(
          dens_efficient.bw, bw_expected, atol=0, rtol=1e-3
      )
  
imports: import numpy
  import numpy.testing
  import pytest
  from numpy.testing import assert_allclose, assert_equal
  import statsmodels.api
label_desc: Use image node_id 9 for calling the TestKDEMultivariateConditional obj's underlying member method code with example usage: obj.test_continuous_cvml_efficient() without return types