code: def test_sample_high_df(self):
      df = np.linspace(10.0, 20.0, 10)
      nc = np.float64(10.0)
      num_samples = int(1e5)
  
      self.assertLess(
          self.evaluate(
              st.min_num_samples_for_dkwm_cdf_test(
                  discrepancy=0.04,
                  false_fail_rate=1e-9,
                  false_pass_rate=1e-9,
              )
          ),
          num_samples,
      )
  
      nc_chi2 = noncentral_chi2.NoncentralChi2(
          df=df, noncentrality=nc, validate_args=True
      )
  
      samples = nc_chi2.sample(num_samples, seed=test_util.test_seed())
  
      self.evaluate(
          st.assert_true_cdf_equal_by_dkwm(
              samples, nc_chi2.cdf, false_fail_rate=1e-9
          )
      )
  
      self.assertAllMeansClose(
          self.evaluate(samples), nc_chi2.mean(), axis=0, rtol=0.01
      )
  
      self.assertAllClose(
          self.evaluate(tf.math.reduce_variance(samples, axis=0)),
          nc_chi2.variance(),
          rtol=0.05,
      )
  
imports: from absl.testing import parameterized
  import numpy
  from scipy import stats
  import tensorflow
  from tensorflow_probability.python.distributions import noncentral_chi2
  from tensorflow_probability.python.distributions.internal import statistical_testing
  from tensorflow_probability.python.internal import test_util
label_desc: Use image node_id 4 for calling the NoncentralChi2SamplingTest obj's underlying member method code with example usage: obj.test_sample_high_df() without return types