code: def testDppSampleStats(self):
      n_points = 5
      _, eigvals, eigvecs = kernel_over_unit_square(
          n_points, dtype=self.param_dtype
      )
      dpp = dpp_lib.DeterminantalPointProcess(eigvals, eigvecs)
  
      n = 500
      samples, expected_marginals = self.evaluate(
          [
              dpp.sample(n, seed=test_util.test_seed()),
              tf.linalg.diag_part(dpp.marginal_kernel()),
          ]
      )
  
      counts = np.zeros(n_points)
      for s in samples:
          counts[np.nonzero(s)[0]] += 1.0
  
      self.assertAllClose(counts / n, expected_marginals, atol=0.1)
  
imports: import contextlib
  from absl.testing import parameterized
  import numpy
  import tensorflow.compat.v2
  from tensorflow_probability.python.distributions import bernoulli
  from tensorflow_probability.python.distributions import dpp
  from tensorflow_probability.python.distributions import uniform
  from tensorflow_probability.python.internal import test_util
  from tensorflow_probability.python.math.psd_kernels import exponentiated_quadratic
label_desc: Use image node_id 13 for calling the _DppTest obj's underlying member method code with example usage: obj.testDppSampleStats() without return types