code: def testXLASample(self):
      self.skip_if_no_xla()
  
      _, eigvals, eigvecs = kernel_over_unit_square(
          20, dtype=self.param_dtype
      )
      for n in 1, 5:
  
          @tf.function(jit_compile=True)
          def f(eigvals):
              return dpp_lib.DeterminantalPointProcess(
                  eigvals, eigvecs
              ).sample(
                  n, seed=test_util.test_seed()
              )  # pylint: disable=cell-var-from-loop
  
          self.evaluate(f(eigvals))
  
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 16 for calling the _DppTest obj's underlying member method code with example usage: obj.testXLASample() and returns: dpp_lib