code: def testEigvecsAsserts(self):
      with self.assertRaisesOpError(r"must be orthonormal"):
          dpp = dpp_lib.DeterminantalPointProcess(
              tf.ones([4], dtype=self.param_dtype),
              tf.ones([4, 4], dtype=self.param_dtype) / 2,
              validate_args=True,
          )
          self.evaluate(dpp.sample(seed=test_util.test_seed()))
  
      v = tf.Variable(tf.ones([4, 4], dtype=self.param_dtype) / 2)
      self.evaluate(v.initializer)
      dpp = dpp_lib.DeterminantalPointProcess(
          tf.ones([4], dtype=self.param_dtype), v, validate_args=True
      )
      with self.assertRaisesOpError(r"must be orthonormal"):
          self.evaluate(dpp.sample(seed=test_util.test_seed()))
  
      with self.assertRaisesOpError(r"must be orthonormal"):
          dpp = dpp.copy(
              eigenvectors=tf.eye(4, dtype=self.param_dtype) * 0.1
          )
          self.evaluate(dpp.sample(seed=test_util.test_seed()))
  
      self.evaluate(v.assign(tf.eye(4, dtype=self.param_dtype) * 0.1))
      dpp = dpp.copy(eigenvectors=v)
      with self.assertRaisesOpError(r"must be orthonormal"):
          self.evaluate(dpp.sample(seed=test_util.test_seed()))
  
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 15 for calling the _DppTest obj's underlying member method code with example usage: obj.testEigvecsAsserts() without return types