code: def testAssertsPositiveConcentration1AfterMutation(self):
      concentration1 = tf.Variable([1.0, 2.0, 3.0])
      self.evaluate(concentration1.initializer)
      d = beta.Beta(
          concentration1=concentration1,
          concentration0=[5.0],
          validate_args=True,
      )
      with self.assertRaisesOpError(
          "Concentration parameter must be positive."
      ):
          with tf.control_dependencies(
              [concentration1.assign([1.0, 2.0, -3.0])]
          ):
              self.evaluate(d.sample(seed=test_util.test_seed()))
  
imports: from absl.testing import parameterized
  import numpy
  from scipy import special
  from scipy import stats
  import tensorflow.compat.v2
  from tensorflow_probability.python.distributions import beta
  from tensorflow_probability.python.distributions import kullback_leibler
  from tensorflow_probability.python.internal import test_util
  from tensorflow_probability.python.math import gradient
label_desc: Use image node_id 33 for calling the BetaTest obj's underlying member method code with example usage: obj.testAssertsPositiveConcentration1AfterMutation() without return types