code: def testAssertsPositiveConcentration1(self):
      concentration1 = tf.Variable([1.0, 2.0, -3.0])
      self.evaluate(concentration1.initializer)
      with self.assertRaisesOpError(
          "Concentration parameter must be positive."
      ):
          d = beta_binomial.BetaBinomial(
              total_count=10,
              concentration1=concentration1,
              concentration0=[5.0],
              validate_args=True,
          )
          self.evaluate(d.sample(seed=test_util.test_seed()))
  
imports: import numpy
  import tensorflow.compat.v1
  import tensorflow.compat.v2
  from tensorflow_probability.python.distributions import bernoulli
  from tensorflow_probability.python.distributions import beta_binomial
  from tensorflow_probability.python.distributions import dirichlet_multinomial
  from tensorflow_probability.python.distributions.internal import statistical_testing
  from tensorflow_probability.python.internal import test_util
  from tensorflow_probability.python.math import gradient
label_desc: Use image node_id 3 for calling the BetaBinomialFromVariableTest obj's underlying member method code with example usage: obj.testAssertsPositiveConcentration1() without return types