code: def testAssertsPositiveRate(self):
      rate = tf.Variable([1.0, 2.0, -3.0])
      self.evaluate(rate.initializer)
      with self.assertRaisesOpError(
          "Argument `rate` must be positive."
      ):
          d = exponential.Exponential(rate=rate, validate_args=True)
          self.evaluate(d.sample(seed=test_util.test_seed()))
  
imports: import numpy
  from scipy import stats
  import tensorflow.compat.v2
  from tensorflow_probability.python.distributions import exponential
  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 13 for calling the ExponentialTest obj's underlying member method code with example usage: obj.testAssertsPositiveRate() without return types