code: def testSampleInBounds(self):
      # Regression test for b/152660887.
      # This number of samples suffices to make the test fail about 45 times out
      # of 100 under --vary_seed (before applying the fix, that is).
      num_samples = 1000000
      total_count = tf.constant([1.0], dtype=tf.float32)
      probs = tf.constant([0.0], dtype=tf.float32)
      dist = binomial_lib.Binomial(
          total_count=total_count, probs=probs, validate_args=True
      )
      samples = self.evaluate(
          dist.sample(num_samples, seed=test_util.test_seed())
      )
      self.assertAllEqual(samples, tf.zeros_like(samples))
  
imports: import numpy
  from scipy import stats
  import tensorflow.compat.v1
  import tensorflow.compat.v2
  from tensorflow_probability.python.distributions import binomial
  from tensorflow_probability.python.internal import implementation_selection
  from tensorflow_probability.python.internal import test_util
  from tensorflow_probability.python.math import gradient
label_desc: Use image node_id 28 for calling the BinomialTest obj's underlying member method code with example usage: obj.testSampleInBounds() without return types