code: def testBinomialMultipleMode(self):
      n = 9.0
      p = [0.1, 0.2, 0.7]
      binom = binomial_lib.Binomial(
          total_count=n, probs=p, validate_args=True
      )
      # For the case where (n + 1) * p is an integer, the modes are:
      # (n + 1) * p and (n + 1) * p - 1. In this case, we get back
      # the larger of the two modes.
      expected_modes = [1.0, 2, 7]
      self.assertEqual((3,), binom.mode().shape)
      self.assertAllClose(expected_modes, self.evaluate(binom.mode()))
  
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 21 for calling the BinomialTest obj's underlying member method code with example usage: obj.testBinomialMultipleMode() without return types