code: def testPmfAndCdfNandCountsAgree(self):
      p = [[0.1, 0.2, 0.7]]
      n = [[5.0]]
      binom = binomial_lib.Binomial(
          total_count=n, probs=p, validate_args=True
      )
      self.evaluate(binom.prob([2.0, 3, 2]))
      self.evaluate(binom.prob([3.0, 1, 2]))
      self.evaluate(binom.cdf([2.0, 3, 2]))
      self.evaluate(binom.cdf([3.0, 1, 2]))
      with self.assertRaisesOpError("Condition x >= 0.*"):
          self.evaluate(binom.prob([-1.0, 4, 2]))
      with self.assertRaisesOpError("Condition x <= y.*"):
          self.evaluate(binom.prob([7.0, 3, 0]))
      with self.assertRaisesOpError("Condition x >= 0.*"):
          self.evaluate(binom.cdf([-1.0, 4, 2]))
      with self.assertRaisesOpError("Condition x <= y.*"):
          self.evaluate(binom.cdf([7.0, 3, 0]))
  
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 9 for calling the BinomialTest obj's underlying member method code with example usage: obj.testPmfAndCdfNandCountsAgree() without return types