code: def testSampleUnbiasedScalarBatch(self):
      dist = multinomial.Multinomial(
          total_count=5.0,
          logits=tf.math.log(
              2.0 * self._rng.rand(4).astype(np.float32)
          ),
          validate_args=True,
      )
      n = int(3e4)
      x = dist.sample(n, seed=test_util.test_seed())
      sample_mean = tf.reduce_mean(x, axis=0)
      x_centered = x - sample_mean  # Already transposed to [n, 2].
      sample_covariance = (
          tf.matmul(x_centered, x_centered, adjoint_a=True) / n
      )
      [
          x_,
          sample_covariance_,
          actual_mean_,
          actual_covariance_,
      ] = self.evaluate(
          [
              x,
              sample_covariance,
              dist.mean(),
              dist.covariance(),
          ]
      )
      self.assertAllEqual([4], sample_mean.shape)
      self.assertAllMeansClose(
          x_, actual_mean_, axis=0, atol=0.0, rtol=0.10
      )
      self.assertAllEqual([4, 4], sample_covariance.shape)
      self.assertAllClose(
          actual_covariance_, sample_covariance_, atol=0.0, rtol=0.20
      )
  
imports: import unittest
  from absl.testing import parameterized
  import hypothesis
  from hypothesis import strategies
  import numpy
  import tensorflow.compat.v1
  import tensorflow.compat.v2
  from tensorflow_probability.python.bijectors import identity
  from tensorflow_probability.python.distributions import binomial
  from tensorflow_probability.python.distributions import hypothesis_testlib
  from tensorflow_probability.python.distributions import multinomial
  from tensorflow_probability.python.distributions.internal import statistical_testing
  from tensorflow_probability.python.internal import hypothesis_testlib
  from tensorflow_probability.python.internal import test_util
  from tensorflow_probability.python.math import gradient
  from tensorflow_probability.python.util import deferred_tensor
label_desc: Use image node_id 25 for calling the MultinomialTest obj's underlying member method code with example usage: obj.testSampleUnbiasedScalarBatch() without return types