code: def testKLScalarToMultivariate(self):
      normal1 = normal.Normal(
          loc=np.float32([-1.0, 1]), scale=np.float32([0.1, 0.5])
      )
      ind1 = independent.Independent(
          distribution=normal1,
          reinterpreted_batch_ndims=1,
          validate_args=True,
      )
  
      normal2 = normal.Normal(
          loc=np.float32([-3.0, 3]), scale=np.float32([0.3, 0.3])
      )
      ind2 = independent.Independent(
          distribution=normal2,
          reinterpreted_batch_ndims=1,
          validate_args=True,
      )
  
      normal_kl = kullback_leibler.kl_divergence(normal1, normal2)
      ind_kl = kullback_leibler.kl_divergence(ind1, ind2)
      self.assertAllClose(
          self.evaluate(tf.reduce_sum(normal_kl, axis=-1)),
          self.evaluate(ind_kl),
      )
  
imports: import functools
  import os
  from absl.testing import parameterized
  import numpy
  from scipy import stats
  import tensorflow.compat.v1
  import tensorflow.compat.v2
  from tensorflow_probability.python.distributions import bernoulli
  from tensorflow_probability.python.distributions import exponential
  from tensorflow_probability.python.distributions import independent
  from tensorflow_probability.python.distributions import kullback_leibler
  from tensorflow_probability.python.distributions import log_prob_ratio
  from tensorflow_probability.python.distributions import logistic
  from tensorflow_probability.python.distributions import lognormal
  from tensorflow_probability.python.distributions import mvn_diag
  from tensorflow_probability.python.distributions import normal
  from tensorflow_probability.python.distributions import poisson
  from tensorflow_probability.python.internal import hypothesis_testlib
  from tensorflow_probability.python.internal import tensorshape_util
  from tensorflow_probability.python.internal import test_util
label_desc: Use image node_id 9 for calling the IndependentDistributionTest obj's underlying member method code with example usage: obj.testKLScalarToMultivariate() without return types