code: def testSliceWithUnslicedMu(self, *slicers):
      # Covers the case where one of the params has lower than minimal event
      # rank (i.e. the native rank for loc is 1, but mu has rank 0).
      mu = self._rng.rand()
      chol, _ = self._random_chol(7, 4, 2, 2)
      dist = mvn_tril.MultivariateNormalTriL(
          mu, chol, validate_args=True
      )
      batch_shape = tensorshape_util.as_list(dist.batch_shape)
      probs = self.evaluate(dist.prob([0, 0]))
      for slicer in slicers:
          batch_shape = slicer(np.zeros(batch_shape)).shape
          probs = slicer(probs)
          dist = slicer(dist)
          self.assertAllEqual(
              batch_shape, self.evaluate(dist.batch_shape_tensor())
          )
          self.assertAllClose(probs, self.evaluate(dist.prob([0, 0])))
  
imports: 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.bijectors import softplus
  from tensorflow_probability.python.bijectors import transform_diagonal
  from tensorflow_probability.python.distributions import kullback_leibler
  from tensorflow_probability.python.distributions import lognormal
  from tensorflow_probability.python.distributions import mvn_tril
  from tensorflow_probability.python.distributions import normal
  from tensorflow_probability.python.internal import tensorshape_util
  from tensorflow_probability.python.internal import test_util
label_desc: Use image node_id 5 for calling the MultivariateNormalTriLSlicingTest obj's underlying member method code with example usage: obj.testSliceWithUnslicedMu() without return types