code: def testSteppedSliceOnBroadcastDim(self, *slicers):
      # Check a failure scenario identified by hypothesis.
      event_dim = 3
      mu = self._rng.rand(1, 1, 6, 1, event_dim)
      chol, _ = self._random_chol(2, 7, 6, 9, event_dim, event_dim)
      slicer = make_slicer[
          44:-52:-3, tf.newaxis, -94::, tf.newaxis, 5, 1
      ]
  
      dist = mvn_tril.MultivariateNormalTriL(
          mu, chol, validate_args=True
      )
      batch_shape = tensorshape_util.as_list(dist.batch_shape)
      probs = self.evaluate(dist.prob([0] * event_dim))
      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] * event_dim))
      )
  
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 4 for calling the MultivariateNormalTriLSlicingTest obj's underlying member method code with example usage: obj.testSteppedSliceOnBroadcastDim() without return types