code: def test_slicing_does_not_modify_the_sliced_distribution(self):
      dist = exponential.Exponential(tf.ones((5, 2, 3)))
      sliced = dist[:4, :, 2]
      self.assertAllEqual([2], sliced[-1].batch_shape_tensor())
      self.assertAllEqual([3], sliced[:-1, 1].batch_shape_tensor())
  
imports: import numpy
  import tensorflow.compat.v1
  import tensorflow.compat.v2
  from tensorflow_probability.python.bijectors import chain
  from tensorflow_probability.python.bijectors import exp
  from tensorflow_probability.python.bijectors import identity
  from tensorflow_probability.python.bijectors import invert
  from tensorflow_probability.python.bijectors import joint_map
  from tensorflow_probability.python.bijectors import scale
  from tensorflow_probability.python.bijectors import scale_matvec_diag
  from tensorflow_probability.python.bijectors import shift
  from tensorflow_probability.python.bijectors import split
  from tensorflow_probability.python.distributions import bernoulli
  from tensorflow_probability.python.distributions import categorical
  from tensorflow_probability.python.distributions import exponential
  from tensorflow_probability.python.distributions import mixture_same_family
  from tensorflow_probability.python.distributions import mvn_diag
  from tensorflow_probability.python.distributions import normal
  from tensorflow_probability.python.distributions import transformed_distribution
  from tensorflow_probability.python.internal import slicing
  from tensorflow_probability.python.internal import tensorshape_util
  from tensorflow_probability.python.internal import test_util
label_desc: Use image node_id 23 for calling the SlicingTest obj's underlying member method code with example usage: obj.test_slicing_does_not_modify_the_sliced_distribution() without return types