code: def test_slice_single_param_atomic(self):
      sliced = slicing._slice_single_param(
          identity.Identity(),
          param_event_ndims=0,
          slices=make_slices[..., tf.newaxis, 2:, tf.newaxis],
          batch_shape=tf.constant([7, 4, 3]),
      )
      self.assertAllEqual([], sliced.experimental_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 18 for calling the SlicingTest obj's underlying member method code with example usage: obj.test_slice_single_param_atomic() without return types