code: def test_single_param_slice_tensor_broadcastdim(self):
      param = tf1.placeholder_with_default(
          tf.zeros([7, 1, 5, 4, 3]), shape=None
      )
      idx = tf1.placeholder_with_default(
          tf.constant(2, dtype=tf.int32), shape=[]
      )
      sliced = slicing._slice_single_param(
          param,
          param_event_ndims=2,
          slices=make_slices[:, idx],
          batch_shape=tf.constant([7, 6, 5]),
      )
      self.assertAllEqual((7, 5, 4, 3), self.evaluate(sliced).shape)
  
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 12 for calling the SlicingTest obj's underlying member method code with example usage: obj.test_single_param_slice_tensor_broadcastdim() without return types