code: def testSlice(self):
      logits = self.evaluate(
          tf.random.normal([20, 3, 1, 5], seed=test_util.test_seed())
      )
      dist = continuous_bernoulli.ContinuousBernoulli(
          logits=logits, validate_args=True
      )
      batch_shape = tensorshape_util.as_list(dist.batch_shape)
      dist_noshape = continuous_bernoulli.ContinuousBernoulli(
          logits=tf1.placeholder_with_default(logits, shape=None),
          validate_args=True,
      )
  
      def check(*slicers):
          for ds, assert_static_shape in (dist, True), (
              dist_noshape,
              False,
          ):
              bs = batch_shape
              prob = self.evaluate(dist.prob(0))
              for slicer in slicers:
                  ds = slicer(ds)
                  bs = slicer(np.zeros(bs)).shape
                  prob = slicer(prob)
                  if assert_static_shape or tf.executing_eagerly():
                      self.assertAllEqual(bs, ds.batch_shape)
                  else:
                      self.assertIsNone(
                          tensorshape_util.rank(ds.batch_shape)
                      )
                  self.assertAllEqual(
                      bs, self.evaluate(ds.batch_shape_tensor())
                  )
                  self.assertAllClose(prob, self.evaluate(ds.prob(0)))
  
      check(make_slicer[3])
      check(make_slicer[tf.newaxis])
      check(make_slicer[3::7])
      check(make_slicer[:, :2])
      check(make_slicer[tf.newaxis, :, ..., 0, :2])
      check(make_slicer[tf.newaxis, :, ..., 3:, tf.newaxis])
      check(make_slicer[..., tf.newaxis, 3:, tf.newaxis])
      check(make_slicer[..., tf.newaxis, -3:, tf.newaxis])
      check(make_slicer[tf.newaxis, :-3, tf.newaxis, ...])
  
      def halfway(x):
          if isinstance(x, continuous_bernoulli.ContinuousBernoulli):
              return x.batch_shape_tensor()[0] // 2
          return x.shape[0] // 2
  
      check(lambda x: x[halfway(x)])
      check(lambda x: x[: halfway(x)])
      check(lambda x: x[halfway(x) :])
      check(
          make_slicer[:-3, tf.newaxis],
          make_slicer[..., 0, :2],
          make_slicer[::2],
      )
      if tf.executing_eagerly():
          return
      with self.assertRaisesRegexp(
          (ValueError, tf.errors.InvalidArgumentError),
          "Index out of range.*input has only 4 dims",
      ):
          check(make_slicer[19, tf.newaxis, 2, ..., :, 0, 4])
      with self.assertRaisesRegexp(
          (ValueError, tf.errors.InvalidArgumentError),
          "slice index.*out of bounds",
      ):
          check(make_slicer[..., 2, :])
  
imports: import numpy
  from scipy import special
  import tensorflow.compat.v1
  import tensorflow.compat.v2
  from tensorflow_probability.python.distributions import continuous_bernoulli
  from tensorflow_probability.python.distributions import kullback_leibler
  from tensorflow_probability.python.internal import tensorshape_util
  from tensorflow_probability.python.internal import test_util
  from tensorflow_probability.python.math import gradient
label_desc: Use image node_id 2 for calling the ContinuousBernoulliSlicingTest obj's underlying member method code with example usage: obj.testSlice() and returns: unknown, unknown