code: def test_rectifies_distribution_batch_shapes(self):
      def fn(scale):
          d = normal.Normal(loc=0, scale=[scale])
          x = d.sample()
          return d, x, d.log_prob(x)
  
      polymorphic_fn = vectorization_util.make_rank_polymorphic(
          fn, core_ndims=(0)
      )
      batch_scale = tf.constant(
          [[4.0, 2.0, 5.0], [1.0, 2.0, 1.0]], dtype=tf.float32
      )
      d, x, lp = polymorphic_fn(batch_scale)
      self.assertAllEqual(d.batch_shape.as_list(), x.shape.as_list())
      lp2 = d.log_prob(x)
      self.assertAllClose(*self.evaluate((lp, lp2)))
  
imports: import warnings
  from absl.testing import parameterized
  import numpy
  import tensorflow.compat.v2
  from tensorflow_probability.python.distributions import lognormal
  from tensorflow_probability.python.distributions import normal
  from tensorflow_probability.python.distributions import poisson
  from tensorflow_probability.python.internal import test_util
  from tensorflow_probability.python.internal import vectorization_util
label_desc: Use image node_id 12 for calling the VectorizationTest obj's underlying member method code with example usage: obj.test_rectifies_distribution_batch_shapes() and returns: d,  x,  d