code: def test_can_take_structured_input_and_output(self):
      # Dummy function that takes a (tuple, dict) pair
      # and returns a (dict, scalar) pair.
      def fn(x, y):
          a, b, c = x
          d, e = y["d"], y["e"]
          return {"r": a * b + c}, d + e
  
      vectorized_fn = vectorization_util.make_rank_polymorphic(
          fn, core_ndims=0
      )
  
      x = (
          np.array([[2.0], [3.0]]),
          np.array(2.0),
          np.array([5.0, 6.0, 7.0]),
      )
      y = {"d": np.array([[1.0]]), "e": np.array([2.0, 3.0, 4.0])}
      vectorized_result = self.evaluate(vectorized_fn(x, y))
      result = tf.nest.map_structure(
          lambda a, b: a * np.ones(b.shape), fn(x, y), vectorized_result
      )
      self.assertAllCloseNested(result, vectorized_result)
  
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 4 for calling the VectorizationTest obj's underlying member method code with example usage: obj.test_can_take_structured_input_and_output() and returns: dict,  unknown