code: def test_cdf_icdf(continuous_dist):
      Dist = continuous_dist.pyro_dist
      for i in range(continuous_dist.get_num_test_data()):
          d = Dist(**continuous_dist.get_dist_params(i))
          if d.event_shape.numel() != 1:
              continue  # only valid for univariate distributions
          u = torch.empty((100,) + d.shape()).uniform_()
          with xfail_if_not_implemented():
              x = d.icdf(u)
              u2 = d.cdf(x)
          assert_equal(u, u2)
  
imports: import numpy
  import pytest
  import torch
  import pyro
  import pyro.distributions
  from pyro.distributions import TorchDistribution
  from pyro.distributions.testing.gof import auto_goodness_of_fit
  from pyro.distributions.util import broadcast_shape
  from tests.common import TEST_FAILURE_RATE, assert_close, assert_equal, xfail_if_not_implemented
label_desc: Use image node_id 13 for calling a global function with example usage: test_cdf_icdf(continuous_dist) without return types