code: def test_log_cdf():
      rng = numpy.random.default_rng(0xFEEEFEEE)
  
      x = rng.random((32,), dtype=numpy.float64)
  
      actual = log_cdf(
          torch.tensor(x, dtype=torch.float), Normal(0.0, 1.0)
      )
  
      assert torch.isfinite(actual).all()
  
      torch.testing.assert_close(
          actual,
          torch.tensor(scipy.special.log_ndtr(x), dtype=torch.float),
      )
  
      actual = log_cdf(
          torch.tensor(x, dtype=torch.float), Uniform(0.0, 1.0)
      )
  
      assert torch.isfinite(actual).all()
  
      torch.testing.assert_close(
          actual,
          torch.tensor(
              scipy.stats.uniform.logcdf(x), dtype=torch.float
          ),
      )
  
imports: import numpy.random
  import scipy.special
  import scipy.stats
  import torch
  import torch.testing
  from torch.distributions import Normal, Uniform
  from neuralcompression.functional import log_cdf
label_desc: Use image node_id 1 for calling a global function with example usage: test_log_cdf() without return types