code: def testLogBesselIveTestNonInf(self):
      # Test that log_bessel_ive(v, z) has more resolution than simply computing
      # log(bessel_ive(v, z)). The inputs below will return -inf in naive float64
      # computation.
      v = np.array([10.0, 12.0, 30.0, 50.0], np.float32)
      z = (
          np.logspace(-10.0, -1.0, 20)
          .reshape((20, 1))
          .astype(np.float32)
      )
      self.assertAllFinite(self.evaluate(bessel.log_bessel_ive(v, z)))
  
imports: import functools
  from absl.testing import parameterized
  import numpy
  from scipy import constants
  from scipy import special
  import tensorflow.compat.v2
  from tensorflow_probability.python.internal import test_util
  from tensorflow_probability.python.math import bessel
  from tensorflow_probability.python.math import gradient
  from tensorflow_probability.python.math.bessel import _compute_general_continued_fraction
label_desc: Use image node_id 19 for calling the BesselIveTest obj's underlying member method code with example usage: obj.testLogBesselIveTestNonInf() without return types