code: def testJacobian(self):
      cholesky_to_vector = invert.Invert(
          fill_scale_tril.FillScaleTriL(
              diag_bijector=exp.Exp(), diag_shift=None
          )
      )
      bijector = cholesky_to_inv_cholesky.CholeskyToInvCholesky()
      for x in [
          np.array([[2.0]], dtype=np.float64),
          np.array([[2.0, 0.0], [3.0, 4.0]], dtype=np.float64),
          np.array(
              [[2.0, 0.0, 0.0], [3.0, 4.0, 0.0], [5.0, 6.0, 7.0]],
              dtype=np.float64,
          ),
      ]:
          fldj = bijector.forward_log_det_jacobian(x, event_ndims=2)
          fldj_numerical = self._get_fldj_numerical(
              bijector,
              x,
              event_ndims=2,
              input_to_vector=cholesky_to_vector,
              output_to_vector=cholesky_to_vector,
          )
          fldj_, fldj_numerical_ = self.evaluate([fldj, fldj_numerical])
          self.assertAllClose(fldj_, fldj_numerical_, rtol=1e-2)
  
file_path: probability/tensorflow_probability/python/bijectors/cholesky_to_inv_cholesky_test.py
imports: import numpy
  import tensorflow.compat.v2
  from tensorflow_probability.python.bijectors import cholesky_to_inv_cholesky
  from tensorflow_probability.python.bijectors import exp
  from tensorflow_probability.python.bijectors import fill_scale_tril
  from tensorflow_probability.python.bijectors import identity
  from tensorflow_probability.python.bijectors import invert
  from tensorflow_probability.python.internal import test_util
label_desc: Use image node_id 4 for calling the CholeskyToInvCholeskyTest obj's underlying member method code with example usage: obj.testJacobian() without return types