code: def testTheoreticalFldjSimple(self):
      bijector = rational_quadratic_spline.RationalQuadraticSpline(
          bin_widths=[1.0, 1],
          bin_heights=[np.sqrt(0.5), 2 - np.sqrt(0.5)],
          knot_slopes=1,
      )
      self.assertEqual(tf.float64, bijector.dtype)
      dim = 5
      x = np.linspace(
          -1.05, 1.05, num=2 * dim, dtype=np.float64
      ).reshape(2, dim)
      y = self.evaluate(bijector.forward(x))
      bijector_test_util.assert_bijective_and_finite(
          bijector,
          x,
          y,
          eval_func=self.evaluate,
          event_ndims=0,
          inverse_event_ndims=0,
          rtol=1e-5,
      )
      fldj = bijector.forward_log_det_jacobian(x, event_ndims=0)
      fldj_theoretical = bijector_test_util.get_fldj_theoretical(
          bijector, x, event_ndims=0
      )
      self.assertAllClose(
          self.evaluate(fldj_theoretical),
          self.evaluate(fldj),
          atol=1e-5,
          rtol=1e-5,
      )
  
imports: import functools
  import hypothesis
  from hypothesis import strategies
  import numpy
  import tensorflow.compat.v2
  from tensorflow_probability.python.bijectors import bijector_test_util
  from tensorflow_probability.python.bijectors import hypothesis_testlib
  from tensorflow_probability.python.bijectors import identity
  from tensorflow_probability.python.bijectors import rational_quadratic_spline
  from tensorflow_probability.python.bijectors import real_nvp
  from tensorflow_probability.python.internal import hypothesis_testlib
  from tensorflow_probability.python.internal import prefer_static
  from tensorflow_probability.python.internal import test_util
  from tensorflow_probability.python.internal import tf_keras
label_desc: Use image node_id 3 for calling the RationalQuadraticSplineTest obj's underlying member method code with example usage: obj.testTheoreticalFldjSimple() without return types