code: def test_log_prob_matches(self):
      # Pearce has a set `noise_scale` of 0.005 ** 0.5 that we must account for.
      (
          linear_bnn,
          constant_params,
          x_train,
          y_train,
      ) = self.get_bnn_and_params()
      diff = lognormal_lib.LogNormal(
          linear_bnn.noise_min, linear_bnn.log_noise_scale
      ).log_prob(0.005**0.5)
      self.assertAlmostEqual(
          linear_bnn.log_prob(constant_params, x_train, y_train) - diff,
          -14505.76,  # Hardcoded from reference implementation.
          places=2,
      )
  
imports: from absl.testing import parameterized
  import jax
  import jax.numpy
  import numpy
  from tensorflow_probability.python.experimental.autobnn import kernels
  from tensorflow_probability.python.experimental.autobnn import util
  from tensorflow_probability.substrates.jax.distributions import lognormal
  from absl.testing import absltest
label_desc: Use image node_id 4 for calling the ReproduceExperimentTest obj's underlying member method code with example usage: obj.test_log_prob_matches() without return types