code: def get_bnn_and_params(self):
      x_train, y_train = util.load_fake_dataset()
      linear_bnn = kernels.OneLayerBNN(width=50)
      seed = jax.random.PRNGKey(0)
      init_params = linear_bnn.init(seed, x_train)
      constant_params = jax.tree_map(
          lambda x: jnp.full(x.shape, 0.1), init_params
      )
      constant_params["params"]["noise_scale"] = jnp.array(
          [0.005**0.5]
      )
      return linear_bnn, constant_params, x_train, y_train
  
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 1 for calling the ReproduceExperimentTest obj's underlying member method code with example usage: obj.get_bnn_and_params() and returns: linear_bnn,  constant_params,  x_train,  y_train