code: def test_train(self):
      seed = jax.random.PRNGKey(20231018)
      x_train, y_train = util.load_fake_dataset()
  
      leaf1 = kernels.PeriodicBNN(
          width=5, period=0.1, going_to_be_multiplied=True
      )
      leaf2 = kernels.LinearBNN(width=5, going_to_be_multiplied=True)
      bnn = operators.Multiply(bnns=[leaf1, leaf2])
  
      _, diagnostics = training_util.fit_bnn_map(
          bnn, seed, x_train, y_train, num_particles=8, num_iters=100
      )
      self.assertEqual(diagnostics["loss"].shape, (8, 100))
  
imports: import chex
  import jax
  import jax.numpy
  import numpy
  from tensorflow_probability.python.experimental.autobnn import kernels
  from tensorflow_probability.python.experimental.autobnn import operators
  from tensorflow_probability.python.experimental.autobnn import training_util
  from tensorflow_probability.python.experimental.autobnn import util
  from tensorflow_probability.python.internal import test_util
label_desc: Use image node_id 3 for calling the TrainingUtilTest obj's underlying member method code with example usage: obj.test_train() without return types