code: def test_plot(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])
  
      params, diagnostics = training_util.fit_bnn_map(
          bnn,
          seed,
          x_train,
          y_train,
          width=5,
          num_particles=8,
          num_iters=100,
      )
      preds = training_util.make_predictions(params, bnn, x_train)
      fig = training_util.plot_results(
          x_train.squeeze(),
          preds,
          dates_train=x_train.squeeze(),
          y_train=y_train,
          dates_test=x_train.squeeze(),
          y_test=y_train,
          diagnostics=diagnostics,
      )
      self.assertLen(fig.axes, 2)
  
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 4 for calling the TrainingUtilTest obj's underlying member method code with example usage: obj.test_plot() without return types