code: def testWithKeras(self):
      # NOTE: Keras throws an error below if we use
      # tf_keras.tf1_layers.BatchNormalization() here.
      layer = None
  
      dist = transformed_distribution.TransformedDistribution(
          distribution=sample.Sample(normal.Normal(0.0, 1.0), [1]),
          bijector=batch_normalization.BatchNormalization(
              batchnorm_layer=layer
          ),
          validate_args=True,
      )
  
      x_ = tf_keras.Input(shape=(1,))
      log_prob_ = dist.log_prob(x_)
      model = tf_keras.Model(x_, log_prob_)
  
      model.compile(
          optimizer="adam", loss=lambda _, log_prob: -log_prob
      )
  
      model.fit(
          x=np.random.normal(size=(32, 1)).astype(np.float32),
          y=np.zeros((32, 0)),
          batch_size=16,
          epochs=1,
          steps_per_epoch=2,
      )
  
imports: from absl.testing import parameterized
  import numpy
  import tensorflow.compat.v1
  import tensorflow.compat.v2
  from tensorflow_probability.python.bijectors import batch_normalization
  from tensorflow_probability.python.bijectors import bijector_test_util
  from tensorflow_probability.python.bijectors import invert
  from tensorflow_probability.python.bijectors import reshape
  from tensorflow_probability.python.distributions import mvn_diag
  from tensorflow_probability.python.distributions import normal
  from tensorflow_probability.python.distributions import sample
  from tensorflow_probability.python.distributions import transformed_distribution
  from tensorflow_probability.python.internal import test_util
  from tensorflow_probability.python.internal import tf_keras
label_desc: Use image node_id 6 for calling the BatchNormTest obj's underlying member method code with example usage: obj.testWithKeras() without return types