code: def testDenseLayersInSequential(self):
      data_size, batch_size, in_size, out_size = 10, 2, 3, 4
      x = np.random.uniform(
          -1.0, 1.0, size=(data_size, in_size)
      ).astype(np.float32)
      y = np.random.uniform(
          -1.0, 1.0, size=(data_size, out_size)
      ).astype(np.float32)
  
      model = tf_keras.Sequential(
          [
              dense_variational.DenseReparameterization(
                  6, activation=tf.nn.relu
              ),
              dense_variational.DenseFlipout(6, activation=tf.nn.relu),
              dense_variational.DenseLocalReparameterization(out_size),
          ]
      )
  
      model.compile(loss="mse", optimizer="adam")
      model.fit(x, y, batch_size=batch_size, epochs=3)
  
      batch_input = tf.random.uniform([batch_size, in_size])
      batch_output = self.evaluate(model(batch_input))
      self.assertAllEqual(batch_output.shape, [batch_size, out_size])
  
imports: import numpy
  import tensorflow.compat.v2
  from tensorflow_probability.python.distributions import independent
  from tensorflow_probability.python.distributions import normal
  from tensorflow_probability.python.internal import test_util
  from tensorflow_probability.python.internal import tf_keras
  from tensorflow_probability.python.layers import dense_variational
  from tensorflow_probability.python.layers import util
  from tensorflow_probability.python.random import random_ops
  from tensorflow_probability.python.util import seed_stream
label_desc: Use image node_id 18 for calling the DenseVariational obj's underlying member method code with example usage: obj.testDenseLayersInSequential() without return types