code: def _testDenseSetUp(
      self, layer_class, batch_size, in_size, out_size, **kwargs
  ):
      seed = Counter()
      inputs = tf.random.uniform([batch_size, in_size], seed=seed())
  
      kernel_size = [in_size, out_size]
      kernel_posterior = MockDistribution(
          loc=tf.random.uniform(kernel_size, seed=seed()),
          scale=tf.random.uniform(kernel_size, seed=seed()),
          result_log_prob=tf.random.uniform(kernel_size, seed=seed()),
          result_sample=tf.random.uniform(kernel_size, seed=seed()),
      )
      kernel_prior = MockDistribution(
          result_log_prob=tf.random.uniform(kernel_size, seed=seed()),
          result_sample=tf.random.uniform(kernel_size, seed=seed()),
      )
      kernel_divergence = MockKLDivergence(
          result=tf.random.uniform([], seed=seed())
      )
  
      bias_size = [out_size]
      bias_posterior = MockDistribution(
          result_log_prob=tf.random.uniform(bias_size, seed=seed()),
          result_sample=tf.random.uniform(bias_size, seed=seed()),
      )
      bias_prior = MockDistribution(
          result_log_prob=tf.random.uniform(bias_size, seed=seed()),
          result_sample=tf.random.uniform(bias_size, seed=seed()),
      )
      bias_divergence = MockKLDivergence(
          result=tf.random.uniform([], seed=seed())
      )
  
      layer = layer_class(
          units=out_size,
          kernel_posterior_fn=lambda *args: kernel_posterior,
          kernel_posterior_tensor_fn=lambda d: d.sample(seed=42),
          kernel_prior_fn=lambda *args: kernel_prior,
          kernel_divergence_fn=kernel_divergence,
          bias_posterior_fn=lambda *args: bias_posterior,
          bias_posterior_tensor_fn=lambda d: d.sample(seed=43),
          bias_prior_fn=lambda *args: bias_prior,
          bias_divergence_fn=bias_divergence,
          **kwargs,
      )
  
      outputs = layer(inputs)
  
      kl_penalty = layer.losses
      return (
          kernel_posterior,
          kernel_prior,
          kernel_divergence,
          bias_posterior,
          bias_prior,
          bias_divergence,
          layer,
          inputs,
          outputs,
          kl_penalty,
      )
  
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 4 for calling the DenseVariational obj's underlying member method code with example usage: obj._testDenseSetUp(layer_class, batch_size, in_size, out_size) and returns: kernel_posterior,  kernel_prior,  kernel_divergence,  bias_posterior,  bias_prior,  bias_divergence,  layer,  inputs,  outputs,  kl_penalty