@docstring_util.expand_docstring(args=doc_args)<EOL><INDENT>def __init__(<EOL>self,<EOL>units,<EOL>activation=None,<EOL>activity_regularizer=None,<EOL>kernel_posterior_fn=tfp_layers_util.default_mean_field_normal_fn(),<EOL>kernel_posterior_tensor_fn=lambda d: d.sample(),<EOL>kernel_prior_fn=tfp_layers_util.default_multivariate_normal_fn,<EOL>kernel_divergence_fn=lambda q, p, ignore: tfd.kl_divergence(q, p),<EOL>bias_posterior_fn=tfp_layers_util.default_mean_field_normal_fn(is_singular=True),  <EOL>bias_posterior_tensor_fn=lambda d: d.sample(),<EOL>bias_prior_fn=None,<EOL>bias_divergence_fn=lambda q, p, ignore: tfd.kl_divergence(q, p),<EOL>**kwargs):<EOL><DEDENT>
<EOL>super(_DenseVariational, self).__init__(<EOL>activity_regularizer=activity_regularizer,<EOL>**kwargs)<EOL>self.units = units<EOL>self.activation = tf.keras.activations.get(activation)<EOL>self.input_spec = tf.keras.layers.InputSpec(min_ndim=<NUM_LIT:2>)<EOL>self.kernel_posterior_fn = kernel_posterior_fn<EOL>self.kernel_posterior_tensor_fn = kernel_posterior_tensor_fn<EOL>self.kernel_prior_fn = kernel_prior_fn<EOL>self.kernel_divergence_fn = kernel_divergence_fn<EOL>self.bias_posterior_fn = bias_posterior_fn<EOL>self.bias_posterior_tensor_fn = bias_posterior_tensor_fn<EOL>self.bias_prior_fn = bias_prior_fn<EOL>self.bias_divergence_fn = bias_divergence_fn<EOL>
Construct layer.

        Args:
          ${args}