class_docstr: Abstract nD convolution layer (private, used as implementation base).
  
  This layer creates a convolution kernel that is convolved
  (actually cross-correlated) with the layer input to produce a tensor of
  outputs. It may also include a bias addition and activation function
  on the outputs. It assumes the `kernel` and/or `bias` are drawn from
  distributions.
  
  By default, the layer implements a stochastic forward pass via
  sampling from the kernel and bias posteriors,
  ```none
  outputs = f(inputs; kernel, bias), kernel, bias ~ posterior
  ```
  where f denotes the layer's calculation. It uses the reparameterization
  estimator [(Kingma and Welling, 2014)][1], which performs a Monte Carlo
  approximation of the distribution integrating over the `kernel` and `bias`.
  
  The arguments permit separate specification of the surrogate posterior
  (`q(W|x)`), prior (`p(W)`), and divergence for both the `kernel` and `bias`
  distributions.
  
  #### References
  
  [1]: Diederik Kingma and Max Welling. Auto-Encoding Variational Bayes. In
       _International Conference on Learning Representations_, 2014.
       https://arxiv.org/abs/1312.6114
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code: def _apply_variational_kernel(self, inputs):
      self.kernel_posterior_tensor = self.kernel_posterior_tensor_fn(
          self.kernel_posterior
      )
      self.kernel_posterior_affine = None
      self.kernel_posterior_affine_tensor = None
      outputs = self._convolution_op(
          inputs, self.kernel_posterior_tensor
      )
      return outputs
  
imports: import tensorflow.compat.v2
  from tensorflow_probability.python import random
  from tensorflow_probability.python.distributions import independent
  from tensorflow_probability.python.distributions import kullback_leibler
  from tensorflow_probability.python.distributions import normal
  from tensorflow_probability.python.internal import docstring_util
  from tensorflow_probability.python.internal import tf_keras
  from tensorflow_probability.python.layers import util
  from tensorflow_probability.python.util.seed_stream import SeedStream
  from tensorflow.python.ops import nn_ops
label_desc: Use image node_id 2 for calling the _ConvReparameterization obj's underlying member method code with example usage: obj._apply_variational_kernel(inputs) and returns: outputs