@docstring_util.expand_docstring(args=doc_args)<EOL><INDENT>def __init__(<EOL>self,<EOL>filters,<EOL>kernel_size,<EOL>strides=(<NUM_LIT:1>, <NUM_LIT:1>, <NUM_LIT:1>),<EOL>padding='<STR_LIT>',<EOL>data_format='<STR_LIT>',<EOL>dilation_rate=(<NUM_LIT:1>, <NUM_LIT:1>, <NUM_LIT:1>),<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(Conv3DReparameterization, self).__init__(<EOL>rank=<NUM_LIT:3>,<EOL>filters=filters,<EOL>kernel_size=kernel_size,<EOL>strides=strides,<EOL>padding=padding,<EOL>data_format=data_format,<EOL>dilation_rate=dilation_rate,<EOL>activation=tf.keras.activations.get(activation),<EOL>activity_regularizer=activity_regularizer,<EOL>kernel_posterior_fn=kernel_posterior_fn,<EOL>kernel_posterior_tensor_fn=kernel_posterior_tensor_fn,<EOL>kernel_prior_fn=kernel_prior_fn,<EOL>kernel_divergence_fn=kernel_divergence_fn,<EOL>bias_posterior_fn=bias_posterior_fn,<EOL>bias_posterior_tensor_fn=bias_posterior_tensor_fn,<EOL>bias_prior_fn=bias_prior_fn,<EOL>bias_divergence_fn=bias_divergence_fn,<EOL>**kwargs)<EOL>
Construct layer.

        Args:
          filters: Integer, the dimensionality of the output space (i.e. the number
            of filters in the convolution).
          kernel_size: An integer or tuple/list of 3 integers, specifying the
            depth, height and width of the 3D convolution window.
            Can be a single integer to specify the same value for
            all spatial dimensions.
          strides: An integer or tuple/list of 3 integers,
            specifying the strides of the convolution along the depth,
            height and width.
            Can be a single integer to specify the same value for
            all spatial dimensions.
            Specifying any stride value != 1 is incompatible with specifying
            any `dilation_rate` value != 1.
          padding: One of `"valid"` or `"same"` (case-insensitive).
          data_format: A string, one of `channels_last` (default) or
            `channels_first`. The ordering of the dimensions in the inputs.
            `channels_last` corresponds to inputs with shape `(batch, depth,
            height, width, channels)` while `channels_first` corresponds to inputs
            with shape `(batch, channels, depth, height, width)`.
          dilation_rate: An integer or tuple/list of 3 integers, specifying
            the dilation rate to use for dilated convolution.
            Can be a single integer to specify the same value for
            all spatial dimensions.
            Currently, specifying any `dilation_rate` value != 1 is
            incompatible with specifying any stride value != 1.
          ${args}