class_docstr: Variational flipout linear layer.
code: def __init__(
      self,
      posterior,
      prior,
      apply_kernel_fn,
      activation_fn=None,
      posterior_value_fn=tfd.Distribution.sample,
      unpack_weights_fn=unpack_kernel_and_bias,
      seed=None,
      dtype=tf.float32,
      validate_args=False,
      name=None,
  ):
      super(VariationalFlipoutKernelBiasLayer, self).__init__(
          posterior,
          prior,
          activation_fn=activation_fn,
          posterior_value_fn=posterior_value_fn,
          seed=seed,
          dtype=dtype,
          validate_args=validate_args,
          name=name,
      )
      self._apply_kernel_fn = apply_kernel_fn
      self._unpack_weights_fn = unpack_weights_fn
  
imports: import collections
  import tensorflow.compat.v2
  from tensorflow_probability.python import random
  from tensorflow_probability.python.distributions import distribution
  from tensorflow_probability.python.distributions import independent
  from tensorflow_probability.python.distributions import mvn_diag
  from tensorflow_probability.python.distributions import normal
  from tensorflow_probability.python.experimental.nn import layers
  from tensorflow_probability.python.internal import prefer_static
  from tensorflow_probability.python.util.seed_stream import SeedStream
label_desc: Use image node_id 1 to create a new VariationalFlipoutKernelBiasLayer object from inherited base classes: VariationalLayer with example: obj = VariationalFlipoutKernelBiasLayer(posterior, prior, apply_kernel_fn, activation_fn, posterior_value_fn, unpack_weights_fn, seed, dtype, validate_args, name)