class_docstr: Dummy prior on sparse regression weights.
code: def _parameter_control_dependencies(self, is_init):
      if not is_init:
          raise ValueError(
              "Cannot explicitly operate on a spike-and-slab prior; "
              "only Gibbs sampling is supported."
          )
      return []
  
imports: import collections
  import itertools
  import numpy
  import six
  import tensorflow.compat.v2
  from tensorflow_probability.python.bijectors import identity
  from tensorflow_probability.python.bijectors import invert
  from tensorflow_probability.python.bijectors import square
  from tensorflow_probability.python.distributions import categorical
  from tensorflow_probability.python.distributions import distribution
  from tensorflow_probability.python.distributions import gamma
  from tensorflow_probability.python.distributions import inverse_gamma
  from tensorflow_probability.python.distributions import mixture_same_family
  from tensorflow_probability.python.distributions import mvn_diag
  from tensorflow_probability.python.distributions import mvn_linear_operator
  from tensorflow_probability.python.distributions import normal
  from tensorflow_probability.python.distributions import normal_conjugate_posteriors
  from tensorflow_probability.python.distributions import transformed_distribution
  from tensorflow_probability.python.experimental.distributions import mvn_precision_factor_linop
  from tensorflow_probability.python.experimental.sts_gibbs import dynamic_spike_and_slab
  from tensorflow_probability.python.experimental.sts_gibbs import sample_parameters
  from tensorflow_probability.python.experimental.sts_gibbs import spike_and_slab
  from tensorflow_probability.python.internal import distribution_util
  from tensorflow_probability.python.internal import dtype_util
  from tensorflow_probability.python.internal import prefer_static
  from tensorflow_probability.python.internal import reparameterization
  from tensorflow_probability.python.internal import samplers
  from tensorflow_probability.python.sts.components import local_level
  from tensorflow_probability.python.sts.components import local_linear_trend
  from tensorflow_probability.python.sts.components import regression
  from tensorflow_probability.python.sts.components import seasonal
  from tensorflow_probability.python.sts.components import sum
  from tensorflow_probability.python.sts.internal import util
label_desc: Use image node_id 3 for calling the DummySpikeAndSlabPrior obj's underlying member method code with example usage: obj._parameter_control_dependencies(is_init) and returns: list