class_docstr: Sampler for Bayesian regression with a spike-and-slab prior on weights.
  
  This implementation follows the sampler described in section 3.2
  of Scott and Varian, 2013 [1]. It differs from the SpikeSlabSampler in that
  it selects the submatrix of the design matrix that is active (given by the
  sparsity pattern of the coefficients), before computing a Cholesky
  decomposition. This may provide a speedup by performing a Cholesky on a (much)
  smaller matrix, at the cost of not being able to jit compile the program, or
  to batch (since each batch element may have a different sparsity pattern, so
  the dynamically sized matrices would all be different sizes).
  
  ### Model
  
  This sampler assumes the regression model
  
  ```
  y ~ Normal(loc=matvec(design_matrix,  # `X` in [1].
                        weights),       # `beta` in `[1]`.
             scale=observation_noise_scale)  # `sigma_epsilon` in [1].
  ```
  
  where the design matrix has shape `[num_outputs, num_features]`, with a
  conjugate InverseGamma prior on the noise variance (eqn (6) of [1]):
  
  ```
  observation_noise_scale**2 ~ InverseGamma(
    concentration=observation_noise_variance_prior_concentration,
    scale=observation_noise_variance_prior_scale)
  ```
  
  and a spike-and-slab prior on the weights (eqns (5) and (6) of [1]):
  
  ```
  slab_weights ~ MultivariateNormal(
     loc=0.,  # `b` from [1].
     precision=(weights_prior_precision  # `Omega^{-1}` from [1].
                / observation_noise_scale**2))
  nonzeros ~ Bernoulli(probs=nonzero_prior_prob)  # `gamma` from [1].
  weights = slab_weights * nonzeros
  ```
  
  ### Example
  
  Constructing a sampler instance specifies the model priors:
  
  ```python
  sampler = spike_and_slab.DynamicSpikeSlabSampler(
    design_matrix=design_matrix,
    observation_noise_variance_prior_concentration=1.,
    observation_noise_variance_prior_scale=1.
    nonzero_prior_prob=0.1)
  ```
  
  The sampler instance itself is stateless, though some internal methods take
  or accept `DynamicSpikeSlabSamplerState` tuples representing posterior
  quantities maintained within a sampling pass. The sampler is
  invoked by passing the regression targets (`y`) and the initial sparsity
  pattern (`nonzeros`):
  
  ```
  (observation_noise_variance,
   weights) = sampler.sample_noise_variance_and_weights(
     targets=y, initial_nonzeros=tf.ones([num_features], dtype=tf.bool))
  ```
  
  This implements the stochastic search variable selection (SSVS) algorithm [2],
  sweeping over the features in random order to resample their sparsity
  indicators one by one. It then returns a sample from the joint posterior
  on the regression weights and the observation noise variance, conditioned
  on the resampled sparsity pattern.
  
  #### References
  
  [1] Steven L. Scott and Hal Varian. Predicting the Present with Bayesian
      Structural Time Series. __International Journal of Mathematical Modelling
      and Numerical Optimisation 5.1-2 (2014): 4-23.__
      https://people.ischool.berkeley.edu/~hal/Papers/2013/pred-present-with-bsts.pdf
  
  [2] George, E. I. and McCulloch, R. E. Approaches for Bayesian variable
      selection. __Statistica Sinica 7, 339–374 (1997)__.
docstr: Proposes flipping the sparsity indicator of the `idx`th feature.
  
  This method computes the sampler state (including factorized precision
  matrices) for a given sparsity pattern, given the state for a
  related sparsity pattern that differs in a single position. This is
  achieved using rank-1 Cholesky updates running in time
  proportional to `num_features**2`, and so is typically more efficient than
  recomputing the equivalent state from scratch using
  `_initialize_sampler_state`.
  
  Args:
    sampler_state: instance of `DynamicSpikeSlabSamplerState` collecting
      Tensor quantities relevant to the sampler. See the
      `DynamicSpikeSlabSamplerState` definition for details.
    idx: scalar int `Tensor` index in `[0, num_features)`.
  
  Returns:
    updated_sampler_state: instance of `DynamicSpikeSlabSamplerState`
      equivalent to `self._initialize_sampler_state(targets, new_nonzeros)`,
      where `new_nonzeros` is equal to `nonzeros` with the `idx`th entry
      negated.
code: def _flip_feature(self, sampler_state, idx):
      """Proposes flipping the sparsity indicator of the `idx`th feature.
  
      This method computes the sampler state (including factorized precision
      matrices) for a given sparsity pattern, given the state for a
      related sparsity pattern that differs in a single position. This is
      achieved using rank-1 Cholesky updates running in time
      proportional to `num_features**2`, and so is typically more efficient than
      recomputing the equivalent state from scratch using
      `_initialize_sampler_state`.
  
      Args:
        sampler_state: instance of `DynamicSpikeSlabSamplerState` collecting
          Tensor quantities relevant to the sampler. See the
          `DynamicSpikeSlabSamplerState` definition for details.
        idx: scalar int `Tensor` index in `[0, num_features)`.
  
      Returns:
        updated_sampler_state: instance of `DynamicSpikeSlabSamplerState`
          equivalent to `self._initialize_sampler_state(targets, new_nonzeros)`,
          where `new_nonzeros` is equal to `nonzeros` with the `idx`th entry
          negated.
      """
      with tf.name_scope("flip_feature_indicator"):
          was_nonzero = tf.gather(sampler_state.nonzeros, idx, axis=-1)
          new_nonzeros = _set_vector_index(
              sampler_state.nonzeros, idx, tf.logical_not(was_nonzero)
          )
          # Update the weight posterior mean and precision for the new nonzeros.
          # (and also update the prior, used to compute the marginal likelihood).
          indices = tf.where(new_nonzeros)[:, 0]
          conditional_prior_precision_chol = tf.linalg.cholesky(
              tf.gather(
                  tf.gather(self.weights_prior_precision, indices),
                  indices,
                  axis=1,
              )
          )
          conditional_posterior_precision_chol = tf.linalg.cholesky(
              tf.gather(
                  tf.gather(
                      sampler_state.weights_posterior_precision, indices
                  ),
                  indices,
                  axis=1,
              )
          )
          sub_x_transpose_y = tf.gather(
              sampler_state.x_transpose_y, indices
          )
          conditional_weights_mean = tf.linalg.cholesky_solve(
              conditional_posterior_precision_chol,
              sub_x_transpose_y[..., tf.newaxis],
          )[..., 0]
          return self._compute_log_prob(
              nonzeros=new_nonzeros,
              y_transpose_y=sampler_state.y_transpose_y,
              conditional_prior_precision_chol=conditional_prior_precision_chol,
              conditional_posterior_precision_chol=(
                  conditional_posterior_precision_chol
              ),
              weights_posterior_precision=sampler_state.weights_posterior_precision,
              observation_noise_variance_posterior_scale=(
                  self.observation_noise_variance_prior_scale
                  + (
                      sampler_state.y_transpose_y
                      - tf.reduce_sum(
                          conditional_weights_mean * sub_x_transpose_y,
                          axis=-1,
                      )
                  )
                  / 2
              ),
              x_transpose_y=sampler_state.x_transpose_y,
          )
  
imports: import collections
  import tensorflow.compat.v2
  from tensorflow_probability.python.bijectors import softplus
  from tensorflow_probability.python.distributions import bernoulli
  from tensorflow_probability.python.distributions import gamma
  from tensorflow_probability.python.distributions import inverse_gamma
  from tensorflow_probability.python.distributions import joint_distribution_auto_batched
  from tensorflow_probability.python.distributions import sample
  from tensorflow_probability.python.experimental.distributions.mvn_precision_factor_linop import MultivariateNormalPrecisionFactorLinearOperator
  from tensorflow_probability.python.internal import dtype_util
  from tensorflow_probability.python.internal import parameter_properties
  from tensorflow_probability.python.internal import prefer_static
  from tensorflow_probability.python.internal import samplers
  from tensorflow_probability.python.internal import vectorization_util
  from tensorflow_probability.python.mcmc.internal import util
label_desc: Use image node_id 4 for calling the DynamicSpikeSlabSampler obj's underlying member method code with example usage: obj._flip_feature(sampler_state, idx) and returns: self