class_docstr: Sequential Monte Carlo transition kernel.
  
  Sequential Monte Carlo maintains a population of weighted particles
  representing samples from a sequence of target distributions. It is
  *not* a calibrated MCMC kernel: the transitions step through a sequence of
  target distributions, rather than trying to maintain a stationary
  distribution.
class_docstr_tok: ["Sequential","Monte","Carlo","transition","kernel",".","Sequential","Monte","Carlo","maintains","a","population","of","weighted","particles","representing","samples","from","a","sequence","of","target","distributions",".","It","is","*","not","*","a","calibrated","MCMC","kernel",":","the","transitions","step","through","a","sequence","of","target","distributions",",","rather","than","trying","to","maintain","a","stationary","distribution","."]
code: def bootstrap_results(self, init_state):
      with tf.name_scope(self.name):
          with tf.name_scope("bootstrap_results"):
              init_state = WeightedParticles(*init_state)
  
              particles_shape = ps.shape(init_state.log_weights)
              weights_shape = ps.concat(
                  [
                      particles_shape[: self.particles_dim],
                      particles_shape[self.particles_dim + 1 :],
                  ],
                  axis=0,
              )
              batch_zeros = tf.zeros(
                  weights_shape, dtype=init_state.log_weights.dtype
              )
  
              return SequentialMonteCarloResults(
                  steps=0,
                  parent_indices=_dummy_indices_like(
                      init_state.log_weights
                  ),
                  incremental_log_marginal_likelihood=batch_zeros,
                  accumulated_log_marginal_likelihood=batch_zeros,
                  seed=samplers.zeros_seed(),
              )
  
imports: import collections
  import tensorflow.compat.v2
  from tensorflow_probability.python.experimental.mcmc import weighted_resampling
  from tensorflow_probability.python.internal import prefer_static
  from tensorflow_probability.python.internal import samplers
  from tensorflow_probability.python.mcmc import kernel
  from tensorflow_probability.python.mcmc.internal import util
label_desc: Use image node_id 10 for calling the SequentialMonteCarlo obj's underlying member method code with example usage: obj.bootstrap_results(init_state) and returns: SequentialMonteCarloResults