def __init__(self,<EOL>target_log_prob_fn,<EOL>step_size,<EOL>max_doublings,<EOL>seed=None,<EOL>name=None):
self._seed_stream = distributions.SeedStream(<EOL>seed, salt='<STR_LIT>')<EOL>self._parameters = dict(<EOL>target_log_prob_fn=target_log_prob_fn,<EOL>step_size=step_size,<EOL>max_doublings=max_doublings,<EOL>seed=seed,<EOL>name=name)<EOL>
Initializes this transition kernel.

        Args:
          target_log_prob_fn: Python callable which takes an argument like
            `current_state` (or `*current_state` if it is a list) and returns its
            (possibly unnormalized) log-density under the target distribution.
          step_size: Scalar or `tf.Tensor` with same dtype as and shape compatible
            with `x_initial`. The size of the initial interval.
          max_doublings: Scalar positive int32 `tf.Tensor`. The maximum number of
          doublings to consider.
          seed: Python integer to seed the random number generator.
          name: Python `str` name prefixed to Ops created by this function.
            Default value: `None` (i.e., 'slice_sampler_kernel').

        Returns:
          next_state: Tensor or Python list of `Tensor`s representing the state(s)
            of the Markov chain(s) at each result step. Has same shape as
            `current_state`.
          kernel_results: `collections.namedtuple` of internal calculations used to
            advance the chain.