def slice_sampler_one_dim(target_log_prob, x_initial, step_size=<NUM_LIT>,<EOL>max_doublings=<NUM_LIT:30>, seed=None, name=None):
with tf.compat.v1.name_scope(name, '<STR_LIT>',<EOL>[x_initial, step_size, max_doublings]):<EOL><INDENT>x_initial = tf.convert_to_tensor(value=x_initial)<EOL>dtype = x_initial.dtype.base_dtype<EOL>log_slice_heights = target_log_prob(x_initial) - tf.random.gamma(<EOL>tf.shape(input=x_initial), alpha=<NUM_LIT:1>, dtype=dtype, seed=seed)<EOL>upper_bounds, lower_bounds, bounds_satisfied = slice_bounds_by_doubling(<EOL>x_initial, target_log_prob, log_slice_heights, max_doublings, step_size,<EOL>seed=seed)<EOL>retval = _sample_with_shrinkage(x_initial, target_log_prob=target_log_prob,<EOL>log_slice_heights=log_slice_heights,<EOL>step_size=step_size,<EOL>lower_bounds=lower_bounds,<EOL>upper_bounds=upper_bounds, seed=seed)<EOL>return (retval, target_log_prob(retval), bounds_satisfied,<EOL>upper_bounds, lower_bounds)<EOL><DEDENT>
For a given x position in each Markov chain, returns the next x.

    Applies the one dimensional slice sampling algorithm as defined in Neal (2003)
    to an input tensor x of shape (num_chains,) where num_chains is the number of
    simulataneous Markov chains, and returns the next tensor x of shape
    (num_chains,) when these chains are evolved by the slice sampling algorithm.

    Args:
      target_log_prob: Callable accepting a tensor like `x_initial` and returning
        a tensor containing the log density at that point of the same shape.
      x_initial: A tensor of any shape. The initial positions of the chains. This
        function assumes that all the dimensions of `x_initial` are batch
        dimensions (i.e. the event shape is `[]`).
      step_size: A tensor of shape and dtype compatible with `x_initial`. The min
        interval size in the doubling algorithm.
      max_doublings: Scalar tensor of dtype `tf.int32`. The maximum number of
        doublings to try to find the slice bounds.
      seed: (Optional) positive int. The random seed. If None, no seed is set.
      name: Python `str` name prefixed to Ops created by this function.
        Default value: `None` (i.e., 'find_slice_bounds').

    Returns:
      retval: A tensor of the same shape and dtype as `x_initial`. The next state
        of the Markov chain.
      next_target_log_prob: The target log density evaluated at `retval`.
      bounds_satisfied: A tensor of bool dtype and shape batch dimensions.
      upper_bounds: Tensor of the same shape and dtype as `x_initial`. The upper
        bounds for the slice found.
      lower_bounds: Tensor of the same shape and dtype as `x_initial`. The lower
        bounds for the slice found.