def __init__(self,<EOL>inner_kernel,<EOL>num_adaptation_steps,<EOL>target_accept_prob=<NUM_LIT>,<EOL>adaptation_rate=<NUM_LIT>,<EOL>step_size_setter_fn=_hmc_like_step_size_setter_fn,<EOL>step_size_getter_fn=_hmc_like_step_size_getter_fn,<EOL>log_accept_prob_getter_fn=_hmc_like_log_accept_prob_getter_fn,<EOL>validate_args=False,<EOL>name=None):
inner_kernel = mcmc_util.enable_store_parameters_in_results(inner_kernel)<EOL>with tf.compat.v1.name_scope(<EOL>mcmc_util.make_name(name, '<STR_LIT>', '<STR_LIT>'),<EOL>values=[target_accept_prob, adaptation_rate,<EOL>num_adaptation_steps]) as name:<EOL><INDENT>dtype = dtype_util.common_dtype([target_accept_prob, adaptation_rate],<EOL>tf.float32)<EOL>target_accept_prob = tf.convert_to_tensor(<EOL>value=target_accept_prob, dtype=dtype, name='<STR_LIT>')<EOL>adaptation_rate = tf.convert_to_tensor(<EOL>value=adaptation_rate, dtype=dtype, name='<STR_LIT>')<EOL>num_adaptation_steps = tf.convert_to_tensor(<EOL>value=num_adaptation_steps,<EOL>dtype=tf.int32,<EOL>name='<STR_LIT>')<EOL>target_accept_prob = _maybe_validate_target_accept_prob(<EOL>target_accept_prob, validate_args)<EOL><DEDENT>self._parameters = dict(<EOL>inner_kernel=inner_kernel,<EOL>num_adaptation_steps=num_adaptation_steps,<EOL>target_accept_prob=target_accept_prob,<EOL>adaptation_rate=adaptation_rate,<EOL>step_size_setter_fn=step_size_setter_fn,<EOL>step_size_getter_fn=step_size_getter_fn,<EOL>log_accept_prob_getter_fn=log_accept_prob_getter_fn,<EOL>name=name,<EOL>)<EOL>
Creates the step size adaptation kernel.

        The default setter_fn and the getter_fn callbacks assume that the inner
        kernel produces kernel results structurally the same as the
        `HamiltonianMonteCarlo` kernel.

        Args:
          inner_kernel: `TransitionKernel`-like object.
          num_adaptation_steps: Scalar `int` `Tensor` number of initial steps to
            during which to adjust the step size. This may be greater, less than, or
            equal to the number of burnin steps.
          target_accept_prob: A floating point `Tensor` representing desired
            acceptance probability. Must be a positive number less than 1. This can
            either be a scalar, or have shape [num_chains]. Default value: `0.75`
            (the [center of asymptotically optimal rate for HMC][1]).
          adaptation_rate: `Tensor` representing amount to scale the current
            `step_size`.
          step_size_setter_fn: A callable with the signature
            `(kernel_results, new_step_size) -> new_kernel_results` where
            `kernel_results` are the results of the `inner_kernel`, `new_step_size`
            is a `Tensor` or a nested collection of `Tensor`s with the same
            structure as returned by the `step_size_getter_fn`, and
            `new_kernel_results` are a copy of `kernel_results` with the step
            size(s) set.
          step_size_getter_fn: A callable with the signature
            `(kernel_results) -> step_size` where `kernel_results` are the results
            of the `inner_kernel`, and `step_size` is a floating point `Tensor` or a
            nested collection of such `Tensor`s.
          log_accept_prob_getter_fn: A callable with the signature
            `(kernel_results) -> log_accept_prob` where `kernel_results` are the
            results of the `inner_kernel`, and `log_accept_prob` is a floating point
            `Tensor`. `log_accept_prob` can either be a scalar, or have shape
            [num_chains]. If it's the latter, `step_size` should also have the same
            leading dimension.
          validate_args: Python `bool`. When `True` kernel parameters are checked
            for validity. When `False` invalid inputs may silently render incorrect
            outputs.
          name: Python `str` name prefixed to Ops created by this class. Default:
            'simple_step_size_adaptation'.

        #### References

        [1]: Betancourt, M. J., Byrne, S., & Girolami, M. (2014). _Optimizing The
             Integrator Step Size for Hamiltonian Monte Carlo_.
             http://arxiv.org/abs/1411.6669