# Path: blackjax/types.py



from typing import Callable, NamedTuple, Tuple
from blackjax.types import PRNGKey, PyTree
import blackjax.smc as smc

    Parameters
    ----------
    logprior_fn
        A function that computes the log density of the prior distribution
    loglikelihood_fn
        A function that returns the probability at a given
        position.
    mcmc_kernel_factory
        A function that creates a mcmc kernel from a log-probability density function.
    make_mcmc_state: Callable
        A function that creates a new mcmc state from a position and a
        log-probability density function.
    resampling_fn
        A random function that resamples generated particles based of weights
    num_mcmc_iterations
        Number of iterations in the MCMC chain.

    Returns
    -------
    A callable that takes a rng_key and a TemperedSMCState that contains the current state
    of the chain and that returns a new state of the chain along with
    information about the transition.

    """
    kernel = smc.base.kernel(
        mcmc_kernel_factory, make_mcmc_state, resampling_fn, num_mcmc_iterations
    )

    def one_step(

# Path: blackjax/types.py



# Path: blackjax/smc/tempered.py
from typing import Callable, NamedTuple, Tuple
from blackjax.types import PRNGKey, PyTree
import blackjax.smc as smc


    Parameters
    ----------
    logprior_fn
        A function that computes the log density of the prior distribution
    loglikelihood_fn
        A function that returns the probability at a given
        position.
    mcmc_kernel_factory
        A function that creates a mcmc kernel from a log-probability density function.
    make_mcmc_state: Callable
        A function that creates a new mcmc state from a position and a
        log-probability density function.
    resampling_fn
        A random function that resamples generated particles based of weights
    num_mcmc_iterations
        Number of iterations in the MCMC chain.

    Returns
    -------
    A callable that takes a rng_key and a TemperedSMCState that contains the current state
    of the chain and that returns a new state of the chain along with
    information about the transition.

    """
    kernel = smc.base.kernel(
        mcmc_kernel_factory, make_mcmc_state, resampling_fn, num_mcmc_iterations
    )

    def one_step(

        rng_key: PRNGKey, state: TemperedSMCState, lmbda: float