# Path: blackjax/types.py



from typing import Callable, NamedTuple, Optional, Tuple
from blackjax.types import Array, PRNGKey, PyTree
import jax
import jax.numpy as jnp
import blackjax.mcmc.random_walk as random_walk


def init(position: PyTree, logprob_fn: Callable) -> RMHState:
    """Create a chain state from a position.

    Parameters:
    -----------
    position: PyTree
        The initial position of the chain
    logprob_fn: Callable
        Log-probability density function of the distribution we wish to sample
        from.

    """
    return RMHState(position, logprob_fn(position))


def kernel():
    """Build a Random Walk Rosenbluth-Metropolis-Hastings kernel with a gaussian
    proposal distribution.

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

    """

    def one_step(

# Path: blackjax/types.py



# Path: blackjax/mcmc/rmh.py
from typing import Callable, NamedTuple, Optional, Tuple
from blackjax.types import Array, PRNGKey, PyTree
import jax
import jax.numpy as jnp
import blackjax.mcmc.random_walk as random_walk



def init(position: PyTree, logprob_fn: Callable) -> RMHState:
    """Create a chain state from a position.

    Parameters:
    -----------
    position: PyTree
        The initial position of the chain
    logprob_fn: Callable
        Log-probability density function of the distribution we wish to sample
        from.

    """
    return RMHState(position, logprob_fn(position))


def kernel():
    """Build a Random Walk Rosenbluth-Metropolis-Hastings kernel with a gaussian
    proposal distribution.

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

    """

    def one_step(

        rng_key: PRNGKey, state: RMHState, logprob_fn: Callable, sigma: Array