# Path: blackjax/types.py



from typing import Callable, NamedTuple, Tuple
from blackjax.types import Array, PRNGKey, PyTree
import jax
import blackjax.mcmc.integrators as integrators
import blackjax.mcmc.metrics as metrics
import blackjax.mcmc.proposal as proposal
import blackjax.mcmc.trajectory as trajectory
    potential_energy, potential_energy_grad = jax.value_and_grad(potential_fn)(position)
    return HMCState(position, potential_energy, potential_energy_grad)


def kernel(
    integrator: Callable = integrators.velocity_verlet,
    divergence_threshold: float = 1000,
):
    """Build a HMC kernel.

    Parameters
    ----------
    integrator
        The symplectic integrator to use to integrate the Hamiltonian dynamics.
    divergence_threshold
        Value of the difference in energy above which we consider that the transition is divergent.

    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: HMCState,
        logprob_fn: Callable,
        step_size: float,

# Path: blackjax/types.py



# Path: blackjax/mcmc/hmc.py
from typing import Callable, NamedTuple, Tuple
from blackjax.types import Array, PRNGKey, PyTree
import jax
import blackjax.mcmc.integrators as integrators
import blackjax.mcmc.metrics as metrics
import blackjax.mcmc.proposal as proposal
import blackjax.mcmc.trajectory as trajectory

    potential_energy, potential_energy_grad = jax.value_and_grad(potential_fn)(position)
    return HMCState(position, potential_energy, potential_energy_grad)


def kernel(
    integrator: Callable = integrators.velocity_verlet,
    divergence_threshold: float = 1000,
):
    """Build a HMC kernel.

    Parameters
    ----------
    integrator
        The symplectic integrator to use to integrate the Hamiltonian dynamics.
    divergence_threshold
        Value of the difference in energy above which we consider that the transition is divergent.

    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: HMCState,
        logprob_fn: Callable,
        step_size: float,

        inverse_mass_matrix: Array,