human
Complete the below
def leapfrog(H, q, p, epsilon, n_steps):

    ''''Leapfrog integrator.
    Estimates `p(t)` and `q(t)` at time :math:`t = n \cdot e`, by integrating the
    Hamiltonian equations
    .. math::
    \frac{dq_i}{dt} = \frac{\partial H}{\partial p_i}
    \frac{dp_i}{dt} = \frac{\partial H}{\partial q_i}
    with :math:`p(0) = p`, :math:`q(0) = q`
    Parameters
    H : Hamiltonian instance.
    Tuple of `logp, dlogp, potential`.
    q : Theano.tensor
    initial position vector
    p : Theano.tensor
    initial momentum vector
    epsilon : float, step size
    n_steps : int, number of iterations
    Returns
    position : Theano.tensor
    position estimate at time :math:`n \cdot e`.
    momentum : Theano.tensor
    momentum estimate at time :math:`n \cdot e`.'
    '''

=====
Write me a the Python program you requested?
assistant
#Here is the Python program you requested.
    def full_update(p, q):
        p = (p + (epsilon * H.dlogp(q)))
        q += (epsilon * H.pot.velocity(p))
        return (p, q)
    p = (p + ((0.5 * epsilon) * H.dlogp(q)))
    q += (epsilon * H.pot.velocity(p))
    if tt.gt(n_steps, 1):
        ((p_seq, q_seq), _) = theano.scan(full_update, outputs_info=[p, q], n_steps=(n_steps - 1))
        (p, q) = (p_seq[(-1)], q_seq[(-1)])
    p += ((0.5 * epsilon) * H.dlogp(q))
    return (q, p)