human
Complete the below
def line_search_wolfe2(f, myfprime, xk, pk, gfk=None, old_fval=None, old_old_fval=None, args=(), c1=0.0001, c2=0.9, amax=None, extra_condition=None, maxiter=10):

    ''''Find alpha that satisfies strong Wolfe conditions.
    Parameters
    f : callable f(x,*args)
    Objective function.
    myfprime : callable f\'(x,*args)
    Objective function gradient.
    xk : ndarray
    Starting point.
    pk : ndarray
    Search direction.
    gfk : ndarray, optional
    Gradient value for x=xk (xk being the current parameter
    estimate). Will be recomputed if omitted.
    old_fval : float, optional
    Function value for x=xk. Will be recomputed if omitted.
    old_old_fval : float, optional
    Function value for the point preceding x=xk
    args : tuple, optional
    Additional arguments passed to objective function.
    c1 : float, optional
    Parameter for Armijo condition rule.
    c2 : float, optional
    Parameter for curvature condition rule.
    amax : float, optional
    Maximum step size
    extra_condition : callable, optional
    A callable of the form ``extra_condition(alpha, x, f, g)``
    returning a boolean. Arguments are the proposed step ``alpha``
    and the corresponding ``x``, ``f`` and ``g`` values. The line search
    accepts the value of ``alpha`` only if this
    callable returns ``True``. If the callable returns ``False``
    for the step length, the algorithm will continue with
    new iterates. The callable is only called for iterates
    satisfying the strong Wolfe conditions.
    maxiter : int, optional
    Maximum number of iterations to perform
    Returns
    alpha : float or None
    Alpha for which ``x_new = x0 + alpha * pk``,
    or None if the line search algorithm did not converge.
    fc : int
    Number of function evaluations made.
    gc : int
    Number of gradient evaluations made.
    new_fval : float or None
    New function value ``f(x_new)=f(x0+alpha*pk)``,
    or None if the line search algorithm did not converge.
    old_fval : float
    Old function value ``f(x0)``.
    new_slope : float or None
    The local slope along the search direction at the
    new value ``<myfprime(x_new), pk>``,
    or None if the line search algorithm...'''

=====
What is a Python code to find alpha that satisfies strong Wolfe conditions?
assistant
#Here is Python code to find alpha that satisfies strong Wolfe conditions.
    fc = [0]
    gc = [0]
    gval = [None]
    gval_alpha = [None]
    def phi(alpha):
        fc[0] += 1
        return f((xk + (alpha * pk)), *args)
    if isinstance(myfprime, tuple):
        def derphi(alpha):
            fc[0] += (len(xk) + 1)
            eps = myfprime[1]
            fprime = myfprime[0]
            newargs = ((f, eps) + args)
            gval[0] = fprime((xk + (alpha * pk)), *newargs)
            gval_alpha[0] = alpha
            return np.dot(gval[0], pk)
    else:
        fprime = myfprime
        def derphi(alpha):
            gc[0] += 1
            gval[0] = fprime((xk + (alpha * pk)), *args)
            gval_alpha[0] = alpha
            return np.dot(gval[0], pk)
    if (gfk is None):
        gfk = fprime(xk, *args)
    derphi0 = np.dot(gfk, pk)
    if (extra_condition is not None):
        def extra_condition2(alpha, phi):
            if (gval_alpha[0] != alpha):
                derphi(alpha)
            x = (xk + (alpha * pk))
            return extra_condition(alpha, x, phi, gval[0])
    else:
        extra_condition2 = None
    (alpha_star, phi_star, old_fval, derphi_star) = scalar_search_wolfe2(phi, derphi, old_fval, old_old_fval, derphi0, c1, c2, amax, extra_condition2, maxiter=maxiter)
    if (derphi_star is None):
        warn('The    line    search    algorithm    did    not    converge', LineSearchWarning)
    else:
        derphi_star = gval[0]
    return (alpha_star, fc[0], gc[0], phi_star, old_fval, derphi_star)