inputs: Complete the below
def _root_hybr(func, x0, args=(), jac=None, col_deriv=0, xtol=1.49012e-08, maxfev=0, band=None, eps=None, factor=100, diag=None, **unknown_options):

    ''''Find the roots of a multivariate function using MINPACK\'s hybrd and
    hybrj routines (modified Powell method).
    Options
    col_deriv : bool
    Specify whether the Jacobian function computes derivatives down
    the columns (faster, because there is no transpose operation).
    xtol : float
    The calculation will terminate if the relative error between two
    consecutive iterates is at most `xtol`.
    maxfev : int
    The maximum number of calls to the function. If zero, then
    ``100*(N+1)`` is the maximum where N is the number of elements
    in `x0`.
    band : tuple
    If set to a two-sequence containing the number of sub- and
    super-diagonals within the band of the Jacobi matrix, the
    Jacobi matrix is considered banded (only for ``fprime=None``).
    eps : float
    A suitable step length for the forward-difference
    approximation of the Jacobian (for ``fprime=None``). If
    `eps` is less than the machine precision, it is assumed
    that the relative errors in the functions are of the order of
    the machine precision.
    factor : float
    A parameter determining the initial step bound
    (``factor * || diag * x||``).  Should be in the interval
    ``(0.1, 100)``.
    diag : sequence
    N positive entries that serve as a scale factors for the
    variables.'
    '''
targets: _check_unknown_options(unknown_options)
    epsfcn = eps
    x0 = asarray(x0).flatten()
    n = len(x0)
    if (not isinstance(args, tuple)):
        args = (args,)
    (shape, dtype) = _check_func('fsolve', 'func', func, x0, args, n, (n,))
    if (epsfcn is None):
        epsfcn = finfo(dtype).eps
    Dfun = jac
    if (Dfun is None):
        if (band is None):
            (ml, mu) = ((-10), (-10))
        else:
            (ml, mu) = band[:2]
        if (maxfev == 0):
            maxfev = (200 * (n + 1))
        retval = _minpack._hybrd(func, x0, args, 1, xtol, maxfev, ml, mu, epsfcn, factor, diag)
    else:
        _check_func('fsolve', 'fprime', Dfun, x0, args, n, (n, n))
        if (maxfev == 0):
            maxfev = (100 * (n + 1))
        retval = _minpack._hybrj(func, Dfun, x0, args, 1, col_deriv, xtol, maxfev, factor, diag)
    (x, status) = (retval[0], retval[(-1)])
    errors = {0: 'Improper    input    parameters    were    entered.', 1: 'The    solution    converged.', 2: ('The    number    of    calls    to    function    has    reached    maxfev    =    %d.' % maxfev), 3: ('xtol=%f    is    too    small,    no    further    improvement    in    the    approximate\n        solution    is    possible.' % xtol), 4: 'The    iteration    is    not    making    good    progress,    as    measured    by    the    \n        improvement    from    the    last    five    Jacobian    evaluations.', 5: 'The    iteration    is    not    making    good    progress,    as    measured    by    the    \n        improvement    from    the    last    ten    iterations.', 'unknown': 'An    error    occurred.'}
    info = retval[1]
    info['fun'] = info.pop('fvec')
    sol = OptimizeResult(x=x, success=(status == 1), status=status)
    sol.update(info)
    try:
        sol['message'] = errors[status]
    except KeyError:
        info['message'] = errors['unknown']
    return sol