human
Can you provide a the Python program you requested given the following:
Complete the below
def lbfgs(opfunc, x, config, state=None):

    ''''An implementation of L-BFGS, heavily inspired by minFunc (Mark Schmidt)
    This implementation of L-BFGS relies on a user-provided line
    search function (state.lineSearch). If this function is not
    provided, then a simple learningRate is used to produce fixed
    size steps. Fixed size steps are much less costly than line
    searches, and can be useful for stochastic problems.
    The learning rate is used even when a line search is provided.
    This is also useful for large-scale stochastic problems, where
    opfunc is a noisy approximation of f(x). In that case, the learning
    rate allows a reduction of confidence in the step size.
    Args:
    - `opfunc` : a function that takes a single input (X), the point of
    evaluation, and returns f(X) and df/dX
    - `x` : the initial point
    - `state` : a table describing the state of the optimizer.
after each
    call the state is modified
    - `state.maxIter` : Maximum number of iterations allowed
    - `state.maxEval` : Maximum number of function evaluations
    - `state.tolFun` : Termination tolerance on the first-order optimality
    - `state.tolX` : Termination tol on progress in terms of func/param changes
    - `state.lineSearch` : A line search function
    - `state.learningRate` : If no line search provided, then a fixed step size is used
    Returns:
    - `x*` : the new `x` vector, at the optimal point
    - `f`  : a table of all function values:
    `f[1]` is the value of the function before any optimization and
    `f[#f]` is the final fully optimized value, at `x*`
    (Clement Farabet, 2012)'
    '''
assistant
#Here is the Python program you requested.
    if ((config is None) and (state is None)):
        raise ValueError('lbfgs    requires    a    dictionary    to    retain    state    between    iterations')
    state = (state if (state is not None) else config)
    maxIter = config.get('maxIter', 20)
    maxEval = config.get('maxEval', (maxIter * 1.25))
    tolFun = config.get('tolFun', 1e-05)
    tolX = config.get('tolX', 1e-09)
    nCorrection = config.get('nCorrection', 100)
    lineSearch = config.get('lineSearch')
    lineSearchOptions = config.get('lineSearchOptions')
    learningRate = config.get('learningRate', 1)
    isverbose = config.get('verbose', False)
    state.setdefault('funcEval', 0)
    state.setdefault('nIter', 0)
    if isverbose:
        def verbose(*args):
            args = (('<optim.lbfgs>',) + args)
            print args
    else:
        def verbose(*args):
            pass
    (f, g) = opfunc(x)
    f_hist = [f]
    currentFuncEval = 1
    state['funcEval'] += 1
    p = g.size(0)
    if ('tmp1' not in state):
        state['tmp1'] = g.new(g.size()).zero_()
    tmp1 = state['tmp1']
    tmp1.copy_(g).abs_()
    if (tmp1.sum() <= tolFun):
        verbose('optimality    condition    below    tolFun')
        return (x, f_hist)
    if ('dir_bufs' not in state):
        verbose('creating    recyclable    direction/step/history    buffers')
        state['dir_bufs'] = list(g.new((nCorrection + 1), p).split(1))
        state['stp_bufs'] = list(g.new((nCorrection + 1), p).split(1))
        for i in range(len(state['dir_bufs'])):
            state['dir_bufs'][i] = state['dir_bufs'][i].squeeze(0)
            state['stp_bufs'][i] = state['stp_bufs'][i].squeeze(0)
    d = state.get('d')
    t = state.get('t')
    old_dirs = state.get('old_dirs')
    old_stps = state.get('old_stps')
    Hdiag = state.get('Hdiag')
    g_old = state.get('g_old')
    f_old = state.get('f_old')
    nIter = 0
    while (nIter < maxIter):
        nIter += 1
        state['nIter'] += 1
        if (state['nIter'] == 1):
            d = g.neg()
           ...