human
Solve the following python programming problem given the following code:
def _fit(self, X) : 
    X = np.array (X.T) 
    X -= X.mean (0) 
    def objective_fct(w) : 
        W = w.reshape (self.n_features, X.shape [0]) 
        (F, Fs, L2Fs, NFs, L2Fn, Fhat) = self._determine_features (X, W) 
        obj = np.apply_along_axis (np.linalg.norm, 1, Fhat, 1).sum () 
        deltaW = l2grad (NFs.T, Fhat, L2Fn, np.ones_like (Fhat)) 
        deltaW = l2grad (Fs, NFs, L2Fs, deltaW.T) 
        deltaW = (deltaW * (F / Fs)).dot (X.T) 
        return (obj, deltaW.flatten ()) 
def l2grad(X, Y, N, D) : 
        return ((D / N [:, None]) - ((Y * (D * X).sum (1) [:, None]) / (N ** 2) [:, None])) 
w0 = ((np.random.random ((X.shape [0] * self.n_features)) * 2) - 1) 
    (w, s, d) = fmin_l_bfgs_b (d, w0, iprint = self.iprint, maxfun = self.maxfun) 
    return w.reshape (self.n_features, X.shape [0]) 

    
    

    Fix the buggy line: (w, s, d) = fmin_l_bfgs_b (d, w0, iprint = self.iprint, maxfun = self.maxfun)
assistant
#Here is Python code to fix the buggy line: (w, s, d) = fmin_l_bfgs_b (d, w0, iprint = self.

(w, s, d) = fmin_l_bfgs_b (def objective_fct(, w0, iprint = self.iprint, maxfun = self.maxfun)