human
Solve the following python programming problem given the following code:
def apply(self, input_labeled, target_labeled, input_unlabeled) : 
    self.layer_counter = 0 
    input_dim = self.p.encoder_layers [0] 
    layers = self.layers 
    self.layer_dims = { 
        0 : input_dim, 
} 
    self.lr = self.shared (self.default_lr, 'learning_rate', role = None) 
    self.costs = costs = AttributeDict () 
    self.costs.denois = AttributeDict () 
    self.act = AttributeDict () 
    self.error = AttributeDict () 
    top = (len (layers) - 1) 
    N = input_labeled.shape [0] 
    self.join = (lambda l, u : T.concatenate ([l, u], axis = 0)) 
    self.labeled = (lambda x : (x [: N] if (x is not None) else x)) 
    self.unlabeled = (lambda x : (x [N :] if (x is not None) else x)) 
    self.split_lu = (lambda x : (self.labeled (x), self.unlabeled (x))) 
    input_concat = self.join (input_labeled, input_unlabeled) 
    def encoder(input_, path_name, input_noise_std = 0, noise_std = []) : 
        h = input_ 
        logger.info (('  0: noise %g' % input_noise_std)) 
        if (input_noise_std > 0.0) : 
            h = (h + (self.noise_like (h) * input_noise_std)) 
d = AttributeDict () 
        d.unlabeled = self.new_activation_dict () 
        d.labeled = self.new_activation_dict () 
        d.labeled.z [0] = self.labeled (h) 
        d.unlabeled.z [0] = self.unlabeled (h) 
        prev_dim = input_dim 
        for (i, (spec, _, act_f)) in layers [1 :] : 
            (d.labeled.h [(i - 1)], d.unlabeled.h [(i - 1)]) = self.split_lu (h) 
            noise = (noise_std [i] if (i < len (noise_std)) else 0.0) 
            (curr_dim, z, m, s, h) = self.f (h, prev_dim, spec, i, act_f, path_name = path_name, noise_std = noise) 
            assert (self.layer_dims.get (i) in (None, curr_dim)) 
            self.layer_dims [i] = curr_dim 
            (d.labeled.z [i], d.unlabeled.z [i]) = self.split_lu (z) 
            d.unlabeled.s [i] = s 
            d.unlabeled.m [i] = m 
            prev_dim = curr_dim 
(d.labeled.h [i], d.unlabeled.h [i]) = self.split_lu (h) 
        return d 
logger.info...

    
    

    Fix the buggy line: costs.total += (costs.denois [costs] * self.p.denoising_cost_x [i])
assistant
#Here is Python program to fix the buggy line: costs.

costs.total += (costs.denois [i] * self.p.denoising_cost_x [i])