human
Solve the following python programming problem given the following code:
@ overrides 
def optimize_policy(self, itr, samples_data) : 
    rewards = samples_data ['rewards'] 
    actions = samples_data ['actions'] 
    observations = samples_data ['observations'] 
    agent_infos = samples_data ['agent_infos'] 
    state_info_list = [agent_infos [k] for k in self.policy.state_info_keys] 
    dist_info_list = [agent_infos [k] for k in self.policy.distribution.dist_info_keys] 
    if self.policy.recurrent : 
        recurrent_vals = [samples_data ['valids']] 
else : 
        recurrent_vals = [] 
feat_diff = [] 
    for path in samples_data ['paths'] : 
        feats = self._features (path) 
        feats = np.vstack ([feats, np.zeros (feats.shape [1])]) 
        feat_diff.append ((feats [1 :] - feats [: (- 1)])) 
if self.policy.recurrent : 
        max_path_length = max ([len (path ['advantages']) for path in samples_data ['paths']]) 
        feat_diff = np.array ([tensor_utils.pad_tensor (fd, max_path_length) for fd in f_dual]) 
else : 
        feat_diff = np.vstack (feat_diff) 
f_dual = self.opt_info ['f_dual'] 
    f_dual_grad = self.opt_info ['f_dual_grad'] 
    def eval_dual(input) : 
        param_eta = input [0] 
        param_v = input [1 :] 
        val = f_dual (* ((([rewards, feat_diff] + state_info_list) + recurrent_vals) + [param_eta, param_v])) 
        return val.astype (np.float64) 
def eval_dual_grad(input) : 
        param_eta = input [0] 
        param_v = input [1 :] 
        grad = f_dual_grad (* ((([rewards, feat_diff] + state_info_list) + recurrent_vals) + [param_eta, param_v])) 
        eta_grad = np.float (grad [0]) 
        v_grad = grad [1] 
        return np.hstack ([eta_grad, v_grad]) 
x0 = np.hstack ([self.param_eta, self.param_v]) 
    bounds = [((- np.inf), np.inf) for _ in x0] 
    bounds [0] = (0.0, np.inf) 
    logger.log ('optimizing dual') 
    eta_before = x0 [0] 
    dual_before = eval_dual (x0) 
    (params_ast, _, _) = self.optimizer (func = eval_dual, x0 = x0, fprime = eval_dual_grad, bounds = bounds, maxiter = self.max_opt_itr, disp = 0) 
 ...

    
    

    Fix the buggy line: feat_diff = np.array ([tensor_utils.pad_tensor (fd, max_path_length) for fd in f_dual])
assistant
#Here is Python code to fix the buggy line: feat_diff = np.

feat_diff = np.array ([tensor_utils.pad_tensor (fd, max_path_length) for fd in feat_diff])