问AttributeError:DataLoader属性缺失'persistent_workers'？

  File "C:\Users\26001\AppData\Local\Programs\Python\Python38\lib\site-packages\torch\utils\data\dataloader.py", line 428, in __iter__
    if self.persistent_workers and self.num_workers > 0:
AttributeError: 'DataLoader' object has no attribute 'persistent_workers'

Traceback (most recent call last):
  File "H:\Users\Administrator\Desktop\fakeNewsDetection\FND\_023_modelingMain.py", line 93, in <module>
    mainFunc()
  File "H:\Users\Administrator\Desktop\fakeNewsDetection\FND\_023_modelingMain.py", line 86, in mainFunc
    net.trainTVT()
  File "H:\Users\Administrator\Desktop\fakeNewsDetection\FND\_022_train.py", line 71, in trainTVT
    for it,(text,mask,label,domainLabel) in enumerate(self.dataLoaderTra):
  File "C:\Users\26001\AppData\Local\Programs\Python\Python38\lib\site-packages\torch\utils\data\dataloader.py", line 428, in __iter__
    if self.persistent_workers and self.num_workers > 0:
AttributeError: 'DataLoader' object has no attribute 'persistent_workers'

        for i in range(self.start_epoch, self.final_epoch):
            timeStart=time.time()            
            # train
            self.net.train()
            correct=0.0
            total=0.0
            for it,(text,mask,label,domainLabel) in enumerate(self.dataLoaderTra):
                text,label=text.long(),label.long()
                text,mask,label,domainLabel=(text.to(self.device),
                                             mask.to(self.device),
                                             label.to(self.device),
                                             domainLabel.to(self.device))
                self.optimizer.zero_grad()
                outputs=self.net(text, mask)
                outputsLog=torch.log(outputs)
                loss=self.loss(outputsLog,label)                
                loss.backward()

class Network(object):
    def __init__(self, opt):
        self.seed=opt.seed
        setupSeed(self.seed)
        self.batch=opt.batch
        self.lr=opt.lr
        self.start_epoch=opt.start_epoch
        self.final_epoch=opt.final_epoch
        self.inter=opt.inter
        self.mode=opt.mode
        self.models=opt.models
        self.plots=opt.plots
        # define dataloader
        self.dataLoaderTra, self.dataLoaderVal, self.dataLoaderTes, self.weightAr, self.coreList = getDatasetTVT(opt.traValTesName)
        # add weightAr to opt
        opt.weightAr = self.weightAr

def getDatasetTVT(traValTesName):
    dataLoaderTraValTesWeightAr=pickle.load(open(traValTesName,'rb'))
    dataLoaderTra, dataLoaderVal, dataLoaderTes, weightArray,coreListTra=dataLoaderTraValTesWeightAr
    return dataLoaderTra, dataLoaderVal, dataLoaderTes, weightArray,coreListTra

    def __iter__(self) -> '_BaseDataLoaderIter':
        # When using a single worker the returned iterator should be
        # created everytime to avoid reseting its state
        # However, in the case of a multiple workers iterator
        # the iterator is only created once in the lifetime of the
        # DataLoader object so that workers can be reused
        if self.persistent_workers and self.num_workers > 0:
            if self._iterator is None:
                self._iterator = self._get_iterator()
            else:
                self._iterator._reset(self)
            return self._iterator
        else:
            return self._get_iterator()