List Index out of Range -DataLoaders

Can any one help please why do i get the error in subject

else self.items.__array__()[(i,)] if hasattr(self.items,'__array__')
--> 191                 else [self.items[i_] for i_ in i])

IndexError: list index out of range

#add stratified k fold split.
    def get_dls(sz,bs):
        item_tfms = AlbumentationsTransform(get_train_aug(sz), get_valid_aug(sz))
        batch_tfms = [Normalize.from_stats(*imagenet_stats)]
        dls = ImageDataLoaders.from_df(train_df, #pass in train DataFrame
                                       #valid_pct=0.2, #80-20 train-validation random split
                                       seed=42, #seed
                                       label_col=0, #label is in the first column of the DataFrame
                                       fn_col=2, #filename/path is in the second column of the DataFrame
                                       bs=bs, #pass in batch size
                                       item_tfms=item_tfms, #pass in item_tfms
                                       batch_tfms=batch_tfms,val_idxs=val_idx) #pass in batch_tfms
        return dls

This is source of error

class ProgressiveLabelCorrection(Callback):
    def __init__(self, num_classes=5, theta_start=0.3, theta_end=0.05, warm_up=0.2, sched_func=sched_lin):
        self.theta = theta_start

    def before_fit(self):
        self.eye = torch.eye(self.num_classes).to(self.dls.device)
    def after_step(self):        
        if self.pct_train > self.warm_up:
            # get batch items
            self.idxs = self.dl._DataLoader__idxs
            self.b_items = L(self.dl.items)[self.idxs[self.iter*

            # get incorrectly classified item boolean mask
            preds_max = self.pred.argmax(-1)
            mislabeled_idxs = preds_max != self.y
            tot_mislabeled = sum(mislabeled_idxs)
            if tot_mislabeled > 0:
                # get probas and targs for incorrect batch items
                mislabeled_probas = self.pred[mislabeled_idxs].softmax(-1)
                mislabeled_targs = self.y[mislabeled_idxs]

                # get predicted and actual probas and targ
                predicted_probas = mislabeled_probas.max(-1).values
                predicted_targs = mislabeled_probas.max(-1).indices
                actual_probas = mislabeled_probas[self.eye[mislabeled_targs].bool()]

                # update labels or theta 
                msk = torch.abs(predicted_probas - actual_probas) > self.theta
                self.epoch_tot_updated += sum(msk)
                new_targs = self.dl.tfms[1][1].vocab[predicted_targs[msk]]
                items_to_change = self.b_items[mislabeled_idxs][msk]

                update_dict = dict(zip(items_to_change, new_targs))
    def before_epoch(self): self.epoch_tot_updated = 0
    def after_epoch(self):   
        print(f"Total changed items in this epoch: {self.epoch_tot_updated}")
        if (self.pct_train > self.warm_up) and (self.epoch_tot_updated == 0):
            self.theta = self.sched_func(self.theta_start, self.theta_end, self.pct_train)
            print(f"Reduced theta to: {self.theta}")