Learn.predict on SSD

Hope someone can help me see what I am missing. I am trying to predict an image on SSD, and it tells me not enough values to unpack (expected 2, got 1)

` def analyze_pred(self, pred, thresh=0.5, nms_overlap=0.1, ssd=None):
# def analyze_pred(pred, anchors, grid_sizes, thresh=0.5, nms_overlap=0.1, ssd=None):
b_clas, b_bb = pred
a_ic = ssd._actn_to_bb(b_bb, ssd._anchors.cpu(), ssd._grid_sizes.cpu())
conf_scores, clas_ids = b_clas[:, 1:].max(1)
conf_scores = b_clas.t().sigmoid()

    out1, bbox_list, class_list = [], [], []

    for cl in range(1, len(conf_scores)):
        c_mask = conf_scores[cl] > thresh
        if c_mask.sum() == 0: 
            continue
        scores = conf_scores[cl][c_mask]
        l_mask = c_mask.unsqueeze(1)
        l_mask = l_mask.expand_as(a_ic)
        boxes = a_ic[l_mask].view(-1, 4) # boxes are now in range[ 0, 1]
        boxes = (boxes-0.5) * 2.0        # putting boxes in range[-1, 1]
        ids, count = nms(boxes.data, scores, nms_overlap, 50) # FIX- NMS overlap hardcoded
        ids = ids[:count]
        out1.append(scores[ids])
        bbox_list.append(boxes.data[ids])
        class_list.append(torch.tensor([cl]*count))

    if len(bbox_list) == 0:
        return None #torch.Tensor(size=(0,4)), torch.Tensor()

    return torch.cat(bbox_list, dim=0), torch.cat(class_list, dim=0) # torch.cat(out1, dim=0), `