def METHOD_NAME(self, device):
    """Tests trident roi head predict."""
    if device == 'cuda':
        if not torch.cuda.is_available():
            return unittest.skip('test requires GPU and torch+cuda')
    roi_head = MODELS.build(self.roi_head_cfg)
    roi_head = roi_head.to(device=device)
    s = 256
    feats = []
    for i in range(len(roi_head.bbox_roi_extractor.featmap_strides)):
        feats.append(
            torch.rand(1, 256, s // (2**(i + 2)),
                       s // (2**(i + 2))).to(device=device))
    image_shapes = [(3, s, s)]
    batch_data_samples = demo_mm_inputs(
        batch_size=1,
        image_shapes=image_shapes,
        num_items=[1],
        num_classes=4,
        with_mask=True,
        device=device)['data_samples']
    proposals_list = demo_mm_proposals(
        image_shapes=image_shapes, num_proposals=100, device=device)
    out = roi_head.loss(feats, proposals_list, batch_data_samples)
    loss_cls = out['loss_cls']
    loss_grid = out['loss_grid']
    self.assertGreater(loss_cls.sum(), 0, 'cls loss should be non-zero')
    self.assertGreater(loss_grid.sum(), 0, 'grid loss should be non-zero')
    batch_data_samples = demo_mm_inputs(
        batch_size=1,
        image_shapes=image_shapes,
        num_items=[0],
        num_classes=4,
        with_mask=True,
        device=device)['data_samples']
    proposals_list = demo_mm_proposals(
        image_shapes=image_shapes, num_proposals=100, device=device)
    out = roi_head.loss(feats, proposals_list, batch_data_samples)
    empty_cls_loss = out['loss_cls']
    self.assertGreater(empty_cls_loss.sum(), 0,
                       'cls loss should be non-zero')
    self.assertNotIn(
        'loss_grid', out,
        'grid loss should be passed when there are no true boxes')