def METHOD_NAME(self):
    """Tests cascade rpn head predict function."""
    cascade_rpn_head = CascadeRPNHead(**cascade_rpn_config)
    s = 256
    feats = [
        torch.rand(1, 1, s // stride[1], s // stride[0])
        for stride in cascade_rpn_head.stages[0].prior_generator.strides
    ]
    img_metas = {
        'img_shape': (s, s),
        'pad_shape': (s, s),
        'scale_factor': 1,
    }
    sample = DetDataSample()
    sample.set_metainfo(img_metas)
    cascade_rpn_head.predict(feats, [sample])