def METHOD_NAME(self):
    s = 256
    img_metas = [{
        'img_shape': (s, s),
        'pad_shape': (s, s),
        'scale_factor': (1, 1)
    }]
    ga_rpn_head = GARPNHead(**ga_rpn_config)
    feats = (
        torch.rand(1, 4, s // stride[1], s // stride[0])
        for stride in ga_rpn_head.square_anchor_generator.strides)
    outs = ga_rpn_head(feats)
    cfg = ConfigDict(
        dict(
            nms_pre=2000,
            nms_post=1000,
            max_per_img=300,
            nms=dict(type='nms', iou_threshold=0.7),
            min_bbox_size=0))
    ga_rpn_head.predict_by_feat(
        *outs, batch_img_metas=img_metas, cfg=cfg, rescale=True)