def METHOD_NAME(self):
    anchors = []
    for k, f in enumerate(self.feature_maps):
        min_sizes = self.min_sizes[k]
        for i, j in product(range(f[0]), range(f[1])):
            for min_size in min_sizes:
                s_kx = min_size / self.image_size[1]
                s_ky = min_size / self.image_size[0]
                dense_cx = [
                    x * self.steps[k] / self.image_size[1]
                    for x in [j + 0.5]
                ]
                dense_cy = [
                    y * self.steps[k] / self.image_size[0]
                    for y in [i + 0.5]
                ]
                for cy, cx in product(dense_cy, dense_cx):
                    anchors += [cx, cy, s_kx, s_ky]
    # back to torch land
    output = torch.Tensor(anchors).view(-1, 4)
    if self.clip:
        output.clamp_(max=1, min=0)
    return output