def METHOD_NAME(cls, poses: Tensor, edge_links: Tensor) -> Tensor:
    """
    Compute the feature vector input to the rescoring network.
    :param poses: [N, J, 3] Predicted poses
    :param edge_links: [L,2] List of joint indices
    :return: [N, L*2+L+J] Feature vector
    """
    joint_xy = poses[..., :2]
    visibility = poses[..., 2]
    joint_1 = edge_links[:, 0]
    joint_2 = edge_links[:, 1]
    # To get the Delta x Delta y
    joint_relate = joint_xy[..., joint_1, :] - joint_xy[..., joint_2, :]  # [N, L, 2]
    joint_length = ((joint_relate**2)[..., 0] + (joint_relate**2)[..., 1]) ** (0.5)  # [N, L]
    # To use the torso distance to normalize
    normalize = (joint_length[..., 9] + joint_length[..., 11]) / 2  # [N] # NOTE: THIS IS COCO-SPECIFIC
    normalize_tiled = torch.tile(normalize, (len(joint_1), 2, 1)).permute(2, 0, 1)
    normalize_tiled = normalize_tiled.clamp_min(1)
    joint_length = joint_length / normalize_tiled[..., 0]
    joint_relate = joint_relate / normalize_tiled
    joint_relate = torch.flatten(joint_relate, start_dim=-2)  # .reshape((-1, len(joint_1) * 2))
    feature = [joint_relate, joint_length, visibility]
    feature = torch.cat(feature, dim=-1)
    return feature