(feat_a_pre, w_a_pre, re_feat_bp_pre)
return feat_a_pre*w_a_pre + re_feat_bp_pre*(1-w_a_pre)
Compute the l-1 th Recovered by weighted blend
    Params:
        feat_a_pre(torch.Tensor): l-1 layer feat_a F_A^[L-1]
        w_a_pre(torch.Tensor): W_A^[L-1]
        re_feat_bp_pre(torch.Tensor): R_BP^[L-1]
    Return:
        F_AP^[L-1]
Compute the l-1 th Recovered by weighted blend
    Params:
        feat_a_pre(torch.Tensor): l-1 layer feat_a F_A^[L-1]
        w_a_pre(torch.Tensor): W_A^[L-1]
        re_feat_bp_pre(torch.Tensor): R_BP^[L-1]
    Return:
        F_AP^[L-1]
Compute the l - 1 th Recovered by weighted blend Params : feat_a_pre ( torch . Tensor ) : l - 1 layer feat_a F_A^ [ L - 1 ] w_a_pre ( torch . Tensor ) : W_A^ [ L - 1 ] re_feat_bp_pre ( torch . Tensor ) : R_BP^ [ L - 1 ] Return : F_AP^ [ L - 1 ]
def weighted_blend(feat_a_pre, w_a_pre, re_feat_bp_pre):
    """
    Compute the l-1 th Recovered by weighted blend
    Params:
        feat_a_pre(torch.Tensor): l-1 layer feat_a F_A^[L-1]
        w_a_pre(torch.Tensor): W_A^[L-1]
        re_feat_bp_pre(torch.Tensor): R_BP^[L-1]
    Return:
        F_AP^[L-1]
    """
    return feat_a_pre*w_a_pre + re_feat_bp_pre*(1-w_a_pre)
def weighted_blend ( feat_a_pre , w_a_pre , re_feat_bp_pre ) : return feat_a_pre * w_a_pre + re_feat_bp_pre * ( 1 - w_a_pre )