【函数讲解】botorch中的函数 is_non_dominated()：用于计算非支配（non-dominated）前沿

        # 获取训练目标值，计算Pareto前沿（非支配解集合），然后从样本中提取出Pareto最优解。
        train_obj = self.samples[1]
        pareto_mask = is_non_dominated(train_obj)
        pareto_y = train_obj[pareto_mask]

这里用到了一个函数 is_non_dominated()，来看下该函数的源码：

def is_non_dominated(Y: Tensor, deduplicate: bool = True) -> Tensor:
    r"""Computes the non-dominated front.

    Note: this assumes maximization.

    Args:
        Y: A `(batch_shape) x n x m`-dim tensor of outcomes.
        deduplicate: A boolean indicating whether to only return
            unique points on the pareto frontier.

    Returns:
        A `(batch_shape) x n`-dim boolean tensor indicating whether
        each point is non-dominated.
    """
    Y1 = Y.unsqueeze(-3)
    Y2 = Y.unsqueeze(-2)
    dominates = (Y1 >= Y2).all(dim=-1) & (Y1 > Y2).any(dim=-1)
    nd_mask = ~(dominates.any(dim=-1))
    if deduplicate:
        # remove duplicates
        # find index of first occurrence  of each unique element
        indices = (Y1 == Y2).all(dim=-1).long().argmax(dim=-1)
        keep = torch.zeros_like(nd_mask)
        keep.scatter_(dim=-1, index=indices, value=1.0)
        return nd_mask & keep
    return nd_mask