def METHOD_NAME(pred: Tensor,
                        gaussian_target: Tensor,
                        alpha: float = 2.0,
                        gamma: float = 4.0,
                        pos_weight: float = 1.0,
                        neg_weight: float = 1.0) -> Tensor:
    """`Focal Loss <https://arxiv.org/abs/1708.02002>`_ for targets in gaussian
    distribution.
    Args:
        pred (torch.Tensor): The prediction.
        gaussian_target (torch.Tensor): The learning target of the prediction
            in gaussian distribution.
        alpha (float, optional): A balanced form for Focal Loss.
            Defaults to 2.0.
        gamma (float, optional): The gamma for calculating the modulating
            factor. Defaults to 4.0.
        pos_weight(float): Positive sample loss weight. Defaults to 1.0.
        neg_weight(float): Negative sample loss weight. Defaults to 1.0.
    """
    eps = 1e-12
    pos_weights = gaussian_target.eq(1)
    neg_weights = (1 - gaussian_target).pow(gamma)
    pos_loss = -(pred + eps).log() * (1 - pred).pow(alpha) * pos_weights
    neg_loss = -(1 - pred + eps).log() * pred.pow(alpha) * neg_weights
    return pos_weight * pos_loss + neg_weight * neg_loss