def METHOD_NAME(pred,
                   target,
                   alpha=0.75,
                   gamma=2.0,
                   iou_weighted=True,
                   use_sigmoid=True):
    """`Varifocal Loss <https://arxiv.org/abs/2008.13367>`_
    Args:
        pred (Tensor): The prediction with shape (N, C), C is the
            number of classes
        target (Tensor): The learning target of the iou-aware
            classification score with shape (N, C), C is the number of classes.
        alpha (float, optional): A balance factor for the negative part of
            Varifocal Loss, which is different from the alpha of Focal Loss.
            Defaults to 0.75.
        gamma (float, optional): The gamma for calculating the modulating
            factor. Defaults to 2.0.
        iou_weighted (bool, optional): Whether to weight the loss of the
            positive example with the iou target. Defaults to True.
    """
    # pred and target should be of the same size
    assert pred.shape == target.shape
    if use_sigmoid:
        pred_new = F.sigmoid(pred)
    else:
        pred_new = pred
    target = target.cast(pred.dtype)
    if iou_weighted:
        focal_weight = target * (target > 0.0).cast('float32') + \
            alpha * (pred_new - target).abs().pow(gamma) * \
            (target <= 0.0).cast('float32')
    else:
        focal_weight = (target > 0.0).cast('float32') + \
            alpha * (pred_new - target).abs().pow(gamma) * \
            (target <= 0.0).cast('float32')
    if use_sigmoid:
        loss = F.binary_cross_entropy_with_logits(
            pred, target, reduction='none') * focal_weight
    else:
        loss = F.binary_cross_entropy(
            pred, target, reduction='none') * focal_weight
        loss = loss.sum(axis=1)
    return loss