def METHOD_NAME(inputs,
                       targets,
                       num_masks,
                       alpha: float = 0.25,
                       gamma: float = 2):
    """
    Loss used in RetinaNet for dense detection: https://arxiv.org/abs/1708.02002.
    Args:
        inputs: A float tensor of arbitrary shape.
                The predictions for each example.
        targets: A float tensor with the same shape as inputs. Stores the binary
                 classification label for each element in inputs
                (0 for the negative class and 1 for the positive class).
        alpha: (optional) Weighting factor in range (0,1) to balance
                positive vs negative examples. Default = -1 (no weighting).
        gamma: Exponent of the modulating factor (1 - p_t) to
               balance easy vs hard examples.
    Returns:
        Loss tensor
    """
    prob = inputs.sigmoid()
    ce_loss = F.binary_cross_entropy_with_logits(
        inputs, targets, reduction='none')
    p_t = prob * targets + (1 - prob) * (1 - targets)
    loss = ce_loss * ((1 - p_t)**gamma)
    if alpha >= 0:
        alpha_t = alpha * targets + (1 - alpha) * (1 - targets)
        loss = alpha_t * loss
    return loss.mean(1).sum() / num_masks