class_docstr: Quality Focal Loss (QFL) is a variant of `Generalized Focal Loss:
  Learning Qualified and Distributed Bounding Boxes for Dense Object
  Detection <https://arxiv.org/abs/2006.04388>`_.
  
  Args:
      use_sigmoid (bool): Whether sigmoid operation is conducted in QFL.
          Defaults to True.
      beta (float): The beta parameter for calculating the modulating factor.
          Defaults to 2.0.
      reduction (str): Options are "none", "mean" and "sum".
      loss_weight (float): Loss weight of current loss.
code: def __init__(
      self,
      use_sigmoid=True,
      beta=2.0,
      reduction="mean",
      loss_weight=1.0,
  ):
      super(QualityFocalLoss, self).__init__()
      assert use_sigmoid is True, "Only sigmoid in QFL supported now."
      self.use_sigmoid = use_sigmoid
      self.beta = beta
      self.reduction = reduction
      self.loss_weight = loss_weight
  
file_path: insightface/detection/scrfd/mmdet/models/losses/gfocal_loss.py
imports: import torch.nn
  import torch.nn.functional
  from ..builder import LOSSES
  from .utils import weighted_loss
label_desc: Use image node_id 1 to create a new QualityFocalLoss object from inherited base classes: nn with example: obj = QualityFocalLoss(use_sigmoid, beta, reduction, loss_weight)