class_docstr: GaussianFocalLoss is a variant of focal loss.
  
  More details can be found in the `paper
  <https://arxiv.org/abs/1808.01244>`_
  Code is modified from `kp_utils.py
  <https://github.com/princeton-vl/CornerNet/blob/master/models/py_utils/kp_utils.py#L152>`_  # noqa: E501
  Please notice that the target in GaussianFocalLoss is a gaussian heatmap,
  not 0/1 binary target.
  
  Args:
      alpha (float): Power of prediction.
      gamma (float): Power of target for negtive samples.
      reduction (str): Options are "none", "mean" and "sum".
      loss_weight (float): Loss weight of current loss.
docstr: Forward function.
  
  Args:
      pred (torch.Tensor): The prediction.
      target (torch.Tensor): The learning target of the prediction
          in gaussian distribution.
      weight (torch.Tensor, optional): The weight of loss for each
          prediction. Defaults to None.
      avg_factor (int, optional): Average factor that is used to average
          the loss. Defaults to None.
      reduction_override (str, optional): The reduction method used to
          override the original reduction method of the loss.
          Defaults to None.
docstr_tok: ["Forward","function",".","Args",":","pred","(","torch.Tensor",")",":","The","prediction",".","target","(","torch.Tensor",")",":","The","learning","target","of","the","prediction","in","gaussian","distribution",".","weight","(","torch.Tensor",",","optional",")",":","The","weight","of","loss","for","each","prediction",".","Defaults","to","None",".","avg_factor","(","int",",","optional",")",":","Average","factor","that","is","used","to","average","the","loss",".","Defaults","to","None",".","reduction_override","(","str",",","optional",")",":","The","reduction","method","used","to","override","the","original","reduction","method","of","the","loss",".","Defaults","to","None","."]
code: def forward(
      self,
      pred,
      target,
      weight=None,
      avg_factor=None,
      reduction_override=None,
  ):
      """Forward function.
  
      Args:
          pred (torch.Tensor): The prediction.
          target (torch.Tensor): The learning target of the prediction
              in gaussian distribution.
          weight (torch.Tensor, optional): The weight of loss for each
              prediction. Defaults to None.
          avg_factor (int, optional): Average factor that is used to average
              the loss. Defaults to None.
          reduction_override (str, optional): The reduction method used to
              override the original reduction method of the loss.
              Defaults to None.
      """
      assert reduction_override in (None, "none", "mean", "sum")
      reduction = (
          reduction_override if reduction_override else self.reduction
      )
      loss_reg = self.loss_weight * gaussian_focal_loss(
          pred,
          target,
          weight,
          alpha=self.alpha,
          gamma=self.gamma,
          reduction=reduction,
          avg_factor=avg_factor,
      )
      return loss_reg
  
file_path: insightface/detection/scrfd/mmdet/models/losses/gaussian_focal_loss.py
imports: import torch.nn
  from ..builder import LOSSES
  from .utils import weighted_loss
label_desc: Use image node_id 2 for calling the GaussianFocalLoss obj's underlying member method code with example usage: obj.forward(pred, target, weight, avg_factor, reduction_override) and returns: loss_reg