class_docstr: A wrapper around paddle.nn.functional.sigmoid_focal_loss.
  Args:
      use_sigmoid (bool): currently only support use_sigmoid=True
      alpha (float): parameter alpha in Focal Loss
      gamma (float): parameter gamma in Focal Loss
      loss_weight (float): final loss will be multiplied by this
class_docstr_tok: ["A","wrapper","around","paddle.nn.functional.sigmoid_focal_loss",".","Args",":","use_sigmoid","(","bool",")",":","currently","only","support","use_sigmoid=True","alpha","(","float",")",":","parameter","alpha","in","Focal","Loss","gamma","(","float",")",":","parameter","gamma","in","Focal","Loss","loss_weight","(","float",")",":","final","loss","will","be","multiplied","by","this"]
code: def __init__(
      self,
      use_sigmoid=True,
      alpha=0.25,
      gamma=2.0,
      loss_weight=1.0,
      reduction="mean",
  ):
      super(FocalLoss, self).__init__()
      assert (
          use_sigmoid == True
      ), "Focal Loss only supports sigmoid at the moment"
      self.use_sigmoid = use_sigmoid
      self.alpha = alpha
      self.gamma = gamma
      self.loss_weight = loss_weight
      self.reduction = reduction
  
file_path: PaddleDetection/ppdet/modeling/losses/focal_loss.py
imports: from __future__ import absolute_import
  from __future__ import division
  from __future__ import print_function
  import paddle
  import paddle.nn.functional
  import paddle.nn
  from ppdet.core.workspace import register
label_desc: Use image node_id 1 to create a new Weighted_FocalLoss object from inherited base classes: FocalLoss with example: obj = Weighted_FocalLoss(use_sigmoid, alpha, gamma, loss_weight, reduction)