class_docstr: Distribution Focal Loss (DFL) is a variant of `Generalized Focal Loss:
  Learning Qualified and Distributed Bounding Boxes for Dense Object
  Detection <https://arxiv.org/abs/2006.04388>`_.
  
  Args:
      reduction (str): Options are `'none'`, `'mean'` and `'sum'`.
      loss_weight (float): Loss weight of current loss.
class_docstr_tok: ["Distribution","Focal","Loss","(","DFL",")","is","a","variant","of","`","Generalized","Focal","Loss",":","Learning","Qualified","and","Distributed","Bounding","Boxes","for","Dense","Object","Detection","<","https",":","\/\/arxiv.org\/abs\/2006.04388",">","`","_",".","Args",":","reduction","(","str",")",":","Options","are","`","'none","'","`",",","`","'mean","'","`","and","`","'sum","'","`",".","loss_weight","(","float",")",":","Loss","weight","of","current","loss","."]
code: def __init__(self, reduction="mean", loss_weight=1.0):
      super(DistributionFocalLoss, self).__init__()
      self.reduction = reduction
      self.loss_weight = loss_weight
  
file_path: mmdetection/mmdet/models/losses/gfocal_loss.py
imports: from functools import partial
  import torch
  import torch.nn
  import torch.nn.functional
  from mmdet.models.losses.utils import weighted_loss
  from mmdet.registry import MODELS
label_desc: Use image node_id 1 to create a new DistributionFocalLoss object from inherited base classes: nn with example: obj = DistributionFocalLoss(reduction, loss_weight)