class_docstr: Losses of YOLO v3.
  
  Parameters
  ----------
  batch_axis : int, default 0
      The axis that represents mini-batch.
  weight : float or None
      Global scalar weight for loss.
class_docstr_tok: ["Losses","of","YOLO","v3",".","Parameters","--","--","--","--","--","batch_axis",":","int",",","default","0","The","axis","that","represents","mini-batch",".","weight",":","float","or","None","Global","scalar","weight","for","loss","."]
code: def __init__(self, batch_axis=0, weight=None, **kwargs):
      super(YOLOV3Loss, self).__init__(weight, batch_axis, **kwargs)
      self._sigmoid_ce = gluon.loss.SigmoidBinaryCrossEntropyLoss(
          from_sigmoid=False
      )
      self._l1_loss = gluon.loss.L1Loss()
  
imports: from __future__ import absolute_import
  from mxnet import gluon
  from mxnet import nd
  from mxnet.gluon.loss import Loss, _apply_weighting, _reshape_like
label_desc: Use image node_id 1 to create a new YOLOV3Loss object from inherited base classes: Loss with example: obj = YOLOV3Loss(batch_axis, weight)