class_docstr: Weighted l1 loss and cross entropy loss for SiamRPN training
  
  Parameters
  ----------
  batch_size : int, default 128
      training batch size per device (CPU/GPU).
class_docstr_tok: ["Weighted","l1","loss","and","cross","entropy","loss","for","SiamRPN","training","Parameters","--","--","--","--","--","batch_size",":","int",",","default","128","training","batch","size","per","device","(","CPU\/GPU",")","."]
code: def __init__(self, batch_size=128, **kwargs):
      super(SiamRPNLoss, self).__init__(**kwargs)
      self.conf_loss = gluon.loss.SoftmaxCrossEntropyLoss()
      self.h = 17
      self.w = 17
      self.b = batch_size
      self.loc_c = 10
      self.cls_c = 5
  
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 SiamRPNLoss object from inherited base classes: gluon with example: obj = SiamRPNLoss(batch_size)