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",")","."]
docstr: Compute weight_l1_loss
docstr_tok: ["Compute","weight_l1_loss"]
code: def weight_l1_loss(self, F, pred_loc, label_loc, loss_weight):
      """Compute weight_l1_loss"""
      pred_loc = pred_loc.reshape((self.b, 4, -1, self.h, self.w))
      diff = F.abs((pred_loc - label_loc))
      diff = F.sum(diff, axis=1).reshape((self.b, -1, self.h, self.w))
      loss = diff * loss_weight
      return F.sum(loss) / self.b
  
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 2 for calling the SiamRPNLoss obj's underlying member method code with example usage: obj.weight_l1_loss(F, pred_loc, label_loc, loss_weight) and returns: unknown