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 loss
docstr_tok: ["Compute","loss"]
code: def hybrid_forward(
      self,
      F,
      cls_pred,
      loc_pred,
      label_cls,
      pos_index,
      neg_index,
      label_loc,
      label_loc_weight,
  ):
      """Compute loss"""
      loc_loss = self.weight_l1_loss(
          F, loc_pred, label_loc, label_loc_weight
      )
      cls_loss = self.cross_entropy_loss(
          F, cls_pred, label_cls, pos_index, neg_index
      )
      return cls_loss, loc_loss
  
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 5 for calling the SiamRPNLoss obj's underlying member method code with example usage: obj.hybrid_forward(F, cls_pred, loc_pred, label_cls, pos_index, neg_index, label_loc, label_loc_weight) and returns: cls_loss,  loc_loss