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 SoftmaxCrossEntropyLoss
docstr_tok: ["Compute","SoftmaxCrossEntropyLoss"]
code: def get_cls_loss(self, F, pred, label, select):
      """Compute SoftmaxCrossEntropyLoss"""
      if len(select) == 0:
          return 0
      pred = F.gather_nd(pred, select.reshape(1, -1))
      label = F.gather_nd(
          label.reshape(-1, 1), select.reshape(1, -1)
      ).reshape(-1)
      return self.conf_loss(pred, label).mean()
  
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 3 for calling the SiamRPNLoss obj's underlying member method code with example usage: obj.get_cls_loss(F, pred, label, select) and returns: self, int