class_docstr: A mixture of SequenceLoss and CrossEntropyLoss.
  Loss = SequenceLoss + alpha * CELoss
class_docstr_tok: ["A","mixture","of","SequenceLoss","and","CrossEntropyLoss",".","Loss","=","SequenceLoss","+","alpha","*","CELoss"]
code: def forward(
      self, seq_inputs, seq_targets, class_inputs, class_targets
  ):
      sl = self.seq_loss(seq_inputs, seq_targets)
      cel = self.ce_loss(class_inputs, class_targets)
      loss = sl + self.alpha * cel
      return loss
  
file_path: stanza/stanza/models/common/loss.py
imports: import logging
  import numpy
  import torch
  import torch.nn
  import stanza.models.common.seq2seq_constant
label_desc: Use image node_id 2 for calling the MixLoss obj's underlying member method code with example usage: obj.forward(seq_inputs, seq_targets, class_inputs, class_targets) and returns: loss