class_docstr: CrossEntropy loss
class_docstr_tok: ["CrossEntropy","loss"]
code: def forward(self, output, target):
      output = output.transpose(1, 2)
      target = target.long()
  
      criterion = nn.CrossEntropyLoss()
      return criterion(output, target)
  
file_path: audio/examples/pipeline_wavernn/losses.py
imports: import math
  import torch
  from torch import nn
  from torch.nn import functional
label_desc: Use image node_id 2 for calling the LongCrossEntropyLoss obj's underlying member method code with example usage: obj.forward(output, target) and returns: criterion