code: def forward(self, logits, labels):
      log_probs = self.taylor_softmax(logits)
      loss = F.nll_loss(
          log_probs,
          labels,
          reduction=self.reduction,
          ignore_index=self.ignore_index,
      )
      return loss
  
imports: import torch
  import torch.nn
  import torch.nn.functional
  from torch.autograd import Variable
label_desc: Use image node_id 2 for calling the TaylorCrossEntropyLossV1 obj's underlying member method code with example usage: obj.forward(logits, labels) and returns: loss