code: def __call__(self, input, target):
      input = input.transpose(0, 1).contiguous()
      shift_logits = input[:-1] if self.causal else input
      shift_labels = target[1:] if self.causal else target
      loss_fct = torch.nn.CrossEntropyLoss(ignore_index=-1)
      loss = loss_fct(
          shift_logits.view(-1, shift_logits.size(-1)),
          shift_labels.view(-1),
      )
      return loss
  
imports: import torch
  from pytorch_pretrained_bert import BertTokenizer
  from transfer_nlp.plugins.config import register_plugin
label_desc: Use image node_id 2 for calling the LMLoss obj's underlying member method code with example usage: obj.__call__(input, target) and returns: loss