code: def __call__(self, y_pred, y, norm):
      y_pred = y_pred.contiguous().view(-1, y_pred.shape[-1])
      y = y.contiguous().view(-1)
      self.loss = self.criterion(y_pred, y) / norm
      if self.opt is not None:
          self.backward_and_step()
      self.n_correct += (
          (
              (y_pred.max(dim=-1)[1] == y)
              & (y != self.criterion.padding_idx)
          )
          .sum()
          .item()
      )
      self.acc_loss += self.loss.item() * norm
      self.norm_term += norm
      return self.loss.item() * norm
  
imports: import torch
  import torch.distributed
  import torch.nn
  import torch.nn.functional
label_desc: Use image node_id 8 for calling the SimpleLossCompute obj's underlying member method code with example usage: obj.__call__(y_pred, y, norm) and returns: unknown