code: def forward(self, out, target):
      loss = self.loss_funct(out, target)
      pred = out.max(dim=1, keepdim=True)[1]
      correct = pred.eq(target.view_as(pred)).sum()
      correct = correct.float()
      correct = correct / out.shape[0]
      return loss, correct
  
file_path: autogluon/tabular/src/autogluon/tabular/models/tab_transformer/pretexts.py
imports: from copy import deepcopy
  import numpy
  import torch
  import torch.nn
  from autogluon.core.constants import REGRESSION
label_desc: Use image node_id 2 for calling the SupervisedPretext obj's underlying member method code with example usage: obj.forward(out, target) and returns: loss,  correct