class_docstr: This is the current default pretext task module.
  
  Functionality:
  
      self.get:
          inputs: (data, target) (target will often be None)
          outputs: (pretext_data, pretext_target)
  
          called before the forward pass through the TabTransformer to create the
          input and label for a BERT-style pretext task.
  
      self.forward:
          inputs: out (embedding for TabTransformer), target (pretext task label)
          outputs: loss, % accuracy on pretext task
  
          given the embedding it passes it through a classifier which learns to
          predict the pretext label.
code: def forward(self, out, target):
      prob = torch.cat(
          [
              self.predicters[col](out[:, col, :]).unsqueeze(1)
              for col in range(self.n_cat_feats)
          ],
          dim=1,
      )
      prob = prob.view(-1, 2)
      target = target.view(-1)
  
      loss = self.loss_funct(prob, target)
      pred = prob.max(dim=1, keepdim=True)[1]
      correct = pred.eq(target.view_as(pred)).sum()
      correct = correct.float()
      correct = correct / pred.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 BERTPretext obj's underlying member method code with example usage: obj.forward(out, target) and returns: loss,  correct