docstr: Returns: res, embedded_x, obf_vars
      res : output of reconstruction
      embedded_x : embedded input
      obf_vars : which variable where obfuscated
docstr_tok: ["Returns",":","res",",","embedded_x",",","obf_vars","res",":","output","of","reconstruction","embedded_x",":","embedded","input","obf_vars",":","which","variable","where","obfuscated"]
code: def forward(self, x):
      """
      Returns: res, embedded_x, obf_vars
          res : output of reconstruction
          embedded_x : embedded input
          obf_vars : which variable where obfuscated
      """
      embedded_x = self.embedder(x)
      if self.training:
          masked_x, obfuscated_groups, obfuscated_vars = self.masker(
              embedded_x
          )
          # set prior of encoder with obfuscated groups
          prior = 1 - obfuscated_groups
          steps_out, _ = self.encoder(masked_x, prior=prior)
          res = self.decoder(steps_out)
          return res, embedded_x, obfuscated_vars
      else:
          steps_out, _ = self.encoder(embedded_x)
          res = self.decoder(steps_out)
          return (
              res,
              embedded_x,
              torch.ones(embedded_x.shape).to(x.device),
          )
  
imports: import torch
  from torch.nn import Linear, BatchNorm1d, ReLU
  import numpy
  from pytorch_tabnet import sparsemax
label_desc: Use image node_id 2 for calling the TabNetPretraining obj's underlying member method code with example usage: obj.forward(x) and returns: res,  embedded_x,  obfuscated_vars, res,  embedded_x,  torch