code: def forward(self, steps_output):
      res = 0
      for step_nb, step_output in enumerate(steps_output):
          x = self.feat_transformers[step_nb](step_output)
          res = torch.add(res, x)
      res = self.reconstruction_layer(res)
      return res
  
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 TabNetDecoder obj's underlying member method code with example usage: obj.forward(steps_output) and returns: res