code: def forward(self, x):
      res = 0
      steps_output, M_loss = self.encoder(x)
      res = torch.sum(torch.stack(steps_output, dim=0), dim=0)
  
      if self.is_multi_task:
          # Result will be in list format
          out = []
          for task_mapping in self.multi_task_mappings:
              out.append(task_mapping(res))
      else:
          out = self.final_mapping(res)
      return out, M_loss
  
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 TabNetNoEmbeddings obj's underlying member method code with example usage: obj.forward(x) and returns: out,  M_loss