code: def forward(self, x: Dict):
      # unpacking into a tuple
      x = self.unpack_input(x)
      # Making two parameters to the right device.
      self.tabnet.embedder.embedding_group_matrix = (
          self.tabnet.embedder.embedding_group_matrix.to(x.device)
      )
      self.tabnet.tabnet.encoder.group_attention_matrix = (
          self.tabnet.tabnet.encoder.group_attention_matrix.to(x.device)
      )
      # Returns output and Masked Loss. We only need the output
      x, _ = self.tabnet(x)
      return x
  
imports: from typing import Dict
  import torch
  import torch.nn
  from omegaconf import DictConfig
  from pytorch_tabnet.tab_network import TabNet
  from pytorch_tabnet.utils import create_group_matrix
  from ..base_model import BaseModel
label_desc: Use image node_id 4 for calling the TabNetBackbone obj's underlying member method code with example usage: obj.forward(x) and returns: x