code: def unpack_input(self, x: Dict):
      # unpacking into a tuple
      x = x["categorical"], x["continuous"]
      # eliminating None in case there is no categorical or continuous columns
      x = (item for item in x if len(item) > 0)
      x = torch.cat(tuple(x), dim=1)
      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 3 for calling the TabNetBackbone obj's underlying member method code with example usage: obj.unpack_input(x) and returns: x