code: def encode(self, feature_encoders):
      if self.encoders is not None:
          self.feature_encoders = feature_encoders
  
          self.cat_feat_origin_cards = []
          cat_features = []
          self.cont_feat_origin = []
          cont_features = []
          for c in self.columns:
              enc = feature_encoders[c["name"]]
              col = self.raw_data[c["name"]]
              cat_feats = enc.enc_cat(col)
              if cat_feats is not None:
                  self.cat_feat_origin_cards += [
                      (f'{c["name"]}_{i}_{c["type"]}', card)
                      for i, card in enumerate(enc.cat_cards)
                  ]
                  cat_features.append(cat_feats)
              cont_feats = enc.enc_cont(col)
              if cont_feats is not None:
                  self.cont_feat_origin += [c["name"]] * enc.cont_dim
                  cont_features.append(cont_feats)
          if cat_features:
              self.cat_data = torch.cat(cat_features, dim=1)
          else:
              self.cat_data = None
          if cont_features:
              self.cont_data = torch.cat(cont_features, dim=1)
          else:
              self.cont_data = None
  
file_path: autogluon/tabular/src/autogluon/tabular/models/tab_transformer/utils.py
imports: import logging
  import torch
  from torch.utils.data import DataLoader, Dataset
  from .None import tab_transformer_encoder
  from .tab_transformer_encoder import NullEnc, WontEncodeError
label_desc: Use image node_id 4 for calling the TabTransformerDataset obj's underlying member method code with example usage: obj.encode(feature_encoders) without return types