code: def test_tokens_dictfeat(self):
      model = Seq2SeqModel.from_config(
          Seq2SeqModel.Config(
              source_embedding=WordEmbedding.Config(embed_dim=512),
              target_embedding=WordEmbedding.Config(embed_dim=512),
              inputs=Seq2SeqModel.Config.ModelInput(
                  dict_feat=GazetteerTensorizer.Config(
                      text_column="source_sequence"
                  )
              ),
              encoder_decoder=RNNModel.Config(
                  encoder=LSTMSequenceEncoder.Config(embed_dim=612)
              ),
              dict_embedding=DictEmbedding.Config(),
          ),
          get_tensorizers(add_dict_feat=True),
      )
      model.eval()
      ts_model = model.torchscriptify()
      res = ts_model(
          ["call", "mom"], (["call", "mom"], [0.42, 0.17], [4, 3])
      )
      assert res is not None
  
imports: import unittest
  import torch
  from pytext.common.constants import Stage
  from pytext.data import Batcher
  from pytext.data.data import Data
  from pytext.data.sources.data_source import Gazetteer
  from pytext.data.sources.tsv import TSVDataSource
  from pytext.data.tensorizers import ByteTokenTensorizer, GazetteerTensorizer, initialize_tensorizers, TokenTensorizer
  from pytext.models.embeddings import ContextualTokenEmbedding, DictEmbedding, WordEmbedding
  from pytext.models.seq_models.rnn_encoder import LSTMSequenceEncoder
  from pytext.models.seq_models.rnn_encoder_decoder import RNNModel
  from pytext.models.seq_models.seq2seq_model import Seq2SeqModel
  from pytext.utils.test import import_tests_module
label_desc: Use image node_id 4 for calling the Seq2SeqModelExportTests obj's underlying member method code with example usage: obj.test_tokens_dictfeat() without return types