code: def forward(
      self, word_tokens: torch.Tensor, seq_lens: torch.Tensor
  ) -> List[torch.Tensor]:
      embedding = self.embedding(word_tokens)
      representation = self.representation(embedding, seq_lens)
  
      # some LSTM representations return extra tensors
      if isinstance(representation, tuple):
          representation = representation[0]
  
      return self.decoder(representation)
  
file_path: pytext/demo/my_tagging/model.py
imports: from typing import List
  import torch
  from pytext.config.pytext_config import ConfigBase
  from pytext.data.tensorizers import TokenTensorizer
  from pytext.loss import CrossEntropyLoss
  from pytext.models.decoders.mlp_decoder import MLPDecoder
  from pytext.models.embeddings import WordEmbedding
  from pytext.models.model import Model
  from pytext.models.module import create_module
  from pytext.models.representations.bilstm_slot_attn import BiLSTMSlotAttention
  from .output import MyTaggingOutputLayer
label_desc: Use image node_id 4 for calling the MyTaggingModel obj's underlying member method code with example usage: obj.forward(word_tokens, seq_lens) and returns: self