code: def create_tgt_embedding_list(config, tensorizers):
      trg_tokens = tensorizers["trg_seq_tokens"]
      target_embedding = ScriptableEmbeddingList(
          [
              create_module(
                  config.target_embedding, tensorizer=trg_tokens
              )
          ]
      )
      return target_embedding, trg_tokens.vocab
  
imports: from typing import Dict, List, Optional, Tuple, Union
  import torch
  from pytext.common.constants import Stage
  from pytext.data.masked_tensorizer import MaskedTokenTensorizer
  from pytext.data.tensorizers import GazetteerTensorizer, Tensorizer, TokenTensorizer
  from pytext.data.utils import Vocabulary
  from pytext.models.embeddings.scriptable_embedding_list import ScriptableEmbeddingList
  from pytext.models.model import Model
  from pytext.models.module import create_module
  from .conv_model import CNNModel, DecoupledCNNModel
  from .mask_generator import MaskedSequenceGenerator
  from .nar_length import ConvLengthPredictionModule, MaskedLengthPredictionModule
  from .nar_output_layer import NARSeq2SeqOutputLayer
  from .seq2seq_model import Seq2SeqModel
label_desc: Use image node_id 3 for calling a global function with example usage: create_tgt_embedding_list(config, tensorizers) and returns: target_embedding,  trg_tokens