code: def create_src_embedding_list(config, tensorizers):
      src_tokens = tensorizers["src_seq_tokens"]
      src_embedding_list = [
          create_module(config.source_embedding, tensorizer=src_tokens)
      ]
      source_vocab = src_tokens.vocab
      gazetteer_tensorizer = tensorizers.get("dict_feat")
      dict_vocab, dict_embedding = None, None
      if gazetteer_tensorizer:
          dict_embedding = create_module(
              config.dict_embedding, tensorizer=gazetteer_tensorizer
          )
          src_embedding_list.append(dict_embedding)
          dict_vocab = gazetteer_tensorizer.vocab
      source_embedding = ScriptableEmbeddingList(src_embedding_list)
      return source_embedding, source_vocab, dict_embedding, dict_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 2 for calling a global function with example usage: create_src_embedding_list(config, tensorizers) and returns: source_embedding,  source_vocab,  dict_embedding,  dict_vocab