code: def build_src_emb(model_opt, vocabs):
      # Build embeddings.
      if model_opt.model_type == "text":
          src_emb = build_embeddings(model_opt, vocabs)
      else:
          src_emb = None
      return src_emb
  
imports: import torch
  import torch.nn
  from torch.nn.utils import skip_init
  from torch.nn.init import xavier_uniform_, zeros_, uniform_
  from onmt.models.model import NMTModel, LanguageModel
  from onmt.encoders import str2enc
  from onmt.decoders import str2dec
  from onmt.inputters.inputter import dict_to_vocabs
  from onmt.modules import Embeddings, CopyGenerator
  from onmt.utils.misc import use_gpu
  from onmt.utils.logging import logger
  from onmt.utils.parse import ArgumentParser
  from onmt.models.model_saver import load_checkpoint
  from onmt.constants import DefaultTokens, ModelTask
  from onmt.modules.lora import replace_lora_linear, replace_lora_embedding, mark_only_lora_as_trainable
label_desc: Use image node_id 5 for calling a global function with example usage: build_src_emb(model_opt, vocabs) and returns: src_emb