code: def load_embedding(embedding, dictionary, pretrained_embed):
      """Loads pretrained embeddings.
  
      Loads pretrained embeddings into a nn.Embedding layer. pretrained_embed
      can either be a nn.Embedding layer, in which case the embedding is set
      to the pretrained_embed argument, or a path to an embedding file.
  
      Arguments:
          embedding (pytorch_translate.common_layers.Embedding):
              Embedding layer whose weights are to be set.
          dictionary (fairseq.data.dictionary.Dictionary): dictionary with the
              same vocabulary size as the embedding argument.
          pretrained_embed (Union(string, nn.Embedding)): source of the
              weights to be loaded.
      """
      if pretrained_embed is None:
          return
  
      if isinstance(pretrained_embed, torch.nn.Embedding):
          embedding.weight = pretrained_embed.weight
      else:
          embed_dict = utils.parse_embedding(pretrained_embed)
          utils.load_embedding(embed_dict, dictionary, embedding)
  
      embedding.init_normalization_if_needed()
  
imports: import argparse
  import ast
  import os
  import signal
  import threading
  import time
  from typing import Dict, List, Optional, Union
  import torch
  from fairseq import distributed_utils, tasks, utils
  from pytorch_translate.file_io import PathManager
label_desc: Use image node_id 7 for calling a global function with example usage: load_embedding(embedding, dictionary, pretrained_embed) without return types