code: def load_embedding(path):
      """
      return embedding for a specif file by given file path.
      """
      EMBEDDING_DIM = 300
      embedding_dict = {}
      with open(path, "r", encoding="utf-8") as file:
          pairs = [
              line.strip("\r\n").split() for line in file.readlines()
          ]
          for pair in pairs:
              if len(pair) == EMBEDDING_DIM + 1:
                  embedding_dict[pair[0]] = [float(x) for x in pair[1:]]
      logger.debug("embedding_dict size: %d", len(embedding_dict))
      return embedding_dict
  
imports: import argparse
  import heapq
  import json
  import os
  import pickle
  import logging
  import numpy
  from tensorflow.train import init_from_checkpoint
  import graph
  from util import Timer
  import nni
  import data
  import evaluate
  from train_model import *
label_desc: Use image node_id 3 for calling a global function with example usage: load_embedding(path) and returns: embedding_dict