code: def load_embeddings_from_file(name):
      print("loading", name + "_embeddings.npy")
      embed = torch.from_numpy(
          np.load(name + "_embeddings.npy")
      ).float()
      print(embed.size())
      print("loading", name + "_vocabulary.txt")
      with io.open(
          name + "_vocabulary.txt", "r", encoding="utf-8"
      ) as f:
          voc = [line.strip() for line in f]
      return embed, voc
  
imports: import os
  import io
  import numpy
  import torch
  import torch.utils.data
  from torch.utils.data.sampler import Sampler
  from torch.utils.data import Dataset
  from neuralcoref.train.utils import encode_distance, BATCH_SIZE_PATH, SIZE_FP, SIZE_FP_COMPRESSED, SIZE_FS, SIZE_FS_COMPRESSED, SIZE_GENRE, SIZE_PAIR_IN, SIZE_SINGLE_IN
  from neuralcoref.train.conllparser import FEATURES_NAMES
label_desc: Use image node_id 1 for calling a global function with example usage: load_embeddings_from_file(name) and returns: embed,  voc