code: def read_embeddings(path, skip_lines=0, filter_set=None):
      """
      Read an embeddings file in the glove format.
      """
      embs = dict()
      total_vectors_in_file = 0
      with open(path, "rb") as f:
          for i, line in enumerate(f):
              if i < skip_lines:
                  continue
              if not line:
                  break
              if len(line) == 0:
                  # is this reachable?
                  continue
  
              l_split = line.decode("utf8").strip().split(" ")
              if len(l_split) == 2:
                  continue
              total_vectors_in_file += 1
              if (
                  filter_set is not None
                  and l_split[0] not in filter_set
              ):
                  continue
              embs[l_split[0]] = [float(em) for em in l_split[1:]]
      return embs, total_vectors_in_file
  
imports: import math
  import warnings
  import torch
  import torch.nn
  from torch.nn.utils import skip_init
  from onmt.modules.util_class import Elementwise
  from onmt.utils.logging import logger
label_desc: Use image node_id 1 for calling a global function with example usage: read_embeddings(path, skip_lines, filter_set) and returns: embs,  total_vectors_in_file