code: def read_embedding(
      file_path: str, vector_dim: int
  ) -> Dict[str, np.array]:
      """
      Reads an embedding file in glove format into a dictionary mapping tokens to vectors.
      """
  
      glove = {}
      with open(file_path, encoding="utf-8") as f:
          for line in f:
              values = line.split()
              # handle weird whitespaces in tokens
              if len(values[1:]) > vector_dim:
                  wordcount = len(values) - vector_dim
                  vec = np.asarray(values[wordcount:], dtype="float32")
              else:
                  vec = np.asarray(values[1:], dtype="float32")
              token = values[0]
              glove[token] = vec
      return glove
  
label_desc: Use image node_id 1 for calling a global function with example usage: read_embedding(file_path, vector_dim) and returns: glove