code: def load(file_path, parse_line_fn):
    
    vocabulary = []
    embeddings = []
    embeddings_dim = None
    for line in tf.gfile.GFile(file_path):
      token, embedding = parse_line_fn(line)
      if not embeddings_dim:
        embeddings_dim = len(embedding)
      elif embeddings_dim != len(embedding):
        raise ValueError(
            "Inconsistent embedding dimension detected, %d != %d for token %s",
            embeddings_dim, len(embedding), token)
  
      vocabulary.append(token)
      embeddings.append(embedding)
  
    return vocabulary, np.array(embeddings)
docs: Loads a text embedding into memory as a numpy matrix.
  
    Args:
      file_path: Path to the text embedding file.
      parse_line_fn: callback function to parse each file line.
  
    Returns:
      A tuple of (list of vocabulary tokens, numpy matrix of embedding vectors).
  
    Raises:
      ValueError: if the data in the sstable is inconsistent.