code: def load(file_path, parse_line_fn):
      """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.
      """
      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)
  
file_path: hub/examples/text_embeddings/export.py
imports: import argparse
  import os
  import shutil
  import sys
  import tempfile
  import numpy
  import tensorflow.compat.v1
  import tensorflow_hub
label_desc: Use image node_id 2 for calling a global function with example usage: load(file_path, parse_line_fn) and returns: vocabulary,  np