code: def load(
      file_path, parse_line_fn, num_lines_to_ignore, num_lines_to_use
  ):
      """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.
        num_lines_to_ignore: number of lines to ignore.
        num_lines_to_use : number of lines to use. Offset by num_lines_to_ignore if
          used together.
  
      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
      with tf.io.gfile.GFile(file_path) as f:
          for index, line in enumerate(f):
              if index >= num_lines_to_ignore:
                  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)
                  if (
                      num_lines_to_use
                      and index
                      >= num_lines_to_ignore + num_lines_to_use - 1
                  ):
                      break
      return vocabulary, np.array(embeddings)
  
file_path: hub/examples/text_embeddings_v2/export_v2.py
imports: import argparse
  import os
  import sys
  import tempfile
  from absl import app
  import numpy
  import tensorflow
label_desc: Use image node_id 2 for calling a global function with example usage: load(file_path, parse_line_fn, num_lines_to_ignore, num_lines_to_use) and returns: vocabulary,  np