code: def export(
      export_path,
      vocabulary,
      embeddings,
      num_oov_buckets,
      preprocess_text,
  ):
      """Exports a TF-Hub module that performs embedding lookups.
  
      Args:
        export_path: Location to export the module.
        vocabulary: List of the N tokens in the vocabulary.
        embeddings: Numpy array of shape [N+K,M] the first N rows are the
          M dimensional embeddings for the respective tokens and the next K
          rows are for the K out-of-vocabulary buckets.
        num_oov_buckets: How many out-of-vocabulary buckets to add.
        preprocess_text: Whether to preprocess the input tensor by removing
          punctuation and splitting on spaces.
      """
      # Write temporary vocab file for module construction.
      tmpdir = tempfile.mkdtemp()
      vocabulary_file = os.path.join(tmpdir, "tokens.txt")
      with tf.gfile.GFile(vocabulary_file, "w") as f:
          f.write("\n".join(vocabulary))
      vocab_size = len(vocabulary)
      embeddings_dim = embeddings.shape[1]
      spec = make_module_spec(
          vocabulary_file,
          vocab_size,
          embeddings_dim,
          num_oov_buckets,
          preprocess_text,
      )
  
      try:
          with tf.Graph().as_default():
              m = hub.Module(spec)
              # The embeddings may be very large (e.g., larger than the 2GB serialized
              # Tensor limit).  To avoid having them frozen as constant Tensors in the
              # graph we instead assign them through the placeholders and feed_dict
              # mechanism.
              p_embeddings = tf.placeholder(tf.float32)
              load_embeddings = tf.assign(
                  m.variable_map[EMBEDDINGS_VAR_NAME], p_embeddings
              )
  
              with tf.Session() as sess:
                  sess.run(
                      [load_embeddings],
                      feed_dict={p_embeddings: embeddings},
                  )
                  m.export(export_path, sess)
      finally:
          shutil.rmtree(tmpdir)
  
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 4 for calling a global function with example usage: export(export_path, vocabulary, embeddings, num_oov_buckets, preprocess_text) without return types