code: def embed_glove(
      xs, glove_path, max_sentence_length=1000, mask_constant=1000.0
  ):
      """Embed a list of string arrays into GloVe word embeddings.
  
      Adapted from https://keras.io/examples/pretrained_word_embeddings/.
  
      Args:
        xs:
          list of string numpy arrays to embed.
  
        glove_path:
          path to the GloVe embedding file.
  
        max_sentence_length:
          pad/truncate embeddings to this length.
  
        mask_constant:
          mask padding with this constant.
  
      Returns:
        xs with words replaced by word embeddings, padded/truncated to a fixed
          length, with padding masked with the given constant.
      """
      xs = list(map(_decode, xs))
      tokenizer = tf.keras.preprocessing.text.Tokenizer()
      tokenizer.fit_on_texts(np.concatenate(xs))
      glove_embedding_layer = _get_glove_embedding_layer(
          tokenizer, glove_path, max_sentence_length
      )
  
      def embed(x):
          # Replace strings with sequences of integer tokens.
          x_tok = tokenizer.texts_to_sequences(x)
          lenghts = np.array([len(s) for s in x_tok])
  
          # Pad all sentences to a fixed max sentence length.
          x_tok = tf.keras.preprocessing.sequence.pad_sequences(
              x_tok,
              max_sentence_length,
              padding="post",
              truncating="post",
          )
  
          # Replace integer tokens with word embeddings.
          x_emb = glove_embedding_layer(x_tok).numpy()
  
          # Mask padding tokens.
          mask = (
              np.arange(max_sentence_length)[None, :]
              >= lenghts[:, None]
          )
          x_emb[mask, ...] = mask_constant
          return x_emb
  
      return map(embed, xs)
  
file_path: neural-tangents/examples/datasets.py
imports: import gzip
  import os
  import shutil
  import urllib.request
  from jax import random
  import numpy
  import tensorflow
  import tensorflow_datasets
label_desc: Use image node_id 5 for calling a global function with example usage: embed_glove(xs, glove_path, max_sentence_length, mask_constant) and returns: map, x_emb