code: def _get_glove_embedding_layer(
      tokenizer, glove_path, max_sentence_length
  ):
      """Get a Keras embedding layer for a given GloVe embeddings.
  
      Adapted from https://keras.io/examples/pretrained_word_embeddings/.
  
      Args:
        tokenizer:
          the `keras.preprocessing.text.Tokenizer` used to tokenize inputs.
  
        glove_path:
          path to the GloVe embedding file.
  
        max_sentence_length:
          pad/truncate embeddings to this length.
  
      Returns:
        Keras embedding layer for a given GloVe embeddings.
      """
      embedding_dim = 50
      word_index = tokenizer.word_index
      print("Loading the embedding model")
      embeddings_index = {}
  
      if not os.path.exists(glove_path):
          if not os.path.exists(f"{glove_path}.gz"):
              print(
                  f"Did not find {glove_path} word embeddings, downloading..."
              )
              url = "https://github.com/icml2020-attention/glove/raw/main/glove.6B.50d.txt.gz"
              urllib.request.urlretrieve(url, f"{glove_path}.gz")
  
          with gzip.open(f"{glove_path}.gz", "rt") as f_in:
              with open(glove_path, "wt") as f_out:
                  shutil.copyfileobj(f_in, f_out)
  
      with open(glove_path) as f:
          for line in f:
              word, coefs = line.split(sep=" ", maxsplit=1)
              coefs = np.fromstring(coefs, "f", sep=" ")
              embeddings_index[word] = coefs
  
      print(f"Found {len(embeddings_index)} word vectors.")
      print(f"Found {len(word_index)} unique tokens.")
      num_words = len(word_index) + 1
  
      emb_mat = np.zeros((num_words, embedding_dim))
      for word, i in word_index.items():
          emb_vector = embeddings_index.get(word)
          if emb_vector is not None:
              # words not found in embedding index will be all-zeros.
              emb_mat[i] = emb_vector
          embedding_layer = tf.keras.layers.Embedding(
              num_words,
              embedding_dim,
              embeddings_initializer=tf.keras.initializers.Constant(
                  emb_mat
              ),
              input_length=max_sentence_length,
              trainable=False,
          )
  
      return embedding_layer
  
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 6 for calling a global function with example usage: _get_glove_embedding_layer(tokenizer, glove_path, max_sentence_length) and returns: embedding_layer