code: def _create_pretrained_emb_from_txt(
      vocab_file,
      embed_file,
      num_trainable_tokens=3,
      dtype=tf.float32,
      scope=None,
  ):
      """Load pretrain embeding from embed_file, and return an embedding matrix.
  
      Args:
        embed_file: Path to a Glove formated embedding txt file.
        num_trainable_tokens: Make the first n tokens in the vocab file as trainable
          variables. Default is 3, which is "<unk>", "<s>" and "</s>".
      """
      vocab, _ = vocab_utils.load_vocab(vocab_file)
      trainable_tokens = vocab[:num_trainable_tokens]
  
      utils.print_out("# Using pretrained embedding: %s." % embed_file)
      utils.print_out("  with trainable tokens: ")
  
      emb_dict, emb_size = vocab_utils.load_embed_txt(embed_file)
      for token in trainable_tokens:
          utils.print_out("    %s" % token)
          if token not in emb_dict:
              emb_dict[token] = [0.0] * emb_size
  
      emb_mat = np.array(
          [emb_dict[token] for token in vocab],
          dtype=dtype.as_numpy_dtype(),
      )
      emb_mat = tf.constant(emb_mat)
      emb_mat_const = tf.slice(
          emb_mat, [num_trainable_tokens, 0], [-1, -1]
      )
      with tf.variable_scope(
          scope or "pretrain_embeddings", dtype=dtype
      ) as scope:
          with tf.device(_get_embed_device(num_trainable_tokens)):
              emb_mat_var = tf.get_variable(
                  "emb_mat_var", [num_trainable_tokens, emb_size]
              )
      return tf.concat([emb_mat_var, emb_mat_const], 0)
  
imports: from __future__ import print_function
  import collections
  import os
  import time
  import numpy
  import six
  import tensorflow
  from tensorflow.python.ops import lookup_ops
  from .utils import misc_utils, vocab_utils, iterator_utils
label_desc: Use image node_id 7 for calling a global function with example usage: _create_pretrained_emb_from_txt(vocab_file, embed_file, num_trainable_tokens, dtype, scope) and returns: tf