code: def add_pretrained_word_embeddings(self, dim=100):
      self._word_embeddings_added = True
  
      with tf.compat.v1.device("/gpu:{}".format(self.use_gpu_device)):
          with tf.compat.v1.variable_scope("word_repr"):
              # shape = (batch size, sentence, dim)
              self.word_embeddings = tf.compat.v1.placeholder(
                  tf.float32,
                  shape=[None, None, dim],
                  name="word_embeddings",
              )
  
              if self.word_repr is not None:
                  self.word_repr = tf.concat(
                      [self.word_repr, self.word_embeddings], axis=-1
                  )
              else:
                  self.word_repr = self.word_embeddings
  
imports: import math
  import random
  import sys
  import numpy
  import tensorflow
  from .sentence_grouper import SentenceGrouper
  from ..tf2contrib import *
label_desc: Use image node_id 4 for calling the NerModel obj's underlying member method code with example usage: obj.add_pretrained_word_embeddings(dim) without return types