def embeddings_with_init(vocab_size, embedding_dim, name):
 return tf.get_variable(name=name, shape=[vocab_size, embedding_dim], initializer=tf.random_normal_initializer(stddev=math.sqrt((1.0 / embedding_dim))))

def embeddings_with_init ( vocab_size , embedding_dim , name ) : return tf . get_variable ( name = name , shape = [ vocab_size , embedding_dim ] , initializer = tf . random_normal_initializer ( stddev = math . sqrt ( ( 1.0 / embedding_dim ) ) ) )
creates and initializes the embedding tensors .