def build(self, input_shape: tf.TensorShape):
    with tf.name_scope('word_embeddings'):
        self.weight = self.add_weight(name='weight', shape=[self.vocab_size, self.embedding_size], initializer=get_initializer(self.initializer_range))
    with tf.name_scope('token_type_embeddings'):
        self.token_type_embeddings = self.add_weight(name='embeddings', shape=[self.type_vocab_size, self.embedding_size], initializer=get_initializer(self.initializer_range))
    with tf.name_scope('position_embeddings'):
        self.position_embeddings = self.add_weight(name='embeddings', shape=[self.max_position_embeddings, self.embedding_size], initializer=get_initializer(self.initializer_range))
    super().build(input_shape)
def build(self, input_shape: tf.TensorShape):
    with tf.name_scope('word_embeddings'):
        self.weight = self.add_weight(name='weight', shape=[self.vocab_size, self.embedding_size], initializer=get_initializer(self.initializer_range))
    with tf.name_scope('token_type_embeddings'):
        self.token_type_embeddings = self.add_weight(name='embeddings', shape=[self.type_vocab_size, self.embedding_size], initializer=get_initializer(self.initializer_range))
    with tf.name_scope('position_embeddings'):
        self.position_embeddings = self.add_weight(name='embeddings', shape=[self.max_position_embeddings, self.embedding_size], initializer=get_initializer(self.initializer_range))
    super().build(input_shape)