def call(
        self,
        input_ids: tf.Tensor = None,
        position_ids: tf.Tensor = None,
        token_type_ids: tf.Tensor = None,
        inputs_embeds: tf.Tensor = None,
        past_key_values_length=0,
        training: bool = False,
    ) -> tf.Tensor:
        """
        Applies embedding based on inputs tensor.

        Returns:
            final_embeddings (`tf.Tensor`): output embedding tensor.
        """
        if input_ids is None and inputs_embeds is None:
            raise ValueError("Need to provide either `input_ids` or `input_embeds`.")

        if input_ids is not None:
            check_embeddings_within_bounds(input_ids, self.config.vocab_size)
            inputs_embeds = tf.gather(params=self.weight, indices=input_ids)

        input_shape = shape_list(inputs_embeds)[:-1]

        if token_type_ids is None:
            token_type_ids = tf.fill(dims=input_shape, value=0)

        if position_ids is None:
            position_ids = tf.expand_dims(
                tf.range(start=past_key_values_length, limit=input_shape[1] + past_key_values_length), axis=0
            )

        position_embeds = tf.gather(params=self.position_embeddings, indices=position_ids)
        token_type_embeds = tf.gather(params=self.token_type_embeddings, indices=token_type_ids)
        final_embeddings = inputs_embeds + position_embeds + token_type_embeds
        final_embeddings = self.LayerNorm(inputs=final_embeddings)
        final_embeddings = self.dropout(inputs=final_embeddings, training=training)

        return final_embeddings

        Applies embedding based on inputs tensor.

        Returns:
            final_embeddings (`tf.Tensor`): output embedding tensor.
        
dpkd/transformers/src/transformers/models/bert/modeling_tf_bert.py