def create_position_ids_from_inputs_embeds(self, inputs_embds: tf.Tensor) -> tf.Tensor:
        """
        We are provided embeddings directly. We cannot infer which are padded, so just generate sequential position
        ids.
        """
        input_shape = tf.shape(inputs_embds)
        sequence_length = input_shape[1]
        start_index = self.padding_token_index + 1
        end_index = self.padding_token_index + sequence_length + 1
        position_ids = tf.range(start_index, end_index, dtype=tf.int32)
        batch_size = input_shape[0]
        position_ids = tf.reshape(position_ids, (1, sequence_length))
        position_ids = tf.tile(position_ids, (batch_size, 1))
        return position_ids

        We are provided embeddings directly. We cannot infer which are padded, so just generate sequential position
        ids.
        
dpkd/transformers/src/transformers/models/layoutlmv3/modeling_tf_layoutlmv3.py