1a7fa6010b7234bb9efddf761c9bf52af07dc347,texar/modules/embedders/position_embedders.py,PositionEmbedder,_build,#PositionEmbedder#Any#Any#Any#,105

Before Change

                    "Either `positions` or `sequence_length` is required.")
            max_length = tf.reduce_max(sequence_length)
            single_inputs = tf.range(start=0, limit=max_length, dtype=tf.int32)
            inputs = tf.tile(tf.expand_dims(single_inputs, 0),
                             [get_batch_size(sequence_length), 1])

        embedding = self._embedding
        dropout_layer = self._get_dropout_layer(self._hparams, inputs)
        if dropout_layer:

After Change

            inputs = expander * tf.expand_dims(single_inputs, 0)

        st = self._hparams.dropout_strategy
        if positions is None and st == "item":
            // If `inputs` is based on `sequence_length`, then dropout
            // strategies "item" and "item_type" have the same effect, we
            // use "item_type" to avoid unknown noise_shape in the "item"
            // strategy
            st = "item_type"
        dropout_layer = self._get_dropout_layer(self._hparams, inputs, st)

        embedding = self._embedding
        if dropout_layer:

Instances