import tensorflow as tf

                  features,
                  filters=depth,
                  rate=rate,
                  weight_decay=weight_decay,
                  scope=scope)
            else:
              aspp_features = slim.conv2d(
                  features, depth, 3, rate=rate, scope=scope)
            branch_logits.append(aspp_features)

        # Merge branch logits.
        concat_logits = tf.concat(branch_logits, 3)
        concat_logits = slim.conv2d(
            concat_logits, depth, 1, scope=_CONCAT_PROJECTION_SCOPE)
        concat_logits = slim.dropout(
            concat_logits,
            keep_prob=0.9,
            is_training=is_training,
            scope=_CONCAT_PROJECTION_SCOPE + '_dropout')

        return concat_logits, end_points

