import tensorflow as tf

                                          1. / model_options.output_stride)
            image_feature = slim.avg_pool2d(
                features, [pool_height, pool_width], [pool_height, pool_width],
                padding='VALID')
          else:
            pool_height = tf.shape(features)[1]
            pool_width = tf.shape(features)[2]
            image_feature = tf.reduce_mean(features, axis=[1,2])[:, tf.newaxis, tf.newaxis, :]
          image_feature = slim.conv2d(
              image_feature, depth, 1, scope=_IMAGE_POOLING_SCOPE)
          image_feature = tf.image.resize_bilinear(
              image_feature, [pool_height, pool_width], align_corners=True)
