import tensorflow as tf

      with tf.variable_scope(tf.get_variable_scope(), reuse=True):
        outputs_to_scales_to_logits_reversed = multi_scale_logits(
            tf.reverse_v2(images, [2]),
            model_options=model_options,
            image_pyramid=[image_scale],
            is_training=False,
            fine_tune_batch_norm=False)

    for output in sorted(outputs_to_scales_to_logits):
      scales_to_logits = outputs_to_scales_to_logits[output]
      logits = tf.image.resize_bilinear(
          scales_to_logits[_MERGED_LOGITS_SCOPE],
          tf.shape(images)[1:3],
          align_corners=True)
      outputs_to_predictions[output].append(
          tf.expand_dims(tf.nn.softmax(logits), 4))

      if add_flipped_images:
        scales_to_logits_reversed = (
            outputs_to_scales_to_logits_reversed[output])
        logits_reversed = tf.image.resize_bilinear(
            tf.reverse_v2(scales_to_logits_reversed[_MERGED_LOGITS_SCOPE], [2]),
            tf.shape(images)[1:3],
            align_corners=True)
