import tensorflow as tf

  }

  with tf.variable_scope(tf.get_variable_scope(), reuse=None):
    outputs_to_scales_to_logits = multi_scale_logits(
        images,
        model_options=model_options,
        is_training=False,
        fine_tune_batch_norm=False)

  if add_flipped_images:
    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,
          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 = _resize_bilinear(
        scales_to_logits[MERGED_LOGITS_SCOPE],
        tf.shape(images)[1:3],
        scales_to_logits[MERGED_LOGITS_SCOPE].dtype)
    outputs_to_predictions[output].append(
