import tensorflow as tf

      prediction) and values storing Tensors representing predictions (argmax
      over channels). Each prediction has size [batch, height, width].
  """
  outputs_to_predictions = {
      output: []
      for output in model_options.outputs_to_num_classes
  }

  for i, image_scale in enumerate(eval_scales):
    with tf.variable_scope(tf.get_variable_scope(), reuse=True if i else None):
      outputs_to_scales_to_logits = multi_scale_logits(
          images,
          model_options=model_options,
          image_pyramid=[image_scale],
          is_training=False,
          fine_tune_batch_norm=False)

    if add_flipped_images:
