import tensorflow as tf

        decoder_use_sum_merge=model_options.decoder_use_sum_merge,
        decoder_filters=model_options.decoder_filters,
        decoder_output_is_logits=model_options.decoder_output_is_logits,
        weight_decay=weight_decay,
        reuse=reuse,
        is_training=is_training,
        fine_tune_batch_norm=fine_tune_batch_norm,
        use_bounded_activation=model_options.use_bounded_activation)

  outputs_to_logits = {}
  for output in sorted(model_options.outputs_to_num_classes):
    if model_options.decoder_output_is_logits:
      outputs_to_logits[output] = tf.identity(features,
                                              name=output)
    else:
      outputs_to_logits[output] = get_branch_logits(
          features,
          model_options.outputs_to_num_classes[output],
          model_options.atrous_rates,
          weight_decay=weight_decay,
          reuse=reuse,
          scope_suffix=output)

  return outputs_to_logits
