import tensorflow as tf

      'is_training': is_training and fine_tune_batch_norm,
      'decay': 0.9997,
      'epsilon': 1e-5,
      'scale': True,
  }

  with slim.arg_scope(
      [slim.conv2d, slim.separable_conv2d],
      weights_regularizer=slim.l2_regularizer(weight_decay),
      activation_fn=tf.nn.relu,
      normalizer_fn=slim.batch_norm,
      padding='SAME',
      stride=1,
      reuse=reuse):
    with slim.arg_scope([slim.batch_norm], **batch_norm_params):
      with tf.variable_scope(_DECODER_SCOPE, _DECODER_SCOPE, [features]):
        feature_list = feature_extractor.networks_to_feature_maps[
            model_variant][feature_extractor.DECODER_END_POINTS]
        if feature_list is None:
          tf.logging.info('Not found any decoder end points.')
          return features
        else:
          decoder_features = features
          for i, name in enumerate(feature_list):
            decoder_features_list = [decoder_features]
            feature_name = '{}/{}'.format(
                feature_extractor.name_scope[model_variant], name)
            decoder_features_list.append(
                slim.conv2d(
                    # end_points["refinement_net/" + feature_name],
