import tensorflow as tf

      output_stride=model_options.output_stride,
      depth_multiplier=model_options.depth_multiplier,
      divisible_by=model_options.divisible_by,
      weight_decay=weight_decay,
      reuse=reuse,
      is_training=is_training,
      preprocess_images=model_options.preprocess_images,
      preprocessed_images_dtype=model_options.preprocessed_images_dtype,
      fine_tune_batch_norm=fine_tune_batch_norm,
      nas_architecture_options=model_options.nas_architecture_options,
      nas_training_hyper_parameters=nas_training_hyper_parameters,
      use_bounded_activation=model_options.use_bounded_activation)

  if model_options.dense_prediction_cell_config is not None:
    tf.logging.info('Using dense prediction cell config.')
    dense_prediction_layer = dense_prediction_cell.DensePredictionCell(
        config=model_options.dense_prediction_cell_config,
        hparams={
            'conv_rate_multiplier': 16 // model_options.output_stride,
        })
    concat_logits = dense_prediction_layer.build_cell(
        features,
        output_stride=model_options.output_stride,
        crop_size=model_options.crop_size,
        image_pooling_crop_size=model_options.image_pooling_crop_size,
        weight_decay=weight_decay,
        reuse=reuse,
        is_training=is_training,
