import tensorflow as tf

              1. / model_options.output_stride)
          resize_width = scale_dimension(
              model_options.crop_size[1],
              1. / model_options.output_stride)
        else:
          # If crop_size is None, we simply do global pooling.
          pool_height = tf.shape(features)[1]
          pool_width = tf.shape(features)[2]
          image_feature = tf.reduce_mean(
              features, axis=[1, 2], keepdims=True)
          resize_height = pool_height
          resize_width = pool_width
        image_feature_activation_fn = tf.nn.relu
        image_feature_normalizer_fn = batch_norm
        if model_options.aspp_with_squeeze_and_excitation:
          image_feature_activation_fn = tf.nn.sigmoid
