import tensorflow as tf

          true_image_shapes: int32 tensor of shape [batch, 3] where each row is
            of the form [height, width, channels] indicating the shapes
            of true images in the resized images, as resized images can be padded
            with zeros.

        Returns:
          prediction_dict: a dictionary holding prediction tensors to be
            passed to the Loss or Postprocess functions.
        """
        flattened_inputs = tf.contrib.layers.flatten(preprocessed_inputs)
        class_prediction = tf.contrib.layers.fully_connected(
            flattened_inputs, self._num_classes)
        box_prediction = tf.contrib.layers.fully_connected(flattened_inputs, 4)

        return {
            'class_predictions_with_background': tf.reshape(
                class_prediction, [-1, 1, self._num_classes]),
            'box_encodings': tf.reshape(box_prediction, [-1, 1, 4])
