import tensorflow as tf

      If predict_keypoints is True the dictionary also contains:
        keypoints: [batch_size, 1, num_keypoints, 2]

    Raises:
      ValueError: if num_predictions_per_location is not 1.
    """
    if num_predictions_per_location != 1:
      raise ValueError('Currently FullyConnectedBoxPredictor only supports '
                       'predicting a single box per class per location.')
    spatial_averaged_image_features = tf.reduce_mean(image_features, [1, 2],
                                                     keep_dims=True,
                                                     name='AvgPool')
    flattened_image_features = slim.flatten(spatial_averaged_image_features)
    if self._use_dropout:
      flattened_image_features = slim.dropout(flattened_image_features,
                                              keep_prob=self._dropout_keep_prob,
                                              is_training=self._is_training)
    with slim.arg_scope(self._fc_hyperparams):
