import tensorflow as tf

                'fast_rcnn_box_loss', tf.reduce_mean(fast_rcnn_box_loss),
                step=global_step)
            if params['include_mask']:
              tf.contrib.summary.scalar(
                  'mask_loss', tf.reduce_mean(mask_loss), step=global_step)
            tf.contrib.summary.scalar(
                'learning_rate', tf.reduce_mean(learning_rate),
                step=global_step)

            return tf.contrib.summary.all_summary_ops()

      # To log the loss, current learning rate, and epoch for Tensorboard, the
      # summary op needs to be run on the host CPU via host_call. host_call
      # expects [batch_size, ...] Tensors, thus reshape to introduce a batch
      # dimension. These Tensors are implicitly concatenated to
      # [params['batch_size']].
      global_step_t = tf.reshape(global_step, [1])
      total_loss_t = tf.reshape(total_loss, [1])
