import tensorflow as tf

      if self.task_index == 0 and FLAGS.summary_verbosity > 0:
        tf.summary.scalar('learning_rate', learning_rate)
        tf.summary.scalar('total_loss', total_loss)
        for grad, var in avg_grads:
          if grad is not None:
            tf.summary.histogram(var.op.name + '/gradients', grad)
        for var in tf.trainable_variables():
          tf.summary.histogram(var.op.name, var)
    fetches = [train_op, total_loss] + enqueue_ops
    return (enqueue_ops, fetches)

  def add_forward_pass_and_gradients(
