import tensorflow as tf

          # Training
          start_time = time.time()
          for i in range(hparams.n_iters):
            _, loss_np = sess.run([train_op, loss])
            print("Iteration %d: loss %.4f" % (i, loss_np))
          wall_time = (time.time() - start_time) / hparams.n_iters
          examples_per_sec = hparams.n_samples / wall_time

          self.report_benchmark(
              name="graph_train_%s_%d" %
              ("gpu" if tf.test.is_gpu_available() else "cpu", sample_size),
              iters=hparams.n_iters,
              extras={"examples_per_sec": examples_per_sec},
              wall_time=wall_time)

  def benchmark_eager(self):
    self._benchmark_eager()

  def benchmark_eager_defun(self):
    self._benchmark_eager(defun=True)
