message: Use autograph.stack now that the list format has changed.
diff: " autograph.utils.set_element_type(numbers, tf.int32)\n",
  " for i in range(n):\n",
  " numbers.append(i)\n",
  - " return numbers.stack() # Stack the list so that it can be used as a Tensor\n",
  + " return autograph.stack(numbers) # Stack the list so that it can be used as a Tensor\n",
  "\n",
  "\n",
  "tf_f = autograph.to_graph(f)\n",
  " if not is_prime:\n",
  " continue\n",
  " primes.append(i)\n",
  - " all_primes = primes.stack()\n",
  + " all_primes = autograph.stack(primes)\n",
  "\n",
  " print('The prime numbers less than', n, 'are:')\n",
  " print(all_primes)\n",
  " train_accuracies.append(step_train_accuracy)\n",
  " test_accuracies.append(step_test_accuracy)\n",
  " i += 1\n",
  - " return (train_losses.stack(), test_losses.stack(), train_accuracies.stack(),\n",
  - " test_accuracies.stack())"
  + " return (autograph.stack(train_losses), autograph.stack(test_losses),\n",
  + " autograph.stack(train_accuracies),\n",
  + " autograph.stack(test_accuracies))"
  ],
  "execution_count": 0,
  "outputs": []
  " cell_output, (state, output) = cell.call(ch, (state, output))\n",
  " hidden_outputs.append(cell_output)\n",
  " i += 1\n",
  - " hidden_outputs = hidden_outputs.stack()\n",
  + " hidden_outputs = autograph.stack(hidden_outputs)\n",
  " if training:\n",
  " hidden_outputs = tf.nn.dropout(hidden_outputs, 0.5)\n",
  " return hidden_outputs\n",
  