diff --git a/tensorflow/contrib/autograph/examples/notebooks/algorithms.ipynb b/tensorflow/contrib/autograph/examples/notebooks/algorithms.ipynb
index bf824e2760e..c51d2124920 100644
--- a/tensorflow/contrib/autograph/examples/notebooks/algorithms.ipynb
+++ b/tensorflow/contrib/autograph/examples/notebooks/algorithms.ipynb
@@ -18,18 +18,29 @@
       "cell_type": "code",
       "execution_count": 0,
       "metadata": {
-        "colab": {
-          "autoexec": {
-            "startup": false,
-            "wait_interval": 0
-          }
-        },
+        "colab": {},
         "colab_type": "code",
         "id": "TuWj26KWz1fZ"
       },
       "outputs": [],
       "source": [
-        "!pip install -U -q tf-nightly"
+        "!pip install -U -q tf-nightly-2.0-preview"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 0,
+      "metadata": {
+        "colab": {},
+        "colab_type": "code",
+        "id": "Cp7iTarmz62Y"
+      },
+      "outputs": [],
+      "source": [
+        "import tensorflow as tf\n",
+        "\n",
+        "tf = tf.compat.v2\n",
+        "tf.enable_v2_behavior()"
       ]
     },
     {
@@ -41,25 +52,21 @@
       "source": [
         "### Fibonacci numbers\n",
         "\n",
-        "https://en.wikipedia.org/wiki/Fibonacci_number"
+        "https://en.wikipedia.org/wiki/Fibonacci_number\n"
       ]
     },
     {
       "cell_type": "code",
-      "execution_count": 2,
+      "execution_count": 11,
       "metadata": {
         "colab": {
-          "autoexec": {
-            "startup": false,
-            "wait_interval": 0
-          },
-          "height": 197
+          "height": 187
         },
         "colab_type": "code",
         "executionInfo": {
-          "elapsed": 7512,
+          "elapsed": 709,
           "status": "ok",
-          "timestamp": 1532101577266,
+          "timestamp": 1563825398552,
           "user": {
             "displayName": "",
             "photoUrl": "",
@@ -68,7 +75,7 @@
           "user_tz": 240
         },
         "id": "H7olFlMXqrHe",
-        "outputId": "472dbfe0-9449-4f93-e908-1a0785188a92"
+        "outputId": "25243e7b-99a7-4a6d-ad00-e97c52be7d97"
       },
       "outputs": [
         {
@@ -89,25 +96,19 @@
         }
       ],
       "source": [
-        "import tensorflow as tf\n",
-        "from tensorflow.contrib import autograph as ag\n",
-        "\n",
-        "\n",
+        "@tf.function\n",
         "def fib(n):\n",
         "  f1 = 0\n",
         "  f2 = 1\n",
-        "  for i in range(n):\n",
+        "  for i in tf.range(n):\n",
         "    tmp = f2\n",
         "    f2 = f2 + f1\n",
         "    f1 = tmp\n",
-        "    print(i, ': ', f2)\n",
+        "    tf.print(i, ': ', f2)\n",
         "  return f2\n",
         "\n",
         "\n",
-        "with tf.Graph().as_default():\n",
-        "  final_fib = ag.to_graph(fib)(tf.constant(10))\n",
-        "  with tf.Session() as sess:\n",
-        "    sess.run(final_fib)"
+        "_ = fib(tf.constant(10))"
       ]
     },
     {
@@ -122,68 +123,15 @@
     },
     {
       "cell_type": "code",
-      "execution_count": 3,
+      "execution_count": 0,
       "metadata": {
-        "colab": {
-          "autoexec": {
-            "startup": false,
-            "wait_interval": 0
-          },
-          "height": 541
-        },
+        "colab": {},
         "colab_type": "code",
-        "executionInfo": {
-          "elapsed": 103,
-          "status": "ok",
-          "timestamp": 1532101577412,
-          "user": {
-            "displayName": "",
-            "photoUrl": "",
-            "userId": ""
-          },
-          "user_tz": 240
-        },
-        "id": "UeWjK8rHq6Cj",
-        "outputId": "73ece895-12fb-489a-e52c-032945d7ed7a"
+        "id": "UeWjK8rHq6Cj"
       },
-      "outputs": [
-        {
-          "name": "stdout",
-          "output_type": "stream",
-          "text": [
-            "from __future__ import print_function\n",
-            "import tensorflow as tf\n",
-            "\n",
-            "def tf__fib(n):\n",
-            "  try:\n",
-            "    with tf.name_scope('fib'):\n",
-            "      f1 = 0\n",
-            "      f2 = 1\n",
-            "\n",
-            "      def extra_test(f1_1, f2_1):\n",
-            "        with tf.name_scope('extra_test'):\n",
-            "          return True\n",
-            "\n",
-            "      def loop_body(i, f1_1, f2_1):\n",
-            "        with tf.name_scope('loop_body'):\n",
-            "          tmp = f2_1\n",
-            "          f2_1 = f2_1 + f1_1\n",
-            "          f1_1 = tmp\n",
-            "          with ag__.utils.control_dependency_on_returns(ag__.utils.\n",
-            "              dynamic_print(i, ': ', f2_1)):\n",
-            "            f2, i_1 = ag__.utils.alias_tensors(f2_1, i)\n",
-            "            return f1_1, f2\n",
-            "      f1, f2 = ag__.for_stmt(ag__.utils.dynamic_builtin(range, n),\n",
-            "          extra_test, loop_body, (f1, f2))\n",
-            "      return f2\n",
-            "  except:\n",
-            "    ag__.rewrite_graph_construction_error(ag_source_map__)\n",
-            "\n"
-          ]
-        }
-      ],
+      "outputs": [],
       "source": [
-        "print(ag.to_code(fib))"
+        "print(tf.autograph.to_code(fib.python_function))"
       ]
     },
     {
@@ -200,20 +148,16 @@
     },
     {
       "cell_type": "code",
-      "execution_count": 4,
+      "execution_count": 12,
       "metadata": {
         "colab": {
-          "autoexec": {
-            "startup": false,
-            "wait_interval": 0
-          },
-          "height": 125
+          "height": 119
         },
         "colab_type": "code",
         "executionInfo": {
-          "elapsed": 233,
+          "elapsed": 663,
           "status": "ok",
-          "timestamp": 1532101577681,
+          "timestamp": 1563825401385,
           "user": {
             "displayName": "",
             "photoUrl": "",
@@ -222,7 +166,7 @@
           "user_tz": 240
         },
         "id": "33CAheYsrEQ7",
-        "outputId": "82a493ee-15b5-419d-8c9c-5f4159090a05"
+        "outputId": "2a88b65d-4fed-4d96-8770-0c68ffece861"
       },
       "outputs": [
         {
@@ -240,8 +184,9 @@
       ],
       "source": [
         "import tensorflow as tf\n",
-        "from tensorflow.contrib import autograph as ag\n",
         "\n",
+        "\n",
+        "@tf.function(experimental_autograph_options=tf.autograph.experimental.Feature.EQUALITY_OPERATORS)\n",
         "def fizzbuzz(i, n):\n",
         "  while i \u003c n:\n",
         "    msg = ''\n",
@@ -251,14 +196,11 @@
         "      msg += 'Buzz'\n",
         "    if msg == '':\n",
         "      msg = tf.as_string(i)\n",
-        "    print(msg)\n",
+        "    tf.print(msg)\n",
         "    i += 1\n",
         "  return i\n",
         "\n",
-        "with tf.Graph().as_default():\n",
-        "  final_i = ag.to_graph(fizzbuzz)(tf.constant(10), tf.constant(16))\n",
-        "  with tf.Session() as sess:\n",
-        "    sess.run(final_i)"
+        "_ = fizzbuzz(tf.constant(10), tf.constant(16))"
       ]
     },
     {
@@ -273,98 +215,15 @@
     },
     {
       "cell_type": "code",
-      "execution_count": 5,
+      "execution_count": 0,
       "metadata": {
-        "colab": {
-          "autoexec": {
-            "startup": false,
-            "wait_interval": 0
-          },
-          "height": 1081
-        },
+        "colab": {},
         "colab_type": "code",
-        "executionInfo": {
-          "elapsed": 289,
-          "status": "ok",
-          "timestamp": 1532101578003,
-          "user": {
-            "displayName": "",
-            "photoUrl": "",
-            "userId": ""
-          },
-          "user_tz": 240
-        },
-        "id": "bBhFIIaZrxvx",
-        "outputId": "d076a7ea-e643-4689-f90a-57f5d086dedc"
+        "id": "bBhFIIaZrxvx"
       },
-      "outputs": [
-        {
-          "name": "stdout",
-          "output_type": "stream",
-          "text": [
-            "from __future__ import print_function\n",
-            "import tensorflow as tf\n",
-            "\n",
-            "def tf__fizzbuzz(i, n):\n",
-            "  try:\n",
-            "    with tf.name_scope('fizzbuzz'):\n",
-            "\n",
-            "      def loop_test(i_1):\n",
-            "        with tf.name_scope('loop_test'):\n",
-            "          return tf.less(i_1, n)\n",
-            "\n",
-            "      def loop_body(i_1):\n",
-            "        with tf.name_scope('loop_body'):\n",
-            "          msg = ''\n",
-            "\n",
-            "          def if_true():\n",
-            "            with tf.name_scope('if_true'):\n",
-            "              msg_1, = msg,\n",
-            "              msg_1 += 'Fizz'\n",
-            "              return msg_1,\n",
-            "\n",
-            "          def if_false():\n",
-            "            with tf.name_scope('if_false'):\n",
-            "              return msg,\n",
-            "          msg = ag__.utils.run_cond(tf.equal(i_1 % 3, 0), if_true, if_false)\n",
-            "\n",
-            "          def if_true_1():\n",
-            "            with tf.name_scope('if_true_1'):\n",
-            "              msg_2, = msg,\n",
-            "              msg_2 += 'Buzz'\n",
-            "              return msg_2,\n",
-            "\n",
-            "          def if_false_1():\n",
-            "            with tf.name_scope('if_false_1'):\n",
-            "              return msg,\n",
-            "          msg = ag__.utils.run_cond(tf.equal(i_1 % 5, 0), if_true_1, if_false_1\n",
-            "              )\n",
-            "\n",
-            "          def if_true_2():\n",
-            "            with tf.name_scope('if_true_2'):\n",
-            "              msg_3, = msg,\n",
-            "              msg_3 = tf.as_string(i_1)\n",
-            "              return msg_3,\n",
-            "\n",
-            "          def if_false_2():\n",
-            "            with tf.name_scope('if_false_2'):\n",
-            "              return msg,\n",
-            "          msg = ag__.utils.run_cond(tf.equal(msg, ''), if_true_2, if_false_2)\n",
-            "          with ag__.utils.control_dependency_on_returns(ag__.utils.\n",
-            "              dynamic_print(msg)):\n",
-            "            msg_4 = ag__.utils.alias_tensors(msg)\n",
-            "            i_1 += 1\n",
-            "            return i_1,\n",
-            "      i = ag__.while_stmt(loop_test, loop_body, (i,), (tf, n, ag__, i))\n",
-            "      return i\n",
-            "  except:\n",
-            "    ag__.rewrite_graph_construction_error(ag_source_map__)\n",
-            "\n"
-          ]
-        }
-      ],
+      "outputs": [],
       "source": [
-        "print(ag.to_code(fizzbuzz))"
+        "print(tf.autograph.to_code(fizzbuzz.python_function))"
       ]
     },
     {
@@ -393,12 +252,7 @@
       "cell_type": "code",
       "execution_count": 0,
       "metadata": {
-        "colab": {
-          "autoexec": {
-            "startup": false,
-            "wait_interval": 0
-          }
-        },
+        "colab": {},
         "colab_type": "code",
         "id": "7moIlf8VABkl"
       },
@@ -414,44 +268,47 @@
         "id": "QlEvfIQPAYF5"
       },
       "source": [
-        "#### Game of Life for AutoGraph"
+        "#### Game of Life for AutoGraph\n",
+        "\n",
+        "Note: the code may take a while to run."
       ]
     },
     {
       "cell_type": "code",
       "execution_count": 0,
       "metadata": {
-        "colab": {
-          "autoexec": {
-            "startup": false,
-            "wait_interval": 0
-          }
-        },
+        "colab": {},
         "colab_type": "code",
         "id": "5pCK2qQSAAK4"
       },
       "outputs": [],
       "source": [
         "#@test {\"skip\": true} \n",
-        "NUM_STEPS = 100"
+        "NUM_STEPS = 75"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "colab_type": "text",
+        "id": "GPZANPdhMagD"
+      },
+      "source": [
+        "Note: This code uses a non-vectorized algorithm, which is quite slow. For 75 steps, it will take a few minutes to run. "
       ]
     },
     {
       "cell_type": "code",
-      "execution_count": 8,
+      "execution_count": 10,
       "metadata": {
         "colab": {
-          "autoexec": {
-            "startup": false,
-            "wait_interval": 0
-          },
-          "height": 308
+          "height": 309
         },
         "colab_type": "code",
         "executionInfo": {
-          "elapsed": 14892,
+          "elapsed": 147654,
           "status": "ok",
-          "timestamp": 1532101593030,
+          "timestamp": 1563825336196,
           "user": {
             "displayName": "",
             "photoUrl": "",
@@ -460,15 +317,15 @@
           "user_tz": 240
         },
         "id": "hC3qMqryPDHS",
-        "outputId": "8405c0e9-e518-41d6-f5bc-e78df6474169"
+        "outputId": "56a095a3-28a3-455d-e95e-2c4c9dcd97d2"
       },
       "outputs": [
         {
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               "\"\u003e\n",
               "  Your browser does not support the video tag.\n",
               "\u003c/video\u003e"
             ],
             "text/plain": [
-              "\u003cIPython.core.display.HTML at 0x7f84b2253b50\u003e"
+              "\u003cIPython.core.display.HTML at 0x7f1286b190b8\u003e"
             ]
           },
           "metadata": {
@@ -1209,15 +790,15 @@
       "source": [
         "import time\n",
         "import traceback\n",
+        "import sys\n",
         "\n",
         "from matplotlib import pyplot as plt\n",
         "from matplotlib import animation as anim\n",
-        "import tensorflow as tf\n",
-        "from tensorflow.contrib import autograph as ag\n",
+        "import numpy as np\n",
         "from IPython import display\n",
         "\n",
         "\n",
-        "@ag.do_not_convert(ag.RunMode.PY_FUNC)\n",
+        "@tf.autograph.experimental.do_not_convert\n",
         "def render(boards):\n",
         "  fig = plt.figure()\n",
         "\n",
@@ -1237,74 +818,71 @@
         "  except RuntimeError:\n",
         "    print('Coult not render animation:')\n",
         "    traceback.print_exc()\n",
+        "    return 1\n",
+        "  return 0\n",
         "\n",
         "\n",
         "def gol_episode(board):\n",
-        "  directions = tf.constant(\n",
-        "      ((-1, -1), (-1, 0), (-1, 1), (0, -1), (0, 1), (1, -1), (1, 0), (1, 1)))\n",
+        "  new_board = tf.TensorArray(tf.int32, 0, dynamic_size=True)\n",
         "\n",
-        "  new_board = []\n",
-        "  ag.set_element_type(new_board, tf.int32)\n",
-        "\n",
-        "  for i in range(len(board)):\n",
-        "    for j in range(len(board[i])):\n",
-        "      num_neighbors = 0\n",
-        "      for d in directions:\n",
-        "        ni = i + d[0]\n",
-        "        nj = j + d[1]\n",
-        "        if ni \u003e= 0 and nj \u003e= 0 and ni \u003c len(board) and nj \u003c len(board[i]):\n",
-        "          num_neighbors += board[ni][nj]\n",
+        "  for i in tf.range(len(board)):\n",
+        "    for j in tf.range(len(board[i])):\n",
+        "      num_neighbors = tf.reduce_sum(\n",
+        "          board[tf.maximum(i-1, 0):tf.minimum(i+2, len(board)),\n",
+        "                tf.maximum(j-1, 0):tf.minimum(j+2, len(board[i]))]\n",
+        "      ) - board[i][j]\n",
         "      \n",
-        "      new_cell = 0\n",
         "      if num_neighbors == 2:\n",
         "        new_cell = board[i][j]\n",
         "      elif num_neighbors == 3:\n",
         "        new_cell = 1\n",
+        "      else:\n",
+        "        new_cell = 0\n",
         "      \n",
         "      new_board.append(new_cell)\n",
-        "  final_board = ag.stack(new_board)\n",
+        "  final_board = new_board.stack()\n",
         "  final_board = tf.reshape(final_board, board.shape)\n",
         "  return final_board\n",
         "  \n",
         "\n",
+        "@tf.function(experimental_autograph_options=(\n",
+        "    tf.autograph.experimental.Feature.EQUALITY_OPERATORS,\n",
+        "    tf.autograph.experimental.Feature.BUILTIN_FUNCTIONS,\n",
+        "    tf.autograph.experimental.Feature.LISTS,\n",
+        "    ))\n",
         "def gol(initial_board):\n",
         "  board = initial_board\n",
-        "  boards = []\n",
-        "  ag.set_element_type(boards, tf.int32)\n",
-        "  # We are being explicit about tensor constants to ensure the loop\n",
-        "  # is not unrolled in the graph. This may change in the future.\n",
-        "  for i in range(tf.constant(NUM_STEPS)):\n",
+        "  boards = tf.TensorArray(tf.int32, size=0, dynamic_size=True)\n",
+        "\n",
+        "  i = 0\n",
+        "  for i in tf.range(NUM_STEPS):\n",
         "    board = gol_episode(board)\n",
         "    boards.append(board)\n",
-        "  boards = ag.stack(boards)\n",
-        "  render(boards)\n",
-        "  return tf.no_op()\n",
+        "  boards = boards.stack()\n",
+        "  tf.py_function(render, (boards,), (tf.int64,))\n",
+        "  return i\n",
         " \n",
         "\n",
-        "with tf.Graph().as_default():\n",
-        "  # Gosper glider gun\n",
-        "  # Adapted from http://www.cplusplus.com/forum/lounge/75168/\n",
-        "  _ = 0\n",
-        "  initial_board = tf.constant((\n",
-        "      ( _,_,_,_,_,_,_,_,_,_,_,_,_,_,_,_,_,_,_,_,_,_,_,_,_,_,_,_,_,_,_,_,_,_,_,_,_,_ ),\n",
-        "      ( _,_,_,_,_,_,_,_,_,_,_,_,_,_,_,_,_,_,_,_,_,_,_,_,_,1,_,_,_,_,_,_,_,_,_,_,_,_ ),\n",
-        "      ( _,_,_,_,_,_,_,_,_,_,_,_,_,_,_,_,_,_,_,_,_,_,_,1,_,1,_,_,_,_,_,_,_,_,_,_,_,_ ),\n",
-        "      ( _,_,_,_,_,_,_,_,_,_,_,_,_,1,1,_,_,_,_,_,_,1,1,_,_,_,_,_,_,_,_,_,_,_,_,1,1,_ ),\n",
-        "      ( _,_,_,_,_,_,_,_,_,_,_,_,1,_,_,_,1,_,_,_,_,1,1,_,_,_,_,_,_,_,_,_,_,_,_,1,1,_ ),\n",
-        "      ( _,1,1,_,_,_,_,_,_,_,_,1,_,_,_,_,_,1,_,_,_,1,1,_,_,_,_,_,_,_,_,_,_,_,_,_,_,_ ),\n",
-        "      ( _,1,1,_,_,_,_,_,_,_,_,1,_,_,_,1,_,1,1,_,_,_,_,1,_,1,_,_,_,_,_,_,_,_,_,_,_,_ ),\n",
-        "      ( _,_,_,_,_,_,_,_,_,_,_,1,_,_,_,_,_,1,_,_,_,_,_,_,_,1,_,_,_,_,_,_,_,_,_,_,_,_ ),\n",
-        "      ( _,_,_,_,_,_,_,_,_,_,_,_,1,_,_,_,1,_,_,_,_,_,_,_,_,_,_,_,_,_,_,_,_,_,_,_,_,_ ),\n",
-        "      ( _,_,_,_,_,_,_,_,_,_,_,_,_,1,1,_,_,_,_,_,_,_,_,_,_,_,_,_,_,_,_,_,_,_,_,_,_,_ ),\n",
-        "      ( _,_,_,_,_,_,_,_,_,_,_,_,_,_,_,_,_,_,_,_,_,_,_,_,_,_,_,_,_,_,_,_,_,_,_,_,_,_ ),\n",
-        "      ( _,_,_,_,_,_,_,_,_,_,_,_,_,_,_,_,_,_,_,_,_,_,_,_,_,_,_,_,_,_,_,_,_,_,_,_,_,_ ),\n",
-        "  ))\n",
-        "  initial_board = tf.pad(initial_board, ((0, 20), (0, 10)))\n",
-        "  \n",
-        "  tf_gol = ag.to_graph(gol)\n",
-        "  game_ops = tf_gol(initial_board)\n",
-        "  with tf.Session() as sess:\n",
-        "    sess.run(game_ops)\n"
+        "# Gosper glider gun\n",
+        "# Adapted from http://www.cplusplus.com/forum/lounge/75168/\n",
+        "_ = 0\n",
+        "initial_board = tf.constant((\n",
+        "    ( _,_,_,_,_,_,_,_,_,_,_,_,_,_,_,_,_,_,_,_,_,_,_,_,_,_,_,_,_,_,_,_,_,_,_,_,_,_ ),\n",
+        "    ( _,_,_,_,_,_,_,_,_,_,_,_,_,_,_,_,_,_,_,_,_,_,_,_,_,1,_,_,_,_,_,_,_,_,_,_,_,_ ),\n",
+        "    ( _,_,_,_,_,_,_,_,_,_,_,_,_,_,_,_,_,_,_,_,_,_,_,1,_,1,_,_,_,_,_,_,_,_,_,_,_,_ ),\n",
+        "    ( _,_,_,_,_,_,_,_,_,_,_,_,_,1,1,_,_,_,_,_,_,1,1,_,_,_,_,_,_,_,_,_,_,_,_,1,1,_ ),\n",
+        "    ( _,_,_,_,_,_,_,_,_,_,_,_,1,_,_,_,1,_,_,_,_,1,1,_,_,_,_,_,_,_,_,_,_,_,_,1,1,_ ),\n",
+        "    ( _,1,1,_,_,_,_,_,_,_,_,1,_,_,_,_,_,1,_,_,_,1,1,_,_,_,_,_,_,_,_,_,_,_,_,_,_,_ ),\n",
+        "    ( _,1,1,_,_,_,_,_,_,_,_,1,_,_,_,1,_,1,1,_,_,_,_,1,_,1,_,_,_,_,_,_,_,_,_,_,_,_ ),\n",
+        "    ( _,_,_,_,_,_,_,_,_,_,_,1,_,_,_,_,_,1,_,_,_,_,_,_,_,1,_,_,_,_,_,_,_,_,_,_,_,_ ),\n",
+        "    ( _,_,_,_,_,_,_,_,_,_,_,_,1,_,_,_,1,_,_,_,_,_,_,_,_,_,_,_,_,_,_,_,_,_,_,_,_,_ ),\n",
+        "    ( _,_,_,_,_,_,_,_,_,_,_,_,_,1,1,_,_,_,_,_,_,_,_,_,_,_,_,_,_,_,_,_,_,_,_,_,_,_ ),\n",
+        "    ( _,_,_,_,_,_,_,_,_,_,_,_,_,_,_,_,_,_,_,_,_,_,_,_,_,_,_,_,_,_,_,_,_,_,_,_,_,_ ),\n",
+        "    ( _,_,_,_,_,_,_,_,_,_,_,_,_,_,_,_,_,_,_,_,_,_,_,_,_,_,_,_,_,_,_,_,_,_,_,_,_,_ ),\n",
+        "))\n",
+        "initial_board = tf.pad(initial_board, ((0, 10), (0, 5)))\n",
+        "\n",
+        "_ = gol(initial_board)"
       ]
     },
     {
@@ -1319,179 +897,21 @@
     },
     {
       "cell_type": "code",
-      "execution_count": 9,
+      "execution_count": 0,
       "metadata": {
-        "colab": {
-          "autoexec": {
-            "startup": false,
-            "wait_interval": 0
-          },
-          "height": 2323
-        },
+        "colab": {},
         "colab_type": "code",
-        "executionInfo": {
-          "elapsed": 753,
-          "status": "ok",
-          "timestamp": 1532101593840,
-          "user": {
-            "displayName": "",
-            "photoUrl": "",
-            "userId": ""
-          },
-          "user_tz": 240
-        },
-        "id": "hIGYeX0Cxs3i",
-        "outputId": "e0b62eb1-3e12-4e53-dc54-8a3fa56d823d"
+        "id": "hIGYeX0Cxs3i"
       },
-      "outputs": [
-        {
-          "name": "stdout",
-          "output_type": "stream",
-          "text": [
-            "from __future__ import print_function\n",
-            "import tensorflow as tf\n",
-            "\n",
-            "def tf__gol_episode(board):\n",
-            "  try:\n",
-            "    with tf.name_scope('gol_episode'):\n",
-            "      directions = tf.constant(((-1, -1), (-1, 0), (-1, 1), (0, -1), (0, 1),\n",
-            "          (1, -1), (1, 0), (1, 1)))\n",
-            "      new_board = ag__.new_list([])\n",
-            "\n",
-            "      def extra_test_2(new_board_2):\n",
-            "        with tf.name_scope('extra_test_2'):\n",
-            "          return True\n",
-            "\n",
-            "      def loop_body_2(i, new_board_2):\n",
-            "        with tf.name_scope('loop_body_2'):\n",
-            "\n",
-            "          def extra_test_1(new_board_1):\n",
-            "            with tf.name_scope('extra_test_1'):\n",
-            "              return True\n",
-            "\n",
-            "          def loop_body_1(j, new_board_1):\n",
-            "            with tf.name_scope('loop_body_1'):\n",
-            "              num_neighbors = 0\n",
-            "\n",
-            "              def extra_test(num_neighbors_2):\n",
-            "                with tf.name_scope('extra_test'):\n",
-            "                  return True\n",
-            "\n",
-            "              def loop_body(d, num_neighbors_2):\n",
-            "                with tf.name_scope('loop_body'):\n",
-            "                  ni = i + ag__.get_item(d, (0), opts=ag__.GetItemOpts(\n",
-            "                      element_dtype=None))\n",
-            "                  nj = j + ag__.get_item(d, (1), opts=ag__.GetItemOpts(\n",
-            "                      element_dtype=None))\n",
-            "\n",
-            "                  def if_true():\n",
-            "                    with tf.name_scope('if_true'):\n",
-            "                      num_neighbors_1, = num_neighbors_2,\n",
-            "                      num_neighbors_1 += ag__.get_item(ag__.get_item(board,\n",
-            "                          (ni), opts=ag__.GetItemOpts(element_dtype=None)),\n",
-            "                          (nj), opts=ag__.GetItemOpts(element_dtype=None))\n",
-            "                      return num_neighbors_1,\n",
-            "\n",
-            "                  def if_false():\n",
-            "                    with tf.name_scope('if_false'):\n",
-            "                      return num_neighbors_2,\n",
-            "                  num_neighbors_2 = ag__.utils.run_cond(tf.logical_and(tf.\n",
-            "                      greater_equal(ni, 0), tf.logical_and(tf.greater_equal\n",
-            "                      (nj, 0), tf.logical_and(tf.less(ni, ag__.utils.\n",
-            "                      dynamic_builtin(len, board)), tf.less(nj, ag__.utils.\n",
-            "                      dynamic_builtin(len, ag__.get_item(board, (i), opts=\n",
-            "                      ag__.GetItemOpts(element_dtype=None))))))), if_true,\n",
-            "                      if_false)\n",
-            "                  return num_neighbors_2,\n",
-            "              num_neighbors = ag__.for_stmt(directions, extra_test,\n",
-            "                  loop_body, (num_neighbors,))\n",
-            "              new_cell = 0\n",
-            "\n",
-            "              def if_true_2():\n",
-            "                with tf.name_scope('if_true_2'):\n",
-            "                  new_cell_2, = new_cell,\n",
-            "                  new_cell_2 = ag__.get_item(ag__.get_item(board, (i), opts\n",
-            "                      =ag__.GetItemOpts(element_dtype=None)), (j), opts=\n",
-            "                      ag__.GetItemOpts(element_dtype=None))\n",
-            "                  return new_cell_2,\n",
-            "\n",
-            "              def if_false_2():\n",
-            "                with tf.name_scope('if_false_2'):\n",
-            "                  new_cell_3, = new_cell,\n",
-            "\n",
-            "                  def if_true_1():\n",
-            "                    with tf.name_scope('if_true_1'):\n",
-            "                      new_cell_1, = new_cell_3,\n",
-            "                      new_cell_1 = 1\n",
-            "                      return new_cell_1,\n",
-            "\n",
-            "                  def if_false_1():\n",
-            "                    with tf.name_scope('if_false_1'):\n",
-            "                      return new_cell_3,\n",
-            "                  new_cell_3 = ag__.utils.run_cond(tf.equal(num_neighbors, \n",
-            "                      3), if_true_1, if_false_1)\n",
-            "                  return new_cell_3,\n",
-            "              new_cell = ag__.utils.run_cond(tf.equal(num_neighbors, 2),\n",
-            "                  if_true_2, if_false_2)\n",
-            "              new_board_1 = ag__.list_append(new_board_1, new_cell)\n",
-            "              return new_board_1,\n",
-            "          new_board_2 = ag__.for_stmt(ag__.utils.dynamic_builtin(range,\n",
-            "              ag__.utils.dynamic_builtin(len, ag__.get_item(board, (i),\n",
-            "              opts=ag__.GetItemOpts(element_dtype=None)))), extra_test_1,\n",
-            "              loop_body_1, (new_board_2,))\n",
-            "          return new_board_2,\n",
-            "      new_board = ag__.for_stmt(ag__.utils.dynamic_builtin(range, ag__.\n",
-            "          utils.dynamic_builtin(len, board)), extra_test_2, loop_body_2, (\n",
-            "          new_board,))\n",
-            "      final_board = ag__.list_stack(new_board, opts=ag__.ListStackOpts(\n",
-            "          element_dtype=tf.int32, original_call=ag.stack))\n",
-            "      final_board = tf.reshape(final_board, board.shape)\n",
-            "      return final_board\n",
-            "  except:\n",
-            "    ag__.rewrite_graph_construction_error(ag_source_map__)\n",
-            "\n",
-            "def tf__gol(initial_board):\n",
-            "  try:\n",
-            "    with tf.name_scope('gol'):\n",
-            "      board = initial_board\n",
-            "      boards = ag__.new_list([])\n",
-            "\n",
-            "      def extra_test(board_1, boards_1):\n",
-            "        with tf.name_scope('extra_test'):\n",
-            "          return True\n",
-            "\n",
-            "      def loop_body(i, board_1, boards_1):\n",
-            "        with tf.name_scope('loop_body'):\n",
-            "          board_1 = tf__gol_episode(board_1)\n",
-            "          boards_1 = ag__.list_append(boards_1, board_1)\n",
-            "          return board_1, boards_1\n",
-            "      board, boards = ag__.for_stmt(ag__.utils.dynamic_builtin(range, tf.\n",
-            "          constant(NUM_STEPS)), extra_test, loop_body, (board, boards))\n",
-            "      boards = ag__.list_stack(boards, opts=ag__.ListStackOpts(\n",
-            "          element_dtype=tf.int32, original_call=ag.stack))\n",
-            "      with ag__.utils.control_dependency_on_returns(render(boards)):\n",
-            "        boards_2 = ag__.utils.alias_tensors(boards)\n",
-            "        return tf.no_op()\n",
-            "  except:\n",
-            "    ag__.rewrite_graph_construction_error(ag_source_map__)\n",
-            "\n"
-          ]
-        }
-      ],
+      "outputs": [],
       "source": [
-        "print(ag.to_code(gol))"
+        "print(tf.autograph.to_code(gol.python_function))"
       ]
     }
   ],
   "metadata": {
     "colab": {
-      "collapsed_sections": [
-        "p8zZyj-tq4K3",
-        "Lkq3DBGOv3fA",
-        "r8_0ioEuAI-a",
-        "7NgrSPCZxs3h"
-      ],
-      "default_view": {},
+      "collapsed_sections": [],
       "last_runtime": {
         "build_target": "",
         "kind": "local"
@@ -1503,8 +923,11 @@
           "timestamp": 1528465909719
         }
       ],
-      "version": "0.3.2",
-      "views": {}
+      "version": "0.3.2"
+    },
+    "kernelspec": {
+      "display_name": "Python 3",
+      "name": "python3"
     }
   },
   "nbformat": 4,