eager: Update sample notebooks with API changes in the last few releases.

Most notably:
- Avoid using tf.contrib.eager since equivalent functionality if available outside tf.contrib
- Datasets can be directly iterated on.
- Use tf.GradientTape instead of tf.contrib.eager.implicit_gradients

PiperOrigin-RevId: 194971115

4 files changed, 278 insertions, 613 deletions

diff --git a/tensorflow/contrib/eager/README.md b/tensorflow/contrib/eager/README.md
index 9a3b780af8..762685db14 100644
--- a/tensorflow/contrib/eager/README.md
+++ b/tensorflow/contrib/eager/README.md
@@ -37,7 +37,7 @@ support for distributed and multi-GPU training and performance.
 
 ## Installation
 
-Eager execution is included in TensorFlow versions 1.7 and above.
+For eager execution, we recommend using TensorFlow version 1.8 or newer.
 Installation instructions at https://www.tensorflow.org/install/
 
 ## Documentation
@@ -48,12 +48,3 @@ For an introduction to eager execution in TensorFlow, see:
 - Notebook: [Basic Usage](python/examples/notebooks/1_basics.ipynb)
 - Notebook: [Gradients](python/examples/notebooks/2_gradients.ipynb)
 - Notebook: [Importing Data](python/examples/notebooks/3_datasets.ipynb)
-
-## Changelog
-
-- 2017/10/31: Initial preview release (in TensorFlow 1.5)
-- 2017/12/01: Example of dynamic neural network:
-  [SPINN: Stack-augmented Parser-Interpreter Neural Network](https://arxiv.org/abs/1603.06021).
-  See [README.md](python/examples/spinn/README.md) for details.
-- 2017/03: Core functionality moved out of the experimental tf.contrib namespace
-  in TensorFlow 1.7.
diff --git a/tensorflow/contrib/eager/python/examples/notebooks/1_basics.ipynb b/tensorflow/contrib/eager/python/examples/notebooks/1_basics.ipynb
index 459f2f4a7d..0279db80fa 100644
--- a/tensorflow/contrib/eager/python/examples/notebooks/1_basics.ipynb
+++ b/tensorflow/contrib/eager/python/examples/notebooks/1_basics.ipynb
@@ -1,11 +1,27 @@
 {
+  "nbformat": 4,
+  "nbformat_minor": 0,
+  "metadata": {
+    "colab": {
+      "name": "Eager Execution Tutorial: Basics",
+      "version": "0.3.2",
+      "views": {},
+      "default_view": {},
+      "provenance": [
+        {
+          "file_id": "0B0kLcpwLFwKEVm9XNkFueGk4bTg",
+          "timestamp": 1504118841551
+        }
+      ]
+    }
+  },
   "cells": [
     {
-      "cell_type": "markdown",
       "metadata": {
-        "colab_type": "text",
-        "id": "U9i2Dsh-ziXr"
+        "id": "U9i2Dsh-ziXr",
+        "colab_type": "text"
       },
+      "cell_type": "markdown",
       "source": [
         "# Eager Execution Tutorial: Basics\n",
         "\n",
@@ -21,11 +37,11 @@
       ]
     },
     {
-      "cell_type": "markdown",
       "metadata": {
-        "colab_type": "text",
-        "id": "z1JcS5iBXMRO"
+        "id": "z1JcS5iBXMRO",
+        "colab_type": "text"
       },
+      "cell_type": "markdown",
       "source": [
         "# Step 1: Import Eager\n",
         "\n",
@@ -33,34 +49,34 @@
       ]
     },
     {
-      "cell_type": "code",
-      "execution_count": 0,
       "metadata": {
-        "cellView": "code",
+        "id": "RlIWhyeLoYnG",
+        "colab_type": "code",
         "colab": {
           "autoexec": {
             "startup": false,
             "wait_interval": 0
           }
         },
-        "colab_type": "code",
-        "id": "RlIWhyeLoYnG"
+        "cellView": "code"
       },
-      "outputs": [],
+      "cell_type": "code",
       "source": [
         "# Import TensorFlow.\n",
         "import tensorflow as tf\n",
         "\n",
         "# Import TensorFlow eager execution support (subject to future changes).\n",
-        "import tensorflow.contrib.eager as tfe"
-      ]
+        "tfe = tf.contrib.eager"
+      ],
+      "execution_count": 0,
+      "outputs": []
     },
     {
-      "cell_type": "markdown",
       "metadata": {
-        "colab_type": "text",
-        "id": "H9UySOPLXdaw"
+        "id": "H9UySOPLXdaw",
+        "colab_type": "text"
       },
+      "cell_type": "markdown",
       "source": [
         "# Step 2: Enable eager execution\n",
         "\n",
@@ -69,30 +85,30 @@
       ]
     },
     {
-      "cell_type": "code",
-      "execution_count": 0,
       "metadata": {
-        "cellView": "code",
+        "id": "WPTUfGq6kJ5w",
+        "colab_type": "code",
         "colab": {
           "autoexec": {
             "startup": false,
             "wait_interval": 0
           }
         },
-        "colab_type": "code",
-        "id": "WPTUfGq6kJ5w"
+        "cellView": "code"
       },
-      "outputs": [],
+      "cell_type": "code",
       "source": [
-        "tfe.enable_eager_execution()"
-      ]
+        "tf.enable_eager_execution()"
+      ],
+      "execution_count": 0,
+      "outputs": []
     },
     {
-      "cell_type": "markdown",
       "metadata": {
-        "colab_type": "text",
-        "id": "twBfWd5xyu_d"
+        "id": "twBfWd5xyu_d",
+        "colab_type": "text"
       },
+      "cell_type": "markdown",
       "source": [
         "# Step 3: Interactively Use TensorFlow!\n",
         "\n",
@@ -102,20 +118,18 @@
       ]
     },
     {
-      "cell_type": "code",
-      "execution_count": 0,
       "metadata": {
-        "cellView": "code",
+        "id": "ngUe237Wt48W",
+        "colab_type": "code",
         "colab": {
           "autoexec": {
             "startup": false,
             "wait_interval": 0
           }
         },
-        "colab_type": "code",
-        "id": "ngUe237Wt48W"
+        "cellView": "code"
       },
-      "outputs": [],
+      "cell_type": "code",
       "source": [
         "print(tf.add(1, 2))\n",
         "print(tf.add([1, 2], [3, 4]))\n",
@@ -131,32 +145,32 @@
         "# Most TensorFlow ops are directly usable with eager execution, giving\n",
         "# results immediately.\n",
         "print(tf.contrib.signal.hamming_window(x * y + 1))"
-      ]
+      ],
+      "execution_count": 0,
+      "outputs": []
     },
     {
-      "cell_type": "markdown",
       "metadata": {
-        "colab_type": "text",
-        "id": "IDY4WsYRhP81"
+        "id": "IDY4WsYRhP81",
+        "colab_type": "text"
       },
+      "cell_type": "markdown",
       "source": [
         "Numpy arrays are supported, too:"
       ]
     },
     {
-      "cell_type": "code",
-      "execution_count": 0,
       "metadata": {
+        "id": "lCUWzso6mbqR",
+        "colab_type": "code",
         "colab": {
           "autoexec": {
             "startup": false,
             "wait_interval": 0
           }
-        },
-        "colab_type": "code",
-        "id": "lCUWzso6mbqR"
+        }
       },
-      "outputs": [],
+      "cell_type": "code",
       "source": [
         "import numpy as np\n",
         "\n",
@@ -168,14 +182,16 @@
         "\n",
         "print(\"Multiplied by 42:\")\n",
         "print(tf.multiply(ones, 42))"
-      ]
+      ],
+      "execution_count": 0,
+      "outputs": []
     },
     {
-      "cell_type": "markdown",
       "metadata": {
-        "colab_type": "text",
-        "id": "PBNP8yTRfu_X"
+        "id": "PBNP8yTRfu_X",
+        "colab_type": "text"
       },
+      "cell_type": "markdown",
       "source": [
         "# Step 4: Define and Print TensorFlow Variables\n",
         "\n",
@@ -183,73 +199,66 @@
       ]
     },
     {
-      "cell_type": "code",
-      "execution_count": 0,
       "metadata": {
-        "cellView": "code",
+        "id": "3Twf_Rw-gQFM",
+        "colab_type": "code",
         "colab": {
           "autoexec": {
             "startup": false,
             "wait_interval": 0
           }
         },
-        "colab_type": "code",
-        "id": "3Twf_Rw-gQFM"
+        "cellView": "code"
       },
-      "outputs": [],
+      "cell_type": "code",
       "source": [
-        "x = tf.get_variable(name=\"x\", shape=[], dtype=tf.float32, initializer=tf.zeros_initializer)"
-      ]
+        "x = tfe.Variable(0.)"
+      ],
+      "execution_count": 0,
+      "outputs": []
     },
     {
-      "cell_type": "markdown",
       "metadata": {
-        "colab_type": "text",
-        "id": "45G7094TxsMb"
+        "id": "45G7094TxsMb",
+        "colab_type": "text"
       },
+      "cell_type": "markdown",
       "source": [
         "## Printing TensorFlow Variables"
       ]
     },
     {
-      "cell_type": "code",
-      "execution_count": 0,
       "metadata": {
-        "cellView": "code",
+        "id": "UJBJeZ5XxuwA",
+        "colab_type": "code",
         "colab": {
           "autoexec": {
             "startup": false,
             "wait_interval": 0
           }
         },
-        "colab_type": "code",
-        "id": "UJBJeZ5XxuwA"
+        "cellView": "code"
       },
-      "outputs": [],
+      "cell_type": "code",
       "source": [
         "# This does NOT print the Variable's actual value:\n",
         "print(\"Printing a TensorFlow Variable:\")\n",
         "print(x)\n",
         "print(\"\")\n",
         "\n",
-        "# A TensorFlow variable represents a reference to a tensor.\n",
-        "# The `read_value()` method provides access to the current value of the\n",
-        "# variable. Tensorflow Variables are automatically initialized according to the\n",
-        "# semantics defined in tf.get_variable().\n",
-        "print(\"Printing a TensorFlow Variable's value using .read_value():\")\n",
-        "print(x.read_value())\n",
-        "print(\"\")\n",
         "\n",
-        "print(\"Printing a TensorFlow Variable's value using .read_value().numpy():\")\n",
-        "print(x.read_value().numpy())"
-      ]
+        "print(\"Printing a TensorFlow Variable's value as a numpy array:\")\n",
+        "print(x.numpy())"
+      ],
+      "execution_count": 0,
+      "outputs": []
     },
     {
-      "cell_type": "markdown",
       "metadata": {
-        "colab_type": "text",
-        "id": "2njjWHcTpBEn"
+        "id": "2njjWHcTpBEn",
+        "colab_type": "text"
       },
+      "cell_type": "markdown",
       "source": [
         "## Changing a TensorFlow Variable's value\n",
         "\n",
@@ -257,64 +266,64 @@
       ]
     },
     {
-      "cell_type": "code",
-      "execution_count": 0,
       "metadata": {
+        "id": "v3wr6Erbo_hB",
+        "colab_type": "code",
         "colab": {
           "autoexec": {
             "startup": false,
             "wait_interval": 0
           }
-        },
-        "colab_type": "code",
-        "id": "v3wr6Erbo_hB"
+        }
       },
-      "outputs": [],
+      "cell_type": "code",
       "source": [
         "x.assign(42)\n",
-        "print(x.read_value())\n",
+        "print(x)\n",
         "\n",
         "x.assign_add(3)\n",
-        "print(x.read_value())"
-      ]
+        "print(x)"
+      ],
+      "execution_count": 0,
+      "outputs": []
     },
     {
-      "cell_type": "markdown",
       "metadata": {
-        "colab_type": "text",
-        "id": "uhtynjHVpTB5"
+        "id": "uhtynjHVpTB5",
+        "colab_type": "text"
       },
+      "cell_type": "markdown",
       "source": [
         "## Use a Variable just like any other Tensor"
       ]
     },
     {
-      "cell_type": "code",
-      "execution_count": 0,
       "metadata": {
+        "id": "7PbktdnHoehR",
+        "colab_type": "code",
         "colab": {
           "autoexec": {
             "startup": false,
             "wait_interval": 0
           }
-        },
-        "colab_type": "code",
-        "id": "7PbktdnHoehR"
+        }
       },
-      "outputs": [],
+      "cell_type": "code",
       "source": [
         "print(x + 3)\n",
         "\n",
         "# This code will broadcast the value across the list of numbers:\n",
         "print(x * [1, 2, 4])"
-      ]
+      ],
+      "execution_count": 0,
+      "outputs": []
     },
     {
-      "cell_type": "markdown",
       "metadata": {
-        "colab_type": "text",
-        "id": "GVChqwlwy1SI"
+        "id": "GVChqwlwy1SI",
+        "colab_type": "text"
       },
+      "cell_type": "markdown",
       "source": [
         "# Step 5: Debug Errors with Instant Feedback\n",
         "\n",
@@ -326,60 +335,58 @@
       ]
     },
     {
-      "cell_type": "code",
-      "execution_count": 0,
       "metadata": {
-        "cellView": "code",
+        "id": "23ap04N0v4k0",
+        "colab_type": "code",
         "colab": {
           "autoexec": {
             "startup": false,
             "wait_interval": 0
           }
         },
-        "colab_type": "code",
-        "id": "23ap04N0v4k0"
+        "cellView": "code"
       },
-      "outputs": [],
+      "cell_type": "code",
       "source": [
         "vector = tf.constant([10.0, 20.0, 30.0, 40.0])"
-      ]
+      ],
+      "execution_count": 0,
+      "outputs": []
     },
     {
-      "cell_type": "code",
-      "execution_count": 0,
       "metadata": {
-        "cellView": "code",
+        "id": "FCUMsIYxxRRa",
+        "colab_type": "code",
         "colab": {
           "autoexec": {
             "startup": false,
             "wait_interval": 0
           }
         },
-        "colab_type": "code",
-        "id": "FCUMsIYxxRRa"
+        "cellView": "code"
       },
-      "outputs": [],
+      "cell_type": "code",
       "source": [
         "# Works, because the values of `begin` and `size` (the 2nd and 3rd input\n",
         "# arguments) are within the bound of `vector`.\n",
         "print(tf.slice(vector, [1], [3]))"
-      ]
+      ],
+      "execution_count": 0,
+      "outputs": []
     },
     {
-      "cell_type": "code",
-      "execution_count": 0,
       "metadata": {
-        "cellView": "code",
+        "id": "T8me2oCNxpFp",
+        "colab_type": "code",
         "colab": {
           "autoexec": {
             "startup": false,
             "wait_interval": 0
           }
         },
-        "colab_type": "code",
-        "id": "T8me2oCNxpFp"
+        "cellView": "code"
       },
-      "outputs": [],
+      "cell_type": "code",
       "source": [
         "# The following does NOT work, because the value of `size` (the 3rd\n",
         "# argument) causes the indices to go out of the bounds of `vector`. The\n",
@@ -388,87 +395,86 @@
         "  print(tf.slice(vector, [1], [4]))\n",
         "except tf.OpError as e:\n",
         "  print(\"Caught error: %s\" % e)"
-      ]
+      ],
+      "execution_count": 0,
+      "outputs": []
     },
     {
-      "cell_type": "markdown",
       "metadata": {
-        "colab_type": "text",
-        "id": "irxJhAgar84v"
+        "id": "irxJhAgar84v",
+        "colab_type": "text"
       },
+      "cell_type": "markdown",
       "source": [
         "# Step 6: Using the GPU\n",
         "\n",
-        "You can place Tensors on the GPU by calling a Tensor's `.gpu()` method.\n",
+        "You can explicitly place Tensors on the GPU by calling a Tensor's `.gpu()` method. The `.device` property tells you whether the Tensor is backed by CPU or GPU memory.\n",
         "\n",
         "The first operation executing on the GPU may be slow as TensorFlow initializes. Subsequent uses will be much faster."
       ]
     },
     {
-      "cell_type": "code",
-      "execution_count": 0,
       "metadata": {
+        "id": "7J4N9baqaKCL",
+        "colab_type": "code",
         "colab": {
           "autoexec": {
             "startup": false,
             "wait_interval": 0
           }
-        },
-        "colab_type": "code",
-        "id": "7J4N9baqaKCL"
+        }
       },
-      "outputs": [],
+      "cell_type": "code",
       "source": [
-        "# The example code from here on will work only if your notebook\n",
-        "# is running on a machine with a functional CUDA GPU. The following\n",
-        "# line checks that.\n",
-        "is_gpu_available = tfe.num_gpus() \u003e 0\n",
-        "\n",
         "# Create some Tensors\n",
         "SIZE = 1000\n",
-        "cpu_tensor = tf.random_normal([SIZE, SIZE])\n",
+        "tensor = tf.random_normal([SIZE, SIZE])\n",
+        "print(tensor.device)\n",
         "\n",
-        "if is_gpu_available:\n",
-        "  gpu_tensor = cpu_tensor.gpu()\n",
+        "\n",
+        "if tf.test.is_gpu_available():\n",
+        "  gpu_tensor = tensor.gpu()\n",
+        "  cpu_tensor = tensor.cpu()\n",
         "else:\n",
-        "  print(\"GPU not available.\")"
-      ]
+        "  print(\"GPU not available.\")\n",
+        "  cpu_tensor = tensor"
+      ],
+      "execution_count": 0,
+      "outputs": []
     },
     {
-      "cell_type": "code",
-      "execution_count": 0,
       "metadata": {
+        "id": "4E-2n7VbzY1n",
+        "colab_type": "code",
         "colab": {
           "autoexec": {
             "startup": false,
             "wait_interval": 0
           }
-        },
-        "colab_type": "code",
-        "id": "4E-2n7VbzY1n"
+        }
       },
-      "outputs": [],
+      "cell_type": "code",
       "source": [
         "# Time a CPU-based matrix multiplication\n",
         "\n",
         "print(\"Time to conduct matmul on CPU:\")\n",
         "%time tf.matmul(cpu_tensor, cpu_tensor)"
-      ]
+      ],
+      "execution_count": 0,
+      "outputs": []
     },
     {
-      "cell_type": "code",
-      "execution_count": 0,
       "metadata": {
+        "id": "vbSFW-T5zhZF",
+        "colab_type": "code",
         "colab": {
           "autoexec": {
             "startup": false,
             "wait_interval": 0
           }
-        },
-        "colab_type": "code",
-        "id": "vbSFW-T5zhZF"
+        }
       },
-      "outputs": [],
+      "cell_type": "code",
       "source": [
         "# Time GPU-based matrix multiplications.\n",
         "\n",
@@ -481,51 +487,9 @@
         "  # Subsequent uses are much faster:\n",
         "  print(\"Time to conduct second matmul on GPU:\")\n",
         "  %time tf.matmul(gpu_tensor, gpu_tensor)"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": 0,
-      "metadata": {
-        "colab": {
-          "autoexec": {
-            "startup": false,
-            "wait_interval": 0
-          }
-        },
-        "colab_type": "code",
-        "id": "E5pIOe3Rz7iW"
-      },
-      "outputs": [],
-      "source": [
-        "# Second timing demo for GPUs, after it has been used once:\n",
-        "\n",
-        "cpu_tensor = tf.random_normal([SIZE, SIZE])\n",
-        "print(\"Time to conduct CPU matmul:\")\n",
-        "%time tf.matmul(cpu_tensor, cpu_tensor)\n",
-        "print()\n",
-        "\n",
-        "if is_gpu_available:\n",
-        "  gpu_tensor = cpu_tensor.gpu()\n",
-        "  print(\"Time to conduct GPU matmul:\")\n",
-        "  %time tf.matmul(gpu_tensor, gpu_tensor)"
-      ]
-    }
-  ],
-  "metadata": {
-    "colab": {
-      "default_view": {},
-      "name": "Eager Execution Tutorial: Basics",
-      "provenance": [
-        {
-          "file_id": "0B0kLcpwLFwKEVm9XNkFueGk4bTg",
-          "timestamp": 1504118841551
-        }
       ],
-      "version": "0.3.2",
-      "views": {}
+      "execution_count": 0,
+      "outputs": []
     }
-  },
-  "nbformat": 4,
-  "nbformat_minor": 0
-}
+  ]
+}
\ No newline at end of file
diff --git a/tensorflow/contrib/eager/python/examples/notebooks/2_gradients.ipynb b/tensorflow/contrib/eager/python/examples/notebooks/2_gradients.ipynb
index e6c7c11733..1e65b27bc8 100644
--- a/tensorflow/contrib/eager/python/examples/notebooks/2_gradients.ipynb
+++ b/tensorflow/contrib/eager/python/examples/notebooks/2_gradients.ipynb
@@ -43,11 +43,9 @@
         "# Import TensorFlow.\n",
         "import tensorflow as tf\n",
         "\n",
-        "# Import TensorFlow eager execution support (subject to future changes).\n",
-        "import tensorflow.contrib.eager as tfe\n",
         "\n",
         "# Enable eager execution.\n",
-        "tfe.enable_eager_execution()"
+        "tf.enable_eager_execution()"
       ]
     },
     {
@@ -106,7 +104,7 @@
     },
     {
       "cell_type": "code",
-      "execution_count": 3,
+      "execution_count": 0,
       "metadata": {
         "cellView": "code",
         "colab": {
@@ -114,34 +112,30 @@
             "startup": false,
             "wait_interval": 0
           },
-          "height": 360,
-          "output_extras": [
-            {
-              "item_id": 1
-            }
-          ]
+          "base_uri": "https://localhost:8080/",
+          "height": 347
         },
         "colab_type": "code",
         "executionInfo": {
-          "elapsed": 127,
+          "elapsed": 374,
           "status": "ok",
-          "timestamp": 1505502830690,
+          "timestamp": 1525154227149,
           "user": {
             "displayName": "",
             "photoUrl": "",
             "userId": ""
           },
-          "user_tz": 240
+          "user_tz": 420
         },
         "id": "O4lsC4ckAcar",
-        "outputId": "2f760690-cafb-4777-b970-91d839f99faf"
+        "outputId": "f8becb3f-498b-4cb7-9ef3-608a68cb65d0"
       },
       "outputs": [
         {
           "data": {
-            "image/png": 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++y7Hbvewb18ql1/+BCUlz7FtWwuqMxiAhUDAjlH8X3stgaSkROLjm4GvEmnr\n+S7gQtP2hs9lfnF4hvb2NPr7LwJmoWnX0N9fBNRgtT5KYWElKhltOSpLfDmTJ3cDsGzZW+F72LSp\njEmTfhnabwnwMzIz/5NJk+5AJao9BbiYNy+DsrL1nHfes5SVrT8h13ZHh5vy8k2Ulm4b9BkKwrEQ\ny1oQhEFxOEro61vHK68ECAS6cLu7qK6eyvbtf+Svf/22KX5tdJmDhtO5EZXBvQG4FiX6jRgt1N7e\nZKqrl2Ox3INxUIYS2Pvwem+jokJ1MTO72l1EN1BRMenFTJv2JJ98YkH913Ynykp2091dSHV1PHBZ\nKCb9MHl5ZzNp0gy83sgLRFzcJLzefwuvXVhYyUMPXXfStdLDDSkIwlCIWAuCMCg2m5Xk5CQCAWPj\nko20ta3hllseISkpNRx/bmxMY2AfbjXVCjaj6qKTgcdR86SnAN9ATbmaiu76jhxfAPyOLVuslJc/\nw+rVc9Bd7Q0NGVHJY16U21qjo6MJj2cFSvwvBHYBd4X214UdnE7V5ASexBzbzjZdR17e2SPS1ORE\nk/AEQUfEWhCEIYkWGV2Et2/vwu3+HrqlOGXKHShhzkANuuhFiV8Lqq92I5oWaRmqLG5QrmY/8L+Y\nG5skAT+jr+9RqqsTqav7G8XF8Tz99Bxuuul5tm0zCmwLaWk9/PM/r6exsQin02ilw8Dr7yISz+4h\nM/NeZs48C7vdg8/Xbyq9Gqn49FBJeIIwXESsBeEURs/YdjptFBZ2DCjPihYZFVceaIEePpyAcRBG\nQsJdBAIbUNnZk5k82YXbbRRNN/BrlKtcubaTk9fg988kGExBNTbRXd7fwe22UF2t3MdJSQA/B+ag\nLOrvEx9fFWoN+gy7dycSEWM9acyGPiHL6+3E6707fK3p6ZVs3apmTbtc7lEpvRoqCU8QhouItSCc\nwkTHmqNjqQ5HCUeOPMJrr2kEAk7i4rK45JKH2Lv3CGoaVTdwMYFAPkbxPuusc5g5s4emptcGtVhV\n1na84RgbfX3TgY+BuahxlQuAHAa6jzNRLvUl4evs6ckIX++WLY/Q16eL8SLgDmy2WcyfH4fDsZxv\nfGMnu3dH1szOLgqvM1Q51skyWusKpw4i1oJwCjBUg5NjxVJtNitPPfUvpm3l5ZtoabkFXXgtltvR\ntHOIxH5d7NnzNh99VITNtodHHilD0+CVV+7E75+NillfS2Lievx+o4A3Ar8wrZuRkYHHE+0+1qiv\nbzadr7+8jtPXAAAgAElEQVT/AKWl27DbO5k58wzee89oxV/A7NkJVFVdBsDMmUdCAzkiDVIEIdYR\nsRaEcc5wOo0NlY18tFjqcAVe07JQFnEVqnd3F8FgJb29quPX0qWVzJ07Db//34kI8wbmz8/kf/7n\nDrzeuSh39udN606ePJudO6/kllseYfv2LiAbn6+fn/98Hps3dxAMRlzdmvYL6uvVveXn/wJz0th7\nfPRRIeXlz+BwlIhLWhiXiFgLwjgnWojr6h6guDjPJNpDWdC6cKmYtcskXMMVeNU0pNLw+bemc7lc\nBQPOHxfnYc8eNxbLLJQrfSHK9R1Zd9IkJwBJSanhZLaaGo2kpPV85Svp1NQsN5wzsnZPTz6RjmgN\nwApcLls45l1VtXTYLunhtlwVhNFGxFoQxjnRQuh2n0l19SKM8eehLGg9ljrYoITIum6ghq1bCZdR\n9fWtY8eOOI4c2Y/ff6bp/Kq1Z+RcmtaI3T47dP5O4EWCwSAtLbOAr6FqoTcCC4iLu51g8MvAEVpa\nfkBFxeZBXzSefnoO77xTidM5DeVWj2SS9/a2ohLXQL0IbEHPAt+3L/64BFjqo4VYQcRaEMY5g2ds\nm+PPJ+L6LShopr7+KVRJViK9vUuorp7MSy+t4Z/+yYbb/R3gEeAAZrezF2OrT6/XRm1tVyi2nQXc\nZth3I3ANKSl9XHbZ0/zP/2Tg8fhQXczcvPiik/nzC03rt7Q0cMstzbhc01D/hX0/tE4asAO/f7ph\n//0YG6h89NEdfPnLTtzunzAcAZb6aCFWELEWhHGOLsR1dQHc7kkol7Kyns1WpMbTT885DjduIsZy\nLF1Yvd6LeP31N1EW60qUtfwEaiBHO6qLsdFFvQGP59rQPtEzoPuA5wgEGnj11UmGLO6rgY34/d+n\noeEOpky5k9bWTCCHlpZp1NT0oCzq7xPp7f0ukAt8E3gUVfaVazqf13s+Xm8iwxVgqY8WYgURa0EY\n5+iubJfLTUVFbbhcyuG4nIqKod24RiEvKurh7rsvNQl5c7O5bEpZyhpwhP5+O5GuY1ZUE5PridRG\n3wecg5o9nQc0AR+iyq6Mk7KSgCX4/Xpf8IENWDyeWSQnt2O2yO8AZgPVqBeEbuAW8vP/MzQ+MxX4\nDip2bbT6+1CW//AEWJLRhFhBxFoQJgjRtbwdHW7q6gIMZUVGx2O3blWJafooy/37WzAL3QcoUfwq\nmnY/0EYkVmxsF2pDCfWi0P7LUeJ9F8oK3xD6eRi4OXSMGo9pPp9qwKJp+4AZmIXc+HKgERdXyeLF\nm1m9+uusXbuerVuht9eC8jJsJDXVj9V6EKdzRegY87jM4T5TQRgrRKwFYYKycmUtbncyRgFsa3sX\nl2vOoCVYbvdsqqt7eeGFZwkEfoBqevI4CQkHiY930dcHyjLdiKaBGtDxBMpSNSd5KYu6m0gnsjwi\nVvi1obUnh36BalGqhFUJ//+GjrkTTZvOpEmfmu7DYslG0yLXnpmZHxbVqio75eXPhLK/rcByFi3a\nyN13XxdOWLPbzeMyBSHWEbEWhAmKEuPLiCR7fYDTuWKISVYaKuY7hUAgDlV+dROwhUDgCwQC/wvM\nAv7VsP8TKAs3A9iHxXIP8fF5BAKTQts04K+AhylTPqa11Xiut4F+LJZ7yMgoJDHxQw4f/hg4HdUi\nVG9peit9fRZaWlwUFlaSl3c2druH7m6LqT/4vHlB071Hu68ffngJ/f3xYiUL4xYRa0GYYOix6P37\nA8ALRMqjGgAL+/bFU16+iX37EigsrKS7Ow+PJxXoR8V6VwHPM3Bs5YOYXdFeIq7opSQn34HFkkIg\ncD1qulYkOe3ccx8hGFxDe7sVSEFlmF+IpnnxeBaQmPhbIuVWoCzvFoyu9by8s009vCsqjLHkr5ie\nQbT7OitrYGmaIIwnRKwFYYKgi3RdXWu4NElZqA8CU1GZ0y/S0dFEQ8Oq8PcLFqwjI8PCn/6Ui7KI\nLaj4cXTCVzbmmHIbcC9wJtCL16u3En0KJeSRY1991UJcXAoqSWwjymqPZJn7/YVRax9BxbUHTwST\nWLJwqiFiLQgThEjC2POYRfZzKMsYrFYv2dlFoVnO6vvm5hxefPEqtmypxOPJRAnkQuBhzHHoD4B7\nUOVQn6Cs9dNQ/43obvR7UWIcB/wB1VAlm2CwjWBwNsYs78j1pQE+EhPvxO+/ECXUXwX+jB7DLixs\nwOG4bljPYbCmJ7m5GcN/kIIQg4hYC8I4xihMjY3NKGs0Oqv6g9C2i0lNbaGpqdXwvYuWlgYuuiie\n1FQfHs8hIsM0koG1wNmo+dR+4N8M696DuQ57PxExVo1U4EbD9/eGfkZf35vArcyf/0fee68Bl6uA\nYPBBEhPzSEhwMW9eBg89dJ0pGexoXcgG6zr27LPXj+RjF4TPHBFrQRjHDBxxuQFlFW/AYulE0yaj\nksImAz/B6ZyDsnZ19/X7tLTcTkuLGiepYtjxeDyRrl9qNvVUVDmWbhF3hn4+jxLfhahY9FMoV/jn\nQvsaLegzQ9fXgYpPzwQ+4owzTufsszfj82XidN4aPm9f30ZgOe+8U3nU+46uH5euY8JEJG6sL0AQ\nhBMnWpiURfssYCEpCVSZlJVIzPkaVLz4Z6i4snnSVV7e2cTFTQltawLuIxA4DTW+8gPUCwGooRv/\nhnKTXwO8SE5OV+j35aiMbo9hfw34O6rL2b+EzvuvQCVnn51OVdXSIZqwdOJ0JvGlL71MefkzuFzu\nQe/bKMh2ux7rVueVrmPCREAsa0EYx+Tnt2N2KX+KsliXk529FqfT+F02A8XQQ3QS1/79h4hY6SsN\nx98R+mVHZY4b1+rC69Vbieq11A+jeocfRjVKuZWMjN/j9f4Wv/8H4WN1oR28x/mLwG243Zbw1Kyf\n/ewC3n//TZStoWq5jYIsXceEiYiItSCMA4aK0VosAZRL+xxUYtZNJCb+ioUL13PTTZdTVnYHXu8Z\nKBHXk8f0lqC7gHxgLSkpUykuDuDz+QkGe1FCrTcyIfTzDOA6VH11E+aXhAz6+oyNS14GvoDKLs9A\nxbxtBAIF5OYewOmcjHLHv0hjo5fzzvt/ZGbmUlhYSUdHPl7vx8BZKE+B2YK++urn8HrvDp970qQ7\ncDi+G35WkikuTERErAVhHDBUjLa5uQBlyR5BWco1zJ49i6qqpZSXb8LrvQtdnJOSKklIuJ2enjhg\nGiqurGqws7PvIzm5kOrqG9HHWMI+zILsDH13AGVdr0G9JAAsJCnpMIHAGjStCJVsdhvKotbLxzR6\nexPp7b2JwsJKenoScbt/gsdjwePRcDo3AuUUFq7F6bwRlQneHzpeXdP+/V48Hv2zcu9bLGdIJzJh\nwiNiLQjjgH374ol0IusKfdZdx06MYyA7OytDx6QSsUq34PPdh893H2bX9qNAKocPF1BX10JEBK8F\nqlCZ4fkogc5CDc643XD8htC+GoFAK5p2t+E7NaVLfc5An1kNVvLyzgagvn7g4I7U1Azi4n5PMHgm\nyoJ/mMREF37/atzugee12Q6OwBMWhNhGxFoQxgEdHU2ozmJKrDo6lCA7HCVs2/Ys3d0RIXc6M7nh\nho20tzehRk0aB20UYnZtu4Dv0NtrobdXL69agcoeTw/9Mo67/H3U8T5SUp6gtBS2bp0V9V1a6Pca\neXlO2toy0NuPFhR4SEpKHSRGrXHwYDvB4C8M2+8jIeE0/P7I2gkJXSQmPoHNdpBNm5aMwBMWhNhG\nxFoQxgHRjUy6u/MpLd2G3d5JWlob3d03YxS3mpofkJFRScQa34PK3Nbjyrqr20qk3MsKTCUh4X7i\n4vz4fBehksOMAtwedbwPTfuE1auXs2tXdUjw9VjyLs48Mxjq5Z3Ntm3Gmux14USwxsZUDh/eS1aW\nnVmz1g8i+oXYbAdMa3/taykSlxZOKUSsBWEcMHPmEXbvjoiVxzOJ+vqrqK/XyMy8n4Edyyx0dWWh\nOoFtQcWoVwE5KDd2GrAas8t6OZBIIHAPSsD/GZXR/RyRCVr5qASzT9AbpHi9LoqLf8mMGbPp6FiD\nxTILm62ZTZuWMWOGHYDS0m2ma2xuzglN7oL4+ATmzp2KwzEfm83Keef9P5Mwx8W9z2OPLeI3v5EM\nb+HURcRaEGIUYwZ4QcERFixYR3NzDvv3f4jbXR7ay0JcXA4DO5Y9h7Ki70fVNH+CyuZuAaaj/ukb\nBb6XSExZjzHXYIyFq45lFtRkrFzD8Vvweu/ivffUfmVl66mq+qHpXqLLsvLzD1FSsh6n8/NAN/X1\nSwA1DWzTpjKKi+/A650LHCEY/Cm/+c1msaSFUxoRa0GIUaIzwBcseCRUB52NcZqWGg+5jr/+tZfu\n7k+Bi1CW8OmoxiMbMVvRG1ATuIwCvxeoNHzuIjLUg9DPPOC7od8/aTg+zbSfPtXLWGbmcJTQ17eO\nHTvigMP8/e+dtLYas8U3huutZ8ywc+aZc0ICrpAuZMKpjoi1IMQI0bXU+/aZrd/t27twu7+HLqiZ\nmfeTnh7gwAE7s2YFuPRSqKkxCq4+0jJ6cEYGkEpy8hr6+opQJVnXAveRklLIxRd3smePO9yCNLJe\nu2Gdr6EsbTvwIcaBH8apXvX1Gjt33oPXO4nu7kwCgRRUh7PJRJLZrEAaBQVt4WcRbYlLFzLhVEfE\nWhDGEKNAt7Xtwem8CbBRX69RWFiJ2fo1dyCLi8vB6VyK07mFhgYbCQnNmEVZH2kZPTiji4yMOI4c\nyUEN2zgHZWmfTmlpAJhMS8vNqCSyDajGJMmobmcuVAw8DeU670MN67gPOJ1Jk97D5TIniLW0nAbc\nYDi/XtJ1DsrVvhyVABeplT6RLmRHG+4hCOMdEWtBGEPMgzjK0OueIZ3u7ngWLPgdzc0F2O0efL5+\namoiohsMtqISwFTcNxBIxizKHwLrAB8JCXeQmmqnt7cVvz+Prq6bQsdGyrL0TmDLlr2FuW3oY6hE\ntXZUDFwvq1qMst5/HfrcH2rCsiHqOj7GPPAjncjMaj9KvFfQ3Pxa+LmcSBeyW255iS1b1JSv+noN\nn28djz++7LjWEIRYRcRaEMaQgYM4VN0zWPB4FvHOO5U888ylVFa+zYEDqRQWVpKdXcTMmT3s2NGD\nx6N3KFPlUCrTuwg4hEokawb6uPLKqTz++DJKS7dRX38VqtXnFNO5LZaZVFS8SkGBL6r+OQnV4/t7\nqKYo0Znnt6EEWo9xL0QJsB/1wvBjIrHpDSi3ezfqBaAGZWWfvKtbxcONYQOZUyRMHESsBWEM0F22\n+/cHUMlaKlksISGDQCAiOE7n5/n615/D6VyFXtvc3d3K4cOddHbOwFwjnQccxOxyvg/4N/7+919Q\nWrqNtrY9QDHKlW22xHt7J1Fd/VVycx8gLq6SYPBclKguBJ4hMfGXTJ6sceiQUcg7iMTBdXe7FWWx\nbwQuQAm1up+MjB4uuSSN5uYUCgr+Avhpbn52hMqx9AEk+rUdPsn1BCF2ELEWhDEgeg611foAxcVT\n8PniTK5u2EN7+ySU8H0K3IbHsxGP5ybMMWA97mu2llUi1ye0tEyipUUD4oiLewzoIRj8FuamKWnA\nb2lvn40S/UuIWMQp+P134fGsJGJFd6GsZz0uvjD0nQc129ofWidyPxkZbTz00HWjEkueNy+dmprI\ntc2blz7i5xCEsULEWhDGgGj39/TpZ1BVdQUul5va2kiNMXyf/v77gVtRcd9OlGgbY8CHgZ8Dp6Hi\nwy4iItsM/BfG0q1g8FGUqNeiXNyXoBLMsgFzJzRlraeg11/7fJ9HxbHdKBd2H6rZyixUL3Er8+f3\n8NFHHQZvQCRJzelcQUXF6NRMP/TQYpKSamlq6sduD+BwLBrxcwjCWCFiLQhjwGClSR0dbm699QW8\n3lTgXVQf799hsVhD+3Whz3c210w7iSR96f29P48S4FuBNxgqLq72vxPlEg9gdqufTWLiLvx+Y1xc\nb1eqZ3FvBCJWfn7+PVRV/V+WLXsr1B5VT1J7At3CHq2aaRmNKUxkRKwF4SQYrFxI0zhmCdGqVXPY\ntasSl2saNtsBVq8uY+XKWmpqMlGCGBHI/v5VKKH7J1Ss2Si8XSir1rgtK7R/AcrCPow5lpsVtX8i\ng7ce3cP8+Vbee68y1GnsCPA14uJuJxiczWA13IcO5bFs2VuG2Lhu4SeG1tyA3R44mUcuCKckItaC\ncBJEdxnbseNOLJZEWlq+SHQbTSOVlW+H3MRq2tXatetDFmc8oAshqCztIpYsWU9dXStudwCz8LpQ\n7m/jtkmoGHRC6LNuMetx5vej9j8Ns3gfAdYQF5cNTGLTpq+wdu3bNDVl8v77/43X+wsi5VnmGu5A\noIv6+u8BZRQWqpeR3t5EdDe61erF4bhyJB69IJxSiFgLwkkQHXtubc3E7KbeyL598dxww5Ns394F\nZDNvXj8HD9pMx+lWeH19AsqtHRHA5OSPqaqqoKTkJdzuuURiyZ+gBnTEoVzZXyAu7m0SE0/Dau0h\nP9/PO++sQpVyAVyKcksfQrnN1QuFwije7cDdBIMWtm1TLxL6y4YqrzKWZx1Cud3PRDVJ0T0IFvLy\nzmbu3E6qqyO13MXFCdKoRBBOABFrQTgJomPPaqqV0UpN49Chf9DQMBNVp2yhpkajsHAtRoFsa3uX\nRx5ZwvPPr6e/H2ANMAP4kOeeUz2yOzo+QM2n/hmq3KsIVaOs1igsrKS2dkVYDE8/3YHRnR5xbx9C\nJY3prURdpKTcyec+d0FoSMh0ol8kdDIzP6S39ymUlR4EWiksTCMvz0Jb236czhWhPbVQOdbxdyIT\nBGEgItaCcBI4HCXs2mWM6YJRhAsLG+juzid6KEZW1nSCwXtCrTgP4XTm8POf/5WMjM/hdn8ntJ+b\nxMTf8KMfHaCx8QX6+rJRTU+CqCEdlqg1i/jRj14KNQc5hNc7jYHu7TtQGdr5JCSsITGxCJvtIK+/\nfiOZmVmUl3dSXR003YOxWcm5506ltdU8lzovL4etW6/A5ZpDRcVmkzBL0pcgjAwi1oJwEthsVmpr\nr6OiQh9l6QbWceCAlY6OvWRl2Wlvfx9lyUYEsKOjiba2eIwNTF555U5SUuyoEqgkQMPvn857730F\n+CbKMs4Grg8d86RpzY8+eoeGBqMlvQqze/sQytJ+ArievLxK6uuVkObmZtDe3oXDUUJX1yb++te1\n9PfnkJfXyurVXw/fb0tLtOcgKyzmgwmz9OsWhJFBxFo45TlZQRlMpMrLN9HQsCpUvuQCfoXqo51D\ncvJenM6fAq9hFD6//zz8/q8DT2F0b0cGX6SjMrvNk6+s1qmkprbgdJ6FWUhPJ9L0pBs1IUvPFrdw\n6JCV8877T7KzizjrLB93330pNpuVjAwrfv8PUUM4VMz6vvsms3JlLR988AnGF4BJk/6Ow/HdIZ9N\ndAIerBdLWxBOABFr4ZTnZAVlMLGPTjxLTEwmISEPm+0AaWmz+fBDGwOzsveimo34MIuuPviiG5X8\npR8zGYsFtmy5iNLSF4nUQOvrdaJGUBpFXwM+ADz4fB04nbfjdFrYvVtj69YHKC7OGzCas6kp0/CM\nzE1OZs8+86gvNtHPQeZSC8KJIWItnPKcrKAMJvYFBUeor9cTsRrw+1fj96syrbi421GiOR01ZcuF\nSkzTUIMyEjGKrsXyDzTtDSAXaMNYhlVSMpnKyrfxeH6KEtInAC8WSxslJalYLI/wt78l0Nv7CX7/\n5NCx/4pqQ/p703273WdSXb1owGhOu91jeEZ6k5PNwCJmzVp/1Gcjc6kFYWQQsRZOeU5WUAYT+4IC\nH2ZXduT7YLAIZeUeRDUNKUSJbyJKjL9NxH39Ppr2A5S4bgD+D/AUcXFTyM9vZu3aMr73vY9QQl2D\ncnHXk5CQQnp6DqtWzaGy8m2ami6goaGVQOBaw5V7MFvi3YCFtjYbmZn3Ehc3hXnzgjgcX6Gi4lXT\nM7Ja36e42HXM7G7JBheEkUHEWjjlOVlBGUzsm5qMiVjdmEVxHzAXZSk3oTK0dSt6DZo2GX1spGpu\nYkW5x53AfwM/Ixi04HSqeLLdrlFf/yKq8cgW4Iv4/X+jujqVHTueprVVTzp7LOo6JqFqtnWL/VpU\nYxM3Hk8u8G2SktZjs1kHeUbLhxXXl2xwQRgZRKyFU56TFZTBxN5siS5g0iR9OEcD5vnOD2O0ujVt\nKuaksNND3+k9wfVhHjVAOi+++AlPPTWX6upGlFDrDUgWAxtob3cb1r8KPclNZZtnYh7c8SAwFfg+\najZ2JCQgoisIY8uoi/Xrr7/O2rVr0TSNq6++mu9+d+jMUUGINYzJY0VFPeGM6ejv7HaNp5+eg6ZB\nRUUtH3zQR1LSSgKByWhaFgkJCeTkvMGhQzMwzneO7tsdH3+Q/v7lKOFNA/4GrEe5rI3DPJSL3e9f\nxHXX3YHqIKYnkaWH9rMQF5dNMOgCnkPVWbtRZWT7UF3QjIlsn0OJPOgxdIkxC0JsMKpiHQwGufvu\nu3nsscfIy8vjG9/4BldccQWzZs0azdMKwogRnTzW1xfJFDd/52LXrofp6cnH7U5GtQA9D11Uu7s1\nurvvZaBLvBdju87MzG5crl+i3OTdKGv6d8TFHSYYfCp0nC7cABb6+magyrgexNyx7A4CgWyUq/si\nlBv9bsP390ZdS1doTY3MzGYuv3y9xJgFIUYYVbH+xz/+gd1uZ+rUqQB87WtfY9u2bSLWwrjhaJni\n5u+2hAdzRFzKUzBbrlNR85/10qckoAKYTGbm/Vx+eT61tfmodqLGcqtzCAZ3EElYM2drJyU10tc3\nGbgg6nznh873o9Dn56K+n0JcXCWZmflcfHEQTfPT3PxsyJX/LWleIggxxKiKdWtrKwUFBeHPU6ZM\nYffu3aN5SkE4LvQZ0sYhGw899NWwUB0tU9z8XRpmIcxhYLb1p6h48JbQfsbM7CyqqpYye/bTUesk\nomZbzyYya/paVO/wmUAj55+vMWXKeurqWnC7jefrwzzCMtqqn0QwuBq3WyM9fSO//vWiE3+QgiCM\nKqMq1pqmjebygnDSRGZIR4ZsJCVFXN3G5LGiol7uvjviFjZ+19a2B6fzUpT1qgGfEB9/iJSU/Xi9\nOaSmdjB3bhJJSX/hwAErDQ31GIWzp6cJgNTUZjweo6C+jZqQFT2M42z07O3333+A555bSmNjE5dd\npiey7UG9GNQYzrMAWE1CwukEgx0Egz8I3YmFl1/uw+VyizUtCDHKqIp1fn4+Tqcz/Lm1tZW8vLyj\nHpObmzGalzTmyP3FFk6nMdlL/Xz5Zbj55s08/PBCiopO49lnrx/02I8//pitWz/C651OUpKb5OT7\n6euLCGt//4P097t5//2vMmuWPXzcsmUbaGiwY8z61rQscnMzyM+fTUuLMRu8ELOl3YNyg98U3max\n5JKbm8HNN+8OCfUSYD5KqA9jjImXlc3i2Wf/lWXLnuJPf5ocWkPD5UpizZo3ePrpa07mccY04+3v\n5vEi9zexGVWxPvfcc/nkk0/49NNPyc3N5YUXXuCXv/zlUY9pb+866vfjGX1YwkTls7y/kRoQUVjY\ngbI8jVauxp/+dA11dXdywQWn09ycg93eyaOPltHfHx8+trj4v/F6VUJXX9/AMiz4HL29izjnnDWc\nddaF4evcuzcF1RAl0go0Le1+PvjgAG1tjcBqIpb0PZhd1wdRLvGI0H75ywHa27tC6+qubiuwHKv1\nAdzuVeFrfuGFRzj33Cc57bROMjPvx+M5K3TMQvbufW3C/v2Uf3vjm1Ph/o7FqIp1fHw8a9as4Tvf\n+Q6apvGNb3xDksuEESE6S9vne4SkpNTjFm+Ho4QdO35Pa2ukhSf4AQutrZnU1NwYPseKFea4rsrC\nNoqzLvzmjmB9fRdRX78k3IpUNTHRO5KloHqEZ1BS8gRO57+gLO40EhPfRNM6CQSM1xYAesOJYfPm\nBXnooa8Aegx9Sfj4KVPexGJJQLnmu4EFBAIZNDRYaGhYQWHhWjyeReHrLShoobx8k0zIEoQYZNTr\nrOfPn8/8+fNH+zTCKUZ0lvb27V243SrufDzDOGw2K7m5X6S19RuGrZtRYmseB/nxx+mmYxMT38Pn\n08up9gNWLJZVaNoUVCb4wtA6R8JrNDVl8vTTc/D5nmf79k85csSH378aj8cSilXrE7bgnHOCfPCB\nJ6pF6BPAdSxePPD+VAxdnyftxuc7Pfyyoa7j58A09KSzrKzpzJ0bicd3dSXIhCxBiFGkg5kwLonO\n0lZzno8+jGMo13lHxweYLeJ/oCxRv2n71KkdpvXmzTuNurprUAKryq1UUuUToWP+GlrrJlQzkhfZ\nv99LRcWr3HnnpVRWvs3WreD361neVlRWOeiZ521tB+jtjVxDYuJHLFwYqX8+WjigtHQbZsv/QmAR\nqu5aY9as/rAY5+ZmcP75zx7zGQqCMDaIWAvjkugWnz5fPzU1Rx/GMdQozKys6TidxqQuG5BGUtLf\n8fkiLmhN8wMRgfzb3zKJuLKNomhDJXlp5OfXc/75f2H7dhdu909wuy1UV2vs2lUZVZetZ3nvITPz\nAOnpnTQ2FnHWWekEg/fQ2WnHZjvIpk3fZMaMSLLa0cZ75ucbx2lG3PLJyZlkZ1fS2FhEefkzOBwl\n5OZmyIQsQYhhRKyFcUl0r2qXy01SkhLv/PxD+Hx+Skqeo6OjiezsImbOPGKY0+wGati6FcrLn+G0\n047Q0BA993k+gUAzxlpop3MzYBbIwTuBvQtYsFrfp67u/2KzWSkt3UZ9fUTQW1ryMQu8L3TeFfT2\n/gaPZzVOp1qvrGxod/TRmrZYLAHMDViUWz47243TuSo8xxrW8+yz18uELEGIYUSshQmBUbzLyzdR\nXX0jSvwiohSZ01wDLKe3V1m5Cxaso6xsPXV1AdzuScDngQcJBmcCT6JaeU5mxoxuYKBAKsv7DlQ8\nuAOYDnhITvawbNlb2O2dFBT4TFZrMNiIWeCdwCpAw++fynDd0UezhpubC1DDO9TLSUrKc5SWQmNj\nUagP95kAAB2VSURBVOhFwLy+DOsQhNhFxFqYcETE1Ni9y0J2dhFz565n61bo7Y1s37LFD8SRlLSP\nSy9NYceO9/H7jT221wCn8cYbbXz8cdMQ8fJ/Ae4HvoxyN/8Tra1NtLbGU1+vYbM1oAR9BtCIGk/5\nKOACckhI8HHmmU9y8KATtzsXo5C3tb2Ly6WGhETHp49mDUeuU5VxlZYqC728/JmQRS3ubkEYL4hY\nCxOOiEh1YU4QcwNJJCe3mJK21Pzoa+nr0/jb39bQ3z8Ts+V8EbAEp1Nj6dJKamuvo69vHVu39hMM\ndqFqnp/D3GnsTuDfw59drmYiPb9dwC9DP28DLAQCGtOmraOjw4/bnYkS9rMAC07nCioqlAteud87\nqa9/kc2bXyQ//xCbNpWZ4tg6Qwm5uLsFYfwhYi1MOHQx2rcvno6OylDMugefzx9yj3cCG7Bavbjd\nzcC3ULHddPr6JqHKsIyWc6T0yuWahs1mJTk5iWBQj1u7gD9hFvjZUZ+Nru0tqOlYz5v22bEjLtTA\nxAIsxVjGFXGFW1Bu/GsIBi3hF4j6+h8OeA5DubXF3S0I44+4sb4AQTgROjrclJdvorR0G+Xlz+By\nucPf6WL05z/PZ+7cacTHJwAaBw7o7nErcC3Tp2eRnNwN/A8qE/tS1HCM6cDtKOt3FZAMPAW4sNkO\nAkZXuxvVuSwdJewQGdox1Gd96EdX1D6HMQu8uYzLbu8M7Wd277tc007gCQqCMJ4Qy1oYM06mZejR\nSpaG2ieSYBaJ1WZm5vL66z6MFmvEoq4M/VKfU1LuZNOmbwJGV3sNKiFtPpFe3/XAdeidxPLz/8E5\n56Tw1lsPEAza6OvbT1/fYlR2trLwi4sT8PnSTOVn8CbgJjHxQ1avXoamESr5ysKY+Ka/QAiCMHER\nsRbGjOEI7lAcrWRpqH2ys4v4whfWsWNHHHAYny8Nl+t0Iv20zRYrmMurZs/+AmvXvk1T00cUFPhY\nsOB3vPZaGr293ai49TWhdeopK3s93EnM4bjB9BLicrmpqNBjxgEcjiux2azh8jOVld4K3ArY8Ps1\n1q5dD2CqzY6LqyQ/HzZtWjKsZyYIwvhFxFoYM4YjuDC4BR6dkd3W9i6NjbOprHw7FKtuort7CkYL\n9PDhvRw4kIjb/RNAjcMsLFwL5KFi1p+iOnzplu1HGC3xDz98h927fwxsob5+Cvn573DxxbBt23J0\nK1qNpnRTVXXLkPetu+n1+9LLuxyOEqqqluJyufnSl17G7Y5MBDPHrNXPL3zhbLZuveL4HrogCOMS\nEWthzBhux6zBLHCHoyTkEv48cASncwVf//rDIctT1Vfr61qtD5Ca6sfpXAG8gVHwsrKm09PTh9t9\nLSr+vBHoBeJJT7fS3R3pbOb1TkElhy0HOmlp6aalxQU4UAlk76EakMwIdwY7mls/+r5eeukOzjjj\ni8yceYR587yDdGTTpMOYIJyiiFgLY8ZwSog6OtzU1bWiMqe7gIXU1QUAyMs7G6cz4gJWiVadKAs5\nsj9kk5WVHJpdbSznctHR0YT6Z2BM9Ipj0qQP+dKXckNWs3EQhje0dgORUiy9i9mtqBj2tVRXR9z6\nQ8Xmoz0LXu9cdu9ewu7dkUYtA5+NlFwJwqmIiLUwZgynhGjlytqw21qJ4gbc7klUVNSGRk1GLE2b\n7QC9vS+i1y4b909N3R/6HEnqSk1tCVnincCDoTOqY71eDYvlEcrK9CYqicBpgHGKlTG+fQ7K6s4I\nb9Nd10PF5gc2V4mUiG3fHsfOnZcPsMyl5EoQTk2kdEuIaQa29vQBC2lqysThKKGsbD3nnfcsZWXr\n2bSpDKvVO+j+2dlFoX1fo6wswM6dV5KXdzaRUq5CoMh07JtvJlFVtZTSUg3l+p5i+F5PSoOI0Kah\nLHe1TXUecw8am+/ocOPz9ZCYeCeqocq9wFfDx+ovJIIgCCCWtRCj6K7j/ftbMDcoSQYmY7d7BrXM\ni4vfCrmgzfvPnNkzYF+zZbsA1S50cfjYI0eacbnchvi4RiQBbQEqLn4xSqi/Sn7+b9C0Plpbn0OP\no1dUbB7gAbDbPaxcWUtNzfdRVv2LZGZm0Nv7K/z+81Gu9oU0Nb02sg9VEIRxi4i1EJNEXMeq21hm\nppf09BaysuzMmrWeVasuoLx804A4sB4Hb2xM5fDhvUPuv2LFGezc+Qlxcb9H09rQtGyU5RwZien3\n9/GlL71McXE8mzYtYfHi/6Kt7V5UMtmnXHppOllZ7tCam3E4bmDZsrdobdXj6CrePm1aIYWFkU5q\nDsflLFv2FsYGLTNnPovdnkF19VVE9wQfbu25IAgTFxFrIWYwJmIpi7oTo5ht3fp/wvuqyVqROPCO\nHXdywQX/v717D66yvvM4/s4dSAI5QIBEuiGAEay2TC11YVxCsY0SwKBopXWkRZuV0sEx7Qw3124t\n3VBTrbZDhyJip1AqWNYkUAhVA4RWKcvWTTEqZYg0CLmS5DQJhlzI2T8eTs41yUlyDufJyef1jyR5\n8jy/x4if/G7f379QVTWelBQb+/bdicVyj5frjbra+/f/DZttKvZtXUbFskSMFd23AGeBWVitVyks\nXAgcYO7cmRQUrMAepnFxO/rorR/qPsMabMye7VhwVlv7IcYsVAuw8PqCMc8V7mvXHtA8tYgorMU8\nPM+Jfg3jPGnPbUru88A1NaMpKjIWf5WW2igpeZ709AleVl4bVcpsNvszXgVGYdTyjsGo8x2O8yEc\nsIeKitFERUW4PLOqarzHO2zYcAenTm2msXEyHR2tdHZ67iNft+6oS3GT5OTN5OU9isWS4LHCvbfj\nMUVk+NACMzEN9wBOSLjavXjMfZuSo0421/853uV7rdYZFBau6F6k1VNdbSOclwMP4Dhw4zxGr95+\nTSy1tR9y7twZl2c6/wJhr1V+773/Q2VlCq2t99HZGef1evf3nDDh1u6hbvf30l5qEQH1rMVE3Lcy\n/eu/dhET00RFxWjWrj3iUmTEfcjY4LywrAXn3qx9LrukpBqr1blKWTze64I7evUjRpyisvJx4G3g\nBSIj4/nqVyPIy3MMs3uOCuwBFpGQ8DyTJ6fS0HCW8vIUsrPfICmpvcfiJjq+UkS8UViLaTiOthxF\nQ8NZ3n13NE1NI4H5lJaOwbl2uMWSwNGjj/LUUwc5caKZrq6RjBr1X3z6adL178nEOQjtK8dd63I3\n0dLSRXGxZ4979OirhIe/CtTT1TWRq1dPYN9j3dlp429/20xj4z9Zu9Y+x96Ja489DhhDevpE4FPK\nyjZQWRlGWZmNhQt/1UPBEx1fKSLeKazFNOxBlZ2dT1mZY07Xfq6z+/ytxZJAdPQorNYngDCamowg\njI6OoqLimNeeqc3m8hG5uf9Gbu4ujh6toqnJ0eP+9NOP6ezcdP3j3TiOtQQIo7LyNpYuzae6ehoQ\nAdTg3LNPSDhDenqj28pv43urqpJU01tE+kVhLUHR2/GYnoVQjLlfb/O37td6C0LnZ9XWfkBl5SPA\nCUpLLZw6Vcirr36ZoqIyjCpmxtx3Z2eM030XERb2PDabYw82NFJdzfW2NQNfJyrqP/nsZ79w/ZeE\n5S7z0KrpLSKDobCWoOjteEz3cHPupdo5iqZ0Ar/AmKOezJkzZzl/fjqpqSlenwVZwHPAOowe8hKW\nLv0B7e3P4dqTHwf8DmNOu4nY2Gu0tPwEo6zoFYzKaP/h8j2xsVO89pg1Dy0ig6WwlqDo7XhMz3Bb\n7lIYpKHByoIFu64vLmsB/oF9q9XVqzbuv38zpaVrenyWUVrU8XFbW6rb12MxVoR/B8ee6k20tKzC\nqP8dS2Rkk8u2LIhlzhz7QjdXmocWkcFSWEvA+XIetfPQcF/h5r5PGTbjHLaNjZNdnlldfRqoAyYB\nTURHv097u+PZMTEfc/Wq4+Pw8L8QE3Mzra2OeyYm3sq8eYc5e3YkKSlW2tvDXY6wTE4u46WXHvXr\nvyNVLhMRO4W1BFxP51E79557Kh/qjWtP+Z/ANYzDMIxqYBbLRS9D369h1P22MW9eM7Gxjmd/97uZ\nfOtbRiETi+Ui+fnfIDfXtcb41KmfsnfvCurqjIM6GhutREc79/4fHVS49jYtICKisJaA8zbk7d57\ndi8f2ltYTZpUh2Pl9SGc545HjPgB+fkP88QT53Ad2o4HrEAR77wziowMG3v3Oupul5be7vKMvDxj\nq1hP88z+HtrubVpAREQVzCTgfKnK5R5Wb74J2dlv0Nho7b7GXiXs3XerMHrKBzAWejm+b8aMO0hN\nTfFS4awZo/DJclpbV7hUN3O/f0ZG8fUiLF9mz547AFi27CSf+cxmFizY79Euf1DlMhHpjXrWEnB9\nrYb2drBFa2sUhYXLgV0899yXWbfuKCUlnVitMcDNGNXGwFix7Riurq39kIwMSEq6wsKFO6iqGk9S\n0mWgg2PHYl3mod17r84nfZWWHqKk5C1Gjap2mR+/eHEPZWUr8PcwtVaMi0hvFNYyIN4WRCUmxvf6\n9Z7mdD0XjD0HrMIeqJ6lPH+CUdP7MAAjRjzDzTfPor7+LJWV36Gy0kJpqY2srF28+ebd3W2Jiemk\ntXU39pO2ej4cxCg9arWGYbXux3PPt/+HqbViXER6o7CWAfG2IMo4PrLnr/cURp5bq27FOBrTGA72\n/PoM4JcYx1oa27UmT95BRMStVFZauq9zPuXKOewTEp4nPX2i18NBjLY6lx5twbPmuIapReTGUljL\ngHhbEFVfbyU7e7+X86h774m6b+OaNOk0V69eBuppb48lKanNrUjKOVpaEl32OZ84EU56uvftYO5t\nnTLlZrZv77l4iethHwtJTt7MuHFpNDaeIyHhM0yb5jgFTFuuRORGUFjLgHjbJ716dZHP51E7y8tb\nQFvbDv7yl3CgHputDat1JRBGUZG3gy+Wc+edr2G1Ovd468nLM+a43ed9fS332dNhH/ZtWYmJD3Zv\n3bLTlisRuREU1jIg3vZJL1z4v7ifRz1lSkGfC6YslgRiYqKxWpdgzEN3YAR9JpDgtd73nDlxFBW9\nhrElq5k5c+KwWBJYv/4LLFu2n7//PYk//nEbqak3M2WKY7GZL4u3+jN/rC1XInIjKKxlQLztk25s\njMJ5fjc9PdLrcLOd8xCyMWy+H1iBo7e8B1jutSf80ktLiI4+SkXFNVJSOsnLWwzAsmX7XRarffTR\nHj76aEX3YrPBcB7m96USm4iIvyisxS+MHuV8jIAdQVTU/1FefgvZ2W/0OI9rDCHbe9MzgCqce6kj\nR3aQkbGrx+pm3nq/jY2TXe7hz9XbzsP8PVVi05YrEQkEhbX4hdHDHIOx//l3dHQ8S1lZGGVlrvO4\nnr3p/wYex3FutKOXmpFB9/nWvs4LWyyf0NoamNXb5887rxL3XolNRCQQFNbiF3l5CwgL28mxY9do\nauqgq8v7PK7nnukXcD43Oioql8jIz2CxXGTjxvsAKC8fhXNIfvzxKI/n238JGDNmOg0Nz2CzJREW\nVk1q6nTS0nb5pcebmtrMqVMa8haRG09hLT5raLDy1FN/vL5q+zJz5sTx0ktLsFgSsFgSiI6Oxmpd\njrE4zHuouS/IioyMp7PTfu0YOjpS6ej4Bq2tNnJzd7F9ewoNDX93uV99/VngHpe2uf4S8DWysnax\nffsK/Gnr1kza2jTkLSI3nsJafLZu3VEOH7YPWdsoKnqN6Oij3cPAjmHiTGDP9TlnXELNfUHWqFEN\nxMUZ+5g/+eQ8Vms29gM39u/v5NSpXxAbm4QxFx4HtDB2bIpH227EquyxYzXkLSLBobAWn3lWEoun\nouJa99cdw8QJwHIyMjznlh2FRzqxWkfQ1PQdmprGMHv2LqZOnUBh4Rjsq8BttjAqK22MGPEMsAl7\nwE+btsujbVqVLSKhTGEtPjMC0V6TOxb4gKQkxypvX4aJ7QuyMjKKKS1d2v35iorR7N17B7CLwsJm\nHD3pZq5ds7gVRfG8r1Zli0goU1iLz/LyFnDy5C+prjZqcsMSYEf31/szTOzeE66t/ZCHH4aUFBsx\nMVW0ta3u/lpExA/Yvv3fe72fVmWLSChTWIvPLJYEJk26jepqx1B4VdX4Ad3LuSdcW/uhy2lZ8fHb\naWtzPCM19TZ/NF9EZMhSWIvPvJ07PdC5YeeecEYGLqdlRURYcV79nZbWNui2i4gMZQpr8Zn7udPJ\nyZvJy3t00Pd1HxKfMyee6GjNP4uI2CmsxWfuq8EnTLgViyWhuyBJZaWF5OSGfh8T6bk4bLGOmRQR\ncaKwHqYGcg5zT9ujPKuS9e+YyIEsDtM50iIynCish6mBnMPc0/aoG3VMpHNA19Z+QGXlasCic6RF\nJOQprIcJ957oxx/H0t+A7akH3FOP29+9X9cefBbGXuyv+9x+EZGhSmE9TLj3pJOTc+mpfndf3EN4\n40ajmIkxZ93Y3eMeSO+9N54V1GKv/1kVy0QktAUsrLds2cLrr7/OuHHjAMjJyWHevHmBepz0wT3o\nxo6dwuzZA1tx3VMIJybGU1fX3OMzB9v7de/BJyeXMWFCl1aMi0jIC2jPeuXKlaxcuTKQjxAfuQfd\ntGnXBtzL9TWE+1uvu69hc88580e1qExEhoWAhrXNZgvk7aUf/Fk729cQ7u8z+xo2V0lRERmuAhrW\nu3fvprCwkNtuu43169cTHx8fyMdJL/wZdL6GcH+feaNWlYuIDDVhtkF0f1euXMnly5c9Pp+Tk8Os\nWbOwWCyEhYXx4osvUldXR25u7qAaKwNXX29l9eoizp+PIzW1ma1bMxk71lxDyA8//Dtef91Y3Q02\nvva1Pezd+/VgN0tEJOgGFda+unTpEqtWreLAgQN9Xuu8QCnUuC/AupGys/NdCpdkZfl/X/Jg36+x\n0cratUddeuxmmpMO5s8v0EL53UDvN9QNh/frS8CGwevq6khMTATgrbfeIi0tLVCPEh84hpitQBFv\nvgnZ2W+YqvKX5qRFRLwLWFj/9Kc/5aOPPiI8PJybbrqJH/3oR4F6lPjAsSisCFhOa2sYhYUD3/vs\nbeW2L78diohI/wUsrPPy8gJ1axmADRvu4NSpzVRVTcJmG/wiLm8rtwsKVviruSIi4kQVzIaJzZvf\nu3685Wv0VrnM3mMuL4+goaGCcePSmDr1isdwuVZui4jcOArrYcIRrpnAHkaO7CAjA49tV44e8x5g\nA5WVYbz/vudwube91vX1VrKz9+skLBERP1NYDxOOcE0AlpOR4Qhf5/nnf/yjEyOA43DuOZeXjyI7\nO9+jHrh95faGDV9g1qxfcfHiOvpbC1zHXYqI9E5hPUz0VsjE9TSr3RjD5M04D5dfvnyGsrKnsQdx\ne/sOfvObh7vvkZ2dz8WLtzKQoXF/H/ghIhJqFNbDRG/bolznnxeRkPA8kycn09Cw+fqc9accPZqA\ncxCfOBHu5R4tDOQkL81/i4j0TmEtbvPPY0hPn8j27fe5XJOWthXnIIZ6L/e4D2OuO5bk5DLy8h4d\nwPN13KWIiDuFtfhU63vOnDiKil4D4oFm5syJ87hHTMxhzp4dSUqKtV8nYvnzkBERkVB0Q8qN9keo\nl5Qbqu/nSynQofx+vgjl9wvldwO931A3HN6vL+pZB0ioVfhSKVARkeBRWAeIKnyJiIi/hPd9iQyE\nVjiLiIi/KKwDJCXlnxirpkErnEVEZDA0DB4gWuGsymQiIv6isA6Q/i7ICsVgU2UyERH/UFibRCgG\nm+btRUT8Q3PWJhGKwaZ5exER/1DP2iRCseSm5u1FRPxDYW0SoRhsKqQiIuIfCmuTULCJiEhPNGct\nIiJicgprERERk1NYi4iImJzCWkRExOQU1iIiIiansBYRETE5hbWIiIjJKaxFRERMTmEtIiJicgpr\nERERk1NYi4iImJzCWkRExOQU1iIiIiansBYRETE5hbWIiIjJKaxFRERMTmEtIiJicgprERERk1NY\ni4iImJzCWkRExOQU1iIiIiansBYRETE5hbWIiIjJKaxFRERMTmEtIiJicgprERERk1NYi4iImJzC\nWkRExOQU1iIiIiansBYRETE5hbWIiIjJKaxFRERMTmEtIiJicoMK68OHD7N48WJmzpzJBx984PK1\nbdu2kZGRwcKFC/nzn/88qEaKiIgMZ4MK67S0NLZs2cLs2bNdPl9eXk5RURGHDh1i+/btPPvss9hs\ntkE1VEREZLgaVFhPnTqVKVOmeARxcXExmZmZREZGMnnyZFJSUjh9+vSgGioiIjJcBWTOuqamhqSk\npO6PJ06cSE1NTSAeJSIiEvIi+7pg5cqVXL582ePzOTk5LFiwwOv3eBvyDgsLG0DzREREpM+w/vWv\nf93vm06aNImqqqruj6urq5kwYYJP35uYGN/v5w0ler+hLZTfL5TfDfR+Q12ov19f/DYM7tybXrBg\nAYcOHaK9vZ1PPvmECxcu8LnPfc5fjxIRERlWwmyDWKb99ttvs2nTJhobGxk9ejQzZszglVdeAYyt\nW/v27SMyMpKnn36au+66y2+NFhERGU4GFdYiIiISeKpgJiIiYnIKaxEREZNTWIuIiJicacN6x44d\nzJgxA6vVGuym+NXPf/5z7rvvPpYuXcrjjz9OXV1dsJvkV3l5eSxcuJCsrCzWrFlDS0tLsJvkN73V\nwh/Kjh8/zr333ss999zDyy+/HOzm+NXGjRuZO3cuS5YsCXZTAqK6upoVK1aQmZnJkiVL2LlzZ7Cb\n5Dft7e089NBDLF26lCVLlrBly5ZgNykgurq6uP/++1m1alWv15kyrKurq3n33XdJTk4OdlP87tvf\n/jb79++noKCA+fPnh9x/gHfddRcHDx6ksLCQlJQUtm3bFuwm+U1PtfCHsq6uLjZt2sSOHTv4wx/+\nwMGDBykvLw92s/zmgQceYMeOHcFuRsBERESwYcMGDh06xJ49e9i9e3fI/Pyio6PZuXMnBQUFFBQU\ncPz48ZAsW71z506mTZvW53WmDOvc3FzWrl0b7GYERGxsbPefW1tbCQ835Y9gwObOndv9TrNmzaK6\nujrILfKfnmrhD2WnT58mJSWFm266iaioKBYtWkRxcXGwm+U3X/ziFxk9enSwmxEwiYmJzJw5EzD+\n3zJt2jRqa2uD3Cr/GTlyJGD0sjs7O4PcGv+rrq6mpKSEhx56qM9r+6xgdqMdOXKEpKQkbrnllmA3\nJWBefPFFCgsLiY+PD6lhK3f79u1j0aJFwW6G9MJbHf/3338/iC2Sgbp48SJnzpwJqQJUXV1dPPDA\nA1y4cIFHHnkkpN4NHB3T5ubmPq8NSlj3VG/8qaeeYtu2bbz66qvdnxuKvZi+6qnn5OSQk5PDyy+/\nzG9/+1vWrFkThFYOnC/14rdu3UpUVNSQmyscSC38oWwo/v0ST1euXOHJJ59k48aNLqN3Q114eDgF\nBQW0tLSwevVqzp07x/Tp04PdLL84duwY48ePZ+bMmZw8ebLP64MS1j3VGz979iyXLl0iKysLm81G\nTU0Ny5Yt4/e//z3jxo27wa0cOF/rqS9evJgnnnhiyIV1X++Xn59PSUnJkBw1GEgt/KFs0qRJVFZW\ndn9cU1Pjcx1/MYfOzk6efPJJsrKy+MpXvhLs5gREXFwcX/rSl/jTn/4UMmH93nvvceTIEUpKSmhr\na+PKlSusXbuWvLw8r9ebasI0LS2Nd955h+LiYo4cOcLEiRPJz88fUkHdl4qKiu4/FxcXM3Xq1CC2\nxv+OHz/OK6+8wtatW4mOjg52cwImVHqkt99+OxcuXODSpUu0t7dz8OBB7r777mA3y69C5WfVk40b\nNzJ9+nS++c1vBrspftXQ0NA9PHz16lVOnDgRUv+//N73vsexY8coLi7mZz/7GXfeeWePQQ0mnLN2\nFhYWFnJ/0V544QXOnz9PeHg4ycnJPPvss8Fukl/9+Mc/pqOjg8ceewyAz3/+8/zwhz8MbqP8xLkW\n/qpVq1xq4Q9VERERPPPMMzz22GPYbDYefPBBn1amDhXf//73OXnyJFarlfnz57NmzRqWLVsW7Gb5\nzV//+lcOHDhAWloaS5cuJSwsjJycHObNmxfspg1aXV0d69evp6uri66uLjIzM0lPTw92s4JGtcFF\nRERMzlTD4CIiIuJJYS0iImJyCmsRERGTU1iLiIiYnMJaRETE5BTWIiIiJqewFhERMTmFtYiIiMn9\nPyQ+uNKCpR6MAAAAAElFTkSuQmCC\n",
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vvPMOfD4fnnnmGfz+97+HyWTCmjVr8Mwzz+Cxxx4DANx+++24/vrrM3bRRJS+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\ny5mIiKjCsJyJiIgqzC8iivHPF8qqogAAAABJRU5ErkJggg==\n",
             "text/plain": [
-              "\u003cmatplotlib.figure.Figure at 0xa813090\u003e"
+              "\u003cmatplotlib.figure.Figure at 0x7f7a18dfb8d0\u003e"
             ]
           },
           "metadata": {
@@ -155,7 +149,7 @@
         "\n",
         "import matplotlib.pyplot as plt\n",
         "\n",
-        "plt.scatter(inputs.numpy(), labels.numpy())\n",
+        "plt.scatter(inputs, labels)\n",
         "plt.show()"
       ]
     },
@@ -168,14 +162,12 @@
       "source": [
         "## Step 2: Define our TensorFlow variables\n",
         "\n",
-        "We'll use Keras's object-oriented [`Dense`](https://www.tensorflow.org/api_docs/python/tf/contrib/keras/layers/Dense) layer to create our variables. In this case, we'll create a `Dense` layer with a single weight and bias.\n",
-        "\n",
-        "(**Note**: We're using the implementation of `Dense` found in `tf.layers.Dense` though the documentation link is for `tf.contrib.keras.layers.Dense`. When TensorFlow 1.4 is released, the documentation will also be in `tf.layers.Dense`) "
+        "We'll use Keras's object-oriented [`Dense`](https://www.tensorflow.org/api_docs/python/tf/keras/layers/Dense) layer to create our variables. In this case, we'll create a `Dense` layer with a single weight and bias."
       ]
     },
     {
       "cell_type": "code",
-      "execution_count": 4,
+      "execution_count": 0,
       "metadata": {
         "cellView": "code",
         "colab": {
@@ -183,27 +175,23 @@
             "startup": false,
             "wait_interval": 0
           },
-          "height": 34,
-          "output_extras": [
-            {
-              "item_id": 1
-            }
-          ]
+          "base_uri": "https://localhost:8080/",
+          "height": 34
         },
         "colab_type": "code",
         "executionInfo": {
-          "elapsed": 22,
+          "elapsed": 332,
           "status": "ok",
-          "timestamp": 1505502830753,
+          "timestamp": 1525154229931,
           "user": {
             "displayName": "",
             "photoUrl": "",
             "userId": ""
           },
-          "user_tz": 240
+          "user_tz": 420
         },
         "id": "z9r-ZeyrXu3A",
-        "outputId": "6230a7a3-29fe-4d08-f101-da80425bad82"
+        "outputId": "e19a698e-5892-4fcd-80d3-1394605ee72c"
       },
       "outputs": [
         {
@@ -212,7 +200,7 @@
               "[]"
             ]
           },
-          "execution_count": 4,
+          "execution_count": 48,
           "metadata": {
             "tags": []
           },
@@ -222,7 +210,7 @@
       "source": [
         "# Create TensorFlow Variables using Keras's Dense layer.\n",
         "\n",
-        "wb = tf.layers.Dense(units=1, use_bias=True)\n",
+        "wb = tf.keras.layers.Dense(units=1, use_bias=True)\n",
         "\n",
         "# We can access the underlying TensorFlow variables using wb.variables.\n",
         "# However, the variables won't exist until the dimensions of the input\n",
@@ -240,7 +228,7 @@
         "id": "docKLUaonYG_"
       },
       "source": [
-        "## Step 3: Define our loss function\n",
+        "## Step 3: *Define the loss function*\n",
         "\n",
         "Our loss function is the standard L2 loss (where we reduce the loss to its mean across its inputs)."
       ]
@@ -261,15 +249,14 @@
       },
       "outputs": [],
       "source": [
-        "def loss_fn(inputs, labels, wb):\n",
+        "def loss_fn(predictions, labels):\n",
         "  \"\"\"Calculates the mean L2 loss for our linear model.\"\"\"\n",
-        "  predictions = wb(inputs)\n",
         "  return tf.reduce_mean(tf.square(predictions - labels))"
       ]
     },
     {
       "cell_type": "code",
-      "execution_count": 6,
+      "execution_count": 0,
       "metadata": {
         "cellView": "code",
         "colab": {
@@ -277,36 +264,32 @@
             "startup": false,
             "wait_interval": 0
           },
-          "height": 34,
-          "output_extras": [
-            {
-              "item_id": 1
-            }
-          ]
+          "base_uri": "https://localhost:8080/",
+          "height": 34
         },
         "colab_type": "code",
         "executionInfo": {
-          "elapsed": 24,
+          "elapsed": 348,
           "status": "ok",
-          "timestamp": 1505502830875,
+          "timestamp": 1525154234538,
           "user": {
             "displayName": "",
             "photoUrl": "",
             "userId": ""
           },
-          "user_tz": 240
+          "user_tz": 420
         },
         "id": "RkNbXoXkpjVH",
-        "outputId": "c36fc98d-3a57-4074-901d-c10ae017ae3f"
+        "outputId": "e4688f3c-e29f-416d-f541-6d81953b5660"
       },
       "outputs": [
         {
           "data": {
             "text/plain": [
-              "\u003ctf.Tensor: id=40, shape=(), dtype=float32, numpy=7.3549819\u003e"
+              "\u003ctf.Tensor: id=1252, shape=(), dtype=float32, numpy=16.979801\u003e"
             ]
           },
-          "execution_count": 6,
+          "execution_count": 50,
           "metadata": {
             "tags": []
           },
@@ -316,47 +299,43 @@
       "source": [
         "# Test loss function (optional).\n",
         "\n",
-        "loss_fn(inputs, labels, wb)"
+        "loss_fn(wb(inputs), labels)"
       ]
     },
     {
       "cell_type": "code",
-      "execution_count": 7,
+      "execution_count": 0,
       "metadata": {
         "colab": {
           "autoexec": {
             "startup": false,
             "wait_interval": 0
           },
-          "height": 51,
-          "output_extras": [
-            {
-              "item_id": 1
-            }
-          ]
+          "base_uri": "https://localhost:8080/",
+          "height": 51
         },
         "colab_type": "code",
         "executionInfo": {
-          "elapsed": 57,
+          "elapsed": 418,
           "status": "ok",
-          "timestamp": 1505502830981,
+          "timestamp": 1525154260083,
           "user": {
             "displayName": "",
             "photoUrl": "",
             "userId": ""
           },
-          "user_tz": 240
+          "user_tz": 420
         },
         "id": "K_7beXoHOU7t",
-        "outputId": "1ad0856a-02ec-4117-a6c0-b41030981d87"
+        "outputId": "8f55c028-fe2b-4edb-ad68-a849afc60623"
       },
       "outputs": [
         {
           "name": "stdout",
           "output_type": "stream",
           "text": [
-            "w: tf.Tensor([[ 1.56891453]], shape=(1, 1), dtype=float32)\n",
-            "b: tf.Tensor([ 0.], shape=(1,), dtype=float32)\n"
+            "w: -0.311619\n",
+            "b: 0.000000\n"
           ]
         }
       ],
@@ -364,31 +343,20 @@
         "# At this point, the variables exist, and can now be queried:\n",
         "\n",
         "w, b = wb.variables\n",
-        "print(\"w: \" + str(w.read_value()))\n",
-        "print(\"b: \" + str(b.read_value()))"
+        "print(\"w: %f\" % w.numpy())\n",
+        "print(\"b: %f\" % b.numpy())"
       ]
     },
     {
       "cell_type": "markdown",
       "metadata": {
         "colab_type": "text",
-        "id": "YIlebeb_qYtC"
+        "id": "JVDWpL9VYWdP"
       },
       "source": [
-        "## Step 4: Create our gradients function using `implicit_value_and_gradients()`\n",
-        "\n",
-        "With a loss function defined, we can calculate gradients and apply them to our variables to update them.\n",
+        "## Step 4: Create an optimizer\n",
         "\n",
-        "To calculate the gradients, we wrap our loss function using the `implicit_value_and_gradients()` function.\n",
-        "\n",
-        "`implicit_value_and_gradients()` returns a function that accepts the same inputs as the function passed in, and returns a tuple consisting of:\n",
-        "\n",
-        "1. the value returned by the function passed in (in this case, the loss calculated by `loss_fn()`), and\n",
-        "1. a list of tuples consisting of:\n",
-        "  1. The value of the gradient (a `tf.Tensor`) with respect to a given variable\n",
-        "  1. The corresponding variable (`tf.Variable`)\n",
-        "\n",
-        "Test it out below to get a feel for what it does. Notice how the first value of the returned tuple (the loss) is the same as the value returned in the cell above that tests our loss function."
+        "We'll use a `GradientDescentOptimizer` to fit our model."
       ]
     },
     {
@@ -403,87 +371,29 @@
           }
         },
         "colab_type": "code",
-        "id": "v1spZQ4NwW1U"
+        "id": "DudNEebMKDWN"
       },
       "outputs": [],
       "source": [
-        "# Produce our gradients function. See description above for details about\n",
-        "# the returned function's signature.\n",
-        "\n",
-        "value_and_gradients_fn = tfe.implicit_value_and_gradients(loss_fn)"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": 9,
-      "metadata": {
-        "cellView": "code",
-        "colab": {
-          "autoexec": {
-            "startup": false,
-            "wait_interval": 0
-          },
-          "height": 153,
-          "output_extras": [
-            {
-              "item_id": 1
-            }
-          ]
-        },
-        "colab_type": "code",
-        "executionInfo": {
-          "elapsed": 46,
-          "status": "ok",
-          "timestamp": 1505502831114,
-          "user": {
-            "displayName": "",
-            "photoUrl": "",
-            "userId": ""
-          },
-          "user_tz": 240
-        },
-        "id": "21WMcpsmFFLd",
-        "outputId": "f51b3171-33f5-4f87-8bf7-0be2dc8edc8a"
-      },
-      "outputs": [
-        {
-          "name": "stdout",
-          "output_type": "stream",
-          "text": [
-            "Outputs of value_and_gradients_fn:\n",
-            "Loss: tf.Tensor(7.35498, shape=(), dtype=float32)\n",
-            "\n",
-            "Gradient: tf.Tensor([[-3.00773573]], shape=(1, 1), dtype=float32)\n",
-            "Variable: \u003ctf.Variable 'dense/kernel:0' shape=(1, 1) dtype=float32\u003e\n",
-            "\n",
-            "Gradient: tf.Tensor([-4.06519032], shape=(1,), dtype=float32)\n",
-            "Variable: \u003ctf.Variable 'dense/bias:0' shape=(1,) dtype=float32\u003e\n"
-          ]
-        }
-      ],
-      "source": [
-        "# Show outputs of value_and_gradients_fn.\n",
-        "\n",
-        "print(\"Outputs of value_and_gradients_fn:\")\n",
-        "\n",
-        "value, grads_and_vars = value_and_gradients_fn(inputs, labels, wb)\n",
-        "\n",
-        "print('Loss: {}'.format(value))\n",
-        "for (grad, var) in grads_and_vars:\n",
-        "  print(\"\")\n",
-        "  print('Gradient: {}\\nVariable: {}'.format(grad, var))"
+        "optimizer = tf.train.GradientDescentOptimizer(learning_rate=0.1)"
       ]
     },
     {
       "cell_type": "markdown",
       "metadata": {
         "colab_type": "text",
-        "id": "JVDWpL9VYWdP"
+        "id": "YBeJYxY8YaiO"
       },
       "source": [
-        "## Step 5: Create an optimizer\n",
+        "### Step 5: Define a training step\n",
         "\n",
-        "We'll use a `GradientDescentOptimizer` to fit our model."
+        "To fit model variables to the data we'll need to:\n",
+        "\n",
+        "1. Calculate the gradients of the loss with respect to the model variables.\n",
+        "2. Use `optimizer` to compute updates to the variable values based on those gradients.\n",
+        "\n",
+        "To calculate the gradients, we use the [`tf.GradientTape`](https://www.tensorflow.org/api_docs/python/tf/GradientTape) context manager\n",
+        "and its `gradient` function to compute gradients through computation conducted within its context:\n"
       ]
     },
     {
@@ -498,94 +408,72 @@
           }
         },
         "colab_type": "code",
-        "id": "DudNEebMKDWN"
+        "id": "diDZfrMJM3OC"
       },
       "outputs": [],
       "source": [
-        "optimizer = tf.train.GradientDescentOptimizer(learning_rate=0.1)"
+        "def run_step(inputs, labels):\n",
+        "  with tf.GradientTape() as g:\n",
+        "    loss = loss_fn(wb(inputs), labels)\n",
+        "  # Compute the partial derivatives of loss with respect to the variables\n",
+        "  grads = g.gradient(loss, wb.variables)\n",
+        "  optimizer.apply_gradients(zip(grads, wb.variables))\n",
+        "  return loss"
       ]
     },
     {
       "cell_type": "markdown",
       "metadata": {
         "colab_type": "text",
-        "id": "YBeJYxY8YaiO"
+        "id": "1WWepgmJQOzc"
       },
       "source": [
-        "### Step 5a: Test Our Optimizer\n",
-        "\n",
-        "Now we have everything needed to start fitting our variables to the data!\n",
-        "\n",
-        "In the next cell, we'll demo these capabilities. We'll:\n",
-        "\n",
-        "1. Print the current values of `w` and `b`\n",
-        "1. Calculate the loss and gradients\n",
-        "1. Apply the gradients\n",
-        "1. Print out the new values of `w` and `b`\n",
-        "\n",
-        "You can run the cell multiple times. Each time, you should see the values of `w` and `b` get closer to their true values of 3 and 2."
+        "Repeatedly running the training step will nudge the variables towards the values that best fit the data (i.e., \"w\" will move closer to 3.0, while \"b\" will tend to 2.0):\n",
+        "\n"
       ]
     },
     {
       "cell_type": "code",
-      "execution_count": 11,
+      "execution_count": 0,
       "metadata": {
-        "cellView": "code",
         "colab": {
           "autoexec": {
             "startup": false,
             "wait_interval": 0
           },
-          "height": 102,
-          "output_extras": [
-            {
-              "item_id": 1
-            }
-          ]
+          "base_uri": "https://localhost:8080/",
+          "height": 51
         },
         "colab_type": "code",
         "executionInfo": {
-          "elapsed": 103,
+          "elapsed": 380,
           "status": "ok",
-          "timestamp": 1505502831285,
+          "timestamp": 1525154412590,
           "user": {
             "displayName": "",
             "photoUrl": "",
             "userId": ""
           },
-          "user_tz": 240
+          "user_tz": 420
         },
-        "id": "diDZfrMJM3OC",
-        "outputId": "d585fff0-ecb3-4e98-9b33-bbae07a95d8c"
+        "id": "ya5Qxz5XQlhU",
+        "outputId": "8dd47155-a6c1-44c5-c279-617c803f1723"
       },
       "outputs": [
         {
           "name": "stdout",
           "output_type": "stream",
           "text": [
-            "Values of w, b, BEFORE applying gradients:\n",
-            "(array([[ 1.56891453]], dtype=float32), array([ 0.], dtype=float32))\n",
-            "()\n",
-            "Values of w, b, AFTER applying gradients:\n",
-            "(array([[ 1.86968815]], dtype=float32), array([ 0.40651903], dtype=float32))\n"
+            "Values of w, b BEFORE applying gradients: 2.725763, 1.894334\n",
+            "Values of w, b AFTER  applying gradients: 2.774932, 1.922555\n"
           ]
         }
       ],
       "source": [
-        "# Test the optimizer.\n",
-        "\n",
-        "print(\"Values of w, b, BEFORE applying gradients:\")\n",
         "w, b = wb.variables\n",
-        "print(w.read_value().numpy(), b.read_value().numpy())\n",
-        "print()\n",
-        "\n",
-        "# Calculate the gradients:\n",
-        "empirical_loss, gradients_and_variables = value_and_gradients_fn(\n",
-        "    inputs, labels, wb)\n",
-        "optimizer.apply_gradients(gradients_and_variables)\n",
-        "\n",
-        "print(\"Values of w, b, AFTER applying gradients:\")\n",
-        "print(w.read_value().numpy(), b.read_value().numpy())"
+        "print(\"Values of w, b BEFORE applying gradients: %f, %f\" % (w.numpy(), b.numpy()))\n",
+        "run_step(inputs, labels)\n",
+        "print(\"Values of w, b AFTER  applying gradients: %f, %f\" % (w.numpy(), b.numpy()))\n"
       ]
     },
     {
@@ -602,51 +490,44 @@
     },
     {
       "cell_type": "code",
-      "execution_count": 12,
+      "execution_count": 0,
       "metadata": {
         "colab": {
           "autoexec": {
             "startup": false,
             "wait_interval": 0
           },
-          "height": 397,
-          "output_extras": [
-            {
-              "item_id": 1
-            },
-            {
-              "item_id": 2
-            }
-          ]
+          "base_uri": "https://localhost:8080/",
+          "height": 364
         },
         "colab_type": "code",
         "executionInfo": {
-          "elapsed": 225,
+          "elapsed": 580,
           "status": "ok",
-          "timestamp": 1505502831550,
+          "timestamp": 1525154278709,
           "user": {
             "displayName": "",
             "photoUrl": "",
             "userId": ""
           },
-          "user_tz": 240
+          "user_tz": 420
         },
         "id": "VukGe-huNaJ4",
-        "outputId": "f0a8d665-1910-477c-d8ab-c94ccdc4afcd"
+        "outputId": "c79c8e63-c781-451e-f74f-20815d8da49f"
       },
       "outputs": [
         {
           "name": "stdout",
           "output_type": "stream",
           "text": [
-            "[2.111051321029663, 2.3047544956207275, 2.4602210521698, 2.5850086212158203, 2.6851789951324463, 2.7655951976776123, 2.830157995223999, 2.8819968700408936, 2.9236228466033936, 2.9570505619049072]\n"
+            "[0.9409681558609009, 1.3733772039413452, 1.7128530740737915, 1.9793939590454102, 2.188689708709717, 2.3530514240264893, 2.4821391105651855, 2.583533763885498, 2.6631851196289062, 2.7257626056671143]\n"
           ]
         },
         {
           "data": {
-            "image/png": 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8lHDsTLHDvZo3KPZ1WcerhCI9VGY9XwFwGDkan3++Cr16PY2tW3ejevUaFd9+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\nAAAAAElFTkSuQmCC\n",
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Dq1atkLscIqJqjeFbTeQfflSjRg3MnDkVp0+fkrskIqJqi+FbjQQF1cKnny5G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\nviIiIln2BzQKNGAGnBgwAAAAAElFTkSuQmCC\n",
             "text/plain": [
-              "\u003cmatplotlib.figure.Figure at 0xc1dc310\u003e"
+              "\u003cmatplotlib.figure.Figure at 0x7f7a18df6b50\u003e"
             ]
           },
           "metadata": {
@@ -668,13 +549,10 @@
         "  w_at_step = []\n",
         "  b_at_step = []\n",
         "  for step_num in range(num_training_steps):\n",
-        "    loss, gradients_and_variables = value_and_gradients_fn(inputs, labels, wb)\n",
-        "    loss_at_step.append(np.asscalar(loss.numpy()))\n",
-        "    \n",
-        "    optimizer.apply_gradients(gradients_and_variables)\n",
+        "    loss_at_step.append(run_step(inputs, labels))\n",
         "    w, b = wb.variables\n",
-        "    w_at_step.append(np.asscalar(w.read_value().numpy()))\n",
-        "    b_at_step.append(np.asscalar(b.read_value().numpy()))\n",
+        "    w_at_step.append(np.asscalar(w.numpy()))\n",
+        "    b_at_step.append(np.asscalar(b.numpy()))\n",
         "\n",
         "  print(w_at_step)\n",
         "  t = range(0, num_training_steps)\n",
@@ -688,171 +566,12 @@
         "\n",
         "train_model(inputs, labels, wb, optimizer, num_training_steps)"
       ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {
-        "colab_type": "text",
-        "id": "UNurY9VJ-hpH"
-      },
-      "source": [
-        "## Other Ways to Compute Gradients\n",
-        "\n",
-        "Using our loss function as an example (`loss_fn()`), there are several other ways we could compute gradients:\n",
-        "\n",
-        "1. `tfe.implicit_gradients()`\n",
-        "1. `tfe.gradients_function()`\n",
-        "1. `tfe.implicit_value_and_gradients()`\n",
-        "1. `tfe.value_and_gradients_function()`\n",
-        "\n",
-        "Each of these functions does the following:\n",
-        "* Wraps a function.\n",
-        "* Returns a function with the same input signature as the wrapped function.\n",
-        "\n",
-        "They differ only in what information they return.\n",
-        "\n",
-        "### Gradients-only functions\n",
-        "\n",
-        "The following two functions return a function that returns only the variables' gradients:\n",
-        "\n",
-        "1. `tfe.gradients_function()`: Returns the partial derivatives of the function `f()` with respect to the parameters of `f()`.\n",
-        "1. `tfe.implicit_gradients()`: Returns the partial derivatives of the function `f()` with respect to the trainable parameters (`tf.Variable`) used by `f()`.\n",
-        "\n",
-        "In our example above, the `tf.layers.Dense` object encapsulates the trainable parameters.\n",
-        "\n",
-        "### Value and gradients functions\n",
-        "\n",
-        "The following two functions are identical to their counterparts above, except that they also return the value of the wrapped function.\n",
-        "\n",
-        "1. `tfe.implicit_value_and_gradients()`\n",
-        "1. `tfe.value_and_gradients_function()`\n",
-        "\n",
-        "### Gradient demos\n",
-        "\n",
-        "In the demos below, we show examples for the `implicit_*` functions, since our existing loss function works seamlessly with these versions. (The other versions require that your parameters are tensors and tensors only; in our example, we're using a `Dense` layer.)\n"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": 13,
-      "metadata": {
-        "colab": {
-          "autoexec": {
-            "startup": false,
-            "wait_interval": 0
-          },
-          "height": 85,
-          "output_extras": [
-            {
-              "item_id": 1
-            }
-          ]
-        },
-        "colab_type": "code",
-        "executionInfo": {
-          "elapsed": 100,
-          "status": "ok",
-          "timestamp": 1505502831671,
-          "user": {
-            "displayName": "",
-            "photoUrl": "",
-            "userId": ""
-          },
-          "user_tz": 240
-        },
-        "id": "aEoCftnfAIH5",
-        "outputId": "72f1c1dc-a574-463f-f860-c4e5f48fcdaa"
-      },
-      "outputs": [
-        {
-          "data": {
-            "text/plain": [
-              "[(\u003ctf.Tensor: id=673, shape=(1, 1), dtype=float32, numpy=array([[-0.26846504]], dtype=float32)\u003e,\n",
-              "  \u003ctf.Variable 'dense/kernel:0' shape=(1, 1) dtype=float32\u003e),\n",
-              " (\u003ctf.Tensor: id=671, shape=(1,), dtype=float32, numpy=array([-0.32890949], dtype=float32)\u003e,\n",
-              "  \u003ctf.Variable 'dense/bias:0' shape=(1,) dtype=float32\u003e)]"
-            ]
-          },
-          "execution_count": 13,
-          "metadata": {
-            "tags": []
-          },
-          "output_type": "execute_result"
-        }
-      ],
-      "source": [
-        "# tfe.implicit_gradients() demo\n",
-        "gradients_fn = tfe.implicit_gradients(loss_fn)\n",
-        "\n",
-        "# Returns only gradients and variables:\n",
-        "gradients_fn(inputs, labels, wb)"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": 14,
-      "metadata": {
-        "colab": {
-          "autoexec": {
-            "startup": false,
-            "wait_interval": 0
-          },
-          "height": 102,
-          "output_extras": [
-            {
-              "item_id": 1
-            }
-          ]
-        },
-        "colab_type": "code",
-        "executionInfo": {
-          "elapsed": 88,
-          "status": "ok",
-          "timestamp": 1505502831785,
-          "user": {
-            "displayName": "",
-            "photoUrl": "",
-            "userId": ""
-          },
-          "user_tz": 240
-        },
-        "id": "bbgCUdCzAVhH",
-        "outputId": "152aa9b6-9e42-4b7e-848a-9423c0b1929c"
-      },
-      "outputs": [
-        {
-          "data": {
-            "text/plain": [
-              "(\u003ctf.Tensor: id=688, shape=(), dtype=float32, numpy=1.0623235\u003e,\n",
-              " [(\u003ctf.Tensor: id=720, shape=(1, 1), dtype=float32, numpy=array([[-0.26846504]], dtype=float32)\u003e,\n",
-              "   \u003ctf.Variable 'dense/kernel:0' shape=(1, 1) dtype=float32\u003e),\n",
-              "  (\u003ctf.Tensor: id=718, shape=(1,), dtype=float32, numpy=array([-0.32890949], dtype=float32)\u003e,\n",
-              "   \u003ctf.Variable 'dense/bias:0' shape=(1,) dtype=float32\u003e)])"
-            ]
-          },
-          "execution_count": 14,
-          "metadata": {
-            "tags": []
-          },
-          "output_type": "execute_result"
-        }
-      ],
-      "source": [
-        "# tfe.implicit_value_and_gradients() demo\n",
-        "value_gradients_fn = tfe.implicit_value_and_gradients(loss_fn)\n",
-        "\n",
-        "# Returns the value returned by the function passed in, gradients, and variables:\n",
-        "value_gradients_fn(inputs, labels, wb)"
-      ]
     }
   ],
   "metadata": {
     "colab": {
+      "collapsed_sections": [],
       "default_view": {},
-      "last_runtime": {
-        "build_target": "",
-        "kind": "local"
-      },
       "name": "Eager Execution Tutorial: Working with Gradients",
       "provenance": [],
       "version": "0.3.2",
diff --git a/tensorflow/contrib/eager/python/examples/notebooks/3_datasets.ipynb b/tensorflow/contrib/eager/python/examples/notebooks/3_datasets.ipynb
index 0088da5c4b..bfcc7feb07 100644
--- a/tensorflow/contrib/eager/python/examples/notebooks/3_datasets.ipynb
+++ b/tensorflow/contrib/eager/python/examples/notebooks/3_datasets.ipynb
@@ -16,7 +16,9 @@
         "\n",
         "We recommend using the `Dataset`s API for building performant, complex input pipelines from simple, re-usable pieces that will feed your model's training or evaluation loops.\n",
         "\n",
-        "If you're familiar with TensorFlow graphs, the API for constructing the `Dataset` object remains exactly the same when eager execution is enabled, but the process of iterating over elements of the dataset is slightly different.  You will use a Pythonic `Iterator()` class instead of using `make_one_shot_iterator()` and `get_next()`. As a result, the discussion on iterators in the [Programmer's Guide](https://www.tensorflow.org/programmers_guide/datasets) is not relevant when eager execution is enabled."
+        "If you're familiar with TensorFlow graphs, the API for constructing the `Dataset` object remains exactly the same when eager execution is enabled, but the process of iterating over elements of the dataset is slightly simpler.\n",
+        "You can use Python iteration over the `tf.data.Dataset` object and do not need to explicitly create an `tf.data.Iterator` object.\n",
+        "As a result, the discussion on iterators in the [Programmer's Guide](https://www.tensorflow.org/programmers_guide/datasets) is not relevant when eager execution is enabled."
       ]
     },
     {
@@ -48,11 +50,8 @@
         "# Import TensorFlow.\n",
         "import tensorflow as tf\n",
         "\n",
-        "# Import TensorFlow eager execution support (subject to future changes).\n",
-        "import tensorflow.contrib.eager as tfe\n",
-        "\n",
         "# Enable eager execution\n",
-        "tfe.enable_eager_execution()"
+        "tf.enable_eager_execution()"
       ]
     },
     {
@@ -137,32 +136,27 @@
       "source": [
         "# Step 3: Iterate\n",
         "\n",
-        "Use `tfe.Iterator` on the `Dataset` object to get a Python iterator over the contents of the dataset.\n",
-        "\n",
-        "If you're familiar with the use of `Dataset`s in TensorFlow graphs, note that this process of iteration is different. Here there are no calls to `Dataset.make_one_shot_iterator()` and no `get_next()` calls."
+        "When eager execution is enabled `Dataset` objects support iteration.\n",
+        "If you're familiar with the use of `Dataset`s in TensorFlow graphs, note that there is no need for calls to `Dataset.make_one_shot_iterator()` or `get_next()` calls."
       ]
     },
     {
       "cell_type": "code",
-      "execution_count": 5,
+      "execution_count": 0,
       "metadata": {
         "colab": {
           "autoexec": {
             "startup": false,
             "wait_interval": 0
           },
-          "height": 153,
-          "output_extras": [
-            {
-              "item_id": 1
-            }
-          ]
+          "base_uri": "https://localhost:8080/",
+          "height": 153
         },
         "colab_type": "code",
         "executionInfo": {
-          "elapsed": 201,
+          "elapsed": 388,
           "status": "ok",
-          "timestamp": 1505952405928,
+          "timestamp": 1525154629129,
           "user": {
             "displayName": "",
             "photoUrl": "",
@@ -171,7 +165,7 @@
           "user_tz": 420
         },
         "id": "lCUWzso6mbqR",
-        "outputId": "ec027d30-96c6-4ea4-9ee1-ef74ec1ae29a"
+        "outputId": "8e4b0298-d27d-4ac7-e26a-ef94af0594ec"
       },
       "outputs": [
         {
@@ -179,9 +173,9 @@
           "output_type": "stream",
           "text": [
             "Elements of ds_tensors:\n",
-            "tf.Tensor([4 9], shape=(2,), dtype=int32)\n",
+            "tf.Tensor([1 9], shape=(2,), dtype=int32)\n",
             "tf.Tensor([16 25], shape=(2,), dtype=int32)\n",
-            "tf.Tensor([36  1], shape=(2,), dtype=int32)\n",
+            "tf.Tensor([ 4 36], shape=(2,), dtype=int32)\n",
             "\n",
             "Elements in ds_file:\n",
             "tf.Tensor(['Line 1' 'Line 2'], shape=(2,), dtype=string)\n",
@@ -191,22 +185,19 @@
       ],
       "source": [
         "print('Elements of ds_tensors:')\n",
-        "for x in tfe.Iterator(ds_tensors):\n",
+        "for x in ds_tensors:\n",
         "  print(x)\n",
         "\n",
         "print('\\nElements in ds_file:')\n",
-        "for x in tfe.Iterator(ds_file):\n",
+        "for x in ds_file:\n",
         "  print(x)"
       ]
     }
   ],
   "metadata": {
     "colab": {
+      "collapsed_sections": [],
       "default_view": {},
-      "last_runtime": {
-        "build_target": "",
-        "kind": "local"
-      },
       "name": "Eager Execution Tutorial: Importing Data",
       "provenance": [],
       "version": "0.3.2",