diff --git a/colab/edge_detection.ipynb b/colab/edge_detection.ipynb
index e34c39a..eaa26b6 100644
--- a/colab/edge_detection.ipynb
+++ b/colab/edge_detection.ipynb
@@ -1,76 +1,151 @@
 {
-  "nbformat": 4,
-  "nbformat_minor": 0,
-  "metadata": {
-    "colab": {
-      "name": "edge_detection.ipynb",
-      "provenance": [],
-      "collapsed_sections": []
-    },
-    "kernelspec": {
-      "name": "python3",
-      "display_name": "Python 3"
-    }
-  },
   "cells": [
     {
       "cell_type": "markdown",
       "metadata": {
-        "id": "h5s6ncerSpc5",
-        "colab_type": "text"
+        "colab_type": "text",
+        "id": "j_IgB6_3npkE"
       },
       "source": [
-        "# Image edge detection module\n",
+        "##### Copyright 2020 Google LLC.\n",
         "\n",
-        "## High level overview:\n",
-        "\n",
-        "1.  Define a `tf.Module` containing a `@tf.function` that performs edge detection\n",
-        "2.  Save the `tf.Module` as a `SavedModel`\n",
-        "3.  Use IREE's python bindings to load the `SavedModel` into MLIR in the `mhlo` dialect\n",
-        "4.  Save the MLIR to a file (can stop here to use it from another application)\n",
-        "5.  Compile the `mhlo` MLIR into a VM module for IREE to execute\n",
-        "6.  Run the VM module through IREE's runtime to test the edge detection function"
+        "Licensed under the Apache License, Version 2.0 (the \"License\");"
       ]
     },
     {
       "cell_type": "code",
+      "execution_count": null,
       "metadata": {
-        "id": "s2bScbYkP6VZ",
+        "cellView": "form",
+        "colab": {},
         "colab_type": "code",
-        "cellView": "both",
-        "colab": {}
+        "id": "mIAGj4IcntD-"
+      },
+      "outputs": [],
+      "source": [
+        "#@title License header\n",
+        "# Copyright 2020 Google LLC\n",
+        "#\n",
+        "# Licensed under the Apache License, Version 2.0 (the \"License\");\n",
+        "# you may not use this file except in compliance with the License.\n",
+        "# You may obtain a copy of the License at\n",
+        "#\n",
+        "#      https://www.apache.org/licenses/LICENSE-2.0\n",
+        "#\n",
+        "# Unless required by applicable law or agreed to in writing, software\n",
+        "# distributed under the License is distributed on an \"AS IS\" BASIS,\n",
+        "# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n",
+        "# See the License for the specific language governing permissions and\n",
+        "# limitations under the License."
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "colab_type": "text",
+        "id": "h5s6ncerSpc5"
       },
       "source": [
-        "#@title Imports and common setup\n",
+        "# Image edge detection module"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "colab_type": "text",
+        "id": "hHCmr6iGjAJN"
+      },
+      "source": [
+        "## Setup"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 7,
+      "metadata": {
+        "cellView": "both",
+        "colab": {},
+        "colab_type": "code",
+        "executionInfo": {
+          "elapsed": 693,
+          "status": "ok",
+          "timestamp": 1598547082775,
+          "user": {
+            "displayName": "",
+            "photoUrl": "",
+            "userId": ""
+          },
+          "user_tz": 420
+        },
+        "id": "s2bScbYkP6VZ"
+      },
+      "outputs": [],
+      "source": [
+        "#@title Imports\n",
         "\n",
         "import os\n",
+        "import tempfile\n",
+        "\n",
         "from matplotlib import pyplot as plt\n",
         "import numpy as np\n",
         "import tensorflow as tf\n",
         "from pyiree.tf import compiler as ireec\n",
-        "from pyiree import rt as ireert\n",
-        "\n",
-        "SAVE_PATH = os.path.join(os.environ[\"HOME\"], \"saved_models\")\n",
-        "os.makedirs(SAVE_PATH, exist_ok=True)"
-      ],
-      "execution_count": 0,
-      "outputs": []
+        "from pyiree.tf.support import tf_utils\n",
+        "from pyiree import rt as ireert"
+      ]
     },
     {
       "cell_type": "code",
+      "execution_count": 10,
       "metadata": {
-        "id": "6YGqN2uqP_7P",
+        "colab": {},
         "colab_type": "code",
-        "outputId": "6a5f982c-17d9-485b-f95c-9656a1583c9b",
-        "colab": {
-          "base_uri": "https://localhost:8080/",
-          "height": 326
-        }
+        "executionInfo": {
+          "elapsed": 36,
+          "status": "ok",
+          "timestamp": 1598547092834,
+          "user": {
+            "displayName": "",
+            "photoUrl": "",
+            "userId": ""
+          },
+          "user_tz": 420
+        },
+        "id": "z5o-gbE30tmJ"
       },
+      "outputs": [],
       "source": [
-        "#@title Construct a module containing the edge detection function\n",
+        "#@title Setup Artifacts Directory\n",
         "\n",
+        "# Used in the low-level compilation section.\n",
+        "ARITFACTS_DIR = os.path.join(tempfile.gettempdir(), \"iree\", \"colab_artifacts\")\n",
+        "os.makedirs(ARITFACTS_DIR, exist_ok=True)"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 11,
+      "metadata": {
+        "colab": {},
+        "colab_type": "code",
+        "executionInfo": {
+          "elapsed": 77,
+          "status": "ok",
+          "timestamp": 1598547096882,
+          "user": {
+            "displayName": "",
+            "photoUrl": "",
+            "userId": ""
+          },
+          "user_tz": 420
+        },
+        "id": "hwApbPstraWZ"
+      },
+      "outputs": [],
+      "source": [
+        "#@title Define the EdgeDetectionModule\n",
         "class EdgeDetectionModule(tf.Module):\n",
+        "\n",
         "  @tf.function(input_signature=[tf.TensorSpec([1, 128, 128, 1], tf.float32)])\n",
         "  def edge_detect_sobel_operator(self, image):\n",
         "    # https://en.wikipedia.org/wiki/Sobel_operator\n",
@@ -86,103 +161,48 @@
         "    gy = tf.nn.conv2d(image, sobel_y, 1, \"SAME\")\n",
         "    return tf.math.sqrt(gx * gx + gy * gy)\n",
         "\n",
-        "tf_module = EdgeDetectionModule()\n",
-        "saved_model_path = os.path.join(SAVE_PATH, \"edge_detection.sm\")\n",
-        "save_options = tf.saved_model.SaveOptions(save_debug_info=True)\n",
-        "tf.saved_model.save(tf_module, saved_model_path, options=save_options)\n",
-        "\n",
-        "# Compile from SavedModel to MLIR mhlo, then save to a file.\n",
-        "# \n",
-        "# Do *not* further compile to a bytecode module for a particular backend.\n",
-        "# \n",
-        "# By stopping at mhlo in text format, we can more easily take advantage of\n",
-        "# future compiler improvements within IREE and can use iree_bytecode_module to\n",
-        "# compile and bundle the module into a sample application. For a production\n",
-        "# application, we would probably want to freeze the version of IREE used and\n",
-        "# compile as completely as possible ahead of time, then use some other scheme\n",
-        "# to load the module into the application at runtime.\n",
-        "compiler_module = ireec.tf_load_saved_model(saved_model_path)\n",
-        "print(\"Edge Detection MLIR:\", compiler_module.to_asm())\n",
-        "\n",
-        "edge_detection_mlir_path = os.path.join(SAVE_PATH, \"edge_detection.mlir\")\n",
-        "with open(edge_detection_mlir_path, \"wt\") as output_file:\n",
-        "  output_file.write(compiler_module.to_asm())\n",
-        "print(\"Wrote MLIR to path '%s'\" % edge_detection_mlir_path)"
-      ],
-      "execution_count": 2,
-      "outputs": [
-        {
-          "output_type": "stream",
-          "text": [
-            "INFO:tensorflow:Assets written to: /usr/local/google/home/scotttodd/saved_models/edge_detection.sm/assets\n",
-            "Edge Detection MLIR: \n",
-            "\n",
-            "module attributes {tf.versions = {bad_consumers = [], min_consumer = 12 : i32, producer = 175 : i32}} {\n",
-            "  func @edge_detect_sobel_operator(%arg0: tensor<1x128x128x1xf32>) -> tensor<1x128x128x1xf32> attributes {iree.module.export, iree.reflection = {abi = \"sip\", abiv = 1 : i32, sip = \"I8!S5!k0_0R3!_0\"}, tf._input_shapes = [\"tfshape$dim { size: 1 } dim { size: 128 } dim { size: 128 } dim { size: 1 }\"]} {\n",
-            "    %0 = mhlo.constant dense<[[[[-1.000000e+00]], [[0.000000e+00]], [[1.000000e+00]]], [[[-2.000000e+00]], [[0.000000e+00]], [[2.000000e+00]]], [[[-1.000000e+00]], [[0.000000e+00]], [[1.000000e+00]]]]> : tensor<3x3x1x1xf32>\n",
-            "    %1 = mhlo.constant dense<[[[[1.000000e+00]], [[2.000000e+00]], [[1.000000e+00]]], [[[0.000000e+00]], [[0.000000e+00]], [[0.000000e+00]]], [[[-1.000000e+00]], [[-2.000000e+00]], [[-1.000000e+00]]]]> : tensor<3x3x1x1xf32>\n",
-            "    %2 = \"mhlo.convolution\"(%arg0, %0) {batch_group_count = 1 : i64, dimension_numbers = {input_batch_dimension = 0 : i64, input_feature_dimension = 3 : i64, input_spatial_dimensions = dense<[1, 2]> : tensor<2xi64>, kernel_input_feature_dimension = 2 : i64, kernel_output_feature_dimension = 3 : i64, kernel_spatial_dimensions = dense<[0, 1]> : tensor<2xi64>, output_batch_dimension = 0 : i64, output_feature_dimension = 3 : i64, output_spatial_dimensions = dense<[1, 2]> : tensor<2xi64>}, feature_group_count = 1 : i64, padding = dense<1> : tensor<2x2xi64>, rhs_dilation = dense<1> : tensor<2xi64>, window_strides = dense<1> : tensor<2xi64>} : (tensor<1x128x128x1xf32>, tensor<3x3x1x1xf32>) -> tensor<1x128x128x1xf32>\n",
-            "    %3 = mhlo.multiply %2, %2 : tensor<1x128x128x1xf32>\n",
-            "    %4 = \"mhlo.convolution\"(%arg0, %1) {batch_group_count = 1 : i64, dimension_numbers = {input_batch_dimension = 0 : i64, input_feature_dimension = 3 : i64, input_spatial_dimensions = dense<[1, 2]> : tensor<2xi64>, kernel_input_feature_dimension = 2 : i64, kernel_output_feature_dimension = 3 : i64, kernel_spatial_dimensions = dense<[0, 1]> : tensor<2xi64>, output_batch_dimension = 0 : i64, output_feature_dimension = 3 : i64, output_spatial_dimensions = dense<[1, 2]> : tensor<2xi64>}, feature_group_count = 1 : i64, padding = dense<1> : tensor<2x2xi64>, rhs_dilation = dense<1> : tensor<2xi64>, window_strides = dense<1> : tensor<2xi64>} : (tensor<1x128x128x1xf32>, tensor<3x3x1x1xf32>) -> tensor<1x128x128x1xf32>\n",
-            "    %5 = mhlo.multiply %4, %4 : tensor<1x128x128x1xf32>\n",
-            "    %6 = mhlo.add %3, %5 : tensor<1x128x128x1xf32>\n",
-            "    %7 = \"mhlo.sqrt\"(%6) : (tensor<1x128x128x1xf32>) -> tensor<1x128x128x1xf32>\n",
-            "    return %7 : tensor<1x128x128x1xf32>\n",
-            "  }\n",
-            "}\n",
-            "Wrote MLIR to path '/usr/local/google/home/scotttodd/saved_models/edge_detection.mlir'\n"
-          ],
-          "name": "stdout"
-        }
+        "tf_module = EdgeDetectionModule()"
       ]
     },
     {
       "cell_type": "code",
+      "execution_count": 12,
       "metadata": {
-        "id": "Ytvb5Gx_EFJl",
-        "colab_type": "code",
-        "outputId": "0f46bfc7-ebe6-4eb1-c65a-fc8b65adcada",
+        "cellView": "form",
         "colab": {
-          "base_uri": "https://localhost:8080/",
-          "height": 51
-        }
+          "height": 238
+        },
+        "colab_type": "code",
+        "executionInfo": {
+          "elapsed": 582,
+          "status": "ok",
+          "timestamp": 1598547098698,
+          "user": {
+            "displayName": "",
+            "photoUrl": "",
+            "userId": ""
+          },
+          "user_tz": 420
+        },
+        "id": "Q2no8DO_s125",
+        "outputId": "3a964b3c-9719-47e5-8bf6-34a6f11c53dc"
       },
-      "source": [
-        "#@title Prepare to test the edge detection module\n",
-        "\n",
-        "TARGET_BACKENDS = (\"vulkan-spirv\",)\n",
-        "DRIVER_NAME = \"vulkan\"\n",
-        "\n",
-        "flatbuffer_blob = compiler_module.compile(target_backends=TARGET_BACKENDS)\n",
-        "vm_module = ireert.VmModule.from_flatbuffer(flatbuffer_blob)\n",
-        "\n",
-        "# Register the module with a runtime context.\n",
-        "config = ireert.Config(DRIVER_NAME)\n",
-        "ctx = ireert.SystemContext(config=config)\n",
-        "ctx.add_module(vm_module)"
-      ],
-      "execution_count": 3,
       "outputs": [
         {
-          "output_type": "stream",
-          "text": [
-            "Created IREE driver vulkan: <pyiree.rt.binding.HalDriver object at 0x7ff712a0f0b0>\n",
-            "SystemContext driver=<pyiree.rt.binding.HalDriver object at 0x7ff712a0f0b0>\n"
-          ],
-          "name": "stderr"
+          "data": {
+            "image/png": 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birJQVCoVxzfihyXUcc3ruul2o/hpbBRaIW8hsPaO3IAZaKI5c6lUygnRnpyc\nxE/+5E/ac44ePQqgcyCp8kIFyh9QpPcOhUI4fvw4AODQoUN2QO3duxf9/f3YsmWL3evChQvrNlGF\nIPlvVjZQLk4/OzfD1oG1uc8+U6iYMLoGK+gBR0VI/Ur+nDK/P08PaOXEY4CBBmAoiziw3r+tfam/\n5aHC99DwfroKlB1F36ndbjsuFf0t9z7dMxUCZBoO20Vln+3QvuIedLlcLb90xQFFhxqwRm2kg8cO\npBNXkzgrlYo55ubm5gB0OKWAjsMul8vhTW96E4DOwNMxm0gkkM/n7V7Mt9ENUS0KCh35jHihtNtt\n8yvVajX09fVZ1JLfotLJqsESQGez5qZB0VpK/ugh1Wo0ko7P8Udm6eTWBci/a7+rH83zPIerz+//\nq9frNg7EtTVaTg9svqf6oHSRXS6aSjWzYrFov6V/i/NHLaZkMonl5WWHCofBIPwt28s2tVprZMP6\nHEZGKa1UN4pGTgFuTlkwuEYp5NdiqQHrdVqNQOfguOOOO2xTf/DBB83/eP3116Ovrw/T09MAOlaP\nRodxrvg3In4eHh7GuXPnAABnz55FOBzGa1/7WgDAxMQEtmzZgvPnz1s7/cm1nCtcC4qKeJ5na9af\n69jX12f5fACc+U1lTJNtg8G1Ui7qFwc688Ffw04PJLUgA4GAY/XQalHExW958NrGjRuxadMmDA4O\nAujsW5oHRauFvvKTJ0/izJkzdq9MJuNYjEqjxHHiXhSPxx20QedWMBh0yIi5DnX/0fmjBAtEkF6M\nuJvS80H1pCc96UlPulK6woLSPBbCEpqP44/uUp+OsgaTXZs09oFAAK9//esdDYth5N/61rfw9NNP\n49WvfjUAYHR01KHjUOiA99Iot5mZGbuXHwp63eteh69//etmptOS8bMxUJRiiXCYZncHg0FHC1RI\n0G/l0S9H0ZyIVCrl0LOQmBfomOuaQ0WoQCOLlP6FmqzSl7AdDOVVuOSGG24wC9MfTQXAsbAOHTpk\nGnMsFsPq6qpDpKmWLglw/VYn30H7m8/XvChNaaA1rpFqnFuJRMKKsnFMu1HUh8CIV9XcdZ0pzELr\nSSG9sbEx3HDDDQCAPXv2YGlpCQ8//DCAzryjP/bAgQOYmprCjTfeCKBjQSk8ROhcrYJoNOrA3Byb\nxcVFpFIpnDp1CgCwefNmDAwMmHVGqFBLqmjOlBKUktpHrV5tA0PBLxeppnsKsMYyoi4AhfXYJ8Da\nmlVXhP/9dY2Tzkz3C87RTCaD/fv3Y3JyEkDH96MwJd+B4xqLxZBMJm0PvPrqq239r66uYnp6GkeO\nHAEAzM/PIxQKOf3jh8t17yQhe/C4AAAgAElEQVTrOj+ru6XVajntAuAw4gBuTqHneU7h0CtJVxxQ\nWiGV0AB9Ln5ISvMc2GnqbE8mkzh48CAAYHx8HPl83jqNmwzQ2dgOHTqEe+65B8DapqrYfLvddg7H\n1dVV68xkMmmHxuDgINLpNC5dugQAmJqacpLWCKXoxuEP/VZzPx6PO0zbHHy+o4bah0IhOwj8TmDe\nT53Efv41zVNQJ3AwGHQOHZYXUMwZWMPxFS5KJBK46aabLOyY8KAqHRpw4HmdEhp89q233mrtbzab\nOHnyJJ577jkAsFLZGhRBfyHvzff1lwRpt9uoVCoOXZEfplV4TEN/A4GAlVBnm7tRqEgBsHmkfoEr\n5TWxLzTx9MCBAzZGjz76KM6ePWtzbWhoCPv37wcAbNiwAc899xx27twJoDMndC0xWInzg2Ot64Pw\nMA8rrn8qRhxfHrIaNKB5T5rnRiVI54oeHNw/tDoC0ztisZhDz8P26+GnkDgPQxWFsP2pIKpUMriA\nfRsIBCwk/9prr0UkEsGzzz4LoKMYK9s54T/O22KxiGq1irGxMQDA/v37sXnzZgAdv/HOnTtxyy23\nAADOnTuHJ5980nyJ/mCmgYEBJwClUqmYIkjWfHVb6Pzy5w0qxRrD9f2xApeTrjigVCNaXl522AC0\nE/wYciKRcHJVgsEgrr32WrvXM888g4mJCbuuxJ9f+MIXcPXVVzsWQK1Ws0XDoAlqH4VCYR0bAjuY\n0XMURgYpx5tuBrrgWNKBi5fJohpgoQuSvi4etO1227EYNWeI2pQ6zpnMDMA5+OjIVn8MxwboLATN\nPwqHww7Lh270N910E4rFokUi0hrVQ1jLQsTjcYfkVosKMtprz549Ng6PPvqoadSZTMY5VOLxuJPw\nqKwD7C8uQLJIcHNkuQE95NUnoRGC3SrK8Qa4Plw/p6Fq9bQkuEHfdtttKJfL+Md//EcAwL59+zA8\nPGz+3VgshptvvhkA8OUvfxlve9vb8O1vfxsALCBIIx0jkYgpLLSguZZWV1etX/v7+1Euly2QpVAo\noNVqGSrAQ0P9m5zfPHw4J6noar4R4PLttdttG3/1A7FUibLB6MHi94P5WTna7bbTLn2uPxCM/liO\nxeDgoOWYnTx5Elu3bsXLX/5yu1ehUHDWdLlctrXGoCuuidOnT+PYsWMAOnvrNddcg7179wLoWKdb\ntmzBxo0bAXRQpUqlYm3TtcN8Qg1e0ohRfwI1A984B6jwclz00H6xNdXzQfWkJz3pSU+6UrrCgtKI\nuNHRUXieZ5p6tVo17YlhoDzhyfxNC+KVr3wl2u224ddnz55FLpdz8nN46j/66KP4vd/7vXW+CzX3\nNRdpcXER2WzWNMrt27eblpPP55HL5SyyZnx8HM1m00pv0/JTBgfNJ4rH404bFUqgUGNkX1HT9VOV\n+HNdlLWCLAu8dzQaXceyrJBmIBBwIDzNi4nFYkin0w5/2LZt2wAA3/jGN9Buty3sPplMIpfLmVa8\nurqKXC5n40rrRC0ftjkUCiGVSlk7+/r68JM/+ZOGo585cwazs7MOR5jmk3ie57TR72/IZrMO7KWR\neqR+AdbDNN2aBwWsIQUM9/dbMsAaQqDhzO1222C7ubk5PPbYY+ajXV1dNesG6PQzYai/+7u/w+/8\nzu/gQx/6EIA1BnK1HBS2LhQK2LFjh1lUg4ODxjoxPz+PxcVFs9QSiYTjU2VblaVB4S4/ZRCjEfld\n9XXSoiSKkkqlDJngOlKfnMJf6p/kvXWuaL/7o/Toy1YfVSAQsDkfjUaNWePuu+/G4OCgReUx2lL9\npuo7YnQd9xNtYzKZxMLCAj7/+c8D6KylO++806yzTZs24dChQ/jOd75j31erJ5lM2tzi/dVnpT6n\nXC5n0bu8l7ZDIb8Xs6C64oAql8vOYEajUafksWLIKysr63IACDXMzc1heXnZDp3V1VXUajULsxwd\nHbXQ75e//OXOhGNnKlzUbrdtwo6NjeHZZ5+164cPH8Ztt90GAFbCgQsjl8s5IcoM3VQIgPcl2SN/\nyxIWGsaaTCbX5fbwUNKwznK57Bx2mqTL72rynIb++g8g5gfxwEqlUk7+Fv056v9iYMOxY8dQKpUs\nkbNcLuPixYsGpRCGuhz0AcApgUEeMo45E3WZ5Ll79248/PDDOHv2rPWfUhuxn/kc3cD8/GKETjQ5\nlxsB+0Y3w24UHTOGEHPu+J30wWDQNpwNGzbg+uuvtwTZS5cuYffu3aagTE9PI5fLWd9Vq1U89dRT\nAIC77roLzz33nI0B15Um1LdaLVMkBwYGMDw8jBMnTgCAc1jl83ns2LHDNmT6Y9jv5KlUOiGFlTQv\nzs+X2Gw2HV8n/XOqHPrzE/WA4vOANZJnigaYrKysOJyIvI/OMw1Yoe+MCnGr1TIf0jPPPIMTJ04Y\nDLdp0yZHqaKfh2NM/6xC81RmeYCzLyuVCj7zmc/g2muvBdDx/d5zzz22fv7pn/7J1iwJE3hf5naq\nIq215gi3+pU/jgPz7DjGV5KuWGW68BmRwgmoUTic+JoDdPDgQeuECxcuIJPJOJvH8vKyner5fN4m\n42tf+1qUy2XTAukE571KpRIGBgYsUqlarWJ5edkmXSaTceoMraysOJp3s9nE+Pg4gI5jUydGrVZb\nt0lq+XjivcCapsaNpK+vz4m+mp2dte9SK9GNX7VN+lD8ASnAGtmlLpp2u23O6sXFRScyT0k4AZeI\n95prrkG9Xjfse3Z2FrlczhZgKpVCPp+3TYl5HFq0Ut+hXC5jeHjYxjObzTqW3N13342HHnoIAHDx\n4kXboD3Pc+pDUavXujj+emOaqKiBLTyQ/UEi3SahUMg08VarhVqt5mzYnDdKkAsA27ZtQzAYNGd8\nJpPB9PS0rQ++98WLFwF03v9zn/scgM54P/roo+uClzRRPhKJmEX9ile8AsPDw6apBwIBOxh5oHK8\nZ2ZmcObMGWOwyOVymJ6edvysfgZ2zdVLJBLOIVMsFp2gKWDNqmQZc76vBnLwkNV7qSVTLBbtICDr\nhAbjAG5koJ+lQwMuWAyQ47N3717bw44dO4ZWq2XozPDwMLZs2WIRxel0GsVi0Tmg2B/VatV5h2Qy\nieHhYRvz6elpvPOd78Tdd99tfXD48GG7jwakcO/g/KFvkO1eXV11chJpgfO+xWLR8e9eSXo+qJ70\npCc96UlXSldYUOqTIY6p4ZyEGfzsDdTqCOMMDQ1heXnZCffetm2bE7vP03r79u2OD6ZcLjsm/fDw\nMNrttrFTTE1N4eTJk2bJ3HXXXaZF12o1i/IDOiXMX/GKV+DkyZMAOtpHtVo16DGXyznalEZTlUol\n5HI5B8bMZrMO9qt0SBoqXq1WUSgUzOqhRsl3Jszop/bhb5XahuOiOUOau0Q6FTX51dyPx+N42cte\nZm2MRCIWeddsNrGysmLjFovF0NfXZya/QhbhcKcWDjVs1pmh9ktN9w1veAMA4POf/7y1Y3l52Xlf\nhvMSamAujlb2zGQy5v9otVpm5ZFFWqMpu1GUi5IpGRoRq3REyuweCoUwNTWF7du3A4CxAigMk81m\nHdiM7A8TExP2b6BTqqZUKpnFFAgEcPvtt+Nd73oXgE6E4MzMDLZu3Qqgo7k//vjj1q58Pm9UR7lc\nbh0nnMJp+m+gs374/kQL1CepodPz8/PO2tM0EkLafH9lmaDoXqQpGfT9KmWQuhNoJeoexzwr/p5o\nw/HjxzE8PGz+3auvvhrz8/OOL/GZZ57BE088AaCzPjZt2mR7HiNVgTV4kvsD20HLb3FxEZ/+9Kfx\njne8A0DHDULf4JEjR9axd2j+Ktc/+5auBq5TppZQ1Nrq+jyoZrNpOUXnz5/H0NCQfc5kMnZA+XnZ\nMpkMFhYWzK+0srLiDAi5oGgOa4cNDg4iGAzaxkffAyd7sVhEvV7HRz7yEQCdzeuTn/ykcZHddNNN\n5mMpFAro6+uzRbq6uopvfvObjoNRoUnP85zABYW0otEoCoWCLQxu7lp0jlAUn8U2M9xd618ptEjn\nqcIYFFIo8Vomk3F8Y7FYzCHPHRgYcJKk1V+TSCQcv2Kj0cD09LT9Np1OY2hoyOFEO3funMM3SOhg\nZGQE09PTDnzkeZ7BpzzAODfe+MY34sEHH7RrmhdFHJxQA/nF1AehFCx9fX3Oc0nJBMD6v9vEHxSg\nG7DSHjHZWclOp6amzGHebDaxuLhofkVSV6lCR1ipWq3i/PnzdpgzQID9fuONN+LjH/+4wYXLy8v4\n2te+Zs8aGRlZxx3H8R8fH8c999xjwUnnz593UgXa7bYpb/39/UYYzWt6QLVaLZRKJduQqQiqT1ZT\nH5imAKyVcWe7qGwqPZHCp5ogzTmqnIi6WdN3o7RBet/z58/bQcEDm2OcTqeRTCYtYCsQCOD06dPW\nvpGREfNnkZqI871SqTgwXTabxezsrAVR3HPPPebfn56edsLbma6ih7TCh1ScNQBFg850L3kx6UF8\nPelJT3rSk66UrrCggDVH2fj4OKrV6mW1aX8p9VqthsnJSZw+fRpAxxmfzWbtel9fn8NAriHYs7Oz\nDrNvo9FAoVCwkNZms4mPfexjBtORBkeL3VELGhoachi4y+Uyfv/3fx8f/ehHAaxFzyjUpsEY1WrV\nLCpS+fC7yWTSAin4Tkp9pLCcZoADHU2tWCyaNsUsfWpBGtHHUG61atSp7ieT5dhQ/OUD/KziAwMD\nDsShkVhjY2OOU31+ft6sXkIJtJhWVlYwNDRkVjVLfih7AEOln3/+eYRCIaeUi0INhG38JVc0hF+h\n1kwmsw7m6TZRpgxC5X6SXgBWhoJjxNIL7Oevf/3rTnXmHTt2YGlpyXFs03KZmJhAKpWyAIpGo4HB\nwUGzmCYnJzEzM4PPfvazADpUSM8++6xRI8ViMQtGOnPmDPr7+y1Kc+fOnTh8+LBFsf3zP/+zo3kn\nk0lnfS8tLTkWkQbfVCoVh1aMycQa+MB7FwoFJzw/kUg4jDfZbBZLS0tO0IyuFYWACYVxHAYHB7Fx\n40azbPr6+hCNRp2IYrWg9HO1WrVQfGAtLJ/3HhgYwJYtWxxr7emnnwbQWWcbN250gjf8jB6xWMwI\ntQ8dOmSsPFu2bMGRI0fWsazzOaSR8hMV03q9XIUKjYi8knTFAUVoDlhjuuWGrRANDwV2wuLiIpaW\nlqzDV1dXnfLfO3fuRKlUssNO2XZnZmbMPAbWWAdIqbNhwwbceOONNvCf+9znsHXrVidUmhM4Fos5\nrOrf+MY3cODAAcdfw5pHQGchcHFrtA3Q2ZCz2ew6vwmFi0zhOfUxaFkPbkKcJKQz0josmvekTNCe\n5znM6n19fdizZw8mJibsXnw3Pltzu/SwO3/+PM6dO2fvsbCwgP7+flsYzWbTibaLxWK2qRw6dMhh\nnWAYtUJC9XrdyanYt28fAODo0aNotVoOYwH7mM9RGIchy1xMSinDMfpuIo9eSmH0FNDxo2qJhIWF\nBfOpkmGC8Hir1cLQ0JBTqkI3Ws/zcPbsWSeEnUoCIW4qc0wToJLxxBNP4KabbsLrX/96AB2oaefO\nnZiamrI2UykoFovIZDI2zwYHB9HX12dK18TEBFZWVgzy0jnKumo6nmw7/x8IrFV+zmazKJfLDnMC\nv8t8Oh7gXA+cs6xGre4GZaGIRCIGu1199dXYtGmTjQN9u3yHcrls1FOAy4HHcG22iywTnIeFQgEv\nvPCCHULnz5/HzMyMtXt4eNiYIxYWFnDy5EnzZ23YsAG1Ws0OO8CtDHz48GFjcDlw4ABOnz5tijH7\nRPtLy4SoLw9wfXice7yXKlB+6YoDSh3ZpVIJi4uLtuFks1kLOQ0GgxgeHnY0YpZABzqdpHkw/A41\nO/379PQ09u7da50zOzuLRqPhkFbedtttVhJ+cHDQFjHQ6XAehNTuqH2+/e1vx8WLF80a27t3Lw4d\nOuRYa/w3cxr4mQEQyvmXyWRsEZHnjBuFWkEM1+fAM69Dc5f8RfeUbqTRaNgh+/KXvxypVMqx+hKJ\nhOWnVKtV8+UAbnl0DS8FOpvKpk2b7KAMh8N46qmnzGfHfAm+B4uhAR3NPZ/P2xiurKyso1yh1e0f\n45tvvhnHjx+357LUhs6t5eVlx7epG55uWPwe31kPrm4SHQfmrnBOJxIJ20CKxaIlQQOdIKB0Om2H\nxqZNm3Du3DlbW7Ozs+u0Yw0p3r59u/VJNBrFpUuXrK/OnDmDBx54AL/wC78AADh48CCmpqYsb2pq\nasoJ7FhaWrLx3r59O7LZrAU+DQ8PY9++fbb22u21QoFzc3O4cOGCQ5OlJWjog9R0hkwm42yUXA9E\nMZQ2SQ8R+rO5XiYnJ62vstms+bD4XQ0rn52dxTPPPGPJuPQN+X1Y/K3mdrHUBvfEgYEB7N+/35Sy\nYrGIF154wULHqcSz70qlEp588kkAHd8gD3wApqDyHYvFoqWKvPKVr8R1112Hf/7nf7Y2qo+pVCqt\n49drtVp2b6WZo6KsgRxXkp4Pqic96UlPetKV0hUWVDgcdqCD8fFxJ0pHMVKWCwc6lsjp06etGueu\nXbtQKBQMzy6VSigUCo5P5nJsBUDHIkomkwY1NBoNLC8v27Pe+MY3Ynp62mjv1Vfhh6yuueYabN68\n2eCUVCqFl73sZQ6UwPc7ffo0lpeX7bf9/f1YWloyTXdxcdEhQPVTrCjVETPIlR1CLShCG7z33r17\nHe06Ho+btUFsWst6UJsDYCUQ2LeLi4uO/65er9u1crmM5eVl0zZLpRL2799vEUKtVgunTp3C17/+\ndWsnx4k+KPb12bNnrTIq2610Nyo7duzA4cOHnWRbEuiyXUq8S8tVSyxQG8/lck5IP6Pbuk3UlxeN\nRp3Kx4VCweY8I8VoLV68eBFbtmwx7Xr37t0IBoOmfRPu5BwmjRKwVpmYa3h5eRlbtmyxviqVSnj6\n6afx/ve/H0CHpHTjxo327LNnz9qcZrj2//7f/xtAJ5R9dXXVvjs9PY1KpWLWmUb8XXXVVdi8ebPN\n6VQqhVqtZmP1wgsvGAIBrEX1KWO3kueqH3X37t3YsGGDg9aoj5aQKK+Vy2Xzjb/wwgvrLCQtmcOU\nDfUzUWKxmNGOAR2ygfn5eWMg7+vrw6ZNmyw9oK+vD6961avMh3f48GHbh2ZnZx2m87Nnz2J8fNzW\nZbVadUhoPc+z5+zduxfbtm2zdAB+j/sFq/NqCLtGvfpJvskKBPwQMEkoTZCyiwOu85Fmo/JhtVot\nc+rRKUqTvVAoOPxR6XTaNuuzZ89i48aNji9jw4YNTn5RrVYzSCuTyWBsbMw24bm5OVs0xG1572w2\ni7m5OZvcLBHBiXH+/HkbuI0bN9pmAHQmmOd5ljM0NzeHmZmZdfRE/L4eIv5s73Q6jauvvtqZgGqG\n++tuKZMxa+so55fSNZ09e9YpOaKwHvud1xioor4wf/nxq666yvonn8/jK1/5CoA1bJuLf+fOnVZS\nAOjQVymtjj/0dXh42DYoZcrgGCvLNvtR82bUtxWLxRxFoRuFdYqAtXnHd1YqGvarBsVcvHjRFIGp\nqSksLS0ZlJTNZh3+tHA4bIcEQ7DZJ1T+CMMNDQ0hnU7bnF5YWMD58+dtXdZqNRsDbnT0bx05cgTD\nw8N2r3K5jKNHj9pcGxwctDk7MDCAXbt2OWsjEolYGZDdu3djZWXF1i2Da7RqrlaQ1f+TH1PzsTzP\nM5j64sWL1mYGRajbQnkgyZ+nz1DFG1jzmzWbTYf6jXltPITb7TbOnDlj0Gy5XMaePXuMeeMnfuIn\nbD587Wtfw/nz5+19E4kEpqenLV2mWCxicXHR1pZSsl26dAl79uwxN8YLL7zghMazz5UtX/2Byj2Y\nz+cdeJnvcjnpigOKpK/AWs4QJ1E2m7VJUywWHfJX/o0awpYtW5DP521jnJubw/DwsJ3q/jpLmgQ8\nNDTkRNqxkJfmJ2micDwetw7mxOZgDg8PY8OGDQ4RLaMEeS/NRRgaGnJKcwCwxT85OYl8Pu9QEGnN\nn3K5bJuK9guwxmuoeVFK9cNDCOhoV1qsj0SwanVp0icXoeLVWpBQfVCFQsHJsVpaWkI6nXYWYTab\ntbHIZrOWLPjQQw85Vo3mkwGdTSkSiTi5Pipbt241fwYTE7kxMthCrUYtpOcvT6FEsuy3bhPN1WG+\nDfsuFovZHGXtJ86NdDrtWAxnz57F1q1bbUwZFMS+mp+ft+9eunTJSZAdHh7G/Py8Y6mo5To+Po5y\nuWzX/YXvyuWyWSr1eh1zc3MOPZWWItcoxVKphFOnTtk84m84ZpOTkxgbG3PIZFWzZ+kb3lcDZhYW\nFjA/P78uUVuVZ41SVfJYf4E+v8WvCiO/r1FymrhOhV33C+W8i0ajeOaZZyxgZevWrbj99tsBAG9+\n85vxwgsvmEI/OzuLpaUls7ZisZj5LfkelOeffx579uyx5GrymyrNVLlcdkhhV1dXnaAJ9U81Gg3r\n2xeL4uv5oHrSk570pCddKV1hQWn2N0N/tTCaEoeWSiU7lWdnZ52CcyR4PX78OICOKVmpVCxUkqHU\nQCfEUksUEyZSGCoWi1moaDqdxoYNGwyTB9Y0HxZYpOZx6tQpTExMmEZVKpVQqVRMG1XNvNFo4NSp\nU+Y3Y/QQ359Z+fw+YSo+K51OG/xBf4xaJgqlRSKRdRg87xOLxZxMbzKV6zu0220HR1cMPhKJWNgs\noVZaKqQjUhhXc9D4bMI8WhjuTW96Ey5duuTAtouLizbmfkhGYYdQKISxsTGHRV7hJFJDUYrFIgYH\nB+37jUbDqZhKmItt7EbRirGEUTQiVP0biUTCoKN4PI75+XmLUt2yZQvGx8cNdn3iiSdwzTXXWL8z\nAo33IREpAGesgY5/MhqN2m/7+vowPT1tyIbC+J7noa+vzyyo0dFRg7GAjvVdqVTMoigWi4Y2sFy8\nUn0tLy+bRbWwsOBQcinrDNBZx2oRaORZKBRy6Li4HmkFKA0WkQnuU8w91PnOQqQALN/M/3s+l9Rg\nwBpDPX/rZ1VnBWLOW6Vn+7Ef+zFkMhmDbfv6+pwI4cXFReedFY24ePGi478ia4RCxslk0lkvZIFh\n31LIQqFMO1eSrjigisWiDebKyorjR1DHPMPIlQOPybtAx080Ojpqm9YXv/hFZ8KSEw/o+IF27Njh\n8Nhp3geTWjn4IyMjiMfjFu6qcf3EV3m4nTt3DmfPnrVDZ3JyEu122/xZygcGdOBFwoH0e3AT5SGg\ntEHAmvmtpjJzqDTAYGZmxmFvrtVq9ux0Om2Le3V11UmeIy+ZYu6avJjJZBy/mjp9mQxLf12pVEI6\nnbb3J/2UQi3qgPdXhR0fH3cSSm+55RYn10sTmwHXP6SJuf5yCwyN1VIv8/PzhrMrdyMPvhdbTN0g\nmirhb7NCskzRoNAfx/GenJzE0tISbr31VgCdftaQ9UAgYONL35Ru/LqW/KVbzpw5Y4cVsAYXAzCK\nJM5/smAzd2d+fh4DAwMONKnQGhNG2cZ4PO5UgVYqKG6y/D2TldkOz1urJcaAC61Fpz68aDS6rvaR\ncvzFYjHrL+ZiKYSmNd7C4bApwlSStBJCPB53GPu1tHomk3F8ss1m0yDuv/u7v8Pk5KTj8ykUCqaY\nsjKC0pdRlEeUY6aQaCqVclwCpJnjO+m99HD1X/NLVxxQ7XbbOPEYzaI1oNgp6qcAOhuX1lFZWVnB\n6OioDd7tt9+O48ePO5gqDxhGx+gE06zqTCaDbDbrOCOZt8G2cGJzk1TNvVAomHZOfJbXFxcXbcLR\ngUpeMzJlsM3UtBS/1sAP5ccitstrJNJUAkdNKFYyXE5ojaZSrW9lZcVJIF5aWkIikbAAhFqtZopA\nKpVyDuBUKoUzZ844G1p/f7+j2XIi8z10EmvJFTqmeTACL148UNvRaDRQKpVs3NLpNBYXFx3LVqMk\ni8Wiw4moJdG13HU3SbVadRIv0+m0cwCz/fl8HuFw2DbcQqFgVhQA3HDDDTh27JgV/6zVak5C/bZt\n2+wQIeehBhhpVBqTsGl19fX1YXh42CIGx8bGnFpIqVTK5ka9Xsd3vvMd86MwgZft1qAoBghxLi8s\nLDgRwvSDKmoCYJ3fBOis/2Aw6KAz6uj3W9DpdNosk2KxaCV3gPUHJ/+mCq7mNvL57Hd+h32nwQeN\nRgPlctm+xyArtjMWi9l9GVCmc5q5f0Bn71ELU0m7edCzXSx2qf2hPJaVSgXVatVJxqdQadT8zStJ\nzwfVk570pCc96UrpCgtqdHTUKfylTNeazU9rgdqWwllAB1q77rrrjL5ldXUV+/fvN20rm82aRhiP\nx50onaWlJWQyGafYm1LCX7p0yYGllLmXtB30wWzZsgVPPfWUae+nT5922IzpCwI6vrALFy4Y9k+L\nh1odfVdaJVe1rUwmY/h8IpHA6uqq9R19DtTU8vk8+vv7HbobWgJDQ0MGHwAdC0FZOTzPs/BxCkPT\n+U4UMoGof0t9Uul0GktLS047ldKpXq+b5pXL5QwiBNZYkbU0QaFQMMvAr40tLCyYD/KJJ55APp+3\n75LpXSGNgYEB00b7+/sdnxP7gO/UjRKJREyT1wg+wGW+J0+lnwZLy5Iz6hPocOD19/fbHL/uuuus\nH8fGxjAzM2M5guR1VEs+Go06Vu/p06cd6FVheoWgCLPyudTKCU9qEU1aF/RfLS4uOtFzOicBmAWp\nnHDKVq6sE+12G+Fw2KAuQlrsz3w+b/sS0yKUWUOLjhIO57OSySSKxaJdJ0UZsMaqTmGOoUYT6z5W\nq9Wc/CNSNPH9tOxPNptFPp93ODMjkYhTlFShVy3KOj4+jnPnzjlsGWqRkx1IfdZqball/2L8ll1x\nQKljl+HH7KSVlRXbuJaXlzE6OmrQQjKZdBbhzp07LTwaWHP0c+ArlYpt5ps3b8bU1JQ9hyGkHJBs\nNouzZ886h4o6yTXkmHk5/O5DDz2EnTt3OnQ8Kysr5u9RPwaTFAmtjI+PO4uq0Wg4gQ7E8vXQ4UbB\n99XqxDq5GVxB2EJLArPwxLEAACAASURBVFQqFXMq6734TvF4HLFYzOAjHpRKxMkFygXHCUjHrZYy\nUX49Vn3lZw39JX+iwqfqROah6i/FwvsMDAzgkUcesTYrrEvIR/tIE47VX0d49HLJlN0k8XjcGQdN\nxm02m/ZvrjE9rLlxUBiwBHTy9crlss3T4eFhg+WpXHENnzp1CoODg9aPi4uLDnQ6PT3t5O8BsDlZ\nLpdRLBYt8bRSqWByctI+79q1CysrK0bl8/Wvf93WCg8mrrPBwUHMzc3ZOxHeU+gtlUpZO7VSted5\nyGQyTu4Sq24Da2tH/Ui6rhXmXlxcdPgzo9Go5ajxWXp4Kqk1w741t1Fhe/IBqj+XewbbpGs4kUjY\nOsvn84jFYg4VmIbAM/UA6OwdExMT9hwehJpsDKxB6vSdaSl6hUUZT8C+vJJ0xQGlmjujeNhozVgf\nHR11nJH0/WjxLmaxUxqNhgUrHDlyxJLSotEoZmZmbDA2btzoaIw8/TkgHDhu4PV63drIujL87vT0\nNAYHB62uypkzZ3DLLbfY4v6N3/gNm8ylUsnJIanX60ilUnYQ+BMkqcVTg2w0Gk7BPdWAEokElpeX\nbXKHw2Enik+dqcCaMxfobE6a2Fyr1RzNjtaXWpGK9SeTSYdRmQucv1XLOBKJoK+vzyGTVH9GPp93\nnMBjY2OOL7FcLjv5a8ruvry8bFbz6Oioo33SR6cadjKZtL4dGRmxcalWq06OVLeymuvmVi6Xnbpc\nfp+aEhiTg5BjUiwWUalUzNoeHh5GPp8364z3B4CnnnoKAwMDNu/6+/uRTqdtPvBgpL+yr68PzWbT\nLKrV1VX7LqPyyG956NAhXH311XjjG98IoDPec3NzzqFy5MgRAGuWt0YE++eZjiE3dj0otRBmIBAw\npIMHOJVfPRQAONZUNBp1+DZZe4vWNyPvNBBM15LuOzy4NBlf/VuRSATpdNruxXFV/5oqd8onyP1C\nn6sBWcFg0PbadDqNXC5n7BgXL150Dnseolp0VANSlFgXWFtPvHYl6fmgetKTnvSkJ10pXWFBEaoD\nYDlAWmtGGRm0ZAY52/jbubk57N271+ACaj3UIHbt2mXa1fT0NBYWFuy7ly5dwo4dOyxTGuhoBbR6\nqJny96qpF4tF1Go1a3MoFMLRo0ctauncuXMYGBiwSD3FiMlYobCcZvTTXKflQrohaiMKFTAih5qb\nVhLVvlNuQ9VcVWMiJKPmv1oNzLHR/Cxqm4w81JD9VqvlQGcKh7BUAe+lFnU+n3fysRSyAjqaq2qF\ngUDAIJ1arYbZ2VmrD8XMdt6bocLs21wuZ7AHsD7SihY7+7obRSPNyI2mFZd1rgBrUCX54DgutVoN\nW7duNUYCciJqqDT/fejQIbRaLUMqxsfHEY/HDQUgyqHVezXniNGVgFsRGOhYY0ePHrV3+F//63/h\nne98J97whjcAAI4dO2bjPTc3t86nqFF7hLy1tI2/arRWGSiVSg5dmUKgzNVTC0Fz8tSSox+JEXCN\nRsOJiKTVohGniookEgmH+VstpEqlgnK5bHOaLhLNHfWnZPBaOp1GsVh0GMk1bUfHeHR0FKdPnzZq\np02bNhn6w3YBaxYoYXjuH0rBRmuTa6vr60Ep1YmWZQfgONNZnpvfjUajWFlZsUnEPB6l46GzF+gM\nAPFy1pQhZLd161ZcunTJymuwtpRSmUQiEcuRqdfrdiDFYjGMjIyY+T85OYnp6WnLIfnmN7+JhYUF\n/Pqv/zqADmZPGInl2/0Fyvhcwn+a96TEnNPT0zYJGJ7LRTY0NOTkJqTTaSc8Wv0oPBQ44eiPY9/n\n83nE43FbKFq8DVjLe9BxYv+QBoaHfTabdZQSwlCX2/RJG8Nx6u/vRygUsnGcmZlxSmgw7JZ9l8/n\nbfxzuZzj+OUi0VQCdaIrKS0DRnSz60bxcw22Wi1nw1aeNQ0LBtZ8IwCskCMhPpZAIUy1vLxsG+4t\nt9yCxx9/3O69detW7N69G4cPHwbQOTjGxsbsAKtUKo6SoRBbf3+/Mw+2bNmCSqViQRITExO46667\nrMbR7/7u767LO1LqqkQiYWsnk8kgn887989ms45vSAM3gsGgzbNGo+GsLYZJK48f1zD9tRwLrkFV\nsnS+0y/E32tKSrvdtlI//JzL5Rz4NJ/PO0ERGnCk70SIT+dIuVw2n93IyIgTsJJIJCzwJR6PY2pq\nyiC+ubk5p24VcyxVcaa/GFirt6dtejGSWEp3qoE96UlPetKTf/PSFRaUwnZMgGVEjkZwkSTRX2RP\nYabFxUXTAkdGRizBEuiEwyr7LpN1gbVS4koBPzAw4ETIaVJgq9UyzYNs3dTEG40Gnn76adP6Zmdn\nMTo6imuvvdY+831HRkawsrLiOC7V/GU5CGpM4XDYYYcA1pyMuVwOs7OzZrmsrKxYMTRgTSvUZ6nT\nnNV6+RxlxyB0oJWAadVwnHjf5eVlx5JJpVKYnZ11yjEoeWwsFnOimJT9gZqrsoGEw2Frx6VLl5yQ\n/1qtZvQqTHrmtUKh4DicU6mUU8ZcKyTz9/wtw4r90Fi3iVZMpqNa0QmFaf0OcSU8XV1dRb1eN4iH\nQUUco/7+fvvt+Pg4br75ZtOun332WbzsZS8ztGFkZATLy8tONG04HHZYBtivsVjMKa9x4MABpyTK\nsWPHcMcdd1jhvIWFBQeSLRQK1mYGAPCdmbSqhNGVSsVhpeA8q1QqiEQieP755+19/ekNGryUSCQc\nKi9lrWGAENcD26DWOEO8gU70oQb6aHg7g5MUelfEiQEWGgil0bEa2RsMBpHJZBxmmcXFRQsyq1ar\nlrIDdNwAbIcGbQBrofO6hnV+KdNIJBKx/uV3ryRdcUBp1jmwVsabwrDZgYEB89kAsIg1zULXCccw\nYTXhya3nr+dy7tw5zM3NGW39tm3bHHiMfiEtTa01gZaWlpzQz3w+78Bpd955px1uMzMzTrimsrcz\nek55qpRDLRKJIJPJWLtGR0ft/ev1ugORMGyUhxCjdJRlmfclJRSZjsPhMJaXlx1zXxUJwmEaKsr3\nZZl5Pmd2dhaDg4MOLYq/OitZ3dm3utj1gDp37hw2btxo7bjhhhvwyCOPWFRff3+/E024tLRk709a\nGz67WCw61Xv5HvSdKOM6w+qplHAT7EZRHwuruVI4N+gTUl+GQjaLi4tOVBbhMvbNhQsXbBMcHx9H\nLBazUPCZmRk8+eSTdkDt378fs7OzBhcyl0lLeRD+5abPdmzYsAFHjx7FN7/5TQCd8hHZbBb/9//+\nXwBrjCdsoyo+5Kmkb7Tdbjt+OM4Hvc41OjQ0hHK57Iyz+omYnqApCxqZm0wmnb7Vz/Sd69zyU6fR\nXcAxU/Z0bYeyzACwaEC/34n3USZ07mdMF9i6dSs2bNhgeyAjJvm+Cj2y7brnqR8ZWIOY+Wzd87Q/\nut4HValUHJJGUm4AnRcjvQkdtXwh0rooTc709LRpACz3zHuxvDrQCSvPZDI2WAMDA4jH4xbYQMJT\nWnIjIyMIhUI2GZT8le0mf9bp06dRLBZx4403AgCefPJJyyMBOj4qTgJSzHDR8ABVPDsaja7zHXEC\nM+eIv+EBR1FrhOGu6vjVXC5y+QEdpSCTyTiWjAYoMFeL7dL6LqQTYruCwSDOnz/vBCeo5sdQWN5L\ny3DXajUMDw87tXa0yFy9Xsfo6KhZTSsrK07y6fDwsGP1qo+SlphqweFw2EKcy+Wyae7hcNh8emxX\nN4qfuiYSidgYM4GW15SHjdc04GjHjh02D0+ePImJiQlTFJaXl508v1QqZeM5MTGBF154wRzqBw8e\nRDKZxFVXXQWgc/g9+eSTToqDbl5KybS4uIjz588bGrFr1y6cPXsWhw4dAuAWIZ2fnzerGeisd63h\n1Wq1MDQ05Ch7zG8EXGqfdruNYrHo8BUyLYH3Ul+PP1dJlV+Wn9HPanGx7If6v/zP0RQNDelvNptO\n2DnzRtVq4juQlFtD50OhkO0XZ86cwY//+I/bPjY1NWVBZLVazQnZVz8/26F9T/+t7ic8zJnHpSH7\nV5KeD6onPelJT3rSldIVFtTw8LBpvSSEpcaQSCTstKYfgBqAZkUDa+U4CB3k83lcunTJTMmRkRE7\n0UulEgYHB80i8Ee4Pf7443jFK16Bv/3bvwUA3HXXXRgfH7fn9ff3m7ZQq9UwNzdnWnylUsG+ffvw\nuc99DgBw44034tprr3WgJWpPyWRyXcFGDZUmrERGYpIwUubn550QU03cJYSn1qlqyf7ihQp/ZbNZ\nLC0tOVaPMmP7Q1YBmFU4NDSEQqHghLNXq1XT1gcHBx34hJGMHKd6vW6aejabRTabNS2Q1C5K679r\n1y6D+KLRqI1poVBwwnvz+byVbgfWcHRlw1Cm7FKp5JRyUD/Bi8ESL6VoIjsjMf0MHrzmD8FWWpxK\npYLR0VGD3iYmJtBoNAzNoOYOrI27+nrvuOMOg/hSqRROnjxp8/Ad73gHTpw4gT/+4z8G4DLh9/X1\nYcuWLYZcPP30004S60033YTZ2Vk89dRTANbmEgBDONS3qVWEy+UyMpmMWRsXLlxw/Ej1et1ZK2q5\nkKHdz5zg/z6whiCob1wtKDK08DP921xrqVTK+pJWC+d0tVrF8vKyrQdaZwrdKsymjC3xeNwpp0FX\nCvdX+nuJIJw+fdp8UP6kdvrvlYJNoUVGhPIdNZqQicr+4o+Xk65ZZVw4Fy9eRCgUcspA8EBi7oSa\nlRr6S2oNHlCnTp1CLpczqOHcuXPrqGt475mZGXz729826GBoaAirq6vGPPGe97wHP/3TP43Xv/71\nADqdzENkcXER9Xrd/DdLS0tYWVnBnXfeCQA4fvw4Nm3aZAeYhqumUimHnZyUKP5wZ8WctcSAHhL+\n7HYGLugiKhaLDrOE5pTxfvys2e4s3aCVfEulksFnOtFnZmac2lFsh/JyJRIJgw+CwSD6+/sdZUHh\nH62+ysWsdE2ELoG1cFf2rZZIyOVyDotypVJBu922sSCbt+brcPPzV03moddtEgwGnTwXzeVSJzaZ\nI5QJRH0GQAf2ZpDAwMAAjhw5YuM9OTlpod4TExMYHh52cvfm5ubw13/91wA6/R4IBPDMM88AAB57\n7DH8zM/8DO677z4AHSWL867dbiOfzxvUPj8/jwMHDuC2224DAAt91wALBuvQt6EHFGui8Z39dYj8\ndEW6aerGr/mPwJoPRh3/yulXrVaddamKNH1KqqQq5Oev5qD0ZSsrK84YpVIpR7HOZrMOR6YGldHP\nqG2JxWK27vh89q1WXOb7KNMOXQrsL83Bo6uG+2skErF9m+Pi7/PLSVccUJVKxYn+UNLBYDDokF9q\nQTqluAHWsHD1z6hFFQgE8J3vfAdAp+BaqVSyEhqpVAqpVApvectbAHQGQB2wv/7rv45jx47h4Ycf\ntmcRq61WqxgaGrJD9pZbbsHu3bvx+OOPA+hYbnNzc7aJJpNJczaPjY2hUCjYe1Aj0mTTRCJh1gax\nXN3Mebj19fVBy3JriWU+119kTOs7aWADowm1lAEAm3D+HCN1EpMMln3Hd9SESFqO7B9dsKurq9YO\nBk7w+f5SDiw1QOtsYWHBuBpJYEotkM5l3puUWlqHSrnJJicnHX9WNBrtei4+JnICaz5GTdxVbjnA\nVUiULNZfCI8BFVqWhdbVpUuX0Gw2TRH0PA833XQTDh48CKDjzzp69Kgd7p7n4S//8i/xqle9CkBn\nXpIj86mnnkJfX58pd3fffTdSqZQ998yZM3j++edtXAYGBqzN5A7UMuN6yHDdqHNeKcqU0NRfGJR1\nw7gh06rjdT0o/CU56DvXiDalgqKFxP5st9v2W27sGvhUqVRsPyAnIudjqVQya59jTmWKCiavcY9T\nJbVerztKqBLaMlmZ7aAPC+ggShqpyLmk60TpmDR46cUsqJ4Pqic96UlPetKV0hUWlJrDhHo0l0O1\ndDU7ac7zMyEcnszMoVLqDlpTpL/fvXs3ADihycAaizAtiGeeeQYnTpwwDWHDhg2WGb9nzx6nxHMu\nl8Pq6qpp8gx915BMZWRQpnOWc1atV38bCoWcsh/lctmJyiMECKxpkHx/flZIQ8Nm/VFKSk9Di4kW\nFLVzjS5Sn5xqmzMzMxbyTtmwYYPDjqGRRwpbxuNxLC0tORqhv2hluVzGzp07AXT8Cryey+UstJjP\n0ShGWtgK+2quUKFQcKIp5+bm1s2TbhMl4aRwHJSNnRF+GjasFoSf+Z6WmEI6nO8sQ06LiTlTf//3\nfw+gA0stLCyYb+P666/H9PQ0/umf/glAB80gceyrXvUqI/kFOtRGpVLJGA3a7TYOHz7sVJGmNUGN\nnf4rws7KBqOh9JlMBsvLyw6rhfaP7i2ErPw+b83J1BQUf7Vc9fcR0uM7rK6uOqS+Cq1zL2AbFxYW\nkMlkbE5zPDT3UUvBkCoJWCuU6q9uy/nieR7y+bzRvV177bU4ceKEPYfwOrCWy8bfLiwsOCXfyUyj\n4fIKtapVrCiYX7rigALWfCD0K12pHLD6I4D1HE+NRsNhAp+amsKOHTsArGHOAIxzipOCdYZ0wumz\njh8/7pjaSplTr9ed0O58Pu+wd7PEu4asEpIiTKChzsrg7s+38G+QejDQB6V0NvF43K7z8OYGrjWa\nBgYG1vm9dJGRcshfH4mTv1qtGtcg6zPxcKfvgwc6v8OFxARoLXnNzY85JXwHQnj8TIbuAwcOAOj4\nHflb5sGof8OfBOw/0JWVXZ3kLGui/qtuFA1R5kavDPXaF1pexr/OmH/Gg2FxcRHDw8PWV0tLS+Zz\njcVieO6558xf5XkeRkZGbBwSiQTGxsZsrhw6dAiNRsNSKoaGhkxBO3PmDC5cuGD9yw3wscceA9AJ\nimg0GnYv9bv600rK5bIdnsBaqoRWKGCfsZ267zC1gN/xQ1zq6C8UCk4AjfpcSDGklY01yIT5lqos\naPUCMrzz3iMjI6ZkLSwsOFydntepCq4+Wq5DKh1a86pUKtm6SyaTOHnyJK6++moAnVQcBsDk83mr\nc8V38EOc5OAE1nLd9LD0l7nhwdT1ibqaccy4f27gmqTmT0SlZaI5FO122xJot23bhna7bQtJy0cM\nDg4aswDQCaCoVCqmuVWrVbTbbfNfpNNpTExMONgwJwEdt5o/oxntJFql9TY9PW3W1dzcnHPIMvJF\nnbGqtc3OzjqknvrdVCqF5eVlJ8JNncAkd1UKfY3Si0QiNpnZ98oZqHlS/kWl2jg539QXoL4f+qQ0\nt0mZKNRv4mdDYB6LMjxoralyuWwHJXFuTVRNp9MOQaqWwM5kMo7ll8/nTbPXUgrs924UPYA9z3MS\nIvk3fq+vr8+ZR0pi7Hkezp49i3379gHo+IZYmBDoKAZcGxMTExgYGLBDheXgOZdWVlZQq9XM3xuJ\nRDA4OGhrb2hoyNqxuLhoSAnQGV/1K66srCAQCDhrT309RFGANSWO64eJpvzt8vKyQ0SsxMGAW8Mo\nGo06my3/7d90eR/1BdIH5S/PocEJGsziRzg0R4r7I/fES5cuOawMfJbeg+NCv5DWh1IfEQ9SijJY\n0GfM9yZ5ABVcJuLzel9fH1ZXVx0eSP+a8Sf7Xk56Pqie9KQnPelJV0pXWFAaGgzAye5Xunjm4ai/\nRgvdMfeAOUPj4+PYtGmT0XWoBpROp5FIJCxyBuhkyysj9+rqquVyAB0tidjvhg0b7N/VahUDAwP2\nnJWVFfT19Tk5E8oErdAarSdlxxgYGLDrzPvR0FktNJfL5ex9h4aG1kW4KQTy7LPPor+/36AFDbEm\nY7JyojWbTbP6zp8/b5VxgQ58qhARmcKBNdiS18rlslPmYmFhAaFQyCnTPjg4aFaz+qsIwbHv1E8G\ndKCV0dFRe9aWLVscuJjh8myj+htKpZLDQr+6uurAq0r9xCx89YV1o2jkKVEApbpRi1er3DabTYf6\nKBQKYWpqynx7lUrFgYcV3mJOG+fVwsIC5ufnDR4i3Zj6/prNpjP+zGOjT5ZWzvz8PFKplFPcT3Pm\n1JqmFa4lIJQ2iFC55sEpe0S9Xrf3Y7FKPlcj2vhbZf4mrMdryvbCeaNcj8o3yDByRT4o7COF/JRi\njIw3/G0ul3Po3nTOxuNx+z7fV+nOmEfJ/WTnzp3G/vH88887EF6hUEAgEHD8m8pqw/QW9sHw8LDt\nl57nYXFx0cmTvJJ0xQHVarUc3Dgej9tkIH8c0NkwtOy4VnME1ri4iJMzbl8Hl5Pz9OnTjlOP4eyK\nxQ8MDNjnWCyGXC5nmLT6SThRNdlYoTXi+Vofidj+zMyMTTred3V11Rad53lYWFhw6s5UKhUHaqJT\nOJfLIRgMGvTS19fn1KzJZrMGAQBuqHQ+n3dq9DB/RA8zwkJ8B02iVs4vBjno5CwWi06+xtjYmOMU\nrtVqBomqc5Whw0rtEo1G7TqTC9nu173udfjUpz5l/aFO4aWlJSeRkUESvDcrk7LvlXi3WCxiYGDA\n3l95GLtJ/CVBotGok1+jh7vCwf5SDAxBZj8zgVlDuqkocmPXDTyRSNiBxERc5Z7L5XI255eWlpwy\nHso9SR+swmWaz6Vh4qTB0gNKIVsqexpkoz5IraitgUrAGr8kD3/mE2peENuhJNMAbE9S/7Um7pLm\nyx/Mw75jfwJrML4qcH19fba2eG9V6imE8JUEQCmH6P9nKs7+/ftx/fXXA+gEH+k+HYlEsLq66vAg\nKuUS+1uhce7b3JO1RM6VpAfx9aQnPelJT7pSusKCUvOYmc2amKZVGgkXUZTahOSE/D6j6wjT8P78\nfzAYdKLvFMIgrKZZ1UNDQ5e17FKpFKanpx3LhOSywFpSH9ut5K/UJJR0UqGFUqmEaDRqScF0iLJd\n9XrdKWOhpJ2saMn+YfirssErTKMRf6p1833VAc9yAXRekyoJWINO1ApkmQCgA59oWC3bpNnzCseq\nRsjQX03yCwaDzrioFjw/P+9Y52NjYzZujUYDq6urZhWREYSWAS12oAP/sWgbAGcOdpNopCWTMnUe\nakSnCq1h9jMRAC0yWa1WjepIrYlwOIyFhQXHEq1UKg4djzI48Fmc0/V63TRvJk/TgiKUrCHKGhSj\nbN5M+FeGfbWwlNoLWENeuPZ0zpJhXJlSVOvnbzUSjfOK6ALnFRPxNYFayZXL5TKy/x97b/LbeHpd\nDR+RoihxnknNU6mkGrqrem64ncbrIU7gTWwkSBZZBAkCBEj+kayCrLLJJtkFMBA7iBMgbRtuu92D\ne6geqrpGSVWlgaRIcR5FUtS7IM7lvSx349uFLz4+my62yN/wjHc495xA4BnmfGA47/j+LJ5n6F0r\nG7BdXFwYMAyfUYsrclw0ErfZbJrwuhZ4ZdSGIDKOoaYy0iAbFrbzc6VSkb2i3++jUCjIuGiPdrSN\nxQEFwHCthcNh6UQdM26324ZhmwgljdLRobbd3V0899xzhl5f09hkMhmButbrdezt7YlkQLvdFpcX\nGPL+aV0inb/SyDNS53MSU9JBV+Hr+iH+ns/V6/UktOT3+5FOp43kO+GzfA5NRzI1NSXPmM/nTa0S\nFwFzZVpB1el0CvoQGGwyuuaMujoMB7DuSyOXNCuBjoNTeVSziMdiMVk4DOnq7+tqf725ORwOQ5VC\ntBAXe6lUkjqO4+Njs/BTqRS63a5ca3p6GvF43IRpgsGgYTXhd0OhkGFn1gfoODVt3IzCeFdWViT8\n2263DaKx0WhIKYH+LefKxsYGPv/8c2xvbwMY5GvZT+vr66b+7NVXX5V8JwDJN3FedjodtNtt+b5W\n52XtoTYANLMCJd01bRYbawh1KYDm+eMa1DLk/B37iXP49PRUmFmAwRzVhz2bzp/wOkQHc+/w+/3G\nuGu1WgZB6ff7DWKu3W7LfaLRqAm1k6Fdpz10qJ61bFpGR0umaDQhD2ytWxWPx+WdDw4OJAe5s7Mj\nCGI21opyTGdnZ+VeZG3RBpHeh7VauQ6ljraxOKB03J/FoaQ+0bFuimsx53J8fGzkzwlG0HmRmZkZ\ngQqTew0YinPp+iKPxyMbT7vdhtfrlXsx3jxauAcMC0L1YGkvp1KpIBAIyACxBovPoS035m605e50\nOgVAwMnKHJaGhROIoA+ZSqUii71QKMDj8YgVzI0CGMpnaLCGBmPMzs4iHo/LwckkuYa36jg4ZTL4\nTiSQ5ed2u2341LSwHHV6gMGC1PkpLn4uMoo9arAH5VbK5TI8Ho/RC9P5C/azLj3Qi0pvRrSeR2tn\nxq1pElLmn9ivOjJBqRldf6cBBjQomDC/du0aAIgVrIuY6Q0wGhEOhw2wpV6vY3V1Vb6fy+XMc5FG\nB8AzhhvzmZoAV3sIuoiVOWjtUREMwr+7XC4DDNFlHBqyzugC1yFrrHROh+8GDD0GYCgMyXnFOj7N\niafpywhWGuWu5LW0AUYpIhr0oVAILtdQ7JORHR4kes7SuNTac6Q34vt3Oh357Z07d7C5uQlgYAju\n7u4aI/z8fCjaSp5D7TWPljxw7+A76MjXV7VJDmrSJm3SJm3SxrKNhQel0V31el1cTWBIcAoMiRI1\nGkRb/QwTaZZxv99vZB1o8QQCAaOwS9VXVsMvLCyYPBIwdM2BIfwVGFI10RJhPoaWAa1VPpdmrGBY\ngZZGtVo13hnRT3zn+fl51Ot1Ux3OdyIdvs6xsCgWgBRPsthSV7ufnJzA5/OJtwEMPDBNM6Xj5ixq\nHKVK4jjoUAq9Fo5Do9GQynPeR0uMaKojxut1SFMzo5Okk/1ZKpXEY2bhIN+BHjWvncvlsLi4aEKv\nmrVCj1mr1UKz2TTif+PYdASBYWZNCMyWzWaN1c/5q5m+tZwC2SA4X5LJpMzJ09NT4/Wen59jfn5e\n1tLly5dxfn4uUHIqGfPaZOzm8+uyk7OzM/j9fiMToqHSLpfLQJv7/b5Y6iy54H3oqfP7fEdNFsvv\nEimqVaK12CXpyvRzaei3joJ0Oh2USiXzWy20Oj09bXJ6qVRK5juLzfWa7veHyr/BYNB4oLVazVAS\nMUfLf+v+oBTJBCgnMQAAIABJREFUqGq2jlDR61pYWMBnn31miHB1rjifzxu2EIZE9f6g916dK/y6\ncPlYHFB6cnPDpeuocxuUqeDfjo+PTfKdIRq6mU6n08hW6xwKN1vtXurDkLBbTT/i9XolsauT+sBQ\nX4rvo2uMGJ/l4Ho8HplgDBvwPsVi8Rk58Xa7bQAY+p11FTmZ2zVE9ejoSDZZ5hM0N5/OT2kmb9aX\n6FoVHVrzer1GmqJUKsnhFg6H4ff75X39fj8qlYpsaMlk0uj48Dm42DWbfTgcNvyKrO3RMXZteKRS\nKXkml8sl8Hn2h4ZSx+NxoxLK+aXDGBoK3O125Rl1/nGc2tzcnCS2+a5aBoV9zhwI+5FJfs5/MpRw\n8wgEAmajTCaTAj6iscYNp9PpIBKJSL8vLCzg8ePHpu5Fj6E27qizptV6tUQMmfLZNMP8xcUFfD6f\nOSj0oTKq8aS5BPl3nfvWJQpTU1OG6oos+Ho/0MATLZ/BEhYNMNIhbYa7NWsJD9lkMmmYdsi6QlYO\ngmI06EozlrMf+F0NpWeNpA5x8hAHBnWV7KtYLGbej6FRfYCT4o1zQB9+VBHmfXX92deF+MbigPJ4\nPDJZWq0WUqmUDLwWyWLMnIPH+KsmOO10OtJpPp9P0GbAEBEEDOOgHIBQKIRwOCzS0pFIBLFYzGyi\n/X5fDsd6vS7Xzefz8Pl8MtA86PQkmZmZkedmvofPqK06DhY3+3g8jlKpZGoVqtWqHIajFlK1WjXJ\nV10wyyJf9rXWghktpgyFQsaDYk5Oc491Oh0Zp2azKd5lNBo1XiHrITRaqlwuGw68eDxuakw4mUf1\nnriwuXFq6hb2ATecWq2GYDBouNh0YSKRSPw98yS8l/YmaWSMglrGrZFcFxjmoDi3/H6/RAwoVcN3\nZ55UG056s6e3pYvNOY/6/T5mZ2flPtVq1axpejKcW+St4yGkPeZ6vS6bMjCkAuMYulwuA+bQtV0E\nWNFT6/V6xpOhxzBKRK0JcjXoQtdjNhoN40HTO9UeFNek0+k0tYusvdJeoaYoC4VCCAaD8vfHjx8L\nxdTFxYUhSy6Xy0bap1wuS70j32Fubk72Gp1n5Hhr8lxgmO/jPqRBNhrN6/V6jXfe6XQMIlBHQbxe\nrylk1tRnumaSz/xVbZKDmrRJm7RJm7SxbGPhQWlI8szMDMrlsqnm1ye9RmzRstY5Ay2nXq1WjZAe\nrwEMTnxNmcIQEy2AaDRqQhxUtdW1PRq/PzU1ZUKJWsIZGCrUAjAINlolrC+g1cr7ZjIZE/IijH4U\n0g1ArFj+jZYqc06JRAKlUsnAf+kx+v1+BINB6R+i+DTxppbHJhu2hr8yRMGYOEOV09PTqFQqxoLW\nXiClPPh9LTqp78XxG/WaNVKJNSgcI01YGY/HhbgXGNLHaCisZgOfnp4WbyQUCqFWq0n/0Fsct6ar\n/RmiHGUhAYZ1Ttoz1RYxUXi06ul9amQe70OkJUPrxWIRPp/PeMhaapz35/pZX183aykcDov3StSe\n3gO096o9BEYHdC5Me8iEZGuSUu1B6XQC0Zw6tKhD3KNqCL1eT96fuTp+ZqhVs+Q7nU7pn0gkgkql\nImtxa2tL7vP06VPUajVZswBM7dLTp0/h8/kMma7OLWp6MlJZjdZnagSgTh/oyE4oFDJ7K1Mc2uPU\nbCHEB2hEqcYaaE927GHmelEVi0XhZgOGNEHAYJKwowAIk7keDB3PZChN11BwE+R/WRdChUhCsBcW\nFowKbLvdliQiMBgMHabTLOtMAHMxc9Fouhadv9K1GqR60fFqDRVn+EKzKPPf1IrhwiCVkw6XOBwO\nE6bhxOcC1O+nefxarRYcDodsNMViEfV6Xaj5yQzNZ9MbI+/BdyLDOCHMZB9nn2iNGiZ8tS7TqM6M\n5jnUG1gikUAmkxE+xXa7jXK5bIp62+223LdUKkldFTDY0DkfqJo8qr8zbk3X0JH/TYdh2I/BYNDM\nb+YMuNExXK6LOH0+nxQqn5ycmNCZzg2fnZ1JeBUY5Ip1npW1TNo45HiurKyYYmNKZOg6OHJuAsM8\nMzDMz+qaKk0CwHCZ3iM0JFuvJYao9aaqn5kF45xLnU7HcHOSgojjoI1kHgT8O4Fh169fBzBYt1qt\nm2E7joOmEIpEIshkMmIc0iCnAXx6emr6in3Ia+u6KNaJam4+7o/xeNyU9LRaLVHCZl9ryD/5RTW2\nQIcHNTR+7HNQfr/fIDwqlYrZcNjZc3NzKJfLMnldLhcajYZITYdCIUMeeXx8jKOjI1MrpAkrdfKx\n1Wphfn7ebNA6v6GLh4Eh+wH/Njs7KxO0UqkY3Rla5vqzttrdbrfJQTkcQ0l3/j+N+mu1WpIk9fl8\nplBXFxvSW9BWDWPD7HdOGorZaU9N12Mw9q8RgBsbG3JwbmxsGHJYp9MpC5CIJ943nU6b/BcLefVz\na0lyrUPDuDbHuFarodFoyMaqJz75zrSHrdFSRANq7sZ8Pi/jmE6nZTEvLCxI8TafYxybtuq52dBA\n0Xx5p6enCIfDZm5oAMmoRMr+/j6SyaRwEDYaDRlfSmDQ0GABMEEx6XTajOnohq11g5ivoZHF2iQN\nAuD3AMtgwv+nBfpGc8Fa0NHtdkutHK+lgU2jxcI6p0LQE6/t9Xpl72D9IOedx+MxRe/0eGhsLi8v\nY2Njw9Sr6XpDfZ9sNoupqSlTG0ovivdiv+nfc4y14UgQmOb1PDs7Mywt9GSZ69bvS25LYKgXx88k\nXtZ5Ru0xaTShngujbZKDmrRJm7RJm7SxbGPhQWkLilT8mpJI5xtarZZYKmSWYLiOFjN/u7S0hMeP\nHws9S7PZFCuFVPFagE9T7BDnz1OfbORaQVJbm7pWh6EUWuoM2WkZBFqfZGum1XJ+fo5isShQ4XQ6\nLRxZfEfdP/pao9QhdLs1R1632xXrtNVqyfuFQiETF+Z3dQX/wcGBeBTXr1830FqN2qM1xfsWCgXx\nyIBBeIk0TMAg9KhlQHTtEuHdHGPmHLSH5Xa7xUvWHtHCwoJY0QCEp1Fb3MfHx9L3DCfqOjv26dHR\nkVBY8drj2rSnToZvfubzx2IxQ6/DPOco9J9tf3/f8DzqvBAwzPcCQw7Ihw8fAhiKWWq2FF1jpvMz\njCRoSLMO8WlFAn5P16Tpa7GGiu/I3CafgyE8vrMuK6D8jJZu0eFEfl+LXfK5NKM6MJSe55qmCOPK\nygoAYHt7GycnJ8bj5N7CvBFz1KFQyDDgJBIJVKtVCQlGo1ETMtNQcCLxNCxfy4Dw/2mPiyE+h8OB\ncDgs/dFoNAyPKaM+epxGOUU1dsDr9UpfFgoFoScbbWNxQJHuh03ToOgEGut4NPeejrEzScfJEQgE\nDGhAgxy42XCCUaNFh950/Hpubk7i8HxGXTOiFw7de26qJKXVh6MugNSUQgz5cYMmQEDnr6huyd/z\nEIlGo8/EervdruGP0xtLvV6XfxP2rROmuk6G3GQMpfn9fqlZAWDCHwzZ6vizlszg3/Vznp2dGVJX\nDY3VIR6GVngvTnyOI7nI+N1Op/NMuFS/I2HXAIR+hc9RqVRMElxvmuNaB6XzTKTU0TRC3ECYJ2I4\nkzyWmotPc88xT8JNdn9//xmlXvazpi4DBn2nQ0nM9/IgaTabJhemqY1Yp6PzTDQIAWvAEuTA65J+\nSYeQNKiIBguNGx0OJMkyD0xqNnEOU+KFz3F0dCR9wxpLfchoOfn9/X1sbm7K3Ds+PsbDhw9/Jxcf\nofEaVj41NSUHByVg+I6kQdOlFDr0qI1SbTDw7xwvNs3TR6kP/Tf+1ufzwe12m5pMTZCt6d2Y69NS\nPl/VxuKA0mi3drtt4sq6eJQiYhxYrWcDDA82LY+si2L1fWq12jOChdpj0iy8wLCCWx8MbCws1ASW\nusaEuQ1dbKuLcfWBTA+H1yIgYLRAVnOR8VkoGqYTu5rRQQv3AUOePz6z1qhhRbomsd3c3DTWFb1O\n9oGuxzo+PjbV7r1ez+R+dE5vd3fXkOuGQiGjp6Pj6PTU9DjNzc0ZHkS+Qz6fx8bGhikg1e9ULpdR\nrVZNjtPpHAopJhIJcxCmUikZ/1FOtnFp+oBmPo7joo0ocivqQ1cfwizE1JtstVqV3IfeYDgmnIf0\npnRux+v1GsNyVKSTY6SteTbNUsLDTCP59GFIFnLeV6sf0OjkoUMtKM5LnQtmX/Ba7DcNBMrn8wLm\n0uuO+4Fmx2i322Jg37hxA4FAQPLI1WrVkMvqg5JzUAs0alBJNptFPB6XKFEmkzH5QG0Msy/1/NDz\nmLVM2uPUxNN6X2GeWXujOkrComY+Zz6fl+8S/af3+K9qkxzUpE3apE3apI1lGwsPiozUbKTVAQan\nrdYv0ppFjHtqfiitD8Waofv37wMAvv3tb4uL2u12TZU9IZa04HK5nEEb6RwZYJU8NaIIGFhZpVLJ\nKMzqHNXvql6n9cQYOlE8rEpnaI3xey3lQQuQ7rymBSIbMjCwmnXNkJZboMyBrpnQ7/3iiy8aL7Hf\n7xutKSIq+cyVSkX4FDWKks8ZDAbFO00mk9jb25Nr6VAjWcS1HpRGRFErR3un/C0ZKXgfyp7wHan3\npMOnc3NzwiEXDAbFm2LIgrF+HX8fp6aRd6xx0Ugqrh1KbrMvWPfHOcw6Qa0HlslkhEU/lUoJopO/\no4VcKpVMOIxzlGEpehdae0zXOWnk4Wg+UzOS8LcadaYVkr1eL3K5nEQMZmZm4PF4TM5Rh940NQ/v\nq8PWOudGKiPuJ3qOxuNxo+FUrVbhcrmEHWJmZgYHBweCPqb8DBtlX4DB2tCQdb/fb9hiCoUC6vW6\n5EQXFxfx85//XNjndQ5ulJ7N4XCgXC4b6iONPmw0GlKiwVy57gPSKgFDJV8ditdMG8vLy4alREv9\njGqT6TYWB5QO0TAUpIttOYE4+XTdj4ZN06XkC3Nz4eDq5KXOPQGDRUYyVT5TtVo1bjdddf5e50l0\nvJ76RTpxz9ofYLARsgYolUrB7XYbmQ9N1xSJREyYjnB2LvZyuWwmiY7Pk6OO/cXEt/7MlslkTH+Q\n6FEvHH0wMKFOFz6RSAgE+eLiAqVSCT/60Y8AAN/97ndRKpVksk9PT8Pn80kYgjIJPEgorMjv6jox\nbUTwtxo6q/u91+vh/v37Ust0fn6ORqMh12J4RHMk6tzIxcWFwGxJ8zPuZLE6jMd5OSo6CUAosHQh\nuubA43zn90OhEEqlkpFE0fLfOjx8enqKRCJhtNNOT09NjlH/nuSqfC7ArktNT8UaKh46rBMCBuNX\nrVbNgaQNOIbK+JyUWddCgpzv5+fnEubmO2ghReqDadogHWovl8uGi3JpaUnW6dHREY6Pj6V2KRqN\nIhAIyHPr9AFBLpzzL7/8sskVlstl3Lt3D59++imAgRbXyy+/LJ93dnZkrlYqFVPLREi/ThdoKLnO\n19Ho5juw1lPL8czNzZlcmQZr1Go1Me64t+i95qvaJMQ3aZM2aZM2aWPZxsKDGmUh0Mgj0tMAQxio\nrnSenp6WRGU2mxUFWmCgCLm8vGwsc1qArCgntDMcDhuUH9Ffmgm70+mINQ4MK7NZZDoqN6Epc2q1\nmngQ9+/fN6quv8tV1hZSPB43rnWv1xPr1OFwSPKVTNa0COlBMsRXqVQEPgvAJJCJgOJvI5EIksmk\nIfzUAJT9/X1z7w8//FBgs/fu3YPT6ZQQ349//GNEo1GBHScSCTz33HMStpyZmUEikZBrHx0dYXV1\nFcDAutSEv5VKBYlEwiDTRgv92O/Xr1+X8AowsLA1Uz6vo73kL774As8//7z0MxPZDAVpho9xbBqc\nUK/XEQwGn6HcAoYF3pruSatCh8Nhw6oxNTVl0GPAEPXlcrmwtLQk3ke73TYs+gzB6lC9hoNrCDJg\nhSLp5Wn2AyrUAjDs/Yx68G9erxflctmgx7SAIftDzy2ObzAYNLRYzWYTkUhE6Iii0ShmZ2flWpqU\nmc/JsBvXBfclsr9TDDAUCmFubs5I2Wg292KxaAp3w+GwrJ2rV69iZ2cHb7/9NgDg448/xtbWloxF\nNpsVUFmxWBQJImBQaqAVG4i01JEVRhD8fj+2t7cl0sKIjvaS9f8fVTvQLCVM0+jUxFe1sTigpqam\nTAjL5/OZuLJGC2mkCPMNPLBSqRRcLpcs0HA4bODOrLcBBi5nvV7HF198AWAQI43H4zIgxWIR8/Pz\nMgClUgm1Ws3AO/XkrNfr4qKz3kDHsykLAQzl1fldDW32+XwGpss8ga7YZo0BAONWU2NF1/loFgYi\nJHXsVx+y3W5XwqGRSATRaNRUe/f7fXn/fD6PYrEoEzYajeLXv/619JWWX2BuTEPWK5UKXnzxRQCD\nTcbv9xtjQNOg8GDhHBgN+WkmbR2yWVxcfEbWYTSHyX4CBvQ9a2trklfodDoShgyHw0KFBHx9WOJ/\ns2lKoVHdnX6/b1jwy+WygfprRdl6vW7kwqemplAulyWMu7CwICGbRqOBa9euyZikUilUq1WZ48Vi\nEZFIRIyZRCKBQqFgEHKaUbvf78ucLpVKBnlGtKBGgOl1Va/XTRje7XabkFa5XDZzRbdRtKCm3OLf\n9VrS3IW6HpGHE/N1U1NT2N3dFWN4YWEB6+vrQhPGED7XFvn0AJgDFhgY3RcXF/Lsly5dwurqqtAk\nnZ2d4ejoSA6Dhw8fYmtrC8CQcox9Sa01zn/CxDXfplbhXV1dFaor7nmjtZf8LTkPNQpWlzho5OXX\n8VqOxQE1Nzf3DLxUb8C0vP1+v4kpEyaqDzAtqdHv9yVRCgw2JFozpVLJEImyoJMTf3V11SSRy+Wy\nsTCBoTXKuLjudJJxsvEg4rvpglh96LJGiFbFKHluuVw2PIBaMoSeGN+J+Qf+loekFiGjRUiROXqI\nusCP/VMoFEQD6OHDh+j1enKg64NRe2J8Dl1DRr44Ggdra2sIhUIGDs2JzSLmUSJebY3ra2tafx5e\nvBYtVz5bvV6H3++XPmBf8tDd3t6W92P8nX05rlx8ozV0sVjMUD3pfIuumQNgIPaMRHBMmNTm3Ll8\n+bLRe2q1WuIhMD/ByMXc3JwQyLLpInA9niR41cbDqAinHofRgl7mf4CBkdVoNMRw9Pl8mJmZkYOV\nXJ1ci3Nzc+Yw0wAs3kvXjY3mWTj/PR4P1tfXZf3s7u4aY8fj8SAWi8m1Hj16hN3dXXlulnAAg3nG\nseL4atBEq9UyNVi8Jg87bezF43Gcnp4+Q4DN+cHSG10nqMFHwWBQ1goBV5pfUdev0dPlHPD7/fJO\noVBIQFn87Ve1SQ5q0iZt0iZt0sayjYUHpenkq9UqGo2GWCapVArr6+sAIKEueheNRgO5XO4Z5gBa\nLqenp8Yq1EV8DocDn332GV5//XUAwJ07dwRaCwzZDLTo2Kg1NQqFpYVAa3PUpdWFu1pNU8fUeR8+\nB4tUtYx1r9fD8vIygIEFxVwPaXq0oqpW3CXaSlMMaaQRBSD5frp4lu/w5ZdfAhjmLHitVqtlWAg0\nfRVDNrovdV6k0+kYuY1AICBhNiKtOOadTsd4oxRV03k2zf6gQ3469wYMLDdCovm53W5LrlAjSIl2\n0nD/cWw6t0O0KJ+ZxebAsCyA60wL0wFDGh9axAwH0cNOJpOS29je3sbHH38sVnur1cLy8rJY20Rt\n6SJwDWmnZAowJK3VBeHaoyJyTkOhdXhQEyCzJIOfSQRMT44IQY6xLjzv9XqGjolzltdi6YcOgRKZ\ne/nyZXS7Xdy5c0f6KpFIiOeWzWbxjW98Qz6//fbbphi/0WjIc5BoVu8PHo/nGUoy7j0nJyfIZrPS\nt5FIRMgIrl27hkwmI8/Jchc9XzTtmiaDrdVq8Hg8Jq9NWDowZJXgtQiz515E5QmOgybi/Tri5bE4\noLxer8RnY7EYfD6fbEjtdht7e3sABguh2WyKa0g4ulZ5JZsCMKT2Ydz86dOnsrFzojGmyvyTDh1V\nKhXD+6c3e838Tc42nZOamZkx7M2aNkWHOjhJOCn29vYQDoflt6Q90YtKMzLrEB7pV7SL7vP5jNrm\n2dmZhDx9Pp+ECigJwk3k+PjY0CQlEgnk83mjy6S56ggdB4a0KKMgEq2B5fP5JEa/vb2Np0+fSq7E\n5/PJAVWpVLCysmImsQ4BMqHM+aMVdKlfw40zEAigWq1KX5Nyh2NMYAvnXjgclsVNrkJeW1NzjVOr\n1Wqmti0QCBhWbY691+s1Gx2/z9Zut9FqtZ6Ro2HfHB8fy3gy78s6n2w2i36/L+wG9XodnU5HrkU2\nehoOGkZNA0qvLQAm2Q4MjYyzszN5DubRNMBgtKZSG4o0qjT/pl7vOl9LSDb3ltXVVaO43Ww2xVA8\nPz9HqVSSzwx58jmz2SyCwaBRINbvFgqFzJrVHKIEVXEcXC4XIpGI3KvT6eDSpUvY39+Xzwy9Xr9+\nHYlEQj4z38f3DwaDJpfmdDplr9V9AUAOa10XqkElxWIRiURC9oRSqWRUKHQdKfv8d7WxOKC0Ndpo\nNExcuFaryUSnZaE3SU1VxEWngRDBYFCK1q5duyZWHeutHj9+DGCQB6nVahIzJQWKRttprSGN/tGT\nBRjy1umi33A4bJAu/G00GjUDRM9Fo4kCgYDkRTqdDqanp81g89/BYNAkvtmXmqqfXiUAY8U0m03U\n63XZRGiZ0cqjF0PvgqARvqNGNFHoUNff6IOUxZI0FhwOB3Z2dvD+++/Lc2vkEXWuOG78DTDIjSUS\nCfl+tVqVRRWPx41WzsHBgclBkcpFAwceP34sm2M0GpW/nZ2dIZvNyuG2s7ODcW1as0fXQVFPDRgi\nWvldFjHrAlBgWO9FklV+v9fryVzx+XyIxWJiCIbDYVOf1Gq1kMlkZIMmt6KeL9q71sTDtLw1TZIu\neudvgGHdGkEB/I7OUWuNKxqSus5SF6KPys0EAgEj61IsFsWQisfjBhSlCQMePnwIl8slfZ9MJvHo\n0SOZ/5cvX8be3p55Z41w04YRPShu/LOzs8ZL+uCDD7C9vY21tTUzjsBgLQWDQVlLxWLRiDSGw2EU\ni0Wz5vl+RFrrdakRk6wT1PJEOs+kc1sE8vAg/LpC3fGMU0zapE3apE3a/+/bWHhQupaJlglP5qWl\nJbECqtWqIGSAgVW7vr4uFiIr3Xkix+NxPH78WKyPSCQilsnGxgaOjo7Eerpz5w6ef/55sbY6nQ7m\n5+cNCSmly4GBt6JrYqamhqquoVDIMJCTCZ3vpBEtlARgvc2olUeGBe3ia4kADaNliFOHJdrttsnn\neDwew1qsw2pnZ2fyeX5+3kDFK5UKSqUSXn75ZQDAf/7nfxrmAf3MhKtqy2hmZsaoGa+uropFXSqV\n0Gg0xPM9Ojp6hqRUsyb3+31DMXNycmIUmPm3arWKN954Qyx7srHfunVLrj09PS3hkHA4DJfLJZZt\nrVaTfg6Hw1hZWTFS3OPYQqGQkTzQOalutyuWd71eRzKZFI+Bism0an0+HwqFgpE10Uzwt2/fFmu6\n0+lgeXlZQkcnJydIJpOyVpxOp0GMjlLmAEPPmJ46n7NQKBgKHdYqarYUzWbAUBcwiFxoGDrDUBrJ\nqOsXQ6GQKdnQ8HfmW/ncmUwGxWJRap2mp6eN6GKr1TL7ztTUlEETf/zxx/KO165dw8OHD+XeOudG\nZnRei2Ul9GyDwSD6/T4++ugjABDhQ80GwXXHnCPHmJEOrtN6vY5IJGKUrhl+3N3dNWoG6XRavCZ+\nd3Z21qCew+Gw8VZ1SYPP5zOitF/VxuKACgaDshA4iJxE+XxeQgnJZFKK8YDBonn48KFoiTDezg4v\nFAq4du2aYQrnpHC73YjFYhIHDoVC+PDDDyV0c3JyYsIFGtYNDCaOlnHQiygUCpl8zuzsLA4PD2UT\n1Wq8DCNqShEm6/ldDUtnqIWDraGuzPtwMmezWczMzMhznp2dGYqhWCwm78McCwtkyWFGvrWlpSUD\nhKBODe+tE6SsPdK1KrouLBgMIhaLmcWuqU/C4bDA2a9cuYJisWiYkEOhkKFn0eGUubk56RvenwdW\nu91GJpORMWZ+gv3BvADn2+rqqowplVe5CfM349Z0zZjf75fCbwBGLmF+fh7dblc+ezwetNttIxev\nC2QJLtAwY4aKvvzyS4TDYSk8Ze0S5xLrxzhXTk5OMDs7ayDdmiasWCwa8A5pmICh2jI3ylwuJ2uT\nGyzfP5fLmbo3Htg6f6lZ2XVhPp9La9Gx0BUYzNHXX39d+oDUSMCwjITPcXJyYmiRWPrAdZlMJqW4\nn9fSwB7NGs5icd0/t2/floPy7OwM6XRajIelpSVzuNdqNel37g28NlUSCIQplUqyXzK0yv4hTx/X\nAcOFXMM8rNgHWm2XoWRd2/ZVbSwOqMPDQ9OhjUbDAB3YSdx8dKGulkDgBs0JOT8/b+LVlUrFSCJf\nunRJclCnp6fC3QYMCuBarZYsYMZ+eW+HwyGTgoKCnFSnp6dYWFgwDA+jYmeadSKfz8tAMn6vCRwb\njYa8I+U3RqvyAcj3NI+dRp45nU5cvnzZiC6yTU1NIZVKyQYcCoWQz+dlslLCnfdKJpM4Pj42sXIt\nW691p0isScttZmYGPp8Pv/rVr+RaPp9PACunp6cGjKAXbDwel0Jo3X+0+vx+v8TfW62WQWYFg0GE\nQiEjjlcqlQyjgV5Io0TF9IyBr5ep/t9uOsGu5UfOz8/Fa6UXyu8yisEDqtFoSLElAMmDas+GczIS\nieCzzz7DzZs3AQzycx988IHx3EYLczVQSBfIco3rNavrj0ZzkFq8cn5+HqlUStYsjSqte6SLkQko\n0rkRen2RSAQOh8MwOPj9fslncl3wwE6n02LYeL1egzQkIEQblYyqAIP5f/36ddy9exeA5UwkwEqD\nWRqNhmGSefDgAV566SUAQ6OU7eTkRObw6enpMzLsBDCx7zWadm5uzsjSBwIB7O7uAoBoUBHVy7oo\nXeQbiUSMt6p5DXU+/+vaJAc1aZM2aZM2aWPZxsKDikajJhdSrVaN1UerdTT/kEwmDdURvQeGC0bp\n9AOBgHgT25oFAAAgAElEQVRQmUzGwDNrtRrS6bRYTOVyWaDmwKBavtfriYXAXBAwFCujxRQMBtFs\nNsVCqNVqojgJDCxGWpCffPKJ8FbxmTWrBmHofIdarSaKlPxMz4QoGr5/JBJBr9cTD4o5Hn6/UqmY\nmpHbt2+LZ6LplICB+6/pnX74wx/in/7pn0z4RHuIuhEKPiqOR2u0Wq0KMz0Agxa8ffs2tra2DPuD\nrr86Pz+H3+8Xb1XT0zDsolkpNNzV6/UiFArJfavVqtDwAAPLn/OSPGWjTPnj2DTyLBwOy1wpFovG\nqtdebrPZxNnZmawlso1rT2WUVohhWDIDcA5vbGxge3tbrOu1tTWcnp7KuPj9ftRqNVOfyPuSMZ6f\niQ5lLiOfz2NpackgeYnyXV9fF8+Pf9MeMZG7XLeMauiaK+1tVSoVWTvRaBSpVEru+/HHHxvoNcPn\nAIT/UNcm6fpDv9+Pi4sLfPbZZwAGfHovvPCCMJDrHBvZ6bXcj+bKo+go34n0Q7x3MBiU0OPR0RFu\n3rwpHpTf75faUY5xMBgU5p7NzU2JRng8HnS7Xdkf+VzaK9Yqwo1Gw/Bg9vt9GUNKrWjG+q9qY3FA\n6dqTWq1m6iJ0GGqU5iaTyUh4DRhOfA6Ox+MxoYWLiwtT16Jl2SmJQTf96OgI169fl0nn8/lwenoq\nEExeA4DIl3NwvF6vqc2hVDYHQmvFlMtlo1nTbreNthIBFTpMpeGbOik+CkGdmprC2tqa9OXBwQE6\nnY4c0vF43NQbeTweeT/yEHJCcnHz2gcHByaMqcMwXDB67Bhe47+pRwQMDk6/3y9hOq/XK8AFr9eL\np0+fykbJ2gpuDKzH4uFbr9cFks8NVkOjdQJ+amoKgUBAKGYuLi7kvvys63NorAADwt8/+qM/wrg1\nXbvmcDhwcnJiSFzZWEzODZlhWV0KMSoZo2v/NNCBeUHmK6enpxEOh01OZm1tzeRGNU2Oz+eT6xKs\nofkStUw5a+g4Fr1eD5cuXQIwCDt+8sknRlqeBh2/q4EO3GB1sTKf0e12Y2try4SDnzx5IiGvcDgM\nj8cjB9jx8bH03ePHj+FyuWT+U2uOc5aGMz/fu3cPV65cEXBOPp8376/lNRwOh4HGP3nyBOfn54Yj\nVPMxVioVw2vabreN/Ewul5N9iv3OXPnu7q7MnXq9jvn5eVk7tVpN8r/AsCxFly3ovibIir/t9/uS\n1vm6A2oS4pu0SZu0SZu0sWxj4UHlcjkTLtPyEtqa6nQ6xg3naa+ZEzRU1OPxGAVZDbkkjQ9/G4vF\ncP/+fbEeSqUS7ty5I7Dqubk541FoZmO6ybxWqVRCLBYTy4xgCp2gZviDleBsDJfQqqhUKojFYmLl\nsJ94LYZmgKH0ANFTlUoFh4eH8s5khaaXpOmHpqenjWAjw5Z8J3oe9ECDwSC2t7fFg9JhSTJh63CS\nVtgloS3LBYrFIsrlslhn0WhUxjidThtkXSwWM4z1FCvU4BV6CkQG6lDLysqKsaApu8I5oT1fJsbZ\nyKzOa49jK5VK8swMl+kCUo2kCofDJsyi1XcJP9bkuGRHB4beKABBZGly4dnZWWEKuXv3Lr75zW8a\nZom7d+/KuOioiKbLAoaAD4ahVldXDeXYpUuXxLt+/PgxIpGImYu6gJyK0fw7JXV0mE6Dk+LxuKwH\nLTPC56xUKgIMIokrn310roxSpel7ZbNZXLp0SQgF/vu//9uQ4Wo2DMqpcJyePHmCbrcrz0nYPPuW\n7O/AwPu8d++e7A+JRALxeNx8t1wui8cVCAQkkuNwOOB0OiX0Xi6XEYvFDCVTp9MRlWAqTWhFcg04\nmp6eNn33VW0sDigAJqdycXEhIRstj07FXH5uNBp48OCB0NYDVlG01+vh6OhIBp48bsCQ1oMu7Onp\nKZaWlkTumxLPH3/8MYDBJFteXn5GP4f3oesNDJFI3CgYltO1DXyHYDBoQmekJ9KsCxqyXSgUsLS0\nJAeDZoPodrvmcD8/P0ckEjG1HYQeA4NJpOlXCoWCTE6fz/dMSKdQKMgmQ20phle+/PJLwyqh+QbJ\n2aXZrd1ut4SEcrmcORwrlYqMUywWM/VZlG7gZsiwlGYa4Dsxn6cXwuPHj2VuFYtF+P1+WbC9Xk/C\nEHxuPlOtVhOuN77DOLazszOTQ9PoUo1w1SE1YDC+rVZL8oIMM+sDQ/PFMYQDDIyI2dlZGROWIxCC\nXKlU8Jvf/Ebm1ne+8x3U63UZfx0KJuMAjSqWKOhDRnNEEtUJDMZeHwKjcGaHwyGKCMBgLZH+CxjM\nNc4N5u40Elfnfihdwt/qXGcymUSn0zHUZgxF87t6XDKZDL788ktBQW5tbUkokSrYugyl2WxK/8Ri\nMZO/PTs7M1LrWiKHOXcil69fvy55KGDIc6gRhGxnZ2emTKHdbuPhw4eyxl0uF2KxmBjwRPBp/TH2\nHw/Rr+PgYxuLA8rv95u6oGAwKFa+pggiXxwX4MbGhhG6A4abIzCYoDdu3DAy5bSuqXXEgXQ4HKYw\nrVAomHu99dZb+Iu/+AvxsDRnFROvmuBVF0jS69N5Nb5Tv983fGmlUsnE50lrxIVCzRsusrm5OVlU\nMzMziEaj4uXE43FDOUOPQG8k7I+1tTXEYjGTqF1bWzPeBDWEgMGh4vF4cOPGDQCDeDU3cwJCNFRc\nS4Rks1m88sorku+5fPky7t+/bzjwOP4zMzOYn5/HgwcPAAzIg1kECAz507TECsdwYWHBwH2vX79u\nDijSyHDzY7kD+6fb7YrFSOltjrHerMap+Xw+I2syCiXXa8Xj8RjOO5/PJ4aA0+k0tXz0tjU3G8fX\n4/EY2RJqrXG+sy7wnXfeATBYez/84Q/xL//yLwCs6CTBBVwfU1NTJjdULBZNTopky8CwRpDvFIvF\nsLGxIUZoo9FANpuV5/T5fAIMAGCIZqn9xgOZfHjcVOv1uhH8ZP/ymXV9VTKZRKFQMLV7FxcXhnDg\nnXfekXrOF198USDnyWQSpVLJeFTtdlvWzvb2tomSMArE+el0OuUZK5UKVldXxQhfXl5GMBgUo+Tk\n5MRIwGsapH6/b4BhPp/PgGjoQemcraZ30h7jqObXJAc1aZM2aZM2af/PtbHwoHQMmgWfPF010oii\neMxlHB8fS2wUGLKX08oLBAKoVCpi9c/Pz4uVTuScVm6l98bPlUrFKLf++7//O/78z/8cwJAMkX9j\nfgwYFuppiO5ooRqtvmw2i0gkYqyrUqkkiLaZmRkEAgEjJEhlYQAmZHd+fo58Pi+f9/b2kEwmDSmn\nRmalUilDP7O/vy/WVKPRwN7enhmHXq+He/fuSd8CkDDGxsaGFPG1Wi2Ew2H5TjweN1RGt27dwg9+\n8AP88pe/BABBFjF8MDs7a5Rru92uWJf5fB4ul0vCFgy7clz7/b48E/uLuZBqtWoq+mlp0npnKFjD\nlJkrXF1dNXNxXCXfWbgMDN5dh1YuLi6kXzn3dWGu9phcLheSyaTJ9+q1pgUbCdXXLOq6+BwYrBd6\nnz/5yU/wd3/3d/izP/szAMCPf/xjeWa32/2MJa7RYq1WC8FgUOaSDtu73W7JFQODtVMqlcQbIXxb\n54I5v4BBmJ/hXlIuca2cn5+b4lp6QVzTOodKL5Re3uHhoekfFs9r1o7p6WmR57h69aqE+wqFgqEz\no9fHovbr169jfX1d9s9kMmlKc3TRf7PZNKrQ9+/fx8rKinwmUlfL8+i1oJG3R0dHcLvdRi5eM8vM\nzs7i5ORE9jWd8qB0C9esLkIebWNxQGldEcpp6HCYDkmdnJxIHmRxcVESncDAtT46OjIhPm7KwGBi\nMP66s7Nj6ny4+WhdGmpCARCABGsXvv3tb8vfSqUSHA6HLEC61ZqupNvtyqTSk/X8/BzZbNYcdvl8\nXuh4kskkDg8P5aCoVqvodrsCrCBMndcFhvD3S5cuYW9vT8KSlFvQcXWdY9DhUWC4cIAhHYmmOpqe\nnpaD9Nq1a8J5x3Agf+tyuQxHWqPRwO3btyV8xv5lWE9vOvV6Haurq5KvqNVqwmDO/lheXjbxbL5/\ntVo1kH6/328S351OB4VCQTaDXC6Hw8NDuXar1RIFZrJIMATEsNE4Nk05xdIMYJhHAgbj2Ov1ZFwK\nhQKCwaCMLyUydNiWuRNgKMcBDEsdtJFQrVZlA45GowiFQiZs+9Zbb+Gv/uqvAAxCWpyTNCB0LaOu\n14tEInjttdfkXZvNpnmOer0uB2MqlZIwP5+5Xq9LLrRerxtlgOXlZaP3VKvVxGjy+Xyo1+sytwAY\nxV09z8j5p3XGdM0QlWZ1znp2dlYMPJfLhTfffBPA4DDXEiG8DvfA9957D2+++aaE5pvNpsm7OZ1O\nE+LN5XJCSXV4eIjZ2VkZ81QqhZmZGdmnNI0a8+Y0DOLxOI6Pjw19UyQSMdyNGvxAiRX2OzDMn38d\nK8tYHFD7+/uyWblcLqysrIjVwwQjMNiANYqHtBxaDnxlZUW8AlLdsNis0+mIJV6r1YylwUJSbvwX\nFxdGCKzVaqFarUrs9w/+4A+MzEatVjPUPjpG7/V68emnn8rALy8viwdAaRGCDc7OzmQSAIOFEo1G\nDeWMLiDWA08EHw/sdDqNlZUVeeatrS04nU5ZsCwgBobFhDrhrOtTOInITUj0kyYb/du//VsAwD/8\nwz8YEEGj0cDi4qJs/Ds7O3jnnXfwN3/zNwCAH/3oR3A4HDIHstmsLOb5+XkcHR2ZheJ0Ok1Nxfn5\nufSZz+czJL4Oh0OQRdFoFPF4XP6+sLCA5eVlg8i7fv269NfMzIwsJkqo0NLPZrP41re+hXFrmjy1\n3+8bwmOdyJ+bm0MoFJIcI/OLmnMyEAgYUUGfzyebsEatMZdLz4QIN71Jzs3NGXqvg4MDmcNLS0vm\nvpVKxUgx6BzUjRs38Nxzz+Hdd98FMCDt5Ry9uLhANBoV44EHw+3btwEMEZ56w9ZAoWq1anj6gsGg\nzDOiFmksJxIJ1Go1s/dwQ6Yxw74aJZrmoTBaq6SNY873P/7jP8Y///M/y3qihhv3uJ2dHZMP5Zgx\naqQBWKOCnZTToGdDkBXHqVwuG0O60+kYxJ+OzrDIm31br9fNoa2fkYYE9xSN/hxtkxzUpE3apE3a\npI1lGwsPKpVKSSiBVsj169cBDKxvnrQMVdHiOTg4MLkdYGBh6NyHPtU1fJk0L7SeKY/BezidTlQq\nFbEm6La/8sorAAZen4av6zoQt9ttaOwB4LPPPhMr6Pz83MB5tac2OzsLt9stVj+tOL5Tr9dDpVIR\nD6PZbEp+6v79+3A4HOIx9vt9wyTw5MkTrK6uivVFyhVgKKqo4/NayZdoOY4TFUTpFS0uLoq38dd/\n/df413/9V7GCW62WYX5fW1vDz372MyGLXVxchNPpFKtPI9Ha7bbU7LCvj4+PJTTl8/lQLBZlHIvF\noiEizeVyQqTJuhW+PyvwmXcgu7dmiqasxs7OjhkHPuu4NR2GdLvdSKfTBl3Gf5dKJTx58sTQS2mP\nmTViGqJOrxqACWETDapRq6enpzIvR+XAKfXBkHCxWJSwE5ntuZboudDKf/755w2Tiq7NmpmZQblc\nlt/qsBqbXvMMJ2rZBy1HEo1GxZMhwakWJU0kErKmNW0UJe61yrOW0OC99X3Jlg8M9jXSDf3whz/E\nm2++id/85jcABvvD5uamzMuNjQ289dZbePXVVwEM1vjJyYk8CyV3OB80nRXZP3QYXxPNjtKdlUol\nw04ODNcBCbG1zIfD4Xgmt87vTk1NGTaYr2pjcUBp3D7rKTi4y8vLJgx3eHgoVDYzMzPIZrOSBM/l\ncnC5XJIXId8XY+GaQXdrawu3b982bqaGReqQHZ+RNETAkJoeGFIM6foaHe+/e/eu4e3SSp10nTnR\nmc8iDQjronQtj97so9GohAuvXr2KTCZjWJU1f1g0GsXjx49lc79165bUkJXLZTidTvntxsYG/H6/\n5NyWlpaM/EIoFEI6nZZDaH9/38i/v/LKK0bjKpfLiST49PQ0VldX5R2vXLmC9957zyjZMud0fn6O\nhYUFef98Po9YLCahNo/Hg0ajIRtRKBSSfvb7/Tg9PZV34vjwkJmfnxcQCsdc18Zls1k57Hd3d+Hz\n+Qyd0zg2TbEDwFDq6ILmZrNplACazaape2HCW+dNHj16ZOrNaJw0Gg1UKhWZl6Ps2y6Xy0g1UPKc\nY9zv92UenZycwOPxyJgR2MTDLhwO4/3335e5UygUZC4wr8YN1+12o16vG9VXgjCAYe0X14OWoQ8E\nAigUCoa9XVP3TE1NIZ1OG448TRhAbTG+X7FYNJRDOp1AMgJtHHIfevfdd7G4uCj9SX0s5qiPj4+R\nSqVkXj548ADf+MY3JCel6zXb7TZ8Pp8Jv2vV3NXVVZTLZUN3paVaZmdnjWE2Nzcnh1273RZtO2Co\np8Vr6TnR7XYNyOzragrH5oDigMzMzJiiNm3R9vt9xGIxI8uuOZ5YdEcrIBgMIplMmk6jdZXJZBAO\nh43VR8uHnzVrRb1el8QgMDgMuKj4fDwIS6USzs/PZWM4PT3F/fv38b3vfQ8ATOEt/6s1qyKRiJGr\nJlEtMNTD0fLRuq/C4bC8P7m3NFloKBSShaMLfq9fv47Z2VlZkO+99x62t7flAKP3yTxRrVaD0+kU\nY2Fzc1OKCwOBgNkYut0ugsGgfPfy5cu4evUq/uM//gPAsBiX45pIJEz9Ta/Xk77e3NxEt9sV6zuX\ny2Ftbc0kXjXSrNFoGKtP63JlMhlDNsyqem7UGi25sLBgal5oFI1bu7i4MHUvmjNPF0/Pzs6KlwwM\n1kqj0TCbua6J0XkawApD1ut1bG5uikcAwBwElIThvKxUKvD5fLIWY7GYKa6v1Woyz5xOJ/L5vHmn\nDz/8UEAEX375peHaJEAHGOZRtWChPgz5XDw4Dg4O5D7ky9O1Ox6Px9RjUUaGz8m+XV9fN7mwhw8f\nGtLq8/Nz9Pt9eX/Kz2jCaE2sy3whMAA2FItFOVR/8pOf4Pvf/76Av1599VVzOGqCY2pwsX9yuZzh\npuQBxvUQiUQM9yjHAnhWyoVyG1qKXvMgav0vt9tt6uY088dom+SgJm3SJm3SJm0s21h4UGdnZ9jb\n2wMwyEfoUFoymRS38vj42MB73W43QqGQySnoeoTZ2Vk8fPhQXNxgMCgWI6lGaCFRBFDXW2ierunp\naVy7ds3ILGh4u1bUpfXIkFKz2cTOzo6pHdLXyefzYjGS009DhTW66PDwUGiHgIF3xnwWYeKa8+rs\n7Ew8l6tXr+L09NS47RqevbS0JF7Q66+/jlKpZJBHTqdTwnZOpxMbGxtiURUKBQNnr1ar8k4ff/wx\nEomEfDeRSMDtdkslfaVSwQ9+8AP827/9G4BB/RYtxtPTU1Obsbe3h8uXL0uO8s6dOyZnReQiMPCw\ndWzf5XIZS5+Cl0TtMTykWbZ53VKpZBCAzBGOW9My9cAwzwIM0WQA5D01M4BmZSkWiyacXKlUTJ5R\nizkuLi4aii0t+w0AKysrQlMGDEK4brdbxEF1iId8mpyX9AK1LMyLL74oHnS5XDYw81arJe/EHIh+\nf537oGev4e+8D3NVOq+k+4MhKl0Co/M1+/v7RtxSe6+sx6I3xhpDzjXNGRqJRIwskNfrxYMHD2Qf\nfOONN9But2Xdvvbaa3j77bfFw3zrrbdknEg9RNZ01gwSmfv555/j+eefFy/Z4/FIKJXe1gsvvABg\nEKZtt9syxpRI4TplaQFDtZozNBgMwuv1yn7xdawsY3FARaNR2UQYm2Z8tt1uy98KhYJQAQGDgdYu\na7lcxurqqmzejx49QjKZNHklTvzj42O4XC5cvXoVAKQYjp3FQ0FDxXW9VrvdFje80+kIyAIYknJy\ncs/Pz+OFF16QSbS2tiZudCaTEXVKYAhc4DvwvjxIL126BLfbLeGVcDgsGz+T+NTpYRGePhhDoZAc\nMposdG9vzxDeMmHMvnv06BECgYCEQxwOB548eWIS4dy81tfXMTc3J8/1zW9+E5988onhU9ve3pZ3\n+p//+R/Mzc1JbceHH34oEz+VSsHr9UpolTBbblB+vx/1et3Q+/A5gsEg1tfXJQH/xRdfGJJfAlT4\n21wuh36/Lwe+x+OR0InL5cLbb78tOSldDzNObXp62hSKlstl2WQymYzR5Mlms5JHIt8bjSqGh7XE\n99zcnAHrcA6TiofjybCx1ger1+sypqFQCJVKRYzS7e3tZzggOYYMtXEcPv30U8zMzMj46+JiFt7y\nHaamptBoNMzhpktLOHf43GdnZ7LxsyhZG8qZTMZItyQSCfn7xcWFHCIXFxfY2NiQzXlqakoOfDZN\nakxSXq75WCwme97R0ZFRNp6fn4fX6xXDcXl5GT/96U/lQNrb28PNmzdlHWteP5a+aP28YDAoa2t1\ndRUHBwdm72V/eDwelMtlOdzK5bI4BuwPTa788OHDZ4gRdF59enparp3JZORvo20sDiiN4rh16xYW\nFxfNQuLAffe738WvfvUr2cxjsZhBJjFmyo0wHo+jUCiItRuJRGSBJRIJgwDqdDqipQIM9W9GZcvZ\ntDXabrdRKpXk76wy1+J2iUTCMHRzo3M4HMhms7LRk0GYk/3k5ATBYFB+W6lUjPVaLpcNp92TJ09k\n4Hu9Hk5OToy3pmsZNGrr4uICR0dHsridTqcwErMvqdUFDA4hnXMIBAKGa+/s7MwcFO12Wzyq4+Nj\nzM7OyqR0u93C2g4MNgOOf6PRQD6fl0Xl8Xhw69YtqWdbWVnBu+++K2O+tLQkVm0ul8MLL7wgz/nk\nyROjS+T1epHJZGT+ud1urK2tSd+2Wi3ZKLrdrnyfvx3H5nK5DBlqKpWS9aINLLfbjXg8bjTNNMq1\nXC7j4uJC/h4Oh1EqlcT40SzpFA6l5Z3NZqUmiU0X/errAzCCnYFAAMfHx4apgu8FDABHa2trJlfG\nA4igGM6rw8NDxGIxmQ963fCdWbPEe2sEoJ7/brcb29vbsl5yuZwhPNb70N27d43OFFkUuKEvLCwY\nYA+5PNnX5XLZrJ2LiwvpD+qq8VDx+/14+eWX5bmJKL516xaAwR6pgWAzMzNyrbOzM6mrBIZ8kxqZ\nyOteu3YNDx48wIsvvghgsC4fP35sUM6dTkeuvbi4aAReNfdmoVAwShJaZ220TXJQkzZpkzZpkzaW\nbSw8KB2DvHz5sqBrgEH4gCGad955BxcXF2KJR6NRpNNpsVRY2a2tK005QikPABLzHQ0l8PPFxYWp\neyCtDz2beDxuLA2fz2dQS7VaTb7Lug4N59T8eLom5Nq1a3j8+LHRktL6R1TBZJ80m02h6imVSuJq\nA4M4eCwWEwqVV155BY8ePTK5IlovKysrpu6FeSSt0eR0OsWDIns76zVSqZT0M0ON/JzL5XDlyhWx\nIImGogX5ne98Bw8fPjTUSJpfkKE6YBA60BDvWq2GaDQqHlahUJC+Ileapr6p1+sy3zKZjNEPopX/\n+eefAxiEV/7P//k/8r5Xr141bO7j2LQGTyQSMXWEOkQbCoXw+eefy1p68OABUqmUoRwCIPkZ8lRq\nuRla1wwzs54sk8kgFApJuMfhcBgPamlpCa1WyzBPaDbzi4sLg3K9uLgweVXWAgJD3TK+XzAYNIhA\nrXTNPBA9JpYr/C5pn9nZWeRyOfluJBIx5R+tVstwM+ocNMPwjPzkcjk0Gg2jvptKpcT7isfjqNfr\n4n34/X7p50ajIYhZXiuVSsmaXl5eRigUwvvvvw9gUCf2zjvvGIVp3T8aIex0OhEKhWS/5F7EdRqP\nx2X8p6enkUgkZF2Sf5TPSbYQvgP5VIl2PTk5MZyA1WrV6Ol9VRuLA4p8W8DAlU4kEtJJsVhMNJkI\nT+TiKRQKBsjATZADQogt49ebm5sSF04kElhcXJQB8Hq9EmcFbCgMGAy8Lk48PT01IQpdE0Epca3L\nVCqVJIyXz+efKTRl0W+r1cLm5qZcq1qtivAcMFhky8vLspEkk0npn3A4LMSswGBC3r9/X+LG6XQa\n8/Pz8s664DcUCplQIOshuEAp28BN5/79+9ja2pJQZa1Wk6T3a6+9ZsQOObF5mDEcyHELBAJIJpMS\nK5+ampJx2traQiAQwHvvvSd9nUql5L7pdBoej0dCb5T6AAahFS15fX5+jpmZGSPxvbe3J6FFjhk3\nkvn5eVmAlUoFBwcHhptxHFu1WpX39/l8UoANDMJDHM+7d+8iHA7LXLlx44YURfM6oyTGpPsCYMo5\n1tbWcHBwIJvRkydP0Gq1xLghpZAO2+twog6Xjxa0jgIblpeX0W63Ta5Y/xsYiguS9owbIeXiuT8Q\nVs93nJubk0M2l8sZQ8jn8xkBUxp3OozHuXFycmLKFYCh5A4wmFcMewODfczv95uCWr5TOBxGrVaT\nw73VamFpaUkMC8L3+dz7+/t44YUXRJ6m3+/LfKahTJj97u6uWXfUsWK4NRwOyxhSykTnDbe2tiR9\nQt065m/L5TJcLpcY5alUSp6p1+vB7XablMhXtUmIb9ImbdImbdLGso2FB6WTa5ubm6hWq8ZS57/n\n5+dx//59Qxbb7XbFQ9jb20M6nRaJ9WKxiOnpaQNn1qGypaUlk+TM5/NiQWoIOTBI/G5sbEi4KBAI\nPEProSHrWn0yEokYZmCK7rGtrq7ik08+ATCwNi8uLsTbyuVyiMViphixVCqJlaw9gnQ6jfX1dbFk\n2Rf8rdfrNQqivV7P0CBFo1GxkObn51EulwVWWq1W8dZbb8nn7e1t9Ho9039EhFGGmqHEarUqiEt+\nPj4+FktubW0Nly5dMiJtfK5Wq4VHjx4Z4bNHjx6JV1StVg36KpfLyZgxlKqJN//rv/5LvHUWSLIv\n6VHSel1YWJDELpk0aG3+f1ED/d9orVZL5p3D4cDU1JTM08PDQ1lL/X4fjUZD3kOL0QGDd69Wq+KN\nUHSQXrYuNg+FQvjss89kHRLCrmHVtVpNxv/8/BwnJyfSz5qeCRjMJX6X7A662FYTKudyOSPHo9cK\nQ3utKEIAACAASURBVFZca+fn5/B4PEZOQjN0d7tdeb/Hjx8bJKrX68VLL730DGSbc57M4MAQWagF\nO/1+v0RkvvGNbyCTyUh/3bt3TyIDwGD9aDZ3zWDBa/KZDw8P4fP5TERBizhSFoO/mZubkzF1OBwG\nAUtIP9dSuVyW6zLsSvDF2toaLl++LKmJTqeDeDwu16Y3Ts8vl8sZgUtNCKxVrEfbWBxQ3W7XqF62\n223pVL/fbybB6uqqbBKUU+DkzmQyiEajsnnV63XUajVhEWeIEBjE3H/v937P8L+xwhsYdGKtVpOJ\nQOZn/v709NRQ4PN5gMHm5fV6DfdeIpEwrBGaBkXDeePxOLLZrHze2dkxm2G320U2mxVINjAIP/L+\nh4eHMin8fj+2trZM+IS5FAD44IMP5IBh+Ofy5csABhOKOT72PeuKgMGE1awMvV5Prnv//n3E43HZ\n8BYXF1GtVmWMeUAQ7s5Ng/3DUCAwmLypVEoMBrLXc1Izr8KDdWZmxtBIZTIZA9nVOamlpSXEYjHz\nHM1mU36/u7trQjiNRkM2Xc04P05N13GRgZzzUB9AnU4HgUBAwr2sxeNaIZxfa5ppw0jXpgEDI4zX\nJ2Rd1xBqlVyGw7V6r9aSmpmZkTnPXKX++9TUlMnXaJ21cDgsYScqKvPQyefzKJfL8k6cz/qgpN6Z\nx+NBOp2WOfv48WPs7e1JKI3rRufhtFYUD0OOg9PplHcqFApmL+l2u6jX67LW5ufnJWQ9Pz9vws5E\n4mkqpGg0KnWBb7zxBt5++20xUqempmT88/m8MeCoisD3bzQaSCQScu29vT1By3JfpPG/u7uLK1eu\nSE6u1+sZGD61uHj4aSqtQCCA5eVlMf6+TlttLA4ov98vA3J+fo7l5WV88MEHAAZwZj0JyuWyyZvM\nz8/LwJK6iJN5ZmYG7XZbOlVbjCRKZGPtEhcND0lO5nw+D7/fL7mfGzduyEY2PT1tNnNygHFyU8aC\nm90onHtvb8/w2G1tbckmSionWlfn5+d46aWXREjv4uJCJkm5XDbx65WVFbz33nsyuZ977jmUSiXZ\nzFOplDxHNptFPB6X3B8LfrmpxONxrK+vi4Xk8/lw8+ZNsaA0VcvFxQXu3bsnEiLcRLggi8Ui0um0\n9O3c3BxeeuklObgKhYK8g9frNSAaeqec1J1OB+VyWQ7sL7/80niurIVhv//lX/4l/v7v/x7A4CAM\nhULyjk+fPsXKyoo8FzXAOHfS6bQYS+Oag/J6vbLBUFeLB0kqlTKikFqza3NzE0dHR7JWTk9Pja4Q\n5SO42evrZrNZY7CxYFzD27Xxd3h4aIiIWeTJpg8+ekt6Duh6G0rCA1ZTjO/QbDblHRKJhKHnyefz\nptRC1xR2u10kEglZh7FYDOFwWAwyggD4zrlcTvYlCmjyM2Hj9Bi++OILRKNRI/bp8XjMmtfcg/V6\nXdZ/Pp/H8fGxrK319XXhLwQGBi1lM4DBnNbvRN03YHAIezweU8icz+dlnFKplCEfaLVaUmbQbrex\ns7ODP/mTPwEA/OM//qPJq/V6PczPz4tBowmxyY/Kw34i+T5pkzZpkzZp/8+1sfCg8vm8uKiEWFMM\nTheWEQ1E6yAWi4nyJTCwapaWlsQSIQU8rfyNjQ1D89NoNAz5JcXvAEgxHPNXJD6k5VKr1QzkUudF\n6C3p+L6W0PB4PPIclIPmO6XTaUP42ul0TLFdtVo1DNWJRMKELFKplIRpbt26ZQhg0+m0kTifnp4W\nizqfz2N5eVnQQcViEaVSSSxS5mZofdECp0Xt8XjEuqLyKsflF7/4BV599VV550ajAbfbLZ5dsVhE\nr9cTl7/b7UqIolwu4/DwUCzI/f19+P1+sQq3trbQ7XaNjD3HnyJwtBBv3ryJJ0+e4E//9E8BAL/8\n5S/RaDQMGs3lcolXMTMzI569Zp4GIP9/3Fo8HhdrmtY35yGlLACId8A5e+/ePaytrRlhvF6vJ1Dp\nZrOJ2dlZw7it81GdTkf6nXOFnivDjrSgCaPW60Vb9Q6Hw0itU24eGHgnevzb7bbch7Lkms3c7/dL\nUStJWGnlz8/PG+YNMsIAQ+kVehP8jp5nFxcXsj4WFxcNm3sikTDkwpp5YRRd6vP5kM1mZR5qQmeG\nQrk2WMKi+6Ner8s41et1+P1+8dY0wS1RuYxUdLtdVKtVue/Z2Rn6/b5EglZXV+X9qc7AkF+z2cQH\nH3yAH/zgBwCA733ve/joo48M00y73Zb91e12G0Ybt9v9tUKFbGNxQGnG6e3tbZM38vl8ElZiUpOT\ngguIGy6lGOgysnqbG7QOB7zyyiu4e/euUb3U8hqsl+HgZjIZQzlC95jPrycNef4028Pp6amRKuCG\nd+PGDTx48EAOL27smi1bK5/W63XZpIFBOEC7/++//76E7TiZGJbMZrOGvmV2dlYmVDKZxGeffWYO\npCtXrsjGkUgkcOvWLQkB/frXv8bq6qroYx0eHgpUfnp6Gk+ePJH+WF9fx+HhodyXBwfHglB7Ln6n\n04k7d+4AGBxAmlX5W9/6Ft59913Ju7F+jU3XjJHKiBtWNBpFsViUxZvL5RCPxyUUe3BwgLW1Nakx\nSSaTRsrl5s2bUrLAPh+3phlPjo6OEAqFZF5rjrtisWhCdsFg0NSQPX36FJcuXZK/j+qnUXEWGBiV\nL7zwAn77298CGPI2arXVUdqgZrMp4TKfzyfzLBAImJoZ1kTqcKGm2JmampI5OSopXi6XDTM+QQB6\nXWjNo2g0KgfU888/j0ajIWkAl8slhyUw2A/q9bphsdDqw8fHxzIOVDbgczCkybFoNptYX18XAy+b\nzQqfJGut+F2GUmkgTU9Pw+VyGUWDZrMpm79mqMhkMojH4zIuFxcXiMVisraopcVrZbNZeV9SwbE9\nePAA4XBY9qWZmRmR1eG1NS+orgM7Pz9HpVKR/vg6wNFYHFCLi4vyIg8fPhRiTmDw8FwknOCcCNVq\nFdFo1CRj0+m0TMAnT56IOBa/Tyz+tWvX8OTJE5lEnDTs8IuLCyNYNzU1Ba/XK4vj5s2bhnSUKCdg\nSG3C583lclheXhYLUtdQkdOMB87Z2RnW1tZkgrXbbfHAgGFOhs+ZSCRkEr333nuSZ2K/FotFmXC9\nXg/Xrl2TheDxeMzBqOuL6vW66PwAQ4QbN7ClpSVsbW3hZz/7mfQnJ9rBwYGhmKGAGvuDgAxNSeXz\n+eSzpnqi58lD5OTkBNevX5eNg/UUHAuXyyX3SafTpoD4zp072NzclELchYUFTE9PG0RluVyWA49g\nF2CwMb777ruCvBql4hmXpjWMFhcX8eTJE7GI9UamkZ7AcB5y7dy4cUM0woAh6azmVtMWMXXLgMHh\nl0qljLdJoAQwBBhoCXg26grpIk6n0ynzodPpYH9/XzbddrstBlk+n8fW1pYgYpeXl1GtVo03Agy9\nII/HY2RA2u225BgfPXqEVColBiwBJdrrIbE134lrg8KJzA1PT0+jWCzKtXO5nDk4WH+phQT1/H/0\n6JH8jZyHjAoFg0HUajUjLKoLsiuViuEI5L4GDAqGdaSDuUOdG6QRQgAKAUzBYBC9Xk9yZa1WSw5V\nALLHsGnpmpWVFRwdHUm/a09ztE1yUJM2aZM2aZM2lm0sPKhKpSI1MOFwGH6/34QHePLGYjFEo1H5\n7vT0NPL5vLiOW1tbqFQq8tv19XX89re/ld//3u/9nmHj7XQ6Ykm2223kcjmxag4ODrC1tWXUOpvN\npiBPDg8P8c1vfhMAxJvRqD7d+v0+MpmMWHCxWEysyOeeew7b29ti1VO2gHHznZ0dPH36VMIhLpcL\nqVRKrKJutyvu/rVr1/Dyyy+LmuaDBw+wtbUlbvfm5iba7bZYOhrxdH5+jo8++khUb3d2dvDxxx+L\nhX316lVTQxKNRk2dVCwWE/Xdzz77DH/4h38oz5zL5VCr1cQqDoVCyOfz0l/pdBqbm5tGwE2rIG9s\nbIi1HQgEUCqVZA689NJL8Pv9YkW/++67YuWnUqlnKKj8fr9Y7Ldu3UK5XBa5bNbFMQ/35MkTg4AK\nh8PiYYxaiOPSjo+PxSsm4lEzM9DrIW0V50Kz2USlUpExYA6G403BPa4lr9crlvejR49EugIYrC0N\nQ6cMwygTONeNlmlgPksjcR0Oh5Fm0B6XZjvhs9NDmJubw8nJicwzipLye6enp0LGCgy8M/52fX1d\nctrAcA4zWhGNRoVaDBiE05mWYK6Le8nDhw9x9epVPHr0CMBgrTgcDlnjfD6d3+N8vnPnDhYWFmQd\nkjWF3yUKUedzRiXUtcJ0oVCQqAgZa7gXzc/PG7LdfD4vYxwKhTA7OyvXIsEvPSgyxXA+EfXJvJxG\nxJ6fn2N1dVU+c//+XW0sDiiHwyEbfzweF0Va/o0uIClSuEk4nU6ziMh8rWPK0WhUBvP09FRADyzy\n1RxgZBIHBgfS3t6eKeJ88803ZfMPhULmGQOBgHzWUhz87dLSkkzgbrcrm2Amk8H5+blRyD09PZVN\ntlarYWdnBz//+c8BDHJnmnJndnZWFlGz2cTBwYGENAnL15IJq6urEqPX8fxXXnkFm5ubcmiRK41a\nMYeHh7h+/bqET+LxuIEGEzoOAN///vfRbDalL9PpNGZnZ6XGirF89hf5v3iw0xgABmEJPS4ffvgh\ntre3ZRybzSZyuZxRWWZ+qNfrmbAMpUj422AwaOitqGnFAz8YDEq4lMrFWh9rHJvH45EwbSaTQa1W\nk/VTLpdlnjUaDRQKBTFeWJ7BDdfj8SAcDpuDolAoyO+DwaDMo7W1NWSzWckFkWFcS8hoCY1wOIyn\nT5/KtXT+pt1uG4oxquTqtXVxcSFzRed+CcTgXKHEgy6u7/V6Mt7xeNwwuvd6PUkB8Pk4l/ju7B/W\nxXFz7ff7JrTa6/UkPRCPx3Hnzh251tnZGbLZrMnZ0iBg/9DY29zcxEcffSTK1gwVall2rWOXTqcx\nNzcn9VykhgIGa5qSG8CA7srpdMo4ZbNZBINBMXCmpqZM0a7P55MDnIc3S1bi8Tg6nY7MJ+YOOTY6\nf99ut5HJZMT4+zqY+VgcUJpIlDUDnFSbm5smD9Lr9WTBnZ6ewufzmVN+amrK8FTdvXsXv//7vw8A\nJgEIWKADDywulM3NTdy9e1cmYCwWw6effirWRjKZlIOA0th6AKanpw3RqkamPX361MSri8Wi3Pfs\n7Aynp6eGrLPZbAo57OPHjxGNRvGLX/wCwMBr5LUobshJdefOHaysrMhC2d7extHRkTxnNpsVL9Dp\ndCKXy8kzvvvuu4bwM5VK4eDgQKzkd955By+++KIc/rlcTqj4+/0+er2eTFb2MzcGxrr1Invy5Il4\nhdTpYf9QJgMYLLjDw0PDLNDpdAQFqlFcLP7VhYhat+qNN97AgwcPZKEsLi6i2+0aTSz2LTd6Hkxf\nV/3+v9lCoZB4d06nEwsLC/I+mltvZmYG8Xhc+qZQKMDhcMi8c7lcyOfzJk9ycHBgwAucs0T86ULd\nqakp2Xjm5+dRq9UMajMajZqaOp3U7/V6pmZubm7O1Nc0Gg15Li0iCVgPiyAQfUBpIlrWedGzuXbt\nmqyzs7MzVCoVo7s0PT0t867f7xuEbCaTkWfe29szxfi60BaAsGrw/+3t7WFra0vGgmTLwED/Sntn\nzNWyv9ivnPP37t3D5cuXZQ4Eg0FZV5Qu4n0JjOGBlM/n0Wq1ZP8EYPLVGoBGpDIPUhI2s7/C4bA5\nDDURb61Ww8zMjGEW+ao2yUFN2qRN2qRN2li2sfCgaMECEGg3raJHjx6Z2HUwGJRTfWdnB81mUyyk\ndDotrA3AwIK8fPmyQINXV1fF8mJIaZSxmJbxe++9J54QMIi5xmIxyaN0u10JJczNzcHhcIhF6Ha7\n4fF45O+s06G1denSJXnmarVqBNuuXr2Ker1upOe19UEaGdIKuVwusa60pcm/ffHFFxLS0iwZ7E/+\nFhhYiXT3t7a2UCwWJdezsbFh8giBQAC1Ws3ExonwuXLliqmvIM2TtmRDoZC8UzKZRLvdlrDEzZs3\nxcojIwfHgdYlLbVEIoFisSje2dramoQpydPGftchXWBgbRYKBcNzSColYDAv6UGRgZl9Ry913Jqm\neqInqyVluDb8fr9IkQPDcCajAsVi0UhVzM7OGloxho95T4fDIXPY5/Oh2+3KWmPkg83hcKDRaJgw\nnb6vLkFgWJ85KMo66NwwxyuVSol1DsCgM4EhgwvfuVarodlsSp9ozsdisYhQKCTXOjg4QLfbNXWA\nTqdT1s/MzIzxAqPRqBFo5PXYdwy3AYN1mclkxJObmpqS0Do5MXWOrlwuG6qtbrcr83RlZQWpVEr2\nPE0LRkQjn6tUKmFhYUG8xnQ6bZg3isWipFNmZ2eRSCSkT09PT40cD1kktOqCZnTXnKGcC5oT9Kva\nWBxQmvPK4XBgYWFBXNyZmRlZYE6n0/Djvf/++3j99dclX0MpaS238PDhQ9H0yefzphBPa8dUq1UE\nAgFZNNeuXZPQBTBImLvdbqMYyefqdrtGd4pqkZyw/X7fbG5+v1+kKa5evYqjoyN873vfAzAIy2ly\nzJdffhk//elPJflIyXZea2pqShbc4uIi7t69Kwf4/Pw8XnnlFUnGNptNnJ2diQu/trYm7n+hUMDp\n6amh+T87O5ONoVAoYGlpSRYR64cIOqnVatJXu7u7cLlckmyt1WpotVpyOCwuLqLZbMq4MSnMGH2r\n1cLt27cBDA67fr8vz1ypVLCzsyMTP51OI5lMyve9Xq8k3+/evYtkMmkKRLV+UCwWMyGPRqOBhYUF\nOZQ9Ho+Enp1Opzw3/zaOrdfryebtdrtN/VG325WDgAABjkEkEjEbCuHLHDPmELQ0AtcSddg4J6nn\nxb5iUS4NBx5OmhCVz8icKech/6YJkVkPCQzGkOPbbrdNTurKlSsoFouyX5CeSZddkIyV9+IcbrVa\niEQiRgVX1x9dXFyYA1xTOfHduGZJwMrNnWSymmhZk6c6nU6pxWNJhuYI1YrTPp8PDx48kBx+rVbD\n1taWkfph6L1er0s9JzDYD8rlsjynw+FAqVSS50wkEtLvDx48wOXLl03tIjXC+A46jxaJRAwZdTab\nlX4PhUImRP51BbtjcUCxcBMYTDKHw2EGU1uxnU5HNsmVlRWUSiVTKe12u6VGIB6P4/nnn5dDRUtF\nE+/PA4knPicouaM0I/Ps7Kyx9LSODIvx+MwamZTL5fDcc8/JxMhkMpJQJnklk7P7+/tYWVmRzX1/\nfx8LCwuyiEgUy0Xp9XpFrKxYLBpm72g0akQYw+GwIa3s9XoyUZaXl7G7uyubdTqdxu7urqD6mJ+j\ntRqLxbC/vy8oHqJ2gIFRcXZ2JpYaNbu0XDYAqQvhwmBft1otOcBZ5/TLX/4SwGDBvv/++xI3n52d\nRb1ex2uvvQZggMyjgCM9Qh03H9XtuXz5suToWKzN+eb3++V9c7kcOp2OHH5aO2ucWrvdNgeu/qzF\nDBOJBFqtlnj9FArk+uDhxjE8OTnBzMyMjKG+FuXLRzcrbcB1u115Dp/PB6/XK95Io9GQteRwOBAO\nh2WO+ny+Z2TsI5GIrHFdx0Y+Tc5/Gp08CMgew+bz/d/23jRIzqs6H3967+npvad79k2zarRamxfZ\nEhgbBwIFmAQIZKtAFhIKKvmQVCqVfE5CgStLUalQFSqVBBfELLExYGOEjGUtaPFIGkmj2fdepqe7\np9fp/ffhrfPovYJQ/2/pqv97vtij6Xn7vveec+5Zn+NWLHspogBANHbBeBREE71Bp/dcgAfVg1ar\nFdlsltGFvr4+BWg3k8nAZDIpVYxOp5Me1srKCt9X0C3kQgoEAkSTAUDjTfhUj8YveyIVwiaTCfV6\nXcEMDYVCvCDW19cRDAZ5buVymd8zODioNHKLZ6vvISuVSgqunyBmyLrkvEulEprNJvdaH9V4mIwc\nlEEGGWSQQS1JLeFBJZNJWvJTU1PY2dnhzR2LxVihtba2hnw+T6tdutPF6pufn0c+n6dLq0fmBqB0\nd7e1tdHqkJ/1sElOpxOBQIDWl1iIgsXX09NDq0+q5yS0IiWXegtpZWWF4TCLxUJvq7OzEysrK/So\nYrEY2traGFq7ffs2urq6+Ky+vj5ks1m+0+zsLHELJaYs8etoNIp0Oo3JyUkAmoeVSqWUaqqHx4fI\n905PT2NkZESBdtrZ2VEmDttsNlpjo6OjtNyazSaRKADNgl5bW6M3pg9PyH40m01a4ADwR3/0RwCA\n69evIxwOs8z21q1bCAaDXHc0GkVbWxu9soMHDzLcIaM1xEL0+/1oa2tjuCQSiWBjY4Me1eTkJO7c\nuUPPTj9VWfhDwpC/zOr7vySBzQG0/hs9Flu5XFYw78rlMsM7uVxO8S6sVqtSTSp7KHw3Pj6uVNrp\n/zs0NIS9vT3Kn1j1+mrbtbU1eifSJyjr8ng89JDFQ9AjhXd2dlJf6D3i9vZ2VCoVxWrv7Ozks00m\nE7Hq5FnZbJZymU6nFVisdDrN6IuM5RD+F0RufShN9k94RnhERmuIrITDYWWKuJR663NpokvMZrMC\nG5XJZBAMBnnGIg/6ab3Ag7xOIpFgBaykDmTNLpeLcgOA0xvkWXp8za6uLqyurjInFQwGkc1mFY+8\nvb1dCftWKhWuIxAI8LkS+hMdKF7pL6KWuKA2Nzdx5MgRAA8AYUURBAIBfP/73wegJer1fRJnzpzB\n9evXKWRDQ0Mol8tKE2U4HGbyXuYjAQ9mo4hCjkQiissqww6F6fr6+rC+vk739+mnn+bhVqtVpXzd\n6XQiGo0quFSlUolMV6/XeXB37tyBw+GgMEtIQwTD6XRie3ubStPhcCCRSPBw9/b2KMxdXV2Ix+PM\noUgIU8I4cjHK3mazWV58HR0dmJiYoDD39fVxJo58TygU4l5LP4n8vb53rVQqwel0UiE1Gg0cPHiQ\nF+k777zzc4PjotEomV/2BHiQV5Q+sFqtRqxD+dtUKsV1pdNpCr7kvmTfJTwhZ65v8AUeNFTKWRQK\nBaU0fmBggOfw05/+FK1I+vJmyYvKmQoWIaDxfzgc5vvH43FlXAKg7b28v9VqVYbw6YcKCqyR5Dqb\nzSbD3sCDsKOsS7D39OEzPY6dHsA3l8vB6/VSlmS+l14+9CMd9LBgJpNJKVmXHJQYSFtbW+jt7aUR\nqod6isfjSKfTVO4OhwOpVIq6p6OjQ5lL1dPTowwCrNfrSsOr3W6ncSvl7fpQezAYpCx1d3dTn1Sr\nVezs7PCcdnZ28KEPfYj9R/K+og+k70m+S4xFeV+3243HH38cgCb/LpeL+3Hv3j1YLBZ+d7FY5DuI\nLpAztdvtiEQi5K2enh5MTU0x9Lq4uIienh4lPSNGhRiz8vMvu6CMEJ9BBhlkkEEtSS3hQZ06dYrW\nw8bGBpPWgHZzCwKBw+GA3+9X0B38fj+tK6m6E1dZmn7F0tMnbqVpV274bDaLQqFAKy8YDCqj6BcW\nFvDII4/QK9A37ZVKJbjdbobppHlUP+J8aGiIFlZbWxstjfHxcWWsvRRF6K0+vSczPDysWIFut5tW\n78LCAvr6+miR9PT04LXXXlPGp3s8Hoa44vE4LaSNjQ2Mj48zzHbz5k309vbSynE4HGg2m0QZn5yc\nJFq8vLMe7LWjo4P78frrrzMBDGihGH3TK6BVGF27do17L15PW1sb9u3bh29+85sAgGeffRZXr17l\nXvb19eHkyZNKpacMc6zX67BardwrGYGiR5bo7e3l3haLRdy+fZtnc/DgQZ7/wMAAUdrlWa1KYl3H\nYjH4/X5l78USF8RrefdKpQKXy0ULWQBO5W+FD+Tnh0fL60GZxTuQfZcx7cIfbW1tjBQAmlcgPDs/\nP6+McZcWBX1Lws7ODnnLYrHQc/N4PMoIHQmN6Yso9CDFUgGol1PhO4E90hccTE1N8VmpVAoul4vP\nkukHsnflcpmh9J2dHeTzeXr20pIi3vjDEE27u7tK9eSzzz5L1InR0VEUi0UWDQnKvN4bKRQKlEt9\ntGZgYADNZpMFSCMjI4qXPDk5qURnNjc3eS7r6+sYGBhQiiAkugNovKY/l2QyiY2NDb6j3+9nSN/l\nchGMF9B4S8LyD1NLXFD6EJnf78fZs2e5SXo0Yuls18dFvV4vmWR1dRVDQ0P82WKxwOFwUCj1FVqA\nOq01lUqhp6eHl93CwgKOHTtG5hGMPzl4/VwVn8+n9P3ICABRdMLIEmq0Wq1KCbYeD2xqagqlUomh\nhP7+fqyurvLZKysrGBsbY1hibW2NTCQd+3qMND0EUXt7O3p7e6nAg8EgL6RAIIBcLsd1iGLTj9u4\nffs2QwflchkOh4M/65GOBYrlueeeAwB88pOfBAClfDscDislvWazme8xOjpKIYrFYohGozh9+jQA\nTUE999xzXKfT6cT8/LyCdi1KdH19Hfv27VMQqE0mkzK+en19nRdjuVzG8ePHmfPU98iVy2VO1ZUz\nb0WSsBaglfNbrVbulVSmyef0JfkCzyPykM/nlYtNkMwFaWR4eJgyWigUOLoCAOVCRrFIu4coKBk3\nL+FzfS+SoCSIIpQeQ9nvUqlEWQWgjJ6Rijy5oFwulzIxOZfLKZO0BQZJzx/CG+3t7RgYGGA1Xblc\nRjqdpkKW6kGR41QqxTXv7OwgEoko4dJAIMBnOxwOdHd3U7cEg0FWL8s7Cp08eRJPPfUUQ7Orq6tY\nX19XRmjo99xisSiTn5PJpGL86mHWNjc3kcvl+GxBPpf90OeoNzY2cOzYMfKPtNLoz+3o0aM0HOv1\nOg4dOsTP69GB0uk0CoUCL9W1tTVW3j5MLXFBjY6O0uorlUpKU2tHRwdLsAcHBxEKhZggvHLlCrLZ\nLF/uyJEjyOfzSt9HvV6nx/Wxj32Mg7+OHDnCPBTwANNKlGgmk0FHR4diFW1sbFBAk8mk0jzq8/l4\niRQKBcXrkfirMIZA4gMag+l7cYrFIh577DEqmc3NTfT39/PgC4UCVlZWlMGC8r737t1DOp2m4r9q\naAAAIABJREFUgi0Wi+jp6SEDioWsbyiWdxDoEmmWPXTokFLo4PF4MDIyQiy+iYkJ/PCHP8RnPvMZ\nABoD63uiPvGJT/CinJubQ6VS4ZqXlpawtrZGq/Du3bvweDzc25/97Gd4+umn+bcyvgAALl++jLGx\nMbzyyisAgCeeeALz8/MUan1zpXgJctGIUhZF0NnZiW984xtUdpubm8rgPdknQLNMxUIHWrfMXD9U\nUABeJSLR1tbGS8Tj8ZA/gAcJcVFsMmvt4fcUS1efNyoWi6hWqyzGCQQCLEMGNKXa29tLAwZ4kAcE\nHlz+QvpycL0RCGhQXyaTiXLb2dlJ/p6cnFTmrjmdTqU1wu12IxqN8h0F1kfOu9FoKI3MMjIe0Ixj\nfcOs2WyG2WzmZajvD/N6vQgEAlxXIBDA+vo6lb3Akekb5q1WK89iY2OD886mpqZw8+ZNNt6eOnUK\n29vb1A/SSiHrArTCKj3MmBj0fX199IxlzSaTSRmpkUwmqYtEz8petre38z2llUb2a3R0FBaLBRcu\nXACg6Ra3280LvaOjg/wifY/CA/qL/GEyclAGGWSQQQa1JLWEB6WHJHnnnXcwMTFBKzgajTJW6ff7\ncePGDZYr79+/nw1zwAOofrFqHgaS1Hdky60tlsbS0hJisRhL2Lu6urC1tUXvTMJ18nl9U5p0levL\nS+12u9KMm81m+fvx8XF6jHqUY6Fz587Rs0kmkzhw4ADdYavVinw+T6u4VqsR9igWi2FychIXL14E\nADzyyCNYXV0llMmVK1cwNDREgNilpSV6l1I9pZ826/V66fVIXkBK+B0OB973vvcxXLK7u8tS8IGB\nAczNzdF6unr1Kp5//nlWG7rdbty9e5eVd/Pz8xxEB2ihysuXL/PMHQ4Hrd5QKIStrS088cQTADRL\n9vjx47R0NzY2eObhcFgZrd1sNtHe3k4eqNVq6OzsZLw/Ho8rcE9Op5P5jd7eXmxvb9NS/fGPf4zP\nf/7zaDVyOBx839XVVQWSaXt7m+eZy+XQ0dGhoFXrPSqpDtOHjprNJv9+bm6Ocivek96DymQyDCUD\n2rnId9VqNaytrXFUi8vl4j4L6orkryTXJaE0gRjTT7r+4Ac/CEDzHCqVCj2EcDiMrq4uehvZbBZj\nY2Nct4TsxIMCHpSGS8WjvqncZrMpLQtOp5Oenr5KrVgsIhwOK9A+3d3dlH+v14toNMoKQWmTEM/l\nxIkTlNlLly6xChbQINj0Y+uHhoaUgZ5+vx/9/f0KSodUz7pcLiwuLirj69vb27k/fr8f1WqVcuv3\n+5kekdE1Ijui+2Q/BGZNniWjSSRnOzQ0xGfJ8FPRcb+sZaMlLqhcLkeGfPLJJ2EymXgheL1eCk08\nHkc4HKYCFuUtYanu7m5YLBZlsmez2aRy29nZofAKppUIoChE2eDnn38e58+fp3t8+vRpnD9/XnHx\nZWMllyUuvAiUKHuB9ZGQn9VqZS5Lcj36Sb56xIpDhw7h5s2b/N6pqSmYzWa+0+zsLF577TUA2sWQ\nSCTI3LFYjKFLAPjUpz6FixcvKqjrIsw9PT2IRCJk9LfffhuTk5NE7XC5XPB6vQyPNZtNvPzyywzr\nPSyQW1tbRDr+6Ec/isXFRb6D4PZJmXEsFsPS0hIvzvX1dZ7hN77xDfzWb/0WL/TJyUnMzc0x/NDX\n14e7d+9yP6emphgSjkajGB0d5boajQZhZoAHJfuyl52dnVhbWyO/9fb2co2SBxShEoT8ViOHw8G9\nEgR5MbqAB4aZx+NR0Ayy2awyEsPv9yt9LHa7HWNjY8x96BV7MBhEV1cXDYO33noLmUyGrSOvvvoq\nTCYTw1YXL15EKBQiP8joClnzw7062WyW65Y2Cf2ZitKMRqMKCoW0hYjM12o1xGIx/iyXnvysN2YX\nFhYwOjqqzLjK5/PMua6uriqwSsPDw9RhXq8XCwsL5Cu73U7oIgCcPizhsbGxMbS1tTGftH//fqWA\nYnR0lOHR/v5+hsKFBBdQ1im6E4DSu5XNZjEwMMB1yr/ri8r0xkE0GlXGvz/33HMK+oO+tcbhcCij\n5vft24fZ2VmW7SeTSZ7pvn374PV6ceXKFQAP+iJ/EbXEBXXx4kV6KuVyGVtbW7w4RkdHGdf0+/2E\nBQG0eGwwGCRziwehTxAWCgV8+tOfBqD1AYhC9vl88Pl8tNx+93d/Vxlutrq6SnwyALh27RrMZjMV\nllSIybMEVgbQGNThcCjzXdbW1ngxvPPOO7REl5eXkUgkqBSlqU8Os1QqKZddPp9HJBLh5Vir1ag0\nZLyCXJTVahW3b9/mRRGNRpFKpZQKOKkGEsgl8ZCef/553L9/n1ZPMBiEyWSi57a2toYjR44ofQ7S\nG9Td3Y14PM7v+epXv6rMJTp58iTu3LmjwKR0dnYy6e52u2kYPP7447h+/Tr71yShev78eQCaZf7k\nk0/yIp2ZmcGJEycAaAaN3usxm80Kfli9XkdbW5ty+W9vbzPeH4lEKET1eh0XLlzABz7wAQBQQHZb\nibLZLD0Zj8eD7u5uegGNRoM5pXQ6zfcEtJysPhd67do1TExM4MaNGwC03NMTTzxBZabPT/r9fuzb\nt4/FJqVSCZFIhB6U5BDF23a73djd3aX8NJtN8obAi4mB0tbWxuZdAJQ/4a1sNkvjZGJiAnNzc8qI\nd4EKArTLzmQyMfcRi8Vgt9upIG02G/luaGgI1WqVxu/w8LACq9Te3o5yuazAYgkeZCqVwpEjR3hx\n7uzsYHx8nBeHnIms880338Sjjz7Kzz8sG5ubm/xbv9+P+fl57p1EDMTQaG9vR3d3N/daGn0BTdfo\nhwhK0Zjs5cDAAO7evcsI1a1bt6iHBwcHUSgUaOyIHpZ19vT0KFGj2dlZuFwuGsdutxvHjx8n79y/\nf58e5MMDXvVk5KAMMsgggwxqSWoJD2rfvn0KsOjGxgZdyVAoxNzEvn37UK/XWVIsyMRiXc3PzyOb\nzSo9G9evX1eGAeqHiNlsNnoqgvwgFsLa2hrcbjdDSQL2KK762NgYrclSqYS9vT1+NpvNKh3sly5d\nQrlcpjfy+OOP0wI/c+YMzp07R+/K7XYjHo/z2VarFcVika5yLpcj2CQApUrp0qVLsNvtjO3H43F0\ndXWx8mplZUWBzI/FYrRennnmGfzt3/4t82E2mw0ul4vnYjKZlJ6zI0eOYP/+/ZwwrEdjlmogsdb7\n+vowOztLQNednR184QtfwLlz5wBoHufhw4e5t8FgkOGXO3fuKD00Pp8P3d3dDB+JZa6vNhNwzImJ\nCUxPTyshTZPJRO/T7Xbj/PnzDNtIvkue5XQ6yXv9/f147LHHuO9SgdhqFI/Hye9OpxNvvvkmrfyN\njQ2Glfx+P3K5HPn93r17qFarlC0BTpZzKBQKOHLkCD0qt9tNPnI4HDCZTIxGzM3NKf1Gdrsdg4OD\n9L5lJIbsZWdnJ8OyevBmQDvfyclJej0bGxu4f/8+85e7u7v0cqREWp6Rz+dhsViUoYP6MKb0QUrO\nWo+qLqkBkZ1isYhGo0Fvw2QyweFw8J3q9Tplx+l04urVq/jIRz7CNebzecqSPicKgAj6wneZTAZv\nv/02AM2LX1xcZHRmdXUVmUyGsrS8vIyuri4ll3j79m2lf0vO/8CBA5ifn6eHtLi4iPX1dfaZSoWj\nRDL0iBZSXS17u7e3p3i23d3dyrDP9vZ2VkLL/skZt7e3w+fzKYMj/zdqiQsqkUgoYy2mpqb4YvV6\nncoon8/D5XIpSN4rKys86NHRUYb5AG1TX375ZYb19AUU2WwW9+7dI1NJfkUO02KxYHBwkHFhCQXI\n4cmIbAAcpaFn/GKxyBBKb28vVlZWGEq4dOkS11EsFhEIBOiiDw0NweVyUdnfuHEDu7u7vBgEE0zy\nTC6XS2nEM5vNDOkJXpgo2XK5jEOHDinozeJ2nz9/Hp/4xCcocDINU9/noUdRrlaruHnzpjKtU+iN\nN95AZ2cn49GFQgHJZBI/+tGPAGiKYnFxkXufyWRw69Yt7mcul+PvTpw4gR/+8IcUhN7eXvzkJz/h\n+2ezWeTzeSqwiYkJBVX9ySefZLhImhb14aPDhw/z8t/a2lIww/QQXENDQ7h9+zaVsD7+3kq0u7tL\nXtre3kYoFGKYRSYZAw+m3kpCPJ/PY3BwkLxz4sQJnDp1iuc/MTGBer2Ot956C4DaQxcOh5FOpxke\nk/YF+V7pCxIKBoMIBAIMJ7a3t/OyqtVqsFgsSqHL+Pg4z0TwOEUeJiYmKP/JZFLBWhQcQsmTtre3\nK6N9vF4vGo0G5VbfI1ev1/HOO+8wtNzV1aUUK+XzeVSrVQViSx86PnjwIH7wgx8AeFDeLTIiU4Il\nbBcIBNDb20vZk5FDgHZhx2IxZQL3yMgI9+PRRx9FLBbjsyRnJb+3WCz8XXt7OxwOhwJBNTg4yBBp\nKpVSRteMjo4qI1JsNhsv6GazqZyxQJWJEb61tYWuri7urcfjoS6tVCpoa2ujTP8yWWqJC2piYoKC\n/8EPfhCrq6u8VQXzCtA29PHHH2cTZ71eR39/P77xjW8AAP7kT/4ENpuN1oUMTtMfiBxGV1cXc1SA\nxsx7e3tUbs1mk3FcQPMoZmdnebF84AMfUHog/H4/mXt7exvlcplVa2azGZ2dnfyuZDJJoZGeBvnb\n2dlZnDhxgonuzs5ONBoNZX6KfhT32toahSiTyRDkFgB+8IMf4LnnnlOGv/l8Puad9FVcJ06cQDKZ\nZG5M8oAP973IRZBIJJTejuXlZeWCFmh/+d3U1BSt70gkongyjUYDzWaTzD84OEhD4NatWzh79iz5\nY3t7GxaLhcqw0WgwbynvpJ/hVK/XyT8mk0nxtBqNBn7/938fL774IgBNiKrVKoUuGAwy/2m1WrG5\nucnmY7n0W42y2Sz3zmw2Y2trSwH/lPyt2WxGKpWiIRAOh5FIJPDZz34WgOblN5tN8viVK1fw4osv\nkm/b2tqocMPhMPL5vDKqJRQKKf1n5XJZaa7e2tpisn96eprPkr5F4dlHHnkEHR0d9K5zuRwajQYv\n1tXVVb5TKBRiNAPQjBk9rqdgT4qBGw6HOUYHeJAbk88K4gWg8bsescHv92N5eZnyceLECUUBb21t\n8X1XVlbg9/t5gdntdqVhNhAIYG9vj8ae4FwCmnzrjSapfhP+KxQKvOAATU4lByj7d/bsWQAafmQ8\nHlewGmdnZ5Vn6xEwFhYWKAtSAatv1NWPI3rxxRfxuc99jhf2rVu3lNlbjUaDBqvP51OKk/TGy8PU\nmmagQQYZZJBB/7+nlvCgNjY2mDeIxWIYGRmhBbGzs0OL0GQy4fLly/zswMAAQqEQXn75ZQAPEJeF\npARVPBebzUar7o033iAyMoCfK70UC04siLW1Nfh8Pvz1X/81gAd4Y4Dm3sdiMVoe0WgU165do8Uk\nMWj9mAexPKLRqGJduVwuzM7O8p0TiQQ8Hg8tkZmZGQwNDXGdjUZDGdBYLpdp5f7BH/wByuUywyGh\nUAjr6+uMgYfDYeZftra2MD8/z30IhUIIh8P0VrPZLPx+Pz2ZWCyG/v5+lr/6fD6GJaanp1GtVvm3\nR44cgdfrZex7a2sL1WqVIQ0pG5b9+cpXvkLr+oMf/CBmZma4P7VaDT09Pdyf5eVlDA8PK+Mg9NNF\nd3d36eWVy2VOhgU0i1Hi74AWEt7a2mKl2PT0NKsay+UyOjs76eW16kTdQqFA3pKwkXgM+gFyoVAI\n+/fvV8Kdn//857l3b775Ji5dusQw9erqKgYHB5U+Fz3C/szMDHm0p6dHQYDp6elBPB6n118oFPDZ\nz36We//yyy/Tsy2VSrBarbTqOzo6sLGxwYpAQU0QuXQ4HPTcxTsSL18wMcXbrtVqCIfD1BHpdBpu\nt1vB/tQPXfR4PJQzQT2X7yqVSgr/Dw0NKVW9grcne5tKpdgnqB/eB2jIKeFwmB6Yw+GgJzIwMIDt\n7W16jI8++iimp6fpbXk8HlgsFn631WrF7u4u9/bb3/42I0oSepXITn9/Pzo7O+lRSUuKvKPVauXv\nuru7MTk5SU+22Wwik8lQT167dg02m40VtNPT0+jo6FBGssj5b21twePx0NNt+RyUyWRiI5qM1ZbY\ndzKZpOIDNLdTyhPn5ubwwgsvUDBE+ehBKlOplJLok4NfXFxEIBCg8C4sLODu3btkokgkgrGxMfzr\nv/4rAK0PKpFIcF36MmOBKhHFZ7fb4XA4yAher1eJ9+tjt0eOHMHm5iafV6/XMTw8zIvD7XbD6/VS\nsUjYQhRkV1cXhejOnTs4evQoBVJyAxLimJ6exr59+xi6PHr0KL7yla8A0Bjf7XYr47Gj0SiFJhKJ\n4OrVq2S4oaEhTE5OKuWscsEIBpcou2q1qpSKj42N4cKFCxRSAXCVfNjAwAAFY3R0VGlyttls6O3t\n5RThAwcOsOQX0EKAYig0Gg385V/+Jdcl4KH6mHe1WmVf3bvf/W7Mzc3x84FAAN/5zncAaEp2aGiI\nodf/Ddzy/5pCoRALOBqNhgKurO/bicViSCQS3PM///M/h8Viwcc//nEAYAhWFN/+/fvhdDp5QVmt\nVhocJpOJcFaA1qumn9smBpF+dtLx48eZK0ulUuQFp9PJvKz8TSqVomJ0u90oFotUjH6/n/y9traG\nzs5O8qhMRJYWhEgkohTBeDwe5HI5riOXyynhUb/fzxDn22+/Da/Xq0zvttlsvChmZmb4vslkUsEe\ntFqtcLvdBFr2er0seQe08OD6+jr3oFgsks8kByfyfvXqVQwODrLgIhKJYGdnhxdrZ2enUjr/F3/x\nF3yn7373u3A4HJSlUqkEu91OeXG5XMjn8+QR/TTz3/iN34DdbqfcFQoFJVxutVrh8Xj4rL29Pdy7\nd4+6W2/8SiGG6DT9eKSHyQjxGWSQQQYZ1JLUEh5UT08PrZh6vQ6LxcLbdmNjg1aMTFIVF/4rX/kK\nzGYzk+LZbBajo6MKbMrDoJZSvvm9730P/f39tHoEhFTvGW1tbbFabG1tDVtbWwwl2e12BY05EAiw\n3FWKLSRMFQwGlaTw7OwsrSex1MRj2N3dRa1W4zrELRfr6tChQ/D5fCw4SCQSfK7NZsP8/Dyhj6rV\nKjo6Oriup556ShkUuLGxoaCTF4tFhgffeecd9Pb2KondQ4cO0bLr7e3F+vo6LSw9wK8ASQrawptv\nvqkgach4EvFmR0ZGsLy8zHMaHx9Xwqdra2u01iqVCjY3NxmGeOutt3DgwAG+R71e59788z//M/b2\n9hS4Ho/Ho6DONxoNfPjDHwagVVdGIhGl6kveTyxcfYK5FUlKnAHN+t7e3qa3p/eAent7sbOzQ15J\np9N44YUXfi7ZLj/LvkmI/OjRo3jf+94HALhw4QJOnTpFq35xcVFpYgU0i1ss+ZGREXR1dXGdzWaT\nZyJI9+9617sAaDw9MzNDL9BisSAQCCijXeS5MoBSD/bb2dlJD06S9npUfafTyVBlo9FQKlz39vbo\nQXo8Hng8Hn5XKBRCuVxWpi5IhMThcCAUCpHvAM0bF49T9kmKiObm5pDP5/mO+lRFIpFQ0DAOHz6M\nzc1N6stGo4FwOMy9jsVi2Nvbo547f/4892Pfvn0IhUL0MAVgQCIw4XAYp0+fZtgyk8ngzJkzfP+/\n+Zu/UYokGo0G+SmTyWB1dZXrnJycVFDo9bxw584ddHR0UI/roe4eppa4oJxOJ+PAY2NjsNlsvGT2\n799Pl3R6ehqRSITK+v79+/D5fHThh4eHlXkwAuUjP29vbxOBQOYuvfTSSwC0UFu1WlWUu8vlUspO\nX3jhBYWJJKa6t7eHxcVFKu+LFy9ibGyMTNTW1oZIJMKczOjoKEOFd+/e5b8DmuKoVCqsrpPn6pHC\nM5kMhUE63oEHPUJC0skt8eharYb79++zEmloaIj7nslk0N3dTXd7eHgYdrude51KpXD16lWuWybo\nihAKMjSgKQ2Px8NzGRkZwfb2Ni/SpaUlHDx4ELdu3QLwoP9CQgubm5sM8dy7dw9jY2Msfe3v70c6\nnebl97WvfQ1zc3MMAelR0c1mMy5fvkwhk14UUUhms5lVYQBw9uxZFAoFCo5+vIbdbsf09DT3PRqN\n4g//8A/RaiR5FkA7f5kUDWjvK0bEvXv3MDExQWVZrVbhdDoZwhoeHlYutEajgYGBAZbdd3Z2Mhf8\n5S9/GW1tbdyb8fFxyhygnWc2m6Xyeuqpp9Df348vfvGL/G75fHd3NwYHB8nv9+/fh9lsJm/JBGXh\ny1KppORj9XBmHo8Hu7u7yju2tbXxb8vlMsxmM2VaP/9L4IPEUKxUKsjlcspI89HRUcqSTAOQ5+rn\nxQmfiU6THJt8XpDl5ZwcDgflv1wuw+120yC6ceMGRkZGuObt7W3mwGVdgrQu+ylpi2QyiaWlJWVE\nRiAQ4OWxu7uLixcvKtBwAk+1sLCAWq1GQ7FQKChGmkA1ycUaj8dRq9WYw9T32NlsNmQyGZ5Ly19Q\n8/PzzE+sr6+jq6uLnorX66XSHBgYwNGjR6k0zp8/z/lJgJZs7OvrIzNHo1GsrKywd+PWrVtk/K6u\nLoTDYR7ewYMHsba2psxomZubY77r+vXrCIVCCjS8fDYQCCi4ZolEAh0dHWSie/fucdAgoMWRxbJo\nb2/H0aNHmevZ3NzE0aNHyXAysE28DSkTFbBU4EHDn9lsVsbUCzioMIDP50N7ezvfIRaLUVg7OztR\nKpW4LikWEO9ULEC5dLa3tzE1NaWUcAssUFdXF8rlMr93ZGQEfX19/N5UKoXl5WVa58FgEJcuXaKF\nKUIHgL0Xcg7SQCyKY3R0FG63mwl5fQ6uUqng9OnTVARShq+f86Sf6yS9LSJUJ06coFLp7+/H/Pw8\nLz992X8rkfALoPGKeAKAdsaiFE6dOoVCoYDvfe97AIDjx4/jox/9KA2WRqMBs9ms5DP1l9B///d/\nU5mfPHkS8/PzCn7enTt3qOh6e3vR09PDy+69730vrl+/zgtOmjoBbZ/Hx8epRF955RWMjY2xH0fm\nsEmvYzabJf8L1qLInbQv6KMVExMTjChIYYPok3g8znVYLBZ4vV6uo7u7W1mnAOlK3k0/OyqRSGB7\ne5vfa7PZUC6XaQiVy2UCGQPaxVEqlfgsAW6W79Xjfh49ehRut5sFGIFAgKNB9Hsga7FarfSIUqkU\nvF4vDev29nbMzc0phmVXVxd10YEDB3jegFawIuvo6+uDy+XiZ48dO4aRkREWs+RyOUSjURpE8Xic\nRufk5CRsNhtlSeoPfhEZOSiDDDLIIINaklrCg6rX6+zmv337NrxeLytz7ty5QytfKtDEqonH4/ir\nv/or3sRerxe3bt2idXXz5k1lUuWhQ4foOvf19aFerzMe7ff7lYF0iUQCmUyG1sY3v/lNVCoVWkF2\nu50eUaVSwd7eHn/X29sLh8NBy0AAHSWfpUdoX1hYwMrKCi2VcDiMlZUVekwyWkIs3ytXriigjT6f\nj+Gul156CXa7nZNM0+k0FhcXWSr905/+FF6vl5U2Bw8epEUUi8WwsbHB921ra1Pgaur1OsN6gOZd\nFAoFelRPPvmk4rlVq1U+K5vN4tVXX+UQQukqF893d3cXExMTtCgFEBcAx2NIOCEYDKJSqfB7bTYb\nuru7FeinX/3VXyUvHTt2jOcyPT2N97///Yql+qUvfUkZW3/06FGe6/T0NMtm79y5g76+PsJZ/bLS\n2P9L0kcfZEquWP3lcpmeaKFQwOLiImXj7/7u7/DII4+Qz2q1GiYmJhjSbTabmJ6epoUs06wBLWTr\n9/vJw9VqFf39/YrHnM/n8Q//8A8AtArIf/mXf6HnI+FFQPMYHA4HvVj99AH5bHt7Oz1YkSlAi5hY\nLBZGMtxuN6xWK89QKs0karK3t4eNjQ3qmq6uLobbQ6EQCoUCPTcZ5SM/yxqETyuVijJaQmQM0GS4\nVqvRsxPwAHlnn8/HcSeAFo3QR4H00GYulwttbW2KB69HePH5fKhUKkruVORQqo3Fq1leXsaBAweU\nFo18Pk+57O3tZeWhy+XCe97zHmU/9u3bRw/7yJEjsFqt+NnPfgZA4zW9xxkMBtlcHA6Hsbe3x/aY\n3/md38H/Ri1zQcnipcxcEnmDg4MMw3R1deHGjRuMZR4+fBj1ep2XzsbGBtrb29n9r+8MBzTGEVdY\nJuRKzsnv9+PSpUt4/PHHuabJyUmlH0Pi34AWApBcxezsLObn58mAXq+XmFiAFg7QM//AwADfNxQK\ncYwIoLnK//Ef/0FhLxaLSKVSZKrDhw8rCM25XI5McejQIWxvb5PhbDYbTp8+zXUNDQ3B5/Ox/H19\nfV2ZZLq+vk7Gn5qawvr6OhWY3W5HOBymUOVyOYTDYV4Uq6urFPz5+Xll9HYymcTHP/5xhnhisRge\neeQRhlosFgu6u7tZ3NDf368I5OXLl2mE7Ozs4KmnnuLvrVYrKpUKFdnKygovf4vFgs3NTfY1+f1+\n/NM//RM+9KEPAdBCfpFIhAIpl5/wnh4p3+FwIBgMMs8on2k1KhaLTIp3dHQgGo3ykhGYLECTlc7O\nTvJRKBTCzs4OeUOQvEXRdnZ24vr16zT+VlZWuDdWq1XJf9brdY51B7SQ3vPPP08j9I033sDrr79O\nntajKLz73e9W8PWKxaLSm1ar1bC+vk7FJ2FbQDNshoeHqZwlZCttBH6/HwMDA9yfzc1N6hBAM/6E\njyRXI5doPB5HKBSiPNRqNY6UALQ2DX3eaGFhgRdfe3u7UkQk43b0k79dLhdl/tChQwzxHzhwALFY\njD/b7XZ4PB7KqfR6imExNDSEO3fuUAbkwgcelNXr5+UlEgllIkE8HlfGs8gaa7UaxsbGGAJOp9Ow\nWCz4tV/7NZ7hq6++yonbEmaU/ZRnyGf1iBW/jFriggqFQrwI2tvb4Xa7iQlmsVhoAezs7GBvb4+N\npzL/SA7HYrFgdXWVHsRLL72EYDBID+PGjRv822AwqNTxu1wufOxjH2Od/0c/+lG8//0Xs++YAAAe\nMklEQVTvp+AAmuDJWmQgF6B5Gz/60Y+YQ4lGo9i/fz/zIn6/H1arlc9qNps8+GQyiampKSqGc+fO\n4emnn6YCXllZwb59+1gEkEgk4Pf7aTkmEgleSFJ1KMUHExMTuHnzJhkjHA6jo6ODwr2xscG/TafT\nGB0d5QU+Pj6O+/fvK/OCDh48SEER/EFhZr2QnDhxAteuXVMaOfXjFvr6+nDv3j16ievr68jlclxL\nOBxmrnBxcREf+chH6OU4HA5cuHCBQiYWoySkc7kcPysjAUQhSUXbP/7jPwLQLMZgMKgonZs3b/Ly\n0c/siUQieP3119k3JMZHq1E2m6Usra+vw+fzkZf087wcDofSq7axsaH0xEjxgXjQxWIRQ0NDymgL\nsYCj0SiSyaSSrzt27BiLSCYmJpBOp4nF+PWvf10BZtVjYJ44cQLf+ta3WOEXDAaxsbHBMxwYGEBP\nTw95KZVK8WKQ3Iy8g1RpSiRDDw4NaHpHn3c2m81UwDJKQrzvRqMBk8mk6IP5+XleBCsrK5SV+fl5\nxbOTxlzxsDo7OxGJRMhnMptOaHd3V2nM18+HKxaLyGQyXJeM09BHfo4dO6ZAuMk7VSoVjs2Rzz7z\nzDN8VjKZZBUxoPGPGPgrKyuYnJxUwIPj8TgN9nK5jLm5OXq6MmhSdGxHRwd1ZywWw9bWFvdLvu8X\nkZGDMsgggwwyqCWpJTwoGegHaN6F2+1muEBfWSYAr+I2Li4uYmdnhzezQPNL/HpwcBAul4tgsnoP\nqFQqIRwO0xKbnp7G8PAwvvvd7wLQbvWHb3apkAEegG0CmvfR29tLi+j06dO4evUqPQixOvXgkLJG\nm82G5eVljq14/PHHFfc/EomgVqvRO6tUKrBYLHxmsViklWK327G1tUWvRlxs/fCzubk57oF8HtCq\n4fTDzW7duoWOjg5arjISQEIeu7u7HJAIaJa7rPnWrVvo6+uj12e322G32xnikcmdeoimN954QwHA\nlXWFQiEsLCzQQ+rt7YXdbmdl5oc+9CGlrDYYDNLKlbJyPbDrjRs3GGrx+XzI5/P0ktxuN7q6ulha\n+84779ArDAQCePTRR2mdyz60GjUaDb6fAN/qQ03ieQpckHj5XV1dKBQK9CYajQY6Ojr4nsViEYlE\ngt7Z6uoq8w8yEVr48OTJk/izP/sz8sOLL76I73znOzyHUqkEp9PJdZpMJvJ3KBTC1tYWPbu9vT34\nfD6ew/3795X+xXw+r1Se6kfECCip8HAgEFDK381mM1G5Ac0bExk2mUzIZrNs0VhfX4fNZlPQYE6e\nPMk98Hg8lEmZgiB5SqfTCYfDwfetVCpoNpvk02QyqQxtXF9fp1xJ6bzsrfSICs/qpw8Dmqxtb2/T\n85EqPwCclith3VQqBbvdzmniIyMjKJVKSshb9q5Wq+FnP/sZ0xrd3d0/N6R0b29PCVtaLBbut8lk\nIm9VKhVl3IrI9i+ilrigstksk7eA5gLKAenRqSuVCiYnJ8mAs7OzOHjwIF80m80ilUopB5ZIJHDq\n1CkA2gUlMWOz2Yz19XWWt6fTaXz1q1/l4Qkkjmyqw+GAxWKh4kyn01zjv//7vyuYgaFQCMlkks8W\nJGA5eJfLRWWdSCTQ3t6uhOwikQjDASdOnIDJZGIcPRAIKDOdRkdHuT9Ski9MJPukL+aYmZmh8Pv9\nfiUuLiPiAS0XNjMzo/Q1PPPMM7xELly4gEwmo7yzFJz09fUhn88zXxGLxdDR0cFL2el0oqenh+uT\n4gy50PP5PPlBcnsi7Ol0Gp2dnYTkKRaL7KsQkucmk0nY7XYK9/z8PKrVKhV2rVZTclCSqJbw8t7e\nHgs7ZmZmUKvVWEqvh99qJWpra6NycrvdSitBOp0m/9ZqNdhsNl5Ai4uLNCQAEMlcft9sNuFwOBji\n0Y95sVgsKJVKOH36NADghRdeQCwWw/PPPw9AU5qpVErBQAyHw1TQ6XSaYbhsNouLFy8yVCTYchLG\nq1QqSKfTSmOvvmCl0Wjwbx8usHE4HBgaGqJyr9frWFtbo3ETCoUo/9IzJuEvKZAS49jlcuH69eu8\nKKvVKtdRqVTg9/v5PYVCAcFgkAaqhF310FALCwv87kqlQsNZQmP6KdmxWExB3Nc32Iv+kM/H43Gu\nIxAIIJPJ8B18Ph9WVlaoe3Z2dmC32ykPlUqF+mBoaAipVIr7sbu7i2QySVmSWWvyXYJgL6XlUjQh\nv5uamuIZinPyi6glLijgQbOWxC4FIl5fFJFKpbC7u6t0K1utViZUx8fH4fF4aKkNDg4iFotRMPSA\nrcePH8fi4iIthK997Wsol8tUolKlos9P5HI5Hp4kFAFt8ycnJymw9Xodo6Oj7BFwu90olUq0kO7e\nvcukv9VqRalU4rN6e3uV2TIbGxuKZSf/LsofgOJRTkxMKIPAHnnkETKAzIqRS0Y/C2drawvBYJDv\nsLGxoeAHdnZ2IpPJUPlJT5VccDJ7SD6rJ3muXJQ+nw9Wq5XGgtvtVvJbjUaDxQgyw0nOYXh4GDdv\n3qTCstvtyOVy3AO3283Ytgxo1M+H0uPLpdNp3L9/n82nDocDPp9P6f2SqrXe3l5cuXKFl6h+IFwr\nkVy6gGbVe71eKhm3282zF56UKEBHRwcKhQL3anBwEFtbW0ruIxQK8VxGR0dpnAwODmJ0dBRf+MIX\nAGgGyWc+8xkaBgLwK95Gs9mE2WymzO/u7lLRy6w0yaGUy2X4/X6ef6FQgMVioSc7NDTENdtsNhQK\nBWWonh53sVwuK8gqkUgEbrebBRgirwDotcizJyYmEI1GKX+CYyceQrVa5fs2Gg14vV4qYMmhSf7b\n5/PB4/EwglCr1ZBMJinTw8PDlIVischLGXgw7E/20uVyEX0HAKsphT/1nqoYK/rLThqQAc24GR8f\n5zrT6TT1QzqdhtlsVnrqhoaG+FlBA5F1lMtlOByOn2uKBjReS6VS1DW/rCLWyEEZZJBBBhnUktQS\nHpTekgc0S0gq0SqVCi0NyfuIteV2uzE7O6ugl4u1Bmh5pV/5lV9h6aMeGX1nZwejo6P4xCc+AUAL\nUzQaDXo29+7dY+c1oFk5EjuVNUoYLpVKYXR0lHmRTCaDJ598ktZVR0cHZmdn6X35fD7mZzKZDI4c\nOUIXfWBgAAsLC7SAxAIWS/bgwYNYWFjAf/7nfwIAfvu3f5vvb7Vacf/+fSWkI9aR/Oz3+7lur9dL\nRIq7d+/im9/8JnsSAoEAtra2uF8bGxtKd3yz2cTMzAx7rD71qU/h29/+NgDNcymXy7Tks9ks4vE4\nQwnVahXb29sM65jNZgUDsa+vD6+99hoA4Nlnn1Xyjm+//Tbcbjer6DweD27fvs33qFQqDH8MDg6i\nUqkwzOBwOBSkif7+fjzxxBMslXc6nbh79y6efPJJAFouQCzGy5cvY3x8nN6I/LfVSJ+vkFyfeO7h\ncJjeZT6fRzqdZmvAzMwM+vr66G23tbX9XJXmzs4O+8BisRg94kQigZ6eHly7dg0A8OMf/xiZTIah\n5MXFRQwODnLffT4fisUiw0Nnz55lRecXv/hFZDIZVuLeu3cPFouFfGy32+Hz+ZRJr+JtmEwmuN1u\nejmFQkFpNRFcRkF7j8ViOHToEGXxwIED1DuCIqH3wPRTb71eLwdcAlofkPAZoPUcPvLIIwC00LI+\nV57L5TAzM8OQXyQSwejoKMdivPzyy9yP7e1teL1eBc0/mUzyXHZ2dlCv15WKZH2VcF9fn1I9qx/S\nOjY2Rjg4WVc8HidPRKNRJYLkdrvpNQteqMhHf38/LBYLI1AOhwPlcpl6TI8ML1icsmZ5119ELXFB\nzc3N8aC7urqwvb3NRV+6dIn5mpGREWxubjLMIKEhgUwRDDtxHY8fP47Z2VkKgn5cfCaTQSKRYA+R\ny+VizwGgNbGFw2FeSPIcUUwXL14krp8oULncenp6lEslk8koIzMEEBPQhCidTvOCkj4eYYxbt25h\nfHycP5vNZnR1deEjH/kIny3hLkATYD1GoL6B8tlnn8Xc3BxLyX/913+de9fR0YFPfepTP4ePJQx4\n9uxZrKys0JBYXl5mXg7QGlnlsxaLBfl8nqGFUqkEj8ejNCrqwycC4iphm1wuh8cee4zf/3D57/Xr\n1/HMM8/w2c888wwFoVQq8f8TiQTOnDmjJHJXV1eVPjGZ1SV7J58BNIUk+yAj36XMXPJUrUY7Ozvk\nLWkg1491lzPJZrPo7u5WsAaXl5ep3KPRKLq7u6m8dnd34Xa7lRCgFFgcOnQITz/9NPOGv/d7v4dM\nJkPFL7Biss82mw0ej4cN1X19fZzy/Morr2B4eJg8HAwGkclklNHhNptNSeTrZ7fp2z/K5TKq1SoN\nJZkYrR/z4nK5lHyN7FVfXx+azSZ/19PTo4SxrVYrurq6qLecTifDcIODgzh27Bj3TkaGyN4LpJAe\nXFkfWu7s7FRyv+l0mt/j9/vh9/up42SCsv7ydzgcvKCazSZD2JLPlYtibW0NwWCQ+yMAv3rZl3UE\ng0Fks1mlSEwKIwBNp+3u7lKPZTIZ2Gw26qZEIqEALdfrde61/mJ/mFriglpaWiIjSCGEWNSPPfYY\nb/xYLIYzZ87wcKLRKK5cuUKL98aNGwqmld/vR7FYZIXQ4uIiN3R1dZVD9wBN4fb09NDqKxaLeOWV\nV9jUub29jb29PVa8nDt3jjHjZrNJDD1AKyAQAQM0paEXKrPZzDi45Lnk0pU4rijA3t5e9jYAIIMI\nE0UiEX722LFjWFpaosX89NNPK0njdDqNSqXC4o2rV6/SipMGSBGavr4+3L59m9bm4uIiAUTlXNbX\n15nTu3btGvdueXkZhw8f5kU4MjKCRqNBxREOh7G1tUVl6HA42OUua3nPe94DAHj99dfR1tZGpRsK\nhXDw4EEKbCKRUAofqtUqvYQzZ87g7t27/B6v14ulpSUFoVpQLwBNGZRKJX63ICAAmvAePnyYFqLe\nYm0lisfj7Eey2+1IJBK/ELEhEomgo6ODP29sbKBUKiloBnrlL8DBotzsdjuVTyaTwf/8z/9wH7u7\nu9HV1UWFnclkMDExgU9+8pMAtDN0Op00hl588UUaSiaTCW1tbfSQt7a2lAIaWYcot1KpxLM3m83M\nMwLgiHJZlwC0yjsmEglEo1G+k9Vq5SUbj8fR0dFBBa3XQYCmkPUYeVarld4XoCl/eW69Xkd3dzd5\nSfI1ehQGi8WCV199FcCDCkv5np2dHUZJisUivRlAuxi7u7upF6T4QtaVTCZZvLR//36k02nK/MjI\nCPb29ogWIecpuktfHVur1eD3+/kOgqwh5Pf7sbe3RxkXT1UPgCv8IPIn9Muq+IwclEEGGWSQQS1J\nLeFBHT9+nFZed3c3dnZ2aH1UKhVW2QCaFSihoHK5jLW1NXoQbW1tWFpaosck6AbirSSTSVpihw4d\nwpkzZ2gh+Xw+TExMMBx09uxZXL58Gf/1X/8FQAu93b9/H0899RQAzY0Xz2R3d1cJD9hsNtjtdr7T\ngQMHsLGxwe/Wj0q+e/cuvF4vQwfi5kt+RvI1+jHMAJTQo8Sjt7e3lREA2WwWfX19/Ozm5iYcDger\nlYaGhmgFeb1elMtlfs/s7Cze9a530brZ29vDW2+9xRyEyWQiUgWghSXEiuvs7MTGxgYrfGKxGNLp\nNEOg/f39WFhY4M8SB5f92tvbw+uvvw5As8w2NjYUz25ra0vB7Wtra6Nn29fXR2/87t27sNlstOou\nX76MqakpWsFLS0sYGhpSZlodPnyY+7exsaFUT5pMJq5Rys9bjU6ePMl9FxQROX+BtgG0fctms7SW\nOzo6cPHiRf5cLpeRSCToYdXrdSQSCVrfxWKRnuqxY8dgsVhYlbm2toZoNKq0M8zMzDAHGYvFcPv2\nbaJsFwoFeuLDw8NIJpOMEIjlLusIh8MKlmU2m6Unls1mYTKZGBWRSkH9PKhYLMZ31IerAE0u9emA\nlZUVfmZsbAyxWIy/j8fjWF1dVUrn5X0zmQzC4TA9tcHBQdhsNub3RkZG4Pf7GcaOx+OYm5vjurPZ\nLPnb4/HA7/fTg8tms8pUYGkH0Ecfcrkc+VZydkLDw8OUh7fffht9fX0KRFMikVBgxPTIGbFYjHpK\nPDV9eLzRaHAdPT09qNfrjKL09fUx8tXR0YGVlZX/T1EIU7MFOg6/9KUvkUmmp6cRCoXIGAJnBGju\nfHt7O3+WmKd8tlqtMlYMaBuxsLDAjSiVSvyshC+EaQYGBlCpVBTYev1F0t7ejmAwyDBdMplk7HR1\ndRWlUonKW+BWhJnD4bAyK0VCDvL/IpyAdjG9/fbbvHQEBkWELBwOY3V1lfHtSCTC30n+RYT3/v37\n2LdvH79XwF71QJQi3P39/SgWi9yPSCSCfD7Pd3S73bhw4QLzgbVaTYFGqlQqZF49RJCQfhxJe3u7\n8nOz2cTIyAiFcGhoiO8Ui8XYzyPPWVpaosKyWq2Yn5/nhb6+vq40W+rH2AcCASwuLiq5QKvVynxn\nPp9XcCDNZjOVbnt7Ox5//HGGWs6dO4cvf/nLaDX6+te/jj/90z8F8IDf5QJuNpvkXzGgxNizWCyI\nRqMM6fr9fmX0SKlUUkZGmM1m5hQES1LOz+v1KhiP5XIZPp+P+y6GmhQ/Wa1WrkMarUVGYrEY8vk8\nlfeNGzdw6NAh8qnH41HGzYTDYYVnq9UqQ28S8tS/c71ep1LNZDKUu7W1NdRqNb5HJBLhkE5Au+z0\nAw0tFovSED8+Ps78nhSRCDmdTqyvr3OdErLTl8eLXKbTabS1tZEnrVYrB4ICD9IH8iyTyQSn00nD\nUt/LZTablWGo0iysLx2v1+vKgFd5p3g8TnmSzwqEmeyHjA0RHhgZGaEBPDs7q+jSrq4uyvDf//3f\nM4XxMBkhPoMMMsggg1qSWiLEt7q6SisvFAohGAwq1XRiqdXrdTz66KMK4vJLL72ET3/60wAedJGL\nW7qzs8Mx8YBmMcho8Pn5eQVxuVwuw2az0Zre29tTUMcHBgYQjUZZ4TU5OcnCho6ODvT09NCz279/\nP65cuUIrqFgssikUAD784Q/jW9/6FgDN5RbPS97x1KlTtHKSySQsFgstlTt37uDkyZP0OFdWVphQ\n/cAHPoDXXnuNZaJdXV2YmZnBe9/7XgBaSMvtdnMc9NDQEBui6/U65ufnaUFKlY1YdX6/H11dXcpI\ncP3o6Y6ODsWC3NnZUZLo1WqVxRlLS0vY29uj1TQ7O8uKM0ALrYmV63a7sbW1xb30er0YHR1VUDKO\nHDlC69zpdLJR2GKxYHFxkVad1WpFf38/fz548CCmp6cZVnW5XHjjjTdYqXfw4EEW4CSTSXznO9+h\nl9iqjbo2m41Wa39/P61mQAsBiwU8NjZGVA5A45XFxUXK2ttvvw2r1cr3FO9L+E5foSVQR/rqUT3C\nvNVqRXt7O0Ng2WwWTqeTxRz1ep3fk8vlsLu7S1lyOBxK6Gjfvn3o6OhQUNn1SOf5fF6B59F7JsvL\nywgEAtQt4n2JR1er1ZSQbrFYpF5qb2+Hw+HgZ3t6evDOO++Q/0+cOMF9TqfTyGQy9OyOHTsGq9XK\ndckId4mw5PN5VCoV8qXei/H7/Wg2m+RvgTaT73K5XKhWq8q0XpPJxLNxOp08M2noFR5IJpOoVCp8\ndi6Xg8ViYcSlUqkwLCsRJlljOp1WPCqZkCs84PP5cP/+fUJFyYQH+Z3b7ebPS0tL/6sH1RIXVE9P\nD0Nvgr0mMf54PE54GXFHJUdw9OhRfO5znyPy8cGDB3Hv3j3GXEXA9JVYEhp7+umn8ZOf/IRVO+l0\nGjabjbmvYrEIq9VK5vd6vYhGo2SqcrlMBqzX68QqA7TQ4/79+6n4JKwm/TaVSoV4b+vr61hdXeVz\nL1++rOD8jY+Pw+/3E9Hgve99L7a3t3kZBINBuvMvvfQSRkZGOLair68P4+PjDI8cO3aMuF+ApsAk\nRFetVpFKpRgqOHDgAA4cOEDhnp+fh9frpeAcPnxYyWFcv36d+3779m2Ew2HuZTAYpCABmrKbmJhg\n3mhychKpVEpBPJC8YVtbG0qlEr9HwrCiZATZWgRhbW2NSmP//v3o7u6mkZFIJNDb28vPJhIJtLW1\n8YIvl8v4zd/8TbYe6NGcQ6EQcrkcKzdFMFuN9JAyKysrMJvNVFajo6N8n3A4jFu3bjEfWSwWlbzq\n1NQU8vk8jcFGo0FUbkC7OETxSWWZ/K2MKRfjL5fLIRaLUbkVi0UcOHCAF1ahUODF2NPTg9u3b9NQ\nlBE6+kq0WCym5Enkb1dWVhSIIUGOkAt7bGwMm5ubyngJmc0EaPk7MSLT6TScTiefnc/n4fF4qEt2\nd3dZnQpAyW0FAgEkk0nuz+XLlwkVBWjKXt8esri4qFwqmUyGF5Db7UY+n1eq+qSfEdCMgWq1qkAu\n6SuZs9ks17G3t4fl5WXyh8ViUd7fbDYTIUbORY9TGAgEyD8mk0mZlyXvLTJssVhw6tQpyqLkx4Uf\nlpeXaYT+MjTzlrigFhYWqHBrtRqi0SiV7MDAAG/x4eFhFAoFWjkXL15U4urT09PKrS5wGnrFJxbj\na6+9hkgkwg2XBKAIbCgUUoAnc7kcBgYGuOH1ep3fE41G8dRTTynjovUW1Pnz5/HHf/zHbNzL5XJ8\nzr59+wjvBGhKpFQqsQQ1Fosp+a6rV6/CbDYrlr2+gGJ7e5sNf0tLS0gmkzh37hwArQCl2WxSya6u\nrnIdAhYr9NZbb2FtbY0elgisrGN9fR1LS0tU9oFAgOWq0hCqxy3Tj/YYGRnB7du3eY7z8/MYHR3l\nBb+2tsbLrtFooL+/n3stI6rFSy6VSnjyySeVOLoo0WKxiFKpxP2ZmprC4uIiz/Sxxx5T5ieFQiE0\nm028+93v5t4KP4TDYZw5c4YCq4fFaSWq1WrcO2ltEMW3tramtAXo+08mJiYQDodpCN2+fRuVSkWB\nqpEeG0AzBuWz0g8nMux0OmGxWChbtVoNfX19lL1SqYTl5WUaR52dnczRyOgNvbK2Wq3kTRlvri98\nkJxzrVZDKpXiZ8vlMiqVCnlleXlZMSyazSYCgQANyXw+z/xsf38/CoUCz1mwNOVi2N7exvb2NmXc\n6/XyObu7u3C5XNzriYkJZcigNPzL/hw4cAB7e3s00iORCL83FotxeCSgyfTZs2fJh4VCAZlMRhlH\ns7e3R52Yy+V4WXV3d+PAgQPUU/l8HiaTSQGm1XvghUKBZypFVPpBiU6nk+eQy+UUoz2dTuOnP/0p\n//7QoUOMOIVCIXR2dioRqP+NjByUQQYZZJBBLUktUcX32c9+li59PB5Xyq71EzPL5TI2Nzc5BXdn\nZwcLCwu0GCcmJrC1taVMCdUjgz/11FO0WmTAmljekUgEqVRKAVmcmZmhdVGpVOB2uxXAQ7EOtre3\n4fP5aBFKQ6NU/Jw+fRrJZJLhxJ2dHbrG4vrq47P5fJ5QL9lsFhcuXGBYwmw2w+/309vw+/10lc1m\nM6xWq2LlJJNJZWBje3u7EtbTv1+z2VSANzOZDK3eVCoFm81GbyOVSuHw4cPc2+3tbWV0diKRYGhx\nfHwc3//+9xkucrvdSgd/KBRSUA4ajYZS/j44OKjE4PXjsqVCSo9wL6GETCaDSqVCj/Lq1avo6elh\nefva2hpyuRxzgGIVS+gpHA4ztOR2uzE0NEQv8OWXX8b3v/99tBr927/9GyvPpJpLrP5QKKRYxPoJ\n0dlsFul0WoGycTqd5DsJlYt8dHZ2sqKxUqmgXq8z7FYqlVCtVvksmQIrVr94RGLl63NONpsN/f39\n/NtEIoF6vc6QrgzrFE9Och+A5hGZTCb+TsKyEo2QXJgeZdzj8VAu9ZBKUqEneyeDAoVH9QC2gMY7\n+gpJPQ/v7u6iWq3Sk7NYLLBYLPRgi8WiUhqul8NarYZGo0F9aLPZYLPZ6DHZ7XYUCgV+lwC0Cg/o\nIz0+nw8+n4+emuSc9E2+Ho+H77Wzs8MzbWtrUwaDCkizeImSfpF1S3m8nEU8HucZivyLDFssFkYt\nHqaWuKAMMsgggwwy6GEyQnwGGWSQQQa1JBkXlEEGGWSQQS1JxgVlkEEGGWRQS5JxQRlkkEEGGdSS\nZFxQBhlkkEEGtSQZF5RBBhlkkEEtScYFZZBBBhlkUEuScUEZZJBBBhnUkmRcUAYZZJBBBrUkGReU\nQQYZZJBBLUnGBWWQQQYZZFBLknFBGWSQQQYZ1JJkXFAGGWSQQQa1JBkXlEEGGWSQQS1JxgVlkEEG\nGWRQS5JxQRlkkEEGGdSSZFxQBhlkkEEGtSQZF5RBBhlkkEEtScYFZZBBBhlkUEuScUEZZJBBBhnU\nkmRcUAYZZJBBBrUkGReUQQYZZJBBLUnGBWWQQQYZZFBL0v8DccRM6vpetr0AAAAASUVORK5CYII=\n",
+            "text/plain": [
+              "\u003cFigure size 600x400 with 2 Axes\u003e"
+            ]
+          },
+          "metadata": {
+            "tags": []
+          },
+          "output_type": "display_data"
         }
-      ]
-    },
-    {
-      "cell_type": "code",
-      "metadata": {
-        "id": "OUUXxol7wl-f",
-        "colab_type": "code",
-        "colab": {}
-      },
+      ],
       "source": [
-        "#@title Load a test image of a [labrador](https://commons.wikimedia.org/wiki/File:YellowLabradorLooking_new.jpg)\n",
-        "\n",
+        "#@title Load a test image of a [labrador](https://commons.wikimedia.org/wiki/File:YellowLabradorLooking_new.jpg) and run the module with TF\n",
         "def load_image(path_to_image):\n",
         "  image = tf.io.read_file(path_to_image)\n",
         "  image = tf.image.decode_image(image, channels=1)\n",
@@ -194,83 +214,336 @@
         "content_path = tf.keras.utils.get_file(\n",
         "    'YellowLabradorLooking_new.jpg',\n",
         "    'https://storage.googleapis.com/download.tensorflow.org/example_images/YellowLabradorLooking_new.jpg')\n",
-        "content_image = load_image(content_path)"
-      ],
-      "execution_count": 0,
-      "outputs": []
+        "image = load_image(content_path).numpy()\n",
+        "\n",
+        "def show_images(image, edges):\n",
+        "  fig, axs = plt.subplots(1, 2)\n",
+        "\n",
+        "  axs[0].imshow(image.reshape(128, 128), cmap=\"gray\")\n",
+        "  axs[0].set_title(\"Input image\")\n",
+        "  axs[1].imshow(edges.reshape(128, 128), cmap=\"gray\")\n",
+        "  axs[1].set_title(\"Output image\")\n",
+        "\n",
+        "  axs[0].axis(\"off\")\n",
+        "  axs[1].axis(\"off\")\n",
+        "  fig.tight_layout()\n",
+        "  fig.show()\n",
+        "\n",
+        "# Invoke the function with the image as an argument\n",
+        "tf_edges = tf_module.edge_detect_sobel_operator(image).numpy()\n",
+        "\n",
+        "# Plot the input and output images\n",
+        "show_images(image, tf_edges)"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "colab_type": "text",
+        "id": "-F1huDW5rROF"
+      },
+      "source": [
+        "## High Level Compilation With IREE"
+      ]
     },
     {
       "cell_type": "code",
+      "execution_count": 13,
       "metadata": {
-        "id": "qW32e6spORCo",
+        "colab": {},
         "colab_type": "code",
-        "outputId": "7b0070f3-85ac-46d4-e3f4-e990c5b7c501",
-        "colab": {
-          "base_uri": "https://localhost:8080/",
-          "height": 570
-        }
+        "executionInfo": {
+          "elapsed": 42,
+          "status": "ok",
+          "timestamp": 1598547100203,
+          "user": {
+            "displayName": "",
+            "photoUrl": "",
+            "userId": ""
+          },
+          "user_tz": 420
+        },
+        "id": "rTqseoRXxl6v"
       },
+      "outputs": [],
       "source": [
-        "#@title Test the \"edge_detect_sobel_operator\" function\n",
+        "#@markdown ### Backend Configuration\n",
         "\n",
-        "edge_detect_sobel_operator_f = ctx.modules.module[\"edge_detect_sobel_operator\"]\n",
-        "\n",
-        "# Invoke the function with the image as an argument\n",
-        "print(\"Invoke edge_detect_sobel_operator\")\n",
-        "result = edge_detect_sobel_operator_f(content_image.numpy())\n",
-        "\n",
-        "# Plot the input and output images\n",
-        "print(\"Input:\")\n",
-        "plt.imshow(content_image.numpy().reshape(128, 128), cmap=\"gray\")\n",
-        "plt.show()\n",
-        "print(\"Output:\")\n",
-        "plt.imshow(result.reshape(128, 128), cmap=\"gray\")\n",
-        "plt.show()"
-      ],
-      "execution_count": 5,
+        "backend_choice = \"iree_vmla (CPU)\" #@param [ \"iree_vmla (CPU)\", \"iree_llvmjit (CPU)\", \"iree_vulkan (GPU/SwiftShader)\" ]\n",
+        "backend_choice = backend_choice.split(\" \")[0]\n",
+        "backend = tf_utils.BackendInfo(backend_choice)"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 14,
+      "metadata": {
+        "colab": {
+          "height": 272
+        },
+        "colab_type": "code",
+        "executionInfo": {
+          "elapsed": 1580,
+          "status": "ok",
+          "timestamp": 1598547102184,
+          "user": {
+            "displayName": "",
+            "photoUrl": "",
+            "userId": ""
+          },
+          "user_tz": 420
+        },
+        "id": "2OPBr0eGroE0",
+        "outputId": "9e7cd67c-7203-4052-d4ea-c06c1656a57b"
+      },
       "outputs": [
         {
+          "name": "stderr",
           "output_type": "stream",
           "text": [
-            "Invoke edge_detect_sobel_operator\n",
-            "Input:\n"
-          ],
-          "name": "stdout"
+            "Created IREE driver vmla: \u003ciree.bindings.python.pyiree.rt.binding.HalDriver object at 0x7f1c7765bdc0\u003e\n",
+            "SystemContext driver=\u003ciree.bindings.python.pyiree.rt.binding.HalDriver object at 0x7f1c7765bdc0\u003e\n"
+          ]
         },
         {
-          "output_type": "display_data",
           "data": {
+            "image/png": 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birJQVCoVxzfihyXUcc3ruul2o/hpbBRaIW8hsPaO3IAZaKI5c6lUygnRnpyc\nxE/+5E/ac44ePQqgcyCp8kIFyh9QpPcOhUI4fvw4AODQoUN2QO3duxf9/f3YsmWL3evChQvrNlGF\nIPlvVjZQLk4/OzfD1oG1uc8+U6iYMLoGK+gBR0VI/Ur+nDK/P08PaOXEY4CBBmAoiziw3r+tfam/\n5aHC99DwfroKlB1F36ndbjsuFf0t9z7dMxUCZBoO20Vln+3QvuIedLlcLb90xQFFhxqwRm2kg8cO\npBNXkzgrlYo55ubm5gB0OKWAjsMul8vhTW96E4DOwNMxm0gkkM/n7V7Mt9ENUS0KCh35jHihtNtt\n8yvVajX09fVZ1JLfotLJqsESQGez5qZB0VpK/ugh1Wo0ko7P8Udm6eTWBci/a7+rH83zPIerz+//\nq9frNg7EtTVaTg9svqf6oHSRXS6aSjWzYrFov6V/i/NHLaZkMonl5WWHCofBIPwt28s2tVprZMP6\nHEZGKa1UN4pGTgFuTlkwuEYp5NdiqQHrdVqNQOfguOOOO2xTf/DBB83/eP3116Ovrw/T09MAOlaP\nRodxrvg3In4eHh7GuXPnAABnz55FOBzGa1/7WgDAxMQEtmzZgvPnz1s7/cm1nCtcC4qKeJ5na9af\n69jX12f5fACc+U1lTJNtg8G1Ui7qFwc688Ffw04PJLUgA4GAY/XQalHExW958NrGjRuxadMmDA4O\nAujsW5oHRauFvvKTJ0/izJkzdq9MJuNYjEqjxHHiXhSPxx20QedWMBh0yIi5DnX/0fmjBAtEkF6M\nuJvS80H1pCc96UlPulK6woLSPBbCEpqP44/uUp+OsgaTXZs09oFAAK9//esdDYth5N/61rfw9NNP\n49WvfjUAYHR01KHjUOiA99Iot5mZGbuXHwp63eteh69//etmptOS8bMxUJRiiXCYZncHg0FHC1RI\n0G/l0S9H0ZyIVCrl0LOQmBfomOuaQ0WoQCOLlP6FmqzSl7AdDOVVuOSGG24wC9MfTQXAsbAOHTpk\nGnMsFsPq6qpDpKmWLglw/VYn30H7m8/XvChNaaA1rpFqnFuJRMKKsnFMu1HUh8CIV9XcdZ0pzELr\nSSG9sbEx3HDDDQCAPXv2YGlpCQ8//DCAzryjP/bAgQOYmprCjTfeCKBjQSk8ROhcrYJoNOrA3Byb\nxcVFpFIpnDp1CgCwefNmDAwMmHVGqFBLqmjOlBKUktpHrV5tA0PBLxeppnsKsMYyoi4AhfXYJ8Da\nmlVXhP/9dY2Tzkz3C87RTCaD/fv3Y3JyEkDH96MwJd+B4xqLxZBMJm0PvPrqq239r66uYnp6GkeO\nHAEAzM/PIxQKOf3jh8t17yQhe/C4AAAgAElEQVTrOj+ru6XVajntAuAw4gBuTqHneU7h0CtJVxxQ\nWiGV0AB9Ln5ISvMc2GnqbE8mkzh48CAAYHx8HPl83jqNmwzQ2dgOHTqEe+65B8DapqrYfLvddg7H\n1dVV68xkMmmHxuDgINLpNC5dugQAmJqacpLWCKXoxuEP/VZzPx6PO0zbHHy+o4bah0IhOwj8TmDe\nT53Efv41zVNQJ3AwGHQOHZYXUMwZWMPxFS5KJBK46aabLOyY8KAqHRpw4HmdEhp89q233mrtbzab\nOHnyJJ577jkAsFLZGhRBfyHvzff1lwRpt9uoVCoOXZEfplV4TEN/A4GAlVBnm7tRqEgBsHmkfoEr\n5TWxLzTx9MCBAzZGjz76KM6ePWtzbWhoCPv37wcAbNiwAc899xx27twJoDMndC0xWInzg2Ot64Pw\nMA8rrn8qRhxfHrIaNKB5T5rnRiVI54oeHNw/tDoC0ztisZhDz8P26+GnkDgPQxWFsP2pIKpUMriA\nfRsIBCwk/9prr0UkEsGzzz4LoKMYK9s54T/O22KxiGq1irGxMQDA/v37sXnzZgAdv/HOnTtxyy23\nAADOnTuHJ5980nyJ/mCmgYEBJwClUqmYIkjWfHVb6Pzy5w0qxRrD9f2xApeTrjigVCNaXl522AC0\nE/wYciKRcHJVgsEgrr32WrvXM888g4mJCbuuxJ9f+MIXcPXVVzsWQK1Ws0XDoAlqH4VCYR0bAjuY\n0XMURgYpx5tuBrrgWNKBi5fJohpgoQuSvi4etO1227EYNWeI2pQ6zpnMDMA5+OjIVn8MxwboLATN\nPwqHww7Lh270N910E4rFokUi0hrVQ1jLQsTjcYfkVosKMtprz549Ng6PPvqoadSZTMY5VOLxuJPw\nqKwD7C8uQLJIcHNkuQE95NUnoRGC3SrK8Qa4Plw/p6Fq9bQkuEHfdtttKJfL+Md//EcAwL59+zA8\nPGz+3VgshptvvhkA8OUvfxlve9vb8O1vfxsALCBIIx0jkYgpLLSguZZWV1etX/v7+1Euly2QpVAo\noNVqGSrAQ0P9m5zfPHw4J6noar4R4PLttdttG3/1A7FUibLB6MHi94P5WTna7bbTLn2uPxCM/liO\nxeDgoOWYnTx5Elu3bsXLX/5yu1ehUHDWdLlctrXGoCuuidOnT+PYsWMAOnvrNddcg7179wLoWKdb\ntmzBxo0bAXRQpUqlYm3TtcN8Qg1e0ohRfwI1A984B6jwclz00H6xNdXzQfWkJz3pSU+6UrrCgtKI\nuNHRUXieZ5p6tVo17YlhoDzhyfxNC+KVr3wl2u224ddnz55FLpdz8nN46j/66KP4vd/7vXW+CzX3\nNRdpcXER2WzWNMrt27eblpPP55HL5SyyZnx8HM1m00pv0/JTBgfNJ4rH404bFUqgUGNkX1HT9VOV\n+HNdlLWCLAu8dzQaXceyrJBmIBBwIDzNi4nFYkin0w5/2LZt2wAA3/jGN9Buty3sPplMIpfLmVa8\nurqKXC5n40rrRC0ftjkUCiGVSlk7+/r68JM/+ZOGo585cwazs7MOR5jmk3ie57TR72/IZrMO7KWR\neqR+AdbDNN2aBwWsIQUM9/dbMsAaQqDhzO1222C7ubk5PPbYY+ajXV1dNesG6PQzYai/+7u/w+/8\nzu/gQx/6EIA1BnK1HBS2LhQK2LFjh1lUg4ODxjoxPz+PxcVFs9QSiYTjU2VblaVB4S4/ZRCjEfld\n9XXSoiSKkkqlDJngOlKfnMJf6p/kvXWuaL/7o/Toy1YfVSAQsDkfjUaNWePuu+/G4OCgReUx2lL9\npuo7YnQd9xNtYzKZxMLCAj7/+c8D6KylO++806yzTZs24dChQ/jOd75j31erJ5lM2tzi/dVnpT6n\nXC5n0bu8l7ZDIb8Xs6C64oAql8vOYEajUafksWLIKysr63IACDXMzc1heXnZDp3V1VXUajULsxwd\nHbXQ75e//OXOhGNnKlzUbrdtwo6NjeHZZ5+164cPH8Ztt90GAFbCgQsjl8s5IcoM3VQIgPcl2SN/\nyxIWGsaaTCbX5fbwUNKwznK57Bx2mqTL72rynIb++g8g5gfxwEqlUk7+Fv056v9iYMOxY8dQKpUs\nkbNcLuPixYsGpRCGuhz0AcApgUEeMo45E3WZ5Ll79248/PDDOHv2rPWfUhuxn/kc3cD8/GKETjQ5\nlxsB+0Y3w24UHTOGEHPu+J30wWDQNpwNGzbg+uuvtwTZS5cuYffu3aagTE9PI5fLWd9Vq1U89dRT\nAIC77roLzz33nI0B15Um1LdaLVMkBwYGMDw8jBMnTgCAc1jl83ns2LHDNmT6Y9jv5KlUOiGFlTQv\nzs+X2Gw2HV8n/XOqHPrzE/WA4vOANZJnigaYrKysOJyIvI/OMw1Yoe+MCnGr1TIf0jPPPIMTJ04Y\nDLdp0yZHqaKfh2NM/6xC81RmeYCzLyuVCj7zmc/g2muvBdDx/d5zzz22fv7pn/7J1iwJE3hf5naq\nIq215gi3+pU/jgPz7DjGV5KuWGW68BmRwgmoUTic+JoDdPDgQeuECxcuIJPJOJvH8vKyner5fN4m\n42tf+1qUy2XTAukE571KpRIGBgYsUqlarWJ5edkmXSaTceoMraysOJp3s9nE+Pg4gI5jUydGrVZb\nt0lq+XjivcCapsaNpK+vz4m+mp2dte9SK9GNX7VN+lD8ASnAGtmlLpp2u23O6sXFRScyT0k4AZeI\n95prrkG9Xjfse3Z2FrlczhZgKpVCPp+3TYl5HFq0Ut+hXC5jeHjYxjObzTqW3N13342HHnoIAHDx\n4kXboD3Pc+pDUavXujj+emOaqKiBLTyQ/UEi3SahUMg08VarhVqt5mzYnDdKkAsA27ZtQzAYNGd8\nJpPB9PS0rQ++98WLFwF03v9zn/scgM54P/roo+uClzRRPhKJmEX9ile8AsPDw6apBwIBOxh5oHK8\nZ2ZmcObMGWOwyOVymJ6edvysfgZ2zdVLJBLOIVMsFp2gKWDNqmQZc76vBnLwkNV7qSVTLBbtICDr\nhAbjAG5koJ+lQwMuWAyQ47N3717bw44dO4ZWq2XozPDwMLZs2WIRxel0GsVi0Tmg2B/VatV5h2Qy\nieHhYRvz6elpvPOd78Tdd99tfXD48GG7jwakcO/g/KFvkO1eXV11chJpgfO+xWLR8e9eSXo+qJ70\npCc96UlXSldYUOqTIY6p4ZyEGfzsDdTqCOMMDQ1heXnZCffetm2bE7vP03r79u2OD6ZcLjsm/fDw\nMNrttrFTTE1N4eTJk2bJ3HXXXaZF12o1i/IDOiXMX/GKV+DkyZMAOtpHtVo16DGXyznalEZTlUol\n5HI5B8bMZrMO9qt0SBoqXq1WUSgUzOqhRsl3Jszop/bhb5XahuOiOUOau0Q6FTX51dyPx+N42cte\nZm2MRCIWeddsNrGysmLjFovF0NfXZya/QhbhcKcWDjVs1pmh9ktN9w1veAMA4POf/7y1Y3l52Xlf\nhvMSamAujlb2zGQy5v9otVpm5ZFFWqMpu1GUi5IpGRoRq3REyuweCoUwNTWF7du3A4CxAigMk81m\nHdiM7A8TExP2b6BTqqZUKpnFFAgEcPvtt+Nd73oXgE6E4MzMDLZu3Qqgo7k//vjj1q58Pm9UR7lc\nbh0nnMJp+m+gs374/kQL1CepodPz8/PO2tM0EkLafH9lmaDoXqQpGfT9KmWQuhNoJeoexzwr/p5o\nw/HjxzE8PGz+3auvvhrz8/OOL/GZZ57BE088AaCzPjZt2mR7HiNVgTV4kvsD20HLb3FxEZ/+9Kfx\njne8A0DHDULf4JEjR9axd2j+Ktc/+5auBq5TppZQ1Nrq+jyoZrNpOUXnz5/H0NCQfc5kMnZA+XnZ\nMpkMFhYWzK+0srLiDAi5oGgOa4cNDg4iGAzaxkffAyd7sVhEvV7HRz7yEQCdzeuTn/ykcZHddNNN\n5mMpFAro6+uzRbq6uopvfvObjoNRoUnP85zABYW0otEoCoWCLQxu7lp0jlAUn8U2M9xd618ptEjn\nqcIYFFIo8Vomk3F8Y7FYzCHPHRgYcJKk1V+TSCQcv2Kj0cD09LT9Np1OY2hoyOFEO3funMM3SOhg\nZGQE09PTDnzkeZ7BpzzAODfe+MY34sEHH7RrmhdFHJxQA/nF1AehFCx9fX3Oc0nJBMD6v9vEHxSg\nG7DSHjHZWclOp6amzGHebDaxuLhofkVSV6lCR1ipWq3i/PnzdpgzQID9fuONN+LjH/+4wYXLy8v4\n2te+Zs8aGRlZxx3H8R8fH8c999xjwUnnz593UgXa7bYpb/39/UYYzWt6QLVaLZRKJduQqQiqT1ZT\nH5imAKyVcWe7qGwqPZHCp5ogzTmqnIi6WdN3o7RBet/z58/bQcEDm2OcTqeRTCYtYCsQCOD06dPW\nvpGREfNnkZqI871SqTgwXTabxezsrAVR3HPPPebfn56edsLbma6ih7TCh1ScNQBFg850L3kx6UF8\nPelJT3rSk66UrrCggDVH2fj4OKrV6mW1aX8p9VqthsnJSZw+fRpAxxmfzWbtel9fn8NAriHYs7Oz\nDrNvo9FAoVCwkNZms4mPfexjBtORBkeL3VELGhoachi4y+Uyfv/3fx8f/ehHAaxFzyjUpsEY1WrV\nLCpS+fC7yWTSAin4Tkp9pLCcZoADHU2tWCyaNsUsfWpBGtHHUG61atSp7ieT5dhQ/OUD/KziAwMD\nDsShkVhjY2OOU31+ft6sXkIJtJhWVlYwNDRkVjVLfih7AEOln3/+eYRCIaeUi0INhG38JVc0hF+h\n1kwmsw7m6TZRpgxC5X6SXgBWhoJjxNIL7Oevf/3rTnXmHTt2YGlpyXFs03KZmJhAKpWyAIpGo4HB\nwUGzmCYnJzEzM4PPfvazADpUSM8++6xRI8ViMQtGOnPmDPr7+y1Kc+fOnTh8+LBFsf3zP/+zo3kn\nk0lnfS8tLTkWkQbfVCoVh1aMycQa+MB7FwoFJzw/kUg4jDfZbBZLS0tO0IyuFYWACYVxHAYHB7Fx\n40azbPr6+hCNRp2IYrWg9HO1WrVQfGAtLJ/3HhgYwJYtWxxr7emnnwbQWWcbN250gjf8jB6xWMwI\ntQ8dOmSsPFu2bMGRI0fWsazzOaSR8hMV03q9XIUKjYi8knTFAUVoDlhjuuWGrRANDwV2wuLiIpaW\nlqzDV1dXnfLfO3fuRKlUssNO2XZnZmbMPAbWWAdIqbNhwwbceOONNvCf+9znsHXrVidUmhM4Fos5\nrOrf+MY3cODAAcdfw5pHQGchcHFrtA3Q2ZCz2ew6vwmFi0zhOfUxaFkPbkKcJKQz0josmvekTNCe\n5znM6n19fdizZw8mJibsXnw3Pltzu/SwO3/+PM6dO2fvsbCwgP7+flsYzWbTibaLxWK2qRw6dMhh\nnWAYtUJC9XrdyanYt28fAODo0aNotVoOYwH7mM9RGIchy1xMSinDMfpuIo9eSmH0FNDxo2qJhIWF\nBfOpkmGC8Hir1cLQ0JBTqkI3Ws/zcPbsWSeEnUoCIW4qc0wToJLxxBNP4KabbsLrX/96AB2oaefO\nnZiamrI2UykoFovIZDI2zwYHB9HX12dK18TEBFZWVgzy0jnKumo6nmw7/x8IrFV+zmazKJfLDnMC\nv8t8Oh7gXA+cs6xGre4GZaGIRCIGu1199dXYtGmTjQN9u3yHcrls1FOAy4HHcG22iywTnIeFQgEv\nvPCCHULnz5/HzMyMtXt4eNiYIxYWFnDy5EnzZ23YsAG1Ws0OO8CtDHz48GFjcDlw4ABOnz5tijH7\nRPtLy4SoLw9wfXice7yXKlB+6YoDSh3ZpVIJi4uLtuFks1kLOQ0GgxgeHnY0YpZABzqdpHkw/A41\nO/379PQ09u7da50zOzuLRqPhkFbedtttVhJ+cHDQFjHQ6XAehNTuqH2+/e1vx8WLF80a27t3Lw4d\nOuRYa/w3cxr4mQEQyvmXyWRsEZHnjBuFWkEM1+fAM69Dc5f8RfeUbqTRaNgh+/KXvxypVMqx+hKJ\nhOWnVKtV8+UAbnl0DS8FOpvKpk2b7KAMh8N46qmnzGfHfAm+B4uhAR3NPZ/P2xiurKyso1yh1e0f\n45tvvhnHjx+357LUhs6t5eVlx7epG55uWPwe31kPrm4SHQfmrnBOJxIJ20CKxaIlQQOdIKB0Om2H\nxqZNm3Du3DlbW7Ozs+u0Yw0p3r59u/VJNBrFpUuXrK/OnDmDBx54AL/wC78AADh48CCmpqYsb2pq\nasoJ7FhaWrLx3r59O7LZrAU+DQ8PY9++fbb22u21QoFzc3O4cOGCQ5OlJWjog9R0hkwm42yUXA9E\nMZQ2SQ8R+rO5XiYnJ62vstms+bD4XQ0rn52dxTPPPGPJuPQN+X1Y/K3mdrHUBvfEgYEB7N+/35Sy\nYrGIF154wULHqcSz70qlEp588kkAHd8gD3wApqDyHYvFoqWKvPKVr8R1112Hf/7nf7Y2qo+pVCqt\n49drtVp2b6WZo6KsgRxXkp4Pqic96UlPetKV0hUWVDgcdqCD8fFxJ0pHMVKWCwc6lsjp06etGueu\nXbtQKBQMzy6VSigUCo5P5nJsBUDHIkomkwY1NBoNLC8v27Pe+MY3Ynp62mjv1Vfhh6yuueYabN68\n2eCUVCqFl73sZQ6UwPc7ffo0lpeX7bf9/f1YWloyTXdxcdEhQPVTrCjVETPIlR1CLShCG7z33r17\nHe06Ho+btUFsWst6UJsDYCUQ2LeLi4uO/65er9u1crmM5eVl0zZLpRL2799vEUKtVgunTp3C17/+\ndWsnx4k+KPb12bNnrTIq2610Nyo7duzA4cOHnWRbEuiyXUq8S8tVSyxQG8/lck5IP6Pbuk3UlxeN\nRp3Kx4VCweY8I8VoLV68eBFbtmwx7Xr37t0IBoOmfRPu5BwmjRKwVpmYa3h5eRlbtmyxviqVSnj6\n6afx/ve/H0CHpHTjxo327LNnz9qcZrj2//7f/xtAJ5R9dXXVvjs9PY1KpWLWmUb8XXXVVdi8ebPN\n6VQqhVqtZmP1wgsvGAIBrEX1KWO3kueqH3X37t3YsGGDg9aoj5aQKK+Vy2Xzjb/wwgvrLCQtmcOU\nDfUzUWKxmNGOAR2ygfn5eWMg7+vrw6ZNmyw9oK+vD6961avMh3f48GHbh2ZnZx2m87Nnz2J8fNzW\nZbVadUhoPc+z5+zduxfbtm2zdAB+j/sFq/NqCLtGvfpJvskKBPwQMEkoTZCyiwOu85Fmo/JhtVot\nc+rRKUqTvVAoOPxR6XTaNuuzZ89i48aNji9jw4YNTn5RrVYzSCuTyWBsbMw24bm5OVs0xG1572w2\ni7m5OZvcLBHBiXH+/HkbuI0bN9pmAHQmmOd5ljM0NzeHmZmZdfRE/L4eIv5s73Q6jauvvtqZgGqG\n++tuKZMxa+so55fSNZ09e9YpOaKwHvud1xioor4wf/nxq666yvonn8/jK1/5CoA1bJuLf+fOnVZS\nAOjQVymtjj/0dXh42DYoZcrgGCvLNvtR82bUtxWLxRxFoRuFdYqAtXnHd1YqGvarBsVcvHjRFIGp\nqSksLS0ZlJTNZh3+tHA4bIcEQ7DZJ1T+CMMNDQ0hnU7bnF5YWMD58+dtXdZqNRsDbnT0bx05cgTD\nw8N2r3K5jKNHj9pcGxwctDk7MDCAXbt2OWsjEolYGZDdu3djZWXF1i2Da7RqrlaQ1f+TH1PzsTzP\nM5j64sWL1mYGRajbQnkgyZ+nz1DFG1jzmzWbTYf6jXltPITb7TbOnDlj0Gy5XMaePXuMeeMnfuIn\nbD587Wtfw/nz5+19E4kEpqenLV2mWCxicXHR1pZSsl26dAl79uwxN8YLL7zghMazz5UtX/2Byj2Y\nz+cdeJnvcjnpigOKpK/AWs4QJ1E2m7VJUywWHfJX/o0awpYtW5DP521jnJubw/DwsJ3q/jpLmgQ8\nNDTkRNqxkJfmJ2micDwetw7mxOZgDg8PY8OGDQ4RLaMEeS/NRRgaGnJKcwCwxT85OYl8Pu9QEGnN\nn3K5bJuK9guwxmuoeVFK9cNDCOhoV1qsj0SwanVp0icXoeLVWpBQfVCFQsHJsVpaWkI6nXYWYTab\ntbHIZrOWLPjQQw85Vo3mkwGdTSkSiTi5Pipbt241fwYTE7kxMthCrUYtpOcvT6FEsuy3bhPN1WG+\nDfsuFovZHGXtJ86NdDrtWAxnz57F1q1bbUwZFMS+mp+ft+9eunTJSZAdHh7G/Py8Y6mo5To+Po5y\nuWzX/YXvyuWyWSr1eh1zc3MOPZWWItcoxVKphFOnTtk84m84ZpOTkxgbG3PIZFWzZ+kb3lcDZhYW\nFjA/P78uUVuVZ41SVfJYf4E+v8WvCiO/r1FymrhOhV33C+W8i0ajeOaZZyxgZevWrbj99tsBAG9+\n85vxwgsvmEI/OzuLpaUls7ZisZj5LfkelOeffx579uyx5GrymyrNVLlcdkhhV1dXnaAJ9U81Gg3r\n2xeL4uv5oHrSk570pCddKV1hQWn2N0N/tTCaEoeWSiU7lWdnZ52CcyR4PX78OICOKVmpVCxUkqHU\nQCfEUksUEyZSGCoWi1moaDqdxoYNGwyTB9Y0HxZYpOZx6tQpTExMmEZVKpVQqVRMG1XNvNFo4NSp\nU+Y3Y/QQ359Z+fw+YSo+K51OG/xBf4xaJgqlRSKRdRg87xOLxZxMbzKV6zu0220HR1cMPhKJWNgs\noVZaKqQjUhhXc9D4bMI8WhjuTW96Ey5duuTAtouLizbmfkhGYYdQKISxsTGHRV7hJFJDUYrFIgYH\nB+37jUbDqZhKmItt7EbRirGEUTQiVP0biUTCoKN4PI75+XmLUt2yZQvGx8cNdn3iiSdwzTXXWL8z\nAo33IREpAGesgY5/MhqN2m/7+vowPT1tyIbC+J7noa+vzyyo0dFRg7GAjvVdqVTMoigWi4Y2sFy8\nUn0tLy+bRbWwsOBQcinrDNBZx2oRaORZKBRy6Li4HmkFKA0WkQnuU8w91PnOQqQALN/M/3s+l9Rg\nwBpDPX/rZ1VnBWLOW6Vn+7Ef+zFkMhmDbfv6+pwI4cXFReedFY24ePGi478ia4RCxslk0lkvZIFh\n31LIQqFMO1eSrjigisWiDebKyorjR1DHPMPIlQOPybtAx080Ojpqm9YXv/hFZ8KSEw/o+IF27Njh\n8Nhp3geTWjn4IyMjiMfjFu6qcf3EV3m4nTt3DmfPnrVDZ3JyEu122/xZygcGdOBFwoH0e3AT5SGg\ntEHAmvmtpjJzqDTAYGZmxmFvrtVq9ux0Om2Le3V11UmeIy+ZYu6avJjJZBy/mjp9mQxLf12pVEI6\nnbb3J/2UQi3qgPdXhR0fH3cSSm+55RYn10sTmwHXP6SJuf5yCwyN1VIv8/PzhrMrdyMPvhdbTN0g\nmirhb7NCskzRoNAfx/GenJzE0tISbr31VgCdftaQ9UAgYONL35Ru/LqW/KVbzpw5Y4cVsAYXAzCK\nJM5/smAzd2d+fh4DAwMONKnQGhNG2cZ4PO5UgVYqKG6y/D2TldkOz1urJcaAC61Fpz68aDS6rvaR\ncvzFYjHrL+ZiKYSmNd7C4bApwlSStBJCPB53GPu1tHomk3F8ss1m0yDuv/u7v8Pk5KTj8ykUCqaY\nsjKC0pdRlEeUY6aQaCqVclwCpJnjO+m99HD1X/NLVxxQ7XbbOPEYzaI1oNgp6qcAOhuX1lFZWVnB\n6OioDd7tt9+O48ePO5gqDxhGx+gE06zqTCaDbDbrOCOZt8G2cGJzk1TNvVAomHZOfJbXFxcXbcLR\ngUpeMzJlsM3UtBS/1sAP5ccitstrJNJUAkdNKFYyXE5ojaZSrW9lZcVJIF5aWkIikbAAhFqtZopA\nKpVyDuBUKoUzZ844G1p/f7+j2XIi8z10EmvJFTqmeTACL148UNvRaDRQKpVs3NLpNBYXFx3LVqMk\ni8Wiw4moJdG13HU3SbVadRIv0+m0cwCz/fl8HuFw2DbcQqFgVhQA3HDDDTh27JgV/6zVak5C/bZt\n2+wQIeehBhhpVBqTsGl19fX1YXh42CIGx8bGnFpIqVTK5ka9Xsd3vvMd86MwgZft1qAoBghxLi8s\nLDgRwvSDKmoCYJ3fBOis/2Aw6KAz6uj3W9DpdNosk2KxaCV3gPUHJ/+mCq7mNvL57Hd+h32nwQeN\nRgPlctm+xyArtjMWi9l9GVCmc5q5f0Bn71ELU0m7edCzXSx2qf2hPJaVSgXVatVJxqdQadT8zStJ\nzwfVk570pCc96UrpCgtqdHTUKfylTNeazU9rgdqWwllAB1q77rrrjL5ldXUV+/fvN20rm82aRhiP\nx50onaWlJWQyGafYm1LCX7p0yYGllLmXtB30wWzZsgVPPfWUae+nT5922IzpCwI6vrALFy4Y9k+L\nh1odfVdaJVe1rUwmY/h8IpHA6uqq9R19DtTU8vk8+vv7HbobWgJDQ0MGHwAdC0FZOTzPs/BxCkPT\n+U4UMoGof0t9Uul0GktLS047ldKpXq+b5pXL5QwiBNZYkbU0QaFQMMvAr40tLCyYD/KJJ55APp+3\n75LpXSGNgYEB00b7+/sdnxP7gO/UjRKJREyT1wg+wGW+J0+lnwZLy5Iz6hPocOD19/fbHL/uuuus\nH8fGxjAzM2M5guR1VEs+Go06Vu/p06cd6FVheoWgCLPyudTKCU9qEU1aF/RfLS4uOtFzOicBmAWp\nnHDKVq6sE+12G+Fw2KAuQlrsz3w+b/sS0yKUWUOLjhIO57OSySSKxaJdJ0UZsMaqTmGOoUYT6z5W\nq9Wc/CNSNPH9tOxPNptFPp93ODMjkYhTlFShVy3KOj4+jnPnzjlsGWqRkx1IfdZqball/2L8ll1x\nQKljl+HH7KSVlRXbuJaXlzE6OmrQQjKZdBbhzp07LTwaWHP0c+ArlYpt5ps3b8bU1JQ9hyGkHJBs\nNouzZ886h4o6yTXkmHk5/O5DDz2EnTt3OnQ8Kysr5u9RPwaTFAmtjI+PO4uq0Wg4gQ7E8vXQ4UbB\n99XqxDq5GVxB2EJLArPwxLEAACAASURBVFQqFXMq6734TvF4HLFYzOAjHpRKxMkFygXHCUjHrZYy\nUX49Vn3lZw39JX+iwqfqROah6i/FwvsMDAzgkUcesTYrrEvIR/tIE47VX0d49HLJlN0k8XjcGQdN\nxm02m/ZvrjE9rLlxUBiwBHTy9crlss3T4eFhg+WpXHENnzp1CoODg9aPi4uLDnQ6PT3t5O8BsDlZ\nLpdRLBYt8bRSqWByctI+79q1CysrK0bl8/Wvf93WCg8mrrPBwUHMzc3ZOxHeU+gtlUpZO7VSted5\nyGQyTu4Sq24Da2tH/Ui6rhXmXlxcdPgzo9Go5ajxWXp4Kqk1w741t1Fhe/IBqj+XewbbpGs4kUjY\nOsvn84jFYg4VmIbAM/UA6OwdExMT9hwehJpsDKxB6vSdaSl6hUUZT8C+vJJ0xQGlmjujeNhozVgf\nHR11nJH0/WjxLmaxUxqNhgUrHDlyxJLSotEoZmZmbDA2btzoaIw8/TkgHDhu4PV63drIujL87vT0\nNAYHB62uypkzZ3DLLbfY4v6N3/gNm8ylUsnJIanX60ilUnYQ+BMkqcVTg2w0Gk7BPdWAEokElpeX\nbXKHw2Enik+dqcCaMxfobE6a2Fyr1RzNjtaXWpGK9SeTSYdRmQucv1XLOBKJoK+vzyGTVH9GPp93\nnMBjY2OOL7FcLjv5a8ruvry8bFbz6Oioo33SR6cadjKZtL4dGRmxcalWq06OVLeymuvmVi6Xnbpc\nfp+aEhiTg5BjUiwWUalUzNoeHh5GPp8364z3B4CnnnoKAwMDNu/6+/uRTqdtPvBgpL+yr68PzWbT\nLKrV1VX7LqPyyG956NAhXH311XjjG98IoDPec3NzzqFy5MgRAGuWt0YE++eZjiE3dj0otRBmIBAw\npIMHOJVfPRQAONZUNBp1+DZZe4vWNyPvNBBM15LuOzy4NBlf/VuRSATpdNruxXFV/5oqd8onyP1C\nn6sBWcFg0PbadDqNXC5n7BgXL150Dnseolp0VANSlFgXWFtPvHYl6fmgetKTnvSkJ10pXWFBEaoD\nYDlAWmtGGRm0ZAY52/jbubk57N271+ACaj3UIHbt2mXa1fT0NBYWFuy7ly5dwo4dOyxTGuhoBbR6\nqJny96qpF4tF1Go1a3MoFMLRo0ctauncuXMYGBiwSD3FiMlYobCcZvTTXKflQrohaiMKFTAih5qb\nVhLVvlNuQ9VcVWMiJKPmv1oNzLHR/Cxqm4w81JD9VqvlQGcKh7BUAe+lFnU+n3fysRSyAjqaq2qF\ngUDAIJ1arYbZ2VmrD8XMdt6bocLs21wuZ7AHsD7SihY7+7obRSPNyI2mFZd1rgBrUCX54DgutVoN\nW7duNUYCciJqqDT/fejQIbRaLUMqxsfHEY/HDQUgyqHVezXniNGVgFsRGOhYY0ePHrV3+F//63/h\nne98J97whjcAAI4dO2bjPTc3t86nqFF7hLy1tI2/arRWGSiVSg5dmUKgzNVTC0Fz8tSSox+JEXCN\nRsOJiKTVohGniookEgmH+VstpEqlgnK5bHOaLhLNHfWnZPBaOp1GsVh0GMk1bUfHeHR0FKdPnzZq\np02bNhn6w3YBaxYoYXjuH0rBRmuTa6vr60Ep1YmWZQfgONNZnpvfjUajWFlZsUnEPB6l46GzF+gM\nAPFy1pQhZLd161ZcunTJymuwtpRSmUQiEcuRqdfrdiDFYjGMjIyY+T85OYnp6WnLIfnmN7+JhYUF\n/Pqv/zqADmZPGInl2/0Fyvhcwn+a96TEnNPT0zYJGJ7LRTY0NOTkJqTTaSc8Wv0oPBQ44eiPY9/n\n83nE43FbKFq8DVjLe9BxYv+QBoaHfTabdZQSwlCX2/RJG8Nx6u/vRygUsnGcmZlxSmgw7JZ9l8/n\nbfxzuZzj+OUi0VQCdaIrKS0DRnSz60bxcw22Wi1nw1aeNQ0LBtZ8IwCskCMhPpZAIUy1vLxsG+4t\nt9yCxx9/3O69detW7N69G4cPHwbQOTjGxsbsAKtUKo6SoRBbf3+/Mw+2bNmCSqViQRITExO46667\nrMbR7/7u767LO1LqqkQiYWsnk8kgn887989ms45vSAM3gsGgzbNGo+GsLYZJK48f1zD9tRwLrkFV\nsnS+0y/E32tKSrvdtlI//JzL5Rz4NJ/PO0ERGnCk70SIT+dIuVw2n93IyIgTsJJIJCzwJR6PY2pq\nyiC+ubk5p24VcyxVcaa/GFirt6dtejGSWEp3qoE96UlPetKTf/PSFRaUwnZMgGVEjkZwkSTRX2RP\nYabFxUXTAkdGRizBEuiEwyr7LpN1gbVS4koBPzAw4ETIaVJgq9UyzYNs3dTEG40Gnn76adP6Zmdn\nMTo6imuvvdY+831HRkawsrLiOC7V/GU5CGpM4XDYYYcA1pyMuVwOs7OzZrmsrKxYMTRgTSvUZ6nT\nnNV6+RxlxyB0oJWAadVwnHjf5eVlx5JJpVKYnZ11yjEoeWwsFnOimJT9gZqrsoGEw2Frx6VLl5yQ\n/1qtZvQqTHrmtUKh4DicU6mUU8ZcKyTz9/wtw4r90Fi3iVZMpqNa0QmFaf0OcSU8XV1dRb1eN4iH\nQUUco/7+fvvt+Pg4br75ZtOun332WbzsZS8ztGFkZATLy8tONG04HHZYBtivsVjMKa9x4MABpyTK\nsWPHcMcdd1jhvIWFBQeSLRQK1mYGAPCdmbSqhNGVSsVhpeA8q1QqiEQieP755+19/ekNGryUSCQc\nKi9lrWGAENcD26DWOEO8gU70oQb6aHg7g5MUelfEiQEWGgil0bEa2RsMBpHJZBxmmcXFRQsyq1ar\nlrIDdNwAbIcGbQBrofO6hnV+KdNIJBKx/uV3ryRdcUBp1jmwVsabwrDZgYEB89kAsIg1zULXCccw\nYTXhya3nr+dy7tw5zM3NGW39tm3bHHiMfiEtTa01gZaWlpzQz3w+78Bpd955px1uMzMzTrimsrcz\nek55qpRDLRKJIJPJWLtGR0ft/ev1ugORMGyUhxCjdJRlmfclJRSZjsPhMJaXlx1zXxUJwmEaKsr3\nZZl5Pmd2dhaDg4MOLYq/OitZ3dm3utj1gDp37hw2btxo7bjhhhvwyCOPWFRff3+/E024tLRk709a\nGz67WCw61Xv5HvSdKOM6w+qplHAT7EZRHwuruVI4N+gTUl+GQjaLi4tOVBbhMvbNhQsXbBMcHx9H\nLBazUPCZmRk8+eSTdkDt378fs7OzBhcyl0lLeRD+5abPdmzYsAFHjx7FN7/5TQCd8hHZbBb/9//+\nXwBrjCdsoyo+5Kmkb7Tdbjt+OM4Hvc41OjQ0hHK57Iyz+omYnqApCxqZm0wmnb7Vz/Sd69zyU6fR\nXcAxU/Z0bYeyzACwaEC/34n3USZ07mdMF9i6dSs2bNhgeyAjJvm+Cj2y7brnqR8ZWIOY+Wzd87Q/\nut4HValUHJJGUm4AnRcjvQkdtXwh0rooTc709LRpACz3zHuxvDrQCSvPZDI2WAMDA4jH4xbYQMJT\nWnIjIyMIhUI2GZT8le0mf9bp06dRLBZx4403AgCefPJJyyMBOj4qTgJSzHDR8ABVPDsaja7zHXEC\nM+eIv+EBR1FrhOGu6vjVXC5y+QEdpSCTyTiWjAYoMFeL7dL6LqQTYruCwSDOnz/vBCeo5sdQWN5L\ny3DXajUMDw87tXa0yFy9Xsfo6KhZTSsrK07y6fDwsGP1qo+SlphqweFw2EKcy+Wyae7hcNh8emxX\nN4qfuiYSidgYM4GW15SHjdc04GjHjh02D0+ePImJiQlTFJaXl508v1QqZeM5MTGBF154wRzqBw8e\nRDKZxFVXXQWgc/g9+eSTToqDbl5KybS4uIjz588bGrFr1y6cPXsWhw4dAuAWIZ2fnzerGeisd63h\n1Wq1MDQ05Ch7zG8EXGqfdruNYrHo8BUyLYH3Ul+PP1dJlV+Wn9HPanGx7If6v/zP0RQNDelvNptO\n2DnzRtVq4juQlFtD50OhkO0XZ86cwY//+I/bPjY1NWVBZLVazQnZVz8/26F9T/+t7ic8zJnHpSH7\nV5KeD6onPelJT3rSldIVFtTw8LBpvSSEpcaQSCTstKYfgBqAZkUDa+U4CB3k83lcunTJTMmRkRE7\n0UulEgYHB80i8Ee4Pf7443jFK16Bv/3bvwUA3HXXXRgfH7fn9ff3m7ZQq9UwNzdnWnylUsG+ffvw\nuc99DgBw44034tprr3WgJWpPyWRyXcFGDZUmrERGYpIwUubn550QU03cJYSn1qlqyf7ihQp/ZbNZ\nLC0tOVaPMmP7Q1YBmFU4NDSEQqHghLNXq1XT1gcHBx34hJGMHKd6vW6aejabRTabNS2Q1C5K679r\n1y6D+KLRqI1poVBwwnvz+byVbgfWcHRlw1Cm7FKp5JRyUD/Bi8ESL6VoIjsjMf0MHrzmD8FWWpxK\npYLR0VGD3iYmJtBoNAzNoOYOrI27+nrvuOMOg/hSqRROnjxp8/Ad73gHTpw4gT/+4z8G4DLh9/X1\nYcuWLYZcPP30004S60033YTZ2Vk89dRTANbmEgBDONS3qVWEy+UyMpmMWRsXLlxw/Ej1et1ZK2q5\nkKHdz5zg/z6whiCob1wtKDK08DP921xrqVTK+pJWC+d0tVrF8vKyrQdaZwrdKsymjC3xeNwpp0FX\nCvdX+nuJIJw+fdp8UP6kdvrvlYJNoUVGhPIdNZqQicr+4o+Xk65ZZVw4Fy9eRCgUcspA8EBi7oSa\nlRr6S2oNHlCnTp1CLpczqOHcuXPrqGt475mZGXz729826GBoaAirq6vGPPGe97wHP/3TP43Xv/71\nADqdzENkcXER9Xrd/DdLS0tYWVnBnXfeCQA4fvw4Nm3aZAeYhqumUimHnZyUKP5wZ8WctcSAHhL+\n7HYGLugiKhaLDrOE5pTxfvys2e4s3aCVfEulksFnOtFnZmac2lFsh/JyJRIJgw+CwSD6+/sdZUHh\nH62+ysWsdE2ELoG1cFf2rZZIyOVyDotypVJBu922sSCbt+brcPPzV03moddtEgwGnTwXzeVSJzaZ\nI5QJRH0GQAf2ZpDAwMAAjhw5YuM9OTlpod4TExMYHh52cvfm5ubw13/91wA6/R4IBPDMM88AAB57\n7DH8zM/8DO677z4AHSWL867dbiOfzxvUPj8/jwMHDuC2224DAAt91wALBuvQt6EHFGui8Z39dYj8\ndEW6aerGr/mPwJoPRh3/yulXrVaddamKNH1KqqQq5Oev5qD0ZSsrK84YpVIpR7HOZrMOR6YGldHP\nqG2JxWK27vh89q1WXOb7KNMOXQrsL83Bo6uG+2skErF9m+Pi7/PLSVccUJVKxYn+UNLBYDDokF9q\nQTqluAHWsHD1z6hFFQgE8J3vfAdAp+BaqVSyEhqpVAqpVApvectbAHQGQB2wv/7rv45jx47h4Ycf\ntmcRq61WqxgaGrJD9pZbbsHu3bvx+OOPA+hYbnNzc7aJJpNJczaPjY2hUCjYe1Aj0mTTRCJh1gax\nXN3Mebj19fVBy3JriWU+119kTOs7aWADowm1lAEAm3D+HCN1EpMMln3Hd9SESFqO7B9dsKurq9YO\nBk7w+f5SDiw1QOtsYWHBuBpJYEotkM5l3puUWlqHSrnJJicnHX9WNBrtei4+JnICaz5GTdxVbjnA\nVUiULNZfCI8BFVqWhdbVpUuX0Gw2TRH0PA833XQTDh48CKDjzzp69Kgd7p7n4S//8i/xqle9CkBn\nXpIj86mnnkJfX58pd3fffTdSqZQ998yZM3j++edtXAYGBqzN5A7UMuN6yHDdqHNeKcqU0NRfGJR1\nw7gh06rjdT0o/CU56DvXiDalgqKFxP5st9v2W27sGvhUqVRsPyAnIudjqVQya59jTmWKCiavcY9T\nJbVerztKqBLaMlmZ7aAPC+ggShqpyLmk60TpmDR46cUsqJ4Pqic96UlPetKV0hUWlJrDhHo0l0O1\ndDU7ac7zMyEcnszMoVLqDlpTpL/fvXs3ADihycAaizAtiGeeeQYnTpwwDWHDhg2WGb9nzx6nxHMu\nl8Pq6qpp8gx915BMZWRQpnOWc1atV38bCoWcsh/lctmJyiMECKxpkHx/flZIQ8Nm/VFKSk9Di4kW\nFLVzjS5Sn5xqmzMzMxbyTtmwYYPDjqGRRwpbxuNxLC0tORqhv2hluVzGzp07AXT8Cryey+UstJjP\n0ShGWtgK+2quUKFQcKIp5+bm1s2TbhMl4aRwHJSNnRF+GjasFoSf+Z6WmEI6nO8sQ06LiTlTf//3\nfw+gA0stLCyYb+P666/H9PQ0/umf/glAB80gceyrXvUqI/kFOtRGpVLJGA3a7TYOHz7sVJGmNUGN\nnf4rws7KBqOh9JlMBsvLyw6rhfaP7i2ErPw+b83J1BQUf7Vc9fcR0uM7rK6uOqS+Cq1zL2AbFxYW\nkMlkbE5zPDT3UUvBkCoJWCuU6q9uy/nieR7y+bzRvV177bU4ceKEPYfwOrCWy8bfLiwsOCXfyUyj\n4fIKtapVrCiYX7rigALWfCD0K12pHLD6I4D1HE+NRsNhAp+amsKOHTsArGHOAIxzipOCdYZ0wumz\njh8/7pjaSplTr9ed0O58Pu+wd7PEu4asEpIiTKChzsrg7s+38G+QejDQB6V0NvF43K7z8OYGrjWa\nBgYG1vm9dJGRcshfH4mTv1qtGtcg6zPxcKfvgwc6v8OFxARoLXnNzY85JXwHQnj8TIbuAwcOAOj4\nHflb5sGof8OfBOw/0JWVXZ3kLGui/qtuFA1R5kavDPXaF1pexr/OmH/Gg2FxcRHDw8PWV0tLS+Zz\njcVieO6558xf5XkeRkZGbBwSiQTGxsZsrhw6dAiNRsNSKoaGhkxBO3PmDC5cuGD9yw3wscceA9AJ\nimg0GnYv9bv600rK5bIdnsBaqoRWKGCfsZ267zC1gN/xQ1zq6C8UCk4AjfpcSDGklY01yIT5lqos\naPUCMrzz3iMjI6ZkLSwsOFydntepCq4+Wq5DKh1a86pUKtm6SyaTOHnyJK6++moAnVQcBsDk83mr\nc8V38EOc5OAE1nLd9LD0l7nhwdT1ibqaccy4f27gmqTmT0SlZaI5FO122xJot23bhna7bQtJy0cM\nDg4aswDQCaCoVCqmuVWrVbTbbfNfpNNpTExMONgwJwEdt5o/oxntJFql9TY9PW3W1dzcnHPIMvJF\nnbGqtc3OzjqknvrdVCqF5eVlJ8JNncAkd1UKfY3Si0QiNpnZ98oZqHlS/kWl2jg539QXoL4f+qQ0\nt0mZKNRv4mdDYB6LMjxoralyuWwHJXFuTVRNp9MOQaqWwM5kMo7ll8/nTbPXUgrs924UPYA9z3MS\nIvk3fq+vr8+ZR0pi7Hkezp49i3379gHo+IZYmBDoKAZcGxMTExgYGLBDheXgOZdWVlZQq9XM3xuJ\nRDA4OGhrb2hoyNqxuLhoSAnQGV/1K66srCAQCDhrT309RFGANSWO64eJpvzt8vKyQ0SsxMGAW8Mo\nGo06my3/7d90eR/1BdIH5S/PocEJGsziRzg0R4r7I/fES5cuOawMfJbeg+NCv5DWh1IfEQ9SijJY\n0GfM9yZ5ABVcJuLzel9fH1ZXVx0eSP+a8Sf7Xk56Pqie9KQnPelJV0pXWFAaGgzAye5Xunjm4ai/\nRgvdMfeAOUPj4+PYtGmT0XWoBpROp5FIJCxyBuhkyysj9+rqquVyAB0tidjvhg0b7N/VahUDAwP2\nnJWVFfT19Tk5E8oErdAarSdlxxgYGLDrzPvR0FktNJfL5ex9h4aG1kW4KQTy7LPPor+/36AFDbEm\nY7JyojWbTbP6zp8/b5VxgQ58qhARmcKBNdiS18rlslPmYmFhAaFQyCnTPjg4aFaz+qsIwbHv1E8G\ndKCV0dFRe9aWLVscuJjh8myj+htKpZLDQr+6uurAq0r9xCx89YV1o2jkKVEApbpRi1er3DabTYf6\nKBQKYWpqynx7lUrFgYcV3mJOG+fVwsIC5ufnDR4i3Zj6/prNpjP+zGOjT5ZWzvz8PFKplFPcT3Pm\n1JqmFa4lIJQ2iFC55sEpe0S9Xrf3Y7FKPlcj2vhbZf4mrMdryvbCeaNcj8o3yDByRT4o7COF/JRi\njIw3/G0ul3Po3nTOxuNx+z7fV+nOmEfJ/WTnzp3G/vH88887EF6hUEAgEHD8m8pqw/QW9sHw8LDt\nl57nYXFx0cmTvJJ0xQHVarUc3Dgej9tkIH8c0NkwtOy4VnME1ri4iJMzbl8Hl5Pz9OnTjlOP4eyK\nxQ8MDNjnWCyGXC5nmLT6SThRNdlYoTXi+Vofidj+zMyMTTred3V11Rad53lYWFhw6s5UKhUHaqJT\nOJfLIRgMGvTS19fn1KzJZrMGAQBuqHQ+n3dq9DB/RA8zwkJ8B02iVs4vBjno5CwWi06+xtjYmOMU\nrtVqBomqc5Whw0rtEo1G7TqTC9nu173udfjUpz5l/aFO4aWlJSeRkUESvDcrk7LvlXi3WCxiYGDA\n3l95GLtJ/CVBotGok1+jh7vCwf5SDAxBZj8zgVlDuqkocmPXDTyRSNiBxERc5Z7L5XI255eWlpwy\nHso9SR+swmWaz6Vh4qTB0gNKIVsqexpkoz5IraitgUrAGr8kD3/mE2peENuhJNMAbE9S/7Um7pLm\nyx/Mw75jfwJrML4qcH19fba2eG9V6imE8JUEQCmH6P9nKs7+/ftx/fXXA+gEH+k+HYlEsLq66vAg\nKuUS+1uhce7b3JO1RM6VpAfx9aQnPelJT7pSusKCUvOYmc2amKZVGgkXUZTahOSE/D6j6wjT8P78\nfzAYdKLvFMIgrKZZ1UNDQ5e17FKpFKanpx3LhOSywFpSH9ut5K/UJJR0UqGFUqmEaDRqScF0iLJd\n9XrdKWOhpJ2saMn+YfirssErTKMRf6p1833VAc9yAXRekyoJWINO1ApkmQCgA59oWC3bpNnzCseq\nRsjQX03yCwaDzrioFjw/P+9Y52NjYzZujUYDq6urZhWREYSWAS12oAP/sWgbAGcOdpNopCWTMnUe\nakSnCq1h9jMRAC0yWa1WjepIrYlwOIyFhQXHEq1UKg4djzI48Fmc0/V63TRvJk/TgiKUrCHKGhSj\nbN5M+FeGfbWwlNoLWENeuPZ0zpJhXJlSVOvnbzUSjfOK6ALnFRPxNYFayZXL5TKy/x97b/LbeHpd\nDR+RoihxnknNU6mkGrqrem64ncbrIU7gTWwkSBZZBAkCBEj+kayCrLLJJtkFMBA7iBMgbRtuu92D\ne6geqrpGSVWlgaRIcR5FUtS7IM7lvSx349uFLz4+my62yN/wjHc495xA4BnmfGA47/j+LJ5n6F0r\nG7BdXFwYMAyfUYsrclw0ErfZbJrwuhZ4ZdSGIDKOoaYy0iAbFrbzc6VSkb2i3++jUCjIuGiPdrSN\nxQEFwHCthcNh6UQdM26324ZhmwgljdLRobbd3V0899xzhl5f09hkMhmButbrdezt7YlkQLvdFpcX\nGPL+aV0inb/SyDNS53MSU9JBV+Hr+iH+ns/V6/UktOT3+5FOp43kO+GzfA5NRzI1NSXPmM/nTa0S\nFwFzZVpB1el0CvoQGGwyuuaMujoMB7DuSyOXNCuBjoNTeVSziMdiMVk4DOnq7+tqf725ORwOQ5VC\ntBAXe6lUkjqO4+Njs/BTqRS63a5ca3p6GvF43IRpgsGgYTXhd0OhkGFn1gfoODVt3IzCeFdWViT8\n2263DaKx0WhIKYH+LefKxsYGPv/8c2xvbwMY5GvZT+vr66b+7NVXX5V8JwDJN3FedjodtNtt+b5W\n52XtoTYANLMCJd01bRYbawh1KYDm+eMa1DLk/B37iXP49PRUmFmAwRzVhz2bzp/wOkQHc+/w+/3G\nuGu1WgZB6ff7DWKu3W7LfaLRqAm1k6Fdpz10qJ61bFpGR0umaDQhD2ytWxWPx+WdDw4OJAe5s7Mj\nCGI21opyTGdnZ+VeZG3RBpHeh7VauQ6ljraxOKB03J/FoaQ+0bFuimsx53J8fGzkzwlG0HmRmZkZ\ngQqTew0YinPp+iKPxyMbT7vdhtfrlXsx3jxauAcMC0L1YGkvp1KpIBAIyACxBovPoS035m605e50\nOgVAwMnKHJaGhROIoA+ZSqUii71QKMDj8YgVzI0CGMpnaLCGBmPMzs4iHo/LwckkuYa36jg4ZTL4\nTiSQ5ed2u2341LSwHHV6gMGC1PkpLn4uMoo9arAH5VbK5TI8Ho/RC9P5C/azLj3Qi0pvRrSeR2tn\nxq1pElLmn9ivOjJBqRldf6cBBjQomDC/du0aAIgVrIuY6Q0wGhEOhw2wpV6vY3V1Vb6fy+XMc5FG\nB8AzhhvzmZoAV3sIuoiVOWjtUREMwr+7XC4DDNFlHBqyzugC1yFrrHROh+8GDD0GYCgMyXnFOj7N\niafpywhWGuWu5LW0AUYpIhr0oVAILtdQ7JORHR4kes7SuNTac6Q34vt3Oh357Z07d7C5uQlgYAju\n7u4aI/z8fCjaSp5D7TWPljxw7+A76MjXV7VJDmrSJm3SJm3SxrKNhQel0V31el1cTWBIcAoMiRI1\nGkRb/QwTaZZxv99vZB1o8QQCAaOwS9VXVsMvLCyYPBIwdM2BIfwVGFI10RJhPoaWAa1VPpdmrGBY\ngZZGtVo13hnRT3zn+fl51Ot1Ux3OdyIdvs6xsCgWgBRPsthSV7ufnJzA5/OJtwEMPDBNM6Xj5ixq\nHKVK4jjoUAq9Fo5Do9GQynPeR0uMaKojxut1SFMzo5Okk/1ZKpXEY2bhIN+BHjWvncvlsLi4aEKv\nmrVCj1mr1UKz2TTif+PYdASBYWZNCMyWzWaN1c/5q5m+tZwC2SA4X5LJpMzJ09NT4/Wen59jfn5e\n1tLly5dxfn4uUHIqGfPaZOzm8+uyk7OzM/j9fiMToqHSLpfLQJv7/b5Y6iy54H3oqfP7fEdNFsvv\nEimqVaK12CXpyvRzaei3joJ0Oh2USiXzWy20Oj09bXJ6qVRK5juLzfWa7veHyr/BYNB4oLVazVAS\nMUfLf+v+oBTJBCgnMQAAIABJREFUqGq2jlDR61pYWMBnn31miHB1rjifzxu2EIZE9f6g916dK/y6\ncPlYHFB6cnPDpeuocxuUqeDfjo+PTfKdIRq6mU6n08hW6xwKN1vtXurDkLBbTT/i9XolsauT+sBQ\nX4rvo2uMGJ/l4Ho8HplgDBvwPsVi8Rk58Xa7bQAY+p11FTmZ2zVE9ejoSDZZ5hM0N5/OT2kmb9aX\n6FoVHVrzer1GmqJUKsnhFg6H4ff75X39fj8qlYpsaMlk0uj48Dm42DWbfTgcNvyKrO3RMXZteKRS\nKXkml8sl8Hn2h4ZSx+NxoxLK+aXDGBoK3O125Rl1/nGc2tzcnCS2+a5aBoV9zhwI+5FJfs5/MpRw\n8wgEAmajTCaTAj6iscYNp9PpIBKJSL8vLCzg8ePHpu5Fj6E27qizptV6tUQMmfLZNMP8xcUFfD6f\nOSj0oTKq8aS5BPl3nfvWJQpTU1OG6oos+Ho/0MATLZ/BEhYNMNIhbYa7NWsJD9lkMmmYdsi6QlYO\ngmI06EozlrMf+F0NpWeNpA5x8hAHBnWV7KtYLGbej6FRfYCT4o1zQB9+VBHmfXX92deF+MbigPJ4\nPDJZWq0WUqmUDLwWyWLMnIPH+KsmOO10OtJpPp9P0GbAEBEEDOOgHIBQKIRwOCzS0pFIBLFYzGyi\n/X5fDsd6vS7Xzefz8Pl8MtA86PQkmZmZkedmvofPqK06DhY3+3g8jlKpZGoVqtWqHIajFlK1WjXJ\nV10wyyJf9rXWghktpgyFQsaDYk5Oc491Oh0Zp2azKd5lNBo1XiHrITRaqlwuGw68eDxuakw4mUf1\nnriwuXFq6hb2ATecWq2GYDBouNh0YSKRSPw98yS8l/YmaWSMglrGrZFcFxjmoDi3/H6/RAwoVcN3\nZ55UG056s6e3pYvNOY/6/T5mZ2flPtVq1axpejKcW+St4yGkPeZ6vS6bMjCkAuMYulwuA+bQtV0E\nWNFT6/V6xpOhxzBKRK0JcjXoQtdjNhoN40HTO9UeFNek0+k0tYusvdJeoaYoC4VCCAaD8vfHjx8L\nxdTFxYUhSy6Xy0bap1wuS70j32Fubk72Gp1n5Hhr8lxgmO/jPqRBNhrN6/V6jXfe6XQMIlBHQbxe\nrylk1tRnumaSz/xVbZKDmrRJm7RJm7SxbGPhQWlI8szMDMrlsqnm1ye9RmzRstY5Ay2nXq1WjZAe\nrwEMTnxNmcIQEy2AaDRqQhxUtdW1PRq/PzU1ZUKJWsIZGCrUAjAINlolrC+g1cr7ZjIZE/IijH4U\n0g1ArFj+jZYqc06JRAKlUsnAf+kx+v1+BINB6R+i+DTxppbHJhu2hr8yRMGYOEOV09PTqFQqxoLW\nXiClPPh9LTqp78XxG/WaNVKJNSgcI01YGY/HhbgXGNLHaCisZgOfnp4WbyQUCqFWq0n/0Fsct6ar\n/RmiHGUhAYZ1Ttoz1RYxUXi06ul9amQe70OkJUPrxWIRPp/PeMhaapz35/pZX183aykcDov3StSe\n3gO096o9BEYHdC5Me8iEZGuSUu1B6XQC0Zw6tKhD3KNqCL1eT96fuTp+ZqhVs+Q7nU7pn0gkgkql\nImtxa2tL7vP06VPUajVZswBM7dLTp0/h8/kMma7OLWp6MlJZjdZnagSgTh/oyE4oFDJ7K1Mc2uPU\nbCHEB2hEqcYaaE927GHmelEVi0XhZgOGNEHAYJKwowAIk7keDB3PZChN11BwE+R/WRdChUhCsBcW\nFowKbLvdliQiMBgMHabTLOtMAHMxc9Fouhadv9K1GqR60fFqDRVn+EKzKPPf1IrhwiCVkw6XOBwO\nE6bhxOcC1O+nefxarRYcDodsNMViEfV6Xaj5yQzNZ9MbI+/BdyLDOCHMZB9nn2iNGiZ8tS7TqM6M\n5jnUG1gikUAmkxE+xXa7jXK5bIp62+223LdUKkldFTDY0DkfqJo8qr8zbk3X0JH/TYdh2I/BYNDM\nb+YMuNExXK6LOH0+nxQqn5ycmNCZzg2fnZ1JeBUY5Ip1npW1TNo45HiurKyYYmNKZOg6OHJuAsM8\nMzDMz+qaKk0CwHCZ3iM0JFuvJYao9aaqn5kF45xLnU7HcHOSgojjoI1kHgT8O4Fh169fBzBYt1qt\nm2E7joOmEIpEIshkMmIc0iCnAXx6emr6in3Ia+u6KNaJam4+7o/xeNyU9LRaLVHCZl9ryD/5RTW2\nQIcHNTR+7HNQfr/fIDwqlYrZcNjZc3NzKJfLMnldLhcajYZITYdCIUMeeXx8jKOjI1MrpAkrdfKx\n1Wphfn7ebNA6v6GLh4Eh+wH/Njs7KxO0UqkY3Rla5vqzttrdbrfJQTkcQ0l3/j+N+mu1WpIk9fl8\nplBXFxvSW9BWDWPD7HdOGorZaU9N12Mw9q8RgBsbG3JwbmxsGHJYp9MpC5CIJ943nU6b/BcLefVz\na0lyrUPDuDbHuFarodFoyMaqJz75zrSHrdFSRANq7sZ8Pi/jmE6nZTEvLCxI8TafYxybtuq52dBA\n0Xx5p6enCIfDZm5oAMmoRMr+/j6SyaRwEDYaDRlfSmDQ0GABMEEx6XTajOnohq11g5ivoZHF2iQN\nAuD3AMtgwv+nBfpGc8Fa0NHtdkutHK+lgU2jxcI6p0LQE6/t9Xpl72D9IOedx+MxRe/0eGhsLi8v\nY2Njw9Sr6XpDfZ9sNoupqSlTG0ovivdiv+nfc4y14UgQmOb1PDs7Mywt9GSZ69bvS25LYKgXx88k\nXtZ5Ru0xaTShngujbZKDmrRJm7RJm7SxbGPhQWkLilT8mpJI5xtarZZYKmSWYLiOFjN/u7S0hMeP\nHws9S7PZFCuFVPFagE9T7BDnz1OfbORaQVJbm7pWh6EUWuoM2WkZBFqfZGum1XJ+fo5isShQ4XQ6\nLRxZfEfdP/pao9QhdLs1R1632xXrtNVqyfuFQiETF+Z3dQX/wcGBeBTXr1830FqN2qM1xfsWCgXx\nyIBBeIk0TMAg9KhlQHTtEuHdHGPmHLSH5Xa7xUvWHtHCwoJY0QCEp1Fb3MfHx9L3DCfqOjv26dHR\nkVBY8drj2rSnToZvfubzx2IxQ6/DPOco9J9tf3/f8DzqvBAwzPcCQw7Ihw8fAhiKWWq2FF1jpvMz\njCRoSLMO8WlFAn5P16Tpa7GGiu/I3CafgyE8vrMuK6D8jJZu0eFEfl+LXfK5NKM6MJSe55qmCOPK\nygoAYHt7GycnJ8bj5N7CvBFz1KFQyDDgJBIJVKtVCQlGo1ETMtNQcCLxNCxfy4Dw/2mPiyE+h8OB\ncDgs/dFoNAyPKaM+epxGOUU1dsDr9UpfFgoFoScbbWNxQJHuh03ToOgEGut4NPeejrEzScfJEQgE\nDGhAgxy42XCCUaNFh950/Hpubk7i8HxGXTOiFw7de26qJKXVh6MugNSUQgz5cYMmQEDnr6huyd/z\nEIlGo8/EervdruGP0xtLvV6XfxP2rROmuk6G3GQMpfn9fqlZAWDCHwzZ6vizlszg3/Vznp2dGVJX\nDY3VIR6GVngvTnyOI7nI+N1Op/NMuFS/I2HXAIR+hc9RqVRMElxvmuNaB6XzTKTU0TRC3ECYJ2I4\nkzyWmotPc88xT8JNdn9//xmlXvazpi4DBn2nQ0nM9/IgaTabJhemqY1Yp6PzTDQIAWvAEuTA65J+\nSYeQNKiIBguNGx0OJMkyD0xqNnEOU+KFz3F0dCR9wxpLfchoOfn9/X1sbm7K3Ds+PsbDhw9/Jxcf\nofEaVj41NSUHByVg+I6kQdOlFDr0qI1SbTDw7xwvNs3TR6kP/Tf+1ufzwe12m5pMTZCt6d2Y69NS\nPl/VxuKA0mi3drtt4sq6eJQiYhxYrWcDDA82LY+si2L1fWq12jOChdpj0iy8wLCCWx8MbCws1ASW\nusaEuQ1dbKuLcfWBTA+H1yIgYLRAVnOR8VkoGqYTu5rRQQv3AUOePz6z1qhhRbomsd3c3DTWFb1O\n9oGuxzo+PjbV7r1ez+R+dE5vd3fXkOuGQiGjp6Pj6PTU9DjNzc0ZHkS+Qz6fx8bGhikg1e9ULpdR\nrVZNjtPpHAopJhIJcxCmUikZ/1FOtnFp+oBmPo7joo0ocivqQ1cfwizE1JtstVqV3IfeYDgmnIf0\npnRux+v1GsNyVKSTY6SteTbNUsLDTCP59GFIFnLeV6sf0OjkoUMtKM5LnQtmX/Ba7DcNBMrn8wLm\n0uuO+4Fmx2i322Jg37hxA4FAQPLI1WrVkMvqg5JzUAs0alBJNptFPB6XKFEmkzH5QG0Msy/1/NDz\nmLVM2uPUxNN6X2GeWXujOkrComY+Zz6fl+8S/af3+K9qkxzUpE3apE3apI1lGwsPiozUbKTVAQan\nrdYv0ppFjHtqfiitD8Waofv37wMAvv3tb4uL2u12TZU9IZa04HK5nEEb6RwZYJU8NaIIGFhZpVLJ\nKMzqHNXvql6n9cQYOlE8rEpnaI3xey3lQQuQ7rymBSIbMjCwmnXNkJZboMyBrpnQ7/3iiy8aL7Hf\n7xutKSIq+cyVSkX4FDWKks8ZDAbFO00mk9jb25Nr6VAjWcS1HpRGRFErR3un/C0ZKXgfyp7wHan3\npMOnc3NzwiEXDAbFm2LIgrF+HX8fp6aRd6xx0Ugqrh1KbrMvWPfHOcw6Qa0HlslkhEU/lUoJopO/\no4VcKpVMOIxzlGEpehdae0zXOWnk4Wg+UzOS8LcadaYVkr1eL3K5nEQMZmZm4PF4TM5Rh940NQ/v\nq8PWOudGKiPuJ3qOxuNxo+FUrVbhcrmEHWJmZgYHBweCPqb8DBtlX4DB2tCQdb/fb9hiCoUC6vW6\n5EQXFxfx85//XNjndQ5ulJ7N4XCgXC4b6iONPmw0GlKiwVy57gPSKgFDJV8ditdMG8vLy4alREv9\njGqT6TYWB5QO0TAUpIttOYE4+XTdj4ZN06XkC3Nz4eDq5KXOPQGDRUYyVT5TtVo1bjdddf5e50l0\nvJ76RTpxz9ofYLARsgYolUrB7XYbmQ9N1xSJREyYjnB2LvZyuWwmiY7Pk6OO/cXEt/7MlslkTH+Q\n6FEvHH0wMKFOFz6RSAgE+eLiAqVSCT/60Y8AAN/97ndRKpVksk9PT8Pn80kYgjIJPEgorMjv6jox\nbUTwtxo6q/u91+vh/v37Ust0fn6ORqMh12J4RHMk6tzIxcWFwGxJ8zPuZLE6jMd5OSo6CUAosHQh\nuubA43zn90OhEEqlkpFE0fLfOjx8enqKRCJhtNNOT09NjlH/nuSqfC7ArktNT8UaKh46rBMCBuNX\nrVbNgaQNOIbK+JyUWddCgpzv5+fnEubmO2ghReqDadogHWovl8uGi3JpaUnW6dHREY6Pj6V2KRqN\nIhAIyHPr9AFBLpzzL7/8sskVlstl3Lt3D59++imAgRbXyy+/LJ93dnZkrlYqFVPLREi/ThdoKLnO\n19Ho5juw1lPL8czNzZlcmQZr1Go1Me64t+i95qvaJMQ3aZM2aZM2aWPZxsKDGmUh0Mgj0tMAQxio\nrnSenp6WRGU2mxUFWmCgCLm8vGwsc1qArCgntDMcDhuUH9Ffmgm70+mINQ4MK7NZZDoqN6Epc2q1\nmngQ9+/fN6quv8tV1hZSPB43rnWv1xPr1OFwSPKVTNa0COlBMsRXqVQEPgvAJJCJgOJvI5EIksmk\nIfzUAJT9/X1z7w8//FBgs/fu3YPT6ZQQ349//GNEo1GBHScSCTz33HMStpyZmUEikZBrHx0dYXV1\nFcDAutSEv5VKBYlEwiDTRgv92O/Xr1+X8AowsLA1Uz6vo73kL774As8//7z0MxPZDAVpho9xbBqc\nUK/XEQwGn6HcAoYF3pruSatCh8Nhw6oxNTVl0GPAEPXlcrmwtLQk3ke73TYs+gzB6lC9hoNrCDJg\nhSLp5Wn2AyrUAjDs/Yx68G9erxflctmgx7SAIftDzy2ObzAYNLRYzWYTkUhE6Iii0ShmZ2flWpqU\nmc/JsBvXBfclsr9TDDAUCmFubs5I2Wg292KxaAp3w+GwrJ2rV69iZ2cHb7/9NgDg448/xtbWloxF\nNpsVUFmxWBQJImBQaqAVG4i01JEVRhD8fj+2t7cl0sKIjvaS9f8fVTvQLCVM0+jUxFe1sTigpqam\nTAjL5/OZuLJGC2mkCPMNPLBSqRRcLpcs0HA4bODOrLcBBi5nvV7HF198AWAQI43H4zIgxWIR8/Pz\nMgClUgm1Ws3AO/XkrNfr4qKz3kDHsykLAQzl1fldDW32+XwGpss8ga7YZo0BAONWU2NF1/loFgYi\nJHXsVx+y3W5XwqGRSATRaNRUe/f7fXn/fD6PYrEoEzYajeLXv/619JWWX2BuTEPWK5UKXnzxRQCD\nTcbv9xtjQNOg8GDhHBgN+WkmbR2yWVxcfEbWYTSHyX4CBvQ9a2trklfodDoShgyHw0KFBHx9WOJ/\ns2lKoVHdnX6/b1jwy+WygfprRdl6vW7kwqemplAulyWMu7CwICGbRqOBa9euyZikUilUq1WZ48Vi\nEZFIRIyZRCKBQqFgEHKaUbvf78ucLpVKBnlGtKBGgOl1Va/XTRje7XabkFa5XDZzRbdRtKCm3OLf\n9VrS3IW6HpGHE/N1U1NT2N3dFWN4YWEB6+vrQhPGED7XFvn0AJgDFhgY3RcXF/Lsly5dwurqqtAk\nnZ2d4ejoSA6Dhw8fYmtrC8CQcox9Sa01zn/CxDXfplbhXV1dFaor7nmjtZf8LTkPNQpWlzho5OXX\n8VqOxQE1Nzf3DLxUb8C0vP1+v4kpEyaqDzAtqdHv9yVRCgw2JFozpVLJEImyoJMTf3V11SSRy+Wy\nsTCBoTXKuLjudJJxsvEg4rvpglh96LJGiFbFKHluuVw2PIBaMoSeGN+J+Qf+loekFiGjRUiROXqI\nusCP/VMoFEQD6OHDh+j1enKg64NRe2J8Dl1DRr44Ggdra2sIhUIGDs2JzSLmUSJebY3ra2tafx5e\nvBYtVz5bvV6H3++XPmBf8tDd3t6W92P8nX05rlx8ozV0sVjMUD3pfIuumQNgIPaMRHBMmNTm3Ll8\n+bLRe2q1WuIhMD/ByMXc3JwQyLLpInA9niR41cbDqAinHofRgl7mf4CBkdVoNMRw9Pl8mJmZkYOV\nXJ1ci3Nzc+Yw0wAs3kvXjY3mWTj/PR4P1tfXZf3s7u4aY8fj8SAWi8m1Hj16hN3dXXlulnAAg3nG\nseL4atBEq9UyNVi8Jg87bezF43Gcnp4+Q4DN+cHSG10nqMFHwWBQ1goBV5pfUdev0dPlHPD7/fJO\noVBIQFn87Ve1SQ5q0iZt0iZt0sayjYUHpenkq9UqGo2GWCapVArr6+sAIKEueheNRgO5XO4Z5gBa\nLqenp8Yq1EV8DocDn332GV5//XUAwJ07dwRaCwzZDLTo2Kg1NQqFpYVAa3PUpdWFu1pNU8fUeR8+\nB4tUtYx1r9fD8vIygIEFxVwPaXq0oqpW3CXaSlMMaaQRBSD5frp4lu/w5ZdfAhjmLHitVqtlWAg0\nfRVDNrovdV6k0+kYuY1AICBhNiKtOOadTsd4oxRV03k2zf6gQ3469wYMLDdCovm53W5LrlAjSIl2\n0nD/cWw6t0O0KJ+ZxebAsCyA60wL0wFDGh9axAwH0cNOJpOS29je3sbHH38sVnur1cLy8rJY20Rt\n6SJwDWmnZAowJK3VBeHaoyJyTkOhdXhQEyCzJIOfSQRMT44IQY6xLjzv9XqGjolzltdi6YcOgRKZ\ne/nyZXS7Xdy5c0f6KpFIiOeWzWbxjW98Qz6//fbbphi/0WjIc5BoVu8PHo/nGUoy7j0nJyfIZrPS\nt5FIRMgIrl27hkwmI8/Jchc9XzTtmiaDrdVq8Hg8Jq9NWDowZJXgtQiz515E5QmOgybi/Tri5bE4\noLxer8RnY7EYfD6fbEjtdht7e3sABguh2WyKa0g4ulZ5JZsCMKT2Ydz86dOnsrFzojGmyvyTDh1V\nKhXD+6c3e838Tc42nZOamZkx7M2aNkWHOjhJOCn29vYQDoflt6Q90YtKMzLrEB7pV7SL7vP5jNrm\n2dmZhDx9Pp+ECigJwk3k+PjY0CQlEgnk83mjy6S56ggdB4a0KKMgEq2B5fP5JEa/vb2Np0+fSq7E\n5/PJAVWpVLCysmImsQ4BMqHM+aMVdKlfw40zEAigWq1KX5Nyh2NMYAvnXjgclsVNrkJeW1NzjVOr\n1Wqmti0QCBhWbY691+s1Gx2/z9Zut9FqtZ6Ro2HfHB8fy3gy78s6n2w2i36/L+wG9XodnU5HrkU2\nehoOGkZNA0qvLQAm2Q4MjYyzszN5DubRNMBgtKZSG4o0qjT/pl7vOl9LSDb3ltXVVaO43Ww2xVA8\nPz9HqVSSzwx58jmz2SyCwaBRINbvFgqFzJrVHKIEVXEcXC4XIpGI3KvT6eDSpUvY39+Xzwy9Xr9+\nHYlEQj4z38f3DwaDJpfmdDplr9V9AUAOa10XqkElxWIRiURC9oRSqWRUKHQdKfv8d7WxOKC0Ndpo\nNExcuFaryUSnZaE3SU1VxEWngRDBYFCK1q5duyZWHeutHj9+DGCQB6nVahIzJQWKRttprSGN/tGT\nBRjy1umi33A4bJAu/G00GjUDRM9Fo4kCgYDkRTqdDqanp81g89/BYNAkvtmXmqqfXiUAY8U0m03U\n63XZRGiZ0cqjF0PvgqARvqNGNFHoUNff6IOUxZI0FhwOB3Z2dvD+++/Lc2vkEXWuOG78DTDIjSUS\nCfl+tVqVRRWPx41WzsHBgclBkcpFAwceP34sm2M0GpW/nZ2dIZvNyuG2s7ODcW1as0fXQVFPDRgi\nWvldFjHrAlBgWO9FklV+v9fryVzx+XyIxWJiCIbDYVOf1Gq1kMlkZIMmt6KeL9q71sTDtLw1TZIu\neudvgGHdGkEB/I7OUWuNKxqSus5SF6KPys0EAgEj61IsFsWQisfjBhSlCQMePnwIl8slfZ9MJvHo\n0SOZ/5cvX8be3p55Z41w04YRPShu/LOzs8ZL+uCDD7C9vY21tTUzjsBgLQWDQVlLxWLRiDSGw2EU\ni0Wz5vl+RFrrdakRk6wT1PJEOs+kc1sE8vAg/LpC3fGMU0zapE3apE3a/+/bWHhQupaJlglP5qWl\nJbECqtWqIGSAgVW7vr4uFiIr3Xkix+NxPH78WKyPSCQilsnGxgaOjo7Eerpz5w6ef/55sbY6nQ7m\n5+cNCSmly4GBt6JrYqamhqquoVDIMJCTCZ3vpBEtlARgvc2olUeGBe3ia4kADaNliFOHJdrttsnn\neDwew1qsw2pnZ2fyeX5+3kDFK5UKSqUSXn75ZQDAf/7nfxrmAf3MhKtqy2hmZsaoGa+uropFXSqV\n0Gg0xPM9Ojp6hqRUsyb3+31DMXNycmIUmPm3arWKN954Qyx7srHfunVLrj09PS3hkHA4DJfLJZZt\nrVaTfg6Hw1hZWTFS3OPYQqGQkTzQOalutyuWd71eRzKZFI+Bism0an0+HwqFgpE10Uzwt2/fFmu6\n0+lgeXlZQkcnJydIJpOyVpxOp0GMjlLmAEPPmJ46n7NQKBgKHdYqarYUzWbAUBcwiFxoGDrDUBrJ\nqOsXQ6GQKdnQ8HfmW/ncmUwGxWJRap2mp6eN6GKr1TL7ztTUlEETf/zxx/KO165dw8OHD+XeOudG\nZnRei2Ul9GyDwSD6/T4++ugjABDhQ80GwXXHnCPHmJEOrtN6vY5IJGKUrhl+3N3dNWoG6XRavCZ+\nd3Z21qCew+Gw8VZ1SYPP5zOitF/VxuKACgaDshA4iJxE+XxeQgnJZFKK8YDBonn48KFoiTDezg4v\nFAq4du2aYQrnpHC73YjFYhIHDoVC+PDDDyV0c3JyYsIFGtYNDCaOlnHQiygUCpl8zuzsLA4PD2UT\n1Wq8DCNqShEm6/ldDUtnqIWDraGuzPtwMmezWczMzMhznp2dGYqhWCwm78McCwtkyWFGvrWlpSUD\nhKBODe+tE6SsPdK1KrouLBgMIhaLmcWuqU/C4bDA2a9cuYJisWiYkEOhkKFn0eGUubk56RvenwdW\nu91GJpORMWZ+gv3BvADn2+rqqowplVe5CfM349Z0zZjf75fCbwBGLmF+fh7dblc+ezwetNttIxev\nC2QJLtAwY4aKvvzyS4TDYSk8Ze0S5xLrxzhXTk5OMDs7ayDdmiasWCwa8A5pmICh2jI3ylwuJ2uT\nGyzfP5fLmbo3Htg6f6lZ2XVhPp9La9Gx0BUYzNHXX39d+oDUSMCwjITPcXJyYmiRWPrAdZlMJqW4\nn9fSwB7NGs5icd0/t2/floPy7OwM6XRajIelpSVzuNdqNel37g28NlUSCIQplUqyXzK0yv4hTx/X\nAcOFXMM8rNgHWm2XoWRd2/ZVbSwOqMPDQ9OhjUbDAB3YSdx8dKGulkDgBs0JOT8/b+LVlUrFSCJf\nunRJclCnp6fC3QYMCuBarZYsYMZ+eW+HwyGTgoKCnFSnp6dYWFgwDA+jYmeadSKfz8tAMn6vCRwb\njYa8I+U3RqvyAcj3NI+dRp45nU5cvnzZiC6yTU1NIZVKyQYcCoWQz+dlslLCnfdKJpM4Pj42sXIt\nW691p0isScttZmYGPp8Pv/rVr+RaPp9PACunp6cGjKAXbDwel0Jo3X+0+vx+v8TfW62WQWYFg0GE\nQiEjjlcqlQyjgV5Io0TF9IyBr5ep/t9uOsGu5UfOz8/Fa6UXyu8yisEDqtFoSLElAMmDas+GczIS\nieCzzz7DzZs3AQzycx988IHx3EYLczVQSBfIco3rNavrj0ZzkFq8cn5+HqlUStYsjSqte6SLkQko\n0rkRen2RSAQOh8MwOPj9fslncl3wwE6n02LYeL1egzQkIEQblYyqAIP5f/36ddy9exeA5UwkwEqD\nWRqNhmGSefDgAV566SUAQ6OU7eTkRObw6enpMzLsBDCx7zWadm5uzsjSBwIB7O7uAoBoUBHVy7oo\nXeQbiUSMt6p5DXU+/+vaJAc1aZM2aZM2aWPZxsKDikajJhdSrVaN1UerdTT/kEwmDdURvQeGC0bp\n9AOBgHgT25oFAAAgAElEQVRQmUzGwDNrtRrS6bRYTOVyWaDmwKBavtfriYXAXBAwFCujxRQMBtFs\nNsVCqNVqojgJDCxGWpCffPKJ8FbxmTWrBmHofIdarSaKlPxMz4QoGr5/JBJBr9cTD4o5Hn6/UqmY\nmpHbt2+LZ6LplICB+6/pnX74wx/in/7pn0z4RHuIuhEKPiqOR2u0Wq0KMz0Agxa8ffs2tra2DPuD\nrr86Pz+H3+8Xb1XT0zDsolkpNNzV6/UiFArJfavVqtDwAAPLn/OSPGWjTPnj2DTyLBwOy1wpFovG\nqtdebrPZxNnZmawlso1rT2WUVohhWDIDcA5vbGxge3tbrOu1tTWcnp7KuPj9ftRqNVOfyPuSMZ6f\niQ5lLiOfz2NpackgeYnyXV9fF8+Pf9MeMZG7XLeMauiaK+1tVSoVWTvRaBSpVEru+/HHHxvoNcPn\nAIT/UNcm6fpDv9+Pi4sLfPbZZwAGfHovvPCCMJDrHBvZ6bXcj+bKo+go34n0Q7x3MBiU0OPR0RFu\n3rwpHpTf75faUY5xMBgU5p7NzU2JRng8HnS7Xdkf+VzaK9Yqwo1Gw/Bg9vt9GUNKrWjG+q9qY3FA\n6dqTWq1m6iJ0GGqU5iaTyUh4DRhOfA6Ox+MxoYWLiwtT16Jl2SmJQTf96OgI169fl0nn8/lwenoq\nEExeA4DIl3NwvF6vqc2hVDYHQmvFlMtlo1nTbreNthIBFTpMpeGbOik+CkGdmprC2tqa9OXBwQE6\nnY4c0vF43NQbeTweeT/yEHJCcnHz2gcHByaMqcMwXDB67Bhe47+pRwQMDk6/3y9hOq/XK8AFr9eL\np0+fykbJ2gpuDKzH4uFbr9cFks8NVkOjdQJ+amoKgUBAKGYuLi7kvvys63NorAADwt8/+qM/wrg1\nXbvmcDhwcnJiSFzZWEzODZlhWV0KMSoZo2v/NNCBeUHmK6enpxEOh01OZm1tzeRGNU2Oz+eT6xKs\nofkStUw5a+g4Fr1eD5cuXQIwCDt+8sknRlqeBh2/q4EO3GB1sTKf0e12Y2try4SDnzx5IiGvcDgM\nj8cjB9jx8bH03ePHj+FyuWT+U2uOc5aGMz/fu3cPV65cEXBOPp8376/lNRwOh4HGP3nyBOfn54Yj\nVPMxVioVw2vabreN/Ewul5N9iv3OXPnu7q7MnXq9jvn5eVk7tVpN8r/AsCxFly3ovibIir/t9/uS\n1vm6A2oS4pu0SZu0SZu0sWxj4UHlcjkTLtPyEtqa6nQ6xg3naa+ZEzRU1OPxGAVZDbkkjQ9/G4vF\ncP/+fbEeSqUS7ty5I7Dqubk541FoZmO6ybxWqVRCLBYTy4xgCp2gZviDleBsDJfQqqhUKojFYmLl\nsJ94LYZmgKH0ANFTlUoFh4eH8s5khaaXpOmHpqenjWAjw5Z8J3oe9ECDwSC2t7fFg9JhSTJh63CS\nVtgloS3LBYrFIsrlslhn0WhUxjidThtkXSwWM4z1FCvU4BV6CkQG6lDLysqKsaApu8I5oT1fJsbZ\nyKzOa49jK5VK8swMl+kCUo2kCofDJsyi1XcJP9bkuGRHB4beKABBZGly4dnZWWEKuXv3Lr75zW8a\nZom7d+/KuOioiKbLAoaAD4ahVldXDeXYpUuXxLt+/PgxIpGImYu6gJyK0fw7JXV0mE6Dk+LxuKwH\nLTPC56xUKgIMIokrn310roxSpel7ZbNZXLp0SQgF/vu//9uQ4Wo2DMqpcJyePHmCbrcrz0nYPPuW\n7O/AwPu8d++e7A+JRALxeNx8t1wui8cVCAQkkuNwOOB0OiX0Xi6XEYvFDCVTp9MRlWAqTWhFcg04\nmp6eNn33VW0sDigAJqdycXEhIRstj07FXH5uNBp48OCB0NYDVlG01+vh6OhIBp48bsCQ1oMu7Onp\nKZaWlkTumxLPH3/8MYDBJFteXn5GP4f3oesNDJFI3CgYltO1DXyHYDBoQmekJ9KsCxqyXSgUsLS0\nJAeDZoPodrvmcD8/P0ckEjG1HYQeA4NJpOlXCoWCTE6fz/dMSKdQKMgmQ20phle+/PJLwyqh+QbJ\n2aXZrd1ut4SEcrmcORwrlYqMUywWM/VZlG7gZsiwlGYa4Dsxn6cXwuPHj2VuFYtF+P1+WbC9Xk/C\nEHxuPlOtVhOuN77DOLazszOTQ9PoUo1w1SE1YDC+rVZL8oIMM+sDQ/PFMYQDDIyI2dlZGROWIxCC\nXKlU8Jvf/Ebm1ne+8x3U63UZfx0KJuMAjSqWKOhDRnNEEtUJDMZeHwKjcGaHwyGKCMBgLZH+CxjM\nNc4N5u40Elfnfihdwt/qXGcymUSn0zHUZgxF87t6XDKZDL788ktBQW5tbUkokSrYugyl2WxK/8Ri\nMZO/PTs7M1LrWiKHOXcil69fvy55KGDIc6gRhGxnZ2emTKHdbuPhw4eyxl0uF2KxmBjwRPBp/TH2\nHw/Rr+PgYxuLA8rv95u6oGAwKFa+pggiXxwX4MbGhhG6A4abIzCYoDdu3DAy5bSuqXXEgXQ4HKYw\nrVAomHu99dZb+Iu/+AvxsDRnFROvmuBVF0jS69N5Nb5Tv983fGmlUsnE50lrxIVCzRsusrm5OVlU\nMzMziEaj4uXE43FDOUOPQG8k7I+1tTXEYjGTqF1bWzPeBDWEgMGh4vF4cOPGDQCDeDU3cwJCNFRc\nS4Rks1m88sorku+5fPky7t+/bzjwOP4zMzOYn5/HgwcPAAzIg1kECAz507TECsdwYWHBwH2vX79u\nDijSyHDzY7kD+6fb7YrFSOltjrHerMap+Xw+I2syCiXXa8Xj8RjOO5/PJ4aA0+k0tXz0tjU3G8fX\n4/EY2RJqrXG+sy7wnXfeATBYez/84Q/xL//yLwCs6CTBBVwfU1NTJjdULBZNTopky8CwRpDvFIvF\nsLGxIUZoo9FANpuV5/T5fAIMAGCIZqn9xgOZfHjcVOv1uhH8ZP/ymXV9VTKZRKFQMLV7FxcXhnDg\nnXfekXrOF198USDnyWQSpVLJeFTtdlvWzvb2tomSMArE+el0OuUZK5UKVldXxQhfXl5GMBgUo+Tk\n5MRIwGsapH6/b4BhPp/PgGjoQemcraZ30h7jqObXJAc1aZM2aZM2af/PtbHwoHQMmgWfPF010oii\neMxlHB8fS2wUGLKX08oLBAKoVCpi9c/Pz4uVTuScVm6l98bPlUrFKLf++7//O/78z/8cwJAMkX9j\nfgwYFuppiO5ooRqtvmw2i0gkYqyrUqkkiLaZmRkEAgEjJEhlYQAmZHd+fo58Pi+f9/b2kEwmDSmn\nRmalUilDP7O/vy/WVKPRwN7enhmHXq+He/fuSd8CkDDGxsaGFPG1Wi2Ew2H5TjweN1RGt27dwg9+\n8AP88pe/BABBFjF8MDs7a5Rru92uWJf5fB4ul0vCFgy7clz7/b48E/uLuZBqtWoq+mlp0npnKFjD\nlJkrXF1dNXNxXCXfWbgMDN5dh1YuLi6kXzn3dWGu9phcLheSyaTJ9+q1pgUbCdXXLOq6+BwYrBd6\nnz/5yU/wd3/3d/izP/szAMCPf/xjeWa32/2MJa7RYq1WC8FgUOaSDtu73W7JFQODtVMqlcQbIXxb\n54I5v4BBmJ/hXlIuca2cn5+b4lp6QVzTOodKL5Re3uHhoekfFs9r1o7p6WmR57h69aqE+wqFgqEz\no9fHovbr169jfX1d9s9kMmlKc3TRf7PZNKrQ9+/fx8rKinwmUlfL8+i1oJG3R0dHcLvdRi5eM8vM\nzs7i5ORE9jWd8qB0C9esLkIebWNxQGldEcpp6HCYDkmdnJxIHmRxcVESncDAtT46OjIhPm7KwGBi\nMP66s7Nj6ny4+WhdGmpCARCABGsXvv3tb8vfSqUSHA6HLEC61ZqupNvtyqTSk/X8/BzZbNYcdvl8\nXuh4kskkDg8P5aCoVqvodrsCrCBMndcFhvD3S5cuYW9vT8KSlFvQcXWdY9DhUWC4cIAhHYmmOpqe\nnpaD9Nq1a8J5x3Agf+tyuQxHWqPRwO3btyV8xv5lWE9vOvV6Haurq5KvqNVqwmDO/lheXjbxbL5/\ntVo1kH6/328S351OB4VCQTaDXC6Hw8NDuXar1RIFZrJIMATEsNE4Nk05xdIMYJhHAgbj2Ov1ZFwK\nhQKCwaCMLyUydNiWuRNgKMcBDEsdtJFQrVZlA45GowiFQiZs+9Zbb+Gv/uqvAAxCWpyTNCB0LaOu\n14tEInjttdfkXZvNpnmOer0uB2MqlZIwP5+5Xq9LLrRerxtlgOXlZaP3VKvVxGjy+Xyo1+sytwAY\nxV09z8j5p3XGdM0QlWZ1znp2dlYMPJfLhTfffBPA4DDXEiG8DvfA9957D2+++aaE5pvNpsm7OZ1O\nE+LN5XJCSXV4eIjZ2VkZ81QqhZmZGdmnNI0a8+Y0DOLxOI6Pjw19UyQSMdyNGvxAiRX2OzDMn38d\nK8tYHFD7+/uyWblcLqysrIjVwwQjMNiANYqHtBxaDnxlZUW8AlLdsNis0+mIJV6r1YylwUJSbvwX\nFxdGCKzVaqFarUrs9w/+4A+MzEatVjPUPjpG7/V68emnn8rALy8viwdAaRGCDc7OzmQSAIOFEo1G\nDeWMLiDWA08EHw/sdDqNlZUVeeatrS04nU5ZsCwgBobFhDrhrOtTOInITUj0kyYb/du//VsAwD/8\nwz8YEEGj0cDi4qJs/Ds7O3jnnXfwN3/zNwCAH/3oR3A4HDIHstmsLOb5+XkcHR2ZheJ0Ok1Nxfn5\nufSZz+czJL4Oh0OQRdFoFPF4XP6+sLCA5eVlg8i7fv269NfMzIwsJkqo0NLPZrP41re+hXFrmjy1\n3+8bwmOdyJ+bm0MoFJIcI/OLmnMyEAgYUUGfzyebsEatMZdLz4QIN71Jzs3NGXqvg4MDmcNLS0vm\nvpVKxUgx6BzUjRs38Nxzz+Hdd98FMCDt5Ry9uLhANBoV44EHw+3btwEMEZ56w9ZAoWq1anj6gsGg\nzDOiFmksJxIJ1Go1s/dwQ6Yxw74aJZrmoTBaq6SNY873P/7jP8Y///M/y3qihhv3uJ2dHZMP5Zgx\naqQBWKOCnZTToGdDkBXHqVwuG0O60+kYxJ+OzrDIm31br9fNoa2fkYYE9xSN/hxtkxzUpE3apE3a\npI1lGwsPKpVKSSiBVsj169cBDKxvnrQMVdHiOTg4MLkdYGBh6NyHPtU1fJk0L7SeKY/BezidTlQq\nFbEm6La/8sorAAZen4av6zoQt9ttaOwB4LPPPhMr6Pz83MB5tac2OzsLt9stVj+tOL5Tr9dDpVIR\nD6PZbEp+6v79+3A4HOIx9vt9wyTw5MkTrK6uivVFyhVgKKqo4/NayZdoOY4TFUTpFS0uLoq38dd/\n/df413/9V7GCW62WYX5fW1vDz372MyGLXVxchNPpFKtPI9Ha7bbU7LCvj4+PJTTl8/lQLBZlHIvF\noiEizeVyQqTJuhW+PyvwmXcgu7dmiqasxs7OjhkHPuu4NR2GdLvdSKfTBl3Gf5dKJTx58sTQS2mP\nmTViGqJOrxqACWETDapRq6enpzIvR+XAKfXBkHCxWJSwE5ntuZboudDKf/755w2Tiq7NmpmZQblc\nlt/qsBqbXvMMJ2rZBy1HEo1GxZMhwakWJU0kErKmNW0UJe61yrOW0OC99X3Jlg8M9jXSDf3whz/E\nm2++id/85jcABvvD5uamzMuNjQ289dZbePXVVwEM1vjJyYk8CyV3OB80nRXZP3QYXxPNjtKdlUol\nw04ODNcBCbG1zIfD4Xgmt87vTk1NGTaYr2pjcUBp3D7rKTi4y8vLJgx3eHgoVDYzMzPIZrOSBM/l\ncnC5XJIXId8XY+GaQXdrawu3b982bqaGReqQHZ+RNETAkJoeGFIM6foaHe+/e/eu4e3SSp10nTnR\nmc8iDQjronQtj97so9GohAuvXr2KTCZjWJU1f1g0GsXjx49lc79165bUkJXLZTidTvntxsYG/H6/\n5NyWlpaM/EIoFEI6nZZDaH9/38i/v/LKK0bjKpfLiST49PQ0VldX5R2vXLmC9957zyjZMud0fn6O\nhYUFef98Po9YLCahNo/Hg0ajIRtRKBSSfvb7/Tg9PZV34vjwkJmfnxcQCsdc18Zls1k57Hd3d+Hz\n+Qyd0zg2TbEDwFDq6ILmZrNplACazaape2HCW+dNHj16ZOrNaJw0Gg1UKhWZl6Ps2y6Xy0g1UPKc\nY9zv92UenZycwOPxyJgR2MTDLhwO4/3335e5UygUZC4wr8YN1+12o16vG9VXgjCAYe0X14OWoQ8E\nAigUCoa9XVP3TE1NIZ1OG448TRhAbTG+X7FYNJRDOp1AMgJtHHIfevfdd7G4uCj9SX0s5qiPj4+R\nSqVkXj548ADf+MY3JCel6zXb7TZ8Pp8Jv2vV3NXVVZTLZUN3paVaZmdnjWE2Nzcnh1273RZtO2Co\np8Vr6TnR7XYNyOzragrH5oDigMzMzJiiNm3R9vt9xGIxI8uuOZ5YdEcrIBgMIplMmk6jdZXJZBAO\nh43VR8uHnzVrRb1el8QgMDgMuKj4fDwIS6USzs/PZWM4PT3F/fv38b3vfQ8ATOEt/6s1qyKRiJGr\nJlEtMNTD0fLRuq/C4bC8P7m3NFloKBSShaMLfq9fv47Z2VlZkO+99x62t7flAKP3yTxRrVaD0+kU\nY2Fzc1OKCwOBgNkYut0ugsGgfPfy5cu4evUq/uM//gPAsBiX45pIJEz9Ta/Xk77e3NxEt9sV6zuX\ny2Ftbc0kXjXSrNFoGKtP63JlMhlDNsyqem7UGi25sLBgal5oFI1bu7i4MHUvmjNPF0/Pzs6KlwwM\n1kqj0TCbua6J0XkawApD1ut1bG5uikcAwBwElIThvKxUKvD5fLIWY7GYKa6v1Woyz5xOJ/L5vHmn\nDz/8UEAEX375peHaJEAHGOZRtWChPgz5XDw4Dg4O5D7ky9O1Ox6Px9RjUUaGz8m+XV9fN7mwhw8f\nGtLq8/Nz9Pt9eX/Kz2jCaE2sy3whMAA2FItFOVR/8pOf4Pvf/76Av1599VVzOGqCY2pwsX9yuZzh\npuQBxvUQiUQM9yjHAnhWyoVyG1qKXvMgav0vt9tt6uY088dom+SgJm3SJm3SJm0s21h4UGdnZ9jb\n2wMwyEfoUFoymRS38vj42MB73W43QqGQySnoeoTZ2Vk8fPhQXNxgMCgWI6lGaCFRBFDXW2ierunp\naVy7ds3ILGh4u1bUpfXIkFKz2cTOzo6pHdLXyefzYjGS009DhTW66PDwUGiHgIF3xnwWYeKa8+rs\n7Ew8l6tXr+L09NS47RqevbS0JF7Q66+/jlKpZJBHTqdTwnZOpxMbGxtiURUKBQNnr1ar8k4ff/wx\nEomEfDeRSMDtdkslfaVSwQ9+8AP827/9G4BB/RYtxtPTU1Obsbe3h8uXL0uO8s6dOyZnReQiMPCw\ndWzf5XIZS5+Cl0TtMTykWbZ53VKpZBCAzBGOW9My9cAwzwIM0WQA5D01M4BmZSkWiyacXKlUTJ5R\nizkuLi4aii0t+w0AKysrQlMGDEK4brdbxEF1iId8mpyX9AK1LMyLL74oHnS5XDYw81arJe/EHIh+\nf537oGev4e+8D3NVOq+k+4MhKl0Co/M1+/v7RtxSe6+sx6I3xhpDzjXNGRqJRIwskNfrxYMHD2Qf\nfOONN9But2Xdvvbaa3j77bfFw3zrrbdknEg9RNZ01gwSmfv555/j+eefFy/Z4/FIKJXe1gsvvABg\nEKZtt9syxpRI4TplaQFDtZozNBgMwuv1yn7xdawsY3FARaNR2UQYm2Z8tt1uy98KhYJQAQGDgdYu\na7lcxurqqmzejx49QjKZNHklTvzj42O4XC5cvXoVAKQYjp3FQ0FDxXW9VrvdFje80+kIyAIYknJy\ncs/Pz+OFF16QSbS2tiZudCaTEXVKYAhc4DvwvjxIL126BLfbLeGVcDgsGz+T+NTpYRGePhhDoZAc\nMposdG9vzxDeMmHMvnv06BECgYCEQxwOB548eWIS4dy81tfXMTc3J8/1zW9+E5988onhU9ve3pZ3\n+p//+R/Mzc1JbceHH34oEz+VSsHr9UpolTBbblB+vx/1et3Q+/A5gsEg1tfXJQH/xRdfGJJfAlT4\n21wuh36/Lwe+x+OR0InL5cLbb78tOSldDzNObXp62hSKlstl2WQymYzR5Mlms5JHIt8bjSqGh7XE\n99zcnAHrcA6TiofjybCx1ger1+sypqFQCJVKRYzS7e3tZzggOYYMtXEcPv30U8zMzMj46+JiFt7y\nHaamptBoNMzhpktLOHf43GdnZ7LxsyhZG8qZTMZItyQSCfn7xcWFHCIXFxfY2NiQzXlqakoOfDZN\nakxSXq75WCwme97R0ZFRNp6fn4fX6xXDcXl5GT/96U/lQNrb28PNmzdlHWteP5a+aP28YDAoa2t1\ndRUHBwdm72V/eDwelMtlOdzK5bI4BuwPTa788OHDZ4gRdF59enparp3JZORvo20sDiiN4rh16xYW\nFxfNQuLAffe738WvfvUr2cxjsZhBJjFmyo0wHo+jUCiItRuJRGSBJRIJgwDqdDqipQIM9W9GZcvZ\ntDXabrdRKpXk76wy1+J2iUTCMHRzo3M4HMhms7LRk0GYk/3k5ATBYFB+W6lUjPVaLpcNp92TJ09k\n4Hu9Hk5OToy3pmsZNGrr4uICR0dHsridTqcwErMvqdUFDA4hnXMIBAKGa+/s7MwcFO12Wzyq4+Nj\nzM7OyqR0u93C2g4MNgOOf6PRQD6fl0Xl8Xhw69YtqWdbWVnBu+++K2O+tLQkVm0ul8MLL7wgz/nk\nyROjS+T1epHJZGT+ud1urK2tSd+2Wi3ZKLrdrnyfvx3H5nK5DBlqKpWS9aINLLfbjXg8bjTNNMq1\nXC7j4uJC/h4Oh1EqlcT40SzpFA6l5Z3NZqUmiU0X/errAzCCnYFAAMfHx4apgu8FDABHa2trJlfG\nA4igGM6rw8NDxGIxmQ963fCdWbPEe2sEoJ7/brcb29vbsl5yuZwhPNb70N27d43OFFkUuKEvLCwY\nYA+5PNnX5XLZrJ2LiwvpD+qq8VDx+/14+eWX5bmJKL516xaAwR6pgWAzMzNyrbOzM6mrBIZ8kxqZ\nyOteu3YNDx48wIsvvghgsC4fP35sUM6dTkeuvbi4aAReNfdmoVAwShJaZ220TXJQkzZpkzZpkzaW\nbSw8KB2DvHz5sqBrgEH4gCGad955BxcXF2KJR6NRpNNpsVRY2a2tK005QikPABLzHQ0l8PPFxYWp\neyCtDz2beDxuLA2fz2dQS7VaTb7Lug4N59T8eLom5Nq1a3j8+LHRktL6R1TBZJ80m02h6imVSuJq\nA4M4eCwWEwqVV155BY8ePTK5IlovKysrpu6FeSSt0eR0OsWDIns76zVSqZT0M0ON/JzL5XDlyhWx\nIImGogX5ne98Bw8fPjTUSJpfkKE6YBA60BDvWq2GaDQqHlahUJC+Ileapr6p1+sy3zKZjNEPopX/\n+eefAxiEV/7P//k/8r5Xr141bO7j2LQGTyQSMXWEOkQbCoXw+eefy1p68OABUqmUoRwCIPkZ8lRq\nuRla1wwzs54sk8kgFApJuMfhcBgPamlpCa1WyzBPaDbzi4sLg3K9uLgweVXWAgJD3TK+XzAYNIhA\nrXTNPBA9JpYr/C5pn9nZWeRyOfluJBIx5R+tVstwM+ocNMPwjPzkcjk0Gg2jvptKpcT7isfjqNfr\n4n34/X7p50ajIYhZXiuVSsmaXl5eRigUwvvvvw9gUCf2zjvvGIVp3T8aIex0OhEKhWS/5F7EdRqP\nx2X8p6enkUgkZF2Sf5TPSbYQvgP5VIl2PTk5MZyA1WrV6Ol9VRuLA4p8W8DAlU4kEtJJsVhMNJkI\nT+TiKRQKBsjATZADQogt49ebm5sSF04kElhcXJQB8Hq9EmcFbCgMGAy8Lk48PT01IQpdE0Epca3L\nVCqVJIyXz+efKTRl0W+r1cLm5qZcq1qtivAcMFhky8vLspEkk0npn3A4LMSswGBC3r9/X+LG6XQa\n8/Pz8s664DcUCplQIOshuEAp28BN5/79+9ja2pJQZa1Wk6T3a6+9ZsQOObF5mDEcyHELBAJIJpMS\nK5+ampJx2traQiAQwHvvvSd9nUql5L7pdBoej0dCb5T6AAahFS15fX5+jpmZGSPxvbe3J6FFjhk3\nkvn5eVmAlUoFBwcHhptxHFu1WpX39/l8UoANDMJDHM+7d+8iHA7LXLlx44YURfM6oyTGpPsCYMo5\n1tbWcHBwIJvRkydP0Gq1xLghpZAO2+twog6Xjxa0jgIblpeX0W63Ta5Y/xsYiguS9owbIeXiuT8Q\nVs93nJubk0M2l8sZQ8jn8xkBUxp3OozHuXFycmLKFYCh5A4wmFcMewODfczv95uCWr5TOBxGrVaT\nw73VamFpaUkMC8L3+dz7+/t44YUXRJ6m3+/LfKahTJj97u6uWXfUsWK4NRwOyxhSykTnDbe2tiR9\nQt065m/L5TJcLpcY5alUSp6p1+vB7XablMhXtUmIb9ImbdImbdLGso2FB6WTa5ubm6hWq8ZS57/n\n5+dx//59Qxbb7XbFQ9jb20M6nRaJ9WKxiOnpaQNn1qGypaUlk+TM5/NiQWoIOTBI/G5sbEi4KBAI\nPEProSHrWn0yEokYZmCK7rGtrq7ik08+ATCwNi8uLsTbyuVyiMViphixVCqJlaw9gnQ6jfX1dbFk\n2Rf8rdfrNQqivV7P0CBFo1GxkObn51EulwVWWq1W8dZbb8nn7e1t9Ho9039EhFGGmqHEarUqiEt+\nPj4+FktubW0Nly5dMiJtfK5Wq4VHjx4Z4bNHjx6JV1StVg36KpfLyZgxlKqJN//rv/5LvHUWSLIv\n6VHSel1YWJDELpk0aG3+f1ED/d9orVZL5p3D4cDU1JTM08PDQ1lL/X4fjUZD3kOL0QGDd69Wq+KN\nUHSQXrYuNg+FQvjss89kHRLCrmHVtVpNxv/8/BwnJyfSz5qeCRjMJX6X7A662FYTKudyOSPHo9cK\nQ3utKEIAACAASURBVFZca+fn5/B4PEZOQjN0d7tdeb/Hjx8bJKrX68VLL730DGSbc57M4MAQWagF\nO/1+v0RkvvGNbyCTyUh/3bt3TyIDwGD9aDZ3zWDBa/KZDw8P4fP5TERBizhSFoO/mZubkzF1OBwG\nAUtIP9dSuVyW6zLsSvDF2toaLl++LKmJTqeDeDwu16Y3Ts8vl8sZgUtNCKxVrEfbWBxQ3W7XqF62\n223pVL/fbybB6uqqbBKUU+DkzmQyiEajsnnV63XUajVhEWeIEBjE3H/v937P8L+xwhsYdGKtVpOJ\nQOZn/v709NRQ4PN5gMHm5fV6DfdeIpEwrBGaBkXDeePxOLLZrHze2dkxm2G320U2mxVINjAIP/L+\nh4eHMin8fj+2trZM+IS5FAD44IMP5IBh+Ofy5csABhOKOT72PeuKgMGE1awMvV5Prnv//n3E43HZ\n8BYXF1GtVmWMeUAQ7s5Ng/3DUCAwmLypVEoMBrLXc1Izr8KDdWZmxtBIZTIZA9nVOamlpSXEYjHz\nHM1mU36/u7trQjiNRkM2Xc04P05N13GRgZzzUB9AnU4HgUBAwr2sxeNaIZxfa5ppw0jXpgEDI4zX\nJ2Rd1xBqlVyGw7V6r9aSmpmZkTnPXKX++9TUlMnXaJ21cDgsYScqKvPQyefzKJfL8k6cz/qgpN6Z\nx+NBOp2WOfv48WPs7e1JKI3rRufhtFYUD0OOg9PplHcqFApmL+l2u6jX67LW5ufnJWQ9Pz9vws5E\n4mkqpGg0KnWBb7zxBt5++20xUqempmT88/m8MeCoisD3bzQaSCQScu29vT1By3JfpPG/u7uLK1eu\nSE6u1+sZGD61uHj4aSqtQCCA5eVlMf6+TlttLA4ov98vA3J+fo7l5WV88MEHAAZwZj0JyuWyyZvM\nz8/LwJK6iJN5ZmYG7XZbOlVbjCRKZGPtEhcND0lO5nw+D7/fL7mfGzduyEY2PT1tNnNygHFyU8aC\nm90onHtvb8/w2G1tbckmSionWlfn5+d46aWXREjv4uJCJkm5XDbx65WVFbz33nsyuZ977jmUSiXZ\nzFOplDxHNptFPB6X3B8LfrmpxONxrK+vi4Xk8/lw8+ZNsaA0VcvFxQXu3bsnEiLcRLggi8Ui0um0\n9O3c3BxeeuklObgKhYK8g9frNSAaeqec1J1OB+VyWQ7sL7/80niurIVhv//lX/4l/v7v/x7A4CAM\nhULyjk+fPsXKyoo8FzXAOHfS6bQYS+Oag/J6vbLBUFeLB0kqlTKikFqza3NzE0dHR7JWTk9Pja4Q\n5SO42evrZrNZY7CxYFzD27Xxd3h4aIiIWeTJpg8+ekt6Duh6G0rCA1ZTjO/QbDblHRKJhKHnyefz\nptRC1xR2u10kEglZh7FYDOFwWAwyggD4zrlcTvYlCmjyM2Hj9Bi++OILRKNRI/bp8XjMmtfcg/V6\nXdZ/Pp/H8fGxrK319XXhLwQGBi1lM4DBnNbvRN03YHAIezweU8icz+dlnFKplCEfaLVaUmbQbrex\ns7ODP/mTPwEA/OM//qPJq/V6PczPz4tBowmxyY/Kw34i+T5pkzZpkzZp/8+1sfCg8vm8uKiEWFMM\nTheWEQ1E6yAWi4nyJTCwapaWlsQSIQU8rfyNjQ1D89NoNAz5JcXvAEgxHPNXJD6k5VKr1QzkUudF\n6C3p+L6W0PB4PPIclIPmO6XTaUP42ul0TLFdtVo1DNWJRMKELFKplIRpbt26ZQhg0+m0kTifnp4W\nizqfz2N5eVnQQcViEaVSSSxS5mZofdECp0Xt8XjEuqLyKsflF7/4BV599VV550ajAbfbLZ5dsVhE\nr9cTl7/b7UqIolwu4/DwUCzI/f19+P1+sQq3trbQ7XaNjD3HnyJwtBBv3ryJJ0+e4E//9E8BAL/8\n5S/RaDQMGs3lcolXMTMzI569Zp4GIP9/3Fo8HhdrmtY35yGlLACId8A5e+/ePaytrRlhvF6vJ1Dp\nZrOJ2dlZw7it81GdTkf6nXOFnivDjrSgCaPW60Vb9Q6Hw0itU24eGHgnevzb7bbch7Lkms3c7/dL\nUStJWGnlz8/PG+YNMsIAQ+kVehP8jp5nFxcXsj4WFxcNm3sikTDkwpp5YRRd6vP5kM1mZR5qQmeG\nQrk2WMKi+6Ner8s41et1+P1+8dY0wS1RuYxUdLtdVKtVue/Z2Rn6/b5EglZXV+X9qc7AkF+z2cQH\nH3yAH/zgBwCA733ve/joo48M00y73Zb91e12G0Ybt9v9tUKFbGNxQGnG6e3tbZM38vl8ElZiUpOT\ngguIGy6lGOgysnqbG7QOB7zyyiu4e/euUb3U8hqsl+HgZjIZQzlC95jPrycNef4028Pp6amRKuCG\nd+PGDTx48EAOL27smi1bK5/W63XZpIFBOEC7/++//76E7TiZGJbMZrOGvmV2dlYmVDKZxGeffWYO\npCtXrsjGkUgkcOvWLQkB/frXv8bq6qroYx0eHgpUfnp6Gk+ePJH+WF9fx+HhodyXBwfHglB7Ln6n\n04k7d+4AGBxAmlX5W9/6Ft59913Ju7F+jU3XjJHKiBtWNBpFsViUxZvL5RCPxyUUe3BwgLW1Nakx\nSSaTRsrl5s2bUrLAPh+3phlPjo6OEAqFZF5rjrtisWhCdsFg0NSQPX36FJcuXZK/j+qnUXEWGBiV\nL7zwAn77298CGPI2arXVUdqgZrMp4TKfzyfzLBAImJoZ1kTqcKGm2JmampI5OSopXi6XDTM+QQB6\nXWjNo2g0KgfU888/j0ajIWkAl8slhyUw2A/q9bphsdDqw8fHxzIOVDbgczCkybFoNptYX18XAy+b\nzQqfJGut+F2GUmkgTU9Pw+VyGUWDZrMpm79mqMhkMojH4zIuFxcXiMVisraopcVrZbNZeV9SwbE9\nePAA4XBY9qWZmRmR1eG1NS+orgM7Pz9HpVKR/vg6wNFYHFCLi4vyIg8fPhRiTmDw8FwknOCcCNVq\nFdFo1CRj0+m0TMAnT56IOBa/Tyz+tWvX8OTJE5lEnDTs8IuLCyNYNzU1Ba/XK4vj5s2bhnSUKCdg\nSG3C583lclheXhYLUtdQkdOMB87Z2RnW1tZkgrXbbfHAgGFOhs+ZSCRkEr333nuSZ2K/FotFmXC9\nXg/Xrl2TheDxeMzBqOuL6vW66PwAQ4QbN7ClpSVsbW3hZz/7mfQnJ9rBwYGhmKGAGvuDgAxNSeXz\n+eSzpnqi58lD5OTkBNevX5eNg/UUHAuXyyX3SafTpoD4zp072NzclELchYUFTE9PG0RluVyWA49g\nF2CwMb777ruCvBql4hmXpjWMFhcX8eTJE7GI9UamkZ7AcB5y7dy4cUM0woAh6azmVtMWMXXLgMHh\nl0qljLdJoAQwBBhoCXg26grpIk6n0ynzodPpYH9/XzbddrstBlk+n8fW1pYgYpeXl1GtVo03Agy9\nII/HY2RA2u225BgfPXqEVColBiwBJdrrIbE134lrg8KJzA1PT0+jWCzKtXO5nDk4WH+phQT1/H/0\n6JH8jZyHjAoFg0HUajUjLKoLsiuViuEI5L4GDAqGdaSDuUOdG6QRQgAKAUzBYBC9Xk9yZa1WSw5V\nALLHsGnpmpWVFRwdHUm/a09ztE1yUJM2aZM2aZM2lm0sPKhKpSI1MOFwGH6/34QHePLGYjFEo1H5\n7vT0NPL5vLiOW1tbqFQq8tv19XX89re/ld//3u/9nmHj7XQ6Ykm2223kcjmxag4ODrC1tWXUOpvN\npiBPDg8P8c1vfhMAxJvRqD7d+v0+MpmMWHCxWEysyOeeew7b29ti1VO2gHHznZ0dPH36VMIhLpcL\nqVRKrKJutyvu/rVr1/Dyyy+LmuaDBw+wtbUlbvfm5iba7bZYOhrxdH5+jo8++khUb3d2dvDxxx+L\nhX316lVTQxKNRk2dVCwWE/Xdzz77DH/4h38oz5zL5VCr1cQqDoVCyOfz0l/pdBqbm5tGwE2rIG9s\nbIi1HQgEUCqVZA689NJL8Pv9YkW/++67YuWnUqlnKKj8fr9Y7Ldu3UK5XBa5bNbFMQ/35MkTg4AK\nh8PiYYxaiOPSjo+PxSsm4lEzM9DrIW0V50Kz2USlUpExYA6G403BPa4lr9crlvejR49EugIYrC0N\nQ6cMwygTONeNlmlgPksjcR0Oh5Fm0B6XZjvhs9NDmJubw8nJicwzipLye6enp0LGCgy8M/52fX1d\nctrAcA4zWhGNRoVaDBiE05mWYK6Le8nDhw9x9epVPHr0CMBgrTgcDlnjfD6d3+N8vnPnDhYWFmQd\nkjWF3yUKUedzRiXUtcJ0oVCQqAgZa7gXzc/PG7LdfD4vYxwKhTA7OyvXIsEvPSgyxXA+EfXJvJxG\nxJ6fn2N1dVU+c//+XW0sDiiHwyEbfzweF0Va/o0uIClSuEk4nU6ziMh8rWPK0WhUBvP09FRADyzy\n1RxgZBIHBgfS3t6eKeJ88803ZfMPhULmGQOBgHzWUhz87dLSkkzgbrcrm2Amk8H5+blRyD09PZVN\ntlarYWdnBz//+c8BDHJnmnJndnZWFlGz2cTBwYGENAnL15IJq6urEqPX8fxXXnkFm5ubcmiRK41a\nMYeHh7h+/bqET+LxuIEGEzoOAN///vfRbDalL9PpNGZnZ6XGirF89hf5v3iw0xgABmEJPS4ffvgh\ntre3ZRybzSZyuZxRWWZ+qNfrmbAMpUj422AwaOitqGnFAz8YDEq4lMrFWh9rHJvH45EwbSaTQa1W\nk/VTLpdlnjUaDRQKBTFeWJ7BDdfj8SAcDpuDolAoyO+DwaDMo7W1NWSzWckFkWFcS8hoCY1wOIyn\nT5/KtXT+pt1uG4oxquTqtXVxcSFzRed+CcTgXKHEgy6u7/V6Mt7xeNwwuvd6PUkB8Pk4l/ju7B/W\nxXFz7ff7JrTa6/UkPRCPx3Hnzh251tnZGbLZrMnZ0iBg/9DY29zcxEcffSTK1gwVall2rWOXTqcx\nNzcn9VykhgIGa5qSG8CA7srpdMo4ZbNZBINBMXCmpqZM0a7P55MDnIc3S1bi8Tg6nY7MJ+YOOTY6\nf99ut5HJZMT4+zqY+VgcUJpIlDUDnFSbm5smD9Lr9WTBnZ6ewufzmVN+amrK8FTdvXsXv//7vw8A\nJgEIWKADDywulM3NTdy9e1cmYCwWw6effirWRjKZlIOA0th6AKanpw3RqkamPX361MSri8Wi3Pfs\n7Aynp6eGrLPZbAo57OPHjxGNRvGLX/wCwMBr5LUobshJdefOHaysrMhC2d7extHRkTxnNpsVL9Dp\ndCKXy8kzvvvuu4bwM5VK4eDgQKzkd955By+++KIc/rlcTqj4+/0+er2eTFb2MzcGxrr1Invy5Il4\nhdTpYf9QJgMYLLjDw0PDLNDpdAQFqlFcLP7VhYhat+qNN97AgwcPZKEsLi6i2+0aTSz2LTd6Hkxf\nV/3+v9lCoZB4d06nEwsLC/I+mltvZmYG8Xhc+qZQKMDhcMi8c7lcyOfzJk9ycHBgwAucs0T86ULd\nqakp2Xjm5+dRq9UMajMajZqaOp3U7/V6pmZubm7O1Nc0Gg15Li0iCVgPiyAQfUBpIlrWedGzuXbt\nmqyzs7MzVCoVo7s0PT0t867f7xuEbCaTkWfe29szxfi60BaAsGrw/+3t7WFra0vGgmTLwED/Sntn\nzNWyv9ivnPP37t3D5cuXZQ4Eg0FZV5Qu4n0JjOGBlM/n0Wq1ZP8EYPLVGoBGpDIPUhI2s7/C4bA5\nDDURb61Ww8zMjGEW+ao2yUFN2qRN2qRN2li2sfCgaMECEGg3raJHjx6Z2HUwGJRTfWdnB81mUyyk\ndDotrA3AwIK8fPmyQINXV1fF8mJIaZSxmJbxe++9J54QMIi5xmIxyaN0u10JJczNzcHhcIhF6Ha7\n4fF45O+s06G1denSJXnmarVqBNuuXr2Ker1upOe19UEaGdIKuVwusa60pcm/ffHFFxLS0iwZ7E/+\nFhhYiXT3t7a2UCwWJdezsbFh8giBQAC1Ws3ExonwuXLliqmvIM2TtmRDoZC8UzKZRLvdlrDEzZs3\nxcojIwfHgdYlLbVEIoFisSje2dramoQpydPGftchXWBgbRYKBcNzSColYDAv6UGRgZl9Ry913Jqm\neqInqyVluDb8fr9IkQPDcCajAsVi0UhVzM7OGloxho95T4fDIXPY5/Oh2+3KWmPkg83hcKDRaJgw\nnb6vLkFgWJ85KMo66NwwxyuVSol1DsCgM4EhgwvfuVarodlsSp9ozsdisYhQKCTXOjg4QLfbNXWA\nTqdT1s/MzIzxAqPRqBFo5PXYdwy3AYN1mclkxJObmpqS0Do5MXWOrlwuG6qtbrcr83RlZQWpVEr2\nPE0LRkQjn6tUKmFhYUG8xnQ6bZg3isWipFNmZ2eRSCSkT09PT40cD1kktOqCZnTXnKGcC5oT9Kva\nWBxQmvPK4XBgYWFBXNyZmRlZYE6n0/Djvf/++3j99dclX0MpaS238PDhQ9H0yefzphBPa8dUq1UE\nAgFZNNeuXZPQBTBImLvdbqMYyefqdrtGd4pqkZyw/X7fbG5+v1+kKa5evYqjoyN873vfAzAIy2ly\nzJdffhk//elPJflIyXZea2pqShbc4uIi7t69Kwf4/Pw8XnnlFUnGNptNnJ2diQu/trYm7n+hUMDp\n6amh+T87O5ONoVAoYGlpSRYR64cIOqnVatJXu7u7cLlckmyt1WpotVpyOCwuLqLZbMq4MSnMGH2r\n1cLt27cBDA67fr8vz1ypVLCzsyMTP51OI5lMyve9Xq8k3+/evYtkMmkKRLV+UCwWMyGPRqOBhYUF\nOZQ9Ho+Enp1Opzw3/zaOrdfryebtdrtN/VG325WDgAABjkEkEjEbCuHLHDPmELQ0AtcSddg4J6nn\nxb5iUS4NBx5OmhCVz8icKech/6YJkVkPCQzGkOPbbrdNTurKlSsoFouyX5CeSZddkIyV9+IcbrVa\niEQiRgVX1x9dXFyYA1xTOfHduGZJwMrNnWSymmhZk6c6nU6pxWNJhuYI1YrTPp8PDx48kBx+rVbD\n1taWkfph6L1er0s9JzDYD8rlsjynw+FAqVSS50wkEtLvDx48wOXLl03tIjXC+A46jxaJRAwZdTab\nlX4PhUImRP51BbtjcUCxcBMYTDKHw2EGU1uxnU5HNsmVlRWUSiVTKe12u6VGIB6P4/nnn5dDRUtF\nE+/PA4knPicouaM0I/Ps7Kyx9LSODIvx+MwamZTL5fDcc8/JxMhkMpJQJnklk7P7+/tYWVmRzX1/\nfx8LCwuyiEgUy0Xp9XpFrKxYLBpm72g0akQYw+GwIa3s9XoyUZaXl7G7uyubdTqdxu7urqD6mJ+j\ntRqLxbC/vy8oHqJ2gIFRcXZ2JpYaNbu0XDYAqQvhwmBft1otOcBZ5/TLX/4SwGDBvv/++xI3n52d\nRb1ex2uvvQZggMyjgCM9Qh03H9XtuXz5suToWKzN+eb3++V9c7kcOp2OHH5aO2ucWrvdNgeu/qzF\nDBOJBFqtlnj9FArk+uDhxjE8OTnBzMyMjKG+FuXLRzcrbcB1u115Dp/PB6/XK95Io9GQteRwOBAO\nh2WO+ny+Z2TsI5GIrHFdx0Y+Tc5/Gp08CMgew+bz/d/23jRIzqs6H3967+npvad79k2zarRamxfZ\nEhgbBwIFmAQIZKtAFhIKKvmQVCqVfE5CgStLUalQFSqVBBfELLExYGOEjGUtaPFIGkmj2fdepqe7\np9fp/ffhrfPovYJQ/2/pqv97vtij6Xn7vveec+5Zn+NWLHspogBANHbBeBREE71Bp/dcgAfVg1ar\nFdlsltGFvr4+BWg3k8nAZDIpVYxOp5Me1srKCt9X0C3kQgoEAkSTAUDjTfhUj8YveyIVwiaTCfV6\nXcEMDYVCvCDW19cRDAZ5buVymd8zODioNHKLZ6vvISuVSgqunyBmyLrkvEulEprNJvdaH9V4mIwc\nlEEGGWSQQS1JLeFBJZNJWvJTU1PY2dnhzR2LxVihtba2hnw+T6tdutPF6pufn0c+n6dLq0fmBqB0\nd7e1tdHqkJ/1sElOpxOBQIDWl1iIgsXX09NDq0+q5yS0IiWXegtpZWWF4TCLxUJvq7OzEysrK/So\nYrEY2traGFq7ffs2urq6+Ky+vj5ks1m+0+zsLHELJaYs8etoNIp0Oo3JyUkAmoeVSqWUaqqHx4fI\n905PT2NkZESBdtrZ2VEmDttsNlpjo6OjtNyazSaRKADNgl5bW6M3pg9PyH40m01a4ADwR3/0RwCA\n69evIxwOs8z21q1bCAaDXHc0GkVbWxu9soMHDzLcIaM1xEL0+/1oa2tjuCQSiWBjY4Me1eTkJO7c\nuUPPTj9VWfhDwpC/zOr7vySBzQG0/hs9Flu5XFYw78rlMsM7uVxO8S6sVqtSTSp7KHw3Pj6uVNrp\n/zs0NIS9vT3Kn1j1+mrbtbU1eifSJyjr8ng89JDFQ9AjhXd2dlJf6D3i9vZ2VCoVxWrv7Ozks00m\nE7Hq5FnZbJZymU6nFVisdDrN6IuM5RD+F0RufShN9k94RnhERmuIrITDYWWKuJR663NpokvMZrMC\nG5XJZBAMBnnGIg/6ab3Ag7xOIpFgBaykDmTNLpeLcgOA0xvkWXp8za6uLqyurjInFQwGkc1mFY+8\nvb1dCftWKhWuIxAI8LkS+hMdKF7pL6KWuKA2Nzdx5MgRAA8AYUURBAIBfP/73wegJer1fRJnzpzB\n9evXKWRDQ0Mol8tKE2U4HGbyXuYjAQ9mo4hCjkQiissqww6F6fr6+rC+vk739+mnn+bhVqtVpXzd\n6XQiGo0quFSlUolMV6/XeXB37tyBw+GgMEtIQwTD6XRie3ubStPhcCCRSPBw9/b2KMxdXV2Ix+PM\noUgIU8I4cjHK3mazWV58HR0dmJiYoDD39fVxJo58TygU4l5LP4n8vb53rVQqwel0UiE1Gg0cPHiQ\nF+k777zzc4PjotEomV/2BHiQV5Q+sFqtRqxD+dtUKsV1pdNpCr7kvmTfJTwhZ65v8AUeNFTKWRQK\nBaU0fmBggOfw05/+FK1I+vJmyYvKmQoWIaDxfzgc5vvH43FlXAKg7b28v9VqVYbw6YcKCqyR5Dqb\nzSbD3sCDsKOsS7D39OEzPY6dHsA3l8vB6/VSlmS+l14+9CMd9LBgJpNJKVmXHJQYSFtbW+jt7aUR\nqod6isfjSKfTVO4OhwOpVIq6p6OjQ5lL1dPTowwCrNfrSsOr3W6ncSvl7fpQezAYpCx1d3dTn1Sr\nVezs7PCcdnZ28KEPfYj9R/K+og+k70m+S4xFeV+3243HH38cgCb/LpeL+3Hv3j1YLBZ+d7FY5DuI\nLpAztdvtiEQi5K2enh5MTU0x9Lq4uIienh4lPSNGhRiz8vMvu6CMEJ9BBhlkkEEtSS3hQZ06dYrW\nw8bGBpPWgHZzCwKBw+GA3+9X0B38fj+tK6m6E1dZmn7F0tMnbqVpV274bDaLQqFAKy8YDCqj6BcW\nFvDII4/QK9A37ZVKJbjdbobppHlUP+J8aGiIFlZbWxstjfHxcWWsvRRF6K0+vSczPDysWIFut5tW\n78LCAvr6+miR9PT04LXXXlPGp3s8Hoa44vE4LaSNjQ2Mj48zzHbz5k309vbSynE4HGg2m0QZn5yc\nJFq8vLMe7LWjo4P78frrrzMBDGihGH3TK6BVGF27do17L15PW1sb9u3bh29+85sAgGeffRZXr17l\nXvb19eHkyZNKpacMc6zX67BardwrGYGiR5bo7e3l3haLRdy+fZtnc/DgQZ7/wMAAUdrlWa1KYl3H\nYjH4/X5l78USF8RrefdKpQKXy0ULWQBO5W+FD+Tnh0fL60GZxTuQfZcx7cIfbW1tjBQAmlcgPDs/\nP6+McZcWBX1Lws7ODnnLYrHQc/N4PMoIHQmN6Yso9CDFUgGol1PhO4E90hccTE1N8VmpVAoul4vP\nkukHsnflcpmh9J2dHeTzeXr20pIi3vjDEE27u7tK9eSzzz5L1InR0VEUi0UWDQnKvN4bKRQKlEt9\ntGZgYADNZpMFSCMjI4qXPDk5qURnNjc3eS7r6+sYGBhQiiAkugNovKY/l2QyiY2NDb6j3+9nSN/l\nchGMF9B4S8LyD1NLXFD6EJnf78fZs2e5SXo0Yuls18dFvV4vmWR1dRVDQ0P82WKxwOFwUCj1FVqA\nOq01lUqhp6eHl93CwgKOHTtG5hGMPzl4/VwVn8+n9P3ICABRdMLIEmq0Wq1KCbYeD2xqagqlUomh\nhP7+fqyurvLZKysrGBsbY1hibW2NTCQd+3qMND0EUXt7O3p7e6nAg8EgL6RAIIBcLsd1iGLTj9u4\nffs2QwflchkOh4M/65GOBYrlueeeAwB88pOfBAClfDscDislvWazme8xOjpKIYrFYohGozh9+jQA\nTUE999xzXKfT6cT8/LyCdi1KdH19Hfv27VMQqE0mkzK+en19nRdjuVzG8ePHmfPU98iVy2VO1ZUz\nb0WSsBaglfNbrVbulVSmyef0JfkCzyPykM/nlYtNkMwFaWR4eJgyWigUOLoCAOVCRrFIu4coKBk3\nL+FzfS+SoCSIIpQeQ9nvUqlEWQWgjJ6Rijy5oFwulzIxOZfLKZO0BQZJzx/CG+3t7RgYGGA1Xblc\nRjqdpkKW6kGR41QqxTXv7OwgEoko4dJAIMBnOxwOdHd3U7cEg0FWL8s7Cp08eRJPPfUUQ7Orq6tY\nX19XRmjo99xisSiTn5PJpGL86mHWNjc3kcvl+GxBPpf90OeoNzY2cOzYMfKPtNLoz+3o0aM0HOv1\nOg4dOsTP69GB0uk0CoUCL9W1tTVW3j5MLXFBjY6O0uorlUpKU2tHRwdLsAcHBxEKhZggvHLlCrLZ\nLF/uyJEjyOfzSt9HvV6nx/Wxj32Mg7+OHDnCPBTwANNKlGgmk0FHR4diFW1sbFBAk8mk0jzq8/l4\niRQKBcXrkfirMIZA4gMag+l7cYrFIh577DEqmc3NTfT39/PgC4UCVlZWlMGC8r737t1DOp2m4r9q\naAAAIABJREFUgi0Wi+jp6SEDioWsbyiWdxDoEmmWPXTokFLo4PF4MDIyQiy+iYkJ/PCHP8RnPvMZ\nABoD63uiPvGJT/CinJubQ6VS4ZqXlpawtrZGq/Du3bvweDzc25/97Gd4+umn+bcyvgAALl++jLGx\nMbzyyisAgCeeeALz8/MUan1zpXgJctGIUhZF0NnZiW984xtUdpubm8rgPdknQLNMxUIHWrfMXD9U\nUABeJSLR1tbGS8Tj8ZA/gAcJcVFsMmvt4fcUS1efNyoWi6hWqyzGCQQCLEMGNKXa29tLAwZ4kAcE\nHlz+QvpycL0RCGhQXyaTiXLb2dlJ/p6cnFTmrjmdTqU1wu12IxqN8h0F1kfOu9FoKI3MMjIe0Ixj\nfcOs2WyG2WzmZajvD/N6vQgEAlxXIBDA+vo6lb3Akekb5q1WK89iY2OD886mpqZw8+ZNNt6eOnUK\n29vb1A/SSiHrArTCKj3MmBj0fX199IxlzSaTSRmpkUwmqYtEz8petre38z2llUb2a3R0FBaLBRcu\nXACg6Ra3280LvaOjg/wifY/CA/qL/GEyclAGGWSQQQa1JLWEB6WHJHnnnXcwMTFBKzgajTJW6ff7\ncePGDZYr79+/nw1zwAOofrFqHgaS1Hdky60tlsbS0hJisRhL2Lu6urC1tUXvTMJ18nl9U5p0levL\nS+12u9KMm81m+fvx8XF6jHqUY6Fz587Rs0kmkzhw4ADdYavVinw+T6u4VqsR9igWi2FychIXL14E\nADzyyCNYXV0llMmVK1cwNDREgNilpSV6l1I9pZ826/V66fVIXkBK+B0OB973vvcxXLK7u8tS8IGB\nAczNzdF6unr1Kp5//nlWG7rdbty9e5eVd/Pz8xxEB2ihysuXL/PMHQ4Hrd5QKIStrS088cQTADRL\n9vjx47R0NzY2eObhcFgZrd1sNtHe3k4eqNVq6OzsZLw/Ho8rcE9Op5P5jd7eXmxvb9NS/fGPf4zP\nf/7zaDVyOBx839XVVQWSaXt7m+eZy+XQ0dGhoFXrPSqpDtOHjprNJv9+bm6Ocivek96DymQyDCUD\n2rnId9VqNaytrXFUi8vl4j4L6orkryTXJaE0gRjTT7r+4Ac/CEDzHCqVCj2EcDiMrq4uehvZbBZj\nY2Nct4TsxIMCHpSGS8WjvqncZrMpLQtOp5Oenr5KrVgsIhwOK9A+3d3dlH+v14toNMoKQWmTEM/l\nxIkTlNlLly6xChbQINj0Y+uHhoaUgZ5+vx/9/f0KSodUz7pcLiwuLirj69vb27k/fr8f1WqVcuv3\n+5kekdE1Ijui+2Q/BGZNniWjSSRnOzQ0xGfJ8FPRcb+sZaMlLqhcLkeGfPLJJ2EymXgheL1eCk08\nHkc4HKYCFuUtYanu7m5YLBZlsmez2aRy29nZofAKppUIoChE2eDnn38e58+fp3t8+vRpnD9/XnHx\nZWMllyUuvAiUKHuB9ZGQn9VqZS5Lcj36Sb56xIpDhw7h5s2b/N6pqSmYzWa+0+zsLF577TUA2sWQ\nSCTI3LFYjKFLAPjUpz6FixcvKqjrIsw9PT2IRCJk9LfffhuTk5NE7XC5XPB6vQyPNZtNvPzyywzr\nPSyQW1tbRDr+6Ec/isXFRb6D4PZJmXEsFsPS0hIvzvX1dZ7hN77xDfzWb/0WL/TJyUnMzc0x/NDX\n14e7d+9yP6emphgSjkajGB0d5boajQZhZoAHJfuyl52dnVhbWyO/9fb2co2SBxShEoT8ViOHw8G9\nEgR5MbqAB4aZx+NR0Ayy2awyEsPv9yt9LHa7HWNjY8x96BV7MBhEV1cXDYO33noLmUyGrSOvvvoq\nTCYTw1YXL15EKBQiP8joClnzw7062WyW65Y2Cf2ZitKMRqMKCoW0hYjM12o1xGIx/iyXnvysN2YX\nFhYwOjqqzLjK5/PMua6uriqwSsPDw9RhXq8XCwsL5Cu73U7oIgCcPizhsbGxMbS1tTGftH//fqWA\nYnR0lOHR/v5+hsKFBBdQ1im6E4DSu5XNZjEwMMB1yr/ri8r0xkE0GlXGvz/33HMK+oO+tcbhcCij\n5vft24fZ2VmW7SeTSZ7pvn374PV6ceXKFQAP+iJ/EbXEBXXx4kV6KuVyGVtbW7w4RkdHGdf0+/2E\nBQG0eGwwGCRziwehTxAWCgV8+tOfBqD1AYhC9vl88Pl8tNx+93d/Vxlutrq6SnwyALh27RrMZjMV\nllSIybMEVgbQGNThcCjzXdbW1ngxvPPOO7REl5eXkUgkqBSlqU8Os1QqKZddPp9HJBLh5Vir1ag0\nZLyCXJTVahW3b9/mRRGNRpFKpZQKOKkGEsgl8ZCef/553L9/n1ZPMBiEyWSi57a2toYjR44ofQ7S\nG9Td3Y14PM7v+epXv6rMJTp58iTu3LmjwKR0dnYy6e52u2kYPP7447h+/Tr71yShev78eQCaZf7k\nk0/yIp2ZmcGJEycAaAaN3usxm80Kfli9XkdbW5ty+W9vbzPeH4lEKET1eh0XLlzABz7wAQBQQHZb\nibLZLD0Zj8eD7u5uegGNRoM5pXQ6zfcEtJysPhd67do1TExM4MaNGwC03NMTTzxBZabPT/r9fuzb\nt4/FJqVSCZFIhB6U5BDF23a73djd3aX8NJtN8obAi4mB0tbWxuZdAJQ/4a1sNkvjZGJiAnNzc8qI\nd4EKArTLzmQyMfcRi8Vgt9upIG02G/luaGgI1WqVxu/w8LACq9Te3o5yuazAYgkeZCqVwpEjR3hx\n7uzsYHx8nBeHnIms880338Sjjz7Kzz8sG5ubm/xbv9+P+fl57p1EDMTQaG9vR3d3N/daGn0BTdfo\nhwhK0Zjs5cDAAO7evcsI1a1bt6iHBwcHUSgUaOyIHpZ19vT0KFGj2dlZuFwuGsdutxvHjx8n79y/\nf58e5MMDXvVk5KAMMsgggwxqSWoJD2rfvn0KsOjGxgZdyVAoxNzEvn37UK/XWVIsyMRiXc3PzyOb\nzSo9G9evX1eGAeqHiNlsNnoqgvwgFsLa2hrcbjdDSQL2KK762NgYrclSqYS9vT1+NpvNKh3sly5d\nQrlcpjfy+OOP0wI/c+YMzp07R+/K7XYjHo/z2VarFcVika5yLpcj2CQApUrp0qVLsNvtjO3H43F0\ndXWx8mplZUWBzI/FYrRennnmGfzt3/4t82E2mw0ul4vnYjKZlJ6zI0eOYP/+/ZwwrEdjlmogsdb7\n+vowOztLQNednR184QtfwLlz5wBoHufhw4e5t8FgkOGXO3fuKD00Pp8P3d3dDB+JZa6vNhNwzImJ\nCUxPTyshTZPJRO/T7Xbj/PnzDNtIvkue5XQ6yXv9/f147LHHuO9SgdhqFI/Hye9OpxNvvvkmrfyN\njQ2Glfx+P3K5HPn93r17qFarlC0BTpZzKBQKOHLkCD0qt9tNPnI4HDCZTIxGzM3NKf1Gdrsdg4OD\n9L5lJIbsZWdnJ8OyevBmQDvfyclJej0bGxu4f/8+85e7u7v0cqREWp6Rz+dhsViUoYP6MKb0QUrO\nWo+qLqkBkZ1isYhGo0Fvw2QyweFw8J3q9Tplx+l04urVq/jIRz7CNebzecqSPicKgAj6wneZTAZv\nv/02AM2LX1xcZHRmdXUVmUyGsrS8vIyuri4ll3j79m2lf0vO/8CBA5ifn6eHtLi4iPX1dfaZSoWj\nRDL0iBZSXS17u7e3p3i23d3dyrDP9vZ2VkLL/skZt7e3w+fzKYMj/zdqiQsqkUgoYy2mpqb4YvV6\nncoon8/D5XIpSN4rKys86NHRUYb5AG1TX375ZYb19AUU2WwW9+7dI1NJfkUO02KxYHBwkHFhCQXI\n4cmIbAAcpaFn/GKxyBBKb28vVlZWGEq4dOkS11EsFhEIBOiiDw0NweVyUdnfuHEDu7u7vBgEE0zy\nTC6XS2nEM5vNDOkJXpgo2XK5jEOHDinozeJ2nz9/Hp/4xCcocDINU9/noUdRrlaruHnzpjKtU+iN\nN95AZ2cn49GFQgHJZBI/+tGPAGiKYnFxkXufyWRw69Yt7mcul+PvTpw4gR/+8IcUhN7eXvzkJz/h\n+2ezWeTzeSqwiYkJBVX9ySefZLhImhb14aPDhw/z8t/a2lIww/QQXENDQ7h9+zaVsD7+3kq0u7tL\nXtre3kYoFGKYRSYZAw+m3kpCPJ/PY3BwkLxz4sQJnDp1iuc/MTGBer2Ot956C4DaQxcOh5FOpxke\nk/YF+V7pCxIKBoMIBAIMJ7a3t/OyqtVqsFgsSqHL+Pg4z0TwOEUeJiYmKP/JZFLBWhQcQsmTtre3\nK6N9vF4vGo0G5VbfI1ev1/HOO+8wtNzV1aUUK+XzeVSrVQViSx86PnjwIH7wgx8AeFDeLTIiU4Il\nbBcIBNDb20vZk5FDgHZhx2IxZQL3yMgI9+PRRx9FLBbjsyRnJb+3WCz8XXt7OxwOhwJBNTg4yBBp\nKpVSRteMjo4qI1JsNhsv6GazqZyxQJWJEb61tYWuri7urcfjoS6tVCpoa2ujTP8yWWqJC2piYoKC\n/8EPfhCrq6u8VQXzCtA29PHHH2cTZ71eR39/P77xjW8AAP7kT/4ENpuN1oUMTtMfiBxGV1cXc1SA\nxsx7e3tUbs1mk3FcQPMoZmdnebF84AMfUHog/H4/mXt7exvlcplVa2azGZ2dnfyuZDJJoZGeBvnb\n2dlZnDhxgonuzs5ONBoNZX6KfhT32toahSiTyRDkFgB+8IMf4LnnnlOGv/l8Puad9FVcJ06cQDKZ\nZG5M8oAP973IRZBIJJTejuXlZeWCFmh/+d3U1BSt70gkongyjUYDzWaTzD84OEhD4NatWzh79iz5\nY3t7GxaLhcqw0WgwbynvpJ/hVK/XyT8mk0nxtBqNBn7/938fL774IgBNiKrVKoUuGAwy/2m1WrG5\nucnmY7n0W42y2Sz3zmw2Y2trSwH/lPyt2WxGKpWiIRAOh5FIJPDZz34WgOblN5tN8viVK1fw4osv\nkm/b2tqocMPhMPL5vDKqJRQKKf1n5XJZaa7e2tpisn96eprPkr5F4dlHHnkEHR0d9K5zuRwajQYv\n1tXVVb5TKBRiNAPQjBk9rqdgT4qBGw6HOUYHeJAbk88K4gWg8bsescHv92N5eZnyceLECUUBb21t\n8X1XVlbg9/t5gdntdqVhNhAIYG9vj8ae4FwCmnzrjSapfhP+KxQKvOAATU4lByj7d/bsWQAafmQ8\nHlewGmdnZ5Vn6xEwFhYWKAtSAatv1NWPI3rxxRfxuc99jhf2rVu3lNlbjUaDBqvP51OKk/TGy8PU\nmmagQQYZZJBB/7+nlvCgNjY2mDeIxWIYGRmhBbGzs0OL0GQy4fLly/zswMAAQqEQXn75ZQAPEJeF\npARVPBebzUar7o033iAyMoCfK70UC04siLW1Nfh8Pvz1X/81gAd4Y4Dm3sdiMVoe0WgU165do8Uk\nMWj9mAexPKLRqGJduVwuzM7O8p0TiQQ8Hg8tkZmZGQwNDXGdjUZDGdBYLpdp5f7BH/wByuUywyGh\nUAjr6+uMgYfDYeZftra2MD8/z30IhUIIh8P0VrPZLPx+Pz2ZWCyG/v5+lr/6fD6GJaanp1GtVvm3\nR44cgdfrZex7a2sL1WqVIQ0pG5b9+cpXvkLr+oMf/CBmZma4P7VaDT09Pdyf5eVlDA8PK+Mg9NNF\nd3d36eWVy2VOhgU0i1Hi74AWEt7a2mKl2PT0NKsay+UyOjs76eW16kTdQqFA3pKwkXgM+gFyoVAI\n+/fvV8Kdn//857l3b775Ji5dusQw9erqKgYHB5U+Fz3C/szMDHm0p6dHQYDp6elBPB6n118oFPDZ\nz36We//yyy/Tsy2VSrBarbTqOzo6sLGxwYpAQU0QuXQ4HPTcxTsSL18wMcXbrtVqCIfD1BHpdBpu\nt1vB/tQPXfR4PJQzQT2X7yqVSgr/Dw0NKVW9grcne5tKpdgnqB/eB2jIKeFwmB6Yw+GgJzIwMIDt\n7W16jI8++iimp6fpbXk8HlgsFn631WrF7u4u9/bb3/42I0oSepXITn9/Pzo7O+lRSUuKvKPVauXv\nuru7MTk5SU+22Wwik8lQT167dg02m40VtNPT0+jo6FBGssj5b21twePx0NNt+RyUyWRiI5qM1ZbY\ndzKZpOIDNLdTyhPn5ubwwgsvUDBE+ehBKlOplJLok4NfXFxEIBCg8C4sLODu3btkokgkgrGxMfzr\nv/4rAK0PKpFIcF36MmOBKhHFZ7fb4XA4yAher1eJ9+tjt0eOHMHm5iafV6/XMTw8zIvD7XbD6/VS\nsUjYQhRkV1cXhejOnTs4evQoBVJyAxLimJ6exr59+xi6PHr0KL7yla8A0Bjf7XYr47Gj0SiFJhKJ\n4OrVq2S4oaEhTE5OKuWscsEIBpcou2q1qpSKj42N4cKFCxRSAXCVfNjAwAAFY3R0VGlyttls6O3t\n5RThAwcOsOQX0EKAYig0Gg385V/+Jdcl4KH6mHe1WmVf3bvf/W7Mzc3x84FAAN/5zncAaEp2aGiI\nodf/Ddzy/5pCoRALOBqNhgKurO/bicViSCQS3PM///M/h8Viwcc//nEAYAhWFN/+/fvhdDp5QVmt\nVhocJpOJcFaA1qumn9smBpF+dtLx48eZK0ulUuQFp9PJvKz8TSqVomJ0u90oFotUjH6/n/y9traG\nzs5O8qhMRJYWhEgkohTBeDwe5HI5riOXyynhUb/fzxDn22+/Da/Xq0zvttlsvChmZmb4vslkUsEe\ntFqtcLvdBFr2er0seQe08OD6+jr3oFgsks8kByfyfvXqVQwODrLgIhKJYGdnhxdrZ2enUjr/F3/x\nF3yn7373u3A4HJSlUqkEu91OeXG5XMjn8+QR/TTz3/iN34DdbqfcFQoFJVxutVrh8Xj4rL29Pdy7\nd4+6W2/8SiGG6DT9eKSHyQjxGWSQQQYZ1JLUEh5UT08PrZh6vQ6LxcLbdmNjg1aMTFIVF/4rX/kK\nzGYzk+LZbBajo6MKbMrDoJZSvvm9730P/f39tHoEhFTvGW1tbbFabG1tDVtbWwwl2e12BY05EAiw\n3FWKLSRMFQwGlaTw7OwsrSex1MRj2N3dRa1W4zrELRfr6tChQ/D5fCw4SCQSfK7NZsP8/Dyhj6rV\nKjo6Oriup556ShkUuLGxoaCTF4tFhgffeecd9Pb2KondQ4cO0bLr7e3F+vo6LSw9wK8ASQrawptv\nvqkgach4EvFmR0ZGsLy8zHMaHx9Xwqdra2u01iqVCjY3NxmGeOutt3DgwAG+R71e59788z//M/b2\n9hS4Ho/Ho6DONxoNfPjDHwagVVdGIhGl6kveTyxcfYK5FUlKnAHN+t7e3qa3p/eAent7sbOzQ15J\np9N44YUXfi7ZLj/LvkmI/OjRo3jf+94HALhw4QJOnTpFq35xcVFpYgU0i1ss+ZGREXR1dXGdzWaT\nZyJI9+9617sAaDw9MzNDL9BisSAQCCijXeS5MoBSD/bb2dlJD06S9npUfafTyVBlo9FQKlz39vbo\nQXo8Hng8Hn5XKBRCuVxWpi5IhMThcCAUCpHvAM0bF49T9kmKiObm5pDP5/mO+lRFIpFQ0DAOHz6M\nzc1N6stGo4FwOMy9jsVi2Nvbo547f/4892Pfvn0IhUL0MAVgQCIw4XAYp0+fZtgyk8ngzJkzfP+/\n+Zu/UYokGo0G+SmTyWB1dZXrnJycVFDo9bxw584ddHR0UI/roe4eppa4oJxOJ+PAY2NjsNlsvGT2\n799Pl3R6ehqRSITK+v79+/D5fHThh4eHlXkwAuUjP29vbxOBQOYuvfTSSwC0UFu1WlWUu8vlUspO\nX3jhBYWJJKa6t7eHxcVFKu+LFy9ibGyMTNTW1oZIJMKczOjoKEOFd+/e5b8DmuKoVCqsrpPn6pHC\nM5kMhUE63oEHPUJC0skt8eharYb79++zEmloaIj7nslk0N3dTXd7eHgYdrude51KpXD16lWuWybo\nihAKMjSgKQ2Px8NzGRkZwfb2Ni/SpaUlHDx4ELdu3QLwoP9CQgubm5sM8dy7dw9jY2Msfe3v70c6\nnebl97WvfQ1zc3MMAelR0c1mMy5fvkwhk14UUUhms5lVYQBw9uxZFAoFCo5+vIbdbsf09DT3PRqN\n4g//8A/RaiR5FkA7f5kUDWjvK0bEvXv3MDExQWVZrVbhdDoZwhoeHlYutEajgYGBAZbdd3Z2Mhf8\n5S9/GW1tbdyb8fFxyhygnWc2m6Xyeuqpp9Df348vfvGL/G75fHd3NwYHB8nv9+/fh9lsJm/JBGXh\ny1KppORj9XBmHo8Hu7u7yju2tbXxb8vlMsxmM2VaP/9L4IPEUKxUKsjlcspI89HRUcqSTAOQ5+rn\nxQmfiU6THJt8XpDl5ZwcDgflv1wuw+120yC6ceMGRkZGuObt7W3mwGVdgrQu+ylpi2QyiaWlJWVE\nRiAQ4OWxu7uLixcvKtBwAk+1sLCAWq1GQ7FQKChGmkA1ycUaj8dRq9WYw9T32NlsNmQyGZ5Ly19Q\n8/PzzE+sr6+jq6uLnorX66XSHBgYwNGjR6k0zp8/z/lJgJZs7OvrIzNHo1GsrKywd+PWrVtk/K6u\nLoTDYR7ewYMHsba2psxomZubY77r+vXrCIVCCjS8fDYQCCi4ZolEAh0dHWSie/fucdAgoMWRxbJo\nb2/H0aNHmevZ3NzE0aNHyXAysE28DSkTFbBU4EHDn9lsVsbUCzioMIDP50N7ezvfIRaLUVg7OztR\nKpW4LikWEO9ULEC5dLa3tzE1NaWUcAssUFdXF8rlMr93ZGQEfX19/N5UKoXl5WVa58FgEJcuXaKF\nKUIHgL0Xcg7SQCyKY3R0FG63mwl5fQ6uUqng9OnTVARShq+f86Sf6yS9LSJUJ06coFLp7+/H/Pw8\nLz992X8rkfALoPGKeAKAdsaiFE6dOoVCoYDvfe97AIDjx4/jox/9KA2WRqMBs9ms5DP1l9B///d/\nU5mfPHkS8/PzCn7enTt3qOh6e3vR09PDy+69730vrl+/zgtOmjoBbZ/Hx8epRF955RWMjY2xH0fm\nsEmvYzabJf8L1qLInbQv6KMVExMTjChIYYPok3g8znVYLBZ4vV6uo7u7W1mnAOlK3k0/OyqRSGB7\ne5vfa7PZUC6XaQiVy2UCGQPaxVEqlfgsAW6W79Xjfh49ehRut5sFGIFAgKNB9Hsga7FarfSIUqkU\nvF4vDev29nbMzc0phmVXVxd10YEDB3jegFawIuvo6+uDy+XiZ48dO4aRkREWs+RyOUSjURpE8Xic\nRufk5CRsNhtlSeoPfhEZOSiDDDLIIINaklrCg6rX6+zmv337NrxeLytz7ty5QytfKtDEqonH4/ir\nv/or3sRerxe3bt2idXXz5k1lUuWhQ4foOvf19aFerzMe7ff7lYF0iUQCmUyG1sY3v/lNVCoVWkF2\nu50eUaVSwd7eHn/X29sLh8NBy0AAHSWfpUdoX1hYwMrKCi2VcDiMlZUVekwyWkIs3ytXriigjT6f\nj+Gul156CXa7nZNM0+k0FhcXWSr905/+FF6vl5U2Bw8epEUUi8WwsbHB921ra1Pgaur1OsN6gOZd\nFAoFelRPPvmk4rlVq1U+K5vN4tVXX+UQQukqF893d3cXExMTtCgFEBcAx2NIOCEYDKJSqfB7bTYb\nuru7FeinX/3VXyUvHTt2jOcyPT2N97///Yql+qUvfUkZW3/06FGe6/T0NMtm79y5g76+PsJZ/bLS\n2P9L0kcfZEquWP3lcpmeaKFQwOLiImXj7/7u7/DII4+Qz2q1GiYmJhjSbTabmJ6epoUs06wBLWTr\n9/vJw9VqFf39/YrHnM/n8Q//8A8AtArIf/mXf6HnI+FFQPMYHA4HvVj99AH5bHt7Oz1YkSlAi5hY\nLBZGMtxuN6xWK89QKs0karK3t4eNjQ3qmq6uLobbQ6EQCoUCPTcZ5SM/yxqETyuVijJaQmQM0GS4\nVqvRsxPwAHlnn8/HcSeAFo3QR4H00GYulwttbW2KB69HePH5fKhUKkruVORQqo3Fq1leXsaBAweU\nFo18Pk+57O3tZeWhy+XCe97zHmU/9u3bRw/7yJEjsFqt+NnPfgZA4zW9xxkMBtlcHA6Hsbe3x/aY\n3/md38H/Ri1zQcnipcxcEnmDg4MMw3R1deHGjRuMZR4+fBj1ep2XzsbGBtrb29n9r+8MBzTGEVdY\nJuRKzsnv9+PSpUt4/PHHuabJyUmlH0Pi34AWApBcxezsLObn58mAXq+XmFiAFg7QM//AwADfNxQK\ncYwIoLnK//Ef/0FhLxaLSKVSZKrDhw8rCM25XI5McejQIWxvb5PhbDYbTp8+zXUNDQ3B5/Ox/H19\nfV2ZZLq+vk7Gn5qawvr6OhWY3W5HOBymUOVyOYTDYV4Uq6urFPz5+Xll9HYymcTHP/5xhnhisRge\neeQRhlosFgu6u7tZ3NDf368I5OXLl2mE7Ozs4KmnnuLvrVYrKpUKFdnKygovf4vFgs3NTfY1+f1+\n/NM//RM+9KEPAdBCfpFIhAIpl5/wnh4p3+FwIBgMMs8on2k1KhaLTIp3dHQgGo3ykhGYLECTlc7O\nTvJRKBTCzs4OeUOQvEXRdnZ24vr16zT+VlZWuDdWq1XJf9brdY51B7SQ3vPPP08j9I033sDrr79O\nntajKLz73e9W8PWKxaLSm1ar1bC+vk7FJ2FbQDNshoeHqZwlZCttBH6/HwMDA9yfzc1N6hBAM/6E\njyRXI5doPB5HKBSiPNRqNY6UALQ2DX3eaGFhgRdfe3u7UkQk43b0k79dLhdl/tChQwzxHzhwALFY\njD/b7XZ4PB7KqfR6imExNDSEO3fuUAbkwgcelNXr5+UlEgllIkE8HlfGs8gaa7UaxsbGGAJOp9Ow\nWCz4tV/7NZ7hq6++yonbEmaU/ZRnyGf1iBW/jFriggqFQrwI2tvb4Xa7iQlmsVhoAezs7GBvb4+N\npzL/SA7HYrFgdXWVHsRLL72EYDBID+PGjRv822AwqNTxu1wufOxjH2Od/0c/+lG8//0Xs++YAAAe\nMklEQVTvp+AAmuDJWmQgF6B5Gz/60Y+YQ4lGo9i/fz/zIn6/H1arlc9qNps8+GQyiampKSqGc+fO\n4emnn6YCXllZwb59+1gEkEgk4Pf7aTkmEgleSFJ1KMUHExMTuHnzJhkjHA6jo6ODwr2xscG/TafT\nGB0d5QU+Pj6O+/fvK/OCDh48SEER/EFhZr2QnDhxAteuXVMaOfXjFvr6+nDv3j16ievr68jlclxL\nOBxmrnBxcREf+chH6OU4HA5cuHCBQiYWoySkc7kcPysjAUQhSUXbP/7jPwLQLMZgMKgonZs3b/Ly\n0c/siUQieP3119k3JMZHq1E2m6Usra+vw+fzkZf087wcDofSq7axsaH0xEjxgXjQxWIRQ0NDymgL\nsYCj0SiSyaSSrzt27BiLSCYmJpBOp4nF+PWvf10BZtVjYJ44cQLf+ta3WOEXDAaxsbHBMxwYGEBP\nTw95KZVK8WKQ3Iy8g1RpSiRDDw4NaHpHn3c2m81UwDJKQrzvRqMBk8mk6IP5+XleBCsrK5SV+fl5\nxbOTxlzxsDo7OxGJRMhnMptOaHd3V2nM18+HKxaLyGQyXJeM09BHfo4dO6ZAuMk7VSoVjs2Rzz7z\nzDN8VjKZZBUxoPGPGPgrKyuYnJxUwIPj8TgN9nK5jLm5OXq6MmhSdGxHRwd1ZywWw9bWFvdLvu8X\nkZGDMsgggwwyqCWpJTwoGegHaN6F2+1muEBfWSYAr+I2Li4uYmdnhzezQPNL/HpwcBAul4tgsnoP\nqFQqIRwO0xKbnp7G8PAwvvvd7wLQbvWHb3apkAEegG0CmvfR29tLi+j06dO4evUqPQixOvXgkLJG\nm82G5eVljq14/PHHFfc/EomgVqvRO6tUKrBYLHxmsViklWK327G1tUWvRlxs/fCzubk57oF8HtCq\n4fTDzW7duoWOjg5arjISQEIeu7u7HJAIaJa7rPnWrVvo6+uj12e322G32xnikcmdeoimN954QwHA\nlXWFQiEsLCzQQ+rt7YXdbmdl5oc+9CGlrDYYDNLKlbJyPbDrjRs3GGrx+XzI5/P0ktxuN7q6ulha\n+84779ArDAQCePTRR2mdyz60GjUaDb6fAN/qQ03ieQpckHj5XV1dKBQK9CYajQY6Ojr4nsViEYlE\ngt7Z6uoq8w8yEVr48OTJk/izP/sz8sOLL76I73znOzyHUqkEp9PJdZpMJvJ3KBTC1tYWPbu9vT34\nfD6ew/3795X+xXw+r1Se6kfECCip8HAgEFDK381mM1G5Ac0bExk2mUzIZrNs0VhfX4fNZlPQYE6e\nPMk98Hg8lEmZgiB5SqfTCYfDwfetVCpoNpvk02QyqQxtXF9fp1xJ6bzsrfSICs/qpw8Dmqxtb2/T\n85EqPwCclith3VQqBbvdzmniIyMjKJVKSshb9q5Wq+FnP/sZ0xrd3d0/N6R0b29PCVtaLBbut8lk\nIm9VKhVl3IrI9i+ilrigstksk7eA5gLKAenRqSuVCiYnJ8mAs7OzOHjwIF80m80ilUopB5ZIJHDq\n1CkA2gUlMWOz2Yz19XWWt6fTaXz1q1/l4Qkkjmyqw+GAxWKh4kyn01zjv//7vyuYgaFQCMlkks8W\nJGA5eJfLRWWdSCTQ3t6uhOwikQjDASdOnIDJZGIcPRAIKDOdRkdHuT9Ski9MJPukL+aYmZmh8Pv9\nfiUuLiPiAS0XNjMzo/Q1PPPMM7xELly4gEwmo7yzFJz09fUhn88zXxGLxdDR0cFL2el0oqenh+uT\n4gy50PP5PPlBcnsi7Ol0Gp2dnYTkKRaL7KsQkucmk0nY7XYK9/z8PKrVKhV2rVZTclCSqJbw8t7e\nHgs7ZmZmUKvVWEqvh99qJWpra6NycrvdSitBOp0m/9ZqNdhsNl5Ai4uLNCQAEMlcft9sNuFwOBji\n0Y95sVgsKJVKOH36NADghRdeQCwWw/PPPw9AU5qpVErBQAyHw1TQ6XSaYbhsNouLFy8yVCTYchLG\nq1QqSKfTSmOvvmCl0Wjwbx8usHE4HBgaGqJyr9frWFtbo3ETCoUo/9IzJuEvKZAS49jlcuH69eu8\nKKvVKtdRqVTg9/v5PYVCAcFgkAaqhF310FALCwv87kqlQsNZQmP6KdmxWExB3Nc32Iv+kM/H43Gu\nIxAIIJPJ8B18Ph9WVlaoe3Z2dmC32ykPlUqF+mBoaAipVIr7sbu7i2QySVmSWWvyXYJgL6XlUjQh\nv5uamuIZinPyi6glLijgQbOWxC4FIl5fFJFKpbC7u6t0K1utViZUx8fH4fF4aKkNDg4iFotRMPSA\nrcePH8fi4iIthK997Wsol8tUolKlos9P5HI5Hp4kFAFt8ycnJymw9Xodo6Oj7BFwu90olUq0kO7e\nvcukv9VqRalU4rN6e3uV2TIbGxuKZSf/LsofgOJRTkxMKIPAHnnkETKAzIqRS0Y/C2drawvBYJDv\nsLGxoeAHdnZ2IpPJUPlJT5VccDJ7SD6rJ3muXJQ+nw9Wq5XGgtvtVvJbjUaDxQgyw0nOYXh4GDdv\n3qTCstvtyOVy3AO3283Ytgxo1M+H0uPLpdNp3L9/n82nDocDPp9P6f2SqrXe3l5cuXKFl6h+IFwr\nkVy6gGbVe71eKhm3282zF56UKEBHRwcKhQL3anBwEFtbW0ruIxQK8VxGR0dpnAwODmJ0dBRf+MIX\nAGgGyWc+8xkaBgLwK95Gs9mE2WymzO/u7lLRy6w0yaGUy2X4/X6ef6FQgMVioSc7NDTENdtsNhQK\nBWWonh53sVwuK8gqkUgEbrebBRgirwDotcizJyYmEI1GKX+CYyceQrVa5fs2Gg14vV4qYMmhSf7b\n5/PB4/EwglCr1ZBMJinTw8PDlIVischLGXgw7E/20uVyEX0HAKsphT/1nqoYK/rLThqQAc24GR8f\n5zrT6TT1QzqdhtlsVnrqhoaG+FlBA5F1lMtlOByOn2uKBjReS6VS1DW/rCLWyEEZZJBBBhnUktQS\nHpTekgc0S0gq0SqVCi0NyfuIteV2uzE7O6ugl4u1Bmh5pV/5lV9h6aMeGX1nZwejo6P4xCc+AUAL\nUzQaDXo29+7dY+c1oFk5EjuVNUoYLpVKYXR0lHmRTCaDJ598ktZVR0cHZmdn6X35fD7mZzKZDI4c\nOUIXfWBgAAsLC7SAxAIWS/bgwYNYWFjAf/7nfwIAfvu3f5vvb7Vacf/+fSWkI9aR/Oz3+7lur9dL\nRIq7d+/im9/8JnsSAoEAtra2uF8bGxtKd3yz2cTMzAx7rD71qU/h29/+NgDNcymXy7Tks9ks4vE4\nQwnVahXb29sM65jNZgUDsa+vD6+99hoA4Nlnn1Xyjm+//Tbcbjer6DweD27fvs33qFQqDH8MDg6i\nUqkwzOBwOBSkif7+fjzxxBMslXc6nbh79y6efPJJAFouQCzGy5cvY3x8nN6I/LfVSJ+vkFyfeO7h\ncJjeZT6fRzqdZmvAzMwM+vr66G23tbX9XJXmzs4O+8BisRg94kQigZ6eHly7dg0A8OMf/xiZTIah\n5MXFRQwODnLffT4fisUiw0Nnz55lRecXv/hFZDIZVuLeu3cPFouFfGy32+Hz+ZRJr+JtmEwmuN1u\nejmFQkFpNRFcRkF7j8ViOHToEGXxwIED1DuCIqH3wPRTb71eLwdcAlofkPAZoPUcPvLIIwC00LI+\nV57L5TAzM8OQXyQSwejoKMdivPzyy9yP7e1teL1eBc0/mUzyXHZ2dlCv15WKZH2VcF9fn1I9qx/S\nOjY2Rjg4WVc8HidPRKNRJYLkdrvpNQteqMhHf38/LBYLI1AOhwPlcpl6TI8ML1icsmZ5119ELXFB\nzc3N8aC7urqwvb3NRV+6dIn5mpGREWxubjLMIKEhgUwRDDtxHY8fP47Z2VkKgn5cfCaTQSKRYA+R\ny+VizwGgNbGFw2FeSPIcUUwXL14krp8oULncenp6lEslk8koIzMEEBPQhCidTvOCkj4eYYxbt25h\nfHycP5vNZnR1deEjH/kIny3hLkATYD1GoL6B8tlnn8Xc3BxLyX/913+de9fR0YFPfepTP4ePJQx4\n9uxZrKys0JBYXl5mXg7QGlnlsxaLBfl8nqGFUqkEj8ejNCrqwycC4iphm1wuh8cee4zf/3D57/Xr\n1/HMM8/w2c888wwFoVQq8f8TiQTOnDmjJHJXV1eVPjGZ1SV7J58BNIUk+yAj36XMXPJUrUY7Ozvk\nLWkg1491lzPJZrPo7u5WsAaXl5ep3KPRKLq7u6m8dnd34Xa7lRCgFFgcOnQITz/9NPOGv/d7v4dM\nJkPFL7Biss82mw0ej4cN1X19fZzy/Morr2B4eJg8HAwGkclklNHhNptNSeTrZ7fp2z/K5TKq1SoN\nJZkYrR/z4nK5lHyN7FVfXx+azSZ/19PTo4SxrVYrurq6qLecTifDcIODgzh27Bj3TkaGyN4LpJAe\nXFkfWu7s7FRyv+l0mt/j9/vh9/up42SCsv7ydzgcvKCazSZD2JLPlYtibW0NwWCQ+yMAv3rZl3UE\ng0Fks1mlSEwKIwBNp+3u7lKPZTIZ2Gw26qZEIqEALdfrde61/mJ/mFriglpaWiIjSCGEWNSPPfYY\nb/xYLIYzZ87wcKLRKK5cuUKL98aNGwqmld/vR7FYZIXQ4uIiN3R1dZVD9wBN4fb09NDqKxaLeOWV\nV9jUub29jb29PVa8nDt3jjHjZrNJDD1AKyAQAQM0paEXKrPZzDi45Lnk0pU4rijA3t5e9jYAIIMI\nE0UiEX722LFjWFpaosX89NNPK0njdDqNSqXC4o2rV6/SipMGSBGavr4+3L59m9bm4uIiAUTlXNbX\n15nTu3btGvdueXkZhw8f5kU4MjKCRqNBxREOh7G1tUVl6HA42OUua3nPe94DAHj99dfR1tZGpRsK\nhXDw4EEKbCKRUAofqtUqvYQzZ87g7t27/B6v14ulpSUFoVpQLwBNGZRKJX63ICAAmvAePnyYFqLe\nYm0lisfj7Eey2+1IJBK/ELEhEomgo6ODP29sbKBUKiloBnrlL8DBotzsdjuVTyaTwf/8z/9wH7u7\nu9HV1UWFnclkMDExgU9+8pMAtDN0Op00hl588UUaSiaTCW1tbfSQt7a2lAIaWYcot1KpxLM3m83M\nMwLgiHJZlwC0yjsmEglEo1G+k9Vq5SUbj8fR0dFBBa3XQYCmkPUYeVarld4XoCl/eW69Xkd3dzd5\nSfI1ehQGi8WCV199FcCDCkv5np2dHUZJisUivRlAuxi7u7upF6T4QtaVTCZZvLR//36k02nK/MjI\nCPb29ogWIecpuktfHVur1eD3+/kOgqwh5Pf7sbe3RxkXT1UPgCv8IPIn9Muq+IwclEEGGWSQQS1J\nLeFBHT9+nFZed3c3dnZ2aH1UKhVW2QCaFSihoHK5jLW1NXoQbW1tWFpaosck6AbirSSTSVpihw4d\nwpkzZ2gh+Xw+TExMMBx09uxZXL58Gf/1X/8FQAu93b9/H0899RQAzY0Xz2R3d1cJD9hsNtjtdr7T\ngQMHsLGxwe/Wj0q+e/cuvF4vQwfi5kt+RvI1+jHMAJTQo8Sjt7e3lREA2WwWfX19/Ozm5iYcDger\nlYaGhmgFeb1elMtlfs/s7Cze9a530brZ29vDW2+9xRyEyWQiUgWghSXEiuvs7MTGxgYrfGKxGNLp\nNEOg/f39WFhY4M8SB5f92tvbw+uvvw5As8w2NjYUz25ra0vB7Wtra6Nn29fXR2/87t27sNlstOou\nX76MqakpWsFLS0sYGhpSZlodPnyY+7exsaFUT5pMJq5Rys9bjU6ePMl9FxQROX+BtgG0fctms7SW\nOzo6cPHiRf5cLpeRSCToYdXrdSQSCVrfxWKRnuqxY8dgsVhYlbm2toZoNKq0M8zMzDAHGYvFcPv2\nbaJsFwoFeuLDw8NIJpOMEIjlLusIh8MKlmU2m6Unls1mYTKZGBWRSkH9PKhYLMZ31IerAE0u9emA\nlZUVfmZsbAyxWIy/j8fjWF1dVUrn5X0zmQzC4TA9tcHBQdhsNub3RkZG4Pf7GcaOx+OYm5vjurPZ\nLPnb4/HA7/fTg8tms8pUYGkH0Ecfcrkc+VZydkLDw8OUh7fffht9fX0KRFMikVBgxPTIGbFYjHpK\nPDV9eLzRaHAdPT09qNfrjKL09fUx8tXR0YGVlZX/T1EIU7MFOg6/9KUvkUmmp6cRCoXIGAJnBGju\nfHt7O3+WmKd8tlqtMlYMaBuxsLDAjSiVSvyshC+EaQYGBlCpVBTYev1F0t7ejmAwyDBdMplk7HR1\ndRWlUonKW+BWhJnD4bAyK0VCDvL/IpyAdjG9/fbbvHQEBkWELBwOY3V1lfHtSCTC30n+RYT3/v37\n2LdvH79XwF71QJQi3P39/SgWi9yPSCSCfD7Pd3S73bhw4QLzgbVaTYFGqlQqZF49RJCQfhxJe3u7\n8nOz2cTIyAiFcGhoiO8Ui8XYzyPPWVpaosKyWq2Yn5/nhb6+vq40W+rH2AcCASwuLiq5QKvVynxn\nPp9XcCDNZjOVbnt7Ox5//HGGWs6dO4cvf/nLaDX6+te/jj/90z8F8IDf5QJuNpvkXzGgxNizWCyI\nRqMM6fr9fmX0SKlUUkZGmM1m5hQES1LOz+v1KhiP5XIZPp+P+y6GmhQ/Wa1WrkMarUVGYrEY8vk8\nlfeNGzdw6NAh8qnH41HGzYTDYYVnq9UqQ28S8tS/c71ep1LNZDKUu7W1NdRqNb5HJBLhkE5Au+z0\nAw0tFovSED8+Ps78nhSRCDmdTqyvr3OdErLTl8eLXKbTabS1tZEnrVYrB4ICD9IH8iyTyQSn00nD\nUt/LZTablWGo0iysLx2v1+vKgFd5p3g8TnmSzwqEmeyHjA0RHhgZGaEBPDs7q+jSrq4uyvDf//3f\nM4XxMBkhPoMMMsggg1qSWiLEt7q6SisvFAohGAwq1XRiqdXrdTz66KMK4vJLL72ET3/60wAedJGL\nW7qzs8Mx8YBmMcho8Pn5eQVxuVwuw2az0Zre29tTUMcHBgYQjUZZ4TU5OcnCho6ODvT09NCz279/\nP65cuUIrqFgssikUAD784Q/jW9/6FgDN5RbPS97x1KlTtHKSySQsFgstlTt37uDkyZP0OFdWVphQ\n/cAHPoDXXnuNZaJdXV2YmZnBe9/7XgBaSMvtdnMc9NDQEBui6/U65ufnaUFKlY1YdX6/H11dXcpI\ncP3o6Y6ODsWC3NnZUZLo1WqVxRlLS0vY29uj1TQ7O8uKM0ALrYmV63a7sbW1xb30er0YHR1VUDKO\nHDlC69zpdLJR2GKxYHFxkVad1WpFf38/fz548CCmp6cZVnW5XHjjjTdYqXfw4EEW4CSTSXznO9+h\nl9iqjbo2m41Wa39/P61mQAsBiwU8NjZGVA5A45XFxUXK2ttvvw2r1cr3FO9L+E5foSVQR/rqUT3C\nvNVqRXt7O0Ng2WwWTqeTxRz1ep3fk8vlsLu7S1lyOBxK6Gjfvn3o6OhQUNn1SOf5fF6B59F7JsvL\nywgEAtQt4n2JR1er1ZSQbrFYpF5qb2+Hw+HgZ3t6evDOO++Q/0+cOMF9TqfTyGQy9OyOHTsGq9XK\ndckId4mw5PN5VCoV8qXei/H7/Wg2m+RvgTaT73K5XKhWq8q0XpPJxLNxOp08M2noFR5IJpOoVCp8\ndi6Xg8ViYcSlUqkwLCsRJlljOp1WPCqZkCs84PP5cP/+fUJFyYQH+Z3b7ebPS0tL/6sH1RIXVE9P\nD0Nvgr0mMf54PE54GXFHJUdw9OhRfO5znyPy8cGDB3Hv3j3GXEXA9JVYEhp7+umn8ZOf/IRVO+l0\nGjabjbmvYrEIq9VK5vd6vYhGo2SqcrlMBqzX68QqA7TQ4/79+6n4JKwm/TaVSoV4b+vr61hdXeVz\nL1++rOD8jY+Pw+/3E9Hgve99L7a3t3kZBINBuvMvvfQSRkZGOLair68P4+PjDI8cO3aMuF+ApsAk\nRFetVpFKpRgqOHDgAA4cOEDhnp+fh9frpeAcPnxYyWFcv36d+3779m2Ew2HuZTAYpCABmrKbmJhg\n3mhychKpVEpBPJC8YVtbG0qlEr9HwrCiZATZWgRhbW2NSmP//v3o7u6mkZFIJNDb28vPJhIJtLW1\n8YIvl8v4zd/8TbYe6NGcQ6EQcrkcKzdFMFuN9JAyKysrMJvNVFajo6N8n3A4jFu3bjEfWSwWlbzq\n1NQU8vk8jcFGo0FUbkC7OETxSWWZ/K2MKRfjL5fLIRaLUbkVi0UcOHCAF1ahUODF2NPTg9u3b9NQ\nlBE6+kq0WCym5Enkb1dWVhSIIUGOkAt7bGwMm5ubyngJmc0EaPk7MSLT6TScTiefnc/n4fF4qEt2\nd3dZnQpAyW0FAgEkk0nuz+XLlwkVBWjKXt8esri4qFwqmUyGF5Db7UY+n1eq+qSfEdCMgWq1qkAu\n6SuZs9ks17G3t4fl5WXyh8ViUd7fbDYTIUbORY9TGAgEyD8mk0mZlyXvLTJssVhw6tQpyqLkx4Uf\nlpeXaYT+MjTzlrigFhYWqHBrtRqi0SiV7MDAAG/x4eFhFAoFWjkXL15U4urT09PKrS5wGnrFJxbj\na6+9hkgkwg2XBKAIbCgUUoAnc7kcBgYGuOH1ep3fE41G8dRTTynjovUW1Pnz5/HHf/zHbNzL5XJ8\nzr59+wjvBGhKpFQqsQQ1Fosp+a6rV6/CbDYrlr2+gGJ7e5sNf0tLS0gmkzh37hwArQCl2WxSya6u\nrnIdAhYr9NZbb2FtbY0elgisrGN9fR1LS0tU9oFAgOWq0hCqxy3Tj/YYGRnB7du3eY7z8/MYHR3l\nBb+2tsbLrtFooL+/n3stI6rFSy6VSnjyySeVOLoo0WKxiFKpxP2ZmprC4uIiz/Sxxx5T5ieFQiE0\nm028+93v5t4KP4TDYZw5c4YCq4fFaSWq1WrcO2ltEMW3tramtAXo+08mJiYQDodpCN2+fRuVSkWB\nqpEeG0AzBuWz0g8nMux0OmGxWChbtVoNfX19lL1SqYTl5WUaR52dnczRyOgNvbK2Wq3kTRlvri98\nkJxzrVZDKpXiZ8vlMiqVCnlleXlZMSyazSYCgQANyXw+z/xsf38/CoUCz1mwNOVi2N7exvb2NmXc\n6/XyObu7u3C5XNzriYkJZcigNPzL/hw4cAB7e3s00iORCL83FotxeCSgyfTZs2fJh4VCAZlMRhlH\ns7e3R52Yy+V4WXV3d+PAgQPUU/l8HiaTSQGm1XvghUKBZypFVPpBiU6nk+eQy+UUoz2dTuOnP/0p\n//7QoUOMOIVCIXR2dioRqP+NjByUQQYZZJBBLUktUcX32c9+li59PB5Xyq71EzPL5TI2Nzc5BXdn\nZwcLCwu0GCcmJrC1taVMCdUjgz/11FO0WmTAmljekUgEqVRKAVmcmZmhdVGpVOB2uxXAQ7EOtre3\n4fP5aBFKQ6NU/Jw+fRrJZJLhxJ2dHbrG4vrq47P5fJ5QL9lsFhcuXGBYwmw2w+/309vw+/10lc1m\nM6xWq2LlJJNJZWBje3u7EtbTv1+z2VSANzOZDK3eVCoFm81GbyOVSuHw4cPc2+3tbWV0diKRYGhx\nfHwc3//+9xkucrvdSgd/KBRSUA4ajYZS/j44OKjE4PXjsqVCSo9wL6GETCaDSqVCj/Lq1avo6elh\nefva2hpyuRxzgGIVS+gpHA4ztOR2uzE0NEQv8OWXX8b3v/99tBr927/9GyvPpJpLrP5QKKRYxPoJ\n0dlsFul0WoGycTqd5DsJlYt8dHZ2sqKxUqmgXq8z7FYqlVCtVvksmQIrVr94RGLl63NONpsN/f39\n/NtEIoF6vc6QrgzrFE9Och+A5hGZTCb+TsKyEo2QXJgeZdzj8VAu9ZBKUqEneyeDAoVH9QC2gMY7\n+gpJPQ/v7u6iWq3Sk7NYLLBYLPRgi8WiUhqul8NarYZGo0F9aLPZYLPZ6DHZ7XYUCgV+lwC0Cg/o\nIz0+nw8+n4+emuSc9E2+Ho+H77Wzs8MzbWtrUwaDCkizeImSfpF1S3m8nEU8HucZivyLDFssFkYt\nHqaWuKAMMsgggwwy6GEyQnwGGWSQQQa1JBkXlEEGGWSQQS1JxgVlkEEGGWRQS5JxQRlkkEEGGdSS\nZFxQBhlkkEEGtSQZF5RBBhlkkEEtScYFZZBBBhlkUEuScUEZZJBBBhnUkmRcUAYZZJBBBrUkGReU\nQQYZZJBBLUnGBWWQQQYZZFBLknFBGWSQQQYZ1JJkXFAGGWSQQQa1JBkXlEEGGWSQQS1JxgVlkEEG\nGWRQS5JxQRlkkEEGGdSSZFxQBhlkkEEGtSQZF5RBBhlkkEEtScYFZZBBBhlkUEuScUEZZJBBBhnU\nkmRcUAYZZJBBBrUkGReUQQYZZJBBLUnGBWWQQQYZZFBL0v8DccRM6vpetr0AAAAASUVORK5CYII=\n",
             "text/plain": [
-              "<Figure size 432x288 with 1 Axes>"
-            ],
-            "image/png": 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\n"
+              "\u003cFigure size 600x400 with 2 Axes\u003e"
+            ]
           },
           "metadata": {
-            "tags": [],
-            "needs_background": "light"
-          }
-        },
-        {
-          "output_type": "stream",
-          "text": [
-            "Output:\n"
-          ],
-          "name": "stdout"
-        },
-        {
-          "output_type": "display_data",
-          "data": {
-            "text/plain": [
-              "<Figure size 432x288 with 1 Axes>"
-            ],
-            "image/png": 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\n"
+            "tags": []
           },
-          "metadata": {
-            "tags": [],
-            "needs_background": "light"
-          }
+          "output_type": "display_data"
         }
+      ],
+      "source": [
+        "#@title Compile and Run the EdgeDetectionModule with IREE.\n",
+        "module = backend.compile(EdgeDetectionModule)\n",
+        "\n",
+        "# Compute the edges using the compiled module and display the result.\n",
+        "iree_edges = module.edge_detect_sobel_operator(image)\n",
+        "show_images(image, iree_edges)"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "colab_type": "text",
+        "id": "0FSaDdlMrjzx"
+      },
+      "source": [
+        "## Low-Level Compilation\n",
+        "\n",
+        "Overview:\n",
+        "\n",
+        "1.  Save the `tf.Module` as a `SavedModel`\n",
+        "2.  Use IREE's python bindings to load the `SavedModel` into MLIR in the `mhlo` dialect\n",
+        "3.  Save the MLIR to a file (can stop here to use it from another application)\n",
+        "4.  Compile the `mhlo` MLIR into a VM module for IREE to execute\n",
+        "5.  Run the VM module through IREE's runtime to test the edge detection function"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 16,
+      "metadata": {
+        "colab": {
+          "height": 309
+        },
+        "colab_type": "code",
+        "executionInfo": {
+          "elapsed": 644,
+          "status": "ok",
+          "timestamp": 1598547122681,
+          "user": {
+            "displayName": "",
+            "photoUrl": "",
+            "userId": ""
+          },
+          "user_tz": 420
+        },
+        "id": "6YGqN2uqP_7P",
+        "outputId": "a9269281-ff28-4584-be67-588e96c4b9d6"
+      },
+      "outputs": [
+        {
+          "name": "stdout",
+          "output_type": "stream",
+          "text": [
+            "Edge Detection MLIR: \n",
+            "\n",
+            "module attributes {tf.versions = {bad_consumers = [], min_consumer = 12 : i32, producer = 506 : i32}} {\n",
+            "  func @edge_detect_sobel_operator(%arg0: tensor\u003c1x128x128x1xf32\u003e {tf._user_specified_name = \"image\"}) -\u003e tensor\u003c1x128x128x1xf32\u003e attributes {iree.module.export, iree.reflection = {abi = \"sip\", abiv = 1 : i32, sip = \"I8!S5!k0_0R3!_0\"}, tf._input_shapes = [#tf.shape\u003c1x128x128x1\u003e]} {\n",
+            "    %0 = mhlo.constant dense\u003c[[[[-1.000000e+00]], [[0.000000e+00]], [[1.000000e+00]]], [[[-2.000000e+00]], [[0.000000e+00]], [[2.000000e+00]]], [[[-1.000000e+00]], [[0.000000e+00]], [[1.000000e+00]]]]\u003e : tensor\u003c3x3x1x1xf32\u003e\n",
+            "    %1 = mhlo.constant dense\u003c[[[[1.000000e+00]], [[2.000000e+00]], [[1.000000e+00]]], [[[0.000000e+00]], [[0.000000e+00]], [[0.000000e+00]]], [[[-1.000000e+00]], [[-2.000000e+00]], [[-1.000000e+00]]]]\u003e : tensor\u003c3x3x1x1xf32\u003e\n",
+            "    %2 = \"mhlo.convolution\"(%arg0, %0) {batch_group_count = 1 : i64, dimension_numbers = {input_batch_dimension = 0 : i64, input_feature_dimension = 3 : i64, input_spatial_dimensions = dense\u003c[1, 2]\u003e : tensor\u003c2xi64\u003e, kernel_input_feature_dimension = 2 : i64, kernel_output_feature_dimension = 3 : i64, kernel_spatial_dimensions = dense\u003c[0, 1]\u003e : tensor\u003c2xi64\u003e, output_batch_dimension = 0 : i64, output_feature_dimension = 3 : i64, output_spatial_dimensions = dense\u003c[1, 2]\u003e : tensor\u003c2xi64\u003e}, feature_group_count = 1 : i64, padding = dense\u003c1\u003e : tensor\u003c2x2xi64\u003e, rhs_dilation = dense\u003c1\u003e : tensor\u003c2xi64\u003e, window_strides = dense\u003c1\u003e : tensor\u003c2xi64\u003e} : (tensor\u003c1x128x128x1xf32\u003e, tensor\u003c3x3x1x1xf32\u003e) -\u003e tensor\u003c1x128x128x1xf32\u003e\n",
+            "    %3 = mhlo.multiply %2, %2 : tensor\u003c1x128x128x1xf32\u003e\n",
+            "    %4 = \"mhlo.convolution\"(%arg0, %1) {batch_group_count = 1 : i64, dimension_numbers = {input_batch_dimension = 0 : i64, input_feature_dimension = 3 : i64, input_spatial_dimensions = dense\u003c[1, 2]\u003e : tensor\u003c2xi64\u003e, kernel_input_feature_dimension = 2 : i64, kernel_output_feature_dimension = 3 : i64, kernel_spatial_dimensions = dense\u003c[0, 1]\u003e : tensor\u003c2xi64\u003e, output_batch_dimension = 0 : i64, output_feature_dimension = 3 : i64, output_spatial_dimensions = dense\u003c[1, 2]\u003e : tensor\u003c2xi64\u003e}, feature_group_count = 1 : i64, padding = dense\u003c1\u003e : tensor\u003c2x2xi64\u003e, rhs_dilation = dense\u003c1\u003e : tensor\u003c2xi64\u003e, window_strides = dense\u003c1\u003e : tensor\u003c2xi64\u003e} : (tensor\u003c1x128x128x1xf32\u003e, tensor\u003c3x3x1x1xf32\u003e) -\u003e tensor\u003c1x128x128x1xf32\u003e\n",
+            "    %5 = mhlo.multiply %4, %4 : tensor\u003c1x128x128x1xf32\u003e\n",
+            "    %6 = mhlo.add %3, %5 : tensor\u003c1x128x128x1xf32\u003e\n",
+            "    %7 = \"mhlo.sqrt\"(%6) : (tensor\u003c1x128x128x1xf32\u003e) -\u003e tensor\u003c1x128x128x1xf32\u003e\n",
+            "    return %7 : tensor\u003c1x128x128x1xf32\u003e\n",
+            "  }\n",
+            "}\n",
+            "Wrote MLIR to path '/tmp/iree/colab_artifacts/edge_detection.mlir'\n"
+          ]
+        }
+      ],
+      "source": [
+        "#@title Construct a module containing the edge detection function\n",
+        "\n",
+        "# Do *not* further compile to a bytecode module for a particular backend.\n",
+        "# \n",
+        "# By stopping at mhlo in text format, we can more easily take advantage of\n",
+        "# future compiler improvements within IREE and can use iree_bytecode_module to\n",
+        "# compile and bundle the module into a sample application. For a production\n",
+        "# application, we would probably want to freeze the version of IREE used and\n",
+        "# compile as completely as possible ahead of time, then use some other scheme\n",
+        "# to load the module into the application at runtime.\n",
+        "compiler_module = ireec.tf_module_to_compiler_module(EdgeDetectionModule())\n",
+        "print(\"Edge Detection MLIR:\", compiler_module.to_asm())\n",
+        "\n",
+        "edge_detection_mlir_path = os.path.join(ARITFACTS_DIR, \"edge_detection.mlir\")\n",
+        "with open(edge_detection_mlir_path, \"wt\") as output_file:\n",
+        "  output_file.write(compiler_module.to_asm())\n",
+        "print(f\"Wrote MLIR to path '{edge_detection_mlir_path}'\")"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 17,
+      "metadata": {
+        "colab": {},
+        "colab_type": "code",
+        "executionInfo": {
+          "elapsed": 51,
+          "status": "ok",
+          "timestamp": 1598547136735,
+          "user": {
+            "displayName": "",
+            "photoUrl": "",
+            "userId": ""
+          },
+          "user_tz": 420
+        },
+        "id": "3SIl8c_Q4adz"
+      },
+      "outputs": [],
+      "source": [
+        "#@markdown ### Backend Configuration\n",
+        "\n",
+        "backend_choice = \"iree_vmla (CPU)\" #@param [ \"iree_vmla (CPU)\", \"iree_llvmjit (CPU)\", \"iree_vulkan (GPU/SwiftShader)\" ]\n",
+        "backend_choice = backend_choice.split(\" \")[0]\n",
+        "backend = tf_utils.BackendInfo(backend_choice)"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 18,
+      "metadata": {
+        "colab": {
+          "height": 51
+        },
+        "colab_type": "code",
+        "executionInfo": {
+          "elapsed": 503,
+          "status": "ok",
+          "timestamp": 1598547140813,
+          "user": {
+            "displayName": "",
+            "photoUrl": "",
+            "userId": ""
+          },
+          "user_tz": 420
+        },
+        "id": "Ytvb5Gx_EFJl",
+        "outputId": "5903c96c-fc24-43c8-f460-a02660116f3c"
+      },
+      "outputs": [
+        {
+          "name": "stderr",
+          "output_type": "stream",
+          "text": [
+            "Created IREE driver vmla: \u003ciree.bindings.python.pyiree.rt.binding.HalDriver object at 0x7f1bf0c69ae8\u003e\n",
+            "SystemContext driver=\u003ciree.bindings.python.pyiree.rt.binding.HalDriver object at 0x7f1bf0c69ae8\u003e\n"
+          ]
+        }
+      ],
+      "source": [
+        "#@title Prepare to test the edge detection module\n",
+        "\n",
+        "flatbuffer_blob = compiler_module.compile(\n",
+        "    target_backends=backend.compiler_targets)\n",
+        "vm_module = ireert.VmModule.from_flatbuffer(flatbuffer_blob)\n",
+        "\n",
+        "# Register the module with a runtime context.\n",
+        "config = ireert.Config(backend.driver)\n",
+        "ctx = ireert.SystemContext(config=config)\n",
+        "ctx.add_module(vm_module)"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 19,
+      "metadata": {
+        "colab": {
+          "height": 238
+        },
+        "colab_type": "code",
+        "executionInfo": {
+          "elapsed": 1107,
+          "status": "ok",
+          "timestamp": 1598547143487,
+          "user": {
+            "displayName": "",
+            "photoUrl": "",
+            "userId": ""
+          },
+          "user_tz": 420
+        },
+        "id": "OUUXxol7wl-f",
+        "outputId": "758ee44d-6c7d-45ec-e5f0-94e98ef161bb"
+      },
+      "outputs": [
+        {
+          "data": {
+            "image/png": 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birJQVCoVxzfihyXUcc3ruul2o/hpbBRaIW8hsPaO3IAZaKI5c6lUygnRnpyc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\n",
+            "text/plain": [
+              "\u003cFigure size 600x400 with 2 Axes\u003e"
+            ]
+          },
+          "metadata": {
+            "tags": []
+          },
+          "output_type": "display_data"
+        }
+      ],
+      "source": [
+        "edge_detect_sobel_operator_f = ctx.modules.module[\"edge_detect_sobel_operator\"]\n",
+        "\n",
+        "low_level_iree_edges = edge_detect_sobel_operator_f(image)\n",
+        "\n",
+        "show_images(image, low_level_iree_edges)"
       ]
     }
-  ]
-}
\ No newline at end of file
+  ],
+  "metadata": {
+    "colab": {
+      "collapsed_sections": [],
+      "last_runtime": {
+        "build_target": "",
+        "kind": "local"
+      },
+      "name": "edge_detection.ipynb",
+      "provenance": []
+    },
+    "kernelspec": {
+      "display_name": "Python 3",
+      "name": "python3"
+    }
+  },
+  "nbformat": 4,
+  "nbformat_minor": 0
+}
diff --git a/colab/mnist_tensorflow.ipynb b/colab/mnist_tensorflow.ipynb
index 12c8a4e..2c7eb6e 100644
--- a/colab/mnist_tensorflow.ipynb
+++ b/colab/mnist_tensorflow.ipynb
@@ -1,25 +1,10 @@
 {
-  "nbformat": 4,
-  "nbformat_minor": 0,
-  "metadata": {
-    "colab": {
-      "name": "mnist_tensorflow.ipynb",
-      "provenance": [],
-      "collapsed_sections": [
-        "PZtRtMMUZHJS"
-      ]
-    },
-    "kernelspec": {
-      "name": "python3",
-      "display_name": "Python 3"
-    }
-  },
   "cells": [
     {
       "cell_type": "markdown",
       "metadata": {
-        "id": "PZtRtMMUZHJS",
-        "colab_type": "text"
+        "colab_type": "text",
+        "id": "PZtRtMMUZHJS"
       },
       "source": [
         "##### Copyright 2020 Google LLC.\n",
@@ -29,12 +14,14 @@
     },
     {
       "cell_type": "code",
+      "execution_count": null,
       "metadata": {
-        "id": "TouZL3JZZSQe",
+        "cellView": "form",
+        "colab": {},
         "colab_type": "code",
-        "cellView": "both",
-        "colab": {}
+        "id": "TouZL3JZZSQe"
       },
+      "outputs": [],
       "source": [
         "#@title License header\n",
         "# Copyright 2020 Google LLC\n",
@@ -50,15 +37,13 @@
         "# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n",
         "# See the License for the specific language governing permissions and\n",
         "# limitations under the License."
-      ],
-      "execution_count": 1,
-      "outputs": []
+      ]
     },
     {
       "cell_type": "markdown",
       "metadata": {
-        "id": "O6c3qfq5Zv57",
-        "colab_type": "text"
+        "colab_type": "text",
+        "id": "O6c3qfq5Zv57"
       },
       "source": [
         "# MNIST Model TensorFlow Training, IREE Execution\n",
@@ -69,7 +54,7 @@
         "\n",
         "## Running Locally\n",
         "\n",
-        "*  Refer to [iree/docs/using_colab.md](https://github.com/google/iree/blob/main/docs/using_colab.md) for general information\n",
+        "*  Refer to [using_colab.md](https://google.github.io/iree/using-iree/using-colab) for general information\n",
         "*  Ensure that you have a recent version of TensorFlow 2.0 [installed on your system](https://www.tensorflow.org/install)\n",
         "*  Enable IREE/TF integration by adding to your user.bazelrc: `build --define=iree_tensorflow=true`\n",
         "*  Start colab by running `python colab/start_colab_kernel.py` (see that file for additional instructions)\n",
@@ -77,110 +62,62 @@
       ]
     },
     {
-      "cell_type": "markdown",
-      "metadata": {
-        "id": "wBXlE69Ia2QU",
-        "colab_type": "text"
-      },
-      "source": [
-        "# Setup Steps"
-      ]
-    },
-    {
       "cell_type": "code",
+      "execution_count": 1,
       "metadata": {
-        "id": "EPF7RGQDYK-M",
-        "colab_type": "code",
+        "cellView": "both",
         "colab": {
-          "base_uri": "https://localhost:8080/",
           "height": 51
         },
-        "outputId": "fe0d703a-2ad7-4d14-9aef-c69b4c342a16"
+        "colab_type": "code",
+        "executionInfo": {
+          "elapsed": 6750,
+          "status": "ok",
+          "timestamp": 1598547312105,
+          "user": {
+            "displayName": "",
+            "photoUrl": "",
+            "userId": ""
+          },
+          "user_tz": 420
+        },
+        "id": "EPF7RGQDYK-M",
+        "outputId": "4aa093c1-5147-4928-9eb3-ba571c3c5326"
       },
+      "outputs": [
+        {
+          "name": "stdout",
+          "output_type": "stream",
+          "text": [
+            "TensorFlow version:  2.4.0\n",
+            "Numpy version:  1.16.4\n"
+          ]
+        }
+      ],
       "source": [
-        "import os\n",
+        "#@title Imports\n",
+        "\n",
+        "from pyiree import rt as ireert\n",
+        "from pyiree.tf import compiler as ireec\n",
+        "from pyiree.tf.support import tf_utils\n",
+        "\n",
+        "from matplotlib import pyplot as plt\n",
         "import numpy as np\n",
         "import tensorflow as tf\n",
-        "from matplotlib import pyplot as plt\n",
-        "from pyiree.tf import compiler as ireec\n",
-        "from pyiree import rt as ireert\n",
         "\n",
-        "tf.compat.v1.enable_eager_execution()\n",
-        "\n",
-        "SAVE_PATH = os.path.join(os.environ[\"HOME\"], \"saved_models\")\n",
-        "os.makedirs(SAVE_PATH, exist_ok=True)\n",
+        "plt.style.use(\"seaborn-whitegrid\")\n",
+        "plt.rcParams[\"font.family\"] = \"monospace\"\n",
         "\n",
         "# Print version information for future notebook users to reference.\n",
         "print(\"TensorFlow version: \", tf.__version__)\n",
         "print(\"Numpy version: \", np.__version__)"
-      ],
-      "execution_count": 2,
-      "outputs": [
-        {
-          "output_type": "stream",
-          "text": [
-            "TensorFlow version:  2.5.0-dev20200626\n",
-            "Numpy version:  1.18.4\n"
-          ],
-          "name": "stdout"
-        }
-      ]
-    },
-    {
-      "cell_type": "code",
-      "metadata": {
-        "id": "43BH_9YcsGs8",
-        "colab_type": "code",
-        "cellView": "form",
-        "colab": {
-          "base_uri": "https://localhost:8080/",
-          "height": 34
-        },
-        "outputId": "46a560cb-5947-4cfa-f71e-9065bf2c07ab"
-      },
-      "source": [
-        "#@title Notebook settings { run: \"auto\" }\n",
-        "\n",
-        "#@markdown -----\n",
-        "#@markdown ### Configuration\n",
-        "\n",
-        "backend_choice = \"GPU (vulkan-spirv)\" #@param [ \"GPU (vulkan-spirv)\", \"CPU (VMLA)\" ]\n",
-        "\n",
-        "if backend_choice == \"GPU (vulkan-spirv)\":\n",
-        "  backend_name = \"vulkan-spirv\"\n",
-        "  driver_name = \"vulkan\"\n",
-        "else:\n",
-        "  backend_name = \"vmla\"\n",
-        "  driver_name = \"vmla\"\n",
-        "tf.print(\"Using IREE compiler backend '%s' and runtime driver '%s'\" % (backend_name, driver_name))\n",
-        "\n",
-        "#@markdown -----\n",
-        "#@markdown ### Training Parameters\n",
-        "\n",
-        "#@markdown <sup>Batch size used to subdivide the training and evaluation samples</sup>\n",
-        "batch_size = 200  #@param { type: \"slider\", min: 10, max: 400 }\n",
-        "\n",
-        "#@markdown <sup>Epochs for training/eval. Higher values take longer to run but generally produce more accurate models</sup>\n",
-        "num_epochs = 5    #@param { type: \"slider\", min:  1, max:  20 }\n",
-        "\n",
-        "#@markdown -----"
-      ],
-      "execution_count": 3,
-      "outputs": [
-        {
-          "output_type": "stream",
-          "text": [
-            "Using IREE compiler backend 'vulkan-spirv' and runtime driver 'vulkan'\r\n"
-          ],
-          "name": "stdout"
-        }
       ]
     },
     {
       "cell_type": "markdown",
       "metadata": {
-        "id": "5vkQOMOMbXdy",
-        "colab_type": "text"
+        "colab_type": "text",
+        "id": "5vkQOMOMbXdy"
       },
       "source": [
         "# Create and Train MNIST Model in TensorFlow\n",
@@ -190,306 +127,217 @@
     },
     {
       "cell_type": "code",
+      "execution_count": 2,
       "metadata": {
-        "id": "GXZIrReTbTHN",
-        "colab_type": "code",
-        "cellView": "form",
+        "cellView": "both",
         "colab": {
-          "base_uri": "https://localhost:8080/",
-          "height": 486
+          "height": 316
         },
-        "outputId": "9c01fab1-f8cb-4a63-fff1-6b82a9b6b49d"
+        "colab_type": "code",
+        "executionInfo": {
+          "elapsed": 2696,
+          "status": "ok",
+          "timestamp": 1598547323963,
+          "user": {
+            "displayName": "",
+            "photoUrl": "",
+            "userId": ""
+          },
+          "user_tz": 420
+        },
+        "id": "XPo8ATGqqZbW",
+        "outputId": "8a98c0eb-5ba9-41d1-a702-8c15568bd946"
       },
+      "outputs": [
+        {
+          "name": "stdout",
+          "output_type": "stream",
+          "text": [
+            "Sample image from the dataset:\n"
+          ]
+        },
+        {
+          "data": {
+            "image/png": "iVBORw0KGgoAAAANSUhEUgAAAQwAAAEaCAYAAADkA2kVAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\n",
+            "text/plain": [
+              "\u003cFigure size 600x400 with 1 Axes\u003e"
+            ]
+          },
+          "metadata": {
+            "tags": []
+          },
+          "output_type": "display_data"
+        }
+      ],
       "source": [
         "#@title Load MNIST dataset, setup training and evaluation\n",
         "\n",
-        "NUM_CLASSES = 10  # One per digit [0, 1, 2, ..., 9]\n",
-        "IMG_ROWS, IMG_COLS = 28, 28\n",
+        "# Keras datasets don't provide metadata.\n",
+        "NUM_CLASSES = 10\n",
+        "NUM_ROWS, NUM_COLS = 28, 28\n",
         "\n",
         "(x_train, y_train), (x_test, y_test) = tf.keras.datasets.mnist.load_data()\n",
-        "tf.print(\"Loaded MNIST dataset!\")\n",
         "\n",
-        "x_train = x_train.reshape(x_train.shape[0], IMG_ROWS, IMG_COLS, 1)\n",
-        "x_test = x_test.reshape(x_test.shape[0], IMG_ROWS, IMG_COLS, 1)\n",
-        "input_shape = (IMG_ROWS, IMG_COLS, 1)\n",
+        "# Reshape into grayscale images:\n",
+        "x_train = np.reshape(x_train, (-1, NUM_ROWS, NUM_COLS, 1))\n",
+        "x_test = np.reshape(x_test, (-1, NUM_ROWS, NUM_COLS, 1))\n",
         "\n",
-        "# Scale pixel values from [0, 255] integers to [0.0, 1.0] floats.\n",
-        "x_train = x_train.astype(\"float32\") / 255\n",
-        "x_test = x_test.astype(\"float32\") / 255\n",
+        "# Rescale uint8 pixel values into floats:\n",
+        "x_train = x_train / 255\n",
+        "x_test = x_test / 255\n",
         "\n",
-        "steps_per_epoch = int(x_train.shape[0] / batch_size)\n",
-        "steps_per_eval = int(x_test.shape[0] / batch_size)\n",
+        "# Explicitly cast to float32 because numpy defaults to double precision and\n",
+        "# IREE uses single precision:\n",
+        "x_train = x_train.astype(np.float32)\n",
+        "x_test = x_test.astype(np.float32)\n",
         "\n",
-        "# Convert class vectors to binary class matrices.\n",
-        "y_train = tf.keras.utils.to_categorical(y_train, NUM_CLASSES)\n",
-        "y_test = tf.keras.utils.to_categorical(y_test, NUM_CLASSES)\n",
-        "\n",
-        "# Construct batched datasets for training/evaluation.\n",
-        "train_dataset = tf.data.Dataset.from_tensor_slices((x_train, y_train))\n",
-        "train_dataset = train_dataset.batch(batch_size, drop_remainder=True)\n",
-        "test_dataset = tf.data.Dataset.from_tensor_slices((x_test, y_test))\n",
-        "test_dataset = test_dataset.batch(batch_size, drop_remainder=True)\n",
-        "\n",
-        "# Create a distribution strategy for the dataset (single machine).\n",
-        "strategy = tf.distribute.experimental.CentralStorageStrategy()\n",
-        "train_dist_ds = strategy.experimental_distribute_dataset(train_dataset)\n",
-        "test_dist_ds = strategy.experimental_distribute_dataset(test_dataset)\n",
-        "\n",
-        "tf.print(\"Configured data for training and evaluation!\")\n",
-        "tf.print(\"  sample shape: %s\" % str(x_train[0].shape))\n",
-        "tf.print(\"  training samples: %s\" % x_train.shape[0])\n",
-        "tf.print(\"  test     samples: %s\" % x_test.shape[0])\n",
-        "tf.print(\"  epochs: %s\" % num_epochs)\n",
-        "tf.print(\"  steps/epoch: %s\" % steps_per_epoch)\n",
-        "tf.print(\"  steps/eval : %s\" % steps_per_eval)\n",
-        "\n",
-        "tf.print(\"\")\n",
-        "tf.print(\"Sample image from the dataset:\")\n",
-        "SAMPLE_EXAMPLE_INDEX = 1\n",
-        "sample_image = x_test[SAMPLE_EXAMPLE_INDEX]\n",
-        "sample_image_batch = np.expand_dims(sample_image, axis=0)\n",
-        "sample_label = y_test[SAMPLE_EXAMPLE_INDEX]\n",
-        "plt.imshow(sample_image.reshape(IMG_ROWS, IMG_COLS))\n",
-        "plt.show()\n",
-        "tf.print(\"\\nGround truth labels: %s\" % str(sample_label))"
-      ],
-      "execution_count": 4,
-      "outputs": [
-        {
-          "output_type": "stream",
-          "text": [
-            "Loaded MNIST dataset!\r\n",
-            "INFO:tensorflow:ParameterServerStrategy (CentralStorageStrategy if you are using a single machine) with compute_devices = ['/job:localhost/replica:0/task:0/device:CPU:0'], variable_device = '/job:localhost/replica:0/task:0/device:CPU:0'\n",
-            "Configured data for training and evaluation!\n",
-            "  sample shape: (28, 28, 1)\n",
-            "  training samples: 60000\n",
-            "  test     samples: 10000\n",
-            "  epochs: 5\n",
-            "  steps/epoch: 300\n",
-            "  steps/eval : 50\n",
-            "\n",
-            "Sample image from the dataset:\n"
-          ],
-          "name": "stdout"
-        },
-        {
-          "output_type": "display_data",
-          "data": {
-            "text/plain": [
-              "<Figure size 432x288 with 1 Axes>"
-            ],
-            "image/png": "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\n"
-          },
-          "metadata": {
-            "tags": [],
-            "needs_background": "light"
-          }
-        },
-        {
-          "output_type": "stream",
-          "text": [
-            "\n",
-            "Ground truth labels: [0. 0. 1. 0. 0. 0. 0. 0. 0. 0.]\r\n"
-          ],
-          "name": "stdout"
-        }
+        "print(\"Sample image from the dataset:\")\n",
+        "sample_index = np.random.randint(x_train.shape[0])\n",
+        "plt.imshow(x_train[sample_index].reshape(NUM_ROWS, NUM_COLS), cmap=\"gray\")\n",
+        "plt.title(f\"Sample #{sample_index}, label: {y_train[sample_index]}\")\n",
+        "plt.axis(\"off\")\n",
+        "plt.tight_layout()"
       ]
     },
     {
       "cell_type": "code",
+      "execution_count": 3,
       "metadata": {
-        "id": "tHq96SIJcNfx",
-        "colab_type": "code",
         "cellView": "both",
-        "colab": {}
+        "colab": {},
+        "colab_type": "code",
+        "executionInfo": {
+          "elapsed": 47,
+          "status": "ok",
+          "timestamp": 1598547329812,
+          "user": {
+            "displayName": "",
+            "photoUrl": "",
+            "userId": ""
+          },
+          "user_tz": 420
+        },
+        "id": "tHq96SIJcNfx"
       },
+      "outputs": [],
       "source": [
-        "#@title Define MNIST model architecture using tf.keras API\n",
+        "#@title Define a DNN model using tf.keras API\n",
         "\n",
-        "def simple_mnist_model(input_shape):\n",
-        "  \"\"\"Creates a simple (multi-layer perceptron) MNIST model.\"\"\"\n",
+        "def simple_dnn(num_classes):\n",
+        "  \"\"\"Creates a simple multi-layer perceptron model.\"\"\"\n",
         "\n",
         "  model = tf.keras.models.Sequential()\n",
-        "  # Flatten to a 1d array (e.g. 28x28 -> 784)\n",
-        "  model.add(tf.keras.layers.Flatten(input_shape=input_shape))\n",
-        "  # Fully-connected neural layer with 128 neurons, RELU activation\n",
-        "  model.add(tf.keras.layers.Dense(128, activation='relu'))\n",
-        "  # Fully-connected neural layer returning probability scores for each class\n",
-        "  model.add(tf.keras.layers.Dense(10, activation='softmax'))\n",
+        "  # Flatten to a 1d array (e.g. 28x28x1 -\u003e 784).\n",
+        "  model.add(tf.keras.layers.Flatten())\n",
+        "  # Fully-connected neural layer with 128 neurons, RELU activation.\n",
+        "  model.add(tf.keras.layers.Dense(128, activation=\"relu\"))\n",
+        "  # Fully-connected neural layer returning probability scores for each class.\n",
+        "  model.add(tf.keras.layers.Dense(num_classes, activation=\"softmax\"))\n",
         "  return model"
-      ],
-      "execution_count": 5,
-      "outputs": []
+      ]
     },
     {
       "cell_type": "code",
+      "execution_count": 4,
       "metadata": {
-        "id": "7Gdxh7qWcPSO",
+        "cellView": "both",
+        "colab": {},
         "colab_type": "code",
-        "cellView": "form",
-        "colab": {
-          "base_uri": "https://localhost:8080/",
-          "height": 374
+        "executionInfo": {
+          "elapsed": 232,
+          "status": "ok",
+          "timestamp": 1598547335213,
+          "user": {
+            "displayName": "",
+            "photoUrl": "",
+            "userId": ""
+          },
+          "user_tz": 420
         },
-        "outputId": "50b0aede-9a8f-4ce5-b340-783be5fbfc06"
+        "id": "43BH_9YcsGs8"
       },
+      "outputs": [],
+      "source": [
+        "#@markdown ### Training Parameters\n",
+        "\n",
+        "batch_size = 32  #@param { type: \"slider\", min: 10, max: 400 }\n",
+        "num_epochs = 8    #@param { type: \"slider\", min:  1, max:  20 }"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 5,
+      "metadata": {
+        "cellView": "both",
+        "colab": {
+          "height": 306
+        },
+        "colab_type": "code",
+        "executionInfo": {
+          "elapsed": 75641,
+          "status": "ok",
+          "timestamp": 1598547494584,
+          "user": {
+            "displayName": "",
+            "photoUrl": "",
+            "userId": ""
+          },
+          "user_tz": 420
+        },
+        "id": "7Gdxh7qWcPSO",
+        "outputId": "9dd7a26a-1fad-41a8-e03e-0c775be0ee25"
+      },
+      "outputs": [
+        {
+          "name": "stdout",
+          "output_type": "stream",
+          "text": [
+            "Epoch 1/8\n",
+            "1688/1688 [==============================] - 11s 6ms/step - loss: 1.0551 - accuracy: 0.7235 - val_loss: 0.3218 - val_accuracy: 0.9170\n",
+            "Epoch 2/8\n",
+            "1688/1688 [==============================] - 9s 5ms/step - loss: 0.3732 - accuracy: 0.8962 - val_loss: 0.2602 - val_accuracy: 0.9283\n",
+            "Epoch 3/8\n",
+            "1688/1688 [==============================] - 9s 5ms/step - loss: 0.3121 - accuracy: 0.9130 - val_loss: 0.2342 - val_accuracy: 0.9352\n",
+            "Epoch 4/8\n",
+            "1688/1688 [==============================] - 9s 5ms/step - loss: 0.2750 - accuracy: 0.9227 - val_loss: 0.2102 - val_accuracy: 0.9427\n",
+            "Epoch 5/8\n",
+            "1688/1688 [==============================] - 9s 5ms/step - loss: 0.2492 - accuracy: 0.9292 - val_loss: 0.1960 - val_accuracy: 0.9453\n",
+            "Epoch 6/8\n",
+            "1688/1688 [==============================] - 9s 5ms/step - loss: 0.2303 - accuracy: 0.9359 - val_loss: 0.1819 - val_accuracy: 0.9510\n",
+            "Epoch 7/8\n",
+            "1688/1688 [==============================] - 9s 5ms/step - loss: 0.2173 - accuracy: 0.9392 - val_loss: 0.1719 - val_accuracy: 0.9527\n",
+            "Epoch 8/8\n",
+            "1688/1688 [==============================] - 9s 5ms/step - loss: 0.2002 - accuracy: 0.9432 - val_loss: 0.1620 - val_accuracy: 0.9568\n"
+          ]
+        },
+        {
+          "data": {
+            "text/plain": [
+              "\u003ctensorflow.python.keras.callbacks.History at 0x7fb369d3b208\u003e"
+            ]
+          },
+          "execution_count": 5,
+          "metadata": {
+            "tags": []
+          },
+          "output_type": "execute_result"
+        }
+      ],
       "source": [
         "#@title Train the Keras model\n",
         "\n",
-        "with strategy.scope():\n",
-        "  model = simple_mnist_model(input_shape)\n",
-        "  tf.print(\"Constructed Keras MNIST model, training...\")\n",
-        "\n",
-        "  optimizer = tf.keras.optimizers.SGD(learning_rate=0.05)\n",
-        "  training_loss = tf.keras.metrics.Mean(\"training_loss\", dtype=tf.float32)\n",
-        "  training_accuracy = tf.keras.metrics.CategoricalAccuracy(\n",
-        "      \"training_accuracy\", dtype=tf.float32)\n",
-        "  test_loss = tf.keras.metrics.Mean(\"test_loss\", dtype=tf.float32)\n",
-        "  test_accuracy = tf.keras.metrics.CategoricalAccuracy(\n",
-        "      \"test_accuracy\", dtype=tf.float32)\n",
-        "\n",
-        "  @tf.function\n",
-        "  def train_step(iterator):\n",
-        "    \"\"\"Training StepFn.\"\"\"\n",
-        "\n",
-        "    def step_fn(inputs):\n",
-        "      \"\"\"Per-Replica StepFn.\"\"\"\n",
-        "      images, labels = inputs\n",
-        "      with tf.GradientTape() as tape:\n",
-        "        logits = model(images, training=True)\n",
-        "        loss = tf.keras.losses.categorical_crossentropy(labels, logits)\n",
-        "        loss = tf.reduce_mean(loss) / strategy.num_replicas_in_sync\n",
-        "      grads = tape.gradient(loss, model.trainable_variables)\n",
-        "      optimizer.apply_gradients(zip(grads, model.trainable_variables))\n",
-        "      training_loss.update_state(loss)\n",
-        "      training_accuracy.update_state(labels, logits)\n",
-        "\n",
-        "    strategy.run(step_fn, args=(next(iterator),))\n",
-        "\n",
-        "  @tf.function\n",
-        "  def test_step(iterator):\n",
-        "    \"\"\"Evaluation StepFn.\"\"\"\n",
-        "\n",
-        "    def step_fn(inputs):\n",
-        "      images, labels = inputs\n",
-        "      logits = model(images, training=False)\n",
-        "      loss = tf.keras.losses.categorical_crossentropy(labels, logits)\n",
-        "      loss = tf.reduce_mean(loss) / strategy.num_replicas_in_sync\n",
-        "      test_loss.update_state(loss)\n",
-        "      test_accuracy.update_state(labels, logits)\n",
-        "\n",
-        "    strategy.run(step_fn, args=(next(iterator),))\n",
-        "\n",
-        "  for epoch in range(0, num_epochs):\n",
-        "    tf.print(\"Running epoch #%s\" % (epoch + 1))\n",
-        "\n",
-        "    train_iterator = iter(train_dist_ds)\n",
-        "    for step in range(steps_per_epoch):\n",
-        "      train_step(train_iterator)\n",
-        "    tf.print(\"  Training loss: %f, accuracy: %f\" % (training_loss.result(), training_accuracy.result() * 100))\n",
-        "    training_loss.reset_states()\n",
-        "    training_accuracy.reset_states()\n",
-        "\n",
-        "    test_iterator = iter(test_dist_ds)\n",
-        "    for step in range(steps_per_eval):\n",
-        "      test_step(test_iterator)\n",
-        "    tf.print(\"  Test loss    : %f, accuracy: %f\" % (test_loss.result(), test_accuracy.result() * 100))\n",
-        "    test_loss.reset_states()\n",
-        "    test_accuracy.reset_states()\n",
-        "\n",
-        "  tf.print(\"Completed training!\")\n",
-        "  tf.print(\"\")\n",
-        "\n",
-        "  # Run a single prediction on the trained model\n",
-        "  tf_prediction = model(sample_image_batch, training=False)\n",
-        "  tf.print(\"Sample prediction:\")\n",
-        "  tf.print(tf_prediction[0] * 100.0, summarize=100)\n",
-        "  tf.print(\"\")"
-      ],
-      "execution_count": 6,
-      "outputs": [
-        {
-          "output_type": "stream",
-          "text": [
-            "Constructed Keras MNIST model, training...\n",
-            "Running epoch #1\n",
-            "  Training loss: 0.732439, accuracy: 81.403336\n",
-            "  Test loss    : 0.390855, accuracy: 89.490005\n",
-            "Running epoch #2\n",
-            "  Training loss: 0.365308, accuracy: 89.811668\n",
-            "  Test loss    : 0.315630, accuracy: 91.119995\n",
-            "Running epoch #3\n",
-            "  Training loss: 0.312111, accuracy: 91.129997\n",
-            "  Test loss    : 0.281829, accuracy: 92.040001\n",
-            "Running epoch #4\n",
-            "  Training loss: 0.281028, accuracy: 92.038330\n",
-            "  Test loss    : 0.258432, accuracy: 92.629997\n",
-            "Running epoch #5\n",
-            "  Training loss: 0.257909, accuracy: 92.753334\n",
-            "  Test loss    : 0.240058, accuracy: 93.229996\n",
-            "Completed training!\n",
-            "\n",
-            "Sample prediction:\n",
-            "[0.243134052 0.00337268948 95.5214081 0.925373673 2.25061958e-05 0.992091119 2.20864391 3.87712953e-06 0.105901182 4.44369543e-05]\n",
-            "\n"
-          ],
-          "name": "stdout"
-        }
-      ]
-    },
-    {
-      "cell_type": "code",
-      "metadata": {
-        "id": "DmespEaFcSEL",
-        "colab_type": "code",
-        "colab": {
-          "base_uri": "https://localhost:8080/",
-          "height": 153
-        },
-        "outputId": "3c8579db-7b3c-4164-ff91-394346595107"
-      },
-      "source": [
-        "#@title Export the trained model as a SavedModel, with IREE-compatible settings\n",
-        "\n",
-        "# Since the model was written in sequential style, explicitly wrap in a module.\n",
-        "saved_model_dir = \"/tmp/mnist.sm\"\n",
-        "inference_module = tf.Module()\n",
-        "inference_module.model = model\n",
-        "# Hack: Convert to static shape. Won't be necessary once dynamic shapes are in.\n",
-        "dynamic_input_shape = list(model.inputs[0].shape)\n",
-        "dynamic_input_shape[0] = 1  # Make fixed (batch=1)\n",
-        "# Produce a concrete function.\n",
-        "inference_module.predict = tf.function(\n",
-        "    input_signature=[\n",
-        "        tf.TensorSpec(dynamic_input_shape, model.inputs[0].dtype)])(\n",
-        "            lambda x: model.call(x, training=False))\n",
-        "save_options = tf.saved_model.SaveOptions(save_debug_info=True)\n",
-        "tf.print(\"Exporting SavedModel to %s\" % saved_model_dir)\n",
-        "tf.saved_model.save(inference_module, saved_model_dir, options=save_options)"
-      ],
-      "execution_count": 7,
-      "outputs": [
-        {
-          "output_type": "stream",
-          "text": [
-            "Exporting SavedModel to /tmp/mnist.sm\r\n",
-            "WARNING:tensorflow:From c:\\users\\scott\\scoop\\apps\\python\\current\\lib\\site-packages\\tensorflow\\python\\training\\tracking\\tracking.py:111: Model.state_updates (from tensorflow.python.keras.engine.training) is deprecated and will be removed in a future version.\n",
-            "Instructions for updating:\n",
-            "This property should not be used in TensorFlow 2.0, as updates are applied automatically.\n",
-            "WARNING:tensorflow:From c:\\users\\scott\\scoop\\apps\\python\\current\\lib\\site-packages\\tensorflow\\python\\training\\tracking\\tracking.py:111: Layer.updates (from tensorflow.python.keras.engine.base_layer) is deprecated and will be removed in a future version.\n",
-            "Instructions for updating:\n",
-            "This property should not be used in TensorFlow 2.0, as updates are applied automatically.\n",
-            "INFO:tensorflow:Assets written to: /tmp/mnist.sm\\assets\n"
-          ],
-          "name": "stdout"
-        }
+        "tf_model = simple_dnn(NUM_CLASSES)\n",
+        "# Stateful optimizers like Adam create variable incompatible with compilation as\n",
+        "# currently implemented.\n",
+        "tf_model.compile(optimizer=\"sgd\", loss=\"sparse_categorical_crossentropy\", metrics=\"accuracy\")\n",
+        "tf_model.fit(x_train, y_train, batch_size, num_epochs, validation_split=0.1)"
       ]
     },
     {
       "cell_type": "markdown",
       "metadata": {
-        "id": "nZdVUd_dgTtc",
-        "colab_type": "text"
+        "colab_type": "text",
+        "id": "nZdVUd_dgTtc"
       },
       "source": [
         "# Compile and Execute MNIST Model using IREE"
@@ -497,157 +345,265 @@
     },
     {
       "cell_type": "code",
+      "execution_count": 7,
       "metadata": {
-        "id": "rqwIx4j4gS1a",
-        "colab_type": "code",
         "colab": {
-          "base_uri": "https://localhost:8080/",
-          "height": 836
+          "height": 819
         },
-        "outputId": "24cded90-c436-47ce-b4f4-7a5da46ea38a"
+        "colab_type": "code",
+        "executionInfo": {
+          "elapsed": 2462,
+          "status": "ok",
+          "timestamp": 1598547512056,
+          "user": {
+            "displayName": "",
+            "photoUrl": "",
+            "userId": ""
+          },
+          "user_tz": 420
+        },
+        "id": "DmespEaFcSEL",
+        "outputId": "72c57513-92d3-4989-bb3b-4cac42968038"
       },
-      "source": [
-        "#@title Load the SavedModel into IREE's compiler as MLIR mhlo\n",
-        "\n",
-        "compiler_module = ireec.tf_load_saved_model(\n",
-        "    saved_model_dir, exported_names=[\"predict\"])\n",
-        "tf.print(\"Imported MLIR:\\n\", compiler_module.to_asm(large_element_limit=100))\n",
-        "\n",
-        "# Write to a file for use outside of this notebook.\n",
-        "mnist_mlir_path = os.path.join(SAVE_PATH, \"mnist.mlir\")\n",
-        "with open(mnist_mlir_path, \"wt\") as output_file:\n",
-        "  output_file.write(compiler_module.to_asm())\n",
-        "print(\"Wrote MLIR to path '%s'\" % mnist_mlir_path)"
-      ],
-      "execution_count": 8,
       "outputs": [
         {
+          "name": "stdout",
           "output_type": "stream",
           "text": [
             "Imported MLIR:\n",
             " \n",
             "\n",
-            "module attributes {tf.versions = {bad_consumers = [], min_consumer = 12 : i32, producer = 443 : i32}} {\n",
-            "  flow.variable @\"__iree_flow___sm_node14__model.layer-1.kernel\" opaque<\"\", \"0xDEADBEEF\"> : tensor<784x128xf32> attributes {sym_visibility = \"private\"}\n",
-            "  flow.variable @\"__iree_flow___sm_node15__model.layer-1.bias\" opaque<\"\", \"0xDEADBEEF\"> : tensor<128xf32> attributes {sym_visibility = \"private\"}\n",
-            "  flow.variable @\"__iree_flow___sm_node20__model.layer-2.kernel\" opaque<\"\", \"0xDEADBEEF\"> : tensor<128x10xf32> attributes {sym_visibility = \"private\"}\n",
-            "  flow.variable @\"__iree_flow___sm_node21__model.layer-2.bias\" dense<[-0.114143081, 0.0953421518, 4.84912744E-5, -0.0384164825, 0.0063888072, 0.218958765, 0.0256200824, 0.0551806651, -0.22108613, -0.0278935507]> : tensor<10xf32> attributes {sym_visibility = \"private\"}\n",
-            "  func @predict(%arg0: tensor<1x28x28x1xf32> {tf._user_specified_name = \"x\"}) -> tensor<1x10xf32> attributes {iree.module.export, iree.reflection = {abi = \"sip\", abiv = 1 : i32, sip = \"I8!S5!k0_0R3!_0\"}, tf._input_shapes = [#tf.shape<1x28x28x1>, #tf.shape<*>, #tf.shape<*>, #tf.shape<*>, #tf.shape<*>], tf.signature.is_stateful} {\n",
-            "    %0 = flow.variable.address @\"__iree_flow___sm_node14__model.layer-1.kernel\" : !iree.ptr<tensor<784x128xf32>>\n",
-            "    %1 = flow.variable.address @\"__iree_flow___sm_node15__model.layer-1.bias\" : !iree.ptr<tensor<128xf32>>\n",
-            "    %2 = flow.variable.address @\"__iree_flow___sm_node20__model.layer-2.kernel\" : !iree.ptr<tensor<128x10xf32>>\n",
-            "    %3 = flow.variable.address @\"__iree_flow___sm_node21__model.layer-2.bias\" : !iree.ptr<tensor<10xf32>>\n",
-            "    %4 = mhlo.constant dense<0xFF800000> : tensor<f32>\n",
-            "    %5 = mhlo.constant dense<0.000000e+00> : tensor<f32>\n",
-            "    %6 = flow.variable.load.indirect %3 : !iree.ptr<tensor<10xf32>> -> tensor<10xf32>\n",
-            "    %7 = flow.variable.load.indirect %2 : !iree.ptr<tensor<128x10xf32>> -> tensor<128x10xf32>\n",
-            "    %8 = flow.variable.load.indirect %1 : !iree.ptr<tensor<128xf32>> -> tensor<128xf32>\n",
-            "    %9 = flow.variable.load.indirect %0 : !iree.ptr<tensor<784x128xf32>> -> tensor<784x128xf32>\n",
-            "    %10 = \"mhlo.reshape\"(%arg0) : (tensor<1x28x28x1xf32>) -> tensor<1x784xf32>\n",
-            "    %11 = \"mhlo.dot\"(%10, %9) : (tensor<1x784xf32>, tensor<784x128xf32>) -> tensor<1x128xf32>\n",
-            "    %12 = \"mhlo.broadcast_in_dim\"(%8) {broadcast_dimensions = dense<1> : tensor<1xi64>} : (tensor<128xf32>) -> tensor<1x128xf32>\n",
-            "    %13 = mhlo.add %11, %12 : tensor<1x128xf32>\n",
-            "    %14 = \"mhlo.broadcast_in_dim\"(%5) {broadcast_dimensions = dense<[]> : tensor<0xi64>} : (tensor<f32>) -> tensor<1x128xf32>\n",
-            "    %15 = mhlo.maximum %14, %13 : tensor<1x128xf32>\n",
-            "    %16 = \"mhlo.dot\"(%15, %7) : (tensor<1x128xf32>, tensor<128x10xf32>) -> tensor<1x10xf32>\n",
-            "    %17 = \"mhlo.broadcast_in_dim\"(%6) {broadcast_dimensions = dense<1> : tensor<1xi64>} : (tensor<10xf32>) -> tensor<1x10xf32>\n",
-            "    %18 = mhlo.add %16, %17 : tensor<1x10xf32>\n",
-            "    %19 = \"mhlo.reduce\"(%18, %4) ( {\n",
-            "    ^bb0(%arg1: tensor<f32>, %arg2: tensor<f32>):  // no predecessors\n",
-            "      %26 = mhlo.maximum %arg1, %arg2 : tensor<f32>\n",
-            "      \"mhlo.return\"(%26) : (tensor<f32>) -> ()\n",
-            "    }) {dimensions = dense<1> : tensor<1xi64>} : (tensor<1x10xf32>, tensor<f32>) -> tensor<1xf32>\n",
-            "    %20 = \"mhlo.broadcast_in_dim\"(%19) {broadcast_dimensions = dense<0> : tensor<1xi64>} : (tensor<1xf32>) -> tensor<1x10xf32>\n",
-            "    %21 = mhlo.subtract %18, %20 : tensor<1x10xf32>\n",
-            "    %22 = \"mhlo.exponential\"(%21) : (tensor<1x10xf32>) -> tensor<1x10xf32>\n",
-            "    %23 = \"mhlo.reduce\"(%22, %5) ( {\n",
-            "    ^bb0(%arg1: tensor<f32>, %arg2: tensor<f32>):  // no predecessors\n",
-            "      %26 = mhlo.add %arg1, %arg2 : tensor<f32>\n",
-            "      \"mhlo.return\"(%26) : (tensor<f32>) -> ()\n",
-            "    }) {dimensions = dense<1> : tensor<1xi64>} : (tensor<1x10xf32>, tensor<f32>) -> tensor<1xf32>\n",
-            "    %24 = \"mhlo.broadcast_in_dim\"(%23) {broadcast_dimensions = dense<0> : tensor<1xi64>} : (tensor<1xf32>) -> tensor<1x10xf32>\n",
-            "    %25 = mhlo.divide %22, %24 : tensor<1x10xf32>\n",
-            "    return %25 : tensor<1x10xf32>\n",
+            "module attributes {tf.versions = {bad_consumers = [], min_consumer = 12 : i32, producer = 506 : i32}} {\n",
+            "  flow.variable @\"__iree_flow___sm_node15__model.layer-1.kernel\" opaque\u003c\"\", \"0xDEADBEEF\"\u003e : tensor\u003c784x128xf32\u003e attributes {sym_visibility = \"private\"}\n",
+            "  flow.variable @\"__iree_flow___sm_node16__model.layer-1.bias\" opaque\u003c\"\", \"0xDEADBEEF\"\u003e : tensor\u003c128xf32\u003e attributes {sym_visibility = \"private\"}\n",
+            "  flow.variable @\"__iree_flow___sm_node21__model.layer-2.kernel\" opaque\u003c\"\", \"0xDEADBEEF\"\u003e : tensor\u003c128x10xf32\u003e attributes {sym_visibility = \"private\"}\n",
+            "  flow.variable @\"__iree_flow___sm_node22__model.layer-2.bias\" dense\u003c[-0.0863539576, 0.0952052548, 0.0697797537, -0.078638956, -0.0109204706, 0.178583801, -0.0201483201, 0.145516276, -0.258134842, -0.0348887108]\u003e : tensor\u003c10xf32\u003e attributes {sym_visibility = \"private\"}\n",
+            "  func @predict(%arg0: tensor\u003c1x28x28x1xf32\u003e {tf._user_specified_name = \"x\"}) -\u003e tensor\u003c1x10xf32\u003e attributes {iree.module.export, iree.reflection = {abi = \"sip\", abiv = 1 : i32, sip = \"I8!S5!k0_0R3!_0\"}, tf._input_shapes = [#tf.shape\u003c1x28x28x1\u003e, #tf.shape\u003c*\u003e, #tf.shape\u003c*\u003e, #tf.shape\u003c*\u003e, #tf.shape\u003c*\u003e], tf.signature.is_stateful} {\n",
+            "    %0 = flow.variable.address @\"__iree_flow___sm_node15__model.layer-1.kernel\" : !iree.ptr\u003ctensor\u003c784x128xf32\u003e\u003e\n",
+            "    %1 = flow.variable.address @\"__iree_flow___sm_node16__model.layer-1.bias\" : !iree.ptr\u003ctensor\u003c128xf32\u003e\u003e\n",
+            "    %2 = flow.variable.address @\"__iree_flow___sm_node21__model.layer-2.kernel\" : !iree.ptr\u003ctensor\u003c128x10xf32\u003e\u003e\n",
+            "    %3 = flow.variable.address @\"__iree_flow___sm_node22__model.layer-2.bias\" : !iree.ptr\u003ctensor\u003c10xf32\u003e\u003e\n",
+            "    %4 = mhlo.constant opaque\u003c\"\", \"0xDEADBEEF\"\u003e : tensor\u003c1x128xf32\u003e\n",
+            "    %5 = mhlo.constant dense\u003c0xFF800000\u003e : tensor\u003cf32\u003e\n",
+            "    %6 = mhlo.constant dense\u003c0.000000e+00\u003e : tensor\u003cf32\u003e\n",
+            "    %7 = flow.variable.load.indirect %3 : !iree.ptr\u003ctensor\u003c10xf32\u003e\u003e -\u003e tensor\u003c10xf32\u003e\n",
+            "    %8 = flow.variable.load.indirect %2 : !iree.ptr\u003ctensor\u003c128x10xf32\u003e\u003e -\u003e tensor\u003c128x10xf32\u003e\n",
+            "    %9 = flow.variable.load.indirect %1 : !iree.ptr\u003ctensor\u003c128xf32\u003e\u003e -\u003e tensor\u003c128xf32\u003e\n",
+            "    %10 = flow.variable.load.indirect %0 : !iree.ptr\u003ctensor\u003c784x128xf32\u003e\u003e -\u003e tensor\u003c784x128xf32\u003e\n",
+            "    %11 = \"mhlo.reshape\"(%arg0) : (tensor\u003c1x28x28x1xf32\u003e) -\u003e tensor\u003c1x784xf32\u003e\n",
+            "    %12 = \"mhlo.dot\"(%11, %10) : (tensor\u003c1x784xf32\u003e, tensor\u003c784x128xf32\u003e) -\u003e tensor\u003c1x128xf32\u003e\n",
+            "    %13 = \"mhlo.broadcast_in_dim\"(%9) {broadcast_dimensions = dense\u003c1\u003e : tensor\u003c1xi64\u003e} : (tensor\u003c128xf32\u003e) -\u003e tensor\u003c1x128xf32\u003e\n",
+            "    %14 = mhlo.add %12, %13 : tensor\u003c1x128xf32\u003e\n",
+            "    %15 = mhlo.maximum %14, %4 : tensor\u003c1x128xf32\u003e\n",
+            "    %16 = \"mhlo.dot\"(%15, %8) : (tensor\u003c1x128xf32\u003e, tensor\u003c128x10xf32\u003e) -\u003e tensor\u003c1x10xf32\u003e\n",
+            "    %17 = \"mhlo.broadcast_in_dim\"(%7) {broadcast_dimensions = dense\u003c1\u003e : tensor\u003c1xi64\u003e} : (tensor\u003c10xf32\u003e) -\u003e tensor\u003c1x10xf32\u003e\n",
+            "    %18 = mhlo.add %16, %17 : tensor\u003c1x10xf32\u003e\n",
+            "    %19 = \"mhlo.reduce\"(%18, %5) ( {\n",
+            "    ^bb0(%arg1: tensor\u003cf32\u003e, %arg2: tensor\u003cf32\u003e):  // no predecessors\n",
+            "      %26 = mhlo.maximum %arg1, %arg2 : tensor\u003cf32\u003e\n",
+            "      \"mhlo.return\"(%26) : (tensor\u003cf32\u003e) -\u003e ()\n",
+            "    }) {dimensions = dense\u003c1\u003e : tensor\u003c1xi64\u003e} : (tensor\u003c1x10xf32\u003e, tensor\u003cf32\u003e) -\u003e tensor\u003c1xf32\u003e\n",
+            "    %20 = \"mhlo.broadcast_in_dim\"(%19) {broadcast_dimensions = dense\u003c0\u003e : tensor\u003c1xi64\u003e} : (tensor\u003c1xf32\u003e) -\u003e tensor\u003c1x10xf32\u003e\n",
+            "    %21 = mhlo.subtract %18, %20 : tensor\u003c1x10xf32\u003e\n",
+            "    %22 = \"mhlo.exponential\"(%21) : (tensor\u003c1x10xf32\u003e) -\u003e tensor\u003c1x10xf32\u003e\n",
+            "    %23 = \"mhlo.reduce\"(%22, %6) ( {\n",
+            "    ^bb0(%arg1: tensor\u003cf32\u003e, %arg2: tensor\u003cf32\u003e):  // no predecessors\n",
+            "      %26 = mhlo.add %arg1, %arg2 : tensor\u003cf32\u003e\n",
+            "      \"mhlo.return\"(%26) : (tensor\u003cf32\u003e) -\u003e ()\n",
+            "    }) {dimensions = dense\u003c1\u003e : tensor\u003c1xi64\u003e} : (tensor\u003c1x10xf32\u003e, tensor\u003cf32\u003e) -\u003e tensor\u003c1xf32\u003e\n",
+            "    %24 = \"mhlo.broadcast_in_dim\"(%23) {broadcast_dimensions = dense\u003c0\u003e : tensor\u003c1xi64\u003e} : (tensor\u003c1xf32\u003e) -\u003e tensor\u003c1x10xf32\u003e\n",
+            "    %25 = mhlo.divide %22, %24 : tensor\u003c1x10xf32\u003e\n",
+            "    return %25 : tensor\u003c1x10xf32\u003e\n",
             "  }\n",
-            "}\r\n",
-            "Wrote MLIR to path 'C:\\Users\\Scott\\saved_models\\mnist.mlir'\n"
-          ],
-          "name": "stdout"
+            "}\n"
+          ]
         }
+      ],
+      "source": [
+        "#@title Wrap the model in a tf.Module with IREE-compatible settings and convert to MLIR.\n",
+        "\n",
+        "# Since the model was written in sequential style, explicitly wrap in a module.\n",
+        "inference_module = tf.Module()\n",
+        "inference_module.model = tf_model\n",
+        "\n",
+        "# Hack: Convert to static shape. Won't be necessary once dynamic shapes are in.\n",
+        "dynamic_input_shape = list(tf_model.inputs[0].shape)\n",
+        "dynamic_input_shape[0] = 1  # Make fixed (batch=1)\n",
+        "\n",
+        "# Produce a concrete function to compile.\n",
+        "inference_module.predict = tf.function(input_signature=[\n",
+        "    tf.TensorSpec(dynamic_input_shape, tf_model.inputs[0].dtype)\n",
+        "])(lambda x: tf_model.call(x, training=False))\n",
+        "\n",
+        "# Include the function to compile in the list of exported_names.\n",
+        "compiler_module = ireec.tf_module_to_compiler_module(\n",
+        "    inference_module, exported_names=[\"predict\"])\n",
+        "\n",
+        "print(\"Imported MLIR:\\n\", compiler_module.to_asm(large_element_limit=100))"
       ]
     },
     {
       "cell_type": "code",
+      "execution_count": 8,
       "metadata": {
-        "id": "IDHI7h3khJr9",
+        "colab": {},
         "colab_type": "code",
+        "executionInfo": {
+          "elapsed": 48,
+          "status": "ok",
+          "timestamp": 1598547515930,
+          "user": {
+            "displayName": "",
+            "photoUrl": "",
+            "userId": ""
+          },
+          "user_tz": 420
+        },
+        "id": "G7v-2EbjyggO"
+      },
+      "outputs": [],
+      "source": [
+        "#@markdown ### Backend Configuration\n",
+        "\n",
+        "backend_choice = \"iree_vmla (CPU)\" #@param [ \"iree_vmla (CPU)\", \"iree_llvmjit (CPU)\", \"iree_vulkan (GPU/SwiftShader)\" ]\n",
+        "backend_choice = backend_choice.split(\" \")[0]\n",
+        "backend = tf_utils.BackendInfo(backend_choice)"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 9,
+      "metadata": {
         "colab": {
-          "base_uri": "https://localhost:8080/",
           "height": 51
         },
-        "outputId": "b8958b7f-c7bb-4fbd-b800-e58c46134086"
+        "colab_type": "code",
+        "executionInfo": {
+          "elapsed": 727,
+          "status": "ok",
+          "timestamp": 1598547519894,
+          "user": {
+            "displayName": "",
+            "photoUrl": "",
+            "userId": ""
+          },
+          "user_tz": 420
+        },
+        "id": "IDHI7h3khJr9",
+        "outputId": "91b29a57-8584-4980-b4b0-24a1d7568805"
       },
-      "source": [
-        "#@title Compile the mhlo MLIR and prepare a context to execute it\n",
-        "\n",
-        "# Compile the MLIR module into a VM module for execution\n",
-        "flatbuffer_blob = compiler_module.compile(target_backends=[backend_name])\n",
-        "vm_module = ireert.VmModule.from_flatbuffer(flatbuffer_blob)\n",
-        "\n",
-        "# Register the module with a runtime context\n",
-        "config = ireert.Config(driver_name)\n",
-        "ctx = ireert.SystemContext(config=config)\n",
-        "ctx.add_module(vm_module)"
-      ],
-      "execution_count": 9,
       "outputs": [
         {
+          "name": "stderr",
           "output_type": "stream",
           "text": [
-            "Created IREE driver vulkan: <pyiree.rt.binding.HalDriver object at 0x000001DC44C47370>\n",
-            "SystemContext driver=<pyiree.rt.binding.HalDriver object at 0x000001DC44C47370>\n"
-          ],
-          "name": "stderr"
+            "Created IREE driver vmla: \u003ciree.bindings.python.pyiree.rt.binding.HalDriver object at 0x7fb362fde928\u003e\n",
+            "SystemContext driver=\u003ciree.bindings.python.pyiree.rt.binding.HalDriver object at 0x7fb362fde928\u003e\n"
+          ]
         }
+      ],
+      "source": [
+        "#@title Compile the mhlo MLIR to an IREE backend and prepare a context to execute it\n",
+        "\n",
+        "# Compile the MLIR module into a VM module for execution.\n",
+        "flatbuffer_blob = compiler_module.compile(\n",
+        "    target_backends=backend.compiler_targets)\n",
+        "vm_module = ireert.VmModule.from_flatbuffer(flatbuffer_blob)\n",
+        "\n",
+        "# Register the module with a runtime context.\n",
+        "config = ireert.Config(backend.driver)\n",
+        "ctx = ireert.SystemContext(config=config)\n",
+        "ctx.add_module(vm_module)"
       ]
     },
     {
       "cell_type": "code",
+      "execution_count": 10,
       "metadata": {
-        "id": "SKflpnLtkLYE",
-        "colab_type": "code",
         "colab": {
-          "base_uri": "https://localhost:8080/",
-          "height": 102
+          "height": 299
         },
-        "outputId": "337ddf79-6746-4685-89f5-65787280c0af"
+        "colab_type": "code",
+        "executionInfo": {
+          "elapsed": 1212,
+          "status": "ok",
+          "timestamp": 1598547527476,
+          "user": {
+            "displayName": "",
+            "photoUrl": "",
+            "userId": ""
+          },
+          "user_tz": 420
+        },
+        "id": "S2FYao92Xd6r",
+        "outputId": "61315fd3-f3ec-4991-c10f-a719039d2d06"
       },
+      "outputs": [
+        {
+          "data": {
+            "image/png": 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eYwGoOr3HqpmEEEL0TV+fLOF0STUrrhve7rJH3ZUQNYi6BjO7smQWVktpt8Aa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\nEELYiRRYQgghhBBCCGEn/x8sF59rHCh5bwAAAABJRU5ErkJggg==\n",
+            "text/plain": [
+              "\u003cFigure size 1200x400 with 2 Axes\u003e"
+            ]
+          },
+          "metadata": {
+            "tags": []
+          },
+          "output_type": "display_data"
+        }
+      ],
       "source": [
         "#@title Execute the compiled module and compare the results with TensorFlow\n",
         "\n",
         "# Invoke the 'predict' function with a single image as an argument\n",
-        "iree_prediction = ctx.modules.module.predict(sample_image_batch)\n",
+        "iree_prediction = ctx.modules.module.predict(x_train[sample_index][None, :])[0]\n",
+        "tf_prediction = tf_model.predict(x_train[sample_index][None, :])[0]\n",
+        "error = tf_prediction - iree_prediction\n",
         "\n",
-        "tf.print(\"IREE prediction ('%s' backend, '%s' driver):\" % (backend_name, driver_name))\n",
-        "tf.print(tf.convert_to_tensor(iree_prediction[0]) * 100.0, summarize=100)\n",
-        "tf.print(\"\")\n",
-        "tf.print(\"TensorFlow prediction:\")\n",
-        "tf.print(tf_prediction[0] * 100.0, summarize=100)"
-      ],
-      "execution_count": 10,
-      "outputs": [
-        {
-          "output_type": "stream",
-          "text": [
-            "IREE prediction ('vulkan-spirv' backend, 'vulkan' driver):\n",
-            "[0.243133873 0.00337268622 95.5214233 0.92537272 2.25061631e-05 0.992090821 2.20864058 3.87712225e-06 0.105901062 4.44369434e-05]\n",
-            "\n",
-            "TensorFlow prediction:\n",
-            "[0.243134052 0.00337268948 95.5214081 0.925373673 2.25061958e-05 0.992091119 2.20864391 3.87712953e-06 0.105901182 4.44369543e-05]\n"
-          ],
-          "name": "stdout"
-        }
+        "fig, axs = plt.subplots(1, 2)\n",
+        "fig.set_figwidth(12)\n",
+        "\n",
+        "ax = axs[0]\n",
+        "ax.plot(iree_prediction, linewidth=2, label=backend.name)\n",
+        "ax.plot(tf_prediction, linewidth=2, label=\"tf\")\n",
+        "\n",
+        "ax.set_title(\"Predictions\")\n",
+        "ax.set_ylabel(\"Softmax 'Probability'\")\n",
+        "ax.set_xlabel(\"Digit\")\n",
+        "ax.set_ylim(0, 1)\n",
+        "ax.set_xlim(0, 9)\n",
+        "ax.legend(frameon=True)\n",
+        "\n",
+        "\n",
+        "ax = axs[1]\n",
+        "ax.plot(error)\n",
+        "\n",
+        "ax.set_title(\"Error\")\n",
+        "ax.set_ylabel(\"Numerical between TF and IREE\")\n",
+        "ax.set_xlabel(\"Digit\")\n",
+        "ylim = 1.25 * np.max(np.abs(error))\n",
+        "ax.set_ylim(-ylim, ylim)\n",
+        "ax.set_xlim(0, 9)\n",
+        "\n",
+        "fig.tight_layout()"
       ]
     }
-  ]
-}
\ No newline at end of file
+  ],
+  "metadata": {
+    "colab": {
+      "collapsed_sections": [],
+      "last_runtime": {
+        "build_target": "",
+        "kind": "local"
+      },
+      "name": "mnist_tensorflow.ipynb",
+      "provenance": []
+    },
+    "kernelspec": {
+      "display_name": "Python 3",
+      "name": "python3"
+    }
+  },
+  "nbformat": 4,
+  "nbformat_minor": 0
+}
diff --git a/colab/resnet.ipynb b/colab/resnet.ipynb
index efc8322..d060b12 100644
--- a/colab/resnet.ipynb
+++ b/colab/resnet.ipynb
@@ -1,25 +1,10 @@
 {
-  "nbformat": 4,
-  "nbformat_minor": 0,
-  "metadata": {
-    "colab": {
-      "name": "resnet.ipynb",
-      "provenance": [],
-      "collapsed_sections": [
-        "RJdPhTjpZwYn"
-      ]
-    },
-    "kernelspec": {
-      "name": "python3",
-      "display_name": "Python 3"
-    }
-  },
   "cells": [
     {
       "cell_type": "markdown",
       "metadata": {
-        "id": "RJdPhTjpZwYn",
-        "colab_type": "text"
+        "colab_type": "text",
+        "id": "RJdPhTjpZwYn"
       },
       "source": [
         "##### Copyright 2020 Google LLC.\n",
@@ -29,12 +14,14 @@
     },
     {
       "cell_type": "code",
+      "execution_count": null,
       "metadata": {
-        "id": "kn6yY883Z1Oq",
-        "colab_type": "code",
         "cellView": "form",
-        "colab": {}
+        "colab": {},
+        "colab_type": "code",
+        "id": "kn6yY883Z1Oq"
       },
+      "outputs": [],
       "source": [
         "#@title License header\n",
         "# Copyright 2020 Google LLC\n",
@@ -50,15 +37,13 @@
         "# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n",
         "# See the License for the specific language governing permissions and\n",
         "# limitations under the License."
-      ],
-      "execution_count": 0,
-      "outputs": []
+      ]
     },
     {
       "cell_type": "markdown",
       "metadata": {
-        "id": "h5s6ncerSpc5",
-        "colab_type": "text"
+        "colab_type": "text",
+        "id": "h5s6ncerSpc5"
       },
       "source": [
         "# ResNet\n",
@@ -67,45 +52,63 @@
         "\n",
         "This notebook\n",
         "\n",
-        "* Constructs a [ResNet50](https://www.tensorflow.org/api_docs/python/tf/keras/applications/ResNet50) model using `tf.keras`, with weights pretrained using the[ImageNet](http://www.image-net.org/) database\n",
-        "* Saves that model using `tf.saved_model` then compiles it with IREE\n",
+        "* Constructs a [ResNet50](https://www.tensorflow.org/api_docs/python/tf/keras/applications/ResNet50) model using `tf.keras`, with weights pretrained using the[ImageNet](http://www.image-net.org/) dataset\n",
+        "* Compiles that model with IREE\n",
         "* Tests TensorFlow and IREE execution of the model on a sample image"
       ]
     },
     {
       "cell_type": "code",
+      "execution_count": 1,
       "metadata": {
-        "id": "s2bScbYkP6VZ",
-        "colab_type": "code",
         "cellView": "both",
-        "colab": {}
+        "colab": {},
+        "colab_type": "code",
+        "executionInfo": {
+          "elapsed": 6576,
+          "status": "ok",
+          "timestamp": 1598547654354,
+          "user": {
+            "displayName": "",
+            "photoUrl": "",
+            "userId": ""
+          },
+          "user_tz": 420
+        },
+        "id": "s2bScbYkP6VZ"
       },
+      "outputs": [],
       "source": [
         "#@title Imports and common setup\n",
         "\n",
-        "import os\n",
-        "import tensorflow as tf\n",
-        "from matplotlib import pyplot as plt\n",
-        "from pyiree.tf import compiler as ireec\n",
         "from pyiree import rt as ireert\n",
+        "from pyiree.tf import compiler as ireec\n",
+        "from pyiree.tf.support import tf_utils\n",
         "\n",
-        "SAVE_PATH = os.path.join(os.environ[\"HOME\"], \"saved_models\")\n",
-        "os.makedirs(SAVE_PATH, exist_ok=True)"
-      ],
-      "execution_count": 0,
-      "outputs": []
+        "import tensorflow as tf\n",
+        "from matplotlib import pyplot as plt"
+      ]
     },
     {
       "cell_type": "code",
+      "execution_count": 2,
       "metadata": {
-        "id": "9foDFPfEieIO",
+        "colab": {},
         "colab_type": "code",
-        "outputId": "506eaa47-f582-4cbc-b8f2-5d17909eb95d",
-        "colab": {
-          "base_uri": "https://localhost:8080/",
-          "height": 68
-        }
+        "executionInfo": {
+          "elapsed": 3603,
+          "status": "ok",
+          "timestamp": 1598547657962,
+          "user": {
+            "displayName": "",
+            "photoUrl": "",
+            "userId": ""
+          },
+          "user_tz": 420
+        },
+        "id": "9foDFPfEieIO"
       },
+      "outputs": [],
       "source": [
         "#@title Construct a pretrained ResNet model with ImageNet weights\n",
         "\n",
@@ -118,119 +121,126 @@
         "tf_model = tf.keras.applications.resnet50.ResNet50(\n",
         "    weights=\"imagenet\", include_top=True, input_shape=tuple(INPUT_SHAPE[1:]))\n",
         "\n",
-        "tf_module = tf.Module()\n",
-        "tf_module.m = tf_model\n",
-        "tf_module.predict = tf.function(\n",
-        "    input_signature=[tf.TensorSpec(INPUT_SHAPE, tf.float32)])(tf_model.call)"
-      ],
+        "# Wrap the model in a tf.Module to compile it with IREE.\n",
+        "class ResNetModule(tf.Module):\n",
+        "\n",
+        "  def __init__(self):\n",
+        "    super(ResNetModule, self).__init__()\n",
+        "    self.m = tf_model\n",
+        "    self.predict = tf.function(\n",
+        "        input_signature=[tf.TensorSpec(INPUT_SHAPE, tf.float32)])(tf_model.call)"
+      ]
+    },
+    {
+      "cell_type": "code",
       "execution_count": 3,
-      "outputs": [
-        {
-          "output_type": "stream",
-          "text": [
-            "WARNING:tensorflow:From <ipython-input-3-3c8b63a0281a>:3: set_learning_phase (from tensorflow.python.keras.backend) is deprecated and will be removed after 2020-10-11.\n",
-            "Instructions for updating:\n",
-            "Simply pass a True/False value to the `training` argument of the `__call__` method of your layer or model.\n"
-          ],
-          "name": "stdout"
-        }
-      ]
-    },
-    {
-      "cell_type": "code",
       "metadata": {
-        "id": "UvrDHh0FW8I1",
+        "colab": {},
         "colab_type": "code",
-        "outputId": "f00defdd-b8e9-499b-873f-5489439a2b77",
-        "colab": {
-          "base_uri": "https://localhost:8080/",
-          "height": 85
-        }
-      },
-      "source": [
-        "#@title Export as a SavedModel.\n",
-        "saved_model_path = os.path.join(SAVE_PATH, \"resnet.sm\")\n",
-        "save_options = tf.saved_model.SaveOptions(save_debug_info=True)\n",
-        "tf.saved_model.save(tf_module, saved_model_path, options=save_options)"
-      ],
-      "execution_count": 4,
-      "outputs": [
-        {
-          "output_type": "stream",
-          "text": [
-            "WARNING:tensorflow:From c:\\users\\scott\\scoop\\apps\\python\\current\\lib\\site-packages\\tensorflow\\python\\training\\tracking\\tracking.py:105: Network.state_updates (from tensorflow.python.keras.engine.network) is deprecated and will be removed in a future version.\n",
-            "Instructions for updating:\n",
-            "This property should not be used in TensorFlow 2.0, as updates are applied automatically.\n",
-            "INFO:tensorflow:Assets written to: C:\\Users\\Scott\\saved_models\\resnet.sm\\assets\n"
-          ],
-          "name": "stdout"
-        }
-      ]
-    },
-    {
-      "cell_type": "code",
-      "metadata": {
-        "id": "6YGqN2uqP_7P",
-        "colab_type": "code",
-        "colab": {}
-      },
-      "source": [
-        "#@title Compile the SavedModel to an IREE compiler module.\n",
-        "\n",
-        "TARGET_BACKENDS = (\"vmla\",)  # others: vulkan-spirv, llvm-ir\n",
-        "DRIVER_NAME = \"vmla\"         # others: vulkan      , llvm\n",
-        "\n",
-        "compiler_context = ireec.Context()\n",
-        "compiler_module = ireec.tf_compile_saved_model( \n",
-        "    saved_model_dir=saved_model_path,\n",
-        "    compiler_context=compiler_context,\n",
-        "    exported_names=[\"predict\"],\n",
-        "    target_backends=TARGET_BACKENDS)"
-      ],
-      "execution_count": 0,
-      "outputs": []
-    },
-    {
-      "cell_type": "code",
-      "metadata": {
-        "id": "nckd6xAaXWHm",
-        "colab_type": "code",
-        "outputId": "b4fc8ed9-b76f-4de2-a22d-de5e14b5626e",
-        "colab": {
-          "base_uri": "https://localhost:8080/",
-          "height": 51
-        }
-      },
-      "source": [
-        "#@title Register the compiler module with a runtime context.\n",
-        "vm_module = ireert.VmModule.from_flatbuffer(compiler_module)\n",
-        "rt_config = ireert.Config(DRIVER_NAME)\n",
-        "rt_context = ireert.SystemContext(config=rt_config)\n",
-        "rt_context.add_module(vm_module)"
-      ],
-      "execution_count": 6,
-      "outputs": [
-        {
-          "output_type": "stream",
-          "text": [
-            "Created IREE driver vmla: <pyiree.rt.binding.HalDriver object at 0x0000024D123451F0>\n",
-            "SystemContext driver=<pyiree.rt.binding.HalDriver object at 0x0000024D123451F0>\n"
-          ],
-          "name": "stderr"
-        }
-      ]
-    },
-    {
-      "cell_type": "code",
-      "metadata": {
-        "id": "d0QjgzR-aiqj",
-        "colab_type": "code",
-        "colab": {
-          "base_uri": "https://localhost:8080/",
-          "height": 286
+        "executionInfo": {
+          "elapsed": 27,
+          "status": "ok",
+          "timestamp": 1598547657992,
+          "user": {
+            "displayName": "",
+            "photoUrl": "",
+            "userId": ""
+          },
+          "user_tz": 420
         },
-        "outputId": "f9b2cf63-a111-4154-a835-0574922aeddd"
+        "id": "0x7NqMybvPg0"
       },
+      "outputs": [],
+      "source": [
+        "#@markdown ### Backend Configuration\n",
+        "\n",
+        "backend_choice = \"iree_vmla (CPU)\" #@param [ \"iree_vmla (CPU)\", \"iree_llvmjit (CPU)\", \"iree_vulkan (GPU/SwiftShader)\" ]\n",
+        "backend_choice = backend_choice.split(\" \")[0]\n",
+        "backend = tf_utils.BackendInfo(backend_choice)"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 4,
+      "metadata": {
+        "colab": {
+          "height": 51
+        },
+        "colab_type": "code",
+        "executionInfo": {
+          "elapsed": 165013,
+          "status": "ok",
+          "timestamp": 1598547823017,
+          "user": {
+            "displayName": "",
+            "photoUrl": "",
+            "userId": ""
+          },
+          "user_tz": 420
+        },
+        "id": "Kk3I2mkOvdp1",
+        "outputId": "8ad45e22-ba41-4660-b1aa-e6a88502b01d"
+      },
+      "outputs": [
+        {
+          "name": "stderr",
+          "output_type": "stream",
+          "text": [
+            "Created IREE driver vmla: \u003ciree.bindings.python.pyiree.rt.binding.HalDriver object at 0x7fef48c98298\u003e\n",
+            "SystemContext driver=\u003ciree.bindings.python.pyiree.rt.binding.HalDriver object at 0x7fef48c98298\u003e\n"
+          ]
+        }
+      ],
+      "source": [
+        "#@title Compile ResNet with IREE\n",
+        "# This may take a few minutes.\n",
+        "iree_module = backend.compile(ResNetModule, [\"predict\"])"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 7,
+      "metadata": {
+        "cellView": "form",
+        "colab": {
+          "height": 314
+        },
+        "colab_type": "code",
+        "executionInfo": {
+          "elapsed": 463,
+          "status": "ok",
+          "timestamp": 1598547878345,
+          "user": {
+            "displayName": "",
+            "photoUrl": "",
+            "userId": ""
+          },
+          "user_tz": 420
+        },
+        "id": "d0QjgzR-aiqj",
+        "outputId": "f5d1ce6f-b41a-42ba-f216-42f9872f3ba3"
+      },
+      "outputs": [
+        {
+          "name": "stdout",
+          "output_type": "stream",
+          "text": [
+            "Test image:\n"
+          ]
+        },
+        {
+          "data": {
+            "image/png": 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GbgcpBmrZWNLYI9s1k0tHtOB0x1nAW0JTxbvOvn/8dA0pYP2JlBLQH93CL4rvReJR0bFh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eljzEWxLEustdzd3VM3LUZbxrHHGIN6ODL4jCQNMdDPfS6uS1hH2hMXuz1uFZZO40R0gXpfMy5T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/RMiAJFnSdhItXE4Xp5S1/S4cp+fzdEocTWlbAyak7Q2HfQGmwjOC483oy4ta9mWJn4asVidI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\n4FlaWnrtlsGztLT02v1/qtOoQ5PQVzwAAAAASUVORK5CYII=\n",
+            "text/plain": [
+              "\u003cFigure size 600x400 with 1 Axes\u003e"
+            ]
+          },
+          "metadata": {
+            "tags": []
+          },
+          "output_type": "display_data"
+        }
+      ],
       "source": [
         "#@title Load a test image of a [labrador](https://commons.wikimedia.org/wiki/File:YellowLabradorLooking_new.jpg)\n",
         "\n",
@@ -248,108 +258,135 @@
         "\n",
         "print(\"Test image:\")\n",
         "plt.imshow(content_image.numpy().reshape(224, 224, 3) / 255.0)\n",
-        "plt.show()"
-      ],
-      "execution_count": 19,
-      "outputs": [
-        {
-          "output_type": "stream",
-          "text": [
-            "Test image:\n"
-          ],
-          "name": "stdout"
-        },
-        {
-          "output_type": "display_data",
-          "data": {
-            "text/plain": [
-              "<Figure size 432x288 with 1 Axes>"
-            ],
-            "image/png": 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\n"
-          },
-          "metadata": {
-            "tags": [],
-            "needs_background": "light"
-          }
-        }
+        "plt.axis(\"off\")\n",
+        "plt.tight_layout()"
       ]
     },
     {
       "cell_type": "code",
+      "execution_count": 8,
       "metadata": {
-        "id": "U7XcCuw1qE1U",
+        "cellView": "form",
+        "colab": {},
         "colab_type": "code",
-        "colab": {}
+        "executionInfo": {
+          "elapsed": 86,
+          "status": "ok",
+          "timestamp": 1598547882207,
+          "user": {
+            "displayName": "",
+            "photoUrl": "",
+            "userId": ""
+          },
+          "user_tz": 420
+        },
+        "id": "U7XcCuw1qE1U"
       },
+      "outputs": [],
       "source": [
         "#@title Model pre- and post-processing\n",
         "input_data = tf.keras.applications.resnet50.preprocess_input(content_image)\n",
         "\n",
         "def decode_result(result):\n",
         "  return tf.keras.applications.resnet50.decode_predictions(result, top=3)[0]"
-      ],
-      "execution_count": 0,
-      "outputs": []
+      ]
     },
     {
       "cell_type": "code",
+      "execution_count": 9,
       "metadata": {
-        "id": "OKDPhohd1LWN",
-        "colab_type": "code",
-        "outputId": "e0b7b6c2-5bed-4fe2-d75d-22cbf4d12abe",
         "colab": {
-          "base_uri": "https://localhost:8080/",
           "height": 51
-        }
+        },
+        "colab_type": "code",
+        "executionInfo": {
+          "elapsed": 5332,
+          "status": "ok",
+          "timestamp": 1598547889343,
+          "user": {
+            "displayName": "",
+            "photoUrl": "",
+            "userId": ""
+          },
+          "user_tz": 420
+        },
+        "id": "OKDPhohd1LWN",
+        "outputId": "ea74a9d6-fc36-4bd8-f18f-1a96a7693d80"
       },
+      "outputs": [
+        {
+          "name": "stdout",
+          "output_type": "stream",
+          "text": [
+            "TF prediction:\n",
+            "[('n02091244', 'Ibizan_hound', 0.12879108), ('n02099712', 'Labrador_retriever', 0.12632962), ('n02091831', 'Saluki', 0.09625229)]\n"
+          ]
+        }
+      ],
       "source": [
         "#@title Run TF model\n",
         "\n",
         "print(\"TF prediction:\")\n",
         "tf_result = tf_model.predict(input_data)\n",
         "print(decode_result(tf_result))"
-      ],
-      "execution_count": 8,
-      "outputs": [
-        {
-          "output_type": "stream",
-          "text": [
-            "TF prediction:\n",
-            "[('n02091244', 'Ibizan_hound', 0.12879111), ('n02099712', 'Labrador_retriever', 0.12632939), ('n02091831', 'Saluki', 0.09625213)]\n"
-          ],
-          "name": "stdout"
-        }
       ]
     },
     {
       "cell_type": "code",
+      "execution_count": 10,
       "metadata": {
-        "id": "QBaypvkMetjw",
-        "colab_type": "code",
-        "outputId": "2198aedd-cbc0-4b7c-80da-e04e182972d6",
         "colab": {
-          "base_uri": "https://localhost:8080/",
           "height": 51
-        }
+        },
+        "colab_type": "code",
+        "executionInfo": {
+          "elapsed": 137315,
+          "status": "ok",
+          "timestamp": 1598548090070,
+          "user": {
+            "displayName": "",
+            "photoUrl": "",
+            "userId": ""
+          },
+          "user_tz": 420
+        },
+        "id": "QBaypvkMetjw",
+        "outputId": "59ebec4e-b0a3-4562-dd7c-3d16f4692641"
       },
-      "source": [
-        "#@title Run IREE model\n",
-        "\n",
-        "print(\"IREE prediction:\")\n",
-        "iree_result = rt_context.modules.module.predict(input_data)\n",
-        "print(decode_result(iree_result))"
-      ],
-      "execution_count": 9,
       "outputs": [
         {
+          "name": "stdout",
           "output_type": "stream",
           "text": [
             "IREE prediction:\n",
-            "[('n02091244', 'Ibizan_hound', 0.12879062), ('n02099712', 'Labrador_retriever', 0.12632969), ('n02091831', 'Saluki', 0.096252546)]\n"
-          ],
-          "name": "stdout"
+            "[('n02091244', 'Ibizan_hound', 0.12879075), ('n02099712', 'Labrador_retriever', 0.1263297), ('n02091831', 'Saluki', 0.09625255)]\n"
+          ]
         }
+      ],
+      "source": [
+        "#@title Run the model compiled with IREE\n",
+        "\n",
+        "print(\"IREE prediction:\")\n",
+        "iree_result = iree_module.predict(input_data)\n",
+        "print(decode_result(iree_result))"
       ]
     }
-  ]
-}
\ No newline at end of file
+  ],
+  "metadata": {
+    "colab": {
+      "collapsed_sections": [],
+      "last_runtime": {
+        "build_target": "",
+        "kind": "local"
+      },
+      "name": "resnet.ipynb",
+      "provenance": []
+    },
+    "kernelspec": {
+      "display_name": "Python 3",
+      "name": "python3"
+    }
+  },
+  "nbformat": 4,
+  "nbformat_minor": 0
+}
diff --git a/colab/simple_tensorflow_module_import.ipynb b/colab/simple_tensorflow_module_import.ipynb
index 600afc1..b1552c4 100644
--- a/colab/simple_tensorflow_module_import.ipynb
+++ b/colab/simple_tensorflow_module_import.ipynb
@@ -1,23 +1,49 @@
 {
-  "nbformat": 4,
-  "nbformat_minor": 0,
-  "metadata": {
-    "colab": {
-      "name": "simple_tensorflow_module_import.ipynb",
-      "provenance": [],
-      "collapsed_sections": []
-    },
-    "kernelspec": {
-      "name": "python3",
-      "display_name": "Python 3"
-    }
-  },
   "cells": [
     {
       "cell_type": "markdown",
       "metadata": {
-        "id": "h5s6ncerSpc5",
-        "colab_type": "text"
+        "colab_type": "text",
+        "id": "vQTT2EYu4q_W"
+      },
+      "source": [
+        "##### Copyright 2020 Google LLC.\n",
+        "\n",
+        "Licensed under the Apache License, Version 2.0 (the \"License\");"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "cellView": "form",
+        "colab": {},
+        "colab_type": "code",
+        "id": "BgQ7yyp84qDj"
+      },
+      "outputs": [],
+      "source": [
+        "#@title License header\n",
+        "# Copyright 2020 Google LLC\n",
+        "#\n",
+        "# Licensed under the Apache License, Version 2.0 (the \"License\");\n",
+        "# you may not use this file except in compliance with the License.\n",
+        "# You may obtain a copy of the License at\n",
+        "#\n",
+        "#      https://www.apache.org/licenses/LICENSE-2.0\n",
+        "#\n",
+        "# Unless required by applicable law or agreed to in writing, software\n",
+        "# distributed under the License is distributed on an \"AS IS\" BASIS,\n",
+        "# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n",
+        "# See the License for the specific language governing permissions and\n",
+        "# limitations under the License."
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "colab_type": "text",
+        "id": "h5s6ncerSpc5"
       },
       "source": [
         "# Defines a simple TF module, saves it and loads it in IREE.\n",
@@ -26,132 +52,253 @@
         "*   [Install a TensorFlow2 nightly pip](https://www.tensorflow.org/install) (or bring your own)\n",
         "*   Enable IREE/TF integration by adding to your user.bazelrc: `build --define=iree_tensorflow=true`\n",
         "*   *Optional:* Prime the build: `bazel build bindings/python/pyiree`\n",
-        "*   Start colab by running `python colab/start_colab_kernel.py` (see that file for initial setup instructions)\n",
-        "\n",
-        "## TODO:\n",
-        "\n",
-        "* This is just using low-level binding classes. Change to high level API.\n",
-        "* Plumg through ability to run TF compiler lowering passes and import directly into IREE\n"
+        "*   Start colab by running `python colab/start_colab_kernel.py` (see that file for initial setup instructions)"
       ]
     },
     {
       "cell_type": "code",
+      "execution_count": 1,
       "metadata": {
-        "id": "s2bScbYkP6VZ",
+        "colab": {},
         "colab_type": "code",
-        "colab": {}
+        "executionInfo": {
+          "elapsed": 6652,
+          "status": "ok",
+          "timestamp": 1598480165652,
+          "user": {
+            "displayName": "",
+            "photoUrl": "",
+            "userId": ""
+          },
+          "user_tz": 420
+        },
+        "id": "s2bScbYkP6VZ"
       },
+      "outputs": [],
       "source": [
-        "import os\n",
         "import tensorflow as tf\n",
-        "from pyiree.tf import compiler as ireec\n",
-        "\n",
-        "SAVE_PATH = os.path.join(os.environ[\"HOME\"], \"saved_models\")\n",
-        "os.makedirs(SAVE_PATH, exist_ok=True)"
-      ],
-      "execution_count": 0,
-      "outputs": []
+        "from pyiree.tf import compiler as ireec"
+      ]
     },
     {
       "cell_type": "code",
+      "execution_count": 2,
       "metadata": {
-        "id": "6YGqN2uqP_7P",
+        "colab": {},
         "colab_type": "code",
-        "outputId": "ec634eb9-25e7-42c8-dd44-2aa035fa80e0",
-        "colab": {
-          "base_uri": "https://localhost:8080/",
-          "height": 802
-        }
+        "executionInfo": {
+          "elapsed": 623,
+          "status": "ok",
+          "timestamp": 1598480319071,
+          "user": {
+            "displayName": "",
+            "photoUrl": "",
+            "userId": ""
+          },
+          "user_tz": 420
+        },
+        "id": "6YGqN2uqP_7P"
       },
+      "outputs": [],
       "source": [
         "class MyModule(tf.Module):\n",
+        "\n",
         "  def __init__(self):\n",
         "    self.v = tf.Variable([4], dtype=tf.float32)\n",
-        "  \n",
-        "  @tf.function(\n",
-        "      input_signature=[tf.TensorSpec([4], tf.float32), tf.TensorSpec([4], tf.float32)]\n",
-        "  )\n",
+        "\n",
+        "  @tf.function(input_signature=[\n",
+        "      tf.TensorSpec([4], tf.float32),\n",
+        "      tf.TensorSpec([4], tf.float32)\n",
+        "  ])\n",
         "  def add(self, a, b):\n",
-        "    return tf.tanh(self.v * a + b)\n",
-        "\n",
-        "my_mod = MyModule()\n",
-        "\n",
-        "saved_model_path = os.path.join(SAVE_PATH, \"simple.sm\")\n",
-        "\n",
-        "options = tf.saved_model.SaveOptions(save_debug_info=True)\n",
-        "tf.saved_model.save(my_mod, saved_model_path, options=options)\n",
-        "\n",
-        "input_module = ireec.tf_load_saved_model(saved_model_path, pass_pipeline=[])\n",
-        "print('LOADED ASM:', input_module.to_asm())\n",
-        "\n",
-        "# Canonicalize the TF import.\n",
-        "input_module.run_pass_pipeline([\n",
+        "    return tf.tanh(self.v * a + b)"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 10,
+      "metadata": {
+        "colab": {
+          "height": 326
+        },
+        "colab_type": "code",
+        "executionInfo": {
+          "elapsed": 954,
+          "status": "ok",
+          "timestamp": 1598480413200,
+          "user": {
+            "displayName": "",
+            "photoUrl": "",
+            "userId": ""
+          },
+          "user_tz": 420
+        },
+        "id": "r2H2BOpn2SpG",
+        "outputId": "9d6ad89a-d491-4dc4-f6b5-a26a8ef24fdb"
+      },
+      "outputs": [
+        {
+          "name": "stdout",
+          "output_type": "stream",
+          "text": [
+            "LOADED ASM: \n",
+            "\n",
+            "module attributes {tf.versions = {bad_consumers = [], min_consumer = 12 : i32, producer = 504 : i32}, tf_saved_model.semantics} {\n",
+            "  \"tf_saved_model.global_tensor\"() {is_mutable, sym_name = \"__sm_node1__v\", tf_saved_model.exported_names = [\"v\"], type = tensor\u003c1xf32\u003e, value = dense\u003c4.000000e+00\u003e : tensor\u003c1xf32\u003e} : () -\u003e ()\n",
+            "  func @__inference_add_160(%arg0: tensor\u003c4xf32\u003e {tf._user_specified_name = \"a\", tf_saved_model.index_path = [0]}, %arg1: tensor\u003c4xf32\u003e {tf._user_specified_name = \"b\", tf_saved_model.index_path = [1]}, %arg2: tensor\u003c!tf.resource\u003ctensor\u003c1xf32\u003e\u003e\u003e {tf_saved_model.bound_input = @__sm_node1__v}) -\u003e (tensor\u003c4xf32\u003e {tf_saved_model.index_path = []}) attributes {tf._input_shapes = [#tf.shape\u003c4\u003e, #tf.shape\u003c4\u003e, #tf.shape\u003c*\u003e], tf.signature.is_stateful, tf_saved_model.exported_names = [\"add\"]} {\n",
+            "    %0 = \"tf.Cast\"(%arg2) {Truncate = false} : (tensor\u003c!tf.resource\u003ctensor\u003c1xf32\u003e\u003e\u003e) -\u003e tensor\u003c*x!tf.resource\u003e\n",
+            "    %1 = tf_executor.graph {\n",
+            "      %outputs, %control = tf_executor.island wraps \"tf.ReadVariableOp\"(%0) {device = \"\"} : (tensor\u003c*x!tf.resource\u003e) -\u003e tensor\u003c1xf32\u003e\n",
+            "      %outputs_0, %control_1 = tf_executor.island wraps \"tf.Mul\"(%outputs, %arg0) {device = \"\"} : (tensor\u003c1xf32\u003e, tensor\u003c4xf32\u003e) -\u003e tensor\u003c4xf32\u003e\n",
+            "      %outputs_2, %control_3 = tf_executor.island wraps \"tf.AddV2\"(%outputs_0, %arg1) {device = \"\"} : (tensor\u003c4xf32\u003e, tensor\u003c4xf32\u003e) -\u003e tensor\u003c4xf32\u003e\n",
+            "      %outputs_4, %control_5 = tf_executor.island wraps \"tf.Tanh\"(%outputs_2) {device = \"\"} : (tensor\u003c4xf32\u003e) -\u003e tensor\u003c4xf32\u003e\n",
+            "      %outputs_6, %control_7 = tf_executor.island wraps \"tf.Identity\"(%outputs_4) {device = \"\"} : (tensor\u003c4xf32\u003e) -\u003e tensor\u003c4xf32\u003e\n",
+            "      tf_executor.fetch %outputs_6 : tensor\u003c4xf32\u003e\n",
+            "    }\n",
+            "    return %1 : tensor\u003c4xf32\u003e\n",
+            "  }\n",
+            "}\n"
+          ]
+        }
+      ],
+      "source": [
+        "#@title Compile to MLIR (mhlo).\n",
+        "compiler_module = ireec.tf_module_to_compiler_module(MyModule(),\n",
+        "                                                     pass_pipeline=())\n",
+        "print('LOADED ASM:', compiler_module.to_asm())"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 12,
+      "metadata": {
+        "colab": {
+          "height": 258
+        },
+        "colab_type": "code",
+        "executionInfo": {
+          "elapsed": 82,
+          "status": "ok",
+          "timestamp": 1598480427027,
+          "user": {
+            "displayName": "",
+            "photoUrl": "",
+            "userId": ""
+          },
+          "user_tz": 420
+        },
+        "id": "S7IrzODx2RIF",
+        "outputId": "a96b729d-e64d-497e-bd0f-7c0e2fa31d4c"
+      },
+      "outputs": [
+        {
+          "name": "stdout",
+          "output_type": "stream",
+          "text": [
+            "LOWERED TF ASM: \n",
+            "\n",
+            "module attributes {tf.versions = {bad_consumers = [], min_consumer = 12 : i32, producer = 504 : i32}, tf_saved_model.semantics} {\n",
+            "  \"tf_saved_model.global_tensor\"() {is_mutable, sym_name = \"__sm_node1__v\", tf_saved_model.exported_names = [\"v\"], type = tensor\u003c1xf32\u003e, value = dense\u003c4.000000e+00\u003e : tensor\u003c1xf32\u003e} : () -\u003e ()\n",
+            "  func @__inference_add_160(%arg0: tensor\u003c4xf32\u003e {tf._user_specified_name = \"a\", tf_saved_model.index_path = [0]}, %arg1: tensor\u003c4xf32\u003e {tf._user_specified_name = \"b\", tf_saved_model.index_path = [1]}, %arg2: tensor\u003c!tf.resource\u003ctensor\u003c1xf32\u003e\u003e\u003e {tf_saved_model.bound_input = @__sm_node1__v}) -\u003e (tensor\u003c4xf32\u003e {tf_saved_model.index_path = []}) attributes {tf._input_shapes = [#tf.shape\u003c4\u003e, #tf.shape\u003c4\u003e, #tf.shape\u003c*\u003e], tf.signature.is_stateful, tf_saved_model.exported_names = [\"add\"]} {\n",
+            "    %0 = \"tf.ReadVariableOp\"(%arg2) : (tensor\u003c!tf.resource\u003ctensor\u003c1xf32\u003e\u003e\u003e) -\u003e tensor\u003c1xf32\u003e\n",
+            "    %1 = \"tf.Mul\"(%0, %arg0) {device = \"\"} : (tensor\u003c1xf32\u003e, tensor\u003c4xf32\u003e) -\u003e tensor\u003c4xf32\u003e\n",
+            "    %2 = \"tf.AddV2\"(%1, %arg1) {device = \"\"} : (tensor\u003c4xf32\u003e, tensor\u003c4xf32\u003e) -\u003e tensor\u003c4xf32\u003e\n",
+            "    %3 = \"tf.Tanh\"(%2) {device = \"\"} : (tensor\u003c4xf32\u003e) -\u003e tensor\u003c4xf32\u003e\n",
+            "    %4 = \"tf.Identity\"(%3) {device = \"\"} : (tensor\u003c4xf32\u003e) -\u003e tensor\u003c4xf32\u003e\n",
+            "    return %4 : tensor\u003c4xf32\u003e\n",
+            "  }\n",
+            "}\n"
+          ]
+        }
+      ],
+      "source": [
+        "#@title Canonicalize the TF import.\n",
+        "compiler_module.run_pass_pipeline([\n",
         "  \"tf-executor-graph-pruning\",\n",
         "  \"tf-standard-pipeline\",\n",
         "  \"canonicalize\",\n",
         "])\n",
-        "print(\"LOWERED TF ASM:\", input_module.to_asm())\n",
-        "\n",
-        "# Legalize to XLA (high-level).\n",
-        "input_module.run_pass_pipeline([\n",
-        "  \"xla-legalize-tf{allow-partial-conversion=true}\",\n",
-        "])\n",
-        "print(\"XLA ASM:\", input_module.to_asm())"
-      ],
-      "execution_count": 15,
+        "print(\"LOWERED TF ASM:\", compiler_module.to_asm())"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 13,
+      "metadata": {
+        "colab": {
+          "height": 445
+        },
+        "colab_type": "code",
+        "executionInfo": {
+          "elapsed": 47,
+          "status": "ok",
+          "timestamp": 1598480427942,
+          "user": {
+            "displayName": "",
+            "photoUrl": "",
+            "userId": ""
+          },
+          "user_tz": 420
+        },
+        "id": "BVmeZrRx2Uh9",
+        "outputId": "cfaa4c01-8f47-40ed-edbe-19f7cbf2c43c"
+      },
       "outputs": [
         {
+          "name": "stdout",
           "output_type": "stream",
           "text": [
-            "INFO:tensorflow:Assets written to: /usr/local/google/home/scotttodd/saved_models/simple.sm/assets\n",
-            "LOADED ASM: \n",
-            "\n",
-            "module attributes {tf_saved_model.semantics} {\n",
-            "  \"tf_saved_model.global_tensor\"() {is_mutable, sym_name = \"__sm_node1__v\", tf_saved_model.exported_names = [\"v\"], type = tensor<1xf32>, value = dense<4.000000e+00> : tensor<1xf32>} : () -> ()\n",
-            "  func @__inference_add_10820(%arg0: tensor<4xf32> {tf_saved_model.index_path = [0]}, %arg1: tensor<4xf32> {tf_saved_model.index_path = [1]}, %arg2: tensor<*x!tf.resource> {tf_saved_model.bound_input = @__sm_node1__v}) -> (tensor<4xf32> {tf_saved_model.index_path = []}) attributes {tf._input_shapes = [\"tfshape$dim { size: 4 }\", \"tfshape$dim { size: 4 }\", \"tfshape$unknown_rank: true\"], tf.signature.is_stateful, tf_saved_model.exported_names = [\"add\"]} {\n",
-            "    %0 = tf_executor.graph {\n",
-            "      %outputs, %control = tf_executor.island wraps \"tf.ReadVariableOp\"(%arg2) {_output_shapes = [\"tfshape$dim { size: 1 }\"], device = \"\", dtype = f32} : (tensor<*x!tf.resource>) -> tensor<1xf32>\n",
-            "      %outputs_0, %control_1 = tf_executor.island wraps \"tf.Mul\"(%outputs, %arg0) {T = f32, _output_shapes = [\"tfshape$dim { size: 4 }\"], device = \"\"} : (tensor<1xf32>, tensor<4xf32>) -> tensor<4xf32>\n",
-            "      %outputs_2, %control_3 = tf_executor.island wraps \"tf.AddV2\"(%outputs_0, %arg1) {T = f32, _output_shapes = [\"tfshape$dim { size: 4 }\"], device = \"\"} : (tensor<4xf32>, tensor<4xf32>) -> tensor<4xf32>\n",
-            "      %outputs_4, %control_5 = tf_executor.island wraps \"tf.Tanh\"(%outputs_2) {T = f32, _output_shapes = [\"tfshape$dim { size: 4 }\"], device = \"\"} : (tensor<4xf32>) -> tensor<4xf32>\n",
-            "      %outputs_6, %control_7 = tf_executor.island(%control) wraps \"tf.Identity\"(%outputs_4) {T = f32, _output_shapes = [\"tfshape$dim { size: 4 }\"], device = \"\"} : (tensor<4xf32>) -> tensor<4xf32>\n",
-            "      tf_executor.fetch %outputs_6, %control : tensor<4xf32>, !tf_executor.control\n",
-            "    }\n",
-            "    return %0 : tensor<4xf32>\n",
-            "  }\n",
-            "}\n",
-            "\n",
-            "LOWERED TF ASM: \n",
-            "\n",
-            "module attributes {tf_saved_model.semantics} {\n",
-            "  \"tf_saved_model.global_tensor\"() {is_mutable, sym_name = \"__sm_node1__v\", tf_saved_model.exported_names = [\"v\"], type = tensor<1xf32>, value = dense<4.000000e+00> : tensor<1xf32>} : () -> ()\n",
-            "  func @__inference_add_10820(%arg0: tensor<4xf32> {tf_saved_model.index_path = [0]}, %arg1: tensor<4xf32> {tf_saved_model.index_path = [1]}, %arg2: tensor<*x!tf.resource> {tf_saved_model.bound_input = @__sm_node1__v}) -> (tensor<4xf32> {tf_saved_model.index_path = []}) attributes {tf._input_shapes = [\"tfshape$dim { size: 4 }\", \"tfshape$dim { size: 4 }\", \"tfshape$unknown_rank: true\"], tf.signature.is_stateful, tf_saved_model.exported_names = [\"add\"]} {\n",
-            "    %0 = \"tf.ReadVariableOp\"(%arg2) {_output_shapes = [\"tfshape$dim { size: 1 }\"], device = \"\", dtype = f32} : (tensor<*x!tf.resource>) -> tensor<1xf32>\n",
-            "    %1 = \"tf.Mul\"(%0, %arg0) {T = f32, _output_shapes = [\"tfshape$dim { size: 4 }\"], device = \"\"} : (tensor<1xf32>, tensor<4xf32>) -> tensor<4xf32>\n",
-            "    %2 = \"tf.AddV2\"(%1, %arg1) {T = f32, _output_shapes = [\"tfshape$dim { size: 4 }\"], device = \"\"} : (tensor<4xf32>, tensor<4xf32>) -> tensor<4xf32>\n",
-            "    %3 = \"tf.Tanh\"(%2) {T = f32, _output_shapes = [\"tfshape$dim { size: 4 }\"], device = \"\"} : (tensor<4xf32>) -> tensor<4xf32>\n",
-            "    %4 = \"tf.Identity\"(%3) {T = f32, _output_shapes = [\"tfshape$dim { size: 4 }\"], device = \"\"} : (tensor<4xf32>) -> tensor<4xf32>\n",
-            "    return %4 : tensor<4xf32>\n",
-            "  }\n",
-            "}\n",
-            "\n",
             "XLA ASM: \n",
             "\n",
-            "module attributes {tf_saved_model.semantics} {\n",
-            "  \"tf_saved_model.global_tensor\"() {is_mutable, sym_name = \"__sm_node1__v\", tf_saved_model.exported_names = [\"v\"], type = tensor<1xf32>, value = dense<4.000000e+00> : tensor<1xf32>} : () -> ()\n",
-            "  func @__inference_add_10820(%arg0: tensor<4xf32> {tf_saved_model.index_path = [0]}, %arg1: tensor<4xf32> {tf_saved_model.index_path = [1]}, %arg2: tensor<*x!tf.resource> {tf_saved_model.bound_input = @__sm_node1__v}) -> (tensor<4xf32> {tf_saved_model.index_path = []}) attributes {tf._input_shapes = [\"tfshape$dim { size: 4 }\", \"tfshape$dim { size: 4 }\", \"tfshape$unknown_rank: true\"], tf.signature.is_stateful, tf_saved_model.exported_names = [\"add\"]} {\n",
-            "    %0 = \"tf.ReadVariableOp\"(%arg2) {_output_shapes = [\"tfshape$dim { size: 1 }\"], device = \"\", dtype = f32} : (tensor<*x!tf.resource>) -> tensor<1xf32>\n",
-            "    %1 = \"mhlo.multiply\"(%0, %arg0) : (tensor<1xf32>, tensor<4xf32>) -> tensor<4xf32>\n",
-            "    %2 = mhlo.add %1, %arg1 : tensor<4xf32>\n",
-            "    %3 = \"mhlo.tanh\"(%2) : (tensor<4xf32>) -> tensor<4xf32>\n",
-            "    return %3 : tensor<4xf32>\n",
+            "module attributes {tf.versions = {bad_consumers = [], min_consumer = 12 : i32, producer = 504 : i32}, tf_saved_model.semantics} {\n",
+            "  \"tf_saved_model.global_tensor\"() {is_mutable, sym_name = \"__sm_node1__v\", tf_saved_model.exported_names = [\"v\"], type = tensor\u003c1xf32\u003e, value = dense\u003c4.000000e+00\u003e : tensor\u003c1xf32\u003e} : () -\u003e ()\n",
+            "  func @__inference_add_160(%arg0: tensor\u003c4xf32\u003e {tf._user_specified_name = \"a\", tf_saved_model.index_path = [0]}, %arg1: tensor\u003c4xf32\u003e {tf._user_specified_name = \"b\", tf_saved_model.index_path = [1]}, %arg2: tensor\u003c!tf.resource\u003ctensor\u003c1xf32\u003e\u003e\u003e {tf_saved_model.bound_input = @__sm_node1__v}) -\u003e (tensor\u003c4xf32\u003e {tf_saved_model.index_path = []}) attributes {tf._input_shapes = [#tf.shape\u003c4\u003e, #tf.shape\u003c4\u003e, #tf.shape\u003c*\u003e], tf.signature.is_stateful, tf_saved_model.exported_names = [\"add\"]} {\n",
+            "    %0 = \"tf.ReadVariableOp\"(%arg2) : (tensor\u003c!tf.resource\u003ctensor\u003c1xf32\u003e\u003e\u003e) -\u003e tensor\u003c1xf32\u003e\n",
+            "    %1 = shape.shape_of %0 : tensor\u003c1xf32\u003e -\u003e tensor\u003c?xindex\u003e\n",
+            "    %2 = shape.shape_of %arg0 : tensor\u003c4xf32\u003e -\u003e tensor\u003c?xindex\u003e\n",
+            "    %3 = shape.cstr_broadcastable %1, %2 : tensor\u003c?xindex\u003e, tensor\u003c?xindex\u003e\n",
+            "    %4 = shape.assuming %3 -\u003e (tensor\u003c4xf32\u003e) {\n",
+            "      %7 = shape.const_shape [1] : tensor\u003c?xindex\u003e\n",
+            "      %8 = shape.const_shape [4] : tensor\u003c?xindex\u003e\n",
+            "      %9 = shape.const_shape [4] : tensor\u003c?xindex\u003e\n",
+            "      %10 = tensor_cast %9 : tensor\u003c?xindex\u003e to tensor\u003c1xindex\u003e\n",
+            "      %11 = \"mhlo.dynamic_broadcast_in_dim\"(%0, %10) {broadcast_dimensions = dense\u003c0\u003e : tensor\u003c1xi64\u003e} : (tensor\u003c1xf32\u003e, tensor\u003c1xindex\u003e) -\u003e tensor\u003c4xf32\u003e\n",
+            "      %12 = \"mhlo.dynamic_broadcast_in_dim\"(%arg0, %10) {broadcast_dimensions = dense\u003c0\u003e : tensor\u003c1xi64\u003e} : (tensor\u003c4xf32\u003e, tensor\u003c1xindex\u003e) -\u003e tensor\u003c4xf32\u003e\n",
+            "      %13 = mhlo.multiply %11, %12 : tensor\u003c4xf32\u003e\n",
+            "      shape.assuming_yield %13 : tensor\u003c4xf32\u003e\n",
+            "    }\n",
+            "    %5 = mhlo.add %4, %arg1 : tensor\u003c4xf32\u003e\n",
+            "    %6 = \"mhlo.tanh\"(%5) : (tensor\u003c4xf32\u003e) -\u003e tensor\u003c4xf32\u003e\n",
+            "    return %6 : tensor\u003c4xf32\u003e\n",
             "  }\n",
-            "}\n",
-            "\n"
-          ],
-          "name": "stdout"
+            "}\n"
+          ]
         }
+      ],
+      "source": [
+        "#@title Legalize to XLA (high-level).\n",
+        "compiler_module.run_pass_pipeline([\n",
+        "  \"xla-legalize-tf{allow-partial-conversion=true}\",\n",
+        "])\n",
+        "print(\"XLA ASM:\", compiler_module.to_asm())"
       ]
     }
-  ]
-}
\ No newline at end of file
+  ],
+  "metadata": {
+    "colab": {
+      "collapsed_sections": [],
+      "last_runtime": {
+        "build_target": "",
+        "kind": "local"
+      },
+      "name": "simple_tensorflow_module_import.ipynb",
+      "provenance": []
+    },
+    "kernelspec": {
+      "display_name": "Python 3",
+      "name": "python3"
+    }
+  },
+  "nbformat": 4,
+  "nbformat_minor": 0
+}