From 194512d757b4c3f0985ee159273d7d7fe53d7419 Mon Sep 17 00:00:00 2001
From: Isaac Miller <17116851+isaacbmiller@users.noreply.github.com>
Date: Fri, 25 Oct 2024 13:45:54 -0700
Subject: [PATCH] [Fix] Update pinned DSPy dependency version (#387)

Signed-off-by: Isaac Miller <isaac.miller@anyscale.com>
Signed-off-by: SumanthRH <sumanthrh@anyscale.com>
Co-authored-by: Isaac Miller <isaac.miller@anyscale.com>
Co-authored-by: Sumanth R Hegde <39546518+SumanthRH@users.noreply.github.com>
Co-authored-by: SumanthRH <sumanthrh@anyscale.com>
---
 templates/e2e-dspy-workflow/README.ipynb      | 165 ++++++++----------
 templates/e2e-dspy-workflow/README.md         |  85 ++++-----
 .../README_files/README_63_0.png              | Bin 54081 -> 54823 bytes
 .../README_files/README_66_0.png              | Bin 54400 -> 55407 bytes
 4 files changed, 99 insertions(+), 151 deletions(-)

diff --git a/templates/e2e-dspy-workflow/README.ipynb b/templates/e2e-dspy-workflow/README.ipynb
index b0a4ce1a9..264a6ce18 100644
--- a/templates/e2e-dspy-workflow/README.ipynb
+++ b/templates/e2e-dspy-workflow/README.ipynb
@@ -87,7 +87,7 @@
     "\n",
     "if importlib.util.find_spec(\"dspy\") is None:\n",
     "    print(\"Installing dspy\")\n",
-    "    !pip install git+https://github.com/stanfordnlp/dspy.git@40d5e0229a3abadd313ff9b857f40e638effae56\n",
+    "    !pip install git+https://github.com/stanfordnlp/dspy.git@c5b4c6d4240d66cc23113c55addfd47313dd25fb\n",
     "\n",
     "else:\n",
     "    print(\"dspy is already installed\")\n",
@@ -497,7 +497,7 @@
        "        <span style=\"color: #008000; text-decoration-color: #008000\">'model_id'</span>: <span style=\"color: #008000; text-decoration-color: #008000\">'meta-llama/Meta-Llama-3.1-70B-Instruct'</span>,\n",
        "        <span style=\"color: #008000; text-decoration-color: #008000\">'model_source'</span>: <span style=\"color: #008000; text-decoration-color: #008000\">'meta-llama/Meta-Llama-3.1-70B-Instruct'</span>\n",
        "    <span style=\"font-weight: bold\">}</span>,\n",
-       "    <span style=\"color: #008000; text-decoration-color: #008000\">'runtime_env'</span>: <span style=\"font-weight: bold\">{</span><span style=\"color: #008000; text-decoration-color: #008000\">'env_vars'</span>: <span style=\"font-weight: bold\">{</span><span style=\"color: #008000; text-decoration-color: #008000\">'HUGGING_FACE_HUB_TOKEN'</span>: <span style=\"color: #008000; text-decoration-color: #008000\">'Add your HF token here'</span><span style=\"font-weight: bold\">}}</span>,\n",
+       "    <span style=\"color: #008000; text-decoration-color: #008000\">'runtime_env'</span>: <span style=\"font-weight: bold\">{</span><span style=\"color: #008000; text-decoration-color: #008000\">'env_vars'</span>: <span style=\"font-weight: bold\">{</span><span style=\"color: #008000; text-decoration-color: #008000\">'HUGGING_FACE_HUB_TOKEN'</span>: <span style=\"color: #008000; text-decoration-color: #008000\">'REDACTED'</span><span style=\"font-weight: bold\">}}</span>,\n",
        "    <span style=\"color: #008000; text-decoration-color: #008000\">'tensor_parallelism'</span>: <span style=\"font-weight: bold\">{</span><span style=\"color: #008000; text-decoration-color: #008000\">'degree'</span>: <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">4</span><span style=\"font-weight: bold\">}</span>\n",
        "<span style=\"font-weight: bold\">}</span>\n",
        "</pre>\n"
@@ -544,7 +544,7 @@
        "        \u001b[32m'model_id'\u001b[0m: \u001b[32m'meta-llama/Meta-Llama-3.1-70B-Instruct'\u001b[0m,\n",
        "        \u001b[32m'model_source'\u001b[0m: \u001b[32m'meta-llama/Meta-Llama-3.1-70B-Instruct'\u001b[0m\n",
        "    \u001b[1m}\u001b[0m,\n",
-       "    \u001b[32m'runtime_env'\u001b[0m: \u001b[1m{\u001b[0m\u001b[32m'env_vars'\u001b[0m: \u001b[1m{\u001b[0m\u001b[32m'HUGGING_FACE_HUB_TOKEN'\u001b[0m: \u001b[32m'Add your HF token here'\u001b[0m\u001b[1m}\u001b[0m\u001b[1m}\u001b[0m,\n",
+       "    \u001b[32m'runtime_env'\u001b[0m: \u001b[1m{\u001b[0m\u001b[32m'env_vars'\u001b[0m: \u001b[1m{\u001b[0m\u001b[32m'HUGGING_FACE_HUB_TOKEN'\u001b[0m: \u001b[32m'REDACTED'\u001b[0m\u001b[1m}\u001b[0m\u001b[1m}\u001b[0m,\n",
        "    \u001b[32m'tensor_parallelism'\u001b[0m: \u001b[1m{\u001b[0m\u001b[32m'degree'\u001b[0m: \u001b[1;36m4\u001b[0m\u001b[1m}\u001b[0m\n",
        "\u001b[1m}\u001b[0m\n"
       ]
@@ -629,7 +629,13 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "Program input: Example({'text': 'I still have not received an answer as to why I was charged $1.00 in a transaction?'}) (input_keys={'text'})\n",
+      "Program input: Example({'text': 'I still have not received an answer as to why I was charged $1.00 in a transaction?'}) (input_keys={'text'})\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
       "Program output label: extra_charge_on_statement\n"
      ]
     }
@@ -659,7 +665,15 @@
    "cell_type": "code",
    "execution_count": null,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Average Metric: 4163 / 4171  (99.8): 100%|██████████| 4171/4171 [07:13<00:00,  9.62it/s]\n"
+     ]
+    }
+   ],
    "source": [
     "from dspy.teleprompt.finetune_teleprompter import bootstrap_data, convert_to_module_level_message_data\n",
     "from src import NUM_THREADS, get_valid_label_metric_fn\n",
@@ -1118,7 +1132,7 @@
        "    <span style=\"color: #008000; text-decoration-color: #008000\">'input_modality'</span>: <span style=\"color: #008000; text-decoration-color: #008000\">'text'</span>,\n",
        "    <span style=\"color: #008000; text-decoration-color: #008000\">'json_mode'</span>: <span style=\"font-weight: bold\">{</span><span style=\"color: #008000; text-decoration-color: #008000\">'enabled'</span>: <span style=\"color: #ff0000; text-decoration-color: #ff0000; font-style: italic\">False</span><span style=\"font-weight: bold\">}</span>,\n",
        "    <span style=\"color: #008000; text-decoration-color: #008000\">'llm_engine'</span>: <span style=\"color: #008000; text-decoration-color: #008000\">'VLLMEngine'</span>,\n",
-       "    <span style=\"color: #008000; text-decoration-color: #008000\">'lora_config'</span>: <span style=\"font-weight: bold\">{</span><span style=\"color: #008000; text-decoration-color: #008000\">'dynamic_lora_loading_path'</span>: <span style=\"color: #008000; text-decoration-color: #008000\">'none'</span>, <span style=\"color: #008000; text-decoration-color: #008000\">'max_num_adapters_per_replica'</span>: <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">32</span><span style=\"font-weight: bold\">}</span>,\n",
+       "    <span style=\"color: #008000; text-decoration-color: #008000\">'lora_config'</span>: <span style=\"font-weight: bold\">{</span><span style=\"color: #008000; text-decoration-color: #008000\">'dynamic_lora_loading_path'</span>: <span style=\"color: #008000; text-decoration-color: #008000\">'none'</span>, <span style=\"color: #008000; text-decoration-color: #008000\">'max_num_adapters_per_replica'</span>: <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">64</span><span style=\"font-weight: bold\">}</span>,\n",
        "    <span style=\"color: #008000; text-decoration-color: #008000\">'max_request_context_length'</span>: <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">8192</span>,\n",
        "    <span style=\"color: #008000; text-decoration-color: #008000\">'model_loading_config'</span>: <span style=\"font-weight: bold\">{</span>\n",
        "        <span style=\"color: #008000; text-decoration-color: #008000\">'model_id'</span>: <span style=\"color: #008000; text-decoration-color: #008000\">'meta-llama/Llama-3.2-1B-Instruct'</span>,\n",
@@ -1160,7 +1174,7 @@
        "    \u001b[32m'input_modality'\u001b[0m: \u001b[32m'text'\u001b[0m,\n",
        "    \u001b[32m'json_mode'\u001b[0m: \u001b[1m{\u001b[0m\u001b[32m'enabled'\u001b[0m: \u001b[3;91mFalse\u001b[0m\u001b[1m}\u001b[0m,\n",
        "    \u001b[32m'llm_engine'\u001b[0m: \u001b[32m'VLLMEngine'\u001b[0m,\n",
-       "    \u001b[32m'lora_config'\u001b[0m: \u001b[1m{\u001b[0m\u001b[32m'dynamic_lora_loading_path'\u001b[0m: \u001b[32m'none'\u001b[0m, \u001b[32m'max_num_adapters_per_replica'\u001b[0m: \u001b[1;36m32\u001b[0m\u001b[1m}\u001b[0m,\n",
+       "    \u001b[32m'lora_config'\u001b[0m: \u001b[1m{\u001b[0m\u001b[32m'dynamic_lora_loading_path'\u001b[0m: \u001b[32m'none'\u001b[0m, \u001b[32m'max_num_adapters_per_replica'\u001b[0m: \u001b[1;36m64\u001b[0m\u001b[1m}\u001b[0m,\n",
        "    \u001b[32m'max_request_context_length'\u001b[0m: \u001b[1;36m8192\u001b[0m,\n",
        "    \u001b[32m'model_loading_config'\u001b[0m: \u001b[1m{\u001b[0m\n",
        "        \u001b[32m'model_id'\u001b[0m: \u001b[32m'meta-llama/Llama-3.2-1B-Instruct'\u001b[0m,\n",
@@ -1492,20 +1506,20 @@
      "data": {
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-       "    <span style=\"color: #008000; text-decoration-color: #008000\">'openai/meta-llama/Llama-3.2-1B-Instruct:isaac:fijif'</span>: <span style=\"font-weight: bold\">{</span>\n",
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-       "        <span style=\"color: #008000; text-decoration-color: #008000\">'bfrs'</span>: <span style=\"font-weight: bold\">{</span><span style=\"color: #008000; text-decoration-color: #008000\">'devset'</span>: <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">53.5</span><span style=\"font-weight: bold\">}</span>\n",
+       "    <span style=\"color: #008000; text-decoration-color: #008000\">'base'</span>: <span style=\"font-weight: bold\">{</span><span style=\"color: #008000; text-decoration-color: #008000\">'vanilla'</span>: <span style=\"font-weight: bold\">{</span><span style=\"color: #008000; text-decoration-color: #008000\">'devset'</span>: <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">0.0</span><span style=\"font-weight: bold\">}</span>, <span style=\"color: #008000; text-decoration-color: #008000\">'bfrs'</span>: <span style=\"font-weight: bold\">{</span><span style=\"color: #008000; text-decoration-color: #008000\">'devset'</span>: <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">36.7</span><span style=\"font-weight: bold\">}}</span>,\n",
+       "    <span style=\"color: #008000; text-decoration-color: #008000\">'openai/meta-llama/Llama-3.2-1B-Instruct:isaac:qmmwc'</span>: <span style=\"font-weight: bold\">{</span>\n",
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+       "        <span style=\"color: #008000; text-decoration-color: #008000\">'bfrs'</span>: <span style=\"font-weight: bold\">{</span><span style=\"color: #008000; text-decoration-color: #008000\">'devset'</span>: <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">58.0</span><span style=\"font-weight: bold\">}</span>\n",
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        "<span style=\"font-weight: bold\">}</span>\n",
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       "text/plain": [
        "\u001b[1m{\u001b[0m\n",
-       "    \u001b[32m'base'\u001b[0m: \u001b[1m{\u001b[0m\u001b[32m'vanilla'\u001b[0m: \u001b[1m{\u001b[0m\u001b[32m'devset'\u001b[0m: \u001b[1;36m0.1\u001b[0m\u001b[1m}\u001b[0m, \u001b[32m'bfrs'\u001b[0m: \u001b[1m{\u001b[0m\u001b[32m'devset'\u001b[0m: \u001b[1;36m37.2\u001b[0m\u001b[1m}\u001b[0m\u001b[1m}\u001b[0m,\n",
-       "    \u001b[32m'openai/meta-llama/Llama-3.2-1B-Instruct:isaac:fijif'\u001b[0m: \u001b[1m{\u001b[0m\n",
-       "        \u001b[32m'vanilla'\u001b[0m: \u001b[1m{\u001b[0m\u001b[32m'devset'\u001b[0m: \u001b[1;36m54.2\u001b[0m\u001b[1m}\u001b[0m,\n",
-       "        \u001b[32m'bfrs'\u001b[0m: \u001b[1m{\u001b[0m\u001b[32m'devset'\u001b[0m: \u001b[1;36m53.5\u001b[0m\u001b[1m}\u001b[0m\n",
+       "    \u001b[32m'base'\u001b[0m: \u001b[1m{\u001b[0m\u001b[32m'vanilla'\u001b[0m: \u001b[1m{\u001b[0m\u001b[32m'devset'\u001b[0m: \u001b[1;36m0.0\u001b[0m\u001b[1m}\u001b[0m, \u001b[32m'bfrs'\u001b[0m: \u001b[1m{\u001b[0m\u001b[32m'devset'\u001b[0m: \u001b[1;36m36.7\u001b[0m\u001b[1m}\u001b[0m\u001b[1m}\u001b[0m,\n",
+       "    \u001b[32m'openai/meta-llama/Llama-3.2-1B-Instruct:isaac:qmmwc'\u001b[0m: \u001b[1m{\u001b[0m\n",
+       "        \u001b[32m'vanilla'\u001b[0m: \u001b[1m{\u001b[0m\u001b[32m'devset'\u001b[0m: \u001b[1;36m34.0\u001b[0m\u001b[1m}\u001b[0m,\n",
+       "        \u001b[32m'bfrs'\u001b[0m: \u001b[1m{\u001b[0m\u001b[32m'devset'\u001b[0m: \u001b[1;36m58.0\u001b[0m\u001b[1m}\u001b[0m\n",
        "    \u001b[1m}\u001b[0m\n",
        "\u001b[1m}\u001b[0m\n"
       ]
@@ -1525,7 +1539,7 @@
    "outputs": [
     {
      "data": {
-      "image/png": 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",
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fZHn/5TNnztxKOTfVtGlT+fj46P3337c5BDg3ltu4cWO5urpqwoQJNnsOZ8yYofj4+Cyv4J7b0vdkZlx+SkqKJk+efMtjtmrVSrGxsZle+4zLadOmjdLS0jRs2LBM86Smpma6fdX1Bg0aJMMw9Pzzz+vixYuZpm/evFmzZ8+WZP+2mFtSU1M1bdo083FKSoqmTZumwMBAValSRVLW633Dhg1at27dLS/30qVLSkpKsomFh4fL29tbycnJkm6PbQ4AcA17ugEAWcru8O6MWrRooQYNGuh///ufjhw5okqVKumXX37R999/rx49eph7kCtXrqx27dpp8uTJio+PV61atbRixQodOHAg05gffPCBVq1apRo1auill15SuXLldO7cOf31119avnx5pvtP5wYfHx9NmTJFzz//vB544AG1bdtWgYGBOnr0qH788UfVrl07y+bSHoGBgerfv7+GDBmihx9+WI899pj27t2ryZMnq1q1ajYXsHKUWrVqqUCBAurQoYNef/11WSwWzZ07918dPty+fXvNmTNHvXr10p9//qm6desqMTFRy5cvV9euXfX444+rXr166tKli4YPH66tW7fqoYceUr58+bR//34tWLBA48ePz/Z6Ael5T5o0SV27dlVERISef/55lS5dWhcuXNDq1au1aNEivfvuu5Ls3xZzS2hoqEaMGKEjR46oTJky+vLLL7V161Z9/PHH5p7/Rx99VN9++61atmypRx55RIcPH9bUqVNVrly5LL9EsMe+ffvUqFEjtWnTRuXKlZOLi4u+++47nTp1Sm3btpV0e2xzAIBraLoBALfMyclJixYt0sCBA/Xll19q5syZCgsL06hRo9S7d2+beT/99FMFBgZq3rx5WrhwoRo2bKgff/wx02HDwcHB+vPPPzV06FB9++23mjx5sgICAlS+fHmNGDHCYbU888wzCg0N1QcffKBRo0YpOTlZhQsXVt26dbO9mri9Bg8erMDAQE2cOFE9e/aUv7+/Xn75Zb3//vvZHpqdmwICArR48WL17t1b77zzjgoUKKDnnntOjRo1UtOmTW9pTGdnZ/3000967733NH/+fH3zzTcKCAhQnTp1VKFCBXO+qVOnqkqVKpo2bZrefvttubi4KCwsTM8995xq16590+V06dJF1apV0+jRozVnzhydOXNGXl5eeuCBBzRz5kyzgczJtpgbChQooNmzZ6t79+6aPn26goODNXHiRL300kvmPB07dlRMTIymTZumpUuXqly5cvrss8+0YMECrV69+paWW7RoUbVr104rVqzQ3Llz5eLiooiICH311Vdq1aqVOV9eb3MAgGssBlfJAAAAsFv9+vUVGxub5SkQAABcj3O6AQAAAABwEJpuAAAAAAAchKYbAAAAAAAH4ZxuAAAAAAAchD3dAAAAAAA4CE03AAAAAAAOQtMNALhlI0eOVEREhKxWa16nckP169fXfffd958sa9asWbJYLDpy5Mh/sjzgRqZOnapixYopOTk5r1MBgHsWTTcA4JYkJCRoxIgR6tu3r5ycrv06uXjxogYNGqT77rtPnp6eCggIUOXKlfXGG28oOjraYflER0dr8ODB2rp1q8OWkdH777+vhQsXOnQZ6Q18+o+7u7tCQ0PVtGlTTZgwQRcuXHDo8m/kzJkzeuONNxQRESEPDw8FBQWpevXq6tu3ry5evJjj8f744w8NHjxYcXFx/yqvsLAwWSwWde/ePdO01atXy2Kx6Ouvv/5Xy5CkkydPql+/fmrQoIG8vb1lsVi0evXqbOf/448/VKdOHeXPn18hISF6/fXXs1xPycnJ6tu3r0JDQ+Xh4aEaNWpo2bJltzxmx44dlZKSomnTpv2regEAt46mGwBwSz799FOlpqaqXbt2ZuzKlSt68MEHNWrUKNWtW1djxozR22+/rQceeEDz58/Xvn37HJZPdHS0hgwZkudN9/PPP6/Lly+rePHiubasoUOHau7cuZoyZYrZTPbo0UMVKlTQ9u3bc2059jp37pyqVq2qOXPm6JFHHtGECRPUq1cvlSpVSlOmTFFsbGyOx/zjjz80ZMiQf910p5s+fbpDv+TZu3evRowYoRMnTqhChQo3nHfr1q1q1KiRLl26pDFjxujFF1/Uxx9/rKeeeirTvB07dtSYMWP07LPPavz48XJ2dlbz5s31+++/39KY7u7u6tChg8aMGSOunQsAecMlrxMAANyZZs6cqccee0zu7u5mbOHChdqyZYvmzZunZ555xmb+pKQkpaSk/Ndp/uecnZ3l7Oycq2M2a9ZMVatWNR/3799fK1eu1KOPPqrHHntMu3fvloeHR64u80ZmzJiho0ePau3atapVq5bNtISEBLm6uv5nuWSlfPny2rt3rz744ANNmDDBIcuoUqWKzp49K39/f3399ddZNtDp3n77bRUoUECrV6+Wj4+PpKt75F966SX98ssveuihhyRJf/75p7744guNGjVKffr0kSS1b99e9913n9566y398ccfOR5Tktq0aaORI0dq1apVatiwYa6vCwDAjbGnGwCQY4cPH9b27dvVuHFjm/jBgwclSbVr1870HHd3d7M5mDlzpiwWi7Zs2ZJpvvfff1/Ozs46ceKEpGvnY+/atUsNGjRQ/vz5VbhwYY0cOdJ8zurVq1WtWjVJUqdOnczDsWfNmmUz9o3GSJecnKxBgwapVKlScnNzU9GiRfXWW2/ZnBNrsViUmJio2bNnm8vq2LGjpOzP6f75559Vr149eXt7y8fHR9WqVdP8+fOzWr12adiwoQYMGKB//vlHn332mc20PXv2qHXr1vL395e7u7uqVq2qRYsWmdM3bdoki8Wi2bNnZxp36dKlslgsWrx4cbbLPnjwoJydnVWzZs1M03x8fGy+iJGkDRs26OGHH5avr6/y58+vevXqae3ateb0wYMH680335QklShRwlyn6eswNjZWe/bs0aVLl26+YnS1+Wzfvr1D93Z7e3vL39//pvMlJCRo2bJleu6558ztX7raTHt5eemrr74yY19//bWcnZ318ssvmzF3d3d17txZ69at07Fjx3I8pnT1CwJ/f399//33t1wvAODW0XQDAHIsfY/bAw88YBNPP6R6zpw5NzyUtXXr1vLw8NC8efMyTZs3b57q16+vwoULm7Hz58/r4YcfVqVKlTR69GhFRESob9+++vnnnyVJkZGRGjp0qCTp5Zdf1ty5czV37lw9+OCDdo8hSVarVY899pg+/PBDtWjRQh999JGeeOIJjR07Vk8//bQ539y5c+Xm5qa6deuay+rSpUu29c6aNUuPPPKIzp07p/79++uDDz5Q5cqVtWTJkmyfY4/nn39ekvTLL7+Ysb///ls1a9bU7t271a9fP40ePVqenp564okn9N1330mSqlatqpIlS2ZqziTpyy+/VIECBdS0adNsl1u8eHGlpaVp7ty5N81x5cqVevDBB5WQkKBBgwbp/fffV1xcnBo2bKg///xTkvTkk0+apymMHTvWXKeBgYGSpIkTJyoyMtKc3x7/+9//lJqaqg8++OCG8125ckWxsbF2/dzKBQN37Nih1NRUmyMVJMnV1VWVK1e2+eJpy5YtKlOmjE0jLUnVq1eXJPPUiZyMme6BBx6w+aIDAPAfMgAAyKF33nnHkGRcuHDBJn7p0iWjbNmyhiSjePHiRseOHY0ZM2YYp06dyjRGu3btjNDQUCMtLc2M/fXXX4YkY+bMmWasXr16hiRjzpw5Ziw5OdkICQkxWrVqZcY2btyY6bk5HWPu3LmGk5OT8dtvv9k8f+rUqYYkY+3atWbM09PT6NChQ6ZlzZw505BkHD582DAMw4iLizO8vb2NGjVqGJcvX7aZ12q1Znp+VmNt3Lgx23l8fX2N+++/33zcqFEjo0KFCkZSUpLNcmrVqmWULl3ajPXv39/Ily+fce7cOTOWnJxs+Pn5GS+88MIN84qJiTECAwMNSUZERITxyiuvGPPnzzfi4uIy1Ve6dGmjadOmNrVeunTJKFGihNGkSRMzNmrUKJv1ltGgQYMMScaqVatumJdhGEbx4sWNRx55xDAMw+jUqZPh7u5uREdHG4ZhGKtWrTIkGQsWLDDnT4/Z85NVboZhGAsWLMg2v/Rpv/76a6ZpTz31lBESEmI+Ll++vNGwYcNM8/3999+GJGPq1Kk5HjPdyy+/bHh4eGSZPwDAsdjTDQDIsbNnz8rFxUVeXl42cQ8PD23YsME8VHjWrFnq3LmzChUqpO7du9scot2+fXtFR0dr1apVZmzevHny8PBQq1atbMb18vLSc889Zz52dXVV9erVdejQIbtztmeMBQsWKDIyUhERETZ7ONPPg82Yq72WLVumCxcuqF+/fpkOu7ZYLDke73peXl7mVczPnTunlStXqk2bNrpw4YKZ/9mzZ9W0aVPt37/fPGz/6aef1pUrV/Ttt9+aY/3yyy+Ki4uz2aufleDgYG3btk2vvPKKzp8/r6lTp+qZZ55RUFCQhg0bZh7lsHXrVu3fv1/PPPOMzp49a+aTmJioRo0a6ddff7Vr7/HgwYNlGIbq16+fo3Xzzjvv3HRvd6VKlbRs2TK7fkJCQnK0fEm6fPmyJMnNzS3TNHd3d3N6+rzZzZdxrJyMma5AgQK6fPmy3YfoAwByDxdSAwDkKl9fX40cOVIjR47UP//8oxUrVujDDz/UxIkT5evrq3fffVeS1KRJExUqVEjz5s1To0aNZLVa9fnnn+vxxx+Xt7e3zZhFihTJ1KAWKFAgR1futmeM/fv3a/fu3eZhzdc7ffq03ctLl36eu6PuE37x4kUFBQVJkg4cOCDDMDRgwAANGDAgy/lPnz6twoULq1KlSoqIiNCXX36pzp07S7p6aHnBggXtuthWoUKFNGXKFE2ePFn79+/X0qVLNWLECA0cOFCFChXSiy++qP3790uSOnTokO048fHxKlCgQE7LtkvJkiX1/PPP6+OPP1a/fv2ynKdAgQKZrk2Qm9IvcJfVfbKTkpJsLoDn4eGR7XwZx8rJmOnSvwjJjS96AAA5Q9MNAMixgIAApaam6sKFC5ka5IyKFy+uF154QS1btlTJkiU1b948s+l2dnbWM888o+nTp2vy5Mlau3atoqOjbfZGp8vuauBGDm6BZM8YVqtVFSpU0JgxY7Kct2jRonYv779w/PhxxcfHq1SpUpJk7jXu06dPtudkp88rXd3b/d577yk2Nlbe3t5atGiR2rVrJxcX+/88sFgsKlOmjMqUKaNHHnlEpUuX1rx58/Tiiy+a+YwaNUqVK1fO8vnXHy2R2/73v/9p7ty5GjFihJ544olM01NSUnTu3Dm7xgoMDMzxlekLFSok6ep9va938uRJhYaG2sybfiTC9fNJMufNyZjpzp8/r/z58/+nV7kHAFxF0w0AyLGIiAhJV69iXrFixZvOX6BAAYWHh2vnzp028fbt22v06NH64Ycf9PPPPyswMPCGF/C6kdzYgxceHq5t27apUaNGNx3P3uWFh4dLknbu3GnT8OaG9AuZpa+zkiVLSpLy5ctn197bp59+WkOGDNE333yj4OBgJSQkqG3btrecT8mSJVWgQAGzGUyv3cfH56b5OGoPbHh4uJ577jlNmzZNNWrUyDT9jz/+UIMGDewa6/DhwwoLC8vR8u+77z65uLho06ZNatOmjRlPSUnR1q1bbWKVK1fWqlWrlJCQYHMxtQ0bNpjTczpmxtwjIyNzlDsAIHdwTjcAIMeioqIkXb31VEbbtm1TbGxspvn/+ecf7dq1S2XLlrWJV6xYURUrVtQnn3yib775Rm3bts3RXtaMPD09JUlxcXG39Hzp6v2MT5w4oenTp2eadvnyZSUmJtosz55lPfTQQ/L29tbw4cPNw4TT5WRP/fVWrlypYcOGqUSJEnr22WclSUFBQapfv76mTZuW5V7QM2fO2DyOjIxUhQoV9OWXX+rLL79UoUKFbK74np0NGzbYrIt0f/75p86ePWu+zlWqVFF4eLg+/PBDXbx48Yb55Mbrl5133nlHV65cyfIWcY4+p9vX11eNGzfWZ599Zp57L139wuTixYs29/du3bq10tLS9PHHH5ux5ORkzZw5UzVq1DCPtMjJmOn++uuvTPdUBwD8N9jTDQDIsZIlS+q+++7T8uXL9cILL5jxZcuWadCgQXrsscdUs2ZNeXl56dChQ/r000+VnJyswYMHZxqrffv26tOnjyRleWi5vcLDw+Xn56epU6fK29tbnp6eqlGjhkqUKGH3GM8//7y++uorvfLKK1q1apVq166ttLQ07dmzR1999ZWWLl1q3qapSpUqWr58ucaMGaPQ0FCVKFEiyz2pPj4+Gjt2rF588UVVq1ZNzzzzjAoUKKBt27bp0qVLWd4r+3o///yz9uzZo9TUVJ06dUorV67UsmXLVLx4cS1atMjmAm2TJk1SnTp1VKFCBb300ksqWbKkTp06pXXr1un48ePatm2bzdhPP/20Bg4caN4P2snp5t/Hz507V/PmzVPLli1VpUoVubq6avfu3fr000/l7u6ut99+W5Lk5OSkTz75RM2aNVP58uXVqVMnFS5cWCdOnNCqVavk4+OjH374wVyf0tXDwdu2bat8+fKpRYsW8vT01ODBgzVkyBCtWrUqxxdTk67t7c5qXf+bc7rTT5X4+++/JV1dL7///rukq41+uvfee0+1atVSvXr19PLLL+v48eMaPXq0HnroIT388MPmfDVq1NBTTz2l/v376/Tp0ypVqpRmz56tI0eOaMaMGTbLtndMSdq8ebPOnTunxx9//JbqBAD8S3l45XQAwB1szJgxhpeXl3Hp0iUzdujQIWPgwIFGzZo1jaCgIMPFxcUIDAw0HnnkEWPlypVZjnPy5EnD2dnZKFOmTJbT69WrZ5QvXz5TvEOHDkbx4sVtYt9//71Rrlw5w8XFxeb2YTkZIyUlxRgxYoRRvnx5w83NzShQoIBRpUoVY8iQIUZ8fLw53549e4wHH3zQ8PDwMCSZtw+7/pZh6RYtWmTUqlXL8PDwMHx8fIzq1asbn3/+eZY1p0sfK/3H1dXVCAkJMZo0aWKMHz/eSEhIyPJ5Bw8eNNq3b2+EhIQY+fLlMwoXLmw8+uijxtdff51p3v3795vj//777zfMJ9327duNN99803jggQcMf39/w8XFxShUqJDx1FNPGX/99Vem+bds2WI8+eSTRkBAgOHm5mYUL17caNOmjbFixQqb+YYNG2YULlzYcHJyslmHvXv3NiwWi7F79+6b5pbxlmHX1+ns7JzplmH/hm5we7Hr/fbbb0atWrUMd3d3IzAw0OjWrVuWr9/ly5eNPn36GCEhIYabm5tRrVo1Y8mSJVku394x+/btaxQrVuymt6gDADiGxTD+xbFtAIB7Vnx8vEqWLKmRI0eaV7++FbGxsSpUqJAGDhyY7RW3cW+rXr26ihcvrgULFuR1Knec5ORkhYWFqV+/fnrjjTfyOh0AuCdxTjcA4Jb4+vrqrbfe0qhRo+y613J2Zs2apbS0ND3//PO5mB3uFgkJCdq2bZuGDh2a16nckWbOnKl8+fLplVdeyetUAOCexZ5uAECeWLlypXbt2qUBAwaoQYMG+vbbb/M6JQAAgFxH0w0AyBP169fXH3/8odq1a+uzzz5T4cKF8zolAACAXEfTDQAAAACAg3BONwAAAAAADkLTDQAAAACAg7jkdQKOZrVaFR0dLW9vb1kslrxOBwAAAABwFzAMQxcuXFBoaKicnLLfn33XN93R0dEqWrRoXqcBAAAAALgLHTt2TEWKFMl2ep433SdOnFDfvn31888/69KlSypVqpRmzpypqlWrSrr67cGgQYM0ffp0xcXFqXbt2poyZYpKly5t1/je3t6Srq4IHx8fh9UBAAAAALh3JCQkqGjRombPmZ08bbrPnz+v2rVrq0GDBvr5558VGBio/fv3q0CBAuY8I0eO1IQJEzR79myVKFFCAwYMUNOmTbVr1y65u7vfdBnph5T7+PjQdAMAAAAActXNTmPO01uG9evXT2vXrtVvv/2W5XTDMBQaGqrevXurT58+kqT4+HgFBwdr1qxZatu27U2XkZCQIF9fX8XHx9N0AwAAAAByhb29Zp5evXzRokWqWrWqnnrqKQUFBen+++/X9OnTzemHDx9WTEyMGjdubMZ8fX1Vo0YNrVu3Li9SBgAAAADAbnl6ePmhQ4c0ZcoU9erVS2+//bY2btyo119/Xa6ururQoYNiYmIkScHBwTbPCw4ONqddLzk5WcnJyebjhIQESVJqaqpSU1MlSU5OTnJycpLVapXVajXnTY+npaUp4wEA2cWdnZ1lsVjMcTPGJSktLc2uuIuLiwzDsIlbLBY5OztnyjG7ODVREzVREzVREzVREzVREzVREzX9dzVdX0d28rTptlqtqlq1qt5//31J0v3336+dO3dq6tSp6tChwy2NOXz4cA0ZMiRTfMuWLfL09JQkBQYGKjw8XIcPH9aZM2fMeUJCQhQSEqKDBw/qwoULZrxo0aIKCAjQnj17lJSUZMZLliwpHx8f7dixw+YFL1u2rPLly6edO3fa5HDffffpypUr2rt3rxlzdnZWhQoVlJCQoEOHDplxd3d3RURE6OzZszp27JgZ9/b2Vnh4uGJiYmy+ePD391exYsV09OhRnTt3jpqoiZpu45oqVKggNzc3bdq0yaamqlWrKiUlRdu3b7epqVq1aoqPj9eePXvMuIeHhypVqqTY2FibWn19fRUZGano6GgdP37cjGf3uVekSBEVKVJE+/btU3x8vE3uQUFB2rlzpy5fvmzGIyIi5Ofnpy1bttjUVLFiRbm6ulITNVETNVETNd1RNb3++uuaMWOGzbTSpUtrzpw5kqSzZ89qypQp2rRpk3nRrI4dO6pBgwY3rGnJkiUaPny4zpw5o1KlSqlXr15q2rQpr9NdVlNiYqLskafndBcvXlxNmjTRJ598YsamTJmid999VydOnNChQ4cUHh6uLVu2qHLlyuY89erVU+XKlTV+/PhMY2a1p7to0aI6e/aseZx9xm810tLSdPr0aXOFWiwWZbVKsoqnnzDvyLi9ueRWnJqoiZocX5Ozs7PCwsLMb28zxqXb99vc9Bzvpm+oqYmaqImaqOnermnQoEH69ttvtWTJErMmZ2dn88LOzZo1U1xcnCZNmiR/f3/Nnz9fQ4YM0fr16/XAAw9kWdOCBQvUsWNHTZ48WdWqVdOECRP0zTffaPfu3QoJCeF1uotqSkhIUEBAwE3P6c7TpvuZZ57RsWPHbC6k1rNnT23YsEF//PGHDOPqhdT69Omj3r17S7paWFBQUK5dSO3kyZOKi4tTUFCQ8ufPb/6RDACOYLVaFR0drXz58qlYsWJ85gAAkIcGDx6shQsXauvWrVlO9/Ly0pQpU/T888+bsYCAAI0YMUIvvvhils+pUaOGqlWrpokTJ0q6+ru/aNGi6t69u/r165frNSDv2HshtTw9vLxnz56qVauW3n//fbVp00Z//vmnPv74Y3388ceSrn4r0aNHD7377rsqXbq0ecuw0NBQPfHEE/96+WlpaWbDHRAQ8K/HAwB7BAYGKjo6WqmpqcqXL19epwMAwD1t//79Cg0Nlbu7u6KiojR8+HAVK1ZMklSrVi19+eWXeuSRR+Tn56evvvpKSUlJql+/fpZjpaSkaPPmzerfv78Zc3JyUuPGjbkQ9D0sT5vuatWq6bvvvlP//v01dOhQlShRQuPGjdOzzz5rzvPWW28pMTFRL7/8suLi4lSnTh0tWbLErnt038yVK1ckSfnz5//XYwGAvVxdXSVd/eKPphsAgLxTo0YNzZo1S2XLltXJkyc1ZMgQ1a1bVzt37pS3t7e++uorPf300woICJCLi4vy58+v7777TqVKlcpyvNjYWKWlpWV5IeiM5x/j3pKnTbckPfroo3r00UeznW6xWDR06FANHTrUYTlweCeA/xKfOQAA3B6aNWtm/r9ixYqqUaOGihcvrq+++kqdO3fWgAEDFBcXp+XLl6tgwYJauHCh2rRpo99++00VKlTIw8xxJ8nzphsAAAAAbgd+fn4qU6aMDhw4oIMHD2rixInauXOnypcvL0mqVKmSfvvtN02aNElTp07N9PyCBQvK2dlZp06dsomfOnVKISEh/0kNuP045XUCwN0kLCxM48aN+1djDB482OZq/Y5w5MgRWSyWbC8acq/KjdcPAADcuS5evKiDBw+qUKFCunTpkqSr52RnlH4l7Ky4urqqSpUqWrFihRmzWq1asWKFoqKiHJc4bmvs6c7CB1ti/9Pl9bu/YI7m79ixo2bPnq3hw4fbXAFx4cKFatmyZZa3M7LXrFmz1KlTJ0lXD4ENDQ1VkyZNNGLECAUFBd3yuI5ksVj03Xff2XVxvcWLF2vUqFH666+/lJaWpvLly6tbt27q2LFjjpY5a9Ys9ejRQ3FxcTbxjRs3mveDv1V9+vRR9+7d/9UYGXXs2FFxcXFauHChGStatKhOnjypggVztu3Zq0SJEpo+fbpefPFF/fPPP9nOd+TIERUvXtwhOeSVrNY3AAC4PfXp00ctWrRQ8eLFFR0drUGDBsnZ2Vnt2rWTn5+fSpUqpS5duujDDz9UQECAFi5cqGXLlmnx4sXmGI0aNVLLli312muvSZJ69eqlDh06qGrVqqpevbrGjRunxMRE829s3Htouu9Q7u7uGjFihLp06WLeRzC3+Pj4aO/evbJardq2bZs6deqk6OhoLV26NNO8aWlpslgsmb4BvB199NFH6tGjh/r27aspU6bI1dVV33//vV555RXt3LlTH3744b9eRmBg4L8ew8vLS15eXv96nBtxdnZ22CFO27dv1/nz51WvXj1t3Lgx030V4+Li1KhRI1WpUsW8MmhO3UnbHQAAuH0dP35c7dq109mzZxUYGKg6depo/fr15t90P/30k/r166cWLVro4sWLKlWqlGbPnq3mzZubYxw8eFCxsdd22j399NM6c+aMBg4cqJiYGFWuXFlLlizJdHE13Dv4i/UO1bhxY4WEhGj48OE3nO+bb75R+fLl5ebmprCwMI0ePfqmY1ssFoWEhCg0NFTNmjXT66+/ruXLl+vy5cuaNWuW/Pz8tGjRIpUrV05ubm46evSozp8/r/bt26tAgQLKnz+/mjVrpv3795tjpj9v8eLFKlu2rPLnz6/WrVvr0qVLmj17tsLCwlSgQAG9/vrrNk1aWFiYhg0bpnbt2snT01OFCxfWpEmTbKZLUsuWLWWxWMzH1zt27Jh69+6tHj166P3331e5cuVUqlQp9e7dW6NGjdLo0aO1YcMGSdLq1atlsVj0448/qmLFinJ3d1fNmjW1c+dOc3qnTp0UHx8vi8Uii8WiwYMHm/lkPDzZYrFo2rRpevTRR5U/f35FRkZq3bp1OnDggOrXry9PT0/VqlVLBw8eNJ9z/eHl6cvI+JNeZ1pamjp37qwSJUrIw8NDZcuW1fjx423Gmj17tr7//nvzuatXr87y8PI1a9aoevXqcnNzU6FChdSvXz+lpqaa0+vXr6/XX39db731lvz9/RUSEmLWndH333+vhx9+WPny5VNgYKBCQkLMn6CgIPXo0UO+vr6aN2+eeUGx5ORk9enTR4ULF5anp6dq1Kih1atXZ9p+crrdXc8wDA0ePFjFihWTm5ubQkND9frrr9vMc+nSJb3wwgvy9vZWsWLFzFsYptuxY4caNmwoDw8PBQQE6OWXX9bFixdvuL4BAMDt6YsvvlB0dLSSk5N1/PhxffHFFwoPDzenly5dWt98841OnTqlxMREbdu2zeae3dLVI/eu/5votdde0z///KPk5GRt2LBBNWrU+C/KwW2KpvsO5ezsrPfff18fffSRjh8/nuU8mzdvVps2bdS2bVvt2LFDgwcP1oABAzRr1qwcLcvDw0NWq9VswC5duqQRI0bok08+0d9//62goCB17NhRmzZt0qJFi7Ru3ToZhqHmzZubt2VLf96ECRP0xRdfaMmSJVq9erVatmypn376ST/99JPmzp2radOm6euvv7ZZ/qhRo1SpUiVt2bJF/fr10xtvvKFly5ZJuno4tyTNnDlTJ0+eNB9f7+uvv9aVK1fUp0+fTNO6dOkiLy8vff755zbxN998U6NHj9bGjRsVGBioFi1a6MqVK6pVq5bGjRsnHx8fnTx5UidPnsxy3HTDhg1T+/bttXXrVkVEROiZZ55Rly5d1L9/f23atEmGYZiHI2UlfRknT57UgQMHVKpUKT344IOSrp4jVKRIES1YsEC7du3SwIED9fbbb+urr76SdPWQqTZt2ujhhx82x6hVq1amZZw4cULNmzdXtWrVtG3bNk2ZMkUzZszQu+++azPf7Nmz5enpqQ0bNmjkyJEaOnSo+VqkW7RokR5//PEsa+nXr582bNig77//Xt7e3mb8tdde07p16/TFF19o+/bteuqpp/Twww/bNNC3ut1l9M0332js2LGaNm2a9u/fr4ULF2a68ujo0aNVtWpVbdmyRV27dtWrr76qvXv3SpISExPVtGlTFShQQBs3btSCBQu0fPly8/Wzd30DAADg3sHh5Xewli1bqnLlyho0aJBmzJiRafqYMWPUqFEjDRgwQJJUpkwZ7dq1S6NGjbL7HOb9+/dr6tSpqlq1qtkkXblyRZMnT1alSpXMeRYtWqS1a9eaDca8efNUtGhRLVy4UE899ZT5vClTppjfHrZu3Vpz587VqVOn5OXlpXLlyqlBgwZatWqVnn76aTOH2rVrm+eulylTRmvXrtXYsWPVpEkT89AfPz+/Gx4uvW/fPvn6+qpQoUKZprm6uqpkyZLat2+fTXzQoEFq0qSJpKvNZpEiRfTdd9+pTZs28vX1NY8IuJlOnTqpTZs2kqS+ffsqKipKAwYMUNOmTSVJb7zxxg3P8UlfhmEYatWqlXx9fTVt2jRJUr58+TRkyBBz3hIlSmjdunX66quv1KZNG3l5ecnDw0PJyck3zHXy5MkqWrSoJk6cKIvFooiICEVHR6tv374aOHCgeRh3xYoVNWjQIElXv/mdOHGiVqxYYa6nEydOaPv27Ta330j3+eefa8yYMfrxxx9VunRpM3706FHNnDlTR48eVWhoqKSrzeuSJUs0c+ZMvf/++5JufbvL6OjRowoJCVHjxo2VL18+FStWTNWrV7eZp3nz5urataukq6/X2LFjtWrVKpUtW1bz589XUlKS5syZY567P3HiRLVo0UIjRoxQcHCwXesbAAAA9w6a7jvciBEj1LBhwyz3tO7evTvTHsfatWtr3LhxSktLk7Ozc5ZjxsfHy8vLS1arVUlJSapTp44++eQTc7qrq6sqVqxosxwXFxebw2YCAgJUtmxZ7d6924zlz5/f5nCd4OBghYWF2Zy/HBwcrNOnT9vkc/2VHqOiov6TK0xnXK6/v3+meuyVcV2ln8uTce9qcHCwkpKSlJCQIB8fn2zHefvtt7Vu3Tpt2rRJHh4eZnzSpEn69NNPdfToUV2+fFkpKSk5vvr57t27FRUVZXP/6Nq1a+vixYs6fvy4ee51xlokqVChQjav16JFi1SnTh35+fnZzPfXX3+pc+fO+uCDD8wvG9Lt2LFDaWlpKlOmjE08OTlZAQEB5uNb3e4yeuqppzRu3DiVLFlSDz/8sJo3b64WLVrIxeXaR2HGZaR/sZJe4+7du1WpUiWbi+XVrl1bVqtVe/fu5VwtAMBtY/z58TefCbiNvVHgjbxOIdfQdN/hHnzwQTVt2lT9+/fP8RW4s+Pt7a2//vpLTk5OKlSokE2DJ1093Dxjc2avfPny2Ty2WCxZxrK7BcO/UaZMGcXHxys6Otrcm5ouJSVFBw8eVIMGDXJ9uZJt3enrLavYjer+7LPPNHbsWK1evVqFCxc241988YX69Omj0aNHKyoqSt7e3ho1apR5fnpuu9nrtWjRIj322GM285w5c0YtW7ZUq1atsvxy6OLFi3J2dtbmzZszfRGU8QuZW93uMipatKj27t2r5cuXa9myZeratatGjRqlNWvWmLX9V9skAAAA7g2c030X+OCDD/TDDz9o3bp1NvHIyEitXbvWJrZ27VqVKVMm273c0tV7EZYqVUolS5bM1HBnJTIyUqmpqTaN3tmzZ7V3716VK1cuh9Vktn79+kyPIyMjzcf58uXLdIXs67Vq1Ur58uXL8kJyU6dOVWJiotq1a5ftcs+fP699+/aZy3V1db3pMnPLunXr9OKLL2ratGmqWbOmzbT0Q6u7du2q+++/X6VKlbK5KJu9uaZf4C3j7ebWrl0rb29vFSlSxK48L168qFWrVtkcXXHlyhW1bt1aQUFBmj59epbPu//++5WWlqbTp0+rVKlSNj83OkT7Vrc7Dw8PtWjRQhMmTNDq1au1bt067dixw64aIyMjtW3bNiUmJpqxtWvXysnJSWXLlpX0324bAAAAuP3RdN8FKlSooGeffVYTJkywiffu3VsrVqzQsGHDtG/fPs2ePVsTJ0684UW/bkXp0qX1+OOP66WXXtLvv/+ubdu26bnnnlPhwoWzvaBWTqxdu1YjR47Uvn37NGnSJC1YsEBvvHHtcJOwsDCtWLFCMTExOn/+fJZjFCtWTCNHjtS4ceP0v//9T3v27NHBgwc1ZswYvfXWW+rdu3emq0oOHTpUK1as0M6dO9WxY0cVLFjQvBd4WFiYLl68qBUrVig2NlaXLl3613VmJSYmRi1btlTbtm3VtGlTxcTEKCYmRmfOnJF0dd1v2rRJS5cu1b59+zRgwIBMF5MLCwvT9u3btXfvXsXGxmZ5kbGuXbvq2LFj6t69u/bs2aPvv/9egwYNUq9evey+LdeSJUtUpkwZmyvI9+jRw7wwW1xcnJl/+k9KSorKlCmjZ599Vu3bt9e3336rw4cP688//9Tw4cP1448/Zru8W9nuZs2apRkzZmjnzp06dOiQPvvsM3l4eNh9r/Bnn31W7u7u6tChg3bu3KlVq1ape/fuev75581Dy+1Z3wAAALh30HTfJYYOHZrpENgHHnhAX331lb744gvdd999GjhwoIYOHZprh6FnNHPmTFWpUkWPPvqooqKiZBiGfvrpp0yH6t6K3r17a9OmTbr//vv17rvvasyYMTbnBY8ePVrLli1T0aJFdf/992c7To8ePfTdd9/pt99+U9WqVXXfffdp/vz5mjJlSpb36P7ggw/0xhtvqEqVKoqJidEPP/wgV1dXSVKtWrX0yiuv6Omnn1ZgYKBGjhz5r+vMyp49e3Tq1CnNnj1bhQoVMn+qVasm6eqV15988kk9/fTTqlGjhs6ePWteBCzdSy+9pLJly6pq1aoKDAzMdPSDJBUuXFg//fST/vzzT1WqVEmvvPKKOnfurHfeecfuXL///vtMh5ZPnjxZ8fHxqlatmk3+6T9//PGHpKvbT/v27dW7d2+VLVtWTzzxhDZu3HjT+3jndLvz8/PT9OnTVbt2bVWsWFHLly/XDz/8YHPu+I3kz59fS5cu1blz51StWjW1bt1ajRo10sSJE8157FnfAAAAuHdYjIzHk96FEhIS5Ovrq/j4+EwXqUpKStLhw4dVokQJubu751GGuJGwsDD16NFDPXr0+M+WuXr1ajVo0EDnz5/PdEEwZC01NVXBwcH6+eefM10NHJnx2QMAcDQupIY73Z1wIbUb9ZoZsacbwL927tw59ezZ09wDDwAAAOAqrl4O4F8LCgrK0aHoAAAAwL2Cphu3tSNHjvzny6xfv77u8rMuAAAAAPxHOLwcAAAAAAAHoekGAAAAAMBBaLoBAAAAAHAQmm4AAAAAAByEphsAAAAAAAeh6QYAAAAAwEFouoFcFhYWpnHjxuV1Grlu1qxZ8vPzy+s08tTgwYNVuXLlvE4DAAAAdxDu052F8efH/6fLe6PAGzl+TseOHTV79mzzsb+/v6pVq6aRI0eqYsWKuZLXkSNHVKJECW3ZssXuRmPw4MFauHChtm7dmis52OP6dZFu//79KlWqVK4u69KlSxo2bJi++uornThxQt7e3ipXrpx69eqlxx9/PNeW07FjR8XFxWnhwoU2cYvFYv7f29tbZcuW1TvvvJOry85raWlpGjVqlGbNmqV//vlHHh4eKl26tF566SW9+OKLeZ0eAAAAkCPs6b6DPfzwwzp58qROnjypFStWyMXFRY8++mhep2WXK1eu5Op4GddF+k+JEiVydRmS9Morr+jbb7/VRx99pD179mjJkiVq3bq1zp49m+vLys7MmTN18uRJbdq0SbVr11br1q21Y8eO/2z5jjZkyBCNHTtWw4YN065du7Rq1Sq9/PLLiouLc+hyU1JSHDo+AAAA7k003XcwNzc3hYSEKCQkRJUrV1a/fv107NgxnTlzRpK0Y8cONWzYUB4eHgoICNDLL7+sixcvms+3Wq0aOnSoihQpIjc3N1WuXFlLliwxp6c3rffff78sFovq168vSVq9erWqV68uT09P+fn5qXbt2vrnn380a9YsDRkyRNu2bZPFYpHFYtGsWbMkXd1DO2XKFD322GPy9PTUe++9p7S0NHXu3FklSpSQh4eHypYtq/HjbY8y6Nixo5544gkNGTJEgYGB8vHx0SuvvJKpQcq4LtJ/nJ2dJUnff/+9HnjgAbm7u6tkyZIaMmSIUlNTJUl9+vSx+aJi3LhxslgsNuuhVKlS+uSTTyRJixYt0ttvv63mzZsrLCxMVapUUffu3fXCCy/Y5HPp0iW98MIL8vb2VrFixfTxxx/bTL/RazN48GDNnj1b33//vbkeV69ebT7Xz89PISEhKlOmjIYNG6bU1FStWrXKnL5kyRLVqVNHfn5+CggI0KOPPqqDBw+a048cOSKLxaJvv/1WDRo0UP78+VWpUiWtW7fOJsdZs2apWLFiyp8/v1q2bJnlFwtTpkxReHi4XF1dVbZsWc2dO9dmusVi0bRp0/Too48qf/78ioyM1Lp163TgwAHVr19fnp6eqlWrlk1+ixYtUteuXfXUU0+pRIkSqlSpkjp37qw+ffqY81itVg0fPtzcdipVqqSvv/7anJ6Tbeu9995TaGioypYtK0k6fvy42rVrJ39/f3l6eqpq1arasGGDzXPnzp2rsLAw+fr6qm3btrpw4UKmdQMAAABINN13jYsXL+qzzz5TqVKlFBAQoMTERDVt2lQFChTQxo0btWDBAi1fvlyvvfaa+Zzx48dr9OjR+vDDD7V9+3Y1bdpUjz32mPbv3y9J+vPPPyVJy5cv18mTJ/Xtt98qNTVVTzzxhOrVq6ft27dr3bp1evnll2WxWPT000+rd+/eKl++vLm3+emnnzaXN3jwYLVs2VI7duzQCy+8IKvVqiJFimjBggXatWuXBg4cqLfffltfffWVTW0rVqzQ7t27tXr1an3++ef69ttvNWTIELvWy2+//ab27dvrjTfe0K5duzRt2jTNmjVL7733niSpXr16+v3335WWliZJWrNmjQoWLGg2uSdOnNDBgwfNLxxCQkL0008/3bTJGj16tKpWraotW7aoa9euevXVV7V3715Juulr06dPH7Vp08Zm732tWrUyLSM1NVUzZsyQJLm6uprxxMRE9erVS5s2bdKKFSvk5OSkli1bymq12jz/f//7n/r06aOtW7eqTJkyateunfllxIYNG9S5c2e99tpr2rp1qxo0aKB3333X5vnfffed3njjDfXu3Vs7d+5Uly5d1KlTJ5svACRp2LBhat++vbZu3aqIiAg988wz6tKli/r3769NmzbJMAyb7TIkJEQrV640vzzKyvDhwzVnzhxNnTpVf//9t3r27KnnnntOa9askaQcbVt79+7VsmXLtHjxYl28eFH16tXTiRMntGjRIm3btk1vvfWWzbo7ePCgFi5cqMWLF2vx4sVas2aNPvjgg2xzBQAAwL3NYhiGkddJOFJCQoJ8fX0VHx8vHx8fm2lJSUk6fPiwSpQoIXd3dzN+p5zT/dlnn5l5JyYmqlChQlq8eLEeeOABTZ8+XX379tWxY8fk6ekpSfrpp5/UokULRUdHKzg4WIULF1a3bt309ttvm+NWr15d1apV06RJk7I8p/vcuXMKCAjQ6tWrVa9evUx5ZXdOt8ViUY8ePTR27Ngb1vXaa68pJibG3GvZsWNH/fDDDzp27Jjy588vSZo6darefPNNxcfHy8nJKdO6kKRmzZppwYIFaty4sRo1aqT+/fub0z777DO99dZbio6OVlxcnAICArRhwwZVqVJFBQsW1JtvvqmFCxdq/fr1mjdvnvr27avjx49Lkn799Vc9++yzOnXqlCpVqqQ6deqodevWql27tjl+WFiY6tata+71NQxDISEhGjJkiF555RW7XpsbndPt7u4uZ2dnXb58WVarVWFhYdq8ebP8/f2zXKexsbEKDAzUjh07dN9995mv6yeffKLOnTtLknbt2qXy5ctr9+7dZmMcHx+vH3/80Rynbdu2WrJkiXmYd+3atVW+fHmbvfht2rRRYmKi+TyLxaJ33nlHw4YNkyStX79eUVFRmjFjhnl0wBdffKFOnTrp8uXLZi6tW7fW3r17Vb58edWqVUuPP/64mjVrJklKTk6Wv7+/li9frqioKHPZL774oi5duqT58+dnuR6y2raWLFmio0ePml9afPzxx+rTp4+OHDmS5focPHiwRo0apZiYGHl7e0uS3nrrLf36669av359lsvNSnafPQAA5Jb/+u9ZILfdSo/0X7tRr5kRe7rvYA0aNNDWrVu1detW/fnnn2ratKmaNWumf/75R7t371alSpXMpk662iRZrVbt3btXCQkJio6OtmkW0+fZvXt3tsv09/dXx44d1bRpU7Vo0ULjx4/XyZMn7cq3atWqmWKTJk1SlSpVFBgYKC8vL3388cc6evSozTyVKlUyG25JioqK0sWLF3Xs2LEs18XWrVs1YcIESdK2bds0dOhQeXl5mT8vvfSSTp48qUuXLsnPz0+VKlXS6tWrtWPHDrm6uurll1/Wli1bdPHiRa1Zs8bmy4UHH3xQhw4d0ooVK9S6dWv9/fffqlu3rtlUpst4MTuLxaKQkBCdPn1akm762tzM2LFjtXXrVv38888qV66cPvnkE5sGcf/+/WrXrp1KliwpHx8fhYWFSVKm9Zoxx0KFCkmSTY41atSwmT9jg5s+jz3bT8blBAcHS5IqVKhgE0tKSlJCQoIkqVy5ctq5c6fWr1+vF154QadPn1aLFi3Mi6gdOHBAly5dUpMmTWxe1zlz5tgcpm7PtlWhQgWbowS2bt2q+++/P9svMKSrX6qkN9zp6y59vQEAAADX4+rldzBPT0+bq3N/8skn8vX11fTp0x263JkzZ+r111/XkiVL9OWXX+qdd97RsmXLVLNmzZvmm9EXX3yhPn36aPTo0YqKipK3t7dGjRqV6fxZe1y/LtJdvHhRQ4YM0ZNPPplpWvoexvr162v16tVyc3NTvXr15O/vr8jISP3+++9as2aNevfubfO8fPnyqW7duqpbt6769u2rd999V0OHDlXfvn3NBi5fvnw2z7FYLJkO775VISEhKlWqlEqVKqWZM2eqefPm2rVrl4KCgiRJLVq0UPHixTV9+nSFhobKarXqvvvuy3QefMYc06+Knls53mw5N1u2k5OTqlWrpmrVqqlHjx767LPP9Pzzz+t///ufee77jz/+qMKFC9ssy83NTZL929b126SHh0eO6knP3xHrDQAAAHcH9nTfRSwWi5ycnHT58mVFRkZq27ZtSkxMNKevXbtWTk5OKlu2rHx8fBQaGqq1a9fajLF27VqVK1dO0rXzhNPPd87o/vvvV//+/fXHH3/ovvvuMw/pdXV1zXL+rKxdu1a1atVS165ddf/996tUqVI2eyrTbdu2zTz0WLp6iLKXl5eKFi1602U88MAD2rt3r9mkZvxxcrq6+aef171ixQrz3O369evr888/1759+8xYdsqVK6fU1FQlJSXZVffNXhvJ/vVYvXp1ValSxTxH/ezZs9q7d6/eeecdNWrUSJGRkTp//rxdeV2f4/UN6vWHT0dGRt5w+8lN6WMmJiaqXLlycnNz09GjRzO9punbhL3b1vUqVqyorVu36ty5c7leAwAAAO5NNN13sOTkZMXExCgmJka7d+9W9+7ddfHiRbVo0ULPPvus3N3d1aFDB+3cuVOrVq1S9+7d9fzzz5uH+L755psaMWKEvvzyS+3du1f9+vXT1q1b9cYbV8+fCAoKkoeHh5YsWaJTp04pPj5ehw8fVv/+/bVu3Tr9888/+uWXX7R//35FRkZKunro7eHDh7V161bFxsYqOTk52/xLly6tTZs2aenSpdq3b58GDBigjRs3ZpovJSVFnTt31q5du/TTTz9p0KBBeu2118ym+UYGDhyoOXPmaMiQIfr777+1e/duffHFF3rnnXfMeR588EFduHBBixcvtmm6582bp0KFCqlMmTLmvPXr19e0adO0efNmHTlyRD/99JPefvttNWjQ4IbncWRkz2sTFham7du3a+/evYqNjb3hLdZ69OihadOm6cSJEypQoIACAgL08ccf68CBA1q5cqV69eplV14ZpR/J8OGHH2r//v2aOHGizRXdpavbz6xZszRlyhTt379fY8aM0bfffmtzlfFb0bp1a40dO1YbNmzQP//8o9WrV6tbt24qU6aMIiIi5O3trT59+qhnz56aPXu2Dh48qL/++ksfffSReb92e7et67Vr104hISF64okntHbtWh06dEjffPNNpiu7AwAAAPai6b6DLVmyRIUKFVKhQoVUo0YN80rY9evXV/78+bV06VKdO3dO1apVU+vWrdWoUSNNnDjRfP7rr7+uXr16qXfv3qpQoYKWLFmiRYsWqXTp0pIkFxcXTZgwQdOmTVNoaKgef/xx5c+fX3v27FGrVq1UpkwZvfzyy+rWrZu6dOkiSWrVqpUefvhhNWjQQIGBgfr888+zzb9Lly568skn9fTTT6tGjRo6e/asunbtmmm+Ro0aqXTp0nrwwQf19NNP67HHHtPgwYPtWkdNmzbV4sWL9csvv6hatWqqWbOmxo4dq+LFi5vzFChQQBUqVFBgYKAiIiIkXW3ErVZrpovFNW3aVLNnz9ZDDz2kyMhIde/eXU2bNs10Vewbsee1eemll1S2bFlVrVpVgYGBmfYoZ/Twww+rRIkSeu+99+Tk5KQvvvhCmzdv1n333aeePXtq1KhRdueWrmbNmpo+fbrGjx+vSpUq6ZdffrH5okKSnnjiCY0fP14ffvihypcvr2nTpmnmzJk3PTLgZpo2baoffvhBLVq0UJkyZdShQwdFRETol19+kYvL1TNihg0bpgEDBmj48OGKjIzUww8/rB9//NG8zZ2929b1XF1d9csvvygoKEjNmzdXhQoV9MEHH5i3nwMAAAByiquXcwXh21p2V/EG7mR89gAAHI2rl+NOx9XLAQAAAADATdF0AwAAAADgINwyDLe1WbNm5XUKAAAAAHDL2NMNAAAAAICD0HRLusuvJQfgNsNnDgAAwL3jnm668+XLJ0m6dOlSHmcC4F6SkpIiSdyKDAAA4B5wT5/T7ezsLD8/P50+fVrS1fsnWyyWPM4KwN3MarXqzJkzyp8/v3nfcQAAANy97vm/+EJCQiTJbLwBwNGcnJxUrFgxvuQDAAC4B9zzTbfFYlGhQoUUFBSkK1eu5HU6AO4Brq6ucnK6p8/uAQAAuGfc8013OmdnZ86vBAAAAADkKna1AAAAAADgIDTdAAAAAAA4CE03AAAAAAAOQtMNAAAAAICD0HQDAAAAAOAgNN0AAAAAADgITTcAAAAAAA5C0w0AAAAAgIPQdAMAAAAA4CA03QAAAAAAOEieNt2DBw+WxWKx+YmIiDCnJyUlqVu3bgoICJCXl5datWqlU6dO5WHGAAAAAADYL8/3dJcvX14nT540f37//XdzWs+ePfXDDz9owYIFWrNmjaKjo/Xkk0/mYbYAAAAAANjPJc8TcHFRSEhIpnh8fLxmzJih+fPnq2HDhpKkmTNnKjIyUuvXr1fNmjX/61QBAAAAAMiRPG+69+/fr9DQULm7uysqKkrDhw9XsWLFtHnzZl25ckWNGzc2542IiFCxYsW0bt26bJvu5ORkJScnm48TEhIkSampqUpNTZUkOTk5ycnJSVarVVar1Zw3PZ6WlibDMG4ad3Z2lsViMcfNGJektLQ0u+IuLi4yDMMmbrFY5OzsnCnH7OLURE3URE3URE3URE3URE3puVvSLDKcDMkiWawW6VpJ1+JpFpscDaf/f67VzrizIRnXxS3/P392catkMa7FDYtx9djbbOLZ5k5Nd31Nqampt837Kbv49Z8L2cnTprtGjRqaNWuWypYtq5MnT2rIkCGqW7eudu7cqZiYGLm6usrPz8/mOcHBwYqJicl2zOHDh2vIkCGZ4lu2bJGnp6ckKTAwUOHh4Tp8+LDOnDljzlOkSBEVKVJE+/btU3x8vBkvWbKkgoKCtHPnTl2+fNmMR0REyM/PT1u2bLF5wStWrChXV1dt2rTJJoeqVasqJSVF27dvN2POzs6qVq2a4uPjtWfPHjPu4eGhSpUqKTY2VocOHTLjvr6+ioyMVHR0tI4fP27GqYmaqImaqImaqImaqIma0msKvBKohNAEJfknqcDBAnJJvvZnf1zxOKV4p6jg3oI2Tc7ZUmdlzWdV4O5Am5rORJ6R0xUnBRwIMGOGk6Ez5c7I9aKr/P7xM+Opbqk6V/qc3M+7yyfax4yneKUoLixOnrGe8jztacYvF7isC4UvyPuktzzOe5jxxKBEJQYlyveor1wvuppxarp3atqUb9Nt836Ssv6MSExMlD0sRsb2PY/FxcWpePHiGjNmjDw8PNSpUyebvdaSVL16dTVo0EAjRozIcoys9nQXLVpUZ8+elY/P1Q3qXv/mk5qoiZqoiZqoiZqoiZru7pomx01mDyo13dE1dfXretu8n7KLJyQkKCAgQPHx8WavmZU8P7w8Iz8/P5UpU0YHDhxQkyZNlJKSori4OJu93adOncryHPB0bm5ucnNzyxR3cXGRi4ttuekr7nrpL6698evHvZW4xWLJMp5djjmNUxM1ZRenJmqSqCm7HHMapyZqkqgpuxxzGqemf1eT4XytWUhvaK6XcZ5bjltyGHeSDNkfzzZ3arrra8r4Xsnr91N28eze/5mWYddc/5GLFy/q4MGDKlSokKpUqaJ8+fJpxYoV5vS9e/fq6NGjioqKysMsAQAAAACwT57u6e7Tp49atGih4sWLKzo6WoMGDZKzs7PatWsnX19fde7cWb169ZK/v798fHzUvXt3RUVFceVyAAAAAMAdIU+b7uPHj6tdu3Y6e/asAgMDVadOHa1fv16BgVdPoB87dqycnJzUqlUrJScnq2nTppo8eXJepgwAAAAAgN1uqwupOUJCQoJ8fX1venI7AAAAcLcYf358XqcA/CtvFHgjr1O4KXt7zdvqnG4AAAAAAO4mNN0AAAAAADgITTcAAAAAAA5C0w0AAAAAgIPQdAMAAAAA4CA03QAAAAAAOAhNNwAAAAAADkLTDQAAAACAg9B0AwAAAADgIDTdAAAAAAA4CE03AAAAAAAOQtMNAAAAAICD0HQDAAAAAOAgNN0AAAAAADgITTcAAAAAAA5C0w0AAAAAgIPQdAMAAAAA4CA03QAAAAAAOAhNNwAAAAAADkLTDQAAAACAg9B0AwAAAADgIDTdAAAAAAA4CE03AAAAAAAOQtMNAAAAAICD0HQDAAAAAOAgNN0AAAAAADgITTcAAAAAAA5C0w0AAAAAgIPQdAMAAAAA4CA03QAAAAAAOAhNNwAAAAAADkLTDQAAAACAg9B0AwAAAADgIDTdAAAAAAA4CE03AAAAAAAOQtMNAAAAAICD0HQDAAAAAOAgNN0AAAAAADgITTcAAAAAAA5C0w0AAAAAgIPQdAMAAAAA4CA03QAAAAAAOAhNNwAAAAAADkLTDQAAAACAg9B0AwAAAADgIDTdAAAAAAA4CE03AAAAAAAOQtMNAAAAAICD0HQDAAAAAOAgNN0AAAAAADgITTcAAAAAAA5C0w0AAAAAgIPQdAMAAAAA4CA03QAAAAAAOMht03R/8MEHslgs6tGjhxlLSkpSt27dFBAQIC8vL7Vq1UqnTp3KuyQBAAAAAMiB26Lp3rhxo6ZNm6aKFSvaxHv27KkffvhBCxYs0Jo1axQdHa0nn3wyj7IEAAAAACBn8rzpvnjxop599llNnz5dBQoUMOPx8fGaMWOGxowZo4YNG6pKlSqaOXOm/vjjD61fvz4PMwYAAAAAwD4ueZ1At27d9Mgjj6hx48Z69913zfjmzZt15coVNW7c2IxFRESoWLFiWrdunWrWrJnleMnJyUpOTjYfJyQkSJJSU1OVmpoqSXJycpKTk5OsVqusVqs5b3o8LS1NhmHcNO7s7CyLxWKOmzEuSWlpaXbFXVxcZBiGTdxiscjZ2TlTjtnFqYmaqImaqImaqImaqIma0nO3pFlkOBmSRbJYLdK1kq7F0yw2ORpO//9cq51xZ0Myrotb/n/+7OJWyWJcixsW4+puwGzi2eZOTXd9TampqbfN+ym7+PWfC9nJ06b7iy++0F9//aWNGzdmmhYTEyNXV1f5+fnZxIODgxUTE5PtmMOHD9eQIUMyxbds2SJPT09JUmBgoMLDw3X48GGdOXPGnKdIkSIqUqSI9u3bp/j4eDNesmRJBQUFaefOnbp8+bIZj4iIkJ+fn7Zs2WLzglesWFGurq7atGmTTQ5Vq1ZVSkqKtm/fbsacnZ1VrVo1xcfHa8+ePWbcw8NDlSpVUmxsrA4dOmTGfX19FRkZqejoaB0/ftyMUxM1URM1URM1URM1URM1pdcUeCVQCaEJSvJPUoGDBeSSfO3P/rjicUrxTlHBvQVtmpyzpc7Kms+qwN2BNjWdiTwjpytOCjgQYMYMJ0Nnyp2R60VX+f3jZ8ZT3VJ1rvQ5uZ93l0+0jxlP8UpRXFicPGM95Xna04xfLnBZFwpfkPdJb3mc9zDjiUGJSgxKlO9RX7ledDXj1HTv1LQp36bb5v0kZf0ZkZiYKHtYjIzt+3/o2LFjqlq1qpYtW2aey12/fn1VrlxZ48aN0/z589WpUyebvdaSVL16dTVo0EAjRozIctys9nQXLVpUZ8+elY/P1Q3qXv/mk5qoiZqoiZqoiZqoiZru7pomx01mDyo13dE1dfXretu8n7KLJyQkKCAgQPHx8WavmZU8a7oXLlyoli1bmitSuroyLRaLnJyctHTpUjVu3Fjnz5+32dtdvHhx9ejRQz179rRrOQkJCfL19b3pigAAAADuFuPPj8/rFIB/5Y0Cb+R1Cjdlb6+ZZ4eXN2rUSDt27LCJderUSREREerbt6+KFi2qfPnyacWKFWrVqpUkae/evTp69KiioqLyImUAAAAAAHIkz5pub29v3XfffTYxT09PBQQEmPHOnTurV69e8vf3l4+Pj7p3766oqKhsL6IGAAAAAMDtJM+vXn4jY8eOlZOTk1q1aqXk5GQ1bdpUkydPzuu0AAAAAACwS56d0/1f4ZxuAAAA3Gs4pxt3urvpnG6n/zAnAAAAAADuKTTdAIAbmjJliipWrCgfHx/5+PgoKipKP//8s80869atU8OGDeXp6SkfHx89+OCDNve5vV5YWJgsFkumn27dujm6HAAAgP/UbX1ONwAg7xUpUkQffPCBSpcuLcMwNHv2bD3++OPasmWLypcvr3Xr1unhhx9W//799dFHH8nFxUXbtm2Tk1P23+tu3LjR5t6aO3fuVJMmTfTUU0/9FyUBAAD8Z/71Od0JCQlauXKlypYtq8jIyNzKK9dwTjcA5D5/f3+NGjVKnTt3Vs2aNdWkSRMNGzbslsfr0aOHFi9erP3798tiseRipgBwb+Kcbtzp7ulzutu0aaOJEydKki5fvqyqVauqTZs2qlixor755ptbzxgAcNtLS0vTF198ocTEREVFRen06dPasGGDgoKCVKtWLQUHB6tevXr6/fff7R4zJSVFn332mV544QUabgAAcNfJcdP966+/qm7dupKk7777ToZhKC4uThMmTNC7776b6wkCAPLejh075OXlJTc3N73yyiv67rvvVK5cOR06dEiSNHjwYL300ktasmSJHnjgATVq1Ej79++3a+yFCxcqLi5OHTt2dGAFAAAAeSPHTXd8fLz8/f0lSUuWLFGrVq2UP39+PfLII3b/gQUAuLOULVtWW7du1YYNG/Tqq6+qQ4cO2rVrl6xWqySpS5cu6tSpk+6//36NHTtWZcuW1aeffmrX2DNmzFCzZs0UGhrqyBIAAADyRI4vpFa0aFGtW7dO/v7+WrJkib744gtJ0vnz5+Xu7p7rCQIA8p6rq6tKlSolSapSpYo2btyo8ePHq1+/fpKkcuXK2cwfGRmpo0eP3nTcf/75R8uXL9e3336b+0kDAADcBnK8p7tHjx569tlnVaRIERUqVEj169eXdPWw8woVKuR2fgCA25DValVycrLCwsIUGhqqvXv32kzft2+fihcvftNxZs6cqaCgID3yyCOOShUAACBP5XhPd9euXVW9enUdO3ZMTZo0MW8JU7JkSc7pBoC7UP/+/dWsWTMVK1ZMFy5c0Pz587V69WotXbpUFotFb775pgYNGqRKlSqpcuXKmj17tvbs2aOvv/7aHKNRo0Zq2bKlXnvtNTNmtVo1c+ZMdejQQS4u3MESAADcnW7pr5yqVauqYsWKOnz4sMLDw+Xi4sJeCgC4S50+fVrt27fXyZMn5evrq4oVK2rp0qVq0qSJpKtHQCUlJalnz546d+6cKlWqpGXLlik8PNwc4+DBg4qNjbUZd/ny5Tp69KheeOGF/7QeAACA/1KO79N96dIlde/eXbNnz5Z09RDCkiVLqnv37ipcuLB5ft/tgvt0AwAA4F7Dfbpxp7un79Pdv39/bdu2TatXr7a5cFrjxo315Zdf3lq2AAAAAADchXJ8ePnChQv15ZdfqmbNmrJYLGa8fPnyOnjwYK4mBwAAAADAnSzHTfeZM2cUFBSUKZ6YmGjThAPA7Y5D73AnuxMOuwMAALdweHnVqlX1448/mo/TG+1PPvlEUVFRuZcZAAAAAAB3uBzv6X7//ffVrFkz7dq1S6mpqRo/frx27dqlP/74Q2vWrHFEjgAAAAAA3JFyvKe7Tp062rZtm1JTU1WhQgX98ssvCgoK0rp161SlShVH5AgAAAAAwB0pR3u6r1y5oi5dumjAgAGaPn26o3ICAAAAAOCukKM93fny5dM333zjqFwAAADueVOmTFHFihXl4+MjHx8fRUVF6eeff840n2EYatasmSwWixYuXHjDMQ3D0MCBA1WoUCF5eHiocePG2r9/v4MqAABklOPDy5944ombfrADAADg1hQpUkQffPCBNm/erE2bNqlhw4Z6/PHH9ffff9vMN27cOLvvHDNy5EhNmDBBU6dO1YYNG+Tp6ammTZsqKSnJESUAADLI8YXUSpcuraFDh2rt2rWqUqWKPD09baa//vrruZYcAADAvaZFixY2j9977z1NmTJF69evV/ny5SVJW7du1ejRo7Vp0yYVKlTohuMZhqFx48bpnXfe0eOPPy5JmjNnjoKDg7Vw4UK1bdvWMYUAACTdQtM9Y8YM+fn5afPmzdq8ebPNNIvFQtMNAACQS9LS0rRgwQIlJiaat2a9dOmSnnnmGU2aNEkhISE3HePw4cOKiYlR48aNzZivr69q1KihdevW0XQDgIPluOk+fPiwI/IAAADA/9uxY4eioqKUlJQkLy8vfffddypXrpwkqWfPnqpVq5a51/pmYmJiJEnBwcE28eDgYHMaAMBxctx0Z2QYhiTZfT4RAAAAbq5s2bLaunWr4uPj9fXXX6tDhw5as2aNDhw4oJUrV2rLli15nSIAwE45vpCadPU8oAoVKsjDw0MeHh6qWLGi5s6dm9u5AQAA3JNcXV1VqlQpValSRcOHD1elSpU0fvx4rVy5UgcPHpSfn59cXFzk4nJ1/0mrVq1Uv379LMdKPwT91KlTNvFTp07ZdXg6AODfyfGe7jFjxmjAgAF67bXXVLt2bUnS77//rldeeUWxsbHq2bNnricJAABwL7NarUpOTtaQIUP04osv2kyrUKGCxo4dm+kCbOlKlCihkJAQrVixQpUrV5YkJSQkaMOGDXr11VcdnToA3PNy3HR/9NFHmjJlitq3b2/GHnvsMZUvX16DBw+m6QYAAPgX+vfvr2bNmqlYsWK6cOGC5s+fr9WrV2vp0qUKCQnJcu90sWLFVKJECfNxRESEhg8frpYtW8pisahHjx569913Vbp0aZUoUUIDBgxQaGionnjiif+wMgC4N+W46T558qRq1aqVKV6rVi2dPHkyV5ICAAC4V50+fVrt27fXyZMn5evrq4oVK2rp0qVq0qSJ3WPs3btX8fHx5uO33npLiYmJevnllxUXF6c6depoyZIlcnd3d0QJAIAMctx0lypVSl999ZXefvttm/iXX36p0qVL51piAAAA96IZM2bkaP70C9veKGaxWDR06FANHTr0X+UGAMi5HDfdQ4YM0dNPP61ff/3VPKd77dq1WrFihb766qtcTxAAAAAAgDtVjq9e3qpVK23YsEEFCxbUwoULtXDhQhUsWFB//vmnWrZs6YgcAQAAAAC4I93SfbqrVKmizz77LLdzAQAAd7EPtsTmdQrALet3f8G8TgHAHSrHe7p/+uknLV26NFN86dKl+vnnn3MlKQAAAAAA7gY5brr79euntLS0THHDMNSvX79cSQoAAAAAgLtBjpvu/fv3q1y5cpniEREROnDgQK4kBQAAAADA3SDHTbevr68OHTqUKX7gwAF5enrmSlIAAAAAANwNctx0P/744+rRo4cOHjxoxg4cOKDevXvrsccey9XkAAAAAAC4k+W46R45cqQ8PT0VERGhEiVKqESJEoqMjFRAQIA+/PBDR+QIAAAAAMAdKce3DPP19dUff/yhZcuWadu2bfLw8FDFihX14IMPOiI/AAAAAADuWLd0n26LxaKHHnpIDz30UG7nAwAAAADAXcPuw8vXrVunxYsX28TmzJmjEiVKKCgoSC+//LKSk5NzPUEAAAAAAO5UdjfdQ4cO1d9//20+3rFjhzp37qzGjRurX79++uGHHzR8+HCHJAkAAAAAwJ3I7qZ769atatSokfn4iy++UI0aNTR9+nT16tVLEyZM0FdffeWQJAEAAAAAuBPZ3XSfP39ewcHB5uM1a9aoWbNm5uNq1arp2LFjuZsdAAAAAAB3MLub7uDgYB0+fFiSlJKSor/++ks1a9Y0p1+4cEH58uXL/QwBAAAAALhD2d10N2/eXP369dNvv/2m/v37K3/+/Kpbt645ffv27QoPD3dIkgAAAAAA3InsvmXYsGHD9OSTT6pevXry8vLS7Nmz5erqak7/9NNPuYUYAAAAAAAZ2N10FyxYUL/++qvi4+Pl5eUlZ2dnm+kLFiyQl5dXricIAAAAAMCdyu6mO52vr2+WcX9//3+dDAAAAAAAdxO7z+kGAAAAAAA5Q9MNAAAAAICD0HQDAAAAAOAgOW66f/31V6WmpmaKp6am6tdff83RWFOmTFHFihXl4+MjHx8fRUVF6eeffzanJyUlqVu3bgoICJCXl5datWqlU6dO5TRlAAAAAADyRI6b7gYNGujcuXOZ4vHx8WrQoEGOxipSpIg++OADbd68WZs2bVLDhg31+OOP6++//5Yk9ezZUz/88IMWLFigNWvWKDo6Wk8++WROUwYAAAAAIE/k+OrlhmHIYrFkip89e1aenp45GqtFixY2j9977z1NmTJF69evV5EiRTRjxgzNnz9fDRs2lCTNnDlTkZGRWr9+vWrWrJnT1AEAAAAA+E/Z3XSn72G2WCzq2LGj3NzczGlpaWnavn27atWqdcuJpKWlacGCBUpMTFRUVJQ2b96sK1euqHHjxuY8ERERKlasmNatW0fTDQAAAAC47dnddKffn9swDHl7e8vDw8Oc5urqqpo1a+qll17KcQI7duxQVFSUkpKS5OXlpe+++07lypXT1q1b5erqKj8/P5v5g4ODFRMTk+14ycnJSk5ONh8nJCRIunrOefq56E5OTnJycpLVapXVajXnTY+npaXJMIybxp2dnWWxWDKd4+7s7Czp6hcJ9sRdXFxkGIZN3GKxyNnZOVOO2cWpiZqo6RZqslpkOBmSVbIY147gMSzG1ZNvsolbrBbpWupXx7DcIJ5me3SQ4WSYy7cr7mxIxnVxy//Pn12cmu76mlJTU2+v95MdnxEW67VxDItFsjjJYlilDLkbFifJYsk+brXN0bBcPVPOYljtizs5S4ZhG7dYrs6fbdwqi00uV3PPLk5Nd2dNt9v76Wa/cy1plrvuc88md2q662tKTU29bd5P2cWzutZZVuxuumfOnClJCgsLU58+fXJ8KHl2ypYtq61btyo+Pl5ff/21OnTooDVr1tzyeMOHD9eQIUMyxbds2WLmHBgYqPDwcB0+fFhnzpwx5ylSpIiKFCmiffv2KT4+3oyXLFlSQUFB2rlzpy5fvmzGIyIi5Ofnpy1btti84BUrVpSrq6s2bdpkk0PVqlWVkpKi7du3mzFnZ2dVq1ZN8fHx2rNnjxn38PBQpUqVFBsbq0OHDplxX19fRUZGKjo6WsePHzfj1ERN1HQLNbn5Ki4sTp6xnvI8fe0z7XKBy7pQ+IK8T3rL4/y1LxgTgxKVGJQo36O+cr3oasYTQhOU5J+kAgcLyCX52sdqXPE4pXinqODegja/RM6WOitrPqsCdwfa1HQm8oycrjgp4ECAGTOcDJ0pd0auF13l94+fGU91S9W50ufkft5dPtE+ZjzFK4Wa7pGaNuXbdHu9n+z4jCgce+La6+EZqATPQAXEH5N7SqIZP+9dSIkeBRR8/rBcUq99iR7rV0xJrl4KPbdflgx/HMX4hyvNyUWFY/fa1HSiYFk5W1MVcu6gGTOcnHSiYITcrySqYNxRM57q4qYY/3B5JsWpwIWTZjzJ1VOxfsXlc+msfBKvfb4levjpvHeoClyMkeflOGq6R2q63d5PN/udG3gl8K773JPuvs9yasq+pk35Nt027ycp679hExOvfYbciMXI2L7bKTU1VatXr9bBgwf1zDPPyNvbW9HR0fLx8ZGXl1dOh7PRuHFjhYeH6+mnn1ajRo10/vx5m73dxYsXV48ePdSzZ88sn5/Vnu6iRYvq7Nmz8vG5ukHd0Xvm7sa9jdRETXlU0+T4ybfNt7k3jN+h31BTk2Nr6urX9bZ6P9nzGTFqy7U/atiDSk13Wk19KvjZ5JjX76eb/c6dHDf5rvvcs8mdmu76mrr6db1t3k/ZxRMSEhQQEKD4+Hiz18xKji+k9s8//+jhhx/W0aNHlZycrCZNmsjb21sjRoxQcnKypk6dmtMhbVitViUnJ6tKlSrKly+fVqxYoVatWkmS9u7dq6NHjyoqKirb57u5udmcb57OxcVFLi625aavuOulv7j2xq8f91biFosly3h2OeY0Tk3UlF38Xq4p/QNeTpKR8TeC+YSs4+bz7I0750LcksM4Nd31NWXcxm+H95M9ccMpc55XG5rMuWQbz2KMq/PnIG6x5DDuJCOLXLKLU9PdWdPt9n662e/cjJ85d8vnnl1xarprasr4Xsnr91N28eze/9fLcdP9xhtvqGrVqtq2bZsCAq4dutCyZcscn9Pdv39/NWvWTMWKFdOFCxc0f/58rV69WkuXLpWvr686d+6sXr16yd/fXz4+PurevbuioqK4iBoAAAAA4I6Q46b7t99+0x9//CFXV1ebeFhYmE6cOJHNs7J2+vRptW/fXidPnpSvr68qVqyopUuXqkmTJpKksWPHysnJSa1atVJycrKaNm2qyZMn5zRlAAAAAADyRI6bbqvVmumYekk6fvy4vL29czTWjBkzbjjd3d1dkyZN0qRJk3I0LgAAAAAAt4PMB6/fxEMPPaRx48aZjy0Wiy5evKhBgwapefPmuZkbAAAAAAB3tBzv6R49erSaNm2qcuXKKSkpSc8884z279+vggUL6vPPP3dEjgAAAAAA3JFy3HQXKVJE27Zt05dffqlt27bp4sWL6ty5s5599ll5eHjcfAAAAAAAAO4ROW66pauXRn/22Wf17LPP5nY+AAAAAADcNew+p3vfvn36888/bWIrVqxQgwYNVL16db3//vu5nhwAAAAAAHcyu5vuvn37avHixebjw4cPq0WLFnJ1dVVUVJSGDx9uc4E1AAAAAADudXYfXr5p0ya99dZb5uN58+apTJkyWrp0qSSpYsWK+uijj9SjR49cTxIAAAAAgDuR3Xu6Y2NjVaRIEfPxqlWr1KJFC/Nx/fr1deTIkVxNDgAAAACAO5ndTbe/v79OnjwpSbJardq0aZNq1qxpTk9JSZFhGLmfIQAAAAAAdyi7m+769etr2LBhOnbsmMaNGyer1ar69eub03ft2qWwsDAHpAgAAAAAwJ3J7nO633vvPTVp0kTFixeXs7OzJkyYIE9PT3P63Llz1bBhQ4ckCQAAAADAncjupjssLEy7d+/W33//rcDAQIWGhtpMHzJkiM053wAAAAAA3OvsbrolycXFRZUqVcpyWnZxAAAAAADuVXaf0w0AAAAAAHKGphsAAAAAAAeh6QYAAAAAwEFougEAAAAAcJAcN92lSpXS4MGDtW/fPkfkAwAAAADAXSPHTXe3bt30448/KjIyUtWqVdP48eMVExPjiNwAAAAAALij5bjp7tmzpzZu3Kjdu3erefPmmjRpkooWLaqHHnpIc+bMcUSOAAAAAADckW75nO4yZcpoyJAh2rdvn3777TedOXNGnTp1ys3cAAAAAAC4o7n8myf/+eefmj9/vr788kslJCToqaeeyq28AAAAAAC44+W46d63b5/mzZunzz//XIcPH1bDhg01YsQIPfnkk/Ly8nJEjgAAAAAA3JFy3HRHRESoWrVq6tatm9q2bavg4GBH5AUAAAAAwB0vx0333r17Vbp0aUfkAgAAAADAXSXHF1IrXbq04uLi9Mknn6h///46d+6cJOmvv/7SiRMncj1BAAAAAADuVDne0719+3Y1atRIfn5+OnLkiF566SX5+/vr22+/1dGjR7ltGAAAAAAA/++W7tPdqVMn7d+/X+7u7ma8efPm+vXXX3M1OQAAAAAA7mQ53tO9adMmffzxx5nihQsXVkxMTK4kBQAAAADA3SDHe7rd3NyUkJCQKb5v3z4FBgbmSlIAAAAAANwNctx0P/bYYxo6dKiuXLkiSbJYLDp69Kj69u2rVq1a5XqCAAAAAADcqXLcdI8ePVoXL15UUFCQLl++rHr16qlUqVLy9vbWe++954gcAQAAAAC4I+X4nG5fX18tW7ZMv//+u7Zv366LFy/qgQceUOPGjR2RHwAAAAAAd6wcN93p6tSpozp16uRmLgAAAAAA3FVy1HRbrVbNmjVL3377rY4cOSKLxaISJUqodevWev7552WxWByVJwAAAAAAdxy7z+k2DEOPPfaYXnzxRZ04cUIVKlRQ+fLl9c8//6hjx45q2bKlI/MEAAAAAOCOY/ee7lmzZunXX3/VihUr1KBBA5tpK1eu1BNPPKE5c+aoffv2uZ4kAAAAAAB3Irv3dH/++ed6++23MzXcktSwYUP169dP8+bNy9XkAAAAAAC4k9nddG/fvl0PP/xwttObNWumbdu25UpSAAAAAADcDexuus+dO6fg4OBspwcHB+v8+fO5khQAAAAAAHcDu5vutLQ0ubhkfwq4s7OzUlNTcyUpAAAAAADuBnZfSM0wDHXs2FFubm5ZTk9OTs61pAAAAAAAuBvY3XR36NDhpvNw5XIAAAAAAK6xu+meOXOmI/MAAAAAAOCuY/c53QAAAAAAIGdougEAAAAAcBCabgAAAAAAHISmGwAAAAAAB6HpBgAAAADAQWi6AQAAAABwEJpuAAAAAAAchKYbAAAAAAAHoekGAAAAAMBBaLoBAAAAAHCQPG26hw8frmrVqsnb21tBQUF64okntHfvXpt5kpKS1K1bNwUEBMjLy0utWrXSqVOn8ihjAAAAAADsl6dN95o1a9StWzetX79ey5Yt05UrV/TQQw8pMTHRnKdnz5764YcftGDBAq1Zs0bR0dF68skn8zBrAAAAAADs45KXC1+yZInN41mzZikoKEibN2/Wgw8+qPj4eM2YMUPz589Xw4YNJUkzZ85UZGSk1q9fr5o1a+ZF2gAAAAAA2CVPm+7rxcfHS5L8/f0lSZs3b9aVK1fUuHFjc56IiAgVK1ZM69aty7LpTk5OVnJysvk4ISFBkpSamqrU1FRJkpOTk5ycnGS1WmW1Ws150+NpaWkyDOOmcWdnZ1ksFnPcjHFJSktLsyvu4uIiwzBs4haLRc7OzplyzC5OTdRETbdQk9Uiw8mQrJLFsJhxw2JcPQ4om7jFapGupX51DMsN4mnXxjDj/798u+LOhmRcF7f8//zZxanprq8pNTX19no/2fEZYbFeG8ewWCSLkyyGVcqQu2FxkiyW7ONW2xwNy9WD9iyG1b64k7NkGLZxi+Xq/NnGrbLY5HI19+zi1HR31nS7vZ9u9jvXkma56z73bHKnpru+ptTU1Nvm/ZRd/PrPhezcNk231WpVjx49VLt2bd13332SpJiYGLm6usrPz89m3uDgYMXExGQ5zvDhwzVkyJBM8S1btsjT01OSFBgYqPDwcB0+fFhnzpwx5ylSpIiKFCmiffv2mV8ASFLJkiUVFBSknTt36vLly2Y8IiJCfn5+2rJli80LXrFiRbm6umrTpk02OVStWlUpKSnavn27GXN2dla1atUUHx+vPXv2mHEPDw9VqlRJsbGxOnTokBn39fVVZGSkoqOjdfz4cTNOTdRETbdQk5uv4sLi5BnrKc/Tnmb8coHLulD4grxPesvjvIcZTwxKVGJQonyP+sr1oqsZTwhNUJJ/kgocLCCX5Gsfq3HF45TinaKCewva/BI5W+qsrPmsCtwdaFPTmcgzcrripIADAWbMcDJ0ptwZuV50ld8/fmY81S1V50qfk/t5d/lE+5jxFK8UarpHatqUb9Pt9X6y4zOicOyJa6+HZ6ASPAMVEH9M7inXTis7711IiR4FFHz+sFxSr32JHutXTEmuXgo9t1+WDH8cxfiHK83JRYVjba8Jc6JgWTlbUxVy7qAZM5ycdKJghNyvJKpg3FEznuriphj/cHkmxanAhZNmPMnVU7F+xeVz6ax8Eq99viV6+Om8d6gKXIyR5+U4arpHarrd3k83+50beCXwrvvck+6+z3Jqyr6mTfk23TbvJynrv2EznhZ9IxYjY/ueh1599VX9/PPP+v3331WkSBFJ0vz589WpUyebPdeSVL16dTVo0EAjRozINE5We7qLFi2qs2fPysfn6gZ1R++Zuxv3NlITNeVRTZPjJ9823+beMH6HfkNNTY6tqatf19vq/WTPZ8SoLdf+qGEPKjXdaTX1qeBnk2Nev59u9jt3ctzku+5zzyZ3arrra+rq1/W2eT9lF09ISFBAQIDi4+PNXjMrt8We7tdee02LFy/Wr7/+ajbckhQSEqKUlBTFxcXZ7O0+deqUQkJCshzLzc1Nbm5umeIuLi5ycbEtN33FXS/9xbU3fv24txK3WCxZxrPLMadxaqKm7OL3ck3pH/BykoyMvxHMJ2QdN59nb9w5F+KWHMap6a6vKeM2fju8n+yJG06Z87za0GTOJdt4FmNcnT8HcYslh3EnGVnkkl2cmu7Omm6399PNfudm/My5Wz737IpT011TU8b3Sl6/n7KLZ/f+z7QMu+ZyEMMw9Nprr+m7777TypUrVaJECZvpVapUUb58+bRixQoztnfvXh09elRRUVH/dboAAAAAAORInu7p7tatm+bPn6/vv/9e3t7e5nnavr6+8vDwkK+vrzp37qxevXrJ399fPj4+6t69u6KiorhyOQAAAADgtpenTfeUKVMkSfXr17eJz5w5Ux07dpQkjR07Vk5OTmrVqpWSk5PVtGlTTZ48+T/OFAAAAACAnMvTptuea7i5u7tr0qRJmjRp0n+QEQAAAAAAuSdPz+kGAAAAAOBuRtMNAAAAAICD0HQDAAAAAOAgNN0AAAAAADgITTcAAAAAAA5C0w0AAAAAgIPQdAMAAAAA4CA03QAAAAAAOAhNNwAAAAAADkLTDQAAAACAg9B0AwAAAADgIDTdAAAAAAA4CE03AAAAAAAOQtMNAAAAAICD0HQDAAAAAOAgNN0AAAAAADgITTcAAAAAAA5C0w0AAAAAgIPQdAMAAAAA4CA03QAAAAAAOAhNNwAAAAAADkLTDQAAAACAg9B0AwAAAADgIDTdAAAAAAA4CE03AAAAAAAOQtMNAAAAAICD0HQDAAAAAOAgNN0AAAAAADgITTcAAAAAAA5C0w0AAAAAgIPQdAMAAAAA4CA03QAAAAAAOAhNNwAAAAAADkLTDQAAAACAg9B0AwAAAADgIDTdAAAAAAA4CE03AAAAAAAOQtMNAAAAAICD0HQDAAAAAOAgNN0AAAAAADgITTcAAAAAAA5C0w0AAAAAgIPQdAMAAAAA4CA03QAAAAAAOAhNNwAAAAAADkLTDQAAAACAg9B0AwAAAADgIDTdAAAAAAA4CE03AAAAAAAOQtMNAAAAAICD0HQDAAAAAOAgNN0AAAAAADgITTcAAAAAAA6Sp033r7/+qhYtWig0NFQWi0ULFy60mW4YhgYOHKhChQrJw8NDjRs31v79+/MmWQAAAAAAcihPm+7ExERVqlRJkyZNynL6yJEjNWHCBE2dOlUbNmyQp6enmjZtqqSkpP84UwAAAAAAcs4lLxferFkzNWvWLMtphmFo3Lhxeuedd/T4449LkubMmaPg4GAtXLhQbdu2/S9TBQAAAAAgx27bc7oPHz6smJgYNW7c2Iz5+vqqRo0aWrduXR5mBgAAAACAffJ0T/eNxMTESJKCg4Nt4sHBwea0rCQnJys5Odl8nJCQIElKTU1VamqqJMnJyUlOTk6yWq2yWq3mvOnxtLQ0GYZx07izs7MsFos5bsa4JKWlpdkVd3FxkWEYNnGLxSJnZ+dMOWYXpyZqoqZbqMlqkeFkSFbJYljMuGExrn4lmU3cYrVI11K/OoblBvG0a2OY8f9fvl1xZ0Myrotb/n/+7OLUdNfXlJqaenu9n+z4jLBYr41jWCySxUkWwyplyN2wOEkWS/Zxq22OhuXq/gOLYbUv7uQsGYZt3GK5On+2cassNrlczT27ODXdnTXdbu+nm/3OtaRZ7rrPPZvcqemuryk1NfW2eT9lF7/+cyE7t23TfauGDx+uIUOGZIpv2bJFnp6ekqTAwECFh4fr8OHDOnPmjDlPkSJFVKRIEe3bt0/x8fFmvGTJkgoKCtLOnTt1+fJlMx4RESE/Pz9t2bLF5gWvWLGiXF1dtWnTJpscqlatqpSUFG3fvt2MOTs7q1q1aoqPj9eePXvMuIeHhypVqqTY2FgdOnTIjPv6+ioyMlLR0dE6fvy4GacmaqKmW6jJzVdxYXHyjPWU52lPM365wGVdKHxB3ie95XHew4wnBiUqMShRvkd95XrR1YwnhCYoyT9JBQ4WkEvytY/VuOJxSvFOUcG9BW1+iZwtdVbWfFYF7g60qelM5Bk5XXFSwIEAM2Y4GTpT7oxcL7rK7x8/M57qlqpzpc/J/by7fKJ9zHiKVwo13SM1bcq36fZ6P9nxGVE49sS118MzUAmegQqIPyb3lEQzft67kBI9Cij4/GG5pF77Ej3Wr5iSXL0Uem6/LBn+OIrxD1eak4sKx+61qelEwbJytqYq5NxBM2Y4OelEwQi5X0lUwbijZjzVxU0x/uHyTIpTgQsnzXiSq6di/YrL59JZ+SRe+3xL9PDTee9QFbgYI8/LcdR0j9R0u72fbvY7N/BK4F33uSfdfZ/l1JR9TZvybbpt3k9S1n/DJiZe+wy5EYuRsX3PQxaLRd99952eeOIJSdKhQ4cUHh6uLVu2qHLlyuZ89erVU+XKlTV+/Pgsx8lqT3fRokV19uxZ+fhc3aDu6D1zd+PeRmqipjyqaXL85Nvm29wbxu/Qb6ipybE1dfXrelu9n+z5jBi15dofNexBpaY7raY+Ffxscszr99PNfudOjpt8133u2eROTXd9TV39ut4276fs4gkJCQoICFB8fLzZa2bltt3TXaJECYWEhGjFihVm052QkKANGzbo1VdfzfZ5bm5ucnNzyxR3cXGRi4ttuekr7nrpL6698evHvZW4xWLJMp5djjmNUxM1ZRe/l2tK/4CXk2Rk/I1gPiHruPk8e+POuRC35DBOTXd9TRm38dvh/WRP3HDKnOfVhiZzLtnGsxjj6vw5iFssOYw7ycgil+zi1HR31nS7vZ9u9js342fO3fK5Z1ecmu6amjK+V/L6/ZRdPLv3//XytOm+ePGiDhw4YD4+fPiwtm7dKn9/fxUrVkw9evTQu+++q9KlS6tEiRIaMGCAQkNDzb3hAAAAAADczvK06d60aZMaNGhgPu7Vq5ckqUOHDpo1a5beeustJSYm6uWXX1ZcXJzq1KmjJUuWyN3dPa9SBgAAAADAbnnadNevX183OqXcYrFo6NChGjp06H+YFQAAAAAAuSPzwesAAAAAACBX0HQDAAAAAOAgNN0AAAAAADgITTcAAAAAAA5C0w0AAAAAgIPQdAMAAAAA4CA03QAAAAAAOAhNNwAAAAAADkLTDQAAAACAg9B0AwAAAADgIDTdAAAAAAA4CE03AAAAAAAOQtMNAAAAAICD0HQDAAAAAOAgNN0AAAAAADgITTcAAAAAAA5C0w0AAAAAgIPQdAMAAAAA4CA03QAAAAAAOAhNNwAAAAAADkLTDQAAAACAg9B0AwAAAADgIDTdAAAAAAA4CE03AAAAAAAOQtMNAAAAAICD0HQDAAAAAOAgNN0AAAAAADgITTcAAAAAAA5C0w0AAAAAgIPQdAMAAAAA4CA03QAAAAAAOAhNNwAAAAAADkLTDQDA/7V33/E5X///xx9XIomVGBFJbFqjRqkRo/n6tLWVhgStvaP2CKJaI0EoQuyIVm2xWqnWik1rtBQVWsrHCmKvBBnX9fvDL+9PYlQoLtHn/S/e1/t9bq/rdsu5znmdc97niIiIiLwgSrpFREREREREXhAl3SIiIiIiIiIviJJuERERERERkRdESbeIiIiIiIjIC6KkW0REREREROQFUdItIiIiIiIi8oIo6RYRERERERF5QZR0i4iIiIiIiLwgSrpFREREREREXhAl3SIiIiIiIiIviJJuERERERERkRdESbeIiIiIiIjIC6KkW14b06ZNo1ChQmTMmJHKlSuzZ8+ev71/2bJllChRgowZM1KmTBlWr179kiIVEREREZF/CyXd8lpYsmQJ/fr1Y9iwYezbt4+yZctSp04dLl68+Mj7f/75Z5o3b07Hjh357bffaNSoEY0aNeLQoUMvOXIREREREXmdKemW18KECRPo3Lkz7du3p2TJkoSGhpI5c2Zmz579yPsnTZpE3bp1GTBgAG+99RYjRoygfPnyTJ069SVHLiIiIiIirzMl3ZLuxcfHs3fvXmrWrGlcs7GxoWbNmuzcufORz+zcuTPV/QB16tR57P0iIiIiIiLPQkm3pHuXL18mKSkJV1fXVNddXV25cOHCI5+5cOHCU90vIiIiIiLyLJR0i4iIiIiIiLwgSrol3cuVKxe2trbExMSkuh4TE4Obm9sjn3Fzc3uq+0VERERERJ6Fkm5J9+zt7alQoQIbN240rpnNZjZu3EjVqlUf+UzVqlVT3Q8QGRn52PtFRERERESeRQZrByDyPPTr14+2bdtSsWJFPDw8CAkJITY2lvbt2wPQpk0b8ubNy+jRowHo3bs3//nPfwgODubDDz8kPDycX3/9lbCwMGt+DRERERERec2ki5nuadOmUahQITJmzEjlypXZs2ePtUOSV8zHH3/M+PHjGTp0KOXKlWP//v2sXbvW2Czt9OnTnD9/3ri/WrVqLFq0iLCwMMqWLcvy5ctZuXIlpUuXttZXEBERERGR19ArP9O9ZMkS+vXrR2hoKJUrVyYkJIQ6derw559/kjt3bmuHJ6+QHj160KNHj0d+tmXLloeuNW3alKZNm77gqERERERE5N/slZ/pnjBhAp07d6Z9+/aULFmS0NBQMmfOzOzZs60dmoiIiIiIiMjfeqVnuuPj49m7dy+fffaZcc3GxoaaNWuyc+fORz5z79497t27Z/z/xo0bAFy9epXExESjDBsbG8xmM2azOVXZNjY2JCUlYbFYnnjd1tYWk8lklJvyOkBSUlKarmfIkAGLxZLquslkwtbW9qEYH3dd30nfSd/p6b/TvZv3sJgsYAGTxWRct5gsYOKx100WE/wv9CdfN/+vDOM6qcv+2+s2D8eIiUfG/rjr+k6v33e6arn6StWntPxG3Lt5PcV3MoHJBpPFDClit5hswGR6/HVz6hgtpvvzByaLOW3XbWzBYkl93WS6f/9jr5sxpYrlfuyPu67v9Hp+p6tXU89VWbs+PanNvXfj3mv3u5em6/pOr813umq5+srUp8ddv3nz5v34U9z3KK900n358mWSkpKM93KTubq68scffzzymdGjRxMQEPDQ9cKFC7+QGEVERKzBH39rhyDyr/Jw71JEXqT01M7dunWLbNmyPfbzVzrpfhafffYZ/fr1M/5vNpu5evUqzs7OmEymv3lSXmc3b94kf/78nDlzBicnJ2uHI/LaU50TeblU50ReLtU5gfsz3Ldu3SJPnjx/e98rnXTnypULW1tbYmJiUl2PiYnBzc3tkc84ODjg4OCQ6lr27NlfVIiSzjg5OemHUeQlUp0TeblU50ReLtU5+bsZ7mSv9EZq9vb2VKhQgY0bNxrXzGYzGzdupGrVqlaMTEREREREROTJXumZboB+/frRtm1bKlasiIeHByEhIcTGxtK+fXtrhyYiIiIiIiLyt175pPvjjz/m0qVLDB06lAsXLlCuXDnWrl370OZqIn/HwcGBYcOGPfTqgYi8GKpzIi+X6pzIy6U6J0/DZHnS/uYiIiIiIiIi8kxe6Xe6RURERERERNIzJd0iIiIiIiIiL4iSbhEREREREZEXREm3iIiIiIiIyAuipFtERETkFaY9b0VeHrPZbO0Q5DWkpFvSpYSEBP76669U1/QjKfJiJXf8VddEXqy4uDi++uorLly4gNlsxmQyWTskkddefHw8N27cwMbmf+mRBrzkeVHSLelOUlISAQEBNGzYkN69e/Pdd98BpPqRFJHn586dOwwaNIigoCDOnDlDUlKStUMSea0dPHiQ4OBgOnfuTO3atdm/fz+3bt2ydlgir63kvqWHhweTJk0iMjISQANe8tzonG5Jl86cOcO1a9cYPXo0J0+eJGPGjMyfP598+fJZOzSR186dO3cYMWIEp06dYvPmzXz88cfUr1+fWrVqWTs0kdfWvXv3+O2335gwYQLbt2+nbdu2tG7dmlKlSlk7NJHX0pkzZ9i8eTMbNmxg8+bNNGzYkM8//5y8efNaOzR5DSjplnQn+U/WZDJx48YNjhw5Qr9+/Th79ixz5szhvffe06y3yHNisVhSjfQvWLCA9evXs2nTJoYMGUKXLl2sGJ3I6ycpKQlbW9tU1yZPnszSpUtxc3Nj8ODBlC9f3krRibw+Hmzfkl25coV9+/YZg1xDhw7lP//5jxUilNeJkm5JV8xms5FQnz9/Hnd3d+D+D2ejRo3YtWsXP/zwA5UqVUp1r4ikXcqOyF9//UW2bNlwcXExPj958iTz589n2LBhBAcH07dvX2uFKvJaiY6ONmbVNm7cSI0aNYzPli1bxsyZM8mfPz8BAQEUKFDAWmGKpHsp27m1a9fi6OjIu+++m+qe8+fPU79+fbJly8aECRM02CX/iDISSTdCQkL49ddfAfDy8mL48OGYzWZjk5mIiAjeeecd2rVrB9x/x1tjSiJPJ2VHZPz48fTr149ly5YRFxdn1KdChQrRr18/xo8fz2effcaSJUusGbLIa2H27Nn07duXo0ePUqtWLbp3787du3eNjQubNm1KmzZt2LNnDzt27AC0yZPIs0jZzgUEBODv709oaCgxMTFGnUpKSsLd3Z1169YRHR3NyJEjrRmyvAaUdEu6EB8fz7p166hbty7vvvsuR44cYfTo0djY2GBjY2Ns7DR79mwyZMiAn58foA0wRJ5Wcp0ZOHAgISEhNGjQgNq1a5M5c2ZMJpORAGTJkoV27drRtWtXvvrqK44ePWrNsEXSvUyZMnHixAlq1qzJqVOn2L9/PxkzZsRkMhmJQJs2bahVqxb9+/fn1q1bauNEnkFyvRk8eDAzZsxg/PjxjB07FldXV6Ods7W1JSEhgdy5cxMREcGGDRuYNGmSlSOX9ExJt6QL9vb2rFmzxthYZtKkSeTMmdP4PPn9N1dXVzp37szx48eJi4uzVrgi6drMmTNZtGgR3333Hb6+vrz55pvGZyln1nLmzEnjxo25ffs2x44de+hzEUm75s2b4+7uzoULFyhfvjz//e9/gfsJQsoBr+DgYN5++22WLl1qzXBF0rW1a9eyZMkSvv32W2rVqmW8rghw6dIlAOzs7AAoWbIkQ4cOZevWrdy8eVPHZsozUdIt6YLFYuHmzZtUq1aN9957j3bt2hnL65I/h/vJt4+PD7/++is//PCDtcIVSZcsFgtms5kdO3bQrl07KlWqZFzfuXMnQ4YMoVu3bsYxfQDVq1fH09OTIUOGcPfuXc28iTyFlEtZzWYzTZo0Ydq0afz3v/9l9OjR7Nu3z7gveY8Sk8lEkSJF2L17t9XiFknvjh8/TsGCBalWrRpwv47NnTsXHx8fihcvjre3Nxs3bjTu/7//+z8OHDjA6dOntV+QPBP91cgr68GzgJ2cnIiMjGT16tVUrlwZb29vfvrpJ+B+JyQuLo5Tp07h7u7OwIEDSUhI0KybyFMwmUwkJSVx4cIFEhISjOtdu3Zl0KBBzJkzh6NHj+Lj48P8+fONz319fSlWrBjXr1+3QtQi6VNSUpIxSBUXF4eNjQ1t27alc+fO9OzZk8OHDzNp0iT279+PyWTi1q1brFy5EhsbGwYNGoSrqyt379618rcQSZ/s7e05efIku3fvJi4ujsaNG/P111+TmJjI+PHj2b9/P8HBwdy7dw+AypUr06hRI/bu3au+pTwT7V4ur6SUR6aEhYVx5swZ8uTJQ9myZY1RSS8vL/bs2cPcuXMpUqQIzZs3p27duowYMYLDhw+TO3ducuXKZc2vIZIu9e/fn9mzZ1OzZk1++ukn8ubNS8eOHWnRogWOjo60adOGY8eOsXHjRjJnzkx8fDxTp06lQ4cOZM+e3drhi7zyUrZx/fr1Y9++fZhMJgoXLsxXX32FjY0NixcvJiQkhHz58tGgQQO+/PJLChQowPr167FYLMTGxpI1a1YrfxORV9vjjgXbu3cvQ4cOZffu3SQmJlKyZEkGDhzIBx98gJOTE+vWraN+/focOHCA0qVLA3Dw4EEKFCigdk6eiZJueaU1atSIgwcP8uabb3LkyBHc3Nxo0qQJ/v7+wP3dXFetWkXevHkpXrw4q1evtnLEIulDyo7I3bt3SUhIwNHR0fh86NChXL58GUdHRwYOHIiTkxN2dnZYLBZ69OjB9evXWbhwoXE03+M6NiLyeE2aNCEqKgpfX18uXbrEkiVLyJo1Kxs2bMDFxYWlS5cyf/58Tp48Sfny5Zk7d661QxZJN1K2S4cOHSIhIYEsWbJQrFgxAPbv309MTAxXr16lefPmqZ5dsWIFEyZMYNmyZeTJk+elxy6vHyXd8sqaMmUK06ZNY/369RQoUIAzZ84wbdo0IiMj8fX1pUuXLgCsWbMGGxsb6tSpA6DzuUWeIGVHJCQkhC1bthAVFUX37t1p0qQJ+fLle+yz58+fp2HDhjRp0oRBgwa9rJBFXju7du2iVatWfPvtt7z99tsAnD59mkaNGmFnZ2e8s33lyhXi4+ONjZ7Uxok8Wcp2btiwYXz33XccP36ckiVL0rZtW3r06PHYZ5PbuSpVqjB16tSXFbK85vSrLa+sEydOULBgQQoUKABA/vz56dq1K8WKFTOW1wHUq1dPCbdIGqXsiCSftV2qVCn+7//+Dz8/PxYtWmTcl9KNGzf45ZdfqFOnDnnz5jUSbo3bijyby5cvc/nyZaONM5vNFChQgFmzZhEdHc33338P3D8lIDnhTrmhmog8Wsp2rm/fvsyYMYMxY8awbNkyAKZOncpff/2V6n6As2fPsmzZMurXr4+7u7uRcKudk+chg7UDEHlQcuKcK1cu7ty5w9WrV43jwQoWLIiPjw/Nmzfn1KlTFCpUKNWz6oyI/L3kjkj//v2ZN28eW7ZsoXTp0iQlJfH7778zdepUunXrRtasWY26ePLkSXx9fYmLi+Odd94xlrhqkEskbR5VVypVqoSjoyPz58+nZ8+exudubm4AxiZpKV/b0CscIk+WXE8CAwOZMmUKR44coWjRogAcPnyYgQMHEhMTYxyHaTKZuHr1KhMmTCAqKorq1asbZ3KrnZPnRUm3WFXyDuXJG8rA/xLnKlWqEBAQwNdff02vXr1wcHAAwNHRkXLlyhn/F5Gns3PnTiZMmEBgYKCxQUxiYiJXrlzBZDJx6tQpbGxsePPNN7GxsaFQoUK0adMGW1tb4703dURE0iYxMZEMGe53t65cuYKDgwNZs2YlS5Ys1K1bl4iICJydnWnRogVwPwHImjUr9vb21gxbJN1JnuE2m81cuHCBpUuXUq1aNc6fP28k3eHh4ZhMJtatW8eePXsoUqQIXl5e5MyZE39/f86dO8c777wDqJ2T50vvdItV3L17F3t7+1Q/ZgEBAVy/fh0nJyf69++Po6MjU6ZMoXfv3vTv35/KlSuTJ08eOnbsyDvvvMPChQut+A1E0o9HbXI2aNAgpkyZwqJFi2jQoAGVK1cmMTGRokWL4uTkxNKlS3nvvfcoXrw4Xl5eeHp6GmVo0zSRvxcbG0tkZCQ1atQwNihs27Ytx44d49y5c4waNYqWLVvy3//+l4EDB/Lnn39StGhRypYty8KFCylWrBirVq2y8rcQST9StkuxsbFkyZKFn376iS+++IKcOXPSsWNHhg8fjqOjIz4+PuTMmZOvvvqK06dPkyFDBgoXLszs2bNxdXV9qDyR50FJt7x0ZrOZjz/+mKioKKKiojCZTHh5eXH48GEKFy5MdHQ0FouFnTt3ki1bNmbPnk1ISAgxMTHkzp2bMmXKpHrvVD+KIo+Xso6MGjWKbNmyGRvIDBo0iAkTJuDi4kKFChVYuHChkSDs3LmTHTt2MG7cOFq0aEFISIi1voJIuhMSEkK/fv2YNWsWbdu2ZdCgQaxevZr+/fuza9cu5s2bx+DBgxk8eDAXL15k9erVzJ07F3d3dwoVKsTYsWMBzbSJpEXKdq5jx44cOnSIn376iQwZMvDzzz8zaNAgDh8+TMGCBfnll1+MOhUXF0d8fDxffvkl9vb2BAQEWPNryOvOIvKSJSYmWiIiIizlypWzVK9e3XL69GlLu3btLJcvX7YkJCRYfv31V0ulSpUshQsXtly7ds1isVgsp0+ftpw8edJy5MgRo5ykpCQrfQOR9MFsNhv/7t27tyVr1qyp6pDFYrGMHDnSYjKZLGFhYca1lHUrMTHxxQcq8hoaNGiQxd7e3jJ//nxL7969Lfv27TM+mzBhgsXR0dEyfPhwy40bN4zrKeub2jiRJ0vZzvXp08eSK1cuy6+//prqnl27dlk8PT0t9erVs0RGRhrXExIS/rY8kedJ73TLS2dra0v9+vXJmDEj/fv357333qNw4cJkzJiRDBkyUL58eWbNmkXnzp2pWLEie/fuJX/+/KnKsGgHV5EnSrlL+bx589ixYwclSpRIdc/nn39ObGws3bt3J3PmzLRs2dKoW2az2dhvwaJVJSJpkvwO9+jRo7l37x5t2rQhW7Zs+Pj4GPf07dsXk8nE8OHDsbGxoUOHDuTNmzdVfVMbJ/Jkye2Sn58f8+fPZ+PGjZQtWxa434YlJiZSuXJlxo4di7+/P5MnT8ZsNlO7dm0yZMjw0GoStXPyougXXV6q5I3TMmTIQI0aNRg3bhzu7u5ERUUZm8aYTCbKli3LV199hbOzM3nz5jV2cU2mH0WRtPH39ycsLIzt27cbHZGkpCQmTpzIrVu3AAgKCsLPz49OnToZr24A6oiIPKWkpCRj0zTA2LDwxo0b/Pzzz9y+fdv4rE+fPgwfPpxhw4bx66+/pipH9U0k7aZOncrEiRP55ptvjHYuMTGRZs2a8cMPP2CxWKhatSpjxozh5s2bTJs2zTiST4Nb8rLoL01emqSkJGMU/+eff+bOnTvUqlWLoUOH4ujoyHvvvWck5QBvv/0206ZNo3///mTMmNFaYYukW3fv3mXnzp3GTslwf+S/fPnyrFy5MtXuyKNHj6ZPnz60atWKnTt3WitkkXQrZRs3bdo0Vq5cCcAXX3yBv78/Q4YMITw8nLi4OOOZPn36sGbNGry8vKwRsshrwWKx8NZbb7FlyxYuXboEQOXKlbl27Rp16tQxBrGqVavG6NGjOXr0KMePH7dmyPIvpI3U5KVI2Rlp3rw5Z8+eZdSoUVSvXp3ExEQ2bdqEn58f2bNnZ8uWLamOEEumDWVE0i55OfjVq1fx8vLi6tWrLF26lA4dOpArVy4WL16Mk5PTQ88tXbqUZs2aWSFikfQrZfvUuHFjTp48ib+/P7Vq1cLZ2Rm4v/x1ypQpzJgxg+bNm5M5c+bHliEiT2fatGnMnj2bypUrs3XrVt58803mzZtHtmzZHrr3r7/+Ms7oFnlZlHTLS/Xxxx9z5MgR5syZQ/HixVPNvm3ZsoXevXuTI0cONm3alGqJnoj8vQffuU450HXt2jUaNGjAzp078fT0ZO3atQ91+B+kBEDk6XXr1o1t27axevVqChQoAEB8fLyxqqR///5MmTKFcePG8emnn+osbpGnkLKdu3TpEomJibi7uxufT506lZCQEBITE/nuu++M87bTUp7Ii6Yelbw0O3bsYP/+/YSHh1O+fHliY2PZvn07ISEhbN26lQ8++IBJkybx559/MmbMGGuHK5JupOw4zJ49m969e9OmTRt+//13AHLkyMGqVauoW7cu0dHRREdHP7FMJdwiT+fSpUvs37+f4cOHU6BAAQ4cOMCsWbOoXbs2zZs3B2D8+PG0adOG3377TQm3yFNI2c4FBQXRpEkTSpUqRf/+/blw4QIAPXr0wN/fnxw5cjB79myOHTv2t2Uq4ZaXSTPd8tJs2rQJX19fli5dyp9//snatWv56aefsLe3x87OjqlTp1KtWjUOHz5MmTJlrB2uSLqQsiPi5+fHsmXLqFatGmfPnuXYsWP88ssvxozbtWvXqF+/PlevXiUiIuKhncxF5NnduXOHjz76iBw5clC+fHkiIyOB+/uTLFq0iFq1arFgwQIrRymS/qRs5/r168eiRYsYMWIEd+7coU+fPsyePZt27doZ90+aNIm5c+fy7rvv0rt3by0ll1eC1u/KS1OlShVsbW1p0aIF0dHR9OvXjx49epA7d25q1qzJ5cuXsbW1NRJuLW8VebKUHZG5c+eyceNG3n77baKiomjevDn37t3j3r17ODg4kCNHDtauXcuHH35Io0aNWLZsmQa4RJ6TTJky4ePjw9q1a5k6dSpffPEF1atXp2TJkgCpNgoFLW0VSauU7dzChQtZt24d5cqVA+DXX3/l6tWr7N+/n8yZM1OsWDF69+6NjY0Nc+bM4dq1awQHB+Pq6mrFbyCipFtekqSkJDJnzsxvv/1mbHBRtGhRAC5fvkymTJkeekYJt0jajB8/npCQELZv3250RPLly8eVK1fo06cPhw4domXLlrRq1YqSJUvyww8/ULlyZebPn8/YsWOtG7zIayA5gf70009p27YtCQkJxkaF165dIzIykk8++STVM0q4RdJu9erVhISEMG/ePKOdS0pKYv369Rw6dIiBAwdSrFgxfHx8GDFiBD179iQuLo7bt28r4ZZXgpaXy0uTcmMngIsXL3Lp0iU+/vhjihYtynfffWfF6ETSl5SzZN27d2fLli106NABPz8/AMqXL4+DgwOffPIJMTExjBkzhn79+jF+/HgA4uLinriZmoik3YMz1+fPn2fv3r0MGjSIIkWKGOcCi0jaJCQkYGdnB8DevXsZM2YMu3btYuPGjRQrVoyKFSuSNWtWpk6dyvXr1wkNDWXHjh3MmzeP6tWrpypLK0vE2pR0y3PzuOXgj7qelJREUFAQ4eHhlCtXjoULF/5tGSLyP/Hx8UyaNIlOnTqRI0cO4P6yu59++omGDRuyfPlyChQowPz5843jUvr06cOcOXP4888/U436qyMikjYPDhw/yYoVK/juu+/Inj07U6dOBdTGiaSVxWLBy8uL5s2bGxsRHj58mGHDhrFt2zYcHR0pW7YsX331ldEObty4kbp16/Ljjz9Su3btVGWpnRNr0/JyeS5Sdkb27NmD2WwmR44cFC9eHBsbm4c6K7a2tvTs2ZPSpUvTuHFjQJ0RkbT67bffCAsLo3379sa1CRMm0KdPH6ZPn07WrFmZPn062bJlM44ryps3L2XLliVjxoypylJHROTJUrZhoaGhXLt2jfj4eDp37oybm9sj2y4fHx/Kli1rbOKkNk7k6eTLl4+4uDjj/yVLlmT48OGMGzeOBQsWGAl38r4l2bNnp0SJEg+t4lI7J68CzXTLP5ZyBLFZs2YcPnyY//73vxQpUoRq1aoxc+ZMIHWn5cFRR3VGRJ5Os2bNKFKkCCNHjsTW1taoTwMGDGDTpk00bdqULl26kCNHDq5cuUKNGjV47733CAkJsW7gIunYRx99xOHDh8mbNy9nzpzBbDYzduxYGjZsmGpvkgfbOM20iTy9sLAwhg4dyi+//EL+/PmN67///jsjRoxg27ZtfP/993h4eHD58mXef/99ypQpw6JFi6wYtcijKcuRfyy5I9G1a1cOHDhAeHg4mzdvpk+fPnz33XfGsqCUM90Pdj6UcIukTfI4qaenJ7t37+bixYuYTCbi4+MBGDduHJ6enixfvpyvv/6aU6dO8f777+Pu7m4k3BprFXl6EyZM4PDhw+zevZvIyEhOnDhBlSpVGDRoELt37wbuDyDDw22cEm6Rp9eiRQuqVKlCaGgosbGxxvUyZcowbNgwqlevTuPGjdm0aRN169Ylb968RsKtdk5eNcp05Jkldy7g/vmkhw8f5osvvqB06dJ4eHjQunVr5syZw5YtW4z32UTkn0nuvHfp0oXTp08zYMAAAOzt7bl37x5w/4zS//u//2Px4sWUKFGCPHnysGbNGuB+vVUCIPJkDx7xFR0dTalSpXB2djbqUHh4OEWLFuWzzz4DNIAs8jxlzZoVDw8P1q5dy8aNG0lMTDQ+K1WqFMOHD8fT05OaNWvi7OzM2rVrAbVz8mpS6yDPJOVy8I0bN2JjY8Pp06c5cuSIcY+9vT01atSgWrVqHDhwwFqhirx2kpKScHBw4JtvvuH777+nb9++ADg4OBidkokTJ1KxYkXatGmTqiOipEDkySwWi7E6a+DAgcD9DQxPnjwJgJ2dHXfu3AHAz8+P06dPc+rUKavEKvI6Sp6pHjx4MG5ubgwcOJDt27cbq7rg/jveAwcOZNasWaxbtw5QOyevLv1VylOzWCzGD9rHH3/MuHHjuH79Ol5eXuzbt49ffvnFuNfBwYF8+fJx/fp1LfUReUrJdebBupOcDFSpUoVJkybxzTff0K1bNwAyZPjf/pgzZ8409lRQR0QkbZKSkoxZMn9/f6ZPn86lS5fw9fXl8uXLxiBX8jvcd+/eJUeOHNjb21stZpH0KuXsdUomk8lYbfLjjz+SO3duevbsyTfffMOVK1eM+ypUqEDHjh0BtXPyatNfpjy15M7I5s2buXXrFqGhobi6utKkSROio6OZNm0amzdvBuC///0v69evp2TJklrqI/IU7t69i5+fH/v27cNkMj1y0Mre3p5mzZoRFhZGeHg4tWrVYtWqVcYy82QpB8pE5O8lD2otW7YMs9nMhg0bcHFx4c0332TQoEH8+OOPtG3bln379rF582a++OILKlSogLu7u5UjF0lfEhMT8fDwYMSIEY/83NbW1kjKt23bRrVq1QgPD+eTTz4x9jRJSe2cvMq0e7k8kwEDBrB7926yZ89ORESEkVCvX7+egIAAzp07Z+yoXKJECVatWgVoB1eRtFq3bh3+/v688cYbDB8+nDJlyvxt/YmJiaFbt27ExsZy/vx5hg8fjoeHB3nz5n3JkYukf99++y1du3YlMTGRHTt28NZbbwFw7do11q5dS0BAAJcvXyZXrlyUL18+1eZNauNE0iY+Pp4vv/ySESNG8OWXXxqrSB6U8vSbjRs3smrVKr7//nsqVapE69atadCgwcsMW+SZKOmWZ/LVV18xcOBAHB0dWblyJe+8847x2YkTJzh79iz79+8nX758eHt7A1r2I/K0li5dysyZM3FyciIwMPCxiXdy3UpKSuLcuXMsXrwYR0dHmjVrhrOzs5WiF0k/HqxXx48f55tvvmHChAn07NmTL7/88qFnoqKiyJgxI2+88QagNk7kWSQlJTF58mQGDBjAxIkT6dmz5yPve7B+HT58mMTERFxcXLTKRNIFJd3yRClHGFNaunQpvXr1olGjRvj5+VG0aNHHlqHOiEjapawv4eHhzJo1K82J9+P+LyKP9mAbl5CQgJ2dHZcvX2bChAksWrSILl26GDuUx8fHP/T+tma4RZ5OYmKisQfJxo0bCQsLY9myZUyfPp1PP/30sc+prkl6leHJt8i/WcrOyPz587l+/Tr37t2jT58+NGvWjMTERAYOHIiDgwM9evQwEu8HfxTV+RdJm5Sz1ra2tnzyySckJSXx9ddfM3To0Mcm3g/WMdU5kSdL2cb5+/tz5swZjhw5Qr9+/ahZsyYDBgzAxsaGefPmYWNjg7+/P/b29g/VPyUBIk8nOeH28fEhJiaGN998k9KlS9OtWzfu3Lnz2KXmqmuSXinplr+V3Bnx9vbm+PHjuLi4EBsby9ixY9m6dSstWrTAYrEYMwCffvopb731ln4URZ5BygTg2rVr3LhxgzfeeIOWLVuSKVMmpkyZ8reJt4g8nZRt3B9//EHPnj0pWrQoXbp0oUePHowdO5YuXbpgMpmYM2cON2/eZNSoUap3Is9BaGgoP//8M7t27aJgwYKcP3+e2bNn4+fnh62tLb169bJ2iCLPjZJueazkDv3QoUOJiopix44duLi4MGrUKIYNG8atW7cAaNmyJUlJSXTo0IFKlSoZG86ISNqZzWYjAejcuTMHDx7k9OnTlClThsGDB+Pt7U1iYiIzZ85k2LBhBAQEUKZMGStHLZJ+PDhIlby8denSpfzxxx9s3boVFxcXY1b7ww8/BCB//vx069aNGzduPPZ4IxF5eskz3AULFgTA3d2dHj16EBMTQ58+fciSJYtxHJhIeqf1hwKkPgf46NGjwP+W8Jw5c4b+/fvj4uJCUFAQEyZMYM2aNXh4eHD69GksFgtt2rRh69attGrVyirxi6R3ycvBW7Zsye7duxk5ciQHDhzgp59+Yty4cdy4cYNmzZrh6+vLjRs36N69O2fPnrVy1CLpQ8qE+4cffgD+t7z1zp07vPnmm7i4uBAYGEifPn1YsWIF//nPf4iOjmbHjh24u7szfPhwY0M1bYcj8nQeVWeKFSvGkSNHOHLkiHEtW7ZsfPDBB8D9Aeg1a9a8tBhFXiQl3ZKqMzJ27Fg6d+5szGIDnD9/nkuXLjF16lTGjx/PwoULqVWrljHrFhISAsC7774L3J+xE5G/l5SUREJCQqprhw4dIioqyqhjERER2NnZ0bt3b7JlywbAxx9/TMuWLalfvz758uWzRugi6UrKNi4oKIiPPvqIZcuWGZ/fvXuX06dPM3nyZCZPnsyCBQuoU6cOAGvXrmX27NlcvnyZnDlzPlSeiDxZUlKSUWcSEhKwWCxYLBY8PT0pUaIEwcHBqRLvXLly0bVrVzZu3Ei9evWsFbbIc6Wk+18uZedhwIABDBo0iO3bt3P58mXjnnr16rF48WIGDx7M4sWLqVu3LnB/BnzTpk04ODikKlMbOIn8vXv37vGf//yH8PBw4uPjjevXr18nPj6eMmXKMH78ePz9/Vm6dCm1a9cmOjqamTNnAtChQwcGDRoEaMZN5O+kbON69epFUFAQdnZ23L5927inWbNmZM6cmT59+jB37lzq168P3B9wnjFjBrly5SJXrlzG/Uq4RdLuwc0KmzdvTo0aNZg4cSI5c+akb9++HDlyhL59+zJ37lxWrVpF165dAXj//fcBTebI60HvdP+LpeyM9OnTh3nz5jF//nxGjBjB3bt3jfsaN25MREQE9vb23Lx5kwsXLnDixAm6dOlC8eLF6datm7W+gki65ODggLOzM7179yZTpkw0bNgQBwcHihcvjsViwdvbm61bt7JkyRJq1aoF3D83eP78+VSsWJEKFSoYZSkBEHm0lG1ccod+3759DBgwwHiNymw2ky1bNvr06cPo0aMJCgrizp07XLhwgbCwMAoUKMDYsWMfKk9E0iblZoWHDx/ms88+48SJE4wdO5Y//viDsLAwzGYza9asoUuXLhQrVoy33nqLadOmAffrnSZz5HWgv+J/qQc7I3PmzGHHjh20bNmSq1ev8tdffwH3N5opUKAAM2fOJFeuXAwZMoRixYrRs2dPypQpw/LlywGNQoqkVXJdiYiIwMvLiw4dOrBq1Spu3bqFs7MzjRs3Ztu2bXTo0IFatWphsVi4cOECvXv3plSpUqkSbhF5vOQ2rl+/fsyZM4dt27ZRrFgxkpKSOHXqlHGfjY0NXl5efPnll2TPnh0/Pz8iIiKoXbu28f632WxWwi3yjFauXMnx48fZvHkzbdu2JXv27CQkJNC0aVMAmjZtyuzZszl27Bhr165l6dKlgOqdvF400/0vlfwj9umnn7Js2TK2bdtGyZIluX79Ora2tly5cgW4v9FMUlISRYsWJTw8nGvXrnHs2DEKFixIiRIlgP+dKywiT5ZyOfj48ePZv3+/ceRekyZNaNeuHSdPnmTJkiWcOnUKR0dHdu/eTeHChY3l5ZpxE0mbDh06sHLlSrZu3crbb78NQL58+YiJiQHuJ9wWiwUHBwdq165N7dq1uXz5MtmzZzc2WlMbJ/LPXL9+HUdHR9zd3QkKCmL8+PEsXryYWrVqce7cOfbs2UO9evXInz+/8YxmuOV1o6T7Xyi5w75nzx52797Nhg0bePvtt0lMTCR79uwUKFDA6JCk/NFzdHQkR44cFClSJFVZ+lEUSbvkpXbJxxG5ublx9OhROnXqhMVioWnTpgQFBbFt2zbmzZuHq6sr7du3x8/PD1ACIPIkCQkJ2NnZAfdXa0VGRvL2228b75YWL16cffv2kZiYaNRHgJs3b2JnZ5fq/W21cSJPJ+WgcHKdy5AhA05OTgQHBzN+/HgWLVpkbFa4ZcsWduzYQZUqVXBzczPK0cCyvG7UkvzLxMfHM378eGJjY3nzzTfZtGkT77zzDhaLxRjVz5EjR6r33UwmE9euXaNBgwZs3rw5VXn6URR5el9++SW///4733zzDcuXLycqKor69evToUMHli9fTr58+WjTpg0bNmxgypQpSrhF0shiseDj48PChQsBmDdvHhUqVMBisRgJtpOTEydPniQuLg6TyWS0ccWLF2fy5MmpylMbJ5J2KXcph/9trNuwYUN+//13BgwYQHh4uLEh7/nz5xk/fjxZsmRJlXCLvI400/0v89tvvzFz5kzatWuHi4uLcd1kMpGYmEiGDBnInTu3sbOrra0t165do1q1ahQsWNDYSVJEnt2NGzcoU6YMuXPnNmYCFi1ahJeXF3379sVkMlG3bl2yZMmS6jkl3CJPli9fPmMz0OT6ZTKZjBm44sWL4+DgwL179wC4du0a1atX5+2338bf39+aoYukWykHtvz9/Tl27BiFChWiUaNGVK9encWLF+Pt7U1ISAh//fUXNjY2TJkyhSJFijBu3DijDA10yetKPbh/mcqVK1O+fHmCg4NJTExM9Vlyh75UqVLG8vIrV67g6elJwYIFWbt2LaBN00SeVfL73BaLhcOHDwP3B7aSE4QWLVoQHR1N06ZNjc9FJO1MJhPlypXj888/58yZM9ja2hr1LrkzX7BgQW7evMm1a9e4evUq7777Lnny5GHdunWA2jiRp5VyhrtFixZERETg6OjI3r178fX15ccff8TT05N169YRGxvLzJkzWblyJfXr12fVqlWANk2T15+S7n+R5I6Hp6cnu3fv5uLFi8D/OhjJSXfWrFk5d+4cp06d4v333ydfvnypEm7NtomkTVJSUqr/J3counXrhq2tLa1atQIgY8aMAGTPnp3Ro0cTGhpKpUqVXm6wIq+JFi1aUKVKFUJDQ4mNjU3VkU9u7zJnzsyJEyeoXbs2BQoUSJVwq40TeTrJM9y7du3CxcWFyMhI5s6dy5QpU/D09KRr166sWrWKd955hw0bNrBt2za+/fZbY4Zb9U7+DfQX/i+S3PHo0qULp0+fZsCAAcD9ZDtlclCsWDFu3bpF5cqVcXd3V2dE5BkkL2sFmD59On5+foSEhLBhwwby589PQEAA+/bto379+uzatYt169bRt29fbt++ja+vL6AZN5FnkTVrVjw8PFi7di0bN25MtarLZDKRO3duMmfOTP369XF1ddWgsshzMGPGDD788EO2bt1Kzpw5AXj77bfp27cvtWvXpmfPnkRERGBnZ0e2bNnInDkzoM0K5d/DZEl5fo289pITgW3btvHhhx/SqVMnJk6cCPzvXZoTJ07w5ptv0qRJk1RnJepHUeTpNWrUiEOHDlGiRAnOnDlDbGwsXbt2xc/Pj3Xr1jFkyBCOHz+Ok5MTlSpVMuqciDy9lO+Efvjhhxw/fpwZM2bw7rvvYm9vb3xeo0YNsmTJwvfffw+ojRN5WikHlgF++OEH5syZw+rVq9m8eTOVK1c2PouKiiIkJIS5c+eyZ88eypUrZ4WIRaxLSfdrKrlj8bhNKeLj41mwYAH9+vWjRYsWTJ8+3fgsJiaGTZs20bx5c0CdEZGnkbLOhYWFMW7cOCIjIylUqBBnz55l4cKFTJ06lcGDB9O1a1cA/vjjD+zt7Y3j+FTnRP5e8safj5IyGahevTpXr16lZ8+eNGnSBGdnZ+D+ZobZsmUDVN9E/ok5c+bQrl07ALZt28aYMWOIjo4mNDSUqlWrGvft37+fvXv30rFjRytFKmJdSrpfQ3fv3mXw4MG0atWK8uXLPzbxvn37NqtXr+bTTz+lQoUK9OrVi1q1ahnvl4I6IyJP8qj6lZwQ+Pv7s3v3brZs2WJ8duHCBcaMGcMff/xBeHg42bNnT1WGdm8V+XuJiYl4eHjQuHFjhgwZ8th7kpNyX19fjh07RoYMGRgxYgRFihQhd+7cgNo4kX9i9+7dVK1alY4dOzJr1iwANm7cyPTp0zlx4gQzZsygSpUqDz2neif/RvqLfw1t3bqVTZs2MWrUKH7//XdjxvtBWbNmpVmzZhw5cgQnJyemTZuGh4cH3333HWfPngV0RJHIkyQnyEOGDKFp06YARme/UKFC3Lp1i5MnTxr3u7m54enpyZYtW7hy5UqqMh78t4g8zGw207hxY0aMGGG8HvWgDBkyGHuVhIWF8cUXX1CqVClatGhBz549+eGHHwC1cSJP48G+ZNmyZVm4cCFLliyhc+fOANSoUYNu3brx5ptv0q1bN7Zt2/ZQOap38m+kc7pfQ3Xq1OHGjRvMnDmToUOHEhgYSJkyZR45g2Y2m3F1dWXp0qWcO3eOxYsXc+HCBTJlymSl6EXSn4MHDzJq1CgAPvnkE8LDwwEoXrw4Fy5cYP78+fj6+uLq6gqAq6srpUqVUsdD5BnY29szePBgsmbNyoABA8iQIQM9e/Z86D5bW1tjRq1GjRrUqFEDX19fEhMTcXFxsULkIunXo/qQGTNmxNvbG4vFYiTds2bNokaNGgCMGDGCyMhIqlev/tLjFXnVaHn5ayblkp3w8HBmzZqFk5PTExPvlJ1/LfsReTqnT5+mdevWuLm5cfHiRZycnIiIiABg8uTJDBkyhDZt2lCpUiUKFChAt27dKFu2LIsXL7Zy5CLpS8pl4xs3biQsLIxly5Yxffp0Pv3008c+p9c2RJ5dyvrTvXt3MmXKxPjx443P7969y/Lly2nfvj09evQwVqAcPnyYkiVLWiVmkVeNMqvXSHKynLyk7pNPPqFDhw7cuHGDoUOHPnap+YMJthJukbRJrksFChSgbt26/PTTTzRp0oTo6GgaNWoEQK9evQgODubo0aP06tULPz8/3nnnHSPh1rinSNolJ9w+Pj4MGzaMTJkyUbp0abp16/bYpeag1zZEnsaD7VJy/blz5w4uLi58/fXXjBgxwvg8Y8aM+Pj40LBhQyZNmmRsxJuccKudE9Hy8tdGyt1ar127xo0bN3jjjTdo2bIlmTJlYsqUKU9cai4if+/BepPy3x07duTAgQPkypULPz8/AgMDadSoEStXrqRTp040atSIu3fvkpCQQOHChQGtKhF5FqGhofz888/s2rWLggULcv78eWbPno2fnx+2trb06tXL2iGKpGvJbVtERASFChWibNmy1KtXjx49etCrVy9jBaXFYmHo0KEAZMqUibfeegsnJyfjDO4HyxP5N1PS/Rowm81Gwt25c2cOHjzI6dOnKVOmDIMHD8bb25vExERmzpzJsGHDCAgIoEyZMlaOWiT9Se44BAQEsH37dgIDA3F3d6dw4cI4OztjZ2dHREQEixYtwsbGhhEjRuDj48OKFSvIlStXqrIsFosSbpFnEBMTw5tvvknBggUBcHd3p0ePHsTExNCnTx+yZMmiY4lE/qHDhw8zcOBAqlatyqlTpzh+/DhVqlQhZ86ctG3bFovFwsiRI7FYLHzxxRdER0ezc+dOevXqZaz00gSPyP/one7XSMuWLfn9998JDg6mbNmyFC5cmPfee49FixaRLVs2lixZQlhYGAkJCSxatIh8+fJZO2SRdOeXX37B09OThIQEfHx8uHr1Kl5eXnTt2pVr165Rp04dgoKCqFGjBsuXL2fMmDE4OzuzefNma4cuku48qtO+ePFievbsyfbt23nrrbeM6ytXrsTb2xuAH3/8kXr16r3UWEVeN6tWraJNmzYkJSXx3XffUaNGDaNOXr16lcWLF+Pn54ebmxt3797Fw8OD77//HlDCLfIgzXSnQ0lJSZjNZuzs7Ixrhw4dIioqioULF1KmTBlmzZqFnZ0dvXv3Jlu2bAB8/PHHxMbGcvHiRSXcIs+oQIECfPnll0ybNg2z2Uz//v3p06cP69evx8XFhRIlSrB3717q1atH48aNiYuL48SJE9YOWyTdSfnaVEJCgvE+t6enJyVKlCA4OBg/Pz8j8c6VKxddu3alSZMmvP/++1aLWyS9S371ycbGhpIlSxIXF8eCBQtwcnKiUqVKAOTMmZNu3bpRq1YtNmzYQI4cOYx3ufXqlMjDNNOdzty7d48aNWrQpUsXPv74Y+zt7QHYsWMHvr6+HD58mPHjxxMUFER4eDi1a9cmOjqaH374gS5duqQqS6OQIs/m0qVLLFiwgMDAQIKCgvD19eWHH35gxowZrF+/Hg8PD7Zu3YqDgwMJCQnGAJnqnEjapEy4/f39OX78OFevXqVBgwZ06dKFtWvXMmHCBBwdHWnevDk5c+Zk8ODBVK9enWnTpgHq+Iv8U8l1KCIighEjRlCiRAn69OlDxYoVgfu7lmfMmPGRz4hIaprpTmccHBxwdnamd+/eZMqUiYYNG+Lg4EDx4sWxWCx4e3uzdetWlixZQq1atQA4fvw48+fPp2LFilSoUMEoS51/kWfj4uJC69atSUpKYuDAgdy8eRN/f3+8vLxYu3YtHh4eODg4YLFYUq1IUZ0TSZvkhNvb25vDhw/z2WefceLECcaOHcsff/xBWFgYZrOZNWvW0KVLF4oVK8Zbb71lJNzaM0Hkn0k5YOzl5YXJZGLEiBFMmzaNLl26UKFCBcqXL8/EiROpU6eO8ZzqncijKelOR1KOOLZv354OHTowe/Zs6tSpg7OzM40bNyYsLIwOHTpQq1YtLBYLMTEx9O7dGw8Pj1QJt4j8M7ly5aJ9+/ZGRyQ2NpbAwEDq1q0LaLRf5J9auXIlx48fZ/Pmzbi7uzNx4kQSEhJo2rQpAE2bNqVp06YEBARga2tLnjx5ANU9kX8q5SuMM2fOxMXFBW9vbxISEggODsbX15dbt25RqFChVAm3iDyeku50JOWbAOPHj2f//v189tlnADRp0oR27dpx8uRJlixZwqlTp3B0dGT37t0ULlyYmTNnGmVotk3k+XB2dqZdu3YABAUFkSFDBuP4FHX6Rf6Z69ev4+joiLu7O0FBQYwfP57FixdTq1Ytzp07x549e6hXrx758+c3ntEMt8jTedQgVXI/ccyYMQwfPpzIyEgAfHx8cHV15a+//uL27dv06NHjsWWISGp6pzsd+vDDD4H7P3JHjx7lypUrzJo1i6ZNm3Ly5Em2bdvGvHnzeOuttyhUqBB+fn7G/fpRFEmbR9WXxw1aXb16lW+++YYBAwbw7bffGseliEjapKxbye9zL1iwgEWLFlGjRg1GjRrFokWLjJUkixYtYseOHQwdOhQ3Nzdrhi6SbqXcO+Hs2bPcvn2bEiVKALB+/Xrq16/P6tWrqV279mP7kOpbiqSNku50JnnX5F9//ZUsWbJga2tLhw4dWLVqFd988w2NGjUydnhNST+KImmXsiNy8OBBbG1tyZo1q3Eu8KNcunSJX3/9VccUiTyllPUN/peA37hxg9KlSxMdHc3atWupXbs2AOfPn+fDDz+kRo0ajBs3zlphi6RrKfuFHTp04LfffiMmJob8+fMzcOBASpQogclkomTJklaOVOT1oKQ7nRk8eDAHDhzgxx9/TNVR8fLyYt++fYSEhFC3bl2yZMli5UhF0qeUHZG2bduyf/9+7t27x40bNxg3bhwtW7Z84isaGuQSSZuUM9z+/v4cO3aMQoUK0ahRI6pXr86OHTvw9vamYsWKNGjQABsbG6ZMmUKRIkVYtWrVQ2WIyNNp06YN+/fvZ8aMGXh4eJAvXz7KlCnDwoULcXV1tXZ4Iq8N9QrTieSxEYvFwuHDh4H7u7vevXsXgBYtWhAdHU3Tpk2Nz0Xk6aVMuPfs2cOiRYvYtWsXhQoVYvDgwVy8eDHNZYjI4yUlJRnJcosWLYiIiMDR0ZG9e/fi6+vLjz/+iKenJ+vWrSM2NpaZM2eycuVK6tevbyTcZrNZCbfIMzp+/DiHDx9m9uzZvPvuu8yaNYv4+HgGDBiAq6sriYmJ1g5R5LWhjdReUQ8ut0vuVHTr1o1ly5bRqlUrFixYYJyPmD17dkaPHk2OHDmoVKmSVWIWeV1cunSJ06dPM3/+fEqVKsXYsWM5fvw48+bNw9XV1TibVDNsIs8uuY3btWsXLi4uREZGkj9/fg4ePMjkyZPp2rUr06ZNo2HDhmzYsIG4uDjs7OzInDkzoBUlIv/UjRs3uHbtGhUrViQkJITAwECWLFlC7dq1OX/+PEuXLqVDhw44OjpaO1SRdE+t1SsoZcI9ffp0/Pz8CAkJYcOGDeTPn5+AgAD27dtH/fr12bVrF+vWraNv377cvn0bX19f4H5nRETS5sG3bK5cucKePXvIkycP06ZNY8yYMcyfP5+6dety5coVBg8ezKlTp5Rwi/xDM2bM4MMPP2Tr1q3kzJkTgLfffpu+fftSu3ZtevbsSUREBHZ2dmTLls1IuLVLucjTeVS/sHTp0jg5OVGzZk0CAwNZvny5sXfCmTNnCA8P5/fff3/ZoYq8ljTT/QpKTrgbNWrEoUOHKFGiBBs2bCA2NpauXbvi5+dHrly5GDJkCB9++CFOTk5UqlSJESNGGGWoMyKSNikHuW7evImTkxMlSpSgYcOGdOrUiZ9//pmVK1fy3nvvAfd3eN27dy/Hjh37243VRORhD67iyp8/P++//z6rV6/m0KFDVK5cGYBSpUrRt29fTCYTTZs2Zc+ePZQrV854TgNeImmXst6dOnUKJycncuTIgb29PZ988gkhISE0a9aMDz74AIvFwvnz5+ncuTOVKlWiWrVqVo5e5PWgpPsVknKpalhYGFFRUWzYsIFChQpx9uxZFi5cSEhICJkzZ6Zr167UqVOHP/74A3t7e4oUKQJouZ3I00jZEenevTvu7u60adOGAgUKULJkSYKDg2nVqhX/+c9/gPudldatW/POO+9Qs2ZNa4Yuki4l17c5c+bQrl07GjRogJOTE3Fxcfj6+hIaGkrVqlWB+4l39+7dqVKlSqqEW0TSzmKxGPWuVatWREVFce7cOQYPHswnn3xCp06dOHHiBGvWrOGDDz7AxcWFQ4cOUaRIEb766iujDA10ifwz2r3cih71I5aYmEiGDBnw9/dn9+7dbNmyxfjswoULjBkzhj/++IPw8HCyZ8+eqgz9KIo8m8aNG3P06FHGjBmDp6cnOXLkAKBr165s2rSJTJkyUbBgQY4dO0bRokWJiIgAVOdEnsXu3bupWrUqHTt2ZNasWQBs3LiR6dOnc+LECWbMmEGVKlUeek6DyiJpZ7FYMJvNRsI9bNgwli9fzqhRo9i2bRsrVqzAy8uLzz//nIwZM7J9+3bmzp3LG2+8Qf78+enevTugeifyvGim24qSO+tDhgzhjz/+YNmyZcYZ24UKFWLDhg2cPHmSQoUKAeDm5oanpyehoaFcuXKF7Nmzp+rwq/Mv8vTGjBlDVFQUe/bsIXv27ACcPn2azJkzM2PGDDZt2sSmTZuws7PDx8eHNm3aAOqIiKTVg4NTZcuWZeHChXTp0gWAWbNmUaNGDQBCQ0Pp1q0bISEhVK9ePVU5qm8iT5a8gstkMhkJ9+bNm7lw4QIzZ87E09OTRo0aUaJECYKDg7FYLPTr148GDRrQoEGDVGWpnRN5fpR0W9nBgwcZNWoUAJ988gnh4eEAFC9enAsXLjB//nx8fX2NsxJdXV0pVaqUfgRFnpObN2/i6elJ9uzZ2b59O+vXr2fq1KkUKVKEOnXqMHLkSD744INUz6gjIpI2j1oNkjFjRry9vbFYLHTu3BlInXiPGDGCyMjIh5JuEfl7cXFxdOnShZ49e+Lh4QHAihUraNq0KVmzZqVRo0bGvb6+vphMJiZOnIiNjQ2dO3emdOnSqcpTOyfy/CjptrLs2bPzf//3f7i5uRETE4OXlxcRERF88MEH+Pv7M2TIEC5evEilSpUoUKAA3bp1o2zZshQuXNjaoYukO497HePbb7/l9u3bHDhwgGrVqjFhwgSioqJYt24dfn5+ODs7pypHHRGRJ0tZx7p3706mTJkYP348AA4ODnh7e2M2m2nfvj1Zs2Zl4sSJ1KhRA3d3d0qWLGnN0EXSleS6dvLkSQoXLmwk3AA+Pj6MGzeOgIAA1q1bR9myZcmTJw+AMeg1cOBAKlWq9FDSLSLPj5JuK0n+gSxQoAB169Zl2rRpfPbZZ3zzzTc0atSIlStX0qtXLzJnzsyyZcuYP38+b7zxBu+88w4LFy5MVYaIPFnKTdPMZrOxuUxQUBB2dnacP3+eyZMnU6ZMGfLkycO6devYsWMH8fHxVo5cJH14sE1K/vedO3dwcXFh0qRJZMuWjSFDhgD3Z7x9fHz49ttvmTRpEhcuXGDx4sVGwq02TuTJUtaT69evExAQAEBwcDDu7u60aNECPz8/YmNjmTVrFrlz56ZDhw64ubkB9xPvokWLGid0iMiLoaT7JXlcZwSgY8eOHDhwgFy5cuHn50dgYKCReHfq1IlGjRpx9+5dEhISjBluLW8VSbuUCXdgYCD79+8nW7ZseHh40LVrVwICAoiPj8fe3h6z2cy5c+cYMGAAVapUwd3d3crRi6QPye1aREQEhQoVomzZstSrV48ePXrQq1cvnJycCAwMxGKxMHToUAAyZcrEW2+9hZOTk3EG94Plicijpexb9ujRg9DQUM6ePUtCQgLz5s0jb968ZMqUicaNGzN06FCSkpKYMWMGJpOJ9u3bG4l3csKtvqXIi6Ok+yVJ/lEMCAhg+/btBAYG4u7uTuHChXF2dsbOzo6IiAgWLVqEjY0NI0aMwMfHhxUrVpArV65UZVksFv0oiqRRyuNSvL29OXr0KA0bNiQhIYFhw4Zx+fJlhgwZgr29PdHR0UyZMoXIyEgKFy5MWFiYUYYSAJEnO3z4MAMHDqRq1aqcOnWK48ePU6VKFXLmzEnbtm2xWCyMHDkSi8XCF198QXR0NDt37qRXr17G+6aqbyJPlrKe9O3bl/DwcPbu3Wsk0vPmzaN///6EhoZisVjw9vYmICAAGxsbZsyYwc2bNxk0aBDZsmUzylTfUuTF0ZFhL9Evv/yCp6cnCQkJ+Pj4cPXqVby8vOjatSvXrl2jTp06BAUFUaNGDZYvX86YMWNwdnZm8+bN1g5dJN0bOXIkK1eu5PvvvydPnjyMHDmS0aNHA9CzZ0/GjBkD3N/N/N69ewwbNgzQyL/I01q1ahVt2rQhKSmJ7777jho1ahgJwtWrV1m8eDF+fn64ublx9+5dPDw8+P777wEl3CJPy9/fn1mzZrFr1y6KFSsG3D9+9uzZsyQmJtKlSxfs7e3p3Lkz3t7eAPTr1w+z2UxISIgVIxf5d9FM90tUoEABvvzyS6ZNm4bZbKZ///706dOH9evX4+LiQokSJdi7dy/16tWjcePGxMXFceLECWuHLZJuPKrDnpiYCEBCQgKffvopefLkYeLEiUyaNIk5c+YQFRVFYGAgWbJkYciQIQwaNMh4Vgm3SNol1xcbGxtKlixJXFwcCxYswMnJiUqVKgGQM2dOunXrRq1atdiwYQM5cuSgefPmqZ4XkbSJjIxk8uTJdOrUyUi44+Pj8fT0pGrVqkyaNIng4GD8/Pz4+uuvMZlMNG7cmAkTJhhlaKBL5OXQTPdLdunSJRYsWEBgYCBBQUH4+vryww8/MGPGDNavX4+Hhwdbt27FwcGBhIQE7OzsAP0oijxJyjpy4sQJ7ty5Q6lSpYzP4+LiiI2N5fz58zRr1ozAwECaNWtGZGQkPj4+3L59m3nz5tGqVauHyhORtEtOniMiIhgxYgQlSpSgT58+VKxYEYC7d++SMWPGRz4jIml3/vx5Ro4cycGDB2natCm9evWiSpUqODo6smLFCpycnAA4cOAAfn5+3Lx5k+nTpxt1Ue2cyMujFu4lc3FxoXXr1nz++ecMHDiQ8ePH4+Xlxdq1a1m9ejWrV6/GwcEBi8ViJNygDWVE/k7KjkNgYCAfffQR77//PqVLl+bKlSsAZM6cGRcXF6KiorC1taVBgwYA2NnZ0bRpU7Zv324k3KA6J/IsEhISjOTZy8uLoUOH8ueffzJt2jR27dpFQkIC5cuXZ926dameU8It8vTc3d0ZMmQI5cqVY+HChbi6upIzZ07WrFljJNwWi4WyZcsyevRovL29jYQb1M6JvExaXm4FuXLlon379phMJkaMGEFsbCyBgYHUrVsX0Ii/yNNImXD36dOHhQsXEhoaipOTE35+fvTu3ZsFCxYY97u5uREdHU1oaCjvv/8+vXr1om7durz77ruA6p/IszKbzcZg8cyZM3FxccHb25uEhASCg4Px9fXl1q1bFCpUiDp16lg5WpHXg5ubG59//jmjR48mJiaGypUrkyHD/e598skdFouFSpUqGa95aIZb5OVT0m0lzs7OtGvXDoCgoCAyZMhgHKGiDr9I2iV3HAYNGsS8efNSbSbz0Ucfce/ePdavX0+ePHlwd3fn3Xff5dNPPyUgIICpU6dSrlw5xo4dC+hkAJG0etTgVHJdHDNmDMOHDycyMhIAHx8fXF1d+euvv7h9+zY9evR4bBki8vTc3NwYPHgwZrOZNWvWkDFjRvz9/bG1tU11ZGYyJdwiL5/e6X4BHtWReNyo4tWrV/nmm28YMGAA3377rXFkioikXWRkJB999BEdO3Zk6tSpxvU33ngDs9nM3bt3uXHjBq1btyY4OBhbW1tiYmK4ePEiHh4egBIAkbRK2Yk/e/Yst2/fpkSJEgCsX7+e+vXrs3r1amrXrv3YeqX6JvL8XbhwgVGjRrF37168vLzw9/e3dkgi8v8p6X7OUnZGDh48iK2tLVmzZqVgwYKPfebSpUv8+uuv1KtX72WFKfJa+bvNZKZOnUqxYsUYNmwYY8aMYc2aNdSoUSPV81pqJ5I2KZPlDh068NtvvxETE0P+/PkZOHAgJUqUwGQyUbJkSStHKvLvdOHCBYKCgvjxxx+ZPHkyH374obVDEhGUdD9XKTsjbdu2Zf/+/dy7d48bN24wbtw4WrZs+cSOvUb/RZ5N8gj/nj17OHnyJBUqVCAiIgJbW1tsbGy4ffs2BQsWJCgoiC5dulg7XJF0rU2bNuzfv58ZM2bg4eFBvnz5KFOmjLGZk4hYT3R0NJs2baJ169bWDkVE/j9ld89RyoR7z549LFq0iF27dlGoUCEGDx7MxYsX01yGiDyd5M1kqlSpQqZMmahcuTJ2dnZGnTp58iTOzs7kz5/fypGKpG/Hjx/n8OHDzJ49m3fffZdZs2YRHx/PgAEDcHV1JTEx0dohivyr5c2b10i4Nbcm8mpQhvecXbp0idOnTzN//nxKlSpFWFgYx48fJywsDFdXV+7evQvoR1DkRUjeTKZhw4asWbOG0aNHA/fPBW7RogXlypWjfv36Vo5SJH27ceMG165do2LFioSEhPDFF1+wZMkS6tSpw/nz55k2bRq3bt2ydpgigjZNE3lVKOn+hx5Mnq9cucKePXvIkycP06ZNY8yYMcyfP5+6dety5coVBg8ezKlTp/QjKPKCuLq68vnnn1OpUiV++OEHhg0bRsWKFXF3d2fp0qXA/dc4ROTJHlVXSpcujZOTEzVr1iQwMJDly5dTu3ZtAM6cOUN4eDi///77yw5VRETklaWk+x9ISkoykuebN28CUKJECRo2bEinTp34/PPP+fbbb43zSM+ePcvevXs5duyY1WIW+TdIXmpeoUIFvvzyS4oUKcK6desA7ZsgklZJSUlGXTl16hTXrl0DwN7enk8++YSoqCiaNWvGBx98gMVi4dy5c3Tu3JlSpUpRrVo1a4YuIiLyStFGas8o5S7l3bt3x93dnTZt2lCgQAECAwMJDg6mVatWTJ06FZPJxKlTp2jYsCHvvPMOc+fOtXL0Iv8O58+fZ+PGjbRq1QpQwi2SVil39G/VqhVRUVGcO3eOwYMH88knn5AhQwYGDx7M2rVreeONN3BxceHQoUMUKVKEVatWPVSGiIjIv5mS7n+ocePGHD16lDFjxuDp6UmOHDkA6Nq1K5s2bSJTpkwULFiQY8eOUbRoUSIiIgB1RkReNiXcIk9msVgwm83GoPKwYcNYvnw5o0aNYtu2baxYsQIvLy8+//xzMmbMyPbt25k7dy5vvPEG+fPnp3v37oDqm4iISEpKuv+BMWPGMHv2bPbs2UP27NkBOH36NJkzZyZXrlxs2rSJTZs2YWdnR+HChWnTpg2gzoiIiLxaUq7eSrZ582bCw8Np3bo1np6eAISFhREcHEzt2rXp168fhQsXfqgstXEiIiKpZbB2AOnZzZs38fT0JHv27Gzfvp3169czdepUihQpQp06dRg5ciQffPBBqmfUGRERkVdJXFwcXbp0oWfPnnh4eACwYsUKmjZtStasWWnUqJFxr6+vLyaTiYkTJ2JjY0Pnzp0pXbp0qvLUxomIiKSmljGNUi4ISPnvb7/9lmbNmtGpUyfOnj3LhAkTeP/991m3bp2x6UxK6oyIiMirILktO3nyJIULFzYSbgAfHx/GjRsHwLp16zh37pzxWefOnenbty/z5s1j//79LzVmERGR9Egz3WmQctmd2WzGYrFga2tLUFAQdnZ2nD9/nsmTJ1OmTBny5MnDunXr2LFjB/Hx8VaOXERE5GEp9xW5fv06AQEBAAQHB+Pu7k6LFi3w8/MjNjaWWbNmkTt3bjp06ICbmxtwP/EuWrQo7733nrW+goiISLqhpPsJUibcgYGB7N+/n2zZsuHh4UHXrl0JCAggPj4ee3t7zGYz586dY8CAAVSpUgV3d3crRy8iIpJayoS7R48ehIaGcvbsWRISEpg3bx558+YlU6ZMNG7cmKFDh5KUlMSMGTMwmUy0b9/eSLyTE269NiUiIvL3lHT/jeQZbQBvb2+OHj1Kw4YNSUhIYNiwYVy+fJkhQ4Zgb29PdHQ0U6ZMITIyksKFCxMWFmaUoV3KRUTkVZCyTerbty/h4eHs3bvXSKTnzZtH//79CQ0NxWKx4O3tTUBAADY2NsyYMYObN28yaNAgsmXLZpSphFtEROTvqaX8G8kdk5EjR3L69GnWr1/P6NGjyZ49O7GxsYwZM4ZBgwYBkDdvXrJnz85HH31kHAtmNpuVcIuIyCsjuU3y9/dn7ty5/Pzzz5QtWxaAxMREsmXLxowZM0hMTGTWrFl8++23wP2jw5o0acKdO3dSJdwiIiLyZJrp/v8eNSOdmJgIQEJCAp9++il58uRh4sSJTJo0iTlz5hAVFUVgYCBZsmRhyJAhRgIOWm4nIiKvpsjISCZPnkynTp0oVqwYAPHx8Xh6elK1alUmTZpEcHAwfn5+fP3115hMJho3bsyECROMMrSKS0REJO10TjepOw8nTpzgzp07lCpVyvg8Li6O2NhYzp8/T7NmzQgMDKRZs2ZERkbi4+PD7du3mTdvHq1atXqoPBERkVfJ+fPnGTlyJAcPHqRp06b06tWLKlWq4OjoyIoVK3BycgLgwIED+Pn5cfPmTaZPn07FihUBtXEiIiJP618/FZuy8xAYGMhHH33E+++/T+nSpbly5QoAmTNnxsXFhaioKGxtbWnQoAEAdnZ2NG3alO3btxsJN6DOiIiIvLLc3d0ZMmQI5cqVY+HChbi6upIzZ07WrFljJNwWi4WyZcsyevRovL29jYQb1MaJiIg8rX910p0y4e7Tpw9TpkwhICCAhQsXYmNjQ+/evVPd7+bmRnR0NKGhofz222/06tULZ2dn3n33XeD+knIREZFXnZubG59//jlVqlQhU6ZMVK5cmQwZ7r9xlpSUhMlkwmKxUKlSJePVKS2MExEReTZaXg4MGjSIsLAwdu3aZbzf9sUXX3Dv3j1q1apFnjx5cHd3x9HRkaFDhzJjxgycnZ0pV66cscmMltuJiEh6ExMTw8iRI/nll19o3Lgx/v7+QOrjMkVEROSf+dcn3ZGRkXz00Ud07NiRqVOnGtffeOMNzGYzd+/e5caNG7Ru3Zrg4GBsbW2JiYnh4sWLeHh4ANo0TURE0q8LFy4watQo9u7di5eXl5F4i4iIyPPxr88US5cuTYcOHThw4ACTJ08GoEqVKhQpUoS1a9dy7tw5+vfvzzfffMPu3bvJlCkThQoVMhJui8WihFtERNKt5KXmFStWJCwsjB9//NHaIYmIiLxW/vUz3fC/Uf49e/Zw8uRJKlSoQEREBLa2ttjY2HD79m0KFixIUFAQXbp0sXa4IiIiz110dDSbNm2idevW1g5FRETktaIpWh69oYydnZ0xg33y5EmcnZ3Jnz+/lSMVERF5MfLmzWsk3BqPFxEReX4yWDuAV4WbmxuDBw/GbDazZs0a7O3t+eyzz7h79y4tWrSgXLly1K9f39phioiIvHDaGFREROT50fLyByQvNd+3bx81a9ZkxYoV5M2bl3Xr1gHaNE1ERERERETSTkn3I1y4cIGgoCDCwsKoXbs233//PaCEW0RERERERJ6Oku7HOH/+PBs3bqRVq1aAEm4RERERERF5ekq600AJt4iIiIiIiDwLJd0iIiIiIiIiL4imb0VEREREREReECXdIiIiIiIiIi+Ikm4RERERERGRF0RJt4iIiIiIiMgLoqRbRERERERE5AVR0i0iIiIiIiLygijpFhEREREREXlBlHSLiIiIiIiIvCBKukVEREREREReECXdIiIiIiIiIi/I/wM2T9PNLHwCoQAAAABJRU5ErkJggg==",
       "text/plain": [
        "<Figure size 1000x600 with 1 Axes>"
       ]
@@ -1537,8 +1551,8 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "Highest Dev Set Score (No Opt): 54.2, Model: Fine-tuned\n",
-      "Highest Dev Set Score (BFRS): 53.5, Model: Fine-tuned\n"
+      "Highest Dev Set Score (No Opt): 34.0, Model: Fine-tuned\n",
+      "Highest Dev Set Score (BFRS): 58.0, Model: Fine-tuned\n"
      ]
     }
    ],
@@ -1588,7 +1602,7 @@
    "outputs": [
     {
      "data": {
-      "image/png": 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Uf/7znzTvv3zx4sX7KeeewsLC5O3trffff9/mEOCsWG6zZs3k7OysDz/80GbP4ccff6zo6Og0r+Ce1ZL3ZKZcfkJCgmbNmnXfY3bs2FGXLl1K9dqnXE7nzp2VlJSkd955J9U8iYmJqW5fdafRo0fLMAw999xzun79eqrpu3bt0uLFiyVlfFvMKomJiZo7d675OCEhQXPnzpW/v79q1qwpKe31/uuvv2rbtm33vdy4uDjdvHnTJlamTBl5eXkpPj5eUs7Y5gAAf2NPNwAgTekd3p1S69at1bhxY73xxhs6fvy4qlWrph9//FGrVq3SoEGDzD3I1atXV9euXTVr1ixFR0fr4Ycf1oYNG/Tnn3+mGnP8+PHatGmT6tatq969e6tixYq6cuWKfv/9d/3000+p7j+dFby9vTV79mw999xzeuihh9SlSxf5+/vr5MmT+u6771S/fv00m8uM8Pf318iRIzV27Fi1aNFCbdq00eHDhzVr1izVrl3b5gJW9vLwww8rf/786t69u1555RVZLBZ99tln/+jw4W7duunTTz/VkCFD9Ntvv+mRRx5RbGysfvrpJ/Xt21dt27ZVw4YN1adPH40bN07h4eF67LHHlC9fPh09elTLli3T9OnT071eQHLeM2fOVN++fRUcHKznnntO5cqV07Vr17R582atXr1a7777rqSMb4tZJTAwUBMmTNDx48dVvnx5ffXVVwoPD9e8efPMPf9PPPGEli9frvbt26tVq1aKiIjQnDlzVLFixTS/RMiII0eOqGnTpurcubMqVqwoJycnrVixQufPn1eXLl0k5YxtDgDwN5puAMB9c3Bw0OrVqzVq1Ch99dVXWrhwoYKCgjRp0iQNHTrUZt5PPvlE/v7++uKLL7Ry5Uo1adJE3333XarDhgsVKqTffvtNb7/9tpYvX65Zs2bJz89PlSpV0oQJE+xWy9NPP63AwECNHz9ekyZNUnx8vIoWLapHHnkk3auJZ9SYMWPk7++vGTNmaPDgwSpQoIBefPFFvf/+++kemp2V/Pz89O2332ro0KF68803lT9/fj377LNq2rSpwsLC7mtMR0dHff/993rvvfe0ZMkS/ec//5Gfn58aNGigKlWqmPPNmTNHNWvW1Ny5c/X666/LyclJQUFBevbZZ1W/fv17LqdPnz6qXbu2Jk+erE8//VQXL16Up6enHnroIS1cuNBsIDOzLWaF/Pnza/HixRowYIDmz5+vQoUKacaMGerdu7c5T48ePRQZGam5c+dq3bp1qlixoj7//HMtW7ZMmzdvvq/lFi9eXF27dtWGDRv02WefycnJScHBwfr666/VsWNHc77s3uYAAH+zGFwlAwAAIMMaNWqkS5cupXkKBAAAd+KcbgAAAAAA7ISmGwAAAAAAO6HpBgAAAADATjinGwAAAAAAO2FPNwAAAAAAdkLTDQAAAACAndB0AwBylIkTJyo4OFhWqzW7U5F0+/ZQjRo1yu40kMddvnxZHh4e+v7777M7FQBAFqPpBgDkGDExMZowYYJGjBghB4e/f0VZLBabH29vbzVs2FDfffddNmZ725gxY1Lll9ZPVjXu33//vcaMGZPh+a1Wqz799FPVrVtXBQoUkJeXl8qXL69u3bpp+/btmV5+XFycxowZo82bN2f6uSklr7dChQopLi4u1fSgoCA98cQT/2gZyWbPnq0nn3xSJUqUkMViUY8ePdKdNyoqSi+++KL8/f3l4eGhxo0b6/fff09z3tWrV+uhhx6Sq6urSpQoodGjRysxMfG+xvTz89MLL7ygt9566x/VCgDIeZyyOwEAAJJ98sknSkxMVNeuXVNNa968ubp16ybDMHTixAnNnj1brVu31g8//KCwsLBsyPa2Dh06qGzZsubj69ev6+WXX1b79u3VoUMHM16oUKEsWd7333+vmTNnZrjxfuWVVzRz5ky1bdtWzzzzjJycnHT48GH98MMPKl26tOrVq5ep5cfFxWns2LGSlCVfJFy4cEGzZ8/W0KFD//FY6ZkwYYKuXbumOnXq6Ny5c+nOZ7Va1apVK+3Zs0evvvqqChYsqFmzZqlRo0batWuXypUrZ877ww8/qF27dmrUqJE++ugj7du3T++++65Zz/2M+dJLL+nDDz/Uxo0b1aRJE/usDADAv88AACCHqFq1qvHss8+miksy+vXrZxM7cOCAIclo2bKlXXNq2LCh0bBhwwzPf/HiRUOSMXr0aLvk069fPyOjv74jIyMNi8Vi9O7dO9U0q9VqnD9/PtPLz6r6Ro8ebUgyqlevbhQqVMiIi4uzmV6yZEmjVatW/2gZyY4fP25YrVbDMAzDw8PD6N69e5rzffXVV4YkY9myZWbswoULhq+vr9G1a1ebeStWrGhUq1bNuHXrlhl74403DIvFYhw8ePC+xjQMw6hcubLx3HPP3VedAICcicPLAQA5QkREhPbu3atmzZplaP6QkBAVLFhQx44ds4nHx8dr9OjRKlu2rFxcXFS8eHENHz5c8fHxNvMtXLhQTZo0UUBAgFxcXFSxYkWbPZRZ7dChQ+rUqZMKFCggV1dX1apVS6tXr7aZ59atWxo7dqzKlSsnV1dX+fn5qUGDBlq/fr0kqUePHpo5c6Yk20Pu0xMRESHDMFS/fv1U0ywWiwICAmxiUVFRGjRokIoXLy4XFxeVLVtWEyZMMM+vP378uPz9/SVJY8eONZefvNf91q1bOnTo0F33Jt9p1KhROn/+vF3XfcmSJe+6npJ98803KlSokM0RCv7+/urcubNWrVplbkMHDhzQgQMH9OKLL8rJ6e+DBvv27SvDMPTNN99kesxkzZs315o1a2RwR1cAyDNougEAOcIvv/wiSXrooYcyNH90dLSuXr2q/PnzmzGr1ao2bdrogw8+UOvWrfXRRx+pXbt2mjp1qp566imb58+ePVslS5bU66+/rsmTJ6t48eLq27ev2dRmpT/++EP16tXTwYMH9dprr2ny5Mny8PBQu3bttGLFCnO+MWPGaOzYsWrcuLFmzJihN954QyVKlDDP/+3Tp4+aN28uSfrss8/Mn/SULFlSkrRs2bI0z5tOKS4uTg0bNtTnn3+ubt266cMPP1T9+vU1cuRIDRkyRNLtZjG5OW7fvr25/OSG8syZMwoJCdHIkSMzvG4eeeQRNWnSRBMnTtSNGzfuOu/Vq1d16dKle/7cq9b07N69Ww899JDN9QQkqU6dOoqLi9ORI0fM+SSpVq1aNvMFBgaqWLFi5vTMjJmsZs2aioqK0h9//HFfNQAAch7O6QYA5AiHDh2SJJUqVSrN6Tdv3tSlS5dkGIZOnjypN998U0lJSerUqZM5z5IlS/TTTz9py5YtatCggRmvXLmyXnrpJf3yyy96+OGHJUlbtmyRm5ubOU///v3VokULTZkyRf369cvS2gYOHKgSJUpox44dcnFxkXR7r2iDBg00YsQItW/fXpL03Xff6fHHH9e8efPSHCc0NFTly5fX+vXr9eyzz95zuUWKFFG3bt306aefqlixYmrUqJHq16+vVq1aKTg42GbeKVOm6NixY9q9e7d5nnGfPn0UGBioSZMmaejQoSpevLg6deqkl19+WVWrVs1QDhkxevRoNWzYUHPmzNHgwYPTna9GjRo6ceJEhsbLzMXmkp07d06PPvpoqniRIkUkSWfPnlWVKlXMPfnJ8TvnPXv2bKbHTFa6dGlJt/emV65cOdM1AAByHppuAECOcPnyZTk5OcnT0zPN6R9//LE+/vhj83G+fPk0fPhwcy+sdHuPbkhIiIKDg3Xp0iUznnxRqk2bNplNd8qGOzo6Wrdu3VLDhg21bt06RUdHy8fHJ0vqunLlijZu3Ki3335b165d07Vr18xpYWFhGj16tM6cOaOiRYvK19dXf/zxh44ePWpzga1/YuHChapTp44++eQTrVixQitWrNCwYcPUpEkTffrppypatKik2+vukUceUf78+W3WXbNmzTR+/Hj9/PPPeuaZZ+66rKCgoPs6LPrRRx9V48aNNXHiRL300ks2r01KX3zxxT33hkt/N66ZdePGDfNLkZRcXV3N6Sn/TW/emJiYTI+ZLPnIjZSvAQAgd6PpBgDkCm3btlX//v2VkJCgHTt26P3331dcXJzNYbtHjx7VwYMHzfOO73ThwgXz/1u3btXo0aO1bdu2VIcjZ2XT/eeff8owDL311lvp3g7qwoULKlq0qN5++221bdtW5cuXV+XKldWiRQs999xzqlq16n0v38HBQf369VO/fv10+fJlbd26VXPmzNEPP/ygLl266L///a+k2+tu7969GVp39jBmzJh77u1O69z0rOTm5pbqHGvp9lEWydNT/pvevCm/NMjomMmSv7TIyDnoAIDcgaYbAJAj+Pn5KTExUdeuXZOXl1eq6cWKFTMvsvb444+rYMGC6t+/vxo3bmyeU2y1WlWlShVNmTIlzWUUL15cknTs2DE1bdpUwcHBmjJliooXLy5nZ2d9//33mjp1qnnhsKyQPNawYcPSvbVZ8i3HHn30UR07dkyrVq3Sjz/+qAULFmjq1KmaM2eOXnjhhX+ci5+fn9q0aaM2bdqoUaNG2rJli06cOKGSJUvKarWqefPmGj58eJrPLV++/D9e/t08+uijatSokbm3Oy0XL15UUlLSPcfy9PRM94iJuylSpEiaF4FLjgUGBprzJceTt6mU89apUyfTYya7evWqJKlgwYKZzh8AkDPRdAMAcoTkc4wjIiIytGe3T58+mjp1qt588021b99eFotFZcqU0Z49e9S0adO77ilcs2aN4uPjtXr1apUoUcKMb9q06Z8XcofkQ53z5cuXoSuzFyhQQD179lTPnj11/fp1PfrooxozZozZdGfVHtBatWppy5YtOnfunEqWLKkyZcro+vXr98zRnntgx4wZo0aNGmnu3LlpTq9du7Zdz+muXr26/vvf/8pqtdocQfHrr7/K3d3d/OKhevXqkqSdO3faNNhnz57V6dOn9eKLL2Z6zGQRERGSbl+dHwCQN3D1cgBAjhAaGirpdiOTEU5OTho6dKgOHjyoVatWSZI6d+6sM2fOaP78+anmv3HjhmJjYyVJjo6OkmRz/nF0dLQWLlz4j2pIS0BAgNlIprXH8+LFi+b/L1++bDPN09NTZcuWtTk82cPDQ9Lt23vdS2RkpA4cOJAqnpCQoA0bNsjBwcHcy965c2dt27ZN69atSzV/VFSUEhMTJUnu7u4ZXn5mNWzYUI0aNdKECRPMw69T+uKLL7R+/fp7/nTr1u2+lt+pUyedP39ey5cvN2OXLl3SsmXL1Lp1a/Pc7EqVKik4OFjz5s2z2fM+e/ZsWSwWm4v7ZXTMZLt27ZKPj48qVap0XzUAAHIe9nQDAHKE0qVLq3Llyvrpp5/0/PPPZ+g5PXr00KhRozRhwgS1a9dOzz33nL7++mu99NJL2rRpk+rXr6+kpCQdOnRIX3/9tdatW6datWrpsccek7Ozs1q3bq0+ffro+vXrmj9/vgICAjJ1j+mMmjlzpho0aKAqVaqod+/eKl26tM6fP69t27bp9OnT2rNnjySpYsWKatSokWrWrKkCBQpo586d+uabb9S/f39zrJo1a0qSXnnlFYWFhcnR0VFdunRJc7mnT59WnTp11KRJEzVt2lSFCxfWhQsX9OWXX2rPnj0aNGiQeRjzq6++qtWrV+uJJ55Qjx49VLNmTcXGxmrfvn365ptvdPz4cRUsWFBubm6qWLGivvrqK5UvX14FChRQ5cqVVblyZR0/flylSpVS9+7dtWjRovtaV6NHj1bjxo3TnHa/53SvWbPGXMe3bt3S3r179e6770qS2rRpYx5Z0alTJ9WrV089e/bUgQMHVLBgQc2aNUtJSUkaO3aszZiTJk1SmzZt9Nhjj6lLly7av3+/ZsyYoRdeeMFmL3VmxpSk9evXq3Xr1pzTDQB5iQEAQA4xZcoUw9PT04iLi7OJSzL69euX5nPGjBljSDI2bdpkGIZhJCQkGBMmTDAqVapkuLi4GPnz5zdq1qxpjB071oiOjjaft3r1aqNq1aqGq6urERQUZEyYMMH45JNPDElGRESEOV/Dhg2Nhg0bZriGixcvGpKM0aNH28SPHTtmdOvWzShcuLCRL18+o2jRosYTTzxhfPPNN+Y87777rlGnTh3D19fXcHNzM4KDg4333nvPSEhIMOdJTEw0BgwYYPj7+xsWi8W426/ymJgYY/r06UZYWJhRrFgxI1++fIaXl5cRGhpqzJ8/37BarTbzX7t2zRg5cqRRtmxZw9nZ2ShYsKDx8MMPGx988IFNDr/88otRs2ZNw9nZ2abWffv2GZKM11577Z7rafTo0YYk4+LFi6mmNWzY0JBktGrV6p7jZET37t0NSWn+LFy40GbeK1euGL169TL8/PwMd3d3o2HDhsaOHTvSHHfFihVG9erVDRcXF6NYsWLGm2++abOeMjvmwYMHDUnGTz/9lCV1AwByBoth3Me9PQAAsIPo6GiVLl1aEydOVK9evbI7HWTSrFmzNHz4cB07dkyFChXK7nRynUGDBunnn3/Wrl272NMNAHkI53QDAHIMHx8fDR8+XJMmTcrSK4jj37Fp0ya98sorNNz34fLly1qwYIHeffddGm4AyGPY0w0AAAAAgJ2wpxsAAAAAADuh6QYAAAAAwE5ougEAAAAAsBOabgAAAAAA7MQpuxOwN6vVqrNnz8rLy4urgQIAAAAAsoRhGLp27ZoCAwPl4JD+/uw833SfPXtWxYsXz+40AAAAAAB50KlTp1SsWLF0p+f5ptvLy0vS7RXh7e2dzdkAAAAAAPKCmJgYFS9e3Ow505Pnm+7kQ8q9vb1pugEAAAAAWepepzFzITUAAAAAAOyEphsAAAAAADuh6QYAAAAASePHj5fFYtGgQYPM2LFjx9S+fXv5+/vL29tbnTt31vnz5+851pkzZ/Tss8/Kz89Pbm5uqlKlinbu3GnH7JFT5flzujMqKSlJt27dyu40ADwAnJ2d73pbCQAA8O/bsWOH5s6dq6pVq5qx2NhYPfbYY6pWrZo2btwoSXrrrbfUunVrbd++Pd3f51evXlX9+vXVuHFj/fDDD/L399fRo0eVP3/+f6UW5CwPfNNtGIYiIyMVFRWV3akAeEA4ODioVKlScnZ2zu5UAACApOvXr+uZZ57R/Pnz9e6775rxrVu36vjx49q9e7d5UebFixcrf/782rhxo5o1a5bmeBMmTFDx4sW1cOFCM1aqVCn7FoEc64FvupMb7oCAALm7u9/zynMA8E9YrVadPXtW586dU4kSJfjMAQAgB+jXr59atWqlZs2a2TTd8fHxslgscnFxMWOurq5ycHDQ//73v3Sb7tWrVyssLExPPvmktmzZoqJFi6pv377q3bu33WtBzvNAN91JSUlmw+3n55fd6QB4QPj7++vs2bNKTExUvnz5sjsdAAAeaEuXLtXvv/+uHTt2pJpWr149eXh4aMSIEXr//fdlGIZee+01JSUl6dy5c+mO+ddff2n27NkaMmSIXn/9de3YsUOvvPKKnJ2d1b17d3uWgxzogT6pMPkcbnd392zOBMCDJPmw8qSkpGzOBACAB9upU6c0cOBAffHFF3J1dU013d/fX8uWLdOaNWvk6ekpHx8fRUVF6aGHHrrr9VmsVqseeughvf/++6pRo4ZefPFF9e7dW3PmzLFnOcihsrXpHjNmjCwWi81PcHCwOf3mzZvq16+f/Pz85OnpqY4dO2boSoGZxeGdAP5NfOYAAJAz7Nq1SxcuXNBDDz0kJycnOTk5acuWLfrwww/l5OSkpKQkPfbYYzp27JguXLigS5cu6bPPPtOZM2dUunTpdMctUqSIKlasaBMLCQnRyZMn7V0ScqBsP7y8UqVK+umnn8zHTk5/pzR48GB99913WrZsmXx8fNS/f3916NBBW7duzY5UAQAAAOQhTZs21b59+2xiPXv2VHBwsEaMGCFHR0czXrBgQUnSxo0bdeHCBbVp0ybdcevXr6/Dhw/bxI4cOaKSJUtmYfbILbL98HInJycVLlzY/EnemKOjo/Xxxx9rypQpatKkiWrWrKmFCxfql19+0fbt27M5ayBtQUFBmjZt2j8aY8yYMapevXqW5JOe48ePy2KxKDw83K7LyW2y4vUDAAC5h5eXlypXrmzz4+HhIT8/P1WuXFmStHDhQm3fvl3Hjh3T559/rieffFKDBw9WhQoVzHGaNm2qGTNmmI8HDx6s7du36/3339eff/6pJUuWaN68eerXr9+/XiOyX7bv6T569KgCAwPl6uqq0NBQjRs3TiVKlNCuXbt069YtmysCBgcHq0SJEtq2bZvq1auX5njx8fGKj483H8fExEiSEhMTlZiYKOn27XocHBxktVplGIb5I90+7HP87kv2KjdNr9UoaC4/JYvFkma8Z8+eWrx4sd5//3299tprZnzVqlVq3769rFZrhsZJK75o0SI9//zz5vTAwEA1b95c48ePV0BAQIZryswy/2ncwcFBy5cvV7t27e45/7fffqsPPvhAv//+u5KSklSpUiX17dtXPXr0yFRNixYt0uDBg3X16lWb+G+//SYPDw8ZhnHfNQ0dOlT9+/e32SYzM86devbsqaioKK1YscKMFStWTGfPnpW/v79dXqfSpUtr3rx56t27t06cOJFubhERETbf+Ga0prv5p7knfx7c7zh3ru+05k9+nPJzSZL5bfqd53o7OTnJMAybuMVikaOjo6xWq817Pr14ys+9tOJJSUk2eaYXd3R0lMViscn7brlTEzVREzVREzXltpqk2+dkJ4918OBBjRw5UleuXFFQUJBee+01DRo0SElJSWZNyYefJyYmysHBQbVr19Z//vMfvf7663r77bdVqlQpTZkyRc888wyvUx6q6c460pOtTXfdunW1aNEiVahQQefOndPYsWP1yCOPaP/+/YqMjJSzs7N8fX1tnlOoUCFFRkamO+a4ceM0duzYVPHdu3fLw8ND0u0LIpQpU0anT59WQkKC4uLilJSUJGdn52y7b25SUpJu3rxpPnZwcJC7u7sSExNtvkRI3vBcXV01ceJEPfvss8qfP7/NYfmxsbHm/5Nrunnzps1G6eLionz58unGjRs2G5nVapW3t7d2796tpKQk7du3Ty+//LLOnDmjdevW2YydnIdhGDY5WiwWeXh4ZKomNzc33bp1SwkJCWbcyclJrq6uio+Pt9mg06rp5s2b5pWg76zJ1dVVTk5Omjx5skaMGKHBgwdr8uTJ8vLy0po1a/Tyyy9r9+7dev/99yVJHh4eslqtunHjRro1Jed/48YNm5rc3d1lGIZu3rx53zVZLBa5uroqNjY23dcpuaa4uDibDwM3Nzc5ODjYvE7JFwy8syZvb285OTkpMTExS1+n/fv36+rVq3r44Ye1Y8cOXb9+3Sb3uLg4tWjRQtWrV5efn5+Za2ZqSkpKkqenp/kapPc6ZbYmwzCUkJCg+Pj4DG97ku376datW0pMTFRsbGy6NSU34vv371dKtWrVUkJCgvbu3WuTY+3atRUdHa1Dhw7ZrJdq1arp0qVL+uuvv8y4j4+PQkJCdPbsWZ0+fdqMJ3/uRURE6OLFi2a8WLFiKlasmI4cOaLo6GgzXrp0aQUEBGj//v026zg4OFi+vr7mZ0SyqlWrytnZWTt37qQmaqImaqImasrVNa1cuVKHDh0yn/PMM89o4sSJunDhglnTrl27bGpaunSpJGnnzp1mTZUqVdKCBQtsapLE65SHarqzN0qXkYNcvXrV8Pb2NhYsWGB88cUXhrOzc6p5ateubQwfPjzdMW7evGlER0ebP6dOnTIkGZcvXzZu3bpl3Lp1y0hKSjIMwzBiY2ONP/74w4iLizOsVqthtVoNwzCMcb9f/Fd/DMMwl5/yJ7149+7djSeeeMIIDg42hg0bZsZXrFhhSLKZd9myZUbFihUNZ2dno2TJksakSZPuOv4nn3xi+Pj42MTeffddw8HBwYiNjTWnr1y50ggJCTEcHR2Nv/76y7h8+bLx3HPPGb6+voabm5vRokUL4/Dhw6nGXbNmjVG+fHnDzc3N6Nixo3H9+nVj4cKFRsmSJQ1fX1+jf//+xq1bt8znlSxZ0hg7dqzRpUsXw93d3QgMDDQ++ugjM/eSJUsaksyfkiVLprnOTp48aeTLl88YPHhwqmnTp083JBnbtm0zrFarsXHjRkOSsWbNGqNKlSqGi4uLUbduXWPfvn0201P+jBo1ysxnypQp5vqVZMyePdto1aqV4ebmZgQHBxu//PKLceTIEaNhw4aGu7u7ERoaahw9etSsadSoUUa1atXM/O5cVnKdhmEYt27dMnr27GkEBQUZrq6uRvny5Y2pU6eazx01alSq527cuNH466+/DEnG7t27zXk3bdpk1K5d23B2djYKFy5sDB8+3EhISDCnN2zY0BgwYIAxbNgwI3/+/EahQoXMulNuS2PHjjWeeuqpNLexxMREIywszAgJCTGio6PN+I0bN4whQ4YYgYGBhru7u1GnTh1j48aNqbafjGx3R44cSff9lJSUZIwaNcooXry44ezsbBQpUsQYMGCAzfb27rvvGj169DA8PT2N4sWLG3PmzLEZZ+/evUbjxo0NV1dXo0CBAsYLL7xgxMTEGIZhpLm+N23alCqXuLg448CBA8a1a9fMz6WU233K2K1bt8z1mDKWmJho1pSRePLnXnrxxMTEDMWT1+WdOaaXOzVREzVREzVREzVRU16t6fLly4YkIzo62ribbD+8PCVfX1+VL19ef/75p5o3b66EhARFRUXZ7O0+f/68ChcunO4YLi4uNjevT5Z8NcKUHBwcbK6cnp3SW356cUdHR73//vt6+umnNXDgQPObs5TP2bVrl5566imNGTNGTz31lH755Rf17dtXBQsWNA+nvnP85Mcp4+7u7rJarUpKSpLFYlFcXJwmTpyoBQsWyM/PT4UKFVLXrl119OhRrV69Wt7e3hoxYoRatWqlAwcOKF++fObzPvzwQy1dulTXrl1Thw4d1KFDB/n6+ur777/XX3/9pY4dO6pBgwZ66qmnzOV/8MEHev311zV27FitW7dOgwYNUoUKFdS8eXPt2LFDAQEBWrhwoVq0aGEesnKnb775Rrdu3dKrr76aavpLL72kN954Q0uXLlW9evXM6cOHD9f06dNVuHBhvf7662rdurWOHDmi+vXra9q0aRo1apR5gQxPT0+bdZdyGe+++66mTJmiKVOmaMSIEXr66adVunRpjRw5UiVKlNDzzz+vAQMG6IcffrB5bvK/Ke8BGRsbqxYtWig0NFTS7cOUixcvrmXLlsnPz0+//PKLXnzxRQUGBqpz58569dVXdejQIcXExGjhwoWSpAIFCujs2bM2r/mZM2fUqlUr9ejRQ59++qkOHTqk3r17y83NTWPGjDHnXbx4sYYMGaJff/1V27ZtU48ePdSgQQM1b97czHfNmjUaMmRImtvSyJEj9euvv+q3336Tt7e3GR8wYIAOHDigpUuXKjAwUCtWrFDLli21b98+lStXLlPb3eOPP25ud3davny5pk2bpqVLl6pSpUqKjIzUnj17bHKcMmWK3nnnHb3xxhv65ptv1LdvXzVq1EgVKlRQbGyswsLCFBoaqh07dujChQt64YUXNGDAAC1atCjd9Z3e+yytz6Xk+J0sFkua8eRDnf5pPOWFYjISTyuXzMapiZokakovx8zGqYmaJGpKL8fMxqmJmqSM1ZRevnfKUU339evXdezYMT333HOqWbOm8uXLpw0bNqhjx46SpMOHD+vkyZNmw/Gga9++vapXr67Ro0fr448/TjV9ypQpatq0qd566y1JUvny5XXgwAFNmjQpQ+cwS7fPuZ8zZ45q1aolLy8vSbcPV541a5aqVatmzrN69Wpt3bpVDz/8sCTpiy++UPHixbVy5Uo9+eST5vNmz56tMmXKSJI6deqkzz77TOfPn5enp6cqVqyoxo0ba9OmTTZNd/369c1z18uXL6+tW7dq6tSpat68ufz9/SXd/sLmbl/GHDlyRD4+PipSpEiqac7OzipdurSOHDliEx89erSaN28u6XazWaxYMa1YsUKdO3eWj4+PLBbLXZeZrGfPnurcubMkacSIEQoNDdVbb72lsLAwSdLAgQPVs2fPdJ+fvAzDMNSxY0f5+Pho7ty5kqR8+fLZnE5RqlQpbdu2TV9//bU6d+4sT09Pubm5KT4+/q65zpo1S8WLF9eMGTPMW/edPXtWI0aM0KhRo8wPoqpVq2r06NGSpHLlymnGjBnasGGDuZ7OnDmjvXv3qmXLlqmW8eWXX2rKlCn67rvvVK5cOTN+8uRJLVy4UCdPnlRgYKAkadiwYVq7dq0WLlxoHvZ/v9tdSidPnlThwoXVrFkz5cuXTyVKlFCdOnVs5nn88cfVt29fSbdfr6lTp2rTpk2qUKGClixZops3b+rTTz81T1eZMWOGWrdurQkTJqhQoUIZWt8AANjb9KvTszsF4B8ZmH9gdqeQZbL16uXDhg3Tli1bdPz4cf3yyy9q3769HB0d1bVrV/n4+KhXr14aMmSINm3apF27dqlnz54KDQ1N9yJqD6IJEyZo8eLFOnjwYKppBw8eVP369W1i9evX19GjR1NdjCCl6OhoeXp6yt3dXRUqVFChQoX0xRdfmNOdnZ1VtWpVm+U4OTmpbt26ZszPz08VKlSwycvd3d1suKXb5+cHBQWZ5+Ymxy5cuGCTz51fsoSGhqZZb1ZLudwCBQqkqiejUq6rQoUKSZKqVKliE7t586Z50b/0vP7669q2bZtWrVolNzc3Mz5z5kzVrFlT/v7+8vT01Lx58zJ9D8iDBw8qNDTUZo9s/fr1df36dZtzX1LWIt2+B2XK12v16tVq0KBBqmsx/P777+rVq5fGjx9vftmQbN++fUpKSlL58uXl6elp/mzZskXHjh0z57vf7S6lJ598Ujdu3FDp0qXVu3dvrVixItUFMFIuI/mLleQaDx48qGrVqpkNd/J6slqtqW4LAgAAAEjZvKf79OnT6tq1qy5fvix/f381aNBA27dvN/deTp06VQ4ODurYsaPi4+MVFhamWbNmZWfKOc6jjz6qsLAwjRw5MsN7r+/Fy8tLv//+uxwcHFSkSBGbBk+6fZGC+zkc/87DfS0WS5ox6x1XX88K5cuXV3R0tM6ePWvuTU2WkJCgY8eOqXHjxlm+XMm27uT1llbsbnV//vnnmjp1qjZv3qyiRYua8aVLl2rYsGGaPHmyQkND5eXlpUmTJunXX3/N6jJS5Z2ce8q8V69eneqelRcvXlT79u3VsWNHDRs2LNWY169fl6Ojo3bt2pXqMJ6UX8jc73aXUvHixXX48GH99NNPWr9+vfr27atJkyZpy5YtZm3/1jYJAACAB0O27uleunSpzp49q/j4eJ0+fVpLly612RPq6uqqmTNn6sqVK4qNjdXy5cs5ZDMN48eP15o1a7Rt2zabeEhIiLZu3WoT27p1q8qXL5/uOQrS7fMaypYtq9KlS6dquNMSEhKixMREm0bv8uXLOnz4sCpWrJjJalK7877s27dvV0hIiPk4X758d91zL0kdO3ZUvnz5NHny5FTT5syZo9jYWHXt2jXd5V69elVHjhwxl+vs7HzPZWaVbdu26YUXXtDcuXNTHeWRfGh13759VaNGDZUtW9Zm73BGcw0JCdG2bdtsrrC9detWeXl52Vwv4G6uX7+uTZs2qW3btmbs1q1b6tSpkwICAjR//vw0n1ejRg0lJSXpwoULKlu2rM3P3d7v97vdubm5qXXr1vrwww+1efNmbdu2Tfv27ctQjSEhIdqzZ4/NlSq3bt0qBwcH816d/+a2AQAAgJwvW5tuZI0qVaromWee0YcffmgTHzp0qDZs2KB33nlHR44c0eLFizVjxow09zb+E+XKlVPbtm3Vu3dv/e9//9OePXv07LPPqmjRojYN2P3aunWrJk6cqCNHjmjmzJlatmyZBg78+xyPoKAgbdiwQZGRkbp69WqaY5QoUUITJ07UtGnT9MYbb+jQoUM6duyYpkyZouHDh2vo0KE2hylL0ttvv60NGzZo//796tGjhwoWLKh27dqZy7x+/bo2bNigS5cuKS4u7h/XmZbIyEi1b99eXbp0UVhYmCIjIxUZGWne1qBcuXLauXOn1q1bpyNHjuitt97Sjh07bMYICgrS3r17dfjwYV26dMm8hVhKffv21alTpzRgwAAdOnRIq1at0ujRozVkyJA0LyyRlrVr16p8+fIKCgoyY4MGDdKePXs0e/ZsRUVFmfkn/yQkJKh8+fJ65pln1K1bNy1fvlwRERH67bffNG7cOH333XfpLu9+trtFixbp448/1v79+/XXX3/p888/l5ubm829wu/mmWeekaurq7p37679+/dr06ZNGjBggJ577jnz1IGMrG8AAAA8OGi684i333471SGwDz30kL7++mstXbpUlStX1qhRo/T2229n2WHoKS1cuFA1a9bUE088odDQUBmGoe+//z7NK0hn1tChQ7Vz507VqFHDvBJ4yvOCJ0+erPXr16t48eKqUaNGuuMMGjRIK1as0H//+1/VqlVLlStX1pIlSzR79mx98MEHqeYfP368Bg4cqJo1ayoyMlJr1qwx7+P+8MMP66WXXtJTTz0lf39/TZw48R/XmZZDhw7p/PnzWrx4sYoUKWL+1K5dW5LUp08fdejQQU899ZTq1q2ry5cvmxcBS9a7d29VqFBBtWrVkr+/f6qjHySpaNGi+v777/Xbb7+pWrVqeumll9SrVy+9+eabGc511apVqQ4tnzVrlqKjo1W7dm2b/JN/fvnlF0m3t59u3bpp6NChqlChgtq1a6cdO3aoRIkSd11mZrc7X19fzZ8/X/Xr11fVqlX1008/ac2aNfLz88tQje7u7lq3bp2uXLmi2rVrq1OnTmratKlmzJhhzpOR9Q0AAIAHh8VIeTxpHhQTEyMfHx9FR0fb3KJIkm7evKmIiAiVKlVKrq6u2ZQh7iYoKEiDBg3SoEGD/rVlbt68WY0bN9bVq1dTXRAMaUtMTFShQoX0ww8/pLoaOFLjswcAYG9cvRy5XW64evndes2U2NMN4B+7cuWKBg8ebO6BBwAAAHBbjrpPN4DcKSAgIFOHogMAAAAPCppu5GjHjx//15fZqFEj5fGzLgAAAAD8Szi8HAAAAAAAO6HpBgAAAADATmi6AQAAAACwE5puAAAAAADshKYbAAAAAAA7oekGAAAAAMBOaLqBLBYUFKRp06ZldxpZbtGiRfL19c3uNLLVmDFjVL169exOAwAAALkI9+lOw/Sr0//V5Q3MPzDTz+nRo4cWL15sPi5QoIBq166tiRMnqmrVqlmS1/Hjx1WqVCnt3r07w43GmDFjtHLlSoWHh2dJDhlx57pIdvToUZUtWzZLlxUXF6d33nlHX3/9tc6cOSMvLy9VrFhRQ4YMUdu2bbNsOT169FBUVJRWrlxpE7dYLOb/vby8VKFCBb355ptZuuzslpSUpEmTJmnRokU6ceKE3NzcVK5cOfXu3VsvvPBCdqcHAAAAZAp7unOxFi1a6Ny5czp37pw2bNggJycnPfHEE9mdVobcunUrS8dLuS6Sf0qVKpWly5Ckl156ScuXL9dHH32kQ4cOae3aterUqZMuX76c5ctKz8KFC3Xu3Dnt3LlT9evXV6dOnbRv375/bfn2NnbsWE2dOlXvvPOODhw4oE2bNunFF19UVFSUXZebkJBg1/EBAADwYKLpzsVcXFxUuHBhFS5cWNWrV9drr72mU6dO6eLFi5Kkffv2qUmTJnJzc5Ofn59efPFFXb9+3Xy+1WrV22+/rWLFisnFxUXVq1fX2rVrzenJTWuNGjVksVjUqFEjSdLmzZtVp04deXh4yNfXV/Xr19eJEye0aNEijR07Vnv27JHFYpHFYtGiRYsk3d5DO3v2bLVp00YeHh567733lJSUpF69eqlUqVJyc3NThQoVNH267VEGPXr0ULt27TR27Fj5+/vL29tbL730UqoGKeW6SP5xdHSUJK1atUoPPfSQXF1dVbp0aY0dO1aJiYmSpGHDhtl8UTFt2jRZLBab9VC2bFktWLBAkrR69Wq9/vrrevzxxxUUFKSaNWtqwIABev75523yiYuL0/PPPy8vLy+VKFFC8+bNs5l+t9dmzJgxWrx4sVatWmWux82bN5vP9fX1VeHChVW+fHm98847SkxM1KZNm8zpa9euVYMGDeTr6ys/Pz898cQTOnbsmDn9+PHjslgsWr58uRo3bix3d3dVq1ZN27Zts8lx0aJFKlGihNzd3dW+ffs0v1iYPXu2ypQpI2dnZ1WoUEGfffaZzXSLxaK5c+fqiSeekLu7u0JCQrRt2zb9+eefatSokTw8PPTwww/b5Ld69Wr17dtXTz75pEqVKqVq1aqpV69eGjZsmDmP1WrVuHHjzG2nWrVq+uabb8zpmdm23nvvPQUGBqpChQqSpNOnT6tr164qUKCAPDw8VKtWLf366682z/3ss88UFBQkHx8fdenSRdeuXUu1bgAAAACJpjvPuH79uj7//HOVLVtWfn5+io2NVVhYmPLnz68dO3Zo2bJl+umnn9S/f3/zOdOnT9fkyZP1wQcfaO/evQoLC1ObNm109OhRSdJvv/0mSfrpp5907tw5LV++XImJiWrXrp0aNmyovXv3atu2bXrxxRdlsVj01FNPaejQoapUqZK5t/mpp54ylzdmzBi1b99e+/bt0/PPPy+r1apixYpp2bJlOnDggEaNGqXXX39dX3/9tU1tGzZs0MGDB7V582Z9+eWXWr58ucaOHZuh9fLf//5X3bp108CBA3XgwAHNnTtXixYt0nvvvSdJatiwof73v/8pKSlJkrRlyxYVLFjQbHLPnDmjY8eOmV84FC5cWN9///09m6zJkyerVq1a2r17t/r27auXX35Zhw8flqR7vjbDhg1T586dbfbeP/zww6mWkZiYqI8//liS5OzsbMZjY2M1ZMgQ7dy5Uxs2bJCDg4Pat28vq9Vq8/w33nhDw4YNU3h4uMqXL6+uXbuaX0b8+uuv6tWrl/r376/w8HA1btxY7777rs3zV6xYoYEDB2ro0KHav3+/+vTpo549e9p8ASBJ77zzjrp166bw8HAFBwfr6aefVp8+fTRy5Ejt3LlThmHYbJeFCxfWxo0bzS+P0jJu3Dh9+umnmjNnjv744w8NHjxYzz77rLZs2SJJmdq2Dh8+rPXr1+vbb7/V9evX1bBhQ505c0arV6/Wnj17NHz4cJt1d+zYMa1cuVLffvutvv32W23ZskXjx49PN1cAAAA82CyGYRjZnYQ9xcTEyMfHR9HR0fL29raZdvPmTUVERKhUqVJydXU147nlnO7PP//czDs2NlZFihTRt99+q4ceekjz58/XiBEjdOrUKXl4eEiSvv/+e7Vu3Vpnz55VoUKFVLRoUfXr10+vv/66OW6dOnVUu3ZtzZw5M81zuq9cuSI/Pz9t3rxZDRs2TJVXeud0WywWDRo0SFOnTr1rXf3791dkZKS517JHjx5as2aNTp06JXd3d0nSnDlz9Oqrryo6OloODg6p1oUktWzZUsuWLVOzZs3UtGlTjRw50pz2+eefa/jw4Tp79qyioqLk5+enX3/9VTVr1lTBggX16quvauXKldq+fbu++OILjRgxQqdPn5Yk/fzzz3rmmWd0/vx5VatWTQ0aNFCnTp1Uv359c/ygoCA98sgj5l5fwzBUuHBhjR07Vi+99FKGXpu7ndPt6uoqR0dH3bhxQ1arVUFBQdq1a5cKFCiQ5jq9dOmS/P39tW/fPlWuXNl8XRcsWKBevXpJkg4cOKBKlSrp4MGDZmMcHR2t7777zhynS5cuWrt2rXmYd/369VWpUiWbvfidO3dWbGys+TyLxaI333xT77zzjiRp+/btCg0N1ccff2weHbB06VL17NlTN27cMHPp1KmTDh8+rEqVKunhhx9W27Zt1bJlS0lSfHy8ChQooJ9++kmhoaHmsl944QXFxcVpyZIlaa6HtLattWvX6uTJk+aXFvPmzdOwYcN0/PjxNNfnmDFjNGnSJEVGRsrLy0uSNHz4cP3888/avn17mstNS3qfPQAAZJV/++9ZIKvdT4/0b7tbr5kSe7pzscaNGys8PFzh4eH67bffFBYWppYtW+rEiRM6ePCgqlWrZjZ10u0myWq16vDhw4qJidHZs2dtmsXkeQ4ePJjuMgsUKKAePXooLCxMrVu31vTp03Xu3LkM5VurVq1UsZkzZ6pmzZry9/eXp6en5s2bp5MnT9rMU61aNbPhlqTQ0FBdv35dp06dSnNdhIeH68MPP5Qk7dmzR2+//bY8PT3Nn969e+vcuXOKi4uTr6+vqlWrps2bN2vfvn1ydnbWiy++qN27d+v69evasmWLzZcLjz76qP766y9t2LBBnTp10h9//KFHHnnEbCqTpbyYncViUeHChXXhwgVJuudrcy9Tp05VeHi4fvjhB1WsWFELFiywaRCPHj2qrl27qnTp0vL29lZQUJAkpVqvKXMsUqSIJNnkWLduXZv5Uza4yfNkZPtJuZxChQpJkqpUqWITu3nzpmJiYiRJFStW1P79+7V9+3Y9//zzunDhglq3bm1eRO3PP/9UXFycmjdvbvO6fvrppzaHqWdk26pSpYrNUQLh4eGqUaNGul9gSLe/VEluuJPXXfJ6AwAAAO7E1ctzMQ8PD5urcy9YsEA+Pj6aP3++XZe7cOFCvfLKK1q7dq2++uorvfnmm1q/fr3q1at3z3xTWrp0qYYNG6bJkycrNDRUXl5emjRpUqrzZzPiznWR7Pr16xo7dqw6dOiQalryHsZGjRpp8+bNcnFxUcOGDVWgQAGFhITof//7n7Zs2aKhQ4faPC9fvnx65JFH9Mgjj2jEiBF699139fbbb2vEiBFmA5cvXz6b51gsllSHd9+vwoULq2zZsipbtqwWLlyoxx9/XAcOHFBAQIAkqXXr1ipZsqTmz5+vwMBAWa1WVa5cOdV58ClzTL4qelbleK/l3GvZDg4Oql27tmrXrq1Bgwbp888/13PPPac33njDPPf9u+++U9GiRW2W5eLiIinj29ad26Sbm1um6knO3x7rDQAAAHkDe7rzEIvFIgcHB924cUMhISHas2ePYmNjzelbt26Vg4ODKlSoIG9vbwUGBmrr1q02Y2zdulUVK1aU9Pd5wsnnO6dUo0YNjRw5Ur/88osqV65sHtLr7Oyc5vxp2bp1qx5++GH17dtXNWrUUNmyZW32VCbbs2ePeeixdPsQZU9PTxUvXvyey3jooYd0+PBhs0lN+ePgcHvzTz6ve8OGDea5240aNdKXX36pI0eOmLH0VKxYUYmJibp582aG6r7XayNlfD3WqVNHNWvWNM9Rv3z5sg4fPqw333xTTZs2VUhIiK5evZqhvO7M8c4G9c7Dp0NCQu66/WSl5DFjY2NVsWJFubi46OTJk6le0+RtIqPb1p2qVq2q8PBwXblyJctrAAAAwIOJpjsXi4+PV2RkpCIjI3Xw4EENGDBA169fV+vWrfXMM8/I1dVV3bt31/79+7Vp0yYNGDBAzz33nHmI76uvvqoJEyboq6++0uHDh/Xaa68pPDxcAwfePn8iICBAbm5uWrt2rc6fP6/o6GhFRERo5MiR2rZtm06cOKEff/xRR48eVUhIiKTbh95GREQoPDxcly5dUnx8fLr5lytXTjt37tS6det05MgRvfXWW9qxY0eq+RISEtSrVy8dOHBA33//vUaPHq3+/fubTfPdjBo1Sp9++qnGjh2rP/74QwcPHtTSpUv15ptvmvM8+uijunbtmr799lubpvuLL75QkSJFVL58eXPeRo0aae7cudq1a5eOHz+u77//Xq+//roaN2581/M4UsrIaxMUFKS9e/fq8OHDunTp0l1vsTZo0CDNnTtXZ86cUf78+eXn56d58+bpzz//1MaNGzVkyJAM5ZVS8pEMH3zwgY4ePaoZM2bYXNFdur39LFq0SLNnz9bRo0c1ZcoULV++3OYq4/ejU6dOmjp1qn799VedOHFCmzdvVr9+/VS+fHkFBwfLy8tLw4YN0+DBg7V48WIdO3ZMv//+uz766CPzfu0Z3bbu1LVrVxUuXFjt2rXT1q1b9ddff+k///lPqiu7AwAAABlF052LrV27VkWKFFGRIkVUt25d80rYjRo1kru7u9atW6crV66odu3a6tSpk5o2baoZM2aYz3/llVc0ZMgQDR06VFWqVNHatWu1evVqlStXTpLk5OSkDz/8UHPnzlVgYKDatm0rd3d3HTp0SB07dlT58uX14osvql+/furTp48kqWPHjmrRooUaN24sf39/ffnll+nm36dPH3Xo0EFPPfWU6tatq8uXL6tv376p5mvatKnKlSunRx99VE899ZTatGmjMWPGZGgdhYWF6dtvv9WPP/6o2rVrq169epo6dapKlixpzpM/f35VqVJF/v7+Cg4OlnS7EbdarakuFhcWFqbFixfrscceU0hIiAYMGKCwsLBUV8W+m4y8Nr1791aFChVUq1Yt+fv7p9qjnFKLFi1UqlQpvffee3JwcNDSpUu1a9cuVa5cWYMHD9akSZMynFuyevXqaf78+Zo+fbqqVaumH3/80eaLCklq166dpk+frg8++ECVKlXS3LlztXDhwnseGXAvYWFhWrNmjVq3bq3y5cure/fuCg4O1o8//ignp9tnxLzzzjt66623NG7cOIWEhKhFixb67rvvzNvcZXTbupOzs7N+/PFHBQQE6PHHH1eVKlU0fvx48/ZzAAAAQGZx9XKuIJyjpXcVbyA347MHAGBvXL0cuR1XLwcAAAAAAPdE0w0AAAAAgJ1wyzDkaIsWLcruFAAAAADgvrGnGwAAAAAAO6HplpTHryUHIIfhMwcAAODB8UA33fny5ZMkxcXFZXMmAB4kCQkJksStyAAAAB4AD/Q53Y6OjvL19dWFCxck3b5/ssViyeasAORlVqtVFy9elLu7u3nfcQAAAORdD/xffIULF5Yks/EGAHtzcHBQiRIl+JIPAADgAfDAN90Wi0VFihRRQECAbt26ld3pAHgAODs7y8HhgT67BwAA4IHxwDfdyRwdHTm/EgAAAACQpdjVAgAAAACAndB0AwAAAABgJzTdAAAAAADYCU03AAAAAAB2QtMNAAAAAICd0HQDAAAAAGAnNN0AAAAAANgJTTcAAAAAAHZC0w0AAAAAgJ3QdAMAAAAAYCc03QAAAAAA2AlNNwAAAAAAdkLTDQAAAACAndB0AwAAAABgJzTdAAAAAADYCU03AAAAAAB2QtMNAAAAAICd0HQDAAAAAGAnNN0AAAAAANgJTTcAAAAAAHZC0w0AAAAAgJ3QdAMAAAAAYCc03QAAAAAA2AlNNwAAAAAAdkLTDQAAAACAndB0AwAAAABgJzmm6R4/frwsFosGDRpkxm7evKl+/frJz89Pnp6e6tixo86fP599SQIAAAAAkAk5ounesWOH5s6dq6pVq9rEBw8erDVr1mjZsmXasmWLzp49qw4dOmRTlgAAAAAAZE62N93Xr1/XM888o/nz5yt//vxmPDo6Wh9//LGmTJmiJk2aqGbNmlq4cKF++eUXbd++PRszBgAAAAAgY7K96e7Xr59atWqlZs2a2cR37dqlW7du2cSDg4NVokQJbdu27d9OEwAAAACATHPKzoUvXbpUv//+u3bs2JFqWmRkpJydneXr62sTL1SokCIjI9MdMz4+XvHx8ebjmJgYSVJiYqISExMlSQ4ODnJwcJDVapXVajXnTY4nJSXJMIx7xh0dHWWxWMxxU8YlKSkpKUNxJycnGYZhE7dYLHJ0dEyVY3pxaqImaqImaqImaqImaqKm5NwtSRYZDoZkkSxWi/R3SX/Hkyw2ORoO//9cawbjjoZk3BG3/P/86cWtksX4O25YjNu7AdOJp5s7NeX5mhITE3PM+ym9+J2fC+nJtqb71KlTGjhwoNavXy9XV9csG3fcuHEaO3Zsqvju3bvl4eEhSfL391eZMmUUERGhixcvmvMUK1ZMxYoV05EjRxQdHW3GS5curYCAAO3fv183btww48HBwfL19dXu3bttXvCqVavK2dlZO3futMmhVq1aSkhI0N69e82Yo6OjateurejoaB06dMiMu7m5qVq1arp06ZL++usvM+7j46OQkBCdPXtWp0+fNuPURE3URE3URE3URE3URE3JNfnf8ldMYIxuFrip/Mfyyyn+7z/7o0pGKcErQQUPF7Rpci6XvSxrPqv8D/rb1HQx5KIcbjnI708/M2Y4GLpY8aKcrzvL94SvGU90SdSVclfketVV3me9zXiCZ4KigqLkcclDHhc8zPiN/Dd0reg1eZ3zkttVNzMeGxCr2IBY+Zz0kfN1ZzNOTQ9OTTvz7cwx7ycp7c+I2NhYZYTFSNm+/4tWrlyp9u3bm99eSLe/wbBYLHJwcNC6devUrFkzXb161WZvd8mSJTVo0CANHjw4zXHT2tNdvHhxXb58Wd7etzeoB/2bT2qiJmqiJmqiJmqiJmrK2zXNiprFHlRqytU19fXtm2PeT+nFY2Ji5Ofnp+joaLPXTEu2Nd3Xrl3TiRMnbGI9e/ZUcHCwRowYoeLFi8vf319ffvmlOnbsKEk6fPiwgoODtW3bNtWrVy9Dy4mJiZGPj889VwQAAACQV0y/Oj27UwD+kYH5B2Z3CveU0V4z2w4v9/LyUuXKlW1iHh4e8vPzM+O9evXSkCFDVKBAAXl7e2vAgAEKDQ3NcMMNAAAAAEB2ytYLqd3L1KlT5eDgoI4dOyo+Pl5hYWGaNWtWdqcFAAAAAECGZNvh5f8WDi8HAADAg4bDy5Hb5aXDy7P9Pt0AAAAAAORVNN0AAAAAANgJTTcAAAAAAHZC0w0AAAAAgJ3QdAMAAAAAYCc03QAAAAAA2AlNNwAAAAAAdkLTDQAAAACAndB0AwAAAABgJzTdAAAAAADYCU03AAAAAAB2QtMNAAAAAICd0HQDAAAAAGAnNN0AAAAAANgJTTcAAAAAAHZC0w0AAAAAgJ3QdAMAAAAAYCc03QAAAAAA2AlNNwAAAAAAdkLTDQAAAACAndB0AwAAAABgJzTdAAAAAADYCU03AAAAAAB2QtMNAAAAAICd0HQDAAAAAGAnNN0AAAAAANgJTTcAAAAAAHZC0w0AAAAAgJ3QdAMAAAAAYCc03QAAAAAA2AlNNwAAAAAAdkLTDQAAAACAndB0AwAAAABgJzTdAAAAAADYCU03AAAAAAB2QtMNAAAAAICd0HQDAAAAAGAnNN0AAAAAANgJTTcAAAAAAHZC0w0AAAAAgJ3QdAMAAAAAYCc03QAAAAAA2EmWNN1RUVFZMQwAIAeaPXu2qlatKm9vb3l7eys0NFQ//PCDOX3evHlq1KiRvL29ZbFYMv07Yfz48bJYLBo0aFDWJg4AAJADZLrpnjBhgr766ivzcefOneXn56eiRYtqz549WZocACD7FStWTOPHj9euXbu0c+dONWnSRG3bttUff/whSYqLi1OLFi30+uuvZ3rsHTt2aO7cuapatWpWpw0AAJAjZLrpnjNnjooXLy5JWr9+vdavX68ffvhBLVu21KuvvprlCQIAslfr1q31+OOPq1y5cipfvrzee+89eXp6avv27ZKkQYMG6bXXXlO9evUyNe7169f1zDPPaP78+cqfP789UgcAAMh2mW66IyMjzab722+/VefOnfXYY49p+PDh2rFjR5YnCADIOZKSkrR06VLFxsYqNDT0H43Vr18/tWrVSs2aNcui7AAAAHIep8w+IX/+/Dp16pSKFy+utWvX6t1335UkGYahpKSkLE8QAJD99u3bp9DQUN28eVOenp5asWKFKlaseN/jLV26VL///jtf1gIAgDwv0013hw4d9PTTT6tcuXK6fPmyWrZsKUnavXu3ypYtm+UJAgCyX4UKFRQeHq7o6Gh988036t69u7Zs2XJfjfepU6c0cOBArV+/Xq6urnbIFgAAIOfIdNM9depUBQUF6dSpU5o4caI8PT0lSefOnVPfvn2zPEEAQPZzdnY2v1itWbOmduzYoenTp2vu3LmZHmvXrl26cOGCHnroITOWlJSkn3/+WTNmzFB8fLwcHR2zLHcAAIDslOmmO1++fBo2bFiq+ODBg7MkIQBAzme1WhUfH39fz23atKn27dtnE+vZs6eCg4M1YsQIGm4AAJCnZLrplqTPPvtMc+fO1V9//aVt27apZMmSmjZtmkqVKqW2bdtmdY4AgGw0cuRItWzZUiVKlNC1a9e0ZMkSbd68WevWrZN0+wKbkZGR+vPPPyXdPv/by8tLJUqUUIECBSTdbrTbt2+v/v37y8vLS5UrV7ZZhoeHh/z8/FLFAQAAcrtMX7189uzZGjJkiFq2bKmoqCjz4mm+vr6aNm1aVucHAMhmFy5cULdu3VShQgU1bdpUO3bs0Lp169S8eXNJt28lWaNGDfXu3VuS9Oijj6pGjRpavXq1OcaxY8d06dKlbMkfAAAgO1kMwzAy84SKFSvq/fffV7t27eTl5aU9e/aodOnS2r9/vxo1apTj/qiKiYmRj4+PoqOj5e3tnd3pAAAAAHY3/er07E4B+EcG5h+Y3SncU0Z7zUzv6Y6IiFCNGjVSxV1cXBQbG5vZ4QAAAAAAyLMy3XSXKlVK4eHhqeJr165VSEhIVuQEAAAAAECekOkLqQ0ZMkT9+vXTzZs3ZRiGfvvtN3355ZcaN26cFixYYI8cAcAuOPQOuVluOOwOAADcR9P9wgsvyM3NTW+++abi4uL09NNPKzAwUNOnT1eXLl3skSMAAAAAALlSppruxMRELVmyRGFhYXrmmWcUFxen69evKyAgwF75AQAAAACQa2XqnG4nJye99NJLunnzpiTJ3d2dhhsAAAAAgHRk+kJqderU0e7du+2RCwAAAAAAeUqmz+nu27evhg4dqtOnT6tmzZry8PCwmV61atUsSw4AAAAAgNws03u6u3TpooiICL3yyiuqX7++qlevrho1apj/Zsbs2bNVtWpVeXt7y9vbW6Ghofrhhx/M6Tdv3lS/fv3k5+cnT09PdezYUefPn89sygAAAAAAZItM7+mOiIjIsoUXK1ZM48ePV7ly5WQYhhYvXqy2bdtq9+7dqlSpkgYPHqzvvvtOy5Ytk4+Pj/r3768OHTpo69atWZYDAAAAAAD2kummu2TJklm28NatW9s8fu+99zR79mxt375dxYoV08cff6wlS5aoSZMmkqSFCxcqJCRE27dvV7169bIsDwAAAAAA7CHTTbckHTt2TNOmTdPBgwclSRUrVtTAgQNVpkyZ+04kKSlJy5YtU2xsrEJDQ7Vr1y7dunVLzZo1M+cJDg5WiRIltG3btnSb7vj4eMXHx5uPY2JiJN2+3VliYqIkycHBQQ4ODrJarbJarea8yfGkpCQZhnHPuKOjoywWizluynhyTRmJOzk5yTAMm7jFYpGjo2OqHNOLUxM1UdN91GS1yHAwJKtkMSxm3LAYt0++SSdusVqkv1O/PYblLvGkv8cw4/+//AzFHQ3JuCNu+f/504tTU56vKTExMWe9n/LiZwQ1UVMursmSZMlzn3s2uVNTnq8pMTExx7yf0ovf+bmQnkw33evWrVObNm1UvXp11a9fX5K0detWVapUSWvWrFHz5s0zNd6+ffsUGhqqmzdvytPTUytWrFDFihUVHh4uZ2dn+fr62sxfqFAhRUZGpjveuHHjNHbs2FTx3bt3mxd98/f3V5kyZRQREaGLFy+a8xQrVkzFihXTkSNHFB0dbcZLly6tgIAA7d+/Xzdu3DDjwcHB8vX11e7du21e8KpVq8rZ2Vk7d+60yaFWrVpKSEjQ3r17zZijo6Nq166t6OhoHTp0yIy7ubmpWrVqunTpkv766y8z7uPjo5CQEJ09e1anT58249RETdR0HzW5+CgqKEoelzzkceHvi0LeyH9D14pek9c5L7lddTPjsQGxig2Ilc9JHzlfdzbjMYExulngpvIfyy+n+L8/VqNKRinBK0EFDxe0+SVyuexlWfNZ5X/Q36amiyEX5XDLQX5/+pkxw8HQxYoX5XzdWb4nfM14okuirpS7IterrvI+623GEzwTqOkBqWlnvp056/2UFz8jqImacnFN/rf889znnpT3PsupKf2adubbmWPeT1LanxGxsbHKCIuRsn3PgBo1aigsLEzjx4+3ib/22mv68ccf9fvvv2dmOCUkJOjkyZOKjo7WN998owULFmjLli0KDw9Xz549bfZaS7dvWda4cWNNmDAhzfHS2tNdvHhxXb58Wd7etzeoB/2bT2qiJmq6bVb0rBzzbe5d47n0G2pqsm9NfX375qj3U178jKAmasrNNc2KmpXnPvdscqemPF9TX9++Oeb9lF48JiZGfn5+io6ONnvNtGS66XZ1ddW+fftUrlw5m/iRI0dUtWpV3bx5MzPDpdKsWTOVKVNGTz31lJo2baqrV6/a7O0uWbKkBg0apMGDB2dovJiYGPn4+NxzRQB48Ey/Oj27UwDu28D8A7M7BQA5GL/jkNvlht9zGe01M33LMH9/f4WHh6eKh4eHKyAgILPDpWK1WhUfH6+aNWsqX7582rBhgznt8OHDOnnypEJDQ//xcgAAAAAAsLdMn9Pdu3dvvfjii/rrr7/08MMPS7p9TveECRM0ZMiQTI01cuRItWzZUiVKlNC1a9e0ZMkSbd68WevWrZOPj4969eqlIUOGqECBAvL29taAAQMUGhrKlcsBAAAAALlCppvut956S15eXpo8ebJGjhwpSQoMDNSYMWP0yiuvZGqsCxcuqFu3bjp37px8fHxUtWpVrVu3zrwY29SpU+Xg4KCOHTsqPj5eYWFhmjVrVmZTBgAAAAAgW2T6nO6Url27Jkny8vLKsoSyGud0A0gP57shN8sN57oByD78jkNulxt+z2W018z0nu6IiAglJiaqXLlyNs320aNHlS9fPgUFBd1XwgAAAAAA5DWZvpBajx499Msvv6SK//rrr+rRo0dW5AQAAAAAQJ6Q6aZ79+7dql+/fqp4vXr10ryqOQAAAAAAD6pMN90Wi8U8lzul6OjoVDctBwAAAADgQZbppvvRRx/VuHHjbBrspKQkjRs3Tg0aNMjS5AAAAAAAyM0yfSG1CRMm6NFHH1WFChX0yCOPSJL++9//KiYmRhs3bszyBAEAAAAAyK0yvae7YsWK2rt3rzp37qwLFy7o2rVr6tatmw4dOqTKlSvbI0cAAAAAAHKlTO/plqTAwEC9//77WZ0LAAAAAAB5Sob3dF+6dEknTpywif3xxx/q2bOnOnfurCVLlmR5cgAAAAAA5GYZbroHDBigDz/80Hx84cIFPfLII9qxY4fi4+PVo0cPffbZZ3ZJEgAAAACA3CjDTff27dvVpk0b8/Gnn36qAgUKKDw8XKtWrdL777+vmTNn2iVJAAAAAAByoww33ZGRkQoKCjIfb9y4UR06dJCT0+3Twtu0aaOjR49meYIAAAAAAORWGW66vb29FRUVZT7+7bffVLduXfOxxWJRfHx8liYHAAAAAEBuluGmu169evrwww9ltVr1zTff6Nq1a2rSpIk5/ciRIypevLhdkgQAAAAAIDfK8C3D3nnnHTVt2lSff/65EhMT9frrryt//vzm9KVLl6phw4Z2SRIAAAAAgNwow0131apVdfDgQW3dulWFCxe2ObRckrp06aKKFStmeYIAAAAAAORWGW66JalgwYJq27ZtmtNatWqVJQkBAAAAAJBXZPicbgAAAAAAkDk03QAAAAAA2AlNNwAAAAAAdkLTDQAAAACAnWS66XZ0dNSFCxdSxS9fvixHR8csSQoAAAAAgLwg0023YRhpxuPj4+Xs7PyPEwIAAAAAIK/I8C3DPvzwQ0mSxWLRggUL5OnpaU5LSkrSzz//rODg4KzPEAAAAACAXCrDTffUqVMl3d7TPWfOHJtDyZ2dnRUUFKQ5c+ZkfYYAAAAAAORSGW66IyIiJEmNGzfW8uXLlT9/frslBQAAAABAXpDpc7o3bdqk/PnzKyEhQYcPH1ZiYqI98gIAAAAAINfLdNN948YN9erVS+7u7qpUqZJOnjwpSRowYIDGjx+f5QkCAAAAAJBbZbrpfu2117Rnzx5t3rxZrq6uZrxZs2b66quvsjQ5AAAAAAByswyf051s5cqV+uqrr1SvXj1ZLBYzXqlSJR07dixLkwMAAAAAIDfL9J7uixcvKiAgIFU8NjbWpgkHAAAAAOBBl+mmu1atWvruu+/Mx8mN9oIFCxQaGpp1mQEAAAAAkMtl+vDy999/Xy1bttSBAweUmJio6dOn68CBA/rll1+0ZcsWe+QIAAAAAECulOk93Q0aNFB4eLgSExNVpUoV/fjjjwoICNC2bdtUs2ZNe+QIAAAAAECulOk93ZJUpkwZzZ8/P6tzAQAAAAAgT8lw052YmKikpCS5uLiYsfPnz2vOnDmKjY1VmzZt1KBBA7skCQAAAABAbpThprt3795ydnbW3LlzJUnXrl1T7dq1dfPmTRUpUkRTp07VqlWr9Pjjj9stWQAAAAAAcpMMn9O9detWdezY0Xz86aefKikpSUePHtWePXs0ZMgQTZo0yS5JAgAAAACQG2W46T5z5ozKlStnPt6wYYM6duwoHx8fSVL37t31xx9/ZH2GAAAAAADkUhluul1dXXXjxg3z8fbt21W3bl2b6devX8/a7AAAAAAAyMUy3HRXr15dn332mSTpv//9r86fP68mTZqY048dO6bAwMCszxAAAAAAgFwqwxdSGzVqlFq2bKmvv/5a586dU48ePVSkSBFz+ooVK1S/fn27JAkAAAAAQG6U4aa7YcOG2rVrl3788UcVLlxYTz75pM306tWrq06dOlmeIAAAAAAAuVWGm25JCgkJUUhISJrTXnzxxSxJCAAAAACAvCLD53QDAAAAAIDMoekGAAAAAMBOaLoBAAAAALATmm4AAAAAAOwk00136dKldfny5VTxqKgolS5dOkuSAgAAAAAgL8h00338+HElJSWlisfHx+vMmTNZkhQAAAAAAHlBhm8Ztnr1avP/69atk4+Pj/k4KSlJGzZsUFBQUJYmBwAAAABAbpbhprtdu3aSJIvFou7du9tMy5cvn4KCgjR58uQsTQ4AAAAAgNwsw0231WqVJJUqVUo7duxQwYIF7ZYUAAAAAAB5QYab7mQRERGpYlFRUfL19c2KfAAAAAAAyDMyfSG1CRMm6KuvvjIfP/nkkypQoICKFi2qPXv2ZGlyAAAAD5qff/5ZrVu3VmBgoCwWi1auXGkz/fz58+rRo4cCAwPl7u6uFi1a6OjRo3cds1GjRrJYLKl+WrVqZcdKAADSfTTdc+bMUfHixSVJ69ev108//aS1a9eqZcuWevXVV7M8QQAAgAdJbGysqlWrppkzZ6aaZhiG2rVrp7/++kurVq3S7t27VbJkSTVr1kyxsbHpjrl8+XKdO3fO/Nm/f78cHR315JNP2rMUAIDu4/DyyMhIs+n+9ttv1blzZz322GMKCgpS3bp1szxBAACAB0nLli3VsmXLNKcdPXpU27dv1/79+1WpUiVJ0uzZs1W4cGF9+eWXeuGFF9J8XoECBWweL126VO7u7jTdAPAvyPSe7vz58+vUqVOSpLVr16pZs2aSbn/zmtb9uwEAAJA14uPjJUmurq5mzMHBQS4uLvrf//6X4XE+/vhjdenSRR4eHlmeIwDAVqab7g4dOujpp59W8+bNdfnyZfOb2N27d6ts2bJZniAAAABuCw4OVokSJTRy5EhdvXpVCQkJmjBhgk6fPq1z585laIzffvtN+/fvT3evOAAga2W66Z46dar69++vihUrav369fL09JQknTt3Tn379s3yBAEAAHBbvnz5tHz5ch05ckQFChSQu7u7Nm3apJYtW8rBIWN/1n388ceqUqWK6tSpY+dsAQDSfTTd+fLl07BhwzR9+nTVqFHDjA8ePDjT35iOGzdOtWvXlpeXlwICAtSuXTsdPnzYZp6bN2+qX79+8vPzk6enpzp27Kjz589nNm0AAIA8oWbNmgoPD1dUVJTOnTuntWvX6vLlyypduvQ9nxsbG6ulS5eqV69e/0KmAADpPppuSfrss8/UoEEDBQYG6sSJE5KkadOmadWqVZkaZ8uWLerXr5+2b9+u9evX69atW3rsscdsrr45ePBgrVmzRsuWLdOWLVt09uxZdejQ4X7SBgAAyDN8fHzk7++vo0ePaufOnWrbtu09n7Ns2TLFx8fr2Wef/RcyBABI99F0z549W0OGDFHLli0VFRVlXjzN19dX06ZNy9RYa9euVY8ePVSpUiVVq1ZNixYt0smTJ7Vr1y5JUnR0tD7++GNNmTJFTZo0Uc2aNbVw4UL98ssv2r59e2ZTBwAAyPGuX7+u8PBwhYeHS5IiIiIUHh6ukydPSrrdOG/evNm8bVjz5s3Vrl07PfbYY+YY3bp108iRI1ON/fHHH6tdu3by8/P7V2oBANxH0/3RRx9p/vz5euONN+To6GjGa9WqpX379v2jZKKjoyX9fVuLXbt26datW+YV0qW/LyCybdu2f7QsAACAnGjnzp2qUaOGeRrfkCFDVKNGDY0aNUrS7evoPPfccwoODtYrr7yi5557Tl9++aXNGCdPnkx1YbXDhw/rf//7H4eWA8C/LNP36Y6IiLA5lzuZi4uLzWHhmWW1WjVo0CDVr19flStXlnT7nuDOzs7y9fW1mbdQoUKKjIxMc5z4+HjzdhqSFBMTI0lKTExUYmKipNu31nBwcJDVapXVajXnTY4nJSXJMIx7xh0dHWWxWMxxU8YlpbqFWnpxJyenVLdcs1gscnR0TJVjenFqoiZquo+arBYZDoZklSyGxYwbFuP2V5LpxC1Wi/R36rfHsNwlnvT3GGb8/5efobijIRl3xC3/P396cWrK8zUlJibmrPdTXvyMyKaaGjZsKKvVmmZNhmGob9++NhevTa4p5TgbNmxIlXuZMmWUmJjI6/SA1GRJsuS5zz2b3Kkpz9eU/HklZf/7Kb34nZ8L6cl0012qVCmFh4erZMmSNvG1a9cqJCQks8OZ+vXrp/3792fqHpNpGTdunMaOHZsqvnv3bvNelP7+/ipTpowiIiJ08eJFc55ixYqpWLFiOnLkiLnXXZJKly6tgIAA7d+/Xzdu3DDjwcHB8vX11e7du21e8KpVq8rZ2Vk7d+60yaFWrVpKSEjQ3r17zZijo6Nq166t6OhoHTp0yIy7ubmpWrVqunTpkv766y8z7uPjo5CQEJ09e1anT58249RETdR0HzW5+CgqKEoelzzkceHve9XeyH9D14pek9c5L7lddTPjsQGxig2Ilc9JHzlfdzbjMYExulngpvIfyy+n+L8/VqNKRinBK0EFDxe0+SVyuexlWfNZ5X/Q36amiyEX5XDLQX5//n3Yp+Fg6GLFi3K+7izfE75mPNElUVfKXZHrVVd5n/U24wmeCdT0gNS0M9/OnPV+youfEdRETbm4Jv9b/nnuc0/Ke5/l1JR+TTvz7cwx7ycp7c+IjO50thgp2/e7ePvttzVs2DAtWbJEY8aM0eTJk9WrVy8tWLBAx44d07hx47RgwQJ16dIlQwtOqX///lq1apV+/vlnlSpVyoxv3LhRTZs21dWrV232dpcsWVKDBg3S4MGDU42V1p7u4sWL6/Lly/L2vr1BPejffFITNVHTbbOiZ+WYb3PvGs+l31BTk31r6uvbN0e9n/LiZwQ1UVNurmlW1Kw897lnkzs15fma+vr2zTHvp/TiMTEx8vPzU3R0tNlrpiXDTbejo6POnTungIAAffHFFxozZoyOHTsmSQoMDNTYsWMzfY6QYRgaMGCAVqxYoc2bN6tcuXI206Ojo+Xv768vv/xSHTt2lHT7fKTg4GBt27ZN9erVu+cyYmJi5OPjc88VAeDBM/3q9OxOAbhvA/MPzO4UMm387kvZnQJw316rUTC7U8gUfscht8sNv+cy2mtm+PDylL35M888o2eeeUZxcXG6fv26AgIC7ivJfv36acmSJVq1apW8vLzM87R9fHzk5uYmHx8f9erVS0OGDFGBAgXk7e2tAQMGKDQ0NEMNNwAAAAAA2SlT53RbLLaHAbi7u8vd3f2+Fz579mxJUqNGjWziCxcuVI8ePSRJU6dOlYODgzp27Kj4+HiFhYVp1qxZ971MAAAAAAD+LZlqusuXL5+q8b7TlStXMjxeRo5sd3V11cyZMzVz5swMjwsAAAAAQE6QqaZ77Nix8vHxsVcuAAAAAADkKZlqurt06XLf528DAAAAAPCgccjojPc6rBwAAAAAANjKcNOdwTuLAQAAAACA/5fhw8tT3jAcAAAAAADcW4b3dAMAAAAAgMyh6QYAAAAAwE5ougEAAAAAsBOabgAAAAAA7ISmGwAAAAAAO6HpBgAAAADATmi6AQAAAACwE5puAAAAAADshKYbAAAAAAA7oekGAAAAAMBOaLoBAAAAALATmm4AAAAAAOyEphsAAAAAADuh6QYAAAAAwE5ougEAAAAAsBOabgAAAAAA7ISmGwAAAAAAO6HpBgAAAADATmi6AQAAAACwE5puAAAAAADshKYbAAAAAAA7oekGAAAAAMBOaLoBAAAAALATmm4AAAAAAOyEphsAAAAAADuh6QYAAAAAwE5ougEAAAAAsBOabgAAAAAA7ISmGwAAAAAAO6HpBgAAAADATmi6AQAAAACwE5puAAAAAADshKYbAAAAAAA7oekGAAAAAMBOaLoBAAAAALATmm4AAAAAAOyEphsAAAAAADuh6QYAAAAAwE5ougEAAAAAsBOabgAAAAAA7ISmGwAAAAAAO6HpBgAAAADATmi6AQAAAACwE5puAAAAAADshKYbAAAAAAA7oekGAAAAAMBOaLoBAAAAALATmm4AAAAAAOyEphsAAAAAADuh6QYAAAAAwE5ougEAAAAAsBOabgAAAAAA7ISmGwAAAAAAO6HpBgAAAADATmi6AQAAAACwE5puAAAAAADsJFub7p9//lmtW7dWYGCgLBaLVq5caTPdMAyNGjVKRYoUkZubm5o1a6ajR49mT7IAAAAAAGRStjbdsbGxqlatmmbOnJnm9IkTJ+rDDz/UnDlz9Ouvv8rDw0NhYWG6efPmv5wpAAAAAACZ55SdC2/ZsqVatmyZ5jTDMDRt2jS9+eabatu2rSTp008/VaFChbRy5Up16dLl30wVAAAAAIBMy9am+24iIiIUGRmpZs2amTEfHx/VrVtX27ZtS7fpjo+PV3x8vPk4JiZGkpSYmKjExERJkoODgxwcHGS1WmW1Ws15k+NJSUkyDOOecUdHR1ksFnPclHFJSkpKylDcyclJhmHYxC0WixwdHVPlmF6cmqiJmu6jJqtFhoMhWSWLYTHjhsW4fRxQOnGL1SL9nfrtMSx3iSf9PYYZ///lZyjuaEjGHXHL/8+fXpya8nxNiYmJOev9lIHPCIv173EMi0WyOMhiWKUUuRsWB8liST9utc3RsNw+aM9iWDMWd3CUDMM2brHcnj/duFUWm1xu555enJryZk057f10r9+5liRLnvvcs8mdmvJ8TYmJiTnm/ZRe/M7PhfTk2KY7MjJSklSoUCGbeKFChcxpaRk3bpzGjh2bKr579255eHhIkvz9/VWmTBlFRETo4sWL5jzFihVTsWLFdOTIEUVHR5vx0qVLKyAgQPv379eNGzfMeHBwsHx9fbV7926bF7xq1apydnbWzp07bXKoVauWEhIStHfvXjPm6Oio2rVrKzo6WocOHTLjbm5uqlatmi5duqS//vrLjPv4+CgkJERnz57V6dOnzTg1URM13UdNLj6KCoqSxyUPeVzwMOM38t/QtaLX5HXOS25X3cx4bECsYgNi5XPSR87Xnc14TGCMbha4qfzH8ssp/u+P1aiSUUrwSlDBwwVtfolcLntZ1nxW+R/0t6npYshFOdxykN+ffmbMcDB0seJFOV93lu8JXzOe6JKoK+WuyPWqq7zPepvxBM8EanpAatqZb2fOej9l4DOi6KUzf78eHv6K8fCXX/QpuSbEmvGrXkUU65Zfha5GyCnx7y/RL/mW0E1nTwVeOSpLij+OIguUUZKDk4peOmxT05mCFeRoTVThK8fMmOHgoDMFg+V6K1YFo06a8UQnF0UWKCOPm1HKf+2cGb/p7KFLviXlHXdZ3rF/f77Fuvnqqleg8l+PlMeNKGp6QGrKae+ne/3O9b/ln+c+96S891lOTenXtDPfzhzzfpLS/hs2Nvbvz5C7sRgp2/dsZLFYtGLFCrVr106S9Msvv6h+/fo6e/asihQpYs7XuXNnWSwWffXVV2mOk9ae7uLFi+vy5cvy9r69QeXqPXN5cW8jNVFTNtU0K3pWjvk2967xXPoNNTXZt6a+vn1z1PspI58Rk3b//UcNe1CpKbfVNKyKr02O2f1+utfv3FlRs/Lc555N7tSU52vq69s3x7yf0ovHxMTIz89P0dHRZq+Zlhy7p7tw4cKSpPPnz9s03efPn1f16tXTfZ6Li4tcXFxSxZ2cnOTkZFtu8oq7U/KLm9H4nePeT9xisaQZTy/HzMapiZrSiz/INSV/wMtBMlL+RjCfkHbcfF5G445ZELdkMk5Neb6mlNt4Tng/ZSRuOKTO83ZDkzqXdONpjHF7/kzELZZMxh1kpJFLenFqyps15bT3071+56b8zMkrn3sZilNTnqkp5Xslu99P6cXTe/+nWkaG5soGpUqVUuHChbVhwwYzFhMTo19//VWhoaHZmBkAAAAAABmTrXu6r1+/rj///NN8HBERofDwcBUoUEAlSpTQoEGD9O6776pcuXIqVaqU3nrrLQUGBpqHoAMAAAAAkJNla9O9c+dONW7c2Hw8ZMgQSVL37t21aNEiDR8+XLGxsXrxxRcVFRWlBg0aaO3atXJ1dc2ulAEAAAAAyLBsbbobNWqku13HzWKx6O2339bbb7/9L2YFAAAAAEDWyLHndAMAAAAAkNvRdAMAAAAAYCc03QAAAAAA2AlNNwAAAAAAdkLTDQAAAACAndB0AwAAAABgJzTdAAAAAADYCU03AAAAAAB2QtMNAAAAAICd0HQDAAAAAGAnNN0AAAAAANgJTTcAAAAAAHZC0w0AAAAAgJ3QdAMAAAAAYCc03QAAAAAA2AlNNwAAAAAAdkLTDQAAAACAndB0AwAAAABgJzTdAAAAAADYCU03AAAAAAB2QtMNAAAAAICd0HQDAAAAAGAnNN0AAAAAANgJTTcAAAAAAHZC0w0AAAAAgJ3QdAMAAAAAYCc03QAAAAAA2AlNNwAAAAAAdkLTDQAAAACAndB0AwAAAABgJzTdAAAAAADYCU03AAAAAAB2QtMNAAAAAICd0HQDAAAAAGAnNN0AAAAAANgJTTcAAAAAAHZC0w0AAAAAgJ3QdAMAAAAAYCc03QAAAAAA2AlNNwAAAAAAdkLTDQAAAACAndB0AwAAAABgJzTdAAAAAADYCU03AAAAAAB2QtMNAAAAAICd0HQDAAAAAGAnNN0AAAAAANgJTTcAAAAAAHZC0w0AAAAAgJ3QdAMAAAAAYCc03QAAAAAA2AlNNwAAAAAAdkLTjTxj5syZCgoKkqurq+rWravffvvtrvMvW7ZMwcHBcnV1VZUqVfT999//S5kCAAAAeFDQdCNP+OqrrzRkyBCNHj1av//+u6pVq6awsDBduHAhzfl/+eUXde3aVb169dLu3bvVrl07tWvXTvv37/+XMwcAAACQl9F0I0+YMmWKevfurZ49e6pixYqaM2eO3N3d9cknn6Q5//Tp09WiRQu9+uqrCgkJ0TvvvKOHHnpIM2bM+JczBwAAAJCX0XQj10tISNCuXbvUrFkzM+bg4KBmzZpp27ZtaT5n27ZtNvNLUlhYWLrzAwAAAMD9oOlGrnfp0iUlJSWpUKFCNvFChQopMjIyzedERkZman4AAAAAuB803QAAAAAA2AlNN3K9ggULytHRUefPn7eJnz9/XoULF07zOYULF87U/AAAAABwP2i6kes5OzurZs2a2rBhgxmzWq3asGGDQkND03xOaGiozfyStH79+nTnBwAAAID74ZTdCQBZYciQIerevbtq1aqlOnXqaNq0aYqNjVXPnj0lSd26dVPRokU1btw4SdLAgQPVsGFDTZ48Wa1atdLSpUu1c+dOzZs3LzvLAAAAAJDH5Io93TNnzlRQUJBcXV1Vt25d/fbbb9mdEnKYp556Sh988IFGjRql6tWrKzw8XGvXrjUvlnby5EmdO3fOnP/hhx/WkiVLNG/ePFWrVk3ffPONVq5cqcqVK2dXCQAAAADyoBy/p/urr77SkCFDNGfOHNWtW1fTpk1TWFiYDh8+rICAgOxODzlI//791b9//zSnbd68OVXsySef1JNPPmnnrAAAAAA8yHL8nu4pU6aod+/e6tmzpypWrKg5c+bI3d1dn3zySXanBgAAAADAXeXoPd0JCQnatWuXRo4cacYcHBzUrFkzbdu2Lc3nxMfHKz4+3nwcHR0tSbpy5YoSExPNMRwcHGS1WmW1Wm3GdnBwUFJSkgzDuGfc0dFRFovFHDdlXJKSkpIyFHdycpJhGDZxi8UiR0fHVDmmF6cmaqKmzNcUHxMvw2JIhmQxLGbcsBiSRenGLYZF+jv1e8etf49hxmU79l3jDqlzlEVp5p5enJryXk1XjCs56v2Ukc+I+JioFDVZJIuDLIZVSpG7YXGQLJb041bbHA3L7f0HFsOasbiDo2QYtnGL5fb86catstjkcjv39OLUlDdrunLFdl9Vdr+f7vU7Nz46Ps997mUoTk15pqYrxpUc835KLx4TE3M7/xTzpSVHN92XLl1SUlKSeV5uskKFCunQoUNpPmfcuHEaO3ZsqnipUqXskiMAANlhhEZkdwrAAyX1X5cA7Ck3/Z67du2afHx80p2eo5vu+zFy5EgNGTLEfGy1WnXlyhX5+fnJYrHc5ZnIy2JiYlS8eHGdOnVK3t7e2Z0OkOfxngP+XbzngH8X7zlIt/dwX7t2TYGBgXedL0c33QULFpSjo6POnz9vEz9//rwKFy6c5nNcXFzk4uJiE/P19bVXishlvL29+WAE/kW854B/F+854N/Few5328OdLEdfSM3Z2Vk1a9bUhg0bzJjVatWGDRsUGhqajZkBAAAAAHBvOXpPtyQNGTJE3bt3V61atVSnTh1NmzZNsbGx6tmzZ3anBgAAAADAXeX4pvupp57SxYsXNWrUKEVGRqp69epau3ZtqourAXfj4uKi0aNHpzr1AIB98J4D/l2854B/F+85ZIbFuNf1zQEAAAAAwH3J0ed0AwAAAACQm9F0AwAAAABgJzTdAAAAAADYCU03AAAAAAB2QtMNAMiQ5OtuWq3WbM4EeDBwrVvg38fvONgDTTdypVu3bunPP/+0ifEhCdjHjRs39Nprr+n999/XqVOnlJSUlN0pAXlaXFycTpw4oejo6OxOBXhgJCYmSpIcHG63R/xdiaxE041cJykpSWPHjlXr1q01cOBArVixQtLfH5IAsp6Dg4MOHDigunXravjw4Vq/fn12pwTkWR999JGaNWumTp06acKECbpw4UJ2pwTkaYmJierevbt69Oihr776SjExMfxdiSzFfbqRK506dUpXr17VuHHjdPz4cbm6uuqzzz5TsWLFsjs1IE8xDEMWi8V8/Pnnn+vHH3/Uxo0b9dZbb6lPnz7ZmB2Qdx08eFDh4eEaMmSIqlevrrCwMA0aNCi70wLypLi4OO3du1eLFi3SiRMndPDgQc2YMUP16tVTwYIFszs95AE03ch1kjdZi8Wi6OhoHTx4UEOGDNHp06e1aNEiNWrUiG8ngX8gZaP9559/ysfHR/7+/ub048eP67PPPtPo0aM1efJkDR48OLtSBfKchIQEOTs7m49PnTqlcePG6ffff1ft2rX10UcfZWN2QN5x55fKybFLly7p7bff1rJly9StWzf17NlTISEh2ZQl8gqabuQqVqvVbKjPnTunIkWKSLr9IdmuXTtt375d3377rWrXrm0zL4CMSflHyAcffKCff/5ZLVq0UI8ePeTm5mZOi42N1dy5c/X6669r8eLFeuqpp7IzbSBPiI+Pl4uLiyTpyJEjKlWqlPLly6erV69qwYIFWrp0qR577DGNGzcumzMFcreUv+u2b9+uypUry9PT02ae2bNna86cOXr44Yf16quvqnTp0tmRKvIIOhLkGtOmTdPOnTslSW3bttWYMWNktVpltVplsVi0atUq1ahRQz169JB0+xxUvlMCMif5j5Dhw4dr2rRpeuKJJ/TYY4/J3d1dFovFvLCMh4eHevTooZdfflkLFizQkSNHsjNtINdbuHChhg8fLklq3LixRo8ebf4ey58/v/r06aPWrVtr27Zt+umnn7I5WyD3Stlwv//+++rdu7fGjBmjW7duSfr7Amovv/yyBg8erI0bN2rNmjXmc4H7QdONXCEhIUHr1q1TixYtVL9+fR08eFDjxo2Tg4ODHBwczKspf/LJJ3JyctLQoUMlKdVhQwDube7cuVqyZIlWrFihF198UWXLljWnpfyDo0CBAmrfvr2uX7+uo0ePppoOIGPi4+N1+PBh/frrrwoODta5c+e0cOFCOTo6ymKxyDAMeXt76+X/a+/O43LM/sePv5KivYRSIYwtWwbJR3ZZx5IlW/Zh7Mq+hoZiNCZLhmyRSLYpBtmbyZIZxKjByE6FrKW03P3+8Ov63o0tZkbF+/lX3fd1ncfp8ehc57zP8r6GDycjI4PAwMC8rrIQBVb22HDy5MksWbIET09PBg0ahJaWFkCOceWAAQPo168fM2bM4Pr160p7FOJ9yfZyUaDo6emRlZXF9u3badu27SvfZ2Zm8uOPP3Lw4EE2bdqErq5uHtRSiIIpKyuLrKws+vfvT9myZZk7d67y+cmTJ9mzZw/37t2jTZs2ODk5KfdNnDiRQ4cOcfz4cYoWLZpX1ReiQFOpVNStW5eoqChGjhypnN3OzMxEU1NTOTJ19uxZHB0d2b59O02aNMnjWgtRMG3cuJHp06ezfft26tatm+O77DanrnPnzhgYGLBu3ToKFy78MasqPhGy0i0KhKysLJ4+fcr//vc/mjZtyoABA4iIiMjxPYCmpiZdu3bl999/Z/fu3XlVXSEKJA0NDTIzM4mPj1e22cHLLXZTpkzB39+fy5cv07VrVwICApTvhw4dSqVKlXj8+HEe1FqIgiu778pud05OTri5uXH69GkmTJjAixcv0NTUJD09nUKFCqFSqahduzZt2rTh2rVreVx7IQquixcv0qZNGyXgzsrKIiQkhGHDhtGgQQPmzp3LpUuXlOtdXFx48OCBBNzig0nQLfKt7K092QwNDTlw4AB79uyhfv36dOnShWPHjgEvg4Xnz59z48YNSpUqxaRJk0hPT5ctQEK8Jy0tLWrVqsWqVatwdnbG0tKSM2fO0Lt3b2JiYjhy5AguLi4sX76c58+fA1C2bFns7OxklVuI95CZmalsc9XU1MTCwoKZM2fi6elJ8+bN+eWXX5gxYwZpaWloaWmRlJRETEwMAA4ODjkCAiHE+7lx44aSJyg9PZ1+/frx/fff88cff1CxYkX8/PzYsGGDMgHdunVr7ty5w4EDB/Ky2qIAk+3lIl9S39rj5+fHrVu3sLCwoFatWvzvf/8DXiZTO3XqFOvXr6d8+fL06tWLNm3a8O233xITE0PJkiXl3YpCvIF6IpnU1FTS09MxMDBQvnd3d+fBgwcYGBgwadIkDA0N0dLSIisri1GjRvH48WMCAwOVLa+ve/WKEOL11Pu4b7/9lr/++ousrCw6dOiAs7MzKSkpzJ8/n4MHD1KzZk1Gjx5N+/bt6dixI4sXLwbg0aNHmJiY5OWfIUSBdeDAAUaNGkVWVhaPHz+mbNmyuLq60rFjRwwMDHB1dWXbtm1cuHABY2NjAE6cOIGNjQ1GRkZ5W3lRIEnQLfK1zp07c/78eb744gv+/PNPzM3N6datG5MnTwage/fu7Nq1C0tLSypXrsyePXvyuMZC5H/qAbKPjw9Hjx4lOjqakSNH0q1bN6ysrN54b1xcHB06dKBbt25MmTLlY1VZiE9S586duXTpEnZ2djx//pzt27fj5uaGt7c3KSkp+Pj4sG3bNuLj42nYsCFbt27N6yoLUWCo93V37tyhSJEiFCpUiGLFipGUlMRvv/3GiRMn0NHRYdSoURQuXFi5fvny5ezevZstW7YoE9IZGRmyvVx8MPnPEfnW0qVLuXjxIkePHqVMmTLcunULX19fgoODMTY25ptvvmHr1q3s3buXQoUK0bp1awB5P7cQb6E+CBk3bhzBwcH079+fYsWKMX78eNLS0pg0adIrK9dPnjzh8uXLDB48mHLlyikBt6xwC/FhgoODiYqKIiIiQpno2rFjBz169MDU1JRp06Yxbtw4+vbtS1xcHHZ2doD0cULkhnrf5OXlRWhoKHfv3qVGjRp4enpSs2ZNmjVrRrNmzV65NyEhgTVr1tCyZcscO8Ak4Bb/hKx0i3zLzc2NmJgYwsLClM9u3LjBlClTSEtLY9u2ba8M9mUwIkTuTJgwAX9/f44ePUr16tXJzMzE3t6ehIQEYmJi0NfXV9rT9evXGTp0KM+fP6dChQqsX78ekPYmxD/x448/4ufnx9mzZ1GpVGRlZaGpqcmKFSuYOnUqx48fp2rVqjnukTYnxLupB9xubm5s3LiRxYsXEx0dzaZNmyhfvjzbt2/H0NAwx/Go+Ph4Ll68yJgxY7C2tiY0NPSV8oT4UDJlI/Kd7EFF8eLFSUlJ4eHDhxQrVgx4mbCpa9eu9OrVixs3bmBtbZ3jXhmMCPFuJ06cYNGiRXh4eFC9enXg5ba5xMRENDQ0uHHjBoUKFeKLL76gUKFCWFtb069fPzQ1NenVqxcgg38h3sfrBu1mZmb8+eef/Pnnn1StWlVJHlqvXj10dXWVRIXqpM0J8W7ZbW3mzJmsXLmSmJgYZbz4119/ERERQVpamtKeNDQ0uHfvHp6envz222/Y29vj5+cHSF8n/j3yXyTyVGZm5itZyrMfbvb29pw8eZI1a9bw4sUL5XsDAwNsbW0pUqTIR62rEAXV3zc0NWjQgEmTJuHl5UVISAiZmZk0bNgQQ0ND7OzsWLRoEXZ2dnTp0oUJEybw66+/0qdPHyXgzsrKkkGIELmUlpamBAHJycnK540bN6Zp06ZMnDiRmJgYJbGajo4Ourq6ZGRk5El9hfgUnD17Fn9/f5o2baokPktPT+fSpUskJSURHBzMoUOHlFfvmZqa0qtXL+bOnSsBt/hPyPZykSdSU1PR1tbO8TCbM2cOjx8/xtDQkAkTJmBgYMDSpUsZO3YsEyZMoH79+lhYWDB48GBq165NYGBgHv4FQhQM6its8+bNw8jIiFGjRgEwZcoUFi1aRIkSJahTpw6BgYHK+bUTJ04QERHBwoUL6d27Nz4+Pnn1JwhR4KSmphIVFUW9evWUYHr8+PFcunQJU1NTRo8eTd26dfn5559ZvHgxCQkJjBgxAi0tLRYtWkSVKlXYtm1bHv8VQhQcr9tNsnTpUrZv307FihWZOXMmzs7OZGRk0KlTJ27evMnFixc5d+4cXbt2pWzZssyePfut5QnxT0jQLT46lUpFjx49iI6OJjo6Gg0NDTp16kRMTAzlypXjzp07ZGVlceLECYyMjFi7di0+Pj4kJCRQsmRJatSowaZNmwB5KArxNurtw9XVlTVr1vDbb79RpUoV5Zp58+YpW/CGDBkC5JzdV3+1kRAid/r27UtUVBQ//PADLVu2ZNiwYezbtw8XFxc2b96MiYkJEydOpEePHpw4cYL169ezbds2qlatStWqVZWVNunjhHg39XYyfPhwzMzMlADa19eXzZs3ExMTQ40aNQgPD1fuS0pKYv/+/QQFBWFoaMjq1avzovriMyFBt/joMjMz+fnnn5k1axaGhoZs3LgRd3d3vL29MTIy4ty5cwwfPpwHDx5w5swZjI2NuXXrFiqVipSUFCVgkG0/QuTOuHHj8Pf358iRI9SqVeuV76dNm4a3tzfr1q2jT58+yufqbUwG/0Lk3p07d+jevTva2tqMGTOGnTt34u7uTsWKFcnIyKB79+7cuXMHNzc3nJ2d0dTU5N69e+jr66OrqwtIHydEbqj3TePHj2fFihVERERQu3Zt5ZrspIVVqlTBx8cHMzMzVCoVGhoaaGhokJycjJ6e3ivlCfFvkqBb5ImMjAwOHz7MhAkTSE5Oply5coSEhKCnp0dWVhbnz59nyJAhPHz4kNOnTyvncbLJQ1GI3Jk8eTK+vr5ERkZSrVo14OXE15IlS/j666+V7eRTp07Fx8eHNWvW0Lt377ysshAFWva7fOPj4/nqq6/IzMwkKyuLsLAwzMzMAHj27Bl9+/YlISGBYcOG0atXL7S1tZUypI8T4v24ubmxYcMGDh06hK2tLZBzp1b2ineFChXw8vLCwsLilfduS7sT/yWZQhUfVXbStMKFC9OiRQsWLlxIqVKliI6OVgYcGhoa1KpVi9WrV2NqaoqlpSWpqak5ypGHohDvlpqayokTJ9DX11dm8VUqFV9++SU//fRTjkG+l5cXrq6uuLi4cOLEibyqshAFWmZmJoULFyYrKwtzc3NCQ0MxNDTk6tWrhIeHo1KpgJcJQTdu3Ii5uTkeHh789ttvOcqRPk6I3Fu0aBGLFy8mPDxcCbhVKhVff/01v//+OwAjR46kV69eXLt2jWnTpnHr1q1X3rst7U78lyToFh+N+ozj8ePHSUlJwdHREXd3dwwMDGjatGmOTOY1a9bE19eXCRMmULRo0byqthAFUlZWFkWLFuWnn36iYsWKtG/fnujoaBo0aICVlRW7du165Q0AXl5eBAUF0aBBgzyqtRAFl3oft2nTJi5fvoyFhQVbt26lWrVq+Pr6cvDgQeV6fX191q9fz5gxY2jYsGFeVVuIAi0lJYWLFy9SvXp1wsLClM+//PJL7t27h42NjfIGj5EjR9KzZ09OnjzJ3r1786rK4jMl28vFR6E+GOnVqxe3b99m3rx5NG7cWNlqPn78eIyNjTl69OhrEzfJ+TYh3uzv2+LU29yjR4/46quvOHHiBA4ODuzbt085N/om0t6EyD319tK9e3du3rzJsGHD6NGjB7q6usTFxdGpUyd0dHSYPn06rVq1eqUM2doqxIdJSEhg/vz5REZG0q5dO0JCQjA3N2fDhg2YmJgAOdvooUOHaNGiRV5WWXyGJOgWH1WPHj34888/8ff3p3Llyjm2vB49epSxY8diYmLC4cOHX9n2I4R4PfXB+tq1azl37hwPHjxgypQp1KhRA4CHDx/i4uLCpUuX2LdvHxUrVszLKgvxSXJxceH06dPs3bsXS0tLtLS0lHOjcXFxdOnShSJFijBu3Dg6duyY19UVokBR7+uSk5PR0dEhKysLTU1N4uPj8fLyYufOnRQuXJg//vgDPT29HBPQf59Mloku8THJMob4aCIiIoiKiiIoKIgvv/yS5ORkfv31V3x8fAgPD6d58+YsXryYS5cuMX/+/LyurhAFwt8zt86ePZuEhARu3LhBy5YtuXnzJgDFihUjMDCQkiVL8tVXX3Hx4sW8rLYQn5wLFy4QExPD1q1bsba25saNG+zcuRMXFxfWrl1LqVKl2L59Ozdu3ODChQt5XV0hChT1vs7Hx4d+/frRpEkTNm7cyNOnTzE3N2fGjBl07dqV4sWLs3TpUgA0NTWVXAp/370lAbf4mGQpUXw0aWlpZGZmkpqayubNm9m3bx/Hjh1DW1sbLS0tli1bRpMmTTh48KCyOieEeLvsQcO4ceNYv349hw4dombNmkRHR9OrVy9evHjBixcvKFKkCCYmJuzbt4/27dvTuXNntm7dKm1NiH+JiYkJCQkJ7N27l+joaLZs2cKVK1ewtrbm66+/Rk9Pjx49enD27FmMjY3zurpCFBh/n1zeuHEjrq6u/PLLL8yZMwdbW1tq1apFiRIlmDx5Ml5eXvz0009kZWUxdepUChUqJKvaIs/JSrf4aOzt7dHU1KR3794MHToUa2trNm/ezN69e3n+/DkPHjxAU1NTCQKyZyaFEG/n7e2Nj48PoaGh2NraUqhQIaysrEhMTMTV1ZVKlSoxbdo0YmJiMDIyYvfu3WRlZREQEJDXVRfik2FoaMjgwYPx8/NjxIgRNGjQgNWrVxMaGkqrVq2IjY0FUAJu6eOEyB31yWV/f3/CwsKYOnUqe/fuRVtbmytXrvDgwQMePXqEubk5kydPxt7enpCQEKZOnZqjDCHyiqx0i48iMzMTXV1dzp49S3h4OF988YVypvTBgwfo6Oi8co8kcRLizdRn7a9du0bVqlU5efKkkgW5RYsWWFtb06pVK2rVqsX8+fNJS0vD29sbY2Njzp49+85kakKI3DMwMGDSpEkMHTqUwoULY25uDsC9e/e4efMmpUqVynG99HFC5N7atWvx8fHhjz/+oFq1asDL12ImJSWxaNEixowZg4WFBStWrKBOnTpMnjyZqVOnyttvRL4hidTER6OezAJeDkTu379Pjx49qFixIjt37szD2glRcKSlpbF48WK+/vprJTPruHHjOHbsGB06dGDbtm2UKVOGgIAAjIyMAHB1dcXf359Lly5hZmamlCVb7oT4b9y/f5/z588zbtw4rK2tCQkJyesqCVGgZPdPGRkZHDlyhL59+9KoUSO2bt0KQN26ddHW1mbu3LnExMSwYcMGkpOTOXDgABYWFjx79gwDA4McZQmRV2SaVfxr3rRVLvtz9YA7MzOTlStX4uzsTK1atZSAW7bbCfFuZ8+exc/PL8d77RctWkSDBg1Yvnw5z58/Z/ny5RgZGZGWlgaApaUltWrVemXWXwYhQuSOenvLlr1u8ff1i7S0NHbv3s2iRYv48ssvlYBb+jghcictLY127dpx584dChcuTIsWLQgODiY8PJwOHTpQv359zM3N2b17N82bN2fUqFH07NmT27dv8+TJEwAJuEW+ItvLxb9CfRX71KlTqFQqTExMqFy5MoUKFXpllVtTU5PRo0dTvXp1nJycAHkvsBC5Vb9+fWrXro23tzdz585FU1MTDQ0NfHx80NLS4vDhw2zcuJFvvvkGExMTEhMTCQwMpGnTpsrKtxAi99T7sJ9//pmnT59iYWFBnTp10NfXR0NDI8fAXltbmzZt2lCzZk3q1KkDSB8nxPvQ1tamcOHCREdHY2lpSaFChWjcuDFbt25lxIgRXLt2jQcPHqCrq0tqaipFixbFxsYGa2vrHONNkMllkT/I9nLxj6kPNJydnYmJieHatWuUL1+e//3vf6xcuRLIOWj5+6yjDEaEyJ3strNkyRJ27txJYGAgFhYWpKWloa2tDcDYsWM5duwYPXv2pHv37nTo0AFLS0v27t2bowwhxLuptxcnJyfOnj2LtrY2V69exdnZmaFDh9K0adNclyGEyB0XFxdSUlLYvn278llmZiYRERH07NmT+vXr89NPPwEvj3M0b94ce3t7Vq1alUc1FuLNJMoR/1j2QGL48OGcO3eOoKAgjhw5gqurKzt37qRXr15Azu3lfx98SMAtRO5kt51vvvmGmzdvMnHiRODlqsCLFy8AWLx4MY0aNWLz5s1UqVIFCwsLJeBWqVQy+BfiPWS3l+nTp3Pp0iXCw8M5d+4c4eHhxMbGsnTpUs6dO5erMoQQ75a9Hujh4cGZM2dYu3at8p2mpiaNGzcmKCiIkydP0q1bN54+fUqLFi2wtLRUAm5ZUxT5jUQ64oOpn01LSUkhJiaGGTNmUL16dezs7Ojbty/+/v4cPXqUZcuW5WFNhfi0ZGZmUqRIEdatW0doaChubm4AFClShIyMDAB++OEH6tatS79+/di3bx8gO0qEeB/ZZ7izB+8XL16kVatWlC1blqJFi9KwYUOWLFnCqVOncqzECSH+mezjGubm5nTo0IGQkBBOnDiR4/smTZqwZcsWjh8/jrGxMZaWljn6OpnoEvmNjL7EB1EfvB86dIhChQpx8+ZN/vzzT+UabW1tWrRowf/+9793rgIIIV71piRN2btG7O3tWbx4MevWrWPEiBEAFC78f6k6Vq5cqRzvkIBbiNzLyspS2tnOnTtJT08nKSmJ1NRUADIyMsjMzKR+/foMHz6cjRs38uzZM1ldE+JfoqGhga6uLr179+b27dv4+fm9MpZs3Lgx/v7+zJgxI8duLunrRH4k/5XivWVlZSkPtB49erBw4UIeP35Mp06dOHPmDL/99ptybZEiRbCysuLx48cyGBHiPaSmpjJ+/HjOnDmjzPr/nba2Ns7Ozvj5+REUFISjoyO7du1StplnU2+zQoi3y8zMVFbJunXrhqurK4ULF6Zz5874+fkRERGBlpaWEpQXLVqUChUqoKOjI6trQrynd40N7e3tmTt3LhEREXz33Xfs3r1b+U5DQ4NmzZrh4eEBSMAt8jf5zxTvLXtQceTIEZ49e8aKFSswMzOjW7du3LlzB19fX44cOQLAtWvX2L9/PzY2NjIYEeI9hIeHc/jwYebNm8cff/zxxsBbX18fZ2dn/vzzTwwNDfH19cXOzo6dO3dy584dQM6TCvE+soPpCxcuUK5cOcLCwtDQ0KB///7079+fNm3aEBISQmxsLLGxsaxZs4bKlSvn2GUihHi31NRU2rZtS2Rk5Fuva9u2LStWrODZs2d4eHgwadIkkpKSSEtLQ0tLS7lOAm6Rn0n2cvFBJk6cSGRkJMbGxoSEhCiD+v379zNnzhzu3r2rvMaoSpUq7Nq1C5AMrkK8j+DgYFauXImhoSEeHh7UqFHjtW0oe3Y/MzOTu3fvsnnzZgwMDHB2dsbU1DSPai9EwbV06VLGjh1L+fLlOXr0KFZWVgDcunWLH374gRUrVlC8eHG0tLSoUaOGkkFZ+jghci81NZVGjRrx4MEDduzYQe3atV+5Rr1NXb16lcjISGbPno2lpSXm5uZ4e3tjYWHxsasuxHuToFt8kNWrVzNp0iQMDAz46aefcjwor169yu3bt4mKisLKyoouXboAsu1HiNxSbytBQUGsWrUq14H3m34XQuTeyZMnWbZsGdu2beOXX37Bzs4uR7s7ffo0T58+BaBZs2aAtDkhPkRqaiqdO3fmwoUL7Nq167WB999lZmZy7Ngxbt++TbNmzShVqtRHqKkQ/4wE3eKd1N+vrS44OJgxY8bQuXNnxo8fT8WKFd9YhgxGhMgd9VXr7HYXGBjImjVrMDIyemvgLYR4f2/q4/7880/GjBlDVFQU4eHh2NjYkJGRoeziUid9nBDvJyMjQzmSceXKFdq1a4e+vj5r1qx5a+D9pvYqRH4nQbd4K/WHW0BAAI8fP+bFixdKYplNmzYxadIkunbtyqhRo5TAWwICId6fent78OABT548oUKFCgDs2LGDpUuXYmxsLIG3EP8S9Tb3/fffc/v2bbKysnBycqJJkyb89ddfjBs3jqioKMLCwpTAW85vC/Hv6Nq1K+np6SQkJHDp0iVMTEzYvn07X375ZV5XTYh/lQTdIle6dOlCbGwsJUqUIDk5mdjYWMLDw6latSqBgYFMnToVJycnhg0bRtWqVfO6ukIUOOorZUOGDOH8+fPcvHmTGjVqMG3aNJo2baqc8TYyMmLOnDnUqFEjj2stxKfBycmJmJgYOnbsyIULF7h06RITJkxgxIgRREVFMXv2bE6fPs3u3bupVatWXldXiE/CjBkzCAgIICIigqJFi/LgwQNGjhzJlStX+OmnnyTwFp8U2Qsl3ih7Psbd3Z3o6GgOHjzIwYMH+eqrr3j48CHPnj0DoE+fPsydOxdfX19Onz6dl1UWosDKDrj79OlDZGQkc+fO5dy5cxw7doyFCxfy5MkTnJ2dGTp0KE+ePGHkyJHcvn07j2stRMHxpjUGX19frly5wq+//srChQtp3LgxT548oWbNmgDY2tri6elJmTJlWLt27cesshCftISEBFq1akXp0qUpUaIEVatWZcuWLZQqVYp+/fpx9uzZvK6iEP8a2R8lgJzbwS9fvkylSpWU32/dusWECRMoUaIEnp6eLFq0iL1792JnZ8fNmzcpXbo0/fr1o0KFCjRs2DAv/wwhCozMzExUKlWO151cuHCB6OhoAgMDqVGjBqtWrUJLS4uxY8diZGQEQI8ePUhOTubevXtKRmUhxNu9ro/L3l0SFxeHnZ0dJUuWZO7cuSxatIjNmzfj4OBAXFwc9+/fp2bNmgQFBVG6dOk8/kuEKPiy22NqairR0dHK55mZmZQoUYLu3bszadIkmjRpwh9//EHZsmXzsLZC/DtkpVvkGIx89913DBkyRFnFBpRBx7Jly/D29iYwMBBHR0cyMjJYuXIlPj4+AErArVKpPvrfIERB8uLFC5o0aUJQUBBpaWnK548fPyYtLY0aNWrg7e3N5MmTCQ4OplWrVty5c4eVK1cCMGjQIKZMmQK8efVOCPGSeh/n7u5OlSpViI2NVXaXPH/+HFNTU9atW8f333/Ppk2baN26NSqViq1bt7J7925evHihBNzS5oR4P5mZmTl+z26Pw4cP5+nTp7i7uwMo+RWsrKyYMWMGHh4eEnCLT4asdH/m1AcjEydO5PvvvwdeJnEyMDAAoG3btqxevZobN26wdetWWrduDbxcAT98+DB9+/bNUaZkcBXi7YoUKYKpqSljx45FR0eHDh06UKRIESpXrkxWVhZdunQhPDycLVu24OjoCEBsbCwBAQHUrVuXOnXqKGVJIjUh3ky9jxs7dixLly4F4OHDh0qSQicnJ5o1a4ZKpeL06dNK5uT4+HjWr1+Pk5MTRYoUUcqUNidE7qknK/Tx8eHSpUsYGxtTv359OnfujIuLC3v37uXBgwdMmjSJhIQEPD096dWrF66uroC8HUB8GuQ/+DOmPhhxdXVlzZo1BAQEUKlSJVJTU5XrnJycKFGiBBUrVuTp06fEx8dz/PhxOnbsiKWlJSNGjMirP0GIAid7J0hISAidOnVi0KBB7Nq1i2fPnmFqaoqTkxO//PILgwYNwtHRkaysLOLj4xk7dizVqlXLEXALId5MvY9zc3MjICCAs2fPUrVqVS5evAi8bI+NGjXiu+++Q1tbm/379/Prr7+yb98+HB0dKV26NDNmzFDKE0K8n+yA28nJidWrV1OoUCEePXpE165d2bRpE25ubvTs2ZMjR45QrVo1unXrRvny5Zk6dapShgTc4lMg2cs/U38fjKxbt47jx49jY2NDyZIlWbNmDR06dFBejfLXX38xatQobty4wd27d6lYsSKVK1dm06ZNgMxCCpFb6rP+iYmJtGzZkqSkJLy8vOjWrRuXL19m9uzZREREYG9vj4GBAZGRkZQrV45du3YB8ko+Id7l732cv78/hw4d4ssvv6R8+fJMnDiR4cOHK9cnJCQQHBzMvHnzKFy4MJaWltSsWZNVq1YB0scJ8U+sWrWKJUuWsG/fPiwtLZkzZw5LliwhKChI2c0FEBERgb6+Pra2toC0O/Fpke3ln6nswciwYcPYunUrv/zyCzY2Njx+/BhNTU0SExMBKFy4MJmZmVSsWJGgoCAePXrEX3/9RdmyZalSpQogD0Uh3kd2wN2+fXsAzM3NuXz5Ml9//TVZWVl0794dT09PfvnlFzZs2ICZmRkDBw5k/PjxgLQ3IXIju48bPXo0gYGBhIeHK9nIK1WqxL1794D/mwQzMzNj9OjRODs7k5ycjJaWlnKGW9qcEP9MbGwsderUwdLSEi8vLxYvXqwE3Ldu3eLmzZs0bNgQBwcH5R5pd+JTI0H3Zyh7BeDUqVNERkZy8OBBatasSUZGBsbGxpQpU4aEhATl2uyHnoGBASYmJpQvXz5HWfJQFOL9LFiwgD/++IPff/8dPT09NDU1GTRoEIMGDUJDQ4POnTvTr18/+vXrl+M+GYQI8W7ZfVxsbCxHjx7lyJEj1KxZU/nc0NCQc+fOAS+3rWa3q5SUFIyNjTEzM8tRlrQ5IT5MdpsrXrw4GhoaeHt7891337FlyxZatWqFSqXi0KFDXL16lRo1amBoaKjcK+1OfGrkP/ozk5aWhre3N8nJyXzxxRccPnyY2rVrk5WVReHCL+dgTExMuHz5MvBykK+hocGjR4/46quvOHLkSI7yZIurEO/vyZMn1KhRg5IlS1K0aFGKFi3Kpk2baN68OW5uboSEhJCcnPzKfTIIEeLt0tLSaNeuHbdu3aJChQpERkZSq1atHOexy5Yty9OnT4H/C6ofPnxIw4YNOXjwYI7ypI8TIvfelKW8TJkyeHt7M336dDZt2kSrVq0AuHv3LkuWLEFbWztHwC3Ep0hGcJ+Zs2fPsnLlSp4/f06xYsUwMTEBXj4YMzIyAChZsiRJSUnAy62wjx494n//+x8AzZo1y5uKC/EJyB74Z2VlERMTA7xsY9mJC3v37s2dO3fo3r278r0QIve0tbUpXLiw0n6ys45raGgoAUC1atW4f/8+aWlpSlKnpk2boqenpxz7EEK8H5VKpRyfWrBgAbNnz+aHH34AwNnZmW+//Zb09HQuXrxIaGgo+/bto1WrVpQpU0aSFYrPgiRS+ww5OztTvnx55s6dq6xuw/9tXV2wYAF79+7l6NGjJCYm0rhxY0qXLs2+fftyXCeEeDv1pGnqbt26RbNmzbC3t2fjxo3K52FhYURFRWFiYsLQoUM/ZlWF+GS4uLiQkpLC9u3bgVcTD27bto1vvvmGxMREHj16hIODA1ZWVoSFhQHSxwnxvtTbTMeOHblw4QJmZmbcu3cPS0tLfvnlFwBmzZrF1q1buXv3LrVq1aJixYqsXr36lTKE+BTJf/dnJHt+xcHBgcjISCWRTPYrjLIfdvr6+ty9e5cbN27QrFkzrKysJOAW4j2pB9zLly9n/Pjx+Pj4cPDgQUqXLs2cOXM4c+YM7dq14+TJk4SFheHm5kZSUpIScGe3TSHEu2X3cR4eHpw5c4a1a9cCL1e51dcXihcvjrGxMdeuXaNRo0YScAvxD2W3mTt37qCrq8vp06fZv38/a9eu5e7du9SvXx+AOXPmsGfPHs6ePcuWLVsk4BafFVnp/gy9ePECGxsb7O3tCQwMBHIGCAcOHKBfv35kZWVRq1YtGYwI8Q907tyZCxcuUKVKFW7dukVycjLDhw9n/PjxhIWFMXPmTGJjYzE0NKRevXoEBwfndZWFKLCysrJISUlhypQp3LhxgylTptCgQQPlOw0NDW7dukWNGjVIS0ujYcOGHDhwAJA+Toh/YtKkSWzevJk6deoQFBRE0aJFyczM5OTJk/Tv35/ixYtz8uTJV+6TV2CKz4X0Lp+ZzMxMihQpwrp16wgNDcXNzQ14ea40e/6lQoUKJCQk0LhxYwm4hXhP6vOYfn5+REdHc/DgQXbv3s3PP//MkCFD8PHx4ccff6R169acOnWKY8eOcejQISXglhVuIT6MhoYGurq69O7dm9u3b+Pn56dkKtfQ0CAzMxMNDQ2ePn1Ku3btJOAW4gOpJ01LS0ujevXqFCtWjEuXLlG0aFHg5diyQYMGrF+/nsTERCpUqPBKORJwi8+F9DCfKPWETeqyV7Pt7e1ZvHgx69atY8SIEcD/Pfj09PQIDAzMEQDIYESI1/t7G1NPShgbG4ulpSXW1tYAWFlZ0b9/f7p27UpISAiPHz8GoHLlysqr+OQVRUK827s26dnb2zN37lwiIiL47rvv2L17N/CyD7SysuLs2bNs27YNkD5OiA+RPZ48f/482tradO3aFXd3dx4+fIiTk5NyXaFChWjQoAErV67M8bkQnxvpZT5BqampjB8/njNnzrxyli2btrY2zs7O+Pn5ERQUhKOjI7t27SI1NRUzMzN69eoFyGBEiHfJnqyaOXMm3bt3B1ASFFpbW/Ps2TOuX7+uXG9ubo6Dg4OSqFC9jL//LIR4VWpqKm3btiUyMvKt17Vt25YVK1bw7NkzPDw8mDRpEs+ePSM1NZVatWoB0scJ8U8EBARga2vLnj170NPTo127dvj6+nLmzBm6deumXFeoUCGaNWuGt7c3IFnKxedJeppPUHh4OIcPH2bevHn88ccfbwy89fX1cXZ25s8//8TQ0BBfX1/s7OzYuXMnt2/fBuS9wELkxvnz55k3bx7bt2+nZ8+eyueVK1cmPj6egIAAEhISlM/NzMyoVq2atC8hPlBiYiI9e/bk7Nmzr/0+u89r0aIFPj4+uLm5ERISQqdOnRg0aBB3794FpI8T4p/o1q0bw4YNw9nZmT179qCjo0P79u35/vvvOXPmjDIRDTK5LIQkUvtEBQcHs3LlSgwNDfHw8KBGjRqvTVaRPcufmZnJ3bt32bx5MwYGBjg7O2NqappHtReiYLl58yZ9+/bF3Nyce/fuYWhoSEhICABLlixh5syZ9OvXj3r16lGmTBlGjBhBrVq12Lx5cx7XXIiCKTU1VUlSuGvXLmrXrv3OezIzMzl27Bi3b9+mWbNmlCpV6iPUVIhPw993hWT/npqaipubG/7+/mzfvp127dqRkpLCzz//jIuLCwsXLmT06NF5WHMh8gcJuj8x6g/FoKAgVq1alevA+02/CyFeT709eXl54evry9SpU1m3bh1WVlb89NNPAKxevZqtW7cSGRlJhQoVqFKlivLmAMncKkTuZWRkKMc3rly5Qrt27dDX12fNmjVvDbzV39AhhPhwo0ePZtCgQdSuXVvpv1JTU3F1dWXDhg2EhITg6OhIcnIyMTEx1KtXL6+rLES+IEH3J0R91Tp7cBEYGMiaNWswMjJ6a+AthHi3t7Wde/fuMWbMGJycnFCpVHh4eFC5cmUl8H7w4AGpqamkp6dTrlw5QCa4hPhQXbt2JT09nYSEBC5duoSJiQnbt2/nyy+/zOuqCVHgvamvy06SFh0dTXh4ONWqVVP6sfv379O0aVPu3bvHqlWr6Ny5s3Kf9HVCyJnuT0ZmZqbyQHv06BGxsbEA9OnTh1GjRvH48WPc3d3fesZbCPF22YOQOXPm0LJlS44fP861a9cAMDU1RUtLi5CQEHr16sXs2bO5cuUKXbt2BaB48eJYWVkpAbdkKRfiw8yYMYPff/8dX19fdu/ezYkTJyhXrhydO3fmzJkzeV09IQq87L4u+5V6AOPGjePWrVts3ryZRo0a0aRJEy5cuKD0Y0ZGRtjY2GBhYUFoaGiO8qSvE0JWuj8J6jOIQ4YM4fz589y8eZMaNWowbdo0mjZtqpzxNjIyYs6cOdSoUSOPay1EwfTbb7/h4OBAeno6Xbt25eHDh3Tq1Inhw4fz6NEjWrdujaenJy1atGDbtm3Mnz8fU1NTjhw5ktdVF+KTMGTIEABWrVqlfHb//n2++uorkpOTCQgIyNUZbyHEm4WHhzN8+HA6dOjA+fPniYqK4s8//8TY2Ji7d+8yfPhwjh07xuHDh7GxseHJkye4uLjg7e1NtWrV8rr6QuQ7MvX0CcgOuPv06UNkZCRz587l3LlzHDt2jIULF/LkyROcnZ0ZOnQoT548YeTIkUp2ciHE+ylTpgwLFiygQoUKqFQqJkyYgK+vL05OTkyePJkqVapw+vRptLW1cXJyYsyYMTRo0CCvqy1EgZe9RpCamsrp06eVzzMzMylRogTdu3cnJiaGJk2acOPGjbyqphCfBAcHB3r37s2PP/7ImTNn+O233zA2NkalUmFhYcGKFSto0aIFdevWpX379tSqVYsiRYooAbes6QmRkwTdBVBmZibp6ek5Prtw4QLR0dEEBgbi6OhISEgIWlpajB07FiMjIwB69OhBnz59aNeuHVZWVnlRdSEKPDMzM/r06cOIESM4fPgw169fJyYmhiFDhhAXF8eWLVvYvXs3L168QEdHh/79++Pp6QnIIESI95GZmZnj9+wtr8OHD+fp06e4u7sDKDlMrKysmDFjBh4eHpQtW/bjVlaIT0h2bqDixYsrR6OWLl3K8+fPKVSoEFlZWZQqVYotW7bw/fffU7t2bUaNGqXkMJHcQUK8SraXFzAvXrygRYsWfPPNN/To0QNtbW0AIiIiGDp0KDExMXh7e+Pp6UlQUBCtWrXizp077N69m2+++SZHWfJQFOLDPXjwAH9/f+bMmcOMGTOYPHkyAPv27cPOzo5ixYpJGxPiA6knBPXx8eHSpUsYGxtTv359OnfuzOzZs9m7dy916tRh0qRJJCQkMGTIEHr16sXUqVMBSd4kxD9169YtdHV1+fHHHwkNDcXBwYF58+aho6Pzxnuk3QnxetIqCpgiRYpgamrK2LFjCQ0N5cWLFwBUrlyZrKwsunTpgpeXF1u2bKFVq1YAxMbGEhAQkGM7HiDBgBD/QPHixRk4cCCzZ8/Gy8tLWXVr06YNxYoVQ6VSSRsT4gNlB9xOTk6sXr2aQoUK8ejRI7p27cqmTZtwc3OjZ8+eHDlyhGrVqtGtWzfKly+vBNwgyZuE+CdUKhWlS5fG1NSUiRMn0rFjR44dO8asWbNIT08nIyODnj178ueff+a4T9qdEK9XOK8rIHIve/YwJCSEgQMHMmjQINauXUvr1q0xNTXFyckJPz8/Bg0ahKOjI1lZWSQkJDB27Fjs7OyoU6dOXv8JQnxSTE1NGTBgAACenp4ULlxYCb5l4CHEP7Nq1SquXLlCWFgYlpaWzJkzB2NjY0qUKIGRkRFubm64ubkRERGBvr4+tra2gKy0CfFPqbeh4OBgKlasyMSJE1GpVISGhtKsWTOePn2Kvr4+VatWzePaClEwSNBdgKifBPD29iYqKkqZ1e/WrRsDBgzg+vXrbNmyhRs3bmBgYEBkZCTlypVj5cqVShmy+ibEu71u4P669mNqasrAgQMBmDhxIjVr1szxflIhxIeJjY2lTp06WFpa4uXlxeLFiwkKCsLR0ZFbt25x8+ZNGjZsiIODg3KPBNxCvJ+/92vqr7OcP38+CxYs4ODBgxQpUoTJkydToUIFTp8+jZ6eHt9+++1ryxBCvErOdBdA7du3B14OLi5fvkxiYiKrVq2ie/fuXL9+nV9++YUNGzZQtWpVrK2tGT9+vHK9DEaEeDf186Tnz59HU1MTfX39tyZnun//Pr///jtt27b9WNUU4pOUPYD39vYmMTERU1NT5s2bpxybUqlUbNiwgatXrzJhwgQMDQ3zuspCFEjqfV1aWhpJSUkUK1YMgBUrVjBixAj27duntLvXjSFlbClE7kjQXcAsWLAAX19ffv/9d/T09NDU1GTQoEHs2rWLdevW0blzZwoXfnUDgzwUhcgd9bbSv39/oqKiePHiBU+ePGHhwoX06dPnnTP60t6EyD31gb+64OBg+vTpQ6FChfjpp5+UCa3bt2/TsWNHunTpwowZMz52dYX4JKj3UyNHjiQ2NpYzZ87wzTff0LZtW/T19Xn8+DGNGzfO45oK8WmQ7eUFzJMnT6hRowYlS5ZUBiqbNm2iU6dOuLm5oaGhQZs2bdDT08txnwQAQuSOesB96tQptm3bhqWlJW3btmXatGk4OjpiZmaWqzKEEG+nUqmUgHvBggWkpKQo57WdnZ25evUq06ZN4+LFi6Snp6Otrc24ceOoVKmSEnDL1lYh3l92P+Xs7ExMTAweHh5oaWnRo0cPrl+/jq+vr+wiEeJfJEF3AZE9qMjKyiImJgZ4md01NTWVokWL0rt3b3bt2kX37t2JjIykXr16eVxjIQqu+/fvc/PmTQICAqhWrRrfffcdsbGxbNiwATMzM6XdyWBfiA+nvtLWsWNHLly4gJmZGffu3WPnzp388ssvTJkyhZSUFFatWsXdu3epVasW//vf/1i9evUrZQgh3s+RI0eIjo5m7969lClThg0bNqCpqcnAgQMxNDQkIyPjtbsnhRDvT3qqfCozMzPH79kD+xEjRqCpqYmLiwsARYsWBcDY2BgvLy9WrFghAbcQ7+nvp2wSExM5deoUFhYW+Pr6Mn/+fAICAmjTpg2JiYlMmzaNGzduSMAtxD+QHSzfuXMHXV1dTp8+zf79+1m7di13796lfv36AMyZM4c9e/Zw9uxZtmzZIgG3EP+S9PR0TExMKFOmDPPmzcPV1ZVt27bRvHlz7t69y549e0hJScnragrxSZDeKh9SP9+2fPlyxo8fj4+PDwcPHqR06dLMmTOHM2fO0K5dO06ePElYWBhubm4kJSUxdOhQ4OVgRAjxbpmZmUrw/PTpUwCqVKlChw4d+Prrr5k+fTo7duygdevWwMvzpKdPn+avv/7KszoL8amYNGkS9vb2pKamoqOjg4GBAQ4ODqxfv57ExETs7e0BsLa2ply5cpibmwM5MywLId7tdSmctLW1uXLlChMnTsTHx4fAwEClr4uMjGT16tXExcV97KoK8UmSPSP5UHbA3blzZy5cuECVKlU4ePAgycnJDB8+nPHjx1O8eHFmzpxJ+/btMTQ0pF69esqrG0DOlAqRG+oTXCNHjqRUqVL069ePMmXKYGNjw/fff4+LiwtNmjQB4MaNG/Tt25fatWvTsmXLvKy6EAXS37MlV69enWLFinHp0iVl55ampiYNGjRg/fr1DBgwgAoVKhAbG5ujHNllIkTuqbe7x48fY2xsDEDTpk1xdHTkhx9+YNmyZUqywvj4eL799lsaNmxI+fLl86raQnxSJOjOR9TPh/r5+REdHc3Bgwextrbm9u3bBAYG4uPjg66uLsOHD6d169ZcvHgRbW1t5aEo2+2EyL3sQYiTkxOXL19m/vz5GBgYAODu7k5cXBwHDx6kdu3alC1blr/++ouKFSuyfv16QBI4CfG+1F/FV7NmTbp27Yqenh4jRozAycmJnTt3Ai8njhs0aMDKlSvZs2dPXlZZiAItKytLaXejR4/mzJkzlCxZkn79+tGpUyfGjRvH/fv3mT17Ng8ePODZs2fKGe+lS5cqZUhfJ8Q/I68My0Ove4hlJ62YPHkykZGRHD16VPkuPj6e+fPnc/HiRYKCgjA2Ns5RhjwUhXh/8+fPZ+3atZw6dUqZ/b958ya6uroUL16cw4cPc/jwYbS0tChXrhz9+vUDZIJLiA8VEBBA//792b17N+3atSMlJYWff/6Z8ePHU69ePbZt26ZcK32cEB9OvZ8aO3Yse/bsYcSIEQQHBwPQrVs3xo0bx7Vr11i5ciVhYWFUqVIFGxsb3N3dXylDCPHhZKU7D2UPHmbOnMnFixfZunWrkiXS2tqagwcPcv36daytrQEwNzfHwcGBFStWkJiYiLGxcY4BiAxGhHh/T58+xcHBAWNjY3799Vf279/PsmXLKF++PK1bt2bu3Lk0b948xz0yCBHiw3Xr1o0TJ07g7OxMcHAw7dq1o3379sDLM97du3dn69atANLHCfGe1N92k91PZR9R3L59OzVr1lSOKgYHB6NSqRg3bhwLFixg5syZ6OvrK2VJXyfEv0daUh47f/488+bNY/v27fTs2VP5vHLlysTHxxMQEEBCQoLyuZmZGdWqVZOHoBAfQH1jj/rPO3bswNnZma+//prbt2+zaNEimjVrRlhYGI8ePXqlHGl/QuTO35N6qlQqdHR0WLRoEX379qVr167s2bMHHR0d2rdvz3fffceuXbuUba1CiNxLTk5m5MiRxMfHK5NUS5YsYdiwYfz666+YmpoCL9984+npSd26ddm5cycLFiwgPT09R8AtyQqF+HfJSnceMzY2plGjRpibm5OQkECnTp0ICQmhefPmTJ48mZkzZ3Lv3j3q1atHmTJlGDFiBLVq1aJcuXJ5XXUhChT1RDIqlUo55+bp6YmWlhZxcXEsWbKEGjVqYGFhQVhYGBEREaSlpeVxzYUouLIH7aNHj2bQoEHUrl2brKwsihYtyg8//EBWVhbdunUjJCQER0dH2rZty6+//iqvvhTiPWSvbu/evRtTU1Mlyz9Ap06dOHbsGAcOHCA4OBg3NzcAjIyM8PLyYtq0aaxdu5aaNWvy1VdfKffJzhIh/l1ypjuPqJ9N8/LywtfXl6lTp7Ju3TqsrKz46aefAFi9ejVbt24lMjKSChUqUKVKFQIDA18pQwjxZuoBt4eHB1FRURgZGWFnZ8fw4cOBl5mUtbW1UalUxMfH06ZNG+zt7fHz88vLqgtRYLypT3r48CFOTk5ER0cTHh5OtWrVlG2r9+/fp2nTpty7d49Vq1bRuXNn5T7Z2irEu6m3u4sXL1KpUiUKFSrE8uXLadq0KTY2NsTFxTFq1Cju3bvH4MGDGTBggHL/48ePCQ0NVfKVCCH+GxJ0fyRvC5Dv3bvHmDFjcHJyQqVS4eHhQeXKlZXA+8GDB6SmppKenq6scMtgRIjcUW97Xbp04fLly3To0IH09HQ2bNjA6NGjmTlzJgB37txh6dKlHDhwACsrK0JCQl4pQwjxdgcOHMDR0RGAcePG0b9/f0qUKMHIkSP59ddfOXr0KNWrVwdeTnb16dOHy5cvU6dOHdauXZuXVReiQFHvm4YMGUJkZCTHjh0jKiqKYcOGUaNGDb799lsqVqzI7du3GT16NImJiQwePJj+/fu/tTwhxL9LoraPJPshNmfOHFq2bMnx48e5du0aAKampmhpaRESEkKvXr2YPXs2V65coWvXrgAUL14cKysrJeCWczZC5F5225s7dy43b95k//79eHl5YWxsTHJyMvPnz2fKlCkAWFpaYmxsTMeOHZWAW6VSySBEiFwKDw9n7NixTJ48mbZt27J582bKli2LhYUFvr6+NGzYkKZNm3L+/HkyMjJ49uwZSUlJbNq0SQJuId6DeoDs5ubGjh07WL9+PQYGBjRq1Ijx48dz584dZs6cyeXLl7GysmLp0qWUKFGCtWvXsnz58lfKlL5OiP+OnOn+iH777Tc8PT1JT0/HxMSEhw8f0qlTJ4YPH873339P69at2bt3L05OTmRmZjJ//nyaNWvGkSNHcpQjD0UhXu9Nr+EDSE9PZ9iwYVhYWPDDDz+wePFi/P39iY6OxsPDAz09PWbOnKkE4CA7SoR4Xw4ODvTu3ZvvvvsOHR0dTp8+jbGxMSqVCgsLC1asWIGrqyt169alWbNmREdHU7duXapVqwbISpsQuZXdTsaNG0dgYCCHDh3C1tZW+X7gwIHo6emxfPly3N3d8fDwoFKlSixevJg+ffq8NkmoEOK/I9vLP6KEhAQ2b96Mr68vNWvW5Ouvv8bV1ZWKFStSokQJUlNTqVatGjNmzCAlJYXAwECuXr2Kp6dnXlddiHxPfbB+9epVUlJSlIE8wPPnz0lOTiYuLg5nZ2c8PDxwdnbmwIEDdO3alaSkJDZs2ICLi8sr5Qkh3i07d8KKFSv47rvvMDExoWXLlsyaNQtdXd0cbWrp0qXcuXMHY2NjZaJL2pwQ72fnzp107dqVVatWMXjwYODlRLOdnR39+vXD1dWVNWvWsGHDBiwtLZk9ezaVKlXi4cOHFCtWLI9rL8TnRYLuj+z+/fts3LgRDw8PPD09GTp0KLt37+bHH39k//792NnZER4eTpEiRUhPT0dLSwuQwYgQb6PePjw8PAgODubevXuULFmS8PBw5TUpAJs3b2bu3Ln89ttv6OrqcvToUQICAhg0aBANGzbMqz9BiE/GrVu30NXV5ccffyQ0NBQHBwfmzZuHjo7OG++RXSVCvL+jR4+yZMkSbt26xapVq7C1taVu3boUL16coKAgjI2NAVi3bh3+/v4UKVKEDRs2KNnNZWwpxMcj28s/shIlStC3b18yMzOZNGkST58+ZfLkyXTq1Il9+/ZhZ2dHkSJFyMrKUgJukC3lQryJ+qDB1dWVwMBAVqxYgaGhIePHj2fs2LFs3LhRud7c3Jw7d+6wYsUKmjVrxpgxY2jTpo0ScMvgX4gPp1KpKF26NAATJ04EYNeuXcyaNYt58+ahoaGBi4sLs2bNomrVqsp90uaEeH9NmzZFS0sLHx8f+vfvz7Nnz6hVqxb+/v4YGRkp/dnAgQNJSkri6dOnOV4nJmNLIT4eWenOI4mJifj7+/Ptt98yZswYPDw8lO9k0C/E+5syZQp+fn6cPHmSSpUqATBjxgxevHiBo6MjFhYWlCpVCgMDA9zd3fnxxx8xNTXF1taWHTt2ADLrL8Q/od53BQcHU7FiRWxsbFiwYAGhoaEULVqUp0+foq+vz/Hjx/O4tkIUbOr9VUREBEuWLGH//v0EBQXRpk0bsof3r0u+K32dEB+fBN15KDvw9vT0ZOzYsbi7u+d1lYQokA4cOEDHjh0ZPHgwy5YtUz6vUKECKpWK1NRUnjx5Qt++ffn+++/R1NQkISGBe/fuYWdnB8hklxDv4++DdvXf58+fz4IFCzh48CB16tThxYsXbNu2jdOnT6Onp8e333772jKEEO9HvQ0dO3aMxYsXc/nyZZYuXUqjRo2U76WtCZH3JOj+D7xu8P6mB97Dhw9Zt24dEydOZMeOHXTu3Pkj1VKIT0dcXBxz587l/PnzdO/enTFjxmBvb4+BgQHLli2jUqVKzJo1i/nz57N3715atGiR434ZkAiRe9kJ0+Dle7aTkpKUpEwrVqxgxIgR7Nu3j1atWr1xMksmuYR4N/WgOSMjI8exw79fA/+34n3lyhUWL15Mo0aNPnaVhRBvIEH3v0x9MHL+/Hk0NTXR19enbNmyb7zn/v37/P7777Rt2/ZjVVOIT058fDzz5s3j1KlTXL9+nTp16hASEoKmpiaFChUiKSmJsmXL4unpyTfffJPX1RWiQFIPlkeOHElsbCxnzpzhm2++oW3btujr6/P48WMaN26cxzUV4tNx//59SpQoAUBoaCiNGjXCxMRE+f51K94REREcPXpUOW4lhMhbkkjtX6RSqZSAu3///kRFRfHixQuePHnCwoUL6dOnz2tX00qUKKEE3DL7L8SHMTc3Z/r06Xh5eZGQkED9+vVzrApcv34dU1NTJcmTEOL9ZfdPzs7OxMTE4OHhgZaWFj169OD69ev4+vpiaGiYx7UU4tOxadMm5s+fT1hYGAMGDCApKYlWrVrluEZ9C3nDhg158eIFjRs3loBbiHxEgu5/UfZgpH///pw6dYpt27ZhaWlJ27ZtmTZtGo6OjpiZmeWqDCHE+zM3N2fatGmoVCr27t2LtrY2U6dOJTU1ld69e2Nra0u7du3yuppCFGhHjhwhOjqavXv3UqZMGTZs2ICmpiYDBw7E0NCQjIwMCheW4YUQ/wYjIyMsLS2xtbXFyMiIy5cvv/Y69cC7efPmNG/eHJDFHCHyC2mF/7L79+9z8+ZNAgICqFatGn5+fsTGxuLn54eZmRmpqakAyK5+If4bZmZmTJ8+nXr16rF7925mzZpF3bp1KVWqFMHBwcDLQYgQ4sOkp6djYmJCmTJlmDdvHq6urmzbto3mzZtz9+5d9uzZQ0pKSl5XU4gCLbufat++PXp6ety/fx8TExNu3bqV43t1r9tNKQG3EPmDtMR/6O/Bc2JiIqdOncLCwgJfX1/mz59PQEAAbdq0ITExkWnTpnHjxg1J2iTEfyh7q3mdOnVYsGAB5cuXJywsDJBZfyHex+smiLW1tbly5QoTJ07Ex8eHwMBAWrduDUBkZCSrV68mLi7uY1dViE9GZmam0k8lJyczcOBAtmzZQvHixenZsyeXL1+mUKFCpKen53FNhRC5JYnU/gH1pGlPnz5VzrH17NmTp0+fcvz4cX766SeaNm0KwLlz5xgzZgwzZ86kZcuWeVVtIT4bcXFxHDp0CBcXF0ACbiHeh3of9/jxY4yNjZXv+vbty+bNm1m2bBnDhg0DXiYzbNeuHQ0bNmTp0qV5UWUhCjz1dufq6oqNjQ0uLi7o6uqye/duli5dSlJSEuvXr+eLL74gKSmJkJAQOnToIPkUhMjHZPT5gdQfiiNHjmTJkiXcvHkTABsbG44dO0afPn1o0qQJADdu3KBv375YW1tLwC3ER1KqVCkJuIX4AFlZWUofN3r0aNq3b4+TkxM7d+5EpVIxbtw4WrZsyezZs5k7dy6TJ0+mVatWWFhYKAG3zOkL8X7U252TkxMHDhzAwMCAFy9eAPDVV18xduxY9PX16dKlC1u3buXLL78kNDRUAm4h8jlZ6f6HnJycuHz5MvPnz8fBwUF5hcPw4cM5fPgwOjo6lC1blr/++ouKFSsSEhICyHuBhRBC5E/qE1Rjx45lz549jBgxQsmJ0K1bN8aNG8e1a9dYuXIlYWFhVKlSBRsbG9zd3V8pQwjxftzd3dmyZQsnT55UxpX3799HX18fHR0dIiMj8fb2JioqCgcHB9atWwfI2FKI/EyC7n9g/vz5rF27llOnTinb7m7evImuri7Fixfn8OHDHD58GC0tLcqVK0e/fv0AGYwIIYTIX7IH6+qD9oMHDxIUFMSYMWOoWbMmqampjB8/nt9//10JvDU1NUlKSkJfX18pS/o4IT5cVlYW33zzDSVKlGDevHkcO3aMo0ePsmTJEqpWrUrv3r0ZOnQoAHfv3sXCwgKQdidEfidB9z8wbdo04uPjWbt2Lb/++iv79+9n2bJllC9fntatWzN37txXHoDyUBRCCJGfJCcnM3HiRNzd3TE3NwdgyZIlLFmyBE1NTQ4fPoylpSUAT548Ydq0aZw9e5avvvqKiRMnoqWlpZQlK21CvB/1cWF2+xkwYAAhISH07t2biIgIqlatSpMmTdi/fz8PHz4kLCyMokWLKmVIuxMi/5PoL5fU5ybUf96xYwfOzs58/fXX3L59m0WLFtGsWTPCwsJ49OjRK+VIwC2EECI/yO7Ldu/ejampqRJwA3Tq1Ik6depw//59ZVs5vHxnsJeXF19++SVr165V3gqQTQb+QuSeepbyhw8fcv36dQD8/f0ZMGAA165dY9asWcyfP5/hw4fTu3dvNDU1ldfPZpN2J0T+JyvduaCeNE2lUuVIdDFr1izi4uLo2rUrNWrUwMLCgrCwMGbNmsXOnTspVapUXlZdCCGEeIX6ytjFixepVKkShQoVYvny5TRt2hQbGxvi4uIYNWoU9+7dY/DgwQwYMEC5//Hjx4SGhirHpoQQ70d9hXvo0KGcP3+eGzduULt2baZNm4aDg0OOa+Lj43F0dKRJkyYsW7YsL6suhPgAEnS/g3rA7eHhQVRUFEZGRtjZ2TF8+HAA0tLS0NbWRqVSER8fT5s2bbC3t8fPzy8vqy6EEEK8Qj3gHjJkCJGRkRw7doyoqCiGDRtGjRo1+Pbbb6lYsSK3b99m9OjRJCYmMnjwYPr37//W8oQQ76d3795cuHCBhQsXUrNmTcqVK4ejoyP+/v6YmpqSkJDAunXrCA4OxsrKitDQUEDanRAFjex1fgv1Fe0uXboQHBxM5cqVMTU1ZdasWXz77bcAaGtrc+fOHaZNm0aHDh0oV66cEnDLnIYQQoj8Qn2g7ubmxo4dO1i/fj0GBgY0atSI8ePHc+fOHWbOnMnly5exsrJi6dKllChRgrVr17J8+fJXypSBvxAf5vz588TExLBp0yZat27Nzz//TNGiRRk9ejSmpqbKSndKSgpt2rRRAm6VSiXtTogCRoLut8h+oM2dO5ebN2+yf/9+vLy8MDY2Jjk5mfnz5zNlyhQALC0tMTY2pmPHjsprweShKIQQIj/J7pPGjRtHYGAghw4donbt2sr3AwcOZNSoUcTFxeHu7q4E3osXLwZ4ba4SIcSbvW7xJTMzE3iZmDA9PZ3q1avj7e3NpEmTCA4OplWrVty+fZsNGzZgYmLCzJkz8fT0BCQhrxAFVeG8rkB+8bptOhkZGQCkp6czbNgwLCws+OGHH1i8eDH+/v5ER0fj4eGBnp4eM2fOVAJwkIeiEEKI/Gnnzp34+PiwatUqbG1tgZf9nZ2dHf369cPV1ZWkpCQ2bNjA7NmzmT17NpUqVWLnzp0UK1YsbysvRAGiPra8evUqKSkpVKtWTdlFWalSJTIzM+nevTuHDx9my5YtODo6AhAbG8vq1auxtbVV2mlWVpaMLYUooKTl8upDMTo6GoDChQtTuHBhJk+eTKdOnTh//jwrV67E19eX7t2707BhQ/T19Zk1axYbN27MUZ48FIUQQuRHJiYmdO7cmRUrVhAVFQWAvb09JUuWVJKlZSdOu3PnDqNGjSI+Pl4JuOXYlBDvpj629PDwoGPHjjRr1ozq1auTmJgIgKGhIZ07d+bw4cMMGTIER0dHsrKyiI+Px9XVlerVqysBN8hRDiEKss9+pfvvD8Xg4GDu3btHyZIlCQ8Px9TUFF1dXXR1dTl48CCampp89dVXAGhpadG9e3cGDRpEw4YNlTLloSiEECK/atq0KVpaWvj4+NC/f3+ePXtGrVq18Pf3x8jISNmpNXDgQJKSknj69GmO14lJHyfE26mPLV1dXQkMDGTFihUYGhoyfvx4xo4dy8aNG9HR0aFPnz5cv36dTZs2cfPmTfT09Dhx4gTlypVjxYoVr5QnhCiYPuvs5e96KNasWTPHCvaRI0dwcnLC3d2dZs2a0b9/f9q0acN3330HyJZyIYQQ+Zt6vxcREcGSJUvYv38/QUFBtGnTRlnFft2OLRn4C/F+pkyZgp+fHydPnqRSpUoAzJgxgxcvXtCiRQusrKyoXLkyiYmJ7Nmzh4CAAGxsbLC2tmbixImAjC2F+FR81kF3trc9FB0dHbGwsKBUqVIYGBjg7u7Ojz/+iKmpKba2tuzYsQOQwYgQQoiCQb2/OnbsGIsXL+by5cssXbqURo0aKd9LvybEhztw4AAdO3Zk8ODBOd6rXaFCBVQqFampqTx+/Jh+/frh5eX12nwJEnAL8en47IPu3DwUnzx5Qt++ffn+++/R1NQkISGBe/fuYWdnB8hDUQghRP6hHjRnZGSgpaX1xmvg/1a8r1y5wuLFi2nUqNHHrrIQn5y4uDjmzp3L+fPn6d69O2PGjMHe3h4DAwOWLVtGpUqVmDVrFgsWLGDPnj20aNFCxpNCfMI++5ZdvXp1Bg0axLlz51iyZAnwMqFM+fLl2bdvH3fv3mXChAmsW7eOyMhIdHR0sLa2VgJuSZomhBAiP8kOph88eKAE3KGhoTle95UdlAM4ODgwduxYvvjiC3r06MHly5c/fqWF+MSUKlWKmTNnYmtrS2BgIGZmZhQrVow9e/ZQsWJFNDQ0mDRpEvr6+ly5cgVAxpNCfMI++9ad24eigYGB8lBUJ1vvhBBC5DebNm2iRYsWxMXF0bp1axYsWICOjk6Oa9QD74YNGzJs2DCmTZumHLMSQvwz5ubmTJ8+HXt7e3R0dKhfvz5aWlpKcH39+nVMTU0pXbp0HtdUCPFf++yzl8P/PRS9vLxISEhQHorZ5KEohBCiIDEyMsLS0hJbW1uMjIzeuHqtfna7efPmNG/eHJBjU0L8W8zNzZk2bRoqlYq9e/eira3N1KlTSU1NpXfv3tja2tKuXbu8rqYQ4j8mPer/l/1Q7NChA3v37sXLywtAHopCCCEKDJVKBUD79u3R09Pj/v37mJiYcOvWrRzfq3vdji0JuIX495iZmTF9+nTq1avH7t27mTVrFnXr1qVUqVIEBwcDr2+bQohPx2efSO3v4uPjmTdvHmfOnKFly5Zs374dS0tLwsLCAJn9F0IIkT9lZmaiqakJQHJyMkePHuX58+f4+/vz+PFj1q1bR6VKlUhPT39tcjUhxH8rPj4eT09P/Pz8aNWqFaGhoYCMLYX4HEjQ/RryUBRCCFGQqAfcrq6u2NjY4OLigq6uLrt372bp0qUkJSWxfv16vvjiC5KSkggJCaFDhw4YGhrmce2F+HzExcVx6NAhXFxcABlbCvG5kKD7DeShKIQQoiBQf/2Xk5MTly9fZsaMGbRp0wYTExMA9uzZw+LFi4mLi2PmzJlMnz6d2rVrs2XLlrysuhCfNRlbCvH5kKA7F+ShKIQQIr9zd3dny5YtnDx5Ugm279+/j76+Pjo6OkRGRuLt7U1UVBQODg6sW7cOyBm0CyGEEOLfJ9nLc0ECbiGEEPlZVlYW8fHxdOvWDRMTE44dO8bRo0dZsmQJVatWpXfv3gwdOpStW7dy9+5dLCwsAJlUFkIIIT4GWekWQgghChj1YDl7pXrAgAGEhITQu3dvIiIiqFq1Kk2aNGH//v08fPiQsLAwihYtqpQhK9xCCCHExyEr3UIIIUQBop407eHDhzx58oRy5crh7++Pm5sbly5dYtasWXz55ZdYW1tTvHhxfvzxR1JTU3ME3RJwCyGEEB+HrHQLIYQQBYT6CvfQoUM5f/48N27coHbt2kybNg0HB4cc18THx+Po6EiTJk1YtmxZXlZdCCGE+GzJSrcQQghRQGQH07179+bChQssXLiQmjVrUq5cOTQ1NalatSqmpqYkJCSwbt06goODKVeunBJwy5ZyIYQQ4uOT7ClCCCFEAXL+/HliYmLYtGkTrVu35ueff6Zo0aKMHj0aU1NTZaU7JSWFNm3aEBoaCrxcJZeAWwghhPj4ZKVbCCGEyGdetyKdfZb7yZMnpKenU716dby9vfH09CQ4OJhWrVpx+/ZtDh48iIuLCzNnzqRw4ZfdvGQpF0IIIfKO9MBCCCFEPqIecF+9epXo6GgAJXlapUqVyMzMpHv37nh5ebFlyxZatWoFQGxsLKtXr+bChQtKwJ2VlSUBtxBCCJGHpBcWQggh8gn1gNvDw4OOHTvSrFkzqlevTmJiIgCGhoZ07tyZw4cPM2TIEBwdHZX3dLu6ulK9enVsbW2VMmVLuRBCCJG3JHu5EEIIkQ+oB9yurq4EBgayYsUKDA0NGT9+PDVr1mTjxo0A/PHHH8ybN4/jx4/j4OCAnp4eJ06coFy5cuzateuV8oQQQgiRdyToFkIIIfKRKVOm4Ofnx8mTJ6lUqRIAM2bM4MWLF7Ro0QIrKysqV65MYmIie/bsISAgABsbG6ytrZk4cSIgZ7iFEEKI/ESCbiGEECKfOHDgAB07dmTw4ME53qtdoUIFVCoVqampPH78mH79+uHl5UWxYsVeKUMCbiGEECJ/kV5ZCCGEyCeqV6/OoEGDOHfuHEuWLAHA3t6e8uXLs2/fPu7evcvEiRPx9/fn7NmzwMsgW50E3EIIIUT+Ij2zEEIIkU+UKlWKmTNnYmtrS2BgIGZmZhQrVow9e/ZQsWJFNDQ0mDRpEvr6+ly5cgWQIFsIIYTI76SnFkIIIfIRc3Nzpk+fjr29PTo6OtSvXx8tLS0luL5+/TqmpqaULl06j2sqhBBCiNwonNcVEEIIIURO5ubmTJs2DZVKxd69e9HW1mbq1KmkpqbSu3dvbG1tadeuXV5XUwghhBC5IInUhBBCiHwqPj6eefPmcebMGVq2bMn27duxtLQkLCwMkKRpQgghREEgQbcQQgiRj8XHx+Pp6Ymfnx+tWrUiNDQUkIBbCCGEKCgk6BZCCCHyubi4OA4dOoSLiwsgAbcQQghRkEjQLYQQQhQgEnALIYQQBYsE3UIIIYQQQgghxH9EpsqFEEIIIYQQQoj/iATdQgghhBBCCCHEf0SCbiGEEEIIIYQQ4j8iQbcQQgghhBBCCPEfkaBbCCGEEEIIIYT4j0jQLYQQQgghhBBC/Eck6BZCCCGEEEIIIf4jEnQLIYQQQgghhBD/EQm6hRBCCCGEEEKI/4gE3UIIIYQQQgghxH/k/wFVT5yMtwEDjQAAAABJRU5ErkJggg==",
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",
       "text/plain": [
        "<Figure size 1000x600 with 1 Axes>"
       ]
@@ -1611,7 +1625,7 @@
      "output_type": "stream",
      "text": [
       "Best testset result: \n",
-      "openai/meta-llama/Llama-3.2-1B-Instruct:isaac:fijif with score: 49.6\n"
+      "openai/meta-llama/Llama-3.2-1B-Instruct:isaac:qmmwc with score: 50.4\n"
      ]
     }
    ],
@@ -1625,7 +1639,7 @@
    "source": [
     "We see that on the true test set, the prompt optimized pipeline using the finetuned model outperforms the prompt optimized pipeline using the non-finetuned model by approximately 15-20% in overall dataset accuracy.\n",
     "\n",
-    "There is a significant drop between the \"Best Fine-tuned (BFRS)\" model's performance on the synthetic devset and on the testset. We believe this to be a gap in the generalized ability of the fine-tuned model: the model has seen the entirety of the synthetic devset before - the best checkpoint is chosen to be the one that minimizes the evaluation loss - but the testset is new and testing its capabilities on unseen data. However, with prompt optimization on top of fine-tuning, we clearly see the best test set performance.\n",
+    "There is a drop between the \"Best Fine-tuned (BFRS)\" model's performance on the synthetic devset and on the testset. We believe this to be a gap in the generalized ability of the fine-tuned model: the model has seen the entirety of the synthetic devset before - the best checkpoint is chosen to be the one that minimizes the evaluation loss - but the testset is new and testing its capabilities on unseen data. However, with prompt optimization on top of fine-tuning, we clearly see the best test set performance.\n",
     "\n",
     "This is one small example of where DSPy optimization can be useful in terms of increasing task specific performance and helping you to get the most out of your LLM.\n",
     "\n",
@@ -1654,7 +1668,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "## Serving a DSPy Pipeline as a Multi-App Serve Application\n",
+    "## Serving a DSPy Pipeline using Ray Serve\n",
     "\n",
     "We can break down our program into two distinct parts: 1) Fine-tuned LLM served behind an OpenAI compatible endpoint and 2) The DSPy program (our business logic tying all components together)\n",
     "\n",
@@ -1703,7 +1717,8 @@
     {
      "data": {
       "text/html": [
-       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"color: #ff4689; text-decoration-color: #ff4689; background-color: #272822\">import</span><span style=\"color: #f8f8f2; text-decoration-color: #f8f8f2; background-color: #272822\"> dspy</span><span style=\"background-color: #272822\">                                                                                                        </span>\n",
+       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"color: #e6db74; text-decoration-color: #e6db74; background-color: #272822\">\"\"\"Ray Serve deployment code for our DSPy application\"\"\"</span><span style=\"background-color: #272822\">                                                           </span>\n",
+       "<span style=\"color: #ff4689; text-decoration-color: #ff4689; background-color: #272822\">import</span><span style=\"color: #f8f8f2; text-decoration-color: #f8f8f2; background-color: #272822\"> dspy</span><span style=\"background-color: #272822\">                                                                                                        </span>\n",
        "<span style=\"color: #ff4689; text-decoration-color: #ff4689; background-color: #272822\">from</span><span style=\"color: #f8f8f2; text-decoration-color: #f8f8f2; background-color: #272822\"> ray </span><span style=\"color: #ff4689; text-decoration-color: #ff4689; background-color: #272822\">import</span><span style=\"color: #f8f8f2; text-decoration-color: #f8f8f2; background-color: #272822\"> serve</span><span style=\"background-color: #272822\">                                                                                              </span>\n",
        "<span style=\"color: #ff4689; text-decoration-color: #ff4689; background-color: #272822\">from</span><span style=\"color: #f8f8f2; text-decoration-color: #f8f8f2; background-color: #272822\"> fastapi </span><span style=\"color: #ff4689; text-decoration-color: #ff4689; background-color: #272822\">import</span><span style=\"color: #f8f8f2; text-decoration-color: #f8f8f2; background-color: #272822\"> FastAPI</span><span style=\"background-color: #272822\">                                                                                        </span>\n",
        "<span style=\"color: #ff4689; text-decoration-color: #ff4689; background-color: #272822\">from</span><span style=\"color: #f8f8f2; text-decoration-color: #f8f8f2; background-color: #272822\"> src </span><span style=\"color: #ff4689; text-decoration-color: #ff4689; background-color: #272822\">import</span><span style=\"color: #f8f8f2; text-decoration-color: #f8f8f2; background-color: #272822\"> MODEL_PARAMETERS</span><span style=\"background-color: #272822\">                                                                                   </span>\n",
@@ -1731,15 +1746,14 @@
        "<span style=\"color: #f8f8f2; text-decoration-color: #f8f8f2; background-color: #272822\">        self</span><span style=\"color: #ff4689; text-decoration-color: #ff4689; background-color: #272822\">.</span><span style=\"color: #f8f8f2; text-decoration-color: #f8f8f2; background-color: #272822\">valid_labels </span><span style=\"color: #ff4689; text-decoration-color: #ff4689; background-color: #272822\">=</span><span style=\"color: #f8f8f2; text-decoration-color: #f8f8f2; background-color: #272822\"> set(labels_in_use)</span><span style=\"background-color: #272822\">                                                                     </span>\n",
        "<span style=\"background-color: #272822\">                                                                                                                   </span>\n",
        "<span style=\"color: #f8f8f2; text-decoration-color: #f8f8f2; background-color: #272822\">    </span><span style=\"color: #66d9ef; text-decoration-color: #66d9ef; background-color: #272822\">def</span><span style=\"color: #f8f8f2; text-decoration-color: #f8f8f2; background-color: #272822\"> </span><span style=\"color: #a6e22e; text-decoration-color: #a6e22e; background-color: #272822\">forward</span><span style=\"color: #f8f8f2; text-decoration-color: #f8f8f2; background-color: #272822\">(self, text):</span><span style=\"background-color: #272822\">                                                                                       </span>\n",
-       "<span style=\"color: #f8f8f2; text-decoration-color: #f8f8f2; background-color: #272822\">        </span><span style=\"color: #66d9ef; text-decoration-color: #66d9ef; background-color: #272822\">for</span><span style=\"color: #f8f8f2; text-decoration-color: #f8f8f2; background-color: #272822\"> i </span><span style=\"color: #ff4689; text-decoration-color: #ff4689; background-color: #272822\">in</span><span style=\"color: #f8f8f2; text-decoration-color: #f8f8f2; background-color: #272822\"> range(</span><span style=\"color: #ae81ff; text-decoration-color: #ae81ff; background-color: #272822\">5</span><span style=\"color: #f8f8f2; text-decoration-color: #f8f8f2; background-color: #272822\">):</span><span style=\"background-color: #272822\">                                                                                         </span>\n",
-       "<span style=\"color: #f8f8f2; text-decoration-color: #f8f8f2; background-color: #272822\">            </span><span style=\"color: #66d9ef; text-decoration-color: #66d9ef; background-color: #272822\">try</span><span style=\"color: #f8f8f2; text-decoration-color: #f8f8f2; background-color: #272822\">:</span><span style=\"background-color: #272822\">                                                                                                   </span>\n",
-       "<span style=\"color: #f8f8f2; text-decoration-color: #f8f8f2; background-color: #272822\">                prediction </span><span style=\"color: #ff4689; text-decoration-color: #ff4689; background-color: #272822\">=</span><span style=\"color: #f8f8f2; text-decoration-color: #f8f8f2; background-color: #272822\"> self</span><span style=\"color: #ff4689; text-decoration-color: #ff4689; background-color: #272822\">.</span><span style=\"color: #f8f8f2; text-decoration-color: #f8f8f2; background-color: #272822\">intent_classifier(intent</span><span style=\"color: #ff4689; text-decoration-color: #ff4689; background-color: #272822\">=</span><span style=\"color: #f8f8f2; text-decoration-color: #f8f8f2; background-color: #272822\">text, temperature</span><span style=\"color: #ff4689; text-decoration-color: #ff4689; background-color: #272822\">=</span><span style=\"color: #ae81ff; text-decoration-color: #ae81ff; background-color: #272822\">0.1</span><span style=\"color: #ff4689; text-decoration-color: #ff4689; background-color: #272822\">*</span><span style=\"color: #f8f8f2; text-decoration-color: #f8f8f2; background-color: #272822\">i)</span><span style=\"background-color: #272822\">                                </span>\n",
-       "<span style=\"color: #f8f8f2; text-decoration-color: #f8f8f2; background-color: #272822\">                sanitized_prediction </span><span style=\"color: #ff4689; text-decoration-color: #ff4689; background-color: #272822\">=</span><span style=\"color: #f8f8f2; text-decoration-color: #f8f8f2; background-color: #272822\"> dspy</span><span style=\"color: #ff4689; text-decoration-color: #ff4689; background-color: #272822\">.</span><span style=\"color: #f8f8f2; text-decoration-color: #f8f8f2; background-color: #272822\">Prediction(label</span><span style=\"color: #ff4689; text-decoration-color: #ff4689; background-color: #272822\">=</span><span style=\"color: #f8f8f2; text-decoration-color: #f8f8f2; background-color: #272822\">prediction</span><span style=\"color: #ff4689; text-decoration-color: #ff4689; background-color: #272822\">.</span><span style=\"color: #f8f8f2; text-decoration-color: #f8f8f2; background-color: #272822\">label</span><span style=\"color: #ff4689; text-decoration-color: #ff4689; background-color: #272822\">.</span><span style=\"color: #f8f8f2; text-decoration-color: #f8f8f2; background-color: #272822\">lower()</span><span style=\"color: #ff4689; text-decoration-color: #ff4689; background-color: #272822\">.</span><span style=\"color: #f8f8f2; text-decoration-color: #f8f8f2; background-color: #272822\">strip()</span><span style=\"color: #ff4689; text-decoration-color: #ff4689; background-color: #272822\">.</span><span style=\"color: #f8f8f2; text-decoration-color: #f8f8f2; background-color: #272822\">replace(</span><span style=\"color: #e6db74; text-decoration-color: #e6db74; background-color: #272822\">\" \"</span><span style=\"color: #f8f8f2; text-decoration-color: #f8f8f2; background-color: #272822\">, </span><span style=\"color: #e6db74; text-decoration-color: #e6db74; background-color: #272822\">\"_\"</span><span style=\"color: #f8f8f2; text-decoration-color: #f8f8f2; background-color: #272822\">), re</span>\n",
-       "<span style=\"color: #f8f8f2; text-decoration-color: #f8f8f2; background-color: #272822\">                </span><span style=\"color: #66d9ef; text-decoration-color: #66d9ef; background-color: #272822\">if</span><span style=\"color: #f8f8f2; text-decoration-color: #f8f8f2; background-color: #272822\"> sanitized_prediction</span><span style=\"color: #ff4689; text-decoration-color: #ff4689; background-color: #272822\">.</span><span style=\"color: #f8f8f2; text-decoration-color: #f8f8f2; background-color: #272822\">label </span><span style=\"color: #ff4689; text-decoration-color: #ff4689; background-color: #272822\">in</span><span style=\"color: #f8f8f2; text-decoration-color: #f8f8f2; background-color: #272822\"> self</span><span style=\"color: #ff4689; text-decoration-color: #ff4689; background-color: #272822\">.</span><span style=\"color: #f8f8f2; text-decoration-color: #f8f8f2; background-color: #272822\">valid_labels:</span><span style=\"background-color: #272822\">                                                </span>\n",
-       "<span style=\"color: #f8f8f2; text-decoration-color: #f8f8f2; background-color: #272822\">                    </span><span style=\"color: #66d9ef; text-decoration-color: #66d9ef; background-color: #272822\">return</span><span style=\"color: #f8f8f2; text-decoration-color: #f8f8f2; background-color: #272822\"> sanitized_prediction</span><span style=\"background-color: #272822\">                                                                    </span>\n",
-       "<span style=\"color: #f8f8f2; text-decoration-color: #f8f8f2; background-color: #272822\">            </span><span style=\"color: #66d9ef; text-decoration-color: #66d9ef; background-color: #272822\">except</span><span style=\"color: #f8f8f2; text-decoration-color: #f8f8f2; background-color: #272822\"> </span><span style=\"color: #a6e22e; text-decoration-color: #a6e22e; background-color: #272822\">Exception</span><span style=\"color: #f8f8f2; text-decoration-color: #f8f8f2; background-color: #272822\"> </span><span style=\"color: #66d9ef; text-decoration-color: #66d9ef; background-color: #272822\">as</span><span style=\"color: #f8f8f2; text-decoration-color: #f8f8f2; background-color: #272822\"> e:</span><span style=\"background-color: #272822\">                                                                                 </span>\n",
-       "<span style=\"color: #f8f8f2; text-decoration-color: #f8f8f2; background-color: #272822\">                </span><span style=\"color: #66d9ef; text-decoration-color: #66d9ef; background-color: #272822\">continue</span><span style=\"background-color: #272822\">                                                                                           </span>\n",
-       "<span style=\"color: #f8f8f2; text-decoration-color: #f8f8f2; background-color: #272822\">        </span><span style=\"color: #66d9ef; text-decoration-color: #66d9ef; background-color: #272822\">return</span><span style=\"color: #f8f8f2; text-decoration-color: #f8f8f2; background-color: #272822\"> dspy</span><span style=\"color: #ff4689; text-decoration-color: #ff4689; background-color: #272822\">.</span><span style=\"color: #f8f8f2; text-decoration-color: #f8f8f2; background-color: #272822\">Prediction(label</span><span style=\"color: #ff4689; text-decoration-color: #ff4689; background-color: #272822\">=</span><span style=\"color: #e6db74; text-decoration-color: #e6db74; background-color: #272822\">\"unknown\"</span><span style=\"color: #f8f8f2; text-decoration-color: #f8f8f2; background-color: #272822\">)</span><span style=\"background-color: #272822\">                                                                    </span>\n",
+       "<span style=\"color: #f8f8f2; text-decoration-color: #f8f8f2; background-color: #272822\">        </span><span style=\"color: #66d9ef; text-decoration-color: #66d9ef; background-color: #272822\">try</span><span style=\"color: #f8f8f2; text-decoration-color: #f8f8f2; background-color: #272822\">:</span><span style=\"background-color: #272822\">                                                                                                       </span>\n",
+       "<span style=\"color: #f8f8f2; text-decoration-color: #f8f8f2; background-color: #272822\">            prediction </span><span style=\"color: #ff4689; text-decoration-color: #ff4689; background-color: #272822\">=</span><span style=\"color: #f8f8f2; text-decoration-color: #f8f8f2; background-color: #272822\"> self</span><span style=\"color: #ff4689; text-decoration-color: #ff4689; background-color: #272822\">.</span><span style=\"color: #f8f8f2; text-decoration-color: #f8f8f2; background-color: #272822\">intent_classifier(intent</span><span style=\"color: #ff4689; text-decoration-color: #ff4689; background-color: #272822\">=</span><span style=\"color: #f8f8f2; text-decoration-color: #f8f8f2; background-color: #272822\">text)</span><span style=\"background-color: #272822\">                                                       </span>\n",
+       "<span style=\"color: #f8f8f2; text-decoration-color: #f8f8f2; background-color: #272822\">            sanitized_prediction </span><span style=\"color: #ff4689; text-decoration-color: #ff4689; background-color: #272822\">=</span><span style=\"color: #f8f8f2; text-decoration-color: #f8f8f2; background-color: #272822\"> dspy</span><span style=\"color: #ff4689; text-decoration-color: #ff4689; background-color: #272822\">.</span><span style=\"color: #f8f8f2; text-decoration-color: #f8f8f2; background-color: #272822\">Prediction(label</span><span style=\"color: #ff4689; text-decoration-color: #ff4689; background-color: #272822\">=</span><span style=\"color: #f8f8f2; text-decoration-color: #f8f8f2; background-color: #272822\">prediction</span><span style=\"color: #ff4689; text-decoration-color: #ff4689; background-color: #272822\">.</span><span style=\"color: #f8f8f2; text-decoration-color: #f8f8f2; background-color: #272822\">label</span><span style=\"color: #ff4689; text-decoration-color: #ff4689; background-color: #272822\">.</span><span style=\"color: #f8f8f2; text-decoration-color: #f8f8f2; background-color: #272822\">lower()</span><span style=\"color: #ff4689; text-decoration-color: #ff4689; background-color: #272822\">.</span><span style=\"color: #f8f8f2; text-decoration-color: #f8f8f2; background-color: #272822\">strip()</span><span style=\"color: #ff4689; text-decoration-color: #ff4689; background-color: #272822\">.</span><span style=\"color: #f8f8f2; text-decoration-color: #f8f8f2; background-color: #272822\">replace(</span><span style=\"color: #e6db74; text-decoration-color: #e6db74; background-color: #272822\">\" \"</span><span style=\"color: #f8f8f2; text-decoration-color: #f8f8f2; background-color: #272822\">, </span><span style=\"color: #e6db74; text-decoration-color: #e6db74; background-color: #272822\">\"_\"</span><span style=\"color: #f8f8f2; text-decoration-color: #f8f8f2; background-color: #272822\">), reason</span>\n",
+       "<span style=\"color: #f8f8f2; text-decoration-color: #f8f8f2; background-color: #272822\">            </span><span style=\"color: #66d9ef; text-decoration-color: #66d9ef; background-color: #272822\">if</span><span style=\"color: #f8f8f2; text-decoration-color: #f8f8f2; background-color: #272822\"> sanitized_prediction</span><span style=\"color: #ff4689; text-decoration-color: #ff4689; background-color: #272822\">.</span><span style=\"color: #f8f8f2; text-decoration-color: #f8f8f2; background-color: #272822\">label </span><span style=\"color: #ff4689; text-decoration-color: #ff4689; background-color: #272822\">in</span><span style=\"color: #f8f8f2; text-decoration-color: #f8f8f2; background-color: #272822\"> self</span><span style=\"color: #ff4689; text-decoration-color: #ff4689; background-color: #272822\">.</span><span style=\"color: #f8f8f2; text-decoration-color: #f8f8f2; background-color: #272822\">valid_labels:</span><span style=\"background-color: #272822\">                                                    </span>\n",
+       "<span style=\"color: #f8f8f2; text-decoration-color: #f8f8f2; background-color: #272822\">                </span><span style=\"color: #66d9ef; text-decoration-color: #66d9ef; background-color: #272822\">return</span><span style=\"color: #f8f8f2; text-decoration-color: #f8f8f2; background-color: #272822\"> sanitized_prediction</span><span style=\"background-color: #272822\">                                                                        </span>\n",
+       "<span style=\"color: #f8f8f2; text-decoration-color: #f8f8f2; background-color: #272822\">        </span><span style=\"color: #66d9ef; text-decoration-color: #66d9ef; background-color: #272822\">except</span><span style=\"color: #f8f8f2; text-decoration-color: #f8f8f2; background-color: #272822\"> </span><span style=\"color: #a6e22e; text-decoration-color: #a6e22e; background-color: #272822\">ValueError</span><span style=\"color: #f8f8f2; text-decoration-color: #f8f8f2; background-color: #272822\"> </span><span style=\"color: #66d9ef; text-decoration-color: #66d9ef; background-color: #272822\">as</span><span style=\"color: #f8f8f2; text-decoration-color: #f8f8f2; background-color: #272822\"> e:</span><span style=\"background-color: #272822\">                                                                                    </span>\n",
+       "<span style=\"color: #f8f8f2; text-decoration-color: #f8f8f2; background-color: #272822\">            </span><span style=\"color: #959077; text-decoration-color: #959077; background-color: #272822\"># If the model is unable to make a prediction in a valid format, return \"unknown\"</span><span style=\"background-color: #272822\">                      </span>\n",
+       "<span style=\"color: #f8f8f2; text-decoration-color: #f8f8f2; background-color: #272822\">            </span><span style=\"color: #66d9ef; text-decoration-color: #66d9ef; background-color: #272822\">return</span><span style=\"color: #f8f8f2; text-decoration-color: #f8f8f2; background-color: #272822\"> dspy</span><span style=\"color: #ff4689; text-decoration-color: #ff4689; background-color: #272822\">.</span><span style=\"color: #f8f8f2; text-decoration-color: #f8f8f2; background-color: #272822\">Prediction(label</span><span style=\"color: #ff4689; text-decoration-color: #ff4689; background-color: #272822\">=</span><span style=\"color: #e6db74; text-decoration-color: #e6db74; background-color: #272822\">\"unknown\"</span><span style=\"color: #f8f8f2; text-decoration-color: #f8f8f2; background-color: #272822\">)</span><span style=\"background-color: #272822\">                                                                </span>\n",
        "<span style=\"background-color: #272822\">                                                                                                                   </span>\n",
        "<span style=\"color: #a6e22e; text-decoration-color: #a6e22e; background-color: #272822\">@serve</span><span style=\"color: #ff4689; text-decoration-color: #ff4689; background-color: #272822\">.</span><span style=\"color: #f8f8f2; text-decoration-color: #f8f8f2; background-color: #272822\">deployment(</span><span style=\"background-color: #272822\">                                                                                                 </span>\n",
        "<span style=\"color: #f8f8f2; text-decoration-color: #f8f8f2; background-color: #272822\">    ray_actor_options</span><span style=\"color: #ff4689; text-decoration-color: #ff4689; background-color: #272822\">=</span><span style=\"color: #f8f8f2; text-decoration-color: #f8f8f2; background-color: #272822\">{</span><span style=\"color: #e6db74; text-decoration-color: #e6db74; background-color: #272822\">\"num_cpus\"</span><span style=\"color: #f8f8f2; text-decoration-color: #f8f8f2; background-color: #272822\">: </span><span style=\"color: #ae81ff; text-decoration-color: #ae81ff; background-color: #272822\">0.1</span><span style=\"color: #f8f8f2; text-decoration-color: #f8f8f2; background-color: #272822\">},</span><span style=\"background-color: #272822\">                                                                           </span>\n",
@@ -1775,6 +1789,7 @@
        "</pre>\n"
       ],
       "text/plain": [
+       "\u001b[38;2;230;219;116;48;2;39;40;34m\"\"\"Ray Serve deployment code for our DSPy application\"\"\"\u001b[0m\u001b[48;2;39;40;34m                                                           \u001b[0m\n",
        "\u001b[38;2;255;70;137;48;2;39;40;34mimport\u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34m \u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34mdspy\u001b[0m\u001b[48;2;39;40;34m                                                                                                        \u001b[0m\n",
        "\u001b[38;2;255;70;137;48;2;39;40;34mfrom\u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34m \u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34mray\u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34m \u001b[0m\u001b[38;2;255;70;137;48;2;39;40;34mimport\u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34m \u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34mserve\u001b[0m\u001b[48;2;39;40;34m                                                                                              \u001b[0m\n",
        "\u001b[38;2;255;70;137;48;2;39;40;34mfrom\u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34m \u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34mfastapi\u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34m \u001b[0m\u001b[38;2;255;70;137;48;2;39;40;34mimport\u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34m \u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34mFastAPI\u001b[0m\u001b[48;2;39;40;34m                                                                                        \u001b[0m\n",
@@ -1803,15 +1818,14 @@
        "\u001b[38;2;248;248;242;48;2;39;40;34m        \u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34mself\u001b[0m\u001b[38;2;255;70;137;48;2;39;40;34m.\u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34mvalid_labels\u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34m \u001b[0m\u001b[38;2;255;70;137;48;2;39;40;34m=\u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34m \u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34mset\u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34m(\u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34mlabels_in_use\u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34m)\u001b[0m\u001b[48;2;39;40;34m                                                                     \u001b[0m\n",
        "\u001b[48;2;39;40;34m                                                                                                                   \u001b[0m\n",
        "\u001b[38;2;248;248;242;48;2;39;40;34m    \u001b[0m\u001b[38;2;102;217;239;48;2;39;40;34mdef\u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34m \u001b[0m\u001b[38;2;166;226;46;48;2;39;40;34mforward\u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34m(\u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34mself\u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34m,\u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34m \u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34mtext\u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34m)\u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34m:\u001b[0m\u001b[48;2;39;40;34m                                                                                       \u001b[0m\n",
-       "\u001b[38;2;248;248;242;48;2;39;40;34m        \u001b[0m\u001b[38;2;102;217;239;48;2;39;40;34mfor\u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34m \u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34mi\u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34m \u001b[0m\u001b[38;2;255;70;137;48;2;39;40;34min\u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34m \u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34mrange\u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34m(\u001b[0m\u001b[38;2;174;129;255;48;2;39;40;34m5\u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34m)\u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34m:\u001b[0m\u001b[48;2;39;40;34m                                                                                         \u001b[0m\n",
-       "\u001b[38;2;248;248;242;48;2;39;40;34m            \u001b[0m\u001b[38;2;102;217;239;48;2;39;40;34mtry\u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34m:\u001b[0m\u001b[48;2;39;40;34m                                                                                                   \u001b[0m\n",
-       "\u001b[38;2;248;248;242;48;2;39;40;34m                \u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34mprediction\u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34m \u001b[0m\u001b[38;2;255;70;137;48;2;39;40;34m=\u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34m \u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34mself\u001b[0m\u001b[38;2;255;70;137;48;2;39;40;34m.\u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34mintent_classifier\u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34m(\u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34mintent\u001b[0m\u001b[38;2;255;70;137;48;2;39;40;34m=\u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34mtext\u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34m,\u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34m \u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34mtemperature\u001b[0m\u001b[38;2;255;70;137;48;2;39;40;34m=\u001b[0m\u001b[38;2;174;129;255;48;2;39;40;34m0.1\u001b[0m\u001b[38;2;255;70;137;48;2;39;40;34m*\u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34mi\u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34m)\u001b[0m\u001b[48;2;39;40;34m                                \u001b[0m\n",
-       "\u001b[38;2;248;248;242;48;2;39;40;34m                \u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34msanitized_prediction\u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34m \u001b[0m\u001b[38;2;255;70;137;48;2;39;40;34m=\u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34m \u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34mdspy\u001b[0m\u001b[38;2;255;70;137;48;2;39;40;34m.\u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34mPrediction\u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34m(\u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34mlabel\u001b[0m\u001b[38;2;255;70;137;48;2;39;40;34m=\u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34mprediction\u001b[0m\u001b[38;2;255;70;137;48;2;39;40;34m.\u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34mlabel\u001b[0m\u001b[38;2;255;70;137;48;2;39;40;34m.\u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34mlower\u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34m(\u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34m)\u001b[0m\u001b[38;2;255;70;137;48;2;39;40;34m.\u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34mstrip\u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34m(\u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34m)\u001b[0m\u001b[38;2;255;70;137;48;2;39;40;34m.\u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34mreplace\u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34m(\u001b[0m\u001b[38;2;230;219;116;48;2;39;40;34m\"\u001b[0m\u001b[38;2;230;219;116;48;2;39;40;34m \u001b[0m\u001b[38;2;230;219;116;48;2;39;40;34m\"\u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34m,\u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34m \u001b[0m\u001b[38;2;230;219;116;48;2;39;40;34m\"\u001b[0m\u001b[38;2;230;219;116;48;2;39;40;34m_\u001b[0m\u001b[38;2;230;219;116;48;2;39;40;34m\"\u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34m)\u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34m,\u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34m \u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34mre\u001b[0m\n",
-       "\u001b[38;2;248;248;242;48;2;39;40;34m                \u001b[0m\u001b[38;2;102;217;239;48;2;39;40;34mif\u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34m \u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34msanitized_prediction\u001b[0m\u001b[38;2;255;70;137;48;2;39;40;34m.\u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34mlabel\u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34m \u001b[0m\u001b[38;2;255;70;137;48;2;39;40;34min\u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34m \u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34mself\u001b[0m\u001b[38;2;255;70;137;48;2;39;40;34m.\u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34mvalid_labels\u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34m:\u001b[0m\u001b[48;2;39;40;34m                                                \u001b[0m\n",
-       "\u001b[38;2;248;248;242;48;2;39;40;34m                    \u001b[0m\u001b[38;2;102;217;239;48;2;39;40;34mreturn\u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34m \u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34msanitized_prediction\u001b[0m\u001b[48;2;39;40;34m                                                                    \u001b[0m\n",
-       "\u001b[38;2;248;248;242;48;2;39;40;34m            \u001b[0m\u001b[38;2;102;217;239;48;2;39;40;34mexcept\u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34m \u001b[0m\u001b[38;2;166;226;46;48;2;39;40;34mException\u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34m \u001b[0m\u001b[38;2;102;217;239;48;2;39;40;34mas\u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34m \u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34me\u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34m:\u001b[0m\u001b[48;2;39;40;34m                                                                                 \u001b[0m\n",
-       "\u001b[38;2;248;248;242;48;2;39;40;34m                \u001b[0m\u001b[38;2;102;217;239;48;2;39;40;34mcontinue\u001b[0m\u001b[48;2;39;40;34m                                                                                           \u001b[0m\n",
-       "\u001b[38;2;248;248;242;48;2;39;40;34m        \u001b[0m\u001b[38;2;102;217;239;48;2;39;40;34mreturn\u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34m \u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34mdspy\u001b[0m\u001b[38;2;255;70;137;48;2;39;40;34m.\u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34mPrediction\u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34m(\u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34mlabel\u001b[0m\u001b[38;2;255;70;137;48;2;39;40;34m=\u001b[0m\u001b[38;2;230;219;116;48;2;39;40;34m\"\u001b[0m\u001b[38;2;230;219;116;48;2;39;40;34munknown\u001b[0m\u001b[38;2;230;219;116;48;2;39;40;34m\"\u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34m)\u001b[0m\u001b[48;2;39;40;34m                                                                    \u001b[0m\n",
+       "\u001b[38;2;248;248;242;48;2;39;40;34m        \u001b[0m\u001b[38;2;102;217;239;48;2;39;40;34mtry\u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34m:\u001b[0m\u001b[48;2;39;40;34m                                                                                                       \u001b[0m\n",
+       "\u001b[38;2;248;248;242;48;2;39;40;34m            \u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34mprediction\u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34m \u001b[0m\u001b[38;2;255;70;137;48;2;39;40;34m=\u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34m \u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34mself\u001b[0m\u001b[38;2;255;70;137;48;2;39;40;34m.\u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34mintent_classifier\u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34m(\u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34mintent\u001b[0m\u001b[38;2;255;70;137;48;2;39;40;34m=\u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34mtext\u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34m)\u001b[0m\u001b[48;2;39;40;34m                                                       \u001b[0m\n",
+       "\u001b[38;2;248;248;242;48;2;39;40;34m            \u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34msanitized_prediction\u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34m \u001b[0m\u001b[38;2;255;70;137;48;2;39;40;34m=\u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34m \u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34mdspy\u001b[0m\u001b[38;2;255;70;137;48;2;39;40;34m.\u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34mPrediction\u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34m(\u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34mlabel\u001b[0m\u001b[38;2;255;70;137;48;2;39;40;34m=\u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34mprediction\u001b[0m\u001b[38;2;255;70;137;48;2;39;40;34m.\u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34mlabel\u001b[0m\u001b[38;2;255;70;137;48;2;39;40;34m.\u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34mlower\u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34m(\u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34m)\u001b[0m\u001b[38;2;255;70;137;48;2;39;40;34m.\u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34mstrip\u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34m(\u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34m)\u001b[0m\u001b[38;2;255;70;137;48;2;39;40;34m.\u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34mreplace\u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34m(\u001b[0m\u001b[38;2;230;219;116;48;2;39;40;34m\"\u001b[0m\u001b[38;2;230;219;116;48;2;39;40;34m \u001b[0m\u001b[38;2;230;219;116;48;2;39;40;34m\"\u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34m,\u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34m \u001b[0m\u001b[38;2;230;219;116;48;2;39;40;34m\"\u001b[0m\u001b[38;2;230;219;116;48;2;39;40;34m_\u001b[0m\u001b[38;2;230;219;116;48;2;39;40;34m\"\u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34m)\u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34m,\u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34m \u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34mreason\u001b[0m\n",
+       "\u001b[38;2;248;248;242;48;2;39;40;34m            \u001b[0m\u001b[38;2;102;217;239;48;2;39;40;34mif\u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34m \u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34msanitized_prediction\u001b[0m\u001b[38;2;255;70;137;48;2;39;40;34m.\u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34mlabel\u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34m \u001b[0m\u001b[38;2;255;70;137;48;2;39;40;34min\u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34m \u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34mself\u001b[0m\u001b[38;2;255;70;137;48;2;39;40;34m.\u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34mvalid_labels\u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34m:\u001b[0m\u001b[48;2;39;40;34m                                                    \u001b[0m\n",
+       "\u001b[38;2;248;248;242;48;2;39;40;34m                \u001b[0m\u001b[38;2;102;217;239;48;2;39;40;34mreturn\u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34m \u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34msanitized_prediction\u001b[0m\u001b[48;2;39;40;34m                                                                        \u001b[0m\n",
+       "\u001b[38;2;248;248;242;48;2;39;40;34m        \u001b[0m\u001b[38;2;102;217;239;48;2;39;40;34mexcept\u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34m \u001b[0m\u001b[38;2;166;226;46;48;2;39;40;34mValueError\u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34m \u001b[0m\u001b[38;2;102;217;239;48;2;39;40;34mas\u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34m \u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34me\u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34m:\u001b[0m\u001b[48;2;39;40;34m                                                                                    \u001b[0m\n",
+       "\u001b[38;2;248;248;242;48;2;39;40;34m            \u001b[0m\u001b[38;2;149;144;119;48;2;39;40;34m# If the model is unable to make a prediction in a valid format, return \"unknown\"\u001b[0m\u001b[48;2;39;40;34m                      \u001b[0m\n",
+       "\u001b[38;2;248;248;242;48;2;39;40;34m            \u001b[0m\u001b[38;2;102;217;239;48;2;39;40;34mreturn\u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34m \u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34mdspy\u001b[0m\u001b[38;2;255;70;137;48;2;39;40;34m.\u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34mPrediction\u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34m(\u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34mlabel\u001b[0m\u001b[38;2;255;70;137;48;2;39;40;34m=\u001b[0m\u001b[38;2;230;219;116;48;2;39;40;34m\"\u001b[0m\u001b[38;2;230;219;116;48;2;39;40;34munknown\u001b[0m\u001b[38;2;230;219;116;48;2;39;40;34m\"\u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34m)\u001b[0m\u001b[48;2;39;40;34m                                                                \u001b[0m\n",
        "\u001b[48;2;39;40;34m                                                                                                                   \u001b[0m\n",
        "\u001b[38;2;166;226;46;48;2;39;40;34m@serve\u001b[0m\u001b[38;2;255;70;137;48;2;39;40;34m.\u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34mdeployment\u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34m(\u001b[0m\u001b[48;2;39;40;34m                                                                                                 \u001b[0m\n",
        "\u001b[38;2;248;248;242;48;2;39;40;34m    \u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34mray_actor_options\u001b[0m\u001b[38;2;255;70;137;48;2;39;40;34m=\u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34m{\u001b[0m\u001b[38;2;230;219;116;48;2;39;40;34m\"\u001b[0m\u001b[38;2;230;219;116;48;2;39;40;34mnum_cpus\u001b[0m\u001b[38;2;230;219;116;48;2;39;40;34m\"\u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34m:\u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34m \u001b[0m\u001b[38;2;174;129;255;48;2;39;40;34m0.1\u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34m}\u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34m,\u001b[0m\u001b[48;2;39;40;34m                                                                           \u001b[0m\n",
@@ -1960,16 +1974,7 @@
    "cell_type": "code",
    "execution_count": null,
    "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "2024-10-23 17:06:59,677\tWARN scripts.py:132 -- The `RAY_AGENT_ADDRESS` env var has been deprecated in favor of the `RAY_DASHBOARD_ADDRESS` env var. The `RAY_AGENT_ADDRESS` is ignored.\n",
-      "2024-10-23 17:06:59,686\tSUCC scripts.py:747 -- Sent shutdown request; applications will be deleted asynchronously.\n"
-     ]
-    }
-   ],
+   "outputs": [],
    "source": [
     "!serve shutdown -y"
    ]
@@ -1985,21 +1990,7 @@
    "cell_type": "code",
    "execution_count": null,
    "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "2024-10-23 17:10:49,026\tINFO scripts.py:489 -- Running config file: 'local_deploy_dspy.yaml'.\n",
-      "2024-10-23 17:10:49,352\tINFO worker.py:1601 -- Connecting to existing Ray cluster at address: 10.0.0.38:6379...\n",
-      "2024-10-23 17:10:49,359\tINFO worker.py:1777 -- Connected to Ray cluster. View the dashboard at https://session-rrffq784rd7isilec3enpn8tqf.i.anyscaleuserdata.com \n",
-      "2024-10-23 17:10:49,364\tINFO packaging.py:359 -- Pushing file package 'gcs://_ray_pkg_7aa1baf78c230c6e25663bb0cf020412097a9e34.zip' (1.32MiB) to Ray cluster...\n",
-      "2024-10-23 17:10:49,377\tINFO packaging.py:372 -- Successfully pushed file package 'gcs://_ray_pkg_7aa1baf78c230c6e25663bb0cf020412097a9e34.zip'.\n",
-      "INFO 2024-10-23 17:10:49,406 serve 83268 api.py:259 - Connecting to existing Serve app in namespace \"serve\". New http options will not be applied.\n",
-      "2024-10-23 17:10:49,413\tSUCC scripts.py:540 -- Submitted deploy config successfully.\n"
-     ]
-    }
-   ],
+   "outputs": [],
    "source": [
     "!serve run local_deploy_dspy.yaml --non-blocking"
    ]
@@ -2027,7 +2018,7 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "card_payment_not_recognised\n"
+      "request_refund\n"
      ]
     }
    ],
@@ -2083,21 +2074,7 @@
    "cell_type": "code",
    "execution_count": null,
    "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "(anyscale +2.7s) Updating existing service 'dspy-rayllm-service'.\n",
-      "(anyscale +4.9s) Uploading local dir '.' to cloud storage.\n",
-      "(anyscale +7.2s) Including workspace-managed pip dependencies.\n",
-      "(anyscale +8.1s) Service 'dspy-rayllm-service' deployed.\n",
-      "(anyscale +8.1s) View the service in the UI: 'https://console.anyscale.com/services/service2_3kg2zf69v85dn3d6l3iluqkk4f'\n",
-      "(anyscale +8.1s) Query the service once it's running using the following curl command:\n",
-      "(anyscale +8.1s) curl https://dspy-rayllm-service-mglb8.cld-tffbxe9ia5phqr1u.s.anyscaleuserdata.com/\n"
-     ]
-    }
-   ],
+   "outputs": [],
    "source": [
     "# Run this if you want to deploy an Anyscale service\n",
     "!anyscale service deploy -f serve_1B.yaml"
@@ -2185,20 +2162,7 @@
    "cell_type": "code",
    "execution_count": null,
    "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "(anyscale +2.4s) Updating existing service 'dspy-service'.\n",
-      "(anyscale +4.5s) Uploading local dir '.' to cloud storage.\n",
-      "(anyscale +8.0s) Service 'dspy-service' deployed.\n",
-      "(anyscale +8.0s) View the service in the UI: 'https://console.anyscale.com/services/service2_l6ivxkmxypevqv5893bs3rq23e'\n",
-      "(anyscale +8.0s) Query the service once it's running using the following curl command:\n",
-      "(anyscale +8.0s) curl https://dspy-service-mglb8.cld-tffbxe9ia5phqr1u.s.anyscaleuserdata.com/\n"
-     ]
-    }
-   ],
+   "outputs": [],
    "source": [
     "!anyscale service deploy -f configs/anyscale_deploy.yaml"
    ]
@@ -2220,6 +2184,13 @@
     "ANYSCALE_DSPY_API_KEY = \"fake-api-key\" # Use the provided API key if query_auth_token_enabled is `True`"
    ]
   },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Note: You may need to way a few minutes for the services to deploy and scale"
+   ]
+  },
   {
    "cell_type": "code",
    "execution_count": null,
@@ -2229,7 +2200,7 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "request_refund\n"
+      "cash_withdrawal_not_recognised\n"
      ]
     }
    ],
diff --git a/templates/e2e-dspy-workflow/README.md b/templates/e2e-dspy-workflow/README.md
index 4ca2f786d..f805853ea 100644
--- a/templates/e2e-dspy-workflow/README.md
+++ b/templates/e2e-dspy-workflow/README.md
@@ -57,7 +57,7 @@ import importlib.util
 
 if importlib.util.find_spec("dspy") is None:
     print("Installing dspy")
-    !pip install git+https://github.com/stanfordnlp/dspy.git@40d5e0229a3abadd313ff9b857f40e638effae56
+    !pip install git+https://github.com/stanfordnlp/dspy.git@c5b4c6d4240d66cc23113c55addfd47313dd25fb
 
 else:
     print("dspy is already installed")
@@ -333,7 +333,7 @@ print_serve_and_model_config("serve_70B.yaml")
         <span style="color: #008000; text-decoration-color: #008000">'model_id'</span>: <span style="color: #008000; text-decoration-color: #008000">'meta-llama/Meta-Llama-3.1-70B-Instruct'</span>,
         <span style="color: #008000; text-decoration-color: #008000">'model_source'</span>: <span style="color: #008000; text-decoration-color: #008000">'meta-llama/Meta-Llama-3.1-70B-Instruct'</span>
     <span style="font-weight: bold">}</span>,
-    <span style="color: #008000; text-decoration-color: #008000">'runtime_env'</span>: <span style="font-weight: bold">{</span><span style="color: #008000; text-decoration-color: #008000">'env_vars'</span>: <span style="font-weight: bold">{</span><span style="color: #008000; text-decoration-color: #008000">'HUGGING_FACE_HUB_TOKEN'</span>: <span style="color: #008000; text-decoration-color: #008000">'Add your HF token here'</span><span style="font-weight: bold">}}</span>,
+    <span style="color: #008000; text-decoration-color: #008000">'runtime_env'</span>: <span style="font-weight: bold">{</span><span style="color: #008000; text-decoration-color: #008000">'env_vars'</span>: <span style="font-weight: bold">{</span><span style="color: #008000; text-decoration-color: #008000">'HUGGING_FACE_HUB_TOKEN'</span>: <span style="color: #008000; text-decoration-color: #008000">'REDACTED'</span><span style="font-weight: bold">}}</span>,
     <span style="color: #008000; text-decoration-color: #008000">'tensor_parallelism'</span>: <span style="font-weight: bold">{</span><span style="color: #008000; text-decoration-color: #008000">'degree'</span>: <span style="color: #008080; text-decoration-color: #008080; font-weight: bold">4</span><span style="font-weight: bold">}</span>
 <span style="font-weight: bold">}</span>
 </pre>
@@ -378,6 +378,8 @@ sanity_check_program(llama_70b, vanilla_program, ft_trainset[0])
 ```
 
     Program input: Example({'text': 'I still have not received an answer as to why I was charged $1.00 in a transaction?'}) (input_keys={'text'})
+
+
     Program output label: extra_charge_on_statement
 
 
@@ -413,6 +415,9 @@ dataset = convert_to_module_level_message_data(collected_data_filtered, program=
 dataset_formatted = [{"messages": item} for item in dataset]
 ```
 
+    Average Metric: 4163 / 4171  (99.8): 100%|██████████| 4171/4171 [07:13<00:00,  9.62it/s]
+
+
 
 ```python
 import rich
@@ -659,7 +664,7 @@ print_serve_and_model_config(serve_config_path)
     <span style="color: #008000; text-decoration-color: #008000">'input_modality'</span>: <span style="color: #008000; text-decoration-color: #008000">'text'</span>,
     <span style="color: #008000; text-decoration-color: #008000">'json_mode'</span>: <span style="font-weight: bold">{</span><span style="color: #008000; text-decoration-color: #008000">'enabled'</span>: <span style="color: #ff0000; text-decoration-color: #ff0000; font-style: italic">False</span><span style="font-weight: bold">}</span>,
     <span style="color: #008000; text-decoration-color: #008000">'llm_engine'</span>: <span style="color: #008000; text-decoration-color: #008000">'VLLMEngine'</span>,
-    <span style="color: #008000; text-decoration-color: #008000">'lora_config'</span>: <span style="font-weight: bold">{</span><span style="color: #008000; text-decoration-color: #008000">'dynamic_lora_loading_path'</span>: <span style="color: #008000; text-decoration-color: #008000">'none'</span>, <span style="color: #008000; text-decoration-color: #008000">'max_num_adapters_per_replica'</span>: <span style="color: #008080; text-decoration-color: #008080; font-weight: bold">32</span><span style="font-weight: bold">}</span>,
+    <span style="color: #008000; text-decoration-color: #008000">'lora_config'</span>: <span style="font-weight: bold">{</span><span style="color: #008000; text-decoration-color: #008000">'dynamic_lora_loading_path'</span>: <span style="color: #008000; text-decoration-color: #008000">'none'</span>, <span style="color: #008000; text-decoration-color: #008000">'max_num_adapters_per_replica'</span>: <span style="color: #008080; text-decoration-color: #008080; font-weight: bold">64</span><span style="font-weight: bold">}</span>,
     <span style="color: #008000; text-decoration-color: #008000">'max_request_context_length'</span>: <span style="color: #008080; text-decoration-color: #008080; font-weight: bold">8192</span>,
     <span style="color: #008000; text-decoration-color: #008000">'model_loading_config'</span>: <span style="font-weight: bold">{</span>
         <span style="color: #008000; text-decoration-color: #008000">'model_id'</span>: <span style="color: #008000; text-decoration-color: #008000">'meta-llama/Llama-3.2-1B-Instruct'</span>,
@@ -888,10 +893,10 @@ rich.print(ft_results)
 
 
 <pre style="white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace"><span style="font-weight: bold">{</span>
-    <span style="color: #008000; text-decoration-color: #008000">'base'</span>: <span style="font-weight: bold">{</span><span style="color: #008000; text-decoration-color: #008000">'vanilla'</span>: <span style="font-weight: bold">{</span><span style="color: #008000; text-decoration-color: #008000">'devset'</span>: <span style="color: #008080; text-decoration-color: #008080; font-weight: bold">0.1</span><span style="font-weight: bold">}</span>, <span style="color: #008000; text-decoration-color: #008000">'bfrs'</span>: <span style="font-weight: bold">{</span><span style="color: #008000; text-decoration-color: #008000">'devset'</span>: <span style="color: #008080; text-decoration-color: #008080; font-weight: bold">37.2</span><span style="font-weight: bold">}}</span>,
-    <span style="color: #008000; text-decoration-color: #008000">'openai/meta-llama/Llama-3.2-1B-Instruct:isaac:fijif'</span>: <span style="font-weight: bold">{</span>
-        <span style="color: #008000; text-decoration-color: #008000">'vanilla'</span>: <span style="font-weight: bold">{</span><span style="color: #008000; text-decoration-color: #008000">'devset'</span>: <span style="color: #008080; text-decoration-color: #008080; font-weight: bold">54.2</span><span style="font-weight: bold">}</span>,
-        <span style="color: #008000; text-decoration-color: #008000">'bfrs'</span>: <span style="font-weight: bold">{</span><span style="color: #008000; text-decoration-color: #008000">'devset'</span>: <span style="color: #008080; text-decoration-color: #008080; font-weight: bold">53.5</span><span style="font-weight: bold">}</span>
+    <span style="color: #008000; text-decoration-color: #008000">'base'</span>: <span style="font-weight: bold">{</span><span style="color: #008000; text-decoration-color: #008000">'vanilla'</span>: <span style="font-weight: bold">{</span><span style="color: #008000; text-decoration-color: #008000">'devset'</span>: <span style="color: #008080; text-decoration-color: #008080; font-weight: bold">0.0</span><span style="font-weight: bold">}</span>, <span style="color: #008000; text-decoration-color: #008000">'bfrs'</span>: <span style="font-weight: bold">{</span><span style="color: #008000; text-decoration-color: #008000">'devset'</span>: <span style="color: #008080; text-decoration-color: #008080; font-weight: bold">36.7</span><span style="font-weight: bold">}}</span>,
+    <span style="color: #008000; text-decoration-color: #008000">'openai/meta-llama/Llama-3.2-1B-Instruct:isaac:qmmwc'</span>: <span style="font-weight: bold">{</span>
+        <span style="color: #008000; text-decoration-color: #008000">'vanilla'</span>: <span style="font-weight: bold">{</span><span style="color: #008000; text-decoration-color: #008000">'devset'</span>: <span style="color: #008080; text-decoration-color: #008080; font-weight: bold">34.0</span><span style="font-weight: bold">}</span>,
+        <span style="color: #008000; text-decoration-color: #008000">'bfrs'</span>: <span style="font-weight: bold">{</span><span style="color: #008000; text-decoration-color: #008000">'devset'</span>: <span style="color: #008080; text-decoration-color: #008080; font-weight: bold">58.0</span><span style="font-weight: bold">}</span>
     <span style="font-weight: bold">}</span>
 <span style="font-weight: bold">}</span>
 </pre>
@@ -911,8 +916,8 @@ graph_devset_results(ft_results)
     
 
 
-    Highest Dev Set Score (No Opt): 54.2, Model: Fine-tuned
-    Highest Dev Set Score (BFRS): 53.5, Model: Fine-tuned
+    Highest Dev Set Score (No Opt): 34.0, Model: Fine-tuned
+    Highest Dev Set Score (BFRS): 58.0, Model: Fine-tuned
 
 
 We see that the our finetuned model significantly outperforms the base model on the synthetic devset, and prompt optimization isn't enough to bring 1B all the way up to FT performance.
@@ -956,12 +961,12 @@ print(f"Best testset result: \n{best_model} with score: {best_score}")
 ```
 
     Best testset result: 
-    openai/meta-llama/Llama-3.2-1B-Instruct:isaac:fijif with score: 49.6
+    openai/meta-llama/Llama-3.2-1B-Instruct:isaac:qmmwc with score: 50.4
 
 
 We see that on the true test set, the prompt optimized pipeline using the finetuned model outperforms the prompt optimized pipeline using the non-finetuned model by approximately 15-20% in overall dataset accuracy.
 
-There is a significant drop between the "Best Fine-tuned (BFRS)" model's performance on the synthetic devset and on the testset. We believe this to be a gap in the generalized ability of the fine-tuned model: the model has seen the entirety of the synthetic devset before - the best checkpoint is chosen to be the one that minimizes the evaluation loss - but the testset is new and testing its capabilities on unseen data. However, with prompt optimization on top of fine-tuning, we clearly see the best test set performance.
+There is a drop between the "Best Fine-tuned (BFRS)" model's performance on the synthetic devset and on the testset. We believe this to be a gap in the generalized ability of the fine-tuned model: the model has seen the entirety of the synthetic devset before - the best checkpoint is chosen to be the one that minimizes the evaluation loss - but the testset is new and testing its capabilities on unseen data. However, with prompt optimization on top of fine-tuning, we clearly see the best test set performance.
 
 This is one small example of where DSPy optimization can be useful in terms of increasing task specific performance and helping you to get the most out of your LLM.
 
@@ -975,7 +980,7 @@ To deploy, you can serve the optimized DSPy program directly: This is the simple
 
 NOTE: As of DSPy 2.5, there are scalability limitations for high throughput scenarios with DSPy. DSPy compiled programs currently use threading for handling multiple queries in parallel, which might not scale as well as a native `async` implementation. A native `async` implementation is in the immediate roadmap for DSPy. If this is a concern, you can always try to stitch together the saved program from DSPy in native Python code. 
 
-## Serving a DSPy Pipeline as a Multi-App Serve Application
+## Serving a DSPy Pipeline using Ray Serve
 
 We can break down our program into two distinct parts: 1) Fine-tuned LLM served behind an OpenAI compatible endpoint and 2) The DSPy program (our business logic tying all components together)
 
@@ -1020,7 +1025,8 @@ pretty_print_py("deploy.py")
 ```
 
 
-<pre style="white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace"><span style="color: #ff4689; text-decoration-color: #ff4689; background-color: #272822">import</span><span style="color: #f8f8f2; text-decoration-color: #f8f8f2; background-color: #272822"> dspy</span><span style="background-color: #272822">                                                                                                        </span>
+<pre style="white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace"><span style="color: #e6db74; text-decoration-color: #e6db74; background-color: #272822">"""Ray Serve deployment code for our DSPy application"""</span><span style="background-color: #272822">                                                           </span>
+<span style="color: #ff4689; text-decoration-color: #ff4689; background-color: #272822">import</span><span style="color: #f8f8f2; text-decoration-color: #f8f8f2; background-color: #272822"> dspy</span><span style="background-color: #272822">                                                                                                        </span>
 <span style="color: #ff4689; text-decoration-color: #ff4689; background-color: #272822">from</span><span style="color: #f8f8f2; text-decoration-color: #f8f8f2; background-color: #272822"> ray </span><span style="color: #ff4689; text-decoration-color: #ff4689; background-color: #272822">import</span><span style="color: #f8f8f2; text-decoration-color: #f8f8f2; background-color: #272822"> serve</span><span style="background-color: #272822">                                                                                              </span>
 <span style="color: #ff4689; text-decoration-color: #ff4689; background-color: #272822">from</span><span style="color: #f8f8f2; text-decoration-color: #f8f8f2; background-color: #272822"> fastapi </span><span style="color: #ff4689; text-decoration-color: #ff4689; background-color: #272822">import</span><span style="color: #f8f8f2; text-decoration-color: #f8f8f2; background-color: #272822"> FastAPI</span><span style="background-color: #272822">                                                                                        </span>
 <span style="color: #ff4689; text-decoration-color: #ff4689; background-color: #272822">from</span><span style="color: #f8f8f2; text-decoration-color: #f8f8f2; background-color: #272822"> src </span><span style="color: #ff4689; text-decoration-color: #ff4689; background-color: #272822">import</span><span style="color: #f8f8f2; text-decoration-color: #f8f8f2; background-color: #272822"> MODEL_PARAMETERS</span><span style="background-color: #272822">                                                                                   </span>
@@ -1048,15 +1054,14 @@ pretty_print_py("deploy.py")
 <span style="color: #f8f8f2; text-decoration-color: #f8f8f2; background-color: #272822">        self</span><span style="color: #ff4689; text-decoration-color: #ff4689; background-color: #272822">.</span><span style="color: #f8f8f2; text-decoration-color: #f8f8f2; background-color: #272822">valid_labels </span><span style="color: #ff4689; text-decoration-color: #ff4689; background-color: #272822">=</span><span style="color: #f8f8f2; text-decoration-color: #f8f8f2; background-color: #272822"> set(labels_in_use)</span><span style="background-color: #272822">                                                                     </span>
 <span style="background-color: #272822">                                                                                                                   </span>
 <span style="color: #f8f8f2; text-decoration-color: #f8f8f2; background-color: #272822">    </span><span style="color: #66d9ef; text-decoration-color: #66d9ef; background-color: #272822">def</span><span style="color: #f8f8f2; text-decoration-color: #f8f8f2; background-color: #272822"> </span><span style="color: #a6e22e; text-decoration-color: #a6e22e; background-color: #272822">forward</span><span style="color: #f8f8f2; text-decoration-color: #f8f8f2; background-color: #272822">(self, text):</span><span style="background-color: #272822">                                                                                       </span>
-<span style="color: #f8f8f2; text-decoration-color: #f8f8f2; background-color: #272822">        </span><span style="color: #66d9ef; text-decoration-color: #66d9ef; background-color: #272822">for</span><span style="color: #f8f8f2; text-decoration-color: #f8f8f2; background-color: #272822"> i </span><span style="color: #ff4689; text-decoration-color: #ff4689; background-color: #272822">in</span><span style="color: #f8f8f2; text-decoration-color: #f8f8f2; background-color: #272822"> range(</span><span style="color: #ae81ff; text-decoration-color: #ae81ff; background-color: #272822">5</span><span style="color: #f8f8f2; text-decoration-color: #f8f8f2; background-color: #272822">):</span><span style="background-color: #272822">                                                                                         </span>
-<span style="color: #f8f8f2; text-decoration-color: #f8f8f2; background-color: #272822">            </span><span style="color: #66d9ef; text-decoration-color: #66d9ef; background-color: #272822">try</span><span style="color: #f8f8f2; text-decoration-color: #f8f8f2; background-color: #272822">:</span><span style="background-color: #272822">                                                                                                   </span>
-<span style="color: #f8f8f2; text-decoration-color: #f8f8f2; background-color: #272822">                prediction </span><span style="color: #ff4689; text-decoration-color: #ff4689; background-color: #272822">=</span><span style="color: #f8f8f2; text-decoration-color: #f8f8f2; background-color: #272822"> self</span><span style="color: #ff4689; text-decoration-color: #ff4689; background-color: #272822">.</span><span style="color: #f8f8f2; text-decoration-color: #f8f8f2; background-color: #272822">intent_classifier(intent</span><span style="color: #ff4689; text-decoration-color: #ff4689; background-color: #272822">=</span><span style="color: #f8f8f2; text-decoration-color: #f8f8f2; background-color: #272822">text, temperature</span><span style="color: #ff4689; text-decoration-color: #ff4689; background-color: #272822">=</span><span style="color: #ae81ff; text-decoration-color: #ae81ff; background-color: #272822">0.1</span><span style="color: #ff4689; text-decoration-color: #ff4689; background-color: #272822">*</span><span style="color: #f8f8f2; text-decoration-color: #f8f8f2; background-color: #272822">i)</span><span style="background-color: #272822">                                </span>
-<span style="color: #f8f8f2; text-decoration-color: #f8f8f2; background-color: #272822">                sanitized_prediction </span><span style="color: #ff4689; text-decoration-color: #ff4689; background-color: #272822">=</span><span style="color: #f8f8f2; text-decoration-color: #f8f8f2; background-color: #272822"> dspy</span><span style="color: #ff4689; text-decoration-color: #ff4689; background-color: #272822">.</span><span style="color: #f8f8f2; text-decoration-color: #f8f8f2; background-color: #272822">Prediction(label</span><span style="color: #ff4689; text-decoration-color: #ff4689; background-color: #272822">=</span><span style="color: #f8f8f2; text-decoration-color: #f8f8f2; background-color: #272822">prediction</span><span style="color: #ff4689; text-decoration-color: #ff4689; background-color: #272822">.</span><span style="color: #f8f8f2; text-decoration-color: #f8f8f2; background-color: #272822">label</span><span style="color: #ff4689; text-decoration-color: #ff4689; background-color: #272822">.</span><span style="color: #f8f8f2; text-decoration-color: #f8f8f2; background-color: #272822">lower()</span><span style="color: #ff4689; text-decoration-color: #ff4689; background-color: #272822">.</span><span style="color: #f8f8f2; text-decoration-color: #f8f8f2; background-color: #272822">strip()</span><span style="color: #ff4689; text-decoration-color: #ff4689; background-color: #272822">.</span><span style="color: #f8f8f2; text-decoration-color: #f8f8f2; background-color: #272822">replace(</span><span style="color: #e6db74; text-decoration-color: #e6db74; background-color: #272822">" "</span><span style="color: #f8f8f2; text-decoration-color: #f8f8f2; background-color: #272822">, </span><span style="color: #e6db74; text-decoration-color: #e6db74; background-color: #272822">"_"</span><span style="color: #f8f8f2; text-decoration-color: #f8f8f2; background-color: #272822">), re</span>
-<span style="color: #f8f8f2; text-decoration-color: #f8f8f2; background-color: #272822">                </span><span style="color: #66d9ef; text-decoration-color: #66d9ef; background-color: #272822">if</span><span style="color: #f8f8f2; text-decoration-color: #f8f8f2; background-color: #272822"> sanitized_prediction</span><span style="color: #ff4689; text-decoration-color: #ff4689; background-color: #272822">.</span><span style="color: #f8f8f2; text-decoration-color: #f8f8f2; background-color: #272822">label </span><span style="color: #ff4689; text-decoration-color: #ff4689; background-color: #272822">in</span><span style="color: #f8f8f2; text-decoration-color: #f8f8f2; background-color: #272822"> self</span><span style="color: #ff4689; text-decoration-color: #ff4689; background-color: #272822">.</span><span style="color: #f8f8f2; text-decoration-color: #f8f8f2; background-color: #272822">valid_labels:</span><span style="background-color: #272822">                                                </span>
-<span style="color: #f8f8f2; text-decoration-color: #f8f8f2; background-color: #272822">                    </span><span style="color: #66d9ef; text-decoration-color: #66d9ef; background-color: #272822">return</span><span style="color: #f8f8f2; text-decoration-color: #f8f8f2; background-color: #272822"> sanitized_prediction</span><span style="background-color: #272822">                                                                    </span>
-<span style="color: #f8f8f2; text-decoration-color: #f8f8f2; background-color: #272822">            </span><span style="color: #66d9ef; text-decoration-color: #66d9ef; background-color: #272822">except</span><span style="color: #f8f8f2; text-decoration-color: #f8f8f2; background-color: #272822"> </span><span style="color: #a6e22e; text-decoration-color: #a6e22e; background-color: #272822">Exception</span><span style="color: #f8f8f2; text-decoration-color: #f8f8f2; background-color: #272822"> </span><span style="color: #66d9ef; text-decoration-color: #66d9ef; background-color: #272822">as</span><span style="color: #f8f8f2; text-decoration-color: #f8f8f2; background-color: #272822"> e:</span><span style="background-color: #272822">                                                                                 </span>
-<span style="color: #f8f8f2; text-decoration-color: #f8f8f2; background-color: #272822">                </span><span style="color: #66d9ef; text-decoration-color: #66d9ef; background-color: #272822">continue</span><span style="background-color: #272822">                                                                                           </span>
-<span style="color: #f8f8f2; text-decoration-color: #f8f8f2; background-color: #272822">        </span><span style="color: #66d9ef; text-decoration-color: #66d9ef; background-color: #272822">return</span><span style="color: #f8f8f2; text-decoration-color: #f8f8f2; background-color: #272822"> dspy</span><span style="color: #ff4689; text-decoration-color: #ff4689; background-color: #272822">.</span><span style="color: #f8f8f2; text-decoration-color: #f8f8f2; background-color: #272822">Prediction(label</span><span style="color: #ff4689; text-decoration-color: #ff4689; background-color: #272822">=</span><span style="color: #e6db74; text-decoration-color: #e6db74; background-color: #272822">"unknown"</span><span style="color: #f8f8f2; text-decoration-color: #f8f8f2; background-color: #272822">)</span><span style="background-color: #272822">                                                                    </span>
+<span style="color: #f8f8f2; text-decoration-color: #f8f8f2; background-color: #272822">        </span><span style="color: #66d9ef; text-decoration-color: #66d9ef; background-color: #272822">try</span><span style="color: #f8f8f2; text-decoration-color: #f8f8f2; background-color: #272822">:</span><span style="background-color: #272822">                                                                                                       </span>
+<span style="color: #f8f8f2; text-decoration-color: #f8f8f2; background-color: #272822">            prediction </span><span style="color: #ff4689; text-decoration-color: #ff4689; background-color: #272822">=</span><span style="color: #f8f8f2; text-decoration-color: #f8f8f2; background-color: #272822"> self</span><span style="color: #ff4689; text-decoration-color: #ff4689; background-color: #272822">.</span><span style="color: #f8f8f2; text-decoration-color: #f8f8f2; background-color: #272822">intent_classifier(intent</span><span style="color: #ff4689; text-decoration-color: #ff4689; background-color: #272822">=</span><span style="color: #f8f8f2; text-decoration-color: #f8f8f2; background-color: #272822">text)</span><span style="background-color: #272822">                                                       </span>
+<span style="color: #f8f8f2; text-decoration-color: #f8f8f2; background-color: #272822">            sanitized_prediction </span><span style="color: #ff4689; text-decoration-color: #ff4689; background-color: #272822">=</span><span style="color: #f8f8f2; text-decoration-color: #f8f8f2; background-color: #272822"> dspy</span><span style="color: #ff4689; text-decoration-color: #ff4689; background-color: #272822">.</span><span style="color: #f8f8f2; text-decoration-color: #f8f8f2; background-color: #272822">Prediction(label</span><span style="color: #ff4689; text-decoration-color: #ff4689; background-color: #272822">=</span><span style="color: #f8f8f2; text-decoration-color: #f8f8f2; background-color: #272822">prediction</span><span style="color: #ff4689; text-decoration-color: #ff4689; background-color: #272822">.</span><span style="color: #f8f8f2; text-decoration-color: #f8f8f2; background-color: #272822">label</span><span style="color: #ff4689; text-decoration-color: #ff4689; background-color: #272822">.</span><span style="color: #f8f8f2; text-decoration-color: #f8f8f2; background-color: #272822">lower()</span><span style="color: #ff4689; text-decoration-color: #ff4689; background-color: #272822">.</span><span style="color: #f8f8f2; text-decoration-color: #f8f8f2; background-color: #272822">strip()</span><span style="color: #ff4689; text-decoration-color: #ff4689; background-color: #272822">.</span><span style="color: #f8f8f2; text-decoration-color: #f8f8f2; background-color: #272822">replace(</span><span style="color: #e6db74; text-decoration-color: #e6db74; background-color: #272822">" "</span><span style="color: #f8f8f2; text-decoration-color: #f8f8f2; background-color: #272822">, </span><span style="color: #e6db74; text-decoration-color: #e6db74; background-color: #272822">"_"</span><span style="color: #f8f8f2; text-decoration-color: #f8f8f2; background-color: #272822">), reason</span>
+<span style="color: #f8f8f2; text-decoration-color: #f8f8f2; background-color: #272822">            </span><span style="color: #66d9ef; text-decoration-color: #66d9ef; background-color: #272822">if</span><span style="color: #f8f8f2; text-decoration-color: #f8f8f2; background-color: #272822"> sanitized_prediction</span><span style="color: #ff4689; text-decoration-color: #ff4689; background-color: #272822">.</span><span style="color: #f8f8f2; text-decoration-color: #f8f8f2; background-color: #272822">label </span><span style="color: #ff4689; text-decoration-color: #ff4689; background-color: #272822">in</span><span style="color: #f8f8f2; text-decoration-color: #f8f8f2; background-color: #272822"> self</span><span style="color: #ff4689; text-decoration-color: #ff4689; background-color: #272822">.</span><span style="color: #f8f8f2; text-decoration-color: #f8f8f2; background-color: #272822">valid_labels:</span><span style="background-color: #272822">                                                    </span>
+<span style="color: #f8f8f2; text-decoration-color: #f8f8f2; background-color: #272822">                </span><span style="color: #66d9ef; text-decoration-color: #66d9ef; background-color: #272822">return</span><span style="color: #f8f8f2; text-decoration-color: #f8f8f2; background-color: #272822"> sanitized_prediction</span><span style="background-color: #272822">                                                                        </span>
+<span style="color: #f8f8f2; text-decoration-color: #f8f8f2; background-color: #272822">        </span><span style="color: #66d9ef; text-decoration-color: #66d9ef; background-color: #272822">except</span><span style="color: #f8f8f2; text-decoration-color: #f8f8f2; background-color: #272822"> </span><span style="color: #a6e22e; text-decoration-color: #a6e22e; background-color: #272822">ValueError</span><span style="color: #f8f8f2; text-decoration-color: #f8f8f2; background-color: #272822"> </span><span style="color: #66d9ef; text-decoration-color: #66d9ef; background-color: #272822">as</span><span style="color: #f8f8f2; text-decoration-color: #f8f8f2; background-color: #272822"> e:</span><span style="background-color: #272822">                                                                                    </span>
+<span style="color: #f8f8f2; text-decoration-color: #f8f8f2; background-color: #272822">            </span><span style="color: #959077; text-decoration-color: #959077; background-color: #272822"># If the model is unable to make a prediction in a valid format, return "unknown"</span><span style="background-color: #272822">                      </span>
+<span style="color: #f8f8f2; text-decoration-color: #f8f8f2; background-color: #272822">            </span><span style="color: #66d9ef; text-decoration-color: #66d9ef; background-color: #272822">return</span><span style="color: #f8f8f2; text-decoration-color: #f8f8f2; background-color: #272822"> dspy</span><span style="color: #ff4689; text-decoration-color: #ff4689; background-color: #272822">.</span><span style="color: #f8f8f2; text-decoration-color: #f8f8f2; background-color: #272822">Prediction(label</span><span style="color: #ff4689; text-decoration-color: #ff4689; background-color: #272822">=</span><span style="color: #e6db74; text-decoration-color: #e6db74; background-color: #272822">"unknown"</span><span style="color: #f8f8f2; text-decoration-color: #f8f8f2; background-color: #272822">)</span><span style="background-color: #272822">                                                                </span>
 <span style="background-color: #272822">                                                                                                                   </span>
 <span style="color: #a6e22e; text-decoration-color: #a6e22e; background-color: #272822">@serve</span><span style="color: #ff4689; text-decoration-color: #ff4689; background-color: #272822">.</span><span style="color: #f8f8f2; text-decoration-color: #f8f8f2; background-color: #272822">deployment(</span><span style="background-color: #272822">                                                                                                 </span>
 <span style="color: #f8f8f2; text-decoration-color: #f8f8f2; background-color: #272822">    ray_actor_options</span><span style="color: #ff4689; text-decoration-color: #ff4689; background-color: #272822">=</span><span style="color: #f8f8f2; text-decoration-color: #f8f8f2; background-color: #272822">{</span><span style="color: #e6db74; text-decoration-color: #e6db74; background-color: #272822">"num_cpus"</span><span style="color: #f8f8f2; text-decoration-color: #f8f8f2; background-color: #272822">: </span><span style="color: #ae81ff; text-decoration-color: #ae81ff; background-color: #272822">0.1</span><span style="color: #f8f8f2; text-decoration-color: #f8f8f2; background-color: #272822">},</span><span style="background-color: #272822">                                                                           </span>
@@ -1145,10 +1150,6 @@ We make use of [application args](https://docs.ray.io/en/latest/serve/advanced-g
 !serve shutdown -y
 ```
 
-    2024-10-23 17:06:59,677	WARN scripts.py:132 -- The `RAY_AGENT_ADDRESS` env var has been deprecated in favor of the `RAY_DASHBOARD_ADDRESS` env var. The `RAY_AGENT_ADDRESS` is ignored.
-    2024-10-23 17:06:59,686	SUCC scripts.py:747 -- Sent shutdown request; applications will be deleted asynchronously.
-
-
 We can now deploy the apps locally with
 
 
@@ -1156,15 +1157,6 @@ We can now deploy the apps locally with
 !serve run local_deploy_dspy.yaml --non-blocking
 ```
 
-    2024-10-23 17:10:49,026	INFO scripts.py:489 -- Running config file: 'local_deploy_dspy.yaml'.
-    2024-10-23 17:10:49,352	INFO worker.py:1601 -- Connecting to existing Ray cluster at address: 10.0.0.38:6379...
-    2024-10-23 17:10:49,359	INFO worker.py:1777 -- Connected to Ray cluster. View the dashboard at https://session-rrffq784rd7isilec3enpn8tqf.i.anyscaleuserdata.com 
-    2024-10-23 17:10:49,364	INFO packaging.py:359 -- Pushing file package 'gcs://_ray_pkg_7aa1baf78c230c6e25663bb0cf020412097a9e34.zip' (1.32MiB) to Ray cluster...
-    2024-10-23 17:10:49,377	INFO packaging.py:372 -- Successfully pushed file package 'gcs://_ray_pkg_7aa1baf78c230c6e25663bb0cf020412097a9e34.zip'.
-    INFO 2024-10-23 17:10:49,406 serve 83268 api.py:259 - Connecting to existing Serve app in namespace "serve". New http options will not be applied.
-    2024-10-23 17:10:49,413	SUCC scripts.py:540 -- Submitted deploy config successfully.
-
-
 ## Query the deployed DSPy service
 
 We can query our app directly using HTTP requests. You may need to wait 1-2 minutes for the deployment to start up.
@@ -1183,7 +1175,7 @@ else:
     print(response.text)
 ```
 
-    card_payment_not_recognised
+    request_refund
 
 
 
@@ -1214,15 +1206,6 @@ Follow the URL in order to find your service URL and API key for your deployed s
 !anyscale service deploy -f serve_1B.yaml
 ```
 
-    (anyscale +2.7s) Updating existing service 'dspy-rayllm-service'.
-    (anyscale +4.9s) Uploading local dir '.' to cloud storage.
-    (anyscale +7.2s) Including workspace-managed pip dependencies.
-    (anyscale +8.1s) Service 'dspy-rayllm-service' deployed.
-    (anyscale +8.1s) View the service in the UI: 'https://console.anyscale.com/services/service2_3kg2zf69v85dn3d6l3iluqkk4f'
-    (anyscale +8.1s) Query the service once it's running using the following curl command:
-    (anyscale +8.1s) curl https://dspy-rayllm-service-mglb8.cld-tffbxe9ia5phqr1u.s.anyscaleuserdata.com/
-
-
 <b style="background-color: yellow;">&nbsp;🔄 REPLACE&nbsp;</b>:
 Replace the following variables with your Anyscale service URL and API key.
 
@@ -1284,14 +1267,6 @@ update_rayllm_config("configs/anyscale_deploy.yaml", new_api_base=ANYSCALE_RAYLL
 !anyscale service deploy -f configs/anyscale_deploy.yaml
 ```
 
-    (anyscale +2.4s) Updating existing service 'dspy-service'.
-    (anyscale +4.5s) Uploading local dir '.' to cloud storage.
-    (anyscale +8.0s) Service 'dspy-service' deployed.
-    (anyscale +8.0s) View the service in the UI: 'https://console.anyscale.com/services/service2_l6ivxkmxypevqv5893bs3rq23e'
-    (anyscale +8.0s) Query the service once it's running using the following curl command:
-    (anyscale +8.0s) curl https://dspy-service-mglb8.cld-tffbxe9ia5phqr1u.s.anyscaleuserdata.com/
-
-
 Great! We should now have a service which runs the compiled DSPy program. Let's query this new service. Make sure to enter the details here: 
 
 
@@ -1300,6 +1275,8 @@ ANYSCALE_DSPY_SERVICE_BASE_URL = None
 ANYSCALE_DSPY_API_KEY = "fake-api-key" # Use the provided API key if query_auth_token_enabled is `True`
 ```
 
+Note: You may need to way a few minutes for the services to deploy and scale
+
 
 ```python
 import requests
@@ -1318,7 +1295,7 @@ else:
     print(response.text)
 ```
 
-    request_refund
+    cash_withdrawal_not_recognised
 
 
 <b style="background-color: yellow;">&nbsp;🛑 IMPORTANT&nbsp;</b>: Please `Terminate` your service from the Service page to avoid depleting your credits.
diff --git a/templates/e2e-dspy-workflow/README_files/README_63_0.png b/templates/e2e-dspy-workflow/README_files/README_63_0.png
index 4d1a833f69ae1f0bfc87161fdd08741ff7145afc..dad8171aadce288f968fa31a532ef4b814e272cd 100644
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