diff --git a/atlas/.gitignore b/atlas/.gitignore
index 5bc5f59..7ee5290 100644
--- a/atlas/.gitignore
+++ b/atlas/.gitignore
@@ -212,3 +212,5 @@ __marimo__/
 ntuple_production/production_status.json
 # preprocess json
 preprocess_output.json
+# Dask reports
+*html
diff --git a/atlas/analysis.ipynb b/atlas/analysis.ipynb
index af4440f..1c34999 100644
--- a/atlas/analysis.ipynb
+++ b/atlas/analysis.ipynb
@@ -2,13 +2,140 @@
  "cells": [
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 33,
    "id": "3f9f15ac-903b-4f33-b775-e7c14a3647a8",
-   "metadata": {},
-   "outputs": [],
+   "metadata": {
+    "scrolled": true
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Requirement already satisfied: atlas_schema in /usr/local/lib/python3.12/site-packages (0.4.1)\n",
+      "Requirement already satisfied: coffea>=2025.7.0 in /usr/local/lib/python3.12/site-packages (from coffea[dask]>=2025.7.0->atlas_schema) (2025.9.0)\n",
+      "Requirement already satisfied: particle>=0.25.0 in /usr/local/lib/python3.12/site-packages (from atlas_schema) (0.25.4)\n",
+      "Requirement already satisfied: aiohttp in /usr/local/lib/python3.12/site-packages (from coffea>=2025.7.0->coffea[dask]>=2025.7.0->atlas_schema) (3.12.15)\n",
+      "Requirement already satisfied: awkward>=2.8.9 in /usr/local/lib/python3.12/site-packages (from coffea>=2025.7.0->coffea[dask]>=2025.7.0->atlas_schema) (2.8.9)\n",
+      "Requirement already satisfied: cachetools in /usr/local/lib/python3.12/site-packages (from coffea>=2025.7.0->coffea[dask]>=2025.7.0->atlas_schema) (6.2.0)\n",
+      "Requirement already satisfied: cloudpickle>=1.2.3 in /usr/local/lib/python3.12/site-packages (from coffea>=2025.7.0->coffea[dask]>=2025.7.0->atlas_schema) (3.1.1)\n",
+      "Requirement already satisfied: correctionlib>=2.6.0 in /usr/local/lib/python3.12/site-packages (from coffea>=2025.7.0->coffea[dask]>=2025.7.0->atlas_schema) (2.7.0)\n",
+      "Requirement already satisfied: dask-awkward>=2025.9.0 in /usr/local/lib/python3.12/site-packages (from coffea>=2025.7.0->coffea[dask]>=2025.7.0->atlas_schema) (2025.9.0)\n",
+      "Requirement already satisfied: dask-histogram>=2025.2.0 in /usr/local/lib/python3.12/site-packages (from coffea>=2025.7.0->coffea[dask]>=2025.7.0->atlas_schema) (2025.2.0)\n",
+      "Requirement already satisfied: dask>=2024.3.0 in /usr/local/lib/python3.12/site-packages (from dask[array]>=2024.3.0->coffea>=2025.7.0->coffea[dask]>=2025.7.0->atlas_schema) (2025.3.0)\n",
+      "Requirement already satisfied: hist>=2 in /usr/local/lib/python3.12/site-packages (from coffea>=2025.7.0->coffea[dask]>=2025.7.0->atlas_schema) (2.9.0)\n",
+      "Requirement already satisfied: lz4 in /usr/local/lib/python3.12/site-packages (from coffea>=2025.7.0->coffea[dask]>=2025.7.0->atlas_schema) (4.4.4)\n",
+      "Requirement already satisfied: matplotlib>=3 in /usr/local/lib/python3.12/site-packages (from coffea>=2025.7.0->coffea[dask]>=2025.7.0->atlas_schema) (3.10.6)\n",
+      "Requirement already satisfied: mplhep>=0.1.18 in /usr/local/lib/python3.12/site-packages (from coffea>=2025.7.0->coffea[dask]>=2025.7.0->atlas_schema) (1.0.0rc7.dev11+g271a85873)\n",
+      "Requirement already satisfied: numba>=0.58.1 in /usr/local/lib/python3.12/site-packages (from coffea>=2025.7.0->coffea[dask]>=2025.7.0->atlas_schema) (0.61.2)\n",
+      "Requirement already satisfied: numpy>=1.22 in /usr/local/lib/python3.12/site-packages (from coffea>=2025.7.0->coffea[dask]>=2025.7.0->atlas_schema) (2.2.6)\n",
+      "Requirement already satisfied: packaging in /usr/local/lib/python3.12/site-packages (from coffea>=2025.7.0->coffea[dask]>=2025.7.0->atlas_schema) (25.0)\n",
+      "Requirement already satisfied: pandas in /usr/local/lib/python3.12/site-packages (from coffea>=2025.7.0->coffea[dask]>=2025.7.0->atlas_schema) (2.3.2)\n",
+      "Requirement already satisfied: pyarrow>=6.0.0 in /usr/local/lib/python3.12/site-packages (from coffea>=2025.7.0->coffea[dask]>=2025.7.0->atlas_schema) (21.0.0)\n",
+      "Requirement already satisfied: requests in /usr/local/lib/python3.12/site-packages (from coffea>=2025.7.0->coffea[dask]>=2025.7.0->atlas_schema) (2.32.4)\n",
+      "Requirement already satisfied: scipy>=1.1.0 in /usr/local/lib/python3.12/site-packages (from coffea>=2025.7.0->coffea[dask]>=2025.7.0->atlas_schema) (1.16.2)\n",
+      "Requirement already satisfied: toml>=0.10.2 in /usr/local/lib/python3.12/site-packages (from coffea>=2025.7.0->coffea[dask]>=2025.7.0->atlas_schema) (0.10.2)\n",
+      "Requirement already satisfied: tqdm>=4.27.0 in /usr/local/lib/python3.12/site-packages (from coffea>=2025.7.0->coffea[dask]>=2025.7.0->atlas_schema) (4.67.1)\n",
+      "Requirement already satisfied: uproot>=5.6.0 in /usr/local/lib/python3.12/site-packages (from coffea>=2025.7.0->coffea[dask]>=2025.7.0->atlas_schema) (5.6.6)\n",
+      "Requirement already satisfied: vector!=1.6.0,>=1.4.1 in /usr/local/lib/python3.12/site-packages (from coffea>=2025.7.0->coffea[dask]>=2025.7.0->atlas_schema) (1.6.3)\n",
+      "Requirement already satisfied: awkward-cpp==50 in /usr/local/lib/python3.12/site-packages (from awkward>=2.8.9->coffea>=2025.7.0->coffea[dask]>=2025.7.0->atlas_schema) (50)\n",
+      "Requirement already satisfied: fsspec>=2022.11.0 in /usr/local/lib/python3.12/site-packages (from awkward>=2.8.9->coffea>=2025.7.0->coffea[dask]>=2025.7.0->atlas_schema) (2025.9.0)\n",
+      "Requirement already satisfied: bokeh!=3.0.*,>=2.4.2 in /usr/local/lib/python3.12/site-packages (from coffea[dask]>=2025.7.0->atlas_schema) (3.8.0)\n",
+      "Requirement already satisfied: distributed>=2024.3.0 in /usr/local/lib/python3.12/site-packages (from coffea[dask]>=2025.7.0->atlas_schema) (2025.3.0)\n",
+      "Requirement already satisfied: Jinja2>=2.9 in /usr/local/lib/python3.12/site-packages (from bokeh!=3.0.*,>=2.4.2->coffea[dask]>=2025.7.0->atlas_schema) (3.1.6)\n",
+      "Requirement already satisfied: contourpy>=1.2 in /usr/local/lib/python3.12/site-packages (from bokeh!=3.0.*,>=2.4.2->coffea[dask]>=2025.7.0->atlas_schema) (1.3.3)\n",
+      "Requirement already satisfied: narwhals>=1.13 in /usr/local/lib/python3.12/site-packages (from bokeh!=3.0.*,>=2.4.2->coffea[dask]>=2025.7.0->atlas_schema) (2.5.0)\n",
+      "Requirement already satisfied: pillow>=7.1.0 in /usr/local/lib/python3.12/site-packages (from bokeh!=3.0.*,>=2.4.2->coffea[dask]>=2025.7.0->atlas_schema) (11.3.0)\n",
+      "Requirement already satisfied: PyYAML>=3.10 in /usr/local/lib/python3.12/site-packages (from bokeh!=3.0.*,>=2.4.2->coffea[dask]>=2025.7.0->atlas_schema) (6.0.2)\n",
+      "Requirement already satisfied: tornado>=6.2 in /usr/local/lib/python3.12/site-packages (from bokeh!=3.0.*,>=2.4.2->coffea[dask]>=2025.7.0->atlas_schema) (6.5.2)\n",
+      "Requirement already satisfied: xyzservices>=2021.09.1 in /usr/local/lib/python3.12/site-packages (from bokeh!=3.0.*,>=2.4.2->coffea[dask]>=2025.7.0->atlas_schema) (2025.4.0)\n",
+      "Requirement already satisfied: pydantic>=2 in /usr/local/lib/python3.12/site-packages (from correctionlib>=2.6.0->coffea>=2025.7.0->coffea[dask]>=2025.7.0->atlas_schema) (2.11.9)\n",
+      "Requirement already satisfied: rich in /usr/local/lib/python3.12/site-packages (from correctionlib>=2.6.0->coffea>=2025.7.0->coffea[dask]>=2025.7.0->atlas_schema) (14.1.0)\n",
+      "Requirement already satisfied: click>=8.1 in /usr/local/lib/python3.12/site-packages (from dask>=2024.3.0->dask[array]>=2024.3.0->coffea>=2025.7.0->coffea[dask]>=2025.7.0->atlas_schema) (8.3.0)\n",
+      "Requirement already satisfied: partd>=1.4.0 in /usr/local/lib/python3.12/site-packages (from dask>=2024.3.0->dask[array]>=2024.3.0->coffea>=2025.7.0->coffea[dask]>=2025.7.0->atlas_schema) (1.4.2)\n",
+      "Requirement already satisfied: toolz>=0.10.0 in /usr/local/lib/python3.12/site-packages (from dask>=2024.3.0->dask[array]>=2024.3.0->coffea>=2025.7.0->coffea[dask]>=2025.7.0->atlas_schema) (1.0.0)\n",
+      "Requirement already satisfied: typing-extensions>=4.8.0 in /usr/local/lib/python3.12/site-packages (from dask-awkward>=2025.9.0->coffea>=2025.7.0->coffea[dask]>=2025.7.0->atlas_schema) (4.15.0)\n",
+      "Requirement already satisfied: boost-histogram>=1.3.2 in /usr/local/lib/python3.12/site-packages (from dask-histogram>=2025.2.0->coffea>=2025.7.0->coffea[dask]>=2025.7.0->atlas_schema) (1.6.1)\n",
+      "Requirement already satisfied: locket>=1.0.0 in /usr/local/lib/python3.12/site-packages (from distributed>=2024.3.0->coffea[dask]>=2025.7.0->atlas_schema) (1.0.0)\n",
+      "Requirement already satisfied: msgpack>=1.0.2 in /usr/local/lib/python3.12/site-packages (from distributed>=2024.3.0->coffea[dask]>=2025.7.0->atlas_schema) (1.1.1)\n",
+      "Requirement already satisfied: psutil>=5.8.0 in /usr/local/lib/python3.12/site-packages (from distributed>=2024.3.0->coffea[dask]>=2025.7.0->atlas_schema) (7.1.0)\n",
+      "Requirement already satisfied: sortedcontainers>=2.0.5 in /usr/local/lib/python3.12/site-packages (from distributed>=2024.3.0->coffea[dask]>=2025.7.0->atlas_schema) (2.4.0)\n",
+      "Requirement already satisfied: tblib>=1.6.0 in /usr/local/lib/python3.12/site-packages (from distributed>=2024.3.0->coffea[dask]>=2025.7.0->atlas_schema) (3.1.0)\n",
+      "Requirement already satisfied: urllib3>=1.26.5 in /usr/local/lib/python3.12/site-packages (from distributed>=2024.3.0->coffea[dask]>=2025.7.0->atlas_schema) (2.5.0)\n",
+      "Requirement already satisfied: zict>=3.0.0 in /usr/local/lib/python3.12/site-packages (from distributed>=2024.3.0->coffea[dask]>=2025.7.0->atlas_schema) (3.0.0)\n",
+      "Requirement already satisfied: histoprint>=2.2.0 in /usr/local/lib/python3.12/site-packages (from hist>=2->coffea>=2025.7.0->coffea[dask]>=2025.7.0->atlas_schema) (2.6.0)\n",
+      "Requirement already satisfied: uhi>=0.2.1 in /usr/local/lib/python3.12/site-packages (from histoprint>=2.2.0->hist>=2->coffea>=2025.7.0->coffea[dask]>=2025.7.0->atlas_schema) (1.0.0)\n",
+      "Requirement already satisfied: MarkupSafe>=2.0 in /usr/local/lib/python3.12/site-packages (from Jinja2>=2.9->bokeh!=3.0.*,>=2.4.2->coffea[dask]>=2025.7.0->atlas_schema) (3.0.2)\n",
+      "Requirement already satisfied: cycler>=0.10 in /usr/local/lib/python3.12/site-packages (from matplotlib>=3->coffea>=2025.7.0->coffea[dask]>=2025.7.0->atlas_schema) (0.12.1)\n",
+      "Requirement already satisfied: fonttools>=4.22.0 in /usr/local/lib/python3.12/site-packages (from matplotlib>=3->coffea>=2025.7.0->coffea[dask]>=2025.7.0->atlas_schema) (4.60.0)\n",
+      "Requirement already satisfied: kiwisolver>=1.3.1 in /usr/local/lib/python3.12/site-packages (from matplotlib>=3->coffea>=2025.7.0->coffea[dask]>=2025.7.0->atlas_schema) (1.4.9)\n",
+      "Requirement already satisfied: pyparsing>=2.3.1 in /usr/local/lib/python3.12/site-packages (from matplotlib>=3->coffea>=2025.7.0->coffea[dask]>=2025.7.0->atlas_schema) (3.2.5)\n",
+      "Requirement already satisfied: python-dateutil>=2.7 in /usr/local/lib/python3.12/site-packages (from matplotlib>=3->coffea>=2025.7.0->coffea[dask]>=2025.7.0->atlas_schema) (2.9.0.post0)\n",
+      "Requirement already satisfied: mplhep-data>=0.0.4 in /usr/local/lib/python3.12/site-packages (from mplhep>=0.1.18->coffea>=2025.7.0->coffea[dask]>=2025.7.0->atlas_schema) (0.0.4)\n",
+      "Requirement already satisfied: llvmlite<0.45,>=0.44.0dev0 in /usr/local/lib/python3.12/site-packages (from numba>=0.58.1->coffea>=2025.7.0->coffea[dask]>=2025.7.0->atlas_schema) (0.44.0)\n",
+      "Requirement already satisfied: pytz>=2020.1 in /usr/local/lib/python3.12/site-packages (from pandas->coffea>=2025.7.0->coffea[dask]>=2025.7.0->atlas_schema) (2025.2)\n",
+      "Requirement already satisfied: tzdata>=2022.7 in /usr/local/lib/python3.12/site-packages (from pandas->coffea>=2025.7.0->coffea[dask]>=2025.7.0->atlas_schema) (2025.2)\n",
+      "Requirement already satisfied: attrs>=19.2 in /usr/local/lib/python3.12/site-packages (from particle>=0.25.0->atlas_schema) (25.3.0)\n",
+      "Requirement already satisfied: hepunits>=2.0.0 in /usr/local/lib/python3.12/site-packages (from particle>=0.25.0->atlas_schema) (2.4.1)\n",
+      "Requirement already satisfied: annotated-types>=0.6.0 in /usr/local/lib/python3.12/site-packages (from pydantic>=2->correctionlib>=2.6.0->coffea>=2025.7.0->coffea[dask]>=2025.7.0->atlas_schema) (0.7.0)\n",
+      "Requirement already satisfied: pydantic-core==2.33.2 in /usr/local/lib/python3.12/site-packages (from pydantic>=2->correctionlib>=2.6.0->coffea>=2025.7.0->coffea[dask]>=2025.7.0->atlas_schema) (2.33.2)\n",
+      "Requirement already satisfied: typing-inspection>=0.4.0 in /usr/local/lib/python3.12/site-packages (from pydantic>=2->correctionlib>=2.6.0->coffea>=2025.7.0->coffea[dask]>=2025.7.0->atlas_schema) (0.4.1)\n",
+      "Requirement already satisfied: six>=1.5 in /usr/local/lib/python3.12/site-packages (from python-dateutil>=2.7->matplotlib>=3->coffea>=2025.7.0->coffea[dask]>=2025.7.0->atlas_schema) (1.17.0)\n",
+      "Requirement already satisfied: cramjam>=2.5.0 in /usr/local/lib/python3.12/site-packages (from uproot>=5.6.0->coffea>=2025.7.0->coffea[dask]>=2025.7.0->atlas_schema) (2.11.0)\n",
+      "Requirement already satisfied: xxhash in /usr/local/lib/python3.12/site-packages (from uproot>=5.6.0->coffea>=2025.7.0->coffea[dask]>=2025.7.0->atlas_schema) (3.5.0)\n",
+      "Requirement already satisfied: aiohappyeyeballs>=2.5.0 in /usr/local/lib/python3.12/site-packages (from aiohttp->coffea>=2025.7.0->coffea[dask]>=2025.7.0->atlas_schema) (2.6.1)\n",
+      "Requirement already satisfied: aiosignal>=1.4.0 in /usr/local/lib/python3.12/site-packages (from aiohttp->coffea>=2025.7.0->coffea[dask]>=2025.7.0->atlas_schema) (1.4.0)\n",
+      "Requirement already satisfied: frozenlist>=1.1.1 in /usr/local/lib/python3.12/site-packages (from aiohttp->coffea>=2025.7.0->coffea[dask]>=2025.7.0->atlas_schema) (1.7.0)\n",
+      "Requirement already satisfied: multidict<7.0,>=4.5 in /usr/local/lib/python3.12/site-packages (from aiohttp->coffea>=2025.7.0->coffea[dask]>=2025.7.0->atlas_schema) (6.6.3)\n",
+      "Requirement already satisfied: propcache>=0.2.0 in /usr/local/lib/python3.12/site-packages (from aiohttp->coffea>=2025.7.0->coffea[dask]>=2025.7.0->atlas_schema) (0.3.1)\n",
+      "Requirement already satisfied: yarl<2.0,>=1.17.0 in /usr/local/lib/python3.12/site-packages (from aiohttp->coffea>=2025.7.0->coffea[dask]>=2025.7.0->atlas_schema) (1.20.1)\n",
+      "Requirement already satisfied: idna>=2.0 in /usr/local/lib/python3.12/site-packages (from yarl<2.0,>=1.17.0->aiohttp->coffea>=2025.7.0->coffea[dask]>=2025.7.0->atlas_schema) (3.10)\n",
+      "Requirement already satisfied: charset_normalizer<4,>=2 in /usr/local/lib/python3.12/site-packages (from requests->coffea>=2025.7.0->coffea[dask]>=2025.7.0->atlas_schema) (3.4.2)\n",
+      "Requirement already satisfied: certifi>=2017.4.17 in /usr/local/lib/python3.12/site-packages (from requests->coffea>=2025.7.0->coffea[dask]>=2025.7.0->atlas_schema) (2025.7.14)\n",
+      "Requirement already satisfied: markdown-it-py>=2.2.0 in /usr/local/lib/python3.12/site-packages (from rich->correctionlib>=2.6.0->coffea>=2025.7.0->coffea[dask]>=2025.7.0->atlas_schema) (4.0.0)\n",
+      "Requirement already satisfied: pygments<3.0.0,>=2.13.0 in /usr/local/lib/python3.12/site-packages (from rich->correctionlib>=2.6.0->coffea>=2025.7.0->coffea[dask]>=2025.7.0->atlas_schema) (2.19.2)\n",
+      "Requirement already satisfied: mdurl~=0.1 in /usr/local/lib/python3.12/site-packages (from markdown-it-py>=2.2.0->rich->correctionlib>=2.6.0->coffea>=2025.7.0->coffea[dask]>=2025.7.0->atlas_schema) (0.1.2)\n",
+      "Requirement already satisfied: mplhep in /usr/local/lib/python3.12/site-packages (1.0.0rc7.dev11+g271a85873)\n",
+      "Collecting mplhep\n",
+      "  Downloading mplhep-1.0.0rc7-py3-none-any.whl.metadata (9.4 kB)\n",
+      "Requirement already satisfied: matplotlib>=3.4 in /usr/local/lib/python3.12/site-packages (from mplhep) (3.10.6)\n",
+      "Requirement already satisfied: mplhep-data>=0.0.4 in /usr/local/lib/python3.12/site-packages (from mplhep) (0.0.4)\n",
+      "Requirement already satisfied: numpy>=1.16.0 in /usr/local/lib/python3.12/site-packages (from mplhep) (2.2.6)\n",
+      "Requirement already satisfied: packaging in /usr/local/lib/python3.12/site-packages (from mplhep) (25.0)\n",
+      "Requirement already satisfied: uhi>=0.2.0 in /usr/local/lib/python3.12/site-packages (from mplhep) (1.0.0)\n",
+      "Requirement already satisfied: contourpy>=1.0.1 in /usr/local/lib/python3.12/site-packages (from matplotlib>=3.4->mplhep) (1.3.3)\n",
+      "Requirement already satisfied: cycler>=0.10 in /usr/local/lib/python3.12/site-packages (from matplotlib>=3.4->mplhep) (0.12.1)\n",
+      "Requirement already satisfied: fonttools>=4.22.0 in /usr/local/lib/python3.12/site-packages (from matplotlib>=3.4->mplhep) (4.60.0)\n",
+      "Requirement already satisfied: kiwisolver>=1.3.1 in /usr/local/lib/python3.12/site-packages (from matplotlib>=3.4->mplhep) (1.4.9)\n",
+      "Requirement already satisfied: pillow>=8 in /usr/local/lib/python3.12/site-packages (from matplotlib>=3.4->mplhep) (11.3.0)\n",
+      "Requirement already satisfied: pyparsing>=2.3.1 in /usr/local/lib/python3.12/site-packages (from matplotlib>=3.4->mplhep) (3.2.5)\n",
+      "Requirement already satisfied: python-dateutil>=2.7 in /usr/local/lib/python3.12/site-packages (from matplotlib>=3.4->mplhep) (2.9.0.post0)\n",
+      "Requirement already satisfied: six>=1.5 in /usr/local/lib/python3.12/site-packages (from python-dateutil>=2.7->matplotlib>=3.4->mplhep) (1.17.0)\n",
+      "Downloading mplhep-1.0.0rc7-py3-none-any.whl (83 kB)\n",
+      "Installing collected packages: mplhep\n",
+      "  Attempting uninstall: mplhep\n",
+      "    Found existing installation: mplhep 1.0.0rc7.dev11+g271a85873\n",
+      "    Uninstalling mplhep-1.0.0rc7.dev11+g271a85873:\n",
+      "      Successfully uninstalled mplhep-1.0.0rc7.dev11+g271a85873\n",
+      "Successfully installed mplhep-1.0.0rc7\n"
+     ]
+    },
+    {
+     "data": {
+      "text/plain": [
+       "'1.0.0rc7.dev11+g271a85873'"
+      ]
+     },
+     "execution_count": 33,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
    "source": [
-    "# ! pip install --upgrade atlas_schema\n",
-    "# ! pip install --upgrade git+https://github.com/scikit-hep/mplhep.git\n",
+    "! pip install --upgrade atlas_schema\n",
+    "! pip install --upgrade --pre mplhep\n",
+    "import mplhep\n",
+    "mplhep.__version__\n",
     "\n",
     "# import importlib\n",
     "# importlib.reload(utils)"
@@ -16,7 +143,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 34,
    "id": "94b8b953-dc21-4d5c-899d-b1f0b03c70b2",
    "metadata": {},
    "outputs": [],
@@ -31,14 +158,13 @@
     "import vector\n",
     "import hist\n",
     "import matplotlib.pyplot as plt\n",
-    "import mplhep\n",
     "import numpy as np\n",
     "import uproot\n",
     "\n",
     "from atlas_schema.schema import NtupleSchema\n",
     "from coffea import processor\n",
     "from coffea.nanoevents import NanoEventsFactory\n",
-    "from dask.distributed import Client, PipInstall\n",
+    "from dask.distributed import Client, PipInstall, performance_report\n",
     "\n",
     "\n",
     "import utils\n",
@@ -62,7 +188,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 35,
    "id": "3dcf6216-0eca-4ea0-921b-eae3eda04af1",
    "metadata": {},
    "outputs": [],
@@ -105,10 +231,33 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 4,
    "id": "6b6c685f-7e9c-4c5b-8f80-19f1543de32f",
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/usr/local/lib/python3.12/site-packages/atlas_schema/schema.py:175: RuntimeWarning: Missing event_ids : ['lumiBlock', 'mcEventWeights', 'dataTakingYear']\n",
+      "  self._build_collections(self._form[\"fields\"], self._form[\"contents\"])\n",
+      "/usr/local/lib/python3.12/site-packages/atlas_schema/schema.py:175: RuntimeWarning: I found a collection with no defined mixin: 'globalTriggerEffSF'. I will assume behavior: 'NanoCollection'. To suppress this warning next time, please define mixins for your custom collections. [mixin-undefined]\n",
+      "  self._build_collections(self._form[\"fields\"], self._form[\"contents\"])\n",
+      "/usr/local/lib/python3.12/site-packages/atlas_schema/schema.py:175: RuntimeWarning: I found a collection with no defined mixin: 'globalTriggerMatch'. I will assume behavior: 'Systematic'. To suppress this warning next time, please define mixins for your custom collections. [mixin-undefined]\n",
+      "  self._build_collections(self._form[\"fields\"], self._form[\"contents\"])\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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t9iei5uOwByIiI6fP4QSR6y+ioLQW7R0sETezq97abQkSiQSffPIJBg4ciHnz5mHr1q0K24OCgpCSkoKMjAz06NFDZTuFhYVITEyEr68vOnbsCKDhJriAgAAkJSUpPe7cuXOxY8cO7Nu3Dzdv3mw07EIWd+fz3crLy9GpUyc88cQTWL9+faPtlpaWWLp0KbZt24bU1FTVHwQR6RWv/BIRkdHq378/5s2bh99++w1r165V2LZkyRLU1NTg5ZdfVtvG4sWLcePGDSxdulT+XkBAAI4fP46CggKV+9XW1sLBwQEuLi5Nyt3e3h4eHh7Yv3+/2rvaa2trOdUZUSti8UtEREZt6dKl8PT0xIoVKxTeHzVqFCZNmoSEhARERETg5s2bCtvLy8vxr3/9C6tXr8aQIUMwdepU+bbJkyejtLQU//znP3H9+vVGx4yPj0dycjLCwsJgYdH0IR2TJ0/GpUuXMGvWLNy6dUthmxACH3/8MS5cuIDw8PAmH4OIdMNhD0REZNRcXFzwwQcfyMfY3mn9+vVo3749PvnkE/j6+qJ///645557cOHCBaSlpeHWrVt46qmn8OmnnyoMT5g5cyYOHTokn3t36NCh6NKlCyoqKvDnn3/i999/R7du3RpdbdbV4sWLcezYMWzevBk//fQTQkND4efnh1u3biE1NRXnzp3DoEGD8PbbbzfrOESkPV75JSIio/f0009j5MiRjd63t7eHVCrF3r17ERYWhszMTHz99dc4deoUBg8ejO+++w7fffcd3NzcGu27ceNG7NmzBw888AAuXryITZs2ITk5GS4uLli1ahVOnz6tMDtEU1haWiIpKQnfffcdgoOD8ddff2Hjxo04fvw4/Pz8sHHjRhw7dgx2dnbNOg4RaU8ihBCGTkKTtLQ0hISEIDU1tdEqPEREpD3ZDW8dHY3/hjciatsMVd+Z1LAHLm9MREREZPyMeXljkyp+4+LieOWXiKgZnuzvirLqejhYc9QbEbUcbS5Gyq78tjaTKn6JiKh5ngxuPPaViMic8Ed/IiIiIjIbrVb8Xr9+HXPmzEHnzp1ha2uLHj16ICYmxmDjPYiIiIjI/Og87GHUqFG4evWqVrEHDx6Ej48PsrOzMXjwYOTk5AAAXF1dce7cOSxduhRJSUk4cOAAbGxsdE2FiIiIiEgnOhe/ly5dQlZWllax7do1XFieOXMmcnJyMGbMGKxfvx7+/v44efIkIiIikJKSgmXLliksO0lERERE1BJ0HvZw8eJFCCFUPnbs2AEAmDdvHry8vHDy5EkkJSXBw8MDW7duhb+/P4CG9drj4+MBAGvWrOHwByKi1vD3GuDU8oZnIiIzpNcxvzdv3sRzzz2HwMBAvP/++wCAhIQEAMD48ePRoUMHhfiBAweiV69eKCoqwtGjR/WZChERKZMhBc680/BMRGSG9Fr8Pv/888jPz8emTZtgb28PAEhJSQEAhIeHK91H9r4sjoiIzE9ycjIkEonGx4svvtho3/T0dMyYMQNdu3aFnZ0d/Pz8MHr0aKxbtw61tbV6yS89PR1PPvkkevbsCXt7ewQGBiIiIgJHjhxRu198fDweeugheHp6wsHBAf369cPcuXNx7do1tfvV19fDx8cHEolE6bLOMjNmzIBEIsGlS5c0noMsVtPj999/19iWJnv37pW3t2HDBpVxEokEDzzwgMb2ZH8/lixZ0uzc9OW+++7DqlWrdNpn48aNkEgkSE5ObqGsSBt6m+d379692Lx5M/7v//4PgwcPlr9//vx5AED37t2V7hcQEAAAyMzM1FcqRERkokJDQxEaGqpy+92F4GeffYYFCxagrq4OvXv3xgMPPIDCwkIcPXoUs2fPxooVK3Dw4EF4eHg0OaevvvoKM2fOhLW1NR588EH5jd+JiYn44YcfsGzZMrz22msK+9TV1eHll1/GypUr0b59ewwdOhQdO3bEqVOn8Pnnn+Obb77Bzz//jEGDBik95tGjR+UF8pEjR3D9+nV4eXk1+Rzu9Oyzz8LZ2Vnldnd392YfY9u2bQqvZ8yY0ew2jcmRI0fw119/GToNaiK9FL9CCPznP/+Bvb19o38A8vPzATTM8KCMm1vDhOu5ubkaj1NaWori4uLmJQvAxsaGs0sQERmh8PBwLF68WKvY5cuX4z//+Q/69euH+Ph4+cUUACgvL8frr7+Ojz76CP/4xz9w9OhRWFhY6JzPtWvXEBUVBX9/fyQnJ8PX11e+LTc3F2PHjsXixYsxbtw49OvXT75t3rx5WLt2LaZOnYp169bByspKvi0hIQGPPfYYnnrqKfz5559wdHRsdNytW7cCAB566CHs27cP8fHxmD9/vs75KxMTEyO//6Yl1NTUYMeOHfD19YWjoyP279+PwsJC+f/3pqqurg4ZGRlISkrC22+/beh0DKqqqgpVVVXNbqe0tFQP2ehOL8Mevv/+e6SlpeH//u//4OnpqbCtvLwcAFT+pZe9L4tTZ+TIkXBxcWn2Y/ny5c08YyIiMqTc3Fy8/fbb6NmzJ1JSUhQKXwCwt7fHihUrEBERgePHj2P//v1NOs6RI0dQXl6ON954Q6HwBQBPT098+OGHqKurw88//yx/PzMzE19++SUeeeQRbNy4UaHwBRrugXnxxReRlZWF3bt3NzpmfX094uPj4eHhIf+1+vbt25uUvyEkJiaisLAQTz75JCZOnIiamhrs3LnT0Gk12549e3Dffffh+eefl0/daq6WL1+ul3pM3ZCeltTs4re+vh4xMTFwcnLCwoULG20XQig83032k3hdXZ3GYyUnJ+PWrVvNfkRHRzfjjImIyNCWL1+O0tJSLF++HHZ2dirj3njjDcyaNQuFhYVNOo5sLK2Tk5PS7YMGDcI777yD3r17y9979913UVtb2+g3oXeaNm0ahg0bhgsXLjTalpycjJycHDz++OO455570KtXLxw5csRkCi7ZVWtZ8QuYVvGuyoABA7Bz507s3LkTy5Yt0xi/cuVKDB48GC4uLhg2bJjasc9xcXEYPXo02rdvj27duuGJJ57AuXPn5Nu//vprSCQSrF27VmG/kJAQSCQSfPbZZwrvBwcHw87ODhUVFfLx0vv378dPP/2E4OBg2NvbIyAgAEuXLtWq/rpbdHS0XuoxQ419bvawh4SEBJw5cwb/+te/Gs3mADT89F1cXIzCwkKlQx9kV3wdHBw0HsvR0VHtOCUiIjIPR44cgZubm7y4UiUkJARffPFFk4/Tq1cvAMDrr7+Obt26ITg4WGG7k5MTXnnlFYX3jh07Bh8fH9x///1q2z18+LDSbbLxsk8++SQAYOLEiTh9+jTi4+MRFRXV5HNpDdXV1di1axf8/f3l45l9fX2RlJRk8kMfPD09ERERAUD1UE6ZGTNm4KuvvoKPjw/+8Y9/4OrVq5g5cyYGDhzYKHbp0qWIiYlBp06d8Mgjj6CoqAgJCQnYu3cv9u7diyFDhiAsLAwAcOjQIcyZMwcAUFZWJr858ciRI5g7dy4AoKSkBH/88QceeOABhR8M9+3bh48//hgPPvggevfujV27diEmJgaWlpY6XxTU1/BRZUN+WkOzi9/169cDAJ555hml293d3eXFb9euXRttz8vLAwB07NixuakQEbVNiSOByjz9tFWRc/s54V79tAkAth5AWPOv4uzZswdFRUUqt8+fPx8BAQHIyMiQF6Yt6eGHH8a4ceOwe/duhISEoFevXnj44Yfx4IMPYvjw4fKZjWSEELhw4QJCQkKadLy6ujrEx8fD09MTI0aMANBQ/C5btgzbt2/XS/H75ptvqryQ1L59e7zxxhtNbnvv3r24deuWvEADgAkTJmD16tXYtWsXpk+f3uS2TUVKSgq++uorDB06FD/99JP8s960aROmTp2qEJudnY233noLffv2xf79++W10LFjxzB69GgsXLgQR44cgZeXF3r16qVwpfT48eOoq6uDp6enwqwjKSkpqKurw0MPPaRwrBUrVmDPnj0YM2YMAODUqVMICgrC999/b3a/EW9W8Xv9+nX89NNP6NatG4YOHao0JjAwEJmZmcjIyGj0EzPQ8OHL4oiISInKPKBC/dRYuqtvgTab75dffsEvv/yicvuECRPg7OyMkpIS+Pn5Ndp+9epVdO7cudH7Xbp0wcWLF3XORyKRYOfOndi8eTM2bdqEw4cP44MPPsAHH3wAGxsbPPzww4iOjpZf5czPz0dFRQV8fHx0PhYAHDhwAPn5+YiKipKvkhocHAx/f38cPnwYOTk56NSpU5Palvnyyy9VbuvSpUuzit87hzzITJw4EatXr8b27dvNoviVfb4rV65U+CHjmWeewWeffYZjx47J3/vmm29QVVWFt956S+Ei4P33348pU6bgyy+/xNmzZ3HPPfcgLCwMK1euRFZWFrp06YJjx47B3t4es2bNwltvvYXLly/D19dX/hsF2dVimYiICHnhCwC9e/dGQEAACgoKWuRzMGbNGvP71Vdfoa6uDk8//bTKGNmUNfv27VO6fe/evQCAIUOGNCcVIqK2y9YDsPPWz0P+z347/bVp592Qox7ExsaqXUV05MiRcHNzg7W1tdIxsI6OjliwYIHCo6mFqIyFhQWmTp0qv5Frz549eOmll+Dl5YWdO3di2LBh8gWd3NzcYGFh0eSC4u4hDzITJkxAfX09vv/++2adCwBkZWWp/Hyb8gOCTGVlJX744Qd07doVAwYMkL8/YsQIdOjQAYmJiWqv6rcV586dg4uLi8JnIPPggw8q/Fk2zavsKv+dZO/JYmT7Hjp0CEDD1eFBgwZh9OjRACBfLOzIkSPw8PBQmH0EgNIpBO/+zYW5aNaVX9nyxGPHjlUZExERgdjYWCQkJKCgoEDhJ5vjx4/j7NmzcHV1VdrxREQEvQwnkEu4t+GKr10nYLxpzlNqaWmJ7t27K9wQJOPi4oKPPvpI4b2ffvoJ1dXVejm2nZ0dxo4di7Fjx+KDDz7A559/jnnz5uH555/H+PHjYWVlBT8/P6U3st3trbfeQmlpKd5++21IJBLU1tbKi1tVd8Fv374d8+bN08u56Nt///tflJSUoKSkBBKJRGnMrl27MG3atFbOrHXl5uaqnJP57vevXbsGW1tbpcNQZLNnXblyBUDD3wkrKyscOnQIzzzzDFJSUjBv3jyEhobCysoKR48exeOPP47jx49j4sSJjfqAw0tva3Lxe/PmTZw8eRLW1tZqxzYFBQUhLCwMiYmJmDRpEjZs2ABfX1+kpaUhMjISALBgwQLY2to2NRUiIjIzQUFB2LJlCw4cOKB2hbDi4mKcP39e6RAJbXTt2hUBAQFISkpqtE0ikWDu3LnYsWMH9u3bh5s3b6J9+/bo06cPEhIScPr0aZXjknNzc/H6669j0KBB8iJl//79uHHjBvr27av0ZrnvvvsOhw4dQm5ubqNpRY2B7Kr1E0880egG+NzcXOzYsQPbt29v88Wvj48PfvvtN6XbZPc5yXh5eaGyshLFxcWNCmBZrKxgdnR0xJAhQ3Do0CGcOXMGRUVFuP/++2Fvb4+QkBAcOXIEaWlpqKioaDTeF4DKH0jMUZOL3/3796O+vh4hISEa7/hbt24dQkNDsX//fvj5+cHV1VX+q4/hw4eb3UBrIiJqnujoaGzduhWLFi1CSkoKLC2V/3e2ePFi1NfXN/k4AQEBOH78eKPfXN6ptrYWDg4OcHFxAQAsWrQIP/zwA2JiYuS/Ib3b119/DQAYNWqU/D1Z8bhkyRJMmDBB6XHWrVuH77//Hs8991yTz6klVFRUICEhAR06dMCWLVsa9UdlZSXc3d3lQx80zZZgyrp37479+/fjt99+azT04e6pvWTzUx8+fBiPPvqo0tg7V8gNCwvDG2+8IZ86TjaUYfjw4fjwww/l80bfPbyCFDV5zK/sp2B1U7nI+Pn5ITU1FbNnz4a3tzcqKioQGBiI2NhYJCYmwtrauqlpEBGRGerduzfmzp2L3377DaNGjcLly5cVtldXV+PNN9/Exx9/3KwpmSZPnozS0lL885//xPXr1xttj4+PR3JyMsLCwuTz1g8dOhTjxo3D999/j9mzZzdaCevgwYNYsmQJ3Nzc8MILLwC4vSqas7MzwsPDleYi+22pMc6Z++OPP6KsrAyPP/640h9EbG1tMW7cOFRXV+OHH34wQIatZ9asWQAafqt956q027dvV1gMBWi4Cc7a2hqvv/46bt68KX//yJEj+OabbxAaGor77rtP/r6sqF29ejXuuece+RX24cOHo76+Hp988gl69erV7HHubV2Tr/x+/vnn+Pzzz7WO9/LyajQ5MxER0Z00TXVmZ2cnX1p2xYoVsLKywqpVq9C9e3f07t0bffv2xc2bN3HixAnk5uZi6dKluHjxotJhC9qYOXMmDh06hK+//hrdu3fH0KFD0aVLF1RUVODPP//E77//jm7dujX6/23Lli149tlnsW7dOuzYsQODBg2Ct7c3MjIycOTIEdja2uKLL76At7c3gNuroj3zzDMqhwGOGTMG7du3x6FDh5CXlwcPj9s3GT733HMq58t///330aVLF/mf1U11BjSMLVV25Vkd2VXrp556SmXME088ge+++w7bt29XmPLr9OnTeOKJJ5Tu06VLF7z//vsKx5HNEnW3Rx991ChmkxgwYACeffZZfPnll+jVqxceeOAB5OTkYP/+/XjkkUfw008/yWP9/Pzw+uuvIyYmBr1798bo0aNRVFSExMRE2NjYNBq/PnDgQLi6uuLmzZsKfTRs2DBIJBLcvHmz0XRqpIQwAampqQKASE1NNXQqRESm7Yd7hNjq3PBsRA4ePCgAaHy4uro22nfPnj3ikUceEe7u7sLa2lr4+PiIKVOmiN9++00IIcTnn38uunTp0qz89uzZIx599FHRvXt3YWtrK3x9fcWIESPEqlWrREVFhcr91q1bJ4YNGybc3NyEg4ODCAoKEs8++6zIyspSiJs2bZoAIHbv3q02jxkzZggA4tNPPxVCCDF9+nSNn1l6errWsQDEggULdPpsSktLhb29vfD09BS1tbUq48rLy4WDg4OwtrYWRUVFQgihMZegoCAhhHZ/P3TNuzlk+axcuVJlzMqVK8XgwYOFk5OTGDJkiFi1apX49ddfBQBx8OBBhdjt27eLUaNGCTc3N+Hv7y8iIyPFuXPnlLY7ceJEAUB88cUXCu/36dNHABA//vij0ly/+uqrRm0FBQU1+7vRHIaq7yRCqFh32IikpaUhJCQEXbt21XhjXFRUlNGvgENEZDDy2R68TXa2ByIyflKpFFKpVG1MZWUlLl68iNTUVKVrQbSUZq/w1pri4uJa9cMxd+n734eoKYbEyhlBYxYaOh0iIiIyEdpcjJRd3GxtJlX8UuvyzV+LDu3ycKPeAwCLX6I2oUcUUFMCWDkZOpNWV1hYiDfffFPr+PDwcLXz2LdlUqkU58+f1yq2uUsi68uJEyewZcsWrWIPHjyILl26KIyFVicmJgZubm7NyI6MCYtfIiJz0nO+oTMwmOLiYqxcuVLreFdXV7MtfuPi4nDw4EGtYpu7JLK+nDlzRqf+TU9P1zp2wYIFLH7bEBa/RERkFvz9/WECt7kYhQMHDhg6BZ1NmzatzS+gQfrR5Hl+iYiIiIhMDYtfIiIiIjIbHPZAKuUfLsKtqipU2xShwyRDZ0NERETUfCx+SaX8w0WoKa6DlXMR7jF0MkSkF9nrVqC2pBiWTs7wm/WSodMhImp1LH6JiMzI5XUfoSrnKmw6+bD4JSKzxDG/RERkcMnJyZBIJBofL774YqN909PTMWPGDHTt2hV2dnbw8/PD6NGjsW7dOtTW1uolv/T0dDz55JPo2bMn7O3tERgYiIiICBw5ckTtfvHx8XjooYfg6ekJBwcH9OvXD3PnzsW1a9fU7ldfXw8fHx9IJBKMHDlSZdyMGTMgkUhw6dIljecgi9X0+P333zW2pcnevXvl7W3YsEFlnEQiwQMPPKCxPdnfjyVLljQ7N3257777sGrVKkOnQU1gUld+IyMjubwxEVEbFhoaitDQUJXb7y4EP/vsMyxYsAB1dXXo3bs3HnjgARQWFuLo0aOYPXs2VqxYgYMHD8LDw6PJOX311VeYOXMmrK2t8eCDD2LUqFG4evUqEhMT8cMPP2DZsmV47bXXFPapq6vDyy+/jJUrV6J9+/YYOnQoOnbsiFOnTuHzzz/HN998g59//hmDBg1SesyjR4/KC+QjR47g+vXr8PLyavI53OnZZ5+Fs7Ozyu3u7u7NPsa2bdsUXs+YMaPZbRqTI0eO4K+/uDy4Otoub2wQwgSkpqYKACI1NdXQqZiVQ30sxX5/iTjUx9LQqRCRnhwZ3Fns95eII4M7GzoVBQcPHhQARGxsrNb7vP322wKA6Nevnzh//rzCtrKyMvHiiy8KAGLw4MGitra2SXldvXpV2Nvbi65du4rs7GyFbTk5OaJfv37CwsJCpKenK2ybM2eOACCmTp0qqqurFbb98MMPwtLSUnTp0kWUlJQoPW5UVJQAIB566CEBQKxevVpp3PTp0wUAkZWVpfFcdIltjurqauHm5iZ8fX3FvffeK6ysrMTNmzeVxgIQo0aN0thmU/5+6Fttba04c+aM+Pjjj0WnTp0EALFy5UqD5dMWGKq+47AHIiIyObm5uXj77bfRs2dPpKSkICAgQGG7vb09VqxYgYiICBw/fhz79+9v0nGOHDmC8vJyvPHGG/D19VXY5unpiQ8//BB1dXX4+eef5e9nZmbiyy+/xCOPPIKNGzfCyspKYb/x48fjxRdfRFZWFnbv3t3omPX19YiPj4eHh4f81+rbt29vUv6GkJiYiMLCQjz55JOYOHEiampqsHPnTkOn1Wx79uzBfffdh+effx45OTmGToeagcUvERGZnOXLl6O0tBTLly+HnZ2dyrg33ngDs2bNQmFhYZOOIxtL6+TkpHT7oEGD8M4776B3797y9959913U1tY2Ggpxp2nTpmHYsGG4cOFCo23JycnIycnB448/jnvuuQe9evXCkSNHTKbg2rp1KwDIi1/AtIp3VQYMGICdO3di586dWLZsmcq4S5cuqRyfPHHiREgkEvmfN27cCIlEgitXruDjjz/G/fffD1dXV4wYMQLffPNNi5wHmdiYXyIiIqDhiqybm5u8uFIlJCQEX3zxRZOP06tXLwDA66+/jm7duiE4OFhhu5OTE1555RWF944dOwYfHx/cf//9ats9fPiw0m2y8bJPPvkkgIaC6fTp04iPjzf6e1qqq6uxa9cu+Pv7y8cz+/r6IikpCYWFhXBzczNwhk3n6emJiIgIAICrq6te246JicHmzZvx4IMPIjw8HImJiXjmmWdw4cIFxMTE6PVYxOKXiMjonRg/ENX5+rnqV5V3Xf58NNRXQ7T2rN07YWDCiWa3s2fPHhQVFancPn/+fAQEBCAjI0NemLakhx9+GOPGjcPu3bsREhKCXr164eGHH8aDDz6I4cOHw97eXiFeCIELFy4gJCSkScerq6tDfHw8PD09MWLECAANxe+yZcuwfft2vRS/b775psob3tq3b4833nijyW3v3bsXt27dwpw5c+TvTZgwAatXr8auXbswffr0Jrfdlm3atAl79+7F6NGjAQBXr17FAw88gLfffhuzZs2Ct7e3gTNsW1j8EhEZuer8HFTlXNVvo/X1+m9TD3755Rf88ssvKrdPmDABzs7OKCkpgZ+fX6PtV69eRefOnRu936VLF1y8eFHnfCQSCXbu3InNmzdj06ZNOHz4MD744AN88MEHsLGxwcMPP4zo6Gj5Vc78/HxUVFTAx8dH52MBwIEDB5Cfn4+oqCi0a9cwMjE4OBj+/v44fPgwcnJy0KlTpya1LfPll1+q3NalS5dmFb93DnmQmThxIlavXo3t27ez+FXhySeflBe+AODj44OlS5di0qRJ2LRpU6PfLlDzsPglIjJy1u7NK3buVJV3HaivB9q1g42HfqbOAvSXY2xsLBYvXqw2pra2FtbW1krHwDo6OmLBggUK7zV3vKmFhQWmTp2KqVOnoqKiAocOHcK+ffvw/fffY+fOnfjxxx8RHx+P8ePHw83NDRYWFigoKGjSse4e8iAzYcIErFq1Ct9//z3mzZvXrPPJysqCv79/s9pQprKyEj/88AO6du2KAQMGyN8fMWIEOnTogMTERBQVFel9yEBbEBYW1ui9Bx98EABw/vz51k6nzWPxS0Rk5PQxnEDmaKhvwwpvHl4Y+stlvbXbmiwtLdG9e3ecO3eu0TYXFxd89NFHCu/99NNPqK6u1sux7ezsMHbsWIwdOxYffPABPv/8c8ybNw/PP/88xo8fDysrK/j5+Sm9ke1ub731FkpLS/H2229DIpGgtrYW33//PYDG8xnLbN++vdnFb0v573//i5KSEpSUlCjc1HWnXbt2Ydq0aa2cmfEQQih9X9kczh06dICNjQ2uXLnS0mmZHRa/RERkcoKCgrBlyxYcOHBA7QphxcXFOH/+vNIhEtro2rUrAgICkJSU1GibRCLB3LlzsWPHDuzbtw83b95E+/bt0adPHyQkJOD06dMqxyXn5ubi9ddfx6BBg+SF4v79+3Hjxg307dtX6c1y3333HQ4dOoTc3Fx4eno26Xxakuyq9RNPPIEOHToobMvNzcWOHTuwfft2sy5+Vf1GID8/v9F7RUVFqKqq0tviJnQbi18iIjI50dHR2Lp1KxYtWoSUlBRYWir/72zx4sWor69v8nECAgJw/PhxFBQUoGPHjkpjamtr4eDgABcXFwDAokWL8MMPPyAmJgbx8fFK9/n6668BAKNGjZK/JyselyxZggkTJig9zrp16/D999/jueeea/I5tYSKigokJCSgQ4cO2LJlS6P+qKyshLu7u1kNfbh79bKKigqcOXNGaeyRI0fw9NNPK7wnm5u6e/fuLZOgGeM8v0REZHJ69+6NuXPn4rfffsOoUaNw+bLiEI7q6mq8+eab+Pjjj2FjY9Pk40yePBmlpaX45z//ievXrzfaHh8fj+TkZISFhcHCwgIAMHToUIwbNw7ff/89Zs+ejaqqKoV9Dh48iCVLlsDNzQ0vvPACAKCmpgY7duyAs7MzwsPDleYSGRkJwDjnzP3xxx9RVlaGxx9/XOkPIra2thg3bhyqq6vxww8/GCDD1tO+fXtIJBIkJSUp/OC1fPlylfNNb9y4Eb/++qv8z9euXcNrr70Ga2trTJkypcVzNjcmdeU3MjIStra2amOioqKMfh5EIiJSTtNUZ3Z2dnj77bcBACtWrICVlRVWrVqF7t27o3fv3ujbty9u3ryJEydOIDc3F0uXLsXFixeVDlvQxsyZM3Ho0CF8/fXX6N69O4YOHYouXbqgoqICf/75J37//Xd069YNa9euVdhvy5YtePbZZ7Fu3Trs2LEDgwYNgre3NzIyMnDkyBHY2triiy++kE9hJVsV7ZlnnlH5/9yYMWPQvn17HDp0CHl5efDw8JBve+655+Dg4KB0v/fffx9dunSR/1ndVGdAw3hjZVee1ZFdtX7qqadUxjzxxBP47rvvsH37dkydOlX+/unTp/HEE08o3adLly54//33FY5z6tQppbGPPvqoUcwm4eTkhAcffBCJiYkYPHgwRo0ahZMnT+LEiROYMGGC0tXuevbsiZEjR2Ls2LGws7NDYmIibty4gcWLF7fIzYmtQSqVQiqVqo25++p4q2nVxZSbyFBrP5u7Q30sxX5/iTjUx9LQqRCRnhwZ3Fns95eII4M7GzoVBQcPHhQAND5cXV0b7btnzx7xyCOPCHd3d2FtbS18fHzElClTxG+//SaEEOLzzz8XXbp0aVZ+e/bsEY8++qjo3r27sLW1Fb6+vmLEiBFi1apVoqKiQuV+69atE8OGDRNubm7CwcFBBAUFiWeffVZkZWUpxE2bNk0AELt371abx4wZMwQA8emnnwohhJg+fbrGzyw9PV3rWABiwYIFOn02paWlwt7eXnh6eora2lqVceXl5cLBwUFYW1uLoqIiIYTQmEtQUJAQQru/H7rm3RyyfFauXKl0e15enpg6darw9PQUXl5e4qmnnhJ//PGHePXVV8WdpddXX30lAIgDBw6Id999VwQFBQknJycxdOhQsWnTptY6HYMxVH0nEULFrYdGJC0tDSEhIUhNTW20ug61nMN9rVBTXAcrZwsM/6PG0OkQkR7IZ3vo5GOysz0QtRUbN27E9OnTcfDgQZUzfLRlhqrvOOaXiIiIiMyGSY35JSKi5vGd9SJqS4ph6aR6zGdbVVhYiDfffFPr+PDwcIwdO7YFMzJeUqlU68UVmrsksr6cOHECW7Zs0Sr24MGD6NKli8JYaHViYmLg5ubWjOzImLD4JSIyI36zXjJ0CgZTXFyMlStXah3v6upqtsVvXFwcDh48qFVsc5dE1pczZ87o1L/p6elaxy5YsIDFbxvC4peIiMyCv7+/yhW2SNGBAwcMnYLOpk2bZnILaJhizm0Bx/wSERERkdlg8UtEREREZoPDHtqIlEXjIcqKIXFwxpD3EgydTqvbllaIsup6OFi3w5PBHJdFREREyrH4bSNq9+yRz8mL9wydTevbdrIIBaW16OhoyeKXiIiIVDKp4pfLGxMREREZP2Ne3tikit+4uDiu8EZERERk5LS5GClb4a218YY3IiIiIjIbLH6JiIiIyGw0ufgVQmDdunUYPHgwnJ2d4efnhyeffBIXLlxQGn/9+nXMmTMHnTt3hq2tLXr06IGYmBiDjfegtmWc1SZMd/wc46w2GToVIiIiMmJNGvMrhMCkSZOwbds2AA1LQObk5GD79u346aefkJycrDCGIzs7G4MHD0ZOTo48/ty5c1i6dCmSkpJw4MAB2NjY6OF0yFyNt96EDrZ5uFHvASDG0OkQERGRkWrSld8PP/wQ27Ztg7e3Nw4fPowbN26gqKgIM2fORFlZGaZPn466ujp5/MyZM5GTk4MxY8YgKysLhYWFSEtLg6+vL1JSUrBs2TK9nRARERERkSo6F79lZWV45513YGVlhd27d2PYsGFo164d7O3t8emnn6JHjx44deoUTpw4AQA4efIkkpKS4OHhga1bt8Lf3x8A0L9/f8THxwMA1qxZw+EPRERERNTidC5+//vf/+LGjRsYPXo0+vfvr7DNysoKL7zwAkaNGoWsrCwAQEJCw2pj48ePR4cOHRTiBw4ciF69eqGoqAhHjx5t4ikQEREREWlH5+J33759AIDHHntM6fZ58+bhwIEDmDRpEgAgJSUFABAeHq40Xva+LI6IiIiIqKXofMPbmTNnAAB9+/bVKv78+fMAgO7duyvdHhAQAADIzMzUNRUiufzDRbhVVYVqmyJ0mGTobIiIiMhY6Vz8yqYyc3d3x7fffovNmzfj+PHjcHR0RP/+/REdHY2BAwfK4/Pz8wE0zPCgjJubGwAgNzdX47FLS0tRXFysa8qN2NjYcHaJNib/cBFqiutg5VyEewydDBERURtWVVWFqqqqZrdTWlqqh2x0p3PxKys+V65ciTVr1gAAPD09cfnyZWRlZeGHH37AqlWr5EvalZeXA7hd5N5N9r4sTp2RI0fqmq5SixcvRmxsrF7aIiIiIjIny5cvx5IlSwydRpPpXPzKZmVYs2YN5s+fjyVLlqB9+/aorKzEypUr8dprr+Hf//43wsLC0KNHDwghAED+fDcLCwsAUJgaTZXk5GQEBQXpmnIjvOpLRERE1DTR0dF46aWXmt1Oenq63i5s6kLn4tfNzQ0FBQWIjIzE6tWr5e/b2tri1Vdfxfnz57F+/Xp8/PHHWLNmDezt7VFcXIzCwkKlQx9kV3wdHBw0HtvR0RHOzs66pkxEREREeqKv4aOOjo56yEZ3Os/20KlTJwDAjBkzlG5/6qmnAAB//PEHgIaxwQBQWFioND4vLw8A0LFjR11TISIiIiLSic7Fr6enJwDAx8dH6XbZ+9evXwcABAYGAgAyMjKUxp86dUohjtq27Lcfw4XoB5H9tvKp8oiIiIhaks7DHnr27In9+/cjIyMD/fr1a7RdNhvEvffeCwAIDQ3Fnj17sG/fPvncv3fau3cvAGDIkCG6pkLNkJ97EeWl+VrHX8r8VWOMvaM73D27qo25vG03qopqYeNqCb//aH14IiIiIr3QufgdN24cPvnkE6xevRpPPPFEo+1r164FAISEhAAAIiIiEBsbi4SEBBQUFCgMbzh+/DjOnj0LV1dXjBgxoqnnQDrKz70Ip4OhcJeoX1I6+47X/qlhGtutFLbIH/WLxgKYiIiIyFB0Ln7Dw8PRp08fHD58GE8//TQ++ugjuLu749atW3jjjTeQkJCATp06YcGCBQCAoKAghIWFITExEZMmTcKGDRvg6+uLtLQ0REZGAgAWLFgAW1tbvZ4YqVZemg93SSX+9P0Yzh73qokcKn91KSRRbZvFeX+hz+XnkVuaD7D4JSIiIiOlc/ErkUiwefNmjBgxAps3b8bmzZvh6emJvLw8CCHQvn17fP3113BxcZHvs27dOoSGhmL//v3w8/ODq6srioqKAADDhw9HdHS03k6ItOfscS/8Awap3K5w5VdNHABcAoDLekmLiIiIqMXofMMbAPTp0wfp6el49tln4ePjg1u3bqFv376YM2cO/vzzT4SFKf6K3M/PD6mpqZg9eza8vb1RUVGBwMBAxMbGIjExEdbW1no5GSIiIiIidXS+8ivj7++P9evXax3v5eUlHw9MhldZWI+qjLMoqdDur0DJqTS126sun0VlYb0+UiMiIiJqMU0ufg0hMjJS49jgqKgo+dLKpFxNbg5++aAM9TXTcUVNnJWzhfz1iXEDNLZ7zQro3CcHCFAdI1voT8WCf0RERNQGSKVSSKVStTGyVYNbm0kVv3FxcQgODjZ0Giav/lYR6msAj+hY+A8dpzIu/Z+D5a8H7v5NbZuXju5G3vJY1N8qUhunablrIiIiMn3aXIxMS0uTzw7Wmkyq+CX9svbrAqfe2v0woSnO+vIpfaRERERE1KKadMMbEREREZEpYvFLRERERGaDxS8RERERmQ0Wv0RERERkNlj8EhEREZHZYPFLRERERGaDxS8RERERmQ0Wv0RERERkNlj8EhEREZHZMKkV3iIjI2Fra6s2Rpvl9IiIiIio5UilUkilUrUxlZWVrZSNIpMqfuPi4hAcrN1yvERERERkGNpcjExLS0NISEgrZXQbhz0QERERkdlg8UtEREREZoPFLxERERGZDRa/RERERGQ2WPwSERERkdlg8UtEREREZoPFLxERERGZDRa/RERERGQ2TGqRC3N19fejKLuWqXV8xn+/Vrv91qljzU2p1eQW1+BWZZ1O+2TkqV8xxsXWAp7OVs1Ji4iIiEyUSRW/5ri88dXfj+Jc5HDU16iPs3K2kL++8tx0je22swIcvAOamV3Lyi2uwbRNl1BZKzTGvvm/ZwFgzreX1cbaWkqw8Rl/FsBEREQthMsb64k5Lm9cdi0T9TWAU9QcuPS+X2Vc7isz5a87f/qVxnYdvAPg02+oPlJsMbcq61BZK/DaWE/4t7dWG5u7vuFZAmDtZF+VcZduVuOtvbm4VVnH4peIiKiFGPPyxiZV/Jozl973o8fDU1Vuv7P4VRdnivzbW6OHh/or/rl3vNYUS0REROaLN7wRERERkdlg8UtEREREZoPDHsioebTLgU1JIWClfsyvgsJ0lZtsSqrh0a4KgOpxwURERNR2sfglo2VZeQUb3SNhl6rj3aCJI1Vu8gew0d0W1yuPAujerPyIiIjI9LD4JaNlUXMTdu0qcf1eKbw691YfvGzg7ddhySrDrl85Ba+/omBRc1NPWRIREZEpYfFLRq/avgfgFqT9Dmpiq29WNzsfIiIiMl284Y2IiIiIzAaLXyIiIiIyGxz2QEatsrAeVRlnUVKh/V/VklNpKrdVXT6LysJ6faRGREREJsikit/IyEjY2qpfvUub5fTINNTk5uCXD8pQXzMdVzTEWjlbyF+fGDdAbew1K6BznxwgQA9JEhERUSNSqRRSqVRtTGWljrM56YlJFb9xcXEIDg42dBrUSupvFaG+BvCIjoX/0HFqY9P/OVj+euDu31TGXTq6G3nLY1F/q0hfaRIREdFdtLkYmZaWhpCQkFbK6DaTKn7JPFn7dYFTb+1/6FEXa335lD5SIiIiIhPVaje8Xb9+HXPmzEHnzp1ha2uLHj16ICYmxmCXvImIiIjI/DSp+H3zzTchkUjUPm7cuCGPz87ORnBwML744gtcvXoVdnZ2OHfuHJYuXYrRo0ejqqpKbydE+lNr4aDwTERERGTqmjTs4dy5cwAAHx8f2NnZKY1p1+52XT1z5kzk5ORgzJgxWL9+Pfz9/XHy5ElEREQgJSUFy5Ytw9KlS5uSCrUgaxsnVKEY1jZOWu9Tf/4XlByz0iq25Ni3GtsiIiIi0qcmFb/nz58HABw6dAjdunVTG3vy5EkkJSXBw8MDW7duRYcOHQAA/fv3R3x8PAYNGoQ1a9bgtdde0ziTAxmxjl3Qzgq49uFnuIbPVIYpzMrwzykam21n1dA2ERERkT40+cqvjY0N/P39NcYmJCQAAMaPHy8vfGUGDhyIXr164fTp0zh69CjGjBnTlHTIGPgPwtfTt+HV4HJ4uai+8ps+d6r89cAtm9U2ef1WDd5Js8er/oP0liYRERGZN52L36KiIty4cQO9evWChYWFxviUlBQAQHh4uNLt4eHhOH36NFJSUlj8mrgLjv2AEF84eai7gn+7+HW6f7La9q7nVeJCxmU9ZUdERETUhOJXNt63R48eiI+PxzfffIOLFy+ia9euGDBgAP7v//4Pzs7O8njZEInu3bsrbS8goGGlgczMTJ2Tp5blO+tF1JYUw9LJWXMwERERkQnQufiVFbM//vgjduzYIX//999/x86dO7F27Vps3boVoaGhAID8/HwAgKurq9L23NzcAAC5ubm6pkItzG/WS4ZOgYiIiEivmnzlt76+HkuWLMFjjz0GX19fpKenY9GiRfj1118xefJknDlzBnZ2digvLwdwu8i9m+x9WZw6paWlKC4u1jXlRmxsbGBjY9PsdqixSzertY7NyFM/x7MubREREVHrqKqq0ss0taWlpXrIRnc6F7+dOnXCpEmTEBERgUmTJsnfHzlyJI4cOYLevXsjIyMDq1evxqJFiyCEAAD5891k44br6uo0HnvkyJG6pqvU4sWLERsbq5e2qIGLrQVsLSV4a6/6K/hv/u9ZAJjzrebxvLaWErjYah5bTkRERK1j+fLlWLJkiaHTaDKdi985c+Zgzpw5SrdZWVlh4cKFmD17Nn75pWGOVnt7exQXF6OwsFDp0AfZFV8HB80LKSQnJyMoKEjXlBvhVV/983S2wsZn/HGrUv0PMbnrG54lANZO9tXYroutBTydtZs3mIiIiFpedHQ0Xnqp+UMj09PT9XZhUxdNmupMnd69ewMAzpw5AwBwd3eXF79du3ZtFJ+XlwcA6Nixo8a2HR0dFW6mI+Pi6WylsVC987pwD7WzQhAREZEx0tfwUUdHRz1ko7smLW+sjr29PQDAyalhVbDAwEAAQEZGhtL4U6dOKcQREREREbUUnYrfsrIy9OnTB3379kVZWZnSGFmRe9999wGAfNaHffv2KY3fu3cvAGDIkCG6pEJEREREpDOdil8HBwd4enrizz//xIYNG5TGSKVSALdvTouIiADQsNJbQUGBQuzx48dx9uxZuLq6YsSIETonT0RERESkC52HPTz33HMAgEWLFmHLli3yWRxyc3Mxffp0HDx4EP3798f06dMBAEFBQQgLC0NBQQEmTZqEy5cb7vBPS0tDZGQkAGDBggWwteX4TyIiIiJqWToXv48//jiioqJQUVGBKVOmwN7eHp6enujUqRM2btyIwMBAbNq0Ce3a3W563bp18PLywv79++Hn5wc3NzeEhITgypUrGD58OKKjo/V6UkREREREyjTphrc1a9Zg586dGDVqFNq3b4/KykoMGzYM//nPf3Dy5En06tVLId7Pzw+pqamYPXs2vL29UVFRgcDAQMTGxiIxMRHW1tZ6ORkiIiIiInWaPNVZRESEfDyvNry8vLB27dqmHo6IiIiIqNn0PtUZEREREZGx0vsiFy0pMjJS441xUVFRiIqKaqWMiIiIiOhuUqlUPgOYKpWVla2UjSKTKn7j4uIQHBxs6DSIiIiISA1tLkampaUhJCSklTK6jcMeiIiIiMhssPglIiIiIrPB4peIiIiIzAaLXyIiIiIyGyx+iYiIiMhssPglIiIiIrNhUlOdkWqW4eGwKCuGxMHZ0KkYhEQiUXgmIiIiUobFbxsx5L0EQ6dgULKal7UvERERqcNhD0RERERkNlj8EhEREZHZMKlhD5GRkbC1tVUbo81yekRERETUcqRSKaRSqdqYysrKVspGkUkVv3FxcQgODjZ0GmSMLB0A3PrfMxERERmSNhcj09LSEBIS0koZ3cZhD9Q2WDoqPhMREREpweKXiIiIiMwGi18iIiIiMhssfomIiIjIbJjUDW9EqvjOehG1JcWwdDLPFe6IiIhIOyx+qU3wm/WSoVMgIiIiE8BhD0RERERkNlj8EhEREZHZYPFLRERERGbDpMb8cnljIiIiIuPH5Y31hMsbm768IWPQrqIY9XaclYGIiKitMubljU2q+CXT9+2gT1FQWouOjpZ43NDJEBERkdnhmF8iIiIiMhssfomIiIjIbLD4JSIiIiKzwTG/1Kqe7O+Ksup6OFgb/89d2etWyJdM5gpyREREbQOLX2pVTwa7GToFrV1e9xGqcq7CppMPi18iIqI2wvgvvxERERER6QmLXyIiIiIyGyx+iYiIiMhssPglIiIiIrNhUje8RUZGwtbWVm2MNsvpEREREVHLkUqlkEqlamMqKytbKRtFJlX8xsXFITg42NBpEBEREZEa2lyMTEtLQ0hISCtldBuHPRARERGR2dBb8fv777/DysoKkydPVrr9+vXrmDNnDjp37gxbW1v06NEDMTExBrvkTURERETmRy/DHurq6jBz5kzU1tYq3Z6dnY3BgwcjJycHAODq6opz585h6dKlSEpKwoEDB2BjY6OPVIiIiIiIVNLLld8PP/wQqampKrfPnDkTOTk5GDNmDLKyslBYWIi0tDT4+voiJSUFy5Yt00caRERERERqNbv4PX/+PGJjY1VuP3nyJJKSkuDh4YGtW7fC398fANC/f3/Ex8cDANasWcPhD2R0qqtKFJ6JiIjI9DWr+BVCYNasWaisrMT06dOVxiQkJAAAxo8fjw4dOihsGzhwIHr16oWioiIcPXq0OakQ6Z1lXZnCMxEREZm+ZhW/a9euRXJyMubMmYNRo0YpjUlJSQEAhIeHK90ue18WR0RERETUUppc/F69ehWLFi2Ct7c33n33XZVx58+fBwB0795d6faAgAAAQGZmZlNTISIiIiLSSpNne3juuedQXFyMjRs3wsXFRWVcfn4+gIYZHpRxc3MDAOTm5mo8ZmlpKYqLi3VP9i42NjacXYKIiIioCaqqqlBVVdXsdkpLS/WQje6aVPx+9913SEhIQGRkJCZMmKA2try8HMDtIvdusvdlceqMHDlSt0RVWLx4sdqb9IiIiIhIueXLl2PJkiWGTqPJdC5+b9y4geeffx6urq5YvXq1xnghhMLz3SwsLAA0zBWsSXJyMoKCgrRPVgVe9SUiIiJqmujoaLz00kvNbic9PV1vFzZ1oXPxu2DBAuTn52P9+vXo1KmTxnh7e3sUFxejsLBQ6dAH2RVfBwcHjW05OjrC2dlZ15SJiIiISE/0NXzU0dFRD9noTqcb3g4dOoRvvvkGo0ePxrPPPqvVPu7u7gCAwsJCpdvz8vIAAB07dtQlFSIiIiIinelU/F68eBEA8PPPP0MikSg8ZPP8fvfdd/L3Ll26hMDAQABARkaG0jZPnToFAPI4IiIiIqKWotOwBycnJ5VTlhUXFyMvLw+Ojo7y4RCWlpYIDQ3Fnj17sG/fPkyaNKnRfnv37gUADBkyRNfciYiIiIh0otOV38ceewznzp1T+njvvfcAAOPGjZO/5+Pjg4iICAANK70VFBQotHf8+HGcPXsWrq6uGDFihJ5OiYiIiIhIuWat8KaNoKAghIWFoaCgAJMmTcLly5cBAGlpaYiMjATQcBOdra1tS6dCRERERGauyYtc6GLdunUIDQ3F/v374efnB1dXVxQVFQEAhg8fjujo6NZIg4iIiIjMXItf+QUAPz8/pKamYvbs2fD29kZFRQUCAwMRGxuLxMREWFtbt0YaRERERGTm9Hbld9q0aZg2bZrK7V5eXli7dq2+DkdEREREpLNWufJLRERERGQMWmXMr75ERkZqvDEuKioKUVFRrZQREREREd1NKpVCKpWqjamsrGylbBSZVPEbFxeH4OBgQ6dBRERERGpoczEyLS0NISEhrZTRbRz2QERERERmg8UvEREREZkNFr9EREREZDZY/BIRERGR2WDxS0RERERmg8UvEREREZkNFr9EREREZDZY/BIRERGR2TCpRS6I9KU6Owslp9K0jtcUa+XWEbY+fs1Ni4iIiFqYSRW/XN6YmqudiyvaWQF5y2ORh1i1sVbOFvLXJ8YNUN+unT1Ck86wACYiIgKXN9YbLm9MzWXl2QmhLzsg208KL9/eamPT/zlY/nrg7t9UxpWd/wtnFjyDmsICFr9EREQw7uWNTar4JdIHW7d2sOlxD5wCtP9Byqk3f+giIiJqC3jDGxERERGZDV75JWpFKYvGQ5QVQ+LgjCHvJRg6HSIiIrPD4peoFdXu2YOa4rqGm+neM3Q2RERE5ofDHoiIiIjIbLD4JSIiIiKzweKXiIiIiMwGi18iIiIiMhssfomIiIjIbHC2BzJLxXl/4ZIO8Zcyf1W5rery2eYnRERERK3CpIrfyMhI2Nraqo3RZjk9Ml/2ju6oFLboc/l54LL62Ow7XvunhqmMK7lShysACm9chRO4EhwREZFUKoVUKlUbU1lZ2UrZKDKp4jcuLg7BwSwuqOncPbsif9QvyC3N1yJ6qPzVpZBElVE3av8LYAkqywubnyAREVEboM3FyLS0NISEhLRSRreZVPFLpA/unl0Bz64a4xSu/AYMUhlXlXEWRc1Pi4iIiFoBb3gjIiIiIrPB4peIiIiIzAaLXyIiIiIyGyx+iYiIiMhssPglIiIiIrPB4peIiIiIzAaLXyIiIiIyGyx+iYiIiMhsmNQiF1zemEiJv9cANSWAlRPQc76hsyEiIuLyxvrC5Y2JlMiQAhXXADtvFr9ERGQUjHl5Yw57ICIiIiKzweKXiIiIiMxGk4vfAwcOYPz48ejWrRucnZ0xePBgREdHo6ysTGn89evXMWfOHHTu3Bm2trbo0aMHYmJiDDbeg4iIiIjMT5PG/L777ruIjo6GEAJWVlZwc3PDr7/+il9//RXffvstjh8/Dk9PT3l8dnY2Bg8ejJycHACAq6srzp07h6VLlyIpKQkHDhyAjY2Nfs6IiIiIiEgFna/8Xrp0CTExMbCwsMAnn3yCsrIy5ObmIjMzE6Ghobh06RJefPFFhX1mzpyJnJwcjBkzBllZWSgsLERaWhp8fX2RkpKCZcuW6e2EiMxNZuINXNhXhczEG4ZOhYiIyOjpXPxKpVJUV1fj6aefxnPPPQcrKysAQLdu3fDtt9/C0tISW7dulQ9nOHnyJJKSkuDh4YGtW7fC398fANC/f3/Ex8cDANasWcPhD0RNdO1gIbKSqnHtYKGhUyEiIjJ6Ohe/f//9NwBg4sSJjbZ16dIF3bt3R319PTIyMgAACQkJAIDx48ejQ4cOCvEDBw5Er169UFRUhKNHj+qcPBERERGRLnQufm/evAlXV1f4+voq3W5nZwcAKC4uBgCkpKQAAMLDw5XGy96XxRERERERtRSdb3g7fPiwym03btzAuXPnYGlpiXvuuQcAcP78eQBA9+7dle4TEBAAAMjMzNQ1FSIiIiIinTR7hbfq6moUFBQgPT0dS5cuRWlpKebOnYuOHTsCAPLz8wE0zPCgjJubGwAgNzdX47FKS0vlV5Sbw8bGhrNLEBERETVBVVUVqqqqmt1OaWmpHrLRXbOL3379+uHs2bPyP0dFRWHFihXyP5eXlwO4XeTeTfa+LE6dkSNHNidVucWLFyM2NlYvbVHb5T7cFdZVJai2cTJ0KkREREZj+fLlWLJkiaHTaLJmF79+fn4oKSnBtWvXIIRAXFwchg0bhkmTJgEAhBAKz3ezsLAAANTV1Wk8VnJyMoKCgpqbMq/6klbch7uiQ7tq3Kh3NXQqRERERiM6OhovvfRSs9tJT0/X24VNXTS7+N27dy8AoKKiAp999hkWLlyIKVOmwM/PD/fffz/s7e1RXFyMwsJCpUMfZFd8HRwcNB7L0dERzs7OzU2ZiIiIiJpIX8NHHR0d9ZCN7pq8vPHd7Ozs8OKLL2L+/Pmor6/HunXrAADu7u4AgMJC5XOQ5uXlAYB8jDARERERUUvRqfjNz8/H559/jm+++UZlzAMPPAAAyMrKAgAEBgYCgHze37udOnVKIY6IiIiIqKXoNOzBwsICc+fOhY2NDaZMmQKJRNIopqioCADg7e0NAAgNDcWePXuwb98++TjgO8mGTQwZMkTX3ImMyq1Tx6D8RzzlMv77tdrtDt4B8Ok3tHlJERERkQKdit/27dujW7duuHDhAg4cOIDRo0c3ipEtWdyvXz8AQEREBGJjY5GQkICCggKF4Q3Hjx/H2bNn4erqihEjRjTnPIgMxsE7AO2sgBLpWpRgrdpYK2cL+esrz01XG9vOCkDcYRbAREREeqTzDW/PPfccFi5ciNmzZ+Orr77CsGHDIJFIcPPmTSxZsgQJCQnw9PTEjBkzAABBQUEICwtDYmIiJk2ahA0bNsDX1xdpaWmIjIwEACxYsAC2trb6PTOiVuLTbygQdxhl1zQv1JL7ykz5686ffqUy7tapYyiRrm1ok8UvERGR3uhc/L744otISEjAoUOHMGLECNjb28PZ2Rk5OTkAABcXF2zcuFHhCu+6desQGhqK/fv3w8/PD66urvLhEcOHD0d0dLR+zobIQHz6DdWqSL2z+O3x8FSVcRmAxqvIREREpDudZ3uwsLDAgQMH8Omnn2L48OFwcXFBWVkZBg4ciPnz5+Pvv//G2LFjFfbx8/NDamoqZs+eDW9vb1RUVCAwMBCxsbFITEyEtbW13k6IiIiIiEiVJs3z265dO8ydOxdz587Veh8vLy+sXcsrWWQ6LrvPQXZNMSRWzuhg6GSIiIhIL5q9yAVRWxU0ZqGhUyAiIiI9M6niNzIyUuONcVFRUYiKimqljIiIiIjoblKpFFKpVG1MZWVlK2WjyKSK37i4OAQHBxs6DSIiIiJSQ5uLkWlpaQgJCWmljG7T2/LGRERERETGjsUvEREREZkNFr9EREREZDZY/BIRERGR2WDxS0RERERmg8UvEREREZkNFr9EREREZDZY/BIRERGR2TCpRS6IzI1lZTZQmK79DppirTsADr7NSYmIiMikmVTxy+WNyVzUWboAANwvvgPUvK/9jokj1W+3sAfCf2UBTERELYrLG+sJlzcmc1Fn4wEAuH7vJ3AK7as+eNnA26/DklXHFWcAx2cD1TdY/BIRUYsy5uWNTar4JTI31Q6BgFuQ9jvoEktERGSGeMMbEREREZkNFr9EREREZDY47IHIiOWcOq1T/KlDKSq3WZedg19hPdTfMkpERNS2sfglMkIunh64YmUH6/fmIE9DrJWzhfx13tShamOvWQE9Q67Aa0BQ85MkIiIyQSx+iYyQb88AYPcfuJWrqfQFCuePkL/2+Pqoyrii1D2oXrUUJfk34aWXLImIiEwPi18iI+XbM6ChCNbg8B2ve48YojIuo+wcrughLyIiIlPGG96IiIiIyGyw+CUiIiIis8Hil4iIiIjMhkmN+Y2MjIStrfqJmrRZTo+IiIiIWo5UKoVUKlUbU1lZ2UrZKDKp4jcuLg7BwcGGToOIiIiI1NDmYmRaWhpCQkJaKaPbTKr4JTJ1luHhsCgrhsTB2dCpEBERmSUWv0StaMh7CYZOgYiIyKzxhjciIiIiMhssfomIiIjIbLD4JSIiIiKzweKXiIiIiMwGi18iIiIiMhssfomIiIjIbLD4JSIiIiKzYVLz/HJ5YyIiIiLjx+WN9YTLGxMREREZP2Ne3pjDHoiIiIjIbDS5+L106RKmT5+Ofv36wdHREb1798b06dNx4cIFpfHXr1/HnDlz0LlzZ9ja2qJHjx6IiYkx2CVvIiIiIjI/TRr2sHfvXjzxxBMoKSmBRCKBh4cHzpw5g9OnTyMuLg4bNmzAE088IY/Pzs7G4MGDkZOTAwBwdXXFuXPnsHTpUiQlJeHAgQOwsbHRzxkREREREamg85XfmpoaREVFoaSkBDNmzEBhYSFycnJQWFiIl156CWVlZZg9ezauXbsm32fmzJnIycnBmDFjkJWVhcLCQqSlpcHX1xcpKSlYtmyZXk+KiIiIiEgZnYvf7du3IzMzEz179sT69evh4uICAHBxccGHH36IyZMn49atW1ixYgUA4OTJk0hKSoKHhwe2bt0Kf39/AED//v0RHx8PAFizZg2HPxA1UQXsFZ6JiIhINZ2L39OnTwMAnn76aUgkkkbbZ8yYAaCh6AWAhIQEAMD48ePRoUMHhdiBAweiV69eKCoqwtGjR3VNhYgAVAoHhWciIiJSTefi9+LFiwAgv4J7N29vbwBAVlYWACAlJQUAEB4erjRe9r4sjoiIiIiopeh8w9sLL7yAKVOmYODAgUq3//rrrwAAX19fAMD58+cBAN27d1caHxAQAADIzMzUNRUiIiIiIp3oXPwOHjxY5baioiK89dZbAG5f0c3PzwfQMMODMm5ubgCA3NxcXVMhIiIiItKJ3lZ4u3DhAp544glkZmbC09MT//rXvwAA5eXlAG4XuXeTvS+LU6e0tBTFxcXNztXGxoZTqxERERE1QVVVFaqqqprdTmlpqR6y0V2zi9+amhqsWLECb775JsrLy2Fvb4+dO3fKi1ohhMLz3SwsLAAAdXV1Go81cuTI5qYLAFi8eDFiY2P10haRwT0ehaKSYlg6OeutyR9i30Lt/9r8R+xremuXiIhM3/Lly7FkyRJDp9FkzSp+z5w5g8mTJ+OPP/4AANx7773Ytm0bevfuLY+xt7dHcXExCgsLlQ59kF3xdXDQfKd6cnIygoKCmpMyAPCqL7UpLVKcxkvhWpKDUqdOAItfIiK6Q3R0NF566aVmt5Oenq63C5u6aHLxu2HDBkRFRaGiogJ2dnZ45ZVX8Morr8DW1lYhzt3dXV78du3atVE7eXl5AICOHTtqPKajoyOcnfV3dYuIiIiIdKOv4aOOjo56yEZ3Ok91BgBxcXGYOXMmKioqMHLkSJw5cwaLFy9uVPgCQGBgIAAgIyNDaVunTp1SiCMiIiIiaik6F7/Z2dmYNm0ahBCYP38+fv75Z3Tp0kVlfGhoKABg3759Srfv3bsXADBkyBBdUyEiIiIi0onOxe/69etRXl6O8PBwrF69Gu3aqW8iIiICQMNKbwUFBQrbjh8/jrNnz8LV1RUjRozQNRUiIiIiIp3oXPxu3boVALBw4UKt4oOCghAWFoaCggJMmjQJly9fBgCkpaUhMjISALBgwQKlQyaIiIiIiPRJpxve6uvrceHCBQDAs88+CysrK5WxvXv3xo4dOwAA69atQ2hoKPbv3w8/Pz+4urqiqKgIADB8+HBER0c3MX0iagm2kjKFZ734ew1QUwJYOQE95+uvXdIbTnFHROZAp+L32rVrqKmpAQBcunRJbeyd05r5+fkhNTUVixcvxo8//ogbN24gMDAQU6ZMwauvvgpra2vdMyeiFmOHctT871lvMqRAxTXAzpvFr7HiFHdEZAZ0Kn47d+6scrEKTby8vLB27dom7UtEREREpA9NmuqMiIiIiMgUNXt549YUGRmp8ca4qKgoREVFtVJGRERERHQ3qVQKqVSqNqaysrKVslFkUsVvXFwcgoODDZ0GEREREamhzcXItLQ0hISEtFJGt5lU8dtW8I5qIjJGLTLLBxGRkWHxawi8o5qIjFCLzPJBRGRkeMMbEREREZkNFr9EREREZDZY/BIRERGR2WDxS0RERERmg8UvEREREZkNzvZgAJxOiMxRZuINSMqrIOxvIGC8obMhIiJzxeLXADidEJmjawcLUVNcByvnQgQYOhkiIjJbHPZARERERGbDpK78RkZGwtbWVm2MNsvpEREREVHLkUqlkEqlamMqKytbKRtFJlX8xsXFITg42NBpEBEREZEa2lyMTEtLQ0hISCtldBuHPRARERGR2WDxS0RERERmg8UvEREREZkNkxrzS0TNl3c2E9UOKVrHnzqkPtbF0wO+PTl5GRERmQYWv0Rmwsm9PdpZAdWrliIPS9XGWjlbyF/nTR2qNvaKlR2w+w8WwEREZBJY/BKZCa+AznB72QHZflJUOwSqjS2cP0L+2uProyrjck6dhvV7c3ArN4/FLxERmQQWv0RmxNatHXqE9gXcgtTGHb7jde8RQ9TG5jU/LSIiolbDG96IiIiIyGzwyi+RuSnO0C2+MF3lJuuyc7efC+300mZDgx0AB1/t29Onv9cANSWAlRPQc75emvwh9i3UlhTD0skZ/4h9TS9t6ury35m4lav9dXpNNzoCvNmRiEyTSRW/XN6YqBmsOwAW9sDx2brtlzhS5SavK3W4AsDrr3lAiYXKOF3aBNCQZ/ivhimAM6RAxTXAzltvxS/ipXAtyUGpUyfAAMXv5b8zcXpcX1jXVKiN0+VGR4A3OxKRalzeWE+4vDFRMzj4NhSU1Tc0xy4bePt1WLLKsOu//AFgOq7f+wmcQvvqpU0UZzQU6NU3DHf1t425lZsH65oK1C9ai069e6mM0/ZGR4A3OxKResa8vLFJFb9E1EwOvroXlGpujqt2qPjfc6DGm+i0bZNaTqfevdTewKjLjY4Ab3YkItPEG96IiIiIyGyw+CUiIiIis8FhD3qm7zuqi85mNjclIiIAOs7KoWFGDtlMH0REpobFrx611B3V7awalqYlIkUtMYVYZuINSMqrIOxvIGC8XpqEraRM4bm1WVQ1/ECu06wcGmbkkM30IWubiMhUsPjVo5a4o9q67Bz8sqNgG9BZb3kStRktMIXYtYOFqCmug5VzIfQ1h4EdylHzv2dDsKi9BQDI7/oqnEaNUx2o7YwcAPIP7gYQI2+biMhUsPhtAXq9o7rQDijh0Gwiar5aWz/tZ9rQEFdr+0ez8yEiMgRWVURERERkNlj8EhEREZHZ4LCHFqDPO6pRnNHsfIhaWs6p0zrFq5vlxLrsHPwK66F+IXMiIqKmManiNzIyEra26v9L1GY5vZbSEndUNzRsD1h3aEZmRC3DxdMDV6zsYP3eHI2rfekyy8k1K6BnyBV4DQhqfpJERNTqpFIppFKp2pjKyspWykaRSRW/cXFxCA4ONnQaKrXEHdUAGgpfXZekJWoFvj0DgN1/aDW3tbaznBSl7kH1qqUoyb8JL71kSURErU2bi5FpaWkICQlppYxuM6ni11To845qImPn2zOgoQjWQNtZTjLKzuGKHvIiIiJSRi83vG3ZsgUSiQR///23ypjr169jzpw56Ny5M2xtbdGjRw/ExMQY7JI3EREREZkfvVz5/fbbb9Vuz87OxuDBg5GTkwMAcHV1xblz57B06VIkJSXhwIEDsLGx0UcqREREREQqNevKrxACn332GXbv3q02bubMmcjJycGYMWOQlZWFwsJCpKWlwdfXFykpKVi2bFlz0iAiPfMe5YYuD1rDe5SboVMhIiLSqyYVv0lJSZg5cyZ69uyJ5557Tm3syZMnkZSUBA8PD2zduhX+/v4AgP79+yM+Ph4AsGbNGg5/IDIiAWEd0O0hGwSEcZYRIiJqW5o07OHHH3/El19+qVVsQkICAGD8+PHo0EHxP9KBAweiV69eOH36NI4ePYoxY8Y0JR0iMlO2kjKFZ6KmSlk0HqKsGBIHZwx5L8HQ6bS6H2LfQm1JMSydnPGP2NcMnQ5Ri2rSld+FCxciPT1d/vD29lYZm5LSMJl9eHi40u2y92VxRETaskO5wjNRU9Xu2YPKHw+jds8eQ6diGPFSuMa/D8Srn5eVqC1o0pVfb29vhYLX2tpaZez58+cBAN27d1e6PSCgYYqkzMzMpqRCRERERKS1Fp/nNz8/H0DDDA/KuLk13FCTm5vb0qkQkbZ6RAE1JYCVk96arIA9LFGCCtjrrU3SL+9RbpCUF0PYOxs6FSKiFtPixW95ecOvI2VF7t1k78vi1CktLUVxcXGzc7KxseHUakTq9Jyv9yYrhQMcUYJK4aD3trVSdhmovqHbPoXpGkOuZpeh8Jb2I8hOHdI8xMvF00OrhUP0LSCsA1BRBdjxRkciUq2qqgpVVVXNbqe0tFQP2eiuxYtfIYTC890sLCwAAHV1dRrbGjlypF5yWrx4MWJjY/XSFhGZgLLLwJ5BQJ2OY4MT1f+bU1lYj3MflKG+Rn0zVs4W8td5U4dqPOwVKztg9x8GKYCJiDRZvnw5lixZYug0mqzFi197e3sUFxejsLBQ6dAH2RVfBwfNV4OSk5MRFBTU7Jx41ZfIzFTfaCh8B38BOPdQH7ts4O3XYclqQ68d3I36mhhYv/AGXEOU39QLAIXzR8hfe3x9VG2bOadOw/q9ObiVm8fil4iMUnR0NF566aVmt5Oenq63C5u6aPHi193dXV78du3atdH2vLw8AEDHjh01tuXo6AhnZ45FI6Imcu4BuAVpH68httb2DwCAxz0B6DFiiMq4w3e87q0mTiZPm9xMhLlPIUbUFulr+Kijo6MestFdixe/gYGByMzMREZGBoKDgxttP3XqlDzOXPCmEiIySi1wo2Ptnj2oKa5rGPrxnt6aJSJqshYvfkNDQ7Fnzx7s27cPkyZNarR97969AIAhQzRfDWkreFMJkWZ5ZzNR7aD9/N/qbiSzLjsHryt1uP7LH6h2qNBLmwBQdLZhikbLymytbo4DoDHOuuycdu20hBa40ZGIyNi0ePEbERGB2NhYJCQkoKCgQGF4w/Hjx3H27Fm4urpixIgRalohInPh5N4e7ayA6lVLkYelamN1uZHsCgBgusbj63pzWjsrwDvvXSDxfY2xADTeROd1pQ5XAFhUtaXBD0RExqPFi9+goCCEhYUhMTERkyZNwoYNG+Dr64u0tDRERkYCABYsWABbW9uWToWITIBXQGe4veyAbD8pqh3UD4fS9kYy67Jz8PprHq7f+4ne2pRxc6mHrZ+GG3Z1uIku/+BuADGwqL2l8dhERKS7Fi9+AWDdunUIDQ3F/v374efnB1dXVxQVFQEAhg8fjujo6NZIg4hMhK1bO/QI7avxhjOtbyQrtANKLOCkzzabSsub6IiIqGW0SvHr5+eH1NRULF68GD/++CNu3LiBwMBATJkyBa+++qra5ZHbpBa4qYTI6D0ehaKSYlg68UZPIiIyHL0UvxcvXtQY4+XlhbVr1+rjcKaPN5WQGfpH7GuGTqFt+XvN7R+izezfFE6fpn+2kjKFZ6K2rFWu/BIRkZ5lSIGKa4Cdt9kVv5w+Tf/sUI6a/z0TtXUmVfxGRkZqvDEuKioKUVFRrZQREREREd1NKpVCKpWqjamsrGylbBSZVPEbFxendKEMIiIiIjIe2lyMTEtLQ0hISCtldFu7Vj8iEREREZGBsPglIiIiIrPB4peIiIiIzIZJjfklIjNSnKFbfGG6/toyAZmJNyApr4Kwv4GA8YbOxrhd/f0oyq5lah2f8d+vNcY4eAfAp5/m5a8N7fLfmbiVq9tS2acOpajd7uLpAd+eAc1Ji8igWPwSkXGx7gBY2APHZ+u2X+JI9dst7Bva1qAC9rBECSpgr9vx1fAe5QZJeTGEvf4W+Lh2sPB/030VgmWIald/P4pzkcNRX6M+zsrZQv76ynPTNbbbzgpA3GGjLoAv/52J0+P6wrqmQmPsneefN1X9OV2xsgN2/8ECmEwWi18iMi4OvkD4r0D1Dc2xywbefh2WrD7WukND2xpUCgc4ogSVwkHz8bUUENYBqKgC7DQX36RfZdcyUV8DOEXNgUvv+1XG5b4yU/6686dfqW3z1qljKJGubbiabMTF763cPFjXVKB+0Vp06t1LbWzh/BHy1x5fH1UZl3PqNKzfm4NbuXksfslksfglIuPj4KtVoarALahFUqG2waX3/ejx8FSV2+8sftXFAUAGgBKYzoqlnXr3Qu8RQ9TGHL7jtaZY3QZREBkf3vBGRERERGaDxS8RERERmQ0OeyAik9USN5Lh8SgUlRTD0kmPbfaIAmpKACsn/bVpJG6dOgZt59JoS7MoEJHpMqniNzIyEra2tmpjtFlOj4jahpa4kewfsa/prS25nvP136aBOXgHoJ0VUCJdq3b8a1ucRcHg/l5z+4epNvh3i9oGqVQKqVSqNqaysrKVslFkUsVvXFwcgoODDZ0GEZHZ8+k3FIg7rHH+3LY4i4LBZUiBimuAnTeLXzJa2lyMTEtLQ0hISCtldJtJFb9ERGQ8fPoN1ViktuVZFIjINPGGNyIiIiIyG7zyS0RkhCwrs9Uv2XwnDXGWldnNzqc16fMmulunjjU/IRNnXXYOKLTTfgc1f5+sy841PyEiA2PxS0RkROosXQAA7hffAWre124nDUs7u1+pQ9YdbRurlryJzsHb/FYjs6hqWI7C6695QImFhug7qPn75HWlDlfuaJvIFLH4JSIyInU2HgCA6/d+AqfQvqoDdVja+fovfwCYLm/bWLXETXSA+U6fZlF7CwCQ3/VVOI0apz5Yy79P+Qd3A4iRt01kilj8EhEZoWqHQO2XbNYQV+1Q0ex8Wou+b6IjoNbWT7flv9XE1tr+0ex8iAyNN7wRERERkdnglV8iMl1teOU0al2W4eGwKCuGxEGPK/uZkBZZLZHISLH4JSLT1YYn+M85dVrr2FOHUjS2xV/zqTfkvQRDp2BQLbFaIpGxMqnil8sbE1Fb5+LpgStWdrB+bw7U3U9/54wHeVPVj5FtB6Dayg4unsZ9wxsRtR1c3lhPuLwxEbV1vj0DgN1/4Fau+qmkCuePkL/2+PqoxnZdPD0a2iYiagVc3piIiLTm2zNAY6F6+I7XvUcMadmEmsHcx9ISkfFh8UtERC3G3MfSEpHxYfFLRETUAlIWjYf431Vvff4QkJl4A5LyKgj7GwgYr7dm9a6lzl/fWiTPv9fcnolGTzfm/hD7FmpLimHp5Ix/xL6mlzZN5dz1jcUvERFRC6jdswc1xXUNNye+p8UOZZeB6hsaw64dLPxfu4UIKExXGWdZma19si1A5/M3kBbJM0MKVFwD7Lz1VwDGS+FakoNSp06Anopfkzl3PWPxS0REZGhll4E9g4C6ct32SxypcpP7lTpkAaizdGlWakRtDYtfIiITVAF7WKIEFbA3dCqtrk3eRFd9o6HwHfwF4NxDfeyygbdfhyWrDLv+yx8ApqPORosp7rhgDJkRFr9ERCaoUjjAESWoFA6GTqXVGfP40WZz7gG4BWkfrya22qFC+3aM9NfTRC2Bi/4QERERkdlg8UtEREREZoPDHoiITNHjUSj637RHRESkPZMqfiMjI2Fra6s2Rpvl9IiITJ2+5vkkImoJ0h8LIX3lPrUxlZWVrZSNIpMqfuPi4hAcHKxye8qi8RBntiFl0R693RBhKpN0ExERtXWm8n9ySyxEYispU3g2VrJzD7e3QdSZM2pj09LSEBIS0kqZ3WZSxa8mLTFZs6lM0k1ERNTWmcr/yQoLkeipTTuUo+Z/z8asJc5d31r1hrfr169jzpw56Ny5M2xtbdGjRw/ExMQY7LI3ERGRqZHN7WyOczwT6UOrXfnNzs7G4MGDkZOTAwBwdXXFuXPnsHTpUiQlJeHAgQOwsbFRum91dbXCMxmfqqoqLF++HNHR0Sr7kQyLfWQa2E/Gr7qmBhuLBOakH9Z6n4z/fq12u2VlNtyv1OH6L39onJ+3st6+YY7nenucOpSiMi7n1OkWu8J169QxZOgQr+n8HbwD4NNvaPOSukNVVRWiX3gOzz4yFNZWVlrvpylPJ/f28ArorFsyapagBoCr2WUovKVbT6nrdwCwqMqDRe0tndrUdO5AE85fw7lXF5xqeG7l+q7Vit+ZM2ciJycHY8aMwfr16+Hv74+TJ08iIiICKSkpWLZsGZYuXap0Xxa/xq+qqgpLlizBSy+9xP+wjRT7yDSwn4yfZQdffH0LiPx0HararVcZZ+VsIX995bnpGtvNAgBojhP27g3P9fXIm6q6YGwHoNrKDi6eWqzwpiUH7wC0swJKpGtRgrVqY3U5/3ZWAOIO660AvvhbMj76fAMe+GkDHNpJ1Mbqmqfbyw6wddOhWFWzBHVlYT3OfVCG+hrNzdyZp7p+14Wuf0d1Pn815w4A1WdqG57bYvF78uRJJCUlwcPDA1u3bkWHDh0AAP3790d8fDwGDRqENWvW4LXXXtM4mwMREZEhefcJBQD4fPQZHO3tVMblvjJT/rrzp19pbLfO0kWrpYhL5z0GAJC0awePr4+qjXXx9IBvT/2NvPTpNxSIO4yya5kaY7U9/1unjqFEurahTT0Vv+U5FwEAjrNnwGfAKLWx2uaZdzYT1auWIttPih6hfdUnoOUS1NcO7kZ9TQysX3gDriHhapssnD9C/lpdv1uXnYPXX/OQ3/VV1Nr6qW1Tl7+jWp+/lucOACfPfIR27b5BREQEtm3bhrCwMLXxeiNawZIlSwQAMXPmTKXbe/XqJQCIpKQkpduTk5MFAJGcnKz2OIf6WIr9/hJxqI+lVnmtWbNGY4wubf7900bxf+0h/v5po1bH15Y2eRq6zVu3bgkA4tatW3pt1xTO3VTaZB/pv82WaJf9ZPxtattHLfF/khBC7OrtJfb7S8Su3l56a1MX2rap7fnr8n+ntm2mxn0mAIjUuM/01uafycfE/7WH+DP5mN7abIlzFzdPijXP2gpx86T+2hTan78ubc58eoIAICwtLUW7du2Eu7u72Ldvn8b9mqtVbnhLSWkYmxIervynGtn7srjWIpVK9d7mrhK9N9kiebZEmy3BVM7dVNpsCaZy7i31ebKfzLPNlmAq524q/3e2hJbIs0Xqhr0tM4ygpfpp7969iImJQUVFBcLDw+Hh4YHExMSWORhaabaH8+fPAwC6d++udHtAQMOvZDIzNf8ahYiIiIjaDldXVyxevBiXL19ulSK4Vcb85ufnA2g4OWXc3NwAALm5uUq3y6ZC27dlLS7/skflcW6W1aO2QsDSoh7Z7/1HZZyVpSWsrCxRcrMAO1a/qTb3GxUCtVUClhUCBRpiS8//jup64PT5LJSmpamN1UVlZSXS9NheS7RZWloKAEhPT4ejo6Pe2jWFczeVNtlH+m+zJdplPxl/m9r2UWb/IRAVpZDYOcJBi+Nrm2fm/RNRV1YKCwdHjfGG/Dy1Pf9L57NQXQ8c37MLp89nqW1T2/+T80//BgD4K/MSoCFXbfM8n/E3quuBwz/9hDMZf6ttM7/cBhZVZagrt8H1dV+pjCv56xiq64Gzp39BqZ36WSlu3usASVU5hI090g5+qzqw7BIqawTSfv8LcK5X26Yuf0fvPP/fT59CTW2t0riCUmtYVJSjrp01Mld/qrbNc2cvKPxZVgS/8MILWLVqFT744AOEh4ejQ4cOiIuLw4gRI1S0pKMWH1ghhLCyshIARFFRkdLte/bsEQDEyJEjlW7fsGGDAMAHH3zwwQcffPDBRxt7pKamKq3/Ll68KLp06SIACBcXFz1VpUK0ypVfIYTC890sLBqm2qirq1O6fdy4cdiwYQO8vb31MhuEtbU1rK2tm90OERERkbmprq7Wy/RkX375JTZu3Njo/aKiIvmV3/Lycnh4eGD79u3NPp5MqxS/9vb2KC4uRmFhodKhD+XlDUv1OTg4KN2/Y8eOmD59egtmSERERESt6dChQwp/vrvo7dixI3bt2oXRo0fr9bitcsObu3vDhNyFhYVKt+fl5QFoKHKJiIiIyHwUFRVhyZIl8PX1xZtvvgl7e3skJiYiNzdX74Uv0EpXfgMDA5GZmYmMjAwEBwc32n7q1Cl5HBERERGZj7Fjx6K+vr7FrvTerVWu/IaGNqyGs2/fPqXb9+7dCwAYMmRIa6RDRERERAY2bNgwWFhYoH379i16pfdurVL8RkREAAASEhJQUFCgsO348eM4e/YsXF1dG01hcf36dcyZMwedO3eGra0tevTogZiYGPnUZ2S82Hctb8uWLZBIJPj7b9VT7jSlH9h3+qVNPzUF+6l5Ll26hOnTp6Nfv35wdHRE7969MX36dFy4cEFpPL9LrU/XPmoK9lHzHThwAOPHj0e3bt3g7OyMwYMHIzo6GmVlZUrj7/zMw8PD0a1bN/zrX//C/fffr/IYeu8nvc0boUFYWJgAIMaMGSOys7OFEEKkpqaKzp07CwAiNjZWIf7SpUuiU6dO8mkwXF1d5a+HDBkiKisrWyt1sydbnlrdo6CgQB7Pvmsd48aNEwDE2bNnlW5vSj+w7/RPUz/p+v0Sgv3UXHv27BFOTk4CgJBIJMLT01NIJBIBQDg4OIht27YpxPO71Pp07SN+jwzjnXfekfeLlZWV8PDwkH+G/v7+IicnRyHeWL5LrVb8Xrp0SXh5eSlNfvjw4aKqqkoh/sEHH5QXy1lZWUIIIdLS0oSvr68AIF5//fXWSt3sPf300wKA8PHxEd27d1f6uHnzpjyefdey6uvrxaeffir//qgqqprSD+w7/dG2n3T9fgnBfmqO6upqERAQIACIGTNmyOefLyoqEi+99JJ8PtGrV6/K9+F3qXU1pY/4PWp9WVlZwtraWlhaWopPPvlEVFdXCyGEyMzMFKGhoQKAmDx5ssI+xvJdarXiVwghrl27JmbPni28vb2FjY2NCAwMFLGxsY2q9rS0NAFAeHh4NPpJ7ddff5UXzxUVFa2ZvtmS/SXOzMzUGMu+azmJiYni2WefFYGBgQpXM5QVVU3pB/adfujST0Lo9v0Sgv3UXJs3bxYARM+ePUV9fX2j7ZMnTxYAxL///W8hBL9LhqBrHwnB75EhLFy4UAAQ06dPb7Tt4sWLwtLSUrRr107+GRrTd6lVxvzKeHl5Ye3atbh69SoqKyuRkZGBxYsXw8bGRiEuISEBADB+/Hh06NBBYdvAgQPRq1cvFBUV4ejRo62Wuzk7d+4cbGxs4O/vrzGWfddyfvzxR3z55Zc4d+6cxtim9AP7Tj906SdAt+8XwH5qrtOnTwMAnn76aUgkkkbbZ8yYAQA4efIkAH6XDEHXPgL4PTIE2X0MEydObLStS5cu6N69O+rr65GRkQHAuL5LrVr8aislJQUAEB4ernS77H1ZHLWcoqIi3LhxA927d5evxKcO+67lLFy4EOnp6fKHt7e3ytim9AP7Tj906Sddv18A+6m5Ll68CAAqiyRZf2VlZQHgd8kQdO0jfo8M4+bNm3B1dYWvr6/S7XZ2dgCA4uJiAMb1XWqVeX51df78eQBA9+7dlW4PCAgAAGRmZrZaTuZKdvWqR48eiI+PxzfffIOLFy+ia9euGDBgAP7v//4Pzs7O8nj2Xcvx9vZWKKTULdHdlH5g3+mHLv2k6/cLYD811wsvvIApU6Zg4MCBSrf/+uuvACD/D53fpdanax/xe2QYhw8fVrntxo0bOHfuHCwtLXHPPfcAMK7vklEWv/n5+QCgdClkAHBzcwMA5ObmtlZKZkv2F+/HH3/Ejh075O///vvv2LlzJ9auXYutW7fK53Jm3xmHpvQD+6716fr9AthPzTV48GCV24qKivDWW28BuH1Fid+l1qdrH/F7ZByqq6tRUFCA9PR0LF26FKWlpZg7d6589V5j+i4Z5bCH8vJyALdP6m6y92Vx1HJkP1HX19djyZIl+PPPP1FUVISDBw9i0KBByM7OxuTJk1FRUQGAfWcsmtIP7LvWp+v3C2A/tZQLFy5gzJgxyMzMhKenJ/71r38B4HfJmKjqI36PjEO/fv3g4+ODRx99FL/88guioqKwatUq+XZj+i4ZZfErhFB4vptsTE9dXV2r5WSuOnXqhEmTJmHTpk2IiYlB79694eLigpEjR+LIkSPo0aMHsrKysHr1agDsO2PRlH5g37U+Xb9fAPtJ32pqavDuu++iT58+SEtLg729PXbu3Cn/T5XfJcPT1Ef8HhkHPz8/+Pj4yG9SjIuLw/fffy/fbkzfJaMsfu3t7QEAhYWFSrfLKnwHB4dWy8lczZkzB99++y0mTZrUaJuVlRUWLlwIAPjll18AsO+MRVP6gX3X+nT9fgHsJ306c+YMBgwYgFdffRXl5eW49957cfz4cYVfj/O7ZFja9BG/R8Zh7969uHLlCsrKyrBixQoUFBRgypQpOHbsGADj+i4ZZfHr7u4OQPXJ5uXlAYB8HAkZTu/evQE0/AMFsO+MRVP6gX1nfO7+fgHsJ33ZsGEDBgwYgD/++AN2dnaIjY1FWlqa/DOX4XfJcLTtI034PWpddnZ2ePHFFzF//nzU19dj3bp1AIzru2SUxW9gYCAAyOeGu9upU6cU4shwZD+VOTk5AWDfGYum9AP7zvjc/f0C2E/6EBcXh5kzZ6KiogIjR47EmTNnsHjxYtja2jaK5XfJMHTpI034PdK//Px8fP755/jmm29UxjzwwAMAbk9JZ0zfJaMsfmW/zti3b5/S7Xv37gUADBkypNVyMkdlZWXo06cP+vbti7KyMqUxsr+Q9913HwD2nbFoSj+w71pXU75fAPupubKzszFt2jQIITB//nz8/PPP6NKli8p4fpdany59xO+RYVhYWGDu3LmYNWuWyvG4RUVFAG7Py2xU3yWd1oNrJSdPnhQARMeOHUV+fr7Ctl9++YXLDraiMWPGCABi9erVSrePGjVKABDr168XQrDvWlOXLl1ULpvblH5g37UMdf2k6/dLCPZTc8XExAgAIjw8XKt4fpdan659xO+RYXTr1k0AEPv371e6ffz48QKAeO+994QQxvVdMsriVwghwsLCBAAxZswYkZ2dLYQQIjU1VXTu3FkAELGxsQbO0DzExcUJAMLOzk5s3rxZvs56Tk6OmDZtmgAg+vfvL+rq6uT7sO9ah7qiSoim9QP7Tv/U9VNTvl9CsJ+ao2fPnmr/w1aG36XWpWsf8XtkGO+//74AILp16yYOHTok/9xv3Lghnn/+eQFAeHp6KhStxvJdMtri99KlS8LLy0sAkFf2stfDhw8XVVVVhk7RbERFRck/e1tbW+Hh4SH/c2BgoDh16pRCPPuudWgqfpvSD+w7/dPUT7p+v4RgPzVVXV2dsLKyEgCEv7+/6N69u8rHhAkT5Pvxu9R6mtpH/B61vtraWjFixAj5Z2Zvby86deok/7OLi4vYs2ePwj7G8l0y2uJXCCGuXbsmZs+eLby9vYWNjY0IDAwUsbGxorKy0tCpmZ2dO3eKUaNGCW9vb+Hs7CyGDRsm/vOf/4jS0lKl8ey7lqepqBKiaf3AvtMvbfpJ1++XEOynprh8+bL8P01NjwEDBijsy+9S62hOH/F71Prq6urEp59+KoYPHy68vLyEk5OTGDhwoJg/f77IyclRuo8xfJckQqgYqUxERERE1MYY5WwPREREREQtgcUvEREREZkNFr9EREREZDZY/BIRERGR2WDxS0RERERmg8UvEREREZkNFr9EREREZDZY/BIRERGR2WDxS0RERERmg8UvEREREZkNFr9EREREZDZY/BIRERGR2WDxS0RERERmg8UvEREREZkNFr9EREREZDZY/BIRERGR2WDxS0RERERmg8UvEREREZkNFr9EREREZDZY/BIRERGR2WDxS0REbc6lS5cgkUggkUiQnJxs6HR0smTJEkgkEnTt2lX+3vbt2+Hr6wtfX1989NFHBsyu9ZhyH5JxY/FLRERk5MrLy3HlyhVcuXIFxcXFhk7HbM2YMQMSiQQPPPCAoVOhZmDxS0RERERmw9LQCRAREZF606ZNw7Rp0wydBlGbwCu/RERERGQ2WPwSEZFZunTpEubOnYvAwEDY29vD09MTQ4YMwcqVK3Hr1i2V+5WVlWH16tUYNmwYunTpAltbW3h5eWHAgAGIjo5Gdna22uPW1tbi/fffR0hICJydneHs7IyQkBB8+OGHqK6uVpmrupu/ZNtSU1MBAJs3b8awYcPQvn17uLm5YfDgwVixYgWqqqqUti+EwJo1azBo0CA4OzvD1dUVI0aMwPbt2wEAb7zxBiQSCSZOnKj23NRp6ufdkm3++OOPePTRR9GpUyfY2dnh3nvvRWRkJH799VeFONlY36+++goAcPDgQflnfunSJXmc7L3Tp0+jrKwMCxYsQKdOnSCRSBode//+/Zg4cSK8vb1hZ2eH++67D5MmTcK6desghFCZc3P7WhMPDw9IJBJs3rwZtbW12LhxI8LCwuDp6YkOHTrggQcewJdfftmkto2GICIiamOysrIEAAFAHDx4sNH2b7/9Vri4uMhj7n54e3uL1NTURvvl5eUJPz8/lfsBEPb29uLEiRNK87p8+bIYOnSoyn0HDBggXnrpJQFAdOnSRevzkW379ddfxcyZM1W2P3ToUFFTU6Owb2FhoRg3bpzKfaKiosRrr70mAIgJEybo2hVN/rxbqg+FEKKyslI8//zzavtx0aJF8vjp06erjMvKymrUD8ePHxeDBg1SiLvz2C+++KKQSCQq23zwwQfFlStXlObenL7W5OLFi/L9d+3aJYKDg1W2P2nSJJ3aNiYsfomIqM1RVzjt2rVLodjctGmTSE1NFfv27ROvvPKKsLGxEQCEk5OTuHz5ssK+//jHPwQA0a5dO7FgwQJx4MABcerUKZGcnCyio6OFlZWVACD69OnTKKf6+nrRv39/+bEjIiLE1q1bxW+//SbWrVsn+vbtKwDI22hK8Tt58mQBQPzjH/8QcXFx4uTJk+Kbb74RgYGB8hipVKqw75gxY+TbZs6cKX744Qfx66+/ijVr1ggfHx8BQPj6+ja5+G3q591SfSiEEHPnzpXv/9hjj4nt27eL9PR0sW3bNjFgwAD5tvj4eCGEELm5ueL8+fPiscceEwDE4MGDxfnz58X58+cVCkzZfgMHDhQAxFNPPSW++eYbcfz4cXnMwoUL5XGDBw8WGzZsEKmpqSI+Pl48/fTT8m2DBg1S+nk2p6812bp1qwAgJBKJuOeee4SlpaV44YUXRHJysvjtt9/EJ598Itzd3eXt79ixQ6f2jQWLXyIianNUFU7V1dXy4uCZZ54RdXV1jfb9448/hIODQ6OrW/X19cLR0VEAEDExMUqP+/bbb8uL4+LiYoVtmzZtkuf02muvNdq3pKREjBw5Uh7TlOIXgPj3v//daPuVK1eEk5OTvGiS2bdvn3y/NWvWKN2va9eu8hhdi9/mfN4t0YdCCPHXX38JS0vLRld3ZSoqKkRQUJAAIO6//36FbbIrwKNGjVJ6vnf2w9q1axttv3jxorwwf+yxx0RVVVWjmBUrVsjb2LZtm9pj6NLX2nj55Zflbdva2oqEhIRGMX///bf8B7SxY8fq1L6xYPFLRERtjqrC6auvvhIAhIuLiygtLVW5/zvvvCMACDs7O3mBcuPGDfH000+Lp59+Wly8eFHpfvv371f663AhhOjXr58AILp166by19EnT55sVvHr5eUlysvLlbb98MMPNyrcZMV2nz59RH19vdL9vvjiiyYXv835vFuiD4UQYurUqQKAcHd3V/lZyX5QsbKyEmVlZfL3tS1+e/XqpfTzfPHFFwUAYW1tLbKzs5W2UV9fL+677z75VW1Vx9C1r7UxYsQIefu7du1SGffEE08IAMLf31/+3qRJkxQKc3WPF154Qae89I1TnRERkdk4efIkAKB///7IyclRGRcQEAAAqKioQHp6OgYNGoT27dtj06ZNats/cuSI0vfr6+tx9uxZAMCsWbNgaan8v9+goCAMGjSo0Q1X2oqIiICdnZ3SbZ6eno3ek30e06dPV3pTFgA888wzmDt3Lurq6nTOpzmfd0u1mZ6eDgB46qmnVH5WkZGR6NOnDwDAwsJC5TFUefjhh5V+nn/99RcAICwsDL6+vkr3lUgkmDFjBhYuXCiPV0bXvtakvr4eaWlpAIAhQ4bgH//4h8rYe+65BwAUPv9+/fqhY8eO8j/n5eVh27Zt6NGjBx566CGF/Z944gmd89MnFr9ERGQ2MjIyADTcrd+9e3et9rlx40aj96qrq5Geno6MjAxcunQJ58+fx4kTJ3D69GmlbVy+fFl+933Pnj3VHq9Hjx5NLn4DAwO1js3Ly5OvFqfus7CxsYGPj4/GWSyU0dfnra82hRA4f/48gNvFsTK2trbo16+fVm0r4+3trfR92bE15d2tWzcADTOL5OTkoFOnTo1idOlrbZw5cwalpaUAgNdee01trCyuffv28vdeffVVhZitW7di27Zt+Oc//4nFixfrNdfmYvFLRERmIysrS+d97pwy6/Lly3jjjTcQHx8vLwBkHBwcMHjwYBw/frxRG7m5ufLXqgojGR8fH51zlOnQoYPWsXd+FpquFHp5eTWp+G3u563vNvPz81FeXg6g4Zxaipubm9L3r1y5otWx7/w7kp2drbT41aWvtXHixAn56xEjRqiN/fvvvwEA/v7+KmN+++03AEBISIgestMvzvNLRERmQ1Z0TJ06FaLhvheNj0mTJgFoKEJCQ0OxceNGVFZWIiIiAu+99x7++9//IiMjA7du3cK7776r9Lh3/or7+vXranNU96t8fbqzoLqzOFcmPz+/ScdozufdEm22b99ePuSkoKCgSeekDVVDSGQ/2Gjq4zv7oyWL9DvJftvg7e0NJycnlXF1dXX4/fffAQDDhw9XGSebh5jFLxERkQH16NEDwO1fnevi7bffxrVr19CxY0f8+eef2LlzJxYuXIjw8HAEBgbCwsICNTU1Svf18vKCvb09gNtXzVQ5d+6czrk1ha+vrzynCxcuqIyrra3F5cuXm3SM5nzeLdGmpaWlfEiBunOur6/Hrl27sGvXLo0/rOhCNtxBNvxBFdl2Ozs7dO7cWW/HV0d25VfT8bZs2YKrV68CgMpFT4QQSEtLg7e3d6sV77pg8UtERGbjvvvuA9Bw09OdK3Pd7fPPP0f//v0RFhYmX20rJSUFAPDoo4/Kb/i5myxGmV69egEAvvjiC9TW1iqNOX36tNo29Ekikchz2rhxo8q4bdu2qSzqNWnO591Sbcr237Ztm8oV9Y4dO4YJEyZgwoQJKCkpUZuPLu69914AwL59++RDIO4mhJCvJHfPPfeovIqsT1VVVfjjjz8AABcvXlQZV1lZiaVLlwIABg0ahCFDhiiNO3/+PG7duoUBAwboP1k9YPFLRERmY/r06fD09ERlZSXmz5+vtPjJysrCG2+8gfT0dPTq1UtefMh+FayqaElPT8eKFSvkf767wF20aBGAhiuOym4AKi8vx4IFCzQWf/okyyk9PR2ff/55o+35+fl48803m9x+cz7vlmrz5ZdfBtDQj2+99VajfYUQWL58OYCGYQqyK813qq+vV3/iKjz//POwtrZGdXU1XnzxRaU/VKxYsQKnTp0CACxcuLBJx9FVenq6PJf8/Hxs2bKlUUxhYSHCwsJw7tw5WFpaQiqVqmxPNt7XWItfzvNLRERtjrp5ce+ct7Z3795iw4YN4rfffhPHjx8XK1as+P/27iYkqjaKA/h/xsLr5OiIEEyYLnShNKI5gbpITEIMQpREQcZAUUSSyEW0kAIFAx00NBEkhEER26QLHRCFgfwAhXEWlpSLWmVKtagElQrPu3jfuTivM+bHOJb3/4NZ3Xue+/g8m+PlueeI2WxW66jurMW6szNXXV2dTE9Py6tXr8TpdEpdXZ0oiiJ6vV69586dO/L69Ws1fnt7W6xWq3q9sLBQnj9/LgsLC+JwONRWst5uaoep8+twOAKuSaAatVlZWWp8TU2NjI6Oitvtlt7eXklISBAAkpKSojZmOKjDrvdx7KFXSUmJGl9SUiIvXryQxcVFcTqdUlBQoF4bHBz0iauqqlLHXVpaki9fvvjt8LbXPnjbVwOQ7Oxs6e/vF4/HI8PDw1JeXq5ey8zM9Fsr+Ch7HUhXV5daOzksLEzCw8OlsbFRpqenxePxSG9vr0/nuJ6enj3H8/6NTqdzX88PNSa/RER06uyVOG1vb0tHR4faacvfz2w2i8fj8Yn7/v27TwLw/5/VapXFxUW1JTAAMZlMPmOsrKz4NBLwN8bY2FhIk9+PHz/K1atX/c7nzJkz8vTpU3nw4IEAEJvNtu89OOp6H8ceen39+nXPpgx6vV7u37+/K669vX3XvTubmexnHzY3N+Xu3bui0+kCPj8/P19WVlb8xh9H8ltRUSEA5NatW/Lo0aOA8zKZTDIwMPDb8bzNU9bW1vb1/FDjsQciItIUnU6HhoYGLCwswGazIT4+HuHh4TCbzcjJyUFbWxvevXuHy5cv+8QZjUa43W48fPgQ6enpMBqNiI6ORlZWFjo6OjA3N4fU1FSMjIwgLS0NBoMBeXl5PmNcuHABLpcLdrsdVqsVUVFRMBgMsFgsePz4MWZnZ2EymUK4Gv9+jOdyudDS0oJLly5BURTExsaisLAQMzMzqK+vVz9w2qsKQCCHXe/jHDM6OhpDQ0MYGhpCXl4eYmNjERERAYvFgrKyMszPz6OtrW1XXH19PWpra3H+/HkoioKEhAScPXv2QOuhKAo6OzsxMTGByspKWK1WnDt3DsnJySgtLUVfXx/Gx8d/WxIvmLyVHq5cuYKmpiY8e/YM6enpMBgMiImJQUZGBpqbm/HmzRvYbLY9x5L/PnaLi4s7VLONUNCJhPBwEREREf11cnNz8fLlS9jtdvXMLJ0O3759Q0xMDEQEk5OTuH79+pHGW15eRnJyMoqKijAyMhKkWQYX3/wSERFpVHd3NyorK/HkyZOA93z+/Fktg2WxWEI1NQoRt9utfmQZjJq8f3JzCy92eCMiItKora0tOBwOGI1G3L5922/XsNbWVmxsbCAyMhK5ubmhnyQdK+8/NomJiQE70x2Et7nFH1vpAXzzS0REpFmlpaUwmUxYX19Hfn4+pqam8PPnT/z69Qtv375FdXU12tvbAQD37t2DoignPGMKtp3nfYPhb3jzyzO/REREGjYxMYHi4mJsbGwAAPR6/a5udTdu3MDw8DCT31Po4sWL+PDhg6bOczP5JSIi0rhPnz7BbrdjamoK79+/x48fP5CUlISkpCTcvHkT5eXlCAsLO+lpUpCtrq6qVSVcLheuXbt2wjMKDSa/RERERKQZPPNLRERERJrB5JeIiIiINIPJLxERERFpBpNfIiIiItIMJr9EREREpBlMfomIiIhIM5j8EhEREZFmMPklIiIiIs1g8ktEREREmsHkl4iIiIg04x+1mmIgGdsGcwAAAABJRU5ErkJggg==",
+      "text/plain": [
+       "<Figure size 800x600 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
    "source": [
     "events = NanoEventsFactory.from_root(\n",
     "    {list(fileset[list(fileset.keys())[0]][\"files\"])[0]: \"reco\"},\n",
@@ -142,22 +291,44 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 5,
    "id": "1abaeac0-ca4c-4a36-8426-10438c4e034e",
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[########################################] | 100% Completed | 21min 48.1s\u001b[2K"
+     ]
+    },
+    {
+     "data": {
+      "text/plain": [
+       "(9007,\n",
+       " [WorkItem(dataset='mc23_13p6TeV.700794.Sh_2214_Ztautau_maxHTpTV2_CVetoBVeto.deriv.DAOD_PHYSLITE.e8514_s4369_r16083_p6697', filename='root://192.170.240.191:1094//root://xrootd.data.net2.mghpcc.org:1094//USATLAS/atlaslocalgroupdisk/rucio/user/alheld/81/f8/user.alheld.47045256._000013.output.root', treename='reco', entrystart=0, entrystop=680, fileuuid=b'\\x9f\\xee\\x19x\\xb2\\x18\\x11\\xf0\\xb8\\xedq\\n\\x02\\n\\xbe\\xef', usermeta={'lumi': 109376.0, 'weight_xs': 1973.0607342, 'dsid': '700794', 'campaign': 'mc23e'}),\n",
+       "  WorkItem(dataset='mc23_13p6TeV.700794.Sh_2214_Ztautau_maxHTpTV2_CVetoBVeto.deriv.DAOD_PHYSLITE.e8514_s4369_r16083_p6697', filename='root://192.170.240.194:1094//root://xrootd.data.net2.mghpcc.org:1094//USATLAS/atlaslocalgroupdisk/rucio/user/alheld/3a/c2/user.alheld.47045256._000012.output.root', treename='reco', entrystart=0, entrystop=11033, fileuuid=b'\\x84\\x0c6\\x9a\\xb2\\x18\\x11\\xf0\\x917\\xa9\\xe7\\xb4\\x9d\\xbe\\xef', usermeta={'lumi': 109376.0, 'weight_xs': 1973.0607342, 'dsid': '700794', 'campaign': 'mc23e'}),\n",
+       "  WorkItem(dataset='mc23_13p6TeV.700794.Sh_2214_Ztautau_maxHTpTV2_CVetoBVeto.deriv.DAOD_PHYSLITE.e8514_s4369_r16083_p6697', filename='root://192.170.240.193:1094//root://xrootd.data.net2.mghpcc.org:1094//USATLAS/atlaslocalgroupdisk/rucio/user/alheld/c1/b4/user.alheld.47045256._000011.output.root', treename='reco', entrystart=0, entrystop=2051, fileuuid=b'f\\x93U\\x88\\xb1\\\\\\x11\\xf0\\x85\\xdaG\\x80\\x8e\\x80\\xbe\\xef', usermeta={'lumi': 109376.0, 'weight_xs': 1973.0607342, 'dsid': '700794', 'campaign': 'mc23e'})])"
+      ]
+     },
+     "execution_count": 5,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
    "source": [
     "run = processor.Runner(\n",
     "    executor = processor.DaskExecutor(client=client),\n",
     "    # executor = processor.IterativeExecutor(),\n",
     "    schema=NtupleSchema,\n",
     "    savemetrics=True,\n",
-    "    chunksize=100_000,\n",
+    "    chunksize=200_000,\n",
     "    skipbadfiles=True,\n",
     "    # maxchunks=1\n",
     ")\n",
     "\n",
-    "preprocess_output = run.preprocess(fileset)\n",
+    "with performance_report(filename=\"preprocess.html\"):\n",
+    "    preprocess_output = run.preprocess(fileset)\n",
     "\n",
     "# write to disk\n",
     "with open(\"preprocess_output.json\", \"w\") as f:\n",
@@ -180,10 +351,42 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 6,
    "id": "dc78d57d-4fe3-4e11-ab4c-0f0afe60ca32",
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[########################################] | 100% Completed | 33min 55.3s\u001b[2K"
+     ]
+    },
+    {
+     "data": {
+      "text/plain": [
+       "{'bytesread': 183286387613,\n",
+       " 'columns': ['pass_ejets_EG_SCALE_ALL__1up',\n",
+       "  'el_e_NOSYS',\n",
+       "  'el_pt_NOSYS',\n",
+       "  'el_pt_EG_SCALE_ALL__1up',\n",
+       "  'el_isol_effSF_tight_NOSYS',\n",
+       "  'el_pt_EG_SCALE_ALL__1down',\n",
+       "  'el_isol_effSF_tight_EG_SCALE_ALL__1up',\n",
+       "  'pass_ejets_EG_SCALE_ALL__1down',\n",
+       "  'pass_ejets_NOSYS',\n",
+       "  'weight_mc_NOSYS',\n",
+       "  'el_isol_effSF_tight_EG_SCALE_ALL__1down'],\n",
+       " 'entries': 1087875546,\n",
+       " 'processtime': 12426.252845525742,\n",
+       " 'chunks': 9007}"
+      ]
+     },
+     "execution_count": 6,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
    "source": [
     "class Analysis(processor.ProcessorABC):\n",
     "    def __init__(self):\n",
@@ -240,7 +443,8 @@
     "\n",
     "\n",
     "t0 = time.perf_counter()\n",
-    "out, report = run(preprocess_output, processor_instance=Analysis())\n",
+    "with performance_report(filename=\"process.html\"):\n",
+    "    out, report = run(preprocess_output, processor_instance=Analysis())\n",
     "t1 = time.perf_counter()\n",
     "report"
    ]
@@ -255,10 +459,22 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 7,
    "id": "9575019b-d1a5-4a8e-9d5a-32d0d8bd0919",
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "data read: 183.29 GB in 9007 chunks\n",
+      "core-average event rate using 'processtime': 87.55 kHz\n",
+      "core-average data rate using 'processtime': 0.12 Gbps\n",
+      "average event rate using walltime: 533.80 kHz\n",
+      "average data rate using walltime: 0.72 Gbps\n"
+     ]
+    }
+   ],
    "source": [
     "print(f\"data read: {report[\"bytesread\"] / 1000**3:.2f} GB in {report[\"chunks\"]} chunks\")\n",
     "\n",
@@ -271,10 +487,21 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 8,
    "id": "982cce52-7f5c-4126-a5cc-6a4dfee70732",
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 800x750 with 2 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
    "source": [
     "mc_stack = []\n",
     "labels = []\n",
@@ -285,7 +512,7 @@
     "\n",
     "    dsids = sorted(set(dsids))\n",
     "    dsids_in_hist = [dc for dc in out[\"hist\"].axes[1] if dc.split(\"_\")[0] in dsids]\n",
-    "    print(f\"{key}:\\n  - expect {dsids}\\n  - have {dsids_in_hist}\")\n",
+    "    # print(f\"{key}:\\n  - expect {dsids}\\n  - have {dsids_in_hist}\")\n",
     "\n",
     "    if key in [\"data\", \"ttbar_H7\", \"ttbar_hdamp\", \"ttbar_pthard\", \"Wt_DS\", \"Wt_H7\", \"Wt_pthard\"] or len(dsids_in_hist) == 0:\n",
     "        continue  # data drawn separately, skip MC modeling variations and skip empty categories\n",
@@ -293,8 +520,14 @@
     "    mc_stack.append(out[\"hist\"][:, :, \"NOSYS\"].integrate(\"dsid_and_campaign\", dsids_in_hist))\n",
     "    labels.append(key)\n",
     "\n",
-    "fig, ax1, ax2 = mplhep.data_model(\n",
-    "    data_hist=out[\"hist\"].integrate(\"dsid_and_campaign\", [dc for dc in out[\"hist\"].axes[1] if \"data\" in dc])[:, \"NOSYS\"],\n",
+    "try:\n",
+    "    data_hist = out[\"hist\"].integrate(\"dsid_and_campaign\", [dc for dc in out[\"hist\"].axes[1] if \"data\" in dc])[:, \"NOSYS\"]\n",
+    "except ValueError:\n",
+    "    print(\"falling back to plotting first entry of categorical axes as \\\"data\\\"\")\n",
+    "    data_hist = out[\"hist\"][:, 0, 0]\n",
+    "\n",
+    "fig, ax1, ax2 = mplhep.comp.data_model(\n",
+    "    data_hist=data_hist,\n",
     "    stacked_components=mc_stack,\n",
     "    stacked_labels=labels,\n",
     "    # https://scikit-hep.org/mplhep/gallery/model_with_stacked_and_unstacked_histograms_components/\n",
@@ -314,7 +547,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 9,
    "id": "b2e3efe0-f724-4206-b233-202a51729014",
    "metadata": {},
    "outputs": [],
@@ -326,10 +559,402 @@
     "    f.write(json.dumps(out[\"hist\"], default=uhi.io.json.default).encode(\"utf-8\"))\n",
     "\n",
     "with gzip.open(\"hist.json.gz\") as f:\n",
-    "    h = hist.Hist(json.loads(f.read(), object_hook=uhi.io.json.object_hook))\n",
+    "    h = hist.Hist(json.loads(f.read(), object_hook=uhi.io.json.object_hook))"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "52f7e192-b188-4d16-93e5-971426ca3521",
+   "metadata": {},
+   "source": [
+    "## ServiceX\n",
+    "### Producing similar histograms from signal samples in the Physlite format"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "148cf1fb-d8ef-40c2-85a9-fbf03d77c1d4",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "!pip install --upgrade servicex_analysis_utils"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 36,
+   "id": "820ccf2a-91b1-44dc-9abd-73eec563e24c",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from servicex import deliver, query \n",
+    "from servicex_analysis_utils import to_awk, ds_type_resolver"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "03b17293-6aa1-4a75-af63-fe2a382a94e4",
+   "metadata": {},
+   "source": [
+    "#### Preparing signal containers"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "id": "2274c7b6-89cd-46fd-b95a-9cbd04a344ad",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "H+ -> cb signal in 40 DSIDs for 9e+06 total events\n"
+     ]
+    }
+   ],
+   "source": [
+    "total_sig=0\n",
+    "signal_containers={}\n",
+    "for container, metadata in dataset_info[\"Hplus_cb\"].items():\n",
+    "    total_sig+=metadata[\"nevts_input\"]\n",
+    "    mass = utils.hplus_signal_mass(container)\n",
+    "    _ , _, campaign = utils.dsid_rtag_campaign(container)\n",
+    "    signal_containers[f\"{mass}_{campaign}\"]=container\n",
+    "print(f\"H+ -> cb signal in {len(signal_containers)} DSIDs for {total_sig:.0e} total events\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "1caf3e3c-1b80-4696-b980-47e234615ef6",
+   "metadata": {},
+   "source": [
+    "#### Building the trasnformation query"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "id": "142ddafc-4b9d-4770-ad0c-c37db1e8242c",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "selection = (\n",
+    "    \"(sum(AnalysisJetsAuxDyn.pt > 25000, axis=1) > 3)\"                          # at least 4 jets\n",
+    "    \" & (any(AnalysisElectronsAuxDyn.pt > 25000, axis=1))\"                      # at least 1 electron\n",
+    "    \" & (any(log(\"\n",
+    "    \"       BTagging_AntiKt4EMPFlowAuxDyn.GN2v01_pb / \"\n",
+    "    \"       (0.2*BTagging_AntiKt4EMPFlowAuxDyn.GN2v01_pc + 0.8*BTagging_AntiKt4EMPFlowAuxDyn.GN2v01_pu + 1e-12)\"\n",
+    "    \"     ) > 2.09, axis=1))\"                                                # at least 1 btag\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 13,
+   "id": "6f2812ee-57e6-4b4f-b563-110b6cb9eaff",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "query_up = query.UprootRaw(\n",
+    "    [\n",
+    "        {\n",
+    "            \"treename\": \"CollectionTree\",\n",
+    "            \"filter_name\": ['AnalysisElectronsAuxDyn.pt'],\n",
+    "            \"cut\": selection\n",
+    "        },\n",
+    "    ]\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 14,
+   "id": "1bfd38ec-2393-43c3-be02-4e70f3429902",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def build_spec(containers, query_c_cut, name_prefix=\"Hplus_cb\"):\n",
+    "    samples = []\n",
+    "    for label, dsid in containers.items():\n",
+    "        samples.append({\n",
+    "            \"Name\": label,\n",
+    "            \"Dataset\": ds_type_resolver(dsid),\n",
+    "            \"Query\": query_c_cut,\n",
+    "        })\n",
+    "    return {\"General\": {\"Delivery\": \"LocalCache\"}, \"Sample\": samples}\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 15,
+   "id": "6495f40c-f46e-402d-bd05-008a8f1eb946",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "transformation_request = build_spec(signal_containers, query_up)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "9898dd81-e5e6-4a4b-96b2-a6bb1111b868",
+   "metadata": {},
+   "source": [
+    "#### Sending the request to the backend"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 16,
+   "id": "b78e4b07-ac15-4d8b-8059-4d655782264d",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "ad160a3b0cd946168fb1f3b182622be6",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Output()"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "text/html": [
+       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"></pre>\n"
+      ],
+      "text/plain": []
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "result_files = deliver(transformation_request, fail_if_incomplete=False, ignore_local_cache=True)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "1c0d3d22-5f47-4218-983f-d5c141c6f3fb",
+   "metadata": {},
+   "source": [
+    "#### Handling the results"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 17,
+   "id": "c3cf4077-6286-43f6-ac2d-47ffde2b4226",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "#Load data into arrays\n",
+    "arrays=to_awk(result_files)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 30,
+   "id": "603ccb15-4543-4f36-9642-1f187c7a26ae",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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yQp8+fbBu3Tp8/PixVuVjGIZhGIb5FtVJI+HXX3/Fpk2bUFhYCBUVFaFVlgFAWloa+fn5WLZsGVsPoIHk5+fjzz//FNr/+vXrMuPX9IFeS0uLW/ei9KJ6IiIi6NixY43SBKpf/tLevHmD4cOHo2/fvvDz88OLFy+QmZmJT58+ITo6GmFhYZg7dy50dXVx+fLlGpfxypUrcHJyQvv27SErKws9PT1YW1tj0aJFSE9Pr3G6DS0sLAw8Hg/W1taNXZQGFxsbyzVAGWH869OyZcvGLkqTEhoaikGDBsHAwAAyMjJo06YNxowZg6ioKIF4X2Pda9my5Xd5L2AYRlCtGwnh4eHYsWMHAGDixIl4+/Yt/Pz8hOKFhIRg9uzZICL4+PjgwYMHtc2aqYSPj0+ZD9Rv3rwR2peRkYGioiK0atVKYFNUVBSI17x5c6E4/AHrRUVFePjwIRfX2NgYcnJyDVL+0u7fv4/27dvj1KlT3D4JCQk0b94cMjIyAnE/ffoER0dHxMXFVatsBQUFGDp0KGxsbHDixAk8fvwYSkpK+PDhA0JDQ7Fs2TIYGBggICBA6Fj+Q8H+/furlWdTcfbsWXh5eeHff/8V2G9tbQ0ej4eJEyc2UskahpeXF7y8vBq7GEwj8vb2hrW1Na5fv46OHTvC2dkZzZs3x4kTJ/DDDz/g4MGDXNyZM2di5syZjVdYhmGY8lAtOTs7E4/HIwsLC27fq1eviMfjkYiIiFD84cOHE4/Ho3HjxtU2669eREQEAaCIiIgGzzsjI4OaNWtGAIS2Pn36VDmdwYMHCxz7/v37cuNGRUUJxHV2dm7w8ufk5JCuri4Xt02bNnT58mUqKiri4ty6dYuMjIwE0vTz86tW+Tw8PAgAaWpq0sGDB+nz589ERFRcXEyPHz+mMWPGEABSUlKi2NhYgWP5ee7bt69aedan0NDQateN8ri4uJR5fn369CEA5OLiUus86lJMTAz3ndSFukzra8C/Pvr6+o1dlCYhIiKCREREyMLCgj5+/CgQFh0dTTo6OiQnJ0eJiYmNVMLK6evr18m9gGGYutFYz5O1fpNw//598Hg8uLu7Vyn+pEmTAEDolStTt9auXYvk5GTus6ysLPfvyn6JL+3+/fvcv3V1daGurl5u3NJdjQDA3Ny8yvl8qabl/+uvv7i3AiIiIrhw4QJsbGwgIvK/qm5paYmtW7cKHPfy5csqly07Oxvr1q0Dj8fDuXPn4OzsDElJSQAlbwnatGmDI0eOYOjQoUhPT8emTZsEjm/dujVat24t9JbmW6enp4fWrVtDS0ursYvCMPUmJCQExcXFWLVqFVRUVATCDA0N4eXlhezsbISHhzdSCRmGYaqm1o2E2NhYAICZmVmV4uvp6QGoWr9ypmYSExOxceNG7rONjQ2srKy4z/Hx8SgsLKw0naSkJCQkJHCfK3voL92gqEr88tSm/GfOnOH+3aJFC66+falt27ZwdHTktrLG0ZQnMjISxcXFaN26Nbp06VJuvClTpnDxS3v69CmePn0KR0fHKuf5Ldi/fz+ePn2KFStWNHZRGKbe8P8mysvLlxlua2uLVatWQUdHB0BJN7wvx3ukpqZi0qRJMDAwgJ6eHiZPnoz379/D1NRUYD2iiRMnQldXFwUFBZg/fz60tbUhLy+P7t2749q1a0J5X7p0CX379oWGhgakpaXRqlUrzJo1i03gwDBMmWrdSODfCFNSUqoUPz4+HgC4X16/ByNGjECbNm0q3LZv315n+Xl5eSEnJ4f7vGzZMoGH5aKiIrx9+7bSdL586O/cuXOF8etq0HJtys+fZQkA3r59i0OHDqG4uFgoXvPmzXH69Glu+3KF8Iq8f/8eAJCXl1dhvJ49e+LYsWP4/fffBfbz++bzHyaAkj/2PB4P79+/R0hICHr27Ak5OTmoqKhg9OjR+PDhA4CSXyl79OgBOTk5aGlpYeTIkULjKfbv319h338vL69qjYmIiorC+PHjYWxsDBkZGairq6Nz587YsGGDwPfEH/y8b98+AICLi4tAPhXlGxUVBWdnZ5iZmUFeXh5du3aFm5ubwNukL8/v8OHDyMnJwdy5c6Gvrw8ZGRmYmppi0aJFyM7OrtK5VaS6+fC/Q77yBqT6+flh0KBB0NbWhqqqKnr27Il9+/ahqKhIKG7Lli1hamoKANi9ezcMDAy4t2L8AcX8xqiPjw/atWsHGRkZ6Ovr46effhKoY6XduHEDw4YNg6GhIaSlpaGtrY0ePXpgz549KCgoqPlFY9C2bVsAgLu7u8D9iK958+aYN28eLCwsyjw+OTkZXbt2hZ+fHwwNDWFlZYWgoCD8+OOPAv+/lTZp0iQcOnQI/fr1Q9euXREeHo6BAwfi1atXXJwbN27A3t4e9+7dw48//oiffvoJioqK2LRpE0aNGlUHZ84wTE1s37690mfEESNGNE7hattfydramkREROiPP/7g9lU0JuH3338nHo9H3bt3r23WX73G6EP27NkzEhMT4/pFDxo0iIiIVq5cKdAHPyQkpNK0PD09BY65cOFCuXHz8vJIUlKSi2tqatoo5TcxMREaw6CpqUmjR4+mzZs3082bNyk3N7dGZeN78uQJl/a8efMoPz+/Wsfz++bHxMRw+/j9+Ldt20YiIiIkLi5OampqXD4dOnSg/fv3E4/HI1FRUYHxGu3bt6fCwkIurX379lXY93/JkiVCYwbKG5Nw9+5dkpCQIAAkJiZGOjo6JC8vz+Vta2vLjfe4c+cOtWrVigtXV1enVq1a0cmTJ8vNl4jIz8+PpKSkCADxeDyBc9PS0qKbN28KxOef3+7du8nCwoIb+yEtLc0dZ29vX+Xvo7wxCdXNx8PDg1q1asWFtWrVilq1asWF5+TkkJOTExeurKxMMjIy3OeBAwcK1U19fX1q3bo1rVq1SuC40uWePHky/frrrwSApKSkSEVFhYvbvHlzoX7xZ86c4cIlJSWpefPmAuWYPHlymdeHjUmomtzcXOrcuTN3Pbt06UJ//vknhYWFlXmv6NOnj8C1nTt3LgGgAwcOcPuSk5OpTZs2BIAcHR25/S4uLiQiIkI//PADpaamcvv5Y6a2bdvG7Zs8eTIBoMuXLwvkb29vTzwej9LT07l9bEwCw3xdGmtMQq0bCXv37iUej0cyMjIUFRVFROU3Em7cuEHi4uIkIiJCmzdvrm3WX73G+FKHDRvG/XHi8XgUGRlJRESHDx8WeHDevXt3pWkNHDhQ4JgPHz6UG/f+/ft1Mmi5tuXnPyxVtImLi1P//v3pyJEjNSrjl+Vs1qwZTZkyhU6ePElpaWmVHltRI0FcXJzmz5/PPSyGhIRwD+kAaOLEiZSRkUFERNeuXePCbt++zaVVl42EXr16EQBydXXl8iUiOn/+PPdg+WX9Lm/gcln5vn37lmtcenh4cA8q8fHx5ODgQADI2Ni4zEaQnp4etWjRgm7evEnFxcVUVFREa9as4a7Vw4cPy/8SSqmskVDdfMpKi4jIy8uLe2jk1+vCwkI6f/48aWtrc43O0vT19UlWVpYkJCRo/vz5lJycLFTuFi1akLS0NO3bt4/y8vKIqKTeyMrKEgDauHGjQJr8gf1//vknN+C+sLCQ9u7dSyIiIgSAUlJShPJhjYSqy83Npe3bt1OPHj0EfvSQlZWln376iZ4+fcrFLd1IKC4uJnl5eerSpYtQmvx74JeNBAAUEBAgEPfWrVsEgJYsWcLtCwoKooMHDwqlO3v2bKH7EWskMMzXpck2EoiIHBwciMfjkZycHK1cuZLOnDnDNRJiY2Pp0qVLNGPGDK6B0L17dyouLq6LrL9qDf2lhoeHCzwMjxw5kgu7fv26QFjpNz/l0dDQ4OK3aNGiwrg7d+4USP/LB5OGKn9WVhb169ev0oYCf3N0dKxRXfz06RNNnz6d+wWcv/FnNVm4cCE9e/aszGMraiTw35yUxp8pqWPHjgKzNBERjR07lgDQ4cOHuX111UjgP7BISUmV+faF31D68sGjOo0E/q+bZc12VlRUxL0ZKj37FP/8SjciS+P/ilv6mlSkskZCdfMpK63379+TnJwcqaurCzzo84WHh5OIiAhJSEhQTk4Ot19fX58A0PTp0yss95YtW4TC+b9IT5kyhduXkpJS4f/PnTp1IgB0/fp1oXxYI6Fm0tPT6fTp0zR9+nTuniovL093794lIsFGwrt37wgA/f7770LpxMbGlttI+LJORUZGCjUS+IqKiuj169d0+fJlWrt2LffmiTUSGObr1WRnNwJK+th26tQJOTk5WLhwIYYNG8b1xW3ZsiXs7Oywc+dOFBYWomXLljh48OBXt3jMt8DDw4P7t6ioqMBc7S1atBCIW9kMR+/eveP63gPVH7Rc2fiFstRF+eXk5HDp0iVcuHABU6ZMETruS2fOnMHRo0erXVYpKSns2LEDiYmJOHToECZMmAAdHR0UFxfj7t27WL58OVq3bo1Ro0ZVq3/8yJEjhfbxF6obPny4wCxNQMlsKQDqpR85j8dDcnIyMjIyIC0tLRT+6dMnACizL31VhYWFAQBmzZolFCYiIgI3NzeBeKX16dMHHTp0ENrP78NfV9ekLvK5fv06srOzMWLECKipqQmFd+vWDW3btkV+fj7u3LkjFM4fd1AWaWlpTJ06VWh/69athcqnoqKCz58/lzmbFxHh8+fPAGr3nTKCFBUV4ejoiB07diA+Ph5//vknsrKyBO53fPzxRWXNIqehoVFuHmXVqS/l5ubi559/hrKyMgwMDNCvXz/s3LmTG0DNMAzzJbG6SERNTQ3379/Hvn374OXlVeZgOVVVVcybNw+//fYbxMXF6yJbppSAgACBFZOdnZ25hwQA0NbWhqioKPfHvyoLkpVW3UHLP/zwQ1WLDqDuy29nZwc7OzsAJQON7927hxs3buD8+fN4/PixQNzbt29j7Nix1Sovn5KSEsaNG4dx48YBKJm5KDAwEIcOHcK///4Lf39/pKenIzg4uErpNW/eXGifqKgoAJT5x5wfVl/4Ewzk5eXh6dOniI6OxosXL3Djxg1cuHChVmkXFRUhJiYGPB6PG+z5Jf6saWXNhtamTZsyj6nra1IX+fAfyv39/XHp0qUy4/BnEis9oxhfRY1dAwODMieCKKt8PB6Pi5uTk4MnT55w32lISAiePHlS+ckw5crNzYWmpiacnJzg6+srFC4mJoalS5fC398fERERQuGampoAUOaA/bL2VYetrS3u37+PP/74A3Z2dmjTpg1kZWXh5eXFpiRnGKZMddJI4HNxcYGLiwsyMjLw4sULxMTEQEtLC8bGxhXOr8/UTlFRERYsWCCwLzg4GEZGRgL7iIj7d3UbCRW9ScjNzRV48DYxManWSsv1Uf7SNDQ04ODgAAcHB6xatQre3t7w9PTkwutypi1TU1OYmppi9uzZ2Lx5M2bNmoVLly7h+vXr6NWrV6XHV/SGra7evpW+jpV59eoVFixYgPPnz3NvDgBAX18fLVu2rNb38KWUlBQUFBRASUkJUlJSZcbhPzSVtSJ2VX49rQt1kQ//h5OUlJRKZ4LLysoS2vflfPulVbd8Dx8+xIIFC3D16lWBtwytW7eGhoaGwBtEpnr4s39duXIFnz9/LrdeFxYWltno19XVhZSUFG7cuCEUdvv27RqXKz4+Hjdu3MC8efOwaNEigbCqzHTHMMz3qU66G31JUVERXbp0gZOTE3r27MkaCPVs//79Qr+OJyUlITo6WmArPRXo+/fvkZubW26a1WkkREZGCnRPqG5Xo7oo/5gxY6CrqwtdXV2hOce/ZG9vL/C59BuLqujTpw/k5eVx69atcuPweDzMnDkT3bt3BwA8ePCgWnnUp9TU1CrFe//+PXr06IG///4bXbt2xbZt23Dz5k0kJyfjzZs3AmtX1ISamhrExcWRkZFR7nSy/AdWfmOhtIbqslgX+fAXkFuxYgWoZCxYudu0adPqrXyPHz9Gr169cOnSJQwYMAC7d+/GvXv3kJ6ejqdPn3JdqJiaGzNmDGJjYzF58mRkZGQIhBERtmzZgtevX3NvOksTERGBq6sr7ty5gyNHjnD709LSsHTp0hqXid/wTEtLE9h/48YNHD9+HAC4rmYMwzB8dfomgWl4nz9/FvhVvDpiYmLK7UpRupGgr68PVVXVctOpzUrLdVX+tLQ0bg0OAHj+/DlMTEzKPO78+fPcv8XFxTFw4MBq5WtkZISwsDDcv3+fawSUR1lZWeC/Dams9SEA4MWLF1U6fu/evXj//j1GjhzJPUiU9uUDUHWJioqiRYsWiI6OxuPHj9GpUyehOI8ePQLwv3EZTRW//M+fPy83TmxsLAoLC6Gnp1dvXTK3bNmCnJwceHh4YPXq1ULhtf1OGcDT0xO3bt3C4cOHERQUhG7dukFPTw8ZGRmIiIjAy5cvYWFhUe6iggsXLuRWcvfz84O2tjauXr2KFi1aoEWLFjV682lsbIx27dph9+7diIyMRPv27fHq1Stcv34d1tbWuHz5MiZOnIitW7fWeBFMhmG+PdVqJPTo0QNJSUng8XjcIjGTJk2qUcY8Hg979uyp0bHM/2zevFng4fjcuXMYNGhQmXEXLlwo8IfpzZs3ZTYS3r59K9AlojrjEYDqNRLqqvzdu3cX6Pc/YsQI7Ny5U2Al5djYWOzcuRNr1qzh9s2YMaPMX6kr0rVrV+zZswcrVqzAmDFj0KxZszLjvX//nnvbUNHKzHVNTKzkf+uyugK9evUKV65cqVI6/C4+ZX3/KSkpZQ4mri4rKytER0dj06ZNOHDggEBYUVERtmzZAgBV6qr1NevevTvExcVx8uRJrFy5knuzwPf8+XO0adMGqqqqZY5JqCsVfafR0dH477//6i3v74WYmBhCQkLg7++P3bt34+nTp7h27Rq0tLRgYGCAP//8E+PGjSt3TIu2tjbu3buH33//HaGhoXj79i1GjhwJb29v6OnpQUFBodplEhUVRUBAAObOnYvQ0FDExMTAwsIC165dQ7du3TBy5Ejcvn273MXaGIb5PlWrkRAXF4f4+HiB19t+fn7Vfh1PRKyRUAdSU1OxatUq7nOXLl3KfcAGILBqMVB+v/7qdDUqK76zs3OFv4SamJjg/PnzdVr+cePGYcuWLfj48SOAkl+ge/XqBSkpKTRr1gypqalCfwCtrKywcuXKCs+tLC4uLti3bx9u3rwJCwsLLF++HAMHDoSioiKAktlkgoOD8ccffyAtLQ3jxo1r0G4c/IbT9evXcezYMYwePRpASePPyckJEhISKCwsrDQdY2NjAMDhw4fh7OwMLS0tEBGuX78Od3d3rttSWTPlAEBiYmKleSxatAiHDh3CwYMH0bx5c8yfPx8KCgp49+4dpk6dihcvXsDY2Ljc1aO/ZklJSVwDVF9fH9OmTcPWrVvh4OCAvXv3cjMmRUZGYuzYsSguLsaUKVO4Rl59MDY2xoULF7Bnzx7Y2NhAWVkZxcXFCAwMhLu7O9dt8OXLl7XuTvY94/F4GDVqVJVWMr527ZrA55iYGIiLiwt0NwJKBi6npqYKjNXy8/ODn5+fUJodOnQQGnvUokUL+Pv7l1mGU6dOCXyuzVgjhmG+HdX6a1TWgEcrKys2nWkjWbFiBdLT07nPlfVZrWkjoaI3CZmZmULdV8qa3ao0GxsbAHVbfkNDQwQEBMDR0REfPnzg9n/+/Flo0KuKigoWLlwINzc3SEhIVJhnWcTExHDkyBEMHToUDx484GY2UlVVhZiYGD58+MD9vzJgwAD89ddf1c6jNn744QfY2dnh4sWLGDNmDBYtWgRJSUk8ffoU2traWL16NX799ddK0xk/fjzWrl2Lf//9F7q6utDU1ERaWhpyc3NhZmaGxYsXw9vbG8uXL8elS5dw9+5dAP8bP+Dp6Ql/f38sXrwYjo6OZebRokUL7NixA7/88gtWrlyJ1atXQ1VVlZvJRUtLC/v27avXB+e6pqmpiaSkJHTo0AE6OjrceJQlS5YgKioKoaGh+OGHH6CoqAgJCQnuXG1tbQWm/a0Pbm5u2LNnDy5dugQNDQ1oaGggOTkZeXl56NWrFxwcHLBt2zZMmTIFwcHB+Pvvv+u1PIwwV1dXREZG4vXr11BSUuL2r1ixAiIiInBycmq8wjEM812p9puEL335KwgjbMSIEeXOcsE3Y8YMzJgxo8ppvn37Ftu2beM+d+/eHba2thUeU9NGQll9xfkiIiKqNVsOUNJ1pD7Kb2lpidjYWBw+fBgnT55EbGws4uPjkZ+fDxUVFZiamsLa2hozZswQ+ONbE3p6erh//z5OnTqFgwcP4vXr13jz5g1ERETQpUsXGBkZYfr06ejRo0et8qkJHo+Hv//+G6tWrcLJkycRExMDZWVluLq6Yvny5QgPD69SOqqqqrhx4wYWLVqE69evIy0tDZ07d8aPP/6IefPmQVJSEtHR0Thz5gx0dXW542bNmoX79+/j5s2bePv2baX1w9XVFebm5lizZg0iIyPx9u1bdOnSBRYWFvD09Cy3O9fXateuXfj9998RHx8v0H9cRUUFV65cwebNmxEUFITIyEgAJf8/TJo0CePHjxdaB6OutWrVCjdv3sTixYtx79495OTkoHfv3hgwYADc3d2Rn5+P58+f4+bNm2VOx8vUv7lz58LBwYFr7Gtra+P69esICQnB+PHjoa+v39hFZBimDm3fvh3bt2+vME5jTSzAo+o+4TFV9uDBA5ibmyMiIqLCB22GYRiG4bty5QpWrlyJf//9F/n5+TAyMoKTkxPmzp1b7w1JhmG+Po31PFnrd/giIiIQERHB7du3a7TKLsMwDMMw/9O3b1/07du3sYvBMMx3rtaNBHV1dSQnJ+Ply5eskcAIoaJCFGYk1WseYoqa4Ik2nT7rDMMwDMMwX7taP1mNHj0aW7Zswfnz5zFmzJi6KFOdO3LkCMaNG4dnz56VO3d+dfz777/o3LkzRowYgaNHj9ZBCb9dBenv8HpJ7a95RQyWPIeEaot6zYNhGIZhGOZ7UutGwooVKxAeHo5jx46hb9++cHV1rYty1am6fJAvKirCpEmTqjSFJAMQMqA1v34H7xLYAlAMwzAMwzB1qdYjoGJjY7Ft2zZ07twZU6ZMQfv27TFv3jxs3rwZBw4cwMGDB8vd6hsRwcfHR2CF3dpav349IiIi6iy9b11xcf2Pi2+IPJivC38laDU1Nejp6WHevHnIz88HULI+gYODA1RUVNCjRw+hxf4AICwsDMOGDYOOjg7U1NRgbW2NixcvVjn/HTt2QFFRkcuzNHd3d1hYWNT85GqAiLB+/Xq0atUKioqKsLW1FZqa2MvLC/r6+jA0NBRYn4Spno8fP2LcuHFQUVFBq1atBGZpY3WP1b3G9PLlS9jY2EBBQQFGRkbYuXMnF8bqpnDdTEpKwtChQ6GsrAx9fX14eXmhuLi43PT279+Pdu3aNUTRvx5USyIiIjXaREVFa5t1uS5fvkyurq5kZGREALjt2bNntUr35cuXJC0tzaU3evToCuNHREQQAIqIiKhVvk1ZUtx9So//kdLjf6QlQWdpavCTOtmWBJ3l0k2Ku9/Yp8k0MDs7O+rduzeFh4dTYGAgGRgY0OzZs4mIyNzcnIYNG0bh4eHk4eFBysrK9PHjR+7YXbt2kZSUFP3xxx90/fp1unnzJv3222/E4/Fo3759Vco/KSmJREREKCgoSChMX1+fVqxYUTcnWkXHjh0jBQUFOnLkCN27d4+cnZ1JT0+PsrOziYhoy5YtpKGhQUFBQXT27FlSVlamvXv3NmgZvxWOjo7Us2dPunPnDvn7+5OsrCydPXuWiFjdY3Wv8RQXF1Pbtm1p1KhRdP/+fTp48CBJSEjQxYsXiYjVzbLq5oABA6hPnz50+/ZtOnv2LKmoqJCPj0+56e3bt4/MzMwaqvgCGut5staNBB6PV+OtvsycOVOgcVAXjYTi4mKysrIiHo9HLi4uTbaRUNZ1qe5WHW+f3aLHTwfT46eDacrxK+Rw4m2dbFOOX+HSffvsVj1dLeZr9OHDBwJAT5484fadP3+elJSU6N69eyQtLc39ESAi6tixI+3atYuIiN69e0fS0tJ04sQJoXTnz59PqqqqlJ+fX6VyWFlZ0eTJkwX2RUVFEQB6+vRpTU6txuzt7cnDw4P7XFBQQFpaWnTmzBkiIjIzMyNfX18ufPPmzWRpadmgZfwWfPr0iURFRSk4OJjbN2rUKHJ2dmZ17/+xutc4/v33XxIVFaW0tDRun52dHU2bNo3Vzf9Xum5mZGQQAIqKiuLCp02bRnZ2duWm9z02Emrd3ai4uLjGW32ZO3cuIiMjuU1bW7vWaf71118ICwvD1KlT0adPn9oXshEkJiZi0aJFoJLGYY23auWZXYi2uyej7e7J2H0nB+dvRdbJtvtODpduYjYbH/I9+fDhA9q0aSMwCYGamhrS09Nx4cIFWFpaQlZWlgvr168fQkNDAQBbt25Fx44dMXz4cKF0PTw84Ovri0+fPgEoeVW9du1aGBsbQ15eHgMHDkRiYiIX38nJCWfPnkVRURG3LyAgAKampmjdunWVzoXH4yEkJASWlpZQVVXFzJkzER8fD2tra8jLy6Nv377cquREhOXLl8PAwADKysoYO3Ys0tLSAACioqKwtLTk0hUTE4OioiISEhKQkpKCR48eoV+/fgLXJDw8HHl5eQCAkJAQdOrUCTIyMtDQ0MAvv/zCxl2VoaCgAEQksBijiooK8vLyEBoayuoeql/3oqOjYWtrC0VFRbRs2bLBV6j/VuTl5WHo0KECddPMzAyfPn1idfP/la6b6enpsLGxgbGxsdD14uPfF5WUlODg4IAPHz5UqfzflAZskDQafX39Wr1JiI+PJwUFBdLW1qb09HTat29fk3yT8Mcff1BiYmKD5hl8+xLh93OVbx0GEAy7lR8+fjOhZWeCnCpBUpag1Zow0IPw+zkKvn2pyuVxcHAo89Xp9OnTydHRsdzj/v33X7K3tycdHR1SUlIiS0tLOn78eLWuxdmzZ8nS0pJkZWVJX1+ffv31V8rIyODC+W+oqrItWbKkWnlfvXqVANCQIUMqjDffYy5pyIAO7VxP+R/jGmwrLiyo1vl8adKkSWRiYkJz584lZ2dngbCNGzdS3759iajk9fLChQurlObGjRvJwMCAgoKC6O7duzRixAjq1q0b5eXlERFRQkICiYiIUFhYGHeMpaVlldMnKnmzZ25uTqGhobRu3ToCQLq6unTq1Ck6fvw4SUhI0KZNm4iIaP369aSjo0MBAQEUGBhI7dq1I1dX1zLT/eeffwgAhYeH0+PHjwkAFRT87xqnpaURAIqLi6OCggJSVFSkyZMn0+3bt+nQoUMkISFBfn5+VT6PulBcWNCgda6m9a9v3740atQoysnJoadPn5KamhodPHiQ1b3/V526l5ubS9ra2jRnzhx68OABHThwgFRUVLi3EF+LgsIiikvLbZStoLCo2uUtLCyk//77jwwMDOjIkSOsbv6/0nWztOLiYoqOjqauXbtyXaKio6NJTEyMZs+eTffu3aNly5aRhITEd/cmodazG/EHIA8bNkyglVqe3NxcnDx5EkpKShg0aFBts28Q06dPR2ZmJvbv3w9FRcXGLk6NpKSkoKioCJqamo1WhlYGoYgWlxQOyPkEvA4DDJsDJuHC4bGJwNGDAI8HtDMEJCUg+uQ1igLXAJkfgJFjq5R/bm4url27Bj8/P4H9qampOHHiBHr0KHsWpoiICHTv3h2ioqKwt7eHnJwcLl++jFGjRiE2NhZz586tNO+//voLP//8M3R0dDB06FDExcVh69atePDgAcLCwiAqKor+/fsL/AoEAHv37gUAoVnDunXrVqVz5rOysoKmpiaCg4ORlZUFeXn5MuP9c+EUro4AED4bL8NnVyuP2jDaGAdxlebVPi49PR2zZs3C/v37cfr0aQQGBkJOTk4gjpycHFJTUwEAMTExGDx4sEC4vr4+3r9/z33evXs3nJ2dsWHDBqxduxYDBgwAABw+fBja2toIDQ1F//79oaWlhR49euDUqVPo3bs3kpOTcefOHWzdurVa5+Di4gIrKytYWVlhw4YNsLe3x9ChQwEAPj4+iI+PBwBs2rQJq1evhoODAwBAQkIC27dvF0rv8OHD+PnnnzFs2DB069YNt27dgpSUFMTE/ne751+j1NRUKCgoYMGCBXBzc4OsrCy6du0KHx8fxMbGVus8aqswIwkvZ+k2aJ581al/vr6+MDExgb+/P4gIAwcOhLOzM6ZOncrqXjXr3qVLl6CoqIg1a9aAx+OhY8eOSE5OxurVqzFkyJBqnUt9SsrKg+7SkEbJO26RDZorSVfrmGbNmiEtLQ1DhgzBmDFjcO3aNVY3v6ibpZmbm+Phw4fo1KkTPDw8AAC7du1Ct27dsG7dOgBA586dERERgZcvX1brPJq6WjcSJkyYAB6Ph549e6Jly5aVxi8oKMCECROgo6PTJBoJx44dQ0BAAEaMGAFHR8capZGdnY3MzMxal0VSUhKSkmU8ZFfBtm3b8Msvv9S6DLWxokMfWHbsxX1OiI9HxJ172LlzM6KycmHb3Ah7xnkIHefsOAI3CDgfdgVmHdoDAK7cCIbL6N+B8CP4lDu0SvkHBwejU6dOUFNTAwDExcUhPDwcq1evRnJycrnHLVy4EEVFRbh9+zY6duwIAMjMzETnzp3h6emJ6dOnC92AS8vMzMSsWbNgaWmJy5cvc43padOmYdeuXfD398eYMWO4rbQzZ84AADZu3FilcyyPiIgIRowYgW3btiEwMBCjR48WivPixQu8evUK6FirrBrMxYsX4eLigsLCQpw+fRpDhgzBzZs3kZCQIBAvKyuLa3xpaWkJvBoHSl4p81+N9+/fH0VFRcjKykJcXBwmTJgAFxcXLm5eXh73xwkoebW+bt06bNq0CYGBgWjevDnMzc2rdR5du3bl/q2ioiLwWVVVFUDJPSQuLk7gj5uNjQ1sbGy4z+/fv8dPP/2Ey5cvY/r06diwYQMAQElJCZ8/f0ZBQQHExcW5a8IPU1BQwIwZMxAUFISoqCg8ePAAt27dEkibKZGWlgZbW1uMGTMG48aNQ2xsLNavX48lS5ZASUmJ1b1q1r0nT57gxYsXkJGR4dJq7B+zvgW3b9/G69ev4eHhgTlz5rC6WUbdLO3UqVOIiYnBkiVLMGrUKJw4cQLPnz8X6lreu3fvajcS8vLyuK51tZGdnV3rNGqizpap5fF4VYp348YNACW/bH/tPn78CHd3dygpKVW7FVyalZVVnZTH09MTS5YsqfZxGRkZyMzMhJ6eXp2Uo6YUxaXQXFYJAJCTkwPd1oJTiUmLiXPhpd0Pv42+ffvCrntvbp+WkgrQ9kfg5iG8jXlbpfzPnTvH/TqVk5NT5etx48YN9O3bl2sgAICCggJcXFywcOFCPH36FF26dCn3+OPHjyM3Nxc7d+4UeNv2559/4vnz53j37l2VylFbo0aNwrZt23DixIkyGwlnzpxByifgvsVqjBlTtbczdUVMsXoPBfv378fEiRPh5OSEbdu2oVmzZgBKVoC/f/++QNyEhARoaGgAAExNTXHz5k2B8FatWgEouZnz/5Dy/zieP38eurqCv27z0wKA4cOH47fffkNERAQCAgK4X7qqQ0REpMLPQMmPK0BJH9uyJCQkwMLCAhISErh27ZrAPUddXR1AyZgkfp3nn6e6ujqSkpJgZWUFAwMD2Nrawt3dvU7+qFWXmKImjDbGNXi+/LyrIjAwEJKSkgJvI42NjeHo6Ih58+axulfNuldYWAgHBwesXr1aIM3y8mosmvKSiFvUOI1mTfmq/TD47t07ZGVloXXr1jA2Nub62ru6uuL3339ndfOLupmSkoJ3796hQ4cO0NfXh76+Pnx8fNC+fXvk5OQIvP3iq+pzbmkrV66El5dXtY/7WlS7kfDll8G/aIaGhlVOg8fjQUdHp7pZN7iZM2ciOTkZvr6+tfplIywsDD/88EOty1PTtwg7duzA9OnTyw0vKirC/v37sWfPHjx//hw8Hg89evTAmjVrar1CNRUVQkuspEEolluA/OSSuZlFiorw9/61XDynCXNRnJfGhfNlZGajVUsdWHZsKRAmlvsGClLFyPz/PCpTXFyM8+fP448//gAASElJcb/SAyj3LVFGRgaMjIzQs2dPoTB+lx2qZDD34cOHYWxsjA4dOgjsb968Oa5du1Zp2SuTm5uLxYsXIyQkBC9fvoS+vj7Gjx+P33//HRISEly8Hj16oHnz5rhw4QJyc3MFfr0DgLNnzwIiohgwciLEVZrVulz1JTU1FdOmTcP8+fOxYsUKgbA+ffpg0aJFyM7O5t7uhISEYNq0aQBKug526NABwcHBsLW1FTh206ZN3GBdJSUlqKurIzU1lftVKj09HbNnz8ayZcu4X+C0tbXRvXt3HDt2DJcuXUJgYGC9nLOysjKaNWuGu3fvQl9fHwAQFRUFV1dX3L59G7NmzYKioiJu3LgBZWVlgWPV1NTQtm1bXL58GZMmTeKuSbdu3bj/D3g8Hi5cuMAdM3/+/Ho5j4rwRMVq1OWsIX369EnoQYHH4+HTp0+wsrKCp6cnq3ulVFb3TExMEBISAmNjY+66HjhwADk5OdxD6tdATFSk2l1+GlpgYCC2b9+Of//9l9uXnZ0NcXFxdl8so24+ePAAo0ePRnJyMvdcm52dDSKCmJgYTExMuIHdfF82pKpiwYIF+P3332t0fqVFRkbW2Q/O1VHtRkJlD0RVVRcXrT79888/OHToEH788cdaryItJycHBQWFOipZ9eTk5CAxMRFGRkblxhk7diz8/f3RsmVLDBw4EC9evMC5c+fw33//4fXr1zVqPfOJ58UhwvTnkg+xwMdS3Zy/HAGQ/z4MHwOFF1u5MBsAXuBj4C5un1ERYPYO+FcKMNWvvPF08+ZNNGvWjLsOoqKiVerzqqioiIcPHwrtLywsxKlTpyAnJ4dOnTpVmEZ0dDTMzc2Rl5eHa9eu4e7du2jWrBmsra2rPNtDeTIyMtCjRw88fvwY1tbWGDVqFG7duoUFCxYgLCwMgYGB3C8wPB4PTk5O2LhxIy5cuCAwk8WHDx9w+/ZtWFtbc7/Kf60uXLgAIsKoUaNKukeV0rlzZ5iZmcHFxQVz587FqVOn8PbtW4wcORIA0LZtWyxYsACDBw/GggULuD90J0+exOXLlwV+7HB3d8fs2bMhJSUFFRUVrF69GvHx8UI/GDg5OWHevHlQUFAod1xLXXBzc8OcOXMgKysLSUlJeHt7w8DAAEBJA2/x4sVITU3l+hkDJb/WysvLY9q0aVi4cCF0dHSQn58PLy8vrgubrKws3r59i4CAAMjKyuLQoUP477//YGRkhIyMjCY7Dqs+2Nvbw8PDA1OmTIGzszPevHmDdevWYeTIkejatSure9Wse+PGjcOiRYvg5uYGV1dX/Pvvv5gxYwaOHDlSb+fyrbK2toabmxu8vb3h6OiIuLg4zJ8/H+PGjWP3xTLqJv/t/6+//oqpU6ciNTUVHh4ecHJygqSkJKZOnYp169Zh9uzZGDduHEJCQhASElLtH7hr0028tIq6NNer6o503r9/v8DG4/FIRESE1qxZIxRW1nbw4EF68OBBHY+/rlhNZjfiz2BU1S0mJkYojYYajf7hwwduvuMvrVu3TmAe4C9dvHiRAJC3t7fAfisrKwJACQkJtSrblau7KMEPlW4AyK5T5fEWjwINtgBpKIEUpEGHfwdduVr2uZc2Z84cmj9/frnhACqc3Yhv3bp1NGrUKNLS0iJFRUW6cOFChfGLi4tJTEyMhg4dSj179hSoM2JiYrRy5coKj9fX1yd9ff1ywxcsWEA8Ho9OnjzJ7SsoKKCJEycSAKEZam7fvl3mzFy7d+8mALRz584Ky/M1WLNmTYVreCQmJpK9vT03C9X9+8KL7fn7+1OfPn1IUVGRTE1NacaMGZSZmUlTp07lZr8qLCwkT09P0tfXJwUFBRoyZAjFxsYKpRUXF0c8Ho8mTZpU7XMBIFA+MzMzgdm3RowYQXPmzOHKs3DhQtLT0yNlZWUaO3YspaSkcOtGlLWVTsvT05P09PSoZcuWtHr1am5/QUEBTZgwgRQUFMjQ0JCWLVtGAQEBpKmpSefOnav2OX3r7t69S3369CF5eXlq3rw5zZw5k5t/ntW96tU9opI59PnX09DQsMLFrJiKnTp1iszMzEhaWpoMDQ1pyZIl3PoGrG4K182wsDDq0qULycrKkp6eHrm7u1NmZiaXz5UrV6hjx46koKBAdnZ25O/v/93NblQni6mJiIjQ69ev66I89aImjYSTJ09Sq1atytzU1dUJAMnJyXH74uPjhdJoiC/12bNntGbNGpKRkRFYPZGoZOGf6dOnV3i8t7c3ASB/f3+B/a9evaLbt29TUVH5069VZcrOI0dWcA/4oRcWU96Hu2VuAGjwAKtyw/lbj64duLRbqoOOVLGRYGRkRLdulb/oWlUbCaUf9Fu1asWtZlme5ORkLn67du0oLCyMsrKy6P79+9S1a1cCQJculT+Fa2WNBAUFBRo4cKDQ/qysLJKQkCAHB4cy05STk6NPnz5x+xwcHEhERKTBp8hlGIZhGKZiTXYK1H379gH43wClb8WwYcMwbNiwMsP2798PFxcXODg44OjRow1cMkEmJiaYO3cuLl68iN27d2PevHlcmK+vb6Vdpdq0aQMAcHZ2xokTJzB48GDY2NjAwMCAe2VXnrKm7PySjKwU8P9jIIskdSDRrPwBviKSyhWGA8CN25HIzs7GX7s9sXv9Bvy0GVivH40frcs/5unTp8jMzBSYHaGmrl+/juzsbNy/fx+//vorHBwcEBoaWunrVCkpKQQEBKBFixYASqZcO3HiBIyMjLB27VqBxYaqKiEhAZmZmfjw4QNmzZolFC4tLY2nT58K7R85ciTWrFmD4OBgDBkyBDk5OQgJCUGvXr3YrCIMwzAMw5Ro0CZJI6nsTULr1q2pdevWdPr06Sql9zUupnb27FnS1dWlwsJCIiLKz8+nKVOmVOnYw4cPU7du3YjH4xEAEhERoSFDhgi9maiJ0t2NKvrFH1X8Jb90uleX/n83JbvuFcZdtWqV0DLxtc2f6H9LzU+cOLHcOPzuRt27l11GCwsL0tLSKvf4it4k8LsOVbSpq6sLHcevl+PHjyeikrdmAGjr1q0VnS5TDenp6SQmJlbhFhwc3NjFZL5BrO4xXytWN2uuyb5J4EtPT8f9+/eF5tqtyPjx4+sq+1p59uwZgJJBoE2Vg4MDZs2ahdOnT2PEiBE4cOBAla/v2LFjMXbsWCQnJyMkJATbtm3D2bNnsXbtWqxcubLc444ePYq7d+9WmHYf69pNu3rjxg34+vri559/FloApXVzQFwUSE2teA2Ks2fPYsGCBXWev5mZGcTFxZGUlFTu8TweD+rq6pCWLntmDDk5uRpPN6mlpQUAWLx4cbWmWOvUqRNatWqFc+fOIT8/H2fPngWPx6vRNHVM2eTl5fHo0aMK4zRv/nXP5MM0TazuMV8rVjebnjppJPz111+YOXNmtR52eDzeV9NI+BaIiIjAzc0NW7ZswdChQ3Hz5k1uyrny+Pn54cGDB1i1ahVkZWXRrFkzjBkzBp07d4axsTFycnIqPP7SpUtcd7PyWFisqDC8MuLi4ti3bx9MTEyEHtIT04CCIkCnefld3d6/f4+oqKgaLwxVUf7v3r1DQUFBhTNHAUDPnj1x9epVfP78GVJSUtz+/Px8REVFoW3btjUqW/PmzSElJYWIiAihsIKCAmzduhWmpqbcypiljRo1CsuXL8fFixcRGBiI7t27N4lpiZsKERGRWk8fzDA1weoe87VidbPpEV6dopquX7+OadOm4fPnz6CSgdBV2oqLi+ui/FXy5s0bEFG5lZNfpgkTJlQpvQkTJoCIGn08wpcmTZqEyMhIzJ8/H6NGjao0/smTJ7Ft2zahRVb4CwWVtTbAl/Eq+541NFVrfkIAOnToAFVVVezcuVNgVeSioiKs+Lvk3106tyn3+PPnz8PGxqbcX/Jrmn9hYSE3l/yX80p/afLkyUhJScGCBQsEphBetGgRkpOTMXZszRYuExERwaRJkxAYGIiAgACBsLVr12L27NnlriTNrx/z5s3Dx48fMWLEiBqVgWEYhmGYb1Ot3yTwV0qUkpLCmjVrMGrUqK9+nvVvlYKCAiZMmIDQ0FCsXbu20vj9+/dHYGAg7OzsuIW27ty5g2fPnmHIkCFwcnJqgFJXTEpKCuvXr4eLiwtMTU3Rr18/SEtL4/LlIMTHA07dga7d2pV7/NmzZ2vVjaa8/G/evIkXL17gp59+gr29PRf/1atX2LZtG1q1aoUZM2YAKFkmfuTIkdi0aROuXbsGc3NzREZG4sGDB7C2tsbUqVNrXL5FixYhMDAQQ4YMQd++fdG8eXOEh4fj+fPnsLGxKbcB0q5dO5iamnIDm8sbpM8wDMMwzPep1m8SHj16BB6Ph7lz58LNzY01EBrZ7NmzsWPHjirFdXd3x6ZNm2BkZITbt2/jwoULUFVVha+vL06ePFmrRdTq0oQJE3Dx4kWYmZnh6tWrOHPmDJSU5LBxErB5SvnH5ebm4urVq3BwcKjz/Js1awY/Pz/s379fIG58fDw2bdqEEydOcPt4PB6OHj0Kb29vSEtL4++/S16BLFu2DJcuXSpzufmq0tDQwMOHD/Hzzz8jISEB/v7+EBMTw/Lly3Hu3Lkyl5bn479NsLCwgJ5e7caONLT3799j5MiRUFNTg56eHubNm4f8/HwAQFJSEhwcHKCiooIePXrg3r17QseHhYVh2LBh0NHRgZqaGqytrXHx4sUq579jxw4oKipyeZbm7u4OCwvhRQHrExFh/fr1aNWqFRQVFWFra4sXL14IxPHy8oK+vj4MDQ2xatWqCtPbv38/2rUrv/HNADExMULXqLK6d+PGDVhaWkJFRQWOjo74+PGjULq7d++GjY0NlJSUoKuri9GjR5c5S1l5Ro4cWWYXQwBo3749PDw8qpxWXcjKysLUqVOhpaUFDQ0NTJkyBZmZ/xtHlp2djXHjxkFNTQ3m5uYICgpq0PJ9qxYtWoRBgwZV65jv7b6YlJSEoUOHQllZGfr6+vDy8mrQXi5NQm1HPsvIyJCIiAjdvn27tkl9cxprNPrXpKqzG30t6TJNg52dHfXu3ZvCw8MpMDCQDAwMaPbs2UREZG5uTsOGDaPw8HDy8PAgZWVlgZm6du3aRVJSUvTHH3/Q9evX6ebNm/Tbb78Rj8cTWLCnIklJSSQiIkJBQUFCYfr6+rRixYq6OdEqOnbsGCkoKNCRI0fo3r175OzsTHp6etwiX1u2bCENDQ0KCgqis2fPkrKyMu3du7fc9Pbt29doiwY1BZmZmTRq1Ciha1RR3Xv37h3Jy8uTp6cn3bp1iwYMGEC9e/cWOH78+PGkpqZG27dvp/v371NwcDDZ2dmRgoIC/ffff1Uqm7+/P4mLi1N6errA/piYGAJQ4Xox9WHatGnUtm1bunbtGoWGhlKnTp1o+PDhXPiwYcOoV69edOPGDdqwYQNJSUnR06dPG7SM35rIyEgSFxcXWCenZ8+eJCUlJbCVXh/pe7wvDhgwgPr06UO3b9+ms2fPkoqKyle7mF+TXUzN2NiYRERE6J9//qmL8nxT+F9qy5YtydTUtMJt27ZtjV3cesEaCUxd46+k+eTJE27f+fPnSUlJie7du0fS0tLcHwEioo4dO3Irkr97946kpaXpxIkTQunOnz+fVFVVuRVKK2NlZSU0tS5/WtyGfsixt7cnDw8P7nNBQQFpaWnRmTNniKhkxVJfX18ufPPmzWRpaVlueqyRUL6lS5eSmJgYARC4RpXVvbVr11KvXr24sI8fP5KYmBg9f/6ciIhOnz5NcnJy9ObNG4H8CgsLqWfPnjR48OAqlS8nJ4dkZGTo0KFDAvu3bt1KWlpaVFxcXK3zrS1ZWVmBh8ZHjx4RAEpLS6OkpCQSFRWlV69eceFDhw6lBQsWNGgZvyWFhYVkbm5OpqamAo0EZWVlOnHiBD179ozb+KsLf4/3xYyMDAJAUVFRXPi0adPIzs6uQctIRLRt27ZKnxFbtmzZKI2EWnc34vdlDg0NrW1S36wTJ07gyZMnFW78/usMw1Tsw4cPaNOmjcBEBGpqakhPT8eFCxdgaWkJWVlZLqxfv37c/Wnr1q3o2LEjhg8fLpSuh4cHfH198enTJwAlr6rXrl0LY2NjyMvLY+DAgQJTPDs5OeHs2bMoKiri9gUEBMDU1BStW7eu0rnweDyEhITA0tISqqqqmDlzJuLj42FtbQ15eXn07dsX6enpXHmWL18OAwMDKCsrY+zYsUhLSwMAiIqKwtLSkktXTEwMioqKSEhIQEpKCh49eiSwYF+/fv0QHh7OzUgXEhKCTp06QUlJCQ4ODvjw4UOVyv89mjx5MiIjI7Fs2TKB/aGhoRXWvdDQUIHvQEVFBZ06deLCV69eDXd3d+jr6wukKyoqir179+Lnn3/m9mVnZ2Py5MnQ1NSEmpoa3NzcuHooIyMDe3t7nD59WiCdgIAAODo6VqkbaVhYGIyMjHDy5Em0bt0aurq6OHDgAK5evQozMzMoKCjAzc2Ni5+ZmYkJEyagWbNm0NHRwZIlSwCUTI2uq6uLDh06cHHV1NQAAImJibh+/TpatGghsHBn6WsGAJs3b4aBgQGkpKRgaGhY5e6036u1a9eiWbNmGDlyJLfv48ePSEtLQ79+/WBiYsJt8vLyAL7P+2J6ejpsbGxgbGzMhZuZmXHnCfzvvigjIwMNDQ388ssvKCwsrNI5VMeMGTMqfUYs3YW5QdW2lZGamkqGhoYkIyNDDx48qG1y3xTW3Yi9SWhqCouKKSW3sMG3wqLa/bo5adIkMjExoblz55Kzs7NA2MaNG6lv375EVPJ6eeHChVVKc+PGjWRgYEBBQUF09+5dGjFiBHXr1o3y8vKIiCghIYFEREQoLCyMO8bS0rLK6ROVLOJnbm5OoaGhtG7dOgJAurq6dOrUKTp+/DhJSEjQpk2biIho/fr1pKOjQwEBARQYGEjt2rUjV1fXMtP9559/CACFh4fT48ePCQAVFBRw4WlpaQSA4uLiKDo6msTExGj27Nl07949WrZsGUlISDT4m4TGqns1rX9fvm2prO516dKF9uzZIxA+ZMgQWrp0KRUXF5OMjAxdvny5SnkPGTKEbG1tKTw8nC5dukTt2rWjWbNmceHHjh0jGRkZys3NJSKirKwskpCQqHL6oaGhJCMjQ46OjhQeHk7jx48ncXFxsrCwoBs3btDChQsJAEVGRhIR0aBBg8jKyopu3rxJfn5+JC8vTwcOHCgzbW9vb1JSUqJPnz7R9u3bqWfPngLhp0+fJkNDQyIiunv3LgGgzZs30927d7l8v3zbUt+KCwso/2Nco2zFhQWVF/D/PX/+nNTU1CgmJoaWLFnCvUkIDw8nWVlZGj58OKmpqdEPP/xAhw8f5o77Hu+LpRUXF1N0dDR17dqV6xJVUFBAioqKNHnyZLp9+zYdOnSIJCQkyM/Pr8rnUZea7GJqysrKCAkJgZOTE3r06IFffvkFo0aNgpGREZSUlGqbPMMwDSg9rxi/XBUeTFnfdvyoClVp0Wofl56ejlmzZmH//v04ffo0AgMDIScnJxBHTk4OqampAEoGmw4ePFggXF9fH+/fv+c+7969G87OztiwYQPWrl3LDQI9fPgwtLW1ERoaiv79+0NLSws9evTAqVOn0Lt3byQnJ+POnTvYunVrtc7BxcUFVlZWsLKywoYNG2Bvb8/NyOXj44P4+HgAwKZNm7B69WpuIL6EhAS2b98ulN7hw4fx888/Y9iwYejWrRtu3boFKSkpgUHs/GuUmpqKQ4cOoVu3bli3bh0AoHPnzoiIiMDLly+rdR611Vh1D6h5/SstPT29wrpXUXhycjJyc3MFFpJ68eKFwC/wAPD8+XMUFBTgwoULiI6Ohq6uLgBg37596Nq1K5YtWwYZGRk4ODiAiBAcHAxHR0cEBwdDVlYWffr0qfL55ObmYunSpTAzM4OKigoOHjyIWbNmoUePHujcuTPWr1+P+Ph4yMnJITAwEG/evIGenh66d++O5ORkvHnzRiC9z58/w8vLC6tWrcKWLVsgJSVV6TWTlpbGX3/9hSlTSmaoaNeuHVavXo3Y2FihNy71qTAjCS9n6TZYfqUZbYyDuErlC4wRESZNmgRPT0+0aNFCIOzly5fIy8uDpaUl5s+fj3/++QcuLi5QUVGBnZ3dd3lfLM3c3BwPHz5Ep06duIH9ubm5WLBgAdzc3CArK4uuXbvCx8cHsbGx1TqPpq7W3Y06dOiAIUOGICsrC58/f8bGjRvRrVs3qKqqQlRUtNytollXGIZhKnPx4kW0bt0aAQEBOH36NIYMGQIlJSVkZWUJxMvKyuJ+sNDS0hJaFT4kJASRkZGIjIyEuro6ioqKkJWVhbi4OEyYMAHS0tKQlpaGoqIiUlNTuT9OQMmrdX63jsDAQDRv3hzm5ubVOo+uXbty/1ZRURH4rKpass5IdnY24uLiBP642djYCHQpef/+PWxtbTF+/HhMmDABR44cAQAoKSnh8+fPKCgoELgm/LDnz58LPTz27t27WufAoNK6V1G4qqoqJCQkBOqmvr4+Vy8vXrzIrUX09OlTFBQUwNjYmKub3bt3R2FhIbf6u6ysLAYMGMDVj4CAAAwaNKhaf3dlZGRgZmYG4H/1kF83JSUluYf7p0+fQltbW2CGtLlz52Lx4sXc53v37sHMzAxbtmyBj48Pfv311ypdMzMzM9jb28PX1xdz585Fr1696qW7x7fAx8cHxcXF+OWXX4TChg4disTERMyePRudO3fG77//DldXV+zevRvA93lfLO3UqVO4du0a5OXluVn/FBQUMGPGDAQFBWHx4sVwcHDArVu3qnUO34JaP6lHRUUJfKZSi0UxDNO0KEmKYMePtVsAr6b5Vsf+/fsxceJEODk5Ydu2bdzUy+rq6kKLAyYkJEBDQwMAYGpqips3bwqEt2rVCgCQl5eHhIQEAOD6054/f577tZaPnxYADB8+HL/99hsiIiIQEBBQozU5vpwCt6wpcfkP+KKiZf/anZCQAAsLC0hISODatWuwsrLiwtTVS1YkT0xM5B7k+Oeprq5e5oNjY0x/3Fh1j593bVVW99TV1bnrXjrc3t4eoqKiMDIyws2bN2FtbQ2g5BdR/rib0g9ghYWF0NTUxLVr14TKUPpNhJOTE3755Rfk5eUhKCgIf/31V7XOp6x6WF7dLK9eAsCVK1cwYMAA9OrVCyEhIQJvAMq7JvxrFhYWhuHDh2Pw4MHo1q0bnJyc8OOPP1brPOqCmKImjDbGNXi+/Lyr4u7du7h79y43JqawsBDFxcWQlpZGREQE2rQRXHS0ffv2OHDgAIDv876YkpKCd+/eoUOHDtDX14e+vj58fHzQvn175OTkICsrC1ZWVjAwMICtrS3c3d25MVzfk1o3Evbt21cHxWAY5msgKsKrdbeL+paamopp06Zh/vz5WLFihUBYnz59sGjRImRnZ3O/dIaEhGDatGkAgOnTp6NDhw4IDg4WWil706ZN3K+USkpKUFdXR2pqKmxsbACUdBeZPXs2li1bxv3Sqa2tje7du+PYsWO4dOkSAgMD6+WclZWV0axZM9y9e5d7yIqKioKrqytu376NWbNmQVFRETdu3ICysrLAsWpqamjbti0uX76MSZMmcdekW7dukJKSgomJidDEE18+MDSEplD3KlJZ3evTpw/Onj3L/cL+8eNHPHz4kHtwcXNzw8KFCzFp0iRoaWkJpM3vCgYAJiYm+PDhAxQUFLh4d+/exeHDh7F582YunoODAyZOnIjVq1cjJyen0pXha8rY2Bjx8fFISEiAtrY2AGDv3r24ffs2du7cifHjx2PkyJE4cOCA0INer1698PbtW7x69QqGhoYASq4Z/83Wzp07MWLECPj4+AAo+X+w9MDShsITFatSl5/GtHLlSsyfP5/7vG3bNjx69Ag+Pj7w8fHBp0+fuOsIlHRnMzIyAvB93hcfPHiA0aNHIzk5mWtkZGdng4ggJiaGM2fOgMfj4cKFC9wxpa/v96LWjYSffvqpLsrBMAxTJRcuXAARYdSoUXj16pVAWOfOnWFmZgYXFxfMnTsXp06dwtu3b7mZPtq2bYsFCxZg8ODBWLBgAfeH7uTJk7h8+TL3oAKULP4ze/ZsSElJQUVFBatXr0Z8fDw0NQV/2XNycsK8efOgoKCAHj161Nt5u7m5Yc6cOZCVlYWkpCS8vb25WWH4D5+pqalcf26g5JdaeXl5TJs2DQsXLoSOjg7y8/Ph5eWFjRs3AgCmTp2KdevWYfbs2Rg3bhxCQkIQEhICHR2dejuXb1FldW/s2LHw9vaGt7c3+vfvD29vb/Tq1Yub8WXKlCk4fvw4zM3N4enpic6dOyM5ORlbtmyBqKgo93bH1NQU1tbWGD16NFauXInU1FTMmTMHdnZ2AuWRk5ODnZ0dVqxYgYEDB0JaWrpezrtt27bo3bs3nJ2dsXz5csTGxmLx4sVYuHAhbt++jcTEREycOFFojEKLFi2goaEBR0dHuLq6YuXKlbhz5w6Cg4O5xf5kZWVx+/Zt3LhxA0lJSdi6dSt4PB7u37+P7t27Q1xcvF7OqSnS1NQUuDepqalBTk4OJiYmGDRoEPr16wc9PT3Y2dnh7t272LNnD/c26nu8L3bp0gUA8Ouvv2Lq1KlITU2Fh4cHnJycICkpCVlZWbx9+xYBAQGQlZXFoUOH8N9//8HIyAgZGRlQVFSst3P6qjToMGkiKioqordv39Lbt28bOusGx2Y3YrMbMXVvzZo1BKDMjYgoMTGR7O3tSUlJiSwtLen+/ftCafj7+1OfPn1IUVGRTE1NacaMGZSZmUlTp07lFg4qLCwkT09P0tfXJwUFBRoyZAjFxsYKpRUXF0c8Ho8mTZpU7XMBIFA+MzMzgYWLRowYQXPmzOHKs3DhQtLT0yNlZWUaO3YspaSkcOtGlLWVTsvT05P09PSoZcuWtHr1aoFyXLlyhTp27EgKCgpkZ2dH/v7+bJ2ESpS1lkRlde/69etkYWFBysrK5OjoKLDIH1HJd7x06VIyNzcneXl5srCwoHXr1lFxcTEZGxtTTEwMEZXMKjhu3DhSU1MjDQ0Ncnd3p8+fPwuV8fDhwwSADh48WK1zCw0NJTk5Oe5zSkoKAeDyJyJSU1Oj8+fPc+GjRo0iFRUV0tHRocWLF1NxcTH5+/uXWzf5aWVlZdGYMWNIVVWVOnbsSBcuXODyePv2LVlZWZGsrCx17NiRzpw5Q15eXqShoUGJiYnVOqfvTenZjYiI/Pz8yMjIiGRkZMjc3FzgOvN9b/fFsLAw6tKlC8nKypKenh65u7tza0cUFBTQhAkTSEFBgQwNDWnZsmUUEBBAmpqadO7cuWqfU2011vMkj6jqgwgmTZoEHo+HVatWcXMdl1ZQUIBnz54BgNBy9XyvX79Gq1atICIi8s0PQHrw4AHMzc0RERGBTp06NXZxGsXVa3/BNLZkfu+nLXbhR+upX3W6DMMwDMMwX5PGep6sVncjPz8/8Hg8LFy4sMxGQlxcHDp06FClBkA12iYMwzAMwzAMwzSg2k/pUAbWAGAY5nuWkZEBcXHxCrdLly41djGZ79C5c+cqrZvf4ywuTP1j98Wmhy1WwDAMU8fk5eXx6NGjCuOUnq6SYRrKjz/+WGndlJCQaKDSMN8Tdl9selgjoQGMGDECUlJSFcaZMWMGZsyY0UAlYhimPomIiHBz3DPM14Q/4w3DNDR2Xyzb9u3by1wlurTPnz83UGkEsUZCAzhx4sR3O3CZYRiGYRiGKVtVfiTmD1xuaPUyJoFhGIZhGIZhmKaLNRIYhmEYhmEYhhHAGgkMwzQ579+/x8iRI6GmpgY9PT3MmzcP+fn5AICkpCQ4ODhARUUFPXr0wL1794SODwsLw7Bhw6CjowM1NTVYW1vj4sWLVc5/x44dUFRU5PIszd3dHRYWFjU/uRogIqxfvx6tWrWCoqIibG1t8eLFC4E4Xl5e0NfXh6GhIbeiLVNzMTExQusBVVb3bty4AUtLS6ioqMDR0REfP34USnf37t2wsbGBkpISdHV1MXr0aDx9+rTK5Ro5ciQGDBhQZlj79u3h4eFR5bTqQlZWFqZOnQotLS1oaGhgypQpyMzM5MKzs7Mxbtw4qKmpwdzcHEFBQRWmN3HiRPz666/1Xewmb9GiRRg0aFC1jvne7otJSUkYOnQolJWVoa+vDy8vLxQXF5eb3v79+8tdA+xbxRoJDMM0OS4uLnj//j3Onz8PHx8fnDhxAn/88QcAwMHBAZKSkggKCkLPnj1ha2uL1NRU7ti//voLdnZ2MDU1xfHjx3Hu3Dl06NAB9vb22L9/f5XyHz58OLKzs3HlyhWhsICAAAwdOrRuTrSK/P394e3tjaVLl+LKlStQV1dHv379kJOTAwDYunUrdu7ciZ07d2Ljxo1Ys2YN/Pz8GrSM35KsrCzMnz9faH9FdS8hIQH29vawtbVFYGAg8vPzMWzYMIHjf/rpJ/zxxx8YNmwYrly5Al9fX2RkZKBbt26IioqqUtmcnJxw5coVZGRkCOyPjY1FVFRUg9dNDw8P3Lp1C0ePHoW/vz8ePHgAV1dXLnzChAmIi4vD2bNn4ezsjOHDh3OLsjI18++//2L16tUC+3r16gVpaWmB7e+//+bCv8f7oqurK9LT03Hx4kVs2bIFW7Zswe7duxu0jF+9ai3PzOORiIgIvX79uszwV69ecXHKU5U434rGWkb7a3Ll6i5K8AMl+IGuXN311afLfP0+fPhAAOjJkyfcvvPnz5OSkhLdu3ePpKWlKTs7mwvr2LEj7dpVUkfevXtH0tLSdOLECaF058+fT6qqqpSfn1+lclhZWdHkyZMF9kVFRREAevr0aU1Orcbs7e3Jw8OD+1xQUEBaWlp05swZIiIyMzMjX19fLnzz5s1kaWnZoGX8VixdupTExMQIAJmZmXH7K6t7a9eupV69enFhHz9+JDExMXr+/DkREZ0+fZrk5OTozZs3AvkVFhZSz549afDgwVUqX05ODsnIyNChQ4cE9m/dupW0tLSouLi4WudbW7KyshQUFMR9fvToEQGgtLQ0SkpKIlFRUXr16hUXPnToUFqwYEG56bm4uJCbm1u9lrkpKywsJHNzczI1NSUHBwduv7KyMp04cYKePXvGbZmZmUT0fd4XMzIyCABFRUVx4dOmTSM7O7ty09u3b5/A//MNqbGeJ2v0JuHdu3eIi4sT2uLj47k48fHxZcZ59+5drRs2DMN8vz58+IA2bdoITKWnpqaG9PR0XLhwAZaWlpCVleXC+vXrh9DQUAAlv6h37NgRw4cPF0rXw8MDvr6++PTpE4CSV9Vr166FsbEx5OXlMXDgQCQmJnLxnZyccPbsWRQVFXH7AgICYGpqitatW1fpXHg8HkJCQmBpaQlVVVXMnDkT8fHxsLa2hry8PPr27Yv09HSuPMuXL4eBgQGUlZUxduxYpKWlAQBERUVhaWnJpSsmJgZFRUUkJCQgJSUFjx49Qr9+/QSuSXh4OLdoVnR0NGxtbaGoqIiWLVvir7/+qlL5v0eTJ09GZGQkli1bJrA/NDS0wroXGhoq8B2oqKigU6dOXPjq1avh7u4OfX19gXRFRUWxd+9e/Pzzz9y+7OxsTJ48GZqamlBTU4ObmxtXD2VkZGBvb4/Tp08LpBMQEABHR0fweLxKzzEsLAxGRkY4efIkWrduDV1dXRw4cABXr16FmZkZFBQU4ObmxsXPzMzEhAkT0KxZM+jo6GDJkiUAgPT0dOjq6qJDhw5cXDU1NQBAYmIirl+/jhYtWsDAwKDMawYAGzduRKtWraChoYHffvsNhYWFlZb/e7Z27Vo0a9YMI0eO5PZ9/PgRaWlp6NevH0xMTLhNXl4ewPd5X0xPT4eNjQ2MjY25cDMzM+48ASAkJASdOnWCkpISHBwc8OHDhyqV/5tSnRYF/w1AbTf2JuH7wd4kNC3FRQVUmB3X4FtxUUGtyj1p0iQyMTGhuXPnkrOzs0DYxo0bqW/fvkRENGDAAFq4cGGV0ty4cSMZGBhQUFAQ3b17l0aMGEHdunWjvLw8IiJKSEggERERCgsL446xtLSscvpERADI3NycQkNDad26dQSAdHV16dSpU3T8+HGSkJCgTZs2ERHR+vXrSUdHhwICAigwMJDatWtHrq6uZab7zz//EAAKDw+nx48fEwAqKPjfNU5LSyMAFBcXR7m5uaStrU1z5syhBw8e0IEDB0hFRYV7C9FQGqvu1bT+ffmrYmV1r0uXLrRnzx6B8CFDhtDSpUupuLiYZGRk6PLly1XKe8iQIWRra0vh4eF06dIlateuHc2aNYsLP3bsGMnIyFBubi4REWVlZZGEhESV0w8NDSUZGRlydHSk8PBwGj9+PImLi5OFhQXduHGDFi5cSAAoMjKSiIgGDRpEVlZWdPPmTfLz8yN5eXk6cOBAmWl7e3uTkpISffr0ibZv3049e/YUCD99+jQZGhoSEdH+/ftJWlqadu3aRXfu3KHRo0eTmJhYg79JKCwqppTcwkbZCouq/ubn+fPnpKamRjExMbRkyRLuTUJ4eDjJysrS8OHDSU1NjX744Qc6fPgwd9z3eF8srbi4mKKjo6lr1660YsUKIiKKjo4mMTExmj17Nt27d4+WLVtGEhIS392bhGqvk0BEddZAYRjm61L8KQkf/tZt8HzVneIgKlv9lTbT09Mxa9Ys7N+/H6dPn0ZgYCDk5OQE4sjJyXH9wmNiYjB48GCBcH19fbx//577vHv3bjg7O2PDhg1Yu3YtNwj08OHD0NbWRmhoKPr37w8tLS306NEDp06dQu/evZGcnIw7d+5g69at1ToHFxcXWFlZwcrKChs2bIC9vT3Xd9fHx4d7Q7tp0yasXr0aDg4OAEpWxS1rAZ7Dhw/j559/xrBhw9CtWzfcunULUlJSEBP73+2ef41SU1Nx6dIlKCoqYs2aNeDxeOjYsSOSk5OxevVqDBkypFrnUhuNVfeAmte/0tLT0yusexWFJycnIzc3V2C12RcvXgj8Ag8Az58/R0FBAS5cuIDo6Gjo6pZcr3379qFr165YtmwZZGRk4ODgACJCcHAwHB0dERwcDFlZWfTp06fK55Obm4ulS5fCzMwMKioqOHjwIGbNmoUePXqgc+fOWL9+PeLj4yEnJ4fAwEC8efMGenp66N69O5KTk/HmzRuB9D5//gwvLy+sWrUKW7ZsgZSUVKXXbNu2bZg9ezamTp0KoGTgaOm3DA0lPa8Yv1wVHmTeEHb8qApVadFK4xERJk2aBE9PT7Ro0UIg7OXLl8jLy4OlpSXmz5+Pf/75By4uLlBRUYGdnd13eV8szdzcHA8fPkSnTp24gf27du1Ct27dsG7dOgBA586dERERgZcvX1brPJq6ajUS+K8QGYZhGtvFixfh4uKCwsJCnD59GkOGDMHNmzeRkJAgEC8rKwtKSkoAAC0tLYFX40DJK2X+q/H+/fujqKgIWVlZiIuLw4QJE+Di4sLFzcvLE+hW6eTkhHXr1mHTpk0IDAxE8+bNq73gTdeuXbl/q6ioCHxWVVUFUNK9JC4uTuCPm42NDWxsbLjP79+/x08//YTLly9j+vTp2LBhAwBASUkJnz9/RkFBAcTFxblrwg978uQJXrx4ARkZGS6toqIiaGpqVus8vndKSkoV1j0lJSXuupcONzY2hqqqKiQkJJCYmMh1ydDX10dkZCSAkllY+vTpAyLC06dPUVBQINBNgohQWFiIpKQkGBgYQFZWFgMGDMDp06fh6OiIgIAADBo0SKChWBkZGRmYmZkB+F895NdNSUlJ7uH+6dOn0NbWhp6eHnfs3LlzBdK6d+8exowZg8TERPj4+HBdp8q7Jvxr9vz5c6xcuZILk5CQEPj/g/kfHx8fFBcX45dffhEKGzp0KAYMGMB19ercuTNevHiB3bt3w87O7ru8L5Z26tQpxMTEYMmSJRg1ahROnDiB58+fCzWqe/fuzRoJFVm8eHF9lYNhmK+AiLQm1J3iGiXf6ti/fz8mTpwIJycnbNu2Dc2aNQMAqKur4/79+wJxExISoKGhAQAwNTXFzZs3BcJbtWoFoOQPHf8hj//H8fz589yvtXz8tICS2Tx+++03RERE1Hj2DhERkQo/A0BBQQGAkj62ZUlISICFhQUkJCRw7do1WFlZcWHq6uoASvqA8x/k+Oeprq6OwsJCODg4CM2GUl5e9aWx6h4/79qqrO6pq6sLNSL4Mx6JiorCyMgIN2/ehLW1NYCSB2L+uJvSD2CFhYXQ1NTEtWvXhMpQ+k2Ek5MTfvnlF+Tl5SEoKKja40zKqofl1c2K6sqVK1cwYMAA9OrVCyEhIQJjLsq7JvxrVlajpipjKuqakqQIdvyo2uD58vOuirt37+Lu3bvcmJjCwkIUFxdDWloaERERaNOmjUD89u3b48CBAwC+z/tiSkoK3r17hw4dOkBfXx/6+vrw8fFB+/btkZOT89XUvcbGpkBlGIbDExGDqGzzBt94IlX/vSI1NRXTpk3D/Pnzcfz4ca6BAAB9+vRBeHg4srOzuX0hISHcL0LTp09HaGgogoODhdLdtGkTNyhSSUkJ6urqSE1N5Qb5aWhoYM2aNQID27S1tdG9e3ccO3YMly5dEprSsq4oKyujWbNmuHv3LrcvKioKXbp0QVFREWbNmgVFRUVEREQI/CEESgaKtm3bFpcvX+b2hYSEoFu3bpCSkoKJiQmio6NhbGzMneudO3cE4jeExqp71a1/5ams7vXp00fgmn78+BEPHz7kvi83Nzds3LhR6BddAFyXBwAwMTHBhw8foKCgwH1fGRkZ2LFjByQkJLh4Dg4O+PTpE1avXo2cnBzY2trW+hzLYmxsjPj4eIGH/b1792Lq1KkoKirC+PHjMXLkSFy+fFloUHavXr3w9u1bvHr1ittX+pqZmJgIdC8qKCgQ+H+goYiK8KAqLdoom6hI1R5MV65ciUePHiEyMhKRkZGYNm0aevfujcjISOzevRvTpk0TiP/ixQsYGRkB+D7viw8ePIC1tbXAAOvs7GwQEcTExITqHgChhtT3oPZ3RqZSI0aMgJSUVIVxZsyYgRkzZjRQiRim6bpw4QKICKNGjRJ4uABKXqObmZnBxcUFc+fOxalTp/D27Vtupo+2bdtiwYIFGDx4MBYsWMC9lj558iQuX74MQ0NDLi13d3fMnj0bUlJSUFFRwerVqxEfHy/UDcfJyQnz5s2DgoICevToUW/n7ebmhjlz5kBWVhaSkpLw9vbmZoU5e/YsFi9ejNTUVIE1IdTV1SEvL49p06Zh4cKF0NHRQX5+Pry8vLBx40YAwLhx47Bo0SK4ubnB1dUV//77L2bMmIEjR47U27l8iyqre2PHjoW3tze8vb3Rv39/eHt7o1evXlz3oilTpuD48eMwNzeHp6cnOnfujOTkZGzZsgWioqLcr5impqawtrbG6NGjsXLlSqSmpmLOnDmws7MTKI+cnBzs7OywYsUKDBw4ENLS0vVy3m3btkXv3r3h7OyM5cuXIzY2FosXL8bChQtx+/ZtJCYmYuLEiUJjFFq0aAENDQ04OjrC1dUVK1euxJ07dxAcHMwt9ufm5obJkydDV1cXnTp1wpYtW7g57hlBmpqaAvcmNTU1yMnJwcTEBIMGDUK/fv2gp6cHOzs73L17F3v27OHeRn2P98UuXboAAH799VdMnToVqamp8PDwgJOTEyQlJTF16lSsW7cOs2fPxrhx4xASEoKQkBDo6OjU+Tls3769zHEUpX3+/LnO862SBh0m/Z1hsxux2Y2YurdmzRoCUOZGRJSYmEj29vakpKRElpaWdP/+faE0/P39qU+fPqSoqEimpqY0Y8YMyszMpKlTp9K+ffuIqGS+cU9PT9LX1ycFBQUaMmQIxcbGCqUVFxdHPB6PJk2aVO1zASBQPjMzMy5/IqIRI0bQnDlzuPIsXLiQ9PT0SFlZmcaOHUspKSncuhFlbaXT8vT0JD09PWrZsiWtXr1aoBxRUVHUp08fkpeXJ0NDQ/Lx8an2uXxvypozvbK6d/36dbKwsCBlZWVydHSkjx8/CoQXFhbS0qVLydzcnOTl5cnCwoLWrVtHxcXFZGxsTDExMURElJqaSuPGjSM1NTXS0NAgd3d3+vz5s1AZDx8+TADo4MGD1Tq30NBQkpOT4z6npKQQAC5/IiI1NTU6f/48Fz5q1ChSUVEhHR0dWrx4MRUXF5O/v3+5dZOfVlZWFo0ZM4ZUVVWpY8eOdOHCBYGybNy4kQwNDalZs2Y0Y8YM+vPPP9k6CVVQenYjIiI/Pz8yMjIiGRkZMjc3F7rORN/ffTEsLIy6dOlCsrKypKenR+7u7tzaEUREV65coY4dO5KCggLZ2dmRv7//dze7EY+ITVdUXx48eABzc3NERESgU6dOjV2cRnH12l8wjS0ZpPa0xS78aD31q06XYRiGYRjma9JYz5NsTALDMAzDMAzDMAK+i0bCkSNHwOPx8Pz58xodHxsbCxcXF3To0AFycnJcv9PXr1/XcUkZhvkWZGRkQFxcvMLt0qVLjV1M5jt07ty5SusmfyVuhqlL7L7Y9HwXA5ePHj1a42ODg4Ph5OSErKws8Hg8qKur48mTJ3j8+DFOnDgBPz8/ODk51WFpGYZp6uTl5fHo0aMK45SerpJhGsqPP/5Yad0sPUsSw9QVdl9ser7pRgIRYdeuXTh//nyNji8oKMCMGTOQlZWFiRMnYuPGjVBUVERGRga8vb2xYcMGTJkyBT169IC2tnYdl55hmKZKRESEm+OeYb4m/BlvGKahsfti0/NNdjcKCQnBpEmTYGJigunTp9c4nb///huvXr2CiYkJfH19oaioCABQVFTE+vXrMWbMGGRkZJS5gh/DMAzDMAzDNFXfZCMhMDAQe/furfXy2Y8fPwYAODs7l7nS3sSJEwEADx8+rFU+DMMwDMMwDPM1+SYbCXPnzuVWHYyMjKxxVyD+4i8tWrQoM5yfbkxMTI3SZximZl6+fAkbGxsoKCjAyMgIO3fu5MKSkpLg4OAAFRUV9OjRA/fu3RM6PiwsDMOGDYOOjg7U1NRgbW2NixcvVjn/HTt2QFFREfn5+UJh7u7usLCwqNmJ1RARYf369WjVqhUUFRVha2uLFy9eCMTx8vKCvr4+DA0NucWqmJqLiYlBu3btBPZVVvdu3LgBS0tLqKiowNHRER8/fhRKd/fu3bCxsYGSkhJ0dXUxevRoPH36tMrlGjlyJAYMGFBmWPv27eHh4VHltOpCVlYWpk6dCi0tLWhoaGDKlCnIzMzkwrOzszFu3DioqanB3NwcQUFBDVq+b9G2bdtgZmYGVVVV/PLLL9x9it0bhe+NSUlJGDp0KJSVlaGvrw8vLy8UFxc3aBm/Zt9kI0FbWxsdOnTgtpoOwvrtt99w/vz5cpez5y8FrqurW+OyMgxTPUSEoUOHQk1NDdeuXYOnpydmzpyJ4OBgAICDgwMkJSURFBSEnj17wtbWVmC1zb/++gt2dnYwNTXF8ePHce7cOXTo0AH29vbYv39/lcowfPhwZGdn48qVK0JhAQEBGDp0aN2cbBX5+/vD29sbS5cuxZUrV6Curo5+/fpxq9Nu3boVO3fuxM6dO7Fx40asWbMGfn5+DVrGb0lWVhbmz58vtL+iupeQkAB7e3vY2toiMDAQ+fn5GDZsmMDxP/30E/744w8MGzYMV65cga+vLzIyMtCtWzdERUVVqWxOTk64cuUKMjIyBPbHxsYiKiqqweumh4cHbt26haNHj8Lf3x8PHjyAq6srFz5hwgTExcXh7NmzcHZ2xvDhw/Hs2bMGLeO3xM/PDytXrsTKlSuxZ88eBAQEcD8KsHuj8L3R1dUV6enpuHjxIrZs2YItW7Zg9+7dDVrGr1qDLt3WSPT19QkAPXv2rM7STEtLI0NDQwJAK1euLDMOW3GZrbjM1L1///2XREVFKS0tjdtnZ2dH06ZNo3v37pG0tDRlZ2dzYR07dqRdu0rqyLt370haWppOnDghlO78+fNJVVWV8vPzq1QOKysrmjx5ssC+qKgoAkBPnz6twZnVnL29PXl4eHCfCwoKSEtLi86cOUNEJSuW+vr6cuGbN28mS0vLBi3jt2Lp0qUkJiZGAARWX62s7q1du5Z69erFhX38+JHExMTo+fPnRER0+vRpkpOTozdv3gjkV1hYSD179qTBgwdXqXw5OTkkIyNDhw4dEti/detW0tLSouLi4mqdb23JyspSUFAQ9/nRo0cEgNLS0igpKYlERUXp1atXXPjQoUNpwYIFDVrGb0nbtm3p7Nmz3OcjR46Qi4sLuzf+v9L3xoyMDAJAUVFRXPi0adPIzs6uQctYFY31PPlNvkmob69fv0bfvn3x6tUraGho4Oeff64wfnZ2NjIzM2u9sbmrGQbIy8vD0KFDoaSkxO0zMzPDp0+fEBoaCktLS8jKynJh/fr1Q2hoKICSX9Q7duyI4cOHC6Xr4eEBX19ffPr0CUDJG4u1a9fC2NgY8vLyGDhwIBITE7n4Tk5OOHv2LIqKirh9AQEBMDU1RevWrat0LjweDyEhIbC0tISqqipmzpyJ+Ph4WFtbQ15eHn379kV6ejpXnuXLl8PAwADKysoYO3Ys0tLSAACioqKwtLTk0hUTE4OioiISEhKQkpKCR48eoV+/fgLXJDw8nLunhISEoFOnTpCRkYGGhgZ++eUXFBYWVukcvjeTJ09GZGQkli1bJrC/sroXGhoq8B2oqKigU6dOXPjq1avh7u4OfX19gXRFRUWxd+9egb8z2dnZmDx5MjQ1NaGmpgY3NzeuHsrIyMDe3h6nT58WSCcgIACOjo5ljq/7UlhYGIyMjHDy5Em0bt0aurq6OHDgAK5evQozMzMoKCjAzc2Ni5+ZmYkJEyagWbNm0NHRwZIlSwAA6enp0NXVRYcOHbi4ampqAIDExERcv34dLVq0gIGBQZnXDAAuXrwIc3NzyMrKonPnzrhx40al5f9eRUdHIzo6GnZ2dty+MWPGwM/Pj90b/1/pe2N6ejpsbGxgbGzMhfP/lvDV9t6Yl5dXJ89/2dnZVc6zTjVok6SR1NWbhPz8fFq1ahXJyMgQAJKRkaHw8PBy4/NbfnW1eXp61qr8jYG9SWhaiosLqajwQ4NvxcWFNSpvYWEh/ffff2RgYEBHjhyhuXPnkrOzs0CcjRs3Ut++fYmIaMCAAbRw4cIqpb1x40YyMDCgoKAgunv3Lo0YMYK6detGeXl5RESUkJBAIiIiFBYWxh1jaWlZ5fSJiACQubk5hYaG0rp16wgA6erq0qlTp+j48eMkISFBmzZtIiKi9evXk46ODgUEBFBgYCC1a9eOXF1dy0z3n3/+IQAUHh5Ojx8/JgBUUFDAhaelpREAiouLo4KCAlJUVKTJkyfT7du36dChQyQhIUF+fn5VPo+60Fh1r6b1b9++fQJvEiqre126dKE9e/YIhA8ZMoSWLl1KxcXFJCMjQ5cvX65S3kOGDCFbW1sKDw+nS5cuUbt27WjWrFlc+LFjx0hGRoZyc3OJiCgrK4skJCSqnH5oaCjJyMiQo6MjhYeH0/jx40lcXJwsLCzoxo0btHDhQgJAkZGRREQ0aNAgsrKyops3b5Kfnx/Jy8vTgQMHykzb29ublJSU6NOnT7R9+3bq2bOnQPjp06fJ0NCQiIgePHhA0tLS5OvrSw8fPiQvLy9SU1Oj169fV+k86kpxUQEVZsc1ylZcVFB5Af/ftWvXSE9Pj3x8fKht27akq6tL7u7ulJuby+6N/6/0vbG04uJiio6Opq5du9KKFSuIiOrk3ujp6Vmnz4EN/Sbhm14noS49efIEY8aMwX///QcAMDU1hb+/P8zMzCo9NiwsDD/88EOtyyApKVnrNBimIlSciqyk0Q2er7zmMfBEm1X7uGbNmiEtLQ1DhgzBmDFjcO3aNcjJyQnEkZOT4/rdxsTEYPDgwQLh+vr6eP/+Pfd59+7dcHZ2xoYNG7B27VpuEOjhw4ehra2N0NBQ9O/fH1paWujRowdOnTqF3r17Izk5GXfu3MHWrVurdQ4uLi6wsrKClZUVNmzYAHt7e67fro+PD+Lj4wEAmzZtwurVq+Hg4ACgZMGr7du3C6V3+PBh/Pzzzxg2bBi6deuGW7duQUpKCmJi/7vd869RamoqFBQUsGDBAri5uUFWVhZdu3aFj48PYmNjq3UetdVYdQ+oef0rLT09vcK6V1F4cnIycnNzBRaSevHihcAv8ADw/PlzFBQU4MKFC4iOjubGw+3btw9du3bFsmXLICMjAwcHBxARgoOD4ejoiODgYMjKyqJPnz5VPp/c3FwsXboUZmZmUFFRwcGDBzFr1iz06NEDnTt3xvr16xEfHw85OTkEBgbizZs30NPTQ/fu3ZGcnMxN/MH3+fNneHl5YdWqVdiyZQukpKQqvWYbN27EhAkTuDEMP/zwA+7fv4/du3djxYoVVT6X2ir+lIQPfzfO2EN1pziIylZtgbH3798jISEB+/fvx7Zt25Cfn4+ZM2eisLAQBQUF7N74xb2xNHNzczx8+BCdOnXiBvfn5ubW+t64YMEC/P7779U677JERkbCysqq1ulUF2skVIGfnx9mzJiBT58+QVpaGvPmzcO8efMgJSVVpePl5OSgoKBQz6VkmO/P7du38fr1a3h4eGDOnDlQUlJCQkKCQJysrCyua5KWlpbAa3Gg5HUy/7V4//79UVRUhKysLMTFxWHChAlwcXHh4ubl5XF/mICS1+rr1q3Dpk2bEBgYiObNm8Pc3Lxa59C1a1fu3yoqKgKfVVVVAZR0L4mLixP4w2ZjYwMbGxvu8/v37/HTTz/h8uXLmD59Ord+i5KSEj5//oyCggKIi4tz14QfpqCggBkzZiAoKAhRUVF48OABbt26JZA2U7nK6p6SkhJ33UuHGxsbQ1VVFRISEkhMTOS6Y+jr6yMyMhJAyQwsffr0ARHh6dOnKCgoEOgiQUQoLCxEUlISDAwMICsriwEDBuD06dNwdHREQEAABg0aJNBQrIyMjAz3Ixi/HvLrpqSkJPfA+fTpU2hra0NPT487du7cuQJp3bt3D2PGjEFiYiJ8fHy4rlPlXRP+NXvy5An+++8/7Nu3jwsvKCgQ6GrI/I+4uDgKCwvx999/Q0dHBwCwefNmDBkyBG5ubuze+MW9sbRTp04hJiYGS5YswahRo3DixIk6uTdKSkrWyQ+8XzbwGgprJFTixIkTmDRpEogIVlZW2Ldvn1CfUYb5VvBEVCCveaxR8q2qd+/eISsrC61bt4axsTH3sOTq6orff/8d9+/fF4ifkJAADQ0NACVvAG/evCkQ3qpVKwAlf+T4f0T5fxjPnz8vNHsZPy2gZCaP3377DRERETWeuUNERKTCz0DJgxFQ0r+2LAkJCbCwsICEhASuXbsm8IuTuro6gJI+4PwHOf55qqurIykpCVZWVjAwMICtrS3c3d0bZfxTY9U9ft61pa6uXmHdU1dXF3pI4894JCoqCiMjI9y8eRPW1tYASn4N5a9OW/rhq7CwEJqamrh27ZpQGUq/iXBycsIvv/yCvLw8BAUF4a+//qrW+ZRVD8urm+XVSwC4cuUKBgwYgF69eiEkJETg72d514R/zQoLC7FkyRKhfvLy8vLVOZVaE5HWhLpTXIPmWTrvqtLQ0ICCggLXQACA1q1b49OnT5XWz+/x3piSkoJ3796hQ4cO0NfXh76+Pnx8fNC+fXvk5OQgKyvrq7g3NiY2cLkCb9++xYQJE0BEcHNzw9WrV1kDgfmm8XiiEBFt1uAbj1f+Q8aXAgMDMWrUKIF92dnZEBcXR58+fRAeHi4wyCskJITrZjF9+nSEhoZy06WWtmnTJm5AmpKSEtTV1ZGamgoTExOYmJhAQ0MDa9asERjUpq2tje7du+PYsWO4dOmS0JSWdUVZWRnNmjXjpl0GgKioKHTp0gVFRUWYNWsWFBUVERERIfRKWk1NDW3btsXly5e5fSEhIejWrRukpKRw5swZ8Hg8XLhwATNnzkT//v3LnL+/vjVW3atu/StPZXWvT58+At/Bx48f8fDhQ+77cnNzw8aNG4V+zQWAdevWcf82MTHBhw8foKCgwNXNjIwM7NixQ2C6bwcHB3z69AmrV69GTk5OuVN515axsTHi4+MFHvb37t2LqVOnoqioCOPHj8fIkSNx+fJlob+fvXr1wtu3b/Hq1StuX+lrZmJigvj4eO48TUxMsH//fjx//rxezqU8PBExiMo2b5SNJ1L133LNzMxQUFAgcD2joqKgqKjI7o1l3BsfPHgAa2trgQHW2dnZICKIiYl9NffGxsTeJFTA19cXubm5sLOzq3ZfOoZh6oe1tTXc3Nzg7e0NR0dHxMXFYf78+Rg3bhw6d+4MMzMzuLi4YO7cuTh16hTevn2LkSNHAgDatm2LBQsWYPDgwViwYAH32vjkyZO4fPkyDA0NuXzc3d0xe/ZsSElJQUVFBatXr0Z8fDw0NQV/2XNycsK8efOgoKCAHj161Nt5u7m5Yc6cOZCVlYWkpCS8vb25WWHOnj2LxYsXIzU1VWDec3V1dcjLy2PatGlYuHAhdHR0kJ+fDy8vL2zcuBEAICsri7dv3yIgIACysrI4dOgQ/vvvPxgZGSEjIwOKior1dk7fksrq3tixY+Ht7Q1vb2/0798f3t7e6NWrF9e9aMqUKTh+/DjMzc3h6emJzp07Izk5GVu2bIGoqCg3K5GpqSmsra0xevRorFy5EqmpqZgzZ47AjDZASfcEOzs7rFixAgMHDoS0tHS9nHfbtm3Ru3dvODs7Y/ny5YiNjcXixYuxcOFC3L59G4mJiZg4caLQGIUWLVpAQ0MDjo6OcHV1xcqVK3Hnzh0EBwdz8/q7u7ujb9++aNu2Lbp16wZ/f3/s2LED06dPr5dzaeoUFRUxduxYjB49Ghs2bEBubi5+/fVXuLu7s3tjGffGLl26AAB+/fVXTJ06FampqfDw8ICTkxMkJSXZvRFgsxsREbVu3Zpat25Np0+fFthvYmJCAOjKlSs1ypetk8BmN2Lqx6lTp8jMzIykpaXJ0NCQlixZws3hnZiYSPb29qSkpESWlpZ0//59oeP9/f2pT58+pKioSKampjRjxgzKzMykqVOn0r59+4ioZOYkT09P0tfXJwUFBRoyZAjFxsYKpRUXF0c8Ho8mTZpU7fMAIFA+MzMzLn8iohEjRtCcOXO48ixcuJD09PRIWVmZxo4dSykpKfThw4dyZ8IonZanpyfp6elRy5YtafXq1dz+goICmjBhAikoKJChoSEtW7aMAgICSFNTk86dO1ftc/pefDm7EVHlde/69etkYWFBysrK5OjoSB8/fhQILywspKVLl5K5uTnJy8uThYUFrVu3joqLi8nY2JhiYmKIiCg1NZXGjRtHampqpKGhQe7u7vT582ehMh4+fJgA0MGDB6t1bqGhoSQnJ8d9TklJIQBc/kREampqdP78eS581KhRpKKiQjo6OrR48WIqLi4mf3//cusmP62srCwaM2YMqaqqUseOHenChQsCZTlx4gS1a9eOZGRkyMLCQmDGHEbY58+fafLkyaSpqUl6enr0559/cjObsXuj8L0xLCyMunTpQrKysqSnp0fu7u6UmZlJRF/XvbGxnid5REQN1SBpLC1btkRMTAyePXvG9fEsjf8Lzb59+zBhwgQAQHFxMaSkpFBQUIAWLVpwA/7KYmZmJjQnNVDyKsvc3BwRERHo1KlTHZ1N03L12l8wjS0ZpPa0xS78aD31q06XYRiGYRjma9JYz5Osu1E5EhISuAExlU13xWZaYBiGYRiGYb4l30Uj4cu+kF8q62VK8+bNy9zPMAxTmYyMDG5l2fIEBgaif//+DVQihilx7ty5MlfVLS07O5uty8PUC3ZvbFq+i0YCwzBMQ5KXl8ejR48qjFN6ukqGaSg//vhjpXWz9CxJDFOX2L2xaWGNBIZhmDomIiJS5vgnhmlscnJyrG4yjYbdG5sWtk4CwzAMwzAMwzAC2JuEBjBixAhISUlVGGfGjBmYMWNGA5WIYRiGYRiGaWzbt2/H9u3bK4zz+fPnBiqNIPYmoQGcOHECT548qXBjDQSGqbqXL1/CxsYGCgoKMDIyws6dO7mwpKQkODg4QEVFBT169MC9e/eEjg8LC8OwYcOgo6MDNTU1WFtb4+LFi1XOf8eOHVBUVER+fr5QmLu7OywsLGp2YjVERFi/fj1atWoFRUVF2Nra4sWLFwJxvLy8oK+vD0NDQ26xqvLs378f7dq1q88iN3kxMTFC1+j+/fvo0aMH5OXl0aZNG+zfv18g/MaNG7C0tISKigocHR3LXL119+7dsLGxgZKSEnR1dTF69Gg8ffq0yuUaOXIkBgwYUGZY+/bt4eHhUeW06kJWVhamTp0KLS0taGhoYMqUKcjMzOTCs7OzMW7cOKipqcHc3BxBQUENWr5v0bZt22BmZgZVVVX88ssv3H2K3RuF741JSUkYOnQolJWVoa+vDy8vLxQXF5ebXn3cG2fMmFHpM+KJEyfqNM+qYo0Epsnj8T6huCi5zjeiosozZxocEWHo0KFQU1PDtWvX4OnpiZkzZyI4OBgA4ODgAElJSQQFBaFnz56wtbUVWGnzr7/+gp2dHUxNTXH8+HGcO3cOHTp0gL29vdBDXXmGDx+O7OxsXLlyRSgsICAAQ4cOrZuTrSJ/f394e3tj6dKluHLlCtTV1dGvXz/k5OQAALZu3YqdO3di586d2LhxI9asWQM/P78GLeO3JCsrC/PnzxfYl52dDXt7e3Ts2BH//PMPZs2ahenTp+P8+fMASqbVtre3h62tLQIDA5Gfn49hw4YJpPHTTz/hjz/+wLBhw3DlyhX4+voiIyMD3bp1Q1RUVJXK5uTkhCtXriAjI0Ngf2xsLKKiohq8bnp4eODWrVs4evQo/P398eDBA7i6unLhEyZMQFxcHM6ePQtnZ2cMHz4cz549a9Ayfkv8/PywcuVKrFy5Env27EFAQAD3owC7NwrfG11dXZGeno6LFy9iy5Yt2LJlC3bv3t2gZfyqNejSbd8ZtuJyw6y4/CSyH6XH/1jnW15+Yp2Vl6k7//77L4mKilJaWhq3z87OjqZNm0b37t0jaWlpys7O5sI6duxIu3aV1L13796RtLQ0nThxQijd+fPnk6qqKrdyc2WsrKxo8uTJAvuioqIIAD19+rQGZ1Zz9vb25OHhwX0uKCggLS0tOnPmDBGVrFbq6+vLhW/evJksLS3LTa+s1YSZEkuXLiUxMTECIHCN/P39SVtbmwoLC7l9c+bMIUdHRyIiWrt2LfXq1YsL+/jxI4mJidHz58+JiOj06dMkJydHb968EcivsLCQevbsSYMHD65S+XJyckhGRoYOHToksH/r1q2kpaVFxcXF1Trf2pKVlaWgoCDu86NHjwgApaWlUVJSEomKitKrV6+48KFDh9KCBQsatIzfkrZt29LZs2e5z0eOHCEXFxd2b/x/pe+NGRkZBICioqK48GnTppGdnV256TXWvbGxnifZmwSmXhWWWmsiPa8I8emf6mRLz6v/X/mTP+fUex5M9eXl5WHo0KECixiamZnh06dPCA0NhaWlJWRlZbmwfv36ITQ0FEDJL+odO3Ysc554Dw8P+Pr64tOnTwBK3lisXbsWxsbGkJeXx8CBA5GYmMjFd3JywtmzZ1FU9L+6GBAQAFNTU7Ru3bpK58Lj8RASEgJLS0uoqqpi5syZiI+Ph7W1NeTl5dG3b1+kp6dz5Vm+fDkMDAygrKyMsWPHIi0tDQAgKioKS0tLLl0xMTEoKioiISEBKSkpePToEfr16ydwTcLDw5GXlwcACAkJQadOnaCkpAQHBwd8+PChSuX/Hk2ePBmRkZFYtmyZwP7s7GzY2NhAVFSU26empoaEhAQAQGhoqMB3oKKigk6dOnF1c/Xq1XB3d4e+vr5AuqKioti7dy9+/vlngbwmT54MTU1NqKmpwc3NjauHMjIysLe3x+nTpwXSCQgIgKOjI3g8XqXnGBYWBiMjI5w8eRKtW7eGrq4uDhw4gKtXr8LMzAwKCgpwc3Pj4mdmZmLChAlo1qwZdHR0sGTJEgBAeno6dHV10aFDB4FrAgCJiYm4fv06WrRoAQMDAy689P+vALB582YYGBhASkoKhoaG2LFjR6Xl/15FR0cjOjoadnZ23L4xY8bAz+//2DvzuBqzP45/WrQvt0WrEqnExS9ZSlJRSkKibNmStYSfNJmGVEyyJYzCZDczGiIpoqayizBiomFITaV9uWm7dX5/3N99dFUqbkWe9+v1vLrPOed+n3Oevvd5zvI93+8R+tn4fxo/G0tLS2FpaQldXV0qn/su4fKtPxvpjcs0HQqr9r1tX/DtEty4Gs8XuYOE3iFuAOezf5IcssU/vjG8rciL1GOrAechIC5aBUjzRexXA7uhHnlVFZ1+XRVxaQgLCrVeEMDw4cPx+++/AwDq6+vx119/ITIyEps3b8bDhw+hpqbGU15VVRWpqakAgD///BMWFhbNypWTk8OUKVOo85CQEISFhWHfvn1QVFTEtm3b4ODggOTkZIiIiMDBwQEeHh64efMmxowZA4DzIvzQhKQ1vL29sXPnTty/fx+enp6IjIxESEgI6urqMHfuXBw7dgyrVq1CcHAwQkNDERYWBkFBQXh7e8PT0xPh4eG4cOECj8zr16/j2bNnMDAwoF5qqqqqPPcEAAoKClBTU4MJEyZg1apVOHjwIOLi4vDDDz/wvDg7g67SPaB9+qeiogIVFRXcv3+fJ33hwoVYuHAhdc5isfDLL79QHZT8/PxmdTM/Px+EEDx+/BgBAQHNXlNHRwc6OjrUubOzM6qrq3H+/HlUVFRg7dq1WLduHXbt2gWA4yzDxcUFVVVVEBcXB4vFQlJSEtatW9emNgIc86iTJ0/i6NGj2L9/P1xdXWFgYIADBw7g0qVL2LJlCxYvXowhQ4bA2dkZ5eXliIqKQkZGBjw8PKCtrY25c+c22U9x8OBBMBgM9OnTB4mJiS3eEwC4d+8eVq9ejZCQEBgbGyMqKgpubm6wtbVtMpjqSAipB2kobr1gByAgKA8BgbbpZnZ2NpSVlXHkyBHs3bsX5eXlmDp1KrZu3dqi/n3Lz0ZNTU1cvXoVAGeg8c8//+DEiRNUW1++fPlFPBu7EnqQQPNV0tBoEWyXbAR/hT/k/Mkhz6ClyOSv7C+cvKoKaERsbr0gn8ly+gG9JBnt/l7Pnj1RUlKCKVOmYNasWUhMTISUlBRPGSkpKcru9vXr15g8eTJPvpaWFt6+fUudHzp0CM7Ozti1axe2b99ObQI9deoU1NTUkJSUhPHjx0NVVRUmJiaIjIzEmDFjUFBQgLt372Lv3r3tasOCBQtgZmYGMzMz7Nq1C7a2tpTdblhYGLKzswEAu3fvRlBQEOzs7ABwAl415xHj1KlTWLp0KRwcHGBkZIRbt25BTEwMwsLvH/fce1RcXIyTJ0/CyMgIO3bsAAAMGzYMqamp+Pvvv9vVjs+lq3QP+HT9a4nnz59j3rx5yM7OpvYulJaWtqibBQUFePfuHU8QqYyMDJ4ZeK7curo6XLp0CS9evICGhgYA4OjRoxg5ciQ2b94MCQkJ2NnZgRCCuLg42NvbIy4uDpKSkjA3N29zG969e4eAgAAwmUzIy8vjxIkTWLNmDUxMTDBs2DDs3LkT2dnZkJKSQkxMDF69egVNTU2MGjUKBQUFePXqFY+86upq+Pn5YevWrdizZw/ExMQ+ek8AQFxcHAcPHsTixYsBAIMGDUJQUBAyMzM7d5DQUIyKvJmddr3GSKv8BgGhnm0q+/btW+Tk5ODYsWPYt28famtrsXr1arDZbNTV1dHPxg+ejY0xNDTEw4cPMXToUGpz/4EDB76IZ2NXQg8SaDqNJQay+NXcki+ybl2rALpmYofmC+LOnTv4559/4OXlBU9PTzAYDMq8g0tFRQVlmqSqqsqzLA5wlpO5y+Ljx49HfX09KioqkJWVhfnz52PBggVU2ZqaGurFBHCW1Xfs2IHdu3cjJiYGvXr1gqGhYbvaMHLkSOqzvLw8z7mCggIAzqx0VlYWz4vN0tISlpbvf09v377FvHnzcPXqVSxfvpyaVWYwGKiurkZdXR169OhB3RNu3vPnz5t0HseMGfNNvQj5BdcMY8OGDdDT08P169epziyDwaDuO5eKigro6upCQUEBIiIiyM3NpcwxtLS08OjRIwAcDyzm5uYghCA9PR11dXU8s5mEELDZbOTl5aFv376QlJTEhAkTcO7cOdjb2yM6OhqTJk3iGSi2hoSEBJhMziQJVw+5uikqKkp1ONPT06GmpgZNTU3qux+uWNy7dw+zZs1Cbm4uwsLCKNOplu4J9/fKZDIhJyeH8PBwPHv2DElJSWCz2W1uw7dGjx49wGaz8fvvv0NdXR0AZ9Z/ypQpcHd3p5+NHzwbGxMZGYnXr19j06ZNmDFjBs6cOUM/G0EPEmg6nPfmRlJitVCTZvFFqqaGGqyuc37ouyxVoNNbs5VvtI1/3tzCgJzOdRH4JaEiLo0spx+65Lpt5d9//0VFRQX69+8PXV1dqrPk4uKC//73v03MQHJycqCsrAwA0NfXx82bN3ny+/XrB4DzkuO+RLkvxosXL1KztVy4sgCOJ49Vq1YhNTX1kz13CAoKfvQcAOrq6gCAx969MTk5ORgxYgRERESQmJgIMzMzKk9JSQkAxwac25HjtlNJSanZjmNb7Nb5TVfpHvfa/MDV1RXHjx/Hxo0b4e3tTQ3KAM69/rCTxvV4JCQkBB0dHdy8eZMy+RAREaEi0zbufLHZbKioqCAxMbHJ9RuvRDg6OmLFihWoqalBbGwsDh482K62NKeHLelmS3oJAAkJCZgwYQJMTU0RHx/PswLQ0j3h/saSk5Mxbdo0TJ48GUZGRnB0dMTYsWPb1Q5+ICAoD2mV3zr9utxrtxVlZWXIyMhQAwQA6N+/P6qqqqCkpEQ/Gz94NhYWFuLff//FkCFDoKWlBS0tLYSFhWHw4MGorKz8Yp6NXQk9SKDpUATw3leyiMAVVORd5ItcGXEpPG1wBQDIqyuhT/+RrXyjbRRX5wI5rZfrrggLCvHV7KIjiImJwU8//YQ///yTSmOxWOjRowfMzc2xYcMGsFgsaqYzPj4ey5YtAwAsX74cQ4YMQVxcHKytrXnk7t69m5qlZDAYUFJSQnFxMTUjVVpairVr12Lz5s3U7JuamhpGjRqF3377DVeuXEFMTEyHtFlOTg49e/ZESkoK1clKS0uDi4sL7ty5gzVr1kBWVhY3btyAnJwcz3cVFRUxcOBAXL16FYsWLaLuiZGREcTExKCnp8ezURRAk85CZ/A16N7HuHTpEo4cOYJLly410S0AMDc3R1RUFDZu3AgAKCoqwsOHD6lOi7u7O3x8fLBo0SKe/SMAKHMHANDT00N+fj5kZGSocikpKTh16hRCQkKocnZ2dli4cCGCgoJQWVnZbJ34ga6uLrKzs5GTk0PZvB8+fBh37txBaGgo5s6dCycnJxw/frxJJ8/U1BRv3rzBy5cvoa2tDYCjm9zZ29DQUEyfPh1hYWEAOL/BxptKOwsBAaE2m/x0JUwmE3V1dTz3My0tDbKysvSzsZln44MHDzBz5kwUFBRQgwwWiwVCCISFhb+YZ2NXQg8SaDoUQuqoz4KkDg18ihooUCMEVeFCAAD7nSRqy97xRS67+v2ghjSQj5Sk6SosLCzg7u4Of39/2NvbIysrC97e3pgzZw6GDRsGJpOJBQsWYN26dYiMjMSbN2/g5OQEABg4cCDWr1+PyZMnY/369dRL7uzZs7h69Sr1YgU4gX/Wrl0LMTExyMvLIygoCNnZ2VBRUeGpj6OjI7777jvIyMjAxMSkw9rt7u4OT09PSEpKQlRUFP7+/pRXGG7ns7i4mMfvuZKSEqSlpbFs2TL4+PhAXV0dtbW18PPzQ3BwMABgyZIl2LFjB9auXYs5c+YgPj4e8fHxPLORNK0TGRmJoUOHol+/fnj58iWVLi4uDjU1NcyePRv+/v7w9/fH+PHj4e/vD1NTU8q8aPHixTh9+jQMDQ3h6+uLYcOGoaCgAHv27IGQkBA1g6mvrw8LCwvMnDkTgYGBKC4uhqenJ49HG4Bjb25jY4Mff/wREydOhLi4eIe0e+DAgRgzZgycnZ2xZcsWZGZmYuPGjfDx8cGdO3eQm5uLhQsXNtmj0Lt3bygrK8Pe3h4uLi4IDAzE3bt3ERcXR/n1l5SUxJ07d3Djxg3k5eVh7969EBAQwP379zFq1CielRoaQFZWFrNnz8bMmTOxa9cuvHv3DitXroSHhwf9bGzm2Th8+HAAwMqVK7FkyRIUFxfDy8sLjo6OEBUVpZ+NAB0noSOh4yQQcv6cNxXP4OERJeozP4/ft28jib6RfDl+27qRknvz9qmuvn00LRAZGUmYTCYRFxcn2traZNOmTZQP79zcXGJra0sYDAYxNjYm9+/fb/L9iIgIYm5uTmRlZYm+vj5xc3Mj5eXlZMmSJeTo0aOEEI5/el9fX6KlpUVkZGTIlClTSGZmZhNZWVlZREBAgCxatKjd7QDAUz8mk0ldnxBCpk+fTjw9Pan6+Pj4EE1NTSInJ0dmz55NCgsLSX5+PgHQ7NFYlq+vL9HU1CR9+vQhQUFBPPVISEggBgYGREZGhtjY2JCIiAg6TkIrfOgv3dbWttn/gbm5OVXm+vXrZMSIEUROTo7Y29uToqIiHplsNpsEBAQQQ0NDIi0tTUaMGEF27NhBGhoaiK6uLnn9+jUhhJDi4mIyZ84coqioSJSVlYmHhweprq5uUsdTp04RAOTEiRPtaltSUhKRkpKizgsLCwkA6vqEEKKoqEguXrxI5c+YMYPIy8sTdXV1snHjRtLQ0EAiIiJa1E2urIqKCjJr1iyioKBADAwMyKVLl6hrvHnzhpiZmRFJSUliYGBAzp8/T/z8/IiysjLJzaXj2DRHdXU1cXV1JSoqKkRTU5P88MMPpK6ujhBCPxubezYmJyeT4cOHE0lJSaKpqUk8PDxIeXk5dZ0v5dnYVf1JAUIIPV3aQTx48ACGhobo06cPxMQ+7qLTzc0Nbm5unVSzziMqcj1GlHNmhXLZ2lAiJXy/RqDirygU+Q9fZCmXxWJ9LceNYXq/Uxg7ejZf5NLQ0NDQ0NDQfMhPP/3UrDemxlRXV+PVq1dITU3F0KFDO6lmtLlRp3DmzJlO/ad+UdS9t0EtyvwvSH3bN2G1FXHZBkCE72JpaGhoaGhoaDqUtkwScyedOxt6kEDz1TNbpQS9hyvwRdbduyLAa76IovmGKSsroyLLtkRMTAzGjx/fSTWioeFw4cKFZqPqNobFYkFUVLSTakTzLUE/G78u6EECTYfS0OhzgW4pNMyd+SI3/684NNzmqK+SYBUUxNsWkbI1xOhfBA0fkJaWxpMnTz5aprG7ShqazmLs2LGt6qaICL00S9Mx0M/Grwu6S0TToVSR93sxkmRtEfWYP96NADOA45gAbuwkPsmkoeEPgoKClI97GpovCSkpKVo3aboM+tn4ddE0MgUNDR/pjF3xDfTeexoaGhoaGhoavkKvJNB0KLUN7w2OJPJ+wUlRCb7ItawThLgqJwjMu/q6VkrT0NDQ0NDQ0NC0B3qQQNNpCNSzUNXQ0HrBNkDq34dGJ3Us1Fdm80Uu6ljvP9PB1GhoaGhoaGi+UWhzI5pOQ1+qJ7KcfuDL4aw5gJLb8OY08n/X4MtRmXHofYXrK7vgLtG0hb///huWlpaQkZGBjo4OQkNDqby8vDzY2dlBXl4eJiYmuHfvXpPvJycnw8HBAerq6lBUVISFhQUuX77c5uvv378fsrKyqK2tbZLn4eGBESNGfFrDPhFCCHbu3Il+/fpBVlYW1tbWyMjI4Cnj5+cHLS0taGtrUxFtaT6d169fY9CgQTxp9+/fh4mJCaSlpTFgwAAcO3aMJ//GjRswNjaGvLw87O3tUVRU1ETuoUOHYGlpCQaDAQ0NDcycORPp6eltrpeTkxMmTJjQbN7gwYPh5eXVZln8oKKiAkuWLIGqqiqUlZWxePFilJeXU/ksFgtz5syBoqIiDA0NERsb+1F5CxcuxMqVKzu62l8lRUVFmDNnDuTl5dGvXz/s27ePyqOfi02fi3l5eZg6dSrk5OSgpaUFPz8/NPBpIrO7QA8SaDqNHkKC6CXJ4Msh3aNHVzeHposghGDq1KlQVFREYmIifH19sXr1asTFxQEA7OzsICoqitjYWIwePRrW1tYoLi6mvn/w4EHY2NhAX18fp0+fxoULFzBkyBDY2to26dS1xLRp08BisZCQkNAkLzo6GlOnTuVPY9tIREQE/P39ERAQgISEBCgpKcHKygqVlZyB7t69exEaGorQ0FAEBwdj27ZtOHLkSKfWsTtRUVEBb29vnjQWiwVbW1sYGBjg2rVrWLNmDZYvX46LFy8CAHJycmBrawtra2vExMSgtrYWDg4OPDLmzZuH77//Hg4ODkhISEB4eDjKyspgZGSEtLS0NtXN0dERCQkJKCsr40nPzMxEWlpap+uml5cXbt26hV9//RURERF48OABXFxcqPz58+cjKysLUVFRcHZ2xrRp0/Ds2bNOrWN3wdXVFW/evMHly5cRGBgIb29vXLhwAQD9XGzuueji4oLS0lJcvnwZe/bswZ49e3Do0KFWpH5jdGp852+Mrgqj/SVx+NQmknMEJOcIyOFTm/gmN+aPSOJ08S1xuviWxFw9QdisLL4c8Vd2UfVN+OMA3+pLwz/+/PNPIiQkREpKSqg0GxsbsmzZMnLv3j0iLi5OWCwWlWdgYEAOHOD8L//9918iLi5Ozpw500Sut7c3UVBQILW1tW2qh5mZGXF1deVJS0tLIwBIenr6J7Ts07G1tSVeXl7UeV1dHVFVVSXnz58nhBDCZDJJeHg4lR8SEkKMjY07tY7dhYCAACIsLEwAECaTSaVHREQQNTU1wmazqTRPT09ib29PCCFk+/btxNTUlMorKioiwsLC5Pnz54QQQs6dO0ekpKTIq1eveK7HZrPJ6NGjyeTJk9tUv8rKSiIhIUFOnjzJk753716iqqpKGhoa2tXez0VSUpLExsZS50+ePCEASElJCcnLyyNCQkLk5cuXVP7UqVPJ+vXrW5S3YMEC4u7u3qF1/hqpqqoiQkJCJC4ujkqbMWMGcXZ2pp+L/6fxc7GsrIwAIGlpaVT+smXLiI2NTafWsa10VX+SXkmg+SoREHivugLC0hCS7MWXQ0BYsgtbRdMWampqMHXqVDAYDCqNyWSiqqoKSUlJMDY2hqTk+/+jlZUVkpKSAHBm1A0MDJoNJuXl5YXw8HBUVVUB4KxYbN++Hbq6upCWlsbEiRORm5tLlXd0dERUVBTq6+uptOjoaOjr66N///5taouAgADi4+NhbGwMBQUFrF69GtnZ2bCwsIC0tDTGjRuH0tJSqj5btmxB3759IScnh9mzZ6OkpAQAICQkBGNjY0qusLAwZGVlkZOTg8LCQjx58gRWVlY89+T27duoqakBALx48QLW1taQlZVFnz59cPDgwTbV/1vE1dUVjx49wubNm3nSWSwWLC0tIST0PmaLoqIicnJyAABJSUk8/wN5eXkMHTqU0s2goCB4eHhAS0uLR66QkBAOHz6MpUuX8lzL1dUVKioqUFRUhLu7O6WHEhISsLW1xblz53jkREdHw97eHgICAmiN5ORk6Ojo4OzZs+jfvz80NDRw/Phx/PHHH2AymZCRkYG7uztVvry8HPPnz0fPnj2hrq6OTZs2AQBKS0uhoaGBIUOG8NwTAMjNzcX169fRu3dv9O3bl8pv/HsFgODgYPTr1w/KyspYtWoV2Gx2q/X/FqmrqwMhhOe5KC8vj5qaGvq5+H8aPxdLS0thaWkJXV1dKp/7HuESHx+PoUOHQkJCAsrKylixYsW3p3+dOiT5xqBXEjpuJSE28Ty1khCbeJ5vchP+OPBNryQ0sOtJTWllpx8N7PpPqi+bzSaPHz8mffv2Jb/88gtZt24dcXZ25ikTHBxMxo0bRwghZMKECcTHx6dNsoODg0nfvn1JbGwsSUlJIdOnTydGRkakpqaGEEJITk4OERQUJMnJydR3jI2N2yyfEEIAEENDQ5KUlER27NhBABANDQ0SGRlJTp8+TURERMju3bsJIYTs3LmTqKurk+joaBITE0MGDRpEXFxcmpV77do1AoDcvn2bPH36lAAgdXV1VH5JSQkBQLKyssi7d++Impoa8fT0JA8ePCDHjx8n8vLy1CpEZ9FVuvep+nf06FGelYQPqaioIIMHDyZLly4lhBAyfPhw8vPPP/OUmTJlCgkICCANDQ1EQkKCXL16tU3XnjJlCrG2tia3b98mV65cIYMGDSJr1qyh8n/77TciISFB3r17R9VFRESkzfKTkpKIhIQEsbe3J7dv3yZz584lPXr0ICNGjCA3btwgPj4+BAB59OgRIYSQSZMmETMzM3Lz5k1y5MgRIi0tTY4fP96sbH9/f8JgMEhVVRX56aefyOjRo3nyz507R7S1tQkhhBw7doyIi4uTAwcOkLt375KZM2cSYWHhTl9JqKtnkyxWSZccdfXs1iv4f8aNG0dmzJhBKisrSXp6OlFUVCQnTpygn4v/p/FzsTENDQ3kxYsXZOTIkeTHH38khHBWHWRlZYmrqyu5c+cOOXnyJBERESFHjhxpczv4SVf1J2nvRp3A9OnTISYm9tEybm5ucHNz66Qa0dA0Tx2rGmlBFzr9uoO+mwwR2fa7x+3ZsydKSkowZcoUzJo1C4mJiZCSkuIpIyUlRdnevn79GpMnT+bJ19LSwtu3b6nzQ4cOwdnZGbt27cL27dupTaCnTp2CmpoakpKSMH78eKiqqsLExASRkZEYM2YMCgoKcPfuXezdu7ddbViwYAHMzMxgZmaGXbt2wdbWlrLdDQsLQ3Y2x3PX7t27ERQUBDs7OwCcqLg//fRTE3mnTp3C0qVL4eDgACMjI9y6dQtiYmIQFn7/uOfeo+LiYly5cgWysrLYtm0bBAQEYGBggIKCAgQFBWHKlCntasvn0FW6B3y6/rXE8+fPMW/ePGRnZ1N7F0pLS1vUzYKCArx7944n0mxGRgbPDDxXbl1dHS5duoQXL15AQ0MDAHD06FGMHDkSmzdvhoSEBOzs7EAIQVxcHOzt7REXFwdJSUmYm5u3uQ3v3r1DQEAAmEwm5OXlceLECaxZswYmJiYYNmwYdu7ciezsbEhJSSEmJgavXr2CpqYmRo0ahYKCArx69YpHXnV1Nfz8/LB161bs2bMHYmJiH70nALBv3z6sXbsWS5YsAQAcO3aMZ5Whs8irqoBGxObWC3YAWU4/oJcko01lw8PDoaenh4iICBBCMHHiRDg7O2PJkiX0c/GD52JjDA0N8fDhQwwdOpTa2P/u3TusX78e7u7ukJSUxMiRIxEWFobMzMx2taMt/PTTT83WuTHV1fwKRNs+6EFCJ3DmzBkMHTq0q6tBQ9PtuHPnDv755x94eXnB09MTDAaDMu/gUlFRQS3Bq6qq8iyNA5wlZe7S+Pjx41FfX4+KigpkZWVh/vz5WLBgAVW2pqaGejkBnKX1HTt2YPfu3YiJiUGvXr1gaGjYrjaMHDmS+iwvL89zrqCgAIBjXpKVlcXzcrO0tISlpSV1/vbtW8ybNw9Xr17F8uXLsWvXLgAAg8FAdXU16urq0OP/G/4rKiqovL/++gsZGRmQkHjfSa6vr4eKikq72kHz3hRjw4YN0NPTw/Xr1ynzIQaDQd13LhUVFdDV1YWCggJERESQm5tLmWRoaWnh0aNHADheWMzNzUEIQXp6Ourq6njMJAghYLPZyMvLQ9++fSEpKYkJEybg3LlzsLe3R3R0NCZNmsQzUGwNCQkJMJlMAO/1kKuboqKiVKczPT0dampq0NTUpL67bt06Hln37t3DrFmzkJubi7CwMMp0qqV7wv29Pn/+HIGBgVSeiIgIz++D5j0lJSWwtrbGrFmzMGfOHGRmZmLnzp3YtGkT/Vxs5rnYmMjISLx+/RqbNm3CjBkzcObMGcjIyMDNzQ2xsbFIS0vDgwcPcOvWLR7Z/KItk8QPHjxo9z3kB/QggYaGhqKHlBgGfTe59YIdcN228u+//6KiogL9+/eHrq4u1VlycXHBf//7X9y/f5+nfE5ODpSVlQEA+vr6uHnzJk9+v379AHBedNwXKfflePHiRWq2lgtXFsDx5rFq1SqkpqZ+svcOQUHBj54DHHtjADz27o3JycnBiBEjICIigsTERJiZmVF5SkpKADg24NyOHLedSkpKYLPZsLOzQ1BQEI/Mlq7VUXSV7nGvzQ9cXV1x/PhxbNy4Ed7e3tSgDODc6w87alyPR0JCQtDR0cHNmzdhYWEBgNMh1tPTAwCeDhibzYaKigoSExObXL/xSoSjoyNWrFiBmpoaxMbGtnufSXN62JJufkxXEhISMGHCBJiamiI+Pp5nz0VL94T7G2tuUNOWPRX8RkVcGllOP3T6dbnXbgsxMTEQFRXl8Vqmq6sLe3t7fPfdd/Rz8YPnYmFhIf79918MGTIEWlpa0NLSQlhYGAYPHozKykpUVFTAzMwMffv2hbW1NTw8PKg9XN8S9MZlGhoaCgEhQYjISnT6ISDU9kdRTEwMZsyYwZPGYrHQo0cPmJub4/bt22Cx3gfFi4+Pp8wsli9fjqSkJMpdamN2795NbUpjMBhQUlJCcXEx9PT0oKenB2VlZWzbto1nY5uamhpGjRqF3377DVeuXGni0pJfyMnJoWfPnkhJSaHS0tLSMHz4cNTX12PNmjWQlZVFamoqz4sQ4GwUHThwIK5evUqlxcfHw8jICGJiYtDT08OLFy+gq6tLtfXu3bs85TuDrtK99upfS1y6dAlHjhzBxYsXsWHDBp4BAgCYm5vz3NOioiI8fPiQ+n+5u7sjODi4yYwuAOzYsYP6rKenh/z8fMjIyFD/r7KyMuzfvx8iIiJUOTs7O1RVVSEoKAiVlZWwtrb+7DY2h66uLrKzs3k6+4cPH8aSJUtQX1+PuXPnwsnJCVevXm2yKdvU1BRv3rzBy5cvqbTGv1c9PT0e86K6ujqe30BnISwoxDf33e09hAXbNlivqqpqMoASEBBAVVUVzMzM6OfiB8/FBw8ewMLCgmeDNYvFAiEEwsLCOH/+PAQEBHDp0iWsXr0a48ePbzauSXfnm1hJ+OWXXzBnzhw8e/aMmplpD7m5ufD19UVsbCwKCwuhqamJmTNn4vvvv291rwENDQ1/sbCwgLu7O/z9/WFvb4+srCx4e3tjzpw5GDZsGJhMJhYsWIB169YhMjISb968gZOTEwBg4MCBWL9+PSZPnoz169dTS8dnz57F1atXoa2tTV3Hw8MDa9euhZiYGOTl5REUFITs7OwmZjiOjo747rvvICMjAxMTkw5rt7u7Ozw9PSEpKQlRUVH4+/tTXmGioqKwceNGFBcX8/g+V1JSgrS0NJYtWwYfHx+oq6ujtrYWfn5+CA4OBgDMmTMHGzZsgLu7O1xcXPDnn3/Czc0Nv/zyS4e1pTsSGRmJoUOHol+/fjydXnFxcaipqWH27Nnw9/eHv78/xo8fD39/f5iamlLmRYsXL8bp06dhaGgIX19fDBs2DAUFBdizZw+EhISoDqC+vj4sLCwwc+ZMBAYGori4GJ6enrCxseGpj5SUFGxsbPDjjz9i4sSJEBcX75B2Dxw4EGPGjIGzszO2bNmCzMxMbNy4ET4+Prhz5w5yc3OxcOHCJnsUevfuDWVlZdjb28PFxQWBgYG4e/cu4uLiqGB/7u7ucHV1hYaGBoYOHYo9e/ZQPu5peLG1tYWXlxcWL14MZ2dnvHr1Cjt27ICTkxNGjhxJPxc/eC4OHz4cALBy5UosWbIExcXF8PLygqOjI0RFRSEpKYk3b94gOjoakpKSOHnyJB4/fgwdHR2UlZVBVla2w9r0RdGp26S7CDs7OwKAPHv2rN3fzczMJCoqKgQAAUAYDAb12djYmFRXV7f4Xdq7Ee3diKZjiIyMJEwmk4iLixNtbW2yadMmyo93bm4usbW1JQwGgxgbG5P79+83+X5ERAQxNzcnsrKyRF9fn7i5uZHy8nKyZMkScvToUUIIx3OSr68v0dLSIjIyMmTKlCkkMzOziaysrCwiICBAFi1a1O52AOCpH5PJpK5PCCHTp08nnp6eVH18fHyIpqYmkZOTI7NnzyaFhYUkPz+feiZ9eDSW5evrSzQ1NUmfPn1IUFAQTz3S0tKIubk5kZaWJtra2iQsLKzdbfnW+NC7ka2tbbP/A3Nzc6rM9evXyYgRI4icnByxt7cnRUVFPDLZbDYJCAgghoaGRFpamowYMYLs2LGDNDQ0EF1dXfL69WtCCCHFxcVkzpw5RFFRkSgrKxMPD49m30WnTp0iAMiJEyfa1bakpCQiJSVFnRcWFhIA1PUJIURRUZFcvHiRyp8xYwaRl5cn6urqZOPGjaShoYFERES0qJtcWRUVFWTWrFlEQUGBGBgYkEuXLvHUJTg4mGhra5OePXsSNzc38sMPP9BxElogJSWF+h336tWLrF69moqNQD8Xmz4Xk5OTyfDhw4mkpCTR1NQkHh4epLy8nBDC8W40f/58IiMjQ7S1tcnmzZtJdHQ0UVFRIRcuXGh3mz6XrupPChBCSEcPRLoKQggOHDiA5cuXA8AnrSRYWVkhPj4e48aNQ3h4OHr37o2HDx9iypQpyMrKwg8//ICAgIBmv8vdaJKamvrNblw+8osfbGo3AQAui2zCwtm+fJF7KSkKRys5/o8XSN7GBHP+eGH5I/Eg9DM5m+rSex/AWIslfJFLQ0NDQ0NDQ/MpdFV/slvuSYiPj8eiRYugp6dHDRA+hYcPHyI+Ph5KSko4ffo0evfuDQAwMDDA2bNnAXBctHWVayoaGhoaGhoaGhqajqBbDhJiYmJw+PBh/P33358lJzo6GgAwadIkyuUWl+HDh2PgwIEoLS1t4hWAhobm26asrAw9evT46HHlypWuribNN8iFCxda1c1v0YsLTcdDPxe/PrrlxuV169bx+PC1tbVt4matLdy+fRsAmmwI42JjY4OnT5/i9u3bGDdu3CfVlYaGpvshLS2NJ0+efLRMY3eVNDSdxdixY1vVzcZekmho+AX9XPz66JaDBDU1NaipqVHnn/rAe/HiBYD3/oI/hLvjv7EnCxoaGhpBQcFP8qRGQ9PRSElJ0bpJ0yXQz8Wvj25pbsQvCgoKAICKSvghcnJyAMATupyGhoaGhoaGhobma6dbriTwi3fv3gF4Pxj4EG46t1xLsFgslJeXf3Z9REVFISoq+tlyaGhoaGhoaGhoOpaamhq+7PFpHAivM6FXEj4C1ztsS15iuaHAG0fsaw4zMzPIysp+9hEYGMjfBtLQdAM2bNiASZMmtes7ycnJcHBwgLq6OhQVFWFhYYHLly+3+fv79++HrKwsamtrm+R5eHhgxIgR7arP50IIwc6dO9GvXz/IysrC2toaGRkZPGX8/PygpaUFbW1tKlgVzafz+vVrDBo0iCft/v37MDExgbS0NAYMGIBjx47x5N+4cQPGxsaQl5eHvb19sxFcDx06BEtLSzAYDGhoaGDmzJlIT09vc72cnJwwYcKEZvMGDx4MLy+vNsviBxUVFViyZAlUVVWhrKyMxYsX80yasVgszJkzB4qKijA0NERsbGyn1q87UVRUhDlz5kBeXh79+vXDvn37qLy8vDzY2dlBXl4eJiYmuHfvXpPvf2vPxby8PEydOhVycnLQ0tKCn58fGhoaWpR37NixJr/51ggMDORL/+/DiNGdBT1I+AgSEhIAgJKSkmbzuSsIkpKSH5WTnJyMsrKyzz7Wr1/P3wbS0Hzl/PnnnwgKCuJJMzU1hbi4OM/x+++/U/kHDx6EjY0N9PX1cfr0aVy4cAFDhgyBra1tk05dS0ybNg0sFgsJCQlN8qKjozF16tTPa1g7iYiIgL+/PwICApCQkAAlJSVYWVlR0Wn37t2L0NBQhIaGIjg4GNu2bcORI0c6tY7diYqKCnh7e/OksVgs2NrawsDAANeuXcOaNWuwfPlyXLx4EQCQk5MDW1tbWFtbIyYmBrW1tXBwcOCRMW/ePHz//fdwcHBAQkICwsPDUVZWBiMjI6SlpbWpbo6OjkhISEBZWRlPemZmJtLS0jpdN728vHDr1i38+uuviIiIwIMHD+Di4kLlz58/H1lZWYiKioKzszOmTZuGZ8+edWoduwuurq548+YNLl++jMDAQHh7e+PChQsAADs7O4iKiiI2NhajR4+GtbU1TxTib/G56OLigtLSUly+fBl79uzBnj17cOjQIb7WYf369Xzp/yUnJ/O1Xm2mU0O3dRFaWlqfFHFZW1v7oxHuDh06RACQOXPmNJtPR1wm5MjJDVQE41Mn1xA2K4svx9WrP5PFUQ/J4qiHJPaPs3yrLx1x+euBzWYTQ0NDoq+vT+zs7Kh0OTk5cubMGfLs2TPq4EbR/Pfff4m4uDg5c+ZME3ne3t5EQUGBitzcGmZmZsTV1ZUnLS0tjQAg6enpn9Gy9mNra0u8vLyo87q6OqKqqkrOn+dEI2cymSQ8PJzKDwkJIcbGxp1ax+5CQEAAERYWJgB4Ii5HREQQNTU1wmazqTRPT09ib29PCCFk+/btxNTUlMorKioiwsLC5Pnz54QQQs6dO0ekpKTIq1eveK7HZrPJ6NGjyeTJk9tUv8rKSiIhIUFOnjzJk753716iqqpKGhoa2tXez0VSUpLExsZS50+ePCEASElJCcnLyyNCQkLk5cuXVP7UqVPJ+vXrO7WO3YGqqioiJCRE4uLiqLQZM2YQZ2dncu/ePSIuLk5FXyaEEAMDA3LgAOcd9y0+F8vKyggAkpaWRuUvW7aM2NjYtCjvwyjrnUlX9SfplYSPoKOjAwBNlu25cF15ccvRNEUE7+3oLOqCkf+7Bl+Ogdmu8Cs2gF+xAXrUN7/SQ9O92b59O3r27AknJycqraioCCUlJbCysoKenh51SEtLA+DMqBsYGGDatGlN5Hl5eSE8PBxVVVUAOEvV27dvh66uLqSlpTFx4kTk5uZS5R0dHREVFcVjbhgdHQ19fX3079+/TW0QEBBAfHw8jI2NoaCggNWrVyM7OxsWFhaQlpbGuHHjUFpaStVny5Yt6Nu3L+Tk5DB79mxqlVNISAjGxsaUXGFhYcjKyiInJweFhYV48uQJrKysqHwrKyvcvn2bspWNj4/H0KFDISEhAWVlZaxYsQJsNrtNbfjWcHV1xaNHj7B582aedBaLBUtLS8oMFQAUFRUp99tJSUk8/wN5eXkMHToUSUlJAICgoCB4eHhAS0uLR66QkBAOHz6MpUuX8lzL1dUVKioqUFRUhLu7O6WHEhISsLW1xblz53jkREdHw97eHgICAq22MTk5GTo6Ojh79iz69+8PDQ0NHD9+HH/88QeYTCZkZGTg7u5OlS8vL8f8+fPRs2dPqKurY9OmTQCA0tJSaGhoYMiQITz3BAByc3Nx/fp19O7dG3379qXyraysqHsCAJcvX4ahoSEkJSUxbNgw3Lhxo9X6f4vU1dWBEMLjaEVeXh41NTVISkqCsbExj9VD4/v8LT4XS0tLYWlpCV1dXSqfyWRS7QTePxcZDAbs7OyQn5/fpvp3J+hBwkcwMjICgBaDe8TFxQEAjxLS0HzNsOsbkF1a1ekHu75lO9DmyMjIwM6dOxEWFsbT6fn7778hKSkJFxcX9OzZEwYGBvjll1+o/D///BMWFhbNypSTk8OUKVMgIyMDAAgJCUFYWBhCQkLwxx9/QEJCAg4ODpS9rYODA4qKiniCKUZHRzcxIWkNb29vbN26Fd9//z1CQkIwatQoeHh4IDw8HDdu3KCW+oODgxEaGoo9e/bg1KlTePLkCTw9PQFwAmTZ29tTMq9fv45nz57BwMCAerGpqqpS+dzPBQUFYLPZmD59OgwNDZGYmIhdu3YhPDwcJ0+ebFc7Ppeu0r326p+KigoGDhzYxJ/7woULecwyWCwWfvnlFxgYGAAA8vPzeVxzA5z/Q35+PgghePz4cYu6qaOjA1tbW+rc2dkZ2dnZOH/+PH799Vdcu3YN69ato/KnT5+OS5cuUR0eFouFpKSkdulmTk4OTp48iaNHj8LCwgKurq5Yv349Dhw4AA8PD/z000/4888/qfpkZmYiKioKW7Zswa5du3DixAkwGAykp6fztPvgwYNgMBjo06fPR+8JADx8+BAODg5wc3PDzZs3MXnyZEydOhWvXr1qczv4AalvQG3Zuy45SBt1U1paGhYWFti1axfevXuHZ8+e4ffff8fkyZNbvc/f4nNRU1MTV69ehYiICAghePnyJU6cOAFra2sAHNf2EyZMwNixY6kByw8//NCuNnQHaO9GH2HKlCnYtGkToqOjUVhYSM2AAMDdu3fx7NkzMBgMjBkzpgtr+fVwS3gx7Kdu5Ius6zd+g17uutYL0rSLvIoaaATEd/p1szZYohdDvE1lCSFYtGgRfH190bt3b568v//+GzU1NTA2Noa3tzeuXbuGBQsWQF5eHjY2Nnj9+jUmT57M8x0tLS0eN8aHDh2Cs7Mzdu3ahe3bt1ObQE+dOgU1NTUkJSVh/PjxUFVVhYmJCSIjIzFmzBgUFBTg7t272Lt3b7vavmDBApiZmcHMzAy7du2Cra0tZbsbFhaG7OxsAMDu3bsRFBQEOzs7AJz4Lz/99FMTeadOncLSpUvh4OAAIyMj3Lp1C2JiYhAWfv+4l5KSAgAUFxdDRkYG69evh7u7OyQlJTFy5EiEhYUhMzOzXe34XLpK94D26V9beP78OebNm4fs7Gxq70JpaSl137lISUmhuLgYBQUFePfuHc/AIyMjg2cGniu3rq4Oly5dwosXL6ChoQEAOHr0KEaOHInNmzdDQkICdnZ2IIQgLi4O9vb2iIuLg6SkJMzNzdvchnfv3iEgIABMJhPy8vI4ceIE1qxZAxMTEwwbNgw7d+5EdnY2pKSkEBMTg1evXkFTUxOjRo1CQUFBk458dXU1/Pz8sHXrVuzZswdiYmIfvScApwM4f/58ag/Df/7zH9y/fx+HDh3Cjz/+2Oa2fC51rGqkBV3otOs1ZtB3kyEiK9GmsuHh4dDT00NERAQIIZg4cSKcnZ2xZMmSj97nb/G52BhDQ0M8fPgQQ4cOpTb2HzhwAEZGRtixYwcAYNiwYUhNTcXff//drnZ87dArCQD09fWhr6+P8+fP86T/5z//gZWVFQoLCzFz5kxkZWUBAB48eIDp06cDAFavXg0xMbHOrvJXSY2AFIQke/HlYAs175aWpvsTFhaGhoYGrFixokne1KlTkZubi7Vr12LYsGH473//CxcXF2ozmqqqKs/SOMBZUn706BEePXoEJSUl1NfXo6KiAllZWZg/fz61+VlWVhbFxcXUywngLK1zzTpiYmLQq1cvGBoatqs9I0eOpD7Ly8vznCsoKADgzARnZWXxvNwsLS15TErevn0La2trzJ07F/Pnz6dWUBgMBqqrq1FXV0eVraiooPJkZGTg5uaG2NhYbNy4EXZ2drh161a72kDDgRCCbdu2YfDgwaiqqsL169cp8yEGg0Hddy4VFRVgMBhQUFCAiIgIj25qaWlRenn58mVUV1eDEIL09HTU1dVBV1eX0s1Ro0aBzWYjLy8PAMeZxoQJEyj9iI6OxqRJk3gGiq0hISEBJpMJ4L0ecnVTVFSU6nRyVwo0NTWp765btw4bN76fELp37x6YTCb27NmDsLAwrFy5stV7AgB//fUXwsPDeZwQxMbG8vwGaTiUlJTA2toas2bNwpUrV/Dzzz/jn3/+waZNm1q9z9/ic7ExkZGRSExMhLS0NGbMmAGAMyD/cFD9LU4I0ysJAOVJ4UNvEADw888/w8jICAkJCdDU1ASDwaBs4UxNTWmPQzTdChVpUWRtsOyS67aVlJQUpKSkUPa1bDYbDQ0NEBcXR2pqKgYMGMBTfvDgwTh+/DgAzoRA42Vw4H1E9ZqaGsp+nGtPe/HiRWq2louysjL1edq0aVi1ahVSU1M/2XuHoKDgR88BUB38xvbujcnJycGIESMgIiKCxMREHnd5SkpKADg24NyOHLedSkpKyMvLg5mZGfr27Qtra2t4eHjwxa93e+kq3eNemx+4urri+PHj2LhxI7y9vdGjRw8qT0lJibrvXLgej4SEhKCjo4ObN29SZh8iIiJUdNrGHTA2mw0VFRUkJiY2uX7jlQhHR0esWLECNTU1iI2NxcGDB9vVlub0sCXdbEkvASAhIQETJkyAqakp4uPjefZctHRPuL8xNpuNTZs2NbGV5+4x6ix6SIlh0HeTWy/YQdduCzExMRAVFeXxWqarqwt7e3t89913uH//Pk/5xvf5W3wuFhYW4t9//8WQIUOgpaUFLS0thIWFYfDgwaisrGx2QN2W/TzdDXqQ0AqamppITU2Fr68vYmJiUFRUBB0dHcyZMwfe3t4QERHp6irS0PANYSFBvppddARc135c9u3bhydPniAsLAxhYWGoqqpCWFgYlZ+RkUE5F1i+fDmGDBmCuLg4yvaUy+7du6nNugwGA0pKSiguLoalJafjWlpairVr12Lz5s3UDJyamhpGjRqF3377DVeuXEFMTEyHtFlOTg49e/ZESkoK1clKS0uDi4sL7ty5gzVr1kBWVhY3btxoEvxRUVERAwcOxNWrV7Fo0SIAnFlCIyMjiImJ4fz58xAQEMClS5eo73zo3rMz+Bp072NcunQJR44cwaVLl5roFgCYm5sjKiqKmmEvKirCw4cPqY6Lu7s7fHx8sGjRIp79IwAokwcA0NPTQ35+PmRkZKhyKSkpOHXqFEJCQqhydnZ2WLhwIYKCglBZWdlsnfiBrq4usrOzkZOTQ9m9Hz58GHfu3EFoaCjmzp0LJycnHD9+vElHz9TUFG/evMHLly+hra0NgKOb3BlcPT09ZGdnU4MlAPj+++9hZWXVxMa+IxEQEmyzyU9XUVVV1aQTKyAggKqqKpiZmcHX1xcsFotaAYqPj8eyZcsAfJvPxQcPHmDmzJkoKCigBhksFguEEAgLC0NPT49nAz2AJgOpb4FvYpDQ2iYn0kKwNC6qqqrtnoWhoaHpGFRUVKCiokKdKyoqQkpKCnp6epg0aRKsrKygqakJGxsbpKSk4Oeff6ZmXQcOHIj169dj8uTJWL9+PfWiO3v2LK5evUp1VABO8J+1a9dCTEwM8vLyCAoKQnZ2Ns+1Ac6M7XfffQcZGRmYmJh0WLvd3d3h6ekJSUlJiIqKwt/fn/IKw+18FhcX8/g+V1JSgrS0NJYtWwYfHx+oq6ujtrYWfn5+CA4OBsAxTXnz5g2io6MhKSmJkydP4vHjx9DR0UFZWRlkZWU7rE3dicjISAwdOhT9+vXDy5cvqXRxcXGoqalh9uzZ8Pf3h7+/P8aPHw9/f3+YmppSHl8WL16M06dPw9DQEL6+vhg2bBgKCgqwZ88eCAkJUR1AfX19WFhYYObMmQgMDERxcTE8PT1hY2PDUx8pKSnY2Njgxx9/xMSJEyEu3jEDsIEDB2LMmDFwdnbGli1bkJmZiY0bN8LHxwd37txBbm4uFi5c2OQ93Lt3bygrK8Pe3h4uLi4IDAzE3bt3ERcXRwX78/DwwLhx4zBw4EAYGRkhIiIC+/fvx/LlyzukLV8ztra28PLywuLFi+Hs7IxXr15hx44dcHJywsiRI8FkMrFgwQKsW7cOkZGRePPmDeUZ7lt8Lg4fPhwAsHLlSixZsgTFxcXw8vKCo6MjREVFsWTJEuzYsQNr167FnDlzEB8fj/j4eKirq3dYW75IOtXh6jcGHSeBkFPHV1FxB34PdyW1RVl8ORKitlFyr8Yd5Ft96TgJXx+bNm3iiZNw5MgRoqOjQyQkJIihoSG5dOlSk+9EREQQc3NzIisrS/T19YmbmxspLy8nS5YsIUePHiWEcPzT+/r6Ei0tLSIjI0OmTJlCMjMzm8jKysoiAgICZNGiRe2uOwBy//596pzJZFLXJ4SQ6dOnE09PT6o+Pj4+RFNTk8jJyZHZs2eTwsJCkp+fTwA0ezSW5evrSzQ1NUmfPn1IUFAQlV5XV0fmz59PZGRkiLa2Ntm8eTOJjo4mKioq5MKFC+1u07fChz7TbW1tm/0fmJubU2WuX79ORowYQeTk5Ii9vT0pKirikclms0lAQAAxNDQk0tLSZMSIEWTHjh2koaGB6OrqktevXxNCCCkuLiZz5swhioqKRFlZmXh4eJDq6uomdTx16hQBQE6cONGutiUlJREpKSnqvLCwkACgrk8IIYqKiuTixYtU/owZM4i8vDxRV1cnGzduJA0NDSQiIqJF3eTKqqioILNmzSIKCgrEwMCgye/1zJkzZNCgQURCQoKMGDGCJCcnt6st3xIpKSnE3NycSEtLk169epHVq1dTsRFyc3OJra0tYTAYxNjYmOe5w+Vbey4mJyeT4cOHE0lJSaKpqUk8PDyomDqEEJKQkEAMDAyIjIwMsbGxIREREd9cnAQBQlqZRqf5ZB48eABDQ0OkpqZi6NChXV2dLiHi8BKYCnI2jZbeAgifTJ0FRAHGKM7nv+S2YdwU/ng6+iPxIPQzOf7I03sfwFiLJXyRS0NDQ0NDQ0PzKXRVf5L2bkTTodQJdLznpxpC7wuhoaGhoaGhoeEn38SehK5m+vTprbpJdXNzg5ubWyfVqGv4ZfQJ5Ioz+SJLteoJVjTM5YssGhp+U1ZWxhNXpTliYmIwfvz4TqoRDQ2HCxcuNBtZtzEsFguiovzx+ERDw4V+LjbPTz/91Gxsh8ZUV1d3Um14oQcJncCZM2e+WXOjegA5hONNgVH/J5zGTuCL3DvJScgp48itpy3maL4wpKWl8eTJk4+W+TBiLw1NZzB27NhWdZP22kfTEdDPxeZpyyQx19yos6EHCTQdSimAYVj6/zMCXAjko3SO3GOkno8yaWg+H0FBQR63jTQ0XwpcT2A0NJ0N/Vz8+qD3JNB0KJ0xyU8PEWhoaGhoaGho+Au9kkDTobDqhIH/r1zrZykjvbIPX+TqS75CusZbAEBFDW1uRENDQ0NDQ0PDT+iVBJoOpRrvO/A92EIAW5QvRw+2UKNrsLuiaTRfCBs2bMCkSZPa9Z3k5GQ4ODhAXV0dioqKsLCwwOXLl9v8/f3790NWVha1tbVN8jw8PDBixIh21edzIYRg586d6NevH2RlZWFtbY2MjAyeMn5+ftDS0oK2tjYVrKoljh07hkGDBnVklb96Xr9+3eQexcbGYsiQIZCSksLw4cNx7do1nvwbN27A2NgY8vLysLe3R1FRURO5hw4dgqWlJRgMBjQ0NDBz5kykp6e3uV5OTk6YMKH5vV+DBw+Gl5dXm2Xxg4qKCixZsgSqqqpQVlbG4sWLUV5eTuWzWCzMmTMHioqKMDQ0RGxsbKfWrzvSnG7m5eXBzs4O8vLyMDExwb1793jyad1sqpspKSkwMjKCtLQ0mEwmzp4926n1+xKgBwk0nUfP14Debf4cPV93dWtovgD+/PNPBAUF8aSZmppCXFyc5/j999+p/IMHD8LGxgb6+vo4ffo0Lly4gCFDhsDW1hbHjh1r03WnTZsGFouFhISEJnnR0dGYOnXq5zWsnURERMDf3x8BAQFISEiAkpISrKysUFlZCQDYu3cvQkNDERoaiuDgYGzbtg1Hjhzp1Dp2JyoqKuDt7c2Tlp2djalTp2LmzJm4ceMGLC0tMWnSJOTn5wMAcnJyYGtrC2tra8TExKC2thYODg48MubNm4fvv/8eDg4OSEhIQHh4OMrKymBkZIS0tLQ21c3R0REJCQkoKyvjSc/MzERaWlqn66aXlxdu3bqFX3/9FREREXjw4AFcXFyo/Pnz5yMrKwtRUVFwdnbGtGnT8OzZs06tY3eiOd0EADs7O4iKiiI2NhajR4+GtbU1FYWY1s2mullZWQk7OzuMHDkS165dw+LFizFjxgw8ffq0U+vY5XRq6LZvDDriMiE/hK0mOLyW4PBa4hm6imSxSvhybDy5mZIb9Esg3+pLR1z+emCz2cTQ0JDo6+vzRFyWk5MjZ86cIc+ePaMObhTNf//9l4iLi5MzZ840keft7U0UFBRIbW1tm65vZmZGXF1dedLS0tIIAJKenv4ZLWs/tra2xMvLizqvq6sjqqqq5Pz584QQTsTS8PBwKj8kJIQYGxu3KO/DaMI07wkICCDCwsIEAM89CgkJIYaGhtR5TU0NkZGRIb/99hshhJDt27cTU1NTKr+oqIgICwuT58+fE0IIOXfuHJGSkiKvXr3iuR6bzSajR48mkydPblP9KisriYSEBDl58iRP+t69e4mqqippaGhoV3s/F0lJSRIbG0udP3nyhAAgJSUlJC8vjwgJCZGXL19S+VOnTiXr16/v1Dp2F1rSzXv37hFxcXEq+jIhhBgYGJADBzjvOFo3OTTWzaioKKKkpETYbDaV379/f7J169ZOrSOXrupP0isJNJ2GmIAAekky+HJICgq1fkGabs327dvRs2dPODk5UWlFRUUoKSmBlZUV9PT0qENaWhoAZ0bdwMCgWT/xXl5eCA8PR1VVFQCOCc/27duhq6sLaWlpTJw4Ebm5uVR5R0dHREVFob7+/db56Oho6Ovro3///m1qg4CAAOLj42FsbAwFBQWsXr0a2dnZsLCwgLS0NMaNG4fS0lKqPlu2bEHfvn0hJyeH2bNno6SkBAAgJCQEY2NjSq6wsDBkZWWRk5ODwsJCPHnyBFZWVlS+lZUVbt++jZoaTgj0+Ph4DB06FAwGA3Z2dtTsN01TXF1d8ejRI2zevJknXUJCAvb29tS5iIgIdHV1KX1KSkri+R/Iy8tj6NChSEpKAgAEBQXBw8MDWlpaPHKFhIRw+PBhLF26lEpjsVhwdXWFiooKFBUV4e7uTumhhIQEbG1tce7cOR450dHRsLe3h4CAQKttTE5Oho6ODs6ePYv+/ftDQ0MDx48fxx9//AEmkwkZGRm4u7tT5cvLyzF//nz07NkT6urq2LRpEwCgtLQUGhoaGDJkCFWW6yc/NzcX169fR+/evdG3b18q38rKironABASEoK+fftCTEwM2tra2L9/f6v1/1ZpSTeTkpJgbGwMSUlJKq3xfaZ1k0Nj3RQQEICTkxOEhN73NZhMJvV7Br4N3aQ3LtPQ0FCQejbYZXmdfl1hWRUICLX9cZSRkYGdO3fi/v37OHr0KJX+999/Q1JSEi4uLkhOTkavXr2wbt06zJ49GwDHPMnCwqJZmXJycpgyZQp1HhISgrCwMOzbtw+KiorYtm0bHBwckJycDBERETg4OMDDwwM3b97EmDFjAHBedh8u07eGt7c31RZPT09ERkYiJCQEdXV1mDt3Lo4dO4ZVq1YhODgYoaGhCAsLg6CgILy9veHp6Ynw8HBcuHCBR+b169fx7NkzGBgYUB1+VVVVKp/7uaCgADU1NZgwYQJWrVqFgwcPIi4uDj/88AN0dXXb1Y7Ppat0D2if/qmoqEBFRQX379/nSXd1daU+19bWIi4uDhkZGZRu5OfnQ01Njec7qqqqyM/PByEEjx8/RkBAQLPX1NHRgY6ODnXu7OyM6upqnD9/HhUVFVi7di3WrVuHXbt2AeAE8HRxcUFVVRXExcXBYrGQlJSEdevWtamNAMcE5eTJkzh69Cj2798PV1dXGBgY4MCBA7h06RK2bNmCxYsXY8iQIXB2dkZ5eTmioqKQkZEBDw8PaGtrY+7cuU1s1g8ePAgGg4E+ffogMTGxxXsCAPfu3cPq1asREhICY2NjREVFwc3NDba2tk06rB0Ju74BeRU1nXa9xqhIi0JYqG3zuS3pZku6l5qa+tH8b1k39fX1qb1ubDYbKSkpSExMxKpVqwB8ObrZ0dCDBBoaGgp2WR7+XqPR6dfVCc5CD/m2BdEhhGDRokXw9fVF7969efL+/vtv1NTUwNjYGN7e3rh27RoWLFgAeXl52NjY4PXr15g8eTLPd7S0tPD27Vvq/NChQ3B2dsauXbuwfft2aqPdqVOnoKamhqSkJIwfPx6qqqowMTFBZGQkxowZg4KCAty9exd79+5tV9sXLFgAMzMzmJmZYdeuXbC1taVsc8PCwpCdnQ0A2L17N4KCgmBnZweAM1PdXJTOU6dOYenSpXBwcICRkRFu3boFMTExCAu/f9xLSXECERYXF+PkyZMwMjLCjh07AADDhg1Damoq/v7773a143PpKt0D2qd/rfH48WNqdjI4OJiaJS8tLaXuOxcpKSkUFxejoKAA79694wkklZGRwTPLCQDPnz9HXV0dLl26hBcvXkBDg3O/jh49ipEjR2Lz5s2QkJCAnZ0dCCGIi4uDvb094uLiICkpCXNz8za34927dwgICACTyYS8vDxOnDiBNWvWwMTEBMOGDcPOnTuRnZ0NKSkpxMTE4NWrV9DU1MSoUaNQUFCAV69e8cirrq6Gn58ftm7dij179kBMTOyj9wQAxMXFcfDgQSxevBgAMGjQIAQFBSEzM7NTO2J5FTXQCIjvtOs1JmuDJXoxxD9LRmv3mdbNprrJpaysDHJyciCEYNWqVRg9ejSAL0c3Oxp6kEDTwbxfPmyAIIqq+BPVoK6hsVyab4mwsDA0NDRgxYoVTfKmTp2KCRMmUMvGw4YNQ0ZGBg4dOgQbGxuoqqrymAwBHFMb7nL4+PHjUV9fj4qKCmRlZWH+/PlYsGABVbampobqtAMck6MdO3Zg9+7diImJQa9evdodFXPkyJHUZ3l5eZ5zBQUFAJwl/KysLBgZGVF5lpaWsLS0pM7fvn2LefPm4erVq1i+fDk1c8dgMFBdXY26ujr06NEDAGdzIzfv+fPnTV7QY8aM6fRBQndBT08PaWlpePjwIdasWQMtLS3Y29uDwWBQ951LRUUFdHV1oaCgABEREeTm5lKmalpaWnj06BEAjmcac3NzEEKQnp6Ouro6npUeQgjYbDby8vLQt29fSEpKYsKECTh37hzs7e0RHR2NSZMm8QwUW0NCQgJMJhPAez3k6qaoqCjVqUxPT4eamho0NTWp7344K3zv3j3MmjULubm5CAsLo8xTWronDAYDAMe8Q05ODuHh4Xj27BmSkpLAZtPe7NoLg8FATk4OT1rj+0zrZlPd5MKNEv3s2TOsWbMG2traWLly5Tejm/QggaZDaWg0SCgVlMeKP5q6VfsUZKpkAKECAEB9Q+t2jDRtQ1hWBTrBWV1y3baSkpKClJQUyr6WzWajoaEB4uLiSE1NxYABA3jKDx48GMePHwcA6Ovr4+bNmzz5/fr1A8AZAHBfpNxBw8WLF6kZMS7KysrU52nTpmHVqlVITU39ZK9GgoKCHz0HgLq6OgDgsY9tTE5ODkaMGAEREREkJibCzMyMylNSUgLAsbPlviy57VRSUmr25dwW22B+01W6x73255Keng5ZWVmoqamByWSCyWTi6dOniIiIgL29PZSUlJp01LheZYSEhKCjo4ObN29S5nAiIiJUdNrGA1M2mw0VFRUkJiY2qUPj2V5HR0esWLECNTU1iI2NxcGDB9vVnub0sCXdbEkvASAhIQETJkyAqakp4uPjeWZZW7on3N9YcnIypk2bhsmTJ8PIyAiOjo4YO3Zsu9rBD1SkRZG1wbL1gh107c9FSUmpiQlS4/tM62ZT3fznn38gICCAPn36YMCAARgwYADy8/Nx/PhxrFy58ovRzY6GHiR0AtOnT+dZvmoONzc3uLm5dVKNOhOCxqsJHXUFGv4gICTMN7OLjiIwMJDHxd++ffvw5MkThIWFISwsDFVVVQgLC6PyMzIyKJvZ5cuXY8iQIYiLi4O1tTWP3N27d1MzQQwGA0pKSiguLqZm60tLS7F27Vps3ryZmoFTU1PDqFGj8Ntvv+HKlSuIiYnpkDbLycmhZ8+eSElJoV5kaWlpcHFxwZ07d7BmzRrIysrixo0bkJOT4/muoqIiBg4ciKtXr2LRokUAOKsnRkZGEBMTg56eHs9GUQBNBlKdwdegex8jICAAPXv2REhICJXGYrGo1Rtzc3NERUVh48aNADib7B8+fEgN6Nzd3eHj44NFixbx7B8BQJmCAZyVivz8fMjIyFDlUlJScOrUKZ5r29nZYeHChQgKCkJlZWUTfecXurq6yM7ORk5ODmXXfvjwYdy5cwehoaGYO3cunJyccPz48SYdOVNTU7x58wYvX76EtrY2AI5ucle2QkNDMX36dOr3XFpayrNxtLMQFhL8bJOfrsTc3BwbNmwAi8WiZtnj4+OxbNkyKp/WTV7d/Pnnn/H06VNERUVRaY1/z/zUzZ9++qlZ09HGVFdXf5Lsz4UeJHQCZ86cwdChQ7u6Gl0CaXjfhZdklcH4z6t8kftE7C3w/75QQy1tcPQtwd2cx0VRURFSUlLQ09PDpEmTYGVlBU1NTdjY2CAlJQU///wzNbM1cOBArF+/HpMnT8b69eupAcDZs2dx9epVqqMCcIKirV27FmJiYpCXl0dQUBCys7N5rg1wZsW+++47yMjIwMTEpMPa7e7uDk9PT0hKSkJUVBT+/v6UvTv3BV9cXEzZGQOcGUJpaWksW7YMPj4+UFdXR21tLfz8/BAcHAwAWLJkCXbs2IG1a9dizpw5iI+PR3x8PNTV1TusLd0Ra2trrFy5EiNHjsSgQYNw9+5dHD58mPLkMnv2bPj7+8Pf3x/jx4+Hv78/TE1NKROOxYsX4/Tp0zA0NISvry+GDRuGgoIC7NmzB0JCQtTqjr6+PiwsLDBz5kwEBgaiuLgYnp6esLGx4amPlJQUbGxs8OOPP2LixIkQF++YTu7AgQMxZswYODs7Y8uWLcjMzMTGjRvh4+ODO3fuIDc3FwsXLmxiB967d28oKyvD3t4eLi4uCAwMxN27dxEXF0cF+5OUlMSdO3dw48YN5OXlYe/evRAQEMD9+/cxatQoqsNG83GGDRsGJpOJBQsWYN26dYiMjMSbN28oz3C0bjbVTWtra2zbtg2hoaEwNTXF06dPERQUhC1btgDgr262ZZL4wYMH7TZl5Qud6nD1G4OOk0CI509rqHgGsyJvEKeLb/lyzIq8Qcn1OeRD2KwsvhwJV7a9j5OQENrVt4+mDWzatIknTsKRI0eIjo4OkZCQIIaGhuTSpUtNvhMREUHMzc2JrKws0dfXJ25ubqS8vJwsWbKEHD16lBDC8QHu6+tLtLS0iIyMDJkyZQrJzMxsIisrK4sICAiQRYsWtbvuAMj9+/epcyaTSV2fEEKmT59OPD09qfr4+PgQTU1NIicnR2bPnk0KCwtJfn4+AWdBrcnRWJavry/R1NQkffr0IUFBQTz1SEhIIAYGBkRGRobY2NiQiIgIOk5CK3wYS6KhoYH4+fmR3r17EwkJCTJkyBAqRgKX69evkxEjRhA5OTlib29PioqKePLZbDYJCAgghoaGRFpamowYMYLs2LGDNDQ0EF1dXfL69WtCCCHFxcVkzpw5RFFRkSgrKxMPDw9SXV3dpI6nTp0iAMiJEyfa1bakpCQiJSVFnRcWFhIA1PUJIURRUZFcvHiRyp8xYwaRl5cn6urqZOPGjaShoYFERES0qJtcWRUVFWTWrFlEQUGBGBgY8Pxe37x5Q8zMzIikpCQxMDAg58+fJ35+fkRZWZnk5ua2q03fEs3FOcnNzSW2traEwWAQY2NjnucOIbRuNqebYWFhpF+/fkRcXJz079+f7N27l7pGZ+tmV/UnBQghtLVGB8Ed+aWmpn6zKwlr9/8Xu8Q5y3izGFNRL6LTyjfahlDt3/i1lDNDt+XdayyU5E+49LdQgjI47veeKWzAaJMlfJHbmPa6+6ShoaGhoaH5dumq/iTdU6HpUN412jHwPO8i0ntI80Wufl0F8P9tHiwihvo6eb7IbRCQBoQ5g4TyGxG4Hsn/4CjG2x9CXLFrXD3S0NDQ0NDQ0LQFepBA06GICtQC4Ngb1qEcVUJ1fJFbV1dFyRWq08DbnI9v+mkrxULFUNXm2AYux3fIlVfki9zG/MOqQx/+i6X5gigrK6PcsLZETEwMxo8f30k1oqHhcOHChWYjjjeGxWJBVPTzverQ0LQHWje/POhBAk2HItLIb/CK3oUYo1fGF7nXnsti+VvObLww6Zpd/58Ku4E/sSJovly4vrU/RmOXgDQ0ncXYsWNb1U0REZFOqg0NzXto3fzyoAcJNB3Me89D9WmFELzEH9eK9b1MACXOIKGgRy3W9P6LL3LNBFlg/v/z0D4piBGU+2j5tiJfyUZxtikAoLjmHV9k0ny5CAoKUn7EaWi+JLiewGhovjRo3fzyoAcJNB2KgMD7PQmlMqqAgAxf5JZJvXdD2SCmCFU1/sSYeFufAxRzXEOKiwoDqOWLXEFh/phZ0dDQ0NDQ0NB0BvQggaZDERd9v5IwdXwhVCcP4otc++oi+FzjBGuRFKkDf+I48zJbQQ3B45bzRdafGX/B7nVH1JKGhoaGhoaGhv/QgwSar54e7DJIVuXxRdbAqsvUZ2mhaig3vOGLXHmBAgAcV7CkgV5VoKGhoaGhofmyEWy9CA0Nf7h4UxnSKr/x5bicotVIsgAqxVX4cgjg/abiPmpP8a58DV8OWbHDlFxS92/n3fRvgA0bNmDSpEnt+k5ycjIcHBygrq4ORUVFWFhY4PLly61/8f/s378fsrKyqK1tao7m4eGBESNGtKs+nwshBDt37kS/fv0gKysLa2trZGRk8JTx8/ODlpYWtLW1qYi2NJ/O69evMWgQ78pobGwshgwZAikpKQwfPhzXrl3jyb9x4waMjY0hLy8Pe3t7FBU1XV08dOgQLC0twWAwoKGhgZkzZyI9Pb3N9XJycsKECROazRs8eDC8vLzaLIsfVFRUYMmSJVBVVYWysjIWL16M8vJyKp/FYmHOnDlQVFSEoaEhYmNjPypv4cKFWLlyZUdX+6umOd3My8uDnZ0d5OXlYWJignv37vHk07rZVDdTUlJgZGQEaWlpMJlMnD378XhM3VE36UECTafxrkYIgkI9+XLU1glRcqWECfaPVeDL0Uf2fSh1gdoqNFRX8+UQqKmCqnAhVIULAUJ7N+IXf/75J4KCgnjSTE1NIS4uznP8/vvvVP7BgwdhY2MDfX19nD59GhcuXMCQIUNga2uLY8eOtem606ZNA4vFQkJCQpO86OhoTJ069fMa1k4iIiLg7++PgIAAJCQkQElJCVZWVqisrAQA7N27F6GhoQgNDUVwcDC2bduGI0eOdGoduxMVFRXw9vbmScvOzsbUqVMxc+ZM3LhxA5aWlpg0aRLy8zlxV3JycmBrawtra2vExMSgtrYWDg4OPDLmzZuH77//Hg4ODkhISEB4eDjKyspgZGSEtLS0NtXN0dERCQkJKCvj9SSXmZmJtLS0TtdNLy8v3Lp1C7/++isiIiLw4MEDuLi4UPnz589HVlYWoqKi4OzsjGnTpuHZs2edWsfuRHO6CQB2dnYQFRVFbGwsRo8eDWtraxQXFwOgdbM53aysrISdnR1GjhyJa9euYfHixZgxYwaePn3aqXXscjo1vvM3BjeMdp8+fYi+vv5Hj3379nV1dTsE34MLCQ6vJTi8lvgeXMg3uYHHVlByA4+t4Jvc6OhQknMEHXrcvfsz3+r7LcNms4mhoSHR19cndnZ2VLqcnBw5c+YMefbsGXWUl5cTQgj5999/ibi4ODlz5kwTed7e3kRBQYHU1ta26fpmZmbE1dWVJy0tLY0AIOnp6Z/RsvZja2tLvLy8qPO6ujqiqqpKzp8/TwghhMlkkvDwcCo/JCSEGBsbd2oduwsBAQFEWFiYACBMJpNKDwkJIYaGhtR5TU0NkZGRIb/99hshhJDt27cTU1NTKr+oqIgICwuT58+fE0IIOXfuHJGSkiKvXr3iuR6bzSajR48mkydPblP9KisriYSEBDl58iRP+t69e4mqqippaGhoV3s/F0lJSRIbG0udP3nyhAAgJSUlJC8vjwgJCZGXL19S+VOnTiXr169vUd6CBQuIu7t7h9b5a6Ul3bx37x4RFxcnLBaLSjMwMCAHDhwghNC6yaWxbkZFRRElJSXCZrOp/P79+5OtW7e2KO9TdXPfvn2t9hH79OlDAJDU1NR2y/8c6JWETuDMmTP466+/Pnq4ufHHOw/N5yLUepHPhDS0XoamdbZv346ePXvCycmJSisqKkJJSQmsrKygp6dHHdLSnEjfe/fuhYGBQbMBe7y8vBAeHo6qqioAHBOe7du3Q1dXF9LS0pg4cSJyc3Op8o6OjoiKikJ9/fuVoejoaOjr66N///5taoOAgADi4+NhbGwMBQUFrF69GtnZ2bCwsIC0tDTGjRuH0tJSqj5btmxB3759IScnh9mzZ6OkpAQAICQkBGNjY0qusLAwZGVlkZOTg8LCQjx58gRWVlZUvpWVFW7fvo2amhoAwIsXL2BtbQ1ZWVn06dMHBw8ebFP9v0VcXV3x6NEjbN68mSddQkIC9vb21LmIiAh0dXUpfUpKSuL5H8jLy2Po0KFISkoCAAQFBcHDwwNaWlo8coWEhHD48GEsXbqUSmOxWHB1dYWKigoUFRXh7u5O6aGEhARsbW1x7tw5HjnR0dGwt7eHgIBAq21MTk6Gjo4Ozp49i/79+0NDQwPHjx/HH3/8ASaTCRkZGbi7u1Ply8vLMX/+fPTs2RPq6urYtGkTAKC0tBQaGhoYMmQIVZYbZDA3NxfXr19H79690bdvXyrfysqKuicAEBwcjH79+kFZWRmrVq0Cu1HsHRpeWtLNpKQkGBsbQ1JSkkprfJ9p3eTQWDcFBATg5OQEIaH3fQImk0n9ngH+6aabm1urfcQzZ858kuzPhd64TEPTiFpBBnzlHwIAlKue4a142zp7raFU8Qfc6tYCAN59wYOE+gaC0prOryBDVBBCgq2/ILhkZGRg586duH//Po4ePUql//3335CUlISLiwuSk5PRq1cvrFu3DrNnzwbAMU+ysLBoVqacnBymTJlCnYeEhCAsLAz79u2DoqIitm3bBgcHByQnJ0NERAQODg7w8PDAzZs3MWbMGACcl92Hy/St4e3tTbXF09MTkZGRCAkJQV1dHebOnYtjx45h1apVCA4ORmhoKMLCwiAoKAhvb294enoiPDwcFy5c4JF5/fp1PHv2DAYGBpS5i6qqKpXP/VxQUAAFBQWYmZlh9uzZ2Lp1K548eYLVq1dDWVmZ5350NF2le0D79E9FRQUqKiq4f/8+T7qrqyv1uba2FnFxccjIyKB0Iz8/H2pqajzfUVVVRX5+PgghePz4MQICApq9po6ODnR0dKhzZ2dnVFdX4/z586ioqMDatWuxbt067Nq1CwAwffp0uLi4oKqqCuLi4mCxWEhKSsK6deva1EaAY4Jy8uRJHD16FPv374erqysMDAxw4MABXLp0CVu2bMHixYsxZMgQODs7o7y8HFFRUcjIyICHhwe0tbUxd+7cJjbrBw8eBIPBQJ8+fZCYmNjiPQGA48ePw8fHB7t378Z//vMfBAcH48yZM1i2bFmb28EPSD0b7DL+OMdoL8KyKhAQaltXrSXdbEn3UlNTP5r/Leumvr4+tdeNzWYjJSUFiYmJWLVqFYAvRzc7GnqQQAMAIKQepKGY73Ibx0n4KhAQQpkQ52EpIVhEff5cxATk+SKnoymtacCKPzrfVev+sQpQEG/bKg4hBIsWLYKvry969+7Nk/f333+jpqYGxsbG8Pb2xrVr17BgwQLIy8vDxsYGr1+/xuTJk3m+o6Wlhbdv31Lnhw4dgrOzM3bt2oXt27dTG+1OnToFNTU1JCUlYfz48VBVVYWJiQkiIyMxZswYFBQU4O7du9i7d2+72r5gwQKYmZnBzMwMu3btgq2tLWWbGxYWhuzsbADA7t27ERQUBDs7OwCcmeqffvqpibxTp05h6dKlcHBwgJGREW7dugUxMTEIC79/3EtJSQEAiouLceXKFcjKymLbtm0QEBCAgYEBCgoKEBQU1KmDhK7SPaB9+tcajx8/pmYng4ODqVny0tJS6r5zkZKSQnFxMQoKCvDu3TueKNwZGRk8s5wA8Pz5c9TV1eHSpUt48eIFNDQ4ASWPHj2KkSNHYvPmzZCQkICdnR0IIYiLi4O9vT3i4uIgKSkJc3PzNrfj3bt3CAgIAJPJhLy8PE6cOIE1a9bAxMQEw4YNw86dO5GdnQ0pKSnExMTg1atX0NTUxKhRo1BQUIBXr17xyKuuroafnx+2bt2KPXv2QExM7KP3BAD27duHtWvXYsmSJQCAY8eO8awydBbssjz8vUaj068LADrBWegh/3nR2Vu7z7RuNtVNLmVlZZCTkwMhBKtWrcLo0aMBfDm62dF020FCbm4ufH19ERsbi8LCQmhqamLmzJn4/vvveRSgLeTn58PX1xc3b97Eq1evoKenh7Fjx2LDhg2UGcPXDmkoRkXeTL7LFRVRA76ifboigoQKEj1GtBijhkvwRe79tFog6/8n5CsbOH1hhIWFoaGhAStWrGiSN3XqVEyYMIFaNh42bBgyMjJw6NAh2NjYQFVVlcdkCADi4+Op5fDx48ejvr4eFRUVyMrKwvz587FgwQKqbE1NDdVpBzgmRzt27MDu3bsRExODXr16wdDQsF3tGTlyJPVZXl6e51xBQQEAZwk/KysLRkZGVJ6lpSUsLS2p87dv32LevHm4evUqli9fTs3cMRgMVFdXo66uDj16cDbmV1RUUHl//fUXMjIyICHxXtfr6+uhovI+YCFN29HT00NaWhoePnyINWvWQEtLC/b29mAwGNR951JRUQFdXV0oKChAREQEubm5lKmalpYWHj16BIDjmcbc3ByEEKSnp6Ourg66urqUHEII2Gw28vLy0LdvX0hKSmLChAk4d+4c7O3tER0djUmTJvEMFFtDQkICTCYn/jxXD7m6KSoqSnUq09PToaamBk1NTeq7H84K37t3D7NmzUJubi7CwsIo85SW7gmDwQDA6XgGBgZSeSIiIjy/D5q2wWAwkJOTw5PW+D7TutlUN7lIS0vjyZMnePbsGdasWQNtbW2sXLnym9HNbjlIePPmDUaOHIm8PM7yIIPBwN9//42AgADEx8cjMTERoqKibZL18OFDjB8/HoWFhRAUFETPnj2RmpqK1NRURERE4Pr169SImebrp/EmnR5Ps/DvnYt8kVsp+y+gzPlMar9ceyOGqCD2j1Xokuu2lZSUFKSkpFD2tWw2Gw0NDRAXF0dqaioGDBjAU37w4ME4fvw4AEBfXx83b97kye/Xrx8AzgCA+yLlDhouXrzY5PetrKxMfZ42bRpWrVqF1NTUT/ZqJCgo+NFzAKir48TWaGwf25icnByMGDECIiIiSExMhJmZGZWnpKQEgDNxwn1ZctuppKQENpsNOzu7Jl6iWrpWR9FVuse99ueSnp4OWVlZqKmpgclkgslk4unTp4iIiIC9vT2UlJSadNS4XmWEhISgo6ODmzdvUuZwIiIi0NPTAwCegSmbzYaKigoSExOb1KHxbK+joyNWrFiBmpoaxMbGtnufSXN62JJufkxXEhISMGHCBJiamiI+Pp7Hrr2le8L9jTXXcWyL3Tq/EZZVgU5wVusFO+jan4uSklITE6TG95nWzaa6+c8//0BAQAB9+vTBgAEDMGDAAOTn5+P48eNYuXLlF6ObHU233Li8aNEi5OXlYdy4cXj9+jVKSkrw4MEDaGho4Pbt20029bQEm82Gi4sLCgsLsWDBApSUlCAvLw+5ubmYMGECMjMzm53N/NqR7PkT3+IZVNe8/yEJChA01Bfw5RD82syYvhKEBAWgIC7U6Ud79iMEBgbiyZMnePToER49eoRly5ZhzJgxePToEQ4dOtTEJjQjI4OymV2+fDmSkpIQFxfXRO7u3bupjWcMBgNKSkooLi6mNj8rKytj27ZtPBvX1NTUMGrUKPz222+4cuVKu/cjtBU5OTn07NkTKSkpVFpaWhqGDx+O+vp6rFmzBrKyskhNTeUZIACczXgDBw7E1atXqbT4+HgYGRlBTEwMenp6ePHiBXR1dam23r17l6d8Z9BVutde/WuJgICAJgMtFotFrd6Ym5vz3NOioiI8fPiQ+n+5u7sjODi4yUoXAOzYsYP6rKenh/z8fMjIyFD/r7KyMuzfvx8iIiJUOTs7O1RVVSEoKAiVlZWwtrb+7DY2h66uLrKzs3k6mYcPH8aSJUtQX1+PuXPnwsnJCVevXm2y8dXU1BRv3rzBy5cvqbT4+HjK9ERPT4/HhKOuro7nN9BZCAgJo4d8ry452rof4WOYm5vj9u3bYLFYVFrj+0zrZlPd/Pnnn7F69WqetMa/5y9FNzuabreS8PDhQ8THx0NJSQmnT5+mlqIMDAxw9uxZjBgxAvv27YOPj0+rZkcpKSl49OgRdHV1cejQIWrkqKKigl9++QV9+vTBxYsXkZuby7Mp8GuEXU/wbwVniU5KTAqCQlKtfKONCLwfh8rL1vDNpElCTBlgccwj+DpgEBEC/u+gQGhobwwyaj7wS3spvl0CdM2+t24Hd3MeF0VFRUhJSUFPTw+TJk2ClZUVNDU1YWNjg5SUFPz888/UzNbAgQOxfv16TJ48GevXr6fMdc6ePYurV69CW1ubkuvh4YG1a9dCTEwM8vLyCAoKQnZ2dhMzHEdHR3z33XeQkZGBiYlJh7Xb3d0dnp6ekJSUhKioKPz9/Sl796ioKGzcuBHFxcWUnTHAmSGUlpbGsmXL4OPjA3V1ddTW1sLPzw/BwcEAgDlz5mDDhg1wd3eHi4sL/vzzT7i5ueGXX37psLZ0R6ytrbFy5UqMHDkSgwYNwt27d3H48GHKk8vs2bPh7+8Pf39/jB8/Hv7+/jA1NaVMOBYvXozTp0/D0NAQvr6+GDZsGAoKCrBnzx4ICQlRs5T6+vqwsLDAzJkzERgYiOLiYnh6esLGxoanPlJSUrCxscGPP/6IiRMnQlxcvEPaPXDgQIwZMwbOzs7YsmULMjMzsXHjRvj4+ODOnTvIzc3FwoULm9iB9+7dG8rKyrC3t4eLiwsCAwNx9+5dxMXFUcH+3N3d4erqCg0NDQwdOhR79uyhYn/QtJ1hw4aByWRiwYIFWLduHSIjI/HmzRvKMxytm01109raGtu2bUNoaChMTU3x9OlTBAUFYcuWLQC+Hd3sdoOE6OhoAMCkSZOoAQKX4cOHY+DAgXj69Clu3ryJcePGfVTW48ePAQBmZmZNlpYYDAYMDQ2RkJCAP//886sfJGSXV2PgIa53jod8k7umn3BneBXlG42XCwVEhSEiy589CUKiX9FN+IoZO3YswsPD8eOPP2LLli3Q19fHmTNnMHz4cKpMQEAABg8ejP3792P37t1QU1PD2LFjcfv2bXh6elLlvL29UVdXh1WrVqG4uBgWFhaIiopqsqQ8bdo0rFmzBpMnT+5QEx0fHx/U1tZixYoVqKiowIQJE7Bnzx4UFxejpqYGPj4+8PHx4fnO0aNHMX/+fLi7u6OwsBBLly6FkJAQvL29MX/+fACAjIwMkpKSsHLlSlhYWEBJSQm7d+/u9MBGXzvz5s1DZmYmvv/+exQUFEBHRwdHjhyhZknV1NQQGxuLtWvXYvfu3TAzM8PJkyep7wsJCSE+Ph6BgYE4dOgQ1q1bB319fTg5OeG///0vj1vdiIgIrFy5ElOmTIGQkBBmzJjRZBUD4Axgz58/3+H/y99//x1ubm6ws7ODuLg4Fi1ahGXLllFuGxvvneHy+vVr9O7dG0ePHsWSJUswefJkaGpqIjIykjJlmT17NvLz87Ft2zaUl5fDyckJK1eupNwC07Sd6OhoLFq0iAokeeXKFcjJyQGgdfNDXr9+DTMzM/z000/YsWMH1q5di969e8PX15faqPyt6KYAId1rF+WECRNw+fJl/P7775g+fXqTfE9PT+zcuRMBAQH44YcfPiprx44dWLduHRYuXIjDhw83yR87diwSExNx9uzZZs0MHjx4AENDQ6SmpmLo0KGf3qhO4N6bvzAi5GXrBdvJmn4XESzG2dwdQIrw/fxtfJG745cf8B2bIzdI+B285u7ni9xLSVE4WsnxN79A8jYmmPPHu8sfiQehn8nZEJXe+wDGWizhi1waGhoaGhqa7k1X9Se73UrCixcvALzfjPghXHOCxjaQLWFgYACAY7v37t07Hu8feXl5uHfvHgDOxsivHfa7d1AVLgQAyPd7iGI+RfySEKwG8H8PUAQQFOrJF7lf1fIEDQ0NDQ0NDc1XRrcbJBQUFAAA5drrQ7jLa439orfE2LFjMXbsWPzxxx+YOnUqtm/fDm1tbTx+/BgeHh5gsViYN29eiwOSrwnB6nyk6rvwXW4EmQSAMzDofvv+aWiap6ysjHLD2hIxMTEYP358J9WIhobDhQsXmo043hgWi9VmD4A0NPyC1s0vj243SHj37h2A94OBD+Gmc8t9DAEBAURGRsLBwQFXrlzBlStXePLnzJnTJtddLBYL5eXlrZZrDVFR0Q77cdRXl3aIXBqabxGub+2P0dglIA1NZzF27NhWdbOxJxoams6iO+pmTU0NampqPltOY89UnUm3GyRwt1i0tNWCu7GQ6we9NS5evIiHDzkbeXv06AFFRUXk5eWBEIIbN27g7t27GDNmzEdlfOiS8FPx9fXFpk2b+CLrQ6rZ7z9npitAtoQ/kU+rDcSB/zuRIt3T4y4NTRMEBQWpzZc0NF8SXE9gNDRfGt1RNwMDA+Hn59fV1fhkut0gQUJCAuXl5SgpKWnW5Ii7gsANxPQxfv/9d8ydOxfy8vL49ddfMX36dAgLC6OyshJ79uzBDz/8AFtbW9y+fRuDBg1qUU5ycjL+85//fGqTKDp0ia3h/aDqnP5GFEl+fODTVtTLG20o7oaBRmhoaGhoaGhommP9+vX473//+9lyHj16xLcJ5/bQ7QYJPXv2pAYJffr0aZKfn58PAK3aCwOcfy4hBIcOHeJx0yUpKYn169ejpKQE27dvx9atW3Hq1KkW5UhJSUFGRuYTWtOJ1L/37zu89hwGCm7li9izxOr9SQeNEYTZ1agrzm69YBsQqiyE9Lv/B14Rb9tqEw0NDQ0NDQ3Nh/DLTFxKik+xq9pJt7P/4EZWzcjIaDafa+/GLdcSJSUlePnyJURERGBnZ9dsGa7b09TU1E+t7heJKGGB0ZDLlwPoeA+7A15dw99rNPhy9IpwxX8vGuC/Fw0gVFXS4XWn+XT27dsHJpMJBQUFrFixArW1tQA4nsfs7OwgLy8PExMTygtZY5KTk+Hg4AB1dXUoKirCwsICly9fbvO19+/fD1lZWeqajfHw8MCIESM+vWGfACEEO3fuRL9+/SArKwtra+smz0A/Pz9oaWlBW1ubClZF8+m8fv26yQpybGwshgwZAikpKQwfPhzXrl3jyb9x4waMjY0hLy8Pe3t7FBU1Nes8dOgQLC0twWAwoKGhgZkzZyI9Pb3N9XJycsKECc0HgRw8eDC8vLzaLIsfVFRUYMmSJVBVVYWysjIWL17Ms0ePxWJhzpw5UFRUhKGhIWJjYzu1ft2R5nTz/v37MDExgbS0NAYMGIBjx47x5NO62VQ3U1JSYGRkBGlpaTCZTJw9e7ZT6/cl0O0GCUZGRgDQZJMxl7i4OACAsbHxR+WIi4tDWFgYhJAW9zdw07/4VYJ2ItF3NpQcs/hyQKjxJiPa3IiGPxw5cgSBgYEIDAzEzz//jOjoaKrja2dnB1FRUcTGxmL06NGwtrbmiUJ88OBBKqDQ6dOnceHCBQwZMgS2trZNXpwtMW3aNLBYLCQkJDTJi46O7vRAZBEREfD390dAQAASEhKgpKQEKysrKgLo3r17ERoaitDQUAQHB2Pbtm04cuRIp9axO1FRUQFvb2+etOzsbEydOhUzZ87EjRs3YGlpiUmTJlGr1zk5ObC1tYW1tTViYmJQW1vbJL7OvHnz8P3338PBwQEJCQkIDw9HWVkZjIyMkJaW1qa6OTo6IiEhAWVlZTzpmZmZSEtL63Td9PLywq1bt/Drr78iIiICDx48gIvLe0968+fPR1ZWFqKiouDs7Ixp06bh2bNnnVrH7kRzuslisWBrawsDAwNcu3YNa9aswfLly3Hx4kUAtG42p5uVlZWws7PDyJEjce3aNSxevBgzZszA06dPO7WOXQ7pZjx8+JAAIIqKiqSgoIAn786dOwQAYTAYpKqqqlVZgwYNIgDImTNnms339PQkAMjSpUubzU9NTSUASGpqavsb0skk/HGA5BwByTkCkvDHAb7JDTi8mODwWoLDa0nA4cX8k/vLGkpu0PHlpLYoiy9HfMR28nQeyNN5IHEXf+ZbfTvq/n6rDBw4kERFRVHnv/zyC1mwYAG5d+8eERcXJywWi8ozMDAgBw5w7vm///5LxMXFm/1Ne3t7EwUFBVJbW9umOpiZmRFXV1eetLS0NAKApKenf0qzPhlbW1vi5eVFndfV1RFVVVVy/vx5QgghTCaThIeHU/khISHE2Ni4U+vYXQgICCDCwsIEAGEymVR6SEgIMTQ0pM5ramqIjIwM+e233wghhGzfvp2YmppS+UVFRURYWJg8f/6cEELIuXPniJSUFHn16hXP9dhsNhk9ejSZPHlym+pXWVlJJCQkyMmTJ3nS9+7dS1RVVUlDQ0O72vu5SEpKktjYWOr8yZMnBAApKSkheXl5REhIiLx8+ZLKnzp1Klm/fn2n1rG70JJuRkREEDU1NcJms6k0T09PYm9vTwihdZNLY92MiooiSkpKPPesf//+ZOvWrZ1aRy5d1Z/sdisJ//nPf2BlZYXCwkLMnDkTWVlZADjR6rgRmFevXg0xMTHqO/r6+tDX18f58+d5ZK1duxYAsGTJEpw+fRpsNscFUGVlJX788Ufs2rULoqKiWLFiRSe0jIaHRpuga3uIo4d8L74c9RLNu86l+XJ48eIFXrx4ARsbGypt1qxZOHLkCJKSkmBsbMzjmMDKygpJSUkAODPqBgYGzfri9vLyQnh4OKqqqgBwVgq3b98OXV1dSEtLY+LEicjNzaXKOzo6IioqisdTWnR0NPT19dG/f/82tUVAQADx8fEwNjaGgoICVq9ejezsbFhYWEBaWhrjxo1DaWkpVZ8tW7agb9++kJOTw+zZs1FSwjGJExIS4lkdFRYWhqysLHJyclBYWIgnT57Ayur9/iArKyvcvn2bcs0XHx+PoUOHQkJCAsrKylixYgX1vKPhxdXVFY8ePcLmzZt50iUkJGBvb0+di4iIQFdXl9KnpKQknv+BvLw8hg4dSulmUFAQPDw8oKWlxSNXSEgIhw8fxtKlS6k0FosFV1dXqKioQFFREe7u7pQeSkhIwNbWFufOneOREx0dDXt7ewi0wYFEcnIydHR0cPbsWfTv3x8aGho4fvw4/vjjDzCZTMjIyMDd3Z0qX15ejvnz56Nnz55QV1envPCVlpZCQ0MDQ4YMocpy9wPm5ubi+vXr6N27N/r27UvlN/69AsDly5dhaGgISUlJDBs2DDdu3Gi1/t8qLekmi8WCpaUl5d0R4PwfcnI4++9o3Xx/TwCObgoICMDJyYnnnjGZTOr3DAAhISHo27cvxMTEoK2tjf37Gzlq6SZ0u43LAPDzzz/DyMgICQkJ0NTUBIPBoF60pqamWL9+PU957tLmh0tg8+fPx927dxEaGoqZM2c2cYEqIiKCffv2dYuIy18zdQ1AdmlV6wXbQHE1wPVeX8+foNNfFaSBjYaqvE6/rqC4CgQE2/Y4ys7OhrKyMo4cOYK9e/eivLwcU6dOxdatW5Gfnw81NTWe8qqqqtS+oT///BMWFhbNypWTk8OUKVOo85CQEISFhWHfvn1QVFTEtm3b4ODggOTkZIiIiMDBwQEeHh64efMm5QY5Ojq6yTJ9a3h7e2Pnzp24f/8+PD09ERkZiZCQENTV1WHu3Lk4duwYVq1aheDgYISGhiIsLAyCgoLw9vaGp6cnwsPDceHCBR6Z169fx7Nnz2BgYECZu6iqqvLcE4ATfFJFRQXTp0+Ho6MjQkND8eLFC7i4uGDEiBFYsGBBu9ryOXSV7gHt0z8VFRWoqKjg/v37POmurq7U59raWsTFxSEjI4PSjZZ0Mz8/H4QQPH78GAEBAc1eU0dHh2cfnbOzM6qrq3H+/HlUVFRg7dq1WLduHXbt2gUAmD59OlxcXFBVVQVxcXGwWCwkJSVh3bp1bWojwDFBOXnyJI4ePYr9+/fD1dUVBgYGOHDgAC5duoQtW7Zg8eLFGDJkCJydnVFeXo6oqChkZGTAw8MD2tramDt3bhOb9YMHD4LBYKBPnz5ITExs8Z4AwMOHD+Hg4IB9+/Zh6NChuHDhAqZOnYqUlJRmHZN0FPUNBKU1XfNCYIgKQkiwbaa6LenmwoULsXDhQuqcxWLhl19+oSYWaN3k0Fg39fX1MWnSJAAAm81GSkoKEhMTsWrVKgDAvXv3sHr1aoSEhMDY2BhRUVFwc3ODra1tk8HU10y3HCRoamoiNTUVvr6+iImJQVFREXR0dDBnzhx4e3u3KxjH/v374eTkhN27d+Pp06fIycnBoEGDYGBggON/TXAAAKI7SURBVB9++KFbRFv+2nlWLgXdbddaL9gGlOpFwd02V/L58U++Ohqq8pD/u0anX1fJMQtCkm0LLvb27Vvk5OTg2LFj2LdvH2pra7F69Wqw2WzU1dU18QIhJSVF7Ul4/fo1Jk+ezJOvpaXFE4H90KFDcHZ2xq5du7B9+3Zqo92pU6egpqaGpKQkjB8/HqqqqjAxMUFkZCTGjBmDgoIC3L17F3v37m1X2xcsWAAzMzOYmZlh165dsLW1pWxzw8LCkJ3N8dy1e/duBAUFUY4URERE8NNPPzWRd+rUKSxduhQODg4wMjLCrVu3ICYmBmHh94977j0qLi6GjIwM1q9fD3d3d0hKSmLkyJEICwtDZmZmu9rxuXSV7gHt07/WePz4MTU7GRwcTM2Sl5aWtqibBQUFePfuHU+AvYyMDJ5ZTgB4/vw56urqcOnSJbx48QIaGpz7dfToUYwcORKbN2+GhIQE7OzsQAhBXFwc7O3tERcXB0lJSZibm7e5He/evUNAQACYTCbk5eVx4sQJrFmzBiYmJhg2bBh27tyJ7OxsSElJISYmBq9evYKmpiZGjRqFgoICvHr1ikdedXU1/Pz8sHXrVuzZswdiYmIfvSfc+zd//nzKTvw///kP7t+/j0OHDuHHH39sc1s+l9KaBqz4gz+xg9rL/rEKUBAXar1gG3n+/DnmzZuH7Oxsau8CrZtNdZNLWVkZ5OTkQAjBqlWrMHr0aACcfasHDx7E4sWLAQCDBg1CUFAQMjMz6UHC14CqqmqboiEDLQde42Jubt4uBabpXBSUlDFJw4AvsqTf5QCcvVyoxdcV2fFboUePHmCz2fj999+hrq4OgDPrP2XKFLi7u1NL6FwqKiqomCmqqqo8JkMAx9SGuxw+fvx41NfXo6KiAllZWZg/fz7PbHpNTQ3VaQc4Jkc7duzA7t27ERMTg169esHQ0LBd7Rk5ciT1WV5enudcQUEBAGfmLysri3LMAACWlpawtLSkzt++fYt58+bh6tWrWL58OTVzx2AwUF1djbq6OvTo0YO6J9w8GRkZuLm5ITY2FmlpaXjw4AFu3brFI5um7ejp6SEtLQ0PHz7EmjVroKWlBXt7ezAYDOq+c6moqICuri4UFBQgIiKC3NxcylRNS0sLjx49AsDx2GVubg5CCNLT01FXVwddXV1KDiEEbDYbeXl56Nu3LyQlJTFhwgScO3cO9vb2iI6OxqRJk3gGiq0hISEBJpMJ4L0ecnVTVFSU6lSmp6dDTU0Nmpqa1Hc/nBW+d+8eZs2ahdzcXISFhVHmKS3dE+7v9a+//sLjx49x9OhRKr+urq7ZGEg0H4drPrlhwwbo6enh+vXrVGeW1s2muslFWloaT548wbNnz7BmzRpoa2tj5cqVYDKZkJOTQ3h4OJ49e4akpKRuaaLZbQcJNN2bHgIEXG9JUqQc7Pqcj3+hjUg1vIXA/10aNzR0vx98awiKq3C8UnXBdduKsrIyZGRkqAECAPTv3x9VVVVQUlJqstSek5MDZWVlAJz9Rzdv3uTJ564G1tTUUAMM7qDh4sWL1IxY4+tzmTZtGlatWoXU1NRP9mokKCj40XOA0zECwGMf25icnByMGDECIiIiSExM5Am6o6SkBIBjZ8t9WXLbqaSkhLy8PJiZmaFv376wtraGh4cHtVehM+kq3eNe+3NJT0+HrKws1NTUwGQywWQy8fTpU0RERMDe3h5KSkpNBrBcrzJCQkLQ0dHBzZs3KXM4ERERKvps44Epm82GiooKEhMTm9Sh8Wyvo6MjVqxYgZqaGsTGxrZ50oxLc3rYkm62pJcAkJCQgAkTJsDU1BTx8fE8s6wt3RPub4zNZmPTpk1N9hBJS0u3pymfDUNUEPvHKnTqNRtfmx+4urri+PHj2LhxI7y9vakJA6Dl/8O3rJv//PMPBAQE0KdPHwwYMAADBgxAfn4+jh8/jpUrVyI5ORnTpk3D5MmTYWRkBEdHR4wdO7Zd7fgaoAcJNF8lwqgFwFkSnFmzHaoV6z/+hfYwivOnvqGQfzK/EgQEhflmdtFRMJlM1NXV4eXLl9DW1gYApKWlQVZWFubm5tiwYQNYLBY1mxQfH49ly5YBAJYvX44hQ4YgLi4O1tbWPHJ3795NzQQxGAwoKSmhuLiYmlEvLS3F2rVrsXnzZmomU01NDaNGjcJvv/2GK1euICYmpkPaLCcnh549eyIlJYV6kaWlpcHFxQV37tzBmjVrICsrixs3bkBOjnfzvaKiIgYOHIirV69i0aJF1D0xMjKCmJgYzp8/DwEBAVy6dIn6zocuFDuDr0H3PkZAQAB69uyJkJAQKo3FYlGdMXNzc0RFRWHjxo0AgKKiIjx8+JAa0Lm7u8PHxweLFi3i2T8CADt27KA+6+npIT8/HzIyMlS5lJQUnDp1iufadnZ2WLhwIYKCglBZWdlE3/mFrq4usrOzkZOTQ9m1Hz58GHfu3EFoaCjmzp0LJycnHD9+vElHztTUFG/evOH5LcfHx1Mr93p6esjOzqY6pADw/fffw8rKqokNfUciJCjAV5OfzubSpUs4cuQILl261Kwe0LrZVDd//vlnPH36FFFRUVRa499zaGgopk+fjrCwMACc90PjTc3dBXqQ0AlMnz6dx8atOdzc3ODm5tZJNepe5BNJENI0qNXnUt+KGRpN1yArK4vZs2dj5syZ2LVrF969e4eVK1fCw8MDw4YNA5PJxIIFC7Bu3TpERkbizZs3cHJyAgAMHDgQ69evx+TJk7F+/XpqAHD27FlcvXqV6qgAnKBoa9euhZiYGOTl5REUFITs7GyoqPDOOjs6OuK7776DjIwMTExMOqzd7u7u8PT0hKSkJERFReHv70/Zu3Nf8MXFxTwxIZSUlCAtLY1ly5bBx8cH6urqqK2thZ+fH4KDgwFwIsi/efMG0dHRkJSUxMmTJ/H48WPo6OigrKwMsrKyHdam7oS1tTVWrlyJkSNHYtCgQbh79y4OHz5MeXKZPXs2/P394e/vj/Hjx8Pf3x+mpqaUCcfixYtx+vRpGBoawtfXF8OGDUNBQQH27NkDISEhyvOLvr4+LCwsMHPmTAQGBqK4uBienp483r4Ajk25jY0NfvzxR0ycOBHi4uId0u6BAwdizJgxcHZ2xpYtW5CZmYmNGzfCx8cHd+7cQW5uLhYuXNjEDrx3795QVlaGvb09XFxcEBgYiLt37yIuLo6KeeLh4YFx48Zh4MCBMDIyQkREBPbv34/ly5d3SFu6K5GRkRg6dCj69euHly9fUuni4uJQU1OjdbMZ3bS2tsa2bdsQGhoKU1NTPH36FEFBQdiyZQsAznPzzp07uHHjBvLy8rB3714ICAjg/v37GDVqFM9KTWv89NNPze4va0x1dXX7bwA/6FSHq98YdJyEjouTsPO0FyW3o46gXwL5Vl86TgJ/qa6uJq6urkRFRYVoamqSH374gdTV1RFCCMnNzSW2traEwWAQY2Njcv/+/Sbfj4iIIObm5kRWVpbo6+sTNzc3Ul5eTpYsWUKOHj1KCOH4APf19SVaWlpERkaGTJkyhWRmZjaRlZWVRQQEBMiiRYva3Q4APPVjMpnU9QkhZPr06cTT05Oqj4+PD9HU1CRycnJk9uzZpLCwkOTn5xNwQps3ORrL8vX1JZqamqRPnz4kKCiISq+rqyPz588nMjIyRFtbm2zevJlER0cTFRUVcuHChXa36Vvh6NGjPL7oGxoaiJ+fH+nduzeRkJAgQ4YMoWIkcLl+/ToZMWIEkZOTI/b29qSoqIgnn81mk4CAAGJoaEikpaXJiBEjyI4dO0hDQwPR1dUlr1+/JoQQUlxcTObMmUMUFRWJsrIy8fDwINXV1U3qeOrUKQKAnDhxol1tS0pKIlJSUtR5YWEhAUBdnxBCFBUVycWLF6n8GTNmEHl5eaKurk42btxIGhoaSERERIu6yZVVUVFBZs2aRRQUFIiBgQG5dOkST13OnDlDBg0aRCQkJMiIESNIcnJyu9ryLfKhbtra2jb7PzA3N6fK0LrZVDfDwsJIv379iLi4OOnfvz/Zu3cvdY03b94QMzMzIikpSQwMDMj58+eJn58fUVZWJrm5ue1qU1voqv6kACH0dGlH8eDBAxgaGiI1NRVDhw7t6up8lD8SD0I/k7NhJ733AYy1WMIXuZuPLMEGcCJSB6AcPyxsn+1hS+z6/XusZfF/9aAxQWKK8JrFH7OLjrq/NDQ0NDQ0NN2brupP0uZGNF8lsgLA0zFPAACXHwyFk60XX+SGRu3HjzXFrRekoaGhoaGhoenG0IMEmq8SIQEBqItxPL7ICwqglySDL3JlBbpdEHKaLqCsrIyK3tkSMTExGD9+fCfViIaGw4ULF5qNON4YFosFUVHRTqoRDQ0HWje/POhBAg0NDQ2f4frW/hiNXQLS0HQWY8eObVU32xNwlIaGX9C6+eVBDxJoaGho+IygoCCP20Yami8FKSkpWjdpvkho3fzyoG0raGhoaGhoaGhoaGh4oFcSaL56agUlUFRVzx9ZeL+USf4f0ZmGhoaGhoaG5luDXkmg+epJlXTAij+K+HL8JTaSkluHtgdDoel89u3bByaTCQUFBaxYsQK1tRyXuHl5ebCzs4O8vDxMTExw7969Jt9NTk6Gg4MD1NXVoaioCAsLC1y+fLnN196/fz9kZWWpazbGw8MDI0aM+PSGfQKEEOzcuRP9+vWDrKwsrK2tkZGRwVPGz88PWlpa0NbWpoJVtcSxY8cwaNCgjqzyV8vbt2/h5OQERUVFaGpq4rvvvqN17yO6l5eXh6lTp0JOTg5aWlrw8/NDQ0NDp9bxW+P169dNfr/379+HiYkJpKWlMWDAABw7down/8aNGzA2Noa8vDzs7e1RVFTURO6hQ4dgaWkJBoMBDQ0NzJw5E+np6W2ul5OTEyZMmNBs3uDBg+HlxR8vhW2loqICS5YsgaqqKpSVlbF48WKUl5dT+SkpKTAyMoK0tDSYTCbOnj37UXkLFy7EypUrO7ranQo9SKChaQH6NfblcuTIEQQGBiIwMBA///wzoqOjqY6vnZ0dREVFERsbi9GjR8Pa2ponCvHBgwdhY2MDfX19nD59GhcuXMCQIUNga2vb5MXZEtOmTQOLxUJCQkKTvOjoaEydOpU/DW0jERER8Pf3R0BAABISEqCkpAQrKytUVlYCAPbu3YvQ0FCEhoYiODgY27Ztw5EjRzq1jt2FBQsW4O3bt7h48SLCwsJw5swZfP/99wBo3WtO91xcXFBaWorLly9jz5492LNnDw4dOtSpdfyWqKiogLc3b3wfFosFW1tbGBgY4Nq1a1izZg2WL1+OixcvAgBycnJga2sLa2trxMTEoLa2Fg4ODjwy5s2bh++//x4ODg5ISEhAeHg4ysrKYGRkhLS0tDbVzdHREQkJCSgrK+NJz8zMRFpaWqfrrpeXF27duoVff/0VERERePDgAVxcXAAAlZWVsLOzw8iRI3Ht2jUsXrwYM2bMwNOnTzu1jl1Op4Zu+8agIy53XMTlU7+vIaXZY0lp9liy7sR2MunMG74czj+HU/XdcDyo9Yq0ETriMn8ZOHAgiYqKos5/+eUXsmDBAnLv3j0iLi5OWCwWlWdgYEAOHODc83///ZeIi4uTM2fONJHp7e1NFBQUSG1tbZvqYGZmRlxdXXnS0tLSCACSnp7+Kc36ZGxtbYmXlxd1XldXR1RVVcn58+cJIZxIzuHh4VR+SEgIMTY2blHehxFbaThwo1v/9ddfVNrFixcJg8Ggde//NNa9srIyAoCkpaVR+cuWLSM2NjadWsdvhYCAACIsLEwA8Px+IyIiiJqaGmGz2VSap6cnsbe3J4QQsn37dmJqakrlFRUVEWFhYfL8+XNCCCHnzp0jUlJS5NWrVzzXY7PZZPTo0WTy5Mltql9lZSWRkJAgJ0+e5Enfu3cvUVVVJQ0NDe1q7+ciKSlJYmNjqfMnT54QAKSkpIRERUURJSUlnnvWv39/snXr1hblLViwgLi7u3dIXbuqP0mvJNB8lQjifaDwU3+rIfrWI74cf2a+X2qsrq7riqbRtMKLFy/w4sUL2NjYUGmzZs3CkSNHkJSUBGNjY0hKSlJ5VlZWSEpKAsCZUTcwMGjWF7eXlxfCw8NRVVUFgGNGsX37dujq6kJaWhoTJ05Ebm4uVd7R0RFRUVGor3+/HyY6Ohr6+vro379/m9oiICCA+Ph4GBsbQ0FBAatXr0Z2djYsLCwgLS2NcePGobS0lKrPli1b0LdvX8jJyWH27NkoKSkBAAgJCcHY2JiSKywsDFlZWeTk5KCwsBBPnjyBlZUVzz25ffs2ampqAADx8fEYOnQoGAwG7Ozs/sfefYdHUXUBHP5tNr0npAMhlFAkivQmTTrSREAUkI4oXYogn1JVQKQjCCJFsNAx0oOASlWKFEEE6QGSENJ7Mt8fa4Ys6TCpnPd59mF3Z/bsnZ07Yc7MLQQFBeWo/M+aoKAgnnvuOaMRWFxcXAgLC2PXrl1S9zCue2FhYbRo0YKKFSuqy/38/NTthEd1z9raGnd3d959912SkpJytA3C2MCBAzlz5gwzZswwej8qKooWLVqg1+vV91xcXAgMDATg4MGDRn8fnJ2dqVGjhlp3Z82axYgRI/Dx8TGKq9fr+frrr3n77beNvmvgwIF4eHjg4uLCsGHD1HpqbW1Nu3bt2Lp1q1Ecf39/OnfujE6XfT/AQ4cO4evry+bNm6lcuTKlS5dm7dq1/Pzzz/j5+WFvb8+wYcPU9SMiIujTpw+urq6ULFmSKVOmABAWFkbp0qWpVq2a0W8CcPfuXXQ6Hd27dzf6zR6vu/PmzaNChQq4u7szcuTIYllvpeNyPujatSuWlpZZrjN06FCGDh2aTyUq+vSmeZ/fmiamb5NZ3ClKMkpK/s84rTNxRqfTZ78icPv2bdzd3Vm1ahWLFi0iIiKCV199lZkzZxIUFISXl5fR+p6enpw8eRKAP//8k2bNmmUY18nJiU6dOqmvFyxYwLJly1i8eDEuLi7Mnj2bLl26cOjQIczNzenSpQsjRozg8OHDNG7cGDD8Z/f4bfrsTJgwgc8//5w//viDsWPHsmXLFhYsWEBiYiK9e/dmzZo1jBw5knnz5rF06VKWLVuGiYkJEyZMYOzYsaxcuZIff/zRKOavv/7KpUuXqF69unrC7+npafSbAAQHBxMfH0/btm0ZOXIky5cvZ8+ePfzvf/8zOrHLDwVV9yDn9a9q1arpmhusWLGCSpUqERkZKXUP47rn7e3Nvn37AEOi8e+///LNN9+o25qUlETXrl3p1q0bS5cu5cqVK/Tv3586derQt2/fXG1LXlJSkkiJvVcg321i5YHOJGenah4eHnh4ePDHH38Yvd+vXz/69eunvo6KiuLbb79Vk7vM/m4GBQWhKApnz55l+vTpGX6nr68vvr6+6utevXoRFxfHtm3biIyMZMyYMYwbN465c+cChvOh/v37Exsbi5WVFVFRURw8eJBx48blaBvB0Dxq3bp1rF69mi+++IKBAwdSvXp1vvzyS3bt2sXHH3/MoEGDqFatGr169SIiIoLt27dz+fJlRowYQfny5endu3e6/hTLly/H0dGRsmXLUqVKFTp06AAY6umJEyc4cOAAI0eOBGDt2rVMmjSJ+fPn8+KLLzJv3jw2bdrEkCFDcrwdqZYsWcKSJUuyXCcuLi7XcbUgSUI+2LRpEzVq1CjoYhQrTlZpegyUPwlxGs3AmBgLGE4WEtLcrXhWKCmhRN7rke/fa+fxPTq9a47WvX//PoGBgaxZs4bFixeTkJDAqFGjSEpKIjExEVtbW6P1bW1t1Xbh169fp2PHjkbLfXx8uH//vvp6xYoV9OrVi7lz5/LZZ5+pHe3Wr1+Pl5cXBw8epFWrVnh6etKwYUO2bNlC48aNCQ4O5vjx4yxatChX2963b1+aNGlCkyZNmDt3Lu3atVPb5i5btozbt28DMH/+fGbNmkX79u0Bw6RCGf3Hsn79et5++226dOlCvXr1OHLkCJaWlpiaPvpzn/obhYaGsm7dOurVq8ecOXMAqFWrFidPnuSff/7J1XY8rYKqe5C7+pcqLCyM0aNHs2bNGrZu3cqOHTuk7j1W99KqWbMmp0+fpkaNGmoH1ZiYGCZOnMiwYcOwsbGhbt26LFu2jBs3buRqO/JaSuw9gjaWLpDvdut2C72NdhMv/v3337z11lvcvn1b7bsQFhaWad0NDg4mJibGaPLHy5cvG12BT42bmJjIrl27uHLlCqVLG36v1atXU7duXWbMmIG1tTXt27dHURT27NlD586d2bNnDzY2NjRt2jTH2xATE8P06dPx8/PD2dmZb775htGjR9OwYUNq1arF559/zu3bt7G1tWXHjh1cu3YNb29vGjRoQHBwMNeuXTOKFxcXx9SpU5k5cyYLFy40uqgbHh6Ok5MTiqIwcuRIXnrpJcAwcMaYMWMYPHgwYBjsIfXOS27l5CLxqVOnqFmz5hPFfxrS3EgUSdYlyqvP3W1i8LKN0uThbB2jxjXTpx89RBQ8MzMzkpKS2LhxI02bNqVVq1YsWLCAVatW4ejoSGRkpNH6kZGRODo6AoarY2mbbYChucOZM2c4c+YMbm5uJCcnExkZya1bt+jTpw9WVlZYWVnh4OBAaGioeuIEhmYfqbfOd+zYQalSpXL9h7xu3Ucjajk7Oxu9LlGiBGC48nfr1i2jE68WLVoY3ba/f/8+rVu3pnfv3vTp04dvv/0WAEdHR+Li4khMfNR8LvU3cnR05O+//073H3Tq1WmRsd27d1O5cmX8/f3ZunUrnTp1krqXQd1La8uWLRw4cAA7Oztef/11AOzt7Rk6dCg7d+7ko48+on379hw5ciRX2yByRlEUZs+ezQsvvEBsbCy//vqr2nwoq7pbokQJzM3Njequj4+PWm93795NXFwciqJw8eJFEhMTqVixolp3GzRoQFJSEvfuGe7E2NjY0LZtW7X++Pv706FDB6OLGNmxtrbGz88PeFRPU+uuhYWFmvBcvHgRLy8vvL291c+OGzeOjz76SH39+++/4+fnx8KFC1m2bFm60Yns7Ow4f/48mzdvZuvWrWoi/vjfTXNzc6Pjp7iQOwmiSHK3fHTV40C9y1msmTt34syo+ovhj4+VmTZzLxQlOhNn7Dy+L5DvzSl3d3fs7e0pWbKk+l7lypWJjY3Fzc0t3a32wMBA3N3dAahSpQqHDx82Wl6hQgUA4uPj1Ta6qW1of/rpJ/WKWNrvT/Xaa68xcuRITp48+cQjy5iYmGT5GlBP8NO2j00rMDCQOnXqYG5uzoEDB2jSpIm6zM3NDTC0s039zzJ1O93c3DL8zzknbYO1VlB1L/W7c2rNmjX069ePbt26sXjxYlxdDXcgpO6lr3shISHcuXOHatWq4ePjg4+PD8uWLeOFF14gOjqayMhImjRpQrly5WjdujUjRoxQ+8kUJiZWHrh1u1Vg362FgQMHsnbtWj766CMmTJiAmdmjIb7d3NzU+pcqdcQjvV6Pr68vhw8fVpvLmZubq/1y0iauSUlJeHh4cODAgXTfn/ZORLdu3Xj33XeJj49n586dLF++PFfbklE9zazuZlZvAfbv30/btm1p1KgRAQEBRn0u/v33X3Q6HWXLluW5557jueeeIygoiLVr1zJ8+PBC83czr0mSIIokE2yzX+kp6VJSuPHvGU1ixYQ/amudXIg7N+l0+lw3u8hvfn5+JCYmcvXqVcqXN9xROnfuHA4ODjRt2pQPP/yQqKgo9WpSQECA2k70nXfeoVq1auzZs4fWrVsbxZ0/f77a8czR0RE3NzdCQ0Np0aIFYLglP2bMGGbMmKFeHfby8qJBgwZ8//337N27lx07duTJNjs5OeHq6sqJEyfU/8jOnTtH//79OXbsGKNHj8bBwYHffvsNJycno8+6uLhQtWpV9u3bx4ABA9TfpF69elhaWlKpUqV0t8kfP5nND0Wh7oWGhjJkyBAmTJjAJ598YrRM6l76unfq1Cl69OhBcHCwerIWFRWFoiiYmpqybds2dDodu3btUj/z+PCdhYHOxFTTJj/5bdeuXaxatYpdu3alq3tgqLvbt29Xr7A/ePCA06dPqwnfsGHDmDRpEgMGDDDq2wSozRQBKlWqRFBQEPb29up6J06cYP369SxYsEBdr3379vTr149Zs2YRHR2dYZm0ULFiRW7fvk1gYKDa5+Lrr7/m2LFjLF26lN69e9O9e3fWrl2bLsn46quvuHDhAtu3b1ffi4qKUpOr1L+bzZs3BwwJyYkTJ4ySoeJAkgRRJJUo4UxipBUAwTEWoNFAXTvPPRqZxDTRnGtrr2Wxds4lm7jAf3c8o6MKpnNmceHg4MCbb75Jjx49mDt3LjExMQwfPpwRI0ZQq1Yt/Pz86Nu3L+PGjWPLli3cvHmT7t27A4aOpxMnTqRjx45MnDhRPQnbvHkz+/btU5MOMExMNWbMGCwtLXF2dmbWrFncvn0bDw/jK3vdunXj/fffx97enoYNG+bZdg8bNoyxY8diY2ODhYUF06ZNo1y5cgDqf/ChoaFG4/K7ublhZ2fHkCFDmDRpEiVLliQhIYGpU6cyb948AAYPHsycOXMYM2YMPXv2JCAggICAAKM7NcJg165dKIrC66+/ztWrV42WSd1LX/dq164NwPDhwxk8eDChoaGMHz+ebt26YWFhgY2NDTdv3sTf3x8bGxvWrVvH2bNn8fX1JTw8HAcHhzzbpmfJli1bqFGjBhUqVDCqt1ZWVnh5efHmm28ybdo0pk2bRqtWrZg2bRqNGjVSR8oaNGgQP/zwAzVr1mTy5MnUqlWL4OBgFi5ciF6vV6+gV6lShWbNmtGjRw8+/fRTQkNDGTt2rNFIdGDo79CmTRs++eQTXnnlFaysrPJku6tWrUrjxo3p1asXH3/8MTdu3OCjjz5i0qRJHDt2jLt379KvX790fRTKlClD69atmT17NkuXLqVRo0ZcuHCBWbNm8fHHHwOGY2LgwIGULl2aGjVqsHDhQnVukOJEkgRRJFk5uqGkGIYi87SJzWbtnItJtFV76oRa27O0tjb/8TokB1Lnv/8/I+OfvWZMWluyZAnDhg2je/fumJub89Zbb6lXwfz9/RkwYIA6adXevXuNrnBOnz6dF154gS+++IL58+fj5eXFyy+/zNGjRxk7dqy63oQJE0hMTGTkyJGEhobSrFkztm/fnu6W8muvvcbo0aPp2LFjlre2n9akSZNISEjg3XffJTIykrZt27Jw4UJCQ0OJj49n0qRJTJo0yegzq1evpk+fPgwbNoyQkBDefvtt9Ho9EyZMoE+fPoChffHu3bsZO3YsX331FQ0aNODLL79k2rRpebYtRVVgYCDx8fG8+OKL6ZYpiiJ1L43Uurdt2zbGjh3L2rVrKVGiBJ07d1aH6HzjjTfYv38/vXr1wtXVlX79+rFt2zYGDRrEL7/8oo4uI55OYGAgJ0+eVJu3pWratCkHDhzAy8uLnTt3MmbMGObPn0+TJk1Yt26dup5erycgIIBPP/2UFStWMG7cOKpUqUL37t157733jIbd3bBhA8OHD6dTp07o9Xpef/11Zs2ala5M3bp1Y9u2bXk+gdrGjRsZOnQo7du3x8rKigEDBjBkyBA2bdoEoCbraV2/fp0mTZqwZMkS9QJKmTJlmDx5stpR+c033yQoKIjZs2cTERFB9+7dGT58uDpscHGhUxTl2RvCJZ+k9kY/efJkoR/d6OcDy6lywzDW8cUyX/Jys8GaxJ2xajAfYg/AdCL4X7/ctT3MTHLSPaLu9wTAxnUJJvoSmsSdsfZDJv/XlGmsiTk3XUZpEtchOZCpodUBCLCcQu8ekzWJK4QQQojiraDOJ+VOgiiS0o5pbqIvgYlGbZn1Oj2pI5/apIQztMztrD+QQ+dO/6Q+l7RcCCGEEIWdJAlCpKHToSYJCSk6ltzQphPSC7GJYG54nixJQrEXHh6uzt6ZmR07dtCqVat8KpF4VkjdE0XVjz/+mOGM5GlFRUVhYaHRvEgiW5IkCJHPYpJTOPdA+9mcKzo4YJGLsaZF3kkdWzsrxW0UDFE4SN0TRdXLL7+cbd01NzfPp9IIkCRBFAMpydqdcJuaKPDfZM62pgpfvKxNX4dtW/Tw31xWf8YH88meqZrETWt3q//xvIub5nFF7pmYmKjjiAuRn6TuiaLK1tZW6m4hI0mCKPKig7Oezjw3rC3dIcoaMCQMJay0GTHEJM2oJJ3LxjOq5B1N4qaVGB8ESJIghBBCiKcnSUI+6Nq1K5aWllmuM3ToUIYO1e5kN7eU5DTDckZHEHv3auYr54JOSYHiNwlhrtnqH/0IU3+rxq0YO82/Y/Wrt6Gkn+ZxhRBCCJE3lixZwpIlS7JcJy4uLp9KY0yShHywadOmQj8Eqi7u0SQgycet+evI75rENSttDlnnR09EZ+KMncf3mseNifuf5jEBlORHh9q2MiPz5DsuJqzLfiUhhBBCFBo5uUicOgRqftNmmloh8plOp8dE76r5A/JmQiJb+7yZUdJIcvGb7TErixcvxs/PjxIlSvDuu++SkJAAwL1792jfvj3Ozs40bNiQ339Pn/AeOnSILl26ULJkSVxcXGjWrBm7d+/O8Xd/8cUXODg4qN+Z1ogRI6hTp86Tb9gTUBSFzz//nAoVKuDg4EDr1q25fPmy0TpTp07Fx8eH8uXLM3PmzHwtX3Fy//59unfvjouLC97e3rz//vtS97Koe/fu3ePVV1/FyckJHx8fpk6dSkpKSqbx1qxZw/PPP58fRS+2rl+/nu433LlzJ9WqVcPW1pbatWvzyy+/GC3/7bffqF+/Ps7OznTu3JkHGQyusWLFClq0aIGjoyOlS5emR48eXLx4Mcfl6t69O23bts1w2QsvvMD48eNzHEsLkZGRDB48GE9PT9zd3Rk0aBARERHq8hMnTlCvXj3s7Ozw8/Nj8+bN+Vq+wkCSBAFASsqjcTkTnwvBpd/zmjziLJIefUdBbFhhYeHMZOfTTHY+zZ/x9TQLezbhBc1iFSWrVq3i008/5dNPP+Wrr77C399fPfFt3749FhYW7Ny5k5deeonWrVsTGhqqfnb58uXqjLg//PADP/74I9WqVaNdu3asWbMmR9//2muvERUVxf79+9Mt8/f3z/NZRB+3YcMGpk2bxvTp09m/fz9ubm60bNmS6GhD4rho0SKWLl3K0qVLmTdvHrNnz2bVqlX5Wsbiom/fvty/f5+ffvqJZcuWsWnTJj744ANA6l5Gda9///6EhYWxe/duFi5cyMKFC1mxYkW+lvFZEhkZyYQJE4zeu337Nq+++io9evTgt99+o0WLFnTo0IGgoCDAMCNzu3btaN26NTt27CAhIYEuXboYxXjrrbf44IMP6NKlC/v372flypWEh4dTr149zp07l6OydevWjf379xMeHm70/o0bNzh37ly+193x48dz5MgRvvvuOzZs2MCpU6fo378/ANHR0bRv3566devyyy+/MGjQIF5//XUuXLiQr2UscEoxFRgYqAwaNEgpWbKkYmFhofj6+ioffvihEhsbm+tYKSkpyooVK5Q6deoodnZ2SunSpZVu3bopV69ezfJzJ0+eVADl5MmTT7oZ+Wb75llK4CqUwFUob0z5QOG9HzV5jFz6rsLXYxS+HqNM/XpwQW9mtmatfUct76y172gWd8fPW5TuP91Xuv90X9mx7xslKeqWJo+AvXPV/bb/5y81K29hV7VqVWX79u3q62+//Vbp27ev8vvvvytWVlZKVFSUuqx69erKl18afps7d+4oVlZWyqZNm9LFnDBhglKiRAklISEhR2Vo0qSJMnDgQKP3zp07pwDKxYsXn2Sznli7du2U8ePHq68TExMVT09PZdu2bYqiKIqfn5+ycuVKdfmCBQuU+vXr52sZi4OgoCAFUP766y/1vZ9++klxdHSUuveftHUvPDxcAZRz586py4cMGaK0adMm03irV69W/Pz88rTMxdX06dMVU1NTBTD6DRcsWKDUrFlTfR0fH6/Y29sr33//vaIoivLZZ58pjRo1Upc/ePBAMTU1Vf7++29FURRl69atiq2trXLt2jWj70tKSlJeeuklpWPHjjkqX3R0tGJtba2sW7fO6P1FixYpnp6eSkpKSq6292nZ2NgoO3fuVF+fP39eAZSHDx8q27dvV9zc3JSkpCR1eeXKlZWZM2fmaxlTFdT5ZLG8k3Dz5k1q1KjBihUruHPnDlZWVvzzzz9Mnz6dl19+mfj4+BzHUhSFHj16MGjQIE6cOIFer+fevXts3LiRF154gZMnT+bhluQfBZnhKy/pdI8ONZ2pHXqbUpo8dKY2BbhVBePKlStcuXKFNm3aqO+98cYbrFq1ioMHD1K/fn1sbB79Li1btuTgwYOA4Yp69erVM5ywZ/z48axcuZLY2FjAcOx/9tlnVKxYETs7O1555RXu3r2rrt+tWze2b99OcppO//7+/lSpUoXKlSvnaFt0Oh0BAQHUr1+fEiVKMGrUKG7fvk2zZs2ws7OjefPmhIWFqeX5+OOPKVeuHE5OTrz55ps8fPgQAL1eT/369dW4pqamODg4EBgYSEhICOfPn6dly5ZGv8nRo0fVv4VXrlyhdevWODg4ULZsWZYvX56j8j9rgoKCeO6554yGaXRxcSEsLIxdu3ZJ3cO47oWFhdGiRQsqVqyoLvfz81O3EyAgIIAaNWrg6OhI+/bt1avbIvcGDhzImTNnmDFjhtH71tbWdO7cWX1tbm5OxYoV1f1w8OBBo78Pzs7O1KhRQ627s2bNYsSIEfj4+BjF1ev1fP3117z99tvqe1FRUQwcOBAPDw9cXFwYNmyYWk+tra1p164dW7duNYrj7+9P586d0emyH+Xk0KFD+Pr6snnzZipXrkzp0qVZu3YtP//8M35+ftjb2zNs2DB1/YiICPr06YOrqyslS5ZkypQpAISFhVG6dGmqVaumrps6CeHdu3fR6XR0794dvf5RE+TH6+6CBQsoV64clpaWlC9fni+++CLb8hc1xbLj8oABA7h37x7Nmzdn5cqVlClThtOnT9OpUyeOHj3KjBkzmD59eo5iff7552zYsAEvLy9++OEHGjRoQFxcHCNGjGDlypX07duXM2fOGFWkoigp5VHi9EqV24x4Tpu2d/tP6yHJ0B7fRCeJSGGXlJLMvdjIfP9eDys7TE1ydgzdvn0bd3d3Vq1axaJFi4iIiODVV19l5syZBAUF4eXlZbS+p6enmsz/+eefNGvWLMO4Tk5OdOrUSX29YMECli1bxuLFi3FxcWH27Nl06dKFQ4cOYW5uTpcuXRgxYgSHDx+mcePGgOE/u8dv02dnwoQJfP755/zxxx+MHTuWLVu2sGDBAhITE+nduzdr1qxh5MiRzJs3j6VLl7Js2TJMTEyYMGECY8eOZeXKlfz4449GMX/99VcuXbpE9erV1ZMuT09Po98EIDg4mBIlStCkSRPefPNNZs6cyfnz5xk1ahTu7u5Gv0deK6i6Bzmvf1WrVk3X3GDFihVUqlSJyMhIqXsY1z1vb2/27dsHGBKNf//9l2+++Ubd1qtXr9K2bVtGjhzJ8uXL2bNnD//73/+MkorCQFGSUVJCs18xD+hMnNHpcva30cPDAw8PD/744w+j9wcOHKg+T0hIYM+ePVy+fFmtO5n93QwKCkJRFM6ePZvpOZOvry++vr7q6169ehEXF8e2bduIjIxkzJgxjBs3jrlz5wKG0R779+9PbGwsVlZWREVFcfDgQcaNG5ejbQRD86h169axevVqvvjiCwYOHEj16tX58ssv2bVrFx9//DGDBg2iWrVq9OrVi4iICLZv387ly5cZMWIE5cuXp3fv3un6UyxfvhxHR0fKli1LlSpV6NChAwBJSUmcOHGCAwcOMHKkYeCR33//nVGjRrFgwQLq16/P9u3bGTp0KO3atUuXTBVlxS5JOH36NAEBAbi5ufHDDz9QooRhMqzq1auzefNm6tSpw+LFi5k0aVK2w5JGR0czc+ZMzMzM+Omnn6hevTpgyIaXLl3Kr7/+yvnz5/n999+pV0+7duYFIT7l0TTnhyxH8+MNryzWzrmyfA4YriLISKiF373YSEpvmJH9ihq71f1/lLJxzNG69+/fJzAwkDVr1rB48WISEhIYNWoUSUlJJCYmYmtra7S+ra2t2i78+vXrdOzY0Wi5j48P9+/fV1+vWLGCXr16MXfuXD777DO1o9369evx8vLi4MGDtGrVCk9PTxo2bMiWLVto3LgxwcHBHD9+nEWLFuVq2/v27UuTJk1o0qQJc+fOpV27dmrb3GXLlnH79m0A5s+fz6xZs2jfvj1guBqY0bB569ev5+2336ZLly7Uq1ePI0eOYGlpiWma2bhTf6PQ0FD27t2Lg4MDs2fPRqfTUb16dYKDg5k1a1a+JgkFVfcgd/UvVVhYGKNHj2bNmjVs3bqVHTt2SN17rO6lVbNmTU6fPk2NGjXUDqpffvkl9erVY86cOQDUqlWLkydP8s8//+RqO/KakhJK5L0eBfLddh7fo9O7ahLr7Nmz6pXzefPmUa5cOcBQlzOru8HBwcTExBjN0n358mWjK/AAf//9N4mJiezatYsrV65QunRpAFavXk3dunWZMWMG1tbWtG/fHkVR2LNnD507d2bPnj3Y2NjQtGnTHG9HTEwM06dPx8/PD2dnZ7755htGjx5Nw4YNqVWrFp9//jm3b9/G1taWHTt2cO3aNby9vWnQoAHBwcFcu3bNKF5cXBxTp05l5syZLFy40OjcMDw8HCcnJxRFYeTIkbz00ksAWFlZsXz5cgYNGgTA888/z6xZs7hx40axShKKXXMjf39/ADp06KAmCKlq165N1apVCQsL4/Dhw9nG2rVrFw8ePODll19WE4RUZmZmjBw5kqZNm3L9+nXNyl9Q4pPyYwxeSRPE0zMzMyMpKYmNGzfStGlTWrVqxYIFC1i1ahWOjo5ERhpfjY6MjMTR0REwXB1L22wDDM0dzpw5w5kzZ3BzcyM5OZnIyEhu3bpFnz59sLKywsrKCgcHB0JDQ9UTJzA0+0i9db5jxw5KlSqV62Hq6tatqz53dnY2ep36NywqKopbt24ZnXi1aNHC6Lb9/fv3ad26Nb1796ZPnz58++23ADg6OhIXF0diYqLRb5K67K+//uLy5ctYW1ur2zphwgSj7RTGdu/eTeXKlfH392fr1q106tRJ6l4GdS+tLVu2cODAAezs7Hj99dcBw4nl4yeHqVe3hfYqVarEuXPnWLt2LTNmzGDbtm0AWdbdEiVKYG5ublR3fXx81Hq7e/du4uLiUBSFixcvkpiYSMWKFdW626BBA5KSkrh37x4ANjY2tG3bVq0//v7+dOjQwegiRnasra3x8zPMCZRaT1PrroWFhZrwXLx4ES8vL7y9vdXPjhs3jo8++kh9/fvvv+Pn58fChQtZtmwZw4cPN/ouOzs7zp8/z+bNm9m6dauaiPv5+dGuXTtWrlzJuHHjaNSoEUlJSRQ3xe5OwtGjRwGM2iun1aZNGy5cuMDRo0dp3rx5lrH27t0LkOkt3HfffZd33333KUpbONX+Zw7P//OzJrF+rtYULA13JXRFrN+DaVIciaHanCjpo0Owiwk0vLBKznrlAuRhZcet7nkzV0R235tT7u7u2NvbU7JkSfW9ypUrExsbi5ubW7pb7YGBgbi7uwNQpUqVdBcIKlSoAEB8fDyBgYZ9lNqG9qefflKviKX9/lSvvfYaI0eO5OTJk088soyJiUmWrwH1BD+zZo2BgYHUqVMHc3NzDhw4QJMmTdRlbm6GWbjv3r2r/meZup1ubm4kJSXRvn17Zs2aZRQzv5tQFlTdS/3unFqzZg39+vWjW7duLF68GFdXw1VeqXvp615ISAh37tyhWrVq+Pj44OPjw7Jly3jhhReIjo7O8MQwJ+3S81tezcuT0+9+WhcvXsTBwQEvLy/8/Pzw8/PjwoULbNiwgc6dO+Pm5qbWv1SpIx7p9Xp8fX05fPiw2lzO3Nxc7ZeTNnFNSkrCw8ODAwcOpCtD2jsR3bp149133yU+Pp6dO3fmug9URvU0s7qb1d+x/fv307ZtWxo1akRAQIDRHYB///0XnU5H2bJlee6553juuecICgpi7dq1DB8+nEOHDvHaa6/RsWNH6tWrR7du3Xj55ZdztR1FQbFLEq5cuQI8+uP7uPLlywOGtpDZ+euvvwDD+L3PFJfSeL2+T5tYAQtIzQ30+qJ14+q5a7/wz+jS2a+YA6WA9/57frv7V5rEzAumJvpcN7vIb35+fiQmJnL16lX1eD537hwODg40bdqUDz/8kKioKPVqUkBAAEOGDAHgnXfeoVq1auzZs4fWrVsbxZ0/f756JcjR0RE3NzdCQ0Np0aIFYLglP2bMGGbMmKFeHfby8qJBgwZ8//337N27lx07duTJNjs5OeHq6sqJEyfU/8jOnTtH//79OXbsGKNHj8bBwYHffvsNJycno8+6uLhQtWpV9u3bx4ABA9TfpF69elhaWlKpUiUCAgKoWLGieoK2du1aoqOjM/07mheKQt0LDQ1lyJAhTJgwgU8++cRomdS99HXv1KlT9OjRg+DgYPVkLSoqCkVRMDU1pVKlSmrn2FQ5ucuf33Q6vWZNfgrC9OnTcXV1ZcGCBep7UVFRmJmZAYa6u337dvUK+4MHDzh9+rSa8A0bNoxJkyYxYMAAo75NgNpUDAx3KoKCgrC3t1fXO3HiBOvXrzf67vbt29OvXz9mzZpFdHR0uuNBKxUrVuT27dsEBgaqfS6+/vprjh07xtKlS+nduzfdu3dn7dq16ZKMr776igsXLrB9+3b1vbS/2dKlS+natSvLli0DDMdo2k7NxUWxSxKCg4MB1D+kj0v9I5a2HWhm/v33XwBcXV357rvvWL9+PcePH8fW1pbq1aszceJEateurU3BCxFTM0u8S1XVJpbeFP67A6crYq3bQkytMLewzX7FXEpWitYdlcLGwcGBN998kx49ejB37lxiYmIYPnw4I0aMoFatWvj5+dG3b1/GjRvHli1buHnzJt27dwcMHU8nTpxIx44dmThxonoStnnzZvbt26cmHWCYmGrMmDFYWlri7OzMrFmzuH37Nh4eHkbl6datG++//z729vY0bNgwz7Z72LBhjB07FhsbGywsLJg2bZrapjj1P/jQ0FCjcfnd3Nyws7NjyJAhTJo0iZIlS5KQkMDUqVOZN28eAD179uTDDz9k2LBh9O/fnz///JOhQ4dm2GTkWbdr1y4UReH1119Pd6FJ6l76upf6/+Pw4cMZPHgwoaGhjB8/nm7dumFhYcHgwYOZM2cOY8aMoWfPngQEBBAQEGB0l1A8vdatWzN8+HDq1q3L888/z/Hjx/n666/VJj9vvvkm06ZNY9q0abRq1Ypp06bRqFEjdaSsQYMG8cMPP1CzZk0mT55MrVq1CA4OZuHChej1evXiQpUqVWjWrBk9evTg008/JTQ0lLFjx6Zr2WFra0ubNm345JNPeOWVV7CyypvJRqtWrUrjxo3p1asXH3/8MTdu3OCjjz5i0qRJHDt2jLt379KvX790fRTKlClD69atmT17NkuXLqVRo0ZcuHCBWbNm8fHHHwOGZlPHjh3jt99+4969eyxatAidTscff/xBgwYN1GSiyMvXAVfzgZmZmQIoYWFhGS7fvXu3AihNmjTJNpaNjY0CKMOGDVMwXA9X3N3dFb1erwCKXq9XFi9enOnnU8e1PXTokBIeHv7Uj7i4uCf9WbL19fop6nj7X6+folncvJp3IK9M/3a0Wt68eqzdulSz8u7/+ctncp6EuLg4ZeDAgYqHh4fi7e2t/O9//1MSExMVRVGUu3fvKu3atVMcHR2V+vXrK3/88Ue6z2/YsEFp2rSp4uDgoFSpUkUZOnSoEhERoQwePFhZvXq1oiiGMcAnT56s+Pj4KPb29kqnTp2UGzdupIt169YtRafTKQMGDMj1dgBG5fPz81O/X1EUpWvXrsrYsWPV8kyaNEnx9vZWnJyclDfffFMJCQlRx+7P6JE21uTJkxVvb2+lbNmyyqxZs4zKce7cOaVp06aKnZ2dUr58eWXZsmW53pZnwezZszP9rRVF6l5Gde/QoUNK7dq1FRsbG8Xb21sZMWKEEhERoX7P/v37lerVqyv29vZKmzZtlA0bNsg8CU/p8bkmUlJSlKlTpyplypRRrK2tlWrVqqlzJKT69ddflTp16ihOTk5K586dlQcPHhgtT0pKUqZPn67UrFlTsbOzU+rUqaPMmTNHSUlJUSpWrKhcv35dURRFCQ0NVXr27Km4uLgo7u7uyogRIzI8d1m/fr0CKN98802utu3gwYOKra2t+jokJEQB1O9XFEVxcXFRfvrpJ3X566+/rjg7OyslS5ZUPvroIyUlJUXZsGFDpnU3NdayZcuUChUqKFZWVkrlypWVRYsWqd9x8+ZNpUmTJoqNjY1SvXp1Zdu2bcrUqVMVd3d35e7du+p6cXFxmpz/HTp0qEDmSdApSvG6rJnaqfHhw4cZ3k0ICAigZcuWvPTSS/z6669ZxjI1NVXbhw4bNoypU6fi7OxMXFwc8+fPZ9KkSZiZmXH27NkMh2w7depUrjuSZWXy5MnqGL9aW/XtVNokGGLvNp9CvzcnaxJ39jfv8n6SNQCzTGMY37twjyM84/sxfBibt21iF9pUYHj3IZrE+vnAcqrcMIxRfbHMl7zcbLAmcYUQQgjxdKZMmcLUqVM1i3fy5Elq1KihWbzsFLvmRtbW1kRERGSaJMTExAAYTXiTGScnJ0JCQujatavR0HKWlpZMmDCBK1eusHLlShYuXMjixYszjXPo0CFefPHFXG/L4ywsLLJfSTwVex1caHwegMV/duSWqTZtJU1irvFj7G4A4pRid9gJIYQQ4jETJ07kvffey37FbJw5c8ZoUID8UuzOVlxdXdUkoWzZsumWp04slDqzXlY8PDwICQmhX79+GS5//fXXWblyJWfPns0yjq2tLfb29jkovSholiYKJS0No3nYm5hhrc/5qCdZMdE9anMZm1L8hkkTxsLDw7P9G7Njxw5atWqVTyUSzwqpe6Ko+vHHHzOckTytqKioInXB1MLCQpPyPj6PRX4pdkmCr68vV69e5fLlyxnekjl//ry6Xnbc3d05f/58pp2oUt9/fOxrUXSZkaA+fxh1Ev/kO5rE9YhRIOu5+0Qxkjq2dlbSDgkohFak7omi6uWXX8627pqbm+dTaQQUwyShXr167N69m71799KjR/oZEvfs2QNA/fr1s41VqVIl9u/fn+HsgvBo9KMqVao8ZalFYWGZZozuZubxfNismyZxV+zdxrSE6P9eFd55EoQ2TExM1HHEhchPUvdEUWVrayt1t5ApdklCp06dmDJlCv7+/oSEhBjddj1+/DiXLl3C0dExR7M6tm/fni+++IJFixbRrVv6k8XUCUC07JwsCpYuzazQTZO/Q9mxWpO4HRVbpmHoYGxJZDZrCyGEEEIUrKI1cH0OvPjii7Rs2ZKQkBB69OjBrVu3AMNIQ127dgVg1KhRWFo+avtRpUoVqlSpok5RnqpNmzY8//zz/Prrr/Tq1UudgyE8PJwRI0bg7++Ph4cHo0aNypdtE0IIIYQQIj8UuyQBDDPleXp6sn//fry9vXFycqJmzZrcvn2bRo0aMXHiRKP1L126xKVLlwgPDzd6X6fTsX79ehwdHVm/fj1ubm54eHjg5OTEokWLcHZ2Zu3atTg4OOTn5ok8FM+jUa8Omg3GrdstTR6/WYwowK0qfh48eEDPnj1xdnamQoUKRqOL3bt3j/bt2+Ps7EzDhg35/fff033+0KFDdOnShZIlS+Li4kKzZs3YvXt3jr//iy++wMHBgYSEhHTLRowYQZ06dZ5sw56Qoih8/vnnVKhQAQcHB1q3bs3ly5eN1pk6dSo+Pj6UL1+emTNn5mv5ipN//vmHFi1aYG9vj6+vL0uXLlWXSd1LX/fu3bvHq6++ipOTEz4+PkydOpWUlJR8LeOz5vr16zz//PNG7+3cuZNq1apha2tL7dq1+eWXX4yW//bbb9SvXx9nZ2c6d+7MgwcP0sVdsWIFLVq0wNHRkdKlS9OjRw8uXryY43J1796dtm3bZrjshRdeYPz48TmOpYXIyEgGDx6Mp6cn7u7uDBo0iIiICHX5iRMnqFevHnZ2dvj5+bF58+Ys4/Xr14/hw4fndbHzVbFMEry9vTl58iSDBg3Cy8uL2NhYfH19mTJlCvv27ctVx5fnn3+eM2fO0L9/f0qWLEl4eDgvvPACgwcP5ty5c7Rs2TIPt0TkN0X36JCI09mhtymlySNep80oScJg4MCB3Lx5k927d/Ppp58yYcIEfvzxR8DQTNDCwoKdO3fy0ksv0bp1a6OZYJcvX06bNm2oUqUKP/zwAz/++CPVqlWjXbt2rFmzJkff/9prrxEVFcX+/fvTLfP39+fVV1/VZkNzaMOGDUybNo3p06ezf/9+3NzcaNmyJdHRhn4wixYtYunSpSxdupR58+Yxe/ZsVq1ala9lLA4UReHVV1/FxcWFAwcOMHnyZEaNGqX2dZO6l77u9e/fn7CwMHbv3s3ChQtZuHAhK1asyNcyPksiIyOZMGGC0Xu3b9/m1VdfpUePHvz222+0aNGCDh06qKM9BgYG0q5dO1q3bs2OHTtISEigS5cuRjHeeustPvjgA7p06cL+/ftZuXIl4eHh1KtXj3PnzuWobN26dWP//v3pLsjeuHGDc+fO5XvdHT9+PEeOHOG7775jw4YNnDp1iv79+wMQHR1N+/btqVu3Lr/88guDBg3i9ddf58KFC/laxgKXr1O3PWNSZ1zO7xnynoTMuGywatMHStjtl5Ww2y8rqzZ9oFncWd9++uh3+PZTzeI+izMux8bGKnq9XtmzZ4/63uuvv6706tVL+f333xUrKyslKipKXVa9enXlyy8Nv82dO3cUKysrZdOmTeniTpgwQSlRooSSkJCQo3I0adJEGThwoNF7586dUwDl4sWLT7JpT6xdu3bK+PHj1deJiYmKp6ensm3bNkVRDLPprly5Ul2+YMECpX79+vlaxuLgzz//VPR6vfLw4UP1vTZt2ihDhgyRuveftHUvPDxcAZRz586py4cMGaK0adMmX8v4rJg+fbpiamqqAEYzLi9YsECpWbOm+jo+Pl6xt7dXZ13+7LPPlEaNGqnLHzx4oJiamip///23oiiKsnXrVsXW1la5du2a0fclJSUpL730ktKxY8cclS86OlqxtrZW1q1bZ/T+okWLFE9PTyUlJSVX2/u0bGxslJ07d6qvz58/rwDKw4cPle3btytubm5KUlKSurxy5crKzJkzM43Xt29fZdiwYXlS1oI6nyyWdxLEE0hJJiUOUuLAPC6cxNDbmjxMk+IKestEMZOYmIiiKEaTJTo7OxMfH8/BgwepX7++0WSJLVu25ODBg4Dhinr16tUzHIt7/PjxrFy5ktjYWMBw1fizzz6jYsWK2NnZ8corrxgNd9ytWze2b9+uzsoOhiu5VapUoXLlyjnaFp1OR0BAAPXr16dEiRKMGjWK27dv06xZM+zs7GjevDlhYWFqeT7++GPKlSuHk5MTb775Jg8fPgRAr9cbjdhmamqKg4MDgYGBhISEcP78eaO7ni1btuTo0aPEx8cDhpnoa9SogbW1Ne7u7rz77rskJcl8Ho+Lj4/n1VdfNap7fn5+xMbGSt37T9q6FxYWRosWLahYsWK63yuV1D3tDBw4kDNnzjBjxgyj962trencubP62tzcnIoVK6r74eDBg0Z/H5ydnalRo4Zad2fNmsWIESPw8fExiqvX6/n66695++231feioqIYOHAgHh4euLi4MGzYMLWeWltb065dO7Zu3WoUx9/fn86dO6NLM7pgZg4dOoSvry+bN2+mcuXKlC5dmrVr1/Lzzz/j5+eHvb09w4YNU9ePiIigT58+uLq6UrJkSaZMmQJAWFgYpUuXNhq5MnWgm7t376LT6ejevTt6vV5d/njdnTdvHhUqVMDd3Z2RI0cWy3pb7EY3Ek/GIiGCiFOG5zWYx5Vj8zSJ61eqLJTukv2KhdDdWD2/njupSayQqFj1aEtMVjSJmReU5BQSo/I/sTOztUSnz9k1Czs7O5o1a8bcuXP5+uuvuXnzJhs3bmTevHmcPXsWLy8vo/U9PT05edKwH//880+aNWuWYVwnJyc6deqkvl6wYAHLli1j8eLFuLi4MHv2bLp06cKhQ4cwNzenS5cujBgxgsOHD6ujpfn7+6e7TZ+dCRMm8Pnnn/PHH38wduxYtmzZwoIFC0hMTKR3796sWbOGkSNHMm/ePJYuXcqyZcswMTFhwoQJjB07lpUrV6pNrVL9+uuvXLp0ierVq6tNCjw9PY1+E4Dg4GA8PDzo2rUr3bp1Y+nSpVy5coX+/ftTp04d+vbtm6tteRoFVfcg5/Wvdu3abNy4EYDk5GT++usvtmzZwowZMzh9+rTUPYzrnre3N/v27QMMica///7LN998o25rUlJSoah72UlKSeZebMGMSudhZYepiT77FTFMAOvh4cEff/xh9P7AgQPV5wkJCezZs4fLly+rdScoKCjDuhsUFISiKJw9e5bp06dn+J2+vr5G80716tWLuLg4tm3bRmRkJGPGjGHcuHHMnTsXgK5du9K/f39iY2OxsrIiKiqKgwcPMm7cuBxtIxiaR61bt47Vq1fzxRdfMHDgQKpXr86XX37Jrl27+Pjjjxk0aBDVqlWjV69eREREsH37di5fvsyIESMoX748vXv3TtefYvny5Tg6OlK2bFmqVKlChw4dAEM9PXHiBAcOHGDkyJEArF27lkmTJjF//nxefPFF5s2bx6ZNmxgyZEiOt6MokCRBAKBXonFsoH3cWPNwiNc+bn746sYLfHA6UJNYlc1toKKhjW54bOG9u5IYFce5WT9mv6LGnn+/I+YO1jlef+XKlVSqVIkNGzagKAqvvPIKvXr1YvDgwelmprS1tVXbhV+/fp2OHTsaLffx8eH+/fvq6xUrVtCrVy/mzp3LZ599pna0W79+PV5eXhw8eJBWrVrh6elJw4YN2bJlC40bNyY4OJjjx4+zaNGiXG173759adKkCU2aNGHu3Lm0a9dObZu7bNkybt++DcD8+fOZNWsW7du3BwxXA5csWZIu3vr163n77bfp0qUL9erV48iRI1haWmJq+ujPfepvFBoair29PRMnTmTYsGHY2NhQt25dli1bxo0bN3K1HU+roOoe5L7+Abi6uvLw4UM6derEG2+8wYEDB6TuPVb30qpZsyanT5+mRo0aagfVmJiYQlH3snMvNpLSG2Zkv2IeuNX9f5SycdQk1tmzZ9Ur5/PmzaNcuXKA4ap6ZnU3ODiYmJgYown4Mpo76u+//yYxMZFdu3Zx5coVSpcuDcDq1aupW7cuM2bMwNramvbt26MoCnv27KFz587s2bMHGxsbmjZtmuPtiImJYfr06fj5+eHs7Mw333zD6NGjadiwIbVq1eLzzz/n9u3b2NrasmPHDq5du4a3tzcNGjQgODiYa9euGcWLi4tj6tSpzJw5k4ULFxqNfhkeHo6TkxOKojBy5EheeuklABYvXsyYMWMYPHgwAGvWrFHvvBQnkiTkg65duxpVuowMHTqUoUOH5lOJ0otIMAWrvP2OBFOZchggJaXw3kkoCh4+fEjr1q1544036NmzJzdu3ODzzz9nypQpODo6EhhonNhFRkaqzUM8PT3TzZAeEBCg3g5v1aoVycnJREZGcuvWLfr06WN0RTM+Pl49cQJDs485c+Ywf/58duzYQalSpXI9b0rdunXV587OzkavS5QoARhu4d+6dcvoxKtFixa0aNFCfX3//n3eeust9u3bxzvvvKNeuXN0dCQuLo7ExETMzMzU3yR1mb29PUOHDmXnzp2cO3eOU6dOceTIEaPYIr1jx47x77//Mn78eMaOHSt1L4O6l9aWLVu4fv06U6ZM4fXXX2fTpk1S9/JZpUqVOHfuHKdPn2b06NH4+PjQuXNnHB0d1b8JqSIjI6lYsSIlSpTA3Nycu3fvqk3ZfHx8OHPmDGAYvapp06YoisLFixdJTEw0al6mKApJSUncu3ePcuXKYWNjQ9u2bdm6dSudO3fG39+fDh06GF3EyI61tTV+fn7Ao3qaWnctLCzUhOfixYt4eXnh7e2tfvbxOxa///47b7zxBnfv3mXZsmVGTafg0Qzmly5dYvTo0ZQvX57hw4fz999/8+mnn6rrmZubGx0/ubFkyZIMk+604uIK5uKiJAn5YNOmTdSoUaOgi5GlCBQCFcOBNfzfkVxN8c7mEzlTq9QNsDTcSojT5e5KXUHwsnk0NN/MKt+giwrSJO6VaFsmYrjyYkqMJjHzgpmtJc+/3zH7FfPge3Nqx44dWFhYGI3OU7FiRTp37sz777+f7lZ7YGAg7u7ugGFOlMOHDxstr1ChAmA4CUs9yUs9cfvpp5/UK2KpUmOBYaSZkSNHcvLkySceWcbExCTL12DohwEYtY9NKzAwkDp16mBubs6BAwdo0qSJuszNzQ0wtLNN/c8ydTvd3Ny4d+8eTZo0oVy5crRu3ZoRI0aofRXyU0HVvdTvzok7d+4QGRlJ5cqVqVixonoy1L9/f9577z2pe4/VvZCQEO7cuUO1atXw8fHBx8eHZcuW8cILLxAdHU1kZGShqHvZ8bCy41b3/xXYdz+tixcv4uDggJeXF35+fvj5+XHhwgU2bNhA586dcXNzS5fgpo54pNfr8fX15fDhw2pzOXNzc3Vm5LSJa1JSEh4eHhw4cCBdGdLeiejWrRvvvvsu8fHx7Ny5U52YNqcyqqeZ1d3M6i3A/v37adu2LY0aNSIgIMCoz8W///6LTqejbNmyPPfcczz33HMEBQWxdu1ahg8fnmFSk5M+FRnJyUXiU6dOFcjEvZIkCAAiMKHWfzMCUy4OuJzl+jm1M81zM5MnO4Dyk63ZozIG2M4kzKKEJnH1dv9AmKGzVnIurpjkN53eJNfNLvJbbGxsuj/GOp2O2NhYmjRpwuTJk4mKilKvJgUEBKjtRN955x2qVavGnj17aN26tVGM+fPnqx3PHB0dcXNzIzQ0VL2qGRYWxpgxY5gxY4Z6ddjLy4sGDRrw/fffs3fvXnbs2JEn2+zk5ISrqysnTpxQ/yM7d+4c/fv359ixY4wePRoHBwd+++03nJycjD7r4uJC1apV2bdvHwMGDFB/k3r16mFpacm2bdvQ6XTs2rVL/czjQyjmh6JQ93bs2MGSJUv4888/1feioqIwMzOjadOmfPjhh1L30jh16hQ9evQgODhYPVmLiopCURRMTU0LTd3LjqmJXrMmPwVh+vTpuLq6smDBAvW91HoL0LRpU7Zv385HH30EGOahOX36tJrwDRs2jEmTJjFgwACjvk0Ac+bMUZ9XqlSJoKAg7O3t1fVOnDjB+vXrjb67ffv29OvXj1mzZhEdHZ3ueNBKxYoVuX37NoGBgWqfi6+//ppjx46xdOlSevfuTffu3Vm7dm26JOOrr77iwoULbN++XX0v7W9WqVIlDh48SPPmzQFDQnLixAmjZKg4KLxnKyJfpZhY5Pl3WDxhlp2fFJM8bnMFIBMJPZV27doxfvx4Bg0aRK9evbh27Rpz5syhe/fu1K1bFz8/P/r27cu4cePYsmULN2/epHv37gBUrVqViRMn0rFjRyZOnKiehG3evJl9+/ZRvnx59XtGjBjBmDFjsLS0xNnZmVmzZnH79m08PDyMytOtWzfef/997O3tadiwYZ5t97Bhwxg7diw2NjZYWFgwbdo0tU1x6n/woaGhRuPyu7m5YWdnx5AhQ5g0aRIlS5YkISGBqVOnMm+eYXACGxsbbt68ib+/PzY2Nqxbt46zZ8/i6+tLeHi4TBaZRrNmzRg2bBjTpk2jc+fO3Lp1iwkTJtCzZ09q1aolde+xule7dm0Ahg8fzuDBgwkNDWX8+PF069YNCwsLqXv5pHXr1gwfPpy6devy/PPPc/z4cb7++mt1lKE333yTadOmMW3aNFq1asW0adNo1KiR2rxo0KBB/PDDD9SsWZPJkydTq1YtgoODWbhwIXq9Xr1oU6VKFZo1a0aPHj349NNPCQ0NZezYsbRp08aoPLa2trRp04ZPPvmEV155BSurvPl/t2rVqjRu3JhevXrx8ccfc+PGDT766CMmTZrEsWPHuHv3Lv369UvXR6FMmTK0bt2a2bNns3TpUho1asSFCxeYNWsWH3/8MWA4JgYOHEjp0qWpUaMGCxcuVOcGKU4kSRDpvGFRh0hbbW5rOScf5/0X1gJw6Ey9bNYueM6Yqc/fPtWZxBBtbn3vKd0U7A3Dq5GUqEnMZ1XJkiXZu3cv48ePp0OHDjg4ONC1a1d12D9/f38GDBigTlq1d+9eoyuc06dP54UXXuCLL75g/vz5eHl58fLLL3P06FHGjh2rrjdhwgQSExMZOXIkoaGhNGvWjO3bt6e7i/Haa68xevRoOnbsmOWt7ac1adIkEhISePfdd4mMjKRt27YsXLiQ0NBQ4uPjmTRpEpMmTTL6zOrVq+nTpw/Dhg0jJCSEt99+G71ez4QJE+jTpw8Ab7zxBvv376dXr164urrSr18/tm3bxqBBg/jll1/UET6EYSSXH374gY8++oiZM2fi5eVF7969+eCDDwCpe2ml1r1t27YxduxY1q5dS4kSJejcubN6rErdyx9vvfUWN27c4IMPPiA4OBhfX19WrVqlXsH38vJi586djBkzhvnz59OkSRPWrVunfl6v1xMQEMCnn37KihUrGDduHFWqVKF79+689957RsPubtiwgeHDh9OpUyf0ej2vv/46s2bNSlembt26sW3btjyfQG3jxo0MHTqU9u3bY2VlxYABAxgyZAibNm0CyLD/y/Xr12nSpAlLlixhzpw5jBkzhjJlyjB58mS1o/Kbb75JUFAQs2fPJiIigu7duzN8+HB12ODiQqcoivSizCOpbchOnjxZ6PskfPjlSGaYG06QJybAqK6jNIm77cByXq9vaIe75Vh9+r1WMCNE5NS/R+dQwttw6ztoye8kRyZoEvdXn3oMdq4FwITYWD59N+tOSjn184HlVLlhaCZ2scyXvNxssCZxhRBCCFE4FNT5pNxJEAYpyfDfVXSr+Gsk+5fOev0csjB5NG63jsKfj7qWqkoKhiTBbWhtzeI2jDODXwzPrSyK34QrQgghhCheJEkQAEan79Ho1JGOnlZEmuY7RYGlS2liQrNf72mkKHA7LDb7FXMgLP7RjKtJclOw0AgPD1dn78zMjh07aNWqVT6VSDwrpO6JourHH3/McEbytKKiorCwyPs+lMJAkgQBQGyaNGG7mRWzeDuLtXOunIk1bxFseKEv/NXNxOzRf642rksw0WszutGO7x6N2BGRaErp6QGaxH3J9iH1yxqeRyXIHYrCInVs7awUt1EwROEgdU8UVS+//HK2ddfc3DyfSiNAkgSRKiURdTa1FDNI1OhA1KW9k1D4RzfS6fKm81984qO4JjqFMtZRmsQtYfkoMVBSCu9Mzs8aExMTdRxxIfKT1D1RVNna2krdLWQkSRAARCemGSP4dmVIqKJNYOd44CAAdunnOinUooO1mwHb0dYdouwBMLWxo2695prEdUiuAqGTAYhL0KaTtRBCCCGEJAkCAKuUaMDQD8HMzBwrnTZ3EizMH7WZN9HnfFZdkXu6OGluJIQQQghtSJIgAEgxeTTTqV/12lQx99UkrqPZA/V5yXyYp+xp6UycsfP4XvO40dET1efOUZG0uXpYk7j3LSLhvwkw9SZJpCQHaxI3LZ2Jc541w3pSDx48YMSIEezatQtnZ2dGjRrFsGHDALh37x4DBw7kyJEjVKlShfnz56uTOqU6dOgQCxYs4Pjx48THx/P888/z/vvvp5v0JzNffPEFEydOJDg4OF0b2REjRnDs2DFOnDihzcbmgKIozJ07l6VLlxIcHEy9evVYtGgRFStWVNeZOnUqq1atQq/XM2jQoCxntl2zZg1z5szh3Llz+VF8IYQQhZAkCfmga9euWFpmfRV96NChDB2qXfOW3DI3f3QV2t4sAiXNyf3TcDANU587OXtmvmIhodPp0eldNY+bbKKH/26qJFkmUaZHWU3i3jy6RX1epuRVIu/10CRuWlZu6zE388h+xXw0cOBAQkJC2L17Nzdu3KBfv354e3vTsWNH2rdvT5kyZdi5cydbt26ldevWXLlyBWdnZwCWL1/OyJEjee+993jvvfcwMTFhw4YNtGvXjlWrVqmTjGXltddeY/jw4ezfv5+2bdsaLfP391cn3MkvGzZsYNq0aSxbtgxfX18WLFhAy5Yt+euvv7CxsWHRokUsXbqUVatWkZiYSN++fXF3d6dfv375Wk4hhBDGlixZwpIlWc+dFBdXMH0OJUnIB5s2bSr0k6nZmD0a3WhElQWUtNR+VmA315Kaxyw6HnXafpCcSLmj67JYN+dGxcXQ8r87NKFxFtyJ1Gbo2rQsLB5S3qXwJAlxcXH4+/uzc+dO6tSpQ506ddi8eTMbN27Ey8uLv/76i0OHDmFjY0O9evXYt28fmzZtYvDgwQQGBjJq1CjWrVtnNNRegwYNsLKyYsyYMbz55puYmWU9dK+7uzuNGjViy5YtRknC+fPnuX79ep7PIvq4tWvXMmTIEN544w0AVq1ahbe3NwEBAXTq1Inly5fzySefqGWdMmUKK1askCRBCCEKWE4uEqdOppbfJEkQ+cbMwrmgi1DsxCbq1UGphh1qxrVoB82/Y/9bQZR30agjuwYSExNRFAVHR0f1PWdnZ0JCQjh48CD169fHxsZGXdayZUsOHjzI4MGDWbRoEdWrV89wLO7x48dTr149YmNjMTMzQ1EU5syZw4oVK7h79y6NGzfmq6++wtPTcEesW7duTJ06lWXLlqHXG5pj+fv7U6VKFSpXrpyjbdHpdOzbt48PP/yQy5cv07t3b8aOHUvv3r35448/1ATI0dERRVH45JNPWLlyJQ8fPqRt27YsWbIEJycn9Ho99evXV+Oampri4OBAYGAgISEhnD9/npYtWxr9JiNHjiQ+Ph4LCwsCAgIYP348//77Ly+99BJNmjTJ1T4RQghR/EiSINLZediD4R2maRIrJeUhMQ8mAWBTiK5G5ze97tGdhBLmFtx67X+axN2zZYnajCmvBEenaDb5W2Y87Cww1eds+Cs7OzuaNWvG3Llz+frrr7l58yYbN25k3rx5nD17Fi8vL6P1PT09OXnyJAB//vknzZo1yzCuk5MTnTp1Ul8vWLCAZcuWsXjxYlxcXJg9ezZdunTh0KFDmJub06VLF0aMGMHhw4dp3LgxYEgSunTpkmH8zEyYMIHPP/+cP/74g7Fjx7JlyxYWLFhAYmIivXv3Zs2aNYwcOZJ58+axdOlSli1bhomJCRMmTGDs2LGsXLmSH3/80Sjmr7/+yqVLl6hevTpBQUHq75D2NwEIDg4mPj6etm3bMnLkSJYvX86ePXv43//+Z9SfQQghxLNHkgSRTlS8CRZO3prESkm2IiHOcIJpYlq0Zl/Wki5NcyNTnQmlbBw1ietpZwVhhuef+f6Fb7W2Wa6fnfvRKbTaFGH0Xo/NUbBZm8nfMnPrwxaUcsx5z/aVK1dSqVIlNmzYgKIovPLKK/Tq1YvBgwdja2vc5MrW1pbQUMM02tevX6djx45Gy318fLh//776esWKFfTq1Yu5c+fy2WefqU101q9fj5eXFwcPHqRVq1Z4enrSsGFDtmzZQuPGjQkODub48eMsWrQoV9vet29fmjRpQpMmTZg7dy7t2rVTmystW7aM27dvAzB//nxmzZpF+/btAcOkQhm1Y12/fj1vv/02Xbp0oV69ehw5cgRLS0tMTR/9uU/9jUJDQ1m3bh316tVjzpw5ANSqVYuTJ0/yzz//5Go7hBBCFC+SJAhRhJmaPLr63iD5Uzj16VPFS0wsAax6ylLlrYcPH9K6dWveeOMNevbsyY0bN/j888+ZMmUKjo6OBAYGGq0fGRmpNk3y9PTk7t27RssDAgJITjbcjmnVqhXJyclERkZy69Yt+vTpQ9++fdV14+Pj1ZN2MDQ5mjNnDvPnz2fHjh2UKlUq1+1G69atqz53dnY2el2ihGHG76ioKG7dukW9evXUZS1atKBFixbq6/v37/PWW2+xb98+3nnnHebOnQuAo6MjcXFxJCYmqn0tIiMj1WV///03TZs2NSpT48aNJUkQQohnnCQJ4j+POi7rTRTNhtJMSdZmlCSRP9xMH/JHZePOrP/6LqX+i+3z9Hs97CxyvO6OHTuwsLBg1apHyUzFihXp3Lkz77//Pn/88YfR+oGBgbi7uwNQpUoVDh82Hn62QoUKgCEBSE0wUpOGn376idKlSxutnxoLDKMcjRw5kpMnT+Lv7/9EHZZNTEyyfA2GfhiA2vfhcYGBgdSpUwdzc3MOHDhg1KfAzc0NgLt37+Lt7a2un7os7R2GVDpd4Z8dXQghRN6SJEEAkLY5uJN9fJ4MpSm0l2jiwGTn0wB0ubgF92j3bD6RPRPghv0V6rrMByDcJCFXTYHyWmxsbLqTWJ1OR2xsLE2aNGHy5MlERUWpTWoCAgIYMmQIAO+88w7VqlVjz549tG7d2ijG/PnzSUoyDAXs6OiIm5sboaGh6tX6sLAwxowZw4wZM9Q7E15eXjRo0IDvv/+evXv3smPHjjzZZicnJ1xdXTlx4gQ+Pj4AnDt3jv79+3Ps2DFGjx6Ng4MDv/32G05OTkafdXFxoWrVquzbt48BAwaov0m9evWwtLSkUqVKHDx40OgzjydSQgghnj2SJAhRlOn0hOsNHXUTUxxJSSqhSdj45KBHL5KjNYmplXbt2jF+/HgGDRpEr169uHbtGnPmzKF79+7UrVsXPz8/+vbty7hx49iyZQs3b96ke/fuAFStWpWJEyfSsWNHJk6cqCYAmzdvZt++fZQvX179nhEjRjBmzBgsLS1xdnZm1qxZ3L59Gw8P4w743bp14/3338fe3p6GDRvm2XYPGzaMsWPHYmNjg4WFBdOmTaNcuXIAbN++nY8++ojQ0FC1/wUY7hTY2dkxZMgQJk2aRMmSJUlISGDq1KnMmzcPgMGDBzNnzhzGjBlDz549CQgIICAggJIln+Uhi4UQQkiSINIJizLPk1mHdSYyBCqABTEkR9/OfsUcMDMJwyHxvzb4Dntwd/kj6w/k0J2k5zSJkxdKlizJ3r17GT9+PB06dMDBwYGuXbsyY8YMwDDC0IABA2jTpg1VqlRh7969RlfXp0+fzgsvvMAXX3zB/Pnz8fLy4uWXX+bo0aOMHTtWXW/ChAkkJiYycuRIQkNDadasGdu3b093F+O1115j9OjRdOzYMdPmQFqYNGkSCQkJvPvuu0RGRtK2bVsWLlxIaGgo8fHxTJo0iUmTJhl9ZvXq1fTp04dhw4YREhLC22+/jV6vZ8KECeqkcT4+PuzevZuxY8fy1Vdf0aBBA7788kumTdNmhDMhhBBFk05RFCX71cSTSJ384uTJk4V+MrXpK/vxkYnhKvS0lAd8OKBwd14tamZ/8y7vJ1kDcNJiFZ4Jodl8IvfOeyzg5Ua5G34zMwePbOS5O+8BcLHMl7zcLH9nEBZCCCGEQUGdT8qdBCGKCZ2pDXqbUprFEkIIIcSzS5IEIfJBHI86/m6N70qXeq9rEvfiX4eoFGEY6jIxKY9nVRM5Fh4ejouLS5br7Nixg1atWuVTiYQQQojckSQhH3Tt2hVLS8ss1xk6dChDhw7NpxKJ/PeoHfsM7JhxbKeGsd8GYE1MpIYxxdOws7Pj/PnzWa5TqpQ2d32EEEIUXUuWLMlwYsy04uLi8qk0xiRJyAebNm0q9H0SRNEn9xEKDxMTEypVqlTQxRBCCFHI5eQicWqfhPwmSYIQ+cDG1IwLDQzzGfzPvw4ngn00ietjdYvDJQ13EKITNAkphBBCCFF8k4S7d+8yefJkdu7cSUhICN7e3vTo0YMPPvgg26Y/2fnzzz+pVasWXbt25bvvvtOoxKI4M9GbUNLSMGtuvM6aO3GumsQtoQ8DDElCigxUJoQQQgiNFMsk4ebNm9StW5d79+4BhtlT//nnH6ZPn05AQAAHDhzAwsLiiWInJyczYMAAdWZWIXLCQpeiPreyD4JKEdoEjo8CzAGIQ+qkEEIIIbRhUtAFyAsDBgzg3r17NG/enOvXr/Pw4UNOnTpF6dKlOXr0qDrp0pP4/PPPOXnypIalFc+CtPNvmeiTwCxBm4deeiIIIYQQQnvF7k7C6dOnCQgIwM3NjR9++IESJQwThFWvXp3NmzdTp04dFi9ezKRJk3Ld7OjKlStMmTIlD0otijvLNPl4SzsrZtftrUnc7w5+x9n4eMMLaW4khBBCCI0UuyTB398fgA4dOqgJQqratWtTtWpVLly4wOHDh2nevHmO4yqKwsCBA4mLi6Nv376sXr1ay2KLYi7tnQQ7j3BiIiZpEreeVxRcqw6AKfGaxBRCCCGEKHZJwtGjRwFo06ZNhsvbtGnDhQsXOHr0aK6ShOXLl3Po0CHefvtt6tevL0mCeGLTfmvD9YdW2a+YAxVML0LZ2wBExadks7YQQgghRM4UuyThypUrAFSoUCHD5eXLlwfg6tWrOY55584dxo8fj5eXF7NmzWLbtm1PXU7xbElR9Orz9jXdCU9y1CSuLiGFc8GGJCEaGQNVCCGEENoodklCcHAwYBjRKCNOTk4A3L9/P8cx33nnHSIiIlizZg0ODg5PXUbx7LEzf9TBeFKl/2kW906cGV8G+wFgairNjYQQQgihjWKXJMTExACPkoHHpb6ful52vv/+e/z9/enatSudO3d+ojJFRUUREfH0Q15aWFg88dCtomB5W5rn+XeYJsmdBCGEEKKwiI+PJz7+6S/gRUVFaVCa3Ct2SYLy3wgvSiYjvej1hmYfycnZDx354MEDRowYgaOjI4sWLXriMjVp0uSJP5vW5MmTZXSlIqqMbQnuzjysedzfq9SH/7o36FNkOFQhhBCisPj000+ZOnVqQRfjiRW7JMHa2pqIiAgePnyYYZOj1DsINjY22cYaNWoUwcHBrFy5Eg8Pjycu06FDh3jxxRef+POp5C5C0WVh50K09eeaxw1P+SvNKxkCVQghhCgsJk6cyHvvvffUcc6cOaPZBefcKHZJgqurq5oklC1bNt3yoKAgAFxcXLKM88svv7Bu3Tpefvll+vfv/1RlsrW1xd7e/qliiKLNyqs8Pv2/BSDml99R4rVpGhRl3g71VgIKKcnBmsSFtM3xZNQkIYQQIre0aiZua2urQWlyr9glCb6+vly9epXLly9To0aNdMvPnz+vrpeVa9euAfDzzz+jSzvIfRrff/8933//PQDXr1+nTJkyT1N0UYyZ2ZfCNC4aAI8eezAxcdYkbtL30x69MEnhxMVxmsQNjY9TnyemxGWxphBCCCGKo2KXJNSrV4/du3ezd+9eevTokW75nj17AKhfv36Wcezs7DIdRjUiIoKgoCBsbW3VZkimpsXupxQa0pk8qh96a09M9K5aRVafRZq4Me/GKE2iOiQH0pAfAHiQkPedroUQQghRuBS7M9tOnToxZcoU/P39CQkJMWpWdPz4cS5duoSjoyONGzfOMk6XLl3o0qVLhsvWrFlD3759ad++Pd99952m5RfFX0ryA81imZjkQz8EaW0khBBCPHOKXZLw4osv0rJlS/bt20ePHj1YtWoVpUuX5tSpU3Tt2hUwdEi2tLRUP1OlShXA0Av9SYc5FSKnooOHahbL0dYdogz9XVxjwnjld21GULpvEQmehudJcUmaxBRCCCFE0VHskgSAr776inr16rF//368vb1xdHQkLCwMgEaNGjFx4kSj9S9dugRAeHh4fhdVCM3oScYuQZsJ1cJNHnWsTk6O1iSmEEIIIYqOYpkkeHt7c/LkSSZPnsyOHTt48OABvr6+9OzZkwkTJmBuLm2sRf7SmThj5/G95nGjoyc8emEWjHtZbe5SRFJNkzhCCCGEKJqKZZIA4OnpyfLly3O0bmYTr2WmT58+9OnT50mKJZ5ROp0enWadldPSq88S9Y6kNNqgSdRrR/dSKfkAABEJJtwOi9UkbloedhaY6k00jyuEEEKIp1dskwQhngU6nU6dQ00XG0XU9BaaxDWp3EHtk/DtVStmfar9XZDjYztT2tVJ87hCCCGEeHqSJAghsrStzMg8iRsdXA5c838GSSGEEEJkT5IEIYoyE506RKnOxgnfebc0CXtpz0KI99ckVmbC4oLyNL4QQgghnpwkCfmga9euRkOuZmTo0KEMHard0JjiWfGoTX+4zpT7FtpM3X7XsiRjLH8DYHKZebg53NUkbtBDPfbnfwUgISFRk5hCCCFEUbVkyRKWLFmS5TpxcXH5VBpjkiTkg02bNlGjRo2CLoYo5j5JtuKTDTM0jGi4K+GQ2IUYu4qaRHSL+I33MCQJMv2CEEKIZ11OLhKfOnWKmjVr5lOJHpEkQQiRpdhkW+JSvDWJFZ/s+OhFSj7MFi2EEEKIJyJJghBFmD0KFxqfB2DlLg923nlOk7iJpslcLfcAAK+HP9A7cYgmcS/EVYLUaUpkkjYhhBCi0JIkQYgiTK+DkpaGtv3jSnzPWFttZlwOVGypxdsAOCUE45iiTZ8EC6WkJnGEEEIIkbckSRCiCLPOh7nIUlxfwq3z15rEsj6yEe78oUksIYQQQuQdSRKEKMJizF2AywAs188gzOp5TeJaxl2AlEAAdCbm6G1KaRJXZ2qjSRwhhBBC5C1JEoQowkx1jzr/ljBTeLNpM03irt99Gf4bcU1Bp0lMIYQQQhQdkiQIUYTpeJQknLVpzy9HwjSJq7esAXE3AEjETJOYQgghhCg68qFFsxCiKEuWkUqFEEKIZ47cSRCiCDNXYtXnAYGbuJuk1yRulZQwUm8gxMXHZrmuEEIIIYofSRKEKMJM0jQ3irtWntgoO03iJtpchbK3AUiJC9MkphBCCCGKDkkShCjCHNJ0F3C30W5yMmerOPW5qV7uJAghhBDPGkkS8kHXrl2xtLTMcp2hQ4cydOjQfCqRKC5cS7iqzw/0/F6zuHfizKj6ix8AZmaJmsUVQgghxCNLlixhyZIlWa4TFxeX5fK8IklCPti0aRM1atQo6GKI4shKm/kLsiY9l4UQQoi8kJOLxKdOnaJmzZr5VKJHJEkQoggra1+ea6vfAOCBfh1xMRGaxD1X1u/Ri5QUEkNvaxI3OT7y0QslWZOYQgghhNCeJAlCFGHWjiVJjr4FgCNDNIt7JeYoWBue6xOTODmljiZxH7iVhf9ufiTGa9eHQgghhBDakiRBiCJMp9dmyNOsRFh5Ma/1GU1iOSQH0iS0OgCJydLXQQghhCisJEkQoggzszXHp8+3AFg5z8BE76RJ3N3bD0Aez7SclJKQp/GFEEII8eQkSRCiCNPpTTC1NTTbSUwYrVlcB1t3+G/OBdeYMF75/bAmce9bRIKn4XlMjEz4LoQQQhRWkiQIIbKkJxm7hHhNYoWbPLp7IGMmCSGEEIWXJAlCFGE6E2fsPLSbHyFVVNwk9fl90yTalbqoSdy3kiNJ7QKdkBjGzdu/axI3LQ83P8zNrTSPK4QQQjxLJEkQogjT6fTo9K7Zr5hLKcqjpkDJuhSCrLQZiSgpzYQwbZLnQsBcTeKmdbvpEcr51Nc8rhBCCPEskSRBCJGOXqdTn5cwt+DWa//TJO7OLfMgj6dHCAu9Dz55+x1CCCFEcSdJghAiHR2PkgRTnQmlbBw1iWtl6sBkh9MAdPnjbWyjrmsSN9regRd8/gEgLDxIk5hCCCHEs0ySBCFEvtFb2xOu9wJglltjLnq+qEncWkoYK/hHk1hCCCGEkCQhX3Tt2hVLS8ss1xk6dChDhw7NpxIJkXNRSQlcu6NNx+WklHhMogwn8wNK+lL3xbqaxP3z7C54uEGTWEIIIUR+WbJkCUuWLMlynbg0/fnykyQJ+WDTpk3UqFGjoIshxBOZjiPT967UMOI1ANpZlMW3bDVNIt66fhweahJKCCGEyDc5uUh86tQpatasmU8lekRmMxJCFIjEpJiCLoIQQgghMiF3EoQQ6ViaWnKhwXkA4o7/jhKfkM0nciYIG9rRC4D7cdoMqyqEEEII7RXbJOHu3btMnjyZnTt3EhISgre3Nz169OCDDz7Itn/A427cuMHkyZM5ffo0V69excfHh1q1avHRRx9Rrly5PNoCIQpOiokFJS0TAWid8hY30eaqv0/8A7AwPI9MkTmXhRBCiMKqWCYJN2/epG7duty7dw8AR0dH/vnnH6ZPn05AQAAHDhzAwsIiR7H27NlDt27diIyMRKfT4ebmxl9//cWFCxfYtGkTq1atolu3bnm5OULkOzszM/V5hIk7YSbadJqKMnnUwlGXnKRJTCGEEEJor1gmCQMGDODevXs0b96clStXUqZMGU6fPk2nTp04evQoM2bMYPr06dnGSUxMZOjQoURGRtKvXz/mzZuHg4MD4eHhTJs2jblz5zJo0CAaNmyIl5dXPmyZEPmjhOmjeRLaezQhPMlRk7i6hOucC94PgLlOmhsJIYQQhVWxSxJOnz5NQEAAbm5u/PDDD5QoUQKA6tWrs3nzZurUqcPixYuZNGlSts2ONm7cyNWrV6lUqRIrV65E998stA4ODnz++efcvXuX7777jrlz5zJnzpw83zYh8otZmsnUJlXSZrZlgDtxZnwZ7AdAiiJ3EoQQQojCqtglCf7+/gB06NBBTRBS1a5dm6pVq3LhwgUOHz5M8+bNs4x14cIFAHr16qUmCGn169eP7777jtOnT2tUeiEKB72lU55/h1lKAsnRt7UJlhT+6LmSok1MIYQQ4hlW7JKEo0ePAtCmTZsMl7dp04YLFy5w9OjRbJOEa9cM47mXKVMmw+WpTYyuX7/+hKUVonCqXtKNs7P/0jzuhYrVwNrw3DwljKCNpTWJ64yb+lxJjNAkphBCCPEsK3ZJwpUrVwCoUKFChsvLly8PwNWrV7ONNXLkSHr27Ent2rUzXH7ixAkASpfW5kRHiMLCwaUCVd82jNwVuuEvkmMTNYn7i+mju3vJOlPCTDw1iRuFI+4pQQBEJMidBCGEEOJpFbskITg4GDCMaJQRJydDM4r79+9nG6tu3bqZLgsLC+Pjjz8GMr9rIURRpTO1QO9g6LPjOki72cIdjigQZXgeblGaKc5ntImbHMjU0OoAxMvQqkIIIcRTK3ZJQkyMYTz31GTgcanvp673JP7991+6devG1atXcXd35+23385y/aioKCIinr4JhIWFRY6HbhXiaZjauYI2o57mu+RY6RAthBCi4MXHxxMfH//UcaKiojQoTe4VuyRBURSjfx+n1+sBSE5OznXsxMRE5s6dy7Rp04iJicHa2ppt27ZlmpCkatKkSa6/KyOTJ09mypQpmsQSIismZm7YeXyvedyo6Inqc+eoSNpcPaxJ3PsWkfBfyyWT5DDtOkSnYWLlgc6k2P3JFEIIkUc+/fRTpk6dWtDFeGLF7n88a2trIiIiePjwYYZNjlLvINjY2OQq7l9//cUbb7zB2bNnAahSpQobNmzAz88v288eOnSIF198MVfflxG5iyDyi06nR6d31Txuik4P/+XvD01jGVrigCZxuwCN/3veNHEuQRvnahI3LedXr2PhkPEgBkIIIcTjJk6cyHvvvffUcc6cOaPZBefcKHZJgqurq5oklC1bNt3yoCBD50YXF5ccx1y1ahVDhw4lNjYWKysr3n//fd5///1s51lIZWtri729fY6/T4jiSm9iAv/1K7azsGDPm9M0ibtzyzzI/c3BXAkMCqWsJAlCCCFySKtm4ra2thqUJveKXZLg6+vL1atXuXz5MjVqpO9wef78eXW9nNi0aRMDBgxAURSaNGnC6tWr8fHx0bLIQjwzdGkmaUskgeSYu5rEjdIlM8V+GwD7zzwkJl6bzstuZg/ZVWEMANfu/kVZ3+qaxBVCCCEKu2KXJNSrV4/du3ezd+9eevTokW75nj17AKhfv362sW7evEmfPn1QFIVhw4axYMECTExMNC+zEM+iKcnOTPlppXYBw38FoIxJZe4muWsXN1VytPYxhRBCiEKq2CUJnTp1YsqUKfj7+xMSEmLUrOj48eNcunQJR0dHGjdunEUUg5UrVxITE0ObNm1YtGhRXhZbCKGRsU4b8PW6okmsO8k+msQRQgghippilyS8+OKLtGzZkn379tGjRw9WrVpF6dKlOXXqFF27dgVg1KhRRv0JqlSpAhh6oXfu3Fl9/4cffgBg3Lhx+bcBQhRj5lhxobGhyd/NNVewCM1+vpKceGBtQ7cqvQBw8KxLi1e+1iTuwSMb4Y42nauFEEKIoqTYJQkAX331FfXq1WP//v14e3vj6OhIWFgYAI0aNWLixIlG61+6dAmA8PBw9b2UlBT+/fdfAPr374+ZmVmm3+fn58fWrVs13gohip9Ec1tKWhpmbz5b1htftweaxE1IetTX4WFiCnqbUprE1ZnmbhQ0IYQQorgolkmCt7c3J0+eZPLkyezYsYMHDx7g6+tLz549mTBhAubm5tnGCAwMJDHRcDJz48aNLNfNbHZnIcRjTB6dzC+L7s+t8JyNEJad0vor4BACQFiMTKYmhBBCPK1imSQAeHp6snz58hytm9HEa6VKlcp0QjYhxJOx5lHH/+2vrtIs7p04M6r+YpizxMY0PJu1hRBCCJEdGapHCJFvPG3y/ip/dIok90IIIcTTKrZ3EoQQhY9ZyWrU+DVR87g1lEj1eQo6zvzzryZx74cnUeW/5wlJKZrEFEIIIYoCSRKEEPmmSYXa+Nuu0DzuT3+UYBOeAMSZO/DpZW1mp3QwaUDT/56HRidoElMIIYQoCiRJyAddu3Y1GnI1I0OHDmXo0KH5VCIhCoatpZU6upGWrE3yvolR8IMQjl/8U/O4L5StiJWlleZxhRBCFH5LlixhyZIlWa4TFxeXT6UxJklCPti0aRM1atQo6GIIUeB0Js7YeXyvedzI6Anqc+eEa/S611STuDd1ldWeWwtuVGTUXzc1iZvW4f5JNKhaU/O4QgghCr+cXCQ+deoUNWvm//8TkiQIIfKNTqdHp3fVPK5iYgrJhudxCoxIrKRJ3DaYU/2/i/z61C/QWEx8WJ7EFUIIIZ6GJAlCiGLg0fwLdayO8Yr+oWaRAxVD/4bV9b/H2UubRCEk1prXt3YCIDlKJmwTQghR+EiSIIQo8vS6R0lC24TuefIdm67totz9vzSJ5QicrLIdgIvJX2oSUwghhNCSzJMghCjy4rHO8+8IRzoXCyGEeHbInQQhRJHnZG7GhQZnNI97P96Ml48b+jfcsmiMW7dtmsQ9fGQ9vncmZL+iEEIIUUAkSRBCFHkOlvo8GVo1rRSTeC5GOWkS63aSC77/PU+QGaKFEEIUQpIkCCGKvIpOltQ40FDzuDVMwtTn8aa2TP8jWpO4Dsk1afbf8wexuizXFUIIIQqCJAlCiCKvcpkabHeZo3ncPefd2BRfGoBIBWKSIzWJq0+OVkdNio3SbiQmIYQQQiuSJAghijw7r2ZUeGiredwd0avU518kJ8ODpZrF/o63AZgdd5OTZzZpFjdVZd8W2Ng4ah5XCCHEs0GSBCFEkac3s0bv1kTzuIpuPeRxl4HGyWvxOvOF5nEvpmygVo1umscVQgjxbJAkQQghMmFvouNCw/MAxB3/HSU+QZO4QdjQjl4AhMbZ4W0TpUnctBI0KqsQQohnkyQJ+aBr165YWlpmuc7QoUMZOnRoPpVICJETZiYm6qhJsbpoFF28NoHT3J04yCvsMtem07VTwkWGMBuAuDhtOlkLIYTIO0uWLGHJkiVZrhMXF5dPpTEmSUI+2LRpEzVq1CjoYhQIJTmFxKi8rdxmtpbo9DIvoNBenLmz+nwCg7mnaFOXSyRHgP6/F+b23Hdop0lcwgG5gSCEEEVGTi4Snzp1ipo1a+ZTiR6RJEHkqcSoOM7N+jFPv+P59zti7pD3M+6KZ4/e9FFn6AWNL2gW906cGd/+UgoAM9N43nE5rUncm9E31SQhISlv540QQghRvEmSIIQQmXA2y/vviDexZ2lIdU1iOVi60zJmMgBJYReIvb1Pk7hpmbs1QG9uo3lcIYQQhYskCSLfVH63JWZ2VprESoyM5dIX2p8ACZFWOQsrji4M0jzulXK+YG54rmVLuRRMCDPxBODF6OWEBWg3ZGsqi0Y/4ly+g+ZxhRBCFC6SJIh8Y2ZnlSfNghIjYzWPCdLXQUDFynVIKbVR87hX/jSD/7o3JBOPfcRRzWLPNTMkBm/HDqWkyR3N4qb68959mpXXPKwQQohCRpIEUeTl1R0F6esgrG1K8u9nJzWPG/FCGfWv75wkHST9qvl3XL03kEvhZTSJ5Wb2kF0VxgCgJKRoElMIIUThJkmCEEJkwsyxJOWnXtE8rumPk/N8krYGNsG0j7fXJFac9W31eVKyDJ8khBDPAkkSRJFkZmvJ8+931Dxu2r4O0oxJ6PSmmDmX0jyus1UJLtQ6q3nc+/FmvHy8kuE77B/QyvaAJnFDdXbq84gHN0mOvp3F2k/GxMoDnYn8lySEEIWF/EUWRZJOb5LnTYGkGZPIK3Y2FuokbXnlIa7MtFmhSSzrlFB6RY4A4Gb0XQLWN9YkbloNXtuNXYmKmscVQgjxZCRJEADo0jR90KfoSQiP0SRuXl2NF6IoK+VWieeWV9E8bp0S/4BdJABNH+yh111tkoS09jpWYE7Uy5rH3XjtHA0lSRBCiEJDkgQBgImiV5+XCffO8wnQCqv8aMYkhF+pWqwP1f5E+4jrS2zDGwCTdpVItiin+Xe8vn8Pq0prNxpTqouRX2oeUwghxJOTJCEfdO3aFUtLyyzXycm03CLv5UczJiHs3SvQcOoJzeOe3Pkp/NeKKbVvgtYW8WeexL33MIwbgf9qHtfLtRRmZuaaxxVCCC0sWbKEJUuWZLlOXFxcPpXGmCQJ+WDTpk3UqFGjoIuRY3ds79Bt0FjN45rZZp0oPSukQ7TIqw7RJawdIDxvRx+6llyeNYGtNYkVTwxeDv4ALPkHgo4f0iRuWj8PrkHl8tU0jyuEEFrIyUXiU6dOUbNmzXwq0SOSJIh0kvTJcjU9D0mHaJFXarqVpL1pG83jOnCO9Q9+A2CupS143tMwehMAlpbcRCfr3zWMa3Dk3FSsLbT/r87TvQJmZhaaxxVCiMJCkgQhigm5QyF8q73OosvbNY/77V+/A7aax03LyipvElybM+u4+UvWt/KfRNxQfyqWr6N5XCGEKCyKbZJw9+5dJk+ezM6dOwkJCcHb25sePXrwwQcfZNs/IC9jiWdTfnSIljsUwtLOBZ+aAzSP6331FBcanNc8btp5HdxtbnE3yVnz73ArGYinT7Tmcf8JOkYFn7Kax9WZOKPT6bNfUQgh8lixTBJu3rxJ3bp1uXfPcEvc0dGRf/75h+nTpxMQEMCBAwewsMjZbWItY4lnl3SIFkWZq0cj+p/QaR63qu7RUMtto1/VPD7APN0/tNEd0zyuSfxO/j7/k+ZxPUsvwtE5bzqdCyFEbhTLJGHAgAHcu3eP5s2bs3LlSsqUKcPp06fp1KkTR48eZcaMGUyfPj3PYyUkJBj9K4qG+Ph4Pv30UyZOnFjoE0CZefqRorTfipqGNdoyx9ZN87g//ukPiUmw4wS8UgfMtP8vqbL9eWKSkjSPq/x7CUeTG5rHPXRjBS+++IrmcT096mBubqNZPDneiibZb0VTQZ1P6hRFUbJfreg4ffo0NWrUwM3Njb/++osSJUqoy37//Xfq1KmDo6Mjd+/ezbap0NPG+uWXX2jSpAmHDh2icWPtZyjV0icrBjLJ1BGAj5PC+GDQVwVboAIUERGBg4MD4eHh2NvbF3RxCkRCeEyez5VRaUgLTe+uRERE4OrtSVjoQxycHDWLK/LOr7s+5lLKGQa338Tyn7pibWOmSdw7EWa8/8BVk1iZmWpxj7LJgZrHrZ54lBIm2g93eLPs15T2bKBZvMioKCrXrEXI/SBKuOXtby20I/+/FU0FdT5Z7O4k+PsbhtPr0KGD0Uk9QO3atalatSoXLlzg8OHDNG/ePN9iCSGM/b0sQNN4UfGGpivRgQ+xMik64+I/yx3Da9brivcNQzLaquRN7O20+S/p29OlNYmTlcnxHoCH5nFnWrriEftQ87jJx74l7OFnmsWLjk8G4I+f11ChsvYzZZeq2BQLazmJFaIgFbsk4ehRw0ygbdpkPAxgmzZtuHDhAkePHs32xF7LWFpbsmRJ3ky+tv80NNG+M15elTfPfoc8UlR+h9RmTEtXfMk7g97WLG5CeAx/Lwtgw8m9dK/ZSrO4af3z9QHuWmjb/yOvyrvh5F4+XDFb8/4qS1d8yYjRIzVPPrSuZ1aOFXDgO6A8DmW+w97eTpO41QLXsiZmGwEBgbRo4aVJTIBbph78L76M4e9k8+qaxVXtP82E5tXBROMkZ/9pPmnjSoKbNslzMjqiYpOBy1wP2cjD09r0V0kBopMsOXDgDg2adcLH43lN4qbasSOAT2Z8jpOLtk3nisrf9bxW1H6Hoha3IBS7JOHKlSsAVKhQIcPl5cuXB+Dq1av5GktreVYJfz4jSUIeKiq/Q2pH6y+/XsHIsaM1i5uafPSqO5XpGxZrFjchPIaTC/KuedTGPEoSNp7cS/dl2sddsPxzBnTvrXnysXjRYgb16qdpzKQoa/XfJBNt2sy/WLE3ntYVmTZlKFMnvI/ORJuT2D1HdrHr+jYG77nJ8nLa9Ue45+ZBP5t6hr+/eZF8/HyGD5pru98gHoAhJnUADdu2mwIHz5FYFzZduKtdXOD3rXtxqvEB1vpkTeMu/HgrcdZ/anZClQSEppjx5cff8YdZICmm2vy+iqIjIcZw4WDrPn/KeXhqEjfVZ3Nm06NNdWwstGk2mGrxgs95Z/AATMy0HU2yqPx/XJCKXZIQHBwMGEYhyoiTkxMA9+/fz7dY3/jP4re/12b7fbkREnaXT1YM1CxeskUyaPt3U4h0UpMPnYlO0xNYM1tLqo5qB3Oh6qh2mre1tdgyVdMO4ql3VPJSXsSPD4nUvK9KajOxC/N3YqvxHaCUiBRcbetpVtc6NK7IrxZlSTGfTXildzSJCXAz8g7tkmP5BYXGydoNFFDKPI7lyU6axctPm+wB+xBtg+qT+NTMUduYhsCMReOmUSYAOiCaG0Ha3GEDSIoz9Hc5HHSJS/G3NIsLEJUQx5c/r0afpO1gF6GRocxcPRTD76Edrc+j8jLutcvaJsw5VeyShJgYw384qSfwj0t9P3W9vIwV99/B+FVgDJhGZvt92dLrwfS/8bMTFSbdiX/6mEbuQ2Iyd26EcOrUKU0jx8XFaR4zr+JGRUUBcObMGWxttZ1Aqij9DkUtbup+++vaZc33W3xCPOevXtIsnpKcQlLr0ijfmpHUWrumJYlRcdzYfIL4pAQu3bumWdxUeRE3JsHwd/Ly/RtYm2t7pTA+KYHtU1ZoGhNAiVZI2q/dKCPlcaU8cCbGlDePlNIsbqwST2mHeyyMT2HEn1GaxU2yTKRctXP0AdY4HcfaWptTieAEU969UAYSk+FG9hfycq0Ixl0d+AB4oF3MeEO9XXHzMlho3HcrIZ5Jd/JgNLwkmHQnD0b1yZPzqMfiJiVDsgZXYAMN55mp55X5RilmTE1NFUB5+PBhhsv37dunAMpLL72U57FWrVqlAPKQhzzkIQ95yEMe8pDHUz1WrVqVw7NhbRS7OwnW1tZERETw8OHDDJsJpV71t7HJvu3r08Zq3749q1atwsvLS5OZmc3NzTE3LzqjtgghhBBCPKsSEhI0mdsgLi6OwMBA2rdvr0Gpcq7YJQmurq7qiX3Zsuk74AYFBQHg4uKS57FcXFzo27dvLkovhBBCCCFEwSt2g3P7+voCcPny5QyXnz9/3mi9/IolhBBCCCFEUVHskoR69eoBsHfv3gyX79mzB4D69evnaywhhBBCCCGKimKXJHTq1AkwzJYcEmI8fNrx48e5dOkSjo6OOZrW+klj3b17l8GDB1OqVCksLS2pWLEiH330Uf73SheakP1ZML799lt0Oh1///13pus8yb6R/Zm3crLfnoTsN+3duHGDvn37Uq1aNWxtbfHz86Nv3778+++/Ga4vx1vhkNv99iRkv2nvwIEDdOjQgXLlymFvb0/dunWZOHEi0dHRGa5fKI63fO0mnU9atmypAErz5s2VmzdvKoqiKCdPnlRKlSqlAMqUKVOM1q9cubJSuXJlZevWrU8d68aNG4qHh4faE93R0VF9Xr9+fSUuLi5vNlrkyNSpU7MdPSAkJERdX/ZnwWnfvr0CKJcuXcpw+ZPsG9mfeS+7/ZbbY1BRZL/lhd27dyt2dnYKoOh0OsXd3V3R6XQKoNjY2CgbNmwwWl+Ot8Iht/tNjrfCYebMmep+MjMzU9zc3NTftEyZMsq9e/eM1i8sx1uxTBJu3LiheHp6ZvhDNWrUSImPjzdaP3XZ6tWrnzpWixYt1KTi+vXriqIoyqlTp5TSpUsrgPK///0v7zZcZKtXr14KoJQsWVKpUKFCho/Q0FB1fdmf+S8lJUVZunSpepxldrL5JPtG9mfeyel+y+0xqCiy37SWkJCglC9fXgGUfv36KWFhYYqiKEpYWJjy3nvvKYDi4OCg3LlzR/2MHG8F70n2mxxvBe/69euKubm5YmpqqnzxxRdKQkKCoiiKcvXqVaVevXoKoLzxxhtGnyksx1uxTBIURVECAwOVQYMGKV5eXoqFhYXi6+urTJkyJcNMKqskITexTp06pQCKm5tbusz8xIkTapIRGxur3YaKXEk9IK9evZrturI/89e+ffuU/v37K76+vkZXuTI62XySfSP7M2/kZr8pSu6OQUWR/ZYX1q9frwBKpUqVlJSUlHTL33jjDQVQxowZoyiKHG+FRW73m6LI8VYYjBs3TgGUvn37plt27do1xdTUVDExMVF/08J0vBW7PgmpPD09Wb58OXfu3CEuLo7Lly8zefJkLCws0q2rGJIl+vTp81Sx/P39AejQoQMlSpQwWla7dm2qVq1KWFgYhw8f1mgrRW79888/WFhYUKZMmWzXlf2Zv3bs2MHXX3/NP//8k+26T7JvZH/mjdzsN8jdMQiy3/LChQsXAOjVqxc6nS7d8n79+gFw+vRpQI63wiK3+w3keCsMUvtnvfrqq+mW+fj4UKFCBVJSUtSRNAvT8VZsk4SCcPToUQDatGmT4fLU91PXE/krLCyMBw8eUKFCBfR6fbbry/7MX+PGjePMmTPqw8vLK9N1n2TfyP7MG7nZb7k9BkH2W164du0aQKYnjqn78Pr164Acb4VFbvebHG+FQ2hoKI6OjpQuXTrD5VZWVgBEREQAhet4K3aTqRWkK1euAFChQoUMl5cvXx6Aq1ev5luZxCOpVzorVqzI5s2bWbduHdeuXaNs2bLUqlWL4cOHY29vr64v+zN/eXl5GZ1gZjW7+JPsG9mfeSM3+y23xyDIfssLI0eOpGfPntSuXTvD5SdOnABQT2rkeCsccrvf5HgrHH799ddMlz148IB//vkHU1NTKleuDBSu402SBA0FBwcD4OjomOFyJycnAO7fv59fRRJppB5EO3bsYOvWrer7f/75J9u2bWP58uX88MMP6vwYsj8LryfZN7I/C15uj0GQ/ZYX6tatm+mysLAwPv74Y+DR1Uc53gqH3O43Od4Kp4SEBEJCQjhz5gzTp08nKiqKIUOG4OLiAhSu402aG2koJiYGeLQzHpf6fup6In+lXlVJSUlh6tSpnDt3jrCwMA4ePEidOnW4efMmb7zxBrGxsYDsz8LsSfaN7M+Cl9tjEGS/5ad///2X5s2bc/XqVdzd3Xn77bcBOd4Ku8z2mxxvhVO1atUoWbIkr7zyCseOHWPo0KEsWLBAXV6YjjdJEjSkKIrRv49LbROYnJycb2USj3h4eNCjRw+++eYbPvroI/z8/HBwcKBJkyb89ttvVKxYkevXr7No0SJA9mdh9iT7RvZnwcvtMQiy3/JDYmIis2bN4vnnn+fUqVNYW1uzbds29cRCjrfCKbv9Jsdb4eTt7U3JkiXVzuebNm1iy5Yt6vLCdLxJkqAha2trAB4+fJjh8tQMzsbGJt/KJB4ZPHgw3333HT169Ei3zMzMjHHjxgFw7NgxQPZnYfYk+0b2Z8HL7TEIst/y2l9//UWtWrWYMGECMTExVKlShePHjxs1QZHjrfDJyX6T461w2rNnD7dv3yY6Opq5c+cSEhJCz549OXLkCFC4jjdJEjTk6uoKZL6TgoKCANR2Z6Jw8fPzAwx/fEH2Z2H2JPtG9mfh9/gxCLLf8tKqVauoVasWZ8+excrKiilTpnDq1Cl1P6SS461wyel+y44cbwXLysqK0aNHM2zYMFJSUvjqq6+AwnW8SZKgIV9fXwB1rNvHnT9/3mg9UbikZuJ2dnaA7M/C7En2jezPwu/xYxBkv+WVTZs2MWDAAGJjY2nSpAl//fUXkydPxtLSMt26crwVHrnZb9mR4y3vBQcH8+WXX7Ju3bpM12nWrBnwaOjawnS8SZKgodTbfHv37s1w+Z49ewCoX79+vpVJGERHR/P888/zwgsvEB0dneE6qQfXc889B8j+LMyeZN/I/ixYT3IMguy3vHDz5k369OmDoigMGzaMn3/+GR8fn0zXl+OtcMjNfpPjrXDQ6/UMGTKEgQMHZtpfICwsDHg0z0WhOt5yNT+zyNLp06cVQHFxcVGCg4ONlh07dkymMy9gzZs3VwBl0aJFGS5v2rSpAigrV65UFEX2Z0Hz8fFRAOXSpUvplj3JvpH9mT+y2m+5PQYVRfZbXvjoo48UQGnTpk2O1pfjrXDI7X6T461wKFeunAIo+/fvz3B5hw4dFECZPXu2oiiF63iTJEFjLVu2VAClefPmys2bNxVFUZSTJ08qpUqVUgBlypQpBVzCZ9emTZsUQLGyslLWr1+vpKSkKIqiKPfu3VP69OmjAEr16tWV5ORk9TOyPwtOViebivJk+0b2Z97Lar89yTGoKLLftFapUqUsT1oyIsdbwcvtfpPjrXD47LPPFEApV66c8ssvv6j74cGDB8qIESMUQHF3dzc6uS8sx5skCRq7ceOG4unpqQBq5pb6vFGjRkp8fHxBF/GZNnToUHV/WFpaKm5ubuprX19f5fz580bry/4sONklCU+yb2R/5r3s9ltuj0FFkf2mpeTkZMXMzEwBlDJlyigVKlTI9NG5c2f1c3K8Fawn3W9yvBW8pKQkpXHjxupvaG1trXh4eKivHRwclN27dxt9prAcb5Ik5IHAwEBl0KBBipeXl2JhYaH4+voqU6ZMUeLi4gq6aEJRlG3btilNmzZVvLy8FHt7e+Wll15SPvjgAyUqKirD9WV/FozsTjYV5cn2jezPvJWT/ZbbY1BRZL9p5datW+qJQ3aPWrVqGX1WjreC8zT7TY63gpecnKwsXbpUadSokeLp6anY2dkptWvXVoYNG6bcu3cvw88UhuNNpyiZ9KQQQgghhBBCPJNkdCMhhBBCCCGEEUkShBBCCCGEEEYkSRBCCCGEEEIYkSRBCCGEEEIIYUSSBCGEEEIIIYQRSRKEEEIIIYQQRiRJEEIIIYQQQhiRJEEIIYQQQghhRJIEIYQQQgghhBFJEoQQQgghhBBGJEkQQgghhBBCGJEkQQghhBBCCGFEkgQhhBBCCCGEEUkShBBCCCGEEEYkSRBCCCGEEEIYkSRBCCGEEEIIYUSSBCGEEEIIIYQRSRKEEEIIIYQQRiRJEEIIIYQQQhiRJEEIIYQQQghhRJIEIYQQQmhi6tSp6HS6XD/69eunxrhx44b6/qFDhwpwa0RBOHToULr64eTklOVnIiMjWbNmDa+99hqVKlXCwcEBR0dHqlevzmuvvcZnn31GeHh4npQ3Li4OR0dHdDodL774Yo4/pygK3t7e6HQ6ypQpg6IorFmzJt22ly1bNk/KnROSJAghhBBCZKFfv37odDqaNWtW0EURj1m5ciUVKlSgb9++bNmyhcuXLxMREUF4eDhnzpxhy5YtjB8/Hm9vbyZOnEhSUpKm329paUm3bt0A+PPPP7lw4UKOPnf8+HFu3boFwJtvvolOp9O0XFowLegCCCGEEKL4OXjwIKVKlcrRunZ2dnlcGlEUpdYhE5P017RTUlIYNmwYS5cuBQwn6z169KB9+/aULVsWRVG4du0aR48eZeXKlYSHhzNz5kyuXbvGt99+m2HMJ/XWW2/x1VdfAfDtt9/y8ccfZ/uZDRs2qM979uwJQJcuXXjppZcAWLhwIQsXLtSsjE9CkgQhhBBCaM7Hx4cyZcoUdDFEEZZVHfrwww/VBKFBgwasW7cuXdOcmjVr0rVrV6ZMmcJbb73Ftm3b+OGHH/Dy8mLu3LmalfOll16ibNmyagIyY8aMLO8MKIrCpk2bAHjhhRfw8/MDDMlyasLs7OysWfmelDQ3EkIIIYQQRcbRo0eZOXMmADVq1GDfvn1Ztt23s7Nj48aNvPzyywB8+eWXmvZR0Ol09OrVC4Dr169z9OjRLNc/duyY2tQo9S5CYSRJghBCCCGKlBs3bjBkyBB8fX2xtrbG3d2d+vXrM3/+/Byd/O3YsYNXXnkFDw8PrKysqFKlCl27duXEiRNG66X2RVi9ejVgaP6S2qH0xo0b6nqp7124cIHo6GhGjRqFh4dHhleT9+/fz6uvvoqXlxdWVlY899xz9OjRg6+++gpFUTItc+p3nDx5EoD169fz0ksv4ezsjJOTE3Xr1mXu3LnEx8fn5CdMx83NDZ1Ox/r160lKSmLNmjW0bNkSd3d3SpQoQbNmzfj666+fKLbWpk+fTkpKCmBolmNtbZ3tZ0xNTZkyZQqmpqYkJCTw3XffZbruk9Sv3r17q8/Xr1+fZVk2btwIGPbpG2+8kW3ZC4wihBBCCKGBKVOmKIACKNevX3+iGNevX1djHDx4MN3y7777TnFwcFDXefzh5eWlnDx5MsPYcXFxyogRIzL9LKCMHz9eXb9v376Zrpd2+1LfO378uFKnTh2j9dJ+9+jRoxWdTpdpzBYtWii3b9/OsOyp65w4cUIZMGBApjEaNmyoJCYm5uo3v3btmvr57du3KzVq1Mg0fo8ePXIVO7cOHjyYZR26du2a+ht269ZN8+9/mvpVv359BVBcXFwy3QcpKSlKqVKlFEBp3LhxpuVIPZZ8fHw02a4nIXcShBBCCFEk/Pjjj7zxxhuEh4dTq1YtvvnmG06ePMnevXt5//33sbCwIDAwkKZNm3L79u10nx81apTaGbRLly5s3LiRM2fOsGHDBmrVqgXA7Nmz2bJlCwCzZs3iypUrdOnSBYC6dety5coVrly5QsmSJdPFHzZsGCdOnOD1119n3bp1HD9+XF324YcfMm/ePBRFoW7duqxatYqTJ0+yefNmtalKQECA+l2ZmTdvHitXrqRjx45s2rSJ06dPs27dOnx9fQE4fPgwy5cvz9XvmnoHRafT8f7773P27FlGjhzJoUOH+OOPP/jiiy9wdXUF4Pvvv2fbtm25iq+lAwcOqHdchgwZomnsp61fb731FgAhISHs3bs3w+84duyY+tnC3NQIkDsJQgghhNBG2jsJBw8eVK5cuZKjR3x8vBojszsJCQkJiq+vrwIovXv3VpKTk9N9/9mzZxUbG5sMr3hfvHhRMTU1TXe3IFVsbKzy4osvKoDSoEEDo2WpdxSaNm2a4XaT5krz8uXL0y2/du2aYmFhoQBKly5djLY31dy5c9UYGzZsyPI7xowZk2757du3FTs7OwVQ3njjjQzLmZmxY8eqsS0tLRV/f/906/z999+KmZmZAiitW7fOVfzcyO5OwpAhQxRA0el0SnR0tGbf+7T1S1EUJTQ0VDE3N1cApWfPnhl+z6hRoxRAMTc3Vx48eJBpeQrDnQRJEoQQQgihibRJQm4eZ86cUWNkliSsXr1aARQHBwclKioq0zLMnDlTARQrKyujk/G33npLARRXV1clJiYmw89+8803CqCYmZkZnYDmNEmoWrWqkpKSkm756NGj1RPDmzdvZhgjJSVFee655xRAqVWrVqbf4enpmWn527Ztm2U5M9O4cWOj5kaZ6datmwIoZcqUMXq/R48eOd7XI0eOzLIs2SUJXbp0UQDFyckpyzgDBw7MshyPn3w/bf16vHw2Njbp4qRtatSpU6csy18YkgQZAlUIIYQQhd7p06cBqF69Ovfu3ct0vfLlywMQGxvLmTNnqFOnDgBnzpwB4PXXX8fKyirDz3bt2pXnn38eAL1en+sytm3bNsPOyhcvXgSgZcuWlC5dOsPPps48PW7cOHX9jHTq1CnT8ru7u+e6zCkpKZw6dQqA+vXr07Fjx0zXrVy5MkC6379atWq4uLior4OCgtiwYQMVK1akVatWRuumTjz2pCIiIgAwMzN7qjiPe9r6leqtt95iy5YtREdHq82XUh09erToNDVC5kkQQgghRB64fv26pvMkXL58GTCMMFShQoUcfebBgwcAKIrClStXgEcneRmxtLSkWrVqT1xGLy+vDN9P/e7syl2uXDkAoqOjuXfvHh4eHunWSe17oJW//vqLqKgoACZNmpTluqnrPT6G/4QJE4xe//DDD2zYsIE333yTyZMna1hacHJyAgyJSGRkZKYT8X3yySfpygWwadOmDN9/mvqVVrt27XBxcSEkJIT169cbJQmpE6jZ2dnRvn37HH1HQZKOy0IIIYQo9K5fv57rz6QOVxkcHExMTAwAnp6eWhbLSOoJ7ONSrx5n991pk4ybN29muE6JEiWesHQZ+/3339XnjRs3znLdv//+GyDb5O+PP/4ADJOZaS1t4pR6Yp8RV1dXypcvn+4RHR2d4fpPU7/SMjMzo0ePHgDs2bOHkJAQAKMJ1Lp06ZLp3aDCRJIEIYQQQhR6qSfYb731FoqhT2W2j9STNWdnZ0xNDY0nUk/a8kJms+ymjoSUVTMWgPv376vP8zKZSSt1ZCMvL69Mr8oDJCcn8+effwLQqFGjLGOmzuWQF0lCgwYN1Oep5cmNs2fPZvj+09Svx6WOcpSUlKTOiXDkyBHu3LkDFI2mRiBJghBCCCGKgIoVKwJZXz3OjKmpqdqU599//810vZSUFLZv38727du5e/fukxU0A6nNV1KbHWUmdbmVlRWlSpXS7PuzknonIbvv+/bbb9WT3FdffTXT9RRF4dSpU3h5eeVJotO8eXO1P8LMmTNJTEzM8Wfj4uLSTZiX6mnq1+Nq165NpUqVgEcTq6UmCx4eHurMz4WdJAlCCCGEKPSee+45wNABOe1sx4/78ssvqV69Oi1btjSawTj18xs2bCAhISHDzx45coTOnTvTuXNnIiMjNSt7lSpVANi7d2+G4+uD4eQ6dWbnypUrZ3pXQkvx8fHqlfVr165lul5cXBzTp08HoE6dOtSvXz/Tda9cuaLOM5AXXF1d1dmN//nnH7744oscf3bq1KmZJn9PW78el3o34ciRI1y7dk1tatSjR48n6hRfECRJEEIIIUSh17dvX9zd3YmLi2PYsGEZnuhfv36dDz/8kDNnzlC1alWjE+2xY8cChv4BH3/8cbrPKorCp59+ChiaB6VeWU4rJSXlico+YsSI/7d3PyFN/3Ecx1+bmmZ/nATBYLgkAg8eKguhoEmmUXkQtIJakF1MGIGUtxCVTlFGQQRFNBD0ppchpZeKXZLMoFGIGEZI0SoLD1EMPh387fvza5vzz7TU5wO+B3Xfz/fj+B723ufz/r60bt06/fr1S42NjQm//W5vb1ckEpEkNTU1Leg68/Xy5UtrLtFoVJ2dnX+8ZmJiQhUVFRoZGVFmZqZu374965jxfoSlKhIkqbm5WS6XS9JUSN3jx49TnvPo0SNdu3Yt6d8Xe3/N5Pf75XA4ZIxRIBCwVmFOnTqVcq7/CooEAACQdmNjYxodHZ3zEYvFZh1v06ZNunLliiQpFAqppKREwWBQg4ODGhgY0I0bN7Rv3z5Fo1G53W5dvHjRdv7+/ftVW1srSWpra9Px48fV3d2tV69eqbe3V0ePHlVvb6+kqdTl6ZzOqY9LIyMjev36tb58+ZJyvtMVFhYqEAhImnq6js/nU0dHh4aGhtTT06PTp09bRUxpaWnSve7pFt96k5eXp4yMDJ07d06XL19WOBzW0NCQ7t69q9LSUoXDYUnSrVu3Un74X8qm5Tiv16tgMKiMjAxNTk7q8OHDunTpkvVBfLrPnz+roaFBR44cUSwWk8/nSzjmYu+vmQoKClRWViZJ1n21Y8cO7d27dxH/+TJbnjgGAACw2i00TE3TgrOShakZMxVG1d7ebqUXJzrcbrd58eJFwvl9+/Zt1uAvp9Npmpqa/jjv+vXrSedrzP9BZ8FgMOl78+PHD3PhwgXjcDiSXr+ystKMj48nPH8u10gV+jbTmTNnjCRTU1Njmpubk87L5XKZjo6OOY3p8/mMJPPx48c5vX6mVGFq04VCIStlOn4UFxebqqoqU1VVZbxer/X7rKwsc+fOHfP+/fukIWWLvb9mevDgge3clpaWOb8P/0KYGisJAABgRXA4HGpsbNTg4KD8fr8KCgqUnZ0tt9utAwcO6OrVqxodHdWuXbsSnp+Xl6euri51dXXp4MGD2rJli9avX6/i4mKdPHlSz549+2MVQZICgYDq6+u1detW5eTkyOv1zjvMKycnRzdv3lRfX5/q6upUUlKiDRs2qKioSCdOnND9+/f18OHDpFkLSyG+krBnzx61trbq3r172rlzp3Jzc5Wfn6/du3erra1Nb968kd/vTzme+a9p2ePxLCjYbb6OHTum4eFh1dfXa+PGjZKkSCSiUCikUCikd+/eKSsrS9XV1YpEIjp//rw8Ho8KCwsTjrfY+2ummpoa5ebmWj+vpK1GkuQwZpauCwAAAKw6379/V35+vowx6u/v16FDhxY95vDwsIqKilRdXa2enp4FjfHkyRNrm858Avl+/vypcDist2/fKhqNavPmzfJ4PCorK7P6F1aS1tZWtbS0aNu2bbM2lS8lEpcBAADWmOfPn1tP50lX/8By9CMkk52drfLycpWXly/7tVcrthsBAACsMfF8hO3btydNip6veIjaUj7ZCMuHlQQAAIA1Zno/QrqkeyVhbGxMsVhMTqczaR/BajI5OalPnz5Jkr5+/fqXZ0ORAAAAsObEVxLSWSQ8ffo0bWNJsnoTXC6XJiYm0jr2v6i7u1tnz57929OwsN0IAABgDfnw4YOV/Pw3+gewMvB0IwAAAAA2rCQAAAAAsKFIAAAAAGBDkQAAAADAhiIBAAAAgA1FAgAAAAAbigQAAAAANhQJAAAAAGwoEgAAAADYUCQAAAAAsKFIAAAAAGBDkQAAAADA5jdNjQNqOjqdigAAAABJRU5ErkJggg==",
+      "text/plain": [
+       "<Figure size 800x550 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "var_key   = \"AnalysisElectronsAuxDyn.pt\"\n",
+    "xlabel    = r\"Electron $p_T$ [GeV]\"\n",
+    "bins      = np.linspace(0, 300, 31)   # 10 GeV bins\n",
+    "normalize = False\n",
+    "\n",
+    "mplhep.style.use(\"ATLAS\")\n",
+    "\n",
+    "hists = {}\n",
+    "edges = None\n",
+    "\n",
+    "for name, arr in arrays.items():\n",
+    "    # keep only electrons with pt > 25 GeV (25000 MeV)\n",
+    "    mask = arr[var_key] > 25_000\n",
+    "    x = ak.to_numpy(ak.ravel(arr[var_key][mask])) / 1000.0  # GeV\n",
+    "    y, edges = np.histogram(x, bins=bins)\n",
+    "    if normalize and y.sum() > 0:\n",
+    "        y = y / y.sum()\n",
+    "    hists[name] = y.astype(float)\n",
+    "\n",
+    "fig, ax = plt.subplots(figsize=(8, 5.5))\n",
+    "for name, y in hists.items():\n",
+    "    mplhep.histplot(y, bins=edges, ax=ax, histtype=\"step\", label=name, linewidth=1.6)\n",
+    "\n",
+    "ax.set_xlabel(xlabel)\n",
+    "ax.set_ylabel(\"Arbitrary units\" if normalize else \"Entries\")\n",
+    "ax.set_xlim(bins[0], bins[-1])\n",
+    "ax.legend(title=\"Signal\", ncol=3, fontsize=10)\n",
+    "mplhep.atlas.label(\"Internal\", ax=ax, data=False, com=\"13/ 13.6 TeV\")\n",
+    "\n",
+    "plt.tight_layout()\n",
+    "plt.show()\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "aa051851-6b64-4dc3-aff8-66a1566f3268",
+   "metadata": {},
+   "source": [
+    "### Using servicex to fetch ntuples with only necessary systematics for Coffea"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 23,
+   "id": "d9e354a7-8bd7-4484-b7cc-04ced8e31ba8",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "needed_branches=report[\"columns\"]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 24,
+   "id": "d18319cd-29c6-4b42-a604-a7dba5b3959f",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "3f7becbd969441bf9509697a5500656b",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Output()"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "text/html": [
+       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"></pre>\n"
+      ],
+      "text/plain": []
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "#1) signal with systematics (old version of ntuplizer) \n",
+    "rucio_did=\"user.acordeir:nominal_trig_overlap_obj_dump_sys_zprime3k.root\"\n",
+    "\n",
+    "#2) Write transformation\n",
+    "ds_query = query.UprootRaw(\n",
+    "    [\n",
+    "        {\n",
+    "            \"treename\": \"reco\",\n",
+    "            \"filter_name\": needed_branches, \n",
+    "            \"cut\": \"num(el_pt_NOSYS)>0\",\n",
+    "        }\n",
+    "    ]\n",
+    ")\n",
+    "\n",
+    "#3) Write deliver config\n",
+    "spec = {\n",
+    "    \"Sample\": [{\"Name\": \"zprime\", \"Dataset\": ds_type_resolver(rucio_did), \"Query\": ds_query}],\n",
+    "}\n",
+    "\n",
+    "#4) Get the data \n",
+    "delivered_file = deliver(spec, ignore_local_cache=True)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 25,
+   "id": "7a3a6935-4953-44db-8ae2-5dfcead921d8",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "['NOSYS', 'EG_SCALE_ALL__1down', 'EG_SCALE_ALL__1up']"
+      ]
+     },
+     "execution_count": 25,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "events = NanoEventsFactory.from_root(\n",
+    "    {delivered_file[\"zprime\"][0]: \"reco\"},\n",
+    "    mode=\"virtual\",\n",
+    "    schemaclass=NtupleSchema,\n",
+    "    entry_stop=1000\n",
+    ").events()\n",
     "\n",
-    "h[:, \"data_data15\", \"NOSYS\"]"
+    "events.systematic_names\n"
    ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 29,
+   "id": "2699d70c-2961-420f-a10c-7b32dd36a679",
+   "metadata": {},
+   "outputs": [
+    {
+     "ename": "ValueError",
+     "evalue": "array does not have azimuthal coordinates (x/px, y/py or rho/pt, phi): pt, e",
+     "output_type": "error",
+     "traceback": [
+      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
+      "\u001b[0;31mValueError\u001b[0m                                Traceback (most recent call last)",
+      "Cell \u001b[0;32mIn[29], line 6\u001b[0m\n\u001b[1;32m      4\u001b[0m     \u001b[38;5;28;01mif\u001b[39;00m variation \u001b[38;5;241m!=\u001b[39m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mNOSYS\u001b[39m\u001b[38;5;124m\"\u001b[39m \u001b[38;5;129;01mand\u001b[39;00m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mEG_SCALE_ALL\u001b[39m\u001b[38;5;124m\"\u001b[39m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;129;01min\u001b[39;00m variation:\n\u001b[1;32m      5\u001b[0m         \u001b[38;5;28;01mcontinue\u001b[39;00m\n\u001b[0;32m----> 6\u001b[0m     h\u001b[38;5;241m.\u001b[39mfill(\u001b[43mevents\u001b[49m\u001b[43m[\u001b[49m\u001b[43mvariation\u001b[49m\u001b[43m]\u001b[49m\u001b[43m[\u001b[49m\u001b[43mcut\u001b[49m\u001b[38;5;241;43m==\u001b[39;49m\u001b[38;5;241;43m1\u001b[39;49m\u001b[43m]\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mel\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mpt\u001b[49m[:, \u001b[38;5;241m0\u001b[39m] \u001b[38;5;241m/\u001b[39m \u001b[38;5;241m1_000\u001b[39m, variation\u001b[38;5;241m=\u001b[39mvariation)\n\u001b[1;32m      8\u001b[0m fig, ax \u001b[38;5;241m=\u001b[39m plt\u001b[38;5;241m.\u001b[39msubplots()\n\u001b[1;32m      9\u001b[0m \u001b[38;5;28;01mfor\u001b[39;00m variation \u001b[38;5;129;01min\u001b[39;00m h\u001b[38;5;241m.\u001b[39maxes[\u001b[38;5;241m1\u001b[39m]:\n",
+      "File \u001b[0;32m/usr/local/lib/python3.12/site-packages/vector/_methods.py:4082\u001b[0m, in \u001b[0;36mPlanarMomentum.pt\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m   4080\u001b[0m \u001b[38;5;129m@property\u001b[39m\n\u001b[1;32m   4081\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[38;5;21mpt\u001b[39m(\u001b[38;5;28mself\u001b[39m) \u001b[38;5;241m-\u001b[39m\u001b[38;5;241m>\u001b[39m ScalarCollection:\n\u001b[0;32m-> 4082\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mrho\u001b[49m\n",
+      "File \u001b[0;32m/usr/local/lib/python3.12/site-packages/vector/_methods.py:3442\u001b[0m, in \u001b[0;36mPlanar.rho\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m   3438\u001b[0m \u001b[38;5;129m@property\u001b[39m\n\u001b[1;32m   3439\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[38;5;21mrho\u001b[39m(\u001b[38;5;28mself\u001b[39m) \u001b[38;5;241m-\u001b[39m\u001b[38;5;241m>\u001b[39m ScalarCollection:\n\u001b[1;32m   3440\u001b[0m     \u001b[38;5;28;01mfrom\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[38;5;21;01mvector\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01m_compute\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mplanar\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[38;5;28;01mimport\u001b[39;00m rho\n\u001b[0;32m-> 3442\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43mrho\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mdispatch\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[43m)\u001b[49m\n",
+      "File \u001b[0;32m/usr/local/lib/python3.12/site-packages/vector/_compute/planar/rho.py:47\u001b[0m, in \u001b[0;36mdispatch\u001b[0;34m(v)\u001b[0m\n\u001b[1;32m     46\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[38;5;21mdispatch\u001b[39m(v: typing\u001b[38;5;241m.\u001b[39mAny) \u001b[38;5;241m-\u001b[39m\u001b[38;5;241m>\u001b[39m typing\u001b[38;5;241m.\u001b[39mAny:\n\u001b[0;32m---> 47\u001b[0m     function, \u001b[38;5;241m*\u001b[39mreturns \u001b[38;5;241m=\u001b[39m _from_signature(\u001b[38;5;18m__name__\u001b[39m, dispatch_map, (\u001b[43m_aztype\u001b[49m\u001b[43m(\u001b[49m\u001b[43mv\u001b[49m\u001b[43m)\u001b[49m,))\n\u001b[1;32m     48\u001b[0m     \u001b[38;5;28;01mwith\u001b[39;00m numpy\u001b[38;5;241m.\u001b[39merrstate(\u001b[38;5;28mall\u001b[39m\u001b[38;5;241m=\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mignore\u001b[39m\u001b[38;5;124m\"\u001b[39m):\n\u001b[1;32m     49\u001b[0m         \u001b[38;5;28;01mreturn\u001b[39;00m v\u001b[38;5;241m.\u001b[39m_wrap_result(\n\u001b[1;32m     50\u001b[0m             _flavor_of(v),\n\u001b[1;32m     51\u001b[0m             v\u001b[38;5;241m.\u001b[39m_wrap_dispatched_function(function)(v\u001b[38;5;241m.\u001b[39mlib, \u001b[38;5;241m*\u001b[39mv\u001b[38;5;241m.\u001b[39mazimuthal\u001b[38;5;241m.\u001b[39melements),\n\u001b[1;32m     52\u001b[0m             returns,\n\u001b[1;32m     53\u001b[0m             \u001b[38;5;241m1\u001b[39m,\n\u001b[1;32m     54\u001b[0m         )\n",
+      "File \u001b[0;32m/usr/local/lib/python3.12/site-packages/vector/_methods.py:4362\u001b[0m, in \u001b[0;36m_aztype\u001b[0;34m(obj)\u001b[0m\n\u001b[1;32m   4357\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[38;5;21m_aztype\u001b[39m(obj: VectorProtocolPlanar) \u001b[38;5;241m-\u001b[39m\u001b[38;5;241m>\u001b[39m \u001b[38;5;28mtype\u001b[39m[Coordinates]:\n\u001b[1;32m   4358\u001b[0m \u001b[38;5;250m    \u001b[39m\u001b[38;5;124;03m\"\"\"\u001b[39;00m\n\u001b[1;32m   4359\u001b[0m \u001b[38;5;124;03m    Determines the Azimuthal type of a vector for use in looking up a\u001b[39;00m\n\u001b[1;32m   4360\u001b[0m \u001b[38;5;124;03m    dispatched function.\u001b[39;00m\n\u001b[1;32m   4361\u001b[0m \u001b[38;5;124;03m    \"\"\"\u001b[39;00m\n\u001b[0;32m-> 4362\u001b[0m     \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28;43mhasattr\u001b[39;49m\u001b[43m(\u001b[49m\u001b[43mobj\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43mazimuthal\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m)\u001b[49m:\n\u001b[1;32m   4363\u001b[0m         \u001b[38;5;28;01mfor\u001b[39;00m t \u001b[38;5;129;01min\u001b[39;00m \u001b[38;5;28mtype\u001b[39m(obj\u001b[38;5;241m.\u001b[39mazimuthal)\u001b[38;5;241m.\u001b[39m\u001b[38;5;18m__mro__\u001b[39m:\n\u001b[1;32m   4364\u001b[0m             \u001b[38;5;28;01mif\u001b[39;00m t \u001b[38;5;129;01min\u001b[39;00m (AzimuthalXY, AzimuthalRhoPhi):\n",
+      "File \u001b[0;32m/usr/local/lib/python3.12/site-packages/vector/backends/awkward.py:1357\u001b[0m, in \u001b[0;36mMomentumAwkward4D.azimuthal\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m   1339\u001b[0m \u001b[38;5;129m@property\u001b[39m\n\u001b[1;32m   1340\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[38;5;21mazimuthal\u001b[39m(\u001b[38;5;28mself\u001b[39m) \u001b[38;5;241m-\u001b[39m\u001b[38;5;241m>\u001b[39m AzimuthalAwkward:\n\u001b[1;32m   1341\u001b[0m \u001b[38;5;250m    \u001b[39m\u001b[38;5;124;03m\"\"\"\u001b[39;00m\n\u001b[1;32m   1342\u001b[0m \u001b[38;5;124;03m    Returns an Azimuthal type object containing the azimuthal coordinates.\u001b[39;00m\n\u001b[1;32m   1343\u001b[0m \n\u001b[0;32m   (...)\u001b[0m\n\u001b[1;32m   1355\u001b[0m \u001b[38;5;124;03m        (<Array [1, 2] type='2 * int64'>, <Array [1.1, 2.2] type='2 * float64'>)\u001b[39;00m\n\u001b[1;32m   1356\u001b[0m \u001b[38;5;124;03m    \"\"\"\u001b[39;00m\n\u001b[0;32m-> 1357\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43mAzimuthalAwkward\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mfrom_momentum_fields\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[43m)\u001b[49m\n",
+      "File \u001b[0;32m/usr/local/lib/python3.12/site-packages/vector/backends/awkward.py:179\u001b[0m, in \u001b[0;36mAzimuthalAwkward.from_momentum_fields\u001b[0;34m(cls, array)\u001b[0m\n\u001b[1;32m    177\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m AzimuthalAwkwardRhoPhi(_touch(array[\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mpt\u001b[39m\u001b[38;5;124m\"\u001b[39m]), _touch(array[\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mphi\u001b[39m\u001b[38;5;124m\"\u001b[39m]))\n\u001b[1;32m    178\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[0;32m--> 179\u001b[0m     \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mValueError\u001b[39;00m(\n\u001b[1;32m    180\u001b[0m         \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124marray does not have azimuthal coordinates (x/px, y/py or rho/pt, phi): \u001b[39m\u001b[38;5;124m\"\u001b[39m\n\u001b[1;32m    181\u001b[0m         \u001b[38;5;124mf\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;132;01m{\u001b[39;00m\u001b[38;5;124m'\u001b[39m\u001b[38;5;124m, \u001b[39m\u001b[38;5;124m'\u001b[39m\u001b[38;5;241m.\u001b[39mjoin(fields)\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;124m\"\u001b[39m\n\u001b[1;32m    182\u001b[0m     )\n",
+      "\u001b[0;31mValueError\u001b[0m: array does not have azimuthal coordinates (x/px, y/py or rho/pt, phi): pt, e"
+     ]
+    }
+   ],
+   "source": [
+    "h = hist.new.Regular(30, 0, 300, label=\"leading electron $p_T$\").StrCat([], name=\"variation\", growth=True).Weight()\n",
+    "\n",
+    "for variation in events.systematic_names:\n",
+    "    if variation != \"NOSYS\" and \"EG_SCALE_ALL\" not in variation:\n",
+    "        continue\n",
+    "    h.fill(events[variation][cut==1].el.pt[:, 0] / 1_000, variation=variation)\n",
+    "\n",
+    "fig, ax = plt.subplots()\n",
+    "for variation in h.axes[1]:\n",
+    "    h[:, variation].plot(histtype=\"step\", label=variation, ax=ax)\n",
+    "_ = ax.legend()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "581554ca-275a-4f9b-908b-22344535d1c2",
+   "metadata": {},
+   "outputs": [],
+   "source": []
   }
  ],
  "metadata": {
diff --git a/atlas/reco.yaml b/atlas/ntuple_production/reco.yaml
similarity index 100%
rename from atlas/reco.yaml
rename to atlas/ntuple_production/reco.yaml
diff --git a/atlas/servicex/TopCP_ServiceX.ipynb b/atlas/servicex/TopCP_ServiceX.ipynb
new file mode 100644
index 0000000..0aa482a
--- /dev/null
+++ b/atlas/servicex/TopCP_ServiceX.ipynb
@@ -0,0 +1,359 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "id": "194df785-f8ad-479e-913b-c0562ffb5127",
+   "metadata": {
+    "scrolled": true
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Collecting servicex_analysis_utils\n",
+      "  Using cached servicex_analysis_utils-1.2.1-py3-none-any.whl.metadata (3.0 kB)\n",
+      "Requirement already satisfied: awkward>=2.6 in /usr/local/lib/python3.12/site-packages (from servicex_analysis_utils) (2.8.9)\n",
+      "Requirement already satisfied: dask-awkward>=2024.12.2 in /usr/local/lib/python3.12/site-packages (from servicex_analysis_utils) (2025.9.0)\n",
+      "Collecting servicex (from servicex_analysis_utils)\n",
+      "  Using cached servicex-3.2.3-py3-none-any.whl.metadata (5.7 kB)\n",
+      "Requirement already satisfied: uproot>=5.0 in /usr/local/lib/python3.12/site-packages (from servicex_analysis_utils) (5.6.6)\n",
+      "Requirement already satisfied: awkward-cpp==50 in /usr/local/lib/python3.12/site-packages (from awkward>=2.6->servicex_analysis_utils) (50)\n",
+      "Requirement already satisfied: fsspec>=2022.11.0 in /usr/local/lib/python3.12/site-packages (from awkward>=2.6->servicex_analysis_utils) (2025.9.0)\n",
+      "Requirement already satisfied: numpy>=1.18.0 in /usr/local/lib/python3.12/site-packages (from awkward>=2.6->servicex_analysis_utils) (2.2.6)\n",
+      "Requirement already satisfied: packaging in /usr/local/lib/python3.12/site-packages (from awkward>=2.6->servicex_analysis_utils) (25.0)\n",
+      "Requirement already satisfied: cachetools in /usr/local/lib/python3.12/site-packages (from dask-awkward>=2024.12.2->servicex_analysis_utils) (6.2.0)\n",
+      "Requirement already satisfied: dask<2025.4.0,>=2023.04.0 in /usr/local/lib/python3.12/site-packages (from dask-awkward>=2024.12.2->servicex_analysis_utils) (2025.3.0)\n",
+      "Requirement already satisfied: typing-extensions>=4.8.0 in /usr/local/lib/python3.12/site-packages (from dask-awkward>=2024.12.2->servicex_analysis_utils) (4.15.0)\n",
+      "Requirement already satisfied: click>=8.1 in /usr/local/lib/python3.12/site-packages (from dask<2025.4.0,>=2023.04.0->dask-awkward>=2024.12.2->servicex_analysis_utils) (8.3.0)\n",
+      "Requirement already satisfied: cloudpickle>=3.0.0 in /usr/local/lib/python3.12/site-packages (from dask<2025.4.0,>=2023.04.0->dask-awkward>=2024.12.2->servicex_analysis_utils) (3.1.1)\n",
+      "Requirement already satisfied: partd>=1.4.0 in /usr/local/lib/python3.12/site-packages (from dask<2025.4.0,>=2023.04.0->dask-awkward>=2024.12.2->servicex_analysis_utils) (1.4.2)\n",
+      "Requirement already satisfied: pyyaml>=5.3.1 in /usr/local/lib/python3.12/site-packages (from dask<2025.4.0,>=2023.04.0->dask-awkward>=2024.12.2->servicex_analysis_utils) (6.0.2)\n",
+      "Requirement already satisfied: toolz>=0.10.0 in /usr/local/lib/python3.12/site-packages (from dask<2025.4.0,>=2023.04.0->dask-awkward>=2024.12.2->servicex_analysis_utils) (1.0.0)\n",
+      "Requirement already satisfied: locket in /usr/local/lib/python3.12/site-packages (from partd>=1.4.0->dask<2025.4.0,>=2023.04.0->dask-awkward>=2024.12.2->servicex_analysis_utils) (1.0.0)\n",
+      "Requirement already satisfied: cramjam>=2.5.0 in /usr/local/lib/python3.12/site-packages (from uproot>=5.0->servicex_analysis_utils) (2.11.0)\n",
+      "Requirement already satisfied: xxhash in /usr/local/lib/python3.12/site-packages (from uproot>=5.0->servicex_analysis_utils) (3.5.0)\n",
+      "Collecting aioboto3>=14.1.0 (from servicex->servicex_analysis_utils)\n",
+      "  Using cached aioboto3-15.5.0-py3-none-any.whl.metadata (8.9 kB)\n",
+      "Collecting filelock>=3.12.0 (from servicex->servicex_analysis_utils)\n",
+      "  Using cached filelock-3.20.0-py3-none-any.whl.metadata (2.1 kB)\n",
+      "Collecting func-adl>=3.2.6 (from servicex->servicex_analysis_utils)\n",
+      "  Using cached func_adl-3.5.0-py3-none-any.whl.metadata (12 kB)\n",
+      "Requirement already satisfied: google-auth>=2.17 in /usr/local/lib/python3.12/site-packages (from servicex->servicex_analysis_utils) (2.41.0)\n",
+      "Collecting httpx-retries>=0.3.2 (from servicex->servicex_analysis_utils)\n",
+      "  Using cached httpx_retries-0.4.5-py3-none-any.whl.metadata (4.1 kB)\n",
+      "Requirement already satisfied: httpx>=0.24 in /usr/local/lib/python3.12/site-packages (from servicex->servicex_analysis_utils) (0.28.1)\n",
+      "Collecting make-it-sync (from servicex->servicex_analysis_utils)\n",
+      "  Using cached make_it_sync-2.1.1-py3-none-any.whl.metadata (3.8 kB)\n",
+      "Requirement already satisfied: pydantic>=2.6.0 in /usr/local/lib/python3.12/site-packages (from servicex->servicex_analysis_utils) (2.11.9)\n",
+      "Collecting qastle>=0.17 (from servicex->servicex_analysis_utils)\n",
+      "  Using cached qastle-0.19.0-py3-none-any.whl.metadata (8.1 kB)\n",
+      "Requirement already satisfied: requests>=2.31 in /usr/local/lib/python3.12/site-packages (from servicex->servicex_analysis_utils) (2.32.4)\n",
+      "Requirement already satisfied: rich>=13.0.0 in /usr/local/lib/python3.12/site-packages (from servicex->servicex_analysis_utils) (14.1.0)\n",
+      "Requirement already satisfied: ruamel-yaml>=0.18.7 in /usr/local/lib/python3.12/site-packages (from servicex->servicex_analysis_utils) (0.18.14)\n",
+      "Collecting tenacity>=9.0.0 (from servicex->servicex_analysis_utils)\n",
+      "  Using cached tenacity-9.1.2-py3-none-any.whl.metadata (1.2 kB)\n",
+      "Collecting tinydb>=4.7 (from servicex->servicex_analysis_utils)\n",
+      "  Using cached tinydb-4.8.2-py3-none-any.whl.metadata (6.7 kB)\n",
+      "Collecting typer>=0.12.1 (from servicex->servicex_analysis_utils)\n",
+      "  Using cached typer-0.20.0-py3-none-any.whl.metadata (16 kB)\n",
+      "Collecting types-pyyaml>=6.0 (from servicex->servicex_analysis_utils)\n",
+      "  Using cached types_pyyaml-6.0.12.20250915-py3-none-any.whl.metadata (1.7 kB)\n",
+      "Collecting aiobotocore==2.25.1 (from aiobotocore[boto3]==2.25.1->aioboto3>=14.1.0->servicex->servicex_analysis_utils)\n",
+      "  Using cached aiobotocore-2.25.1-py3-none-any.whl.metadata (25 kB)\n",
+      "Collecting aiofiles>=23.2.1 (from aioboto3>=14.1.0->servicex->servicex_analysis_utils)\n",
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+      "Requirement already satisfied: aiohttp<4.0.0,>=3.9.2 in /usr/local/lib/python3.12/site-packages (from aiobotocore==2.25.1->aiobotocore[boto3]==2.25.1->aioboto3>=14.1.0->servicex->servicex_analysis_utils) (3.12.15)\n",
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+      "Collecting botocore<1.40.62,>=1.40.46 (from aiobotocore==2.25.1->aiobotocore[boto3]==2.25.1->aioboto3>=14.1.0->servicex->servicex_analysis_utils)\n",
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+      "Collecting s3transfer<0.15.0,>=0.14.0 (from boto3<1.40.62,>=1.40.46->aiobotocore[boto3]==2.25.1->aioboto3>=14.1.0->servicex->servicex_analysis_utils)\n",
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+      "Requirement already satisfied: httpcore==1.* in /usr/local/lib/python3.12/site-packages (from httpx>=0.24->servicex->servicex_analysis_utils) (1.0.9)\n",
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+      "Requirement already satisfied: annotated-types>=0.6.0 in /usr/local/lib/python3.12/site-packages (from pydantic>=2.6.0->servicex->servicex_analysis_utils) (0.7.0)\n",
+      "Requirement already satisfied: pydantic-core==2.33.2 in /usr/local/lib/python3.12/site-packages (from pydantic>=2.6.0->servicex->servicex_analysis_utils) (2.33.2)\n",
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+      "Requirement already satisfied: charset_normalizer<4,>=2 in /usr/local/lib/python3.12/site-packages (from requests>=2.31->servicex->servicex_analysis_utils) (3.4.2)\n",
+      "Requirement already satisfied: markdown-it-py>=2.2.0 in /usr/local/lib/python3.12/site-packages (from rich>=13.0.0->servicex->servicex_analysis_utils) (4.0.0)\n",
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+      "Requirement already satisfied: ruamel.yaml.clib>=0.2.7 in /usr/local/lib/python3.12/site-packages (from ruamel-yaml>=0.18.7->servicex->servicex_analysis_utils) (0.2.8)\n",
+      "Collecting shellingham>=1.3.0 (from typer>=0.12.1->servicex->servicex_analysis_utils)\n",
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+      "Using cached make_it_sync-2.1.1-py3-none-any.whl (4.4 kB)\n",
+      "Installing collected packages: types-pyyaml, tinydb, tenacity, shellingham, qastle, make-it-sync, filelock, aiofiles, func-adl, botocore, typer, s3transfer, httpx-retries, aiobotocore, boto3, aioboto3, servicex, servicex_analysis_utils\n",
+      "\u001b[2K  Attempting uninstall: botocore\n",
+      "\u001b[2K    Found existing installation: botocore 1.40.18\n",
+      "\u001b[2K    Uninstalling botocore-1.40.18:90m╺\u001b[0m\u001b[90m━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m 9/18\u001b[0m [botocore]\n",
+      "\u001b[2K      Successfully uninstalled botocore-1.40.18━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m 9/18\u001b[0m [botocore]\n",
+      "\u001b[2K  Attempting uninstall: aiobotocore[90m╺\u001b[0m\u001b[90m━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m10/18\u001b[0m [typer]re]\n",
+      "\u001b[2K    Found existing installation: aiobotocore 2.24.2━━━━━━━━━━━\u001b[0m \u001b[32m10/18\u001b[0m [typer]\n",
+      "\u001b[2K    Uninstalling aiobotocore-2.24.2:\u001b[0m\u001b[90m━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m10/18\u001b[0m [typer]\n",
+      "\u001b[2K      Successfully uninstalled aiobotocore-2.24.2━━━━━━━━━━━━━\u001b[0m \u001b[32m10/18\u001b[0m [typer]\n",
+      "\u001b[2K   \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m18/18\u001b[0m [servicex_analysis_utils]ex_analysis_utils]\n",
+      "\u001b[1A\u001b[2KSuccessfully installed aioboto3-15.5.0 aiobotocore-2.25.1 aiofiles-25.1.0 boto3-1.40.61 botocore-1.40.61 filelock-3.20.0 func-adl-3.5.0 httpx-retries-0.4.5 make-it-sync-2.1.1 qastle-0.19.0 s3transfer-0.14.0 servicex-3.2.3 servicex_analysis_utils-1.2.1 shellingham-1.5.4 tenacity-9.1.2 tinydb-4.8.2 typer-0.20.0 types-pyyaml-6.0.12.20250915\n"
+     ]
+    }
+   ],
+   "source": [
+    "!pip install servicex_analysis_utils"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "id": "65a2e4f4-4349-44a6-bf97-28cb32c3f975",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from pathlib import Path\n",
+    "import sys\n",
+    "\n",
+    "sys.path.append(str(Path().resolve().parent))\n",
+    "\n",
+    "from servicex import deliver, query \n",
+    "from servicex_analysis_utils import ds_type_resolver, to_awk\n",
+    "import utils\n",
+    "import gzip\n",
+    "import json"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "id": "169e377b-7bdd-430f-8b18-3037b1a364d9",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "fname = \"../ntuple_production/file_metadata.json.gz\"\n",
+    "with gzip.open(fname) as f:\n",
+    "    dataset_info = json.loads(f.read().decode())"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "id": "40ed1c21-945f-4dda-98bf-e2c7194ec2b6",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "H+ -> cb signal in 40 DSIDs for 9e+06 total events\n"
+     ]
+    }
+   ],
+   "source": [
+    "total_sig=0\n",
+    "signal_containers={}\n",
+    "for container, metadata in dataset_info[\"Hplus_cb\"].items():\n",
+    "    evts = metadata[\"nevts_input\"]\n",
+    "    total_sig+=evts\n",
+    "    mass = utils.hplus_signal_mass(container)\n",
+    "    _ , _, campaign = utils.dsid_rtag_campaign(container)\n",
+    "    signal_containers[f\"{mass}_{campaign}\"]={'DSID' : container, 'Events': evts }\n",
+    "print(f\"H+ -> cb signal in {len(signal_containers)} DSIDs for {total_sig:.0e} total events\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "id": "60002f67-86ca-4e4c-bc6e-b9a537c68f95",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "320000"
+      ]
+     },
+     "execution_count": 10,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "signal_containers['20GeV_mc20e']['Events']"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "id": "c47a20b0-add4-4eea-947a-b6311348d12a",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<servicex.dataset_identifier.RucioDatasetIdentifier at 0x7f8969a4e900>"
+      ]
+     },
+     "execution_count": 11,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "sample = ds_type_resolver(signal_containers['20GeV_mc20e']['DSID'])\n",
+    "sample"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 16,
+   "id": "8b4a7df0-2aeb-4d15-886e-23c6b3f3bbae",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "config_path = \"../ntuple_production/reco.yaml\" "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 17,
+   "id": "2ea319c8-8da0-43e2-8400-1381f67b2bb3",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "TopCPQuery(reco='../ntuple_production/reco.yaml', parton=None, particle=None, max_events=200000, no_systematics=True, no_filter=False)"
+      ]
+     },
+     "execution_count": 17,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "request = query.TopCP(reco=config_path, max_events = 200_000)\n",
+    "request"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 18,
+   "id": "03e6751e-1922-4874-ba17-af5283791834",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "spec = {\n",
+    "        \"General\": {\"OutputDirectory\": \".\"},\n",
+    "        \"Sample\": [{\n",
+    "            \"Name\": '20GeV_mc20e',\n",
+    "            \"Dataset\": sample,\n",
+    "            \"Query\": request,     \n",
+    "        }]\n",
+    "    }"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 19,
+   "id": "076b1f77-cd67-47a1-aced-9fe0d2e849ab",
+   "metadata": {
+    "scrolled": true
+   },
+   "outputs": [
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "dc686d2a289b44358d2b37b80b34fa3a",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Output()"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "text/html": [
+       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"></pre>\n"
+      ],
+      "text/plain": []
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "files = deliver(spec)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "17d763bf-71cb-4407-bbea-a83cf5f740ae",
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3 (ipykernel)",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.12.11"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}
diff --git a/atlas/tpcpt_efficiency_summary.txt b/atlas/tpcpt_efficiency_summary.txt
deleted file mode 100644
index 78d2d44..0000000
--- a/atlas/tpcpt_efficiency_summary.txt
+++ /dev/null
@@ -1,56 +0,0 @@
-
-## EVENTS ACCEPTED with current reco.yaml 
-## Signal ZPrimettbar
-# 8 / 110 events
-
-PrimaryVertexSelectorAlg INFO    accepted 110 out of 110 events for filter VertexSelection (vertex selection)
-EventFlagSelectionAlg    INFO    accepted 110 out of 110 events for filter JetCleaning (selecting events passing: DFCommonJets_eventClean_LooseBad,as_char)
-TrigEventSelectionAlg    INFO    accepted 46 out of 110 events for filter TriggerEventSelection (trigger event selection)
-TrigEventSelectionAlgDecoINFO    accepted 46 out of 46 events for filter TriggerEventSelection (trigger event selection)
-CutBookkeeperAlgANALYSIS INFO    CutBookkeeper information will be stored in CutBookkeeper_301333_284500_NOSYS
-TrigGlobalSFAlg_emu      INFO    Events passing selection for at least one systematic: 19 / 46 for global trigger matching
-TrigGlobalSFAlg_emu      INFO    Events passing selection for at nominal: 19 / 46 for global trigger matching
-TrigGlobalSFAlg_emu      INFO    Events passing selection for all systematics: 19 / 46 for global trigger matching
-SUBcommon_SAVE           INFO    Events passing selection for at least one systematic: 19 / 19 for events passing < SUBcommon >
-SUBcommon_SAVE           INFO    Events passing selection for at nominal: 19 / 19 for events passing < SUBcommon >
-SUBcommon_SAVE           INFO    Events passing selection for all systematics: 19 / 19 for events passing < SUBcommon >
-fourjetoneb_el_SAVE      INFO    Events passing selection for at least one systematic: 19 / 19 for events passing < fourjetoneb_el >
-fourjetoneb_el_SAVE      INFO    Events passing selection for at nominal: 19 / 19 for events passing < fourjetoneb_el >
-fourjetoneb_el_SAVE      INFO    Events passing selection for all systematics: 19 / 19 for events passing < fourjetoneb_el >
-fourjetoneb_mu_SAVE      INFO    Events passing selection for at least one systematic: 19 / 19 for events passing < fourjetoneb_mu >
-fourjetoneb_mu_SAVE      INFO    Events passing selection for at nominal: 19 / 19 for events passing < fourjetoneb_mu >
-fourjetoneb_mu_SAVE      INFO    Events passing selection for all systematics: 19 / 19 for events passing < fourjetoneb_mu >
-EventSelectionMergerpa...INFO    Events passing selection for at least one systematic: 9 / 19 for events passing at least one EventSelection algorithm
-EventSelectionMergerpa...INFO    Events passing selection for at nominal: 9 / 19 for events passing at least one EventSelection algorithm
-EventSelectionMergerpa...INFO    Events passing selection for all systematics: 8 / 19 for events passing at least one EventSelection algorithm
-LeakCheckModule          INFO    Memory increase/change during the job:
-LeakCheckModule          INFO      - resident: 1200.7 kB/event (130876 kB total)
-LeakCheckModule          INFO      - virtual : 6917.28 kB/event (753984 kB total)
-
-
-## TTBar bakground (nonallhad)
-# 160/1000 events 
-
-PrimaryVertexSelectorAlg INFO    accepted 1000 out of 1000 events for filter VertexSelection (vertex selection)
-EventFlagSelectionAlg    INFO    accepted 991 out of 1000 events for filter JetCleaning (selecting events passing: DFCommonJets_eventClean_LooseBad,as_char)
-TrigEventSelectionAlg    INFO    accepted 486 out of 991 events for filter TriggerEventSelection (trigger event selection)
-TrigEventSelectionAlgDecoINFO    accepted 486 out of 486 events for filter TriggerEventSelection (trigger event selection)
-TrigGlobalSFAlg_emu      INFO    Events passing selection for at least one systematic: 376 / 486 for global trigger matching
-TrigGlobalSFAlg_emu      INFO    Events passing selection for at nominal: 375 / 486 for global trigger matching
-TrigGlobalSFAlg_emu      INFO    Events passing selection for all systematics: 372 / 486 for global trigger matching
-SUBcommon_SAVE           INFO    Events passing selection for at least one systematic: 376 / 376 for events passing < SUBcommon >
-SUBcommon_SAVE           INFO    Events passing selection for at nominal: 376 / 376 for events passing < SUBcommon >
-SUBcommon_SAVE           INFO    Events passing selection for all systematics: 376 / 376 for events passing < SUBcommon >
-fourjetoneb_el_SAVE      INFO    Events passing selection for at least one systematic: 376 / 376 for events passing < fourjetoneb_el >
-fourjetoneb_el_SAVE      INFO    Events passing selection for at nominal: 376 / 376 for events passing < fourjetoneb_el >
-fourjetoneb_el_SAVE      INFO    Events passing selection for all systematics: 376 / 376 for events passing < fourjetoneb_el >
-fourjetoneb_mu_SAVE      INFO    Events passing selection for at least one systematic: 376 / 376 for events passing < fourjetoneb_mu >
-fourjetoneb_mu_SAVE      INFO    Events passing selection for at nominal: 376 / 376 for events passing < fourjetoneb_mu >
-fourjetoneb_mu_SAVE      INFO    Events passing selection for all systematics: 376 / 376 for events passing < fourjetoneb_mu >
-EventSelectionMergerpa...INFO    Events passing selection for at least one systematic: 206 / 376 for events passing at least one EventSelection algorithm
-EventSelectionMergerpa...INFO    Events passing selection for at nominal: 186 / 376 for events passing at least one EventSelection algorithm
-EventSelectionMergerpa...INFO    Events passing selection for all systematics: 162 / 376 for events passing at least one EventSelection algorithm
-LeakCheckModule          INFO    Memory increase/change during the job:
-LeakCheckModule          INFO      - resident: 82.3664 kB/event (82284 kB total)
-LeakCheckModule          INFO      - virtual : -31.7878 kB/event (-31756 kB total)
-
diff --git a/atlas/utils.py b/atlas/utils.py
index 4a5a6de..c039d5f 100644
--- a/atlas/utils.py
+++ b/atlas/utils.py
@@ -62,6 +62,13 @@ def dsid_rtag_campaign(name: str) -> tuple[str, str, str]:
 
     return dsid, rtag, campaign
 
+def hplus_signal_mass(name: str) -> str:
+    """get the mass from these signal samples"""
+    m = re.search(r'(?i)mhc(\d+)(?=\.)', name)  # case-insensitive, stop at the dot
+    return str(m.group(1)+"GeV") if m else None
+
+    
+
 
 def integrated_luminosity(campaign: str, total=False) -> float:
     """get integrated luminosity in pb for each MC campaign"""