diff --git a/.gitattributes b/.gitattributes
index 887a2c18..f0d45b6b 100644
--- a/.gitattributes
+++ b/.gitattributes
@@ -1,2 +1,9 @@
 # SCM syntax highlighting & preventing 3-way merges
 pixi.lock merge=binary linguist-language=YAML linguist-generated=true
+FAIR_universe_Higgs_tautau/saved_datasets/dataset_*.root filter=lfs diff=lfs merge=lfs -text
+*.h5 filter=lfs diff=lfs merge=lfs -text
+*.png filter=lfs diff=lfs merge=lfs -text
+*.bin filter=lfs diff=lfs merge=lfs -text
+*.onnx filter=lfs diff=lfs merge=lfs -text
+*.npy filter=lfs diff=lfs merge=lfs -text
+*.data filter=lfs diff=lfs merge=lfs -text
diff --git a/.gitignore b/.gitignore
index 8b013c85..8b2bf6af 100644
--- a/.gitignore
+++ b/.gitignore
@@ -189,9 +189,20 @@ cython_debug/
 
 # Ignore cached_data directory
 cached_data/
+saved_datasets/
 
 # Ignore library version files
 src/nsbi_common_utils/_version.py
 # pixi environments
 .pixi/*
 !.pixi/config.toml
+
+*.dag.*
+
+apptainer-build.sub
+
+logs/*
+
+lightning_logs/
+
+data/
diff --git a/FAIR_universe_Higgs_tautau/1_data_loader.ipynb b/FAIR_universe_Higgs_tautau/1_data_loader.ipynb
new file mode 100644
index 00000000..41aea303
--- /dev/null
+++ b/FAIR_universe_Higgs_tautau/1_data_loader.ipynb
@@ -0,0 +1,417 @@
+{
+ "cells": [
+  {
+   "attachments": {},
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Data Loading Notebook\n",
+    "===\n",
+    "\n",
+    "Prepare data for the analysis. The raw data is downloaded from the FAIR Universe HiggsML challenge repository. Use the HiggsML package to download and process the dataset, followed by selections and saving to local cache.\n",
+    "\n",
+    "NB: This notebook is only to be run once to get the FAIR Universe dataset into `.root` ntuples. The rest of the workflow is independent of this.\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 20,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import os, sys\n",
+    "sys.path.append(\"\")\n",
+    "import numpy as np\n",
+    "import pandas as pd\n",
+    "import matplotlib.pyplot as plt\n",
+    "import mplhep as hep\n",
+    "import yaml\n",
+    "import uproot\n",
+    "\n",
+    "import warnings\n",
+    "warnings.simplefilter(action='ignore', category=FutureWarning)\n",
+    "\n",
+    "import HiggsML\n",
+    "from HiggsML.systematics import systematics\n",
+    "from HiggsML.datasets import Data\n",
+    "\n",
+    "hep.style.use(hep.style.ATLAS)\n",
+    "\n",
+    "from HiggsML.datasets import download_dataset\n",
+    "\n",
+    "\n",
+    "import logging\n",
+    "logging.basicConfig(level=logging.INFO, format='%(asctime)s - %(levelname)s - %(message)s')\n",
+    "logger = logging.getLogger(__name__)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def load_config(path: str) -> dict:\n",
+    "    with open(path, \"r\") as f:\n",
+    "        return yaml.safe_load(f)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "data_config = load_config(\"config.pipeline.yaml\")[\"data_loader\"]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "2026-03-19 17:33:40,035 - HiggsML.datasets     - INFO     - Handling as URL: https://zenodo.org/records/15131565/files/FAIR_Universe_HiggsML_data.zip\n",
+      "2026-03-19 17:33:40,035 - HiggsML.datasets     - INFO     - Current working directory: /Users/gauravsharma/Documents/github_projects/feb_05_2k26_macOS_pyt/NSBI-workflow-tutorial/FAIR_universe_Higgs_tautau\n",
+      "2026-03-19 17:33:40,048 - HiggsML.datasets     - INFO     - Total rows: 220099101\n",
+      "2026-03-19 17:33:40,048 - HiggsML.datasets     - INFO     - Test size: 66029730\n"
+     ]
+    }
+   ],
+   "source": [
+    "data = download_dataset(\"https://zenodo.org/records/15131565/files/FAIR_Universe_HiggsML_data.zip\")\n",
+    "\n",
+    "# data_input_dir = data_config['data']['data_input_dir']\n",
+    "# data         = Data(data_input_dir)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "List of production modes to keep: ['htautau', 'ztautau', 'ttbar']\n"
+     ]
+    }
+   ],
+   "source": [
+    "list_of_processes = data_config[\"data\"][\"processes\"]\n",
+    "print(f\"List of production modes to keep: {list_of_processes}\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Event selections: DER_mass_transverse_met_lep <= 250.0 and DER_mass_vis <= 500.0 and DER_sum_pt <= 1000 and DER_pt_tot <= 250 and DER_deltar_had_lep <= 4.5 and DER_pt_h <= 400 and DER_pt_ratio_lep_had <= 9.0\n",
+      "\n"
+     ]
+    }
+   ],
+   "source": [
+    "selections = data_config[\"preprocess\"][\"selections\"]\n",
+    "print(f\"Event selections: {selections}\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "seed = data_config[\"preprocess\"][\"seed\"]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "2026-03-19 17:34:01,344 - HiggsML.datasets     - INFO     - Selected train size: 53924279\n",
+      "2026-03-19 17:35:44,316 - HiggsML.datasets     - INFO     - Data loaded successfully\n"
+     ]
+    }
+   ],
+   "source": [
+    "train_fraction = data_config[\"data\"][\"train_size\"]\n",
+    "\n",
+    "data.load_train_set(train_size=train_fraction)\n",
+    "df_training_full = data.get_train_set()\n",
+    "\n",
+    "del data"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Class balance before processing: \n",
+      "\n",
+      " detailed_labels\n",
+      "ztautau    34423675\n",
+      "htautau    17852544\n",
+      "ttbar       1517774\n",
+      "diboson      130286\n",
+      "Name: count, dtype: int64\n"
+     ]
+    }
+   ],
+   "source": [
+    "print(f\"Class balance before processing: \\n\\n {df_training_full['detailed_labels'].value_counts()}\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def process_data(df: pd.DataFrame, list_of_processes: list, seed: int) -> pd.DataFrame:\n",
+    "    \"\"\"\n",
+    "    Filter to the requested processes and balance by downsampling to the smallest class.\n",
+    "    \"\"\"\n",
+    "\n",
+    "    all_labels = df[\"detailed_labels\"].unique()\n",
+    "    processes_to_exclude = list(set(all_labels) - set(list_of_processes))\n",
+    "    print(f\"Excluding processes: {processes_to_exclude}\")\n",
+    "\n",
+    "    df_filtered = df[~np.isin(df[\"detailed_labels\"], processes_to_exclude)].copy()\n",
+    "\n",
+    "    counts = df_filtered[\"detailed_labels\"].value_counts()\n",
+    "    print(f\"Counts before balancing:\\n{counts}\")\n",
+    "\n",
+    "    # Downsample every process to the size of the smallest one,\n",
+    "    # rescaling weights so the total weight sum is preserved per process.\n",
+    "    min_process = counts.idxmin()\n",
+    "    n_min = counts.min()\n",
+    "    print(f\"Balancing to minimum process count ('{min_process}'): {n_min}\")\n",
+    "\n",
+    "    df_list = []\n",
+    "    for _, df_process in df_filtered.groupby(\"detailed_labels\"):\n",
+    "        weight_sum_orig = df_process[\"weights\"].sum() \n",
+    "        df_sampled = df_process.sample(n=n_min, random_state=seed)\n",
+    "        df_sampled = df_sampled.copy()                  \n",
+    "        df_sampled[\"weights\"] *= weight_sum_orig / df_sampled[\"weights\"].sum()\n",
+    "        df_list.append(df_sampled)\n",
+    "\n",
+    "    return pd.concat(df_list).reset_index(drop=True)\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 14,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Excluding processes: ['diboson']\n",
+      "Counts before balancing:\n",
+      "detailed_labels\n",
+      "ztautau    34423675\n",
+      "htautau    17852544\n",
+      "ttbar       1517774\n",
+      "Name: count, dtype: int64\n",
+      "Balancing to minimum process count ('ttbar'): 1517774\n",
+      "Class balance after processing: \n",
+      "\n",
+      " detailed_labels\n",
+      "htautau    1517774\n",
+      "ttbar      1517774\n",
+      "ztautau    1517774\n",
+      "Name: count, dtype: int64\n"
+     ]
+    }
+   ],
+   "source": [
+    "# Find the \"balanced\" dataset\n",
+    "df_balanced = process_data(df_training_full, list_of_processes, seed)\n",
+    "print(f\"Class balance after processing: \\n\\n {df_balanced['detailed_labels'].value_counts()}\")\n",
+    "\n",
+    "del df_training_full"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 15,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def apply_systematics(df: pd.DataFrame, syst_settings: dict) -> dict:\n",
+    "    \"\"\"Generate the nominal dataset and all systematic variations.\"\"\"\n",
+    "    dataset_dict = {}\n",
+    "\n",
+    "    print(\"Generating nominal dataset...\")\n",
+    "    dataset_dict[\"nominal\"] = systematics(data_set=df, dopostprocess=False)\n",
+    "\n",
+    "    for sample_name, syst_args in syst_settings.items():\n",
+    "        print(f\"Generating systematic variation: {sample_name}\")\n",
+    "        dataset_dict[sample_name] = systematics(data_set=df, dopostprocess=False, **syst_args)\n",
+    "\n",
+    "    return dataset_dict"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 16,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Generating nominal dataset...\n",
+      "Generating systematic variation: TES_up\n",
+      "Generating systematic variation: TES_dn\n",
+      "Generating systematic variation: JES_up\n",
+      "Generating systematic variation: JES_dn\n"
+     ]
+    }
+   ],
+   "source": [
+    "# Inject the shared seed into each systematic's data_config\n",
+    "syst_settings = data_config[\"systematics\"]\n",
+    "for syst_cfg in syst_settings.values():\n",
+    "    syst_cfg[\"seed\"] = seed\n",
+    "\n",
+    "# Apply systematic variations\n",
+    "dataset_dict = apply_systematics(df_balanced, syst_settings)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 18,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def save_root_files(\n",
+    "    dataset_dict: dict,\n",
+    "    output_dir: str,\n",
+    "    processes: list,\n",
+    "    selections: str,\n",
+    ") -> None:\n",
+    "    \"\"\"\n",
+    "    Save each (sample, process) combination to its own ROOT file.\n",
+    "    \"\"\"\n",
+    "    os.makedirs(output_dir, exist_ok=True)  \n",
+    "    print(f\"Output directory: {output_dir}\")\n",
+    "\n",
+    "    for sample, df in dataset_dict.items():\n",
+    "        output_path = os.path.join(output_dir, f\"dataset_{sample}.root\")\n",
+    "        print(f\"Writing {output_path}...\")\n",
+    "\n",
+    "        with uproot.recreate(output_path) as root_file:\n",
+    "\n",
+    "            for process in processes:\n",
+    "                df_process = df[df[\"detailed_labels\"] == process].copy()\n",
+    "                df_process = df_process.query(selections)\n",
+    "\n",
+    "                # Drop the label column — not needed in the output tree\n",
+    "                columns_to_keep = [c for c in df_process.columns if c != \"detailed_labels\"]\n",
+    "                arrays = {col: df_process[col].to_numpy() for col in columns_to_keep}\n",
+    "\n",
+    "                if arrays and len(df_process) > 0:\n",
+    "                    tree_name = f\"tree_{process}\"      \n",
+    "                    root_file[tree_name] = arrays\n",
+    "                    print(f\"  Wrote tree '{tree_name}' with {len(df_process)} events.\")\n",
+    "                else:\n",
+    "                    logger.warning(\n",
+    "                        f\"  No events for process '{process}' in sample '{sample}' \"\n",
+    "                        f\"after selection — tree not written.\"\n",
+    "                    )"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 19,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Output directory: ./saved_datasets\n",
+      "Writing ./saved_datasets/dataset_nominal.root...\n",
+      "  Wrote tree 'tree_htautau' with 1509266 events.\n",
+      "  Wrote tree 'tree_ztautau' with 1517348 events.\n",
+      "  Wrote tree 'tree_ttbar' with 1476884 events.\n",
+      "Writing ./saved_datasets/dataset_TES_up.root...\n",
+      "  Wrote tree 'tree_htautau' with 1509266 events.\n",
+      "  Wrote tree 'tree_ztautau' with 1517348 events.\n",
+      "  Wrote tree 'tree_ttbar' with 1476897 events.\n",
+      "Writing ./saved_datasets/dataset_TES_dn.root...\n",
+      "  Wrote tree 'tree_htautau' with 1509264 events.\n",
+      "  Wrote tree 'tree_ztautau' with 1517347 events.\n",
+      "  Wrote tree 'tree_ttbar' with 1476871 events.\n",
+      "Writing ./saved_datasets/dataset_JES_up.root...\n",
+      "  Wrote tree 'tree_htautau' with 1509166 events.\n",
+      "  Wrote tree 'tree_ztautau' with 1517346 events.\n",
+      "  Wrote tree 'tree_ttbar' with 1476801 events.\n",
+      "Writing ./saved_datasets/dataset_JES_dn.root...\n",
+      "  Wrote tree 'tree_htautau' with 1509347 events.\n",
+      "  Wrote tree 'tree_ztautau' with 1517348 events.\n",
+      "  Wrote tree 'tree_ttbar' with 1476963 events.\n"
+     ]
+    }
+   ],
+   "source": [
+    "save_root_files(\n",
+    "            dataset_dict,\n",
+    "            data_config[\"output\"][\"dir\"],\n",
+    "            list_of_processes,\n",
+    "            selections,\n",
+    "        )"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python (torch-mac-mps)",
+   "language": "python",
+   "name": "torch-mac-mps"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.11.15"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 4
+}
diff --git a/FAIR_universe_Higgs_tautau/2_data_preprocessing.ipynb b/FAIR_universe_Higgs_tautau/2_data_preprocessing.ipynb
new file mode 100644
index 00000000..d98d2923
--- /dev/null
+++ b/FAIR_universe_Higgs_tautau/2_data_preprocessing.ipynb
@@ -0,0 +1,767 @@
+{
+ "cells": [
+  {
+   "attachments": {},
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Data Processing Notebook\n",
+    "===\n",
+    "\n",
+    "Prepare data for the NSBI analysis. \n",
+    "\n",
+    "The ntuples are loaded into `pandas.DataFrame` and saved for NSBI trainings and inference.\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/Users/gauravsharma/Documents/github_projects/feb_05_2k26_macOS_pyt/NSBI-workflow-tutorial/src/nsbi_common_utils/plotting.py:10: FutureWarning: ``set_style`` is deprecated: Naming convention is changing to match mpl. Use ``mplhep.style.use()``.\n",
+      "  hep.set_style(\"ATLAS\")\n"
+     ]
+    }
+   ],
+   "source": [
+    "import os, sys, importlib\n",
+    "import numpy as np\n",
+    "import pandas as pd\n",
+    "import matplotlib.pyplot as plt\n",
+    "import mplhep as hep\n",
+    "import yaml\n",
+    "import uproot\n",
+    "\n",
+    "import nsbi_common_utils\n",
+    "from nsbi_common_utils import configuration\n",
+    "from nsbi_common_utils import datasets\n",
+    "\n",
+    "\n",
+    "import warnings\n",
+    "warnings.simplefilter(action='ignore', category=FutureWarning)\n",
+    "\n",
+    "hep.style.use(hep.style.ATLAS)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def load_config(path: str) -> dict:\n",
+    "    with open(path, \"r\") as f:\n",
+    "        return yaml.safe_load(f)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "data_processing_config = load_config(\"config.pipeline.yaml\")[\"data_preprocessing\"]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Features to use for downstream training: \n",
+      "\n",
+      " ['PRI_lep_pt', 'PRI_lep_eta', 'PRI_lep_phi', 'PRI_had_pt', 'PRI_had_eta', 'PRI_had_phi', 'PRI_met', 'PRI_met_phi', 'DER_mass_transverse_met_lep', 'DER_mass_vis', 'DER_pt_h', 'DER_deltar_had_lep', 'DER_pt_tot', 'DER_sum_pt', 'DER_pt_ratio_lep_had', 'DER_met_phi_centrality', 'PRI_jet_leading_pt', 'PRI_jet_leading_eta', 'PRI_jet_leading_phi', 'PRI_jet_all_pt', 'PRI_jet_subleading_pt', 'PRI_jet_subleading_eta', 'PRI_jet_subleading_phi', 'DER_deltaeta_jet_jet', 'DER_mass_jet_jet', 'DER_prodeta_jet_jet', 'DER_lep_eta_centrality', 'PRI_n_jets']\n"
+     ]
+    }
+   ],
+   "source": [
+    "features = data_processing_config[\"features\"]\n",
+    "\n",
+    "print(f\"Features to use for downstream training: \\n\\n {features}\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Define branches to load\n",
+    "==\n",
+    "\n",
+    "Classified in terms of the number of jets for feature engineering later."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Specify branches to load from the ROOT ntuples\n",
+    "input_features_noJets = ['PRI_lep_pt', 'PRI_lep_eta', 'PRI_lep_phi', 'PRI_had_pt', 'PRI_had_eta',\n",
+    "    'PRI_had_phi', 'PRI_met', 'PRI_met_phi', 'DER_mass_transverse_met_lep',\n",
+    "    'DER_mass_vis', 'DER_pt_h', 'DER_deltar_had_lep', 'DER_pt_tot', 'DER_sum_pt',\n",
+    "    'DER_pt_ratio_lep_had', 'DER_met_phi_centrality']\n",
+    "\n",
+    "for feat in input_features_noJets:\n",
+    "    if feat not in features:\n",
+    "        input_features_noJets.remove(feat)\n",
+    "\n",
+    "input_features_1Jets = ['PRI_jet_leading_pt', 'PRI_jet_leading_eta',\n",
+    "    'PRI_jet_leading_phi',\n",
+    "    'PRI_jet_all_pt']\n",
+    "\n",
+    "for feat in input_features_1Jets:\n",
+    "    if feat not in features:\n",
+    "        input_features_1Jets.remove(feat)\n",
+    "\n",
+    "input_features_2Jets = ['PRI_jet_subleading_pt',\n",
+    "    'PRI_jet_subleading_eta', 'PRI_jet_subleading_phi', 'DER_deltaeta_jet_jet', 'DER_mass_jet_jet',\n",
+    "    'DER_prodeta_jet_jet',\n",
+    "    'DER_lep_eta_centrality']\n",
+    "\n",
+    "for feat in input_features_2Jets:\n",
+    "    if feat not in features:\n",
+    "        input_features_2Jets.remove(feat)\n",
+    "\n",
+    "input_features_nJets = ['PRI_n_jets']\n",
+    "\n",
+    "for feat in input_features_nJets:\n",
+    "    if feat not in features:\n",
+    "        input_features_nJets.remove(feat)\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "['PRI_lep_pt', 'PRI_lep_eta', 'PRI_lep_phi', 'PRI_had_pt', 'PRI_had_eta', 'PRI_had_phi', 'PRI_met', 'PRI_met_phi', 'DER_mass_transverse_met_lep', 'DER_mass_vis', 'DER_pt_h', 'DER_deltar_had_lep', 'DER_pt_tot', 'DER_sum_pt', 'DER_pt_ratio_lep_had', 'DER_met_phi_centrality', 'PRI_jet_leading_pt', 'PRI_jet_leading_eta', 'PRI_jet_leading_phi', 'PRI_jet_all_pt', 'PRI_jet_subleading_pt', 'PRI_jet_subleading_eta', 'PRI_jet_subleading_phi', 'DER_deltaeta_jet_jet', 'DER_mass_jet_jet', 'DER_prodeta_jet_jet', 'DER_lep_eta_centrality', 'PRI_n_jets']\n"
+     ]
+    }
+   ],
+   "source": [
+    "branches_to_load = input_features_noJets \\\n",
+    "                    + input_features_1Jets \\\n",
+    "                    + input_features_2Jets \\\n",
+    "                    + input_features_nJets\n",
+    "\n",
+    "print(branches_to_load)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "input_features_by_jet = {\n",
+    "    1: input_features_1Jets, \n",
+    "    2: input_features_2Jets\n",
+    "}"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Loading the datasets_helper\n",
+    "---\n",
+    "\n",
+    "The `nsbi_common_utils.datasets` library provides a class `datasets`, which is a lightweight helper class for the various utilities related to reading, processing and saving datasets."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "datasets_helper = nsbi_common_utils.datasets.datasets(\n",
+    "                                                        config_path = data_processing_config['fit_config_path'],\n",
+    "                                                        branches_to_load = branches_to_load\n",
+    "                                                    )\n",
+    "datasets_all = datasets_helper.load_datasets_from_config(load_systematics = True)\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Preprocessing\n",
+    "--\n",
+    "\n",
+    "Here we perform some simple feature engineering, particularly focusing on jet variables whose impact on the final likelihood is permutation invariant.\n",
+    "\n",
+    "Kinetmatic features with long tails are also transformed using `log` functions to help the neural network trainings."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def process_data(df: dict, input_features_by_jet: dict, branches: list) -> tuple:\n",
+    "    \"\"\"\n",
+    "    Apply feature engineering to all samples in all regions.\n",
+    "    \n",
+    "    Returns:\n",
+    "        (modified_df_dict, list_of_new_branch_names)\n",
+    "    \"\"\"\n",
+    "    median_feature = {}\n",
+    "\n",
+    "    # 1. Calculate medians from nominal samples\n",
+    "    nominal_data = df[\"Nominal\"]\n",
+    "\n",
+    "    print(\"Computing medians from Nominal samples for imputation...\")\n",
+    "    for sample, sample_dataset in nominal_data.items(): \n",
+    "        median_feature[sample] = {}\n",
+    "        for n_jets, feat_list in input_features_by_jet.items():\n",
+    "            for feature in feat_list:\n",
+    "                # Calculate median for events with n_jets >= threshold\n",
+    "                vals = sample_dataset.loc[sample_dataset['PRI_n_jets'] >= n_jets, feature]\n",
+    "                median_feature[sample][feature] = np.median(vals)\n",
+    "\n",
+    "    print(\"Applying feature engineering to all regions and samples...\")\n",
+    "    branches_to_add = []\n",
+    "\n",
+    "    # 2. Apply engineering to all datasets (systematics/regions)\n",
+    "    for region, sample_datasets in df.items():\n",
+    "        for sample, sample_dataset in sample_datasets.items():\n",
+    "            \n",
+    "            df_modified = sample_dataset.copy()\n",
+    "            \n",
+    "            # --- Categorical jet masks ---\n",
+    "            df_modified['njet_0'] = (df_modified['PRI_n_jets'] == 0).astype(int)\n",
+    "            df_modified['njet_1'] = (df_modified['PRI_n_jets'] == 1).astype(int)\n",
+    "            df_modified['njet_2'] = (df_modified['PRI_n_jets'] >= 2).astype(int)\n",
+    "\n",
+    "            for mask_name in ['njet_0', 'njet_1', 'njet_2']:\n",
+    "                if mask_name not in branches_to_add:\n",
+    "                    branches_to_add.append(mask_name)\n",
+    "\n",
+    "            # --- Jet feature processing ---\n",
+    "            for n_jets, feat_list in input_features_by_jet.items():\n",
+    "                mask_col = f'jet{n_jets}_mask'\n",
+    "                df_modified[mask_col] = (df_modified['PRI_n_jets'] >= n_jets).astype(float)\n",
+    "                \n",
+    "                if mask_col not in branches_to_add:\n",
+    "                    branches_to_add.append(mask_col)\n",
+    "\n",
+    "                for feat in feat_list:\n",
+    "                    df_modified[feat] = df_modified[feat].where(df_modified['PRI_n_jets'] >= n_jets, \n",
+    "                                                                median_feature[sample][feat])\n",
+    "                    \n",
+    "\n",
+    "            # --- Log transformations when distributions spread out ---\n",
+    "            for feat in branches:\n",
+    "                if feat not in df_modified.columns:\n",
+    "                    continue\n",
+    "\n",
+    "                kin = df_modified[feat].to_numpy()\n",
+    "\n",
+    "                # Only apply log if all values are positive and range is large\n",
+    "                if (np.amin(kin) > 0.0) and (np.amax(kin) > 100.0):\n",
+    "                    log_feat = f'log_{feat}'\n",
+    "                    df_modified[log_feat] = np.log(kin + 10.0)\n",
+    "                    \n",
+    "                    if log_feat not in branches_to_add:\n",
+    "                        branches_to_add.append(log_feat)\n",
+    "\n",
+    "            df[region][sample] = df_modified.copy()\n",
+    "\n",
+    "    return df, branches_to_add\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Computing medians from Nominal samples for imputation...\n",
+      "Applying feature engineering to all regions and samples...\n"
+     ]
+    }
+   ],
+   "source": [
+    "datasets_all, new_branches = process_data(\n",
+    "            datasets_all, \n",
+    "            input_features_by_jet, \n",
+    "            branches=branches_to_load\n",
+    "        )"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Append the additional branches from engineered features back into the dataset and save back to the original `.root` ntuples defined in the data_processing_config file."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "DEBUG: \n",
+      "Saved 3 tree(s) to saved_datasets/dataset_nominal.root: ['tree_htautau', 'tree_ttbar', 'tree_ztautau']\n",
+      "DEBUG: \n",
+      "Saved 3 tree(s) to saved_datasets/dataset_JES_up.root: ['tree_htautau', 'tree_ztautau', 'tree_ttbar']\n",
+      "DEBUG: \n",
+      "Saved 3 tree(s) to saved_datasets/dataset_JES_dn.root: ['tree_htautau', 'tree_ztautau', 'tree_ttbar']\n",
+      "DEBUG: \n",
+      "Saved 3 tree(s) to saved_datasets/dataset_TES_up.root: ['tree_htautau', 'tree_ztautau', 'tree_ttbar']\n",
+      "DEBUG: \n",
+      "Saved 3 tree(s) to saved_datasets/dataset_TES_dn.root: ['tree_htautau', 'tree_ztautau', 'tree_ttbar']\n"
+     ]
+    }
+   ],
+   "source": [
+    "datasets_helper.add_appended_branches(new_branches)\n",
+    "\n",
+    "datasets_helper.save_dataset_to_ntuple(datasets_all, save_systematics=True)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Visualize the data\n",
+    "-\n",
+    "\n",
+    "Check for low-stats phase space and effect from systematics before moving to the density ratio training.\n",
+    "\n",
+    "NSBI workflows need precise estimate of the density ratios as a function of the full kinematic phase space. If regions exist with low-stats they can be filtered out by passing query strings to the `Filter` section of the `Regions` entries. E.g.:\n",
+    "\n",
+    "```\n",
+    "Regions:\n",
+    "-   Name: CR\n",
+    "    Filter: kin_feature_xyz<=100.0\n",
+    "```"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def plot_kinematic_features(\n",
+    "    columns,\n",
+    "    nbins,\n",
+    "    variations_to_plot,\n",
+    "    dataset_dict,\n",
+    "    xlabel_dict,\n",
+    "    samples_list\n",
+    "):\n",
+    "    \"\"\"\n",
+    "    For each feature in `columns`, builds weighted histograms for each (label, variation)\n",
+    "    and then plots them on a grid of subplots.\n",
+    "\n",
+    "    Parameters\n",
+    "    ----------\n",
+    "    columns : list of str\n",
+    "        List of dataframe‐column names to histogram.\n",
+    "    nbins : int\n",
+    "        Number of bins to use (based on the 'nominal' variation of each feature).\n",
+    "    variations_to_plot : list of str\n",
+    "        Keys in `dataset_dict` (e.g. ['nominal','TES_up','TES_dn']).\n",
+    "    dataset_dict : dict[str -> pandas.DataFrame]\n",
+    "        Mapping each variation name to a DataFrame.  Each DataFrame must have columns:\n",
+    "            - feature columns in `columns`\n",
+    "            - \"detailed_labels\" (to mask by label)\n",
+    "            - \"weights\"\n",
+    "    xlabel_dict : dict[str->str]\n",
+    "        Mapping each feature name → label for the x‐axis.\n",
+    "    samples_list : list\n",
+    "        The list of sample labels to loop over.\n",
+    "\n",
+    "    Returns\n",
+    "    -------\n",
+    "    fig, axes\n",
+    "        The Figure and array of Axes where the histograms were drawn.\n",
+    "    \"\"\"\n",
+    "    # Compute all histograms and bins\n",
+    "    hist = {feat: {lbl: {} for lbl in samples_list} for feat in columns}\n",
+    "    bins_dict = {}\n",
+    "\n",
+    "    for feature in columns:\n",
+    "        for label in samples_list:\n",
+    "            # Compute bins from the 'nominal' dataset\n",
+    "            arr_nom = dataset_dict['Nominal'][label][feature].to_numpy()\n",
+    "            bins = np.histogram_bin_edges(arr_nom, bins=nbins)\n",
+    "            bins_dict[feature] = bins\n",
+    "            break\n",
+    "\n",
+    "        # For each variation & label, fill the histogram\n",
+    "        for variation in variations_to_plot:\n",
+    "            for label in samples_list:\n",
+    "                df_var = dataset_dict[variation][label]\n",
+    "                vals = df_var[feature].to_numpy()\n",
+    "                wts = df_var[\"weights\"].to_numpy()\n",
+    "                hist_vals, _ = np.histogram(vals, weights=wts, bins=bins)\n",
+    "                hist[feature][label][variation] = hist_vals\n",
+    "\n",
+    "    # Set up a grid of subplots (up to 2 columns, adjusting rows if needed)\n",
+    "    n_plots = len(columns)\n",
+    "    ncols = 2\n",
+    "    nrows = int(np.ceil(n_plots / ncols))\n",
+    "    fig, axes = plt.subplots(nrows, ncols, figsize=(6 * ncols, 5 * nrows))\n",
+    "    axes = axes.flatten()\n",
+    "\n",
+    "    # Prepare color/linestyle maps\n",
+    "    palette = plt.rcParams['axes.prop_cycle'].by_key()['color']\n",
+    "    color_label_map = {lbl: palette[i % len(palette)] for i, lbl in enumerate(samples_list)}\n",
+    "    linestyle_map = {\n",
+    "        variations_to_plot[0]: '-',\n",
+    "        variations_to_plot[1]: '--',\n",
+    "        variations_to_plot[2]: '--'\n",
+    "    }\n",
+    "\n",
+    "    # Plot each feature in its own Axes\n",
+    "    for ax, feature in zip(axes, columns):\n",
+    "        for label in samples_list:\n",
+    "            for variation in variations_to_plot:\n",
+    "                hep.histplot(\n",
+    "                    hist[feature][label][variation],\n",
+    "                    bins=bins_dict[feature],\n",
+    "                    label=(label if variation == 'Nominal' else None),\n",
+    "                    ax=ax,\n",
+    "                    linewidth=1.5,\n",
+    "                    color=color_label_map[label],\n",
+    "                    linestyle=linestyle_map.get(variation, '-')\n",
+    "                )\n",
+    "\n",
+    "        ax.set_yscale('log')\n",
+    "        ax.set_xlabel(xlabel_dict.get(feature, feature), size=14)\n",
+    "        ax.set_ylabel('Events', size=14)\n",
+    "        ax.legend()\n",
+    "\n",
+    "    # If there are unused subplots (e.g. 3 features → 4 subplots), turn off extras\n",
+    "    for i in range(n_plots, len(axes)):\n",
+    "        axes[i].axis('off')\n",
+    "\n",
+    "    plt.tight_layout()\n",
+    "    return fig, axes"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 13,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "samples_list = list(datasets_all[\"Nominal\"].keys())"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 14,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 1200x1000 with 4 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "columns = ['DER_pt_h', 'DER_deltar_had_lep', 'DER_pt_tot', 'DER_sum_pt']\n",
+    "nbins = 50\n",
+    "\n",
+    "xlabel_dict = {\n",
+    "    'PRI_lep_pt': 'lepton $p_T$ [GeV]', \n",
+    "    'PRI_lep_eta': 'lepton $\\\\eta$ [GeV]', \n",
+    "    'PRI_met': 'MET [GeV]', \n",
+    "    'PRI_met_phi': 'MET $\\\\phi$',     \n",
+    "    'PRI_n_jets': '$n_{jets}$'      \n",
+    "}\n",
+    "\n",
+    "variations_to_plot = ['Nominal', 'JES_Up', 'JES_Dn']\n",
+    "\n",
+    "fig, axes = plot_kinematic_features(\n",
+    "    columns=columns,\n",
+    "    nbins=nbins,\n",
+    "    variations_to_plot=variations_to_plot,\n",
+    "    dataset_dict=datasets_all,\n",
+    "    xlabel_dict=xlabel_dict,\n",
+    "    samples_list=samples_list\n",
+    ")\n",
+    "plt.show()\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 15,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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rk/BrAgAA/Sw28LCUqIJMXCJXUlJC27dvpzZt2tA777zjfqxr1660bt06uvTSS8WOKoJMLf0bah3NXxEAAAAYANZMLrw2/MlPfuL3/vzoRz8SjwWCYSkAAADJMSwlqnK5//73v3TjjTeKAFOgXZfvf//7dPDgwWheJmW1+cE/vFOfm288fUQhmS26XiMAAISHszN+85vf0He/+10qKCiggQMHioxexuXjzz33nHsT5gc/+IH7677++mv6xS9+QZdddhnl5ORQx44daeTIkfTBBx+E1cfg008/FffPmzdPfM7PPWXKFHHMP4P5tRXV1dU0Y8YMkWGcm5srNoqGDh3q98s9P5dWad+FCxfE/XwtEBmsmVyOHz9OHTp0EN/f6htnxKckc772cTxIudrHAAAGgzVTaosqk6m4uJisVmvQc+x2O7VujWwcTZKZzBqpz3JTXTR/LQAAoAMu5/nss8/o+uuvFz8fOct31KhRIrhw77330ltvvUX79+8Xx/369RNf09jYKAJK/HUc7Lnpppvo7NmzIjOY+ynx+RwQivQ6nE6n+Ho+/s53vuN+bOzYsbR582bq37+/uM6qqirauHGjyCz597//La4B4gNrJk+Qib+nr7322rT4VpPMuZrHcXktS77mMQCA0WDNlNqiCjINGDCA/vWvf9GJEye8dkoVfD83AOdFM/i7/c7JlJP7qP8Dsp3sF12HD1b+iX49dSbePgBIOhzocNqCb0QYgcmSG5PpcOfOnRMBJWVj5dFHH6U//OEP4ufgb3/7W6qsrBRBI+5V2KVLF3HOxx9/LAJM48ePp9dee839XG+88QbdfvvtIiAUaZCJU6d5g4eDTA8++KA7cMQZU/x8/Mu9Okvqf//3f+maa64RP8/1DjIZub9AtJJxzbRy5UrxvXno0CHq1auX5jkcFJ0zZ44IVpaXl1Pnzp1FMHPWrFmaPZR4uh5n7TEebiJ6DQEApDmsmbBmSiVRBZkWLlwodlu5JIAX00pa/fvvvy8Wzn/84x/F55gwp2313xbTgMGj/O7nqXJla10LsLYjrhWfe/7GWrtL6GS7lcheKyaOYJEGAEbDAaZD9xt/N/2KpbVkyuLGudF54oknvDJ3OTuIg0wVFRUBv+aSSy4Rk1m59FyNp7YyDkzFitlsFq81aNCguL9WKvYXiFYyrplWrVoV9PFTp06JACUHWBl//x85coTmz58vSkV37NhBWVlZfplMnAXfu3dvEbzq1KkTTZgwgWbPnk2ZmZmUamR7g9exq0l3nF5LtnsdRz1CGgASBmsmrJlSSVRBJt6J3bJli+j9MG3aNPf93A+Co7Hf/va3xQIl0O5XOk9JCVflptC7yphABwCgP/5lW417HoXSvXt3cVOCPPwL+hdffEGvvvpqzK+vffv2NHHiRHFcU1NDR48epcOHD9Pf//73mL8WJPeaia9nyZIltH79+qDn8fcTB5iGDRsmvmf5z8jZeqNHj6bdu3fTggULRMBJvWY6efIklZaWir5fPXr0EIEoDqzx9/+f/vQnSjn2Gp/jdvF7LVul93FWq/i9FgBAFLBmSm1RBZkY93XYtWuXWFTwjtTp06dFQ8eePXuKb55YlCCk4pQUAIBUx2VonCWUDNcZC0VFRS0uSZo7d64IMLFWrVq5ezaFGxAIF2eXPPLII6JETwmERdIbJ5LXguRbM/H3Bwe6du7c6f5+DIT/DHw+N49fvXo1tWvnCp707duX1q5dK8oDFy1aJDKUlE03LuP8y1/+IjLROMDEfvjDH4psp6eeekr8f9C2bdsE/EkBAIwFa6bwYM2UBkEmbmrKjUN5UcSLCr6lG2VKyptvvul1f6CJe+Hg8jeRnaSluVzOUXuSyt/+VotfAwAg3vhnQyzK0FIZZ3/cd999okE4T5/j4BKX0HEpFU+hCwf3wQkHZ9Fwk3Eul+MgAAc8Lr30UtffU5jBjXBfC5JzzbRhwwZavnx5WOcqG2n8vasEmBT8vcXlcDxdmINqnOnELBaL6DXm68c//rHIevr888/puuuui8mfBQAgmWDNFBrWTGkSZBo+fLhYDP/sZz+jcePGJWWPBCNOSRH9lTSmzqnJcZ5QAgAA8cdZI7xR8c9//pMkSfLqdRMIT6RT97nZu3dvWK/FzcQZ9wXKz/f0yvrqq6/CbrId7mtBcq6Zpk+fLvojKTgoeebMGc1zuRyOjRgxQvNxvp+DTHyeEmTizC0OJPF7of5+V/pKchYfAACAFqyZkocU7WKEF6rPPvusSIu+4oorxE4UTw4xIk6v4ygx97sIhKek3H///WJ3l9O7OYDEk4ACTbPxnZISESn6nk0AAJBcbDab+/jixYvi54u6tx/3S3rsscfEsfpnj5ItwhPiFDwx7vnnnw/7tbgnzoULF7wCVkp/oFCvxV9npKbUySYZ1kwdO3ak7373u+4bZx4Fwj29mFL25kvpNab+83EmHAefeMqe2ttvvy2ahhuhHxUAABgH1kxpGGT6/e9/L5qGfvrpp2I8My9GOCDDgRlOx+cxxGVlZZRMU1K4VOGVV14RC/ecnBz3lBROcefFuFYmE984LZz//FzewD0FmpqaQl6PlBmbbCRHYzXJTXXiZq8vE9PotG4RB8EAACBmlEbg3BOJp7yxMWPGUFVVlfjlmkfF8+h3/jnCwSAOSHBquBJE4v5/vFHC5/z85z+nBx54QGTD+E6mU78W//x6+umn3a/FPZX69OlDt912G915553UrVs3kW3CGytc/sRj55U+OfwzcOrUqeK6fvWrX4mv4/OKi4vxXZEGa6ZQlGtVT1TUahtw/vx5931cIshBpnvuuUf8f8CtBrgROvdj4vcnWFALAADSB9ZMyS0m0015IgoHVriRKGcJ8U4nL5B5UfqNb3yDbr75ZtITL6pffvnliKak8PQTXvjzqGQer6tMSVFTpqTwn5sX+/z8P/3pT8WfnxdPiVK5vh+dfz1f3MpWl1DZ2ss0b05r4PILAACIL/7FmgNC//73v2nTpk3iPv5Zwb9gc0CHszn4Z8qMGTPo3XffFT2azGazu3SOe9Vwg2VuEs2bJhysePjhh+nFF1/0e61bbrlFBKU++eQTd5kcB5Z4E4UbNXPJHAeX7r77bvroo4/opZdeEtlLSnYKZ6HwzzTurcNZJx988IEIbv3jH/9wlzZBaq6ZwmW1WoP2oFTuV85T8DTDO+64Q3wv8/8TvL7i+ziLPJja2lqR5RftTWvDEAAAjCXd10yNjY0x+ZnHPzv1YHLGYVQMp9xzg9EXXnhBNLrknVc9smgCTUnhiS6+Kdk8JYWzmPgbifsFqJtY8jcTp7bzbh2X0ylTUjh976233vKaksJ4h5L/B+BdPq0pKRy44q/Zs/tdGjBwaIv+bJyZxIGjSHAzcXOIXk8AAABGpvwM5f5QkUzhMyqjrJm0cKYbL+K11k2ZmZliWhxvyGllM/EajHsv8UKf12HR/n3Hypw5c0SQL1E4w5w3AFnx7aVkzolfJqC97jyVvXmJ67VuO0fmvPZxey0AAIifuXPn0rx582L2fIleM0XV+FuNAyqcas/RO15YcH8JjibedNNNdOutt5IeDD8lpamGYjKBzlxIktkV+JLtViK7J2LpqDtNlZtaFsgCAACA2DPimqklpQy8SxooyKRkMOXlxWbC5HvvvSdKNqOlbpqfCJJqUIv6OC6vZfE09HfayskuWwOcmE2SpZBM5tywJ0sCAEDizJw5U7QsiBZnZw0dmvhYQFRBJt7Z4gUSp6vt2bNHpHtzlg+ntvEiiYMtek4KSeUpKYEm0EmZvJhT7ZKZC7WPAQAAIGGMvmaKFPfmUoJMnPHkq7S0VHwsKiqKyevxek7JJA9k8uTJ4gZE5W9/K+Tb0H58LZnEuhEAAIwkKysr5KYI93LkWzCBhpcZOsh05ZVXunepeIHEN+4lEKtdq1hMSeGbIl5TUrhkjus4jTglRclwYs6Gc2S3B8ieMueLHTbsagEAAMSe0ddMkeI+F7wm4mbmWin4Bw4ccJ8XC2vWrEnK8kjZbvM6dm0GAgAARCecjZVYl5wnJMjEjRt5kcSBlkSnHxttSgr3Gxg4cKAop1OiikabkoJdLQAAAH2k0pqJ8ZqHm7FyPylucOpr8+bN4iNPzosFno5otEwm7p0lhqqYW5Nkdq35ZFstkapMjdsWuNmreUUZt+vhjULOTpJlO5GtUvMcp90a1noQAACMbXGqZDJxR3eu6eOUbvbXv/5VfDxx4gR99dVXNHjwYL+vmT17Ni1ZskRk/aTqlJTHHntMdK/nPyf3buL7wumpUFtnFanmiUinAwAA0BNPSonFZC+9JqVEKpXXTGz06NGiMSn3luLrVZfFcTkglwfyxt2QIUNSNpOJA0yRDmGJJ+6vxOVvooFDlnbppb3xovuYSzZjMmYaAAASbnKqZDI9++yz4qOyYFLwRJTnn39ecxoKN7Pken2jU4bsBRq2p/RZ8v0zcv8E/vPzLVLDfui/8xfrSSnuXS17A1GAUjnsagEAQLwtXLgwppNSjC6V10yMm3BzT8qtW7eKTKYVK1ZQp06dxIKWs47YlClTQmYfQYLZyr2PswrwVwAAAMacLpfsEj0lhW1/5+909fd+GPXzBMticu9qiR4A3lPzFPb6Cvdx04XDlJHVWrNfk/s5MY0EAADSbFIK+Fu2bJkom9u+fTt17txZrJ+qq7kkjESmFv+dx4oRy+XUikYfoIz8rn7lcr6TgQEAAGIhZcrlUlmip6Swib/4DeXkzNN/waTKcKpcHzoVHdNIAAAg3SalgD8OLO3du1dkVG/YsIEqKipEo+/x48fTjBkzYtqb0ojlcmq8AccbemjsDQAAiZAy5XKpLNFTUtibq1fS1QOupWSD6SgAAJF577336Pvf/z79+c9/prvvvhtvXxIumNIR948KpUOHDrR06dKEXA8AAKQ+rJmSH4JMOk1JMRJO3y6+9XjgE8z55Gwo9UwjifN0FACAZML9dbj3zLvvvivKuC5cuCDKhiZMmCD61ABA6pfLAQBAaFgzxY6Rs78RZNJpSgq77fbxlJPj6XWk14JJNDUv8C8RVLPba7XH8Uq5JFny3RlOrgCUJ3ilNEwHAABI5gUT6Mvo5XIAAACJNNnA2d8IMuk4JWX1X1+gAYNHUbKp3BRew1WRHRUieAUAAJAMCyYAAAAAiEOQ6cMPP6T58+f73ccWLFhATqdT87FkkMgpKez2OydTTu6jQc9B6jcAgHH94Ac/EGVyjHsude3alU6ePCk+5/5LfFMeV6xevZpefPFFMSWN+/zdeuut9Oijj5IkSV7ZOs8++yy99tpr9OWXX4qG2T169KBf/OIXogzPt2/BBx98IJ7viSeeoJEjRyZtmV6qZTKl8poJXNncmsdGJmVrHwMAxBnWTOkj4iDT7t27xU3L448/rnm/yWSiZJDIKSls9d8WJ00mU8C+TT7lco4L/w070wkAINlxpisHCjjYw8ff+c536OzZs/TSSy/RlVdeSTfeeCNdeuml9NVXX4nz//73v9O2bdtE6fWoUaPo3//+N82aNUtk57z55pvu533ooYdEM+VvfvObNG7cOKqvr6d33nmH7rnnHjKbzXTHHXd4Xccrr7xCr7/+ugg4xbKsO9FSLZMplddMiWbEnkzK+sf32MgkS6HmMQBAvGHNlD4bcxEFmXx3Y5MNpqTEt2+TGNtr7+T+3NFQTlJ2iTiWZTuRrdLva9C3CQCSGf9Ca7fbRZDpwQcfdDf+5iDTgAEDRDYSU4JMPGDib3/7mztIxNmyI0aMEP1mduzYIXb5HA4H/fWvfxVZUZ999pkIKrGvv/6aunXrRm+99ZZfkImzo/7zn/9Q//79E/4eQGqumYwGPZkAAJIb1kzpszEXUZApmXdHIfEqNwwIeQ76NgGkLs7waXSQ4WVlJC57hEuv1QEiLst+7rnnxORSDj5xkKmxsZEWLVpE3/72t90BJmVUPJ9fWekfsOdegggwGQvWTKlPbKCpjj0Fr8Yly7LXcTJcM0A6wJrJH9ZMyQuNv3WEnkwAkMo4wHT35jIyur/cVEzZCfppOGzYML/7uBdgQUEBHTt2THyem5tLEydOFMdWq5WOHj1KR44coY0bN1JZmfb7yQGpVGDk1G8AP+oMbT7OamX8N8lW7n2cVaDn1QBAM6yZ/GHNlLwQZNJRMvVkalHvJnW/JlW5nKPuNPo2AUBauuSSSzTv79ixo1cAaf/+/SIFes+ePSLbgBt/X3311SIYpaVt27aUCoyc+g36MmJPJgAAiB+smZJ3Yw5BJj1JsW0kbuTeTSIdOxl2+AAgpmVonCWUDNeZKKWlpZr3nzlzhq699lpxfODAAXHcvXt30ZuJM524PxP/+8o9mbSgWTSkOvRkAoBUhjWTP6yZkndjDkEmHUmZyTGJJOZUY36dchPZL54IejqagwMkJw58JKoMLVls376dfvvb33rdt2vXLrp48SL16tVLfP7Pf/5TTJPjaXNXXHGF+zybzSYWXBxwAgAAgNSBNZM/rJmSF5b/Ovrp7XdQdk5O2qV+q8f8lr/l+qUqGDQHB4BkwEGgYJ8znkK3cuVKGjdunPi8qqqKHn74YZIkiX7xi1+I+zjgpDym4IlzM2fOFD2aUrknkZFTvwEAACA2sGZKbQgy6ejvq/5KAwYO1vMSkoMUvAcDAICeuFE3mz9/Pn366af0q1/9SgSNtmzZIgJD9913n/vcW265he68805avny5mBbHu3Rnz56lRx991J3J9D//8z/09NNP04gRI2jo0KHUrl072rlzp1iQ9e7dmz766CO69957xXOkGiOnfgMAAEB0sGZKDwgy6ampmtKRyZxL7cfX+jUEV3ParVT+9rfEsWQpTPg1AgCEiwNHPPmNA0acZTNt2jR68sknacmSJfTSSy/RT37yE/e5v/nNb+jGG2+kv/zlL/Svf/1LTIXjc++55x73OdyDae3atSJo9e9//5u6dOlCN910Ey1YsIAOHz5Md999N33++ef4C4K0gsbfAADJD2um9Mj+NjmdTqcur5zGlF3YPe//K+Wmy8WK3FRH5193ldVxQErKzNP7kgAAwEA/Q/fu3Uv9+vXT+3Igjf++7XXnqexN18TI4tvOkTmvPRmdve5rKnvzUnFcfNtXZM77ht6XBAAAKfYzVAz9AjAaHtmtdQwAAABgtB6T6mMjkyytNY8BAABiBeVyOrr9zsmUk/to2jX+Dout3H3oqNpPlNfJ85iU7S6hEwEoWzkm0AEApAAjp34DAAAAQGgIMulo9d8Wo1wuDJWbhoY8BxPoAACSHxp/QzIRfSVVxygPAAAAQLkcAAAAAEDk1INLNIaYGJHcUKp5DAAAECvIZAJDMuV2dmUnaWkul3PUnnRPoAMAAAAAAAAAfSHIpCcpU9eXN7KMjAyigm5Bz5HNuQm7HgAAAAAAAAAIDkEmHUmZBXq+PAAAAEBSGDNmDGVnZwc9J22HpbSQo+40j5jzmjLnW0LHmeVi4w8AAAxlsYGHpSDIpKOf3n4HZefkBD0HCyYAAEgXRl4wgb7WrFlD/fr1w19DDGGwCgBA8pocxsbKvn376KqrrqJEQ5BJR2+sfo2uHnCtnpeQMpx2K8lNdSEbWWJHDgDAuIy8YIL4+MUvfkEff/wxffTRR3iLAQAAUgCCTDqSG5NjEkkyCLcBuGgmHqLXEwAAAMTfli1b6OWXX6arr74ab7deg1U0yuW4jC6cLCcAAAAtkua9AAAAEDP/8z//QyaTKSHv6MMPPyxe68svv0zI6wG0xIULF2jixIlUWFiYvG+glK19bGDcX8lc0M1zy/sGSZl57hvfl5HXSe/LBIA0hjVT8kMmEyT/blyIppWO+nNUufF7rk8kS6IvEwAAICmtXLmSxo8fT4cOHaJevXppnnP27FmaM2cObdy4kcrLy6lz5840duxYmjVrVtBG3b/+9a+pe/fu1KVLF/r8888pGUmWQs1jAACAdIYgEyQtMe1Eo/RNyvS+T8ou8RyrglEAAAAQ2KpVq4K+PadOnaJrrrmGzp07Jz5v3bo1HTlyhObPn0/btm2jHTt2UFZWlt/Xvf322/SPf/yDPvvsM3riiSfwV2DoXw/wqwIAAEQG5XIAAABR4NK0QLd77rlHfPznP//pPpfvU3zxxRciU4SzOTjro1OnTnTbbbfRgQMHvF7jBz/4AXXr1k3zl3V+zr/85S/icz7nueeeE8ddu3YVX6f4+uuvRZPlyy67jHJycqhjx440cuRI+uCDD7yeU7lmX59++qm4f968efh+SXFOp1P0Slq/fn3Q87jcjQNMw4YNo5MnT1JVVZVozM7fx7t376YFCxb4fQ1nO91///30xz/+UfN7OpnIsqx5nOyk7LaaxwAA0cKaKT1gewIAACAKU6ZM8buPJ2Xt2rWL2rRpIx5ft24dHTt2TBwPGDBAnFNRUUHXX3+9+CX9pptuEgGf48ePiwyP999/n/7v//6P2raN7Be8e++9l9566y3av3+/OFZGvjc2Norn58yRoUOHitfjMqd33nmH3nvvPXH+5Zdfju+DNMfZR5y9tHPnTpGRFAx/z/D5JSUltHr1amrXrp24v2/fvrR27Vrxfb5o0SKaPXu2V9kcBzq/+93v0gMPPEBJz1bufZxVoOfVAAAYHtZM6QFBJkh56h5NfOxbTgcA8cuGcNqthn97TebcqJpyP/vss16ff/XVVyK406NHD5o7d65obMxZHhxkUp+7adMmOnPmjPglXJ3x8Yc//IEeffRREaQaNWpURNfy29/+liorK0UA4PHHHxcZUoxHxHOAibOmXnvtNff5b7zxBt1+++20efNmBJmANmzYQMuXLw/rneDAKePvUSXApOjfvz/17t2bDh48KL6POdOJcfCJA5schK2rqxP3NTU1kcPhEJ9nZmaSxYLeiQCQfrBmwpoplSDIpKPb75pMOTmPBj1n8uTJ4gaxwWN5PcxeaeBKMIobiot+TwAQFQ4wnX893/DvYvvxtWTKzIvJc/EvzFzuVltbK7I8gk3O4l/Cly1bRrfccovX/VdeeaX4yMGiWLnkkkvEa914441xf61oLF68WNyCaWhoSNj1pJvp06fThAkT3J9z9hsHQrVwORwbMWKE5uN8PweZ+DwlyMRloBxMUr7v1PLz8+mxxx4T/ZxAX9icA0g8rJmwZkolCDLp6M3Vq+jqAdfqeQlpp3LT0JDniIl1Gg3FAQBCefjhh+nDDz8UPZK+853vBD23T58+4sbOnz9PR48eFSVyf/rTn2L+RvMUL74pASUuheJ+UK+++ioZSTgbK9zz56qrrkrYNaUT7tPFN0WwrCL+fmWcsadF+X7jDD4Fl3DecMMNXuc9+eSTokyUvxe5lxMAAKQHrJlSF4JMAAAQtzI0zhJKhuuMhddff11k4UyaNInuuuuusL6Gm3T/v//3/0SJHSsqKgo4Kj5Qen0k4+i5fE/ptdOqVSt3z6ZYvxakvrKyMvdEOS3cj0wJoCo4iOQbSGrfvr14rmuvxaYbAKQvrJlCw5opeSDIFGPc0JJ7X3C/ATAGLn8T2Ul+POVyjtqTVP72txJ+bQApP0EkRmVoRsdlQDwxizNsnn/++bD7J3EvJi5PuvPOO0UzZO5tw424v//974f1HDypKxycJXLfffeJ/jm8SOPgEpfQffnll2IKXSxfC9KD1Wr1Cib5Uu5XzosWl6DW1NRE/TxZWVniBgBgJFgzBZdua6bGxkZxi8XPTj0gyBRDW7ZsESN/r7766rDOlxuN0QMj1Yn+SiHK3+QYZTIAQPrhX3xvvfVWMUFrzZo1Yf8Cy1O8rrnmGlqxYoXX/adOnQq7F9HevXvDfq0OHTrQP//5T5IkKeRrMV7cqP8s4b4WpAclsy1QhpvS25Cbegfj+/0fCE9FjIU5c+aIjD4AAEg8rJnCs3DhQpo3bx4lKwSZYuTChQs0ceLEoE1eAQAg9fCuGpegrV+/PuQOFzcG5wlayu4S97zhX8KVX8jPnj1LTz31lF9QiXfrzp07JybEKb2euKky934KxGazuY8vXrwonq++vp7y8vLcCz1utKz1Wownzv34xz8Wx19//XXYGVqQHnJzc8X3UFVVlWbJnJLBpHy/RYt3q5UeZtEINwjM/186rYGDsMwpN3k+kbKjvTQAgJSHNVN4Zs6cSVOnTqVoffLJJzHbpIlEWgWZuB8Fj28+dOhQwJ4XvMDnXa6NGzeKNLfOnTvT2LFjadasWWKXOpBf//rXosklj4v+/PPP4/inAAAAo+DMoLfeekv8rNi6dau4qbVt21akePMv5Iw3IzhwM2bMGJH99OKLL4qfR9ddd534hX3Tpk00ePBgOnz4sNjFKigooHHjxokJdDz+nVPC+XP+BZ4//+lPf+oXaFJe65FHHhGvxSnf/Hq/+c1vxGvxYoN/gebsW55wx1O9ODWcU8Efeugh+slPfkLPPPOM+NnHPzM5AMZ/Tp4WFiyoBemluLjYHWTq1s0/W7i0tNTdZyxWv5gEW4fFeiIvB5jK1l4W9vmSBZuMAADBYM0U29JuQ0/kdaaRH/3oR5zT7Tx06JDm419++aXzkksuEefwrXXr1u7jQYMGORsaGjS/7p///KczPz/fefz4ceeECROcV199ddDr2Lt3r3jOPe//KyZ/LoheU81x55kVJG58DAAQjj//+c/unxNat65du4rzPvzwQ+d1113nzMnJcT788MPiPqvV6pw2bZqzS5cuzoKCAueQIUOcr7zyinhs/vz5zpKSEuczzzzjfq1FixY5e/XqJX7eDB061Ll06VLn//3f/4nX4etQHDlyxHnjjTc68/LynLfeequ4z+FwOJ966ilnjx49xP3XXHON8/e//73Tbrc7ly1b5uzQoYNz6tSp7ud44403nN/5znfEuQMHDnT+7ne/c9bV1YnXmjt3rq7fHMrPUP4I8cXfv4HWTSNGjBCPrVq1SvNrH3rooZh8v+j1991U+5V7XRDOzWGrdaaKpppTqjXRKb0vBwBSBNZMiafXz1AT/4dSHP8RlyxZIppys0CZTMOHD6dt27bRsGHDxK4uZyXt37+fRo8eTadPnxZlBfPnz/f6Gs524p1grpl84IEH6J577hENYIM1/lbGL+95/180YPCoOPyJIVL2i6epbG1ncVx86ykyF2CMMgCAESk/Q7lHVCTT8SBynKF08uRJzXUTr3u4txGve5YvX+73td/85jfF13HZ5Y033hj13zdfSyIzmeSmOjr/er44Lhp9QHsKpaXI3eOMH+fGvalA/WfnCaFSmgxwAABIJovDzGQ6ceJEwtdMKV0uxwEjbna6c+dO98jmQDiYxOeXlJTQ6tWr3T0p+vbtK0oSBgwYQIsWLaLZs2d7LXI4cMXd7TnABMlLmTLnewwAAAD+eAOOg0zr1q0TG27qsrg9e/aIABP3ahoyZEhM3j5uqq9XUDEjvysCLQAAYCiTw9hYUTZqEs0zYiYFbdiwQeyuhQowMV4kMR5VqASYFP379xfZStXV1bRr1y73/Rx8euedd0Qz1Lq6OnHjpq7c64KP1U1XIXk4ak+S/eIJjduXYnePb2mQAAgAABAQN+HmDHAOMHH/Ls74Vha03AOMTZkyJWT2EQAAAKSWlM5kmj59umgUqRg5ciSdOXNG89zdu3eLj9zYVAvfz5N8+Dwup2NcFsfBpCuvvNLvfG6kqlVe5801TQiMpfztb4U8h9PHTUgfBwCANLZs2TIaOHAgbd++XTS/58wl3pBj3MCep+PECgeuElou11DqdSxl+jc3T1VyQ7nXMcrlAACMZ7GBG3+ndJCpY8eO4qbgUdGBHD16VHzs0aOH5uM8OY4dO3bMfd+9995LN9xwg9d5Tz75JB0/flz0dOrUKXhfHymrVZh/EgAAAABj4cAS93ngqbycPV5RUUE9e/YUUwlnzJgRdN2VTOVy6UcOcAwAAEYx2cDlcikdZIpEWVmZ+Mi7cFratGkjPp4/f959HweRfANJ7du3F8917bXXxvV6Iba4YSdnJ7l277QXVE57A5W/7Z+1BgAAkIq4WWgoHTp0oKVLlybkegAAAMD4EGRqZrVavYJJvpT7lfNioba2jmpqaqJ+nqysLHGDluOJMFz+FiwlnHsyKZA+DgAQmcbGRnGLVm1tLd76NJTocjkAAAAjW4xyOeNTGjkHauickeHqn8RNvYNZsWJF2K/5g2E3USxwmjpPeIF4Q/o4AEBLLVy4UIy9B2gJlMsBAAB4oFwuCeTm5oqsoqqqKs2SOSWDKS8vcKZLpLa/83e6+ns/jPp5kMUEAABGx02gp06dGvXzfPLJJzR06NCYXBMABOe0W8VUXRaopYApt7N7MxYAAADlcs2Ki4vdQaZu3fwniJSWuqaMFBUVxey7ZuID0yknN/iuLlK/AQAgFYRT2m3k1G/QF8rljDtxt/jW40QF6TN9DwDACBYbeM2EIFMznobCk+MOHz6sOb3kwIED7vNiZfXfFtOAwaNi9nwAAADJzMip35Bm5XKSRfsYAADAACYbeM2EIFOzgQMH0qZNm2jLli00duxYvzdq8+bN4uOgQYMS+zcEAAAAAAklWVprHqcDLn8T2UkkkZTtyeBXl8s56k5T5SaUrQIAgD8EmZqNHj1aNM9et24dlZeXe5XF7dmzhw4dOiR6NQ0ZMoRi5fY7J1NO7qNBz0G5HAAApAsjp34DpAvRX0mj/M17Aq8U4BgAANIdgkzN+vTpQ8OHD6etW7eKTCaeEtepUyeRYsZ9ANiUKVNCjs+NBMrlko1nEeW0N5D94mkisgc+O7uETOZcMplMCbo+AIDkZuTUbwA98fTjxuYBx1a7TPYgw45zM4nMkmvN0mCXyRbg3HbZnunJkVJnOKmPAQAAEGRSWbZsmSib2759O3Xu3FlkLlVXV4vHBg8eLCbjxBYmcSQT9SKq/O0rw/qa9uNryeS18wcAAABGb/wtN5R6HUuZ+ja25gDT3ZvLYvqcf7iuDbXPCx6MiiYQBQAA6Zn9jSCTCgeW9u7dS3PmzKENGzZQRUWFaPQ9fvx4mjFjBlkssW38OPauyZSdkxP0HJTLAQBAujDyggnSrPG3wbKUGmVnzF/3Nx9UhTznhe+3oxLslQEAGM5kA2d/p1WQ6cSJEyHP6dChAy1dujQh1/PG6tfo6gHXJuS1IHpc+saZSQq5oVKzXM5pt4Y18hcAAJJnwQRglCyl54a0pQzJ1KJyufIGB8370JWlHw4OejXY/Z/LYfVcr91aRpZWiEQBAEAaBpkAosG9ldSlb94NMD3kpjq80QAAABAXHGAqyQuvhC3bnOFX/sbZScGCUepA1KMBsp1aO8pobvNxRYODOrRq6Z8GAABSDYJMOrrtpz+jnNz8oOegXC75uDKcPMeBglEAAOAN5XKQyqVwrM4mk8On8q3B7gw7S0kJFLUU91fSKn/zDUZFolFkO7n+DOjtBAAACDLpCNPlUpW6hC7w9DkAAPCGcjkwSuNvvUrhIslSipewsp0qqon+7TpeuLeaqjOC/xnR2wkAIH025hBkAgAAAABDS+XG30YTTraToxYT5wAA9DTZwH0sEWQCAAAAAAi4RG75crmmUXYfPz2kLeVmmPzK5WJVCpdI7bKIKpqP/3BdW8oqLA7a24mn43FJXaByOvGc2a4AFwAAJDcEmXR0+52TKSf30aRL/QYAAEi31G9IL1J2W83jaGRJJmqXmxpBFCnDVULHck0NlOG0ij6U+ao2AQ67TK0drj6V03Y4OIIU9DlRUgcAkBoQZNIRejIBAAAkR+o3AGgrf/tbAd8aZQLd3Lb/S9XUBW8hAEAaQJAJAAAAACBCDoeDKnwS6wqzJK9Jclwyloqk7JKIzn9mSDvKyC/2K5dTl9QBAEBqQJBJV56FCKQmp91K9rqviWRbwHNMuZ3RgwAAAMBA5IZSr2Mps5vfORxg+tW7Smei9GIy51L78bVe93G5nHqqrqPuNFVuGiqOszJMZDabvJqHs2zVpw7ZSaV1wYNy6NsEAGB8CDLpSMpqrefLQwJ294KlkCuKbz1OVOC/eAUAAAAwIpPJRKZM7xF0ku/nqvVQoMwni9mz4TrlfVf/pmDQtwkAwPgQZNLRT2+/g7JzcoKeg8bfAACQLtD4GwIZM2YMZWdnG3bNNGdgayrKzvArl1NPkkuWyXFGZkmNvukAACm9ZkKQSUdvrH6Nrh5wrZ6XAAlIIZdt1X7lclxGp2Q5RdrXAAAgVaHxNwSyZs0a6tevn2HfIA4wleT5lIL5lIaBv6wMor/cVCyObXaZtFpYNcpOmtac5ZStynwCAEhnkw08LAVBJh3JjWh0mA4p5L7p40xuqkvwVQEAAOivpqaGfvvb39K6devo/Pnz1L17d/rVr35FP//5zxPasNtpPeVzr0RSdpHnHOvZhF1PqhKbbKpjrfUQr5myzZ6gXKHG8zTYPelgZfUOypJMmhlOSgCKA1f8vAAAoA8EmXQl6/vyAAAAAAnEwaStW7fS7NmzqVu3brR9+3a6//77xQCMe++9NyHXwAGmsrWXRf086liHRtwD1FncQQaghMIZTgoloykYzoxSAlcAAJB4+CcYwKC7ewAAAKmkoqKC3njjDXr55Zdp0qRJ4r5bbrmFjh07Rn/7298SFmSKVJlVpgzJoVnGpci3oIwrXrRK6AAAwLgQZAJI4t09AACAeFm5ciWNHz+eDh06RL169dI85+zZszRnzhzauHEjlZeXU+fOnWns2LE0a9Ysv0bdlZWVdMMNN9DQoa6x9oouXbrQZ599pstfZNsR71FGXie/crl6awVNfe+8OK7ek0uUUaHL9aUKR91pvz6Uvj0rTbmdRUabL26YzlPlgj6/7HRPp7PLnPmEflgAAHpBkElHt985mXJyHw16DqbLAQBAujDypJR0tGrVqqCPnzp1iq655ho6d+6c+Lx169Z05MgRmj9/Pm3bto127NhBWVlZ7vN79uwpSuWYzWYTQak9e/aIpt4clNIDB5jMBd007s+l6gyMg4uVyk3egUUtxbceJ9L6u8jgpurBv7a0zpPudOqig4qytfs1cQDK2uQJXmkFtQAAIDoIMulo9d8W04DBo/S8BAAAAMMw8qSUdOJ0OmnJkiW0fv36oOdNnDhRBJiGDRtGr776qshI2r9/P40ePZp2795NCxYsEAEnLa+88go9+OCD4njUqFE0ZcoUShx1aVvoMrenh7TVbDbNCrM8zabBGOZ9GN5gHc6OChW8AgCAyCHIBAAAAAAi+4izl3bu3CkykoLhYBKfX1JSQqtXr6Z27VzlTH379qW1a9fSgAEDaNGiRaLBt2/ZnNKL6YorrqC9e/eKYNQDDzwgAk+JoC6LUx8HUpyTQdlmdPaOFJe/iewkr/feu1zOcfFYWFlOAACQPBBk0hWaRAIAAIAxbNiwgZYvXx7WuevWrXNnISkBJkX//v2pd+/edPDgQdq1a5fIdPLVoUMHcePHMjMz6fHHH6eXXnqJzGZjLE1rbLLXcbYZqUqREqVoGuVvCjH0JEZ9KYP1bVKXy52rs9OjH1SJ4/LmjuKByumU50VJHQBAZIzxkzxNSVmt9b4EiFOZQaNqEkqDXSabz2QUh9WzeL3YKFNOjjPguWrZGUSW5oUQj/T1nbiCxRAAALTU9OnTacKECe7PR44cSWfOnNE8l8vh2IgRIzQf5/s5yMTnKUGm119/nX73u9+JkkcOLKl7NdXW1oom4p06cRNuA3AGOAZDCqdvE8ttXkOFW1aHkjoAgMghyAQQYxxguntzWdBzWjsqaW7z8eO7viarqY7M1Bjw/Gq6lFdQIV97zsDWotmlelfONxiFQBQAAGjp2LGjuCksFkvAN+ro0aPiY48ePTQf7969u/h47Ngx932XXnopHThwgD788EMaPHiw+/53332XWrVqJR5PBLmh3OtYZNQAAABATCDIBBAmh8NBFQ1EuZlEZkkKmHlktXuylAKxUlv38ayq0L0I5rb9X6qmLiHPw64cAAAkQllZmXuinJY2bdqIj+fPn3ffx4ElnkY3btw4mjFjBl1yySWi/xP3bvrjH/9IJlOi+h6pf06H/pkNcWQp0j6OE62yOt9yOZ5Op6ynuKQuWDmd8pwoqQMA8ECQSUdy4wU9Xx4iLHHjhUa4E0vUE2kKMk1+z+W0Z5P8VvjPM/eatpSR306zXK4l1wUAABANq9XqFUzypdyvnMckSaK3335bBJi4bK6qqko0/+Y+UHfddVfQ1+Nyupqamqj/0rKysgjdlYyDvycUTuspsttytU+0FIlzTebcqIKRocvqMkRGuAKbdwCgh8bGRnGLFv/s1AOCTLoK0nwHDFfi1hI88rg1R4Z8OJ0F5Bzv+Z9ettVwzr7XOXJTHVX869viuLh1CZl95iNzE9LCMHblOBj1VR125QAAILabM+qPvpTMDs4CVmvfvj2tWLEi4tcbOjQ2E8jmzJlDs6bc6f68zCpThtR8jSaiQovrZ2ejjEZMiVb+9rdCntN+fC2ZUN4IAClu4cKFNG/ePEpWCDLp6PY7J1NO7qNBz5k8ebK4QXyzlOrDKHHzzVDikcahGnVzAEgL78KpF0la/SA4yKS10xfprhwHo4pU14hdOQAwqsWLF4tbMA0N3gF50Edubq7ILOJsJK2SOSWDKS8vNv2O3nvvPerTp09MMpkqqr9yfz53TyVVZxRE/byQOnw377TK5dRZ5DU2BxVmuR6XZZlqm7SfEyV1ABCumTNn0tSpUylan3zyScw2aSKBIJOOVv9tMQ0YPErPS0gLkWYpBSpx818smDDSGAAghsLZWOHJZFdddRXed50VFxe7g0zduvmPqS8tLRUfi4pi02eHp95lZwfYuWmGjbnkw+VvnJ2kldGtcNqtYWU5xUrwzbsMyvfphz/7P5hSBwCxlZWVJW7JujGHIBMkfSPu/ExPpg833bZHmaUUqMQt0cSCS3UczfSbaHbltJpcKs+JXTkAgPTUs2dPMTnu8OHD1K9fP7/HeYqccl4srFmzRvN1ovVQn1ZU1K6dX7kcq7HJRM7AWckQPSWzO9gax153NmbrIQCAVDHZwBtzCDJB0uIA06/erYjoa8LNUjIE9Y5egN29cGFXDgAAYmngwIG0adMm2rJlC40dO9bv8c2bN4uPgwYNisnrjRkzJnaZTKrG0W2yzVSSlxGw3BxSaz0UK+rNO/42yTX7l8thMAsAxBMymVIcp4v/9re/pXXr1olRvd27d6df/epX9POf/1zvS0vpLCX+4R0po2QpAQAAJLPRo0fT3LlzxdqnvLzcqyxuz549dOjQIdGraciQIYbLZGpdWELVqmNIHo66055PLG1Jklz75bKtlkh29QEz5XaOe6Z14M27DMr1KadTGslbbQ7Nfk0KZIgDQCSQyZTiOJi0detWmj17tuhLsH37drr//vvFD6B7771X78tLiywlpRG3VrmcIbOUDEi9K6dVTue7K8cLpga7M2A5nfp5UVYHAJBauAn38OHDxfqHM5l4YlynTp1Eaj5nHbEpU6aEzD7SJZMJklblptANbItvPU5U4N8nLNHUiXDT3q8MeT6vwYINcgEAUEMmUwqrqKigN954g15++WWaNGmSuO+WW24RfQr+9re/BQ0ySVltKZ2zlJSeP6zOJpPD2fIsJc5QyjajEXe8duV8m1yGu2BiWDQBAKSmZcuWibI53lzr3LmzyFyqrnZtRAwePFhMx4mVePVkAogXpYQOACAekMlkICtXrqTx48eLNO5evXppnnP27FmaM2cObdy4UaSA88KJd+lmzZrlt4tWWVlJN9xwg99owC5dutBnn30W1z9LsopHlhIylAAAABKL10d79+4Va6YNGzaIjTdu9M3rrBkzZpDForFDYQA1teVexznZ+bpeDwTH5W8iO8mXqlyuqfpzqtwwwFBvZVYG0V9uKnZ/ru7XpLhgc9BjzdPpHLKTSuuCb7IiOxwAkkHaNf5etWpV0MdPnTpF11xzDZ07d058zrtyR44cofnz59O2bdtox44dXuMEeTHFqeLMZrOJoBT3IuAdNw5KpRun00mNPj8fkaWUeiV1gcrpFDydThnpi0UTAEByOnHiRMhzOnToQEuXLo37tcSyXE52ODSPwZhEyX2I8reMbE9PMCNNzss2B+/XpM7qn4KSOgCIAMrlDBL8WLJkCa1fvz7oeRMnThQBpmHDhtGrr74qMpL2798vGlzu3r2bFixYIAJOWl555RV68MEHxfGoUaNEL4Jg5MYLlMzBo1qbTLKqxE3p0xNuGZU6S8k3EKWGLCUip91K9rrz7qaWgcS72aV2SZ1/OR0WTQAAEEsolwMgyvAMRgSANDc5jI0V7pN41VVXUaKlfCYTZx9x9tLOnTtFRlIwHEzi80tKSmj16tXUrp2rCXLfvn1p7dq1NGDAAFq0aJFo8K21m8a9mK644gqROs7BqAceeEAEngJLnt0zDjDdvbks5s+LXkpBWDw9u8rf/lZY76dRml1GClOiAQAAIN34ltTVNMp+56g3cK0OJzkClNRJJqJ8iySek7OoAAD0kvJBJu4RsHz58rDO5TG8ShaSEmBS9O/fn3r37k0HDx6kXbt2iUwnrZRxvvFjmZmZ9Pjjj9NLL71EZrM56bOUKiNsxM2QpRQdpc9ASwNTRlk0BZo+p1401TU5yR6gZIEDUNw8E4smAAAASJbs75aU1GVr7LrxJF9FONUCvP7yLtMDAEislP8naPr06TRhwgT35yNHjqQzZ85onsvlcGzEiBGaj/P9HGTi85Qg0+uvv06/+93vRCoaB5bUvZpqa2tFE3Ee6WuUAFKNTSaKUYmbVrmcf3NCTHxrKZM5l9qPr3V/LttqNRdMvJhSMp1aFJiK+6JJezodFk0AAKBHTyZIQWmU/Q0AwNCTSUcdO3YUN0WwSSdHjx4VH3v06KH5ePfu3cXHY8eOue+79NJL6cCBA/Thhx+Kcb2Kd999l1q1aiUeb2lgSCtl1jclNljwqCUBpHCgxC1xgRpTpqcBkqQ6VpOb6hJ0RQAAAPpATyYIpkWbbObWSfGmqrPDA/1uoF7vl9U7xFo92O8OyA4HSH6T0ZMpOZSVlbknymlp06aN+Hj+/Hn3fRxY4ml048aNE+N6L7nkEtH/iXs3/fGPfwxaE/1VRS21O1vl9wPAnGGih96tCPu6pUwLZWR6Jt5FI5wsJTTiNhaR4aQ6DhSMMuqiSWukbySLJm6CmacKuGLhBGBMjY2N4hYtzhIGAIg6+9sceOPZSNTZ4VrldAzZ4QBgJMaorTEIq9XqFUzypdyvnMckSaK3335bBJi4bK6qqko0/+Y+UHfddVfQ17v1f8bF5Lqv/Nk0+tb46SHPW3pDETXyCDeUuKUW9SIqRP8BYy6a/Ef6Kqw2T61nuBl56EUAYEwLFy6kefPm6X0ZANS6VXuqVh1D8kP2NwCAcSDI5FOmpv7oS2kO6PBpUNy+fXtasWJFxG/+qKeWkLmHfwNxtQXfaxMw5VXJ3sjKyqJGU2bg4JGJqNA9bcKT8QFgdIEynAAg+cycOZOmTp0a9fN88sknNHTo0JhcEyQP9GSCdM7+Djc7PFirjSbZSVODZYc3/76gfk5MqQMwrsWLF4tbMA0NDaQHBJlUcnNzqaamRmQjaZXMKRlMeXmx+YH0wKBedHX/rkF7LeEfeEhnXJr5wve9Jz1qlcvxSF8l0+n4hSYqys4IWE7H8P8VQOLxhgjfopWfnx+T64Hkgp5MEBOqjG/HxS+8M8DNrd0ldLLdSmSvNcQEukiywwOV07FSa2TZ4cgMBzC2yejJlByKi4vdQaZu3fynTZSWloqPRUVFMXm9yffcTTk5ucHPwaQUSGO8sCsJI6brqPMsnOZ9qBRBBIaFE4AxGXlXDlJHTW2513FONgKX6ahy09D0mkAXpF0GAEAsIZNJpWfPnmJy3OHDh6lfv35+bxZPkVPOi4XXV75G3xt0XUyeCwDCx+nkwXb7AEAfRt6Vg9Qhq9oeqI8B0jo73ERkczjpkeYsp5M1TdSWU78DlNMxZIYDgBYEmVQGDhxImzZtoi1bttDYsWP93qzNmzeLj4MGDaJY+NnYcZSXF3z3DJlMANEtnJRyOe4/oKSHlzc4ApaoKv0M+DmTIUUeIJUgkwkA4onL30R2kpbmcjlH7Un3BDpH3WnV44UkmbO9yumU50yG9UI42eGlqszwObuRGQ4ALYMgk8ro0aNp7ty5tG7dOiovL/cqi9uzZw8dOnRI9GoaMmQIxcLSZS/QTcNHx+S5ANJZOAsndYPLcErqOGgVTqkeAMQOMpkAIJ5EMChE+Ztszk3fkroI1dqQGQ4A/hBkUunTpw8NHz6ctm7dKjKZeGJcp06dRGo+TzVhU6ZMoexs1y4GgCGYVU3qM/JIbqoLerrJnItpIQAAkFQwXQ5A55K65nI5dWZ4ab2DZFWvp8IsySsAxY8hMxwg/bK/EWTysWzZMlE2t337durcubPIXKqudmU9DB48WIxgjpX773uQ8vKCPx/K5SAUZRIKs37+LNUd+F3Q80vGXaQMS/o1OQ20cFKXy2mV1PmV09lkr+aZWDwBpMeCCfSF6XKge0mdqlyuqfoQVW64OuX+UpAZDpA8Jhu4jyWCTD44sLR3716aM2cObdiwgSoqKkSj7/Hjx9OMGTPIYvH8Qh+tpcsWoVwOYipUgElwunoOpZt4LJwYyuoA0mPBBADpIZySuozstgm7HgCAZJN2QaYTJ06EPKdDhw60dOnShFwPQKKZJOM3pwQAAACA5CqpU8rlAg1bUZfT1dlkcjRnhiMrHCC1pF2QyUhQLgexwD2W2o+vJafDRuRs0jzHaa+jsrWX4Q1vwcJJq1yuvN7hznSqbHR4LZp8y+mU502GyTMAekO5HCRC61btqVp1DBANp90q+mHK9gYie43mOckygS4cKKkDgFAQZNIRyuUgFkwmE5ky84j4FoC9vsx9zGN3pSDnprNwFk7Z5gyvIFI4I35RUgcQHpTLAUCyKX/7WyHPSecJdACQfhBk0lFBfoDpDQCxZq/1OS7Ge5xAlQ0OzYkrash2AgCIntPppEZPdQ7VNPr3IWy0OZJuAcx/LqfN6nWfvbaSd440z89omzqZM5A6JXXqtdCpmib6bfNGnVJSF6icTnlOfE8DJIdk+xkLAGCsxVPzSF/fcjleMCkldXPCaCKObCcAgOhxgOnuzZ7sXS2FjrP0RPPxxdpKysk2/sRVDjAduj/867zsyQNkKu4aNBiFQFQMJtCpJs75lss56k5T5aahlI7CyQxvnZUR0bAVrJMAkgeCTDoaP+4OysnJjbp0AACMs3gS5XQA0CLoyQSBjBkzhrKzXb/MB8LrpYmTfhnyTZTI5j52OLR7GRqN/WJ5ROcfnx26hKvH08cpoxglXLGaQOdqRaDK3jF7goJOuYnsF4MPH0qlvk0AkN5rJgSZdPTKssV0w7Cb9bwEANAh20kpl1NnOzXKTmqwOwOW0ynPiQUopDL0ZIJA1qxZQ/369Qv5BlVYPbVyzw1pSxk8vcGHo/Yi0Tuu43ZZSfKeO2WvLCVLcVe/DKWmytP05VPpmTljRJLZs5Fc/lavkOenW98mrZI633K586pBK7xmys8kkiTXOVa7THaH/3NinQTpYnIYySj79u2jq666ihINQSYdOQLU0QNA+mU7KaN+g0GqOAAkO+4t9OKLL9JLL71EJ06coK5du9Kdd95J06ZNI7M5+mWpuocLB5hK8vz/vbXLEikFdVKG55davTgcDnJUnnJ9YpLIXFDkncHklEUASSFl5ZKUlUeWrDy/8jfOTmLmwhLPc6iCUQhEGZgq8ykdhCqp47WS+v9nlNQBJA8EmQDSdtSu1bshuApSto1Jne3k2xBTDTt5AGBUL7zwAj388MP061//mq677jrau3cvzZ07l06dOiWCT+mIA0xHp10Wk1/atcrffINRkBgmcy61H1+r2a9JvSZTptOpM5+gZZDtBGAMCDIBpBmM2jVmqnig6XPqkrpwsp0YMp4AwKiefvppuuOOO+jZZ58Vn9966610ySWX0JQpU0SwqaTEk4EDsafOcJIdTWQrC94nCM3BW85kMpFJ9GnS6Nek/B001UXxCulXUqdVLqdeJyHbCcAYEGTS0f33PUh5eTODnoPG3wDplyqO5uGQrozcxDIdrVy5ksaPH0+HDh2iXr20e8qcPXuW5syZQxs3bqTy8nLq3LkzjR07lmbNmuXXqLu+vp5Onz5N119/vdf9Q4YMEWV0n3/+edRBJi49a+340nVce1GUxrlJFpIsrUX2iFF1fWwXZXf+rl+5nG/gJxaOPxq6TxCag8eXbLd5HbuCUaDAoBWA5IQgk46WLltENw0freclQFqP2vUul0vnUbvJmO3kWy7n20Scm98GKqdTnhfNMcFojNzEMh2tWrUq6ONc4nbNNdfQuXPnxOetW7emI0eO0Pz582nbtm20Y8cOysrydNbmnksff/wx9ejRw+t53n//ffHxG9/4RvQXXX+a5lYOcB2/Q+7eS8nC3LqD6LdklFI3deYTxIHd9XObOS78l8jeyfOYlEuSJd8TjLJXo51BDLKd6u0yNdhdjzfYZbI5tJ8TaySAlkOQCSCtR+0We+4wF2ofQ1JkO6kDUGgiDgDR4KyiJUuW0Pr164OeN3HiRBFgGjZsGL366qvUpUsX2r9/P40ePZp2795NCxYsEAEnRWZmpl+AcNOmTSLr6aabbqKePXsm/C9Oyk6vIIrJkktXLHVtMNmt1USqTBqF3Gil47NdfYIgccLZ6Eu3CXTxyHb6zQdVIZ8TbQcAooMgEwAISoaT7zGkJs52qmxw+I3/VcNOHkB64ewjzl7auXOnyEgKhoNJfD6Xt61evZratXNlEvTt25fWrl1LAwYMoEWLFtHs2bP9yuZYaWkpzZw5k1asWEFXX301vfbaa7H/Aw3dQcVFXfzK5XybM6ddn6Dm7KhAWVJyI/oEAQBAyyHIpKPcHO+FDgBAS2VlEP3lJk9mmtb0OZ5MN/0DV/NwZDsBgK8NGzbQ8uXLw3pj1q1bJz6OGjXKHWBS9O/fn3r37k0HDx6kXbt2iUwntX/84x80adIk0aPpySefpGnTpoksp1jIz2tHSiF4fnEfMucnwVrLJGkf60RkOKmO9S7ZS4t2Blqay+UctSfdQ1u4tYHv40yW7UQ21893TAgOXlKXm0lklvzL5XzbDvCaKVA5nfKcKKkD0IYgk47wDxMYiRivqzpG88nk253ONgdPEecFU6yznRgWWgCpYfr06TRhwgT35yNHjqQzZ85onsvlcGzEiBGaj/P9HGTi89RBpjfeeEM0Bh8+fLgIaMWkD5MK933SOjYyc0GR5rFu1CV0GuV0EN92Br5kVbYdSuoSM2gFG3EA0UmOn74AEH/2Gveh48JB7+aT5nySmhc5Ihhlr8FOWQpkOykNMaPJdmLoXQCQGjp27ChuCovFEvDco0ePio++TbwV3bt3Fx+PHTvmvq+pqYkefPBBMV2Op9Fhsw0AACD1IMiko5/9bBzl5brSXKOZtAMQa9gpS03xyHZSUs8BYmHx4sXiFkxDgyfrEvRTVlbmniinpU2bNuLj+fPn3fd9+OGH4usGDhwo+jn54j5Oytf5qq2tpZoaz2ZIIOWVX5O93nVcf+40FbX1zpTiaXfqiXeJaKLutFndn9trSjUbbQNEXFKnKpdrqjlGleu+611SF6CcTnlOBHm1S+oCldNpldRVNzgCltMpz4n3GVqisbFR3KLFPzv1gCCTjpa+8gLdNHy0npcAABBxtpOyuFIynaoanWRt0l5lKYs1fl4OcgFEu7Gyb98+vwllkHhWqyswEigopNyvnMd4Eh3jPkxa3n33XRo6VHvCVqD7g7vS7545c+bQ3LlzKVE4wHTo/uAbimr22kpD9UBqqjxNUn6ReyS83VpDpCqvZxltEbDQu6Quw1LgPsZGYexK6nw349Sbaiipg3hauHAhzZs3L2nfZASZACB088nmcjl180lI32wn34yncBZaHLhSPy8AJDfO0FF/9KXs3jscngD0bbfdFvD8UN577z3q06dPyPPOlR4n2trX9cnw/XRJyWVejycyi6lFOOPEQL58KnRwr8fTxymjOHgQBCAVKBlOAPE2c+ZMmjp1atTP88knn7RwkyY6WPIDQIuaTwJEoqzeQVmSdiYTsp0Akk9ubq4oX6uqqtIsmVMymPLyYpOVww3Js7Ozg57DWXC3jBlBlNN8R0EBFRYWkp44M0lx2e8OkWQO3OdKyQoCiJTmRqFPuZyj6lN3lpMoqZOySbK4/v+QZZnIVu73nCj1Cp7trTV9jjPAH/2gyvU+y04qrQs+PQUldaAlnNJuI7cYQJAJAABavNAKNt5XXVKHbCeA1FJcXOwOMnXr5r9BUVrq6j1UVBSbaWlr1qyhfv36hTzv7HlPo3GjZSZxgMmSBBk/HOji7CSFVrkcl9GFk+UExtgoFH97eZ6BLiipi2dvS0+205QwMr0xPAVSscUAgkwAANDihVagcrqWNBEPJ9uJob8TgP569uwpJscdPnxYM/hz4MAB93mQfAGLQOVvRuoXBZAKkO0EqQhBJh3JF8vIVnbC/wFzNplzXemrJksumuUCQFKKR7YTQ38nAP3xhLhNmzbRli1baOzYsX6Pb968WXwcNGhQTF5vzJgx4ZfLAUDokjqNcjkuowsnywnCK6kLtP5Rr32Q7QQthXI50FT6xm/o6Nuumt1ArlhaSybsGgFAEopHtlOwjKfsDCKLGdlOAIkwevRoMaVt3bp1VF5e7lUWt2fPHjp06JDo1TRkyJD0LpdLUVxCp3UMyVlSJ35yZnkm1EFsSurCGZ4SDmQ7gRaUy4GmWRuqxC9FwTzcbwk9+FD0neUBYsLsGcPslJvIflEjE8+n4aTJjGw8iC7bSVlgKbt96O+Uuoy8KwfeeNLb8OHDaevWrSKTacWKFdSpUyfR/4GzjtiUKVNCZh/FOpPprrvHU23z5/l57fDXFidKjybfYwAIDdlOkOprJpTL6Wjl63+lQYOu87tfdjTR8Ud7iePL7/+5DlcGoE1STZcrf8v1PRpK+/G1ZMpEDwdoebYTCzWdBVKDkXflwN+yZctE2dz27dupc+fOInOpurpaPDZ48GAxgjlWws1kqm9QQkxEZjOWuQARkbLD30zEBDpDZTtZQiQuQOqZbOA1E3766sjc+hLNKSPqPk1ybTlRDtJXASC98YhfnsASjDrb6evaJirIzAhYTsfQQBwgOhxY2rt3L82ZM4c2bNhAFRUVotH3+PHjacaMGWSxWPAWpygxZU51jIbgqUHp0RTuZqLo8RRkqh3EJtvJZpepIURvp2zV+gZAbwgy6ai2pjzkOTwi1s1sIXNua/en9ppS96hZUWcNEGdc+saZSUy224jsrh1rX067lcrf/hb+PiBm+N+4khAJcepsp1m7tL831dBAHCC4EyeCZzGwDh060NKlS+P+VoZbLvfTn412f15rraacbE+Ztx7MhSWax0nP3qB9DOlFQiA5UdlOnvCfdrZToH6VnOGkBKCwuZZaFqNcLnU5nU568cUX6aWXXhKLsa5du9Kdd95J06ZNC5mmLdvqQj7/l0+FnvDQ4+njAUfNAsT6B55S+iaJj200z7PXexraczDKdS6AsZypa6J8nzR19WKMYUEGYAwtafxtb2qM81UBpPBmYvPEuWAbiU5bNdllm/aTSRaSLK3RmzOOOMNJgX6V6WcyyuVS1wsvvEAPP/ww/frXv6brrrtOpI3ztJVTp06J4BNAWlJlODku/JfI3smvITiTZTuRrVKM1UU2HsS7pI7L5S40eVLLZ36AbCcAAADNzcQAE+fkJs8meThZ6+jNGT9aJXTBhJPtxLDBBtFCuVyUnn76abrjjjvo2WefFZ/feuutdMkll4iJKhxsKimJPDWay984O8n/b8tTLmcrO0nHZ6McCYyvclPobDzU9EOiSuoaHI6IdwlDNSUHAAjEbq32OkbvIgDQq19lONlODO0EIFppFWRauXKlaEZ56NAh6tVLu5nd2bNnRQPLjRs3Unl5uWhqyaN5Z82a5dcLoL6+nk6fPk3XX3+91/1DhgwRZXSff/55y4JMGRkhy9+kLM+ULwAAiM2CjHfzLqqynb6qc1CRQ3u3zy7LZG3yPC+y8QD078l0y5gRxvpr4P6FWscAqVBS1+DqDxuspM5Re5Jk1XRiDzNJ2W3dz8lZVL4cDgc5rad87pVIyi5yfyY3cEmfq2ws3TLjI+1XGS5e3xClz/uYrBajJ5MxrFq1KujjXOJ2zTXX0Llz58TnPIr3yJEjNH/+fNq2bRvt2LGDsrKy3Odzz6WPP/6YevTo4fU877//vvj4jW98Iy5/DgCj4x/yIjtJS3O5HC860BwcjLggs6myneZ9GLqkjs0Z2JqKsoMHoxCIAkhsTyZIDLnRSnJjHckNtSTbrJrnYEhNqvbn1N4Uj1VJHQeYytZeFva1ITO+ZdlOvhtspy46sKZJApPRk0lfnFW0ZMkSWr9+fdDzJk6cKAJMw4YNo1dffZW6dOlC+/fvp9GjR9Pu3btpwYIFIuCkyMzMpKuuusrrOTZt2iSynm666SYxxjeRP9zVE+e04Ac8JIrYRQox0lZ7VwsgOYUTjOJFXqgAFwBAsgmnfQOG1AAYd3OtJRtsvptrDL2dIC3K5Tj7iLOXdu7cKTKSguFgEp/P5W2rV6+mdu1cEd++ffvS2rVracCAAbRo0SKaPXu2Zrp2aWkpzZw5k1asWEFXX301vfbaa5Qo4fZmwg94AIDodv3UC6hzdXZ69APPJMVQyn06dCLbCSC5cSmPo/JUwP6ZrKnydOIvDMAgJXWByumY02Gj8n9eEfZzth3xHmXkdfIrl2uq/JQq37k26muGyIQTiEJvp/SV0kGmDRs20PLly8M6d926deLjqFGj3AEmRf/+/al379508OBB2rVrl8h0UvvHP/5BkyZNEj2annzySZo2bZrIcgolq7B9RH8eAAAwzq5fp3wpZDCKJ7ko6efIdgKIf0+mO+7+GSkFW7l5beL6lnOA6ei08Et5UokypEbKbUuS2fXrhG+5HAfYvnwq9PAPSN2SukDldMx+8YT7uKliP2XkfcOvXxP3dlJwgMmskSWfkdshhleevsLZYFOvacKB3k7xhZ5MOpk+fTpNmDDB/fnIkSPpzJkzmudyORwbMUK7YSTfz0EmPk8dZHrjjTdEY/Dhw4eLgFYkfZiyLMEXS2FNoPPZMfMtl7NfOEcn53/P9YmU0jFFSGK8iOD6fVm2E9m0f3ilWzNHSI1glNaoYACIX0+m+oZad5DJYrbgrY4TrSE1Upb3P4gcgNI6BvBVuWlwGG+KK4sY9FvTdMw1RTQ8xbe3Ew/rzW3OBpdlmWrRrzIq6Mmkk44dO4qbwmIJvNg4evSo+OjbxFvRvXt38fHYMU9DyaamJnrwwQfFdDmeRpfIX4ADTaCz+NxnLvRMtzPn4wc8GFM4TSHRzBHSYWdQXVIXaJKd8rwIugIYR5dZ71Fm205BN/94gzCdKBlOvscALfp+UpXIQeoMT0G/ytSEf/GblZWVuSfKaWnTxpVyff78efd9H374ofi6gQMHin5OvriPk/J1WqwXw083BACA1M92ask0O62dQYZAFKQja0ON13FOdn5CXpcDTL4bfUzrPgAIMYm4uVzOt89TqAwnp72B7Be5D5o94FuMzHhj8e1XiWyn1IAgUzOr1ZVcHSgopNyvnMd4Eh3jPkxa3n33XRo6NHAtek1VKdXUeBZDLZWVlSVuodjKTpKU5fMPtGT2ynAyWXJFHTVAwhYXUi5JFtcvAb7lco6601S5Cf0cAHwl4+5gY2OjuEWrtrY2JtcDqcnWWK95DInHPZrUx77ldJDewplEHGmGU/nbV4Y8H5nx+mVwKwEk9KtMfQgyNXM6nV4ffSllCTxJRHHbbbcFPD8cY+78NRHxLTpz5syhuXPnaj6mTtMOZwrdFUtryYRFAOi0uBB7UVmtVHdkax8DpMGCzLdcjnsbhJvpZEQLFy6kefPm6X0ZADEj5RdpHoOLugm4+hgAUhf6VQJDkKlZbm6uyCqqqqrSLJlTMpjy8mK3C7Pmb3+i4T++O+rnCSeLCSAZSZZCzWOA9Jtml0G9WmsHo9Sp5aV1Dpr+gXd/p0DldIkuqZs5cyZNnTo16uf55JNPgmYJQ3pPl7tljPYAl3iQJEnzGAASh8vo2o9XZ81V+pXL8YAZpf+nlO3pVwvJke0E2jBdLgkUFxe7g0zduvmnbpaWujKCiopit1P18MyFlP/US1F3jQ9rCl0gkpnkxlo6PtOVXmq/WE4WZDJBkuEMQ6f1FJEpg0wZ2e6PTnudeFz5qEA9PqRqMCrbbDJsSV04pd1GXjBBckyXO3veM6AFAFIft/kwZXp+kEmqYwVPMFY4ak+SHKC/kxKA4sAV2ocYJ9tJ2TTLMBHlWTwB/ZpG2X2cjn0oJ4cRJ9i3bx9dddVVlGjIZGrWs2dPMTnu8OHDmouYAwcOuM+LlSXPP0E/vOU+0mMKnZqt7ITnE6fnf1YAvXHWhfo40D4xB5jK1l4W9vOiHh/AmAs3Iy+YAHzZrTVex9ikA0iNicacGaUOXIG+wtk0Uw9ECbSmScdAlF4QZGrGE+I2bdpEW7ZsobFjx/q9UZs3bxYfBw0aRKmsqZInMnAIXyJzgSdrizOclAAUZ0fhf1BICFu593FWAd54gAhT0H3L5U6qejsZLdsJIKnYG7SPAQAgobCeMRYEmZqNHj1aNM9et24dlZeXe5XF7dmzhw4dOiR6NQ0ZMiRmb/6khx6n/MeeiWu5XKS+fCp0nwsuvwuVHQUQa3JDOTnMzb/pmsxkynCV3XDzfX5M0e7m/6WMgsv8yuUwqQ7SQegU9Az3Tl8ss51itUOIcjkAAIhX3yatfk1afZvAmJtmvuuQ4xeaknogSipDkKlZnz59aPjw4bR161aRybRixQrq1KmTSMvnZpNsypQpIZtORuLPK5bQ9TeMitnzAaSyig0Dwh5lm5Hd/ENJnepsytA+BkgzkS7cwl3AxSLjCeVyEK3cvDZkVR0DACh9m7T6NSnsdV+7j2Vbtea5vLHJwSjXOTW8Axr0zUUP0Pj2berVOkOzgbh6TaNuIK5smnG7p3wDtAhIZQgyqSxbtkyUzW3fvp06d+4sMpeqq12L68GDB4vJOLE08b4HKSf3Ud0zmTSbg/uUyzWc+pROLrg2rtcBEE+iIbjGMUC6CWfhVpStzwILmUwQLYvZ4g4y8THoR8ptrXkMYEiyTftYhQNM51/PD/sp0QPUWA3EI+3t5BeMsslETgSiwoEgkwoHlvbu3Utz5syhDRs2UEVFhWj0PX78eJoxYwZZLLFdrLz212V07ZDhpLdwmoObW3dwHzdVfk3mQs/4T3ttJZHsn3qK3k0QLZPFf5pjdtfbqXDgi6JcjsvgSle3D/k1rgeQyQQQy2wn3vnjXUFl0Vbd6KDCrMDldOHsECKTCSB1SKogn/oYIF045SayX1QNWNKAbCdjQW+n2EirINOJE8H/J2cdOnSgpUuXJuR6rBddqXvJ5sunBod1Hno3QbQkS76ooVczSap+TM019l6PBxhLi0wmgNjuDmZzR3GV3+4Ob4ewJEd7Z1CBVHXQwq0LQrUs4CDl2PH/4/7c2lBDOdnhZx0AAIQ79bjof74gk5QZtLdT+Vu9Qr6hyHZK/KaZb4ZSsvZ2Wrx4sbgF09Cgz1CKtAoyGY298aLelwCQFDX0LX1cTWkCzhy1J8mZXax6VCKTOYdMJtdHAIgP7BCClueff54++OADevPNNwO+QWvWrKF+/fqFfAPPnj/mPrY11uMN15HcUOt1LGVhTCWkztRjDjCZC/wrQeQmz3ozLJa2sbgyiENvJ3UwSt3bySgmh9FWh/tLX3XVVZRoCDLpyIjT5cLq2+TTr0ldLtdUeTqsCXUAego0PSSz5DpqO3yTOyOKg1gAEN4OYaBGmuqSulCOrF9Ogx/5K2V4nsYwu3IQH2fPnqXnnnuOrr766qR7i6X8Is1jcJFtVs1jAKPjbCQlWMRNwJUeTTypONxJdsGag6uznZzWr8huCxC8sLQlSXL9ui7baonkwP8foewucb2dRANxE1EhsrIDQpBJR0uef4J+eMt9lAyC9W2yBNiZkhutZKs4rdmvyf28bTujgz8YRlPpB+6GjiW3n6eMHE/vMQCIbGGmlNP5pqprNdIsr3cFonr+6F6a9+RU6lqYGbCc7rNP9tHw68KbNgnGxbur06ZNo927d4vAYVIGmSRJ8xgAUnMzMhzqLPtA0+zU2U7RvJYayu4SJ6IG4ibvYFStTSbZmfrtARBkgth+Q6kagh+fHfofTfRtgoTJ0O7VBAD67hCKYJQqiDQnRG+nqqMXYnh1EI6VK1eKISiHDh2iXr16BcxI4sEpGzdupPLycjFMZezYsTRr1izNXkqFhYV08803i9tLL72EvwgAAEgZ88IIRPEGXKgMqmSFIBMApAXeTfJtEu6LezW505dVPZwAANLZqlWrgj5+6tQpuuaaa+jcuXPi89atW9ORI0do/vz5tG3bNtqxYwdlZbkGNih69OhBjzzyiDhev349JSO7tcbrOFBmNwAYH5ebiWwgXjNmezbN1eVyvue3+LWaS+pClcCFKpdz2C5Q5bq+rk/MrVt8PdCCBuIa5XJKVjYgyKSrZOrJFC6TJZeuWFqr2a9JXUanZDmpM58A4vq9GUaTcDnAZDoASMzibfmSF2nFKy+Jkjr3/5ec5dSc6WR3Oun8BfR2SRSn00lLliwJGQSaOHGiCDANGzaMXn31VerSpQvt37+fRo8eLcrhFixYIAJOKcfeoH0MAElHlC1pNPIOVPIWizVpJM+tda6kKruTzJaYXR+0LCu7XZb/JDvyCUadrGlyZ2yL3k6c2ZulXU6nRX1unU0mh9OYZXfIZNJRMvVkiugfTdVOntauntzo+QfRVnaSpCztX+yVABQHrtCAGQAg9RdvM6b+StwCKbU66I5XdtDWKcMTen3phrOPOHtp586dIiMpGA4m8fklJSW0evVqatfOtcDu27cvrV27lgYMGECLFi2i2bNna5bNQXrgDUZe/7k+amcVo08nAKR6A/G2WZ5gUKyynoxYdocgk46yCjGJJJy+TZwZpQ5cAQBAelLvBkL8bNiwgZYvXx7WuevWrRMfR40a5Q4wKfr370+9e/emgwcP0q5du0SmU7zl5rYiq+oYjAF9OgFiT5TQqY7jkXUF0BIIMukoy4J/CAAAAMBYpk+fThMmTHB/PnLkSDpz5ozmuVwOx0aMGKH5ON/PQSY+LxFBJktmtjvIxMcAAClL3aMpWG8nMG5vJ2pZuVxZvYOmvV/pVXanVU5XWe95LJEQZALd+jbZrdVEdv9Ger59mwKV1En5Re6RwSipg9hQZ0kgYwIA0lPHjh3FTWGxBO71cfToUXcjby3du3cXH48dOxbz6wRj4/I3niIs5bZ294vxLZdrqjxNXz41VMerBAAw2MTdMGSpmlcGK7vTayIvgkw6GnfHPZSXX5hSjb8j6dsUbAqLrepr9zFK6iBRTOYczeNAnHaraIwb8PscjcQBIrJ48WJxC4QbXH5dhcmPRlJWVuaeKKelTZs24uP58+cTcj3Whhqv45zs/BY9j8PhIEflKc3HJEsuSdn5ImACwX+Zyij2bqQsibVfsefz7ELNYwAASF4IMuno5efmplzj75gJkOEEEF+e7CW5oSLAOZ7gUfn6q8lx4ZD2M+V1puKffO76CjOa1wOEI9TGSmmdg+5YhsbfRmK1Wr2CSb6U+5XzWqq2tpZqajwBpEAqykuJ6ps/KS+lTMk7yJSVlSVuoXCA6ei0y1p8vRAeyZKteQwAkM4aGxvFLZBMh4Oe6pfpd3+Bqlzu64tN9MgBfTbmEGQCQ6dYByM7muj4o73Esd1aEzQzCiAc6uyl8revjOpNk+tO0fnXXb/ctB9fK0bVAgCkGiWbM1BWpzJWmTODAtmxY0fI1xk6tCUlVX397pkzZw7NnTu3Bc8FAACQGAsXLqR58+Yl7duNIBMkTYq1L1vZCfdxU+kRInuDZr8mDkApj2E8LgAAQOzk5uaKDKOqqirNkjklgykvL7pA+3vvvUd9+vQJed650uNEW5uDS8P30yUl3tlI4WQx+eoy6z3KbNvJr1yOyXY7ydZKsb6AyKlLDvnYVU4HAJDeZs6cSVOnTo3qOcrqHPTzv75PO2b8hBINQSZICeE2jeTsqFDBK0hfXNbGWUfca6lsXT9yWr/SPE8qvJxMJiloqVy7Ee9T2dqu4nOnvZ4ImUwAkIKKi4vdQaZu3fx/vpaWloqPRUVFUb0OT7vLzg5eTsWllreMGUGkJKUWFFBhYcv6/Ei5bd3HWZd+l8x5rbTP45hVgMcgNHUTcNexp18TAEC6ygqjtDtkH0uZ6OtqlMsBAOhKNOvmYFBmHrW/5YuA5R9Oh5VK/14S+HlE3yZ143A5DlcLAKC/nj17islxhw8fpn79+vk9fuDAAfd50VizZo3m8/s6ez42U+wks1nzGADAMMyttY8hLUw2cB9L/NTU0aSHHqf8x55Ju+lyiejbpC6Xa/j6czo5b0CCrw6SnWjWHeAxZ3PGU8CvNZnIUV/mOZ8zmQAgJCPvyoG2gQMH0qZNm2jLli00duxYv8c3b94sPg4aNCiqt3DMmDHhZzIBAKQByWzRPG4J7pvntPpM1DTnk9Q87Ebm1iP2IMMXvM61EdmryZTb2d2XDxK/ZuK98tM6TeRFkElHS55/AtPl4ty3iZkLPCn6TZWnRQq8sispN9SSbPOeeIO+TRB2xlNQ6uwlZDIBJPuuHGgbPXq0aKS9bt06Ki8v9yqL27NnDx06dEj0ahoyZEhSZTLx+kB9jF5BAJDKOMBUtja2EzWLbz1OVIA2JXqtmRrsTvrx4u3IZEo3WYXR9SeA+PRuQt8mAABjKlSN5gVj4Gbcw4cPp61bt4pMphUrVlCnTp1o3759IvuITZkyJWQWUqzk5rYiq+q4pdQbUL6bUQAARiDbVf9O2a0kaWyAamYoMXMhSeZsd5aSo+50fC8W0goymXSUZcEEDYB0IDeUk8OcRyZznk8JnSfDyZTlKfEEAEgmy5YtE2Vz27dvp86dO4vMperqavHY4MGDxZScaIVbLnfvxLvdQSZLZmICWwAAurDX+hwXxyRDqe2I9ygjr1OLyuUctSep/O1vRfR6EKcWA06ir1EuB5CY3k1a5XJcRhfuhDqASFVsCN0TrPin50jKbY83FwCSDgeW9u7dS3PmzKENGzZQRUWFaPQ9fvx4mjFjBlks0fUKiaRcrl5V5gYAkC68MpFUWUqOhsqIn4sDTGafMjdXllS7kF8rNwelvK5JyiXJku/Vr0kRTd8mHtDDE6FlWSaylQc+UcomyVLo6rdqCtRxNflMNnCLAWQy6ai+1vM/GOjTu0mrx4LcaCW5sU6zX5P7OduikR3EmAPlGACh1NrQ30wPJ06cCHlOhw4daOnSpaQ3qyrIxMc52a5fbMCYpOxCzWMAiEzlpqHhZyhplMups5Q48JOoa4qmbxMHmM6/Hv6/8Ty0J3RPVYgFBJl01NRwQc+XhwCOzw6d4om+TRCKlNeFSm4/73Vf1Y5bqan0A7x5AC0kO/HWpauWTJezNWISodFJlmzNYwCIPa0MpUiylMBYFht4Ii+CTAAAcSD6K+WUeN3X7sYt5HR6MjHUdeu8G+NoqCByOsjpaBAffZksRWSSJK/Gjk57Azl9zg2UDqykFQdjMueQyYTeUABgLImeLgcAYHSccSQygXx5ZSlZ3b2bYpWhFPE1+ZTLOS78N6wsp0gUjT4g1r++eN2bqj2iJqNcDiA5+jZp9WtSoG8TREsEcFSfO7M9DRoj+QFYMrbcHYS6sOs+avxqnffjP6uhjKwCv6+Tm+qodKX//Wrtbv6IMlr1EoGmVKtdBwAAY5FtDV7HWm0MAECb6GUUotTMtTHp3xBcr2sS12NvLtmLkmxTlfhZ2pI5r4PfOfbGi57zZZmwjZoYyGTS0aSHHqf8x56JKkIJ8evb5LvQkXJbax4DJFrp34uCPu7k5ocaQSaSbSGfu2JDf/cxatch0Yyc+g3JVy4XDfzMTwy5ocb7uAAlOwBA5HA4xGQ8rywo2U5kq9Rudi57AtZe1A3BA62Pw+BbDcCTo9VTon2ZomhongprJgSZdLTk+Sfoh7fcp+clQAQks2c6jr3qDMnW6oALU+VckwWZIBCYKavI3bfJZM5zl8mpy+X4h1g40+nCYcrIiuh8p8NGhAaJkEBGTv2G9CqXU//MVx8DAEDscODG3njBK3ikBJBiXVIXjUibjBdH0dA8FdZMCDIBxKk5OLtiaS2ZkPoN4fZt0gjoyM0NxDkIpdDq2eSoL6OKfzX3d3I0kqO+1O+5nPY6rx9+6ud0v15Dmad0z9mEvzsAAAAAiB3J0zspVfslkQEK8/Iz9XttBJni5Pnnn6cPPviA3nzzzXi9BACkaQPxUNlF5f/8ZugnthRp9m0S2UsaxwAA6YT7MqqP0SsIACA2lPK3SJp6Z+R39SuXU4t3U3O5wfO6RT85RKaMbL9yOXWTcSk7eGuLhP0OoRMEmeLg7Nmz9Nxzz9HVV18d9DxLXpt4vDzECZe+cWaSb0NwX7KjiY4/2st13GjFwhQSI8N/okZQgerS1dlLyGQCgynM0n9nEIwtO6uAGlTHLaX+OR/sZz4AAESGB8tw30/3v7FBgkfi/NzOJGVkuHKDslrp9HbbPdeTYSFzQRfNATvggiBTDO3bt4+mTZtGu3fvpoaGhpBBpuycwli+PMQZT9ni0rdQu5m2shPuY7mxNqETHSB98bQO5Qc2l8vVfPhLaji5OuD5Jov+OywAALFu/H3vxLs9QabsCIPvAACQmN+pVFn5+gaPktfiUI2/nURfV6Hxd0KtXLmSxo8fT4cOHaJevVxZJ1oZSXPmzKGNGzdSeXk5de7cmcaOHUuzZs3SXOgUFhbSzTffLG4vvfRSAv4UYHRNlZ6pB1J2IUmWbHeGkysARZTRNv7TByDNfmBn5lHrwX8h57WvBj7fjF++ACD1Gn/Xq8rcAAAAEsU1cc5zzBvA8YTG3wa0atWqoI+fOnWKrrnmGjp37pz4vHXr1nTkyBGaP38+bdu2jXbs2EFZWd6Tmnr06EGPPPKIOF6/fn3Ia6iv1Z5OBqnjy6dCT0Xo8fRxyiiO7/QBSD88SS7SaXKxpox7dcpNXG8S+EQpi0ySWQS+OFgGEEidLfC4YABmVQWZ+DgnO7LeH5BYUla+V7sBdTa4FmzMAUBk/8h4poPyetR+Mfi/MVya1/LNf/UaRaZ0lnblcvxLz5IlS0IGgSZOnCgCTMOGDaNXX32VunTpQvv376fRo0eLcrgFCxaIgFM0mhouRPX1AAB6B5DUuAmiScpw/yCXG6uodHX7sJ+zZNxFyoiwGSSkF079BgjG1lineQzGJGV5smqVfpbBYGMOACL6N8bS2n1c/lbof2N4+jIVYPM/WmkTZOLsI85e2rlzp8hICoaDSXx+SUkJrV69mtq1ayfu79u3L61du5YGDBhAixYtotmzZ4fsDwDph3fZeBHkS10u13DmEJ2cG7xnF4AuMnK0jzWaG5au9G6q2/oH/yBLybXi2Hr4VardPyuy13Zw0ApBJgAACJ35BAAQz8wnaLm0CTJt2LCBli9fHta569atEx9HjRrlDjAp+vfvT71796aDBw/Srl27RKYTgBqnWIYqfzPnt8WbBoZkMkmax9oBIW/VO24J+3VaDX6NsjoOJ7mhzD3uFQAg2sbft4wZgTcxCSf3MtnWQHJDjd853Mfy+Oxv+WU+AQBEPMmuodTvHM7MV9ai6synpG/8LRN9XY3G33E1ffp0mjBhgvvzkSNH0pkzZzTP5XI4NmKE9kKF7+cgE5+HIBMApBRV9pLT0UCOev8fxoyDQ5Eouf08mcx53j2jJLOrX5PyeqpjAICWNP4+e/5YTN44Kbe15jHEZ3KveJ/5Y4H35i6TUfYIALGaZJfZTTM7X+GoPUmyxnAcp8PTW1TKLjHE38fkyZPFLZDSOgfdsWwHbZ0ynBItbTKZOnbsKG4KiyVwKtzRo0fdjby1dO/eXXw8diw2CxlIb65Jc3UBd/AYGl1C4r4h7e7DsjVdwvqSotEHSMou1n5QcvVpCtjUW90QPFhzcACABJLMFs1jSDzZbvM6FsEoAIA4QHZ9bKRNkCkSZWVl7olyWtq0aSM+nj9/PqHXBalJSQEPBo0uIWGckWcTcYApI8cYuzoAAGENLrBZQ5ZogTHI1mrv4zzXOhwAwFjUbSYk0ltupn6vjSCTBqvV6hVM8qXcr5zXUlZrPdXUaGeuRCIrK0vcAACilhG830WbGzZRZru+4rjuiyVU98njIb8GgDU2NopbNC7WOcjegIlhEB0OMB26P/wG0hxwQvYMAEBq923S6tfkYSYpu637fC1SdpHmsV7Mkn6BLgSZAuxwqT9qNXZmDocj4Bu7Y8eOkG/+mDt/TUR8i86cOXNo7ty5UT8PJH4CnXrinO9ualPlafryqaH4a4GEkjLzvBok+jJluMrfWMG3H6X83lMD/rAFUFu4cCHNmzcPbwrEVXZWATWojmNBbuR/EwOUBAMAQEr0bdLq1wQtgyCThtzcXJFhVFVVpVkyp2Qw5eVFVxO+eeNbNPDa6ylayGJKjQl0vg0v1WN6MbIX9GqQGPTcDIu4AYRj5syZNHXq1KjerAa7k8a+8i7tmPETvOmgKTs71xNkytYOgKtL4S77/RckZWSG3BgCAABIJg12WbfXRpBJQ3FxsTvI1K2bf0SztNSVSldUFF0a3KRfPEQ5uXlRdY2H1KUe04uRvZCypCztYwPgkbaBMlpFMA4ZXDEv7Q41jpf/Nk5XolwOYpGZ5MIBJovPpg8AAEDEP1saKr2OJY1NW9ET0G4l2VbDJwV+MktbkiRz4ME5YbAFLrqKOwSZNPTs2VNMjjt8+LDmuNwDBw64z4vGsiXP0LCbbo3qOSA92MpOagaaOMNJfb/J0vJ/iAD0YJLMmseBfii7P5cdQX84m7KKSIqyFr3srSvJUfel5mPm1ldS8U8ORvX8EPnGSoXVQT97RZ9xvKCvMWPGUHa2q7w8EP7eGTf+NvfnDQ1WyskOv/cSAABAy9kDHHvwWvb86+H/XOIWFoEqDEJtzDlkoq+r9dmYQ5BJw8CBA2nTpk20ZcsWGjt2rN/jmzdvFh8HDRoU1Ztvq6uK6ushtXGPpkgm0LErltaSCaN9IQUzmSL9oVxy+3kiTLxLOQ7txDJIA2vWrNHc+PN19vwx93FD40X+1yDOVwYAAGC8jbnSOgfdsUyfjTkEmTSMHj1aNNJet24dlZeXe5XF7dmzhw4dOiR6NQ0ZMiSqN3/SQ49T/mPPBD0H5XLpS2ukcsivsdswAQdSklNuiuz8AGVu4X4tB7Xajni3uUBLg8lMjvoyqtjk+jlQ9KOPNdOiITJG3pWD2Nm6dSstWLCAPv30U7ryyivp0UcfFWsvAC2SxZOxLTuayFZ2ImQPLWVIDwCAURWNPqDZeoHXoOVvh5dgYFQIMmno06cPDR8+XCyCOJNpxYoV1KlTJ9q3b59I12ZTpkwJmbYdypLnn6Af3nJfVM8BqT+BLhgul7PXlLoznWRrNVFemwRdIUAMyI3ax37n2dyHxbcep6bqg1S9fVSQ8yMLSkWTNeX6mjoiBJlSelcOYuM///kPjRo1in784x/Tiy++SO+8845YW23cuFGsvQB8SaqSx+OP9gr5BvHayXe4CgCAEciypxm3KbczmTWmoNrrznvOt9Um5SYmgkwBLFu2TJTNbd++nTp37iwyl6qrq8VjgwcPFlNyABI9gS5UA1OAZOOU7ZrH/uepgkaShSRz8CBQNFPvnPb6FnxNHclNdX4Nwfm5nE7XgiKa5o0AqeLZZ5+lvn370t///nfRN23cuHF09uxZcT+CTBDrzCcAAEOxlXsfawSZSLZqHycRBJkC4MDS3r17ac6cObRhwwaqqKgQjb7Hjx9PM2bMIIsl+rHdKJcDgLTXgkwmPra0HyyaIXqd0lhFZWs6uT4xBR9JHpzslcosZRf7n2LKILn+nDuduWztZeJjRl4XKrntpPu0ineGUFPFxyGbN4ILyuWMZeXKlWLdw20CevXSziDhABGvlTgTiVsM8PqJs8BnzZrll/Fts9noX//6F/3hD3/wasz/s5/9jCZNmkQXL16kggKNBTekNR5qwj0nlbYAImvbh9xodWd1qzOfAAAg8dI2yHTiRPB6btahQwdaunRp3K4B5XIAAB6OutMiS8kkZYqPRCYReOIMJ/GYiknKIJPkHbARE+fcJ8SmHwcHmDICNBB3Nvn3TeOsJUd9OZnMOe7PPdfnOQZtKJczllWrVgV9/NSpU3TNNdfQuXPnxOec9X3kyBGaP38+bdu2jXbs2EFZWZ6G/ufPnxeBJu7DpPbNb36THA4HnTlzJmAwK5ak7ELNYzAmkSHaPNRE4o8abQHkRvRqA4D04XA4yGk95frEXEiS2bWpI/MkZrsrKJ/VJFOBfFaX60vbIBNAKrJfLCdzYUnQ3T40xASjqtw0NLonkBt8jlv2y6PT0aB57CfDvyRDtp6m0tUamU/8XIHSogEMhpvfL1myhNavXx/0vIkTJ4oA07Bhw+jVV1+lLl260P79+0UT7927d4vm3hxwUgeZWJs23kGCtm3bio+lpaWJCTJZsjWPIXnJdrvXcZBhpQAAcSFll2gexwMHmJQs+mDuv0D0D0o8BJl09MCUuZSH6XIQQyfnDQh5DhpiQqpSZzJ5ZTVF/EQO7WMf6K+U+HI5Hhp4ugoZC/HC2UecvbRz506RkRQMB5P4/JKSElq9ejW1a9dO3M/9ltauXUsDBgygRYsW0ezZs0MOSlEaodpVgYKWsmTlkU11DOlBtla6jxu/+pTktp28ejQpJXQcgFLOxaYbAEB8IMiko5WvraBrh2CSCgCkL56swdPi3AKUy6n7NfHXhJfJFF+mrCIqud0zAcRDcpfLOWpPJv0YWiOVyzXYnfTjxdsxXS5OuAfl8uXLwzp33bp14iNPilMCTIr+/ftT79696eDBg7Rr1y6R6cTat28vPlZWegIC6s+5TUG0crPz3UEmPtYi2xq8jkUJFqSML58KLysWm24AkAra/mgfZba63K9c7kLVl0TLf6DLNSHIBJDkeCeOF0r+u3WecrmmytNhL7oAEj1FkQqSc9S0aFwcoF+TQlZNmgMwuunTp9OECRPcn48cOVL0SdLC5XBsxIgRmo/z/Rxk4vPUQSYenML333jjje5zP//8c/H/U8eOHSkR5IYa7+MC7yAZAABA5D9bKr2OpQQNe8nIau1+LddHV9sGm1W/XqAIMumood6/aSxAS35Jzyj2/yU9UHNMAIBkZbWjeXo8cZBHHegJNkn36NGj4mOPHj00H+/evbv4eOzYMa/n48wnLq976KGHRGCJ+z/x5xx0KiyMvgl3Q4PV6zgHk8bSbsPNl3oDznb+GB3/7XcTfHUAkB7UJd927VOkbO1jNUtb7eNImfXrA4ogk47uue9XlF/w26hKBwDCwQssrWOAVOW0W8npsJEpw+Ke8ua013udYzLnavZVMpnzNI9bItLncjqaywMDPp/2NadLTyaHTPR1NXoyGUFZWZl7opwWpbm30uxbMXXqVLr++utp3LhxokE4l+i9//77tGnTpphcV0PjRZ/j+DZfBWNvuPmScjB8AQD0I1kKNY+9zpHMmscRv1bzxDk9IMikoyXPP0E/vOU+PS8B0oSyg+d7DJCquA9S/nfnUEHfueJze/X/+fVGaj++lkwJSmUO14X/TKL6Y38J+HjJ2FLKyNaeXpcKQm2slNY56I5lO9CTyQCsVqvmpDiFcr9ynuJ73/ue6Of0xBNP0AMPPCB6N7311lvukrpAamtrqaYmdAb4xYsXiZR48sWLlJfj/TVZWVnc7Q0AAMCwGpuIbHainJoakjL9h9DYL16ki80/67JqLpLZ6f/z8WJNDdV5WpomFIJMAGnGVnaSpKzcoOnkJktqZ0tAijJlkvFIAY4BkhuXuak/avZb4+wzh//iePjw4eIWiaFDW9JXsK/fPXPmzKFZk+9uwXNBqpEbrSQ31onphnJtueY5mEAHADH9d0eWvY61VoayrZZe2ED0zNtE9MswehX+0nglwAgyAaQBHtmrOD479KSrK5bWkgnTdiDJmCyesh2e+iapPje3/qbIXHLa66h0tWvCVSBOp0nzOFpyI/8S499TSPyS7qgXpXAF/f8fFVz9B1HuR+T/y7tTlslRX0ZSdpE7EMxlgFwOmG6ldaCv3NxckVlUVVWlWTKnZDDl5cUmW/C9996jPn36hDzvXOlxoq3NwaXh++mSksv8MpmoRruZOaSXcNZDmEAHADFlK/c+ztIo4ZWt9KubiSbdRFT040/JXNDV/5SmOip9wxWAKvnpGc0m4/zz8MCrfenW3yX+7xBBJoA0IFu9x0UDpCJ1QIWDSVotorlPk/s4QAaGyeTUPG4Zz1WU//ObFCslYysoI9vVDLLinSHUVPGx3zlGLAeE1FFcXOwOMnXr5t8Lp7S0VHwsKiqKyevx1Lvs7OD9JbjU8pYxI4hymu8oKNBsJu75VwAAAMB4sjJdt8LCAjIX+P8ck5sy6MX/EP3530TmP/2AyOSfE2W326ixinSBIJOOJj30OOU/9kzQc9D4G2Ih2NQVhexoouOP9nId2+0kZeG9h+SizuYpW+udvaDJwZkWSdqjzBm4OXgyQ+Pv5NGzZ08xOe7w4cPUr18/v8cPHDjgPi8W1qxZo/k6vs6e90yzC0TKytc8hjRaD5mzyZzr+sXNt1yuqfI0ffnUUHdJnb3+YsByOuU5lfJQAIBEkO02umcYiVvx7bvJnOPfH/FC5TH68E89aMQ8SjgEmXSExt9gpKkrtrIT3plPea0ScGUAMeTwnh7X0h5OPOFN67hFMjz9z4pvPSFK2Pxez17nDopxmR9PodMql5Mbyvyal7N2P3zfHWALpxzQyND4O3kMHDhQTITbsmULjR071u/xzZs3i4+DBg2KyeuNGTMm/EymENR9CbV6FEJ6rYfE/r966pxqIhNK6gAgXFJ2ieZxXNiracX25kym3w0gkvzXtLLcRPUVpAsEmXSUmY1f4gEAYsWUVSSCNME4HQ1UtqaL65NAY2HVWUJRZgypS/ik7GLNmnmuq3efb85znROizI0DTkJGDpmaU6S5FJCDTGRpG7ShZDLLN2Jv9zQ1evRomjt3rpgUV15e7lUWt2fPHjp06JDo1TRkyJCEZjJVVJ9zl8NZ0FsQWkDJcAIA0JvDdoGk5nUiNwTnfk3i/rrTnkymW7eQucA/maCu+jh98Fx3ZDKlm5x8/0aZAADQMpIkEeUE3zly1JeqP+GmLUn5dmtlNPkyycb483Gwy8lNz02ZZMqwkFN2kJNLFQNknplyA5eeiL9jMARuws0T4rZu3SoymVasWEGdOnWiffv2iawjNmXKlJDZR7GWm53vDjLxsRbZ1uB1LCEYBSo8YZcHoLi/RzSmz9lrSunkEwNdn5gteP8AIC4q1/lPSQ1Xgz3avqIth0wmAPDD/QiC9Stg6EEAyd63SWsiG8ThPW8sj6iEr/jW40QaO3JgPMuWLRNlc9u3b6fOnTuLzKXq6mrx2ODBg2nmzJkxe61wy+XunXh3yOeSG2q8jwvaxeQaITVwBqp6wq5fOR3/AlXo2dCQ66rJZg/QTt5sIXNuaxG4wrRPgNQm21w//1hT1WeUkXMJqXEJnasdQmx4yuVu1CyXQ+PvNNVo85RIABiJ0vAyGIz1haSkzp6JtIdTC2n1YYpZOaBPuZxcfyasLKdEcspNMXsuqx2BQSPhwNLevXtpzpw5tGHDBqqoqBCNvsePH08zZswgiyV2GR7hlstduOjZELE1NVBOgGwmgFgJp28TZ0apA1cAkIJkT7C5cuP3Qp8vWQJmdIsNN2ZuTVJztqS6XI795lai3wTJAD9bcY4+e6EDyuXSzYR7fkn5BbOCnoPpcgAA8SE3lJPD3LzoN5nJlOEaqehUZRdzj6RocH+lS+4IvqFgMmVQdpcx7uMWlwNm5pGzyZOh4bQnJogWyaKraPQBknI7+ZXLOerPiQUZ78q99rubAvbLcshEX1djgyZRTpzwDIQIpEOHDrR06VIyCqv1gtdxqwJPrygAAACjkCzarXNEwEgjo1urr2fQ54/RJmdLoFxOR5guB4Yc66vQKJdTj/UFSHYVGwYk7LWc9gZyOh0BH2/zgzdj9ErqTJ+WZ/04HA5yWk8FPkHKJpM5h0xSpitTSbaJTKtQPZM4qysji/9d8W6sa8p0DcLgBpaPvvIhZWS7mpf7Kq1z0B3LdtDWKcNb8seCJBbL6XIAsezbxP2ZtMiNVneWk63spPYkQ8lM5nzXv3coqQNIbl4ZSBp8J85Fm+m+ePFicQvELsvUiOlyAGC0sb7B+hEAQPiq3r2VGr/aGPDxDhP0a86ohQNMZWsvi+hrRClfiMbrgShZZEyuP0vOpgsaJ2WSbMsis9MgGVqQUOGWy509fywh1wPpTd23yRJg7SQ3erIuUVIHkPoCZSDFS6iKp7MVpfTZC+1RLgcAABBPJoundIZ3m0wa5XKyvZ4uvP8z1/3mJOvnkpGjfRwpyRP0CZdTtrf45XjinCJUT6mHqoneafErAQAYE7KdACCm7BdJLyiXA4AI/sXI1j4GSBLcPDH3il+K44zcjl4ZNGq2s9vERxPFJsOozffXBi2XixWlCbjvccTPowpQtRu1nySLq5zNt1xOtn7tCQrJjS1+PZI9I+UBtKBcDpK1pC5QOR2T7TY6PuMKcYxsJwCIabmc3UaNVaQLBJkAIPx/MJp7NAn2hsALJymDpExXEAo9BsBIOKjUauDixL+uOZtMlDxMGZ6JJ+bCngGbTTptVTGdnOfkruvcFFzzpEwqq6khWto7Jq8J6VsuJ2Xlax4DxKOkLlA5nW9JXVTZTiaJzM1N7rHuAkgPk0OUy12oPEYf/qkHyuUAIHkcnRZevxaM7QVIHKfDpnkcTelbNGVw4ZbwhZycp6hDvx2InvqXdM1f2AESBNlOABAvZkm/7U1kMunInIVGypBcRJYBQJJ/D8uN5eJYyioSO80pxdmkfRzp09it3sdZPuVyLSy9Ux+3RG5mVF8OacCSlUM21TFAumU7AQDoDUEmHeUWaI9oBkhm+d/9EV1y92I6OrWL3pcC4IcDJqV/d2XMtB9fS6YAZWDJyt3I3Oc4qh5JUfZLMklmzeNk25WD5JCbXegOMvFxoD446mOpeUoYgKGznS6Wc2qp5jlyUwMdn3llwq8NAIyrwa5fcgCCTDq64677KCc3L6paS4BE4oU4L3SCMmWQ/cJZ96fctynYDh0ApFkJX5DAXqgmlrLcRPUVMb9ESKHG3/dOvDvkc8nWau/jvDYxuUaAuGY7BQmG2sq/dB9zMCrYuQCQGhaj8TdoeXXZ83T9DT/BmwNJt9ABABCkLO1jFaeqkbfruE2LN1Yqyo/Rx4v0aWIJydH4+wJnezSzNTVQTjYae0MaUGU4NVV86flc1RCc2WsrOX2PMtp2poyMDD2uFABiJNSa6WzFOfrshQ5o/J1uGms8CyEAADAGnqTWYUJy9h9zOho0j+PFZM7VPPYi27SPW8DmiOrLIQ1YrRe8jlupfsEGSAdfPjU05Dk9nj5OGcgyB0hpUqB1WQKgXA4A4qqp8jSR2ULm3NZeJXRq2FEDiBGnQ/s4TkxSpuZx3ETZ0wkAAFwldepeZOpj2VYvMqGk/CLX5E8AgAhhtRYDW7dupQULFtCnn35KV155JT366KM0evToWDw1QNLDjhpA6nLKTZrH8RLtdDoAgFTEm3WcneRHVS5nKztJx2d/SxyfnDcg5HP2/NM5klq1j/3FAkBCyFFmj0cDQaYo/ec//6FRo0bRj3/8Y3rxxRfpnXfeEc0pN27cSMOHD4/N3xIAAMSF0+kUE+e8yI104T+TxGHrwX8jkzl4s2FDkbK1jyN+Hov2sQ+nvd77OMuTsRgPpowk+rsAXRp/3zJmBN55SDvcXylU+ZuUFVnpjO3cF+S0eX4+mgtdk1mZnRvm223IRAfQ0WI0/k5dzz77LPXt25f+/ve/i5TScePG0dmzZ8X9CDJBuvLbUdMol+MyunCynABiySSZKaf73e5jDjCdfz1wY2DndX8mUxL9FZikDM3jiJ8nI1fz2I/s6fvkuHjE87mUJbKOTBkWkm0XKVacAcZ3Q+oLt/H32bOH3ceO6q/J5lPuI+W2JrnRJ7AMkAa4/M3vvry21ON3/+culzv+xCCyffVfcYxMdIAkb/x9/hh99pI+w1LSKpNp5cqVNH78eDp06BD16tVL8xwOEM2ZM0dkIpWXl1Pnzp1p7NixNGvWLL8dNJvNRv/617/oD3/4g1fN8s9+9jOaNGkSXbx4kQoKCuL+5wJIth01C5pNgk5MGVnUevCf3Z87ZXvK/l3IDWXaD0jZIgDldNhc09400qnV2V0cKApH5aYEBI0dnswpAM1vkZpzpIRXv1p8G9Vc8O4B6Euy6NcYFSCRMrLz6YqltX73q/sxXfrL1XR81pX4iwFIAdk6DpBMqyDTqlWrgj5+6tQpuuaaa+jcuXPi89atW9ORI0do/vz5tG3bNtqxYwdlZXlGNJ8/f14EmrgPk9o3v/lNcjgcdObMmYDBLAAA0B9PRGs/vjbo48mEA0eK8re/FZvnNFAgzq+0ESBKUnbgTEaAVGIymcikCihpybzkcu3eTqpyOXVvJwAwLotZv1x8c7r03FiyZAmtX78+6HkTJ04UAaZhw4bRq6++Sl26dKH9+/eLJt67d+8Wzb054KQOMrE2bdp4PU/btm3Fx9LS0qBBJnMWspwgjammRMl2G9nKTgQ9HRPoIG6L7szgi+6k4oxD8225MeBDptzOVHyrxi8kqnI5DlKJ4JDcIM6PRm4CBthBcsvMaU1KUWWXn/+V2l1yuV+5nGT2ZOeZkMkE0OLeTmKCcIB+Te7nbNtZPC8ApI+UDjJx9hFnL+3cuVNkJAXDwSQ+v6SkhFavXk3t2rUT93O/pbVr19KAAQNo0aJFNHv27JCNJ2XZtbyx24Pv/uYWuIJRAOnInO/5/j8+44qQ5/POWqiFD0C6k/K6UMnt50OcFLpcTuAgkUkiU5Z/Hw/3KfyLQ0EY/19mtaJYMEvJ1CEL9JDfuj3VNB8XdulDlkJMxwKIF/RtAjCuBrtTt9dO6SDThg0baPny5WGdu27dOvGRJ8UpASZF//79qXfv3nTw4EHatWuXyHRi7du7Fi6VlZVe5yufd+jQISZ/DgDwznwCAG2iP2COZ0c5UPNsR+0pcZyR31kEkgAAAAAgddgc+r12Sv/WNn36dJowYYL785EjR4o+SVq4HI6NGKE9+pbv5yATn6cOMlksFnH/jTfe6D73888/Fwv9jh07Br2+RptnKg9AuuESBXUDSp4454snACl1/+rMJwBoOae9nsrWurKPuB9VMpUL6rkrB8mhocHqdZxXqOvlAKT+BGGNcrmm0mPuLCcuqePJdsqQJLu1hsju/TsQSuoA4sCco99LUwrjII860MMBoUCOHj0qPvbo0UPz8e7du4uPx44d83o+znzi8rqHHnpI/OPJ/Z/4cw46FRYGX9k01oQoaQBIowaUWhPn5Ma6BF8VQHJz2huoeued4rj14L+RyRy8vDvZ6LkrB/oaM2ZMyHYFPMp59A3fdk+Xq686TVSCMmuAhE4Q5rWdqicTSuoA4mPx4sXiFohdlqmxgnSR0kGmSJSVlbknymlRmnsrzb4VU6dOpeuvv57GjRsnGoRzid77779PmzZtCvmaVms91dQonQNajifeqafeAaQinmaibjYpmCQyFxT5ZUhxAAsgHTmdDmr4co3r+Lo/k5H+T2hsbBS3aFystVJddE8BSWrNmjXUr1+/kOd99cX7CbkeAAAAPU2ePFncAjl/oYY+ebYVjZhHCYcgUzOr1ao5KU6h3K+cp/je974n+jk98cQT9MADD4jeTW+99Za7pC6YMXf+moj4Fp05c+bQ3Llzo34eAKOxXyx3H4c7LpdL8EKN6AWAxFu4cCHNm6fDSgcM7/nnn6cPPviA3nzzTb0vBQDiUFKnVS7HZXThZDkBQMvIgQa7JACCTM24zE390ZcyetPh8M/VHz58uLhFas3f/kTDf3w3RQtZTJCynMog6sgCUyJVGwAMZebMmSL7NxqlF2rosxc70a2/i9llgc7Onj1Lzz33HF199dV6XwoAJKCkTmuNxj04uUWCVr8m93O27ez+fQwAwmC7QHpBkKlZbm6uKF2rqqrSLJlTMpjy8mL3y2tubk7Ivk0A6UyruaRWuRyX0rkznVoQmAKA+ItFabdVdlIeqsNTwr59+2jatGlioEpDQwOCTABpLJxsdV4PBusFBQDGgSBTs+LiYneQqVs3/3/ASktdk6+Kirz7v0Rj0kOPU/5jz0RVawmQzs0lFepeTZzJJIldMpPX/bKt3h2AUqdtA0ASNbF02KmxMqGXlPJWrlxJ48ePp0OHDlGvXr0CZhtxaf7GjRupvLycOnfuTGPHjqVZs2aFbMYdCG+y3XzzzeL20ksvRfmnAAAAALVsHRP/EGRq1rNnTzE57vDhw5qNJQ8cOOA+L1aWPP8E/fCW+2L2fABAdHLegJBvQ88/nSOpVXu8XZCWTJKZcq/4pfvYSEJtrJw9f4w+e6mHLk0sU9WqVauCPn7q1Cm65ppr6Ny5c+JzzvY+cuQIzZ8/n7Zt20Y7duxoUYYaT/N95JFHxPH69etbePUAkOzZ6lr9mhTqvk1cUme/cJ5kmzXoc6KkDsDFYtZv/IuxVpc6GjhwoJgIt2XLFrE752vz5s3i46BBg2L2mg9MmUt5yGQCSDhnkAUKQKozZWRRq4GBs4WMnMkkO2xUj0ymmOAelEuWLAkZ4Jk4caIIMPFAk1dffZW6dOlC+/fvFxN1udRtwYIFIuBkFJk5rUlWHQNA8mSr+/ZrknLbuo9RUgeQPBBkasaLJZ7QxpPiOBVcXRa3Z88ekUbOu3dDhgyJ2Zu/8rUVdO2QyBuGA4A33gVTy2h7KXWft89dLnfyySHU8OU+vG2Q8kzmXGo/vtZ1bMoguaku4LlSpvEa5IfKZKqrPk4fPNcdmUxR4Owjzl7auXOnyEgKhoNJfH5JSQmtXr2a2rVrJ+7v27cvrV27lgYMGECLFi2i2bNnt7hsLtbyW7enGtUxACQvyYxfVQFaymbXHmiWCPg/t1mfPn3EhLitW7eKTKYVK1ZQp06dRGPKMWPGiHOmTJlimEUUAHhkZOfTFUtdv1i7ePdj6vrYB2QrPe7eBWuq/JrMhSXux+21lUSy3estRco1JCOTyUSm5uBR9c4JVH/sL5rn5fWeRnnf/g1JWUXiayB9bNiwgZYvXx7WubzxxkaNGuUOMCn69+9PvXv3poMHD9KuXbtEppOCe1uePHlS8zl5ktxDDz0U1Z8BANKDyZLrtb6TG2r9yuXk+ot0/LffdR07mshWdiLoc2J9B+miwaHfayPIpLJs2TJRNrd9+3bR1JIzl6qrq8VjgwcPFuOXY2ncnRMpLy8/6Dlo/A0Q5i/WGiNxFZIlxyvo9OVTg0M+J6aYQCqrO/i0uHHWkxKUSoZyOYfDRg0ol4vK9OnTacKECe7PR44cSWfOnNE8l8vh2IgRIzQf5/s5yMTnqYNMH330ETkc2qvbgoICiqeGBqvXcR6G+AKkzPrONdjFm9zoydg9/qj28AI1rO8gbZhz9Htp3V7ZgDiwtHfvXjFBhXf6KioqRKNvnroyY8YMslgsMX29l599HI2/AQAgLlp9bwkVhui9xOV1RoLG3/HXsWNHcVMEW9scPXrU3aRbS/fu3cVHHpyiFstJvJGqrzpNGapjKsHIcwDwQLYTpAvJHDyZJZ7SKsh04kTw9EnWoUMHWrp0aUKuBwASP8VEoVUup55iApAKDb75llJMOs7jTUNlZWXiI2d2a2nTpo34eP78+bhfS21tLdXUKN2WArtYW0cZ9a5jR22d39fwJLyWTMMDAOOX1GmV04n7G63ulgnhZDtd9uQBchaWkNyobsXgDWV3EE+NjY3iFo2LNTVUF91TtFhaBZmMZtJDj1M+pssB6DbFJNA0E2VBwinYdms1kd0W+HkxLhcgYeVydoedGlEulzBWq9UrmORLuV85L56GDm3JBsBIv3s4W50HvQBA6pXUaZXT+ZbUhQOT7EBvCxcupHnz5lGyQpBJR0uefwLlcgAGFc4Cg6G2HyBx5XLnL9TQJ8+2wnS5BHE6nV4ftYL3LFD/pXDt2LEj5DnvvfeeGNISytdHdlHG/7qCS44BG+kbPa/1ehxZTADpJ9JsJwC9zZw5k6ZOnRrVc5wrPU4HXu1Lt/6OEg5BJgAAjRK6cPGixFb1NbKdABIgULAD4iM3N1eUm1VVVWmWzCkZTHl58W8en5+fT4WFobt41+TnUUZzr1NHfl5YXwMAqS2cbCf++eI1yU5ktHuXy9nrqunknH6u58nGvy0QP1kxKO2uqy+gPJ2qwxFkAgAIMCqXaZXLedX2h7HrhWwngBhxxL8sCzyKi4vdQaZu3fzLjUtLSxPW6HvMmDGUnZ0d9BzOght9w7fjfi0AkC6T7Iq9zjEXesruJEvwf48A0nkiL4JMOkJPJgBjLzAC92uKrLYfAGLUk8luo8YqvJuJwhN2eXLc4cOHqV8/1+692oEDB9znxduaNWs0r8HXV1+8H/drAQAAMHqLgbrq4/TBc911aTGAIJOOXnnp/9FNP/qZnpcAADHIeNLOdqqj47NdO+pSvn7jvAFSacF0ofIYffinHujJlCADBw6kTZs20ZYtW2js2LF+j2/evFl8HDRokGEymW652dODKSML5SwAEDucya5oOHOIzPltvR7nEjp1hhOvF3kDU6s0z2mzkmxrILkh8NRMKSufpKzcgM8D6W1xiI05WW6i+grSBYJMOsovxC+eAKmQ8RQq20mSpIRdG0AqM0tYZCfS6NGjxSS2devWUXl5uVdZ3J49e+jQoUOiV9OQIUMMk8lUV3OelF/ZCtt2iPt1AUD6UPdoOjn36pDnX/bkAREk8mMy0dFHtCcea+GNTd9Me4DJITbmqsqP0f8u0mdjDkEmAIA4s5Wd1FxkcIaTEoDCLhUAGA1Pcxs+fDht3bpVZDKtWLGCOnXqRPv27ROZRWzKlCkhM4wSmck04U7XdQEA6A3T6iBdWwwgyKSjpqYmPV8eAOLIbq2JaJGBXSqA0BrsmC6XaMuWLRNlc9u3b6fOnTuLzKXq6mrx2ODBg8WY5UQIN5Opqvy0+9hmsxH2/gEgVjLadhbDXALhcjn7hXMRBZcCZTuph8wAtCST6WzFGfrshW8gkynd3DXhPsov8B8JHMk3DwAYlL0hNtlOuW1JMrv2A5DtBKnOyLty6YoDS3v37qU5c+bQhg0bqKKiQjT6Hj9+PM2YMYMsFgsZSV3FCcpQHbcp6qTzFQFAqsjIyKCM4uBlbqbc1kEDUepeS+L8AP2W7HWeH3ay3dY87Q4gfJI5n/SCTCYdLXn+CfrhLffpeQkAoNNuF5MdTXT80V7iGNlOAGHsypWeoM9evAyNv2PoxIkTIc/p0KEDLV26VNdv0XDL5Ubf4Bq4AABg1EBUOGQeKqM+zmsT9XNCmm3MyTI1ovE3AEB6LTJsZaF/uQvUTDwYZDxBqjKZNRqoQloIt1zuqy/eT8j1AAAkSlOlpwxYsuSSlJ3vznBSglG8uclrT0gfk0NtzFVV0mfPt0O5HABAOok02+nwr9qH9byXv1xD5pyCmFwjgJGYMnL0vgQAAICE+vKpoSHP6TLrPcps2ylAMMpOsrUSgah0Y9OvvwDK5QAAUijbicm15UQIMgEAAACkhZYEohiCURAPCDIBABgYl74F841frqKCPqNE43BMIYFU57SHVzIKqQc9mQAgnQTKdlcHhRrPHaETj/eNaSCK8evGoq8U6NuTyeGwUUMl6QJBJgAAA8soKKbLXzgf8HFl+px6Mp1ss7r7N5kys8kkuWr0nXYbOR1NQaeZABiZ027V+xJAJ+jJBADpJJxsd8ul3w7YdkEJRmETMn17MtVVH6cPnuuOnkzp5oEpcynvsWei+uYBgNQmSRJJhSURfc3xWd/ySo3O6XqVCDBVbH6Oyt9+Qtx/2VMHxALE/Tq5rUkyu0aRy40cpKpF7T4knJF35SA5ZGQVah4DAKRjICqzwxVB+38iGAXxgEwmHa18bQVdO2S4npcAACkuUGq0OhAVCNKlIdGMvCsHyaGwbQeqUR0DAKSzcAJRTJ0RD6nBLjt1e20EmQAA0qB3U0u4MppC98BB6R0AGKUn04Q7x+AvAwAgCk2Vp4nM2WTOdWWDyrLsGioTSIBzua8UB7lAn+xvu91GjToNmEOQSUdNTa7eKAAA8ejd5NuPSeaeTDbtnjZKuZy6dj/cRuKXv1xDZkyzgwRosOu3KwfJ0ZOpqvy0+9hms1FenK8LACDVhNsgPBRkxOub/X227Cv6bHEn9GRKN/UXzuh9CQCQLr2bsvIonL2klqRLix0rBJkgAWwOvM0QXF3FCfe/dXzcpsh7XDcAAEA6kCz69SVEJhMAAIQsu2v3o5lU/OPZ4vjMX35JNbv+6p1WzT/MsgtJsrjKWWRbA8kNSmcUpExDjJgkvJUAAAAxxqVtXg3CW1guZys7Qcdnfxt/P2kOQSYAAAhaducbQOp410vU9oYH6eS8AWGnVSNlGmLBlJGDNxIAACCBDcLF9k6YGetSVmyKlB0OBzkqT6me2Ezm/LbuT+01pV7no/+TPw4O6gVBJgAACL/sTgSccslcUBTzJuJoIA6hmMyYfgMAAJDMvAJIZguZc1v7BY84Sz6S3lDYzNRgqyC9IMiko0kPPU75jz0TVUMvAABDpFVrlMs1njnoXiCE00S855/OUUZ2vjg2ZVjIZM4Ux07ZQc6mBvd5CEal8aQUWaZG/dZMkATT5UbfgDINAAAjUG8wqjOPIg0ggTZMlwNNS55/gn54y314dwAgpdKqlXRpuW1kDXeP/PoS93H7n/0/avODSSLAVPf5dvrqT7e4H7vsqQMkNfeO4o9Sc2CKp+fJ1mrX9WFsblIKtbFyruIsffpCR10mpUByTJf76ov3E3I9AAAQXLhTikPpMus9yizq5lcul+7Bqskh1kwV5cfo40U9MF0OAABSt4l44bV3Uce7XxTHZf96kirWLwz4tedXPSJuWo7PCr1oQdp0anLarXpfAgAAAMRI19/+h7I7fSdgr6VAG4eWIBud4JJtNpFeUC6nI8mMBqYAkD5NxCVLPklZrsBT8ejHqd1NU8heUxaznS5Ifdnea0wAPxmZeZrHAACQwHYKAXotaZ0vqQJICB6lBgSZ4uT555+nDz74gN58882A5+S3Dt5cFwAgVZuIc+8mccsv8ptmp9WPyel0ktPmn8WiLpdrPHeETjzeNy5/FjAGi467cpAcCosupRrVMQAA6N9OIS7BI8msfawTv4l4Kko/Uf7otNu4x4NYA/NaOV7sspP0ov/fRgo6e/YsPffcc3T11VfrfSkAAEk/zY5yCsN6rozcVu5j2WYVP8RNZov43CnL5Gyq9zofDcQBAAAAkpO6R5P6WC8cYDo67bKwz+dN1pBr4ChYm0g3CDLF0L59+2jatGm0e/duamhoQJAJAEAn3Lep3Y9mUvGPZ4sAU8Op/X7NIS9/uYbMOQX4O0oiNrt+u3IAAACQXJRMePvFct5x1D7JJJG5oMgr46hFJIPV9EtZur10SgaZVq5cSePHj6dDhw5Rr169AmYbzZkzhzZu3Ejl5eXUuXNnGjt2LM2aNSvkiNxACgsL6eabbxa3l156KeT5NeVftuh1AAAgNG4sHqy5eOOp/USX9FJNp7OTbK30OgdT6oylwaH3FYDRVZ39gjJUx3mF7XW+IgAAiDdb2Ul33083k0SSJYcO/yr8nwNXLK0lU1bL+vlJmZ4YQtc5/+sOXKmDV/baSjo+85viPrmpsUWvE/b1WMKrBIiHlAwyrVq1Kujjp06domuuuYbOnTsnPm/dujUdOXKE5s+fT9u2baMdO3ZQVlbkkb8ePXrQI4+4piGtX7++hVcPAACxmGYXSjhjbzGlzmBaursISW/MmDEhNwF5lPPoG76dsGsCAAD9iOykZoYYIqPKZLKUXEbm/HZ+p8iNdapP7FG93OLFi8UtELssU2MF6cKcaulwS5YsCRngmThxoggwDRs2jF599VXq0qUL7d+/n0aPHi1K3RYsWCACTgAAkJzT7EwZmZr9mGwVX9LJuQN0vU5oOVNGZIFESB1r1qyhfv36hTzvqy/eT8j1AACAzgKVvwVw2ZMH/LOdREZRAx2feWXUl6POZJJUx/HCGyt8C+RsVTV99nwbGjGPEi4lgkycfcTZSzt37hQZScFwMInPLykpodWrV1O7dq4IY9++fWnt2rU0YMAAWrRoEc2ePbvFZXMAAGCwJuLNvZdMee1co3U1ptMp5XJNlafDynKCxDOZEWRK9s3AF198UbQUOHHiBHXt2pXuvPNO0c/SbE6JJSkAACQItzRQr+mC9VoK1m/JVnHafdxw6lMyt+7g9bjZZ20Z6HnUWUpyYx1JLSy7ixmbTmlMqRJk2rBhAy1fvjysc9etWyc+jho1yh1gUvTv35969+5NBw8epF27dolMJ0W3bt3o5MmTms/Jk+QeeuihqP4MAACg32hdd3/EPM+EOgCIrRdeeIEefvhh+vWvf03XXXcd7d27l+bOnSvaGHDwCQAAIBZruoioytZOLrg2rn2b0kVKBJmmT59OEyZMcH8+cuRIOnPmjOa5XA7HRowYofk4389BJj5PHWT66KOPyOHQ7jhaUIDpRAAAqUhutJK9/iLJtZ66fy1oEJ4YTruqlwEknaeffpruuOMOevbZZ8Xnt956K11yySU0ZcoUEWziLHMAAAAj4+bdFo0gk28mk94sOg67S4kgU8eOHcVNYbG4+nBoOXr0qLtJt5bu3buLj8eOHfO6v6jIk2oHAAApzJwdcSNJNAhPDKfD1VsLkm8Kb319PZ0+fZquv/56r/uHDBkiyug+//xzBJkAAMBwZXdcLsfT69xrwigadptUQ2oCDazhn4lOm9VzXmY2mZqbijvtNnI6msIq38s26zcsJSWCTJEoKytzT5TT0qZNG/Hx/HlX89h4ksw5cX8NAACIjDm3MKrAFMSPRXLi7U3SKbzcc+njjz/22+R7/31Xo+5vfOMbFAsZmXmaxwAAAC0tu1M3DOcsdyVTSd13ydnUGPINNqmCQVqBIeZoqKXDD3jWop2nbaKcHoNEoKli83NUtvYxr/Mvf7mGzM29R40i7YJMVqvVK5jkS7lfOS+eTJl5VFNTE/Xz8EIv0sUeAABo4x0hrrdXyLKsWS7HiwxlV0uuqySbvUH7Cc0WMue2DrjTlC4aGxvFLRo2ay3VRfcUaU3PKbyZmZl01VVXed23adMmkRl10003Uc+ePSkWCosupRrVMQAAQCwpaz9e1/VaVOqeUHd2xaSYZDLJPmvOU09rt/nxOl8jyGSX9duYM6fjAkv9USuSyQL1XwoX7/KFctPI/6FY4HR27mUAAADR40CQuqGjxP/R+OGtrrcPp6wu3RtFLly4kObN02GOLhhuCm9paSnNnDmTVqxYQVdffTW99tpr+FsCAICkwiVth+7Pj+xr7DbN43iwelfVJVTaBZlyc3NF9lBVVZVmyZySwZSXF/9fBDZvfIsGXuvdm6AlkMUEAGB89prSwONspQySuOY+SLaTb40+P18yNSPnoMLUqVOjeo6qiuO0d2lfuvV3MbustGCkKbz/+Mc/aNKkSaJH05NPPknTpk0TWU4AAABGFSjrKOLzZVU/pzB6O3Wd+79kadfFry9TY+lROjl3QPAvlvT72Zp2Qabi4mJ3kIkXRFq7a4lq9H3f/b+k/ALt3lCKyZMnixsAABizrC5QOZ1vSd3RaZeFfM7LXzgfcCKJ+rmSsRl5OKXdixcvFrdA7LywqorDxaU4o0zhfeONN0Tz8OHDh4ugV6z6MKlVnf2CMlTHeYXtY/4aAACQXjIKisUaTU29ccjlciR7fgZK+dqxBFnVt0l9HIg5v0g0HveSlSfuD0WyBI8zxFPaBZm45p8nxx0+fJj69evn9/iBAwfc58XbkuefoB/ecl/cXwcAAOJXVheonK4lI2wP/yqGvxBLyfcjPtTGytnS4/TZi91pBKrukm4Kb1NTEz344INiuhxPrDNSlh0AAEAwkiSR5BvsUT8ebjsEObJMpkA4s8n9NE2NIrvdaW8ip8NTgtdUG/8e04Ek3wo0SgMHDhSNJrds2SJ203xt3rxZfBw0aFDcr2XSQ49T/mPPBD0HmUwAAMmf7RSsZj/3ih+Q9VDoPn7hZDx5ZTs5HUFK6kxiUorRmpGHzGRy2KmxMqGXlHbiNYX3ww8/FM/N6zDu+eSLez0FGsrCamtrwxqWcrG2jjLqXceO2jq/r8GwFAAAMDLJkks2B4lbbYODzBo/++y1Vqptjid99sg3NZ/nQt4lVFdMuki7IBNPRuEm2dxzoLy83Gvnbc+ePXTo0CGxsBoyZEjcrwWZTAAA6ZHtpJ5W58tJEplIDq/XkmQmc35bERzyS5/2yZw6+ki3pGtGHmpj5fyFGvrk2VbIZErCKbw8rY5xHyYt7777Lg0dOjTg1wd7LLCRfvdgWAoAABiZlJ1Pr/yX6MXPiOjv0VRXuX7u6iHtgkx9+vQRvQC2bt0qMpl4skmnTp1o3759NGbMGHHOlClTWjwxBQAAoMVp1FzKZKA+SpB+4jWF97bbbgv4nOF47733xBoulK+P7KKM/3UFlxwDNtI3el7r9TiGpQAAgNH9/NtEd19JdPmfzmiuIV39QCvIlJnFn5DT0eQql5M95XLnzvwfHd4wVpdhKWkXZGLLli0T6drbt2+nzp07i8yl6upq8djgwYPFBJxEQLkcAADEozzv6MwryV5xSvOczJKe1FQafIx9S8m2BlGmF+z6gpXnoVxOf0aawqvGjctDbQByFtzoG75NGTmuzx35eVRYWJiYCwQAAIiA3GglR12VCBCpOaw1ZMkgccvPzSVznvbPscWvvx60xYCjqYEaXCGOhEvLIBMHlvbu3StSpnmsb0VFhWj0PX78eJoxY0bQhpixhHI5AACIR3lej4X/JwrxAi1qDv8qcPNKza+x1RM5Zf/7myfrKbtsX780nuo+9++3o7j85RoyB2iSztD4W39GmsKrtmbNGs2BLb6++uL9hFwPAABApEyZzbsgRGFNDJatlUR5rVq0Zqo58wntXtpXlxYDKRlkOnHiRMhzOnToQEuXLiU9SRmZur4+AACkJm7qHYj9YrnXsUUjDZvLmpw2T8+dL58eQfWHP4j6ujggFWgSXzgsUsvLrSD5pvC2hJSRpXkMAACgN5MkZhInhG+GVCKlZJApWeS38YwTBgAASAh1RpJGdpLSQPyLSS0PBsWiN5WWbLNxJuGlKiNN4VXjvpnhlMtNuHMMKXN4WpV0Tci1AQAAhMNkcW0CXvbUQbIUdSGn3Ualax6jqn+/qHm+lF/U4hYDjqZ6lMulozvuuo9ycoMvuEOlwQEAALQUl865JtKZvLKfmipOR/xclz11gMwFxX7BpKbK03RshvZ43UgXTLLcRPUVEV8aJOkU3paUy9XVnE/I9QAAAERKap44zGVzIqspK48uGf8stR/7h6BBqZbECbh8/OCqoSiXSzev/XUZXTtkuN6XAQAAaUrpB2Bu15l6LPzcff/p536seX5Gfjvq+cyXmmV0me26UEZ2vv/X5LUNeR1Ked6ku39Gk+68LeB5NbVn6aOlfXRZMKULo07hjSSTCQAAwMi9M9VMZou4RSrksBRbPTVeIF2gXE5HNeWehToAAIBeeBLdofv9A0TssicPkKXYU3akzlLqMn2Lu+Qu0G6b3NSgedyS8ryaViVEoWNWkCJTeFva+Duj+bjq7BeUV9g+7tcGAACQaJNDZTKdOEgH//ItXTbmEtd5CgAAAHQXLPVaS0ZBkQgsKTc1yZLjvp935zTJDu1jFXWTcTDOFN6f//zn1LFjR6qvrxeNvrmMjjOcEjWFFwAAAJIPMpl0NOmhxyn/sWeCnoOeTAAAEEvqQJFvmdzJJ4dQw5f7Ap4fbysPEa36wv9+S8llZJLM1GS3kU2n1O9UkixTeFtSLjf6hm8n7JoAAAD0sjhUuVxTIzW6kpATDkEmHS15/gn64S336XkJAACQpk0nXbwbfnd97AO/iXORZj5pvZ7WsZrT5CpwGncF0eMbvyRzfju/a+BMKT1Tv0FfkZTLAQAApLrJIcrl/j97dwLfRJn+AfxJmqalF6W0XHIfguABItfKoSKCB4uK7qJ4cCjKggoICuJyiAceu+oKoojgeoCgeAHKISIgAiqHLvBH7kM52lJK6Zkmmf/necukk2RytDkmk/y+n0/oNJkm08nQefPM8zzvnwe20K4Pu6HxNwAAAIS/6aSy/C2UuPdSxWx2zqRSedJ5ImN8YlizpwAAAACijSFO7lAYfshkAgAAgLA4ML55YE9gKwnWpgAAAABELaNJu1APgkwAAACgD3b12ekg+qEnEwAAgP89mezWMio5S5pAkElDDz82lZLR+BsAAMDR+Puzzj2IjOop3lZLMZWh8XdM8rcn04kDWx3LxriEEG8VAABAZPZkOnf8Z9rybmf0ZIo1Cz96j67u2UfrzQAAAAiZqjQO58bf09/eRnGJKaqPnz20gX56vxcaf4NHNes0pQLFMgAAQCyyWW2avTYymQAAACBMs9kFFpQymLxPYQ8AAAAARMUWi2a7AUEmAAAA0GQ2u6qym1D+FKv87ck05N47wrZNAAAAkdqTyWop0azFAIJMGjqfd1zLlwcAANCVMrtR602ACO/J9Of+TSQfJfmnD1ByWt2QbxsAAECk9WT64/Bu2v3fSzVpMYDRmoYku13LlwcAANAXe7nWWwARTrLbVJcBAAAgPJDJpKGHHptCKZhdDgAAwL/U7/JSKsvHzgIAAACIVAgyaejt15+hG29/QMtNAAAAiBiRnPoNAAAAoBcG23nNXhvlcgAAAAAAAAAAUcIQF6fZayOTCQAAAPTBVqr1FkCEzy434PrLwrZNAAAAWpkdwS0GEGQCAAAAfbCXaL0FEOGzy/3x+4awbA8AAEAktxgoOPkrbX67PWaXizUGI6oVAQAA/BVnwKys4GtsFae6DAAAEEskq0Wz10aUQ0OpGY20fHkAAABdSUTMAHxIr9tSdRkAACCW2Kw2zV4bQSYAAADQBYPJe08eAAAAACAqtiCTCQAAAMAru8mMPQQAAADgE2aXi0l3Db6fUlLTA2roBQAAEDMzpVjLqOxsWDcJdCb/9AFHmj4vJ6fV1XiLAAAANGD2HmcIJcwup6G3X3+Gbrz9AS03AQAAIGL4urBy8ug2+m3BVZrMlALauuOOOygx0Xu5JB87A66/zPG9ZNeuHwUAAICmF+bKy6gsnzSBIBMAAADogt2Ozt+x6tNPP6Urr7zS53p//L4hLNsDAAAQyRfm/ji8m3b/91JNLsyh8XeAJEkSEcRLL72UkpOTqV27djRz5kyyWq3BeYcAAADgwjkX18b0rKCggB577DFq3ry5GDNdfvnl9M4772i9WQAAAFHHYCvU7LUxWgvQG2+8QWPHjqVHH32UunfvTtu2baNp06bRsWPH6M033wzOuwQAAABEBgP2go49+OCDtGbNGpo8eTI1a9aM1q5dSyNGjKC4uDgaNmyY1psHAAAQNQxx2uUTIcgUoFdeeYXuueceevXVV8X3AwcOpHr16tGYMWNEsKlOnTrBeJ8AAADAVop9oFNnzpyhJUuW0FtvvUUPPfSQuO/222+ngwcP0gcffIAgEwAAQBAZ47RrMRA15XILFy4kg8FAv//+u8d1Tp48Ka6YNWzYUDSPvPjii2nKlClUWlq9QWtJSQkdP36crrvuOqf7e/bsKcro9uzZU63nBQAAABV2BJn0OmbKy8uj66+/nnr16uV0f5MmTar9nAAAAKAuyaxdPlHUZDItWrTI6+NcvtalSxc6deqU+D49PZ32799PM2bMoG+//ZbWrVtHCQkJVXpNk8lEv/zyC7Vs2dLp/g0bKppOXnTRRV5/3mCMmhgfAABAyMUZMFuYXsdMrVq1EqVyzGKxUG5uLm3dulU09H7qqacoWAzGONVlAACAWCKVl2n22rqPcnDGEKdeL1++3Ot6w4cPF4Ol3r1705EjR+js2bO0fft2atSoEW3evJmeffbZKr92fHw8dezYkWrWrOm4b+XKlWKw1LdvXzGgUsODK5aQUrfKrwnVU1ZWJsoX+SuEB/Z5+GGfY59Hu8Q4IovV+VwK+hgzKXGzb74Qx+VyV199tWgx4In8Pvv7fqfXbam6DP7DuST8sM+xz2MBjvPwkiS7ZmMm3QaZ+EoaD4Jat25NI0eO9Lrujh07xPrcH2nx4sUiNZt16NCBli5dKpZnzZoVULp2dna22J6bbrqJ2rZtSx9++KHHdeU3uby8vNqvB1X/ozZ9+nQEmcII+zz8sM+xz6OdwZRA5ReSmRBk0u+YiYNL/Bovvvgiff/99/Twww8HLcgEgcO5JPywz7HPYwGO8/AqLCnTbMyk2yDTihUraP78+SJ925dly5aJr/3796fatWs7PdapUydq164d5efn06ZNm5we45lPuGeB2u311193rPfZZ5+J5+DB2HPPPSeeJzMzM2i/KwAAABDZTVUr0YLIGzOx+vXriyypJ554QlwA+vjjj8lqvXC5FQAAAHQd6tFtkGnChAm0c+dOx61BgwYe1+XUbtavXz/Vx+X75fVkP//8s0gXV7vxNLyMZ0q544476MorrxQNNCdNmiTK6PxRmF/R6yAQs2fPDvg5Ivm5grlN0byfgvlc2OfY58E+DiLxuXCc63Ofl9nRY0evY6aPPvqILrvsMrcsbm4tUFhYKBqNB8O57COqy5F4PEf6cwUL9jn2eSQ/V7DgOMc+j7jnMtcizUhRomnTphL/Onv37nV7rGXLluKxHTt2qP7sm2++KR4fMmRIlV7TYrFIWVlZUu/evSWr1er3z61fv1683qcf/EcK1CWXXBLwc0TycwXrec6dOyf2OX+NlG2K1OfCPsc+D+ZxEKnPheNcn/v8xJFfpKUTSfw953Mp6GfM9P3334uf27Bhg9P9jz/+uFSzZk3Jbrd7HTP5+34f37teOrGAxI2XI/l4jtTnCuaYKVjbFMznicTnwj7HPg/m8RSpz4XjPLz7/PihXZqNmaJmdjlvcnJyHLOjqKlVqyLKd/r06So975YtW8Rzd+3aVfQWcNW5c2fHcwMAAEBg7PaYGLZE5ZipR48eYsa6u+++myZOnEj16tWjjRs3iv5OL7/8siirAwAAAP2LidFacXGx+Oop4CPfL6/nL3lqX+7DpIabWfbq1cvtfrlZ5u69ByhrwwYKBG8zB7vMZnNAzyNvF88eEwzBeq5gPQ+n4jMuE0hJSYmIbYrU58I+xz4P5nEQqc+F4zz8+6qkpIQ2BHjOyzlxiPYcq9wu0M+YyWg00pdffikCTDNnzhQz1rVp00b0irrvvvs8/pz8PvP52x85x34lw+8Vy5LpVzp02v04jKS/IZH4XMEcMwVrm4L5PJH4XNjn2OfBPJ4i9blwnPuPG3XzeTaQcVPOn3u0GzNJMZD6bTKZxGNnz55V/dk1a9aIx7t37x6GLZWkBQsWiNfDDfsAxwCOARwDOAZwDFTvGOBzKVQPxkz4u4O/OzgGcAzgGMAxEDvHwIIwj5liIpMpKSmJCgoKxFUztfRv+WpccnJyWLbnlltuoQULFojGm4mJiQE/H2cxBSOTCQAAIFT4qlwwptDlq3EnTpwQ51IIPoyZAAAAtGXR+ZgpJoJMWVlZjiATT7HrKjs7W3zNzMwMy/bw6wwZMiQsrwUAAADgL4yZAAAAIBBGigE8PS7bt2+f6uO7du1yWg8AAAAgFmHMBAAAAIGIiSATz/7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+      "text/plain": [
+       "<Figure size 1200x1000 with 4 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "columns = ['DER_mass_transverse_met_lep', 'DER_mass_vis',\n",
+    "       'DER_pt_ratio_lep_had', 'DER_lep_eta_centrality']\n",
+    "\n",
+    "nbins = 50\n",
+    "\n",
+    "xlabel_dict = {\n",
+    "    'DER_mass_transverse_met_lep': 'DER_mass_transverse_met_lep', \n",
+    "    'DER_mass_vis': 'DER_mass_vis', \n",
+    "    'DER_pt_ratio_lep_had': 'DER_pt_ratio_lep_had', \n",
+    "    'DER_lep_eta_centrality': 'DER_lep_eta_centrality',       \n",
+    "}\n",
+    "\n",
+    "variations_to_plot = ['Nominal', 'JES_Up', 'JES_Dn']\n",
+    "\n",
+    "\n",
+    "fig, axes = plot_kinematic_features(\n",
+    "    columns=columns,\n",
+    "    nbins=nbins,\n",
+    "    variations_to_plot=variations_to_plot,\n",
+    "    dataset_dict=datasets_all,\n",
+    "    xlabel_dict=xlabel_dict,\n",
+    "    samples_list=samples_list\n",
+    ")\n",
+    "plt.show()\n",
+    "\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 16,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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+      "text/plain": [
+       "<Figure size 1200x1000 with 4 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "columns = ['DER_deltaeta_jet_jet', 'DER_mass_jet_jet', \n",
+    "           'DER_prodeta_jet_jet', 'DER_lep_eta_centrality']\n",
+    "nbins = 50\n",
+    "\n",
+    "xlabel_dict = {\n",
+    "    'DER_deltaeta_jet_jet': r'$\\Delta \\eta_{jj}$', \n",
+    "    'DER_mass_jet_jet': '$m_{jj}$ [GeV]', \n",
+    "    'DER_prodeta_jet_jet': '$\\eta_{j1}\\eta_{j2}$', \n",
+    "    'DER_lep_eta_centrality': 'X',\n",
+    "}\n",
+    "\n",
+    "variations_to_plot = ['Nominal', 'JES_Up', 'JES_Dn']\n",
+    "\n",
+    "\n",
+    "fig, axes = plot_kinematic_features(\n",
+    "    columns=columns,\n",
+    "    nbins=nbins,\n",
+    "    variations_to_plot=variations_to_plot,\n",
+    "    dataset_dict=datasets_all,\n",
+    "    xlabel_dict=xlabel_dict,\n",
+    "    samples_list=samples_list\n",
+    ")\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 17,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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w6f2DBg0KpbIHqcbfp59+ujz11FNtJolqFTomQwCASAcOHNBZKeq41LlzZxk5cqQOHigDBw6UP//5z6Fj6be+9a3Q96nef6oM/PDDD5dOnTrpY5w6gaIWoojsZ6BOgkT66KOP9O0qG0ZRjz116lQ9Pu200/RzB9XV1clNN92kgxmlpaX6eD5mzJhQ9m+QeiyzMj9VWq5uV/sC682vAADIJOZO+SWpjCZVFqaaZHft2tX0/qKiIj1xVWne+aC6ujq00pyZ4P9z586dbbKmvve978mPf/xjndKt+if8/ve/l6uvvlr69u2bgT0HAOQTdaxQPfzUiQpVgq0CCqoEWx1XL7nkEnnhhRd030A1Dp6waGpq0kEl9X0q4KNWOlULUrzyyiu6v5LaXgWFEt0P1Zhcfb8aG0u9J06cKIsXL9al42o/1SqrL7/8spxzzjnyn//8R+8D0sNq8ysAAJLF3KmAAk1qctzY2BhzG6/XGzVwk2uC/5doE7vg7ZH/ZzXZV6vL3XXXXXLppZfq4NIdd9wh1157bczna2hokPr6+qT3u6SkRF8AAPlBrW6qggnB4+ONN94of/zjH3Xg4Le//a3U1tbqwJEqjwouTPHhhx/qINOkSZPkySefDD3Wc889Jz/5yU90UCjRQJNKn1bHaRVoUgtZBINHKnNKPd4pp5wSli313//+V0466SR9zEtnoEkF1dQlWeo4m4+sNr8CACBZzJ0KKNB04okn6snmpk2bwtLtg9TtatKsAjH5ILjcdLRlp9UZRMXn87W5T51lVpdEpGqSPmPGDJk5c2ZKHgsAskH93g14Yv9hnQtszlLTkrRE/e53vwsLEqgsIRVo2r17d9TvOeSQQ+Sxxx6T73znO2G3H3XUUfpaBadSRZV+q+c6+eST0/5cZtSJm2B5XyGy2vwKAJB6zJ2YO1k20KQmgiplX/WWUGdjg/0Z3nzzTX3m9e6779Zfq+yefKB6UKgMI1UeYHaWMHh2UfVlSgV1Blk1+kwW2UwA8p0KMq29rFxy3ZFzGsRWkvwxQGUFRR5/2qP6BAZ7BapAz7p16+Szzz6Txx9/XFKtV69eMnnyZD1Wx0XVw/Dzzz+Xf/zjH5IJqhF2e1nB8Vi9enVelvhZbX4FAEg95k7MnSwbaFLp/EuWLNGNRK+//vrQ7aq5qIqwfv3rX5dnnnlGhg4dKvmSqh4MNJmdQdy1a5e+7t69e0qeT61IZ2zwCQAoDB09jqjl7VUGqwoyKV26dElo0YloGbtmVHPy6667TpfrBYNhqpQuHc+VrpLwfF351WrzKwAAksXcqYACTYpqErp8+XLdS2Lt2rWydetW6d27twwZMkSfsU1FiUGmqH1WK8qps7ZmE/c1a9aEtgMApLYkTWUL5cN+ZovKXPrFL36hm4arVenUcUqV06lsF7USWTxqamri2k4FOVQ5uCqdU6uoqmP9YYcdpo/p8R7X430uWH9+BQBIPeZO7WPulKeBJrVKjlqJRk12jj/+eH3JZypFXfU8UBNstdpOJNUYVYnsWQEASI4OYKSgJM3KVAaLCjT861//ErvdHrp9y5YtUb9HNdQ2ZgatWLEirudSDcYVVb5lzAr68ssvo36P2+0O+zre54L151cAgNRj7tQ+5k7Z0zpT7YCxY8fKoYceqlPrrTChVEsJKwsWLGhzJvb999/XZxRV76bRo0dnaQ8BAIXE4/GExvv27dPBnAMHDoRuU+Xet9xyS5tAT7du3cJOkARXkrvvvvvifi6/3y979+4NC1oFy7jaey71ffQP6jirza9Svby1akof6zJ79uxs7yYAIEuYO0Wnjo/tHUPVcTbrgaYbbrhBn+m899579QopRx55pNx+++26/CwfqcbcanKngkwqo0mlqSsrV64MveCqX4LL5cryngIArCzYHFwFGtTqb4o6Dqkegqovz6RJk/RxSpXMqYCQOhar9PBgIEmtYqfOdKptLr30Urniiitk+PDhbVasMz7XrFmz5J577gk9l+oFpI6L5513nlx44YW6d+Enn3yiS+jUCZmbb75Zb/vd735XOnXqpJt3q/361a9+pb9Pbad6HyJxVptfpdK8efPk008/jXmZMmVKtncTAJBhzJ3ap46P7R1D1XE264GmP/zhD7qf0UcffSS//e1vxel0yq233ipHHHGELi9TEbPq6mrJJ2pCr0oTVNp6v379pGvXrnpyrsoFRo0apVfCAQAgnS6++GIdFPrPf/6jS7qDQac777xTB3VefPFF2bx5s9x0003yxhtv6J5NDocjVEZ36qmnyrPPPqv7+ai0cXWcvuaaa+TBBx9s81w/+tGPdGBKrdAWLJlTwaVHH31UevbsqcvnVIDp5z//uXzwwQfy0EMP6SwmtRKdolbCe+mll3RPoX//+9/y9ttv6wDX888/L0VFRXxQOsCK8ysAANKJuVNusQWSWRbGhFoJRzUOfeGFF/SEVE18VZbQwoULJReoM7Jqcq7K4KKt1rJ9+3aZMWOG3ufdu3frgJM6S6sm9KlYBUdlSKnglUqHT2S1oHj4fD4JNG4RcVaJ3d7SgsvvaRDxN7bZ1lbajz8CAACWlc7jbabl+vwq3az0XgKZoP7EC3jD5/9+d8sK2uYcYndVGbZVbUT8Uba1i93VvYPb1oqIN+pe2F09O7atp079Y7odf/MAmT/eJr3qXCR19lSVl6n6vvvvv19nBgXPxuaCTZs2tbuNymiaM2eO5CMVZKqef3hc2/Y4d6NI54Fp3ycAAGDt+RWAJE4QK3an2J2V8QWF4thWBZlqXjyGt0VEuo9fI7byAe0GpDRn99CCG35PvXpx293W5ihlJVAgXYEmlcKtejaotPmlS5fqZqXqbNuZZ54p5557bqqeBgAAoGAwvwLyJFAUjd0ldmeFHqqeeuJpXXDIt3+r1C4ak+7dLHjpDrh1/+E6sdljlIrHqDQh2wpWlVSgSZWfqcCS6hWhVmVTvzxVo2zVV0IFl37wgx9Ily5dUre3SDh6X1Q+IOwXGgc0AAByG/MrIA8CSXaXBDy1eZE1FPqboIBK5zL5N0/NC0M6/L1V45ZJoKxv1IAUgSgUZKBJpW8rZWVlOrCkLt/73vf018g+lcZpLy7TFwAAkB+YXwEpDhTZS8XuLNe3+f1eEY8KYERh2NZbv0FqFxybkbej6qx3pLjrN1JWOmekghX2oiKxF8ffMiORvx9ycVv1f9ZtQhLo5ZRI6VyqShPbC4aFBaKCHJVidzhj9uINIlCFvAw0XXDBBTq4NG7cuJQ0yUYGOKvMxwAAICcwvwISb3jt93pEvHVh92cyq0X3AXKUJlQ6ZxYMCn1bQkEheq5G0quemvSiTVWgS332ek1qiLOXU3imkm/fZ3F/LpP9/Ab7U/nVz4m3ZX+NCEQhJwJNamnlMWPG6JI45W9/+1uowfaXX34po0aNavM906dPl0ceeURqasx/qSKzgr/kIscAACA7mF8BHeuBlMmG1yqzpMiYWWIIICnxNITWeTIlndO4l8gU9V7bDgaiEg1e2VzdQ9lWZgEpX8PmlH2u23scnTHV5WixO1z668iAFIEodFRCkYZ7771XXwcDTUFq9ZP77rtPHwwiqabge/bs6fAOAgAAWBnzKxSqmIEkR7kE3LtS9gd3qE9RAqVzxm35gxvpzrYKClQcaR6IirN0LpFMvoRK9xwVhoCUW9WW8nOBqEhpKTD6l5FhTP8mAAAAZIMKMlXPPzwlj9V9wlaxBZrb71OkviiJb7GiRLYFMhWIMjL7Wy7Un8oYkDJkKqUyEKWfJ859RWEh0GQ1dpf5OMgQ8Vb1wWERcJPoOGdvAAAAkG3ReyC1ZCDFU7oGFGqgqiUg1aNNo/S2mUqN4tv7fxnrbQbrItBkMcZacePYTDy/QIhSA0BmLVu2TE477TT5y1/+Ij//+c95+QEUhDY9kBzlYjcElggkAenPmNI9pJxV5qV7BwNSqewhlSrMnXIPgSYAADJE9TOcOnWqvPHGG3pxjb1790plZaVcdNFFMnfuXN4HoB0TJkwQl8skY9tgypQp+oI84Gg9KVrU5WhxdOqW1d0B0H7pnt8sszCNmDulzuzZs/UlFrc7xgqKCSDQZDF62VTDWNeWG7RJlTQ6WDqXi1FqAACAefPmybBhw3ghLCJYrhM5BgCkXjwnYlauXCnDhw9P+rkINFmNpyZ8HLGEajzN5TIdpQYAAAAAANaQcKDpvffek1mzZrW5Tbn99tslEAiY3gcAQCH71re+pUvmFNWDacCAAbJ582b9terHpC7B+4OeffZZefDBB2X16tUyZMgQOffcc+XGG28Uu90eluJ87733ypNPPilffPGFlJSUyODBg+XKK6/UJXmR/Qvefvtt/Xi/+93v5KyzzqJkL0cwv0Ih0ithGcashgzAiLlTAQWa3n33XX0xc+utt5rezgoQAIBCp3rLqJMxKuCjxt/4xjdk+/bt8tBDD8lRRx0l3/nOd+Swww6TL7/8Um//j3/8Q5YuXSqjR4+Ws88+W/7zn//IzTffrFOa//nPf4Ye99e//rXMmTNHvva1r8n5558vBw4ckFdeeUUuvvhicTgccsEFF4Ttx6OPPipPPfWUDjqpx0ZuYH4FK1K/8wJhwSS3iLc+9LVaZj1EL73esioWgPygfr69B3aH/VwbJbuCOXOnAgk0RZ5pBQAgVX+MNPly/7UsKer4yRNVE+/1enWg6aqrrgo1A1eBphNPPFFnJSnBQNOiRYvk73//eyhQVFdXJ+PGjdM9al5//XV9ls/n88nf/vY3nR318ccf68CS8tVXX8nAgQPlhRdeaBNoUllS77zzjowYMSLJVwOpwvwK+R5I0plJOlAUcb+3kb6fgMXmTl5bmTRJS6uVr148Mea2VePekrKqI/Tcye/1iHjrEgpEMXcqkEATZz4BAOmgJko/X1yd8y/uX8/sIa4MdTccNWpUWJBIrU735z//WU4++WQdgFKBpqamJnnggQfk61//eijIpPTu3VtvX1tb2+ZxJ06cSJApxzC/Qq5SwexA45ao9wekSGrm98/oPgHIgblTj03xbbdC5A/VvaREGs0fRi1S1U7/4EQwd8odNAOHyaei0nwMAMiYM844o81tI0eOlM6dO8uGDRv016WlpTJ58mQ9bmxslPXr18u6devk5Zdflupq88mnCkoBQDxUkKl6/uEpebG6j18jNtchMUtsABQWXT5rLxW7s1x/HZn1lOjvBeZOuYNAE9qwO5ymYwBIZ0mayhbKdWo/M+WQQw4xvb1Pnz5hQaRVq1bp1PL3339f/H6/bgZ+wgkn6ICUmaqqqrTtMwALZSw5KsP6K8UVSIqxcnFriUy3juwqgDyZO+nfKwcO9l9zlEkn586w0jkVXKpdNEbfHbyOpmrcMvG5d7c87oFa8Tfv1xfF37xPP5ex9I65U+4g0GQ1dpf5GABymJqAZKokLV/s2rXL9PZt27bJKaecosdr1qzR40GDBuleTSrjSfVrUpMu1aPJDAt0AOhIxlLV9z6UIldEoNpRIXZHy3xTBZn4/QJkTs7OnVQpf0nbOUjLqpJdE3ooFYja/1HLuO6NH8nOHSL1B+Pf7i/mtwTKDaV3zJ1yRy5+NJEEu7PCdJwIlpoFgOx77bXX5Le//W3YbcuXL5d9+/bJ0KFD9df/+te/9CpzahW6I488MrSdx+PRky0VdAJQuKJmLDkqddZ6IhlLxZVHHvxDEQA6TmU36t5MQRGlc769/6/dTKfIlSt9jdv19dIlr8j0m65tfS5HqV4AhblT5hFoQluGVUN8e/8vfBURw5mr4AojyS5bCQCFRgWCYn2tqNXpnn76aTn//PP113v27JFrrrlG7Ha7XHnllfo2NXEK3mf8w3LatGm6Z5Pb7U7z/wRATgWSnFVit7dM7/2eBvHt+yzuP9hUiUpRWd82waigWGVxABAv/XdjlAbgKphtc54SFogq2zlX5JlZUnrKs9Jr3PekeNv/E5lysjR7W0vv6ta2bPvm2+/KQ1eVy49OPvhcY5bL1Kt/qedOl/7sbPHu2yR7d7cEp3ZXb5PA0KE6M4y5U+oRaLIY1Z/DOLYn+XjxTE5SvVoAAFiVat6tzJo1Sz766CP51a9+pSc/S5Ys0cGhX/ziF6Ftf/SjH8mFF14oTzzxhF5FTmU4bd++XW688cZQRtMPf/hDueeee2TcuHEyZswY6datm7z11ls6cHX00UfLBx98IJdccol+DCCXqeDphx9+qD+zyE6zbhVkcjCfA5BjgajOVS0B8DvvfkDWfLZFrrzo+2K3iSxbI3LnP0XON/y5etZwkasfFfnH2yK9uoi8fc0psrNOZMpZIlUff1uqPxYZUy7yJ5vId888Q8acfpZ079GTuVMaJBuHQK7x1JiPAQBZp4JH55xzjqxevVqee+453bj7jjvukPLycnnooYekpqb19/ZvfvMbefDBB3Vm0r///W85/PDDdcDo97//fWgb1ZNp/vz5MnjwYPnPf/4j//3vf+XMM8/UQazHH39c3/7pp59m6X8LxEcFWh9++GFerjRTGUvq5KC+/KRWek1q0Jce5+3Qt7HqG4B8mDu5ug6RWTNukIpuh8nfl3cW/wnPS+WYZ/S2N9/zstz/P7fKgSaRJatF+vUQ+dMlItPPa3284YNEHr1KZEBPkdffWMbcKU1sgUAgkK4Hh7mVK1fK8OHDZcWKFTJs2LCUvkwqHTB4ZktNGjpyZir2CiQtpXO+hs1S8+IxST0PAAD5erxFauzdu1eOOeYYqa+vlyOOOCJqRlMhv5dh87IYpXG69K3rsWH3i7+1BxOtDgAUgph/y6pMUG9j6O/YWKtlFurvzJUpOt5SOoeE6maD/NTpAwBQEFSvsEmTJsnatWtDZZuRVFnnjBkz5OWXX9aZef369ZOJEyfKzTffLC5X9FVwr776ar1qYv/+/cm+S7I8Tpe+lXQJfU3jbgCFqL2/Zb1NLf0tlWDAyQztYZJDoAkAAABRPfNMS0lCNFu2bJGTTjpJduzYob+urKyUdevW6V5kS5culddff12XiUZ68cUX5fnnn5ePP/5Yfve73xXsOxA6++7srnu2KX5PvYjfHbaqEgAgBeJsLxPwN+tqoWgKNeMpXgSaAAAA0IbqrvDII4/ISy+9FPPVmTx5sg4ynXHGGbo3mMpOWrVqlYwfP17effdduf3223XQyUhlPV122WVy9913y8CBhV1+n0hD76qzP5LiikFRS+MAALGp35XGVe2ildXVvGCewRtExlNsBJoAAAAQorKQVBaTWsFQZSbFogJKavuePXvKs88+q1c+VI4//njdqP7EE0+UBx54QKZPnx5WQqdWmTv22GPliiuu4JVPQJGzc6gkjtI4AEhtaZ2/eT8vaYoQaAIAAEDIwoUL9QqH8ViwYIG+Pvvss0NBpqARI0bI0UcfLZ988oksX75cZzwpKgD1yiuv6Mbf+/e3TOqbm5t1CZn6uri4WJxOZ0GWxqnGtEXlA8LuNyJrCQDSRzUGV6tx6t/Tfn+bMjvfgV1S+/JIPaa0LjYCTVZjd5qPU81RYT4GAAB57YYbbpCLLroo9PVZZ50l27ZtM91WlcYp48aNM71f3a4CTWq7YKBpzZo1OqB01FFHtdm+vLxcbrnlljaldoVSGqf+yFGZSmQrAUDm2Ww2sQWzRtU/JZ3D7re7eobG7ZXWdVcr2pUP0GO/e1fB9Xgi0GQxdmel6Tjlz+NwmY4BAEB+69Onj74ExcouWr9+vb4ePHiw6f1qRTllw4YNodsuueQS+fa3vx223R133CEbN27UPZ769u0r+dbLSvX1CPJ76tQ/oa9p5g0Ahacmxop2hdDjiUATAAAAOqS6ujq00pyZrl276uudO3eGblOBpMhgUq9evfRjnXLKKXn3Tqgg086nyuPaltI4ALBGaZ1ZppI6sVC7aEwW9iz3EGhCh/i97rAxKd4AABSexsbGsIBSpODtwe2S1dDQIPX19Uk/TklJib5kGqVxAGCN0jrFXjzQfEU7uzOsuigYkEpnIKqpqUlfUnGcTQUCTRZjjKqqceSHP2W89RHj8AagAADA+lTZmPE6UrD/hGqIHcvcuXPjer4xY1IzQZ8xY4bMnDkz5XMv3ZND/XFhKJ0zopk3ABTeinb2dP1NbnDXXXfJbbfdJrmCQBMAADnkhz/8ofzrX/+K+od7Kl1zzTXy5z//WTZv3iz9+/dP+/PBekpLS3WG0Z49e0zL54KZTGVlrWeAk7Fs2TI57rjjkn6cdGUzqYwlR9mhaXlsAEC+z50cUcbJmzZtmlx77bVJP87q1atTclKHQBMAAAA6pEePHqFA08CBbc/Y7trVku3TvXv3lLzCalW6igpWuwUA5B+7q8p0nAqpKglXx9lUINAEAACADhkyZIheUe7zzz+XYcOGtbl/zZo1oe1SYcKECeJyxV7tdsqUKfoCAABazZ49W19icbtbezEnw56SR0FB87lrxd+8X1+8B3aLd9+msEt7fRkAoKCaSEa5XHzxxfpapX4Ht1W3BX322WcyadIknaat/tBWq3add955oT/kg771rW+ZZpa8+OKL+jH/+te/6q/VNir1WxkwYID+vqCvvvpKrrzySjn88MOlU6dOeqn7s846S95+++2wxwzuc6SPPvpI355LvQKQHiNHjtTXS5YsMb1/8eLF+vrkk09OyfPNmzdPPv3005gXgkwAYB1WmztNfVykT+suZnTupI6P7R1D1XE2FchoQtJqF54Q837ded+kKRoAFJqpU6e2ue2DDz6Q5cuX69W51P0LFizQGSJqfOKJJ+ptdu/eLaeffrrs2LFDzjzzTD1x2bhxozz//PPy5ptvyv/93/9JVVViKdiXXHKJvPDCC7Jq1So9DmajqBVL1ON//PHHukZfPd/27dvllVde0f1x1PZHHHFEil4R5Lvx48frptrqc1tTUxNWIvf+++/L2rVrde+m0aNHZ3U/AQD5yUpzJ3+mFu7KAQSaAABZp5o3BrypWf48nVSjX7MMnnjde++9YV9/+eWXepIyePBg/ce66j2jmkuqyZJx20WLFsm2bdtk+vTpcvvtt4du/+Mf/yg33nijnmydffbZCe3Lb3/7W6mtrdWTn1tvvTXU0PLDDz/UEyV1BvDJJ58Mbf/cc8/JT37yE52hQqAJQaox99ixY+XVV1+ViRMn6tXj1BnjlStX6jI3RU382yt3AwAkhrkTc6dcRqDJauxO83GKqeV5daaS4igXu6NUD/1et4i3Xnz7t0rtotQsQQzA+lSQaedTqWk+mE69JjWIrTg1q2c1Nzfr9O2GhgZZunRpzAbHRx99tDz22GPyox/9KOz2o446Sl+rgFGqHHLIIfq5vvOd76T9uWAN6vOiSuhee+016devn85gqqur0/eNGjVKr4STKjnZoylDcy8AMGLuxNwpl3s0EWiyGLuz0nScakVFRablcHb9B1i3tD0vAFiFWh73vffe03X/3/jGN9rNGgku6b5z505Zv369Tvn+3//935Tv16BBg/QlGFRat26d7nHw+OOPp/y5YA0quLRixQqZMWOGLFy4UJcrqObfKivupptuEqczdcEX1TvCrOl4Icy9AKDQMXdKTjwnYlRG8vDhw5N8JgJNAIAcKUlT2UL5sJ+p8NRTT+kzSpdffrn87Gc/i+t7VPPJ//mf/9HldorqhTN06NCEUuzj9fTTT+tSPhVkUrp06ZLQH/eJPBdy36ZNm9rdpnfv3jJnzpyM7A8AgLlTPJg7ZQ8ZTUjTJ6vcfAwAJvTqISkqSct1aqWTyy67TJ8tuu++++Lup6R6M1100UVy4YUXyrHHHivdunXTDSZPO+20uB5DNWqOh8pc+sUvfqF7PqkJmgowqXK6L774Qq+wksrnAgAAHcPcKTbmTtlFoMlicqWTfbBnU+QYAApZfX29nHvuubrHjCoBKikpiev7nnnmGTnppJN0o2WjLVu2xF1fr0qb4n0ulZ2ilgq22+3tPldwtRXj/yXe5wKsIFfmXgBgRVaeOzk6J/5c+YJAEwAAGaIyklQ52ksvvdRudpBqFl5cXKzHqmG46nPj8/laeuSJ6GVz77zzzjaTI5XppJbyVSvHBXs/ffLJJ7oXVDQejyc03rdvn368AwcOSFlZWWiSd8stt5g+l6JWovvBD36gx1999VXcmVqAJZqBAwDSxlpzJ4d0PZjAv+S15fLDnxyZ0bkTzcCR9/xeT9i4pUk4ABQudZbrhRde0I2T1VLw6mJUVVWl07xLS1uyQCdPnqyDN+oPa3Um78EHH9Q9mU499VQ9eVm0aJFe0evzzz+Xu+66Szp37iznn3++Xplu/vz5uqROfd3Y2Ki//vGPf9xmwhR8ruuuu04/lyqZU8/3m9/8Rj/XmDFj9ARtyZIleuW78vJyXVqnSul+/etfyznnnCN/+tOf9LL2qvGzmsip/+e4ceNiTs4AKzUDBwCkh9XmTr/65S9k3DCROUtEJl38K5m09L2Mzp0y2QxcNexEhq1YsUJ1SdXXqdZcvzGwba7oixpnS67sBwDkir/85S/6d3+0y4ABA/R27733XuDUU08NdOrUKXDNNdfo2xobGwPXX399oH///oHOnTsHRo8eHXj00Uf1fbNmzQr07Nkz8Kc//Sn0XA888EBg6NChgfLy8sCYMWMCc+bMCfzf//2ffh61H0Hr1q0LfOc73wmUlZUFzj33XH2bz+cL3HnnnYHBgwfr20866aTAH/7wh4DX6w089thjgd69eweuvfba0GM899xzgW984xt625EjRwZ+//vfB/bv36+fa+bMmQGrHm+RWbn8XjLnAYD0sNrcyedp0H8fP/JLCXzj68dYeu5kU/8kH65CIoJRQlWHmeqzct59m6R6/uF63OPcjeLonJ0+AbmyHwCAwpXO4y0yK5ffS+Y8AIB4+Jv3y86nWhbKUqst52LVT6qOt62dqgAAAAAAAJByfneN6diKaAaeYldeeaV8+OGH8sEHH6T6oQEAAAoazcABAPnLH2WcGTQDz1Oq4dfDDz8sJ5xwQo7EDokjAgAA68jNZuDMvQAAuW9KBpuBE4lIkb179+ou9xUVFZJNdleV6TibAt5GXY+qV6Lz1pluYyvtF1p2EgAAIF/k4twLAIBsKqhA09NPP62XX167dq1eetDM9u3bZcaMGfLyyy9LTU2NXkpRLdt88803i8vlivrYV199tQwaNEj69+8vn376aRr/F/mn5sVj2t1GNQwXGoYDAAAAAJDXCqoZ+DPPPBPz/i1btuh07EcffVS++uor6dSpk6xbt05mzZolp59+ujQ1NZl+34svvijPP/+8zJ07V+z2gnpJAQAAAAAAQgoiKhIIBHTvpJdeeinmdqr0bceOHXLGGWfI5s2bZc+ePbpGsW/fvvLuu+/K7bff3uZ7VNbTZZddJnfffbcMHDhQss3v3mU6zjRVCqeylHqct0Mv3aguPX5S23LbwUvVuGVZ2z8AAAArzb0AAMgVli6dW7p0qc5ieuutt3RmUiyrVq3S2/fs2VOeffZZ6datm779+OOPl/nz58uJJ54oDzzwgEyfPj2shE6tMnfsscfKFVdckfb/Tz7R/ZYiSuHsxWUi0tVwQ6n5GAAAwASrzgEArJHnY8/4s7PqXIosXLhQnnjiibi2XbBggb4+++yzQ0GmoBEjRsjRRx8tn3zyiSxfvlxnPCkqAPXKK6/IBx98IPv379e3NTc3i8/n018XFxeL0+lM1X/HcuzOctMxAABA/qw6BwBA++yu7qZjK646Z+nSuRtuuEFWr14duvTp0yfqtqo0Thk3bpzp/cHbg9spa9as0QGlo446SsrLy/Xlqaee0tlRaqx6OwEAAAAAABQKS5fOqcCSMbgUK7to/fr1+nrw4MGm96sV5ZQNGzaEbrvkkkvk29/+dth2d9xxh2zcuFEef/xx3dsJ0fn93rCxpaOeAAAAAAAUAEsHmhJRXV2trysrK03v79q1pbfQzp07Q7epQFJkMKlXr176sU455ZR2n7OhoUHq6+uT3HORkpISfck7ntrwcUmXbO4NACBHqFVeo630mgh1nAUAAMgFfndN2Lilh7E1EWg6qLGxMSygFCl4e3C7VBgzZkxKHmfGjBkyc+bMlDwWAADZdtddd8ltt92W7d0AAABIIX+UsfUQaDooEAiEXZuuoiaiG33HMnfu3Lhf/GXLlslxxx0nyQrPZjK+pby9AID8M23aNLn22muTfhzVnzFVJ3WA6Jh7AQBgRCTioNLSUl3GtmfPHtPyuWAmU1lZ6tLbVMPwiooKSSW7q8p0nOt8+7eK2EtDq8/p/k3G0joRsZX2CwX8AADWlaqScHWcBdItX+deAACkC4Gmg3r06BEKNA0cOLDNC7Vr1y593b175pchLAS1i9o/49zj3I0indu+NwAAoDBMmDBBXC5X0ss3AwBQaGbPnq0vsbjd7pQ8F4Gmg4YMGaJXlPv8889l2LBhbV6oNWvWhLYDAABA5s2bN890ngYAACTpEzErV66U4cOHS7IINB00cuRIWbRokSxZskQmTpzY5oVavHixvj755JMll+VTJ3tVCqezlIJMSudUSV082U4AAADZkE9zLwAAMsGekWfJA+PHj9fXCxYskJqa1gmD8v7778vatWt176bRo0dLbsufTvaq35Kj88DWS1kvPTlTF0dJF31bUVnfbO8mAACAJeZeAIBcCb/Yxcqs/b9LgFr9bezYsTrIpDKatm7dGkodU/0AlKlTp7bbFyDXqVXzdu2PfXF7A6FLtFX4AAAAAABAfOyu7qZjK6J0zuCxxx7TJXSvvfaa9OvXT2cw1dXV6ftGjRqll1vOdX5f65m06ka/FNl9LV/YRCqcdqk+EJDr3wxfzS2We0ZXSYndZnpfRUlLnLKkSMRmM98maXaX+RgAAAAAAOQcAk0GKri0YsUKmTFjhixcuFB2796tm39PmjRJbrrpJnE6nZLrdje1jme+Xyt1RZ2Terx4glJ/PbOHuNL0SbI7K0zHAAAAAAAg9xRUoGnTpk3tbtO7d2+ZM2eOFIJo2UolDptctjS8T1Us1Qd8UbOelG6uln5MAAAAAAAUIr+7Nmxs5cUjCirQVGh+fVwX6d6tW1jpnFG0kjfVl0llKTV4/OKP0qKpyd9agtde1tP9p3WTnh38GfL7/WFjmooBAAAAAPKPN8rYegg0WY0hcNTV5ZCeZYlnEqngkyqFczmif69qFB4vnz+gm4zHEjXrydOaWeXbs0rEuAqd3RVWTmdzlKavVxQAAECBryIEAEA8CDRZTGVFT6kzjNNFZUOprKf9Hr/4ArEznqbG0ecpnqyn2kVjYt7fa1KD2CycfggAQKFTKwG3twLwlClT9CVTCmkVIQBA/po9e7a+xOJ2u1PyXASa0CHtZT0lkvGklBcn/0ZQWgcAgLXNmzdPhg0blu3dAAAg70yJ40TMypUrZfjw4Uk/F4EmpDXjSYkn62l3U0AamtuW1/kCh0r5D9aL3W4T1WIqrDTO7pKAp1ZqXjymtcyuJLlV9gAAAAAAQMcRaLKYuvpdYeNOrvKsZjwp8WQ9xW4o3hI8UoErlyO8B5PXn5rUPgAAgI7wu2vCxlZeRQgAgHjQsdBqAgHzMQAAANLAH2UMAEBhIqMJWS+va/T6xRtlUTpjeV31AZ+U2MMzmnyNrRO6AIE1AAAAAEAOsrtaF+syjq2IQBNytqG4Uuv2xSyvq/TVysyDY49fJAU9xQEAAAAAQAcRaEJOi5bpZCbgbRR/83499rtbe1UZ2Ur7SVFR9MAWAAAAAADoOAJNyGndXCL3n9Yt6v1N9fUii1vG+176uuxr5/G6/2iDFFUcntqdBAAAAAAgBr+7Nmxs5cUjCDQhp6nso54xfv52H7CJJ4HHo7wOAAAAAJB53ihj6yHQZDU2m/nYojzOvjKz6r/iF6e47RWh20v9rUsNd/Vtk1/vPSdLewgAAApnEWcWdAYAgECTxVRW9JQ6w9jqepYVyawzhpnc0y808jX0FHmlZbx9v00qisMbP5UWizjs9tBKeKpJOQAAQDzsru6mYwAAChWBJli6tE7Z6e0q/oPj21aJeGy7o2771zN76JXwAAAAAABA4sjvBQAAAAAAQEqQu2Ex9Q01YeNOrnIpdN1KRKoPjv94fIOII5jfFGSX33zQMvqyvotUlDiiltYplNcBAJAdEyZMEJfLFXObKVOm6EumFNIqQgCA/DV79mx9icXtdqfkuQg0WYzf5zMdF7RmQ6ncq98w3eSPB69/885G8dhiTxAprwMAIDvmzZsnw4aZ9WbMpsJZRQgAkL+mxHEiZuXKlTJ8+PCkn4vSOSBBbm9kRhQAAAAAADGCL66epmMrIqMJlmcr7Sc9zt0Y9X5f8wGp/ffRenzPqG5ic5S1KZ3b0xSQ699sSY3ftt8nHkOymKtIxOmwh4JQ6r5urpZG5QAAAAAAFBICTbA8HfDpPDD6Bvs2hYbd7LvFUVbRZpPG5tbI0m3v1bX7nPef1q3d1fAAAAAAALAaSucAAAAAAADSyO+pMx1bERlNgIFv/9bQj4bdVRW6vbJ5p/z5hEDLF536hpXFBUvnqg/4QuV1NW6faWmdx+sXdReldQAAAABQQPwe87EFEWgCDGoXjWn39VD9nhxlbUvxSuy2uMvrKK0DAAAAAFgRpXMWU9mll+kYAAAAqVdIqwgBABAPMppQ8MxXpQsvnWvevUpqF42K+VqpcjiVqRTJrLQOAAAAAAArItCEgtfuqnRq5bqyQ+N6nFgrzRlL64I9nJxFIq6I/k1B9HECAAAAAOQbAk0WU99QEzbu5CrP6v7AXHs9nBT6OAEAkPv87tqwsb04xlknwCICgYA0GU6Qev1+aWyOvr3x5GrktpxcBayHQJPF+H0+0zFSJ+BtFH/zfvG7VVDPH7Ucz7gyHQAAaFVfXy+//e1vZcGCBbJz504ZNGiQ/OpXv5JLL700D18mb5QxkD0+n092u1u/jpVFn0hQKKjJH0hZS4gZIyuluyv+5w5yFImUHtzW7/dLQ5RtCWQBmUegCUiwb37Ni8e0u7Xu+RRRjmfWwynyoP/lfl9c2U4AAOQzFVB69dVXZfr06TJw4EB57bXX5LLLLtMnaS655JJs7x6QN4GkYC/QyACSatOQL3PKdO/n7d+slL6dowekCEQhU+wFtHgEgSYgDnZX96Rfp/Z6OLkcRdLdcHZpX7NPKryxz+hwYAQA5Jvdu3fLc889Jw8//LBcfvnl+rYf/ehHsmHDBvn73/9OoAmIKE1ze/3iichAyqVA0j2jq6RriS2h0rkt+zK3/7e8UxdXRlWRTaTM2Xpyub6ppXKB+TaQOAJNQBxsjlLpNakh7LbI0jnf/q8MK9O1HqQ66ubl9HECAGTf008/LZMmTZK1a9fK0KFDTbfZvn27zJgxQ15++WWpqamRfv36ycSJE+Xmm28Wl+tgTcxBtbW18u1vf1vGjBkTdnv//v3l448/Tuv/Bcjl8rZ0lqb1KStKaemcUTAQU+6Md4+KZGil+WrNqSqdSyQQ1952xtI+vfcRAamSIhGbrXXRHwAEmoC4qIOHLaK5Z2SzT2P6Y8DnFu++TdEfjx5OAIA88cwzz8S8f8uWLXLSSSfJjh079NeVlZWybt06mTVrlixdulRef/11KSkpCW0/ZMgQXTaneDweHZh6//33Zd68eTowBVg9kFRaLOKw26X6QGqCSSqjqEenoqgBpMiMHJVFXxH3oycSQEpdpn/iz10kpU7zlhVmAalUBqLU629cXdqIbCgY+T11YWMrLx5BRhOQBjX/OjLhHk6RB8VoZ3SMB0Z1tsvtDVBaBwBIS+nOI488Ii+99FLM7SZPnqyDTGeccYY8/vjjOjNp1apVMn78eHn33Xfl9ttv10EnM48++qhcddVVenz22WfL1KlTeSeRt4GkYAApWO62LUW9N+8/rUqdpox6f0sww5ZgAMnaogeyWgJSZr1TjZlK1Qd8cQcBY23XEgQMhMrwIhGIKjB+j/nYggg0AXlyUDQ7o9PeAVAdQNs/WwQAQCuVhaSymN566y2dmRSLCiip7Xv27CnPPvusdOvW8ofb8ccfL/Pnz5cTTzxRHnjgAd30O7KELtib6cgjj5QVK1bogNQVV1yhg09ALgWSIgNIwX5JqeyT1FKeFb5icbDRN6VZmc+o6lNqMy3t099rU1lSNrlsqWqjEVt7c/XbTq6UARWGvlAev0ig9X4CUchXBJosprJLL6kzjJGdPk5+d22bJY4D3sbQinVWX2UAAJC/Fi5cKE888URc2y5YsCCUjRQMMgWNGDFCjj76aPnkk09k+fLlOuMpUu/evfVF3VdcXCy33nqrPPTQQ+Jw5M8UtZBWESo0Ksj0qzd2pzyQZBa4IqCQX4EolfH51zN7mGYqJdJfa8a7sQOVnDRGvsqfoziQR32czOpt/c37U/I8wVTfeErrAABI1A033CAXXXRR6OuzzjpLtm3bZrqtKo1Txo0bZ3q/ul0FmtR2wUDTU089Jb///e9l5cqVOrhk7N3U0NCgG4v37duXNw4ZXdnNbHU3NadKJJDUr3ORaeaTEi2QpMrdkJ/zfpfD/P0LBqGMjAGpRObq+jNoE6lwmmc9EaBEriLQBFjiDIt5aZ06o9Lg8cVcsYMDFADAqE+fPvoS5HRG78a7fv16fT148GDT+wcNGqSvN2zYELrtsMMOkzVr1sh7770no0YFV2sVeeONN6RLly76fiCVgaRGr1+8JjGjRDJPgg23IwNIsRpuo7CDUEbGz4OxP5TqIV4eEUSqOdAaiEpoRbyIgJRC2SWyhUCTxexrqA0bd3KVZ3V/0KqlnK5F856PpajTIWEvT2S6vSrF68hSqSrTKSieyRMpuQCAjqqurg6tNGema9eu+nrnzp2h21RwSa1Sd/7558tNN90khxxyiO4HpXo53X333TGPfSrjqb6+Puk3TK2CZ1wJLxmFtIpQvlFBpp8vbvmMJkutKuZytDTcBtJVlqc/X4YeTe1pLxClMqsig16wpqamJn1JljrOpgIfO4vx+ZpNx8gFrT2bal/+Zrtbq35PwVK8RATL6QAASLfGxsawgFKk4O3B7RS73S4vvviiDjKpEro9e/bohuCqL9TPfvazmM83ZsyYlOz3jBkzZObMmSl5rEJaRSjXV30zy1iKl8pY6lxsM81WCmahAJnQZkW8iEylzXubZUacpXeb65ulSqU1GVSUtD4WGU/Wcdddd8ltt90muYJAE5CjOnpWVB0wjHXhxv5NQfUen0x/p65NBhQAAImWJhmvIwVLiVRAwKhXr14yd+7chF/sZcuWyXHHHZf0m5SqbCbkT7NuFUhSWUnR0EoA+dKIfEhlkfmKeAcDUtUHfKGqhvaajZPxlGHO7ubjFJg2bZpce+21ST/O6tWrU3JSh0ATkCG20n7S49yNUe9XpXO+hs2hlek6ela0bV142/5NxjMZ+5oD4on4AyBIZe+WsqwuACCK0tJSXcqmspLMyueCmUxlZakpJysvL5eKigrejwLRXsaSL4GMJdVfSZW+AVYPRMUKqEZSQSmz7YN/K5DxlFr2g4sFRI5TIVUl4eo4mwoEmqzG3xow8NV9JR7jB9jhFEdp6yTQ5uxYDyB0jD6r23lgzG38jtKMv7zx9HHibAcAwEyPHj1CgaaBA9se43bt2qWvu3dP7ZlbFIZUZCyVF7f8QRdRPQQUTuldhGZ/QK49OP9v7+8A/gZARxFoshhf/Q4JHke/nH2e1O9tmeCZOXJOg9hKaFiZq3z7t7ZpEq4bjhoynVSWlNlSuQAAZMKQIUP0inKff/65DBs2rM39anW54HapMGHCBHG5YjfLmTJlir4gvzOWVEZFIj2WyFgC4st42rU/SiOyBDKegig5TYzfUx82zvTiEbNnz9aXWNzuGGmkCSDQBOSo2kXt18bqUrx2sqRi9XEy69/UkSV/AQCFaeTIkbJo0SJZsmSJTJw4sc39ixcv1tcnn3xySp5v3rx5pgEtWD9j6fZvVkqXiMaSwWwlhYwlIDUZT8a/Adr7W4DVqxPkd5uPMySeEzErV66U4cOHJ/1cBJos7JALH5A+A05ovcHhFP/+Otk4vaUHkLd+lzh7JB6kQO5lPalmcsGJlo6UH/zFFS3jqbWPU9v+TUGNhmVX1JmPyL4Gwf5NCvXbAFCYxo8fr1dvW7BggdTU1ISVyL3//vuydu1a3btp9OjRWd1P5L++nYvpsQRkIOPJ7Y0/k9DY9xUwItBkMV17D5VgQl6PoaPFWdEr7H6Pt7Xsqrn2YJBCsTvEUV4V+lIFoZSiKkqzst0wPLJ0zrdvQyjbqb2sp45mPCkNhkynG96mfhsA0JZaAW7s2LHy6quv6owmtZJc37599RlRVeamTJ06td1yt3xmPE4bx0jMjJGV0t1VFPUPWDKWgMwIVj7s9/jFZxJzouoB8SDQVMC+uLP90qzB92yUIrKecqZhuK7j7eBqdAAApMNjjz2mS+hee+016devn85gqqtrWVJ71KhResnlVKFHk7UUGZKle3Uqkm6l9J0Esi1Y+eBS5QtJZjwht9CjCUBCWU/G0rnmPWuk9uWRaa/fVksKTz1Yt21WWqcmj2XO1rORlNcBgDWp4NKKFStkxowZsnDhQtm9e7du/j1p0iS56aabxOmMUqPdAfRoshbjPME4BpC7VKaTcRwtIIXcQ4+mPBMIBOTBBx+Uhx56SDZt2iQDBgyQCy+8UK6//npxODKbNFZf82XYuCyidE6VwqkspTYMpXOe6s2hPk7Iv6ynok49M75iRXuldQrLowJAflJzm/b07t1b5syZI4VIrwhrGGd6FSEAyCRjOZ1ZaR2gUDqXAvfff79cc801cvXVV8upp56qz+qpxphbtmzRAahM8jXvlyLD2Cx40F4pnL2kNE17BwAAYDHGknbK2wEAINCUCvfcc49ccMEFcu+99+qvzz33XDnkkEN080sVcOrZM38bQ3rrtoujonX/vftqRAKt6ZIKDcMLU3uldap0rtHXujxqfROptQCA5NCjyVrU3MA4pgQHgKU5u5uPM4QeTWn09NNP654BarndoUOHmm6zfft23Wfg5Zdf1kv1qt4DaiWVm2++uc2qKQcOHJCtW7fK6aefHna7WsZXldR9+umneR1o2nz7Ke1uQ8PwwtReaZ3iM5TX1bh9Ufs3KfRwAgC0hx5N+UXNhZtaD/9ag8cv/kDbuQGA/KN+hsuLJdQrttHrF68v/MS0bvsBLfg6RY4zhR5NafTMM8/EvF+Vu5100kmyY8cO/bVaOWXdunUya9YsWbp0qbz++utSUlIS2l71YPrwww9l8ODBYY/z5ptv6utDDz00Lf8PICpDdDzgbxbvvk3tNhfPxAHgtvdae1iYoYcTAADWooJMP19cne3dAJCl+b2qfmjvxDSsyVFIZ1QeeeQReemll2JuN3nyZB1kOuOMM+Txxx+X/v37y6pVq2T8+PHy7rvvyu23366DTkHFxcUyfPjwsMdYtGiRzn4688wz9aor+SasYbihSbixdK65dqt8ceeY7O0kojJGx2teMM/aM9Ir2MVoLp4ppMwDAGDd0jgAhSeYtRgt66nQMp78nvqwsZUXj7B8oEllIaksprfeektnJsWiAkpqe1Xq9uyzz0q3bi39Z44//niZP3++nHjiifLAAw/I9OnT25TQKbt27ZJp06bJ3Llz5YQTTpAnn3xS8lGshuHOEuv+MBQsZ2sgMVN9nIKlc9UHfKEeTgAAwLqlcfeMrpIenYrC7o+cMwDIfZHz+8gg0o79vlCmExlPEfxu87EFWT7QtHDhQnniiSfi2nbBggX6+uyzzw4FmYJGjBghRx99tHzyySeyfPlynfFk9Pzzz8vll1+uezbdcccdcv311+tsJ8uy2c3HyDqbo1R6TWpojZqb/BILeBul5sVjWsaNX4rXEyXY46wSu92hH9Nms6W8j1OJvfUxg5NRdVO5oX+T8WxooZ31AADAKqVx6pjvcqgLx3Egn8Wa36ufb2OPJhQuyweabrjhBrnoootCX5911lmybds2021VaZwybtw40/vV7SrQpLYzBpqee+453Sx87NixOqiVzb5MZd0GitswThdH59Y+QH5Po3iqo/cBYlW6zFIBIdvBNMxo6Zj+5v2hcTDgFIsKXAUfM13aO+OhUOcNAIUtJ1edy/IqQtlU76E0DkD7FQ3GrKcv9zXL9Hfqwk40lxaLOA7e7/b6xVOgpXXpxqpzKdSnTx99CXI6nVG3Xb9+vb6ObOwdNGjQIH29YcOG0G3Nzc1y1VVX6VXn1Cp12f5BUP8/dxz/11TaOO2omPezKh0AALDqqnPZXkUo06VxOrhEaRyADlY0VDhb/16mtC6zWHUuS6qrq0MrzZnp2rWrvt65c2fotvfee09/38iRI3V/p0iqr1Pw+woWpXU5W17n9zSI+BtNtzGW12XyjEdk6dzmvc0yI45sJwAAkFqUxgEAOsLypXOJaGxs+YM7WmAoeHtwO0WtUKeovkxm3njjDRkzxnx1toaGBqmvb+0831ElJSX6otTXbg/drsZlFb0kHWzOUjlyTkPYSnRG/qZG2Tj9mDZldsit8rpYKx1497cGVFVAKh2rIrR3xkOpcrWe9aht8klFid30rGoQKbZA/mtqatKXZKnjLJBu+byKUGTGUuRxVR13ASDdJ5qNpXOU1lkDgaaIg63xOlKwLM7naz3onnfeeVG3b0+0AFSiZsyYITNnzmzZt6Z6Cf5ZrsZpDVQcXIHObCU6f1NrDyDkKWOmU5Ssp0yb8S59nIBCcNddd8ltt92W7d0ALL+KUCIZS8FV48xO8iisGgcgHpTWFQYCTQalpaU6w2jPnj2m5XPBTKaystScqVq2bJkcd9xxST9OMJsJSBff/q2tXzgqxe5o6f8VLL2zlfbLen8yANYxbdo0ufbaa5N+nNWrV6fspA5Q6Fg1DgCS5KwyH1sQgSaDHj16hAJNAwe2XbFt165d+rp799SUgpWXl0tFRYVYkbehNmxslvWE/FG7KPYfaj3O3SjSeWDa02sj+zcFz6qqFSviWbUOQH4wloQne5yFteTkqnN5rMGwatyfRldJcZHNNFtJIWMJQCZYubTObneYjjOFVeeyZMiQIXpFuc8//9x0RZM1a9aEtkM7/N7QsLlmU9jXjoqe4QEpv1eKqsiIQeLptS5H2wNHkz8gbm8gZmp/Lh1wAAD5vepc3q0a19QaXAr+kaYU223Ss5TjI4DsorQufVh1LkvUynGLFi2SJUuWyMSJE9vcv3jxYn198sknZ2Hv8tcXd7ZftjD4no1S1CM9GTHoGFUOpzOVIh0snfM1bA6tShe1tM7bqKKJocdLW4DH1jq8/s3WbDoz6gxJew3IAQCwgkR6MAEA0suv2o4Yxvm0eESiKJ0zGD9+vG6qvWDBAqmpqQkrkXv//fdl7dq1unfT6NGjs/FeARmlg0IxyuH8jtK4S+vSXV5XYSinAwAAbUvjAKDQSutyjj/3FltKFwJNBqox99ixY+XVV1/VGU1z586Vvn37ysqVK3VPAGXq1Knt9gaA6FI4laVk+qE7WDrnqd4sG6e3ZMQAySgpEvnrmT3CbjOWzhl7OAVL69Tk2x+jDwXldQAAK5XGhVaNM9xvRA8mAFYrrVO/A6P1b1KY86cPgaYIjz32mC6he+2116Rfv346g6muruUP1FGjRumVcHJZWbeB4jaMs/kLoL1SOHtJa0YMrFdapzTXrZXahSeEl9c5KsTucKW0tM5ms4nLEaN/UwKldQrldQCAuDm7m49zrDSOVeMAFJr2Fgtizp8+BJoiqODSihUrZMaMGbJw4ULZvXu3bv49adIkuemmm8TpbPkDOlep/QsGmnJ9X2Ht0jq9jasqJ1auo7QOAJAu9oNnyiPHmbCf0jgAQA4quEDTpk2b2t2md+/eMmfOnIzsD4DslNZFls7Vun0yo52zHgCA7FKtDNprYRDPqjpW4TMcxyiNA4C2PZwiS+e27W9tqVFoZs+erS+xuN3BtJXkFFygyerqa7eHjcsqemV1f1DYTMvrDKVzxtK6tO5He6V1KuuppPUsdLM/ILv2RzS9iNhWBa/U4wIAMmfevHkybNiwnHrJ/Z76sHGqVxGK1YfJ2IOJ0jgAiN3DSc3/jT2aGrw+qfAWTv+mKXGciFH9qYcPH570cxFoshhfU70UGcb5wt/UKJ49X4l4PTEbjFvth73Qy+uMpXW6h1NY/ya3iDf8M5xMH6dEXBtHHyeVIRUZvAIAFCC/23ychT5MAID4TXs7emYT/ZuSw59JyJrg6nNKPKvPqVXs2mswjvzVXg+ndPdxAgAgF9GHCQAswlllPrYgAk0AENHHKbJ/U1CTPxDXqnUAAGS6D5Mq8wAAxN/DyVUk4nRkrn+T3e4wHVuRtf93yGk2Z6kcOadlaXvF21jXpnROldQFs52MGVCwaA8nk9I5VVIXzHbyuWvF7uoZtbQumfI6Yx+nyP5NQW5v62y/+oBP98OIFOz1RA8nAEA8fD6f7HaLqENP6cE/ePx+vzQ0t9xPHyYASH8Pp1j9mzxevxha4lm2h1MqEWhC1qg/7G0lrT/lTsM4yN+0P8N7hVzp4dTSTLV1xQglnsbh6SyvM5YvtJfZRA8nAEA8VJDpV2/s5sUCgDzp39TRHk5+T0PYONWLR+SS1mWWAABxly8AAAAAQNz8jeZjCyKjCTlNl9MZxmZZTyig8jpHudgdpW1K54zldZmq6TZj7OEUrbSuvFjVZFNeBwCFIpHSuGAPJuP9RvRhAoDM928Kls6p39fp7OFkJQSaLKZT177iMYzznrFnU0T/JhR2eZ1ZaV0m9iVWiuzuxtY/FuJpGk55HQBYfxWhRErj1AkKl0OdpCiSUmcqdxIAkEz/pgqTE8yqf6tZ/yal0Hs4EWiyGJerNBRoUmMraa7dGhrby7uHskK8jfUiXrcUVXWsCTQswFFuPs4wSusAIL0mTJggLlfs5dWmTJmiL5lSSKsIAQDiO6l8fwd6OKXb7Nmz9SUWt9udkufiaIi88cWdsUujBt+zUYp6pKcJNHJbsJwucpxp7ZXWKT5/QKYePDA1ev1RV7gDALQ1b948GTZsWN6+NJTGAQCyJZ4TMStXrpThw4cn/VwEmiymoW5n2LisoldW9wfINF/DZvGbBZvspWJ3lovNUapXPMxGaZ2ya39rbu2O/T7x+sz7NyklRS2rMwIAclciqwhRGgcA+St4UtlZpMrpwvs3KfRwakWgyWKaD9RJkWGc71Q5nMpUihQsnfNUb5aN04/Jyr4hd/gN/btqXoz9eeg1qUFsJn8EBAIBCXhbVn/w+70intp2m5QnW6rZXjNBejgBQB4ooFWEAKCQmZ1UNuvfFOzh1ODxSaNhYQdfoz809vtax1ZEoAk5/8McqxzOXmKtPlToIG9d0hlPKsjUXpDKSK+EZ9KkPJUorQOA3Gf8Y6G60S9Fdl/Y2e4DXmv/MQEAaKF+98fq4eTyeeX3B8e7A12kt4VfOAJNAPKeyi7SgZ8ojEGkRIJJMdlLU97HSZXO7W4KhA5MxrI6AEBu2t3UOp75fq3UFXXO5u4AALIkeIIhGr+9dTlRu2FsRQSaYBn+pv36YlyJLhIr01mTLmGLkV3kb275XMSr+3lfic1v+MvBJGCl+j2lo49TQ3NrdEnVeQeVFos4DvZvcnv94jl4V6EvnQoAAADkAtVfVbW+CPL6/WGlczUNxSKvSEEg0ATL2Dj96+1uw8p0hUk1AFe9mXQvp1hldu00DE80YJWs9no45erSqQBQqH59XBfp3i28UazxDw11cgAAYE3q7wdXWISlSMoNiUtFzQck2FnWEVALSZh1d7IGAk0ACuKXvmoA3rISUNcOP45uEm4Yx06OBQAUmq4lKms1Mss0/A8NAEBhcok7FGhSYysj0ATLrEoXXIkusnSuuXarfHHnmKzuJyzCsBKdb89HImV9W++zu8TubD0rES0rqqM9nIylc1/ua5bp7+T/qpIAAAAArIdAk8V06to3FCVV40Jdlc5ZQi0R0qt2UezgpSrVU1lUqe7hpFQYl7QAAGRVaVlXaTSMAQAw4/UHTMdWRKDJYlyu0tZ0PFfHVsWyGntplekYSCe/35+R0jq1bLbb27ZJuBENwwEgfZwOZyjQpMYAAJgxNgZX4y5iXQSaYHl2R+vH3LvnS/E3tpY/RWJVOsRiK+0nPc7dGOWD5pKApza0Kp14akRK0r/E9W/e3tPuNjQMBwAAAJApBJospqFuZ9i4rKJXVvcn12ycfjAIEAWr0qG9sjbp3LZUM8jrb7+pn8/nk0DjFhG7U+zOytDtfvcu08CWfs4kNbaT9UTGEwAk8TvWXR827uQq5+UEABQ0Ak0W03ygTooMYySG0jqkmwoyVc8/PK5tdfaUSWDL2DDc2CTcGESqcfvktvdafgfc2E7WExlPAPLFhAkTxOVyxdxmypQp+pIpnqYDpmMAAHLJ7Nmz9SUWtzs1q+ERaILl2ZylcuScBvG7G8TvCXZRaOVvagxlOhnL7IBcFa1huMtBk3AA1jZv3jwZNmxYtncDAIDEObuYjzMknhMxK1eulOHDhyf9XPxVDctTS8zbSsrEHmUlOn/T/tDYU71Z7CVtm6jbSyvFfrDBpwpcdWTZehQW34FdYnf1DDUG1z2b1O37t4a26T5+jRSVDwgrnVP3t7eiXTzay3oyZjwBAAAASC+73Sl+w9jKCDSh4KlMp3h7OCkqO0oFroBYal8e2e4LZHOUir245bNkL47e+6kjyHoCAAAAkA2ZWH0byGlm5XRA1hjPblj8TAcAAABQKByBRtOxFZHRhIJXVNVPrzYXi9/XLBtvHNoydjdELcNDYVOrxOkG3ordJXZnRZvSucjtIxlXojOOU02V0wX5/AHZtT9iKboIrEwHAAAAdJxLGsVjGFsZgSYUPFViVNQjdtmSp3pTaEwGFGJ9lsxWidOpoyWdc+qFC/ZsUqa+Wdvu9veMrpLOxb7QqnZmCEYBAAAAINBkMZ269g5FSdUYAFLh+jiCUar5uNlqeABgZaWlXULnpdUYAAAzXn/AdGxFBJosxuWqaE3Hc7WU7SC1mmu3tlmJzt/UKP6mhrByPJ3dAiQouPpccJzqJuFBJUUifz2zR9hKdJGa/IG4AkwAUMicxa0FEGoMAICZxubwsZVPTRBoAhL0xZ3tLz2vej61V44HZJPNZhPXwSOAy2EeFA0EAqFgVLSAVIPXJ9Permt5HGKrAAAAQMEj0GQxDXU7w8ZlFb2yuj8ArBGMihaQqvC29npyOljIFEDhaXTXh407ucqzuj8AAGQbgSaLaT5QJ0WGMdK3Ml1k6VzT9v8LZTup8jq7q0LszpYUekrrAACwJk/TAdMxAACFikATkIKV6ewlZWE9mtorr6O0Dlbh8fpD4237m6U8IuvJWaQyoeyhvlAqSwoAVGnugw8+KA899JBs2rRJBgwYIBdeeKFcf/314nAwPQUAWJCjs/nYgjiSAwA6zG3o2RTs1RSN6vdkLMUDULjuv/9+ueaaa+Tqq6+WU089VVasWCEzZ86ULVu26AAUAABWY3e4xG8YWxlTfiBN5XWRpXPG0joAAArZPffcIxdccIHce++9+utzzz1XDjnkEJk6daoOOPXs2TPbuwgAADqIQBOQgfK6yNI6IL5fy7n/K7qbS+T+07pFvd/nD8jUN2vjKq1TKK8Dcs/TTz8tkyZNkrVr18rQoUNNt9m+fbvMmDFDXn75ZampqZF+/frJxIkT5eabbxaXK/ys7YEDB2Tr1q1y+umnh90+evRoXVL36aefEmgCAFiOI9AoHsNYpEKsKvf/igEsqKU5+H7xe9ziN6xWE5khpYJXKCx2V5XpOFepz2jPsuj379rvi7u0TqG8Dsg9zzzzTMz7VbnbSSedJDt27NBfV1ZWyrp162TWrFmydOlSef3116WkpCS0verB9OGHH8rgwYPDHufNN9/U14ceemha/h8AAGSTS1oDTWpsZQSagCzYOP2YdrehYTgKkdevKtcJsAK5QGUXPfLII/LSSy/F3G7y5Mk6yHTGGWfI448/Lv3795dVq1bJ+PHj5d1335Xbb79dB52CiouLZfjw4WGPsWjRIp39dOaZZ8qQIUPS9n8CAADpR6DJYjp17R2KkqoxAORyaZ0qndvXHJDrD5bXNTaLlDszuIMA2lBZSCqL6a233tKZSbGogJLaXvVUevbZZ6Vbt5af9+OPP17mz58vJ554ojzwwAMyffr0NiV0yq5du2TatGkyd+5cOeGEE+TJJ5/Mu3ektLRL6Ly0GgMAYMbrD5iOrYhAk8W4XBWt6Xgu69Z85nOz8LAm4RGlc821W2kYXuD87l1hY3uxed+vfNFeaZ3i8RmWrgOQdQsXLpQnnngirm0XLFigr88+++xQkCloxIgRcvTRR8snn3wiy5cv1xlPRs8//7xcfvnlumfTHXfcIddff73Odso3zuLWAgg1BgDAjDqhahxb+dQEgSYgi83CVZNw6dw6MbeXlIfGfl+zeKo3xX5M+jgBAFLshhtukIsuuij09VlnnSXbtm0z3VaVxinjxo0zvV/drgJNajtjoOm5557TzcLHjh2rg1r0ZQIAwDoINFnM/r01YeOyil5Z3R8kxl5SGhpvvNF8ZR8j+jgVJp/PJ4HGLTG2sIvd1T30ld+tytK8Ube2ldJ4HkCrPn366EuQ0xm9nnX9+vX6OrKxd9CgQYP09YYNG0K3NTc3y1VXXaVXnVOr1OX7wheN7oawcSdX60kjAAAKEYEmi/E07g610VVjWJsxAwqFQwWZqucfnrLH63HuRpHO+V2iByA7qqurQyvNmenatau+3rlzZ+i29957T3/fyJEjdX+nSKqvU/D78oGnab/pGACAQkWgCcghNmepHDmnwbR/U5C/qTG0ap0xAwqFxC5W1eQPiNsb0KvPGevYjZx2EUeRTRx2m/7aHwhIs6HNU2eniN1u3dcIyCWNjS3diaIFhoK3B7dT1Ap1iurLZOaNN96QMWPGmN7X0NAg9fVtj42JKikp0RcAADLG0dl8nAJNTU36kix1nE0FAk1ADrHZbGJTfZtM+jcF+Y1nTqs3mwab7M5SsbvKdeBKPSasxVgW1338GrE5ShMunQt4G6XmxYMBS1dPyRXB1eeS8cjp3aSyU0p2B0A7AoFA2HWkYFmcKvkNOu+886Ju355oAahEzZgxQ2bOnJmSxwIAIB52h0v8hnEq3XXXXXLbbbflzBtBoAnIM35vcF1BCWU2RaOyo4KBK1hTUfkAsRfHfo/N7vc3twYsfQ2bxd8mWCVtglB+T536J+d7PXmCR3AAaVdaWqozjPbs2WNaPhfMZCorS82xaNmyZXLccccl/ThkMwEArGTatGly7bXXJv04q1evTslJHQJNKfLqq6/K7bffLh999JEcddRRcuONN8r48eNT9fBAiL+xjlfD0lp/LQd8HvHu+0K9622ykVIpmNmUrV5P3Vwi95/WTZxFIi5HS8lbZOlcky8g17/Vku30/YGd5IeDy3Tp3JcNXpm+fE9K9h9A4nr06BEKNA0c2Pbnf9euXfq6e/fWLMtklJeXS0VFRUoeCwCATLIH3K0ZTQG3iKTueJaqknB1nE0FAk0p8M4778jZZ58tP/jBD+TBBx+UV155RSZMmKBXUlHL9gKpVFTVT682F42xh1N7pXUK5XW5xe6qCo1r/nVku9v73TXtZjTlOpUF1bPNf6FIyg0LXakym7+e2UOPVSwq2J/p8C4OfXt1oy8UiAKQOUOGDNEryn3++ecybNiwNvevWbMmtF0qqPmVyxW73GDKlCn6AgBALimVBqk3jEUy275i9uzZ+hKL260CYMkj0JQC9957rxx//PHyj3/8QzegPf/882X79u36dgJNSMcf5UU9omeNePfvibu0TqG8Lt91rE5M9XXqNanBtH9TIqVzmer1pHqNuUyOWPaDt5cU2RJqKK6yqHKhzA/Id2rluEWLFsmSJUtk4sSJbe5fvHixvj755JNT8nzz5s0zDWgBAABJ+kTMypUrZfjw4ZKsggs0Pf300zJp0iRZu3atDB061HQbFSRSTSJVRlJNTY3069dPT55uvvnmNmfRPB6P/Pvf/5Y//vGPYasc/fSnP5XLL79c9u3bJ507p7ajfCwlnXuE/mRUYxQeSuvyWzAAZMxYihVMUn2ROtx4vrgsoWyo9no95VNDcVWq1zaLCkCiVJsA1VR7wYIFes5kLJF7//339XxL9W4aPXq0ZV9cV0lncRvGAACY8fsDpmMrKrhA0zPPPBPz/i1btshJJ50UWnpXTY7WrVsns2bNkqVLl8rrr78eVvu4c+dOHWxSfZmMvva1r+kVVrZt2xY1oJUOpeVVrel45a0lOCgc7ZXWKX5fs2y8seVz6fd6xc4KzzkjGAAKyveyOADWphpzq+xt1atSnZSbO3eu9O3bV58RVWVuytSpU9std8tnLldpa6DJZb6wAgAADc3hYyufmiiYQJPq7/HII4/ISy+9FHO7yZMn6yDTGWecIY8//rj0799fVq1apc/Yvfvuu7rhtwo6GQNNSteuXcMep6qqKtQEM5OBJqC90jrFU70pNG768iPxV/U17d+kglD+xlodvKLMCGZ0OZ1hnK3AWDwNxWvcPrntPZrpA6n22GOP6RK61157TWeBq5N0dXUtP2ujRo3SK+GkCj2aAADoGHo0pZDKQlJZTG+99ZbOTIpFBZTU9j179pRnn31WunXrpm9X/Zfmz58vJ554ojzwwAMyffr0ds/M+f0tpS5eb/TeJ+mwf29N2LisoldGnx/554s721++UmVItRe8QoEy9myK6N+Uaw3FHUXmYwDJUcGlFStW6LYDCxculN27d+vm36pVwU033SROp+EH0YI9mhrdDWHjTgdP1gAAkEvo0ZRCasLzxBNPxLWt6i+gqBXkgkGmoBEjRsjRRx8tn3zyiSxfvlxnPCm9erUEcmprw3uCBL/u3bu3ZJKncbcUGcYAkE2qhDjQuEXEWSV2e0sSrd/ToJqJRe05la7sudKDmU6RYwCxbdrUmgUbjZrvzJkzpyBfSk/TftMxAACFyvKlczfccINcdNFFoa/POuss3TfJjCqNU8aNG2d6v7pdBZrUdsZAkzpTp27/zne+E9r2008/1c3B+/Tpk+L/EZDePk7B0jlP9ebQqnXNtVtbN3C4xFFaEcrc8zfUUFqHqFSQqXr+4XG/Qj3O3SjSmew5AAAAWIyjzHxsQZYPNKlAjzHYEyt9e/369fp68ODBpvcPGjRIX2/YsCHs8VQGlCq1+/Wvf62DS6oflPpaBZ4qKlr+IAfyrY+TvaQ07vI6SuuQD4Ilza1j6ucAAACQGXZHaWgtaTW2MssHmhJRXV2tr1UTSzPBht/BBuBB1157rZx++uly/vnn66bhqlzvzTfflEWLFmVgrwEgP3Qfv0aKyge0KZ3z7d8qtYva7xWW6pU+SlPXNgZAhtAMHACAjqEZeJY0NjaariAXFLw9uF3QN7/5Td3f6Xe/+51cccUVupfTCy+8ECqvi6ahoUHq6+uT3u+SkhJ9ATJSXnewdM5YWgfEw+Yo1avStVmZzlllPkbBampq0pdkqeMsrCUXm4EDABAPe8DdmtEUcItIZqufaAaeJarkzXgdKdigVjW3jTR27Fh9ScSYMak5g69WeZk5c2ZKHguIt7zOWFoX6uFk0r8p9HhV6WvyjPwWbBIeOU6nJl9A3N6AFNlEitU/B3/3Nx389V5SJGKztdyOzLvrrrvktttu46UHAACWUSoNUm8Yi/QUq6J0zqC0tFRnGO3Zs8e0fC6YyVRWlprGXcuWLZPjjjsu6cchmwnZ1l4PJ4U+Thbl7G4+znHXv9WyMuipfUrk0q9X6CDTPk9AfvVGy2qd94yqkpKDAShFDcucrSvV1Te19nuKZNyWgFXHTJs2TZelJ2v16tUpO6kDAACA+BBoMujRo0co0DRwYNtMjl27dunr7t1T88dUeXl5ypuFl3TuIV7DGADSSS2AYDZOhO7ZZBi3Ka1Lo7e3Ncnb21r685kFopL11zN7iIsjbdZKwtVxFkg3V0lncRvGAACY8fsDpmMrYvprMGTIEL2i3Oeff25a/79mzZrQdrmqtLyqNR2vnF4nyHAPp4jSuaYtq0LZTrq8zuEUR2lrtqC3flfY41FaV6AMjcHDxilmSEgCkKdysRm4y1XaGmhyWXsVIQBA6ham6ZzhF5Nm4FkycuRIvVLckiVLZOLEiW3uX7x4sb4++eSTs7B3QJ71cFIxg6q+cZfXUVqXn/ye1gUNmveskaJO4bXmAb/hiGqP/cdhMlTvvEDjlvAbHeWhpWP9XreUefbKQycdvK9TXykuLpJiu6E/kzcgnigVcYmUzvn8AZn6ZmoyogCEoxk4AAAdQzPwLBk/frxuqq1WkKupqQkrkXv//fdl7dq1unfT6NGjJVc1NtSGjcsqemV1fwBYnD94Hl+k9uWRMTe1OytSE0hyVIjd4QoFkMRbL779W6V2Ufy9eHqcu1EcTmOg1CadiuPfH5cjemP7Xft9YQGpWNsCyH9ud2PYuJOLkk0AQGGjdM5ANeZWK8e9+uqrOqNp7ty50rdvX1m5cqVO1VamTp3absp2NjXtq5Yiw1jka1neIxSyNuV1EaVzBzaviKuROKCCTNXzD+eFAJBz3E37IsbWXUUIAIB4EGiK8Nhjj+kSutdee0369eunM5jq6ur0faNGjdIr4QBITXldsaG0zrt3hzgqepr2bwo9Hn2cco6ttJ/ODorK2T3UJNx2sIwt3arGLZOisr5tSud8ez9JKOsJAAAASBlHmfnYggg0RVDBpRUrVsiMGTNk4cKFsnv3bt38e9KkSXLTTTeJ0+nMzjsFWNzmWd9sdxv6OOUe3cC9c/RgYjpUfX+lFHc5Iqx0LjL4FdlYXq9k520NbAIAAACZZHeUSrDLZ/BkqFUVXKBp06ZN7W7Tu3dvmTNnTkb2BwCQmKKSypbAUTCAJN14CYECkYurzgEAkA9mz56tL7G43a39V5NRcIEmADnawymif1OwdK65dit9nKzOUWk+BoAIrDoHAMhXtoDHdJwprDoHQAq9h5PT5HZ/U6P4m/ab9m8KPSZ9nPKO3eE0Haeco9x8DAAAAKRZmdRLvWEs0rrKvdWQ0QQgt9lbf01tnH5Mu5vTxwnRP0qttfDprIu321rHzf6A7HUHq/Hb6uyUULN0AAAAwAoINFmMs7Sb+AxjIN85yquyvQtIM7+nIWwc7L+Ur9lI5c7WwNG1b9bG3PaR07tJZae2t/t8PtmtSuRtIhWGx6v3+EUCbbdXm9jsNikpao1yeXwBCRzcloAWkD7OkjLxGMYAABQ6Ak0WU9ale2s6XhfrpuKhcNicpXLknNZAhBJZOuet2y6bbz9Fj/1ej3hqvhAJmGeRUFqXg/yN5uMUZyOpz4ZxbBrQyrB93oC4vIE2AaQat09ue68uZc8TLaAFIHmlrvJQoEmNAQAwU98UCBtnfyaaPgSaAOQ0m80mtogzxJH9mxwVPUPjjTcdGfPxKK0rYN66iHHXtDxNSZHIX8/socdX/adG9jWbpCAddH07GU+pouJYAAAAQCYQaLKYxobasHFZRa+s7g8ApILf6w4b50I2UqzgqOvg0fWB09tmljZ5A3LZazVxP949o6ukR6ciPTZmPjX5AnL9Wy2/87u57HLXqVWh0rmZ7+6RTfXeFPxvALTH7W4MG3ciqwkAUOAINFlM075qKTKMRb6W5T0CMl9e591XE1Y6F1Za1+yOWVqnUF6Xg7z1EeP86EHnchg6g5tkPLXXe0np5lIrNLY8jssR/A0vEggEQo9js6nHbX2u332zq+zc7wsFogCrmDBhgrhcrqSXb04ld9O+iHFrli0AALli9uzZ+hKL2916cjcZBJoAWK68LrIZa1hp3bSj2n08yuuQqYynIGMAKZnHCXIWhTcGB6xi3rx5MmzYsGzvBgAAiXN0Mh9nSDwnYlauXCnDhw9P+rlYUxkAItn41QgAAAAgdeyGlZONYysiowmAFHppneJvapSN04/RY0dnVmwEAAAAgI4g0ARACr20TvE37Q+NPdWbxV5SavpYwTI8FbxSj4sUcFSaj5GUihK76RgAAACZZwt4TMdWRKAJACIEM5tiURlSxuAVOs7ucJqOU85eaj4GAAAA0qxM6qXeMBaxbhUFpzgBAAXB7iw3HQMAAABIHTKaLMZZ2k18hjGAxPo4eRtqRfxe022MfZyilddRWpc4v7cxbGwvNskUMzZMtHjzxFRp8PjDxh1Z2Q5A+1Q5tidGaTaAtnw+n/hqt4S+tpd3F7u9JQfC21gv4m1dYr2oqp8UFXEMA/IJgSaLKevSvTUdr4t1U/GAdPVxivVHgmf31rjL6yitS4C3IWLco80mdkep6TgRfq8nbGwa0LIQf8B8DCC1Sl3loUCTGgO5IBAISMDTeiIn1ok044myhLdtrBMxHF/j3ba5dqt8ceeYuP4v/W9eJsVVfaMGpAhEIV/UNwXCxlaeiRJoAoB4xZh0IQ946yLGXbO5NwAAxJ3xE2Kzh62Oa7aSbmQWdr5rLyBlDEQF2UurxO5o+VPX724QvyHoFolAFZB6BJosxu2uDxuXVfTK6v4AVqImIoPv2Rj1/kRK6xRWrotfoWUjATA3YcIEcblcMV+eKVOm6EumeJrdYeNOZDWhI8Eku0Mc5VWh+9ybV8kXd44qiNdSBYpK+h0flqnUvGtd3BlP8W4XzeF3rBFbjwHtBqTspZWhRUuM2xKoQr6YPXu2vsTidrce05JBoMliDuzZLkWGsfQckuU9AqxD9Qco6jEw6v2ePV+Fxqxcl2JkI6WF3++XfR4RZ5GIN6C+Fmn2B0xL7YrtInabTYqLWkrxfH4Rrz8gPsO23VwtPydAusybN0+GDRuWUy9wY+PesHEXQ7YJCltk+Zrird8VGidSPpaMI+7fKf7mppwqnTOKDNSoNgZFFT1NT+4FS+fUCb1UZWwl+zhqP2PND4FcEc+JmJUrV8rw4cOTfi4CTQCQKjEmWkAuUkGmy16rSdnj3X9aN+lJshmAAgwkGQNIqS5fCysNi7N0zrityqB22GxxP18iTe3TtW17J/eKex8ZM8u8vdK5VAb51GOZZT0pZK8jjKOT+diCCDQBQIZK6xS/r1k23jhUj91frgk70xfZ5JLJCQAAuUsFmdZelpoG8ANuWS6ufseaBpBilWYV6kqH7QWijOwmr1FwzpZILydjEMn91aey+bYT9ThWwEqV5Zm1UrC7KsTudDHXKzB2R7n4DWMrI9AEABmc9HiqN4XGm383Mua2rFyHTJyND/rT6CopL7YnXDq384BPbnvP0GgdACyWsWSWrRTMWIqXCjg4ewyI+lgq8GGPKB9DZudsZgGpaIxZZbG0l9E2+H82qQ9a1Pvp/4R8RaAJAJBdjgrzMZJSUWI3HRt5DNUWxXabdHGZbxeLsUcTAFhiZTfF4RR7UbF8/qv4FtaJlrkSGUhy0svH0lnsKuvJu2db3CWT668bGF+jco9b/IZFn4z7QW/EPBLwmo8tiEATAOTSynUJlNZZpbzO7nCZjlP/RKXmYyRFZTqZjQEgJwNJDpc4SltOajRXb5KN07+ekudS2UqJZMTAulnsNldF3KsUJ50RRSPyvFIudVJvGIu0rjRpNQSaACCXVq5LoLROobwufnZnuekYyVHldGZjAMg2FWRaf/3hKXmsqBlLDqc4Siv1iR8gnrmeKslU87dg0Mnf1DLuSKNytUJg8KSkWdYTGU/IFgJNFlPcqTLUYEyNAVibt7E+7/s4+L2NYWN7scn/h2ykhO031MWpscth3kg2WapnU5A/EBC3t20tnbOIIBSsy1nSSTyGMbKYsWTIVvL7/WGrgSW0spshgGSlDGLkBvU5sh2cu7VkwfUwb1R+sGF4ZBDJGIjaPPOEmM8V/FzbS8pDgVK/1yP+xpbeigSikC4EmiymvLJXazpeZXw15QDyp7ROlc55d29pTaX2uiXveRsixuETrlRlI/n93rCx1au8jL2T0tlHydg4/Ir/7DbdZtqILnJE12Kd8VRS1DLJBqyi1FURCjSpMXIzYykUSIoIRvkbavSYP7iR843KO3dL+LHay4yi9C6z6psCYeP8PlUcG4EmAMizlev8MZqNIgZPbfi4pEvBv1x2m/k4EWr1ufbc9cHe0PivZ/YQF7MPAAlmLNmdpWJ3tZxo8Hu94m+sTShjSQWZIhtx6xMOnTrzXiBvT0qGZSodzHpKpPTO+DMU/jPWkvVEABYdxVTPYtyGulw1LqsgqwmwsuAEwV5aJXZHy690v7tB/J7WcjQrTBLUhMc4Ni2vQ8LUSnNm40SosrhEeHUKFBlNsA5Pszts3OngH2rIQsZSzyGm2UrBYyFg5ZOSwawns+x4Y0Cqacc62XTr8XrcXkCqTUlpRECKklJEQ6DJYg7s2S5FhrH0HJLlPQKQTvGcsbJEWrS3LmLcNZt7YxmGBQzDxolwOWw6S0kFkKJlN9U3++X6N2vjzoAC8klj496wcZfO3bO6P/mYsWSWraQknLHUtXfoa7KVUKjay44vKu2Ssnkmi9IgGgJNAFDgWU9WyHhCxzhs5uNEqH5LLaVwtnaymNqOARS2RDKWBsz4r7gOPapNMCqIjCUguX6gwYCvp3pzay9QpJajk/nYggg0AUCeMU2JjggiNe34LHQWikaQCPL4AmHNu/d5Wr/w+EQ6Fed3Y3JY34QJE8TlalmFKZopU6boC6zF0bn7wRW6VBmQiJTRZw9IR8ZTce8jYy5M4/c1y8Ybh7aMvd6Wn0fExe4oD60Qr8aZNnv2bH2Jxe1OzUJDBJoAwGITBDURN/ZoApr8Aalv8stDH9fLyl2t/a6MAoHMRIBUGV15s02vQKdEltx1dqoyPquvCYiOmjdvngwbNowX0IIOv2ONOHsMMM1WUshYAnJjnump3hQa659Vgr55Y0ocJ2JWrlwpw4cPT/q5CDQBQAFmPbm/+lQ233ZiRGldpdgdzsw3FHdUmI+RlJKi1lK2YH+kWDwZ6p00ffmemPc/cno3qbR2NjkAE6pRsTpRQrYSAMsKeM3HFkSgCQAK8GyUKkHIlYbidofLdJz6Jyo1H1tUoqvBOXMkiShTAS8AAIBMKpc6qTeMRaos+wYQaAIAFAS7s9x0bFXB1eCM/ZliVcepkrV0KTfp/XTTiC7ytSqnLp17fv1+WbjpQPp2AAAAABlDoMliijtVhhqMqTEAxF9aF146Z2wonk5+rztsbC9uafYasXPmY8SxGlxr4ClbOhXbw4JewYyrlj5NNhnbtxOBJuQtZ0kn8RjGAAAUOgJNFlNe2as1Ha+yV5b3BkCuyqmG4t76iHG3tGQj+f3esHGOVIoVZNArUpE9e0EwIFmlropQoEmN0crn84mvdovpS6KWUvc3sXAFkM/Uz7C/aX/MJv5p6/GZh+oNq/2qscmpVcsg0AQAKAye2vBxCUtjA0A6qSDT+usPj2tblVULIL9snH5M1np85qVAlLEFEWiyGLe7MWxcxok1ACk8YxXfdg2m9yVzVotsJGtz2M3HQD7weD1hY4rnOiZYug0AyH8EmizmwJ6tUmQYS08iyAA6xnh2ub0zVvFI6qwW2UiWVqR7NbUdA/mgcf+esHGXcuuuIpRQaVxJeVhpXP+bl0lxVd/W+52lYne1lkLbnPTfA/Kpz6fxZ9hYOtdcuzXU4zNUWuf3i7+hxvSxKK2zJgJNAABTnF1GppDRBFi/NK7ksK+Lo6xr2vcJQOb7fNpLRKSsbUsCSusi6BfKZGxBBJoAAKbU2eUj57SUwX31yIWyb8ULMV8pfXZLn8FuLZ0zntUCoimglgWAJTKW1O96e0lizbw5eQEgkrd+lzgqeuqxWdaT1TKe7M6K0ArxamxlBJoAAFFXCrOVtKyHcehlT4rfY957Kche3l3sdtVgp3UZe7thBSbjGDDy+c3HKCz33XefvP322/LPf/4z27uCDmQstSmNOxiMCqI0Diis0jpxuMRRWtEmiGQ8Cbn5dyNjPhbNxPMXgSYAQLvsLlWHn3j/DLvTZToOPxKVm49RMLz+gOkYhWP79u3y5z//WU444YRs7wo6SAWZnKwuBRQ809I69U+nzgX/2vgDftOxFRFoAgBkld1RajpO/RO5zMfIOl/AfAzrW7lypVx//fXy7rvvitvtJtCUK6Vxrgp9ciBaM29jtpLf4xa/u15nMQBA3BlPiknWkzHjyVhaF/w6n0vrKqRW6g1jkdaFd6yGQBMAIG3UHyDGsf1gKV42GGvhrV4XD6TD008/LZMmTZK1a9fK0KFDo2YmzZgxQ15++WWpqamRfv36ycSJE+Xmm28Wl6ttgLeiokK+973v6ctDDz3EG5fDpXHRMpb07/XO3dKwdwAKJeMpWtYTpXX5i0CTxRR3qgw1GFNjAMgmdZY7bGzyx4jfawhGed1iLzYJRtlLzccAMuaZZ56Jef+WLVvkpJNOkh07duivKysrZd26dTJr1ixZunSpvP7661JSEr7KzuDBg+W6667T45deeknykbOkk3gM41zPWApmKynehpblyAEgH6nsJ3E4xVFaaZr1lG8ZT1ZCoMliyit7hdLx1BgAcp63PmLcNhhld5abjhOhUrKNY33mDEC7AoGAPPLII+0GgiZPnqyDTGeccYY8/vjj0r9/f1m1apWMHz9el8bdfvvtOuhkNaWuilCgSY2t0MzbGIyiNA5A9krr2gaRjKV17a1snGvNxOs9gbBx9vL8049AEwCgMHhqwsclNKUEYlFZSCqL6a233tKZSbGogJLavmfPnvLss89Kt24tAePjjz9e5s+fLyeeeKI88MADMn36dNMSOmQfpXEAcrm0Tsn7BQcCUcYWRKApBWf5HnzwQd1XYNOmTTJgwAC58MILdWNLhyPzL6/b3Rg2LsutE2sA0CF+vzdsTDaStTjs5mNk18KFC+WJJ56Ia9sFCxbo67PPPjsUZAoaMWKEHH300fLJJ5/I8uXLdcaTlXi8nrBxbhXPtc1YCstWamoUf1ODHtPMG0DeZTyZZD0d2Lyi3UwnpB+BpiTdf//9cs0118jVV18tp556qqxYsUJmzpyp+xSoAFSmHdizVYoMY+mZ51FfAFA8teHjki68LhZSZLOZjpFdN9xwg1x00UWhr8866yzZtm2b6baqNE4ZN26c6f3qdhVoUttZLdDUuH9P2LhLeVX2V40rrRS7wxm2apxZxlLLAg09MrKvAJDujCclWAKsePfusNSqdfmEQFOS7rnnHrngggvk3nvv1V+fe+65csghh8jUqVN1wEmlkAMAogv4W3OH/e4G8QZ2ScDvk0Bza5Nw3/6trdv4WnstwRrIaMpNffr00Zcgp9MZddv169eHmnubGTRokL7esGFDyvezkCXSgwkACs3mWd/MrR5O9hLzsQVZJtCUjiV323PgwAHZunWrnH766WG3jx49WpfUffrppwSaAOAgdWbd17hXAt6msNfEU93a+2X9zd+QUFddI6dI11Nahr66r0QqW/5oBZAbqqurQyvNmenatau+3rlzZ1LP09DQIPX1hgUEOkitfhe5Ah4AAOlkd1aEVohX41RqamrSl2Sp42wqWCbQlI4ld9ujejB9+OGHbc7evfnmm/r60EMPTfj/AQBWYi9pXSFu4/RjzDcyBJHiYbMX6+vIrKfQ/c5SsVF+lVe8fvMx8kdjY2NYQClS8Pbgdh01Zkxq+m6oE48q8zwfmJXHmZXGha0ad/B+Yx8mejABKLgeTnaHOAzlzJGr1nnrtoeX1u2rURPMvCyru+uuu+S2226TXJH3gaZsLrlbXFwsw4cPD7tt0aJFOkPqzDPPlCFDhnTo/wQAVmEvKW1/I0Ngof+0t6Wk+5A2QaSmXR9Lwwfn6LGvqV5PFNxfrJIt97TtB3PEw/Xi6MSKcvnEZyifNI6RX/Mx43Wk4GRdBU2iUSf92rNs2TI57rjjJFn5lM0Ub3lczFXj6MMEoEAksmrd5ttPyWhZnf9gECtynArTpk2Ta6+9NunHWb16dUpO6uRtoCnXltzdtWuXfnPnzp0rJ5xwgjz55JMdehwAsBKVXXTknJYU3IaPF8mXD0xou5HhOOvsMUgcndv2tgs014TGX/xxnHl5XfDhGmpEzAJNdpf5GFnnDZiPkT9KS0t1SduePXtMy+eCmUxlZSro0XHl5eVSUcGSugCA/FMhtVJvGIuYl5tnsyRcHWdTIW8DTZlYcnfgwIGyefNm08f885//LL/+9a/1+Pnnn5fLL79c92y644475Prrr9fZTgBQ6FQJm02fTRfpPGx8KOhkFPDul13/7NWm1C7scZzxLxhuKzL//WushU91XTxQ6Hr06BEKNKn5k9kJOaV79+5Z2DvrCJbHhZXGuRvE72mkNA4AkiytO7B5lXxx5yhex0IONGViyd0PPvggaop3584tZ8ufe+453VB87NixOvCV7b5MRSUVpmMAyDZbkUNfInn9relJAV+z+fe6WicBg2Z9JEWlh4Q/Rn11qAeU7eAfXwAyR7ULUCvKff755zJs2LA2969Zsya0XTImTJjQbvb5lClT9CVTHMUl4jWMU9GDSbGXVond4Qjrw2RWHtdSGgcASLa0rriq9W/5WP2b8rGHkzJ79mx9icXtbtv/tKACTZlYcre9s27Nzc1y1VVX6VXn1Ep2ufBBq6jq3ZqOV9U7y3sDAHHw1kWM2zYTLirpEhoXdxsk9uKyqFlM0TKa/H5DXbzfL3beHCBlRo4cqftULlmyRJ+Ai7R48WJ9ffLJJyf1PPPmzTMNZGVTeWml1BnG6erBBADInFj9mzraw6nBEwgbZ/o0QTwnYlauXNmmD3VBBZpyYcnd9957Tz+2mlypHlCRVO+naKuvKCzRCwApYneYj408NeHjEhqG56pcW6IX7VOLq6hV3FS7gpqamrCTde+//76sXbtWz8NGjx7NywkAKEh+Qx9Kq699UhCBpnQtuatWsVNUXyYzb7zxRsyO7elYotfjaS1BUWOSqQFYQXvZSIHmA+FjVp3LK0X28HGuLdGL9qmV4FQbgVdffVVnNKnFUfr27avPjKpyN2Xq1KkdXnQll3m9XtNxMj2YjKVzCn2YACDD/ZtsdnF07h5WOte8+wv54s6Wv+Gba78KL61rqBXxe/O6tC6VCiLQlIold82cd955UR8zHulYonf/7k1SZBh37d4yWQGAvNZONlLAEIgyjpEfHDZb2DjXluhFfB577DGd5f3aa69Jv379dAZTXV1LUdmoUaP0+5qsXOzR1LB/d9i4c3llXD2YgsGk9now6e3owwQAWe3f5FS/hw09mtprGj64A6V16UaPpjxdcjdRLNELAKnRJqPJbBvDiQHfvhoJ+Esl4DHPZC30s1DZzmgqcebWEr2IjwourVixQmdaq9WBd+/erZt/T5o0SW666aaY/TTzuUdTKnsw2Z2lad8fAECW2IvNxxlCj6YUY8ldAIAYAlAbZpwo0lpp3MbA360UZ68hOhClAlRmASmCUaljizJGbtm0aVO72/Tu3VvmzJmTkf2xIruL4CgA5GVpXUOtNNdsCpXWmbE7K8VvGFtZQZTOZWrJXQBA7vI3x4gsRdh0a/sZE7mYEp2vvH7zMWA1xh5MkX2YFBsZTQCQv6V1hh5Nha4gAk2ZWnIXANAB9lLzcaoZDv79f7NInD2PC8tUUk0eN992YvqeH1H5DEuvGMdAPvRoEn9rj09f3VfisYcvV+D3NYfGxT2HiLNr78ztGwAgK/xNjeJv2h/WJLy5oTHUT9lvOHZkCj2aUowldwEgd9md5abjjvI17hXfgX26V5NqDB7s2dS0Z3vr87iqpLhLr7Dvc3TrL0fcv/PgBg6xOZxtSuea67bLF7efcnAbejilijdgPgbyoUeTr35H6A+HL2efJ/V7d0Xd1lFakbH9AgBkz8bpx7S5zdOpRHqNbBm79vw/ka6jM7pP9GhKsUJechcACoGtqLWh4qZbjzffyC7S9WDZvN3RNnPKbreL3bBMrRbRL8W48pO9mGMGAAAAWjgi55EFrCBK5zK15G4uKCqpMB0DQK7yez1hY3uxyQqgdpf5+CCVfZQRxiwmMpoARMy3+k5+Qnr0OSr810Z5dx3IVujBBADWpX7HHzmnIew2Y+nc3q0fiPfjn0ghKJhAUyaW3M0FFVW9pd4wBoCc562LGHdts4ndWWE6Dt1W3j1U9qazm+yONqVzAW+j1C5uSWMu6trajDcRxiwmMpoARM69KgeeIM6K8LJcAEBhsNlsYjNkv4eahB/UXLc1tLruAZ/ZjNc6LBNoYsldAChcpmVvnTqHfendZ1iavXm3iCvxrE/V1NE4NpbSAQAAANHYOnU1HVuRZQJNaOHxeMLG/AkEwAr8fn/YOHxNJ+S7Irv5GMiHVeeYewEA8sHs2bP1JRa3252S5yLQZDH7d28KrXyixl27d6w8BAByiqcmfFwSnq2UKZEZTWZ8Pp/4areYP4DNLo7O3Vu/dJbqNOtC5zC8BsYxkA+rzjH3AgDkgylxnIhRC6YNHz486eci0AQAQAqpINP66w+Pa1vVMDKylr8QkdEEAACsztfkCSWFqLGVkaAOAACyym4zHwMAAFiGPfYqylZCRhMAAKlkaz2H0//mt6S46tCw+/yeRtk4rWX5c/eWj8RR2TtqaV2hlNc1+yLGxdncGwAAACSDQBMAILvspebjPGUMFHUacHyblek81a2r322+/ZR2H68Qyuv8gYDpGAAAAPmHQBMAIKvsznLTcRqeyHyc0GMUmY/TyNtQK06LB5qa/eZjIB9WnQMAIB5lgd3iNowzjVXnAABIMbuz0nSc0GMUu0zHiSiq6ieD79lofufB0jlP9WbZOP2Yltv83g49D2BFubjqHAAA+WAKq86ho4qKy0zHAJCr/IZAihrb05WNlApxZDSpDKRY2UhFRUVS1GNg7Kcpyf8SQqBQMPcCACAcq85ZTEX3w0zHAJCzPLXm4xRnI6VCXBlNxgwkspEAy2PuBQCIh9tnPrYiAk0AgILgd+8yHSf0GE37TccAAABATJ26mo8tiEATAADIqiK7+RgAAAD5h+mcxezZ/pnpGAAKPRspJfthyGLy1lfrr/2eA+HbRHyN9jlsNtMxkA+YewEAEM4R8TUAAIhDcFU41+CTpd/Uf4vd2Ul/vfmOUbx+CSKjCfGaMGGCuFyupFfVAQAg03xutxQZxpk2e/ZsfYnFnaL9ItAEAEAS3Ovflc+v6pHS19BR0dN0bFV2m/kYiDRv3jwZNmwYLwwAIP8UlZqPMySeEzErV66U4cOHJ/1clM4BABAnm7M0rdsXqmaf+RgAAAD5h4wmAEB22UvNxzmoqHMPOeL+nW3vsNlDpXPGXk728u6Z3L285Q8ETMcAAADIPwSaAABZZXeWm47Te8jr2OHPbreLPY5SNntJmSTD21AbNnYm+Xi5rtlvPgYAALCKssBucRvGVkbpHACgINhdVabjnOT3mo8BAACAHEegyWKKistMxwCQq/yGQIpxHMbuNB8DQJYx9wIAIByBJoup6H6Y6RgAcpan1nxsYHdWmo4BINuYewEA4uH2mY+tiEATAKAg+N27TMcAAABAugVKupiOrYhAEwAAyKoiu/kYAADAKmx2u+nYilh1zmL2bP9MigzjsopeWd4jAEh9NpK9eCAvq4U4bDbTMRBpwoQJ4nK5Yr4wU6ZM0ZdMYe4FAMgHs2fP1pdY3O7gunjJIdAEAACyiowmxGvevHkybNgwXjAAQN7xuT2hpBA1zrR4TsSsXLlShg8fnvRzWTtfCwCAPOSo6Gk6tiq7zXwMAABgGUUu87EFEWgCAABZ1ewzHwMAACD/EGgCAGSX3WU+RsHwBwKmYwAAAOQfejQBALLK7qwwHaf3kJfbhz9vY11o3LznKynuemjY/faSMvH7/eJvqAndZnOWiu1gI21/c5OI39u6fXl3sefw6ibNfvMxAACAVXTy7RGPYWxluT3TBgAgReyuKtNxTvK2NojccOPQNncf9deADjJ9/qv4VhY94v6dYs+BXk/ehtqwAFjo9gNZ2R0AAICMsdv9pmMrItBkMfaiEtMxAOQqlZljHNvzPBspU/zNqVl+NpPWX3+4+A/sbXP7nk6HiZy9Iiv7BCSLuRcAAOGYrVtMl54DpN4wBoCc56kJH5d0zu9spBRQZXBBA2b8Vxydu+tyuSBvw27x7t0Z+rr/zcvEecjQUOlcwFYkvvodsvHmYyQXBAIBCXga1SiubYF8wtwLABAPt898bEUEmgAABcHvrgkb24tbAze5JhgwUjbfdmK7239x55iwr4+c0yC2zj0kV6gg09rLyuPb1qsCUuns1QUAAJB5gZIuYjOMrSx3O4MCAJBSxlp4a9fFhxqCm4xzkd3Qt6moNcYGAABgGTbDwizGsRWR0WQxe7Z/JkWGcVlFfI1iASCX+d27wsb24oFiZWqVONXAO+w2Q+mc6s8U8HkPlqO1XVVOl975W18zswbcmeStb92Xw+9YI84e4aXdNTu/Evl/B79w7xPpQkYT8gdzLwAAwhFoAgAgx6igUaxV4oxBp3j6PBnH2WYvKW2z/w5HcWhc5GtdcQ8AAMAqfG53KClEja3M2vlaAAAUKGOfJ+M4F9lt5mMAAADLKCo1H1sQGU0AACCrmv3mYyDShAkTxOVyxXxhpkyZoi8AAKDV7Nmz9SUWd4oyrQg0AQCyy+4yH6NgmoH7A+ZjINK8efNk2LBhvDAAACQonhMxK1eulOHDh0uyKJ0DAGSV3VlhOk7DM0UZW5SxAXiWm4G3h4wmAABgdZ18e0zHVlQAM20AAFSyVHfTMQAAAJBudrvfdGxFBJosxl5UYjoGgFzl9/tNx9Ervan6jkdOrTpnd5iPAQtg7gUAQDgCTRbTpecA0zEA5CxPjfnYwO6qMh0jP1adc5RXmY5Dt1V0Mx0D+YC5FwAgHm6f+diKCDQBAAqC311jOgYAAADSLeDsbDq2IgJNAIACYSzLs3ZdfL6tOme3mY8BAACswlbkMB1bEYEmi6nbud50DAD5jGyk/F51zruvxnQcZHPvMx0D+YC5FwAA4Qg0WUzA7zMdA0B+K6xspFSwFXcyHWdFwG8+Psju85iOgXzA3AsAENfxwtNkOrYiAk0AAFiQzW43Hecih818DAAAYBV+WyfTsRXl9swTAABYnsdvPgYAAED+IdCUYldeeaWMGDEi1Q8LANZld5qPkZSA12M6zkWBgPkY1vbqq6/KmDFjpLKyUr75zW/Kiy++mO1dAgAAKUCgKYWWLFkiDz/8cCofEgAsz+6sNB2n4ZmijK0p4Gs2HeciMpoKzzvvvCNnn3229OrVSx588EEZNGiQTJgwQQefAACwohJfnenYiqy9pl4G7d27VyZPniwVFRXZ3hUAgAm7q7vpGEDm3XvvvXL88cfLP/7xD7Hb7XL++efL9u3b9e1jx47lLQEAWI7D7hOvYWxlljml+/TTT4vNZpPPPvss6jZqAnPZZZfJYYcdJi6XS4444gi59dZbxe12J/38V199tT4bd84550g22exFpmMAyG+FlY1kuVXnbHbzMQpyjuXxeOTf//63TJw4UQeZgn7605/q7PB9+/ZJPmHuBQBAOMvM9p555pmY92/ZskWGDRsmjz76qHz11VfSqVMnWbduncyaNUtOP/10aWrq+PKCqqfA888/L3Pnzg2bMGVDZa/BpmMAyFV+9y7TsRHZSPm96pyjc3fTcZC9vKvpGNacY+3cuVMHm4466qiw27/2ta+Jz+eTbdu2ST5h7gUAiIfbZz62orwPNAUCAd0X6aWXXoq5nSpr27Fjh5xxxhmyefNm2bNnj6xcuVL69u0r7777rtx+++0dev6amhp9Bu/uu++WgQMHxvU9anJlvIZ1qMn0zJkzkwpcIjfx3uY/v7vWdMx7m33GkzSpPGHD8TY351gq0KR07RoeVKyqqtLXu3a1DTjzXnYcv+OSw+vHa5cNfO6s+foFnJ1Nx7kkVcfbvA00LV26VE9shg4dqld6i2XVqlV6+549e8qzzz4r/fv317er3gDz58/X4wceeKBDJXTquY899li54oor4v4eJkvWpX6Z3XbbbTn3Sw3J4721Aq/p2KrvbT6tOme3mY+TxfE2v+ZYfr9fX3u9xp9V3stkWfV3XKbw+vHa8bnLP7n6c2srcpiOc0nBB5oWLlwoTzzxhE7Nbs+CBQv0tVrdpFu3bmH3jRgxQo4++mipq6uT5cuXh92nMpRUTwKzy3333acnUK+88ooe79+/X1+am5t12rcaZyNjqW7netMxAOQzv7vGdIz8WHXOu6/GdBxkc+83HcOacyy10pxSW9uaWWj8unfv3pJPmHsBAGCRjKYbbrhBVq9eHbr06dMn6rYqbVsZN26c6f3B24PbBX3wwQc6Fdzscumll8qaNWt0QEn1GCgvL9eXp556Sp/dU2PVmyDTAv6WYs+5r7WO02H27Nl5+diZePx0y+fXJ5/3PRN47aN7cM5fDF+1ZD3kk2y8t6lqBp6SfQ/4zccHH9/ua812MY5hzTmWCjQ5nU755JNPwrb99NNPdelkrOdLRrKf5Wjfb5xvxZp7pev5MyHb+57t709WMs+f7f97Pr922f5+Xrv8/eyl7XjhOWA6ztTzZ1LeBprUJESVrAUvasISzfr1LZk9gwebN8dWq8UpGzZsCLu9e/fuejJkdiktLZVLLrlE3n777bDLd7/7XZ1qrsYqGJUtf/lPeh+fP4izJ5+DNfm875nAax/dw48+KfksK4GmFDUDz8TPrcNQLmccw5pzLPV4KgNKldoFy+VUPyj19Xe+8x2pqKiQdMj2xD3b35/N5873708WgabsvHbZ/v58/txZ4fuz+dyzo3y/31ZmOs7U82dSbhYGplh1dbW+rqysNL0/2Iwy2JwyXqrJpboYqSCUer5TTjmlw/sLAEAh8fjNx7DuHOvaa6/VK9Kdf/75Mn78eF2u9+abb8qiRYsysNcAACCdCiLQ1NjYaLq6SVDw9uB26RZsiKnS0VNBnRkMnm3cuflzsW8WafKI/L9PPpdd+8slXf8HtaJMvj12uh+/oaEh9N6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+      "text/plain": [
+       "<Figure size 1200x1000 with 4 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "columns = ['PRI_lep_pt', 'PRI_lep_eta', 'PRI_met', 'PRI_met_phi']\n",
+    "nbins = 50\n",
+    "\n",
+    "xlabel_dict = {\n",
+    "    'PRI_lep_pt': 'lepton $p_T$ [GeV]', \n",
+    "    'PRI_lep_eta': 'lepton $\\\\eta$ [GeV]', \n",
+    "    'PRI_met': 'MET [GeV]', \n",
+    "    'PRI_met_phi': 'MET $\\\\phi$',     \n",
+    "    'PRI_n_jets': '$n_{jets}$'      \n",
+    "}\n",
+    "\n",
+    "variations_to_plot = ['Nominal', 'JES_Up', 'JES_Dn']\n",
+    "\n",
+    "\n",
+    "fig, axes = plot_kinematic_features(\n",
+    "    columns=columns,\n",
+    "    nbins=nbins,\n",
+    "    variations_to_plot=variations_to_plot,\n",
+    "    dataset_dict=datasets_all,\n",
+    "    xlabel_dict=xlabel_dict,\n",
+    "    samples_list=samples_list\n",
+    ")\n",
+    "plt.show()\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 18,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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+      "text/plain": [
+       "<Figure size 1200x1000 with 4 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "columns = ['PRI_had_pt', 'PRI_had_eta', 'PRI_had_phi', 'PRI_jet_all_pt']\n",
+    "# columns = ['PRI_had_pt', 'PRI_had_eta', 'PRI_had_phi']\n",
+    "nbins = 50\n",
+    "\n",
+    "xlabel_dict = {\n",
+    "    'PRI_had_pt': 'hadron $p_T$ [GeV]', \n",
+    "    'PRI_had_eta': 'hadron $\\eta$ [GeV]', \n",
+    "    'PRI_had_phi': 'hadron $\\phi$', \n",
+    "    'PRI_jet_all_pt': 'sum jet pT [GeV]',\n",
+    "}\n",
+    "\n",
+    "variations_to_plot = ['Nominal', 'JES_Up', 'JES_Dn']\n",
+    "\n",
+    "\n",
+    "fig, axes = plot_kinematic_features(\n",
+    "    columns=columns,\n",
+    "    nbins=nbins,\n",
+    "    variations_to_plot=variations_to_plot,\n",
+    "    dataset_dict=datasets_all,\n",
+    "    xlabel_dict=xlabel_dict,\n",
+    "    samples_list=samples_list\n",
+    ")\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 19,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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Fi0z9UmdUoGnlypU0bdo0wwkqKp6Qwjsir732mkjz7tevH02dOpXmzZsXnJxi5LbbbhOp4v3790dDSwAAALAsdWBxpMHFasaz3+/XXc99mB5++GFx6ohNmzbR+eefT12FbCaADpKbjNcAkFBz586lO+64o8v3wz0X43WwpqMyKtDEY3ujOXz4MI0ePZqOHz8eTAnn6SuLFy8WY3/feustw50U7i/w4osv0ieffEL33HNP3J+33FSmW0tZAxI+kU72eYl81brb/E0VdHLtqMAF9G8CAADISB6PR5SyVVVVGZbPqZlMOTnxyX7Izc21VMY4gBXI3mrdGtlOAPHjilOpN39+pkpGBJr4iBunaL/66qtRt+PSNw4yTZgwgZ5++mmRnbRjxw6aPHmymJyyZMkSEXTS4qyn6dOn0+9//3saMCBxAaBkTqSztU6jI+oeuRZb08sJAAAAMkdxcXEw0GS071NWFjhAVlQUnylVU6ZMiZpVzmbMmCFOAJAkstd4DQBJofY4jKapKXWZiJYONHEWEmcxcdNJzkyKhgNKvH1JSYnoHdCjR49g88rVq1fTqFGj6JFHHqH58+frdnZ4yhxPZ7npppsok/jrDpHsMJ5Kh0bhAAAA1jV48GAxUW7Pnj00fPjwNrfv2rUruF08rFq1yvBxAAAAMtWMGA6wbN++nUaMGEGpYOlA09q1a2n58uUxbbtmzRpxfuWVVwaDTKqRI0eKhpWffvqpmJDCGU+MA1Cvv/46ffjhh1RfXy+ua2lpET0J+HJWVlbUKSpmI8u+4BqNwgEAADITT4xbt26dmLbLfSzDrV+/XpyPHTs2Lo+HjCYAAAA9ZDSl0OzZs+naa68NXr7iiivo6NGjhttyaRzj0btG+HoONPF2aqCJj9hxQOncc881rIfkySrhpXam5j0Z23bo3wQAAGBZ3FKAp7fxQTpuIaAtkdu6dasYusK9m8aNGxeXx0NGEwAAgB4ymlKod+/e4qSKll20b98+cT5o0CDD23miHONUcdX1119P3/72t3Xb3XvvvXTgwAHR46lv375kJVwSV3zVAcPbFF9DMMsJ/ZsAAACsiyfATZw4kd544w2R0bRixQqxz8Mp+px9xGbOnNluXyUAAACwJkuXznVEeXm5ODeansK6dw80xj5x4kTwOt6pCg8m9ezZU9zXRRdd1O5j1tXViWaa7fHV1lJtY2Dtqqklh1KTkK707RHjivOMG57LLYHSQQAAgEiam5vFqav48xNS66mnnhIldG+++Sb169dP7D9VVwemUF188cViNHO8oHQOIEWcRcZrAEi5UjQDNwd1FK8aUAqnXq9uFw/jx4/v+A/9v2+0uWrBggUihT1d+je11OwnuzNPv4HkJsmZTzaHR0y2AwCAzLN06VJatGhRqp8GxAEHl7Zt2yb2QbgnZmVlpWj+PW3aNJozZ05ce1SidA4gNSRJMlwDQOrNQDNwc1AURXdumM3D09b8/qj3w+njsdq0aZNIP28PZwuVvRAoASz58VGSsnJ0tycjm6kj/ZtOrmkbDFP1nFZHtrDnDwAAmYGzXO64444u38/OnTs7d7AGYnbw4MF2t+nVqxctW7YMrypABpLcJYZrAACG0rlWHo9HlLFVVVUZls+pmUw5OfELknDD8Pz8/Ha3k1vs1JgdWPP24YEmAAAAM4hXqTd/fkLmQOkcQGrI3hrdGt9BANJHKUrnzKG4uDgYaBowoG0forKyMnGunawCHW8UDgAAANARKJ0DSBG5yXgNACk3I81L51Bs24r7CrA9e/YYvlC7du3SbZdMclOF4TqdcGmhI2+A4YmDUCpZllP6PAEAAAAAoGtkb7XhGgCAIdDUiiensA0bNhj+Zqxfv16cjx07NgW/OdrgjAkDNd5QcMxftYN8tQdDp/pjogcVnyL1xwIAAAAAgDQie43XAADo0RQyefJkMbltzZo1VFFRoSuR27p1K+3evVv0bho3bhx+cbrg5LrIzVvRKBwAAADCoUcTAACAHno0mQRPf5s4cSK98cYbNHXqVDE9rm/fvqKukXdw2MyZM8ntdqf6qQIAAABkDPRoAgAAMFePJkyd03jqqadECd2bb75J/fr1ExlM1dWBmuOLL75YjGW2Or/fT5Wtvf7yXaHKynqvTH5NZVsPd6AvU5cbhcstVPHSkGD/JtRyAgAAAAAAAJgXAk0aHFzatm0bLViwgNauXUuVlZWi+fe0adNozpw55HQ6yQq4F1KzP7Cu88okawJIFU1+WvRB+w39FowpoCJ324AUc9mJbDZb8LIISOW1neTHuE+Ttn8T5fQN3Sg5SXIWiKXN4dHdJwAAAAAAAACkn4wKNB08GApqRNKrVy9atmwZWRkHmX6+vrxL9xEtGPXM5cXk7sRvFvo3AQAAAACkCWeh8RoAoB0ZFWgyL23GUPKKy+4fV0jF2fZg6dyJxtiynXhbtyO2sjoAAACAaNAMHCA1JMlhuAaA1CstLRWnaJqaWnvipAD+YpiA5C4yXHekRE6r2ReqlfvDuELKkoxL0gJ9mAK3ceCowEX08CU92mzHpXPljX6atfmkuMwBKW0/p9ws/nCSDMvqYu3fBAAAAJkJzcAB0pCzyHgNAEmBZuCQMk0+ha7dUBF1Gw4yleTEln3EvZZKcoxvc2mCVR0pq4u1f1NL5Tayo38TAAAAAEBSyN463VrKCn0RUA8ih68BABgymiysRSbLQP8mAAAAAIAkkhuM1wAA7UCgyQTkpgrdWns0IRpna9lb1G3idACCy+yMyuqYX1ZoZmtZ3ZHaFurm1GdQqaV14WV1AAAAAACQfmRvjW4d6/cTAMgMCDSZgjY1qXNpSg9/q4euvE2V56S4iFZWV1YfahR113uxl9WhfxMAAAAAQBqSm4zXAAAINGWOfKdEboe5soVi7d/kq/6M7Ln9NbdKZHNki5XN4UGWFAAAgIlh6hwAAIAeps5BytR4Zd2aJ8elQrSyumZZCU6r66zKtaMi3tZzWh3ZkMoLAABgWpg6BwAAoIepc5A6SoR1kkUrqzvZFCqr+7K2hfLD+jd5sogc6N8EAAAAAAAAYAro0WRBzX6FFCVwnu58oTgTze9I/yZnkeF2WcUXUsG4lVS++oy4Pk8AAAAAAAAAaB8CTSYnyzLVejnyQuRqnTI3992TdFTTgNuKJGeuKIsLZ7NJ5G88Hrzsr/uCSNO/iW9X+zcBAAAAAEAEzkLjNQBAOxBoMgUpwpqoolGhW9+uJLPqbP8mm80WU++lipeH6i47CodTj+9sRpNwAAAAAIAoJMlhuAYAaA/+YpiA5C4yXHeEUx+fShvR+jdVJ6B/k+/kdjrxbCAbCk3CAQAAAAA6QdvGIkJLCwDIXAg0Wcj94wqpODsQjBH9mTQtmvKcZDreTvZvIrsnsU8MAAAAAMDiZG+dbi1pqgkkSTJcAwAwBJosxCXZyO0IZPao55lIchdTydUn2lyv+BqDTcL9dYdIdoQHpCSRMWZzeGLOkAIAAAAAsCS5wXgNANAOBJpMQG46qVtrjyZYWWf7N4mjKtklba731X4RXFe8/LWIj4uyOgAAgPQxZcoUcrvdUbeZMWOGOAFAcsjeGt06U76fAKSL0tJScYqmqamJUgWBJlPwRVhbW/z7N8mJfLoAAACQAKtWraLhw4fjtQVIJ3KT8RoAkiKWAyzbt2+nESNGUCog0ASm1Jn+TTZPPyq+6oDhdoqvIWqWEwAAAAAAAAC0D4Ems9O2EkJboXYzpChvgOFtckt9cI3+TQAAAAAAAACdg0CTyeU7JcO11XW2f1MkclNFcI3+TQAAAIl3880300cffUQffvghXm4AAAALQaAJMq5/kxqoEhlOQejfBAAAkCwbNmygxx9/nC644AK86AAAABaDQBNkZP8mzobSBqpi7d+k+OojhqRsDo+m8TgAAAAYOXXqFN1www2Un5+PFwggnTkKjNcAAO1AoMnk6ryybu12tM3egbZ4Kl1n+jeVPd8z4stZck0t2Z25eLkBAMD0Vq5cSdOmTaPdu3fTkCFDDLc5duwYLViwgF577TWqqKigfv360dSpU2nevHnkdrsj3vdtt91GZ555JvXv358+++yzBP5fAEA0fr+flIbDEW/ng60qyeGMup2v/gSRHNq+DWchSVLgq6fsrdNtywd89ZUGAGB2CDSZgOQuMVwzWTFeZ7JI/Zu0vZscUuz9rBRfY2wb+vkDE4EmAAAwv+eeey7q7YcPH6bRo0fT8ePHxeWCggLau3cvLV68mDZu3EhvvfUWuVyuNj/38ssv04svvkiffPIJ3XPPPQl7/gAZGShy5JLk8Iil7Gsi8tVE/mFHLilNZZ2fuuwsDC67Orm5cNImUnL6tj6vgmBQKzwgZQRBKoD0hECTSTX5AlGlZj+iS7H2b1Jfs46LrX+TzRGhaRQAAIBJKIpCTzzxBL366qtRt+PSNw4yTZgwgZ5++mmRnbRjxw6aPHkyvf/++7RkyRIRdNLirKfp06fT73//exowwDiLGADa/pvUZhZpA0j++iN0ct34lLxkanZSPHTl/6Fo8i5S+EC8r87wdgSiAFIDgSYTUORQoIMDSzabQrf8s4JqWxBk6ogmX+h1/KquhfKy9Cm6bjuR0yGRy066Xks2VxGVXH3C+L3x1VP56oFi7a87RHLrUaRw+JADAIB0xllInMX0zjvviMykaDigxNuXlJTQ888/Tz16BLKIhw0bRqtXr6ZRo0bRI488QvPnz9eV0PGUuW984xt00003Jfz/B8BsgSSZg0kGwRJtr9BE4oAN9xttQ/KQ5Mxtcxtf7jkt9HzbzT4KK53z134elyBZe69N4fe2k63bWYHHNXiNsY8OkBgINJlAeU1lcH3bhr1UYz89pc/HCk3C520xbhLOnrm8mNyafxkSl9ll60sWjfo3RfugE43GI/SAAgAASLW1a9fS8uXLY9p2zZo14vzKK68MBplUI0eOpKFDh9Knn35KW7ZsERlPjANQr7/+On344YdUXx/47GxpaRGlP3w5KyuLnM7IPWAALNsHiexUsbp/lx+Hy8/s3c7pUOmcum1nhtrwtrasUDa/pFm3h7e1uYuMB/HEUDrXkeDbyVeHx1a258gnyeHWvXYIQgF0HgJNJqDIoQiJRL6I2zljbzsEAAAAEDR79my69tprg5evuOIKOnr0qOErxKVxbNKkSYa38/UcaOLt1EDTrl27REDp3HPPbbN9bm4u3XXXXW1K7QBMHUxqDZjIdYfilpEkso7cpxkGkMKDIoHAT9uepeki2iCe9oJXnAWmy6YKy1TqSElhtO10vaMMAnSYOA0QGQJNJnPXyO5UXFws1s1hPYfycCCwU03CwxuFd4SaNiw38c/6Ih5tCW/iDgAAkE569+4tTqpo2UX79u0T54MGDTK8nSfKsf379wevu/766+nb3/62brt7772XDhw4IHo89e0b9mUOIF0DSQYZN/HqlRSxfK1NMCl9A0jJYJxNVRy63dMvlC2ly1QKBKRifb/a2yb29wsg8yDQZDIOyUZuRyCtVT2HrjUJZ9VN/s71b2r9oDM64qItq0P/JgAAsIry8vLgpDkj3bt3F+cnToT6G3IgKTyY1LNnT3FfF110UdTHq6uro5qaKCVAMeIpeEaT8ADaNNwOK9mKVyBJlLe1yZAJBUKQIZPYbCk1IKULRIW9D/4OZKC1t12gUXlvIp9xyw4EoiCS5uZmceoq/vxMFQSaALrQvylW6N8EAABW0dDQoAsohVOvV7frqvHj4zNVa8GCBbRw4cK43BeYtOF2HHr+xBxM0mY+tWbSILCQ/mV7Sv7Zxr2j1NvJRhWrB8SnUfl3/0W2gkA5sezz6gJS+F3JbEuXLqVFixaRmSHQBAAAAAAd+tKuPQ+nlopwuVE0K1asiOnxNm3aROeff36X3yFkM2UuDiSdeDY3LvdVeOUOysofLNZGgSujAEF4aReQaXtHqf2hIjVb71Cj8rWjIt6GQUKZbe7cuXTHHXd0+X527twZt4M1HYVAE0Ci+zd5q3lPRHcb+jcBAIBZeTweUcpWVVVlWD6nZjLl5MQ+hSoablLudgdKiyKZMWOGOAF0lei74ywyzHxSA0lSayCpI5PWwBr0bTN6xNCoXJ+pFK8yTLA2Vwyl3qWlpeIUTVNTE6UKAk0A7fRvqtH0bzpa30K5Dv1RKqed+2WhfxMAAGQGHkqiBpoGDGh75L+srEycFxUVxeXxVq1aRcOHRx9RDmA4+a2Vtv8SGjhD8huVh8qMdf2hJA9JztxgQMp/6t/BIBQHpHS3yz4ib+jAN0rrYEYMB1i2b99OI0aMSMmLhUCTCRR060nVmjUklybORHPfRf8mAADIbIMHDxYT5fbs2WMYANq1a1dwO4Bk4SBT+eqB7W5n8/Qhh6tbUp4TQIcalftCjeLby3pCaR2kOwSaANIYH4Hz1R8jktumPeJIBgAApMKYMWNo3bp1tGHDBpo6dWqb29evXy/Ox44dG5fHmzJlCkrnoH1S9PLKIM4KQaAJAEyuFKVzAJnRv6m80U8uyRbxPrSNIaP1b5Jb6qnyla+LNabVAQBAupk8ebKY3rZmzRqqqKjQlcht3bqVdu/eLXo3jRs3Li6Ph9I5aK80riPlcXygDiAd6crqWFjpnL/qY5TWQRBK56DLautO6tbZ7vhMzYD49m+K1jCcA1Xa+9A3EqQ2gaZY8HbqtrK3Rpf1hGwnAABIFJ4AN3HiRHrjjTdERhNPj+vbt6/oBcHZR2zmzJntZiEBJKI0jtlzz0CjbrDUxDuJ/5OD0jowD5TOmYDf32K4hvTq3xQNNwyPlZrtpNIGkbSTKtSsJyOo2wYAgER66qmnRAndm2++Sf369RMZTNXVgT6GF198sRjNHC8oncsM7WYsydgHBoj53xM3E2eSmyRnvljKskzkrRBrHJQ2v1KUzkG64T8ych3/kbGR5PKQ3MypxkrwXGUv7Kcr94LOl9XxVLquTasAAABIHxxc2rZtGy1YsIDWrl1LlZWVovn3tGnTaM6cOeR0OuP2WCidywwdyViKWBrnLCJJkiKWzQFkSmkdmolb3wxMnYN0w0GmPbe2P71u0P0HyF5snL4J7ZfVNflCQbuO9G+K+QOmdWdKzXry1+5t90MFAAAgFgcPHmx3m169etGyZcvwgkLSoTQOMlFHSusAUg2lcwAJ4uP01E70b+r0SFSDyXQAAABmh9K5DCGFsuAKr/iA7Nkl+ts1B9iQsQQQQ8ZTWOmcr/Zg1NYbYC6lKJ2DdCN7Q1M5Bt73KTnyioOlcy0nD9MX9yErJh4aWuLfvwkAACDToHQu86bGZXX/GloHAMQx4ym8HYfo4YT+TaY2A6VzkHaUUEmX5MwmRz4Hmlppq7tsxqVekLr+TQAAAABW78EEAIkVrd0GhgpBPKB0DnQkp8dwDenfv4mchbrJLJweGw4TJgAAACDVpXEAAGBtCDSZQEG3nlStWSeWLqUpwY+VuRLRv0mSQv+cK14aYrgNjlAAAIDZoEeTNcrjtKVxkabGSe5AXyb0YAJIxtQ69G8ys1L0aAIzkVwewzXEF/o3AQAAxAY9mqxXHoepcQBpNrWuTf8mJ0nOguB1clNZcI0KifQwAz2aIP1EzlqSvY26teSKMZ0GEtK/qbZFIa/fH1NZHR/96zmtrvW9qyOSG4IfFmodNh9NlFvqSfF7iRR9t3KbKzTNBQAAAAAAMk+0/k0MFRIQC5TOmUBt3UndOtudm7isJUU2XkNK+jd1pKzOZrORrfVohPaohFbFy1+LeH/FPz5OkifRpZkAAABgXaGvFoVXvEf27NN0t6I0DgAgMyDQZAJ+f4vhOhkZTWAB9uzYtvOHeicAAAAAdJTkDg0myep+XsQDXwCQ7v2b9KVzLZXb2s10AtBCoCkOFEWhRx99lB577DE6ePAgnXHGGfSzn/2MZs2aRQ5H+r3EyGhKby470TOXF4uG4eG9nGq8fpr/XqA1vEPSZz+F3wdnOKlHD0uuPiECTjabRHWf3Ev1/16a+P8RAACAOEAzcACA1PVvErfn9MVbkGZK0Qzc+h5++GG6/fbb6bbbbqNvfvObtG3bNlq4cCEdPnxYBKDSDfowpTcOELlFfNJOuWGTgPNdoR5KJxr8tPCDSsP7WDGxB3mcgf5Nou9SdmCKC8s7fyHlnjdfrP11h6KW0wEAAKQamoGn18FVdXqc3MTl/b6Ik+UAwJrUnq/hTcJVaBaeHGgGngHuv/9++ulPf0oPPPCAuHzVVVfRaaedRjNnzhQBp5KS0Jf8tIA+TJaw8INAZpORuhYiT1iQSmWzO8WJyZrRwvyh4as/EWwirvsZTz9d43EAAADIPLyvcOLZ2HqF8hdQKStyhgQAmFN7B6nRLBxYxo2YWrlypcgY+fzzzyNuc+zYMZo+fTr16dOH3G43nXXWWXT33XdTU1NTm20bGxvpyJEjdOmll+quHzdunDjq89lnn1G6QR8mCHIU6D40yv9+mhhLHH7iccUAAAAAAAAA7Um/BkIJ9txzz0W9ncvdRo8eTcePHxeXCwoKaO/evbR48WLauHEjvfXWW+RyuYLbcw+mjz76iAYNGqS7n82bN4vz008/Pa4ZSErDSfLV5BluJuXGOJ4eGU2m798kKwq1+PW3nfL6afY7VWLtlxUqqw/bgIh6uFtrsFtJjghpTwAAAABRFP1gdzBD2ghnQwOAxZqFhzUJV0vn/PVH0CwcMjPQxNlFTzzxBL366qtRt7vhhhtEkGnChAn09NNPU//+/WnHjh00efJkev/992nJkiUi6KTKysqiESNG6O5j3bp1NG/ePLr88stp8ODBXX/ujdXB2XCHH/geVZ9qWwvLBv/xOEndujaeXnLlGK4h3fo32Yiy9Le5HKEJgjM3c9+Eth6+pAeVaN5Wm8NDPafVBS/L3rpg6Zz2A0OtxZa594IvtL24D5TVAQAAWIK2BxOTmyr4v8HL/vqvgmub3U2OvP5Jf44AkD7Nwo3KY4PfG9C/KaNZPtDEWUicxfTOO++IzKRoOKDE23NPpeeff5569Oghrh82bBitXr2aRo0aRY888gjNnz9flNSFKysro7lz59KKFSvoggsuoL/+9a9x+X/w5Peixta1q+FUxO0UX3NcHg+sq0VWdDuTzZz0ZPMEs6UcEcYQR6vFRh02AAAkEqbOpWcPJm0ACgAyXSisgO8NyYGpcym2du1aWr58eUzbrlmzRpxfeeWVwSCTauTIkTR06FD69NNPacuWLSLjSevFF1+kG2+8UfRsuvfee2nWrFki2yke+H7UQJMtSlstKatt8KujkNFk/rI6JsuyaAjOKpr8tKi1cfjfPq+jGed3E0GmWq9Ct75daTipDgAAIF1g6hwAQHqT3IWpfgoZZ8aMGeIUzfbt29tUXyWL5TOaZs+eTddee23w8hVXXEFHjx413JZL49ikSZMMb+frOdDE22kDTS+88AJNnTqVJk6cKIJacenLFMGZSz+lnPzQFDt//Unae3trDbxkj3ujcJTPmbGsjtkNp87964SX/rW+PKZJdcFabEdBsJeTWjqnK6uTQ72gFLmFFL83dB8Oj3heAAAAkP4CpXIBRZM/E5/jkTKX0IMJACK14wj8PUH/pkxm+UBT7969xUnldEZuWrhv3z5xHt7YW3XmmWeK8/379weva2lpoVtuuUVMnXvttdcSMgK+5uSx4Lqu/hTlFRuPivVVHye5sUZ/pWQXmU7aRuFRs5bQKNxynFL8arElUVoXyJpSNX+1juye60SQqXHfn6n2w18Gbyv+SQ05XMbN6wEAACDdhIJKNgd6MGU6v99PlWFDt912IqcjsHPp9cnU5G87bAYyDx9YtoW14DDq3wSZw/KBpo4oLy8PTpoz0r17d3F+4sSJ4HUffPCB+LkxY8aI/k7huK+T+nOd5W+uIbtmrSU31wfXB+ZH7qNz1sMnSNJkQsWa0QTml++y0bIJHGgkypIC2UVcOuf1B8rq5m2pCk6ty3eFolI1zaGdzWg7ELX/ulWcDHkriBBoAgAAADAdDjJp2yxEEj5sBiByyAHhh0yBd1qjoSEwZSNSYEi9Xt2O8YQ6xn2ZjLz99ts0fnygxChcXV0d1dSEZSAZqK2rJ3trzMdfV6/7GV9NrQgYdKS1DjKaMgtnsnVr077LRtlZRM3+UHPwu94L9HGKaQfCHmggDgDQEc3NzeLUVfz5CQCdz1JRGg6LrwHavipcNsel8QAA8aT9O6P46shXezDitphmbR0INGlwlof2PJya0cEf0Kof/ehHEbdvT6QAVHRXtLnmN3Nm0d1zZ+uym8TzbKiig3ePaNO/CX2YoKskdzGVXH2Co5ZkkwJ/Rrh0jmQv+eu+oMq1o/AiA0AbS5cupUWLFuGVAUghDjKVrx6I9yCDSt88WUSO1hYaTT5ZHKSORFsaV9sS2nDBmAIqctuDt5c3+mnW5pPiNs6QV/HBb3dYaV0k2m19skwNrYNswqE0zzoqXj436u2YZm0dCDRpeDwekS1UVVVlWD6nZjLl5MQnN3TTpk10/vnnt7vdV3u3kP1fgQCTf9RrdPrgi3S3u1wucrhcbX5Obi4x7N/kq6sw7skEGYc/uDlbidltRDmahk4HTrUEp9XxDoT+dhvVyD1EK4fwD3/FV68bkyy31JMi+/gXUvfYNleobxgAZIa5c+fSHXfc0eX72blzZycP1gAAWAcf7G5uDeQYBZC0k4e7ioNMJTmh/T1XazsGFq/HiOTeCwuoT55xQApBKKvBdwOrQKBJo7i4OBhoGjCgbfOysrJA5/yioqK4vPi5ubmUn5/f7nY1uTlkzw6s/bk5Mf1MrP2b0Icps3GAKFJNPe9QxLIDEa0uv+LlyH3Din98nCRPzw48WwAwOz4wwqd4fH4CQNcVTd5F9twzNBPnNM3APa1TjSHtGnKrmmUlmFVkZfOjtHcIZFpFzpBCICq9JtKF/51RD0yr3xkkd3y+Z0PqIdCkMXjwYDFRbs+ePTR8+PA2L9auXbuC21kGMpog3qQYv0T6Q73OAAAAIpkyZQq53W2aDerMmDFDnKDjXwADE2XVybJg1obc7bl/XCEVZ9s7VDqn3ZYDNpGy4rXiVToXazZWe9ugUXl6TaQz+jvD1Q/QcaWlpeIUTVNThCh1EiDQpMGT49atW0cbNmygqVOntnmx1q9fL87Hjh1LZiDlFolpcyy8f5PKXtgv9kbhkFHCdyC0pXNGdfmcQZ3rlEjO6kndphwXASeXw0HNh1+iU+/+d5v7F+V0mrXib9bt+PKHEgAAwKpVqwwPAEJ7zb5V+qbf/vqv8NKZJGOJeytx1lIs/nhJIdko8r5TILPHRm5H7BOEom0bLSte+/Ox1WGIe6Rcp/F+aHhA6nBt7CWB2v5R4ffFXPZAIATSg7/uEMmO8KFDUjDTCd8ROnaAZfv27TRiRGvP5iRDoElj8uTJtHDhQlqzZg1VVFToSuS2bt1Ku3fvFr2bxo0bl9Q3KafHAGrSrGPF/W+k/FCfJoCOiLYD0X5dPt/upWcu70bZA39C7v4/DH54qKmxjQeeI/vX7hTr5q/WUfXbU4I/XXJNLdmdKI0BAADoKDT7tm7GEvcqyg8bNa02+rZawCTyfqidhhREz6bSHhBtLyD1zOXF5MY34pQKlNO133aDcQmemh0F6Q3dtjS4MffEiRNFkIkzmo4cORKMBHLaNps5c2a76dvx5nQ6DdeJgIwmiCf+oG+W7eS1ecTJZ2ttNkZE9R8voBPP5oqTNsgU3lAcAACsgftg/vKXv6SBAweKwSrnnXcePfnkk6l+WgAJz1gqq297qvP6qcmnxJyxxPrkZYmG3NpTrtNObofNUkGm2IJQbU8FPBXPYdMdEG3PiXpfm/fmZFPgvensZHHoKAynsiLEb8M89dRTooTuzTffpH79+okMpurqQCT84osvFhNzrExuadKtUT4HRozq8tXSOe1RpPAGlUM8Ct0cy0uK/k0AAJbzi1/8gt544w2aP3++GLrC+1rTp08XXxqvv/76VD89SyqctInsOQN0pXNyU2C4DUPD7/TKWAo0tra3yVZScdYStC9S/yg166m2JdRE/VfvVkW8H2Q7JQf/HSq+6kDE2xVfE1W8fG6Sng3ECwJNYTi4tG3bNlqwYAGtXbuWKisrRfPvadOm0Zw5cxKeUWSk5uQx3TonP4GTuuRQHbO37ADJjTXG2U65GE2fyWItqwv3eX0u/aoo8EGy7NvFRMfXt8lmAgAA6+H9qRdeeIEef/xxuvHGG8V1P/zhD8UQlr/85S8INCWIPacvOfL66q6TsmJvwwCd67OUmxVoYcFqonXeDsNBJs7Mga5pr3+U1x/be8IHT6Pt12KiXfzeL8qL/HfJVxuoMmJy00kMLjCJjAs0HTx4sN1tevXqRcuWLaN04W+uIbtmnayMpkOLRkXcjpuMo/8TGGntF27MZiMvBT75a+VscpZcSdlTAr/Tcv0X1Pz618UaqcoAAKm1cuVKcZCN+1MOGTLEcJtjx46JA3OvvfaaaDvAB+u49cC8efPatBk4efIkffvb36bx48frru/fvz998sknCf1/AUiXrCVtxpI2W0md7hY+2Q2Sn/HEpYyRMvPDYaJdsvgirCGdZVygCWLPaIoGJXUQSb7LRssmhBrpq7h1QI1XpjtbP7RvfUu/Q1bgb6SFrWuvTJSFlxgAIGWee+65qLcfPnyYRo8eTcePHxeXudXA3r17afHixbRx40Z66623yOVyBbfn7HAum2Ner1cEpnjQCk+U48AUdGWqnIbkJMXXgJczTUXKWOrIJDhIbMYT92aKFd42gMgQaAIde2E/GnS/cY2s3NJMB+aeI9bNx/eQ3VOgu93mDIykR1ldZuNU8W7ujn94e0kzytShnzjHGU6x7Dhj5CkAQNfw39snnniCXn311ajb3XDDDSLINGHCBHr66adFZtKOHTvEBN/333+flixZIoJORrgB+C233CLWV155pRi0Ah14jxoOU/nqgTFtK7kxfTjRpXENPpkqmvy6rKUz8uy6232tNyNjKf1xHyzuzSTLMtW1RM94qm9RyBelDA+ldfHnrz/S5m8bl9OpmU7c70mU4kHKIdAEOvwP015sXCPrqwk1jzx49/CIrxzK6iAS3jGLpEdOHlFrhrLk8IQ1AWwQ0+naU3JNLdmd7W8HAAB6nIXEWUzvvPOOyEyKhgNKvH1JSQk9//zz1KNHoARl2LBhtHr1aho1ahQ98sgjoum30aRe7s109tlni56YHJC66aabMH0OLNPQm7OWPNxxuhWylcyFD5q7xTdkfh+jHzRFaV3ynVynL78OJ5qKR+n3BMmDQBMAJE12liSOEhlpaq6jxtZefxVVxynLGcppjrUMQPHVEyHQBADQYTwAZfny5TFtu2bNmmA2khpkUo0cOZKGDh1Kn376KW3ZskVkPBn1wuQT35aVlUV33303PfbYY+RwYLe0c1PlNM2+JSdJzgJdpi90LGMp3xVqNlnvlcmvkC5jCSBW9sh9xAEsD5/oEDMuieNsJdEwPKyXk9zcQAfmfy1YQgcQ/ShRW1V1zaE/TOsHU6Qiu/wfHiZHw0HjIxp+9KUAAOiM2bNn07XXXhu8fMUVV9DRo0cNt+XSODZp0iTD2/l6DjTxdmqg6dlnn6Xf/va3tH37dhFc0vZuqqurE43F+/bVT0cDY9qSkaweIzCBKckZS9FK4xjK46ytI6V1DX6F/PUoresqLocTmUoR/g62VH1CJ1+7sMuPA/GFQBPEjD9QI02a89WFpjJ4T+xF/ybouOZTMW1W53OQK38sOS99i7z//JbuNkWOvYEjAACE9O7dW5xUTqdBzUirffv2ifNBgwYZ3n7mmWeK8/379wev69OnD+3atYs++OADuvjii4PXv/3229StWzdxO4AZoDQus6G0LvlEz6Uo5XD27NOS+nwgNgg0mUB2977k1azTkhwaNYn+TdAZPfJ6UCzHEmdtaSSvVEU2ZQhlFR2gAv8RmlcVyG6qaWqmHt0C2ymKTIqvMfhzaBQOABAf5eXlwUlzRrp37y7OT5w4EbyOg0s8pe6aa66hOXPm0GmnnSb6QXEvp9///vfiyxtAOrh/XCEVZ9t1pXNayFiCeEFpXfxxuw25pV6sZW81/yd4GxqFJxcCTSbgdnuCgSZeA1hRVnY36jmtzvC2Zr9C0zeW66bTKTY7eSmHmmz5we28ex8nf/58sfZV76aT60PldcU/qSGHKy/B/xcAANbX0NCgCyiFU69Xt1Ozol9++WURZOISuqqqKtEQnPtC/fd//3fUx+PSupqami4/b5fLJU7pTjtplctzyFuhu93fGBrOwl+kpKwIc9oh4tS48D5M2h5MLonL/AOBTzTyhs6W1rGaZrnTpXWYWNc5FS8HWrmYvVF4c3OzOHUVf36mCgJNEOf+Tc267Kbw/k1cYic3B6LM4eyFGEeZyfhoti3CzrLbodCTk3Jo4ftVdLBG//vlt2lG2e37I5Xt+6PxA/COOgJNAABxCYRoz8Opo6X5y71Wz549acWKFR1+vPHjo08ZitWCBQto4cKFlO5inbQqaI7WQ3x6MAHEqx+pUaAy1ql1D1/Sg0oQQ85YS5cupUWLFpGZIdBkAnXVJ3TrnPyeZNb+TQfmnhPxPgbdf4DsxeaIMkNqPrTvubA7hbdhamx0U9Pq9u8j0hciAADoGI/HIzKMOCvJqHxOzWTKyYnPt6RNmzbR+eef3+X7MUM2EwAAdL5ZuLZJOGd8+mv3Gw8QSnNz586lO+64o8v3s3PnzrgdrOkoBJpMoKWxmuyatemEZThFYsvKTvhTAXNzGhSzK+5cuq0o8OEyMM9GR2pCGXMF/i9pdnVgKlJjQxXZs+uD/ZvIH+jfZHMVBSfHAABA+4qLi4OBpgED2h4gKisLlHYVFRXF5eXkaXhutzvqNjNmzBAnK5C9oTLBov/6nGySJnPX4AsWdAxPjRvYLfSahvdhQg8mSFVpXXWzn37zfuC7noS2dV1uFi7Kik2a9emKodS7tLRUnKJpatLUDCcZAk2QtLI6Fl5a56uroEMLR4m1v65S3Kb4284KRVkdRMt28toCR813cxmyFOpjZldCv0v16y4go6LNkqtPEGUbZ+MBAEBbgwcPFhPl9uzZQ8OHD29zO0+XU7eLh1WrVhk+jmXJoS8GHGRymKSnSDr3YeIv8NqpcWoPJoY+TJAupXXa39lcJw6CAnX5AMv27dtpxIgRlAoINEFKy+rsuaGjnWofJyMoq4PO0PVvigAldQAAHTNmzBhat24dbdiwgaZOndrm9vXr14vzsWPH4qWFpEEfJrCS8ka/aEwfjq/iIBRnRmFaJ6QzBJogpWwoWYI4pSB7/QqFt2Fq9HWneW/togL/VzS7+nLDn5e9dUSe9Ot7BgCQriZPniyaaq9Zs4YqKip0JXJbt26l3bt3i95N48aNi8vjTZkyJaNK5wAAojUKZ7zvq2ZGQWTank3atRWUonQOIDKb00NnL6sjxecVJXPqOfPVltOhRaODJXe+mjLd7SqU1WU2NQVZmwavcvlsVC8Vk0sJ9GMyUtlM1CvBzxEAwEq4MffEiRPpjTfeEBlNPEmub9++IkWfg0Js5syZ7QaHMrl0jsu8lIbDgQuSmyRnvljKskz++iOpfXIWsGhMAZ2h6cNU55WDw0TQgwkgM/nrDpHsCLXYMOp5p05NNYMZKJ0DaGekvSuHiE9hJM11mFYHXVEl9aFftTYMZwX+IzSvynwTKAAA0sVTTz0lSujefPNN6tevn8hgqq4ONLG9+OKLxcQciIyDTOWrB+IlSpBC9GECE2fphzcJVzXLSruZThBZxcuR27QwMbkOPfHiBgl3AJARkz20U2UqTspEbwbWPkWiJp/SZuqMetTTTEc2AACShYNL27ZtowULFtDatWupsrJSNP+eNm0azZkzh5xOZ9weC6VzAJBZWfrG+568vwoJ5IzPpNRkKUXpHHRVdve+5NWsM21anS0rOziNTi2dazl5hL64L5CR4m+qI399VZvSO4ayuswWabKH3OQh9aN6wYcNVC+VG/78w5f0oJK2yXYAAJZ38ODBdrfp1asXLVu2LOHPxYqlc1qF3/0XZRWcGyydI2+FrpQDjKfKhV4konynJLI9AKysxhvKdDp0qkVk7oX/O1ChWXjry+LwUM9pdSR7q7kxa5vXVPE1BDOdeICVmcxA6Rx0ldvtCQaaeJ0pok2rs2WF+j4cvOu8iPeBaXVg/AsUSuaUCRlLAACQOnZ3EUlZgaMa4muOKw9vRxenyhkM6wIwP00sdcEHgVLlSNAsXNOmJSsn+Dc2nNxSH893CDRQOgemZJMQHIDO65HtJPV48aOXFJAtyxMsnats8tOSD+vIb3OJFGU1TbnBJ5PPr7kPlNUBACQFSuego3j8OwBAe2RvjW4dKSCVjkpROgddVVd9QrfOyccodrWsLpjdJPuDpXPe8gPBsjpfXQXZc3twLjopfi8pvhZxjpK6zCZRc3B9arV+5lx3Iprmmkyrc++l2ZtlTq0zvA+U1QEAJIfVS+eg4xaMKaAitWworGRILRsCsBo+yMn7n4ZsRF6/Qne2NgvnMrtIvZ5AQ24yXpvADJTOQVe1NFYHi3t4DdHL6rQ9mg4tHGW4DUrqMpuiRO/jMLz5ZXGa32MX1StF5KQGg/soTOAzBAAAAB1NOVxRtp1KPPgSDZmFB9RE6x1aVh9Kva9o9IdK7SIEY7msDCBRUDoHGUlubiC5OVSTa3O4yGbHPwfQ++MlJWTPyqbqv53W9qXxnyKifLxkAADQcc5C43UGi9jsu5VX0+w7/EszAOgtQg8nSDF8swbL4bI4zljikjru5RQorZOp+cReOrQokOF0YH5guoDq9Jv/RjnnfitYlme2qQPQMZK7mEquDpWkasktdVTx4pli7bbLZFPaZjPFkhUFAADxYcUeTZLkMFxnso40+waArqlDaZ3plaJHE0Dy00rtxQPaXO/IK4r4M189NjW45t5PkcrywBpEIDG7/fe4/AWDTCaVnwNQ3ajFr4gm4lpIRwYAiB/0aMoQqOIBSEwPp9bSufJGP81q7eGkSRAEk5qBHk0A6cHm9MS0XXPZgWBZnVpSFzx3elDPDEKLrFCtV6a/flZLb38Vai7OVkzsQR4nekcAAIAx2VunW5tp0lGiaMvh7h9XSC4pLPKk6TODZt8AHe/h1ObfFEACIVcXMoY9rzg4qS68L1PziX108Dfni/UXi8dGvI+zl9WRzYWdQUuzRw5ISr2+Q/Kx18X67g9qqCFCucOhWj/1yyNytJZgNvlk8vpDR5t4RwAAAKyLy6sVX6j0WpZ9RN5AJgHz1x8JbSwbl2hnsuJsO7kd+FIMAMnDf7N99cciTp+zefphH74DEGiCjBFtUp09O7amzorfF+dnBemGjyr3nBY60qzV2NxANasCv0M+m6tTDRg5pTna0SYAALDGF5YTz+am+mkAAEA0zlBrlYqX9T18wxVfdYAor217FjCGQJMJZHfvRV7NGhLXQDwcZz3566uCzcObj+/R9XqyOZxks2eJNZqIWwOPerVFKGFwtYQCUE9c4iG/s4j7zAsVTX6at6UqWU8TACBjmLEZuLY0DkJZXs2h6etU09z6AdrqpOZGNCoGSKxmWaEmnxL896bt2ZRJ2fdmHgBVimbg0FVud34w0MRrSF4D8XDq1DojaCKeGUEolctuI7umn0RulqJrwOjJCpXOfVnbQvPfi5zlBAAAFmsGrimHK5q8i2yePrrSufByjEzAcaSfry+PaVs0Kk6jElCvpgS0qY5kzWXtAdtMCUxYhdoUPNOz720Oj66SQfbW6ErnuMz55LrxlI5moBk4gLkpPn2j50hsdmfCnwukluKrN1y314AxH43BAQAyFn+Rcbi6EfEJwEQ4yLR7evsloFwVEMsBW4B0r2RoM5jBWWi8hnahdM4E6qpP6NY5+T1T+nwyTaSyOia3NNOBueeItbfyC7I36Eun1NI6lNVB8HeGQrnJPlkhn6ZygKfoaLOmAAAArILLc1R/GFdIWVEmYHHpDqSe7ENvUivhf1dq9n2+S9L92yxr9EftMZqpJM3gH+0a2odXywRaGqvJrllD+pTV+WrKgmt1ap0RlNVZj7/hKNkcOZzKRja7W5fhZHMV6Wq+OfXcqQRu/7K8mfIVJ7V462ndwQb651eNwe3un/gNystGZhwAAFiPthyOg0wlOSi1SndyQ6i8auC9u0SfUrV0ruXkEfrivvQsKQJjkbLv3Q47ylUh7hBoAkgCX005KbKflJa24zJR125OJ1//ZsTbSq4+QZStmXDob6DfVQwMrN8mOtV69bjWk6quZh/lZZ+ZoGcMAACQ3Ibf2qbfPDQDzEtyecjRDVUVkFm0gx143aa0DiJCoAmgC7gkjrOVFL+vTS8nf2NNMMtJnVpnBHXt1hPevylLaaTYOn0BAIAVps5lqo40/AZzkzwFhmuwxkS6ygY/+TVZiFqZNJVOO9hBt04DpZg6B2BdXB4l5WsyVzTkZn2wIRJ/4ykRlOJsp/CsJ2Q7pReeDFR8VWu/rtaSOfXcV/0ZVa41nkqYn6WQutud971dJLmLROmcXyaqqj5Mnne/lbz/CQAAkzHl1LkMVa/pwwTWJjmchmswJ23PpmgT6TJtKl06m4GpcwCZyeb00NnL6oLBI20QqeXUMfpi8UViffA3wyLeB7Kd0os4epNn3K+Lg0cRyaF8Jrcrlxy5odRzry072B6cJxMBAIAFOAqM1xanzYC4f1whFWfbg6VzWmj2DQBgbSidA0jkuExXa7g/O193m6ReDxlCu5MdvsMdmrrTIgcmf3CmU7g8ZyCDDgAA0h+yPYhcko3cDj5lSIlNhpGbNL1rmuqwb2tyPPn4mcuLddmJ2sAx91jDVDroCASaAFLY24nZ7FlEkj2Y9eQ9sTc4xcNXV0F2rnuXHKS0NJIiy+KcoawufcnNVSS7S4K9mvz1X0bcNifbQ+qu2rytTeS1VRhu98SlPaggOyFPFwAAAKBD1Olz4Wsw7wFytyYyEB4g1pbWadcAkSDQZALZ3XuRV7MGi/Z2as160vZoOrTQuOcPQ1ldugl96Fa+OiLmn7LLTbqm4V6bcbYb2l4AAJgHJhWBFUhOj+EaAKA9CEeagNudb7iGzCY3N4iG43xSfGooElLF5uhcupFTCuUl/2AgUpYAACwhjScVAcRKcucargEA2oOMJoA0wyVxnK3EJXU2h7O1tC5QOuetOESHFo0W2x2Y/7Xgz3SfeBsVf3++rjQP/XySy+YqopKrTwRj+Bx4Ukvn1HOxnaef/gf9gVJIdnkfosuGhOrjyxv8NOudwOQPxd9EckuEx3Z4RMozAABAIvn9fqoMJeIG2W1EOU5JjEUHAGtPlOR1xvRecxYar6FdCDSZQP2pCt06Jz80sQqsOdnMXmww2Sw7T2QvGal644/ipDrzd/spq6Cn4SQ8BCQSQwT2ssPKIbNiafoe2im3S0QORyhg1N0dWts+/DmdOLLa8B6Kf3ycpCzjI40IQgEAQLxwkOnWtyvxgmYI2efTrSVXSp8OpJC2MfiJRr/ucm6WfmANNxa3yvcNSXIYrqF9eLVMwNtQSXbNGjIXB4pisf9XZxpef/ayutAkPEh7Ds2H9okGPxVF2K78hdMi3kfPaXVkiyngBQCQnqZMmUJutzvqNjNmzBAnSB9oGGx+csNJ/TqnW0qfD6SH9qbP8fQ6bWNxSIzS0lJxiqapySAFNUnwKwBgIva8Yt20Oi6tYzyNrvnYbjq0KHLzcDC339oeIqnoD8HLOXIFLTiJ9xsArG/VqlU0fPjwVD8NCLNgTAEVue260jktzmoAgMzT4LNOaV06D3aYEcMBlu3bt9OIEbEPKYonBJoAzD6trlVWj7DePwZaTp0ge4RmjujrlH783lp6sDxQArmw8F/UYgtktPF5C4Uah/u+vYs8bjd5/eoVDURvnBdYtrSQMysFTx4AACyNg0wlOdb4MgkdJ3kKDNdgTT3cRA9f0sPwNi6dq2xWaNbmQAacT90ftQIMdug0BJoALEKb7RQkOUhuPEX7Zg0UF/fPNi6pY/yzkYJYkEjaGnZ9PbvTF+rPtjBK9pJj49co0tzBytpK6oUdQABIA4qi0KOPPkqPPfYYHTx4kM444wz62c9+RrNmzSKHA7ukZsBfKI3WkHmk1qz68DVYt4dsSZRknroWK0WXIB7wqQ5g8Wwnnxxq5Ajph5t1G63FZYs0UgQAYA8//DDdfvvtdNttt9E3v/lN2rZtGy1cuJAOHz4sAlCW4igwXpt0qpxKO1UO020BACASBJoAMriBeN7wH1Dt9n+Ita/uZMSpdvbCfuJIBiTkHYqw1uvx3X+RPbd/YCtHjsgM4Emzir+JFIWo2dcSLJ2rqjlOBZsC/Ux8Njc1+RTyywr5QpNpKc+JLwkAkFz3338//fSnP6UHHnhAXL7qqqvotNNOo5kzZ4qAU0mJdbJqzZrtgalyECu5uUG3ljBsBlpVNPl1k+i4Z5NaTsclePhOkRkQaAKwOP7g52lzRuSWpmCg6cDccyLex5lL/0P23meHfk4TkOJAFjJvEpPRpCW5i8ierf8SFujSFOi5pctmtntIPeY8b2szeaXyNve3bEIRdXPrj2IrDYf1G9mzyWb3kE2yk+L3EiktZHMV4Sg2QAZYuXIlTZs2jXbv3k1Dhgwx3ObYsWO0YMECeu2116iiooL69etHU6dOpXnz5rWZEtfY2EhHjhyhSy+9VHf9uHHjROD8s88+s1SgyaxQDgexkpvrwtbFePGg3al03OcpWgkeWAcCTXHyxhtv0JIlS+jjjz+mc889l37961/T5MmT43Lfrrxi8mnWAB3BQSBbhKNMkTKYwh195mbqO+P54OU9twYaVLOzHq8hR3Ye3pROUnyN+rV2moXNbrxuR5ZkC/ZsyqJGIqVtppQsF3L4KvTYDYepfHWgl1c0JVefIAoLeAGA9Tz33HNRb+dyt9GjR9Px48fF5YKCAtq7dy8tXryYNm7cSG+99Ra5XK7g9tyD6aOPPqJBgwbp7mfz5s3i/PTTTycrkXkog2adTpOKotGWw90/rpBcUtvPDx4m5XFImCqXCRxu4zUAQDsQaIqD9957j6688kr6/ve/L3oMvP766zRlyhRxhG/ixIldvn9PbiHVaNYA8cKT5tQG4nxEWfGGdox9dRV0aGGgAXXj7rd1wSUtua6CCIGmLpAjrDm25DZct6ebw0tqDtO9lV8z3MbbcoqI8jv4XLlUL1LbcQCwAv4seOKJJ+jVV1+Nut0NN9wggkwTJkygp59+mvr37087duwQB9nef/99cfCNg06qrKysNiOW161bJ7KfLr/8cho8eDBZiq8ubG2+A4XF2XZyO9ArMJM5PPmGa4D2ptJpS+e+rG2h+e9FznICa0KgKQ6418CwYcPob3/7m/gHdc0114h0cr4+HoEmgGQ3EFen2MWCA1JSdj5JWe42mVIcyEKz0A5mNGl1MqOJ5LD76aDCSZvIntM3WDonNxyhipdbA1ZKS5fuGwDSE2chcRbTO++8IzKTouGAEm/PpW7PP/889egR+HLB+0KrV6+mUaNG0SOPPELz589vU0LHysrKaO7cubRixQq64IIL6K9//WvC/r+gY7iXinbt5vQlAIAuTKXLd5r474hJBzukg4wLNMW754DX66VXXnmFfve73+m+UP/kJz+hG2+8kWpraykvr2tlRfWnKnTrnHzjzBKARPZ20pbZeSu+oEOLAtlOataTkcF/PE5SN/y+JjujiaRQ49ke/7Wf/K5AMLGiwUf3vbuPmm0eWmqP3A+Kg0yOvAHBy4o38rYAYA1r166l5cuXx7TtmjVrxDlnc6tBJtXIkSNp6NCh9Omnn9KWLVtExpPWiy++KPaPuGfTvffeS7NmzRLZTpAe1Ia94WvITLIs69ahbzoAmcGsgx3SQcb9vYil58Dw4cPpySefpK+++oqys7ODPQe4gWVzc7Nu+xMnTohgE/dl0jrnnHNEc92jR492+Tl7GyoN1wCJ7u3EwSb15MgvCZ1iDB5pS/Gg4xRfveG6Pdrm7HZnLmW7AyeXK49O2vtRvVTUtoG7Lct4DQAZYfbs2bRz587gqXfv3hG35dI4NmnSJMPb1evV7VQvvPCCaC3A+1mff/65yGpCkAkgfYn2CAZrAID2ZExGU6J6DnCgiXXv3l13P4WFhcH08EiZUwBm5ejeJ9jbSZDstPf2fm0CSy3VRwPjbm0SSc5sXVYUyuqST5u5HJ7FbHPkGK4BIDNwYEkbXHI6Ix+53bdvnzgPb+ytOvPMM8X5/v37g9e1tLTQLbfcIg7accY4xlunDh8IrWwKfA64HYFjzj5ZpoaWwFhyAIBEaZYVqm7yk9cfuddTOn0+yF5NdYe3zjSDHdKB5QNNyew5YJRq6vOp8+IArN3bacgjZeLcW3GIDswL9PP5Ysk3I94HB6oi9YeCrmU0aclN5YFeSzaJnP4murpmJtlIIUfDQvK1hL5IKpoJSTY7UoMBILLy8vLgpDkj6sE39WAc++CDD8TPjRkzRuxrheN9rPCDdpAYHGS69W1kyEPXSO58wzVAOI8mUX7W5pNRXyBuKB6p11NKyA3Ga2iX5QNNie450LNnoITo5En9Pxr1cq9eveLy/wGQ7kTmkmgPFFs/n+ZjnwcynFqznYLnHOhwetqWdmUADvb4Gzk1XRaNwf31R7p8n8Em3q3Gtp7XvvI3qo3wM35fC0mongOACBoaAjvbkQJD6vXqdoyzxRn3ZTLy9ttv0/jx4w1vq6uro5oadf5u57lcLnGKV1aQ0nDY+EZHri54D2BFktNtuAYI59D0MYbYcLue8JY9ncGfn6niyISeA9dee23w8hVXXBGxb1IsPQc40MTbaQNNnF7O11922WXBbT/77DOR9RGtxwGAFdkL+9Gg+w8ELoQFkZqP/ocOLhwpbvrivnER74ObkNtaA1dWpy1TCw8KpUplQyOdlq3JVJDcxmsAyEjcjkB7Hk4te+BgjOpHP/pRxO3bEykA1VE86GXhwoVxuS8OMpWvHhjbxo70zfa4f1whFWfbg6Vz4SUsAABd5bITPXN5aJp1k0/Wlc5xye6iD6rxQmssXbqUFi1aRGZm+UBTonsO8P1xBhSX2v3yl78UwSXekeLLHHjKz0/fnQuAROAvGPbiAca35eozBSORmxuCGVLQcTZXEZVcremh1Vo6V1bvo99sPkgShX2baOUjNyk2ie7PLtLfH/o3AYCGx+MRGUZVVVWG5XNqJlNOTnz+jm/atInOP//8Lt9PvLKZOkpypG/ExiXZyO3gDGI75aJqGjqA99W0a+y3QSRcpeDWRB3cDnvE0jrtOpPNnTuX7rjjji7fDw/3iNfBmo6yfKAp0T0HGP8ScHPLa665RjQN53K9zZs307p165LwrAFMmu2kas128tVW0oF55wb7PMnNramePP1OlOPxFDyP5crqtEEhDuhwyZxaOsfnwe08/WK+Tw54U3bb/lfFeX66d8LXIzZnVOvmPU79R4NNshuuASAzFRcXBwNNAwa0PbDAg1BYUZE+aN1ZnJneXm/MGTNmiFMqFE7aRPacvqErHLkkOUJl5DbNGsAqgvtpwXUoYwWgs6V1KLOLvdS7tLRUnKJpamqiVEGgqYs9B9iFF14o+jvdc889dNNNN4leTi+99FKwvC6SWHsOeCmbxHdO8YZlt/mZePYcAEhVtpP2yNihRaMypqyuTVAogdMs+PWP1GCxyRe5pEXxe/VrTNyADO45AESDBw8W2d179uyh4cOHt3lJdu3aFdwuHlatWmX4OCmlKYezdxtKjuzYMnYBTEWbjZfGmXkAmWhGDAdYtm/fTiNGjKBUQKCpiz0HVBMnThSnjuhcGtuohPYcAEidzvXugPjg/hz6dShzSfE3hK0xGQoyt+cAkJgcx1nbGzZsoKlTp7Z5SdavXy/Ox45Vxw90zZQpU9Iuo0lbDpdupXG8n8qT5cI57VyyIokMVoBYODz5hmuAeOKeTdp1eGkdGENGk4mka8+B+poyql0TOCqYd+VeygkbCY9sJrBsWR2z2US204F5Q8XF5mO7yZ5TqLtdnXQn5RYFMoSgw7RNYHmt7dWh+EMZKM0tzeSLkP3EzR6tVNYI8WeFngNAok0AH+DibO6KigpdidzWrVtp9+7dYj9q3LjIQx9Mn9GUxjjIdOvblal+GgAAMdE2Bteu04KjwHidBmYgo8k8kt1zIDc3N6Zm4c11X1FeYOo7OaQWNBiHjCur89UE/u2xgwsuiHgfZz18gqSwQCx0nTd0oInu2FxJ1fZcw+14ooi22SNAuHiVevPnJ6QOHyTjLO433nhDZDStWLGC+vbtK1L0OfuIzZw5s90sJDOTfU26tYSSYrAgWZPtzGscyoNE4wl02sbgas8mdVIdT8NUq4ySQXKEjrwqTUfJ5zOejse9VJP5vMwAXwlS2HMgVs215cEiFl4TnZPUxwcwD2TTJIIcY5lFYIcUH7IAmeCpp54SJXRvvvkm9evXT2QwVVcHdsAvvvhikb0WL+lYOke+mrB1evZoWjCmgIrcdl3pnFoizdmr/KUNIBK5rkK/zs7DiwUJtegD40CO6uFLekTsN5poFS9/LeJtxVcdIMozPmCeKCidM5Fk9xwAgNhwSRxnKwUahuuDHrK3MVhWFyizqze8D6tNq0umOk1ZnWpIdwfNG9Wdmv0KTd9YEdzOg/HYABmBg0vbtm0TfSJ52m5lZaU4EDdt2jSaM2cOOZ3x+2OA0rnO4yBTSY7RAQC7rkQaAADMZQZK58wj2T0HACA23HcpUkmcrzZ0tO3Q0m+R96tA5mG4IU/Ukt2NcptYNPtDmUniqLdXpvDh3JKNj4zbqKZZU1cHAJZx8ODBdrfp1asXLVu2LCnPB2LH5SZGa4B4ktz5hmuAjuLMSs5UCqeWzpU3+mnW5pMpeWFtDg/1nFZHsq+ByKefeOtvrqaTrw5vM4kUAlA6p4GeAwAmpIQCHZGCTGIzbwMRAk0xeeLftXTX6MBkuXe+aqJVH1eTOtfyV8MKqKi4iFz49AAASEtqT5PwNUA8SU634Rqgo7i3UbRyOBcf3UwRroawZeW09uEr1t0muevTdvpoOsBXhRT2HAAASEd7q3308/XcDy4gl0LNmz3Z2dTNjS8uAJA8admjCQAAIIVKS0vFKZqmptDgimRDoCmFPQcAIH79m4xwv6Z9swaKta+20rB/k70QUyJYbpTyCp+tdewlEeW5w4voAAASCz2aOoanM2nXbgeGNED8yV7N5EVvE0muFHVoBkgRUU6nWSd7+ugM9GhKL+g5AJCZ/ZsOzDvXcJtB9x8ge3Fyp0Sko+wsiZ65XJ8SrFJ8Hqr+W+t2UYLtPiXUqF1WFDGGVuWyB9KPAQCgaxRFoWbN31d1gpzReHDt32GAeJKbavTrvPScvAiQMNqeTWJtvB+dqZDRZAJOTw/ya9YA0PH+TRAdB4HcET4R/H6f5jXVrMOsOdBAPz83TwSZjtb7af6WquBtKyb2IA/P1gYAsAJHrvE6CTjIpC1vBkgYh9N4DZAizbJC1U3+iEF0bizOPZ8g9RBoMoGcbkVUo1kDQMfL6mRvYzDw5KuroEMLRwW2Qap3uxRfvX7tKjDc7p9HmsTJSF0LkQf7qABgkR5NksNjuE52aRxAIjk8BYZrgGTSTs9sb/ocT6+L1ljcSkrRowkAIL3K6hBc6iDZa7zmLEv0BQeABEOPJj3tkfz7xxVSd5dNVzoXfnQfAMDMMD3TGHo0QZc11J3UrXPye+JVBegCbaDJV3fSsEk4Q6Pw9uW7bLRsQhHZJd4RCPRg4tK5Fn+gT8g8TfkcAIBVyD5NI2RfU9KbwGrHfuc67ZSLjFEAsCju86ntI8pZndqAO+9vLvogMCUe0gdK50ygubac7Jo10TkpfkYA5ia3hL4gHJgb+d8TGoXHljXWrc0RcxtRFvcRCTUHBwCwFF9N2Bo9NMF6fDVlurUTw1MgDfqIhk/SdNuN15BaCDQBQOaRYxvDgxI7AIDUS8ceTQAAkB6cDslwbXWl6NEEAJDGTcI5u0kTeNI2Coeu0R5wCjv41GY8d7R0aT6SBQCZCz2aANKP5Mo1XANkDEe+8TpJZsRwgGX79u00YsQISgVkNAFAxonUJJw5NNcjo6lrPJqjStp1R8Zzc02+Nl0aAMDq/H4/VRoM8HTauWREEuO9AVJNcnkM1wDJpp3Eyevw0rpEkRxuwzUEYPcdACBC/yZfXWXEP5I2pydzMm0kl/G6CzijCQAA2uIg061vV+KlAQCIgbYx+NF6v+4y92xSy+mQJZ9cCDQBAGhpyuj23dE/4msz5IlasrszI1Xc5sgxXLdHluWwdegIU7MvcqBpeImTtpd5NQGpDAnoAQAAmITs1Uxe9DYhCxzSQrTpc/HOkk+X6aPpCoEmAIBOULwNRJkSaJIchuv21LXo1x7N+G1vKAbVhl0TV+KjUtlZHXiyAAAWsmBMARW1jlFSS+eY1ydTk5+oB6o1IEXkphr9Og+TFyHDYPpoVAg0mYDT04P8mjUApB6XzmUKRW4xXHdEs1+hptYsJm0gSXVBiZNuHdZNrOuaZfrwhHHZSHgTcbcjdGdev0Jq6xKkRwNAQjlyjddxxkGmkpy2/Ua4B0nyW89CxnE4jdcAaYQD7g9fYvwd2S8rNHPzyWCAPln9mwCBJlPI6VZENZo1ACRnIp1R/ya1nM5fXxWxR5PV+jcpvgb92lXQ4fuY9U7gQ56N7+Oma84KpRf/cXwhdc+2k7M1AlXTrA9QRWoizsGkP08KNW//w7ZTtKM8UHKHJuIA1jFlyhRyu91dnr4TT5LDY7iOh9YEpjZrgGRzeAoM1wDpxG7ngLzxbWX1oaOTnAVqpQB9aWmpOEXT1GQwWSJJkNEEABDjRDpfTVlwve+OfpnTv0luNl53Eh9dataUznEpiBpk6kjfJ27fdKpZJlfrz2oHMYX3hAIA81q1ahUNHz6cMoXauDZ8DQAA0JEDLNu3b6cRI0ZQKiDQZAJNmhpoXufk90zp8wHIVHJLbEGWTOrfFI0zwvejd482i1NnaPs+cZ+n6RsrIm6n7QkFABBPss+rW6MJLAAAQAgOk5hAY9UxwzUAJJctxv4EmdS/KRorlQ8CAOj4qo3XccB9RIzWAMmmzeTWrgEA2oOMJgCAGNnzimPq36RojnRnsjwn0bIJob5ykkSUJdmCTb15opy6XWfce1F3Ks62B0vnyhv9NKu14SMAgFlxHxGr9hQBAIDMgEATAECc+zfJjadIznJlRKPw9l6vbhH799ooOyvCLZqXJ9pLlZ8lUTdXKDHX1RrEAgAAgMSSXKEWAbK/hbzlByNuay/sJxo2A1hKkqaPmhUCTQAAce7ftG/WwMxpFJ4ATk3ASLtm2ksIKwGAWfn9fqpsCkyVUxt+N/lkkelZoU1pgqTgLFvRX1EjUqmYUdCE30//ycP6DSUHOXIL270/w21rK4iUCGWTNokceUWd27buJDcVi/g0HJqDabxty8kjkZ+yK9Qm4MCvh1A0A+/dRbbiM0Q/M7khcqmp5PSQ1LqPJPt8JDecjMu25HCTw5MfHBYi11VEPACo2OxkU/wRfy8CD24nKcsdPJeb6wP33Xoey7biptwicVAOkqNZVqimya/LGtXq4Q5MsEuH6aNWgEATAEA8RNlx0+FRaWYjuYzXCRIto8npsBmumVpCF74GAEg3HGS69e3KVD+NjKEGDMKDDCq5uYEOzP9aTPd1xm/eI3ff83QBJA7IfHHf+Lg+ZyuJ9bXNNGcs/Bc5clsDgTaJJGd28DyTMuATyamJG7XXXuHhS3pQSU7in1OmQKAJACAOYm4AHmtAKo3YNEdptOtEcWqCRNo1QzAJIPNMmTKF3O6Idbgxj3kGazPMKmqlyH7a/6vBcXmcQ4svjMv9WGG/5+xldWJtlKmE4Fv7Di0cFfG2QfcfiPDCBwJRyIaKjbs1YzQRZF+Tbp3s6aOlpaXiFE1TU+g5JhsCTQAACWwUbsvKJn9dRbCcTvG3mO71tklZhutEafYpurU7LHMpItTVAVjSqlWraPjw4WRV948rFIMN1NK58FIOiA0HmaKVrseKy7ycxWfoSt06EjTpP28TZRX2tUTpnHZbLhnU3bXNRjZX4Iu1xOc53XW38/YcLNGXuKVH6Zyv8rBhllXeqKup188fJSnLJUrcElE613T0s6gBJlV7v8u8zxmpbyiEuOxEz1xerJvkqS2d41LlRR90cnKoryZs3SOpL/2MGA6wbN++nUaMGEGpgECTCWRlF5CsWQOAuRqFc3NwlRkn0imaHU3tOhWiBaEQZwKApJE8xutO4EEG/LfM7UCz5E5lLLUGEbj8LdZAkra/UDgOkEh2OzmLB+iuC2aYOJzk8BREDCBFanytvb/2OFsDOGbZ1gi/Bvaw/2ejgFQkolI/p1v8t+Xndvq5wWws/Y0OEWQKPtcYdWRbV7/hxtlKNkkEDGMNlooAI+9jciDMoC0DGrC3vqw2/tsael3472x+hNI67Rq6DoEmE8gt6Ek1mjUAmIs2i8mMGU2Kr16/dsW2M9dZzX7FcM28smK4Fs9NMXcrLAAwD8mZa7iG+AWSwjNWmr/8uONZRQYZLZ3pfWMUNOlMAAnSgzYbK9mi/S5xBhUHwCJlSPnrT9LBBReI9YF5Q6M+zoCFH5Kr9zmtAVilTUAKgai2pXWJLLPLRAg0AQAkEX/QK7JMttYpI5zhpAaf0rbxo9xsvE4BBJMArOWhhx6id999l/7+97+n+qmARUrfGAeZEAACswbAImVIyR0olTu4cGTU2zmrKlLACyAeEGgygaamGt06Jx9ZTQBmdWDe18SRVne/YSLYVP7KvVT56lJx21mP15AjOy/VT9G0omU7AUD6OXbsGD344IN0wQWBI/Rmwr1mtOtkN4HNdLqMJYPR9eH9hAAs14DdIFOJy+kOLWq//xOE+GQ5bI36uXhBoMkEGquOBX/leU0l8ZmaAQCpESn133t8N1GP/rrrbFluskn29M12SqImn9KmlE4L2U4A5sDNSWfNmkXvv/++mIhjxkAT+arD1rH1nclk7ZXGccNtbSDJddqQiM2eI5X9iFxhHLABi2rTgD2Mo0d/zWAam+hFpg1ItVQejrn8NFM0tOjXuc5UPhtrQaAJACDBOEgUi2hTSPgIVqr6CaSLW96qoFovMpUAkmnlypU0bdo02r17Nw0ZMiRiZtKCBQvotddeo4qKCurXrx9NnTqV5s2bR25327Fp+fn59N3vflecHnvsMcpEmdiAtiOlcZyt5OjWs1PNngEyldFgGl1ASrMLJXsbgxMLea2dXIj+TRAPCDQBACSYPa9Yc4QpwJaVLUrnvOWHDEfsGjWIzEQue2ZncQGk2nPPPRf19sOHD9Po0aPp+PHj4nJBQQHt3buXFi9eTBs3bqS33nqLXK7AFCfVoEGD6M477xTrV199lTKRVRvQqllLRiPttRlLAJB82mmL0ZqJi4zCXme3/kxOMBClniMQ1cqRa7wGAYEmAIAUHGFSOXro+0j0m7WO3P2HiXXlugeocu1vxVrhGvzWnXarc0b4zvXIt4qotlmmW96uFJddGZIFAJAKHNx+4okn2g0E3XDDDSLINGHCBHr66aepf//+tGPHDpo8ebIojVuyZIkIOmXi69fs55Jfmbz+trc3W7SPXKxZS7GUxgFAarRXXodG4gGSw2O4hgAEmgAAUhyE0rLnFJJDDUpp0ph5Ol3KSE7jdaIeTgplMWnbUrkdfCH0ernCsgC0AapIwSoAiI6zkDiL6Z133hGZSdFwQIm3Lykpoeeff5569Oghrh82bBitXr2aRo0aRY888gjNnz/fsITOyjjI9PP15TFtm4kNaFEaB5B8Um5Ra4Z9a/8mTaZSS+UX6N8EcYVAEwBAmkwQEZezQl/GCsbfQJWv/U6sFb+mW2GS2Rw5hutEcWrK5bTr9mibpWd643SAzlq7di0tX748pm3XrFkjzq+88spgkEk1cuRIGjp0KH366ae0ZcsWkfEExqzagHbgvbvIWXxGsHROCxlLAKnPsNf2b7J37yMylcJvU0vnvBUHo/YSzUSYPhodAk0AAGkyQaTNbfYsSgc2KctwDQDWM3v2bLr22muDl6+44go6evSo4bZcGscmTZpkeDtfz4Em3i6TA033jysklyZTM1wPiyZ7ccYEf0kVX1hzMJUPIJ3xFEd78QDD20SwqamWrI5Lmuu8ft0kuvC/1bppl5g+GhUCTSaQlV1AsmYNAJBMiuwzXKdCs1/RrQPldAHaKsSwikQAiFHv3r3FSeV0Rk612bdvX7C5t5EzzzxTnO/fv996r7/kMV7ryuECurtslGuR0XJqs2+V5MoNNhjmo/to+A1gfTytjts8yC1NRLI/eK4yU7Nw7Z/mWZtDfeKMPHxJDyrJ7AHQHYJAkwnkFvSkGs0aACCZFF+Dfu1K7IjpZp8mmOTTB5O0o3l1a/6SIxuvASAxysvLg5PmjHTvHshiOXFCP3Wzo+rq6qimRt0T6jyefhc+Aa+zJGeu4VqlPSJupdK4WJt9A4B1HVo0yjLNwtN16mdzc7M4dRV/fqYKAk0AACah+FpI8XsN+zwltCeR3GS8BoCM1tDQoAsohVOvV7frrPHjo09AitWCBQto4cKFcbkvq9JmLOmylbxNJDfVIGMJIENp+zlZCU8wfubyYl02qvZAQUWTnxZ9oO8xlwxLly6lRYsWkZkh0GQCTU0NunVOfkqfDgAkibYxuCL76eTGh+nEc3e22W7wH4+TI7cH2eyONgGphAeh0ohXMy5cuwaAxFAURXceTi2d4OBFJG+99Va7j7Np0yY6//zzqavilc3EZE0ZMa/T85h4YjOW+s/bRK5e5+hK57RNv9HwG8CKE+s0fZs0pXMtFYeCU+uildalW1kd7yO7dRERuy4DVVtal8wK6Llz59Idd9zR5fvZuXNn3A7WdBQCTSbQWHUkOPSW11RijlREAOgamxT6RNv/q8ERt9t722nU55ZVlHteoCFv9Xt/peN/ukmshzxRS3Z327IOK9J+143wvRcA4sjj8YiStqqqKsPyOTWTKSena0fCc3NzKT8/zY6yeU/q1wkuKU5HWYV9yZEfygRA02+AzJlY1ybLSdOjKVppnZnK6sJL65JZZueKU6k3f36mCgJNAAAmyGhqz5ePTDG8Xm6uz5hAEwAkV3FxcTDQNGBA2y8OZWVl4ryoqKhLjzNlyhRyu6P/PZwxY4Y4QfycsfAjcvc+W1c6p4WMJQCwemldOistLRWnaJqaUtfyAoEmAIA0Jbnz6OxloSZ+VW89YVg6F43i7VpvFACASAYPHiwmyu3Zs4eGDx/e5vZdu3YFt+uKVatWGd4/JJYjtzD45VGc5/XASw4AHS6taz6xj75YcpEpXzntBNHA2h7z9NFEi+UAy/bt22nEiBGUCgg0AQCkKa4bt2mOEBV++1bq/q0bxdrfVCdK5lTa0rnK1/+Pyl9aQJakbTeVGa2nANLWmDFjaN26dbRhwwaaOnVqm9vXr18vzseOHdulx0FGU+IafjPJnU+SM5Ax5qtPftNbALB2aR1n16u4f5M9r5gUn5cbu5Hc0izOVenWwynaBNH2po8mGjKaAAAgLmyOLHESa6dHl+1kc7iCzcDzx/wkGGjy1VWID3rt7R1uEi7pPlUT/m5qn1b4U3TZbYZr5pSM1wCQGJMnTxZT3NasWUMVFRW6ErmtW7fS7t27Re+mcePGZVxGU6oayMa74TcAQDxF699kxh5OqTQDGU0AAJDobCfdbZqA0qGFxh/oHWkSbrN7DNeJ4tQEkLTr9mgDZ5kyaQ8glXgS3MSJE+mNN94QGU0rVqygvn37ilR9zkJiM2fObLe/khWlqoEsAAAkB0/a1K6lLPSp0kLpHABABpIbazoQaHIarpOhxitTsy8UNGqWOz5OjkevN/u5tl4hf6jUvo08ZyD1GwBi99RTT4kSujfffJP69esnMpiqqwPlVxdffLEY0dxVKJ1LnP7zNpGr99Bg6Zy26TeafQNAPPDfEs5UUrPp+VxbOtdScZC+uG+8+V5sX3XYuntSH74UzcChq7KyC0jWrAEA2mvIyHpd/yTlDfu+WNd9/Dodfera4DaKrznmF1HRjKzVrpPh1rcqI97W7FfI7QgFobQxIu2ag0w/X1/e7mMtm1BE3TIv8QKgSzi4tG3bNlqwYAGtXbuWKisrRfPvadOm0Zw5c8jp7HpwOi1L59ppAhu1gWwaySrsSw5Nk280/QaAeOOeS1HL4TQ9miB2KJ2DLsst6Ek1mjUAQDScqcT9m7R9mbpdOI3cAy+gA/O+1uEXT/HV69eu/PR4A8KSmzTf63TrmO9O6Xi2FIDVHTx4sN1tevXqRcuWLaNM0l4T2GgNZJPd7Du86bfcjGmkAJA+OMNJxRlOvpqyNk3C07VZOESG0jkAgAzo38QBJ0nzQd4hcpPxOkFcdqJnLi8WGUvhwaTwUreOevhbPeiV/fX0xuG2/x8IMwGkJ5TOdQyafQOAmWj7ah6Ye07UbdEsPASlc9BlTU0NunVOmiQTAIB58TQ6u6d7cIqd4vcFy+m49C6VvYp4h8Pt4Aa6HWvo7dX0b9KutSUsHnG/xv9vEhqIA6SldCydkzVH2nmN7m4AANaTzhNEZ2DqHHRVY9WRYGU/r6kEIx8BoGsiTaNjZz18gqT8EtO9xNrKN+06vIRl6pAcmjI4kPFV0yzTrW9H7gMFAGDIe1K/dnVL22bf3IdJWzrHuHxObq5Dw28ASDk+wMn7nuFNwtXSOV9tOR1aNFqsbVnZSX1umCDaeSidAwDIEDZHcifGpSuHZCM1qak+NJlWTKUDALASDjI5DZrwiqbfVJyS5wQAoMVZ9NEOcAb+XgXYMB3YNBBoAgDIEPZuvcQRI7VMzmZ3BkvnWk4dp4N3fSPjmmJLNuM1AKQP9GiKrdm3SvaH0jglV9tG5QAAZqLNYvLVnSS5ud5wGw5Cxbv9Q9QJou1MH0009GiyOP5C9uijj9Jjjz0mJrOcccYZ9LOf/YxmzZpFDgfieACQfkeManeupSMPfC/idoo3cyYSafsyoUcTQHpKxx5NZmn2LbmS/+UHACCuND3x2msWHu/2D9EmiLY3fTTTezShd2EXPfzww3TbbbfRxIkT6c9//jNNnjyZFi5cKK4DALAEW5bxOs1kScZrAIBkS+cGsgAAZqJosjTBPJBy00X3338//fSnP6UHHnhAXL7qqqvotNNOo5kzZ4qAU0mJ+RrqAoC15X79cjp7WZ3uupaqo7T/12cZ9nKyOXIM11bITvJpygS1awCAdG0gKzlDWUpnLPgXOfKK9Ld7Ckhq/TvOzXUBAKzQLJxxs/DwwJPcVEcH7jov6c3CMX00Ossc8125cqUYif35559H3ObYsWM0ffp06tOnD7ndbjrrrLPo7rvvpqampk49ZmNjIx05coQuvfRS3fXjxo0TJXWfffZZp+4XACCRbHaHaKyoPdk9oYlJNntY1pIUuiw3VJOvpszwJGvq2M3CLxuvAQDSleQOlWi4Tz9XNPvWnhw53YN/23nfGADA7K0fHPkl4pRV2KfN3zy+zqjMLunTR8GaGU3PPfdc1NsPHz5Mo0ePpuPHj4vLBQUFtHfvXlq8eDFt3LiR3nrrLXK5XB16TO7B9NFHH9GgQYN012/evFmcn3766RQPdle+4RoAIF64MbjRmine2uB6750DieTk1MV3lF0yXjvsxmsAMId0bAau2EL7jFUtWaTU+3W3N/qiNJAFAID4/T3WZDhlUpldaWmpOEXT2YSaeDB9oIkzh5544gl69dVXo253ww03iCDThAkT6Omnn6b+/fvTjh07RE+l999/n5YsWSKCTh2RlZXVprnWunXraN68eXT55ZfT4MGDKR7yC3tRjWYNABB3mgkdvrpKUvze4OWWas10I95MNld2kkdTtqJdA4A5pGMzcJ8jdODvjvdayGurjLhteAPZWPZt1aEMXBIihw1o8DecCq5ln1c3+hsAINNwOZ2quWxfcCqdLctNNskeOnd6LJXlOSPNm4GbNtDEWUicxfTOO++IzKRoOKDE23O/pOeff5569Oghrh82bBitXr2aRo0aRY888gjNnz+/3SNmkZSVldHcuXNpxYoVdMEFF9Bf//rXTt0PAEAqKC2hIx77f6XP0iQnUfeLAsv+c9aRq+cwUmS/+Bn+wnPw7mGBGyXzfaSggTgApBsOMu2eHtsEIy5nppzuCX9OAADpSpvF9MXi1h1WA9yf1IbAfNKY71tBq7Vr19Ly5ctj2nbNmjXi/MorrwwGmVQjR46koUOH0qeffkpbtmwRGU+qAQMG0KFDhwzv88EHH6Rf/vKXYv3iiy/SjTfeKHo23XvvvTRr1iyR7RQvXq9Xt8ZxKwBIFUdOoaiRbzzwIR1cNEp/YzLr4g14ZcVwre0dJYeVsHSmgTgAgM8X+nv3u7EFZHdHbm3Qo3PHMAEAIN6ZTx0MNKH9QgYGmmbPnk3XXntt8PIVV1xBR48eNdyWS+PYpEmTDG/n6znQxNtpA00ffvgh+f36mntVXl6eOH/hhRdo6tSpNHHiRBH4ildfJq36yoPBr0W87l7UN+6PAQCZLdbJRJIrfScYaYfGadd1Lfq1pwMlLAAARurqNaVyvpNUEsesIlkTxBr4v5+TFD6gQcNe2A9vEABkNP47OOj+A22u55I5f91JOjD/a53u36RtuVDfopBPExvwN2gOZGKijHUCTb179xYnldMZ+ZvDvn37xHl4027VmWeeKc7379+vu76oSD8uNlxLSwvdcsstYurca6+9RnY7Gj0CgDlxjw9OKTbiqz1Ila9+PXChdWys+4zhYvuW6uPBUjvFhFPnfJrMJ+0aACBV5IbQ9CIOMvFUJQAAMMbfwe2R/k7KfsNeTp0xa7N+slyB/yQtbF1XNhOhk7JFAk0dUV5eHpw0Z6R798BRqBMnTnTofj/44ANx32PGjBE9oMJx7yf1vo3U1dVRTY3a5juy2rp6sjcG1v66+jY/w9PyOjoxDwBAi5sjRqpbl7yh69UmiqKpIo/O1jQRV1pa/1CZiGQzXkNiNDc3i1NX8ecnZI50nDoHAACZPZHOR5rPJUfgQGwylWLqXOo1NASmdUQK+qjXq9vFiqfYMe7LZOTtt9+m8ePHR/z5aLdFdkWbaxYsWEALF6rxVAAAiBV6NCXX0qVLadGiRUl+VDC7dJw611XcmsF/8jBJnkKSHA7dhLmWk0dS/fQAAIATOuxEz1xeLHp8alsxsFMNWURrA2unK7YBDvGEqXNpgMfEas/DqSVvkfoxRfKjH/0o4n3GYtOmTXT++ee3u91Xe7eQ/V+BAJN/1Gt0+mB9N31kMwFAQtmyjNd8Uds7RLIHR8q2uYskjJTFBLn0x9NZ77jjji7fz86dOzt5sAYgPXCQad+sgal+GgAAEAXvu7rFsQB7mx6fefYsqjbo5QQZVDrn8XhEuVlVVZVh+ZyayZSTk9x5brm5uZSfH3lKiaomN4fsrdl4/tycmH4GACBebI4cw3XgcuhTt6XiEO27o3/KRsp2JjvJpzlYoF1DYsSr1Js/PwEAAABi5autIHtuDx5BTIrfK0rptH2buKl4R3ouy5ppy9o1ZFCgqbi4OBhoGjCgbaOwsrKymJp/AwBkIpvdabi2Au2QEAwMAYBUGHjvLnIWnxEsndPCVDkAgPg4tGhU1Nt5cl3EpuIG5KZK/dpj3A86U2VEoGnw4MFiotyePXsMa/x37doV3C4d2V35hmsAgGRQFNlwHc7Z8yzd5Douo9tza09KFsmmGK4dmoNT2jUAQGc5slzk06y7QnJ5xORPPgEAgDloezbxOi+VTyYNZUSgiafCrVu3jjZs2EBTp05tc/v69evF+dixYykd5Rf2ohrNGgAgmRRfvX7tNP4o5alziisnOH2O05G7X/r/Amtb4iM8smIzXGvr5lFDDwDxkOspCPbm4DUAAKQfzgrlTCVbljswMTnLrSud85btpy/uQ8/HRMiIQNPkyZPFVLY1a9ZQRUWFrkRu69attHv3btG7ady4cSl9ngAAacnfaLwOawYerbFt8X8tIHJGH0+eCmggDgAAAGBN3HMpWjmctkcTxFdGtEfnyW4TJ04UQSbOaDpyJDA2dvv27TRlyhSxnjlzJrnd6fcliHm9XsM1AECqaZuBp1qLppm3ds0jaY3WnW0gDgDg8/kM1wAAYB42h8twDV2XERlN7KmnnhIldG+++Sb169dPZDBVVweSni+++GIxcjld1VceJLtm3b2ob4qfEQBAgM3p0fVlUjgdubV0ruVUGR286+vB6xNN+xDadXgNffh4WgBIb3xQsL2DgTNmzBCnZKmrr9St83JRPgcAYDY2u8NwbQalpaXiFE1TUxOlirlezS7g4NK2bdtowYIFtHbtWqqsrBTNv6dNm0Zz5swhpxPfPAAAOspms5EtvIFtdl6wGbhKDT6lG5+sGK4BIH2sWrXKcJiLqTncxmsAAEgaM2c0zYjhAAtXcI0YMYJSwTKBpoMHD7a7Ta9evWjZsmVJeT4AABlPchiv04hkM14DACSSw5NvuAYAgCTStFrQrWMhuYzXkDk9mgAAIPkkzchv7TqdoEcTQHK98cYbNH78eNHC4MILL6SXX37Zkm+Boigiq9NbdYy85QcNTodS/RQBADIeT59TNZft1f2dbqk6Sr6aMvI3nBJ/z/nvulaWK99wDQHpeYgZAADShy3LeN0B/AGtLaXT3b3TI0rwugoT5ADS23vvvUdXXnklff/736dHH32UXn/9ddF/6bXXXhNDW6xE8TbQ7um5MW3LQwpw5BcAIPkUf6iR56GFo6Juyz1Jte0icpwSVWvWoIdAEwAARGVzeAzX7dEe+dk3a2DE7Qb/8ThJrhzxxSyclFtEkiQlLDvJp3mO2jUAxN8DDzxAw4YNo7/97W/i3/U111xDx44dE9dbLdDUEXJdRbC3HQAAJI/ii32iu+LXTxiNNtUYEGgCAIB22Owuw3V7jAJHRvbedlrE2856+ARJ+SWUKH7ZeA2Q6VauXCkGpuzevZuGDBliuA0HiXjICmckVVRUiMErU6dOpXnz5rWZEuf1eumVV16h3/3ud7rg8U9+8hO68cYbqba2lvLyrBlsGXjvLpJckYP09sJ+SX0+AAAQ+vs76P4Dhi8HNwf311fRgflfE5cVX7N+A2+Ffo1+ezrIaDIBe1aO4RoAIBkURTZct4dL4pJJsimGa7smuUm7BoDInnvuuagvz+HDh2n06NF0/PhxcZl7Lu3du5cWL15MGzdupLfeeotcrlBg+sSJEyLYdO655+ru55xzziG/309Hjx6NGNBKR3Z7Fvk163Dao9uOHv3IgYwlAIC0Y7fbyV48oFMZTTXNim6Nb+l6CDSZQH5RH6rRrAEAkknxNejXztiyDux5xSIjSZAcwYbgXFLnry0PltPxkSSbO59sSuBrW0tNGR2c//XgtrGSFZvhWls3jxp6gOj439wTTzxBr776atTtbrjhBhFkmjBhAj399NPUv39/2rFjB02ePJnef/99WrJkiQg6aQNNrHv37rr7KSwsFOdlZWWmCjTl5RYGe3Pw2rAcTrtGoAkAwHS0WUxtMpogKnStAgCA6PwNxut2cHmMI78kcMotFH2Y+GR354rrVBxwKvvbncFt7ZpGi7GW33WWw2a8Bsg0nIXEwSMO9tx8881Rt+WAEm9fUlJCzz//vAgyMe6/tHr1arF+5JFHqKmpqd3HVTN/fD79kWIAAAAwLwSaAAAgrcgtmiNGkj3mn2vRZD9p19HYJZvhGiDTrF27lpYvXy7K39qzZs0acc4T5Hr06KG7beTIkTR06FCqrq6mLVu2BK/v2bOnOD958qRue/Vyr1694vL/AQAAAKmHQJMJVB373HANAGBW3L+Jx8Sqp17XPhG8zZETKq2RsvQNhaPRDvzQrmuaZcM1AITMnj2bdu7cGTz17t074svDpXFs0qRJhrer16vbqYEmp9NJn376qW7bzz77TGQ/Rnu8dFR96oThGgAAANCjCQAAUsBms5FNUyIXdmPSnodPVgzXAJmGAz3aYA8HhSLZt2+fOB80aJDh7WeeeaY4379/v+7+OAOKS+1++ctfiuAS94Piy5dddhnl5+fH8f8GAAAAUgnNwAEAIGNpY1pJjG8BmFp5eXlw0pwRteG32gBcdccdd9Cll15K11xzjWgazuV6mzdvpnXr1iXhWQMAAHSMze40XAuSy3gNAgJNAACQVuTmesN1Itg10SXtGgAia2hoMJwgp1KvV7dTXXjhhaK/0z333EM33XST6OX00ksvicl10dTV1VFNjTp/t/NcLpc4AQAAxMLmyDJcsyxXPrVo1vHU3NwsTl3Fn5+pgkATAABEZ3MYr9NMlmS8BoD44pI37Xk4uz3QxN/v97e5beLEieLUEePHj6d4WLBgAS1cuJCSwuE0XgMAgCUymnKcElVr1vG0dOlSWrRoEZlZ+n5jAACAtGBz5BiuE0Y7aU6yR8xq4obi3OspuGmEdTQ+zRdl7RoAIvN4PCLDqKqqyrB8Ts1kysmJz9+LTZs20fnnn9/l+0lmNpPDU2C4BgAAa/RYkDWTZ7TreJg7d64oN+8qHu4Rr4M1HYVAEwAARGWzuwzXiaKbNCf7aff0XMPthjxRS3a38W2x8svGawCIrLi4OBhoGjBgQJvby8rKxHlRUVFcXsZrr72W3O7oEyhnzJghTgAAAPGi+JoN14K3Qr/2xK98zhVDqXdpaak4RdPU1ESpgkCTCdizcgzXAADJoC2PiVQqkyj7Zg2MeJvibSDSBJpsNsVwbdccgNKuAaBzBg8eLCbK7dmzh4YPH97m9l27dgW3i4dVq1YZPk4q2e1Z5NesAQDAehS/z3DNapoV3TrZ39JnxHCAZfv27TRixAhKBXSxMIH8oj6GawCAZFB89YbrZMn52uV09rI6cRp0/4GI2ymKzXCtrZuPdw09QCYaM2aMON+wYYPh7evXrxfnY8eOJavKyy00XKt8NWWGawAAsEhGE0SFjCYAAIjO32C8ThDuvaQ66+ETJLnzSXIGymZ4HdpQ08upkxw24zUARDZ58mTRVJsnyFVUVOhK5LZu3Uq7d+8WvZvGjRsXl5dxypQpKJ0DAADQQOkcAABAB+gafLtygkEmcTnCOlpj70jlfrK3iWz+Fk0DcbxNALHgxtw8Oe6NN96gqVOn0ooVK6hv374iRZ+DQmzmzJntBofMXDoHAACQSjPSvHQOGU0mUHXsc7Jr1jn5PVP8jAAAkiN84pz2cngAKVJj7+q6Wt26JDvwF/XIH6+iE3s+IvrBZ4H7bqknyo5fI0cAK3vqqadECd2bb75J/fr1ExlM1dWBQc8XX3yxmJhjZdWnTujW2V0cTAAAAGAlCDQBAEDa2nNrz5ibgUfeLlRTv3/OUDrZ+GXwsuwqCa59zTyZA4EmgFhwcGnbtm20YMECWrt2LVVWVorm39OmTaM5c+aQ0+mM2wuJ0jkAAAA9lM4BAAAkIdsJAOLn4MGD7W7Tq1cvWrZsWcJfdpTOAQBAKtjsTsO1IGUZr5NkBkrnAAAAOtYMnCfMGfFWHqYDc89t3bDjE+T6z9tMvXuGspgqK8qIdgbWdvRoAgAAAAB1n9SRZbhmDlcB+TRr0EPpHAAARGdzGK8T2Azc5soxvM2R2yO4lpzZHZ4gJ5qLa+5buwaA9GTK0jmH03gNAACWyGjKdUpUrVknW2lpqThF09TEbSFSA4EmAACIyubwGK7TjV0zNk67jsavGK8BIH2YsXTO4SkwXAMAgIlIkvE6DcxI89K59Hq1AAAg7djsbsO1FchZbsM1AAAAAGQ2paXJcM3kpjLDNQQg0GQCkt1luAYASAZFUQzXqaBtAB7eDNxmUwzXaXwwCgBMSrLbDdcAAGAdiuw3XLOaZsVwDQEonTOBbiVnUI1mDQCQVP6GsHVuWr4BimIzXOfncLlfg2YNAGaSjj2a8nOLgr05eB3OV1OmWzuLByTtuQEAQOIzmlKtFD2aAADAzBRfveE6JbST5joxdS6c5GsKW+d3+T4BIL7M2KMJAAAgk3s0IaMJAABMQztpLnzqnE9T1qddR6MteEHxCwDEyu8PlVDI3nqSbYEsSl/dSSLZRy0nj+DFBACAjIVAkwlUHfs8+AWI1zn5PVP8jAAA0o9fNl5X1XAWVuBL4MmKcvL0C5X+yd5GIspOi/5TAGAe1V9+HNw3+/dd51H+KTSCBQCwMl9tBdl5iqhkF2V0cl1FqOF1WP8mQKAJAAAsTvFyf6YcsT784JXU5w+7grd99czNROf9Sax9zZpeVAAAAAAArQ4tGqV7LWq6lVD/1qpuf81xol5n4bXSQEYTAACYRrSpc52htGY6BdYAkI7SsRm41unT/0ynD/6mrnROy17YLyXPCwAAEkjWjjXOSvpLXYpm4AAAAOnRDLzvLS/oLve55v+IWhOcHKGYEwCkkXRvBm7LyibJFciadLaeAwCA+fGBgkH3HyCbPYtsDqc4V0vnjh3+nOijcWI7yZn8YTIz0AwcAABMzWY3XqdZM3BtoChS0Mjm0P8MOVzxfYIAAAAAYAl2u53sxQPa3pCdT/kF5aQ2XcjBRJk2uj4bGgAALM3myDFcpxu7ZDNcR+NXjNcAAAAAANA5CDQBAEBUNrvbcG0FfofHcA0AAAAAEIniqzFcQwACTSYg2V2GawCATCOLCXJt10yyKcZrh8NwzWw2m+EaACAa7JsBAGS2RsVhuIYAvCIm0K3kDKrRrAEAkknx1evXWSksn1MU4zUHnhSb4bqgoDtRaxV9YB0iuThDq0mzBoB0k45T57BvBgCQ4STt1Lnk5++UYuocAABYKtBk4qlz4SRfs+EaANJHuk+dAwCADGRzGK+TZEaaT51D6RwAAJhGtKlzPk2Gk3YdjZ0UwzUAAAAAQCSK32u4hgAEmkyg+sQ+wzUAAIT4ZeN11clKwzWTW5oM1wAA2DcDAIBIFFkxXEMAAk0moMh+wzUAAMTyN1Q2XDPZ7zdcAwBg3wwAACLuX/qaDNcQgEATAACYhtzcYLju9P1FWAMAAAAARGaPsAaGQBMAAJiHLcK6kxw24zUAAAAAQCQ2d4HhGgIQaAIAgOhsduN1CkhOj+Ga2W3GawAAAACAeMq1G68hAIEmAACIymb3GK7TjUOyGa6j0fZuRB9HAAAAAICuc8ThPgAAwMJsjmzDtRX4HB7DNQCkjylTppDb7Y66zYwZM8QJAAAgGRRfjeE6WUpLS8Upmqam1DUpR6DJBGyS3XANAJBpZG+D4ZpJNsVwbZMkw7W4bLMZrgEgfaxatYqGDx9O6QT7ZgAAma1RcejWye7SNCOGAyzbt2+nESNGUCqgdM4ECnoOMlwDACSD4qs3XKeEohivRembzXDdvbCH4ZpJLrfhGgAgGuybAQBkOO3By7ADmYBAU9zdfPPNNHLkSPxuAYBlpFWgKc5j52y+ZsM1ACTHQw89RD/60Y/wcgMAgLnYHMZrEBB6i6MNGzbQ448/Hs+7BAAADcnlMVwznybDSbuOxkGK4RoAEu/YsWP04IMP4qUGAADTUfxewzUEINAUJ6dOnaIbbriB8vPzKd6qT+wzXAMAQIhfNl5Xnaw0XDO5pclwDQCJwz0jLr30Uho4cCAdOnTIlC819s0AADKbohlXrF2DxQJNK1euFI1cP//886hHzqZPn059+vQR00vOOussuvvuu+PSjf22226jM888k37wgx9QvCmy33ANAACx/A2VDddM9vsN1wCZIFX7TnxQ7rvf/S4tWbJE7DuZEfbNAAAym+JrMlxDgGWKCZ977rmotx8+fJhGjx5Nx48fF5cLCgpo7969tHjxYtq4cSO99dZb5HK5OvXYL7/8Mr344ov0ySef0D333NOp+wAAgPbJzQ2G687SHoDCwSjINKnadxo0aBDdeeedYv3qq6928tkDAACkknYaPCbDWy6jSVEU0RepvR0VLmvjHaUJEyaINO2qqiqRut23b196//33xVG1zqioqBBH+n7/+9/TgAEDYvoZr9erOwdza25upoULF4pzsAa8p2msk73A/S3NtOvZ37f5d+qQjNeQ3vA5au59p1jhfe4cfIbhNUsW/K7hdcvk3zWbu8BwnU68KYw7mHa3mo+k8Q7QkCFDxKS3aHbs2CG2Lykpoeeff5769+8vrh82bBitXr1arB955JFOpYHzY3/jG9+gm266KeafwY6TtfAfvEWLFqXVHz7oGryn0T4qUvuxITk9hmtmtxmvmdzipc+eux8BfovA56i5951ihfe5c/AZhtcsWfC7htctk3/Xcu3G63TiRaCp49auXUvLly8XKdztWbNmjTi/8sorqUePHrrbRo4cSUOHDqXq6mrasmWL7jbOUOLeBUYnHsfLO1qvv/66WNfX14tTS0sL+f1+sUbGEgBYgc2RY7hONw7JZriOBqVzkEnSYd8JAAAArM+0GU2zZ8+mnTt3Bk+9e/eOuC2nd7NJkyYZ3q5er26n+vDDD0XKuNHpF7/4Be3atUsElM4991zKzc0Vp2effVYcBeQ19zBIJ6WlpXhMi8F7ai3p+n7aHNmG60Q+ZrL47NmGa6u/p1Z4TDDnvlMqxfv3dMWbmfFvKN7PLZ73lymvWzq/B5nyumXKaxbv+7Py6yb7aoLrZctXUDyVpvHrZvlAE+8cccmaenI6nRG33bdvX7D5pBF14sn+/ft11xcVFVHPnj0NTx6Ph66//np69913dafvfOc7IiWd1/HaobJJdsN1R2XKlw8r/MOMVaa8vpnynmbKa9uVx5S9jYZrJtkUw7VNkgzX7d2Wia+vmR4TzLnvlErx/D3l/bE//TO0tvK/IXyJTf3rls7vQaa8bpnymsX7/qz8ujUpoblqT/7lRYqn0jR+3TJu6lw05eXlwWkpRrp37y7OT5w40aH75WaYfNLiHSl+vIsuuojipaDnIKrRrAEAkknx1evXWSksn1Nk47Uog7MZrrsXctlP4O97QfdC3c/YnDwxq1mzBoBE7jtZhXZ/DPtmAACZR+Gy8FQ/iTSWEYGmhoYG3U5ROPV6dbtEUxtnctp6TNvXn6S6zwPr3C3vkztH/0WJj0hGOyqpfVyeFpNMmfCYdXV1wfeTSyaTKRNe30x6T9P1tfXVH6WqQ4F194/+RY6c3gl/zEhaqo7SkcrAuvaTTymre1Xwttpmmar2VYv1zsICynMFMpQaGxuo+kDghz755N909GhgVDs7WV1FVfsC2U+fZtvoWIHx54TV3tNUPSb3LoxH/0L18zORjagzXTrsO3V0f6k9jY2Ncfu30VhXSc1eok8OEeXu/ISyc/V9rFL57zben2Hx/psSz/uL130l4nM/Hf8/431/mfS64d9oal+3dPxdKz++n6h1/7i52RvXf6ONjY20efNmc+8vKRZxxhln8DcFZffu3W1uczgc4raqqirDn33jjTfE7d/85jeT8EwVZcWKFeLxcMJrgN8B/A7gdwC/A/gd6PzvAH+egnX3nbC/hL+P+PuI3wH8DuB3AL8DZMr9pYzIaOKeADU1NVRVVWWYAq4ejcvJSU45yPe+9z1asWKF6JXgdru7fH+xZjQBAACkUrwymvjI3NGjR8XnKVh33wn7SwAAkIm8FthfyohAU3FxcXBnicfuhisrKws2sEwGfpxrr702KY8FAAAAYMZ9J+wvAQAAmJNpp851xODBg8X5nj17DG/ftWuXbjsAAACATIZ9JwAAAOisjAg0jRkzRpxv2LDB8Pb169eL87Fjxyb1eQEAAACkI+w7AQAAQGdlRKBp8uTJ4nzNmjVUUVGhu23r1q20e/du0X9g3LhxCX0ex44do+nTp1OfPn1Eb6azzjqL7r77bkzNSRNffPGFKGn8xje+IaYZfO1rXxOXDxw4ELf3E78DqfPxxx9TVlYW/eQnPzG8He9n+lMUhZ566ikaPXo05efnU79+/ejHP/4x/o2aFJde3XzzzXTeeedRXl4eXXDBBfSrX/2KamtrDbfHv9HM3Hfqyt8A6NznIXRuvzCTYH+2c/A71XX4+2Wiz0slAyansIkTJ4rbJ0yYoBw+fFhct23bNqVPnz7i+oULFyb0+X3xxRfKaaedFuz8XlBQEFyPHTtWaWpqSujjQ3Tr1q1T8vLyxPths9mUnj17inO+nJOTo7zwwgtdfj/xO5A6Pp9PGTFihHh/pk6d2uZ2vJ/pT5Zl5cc//rHu31xWVlbw3+hHH32k2x7vaXrbvn27UlRUJN4/SZLE31z1ve3fv3/wc1qF9zMz953+P3t3AudEef4B/Mkkm82esOyBoFxyqVgVEMQqoCKCWoqtWKloi1JR/3iAQgWxAopiq61axQMPtFW8wKOIAqIIiIjKoUWLcgrKtSfLHtlsMvP/PG92kpnsJJtkc+f3/XxCniSTyexMSN48877P25rPAAjv+xDCaxemE7Rnw4P3VOvh8yu5vi/TJtHEH4odOnQwTAwMHjxYaWhoiOr2XXjhhZ7G2t69ez0N7U6dOon777777qi+PvjncDiU7t27i+Nw7bXXKlVVVeJ+vr799tvF/W3atFF+/vnnVh1PvAfi569//avn/7tRwxrHM/E99NBD4vh17NhRWbduneJyuZTa2lplwoQJ4v5TTz1VNEBUOKaJq7GxUTnjjDPEcRs/frxy9OhRcf/BgweViy++WNz/q1/9SvccHM/0bDu15jMAwvs+hPDahekE7dnQ4T0VGfj8Sq7vy7RJNLEDBw4o119/vdjpmZmZSs+ePcXZuGj3JuIEBG9bSUmJUlZWpnvsiy++8DTe6uvro7odYOyVV14Rx6B3794iA+zr97//vXj8jjvuCPt44j0QPzt27FCysrL8NqxxPBNfTU2NUlhYKM7G8PHybbz16tVLHNsNGzaI+3BME9v69evF8eLjxkknrcrKSk8yg7+zGY5neradWvMZAOF9H0J47cJ0gvZsePCeaj18fiXf92XK1Gjas2ePGIvYu3dvv8t06NCBFixYQD///LOoo8Oz0M2aNYsyMzOjum1c34CNGjWKCgsLdY8NGDC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",
+      "text/plain": [
+       "<Figure size 1200x1000 with 4 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "columns = ['PRI_jet_leading_pt', 'PRI_jet_leading_eta', 'PRI_jet_leading_phi', 'PRI_jet_subleading_pt']\n",
+    "\n",
+    "# columns = ['PRI_had_pt', 'PRI_had_eta', 'PRI_had_phi']\n",
+    "nbins = 50\n",
+    "\n",
+    "xlabel_dict = {\n",
+    "    'PRI_jet_leading_pt': 'leading jet $p_T$ [GeV]', \n",
+    "    'PRI_jet_leading_eta': 'leading jet $\\eta$ [GeV]', \n",
+    "    'PRI_jet_leading_phi': 'leading jet $\\phi$', \n",
+    "    'PRI_jet_subleading_pt': 'subleading jet $p_T$ [GeV]',\n",
+    "}\n",
+    "\n",
+    "variations_to_plot = ['Nominal', 'JES_Up', 'JES_Dn']\n",
+    "\n",
+    "\n",
+    "fig, axes = plot_kinematic_features(\n",
+    "    columns=columns,\n",
+    "    nbins=nbins,\n",
+    "    variations_to_plot=variations_to_plot,\n",
+    "    dataset_dict=datasets_all,\n",
+    "    xlabel_dict=xlabel_dict,\n",
+    "    samples_list=samples_list\n",
+    ")\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python (torch-mac-mps)",
+   "language": "python",
+   "name": "torch-mac-mps"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.11.15"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 4
+}
diff --git a/FAIR_universe_Higgs_tautau/Preselection_withNN.ipynb b/FAIR_universe_Higgs_tautau/3_preselection_network.ipynb
similarity index 74%
rename from FAIR_universe_Higgs_tautau/Preselection_withNN.ipynb
rename to FAIR_universe_Higgs_tautau/3_preselection_network.ipynb
index 4b57cbbb..90c9ee8e 100644
--- a/FAIR_universe_Higgs_tautau/Preselection_withNN.ipynb
+++ b/FAIR_universe_Higgs_tautau/3_preselection_network.ipynb
@@ -20,21 +20,7 @@
    "cell_type": "code",
    "execution_count": 1,
    "metadata": {},
-   "outputs": [
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "2025-11-10 18:19:20.194093: E external/local_xla/xla/stream_executor/cuda/cuda_fft.cc:485] Unable to register cuFFT factory: Attempting to register factory for plugin cuFFT when one has already been registered\n",
-      "2025-11-10 18:19:20.214024: E external/local_xla/xla/stream_executor/cuda/cuda_dnn.cc:8454] Unable to register cuDNN factory: Attempting to register factory for plugin cuDNN when one has already been registered\n",
-      "2025-11-10 18:19:20.220435: E external/local_xla/xla/stream_executor/cuda/cuda_blas.cc:1452] Unable to register cuBLAS factory: Attempting to register factory for plugin cuBLAS when one has already been registered\n",
-      "2025-11-10 18:19:20.236021: I tensorflow/core/platform/cpu_feature_guard.cc:210] This TensorFlow binary is optimized to use available CPU instructions in performance-critical operations.\n",
-      "To enable the following instructions: SSE4.1 SSE4.2 AVX AVX2 AVX512F FMA, in other operations, rebuild TensorFlow with the appropriate compiler flags.\n",
-      "/home/jsandesara_umass_edu/NSBI-workflow-tutorial/src/nsbi_common_utils/plotting.py:10: FutureWarning: ``set_style`` is deprecated: Naming convention is changing to match mpl. Use ``mplhep.style.use()``.\n",
-      "  hep.set_style(\"ATLAS\")\n"
-     ]
-    }
-   ],
+   "outputs": [],
    "source": [
     "import os, sys, pathlib, importlib\n",
     "sys.path.append('../')\n",
@@ -44,53 +30,80 @@
     "from nsbi_common_utils import training, datasets, configuration\n",
     "from nsbi_common_utils.training import preselection_network_trainer\n",
     "\n",
-    "\n",
-    "import tensorflow as tf\n",
-    "tf.config.optimizer.set_jit(False)\n",
-    "from tensorflow import keras\n",
-    "from tensorflow.keras import layers\n",
     "import numpy as np\n",
     "import pandas as pd\n",
     "from sklearn.model_selection import train_test_split\n",
-    "from tensorflow.keras.optimizers import Nadam\n",
     "import mplhep as hep\n",
     "import matplotlib.pyplot as plt\n",
     "import pickle\n",
     "import yaml\n",
     "import random\n",
     "\n",
-    "from utils import calculate_preselection_observable\n",
-    "\n",
     "import warnings\n",
     "warnings.filterwarnings(\"ignore\", category=RuntimeWarning)\n",
     "warnings.filterwarnings(\"ignore\", category=FutureWarning)\n",
     "\n",
-    "hep.style.use(hep.style.ATLAS)"
+    "# hep.style.use(hep.style.ATLAS)\n",
+    "hep.style.use(\"ATLAS\")\n",
+    "\n",
+    "from scripts.utils import calculate_preselection_observable"
    ]
   },
   {
    "cell_type": "code",
    "execution_count": 2,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "torch: 2.10.0\n",
+      "cuda available: False\n",
+      "torch cuda: None\n"
+     ]
+    }
+   ],
    "source": [
-    "## TODOs\n",
-    "## define mutable vs immutable information\n",
-    "\n",
-    "importlib.reload(sys.modules['nsbi_common_utils.configuration'])\n",
-    "from nsbi_common_utils import configuration\n",
-    "\n",
-    "config = nsbi_common_utils.configuration.ConfigManager(file_path_string = './config.yml')"
+    "import torch\n",
+    "print(\"torch:\", torch.__version__)\n",
+    "print(\"cuda available:\", torch.cuda.is_available())\n",
+    "print(\"torch cuda:\", torch.version.cuda)\n",
+    "# print(\"gpu:\", torch.cuda.get_device_name(0))\n",
+    "# print(\"cc:\", torch.cuda.get_device_capability(0))\n"
    ]
   },
   {
    "cell_type": "code",
    "execution_count": 3,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "platform.machine(): arm64\n",
+      "platform.processor(): arm\n",
+      "torch version: 2.10.0\n",
+      "mps available: True\n",
+      "selected device: mps\n",
+      "tensor device: mps:0\n"
+     ]
+    }
+   ],
    "source": [
-    "# Input features for training\n",
-    "features, features_scaling = config.get_training_features()"
+    "import platform, torch\n",
+    "\n",
+    "print(\"platform.machine():\", platform.machine())\n",
+    "print(\"platform.processor():\", platform.processor())\n",
+    "print(\"torch version:\", torch.__version__)\n",
+    "print(\"mps available:\", torch.backends.mps.is_available())\n",
+    "\n",
+    "device = \"mps\" if torch.backends.mps.is_available() else \"cpu\"\n",
+    "x = torch.randn(2, 2).to(device)\n",
+    "\n",
+    "print(\"selected device:\", device)\n",
+    "print(\"tensor device:\", x.device)\n"
    ]
   },
   {
@@ -99,23 +112,213 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "importlib.reload(sys.modules['nsbi_common_utils.datasets'])\n",
-    "from nsbi_common_utils import datasets\n",
+    "def check_apple_GPU():\n",
+    "    import torch\n",
+    "    import pytorch_lightning as pl\n",
+    "    import torch.nn as nn\n",
+    "    from torch.utils.data import DataLoader, TensorDataset\n",
+    "\n",
+    "    x = torch.randn(256, 8)\n",
+    "    y = torch.randint(0, 2, (256,))\n",
+    "\n",
+    "    loader = DataLoader(TensorDataset(x, y), batch_size=32)\n",
+    "\n",
+    "    class ToyModel(pl.LightningModule):\n",
+    "        def __init__(self):\n",
+    "            super().__init__()\n",
+    "            self.layer = nn.Linear(8, 2)\n",
+    "\n",
+    "        def forward(self, x):\n",
+    "            return self.layer(x)\n",
+    "\n",
+    "        def training_step(self, batch, batch_idx):\n",
+    "            x, y = batch\n",
+    "            if batch_idx == 0:\n",
+    "                print(\"training_step model device:\", next(self.parameters()).device)\n",
+    "                print(\"training_step input device:\", x.device)\n",
+    "            loss = nn.CrossEntropyLoss()(self(x), y)\n",
+    "            return loss\n",
+    "\n",
+    "        def configure_optimizers(self):\n",
+    "            return torch.optim.Adam(self.parameters(), lr=1e-3)\n",
     "\n",
-    "datasets_helper = nsbi_common_utils.datasets.datasets(config_path = './config.yml',\n",
-    "                                                branches_to_load = features)"
+    "    model = ToyModel()\n",
+    "\n",
+    "    trainer = pl.Trainer(\n",
+    "        accelerator=\"mps\" if torch.backends.mps.is_available() else \"cpu\",\n",
+    "        devices=1,\n",
+    "        max_epochs=2,\n",
+    "        logger=False,\n",
+    "        enable_checkpointing=False,\n",
+    "    )\n",
+    "\n",
+    "    trainer.fit(model, loader)\n",
+    "\n",
+    "    print(\"trainer root device:\", trainer.strategy.root_device)\n",
+    "    print(\"model device:\", next(model.parameters()).device)\n"
    ]
   },
   {
    "cell_type": "code",
    "execution_count": 5,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "GPU available: True (mps), used: True\n",
+      "TPU available: False, using: 0 TPU cores\n",
+      "💡 Tip: For seamless cloud logging and experiment tracking, try installing [litlogger](https://pypi.org/project/litlogger/) to enable LitLogger, which logs metrics and artifacts automatically to the Lightning Experiments platform.\n"
+     ]
+    },
+    {
+     "data": {
+      "text/html": [
+       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\">┏━━━┳━━━━━━━┳━━━━━━━━┳━━━━━━━━┳━━━━━━━┳━━━━━━━┓\n",
+       "┃<span style=\"color: #800080; text-decoration-color: #800080; font-weight: bold\">   </span>┃<span style=\"color: #800080; text-decoration-color: #800080; font-weight: bold\"> Name  </span>┃<span style=\"color: #800080; text-decoration-color: #800080; font-weight: bold\"> Type   </span>┃<span style=\"color: #800080; text-decoration-color: #800080; font-weight: bold\"> Params </span>┃<span style=\"color: #800080; text-decoration-color: #800080; font-weight: bold\"> Mode  </span>┃<span style=\"color: #800080; text-decoration-color: #800080; font-weight: bold\"> FLOPs </span>┃\n",
+       "┡━━━╇━━━━━━━╇━━━━━━━━╇━━━━━━━━╇━━━━━━━╇━━━━━━━┩\n",
+       "│<span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\"> 0 </span>│ layer │ Linear │     18 │ train │     0 │\n",
+       "└───┴───────┴────────┴────────┴───────┴───────┘\n",
+       "</pre>\n"
+      ],
+      "text/plain": [
+       "┏━━━┳━━━━━━━┳━━━━━━━━┳━━━━━━━━┳━━━━━━━┳━━━━━━━┓\n",
+       "┃\u001b[1;35m \u001b[0m\u001b[1;35m \u001b[0m\u001b[1;35m \u001b[0m┃\u001b[1;35m \u001b[0m\u001b[1;35mName \u001b[0m\u001b[1;35m \u001b[0m┃\u001b[1;35m \u001b[0m\u001b[1;35mType  \u001b[0m\u001b[1;35m \u001b[0m┃\u001b[1;35m \u001b[0m\u001b[1;35mParams\u001b[0m\u001b[1;35m \u001b[0m┃\u001b[1;35m \u001b[0m\u001b[1;35mMode \u001b[0m\u001b[1;35m \u001b[0m┃\u001b[1;35m \u001b[0m\u001b[1;35mFLOPs\u001b[0m\u001b[1;35m \u001b[0m┃\n",
+       "┡━━━╇━━━━━━━╇━━━━━━━━╇━━━━━━━━╇━━━━━━━╇━━━━━━━┩\n",
+       "│\u001b[2m \u001b[0m\u001b[2m0\u001b[0m\u001b[2m \u001b[0m│ layer │ Linear │     18 │ train │     0 │\n",
+       "└───┴───────┴────────┴────────┴───────┴───────┘\n"
+      ]
+     },
+     "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\"><span style=\"font-weight: bold\">Trainable params</span>: 18                                                                                               \n",
+       "<span style=\"font-weight: bold\">Non-trainable params</span>: 0                                                                                            \n",
+       "<span style=\"font-weight: bold\">Total params</span>: 18                                                                                                   \n",
+       "<span style=\"font-weight: bold\">Total estimated model params size (MB)</span>: 0                                                                          \n",
+       "<span style=\"font-weight: bold\">Modules in train mode</span>: 1                                                                                           \n",
+       "<span style=\"font-weight: bold\">Modules in eval mode</span>: 0                                                                                            \n",
+       "<span style=\"font-weight: bold\">Total FLOPs</span>: 0                                                                                                     \n",
+       "</pre>\n"
+      ],
+      "text/plain": [
+       "\u001b[1mTrainable params\u001b[0m: 18                                                                                               \n",
+       "\u001b[1mNon-trainable params\u001b[0m: 0                                                                                            \n",
+       "\u001b[1mTotal params\u001b[0m: 18                                                                                                   \n",
+       "\u001b[1mTotal estimated model params size (MB)\u001b[0m: 0                                                                          \n",
+       "\u001b[1mModules in train mode\u001b[0m: 1                                                                                           \n",
+       "\u001b[1mModules in eval mode\u001b[0m: 0                                                                                            \n",
+       "\u001b[1mTotal FLOPs\u001b[0m: 0                                                                                                     \n"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "8828c78f2e5d4a3fb9dae7adb04f04c2",
+       "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\">training_step model device: mps:0\n",
+       "</pre>\n"
+      ],
+      "text/plain": [
+       "training_step model device: mps:0\n"
+      ]
+     },
+     "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\">training_step input device: mps:0\n",
+       "</pre>\n"
+      ],
+      "text/plain": [
+       "training_step input device: mps:0\n"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/Users/gauravsharma/Documents/github_projects/feb_05_2k26_macOS_pyt/NSBI-workflow-tutorial/.pixi/envs/torch-mac-mps/lib/python3.11/site-packages/pytorch_lightning/trainer/connectors/data_connector.py:434: The 'train_dataloader' does not have many workers which may be a bottleneck. Consider increasing the value of the `num_workers` argument` to `num_workers=15` in the `DataLoader` to improve performance.\n"
+     ]
+    },
+    {
+     "data": {
+      "text/html": [
+       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\">training_step model device: mps:0\n",
+       "</pre>\n"
+      ],
+      "text/plain": [
+       "training_step model device: mps:0\n"
+      ]
+     },
+     "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\">training_step input device: mps:0\n",
+       "</pre>\n"
+      ],
+      "text/plain": [
+       "training_step input device: mps:0\n"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "`Trainer.fit` stopped: `max_epochs=2` reached.\n"
+     ]
+    },
+    {
+     "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"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "trainer root device: mps:0\n",
+      "model device: cpu\n"
+     ]
+    }
+   ],
    "source": [
-    "# Get the dictionary of labels to processes\n",
-    "train_label_sample_dict = {\"htautau\"        : 0,\n",
-    "                            \"ttbar\"         : 1,\n",
-    "                            \"ztautau\"       : 2}"
+    "check_apple_GPU()"
    ]
   },
   {
@@ -124,20 +327,456 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "dataset_incl_dict = datasets_helper.load_datasets_from_config(load_systematics = True)\n",
+    "def load_config(path):\n",
+    "    with open(path, \"r\") as f:\n",
+    "        return yaml.safe_load(f)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "config_workflow        = load_config(\"config.pipeline.yaml\")[\"preselection_network\"]\n",
     "\n",
+    "config_train           = config_workflow[\"training\"]\n",
+    "config_preselection    = config_workflow[\"preselection_observable\"]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "fit_config_path = config_workflow[\"fit_config_path\"]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "fit_config = nsbi_common_utils.configuration.ConfigManager(file_path_string = fit_config_path)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Input features for training\n",
+    "features, features_scaling = fit_config.get_training_features()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "label_dict = config_train[\"labels\"]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "datasets_helper = nsbi_common_utils.datasets.datasets(\n",
+    "                            config_path=fit_config_path,\n",
+    "                            branches_to_load=features\n",
+    "                        )"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 13,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "dataset_incl_dict = datasets_helper.load_datasets_from_config(load_systematics=True)\n",
     "dataset_incl_nominal = dataset_incl_dict[\"Nominal\"].copy()"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 7,
+   "execution_count": 14,
    "metadata": {},
    "outputs": [],
    "source": [
-    "dataset_incl_nominal_training = datasets_helper.merge_dataframe_dict_for_training(dataset_incl_nominal, \n",
-    "                                                                            train_label_sample_dict,\n",
-    "                                                                            samples_to_merge = dataset_incl_nominal.keys())"
+    "dataset_incl_nominal_training = datasets_helper.merge_dataframe_dict_for_training(\n",
+    "        dataset_incl_nominal, \n",
+    "        label_dict,\n",
+    "        samples_to_merge=dataset_incl_nominal.keys()\n",
+    "    )"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 15,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "preselectionTraining = preselection_network_trainer(\n",
+    "        dataset_incl_nominal_training, \n",
+    "        features, \n",
+    "        features_scaling\n",
+    "    )"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 16,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "model_path = config_workflow[\"model_path\"]\n",
+    "force_train = config_workflow[\"force_train\"]\n",
+    "load_trained_models = config_workflow[\"load_trained_models\"]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 17,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Starting Training\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "GPU available: True (mps), used: True\n",
+      "TPU available: False, using: 0 TPU cores\n",
+      "/Users/gauravsharma/Documents/github_projects/feb_05_2k26_macOS_pyt/NSBI-workflow-tutorial/.pixi/envs/torch-mac-mps/lib/python3.11/site-packages/pytorch_lightning/trainer/connectors/logger_connector/logger_connector.py:76: Starting from v1.9.0, `tensorboardX` has been removed as a dependency of the `pytorch_lightning` package, due to potential conflicts with other packages in the ML ecosystem. For this reason, `logger=True` will use `CSVLogger` as the default logger, unless the `tensorboard` or `tensorboardX` packages are found. Please `pip install lightning[extra]` or one of them to enable TensorBoard support by default\n",
+      "💡 Tip: For seamless cloud logging and experiment tracking, try installing [litlogger](https://pypi.org/project/litlogger/) to enable LitLogger, which logs metrics and artifacts automatically to the Lightning Experiments platform.\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "selected accelerator: mps\n"
+     ]
+    },
+    {
+     "data": {
+      "text/html": [
+       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\">┏━━━┳━━━━━━┳━━━━━━━━━━━━┳━━━━━━━━┳━━━━━━━┳━━━━━━━┓\n",
+       "┃<span style=\"color: #800080; text-decoration-color: #800080; font-weight: bold\">   </span>┃<span style=\"color: #800080; text-decoration-color: #800080; font-weight: bold\"> Name </span>┃<span style=\"color: #800080; text-decoration-color: #800080; font-weight: bold\"> Type       </span>┃<span style=\"color: #800080; text-decoration-color: #800080; font-weight: bold\"> Params </span>┃<span style=\"color: #800080; text-decoration-color: #800080; font-weight: bold\"> Mode  </span>┃<span style=\"color: #800080; text-decoration-color: #800080; font-weight: bold\"> FLOPs </span>┃\n",
+       "┡━━━╇━━━━━━╇━━━━━━━━━━━━╇━━━━━━━━╇━━━━━━━╇━━━━━━━┩\n",
+       "│<span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\"> 0 </span>│ mlp  │ Sequential │  3.0 M │ train │     0 │\n",
+       "│<span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\"> 1 </span>│ out  │ Linear     │  3.0 K │ train │     0 │\n",
+       "└───┴──────┴────────────┴────────┴───────┴───────┘\n",
+       "</pre>\n"
+      ],
+      "text/plain": [
+       "┏━━━┳━━━━━━┳━━━━━━━━━━━━┳━━━━━━━━┳━━━━━━━┳━━━━━━━┓\n",
+       "┃\u001b[1;35m \u001b[0m\u001b[1;35m \u001b[0m\u001b[1;35m \u001b[0m┃\u001b[1;35m \u001b[0m\u001b[1;35mName\u001b[0m\u001b[1;35m \u001b[0m┃\u001b[1;35m \u001b[0m\u001b[1;35mType      \u001b[0m\u001b[1;35m \u001b[0m┃\u001b[1;35m \u001b[0m\u001b[1;35mParams\u001b[0m\u001b[1;35m \u001b[0m┃\u001b[1;35m \u001b[0m\u001b[1;35mMode \u001b[0m\u001b[1;35m \u001b[0m┃\u001b[1;35m \u001b[0m\u001b[1;35mFLOPs\u001b[0m\u001b[1;35m \u001b[0m┃\n",
+       "┡━━━╇━━━━━━╇━━━━━━━━━━━━╇━━━━━━━━╇━━━━━━━╇━━━━━━━┩\n",
+       "│\u001b[2m \u001b[0m\u001b[2m0\u001b[0m\u001b[2m \u001b[0m│ mlp  │ Sequential │  3.0 M │ train │     0 │\n",
+       "│\u001b[2m \u001b[0m\u001b[2m1\u001b[0m\u001b[2m \u001b[0m│ out  │ Linear     │  3.0 K │ train │     0 │\n",
+       "└───┴──────┴────────────┴────────┴───────┴───────┘\n"
+      ]
+     },
+     "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\"><span style=\"font-weight: bold\">Trainable params</span>: 3.0 M                                                                                            \n",
+       "<span style=\"font-weight: bold\">Non-trainable params</span>: 0                                                                                            \n",
+       "<span style=\"font-weight: bold\">Total params</span>: 3.0 M                                                                                                \n",
+       "<span style=\"font-weight: bold\">Total estimated model params size (MB)</span>: 12                                                                         \n",
+       "<span style=\"font-weight: bold\">Modules in train mode</span>: 10                                                                                          \n",
+       "<span style=\"font-weight: bold\">Modules in eval mode</span>: 0                                                                                            \n",
+       "<span style=\"font-weight: bold\">Total FLOPs</span>: 0                                                                                                     \n",
+       "</pre>\n"
+      ],
+      "text/plain": [
+       "\u001b[1mTrainable params\u001b[0m: 3.0 M                                                                                            \n",
+       "\u001b[1mNon-trainable params\u001b[0m: 0                                                                                            \n",
+       "\u001b[1mTotal params\u001b[0m: 3.0 M                                                                                                \n",
+       "\u001b[1mTotal estimated model params size (MB)\u001b[0m: 12                                                                         \n",
+       "\u001b[1mModules in train mode\u001b[0m: 10                                                                                          \n",
+       "\u001b[1mModules in eval mode\u001b[0m: 0                                                                                            \n",
+       "\u001b[1mTotal FLOPs\u001b[0m: 0                                                                                                     \n"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "9627c51df8bb4f6ea2a52b88beff8dd0",
+       "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\">/Users/gauravsharma/Documents/github_projects/feb_05_2k26_macOS_pyt/NSBI-workflow-tutorial/.pixi/envs/torch-mac-mps\n",
+       "/lib/python3.11/site-packages/pytorch_lightning/trainer/connectors/data_connector.py:429: Consider setting \n",
+       "`persistent_workers=True` in 'val_dataloader' to speed up the dataloader worker initialization.\n",
+       "</pre>\n"
+      ],
+      "text/plain": [
+       "/Users/gauravsharma/Documents/github_projects/feb_05_2k26_macOS_pyt/NSBI-workflow-tutorial/.pixi/envs/torch-mac-mps\n",
+       "/lib/python3.11/site-packages/pytorch_lightning/trainer/connectors/data_connector.py:429: Consider setting \n",
+       "`persistent_workers=True` in 'val_dataloader' to speed up the dataloader worker initialization.\n"
+      ]
+     },
+     "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\">Epoch    0 | lr = 1.000e-06 | val_loss = 1.099555 | val_acc = 0.3539\n",
+       "</pre>\n"
+      ],
+      "text/plain": [
+       "Epoch    0 | lr = 1.000e-06 | val_loss = 1.099555 | val_acc = 0.3539\n"
+      ]
+     },
+     "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\">/Users/gauravsharma/Documents/github_projects/feb_05_2k26_macOS_pyt/NSBI-workflow-tutorial/.pixi/envs/torch-mac-mps\n",
+       "/lib/python3.11/site-packages/pytorch_lightning/trainer/connectors/data_connector.py:429: Consider setting \n",
+       "`persistent_workers=True` in 'train_dataloader' to speed up the dataloader worker initialization.\n",
+       "</pre>\n"
+      ],
+      "text/plain": [
+       "/Users/gauravsharma/Documents/github_projects/feb_05_2k26_macOS_pyt/NSBI-workflow-tutorial/.pixi/envs/torch-mac-mps\n",
+       "/lib/python3.11/site-packages/pytorch_lightning/trainer/connectors/data_connector.py:429: Consider setting \n",
+       "`persistent_workers=True` in 'train_dataloader' to speed up the dataloader worker initialization.\n"
+      ]
+     },
+     "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\">Epoch    0 | lr = 1.000e-06 | train_loss = 0.511784 | train_acc = 0.7706 | val_loss = 0.558042 | val_acc = 0.7587\n",
+       "</pre>\n"
+      ],
+      "text/plain": [
+       "Epoch    0 | lr = 1.000e-06 | train_loss = 0.511784 | train_acc = 0.7706 | val_loss = 0.558042 | val_acc = 0.7587\n"
+      ]
+     },
+     "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\">Epoch    1 | lr = 1.000e-06 | train_loss = 0.528557 | train_acc = 0.7694 | val_loss = 0.514949 | val_acc = 0.7815\n",
+       "</pre>\n"
+      ],
+      "text/plain": [
+       "Epoch    1 | lr = 1.000e-06 | train_loss = 0.528557 | train_acc = 0.7694 | val_loss = 0.514949 | val_acc = 0.7815\n"
+      ]
+     },
+     "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\">Epoch    2 | lr = 1.000e-06 | train_loss = 0.522432 | train_acc = 0.7871 | val_loss = 0.506022 | val_acc = 0.7861\n",
+       "</pre>\n"
+      ],
+      "text/plain": [
+       "Epoch    2 | lr = 1.000e-06 | train_loss = 0.522432 | train_acc = 0.7871 | val_loss = 0.506022 | val_acc = 0.7861\n"
+      ]
+     },
+     "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\">Epoch    3 | lr = 1.000e-06 | train_loss = 0.516662 | train_acc = 0.7755 | val_loss = 0.501236 | val_acc = 0.7888\n",
+       "</pre>\n"
+      ],
+      "text/plain": [
+       "Epoch    3 | lr = 1.000e-06 | train_loss = 0.516662 | train_acc = 0.7755 | val_loss = 0.501236 | val_acc = 0.7888\n"
+      ]
+     },
+     "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\">Epoch    4 | lr = 1.000e-06 | train_loss = 0.491181 | train_acc = 0.7851 | val_loss = 0.497460 | val_acc = 0.7913\n",
+       "</pre>\n"
+      ],
+      "text/plain": [
+       "Epoch    4 | lr = 1.000e-06 | train_loss = 0.491181 | train_acc = 0.7851 | val_loss = 0.497460 | val_acc = 0.7913\n"
+      ]
+     },
+     "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\">Epoch    5 | lr = 1.000e-06 | train_loss = 0.465231 | train_acc = 0.8127 | val_loss = 0.494060 | val_acc = 0.7932\n",
+       "</pre>\n"
+      ],
+      "text/plain": [
+       "Epoch    5 | lr = 1.000e-06 | train_loss = 0.465231 | train_acc = 0.8127 | val_loss = 0.494060 | val_acc = 0.7932\n"
+      ]
+     },
+     "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\">Epoch    6 | lr = 1.000e-06 | train_loss = 0.484480 | train_acc = 0.8009 | val_loss = 0.490745 | val_acc = 0.7952\n",
+       "</pre>\n"
+      ],
+      "text/plain": [
+       "Epoch    6 | lr = 1.000e-06 | train_loss = 0.484480 | train_acc = 0.8009 | val_loss = 0.490745 | val_acc = 0.7952\n"
+      ]
+     },
+     "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\">Epoch    7 | lr = 1.000e-06 | train_loss = 0.460311 | train_acc = 0.8143 | val_loss = 0.487393 | val_acc = 0.7970\n",
+       "</pre>\n"
+      ],
+      "text/plain": [
+       "Epoch    7 | lr = 1.000e-06 | train_loss = 0.460311 | train_acc = 0.8143 | val_loss = 0.487393 | val_acc = 0.7970\n"
+      ]
+     },
+     "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\">Epoch    8 | lr = 1.000e-06 | train_loss = 0.491496 | train_acc = 0.7901 | val_loss = 0.484077 | val_acc = 0.7988\n",
+       "</pre>\n"
+      ],
+      "text/plain": [
+       "Epoch    8 | lr = 1.000e-06 | train_loss = 0.491496 | train_acc = 0.7901 | val_loss = 0.484077 | val_acc = 0.7988\n"
+      ]
+     },
+     "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\">Epoch    9 | lr = 1.000e-06 | train_loss = 0.471397 | train_acc = 0.7978 | val_loss = 0.481057 | val_acc = 0.8006\n",
+       "</pre>\n"
+      ],
+      "text/plain": [
+       "Epoch    9 | lr = 1.000e-06 | train_loss = 0.471397 | train_acc = 0.7978 | val_loss = 0.481057 | val_acc = 0.8006\n"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "`Trainer.fit` stopped: `max_epochs=10` reached.\n"
+     ]
+    },
+    {
+     "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"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "trainer root device: mps:0\n",
+      "model device: cpu\n",
+      "Exporting ONNX model with softmax output (probabilities)\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/Users/gauravsharma/Documents/github_projects/feb_05_2k26_macOS_pyt/NSBI-workflow-tutorial/src/nsbi_common_utils/training/utils.py:67: UserWarning: Exporting a model while it is in training mode. Please ensure that this is intended, as it may lead to different behavior during inference. Calling model.eval() before export is recommended.\n",
+      "  torch.onnx.export(\n",
+      "/Users/gauravsharma/Documents/github_projects/feb_05_2k26_macOS_pyt/NSBI-workflow-tutorial/src/nsbi_common_utils/training/utils.py:67: UserWarning: # 'dynamic_axes' is not recommended when dynamo=True, and may lead to 'torch._dynamo.exc.UserError: Constraints violated.' Supply the 'dynamic_shapes' argument instead if export is unsuccessful.\n",
+      "  torch.onnx.export(\n",
+      "W0319 19:21:47.421000 26438 site-packages/torch/onnx/_internal/exporter/_compat.py:125] Setting ONNX exporter to use operator set version 18 because the requested opset_version 17 is a lower version than we have implementations for. Automatic version conversion will be performed, which may not be successful at converting to the requested version. If version conversion is unsuccessful, the opset version of the exported model will be kept at 18. Please consider setting opset_version >=18 to leverage latest ONNX features\n",
+      "W0319 19:21:47.879000 26438 site-packages/torch/onnx/_internal/exporter/_registration.py:110] torchvision is not installed. Skipping torchvision::nms\n",
+      "W0319 19:21:47.880000 26438 site-packages/torch/onnx/_internal/exporter/_registration.py:110] torchvision is not installed. Skipping torchvision::roi_align\n",
+      "W0319 19:21:47.881000 26438 site-packages/torch/onnx/_internal/exporter/_registration.py:110] torchvision is not installed. Skipping torchvision::roi_pool\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[torch.onnx] Obtain model graph for `ModelWithSoftmax([...]` with `torch.export.export(..., strict=False)`...\n",
+      "[torch.onnx] Obtain model graph for `ModelWithSoftmax([...]` with `torch.export.export(..., strict=False)`... ✅\n",
+      "[torch.onnx] Run decomposition...\n",
+      "[torch.onnx] Run decomposition... ✅\n",
+      "[torch.onnx] Translate the graph into ONNX...\n",
+      "[torch.onnx] Translate the graph into ONNX... ✅\n",
+      "Training complete. Model saved to ./saved_datasets/preselection_model/\n"
+     ]
+    }
+   ],
+   "source": [
+    "if force_train or not load_trained_models:\n",
+    "    print(f\"Starting Training\")\n",
+    "    preselectionTraining.train(\n",
+    "        test_size=config_train[\"test_size\"], \n",
+    "        random_state=config_train[\"random_state\"], \n",
+    "        path_to_save=model_path,\n",
+    "        batch_size=config_train[\"batch_size\"],\n",
+    "        epochs=config_train[\"epochs\"], \n",
+    "        learning_rate=config_train[\"learning_rate\"]\n",
+    "    )\n",
+    "    print(f\"Training complete. Model saved to {model_path}\")\n",
+    "else:\n",
+    "    print(f\"Using load_trained_models={load_trained_models} from config.\")\n",
+    "    preselectionTraining.assign_trained_model(model_path)"
    ]
   },
   {
@@ -179,26 +818,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 8,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "['DER_mass_transverse_met_lep', 'log_DER_mass_vis', 'log_DER_pt_h', 'DER_deltar_had_lep', 'log_DER_pt_tot', 'log_DER_sum_pt', 'DER_pt_ratio_lep_had', 'DER_met_phi_centrality']\n"
-     ]
-    }
-   ],
-   "source": [
-    "features, features_scaling = config.get_training_features()\n",
-    "\n",
-    "print(features)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 10,
+   "execution_count": null,
    "metadata": {
     "scrolled": true
    },
@@ -343,38 +963,60 @@
     }
    ],
    "source": [
-    "importlib.reload(sys.modules['nsbi_common_utils.training'])\n",
-    "from nsbi_common_utils.training import preselection_network_trainer\n",
+    "# importlib.reload(sys.modules['nsbi_common_utils.training'])\n",
+    "# from nsbi_common_utils.training import preselection_network_trainer\n",
     "\n",
-    "PATH_PRESEL_MODEL = './saved_datasets/preselection_model/'\n",
+    "# PATH_PRESEL_MODEL = './saved_datasets/preselection_model/'\n",
     "\n",
-    "# If the preselection NN has already been trained and saved, load from the saved model\n",
-    "USE_SAVED_MODEL_PRESEL = False\n",
+    "# # If the preselection NN has already been trained and saved, load from the saved model\n",
+    "# USE_SAVED_MODEL_PRESEL = False\n",
     "\n",
     "\n",
-    "preselectionTraining = preselection_network_trainer(dataset_incl_nominal_training, \n",
-    "                                            features, \n",
-    "                                            features_scaling)\n",
+    "# preselectionTraining = preselection_network_trainer(dataset_incl_nominal_training, \n",
+    "#                                             features, \n",
+    "#                                             features_scaling)\n",
     "\n",
-    "if not USE_SAVED_MODEL_PRESEL:\n",
-    "    preselectionTraining.train(test_size=0.2, \n",
-    "                                random_state=42, \n",
-    "                                path_to_save=PATH_PRESEL_MODEL,\n",
-    "                                batch_size=1024,\n",
-    "                                epochs=50, \n",
-    "                                learning_rate=0.1)\n",
+    "# if not USE_SAVED_MODEL_PRESEL:\n",
+    "#     preselectionTraining.train(test_size=0.2, \n",
+    "#                                 random_state=42, \n",
+    "#                                 path_to_save=PATH_PRESEL_MODEL,\n",
+    "#                                 batch_size=1024,\n",
+    "#                                 epochs=50, \n",
+    "#                                 learning_rate=0.1)\n",
     "\n",
-    "else:\n",
-    "    preselectionTraining.assign_trained_model(PATH_PRESEL_MODEL)\n"
+    "# else:\n",
+    "#     preselectionTraining.assign_trained_model(PATH_PRESEL_MODEL)\n"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 11,
+   "execution_count": 18,
    "metadata": {
     "scrolled": true
    },
-   "outputs": [],
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/Users/gauravsharma/Documents/github_projects/feb_05_2k26_macOS_pyt/NSBI-workflow-tutorial/.pixi/envs/torch-mac-mps/lib/python3.11/site-packages/onnxruntime/capi/onnxruntime_inference_collection.py:123: UserWarning: Specified provider 'CUDAExecutionProvider' is not in available provider names.Available providers: 'CoreMLExecutionProvider, AzureExecutionProvider, CPUExecutionProvider'\n",
+      "  warnings.warn(\n"
+     ]
+    },
+    {
+     "ename": "ValueError",
+     "evalue": "cannot reshape array of size 4527228 into shape (1509076,)",
+     "output_type": "error",
+     "traceback": [
+      "\u001b[31m---------------------------------------------------------------------------\u001b[39m",
+      "\u001b[31mValueError\u001b[39m                                Traceback (most recent call last)",
+      "\u001b[36mCell\u001b[39m\u001b[36m \u001b[39m\u001b[32mIn[18]\u001b[39m\u001b[32m, line 6\u001b[39m\n\u001b[32m      1\u001b[39m \u001b[38;5;28;01mfor\u001b[39;00m region_name, dataset_sample_dict \u001b[38;5;129;01min\u001b[39;00m dataset_incl_dict.items():\n\u001b[32m      3\u001b[39m     \u001b[38;5;28;01mfor\u001b[39;00m sample_name, dataset \u001b[38;5;129;01min\u001b[39;00m dataset_sample_dict.items():\n\u001b[32m      4\u001b[39m \n\u001b[32m      5\u001b[39m         \u001b[38;5;66;03m# Get predictions (softmax outputs)\u001b[39;00m\n\u001b[32m----> \u001b[39m\u001b[32m6\u001b[39m         pred_NN_incl = \u001b[43mpreselectionTraining\u001b[49m\u001b[43m.\u001b[49m\u001b[43mpredict\u001b[49m\u001b[43m(\u001b[49m\u001b[43mdataset\u001b[49m\u001b[43m)\u001b[49m\n\u001b[32m      7\u001b[39m         presel_score = calculate_preselection_observable( pred_NN_incl, \n\u001b[32m      8\u001b[39m                 samples_list        =   label_dict,\n\u001b[32m      9\u001b[39m                 signal_processes    =   config_preselection[\u001b[33m\"\u001b[39m\u001b[33msignal_processes\u001b[39m\u001b[33m\"\u001b[39m], \n\u001b[32m     10\u001b[39m                 background_processes=   config_preselection[\u001b[33m\"\u001b[39m\u001b[33mbackground_processes\u001b[39m\u001b[33m\"\u001b[39m],\n\u001b[32m     11\u001b[39m                 pre_factor_dict     =   config_preselection[\u001b[33m\"\u001b[39m\u001b[33mpre_factor_dict\u001b[39m\u001b[33m\"\u001b[39m]\n\u001b[32m     12\u001b[39m             )\n\u001b[32m     14\u001b[39m         dataset_incl_dict[region_name][sample_name][\u001b[33m'\u001b[39m\u001b[33mpresel_score\u001b[39m\u001b[33m'\u001b[39m] = presel_score\n",
+      "\u001b[36mFile \u001b[39m\u001b[32m~/Documents/github_projects/feb_05_2k26_macOS_pyt/NSBI-workflow-tutorial/src/nsbi_common_utils/training/preselection_training.py:244\u001b[39m, in \u001b[36mpreselection_network_trainer.predict\u001b[39m\u001b[34m(self, dataset)\u001b[39m\n\u001b[32m    238\u001b[39m \u001b[38;5;28;01mdef\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[34mpredict\u001b[39m(\u001b[38;5;28mself\u001b[39m, dataset):\n\u001b[32m    239\u001b[39m \u001b[38;5;250m    \u001b[39m\u001b[33;03m'''\u001b[39;00m\n\u001b[32m    240\u001b[39m \u001b[33;03m    Method that evaluates density ratios on provided dataset, using self.model\u001b[39;00m\n\u001b[32m    241\u001b[39m \n\u001b[32m    242\u001b[39m \u001b[33;03m    dataset: the dataset to evaluate trained model on\u001b[39;00m\n\u001b[32m    243\u001b[39m \u001b[33;03m    '''\u001b[39;00m\n\u001b[32m--> \u001b[39m\u001b[32m244\u001b[39m     pred                        = \u001b[43mpredict_with_onnx\u001b[49m\u001b[43m(\u001b[49m\u001b[43mdataset\u001b[49m\u001b[43m[\u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[43m.\u001b[49m\u001b[43mfeatures\u001b[49m\u001b[43m]\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\n\u001b[32m    245\u001b[39m \u001b[43m                                                    \u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[43m.\u001b[49m\u001b[43mscaler\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\n\u001b[32m    246\u001b[39m \u001b[43m                                                    \u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[43m.\u001b[49m\u001b[43mmodel\u001b[49m\u001b[43m)\u001b[49m\n\u001b[32m    248\u001b[39m     \u001b[38;5;28;01mreturn\u001b[39;00m pred\n",
+      "\u001b[36mFile \u001b[39m\u001b[32m~/Documents/github_projects/feb_05_2k26_macOS_pyt/NSBI-workflow-tutorial/src/nsbi_common_utils/training/utils.py:134\u001b[39m, in \u001b[36mpredict_with_onnx\u001b[39m\u001b[34m(dataset, scaler, model, calibration_model, batch_size)\u001b[39m\n\u001b[32m    131\u001b[39m     batch = scaled_dataset[i:end_idx]\n\u001b[32m    132\u001b[39m     preds[i:end_idx] = model.run([output_name], {input_name: batch})[\u001b[32m0\u001b[39m]\n\u001b[32m--> \u001b[39m\u001b[32m134\u001b[39m preds = \u001b[43mpreds\u001b[49m\u001b[43m.\u001b[49m\u001b[43mreshape\u001b[49m\u001b[43m(\u001b[49m\u001b[43mpreds\u001b[49m\u001b[43m.\u001b[49m\u001b[43mshape\u001b[49m\u001b[43m[\u001b[49m\u001b[32;43m0\u001b[39;49m\u001b[43m]\u001b[49m\u001b[43m,\u001b[49m\u001b[43m)\u001b[49m\n\u001b[32m    136\u001b[39m \u001b[38;5;28;01mif\u001b[39;00m calibration_model \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m:\n\u001b[32m    137\u001b[39m     preds = calibration_model.cali_pred(preds)\n",
+      "\u001b[31mValueError\u001b[39m: cannot reshape array of size 4527228 into shape (1509076,)"
+     ]
+    }
+   ],
    "source": [
     "for region_name, dataset_sample_dict in dataset_incl_dict.items():\n",
     "\n",
@@ -382,11 +1024,12 @@
     "\n",
     "        # Get predictions (softmax outputs)\n",
     "        pred_NN_incl = preselectionTraining.predict(dataset)\n",
-    "        presel_score = calculate_preselection_observable(pred_NN_incl, \n",
-    "                                                        train_label_sample_dict, \n",
-    "                                                        signal_processes       = ['htautau'], \n",
-    "                                                        background_processes   = ['ttbar', 'ztautau'], \n",
-    "                                                        pre_factor_dict        = {'htautau': 1.0, 'ttbar': 1.0, 'ztautau': 1.0})\n",
+    "        presel_score = calculate_preselection_observable( pred_NN_incl, \n",
+    "                samples_list        =   label_dict,\n",
+    "                signal_processes    =   config_preselection[\"signal_processes\"], \n",
+    "                background_processes=   config_preselection[\"background_processes\"],\n",
+    "                pre_factor_dict     =   config_preselection[\"pre_factor_dict\"]\n",
+    "            )\n",
     "\n",
     "        dataset_incl_dict[region_name][sample_name]['presel_score'] = presel_score\n"
    ]
@@ -402,12 +1045,29 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 12,
+   "execution_count": 20,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "DEBUG: \n",
+      "Saved 3 tree(s) to saved_datasets/dataset_nominal.root: ['tree_htautau', 'tree_ttbar', 'tree_ztautau']\n",
+      "DEBUG: \n",
+      "Saved 3 tree(s) to saved_datasets/dataset_JES_up.root: ['tree_htautau', 'tree_ztautau', 'tree_ttbar']\n",
+      "DEBUG: \n",
+      "Saved 3 tree(s) to saved_datasets/dataset_JES_dn.root: ['tree_htautau', 'tree_ztautau', 'tree_ttbar']\n",
+      "DEBUG: \n",
+      "Saved 3 tree(s) to saved_datasets/dataset_TES_up.root: ['tree_htautau', 'tree_ztautau', 'tree_ttbar']\n",
+      "DEBUG: \n",
+      "Saved 3 tree(s) to saved_datasets/dataset_TES_dn.root: ['tree_htautau', 'tree_ztautau', 'tree_ttbar']\n"
+     ]
+    }
+   ],
    "source": [
     "datasets_helper.add_appended_branches(['presel_score'])\n",
-    "datasets_helper.save_datasets(dataset_incl_dict, save_systematics = True)"
+    "datasets_helper.save_dataset_to_ntuple(dataset_incl_dict, save_systematics = True)"
    ]
   },
   {
@@ -421,18 +1081,19 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 13,
+   "execution_count": 21,
    "metadata": {},
    "outputs": [
     {
-     "data": {
-      "image/png": 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",
-      "text/plain": [
-       "<Figure size 800x600 with 1 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
+     "ename": "NameError",
+     "evalue": "name 'presel_score' is not defined",
+     "output_type": "error",
+     "traceback": [
+      "\u001b[31m---------------------------------------------------------------------------\u001b[39m",
+      "\u001b[31mNameError\u001b[39m                                 Traceback (most recent call last)",
+      "\u001b[36mCell\u001b[39m\u001b[36m \u001b[39m\u001b[32mIn[21]\u001b[39m\u001b[32m, line 1\u001b[39m\n\u001b[32m----> \u001b[39m\u001b[32m1\u001b[39m min_pred = np.amin(\u001b[43mpresel_score\u001b[49m)\n\u001b[32m      2\u001b[39m max_pred = np.amax(presel_score)\n\u001b[32m      4\u001b[39m bins = np.linspace(min_pred, max_pred, num=\u001b[32m50\u001b[39m)\n",
+      "\u001b[31mNameError\u001b[39m: name 'presel_score' is not defined"
+     ]
     }
    ],
    "source": [
@@ -614,9 +1275,9 @@
  ],
  "metadata": {
   "kernelspec": {
-   "display_name": "Python (pixi: nsbi-env-gpu-new-new-new)",
+   "display_name": "Python (torch-mac-mps)",
    "language": "python",
-   "name": "nsbi-env-gpu-ne-neww"
+   "name": "torch-mac-mps"
   },
   "language_info": {
    "codemirror_mode": {
@@ -628,7 +1289,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.12.12"
+   "version": "3.11.15"
   }
  },
  "nbformat": 4,
diff --git a/FAIR_universe_Higgs_tautau/4_neural_likelihood_ratio_estimation.ipynb b/FAIR_universe_Higgs_tautau/4_neural_likelihood_ratio_estimation.ipynb
new file mode 100644
index 00000000..5fa302cb
--- /dev/null
+++ b/FAIR_universe_Higgs_tautau/4_neural_likelihood_ratio_estimation.ipynb
@@ -0,0 +1,1114 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "id": "85242300-60a2-467d-9f11-8af27bdc312a",
+   "metadata": {},
+   "source": [
+    "Train Neural Networks to estimate Likelihood Ratios\n",
+    "===\n",
+    "\n",
+    "In this notebook we will setup the neural networks that train unbiased and low-variance density ratios to be then used for neural simulation-based inference. "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "id": "0a769dae-97a2-44f9-bc81-e4c74ff7bedb",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/home/jsandesara_umass_edu/NSBI-workflow-tutorial/src/nsbi_common_utils/training.py:833: SyntaxWarning: invalid escape sequence '\\i'\n",
+      "  Test if \\int p_A/p_B x p_B ~ 1\n",
+      "/home/jsandesara_umass_edu/NSBI-workflow-tutorial/src/nsbi_common_utils/plotting.py:10: FutureWarning: ``set_style`` is deprecated: Naming convention is changing to match mpl. Use ``mplhep.style.use()``.\n",
+      "  hep.set_style(\"ATLAS\")\n"
+     ]
+    }
+   ],
+   "source": [
+    "import os, sys, pathlib, importlib\n",
+    "sys.path.append('../')\n",
+    "\n",
+    "\n",
+    "import os\n",
+    "import sys\n",
+    "import argparse\n",
+    "import warnings\n",
+    "import logging\n",
+    "import numpy as np\n",
+    "import yaml\n",
+    "import mplhep as hep\n",
+    "\n",
+    "import nsbi_common_utils\n",
+    "\n",
+    "import warnings\n",
+    "warnings.filterwarnings(\"ignore\", category=RuntimeWarning)\n",
+    "warnings.filterwarnings(\"ignore\", category=FutureWarning)\n",
+    "\n",
+    "hep.style.use(hep.style.ATLAS)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "id": "20d648d1-3b5f-483b-b07a-f230e4a8301c",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "1.24.2\n"
+     ]
+    }
+   ],
+   "source": [
+    "import onnxruntime\n",
+    "print(onnxruntime.__version__)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "id": "f7e8b9a6-ecd8-479c-932e-bab91be39c5e",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def load_config(path):\n",
+    "    with open(path, \"r\") as f:\n",
+    "        return yaml.safe_load(f)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "id": "f78466f0-9ade-4119-a76b-3566bb35ff59",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Load the workflow parameters\n",
+    "config_workflow = load_config(\"config.pipeline.yaml\")[\"neural_likelihood_ratio_estimation\"]\n",
+    "\n",
+    "# Load the fit configuration\n",
+    "nsbi_fit_config_path = config_workflow[\"nsbi_fit_config\"]\n",
+    "fit_config_nsbi = nsbi_common_utils.configuration.ConfigManager(file_path_string=nsbi_fit_config_path)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "id": "ec064a6c",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Load the training features defined in fit configuration file -- this can also be passed separately if just using training APIs\n",
+    "features, features_scaling = fit_config_nsbi.get_training_features()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "af1d065f-de1e-4c32-8adb-6879ff3c412f",
+   "metadata": {},
+   "source": [
+    "Statistical Model\n",
+    "-\n",
+    "\n",
+    "The statistical model for the analysis can be written as: \n",
+    "\n",
+    "$$p(x|\\mu, \\alpha) = \\frac{1}{\\nu(\\mu, \\alpha)} \\sum_c f_c(\\mu) \\cdot \\nu_c(\\alpha) \\cdot p_c\\left(x|\\alpha\\right)$$\n",
+    "\n",
+    "where $c$ stands for the various physics channels that contribute to the final state $x$, **$\\mu$ is the signal-strength parameter and $\\alpha$ is the vector of nuisance parameters associated with the various systematic uncertainties in the model**. Note that we are assuming that the parameter $\\mu$-dependence is known analytically, given by $f(\\mu)$ and that we have simulation models for each of the channels $p_c(x)$. \n",
+    "\n",
+    "Setting up the test\n",
+    "-\n",
+    "\n",
+    "The objective is to build the test statistic for composite hypothesis testing:\n",
+    "\n",
+    "$$t_\\mu = -2 \\cdot \\frac{\\text{Pois}(\\mathcal{N}_\\text{evts}|\\mu, \\hat{\\hat{\\alpha}})}{\\text{Pois}(\\mathcal{N}_\\text{evts}|\\hat{\\mu}, \\hat{\\alpha})} -2 \\cdot \\sum_i^{\\mathcal{N}_\\text{evts}} w_i \\times \\log \\frac{p(x_i|\\mu, \\hat{\\hat{\\alpha}})}{p(x_i|\\hat{\\mu}, \\hat{\\alpha})} + \\sum_m^{N_\\text{systs}} \\alpha_m^2$$\n",
+    "\n",
+    "A direct approach would then be to model the probability density $p(x|\\mu, \\alpha)$. This can be done using generative models like Normalizing Flows. \n",
+    "\n",
+    "In the presence of high-dimensional inputs, including discrete inputs like the number of jets, this is a more difficult task than training probability density *ratios*. We rewrite the test statistic to turn this into a density ratio estimation task:\n",
+    "\n",
+    "$$t_\\mu = -2 \\cdot \\frac{\\text{Pois}(\\mathcal{N}_\\text{evts}|\\mu, \\hat{\\hat{\\alpha}})}{\\text{Pois}(\\mathcal{N}_\\text{evts}|\\hat{\\mu}, \\hat{\\alpha})} -2 \\cdot \\sum_i^{\\mathcal{N}_\\text{evts}} w_i \\times \\log \\frac{p(x_i|\\mu, \\hat{\\hat{\\alpha}})/p_{ref}(x)}{p(x_i|\\hat{\\mu}, \\hat{\\alpha})/p_{ref}(x)} + \\sum_m^{N_\\text{systs}} \\alpha_m^2$$\n",
+    "\n",
+    "\n",
+    "Then using the analytical statistical model:\n",
+    "\n",
+    "$$\\frac{p(x|\\mu, \\alpha)}{p_{ref}(x)} = \\frac{1}{\\nu(\\mu, \\alpha)} \\sum_c f_c(\\mu) \\cdot \\nu_c(\\alpha) \\cdot \\frac{p_c\\left(x|\\alpha\\right)}{p_{ref}(x)}$$\n",
+    "\n",
+    "Further rewriting of the model, factorizing out the $\\alpha$-dependence gives,\n",
+    "\n",
+    "$$\\frac{p(x|\\mu, \\alpha)}{p_{ref}(x)} = \\frac{1}{\\sum_c G_c(\\alpha) \\cdot f_c(\\mu) \\cdot \\nu_c} \\sum_c f_c(\\mu) \\cdot G_c(\\alpha)\\cdot \\nu_c \\cdot g_c(x|\\alpha) \\cdot \\frac{p_c\\left(x\\right)}{p_{ref}(x)}$$\n",
+    "\n",
+    "Factorized approach:\n",
+    "--\n",
+    "\n",
+    "Instead of training NNs parameterized on $\\mu$ and $\\alpha$, we use analytical parameterizations to simplify the problem to training only parameter-independent density ratios.\n",
+    "\n",
+    "Let's set this up for the toy $H\\to \\tau\\tau$ model we are using. **Neglecting the nuisance parameter dependence ($G_c(\\alpha) = g_c(x|\\alpha) = 1$), the probability model can be written as** \n",
+    "\n",
+    "$$\\sum_c f_c(\\mu) \\cdot \\nu_c(\\mu) \\cdot \\frac{p_c\\left(x\\right)}{p_{ref}(x)} = \\mu \\cdot \\nu_{H \\to \\tau\\tau}(\\mu) \\cdot  \\frac{p_{H \\to \\tau\\tau}\\left(x\\right)}{p_{ref}(x)} + \\nu_{t\\bar{t}}(\\mu) \\cdot  \\frac{p_{t\\bar{t}}\\left(x\\right)}{p_{ref}(x)} + \\nu_{Z \\to \\tau\\tau}(\\mu) \\cdot  \\frac{p_{Z \\to \\tau\\tau}\\left(x\\right)}{p_{ref}(x)}$$\n",
+    "\n",
+    "In the NSBI approach presented in this tutorial, the density ratio estimation as a function of the various nuisance parameters is factorized from the nominal density ratio estimation, and will be done in the  `Systematic_Uncertainty_Estimation.ipynb` notebook."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "id": "14831cb9",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "importlib.reload(sys.modules['nsbi_common_utils.datasets'])\n",
+    "from nsbi_common_utils import datasets\n",
+    "\n",
+    "branches_to_load = features + ['presel_score'] # Can be defined independently of config when using just the APIs\n",
+    "    \n",
+    "# datasets library helps with preparation of data, reads metadata from fit configuration file\n",
+    "datasets_helper = nsbi_common_utils.datasets.datasets(\n",
+    "    config_path=nsbi_fit_config_path,\n",
+    "    branches_to_load=branches_to_load\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "id": "51f6cf55",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "dataset_incl_dict = datasets_helper.load_datasets_from_config(load_systematics=False)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "id": "402d7e23-ab82-48d4-84db-252fa5d099e3",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# The loaded dataframe is a dictionary, with \"Nominal\" key referring to the nominal dataset\n",
+    "dataset_incl_nominal = dataset_incl_dict[\"Nominal\"].copy()\n",
+    "\n",
+    "# Get the signal region events to be used for SBI fit\n",
+    "dataset_SR_nominal = datasets_helper.filter_region_dataset(dataset_incl_nominal, region=\"SR\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "94d325c1-c6ba-46f7-bdcb-fb9f4c120986",
+   "metadata": {},
+   "source": [
+    "Density ratio training\n",
+    "===\n",
+    "\n",
+    "Now we train the NNs for $\\frac{p_c}{p_{ref}}(x)$ density ratios to build the full model. Our choice of reference hypothesis, motivated by the search-oriented mixture model described above, would be: \n",
+    "\n",
+    "$$p_{ref}(x) = p_{H \\to \\tau\\tau}(x) = \\frac{1}{\\nu_{H \\to \\tau\\tau}} \\frac{d\\sigma_{H \\to \\tau\\tau}}{dx} $$\n",
+    "\n",
+    "gives the POI $\\mu-$parameterized model:\n",
+    "\n",
+    "$$\\sum_c \\left[f_c(\\mu) \\cdot \\frac{p_c\\left(x\\right)}{p_{ref}(x)} \\right]= \\mu + \\frac{p_{t\\bar{t}}\\left(x\\right)}{p_{H \\to \\tau\\tau}(x)} + \\frac{p_{Z \\to \\tau\\tau}\\left(x\\right)}{p_{H \\to \\tau\\tau}(x)}$$\n",
+    "\n",
+    "But this has shown to cause numerical issues, since the ratio corresponding to the $\\mu$-scaling signal term is exactly 1. In the absence of additional signal production mechansims, we instead use:\n",
+    "\n",
+    "$$p_{ref}(x) = \\frac{1}{\\nu_{H \\to \\tau\\tau} + \\nu_{t\\bar{t}}} \\left[\\frac{d\\sigma_{H \\to \\tau\\tau}}{dx} + \\frac{d\\sigma_{t\\bar{t}}}{dx} \\right]$$\n",
+    "\n",
+    "giving a more numerically stable model:\n",
+    "\n",
+    "$$\\sum_c \\left[f_c(\\mu) \\cdot \\frac{p_c\\left(x\\right)}{p_{ref}(x)} \\right]= \\mu \\cdot \\frac{p_{H \\to \\tau\\tau}\\left(x\\right)}{p_{ref}(x)} + \\frac{p_{t\\bar{t}}\\left(x\\right)}{p_{ref}(x)} + \\frac{p_{Z \\to \\tau\\tau}\\left(x\\right)}{p_{ref}(x)}$$\n",
+    "\n",
+    "The task of estimating the $\\mu$-parameterized density ratio is thus reduced to estimating three $\\mu$-independent density ratios $\\frac{p_{H \\to \\tau\\tau}}{p_{ref}}(x)$, $\\frac{p_{t\\bar{t}}}{p_{ref}}(x)$ and $\\frac{p_{Z \\to \\tau\\tau}}{p_{ref}}(x)$ mixed together with an analytical parameterization (hence the name mixture model).\n",
+    "\n",
+    "**NB**: This is the same as the *morphing-aware* approach of density ratio estimation in MadMiner"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "id": "bb48caa1-44b2-49ce-876b-5b190aeada5d",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "./saved_datasets/\n"
+     ]
+    }
+   ],
+   "source": [
+    "# Get the path where intermediate data from the workflow is saved\n",
+    "path_to_saved_data = config_workflow[\"saved_data_path\"]\n",
+    "if not path_to_saved_data.endswith('/'):\n",
+    "    path_to_saved_data += '/'\n",
+    "\n",
+    "print(path_to_saved_data)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "id": "18c6e060-e5fe-49b4-bdbe-35a5012d45fe",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "./saved_datasets/output_training_nominal/\n"
+     ]
+    }
+   ],
+   "source": [
+    "training_output_dir_name = config_workflow[\"output_training_dir\"]\n",
+    "training_output_path = os.path.join(path_to_saved_data, training_output_dir_name)\n",
+    "if not training_output_path.endswith('/'):\n",
+    "    training_output_path += '/'\n",
+    "\n",
+    "print(training_output_path)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "id": "f8af0576-dfd3-4925-943d-fef80a5e293d",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Basis processes: ['htautau', 'ttbar', 'ztautau']\n",
+      "Reference processes: ['htautau', 'ttbar']\n"
+     ]
+    }
+   ],
+   "source": [
+    "# Get the anchor/basis points used to build the full statistical model\n",
+    "basis_processes = fit_config_nsbi.get_basis_samples()\n",
+    "\n",
+    "print(f\"Basis processes: {basis_processes}\")\n",
+    "\n",
+    "# Basis points making up the reference hypothesis -- this can in principle be not restricted to basis points\n",
+    "ref_processes = fit_config_nsbi.get_reference_samples()\n",
+    "print(f\"Reference processes: {ref_processes}\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "id": "49a5b564-9fd5-43e0-98bf-9ebb4c87d701",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Use log-loss to converge to logLR: False\n",
+      "Delete existing models?: False\n"
+     ]
+    }
+   ],
+   "source": [
+    "use_log_loss = config_workflow[\"use_log_loss\"]\n",
+    "print(f\"Use log-loss to converge to logLR: {use_log_loss}\")\n",
+    "\n",
+    "\n",
+    "# Start afresh? Set delete_existing_models=True\n",
+    "delete_existing = config_workflow[\"delete_existing_models\"]\n",
+    "print(f\"Delete existing models?: {delete_existing}\")\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 13,
+   "id": "53a09258-d11e-4491-a1cd-2dda0b27da85",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Preparing datasets and initializing trainers...\n"
+     ]
+    }
+   ],
+   "source": [
+    "NN_training_mix_model = {}\n",
+    "\n",
+    "path_to_ratios = {}\n",
+    "path_to_figures = {}\n",
+    "path_to_models = {}\n",
+    "\n",
+    "print(\"Preparing datasets and initializing trainers...\")\n",
+    "for process_type in basis_processes:\n",
+    "\n",
+    "    # Prepare dataset to be passed to training\n",
+    "    dataset_mix_model = datasets_helper.prepare_basis_training_dataset(\n",
+    "        dataset_SR_nominal, [process_type], dataset_SR_nominal, ref_processes\n",
+    "    )\n",
+    "\n",
+    "    output_name = f'{process_type}'\n",
+    "    output_dir = os.path.join(training_output_path, f'general_output_{process_type}')\n",
+    "\n",
+    "    path_to_ratios[process_type] = os.path.join(training_output_path, f'output_ratios_{process_type}/')\n",
+    "    path_to_figures[process_type] = os.path.join(training_output_path, f'output_figures_{process_type}/')\n",
+    "    path_to_models[process_type] = os.path.join(training_output_path, f'output_model_params_{process_type}/')\n",
+    "    \n",
+    "    # setup the training of density ratios using density_ratio_trainer API\n",
+    "    NN_training_mix_model[process_type] = nsbi_common_utils.training.density_ratio_trainer(\n",
+    "                                                                                            dataset                 = dataset_mix_model,    # dataframe containing all the relevant features for training\n",
+    "                                                                                            weights                 = dataset_mix_model['weights_normed'].to_numpy(),\n",
+    "                                                                                            training_labels         = dataset_mix_model['train_labels'].to_numpy(),\n",
+    "                                                                                            features                = features,\n",
+    "                                                                                            features_scaling        = features_scaling,\n",
+    "                                                                                            sample_name             = [process_type, 'ref'],\n",
+    "                                                                                            output_dir              = output_dir, \n",
+    "                                                                                            output_name             = output_name,\n",
+    "                                                                                            path_to_figures         = path_to_figures[process_type],\n",
+    "                                                                                            path_to_ratios          = path_to_ratios[process_type],\n",
+    "                                                                                            path_to_models          = path_to_models[process_type],\n",
+    "                                                                                            use_log_loss            = use_log_loss,\n",
+    "                                                                                            delete_existing_models  = delete_existing\n",
+    "                                                                                        )\n",
+    "    \n",
+    "    del dataset_mix_model"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "b5aff422-1d33-448f-8afe-40e257a5dd02",
+   "metadata": {},
+   "source": [
+    "Neural Network architecture\n",
+    "===\n",
+    "\n",
+    "Here we will start with a multi-layer perceptron (simple feed-forward Neural Network with fully-connected layers). This can be supplemented with more complex architectures like transformers. \n",
+    "\n",
+    "After detailed tuning during the off-shell Higgs boson analysis effort, we found that a network with very wide layers ($\\geq 1000$) and a depth of less than 10 works best - alongside batch size of a few hundreds and a gradually declining learning rate that starts with a value just large enough to not blow up."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "9d120caa-aa4d-4135-aa58-df6aef99239d",
+   "metadata": {},
+   "source": [
+    "Starting with the training and validation of the various density ratio:\n",
+    "\n",
+    "$$\\frac{p_{H\\to\\tau\\tau}(x)}{p_\\text{ref}(x)}, \\frac{p_{t\\bar{t}}(x)}{p_\\text{ref}(x)}, \\frac{p_{Z \\to \\tau\\tau}(x)}{p_\\text{ref}(x)}$$"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 14,
+   "id": "ed2716db-abfc-4244-bbf5-2bc5525204ea",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Force retraining despite existing models?: False\n"
+     ]
+    }
+   ],
+   "source": [
+    "# Flag that forces the retraining of density ratios\n",
+    "force_train = config_workflow[\"force_train\"]\n",
+    "print(f\"Force retraining despite existing models?: {force_train}\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 16,
+   "id": "3adce34e-f7c4-4a73-a986-b5d17cc20fbf",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Common training settings: {'htautau': {'hidden_layers': 4, 'neurons': 1000, 'number_of_epochs': 100, 'batch_size': 512, 'learning_rate': 0.0001, 'scalerType': 'MinMax', 'calibration': False, 'recalibrate_output': False, 'type_of_calibration': 'histogram', 'num_bins_cal': None, 'callback': True, 'callback_patience': 30, 'callback_factor': 0.01, 'validation_split': 0.1, 'holdout_split': 0.25, 'num_ensemble_members': 1, 'load_trained_models': True, 'verbose': 1, 'plot_scaled_features': False, 'summarize_model': True}, 'ttbar': {'hidden_layers': 4, 'neurons': 1000, 'number_of_epochs': 100, 'batch_size': 512, 'learning_rate': 0.0001, 'scalerType': 'MinMax', 'calibration': False, 'recalibrate_output': False, 'type_of_calibration': 'histogram', 'num_bins_cal': None, 'callback': True, 'callback_patience': 30, 'callback_factor': 0.01, 'validation_split': 0.1, 'holdout_split': 0.25, 'num_ensemble_members': 1, 'load_trained_models': True, 'verbose': 1, 'plot_scaled_features': False, 'summarize_model': True}, 'ztautau': {'hidden_layers': 4, 'neurons': 1000, 'number_of_epochs': 100, 'batch_size': 512, 'learning_rate': 0.0001, 'scalerType': 'MinMax', 'calibration': False, 'recalibrate_output': False, 'type_of_calibration': 'histogram', 'num_bins_cal': None, 'callback': True, 'callback_patience': 30, 'callback_factor': 0.01, 'validation_split': 0.1, 'holdout_split': 0.25, 'num_ensemble_members': 1, 'load_trained_models': True, 'verbose': 1, 'plot_scaled_features': False, 'summarize_model': True}}\n"
+     ]
+    }
+   ],
+   "source": [
+    "# Get training hyperparameters\n",
+    "training_settings = config_workflow[\"training_settings\"]\n",
+    "print(f\"Common training settings: {training_settings}\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 17,
+   "id": "429b1c6a-2ad3-49f6-86cd-bf4212254791",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Processing htautau...\n",
+      "Using load_trained_models=True from config for htautau.\n",
+      "Starting training/loading for htautau\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "2026-02-21 12:53:23 | INFO | Training Logs | Reading saved models from ./saved_datasets/output_training_nominal/output_model_params_htautau/\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Testing normalization for htautau...\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "2026-02-21 12:53:32 | INFO | Training Logs | \n",
+      "\n",
+      "\n",
+      "The sum of PDFs in ensemble member 0 is 0.923693701130605\n",
+      "\n",
+      "\n",
+      "2026-02-21 12:53:32 | INFO | Training Logs | The sum of PDFs using the whole ensemble is 0.923693701130605\n",
+      "\n",
+      "\n",
+      "\n",
+      "2026-02-21 12:53:32 | INFO | Training Logs | starting ensemble training\n",
+      "2026-02-21 12:53:32 | INFO | Training Logs | Loading existing model for ensemble member 0\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Processing ttbar...\n",
+      "Using load_trained_models=True from config for ttbar.\n",
+      "Starting training/loading for ttbar\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "2026-02-21 12:53:33 | INFO | Training Logs | Reading saved models from ./saved_datasets/output_training_nominal/output_model_params_ttbar/\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Testing normalization for ttbar...\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "2026-02-21 12:53:38 | INFO | Training Logs | \n",
+      "\n",
+      "\n",
+      "The sum of PDFs in ensemble member 0 is 1.0041557357176245\n",
+      "\n",
+      "\n",
+      "2026-02-21 12:53:38 | INFO | Training Logs | The sum of PDFs using the whole ensemble is 1.0041557357176245\n",
+      "\n",
+      "\n",
+      "\n",
+      "2026-02-21 12:53:38 | INFO | Training Logs | starting ensemble training\n",
+      "2026-02-21 12:53:38 | INFO | Training Logs | Loading existing model for ensemble member 0\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Processing ztautau...\n",
+      "Using load_trained_models=True from config for ztautau.\n",
+      "Starting training/loading for ztautau\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "2026-02-21 12:53:39 | INFO | Training Logs | Reading saved models from ./saved_datasets/output_training_nominal/output_model_params_ztautau/\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Testing normalization for ztautau...\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "2026-02-21 12:53:45 | INFO | Training Logs | \n",
+      "\n",
+      "\n",
+      "The sum of PDFs in ensemble member 0 is 1.0497169177894417\n",
+      "\n",
+      "\n",
+      "2026-02-21 12:53:45 | INFO | Training Logs | The sum of PDFs using the whole ensemble is 1.0497169177894417\n",
+      "\n",
+      "\n",
+      "\n"
+     ]
+    }
+   ],
+   "source": [
+    "for process_type in basis_processes:\n",
+    "    print(f\"Processing {process_type}...\")\n",
+    "    \n",
+    "    if process_type not in training_settings:\n",
+    "        print(f\"Settings for process '{process_type}' not found in 'density_ratio_estimation.training_settings'.\")\n",
+    "        raise KeyError(f\"Missing config for {process_type}\")\n",
+    "\n",
+    "    settings = training_settings[process_type].copy()\n",
+    "    \n",
+    "    if force_train:\n",
+    "        print(f\"Force training enabled. Setting load_trained_models=False for {process_type}.\")\n",
+    "        settings['load_trained_models'] = False\n",
+    "    else:\n",
+    "        print(f\"Using load_trained_models={settings['load_trained_models']} from config for {process_type}.\")\n",
+    "    \n",
+    "    print(f\"Starting training/loading for {process_type}\")\n",
+    "    NN_training_mix_model[process_type].train_ensemble(**settings)\n",
+    "    \n",
+    "    print(f\"Testing normalization for {process_type}...\")\n",
+    "    NN_training_mix_model[process_type].test_normalization()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "fc62e325-9a46-4d80-b262-089a8dbc8ece",
+   "metadata": {},
+   "source": [
+    "Check for overtraining by comparing the NN output distributions between training and holdout datasets. The holdout dataset is the subset of events not used during training."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 18,
+   "id": "25a6a41e",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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+      "text/plain": [
+       "<Figure size 1200x600 with 4 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "text/plain": [
+       "<Figure size 800x600 with 0 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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+      "text/plain": [
+       "<Figure size 1200x600 with 4 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "text/plain": [
+       "<Figure size 800x600 with 0 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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+      "text/plain": [
+       "<Figure size 1200x600 with 4 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "text/plain": [
+       "<Figure size 800x600 with 0 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "for process_type in basis_processes:\n",
+    "    NN_training_mix_model[process_type].make_overfit_plots()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "7bed7896-6ca2-4c24-a816-d8e2a645b466",
+   "metadata": {},
+   "source": [
+    "Diagnostic Checks\n",
+    "===\n",
+    "\n",
+    "While traditionally, a NN observable is judged on the basis of its accuracy - for NSBI we are interested in the quality of the density ratios more than the discrimination power. The latter comes from the perfect modelling of the multi-dimensional likelihood ratios.\n",
+    "\n",
+    "To ensure correct modelling, we run two main checks on the training:\n",
+    "\n",
+    "- **Calibration closure test**\n",
+    "\n",
+    "  The NNs are trained using the binary cross-entropy loss, which under ideal conditions leads to the NN converging to the score function:\n",
+    "\n",
+    "  $$\\hat{s}_\\text{pred} = \\frac{p_\\text{ref}(x)}{p_\\text{ref}(x)+p_\\text{c}(x)}$$\n",
+    "\n",
+    "  that can be converted into the probability ratio we desire (likelihood ratio trick):\n",
+    "\n",
+    "  $$\\frac{p_\\text{c}(x)}{p_\\text{ref}(x)} = \\frac{\\hat{s}_\\text{pred}(x)}{1-\\hat{s}_\\text{pred}(x)}$$\n",
+    "\n",
+    "  For the NNs to be well-calibrated, we use the Monte Carlo samples to verify the equality:\n",
+    "\n",
+    "\n",
+    "  $$\\left[\\frac{p_c(x)}{p_c(x)+p_{ref}(x)}\\right]_\\text{NN} \\sim \\left[\\frac{\\mathcal{N}_c^{I(x|\\hat{s}_\\text{pred})}}{\\mathcal{N}_c^{I(x|\\hat{s}_\\text{pred})}+\\mathcal{N}_\\text{ref}^{I(x|\\hat{s}_\\text{pred})}}\\right]_\\text{MC}$$\n",
+    "\n",
+    "  where we bin the events from $p_c$ and $p_\\text{ref}$ MC samples, denoted by $\\mathcal{N}_c^{I(x|\\hat{s}_\\text{pred})}$ and $\\mathcal{N}_\\text{ref}^{I(x|\\hat{s}_\\text{pred})}$ respectively where $I(x|\\hat{s}_\\text{pred})$ returns the index of the $\\hat{s}_\\text{pred}$ bin in which an event $x$ falls.\n",
+    " \n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 19,
+   "id": "25aba043",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 1200x600 with 4 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "text/plain": [
+       "<Figure size 800x600 with 0 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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+      "text/plain": [
+       "<Figure size 1200x600 with 4 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "text/plain": [
+       "<Figure size 800x600 with 0 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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+      "text/plain": [
+       "<Figure size 1200x600 with 4 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "text/plain": [
+       "<Figure size 800x600 with 0 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "num_bins_cal = 50\n",
+    "\n",
+    "for process_type in basis_processes:\n",
+    "    NN_training_mix_model[process_type].make_calib_plots(nbins=num_bins_cal, observable='score')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 20,
+   "id": "169590c0-a4e6-4271-813d-916c8104673b",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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+      "text/plain": [
+       "<Figure size 1200x600 with 4 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "text/plain": [
+       "<Figure size 800x600 with 0 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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+      "text/plain": [
+       "<Figure size 1200x600 with 4 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "text/plain": [
+       "<Figure size 800x600 with 0 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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+      "text/plain": [
+       "<Figure size 1200x600 with 4 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "text/plain": [
+       "<Figure size 800x600 with 0 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "num_bins_cal = 50\n",
+    "\n",
+    "for process_type in basis_processes:\n",
+    "    NN_training_mix_model[process_type].make_calib_plots(nbins=num_bins_cal, observable='llr')"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "58f2a7a2-f890-4ed3-8fc2-764ab989e494",
+   "metadata": {},
+   "source": [
+    "## Density ratio reweighting closure tests\n",
+    "  \n",
+    "  Despite having a well-calibrated output and thus a robust probabilistic interpretation, the trained density ratios might not capture the full multi-dimensional event information $x$. In other words, the NNs might still be biased estimators of the optimal score function, as defined in the CARL paper (link).\n",
+    "\n",
+    "  The next diagnostic involves verifying the following equality using 1D projections of $x$:\n",
+    "\n",
+    "  $$\\frac{p_c(x)}{p_{ref}(x)} \\times p_{ref}(x) \\sim p_c(x)$$\n",
+    "\n",
+    "  We can do this one-by-one for all the observables used to model the density ratios, and also possibly the observables not used directly in the training but can still be well-estimated due to the NN learning the right physics."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 21,
+   "id": "aad88322",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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+      "text/plain": [
+       "<Figure size 1200x600 with 4 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "text/plain": [
+       "<Figure size 800x600 with 0 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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yjIwM04OpZ8+ectddd0m/fv3k1VdfDcWqEwIzqKCm5M450kyn6yt/hvxkWbkhaCEABIfZ5WITsREAAAk0u1x1brvtNunevbtccMEFcuaZZ0p2dnYoVw8gxDTB5Ni3ye/X6ZBoqyTP4zLhqTQAAAAQPlyAA4DghDTJpE477TT55ptvZPjw4bJq1SoKgQOxwGYXe0bTkK5yV2GJmQXiwAwQAAAA0Y8LcABQA0mm9evXmyFy1enUqZN899138tRTT0lpaWkw7QMQAZpgajwytMW575q9TPLSG0t20VY5MqRrBgAAiI4LcP6UEuACHIBEEFCSqVWrVl6X0aFyd9xxRyCrBxAhewqLJetg0KNJIW96//aKpJYUSHFKpixpf5HHZfPTmDUFAADElrz9DsnUe3tjmVL/R6/LF5ZariRTRrLNpyQTF+CA4Kwa3yIsuzA5p4m0mxy+2YMThU9JpnvvvVc6dOggo0ePNj/7W9T7wgsvDKx1AMLKIQeCIQ14tNeRN+saHnuwIpPNp+UBBEd/Ny/3IQEciKyyPHlspP5OAwCcyhwOc++wRHYWHfi/byyTcPLIlsSOBkIQGz1y7FyPyzTcs9L8vaK/l9tzOvu87qzS3fI4n1BkkkwTJ06UIUOGuJJMF198sU+1lpxZfZJMQPTTq2rebKnbXSztDm5ZPi3v/EMWQOC8JXSP+fUZSSvJl/0pWfJNp6t8X3HgM9MCQBw7lCjK2ven16UD+WM2s3R3UC0EEtOB/EO2Y6vcVDjU45KLMs8Tm90mlsOSiwon+LyFA7VkVwTd0kTnU5Jp4MCB0qNHjwpJJ1+STPDNiBEjJD09PehpBoFA2awymXZ2d3YgECXsB//I0UBqcq7n381U226RVL3abpchuVN9ugKYSIHU1KlTzc2ToiIybtGG2AjREBvdsnCY1+UcRYdqMtnTPddkKj8kR4QhOYA/kux2kTKNdhxSx7HZ47KHlX59cOyFeF22PJutLCE+lKlhjo1sln4rokYsWbJE+vTpI4sXL5bevXvzKSAkU+n6o3TfZvNFvcfWSDpf7FvPJADht/WtFuLYtyms2+D3/hDOx9GDzwLhiI38Kc5NbAREJ/4mip3zcUCFvwFE/1S6vrCHZa0AgpWUoVe5w8P5BxQAJFpspFfWrRLPw/iJjYDo1OD08PX+W/lyY8mxtoVt/YnGpyTTl19+GdRG+vfvH9TrATCVLpBICKQAwAc2u9gzmoZ0VzlngDswpBgAELaaTMHUYCorS4yxjUA4aRDVeOQfIV3nXbOXmaLCTKULAABiDbERAMRoksnX2eQAAAAAAACQmHxKMr344ovhbwkAAAAAAABiVlgLf8+bN0+Sk5Pl1FNPDedmgLi2p7BYsg7WCNDhbaGUn9YgpOsDAACIttio92+vSGpJgRSnZMqS9hd5XJbYCACiNMm0Z88eue666yQ/P1+2b98ers0Acc8hB4aqahFKrZ/kTettXx+cP8Umvzf6SwRaCAAAEL2xkS6/PyVTLLH5tDwAoAaSTI888og8++yzsn79+mqXsSxLBgwYEOgmAFSiBbq9SXbsP5hi8m15lVXmeTpfAACAaORLrLOpfh9iIwBeaeL66o9zPS5TWGqZPIfWrM5I9r1udZ00uzx4XL2E+BQCSjK9/vrrctttt5n/N23aVLZs2WJ2dIsWLcxjf/xxYAasE0880SSi4NmIESMkPT3d4zLjxo0zN0Sn3DlHSlnhlrCsO8s68EVns8pk2tndfXiFL8sAgMiq8QfO26GWnNNE2k3+ISp28dSpU83Nk6Kiooi1B74hNoIviI0AhNrOIoePS1om4RSLpoY5NgooyfT888+bzN17771n6i198sknctJJJ8n06dPN/caNG+WMM86Q4uJiadeuXcCNSxSzZs2S3r1713QzEARNMDn2bQrLPrSHZa0AEp0msPN7ea7LtrBolOy31ZY0a68cm/6mz+veI/kSLXy5SLNkyRLp06dPxNoE74iNEu8CnFWS7+odYEvRikveL8ABQCjYLYcrcV0v3R7Snky7ihymJ2U0CXdsFFCSadWqVdK5c2dXQe9BgwZJw4YNZdGiRSbJ1LJlS3n11Vele/fu8s9//tPUZgISgs0u9oymIV2lFrXUrpt7pW5I1wsgUR0IiOzikBxrm8cl29u/P5DpdojXZd1sAkACC/QCnP4xZpV4HsbPBTgAoVS7eKdIqki2Y5vc9Zvnum37N1lilVliS7JJWnPvAU9+WoMDf8vZdKjcckkEASWZdu7cKV26dKnwWMeOHWXNmjWun7t27SqdOnWSOXPmkGRCwtAEU+ORB4aLhorOmqJFKrXmwJEhXTOARFSU2kRspb79idbatlxsYh0olmv3nkDPLNtqklcA4O8FOH96MnEBDkA42GwitlTvcYwtSf+1xJ7qvY9Sjl6ks3TdZZIoAkoyNW7cWDZtqnhlom3btrJ8ecXMnNZrWrx4cXAtBAAAIdNz9I9h25srX27sX48nAHGPC3AAop6VLo5i32oQJWc7DvTYtkQcxT5ctEs90Hs8kQSUZOrWrZvMnz9f3nnnHTnzzDNdPZdmzpxpioA3adLEPLZ27VqvBa0BAAAAAABqQvMr94Zt3SsT8AJcQEOab7jhBtOdVWf+uOKKK8xjQ4cOlZKSEhk5cqSZfe6yyy6T33//Xfr27RvqNgMAAAAAACAeejKdcsop8uabb8qkSZNMzyXVo0cPufzyy2XatGmycOFCk4TKzs6We+65J9RtBgAAAAAAQDwkmZT2WNKbJpOcnnnmGRk4cKB8/fXX0qxZMxk9erSp1QTEu7K8bWJLFindvVlWjW8R0nVbx84N6foAAADCLW+/QzJFZPd+h0z6ONfr8v5MC66zNQEA4izJ5KQngvL/P++888wNSCQFyTmSJbkm6HnUh6RQwz0rD87xbcn2nM6e153ueRpNAACAaFPmOFDo1mGJ7CzyXvQ2taRAilMyJbUkX3amaHrK69ROAIB4TDIBEHHY7GaGAcuWJPm1mnndJfUK1h+YkkBsPi1v2AmoAABArDg02iFr359el+699nXXjE1L2p3v0xYyS3cH1UIAQBQlmZ566imZPXu2bNy4scKQucq0d9OaNWsC3QwQc+qle6+nn9/quEPL+7jeOjkHZm0EAACIFTarTG5ZOMzrco6ifNdwuZM2v+HTupNNbPRDCFoJAKjRJNPDDz8sd9xxh8fkEhBtcuccKWWFBwrVh1qWlesKpJ4+kToBAAAATh2n/MHOAIAEEVCS6YUXXjD3d911l9xwww1Sv379ULcLCDlNMDn2bQrLnvXedwkAACC2L8BZJYd6G9lSsny6AAcASCwBJZk2b94snTt3lnvuuSf0LUpAI0aMkPT0dI/LjBs3ztwQAja72DOahjSQ2lVYYuox7ZW6fEQAEEWmTp1qbp4UFRVFrD3wDbFRdF+A07EMVkmex2W4AAcAiRkbBZRk6t69uxQXFwe8UVQ0a9Ys6d27N7slQjTB1HhkaLtt3zV7meSlN5bsoq1yZEjXDAAIhi8XaZYsWSJ9+vRhR0cRYqMI4wIcACSMcWGOjQJKMg0ZMkTuvfdeWbp0qfTq1SugDQMAAACoeVyAAwCESkBJpttuu02+//57GTx4sBky179/f8nKqn44UcuWLYNpIwAAAAAAAOIxyVRSUiJJSUmyfft2ueaaazwuq/VsSktLA20fAAAAAAAA4jXJdOedd8q7775rEkhdu3aVBg0amP8DAAAAAAAgMQWUZFqwYIEkJyfL/PnzZdCgQaFvFQAAiGmrxrcIy3qTc5pIu8k/hGXdAAAANWXu2n1SWOqQjGS7DGtXK7GSTOvWrTOzoZFgAgAAlWVZuZLfq4HHHfNnSUcR7QRtiTRLWeXzTtwj+exwAAAQUyxbklz9ca7HZXYVOTQsMuHRvHX7fF53nTS7PHhcPYnpJFP37t1l586doW8NAACIYQeGztvFITnWNo9LbrHau5JM3pZ1swkAAICYsrPI4dNylh/LRqOAkkyTJ0+WoUOHyuuvvy7nn39+6FsFhMGewmLRORB3FZbIXbOXeV2+4e5fXJnk7XW6elw2P83zFXsASARFqU3EVmr3admmSetd/8+Tpl6XzyzbapJXAAAAscJuHYhdsh1bZXJu95D28tbeUWqvTXsxLZeYTjLVrVtX/u///k8uvvhimTdvnhx33HGSlaV/vrt34YUXBtNGICQcBy9/6y9jXnpj78vniBRkNJbMwq1S4MPyAJDoeo7+MWzrXvlyY/96PAHwigtwABBetYt3iqT61ss7q2yb2NNEHPtF7L5karRHhHaKsJVJNAkoyXT00Ueb2eQsy5I333xTZs6c6XY5fV6XI8kEX+XOOVLKCreErUaIU3bRVq/LN9zzizTa84tJTdl9HJ6RVZYXTBMBAACCio2sknxXDG5Lqf4isKpt7TjwGi7AAUB4WOniKC7yadGyAoeU7T2YPLL50DP8YPIq2gSUZJo4caI5ceEQPZn/61//kqefftoURm/Tpo1JrmmPL52JD77RIMqxb1NYdpfz19Rmlcm0sz13VTzAl2UAAIA7xEY1Gxvp3yhWieeLX+X/hPHlApzDz+UVF+AAJLLmV2rWKLF6eQecZEJF//znP+XGG2+U66+/3gwfXLx4sUyaNEk2bNhgkk/wk80u9oymIb1ap7WY9ErdXqnLxwEAQJgRG8VWbOTLBbi33ntf9pVukFq17DJy6BC/mg8ASAwBJZluuukmad++vVxzzTWhb1GMevTRR+WCCy6Qxx9/3Px89tlnS5MmTWT8+PEm2dSoUaOabmJM0SCq8cg/QrpOLfattZj0ytuRIV0zAACojNgo/mIjEksAAG98mwKmkueff14ee+wxiUYzZswwV25+/fXXapfZvHmzXHHFFdKiRQtJT0+Xjh07yt/+9jcpKvJtrGRlhYWFsnHjRhk0aFCFxwcMGGCuJP3yyy8BrRcAACBYxEYAACCqezKNHDlSXnrpJVm2bJl07x5ddWveeOMNj8/r8DUtXL5ly4ECinXq1JHVq1fLvffeKwsWLJBPP/1U0tLS/Nqm1lz64YcfpEOHDhUe/+KLL8x98+bN/X4fAAAAoUBsBAAAoron05NPPml67Wiy6cMPPzS9dWqatuGZZ56RuXPnelxu7NixJsF04oknyvr162XXrl2yZMkSadmypSxatEjuu+8+v7edkpIiffr0kZycHNdj8+fPlzvuuENOPfVUOeywwwJ6TwAAAIEiNgIAADGRZBoxYoQZkrZmzRoZMmSI6fmjvXW0TlPlW+XePaGmvY80cdSpUye5+uqrPS67dOlSs7zWR5o5c6a0bt3aPN6rVy+ZPXu2+f9TTz0V8LA5tW3bNtOeoUOHSteuXeW1114LeF0AAAD+IjYCAAAxlWR6//33TQ+msrIyc5WstLTU1Dlat25dldvatWslnObNmyfTp083Q968mTNnjrk//fTTpX79+hWe69u3r3Tr1k12794tCxcurPBc27ZtD8zQ4eb2xBNPuJZ7++23zTo0gXX//feb9TRo0CBk7xUAAMAbYiMAABBTNZkcDodEi5tvvlnGjBnj+ll7EP35559ul9XhcGrw4MFun9fHV6xYYZbT4XRO33//vUmouZOVdWBq2LfeektGjRolJ598skl6UYcJAADUBGIjAAAQU0mmaNKsWTNzc0pNTa12WR3ep6obwqfD+9Rvv/1W4XFvvZFKSkrk2muvNXWq3nvvPUlKSvLrPQAAAIQKsREAAIjZJNOePXvk119/NYkZHTbXsWNH6dy5c4Ui2NFi+/btrhnl3Klbt66537p1q1/r/eabb8y6jznmGFMHobKjjjrKtW4AAIBoQWwEAACiIsmktYseeOABUyh7//79FZ7T3kTXXXed3HnnnVGVbNq3b5+5ry7h43zcuZyvdLY6pXWY3Pnss89k4MCB1b6+oKBA8vLyJFhagF1vsSxvv0My9fja75BJH+d6Xb6w1DJ1wbQ+VkayzeOy+WnUxwKAeKLxR+UYJBB6Hk5UxEYAAMSP/VEQGwWUZNL6RKeccoosXrzYJJROO+00adeundjtdjMk7aOPPpJ//OMf8uWXX5ri1/p4NNBkRPn7ypzD3Kqrv1Sdc845p9p1+sJTAsofEydOlEmTJkksKztY78thiews8l77K7WkQIpTMiW1JF92pmh6ygMbwxgBIJ48+OCDMnny5JpuRkwjNop+ZXnbxJYsUrp7s6wa38Lr8o6ifNcFOHv6gdqh1bGOnRvClgIAalo0xEYBJZkeeeQR+eGHH0xyZMaMGdK0adMKz2/atElGjx5tEkyPPvqo3HLLLRINatWqZXoM7dq1y+2QOefVvNq1a0e0XZ9//rn07Nkz6PXEei+mAw4l67L2uS/gXl7vta+LaAcmS2RJu/N92kJm6e6gWggAiA633367TJgwIej1/PjjjyG74BNriI2in+UoM6GOOBxSumuT76/TlxQG31MeABA7bo+C2CigJNMHH3wg6enpMnv2bKlXr16V53VmtVmzZkmrVq1MIexoSTI1bNjQlWRq27Ztlee3bdvmU6HvUMvMzJTs7OyIbjPa2awyuWXhML+u1p20+Q2f1p2c00REfghBKwEANSlUw8T1PJyoiI2i397UepIluZKf0Uge/2vV2p+V9VrziusC3NIOF3lctsBZSsBOb28AiAdpURAbBZRkWrZsmXTv3t1tgsmpUaNG0qNHD7NstDjssMNMgfJVq1ZJ7969qzy/fPly13KoeR2n/FHTTQAAIK4RG4VG7pwjpazwQI1OX1glhy6S2VI8D2mrLTsPvMaWJHnpjb2ue1Gnqw+WEjhQUsAXydmNfGw5AABhSDJpVksLf3ujvYaysjyfOCNJZ3+bP3++fPjhhzJq1Ci3PbRUv379ItquESNGmJ5hnowbN87c4j2QyrK8F/sGAMBXU6dONTdPioqKEnaHEhuFhsZFjn2+D2UrP6TNKvE8pK18ZdN66d7rnO4yU6iIlKRk+rS8qpMWHfVTAQCxHxsFlGTSHko6DG7RokXVJmT0udWrV5ui4NFi+PDhpjD2nDlzJDc3t8KwuG+//VZWrlxpajUNGDAgou3SoYXuelZFq0gFUgAABMuXizRLliyRPn36JOTOJjYKMZtd7BkVa5W64yjcrMWWfFp+V2GJ6cW0V+rK0yd6L+kwd+0+KSx1SEayXYa1q+VX8wEA8W9cmGOjgJJMV155pcybN0/OPPNMUwT8vPPOk+TkZNfMbFoM3FmHSZeNFlpc++STTzaz32lPphdffFFatmxpdqD2JlLjx4/32qsI/gVS/vRkKh9IAQCA8CI2Ci2NixqP9D7cv2DFY2IV54ktNVsyu3ku0HrX7GVmmFx20VY50oc2kFgCANSkgJJMw4YNMxXLH3vsMbnkkkvksssuM8kaTSJs3LhRSktLTVJBl4mmnkzq+eefN13DP/74Y1OYXHsuOYf+9e/f31RjR2gDKX/4G0gBAIDgEBtFnrfEEgAACZVkUo8++qjpYn3vvffKzz//LOvWrXPNUqLD6e6+++6IDzvzhSaWFi9eLBMnTjS9sXbs2GGKXp5//vly2223SWpqasTbFGs1mQAAiBXUZPKO2AgAgMQxNRprMjlpzx8toq3y8/PNfU0X+nYmuzxp2rSpPPfccxItYq0mEwAAsYKaTMRGAAAgymsyuVPTySUAAAAAAADUHJ+STK+++mpQG7nwwguDej0AAIDKsnLl11ebedwZC9MvkP22TEmzCuTYote87rj160vYuQAAIG5tmz1cHMV7xJ6aI43Ofrfmk0wXX3yxKeodCH0dSSbPqMkEAIBv7OKQ7LLNnmOPsjJJt+eLOBwy+8PN8tInnte5v5i9H22IjQAA8P0C3Kbnkjwusz8pQzKS9krBvtoybbRdXv7c8rh8UUmYk0w33HCDX0mmDRs2yNtvvx14qxIMNZkAAPCsUOqI+BiKWA4Rm/3A/VknNZKzTvK8/Pp122XIZM/BFiKL2AgAAN8vwImX+cuKbbUlw9orxcm15dJT98qlp3pe/uf1IoMnS/iSTI8//rjPK3zmmWdk+vTp5v/t2rWLqgLbAAAgNuWszZLSPXt9W7hZmRQmZ0h6aYHY/0zxuvjeunVEZFfwjQQAAIiQAntDsSzPPZicbFaZ6/+77U29Lm/JFvNvjRb+XrNmjVx22WXy5Zdfit1ulwkTJsi9994rGRkZodoEAABIUO0m/+Dzsh0r/DTF6/LLJtcLqE0AAAA1ZXbbL2T3fodPy/bOfVySbcVSaqXKkgY3el3+pPVdRCS3ZpJMDodDHn30UZk8ebIUFhZK9+7d5fnnn5e+ffsGu2oAAAAg7sxdu08KSx2SkWyXYe1q1XRzAAAx6MHjfL9INnftna7zztM+nHdmf3Wo51NEk0w//fSTjB07VpYuXSopKSlyzz33yG233SbJySHrIAUAAABEtbz9DsnUIQj7HTLpY+9XfncVOcwgBC0zNm/dPo/L5qc1CGFLAQCJaFgEL2gElA0qLi42CaW///3vUlJSIv369ZMXXnhBOnfuHPoWJoBYm0HF30Cq3qavxLJ0pkGRnc2P87gsgRQAIJSmTp1qbh7PPdv3sNOjTKzFRs7BCg5LZGeR96ELqSUFUpySKSklBbLTRFUe2HyrtwEAQDTERn4nmRYuXGhqL61atUpq164t//jHP8wJ3p/Z5xDbM6iUORx+BVLFtdpLQUZjySzcKgXelieQAgCEkC+JiNmT68mISRT+jiaxFhtZB2MjlV201evyGkNpkim1dK+kl3kuaG85DgxZyCzdHYKWAgAS3bgwx0Y+J5n27t1rhsI9/fTTpg7TqaeeKs8++6y0atUqoA0jlh2qMp+170/vS5dLHPmyvCKQAgAAsRYb6ew9N/63h9elF3W8UvanZElaSb70W/WsT1tIrttcZPQfQbcUAIBw8inJNH/+fLnqqqtk48aNUr9+fXn88cfl/PPPD2vDEP00kLpl4TCvy33VbIQUJWeaqaSP+3OWT+tOzmkiIr7PJAQAABANTDLIi36rp2kXJRGb3aflD8VGAADEQZJp6NChruFwnTp1krffftvcfKGv0y7PiE8dp/wR8qmkAQAA4jk22jH/MSkrzJOkjGypP3hCRNoFAEAk+DxcztLKzQdrMvmDWk0AAADAISSWAAAJnWT67LPPwt+SBBZrM6gAABArmF0uNhEbAQAQx7PLDRgwIOANIP5mUAEAIFYwu1xsIjYCACA2YyN7gO0CAAAAAAAA/K/JhNiSO+dIKSvc4vPyVkm+qbulNbRsKVkel82yckPQQgAAgMghNgIAIPxIMsUpTTA59m3y+3Va3t0qyfO4DN3fAABArCE2AgAg/EgyxTubXewZTUPak2lXYYlYtiTZK3VD2FAAAIDoiY0chZtFLIdPyxMbAQBwAEmmOKdBUeORf4R0nXfNXiZ56Y0lu2irHBnSNQMAAERHbFSw4jGxivPElpotmd0meFyW2AgAgANIMgEAAACVeEssAQCAqkgyRYERI0ZIenp60NMMAgCAiqZOnWpunuRv38NuizLERgAAxGZsRJIpCsyaNUt69+5d080AACDu+HKRZvbkejJi0q6ItQneERsBABCbsREThQEAAAAAACBoJJkAAAAAAAAQNJJMAAAAAAAACBpJJgAAAAAAAASNJBMAAAAAAACCxuxycSpvv0MyRWT3fodM+jjX6/KFpZZYliU2m00ykm0el81PaxDClgIAAAAAgHhAkilOOZz3lsjOIudP1UstKZDilExJLcmXnSmanvLAlhSaRgIAAAAAgLhBkilOWY5DiaXsoq1el9dklEkyle6V9LK9XtZdZu4zS3eHoKUAAADRZ8f8x6SsME+SMrKl/uAJNd0cAABiAkmmKDBixAhJT0/3uMy4cePMzXeW+ddmlcmN/+3hdelFHa+U/SlZklaSL/1WPevTFpLrNhcZ/YcfbQIAILKmTp1qbp7kb98TsfagJmMj//z3l61SWOqQjORCuWRw2DYDAEBcxUYkmaLArFmzpHfv3mFbv0kGeXHsxjdcNZnsPixv1pvTJAStAwAgfHxJRMyeXE9GTNrFxxDnsVFZ3jaxJYuU7t4sq8a38Lr8wmPnSn6tZpK170851svy1rFzQ9hSAABiNzYiyZQAOk6htxEAAEhsBck5kiW5ZgKTR31IClkHa1Dq/SNeli9IbxyydgIAEMtIMgEAACDuOWx2U01Ak0baQ8mbzMJDNS19Wd6wMzkKACCxkWQCAABAQqmXbve6THGhf8urOpQSAAAkOJJMAJAAmCUJAMQ1KcrTJzbwujuunJdv7pMzMn1aHkBsITYCwoMkEwAkSCBVumuTmQiAqbgBwLvTuzQ+OLtcJrsLiEPERkB4kGQCAAAAKhnWrhb7BAAAP/k2wBwAgBj19NNPS/v27SU9PV0effRRiQa///672Gw2OeGEE2q6KQAAIMEQGyGcSDIBAOLW0qVL5ZprrjFJncMPP1yaNm0atm21bdvWJI4q00SSPq5tgJh9ofsKAABEHrFR9LHFWWzEcDkAQNxavny5ub/sssvkmWeeqenmAAAA1ChiI4QbPZkAAHHL4XCYe397MO3YsUOWLVsWplYBAADUDGIjhBtJJgBA1Jg8ebLpMvztt9/Khx9+KN27d5ekpCTZs2ePa5ndu3fLjTfeKEcffbRkZmaa7sVjx46VVatWVViXPj5mzBjz/0mTJpn16vp9UVBQIMcdd5wsWLDA67Ivv/yyWff69evNz/p/vX3++efm/rPPPjOPt2nTxu2wuZ07d5r306dPH8nJyZFjjz1W7rvvPiktLXWb/Jo4caLZL3Xr1pWsrCzp3LmzXHfdda7tV677dPXVV0tubq6MGDFCateuLU888YTH93PJJZeY123dulVef/11U8+qXr168tBDD5nHb7755grLb9u2zfWe//Wvf1V47quvvjKPd+3a1fXZKm1rvHUNBwAgHIiNiI1iDUkmAEDU+fjjj+W0004ziZKePXtKcvKB0d3au0h/njJliqxdu9b8f9++fTJ9+nSTpPnkk09c6xg4cKBJxqgOHTrI4MGDzb0v6tevL61bt5ahQ4fKK6+84nHZ5s2bm3VnZGSYn/X/etPEjPPe2Z7yy6m8vDyTzNLET1FRkbRo0UIWLVokd999twwYMMC8Nyd9/tRTT5V77rnH7JcuXbrIEUccIZs3b5annnrKLL99+/Yq7du7d68MGjRI3n33XdNWvflCk2cXXHCBSer17t3btF2V38fqyy+/dP1fE2vlOX8eMmSI6zNQug/0/7pPAACAd8RGxEYxw0KNWbx4saUfQdu2ba0uXbp4vD311FN+rft/LzWy/nxRzD0A/HpDc2vFRWLuo9mkSZPM92JSUpJ15513WsXFxa7nHA6HdfTRR5vn77vvPqu0tNT1+JQpUyybzWY1bdrUys/Pd73mpZdeMsvrev21Z88ea9CgQeb19957r9fl27RpY5at7PjjjzePr1+/3vWY/l8f01uTJk2sH374wfXczz//bM4L+tz999/venzmzJnmsWOPPda0zWnv3r3WCSecYJ7T91t5G7ovdb+tXbvWp/c9ZswY87rU1FRz7tH9q/Re22q3262dO3e6lr/hhhvM8rVq1TLPl3fyySeb5z766CPXY/qz7qtI0ffg7RzbooHdtEvPy6hZxEYAIoXYiNiI2KhLWGIjCn9HgVmzZpmrxACwduKRUrpnS8h3ROnuza77VeNbhHz9yTlNpN3kH0K2Pu2VpEPGypszZ44ZRjdy5Ei58847XY/rsKsbbrjBFLJ8/vnn5a233pJLL7006DZkZ2fL/Pnzzbq0Z5H2HtIpf529qkJFe2Xp+3XS3lcvvviiHH/88fL3v/9dbrvtNrHb7VJSUmJ6/9xyyy2mbU61atWSUaNGyaefflplyJyz9sILL7zg99C0M844Q8aNG1dhP2tPKu3hpD2U9HlnTyYdCqjnsbffftsMW+zYsaNp79dff22G6PXv319qir6H8u/DndmT68mISbsi1iZ4R2wEwInY6ABiI2KjWImNSDIBQBTRBFPprk3h24DlCO/6Q0SHqVX23Xffmftzzz3X7WvOOussk2TS4WahSDKplJQUM1yuZcuW8uCDD8qmTZtMEktrQYWC1lQ655xzqjyuw8g6deokv/76q9mmbv/88883N3fD4f7zn/9Uuw0d9tetW7eQfAaa5NIkkya0NMmUn58vP/30k5x33nnSt29fk2T64osvTJLphx9+MG07/fTTJS0tze/tAwCgiI2qPy8TGxEbRSOSTAAQRbRHUDiYnkyWQ8Rml+Q6/s20VhPtbtKk6vqchb3PPvtsj6/VItfeaI2gyh555BFXDafytAfPAw88IK1atZJrr73WJIDef/99adSokQRLexdpLyV3nEmmNWvWmCST0qTNvHnzTA+h1atXy7p168zz2mvIn33pC3evO/nkk017nXWZNKFXVlZmeiodc8wx5jHt5XTZZZdVqMcEAECgiI2qPy8TGxEbRSOSTAAQRUI55Kw8HSKnPZg0wdRxyh8S7dLT06s85kykaDKjTp061b5WC2J7o8PgKtNhaZ5cddVV5oqhDmXT5Mrw4cMlWNUlmMpPMewsFK49g/7617+aQt+a4NLhaSeddJIZKqgFv3VIn6/70hfuXqcF0bXHku4HnVXOWfRbk0yaFNPnnckl56x6zmLfAAAEgtio+vMysRGxUTQiyQQAiAnOmeE0GRRsgudA/Wn/lr/jjjtMgkl75px44okSCjpDniaT3CWbtBeTOuyww8y9DpXbsmWLacNFF11U4TVacylSNGmktbE0iaRJpoYNG0rnzp3Nc9rLS4fMae+qhQsXmsf9rQUFAAB8Q2xEbBSNqr+ECgBAFOnZs2e1vZDUjBkz5Mwzz3QN5QqV4uJik+B56KGH5PLLL5f//ve/IavJlJeXZ5Iy7qYp1uFwGjxq7yDtqaRd4jVhM2bMmCpJKR0+FynOnkn6OWiPpvJFvU844QRz//jjj0tBQQFD5QAACCNiI2KjaESSCQAQE7Tgtw7Jeu6550yBb2dvJL3XXjVaL0kTH0cccUTItrl7924zo9qbb74p999/v9m2t9nl9uzZU+263NHhbkuWLHH9rIW0nYXLJ02aZO7r1q1ruslrEXBnDye1f/9++cc//mF6N6mdO3dKuB111FFSr149k9QrLCyskGTSGfHK96yqrh6TJtf87U0GAAAqIjYiNopGJJkAADFBkzvTpk0zCRftUdS8eXOT4GjQoIHpQaOJC018aM+fUNiwYYMce+yxppeQzjCnw+U80XY5i2OPHDmyyuM6A5sGg1rLyGnYsGGmnsKRRx4phx9+uLn16tXLbFuXHz16tOu9azFtTSppcfK//OUvpm1aBFRrMU2YMMEUKH/66adN3aZw0l5U+h61Lap8kklnsdPPQ5+rVauWDBgwoMrrdX9oMmzQoEFyzTXXhLWtAADEM2IjYqNoRJIJABAzNKGxbNkyk2TS4tfaA0iLgI8aNcr8X4fLhYL2OtIC49pzSHtHXXDBBV5fo7PTdezY0fRE0npETn/7299Md/bffvvNFMUuPxNcmzZt5PvvvzfvR+n2NPny7LPPyuuvv15hWJz2WHr44YdNjSbdhiZqNKG0ePFiefTRR80MeJrAKSoqkkgNmcvKynJ11Vea6NK6TErfR1paWpXXPvXUU2amPi2evnTp0rC3FQCAeEZsRGwUbWwW/dVrjP5B1KdPH/MHgs4SFEorX24sOdY22WNrJJ0v3hrSdQOIPa7Z5eo2j4nZ5Wra77//bnrhvPfee6Z3DuLb7Mn1ZMSkXWE5H8M/xEYAIoXYyD/ERolldhCxEbPLAQBQifaO+uabb6Rp06bsGwAAkPCIjeArhsuFiNYC0eKt7dq1k9q1a0uPHj1M7RAAQOzJyckhwQQEidgIAOIHsRF8RU+mENF6Gh999JHceeedZoppnX76iiuukKSkJNcsQdXZ8eFg2bom1eMyVkm+mYlH613YUrK8tifLyvX7PQCIX/UHT5CywjxJysiu6aYASBDBxEYAEG7ERkB4kGQKgR07dshbb70lzzzzjFx55ZXmsbPOOssUeX311Ve9BlKOou3i2OfbtnTCZ6skz+tydFEDUDmQAoBYiY0AINyIjYDwiLtchE5frb19fv3112qX2bx5s7mS1qJFC0lPTzezAensP4HOyKMz/Jx00kmuGXWcWrdu7ds6bTax12ru8Sa2gx+Vze51Wb1pwe/d9qZSYGsQ0HsCAADxISZjIwAAEJPirifTG2+84fH5DRs2yNFHHy1btmxxFTBbvXq13HvvvbJgwQL59NNP3U657IlOJ63dwVVxcbHk5ubKt99+K7NmzZI77rjD6+vtaQ2k8UjPsz0VrHhMrOI8saVmS2Y37z0S7pq9TPLSG0t20VY50o/3AgAA4kssxkYAACA2xU1PJq1XpF2y586d63G5sWPHmiDqxBNPlPXr18uuXbvMdLktW7aURYsWyX333RdUO7TYd/PmzU2X8GOPPVbGjx8voaCJpaxek3xKMAEAAMR7bAQAAKJPzCeZ9AqbBkedOnWSq6++2uOyS5cuNcs3atRIZs6cabpsq169esns2bPN/5966qmgunFrAKXbePjhh+Wzzz6Tq666KuB1AQAA+CuRYiPt6Z2/dJK5BwAANS/mk0zz5s2T6dOnm27d3syZM8fcn3766VK/fv0Kz/Xt21e6desmu3fvloULF1Z4TmdEMbO6ubk98cQTFZZt2rSpuRJ4yy23yOTJk+XNN9+U0tLSkLxXAAjU3LX75N+rCsw9gPiWSLHR3hWPScFPk809APiD2AgIj5ivyXTzzTfLmDFjXD8PHTpU/vzzT7fLapdvNXjwYLfP6+MrVqwwy2kw5PT9999LWVmZ29dkZWXJ66+/Lg899JDpWp6SklKhHkFBQYEppqldzgGgpsxbt092FjmkXrpdhrWrxQcBxLF4iI12fDhYtq5J9fpeHYWbXfdb32rhcdksK9fr+gAkDmIjIDxiPsnUrFkzc3NKTa0+IFmzZo2579Chg9vn27dvb+51et3yGjTwPEObzsSyfPly+eabb6R///6ux7VLeE5OjnkeAAAgEuIhNnIUbReHPx0vLYc49m2K7+77AADEgIQ6327fvt01a4o7devWNfdbt271a70aPOmsLOedd55MnTrV1DDQopZTpkyRiRMnmq7jAIDQ0mE3+v368ssvs2uBeIuNbDax12ru9Sa2g6Gsze512T22RrLb3lQKbJ4TZAAQq4iNEA1ivieTP/bt21chYKrM+bhzOV/Z7XZ599135bbbbjNdw3VWls6dO5t6CBdddJHX1+8tckheXp4ES6cX9neKYQCIJp9//rkcf/zxZqjPiy++aB77/fffpU2bNuZxnUodcNq/f7+5BWtfkZWwOzVaY6NCq55kDP7F63Lb3u5shsrZMxpLIzfLl4+N7pq9TPLSG0t20VY50q93AwA1h9gIsRYbJSfaVL7l7ytLSkoy99XVGPCkcePGrj+I/HXm5B0ik3MkWHplcNKkSUGvBwCAWPDggw+aq7YIXPzERptFLqm6PLERACCRPBgFsVFCJZlq1aplegzp1TR33cKdV+lq164d0Xa9M7G+DJqw1uMy89ftk8Iyh2Qk2WVwW/dFe+nFBABIJLfffrtMmDAh6PW8+2Brueih3ZKIYjk28gWxEQAgkdweBbFRQtVkatiwobnXQMqdbdu2+VTMMtRqp9slOzvb4+3T3GR5b3Oyua9uGQIpALHshBNOMEPi1EsvvWRqtlxyySVmqJyzYLA+pstV9sUXX8i5555rignr7a9//at8/PHHbrfz3XffyciRI01B44yMDNPb4phjjjF1YypPq+6sbfDtt9/Khx9+KN27dzc9O/bs2ROWfQD/6HnP2/nTl1ut9MStnRjLsZEvN2IjALGM2AixGBslVE8mnTZXZ0dZtWqV9O7du8rzOguKc7lIuvSxnbL36U4el2k79BLpMOxS2VXkkKs/9j4Fb34aRS0BxJajjjrK9JrQJFDz5s1NQkdvAwcONPUI6tWrZ5bp0aNHhdfNnz9f/v3vf5uTqj6n3/Nz5syRefPmySOPPCI33XSTa9mFCxeagK2kpERat25tkks7d+4029RE0rJly+SZZ56p0jZNWOmwG01K9ezZU5KTE+r0GdM0eag3T/K3J27SMFpjo8se3ym1X+nqcZlx48aZGwDEK2IjxGJslFBRsv4xoX+M6NXoUaNGVXn+gw8+MPf9+vWLaLumXFtX3urzpcdlUksKpFhEUkoKZKdkel+p7UANBQCIFQ8//LCruOXJJ5/squVy9tlnm95MmkB6//33q7zuzTfflDPOOENef/11M/RHPfnkk6ar8B133CFnnXWWtG3b1jx+//33mwTTP/7xjwpdiZcsWSLHHXec6UGlr6085fvf/vY3U8BYE00pKSlh3hMIJV8SEbMn15MRk9z35Il30RobPX9jPTn5Nu+FvwEgnhEbIRZjo4RKMg0fPtwUxtYr3Lm5uRW6fusV7JUrV5p6BAMGDIh423SmE08clkhxSqaklu6V9LK9XtdnOQ4U6MwsTcwaE0Csuv2rnbJ7vyPk69VekM57X3pD+qtOml0ePK6e1IT69evLa6+95kowqeuvv16WLl1qkkaPP/64SRypRo0amaF0N9xwQ4V1aA8O7aG0aNEi2bx5s+nlVF6fPn3kvvvui9A7AiInmmMjAFDERv4jNkJNSqgkk/4BoVfHP/roI3O1Tq+St2zZ0lzBHjFihFlm/Pjxkp6eHtmGWWVy438rDv+o7LFhS829TZed08vnVSfXbS4y+o+gmwggMjTBtPNgQigcdP6ocK6/JmgtJndFia+88kqTZFqxYoXrMf3ZnV9++UV+/PHHarcxdOjQELUWiC5RGxsBwEHERv4jNkJNSqgkk3r++edN13Ctr9GqVStzdW737gO9ffr372+qsUfa+Kf2SJrD80dx9M7rZWD/oyWtZO+BxJGPknOahKCFACJFewSFg/Zg0gSTlvCrm26PmXb7okOHDm4f79TpQK27NWvWVHjcWbPpp59+knXr1snq1avlzz//9LiNJk34Lo1V1GSKzdjI15pMo9rvl7LCPEnKyJb6g4OfTQdA9CE28h+xETyhJlOIafC0ePFiU1dDi8Lu2LHDFLM8//zzTb2NynU4ImHKtTly9sSdHpdZNb6FlK740iSYOk6hZxIQr8I15EyHyGkPJk0wPX1ifE0MYLe7T3A5HAd6bGmxbqd77rnH3PS5Ll26mMLiWoupb9++8tBDD8k333zjdl304ohd1GSKzdjI15pMJj7atcnERySZgPhEbOQ/YiN4Qk0mP+lVaW+aNm0qzz33nMQSDZycV+oAAIdU7qnk9Ouvv1aYFUvrLekf0frz3LlzpWPHjhWWf/TRR9mtiEvxGhupha3Ol8JmDslItkvF32gASFzERqhJNTe+AX4nmRqdOYmrdABQycyZM2Xfvn1V9svTTz9t7nUYkPrqq6/M/YUXXlglwaSv91STCUB0+rr1BfJ5t5vNPQDgAGIj1KSEq8kEAIh+znow3h5T27dvN4mjV1991TXDnM4oN2PGDKlXr55cd9115rFmzZqZ+y+++EJKS0slOfnAKfD333+Xyy+/XAoKCszPO3furDK7HIDIKivINUPhvLGOnXvg3lHmdXnnsgAQi4iNECtIMkUBLfx990zvxS31BgDxrG7duuZ+/vz5csYZZ8iJJ54oF110kdhsNvn555/ltNNOkz59+pi6Sk6XXnqpTJ8+XT744APp0aOH6SKuiSdNOE2bNk2ys7NdM8TprFkLFiyQFi1amJpMWnvmf//7nykSfvbZZ8vs2bNlyJAhct9998lll11WY/sBoUPh79h0xTOWpJRu8rjMaK3tf+yhn7U2EwDEG2IjhBqFvxPAgcLf3otbAkC80yTRTTfdZJJGOqW6zo6Sk5Nj6iU98sgj8sknn0hmZmaF11xzzTVy+umny0svvWTqLunzgwYNkjvvvNMU9i4fpH322Wdy9913m95MWuj4iCOOkLvuuktuvfVWM2SusLDQDKvTpBbiA4W/Y9NzV4q02+t9Nt1Hyv3f2+y7NnvSgf847wEgBhAbIdZiI5tlWTqrNWrAkiVLzBX5WZPqep1dDgBCMbtcvTicXQ4IljOQ0sRj79692aFREhstOG6V1+WLd22WgozGklm4VVLrNvW47K4ih2jQy/cgAEVsBIQnNmK4HAAAAKKLLckkxr0p36/Rl+UBAEB4kWQCAABA1NEeR94UF/q3vKqTxuTKAACEC0mmKEDhbwDhdlrbWlJY6pCMZP64QmKh8HdsGv/PnZL15gCPy2g9iR+bHFjGbhOGAgPwC7EREtXUqVPNzZP87XsCXj81mWoQNZkAAKh51GSK3djoynnrZLfUljqyV549rW1E2ggAQLybHURNJi5pAwAAICbZ07Mq3AMAgJpFkgkAAAAAAABBI8kEAAAAAACAoFH4GwASQMGKx8QqzhNbarZkdptQ080BgJCgcC+AQBEbAeFBkgkAEsDeFY+JY98msddqTpIJQNwY1q5WTTcBQIwiNgLCgyRTFBj/1B65e2ZXr9P06g0AAETPNL0ID2IjAABiMzYiyRQFplybI2dP/KWmmwEAcenpp5+WRx99VDZt2iT33Xef/N///V9NNwkR5MtFGuc0vYgexEYAED7ERoltXJhjI5JMAIC4tXTpUrnmmmskKSlJevbsKU2bNq3pJgEAANQYYiOEG0kmAEDcWr58ubm/7LLL5Jlnnqnp5gAAANQoYiOEmz3sWwAAoIY4HA5zTw8mAAAAYiOEH0kmAEBUmTx5sthsNvn222/lww8/lO7du5vhbnv2HChAuHv3brnxxhvl6KOPlszMTGnbtq2MHTtWVq1aVWE9+viYMWPM/ydNmmTWqesGAACIJcRGiCUkmQAAUenjjz+W0047TX7//XdTTyk5OVmWLVtm/j9lyhRZu3at+f++fftk+vTp0qdPH/nkk09crx84cKBJUKkOHTrI4MGDzT0AAEAsIjZCLKAmUxRgml4AqOpvf/ub3HbbbTJx4kRJSUkRy7Lk8ssvN0knnSVOn9MeTvr4k08+aXo3XXDBBaZHk/Zweumll+Tll182vZn0cV0PEk+4p+lFeBAbAUBVxEaIhdiIJFMUYJpeAE65c46UssItId8hjsLNrvutb7UI+fqTMppIg9N/COk6tWeSJpOc5syZY4bQjRw5Uu68807X4zoM7oYbbjCFLJ9//nl566235NJLLw1pWxC7wj1NL8KD2AiAE7HRIcRGiIXYiCQTAEQRTTA59m0K3wYsR3jXH0JDhw6t8PN3331n7s8991y3y5911lkmybRo0SKSTAAAxAlio0OIjRALSDIBQBTRHkHhYHoyWQ4Rm13sGU1jot1NmlRcp7Ow99lnn+3xdbm5uSFvCwAAqBnERocQGyEWkGQCgCgS6iFnTjpETnswaYKp8cg/JBakp6dX+LmkpMTcH3PMMVKnTp1qX9elS5ewtw0AAEQGsdEhxEaIBSSZAAAxwTkznBb8Hj58eE03BwAAoEYRGyEa2Wu6AQAA+KJnz57mfv78+W6fnzFjhpx55pnyySefsEMBAEDcIzZCNCLJBACICVrwu1OnTvLcc8+ZAt+WZZnH9f6zzz6Ta6+91iSgjjjiiJpuKgAAQNgRGyEakWQCAMSE5ORkmTZtmtStW1cuv/xyad68ufTv318aNGggJ5xwguTl5ZneTPXr16/ppgIAAIQdsRGiEUkmAEDM0KTSsmXLTJKpUaNGsmTJElMEfNSoUeb/OlwOAAAgURAbIdrYLOd4A0Sc/kHUp08fadHALlkNO3lcdty4ceYGAEHNLlereczMLgeEwtSpU83Nk/ztv8ofuQ5ZvHix9O7dmx1fg4iNAEQKsRES1dQwx0bMLhcFplybI2dP/KWmmwEAQNzx5SLN7Mn1ZMSkXRFrE7wjNgIAIDZjI4bLAQA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",
+      "text/plain": [
+       "<Figure size 1200x600 with 4 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "text/plain": [
+       "<Figure size 800x600 with 0 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 1200x600 with 4 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "text/plain": [
+       "<Figure size 800x600 with 0 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "variables_to_plot=['log_DER_pt_h'] # The 1D variable for reweighting closure\n",
+    "yscale_type='log'\n",
+    "num_bins_plotting=21\n",
+    "\n",
+    "for process_type in basis_processes:\n",
+    "\n",
+    "    NN_training_mix_model[process_type].make_reweighted_plots(variables_to_plot, yscale_type, num_bins_plotting)\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 15,
+   "id": "3cc68bf7-a294-47ba-b1d8-38703bdc9d62",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "dataset_combined_SR = Datasets.merge_dataframe_dict_for_training(dataset_SR_nominal, None, \n",
+    "                                                                samples_to_merge = [\"htautau\", \"ztautau\", \"ttbar\"])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 16,
+   "id": "891bd136-e59f-49a5-a853-073482a8e33a",
+   "metadata": {
+    "scrolled": true
+   },
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "2025-11-12 08:51:59 | INFO | Training Logs | Evaluating density ratios\n",
+      "2025-11-12 08:52:22 | INFO | Training Logs | Evaluating density ratios\n",
+      "2025-11-12 08:52:44 | INFO | Training Logs | Evaluating density ratios\n"
+     ]
+    }
+   ],
+   "source": [
+    "path_to_saved_ratios = {}\n",
+    "\n",
+    "for process_type in basis_processes:\n",
+    "    # Evaluate the density ratios p_c/p_ref for the full dataset and save for the inference step\n",
+    "    path_to_saved_ratios[process_type] = NN_training_mix_model[process_type].evaluate_and_save_ratios(dataset_combined_SR, \n",
+    "                                                                                                     aggregation_type = 'median_score')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 17,
+   "id": "f7c18575-9da4-4c4a-b1fb-8289c6287b63",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "{'htautau': './saved_datasets/output_training_nominal/output_ratios_htautau/ratio_htautau.npy',\n",
+       " 'ttbar': './saved_datasets/output_training_nominal/output_ratios_ttbar/ratio_ttbar.npy',\n",
+       " 'ztautau': './saved_datasets/output_training_nominal/output_ratios_ztautau/ratio_ztautau.npy'}"
+      ]
+     },
+     "execution_count": 17,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# Density ratio paths to put in the config file\n",
+    "path_to_saved_ratios"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 18,
+   "id": "36e61653-61ea-4666-9bdf-f7b3e8b99c5b",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Optionally save the dataframe in the same order used to evaluate the density ratios for diagnostics\n",
+    "\n",
+    "path_to_save = f\"{PATH_TO_SAVED_DATA}/dataset_Asimov_SR.root\"\n",
+    "nsbi_common_utils.datasets.save_dataframe_as_root(dataset_combined_SR, \n",
+    "                                                  path_to_save = path_to_save,\n",
+    "                                                  tree_name = \"nominal\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 19,
+   "id": "57b246d6-0de9-4718-8fb1-256ecc3ac376",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "'./saved_datasets//dataset_Asimov_SR.root'"
+      ]
+     },
+     "execution_count": 19,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "path_to_save\n",
+    "# nsbi_common_utils.datasets.load_dataframe_from_root(path_to_save, tree_name = \"nominal\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 20,
+   "id": "0938306e-0630-4a21-a8af-41c9b896300b",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Asimov weights correspond to the expected weight vector in the same shape and order as the evaluated density ratios\n",
+    "# This is used in the weighted NLL fits for expected sensitivity and coverage computations\n",
+    "\n",
+    "path_to_save = f\"{PATH_TO_SAVED_DATA}/asimov_weights.npy\"\n",
+    "np.save(f\"{path_to_save}\", dataset_combined_SR[\"weights\"].to_numpy())"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 21,
+   "id": "bfb877a1-e9e5-4e20-8d5f-757c243f4cfb",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "'./saved_datasets//asimov_weights.npy'"
+      ]
+     },
+     "execution_count": 21,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# Path to put in the config file for inference\n",
+    "path_to_save"
+   ]
+  },
+  {
+   "attachments": {},
+   "cell_type": "markdown",
+   "id": "7aa27cca-fecf-426a-a7c5-a77d82a4de2a",
+   "metadata": {},
+   "source": [
+    "Systematic Uncertainty Modelling\n",
+    "===\n",
+    "\n",
+    "So far we have left out the nuisance parameter piece of the parameterized density ratio decomposition shown before:\n",
+    "\n",
+    "$$g_c(x|\\alpha) = \\frac{p_c(x|\\alpha)}{p_c(x)}$$\n",
+    "\n",
+    "which will be estimated in the `Systematic_Uncertainty_Estimation.ipynb` notebook."
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python (pixi: nsbi-env-gpu-new)",
+   "language": "python",
+   "name": "nsbi-env-gpu"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.12.12"
+  },
+  "pixi-kernel": {
+   "environment": ""
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}
diff --git a/FAIR_universe_Higgs_tautau/Systematic_Uncertainty_Estimation.ipynb b/FAIR_universe_Higgs_tautau/5_systematic_uncertainty_training.ipynb
similarity index 100%
rename from FAIR_universe_Higgs_tautau/Systematic_Uncertainty_Estimation.ipynb
rename to FAIR_universe_Higgs_tautau/5_systematic_uncertainty_training.ipynb
diff --git a/FAIR_universe_Higgs_tautau/Parameter_Fitting_with_Systematics.ipynb b/FAIR_universe_Higgs_tautau/6_parameter_fitting_with_systematics.ipynb
similarity index 100%
rename from FAIR_universe_Higgs_tautau/Parameter_Fitting_with_Systematics.ipynb
rename to FAIR_universe_Higgs_tautau/6_parameter_fitting_with_systematics.ipynb
diff --git a/FAIR_universe_Higgs_tautau/Data_Loader.ipynb b/FAIR_universe_Higgs_tautau/Data_Loader.ipynb
deleted file mode 100644
index 61537755..00000000
--- a/FAIR_universe_Higgs_tautau/Data_Loader.ipynb
+++ /dev/null
@@ -1,272 +0,0 @@
-{
- "cells": [
-  {
-   "attachments": {},
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Data Processing Notebook\n",
-    "===\n",
-    "\n",
-    "Prepare data for the analysis. The raw data is downloaded from the FAIR Universe HiggsML challenge repository. Use the HiggsML package to download and process the dataset, followed by selections and saving to local cache.\n",
-    "\n",
-    "NB: This notebook is only to be run once to get the FAIR Universe dataset into `.root` ntuples. The rest of the workflow is independent of this.\n"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 1,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "import os\n",
-    "import numpy as np\n",
-    "import pandas as pd\n",
-    "import matplotlib.pyplot as plt\n",
-    "import mplhep as hep\n",
-    "import yaml\n",
-    "import uproot\n",
-    "\n",
-    "from utils import plot_kinematic_features\n",
-    "\n",
-    "import warnings\n",
-    "warnings.simplefilter(action='ignore', category=FutureWarning)\n",
-    "\n",
-    "import HiggsML\n",
-    "from HiggsML.systematics import systematics\n",
-    "hep.style.use(hep.style.ATLAS)\n",
-    "\n",
-    "from HiggsML.datasets import download_dataset"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 2,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "2025-11-10 17:44:45,732 - HiggsML.datasets     - INFO     - Handling as URL: https://zenodo.org/records/15131565/files/FAIR_Universe_HiggsML_data.zip\n",
-      "2025-11-10 17:44:45,736 - HiggsML.datasets     - INFO     - Current working directory: /home/jsandesara_umass_edu/NSBI-workflow-tutorial/FAIR_universe_Higgs_tautau\n",
-      "2025-11-10 17:44:45,965 - HiggsML.datasets     - INFO     - Total rows: 220099101\n",
-      "2025-11-10 17:44:45,966 - HiggsML.datasets     - INFO     - Test size: 66029730\n"
-     ]
-    }
-   ],
-   "source": [
-    "data = download_dataset(\"https://zenodo.org/records/15131565/files/FAIR_Universe_HiggsML_data.zip\")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 3,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "2025-11-10 17:44:54,525 - HiggsML.datasets     - INFO     - Selected train size: 53924279\n",
-      "2025-11-10 17:51:04,104 - HiggsML.datasets     - INFO     - Data loaded successfully\n"
-     ]
-    }
-   ],
-   "source": [
-    "data.load_train_set(train_size=0.35)\n",
-    "df_training_full = data.get_train_set()\n",
-    "del data"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 4,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "list_of_processes_to_model = [\"htautau\", \"ztautau\", \"ttbar\"]"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 5,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "['diboson']\n"
-     ]
-    }
-   ],
-   "source": [
-    "process_to_exclude = set(df_training_full[\"detailed_labels\"].unique()) - set(list_of_processes_to_model)\n",
-    "process_to_exclude = list(process_to_exclude)\n",
-    "print(process_to_exclude)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 6,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "detailed_labels\n",
-       "ztautau    34425566\n",
-       "htautau    17854072\n",
-       "ttbar       1515091\n",
-       "Name: count, dtype: int64"
-      ]
-     },
-     "execution_count": 6,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "mask_process_exclusion = ~np.isin(df_training_full[\"detailed_labels\"], process_to_exclude)\n",
-    "\n",
-    "df_training_full = df_training_full[mask_process_exclusion].copy()\n",
-    "df_training_full[\"detailed_labels\"].value_counts()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 7,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# Trim the dataset, so all processes have equal entries\n",
-    "\n",
-    "# get the number of ttbar events (lowest)\n",
-    "n_ttbar = df_training_full.loc[\n",
-    "    df_training_full.detailed_labels=='ttbar'\n",
-    "].shape[0]\n",
-    "\n",
-    "# Trim the other processes to match ttbar number, preserving event weight sums\n",
-    "df_list = []\n",
-    "for _, df_process in df_training_full.groupby('detailed_labels'):\n",
-    "\n",
-    "    weight_sum_orig = df_process.weights.sum()\n",
-    "\n",
-    "    df_sampled = df_process.sample(n = n_ttbar, random_state=42)\n",
-    "\n",
-    "    df_sampled['weights'] *= weight_sum_orig / df_sampled['weights'].sum()\n",
-    "\n",
-    "    df_list.append(df_sampled)\n",
-    "    \n",
-    "    del df_sampled\n",
-    "\n",
-    "df_training = pd.concat(df_list).reset_index(drop=True)\n",
-    "del df_training_full, df_list"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 8,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "syst_settings = {\n",
-    "    'TES_up': {'tes': 1.02, 'seed': 42},\n",
-    "    'TES_dn': {'tes': 0.98, 'seed': 42},\n",
-    "    'JES_up': {'jes': 1.02, 'seed': 42},\n",
-    "    'JES_dn': {'jes': 0.98, 'seed': 42}\n",
-    "}\n",
-    "\n",
-    "\n",
-    "dataset_dict = {}\n",
-    "\n",
-    "dataset_dict['nominal'] = systematics(\n",
-    "        data_set = df_training,\n",
-    "        dopostprocess=False\n",
-    "        )\n",
-    "\n",
-    "for sample_name, syst_args in syst_settings.items():\n",
-    "    dataset_dict[sample_name] = systematics(\n",
-    "        data_set = df_training, \n",
-    "        dopostprocess=False, \n",
-    "        **syst_args\n",
-    "    )\n"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 9,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "saved_datasets = \"./saved_datasets/\""
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 10,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# Some common analysis selections to remove low-stats regions\n",
-    "selections = \"DER_mass_transverse_met_lep <= 250.0 and \\\n",
-    "            DER_mass_vis <= 500.0 \\\n",
-    "            and DER_sum_pt <= 1000 and \\\n",
-    "            DER_pt_tot <= 250 and \\\n",
-    "            DER_deltar_had_lep <= 4.5 and \\\n",
-    "            DER_pt_h <= 400 and \\\n",
-    "            DER_pt_ratio_lep_had <= 9.0\""
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 11,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "for sample in dataset_dict.keys():\n",
-    "\n",
-    "    # Write to ROOT TTree\n",
-    "    with uproot.recreate(f\"{saved_datasets}dataset_{sample}.root\") as ntuple:\n",
-    "\n",
-    "        for process in list_of_processes_to_model:\n",
-    "\n",
-    "            df = dataset_dict[sample]\n",
-    "            \n",
-    "            df_process = df[df[\"detailed_labels\"] == process].copy()\n",
-    "\n",
-    "            df_process = df_process.query(selections).copy()\n",
-    "\n",
-    "            columns_to_keep = df_process.columns.tolist()\n",
-    "\n",
-    "            columns_to_keep = list(set(columns_to_keep) - set([\"detailed_labels\"]))\n",
-    "\n",
-    "            arrays = {col: df_process[col].to_numpy() for col in columns_to_keep}\n",
-    "\n",
-    "            ntuple[f\"tree_{process}\"] = arrays"
-   ]
-  }
- ],
- "metadata": {
-  "kernelspec": {
-   "display_name": "Python (pixi: nsbi-env-gpu-new-new-new)",
-   "language": "python",
-   "name": "nsbi-env-gpu-ne-neww"
-  },
-  "language_info": {
-   "codemirror_mode": {
-    "name": "ipython",
-    "version": 3
-   },
-   "file_extension": ".py",
-   "mimetype": "text/x-python",
-   "name": "python",
-   "nbconvert_exporter": "python",
-   "pygments_lexer": "ipython3",
-   "version": "3.12.12"
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 4
-}
diff --git a/FAIR_universe_Higgs_tautau/Data_Preprocessing.ipynb b/FAIR_universe_Higgs_tautau/Data_Preprocessing.ipynb
deleted file mode 100644
index 7c9b5b03..00000000
--- a/FAIR_universe_Higgs_tautau/Data_Preprocessing.ipynb
+++ /dev/null
@@ -1,574 +0,0 @@
-{
- "cells": [
-  {
-   "attachments": {},
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Data Processing Notebook\n",
-    "===\n",
-    "\n",
-    "Prepare data for the NSBI analysis. \n",
-    "\n",
-    "The ntuples are loaded into `pandas.DataFrame` and saved for NSBI trainings and inference.\n"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 1,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "import os, sys, importlib\n",
-    "import numpy as np\n",
-    "import pandas as pd\n",
-    "import matplotlib.pyplot as plt\n",
-    "import mplhep as hep\n",
-    "import yaml\n",
-    "import uproot\n",
-    "\n",
-    "from utils import plot_kinematic_features\n",
-    "\n",
-    "import nsbi_common_utils\n",
-    "from nsbi_common_utils import configuration\n",
-    "from nsbi_common_utils import datasets\n",
-    "\n",
-    "\n",
-    "import warnings\n",
-    "warnings.simplefilter(action='ignore', category=FutureWarning)\n",
-    "\n",
-    "hep.style.use(hep.style.ATLAS)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "**Define branches to load**\n",
-    "\n",
-    "Classified in terms of the number of jets for feature engineering later."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 2,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# Specify branches to load from the ROOT ntuples\n",
-    "\n",
-    "input_features_noJets = ['PRI_lep_pt', 'PRI_lep_eta', 'PRI_lep_phi', 'PRI_had_pt', 'PRI_had_eta',\n",
-    "       'PRI_had_phi', 'PRI_met', 'PRI_met_phi', 'DER_mass_transverse_met_lep',\n",
-    "       'DER_mass_vis', 'DER_pt_h', 'DER_deltar_had_lep', 'DER_pt_tot', 'DER_sum_pt',\n",
-    "       'DER_pt_ratio_lep_had', 'DER_met_phi_centrality']\n",
-    "\n",
-    "input_features_1Jets = ['PRI_jet_leading_pt', 'PRI_jet_leading_eta',\n",
-    "       'PRI_jet_leading_phi',\n",
-    "       'PRI_jet_all_pt']\n",
-    "\n",
-    "input_features_2Jets = ['PRI_jet_subleading_pt',\n",
-    "       'PRI_jet_subleading_eta', 'PRI_jet_subleading_phi', 'DER_deltaeta_jet_jet', 'DER_mass_jet_jet',\n",
-    "       'DER_prodeta_jet_jet',\n",
-    "       'DER_lep_eta_centrality']\n",
-    "\n",
-    "input_features_nJets = ['PRI_n_jets']\n",
-    "\n",
-    "branches_to_load = input_features_noJets \\\n",
-    "                    + input_features_1Jets \\\n",
-    "                    + input_features_2Jets \\\n",
-    "                    + input_features_nJets"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Loading the datasets_helper\n",
-    "---\n",
-    "\n",
-    "The `nsbi_common_utils.datasets` library provides a class `datasets`, which is a lightweight helper class for the various utilities related to reading, processing and saving datasets."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 3,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "importlib.reload(sys.modules['nsbi_common_utils.datasets'])\n",
-    "from nsbi_common_utils import datasets\n",
-    "\n",
-    "# Pass the configuration file path and branches to load to the datasets module\n",
-    "datasets_helper = nsbi_common_utils.datasets.datasets(config_path = './config.yml',\n",
-    "                                                branches_to_load = branches_to_load)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 4,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# Load the datasets with branches_to_load, converting to Pandas DataFrame for processing\n",
-    "datasets_all = datasets_helper.load_datasets_from_config(load_systematics = True)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Preprocessing\n",
-    "--\n",
-    "\n",
-    "Here we perform some simple feature engineering, particularly focusing on jet variables whose impact on the final likelihood is permutation invariant.\n",
-    "\n",
-    "Kinetmatic features with long tails are also transformed using `log` functions to help the neural network trainings."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 5,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "input_features_by_jet = {\n",
-    "        1   :   input_features_1Jets, \n",
-    "        2   :   input_features_2Jets\n",
-    "    }\n",
-    "\n",
-    "median_feature = {}\n",
-    "\n",
-    "for sample, sample_dataset in datasets_all[\"Nominal\"].items(): \n",
-    "\n",
-    "    median_feature[sample] = {}\n",
-    "\n",
-    "    for feature in input_features_1Jets:\n",
-    "\n",
-    "        median_feature[sample][feature] = np.median(sample_dataset.loc[sample_dataset['PRI_n_jets'] >=1, feature])\n",
-    "\n",
-    "    for feature in input_features_2Jets:\n",
-    "        \n",
-    "        median_feature[sample][feature] = np.median(sample_dataset.loc[sample_dataset['PRI_n_jets'] >=2, feature])\n",
-    "        "
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 6,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "branches_to_add = []\n",
-    "\n",
-    "for region, sample_datasets in datasets_all.items():\n",
-    "\n",
-    "    for sample, sample_dataset in sample_datasets.items():   \n",
-    "        \n",
-    "        sample_dataset['njet_0'] = (sample_dataset['PRI_n_jets'] == 0).astype(int)\n",
-    "        sample_dataset['njet_1'] = (sample_dataset['PRI_n_jets'] == 1).astype(int)\n",
-    "        sample_dataset['njet_2'] = (sample_dataset['PRI_n_jets'] >= 2).astype(int)\n",
-    "\n",
-    "        branches_to_add = branches_to_add + ['njet_0', 'njet_1', 'njet_2']\n",
-    "\n",
-    "        for i, feat_list in input_features_by_jet.items():\n",
-    "            mask_i = (sample_dataset['PRI_n_jets'] >= i).astype(float)\n",
-    "            sample_dataset[f'jet{i}_mask'] = mask_i\n",
-    "\n",
-    "            branches_to_add = branches_to_add + [f'jet{i}_mask']\n",
-    "\n",
-    "            for feat in feat_list:\n",
-    "                sample_dataset[feat]    = sample_dataset[feat].where(sample_dataset['PRI_n_jets'] >= i, median_feature[sample][feat])\n",
-    "\n",
-    "        for feat in branches_to_load.copy():\n",
-    "\n",
-    "            kin = sample_dataset[feat].to_numpy()\n",
-    "            \n",
-    "            if (np.amin(kin) > 0.0) and (np.amax(kin)>100):\n",
-    "                log_feat = 'log_'+feat\n",
-    "                sample_dataset[log_feat] = np.log(kin+10.0)\n",
-    "\n",
-    "                if log_feat not in branches_to_add:\n",
-    "                    branches_to_add         = branches_to_add + [log_feat]\n",
-    "\n",
-    "        datasets_all[region][sample] = sample_dataset\n"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Append the additional branches from engineered features back into the dataset and save back to the original `.root` ntuples defined in the config file."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 7,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "datasets_helper.add_appended_branches(branches_to_add)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 8,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "datasets_helper.save_datasets(datasets_all, \n",
-    "                        save_systematics = True)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Visualize the data\n",
-    "-\n",
-    "\n",
-    "Check for low-stats phase space and effect from systematics before moving to the density ratio training.\n",
-    "\n",
-    "NSBI workflows need precise estimate of the density ratios as a function of the full kinematic phase space. If regions exist with low-stats they can be filtered out by passing query strings to the `Filter` section of the `Regions` entries. E.g.:\n",
-    "\n",
-    "```\n",
-    "Regions:\n",
-    "-   Name: CR\n",
-    "    Filter: kin_feature_xyz<=100.0\n",
-    "```"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 9,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "import sys, importlib\n",
-    "importlib.reload(sys.modules['utils'])\n",
-    "from utils import plot_kinematic_features"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 10,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "samples_list = list(datasets_all[\"Nominal\"].keys())"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 11,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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",
-      "text/plain": [
-       "<Figure size 1200x1000 with 4 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "columns = ['DER_pt_h', 'DER_deltar_had_lep', 'DER_pt_tot', 'DER_sum_pt']\n",
-    "nbins = 50\n",
-    "\n",
-    "xlabel_dict = {\n",
-    "    'PRI_lep_pt': 'lepton $p_T$ [GeV]', \n",
-    "    'PRI_lep_eta': 'lepton $\\\\eta$ [GeV]', \n",
-    "    'PRI_met': 'MET [GeV]', \n",
-    "    'PRI_met_phi': 'MET $\\\\phi$',     \n",
-    "    'PRI_n_jets': '$n_{jets}$'      \n",
-    "}\n",
-    "\n",
-    "variations_to_plot = ['Nominal', 'JES_Up', 'JES_Dn']\n",
-    "\n",
-    "fig, axes = plot_kinematic_features(\n",
-    "    columns=columns,\n",
-    "    nbins=nbins,\n",
-    "    variations_to_plot=variations_to_plot,\n",
-    "    dataset_dict=datasets_all,\n",
-    "    xlabel_dict=xlabel_dict,\n",
-    "    samples_list=samples_list\n",
-    ")\n",
-    "plt.show()\n"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 12,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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",
-      "text/plain": [
-       "<Figure size 1200x1000 with 4 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "columns = ['DER_mass_transverse_met_lep', 'DER_mass_vis',\n",
-    "       'DER_pt_ratio_lep_had', 'DER_lep_eta_centrality']\n",
-    "\n",
-    "nbins = 50\n",
-    "\n",
-    "xlabel_dict = {\n",
-    "    'DER_mass_transverse_met_lep': 'DER_mass_transverse_met_lep', \n",
-    "    'DER_mass_vis': 'DER_mass_vis', \n",
-    "    'DER_pt_ratio_lep_had': 'DER_pt_ratio_lep_had', \n",
-    "    'DER_lep_eta_centrality': 'DER_lep_eta_centrality',       \n",
-    "}\n",
-    "\n",
-    "variations_to_plot = ['Nominal', 'JES_Up', 'JES_Dn']\n",
-    "\n",
-    "\n",
-    "fig, axes = plot_kinematic_features(\n",
-    "    columns=columns,\n",
-    "    nbins=nbins,\n",
-    "    variations_to_plot=variations_to_plot,\n",
-    "    dataset_dict=datasets_all,\n",
-    "    xlabel_dict=xlabel_dict,\n",
-    "    samples_list=samples_list\n",
-    ")\n",
-    "plt.show()\n",
-    "\n"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 13,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "<>:8: SyntaxWarning: invalid escape sequence '\\e'\n",
-      "<>:8: SyntaxWarning: invalid escape sequence '\\e'\n",
-      "/tmp/ipykernel_1494407/2432422146.py:8: SyntaxWarning: invalid escape sequence '\\e'\n",
-      "  'DER_prodeta_jet_jet': '$\\eta_{j1}\\eta_{j2}$',\n"
-     ]
-    },
-    {
-     "data": {
-      "image/png": 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",
-      "text/plain": [
-       "<Figure size 1200x1000 with 4 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "columns = ['DER_deltaeta_jet_jet', 'DER_mass_jet_jet', \n",
-    "           'DER_prodeta_jet_jet', 'DER_lep_eta_centrality']\n",
-    "nbins = 50\n",
-    "\n",
-    "xlabel_dict = {\n",
-    "    'DER_deltaeta_jet_jet': r'$\\Delta \\eta_{jj}$', \n",
-    "    'DER_mass_jet_jet': '$m_{jj}$ [GeV]', \n",
-    "    'DER_prodeta_jet_jet': '$\\eta_{j1}\\eta_{j2}$', \n",
-    "    'DER_lep_eta_centrality': 'X',\n",
-    "}\n",
-    "\n",
-    "variations_to_plot = ['Nominal', 'JES_Up', 'JES_Dn']\n",
-    "\n",
-    "\n",
-    "fig, axes = plot_kinematic_features(\n",
-    "    columns=columns,\n",
-    "    nbins=nbins,\n",
-    "    variations_to_plot=variations_to_plot,\n",
-    "    dataset_dict=datasets_all,\n",
-    "    xlabel_dict=xlabel_dict,\n",
-    "    samples_list=samples_list\n",
-    ")\n",
-    "plt.show()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 14,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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BEARs27Yt5tei8CTz/RUREVEy4b1TcokoowkAfv/73+OBBx5AR0cHAFe/gJdeegmbNm3Cb37zGyxatAgLFiyIeKE9tWHDBqxatSqkudXV1QCAGTNmID8/3+vY2LFjMXLkSOzZswfbt2/H5MmTAQAFBQWe5yhNmTIFAPDPf/4T3/ve9yL6HoiIiCi9JPv9VaKUlpaq9stUCmVXHSIionRTVVWFqqqqgHOUSTWRiCjQtH79esybNw+jRo3C73//e6xduxarV68GAFx33XWoqanBAw88gGHDhuH666+PyoLDNXfuXNx0002er6dPn45jx46pzq2trQUATJs2TfX4tGnTsGfPHtTW1noCTf369YMgCD5/Ie4bQ/ZoIiIionCkwv1Voqxbtw6jR49O9DKIiIhSTigfxNTV1WHMmDERXyui0rlHHnkEhYWF2Lp1K6ZMmYKcnBzPsUGDBqG6uhqFhYV47LHHIl5oT/Xv3x/f//73PX8MBoPfufv37wcADB06VPX4kCFDALhS2d2ysrIwadIkvPjii15z169fD71ez5shIiLy0d7ejt/85jf4/ve/j+zsbJSUlKCmpgYAMHjwYDz66KMAXK+lV111led53377LW677TZccMEFyMzMRP/+/TF9+nR8+OGHXue/+eabIQiCz3U/++wzCIKARYsWAQCuuuoqzJkzBwBw5ZVXYvDgwZ65TU1NuO+++zBs2DCYzWYUFhZi4sSJnuxft0WLFqmmqp89exaCIODmm2/u2Q8pjaXC/RUREVE88d4ptUSU0fTPf/4TM2fORO/evVWP63Q6XHnlldi0aVMkl4mb+vp6AK6d5tS4v8+TJ096PT537lz88Ic/xH//93+jtLQUX3zxBR5++GH8+te/xoABA2K6ZiIiSj2lpaX4/PPPMWnSJBQUFGDr1q2YMWMG/vnPf+KWW27Bq6++ip07d+KWW27xfGBhs9kwffp0fP7555g4cSKmTp2K48eP46233sK2bduwc+dODBs2LOx1yLKMbdu2obS01KvUu6ysDJs3b8bYsWMxadIknDlzBhs3bsS1116Ld955BxMnTozqz4S6aO3+ioiIKFK8d0otEQWaCgoK0NbWFnCOw+HwG7hJNu7vxd+Nnfvx7t/z1KlT8cYbb+Chhx7Cz3/+cwwYMABLly7FXXfdFfB6LS0taG5ujnjdRqMRRqMx4vMQEVF8nDhxAv/85z89r4/33nsv/vCHP2DTpk24//770djYiJ07d+KBBx7wbEzx6aef4vPPP0d5eTleeOEFz7lefvll/OhHP8LmzZvDvlmqqKiAw+HAtm3bcPvtt3tugL799lts3rwZl19+udcnfv/4xz9w6aWX4o033ojpzZLNZoPNZov4PC0tLVFYTfxp7f6KiIgoUrx3Si0RBZrGjRuHN954A4cOHfJKGXM7dOgQNm3ahKlTp0Zymbhxb5nob+tEnU4HAHA6nT7Hpk+fjunTp4d1vWj9oi1cuBCVlZVRORcRUSLIsgzZHviNdTIQDGbVtOpw/e53v/MKElx77bX4wx/+gNOnT/t9znnnnYcVK1bg6quv9np8xIgRAIDGxsaI1+Wm1+uxYsUKXHbZZTG/lpqHHnrIk6KejrR2f0VERNHHeyfeOyWziAJNDz30EN566y2UlJTg3nvv9dQYvv/++/j000/xxz/+EQCwdOnSyFcaB2azGc3NzThz5ozqp4TuTxezsrKicr1t27Zh1KhREZ+H2UxElOpkexv23pr8mydcuLwFgjHy14BLL73U62uz2Rz0OUOGDPH0CmxsbMS+ffvw5ZdfYuXKlRGvp7u+ffti9uzZAIDm5mbs378fX331Ff76179G/Vpq5s2bFzQrOBS7du1KyU8PtXZ/RURE0cd7J947JbOIAk0DBw7Eli1bMGfOHNxzzz2ex6+66irIsozvfve7WLNmDYYPHx7xQuOhoKDAE2hS+wTx1KlTAIA+ffpE5XoWi8WrwScREaWHnr6OvPTSS6isrMS+ffsAAL169Qpr0wl/GbtqampqcPfdd+Pzzz8H4Lqhu/zyy2Nyre6iVRKeqju/au3+ioiIKFK8d0otEQWaAGDs2LHYvn07du7cib179+Lo0aPo168fiouLcemll0YlTS5eiouLceDAAXz11Veqv3y7d+/2zCMiougRDGZcuDz5++kIhuCfnsXKypUr8bOf/QwzZszAo48+itGjR+O8887D119/jUGDBoV0joaGhpDmbdmyBdOnT8dll12G9evXY+zYsTj//PMhCELIr+uhXovUaen+ioiIoo/3TsHx3ilxIgo0bd26FZMmTYIgCLj44otx8cUXR2tdCVFSUoJNmzZhy5YtKCsr8zm+efNmAPCpuyQiosgIghCVtGotW7NmDfr164fXXnsNoih6Hj9y5Ijf59hsNq/MoB07doR0rZdffhkA8NZbb3llBX3zzTd+n2O1Wr2+DvVa5Etr91dERBR9vHcKjvdOiSMGn+LflClT8J3vfAd33323Jn4oM2fOBABUV1f7RBM//vhj7N27F7m5uZgwYUIilkdERGnGbrd7xufOnYPVakV7e7vnsebmZvz2t78F4H2zkp+fD6DrAxLAtRvKY489FvK1JEnC2bNnPY/ZbDZPGVewa509e5b9gyKgtfuraCotLcWIESMC/qmqqkr0MomIKEF47+RfVVVV0NfQ0tLSqFwrokDT3LlzYbFY8Mgjj2DcuHG48MILsWTJEhw4cCAqi4u3UaNGYcqUKWhoaEBZWRmOHj0KAKirq/P8wOfMmQOTyZTIZRIRkca5G1zefffdWLFiBQDXG+wzZ85g+PDhKC8vR1lZGQYNGgRJkmCxWLBy5UrPzdC1114LQRBQVlaGn//85/jlL3+JMWPG+Oy6orzW4sWLsWzZMs+1ZFnGqFGjcP311+PHP/4xBg8ejD179uD8889HdXU15s+fDwD4wQ9+gMzMTNx1110oLy/Hr371K4waNQrnn38+CgoKYv6z0iKt3V9F07p16/DFF18E/FNRUZHoZRIRUZzx3im4ioqKoK+h69ati8q1Igo0/f73v8dXX32Fzz77DPfffz8MBgMeeOABDBs2DJdddhmqqqpQX18flYXGy4oVK9CvXz9s3boVRUVF6N27N8aMGYNvvvkG48ePx7x58xK9RCIi0ribb74ZV199Nd555x1s2rQJgOvG6cEHH0RmZiZef/11HD58GPfddx/ee+89PProo9Dr9Z5U8CuuuAJr165FcXEx1qxZg88++wx33nknnnzySZ9rXXfddbj22muxa9cuT9r39ddfj2effRaFhYV46623sGfPHvz0pz/FJ598gqeeegr5+fnYv38/ANeOLm+++SbGjh2LN954Ax9++CHKysrwyiuvQKfTxeknpi1avL8iIiKKJd47JRdBjnJr83379mH9+vV49dVX8cknn0Cv12PKlCnYsGFDNC/TY4MHD8bhw4exd+9ev7u1HD9+HAsXLsSGDRtw+vRpFBUVoby8HPfdd19UdsGpq6vDmDFjsGPHjrA63hMREVHotPR6m+z3V7Gmpb9LolTgdDoht/nrYyNCNHXtACZZGwBIIc5tBODwe13RVBj2XEFv5gYJRFESrdfbiHed6664uBhz5szBiBEj8Pjjj2Pr1q2eiGIyOHToUNA5/fr1w/Lly+OwmujzelEw5EEUXX/Fkr0FkNq85grmIs1ETImIiLQs2e+viCh8XvftogGiIddzTLKe8v/EcOZCD9GUF/ZcqeUwGl6/KMDc5NHnv76EIGb4Pe4VvLI3AZLd71wY+niaRkv2ZkCyBp3LQBeRr6gFmurr61FdXY033ngDNTU1aG9vh16vx9SpUzFr1qxoXYaCkNuOoH79BSHNLZh1EMgeHOMVERERUU/x/oooeQXO+ukkmiAacgAAkiQB9q4Nh5ytR9G4aWIsl5gWGl5Vr1KJlz7/tQ+CGODD+wAf/vODf9KqiAJNe/fuxRtvvIHXX38dH3/8MSRJgslkwtVXX41Zs2bhmmuuQa9evaK1ViIiIiLN4/0VUXz4BIpEM0SDa1tySXIA9kb/TxZNkO2NKZP1E4m8adugyxqgeCTxpXOyoy1pfvYNrxb3+Ll507ZBdv9sVQJSDERRqooo0DRixAgAQFZWFmbNmoVZs2bhhz/8IbKysqKyOIpM3ozPkJEzBEDXP1b85ISIiCi58f6KKDo8gSQ/AaRkui/Om/53ZPT+nufrRJfOuea6AkjBgh1iRuj/NkVrrizL6Fve4lpnkHK4WJTORSvQFez3zysQ5abPhag3dK7Rtz2KEgNVlCgRBZpuvPFGzJo1C9OmTYtKk2yKLp0h2/MPdDj/qBMREVHi8P6KKDSyLEN2uN5kSw474GjyOh7PQFKfmbsh6M2+BwKUzikJ5iKIioCAmBF6e4vYzU3e9w+CIEDowfuc2AS6gvVy8s5Ucp77MuTfy0h/f9kqhRIlrEDT3XffjYkTJ+Kaa64BAPz5z38G4Gqw/c0332D8+PE+z1mwYAGeeeYZNDSo/6NKRERElM54f0WkLlgPpGiWT+VN2wZd9vCwSufcASQgtJ3PRAAwZkdhtZRokQS6BFMfVwCou86AlDOKjdhlRxsc7fWAo8X/JH0ORL0JACA52rzmMiOKeiqsQNMjjzwCAJ4bIbfHH38cjz32GJxOp89z2tvbcebMmQiWSFFlyFMfExERUULw/orSVcBAkt4C2Xoqam+4+8zcDZ1lkN8AUvc31K6gEHuhUfTpdLqAWUZyzoXqgSi3IKVzTvtZNFZfDAAR///jVbrnFZCyAo5mBqLIr6jtOkepwZ222X1MRERERBRP4eyWHEyf0qMQ5A6/x92laQwgUbILFohSUsumEjtao7aWYKV7LM0jfxhp0BrRpD5W4Ww5DEmtllwRJQ8lDZiIiIiIKJb890ByNfrmPSuRi6A3e/pHAb7lcD66lc45z/4raZrkU+pioEljlLXiyrGbZO/6RyaUVMq+5S2e+mMiIoq9bdu24corr8Rzzz2Hn/70p4leDhFRXORN2wadcnctvQWiIrDEQBJRaJT9owB31lNBSM8VM7IgGPLUS/c6A1LR7CEVLbx3Sj4MNKWbANtfEhFRbD322GOYM2cO3nvvPUycOBFnz55Fbm4ubrrpJqxevTrRyyNKeqWlpTCZAmdsV1RUoKKiIk4roojouz4U1fUaCX1mfgIXQ0RA8NI91YqYGOK9U/RUVVWhqqoq4ByrNcAOimFgoEljJEnyGovdjgvmosDN5QDIUgcaXh3uOoe9Jam3NiUiIqL0sW7dOowePTrRy6AocZfrdB8TEVH0hfJBTF1dHcaMGRPxtRho0hp7g/e42xaqoTSXc5w71PUFM6CIiIiIiIiIKERhB5o++ugjLF682OcxAFiyZAlkWVY9RkRElM6uuuoqvPfeewCAK6+8EoMGDcLhw4cBAM899xyee+45z3G3tWvX4sknn8SuXbtQXFyMWbNm4d5774UoduWrWq1WPPLII3jhhRfw9ddfw2g0YujQobjttttw0003eea5+xd8+OGH2LVrF373u99h+vTpTDtPEry/onQkOdq8xsyiJyIl3julrrADTbW1taitrVU99sADD6g+zsZ9RESU7kpLSyHLMrZt24bS0lJ873vfw/Hjx/HUU09hxIgRuPrqq3H++efjm2++AQD89a9/RU1NDSZMmIAZM2bgnXfewfz581FXV4e//e1vnvPecccdWL58Of7t3/4NN9xwA9rb2/HWW2/h5ptvhl6vx4033ui1jmeffRYvvvgirrzySkyYMCGuPwPyj/dXpEWyLEP2CiZZAUez52tn69GuyY4WhNqwmIiSg+xog6P9tNf/10qCuchVUdNDvHdKXWEFmrpHC4mIiKJBlmXYnIleRXBGXc/f3FdUVMDhcGDbtm24/fbbPQ0tn3rqKYwbNw6PPPIIAHhuljZt2oS//OUvnpudpqYmTJs2DevWrcO7776Lq666Ck6nE3/+858xaNAgfP7559DrXS/r3377LQYPHoxXX33V52Zp7dq1+Pvf/46xY8f29MdAUcb7K0pV7kCSv+3TZUdb0u1ORaQVibp3cghZsMHVEPzb18cFnJs37QNk5Q2DIAiQHHbA0eR1PFggivdOqSusQBOjd0REFAs2J/DTzfWJXkZQz08tgClO3Q3Hjx/vdaOTm5uLRx99FJdddhn+8pe/4KqrroLNZsMTTzyB7373u54bJQDo168fcnNz0djY6HPesrIy3iglGd5fUbJyOp2Q2474PS5Dh4b1A+O4IiJyS+i9U8Gh4HMAYAfw+/q+MEK972/BrINB+weHg/dOyYPNwImIiJLQ5MmTfR4rKSlBdnY2Dhw4AAAwm82YPXs2AKCtrQ379+/Hvn37sHHjRtTXq998fve7343doolIU+S2I6hff0FUztVn5m4IpvMCltgQUXpxth4FRDNEgwUAfLKewv13gfdOyYOBJiIiSjijzpUtlOyMPW8zELbzzjtP9fH+/ft73Qjt3LkTFRUV+PjjjyFJEoxGIy655BJkZ2erPj8vLy8m6yWi1BMwY0mf69VfKZg+M3dD0Jv9Hu8qkckPc5VEpCZZ752cTifk9s7+a/osZBpOepXOOVuPonHTRADw/NefvGnb4LSedp23vRFSRyukjlYAgNRxDk6n06v0jvdOyYOBJq0RTerjcOhz1cdERDEiCELcStJSxalTp1QfP3bsGC6//HIAwO7du3H55ZdjyJAh+POf/4ySkhIMGjQIOp0Ogwerp6KzgTQRuYWTsZT3w0+hM3V7s6XPgah33W8KejP/fSGKo6S9d9LrAaPvPYhrV8neYZ2qcdNEtH7mGje9dx1OngCaO+Pf1q/XuwLlitI73jslj2T81aQIiIYc1XFY59AbPGPZegyObk3blCLdSYCIiNRt3boV999/v9dj27dvx7lz5zB8+HAAwGuvvYb29nb87W9/w4UXXuiZZ7fbcerUKQwaNCieSyaiJOM3Y0mfC1FvCCtjKSP3ws43ikREPSeYi1y9mdy6lc45z/4zaKaTm3vnSmfbcQBAzZa3sOC+u7qupTfj73//O++dEoCBJgoo2E4h0W7gRkSUDux2e8CvAWDbtm146aWXcMMNNwAAzpw5gzvvvBOiKOK2224DAJw7d85zzM3pdGLevHloa2uD1WqN1bdAREnAJ5BkyIMoum7vJXsLnOe+DPkNW960bdBlDeh6oDMY5RaoLI6IKFQ6nc7v+0cxIwuC4XKvQFTWydXAmsUwX74Wfaf9EBnH/glUXIYOR1fpXdNe19z3P6zFU7dbcN1lrq8N04/y3ilBGGjSGEmSvMZirC+o71nWFBFROjKbXW/UFi9ejM8++wy/+tWvIIoitmzZgnnz5uFnP/uZZ+51112HH//4x1i1ahX69euHrVu34vjx47j33ns9n8r913/9F5YtW4Zp06Zh4sSJyM/PxwcffAC73Y6RI0fik08+wS233IJVq1Yl5PslCtVtt92GTz/9FJ988kmil5JSotmsW5c1AHp+eEhECdY9EJWd5wqAP/jHJ7D7yyO47ab/hCgA23YDD/4NuEERS58+Bvj1s8BfPwT69gL+ft9oHD9Zj7l3/gJD+hvgOHcIM6aN571THMQ8DkFxZm9QH4dB0JvRt7wFBT86hYJZB33+9Jm52zPXXZdPRETBXXfddbj22muxa9cuvPzyyzAajVi6dCksFgueeuopNDR0/bv9m9/8Bk8++STa2trwxhtv4IILLsCqVavw8MMPe+aUlJRg/fr1GDp0KN555x384x//wNSpU/HZZ59h5cqVGDp0KL744otEfKtEIduyZQuefvrpRC9D8/Kmbeu6n/tRI/qWt7ju964/gYJZB7nrGxElpe73TqbexVi8cC5y8s/HX7ZnQ7rkFeROXAMAmL9sIx7/3wfQbgO27ALOz6nH/90C3Pm9Z1C//gLUr78AQ76egWcrJAzs3cx7pxgSZFmWE72IdFNXV4cxY8Zgx44dGD16dFTP7Th3yPPJVsGsgzH5ZErqaMXJF111tH3LW1ivT0RESSmWr7cUHWfPnsVFF12E5uZmDBs2zG9GUzr/XXqVxwUojcubtg263t/3Og6pqwcT+2oSUTpQvh8OJtBumen6b2a0Xm9ZOkdhkxSNIyVHGwNNREREGvbSSy+hvLwce/fu9ZRtdnf8+HEsXLgQGzduRENDA4qKilBWVob58+fDZPKf/fzrX/8aQ4YMwcCBA/kJsh+hlsfpsgZAb+zl+Zr3Z0SUjnyajXcjSx1oeNX1WhaoHzF7EUeGgSYKn6Ol27ggYUshIiKi2FqzZk3A40eOHMGll16KEydOAAByc3Oxb98+LF68GDU1NXj33XdhNBp9nvf666/jlVdeweeff47f/e53MVl7KvBkLBn6QBRdXS0kezMguRrSundVIiKi4AI1GwdcGU+hkKWOgHPTNeMpVAw0EREREZEPWZbxzDPP4M033ww4b/bs2Thx4gQmT56MlStXYuDAgdi5cydmzpyJ2tpaLFmyBIsXL/Z6TkNDA2699Vb88Y9/xODB6f2JcTgNvfNmfIaMnCEA1EvjiIgosEAZT7KjzZPl5M568ocZT4Ex0EREREREHjU1NVizZg0++OAD7Nu3L+DcnTt3oqamBoWFhVi7di3y8/MBABdffDHWr1+PcePG4YknnsCCBQu8Suhuu+02fP/738cvf/nLmH4vyUCWZcjKtgOKbCUgvIwlnSHbUxLH0jgiovAFyniSOlrjvBrtYqCJiIiIiDw2bNgQ8rbO1dXVAIAZM2Z4gkxuY8eOxciRI7Fnzx5s374dkydPBgCsX78eb731Fj755BO0trpu6js6OuB0OtHa2oqMjAwYDIYofkeJJTvaPJuoBNNn5m7oLIN8glFuzFoiIood9+7rACBJks8u7s72U2jcWAKApXXBMNCkNaJBfUxEREQUgrlz5+Kmm27yfD19+nQcO3ZMdW5tbS0AYNq0aarHp02bhj179qC2ttYTaNq9ezdaW1sxYsQIn/kWiwW//e1vfUrtUpkkSSHPFfRmiBlZzFYiIkoAQRAguLNGAcCY7XVcNBV6xiytC4yBJo0RDbmqYyIiIqJQ9O/fH/379/d8HSi7aP/+/QCAoUOHqh4fMsTVT+jAgQOex2655Rb8x3/8h9e8pUuX4uDBg1i5ciUGDBjQ47UnJcUn4n1m7oZgyFPNVgKYsUREpBWyow1SRysk6ynV41rPeGKgiYiIiIh6pL6+HoBrpzk1vXv3BgCcPHnS89iAAQN8gkl9+/ZFfX09Lr/88tgsNIbcu8YpP+mW7E2AZHcdV/RgEvRm6LP6xXuJREQUBcrSOgA+QSRn+wk0bvx3APA0FfdH6xlPDDRR+PQ56mMiIiJKK21tribX7oBSd+7H3fMi1dLSgubm5ojPYzQaYTQao7Ci8HaNIyKi1KUsrQMAMcM7UKT8wCHebDYbbDZbxOdpaWkJPikEDDRpjDKqKllP+fzyR4OoN6mOiYiIKL3Isuz13+7cZQFOpzPgeVavXh3S9SZOnBjG6vxbuHAhKisro3IuIiIiwDfjCfB+f+5sPYrGTdF5HevuoYcewqJFi2Jy7p5goImIiCiJ/Nd//Rdee+01v2/co+nOO+/Eo48+isOHD2PgwIExvx5pj9lsRnNzM86cOaNaPufOZMrKik5z623btmHUqFERnyda2Uzdde0a1+QpnVNiDyYiouhLlnun7hlPQPesJ72fceTmzZuHu+66K+Lz7Nq1Kyof6jDQRGGTHFavMXdGISIiSk8FBQWeQNPgwb5Z1KdOuT7J7dOnT1SuZ7FYkJOTvGX73DWOiIj8EU15quNoiFZJuMViicJqGGiinnB09UZwnt0DOBQNPfUWiHozgM6AlKNZ8x31iYiI0lVxcTEOHDiAr776CqNHj/Y5vnv3bs+8aCgtLYXJFLhsv6KiAhUVFVG5HhERkVZUVVWhqqoq4ByrVX1X1HCJUTkLpa3GTRNRv/6Crj9rC3HyRQtOvmhB/do+qF9/AeS2I4leJhFRUhAEwe+fm2++GYIg4LXXXvPMvfnmmz3P/fLLL1FeXo6BAwfCZDJhwIABuP766z1v5N2uuuoq1cyS119/HYIg4PnnnwcADB48GI8++igAYNCgQbjqqqs8c7/99lvcdtttuOCCC5CZmYn+/ftj+vTp+PDDD73O6V5zd5999hkEQUiqXgEUGyUlJQCALVu2qB7fvHkzAOCyyy6LyvXWrVuHL774IuAfBpmIiLRDa/dOc1YC/W+Gj3jcO1VUVAR9DV23bl1UrsWMJiIiojiZM2eOz2OffPIJtm/fjt69e2POnDmorq7GgQMHMGfOHIwbNw4AcPr0aUyaNAknTpzA1KlTMX36dBw8eBCvvPIK3n//ffzrX/9CXl54Kdi33HILXn31VezcuRO33HKLJxvFZrNh+vTp+PzzzzFx4kRMnToVx48fx1tvvYVt27Zh586dGDZsWMQ/C9KGmTNnorKyEtXV1WhoaPAqkfv444+xd+9e5ObmYsKECQlcJRERpSot3TvFY+OuZMFAE4VNMBehYNZB9YOdpXPOlsNoeP2i+C6MiFKWLMuQHdHZ/jyWBL1ZNYMnVI888ojX19988w1Gjx6NoUOHorKyEjk5OTh8+DAOHDjgNXfTpk04duwYFixYgCVLlnge/8Mf/oB7770X27dvx4wZM8Jay/3334/Gxkbs3LkTDzzwgKeh5aefforPP/8c5eXleOGFFzzzX375ZfzoRz/C5s2bGWgij1GjRmHKlCl4++23UVZWhtWrV2PAgAGoq6tDaWkpANebhGDlbkREFB7eO/HeKZkx0KQ1okF9HEU6nQ7IDhx9lTr7NBERhUJ2tOHki9FpPhhLfctbfHYT6amOjg5cf/31aGlpQU1NTcAGxyNHjsSKFStw3XXXeT0+YsQIAEBjY2NU1gQA5513HlasWIGrr7465tcibVixYgVKSkqwdetWFBUVITc3F01NTQCA8ePHY968eVG7VlL2aIrDvRcRUXe8d+K9U7ji2aOJgSaNEQ25qmMiIkoud955Jz766CM8//zz+N73vhdw7qhRozxbup88eRL79+/Hv/71L/zpT3+K+rqGDBmCIUOGAHDdGO3btw9ffvklVq5cGfVrkTYUFRVhx44dWLhwITZs2IDTp0+juLgY5eXluO+++2AwRC/4sm7dOtWm44nEey8iovjgvVNkQvkgpq6uDmPGjIn4Wgw0ERFRwgl6M/qWtyR6GUEJUcrWfPHFF1FVVYVf/OIX+MlPfhLScx599FH87//+L7755hsAru3ihw8fHvI1ZVkOee5LL72EyspK7Nu3DwDQq1evsN7ch3MtSn6HDh0KOqdfv35Yvnx5HFZDREQA751CwXunxGGgiYiIEk4QhKilVSe73bt349Zbb8WYMWPw2GOPhfSc+++/H0uWLMFNN92EH//4x/j+97+P/Px8bNu2DVdeeWVI52hoaAhp3sqVK/Gzn/0MM2bMwKOPPorRo0fjvPPOw9dff41BgwZF9VpERETUM7x3Coz3TonFQJPGpFMneyKiVNPc3IxZs2bBZDJh3bp1MBqNIT1vzZo1uPTSS7F69Wqvx48cOaI6X62+fseOHSFfq1+/fnjttdcgimLQawGu3VaU30uo1yLSAt57ERHFjpbvnfTZ4V8rVTDQRLGht6iPiYjS2E033YR9+/bhzTffDPoJV0dHBzIyMgAALS0tMBgMcDqdrg0ZABw/fhwPPvggAO+bo/z8fJw4cQKff/65p3/Bnj178Pzzz/u9lt1u94zPnTsHq9WK9vZ2ZGW5Piltbm7Gb3/7W9VrAcDmzZtxzTXXAAC+/fbbkD9tJApXUjYDJyKimNHWvZMevTuT0LZs3Y7/+tGFAOJ378Rm4JTyREUtrsgd6IiI8Nprr+HVV19FUVER3n77bbz99ttex/Py8nD//ffDbHb9mzl79mxcc801KC0txaxZs/Dkk09i+PDhuOKKK9Dc3IxNmzZh/Pjx+Oqrr/DQQw8hOzsbN9xwA6677jqsX78eV155JW644Qa0tbVh/fr1+O///m+fGyb3te6++25cc801+NnPfobS0lL85je/wfDhwzFx4kQ4nU5s2bIFI0eOhMViwcqVK3HeeefhjjvuwLXXXov/+7//Q1lZGcrLy6HT6fDaa69h2rRpAW/OiHoqGZuBExFRbGjt3ulX//MzTBsNLN8ClN/8K5TXfBTXe6d4NgOHTHG3Y8cOGYC8Y8eOqJ+7o/mgfGw15GOrIXc0H4z6+UPltLd41uG0tyRsHUREyeK5556TAfj9M2jQIFmWZfmjjz6Sr7jiCjkzM1O+8847ZVmW5ba2Nvmee+6RBw4cKGdnZ8sTJkyQn332WVmWZXnx4sVyYWGh/H//93+eaz3xxBPy8OHDZYvFIk+cOFFevny5/K9//UsGID/33HOeefv27ZOvvvpqOSsrS541a5Ysy7LsdDrlBx98UB46dKiclZUlX3rppfLvf/972eFwyCtWrJD79esn33XXXZ5zvPzyy/L3vvc9OSsrSy4pKZEffvhhubW1VQYgV1ZWxvznGkgsX28pvpL57zJZ7r2IiLRGa/dO7vfIz/wP5O999yJN3zsJsqyx9uYpwB0l3LFjR9Q/lXOcO4T69RcAAApmHYQ+OzF9AhztZ1C/Ns+1jh81Qp/ZOyHrICKi9BXL11uKr2T+u0yWey8iIkpuUkcrTr7oaivTt7wFYhI2c4/W660YfApRDzia1MdEREREREREaUayNqiOtYg9mqLstttuw6effopPPvkk0UshIiIi0hQ2AyciotQl+RnHB5uBp6gtW7bg6aefxiWXXJLAVej9jImIiIhSW3I2A+e9FxERJb94NgPnq2GUnD17FrNnz0ZOTk5C1yGa8lTHieRsPQqIZogGVz2q5LB7ldMJ5iLPlpNEREREqSQZ772IiIgSKa16NL300ksQBAFffvml3znHjx/HrbfeivPPPx8mkwnDhg3DAw88EDSF7Ne//jWGDBmCa6+9NsqrTn2Nmyai/m/n4eSLFpx80YL6tXmoX3+B54/cdiTRSyQiIiIiIiKiKEirQNOaNWsCHj9y5AhGjx6NZ599Ft9++y0yMzOxb98+LF68GJMmTYLNZlN93uuvv45XXnkFq1evhiim1Y+UiIiIiIiIiMgjLaIisizj6aefxptvvhlw3uzZs3HixAlMnjwZhw8fxpkzZ1BXV4cBAwagtrYWS5Ys8XlOQ0MDbr31Vvzxj3/E4MGJ385Wsp5SHcebYC5CwayDXX+uP4G+5S3oW96Cgh81Im/atoStjYiIiChakuXei4iIKFloukdTTU0N1qxZgw8++AD79u0LOHfnzp2oqalBYWEh1q5di/z8fADAxRdfjPXr12PcuHF44oknsGDBAq/dTm677TZ8//vfxy9/+cuYfi+pRqfTAdnqgTcxIwtwDIjzioiIiCjVcdc5IiJKXaKfcXxw17ko2bBhA1atWhXS3OrqagDAjBkzPEEmt7Fjx2LkyJHYs2cPtm/fjsmTJwMA1q9fj7feeguffPIJWltbAQAdHR1wOp1obW1FRkYGDAZDFL8jIiIiovSVnLvOERERBSea+qiO4yWeu85punRu7ty52LVrl+dP//79/c6tra0FAEybNk31uPtx9zwA2L17N1pbWzFixAhYLBZYLBa8+OKL2LlzJywWCxYvXhzF74aIiIiIiIiIKLlpOqOpf//+XsGlQNlF+/fvBwAMHTpU9fiQIUMAAAcOHPA8dsstt+A//uM/vOYtXboUBw8exMqVKzFgAMvD/BLN6mMiIiIiIiIiSlmaDjSFo76+HgCQm5urerx3794AgJMnT3oeGzBggE8wqW/fvqivr8fll18e9JotLS1obm7u4Yq7GI1GGI3GiM8TT6LBojomIiKy2Wx+d3oNR0tLSxRWQ0RERBQ5ydrgNRYzshK4mthioKlTW1sbgK6AUnfux93zomHixIlROc/ChQtRWVkZlXMREREl2kMPPYRFixYlehlEREREUST5GWsPA02dZFn2+m93Op0OAOB0OgOeZ/Xq1SFfc9u2bRg1alTI8/3xzmbS+xknF0lyeI013SyMiIjCMm/ePNx1110Rn2fXrl1R+1CHyL/UuPciIiKKF74adjKbzWhubsaZM2dUy+fcmUxZWdFLb7NYLMjJyYna+QBANOWpjpOOvdEzdJ75DMhSlCCKZk85nSQ5AHsjBHORJ9hHRETaFq2ScIuFpdkUeylz70VERBQnDDR1Kigo8ASaBg8e7HP81KlTAIA+feK/DaHWNW4K/mlzwayDQLbv3wsRERGlj9LSUphMpoBzQtm+mYiIKN1UVVWhqqoq4Byr1RqVazHQ1Km4uBgHDhzAV199hdGjR/sc3717t2ceEREREcXfunXrVO/TiIiIKLBQPoipq6vDmDFjIr4WA02dSkpKsGnTJmzZsgVlZWU+xzdv3gwAuOyyy+K9tLCkSid7wVzkylJS01k652w5jIbXL4rvwoiIiIjCkCr3XkRERPHCHsydZs6cCQCorq5GQ0OD17GPP/4Ye/fuRW5uLiZMmJCI5YUhNTrZ63Q66LMHq//J6gsxIwuC3pzoZRIREREFkRr3XkRElGiin7H2aPu7C8OoUaMwZcoUNDQ0oKysDEePHgXgSh0rLS0FAMyZMydoXwAiIiIiIiIiIiXR1Ed1rEUsnVNYsWIFSkpKsHXrVhQVFSE3NxdNTU0AgPHjx2PevHmJXWAIJGfXJ2n1bRJ0otP1hQDkGLriii12CZLs/zw5xtDm5pvA3eCIiIiIiIiICAADTV6KioqwY8cOLFy4EBs2bMDp06dRXFyM8vJy3HfffTAYDIleYlCnbV3jyo8b0aTLjun1Hr8yH4VsRUBERERERERESLNA06FDh4LO6devH5YvXx6H1WiDTZJhdchotUtwMuuJiIiIiIiIyIdkbfQaa3nziLQKNKWbO0b1Qp/8fNcXUSyda7A6seijJgDAPe83IpgeZz2JXf2wZKkDjnP+A4WCuYjBLCIiIiIiIkpSDj9j7WGgSWsEwTPsbdKjMEs9+GLShx6U6T5XGYQKRbCsJ38ZT6IhxzNueHV4wGsUzDoIZA8Oa11EREREkUufXYSIiIhCwUCTxuTmFKJJMY4Fow54fmqB12Pdg0jhZD2xzxMRERGForS0NOgOwBUVFaioqIjTitJrFyEiIkpdVVVVqKqqCjjHarVG5VoMNFHYBEGAqdtvTjgZUiFfR29G3/IWAIAkSYC9weu4s/0UGjeWuL4QA990EhERUepbt24dRo8enehlEBERpZxQPoipq6vDmDFjIr4WA00UE/kmV6aSssxOmfWkzHhyl9a1OSQ4nN3PZPKcT2f0Lo0TTV0ZW8oyOyIiIiIiIiJKDAaaNKap+ZTXONNkScg6dDqdTzmcv6ynmDYUJyIiIoohydrgNdbyLkJEREShYMdCrZFl9bEGSZKkOiYiIiKKH8nPmIiIKD0xo4kSwl1aZ8kARNEV7+xeOte9vK7R6vQ67mzpyt6S2k8Bxux4LJ2IiIiIiIgoLF6tX0yx2bgrWTDQRAkRSmmdJaNrrFZel+tsRGXn+LQN6BfdJRIRERERERFRmBhooqTlznQKSdtROM6JgGiAaMj1PCxZu7KeBHMRdLro745HRERERERERC4MNFHSMuqA56cWeL72Ka073QS80/nFtqtQH+R8+dfuhy53SLSXSURERERERBSQZG30Gmt58wgGmihpCYIAk+I3tHtpndGmQ1sY52N5HRERERERESWGw89Yexho0hpBUB9rkD5rICrz/uH1mAQDrGKO5+vvdHyOO85eG+eVERERUfoQ/YyJiIjSEwNNGpObU4gmxVjLzAYdHpt+iedrq0OC3ek9p73JAbzd+YUk41RrtwkK+SawhxMRERGFRTT1UR0TERGlKwaaKGUFK60DgNYOHZo7x5WfnEGTLsdnjtvjV+b77IRHRERERERERKFjoInShkFuh0FuhVlqUD3udOYCYEYTERERERERUU8x0KQxzS0NXuNMkyWBq0k8g97gGc8/MzHw5NZ9OKUbHHAKy+uIiIgSp7S0FCaTKeCciooKVFRUxGlF6bWLEBERpa6qqipUVVUFnGO1WqNyLQaaNEZyOlXH6Upn7B3y3GCldQDL64iIiBJp3bp1GD16dKKX0U367CJERESpK5QPYurq6jBmzJiIr8VAE2maoDejb3mL12OS9ZRn3NZ8BK1vXxny+cwZ0VoZERERERERpQvRVKg61iIGmkjTBEGA0C2FXczoKo/LlHVo7Rw/eEUBnMZ8n3PYJBn3vO9Kiz9jk9HWwZ3riIiIiIiIiNQw0ERpTZfZFVjqnV0AMcM3SGR1yJ6xO+DkD0vriIiIiIiIKJ2JiV4AkZaYmMxERERERERE3Uj2JtWxFjGjiaiTs+UwJL2526N6ZBh747nJZlgdEuwwQxAErxnK0rqzHTKsfpqwm3SAQS/CqIPPOYiIiIiIiEjDJLv6WIMYaKK0pmwM3vD6RUHn9y1v8dm2OJzSOgB4fmoBTPw/j4iIiIiIiDSIpXMak9urr+qYiIiIiKIvnXYRIiIiCgXzKiitCeYiFMw6GGCGHrKjBQ2vjwAASNYGn4wmo86VpWR1SLD72ZBOWV5HREREREREpFUMNFFa0+l0QPbggHMc5w4pvpJ8jguCAJMeMOn9dwJvtnZFoI61dsDSba5BB5j0rgRD9nAiIiIiIiKiVMVAk8Y0tzR4jTNNlgSuRnucrUc7RyJEUx/P45K1AYAEwVzkCl51o4gzYd6HTQGvwR5OREREqUOyNnqNu2c+E2mRLMuwKe5vHZKEtg71ufkmqN4fE5F28e2sxkiKHc8kP7ufUc81bpoY8HjBrINBM6SIiIjSXXNzM+6//35UV1fj5MmTGDJkCH71q1/h5z//eaKX1gMOP2OixJEkCecUm1rpREAvujLmnZIMhyJJ39Atm14vAjoBsDn9B5DCaQux9N9zcX62K3M/UEDKvRZ3ln+wuXodYOaOzkRJiYEmojjINwGPX5nv97hTkjGn88U6WGkdwPI6IiJKbT//+c/x9ttvY8GCBRg8eDC2bt2KW2+9FTqdDrfcckuil0eU9JxOJ05bAZMOMHTeI9odkieL/lyHhPnbz/To3P9vWBbGn2/C/7xzOiprXfD3pqicx5+nJ+WhQ/J/X+wOSAGuAFxLt+AVM64oXtJp8wgGmoiCUG8Y3lU652w5jIbXLwp4Dp1Oh8IAmfSnWruyz4KV1gEsryMiotR1+vRpvPzyy3j66afxi1/8AgBw3XXX4cCBA/jLX/7CQBMRvEvT1DacabA6seijpphce81XrVjzVWvI85dNyENvo+CVfRTL9XX3y3ci23BnYUku+phcWVxZhq4PdpttrrQvBqKIwse3qkRBBGsYLunNcVyNi0OSAPAFj4iIYu+ll15CeXk59u7di+HDh6vOOX78OBYuXIiNGzeioaEBRUVFKCsrw/z582EymbzmNjY24j/+4z8wcaJ3OfrAgQPx+eefx+z7IEo27qwkNbHYsfhnF2Vj/HdMcEoyPj5hwzP/PBfS85ZNyINR9J8x5A7EWAzej7mz+cMphwt1bjR/PsECYu5AlFv3gBQrDYh8MdBEFEXO1m+90iBdDUK9+zWoNQwPVlpn0AHnOrpeUNs64PViTkREFCtr1qwJePzIkSO49NJLceLECQBAbm4u9u3bh8WLF6OmpgbvvvsujEajZ35xcTHefvttAIDdbkdDQwM+/vhjrFu3DvPnz4/dN0IUZ/4CSeYMQC+KqG+PTrBk2YQ8FGTqvErnlCwZgCiK0Hv6NAmY8B0jxhQafSd3UvZ06kkgxX82vy6Me1j/c2VZxvNTCwCol8MpqZXOhZNxFWxeoEAcs6FISbI3eY21vHkEA01EUdS4aXzQOWoNw4OV1gGAXdHcvUFxF6H2yQ9f1IiIKFKyLOOZZ57Bm2++GXDe7NmzceLECUyePBkrV67EwIEDsXPnTsycORO1tbVYsmQJFi9erPrcZ599FrfffjsAYMaMGZgzZ060vw2imOkeSHIHkABXudux1uiUjz1+ZR6AYBlFAkx6HXJCPKdeJ6JXCt8qCoKgaCOhgzmM4JXZoP4hrzJTqb7dGXIQMNA8VxBQ9pThdcd79jQj2dXHGsRAE1EKCnbT8viV+UEDV0RERGpqamqwZs0afPDBB9i3b1/AuTt37kRNTQ0KCwuxdu1a5Oe73rhdfPHFWL9+PcaNG4cnnngCCxYs8CmhA1y9mS688ELs2LEDS5YswS9/+Us8++yzMfm+iMLlDiR1DyC5+yVFsw+RqzzLO+DgbvTN0qzoC/Yhb3+z4LfaQCcAep2AW2sagl4nWLBq0WW5GJSj6AtllwC56zgDUZSqGGjSmNxefdGkGFPseTcL72oSDnSVzjlbj6Jx00TV5xMRESWTDRs2YNWqVSHNra6uBuDKRnIHmdzGjh2LkSNHYs+ePdi+fTsmT57s8/x+/fqhX79+mDx5MjIyMvDAAw/gqaeegl6fOreo6bSLULo5bQV+9V50dl5TBpLUAlcMKCSXYIEod+meWqZSOP2jFtY2BTzOD48pVaXOqzhRkgrULDyadbf++ji5S+eUKb7BSuvc5+MNDRERdTd37lzcdNNNnq+nT5+OY8eOqc6tra0FAEybNk31+LRp07Bnzx7U1tZ6Ak0vvvgiHn74YdTV1SEjI8Mzt7i4GC0tLTh+/DgGDBgQpe+GyD/lzm6A7+5uDWoNj/xYWJKLomydauYT4P++y6TnvVgqcpfuqf39KftHuSkDUuFkwjVYnYAA5BjUs554P0/JioEmojiTHW1wnDuK7k3C3dSahQPBP1lRNiEM5cWLn5AQEZGa/v37o3///p6vDQb/zU/2798PABg6dKjq8SFDhgAADhw44Hns/PPPx+7du/HRRx9h/Piu3obvvfceevXqhfPPPz+i9RO5uQNJHZIMSaVFjk2S8at3Q8tYcjfc7h5Acuv+hp8BpPTl3T/KRfn7oPzwWBQAS7cgUkN7VyAqrB3xugWkAO6IR4nDQJPGnGtp9BpnmiwJXA25KVPpG16/KOBctWbhREREyai+vh6Aa6c5Nb179wYAnDx50vPY+PHjcemll+KGG27Afffdh/POOw8ffPABnnjiCfzxj38M+KaopaUFzc3NEa/baDR67YQXiXTaRSjV2JzATzfXR+VcRlGASS8wgEQRC/ThsUmv8+rRFEywQNTzUwt8gl6kTTabDTabLeLztLS0RGE1DDRpjtPZoTom7QtWWgcAJ1sd+M2HZ+K9NCIi0qi2tjYAXQGl7tyPu+cBrm3WX3/9ddx33314+OGHcebMGVx44YVYtWoVfvKTnwS83sSJ0el3uHDhQlRWVkblXOm0i1Cy6b7rW3c2KfR37Msm5CE7Q1DNVgJc91lE8eBzT98tU+nw2Q4sDLH07nBzB/KM3sHRHGPXuZjxpB0PPfQQFi1alOhleDDQRBQHgt6MvuVd0WF3k3C3aDQLD1ZaBwCZ+q4Xlgar02//JoA130REFJwsy17/7c79OuJ0er9779u3L1avXh329bZt24ZRo0aF/bzuopXNRIkVTrPu8f2N+OCY/0/7840CzAbe91DiBbunL87Vqe+I1xmQUvZtDdZsnBlPcWbooz6Ognnz5uGuu+6K+Dy7du2Kyoc6/LUiigNBECAoUum7p9UnYseaYKm27OFERETBmM1mNDc348yZM6rlc+5Mpqys6LygWCwW5OTkROVclPyCZSw5w8hY+unIbPzsu/5/d4yMMVGKCKdvazD17U7V+e6sJ2Y8RZcoiqrjaIhWSbjFEp3WOww0aY3U9Ymhs+lb2JW/wHoD9OZcz5eCwcx/OIiIiKjHCgoKPIGmwYN9+wueOnUKANCnT3Q/uaX0EE7G0rIJeapvmC0Zrjd0fMNM6cJfOw23DknGXZ0ZT+7MJ3+Y8UQ9xV8bjXE2n4D7A5lvqq5H89lTfudeuLwFgpEpK8nG2XIYkt7s87gy00nQ9yxI2P2Fp3vp3JFzoW+3SkREVFxcjAMHDuCrr77C6NGjfY7v3r3bMy8aSktLYTIFbpZTUVGBioqKqFyPUkdBpg4mPQNJRMEynk61+mlEpsJfxpMbW22ER7I3e43jvXlEVVUVqqqqAs6xWgOkkYaBgSaiJKDcsSbYrnQA0Le8xasUL1SBX3h0XdujAmi2Oz1ps5IkoUWltzxfXIiI0ltJSQk2bdqELVu2oKyszOf45s2bAQCXXXZZVK63bt061YAWpaZwSuOW/HsuenXroeTOVgJY+kYUqmAZTzZJ9mQ6Bct4YquNMElW9XGchPJBTF1dHcaMGRPxtRho0rDzfvwE+g+6pOsBvQFSaxMOLnAFMhzNp2Ao8E1zpwRIwl1qFvy9KegcvrgQEaW3mTNnorKyEtXV1WhoaPAqkfv444+xd+9e5ObmYsKECQlcJSWrcErjBmRnMGOJKAqCZTxZHaH3PlPuYEekxECTxvTuNxzuhLyC4RNgyOnrddzu6ApodDQe7Tog6qG35Hm+dDS7Su50eUXMWIkDwVyEglkHA86RpQ40vDocgCsDKt6plkRERN2NGjUKU6ZMwdtvv42ysjKsXr0aAwYMQF1dHUpLSwEAc+bMCVrulsoSsaEHEVGsGHWu3kytdglOlZiTMuOJyB8GmtLY1w8G37Zw6LKD0DHrKeZ0Oh2QHfjn7Dh3yDN2njvgnQVl6OO1c0E0ejjpdYBZ71s612Dt6uNkk2RYHTJL64iI0tiKFStQUlKCrVu3oqioCLm5uWhqagIAjB8/HvPmzYvatdijSVt0iluVxZflIrdb/ZsyW4KlcUTxIQgCTHrApFf/ny6cjCdKLuzRREQBNW4KHCSMfg8nHcwG30dZt01EREVFRdixYwcWLlyIDRs24PTp0yguLkZ5eTnuu+8+GAwqLyA9xB5N2pJl6AokFeWwNI4oFbTaJa+xv4AUJR/2aEoxsizjySefxFNPPYVDhw5h0KBB+PGPf4x77rkHen18f8TNDd94jbO6lc7p8oowdJlKiZaidM5ef9jTx4lSUyJ2MSAiIm06dOhQ0Dn9+vXD8uXL47Ca5KPc0IOl7USkdcpyOrXSOiKAgaaoePzxx3HnnXfi17/+Na644grs2LEDlZWVOHLkCJ588sm4rsXZ0QqdYtydTqcLWgonGs0xWBlFKmAfJ0MfyG1HPDvWOc/t897JQFFaJ9mbAckKwdyz/lvu8rpQSuuIiIg0T1nKnoSbexAREcUbA01RsGzZMtx444145JFHAACzZs3Ceeedhzlz5qCyshKFhanbGNLRdBz6nK71O841AHJXuiSbhcdPsD5ODntXgDBYaR0AV9AqSF8of+vwLYdTL61z93Bqtkm+BzuxjxMREYWKPZq0RXl/0GxjCQ4RaZyhj/o4TtijKYZeeukllJeXY+/evRg+fLjqnOPHj2PhwoXYuHEjGhoaUFRUhLKyMsyfP9/n5qa9vR1Hjx7FpEmTvB6fMGECZFnGF198kdKBpsNLLg94nM3CqTtlY89QdqRgHyciIgoVezSlFlmWYXN6P9ZilyB1lts0WJ2+TyKilNFgdcKSAU/lRJtDgkPxvzU/UPam3LxJOY4X9miKoTVr1gQ8fuTIEVx66aU4ceIEACA3Nxf79u3D4sWLUVNTg3fffRdGo9EzX6/X49NPP8XQoUO9zvP+++8DAL7zne9E+TsgUhestE4URThbDnvK62JF2diTiIiI0pfNCfx0c32il0FEMRKsXQY/UE5faRNokmUZzzzzDN58882A82bPno0TJ05g8uTJWLlyJQYOHIidO3di5syZqK2txZIlS7B48WLP/IyMDJ+I36ZNmzB//nxMnToVxcXFMfl+YsmrYbiiSTjgKp3rOP01vn4weGkWxVew0joAkPSx779l1AHPTy3weqx76VyTzYn7a5sAeGdAERERkXYEKp0nIko3kr3Za6zlzSM0H2iqqanBmjVr8MEHH2Dfvn0B5+7cuRM1NTUoLCzE2rVrkZ+fDwC4+OKLsX79eowbNw5PPPEEFixYoNof4NSpU5g3bx5Wr16NSy65BC+88EJMvqdYC9Qw3GDM8urRRNSdIAgwdfuXpXvPhRxjV9ZTm1OGs9V/6jxTbomIiJJTOKVxyybkoSBT53VcKT9w6y0iShLujYHcupfOnWjt2hiIpXXdKDdrkqLTCylZaT7QtGHDBqxatSqkudXV1QCAGTNmeIJMbmPHjsXIkSOxZ88ebN++HZMnT/Y6/sorr+AXv/gF2tvbsXTpUtxzzz3IyMiIzjdBFANO+zmInTsTSvYWQGrzOt7TXenCFayPE1NuiYiIklM4pXFGUYBJL7DhN1GKU98YyMWk13kFklhal740H2iaO3cubrrpJs/X06dPx7Fjx1Tn1tbWAgCmTZumenzatGnYs2cPamtrvQJNL7/8MsrKyjBlyhSsWrUqoX2ZsvIHw6oYx4TQlY0idVhhb/g6YJYTd6ZLTo3V3w94vKe70kWbyNI6IiLqlJS7ziV4F6FEarYzy52Ies6d9WjOAPSdWU9WhwR7Z7Aq7TKeYoy7zkVR//790b9/f8/XBoPKHuyd9u/fDwA+jb3dhgwZAgA4cOCA57GOjg7cfvvtmDRpEjZu3Jjw/xEMBoMn0BToe42EPrvrJurgvBFB53NnOupO2cdJrX+DTZI9mU7tThmn/JTWiQJgMYgw6lwle0REpG3JuOtconcRiiW10rhmuwSwNI6I/AintI4ZT/HFXecSpL7elfqbm5urerx3794AgJMnT3oe++ijj1BfX4+SkhLU1NT4PGfcuHGe56UtQVs3XanMa2c6Qx5E0fVPgLt0ztl6FI2bYt/oXdnHSS2F3uroukMNVloHuIJW3ftCERERUWRYGkdE4QqntI60i2/NFNraXD1q/AWG3I+75wHAiRMnAABLly5Vfc57772HiRPV37i3tLSgublZ9Vg4jEYjjEYjAKC58bjn8ebG48jK6Rvx+bsTDGZcuLzF87XjXINP6Zxka8PBBRcB8M6AosTytzOd2o4HsqMNUkerav8mt3j1cSKi9GKz2WCz2SI+T0tLS/BJRBFK5V2EumcsKbOVAKCxezoTEVGEumc8Ad6lc9+c68CCvzclYGUUTQw0Kciy7PXf7txvqJ3Orhfd66+/3u/8YPwFoMK1cOFCVFZWAgCctma43/Y7bZEHsdQIggDB2HUTZTD63lBJttaYXJvip+H1i4LOiVUfp2CldYB3eR0RactDDz2ERYsWJXoZRKFJ4V2EwslYcpfGdQ9GubE0johCESjjCQByDF0fYjdYnX77NwHs4ZTMGGhSMJvNaG5uxpkzZ1TL59yZTFlZ0fmkatu2bRg1alTE53FnMxFpRbDSOsC7vK6+3Qlj967hApBjcL0osYcTUWqZN28e7rrrrojPs2vXrqh9qEOU7lgaR0TxprkeToY89bEGMdCkUFBQ4Ak0DR7sm6Vx6tQpAECfPtEpBbNYLMjJyYnKuZKNo6XRa6yW9UTJx9PDSaV/k1u8+jgFo9zpJlhmE3s4EaUWZUl4JCwWSxRWQ8kkKXedS2EtitfS/5uQhwydoJqtBDBjiYiST6rtWud+f9V9HC/cdS5BiouLceDAAXz11VeqO5rs3r3bM4+CkByeYUfDIa+v9TmFXlMFg5nZJklCrYeTT68Jfa76ON56VrFKREQpLBl3nUtmqrvGKUrSlbvGZYgCCs3J84aMiNJT9x5O3YNIx7hrXY9x17kEKSkpwaZNm7BlyxaUlZX5HN+8eTMA4LLLLov30lLa1w8Gzn65cHmLV88nSm6i3uAZy9ZjcDia/M6NZbNwtUaCSnZJxt3s4URERGksnB5MRETJINiudfYU3qNAsrd4jVNp84hwMdCkMHPmTFRWVqK6uhoNDQ1eJXIff/wx9u7di9zcXEyYMCGBq9QeltalrmANw2PVLBwI3kjwVFvXq1CzXWJPCSIiSjvK0jgiIi1I6V3rlDt5+9nVWysYaFIYNWoUpkyZgrfffhtlZWVYvXo1BgwYgLq6OpSWlgIA5syZE7Q3AAG6vCIMXXZQ9Zg+pxD2+sM4uKAzSKEoqyONSZLSuoZ2Z9fXiibhgOsmXJKTr4abiIgomHBK4zy7xvnZzZU9mIgoFXDXutTAQFM3K1asQElJCbZu3YqioiLk5uaiqakJADB+/HjMmzcvsQsMIit/MKyKcaLodDroCvxfXzSa47gaiiZBb0bf8hafJuFusqPNk+mkLLNLpGD12wBruImIqIcMfdTHcRBOaRx3jSOidMMeTonDQFM3RUVF2LFjBxYuXIgNGzbg9OnTKC4uRnl5Oe677z4YDMnxxtkfg8HgCTQl+1opNQmCACEjy29NsdTR2jW2noLsUJknGiF07rQg6NkMnoiIUpcoiqrjeGhlaRwRESWhtAs0HTp0KOicfv36Yfny5XFYDZH2SI6uLKf69RcEnd9n5m4Iev8Zbj1tKO63WbiidK6+3Yl7OhuGK8sLcowsrSMiSkalpaVBWxiEsquOVjgVZeIsjSMiCm/XugarEyYdYND7ltZp8d6/qqoKVVVVAedYrdaAx0OVdoEmrWtuPO41zsrpm8DVUFpytASfoxCrhuLB6rcBVxmBG1NriYiS37p16zB69OhEL8OLZG/2Gkd7F6FAfZiUH5KwNI6IKLxd6wLd/2vx3j+UD2Lq6uowZsyYiK/FQJPGOG3N0CnGqUCytUGytcLR1gQ47KpzdHk9y2qhBAjSn6LX+BdgOO8q1P/tO6GdT58ThUURERHFiGRVH0dJOH2YiIiIkgEDTZRwnt3nAhi67GDA5uKUPHQGC/qW+89qEnRGQNC5Goo72lQzoLwbiscu199feZ27dE5ZWkdERJQI7MNERBQ93e//laVz37Z0YP72pthd3JCnPtYgBpooIfQ5hYleAsWIu1l40HmehuIFPseUDcWdLYchqfVw0lsgdj7e04biwcrrlKV17vIEZf+mVrvk1R9Di7XcREQUW91L4yRJQktH19fK8rhAfZjYg4mIKLhA9//ZGV338d37N9kdEhT/HPfovl8U9apjLdL2d0dJSzCYceFy70yW7qVzHY1H8fWDE+O8MkoGkqOr9CBYDycA6FveElJwKxLBejgB2qzlJiKi2AqnNI59mIiI4oP9WyPDQBMlhCAIEIze/2caun2tzHpiBlSacaRGfzEiIqJItTlYGkdElA4ke4vXONqbRyQTBpqIKOkI5iLXbnMByFIHGl4dDsCVARWLf6jVejh1L5072e4MKduJiIhIjUNRirFsQh7yjYJX6ZwSy+OIiGInUP8mu0PCN60R3vdLbepjDWKgiYiSjk6nA7IDN393nDvkGTvP7gEcA7oOKvo3AbHr4WTS67x6NNkkGVaH64Hu/ZsA9nAiIkpHTqcTp62AXgeYO9+wKPswKXswGUUBZoMOZkMiVkpElN4C3fub9Dr0caofI18MNGlMZu8BsCvGqczR1uQZW7/Z7VM+J1r6QBRdN2yCoWeBBNKGxk2Be3nFo4cTgKA71LGWm4hIg4LsInTaCvzqvdNxXBAREVFiMdCkMSaT2RNoMplUdupKJYrG4Id/VxJw6oXLW3x6PhG5SQ67pmugiYjSRWlpKUymwPVjFRUVqKioiNOK0msXISIicrFJMpqtTq+d6JSSsZKhqqoKVVVVAedYrdaAx0PFV0MiSkkB+zjpLZCtp7p2rHM0Aegdk3Uoa7m7929yyq6SCHctt9XRVVrX5pC8+nIoz5dsL0pERMli3bp1GD16dKKX0WPLJuShIFPnVTqnxB5MRETJy6C4RU/FSoZQPoipq6vDmDFjIr4WA00a09J00mucldM3gauJjC6vCEOX+W8ILTk7cPBeVzNoR1uzz651pG3B+jg5HF27Ojhbj3YdEM0QDRYArkwnVxDKFbjqSYDHXy232tbTcz8M/IIEJOeLEhER+RfOLkJGUYBJLwBgHyYiolRj0ovBJxEABpo0p6O9CTrFOJXpdDroCvwHEuz1Xc2g4YhOih9pU7AeTgBc2VFBGpATERH5SKNdhIiI0plRBzw/tcDztd0heZXOKSsZbJKMFrsTbYrsVWeb5BlLzq6xFjHQREQUQ8rSOksGPA3slaVzyhclIiJKLco3C/VtEnSiEwZd1yff7Q5tv5kgIkoXgiDApIigmPQ65CiOByutMzkdeLhzfFruhX4xWWVyYKCJiDTJbw8nRelcR/M+NFZfHNN1hFNaB7g+/Wi0OlX7NwHs4URElGxO27rGlR83okmXnbjFEBFRwgQrrZPErpppUdR2/TQDTaQJHY1HIVr6eLJFHG3NPuV0urye9eCh1BSshxMA6Ay94rSawCwZXeNUbCxIRERERJTuupfWOSTJq3SuoSUDeCsBC0sABppIE75+MHgPnqHLDgbs+USUKO4AKRERpbY7RvVCn/x8r9I55RsN7ipHRKRd3UvrAB0sisQlXUc77J1jvdwCeBXeaQsDTUREAGRHG6SOVsiyBDjbfSfozBAEAYKYAUHnesWQZQmyo2uuoHfNCVf3Tz+U/ZsA9nAiIkoVvY06FGZ1z572fqNBRETpyQSrJ9BkgrY3s2KgiVKWLq8IQ5d19eBRK53raDwaUrYTUcPrF4U0z/xvdyB79FLIsgRn85c4/eZYz7HC/9cMnTH83hxqjQW9rqkorXNKMk61+mng1Il9nIiIiIiIKFEYaNKYzN4DPFHSzN4DErqWWNPpdH5L4QxGNrGhEAgZwed00/avx9D2r8dUj8n2BqAHgaZg9IrSujlBejgB7ONERBRP5qzeaFOMiYiI1DgkWXWsRQw0aYzJZO5KxzOZE7qWZCPZ2uA4exKSvU31OJuFpx/R3B+FPzoJCK5yOEfTFzi9YVyilxUxc/jxMyIi6iGD3uAJNBn0rJEjIiJ1ysbgbR1AcmxLFBsMNJGmieY8z/jggsClUWwWnn5EUQQyCz1fZ/QZg77lLZ6vZVkGnIrApODdnwnOdjhbvg4YnHI6nZDbjng/KOgg6Eye/8qO1q5Dxj4+zcGVPZysDgl2lco5myR7dqzTs7k4ERERERElCANNGtPSdNJrnJXTN4GrSTxRz19xCp0giBAyutecWQI8I9srSKRGbjuC+vUXhLyGwh+d9Ap+udbV1cOpe/8mN6ujK/3W5lBPxTXoALEHzcqJiMi/Nmuz1zjTFOh1g4iISPv4LlxjOtqboFOM051gMOPC5V0ZKpK1xat0js3CKdk4zu51Ba/8ZD2pZTwBrkwnt1u3Nqiee2FJLi7oleE3K0qJDcWJKBmVlpbCZDIFnFNRUYGKioo4rQiw29pVx0RERMmkqqoKVVVVAedYrdHZDY+BJtI0QRAgKBqDi92ahCtL65RjopCJJvWxirxp26DLGuATRFL2hmrcFDjwqZbxBCBo4AgAFn3UFHxSJzYUJ6JktG7dOowePTrRyyAiIgqfoZf6OE5C+SCmrq4OY8aMifhaDDRRWlOW1jnOfAOpTX1HL9GcC1FvgGAwQ2DpESmIhhzVsYeuqym/Lmc49GbfclbR1CfidRii3JaJDcWJiIiIiKJHFA2QFGMtY6CJqFOwZuEAcOHyFq8MKSIlyXoKssP790O2dZWx+QtSCuYiFMw62O3Brqwnqf0EGl53/X766wmVYxSwfLL/gFWGoj8TG4oTEREREVGsMNBERBQBSerqjRS06bciu8nrYZ0OyPa/46Hc0ez3mJsoiugVuHLPI5SG4kREREREFD16uQ12xRhQqYbQCAaaKK25m4V3bxKuJNnaQsp2ojRlV2+8rSbZyy6VDcWtDslvQIqIiIiIiMJjQlegyQT1955awUATpTV3s/DuTcKVHGdPesaStSXgXEpDhsD9lYyDfoReJU9C0Bkh6NUzmpKFspzuWKvT62uTDjDou8rpjLrkD5wREREREVH8MdCkMZm9+3mipJm9+yV0LVqhzHSynfjS62t3k3A3NgtPPzqDBX3LW/weF0Q9BJ0xjivqOUmSkCXVQ5JFLPt7PQBAJ9sgwreh0/9NvRhZRr6EEBGZzb08n0ubzfHfRYiIiFKDQ5JVx1rEdwkaYzLldKXjmbRb85koXz8YeOt5NgtPP4IgQMiI9d+56GfcRZIkyLYGyI52AJLiiOCVSSXosyDo/QRE7Q1Yejq0MtHG01/BkTfE6zG9COjErvMy64mI0oEho6sAwpARYrM8IiJKO20d3mMtfzTBQBNRFEm2NpbWUdQJ+kzVsZJsa8CptX1DOl/f8hbV4Fi+EQi149TCT21o1QWe/dzVfZCZwUATEREREVE6YaBJY1qaTnqNs3JCe+NJ/unyijB02UHVY6I5F44zxzzNwiVbC4CCOK6O0kPwjKZwyI52QCXQJOq6mn/3mbkboqkAstMGyA4AQP2ZU8A7Ja4JIWQqtXfIyMyIeLlEREmtzdrsNc40WRK4GiIiosRjoEljOtqboFOMKXI6nQ66Av9bz0ttTfFbDKWlUDKalPJ/+A+IJneTclfpnGStR8Pr7rI4SfV5stxVKy4Y+0CXWeh1PFs24Zyfa956UTZK+hnRaJNwz/uNAACHtkvPiYgAAHZbu+qYiIgoXTHQRBRFHY1HAQCiKQeiwdWnQbK1dWY6uejyiqDTcdt4Cp3saPUeq/aE6sow0lkGQZfZg8w6Z5v6uJNRr/cEmh6/qhA6U9eOexmd/ZlaOxhdIqLgZFnGk08+iaeeegqHDh3CoEGD8OMf/xj33HMP9HrenhIRkQbps9XHGsRXcqIoCtYsHACGLjsYMEOKKBZcTcJ9x+EQha4gkkknQ6cPXD7XYHXtVmfQASa9q+TP7pDQ+TDyTWDQlShNPf7447jzzjvx61//GldccQV27NiByspKHDlyBE8++WSil0dERBR1ot7kqSsQ9drePIKBJiKiJOeT0aQ+y8/YTfIzjp1FHzUFPP74lfkoZO98orS0bNky3HjjjXjkkUcAALNmzcJ5552HOXPmoLKyEoWFhUHOQERERMmKgSaiCKk1C+9eOmc7/i9PtlNH41Hv43YrJEUjUZbWUbKSnXbVMRFp20svvYTy8nLs3bsXw4cPV51z/PhxLFy4EBs3bkRDQwOKiopQVlaG+fPnw2Ty/tS2vb0dR48exaRJk7wenzBhAmRZxhdffMFAExERaY5eboNdMQZyErmcmGKgiShCwZqFi8Ysrx5NwcrrWFpHPSE72lTHYRH06mPPiTvUxwr5JlemkpKydO5YawfmfdjUs/URUUKsWbMm4PEjR47g0ksvxYkTJwAAubm52LdvHxYvXoyamhq8++67MBqNnvl6vR6ffvophg4d6nWe999/HwDwne98J8rfARERUeKZ0BVoMqGH9+spIvJ9somIKAkEK50LTtAZVcdqnO31cLafgrP9FBztpz1juf048qQjyJOOIN8EFGbpkGvSwaQXYNILsOiZrUeUKmRZxtNPP40333wz4LzZs2fjxIkTmDx5Mg4fPowzZ86grq4OAwYMQG1tLZYsWeI1PyMjA2PGjEGvXr08j23atAnz58/H1KlTUVxcHJPvh4iIiOKDGU0ak9m7nydKmtm7X0LXQl26l9d1L52zHdsTUiNxItlp9f7a0QZZloNmMQn6LNVxTzVuvDTonIJZB4Fs/9l55zqcyHG4Pu9wSBLaVJKk2DCcKP5qamqwZs0afPDBB9i3b1/AuTt37kRNTQ0KCwuxdu1a5Oe7MhovvvhirF+/HuPGjcMTTzyBBQsW+JTQAcCpU6cwb948rF69GpdccgleeOGFmHxPsWQ29/J8Lm029wo4l4iI0pdDklXHWsRAk8aYTDld6Xgm7dZ8pppA5XWiMQtS3gDP146WRuhzXL0puvdv8pyPfZzSTFfyadO2G1Awc6fn6/pXR8DZ+nVUriIrriPHIeF1/vamoHPYMJwo/jZs2IBVq1aFNLe6uhoAMGPGDE+QyW3s2LEYOXIk9uzZg+3bt2Py5Mlex1955RX84he/QHt7O5YuXYp77rkHGRkZ0fkm4siQ0VUAYcjQ9i5CRETUc8oPVds6AC1/NMFAE1GSOVx5SdA57OOUXgR9puKLUAJAQs+uo9iNTlDZmU4wF7mylABAlwmhcy0ydBDgBAA4zh1A48Z/79H1iSg5zJ07FzfddJPn6+nTp+PYsWOqc2trawEA06ZNUz0+bdo07NmzB7W1tV6BppdffhllZWWYMmUKVq1axb5MREREGsJAk8a0nm3wGmfl9E3gaogoKnRmzzB3wgtwtp/yfJ037T0AMmRHOxpeHwkAEIx9VE4i+hmHsQydLmApHADIjtaAx5XNwpVNwpWlcw1WJxZ91AQAsEkyrA6ZpXVEcdS/f3/079/f87XBYPA7d//+/QDg09jbbciQIQCAAwcOeB7r6OjA7bffjkmTJmHjxo0p//9wm7XFa5xpsiRwNURERInHQJPG2NtOQ6cYU2pQ9nASjRaIRldgQVk619F4lH2c0pQgdGUoNbw2Iqz5XSQ/4yjTZaqP3Q/pdH5K4XSwqLyXvef9xoCXY2kdUWLV19cDcO00p6Z3794AgJMnT3oe++ijj1BfX4+SkhLU1NT4PGfcuHGe56UCu61VdUxERJSuGGgiSgL+ejiJxiwgO9/nccnWBmd7M+QOq88xABAtfSCK3FSSwqPMRgqWmeSPoCjtE0Iq8yOiVNbW5upO5C8w5H7cPQ8ATpw4AQBYunSp6nPee+89TJyo/sFKS0sLmpt9exeGy2g0wmgMvLsmERFRVOmz1cdRYLPZYLPZIj5PS0tL8EkhYKCJKEWIxq5U/IMLLgo4t/hPJyD2YtmkVgh6M/qWt3TuLOdvhwoBgt7smd+dcre67jvXJRt3eV0opXVElFiyLHv9tzt3WZzT6fQ8dv311/udH4y/AFS4Fi5ciMrKyqici4iIKBSi3uSpKxD10d084qGHHsKiRYuies5IMNBElCLc5XShkO2Bt7qn1CIIAoSMLCAjghoxuetNnrPlMASd4sVN0AGyE5K1QeWJYV5GFlXH4VAvr1MvrbNJMto6JHQ4fY+5ZRvADD+iGDGbzWhubsaZM2dUy+fcmUxZWdGpcd22bRtGjRoV8XmYzURERFoyb9483HXXXRGfZ9euXVH5UIeBpih5++23sWTJEnz22WcYMWIE7r33XsycOTPRyyINEQxmXLjclcrY+uX7OPq/0xO8IkpVjZti1+tLECTVcTQZFH2Dg/VwAoBnJuUj17ddFBFFQUFBgSfQNHiwbwn4qVOuzQv69FHbpCB8FosFOTk5UTkXERFRPImytSujSbYCiN7rWbRKwi2W6GxowUBTFPz973/HjBkzcM011+DJJ5/EW2+9hdLSUmzcuBFTpkxJ9PJIIwRBgGB0fSJsuehqT9DJzXH2JPbPde3uIzk7YK8/FPB8uryilN/ph9KTu5wuVPYY9j4nSnfFxcU4cOAAvvrqK4wePdrn+O7duz3zoqG0tBQmU+Byg4qKClRUVETlekRERNFiRguaFWOgMK7Xr6qqQlVVVcA5Vmt0Wmww0BQFjzzyCC6++GL89a9/hSiKuOGGG3D8+HE88sgjDDRRTAiizhN0ctMrejIdvHd40HMMXXZQtQE5aZNgLkLBrINdX+sVvz+dpXNAZ/8m2QnBXNSj68hOu+o4mow64PmpBQCAb1ocWLD9TMD5BsZTiWKmpKQEmzZtwpYtW1BWVuZzfPPmzQCAyy67LCrXW7dunWpAi4iIiAIL5YOYuro6jBkzJuJrpV3TipdeegmCIODLL7/0O+f48eO49dZbcf7558NkMmHYsGF44IEHVKN7drsdb7zxBsrKyrx6gPy///f/sGXLFpw7dy4m34c/xuwC1TFRd6Ih9J5PlPp0Oh302YM9f3SZhV1/TPmesd5S5Dre02w3uUN9HEWCIMCkd/25oJcez08t8PmzfHJXmU64GVBEFDp3m4Dq6mo0NHj3efv444+xd+9e5ObmYsKECYlYXlyYjNmqYyIiIiVJklXHWpR2GU1r1qwJePzIkSO49NJLPVvv5ubmYt++fVi8eDFqamrw7rvvetU+njx5Ena7HSNGjPA6z7/927/B6XTi2LFjGD48eHZJtJgteV3peJa8uF2XEk/Zw0ly2CG1NfnMkWxtnh3rHOcaIAVoGs7SOkoFoiDAlHavZETJY9SoUZgyZQrefvttlJWVYfXq1RgwYADq6upQWloKAJgzZ07QcrdUZjKZYVWMiYiI1LR0eI+1/NFE2tyey7KMZ555Bm+++WbAebNnz8aJEycwefJkrFy5EgMHDsTOnTsxc+ZM1NbWYsmSJVi8eLFn/smTJwEAvXv39jpPXp4ryHPq1Km4BpoofSl7OInGLCCrt88cydbqGbsDTv6wtI4iJVkb4NRnQZZFT2NwWeoAJFdJnWCOXTDTIUndxgyaEsXKihUrUFJSgq1bt6KoqAi5ubloamoCAIwfPx7z5s2L2rXYo4mIiKhn2KMpimpqarBmzRp88MEH2LdvX8C5O3fuRE1NDQoLC7F27Vrk5+cDAC6++GKsX78e48aNwxNPPIEFCxYEvcmROt/kOByO6HwjIWo92+A1zsrpG2A2kX8sraNInd4wLuDxglkHgezYBDPbFJ8YnbHK0Itd6ck6AcjQCTG5LlE6Kioqwo4dO7Bw4UJs2LABp0+fRnFxMcrLy3HffffBYDBE7VrJ2KOpzdriNc40RWfHHiIiomiKZ48mzQeaNmzYgFWrVoU0t7q6GgAwY8YMT5DJbezYsRg5ciT27NmD7du3Y/LkyQCAvn1dgZzGRu8ttt1f9+vXL6L1h8vedtrzub297XRcr03Jz11eJzkckNp8t4VXltZJ9jY4mk+pn0jUQ8wwQjCYIQh8w07J7Z4PvH/XJw8w4ScjsiHLMuxOINsArx57ROTt0KHAu5gCrvud5cuXx2E1yceuyBZWjomIiNKV5gNNc+fOxU033eT5evr06Th27Jjq3NraWgDAtGnTVI9PmzYNe/bsQW1trVegyWAwYM+ePbj66qs9c7/44guIooj+/ftH6Tshipy7vE40Asjq5XNcWVq3/54Lgp7vwuUtPrvfUXrz7G4nmiCIrrC3snTOce4gGjdGZ/epntp61IqtR7vSgp+ZlI/czAQuiIiIiIi0T7nrs17b76E0H2jq37+/V7AnUPr2/v37AQBDhw5VPT5kyBAAwIEDB7zON2PGDKxduxZ33HEHRFGELMtYu3Ytrr76auTk5ETj2yCKC1nR14aoJ3Q6XcByONkRn0/7DWEkKJ22SV6ZeaIIZIiur5n1RERERETRIOrNkBRjLdN8oCkc9fX1AFw7zalxN/x2NwB3u+uuuzBp0iTccMMNmDlzJjZs2ID3338fmzZtiul6iaIuhDK4nH+/Ec1/fyEOiyFNEk3q4yjLMQpYPrmP7+U7g0j17U7c876rpG7+9jNBz/eniXnoZfL+/0MUAAN7PRHFFZuBExER9QybgSdIW5trq/fuO8i5uR93z3P793//d1RXV+N3v/sdfvnLX2LkyJF49dVXPeV1/rS0tKC5uTnidRuNRhiNxojPQyQas3Dh8paAc5z2dk+gyV5/yLXDnZLeBL3ZlcnHHk7Unbucrvs42kRRRK8A70WNYni/l7/e5tvTbGxfA24f5SpBNerA3/UostlssNlsEZ+npSXwv2eUepKxGTgREVEoRNnaldEkWwHEt/qJzcATRJZlr/92596G2+l0+hybMmUKpkyZEtb1Jk6cGOYK1S1cuBCVlZVRORelN3cPp0CUDcIPLvhuwLns4UTJypKh/vhl5xkw+7s5yBAFfNPiwIIA2U6fnLTjp5tdmbDPTy2Aia+oUfPQQw9h0aJFiV4GERERUdSY0YJmxRgoTORyYoq3xQpmsxnNzc04c+aMavmcO5MpKys6b5y3bduGUaNGRXweZjMRUaqQIaqO4y0zQ8TzUwt8HtcJQEZnOVy+MfT12Ry+H1CwtK7n5s2bh7vuuivi8+zatStqH+oQERERUWgYaFIoKCjwBJoGD/ZtZnvqlCuTo08f374fPWGxWKLeLNyYXQCHYkwUbbq8IgxddtDvccnZgYP3DneNJSmBoQRKRgIk1XHc1yEIQTOQevnp8+R6PnDWLnn6PN26tcFnzvf7GHDXmK7dHVleF7polYRbLJYorIYoMJMxG1bFmIiISI0kyapjLWKgSaG4uBgHDhzAV199pVr/v3v3bs+8ZGW25HWl41nyEroW0iadTgddgf9dxez1h7rGx/cCfQZ2HRR1EDO8G+ewjxMlq2B9nqwqWUxKnzV0ldYBLK8jioZkbAZuMpm7Ak0mbe8iREREPdfS4T2O90cTbAaeICUlJdi0aRO2bNmCsrIyn+ObN28GAFx22WXxXhpRSjq8aFzQOcOfOQediVkH6UKWOlTH4XA6nZDbjng/qLd4tomVHFbA0bXRgmAu8vTYiyZDmOl6zTYJJn3sGqATpQM2AyciIuoZNgNPkJkzZ6KyshLV1dVoaGjwKpH7+OOPsXfvXuTm5mLChAkJXGVgbS2NXuOsnL4JXA1RcM5z9Qw0pRPJrj5W8Aok6XMg6l3ZC+4AkrP1KBo3hd53p2DWQSDbfxZeT+UEKa0z6ATUtzs95XVEpE1Wa5vXOJOvaURElOYYaFIYNWoUpkyZgrfffhtlZWVYvXo1BgwYgLq6OpSWlgIA5syZEzRlO5Fs5+qhU4yBf0vkcigNiRb1N96W7/8n+t/6HMQME+z1h3FwwUVxXhmlCrntCOrXX5DoZQQVrLQOAIwiy0KJtM5qO9dtrN1dhIiIiELBQFM3K1asQElJCbZu3YqioiLk5uaiqakJADB+/HjMmzcvsQskSnI6kwUXLm/xPSDoIBpc78pFY1cPi47Go66B3gC9OdfzuKPZ1XxflxebsifShrxp26DLGuBTOuc8uyesrCciIiIiopjSZ6mPNYiBpm6KioqwY8cOLFy4EBs2bMDp06dRXFyM8vJy3HfffTAYDIleIlFSEwQBgjH0fzi/fjBwMGDosoMBm4+TtuX9Zx0yeg0D4Nt7CVDvvyRmZAGOAXFbIxERERFRMKLe7Nlz2f0BqValXaDp0KFDQef069cPy5cvj8NqiCgYT8YTAIh66BW7KTLrKQWJRvWxHzpjritwhM4AEvJjtDAiSgXJuOscERFRKuCuc0Skabq8IgxddtD7QUXpnLKHU7CMJ4BZT6lEEPWqYyKiUHDXOSIiSlWCbFcdxwt3nSMiTdPpdAEDQ8oeTkQ9oreoj+NM2QucfcGJiIiI0lcWmtGsGAPqmyhpAQNNRJR0VDOeAK/SOWXWk+NcA0RjFiCIEA2ZnumSrQ2ADNHSB6IoxmPpFIQMUXUcbcq690TWwFsMouqYiIiIiEirGGjSGIM5H07FmCgVBct4Aryzng4vGhdwbvGfTkDs1Tcqa6PICJ4WiN5jL0mSjRRtzXYJNodvWpMgAAadAKPO1UyfiFKLwZgFu2JMRESU7hho0pisXn260vF6aTcVjygcsr0t0UugMEQjG0ly2L3G7obiifSrd08HPP781AKY+KpMlHLMJosn0GQ2aSc4TkRE0dVsk73Gib87jR3e0hJRShIt3oFU44DvY+C9NZ7SuX33DofzzLeJWBoFIEsO1XHUOZq6jXvH7loB2Bxy8EnuuU7/cwUBMOqY7UREREREyY+BJo1pa2n0GmflsFyItElnsuDC5S1dD3TrzzRw7hYcnD8SACDZ2yDZ2rzK7dz9mzxPN5hZthQPkk19rJzisHqNkyEbqacMusDHB2TrcPScq+D5vg9Oo9GmHmzqn6XDQ1fksbyOKAlZrW1e40xmNRERUZpjoEljbOfqoVOMgX9L5HKIYkYQBAgBemEog04H518EQ78LccGiTwG4gkyHl46H/cSXnjnDnm6GPjM7dgum0Dmau41Tt9+cSS/g+akFfo/bnTJ+XtMAAH6DTABwrNWJn26uZ3kdpb3S0lKYTKaAc0LZvjmarLZz3caFcbs2ERFRqKqqqlBVVRVwjtVqDXg8VLxdJaK0YD++F3tv9f8ps9TSADDQRFEmCAIDQ0RRtG7dOowePTrRyyAiIgqfPlN9HCehfBBTV1eHMWPGRHwt3v4SkSYJhvCaSEv2dt+vZcUOaSytoxgw6lxNwG1OWVnJ6cUmyUEbiYfL7pQh+bkey/OIiIiIok/UWzx7Losa2llZDQNNRKRJuuwCDHv8ZOdXgmp/Jvvpr3G4chwA4Mgfr8bQ37tK6SR7O77+wxTYjuzyPGfIHw9CZ3KV6omKkj1lQEq09IEoirH7prRANKqP05Q748mk9x/YOdXm9IxPtzuRn9nV+Kl7k3B/ASRZlmF3AsbO6zz0jzPYe0a9GTvL84iIiIgoEryVJCJNEkURYo56nwx3oEiytXoec5z5NmBp3YG5FwS9ZvGfTkDsxQb8gQiiXnVMASgCR3e93+h1qCBTxONX5cPWGYuqrD2DQ82R7eYnyzIAgVlPRERERFEkyHbVsRbxLp+I0la45XXByPa24JMoPkSz+liDbE7gp5vro3a+E21O5BmBP+06i92nO1TnPHd1H2RmMNBEREREFKosNKNZMQb6JHI5McVAExGlra7yum6ldZ3lcE5rCw7MHeJ5XMzKw9CH/+XJiPp62TS0f/VhvJed0mSIquNoEw0W1XEqMqj8mPJNIh66PA8Q4OrvFEX3fXgm6By7E8jMiOpliYiIiEgjGGjSGIM5H07FmIj881de5w4k6bILcOHyFtVjADBw7hbYTx3EwQUXAQCkbhlNkt0KyF39ddhQHBAgqY69KJsjarxRYijUfmdOWyXcurUh6HOzMwQ8MakP7E4Z/7vjLADg7jG9YNB5n7PZLoXVcLwjysEtolRmMGbBrhgTpQOn0wln45GAc/Sd91i8/yFKPww0aUxWrz5d6Xi9tJuKRxQPgiBACPCmQTRkemVCHVo4FsOrXCVMUocV31aVofWLGs/xYU83Q5+ZHbsFa4SoN6uOwyE57F5jMSN13/wZdMHn+CW4Go2b9AIW/Xtv/9cQBSyf7P81w6gXvIJRGTq+YSByM5ssnkCT2cTgOCUHWZa9SvodLY2A5L+Hn17xwVsoczvqD3s+aAvmgt9/CVHnPw1WuZmKo60ZcFjDmqvLK4JOF8mLJVF8NNtkr3Hq3p0Gx0ATEVGUyB3tARuKSy0NQJoHmmTFjasc4CY2Yo6mbmP/QZZkZ9ILeH5qgd/j7h3lAFdQqiefGouiiF6mwHNsDgaXiIiShTuQ5DjX4Nn9VkmytYUcCIq1g/cOj+n5B87fhoy8AV6PieY8iHrXW13J2uKTda7EQBVR9DHQpDFWa7PXOCuHO2ARxVI4DcWDlda5z6fp9HLJpj5WTtFQNlI0CIIAU8BXa4H9kihtlJaWwmQKHBWtqKhARUVFnFYE2DusXuNMZjVRiLzKz0Q99JY8zzF/ASQAgCBCNGTiq18lx33+BUt3e2V4A/ENdH394MSInn/B0t0QCgYFDUiJ5lyIegMA7+AVA1WUKqqqqlBVVRVwjtXqP6MwHAw0aUz7mePQKcYoLE7oeoi0rquhuHf/JqnDCkhO2E9/jcOV4wAAB+df5On5JHVYcezZm9Gyq9rrfCyvg6aykbTELsmqY6J4WrduHUaPHp3oZXhpazvrNe6VzdYF5NK9fA0AHM2nPOOOxqMRB0lCMezxk5A6bFEtnVNS+5BMlmXPPY+jrQlw+N/KvSelc/YwSveCifQ83TOqlAEpzX+ASCkllA9i6urqMGbMmIivxUATEVEEgjUUl2ytXo8HKq0DWF5HyUuW1cdEROlKGUhSBpDc4pXVo5ZRBAAQROiz+0AwmKEPI9gRjab2yj6X4Zwv1LkZ/S7E0GUH/R4PVjoXzSBfoPP4/bvpxGyoNKPPVB9rEANNREQxFE5pHRERESWPYDurSc6OqPUfGvTb7TAVfd/zdbDSOb0icy4ds2Z0Oh10BYNDmiuqBK90eUUYuuxgWL2clJlK1m+/wOFF44JeO1ig8YKluyHnFEKytageZyBKW0S9xbPnsqjxnZUZaCIiiqFgpXUAvMrrNE80qI+JiIjizG8gSW+A3pwb1s5qwVywdDcMBYNUM58AV0BBVAQUopFVRP6pBarUAlL+GAeO9p9RpcvA/jsHqB/rJtjv19BlB0MOqBElEwaaiIhiKFhpHeBdXifZ2gL2RlD2MUhFgpihOqbkZxDVx0REqcrZeAT777kgKucKpURK1OlgYNBAEwJlVLn7U0m2NtVMpXBKKl3naIVkt0JSbPrkWQcznlKL7FAfaxADTURECSZautLfg914FP/pBMReybHLTFITzepj6jFlWUa6lWgQUWpSzVjSm6A35wDw7aMYiGogqTPzCUjP8jVS5+5P5fpQscDnuLJROgCfgJSyfxQznrTFgiY0K8ZAXoDZqY2BJiKiBAsnQ6n77jWpRoaoOo420WBRHRMRUfoIJ2PJa+cwRQDJjYEkihZlo3QAfgNSoXC0NHp2AVTLemLGEyUKA00ak5GZ62kwlpGZm8ilEFGIBIPZ88nWsdW/QHPti37nBiutA5K7vE6ApDr2wmykpNQhyapjN1mWYXP6Ps+gA8TON2cOSYaj86/dqGNmFGmDwZgJu2JM8eOTsaTMVpIkdDQeDflcGXkDWNZGScHTqNyUA9FgAuAdRFJmPB2uvCTgudwBVNFo8WTkSQ47pLYmz7UYiKJYYKBJYyy5fbvS8XJZXkOUCpSfbPWfvRL9b37G67iz/Rz23dEPQPAUaiD1y+uikY0kKQJxkuSIYe5U+tArYkJOWYbV4R1ssjlk3Lq1wed5yybkYUC263bj1f2tWLfPlZX3/NQCmHgXQhpgNuV4Ak1mU05C15JuepSx1C0YJbW4/t3S5RXFbJ1E4fDbqDw7P+xzuQNS/rD0Lr6abbLXWMst/3mLR0SURMQMIwBjROdI5vI6WXKqjqPO3ug9NvaK3bXShDL76NfvNQaYSUQUHcqMJdFghmhyffggORyQ2hojzlgSASAzO1rLJYoLd8YTAO9Mpc6sJ2XGUzDK/4e8/x9zZT0x44l6ioEmjbEq6nKt1mZk5aRuVgMRuShL6068dCea3ns24HzRnMSNBSWr+lg5xWH3GosZWv68R5sevzIfOUZXHplBcX/6g8FmT0YTkVbYO6xe40wT+8JFS9gZS4XFqtlKADOWSDv87XjnznpSBqK6jnUFpGwn9uHQAxcDYMYTxQ4DTRrTfuY4dIoxCosTuh4iipyytO68Gx/HeTc84jNHsrXiq191BpadtoA76SR9Q1NHU7dx7wQthJQEdKV7V03KhyXDuyCx2S7hV++eBgAYdQJMet/fMX0y/94R9VBb21mvca/sPgFmk5I7Y0ktWwlA+BlLvft5vma2EqUrf4Eoz3Fz6Fne3f8fVP6/CqTAPSUlDANNREQpJJTSOk/AyY/hz5yDjp+4U5hkdN1IZgi+gSSH1PW1jk2xiCgE4WQsDVr4D5i+MwKAdzDKjRlLRKFRy3gCuoJI9vrDnp6gwTKeLlze4rWDHgWhz1QfaxADTUREGiArys2CT/bdMYwoUnpRUB0TRVNpaSlMJlPAORUVFaioqIjTiihe9Nl9OreBB0QjgCz23iPqiWAZT+4Su1BIDofr/0cKiai3ePZcFvXx/9C3qqoKVVVVAedYreqtLcLFQBMRkQbIzo6Q5wpiAtNNRIP6mFKKTXLtOmfUdTUJ75AUO6nYJdgc3sGmDB3Qoej/LssyAAakKDzr1q3D6NGjE70MioELlu6GoWCQarYSwIwlonjxl/EEuLKeHOcaPBlPUlsjg74pJJQPYurq6jBmzJiIr8VAExGRBoiWPhj2+Em/xyVbq6c8wV//JiHDBEGM7c4igpihOqbkp2zq7e7F9MRV+cg2uAKXf/ninM/xQJhXR0RKotEM0ZjFbCWiBAuW8SQl8e7GSU92qI81iIEmIiINEEURYk6h3+OOs11BKH89nAYv/AcyLxgb9bUlhGhWH1OPqTX7vD2EgJI/EiNNRERElEYsaEKzYgwk8U7REWKgiYgoDYTy6ZPU0Q7J1grJ1up3F5FIs55kWVQdR5tosKiOqeeMOuD5qQUAgD9/cQ5bj/qv4X/8ynwYdb6lcy0dsifbySmpPZOIiIiIUh0DTRqTkZnraTCWkZmbyKUQURIRDMGzeoLtLAIAA+ZUI3PIOIiWPhB70OtJECTVsRdmIyUlQRBg6rxruHlkNn4ywv+24creTUrtiiZNDqY0kUYYjJmwK8ZERETpjoEmjbHk9u1Kx8sNvMU5EaUPtTf9PXH00RkAgGGPnwxYqheJaGQjSZLDa5zA9uealKET0JMOW05ZfUyUysymHE+gyWzKSehako3T6YSz8YjqMdFghmRjrxeiVCbZ2lzZ8AGa+Ot0se3/mUqa7bLXOCuBa4k1BpqIiNKAWkbTwPnbkDnItavE6c2Pon79b2O+Dllyqo6jzt7oPTaysSwRUbw5G494NqIIRjTnxnYxRBR17t3n/Bm67GDAxuJpR/Yz1iAGmjTGam3zGmfxgzUignpGk5iRCdHo+ixF0Bu8jg1ddrDrWGdfpo4z3+LAvcMBALKzo2cLkazqY+UUZiMRUQqxO+xeYxbP9YzY7XWIiFKf41wD9J0Z8JIkQWpp8DrOjCftYqBJY9rPHIVOMUYhI8hE5MpounB5i/djGSbPOP/qO5A3+X+85ncPTgm6rmIpWfHGKuqYjUREKaSt9YzXuJdFu7sIdRewNM5o8SqNGzh/GzLyBnQdN5ghmrrKo0PpJUhEiafLK3J9IKn4f1hZOtfReNTT9/PwonEBz8WMJ+1ioImIKA0IggDB6L8SXNAbfLKaekKWZciKHe4EvRGCzvVSIzsdXsdkWeM5w0REGhdOaZzx/O9Cn9U7xisioljT6XQ+wSHRCCAr/A8GOxqPAnoT9GZXGU73rCfNZTyJRvWxBjHQREREUSPZWvHlL7p2I+t/65+Rc8l1AIDmT1/Bsed+gt6Xu44pg06UHjJE9TERJSd/GUui0QLRGF4zb5bGEaUHd8YTANUgkjLjKdiOx1rLeBINOZ4d4kWDtnvcMNBERERhk+xtkJ2OrmwlRwdkpx2O5nqveceW/wTHlv+k6wHFVmVSRw/7PFHKEhXlmGKUdkIk7Xjsscfw4Ycf4m9/+1uil0KdwslY8imN6wxGubE0jig9qGU8AXD13MzM9nmctImBJiIiCtvB+RfhO7f9FVkjrgIAnP3orzj54h3Bn6islnM4/E4jbcrQqY+Jjh8/jkcffRSXXHJJopeSVtQylkRTDkSDq4efo7Up5HNl5A2AQUOZB0QUG14ZT4BP1pPtyM6gmU6pSpIl1bEWMdBEREQhEfTeteTfPlUWcP4FS3fDUDDIVTKnzGqKB9GkPqaEkmT1MaWvuro63HPPPaitrYXVamWgKc56mrGkzFaS7FZI1mbo8opitk4i0g5/GU+AK+tJUmRGOppPeXatc3/tda4U6+GUg0Y0K8ZAbgJXE1sMNBERUUjEzPBqyXXZfSAas9Cr5P95+jS1f1uH5vcnAABkIXZNepR171qvgU8lTkl9TKnhpZdeQnl5Ofbu3Yvhw4erzjl+/DgWLlyIjRs3oqGhAUVFRSgrK8P8+fNhMvkGfXNycvDDH/4QP/zhD/HUU0/F+lugCPjLWBKNWUB2fgJWRERad/h3JQGPa62Hk5Yw0KQxGZm5ngZjGZm5iVwKEWmMaMzChctbPF+7+zL5nW/pAwAQdHpPLyddRtcbTUGWXDvROWxez5MVqS6ykBmVtVNycCj+bh1MaUo5a9asCXj8yJEjuPTSS3HixAkAQG5uLvbt24fFixejpqYG7777LoxG78zIoUOH4u677wYAvPnmm7FZeIwZjJmwK8apyp2xpCydc2crAWDGEhERhYyBJo2x5Pb1pONZcvsmdC1EpC2CIEAwZnU90INdWUXl8wUB5+pexzdPlHabBPTuLM2Xzp2GQxIhS07IHdaup+oyIOgNEC19IIq+mVGSJHmNucFZcnDK6mNKbrIs45lnngkaCJo9ezZOnDiByZMnY+XKlRg4cCB27tyJmTNnora2FkuWLMHixYvjtOr4MZtyPIEmsym5Mij97hrXGUxS7hqnlrHEbCUiijfvXesM0JtzPccczae8dq1znD2RUqV1zXbZa5wVYG6qY6CJiIjiRjB0ZTQdXDgWCNIP/MD8i4AAJVbDHj8JUXGD4WFv8B4bucsJUbhqamqwZs0afPDBB9i3b1/AuTt37kRNTQ0KCwuxdu1a5Oe7ghMXX3wx1q9fj3HjxuGJJ57AggULVEvoKDbC6cFERJQMAvVw6h4MP7z43wOeK+lK62Q/Yw3ih7wRkmUZVVVVuOiii5CVlYWRI0fi4YcfhiNBuylZrW2qYyKipOBs7xrzFYgoqW3YsAGrVq0KGmQCgOrqagDAjBkzPEEmt7Fjx2LkyJFoamrC9u3bY7LWRLI77KpjIiKidMWMpgg9/vjjuPPOO/HrX/8aV1xxBXbs2IHKykocOXIETz75ZNzX037mKHSKMQqTKIJLRKQzBJ2SP2sepIaHAABDlv4DOstAr9I5x7kGHF40LqbLJCJg7ty5uOmmmzxfT58+HceOHVOdW1tbCwCYNm2a6vFp06Zhz549qK2txeTJk6O+1kRqaz3jNe5lyYvbtf2WxplzIeoNXqVxXrvGKfsw2dog2VrYg4mIUoKWS+u0hIGmCC1btgw33ngjHnnkEQDArFmzcN5552HOnDmorKxEYaFKSQcRUboK0DzcTRSNnmo5XXYf6LO9/x0VFA3FZckZzdURkUL//v3Rv39/z9cGg/9A8f79+wG4mnurGTJkCADgwIEDUVwhhVMaF3DXOBREeWVERLGR0qV1olF9rEGaCTTFYsvdYNrb23H06FFMmjTJ6/EJEyZAlmV88cUXDDQRESlldO3IVPzwbugsg3ymONuPoeG1Sv/nUAaXGGhKKRmi+phSX319PQDXTnNqevfuDQA4efJkRNdpaWlBc3Nz8IlBGI1Gnx3wiIiIYkk05Hg+TBUN0d08wmazwWazBZ8YREtLS/BJIdBMoCkWW+4Go9fr8emnn/p8evf+++8DAL7zne+EdT4iIq0TxK6MCMHYy3sXuk6SPfBLk6A3qI4p+YmCoDqm1NfW5irRcgeUunM/7p7XUxMnTozo+W4LFy5EZWVlVM4Va2rlcUFL4zqPAyyNI6L04lVaJ+qhV5Qz+5TWNR33Lq071wDIkuc8qVRW99BDD2HRokWJXoZHygeaErnlbkZGBsaMGeP12KZNmzB//nxMnToVxcXFYX8/RERaJih6NAkh9GsKRrK1QrK1+jwuOzsiPjdFn15UH1Pqk2XZ67/duW/WnU7/WYjvvvtu0Ots27YNo0aNCn+B3aRSNlOo5XEsjSMiCrO0bsnlfs8Ti7I6SZZUx9Ewb9483HXXXRGfZ9euXVH5UCdlA03JtuXuqVOnMG/ePKxevRqXXHIJXnjhhR6dh4iIAlP2ZfL35qv3Dyq65gup84ZS69JoV9+0Yzab0dzcjDNnzqiWz7kzmbKyfLMYw2GxWJCTE91yAyIionjIQSOaFWMgN2rnjlZJuMViicJqUjjQ5N5yNxShbLm7Z88ebN++3WsnlMGDB+Pw4cOq53z00Udxxx13AABeeeUV/OIXv0B7ezuWLl2Ke+65BxkZGT34roiItE2GTnUc1jk6d58L5MzmKvTu/DBG6OF1KPqckvqYUl9BQYEn0DR4sO8nwKdOuXb66dOnT7yXlvTUS+PyIOpdt+mOcw2ex93lcV6lcdYWSPY2lsYREYUgUGld++Gd+PrB8QlambakbKApHlvufvLJJ35TvLOzswEAL7/8MsrKyjBlyhSsWrUq4X2ZdMYc1TERUTIQ4FQdexFN6mP38wzmsK4pWVuB6Hw4QxFySLLqmFJfcXExDhw4gK+++gqjR4/2Ob57927PvEiUlpYGzT6vqKhARUVFwDnRpM8wwqEYhyvSnePUet0REZG6QKV1GXld7+UD9W8CUq+HEwBUVVWhqqoq4ByrNfgHuqFI2UBTPLbcDfapW0dHB26//XZMmjQJGzduTIpftJy8fl3peHn9EroWIqKeUO7CobYjh/JNVf5/zkPBNQsAALZjX+BQ5Tif+U6VHk6UGE5ZfUypr6SkBJs2bcKWLVtQVlbmc3zz5s0AgMsuuyyi66xbt041kJVIFnMumhRjIiJKfYH6NwE96+HUYpe9xvH+mCCUD2Lq6up8+lD3RMoGmsIRqy13P/roI9TX16OkpAQ1NTU+x8eNG+d39xWAW/QSUfqRFZ8EyT1sgigas3DhctfWq4Iuw7PznGngGM/j7Uc+RPP2zizWjjMRrJgSIdm26KXgZs6cicrKSlRXV6OhocHrw7qPP/4Ye/fuRW5uLiZMmJDAVSa/rtK4rtI5d2kcAJbHERGlMGUyt9YTu9Mi0BSrLXdPnDgBAFi6dKnq8ffeey9gx/ZYbNFrt9s9j9vt9rhHSYmIAnK2q48VJEnyGnffnEwQBAgqpSKCKHoeFzMyAy5DlmXIdsW/+aIeYmfJi88xuMr1BEEIeE6KnmTbopeCGzVqFKZMmYK3334bZWVlWL16NQYMGIC6ujqUlpYCAObMmdPjTVeSmcPhUB33BEvjiIgSx6t/kyBCn931oYnjXAM6Tn+Nrx90vYfvaPzWu7SupRGQHF7nSoaKp0RJi0BTNLbcVXP99df7PWcoYrFFb+vpQ562t62nD6F3nwERn5+IKK7sDd5jY3bYpxDEDNWxm2xvw95buxo39Z78P+j3E1fNuvNcA776VaHX/AuXt6gGtyg2km2LXgrNihUrUFJSgq1bt6KoqAi5ubloamoCAIwfPx7z5s2L+BrJ2KOppfW01zjbkut1XK3Zt5tozoNkC++DTiIiio1A/ZsMxiyvHk3Bmob3pLQu1tijKcriteVuuLhFLxFRbAh69Zc3WZIgd7TDafUuqZKdHZA6ezlJdt+eTpF8qEDhS7Yteik0RUVF2LFjBxYuXIgNGzbg9OnTKC4uRnl5Oe67776A/TRDlYw9moIJp9m3GOZmB0RElEKUH36qfBAaa+zRFGXccpeIKH011jyGwlLXpg8dp4/gUOVYnzlN7z2Lpvee9XsOydYKnYlBi0hliOpjSg2HDh0KOqdfv35Yvnx5HFajTSL/nSEiSloBS+taGtHRcMhTWqdGNORCUoy1LC0CTfHacpeIiIIQMtTHUSYrOiw21a5F07a1kZ3PztKWaBAVfa5E9ryiNORu9u2mbPoNuPrBERFRcgpaWqfo0STZ2jzZ8u7+TR0tbZ42N5IUXtueVJMWgaZ4bblLRESBCTqD6jj6Fwp9avGfTkDuaMf+u5Orjl6L9KL6mChUydijCYo3C86mb2EXxW6HOzzjjMJiGHr3i9vSiIgoMQ4uuMjnsaas/hg8zjV2Nh0DAuxQHwvs0RRl3HKXiCi96HK63sgNWbwLOrPKG7vOnebcGQQXLvfu29Rx9jgOzO3MdNVrb6esRJD9jIlClYw9mpzNJzyfUH9TdT2az57yO1dvZm9OIqJ0ZZa6No/Ikk8HmBkb7NEUZem85S4RUTKRIaqOo01nyvOMM/KHQswIvtlD913lMnp1Baf45jA6nJL6mIiIiCjVKXs46XO6djB2l86dPfoJHJ//KFHLi6u0CDQB8dlyNxnojDmqYyKiZCBAUh17EU3qY0p5TkXvLOWYKJUp77cGzF6Fgv4jvI6Llj4QO8vp2IOJiEi7/PVwMnR+mKk/dxQOn6PalDYdEtxb7v785z9H//790d7ejuLiYlRWVuLtt9+Oypa7ySAnr5/qmIgoVYiGHNVxvEmSpDqmnvv/7N15eFNV3gfw771Jk3SlQBdaoRTZVFwQBIoOuyyiDI7UsYozsgjK1FFwx40qoo46oq9WEVB0VFAHVPZdRUBAoCADiJQdWVvaUrqkaZLz/lGSJuQmTZqkWfr9PE8eTu49uefk3jQ5/O5ZjEI5TRTKbNtb8W1ugCaxjd1DHRkLWRsNWRsNiZPgExE1WpUm5XQ4CpseTVxyl4go+AkhFNO+ZtaftUvLEZ5P9G0uK7SmjUXHICek1+6UVJA17G1FRERERO6RIpsqpsNR2ASaqIbBYLBL1z0rCRFRAzJVKKdtXNqTKBi63h56xn7lkNhumWj10H8DVBuixisYV51j24uIiEIBV52jeis/d9i68kn5ucNomtAqoPUhIvKYodA+rY0NXF2cMRlhriq3PpU0URwSQ9QAgnHVOba9iIgoFHDVOSIiogBzNWnvhbzvsG98jPV5h3fP2K0uQkRERERky1RlsN6YMFUZXOYNdcEwIoGIiCjosIeS76ll5TQRERFR2GtEKyuzRxMRETUcSa2cDkK2PZo6vHsGstZ+5hVj6VkcePzyhq5WSFPZBO9UDOQRERERhaXgbuUTEVFYkVRaxXQwsu3RZFma3Nalz6luKlk5TUREREThg4EmIiIKP7JGOe0JSYXYbpnWNBEFXjCuOkdEROSOaHEOept0Q+Oqc0REFJYEVIppX5M18Yppz46hQ6uH/ut2fttV6JRwZTrAZFZOE7krGFedIyIiCgVcdY7qTRURrZgmIgoGEkyKaTu+6I3kQ84CSJdu//2hJAhDhdPjtHvzEGRtdM3rJBmyJtJuvxyTAFkO7/FkJrNQTBOFMra9iIiI7DHQFGbiElqi1CZNRBRqfNEbyZf2jY+pO5Ncd++suiYO7/DuGchxSe5WKyQZhXKaKJSx7UVERO7Qm5TT4YiBJiIiajBCCMW0r5n1Z+3SckQbv5UFAHKEDh3fO+uw3ZOV6cwuekMRERERUYiLbKqcDkMMNBERUcMxVSing9gVM8sUt5uryrH/n8nW50qr0Nluu/yV3VDHJtoNnTOcO4ojOd1rMvgx8EZERERE1FAYaAozxad+t06vW3zqd0THJbvMT0QUbBq6N1JdlAJIlzKWnnXMJ8l28zipYxOhvmRonNlQaZdfiRDC6dxP7tSNiPyLbS8iIiJ7DDQRERHVg20QyZ3hcYo9nmwmBL90cnALYahwOk/UVZ+yFxQ1LpmZmdDpdC7zuLOqDhERUUMz6fXWGxMmvb7By8/NzUVubq7LPHof1YuBJiIiohBmNuhxYubfAACXjf8Mssb1f8IDSS0rp4ncNX/+fHTp0iXQ1SAiIvKcKko53UDcuRGTl5eHrl27el0WA01ERNRwJLVyOgTJMQno8O4Z5xkuzsNkfarxvkHR4d0zjj2jhAkXts6vSY/7xOsy/EklSYppIiIiIgofod3KJyKikCKptIrpUCTLMuRL5lzylLmqQjFtR1Ih5rqhNWXq4oK6x1JdVLJymoiIiIjCBwNNREQUhtRO0kFGcpK2IWt0SHt0aYNUx9/ceLtEREREYSlanIPeJh3Ogrj1TURE4UZAVkz7mqxrppgONrLNcDq5nkPrhKidENxYdg6yzSTltnksK9dJmihIF4etSRGRkOSa6yCMBghTNeSYBMiy59dGaXU8c7UeMJusz/WG2n0GkxnRfvwMEBEREVFgMNAUZlQR0YppIqJgIMGsmLYja5TTpMg2uHPg0dZeH6/Du2fqNSTQ1ep4FqWaROD23QAAY1kREOnd0EOiYMC2FxERkT3eSgwzcQktFdNERKFC1sQrphsrc1U5fhsXjd/GRcOs0FspWLhTN5PNvFzVTuKMRKGGbS8iInKH3qScDkfs0URERGHHrD9rl5Yj2gSwNs6ZbXojmQ1OJgMHHIakOXP5q79BHeM4VNBcVY4Dj18OAGj35iHrynWWoXPG0rPW/b7Q7s1DUMclOQydO1VYAPzms2KIiIiIQobQNrHOUSm0TQJaF39joImIiBqMMJYrphstm/mV7NIeHaL2daroplArDHuz7W2kjkuyBposLn3uLVkbbX3YUpu0AKoAAJImtFcdJCIiIvKEZDMHplSP+TBDCQNNYab41O9Q2aSj45IDWh8iIk+FSm8k3/B+HTbLxN6Xpm0JkwkRLTpY04HiTl2JXMnMzIROp3OZJzs7G9nZ2Q1UI7a9iIgoNOTm5iI3N9dlHr1e73K/uxhoIiIiChBZG6WY9jVJpUL16f3WNFGomj9/Prp06RLoahAREXnMpDdYb0yYbJfibSDu3IjJy8tD165dvS4rvPtrERERhTlzdZViOhhJxirFNBEREVHYU+mU02GIgSYiImo4kko5TfVnNiqnbQizWTHd0NQQimkiIiIiCh8cOkdERA1Gsrl7I4X5nRx3mKsqFNN2JBlRV/SxputDVFfapyNj7YuI0CmmPSKrlNM2VJJymoiIiIjCBwNNREQUhtRO0kHGjbnAZU0k0if/6N9q2ASGJCdBorrINgEq2Umwynb+b84FTkRERI1JpKkYBpt0OAvi1jcREYWdBho6J+uaKaaDjaypnQDcWHYOEAKAZDcxuNlQCQgzZG204jHMhtrVQczVegghrCu6maurALMR5qpyx9dd3AfAbr8QykPahBAQhgoYy4oUh+gp9cgyG/SAqF3lzlBZDqDmfVSbOHSOiIiIGg9ZNiumwxEDTWFGVmkV00REQcEm6GCXthMivZF87MiUG7w+xqHJV6HDu2ehjksEAJyZ9yiK176vmPfUJw/g/IZPHbabK0qg0sU4bBeGCuwb77jdlT/eG4GyX5dZn5dqEoHbdwMATM6GChKFGLa9iIiI7HEy8DDTJCldMU1EFCpCpTdSqHLWM8pCmKr9VrbJ5j/h1eF9I48aEba9iIjIHXqTcjocNZ5bxURE1GiY9YV2aTnCdXAlUOSYBHR49wyA2iFyng6dkzRREIYKa9o2X/LdbyH5rtetw94kTRSki8P1UkZ9iJS/5wIADAVHcOjZq92u9+XTdtvV0ZY6LslaRsuHFtj1XDvxxxFgn9vFEBEREYUNoW1inZJTaJsEtC7+xkATERE1GGEsV0z7ntlJOrjIsgw5Lsn7AykMdQMAOUILQKuYx3afs6CRM5rE9Dp7RgGArLGfFFzdrCWAKgCAKpa91YiIiKjxkGRZMR2OwvvdNULFp35XTBMRhQqz/qximvxHspmU3DZty1h6VjHtUTk2S81JXHaOwgTbXkRERPYYaCIiImrkGAAiIiIi8i+TXq+YDkcMNBEREVGDEJVlimkiIiKisKeKUk6HIc7RREREDUdSKacpoMzVVYppX1Obq2BpetSkiTyTmZkJnU7nMk92djays7MbqEZEREShITc3F7m5uS7z6H3U04qBJiIiajCSSqeYpsCSVGrFtK+pJOU0kbvmz5+PLl26BLoaREREIcedGzF5eXno2rWr12Vx6BwREYUh2UmalEiySjHta7KknCYiIiIKd5GmYsV0OGLrm4iIGlDDBIBkXYJimgKr2qycJiIiIgp3smxWTIcjBprCjKzSKqaJiIKD2UnaltpJmvxFmKoV03ZktXLaA2ahnCYKZWx7ERER2WOgKcw0SUpXTBMRhQpZ10wxTf4jjAbFtC11TDPFtCfYo4nCEdteRETkDr1JOR2OGGgiIqKwY9YXKqaJiIiIiAJBaGIV0+GIgSYiImowwliumPY9d4boUUOTo+MV00REREThrqFW+Q0GDDSFmZIzBxTTREShgr2RGp4UEamYtmW8UKiY9oSsUimmiUIZ215ERET2GGgKM8JsUkwTEYUO9kZqaJIsK6btCLNymqiRY9uLiIjcIQxViulwxEATERERNQx9hXKaiIiIKMyZpUjFdDhioImIiBqQ7CRNgeTOqnO+oDJVKqaJiIiIKHywle9jEyZMQLdu3QJdDSKioCSpIxXTFGCSrJz2MZWknKbGafXq1ejTpw/i4+Nx4403YuHChYGuEhEREfkAA00+tGrVKsyYMSPQ1SAiIvac8khDrYIiS8ppanx+/vlnDBs2DMnJyXj//ffRtm1bZGZmYvXq1YGuGhERkV9oTSWK6XAU3mvqNaDz589j7NixiIuLC3RViIiCWMMEgGRdgmKaAqvarJymxmf69Om4/vrr8eWXX0KWZdxzzz04deoUpk+fjoEDBwa6ekRERD6nlk0w2qTDWdjc5p07dy4kScLvv//uNM+pU6cwfvx4tGzZEjqdDh06dMALL7wAvV7vdfkPP/ww2rZti9tvv93rY3lDklWKaSKi4ODOinLsjdTQhKlaMW3HB8PrzEI5TcHN120sg8GARYsWISsrC7LNKod33303Vq1ahQsXLvjlffgL215ERET2wqYFP2/ePJf7jx07hi5dumDWrFk4ceIEIiMjkZ+fj6lTp6J///6oqqr/8oILFy7EN998gzlz5tg1mAIhPrmdYpqIKFSwN1LDc2cycHVsgmLaE+zRFJp83cY6c+YMDAYDrrrqKrvtV155JUwmE06ePOnz9+BPbHsREZE79CbldDgK+UCTEAIzZszAkiVLXOYbO3YsTp8+jQEDBuDIkSMoLi5GXl4eWrVqhU2bNuHll1+uV/mFhYUYP3483njjDbRp08at1xgMBrt/KXxUVVUhJyfHq8AlBSde29Bi1hcpppXw2oYv/t56x19trDNnzgAAmjZtare9WbNmAICzZ886lMFrWX/8jvMOz1/98dzVH8+dd4L1/AlNrGI6mPjq9zZkA01r1qzB2LFj0bFjR0yYMMFl3h07dmDNmjVISkrCV199hdatWwMArr/+eixYsAAA8N5779VrCN2ECRNw3XXX4cEHH3T7NWwsha+qqiq8+OKLQfelRt7jtfUNYaxQTPue0UnaEa9tw5Gj4xXT/sLf2/oJVBvLbK7p5mY0Ov7N8lrWH7/jvMPzV388d/XHc+edYD1/DbX4ijcafaBp6dKl+Pjjj5Gfn19n3sWLFwMAhg0bhubNm9vt69atGzp16oSSkhJs3LjRbl+bNm0gSZLi45133sGCBQuwfPlyvPPOOygvL0d5eTmqq6thMplQXl4ekMZQyZkDimkiouAgnKRrmfWFimnyHykiUjFty3ihUDHtCVmlUkxTcPF3Gys5ORkAUFRk39vQ8jwlJcWr+jc0tr2IiIjshWyg6YknnsDOnTutj9TUVKd5N23aBAAYMmSI4n7Ldks+i61bt+L06dOKj3HjxmH37t0oLy/HVVddhZiYGMTExOCLL77Ajh07EBMTg6lTp/ro3bpPmGsGe85ZW5v2h9zc3JA8dkMc399C+fyEct0bAs+9heOE4by2/j22ZDO/oHTJXIPW4wub6yJ8N8FSqF/bcOTvNlZycjI0Gg327Nljl3fv3r2QZdlled7w9rPm7PW27S1XbS9/ld8QAl33QL/eW96UH+j3HsrnLtCv57kL3c+e334vDJWK6YYqvyGFbKApNTUV1113nfWh0Wic5j1woObuUrt2yhM0tm3bFgBw8OBBu+0JCQlITk5WfERFRWHMmDHYsGGD3eOWW25Bx44dsWHDBowbN85H79Zzn3zv3+MH+3+aAnl8fwvl8xPKdW8IPPeBO76/8dpepK9wSIf6tQ1H/m5jaTQaDBs2DF999ZV1uJwQAl999RUGDRqEuLg4X70VO4FuuAf69YEsO9Rf7y0GmgJXPgNNjff1gSzb2evNUrRiuqHKb0jBOTDQxwoKCgAA8fHxivstk1FaJqd0V6tWrdCqVSu7bcnJySgoKMBNN93keUWJiMKe7CRNgeTOqnO+oDJVAoi2SVOoq28b69FHH0X//v1xzz33YPjw4Vi6dCl++uknrFixwq/1JSIiIv9rFIGmioqau6aXrm5iYdluyedvlgkxd+7c6ZPjaTQa693GM0f2Qz4CVBmA/+3Zj7PlMT4p41J6vR55eXkhd2x/H7+srAxAzbWNiQm9c+/v44dy3UP92gbLuTfpi1B0pCbd7H/5UOnOOeQxlp9A8cU8TXfuhjq62OP6Kx3DGV5bwFhagGMXL0XZrj1Qx9Wu+mU5fnXxCRy/mOfCrt2IaOr8nDpTUnACxQdq5oD6n7oS8WeUr63BYPDJPIeW39n6LPZB7qlvG+vGG2/E4sWL8dJLL+HBBx9Ep06d8O2332LAgAGKx/FF26miogI//fQTAPu2k7uc/a1Z2l4AYI5z3vby9m+1vq/3xXdcoOoeDK8P9PnjuQvd989zF7qfPX/V/cyJg7W/F8334myx8mTl9Snftu1k+3vnKZ+1nUSYSE9PFwDEvn37HPap1WoBQBQXFyu+dvXq1QKA+NOf/uTnWtaYM2eOQM0suHzwwQcffPDBh58fc+bMaZDf93AVDG0stp344IMPPvjgo+Ee3radGkWPpqioKJSWlqK4uFixa7flLlt0tPNxkr502223Yc6cOUhNTYVOp/P6ePW5K0dERBSsfNWjSa/X4+TJk7jtttt8UCtS0lBtLLadiIiInAu2tlOjCDQlJiZaG0Ft2rRx2H/2bM0QgYSEhAapT0JCAkaNGtUgZRERERH5S0O1sdh2IiIiCh2NYibW9u3bAwD279+vuH/37t12+YiIiIiobmxjERER0aUaRaApIyMDALBq1SrF/StXrgQA9OzZs8HqRERERBTq2MYiIiKiSzWKQNPw4cMBAIsXL0ZhYaHdvi1btmDfvn2Ij49H7969/VqPU6dOYfz48WjZsiV0Oh06dOiAF154gavhBLG5c+dCkiT8/vvvTvPU57rysxAYR48exahRo3DdddchJiYGV199NUaNGoVDhw4p5ue1DR0//PADhg0bhssvvxxxcXHo0aMHJk+ejPLycsX8vLah69dff0VERATuvvtuxf28tg0rUG0sT//myZ6nv4fkmjvtxcaE36m+xc+X+/jdVn8+/131airxIOJqRRQhhBg4cKAAIAYMGCCOHTsmhBBi+/btomXLlgKAyMnJ8Wv9jh49Klq0aGGdxT0+Pt6a7tmzp9Dr9X4tn+rntttuc/m5qs915WchMFasWCFiY2MFACFJkkhOThaSJAkAIjo6Wnz99dd2+XltQ8drr71mvZYREREiKSnJet5bt24tTp8+bZef1zZ0GY1G0bVrVwFAZGVlOezntfWPYGtjefo3T/Y8/T2kutXVXmxM+J3qe/x8uYffbfXnj9/VRhNoOnr0qEhJSVH80uvVq5eoqqrya/1uvvlmayPsyJEjQggh8vLyRKtWrQQA8dxzz/m1fPKM2WwWH3zwgfUz4uxzVZ/rys9CwzMYDKJt27YCgBg9erQoKSkRQghRUlIiHn30UQFANGnSRJw4ccL6Gl7b0HDkyBGh0WiEWq0W77//vjAYDEIIIQ4ePCgyMjIEAHH33XfbvYbXNnT961//sn4vKwWaeG39I5jaWPX5m6da9fk9JOfcbS82JvxO9R1+vtzH77b689fvaqMJNAkhxMmTJ8W4ceNEamqq0Gq1on379iInJ8fvkfW8vDwBQCQlJYnCwkK7fb/88ou1UVZZWenXelDdVq9eLcaMGSPat29v/VJ39rmqz3XlZyEwvvjiCwFAdOzYUZjNZof9d999twAgHnvsMSEEr20oeeKJJwQAMWrUKId9hw8fFmq1WsiybD3vvLahKz8/X0RGRjoNNPHa+k8wtbE8/Zsne57+HpIyT9qLjQm/U32Dny/P8but/vz1uxo2czQdPnwYQgh07NjRaZ6UlBTMnDkTJ06cgF6vx/79+zFlyhRotVq/1m3x4sUAgGHDhqF58+Z2+7p164ZOnTqhpKQEGzdu9Gs9qG5Lly7Fxx9/jPz8/Drz1ue68rMQGHv27AEA3HvvvZAkyWH/6NGjAQA7duwAwGsbSizzFfzlL39x2Jeeno527drBbDZbV8TitQ1NQgjcf//90Ov1Tpe457X1n2BqY3n6N0/2PP09JGWetBcbE36n+gY/X57jd1v9+et3NWwCTcFs06ZNAIAhQ4Yo7rdst+SjwHniiSewc+dO6yM1NdVp3vpcV34WAuPw4cMAgNatWyvut1znI0eOAOC1DSVFRUWIj49Hq1atFPdHRkYCAEpLSwHw2oaqmTNnYt26dRg/fjz69u2rmIfXtnHw9G+e7Hn6e0jKPGkvNib8TvUNfr48x++2+vPX76ra65pRnQ4cOAAAaNeuneL+tm3bAgAOHjzYYHUiZampqXZf5hqNxmne+lxXfhYC45FHHsHIkSPRrVs3xf2//PILAFi/YHltQ8f69eud7jt37hzy8/OhVqtxxRVXAOC1DUUnTpzAk08+idTUVPzrX//Cd999p5iP17Zx8PRvnux5+ntIyjxpLzYm/E71DX6+PMfvtvrz1+8qA00NoKCgAAAQHx+vuL9p06YAgDNnzjRUlcgH6nNd+VkIjB49ejjdV1JSgmnTpgGovdPGaxu6DAYDCgsLsXPnTkydOhVlZWV48MEHkZCQAIDXNhRNmDABpaWl+PTTT9GkSROn+XhtG6e6/ubJnqe/h0Se4HcqBQq/23zHV7+rDDQ1gIqKCgC1X66Xsmy35KPQUJ/rys9CcDl06BDuvPNOHDx4EMnJyXjggQcA8NqGsuuuuw779u2zPs/OzsZbb71lfc5rG1q+/PJLLF68GJmZmbj99ttd5uW1bZzq+psn9zj7PSTyBL9TKdjwu81zvvpd5RxNDUAIYffvpVQqFQDAZDI1WJ3Ie/W5rvwsBIfq6mr861//wjXXXIO8vDxERUXhu+++szaAeG1DV1paGi677DLrRJDz58/HN998Y93Paxs6zp07h4cffhjx8fF4991368zPa9s41fU3T67V9XtI5Al+p1Kw4Hdb/fnqd5WBpgYQFRUFACguLlbcb4nqR0dHN1idyHv1ua78LATe3r17ccMNN+Dpp59GRUUFrrzySmzZsgUZGRnWPLy2oWvlypX4448/UF5ejrfeeguFhYUYOXIkfv75ZwC8tqFk4sSJKCgowL///W+0aNGizvy8to1TXX/z5Jw7v4dEnuB3KgUDfrd5x1e/qxw61wASExNRWlqK4uJitGnTxmH/2bNnAYDzCYSY+lxXfhYCa86cOcjOzkZlZSUiIyPx1FNP4amnnoJOp7PLx2sb+iIjIzFp0iQcPXoU77zzDmbPno0bb7yR1zZE/PTTT/j888/Rv39/jBkzxq3X8NqGnjVr1mDBggVu5f3ggw9c7nf2Nx/OvDl/7v4ehjtffgaJ36kUePxu8x1vf1cZaGoA7du3x8GDB7F//3506dLFYf/u3but+Sh01Oe68rMQOPPnz8fYsWMhhECfPn3wySefID09XTEvr21oKCgowDfffIPo6Gjce++9inn69euHd955x7qcLa9taLAsU/z9999bu25f6ssvv8SXX34JoGa5Yl7b0PPrr79ixowZbuX94IMP6vU3H848PX8Wnvwehrv6nkNSxu9UCiR+t3nOn7+rHDrXACzd9FatWqW4f+XKlQCAnj17NlidyHv1ua78LATGsWPHcN9990EIgYceegjff/+9yx8eXtvQoFKp8OCDD+L+++93Oh9ESUkJAFiXCea1DQ2xsbFo166d4iMpKQkAEBMTY92mVqt5bUPQY489BiGEWw+gfn/z4czT8wd4/nsY7upzDsk5fqdSoPC7rX78+rsqyO927NghAIiEhARRUFBgt2/z5s0CgIiPjxeVlZUBqiE5k56eLgCIffv2Oeyrz3XlZyEwXnjhBQFADBkyxK38vLah4/LLLxcAxNq1axX3Dxs2TAAQr7/+uhCC1zYcfPLJJwKAyMrKstvOa9s4ePo3T/Y8/T0k97hqLzYm/E71D36+6sbvtvrz1+8qA00NZODAgQKAGDBggDh27JgQQojt27eLli1bCgAiJycnwDUkJXV9sdfnuvKz0PA6duzo8gtUCa9taHjjjTcEAHH55ZeLn376SZjNZiGEEOfOnRMPP/ywACCSk5PtGry8tqHNWaBJCF7bxqA+f/NUqz6/h1Q3BgJq8TvV9/j5qhu/2+rPX7+rDDQ1kKNHj4qUlBQBwBrNt6R79eolqqqqAl1FUlDXF3t9ris/Cw3LZDKJiIgIAUC0bt1atGvXzunj9ttvt76O1zY0GI1G0bt3b+t5joqKEi1atLA+b9KkiVixYoXda3htQ5urQBOvbfirz9881ajv7yHVjYGAWvxO9T1+vlzjd5t3/PW7ykBTAzp58qQYN26cSE1NFVqtVrRv317k5OQIvV4f6KqRE+58sdfnuvKz0HCOHz9u/aKs63HDDTfYvZbXNjSYTCbxwQcfiF69eomUlBQRGxsrunXrJh566CFx+vRpxdfw2oYuV4EmIXhtG4P6/M2Td7+H5BoDAfb4nepb/Hy5xu827/njd1USgrPbERERERERERGR97jqHBERERERERER+QQDTURERERERERE5BMMNBERERERERERkU8w0ERERERERERERD7BQBMREREREREREfkEA01EREREREREROQTDDQREREREREREZFPMNBEREREREREREQ+wUATERERERERERH5BANNRERERERERETkEww0ERERERERERGRTzDQREREREREREREPsFAExERERERERER+QQDTURERERERERE5BMMNBERERERERERkU8w0ERERERERERERD7BQBMREREREREREfkEA01EREREREREROQTDDQREREREREREZFPMNBEREREREREREQ+wUATERGFldGjR0OSpEBXIyz069cPkiRBkiQ0bdrUYf/OnTsxevRotGnTBpGRkUhLS0P//v0xe/ZsGI3Gepf7ww8/QJIk3H777S7zPf3005AkCZ988glefPFFa10lScKvv/5a7/KJiIjIHttX7mHbqQYDTURERB565513IEkS1q1bF+iq+J1Wq8XEiRMxYcIEu+0zZsxARkYGPv/8c8THx+Puu+9G165dsXv3bowbNw7XXnstzp49W68y+/TpgxYtWmDlypW4cOGC03wLFy6EWq3Gn//8Z2RkZGDixInIyMioV5lEREQUWOHSvmLbiYEmIiIiciEyMhLTp0/HK6+8Yt326quvYsKECbjiiiuwb98+7NixAx9//DG+/fZbHDlyBJMmTcJvv/2GP//5zzCZTB6XKcsyMjMzodfrsXTpUsU8+/fvx759+zBgwAA0a9YMgwcPxvTp0zFkyJB6v1ciIiIib7HtxEATEREReeDMmTN45ZVX0LFjR2zatAlt27a12x8VFYW33noLw4cPx5YtW7B27dp6lXPXXXcBAObPn6+4/7vvvgMAZGZm1uv4RERERA2hMbadGGgiIqKwV1FRgccffxydO3dGdHQ0OnXqhNdeew0Gg8EuX5s2bTBu3DgcOnQId999N1JTU9G2bVvcd9991q7M/fr1w8SJEwEAffv2RZs2beyOUVRUhPHjx+Oqq65CXFwcbrzxRkyfPh1CCLt8o0ePRqtWrVBdXY2nn34aqampiI2NxY033ogffvjBrfeVlpaGYcOGYc+ePbjrrruQmJiI5ORk/O1vf8O5c+fqebZce/XVV1FWVoZXX30VkZGRTvM9//zzuP/++1FcXGy33d1rcdNNN6Fly5ZYvnw5KioqHI6/cOFCqFQqDB8+3DdvjIiIiDwSru0rX/O27QS4d66Dqe3EQBMREYW18+fPo3v37vj3v/+NZs2a4a677oLJZMLkyZMxfPhwmM1mu/yHDx9G3759sXXrVgwaNAiJiYn4z3/+gxtuuAEnTpxAZmYm+vTpA6DmjtCYMWOsrz158iQ6d+6MWbNmITExESNGjMC5c+fw6KOP4s4771Ss39ixY/H5559j4MCB6NGjBzZt2oRbb70VBw8edPm+zp49i+PHj6OiogI33HADdu/ejdtuuw3x8fH4/PPP65wMsr42bNiApk2b4i9/+YvLfF27dsWsWbOsd9cAz66FJEm48847UVFRgeXLl9sd++zZs9i8eTP69u2LxMRE375BIiIiqlO4tq8A4MEHH4RKpcIPP/yAYcOGITk5GS1btkR2drZiAKcu3rSdAPfPdVC1nQQREVEYGTVqlLD9eZs8ebKQJEksWLDAuq26ulqMHj1aABBz5syxbk9PTxcAxC233CL0er11++uvvy4AiH/+859CCCHefvttAUD8+OOPdmU/+OCDAoCYNWuWdZvBYBAjRowQAMTq1avt6inLsujcubMoKiqybn/yyScFAPHee++5fJ9Lly4VABzy6vV60b17dwFAbN26ta7T5VLfvn1FfHy83bbY2FiRkZFRr+N5ci2EEGLz5s0CgMjKyrLbPmvWLAFAfPDBBw5l5OTkCABi586d9aojEREROWos7SshhOjcubOQJEnIsiyuv/568Y9//EPcdNNNAoAYO3asy9f6uu0khGfnOljaTgw0ERFRWLm0IRQXFyduvfVWh3wXLlwQGo1G3HbbbdZt6enpQpIk8fvvv9vlNZvN4sorrxRRUVHCbDYrNoRMJpPQ6XSia9euDmUdPnxYyLIs7rrrLod6Ll682C7vzz//LACInJwcl+/T0ijIzs522Ddz5kwBQPznP/9xeYy6XNpYOnv2rAAg/vrXvzrk/eOPP6yBL9tHenq6NY8n18IiPT1dxMTEiMrKSuu22267TciyLE6dOuWQn4EmIiIi32ss7avy8nKhUqlEfHy8WL58uV1db7jhBhEbG+vy9b5uOwnhefspGNpOat/2jyIiIgoeJ0+eRGlpKc6ePYtJkyY57I+MjMRvv/1mty0tLQ0dOnSw2yZJEgYMGID33nsPJ0+eVCzrxIkT0Ov16N27t8O+9PR0pKWlKXbXvnRJ2aioqDrfFwBs27YNarUaTz75pMO+Zs2aAQCio6PdOpa7mjZtCo1Gg9OnTzvsi4mJsc6tYPHf//7Xmq7PtQCAv/71r3j99dexcuVKDB8+HOXl5VizZg169eqFFi1aeP+miIiIyCPh3r4ymUx4/fXX7VZjkyQJ7du3R35+vlvHsfCm7QTU71wHQ9uJgSYiIgpbx48fBwBs3boVW7duVcyj1WrtnqekpCjmu+yyywAAf/zxh+J+SwMpOTlZcX9ycjKOHj3qsD0hIUExf122bduGTp06IS0tzWHfoUOHAMDaoHv//feRnZ3t9Fgff/wxRo8eXWeZarUa7dq1U2xkNWnSBNOnT7fbtmzZMusklfW5FkDNCiqvv/46FixYgOHDh2PlypXQ6/VcbY6IiChAwrl9tXnzZgDAjTfe6LBv79696NSpk0fH86btBNTvXAdD24mTgRMRUdiyNGpeeOEFiJrh4g6PM2fO2L2moKBA8ViWO1HOGkqW7ZbVUy519uxZp6/11IkTJ3D69Gm0a9dOcf+6deug1Wqty+f+4x//sL7fTz/9FElJSXbnwJ0gk0Xnzp1x6tSpOlduKS0txYEDB6zP63MtAKBLly5o164dFi1aBIPBgIULF0KSpDon1CQiIiL/CNf2FVATaIqLi8OVV15pt72iogK7d+9G9+7dPT5mfdtOQP3OdTC0nRhoIiKisNWyZUvodDps377dYV91dTXeeusth1U5jh496nBXTQiBH374AVqtFi1btnRallarxU8//eSw78iRIzh27JjTwJCnLHe0jEajw75ff/0Vy5Ytw/jx4xWX0N2wYQN69epV77InT54MlUqFJ598UrF8iylTptitOFOfa2Fx11134fz581ixYgWWLl2KG2+80XoHlIiIiBpWuLavAGDLli3o0aMHZNk+VJKXlweTyVSvQFN9205A/dtPgW47MdBERERhS5ZljB07FkuXLsXixYvt9r3xxht47LHHHO6wGY1GPPbYY3YNgbfeegu7du3CqFGj7Boetl2bZVnG6NGjsW3bNnz88cd2eSZNmgSTyYT777/fJ+9r27ZtAIA1a9bYzUtw8OBBDB8+HJGRkZg8ebLiazds2IA//elP9S776quvxoMPPoht27ahb9++1i7dFgaDAS+99BL+7//+z64rd32uhYVlmd+nnnoK586d47A5IiKiAArX9tXx48dx8uRJ9OzZ02HfL7/8AgD1CjTVt+0E1L/9FOi2E+doIiKisPb8889j6dKlGD58OAYMGICWLVti06ZN+P3333HzzTfjnnvuscvfqlUrrFmzBtdeey0yMjKwb98+bNq0CZdddhmef/55ALUTSk6dOhW//vorHn/8cbuyxo4di88++wzp6enYuHEj8vPzkZmZiUGDBvnkPW3btg3NmzdHamoqunTpgltvvRUFBQVYv349oqOjsXz5csVu5OfOncO+ffu8CjQBNQ3DiIgIvPPOO2jXrh2uvvpqXHvttSgqKsLWrVtx5swZTJ06FYcPH8aaNWusr/P0Wlhcc801uPLKK62TXd5xxx1e1Z+IiIi8E47tqy1btgCA00BTs2bNrNMSeKq+bSegfu2nQLed2KOJiIjCWnJyMnbs2IEHHngAJ0+exNdffw21Wo1p06Zh0aJFUKvt77m0bdsWmzZtQrt27bBkyRKcOnUKf/vb35CXl2ftcnzHHXfg9ttvx86dO/H1119bX5uamoqdO3di3LhxOH36NBYsWIDmzZvjrbfessvnrW3btqFbt25YuHAhBg4ciNWrV+PXX3/F8OHDsXnzZsWVWQBg48aNiIqKQufOnb0qX6PRYPr06Vi+fDluvvlmHD9+HHPnzsX27dvRv39//PLLL3jmmWfQrVs3u9d5ei1sWe7Mde/eXXECdCIiImo44di+2rx5MyRJQo8ePRz2/fLLL/XqzWRR37YTUP/2UyDbTpIQQjRoiUREREGqTZs2SE9Pr3OyxkA6fPgwLr/8cjz33HOYOnWqR6998sknkZeX53CnzJl+/fph586dKC4urk9VA+LFF19ETk4Odu7cieuuuy7Q1SEiImr0QqF95UpBQQGSkpLwwgsv4MUXX3SZl22nGuzRREREFEIs8zMp3fGqi7fzMxERERE1NpZFWLzp0dTYcI4mIiKiEGIJNN1www0evU6v12P79u113om7VGVlJSZNmoTIyEi88sorHr22Ia1cuRIrVqzA5s2bA10VIiIiCiOWicDdvcnHthMDTURERCFl27ZtSElJQWpqqkev0+l0qKqq8ri8qqoqvP3224iPjw/qxtLmzZvx9ttvB7oaREREFGZ++eUXpKenIykpya38bDtxjiYiIiIiIiIiIkUJCQno37+/TyceD3cMNBERERERERERkU9wMnAiIiIiIiIiIvIJBpqIiIiIiIiIiMgnGGgiIiIiIiIiIiKfYKCJiIiIiIiIiIh8goEmIiIiIiIiIiLyCQaaiIiIiIiIiIjIJxhoIiIiIiIiIiIin2CgiYiIiIiIiIiIfIKBJiIiIiIiIiIi8gkGmoiIiIiIiIiIyCcYaCIiIiIiIiIiIp9goImIiIiIiIiIiHyCgSYiIiIiIiIiIvIJBpqIiIiIiIiIiMgnGGgiIiIiIiIiIiKfYKCJiIiIiIiIiIh8goEmIiIiIiIiIiLyCQaaiIiIiIiIiIjIJxhoIiIiIiIiIiIin1AHugJERERE1LiYzWZUVlY6bFepVNDpdAGoEREREfkKezQRERERUYNavHgxYmJiHB533nlnoKtGREREXmKPJiIiIiJqUIcOHUJKSgr++9//2m1v2rRpgGpEREREvsJAExERERE1qEOHDqFDhw646aabAl0VIiIi8jEOnSMiIiIip+bOnQtJkvD77787zXPq1CmMHz8eLVu2hE6nQ4cOHfDCCy9Ar9cr5j948CAuv/xyAIDJZPJLvYmIiCgwGGgiIiIiIqfmzZvncv+xY8fQpUsXzJo1CydOnEBkZCTy8/MxdepU9O/fH1VVVQ6vOXToEA4dOoROnTpBo9EgPT0dOTk5qK6u9tfbICIiogbCQBMRERERORBCYMaMGViyZInLfGPHjsXp06cxYMAAHDlyBMXFxcjLy0OrVq2wadMmvPzyy3b5zWYzjhw5gl27duHBBx/EkiVL8Ne//hXTpk3DY4895s+3RERERA1AEkKIQFeCiIiIiILDmjVrMG/ePKxfvx75+fnW7fv27UPHjh3t8u7YsQNdunRBUlIS9u7di+bNm1v3bd26Fd27d0d8fDxOnToFnU4HADAYDPj222/RtWtXtGvXzpr/+eefxyuvvIKCggI0a9bMz++SiIiI/IU9moiIiIjIaunSpfj444/tgkzOLF68GAAwbNgwuyATAHTr1g2dOnVCSUkJNm7caN2u0Whw11132QWZAODPf/4zzGYz9u7d64N3QURERIHCQBMRERERWT3xxBPYuXOn9ZGamuo076ZNmwAAQ4YMUdxv2W7JBwDHjx/HypUrYTab7fKqVCoAQJMmTbyqPxEREQWWOtAVICIiIqLgkZqaahdc0mg0TvMeOHAAABx6J1m0bdsWQM0qcxaFhYUYMmQIvv32W9x+++3W7QsXLkR8fLzD8DwiIiIKLQw0EREREVG9FBQUAADi4+MV9zdt2hQAcObMGeu266+/HkOGDMHo0aOxfv16ZGRkYOPGjcjNzUVubq7LwBYREREFPwaaAqCwsBArV65Eeno6IiMjA10dIiKisFRZWYkjR45g8ODBSEhICHR1wlJFRQWA2oDSpSzbLfksvvzySzz33HP46quv8OGHH6JTp0748ssvMWLECMXjFBYWYsmSJUhNTbVOKu4NjUbDgBYREYUNg8EAg8Hg9XH0ej1OnjyJ2267zau2EwNNAbBkyRKMHj060NUgIiJqFObMmYNRo0YFuhphybJ4sbNFjC3zLplMJrvtTZo0wbvvvot3333XrXLYdiIiImo43radGGgKAMu8BzNnzkTXrl29Pl5mZibmz5/v9XEaw7HKysrQp08frFu3DjExMUFTr8ZwLJ77wB3L1+ceCM73GazH4mc/cMfavn07xo8f73Iya/JOVFQUSktLUVxcrDh8ztKTKTo62qtyLNfwnXfeQefOnb06FuBZjyZffrY9EYhyA1GmP36j3MVr61+N7do2ps8Tr23wleurHk07d+7EI4884nXbiYGmALB0+e7YsSO6dOnik+P54jiN4VilpaUAgM6dOyMuLs7r4wXjewzWY/HcB+5Yvj73QHC+z2A9Fj/7gTtWWVmZ9XjkH4mJidZAU5s2bRz2nz17FgC8HrpouYadO3dG7969vTpWfcr21Wc72MsNRJn++I1yF6+tfzW2a9uYPk+8tuFdrqVsb8i+qMSFCxdQVVVlff79999j0qRJ+Pe//41Tp075oggiIiKiRiUU2lft27cHAOzfv19x/+7du+3yERERUfjzqkfT+fPn8fe//x1LlizB5s2b0a1bNyxbtgzDhg2zjtWfPn06NmzYgPT0dF/UlxRkZ2fzWAESrO8xWI/lS8H6HoP1WL4WrO8zWI/lS8H6HoP1WKEolNpXGRkZWLFiBVatWoWsrCyH/StXrgQA9OzZ0yfl/f3vf0dUVJTLPNnZ2WHxeQxEuY3tb4/XNnw1pnPMaxueZfqjXMvqrq5cunhHvQkvTJo0SUiSJG677Tbxxx9/CCGE6NWrl4iPjxc///yz+O6774RarRbjx4/3ppiws27dOgFArFu3LtBVaXTOnz8vAIjz588HuiqNDs994PDcBxbPf+CE6u9tsLWv0tPTBQCxb98+h307duwQAERCQoIoKCiw27d582YBQMTHx4vKykqv6hCq15Lqxu/I8MVrG754bcOXr35vverRtGjRInTo0AGLFy8GAJw7dw4bN27EhAkTrHeuOnfujO+//96bYoiIiIgajVBqX3Xu3BkDBw7E6tWrkZWVhTlz5qBVq1bIy8tDZmYmAGDixImcJ4uIiKgR8WqOplOnTiEjI8P6fP369QCAW265xbrtqquuwokTJ7wpJuxYVjlxd7UT8h2tVospU6ZAq9UGuiqNDs994PDcBxbPf+CE6u9tqLWvZs+ejZSUFKxduxZpaWlo2rQpunbtij/++AO9evXC5MmTvS4jVK8l1Y3fkeGL1zZ88dqGL1/93noVaLLcsbJYvHgxNBoN+vTpY922Z88er1caCTdsLAWOVqtFTk4OvxQDgOc+cHjuA4vnP3BC9fc21NpXaWlp2L59O8aNG4fU1FRUVlaiffv2yMnJwerVq31y/kP1WlLd+B0Zvnhtwxevbfjy1e+tV0Pnhg4dinfeeQfPPvsstFotvvjiC9xyyy2IiYlBSUkJ/vOf/2Dnzp0YPny4V5UkIiIiaiyCrX11+PDhOvOkpKRg5syZfq9LZmZmncPwfD0ZOBERUThwZzJwvV7vk7IkIS4uX1IPRUVFGDJkCLZt2wYAaNasGTZu3IiOHTvipZdeQk5ODpo3b44ffvgBV199tU8qHA7y8vLQtWtXbN++HV26dAl0dYiIiMJSqP7esn3lKFSvJRERUSjx1e+tVz2amjVrhp9//hlbtmxBQUEB/vSnP1m7cffq1QszZszA8OHDkZyc7E0xRERERI0G21dEREQUyrwKNK1fvx4tW7bETTfd5LCvX79+6NevHw4fPoytW7eiW7du3hRFRERE1CiwfUVEREShzKvJwPv27VvnGL9Zs2Zh8ODB3hRDRERE1GiwfUVEREShzOMeTWPHjrWmhRBYuXIliouLFfMaDAasWrUKTZs2rX8NiYiIiMIc21dEREQULjwONM2ZM8ealiQJe/bswZ49e5zml2UZjz32WP1qR0RERNQIsH3lHq46R0REVD8Nueqcx4GmkpISADV325o2bYp//OMfeOWVV5zm1+l00Gg09a4gERERUbhj+8o98+fP56pzRERE9eDOjRjLqnPe8jjQFBcXZ01/8sknuOqqq+y2EREREZFn2L4iIiKicOHVqnN///vffVUPIiIiIgLbV0RERBTavFp1rrKyEhMmTEBycjJUKpXTh1rtVTyLiIiIqNFg+4qIiIhCmVctlGeffRYffvghmjRpgoEDByIqKspX9SIiIiJqlNi+IiIiolDmVaBpwYIFaNGiBXbs2IHk5GRf1YmIiIio0WL7yjmuOkdERFQ/Qb3qnK1z587hrrvuYiOIiIiIyEfYvnKOq84RERHVT0OuOufVHE3XX389jh8/7nUliIiIiKgG21dEREQUyrwKNL355pv4+eef8dlnn/mqPiHr+PHjyMrKQmpqKlJSUvDggw+itLQ00NUiIiKiEMP2FREREYUyr4bO7d27F+PGjcOYMWPw3nvvoUePHkhISIAkSXb5JEnCc88951VFg9nJkyfRq1cvdOjQAf/6179w6NAhTJ8+HQUFBViwYEGgq0dEREQhhO0rIiIiCmVeBZrGjh1rTW/duhVbt25VzBfuDaHc3FwkJSVh2bJl1qWG27Ztiw8++AAXLlxAbGxsgGtIREREoYLtKyIiIgplXgWajhw54qNqNJy5c+di5MiR2LdvHzp27KiY59SpU5gyZQqWLVuGwsJCpKWlISsrC88884ziSidz5szBiy++CLVaDbPZDFmWce+99+Lee+/199shIiKiMBOK7SsiIiIiC68CTWlpab6qR4OZN2+ey/3Hjh1Djx49cPr0aQBAfHw88vPzMXXqVKxZswY//PADtFqtNX9xcTFOnToFnU6HwYMHY8OGDYiOjsaIESPwxhtvICYmxq/vh4iIiMJLKLaviIiIiCy8CjRZHDx4EIsWLcJvv/2GsrIyzJ07F3v37kVsbCxatWrliyK8JoTAhx9+iCVLlrjMN3bsWJw+fRoDBgzARx99hNatW2PHjh0YPnw4Nm3ahJdffhlTp0615j916hQA4IEHHsCoUaPwyCOP4NChQ5gyZQpOnDiBRYsW+fV9ERFR6Bg9ejQ++eQTCCECXRUKAaHQvmpomZmZir3LbbmzfDMREYUGtp18Jzc3F7m5uS7z6PV63xQmvPTaa68JjUYjJEkSkiQJWZaFEEL8+9//FiqVSrz88sveFuGV1atXizFjxoj27dsLANbHvn37HPLm5eUJACIpKUkUFhba7fvll18EABEfHy8qKyut23/++WcBQIwdO9Yu/1dffSUAiF9//dWhnO3btwsAYvv27T56l0REFApGjRol3PnpnThxogAgjhw54vc6vf322wKA+PHHH/1eVkML5d/bYG9fNbRQvpZERFR/bDs1LF/93sreBKkWLFiAyZMno1OnTli5ciXGjBlj3XfHHXdg0KBBeOGFF/Df//7Xm2K8snTpUnz88cfIz8+vM+/ixYsBAMOGDUPz5s3t9nXr1g2dOnVCSUkJNm7caN2emJhofY2tgQMHAgD+97//eVV/IiIialxCoX1FRERE5IxXgabp06cjKSkJa9euxcCBAxEXF2fdl56ejsWLFyMpKQnvvPOO1xWtryeeeAI7d+60PlJTU53m3bRpEwBgyJAhivst2y35ACAlJQWSJDl0MauurgYAztFEREREHgmF9hURERGRM14Fmv73v/9h0KBBaNq0qeJ+lUqFvn37Ys+ePd4U45XU1FRcd9111odGo3Ga98CBAwCAdu3aKe5v27YtgJo5Eyyio6PRv39/fPHFF3Z5FyxYALVajS5dunj7FoiIKMxUVlbiySefxHXXXYfY2FhkZGRgzZo1AIA2bdrg7bffBlATVOjXr5/1dSdOnMCECRNw+eWXIzIyEqmpqRg6dCg2bNhgd/zRo0dDkiSHcn/99VdIkoQXX3wRANCvXz9MnDgRANC3b1+0adPGmrekpARPP/00OnTogKioKCQlJaFPnz7W3r8WL774IiRJwtGjR+22nz9/HpIkYfTo0fU7SY1YKLSviIiIGhLbTqHFq8nAExMTUVFR4TKP0WhEfHy8N8U0mIKCAgBwWl9Lg+/MmTN225944gnceuut+Otf/4rMzEzs3bsXr732Gh5++GGXk3WWlZWhtLTU63prtVq7lfCIiCi4ZWZmYteuXejfvz8SExOxdu1aDBs2DP/73/8wZswYfPvtt9ixYwfGjBljvWFRVVWFoUOHYteuXejTpw8GDx6MU6dOYfny5Vi3bh127NiBDh06eFwPIQTWrVuHzMxMXHvttdZ9WVlZWLlyJbp164b+/fujuLgYy5Ytw+23347vv/8effr08ek5sVVVVYWqqiqvj1NWVuaD2jS8cGtfEREReYttp9DiVaCpe/fuWLRoEQ4fPmwXybM4fPgwVqxYgcGDB3tTTIOxNOqc3UG0bL+08Td48GAsWrQIr776KsaNG4dWrVph2rRpePTRR12W56sP2pQpU5CTk+OTYxERBYIQAsLg+j/WwUDSRCne7fLU6dOn8b///c8aKHjqqafw+uuvY8WKFXj++edRVFSEHTt24IUXXkDr1q0BANu2bcOuXbswcuRIfP7559Zjff3117jrrruwcuVKjxtL2dnZMBqNWLduHR566CHr79KJEyewcuVK3HTTTXZ3/H755Rf06NEDixYt8mtj6dVXX7XeOWyMwq19RUREvse2E9tOwcyrQNOrr76K5cuXIyMjA0899ZS169dPP/2Ebdu24Y033gAATJs2zfuaNgBxcclE4WTpRJVKBQAwmUwO+4YOHYqhQ4d6VN66devQuXNnzyqpgL2ZiCjUCUMF9o0P/jntrphZBkkb7fVxXnrpJbveKLfffjtef/11nDt3zulrWrRogdmzZ2PQoEF226+66ioAQFFRkdf1slCr1Zg9ezZ69uzp97KUTJ48uc6bNe7YuXNnSDbqwq19RUREvse2E9tOwcyrQFPr1q2xatUqTJw4EY8//rh1e79+/SCEwDXXXIN58+ahY8eOXle0IURFRaG0tBTFxcWK3dEtPZmio73/QwFqJgq3neCTiIgahx49etg9j4qKqvM1bdu2tc4VWFRUhPz8fPz+++/46KOPfF6/5ORkjB07FgBQWlqKAwcOYP/+/fjyyy99XpYSXw0JD9UFOcKtfUVEROQttp1Ci1eBJgDo1q0bNm7ciB07dmDfvn04fvw4UlJS0L59e/To0cMn3eQaSmJiojXQpNRV/ezZswCAhISEhq4aEVFYkzRRuGJm8M+nI2nqbtS4o76/I3PnzkVOTg7y8/MBAE2aNPFo0QlnPXaVrFmzBo899hh27doFoKZBd9NNN/mlLHIUTu0rIiLyPbad3MO2U2B4FWhau3Yt+vfvD0mScP311+P666/3Vb0Con379jh48CD279+v+OHbvXu3NR8REfmOJEk+6VYdzj766CPcf//9GDZsGN5++2106dIFLVq0wNGjR5Genu7WMQoLC93Kt2rVKgwdOhQ9e/bEggUL0K1bN7Rs2bLmOrkZ4HC3LHIUbu0rX8rMzIROp3OZJzs7G9nZ2Q1UIyKiwGDbqW5sO9nLzc1Fbm6uyzx6vd4nZXkVaBo4cCBatGiBu+++G/fccw+6du3qk0oFSkZGBlasWIFVq1YhKyvLYf/KlSsBwGHcJRERkb/NmzcPKSkp+O677yDLsnX7sWPHnL6mqqrKbgja9u3b3Srr66+/BgAsX77cbvjZH3/84fQ1lzZM3C2LHIVb+8qX5s+f79GdaCIiarzYdrLnzo2YvLw8n7Q75LqzOPfEE08gJiYG06dPR/fu3XHFFVfg5ZdfxsGDB72uWCAMHz4cALB48WKHaOKWLVuwb98+xMfHo3fv3oGoHhERNTIGg8GavnDhAvR6PSorK63bSktL8dxzzwGwb6w0b94cQO0NEqBmNZR33nnH7bLMZjPOnz9v3VZVVWWdL6iuss6fP8+Jqr0Qbu0rIiKihsK2U3DwKtD0r3/9C/v378evv/6K559/HhqNBi+88AI6dOiAnj17Ijc3FwUFBb6qq9917twZAwcORGFhIbKysnD8+HEANVG9zMxMAMDEiRPr7LJNRETkDcsEl4899hhmz54NoGbIUHFxMTp27IiRI0ciKysL6enpMJvNiImJwUcffWRtDN1+++2QJAlZWVkYN24cHnzwQXTt2tVh1RXbsqZOnYo333zTWpYQAp07d8add96Jv/3tb2jTpg327NmDli1bYvHixXjmmWcAALfccgsiIyPx6KOPYuTIkfjnP/+Jzp07o2XLlkhMTPT7uQpH4da+IiIi8je2nYKLV4Emi2uuuQY5OTnYtWsXfv/9d0ybNg1msxn//Oc/cdlll+HWW2/1RTENYvbs2UhJScHatWuRlpaGpk2bomvXrvjjjz/Qq1cvTJ48OdBVJCKiMDd69GgMGjQI33//PVasWAGgpuH0yiuvIDIyEgsXLsSRI0fw9NNP48cff8Tbb78NtVpt7Qr+pz/9CV999RXat2+PefPm4ddff8WkSZPw/vvvO5R1xx134Pbbb8fOnTut3b7vvPNOzJo1C0lJSVi+fDn27NmD++67D1u3bsUHH3yA5s2b48CBAwBqVnRZsmQJunXrhkWLFmHDhg3IysrCN998A5VK1UBnLDyFU/uKiIjIn9h2Ci6S8MPU5nq9HqtWrcK7776LtWvXQpIkmEwmXxdTL23atMGRI0ewb98+p8sCnzp1ClOmTMHSpUtx7tw5pKWlYeTIkXj66ad9styyZdzj9u3bOc8AERGRn4Tb720wt6/8LdyuJRERUTDy1e+tV5OB2yooKMDixYuxaNEirFmzBpWVlVCr1Rg8eDBGjBjhq2K8dvjw4TrzpKSkYObMmQ1QGyIiIiLnQqV9RURERGThVaBp3759WLRoERYuXIgtW7bAbDZDp9Nh0KBBGDFiBP785z+jSZMmvqorERERUdhj+4qIiIhCmVeBpquuugoAEB0djREjRmDEiBG49dZbER0d7ZPKERERETU2bF8RERFRKPMq0HTvvfdixIgRGDJkiE/mLiIiIiJq7Ni+IiIiolDm0apzjz32GBYtWmR9/p///AfDhw/HyZMnsX79esXXPPvss0hISPCulo2IyWSC8cLhmkflOZiry2GuLoexsqB2u82jsUwCSkREFK7YviIiIqJw4lGPpunTpwMA/vznP9ttf/fdd/HOO+8oBj0qKytRXFzsRRUbF1FxDAULLnc7f+KIQ0BsGz/WiIiIiPyJ7SsiIiIKJz5bdY6IiIiIyJ8yMzOh0+lc5snOzkZ2dnYD1YiIiCg05ObmIjc312UevV7vk7IYaApizW7LQ0STDgAAs7ECMJYBAEzlx1G0ok8gq0ZERETU4ObPn48uXboEuhpEREQhx50bMXl5eejatavXZTHQFEDG8hMwVraBrNYAqAkmmcqPW/ertPGQI2pWmKn5NzEQ1SQiIiIiIiIicotHk4GTbxWv/TMKvmqGM1/E4MwXMSj4Ksm9nkrqOOU0ERGFvHXr1kGSJHz66aeBrgoRERFR0GPbKfgw0BSCZLVOMU1ERMHtnXfegSRJWLduHQDg/PnzkCQJo0ePDnDNiIiIiIIP206hiUPnAqjJjbOQ2GuE3dA5yzxMACBFpQWqakREREREREREHvM40LR582ZMnTrVYRsAvPzyyxBCKO4jR3JkMtSRTWufuzkPk9mot0tb5nEiIiKi0MT2FREREYULjwNNmzZtwqZNmxT3vfDCC4rbJUnytBhyxVhqTZrO7wGMrWr3qWMgq6OsTyV1FM8/EVEQ6NevH3788UcAQN++fZGeno4jR44AAD755BN88skn1v0WX331Fd5//33s3LkT7du3x4gRI/DUU09BlmtHvuv1ekyfPh2ff/45jh49Cq1Wi3bt2mHChAkYNWqUNd+6devQt29fbNiwATt37sRLL72EoUOHYs6cOX5+5+QOtq+IiIjsse0UujwKNF16ESnw6po8PHlkGST2eCIiCrjMzEwIIbBu3TpkZmbi2muvxalTp/DBBx/gqquuwqBBg9CyZUv88ccfAIAvv/wSa9asQe/evTFs2DB8//33eOaZZ5CXl4f//ve/1uM+8sgjmDlzJq688krcc889qKysxPLlyzF69Gio1Wrce++9dvWYNWsWvvjiC/Tt2xe9e/du0HNAyti+IiIicsS2U+jyKNDEk+pjsv8n8ubQOiIKBUIIVJkCXYu6aVX170WSnZ0No9GIdevW4aGHHkKfPn1w/vx5fPDBB+jevTumT58OANbG0ooVK/DZZ59ZGzslJSUYMmQI5s+fjx9++AH9+vWDyWTCf/7zH6Snp2PXrl1Qq2t+1k+cOIE2bdrg22+/dWgsffXVV/j555/RrVu3+p4G8jG2r4iIyFNsO7HtFMw4GXgAyRFRdWdSIEWlIXHEIeWd6hgI/VkULry65rmxFEDz+lWQiKiBVJmA+1YWBLoadfp0cCJ0DfTL2atXL7uGTnx8PN5++2307NkTn332Gfr164eqqiq89957uOaaa6wNJQBISUlBfHw8ioqKHI6blZXFhhIREVGIY9vJEdtOwYOBphCkUqmA2DZO9xttVq4jIqLQNGDAAIdtGRkZiI2NxcGDBwEAUVFRGDt2LACgoqICBw4cQH5+PpYtW4aCAuXG5zXXXOO/ShMREREFCNtOwYOBpgAyG6sDXQUioqCgVdXc8Qp2WlXDldWiRQvF7ampqXYNoR07diA7OxtbtmyB2WyGVqvFDTfcgNjYWMXXN2vWzC/1JSIioobDtpMjtp2CBwNNgWS6EOgaEBEFBUmSGqxbdag4e/as4vaTJ0/ipptuAgDs3r0bN910E9q2bYv//Oc/yMjIQHp6OlQqFdq0Ue75ypXKiIiIQh/bTo7Ydgoect1ZiIiIqKGtXbvWYdvGjRtx4cIFdOzYEQDw3XffobKyEv/9738xcuRItG3bFiqVCgaDwWlji4iIKFwJIWCuLrc+TPoimCrPOjzMZnOgq0p+wLZT8GAMlIiIqIEZDAaXzwFg3bp1mDt3Lu655x4AQHFxMSZNmgRZljFhwgQAwIULF6z7LEwmEyZPnoyKigro9Xp/vQUiqgeTyQRRccxxhzoesloDADAbygBzhfODyDrIkcmQZRnCXA1hqvn+EGYzhKHwYh4tJFkNyFoAAjAbIMzVgNnxu8Y2rySrIamj7O7eCyEgjM7rYzabAUu5ACBrIMkRgKwBIANmPYTZBJgVvo+kCEgqDSBFAJLK/byyGjBVQggzYKp0nheAkHWQbI4nhABMNe9H0iRAVmsgqbTWfWZDWe15tB5PXZNHUte8L1OF3XHs86ogqXSQtAmQ5dp7+nbX/mIey7/CWF5T/sV/oXK8BjDpIakja7dZ8gIA5Ev26SGpdTbPKwAISwUhqaNs9lVe8lpXeS89biWA2oCNpI6222d/XBd5Tfqa82HzHKJmOTUpKq1mftqLzGYzRFWhXR4A1nMmjOUoWHA56pIwfDdEdOuavwlhhDBVAcLomFEVCUmSAVUkYDYC4uLfnFCYAsWTvLIOkqyqWYVcmNzPC7Pbf8+e/O0DAMxVEGYjYK5SyGv7dy3Z573k82rNC8BsNADGEsfjXXTp9VX6jjRV1bR19BWlMFadh/HCEQDO206ffTobd99RM1dTcfF5THz4H5BlGeP+PgzGC4dxvugUAOBcwUkY21wGGAphMpnw9POvoaKiApXl52G8cLim7MpzF99HFYxVF+y/69x4P40ZA01EREQNJCqqpsE+depU/Prrr/jnP/8JWZaxatUqTJ48Gffff7817x133IG//e1v+Pjjj5GSkoK1a9fi1KlTeOqpp6x35f7yl7/gzTffxJAhQ9CnTx80b94c69evh8FgQKdOnbB161aMGTMGH3/8cUDeLxHZExXH3PpPcF0SRxyC0DRFxe8zUJY32Qc1q5Xwl/01//m8SBgralczDkOR7cchrvv0miBT+TEULuzkk+MmjjgEcTGYIozlMJUfR9GKPj45dmPS/NZfgJjWAGoCUKL6gk8+j+H8mQ4lCcN3Q4pJtwaklP5OzL/V/Dtl4p+xsTMw5mbUtJ1WrsBTj03AmL/fBVPFaQDA0K7AqNHjMPN1ILkJsOE34EwJkD0UaLbrZhTsAvpEA29JwC2DByCjI9A0BvhlP2AwAh0vA7Zt34F7h16O6WOBkn01ZZduegAFqgfqfD+JIw65XLSrMWGgKRypY5TTREQUUHfccQeWLVuGtWvXQq/X4/HHH8e0adPw4Ycf4oMPPsDtt99uzfvkk09i0KBB+PTTT7Fo0SJcc801mDZtGkaPHm3Nk5GRgQULFmDq1Kn4/vvv0bp1awwePBgvv/wy9u/fj/vuuw979+4NwDsl8o/MzEzodDqXebKzs5Gdnd1ANQoMXwSrnCn8toPfjh2MKvNnoTJ/ls+P689r1JicW9o90FUgP3In4HdLV2DtLmDDXqCqGphwC/DUHWZ8/mMRZnwwA72jZqDqYkewf9wC9OkEfL0RWLUTuKIl8NQdQFav2uN1bQvMegh4exGw8TegZXOgz9U1+Q6dBh6ZDeSfrOcbkjX1fGHDyM3NRW5urss8vuoNLwkhRN3ZyJfy8vLQtWtXbPlpEbr3Gubz45ury3Hmi5oAU/LIMsgR0XW8goiIKPxYfm+3b9+OLl26BLo65IVwuZbGC4etAYhmQ9ZBFd2qZocbQ+c86VnUfNivUEW1cHv4jDAUu33suIwZkHUJKPkx07HcW3+BHJUaNEPnJHU0hDDDVPo7zi3p5tb7c3g/ugS3h86Z9YVuBUaa3bIB6rj2dkPnlIZ8qRN7oln/7y4+k2E79Cych84ZS/NRtPxPqIv1+gB2w7esw+A09sMXrcM8LXlVURw6V8+hc8JQ5DKYGnvDvxHV8QHFoXPu9u6zfkfKUZA1MTCVHXH7eyph+G67z679e9FB1sQBUBj6W4+8tt/N4fB/b1/93rJHExERERFRA1NFt4JaYYiFs/+kCCGQPLKs9rmz/zwCDvMD1cUc2QJJd525eFwX/9GMaAJZrQUgIXlkmUNe5XLj3K6HZ3mVlyFXIjfvYj13dnNZqeMgXwycKAWOnJ9H5RED5ujWNefR2dxLluNeOo9LRLTD9QUASbIPIDUamuY1Q5DgOiDl6edcBgCt+58bck5o4hw+r7YkVU1gqub7rKn9vqi0mut7MYAEKM/ldOnfiYi7wvq5UGRzvEvnmnP6EsDtz4SzvObqcodtxEBTYMmuu37Xl9losEuHelSViIiIKCzIUcppN0iSBMlPbTpZloHIJM/qI4fOfyMkSbY/d07/Y+ndlBMO59GD6+XP6xtqVCoV57kJct58XpWur1JAyp3XUfAKnV+IMCRHeNbAcJtNNNh0/n+AsZVNobWRXsD9aC8RERERece2DWabJiKi0GTWn7VLyxEMhgEMNIW9usa/Jo8s490TIiIiIiIiIvIJ9we1ks+ZzQoTzjV0HYzKY/uJiIiIyLds237B0A4kIiLyB/ZoCqTqUr8c1jrBmhI5CsJQWDtjv7EEdY2HJSIiIiIfMBTZp7VNAlcXIiIiP2GgKQzVNVGaUWHJXCIiIiIiIiIib3HoHBERERERERER+QQDTURERERERERE5BMMNBEREQWRv/zlL5AkqUHKmjRpEiRJwtGjRxukPCIiIiJfC2jbSdbU7rRNN3IMNBEREREREREReUjWxCumGzsGmoiIiIiIiIiIyCcYaAokWRegcqOU00RE5FeSJDl9jB49GpIk4bvvvrPmHT16tPW1v//+O0aOHInWrVtDp9OhVatWuPPOO7F79267Mvr164c2bRxXHl24cCEkScKnn34KAGjTpg3efvttAEB6ejr69etnzXvixAlMmDABl19+OSIjI5GamoqhQ4diw4YNdse01PlSv/76KyRJwosvvliv80QUttgGIyLySKi0nU4VA/946BG2nS5SB7oCjZkcEZgGhqyJUUwTEZF/TZw40WHb1q1bsXHjRjRt2hQTJ07E4sWLcfDgQUycOBHdu3cHAJw7dw79+/fH6dOnMXjwYAwdOhSHDh3CN998g59++gm//fYbmjVr5lFdxowZg2+//RY7duzAmDFj0KVLFwBAVVUVhg4dil27dqFPnz4YPHgwTp06heXLl2PdunXYsWMHOnTo4PW5IGqM2AYjIvJMsLedzPqzqKoG/jYd2Hv8I7adLmKgiYiIqIFMnz7d7vkff/yBLl26oF27dsjJyUFcXByOHDmCgwcP2uVdsWIFTp48iWeffRYvv/yydfvrr7+Op556Chs3bsSwYcM8qsvzzz+PoqIi7NixAy+88AJat24NANi2bRt27dqFkSNH4vPPP7fm//rrr3HXXXdh5cqVja6xRERERIER7G0n44XD+PUIsPc4cPdfh2PuV99Z8zfmthMDTQFkNhsDXq7JcN5hvyRHQFJxxnwiajhCCAhjRaCrUSdJHeWzVU2qq6tx5513oqysDGvWrEFcXJzTvJ06dcLs2bNxxx132G2/6qqrAABFRUU+qRMAtGjRArNnz8agQYP8XhZRY2PbBjObjZzDgojqjW2n4Gk7JTUB3hwN3PHEE34vK1Qw0BRI1aWBKddQ+0Ev/O9lDrujrnwEsV2mWZ/78suBiEiJMFbgzBfBP4wkeWQZpIhonxxr0qRJ2Lx5Mz799FNce+21LvN27twZnTt3BgCcOXMGBw4cwG+//Yb/+7//80ldbLVt2xZt27YFUNMwys/Px++//46PPvrI52URWUyYMAHbtm3D1q1bA10V/7Jpg8FQBGibBK4uRBTS2HYKnrZTelLNI7FlKttOFzHQRA4qfnsHFb+9Y32eePd5qLXOo8VEROSZL774Arm5uXjggQfw97//3a3XvP322/j3v/+NP/74AwCQkJCAjh07ul2mEMLtvHPnzkVOTg7y8/MBAE2aNLHO4eTrsohWrVqFGTNm4IYbbgh0VYiIKEgFe9vpm03AOy/3R/7BIwDYdmKgqTFSeTgJueEcwEATEfmRpI5C8siyQFejTpLa+0Ucdu/ejfHjx6Nr165455136n4BauYEePnllzFq1Cj87W9/w3XXXYfmzZtj3bp16Nu3r1vHKCwsdCvfRx99hPvvvx/Dhg3D22+/jS5duqBFixY4evQo0tPTfVoW0fnz5zF27FiXwx+IiMgR206uNWTbae5PwONzgNtuaYu3/y+XbScw0NQoSWrlrou2Q+YMZzegePWQhqwWETVikiT5rFt1MCstLcWIESOg0+kwf/58aLVat143b9489OjRA3PmzLHbfuzYMcX8er3eYdv27dvdLislJQXfffcdZLl2BhlnZQE1K9XZvhd3y6LQMHfuXIwcORL79u1zeif41KlTmDJlCpYtW4bCwkKkpaUhKysLzzzzDHQ6ndNjP/zww2jbti1at26NvXv3+ustEBGFHbadXGvIttN3W4DkeGDBvA+hadK2zrKA8G87cQ7CRkiOiEbyyDKHR9wNr0OOiIYcEQ1VbPtAV5OIKOyMGjUK+fn5+Oyzz+q8w1VdXW1Nl5WVobS0FCaTybrt1KlTeOWVVwDYN46aN2+O06dPY9euXdZte/bswaeffuq0LIPBYE1fuHABer0elZWV1m2lpaV47rnnFMsCgJUrV1q3nThxwu27jRQa5s2b53L/sWPH0KVLF8yaNQsnTpxAZGQk8vPzMXXqVPTv3x9VVVWKr1u4cCG++eYbzJkzxy6oSUREZBH8bSc1yvVAVTVQWVm74ENjbzvxV70RkiTJGlCyfdiuNMfJv4mIfOu7777Dt99+i1atWmH16tWYNGmS3WPq1KkAgKiomi7mY8eOxfz58wEAI0aMwG+//YaOHTti1KhRuOOOO9C2bVukpaVBlmW8+uqrmDt3LgBYV1fp27cvHnroIYwZMwY33ngj/vrXvzrUyVLWY489htmzZwMAMjMzUVxcjI4dO2LkyJHIyspCeno6zGYzYmJi8NFHH1kbQ7fffjskSUJWVhbGjRuHBx98EF27dnVYsY5CkxACM2bMwJIlS1zmGzt2LE6fPo0BAwbgyJEjKC4uRl5eHlq1aoVNmzbZLSttUVhYiPHjx+ONN95AmzZt/PUWiIgohIVC20nWNcOtNwAl5UCnG/qz7WQhqMFt375dABBbfloU6Ko4VV16SJycA3FyDkR16aFAV4eIKOR98sknAoDTR3p6uhBCiM2bN4s//elPIjIyUkyaNEkIIURFRYV4/PHHRevWrUVsbKzo3bu3mDVrlhBCiKlTp4qkpCTx1ltvWct67733RMeOHUVMTIzo06ePmDlzpvjtt98EAPHJJ59Y8+Xn54tBgwaJ6OhoMWLECCGEECaTSbzyyiuiXbt2Ijo6WvTo0UP861//EkajUcyePVukpKSIRx991HqMr7/+Wlx77bUiOjpaZGRkiNdee02Ul5cLACInJ8fv59UVy+/t9u3bA1qPULN69WoxZswY0b59e7vP6L59+xzy5uXlCQAiKSlJFBYW2u375ZdfBAARHx8vKisr7fZlZmaKgQMHWp+PGjVK3HDDDU7rFC7Xku0rIiL3hULbyWQoE398BDF5BES7dm3ZdrpIEiLMpjcPAXl5eejatSu2bFiB7jcNDnR1FBkvHEbBgssBAIkjDkEdy7uNREQUWiy/t9u3b/do5ZfGbtKkSXj77bcdtivN0fTSSy9hypQpGDt2rLVXnK2rr74ae/bswZo1azBgwAAAwIIFC3Dfffdh69atSEtLAwA88MAD2Lt3L9avX4+IiAhoNBq744TLtTSWn0LBf1MBAIl3noQ6OiXANSIiIm+Yq8tx5osYAEDyyDLIIT5vlq9+bzl0LoDkiJhAV4GIiIjIzhNPPIGdO3daH6mpqU7zbtq0CQAwZIjyAiKW7ZZ8QM3qQeXl5bjqqqsQExODmJgYfPHFF9ixYwdiYmKsQyHCkayJU0wTEVFoMuvPKqYbO64652MTJkzAtm3bsHXr1kBXxTuyTjlNREREYS01NdUuuHRp7yJbBw4cAAC0a9dOcX/btjWr7xw8eNC6bcyYMbj55pvt8k2bNg2HDh3CRx99hFatWtW77kRERBR4DDT50KpVqzBjxgzccMMNbuU3m81+rlH92d5lk2QVzNXlTvNK6ihOHk5ERNQIFRQUAADi4+MV9zdt2hQAcObMGeu2Vq1aOQSTkpOTUVBQgJtuusk/FQ0Stm0/s9nMoQVERBSWGGjykfPnz2Ps2LGIi/OgG3R1id/q40tnv0p2uT/pngtQaTgMkIiIqLGpqKgAUBtQupRluyWftyzLVXtLq9VCq9X6oEYeMhTap7WxDV8HIiIKO1VVVaiqqvL6OGVlZT6oTSMLNM2dOxcjR45UnMzS4tSpU5gyZQqWLVuGwsJCpKWlISsrC8888wx0OudDyB5++GG0bdsWrVu3xt69e/31FhqMR3PEmw3+qwgREREFLUt7wVm7QaVSAQBMJpPL48yZM8et8vr06eNB7ZybMmUKcnJyfHIsIiKiQHv11Vfx4osvBroaVo0q0DRv3jyX+48dO4YePXrg9OnTAGq6gefn52Pq1KlYs2YNfvjhB8W7XwsXLsQ333yDXbt24aWXXvJL3Rucyf07j5IqAHcEiYiIKOCioqJQWlqK4uJixeFzlp5M0dG+WYVn3bp16Ny5s9fHCUhvJiIiIj+ZPHkyHn30Ua+Ps3PnTp/c1GkUgSYhBD788EMsWbLEZb6xY8fi9OnTGDBgAD766CO0bt0aO3bswPDhw7Fp0ya8/PLLDiuhFBYWYvz48XjjjTfQpk0bf76NhqWKcrlbl34X9Ee+aqDKEBERUTBKTEy0BpqU2kFnz9aswJOQkOCT8mJiYjybpoCIiKgR8NWQ8JgY30yJE9aBpjVr1mDevHlYv3498vPzXebdsWMH1qxZg6SkJHz11Vdo3rw5AOD666/HggUL0L17d7z33nt49tln7YbQTZgwAddddx0efPBBv76XhiZHRCN5pPPxmWZjpTXQxMksiYiIGqf27dvj4MGD2L9/P7p06eKwf/fu3dZ8RERE4UftJN24hfWZWLp0KT7++GO38i5evBgAMGzYMGuQyaJbt27o1KkT9uzZg40bN2LAgAEAgAULFmD58uXYunUrystrVmWrrq6GyWRCeXk5IiIiXC4JHMwkSYIU4bybu1l/tvYJJ7MkIiJqlDIyMrBixQqsWrUKWVlZDvtXrlwJAOjZs6dPysvMzHQ5ZyYAZGdnIzs72yflERERuSLrmimmg1Fubi5yc3Nd5tHr9T4pK6wDTU888QRGjRplfT506FCcPHlSMe+mTZsAAEOGDFHcP2TIEOzZswebNm2yBpp2796N8vJyXHXVVQ75Y2Ji8NxzzzkMtSMiIiIKF8OHD0dOTg4WL16MwsJCuyFyW7Zswb59+xAfH4/evXv7pLz58+cr9pwiIiIi19y5EZOXl4euXbt6XVZYB5pSU1ORmppqfe6qd9GBAwcAAO3atVPc37ZtWwDAwYMHrdvGjBmDm2++2S7ftGnTcOjQIXz00Udo1aqV6wrKodnbiYiIiAgAOnfujIEDB2L16tXIysrCnDlz0KpVK+Tl5SEzMxMAMHHixDp7ITUask45TUREFEbCOtDkiYKCAgBQXDEFAJo2bQoAOHPmjHVbq1atHIJJycnJKCgowE033VRnmXKEbybaCjRhrIAw6iGpaxtM5upyuzySOgqSJDV01YiIiMjPZs+ejYyMDKxduxZpaWmIj49HSUkJAKBXr16YPHlyYCsYRGRNnGKaiIhCk1lfaJeWXUw/05gw0HSRZfldS0DpUpbtlny+UFZWjtLSUq+P46sZ5uurcOHV0Kbdjvhen1u3nfnCPoiWdM8FqDThEVgjIiL/qqqqQlVVldfHKStzvqgF+U5aWhq2b9+OKVOmYOnSpTh37hzat2+PkSNH4umnnw7Z+SqJiIjqZnaSbtwYaLpICGH376VUKhUAwGQyuTzOnDlz3C6z34DBbud1ZcqUKcjJyfHJseqr6th3DsElW8JYDjDQREREbnj11Vfx4osvBroadNHhw4frzJOSkoKZM2f6vS6hPhm42Wy2S3PVXiIiaiicDDwAoqKiUFpaiuLiYsXhc5aeTNHRvusKt3b5l7jhxlu8Pk5AejOpojzLb/JdTzAiIgpvkydPxqOPPur1cXbu3Ik+ffr4oEYULEJ+MnBDoX2aq/YSEVED4WTgAZCYmGgNNLVp08Zh/9mzZwHAbjUVb8VERyEuLjTH50vqmoBbXUPmiIiIPOWrIeExMfxNIiIiImpoDDRd1L59exw8eBD79+9XvFO2e/duaz4C5IhoJI8sgySp7CYBTx5ZOx+GqewIChdeHYjqEREREREREVEAcGj4RRkZGQCAVatWKe5fuXIlAKBnz54NVqdgJkkS5IhouyATUBOAsjwkTbzNDk4ESkRERERERBTuGGi6aPjw4QCAxYsXo7Cw0G7fli1bsG/fPsTHx6N3796BqF5Ikm0CTZIcAXN1udOHs0nYiYiIiIiIiIKT7CTduHHo3EWdO3fGwIEDsXr1amRlZWHOnDlo1aoV8vLykJmZCQCYOHFinSudkLKz89MBU6XT/Un3XICKq9IRERGRC6G+6hwREYUXWZegmA5GXHUuQGbPno2MjAysXbsWaWlpiI+PR0lJCQCgV69emDx5cmArGMpcBJlq9usBMNBEREREzoX8qnNEREQB0pCrzrFvl420tDRs374d48aNQ2pqKiorK9G+fXvk5ORg9erV0Gh8PM+QHOHb4wUZYSx3O6+kjvRjTYiIiIiCgO2clZy/koiIwlSj6tF0+PDhOvOkpKRg5syZDVAbQI6IbZBy6mI0CxjNzvdrVTWTf3tMFeVyd0Tijagu+Nnz4xIRERGFINv5K23TREQUmsz6Qru0HBEdwNoEj0YVaKIaZrMZFwxAhAqQJQlf/l6G5UecD217t29zxGgkmAVgMgNGIWCyCUw11wEqlcrhdXJENJJHljk9rjBW4uxXiV69FyIiIiIiIqLAMDtJN24MNIUhIQSqTDXparOA+ZLP+3mDGU+sL3L7eP/88ZzL/VMy4pESVRO0ilDhkoBUzYSdSsEoo/6sNW0qOwrEtLY+lySZw+mIiIiIiIiIQgwDTQFkrnI/2OOJKhNw38oCvxxbyYubS+rMMyUjHgk6IEKuDUhVVwvr/sKFnezyq5t1QfNbfrI+l9RR9Ru+R0RERBQkzDY32cz6s5Aj2gSwNkRERP7BQFMj92bvZkiMVMFoth8OJ4SAwWwfGLLtqXSmwuRWgMlCKW+86RxynOQ3FuXhzBe1q9Al3XMBKg1XpSMiImrMMjMzodPpXOZxZ1UdIiKixiY3Nxe5ubku8+j1ep+UxUBTGDLbjJV7r28zzM8vx48nqhTzNtdK0KklAJ71ForX1MzdZKEUkDpdYcQLm0qcHqMa7g+NE1WFAANNREREjdr8+fPRpUuXQFeDiIgo5LhzIyYvLw9du3b1uiwGmkKQ7RxMAFBhNMNo87xQX/tEQML918Rh9NXKx9I6zuHtFpVKhaQ6JtSPiVDZBaMA+4DU2fMRwBL710jx10N38zqoZcBYdhSVy66peR+XTjRFREREREREREGHgaYQ5OkcTBEqCRF+rI8zdQWjInWxeDzhkN02ARnV62pWwIs3VVqH1l2orEB8VDkkSYKkjqrNb6yAEDVzPXEeJyIiIiIiImo4spN048ZAUwgyh0nvHkmSYJDq6BZ1UdWKa3AGgDr+KiTevse6vXBJNxhL9gIAkkeWQYpw73hERERERERE3pB1CYrpxo6BphBUVl2bfrN3M0RHSHZD52w1dz1fZkA118FhaJ1aAlSyBLUMFBScB1bbv8ZkMqOk7ALUF4PFJpugm8lkhByIrltEREREREREBICBpsDyQVREK0topqvnREsBVtfQulKt405xYR8q58cp5i8sK0eKromvqkdERETkW7JGOU1ERBRGGGgKIDkiNtBVCGqSJEF48gJTpb+qQkREREEgMzMTOp3r7trurKoTKLImXjFNREShyawvskvLQTyVS25uLnJzc13m0ev1PimLgSYKWs2jo1AIALFXQL55C4CaoXXVa3pAlO5zyF9ebcZ5fc1QOpUMqOWaicFNZgGjGYjVALLMCdqIiIhC1fz589GlS5dAV4OIiOgio5N08HHnRkxeXh66du3qdVkMNAUZk8mEcwpBxDhtbYDkvMHJhExhRq2Nq5ng+9KV5v683brS3JmCQ8CqawEAb+SVoERViAhRCQmOE6b/X/9WaBrFQBMRERERERGRvzDQFEDmqiKHbef0wD9/PBeA2gQfSZIUV5GT1FGQLE9UtfsjRBU0ohz/LBmOVsb/ObyuylACRHEOJyIiIgoMs/6sXVqOaBPA2hAREfkHA00U0uJiE1B6Mf1scS+XeYWxHAADTURERERERET+wkBTEJuSEY+EiyvK2Q6dKzeYYbo4S3Zz1/Nhhj2dWrYGmupSri/Heb3Zbv4msxCovjgSkXM4EREREREREXmHgaYglqBTISla5bBdp3bc1ljJEdFIHlkGADi3vDeMRXlO876RV4Jy+bji/E0A53AiIiIiIiIi8hYDTUFGJSmnSZntPE4JQzdACPsg0tnSUojFqQCACjkBD5y/B+2qf1Y8lqH6PIA4v9aXiIiIiIiIKJwx0BRkojWyYprqJqkjcWlsLjbSZB1a9+6fIlG1QYa5UPn1lpXsiIiIiIiIiOoi65IU040dA00U1mzncKr8LsVl3jJ9GcoMsYrzN1lwHiciIiIiIiIi5xhoCijOteR3Jr3bWadtvQCjZHC63yBF48P+zREf6YuKERERkacyMzOh07leCSU7OxvZ2dkNVCNPqZ2kiYiI/Cs3Nxe5ubku8+j17v//2RX+wgWQrG3isK20ymyX5sTfXlLVsSyfKhIwVQIA7isdj47V651mfTLhEKpMzXxZOyIiIvLA/Pnz0aVLl0BXo95kXTPFNBERhSazvsguLV+cPzgYuXMjJi8vD127dvW6LAaaKKzZrkqnxGy4gIL/1gypcxVkAoDXCy8HTJwwnIiIiIiIiADA6CTduDHQRGHNdlU6JcKDoXUAYBICeqNQnL8J4BxORERERERE1Lgx0BRA5qqiuuPYj4IAAI2TSURBVDORf8naOrNE3n7KOpH4c+v/QLUU5ZDHgChAkjiHExERETll1p+1S8sRbQJYGyIiIv9goIkatbqG1gFAyYXagOC0c1cr5nky4RAMiIbBrLibiIiIiIiIqFFgoIkatbqG1gFArOoMquo4ToSoAADI4MSeRERERERE1Hgx0ERUh7oCUUBtTye1KIVZxMCgMH8TAGhVNcEtIiIiIiIionDEQFMAVZtrJpa2JUvKaQocTwJD1UY9TpRF4vGflOff+mRQAiIjeGGJiIiIiIgoPDHQFEBv5Z2HuqzA+jwmQsKrf6odehWj4eplQUFVO/l34ohDkLSJ1uCTMFWh/MJJlC+9BgDw1E9nUK4SiocBAIMJiIzwb3WJiIiIiIjI/2RdkmK6sWMkI4iUVQv884dzga4GXcK2R1PBgsshSRLkiGjIEdG4sPVRa5AJAKJFETSiHBpRDojagJNKVEEjyiGE8yAUERERERERUahjj6YAMkMV6CqQjz1T3MeafrHZVpTLzXFTihaJ+c/gRv3nkMznAcQFroJEREQUQGonaSIiovDBX7gA+kfXlujePcFhu1Zd04NGyzhUUJDUUUgeWWb33KLJjR8iqtPjOLfoGofXTSnqVpMorN1WUXkBUMUoliPLQIQsccJwIiIiJzIzM6HT6Vzmyc7ORnZ2dgPVyDOyrplimoiIQpPZUGKXlt1YSCpQcnNzkZub6zKPXq/3SVkMNAVQdISMJjqOXgx2kiQ5XXlOUmkh6xLdPlbOzydRonI9SRMnDCciIlI2f/58dOnSJdDVICIiqmE2KKeDkDs3YvLy8tC1a1evy2KgichLkto+CJU44rA1+FT6yyRU5s+y7tOISmhEOaoRCSHVBBlVwgAVqq15qozNOWE4ERERERERhSQGmgLoQnlRoKtAPiBHRDsMrbMMfWuS8S5UHR5G2cUJwy1zOL3a9CcUq1qiewstsgxvwLj//6yvF0bO40RERBSOzPpCu3QwD7EgIiKqLwaaAokrkIWFuobW6bRRKLtk++Ti3jWJQsB4yT69oRx6Y6zdNlkCNCoOpyMiIgptZidpIiKi8MFAE5GfXTq0ri7PbjyHcpX9TPDdkjV4qHMTAOBk4URERERERBS0OBM1kZ9ZAk2R7e9H0l1nkDyyDFFXPuI0v1aUW9MqYYBGlOPX08UYt+IIxq04gspq3gElIiIiIiKi4MQeTUR+Zul9FHPts1BFJtVsk53P9p1i/A1lqpp8A8vfxsDK/7PbX1V1HlEazuFEREREREREwYeBJiI/k9RRSB5ZBkkdad0W22UaYjrnWJ+Xlxy2Thg+7sJo4ILz45VVXEBkZO0cTpy/iYiIiIiIqOHJuiTFdGPHQBORnylNFi6pNJBUGutzXWwrhwnDnXll83EUqWt6RKmEAZ2bS/jn9U3s8mg1UZBljowlIiIiIiKihsVAUyBxQme6SK2NQ/LI2lBT2a5pKP/fq4p5U42/oUh9OQCgd+UsDP/9JZT8bp9HN+I8msZyeB0REVFwkZ2kiYiIwgcDTQEUG90s0FWgIHFpr6fYzjmIufZZ6/Pic4dgWHEtAMAgRTq8/lIGzhdOREQUdGRdgmKaiIhCk9lQYpeWIzxbcTxcMdBEFIQuHVqnjWkJw8V088Rr8Em3KACAMD2CST/+FdVmQCMqMKWoe812IRq6ykRERERERI2L2aCcbuQYaCIKAZGaCOv84HcdvBolB2v35SjkrzaUw2CKs5skXG+sDT5pVbWr4RERERERERH5CgNNAXShvCjQVaAQ4WlQ6IUtpbi8qRmP3xBv3fboj4Uouxhs+nBQGqI0Kl9WkYiIiOpg1hfapTnEgoiIwhEDTYGkMLzJZDLBVHRMOb8kQx1rP55f0kSxZ0pjIKkRc/3LKN87HaLqnGIWued8mDdlAgB66j/HsAPTUHKgdv8LNnn1+hJEaexXqiMiIiJ/MztJExERhQ8GmoKMqegYDjx+udv5O7x7BrLW8W6Y0jYKYcKIsh3PucwSqYtE+cX0sPJpLvMaqisAMNBEREREREREvsVAkw8IIfD+++/jgw8+wOHDh5Geno6//e1vePzxx6FW+/cU7/9nssM2VUxztH/rKAD2eAoXkjoKySPLXOYxGyutgaa6GIUMc3VtboNJwLZ/nVYTBVnmsstERBRcMjMzodPpXObJzs5GdnZ2A9WIiIgoNOTm5iI3N9dlHr1e75OyGGjygXfffReTJk3Cww8/jD/96U/Yvn07cnJycOzYMbz//vueHUyq/c99+nMboY5Psdunjk2AoeAIDj17tdNDmMrOYd/4GADAFTPLILF3U8iTJAlSHfM41LXSnOmGT6Hadh8AYO2mzzHo3ONO8za5qxRRkbGeV5SIiMiP5s+fjy5dugS6GkRERCHHnRsxeXl56Nq1q9dlMdDkA2+++SbuvfdeTJ8+HQAwYsQItGjRAhMnTkROTg6SkpLcPpbtHEy6tOsUh8CpLpmniQioe8LwSG0kLAtuXqgjUq2vNiMq0kcVIyIiIiIiCkeaBOV0I9foAk1z587FyJEjsW/fPnTs2FExz6lTpzBlyhQsW7YMhYWFSEtLQ1ZWFp555hmH7tqVlZU4fvw4+vfvb7e9d+/eEEJg7969HgWa3KGKTUSHd8843W8sL8ahp6+oSZcVQcMeTY1CXcPrKqsqrIGmEeWu53symEw+rBkREREREVH4sZ1uhFOP1Gp0gaZ58+a53H/s2DH06NEDp0+fBgDEx8cjPz8fU6dOxZo1a/DDDz9Aq9Va86vVamzbtg3t2rWzO85PP/0EALjsssucF1bPuZNkWYYc5zx4Za6ymanHbKxXGRR66hpep612PceTHXOVdQ6nS+dvgqyBJEdAq6q7FxURERHZkp2kiYiIwkej+YUTQmDGjBlYsmSJy3xjx47F6dOnMWDAABw5cgTFxcXIy8tDq1atsGnTJrz88st2+SMiItC1a1c0aVK7gteKFSvwzDPPYPDgwWjfvr3TsmKjmzlsM14oVEzXV3XRcRgKDsNQcBjmqnLrw1B8AoaCwzCx50qjIamdB6GibvwP5GEnrc/F4lSc+SIGZ76IQfGXsSixeby/eAbuW1kAvdH1nFBERERkT9YlKKaJiCg0mQ2liunGLux7NK1Zswbz5s3D+vXrkZ+f7zLvjh07sGbNGiQlJeGrr75C8+bNAQDXX389FixYgO7du+O9997Ds88+q7jiydmzZzF58mTMmTMHN9xwAz7//HPPKyzMyul6OvpKH5f72715CKrENl6XQ8HPVe8jjUYHc4QOnqwxYDABkRHe14uIiIiIiCgkmfXK6UYu7ANNS5cuxccff+xW3sWLFwMAhg0bZg0yWXTr1g2dOnXCnj17sHHjRgwYMMBu/zfffIMHHngAlZWVmDZtGh5//HFERLj/v3BzVQUAAbOhwu3XEHnC1RxOkkoLc3mhNdD0StN1KJebK+Y1IQIaUQ4hlPcTERERERFR4xX2gaYnnngCo0aNsj4fOnQoTp48qZh306ZNAIAhQ4Yo7h8yZAj27NmDTZs22QWavv76a2RlZWHgwIH4+OOPXc/LZONC0THrfEoHJl8F47ljbr2uLqpmaWj35iGH7eqL8zoZCo7g0LNX+6QsCh11zuGkqu3x9Eyx655wACCqjqNKioddv7uL8zfVHI9zOBEREdky64vs0rKL32UiIqJQFfaBptTUVKSmplqfazQap3kPHDgAAA4Te1u0bdsWAHDw4EHrturqajz00EPo378/li1bBpVK5XbdTn/2EPZ9PdLt/O5SqVQuh8PJ2iifl0mhz9OgkP67Vg5D7ebHvIoNkWMAAJ8MSkBkBANNREREtYxO0kREROEj7ANNnigoKABQs9KckqZNmwIAzpw5Y922efNmFBQUICMjA2vWrHF4Tffu3a2v85SkYUCIGpDK8fMWlzEDkW3vBQBUnViBkh8zXR9CVEEjanrpVRmbcw4nIiIiIiKiRoaBJhsVFTXzIzkLDFm2W/IBwOnTpwEA06ZNU3zNjz/+iD59lIchRQ16CqnD/m59bq6qBGwWki8XWsildc9cr9VqodVq68ynxFxVYR2+Z6woAYwGhzyqZmke9dSi0KTUo0lSaa3d+iWV4wT4l/pLeQ7+Up4DABDGYgDxPqwhETUWVVVVqKqq8vo4ZWXK89IRERERkf8w0GRDCGH376UswRaTyWTddueddzrNX5e/jn0awNP1eq2tKVOmICcnp16vdWeuJq5M1zgoTRYuqWqHmmovG4zY4QdxYWFb9w5YdQ6VETZfMVKE/fE4hxMROfHqq6/ixRdfDHQ1iIiIiKgeGGiyERUVhdLSUhQXFysOn7P0ZIqO9s3EjfM/+z8M/PN9Xh+nvr2ZiGzVNVm4JKsRGZWICzbbIrv+G9r24wEA1WfWo/yHodZ9+iXt7OZwWhj9An6IyrY+5xxOROTM5MmT8eijj3p9nJ07dzrtVUxERETkNU2CcrqRY6DJRmJiojXQ1KaNYw+es2fPAgASEnzzAYqKikRcXJxPjuUJ21XpLCvRAfZD56qLjuPoK2yckz1Jlu2eR6i1iNTFAABkTSTK63q9MKGlcRcAoKq6PyIj5DpeQUSNkTdDwm3FxMT4oDZEREREymSb/x/JMv9vY8FAk4327dvj4MGD2L9/P7p06eKwf/fu3dZ8oczZqnQaLZfYJdcuHV5nOxROk9wL2lsPoGppzaqNrzRdh3K5uXW/GWpEiSI8VjLk4obz0BudfwXp1OztREREREREFGoYaLKRkZGBFStWYNWqVcjKynLYv3LlSgBAz549fVJeXEJrnxzHH+SYBMU0NW6uhtdJsgpaXTQs0/c+U+y6R9yHeSeRd6GZ0/2fDk7kPE5ERBRWZF2SYpqIiEKT2VBql5ZdTEXSmLBvl43hw4cDABYvXozCwkK7fVu2bMG+ffsQHx+P3r17B6J6DYpdAKk+tCr3g0KHSi643H/fygJUmVxmISIiIiIiChyzXjndyDGCYKNz584YOHAgCgsLkZWVhePHjwMA8vLykJmZCQCYOHEidLq6l3kPJ4aCIzAUHHZ4GCsvwFxVXu9V9yj8SOraCH7M9S8jeWQZkkeWodmQdQ55Y8xFiDQXQyPKoRHl0JovINpcYPewXeGRiIjCR2lpKR555BFcfvnliI6OxrXXXotZs2YFulpERETkAxw6d4nZs2cjIyMDa9euRVpaGuLj41FSUgIA6NWrFyZPnuyzssqKT/rsWL5mrKjtAnjo2atd5r1iZhkkzu9EAOSIaOscTpIcYZ3DSZN0E5JHlqG85DDKll4DAHjcMleTCxfOH4AclwxItccSwgyNqKwZxqeO4tA6IqIQNG7cOKxevRrPPvss2rRpg7Vr12L8+PFQqVQYM2ZMoKvnN2Z9kV2aQyyIiCgcMdB0ibS0NGzfvh1TpkzB0qVLce7cObRv3x4jR47E008/DY1GU/dB3GQ2VfvsWD5nZLc/8pyzOZwkWQVJjoYcEeXR8cSydigB8H3kP7AoZgoAoJnpGF4o6gYASB5Z5nTOKCIiCk7nzp3D119/jRkzZuCBBx4AANxxxx04ePAgPvvss7AONAFGJ2kiIqLw0egCTYcPH64zT0pKCmbOnNkAtQleqmZpaPfmIaf7zVUVdfZ0IrqUVlc7sXzc7QcBdRwkmKBVSRDCDJgqcbboDKQfnE+4rxEV1jSHbRIR+d/cuXMxcuRI7Nu3Dx07dlTMc+rUKUyZMgXLli1DYWEh0tLSkJWVhWeeecZhyoGioiLcfPPN6NPHftGI1q1bY9euXX57H0RERNQwGl2gidyjUqmgSmzjdL+h+JQ1bawohYZD58gNqggdYq6r6ZkUFdsSxtJ8FC60D1jaDoTLafYLKuQEmBBh3VYst7Smq0wCnvWRIiIiT82bN8/l/mPHjqFHjx44ffo0ACA+Ph75+fmYOnUq1qxZgx9++AFardaav3379li9ejUAwGAwoLCwEFu2bMH8+fPxzDPP+O+NEBERUYPgZOBUPzZD68zlRTBXlVsfwmy27hNGAycMJytJpUHs9TmIvT7HOueSK9VSFAxSNExSbV5hMydTsd4MvVHYPfhZIyLyDSEEZsyYgSVLlrjMN3bsWJw+fRoDBgzAkSNHUFxcjLy8PLRq1QqbNm3Cyy+/7PS1s2bNwmWXXYY77rgDN910EyZOnOjjd0FEREQNjT2ayGuXDqG7/JU90CS0hjAacHb+cyj+/n10mFEKdWRsgGpIwUodf6V18nCLyopzKP22NQBAQIUP+0RCq64JLs3ZXYptp2qHzj294RwMcpXd62fdnIA4LScIJyKqrzVr1mDevHlYv3498vPzXebdsWMH1qxZg6SkJHz11Vdo3rw5AOD666/HggUL0L17d7z33nt49tlnFVftveOOO3DFFVdg+/btePnll/Hggw9y9TkiIgodmmbK6UaOgSbyuUPPdHLYZi4rBBhooktIkuwwmbcuUsCy5uG73S/gwvw4VF58/peLD4socQ4Q9kGl8xVamKtlCCEQGwHIKpWTslWQ1I7/6SEiauyWLl2Kjz/+2K28ixcvBgAMGzbMGmSy6NatGzp16oQ9e/Zg48aNGDBggMPrU1JSkJKSggEDBiAiIgIvvPACPvjgA6jVbKISEVHwk2W1Yrqx45mgepE0nBmH/EOyGRp3YXl3l3lzihT2L4Q1MOVq7URd60w07fdfzytIRBTmnnjiCYwaNcr6fOjQoTh58qRi3k2bNgEAhgwZorh/yJAh2LNnDzZt2mQNNH3xxRd47bXXkJeXh4iI2jn42rdvj7KyMpw6dQqtWrXy0bshIiKihsZAUwDFJbQOdBXqTRWbiA7vnnHYLkVEQpJrpv6qOp2Pwy9c39BVI3KLyWxEpb4MWk0UZJnT1RERWaSmpiI1NdX6XKNxPqfegQMHAADt2rVT3N+2bVsAwMGDB63bWrZsid27d2Pz5s3o1auXdfuPP/6IJk2aoGXLlg7HCReyLkkxTUREoclsKLNLyxFcJAtgoInqSZZlyHGuG0iqqCbWtLmqAsJstgahhNEAYaq27pc0UXY9WajxktRRDvM22RJmMy5U10xSKyqOoWr5NfUqp/r4dyj5MhYxfy1FbBSHdRIR1UdBQQGAmpXmlDRt2hQAcOZM7c2pXr16oUePHrjnnnvw9NNPo0WLFli/fj3ee+89vPHGG2wPEBFR6DBXKKcbOQaaqEEcevZqtMn5BdrUqyCMBhQsmoaiFf+27udk4WQhSZLDvE2XitcCJn0BztYzyGTLaOIqdURE9VVRUdOotgSULmXZbskH1NysWrhwIZ5++mm89tprKC4uxhVXXIGPP/4Yf//7312WV1ZWhtLSUpd53KHVaqHVar0+DhERUTCoqqpCVVVV3RnrUFbm/Ia/JxhoCqCykrOBrkKDOpzjfL4dThZO/lA+6CAua1bT827+/jKsOlYze1O0uRBTLs7vVFZ2FrqImp52GrlmEj9JrYPRLGA0A1oVeHediMgJIYTdv5dSXVyQwWQy2W1PTk7GnDlzPC6vT58+Hr9GyZQpU5CTk+OTY3nCbCixS3OIBRER+cKrr76KF198MdDVsGKgKYDMxsq6M4UwThhO/iRrExSH2FWZBMavKQCEwBvRzaFV1QSJVHJtsMj2v0Pqle1RYvNc23Iomt28FN8eKMf8/Ap8OjgROn5TEhEpioqKQmlpKYqLixWHz1l6MkVH+yagsm7dOnTu3Nnr4wSsN5PZoJwmIiLywuTJk/Hoo496fZydO3f65KYO//tEfmM7YbjtJOGW+ZkMhUdx5EXXq4oROeNsiJ1OLTBrSDSEsRwlX8bCMitIv4uPupjMwHm9GYsO1vznqOYuPXs0EREpSUxMtAaa2rRp47D/7Nma3tsJCQk+KS8mJgZxcXE+ORYREVG48NWQ8JiYGB/UhoEm8iOnE4Zra4ID5qpy6yZzVQXMVeUOk4RbjxWTwJXByC2SJEGnBszCveDQs813o1qq7X1nNqggrzmKqYWdAABVVWcQGcFhnUREStq3b4+DBw9i//796NKli8P+3bt3W/MRERFR48BAEwWFQ89e7XJ/+/87DblJcgPVhsKBq9XrKvQXcGFBCgCgWoqCQbLvGaUR5dCiZmir3miG3iigUQHyxbmaLPM3AZzDiYgat4yMDKxYsQKrVq1CVlaWw/6VK1cCAHr27OmT8jIzM6HT6Vzmyc7ORnZ2tk/K8zWzyWxNF1SYoZJr565SSUC0pvammju/L+f1tccTQsBQ+xSyBMTYHK/UYAYE0FxXO3eWO0wmE87pAUhAnM3xygxmmF2spxGnrV/ecoMZrtbpsM0L1H2eqk3C7nhmsxllNvc0YyJgvZlZYTTDaIJTURGA+mJelSRgcnFT69J6mc1mXLhktGTEJW0Ly8dDo/CebNshl7JcI7VUM1WAWq6ZJsBkBoyi9ri2bPPWlA/EamB3Y9dSZ5Vc07fbaAZMorYNZOvSz5X1c2NDJQFqWYJKrvl8Vpscy6yL7XlUyTXHAwCzEKg2Of4dWMgSECFLkOWaf5U+N5Zjy3LN+TE4OZ7S35DS9bWQJECjklBlrPkgVil8wCXU5NGpAa1aht6S11ibV+lcOS1XgnX6CGuZAqgyC/s5JC7SqiTrawwmASEAg7nmX8W8ALRq/+Wtee7Z37b9sexfK4RAlYu/bcv3QoRs8zd5yd+O3T6zgCzVPDdV1VbCrPTHFkRyc3ORm5vrMo9er3e5310MNFHASBGuG4q2hIFLRZJnXK1eFykELlxM39YmCt8ccX6cJzYUwSBVYUpGPC5vEgEAWHa4Al/tr+mR98mgBERGMNBERI3T8OHDkZOTg8WLF6OwsNBuiNyWLVuwb98+xMfHo3fv3j4pb/78+Yo9p0LFOZsFgXK2FKFE5bzH7Ju9m0ErO/6+2AZaHlhbqPR/RpemZMQjwbYJdkkAyRKQsijUm/Di5hIPS2k4zs6TJYA063+l2HDS+5WYLtW/lQ7fH3f+H7KZAxKgVdfcsDKYgAvVZjyzsbje5T3WJQ5XJ2hgFo4BJF9do1duaorYiNqLX1ptxrNu1nnaTU3RIkqyBpDOVprw3M911+nSMgHlgJRZCFSbvT+PFv/XtxmkS6ZGcPf9vtgzHilRkl0AqbTajMd/KvK6Xh3i1Xi2R1M89H0hLlTbn5dXbmyKOI2ARlVbdmGlGY/5oNxg9FbvZohQ+Nu2BKQ+/e0CNjr52/6/Ps2huxiwqjYLmITAwz/65zzpTCa8djF9TjRFil9K8Q13bsTk5eWha9euXpfFQBMFjCouGR3ePQMpQgdJVuHcyrdRsOA5l68RxmoIk/LtAkkTxZ4l5B5TbeBSI8oB1D0W2VkDrsooEBnho3oREYWYzp07Y+DAgVi9ejWysrIwZ84ctGrVCnl5ecjMzAQATJw4sc5eSOTIF/9pVRLMQaP68Nd5qourIBMAjF9b6NPy/p1X6tPjKfEmgONuQMqXZXrDm6DDlE0lvqvIJfaXGHHfygLFfc/8HJhzFSiPevG3/fC6cz6siWtmuTakIssMr1jwTFDAXDqHU8LQJ9B88ETrc0PhERx6pmZInWUOp+IfPsSZeY8pHq/jhxeg0vlm8jJqPIa2kjCk3cU5miQVJJUO5ZVaGBa49/oL1QK6COdD6wAOryOi8DZ79mxkZGRg7dq1SEtLQ3x8PEpKSgAAvXr1wuTJkwNbwSD1SOcmSGje3PpcJQFqlYTxa7wPULzZuxkiVZJ16FxBpcknAZk3ezdDYmTNkKFADp3z1fuZdlNTpEbJbg+dU8kC//jet4EtS4/p8moz/vG99/85nnZTU6REqdweOldhNGPiurrf09t9miFKLdsNnXO3J1VNLzqVtadSudGMSW6U6YkhrXW4o13N/wMK9CY8u7EYb/ZqZu39IktApUm4/bn5v77NoFPJ1qFz9e01dnXzCDxyfROXQ+eqzO7Xq77e7VuzEnOwD53zVc8wb1jOlbtD5wrLNMCSAFU2iDHQFEBxCa0DXYWgIqk1kNQa63NVdDNruq45nICaycUZaCJPFX17uTWtSR2Mpv0WQK3Sw3Iv6cMBCVjxh2wdKnepx9fX/Bg+3a0JrmxW8/ldc6wSn/1WOz8Uh9cRUThLS0vD9u3bMWXKFCxduhTnzp1D+/btMXLkSDz99NPQaDR1H6SRiG+SjJKL6TZJqYjU2c/zIoTAp4MTUVrlep4P23mFlFx6gyM1SsK7fZsrZ65j6Jytmrlpao6rU7s/z5Ov87p8PxfZnqNqs4BZ4ZReOudNXWVbro/tfEqX7jeY7edysgydc1Y3y40qrUrGRwMTFI9rEaGC4tA5WzXXyP05j2LNNcP8nM29ZAkOKc0P1FxX859yV0PdautUe24tZdrmUSrT2fEsbM+j2ma+plithE8HJyrOkzVzQM3w3rrmcrr0/VreKwDIACJUktO5l2zrJV/MA8A6lOtSls+VkkvnFLPM5WQt22bupRi18+8FT256OqtnQ+SN1cB6jZwFpABAI9cEryzvqdosYDLVXlONDLt9aqn2uQQBYTNsMuKS+c88vUFcUV47UsJsLAPAlVEBBpooiMk6z1b64jxO5C3DyZU484V9sFJjLMSf26bjlla1v3SFFSY883Pt3RYBGa9trX1NhKiAxqaVXmVszuF1RBSyDh8+XGeelJQUzJw50+91CfXJwOtiWTnVk8CMO1QqFZKUpy104Ouy/cGT9wMAOvjmZo/l+sCD47l7PiVJQoym4W9KybKMJvUc2er0OtTR5vG4TA/bULL1OnlZrg1XnzlPgi1KJCf1vVgyolzE6r0tO9jU9xr56m+8PiLMlai2SQczTgZOBEDWRuOKmTW9QlwNmbPm1zF6TG5SRbmdVRj1UJkqcP7bdIiqmi7tagCv2+Q5pu6Mt5qutD6fXNQbzczHrc/1VSUwaeOgkjm0jojIG6E+GTgREYUX295QzlaGDBacDLyRKCs5G+gqBDVJkiBpa24dNLv5n2ja7wGHPOaqcvx/e/ceH0V1/g/8MzN7y5UASYDInQAKtHIRxVqBQlV+Xqpi/IrFfmuloharoNKKbSVU0WptlULUUhVtq2gLXoooIIrXLygasEWL3AUVQgIJue51zu+PZSez2dlrdrO3z/v1yosnM2dnTmbZ7OTZc56z6+e9vLG9EW4RfGK9nF8c1fKplLlkcx56zWwOul+1H0Xtau+UurpXRkR8XLNohQQBCP+x2PPfO4Z5Z9owtpcVAPD+13Y89u8mbf8zF5SE+CSLiIgyRVPzcb84h1P+iYgoA/FPmyRS3ak9tC6VSCYzJFPocbN77hgccv+wpTV+xccpe0mSBMkcfMy9cEc+Hn/Hd/bjmV3eTy86jmTyyRXH4HJ2Q5vdO7DW7bKfXO3OS1V7Akj9qQpERNQ5Ho/LMCYiovTk1q104A616kGWYaKJ0ppwR36TJpmsCewJZRTd1LrSq2ogmfwTT/oRTxN7qfhuf28C88S/JAiDUmGVx88E3oRWAHYk/KfeOZ2NyLVEV5OMiIiIiIiSS5YAjy4mLyaaKL0pkf8XFm4HVIfxymEAIFlyWSeHAPjXS5JMeZBPjn5SXYH/f46/NKTT5/PoltTwqMYrsLCOExERERFRakmnGk1diYkmSmv6guFG9DWcfP8GM/zPTVBYK4FCqF01EKqjLurHLe7+Hk4op/hts4hW3HtsFABgW60T3z85oOmjGgceqW4MOAbrOBERZf6qc0RERInCVeeIIqQvGG5EeNyRHyyatpQVLL0maaOZgjGaWgcADo/A7I21AAAhAhdC1s/gVoXACbt3GFNbW6tf/SatDes4ERFx1TkiIkopbnOhYZyKuOocUZwItyPyth5n0Kl1kmIJW4ycModkykWvmc2QJP9VCksqDhi2NZrSZjMJ/GWaNwF1Ys0oqCc+D3q+v/23GU/u9Kaebqu/AA+6twe0MarjJISAcLcXhdL3RXgcEKrbcB8REREREXWegMUwznZMNFFGkyy5Iff3mrkENc/eCiD01LreP1qGHt/nMPxsEWxVunCjmzoewzfVrUkCDMouRa1j/SbV1YwTL7R/clJyxX7IthIAQONH89C2+y/avtIfNkGxcGooERERERElFhNNSZTTrSzZXch4YWs4uexaoikUXyFxFgynSAl3K+peHQ8A6DntHUDJ8duvuppR+4/eAIA/nZsPSbGdfNx6+NJSbW1NkNYPAwC4XM34+Gsnln3aXr8pT63DQt0xa1cPCt4hTysAJpqIiJIpP68nmnUxERGlN9Xd3CFO7elzXYWJpiQymzkVK9HC1XAKtQqdXs3K21Gz8nacurw55PGIfIQQcDecnC6n5IQcDaV++CM4v1kfsF2f0nS/Uob+AB6MbzeJiKgLmUwmw5iIiNKTWW2DSxeTF9/hiKLgbqkPeNFIihmSifNxKblsF+9FYWEpnLqpdZ62o2h6ZQgAQFVVrZR4x/pNPqzjREREREQUOVl37yzzPlrDRFMSNTdGv0w6xZecX4xhS2uC7pcUC1z1X2Pfr7zL0O+Z1y+gTckV96LnBXO97Tm1jmJQNPFZbeqcXpu9CY2r+wAAft1zB5ySf82xKwcB47cOBgC4LT3x7wYzfrf1RPtxPcdQeTJ2OFvhG1R14v9uQNveZwLO12tms2FtKiKiVFFRUQGbLfD3pV4kq+okS3Nrg1+cY+OUZiIi6hpVVVWoqqoK2cZut8flXEw0JZHqjGzaFiWOLMuQC0tDtvG01ofcX7v616hd/WsAwPA/N0HhTSNFQLh1r3/hMZxaZxMCvopMy84bAMnk38bhaEbrVm9827vH4ZSCJ4mcjtag+4iI0sWqVaswduzYZHcjZm6XwzAmIqL05FaFYZyKIvkgprq6GuPGjev0uZhoIgoj3Mp1eu4TNQEjmji1jmIlnA1abLIfhpLnv4BAm5DxsfUKAIBH9+vcJOyQ4YEkdPPorN21sNt3/ozCCd5PM4S7BUdfCL7iIhERERERGZMlwKOLyYuJJqIw5CDFv31T5py1B7SpdXt/UR7QrufFC1Dyg18B4NQ6ipJwaWHdy8MCdhdecRhnOFYDAP5e+Ki2/ZrGmzHaucavrSwrsLt9n7JYAMmb/BRS+ycvQqT2pzBERERERKmENZqMMdFEFIZszcOpy5sDtvtGKsnW0COejr16P469ej8ATq3LJpIkQckboMWJYFXaj/v01Fxtal3zu1a4Dvq3/arJhXverw08iBDI6/kZAKBKykVOQnpKRERERETZgokmojAkSYIUZFQTEOXUusajnFqXJSRTLkqvPNC5gyjt/7dKrtgH2eZfT0w/AsmqSJBN3v9b1ol/gxBPo6HpGBz/8ia7HA47LCJwpTnveXJwwYBcKBJHNBERERERRcptLjSMsx0TTUSdFGxqnW/KnN/UuvlDAtv9vzvQa8bvE9pHSk8dk5L6guHCbYdwN3Z8iPdxJhskAJLcpG0re28YHgx1sqNAS/8TUHMKgjaxKokbnUVERERElG4ELIZxtmOiiaiTOju1TnhccLecgPPobgCAtd9oKCa+NMlf7cuj0OdH7SvV1b99BRxfvRbXc8x7+xu0yT2C7n/6/GLkmJloIqLkqaiogM1mC9kmklV1iIiIsk1VVRWqqqpCtrHb7XE5F/+aTaKcbmXhG1HK6+zUuuMbluD4hiXa94MX74BUMrC9gWyCbLZ2tpvUxYS7DcdenwgA6HHe64DSsfqR7NdWdbWgI+EO3BYNiyUXvreKX/XcAZfk/b948aBc/GCI9//sriNfo/TtU0/2yGN0GI2HM+uIKMlWrVqFsWPHJrsbMcvP64lmXUxEROlNdbd2iFN3+lwkH8RUV1dj3LhxnT4XE01JZDabk90F6gLRTjXyTbPzKZp8PXr/8OH243HlurQghArXsY8BADXPl/jtk63FKKk4oH1fu3pg2OOVVuz3+7775NUQoj0xJJkCE5o2kwzf5Lqq8wZoxcJNMmA6uf5q36JCOMOeHScfx/93RESdYdKNWDZx9DIRUdozeVrg1sXkxXc4ogST84sxbGlNwHZ9EXDn0X3Y9+tvGz6+4e2/oOHtv2jfD3u8EaYQdXQoM/mSRO3fe+swhXyMLiGpLxauZ1MkLdH0h7NzYMvPBZQcSJJ3xJXd6cDP3zoMABCiGAh7ViIiIiKi7KDIkpZoUvihrIaJpiRqbqxLdheoC8iyDLmwNGQbJcx+Pfexg5CDTK0TQkA4WznqKQVIplz0mhlYuyvS/UbtE0H//8T+6hDYAZRcsR+mgoEAgJZPfo0H6/4AAFDdjYCZSU4iolg1tzb4xTm2/OR1hoiIKEGYaEoi1cmhdeRltHKdfspc24FqfHmft95Px6l1hWfPRNlP/gwAUB0t2PXzXhj+5yYovHlNKkmSIJmD1+4CEHZ/sggI2N3egkwuXWGmJocKxdz+vSIBZoUJTSKiSLldDsOYiIjSk1sVhnG2Y6KJKAUYrlynG6lk7tE36GMbNz+Lxs3P+m0TzlaAiaasJ5lyUHzpDi02pLSPlOpWcQQ2az5qnRbcsr4WAJCj3oD78UcAwPz3j8Mptf9hdP6AHMwaxRFORERERJSd9JNIOKGkHRNNRCmgsyvXdeSxN7ePkpIUyJbQS0FTZpIkGebuI8O0aX9HtFnzIZvzILnai4x7pNCLFggh0OT0fnpTYJE4ZZOIiIiIsoYi6Wo08T5Yw0QTURroOLXOaLqc3t75Q7S4YHwF+t38z8R3ktKSRQ7cVpwj45kLvCvlNTRbIV72br/vnB4o1BWil2Wg0SEw+01vvbmnzy9GjplvsERERERE2YyJJqI00HGUiKSYDOs6GfK4oTpa/BJSrOGUHYTHieZ/3wcAyP/2XZAUS0AboxFIsiTBdvLdwWaS0XZy+10f+E+d66jRoWrH09dvEkLAcXKQlFUxPicRERERUbpxK/mGcbZjookoDUiWXP8aTrIpYJ/rxFHsnT844LFN1S9j52z/X3ruxqPtf+xzal3GEqoLzZ8uAgDkjZpvmGgKRzYY8RTMLe8c1+KJp1gxa1QhAMDh5qgnIoqPiooK2Gyh37PmzJmDOXPmdFGPiIgomwnZZhinoqqqKlRVVYVsY7fb43IuJpqI0kCoGk6+fYot8hXM9FPr8kZdgH63rPY/piWXo04IAJBvy4Vy8X8AAMu79UeTC/j5pmNhH6cK4McnC4rrOT1ATuiyT0REQa1atQpjx45NdjeIiIjSTiQfxFRXV2PcuHGdPhcTTUlkLewVvhFRhPQFwwvGXoZTbvy79n3HEU16LTvWB+wftrQGpsLS+HeS0o4sK8grHqV9bzMJrX4TAJywe7SRTGf2tuKnI701nFxC4P1vAqfZCcFlX4koe+XmdUerLiYiovSmuls7xIXJ60wKYaIpTt544w3ce++9+PTTTzFixAj88pe/xKWXXhryMVZOV6I4kq157dPrOkyHCzW1zoj/qnUyZEuOtk91tHg3c9RTVpJ09ZsAQFjb59Z9dMSBj44Er+EEAE1uAZs7eLKJNZyIKJNZTBYt0WQxRT+dmYiIUovJ06KtOmfytCS1L6mEiaY4+L//+z9ccskl+MEPfoBHH30Ur7/+OioqKvDaa6/hvPPOS3b3KEuEml4nW/NgKmwfhdJxutwXN5dCONuz8fqpdbmnTsLABW9r3+++fSA8TXU4dXlz0PNR5ghXUDzapNAd7x4Puf+ZC0r8EllERERERKlKkSUt0aTI/LDUh7fzcfDwww9jzJgxeP755yHLMn74wx/i8OHDePjhh0MmmlqbQv/BRRRP+oSApCgRr1onVI+2ap3qaIGnyVvUWVVVRFEnmtJUuILiVgV+U+mMHGvz4LYwCSYiomzQam/0i3O4AiwREWWgrEs0Pffcc5g5cyZ27tyJ4cOHG7Y5fPgwFi5ciNdeew11dXXo378/ZsyYgbvuuitgpROn04l//etfePDBByHrlme6+uqrccMNN6CpqQkFBQWG53E7muL3gxGF4bdynaT47Ru+7GjQqXVtu943rPHkPnYQcslAg6l1rQAEp9ZliY5T6Yz0sLX/blw+tRhWkwSHbvqcQxVagXFvDSf+vyGizOR0tBnGRESUnjyqMIyzXdYlmlauXBly/8GDB3HWWWfhyJEjAICioiLs3r0b99xzDzZu3IhNmzbBarVq7WtqauB0OjFixAi/45x22mnweDz45ptvgia0iLpSuKl10axaBwD7fuUtEG0bMBYDf/Wutn3PghFwHzuI4X9ugsJPapNKUmzoefFHWpwsiizhrN7e35s5ZgkWRTJckQ7gqnRERERElEakIHGWy5pEkxACf/7zn/Hqq6+GbDdr1iwcOXIEU6dOxZNPPokBAwZg27ZtuPTSS7F582bce++9uOeee7T2NTU1AIDu3f1XDunRowcA4OjRo0w0UVqQ84sxbGlN+waDIuDOui9xYNGZfo+zf1ltPOKp8ahuRJME2Zob0IYSS5IVWIrHR9xeuFugdtimupoD9gvVDaiBRb8la7HfyE4fiyLhtnHdIu6Hj92gaDiLhRMRERFRqlAkXY0m3qNqMj7RtHHjRqxcuRLvvfcedu/eHbLttm3bsHHjRpSWluKFF15Az549AQBjxozB6tWrceaZZ2LZsmX41a9+FTCFriNV9f655na7Q7YjShWyLEMuLA2+35oX1dL0+oLilj6nYvCij/32c2pd6jn6Qq9O7S+9qgbICfw/ZHcLbQTT8u8Xw6pIWD61GAAgSd79P3/bO3XO4RFagun6N2rh7JD5evr8YuSY+f+GiIiIiChVZXyiae3atXjqqaciartmzRoAwCWXXKIlmXzGjx+PkSNH4rPPPsMHH3yAqVOnAgB69fL+4XX8uH+hW9/3ffr06VT/iVKJvoB4sClzRpyHdwaMehq29KjfSngUf8LjRMt/lwAA8k67NaCQdzLM3ljn9/2YEgtu+nah9v3t7x2D0xP88ZxaR0RERESpwqPkG8bZLuMTTfPnz8e1116rfX/hhRfim2++MWy7efNmAMC0adMM90+bNg2fffYZNm/e7Jdoslgs+Oyzz3D++edrbT///HPIsoyysrI4/SREySdb83QFxf2n1pXf/1+4TtQYFhQ34rE36abT+U+tU51tgFA56qmThOpC08e/AADkDv9Z2ERT6VU1kEz+tbpU+1HUrvY+pyVX7INsK/WbOqfaa1H3yqhO9dOihG/jw/8ORERERJQqVNlmGGe7jE80lZWV+SV7LJbgf2jt2bMHAFBeXm64f8gQ71SgvXv3+h3vkksuwQsvvIBbb70VsixDCIEXXngB559/PgoLCw2PRZSOQhcUz/UbodRxuty+hWfAeXin9r1+ap25eACG/uGA9v2B+ybCvv9jnLq8Oej5KD6EqpveK1shm/M67G8f3SlZegbsj4RVAZ65wHj0miwB+gU6lk32rkqnZ3epuOEt36p0xrWbOp6PCUqizFRRURG2fMGcOXMwZ86cLuoRERFReqiqqkJVVVXINna7PS7nyvhEUzRqa701RIqKigz3+wp++wqA+9x2222YMmUKfvjDH+LSSy/F2rVr8e6772LdunUhz+dWCtDY2NjpflutVr+V8IiSRf/HvSTJflPtJCmwSLSPEAKqowWAt+i4ff/H2nZKMH1Rb6MC37JiGGtki3GsP4YkwRbi3cYvcWSQH3Lpds9+sy6wQQfPXFAS8nyU+hwOBxyOwP+P0Wpubg7fiNLKqlWrMHbs2GR3I2a5ed3RqouJiCi9qW57hzh1B5pE8kFMdXU1xo0b1+lz8VZcp7XV+9bfcQU5H992Xzuf73znO1izZg1++9vf4sYbb8TIkSPx0ksvadPrgvnBZTPi0Gtg4cKFqKysjMuxiDpDsuS2T63rkDEYVLk16NQ697GDhivXqY4WKDbOdU5lkmw2jGPVsYYTZaf7778fixYtSnY3iOLOYrJoiSaLKfl184iIqHMUTxM8uhgIvrhSNmGiScc3eiLYKApF8X6a7/EEVqo977zzcN5550V1vvWvvYQJ50yJspeBOJqJUkW4qXWKLbppV56mOii2fL+RUb76TQBXrusKwuP0j2OYOhdPS7/XE4WWwNFxDo9gkiqDLFiwALfddlunj7N9+3ZMmjQpDj0iIiIiCmSSJS3RZJL5d4kPE006ubm5aGxsRH19veH0Od9Ipry8+PyhJakO1nCirCJZ2gt+m3r2R/n9n2vfH1g8EfYvq/3a7/vVKCj5PTH0j19q2758aBradr0PABj+5yaOeEo04TKOfZtUj2EcjVA1nADA4RbalDmLwfu3LAFWhW/smSReU8Lz8/n7gVJLq73RL87hexgREWUgJpp0SkpKtETToEGDAvYfPXoUAFBcXByX87kdTXE5DlG68Fu1rsNKcwN//T4gVDjrDmDfXe2rmHmajxlOqwMA4WwFeJOeXKq9Qxx98jxcDSeHroaTryi43uBuJiycwFonRJT6nI42w5iIiNKTR7eqjT7Odkw06QwdOhR79+7Frl27DAtN7tixQ2tHRNELObXOkgMAUPIjT+R67M2QrXkdptbZAeEdWZPtU+skxYYeF2zS4s4S7haorhZIik0rDC7U9lFOqqsZkinw+RVCAJ6TVUkU73MiyWZIirc+icfjhmg91KHzuv1OCXmqd7EGF3KADs+pLHKgqqrWpq1ZhWqS/I4BocICb1JMshZDloMXpyciIiIiioSQjONsx0STzoQJE7Bu3Tps2LABM2YEFupev349AODss8/u6q4RZQ3ZmgclvycAYMj9n0O25vlNl9PbO38IJEsuhi87qm079Kcr0LLD+1rN9ql1kqzA2mdy3I5Xu9pbyL3HBZu047bt+au2v+7FIREfK//0hSgYUwkAcB/9PxxfH7qOzuIQ+8zFt6Ox+XYsPuYdCWd/GQi1MGvpVTVADgs1EhEREVHnmCRdjaYs/oC7IyaadC699FJUVlZizZo1qKur85si9+GHH2Lnzp0oKirCxIkTk9hLosym2PIxvMq/qLNv9IwR4WwNOrWOq9Z5qa6W4Dsl3bWVo18BSbhjm/ohVKfWL+Hp3PSR9V+24a2jdbi3U0chIiIiIqJ4YKJJZ/To0TjvvPPwxhtvYMaMGVixYgX69euH6upqVFRUAADmzp0Lm63zU1CIKHL9b39dW2nOdeIo9s4fHNHjjFeta59aB2T29DqhutD6xXK07HgQnpaDhm2KJq/SYkk2BzZQ2utolV5VA8mU5zcNL/fU2Wj5zz3a9/lj7kXeiLkAANexT3B8nfFIpZb/3I+W/9wfsL3nRR9BthX7TXtrc7Sg6WXvc758wOf4zXf7a+1vf6cONXYFBerh9oNM2YJuPQb6HUO01eDEK+WGfSEiIiIiioVHd6+sj7MdE00dPPHEE5gwYQLefPNN9O/fH0VFRWhoaAAAnHvuuViwYEFyO0iUhXz1mwDAVOhdnSzUlDmffb/yTqVqL0AOfP34NWiqfln7PpOn1wmPE40f3typY+iTcJIpD7LZvwaT1GEUlKRYtDaSKQfRUvIHQOkwrc0mauBbOuGX43v49eF338uFEED9MQfwunebNbcEufm9/I7R5m4f1eX0CETfMyIiIiIif6qcaxhnOyaaOujfvz8++eQTLFy4EGvXrsWxY8cwdOhQzJw5E3feeScsluinlhBR/OhXrtOPVOp368uA8ASsWgcg6NQ6ILNXrpNMueg1szlkG6G6O3eSDiOeZEuR9r25x1j0mtkMT9tR1L3oHZFUcsU+yDb/RJJwt+DoC70CjuejT3aZFf/RZ9aT31sVCY4Q3RRBYiIiIiIiiq+sSzTt378/bJs+ffpg+fLlCe+LtTDy1bWIyCvYynWyxTudK5pV6wBvHSchhJbMUF0OQJd8SeepdZIkQTIbr/LnE7J+U4Tn0GJTXvsqb/DW1pLkPAi1Z/s2S8+AUVFCsaHnxR8BAOQYRkEBAExW45iIKIXk5nZDqy4mIqL0prrtHeLC5HUmhWRdoimVWC2h/wAkoujpRzkNW1oDyAqgevzauBtrtWl1e+4YjFOXN2vJq8NP34AT7z+jtdXvo9joi7kbFXaXZAWW4vGdOofNZNJGNNlMfGsjotRkMdu0RJPFzJqfRETpTvE0aavOKZ4mAFzZGGCiiYgyjGLLx4hn2idHHVp2JZq2rgrxCO+oJh/h8Z9KJgQnWoUiKVZ0n/qqFhu2kc3IP32hFieFZDaOiYiIiIhiZJIlLdFkktNzFkQiMNGURK1Nx5PdBSICsOvnvYLuczcehWzNy8ipdfEgySbY+l0Uvl2IKXzC40TLf5cAAPJOu9Vv+l2khMdpGGvn10/pi+H4RETx0Gpv9ItzMrRGIBERZTcmmpLI7WgK34iIOuWU2X8Drn/ab5tqb8auW3pH9Pi984eg/A/7YSrwrnZ35Ll5aHj7L9r+TF61Ll6E6kLTx78AAOQO/1lAoifc/kg4PKpfHG6yo9MjoIYYrGZVkNUJRCJKDKejzTAmIqL0pOpmP6icCaFhoomIMpqvSLieZMnVVq4DgG9W3IDGzc8GPcae2wcF3ZfJq9ZFQqgutO31XrucITMNp8YJoRrGEZNMxrG+iWw1jHUd9YsXf9iAnfWuoKf885SesJll/0MIAefJsdEWBbCaJMgnk1FuVcCtOwUTVURERESZTw0SZzsmmogo63Rcua5s1pMo+8mfte9dJ45i7/zBER0r22s4CY8TJz74CQDANvBK4xpMnrYOcUFU59DXfgpWB0rPoQrY3f7Pi9D1wSzacKAxdBLohreOhT3P0u/1RGmut7j581+0YM2+Vm3fMxeUwMZ3WKK4q6iogM0Wuoj2nDlzMGfOnC7qERERZTOTpKvRlOIfMlZVVaGqqipkG7vdHnJ/pHgbTERZTzZbAbQnMMzFA1D+h/0AACW/GDUrb/ObLqfnV0jc7YTwBI6SyfY6Tl1Bn/C76/1jaFH8V7fLU+uw+GTc5BTonWfCgUb/1QiJKPWtWrUKY8eOTXY3iIiI0k4kH8RUV1dj3LhxnT4XE01ERB3IsgxL8UDte0kJvkqZu/4rWEu9o5+OrvoVjr3+UECbU5c3+42goujok0hBR5DpRywhdN0ThyrwqzO7o+OhHKrAzzd5RzIt/34xrIp/crDRqWr7l07uieKc9ql1M4bn4ZLBuZi9sS7sz0NEREREmUFVcg3jbMdEExFRGL2u/iN6XfWg9r2z7gD23TXq5HeSNqrJaDQTwOl1neZp7RAH1sSyKIDjZHz/d3oit8hbvN3pERACaGxyA2u9++/64DhaZCXgGH6MnjLdNqvSXp8J8C5naw1zSCIiIiLKLB451zDOdkw0ERGF0XFqnal7Py3+8r6JYR+f7QXDu4J+ZqLVBNhM3g2LttRj3wk38tR6bepcJGa/yZFJRERERESxYKIpiayFxcnuAhHFQFaiH7oSrH4TkAU1nGSbcXySpNjQ44JNWtzZc1jNOSGb/uHcnsjJLwnY7nCLiBNMza7AdUUcHt3ytqoKgEOciMhfbm43tOpiIiJKb6rqNIyzHRNNSWS1sGYLUTqSLLk4dXkzjq76FY5vWBK+vTkHtWvuQ93Liwz3Z3oNJ0k3TU0ymLImyQqsfSZ36hxWk/E5Fp3dHaoAVLvAiVXebQUWCSZT6MTe8qnFsHZo0+xUMedkjabb3j0e8vEOD8DB00TUkcVs0xJNFnOMiXUiIkoZiuuEtuqc4joBgINJACaaiIiiJkkSJGteyCLhep7mOqj25qD7VVWFHHRvapMUK4om/0OLDdvIZuSNvEOLE9KPICPCLCcLentM7eeNpA9Wk6RNv2sX+bMkSxKcHgE1RHkuqxK830RERESU+kyypCWaTDLv63yYaEqituaGZHeBiDqhtGIxSi6vNNynOlqw6+e9AAB77hgc8jhqcx2E2Rp0ah2QutPrJNmEnIFXhmkkQckfqMUdCdWF1i+WAwByh8+OKRklVJdhrHVBlwQLlhALx6oAz1zgX2RcT79qndUkYdn2Rnx4xNHxMJpnLiiBje/CRFmlVfehQ6u9GTms30dERBmIt7hJ5LKfSHYXiKgTJJMFksnS+QMpZtS99nvUrv510CbD/9wEJU3/IBEeJxo/vBkAkFN+bUAiKdz+yE6iGsdx5jxZh6nAIgUk/pqd7ed1hxrKdJLDLeDSjXoyyYDS4ZMw/agnl0fAI4z3haIKAafHeB9HVRF1LefJVUo7xkRElJ5U3SePKlea1jDRRESUAL46TsGKgKuOFm2k0555/QL2d5SqK9cJ1Q37wZcAALb+l0OSA99WhC7xI2JJAkkm41jfJMyIJaF74xcx3gQ4PMD1G73Fwo1GI3l0P1qrW+Cnowowa2SBXxu7R+CWt72jniIpPP6X7xej0OpNBP31v83Y8GWbti/SEVH/Pe7Cb7c0GO7jqCoiIiKi2KlB4mzH20siogTw1XFCkCLfapSfZEspWjRWeBxoePt/AAC9ZjYbJprgaesQFwS2CSEe097gae0QJzZp55tClwxuVeDNg95rPrV/6BX4iIiIiCh2JklXo4mjxDVMNBERJYNsQskV9+L4+ofhaTZOSvS95UV89afpAIxXa6N28RixZDNJeOGi0nh1KayHJvZAjkmCevLjL5MEuAXw85Mjnh46twdUVeCE3dvgivJcXDIoV9vv/TkDb2jcKvDUZ946MJP65uC0HmatthQAODwCszeGH01FRERERBQLJpqIiJJBdYesyQT4j2IKNgJKMlkhKZn9qzyiJFLAiKWuZ9HlApdPLYY1YNU6oMWl4mdveRNFd7x7POTx7ngv9H6nB8iJoJyVLEl+0+NMMjDz1DwtJiIiIqLYqIrNMM52mf3XCRFRivLVcArF09akxb4V7DrqN+9VFIy+KK59SzldPO0tVvqi2laTBJtBogmIX2bHE0HBcSMmWcIPhhhP6SQiIiKiyHnkfMM42zHRRESUBFoNpxDcjUfDHke47FAdLZAsuVw9rJOcHoFl2xsBADePLoRFif/1tCrwm8bWkQixQpxJBo47VG0klJsLmxARERFRCmKiKYksed2T3QUiSmGSJTdsm6+WVQAATl3eHDZxlTSy1Tg+SVKs6D71VS1OxDkioQrgwyMOAMAst4B+wJA1hhJZblXA3WH5EVUI7DzugtUk4bQeZsgByUEp5HQ4p6e9U05d/SZF9o5UAoAWXabK7lLRcRSVKgQONLoBAMO7m6DInD9H1FVs1gLYdTEREaU3VXUaxtmOiaYksuUUJrsLRJTCohmh5Kz7EqaCYv/H6+o3JXPEk34lOqNV6STZBFu/zk3/C3eOaHUslv3MBSVQJGBSX+/ce6PBThYFWHxOdy1+dV8rnt0ZfHXBZy4o8audFAl9Yur2MDWeAOCGt0Kvfrd8ajG6sZwAJYEQAo8++igee+wx7N+/HwMHDsSPfvQj3HHHHTCZMvf21GbLbU802cJ/mEBERKlNcZ3QVp1TXCcAFIdqnjUy952ciCjNyfnFGLa0xnCfZLLCdfwr7PvVKADAvrtGhjxWokY8SYoF3c5ZocWGbWQTck/9mRYnhWQyjqNgViT87PTgHxDIkoTyogiqcxMRli5dinnz5uGWW27Bd7/7XXzyySeorKzEwYMH8eijjya7e0RERBExyZKWaPKNLicmmpKqrbkh2V0gohQmyzLkwtKg+z0t4Ue0JJokm5E79NpwjWDuOV6LOxKqC217nwUA5AyZCUmOPlkjVJdhrHVBNyUv2PS8UPWTYpk6d+GgXJw/IPiIhViOqV/Z7tEpPWE6OcJJP3Wu1anipk3ekUx/ntoTtg5LyzU6Vfx8U+iRTkSJ9tBDD+Gaa67Bww8/DAC44oor0Lt3b8ydOxeVlZUoLQ3+uy+dtdqb/eIcGwvHEhFR5mGiKYlc9hPJ7gIRpTE5v31obvlD+yB3GLGkuuzYc9sAAIDwuLu0b3rC48SJD34CALANvDIgkRRuf4QnMY6jIElS1FPZQjHJEkxxLn+kn/6YZ5bDrmxnMwW2cbj5aRtF57nnnsPMmTOxc+dODB8+3LDN4cOHsXDhQrz22muoq6tD//79MWPGDNx1112w2fznZ7a1teHQoUOYMmWK3/aJEydCCIHPP/88YxNNTkeLYUxEROlJFcIwznZMNBERpSlZV8TZVFgakGjSr1on3I6E9EGobji+Xg8AsJ5ygeHUOKF70xWxvAFLinHs18RmGMetD2nErati7o2ZWKLOWblyZcj9Bw8exFlnnYUjR44AAIqKirB7927cc8892LhxIzZt2gSrtX0koclkwscff4zy8nK/47z77rsAgFNOOSXOPwEREVFiqEHibMelZoiIMoB+xJLwuKE6WqDqPi1PVHJFeByof/Ni1L95MYQnSDLL02ocRyhcEikinexDOvGoxjFRtIQQePzxx/Hqq6+GbDdr1iwcOXIEU6dOxYEDB1BfX4/q6mr069cPmzdvxr333uvX3mw2Y9y4cejWrZu2bd26dbjrrrtwwQUXYOjQoQn5eYiIiOLNpBttbkrSwjupiIkmIqIM0PTJS37xztn52HPHYG2bp6kOqqMFwq2rZeRxQzgTn4wiovSyceNGzJo1C8OHD8dNN90Usu22bduwceNGlJaW4oUXXsCAAd7pumPGjMHq1asBAMuWLYPdbjd8/NGjRzFr1ixceOGFGDFiBP7+97/H94chIiKiLsdEExFRBug4ba6jfb8ahZ2z83Fi87Patub/rMcXN/dqb+RK35E+wt1iGFMgRTaOiXzWrl2Lp556Crt37w7bds2aNQCASy65BD179vTbN378eIwcORINDQ344IMPAh774osvYuTIkXjhhRewePFifPDBBygu5rLQRESUPlTdaHs11pH3GYi3mEREaUqy5OLU5c04dXkzCsZdrm0vGHc5Tl3ejMH37Qh4jOp2wNN6Au7Go/C0NvjvU1NznhWTSPGlX3qXy/CSkfnz52P79u3aV1lZWdC2mzdvBgBMmzbNcL9vu6+dzz/+8Q9UVFRg7Nix+OKLL7BgwQKYzTEsBEBERJREHjnfMM52LAZORJSmJEmCZDCSSVJMkBQTlPzAkQFHnr4RR56+sX2D7uMGtfkYUNA7EV2lBHB4jKc6OnTFwFX4FwZ3q4DD3bEwOpNN5K+srMwvuWSxWIK23bNnDwAEFPb2GTJkCABg79692jaXy4Wbb74ZU6ZMwWuvvQZFMS7yT0REROmJiaYksuR1T3YXiCiDSalSkFC2GscnSYoVRZP/ocWJOEcmmr2xLmybA41udLd5/4h/bX8rnt3pPyLM7hbI4SAS6oTa2loA3pXmjHTv7r3Xqamp0bZt2bIFtbW1mDBhAjZu3BjwmDPPPFN7XKaxWQtg18VERJTeVNVtGGc7JpqSyJZTmOwuEFEGk/OLMWyp9487SbFAMnkzCsLjhnB7V4hz1OxE4weTAACqs9VbMFz1QLi8fwr5TVuLcWqdJJsMY/22nIFXxnTsSM+RraxK6GSjKzVnS1IaaW311nYLlhjybfe1A4AjR44AABYvXmz4mLfffhuTJk0y3Nfc3IzGxsaY++tjtVphtXZ9Utpmy21PNNlyu/z8REQUX4qrHh5dDPRISj8cDgccjiArQEehubk5Dr1hoomIKGPJsgy5sDRkG09LrRbvrzwT6Jh4kIHuJ//eU1vqoHT4BF5SLCg8a5kWG5FkE3KG/FiLk0JSjOM0Y1WAZy4oCdlGCAHnyTuefN1TcuGgXJw/IBe1rR7c8d7xBPaSsolvtcpgq1b6psV5PB5t25VXXhnzKpfBElDRWrhwISorK+NyLCIiyl4mWdISTcmsfXn//fdj0aJFSTt/R0w0JZG9rfOfyBERdYZkCl57pSOjPwwl2Yy80+aEOYkCa7+LtDjguKob9oMvAQBs/S+PKRkldEOVhcGwZUm3CoiUxiuCSJIEW9jLIxlOhzPJEkxy+FFORNHIzc1FY2Mj6uvrDafP+UYy5eWFXhkzUu+88w5Gjx7d6eMkYzQTANjtrX5xjo2FY4mIqPMWLFiA2267rdPH2b59e1w+1GGiKYmcLfXJ7gIRZTmlsL34d99bXsRXj0wP2lY422I6h/A40PD2/wAAes1sDkgkhdsf2UncxjERJVRJSYmWaBo0aFDA/qNHjwIAiosDFyeIRX5+PgoL07f0gN3R1CEOPeqUiIhSm6r7IFaNcbRuPMRrSnh+fnw+AJHDNyEiokwlye1vA7I5/EgfoXqgOlq0L09bI+wHX4f94OtQPcYJHv1IqJimy0Qw7S1TRix1BbNsHBPFYujQoQCAXbt2Ge7fsWOHXzsiIqJMogaJsx1HNBEREQAgZ+g5OHW5fwFA1/EDOL5+FABAtuTAfqAa+xed2d5AV8Op9MoaIM/g03lPa4c4uk9K4pFE8itq7m4BzPGZxpOOZN1qhHKqrExIaWvChAlYt24dNmzYgBkzZgTsX79+PQDg7LPP7uquERERJZxJ0tVo4n2VhokmIiICAEiyArlDAsZU2F54WrblAyeOdXW3iCiFXXrppaisrMSaNWtQV1fnN0Xuww8/xM6dO1FUVISJEyfG5XwVFRWw2UInnOfMmYM5c8LUjiMiIsoyVVVVqKqqCtnGbreH3B8pJpqIiCgi7sZamHr0RflD+7RtntY6NGzyjnCKdRWpeAgYsURBCQjDmCgWo0ePxnnnnYc33ngDM2bMwIoVK9CvXz9UV1ejoqICADB37tywyaFIrVq1CmPHjo3LsYiIiDpL1Y22V1O8fEMkH8RUV1dj3LhxnT4XE01ERBSUUNuXJN9716jAyee6qXPwuBLTB057iysJkmFMFKsnnngCEyZMwJtvvon+/fujqKgIDQ0NAIBzzz0XCxYsSG4HiYiIEsQj5xvG2Y5lQImIKCjhinz4rGSyJLAnRJSq+vfvj08++QTXX389ysrK0NbWhqFDh6KyshJvvPEGLBb+biAiIsomHNGURGZbt2R3gYgoJDmvvd7K0D/sg2TyH03kaanVioUHP4jFOD5JUizods4KLY6to6HPQe1SZRleSh/79+8P26ZPnz5Yvnx5F/QmvVmseXDqYiIiSm+q6jaMsx0TTUmUk1+U7C4QEYUkye0DX5WC0oBi4ULo3lzdbiiGxzAbxvptuUOv7WQ/Q5+D2rlU45goHaR7MfBcW76WaMq1cYoFEVG6k131WmUJ2VUPoEcyuxMSi4ETEVFaUO1NaN3THsNgoKYkKbANqNDipNCfN1l9IKJOYzFwIiJKJWZZgkMXpzIWA88S9rbGZHeBiKhTJNkCxyFv7GlthLvxqHe7YtZqNqluJyxll3ljyAGjnoTqhuPr9QAA6ykXQJKjf2sSuqHKwmDYsqRbBURK8RVBiChz2e2tfnEORzUREVEGYqIpiZwt9cnuAhFR3ByoPNN4h25lOnPx2VB6DvbbLTwO1L95MQCg18zmmBJNUF3GMRFRCrE7mjrEpcnrDBERdZrQ1bsUrH2p4apzREQUMyE8UAoApSDC9s62GM4iB4nbSaYcw5gCmWTjmIiIiIii4w4SZzuOaOokIQQeffRRPPbYY9i/fz8GDhyIH/3oR7jjjjtgMvHyElFmk3PyUXiGN65/B4AKdJ/6M5RecS8kkwXOui+x7+6RWnvhbjU+UAjxSCIJd4t/bM7e1Z4USTKMidJBuhcDJyKizGKWdDWaUvy+isXA08jSpUsxb9483HLLLfjud7+LTz75BJWVlTh48CAeffTRZHePiCihZGt7fZHhy2ogmfL86jNZ+5yKgb9+ByfemZSsLhJRBmExcCIiotiwGHgaeeihh3DNNdfg4YcfBgBcccUV6N27N+bOnYvKykqUlnLuPRFlLkn3yY1kyYPcYaSQJMuQu2AqW8CIJQpKhTCMiYiIiCg6QrIYxtkuY6ozPPfcc5AkCV988UXQNocPH8bs2bPRt29f2Gw2DBs2DHfffXfMw8Pa2tpw6NAhTJkyxW/7xIkTIYTA559/HtNxiYioHZNI8SVDMoyJiIiIKDpuU6FhnO0yZkTTypUrQ+4/ePAgzjrrLBw5cgQAUFRUhN27d+Oee+7Bxo0bsWnTJlit1qjOaTKZ8PHHH6O8vNxv+7vvvgsAOOWUU6I6HhERERERERFROkv7EU1CCDz++ON49dVXQ7abNWsWjhw5gqlTp+LAgQOor69HdXU1+vXrh82bN+Pee++N+txmsxnjxo1Dt27dtG3r1q3DXXfdhQsuuABDhw4N/Xhbt5D7iYgygqXAOD5JUiwoPGsZCs9aBkmJccixbDGOKQCX4SVKHos1zzAmIqL0pKqqYZzt0jbRtHHjRsyaNQvDhw/HTTfdFLLttm3bsHHjRpSWluKFF17AgAEDAABjxozB6tWrAQDLli3rVIX1o0ePYtasWbjwwgsxYsQI/P3vfw/7mJz8opjPR0SULsz5PQxjH0k2I++0Ocg7bQ4k2RzTOfSPi/UY2cKpGsdElHi5tnzDmIiI0pPsOmYYZ7u0nTq3du1aPPXUUxG1XbNmDQDgkksuQc+ePf32jR8/HiNHjsRnn32GDz74AFOnTtX2DRo0CAcOHDA85iOPPIJbb70VAPDiiy/ihhtuQFtbGxYvXow77rgDZjP/0CGizCfJZuSfvlCLjRsp0N53JaVrOhZADhITUTqpqKiAzWYL2SaSVXWIiIjiwSxLcOjiVFZVVYWqqqqQbToz+EYvbRNN8+fPx7XXXqt9f+GFF+Kbb74xbLt582YAwLRp0wz3T5s2DZ999hk2b97sl2jaunUrPB6P4WMKCrzTP/7xj39gxowZOO+88/DUU09FVZfJ4WRRWyJKb5JiQcGYypBtVKHAftAbexx2SIr/7z4BCe7jHwEALL3OhSRHn4wSqscw1vqpW/lO6oJV8IgoMVatWoWxY8cmuxsxc7rsfnEORzUREVEXieSDmOrqaowbN67T50rbRFNZWRnKysq07y2W4DU59uzZAwABRbt9hgwZAgDYu3ev3/bi4uKQfXC5XLj55psxZcoUvPbaa1CU6P44cjTWRdWeiCgdqY1fo2CMN95zW1+gw3StwrOvgmJ7AQBQ+sMmKJYY/vBSncYxEVEKaW094Rd3Kwh9r0lERKmNtS+NpW2iKRq1tbUAvCvNGenevTsAoKamJqrjbtmyBbW1tZgwYQI2btwYsP/MM8/Ujk1ElImEUOFu+C8AwFR0GiQp+mlpjR++gO6TTn7jagUCEk3hp71xxFLkTLJxTERERETRcQeJs11WJJpaW1sBIGjSx7fd1y5SR44cAQAsXrzYcP/bb7+NSZMmGe7znq8NjY2NUZ3TiNVqhdVq7fRxiIiiJdxtqHtlFACg18xmSGaDVZRk3ac7MgJGNOl52pohmTocQ7TPd481iSTcLf6xUT+zhCJJhnEqcTgccDgc4RuG0dzcHIfeEBERERkzS7oaTSl6X5UMWZFo8g1hCzaUzTflLVg9pmCuvPLKTg2Pq/jRLQBuifnxPgsXLkRlZWWnj0NElAhKfvvUkPL7d0C2lbTvlE1wHduLhk1nAgD2zB8SkIgqGH8ZTCxjklXuv/9+LFq0KNndICIiIqIYZEWiKTc3F42NjaivrzecPucbyZSX17WfcK/6259w3g9+3OnjcDQTEaUySW6fn6UUlEDJKfXb72k63OlzCHerYUyBBIRhnEoWLFiA2267rdPH2b59e8iRxURERESdISSLYZztsiLRVFJSoiWaBg0aFLD/6NGjAMIX/4633NwcFBYWduk5iYhSjWRpnw435L4dMPcY6LdfdRxH7YsvAziZRDKc9iaCxNSRBMkwTiXxmhKen8+hcERERJQ4blOhYZztsiLRNHToUOzduxe7du0yXBJ3x44dWjsiIupaUpj57Kq9vZad6miCklMSojURZbKKigrYbLaQbSJZvpmIiCjbVFVVoaqqKmQbu90el3NlRaJpwoQJWLduHTZs2IAZM2YE7F+/fj0A4Oyzz+7SfpmsBV16PiKipJDMxrGPbEbrHm9Y//aowAFJJqD7ud5QONtiOwdpuAwvpbNVq1YZfmiYLizWHDh1MRERpTdVVQ3jVBTJBzHV1dUYN25cp8+VFQsbX3rppQCANWvWoK6uzm/fhx9+iJ07d6KoqAgTJ07s0n7lFvTo0vMRESWDpFgMY22bbIHjEOA4hJhnvYU7B7VzqsYxESVerq3QMCYiovQkO48ZxtkuK0Y0jR49Gueddx7eeOMNzJgxAytWrEC/fv1QXV2NiooKAMDcuXPDDsUmIiJ/kmxG3sg7tNiwjSTD0muSFgfst+Rq8bClNZCt/jWYnEc/R/2bZ3ayp3KQmIiIiIgoNmZFgkMXk1dWJJoA4IknnsCECRPw5ptvon///igqKkJDQwMA4Nxzz8WCBQu6vE8OZ0uXn5OIKJ4kxYLC8b8P3Ui2oMDXRjYabSSh4Kwp3sgSevXPYFO9hOoxjLUzmHIMYyKiruR02f3iHBsL1hMRUebJmkRT//798cknn2DhwoVYu3Ytjh07hqFDh2LmzJm48847YbF0/VQLR2Nd+EZERGlOeOw49qp3RFKvmc2QZP9kkqQApty3tHjnTzv84RVJjSbVYRwTEaWQ1tYTfnG3gq5d8ZiIiKgrZEyiaf/+/WHb9OnTB8uXL++C3hARZQchVHiaDwIAlPz+hlPj4irICnWSKdcwpkCKbBwTERERUXScqjCMs13GJJqIiKjrCXcbalcPAnBytJI5cOqbcLf4xwZt9E5d3uz3vbN2D+o3jgYASNZuUB2B046Fbiqy0fQ64W71j8P0IZOZdMk6U5DEHRERERGFZ5HbazRZZN5X+TDRREREKaVjMXBz9z5abMrthi9uLoVwtvo/SDe9Dq4O+wD4L2fHT5uIiIiIiBKFiSYiIkobRqOZAPgtJOdpq4dsK/XbHW7EUzbR//zZfi0o/VRUVIRdJXjOnDmYM2dOF/WIiIiymn7V5SArMKeKqqoqVFVVhWxjt9tD7o8UE01ERJTS9NPeds8fDLgD29gGjQawHQCw9zfjAGeHBmFHPGUPSTddTuLUOUozq1atwtixY5PdDSIiIgCAS+lmGKeiSD6Iqa6uxrhx4zp9LpYBJSKiFBd+1I0kK13QDyIiIiIiCocjmpLIZC1IdheIiBJPMhvHvk2yGfmnL9TiUAZXfgRTtwEB210N+9Dw1tkAgCH3VMPcfZjffveJAzj22qiQxxZCaLWfVGcbINSgbeX8YsgyP6shouhYrDnagEuLNSepfSEiIkoUJpqSKLegR7K7QESUcJJiMYz12wrGVEZ0rH2VZxpOndNPjRMud0BBcdmaG/bYwtmKnbPzI+rHsKU1kAtLwzdMQQ6PMIyJKPFybYVaoinXVpjUvhARURzYa/3jbvzdDjDRREREnSDJJuSe+jMtNmwjyTD3PEOLk0MKEhMRERERxcasSHDoYvJioimJHM74VHQnIkoWSbGi24TQq1dAsaLbd5/W4o6EUOFu+C8AwFR0WmAyytw+GmnYH/cFrCgHAI6aHWh4awIAQBWqd3U6SYZs8U5NkUy6EU1KDlRHKzrWftKvaDfkof1QOoyC8tibsXf+kID2+tFTqtMOCA8kSy4LbRNRAKfb6Rdz8hwREWUiJpqSyNFYk+wuEBElnHC3oe4Vb32kXjObIZnzotrvt0qaJS9gWhwAyOb2YuAH7pkAOAHboDMwuHJrYH+crdi/eAoc33wetM+SBJhCTI3b9fNeWjzimfaE1dfLf4Smratw6vJmSAb9JKLs1tpS7xd3y2cZBSIiyjxMNBERUcyEEFAddQAA2VqcoFE84ae9SYpuXIBBDW/9aKWIxFC6SHW0oGnrqugf2MUU2TgmIiIioug4VWEYZzsmmoiIKGbC3Yqjz3tH/hiNRvK18YsN2oSin/bmNwVO38bSnmgqv38HTN0GAropeJ7W437xoMqt6JhNcjcexZ47BgMwLh6uOtu0ePD9n8PSs39UP0eqMOmSgSZO7yMiIiKKmUVur9FkkXlf5cNEExERJZgIEsePfiSVbM2FbM2D6mjVRjKpzvYRTarLoLaSoz0ZFpRoHyolm22GU/iIiIiIiLIdE01ERJT2hOoMiPfeNQKuui+9G01A93O94YF7J8FaOgJD7v9Me8z+yvEhazYBAOT2OlCqyxEwHU//vcfe7FcQXHU5ANXt1z55BcMTn/gjIiIiygqy2TjOckw0ERFRShPuNv/YYOqdZLIYxrHST5Pzkc02Ld634LSQj999S2+/guCHn74BJ95/xq9N8gqGh695RZSqKioqYLPZQraZM2cO5syZ00U9IiKibOZSuhnGqaiqqgpVVaFXi7bb7XE5FxNNRESU4tQgcWhD7vscvhE7rob9OP76t05u/w/MRYP92g6q3Ap3Y41Wo0kyh/5DloiSY9WqVRg7dmyyu0FERJR2Ivkgprq6GuPGjev0uZhoSiKTtSDZXSAiSjzJbBz7Nslm5I28Q4vjdQ59QW/FlucXdyz27avrpB3CYEqbZGl/zLClNQE1mjz2Zuy+pbe2X9++z7V/Rp//rYLqaMGun/eK5icjogxisebAqYuJiIgyERNNSZRb0CPZXSAiSjhJsRjG+m2F43+f0HPEg3D6FwwPVQxctub5Fyg3WwFYE9KvaDk8wjAmosTLtRVqiaZcW2FS+0JERJ0n7LX+cTf+bgeYaCIiok6QZBNyhvxYiw3bSBJMRSO0ODlYl4iIiIiI4suiSHDoYvJioimJHM74FNoiIkoWSbGi6NynQzdSbOg+da0WdySECk/zQe/u/P6QJDnqfgihGsZaP025hnEkVGcb0PGYshJy1Tm/2GUHVM/JvgkMvs+72p0wBV4LVVWhNtd5+2nOgSR7r4VwOyE8Lv8u5BdDlqO/VkSUPE630y/m5DkiIspETDQlkaOxJtldICJKOOFuQ+3qQQCAXjObIXVYNS7c/oh42ozjODj4h/+H1p3v+G37+rGZaNmxPuhjIqnDNGxpDVBY6rdNba6LuIbTsKU1kDs8nohSW2tLvV/cLZ9lFIiIKPMw0URERDETQkC4vbWLJFNu0qbGSaY8wzhWoeovpTtFNo6JiIiIKDpOVRjG2Y6JJiIiiplwt6Lm2XwAwUcj+RJRWhzliKV4JJGEbpSTCDLiSb9KnF7/218PmDonIENCh21CaAXD9Ykq/dQ55/GvcWDh2JPbHQil/Pd7YermHd3kmzrnbqrFvrtGhXxcOCZdMtCUtJpZREREROnPIutqNMm8r/JhoomIiBJMBIm7sguqcawTbDSWbImiiootP/Dx+qSTvq6T6g55KNmW3/7YDB5hRURERESZhYPmiYgo7Ql3m2GcavSjpoKNoEo0oUv2iWQl/oiIiIgygawYx1mOiSYiIkppkSWR1CBxatGPmoqlnpVkzjGMozoGJMOYiIiIiKLjUrobxtmOiSYiIkpx6ZFE6gqSLBvGRERERESpgjWakki2sOYGEWUByWQc+zbJJuSe+jMtTsQ5UoW+ALhhMXD9zx/rtSCilGUyW+HWxURERJmId7FJlF9YnOwuEBElnKRYDWP9tm4TqhJ6jpShKwCu2hv9ioNLllzIuj88ZYM/QvXJKU9Lvd8+yZyjjXLyrVInWXIDpug5PMIwJqLEy88tQoMupsRQVRVqc13Q/ZJihmSyGP6OJCKKhrDX+sfdCpPYm9TBRBMREcVMkhTYBlRosXEbCUreAC0mr313jfL7ftjSo5CtoQuEq20ntHj3vH5++wYv3gFb35EAgNo196Hu5UU4dXkzJK5YR5QyVLV9+q/qbIEqSd4EssEqlHJ+MeQopsgKISCcrVqi2fdvR5LZBklWIJltgOoJaNvxvL7jAjBMzAjZDNHWAMlkBYSA8Dgh3C4IjzPw3CYrJMXkbQtAuB0QHjeEO3CEp2SyeBNCigWQpMjbyjJcxw5h369GBbTrqPzhQ4DBNfKRLbmQT64mqrrdUFuPR9RWqCogSX7XSn8dIZsgmSze50u/3e+AJu8HDrq2gPFzAEmBbLFp37objxqfFwBMFph0SU59W6VHfyiKEvBYvxVTO5JkyJacqJJ27cdtReBqtNLJ90LJ8D2xYxJRtuZBdbYBQtX+7ci3gqtszYPqsgOqx/D1pfXrZBvfvwE/8smfVbLkQridgOr2ex3rr4V+4Q+jtn7HPfmBkWTOAVR3mNdxNG1Dv+aDt1Ujfz2nyGtfuOwAov/96fF44Dl+sD0JrZgB2QThaoNQVQiXt0ao33N7sq0ZLvh+YjOrGmiYaEoilyv4GxsRUTqQTDZ0/94/QzdSctDzkq1a3JEQAqrDe9MoW4tjSkYJIQzj9n7mGcZdLVwBb4/T7hfLUSSJhMv7x4DH3oxjax/0HsPerO333djq66mrQgXAFVKIuoqjof2P+l23DYDVaArtSQMXfgRzj34R/zEpVBV7f1GekH6H0vt/q3Dkr3O6/LzxsKdDwj5d5Y++BGXXr4Bs9iabdv28V0zHGXDXOzD3OHlNTia6Ij3WsKU1ACQAwjiBJEmQLblam3DHNfXsj8GLPglIILmb63Cg8sxofixDAxd+BFNB4OySPXcM7vSxKfk6Pr/+CSQFwmWHUD1aYsp1/BC+vG9STOcyT70J+Sdj1WWQNM5STDQlUduJb5LdBSKihBPuVhx9vhQA0GtmMyRzXlT7I+JpNY5TjO/TbsB7U65PJEmWXLjqDmjfexqPQNF9Qh0uSbXf4MZ79y29A7bV5/QFLvnEe47m40B+bH+QEFH0Wpu+0VK7jtxusJ44GrTtgUWd/2O6K6RbkqnnRXfi2NrfJbsbcdW8fQ12zel8SY5Y/9AGYk9uBeM+dhC7bi6J6zH10uX1RbHpyue38cOVyB/vjX2JK2KiiYiIMkCqjFgKRz9aS7bmhRyx1HFqXflD+72fxHWSrBuuL/OTN0ozFRUVsNlsIdvMmTMHc+akfvKj94+q0P/b/w+qy4Ga52/HifeejtuxBy/eASW3W8TTaFz132D/3WMMj9X3lhfx1Z+mR3TeIQ98ASW3KCWmz/hGKwRMoZFNKLn01wAA1e2E2toQ9OeJduqc6myNaETM4Pt3wtzjlIinznlaG7B3/pCwxw173sU7IOcV+U2dazvwSUQJJjmvB9SW4D9/Mgys/Ai2shERT51THS0Rj1gqf2gfICsRT51zN9dj34JTIzyuiVPnEvDadx0/iH2/Pj2gTbQGVn4Ea2m5NnXO09aMvb8I/vpzy+33Zp4Ur31ZVVWFqqrQdVHt9vgky5hoIiKimKmuFtQ8670J7zWzGbLBaCShm6sl3G1AlCOW4pFEEp42w7irCbfTP46yfpKs++Og44goH3fjUcMb6f6/eAO2ft9GXc0hYJd3m4m1BCjNrFq1CmPHjk12N+LCVFCiJZz7/Phx9PnRsoD6M7H84QlEX59Ezi8+OfVJfzzvH5wwWTH80XrvH4+yGVBD1DSK8rwJkxOuGK/3j13ZmgfkdY/okLIVQF63kG1UVQ24jobH8l0n3SjXkO1zi8IfV1a0qXPBGNVRspWf401+BBzPBFN+D+1bIURgfS5fkuxknSbflLlwU+dka67WJlRtp3AJJKVHf8iKEvE0c9/zE6z2kr6WU7SF4pWCEpy6vDlsOxagTyBbgfH/ZYSfOqfnV6sspyDsc1uz933go2ne86ipPaIpkg9iqqurMW7cuE6fi4kmIiJKMDVI3IX0N6gGN6td1g3dH4eGfyjqioaWP7QPpsLS9n3mHK0YJRB8RJRS0D7VQJ+M8v3RqDSFKOpKRF1HP8LRbAVghQwAOQVd3hVZliHrft8EyCvqsr6ks7DXMcWOCwCKokApGRT7AXTJsmjqCkbSNprjRXTOBF5HSZK4+EaSRfV/OWwyul2451ay5ASUtCcmmoiIKAMIj90wTjfhptZFciMTzTF4Y0RERETUGUqQOLsx0URERCktIIlkNPVOeIzjDCOZbRi08CMtjoWsKyouhykwTkRERETBuU1FWnrJbSpKZldSChNNRESU2rIkiRQJSVaQM3h8J48hG8ZEREREFCUOFTfERFMSyRbO4yWiLCCZjGPfJtmEnCE/1uJEnCNt6H/+WK+FbEK37/64c8cgooRQrIWGMRERpal43LtlIF6JJMovLE52F4iIEk5SrIaxflvRuU8n9BzpwlsQODD2EW4njm1YAgDoef6tkEwWw2Occv3TQc/hcDoBWHQxEXWVwh590KiLiYgovSlqs2Gc7ZhoIiKimEmSAmvfC7U4GNnKxHo8CI8LR1/4BQCgx9SfGSaawh5D9zyJEM8ZEREREYWmKIBHF5MXE01J5HIFLm1NRJROJJMNPb6/NkybXJRUHNDijoQQEO5Wbb9+1bRICSEM4/Y+5BnGXU3SFd+WDApxh/s5IiGEgHCevJ6WwOspyYphTESJ59SNInQ6nWARBSKi9Kb/0C+WDwAzFauAJlHbiW+S3QUiooQT7lbUPJuPmmfztYRSNPsj4mk1jlNMuELcvgRRxzgawtmKnbPzsXN2fszHIKLEaDm23zAmIqL0JFx2wzjbMdFERERpL1VGLKUDWXUZxkREREQUHbdHGMbZjokmIiKKmepqwZG/5+HI3/OguloM2wh3m2EcqXgkkYTHbhh3NeF2GsZdSVGdhjERERERRUdW7YZxtmONJiIi6pTw093UIHEXEh7juMu74TKMiYiIiIgyBUc0ERFR2kuVEUvpQASJiYiIiChacpA4u3FEExERpbSAJJLZYPpcioxYSjTJbMOAOzdpcSxk3Wp3ssHKd0REREQUGbepOxRdTF5MNBERUWrLkiRSJCRZQd5pkzt5jNAr3xERERFRhDhU3BATTUkkm/hJMhFlAUkxjn2bJAW2ARVanIhzpA1ZMY6jISkoGF+hxUSUOhTdiEzFaHQmERGlF9lkHGc5Xok4u+mmm/Dxxx9j69atYdvmF5V2QY+IiJJLUmyGsbbNZEP37/0zoedIF7JuOpxsMDVOuF2of3s5AKD75NmQTObAY1hs6Hdz8OvpcDoBWHQxUWpYsmQJ3n//ffzzn537fZDKCov7olEXExFRelPUZsM423HMfBxt2LABjz/+eLK7QUTUZSRJhqXXJFh6TYIkBX9LkUy5kEy5XdizzCQ8Thz528048rebITyxJYmE7q1f8DaAUsThw4fxyCOPJLsbREREUVEU4zjbcURTnJw4cQKzZs1CYWFhsrtCRNRlJFMOev6/t0O2kc156H1NS9d0KAjJlGcYd3k/dKOUYi3m3ek+KCbDmCgZqqurcccdd2Dz5s2w2+0444wzkt0lIiKiiEkmi2Gc7TLmo8znnnsOkiThiy++CNrm8OHDmD17Nvr27QubzYZhw4bh7rvvht3e+aWwb7nlFgwZMgSXXXZZxI9prPuy0+clIkp1qqsFh5+WcPhpCaorMOEUbn8khLvFME41kq7ukmRQg0l1tBjG0VAdLfj8xxI+/7EU8zGIjCTiXquwsBAXXXQR7r33XgwZMiRRXU8Z9Ye/MIyJiCg9CZfdMM52GZNoWrlyZcj9Bw8exNixY/GXv/wFX3/9NXJycrB7927cc889mDJlChwOR8znfuWVV/Diiy9ixYoVkCNYwcd5siaGy+WK+ZwUG4fDgcrKyk493xQbXvvkyYprr+Qaxykg1a6/pLoM40zke791shZVXCTiXqu8vBy33347br/9dvTr1y/osflcZq5U+x1J8cPnNnPxuW3nVoVhnK7i9X6b9okmIQQef/xxvPrqqyHbzZo1C0eOHMHUqVNx4MAB1NfXo7q6Gv369cPmzZtx7733xnT+uro6zJ49G7///e8xaNCgiB7Tnmhyx3ROip3D4cCiRYv4SzEJeO2TJ5HXXnW1oGZlCWpWlgQdjSQ8dsM4YhEkkSRJMozj1odO0F9/4W5/09bHXcmkOg3jTMTkRHwk+14L4HOZyXh/kLn43GYuPrftZN19pdzF95iJkPWJpo0bN2LWrFkYPnw4brrpppBtt23bho0bN6K0tBQvvPACBgwYAAAYM2YMVq9eDQBYtmxZTFPobrrpJpx++um48cYbo/8hiIgygOqog+qoC95AeIzjCIVLIkWkk32IF+FxGcZEqShV7rWIiIgovaRtomnt2rV46qmnsHv37rBt16xZAwC45JJL0LNnT79948ePx8iRI9HQ0IAPPvjAb9+gQYMgSZLh15IlS7B69Wq8/vrrWLJkCVpaWtDS0gKXywWPx4OWlpYu+9StqqqKx0qSVP0ZU/VY8ZSqP2OqHive4tm3Rx/7c6ePITzeT9RWvNked1aqXv/ndnbu8fpB3U8+/dfOHUwnW/7vZ5OuuNdKB8n6/5iM82bba4/PbebKpmvM5zbZ55SDxIk+b2pL20TT/PnzsX37du2rrKwsaNvNmzcDAKZNm2a437fd185n69atOHLkiOHX9ddfjx07dqClpQUjRoxAfn4+8vPz8eyzz2Lbtm3Iz8/HPffcE6efNrRUvblP1WPFU6r+jKl6rHhK1Z8xVY8Vb/FNNC0PuV+fOAqaRBLeqchPv9Ued1YqXn/JZMWq2n7oN+9VSCZrTMeQdavdrfjrs/HqWtb8388mXXGvlQ6y6Y/HbHvt8bnNXNl0jfncJvecHlN3wzjR5011abuucVlZmd8Nj8USfCnBPXv2APAWnDTiW+Vk7969ftuLi4tD9uG6667D97//fb9tixcvxr59+/Dkk0+GLGpJREQR0ieO4pRESleSYoJsy0fB6ItiP4bfancxTkWkrNAV91pERETpTAjjONulbaIpGrW1tQCAoqIiw/3du3szjzU1NVEdt1+/fgHJpF69eqG2thbnnHNO0Mf56hN8vvsgSt59N6pzGmlra0N1dXWnjwN4+5bJx2pubgYAbN++Hfn5+Z0+Xir+jKl6LF775B0r1LVX3W04dsAb99y2HbIpJ6L9vr6FezwAeBwNOH6yTY/tO6BYi/z22+0O7Gzz/s78Ztu/A0bqeOzH2x+/7d9QbD0CzuFuPYz6A4DDCVR/+jlMufX++5u+Rv3JY3TfvgOmgvqAY6jONhw45o1bt21PyPW3wYmDJ8/RvP3fMOX7/yzu1ob2/f/eAVNuUcDxwvVLdTlQ09N7PVu3/xuy2f96NtQfR/2eyI4VjXgeq62tDe/G4f1x+/btAMC6QF0gUfdaPr7n0PecdpbFYgmZOOt47nj936796hBw4OQ3PQ7haEvw48bzvJFKxjnjfX8QjWT8vMk6L5/bzDxnss7L57ZdzeH9kA94Y7VkJ47Wx69AejQ/q9PpjEvpnrjdO4kMMXDgQAFA7Ny5M2Cf2WwWAERDQ4PhY9etWycAiEmTJnW6H9dee60444wzQrZZsWKFgLdMBr/4xS9+8Ytf/Erw14oVKzr9/k6Jv9eaPHmyqKioMNzHeyd+8Ytf/OIXv7ruq7P3TlkxokmcHMMmgoxlUxTvNAKPp/MrEa1YsSJsm4svvhgrVqxAWVkZbDZb2PbhRPOpHBERUaqL16dydrsd33zzDS6++OI49IpCice91qZNm4Lu470TERFRcKl275QViabc3Fw0Njaivr7ecEh3a2srACAvL69L+lNcXIxrr722S85FRERElGiJvtfivRMREVH6SNtV56JRUlICAKivrzfcf/ToUQDhi38TERERUSDeaxEREZFPViSahg4dCgDYtWuX4f4dO3b4tSMiIiKiyPFei4iIiHyyItE0YcIEAMCGDRsM969fvx4AcPbZZye0H4cPH8bs2bPRt29f2Gw2DBs2DHfffTdXw+mE5557DpIk4YsvvgjaJpbrzufK2Jdffolrr70Wp59+OvLz8zFq1Chce+212Ldvn2F7Xvv42rRpEy655BIMHjwYhYWFOOuss7BgwQK0tLQYtuf1T5xPP/0UZrMZV199teF+Xvvk4bVPjmTfa4V7TVL6iPZeg1IPf6dmLr4+s0en31c7VUo8hYRaCWXbtm0CgCguLha1tbV++7Zs2SIAiKKiItHW1paw/n355Zeid+/eWhX3oqIiLT777LOF3W5P2Lkz2cUXXxz0eRcituvO58rYunXrREFBgQAgJEkSvXr1EpIkCQAiLy9P/OMf//Brz2sfX7/73e+06202m0Vpaal2bQYMGCCOHDni157XP3HcbrcYN26cACBmzJgRsJ/XPn4WLVoUdlWUuro6rT2vfWKl6r1WuNckpY9o7zUo9fB3aubi6zN7xON9NSsSTUIIcd555wkAYurUqeLgwYNCCCE++eQT0bdvXwFAVFZWJrR/3//+97XzHzhwQAghRHV1tejXr58AIH79618n9PyZRlVV8dhjj2lvXMGe91iuO5+rQE6nUwwZMkQAED/5yU+05asbGhrEbbfdJgCIbt26ia+//lp7DK99/Bw4cEBYLBZhMpnEo48+KpxOpxBCiL1794oJEyYIAOLqq6/2ewyvf+I88MAD2u8eozdfXvv4ueaaawQAccopp4jy8nLDr+PHj2vtee0TK1XvtcK9Jik9xHKvQamHv1MzE1+f2SUe76tZk2j68ssvRZ8+fQyz6+eee65wOBwJ61t1dbUAIEpLS/0+eRVCiI8++qhLRlRlijfeeENcd911YujQoX6faBs977Fcdz5Xxp599lkBQAwfPlyoqhqw/+qrrxYAxO233y6E4LWPt/nz5wsA4tprrw3Yt3//fmEymYQsy9q14fVPnN27d4ucnJygb7689vHlS6Tu3bs3bFte+8RLxXutcK9JSh/R3mtQ6uHv1MzF12f2iNf7albUaAKA/v3745NPPsH111+PsrIytLW1YejQoaisrMQbb7wBi8WSsHOvWbMGAHDJJZegZ8+efvvGjx+PkSNHoqGhAR988EHC+pAp1q5di6eeegq7d+8O2zaW687nythnn30GALjmmmsgSVLA/p/85CcAgG3btgHgtY83Xw2yyy+/PGDfwIEDUV5eDlVVtSK8vP6JIYTAT3/6U9jt9qDLrPPax9fu3bthtVoxYMCAsG157ZOvq++1InlNUvqI9l6DUg9/p2Yuvj6zQzzfVzMm0bR//34IITB8+PCgbfr06YPly5fj66+/ht1ux65du7Bw4UJYrdaE9m3z5s0AgGnTphnu9233taPg5s+fj+3bt2tfZWVlQdvGct35XBnbv38/AAT9Y8/3PBw4cAAAr328HT9+HEVFRejXr5/h/pycHABAY2MjAF7/RFm+fDneeecdzJ49G5MnTzZsw2sfPw0NDTh27BjKy8uhKErY9rz2iZdq91qRvCYpfUR7r0Gph79TMxdfn9khnu+rGZNoSmV79uwBAJSXlxvuHzJkCABg7969XdandFVWVobTTz9d+wr16Wgs153PlbFbb70Vr776Ki644ALD/R999BEAaIkQXvv4eu+991BfX48xY8YE7Dt27Bh2794Nk8mEU089FQCvfyJ8/fXX+MUvfoGysjI88MADQdvx2sePb+TqsGHDsHr1alx++eUYPXo0Lr/8cixevFhLrPrw2meXSF+TlD6ivdeg1MPfqZmLr8/MF+/3VVMc+kRh1NbWAgCKiooM93fv3h0AUFNT01VdygqxXHc+V8bOOuusoPsaGhqwePFiAO2fVPHaJ5bT6URdXR22b9+Oe+65B83NzbjxxhtRXFwMgNc/EW666SY0NjbimWeeQbdu3YK247WPH98fLGvXrsVLL72kbf/000/x8ssvY/ny5XjhhRcwYcIEALz22SbS1ySlj2jvNSj18Hdq5uLrM/PF+32VI5q6QGtrK4D2X64d+bb72lF8xHLd+VxFZ9++fZg6dSr27t2LXr164YYbbgDAa59op59+Ok455RRcdNFF2LJlC+bMmYMlS5Zo+3n94+v555/HmjVrUFFRgcsuuyxkW177+PGNaFJVFYsWLcJ//vMfNDQ04O2338aZZ56JgwcP4uqrr0ZbWxsAXvtsEs1rktJfsHsNSj38nZp9+PrMDIl4X2WiqQsIIfz+7chXe8Lj8XRZn7JBLNedz1VkXC4XHnjgAXzrW99CdXU1cnNz8fLLL2s3ELz2idW/f3+ccsopWjHGVatW4cUXX9T28/rHz7Fjx3DLLbegqKgIS5cuDdue1z5+evfujRkzZuBvf/sb7r77bowaNQrdunXDpEmT8P7772PYsGE4cOCA9rzw2meHaF+TlL7C3WtQ6uHv1OzB12fmSNT7KhNNXSA3NxcAUF9fb7jfl9XPy8vrsj5lg1iuO5+r8D7//HOcccYZuPPOO9Ha2orTTjsNH374oTZ9BeC1T7T169fjq6++QktLC/74xz+irq4OM2fOxP/93/8B4PWPp7lz56K2thZ/+MMf0Lt377Dtee3jZ/bs2Vi5ciVmzJgRsM9sNmP+/PkAgC1btgDgtc8W0b4mKT1Fcq9BqYe/U7MDX5+ZJVHvq6zR1AVKSkrQ2NiI+vp6DBo0KGD/0aNHAUCrr0LxEct153MV2ooVKzBnzhy0tbUhJycHv/zlL/HLX/4SNpvNrx2vfdfIycnBvHnz8OWXX2LJkiV44okn8J3vfIfXP07effdd/P3vf8eUKVNw3XXXRfQYXvuuM2rUKADeG16A1z4dbdy4EatXr46o7WOPPRbTa5KSI9rnVi/Sew1KPfydmvn4+swsiXxfZaKpCwwdOhR79+7Frl27MHbs2ID9O3bs0NpR/MRy3flcBbdq1SrMmjULQghMmjQJTz/9NAYOHGjYltc+fmpra/Hiiy8iLy8P11xzjWGb733ve1iyZIm2pCyvf3z4lvJ96623tGmKHT3//PN4/vnnAXiX9OW17zq+T84LCgoA8P99Ovr000/x+OOPR9T2sccei+k1GWwpbkqsaJ9bn2juNSj18HdqZuPrM/Mk8n2VU+e6gG8Y4YYNGwz3r1+/HgBw9tlnd1mfskEs153PlbGDBw/ixz/+MYQQuPnmm/HWW2+FfGPhtY8fRVFw44034qc//WnQmgcNDQ0AgLKyMgC8/vFSUFCA8vJyw6/S0lIAQH5+vrbNZDLx2sdJS0sLvvWtb+Hb3/42WlpaDNvs2rULADBixAgA/H+fjm6//XYIISL6AmJ7TVJyRPvcAtHfa1Dq4e/UzMXXZ2ZK6PuqoITbtm2bACCKi4tFbW2t374tW7YIAKKoqEi0tbUlqYfpa+DAgQKA2LlzZ8C+WK47nytjd999twAgpk2bFlF7Xvv4Gjx4sAAg3nzzTcP9l1xyiQAgHnzwQSEEr39XePrppwUAMWPGDL/tvPbxM3XqVAFALF261HD/5MmTBQDx5JNPCiF47bNdsNckpY9o7zUo9fB3aubi6zP7dPZ9lYmmLnLeeecJAGLq1Kni4MGDQgghPvnkE9G3b18BQFRWVia5h+kpVKJJiNiuO5+rQMOHDw+Z6DDCax8/v//97wUAMXjwYPHuu+8KVVWFEEIcO3ZM3HLLLQKA6NWrl99NHa9/YoV68+W1j49Vq1YJACInJ0c8++yz2v/7I0eOiB//+McCgBgzZozweDzaY3jtsxcTTekvlnsNSj38nZqZ+PrMPkw0pYkvv/xS9OnTRwDQsvm++NxzzxUOhyPZXUxL4RJNsVx3Plf+PB6PMJvNAoAYMGCAKC8vD/p12WWXaY/jtY8ft9stJk6cqF2L3Nxc0bt3b+37bt26iXXr1vk9htc/sUK9+fLax8+cOXO062Cz2URpaan2/dChQ8WOHTv82vPaZy8mmtJbrPcalHr4OzXz8PWZnZhoSiPffPONuP7660VZWZmwWq1i6NChorKyUtjt9mR3LW2FSzQJEdt153PV7tChQ9oNQrivM844w++xvPbx4/F4xGOPPSbOPfdc0adPH1FQUCDGjx8vbr75ZnHkyBHDx/D6J064N19e+/h5+eWXxeTJk0VZWZkoLCwU3/3ud8Vdd90lmpubDdvz2mcnJprSW2fuNSj18HdqZuHrMzt19n1VEiJIdVkiIiIiIiIiIqIocNU5IiIiIiIiIiKKCyaaiIiIiIiIiIgoLphoIiIiIiIiIiKiuGCiiYiIiIiIiIiI4oKJJiIiIiIiIiIiigsmmoiIiIiIiIiIKC6YaCIiIiIiIiIiorhgoomIiIiIiIiIiOKCiSYiIiIiIiIiIooLJpqIiIiIiIiIiCgumGgiIiIiIiIiIqK4YKKJiIiIiIiIiIjigokmIiIiIiIiIiKKCyaaiIiIiIiIiIgoLphoIiIiIiIiIiKiuGCiiYiIiIiIiIiI4oKJJiIiIiIiIiIiigsmmoiIiIiIiIiIKC6YaCIiIkqgRYsWQZIkSJKE3bt3h2w7atQoSJKEMWPG+G33PT7Ul+8xzzzzTETtJUnC9773vaB9eeedd/zaLlmyJKDN9u3b8ZOf/ASDBg1CTk4O+vfvjylTpuCJJ56A2+2O4Wp5bdq0CZIk4bLLLgvZ7s4774QkSXj66af9rrMkSfj0009jPj8RERERxc6U7A4QERFlixdffBG//OUvDfft3r0bn332WdDH9u3bFxUVFUH3n3LKKQCAESNGYO7cuX77NmzYgM8//xzXXXcdCgsLte3l5eVh+zxp0iSMGTMmIPn1+OOPY+7cufB4PBg1ahS+973vob6+Hh988AGuv/56/PGPf8Tbb7+N0tLSsOcwOmfv3r2xfv16NDU1oaCgwLDdK6+8ApPJhB/84AfYunUr5s6diy1btmDLli1Rn5OIiIiI4kQQERFRwlRWVgoAoqCgQIwfPz5ouwceeEBIkiTy8/PF6NGj/fYBEJMnT465D9dee60AIA4cOBDxY95++20BQDzyyCMB++677z4BQJx++uliz549fvtaWlrEvHnzBABx1llnCbfbHVOfb775ZgFArFy50nD/F198IQCICy64wG+773pv3749pvMSERER6fnuLQCIXbt2hWw7cuRIAcDvXs53TxXua+7cuUKI9vu2SL4qKysT+rPHiiOaiIiIusBFF12E559/HocOHUK/fv0C9r/00ks488wzUVNTk4TeRa6mpgb33Xcfhg8fjs2bNyMnJ8dvf25uLv74xz9i3759eOWVV/Dmm2/i/PPPj/o8V111FZYtW4ZVq1ZhxowZAftffvllAAg5youIiIgonjozOn3ChAmYMGFC0P2TJk0CAJx//vkoKiry2/fUU08BAK677rqAY0aroaEBPXr0wM9+9jMsW7Ys6sdHgokmIiKiLjB9+nQ8//zzePHFF3Hrrbf67Tt8+DA+/PBD/O53v8Njjz2WpB5G5v7770dzczPuv//+gCST3m9+8xuUlJSgvr4+YN+jjz6KlStX4tNPP0WPHj1w0UUX4Te/+Q169+6ttTnnnHPQt29fvP7662htbUVubq7fMV555RUoioJLL700fj8cERERURAFBQVYvXp10ETTSy+9BEmSkJeXZ7h/2rRpWLhwYdjzXH311bj66qv9tvk+YHv44Yej67SBjz/+GEIIjB8/vtPHCobFwImIiLrAsGHDMGrUKKxevTpg38svvwwhRNji16ng/fffR/fu3XH55ZeHbDdu3Dj85S9/wVVXXeW3/ZprrsGcOXPQ0NCAK6+8Ev369cOjjz6Ks88+G998843WTpIkXHnllWhtbcXrr7/ud4yjR49iy5YtmDx5MkpKSuL3wxEREREFcdFFF2Hr1q04dOiQ4X7f6PTi4uIu7ll0tm7dCgBMNBEREWWC6dOn44MPPgiYHvfyyy9j5MiRGDZsWNDH7tmzB/PmzQv6tX79+kR3HwCwa9cuDB8+PKbHbtiwAc8++yzmzZuHf//733jyySfx3nvv4cknn8SBAwdw1113+bX3JalWrVrlt/1f//oXVFXltDkiIiLqMtOnTwfgnT7XkW90uq9NqvJ4PHjvvfdgtVoxePDghJ2HiSYiIqIuMn36dKiqqg1/BoATJ05g06ZNYUcIffXVV3jkkUeCfnXFSmu1tbVoampC//79A/Z9/fXXkCQp4GvQoEFam2XLlqF79+544IEHIEmStv26667D2WefjZdeegmqqmrbzzrrLAwcOBCvvvoq7Ha7tv2VV16BLMtpMQKMiIiIMkM6j06vqanB7Nmz0bt3b7z++utwOBzo27cv7rzzTng8nrifjzWaiIiIusjpp5+OwYMHY/Xq1bjhhhsAAK+++ipcLlfYRNPkyZOxadOmruhmUN27d4fFYsGRI0cC9uXn52Pu3Ll+2/75z3/6fb9z507YbDb84he/CHh8Y2MjGhsb8c0336Bv377a9v/5n//Bgw8+iPXr1+PSSy9FS0sLNm7ciHPPPdevphMRERFRok2fPh333nsvampq0KtXL217JKPT161bh4aGhqD7b775ZgwZMiSe3dXO+6Mf/QgtLS0499xzsWHDBpxzzjk4dOgQHnjgAXz99df429/+FtdzMtFERETUhaZPn45HHnkE9fX16N69O1566SX0798fY8eOTXbXwjKZTCgvL8fu3bsD9nXr1i2gQOVrr70Gp9OpfX/o0CHY7XY88sgjQc/R1NTk9/1VV12FBx98EKtXr8all16K9evXw263c9ocERERdbnp06fjt7/9LV5++WXtQ0Pf6PRgRcJ9tmzZEnIE+mWXXRb3RNOnn36KK664AqNHj8bzzz+P7du3Y8OGDbjjjjswdepUTJkyBX//+9+xYMECjBgxIm7n5dQ5IiKiLjR9+nS43W7861//gt1ux7p168KOZkolo0ePxuHDh8OOrmpsbMSePXv8tvXp0wcTJ06EECLo12mnneb3mLFjx6K8vBz/+te/4HQ68corr0CSpLS6ZkRERJQZ9KPTfSIdnV5ZWRnyHmjSpElx7asQAldddRX69euH119/Hf369cNHH30EADjjjDNQUFCARYsWAUDAwiudxUQTERFRF5owYQLKysqwevVqbNiwAS0tLWmVNFmwYAEURcEvfvELuN3uoO0WLlzoV28JAIYOHYrPPvsMDocjoP2zzz6L5cuXGx7rqquuwokTJ7Bu3TqsXbsW3/nOd3DKKad07gchIiIiisH06dOxadMm1NfXA0DKjk5/66238MUXX2Dx4sUoLCwEAHz00Ufo3bu3VqagvLwcAKAoSlzPzUQTERFRF5IkCZdddhk2bNiAv/71ryguLsZ3v/vdZHcrYqNGjcKNN96Ijz/+GJMnTw5Y4tfpdOK3v/0t/vSnP8Fqtfrtu/7663Hs2DH85je/gRBC2/7ee+/hf//3f4MOJ/etPvfLX/4Sx44d47Q5IiIiSpp0GZ2+du1aAMBFF12kbfv4448xfvx47fv9+/cDgN/iLfHAGk1ERERdbPr06Xj00UexevVqXHfddRF9irRnzx7MmzcvZJv58+ejrKwsXt0M6o9//CPMZjOWLFmC8vJyjBo1Ct/+9rdx/PhxbN26FTU1Nbjnnnuwf/9+bNy4UXtcRUUFLrzwQvz+97/Hhg0bMG7cOHz22Wf4+OOP0adPH9x7772G5/vWt76F0047Df/9738BIOWXDiYiIqLMpR+d3r1795Qdnd7a2goAkGXv+KI9e/bg+PHjfommxx57DL169cKFF14Y13Mz0URERNTFJk2ahJ49e+LYsWMR35h89dVXIYtoA8C1117bJYkmi8WChx9+GNOmTcOf/vQnbN26FTt27EBJSQmmTJmCefPmYdy4cVi+fLlfogkA1qxZg4ceegirV6/GP/7xD5SUlOC6667DwoULQ/b9qquuQmVlJc4880z0798/0T8iERERkSHf6PQnn3wSFoslZUen++6rVq1ahR/+8IdafSZfomnJkiV45ZVX8Nhjj8FsNsf13JLQj10nIiIiAvDOO+9g8uTJeOSRR3DrrbcmuzsRW7RoESorK7F9+3acfvrpye4OERERpTmje4s333wT3//+9wEA1113HZ588kmt/aBBg1BUVIRt27YBaL+nmjBhAiZMmBD0PDk5ObjvvvsM9/mmtvmmukXi4MGDGDJkCHJycvDEE0/gvffew7Jly/DOO+/gqaeewjPPPIPrr78+aI3MzuCIJiIiIiIiIiKiCMUyOn3Lli1B61ECQFFRUdBEUyz69++PlStX4oc//KFW7xLw9j03NxdLly7FnDlz4nY+PY5oIiIiogC+T98mTZqEMWPG4PLLL8fEiROT3a2g1q9fj3Xr1mk3cRzRRERERATs3r0bTz/9NO677z6cdtppuO222/CDH/wApaWlCTsnV50jIiKioN555x088sgj2vDvVLVlyxY88sgjIT8pJCIiIso2Q4cOxdVXXw0AuOGGG/DTn/40oUkmgCOaiIiIiIiIiIgy1pNPPomf/vSneP/993HOOeck/Hwc0URERERERERElKE+/PBDKIqCMWPGdMn5OKKJiIiIiIiIiChDjR49GkIIfPrpp11yPiaaiIiIiIiIiIgoLjh1joiIiIiIiIiI4oKJJiIiIiIiIiIiigsmmoiIiIiIiIiIKC6YaCIiIiIiIiIiorhgoomIiIiIiIiIiOKCiSYiIiIiIiIiIooLJpqIiIiIiIiIiCgumGgiIiIiIiIiIqK4YKKJiIiIiIiIiIjigokmIiIiIiIiIiKKCyaaiIiIiIiIiIgoLv4/f06+f+95OBoAAAAASUVORK5CYII=",
-      "text/plain": [
-       "<Figure size 1200x1000 with 4 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "columns = ['PRI_lep_pt', 'PRI_lep_eta', 'PRI_met', 'PRI_met_phi']\n",
-    "nbins = 50\n",
-    "\n",
-    "xlabel_dict = {\n",
-    "    'PRI_lep_pt': 'lepton $p_T$ [GeV]', \n",
-    "    'PRI_lep_eta': 'lepton $\\\\eta$ [GeV]', \n",
-    "    'PRI_met': 'MET [GeV]', \n",
-    "    'PRI_met_phi': 'MET $\\\\phi$',     \n",
-    "    'PRI_n_jets': '$n_{jets}$'      \n",
-    "}\n",
-    "\n",
-    "variations_to_plot = ['Nominal', 'JES_Up', 'JES_Dn']\n",
-    "\n",
-    "\n",
-    "fig, axes = plot_kinematic_features(\n",
-    "    columns=columns,\n",
-    "    nbins=nbins,\n",
-    "    variations_to_plot=variations_to_plot,\n",
-    "    dataset_dict=datasets_all,\n",
-    "    xlabel_dict=xlabel_dict,\n",
-    "    samples_list=samples_list\n",
-    ")\n",
-    "plt.show()\n"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 15,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "<>:7: SyntaxWarning: invalid escape sequence '\\e'\n",
-      "<>:8: SyntaxWarning: invalid escape sequence '\\p'\n",
-      "<>:7: SyntaxWarning: invalid escape sequence '\\e'\n",
-      "<>:8: SyntaxWarning: invalid escape sequence '\\p'\n",
-      "/tmp/ipykernel_1494407/3719328280.py:7: SyntaxWarning: invalid escape sequence '\\e'\n",
-      "  'PRI_had_eta': 'hadron $\\eta$ [GeV]',\n",
-      "/tmp/ipykernel_1494407/3719328280.py:8: SyntaxWarning: invalid escape sequence '\\p'\n",
-      "  'PRI_had_phi': 'hadron $\\phi$',\n"
-     ]
-    },
-    {
-     "data": {
-      "image/png": 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",
-      "text/plain": [
-       "<Figure size 1200x1000 with 4 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "columns = ['PRI_had_pt', 'PRI_had_eta', 'PRI_had_phi', 'PRI_jet_all_pt']\n",
-    "# columns = ['PRI_had_pt', 'PRI_had_eta', 'PRI_had_phi']\n",
-    "nbins = 50\n",
-    "\n",
-    "xlabel_dict = {\n",
-    "    'PRI_had_pt': 'hadron $p_T$ [GeV]', \n",
-    "    'PRI_had_eta': 'hadron $\\eta$ [GeV]', \n",
-    "    'PRI_had_phi': 'hadron $\\phi$', \n",
-    "    'PRI_jet_all_pt': 'sum jet pT [GeV]',\n",
-    "}\n",
-    "\n",
-    "variations_to_plot = ['Nominal', 'JES_Up', 'JES_Dn']\n",
-    "\n",
-    "\n",
-    "fig, axes = plot_kinematic_features(\n",
-    "    columns=columns,\n",
-    "    nbins=nbins,\n",
-    "    variations_to_plot=variations_to_plot,\n",
-    "    dataset_dict=datasets_all,\n",
-    "    xlabel_dict=xlabel_dict,\n",
-    "    samples_list=samples_list\n",
-    ")\n",
-    "plt.show()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 16,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "<>:8: SyntaxWarning: invalid escape sequence '\\e'\n",
-      "<>:9: SyntaxWarning: invalid escape sequence '\\p'\n",
-      "<>:8: SyntaxWarning: invalid escape sequence '\\e'\n",
-      "<>:9: SyntaxWarning: invalid escape sequence '\\p'\n",
-      "/tmp/ipykernel_1494407/2362404005.py:8: SyntaxWarning: invalid escape sequence '\\e'\n",
-      "  'PRI_jet_leading_eta': 'leading jet $\\eta$ [GeV]',\n",
-      "/tmp/ipykernel_1494407/2362404005.py:9: SyntaxWarning: invalid escape sequence '\\p'\n",
-      "  'PRI_jet_leading_phi': 'leading jet $\\phi$',\n"
-     ]
-    },
-    {
-     "data": {
-      "image/png": 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",
-      "text/plain": [
-       "<Figure size 1200x1000 with 4 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "columns = ['PRI_jet_leading_pt', 'PRI_jet_leading_eta', 'PRI_jet_leading_phi', 'PRI_jet_subleading_pt']\n",
-    "\n",
-    "# columns = ['PRI_had_pt', 'PRI_had_eta', 'PRI_had_phi']\n",
-    "nbins = 50\n",
-    "\n",
-    "xlabel_dict = {\n",
-    "    'PRI_jet_leading_pt': 'leading jet $p_T$ [GeV]', \n",
-    "    'PRI_jet_leading_eta': 'leading jet $\\eta$ [GeV]', \n",
-    "    'PRI_jet_leading_phi': 'leading jet $\\phi$', \n",
-    "    'PRI_jet_subleading_pt': 'subleading jet $p_T$ [GeV]',\n",
-    "}\n",
-    "\n",
-    "variations_to_plot = ['Nominal', 'JES_Up', 'JES_Dn']\n",
-    "\n",
-    "\n",
-    "fig, axes = plot_kinematic_features(\n",
-    "    columns=columns,\n",
-    "    nbins=nbins,\n",
-    "    variations_to_plot=variations_to_plot,\n",
-    "    dataset_dict=datasets_all,\n",
-    "    xlabel_dict=xlabel_dict,\n",
-    "    samples_list=samples_list\n",
-    ")\n",
-    "plt.show()"
-   ]
-  }
- ],
- "metadata": {
-  "kernelspec": {
-   "display_name": "Python (pixi: nsbi-env-gpu-new-new-new)",
-   "language": "python",
-   "name": "nsbi-env-gpu-ne-neww"
-  },
-  "language_info": {
-   "codemirror_mode": {
-    "name": "ipython",
-    "version": 3
-   },
-   "file_extension": ".py",
-   "mimetype": "text/x-python",
-   "name": "python",
-   "nbconvert_exporter": "python",
-   "pygments_lexer": "ipython3",
-   "version": "3.12.12"
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 4
-}
diff --git a/FAIR_universe_Higgs_tautau/Neural_Likelihood_Ratio_Estimation.ipynb b/FAIR_universe_Higgs_tautau/Neural_Likelihood_Ratio_Estimation.ipynb
deleted file mode 100644
index 4bc90600..00000000
--- a/FAIR_universe_Higgs_tautau/Neural_Likelihood_Ratio_Estimation.ipynb
+++ /dev/null
@@ -1,1222 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "markdown",
-   "id": "85242300-60a2-467d-9f11-8af27bdc312a",
-   "metadata": {},
-   "source": [
-    "Train Neural Networks to estimate Likelihood Ratios\n",
-    "===\n",
-    "\n",
-    "In this notebook we will setup the neural networks that train unbiased and low-variance density ratios to be then used for neural simulation-based inference. "
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 1,
-   "id": "0a769dae-97a2-44f9-bc81-e4c74ff7bedb",
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "/home/jsandesara_umass_edu/NSBI-workflow-tutorial/src/nsbi_common_utils/plotting.py:10: FutureWarning: ``set_style`` is deprecated: Naming convention is changing to match mpl. Use ``mplhep.style.use()``.\n",
-      "  hep.set_style(\"ATLAS\")\n",
-      "2025-11-12 08:47:02.076767: E external/local_xla/xla/stream_executor/cuda/cuda_fft.cc:485] Unable to register cuFFT factory: Attempting to register factory for plugin cuFFT when one has already been registered\n",
-      "2025-11-12 08:47:02.183161: E external/local_xla/xla/stream_executor/cuda/cuda_dnn.cc:8454] Unable to register cuDNN factory: Attempting to register factory for plugin cuDNN when one has already been registered\n",
-      "2025-11-12 08:47:02.231527: E external/local_xla/xla/stream_executor/cuda/cuda_blas.cc:1452] Unable to register cuBLAS factory: Attempting to register factory for plugin cuBLAS when one has already been registered\n",
-      "2025-11-12 08:47:02.707517: I tensorflow/core/platform/cpu_feature_guard.cc:210] This TensorFlow binary is optimized to use available CPU instructions in performance-critical operations.\n",
-      "To enable the following instructions: SSE4.1 SSE4.2 AVX AVX2 AVX512F FMA, in other operations, rebuild TensorFlow with the appropriate compiler flags.\n"
-     ]
-    }
-   ],
-   "source": [
-    "import os, sys, pathlib, importlib\n",
-    "sys.path.append('../')\n",
-    "\n",
-    "# Load the package and modules for training and plotting\n",
-    "import nsbi_common_utils\n",
-    "from nsbi_common_utils import plotting, training, datasets, configuration\n",
-    "from nsbi_common_utils.training import density_ratio_trainer\n",
-    "\n",
-    "\n",
-    "import tensorflow as tf\n",
-    "from tensorflow import keras\n",
-    "from tensorflow.keras import layers\n",
-    "import numpy as np\n",
-    "import pandas as pd\n",
-    "from sklearn.model_selection import train_test_split\n",
-    "from tensorflow.keras.optimizers import Nadam\n",
-    "import mplhep as hep\n",
-    "import matplotlib.pyplot as plt\n",
-    "import pickle\n",
-    "import yaml\n",
-    "import random\n",
-    "\n",
-    "from utils import preselection_using_score, calculate_preselection_observable\n",
-    "\n",
-    "from coffea.analysis_tools import PackedSelection\n",
-    "\n",
-    "import warnings\n",
-    "warnings.filterwarnings(\"ignore\", category=RuntimeWarning)\n",
-    "warnings.filterwarnings(\"ignore\", category=FutureWarning)\n",
-    "\n",
-    "hep.style.use(hep.style.ATLAS)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 2,
-   "id": "4de8df61",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "importlib.reload(sys.modules['nsbi_common_utils.configuration'])\n",
-    "from nsbi_common_utils import configuration\n",
-    "\n",
-    "# Load the config file to get metadata like training features\n",
-    "config = nsbi_common_utils.configuration.ConfigManager(file_path_string = './config.yml')\n"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 3,
-   "id": "ec064a6c",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# Input features for training\n",
-    "features, features_scaling = config.get_training_features()"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "af1d065f-de1e-4c32-8adb-6879ff3c412f",
-   "metadata": {},
-   "source": [
-    "Statistical Model\n",
-    "-\n",
-    "\n",
-    "The statistical model for the analysis can be written as: \n",
-    "\n",
-    "$$p(x|\\mu, \\alpha) = \\frac{1}{\\nu(\\mu, \\alpha)} \\sum_c f_c(\\mu) \\cdot \\nu_c(\\alpha) \\cdot p_c\\left(x|\\alpha\\right)$$\n",
-    "\n",
-    "where $c$ stands for the various physics channels that contribute to the final state $x$, **$\\mu$ is the signal-strength parameter and $\\alpha$ is the vector of nuisance parameters associated with the various systematic uncertainties in the model**. Note that we are assuming that the parameter $\\mu$-dependence is known analytically, given by $f(\\mu)$ and that we have simulation models for each of the channels $p_c(x)$. \n",
-    "\n",
-    "Setting up the test\n",
-    "-\n",
-    "\n",
-    "The objective is to build the test statistic for composite hypothesis testing:\n",
-    "\n",
-    "$$t_\\mu = -2 \\cdot \\frac{\\text{Pois}(\\mathcal{N}_\\text{evts}|\\mu, \\hat{\\hat{\\alpha}})}{\\text{Pois}(\\mathcal{N}_\\text{evts}|\\hat{\\mu}, \\hat{\\alpha})} -2 \\cdot \\sum_i^{\\mathcal{N}_\\text{evts}} w_i \\times \\log \\frac{p(x_i|\\mu, \\hat{\\hat{\\alpha}})}{p(x_i|\\hat{\\mu}, \\hat{\\alpha})} + \\sum_m^{N_\\text{systs}} \\alpha_m^2$$\n",
-    "\n",
-    "A direct approach would then be to model the probability density $p(x|\\mu, \\alpha)$. This can be done using generative models like Normalizing Flows. \n",
-    "\n",
-    "In the presence of high-dimensional inputs, including discrete inputs like the number of jets, this is a more difficult task than training probability density *ratios*. We rewrite the test statistic to turn this into a density ratio estimation task:\n",
-    "\n",
-    "$$t_\\mu = -2 \\cdot \\frac{\\text{Pois}(\\mathcal{N}_\\text{evts}|\\mu, \\hat{\\hat{\\alpha}})}{\\text{Pois}(\\mathcal{N}_\\text{evts}|\\hat{\\mu}, \\hat{\\alpha})} -2 \\cdot \\sum_i^{\\mathcal{N}_\\text{evts}} w_i \\times \\log \\frac{p(x_i|\\mu, \\hat{\\hat{\\alpha}})/p_{ref}(x)}{p(x_i|\\hat{\\mu}, \\hat{\\alpha})/p_{ref}(x)} + \\sum_m^{N_\\text{systs}} \\alpha_m^2$$\n",
-    "\n",
-    "\n",
-    "Then using the analytical statistical model:\n",
-    "\n",
-    "$$\\frac{p(x|\\mu, \\alpha)}{p_{ref}(x)} = \\frac{1}{\\nu(\\mu, \\alpha)} \\sum_c f_c(\\mu) \\cdot \\nu_c(\\alpha) \\cdot \\frac{p_c\\left(x|\\alpha\\right)}{p_{ref}(x)}$$\n",
-    "\n",
-    "Further rewriting of the model, factorizing out the $\\alpha$-dependence gives,\n",
-    "\n",
-    "$$\\frac{p(x|\\mu, \\alpha)}{p_{ref}(x)} = \\frac{1}{\\sum_c G_c(\\alpha) \\cdot f_c(\\mu) \\cdot \\nu_c} \\sum_c f_c(\\mu) \\cdot G_c(\\alpha)\\cdot \\nu_c \\cdot g_c(x|\\alpha) \\cdot \\frac{p_c\\left(x\\right)}{p_{ref}(x)}$$\n",
-    "\n",
-    "Factorized approach:\n",
-    "--\n",
-    "\n",
-    "Instead of training NNs parameterized on $\\mu$ and $\\alpha$, we use analytical parameterizations to simplify the problem to training only parameter-independent density ratios.\n",
-    "\n",
-    "Let's set this up for the toy $H\\to \\tau\\tau$ model we are using. **Neglecting the nuisance parameter dependence ($G_c(\\alpha) = g_c(x|\\alpha) = 1$), the probability model can be written as** \n",
-    "\n",
-    "$$\\sum_c f_c(\\mu) \\cdot \\nu_c(\\mu) \\cdot \\frac{p_c\\left(x\\right)}{p_{ref}(x)} = \\mu \\cdot \\nu_{H \\to \\tau\\tau}(\\mu) \\cdot  \\frac{p_{H \\to \\tau\\tau}\\left(x\\right)}{p_{ref}(x)} + \\nu_{t\\bar{t}}(\\mu) \\cdot  \\frac{p_{t\\bar{t}}\\left(x\\right)}{p_{ref}(x)} + \\nu_{Z \\to \\tau\\tau}(\\mu) \\cdot  \\frac{p_{Z \\to \\tau\\tau}\\left(x\\right)}{p_{ref}(x)}$$\n",
-    "\n",
-    "In the NSBI approach presented in this tutorial, the density ratio estimation as a function of the various nuisance parameters is factorized from the nominal density ratio estimation, and will be done in the  `Systematic_Uncertainty_Estimation.ipynb` notebook."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 4,
-   "id": "14831cb9",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "importlib.reload(sys.modules['nsbi_common_utils.datasets'])\n",
-    "from nsbi_common_utils import datasets\n",
-    "\n",
-    "branches_to_load = features + ['presel_score']\n",
-    "\n",
-    "Datasets = nsbi_common_utils.datasets.datasets(config_path = './config.yml',\n",
-    "                                                branches_to_load =  branches_to_load)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 5,
-   "id": "51f6cf55",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "dataset_incl_dict      = Datasets.load_datasets_from_config(load_systematics = False)\n",
-    "\n",
-    "dataset_incl_nominal   = dataset_incl_dict[\"Nominal\"].copy()\n",
-    "\n",
-    "dataset_SR_nominal     = Datasets.filter_region_dataset(dataset_incl_nominal,\n",
-    "                                                       region = \"SR\")"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "94d325c1-c6ba-46f7-bdcb-fb9f4c120986",
-   "metadata": {},
-   "source": [
-    "Density ratio training\n",
-    "===\n",
-    "\n",
-    "Now we train the NNs for $\\frac{p_c}{p_{ref}}(x)$ density ratios to build the full model. Our choice of reference hypothesis, motivated by the search-oriented mixture model described above, would be: \n",
-    "\n",
-    "$$p_{ref}(x) = p_{H \\to \\tau\\tau}(x) = \\frac{1}{\\nu_{H \\to \\tau\\tau}} \\frac{d\\sigma_{H \\to \\tau\\tau}}{dx} $$\n",
-    "\n",
-    "gives the POI $\\mu-$parameterized model:\n",
-    "\n",
-    "$$\\sum_c \\left[f_c(\\mu) \\cdot \\frac{p_c\\left(x\\right)}{p_{ref}(x)} \\right]= \\mu + \\frac{p_{t\\bar{t}}\\left(x\\right)}{p_{H \\to \\tau\\tau}(x)} + \\frac{p_{Z \\to \\tau\\tau}\\left(x\\right)}{p_{H \\to \\tau\\tau}(x)}$$\n",
-    "\n",
-    "But this has shown to cause numerical issues, since the ratio corresponding to the $\\mu$-scaling signal term is exactly 1. In the absence of additional signal production mechansims, we instead use:\n",
-    "\n",
-    "$$p_{ref}(x) = \\frac{1}{\\nu_{H \\to \\tau\\tau} + \\nu_{t\\bar{t}}} \\left[\\frac{d\\sigma_{H \\to \\tau\\tau}}{dx} + \\frac{d\\sigma_{t\\bar{t}}}{dx} \\right]$$\n",
-    "\n",
-    "giving a more numerically stable model:\n",
-    "\n",
-    "$$\\sum_c \\left[f_c(\\mu) \\cdot \\frac{p_c\\left(x\\right)}{p_{ref}(x)} \\right]= \\mu \\cdot \\frac{p_{H \\to \\tau\\tau}\\left(x\\right)}{p_{ref}(x)} + \\frac{p_{t\\bar{t}}\\left(x\\right)}{p_{ref}(x)} + \\frac{p_{Z \\to \\tau\\tau}\\left(x\\right)}{p_{ref}(x)}$$\n",
-    "\n",
-    "The task of estimating the $\\mu$-parameterized density ratio is thus reduced to estimating three $\\mu$-independent density ratios $\\frac{p_{H \\to \\tau\\tau}}{p_{ref}}(x)$, $\\frac{p_{t\\bar{t}}}{p_{ref}}(x)$ and $\\frac{p_{Z \\to \\tau\\tau}}{p_{ref}}(x)$ mixed together with an analytical parameterization (hence the name mixture model).\n",
-    "\n",
-    "**NB**: This is the same as the *morphing-aware* approach of density ratio estimation in MadMiner"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 6,
-   "id": "bf0a4b5b-fa08-4c7c-a240-7353809b67a9",
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "['htautau', 'ttbar', 'ztautau']\n",
-      "['htautau', 'ttbar']\n"
-     ]
-    }
-   ],
-   "source": [
-    "# Define the processes that make up the mixture model formula for density ratio estimation\n",
-    "\n",
-    "PATH_TO_SAVED_DATA = './saved_datasets/'\n",
-    "TRAINING_OUTPUT_PATH = f'{PATH_TO_SAVED_DATA}output_training_nominal/'\n",
-    "\n",
-    "# Signal processes in the model\n",
-    "basis_processes = config.get_basis_samples()\n",
-    "print(basis_processes)\n",
-    "\n",
-    "ref_processes = config.get_reference_samples()\n",
-    "print(ref_processes)\n"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 7,
-   "id": "8966e691-14bb-4c04-8de3-880e5ccea5b6",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "importlib.reload(sys.modules['nsbi_common_utils.training'])\n",
-    "from nsbi_common_utils.training import density_ratio_trainer\n",
-    "\n",
-    "NN_training_mix_model = {}\n",
-    "\n",
-    "use_log_loss = False\n",
-    "\n",
-    "# DELETE_EXISTING_MODELS = True\n",
-    "DELETE_EXISTING_MODELS = False\n",
-    "\n",
-    "path_to_ratios = {}\n",
-    "path_to_figures = {}\n",
-    "path_to_models = {}\n",
-    "\n",
-    "for process_type in basis_processes:\n",
-    "\n",
-    "    dataset_mix_model = Datasets.prepare_basis_training_dataset(dataset_SR_nominal, [process_type], dataset_SR_nominal, ref_processes)\n",
-    "\n",
-    "    # Save paths\n",
-    "    output_name                     = f'{process_type}'\n",
-    "    output_dir                      = f'{TRAINING_OUTPUT_PATH}general_output_{process_type}'\n",
-    "    path_to_ratios[process_type]    = f'{TRAINING_OUTPUT_PATH}output_ratios_{process_type}/'\n",
-    "    path_to_figures[process_type]   = f'{TRAINING_OUTPUT_PATH}output_figures_{process_type}/'\n",
-    "    path_to_models[process_type]    = f'{TRAINING_OUTPUT_PATH}output_model_params_{process_type}/'\n",
-    "\n",
-    "    NN_training_mix_model[process_type] = density_ratio_trainer(dataset_mix_model, \n",
-    "                                                                   dataset_mix_model['weights_normed'].to_numpy(),\n",
-    "                                                                   dataset_mix_model['train_labels'].to_numpy(),\n",
-    "                                                                   features, \n",
-    "                                                                   features_scaling, \n",
-    "                                                                   [process_type, 'ref'], \n",
-    "                                                                    output_dir, output_name, \n",
-    "                                                                    path_to_figures=path_to_figures[process_type],\n",
-    "                                                                    path_to_ratios=path_to_ratios[process_type], \n",
-    "                                                                    path_to_models=path_to_models[process_type],\n",
-    "                                                                    use_log_loss = use_log_loss,\n",
-    "                                                                    delete_existing_models = DELETE_EXISTING_MODELS)\n",
-    "\n",
-    "    del dataset_mix_model"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "76ecb1e9-7125-4b9b-ba07-ff625f6bfd83",
-   "metadata": {},
-   "source": [
-    "Neural Network architecture\n",
-    "===\n",
-    "\n",
-    "Here we will start with a multi-layer perceptron (simple feed-forward Neural Network with fully-connected layers). This can be supplemented with more complex architectures like transformers. \n",
-    "\n",
-    "After detailed tuning during the off-shell Higgs boson analysis effort, we found that a network with very wide layers ($\\geq 1000$) and a depth of less than 10 works best - alongside batch size of a few hundreds and a gradually declining learning rate that starts with a value just large enough to not blow up."
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "a16f3dd7-eb37-4440-a316-090ce6136c43",
-   "metadata": {},
-   "source": [
-    "Starting with the training and validation of the various density ratio:\n",
-    "\n",
-    "$$\\frac{p_{H\\to\\tau\\tau}(x)}{p_\\text{ref}(x)}, \\frac{p_{t\\bar{t}}(x)}{p_\\text{ref}(x)}, \\frac{p_{Z \\to \\tau\\tau}(x)}{p_\\text{ref}(x)}$$"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 8,
-   "id": "1973aa36-189e-436d-9b18-add588b946aa",
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Num GPUs Available: 1\n"
-     ]
-    }
-   ],
-   "source": [
-    "print(\"Num GPUs Available:\", len(tf.config.list_physical_devices('GPU')))"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 9,
-   "id": "4de59bde-2332-4488-be38-62fae1ed4aca",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# You can either train your own NNs (in which case set this to False) or you can load the pre-trained NNs. \n",
-    "# Training NNs without GPUs can be very slow, for the kind of NNs we need for unbiased estimation of density ratios\n",
-    "USE_SAVED_MODELS = True\n",
-    "\n",
-    "# Define unique settings for each process type\n",
-    "doCalibration = False\n",
-    "RECALIBRATE = False\n",
-    "num_bins_cal = 500\n",
-    "scaling_type = 'MinMax'\n",
-    "batch_size = 512\n",
-    "validation_split = 0.1\n",
-    "holdout_split = 0.25\n",
-    "\n",
-    "num_epochs = 100\n",
-    "callback_patience = 30\n",
-    "\n",
-    "num_layers = 4\n",
-    "num_neurons_per_layer = 1000\n",
-    "\n",
-    "num_ensemble_members = 10\n",
-    "\n",
-    "verbose_level = 1\n",
-    "\n",
-    "PLOT_SCALED_FEATURES = False\n",
-    "\n",
-    "CALIBRATION_TYPE = \"histogram\"\n",
-    "\n",
-    "training_settings = {\n",
-    "\n",
-    "    'htautau': {\n",
-    "        'hidden_layers':        num_layers,\n",
-    "        'neurons':              num_neurons_per_layer,\n",
-    "        'number_of_epochs':     num_epochs,\n",
-    "        'batch_size':           batch_size,\n",
-    "        'learning_rate':        0.1,\n",
-    "        'scalerType':           scaling_type,\n",
-    "        'calibration':          doCalibration,\n",
-    "        'num_bins_cal':         num_bins_cal,\n",
-    "        'callback':             True,\n",
-    "        'callback_patience':    callback_patience,\n",
-    "        'callback_factor':      0.01,\n",
-    "        'validation_split':     validation_split,\n",
-    "        'holdout_split':        holdout_split,\n",
-    "        'verbose':              verbose_level,\n",
-    "        'plot_scaled_features': PLOT_SCALED_FEATURES,\n",
-    "        'load_trained_models':  USE_SAVED_MODELS,\n",
-    "        'recalibrate_output'   : RECALIBRATE,\n",
-    "        'type_of_calibration'  : CALIBRATION_TYPE,\n",
-    "        'num_ensemble_members': num_ensemble_members,\n",
-    "        'summarize_model': True\n",
-    "    },\n",
-    "    \n",
-    "    'ttbar': {\n",
-    "        'hidden_layers':        num_layers,\n",
-    "        'neurons':              num_neurons_per_layer,\n",
-    "        'number_of_epochs':     num_epochs,\n",
-    "        'batch_size':           batch_size,\n",
-    "        'learning_rate':        0.1,\n",
-    "        'scalerType':           scaling_type,\n",
-    "        'calibration':          doCalibration,\n",
-    "        'num_bins_cal':         num_bins_cal,\n",
-    "        'callback':             True,\n",
-    "        'callback_patience':    callback_patience,\n",
-    "        'callback_factor':      0.01,\n",
-    "        'validation_split':     validation_split,\n",
-    "        'holdout_split':        holdout_split,\n",
-    "        'verbose':              verbose_level,\n",
-    "        'plot_scaled_features': PLOT_SCALED_FEATURES,\n",
-    "        'load_trained_models':  USE_SAVED_MODELS,\n",
-    "        'recalibrate_output'   : RECALIBRATE,\n",
-    "        'type_of_calibration'  : CALIBRATION_TYPE,\n",
-    "        'num_ensemble_members': num_ensemble_members,\n",
-    "        'summarize_model': True\n",
-    "    },\n",
-    "    \n",
-    "    'ztautau': {\n",
-    "        'hidden_layers':        num_layers,\n",
-    "        'neurons':              num_neurons_per_layer,\n",
-    "        'number_of_epochs':     num_epochs,\n",
-    "        'batch_size':           batch_size,\n",
-    "        'learning_rate':        0.1,\n",
-    "        'scalerType':           scaling_type,\n",
-    "        'calibration':          doCalibration,\n",
-    "        'num_bins_cal':         num_bins_cal,\n",
-    "        'callback':             True,\n",
-    "        'callback_patience':    callback_patience,\n",
-    "        'callback_factor':      0.01,\n",
-    "        'validation_split':     validation_split,\n",
-    "        'holdout_split':        holdout_split,\n",
-    "        'verbose':              verbose_level,\n",
-    "        'plot_scaled_features': PLOT_SCALED_FEATURES,\n",
-    "        'load_trained_models':  USE_SAVED_MODELS,\n",
-    "        'recalibrate_output'   : RECALIBRATE,\n",
-    "        'type_of_calibration'  : CALIBRATION_TYPE,\n",
-    "        'num_ensemble_members': num_ensemble_members,\n",
-    "        'summarize_model': True\n",
-    "    }\n",
-    "}"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 10,
-   "id": "65de9378-0c65-4425-b21b-acef0d2a344f",
-   "metadata": {
-    "scrolled": true
-   },
-   "outputs": [
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "2025-11-12 08:47:44 | INFO | Training Logs | Reading saved models from ./saved_datasets/output_training_nominal/output_model_params_htautau/\n",
-      "2025-11-12 08:47:51 | INFO | Training Logs | Loading existing model for ensemble member 1\n",
-      "2025-11-12 08:47:52 | INFO | Training Logs | Reading saved models from ./saved_datasets/output_training_nominal/output_model_params_htautau/\n",
-      "2025-11-12 08:47:58 | INFO | Training Logs | Loading existing model for ensemble member 2\n",
-      "2025-11-12 08:48:00 | INFO | Training Logs | Reading saved models from ./saved_datasets/output_training_nominal/output_model_params_htautau/\n",
-      "2025-11-12 08:48:05 | INFO | Training Logs | Loading existing model for ensemble member 3\n",
-      "2025-11-12 08:48:07 | INFO | Training Logs | Reading saved models from ./saved_datasets/output_training_nominal/output_model_params_htautau/\n",
-      "2025-11-12 08:48:13 | INFO | Training Logs | Loading existing model for ensemble member 4\n",
-      "2025-11-12 08:48:14 | INFO | Training Logs | Reading saved models from ./saved_datasets/output_training_nominal/output_model_params_htautau/\n",
-      "2025-11-12 08:48:20 | INFO | Training Logs | Loading existing model for ensemble member 5\n",
-      "2025-11-12 08:48:22 | INFO | Training Logs | Reading saved models from ./saved_datasets/output_training_nominal/output_model_params_htautau/\n",
-      "2025-11-12 08:48:27 | INFO | Training Logs | Loading existing model for ensemble member 6\n",
-      "2025-11-12 08:48:29 | INFO | Training Logs | Reading saved models from ./saved_datasets/output_training_nominal/output_model_params_htautau/\n",
-      "2025-11-12 08:48:35 | INFO | Training Logs | Loading existing model for ensemble member 7\n",
-      "2025-11-12 08:48:36 | INFO | Training Logs | Reading saved models from ./saved_datasets/output_training_nominal/output_model_params_htautau/\n",
-      "2025-11-12 08:48:42 | INFO | Training Logs | Loading existing model for ensemble member 8\n",
-      "2025-11-12 08:48:44 | INFO | Training Logs | Reading saved models from ./saved_datasets/output_training_nominal/output_model_params_htautau/\n",
-      "2025-11-12 08:48:49 | INFO | Training Logs | Loading existing model for ensemble member 9\n",
-      "2025-11-12 08:48:51 | INFO | Training Logs | Reading saved models from ./saved_datasets/output_training_nominal/output_model_params_htautau/\n",
-      "2025-11-12 08:48:59 | INFO | Training Logs | \n",
-      "\n",
-      "\n",
-      "The sum of PDFs in ensemble member 0 is 1.0012520780021208\n",
-      "\n",
-      "\n",
-      "2025-11-12 08:49:02 | INFO | Training Logs | \n",
-      "\n",
-      "\n",
-      "The sum of PDFs in ensemble member 1 is 1.000179298702787\n",
-      "\n",
-      "\n",
-      "2025-11-12 08:49:05 | INFO | Training Logs | \n",
-      "\n",
-      "\n",
-      "The sum of PDFs in ensemble member 2 is 1.0025290551121426\n",
-      "\n",
-      "\n",
-      "2025-11-12 08:49:08 | INFO | Training Logs | \n",
-      "\n",
-      "\n",
-      "The sum of PDFs in ensemble member 3 is 0.9995786943014111\n",
-      "\n",
-      "\n",
-      "2025-11-12 08:49:11 | INFO | Training Logs | \n",
-      "\n",
-      "\n",
-      "The sum of PDFs in ensemble member 4 is 1.001670733877161\n",
-      "\n",
-      "\n",
-      "2025-11-12 08:49:14 | INFO | Training Logs | \n",
-      "\n",
-      "\n",
-      "The sum of PDFs in ensemble member 5 is 0.9989890402421188\n",
-      "\n",
-      "\n",
-      "2025-11-12 08:49:17 | INFO | Training Logs | \n",
-      "\n",
-      "\n",
-      "The sum of PDFs in ensemble member 6 is 1.0030393955998496\n",
-      "\n",
-      "\n",
-      "2025-11-12 08:49:20 | INFO | Training Logs | \n",
-      "\n",
-      "\n",
-      "The sum of PDFs in ensemble member 7 is 0.9999368149156301\n",
-      "\n",
-      "\n",
-      "2025-11-12 08:49:23 | INFO | Training Logs | \n",
-      "\n",
-      "\n",
-      "The sum of PDFs in ensemble member 8 is 1.004064859061798\n",
-      "\n",
-      "\n",
-      "2025-11-12 08:49:26 | INFO | Training Logs | \n",
-      "\n",
-      "\n",
-      "The sum of PDFs in ensemble member 9 is 1.0013823062033043\n",
-      "\n",
-      "\n",
-      "2025-11-12 08:49:26 | INFO | Training Logs | The sum of PDFs using the whole ensemble is 1.0012622276018324\n",
-      "\n",
-      "\n",
-      "\n",
-      "2025-11-12 08:49:26 | INFO | Training Logs | starting ensemble training\n",
-      "2025-11-12 08:49:26 | INFO | Training Logs | Loading existing model for ensemble member 0\n",
-      "2025-11-12 08:49:27 | INFO | Training Logs | Reading saved models from ./saved_datasets/output_training_nominal/output_model_params_ttbar/\n",
-      "2025-11-12 08:49:30 | INFO | Training Logs | Loading existing model for ensemble member 1\n",
-      "2025-11-12 08:49:31 | INFO | Training Logs | Reading saved models from ./saved_datasets/output_training_nominal/output_model_params_ttbar/\n",
-      "2025-11-12 08:49:35 | INFO | Training Logs | Loading existing model for ensemble member 2\n",
-      "2025-11-12 08:49:36 | INFO | Training Logs | Reading saved models from ./saved_datasets/output_training_nominal/output_model_params_ttbar/\n",
-      "2025-11-12 08:49:39 | INFO | Training Logs | Loading existing model for ensemble member 3\n",
-      "2025-11-12 08:49:41 | INFO | Training Logs | Reading saved models from ./saved_datasets/output_training_nominal/output_model_params_ttbar/\n",
-      "2025-11-12 08:49:44 | INFO | Training Logs | Loading existing model for ensemble member 4\n",
-      "2025-11-12 08:49:45 | INFO | Training Logs | Reading saved models from ./saved_datasets/output_training_nominal/output_model_params_ttbar/\n",
-      "2025-11-12 08:49:49 | INFO | Training Logs | Loading existing model for ensemble member 5\n",
-      "2025-11-12 08:49:50 | INFO | Training Logs | Reading saved models from ./saved_datasets/output_training_nominal/output_model_params_ttbar/\n",
-      "2025-11-12 08:49:53 | INFO | Training Logs | Loading existing model for ensemble member 6\n",
-      "2025-11-12 08:49:54 | INFO | Training Logs | Reading saved models from ./saved_datasets/output_training_nominal/output_model_params_ttbar/\n",
-      "2025-11-12 08:49:58 | INFO | Training Logs | Loading existing model for ensemble member 7\n",
-      "2025-11-12 08:49:59 | INFO | Training Logs | Reading saved models from ./saved_datasets/output_training_nominal/output_model_params_ttbar/\n",
-      "2025-11-12 08:50:03 | INFO | Training Logs | Loading existing model for ensemble member 8\n",
-      "2025-11-12 08:50:04 | INFO | Training Logs | Reading saved models from ./saved_datasets/output_training_nominal/output_model_params_ttbar/\n",
-      "2025-11-12 08:50:07 | INFO | Training Logs | Loading existing model for ensemble member 9\n",
-      "2025-11-12 08:50:08 | INFO | Training Logs | Reading saved models from ./saved_datasets/output_training_nominal/output_model_params_ttbar/\n",
-      "2025-11-12 08:50:14 | INFO | Training Logs | \n",
-      "\n",
-      "\n",
-      "The sum of PDFs in ensemble member 0 is 0.9991518585833828\n",
-      "\n",
-      "\n",
-      "2025-11-12 08:50:16 | INFO | Training Logs | \n",
-      "\n",
-      "\n",
-      "The sum of PDFs in ensemble member 1 is 1.0017495825764366\n",
-      "\n",
-      "\n",
-      "2025-11-12 08:50:17 | INFO | Training Logs | \n",
-      "\n",
-      "\n",
-      "The sum of PDFs in ensemble member 2 is 1.0033738711337539\n",
-      "\n",
-      "\n",
-      "2025-11-12 08:50:19 | INFO | Training Logs | \n",
-      "\n",
-      "\n",
-      "The sum of PDFs in ensemble member 3 is 1.0030635182895413\n",
-      "\n",
-      "\n",
-      "2025-11-12 08:50:21 | INFO | Training Logs | \n",
-      "\n",
-      "\n",
-      "The sum of PDFs in ensemble member 4 is 1.0021202851046334\n",
-      "\n",
-      "\n",
-      "2025-11-12 08:50:23 | INFO | Training Logs | \n",
-      "\n",
-      "\n",
-      "The sum of PDFs in ensemble member 5 is 1.0024350142219314\n",
-      "\n",
-      "\n",
-      "2025-11-12 08:50:25 | INFO | Training Logs | \n",
-      "\n",
-      "\n",
-      "The sum of PDFs in ensemble member 6 is 1.001330724374097\n",
-      "\n",
-      "\n",
-      "2025-11-12 08:50:27 | INFO | Training Logs | \n",
-      "\n",
-      "\n",
-      "The sum of PDFs in ensemble member 7 is 0.9986230752399711\n",
-      "\n",
-      "\n",
-      "2025-11-12 08:50:28 | INFO | Training Logs | \n",
-      "\n",
-      "\n",
-      "The sum of PDFs in ensemble member 8 is 1.0058205782416427\n",
-      "\n",
-      "\n",
-      "2025-11-12 08:50:30 | INFO | Training Logs | \n",
-      "\n",
-      "\n",
-      "The sum of PDFs in ensemble member 9 is 0.9981226687030382\n",
-      "\n",
-      "\n",
-      "2025-11-12 08:50:30 | INFO | Training Logs | The sum of PDFs using the whole ensemble is 1.0015791176468427\n",
-      "\n",
-      "\n",
-      "\n",
-      "2025-11-12 08:50:30 | INFO | Training Logs | starting ensemble training\n",
-      "2025-11-12 08:50:30 | INFO | Training Logs | Loading existing model for ensemble member 0\n",
-      "2025-11-12 08:50:32 | INFO | Training Logs | Reading saved models from ./saved_datasets/output_training_nominal/output_model_params_ztautau/\n",
-      "2025-11-12 08:50:36 | INFO | Training Logs | Loading existing model for ensemble member 1\n",
-      "2025-11-12 08:50:37 | INFO | Training Logs | Reading saved models from ./saved_datasets/output_training_nominal/output_model_params_ztautau/\n",
-      "2025-11-12 08:50:41 | INFO | Training Logs | Loading existing model for ensemble member 2\n",
-      "2025-11-12 08:50:42 | INFO | Training Logs | Reading saved models from ./saved_datasets/output_training_nominal/output_model_params_ztautau/\n",
-      "2025-11-12 08:50:46 | INFO | Training Logs | Loading existing model for ensemble member 3\n",
-      "2025-11-12 08:50:48 | INFO | Training Logs | Reading saved models from ./saved_datasets/output_training_nominal/output_model_params_ztautau/\n",
-      "2025-11-12 08:50:52 | INFO | Training Logs | Loading existing model for ensemble member 4\n",
-      "2025-11-12 08:50:53 | INFO | Training Logs | Reading saved models from ./saved_datasets/output_training_nominal/output_model_params_ztautau/\n",
-      "2025-11-12 08:50:57 | INFO | Training Logs | Loading existing model for ensemble member 5\n",
-      "2025-11-12 08:50:58 | INFO | Training Logs | Reading saved models from ./saved_datasets/output_training_nominal/output_model_params_ztautau/\n",
-      "2025-11-12 08:51:03 | INFO | Training Logs | Loading existing model for ensemble member 6\n",
-      "2025-11-12 08:51:04 | INFO | Training Logs | Reading saved models from ./saved_datasets/output_training_nominal/output_model_params_ztautau/\n",
-      "2025-11-12 08:51:08 | INFO | Training Logs | Loading existing model for ensemble member 7\n",
-      "2025-11-12 08:51:09 | INFO | Training Logs | Reading saved models from ./saved_datasets/output_training_nominal/output_model_params_ztautau/\n",
-      "2025-11-12 08:51:13 | INFO | Training Logs | Loading existing model for ensemble member 8\n",
-      "2025-11-12 08:51:14 | INFO | Training Logs | Reading saved models from ./saved_datasets/output_training_nominal/output_model_params_ztautau/\n",
-      "2025-11-12 08:51:18 | INFO | Training Logs | Loading existing model for ensemble member 9\n",
-      "2025-11-12 08:51:20 | INFO | Training Logs | Reading saved models from ./saved_datasets/output_training_nominal/output_model_params_ztautau/\n",
-      "2025-11-12 08:51:26 | INFO | Training Logs | \n",
-      "\n",
-      "\n",
-      "The sum of PDFs in ensemble member 0 is 0.9995704079345332\n",
-      "\n",
-      "\n",
-      "2025-11-12 08:51:28 | INFO | Training Logs | \n",
-      "\n",
-      "\n",
-      "The sum of PDFs in ensemble member 1 is 1.006239744688583\n",
-      "\n",
-      "\n",
-      "2025-11-12 08:51:30 | INFO | Training Logs | \n",
-      "\n",
-      "\n",
-      "The sum of PDFs in ensemble member 2 is 1.0035100049284593\n",
-      "\n",
-      "\n",
-      "2025-11-12 08:51:32 | INFO | Training Logs | \n",
-      "\n",
-      "\n",
-      "The sum of PDFs in ensemble member 3 is 1.0012328159982484\n",
-      "\n",
-      "\n",
-      "2025-11-12 08:51:34 | INFO | Training Logs | \n",
-      "\n",
-      "\n",
-      "The sum of PDFs in ensemble member 4 is 1.0058449083070204\n",
-      "\n",
-      "\n",
-      "2025-11-12 08:51:37 | INFO | Training Logs | \n",
-      "\n",
-      "\n",
-      "The sum of PDFs in ensemble member 5 is 0.9989713231592503\n",
-      "\n",
-      "\n",
-      "2025-11-12 08:51:39 | INFO | Training Logs | \n",
-      "\n",
-      "\n",
-      "The sum of PDFs in ensemble member 6 is 1.0015353695346148\n",
-      "\n",
-      "\n",
-      "2025-11-12 08:51:41 | INFO | Training Logs | \n",
-      "\n",
-      "\n",
-      "The sum of PDFs in ensemble member 7 is 1.0050043971315916\n",
-      "\n",
-      "\n",
-      "2025-11-12 08:51:43 | INFO | Training Logs | \n",
-      "\n",
-      "\n",
-      "The sum of PDFs in ensemble member 8 is 1.0052064019371687\n",
-      "\n",
-      "\n",
-      "2025-11-12 08:51:45 | INFO | Training Logs | \n",
-      "\n",
-      "\n",
-      "The sum of PDFs in ensemble member 9 is 1.0016147544926886\n",
-      "\n",
-      "\n",
-      "2025-11-12 08:51:45 | INFO | Training Logs | The sum of PDFs using the whole ensemble is 1.002873012811216\n",
-      "\n",
-      "\n",
-      "\n"
-     ]
-    }
-   ],
-   "source": [
-    "# Train the density ratio NN\n",
-    "\n",
-    "for count, process_type in enumerate(basis_processes):\n",
-    "    \n",
-    "    settings = training_settings[process_type]\n",
-    "\n",
-    "    NN_training_mix_model[process_type].train_ensemble(**settings)\n",
-    "    NN_training_mix_model[process_type].test_normalization()\n",
-    "    "
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "fc62e325-9a46-4d80-b262-089a8dbc8ece",
-   "metadata": {},
-   "source": [
-    "Check for overtraining by comparing the NN output distributions between training and holdout datasets. The holdout dataset is the subset of events not used during training."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 11,
-   "id": "25a6a41e",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
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",
-      "text/plain": [
-       "<Figure size 1200x600 with 4 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "data": {
-      "text/plain": [
-       "<Figure size 800x600 with 0 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "data": {
-      "image/png": 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",
-      "text/plain": [
-       "<Figure size 1200x600 with 4 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "data": {
-      "text/plain": [
-       "<Figure size 800x600 with 0 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "data": {
-      "image/png": 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",
-      "text/plain": [
-       "<Figure size 1200x600 with 4 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "data": {
-      "text/plain": [
-       "<Figure size 800x600 with 0 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "for process_type in basis_processes:\n",
-    "    NN_training_mix_model[process_type].make_overfit_plots()"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "7bed7896-6ca2-4c24-a816-d8e2a645b466",
-   "metadata": {},
-   "source": [
-    "Diagnostic Checks\n",
-    "===\n",
-    "\n",
-    "While traditionally, a NN observable is judged on the basis of its accuracy - for NSBI we are interested in the quality of the density ratios more than the discrimination power. The latter comes from the perfect modelling of the multi-dimensional likelihood ratios.\n",
-    "\n",
-    "To ensure correct modelling, we run two main checks on the training:\n",
-    "\n",
-    "- **Calibration closure test**\n",
-    "\n",
-    "  The NNs are trained using the binary cross-entropy loss, which under ideal conditions leads to the NN converging to the score function:\n",
-    "\n",
-    "  $$\\hat{s}_\\text{pred} = \\frac{p_\\text{ref}(x)}{p_\\text{ref}(x)+p_\\text{c}(x)}$$\n",
-    "\n",
-    "  that can be converted into the probability ratio we desire (likelihood ratio trick):\n",
-    "\n",
-    "  $$\\frac{p_\\text{c}(x)}{p_\\text{ref}(x)} = \\frac{\\hat{s}_\\text{pred}(x)}{1-\\hat{s}_\\text{pred}(x)}$$\n",
-    "\n",
-    "  For the NNs to be well-calibrated, we use the Monte Carlo samples to verify the equality:\n",
-    "\n",
-    "\n",
-    "  $$\\left[\\frac{p_c(x)}{p_c(x)+p_{ref}(x)}\\right]_\\text{NN} \\sim \\left[\\frac{\\mathcal{N}_c^{I(x|\\hat{s}_\\text{pred})}}{\\mathcal{N}_c^{I(x|\\hat{s}_\\text{pred})}+\\mathcal{N}_\\text{ref}^{I(x|\\hat{s}_\\text{pred})}}\\right]_\\text{MC}$$\n",
-    "\n",
-    "  where we bin the events from $p_c$ and $p_\\text{ref}$ MC samples, denoted by $\\mathcal{N}_c^{I(x|\\hat{s}_\\text{pred})}$ and $\\mathcal{N}_\\text{ref}^{I(x|\\hat{s}_\\text{pred})}$ respectively where $I(x|\\hat{s}_\\text{pred})$ returns the index of the $\\hat{s}_\\text{pred}$ bin in which an event $x$ falls.\n",
-    " \n"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 12,
-   "id": "25aba043",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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",
-      "text/plain": [
-       "<Figure size 1200x600 with 4 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "data": {
-      "text/plain": [
-       "<Figure size 800x600 with 0 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "data": {
-      "image/png": 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",
-      "text/plain": [
-       "<Figure size 1200x600 with 4 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "data": {
-      "text/plain": [
-       "<Figure size 800x600 with 0 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "data": {
-      "image/png": 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",
-      "text/plain": [
-       "<Figure size 1200x600 with 4 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "data": {
-      "text/plain": [
-       "<Figure size 800x600 with 0 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "num_bins_cal = 50\n",
-    "\n",
-    "for process_type in basis_processes:\n",
-    "    NN_training_mix_model[process_type].make_calib_plots(nbins=num_bins_cal, observable='score')"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 13,
-   "id": "169590c0-a4e6-4271-813d-916c8104673b",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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",
-      "text/plain": [
-       "<Figure size 1200x600 with 4 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "data": {
-      "text/plain": [
-       "<Figure size 800x600 with 0 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "data": {
-      "image/png": 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",
-      "text/plain": [
-       "<Figure size 1200x600 with 4 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "data": {
-      "text/plain": [
-       "<Figure size 800x600 with 0 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "data": {
-      "image/png": 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",
-      "text/plain": [
-       "<Figure size 1200x600 with 4 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "data": {
-      "text/plain": [
-       "<Figure size 800x600 with 0 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "num_bins_cal = 50\n",
-    "\n",
-    "for process_type in basis_processes:\n",
-    "    NN_training_mix_model[process_type].make_calib_plots(nbins=num_bins_cal, observable='llr')"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "58f2a7a2-f890-4ed3-8fc2-764ab989e494",
-   "metadata": {},
-   "source": [
-    "## Density ratio reweighting closure tests\n",
-    "  \n",
-    "  Despite having a well-calibrated output and thus a robust probabilistic interpretation, the trained density ratios might not capture the full multi-dimensional event information $x$. In other words, the NNs might still be biased estimators of the optimal score function, as defined in the CARL paper (link).\n",
-    "\n",
-    "  The next diagnostic involves verifying the following equality using 1D projections of $x$:\n",
-    "\n",
-    "  $$\\frac{p_c(x)}{p_{ref}(x)} \\times p_{ref}(x) \\sim p_c(x)$$\n",
-    "\n",
-    "  We can do this one-by-one for all the observables used to model the density ratios, and also possibly the observables not used directly in the training but can still be well-estimated due to the NN learning the right physics."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 14,
-   "id": "aad88322",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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-      "text/plain": [
-       "<Figure size 1200x600 with 4 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "data": {
-      "text/plain": [
-       "<Figure size 800x600 with 0 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "data": {
-      "image/png": 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",
-      "text/plain": [
-       "<Figure size 1200x600 with 4 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "data": {
-      "text/plain": [
-       "<Figure size 800x600 with 0 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "data": {
-      "image/png": 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WixYtUmRkpK9DbDaC4b4DAAAEOnIjzwmG3IgiEwAAAAAAANzGjb8BAAAAAADgNopMAAAAAAAAcBtFJgAAAAAAALiNIhMAAAAAAADcRpEJAAAAAAAAbqPIBAAAAAAAALdRZAIAAAAAAIDbKDIBAAAAAADAbRSZAAAAAAAA4Lb/D+XaDsUI8PHPAAAAAElFTkSuQmCC",
-      "text/plain": [
-       "<Figure size 1200x600 with 4 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "data": {
-      "text/plain": [
-       "<Figure size 800x600 with 0 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "variables_to_plot=['log_DER_pt_h'] # The 1D variable for reweighting closure\n",
-    "yscale_type='log'\n",
-    "num_bins_plotting=21\n",
-    "\n",
-    "for process_type in basis_processes:\n",
-    "\n",
-    "    NN_training_mix_model[process_type].make_reweighted_plots(variables_to_plot, yscale_type, num_bins_plotting)\n"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 15,
-   "id": "3cc68bf7-a294-47ba-b1d8-38703bdc9d62",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "dataset_combined_SR = Datasets.merge_dataframe_dict_for_training(dataset_SR_nominal, None, \n",
-    "                                                                samples_to_merge = [\"htautau\", \"ztautau\", \"ttbar\"])"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 16,
-   "id": "891bd136-e59f-49a5-a853-073482a8e33a",
-   "metadata": {
-    "scrolled": true
-   },
-   "outputs": [
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "2025-11-12 08:51:59 | INFO | Training Logs | Evaluating density ratios\n",
-      "2025-11-12 08:52:22 | INFO | Training Logs | Evaluating density ratios\n",
-      "2025-11-12 08:52:44 | INFO | Training Logs | Evaluating density ratios\n"
-     ]
-    }
-   ],
-   "source": [
-    "path_to_saved_ratios = {}\n",
-    "\n",
-    "for process_type in basis_processes:\n",
-    "    # Evaluate the density ratios p_c/p_ref for the full dataset and save for the inference step\n",
-    "    path_to_saved_ratios[process_type] = NN_training_mix_model[process_type].evaluate_and_save_ratios(dataset_combined_SR, \n",
-    "                                                                                                     aggregation_type = 'median_score')"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 17,
-   "id": "f7c18575-9da4-4c4a-b1fb-8289c6287b63",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "{'htautau': './saved_datasets/output_training_nominal/output_ratios_htautau/ratio_htautau.npy',\n",
-       " 'ttbar': './saved_datasets/output_training_nominal/output_ratios_ttbar/ratio_ttbar.npy',\n",
-       " 'ztautau': './saved_datasets/output_training_nominal/output_ratios_ztautau/ratio_ztautau.npy'}"
-      ]
-     },
-     "execution_count": 17,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "# Density ratio paths to put in the config file\n",
-    "path_to_saved_ratios"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 18,
-   "id": "36e61653-61ea-4666-9bdf-f7b3e8b99c5b",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# Optionally save the dataframe in the same order used to evaluate the density ratios for diagnostics\n",
-    "\n",
-    "path_to_save = f\"{PATH_TO_SAVED_DATA}/dataset_Asimov_SR.root\"\n",
-    "nsbi_common_utils.datasets.save_dataframe_as_root(dataset_combined_SR, \n",
-    "                                                  path_to_save = path_to_save,\n",
-    "                                                  tree_name = \"nominal\")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 19,
-   "id": "57b246d6-0de9-4718-8fb1-256ecc3ac376",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "'./saved_datasets//dataset_Asimov_SR.root'"
-      ]
-     },
-     "execution_count": 19,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "path_to_save\n",
-    "# nsbi_common_utils.datasets.load_dataframe_from_root(path_to_save, tree_name = \"nominal\")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 20,
-   "id": "0938306e-0630-4a21-a8af-41c9b896300b",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# Asimov weights correspond to the expected weight vector in the same shape and order as the evaluated density ratios\n",
-    "# This is used in the weighted NLL fits for expected sensitivity and coverage computations\n",
-    "\n",
-    "path_to_save = f\"{PATH_TO_SAVED_DATA}/asimov_weights.npy\"\n",
-    "np.save(f\"{path_to_save}\", dataset_combined_SR[\"weights\"].to_numpy())"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 21,
-   "id": "bfb877a1-e9e5-4e20-8d5f-757c243f4cfb",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "'./saved_datasets//asimov_weights.npy'"
-      ]
-     },
-     "execution_count": 21,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "# Path to put in the config file for inference\n",
-    "path_to_save"
-   ]
-  },
-  {
-   "attachments": {},
-   "cell_type": "markdown",
-   "id": "7aa27cca-fecf-426a-a7c5-a77d82a4de2a",
-   "metadata": {},
-   "source": [
-    "Systematic Uncertainty Modelling\n",
-    "===\n",
-    "\n",
-    "So far we have left out the nuisance parameter piece of the parameterized density ratio decomposition shown before:\n",
-    "\n",
-    "$$g_c(x|\\alpha) = \\frac{p_c(x|\\alpha)}{p_c(x)}$$\n",
-    "\n",
-    "which will be estimated in the `Systematic_Uncertainty_Estimation.ipynb` notebook."
-   ]
-  }
- ],
- "metadata": {
-  "kernelspec": {
-   "display_name": "Python (pixi: nsbi-env-gpu-new-higgs)",
-   "language": "python",
-   "name": "nsbi-env-gpu"
-  },
-  "language_info": {
-   "codemirror_mode": {
-    "name": "ipython",
-    "version": 3
-   },
-   "file_extension": ".py",
-   "mimetype": "text/x-python",
-   "name": "python",
-   "nbconvert_exporter": "python",
-   "pygments_lexer": "ipython3",
-   "version": "3.12.12"
-  },
-  "pixi-kernel": {
-   "environment": ""
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 5
-}
diff --git a/FAIR_universe_Higgs_tautau/README.md b/FAIR_universe_Higgs_tautau/README.md
index 7518056a..5b94284c 100644
--- a/FAIR_universe_Higgs_tautau/README.md
+++ b/FAIR_universe_Higgs_tautau/README.md
@@ -4,30 +4,3 @@ FAIR Universe Dataset
 The tabular dataset used in this demonstration is hosted on Zenodo (https://zenodo.org/records/15131565), and is created using the particle physics simulation tools Pythia 8.2 and Delphes 3.5.0. The dataset provides events for the $H\to \tau\tau$ analysis, where the signal process is sub-dominant compared to the very large $Z\to \tau\tau$ and other backgrounds - good challenge to test the sensitivty of NSBI techniques.
 
 **NB** If you need access to pre-trained ensemble NNs, feel free to download the directory [here](https://cernbox.cern.ch/files/spaces/eos/user/j/jsandesa/NSBI_tutorial_data/saved_datasets) and move it inside `NSBI-workflow-tutorial/FAIR_Universe_Higgs_tautau/` in your cloned copy.
-
-Workflow
---
-
-1. [`DataLoader.ipynb`](./DataLoader.ipynb)
-
-This notebook is to download the FAIR Universe data and store it in the form of `.root` ntuples. This step only needs to be done once and is independent of the overall NSBI workflow. 
-
-2. [`DataPreprocessing.ipynb`](./DataPreprocessing.ipynb)
-
-This notebook defines selections and preselects events in the signal and control regions.
-
-3. [`Preselection_withNN.ipynb`](./Preselection_withNN.ipynb) 
-
-This notebook trains a multi-class classification neural network for signal/control region definitions.
-
-4. [`Neural_Likelihood_Ratio_Estimation.ipynb`](./Neural_Likelihood_Ratio_Estimation.ipynb)
-
-This notebook is used to train the desnity ratios to be used for modeling the negative log-likelihood ratios for inference.
-
-5. [`Systematic_Uncertainty_Estimation.ipynb`](./Systematic_Uncertainty_Estimation.ipynb)
-
-This notebook is used to train the density ratios associated with the systematic uncertainty model, then used for modeling the negative log-likelihood ratios for inference.
-
-6. [`Parameter_Fitting_with_Systematics.ipynb`](./Parameter_Fitting_with_Systematics.ipynb)
-
-This notebook is used to create a workspace object and perform parameter fitting with the NSBI model.
diff --git a/FAIR_universe_Higgs_tautau/config.pipeline.yaml b/FAIR_universe_Higgs_tautau/config.pipeline.yaml
new file mode 100644
index 00000000..3c0e630d
--- /dev/null
+++ b/FAIR_universe_Higgs_tautau/config.pipeline.yaml
@@ -0,0 +1,202 @@
+# Data loader settings - these are specific to the Higgs to tau tau example
+data_loader:
+  data:
+    url: "https://zenodo.org/records/15131565/files/FAIR_Universe_HiggsML_data.zip"
+    train_size: 0.35
+    processes:
+      - htautau
+      - ztautau
+      - ttbar
+
+  preprocess:
+    seed: 42
+    selections: >
+      DER_mass_transverse_met_lep <= 250.0 and
+      DER_mass_vis <= 500.0 and
+      DER_sum_pt <= 1000 and
+      DER_pt_tot <= 250 and
+      DER_deltar_had_lep <= 4.5 and
+      DER_pt_h <= 400 and
+      DER_pt_ratio_lep_had <= 9.0
+
+  systematics:
+    TES_up:
+      tes: 1.002
+    TES_dn:
+      tes: 0.998
+    JES_up:
+      jes: 1.002
+    JES_dn:
+      jes: 0.998
+
+  output:
+    dir: "./saved_datasets"
+
+# Data processing settings - these are specific to the example
+data_preprocessing:
+  fit_config_path: "./config_fit_nsbi.yml"
+  features:
+      - PRI_lep_pt
+      - PRI_lep_eta
+      - PRI_lep_phi
+      - PRI_had_pt
+      - PRI_had_eta
+      - PRI_had_phi
+      - PRI_met
+      - PRI_met_phi
+      - DER_mass_transverse_met_lep
+      - DER_mass_vis
+      - DER_pt_h
+      - DER_deltar_had_lep
+      - DER_pt_tot
+      - DER_sum_pt
+      - DER_pt_ratio_lep_had
+      - DER_met_phi_centrality
+      - PRI_jet_leading_pt
+      - PRI_jet_leading_eta
+      - PRI_jet_leading_phi
+      - PRI_jet_all_pt
+      - PRI_jet_subleading_pt
+      - PRI_jet_subleading_eta
+      - PRI_jet_subleading_phi
+      - DER_deltaeta_jet_jet
+      - DER_mass_jet_jet
+      - DER_prodeta_jet_jet
+      - DER_lep_eta_centrality
+      - PRI_n_jets
+
+preselection_network:
+  fit_config_path: "./config_fit_nsbi.yml"
+  
+  model_path: "./saved_datasets/preselection_model/"
+  force_train: false
+  load_trained_models: true
+  
+  training:
+    test_size: 0.2
+    random_state: 42
+    batch_size: 1024
+    epochs: 50
+    learning_rate: 0.000001
+    
+    labels:
+      htautau: 0
+      ttbar: 1
+      ztautau: 2
+      
+  preselection_observable:
+    signal_processes: ["htautau"]
+    background_processes: ["ttbar", "ztautau"]
+    pre_factor_dict:
+      htautau: 1.0
+      ttbar: 1.0
+      ztautau: 1.0
+    
+  output:
+    plots_dir: "./output/plots/preselection_network"
+
+
+# Settings for nominal density ratio estimation
+neural_likelihood_ratio_estimation:
+  nsbi_fit_config: "./config_fit_nsbi.yml"
+  filter_region: "SR"
+  saved_data_path: "./saved_datasets/"
+  output_training_dir: "output_training_nominal"
+  
+  use_log_loss: false
+  delete_existing_models: false
+  force_train: true
+
+  # Some default hyper-parameters -- Quality tested for SBI
+  common_training_params: &common_params
+    hidden_layers: 4
+    neurons: 1000
+    number_of_epochs: 100
+    batch_size: 512
+    learning_rate: 0.00001
+    scalerType: "MinMax"
+    
+    calibration: false
+    recalibrate_output: false
+    type_of_calibration: "histogram"
+    num_bins_cal: null
+    
+    callback: true
+    callback_patience: 30
+    callback_factor: 0.01
+    
+    validation_split: 0.1
+    holdout_split: 0.25
+
+    num_ensemble_members: 1
+    load_trained_models: true  # 'force_train' flag in script overrides this
+    
+    verbose: 1
+    plot_scaled_features: false
+    summarize_model: true
+
+    num_workers: 2
+
+  # Process-specific settings - can be different from the default
+  training_settings:
+    htautau:
+      <<: *common_params
+      
+    ttbar:
+      <<: *common_params
+      
+    ztautau:
+      <<: *common_params
+
+
+
+# Section for Systematic Uncertainty Estimation
+systematic_uncertainty:
+  nsbi_fit_config: "./config_fit_nsbi.yml"
+  saved_data_path: "./saved_datasets/"
+  output_training_dir: "output_training_systematics"
+  filter_region: "SR"
+  force_train: true
+  
+  training_settings:
+    hidden_layers: 4
+    neurons: 1000
+    number_of_epochs: 50
+    batch_size: 10000
+    learning_rate: 0.000001
+    scalerType: "MinMax"
+    
+    calibration: true
+    recalibrate_output: true
+    type_of_calibration: "histogram"
+    num_bins_cal: 10
+    
+    callback: true
+    callback_patience: 10
+    callback_factor: 0.5
+    
+    validation_split: 0.2
+    holdout_split: 0.2
+    
+    verbose: 0
+    plot_scaled_features: false
+    load_trained_models: true
+    num_ensemble_members: 1
+    summarize_model: false
+
+    num_workers: 2
+
+
+# Parameter fit settings
+parameter_fitting:
+  configs:
+    histogram: "./config_fit_histogram.yml"   
+    nsbi: "./config_fit_nsbi.yml" 
+  measurement: "higgs_tautau_signal_strength" 
+  output:
+    plots_dir: "./output/fit_plots/parameter_fitting"
+  scan:
+    parameter: "mu_htautau"
+    range: [0.0, 3.0]
+    steps: 50
+
diff --git a/FAIR_universe_Higgs_tautau/config_hist.yml b/FAIR_universe_Higgs_tautau/config_fit_histogram.yml
similarity index 100%
rename from FAIR_universe_Higgs_tautau/config_hist.yml
rename to FAIR_universe_Higgs_tautau/config_fit_histogram.yml
diff --git a/FAIR_universe_Higgs_tautau/config.yml b/FAIR_universe_Higgs_tautau/config_fit_nsbi.yml
similarity index 93%
rename from FAIR_universe_Higgs_tautau/config.yml
rename to FAIR_universe_Higgs_tautau/config_fit_nsbi.yml
index c91404d2..402343b7 100644
--- a/FAIR_universe_Higgs_tautau/config.yml
+++ b/FAIR_universe_Higgs_tautau/config_fit_nsbi.yml
@@ -24,7 +24,7 @@ Samples:
       UseAsReference    : True
       UseAsBasis        : True
         
-    - Name : ztautau
+    - Name              : ztautau
       Tree              : tree_ztautau
       SamplePath        : saved_datasets/dataset_nominal.root
       Weight            : weights
@@ -166,13 +166,13 @@ Regions:
              - SystName: TES
                ModelsUp: saved_datasets/output_training_systematics/output_model_params_htautau_TES_Up/
                ModelsDn: saved_datasets/output_training_systematics/output_model_params_htautau_TES_Dn/
-               RatiosUp: saved_datasets/output_training_systematics/output_ratios_htautau_TES_Up/ratio_TES_Up.npy
-               RatiosDn: saved_datasets/output_training_systematics/output_ratios_htautau_TES_Dn/ratio_TES_Dn.npy
+               RatiosUp: saved_datasets/output_training_systematics/output_ratios_htautau_TES_Up/ratio_htautau.npy
+               RatiosDn: saved_datasets/output_training_systematics/output_ratios_htautau_TES_Dn/ratio_htautau.npy
              - SystName: JES
                ModelsUp: saved_datasets/output_training_systematics/output_model_params_htautau_JES_Up/
                ModelsDn: saved_datasets/output_training_systematics/output_model_params_htautau_JES_Dn/
-               RatiosUp: saved_datasets/output_training_systematics/output_ratios_htautau_JES_Up/ratio_JES_Up.npy
-               RatiosDn: saved_datasets/output_training_systematics/output_ratios_htautau_JES_Dn/ratio_JES_Dn.npy
+               RatiosUp: saved_datasets/output_training_systematics/output_ratios_htautau_JES_Up/ratio_htautau.npy
+               RatiosDn: saved_datasets/output_training_systematics/output_ratios_htautau_JES_Dn/ratio_htautau.npy
         - SampleName    : ttbar
           Nominal       : 
                 Models  : saved_datasets/output_training_nominal/output_model_params_ttbar/
@@ -181,13 +181,13 @@ Regions:
             - SystName: TES
               ModelsUp: saved_datasets/output_training_systematics/output_model_params_ttbar_TES_Up/
               ModelsDn: saved_datasets/output_training_systematics/output_model_params_ttbar_TES_Dn/
-              RatiosUp: saved_datasets/output_training_systematics/output_ratios_ttbar_TES_Up/ratio_TES_Up.npy
-              RatiosDn: saved_datasets/output_training_systematics/output_ratios_ttbar_TES_Dn/ratio_TES_Dn.npy
+              RatiosUp: saved_datasets/output_training_systematics/output_ratios_ttbar_TES_Up/ratio_ttbar.npy
+              RatiosDn: saved_datasets/output_training_systematics/output_ratios_ttbar_TES_Dn/ratio_ttbar.npy
             - SystName: JES
               ModelsUp: saved_datasets/output_training_systematics/output_model_params_ttbar_JES_Up/
               ModelsDn: saved_datasets/output_training_systematics/output_model_params_ttbar_JES_Dn/ 
-              RatiosUp: saved_datasets/output_training_systematics/output_ratios_ttbar_JES_Up/ratio_JES_Up.npy
-              RatiosDn: saved_datasets/output_training_systematics/output_ratios_ttbar_JES_Dn/ratio_JES_Dn.npy
+              RatiosUp: saved_datasets/output_training_systematics/output_ratios_ttbar_JES_Up/ratio_ttbar.npy
+              RatiosDn: saved_datasets/output_training_systematics/output_ratios_ttbar_JES_Dn/ratio_ttbar.npy
         - SampleName    : ztautau
           Nominal       : 
                 Models  : saved_datasets/output_training_nominal/output_model_params_ztautau/
@@ -196,13 +196,13 @@ Regions:
              - SystName: TES
                ModelsUp: saved_datasets/output_training_systematics/output_model_params_ztautau_TES_Up/
                ModelsDn: saved_datasets/output_training_systematics/output_model_params_ztautau_TES_Dn/
-               RatiosUp: saved_datasets/output_training_systematics/output_ratios_ztautau_TES_Up/ratio_TES_Up.npy
-               RatiosDn: saved_datasets/output_training_systematics/output_ratios_ztautau_TES_Dn/ratio_TES_Dn.npy
+               RatiosUp: saved_datasets/output_training_systematics/output_ratios_ztautau_TES_Up/ratio_ztautau.npy
+               RatiosDn: saved_datasets/output_training_systematics/output_ratios_ztautau_TES_Dn/ratio_ztautau.npy
              - SystName: JES
                ModelsUp: saved_datasets/output_training_systematics/output_model_params_ztautau_JES_Up/
                ModelsDn: saved_datasets/output_training_systematics/output_model_params_ztautau_JES_Dn/
-               RatiosUp: saved_datasets/output_training_systematics/output_ratios_ztautau_JES_Up/ratio_JES_Up.npy
-               RatiosDn: saved_datasets/output_training_systematics/output_ratios_ztautau_JES_Dn/ratio_JES_Dn.npy
+               RatiosUp: saved_datasets/output_training_systematics/output_ratios_ztautau_JES_Up/ratio_ztautau.npy
+               RatiosDn: saved_datasets/output_training_systematics/output_ratios_ztautau_JES_Dn/ratio_ztautau.npy
                            
 
                  
diff --git a/FAIR_universe_Higgs_tautau/htcondor/job_data_loader.sub b/FAIR_universe_Higgs_tautau/htcondor/job_data_loader.sub
new file mode 100644
index 00000000..24fad8f7
--- /dev/null
+++ b/FAIR_universe_Higgs_tautau/htcondor/job_data_loader.sub
@@ -0,0 +1,28 @@
+universe = container
+container_image = file:///staging/jsandesara/nsbi-env-gpu.sif
+
+executable = FAIR_universe_Higgs_tautau/htcondor/run_step.sh
+arguments  = data_loader $(CONFIG)
+
+log    = logs/data_loader.$(Cluster).log
+output = logs/data_loader.$(Cluster).out
+error  = logs/data_loader.$(Cluster).err
+
+request_cpus   = $(CPUS:1)
+request_memory = $(MEM:64GB)
+request_gpus   = $(GPUS:0)
+request_disk   = $(DISK:64GB)
+
+stream_output = True
+stream_error  = True
+
+should_transfer_files = YES
+when_to_transfer_output = ON_EXIT
+
+transfer_input_files = pyproject.toml, src, FAIR_universe_Higgs_tautau, README.md
+transfer_output_files = FAIR_universe_Higgs_tautau/data, FAIR_universe_Higgs_tautau/saved_datasets
+transfer_output_remaps = "data = ./FAIR_universe_Higgs_tautau/data; saved_datasets = ./FAIR_universe_Higgs_tautau/saved_datasets"
+
++SingularityImage = "/staging/jsandesara/nsbi-env-gpu.sif"
+
+queue
diff --git a/FAIR_universe_Higgs_tautau/htcondor/job_data_preprocessing.sub b/FAIR_universe_Higgs_tautau/htcondor/job_data_preprocessing.sub
new file mode 100644
index 00000000..6e9663a7
--- /dev/null
+++ b/FAIR_universe_Higgs_tautau/htcondor/job_data_preprocessing.sub
@@ -0,0 +1,28 @@
+universe = container
+container_image = file:///staging/jsandesara/nsbi-env-gpu.sif
+
+executable = FAIR_universe_Higgs_tautau/htcondor/run_step.sh
+arguments  = data_preprocessing $(CONFIG) $(SKIP)
+
+log    = logs/data_preprocessing.$(Cluster).log
+output = logs/data_preprocessing.$(Cluster).out
+error  = logs/data_preprocessing.$(Cluster).err
+
+request_cpus   = $(CPUS:1)
+request_memory = $(MEM:64GB)
+request_gpus   = $(GPUS:0)
+request_disk   = $(DISK:64GB)
+
+stream_output = True
+stream_error  = True
+
+should_transfer_files = YES
+when_to_transfer_output = ON_EXIT
+
+transfer_input_files = pyproject.toml, src, FAIR_universe_Higgs_tautau, README.md
+transfer_output_files = FAIR_universe_Higgs_tautau/saved_datasets
+transfer_output_remaps = "saved_datasets = ./FAIR_universe_Higgs_tautau/saved_datasets"
+
++SingularityImage = "/staging/jsandesara/nsbi-env-gpu.sif"
+
+queue
diff --git a/FAIR_universe_Higgs_tautau/htcondor/job_density_ratio_eval.sub b/FAIR_universe_Higgs_tautau/htcondor/job_density_ratio_eval.sub
new file mode 100644
index 00000000..6d9fdd0f
--- /dev/null
+++ b/FAIR_universe_Higgs_tautau/htcondor/job_density_ratio_eval.sub
@@ -0,0 +1,38 @@
+universe = container
+container_image = file:///staging/jsandesara/nsbi-env-gpu.sif
+
+executable = FAIR_universe_Higgs_tautau/htcondor/run_step.sh
+arguments  = data_nn_eval $(CONFIG) $(SKIP)
+
+log    = logs/data_nn_eval.$(Cluster).log
+output = logs/data_nn_eval.$(Cluster).out
+error  = logs/data_nn_eval.$(Cluster).err
+
+request_cpus   = $(CPUS:1)
+request_memory = $(MEM:64GB)
+request_gpus   = $(GPUS:1)
+request_disk   = $(DISK:64GB)
+
+stream_output = True
+stream_error  = True
+
+should_transfer_files = YES
+when_to_transfer_output = ON_EXIT
+
+# Require a machine with a modern version of the CUDA driver
+requirements = (GPUs_DriverVersion >= 12.0)
+
+# Don't use CentOS7 to ensure pty support
+requirements = (OpSysMajorVer > 7)
+
+# Optional: specify the GPU hardware architecture required
+# Check against the CUDA GPU Compute Capability for your software
+# e.g. python -c "import torch; print(torch.cuda.get_arch_list())"
+# The listed 'sm_xy' values show the x.y gpu capability supported
+gpus_minimum_capability = 5.0
+
+transfer_input_files = pyproject.toml, src, FAIR_universe_Higgs_tautau, README.md
+transfer_output_files = FAIR_universe_Higgs_tautau/saved_datasets, FAIR_universe_Higgs_tautau/output
+transfer_output_remaps = "saved_datasets = ./FAIR_universe_Higgs_tautau/saved_datasets; output = ./FAIR_universe_Higgs_tautau/output"
+
+queue
diff --git a/FAIR_universe_Higgs_tautau/htcondor/job_density_ratio_training.sub b/FAIR_universe_Higgs_tautau/htcondor/job_density_ratio_training.sub
new file mode 100644
index 00000000..9324957d
--- /dev/null
+++ b/FAIR_universe_Higgs_tautau/htcondor/job_density_ratio_training.sub
@@ -0,0 +1,38 @@
+universe = container
+container_image = file:///staging/jsandesara/nsbi-env-gpu.sif
+
+executable = FAIR_universe_Higgs_tautau/htcondor/run_step.sh
+arguments  = neural_likelihood_ratio_estimation $(CONFIG) $(SKIP)
+
+log    = logs/density_ratio_training.$(Cluster).log
+output = logs/density_ratio_training.$(Cluster).out
+error  = logs/density_ratio_training.$(Cluster).err
+
+request_cpus   = $(CPUS:1)
+request_memory = $(MEM:64GB)
+request_gpus   = $(GPUS:0)
+request_disk   = $(DISK:64GB)
+
+stream_output = True
+stream_error  = True
+
+should_transfer_files = YES
+when_to_transfer_output = ON_EXIT
+
+# Require a machine with a modern version of the CUDA driver
+requirements = (GPUs_DriverVersion >= 12.0)
+
+# Don't use CentOS7 to ensure pty support
+requirements = (OpSysMajorVer > 7)
+
+# Optional: specify the GPU hardware architecture required
+# Check against the CUDA GPU Compute Capability for your software
+# e.g. python -c "import torch; print(torch.cuda.get_arch_list())"
+# The listed 'sm_xy' values show the x.y gpu capability supported
+gpus_minimum_capability = 5.0
+
+transfer_input_files = pyproject.toml, src, FAIR_universe_Higgs_tautau, README.md
+transfer_output_files = FAIR_universe_Higgs_tautau/saved_datasets, FAIR_universe_Higgs_tautau/output
+transfer_output_remaps = "saved_datasets = ./FAIR_universe_Higgs_tautau/saved_datasets; output = ./FAIR_universe_Higgs_tautau/output"
+
+queue
diff --git a/FAIR_universe_Higgs_tautau/htcondor/job_parameter_fits.sub b/FAIR_universe_Higgs_tautau/htcondor/job_parameter_fits.sub
new file mode 100644
index 00000000..12e12bb2
--- /dev/null
+++ b/FAIR_universe_Higgs_tautau/htcondor/job_parameter_fits.sub
@@ -0,0 +1,28 @@
+universe = container
+container_image = file:///staging/jsandesara/nsbi-env-gpu.sif
+
+executable = FAIR_universe_Higgs_tautau/htcondor/run_step.sh
+arguments  = parameter_fitting $(CONFIG) $(SKIP)
+
+log    = logs/parameter_fitting.$(Cluster).log
+output = logs/parameter_fitting.$(Cluster).out
+error  = logs/parameter_fitting.$(Cluster).err
+
+request_cpus   = $(CPUS:1)
+request_memory = $(MEM:64GB)
+request_gpus   = $(GPUS:0)
+request_disk   = $(DISK:64GB)
+
+stream_output = True
+stream_error  = True
+
+should_transfer_files = YES
+when_to_transfer_output = ON_EXIT
+
+transfer_input_files = pyproject.toml, src, FAIR_universe_Higgs_tautau, README.md
+transfer_output_files = FAIR_universe_Higgs_tautau/saved_datasets, FAIR_universe_Higgs_tautau/output
+transfer_output_remaps = "saved_datasets = ./FAIR_universe_Higgs_tautau/saved_datasets; output = ./FAIR_universe_Higgs_tautau/output"
+
++SingularityImage = "/staging/jsandesara/nsbi-env-gpu.sif"
+
+queue
diff --git a/FAIR_universe_Higgs_tautau/htcondor/job_preselection_network.sub b/FAIR_universe_Higgs_tautau/htcondor/job_preselection_network.sub
new file mode 100644
index 00000000..446622a6
--- /dev/null
+++ b/FAIR_universe_Higgs_tautau/htcondor/job_preselection_network.sub
@@ -0,0 +1,26 @@
+universe = container
+container_image = file:///staging/jsandesara/nsbi-env-gpu.sif
+
+executable = FAIR_universe_Higgs_tautau/htcondor/run_step.sh
+arguments  = preselection_network $(CONFIG) $(SKIP)
+
+log    = logs/preselection_network.$(Cluster).log
+output = logs/preselection_network.$(Cluster).out
+error  = logs/preselection_network.$(Cluster).err
+
+request_cpus   = $(CPUS:1)
+request_memory = $(MEM:64GB)
+request_gpus   = $(GPUS:0)
+request_disk   = $(DISK:64GB)
+
+stream_output = True
+stream_error  = True
+
+should_transfer_files = YES
+when_to_transfer_output = ON_EXIT
+
+transfer_input_files = pyproject.toml, src, FAIR_universe_Higgs_tautau, README.md
+transfer_output_files = FAIR_universe_Higgs_tautau/saved_datasets, FAIR_universe_Higgs_tautau/output
+transfer_output_remaps = "saved_datasets = ./FAIR_universe_Higgs_tautau/saved_datasets; output = ./FAIR_universe_Higgs_tautau/output"
+
+queue
diff --git a/FAIR_universe_Higgs_tautau/htcondor/job_systematics_training.sub b/FAIR_universe_Higgs_tautau/htcondor/job_systematics_training.sub
new file mode 100644
index 00000000..c43efda8
--- /dev/null
+++ b/FAIR_universe_Higgs_tautau/htcondor/job_systematics_training.sub
@@ -0,0 +1,26 @@
+universe = container
+container_image = file:///staging/jsandesara/nsbi-env-gpu.sif
+
+executable = FAIR_universe_Higgs_tautau/htcondor/run_step.sh
+arguments  = systematic_uncertainty_training $(CONFIG) $(SKIP)
+
+log    = logs/systematic_uncertainty_training.$(Cluster).log
+output = logs/systematic_uncertainty_training.$(Cluster).out
+error  = logs/systematic_uncertainty_training.$(Cluster).err
+
+request_cpus   = $(CPUS:1)
+request_memory = $(MEM:64GB)
+request_gpus   = $(GPUS:0)
+request_disk   = $(DISK:64GB)
+
+stream_output = True
+stream_error  = True
+
+should_transfer_files = YES
+when_to_transfer_output = ON_EXIT
+
+transfer_input_files = pyproject.toml, src, FAIR_universe_Higgs_tautau, README.md
+transfer_output_files = FAIR_universe_Higgs_tautau/saved_datasets, FAIR_universe_Higgs_tautau/output
+transfer_output_remaps = "saved_datasets = ./FAIR_universe_Higgs_tautau/saved_datasets; output = ./FAIR_universe_Higgs_tautau/output"
+
+queue
diff --git a/FAIR_universe_Higgs_tautau/htcondor/run_step.sh b/FAIR_universe_Higgs_tautau/htcondor/run_step.sh
new file mode 100644
index 00000000..fd02c39e
--- /dev/null
+++ b/FAIR_universe_Higgs_tautau/htcondor/run_step.sh
@@ -0,0 +1,16 @@
+#!/usr/bin/env bash
+set -e
+
+STEP=$1
+CONFIG=$2
+
+export SETUPTOOLS_SCM_PRETEND_VERSION=0.0.0
+python -m pip install --no-deps -e .
+
+echo "PATH=$PATH"
+which python
+python -c "import sys; print(sys.executable)"
+
+cd FAIR_universe_Higgs_tautau
+
+python scripts/${STEP}.py --config ${CONFIG}
diff --git a/FAIR_universe_Higgs_tautau/htcondor/stage_data_processing.dag b/FAIR_universe_Higgs_tautau/htcondor/stage_data_processing.dag
new file mode 100644
index 00000000..4d061152
--- /dev/null
+++ b/FAIR_universe_Higgs_tautau/htcondor/stage_data_processing.dag
@@ -0,0 +1,7 @@
+JOB data_loader FAIR_universe_Higgs_tautau/htcondor/job_data_loader.sub
+VARS data_loader CONFIG="config.pipeline.yaml" CPUS="8" MEM="64GB" GPUS="0" DISK="64GB"
+
+JOB data_preprocessing FAIR_universe_Higgs_tautau/htcondor/job_data_preprocessing.sub
+VARS data_preprocessing CONFIG="config.pipeline.yaml" CPUS="8" MEM="32GB" GPUS="0" DISK="32GB"
+
+PARENT data_loader CHILD data_preprocessing
\ No newline at end of file
diff --git a/FAIR_universe_Higgs_tautau/htcondor/stage_density_ratio_training.dag b/FAIR_universe_Higgs_tautau/htcondor/stage_density_ratio_training.dag
new file mode 100644
index 00000000..a7a75fe7
--- /dev/null
+++ b/FAIR_universe_Higgs_tautau/htcondor/stage_density_ratio_training.dag
@@ -0,0 +1,11 @@
+JOB neural_likelihood_ratio_estimation FAIR_universe_Higgs_tautau/htcondor/job_density_ratio_training.sub
+VARS neural_likelihood_ratio_estimation STEP="neural_likelihood_ratio_estimation" CONFIG="config.pipeline.yaml" CPUS="8" MEM="8GB" GPUS="1" DISK="16GB"
+
+# JOB systematic_uncertainty_training FAIR_universe_Higgs_tautau/htcondor/job_systematics_training.sub
+# VARS systematic_uncertainty_training STEP="systematic_uncertainty_training" CONFIG="config.pipeline.yaml" CPUS="8" MEM="8GB" GPUS="1" DISK="16GB"
+
+JOB data_nn_eval FAIR_universe_Higgs_tautau/htcondor/job_density_ratio_eval.sub
+VARS data_nn_eval STEP="data_nn_eval" CONFIG="config.pipeline.yaml" CPUS="8" MEM="8GB" GPUS="0" DISK="32GB"
+
+PARENT neural_likelihood_ratio_estimation CHILD data_nn_eval
+# PARENT systematic_uncertainty_training CHILD data_nn_eval
diff --git a/FAIR_universe_Higgs_tautau/htcondor/stage_parameter_fitting.dag b/FAIR_universe_Higgs_tautau/htcondor/stage_parameter_fitting.dag
new file mode 100644
index 00000000..e91a68b1
--- /dev/null
+++ b/FAIR_universe_Higgs_tautau/htcondor/stage_parameter_fitting.dag
@@ -0,0 +1,2 @@
+JOB parameter_fitting FAIR_universe_Higgs_tautau/htcondor/job_parameter_fits.sub
+VARS parameter_fitting STEP="parameter_fitting" CONFIG="config.pipeline.yaml" CPUS="8" MEM="16GB" GPUS="0" DISK="16GB"
diff --git a/FAIR_universe_Higgs_tautau/htcondor/stage_preselection_network.dag b/FAIR_universe_Higgs_tautau/htcondor/stage_preselection_network.dag
new file mode 100644
index 00000000..9ffa4a94
--- /dev/null
+++ b/FAIR_universe_Higgs_tautau/htcondor/stage_preselection_network.dag
@@ -0,0 +1,2 @@
+JOB preselection_network FAIR_universe_Higgs_tautau/htcondor/job_preselection_network.sub
+VARS preselection_network STEP="preselection_network" CONFIG="config.pipeline.yaml" CPUS="4" MEM="32GB" GPUS="1" DISK="32GB"
diff --git a/FAIR_universe_Higgs_tautau/htcondor/workflow_full.dag b/FAIR_universe_Higgs_tautau/htcondor/workflow_full.dag
new file mode 100644
index 00000000..fd0f91a4
--- /dev/null
+++ b/FAIR_universe_Higgs_tautau/htcondor/workflow_full.dag
@@ -0,0 +1,20 @@
+JOB likelihood_ratio_estimation FAIR_universe_Higgs_tautau/htcondor/job.sub
+VARS likelihood_ratio_estimation STEP="neural_likelihood_ratio_estimation" CONFIG="config.pipeline.yaml" CPUS="4" MEM="32GB" GPUS="1" DISK="32GB"
+
+JOB systematic_uncertainty FAIR_universe_Higgs_tautau/htcondor/job.sub
+VARS systematic_uncertainty STEP="systematic_uncertainty_training" CONFIG="config.pipeline.yaml" CPUS="4" MEM="32GB" GPUS="1" DISK="32GB"
+
+JOB parameter_fitting FAIR_universe_Higgs_tautau/htcondor/job.sub
+VARS parameter_fitting STEP="parameter_fitting" CONFIG="config.pipeline.yaml" CPUS="8" MEM="16GB" GPUS="0" DISK="16GB"
+
+#########################################################
+
+SPLICE data_processing stage_data_processing.dag
+SPLICE preselection_network stage_preselection_network.dag
+
+PARENT data_processing CHILD preselection_network
+
+PARENT preselection_network CHILD likelihood_ratio_estimation
+PARENT preselection_network CHILD systematic_uncertainty
+
+PARENT likelihood_ratio_estimation systematic_uncertainty CHILD parameter_fitting
diff --git a/FAIR_universe_Higgs_tautau/output/plots/preselection_network/preselection_score_distributions.png b/FAIR_universe_Higgs_tautau/output/plots/preselection_network/preselection_score_distributions.png
new file mode 100644
index 00000000..141c591c
--- /dev/null
+++ b/FAIR_universe_Higgs_tautau/output/plots/preselection_network/preselection_score_distributions.png
@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
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+size 33398
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new file mode 100644
index 00000000..80991809
--- /dev/null
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new file mode 100644
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new file mode 100644
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diff --git a/FAIR_universe_Higgs_tautau/scripts/data_loader.py b/FAIR_universe_Higgs_tautau/scripts/data_loader.py
new file mode 100644
index 00000000..d51260b3
--- /dev/null
+++ b/FAIR_universe_Higgs_tautau/scripts/data_loader.py
@@ -0,0 +1,158 @@
+import os
+import argparse
+import logging
+import numpy as np
+import pandas as pd
+import uproot
+from HiggsML.datasets import download_dataset
+from HiggsML.systematics import systematics
+
+import yaml
+
+logging.basicConfig(level=logging.INFO, format='%(asctime)s - %(levelname)s - %(message)s')
+logger = logging.getLogger(__name__)
+
+def load_config(path: str) -> dict:
+    with open(path, "r") as f:
+        return yaml.safe_load(f)
+
+
+def parse_args() -> argparse.Namespace:
+    parser = argparse.ArgumentParser(description="Download and process HiggsML data for analysis.")
+    parser.add_argument(
+        "--config",
+        type=str,
+        default="config.pipeline.yaml",
+        help="Configuration file path.",
+    )
+    return parser.parse_args()
+
+def download_and_load(url: str, train_size: float) -> pd.DataFrame:
+    """Download the dataset and return the training set as a DataFrame."""
+    logger.info(f"Downloading dataset from {url}...")
+    data = download_dataset(url)
+
+    logger.info(f"Loading training set with size fraction: {train_size}")
+    data.load_train_set(train_size=train_size)
+
+    df = data.get_train_set()
+    del data
+    return df
+
+def process_data(df: pd.DataFrame, list_of_processes: list, seed: int) -> pd.DataFrame:
+    """
+    Filter to the requested processes and balance by downsampling to the smallest class.
+    """
+
+    all_labels = df["detailed_labels"].unique()
+    processes_to_exclude = list(set(all_labels) - set(list_of_processes))
+    logger.info(f"Excluding processes: {processes_to_exclude}")
+
+    df_filtered = df[~np.isin(df["detailed_labels"], processes_to_exclude)].copy()
+
+    counts = df_filtered["detailed_labels"].value_counts()
+    logger.info(f"Counts before balancing:\n{counts}")
+
+    # Downsample every process to the size of the smallest one,
+    # rescaling weights so the total weight sum is preserved per process.
+    min_process = counts.idxmin()
+    n_min = counts.min()
+    logger.info(f"Balancing to minimum process count ('{min_process}'): {n_min}")
+
+    df_list = []
+    for _, df_process in df_filtered.groupby("detailed_labels"):
+        weight_sum_orig = df_process["weights"].sum() 
+        df_sampled = df_process.sample(n=n_min, random_state=seed)
+        df_sampled = df_sampled.copy()                  
+        df_sampled["weights"] *= weight_sum_orig / df_sampled["weights"].sum()
+        df_list.append(df_sampled)
+
+    return pd.concat(df_list).reset_index(drop=True)
+
+
+def apply_systematics(df: pd.DataFrame, syst_settings: dict) -> dict:
+    """Generate the nominal dataset and all systematic variations."""
+    dataset_dict = {}
+
+    logger.info("Generating nominal dataset...")
+    dataset_dict["nominal"] = systematics(data_set=df, dopostprocess=False)
+
+    for sample_name, syst_args in syst_settings.items():
+        logger.info(f"Generating systematic variation: {sample_name}")
+        dataset_dict[sample_name] = systematics(data_set=df, dopostprocess=False, **syst_args)
+
+    return dataset_dict
+
+
+def save_root_files(
+    dataset_dict: dict,
+    output_dir: str,
+    processes: list,
+    selections: str,
+) -> None:
+    """
+    Save each (sample, process) combination to its own ROOT file.
+    """
+    os.makedirs(output_dir, exist_ok=True)  
+    logger.info(f"Output directory: {output_dir}")
+
+    for sample, df in dataset_dict.items():
+        output_path = os.path.join(output_dir, f"dataset_{sample}.root")
+        logger.info(f"Writing {output_path}...")
+
+        with uproot.recreate(output_path) as root_file:
+
+            for process in processes:
+                df_process = df[df["detailed_labels"] == process].copy()
+                df_process = df_process.query(selections)
+
+                # Drop the label column — not needed in the output tree
+                columns_to_keep = [c for c in df_process.columns if c != "detailed_labels"]
+                arrays = {col: df_process[col].to_numpy() for col in columns_to_keep}
+
+                if arrays and len(df_process) > 0:
+                    tree_name = f"tree_{process}"      
+                    root_file[tree_name] = arrays
+                    logger.info(f"  Wrote tree '{tree_name}' with {len(df_process)} events.")
+                else:
+                    logger.warning(
+                        f"  No events for process '{process}' in sample '{sample}' "
+                        f"after selection — tree not written."
+                    )
+
+def main() -> None:
+    args = parse_args()
+    config = load_config(args.config)["data_loader"]
+
+    list_of_processes = config["data"]["processes"]
+    selections = config["preprocess"]["selections"]
+    seed = config["preprocess"]["seed"]
+
+    # Inject the shared seed into each systematic's config
+    syst_settings = config["systematics"]
+    for syst_cfg in syst_settings.values():
+        syst_cfg["seed"] = seed
+
+    try:
+        df_full = download_and_load(config["data"]["url"], config["data"]["train_size"])
+        df_balanced = process_data(df_full, list_of_processes, seed)
+        del df_full
+
+        dataset_dict = apply_systematics(df_balanced, syst_settings)
+
+        save_root_files(
+            dataset_dict,
+            config["output"]["dir"],
+            list_of_processes,
+            selections,
+        )
+
+        logger.info("Data loading workflow completed successfully.")
+
+    except Exception as e:
+        logger.error(f"Workflow failed: {e}", exc_info=True)
+        raise
+
+
+if __name__ == "__main__":
+    main()
\ No newline at end of file
diff --git a/FAIR_universe_Higgs_tautau/scripts/data_nn_eval.py b/FAIR_universe_Higgs_tautau/scripts/data_nn_eval.py
new file mode 100644
index 00000000..610f48c2
--- /dev/null
+++ b/FAIR_universe_Higgs_tautau/scripts/data_nn_eval.py
@@ -0,0 +1,206 @@
+import os
+import sys
+import argparse
+import warnings
+import logging
+import numpy as np
+import yaml
+import mplhep as hep
+import pickle
+
+import nsbi_common_utils
+
+hep.style.use(hep.style.ATLAS)
+
+warnings.filterwarnings("ignore", category=RuntimeWarning)
+warnings.filterwarnings("ignore", category=FutureWarning)
+
+logging.basicConfig(level=logging.INFO, format='%(asctime)s - %(levelname)s - %(message)s')
+logger = logging.getLogger(__name__)
+
+def load_config(path):
+    with open(path, "r") as f:
+        return yaml.safe_load(f)
+
+def parse_args():
+    parser = argparse.ArgumentParser(description="Neural Likelihood Ratio Estimation Trainer")
+    parser.add_argument('--config', type=str, default='config.pipeline.yaml', 
+                        help='Path to the YAML configuration file')
+    return parser.parse_args()
+
+def main():
+    args = parse_args()
+    logger.info("Starting neural network evaluation.")
+
+    logger.info(f"Loading configuration from {args.config}")
+    # Load the workflow parameters
+    config_workflow_nominal         = load_config(args.config)["neural_likelihood_ratio_estimation"]
+    config_workflow_systematics     = load_config(args.config)["systematic_uncertainty"]
+
+    nsbi_fit_config_path = config_workflow_nominal["nsbi_fit_config"]
+    logger.info(f"Initializing NSBI ConfigManager from: {nsbi_fit_config_path}")
+    fit_config_nsbi = nsbi_common_utils.configuration.ConfigManager(file_path_string=nsbi_fit_config_path)
+
+    # Load the training features defined in fit configuration file -- this can also be passed separately if just using training APIs
+    features, features_scaling = fit_config_nsbi.get_training_features()
+    logger.info(f"Training features loaded: {len(features)} features")
+
+    logger.info("Initializing datasets...")
+    branches_to_load = features + ['presel_score'] # Can be defined independently of config when using just the APIs
+    
+    # datasets library helps with preparation of data, reads metadata from fit configuration file
+    datasets_helper = nsbi_common_utils.datasets.datasets(
+        config_path=nsbi_fit_config_path,
+        branches_to_load=branches_to_load
+    )
+
+    logger.info("Loading datasets from paths defined in fit config...")
+    dataset_incl_dict = datasets_helper.load_datasets_from_config(load_systematics=False)
+
+    # The loaded dataframe is a dictionary, with "Nominal" key referring to the nominal dataset
+    dataset_incl_nominal = dataset_incl_dict["Nominal"].copy()
+
+    # Get the signal region events to be used for SBI fit
+    region = config_workflow_nominal["filter_region"]
+    dataset_SR_nominal = datasets_helper.filter_region_dataset(dataset_incl_nominal, region=region)
+
+    # Get the anchor/basis points used to build the full statistical model
+    basis_processes = fit_config_nsbi.get_basis_samples()
+    logger.info(f"Basis processes: {basis_processes}")
+
+    logger.info("Merging dataframes for final evaluation.")
+    dataset_Asimov_SR = datasets_helper.merge_dataframe_dict_for_training(
+        dataset_SR_nominal, None, samples_to_merge=basis_processes
+    )
+
+    # Save Asimov weights for inference
+    weight_save_path = fit_config_nsbi.get_channel_asimov_weight_path(channel_name=region) # Get path to asimov weights from fit config
+    np.save(weight_save_path, dataset_Asimov_SR.weights.to_numpy()) # save the weights
+
+    # Get the path where intermediate data from the workflow is saved
+    path_to_saved_data = config_workflow_nominal["saved_data_path"]
+    if not path_to_saved_data.endswith('/'):
+        path_to_saved_data += '/'
+    
+    # Get the path where trained models were saved
+    training_input_dir_name = config_workflow_nominal["output_training_dir"]
+    trained_models_path = os.path.join(path_to_saved_data, training_input_dir_name)
+    if not trained_models_path.endswith('/'):
+        trained_models_path += '/'
+        
+    logger.info(f"Trained models path: {trained_models_path}")
+
+    # TODO: add support for use_log_loss
+    use_log_loss = config_workflow_nominal["use_log_loss"]
+
+    ensemble_members    = config_workflow_nominal.get("num_ensemble_members", 1)
+    aggregation_type    = config_workflow_nominal.get("ensemble_aggregation_type", "median_score")
+
+    logger.info("Evaluating and saving nominal density ratios on Asimov dataset")
+    for process_type in basis_processes:
+
+        path_to_saving_evaluated_ratios         = os.path.join(trained_models_path, f'output_ratios_{process_type}/')
+        path_to_trained_models                  = os.path.join(trained_models_path, f'output_model_params_{process_type}/')
+
+        score_pred = np.ones((ensemble_members, dataset_Asimov_SR.shape[0]))
+        ratio_pred = np.ones((ensemble_members, dataset_Asimov_SR.shape[0]))
+
+        for ensemble_index in range(ensemble_members):
+
+            path_to_saved_scaler        = f"{path_to_trained_models}model_scaler{ensemble_index}.bin"
+            path_to_saved_model         = f"{path_to_trained_models}model{ensemble_index}.onnx"
+
+            logger.info(f"Reading saved models from {path_to_trained_models}")
+            scaler, model_NN                = nsbi_common_utils.training.load_trained_model(path_to_saved_model, path_to_saved_scaler)
+            score_pred[ensemble_index]      = nsbi_common_utils.training.predict_with_onnx(dataset_Asimov_SR[features], scaler, model_NN, batch_size = 10_000)
+            ratio_pred[ensemble_index]      = nsbi_common_utils.training.convert_score_to_ratio(score_pred[ensemble_index])    
+
+
+        if aggregation_type == 'median_ratio':
+            ratio_ensemble = np.median(ratio_pred, axis=0)
+            
+        elif aggregation_type == 'mean_ratio':
+            ratio_ensemble = np.mean(ratio_pred, axis=0)
+            
+        elif aggregation_type == 'median_score':
+            score_aggregate = np.median(score_pred, axis=0)
+            ratio_ensemble = score_aggregate / (1.0 - score_aggregate)
+            
+        elif aggregation_type == 'mean_score':
+            score_aggregate = np.mean(score_pred, axis=0)
+            ratio_ensemble = score_aggregate / (1.0 - score_aggregate)
+    
+        else:
+            raise Exception("aggregation_type not recognized, please choose between median_ratio, mean_ratio, median_score or mean_score")
+
+        saved_ratio_path = f"{path_to_saving_evaluated_ratios}ratio_{process_type}.npy"
+        np.save(saved_ratio_path, ratio_ensemble)
+
+        logger.info(f"Nominal density ratios for {process_type} basis point saved to: {saved_ratio_path}")
+
+    logger.info("All nominal ratios evaluated on Asimov and saved.")
+
+    logger.info("Running evaluation on Asimov with systematic variation networks...")
+
+    # Get the path where trained models were saved
+    training_input_dir_name = config_workflow_systematics["output_training_dir"]
+    trained_models_path = os.path.join(path_to_saved_data, training_input_dir_name)
+    if not trained_models_path.endswith('/'):
+        trained_models_path += '/'
+        
+    logger.info(f"Trained models path: {trained_models_path}")
+
+    calibration_flag        = config_workflow_systematics["training_settings"].get("calibration", False)
+
+    for process_type in basis_processes:
+
+        ensemble_index = 0 #TODO: support ensemble evaluations for systematics too
+
+        for dict_syst in fit_config_nsbi.config["Systematics"]:
+
+            # Only evaluate norm+shape systematics where the process_type is involved
+            if (process_type not in dict_syst["Samples"]) or (dict_syst["Type"] != "NormPlusShape"): continue
+
+            syst = dict_syst["Name"]
+
+            for direction in ["Up", "Dn"]:
+
+                output_name = f'{process_type}_{syst}_{direction}'
+                        
+                path_to_saving_evaluated_ratios     = os.path.join(trained_models_path, f'output_ratios_{output_name}/')
+                path_to_trained_models              = os.path.join(trained_models_path, f'output_model_params_{output_name}/')
+
+                path_to_saved_scaler        = f"{path_to_trained_models}model_scaler{ensemble_index}.bin"
+                path_to_saved_model         = f"{path_to_trained_models}model{ensemble_index}.onnx"
+
+                logger.info(f"Evaluating and Saving Ratios for {process_type} {syst} {direction}")
+
+                logger.info(f"Reading saved models from {path_to_trained_models}")
+                scaler, model_NN                = nsbi_common_utils.training.load_trained_model(path_to_saved_model, path_to_saved_scaler)
+
+                path_to_calibrator_model    = None
+                if calibration_flag:
+                    path_to_calibrator_model         = f"{path_to_trained_models}model_calibrated_hist{ensemble_index}.obj"
+                    if not os.path.exists(path_to_calibrator_model):
+                        logger.warning(f"No calibration model found with name {path_to_calibrator_model}")
+                        calibration_model = None
+                    else:
+                        file_calibration = open(path_to_calibrator_model, 'rb') 
+                        calibration_model = pickle.load(file_calibration)
+
+                score_pred      = nsbi_common_utils.training.predict_with_onnx(dataset_Asimov_SR[features], 
+                                                                                               scaler, model_NN, 
+                                                                                               calibration_model = calibration_model, 
+                                                                                               batch_size = 10_000)
+                ratio_pred      = nsbi_common_utils.training.convert_score_to_ratio(score_pred)    
+
+                saved_ratio_path = f"{path_to_saving_evaluated_ratios}ratio_{process_type}.npy"
+
+                np.save(saved_ratio_path, ratio_pred)
+
+                logger.info(f"Systematic density ratios for {syst}_{direction} affecting the {process_type} basis point saved to: {saved_ratio_path}")
+
+                logger.info("All systematic density ratios evaluated on Asimov and saved.")
+
+if __name__ == "__main__":
+    main()
diff --git a/FAIR_universe_Higgs_tautau/scripts/data_preprocessing.py b/FAIR_universe_Higgs_tautau/scripts/data_preprocessing.py
new file mode 100644
index 00000000..c239a473
--- /dev/null
+++ b/FAIR_universe_Higgs_tautau/scripts/data_preprocessing.py
@@ -0,0 +1,188 @@
+import os
+import sys
+import argparse
+import numpy as np
+import pandas as pd
+import matplotlib.pyplot as plt
+import mplhep as hep
+import yaml
+import logging
+import warnings
+
+sys.path.append('../src')
+import nsbi_common_utils
+from nsbi_common_utils import datasets
+
+warnings.simplefilter(action='ignore', category=FutureWarning)
+
+logging.basicConfig(level=logging.INFO, format='%(asctime)s - %(levelname)s - %(message)s')
+logger = logging.getLogger(__name__)
+
+hep.style.use(hep.style.ATLAS)
+
+def load_config(path: str) -> dict:
+    with open(path, "r") as f:
+        return yaml.safe_load(f)
+
+def parse_args() -> argparse.Namespace:
+    parser = argparse.ArgumentParser(description="Process HiggsML data features.")
+    parser.add_argument(
+        "--config", 
+        type=str, 
+        default="config.pipeline.yaml",
+        help="Path to configuration file."
+    )
+    return parser.parse_args()
+
+def process_data(df: dict, input_features_by_jet: dict, branches: list) -> tuple:
+    """
+    Apply feature engineering to all samples in all regions.
+    
+    Returns:
+        (modified_df_dict, list_of_new_branch_names)
+    """
+    median_feature = {}
+
+    # 1. Calculate medians from nominal samples
+    nominal_data = df["Nominal"]
+
+    logger.info("Computing medians from Nominal samples for imputation...")
+    for sample, sample_dataset in nominal_data.items(): 
+        median_feature[sample] = {}
+        for n_jets, feat_list in input_features_by_jet.items():
+            for feature in feat_list:
+                # Calculate median for events with n_jets >= threshold
+                vals = sample_dataset.loc[sample_dataset['PRI_n_jets'] >= n_jets, feature]
+                median_feature[sample][feature] = np.median(vals)
+
+    logger.info("Applying feature engineering to all regions and samples...")
+    branches_to_add = []
+
+    # 2. Apply engineering to all datasets (systematics/regions)
+    for region, sample_datasets in df.items():
+        for sample, sample_dataset in sample_datasets.items():
+            
+            df_modified = sample_dataset.copy()
+            
+            # --- Categorical jet masks ---
+            df_modified['njet_0'] = (df_modified['PRI_n_jets'] == 0).astype(int)
+            df_modified['njet_1'] = (df_modified['PRI_n_jets'] == 1).astype(int)
+            df_modified['njet_2'] = (df_modified['PRI_n_jets'] >= 2).astype(int)
+
+            for mask_name in ['njet_0', 'njet_1', 'njet_2']:
+                if mask_name not in branches_to_add:
+                    branches_to_add.append(mask_name)
+
+            # --- Jet feature processing ---
+            for n_jets, feat_list in input_features_by_jet.items():
+                mask_col = f'jet{n_jets}_mask'
+                df_modified[mask_col] = (df_modified['PRI_n_jets'] >= n_jets).astype(float)
+                
+                if mask_col not in branches_to_add:
+                    branches_to_add.append(mask_col)
+
+                for feat in feat_list:
+                    df_modified[feat] = df_modified[feat].where(df_modified['PRI_n_jets'] >= n_jets, 
+                                                                median_feature[sample][feat])
+                    
+
+            # --- Log transformations when distributions spread out ---
+            for feat in branches:
+                if feat not in df_modified.columns:
+                    continue
+
+                kin = df_modified[feat].to_numpy()
+
+                # Only apply log if all values are positive and range is large
+                if (np.amin(kin) > 0.0) and (np.amax(kin) > 100.0):
+                    log_feat = f'log_{feat}'
+                    df_modified[log_feat] = np.log(kin + 10.0)
+                    
+                    if log_feat not in branches_to_add:
+                        branches_to_add.append(log_feat)
+
+            df[region][sample] = df_modified.copy()
+
+    return df, branches_to_add
+
+
+def main() -> None:
+    args = parse_args()
+    config = load_config(args.config)["data_preprocessing"]
+
+    features = config["features"]
+
+     # Specify branches to load from the ROOT ntuples
+    input_features_noJets = ['PRI_lep_pt', 'PRI_lep_eta', 'PRI_lep_phi', 'PRI_had_pt', 'PRI_had_eta',
+        'PRI_had_phi', 'PRI_met', 'PRI_met_phi', 'DER_mass_transverse_met_lep',
+        'DER_mass_vis', 'DER_pt_h', 'DER_deltar_had_lep', 'DER_pt_tot', 'DER_sum_pt',
+        'DER_pt_ratio_lep_had', 'DER_met_phi_centrality']
+    
+    for feat in input_features_noJets:
+        if feat not in features:
+            input_features_noJets.remove(feat)
+
+    input_features_1Jets = ['PRI_jet_leading_pt', 'PRI_jet_leading_eta',
+        'PRI_jet_leading_phi',
+        'PRI_jet_all_pt']
+    
+    for feat in input_features_1Jets:
+        if feat not in features:
+            input_features_1Jets.remove(feat)
+
+    input_features_2Jets = ['PRI_jet_subleading_pt',
+        'PRI_jet_subleading_eta', 'PRI_jet_subleading_phi', 'DER_deltaeta_jet_jet', 'DER_mass_jet_jet',
+        'DER_prodeta_jet_jet',
+        'DER_lep_eta_centrality']
+    
+    for feat in input_features_2Jets:
+        if feat not in features:
+            input_features_2Jets.remove(feat)
+
+    input_features_nJets = ['PRI_n_jets']
+
+    for feat in input_features_nJets:
+        if feat not in features:
+            input_features_nJets.remove(feat)
+
+    branches_to_load = input_features_noJets \
+                        + input_features_1Jets \
+                        + input_features_2Jets \
+                        + input_features_nJets
+    
+
+    input_features_by_jet = {
+        1: input_features_1Jets, 
+        2: input_features_2Jets
+    }
+
+    try:
+        logger.info("Loading dataset into Pandas DataFrames...")
+        datasets_helper = nsbi_common_utils.datasets.datasets(
+                                                                config_path=config['fit_config_path'],
+                                                                branches_to_load=branches_to_load
+                                                            )
+        datasets_all = datasets_helper.load_datasets_from_config(load_systematics = True)
+
+        logger.info("Applying feature engineering...")
+        datasets_all, new_branches = process_data(
+            datasets_all, 
+            input_features_by_jet, 
+            branches=branches_to_load
+        )
+
+        logger.info(f"Adding {len(new_branches)} new engineered features to output schema.")
+        datasets_helper.add_appended_branches(new_branches)
+
+        logger.info("Saving processed datasets...")
+        datasets_helper.save_dataset_to_ntuple(datasets_all, save_systematics=True)
+
+        logger.info("Data preprocessing workflow completed successfully.")
+
+    except Exception as e:
+        logger.error(f"Workflow failed: {e}", exc_info=True)
+        raise
+
+
+if __name__ == "__main__":
+    main()
\ No newline at end of file
diff --git a/FAIR_universe_Higgs_tautau/scripts/ensemble_training.py b/FAIR_universe_Higgs_tautau/scripts/ensemble_training.py
new file mode 100644
index 00000000..143bf5f0
--- /dev/null
+++ b/FAIR_universe_Higgs_tautau/scripts/ensemble_training.py
@@ -0,0 +1,48 @@
+import os
+import sys
+import argparse
+import warnings
+import logging
+import numpy as np
+import tensorflow as tf
+import yaml
+import mplhep as hep
+
+sys.path.append('../src')
+import nsbi_common_utils
+from nsbi_common_utils import datasets, configuration
+from nsbi_common_utils.training import density_ratio_trainer
+
+hep.style.use(hep.style.ATLAS)
+
+warnings.filterwarnings("ignore", category=RuntimeWarning)
+warnings.filterwarnings("ignore", category=FutureWarning)
+
+logging.basicConfig(level=logging.INFO, format='%(asctime)s - %(levelname)s - %(message)s')
+logger = logging.getLogger(__name__)
+
+def load_config(path):
+    with open(path, "r") as f:
+        return yaml.safe_load(f)
+    
+
+def parse_args():
+    parser = argparse.ArgumentParser(description="Neural Likelihood Ratio Estimation Trainer")
+    parser.add_argument('--config', type=str, default='config.pipeline.yaml', 
+                        help='Path to the YAML configuration file')
+    parser.add_argument('--ensemble_size', type=int, default=None,
+                        help='Size of the ensemble.')
+    parser.add_argument('--process', type=str, default=None,
+                        help='Basis point process to train (e.g. htautau, ztautau, ttbar).')
+    return parser.parse_args()
+
+def main():
+    args = parse_args()
+    logger.info("Starting ensemble training.")
+
+    logger.info(f"Loading configuration from {args.config}")
+    config_workflow = load_config(args.config)["neural_likelihood_ratio_estimation"]
+
+    nsbi_config_path = config_workflow["nsbi_config"]
+    logger.info(f"Initializing NSBI ConfigManager from: {nsbi_config_path}")
+    config_nsbi = nsbi_common_utils.configuration.ConfigManager(file_path_string=nsbi_config_path)
\ No newline at end of file
diff --git a/FAIR_universe_Higgs_tautau/scripts/neural_likelihood_ratio_estimation.py b/FAIR_universe_Higgs_tautau/scripts/neural_likelihood_ratio_estimation.py
new file mode 100644
index 00000000..fbbba8c7
--- /dev/null
+++ b/FAIR_universe_Higgs_tautau/scripts/neural_likelihood_ratio_estimation.py
@@ -0,0 +1,172 @@
+import os
+import sys
+import argparse
+import warnings
+import logging
+import numpy as np
+import yaml
+import mplhep as hep
+
+import nsbi_common_utils
+
+hep.style.use(hep.style.ATLAS)
+
+warnings.filterwarnings("ignore", category=RuntimeWarning)
+warnings.filterwarnings("ignore", category=FutureWarning)
+
+logging.basicConfig(level=logging.INFO, format='%(asctime)s - %(levelname)s - %(message)s')
+logger = logging.getLogger(__name__)
+
+def load_config(path):
+    with open(path, "r") as f:
+        return yaml.safe_load(f)
+
+def parse_args():
+    parser = argparse.ArgumentParser(description="Neural Likelihood Ratio Estimation Trainer")
+    parser.add_argument('--config', type=str, default='config.pipeline.yaml', 
+                        help='Path to the YAML configuration file')
+    parser.add_argument('--ensemble_index', type=int, default=None,
+                        help='Ensemble member index.')
+    parser.add_argument('--process', type=str, default=None,
+                        help='Basis point process to train (e.g. htautau, ztautau, ttbar).')
+    return parser.parse_args()
+
+def main():
+    args = parse_args()
+    logger.info("Starting Neural Likelihood Ratio Estimation workflow.")
+
+    logger.info(f"Loading configuration from {args.config}")
+    # Load the workflow parameters
+    config_workflow = load_config(args.config)["neural_likelihood_ratio_estimation"]
+    
+    # Load the fit configuration
+    nsbi_fit_config_path = config_workflow["nsbi_fit_config"]
+    logger.info(f"Initializing NSBI ConfigManager from: {nsbi_fit_config_path}")
+    fit_config_nsbi = nsbi_common_utils.configuration.ConfigManager(file_path_string=nsbi_fit_config_path)
+
+    # Load the training features defined in fit configuration file -- this can also be passed separately if just using training APIs
+    features, features_scaling = fit_config_nsbi.get_training_features()
+    logger.info(f"Training features loaded: {len(features)} features")
+
+    logger.info("Initializing datasets...")
+    branches_to_load = features + ['presel_score'] # Can be defined independently of config when using just the APIs
+    
+    # datasets library helps with preparation of data, reads metadata from fit configuration file
+    datasets_helper = nsbi_common_utils.datasets.datasets(
+        config_path=nsbi_fit_config_path,
+        branches_to_load=branches_to_load
+    )
+
+    logger.info("Loading datasets from paths defined in fit config...")
+    dataset_incl_dict = datasets_helper.load_datasets_from_config(load_systematics=False)
+
+    # The loaded dataframe is a dictionary, with "Nominal" key referring to the nominal dataset
+    dataset_incl_nominal = dataset_incl_dict["Nominal"].copy()
+
+    # Get the signal region events to be used for SBI fit
+    region = config_workflow["filter_region"]
+    dataset_SR_nominal = datasets_helper.filter_region_dataset(dataset_incl_nominal, region=region)
+
+    # Get the path where intermediate data from the workflow is saved
+    path_to_saved_data = config_workflow["saved_data_path"]
+    if not path_to_saved_data.endswith('/'):
+        path_to_saved_data += '/'
+    
+    # Get the path where trained models will be saved
+    training_output_dir_name = config_workflow["output_training_dir"]
+    training_output_path = os.path.join(path_to_saved_data, training_output_dir_name)
+    if not training_output_path.endswith('/'):
+        training_output_path += '/'
+        
+    logger.info(f"Training output path: {training_output_path}")
+    
+    # Get the anchor/basis points used to build the full statistical model
+    basis_processes = fit_config_nsbi.get_basis_samples()
+    logger.info(f"Basis processes: {basis_processes}")
+
+    # Basis points making up the reference hypothesis -- this can in principle be not restricted to basis points
+    ref_processes = fit_config_nsbi.get_reference_samples()
+    logger.info(f"Reference processes: {ref_processes}")
+
+    NN_training_mix_model = {}
+    use_log_loss = config_workflow["use_log_loss"]
+
+    # Start afresh? Set delete_existing_models=True
+    delete_existing = config_workflow["delete_existing_models"]
+
+    if delete_existing:
+        logger.warning("delete_existing_models is True. Old models will be removed.")
+
+    path_to_ratios = {}
+    path_to_figures = {}
+    path_to_models = {}
+
+    logger.info("Preparing datasets and initializing trainers...")
+    for process_type in basis_processes:
+
+        # Prepare dataset to be passed to training
+        dataset_mix_model = datasets_helper.prepare_basis_training_dataset(
+            dataset_SR_nominal, 
+            [process_type], 
+            dataset_SR_nominal, 
+            ref_processes, 
+            denominatorisreferencehypothesis=True
+        )
+
+        output_name = f'{process_type}'
+
+        path_to_ratios[process_type] = os.path.join(training_output_path, f'output_ratios_{process_type}/')
+        path_to_figures[process_type] = os.path.join(training_output_path, f'output_figures_{process_type}/')
+        path_to_models[process_type] = os.path.join(training_output_path, f'output_model_params_{process_type}/')
+        
+        # setup the training of density ratios using density_ratio_trainer API
+        NN_training_mix_model[process_type] = nsbi_common_utils.training.density_ratio_trainer(
+                                                                                                dataset                 = dataset_mix_model,    # dataframe containing all the relevant features for training
+                                                                                                weights                 = dataset_mix_model['weights_normed'].to_numpy(),
+                                                                                                training_labels         = dataset_mix_model['train_labels'].to_numpy(),
+                                                                                                features                = features,
+                                                                                                features_scaling        = features_scaling,
+                                                                                                sample_name             = [process_type, 'ref'],
+                                                                                                output_name             = output_name,
+                                                                                                path_to_figures         = path_to_figures[process_type],
+                                                                                                path_to_ratios          = path_to_ratios[process_type],
+                                                                                                path_to_models          = path_to_models[process_type],
+                                                                                                use_log_loss            = use_log_loss,
+                                                                                                delete_existing_models  = delete_existing
+                                                                                            )
+        
+        del dataset_mix_model
+
+    # Flag that forces the retraining of density ratios
+    force_train = config_workflow["force_train"]
+    
+    # Get training hyperparameters
+    training_settings = config_workflow["training_settings"]
+
+    for process_type in basis_processes:
+        logger.info(f"Processing {process_type}...")
+        
+        if process_type not in training_settings:
+            logger.error(f"Settings for process '{process_type}' not found in 'density_ratio_estimation.training_settings'.")
+            raise KeyError(f"Missing config for {process_type}")
+
+        settings = training_settings[process_type].copy()
+        
+        if force_train:
+            logger.info(f"Force training enabled. Setting load_trained_models=False for {process_type}.")
+            settings['load_trained_models'] = False
+        else:
+            logger.info(f"Using load_trained_models={settings['load_trained_models']} from config for {process_type}.")
+        
+        logger.info(f"Starting training/loading for {process_type}")
+        NN_training_mix_model[process_type].train_ensemble(**settings)
+        
+        logger.info(f"Testing normalization for {process_type}...")
+        NN_training_mix_model[process_type].test_normalization()
+
+    logger.info("Training/Loading complete.")
+
+    logger.info("Workflow completed successfully.")
+
+if __name__ == "__main__":
+    main()
\ No newline at end of file
diff --git a/FAIR_universe_Higgs_tautau/scripts/parameter_fitting.py b/FAIR_universe_Higgs_tautau/scripts/parameter_fitting.py
new file mode 100644
index 00000000..04a47570
--- /dev/null
+++ b/FAIR_universe_Higgs_tautau/scripts/parameter_fitting.py
@@ -0,0 +1,188 @@
+import os, sys
+import argparse
+import logging
+import warnings
+import matplotlib.pyplot as plt
+import mplhep as hep
+import yaml
+import jax
+
+import nsbi_common_utils
+
+jax.config.update("jax_enable_x64", True)
+
+warnings.simplefilter(action='ignore', category=FutureWarning)
+warnings.filterwarnings("ignore", category=RuntimeWarning)
+
+logging.basicConfig(level=logging.INFO, format='%(asctime)s - %(levelname)s - %(message)s')
+logger = logging.getLogger(__name__)
+
+# Set Style
+hep.style.use(hep.style.ATLAS)
+
+def parse_args():
+    parser = argparse.ArgumentParser(description="Run inference and parameter fitting.")
+    parser.add_argument(
+        "--config", 
+        type=str, 
+        default="config.pipeline.yaml",
+        help="Path to the main pipeline configuration file."
+    )
+    return parser.parse_args()
+
+def load_config(path):
+    with open(path, "r") as f:
+        return yaml.safe_load(f)
+
+def save_nll_plot(scan_data, output_dir, parameter_label):
+    """Plots the NLL scans and saves the figure."""
+    if not os.path.exists(output_dir):
+        os.makedirs(output_dir)
+
+    plt.figure(figsize=(10, 8))
+    
+    for item in scan_data:
+        plt.plot(
+            item['points'], 
+            item['nll'], 
+            label=item['label'], 
+            linestyle=item['style'], 
+            color=item['color'],
+            linewidth=2
+        )
+    
+    plt.xlabel(parameter_label, fontsize=20)
+    plt.ylabel(r"$-2\Delta \ln L$", fontsize=20)
+    plt.legend(fontsize=14, frameon=False)
+    plt.ylim(0, 8)
+    plt.xlim(0, 3)
+    
+    plt.axhline(1, color='gray', linestyle=':', alpha=0.5)
+    plt.axhline(4, color='gray', linestyle=':', alpha=0.5)
+    
+    hep.atlas.label(data=False, label="Internal", loc=0)
+    
+    output_path = os.path.join(output_dir, "nll_scan_comparison.png")
+    plt.savefig(output_path, dpi=300, bbox_inches='tight')
+    plt.close()
+    
+    return output_path
+
+def main():
+    args = parse_args()
+    config_workflow = load_config(args.config)["parameter_fitting"]
+    
+    plots_dir = config_workflow["output"]["plots_dir"]
+    
+    logger.info("Starting Inference Pipeline...")
+    logger.info(f"Configurations: Hist={config_workflow['configs']['histogram']}, NSBI={config_workflow['configs']['nsbi']}")
+
+    measurement = config_workflow["measurement"]
+    scan_param = config_workflow["scan"]["parameter"]
+    scan_range = tuple(config_workflow["scan"]["range"])
+    scan_steps = config_workflow["scan"]["steps"]
+
+    try:
+        
+        logger.info("Building Workspaces")
+
+        hist_config_path = config_workflow["configs"]["histogram"]
+        ws_hist = nsbi_common_utils.workspace_builder.WorkspaceBuilder(config_path=hist_config_path).build()
+
+        nsbi_config_path = config_workflow["configs"]["nsbi"]
+        ws_nsbi = nsbi_common_utils.workspace_builder.WorkspaceBuilder(config_path=nsbi_config_path).build()
+        
+        logger.info("Initializing Models")
+
+        model_hist = nsbi_common_utils.model.Model(workspace=ws_hist, 
+                                                   measurement_to_fit=measurement)
+        
+        model_nsbi = nsbi_common_utils.model.Model(workspace=ws_nsbi, 
+                                                   measurement_to_fit=measurement)
+        
+        list_params, init_values = model_hist.get_model_parameters()
+        num_unconstrained = model_hist.num_unconstrained_param
+        
+        inf_hist = nsbi_common_utils.inference.inference(
+            model_nll=model_hist.model,
+            initial_values=init_values,
+            list_parameters=list_params,
+            num_unconstrained_params=num_unconstrained
+        )
+        
+        inf_nsbi = nsbi_common_utils.inference.inference(
+            model_nll=model_nsbi.model,
+            initial_values=init_values,
+            list_parameters=list_params,
+            num_unconstrained_params=num_unconstrained
+        )
+
+        logger.info("\nPerforming Fits (Tables logged to file)")
+        
+        print("\n" + "="*40)
+        print(" NSBI FIT RESULTS ")
+        print("="*40 + "\n")
+        inf_nsbi.perform_fit()
+        
+        print("\n" + "="*40)
+        print(" HISTOGRAM FIT RESULTS ")
+        print("="*40 + "\n")
+        inf_hist.perform_fit()
+
+        logger.info(f"\nRunning Profile Scans for {scan_param}")
+        freeze_params = []
+
+        logger.info("Scanning Histogram Model...")
+        pts_hist, nll_hist, pts_stat_hist, nll_stat_hist = inf_hist.perform_profile_scan(
+            parameter_name=scan_param,
+            freeze_params=freeze_params,
+            bound_range=scan_range,
+            fit_strategy=0,
+            doStatOnly=True,
+            size=scan_steps
+        )
+
+        logger.info("Scanning NSBI Model...")
+        pts_nsbi, nll_nsbi, pts_stat_nsbi, nll_stat_nsbi = inf_nsbi.perform_profile_scan(
+            parameter_name=scan_param,
+            freeze_params=freeze_params,
+            bound_range=scan_range,
+            fit_strategy=0,
+            doStatOnly=True,
+            size=scan_steps
+        )
+
+        
+        logger.info("\nGenerating Plots")
+        
+        plot_data = [
+            {
+                'points': pts_hist, 'nll': nll_hist, 
+                'label': "Histogram Stat+Syst", 'style': "-", 'color': "blue"
+            },
+            {
+                'points': pts_stat_hist, 'nll': nll_stat_hist, 
+                'label': "Histogram Stat Only", 'style': "--", 'color': "blue"
+            },
+            {
+                'points': pts_nsbi, 'nll': nll_nsbi, 
+                'label': "NSBI Stat+Syst", 'style': "-", 'color': "black"
+            },
+            {
+                'points': pts_stat_nsbi, 'nll': nll_stat_nsbi, 
+                'label': "NSBI Stat Only", 'style': "--", 'color': "black"
+            }
+        ]
+        
+        parameter_label_latex = r'$\mu_{h\tau\tau}$' 
+        plot_path = save_nll_plot(plot_data, plots_dir, parameter_label_latex)
+        
+        logger.info(f"Plot saved to: {plot_path}")
+        logger.info("Inference workflow completed successfully.")
+
+    except Exception as e:
+        logger.error(f"An error occurred: {e}", exc_info=True)
+        raise
+
+if __name__ == "__main__":
+    main()
\ No newline at end of file
diff --git a/FAIR_universe_Higgs_tautau/scripts/preselection_network.py b/FAIR_universe_Higgs_tautau/scripts/preselection_network.py
new file mode 100644
index 00000000..acfe6958
--- /dev/null
+++ b/FAIR_universe_Higgs_tautau/scripts/preselection_network.py
@@ -0,0 +1,186 @@
+import os
+import sys
+import argparse
+import logging
+import numpy as np
+import pandas as pd
+import warnings
+import yaml
+import matplotlib.pyplot as plt
+import mplhep as hep
+from sklearn.model_selection import train_test_split
+from sklearn.metrics import roc_curve, auc, confusion_matrix
+from utils import calculate_preselection_observable
+
+warnings.filterwarnings("ignore", category=RuntimeWarning)
+warnings.filterwarnings("ignore", category=FutureWarning)
+
+logging.basicConfig(level=logging.INFO, format='%(asctime)s - %(levelname)s - %(message)s')
+logger = logging.getLogger(__name__)
+
+hep.style.use(hep.style.ATLAS)
+
+sys.path.append('../src')
+import nsbi_common_utils
+from nsbi_common_utils.training import preselection_network_trainer
+
+def load_config(path):
+    with open(path, "r") as f:
+        return yaml.safe_load(f)
+
+def parse_args():
+    parser = argparse.ArgumentParser(description="Preselection Neural Network Training & Inference")
+    parser.add_argument('--config', type=str, default='config.pipeline.yaml', 
+                        help='Path to the YAML configuration file')
+    return parser.parse_args()
+
+
+def plot_score_distribution(dataset_dict, output_dir):
+    """
+    Generates the distribution of preselection scores for various processes.
+    Matches the logic in '3_preselection_network.ipynb'.
+    """
+    if not os.path.exists(output_dir):
+        os.makedirs(output_dir)
+        print(f"Created plot directory: {output_dir}")
+
+    presel_scores = []
+    weights_list = []
+    
+    for sample_name, sample_dataset in dataset_dict["Nominal"].items():
+        if 'presel_score' in sample_dataset.columns:
+            presel_scores.append(sample_dataset['presel_score'].values)
+            weights_list.append(sample_dataset['weights'].values)
+    
+    if not presel_scores:
+        print("No preselection scores found to plot.")
+        return
+
+    all_scores = np.concatenate(presel_scores)
+    min_pred = np.min(all_scores)
+    max_pred = np.max(all_scores)
+    
+    bins = np.linspace(min_pred, max_pred, num=50)
+    
+    plt.figure(figsize=(8, 6))
+    
+    for sample_name, sample_dataset in dataset_dict["Nominal"].items():
+        if 'presel_score' not in sample_dataset.columns:
+            continue
+            
+        scores = sample_dataset['presel_score']
+        weights = sample_dataset['weights']
+        
+        hist, _ = np.histogram(scores, weights=weights, bins=bins)
+        hist_err, _ = np.histogram(scores, weights=weights**2, bins=bins)
+        
+        # Plot using mplhep
+        hep.histplot(hist, bins=bins, 
+                     alpha=0.6, label=sample_name, 
+                     density=True, linewidth=2.0, 
+                     yerr=np.sqrt(hist_err)) 
+
+    plt.xlabel("Preselection Score", size=18)
+    plt.ylabel("Density", size=18)
+    plt.legend()
+    plt.yscale('log')
+    
+  
+    save_path = os.path.join(output_dir, "preselection_score_distributions.png")
+    plt.savefig(save_path)
+    plt.close()
+    print(f"Score distribution plot saved to {save_path}")
+
+
+def main():
+    args = parse_args()
+    logger.info(f"Loading configuration from {args.config}")
+    config_workflow = load_config(args.config)["preselection_network"]
+
+    config_train        = config_workflow["training"]
+    config_preselection = config_workflow["preselection_observable"]
+
+    
+    fit_config_path = config_workflow["fit_config_path"]
+    logger.info(f"Initializing ConfigManager from: {fit_config_path}")
+    
+    fit_config = nsbi_common_utils.configuration.ConfigManager(file_path_string = fit_config_path)
+    
+    features, features_scaling = fit_config.get_training_features()
+    logger.info(f"Training features loaded from NSBI config: {len(features)} features")
+    
+    label_dict = config_train["labels"]
+    
+    logger.info(f"Initializing Datasets...")
+    datasets_helper = nsbi_common_utils.datasets.datasets(
+        config_path=fit_config_path,
+        branches_to_load=features
+    )
+
+    dataset_incl_dict = datasets_helper.load_datasets_from_config(load_systematics=True)
+    dataset_incl_nominal = dataset_incl_dict["Nominal"].copy()
+
+    logger.info("Merging nominal samples for training...")
+    dataset_incl_nominal_training = datasets_helper.merge_dataframe_dict_for_training(
+        dataset_incl_nominal, 
+        label_dict,
+        samples_to_merge=dataset_incl_nominal.keys()
+    )
+
+    preselectionTraining = preselection_network_trainer(
+        dataset_incl_nominal_training, 
+        features, 
+        features_scaling
+    )
+
+    model_path = config_workflow["model_path"]
+    force_train = config_workflow["force_train"]
+    load_trained_models = config_workflow["load_trained_models"]
+    
+    if force_train or not load_trained_models:
+        logger.info(f"Starting Training")
+        preselectionTraining.train(
+            test_size=config_train["test_size"], 
+            random_state=config_train["random_state"], 
+            path_to_save=model_path,
+            batch_size=config_train["batch_size"],
+            epochs=config_train["epochs"], 
+            learning_rate=config_train["learning_rate"]
+        )
+        logger.info(f"Training complete. Model saved to {model_path}")
+    else:
+        logger.info(f"Using load_trained_models={load_trained_models} from config.")
+        preselectionTraining.assign_trained_model(model_path)
+
+    logger.info("Running inference")
+
+    for region_name, dataset_sample_dict in dataset_incl_dict.items():
+        for sample_name, dataset in dataset_sample_dict.items():
+            
+            pred_NN_incl = preselectionTraining.predict(dataset)
+
+            presel_score = calculate_preselection_observable(
+                pred_NN_incl, 
+                samples_list        =   label_dict,
+                signal_processes    =   config_preselection["signal_processes"], 
+                background_processes=   config_preselection["background_processes"],
+                pre_factor_dict     =   config_preselection["pre_factor_dict"]
+            )
+
+            dataset_incl_dict[region_name][sample_name]['presel_score'] = presel_score
+
+    
+    logger.info("Saving datasets with new 'presel_score' branch...")
+    datasets_helper.add_appended_branches(['presel_score'])
+    datasets_helper.save_dataset_to_ntuple(dataset_incl_dict, save_systematics=True)
+
+    logger.info("Saving the 'Preselection Score' plots...")
+    plot_score_distribution(
+        dataset_dict=dataset_incl_dict,
+        output_dir=config_workflow["output"]["plots_dir"]
+    )
+
+    logger.info("Preselection workflow completed successfully.")
+
+if __name__ == "__main__":
+    main()
diff --git a/FAIR_universe_Higgs_tautau/scripts/systematic_uncertainty_training.py b/FAIR_universe_Higgs_tautau/scripts/systematic_uncertainty_training.py
new file mode 100644
index 00000000..bbccac37
--- /dev/null
+++ b/FAIR_universe_Higgs_tautau/scripts/systematic_uncertainty_training.py
@@ -0,0 +1,175 @@
+import os
+import sys
+import argparse
+import logging
+import numpy as np
+import yaml
+import warnings
+import random 
+import mplhep as hep
+
+sys.path.append('../src')
+import nsbi_common_utils
+from nsbi_common_utils import datasets, configuration
+from nsbi_common_utils.training import density_ratio_trainer
+
+hep.style.use(hep.style.ATLAS)
+
+warnings.filterwarnings("ignore", category=RuntimeWarning)
+warnings.filterwarnings("ignore", category=FutureWarning)
+
+logging.basicConfig(level=logging.INFO, format='%(asctime)s - %(levelname)s - %(message)s')
+logger = logging.getLogger(__name__)
+
+def load_config(path):
+    with open(path, "r") as f:
+        return yaml.safe_load(f)
+
+def parse_args():
+    parser = argparse.ArgumentParser(description="Systematic Uncertainty Estimation")
+    parser.add_argument('--config', type=str, default='config.pipeline.yaml', 
+                        help='Path to the main pipeline configuration file')
+    parser.add_argument('--train', action='store_true', 
+                        help='Force training of Neural Network for systematics')
+    return parser.parse_args()
+
+def main():
+    args = parse_args()
+    logger.info("Starting Systematic Uncertainty Estimation workflow.")
+
+    logger.info(f"Loading configuration from {args.config}")
+    config_workflow = load_config(args.config)["systematic_uncertainty"]
+    
+    nsbi_config_path = config_workflow["nsbi_fit_config"]
+    logger.info(f"Initializing NSBI ConfigManager from: {nsbi_config_path}")
+    config_nsbi = nsbi_common_utils.configuration.ConfigManager(file_path_string=nsbi_config_path)
+    
+    features, features_scaling = config_nsbi.get_training_features()
+    logger.info(f"Training features loaded: {len(features)}")
+
+    logger.info("Initializing Datasets...")
+    branches_to_load = features + ['presel_score']
+    
+    datasets_helper = nsbi_common_utils.datasets.datasets(
+        config_path=nsbi_config_path,
+        branches_to_load=branches_to_load
+    )
+    
+    logger.info("Loading full datasets (Nominal + Systematics)...")
+    dataset_incl_dict = datasets_helper.load_datasets_from_config(load_systematics=True)
+    
+    region = config_workflow["filter_region"]
+    logger.info(f"Filtering datasets for region: {region}")
+    dataset_SR_dict = datasets_helper.filter_region_by_type(dataset_incl_dict, region = region)
+    
+    path_to_saved_data = config_workflow["saved_data_path"]
+    if not path_to_saved_data.endswith('/'):
+        path_to_saved_data += '/'
+    
+    if not os.path.exists(path_to_saved_data):
+        os.makedirs(path_to_saved_data)
+
+
+    # =================================================================================
+    # PART 2: SYSTEMATIC TRAINING 
+    # =================================================================================
+    
+    should_train = config_workflow["force_train"]
+    
+    if should_train:
+        logger.info("--- Starting Part 2: Neural Network Training for Systematics ---")
+        
+        # Load training settings directly from pipeline config
+        sys_training_params = config_workflow["training_settings"].copy()
+        
+        # Force training override
+        if should_train:
+            logger.info("Force training enabled. Setting load_trained_models=False.")
+            sys_training_params['load_trained_models'] = False
+
+        NN_training_syst_process  = {}
+        path_to_ratios            = {}
+        path_to_figures           = {}
+        path_to_models            = {}
+
+        for process in config_nsbi.get_basis_samples():
+            
+            NN_training_syst_process[process] = {}
+            path_to_ratios[process]           = {}
+            path_to_figures[process]          = {}
+            path_to_models[process]           = {}
+            
+            # Iterate through systematics defined in NSBI config
+            for dict_syst in config_nsbi.config["Systematics"]:
+                syst = dict_syst["Name"]
+                NN_training_syst_process[process][syst] = {}
+                path_to_ratios[process][syst]           = {}
+                path_to_figures[process][syst]          = {}
+                path_to_models[process][syst]           = {}
+
+                for direction in ["Up", "Dn"]:
+                    samples_to_train = config_nsbi.get_samples_in_syst_for_training(syst, direction)
+                    
+                    if (process not in samples_to_train):
+                        # Clean up if process not affected by this systematic
+                        if not NN_training_syst_process[process][syst]:
+                             del NN_training_syst_process[process][syst]
+                             del path_to_ratios[process][syst]
+                             del path_to_figures[process][syst]
+                             del path_to_models[process][syst]
+                        continue
+
+                    syst_key_name = f"{syst}_{direction}"
+                    if syst_key_name not in dataset_SR_dict: continue
+
+                    logger.info(f"Initializing Trainer: {process} vs {syst_key_name}")
+
+                    # Prepare Dataset: Ratio of Systematic / Nominal 
+                    dataset_syst_nom = datasets_helper.prepare_basis_training_dataset(
+                        dataset_SR_dict[syst_key_name], 
+                        [process], 
+                        dataset_SR_dict["Nominal"], 
+                        [process]
+                    )
+
+                    # Get the path where trained models will be saved
+                    training_output_dir_name = config_workflow["output_training_dir"]
+                    training_output_path = os.path.join(path_to_saved_data, training_output_dir_name)
+                    if not training_output_path.endswith('/'):
+                        training_output_path += '/'
+
+                    logger.info(f"Training output path: {training_output_path}")
+                    
+                    output_name = f'{process}_{syst}_{direction}'
+                    
+                    path_to_ratios[process][syst][direction]    = os.path.join(training_output_path, f'output_ratios_{output_name}/')
+                    path_to_figures[process][syst][direction]   = os.path.join(training_output_path, f'output_figures_{output_name}/')
+                    path_to_models[process][syst][direction]    = os.path.join(training_output_path, f'output_model_params_{output_name}/')
+                    
+                    NN_training_syst_process[process][syst][direction] = nsbi_common_utils.training.density_ratio_trainer(
+                        dataset_syst_nom, 
+                        dataset_syst_nom['weights_normed'].to_numpy(),
+                        dataset_syst_nom['train_labels'].to_numpy(),
+                        features, 
+                        features_scaling,
+                        [syst+'_'+direction, process], 
+                        output_name, 
+                        path_to_figures=path_to_figures[process][syst][direction],
+                        path_to_ratios=path_to_ratios[process][syst][direction], 
+                        path_to_models=path_to_models[process][syst][direction],
+                        delete_existing_models=False
+                    )
+
+        logger.info("Executing Training Loop...")
+
+        for process, process_dict in NN_training_syst_process.items():
+            for syst, syst_dict in process_dict.items():
+                for direction in syst_dict.keys():
+                    
+                    logger.info(f"Training: {process} | {syst} | {direction}")
+                    NN_training_syst_process[process][syst][direction].train_ensemble(**sys_training_params)
+    
+    logger.info("Systematic workflow completed.")
+
+if __name__ == "__main__":
+    main()
\ No newline at end of file
diff --git a/FAIR_universe_Higgs_tautau/utils.py b/FAIR_universe_Higgs_tautau/scripts/utils.py
similarity index 97%
rename from FAIR_universe_Higgs_tautau/utils.py
rename to FAIR_universe_Higgs_tautau/scripts/utils.py
index 1e05a8f5..25760557 100644
--- a/FAIR_universe_Higgs_tautau/utils.py
+++ b/FAIR_universe_Higgs_tautau/scripts/utils.py
@@ -3,7 +3,7 @@
 import mplhep as hep
 import matplotlib.pyplot as plt
 
-def calculate_preselection_observable(pred_NN_incl, samples_list, signal_processes, background_processes, pre_factor_dict = {'htautau': 1.0, 'ttbar': 1.0, 'ztautau': 1.0}):
+def calculate_preselection_observable(pred_NN_incl, samples_list, signal_processes, background_processes, pre_factor_dict = {}):
 
     signal_sum = np.sum(
         [pre_factor_dict[signal] * pred_NN_incl[:, samples_list[signal]] for signal in signal_processes], axis=0
@@ -17,7 +17,6 @@ def calculate_preselection_observable(pred_NN_incl, samples_list, signal_process
 
     return presel_score
 
-
 def preselection_using_score(dataset, channel_selections):
 
     mask_channel = {}
diff --git a/image.def b/image.def
new file mode 100644
index 00000000..0feb8a47
--- /dev/null
+++ b/image.def
@@ -0,0 +1,51 @@
+Bootstrap: docker
+From: ghcr.io/prefix-dev/pixi:latest
+Stage: build
+
+%arguments
+    CUDA_VERSION=12.5
+    ENVIRONMENT=nsbi-env-gpu
+
+%files
+    ./pixi.toml /app/
+    ./pixi.lock /app/
+    ./README.md /app/
+    ./src /app/ 
+    ./pyproject.toml /app/
+
+%post
+    #!/bin/bash
+    export CONDA_OVERRIDE_CUDA={{ CUDA_VERSION }}
+    export SETUPTOOLS_SCM_PRETEND_VERSION_FOR_NSBI_COMMON_UTILS=0.0.0
+    export SETUPTOOLS_SCM_PRETEND_VERSION=0.0.0
+
+    set -e
+    cd /app
+    pixi install --environment {{ ENVIRONMENT }}
+
+    # Build an entrypoint that activates the env then runs the command
+    echo "#!/bin/bash" > /app/entrypoint.sh
+    pixi shell-hook --environment {{ ENVIRONMENT }} -s bash >> /app/entrypoint.sh
+    echo 'exec "$@"' >> /app/entrypoint.sh
+    chmod +x /app/entrypoint.sh
+
+
+Bootstrap: docker
+From: ubuntu:24.04
+Stage: final
+
+%arguments
+    ENVIRONMENT=nsbi-env-gpu
+
+%files from build
+    /app/.pixi/envs/{{ ENVIRONMENT }} /app/.pixi/envs/{{ ENVIRONMENT }}
+    /app/pixi.toml /app/pixi.toml
+    /app/pixi.lock /app/pixi.lock
+    /app/.pixi/.condapackageignore /app/.pixi/.condapackageignore
+
+%environment
+    export PATH=/app/.pixi/envs/{{ ENVIRONMENT }}/bin:$PATH
+
+%runscript
+    #!/bin/bash
+    /app/entrypoint.sh "$@"
diff --git a/pixi.lock b/pixi.lock
index 1be77533..126c7ccf 100644
--- a/pixi.lock
+++ b/pixi.lock
@@ -3,58 +3,151 @@ environments:
   default:
     channels:
     - url: https://conda.anaconda.org/conda-forge/
+    indexes:
+    - https://pypi.org/simple
     packages:
       linux-64:
-      - conda: https://conda.anaconda.org/conda-forge/linux-64/_libgcc_mutex-0.1-conda_forge.tar.bz2
-      - conda: https://conda.anaconda.org/conda-forge/linux-64/_openmp_mutex-4.5-2_gnu.tar.bz2
+      - conda: https://conda.anaconda.org/conda-forge/linux-64/_openmp_mutex-4.5-7_kmp_llvm.conda
       - conda: https://conda.anaconda.org/conda-forge/noarch/_python_abi3_support-1.0-hd8ed1ab_2.conda
+      - conda: https://conda.anaconda.org/conda-forge/noarch/_x86_64-microarch-level-1-3_x86_64.conda
+      - conda: https://conda.anaconda.org/conda-forge/noarch/aiohappyeyeballs-2.6.1-pyhd8ed1ab_0.conda
+      - conda: https://conda.anaconda.org/conda-forge/linux-64/aiohttp-3.13.3-py312h5d8c7f2_0.conda
+      - conda: https://conda.anaconda.org/conda-forge/noarch/aiosignal-1.4.0-pyhd8ed1ab_0.conda
+      - conda: https://conda.anaconda.org/conda-forge/noarch/annotated-types-0.7.0-pyhd8ed1ab_1.conda
+      - conda: https://conda.anaconda.org/conda-forge/noarch/anyio-4.12.1-pyhcf101f3_0.conda
+      - conda: https://conda.anaconda.org/conda-forge/noarch/argon2-cffi-25.1.0-pyhd8ed1ab_0.conda
+      - conda: https://conda.anaconda.org/conda-forge/linux-64/argon2-cffi-bindings-25.1.0-py312h4c3975b_2.conda
+      - conda: https://conda.anaconda.org/conda-forge/noarch/arrow-1.4.0-pyhcf101f3_0.conda
       - conda: https://conda.anaconda.org/conda-forge/noarch/asttokens-3.0.0-pyhd8ed1ab_1.conda
       - conda: https://conda.anaconda.org/conda-forge/noarch/attrs-25.4.0-pyh71513ae_0.conda
-      - conda: https://conda.anaconda.org/conda-forge/linux-64/boost-histogram-1.6.1-py313hc8edb43_0.conda
+      - conda: https://conda.anaconda.org/conda-forge/noarch/awkward-2.8.12-pyhcf101f3_0.conda
+      - conda: https://conda.anaconda.org/conda-forge/linux-64/awkward-cpp-51-py312h1e80e48_0.conda
+      - conda: https://conda.anaconda.org/conda-forge/noarch/awkward-pandas-2023.8.0-pyhd8ed1ab_0.conda
+      - conda: https://conda.anaconda.org/conda-forge/linux-64/aws-c-auth-0.8.6-hd08a7f5_4.conda
+      - conda: https://conda.anaconda.org/conda-forge/linux-64/aws-c-cal-0.8.7-h043a21b_0.conda
+      - conda: https://conda.anaconda.org/conda-forge/linux-64/aws-c-common-0.12.0-hb9d3cd8_0.conda
+      - conda: https://conda.anaconda.org/conda-forge/linux-64/aws-c-compression-0.3.1-h3870646_2.conda
+      - conda: https://conda.anaconda.org/conda-forge/linux-64/aws-c-event-stream-0.5.4-h04a3f94_2.conda
+      - conda: https://conda.anaconda.org/conda-forge/linux-64/aws-c-http-0.9.4-hb9b18c6_4.conda
+      - conda: https://conda.anaconda.org/conda-forge/linux-64/aws-c-io-0.17.0-h3dad3f2_6.conda
+      - conda: https://conda.anaconda.org/conda-forge/linux-64/aws-c-mqtt-0.12.2-h108da3e_2.conda
+      - conda: https://conda.anaconda.org/conda-forge/linux-64/aws-c-s3-0.7.13-h822ba82_2.conda
+      - conda: https://conda.anaconda.org/conda-forge/linux-64/aws-c-sdkutils-0.2.3-h3870646_2.conda
+      - conda: https://conda.anaconda.org/conda-forge/linux-64/aws-checksums-0.2.3-h3870646_2.conda
+      - conda: https://conda.anaconda.org/conda-forge/linux-64/aws-crt-cpp-0.31.0-h55f77e1_4.conda
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-  license: Apache-2.0
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   - mkl <2025
-  - libcblas   3.9.0   37*_openblas
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+  - liblapack  3.9.0   37*_mkl
+  - blas 2.137   mkl
+  - libcblas   3.9.0   37*_mkl
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+  - liblapacke 3.9.0 20_linux64_mkl
+  - mkl <2025
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-  - liblapacke 3.9.0   37*_openblas
-  - liblapack  3.9.0   37*_openblas
+  - liblapacke 3.9.0   37*_mkl
+  - blas 2.137   mkl
+  - liblapack  3.9.0   37*_mkl
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+  license_family: MIT
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   md5: b86c869eb0340028b7f2d23dbd0095b0
@@ -5732,9 +7310,9 @@ packages:
   - pkg:pypi/uhi?source=hash-mapping
   size: 35119
   timestamp: 1755818259613
-- conda: https://conda.anaconda.org/conda-forge/linux-64/unicodedata2-16.0.0-py312h4c3975b_1.conda
-  sha256: cbf7d13819cf526a094f0cfe2da7f7ba22c4fbae4d231c9004520fbbf93f7027
-  md5: 4da303c1e91703d178817252615ca0a7
+- conda: https://conda.anaconda.org/conda-forge/linux-64/unicodedata2-17.0.0-py312h4c3975b_1.conda
+  sha256: 3c812c634e78cec74e224cc6adf33aed533d9fe1ee1eff7f692e1f338efb8c5b
+  md5: a0b8efbe73c90f810a171a6c746be087
   depends:
   - __glibc >=2.17,<3.0.a0
   - libgcc >=14
@@ -5744,13 +7322,13 @@ packages:
   license_family: Apache
   purls:
   - pkg:pypi/unicodedata2?source=hash-mapping
-  size: 404974
-  timestamp: 1756494558558
-- conda: https://conda.anaconda.org/conda-forge/noarch/uproot-5.6.6-pyhcf101f3_0.conda
-  sha256: fba01c12e72e43d794be984107e06e66e411e6d9558dfa55224fef7e6287e5d5
-  md5: bc7685361e02e8edbdce68201112cfda
+  size: 408399
+  timestamp: 1763054875733
+- conda: https://conda.anaconda.org/conda-forge/noarch/uproot-5.7.1-pyhcf101f3_0.conda
+  sha256: 8f53ea0cb6351807d26dc665725c0445ad6f7e5140e5da3284741805e3361011
+  md5: cfc636ae1c0d967b3bf5437fd234485c
   depends:
-  - python >=3.9
+  - python >=3.10
   - awkward >=2.4.6
   - cramjam >=2.5.0
   - python-xxhash
@@ -5763,8 +7341,8 @@ packages:
   license_family: BSD
   purls:
   - pkg:pypi/uproot?source=hash-mapping
-  size: 273946
-  timestamp: 1758498073051
+  size: 278950
+  timestamp: 1769841805875
 - conda: https://conda.anaconda.org/conda-forge/noarch/uri-template-1.3.0-pyhd8ed1ab_1.conda
   sha256: e0eb6c8daf892b3056f08416a96d68b0a358b7c46b99c8a50481b22631a4dfc0
   md5: e7cb0f5745e4c5035a460248334af7eb
@@ -5791,20 +7369,20 @@ packages:
   - pkg:pypi/urllib3?source=hash-mapping
   size: 101735
   timestamp: 1750271478254
-- conda: https://conda.anaconda.org/conda-forge/noarch/vector-1.7.0-pyhcf101f3_0.conda
-  sha256: 7453921c481536b0f657ffad0ca126a3eba5bbf2ed4e45f92672c7f6b51ecaec
-  md5: f36ffcac809eb6ddbebd646e3c1d4b34
+- conda: https://conda.anaconda.org/conda-forge/noarch/vector-1.8.0-pyhcf101f3_0.conda
+  sha256: 1ce72cfb9584b4fd9fd611b9bac7870737977db7a0ae418d2d6fc73066c85e08
+  md5: 45c91eb952e5ddf52f536491efe1b9e4
   depends:
   - numpy >=1.19.3
-  - python >=3.9
+  - python >=3.10
   - packaging >=20
   - python
   license: BSD-3-Clause
   license_family: BSD
   purls:
   - pkg:pypi/vector?source=hash-mapping
-  size: 98352
-  timestamp: 1761068383816
+  size: 98730
+  timestamp: 1770227453419
 - conda: https://conda.anaconda.org/conda-forge/noarch/wcwidth-0.2.14-pyhd8ed1ab_0.conda
   sha256: e311b64e46c6739e2a35ab8582c20fa30eb608da130625ed379f4467219d4813
   md5: 7e1e5ff31239f9cd5855714df8a3783d
@@ -5816,17 +7394,28 @@ packages:
   - pkg:pypi/wcwidth?source=hash-mapping
   size: 33670
   timestamp: 1758622418893
-- conda: https://conda.anaconda.org/conda-forge/noarch/webcolors-24.11.1-pyhd8ed1ab_0.conda
-  sha256: 08315dc2e61766a39219b2d82685fc25a56b2817acf84d5b390176080eaacf99
-  md5: b49f7b291e15494aafb0a7d74806f337
+- conda: https://conda.anaconda.org/conda-forge/noarch/wcwidth-0.5.3-pyhd8ed1ab_0.conda
+  sha256: 2395599ec9e37e6f21838bb26e7f2336fa03a4b1460ba10897ec856b21ac7d59
+  md5: 36432484e9ce3b073a51bf138767a593
   depends:
-  - python >=3.9
+  - python >=3.10
+  license: MIT
+  license_family: MIT
+  purls:
+  - pkg:pypi/wcwidth?source=compressed-mapping
+  size: 70539
+  timestamp: 1769858722627
+- conda: https://conda.anaconda.org/conda-forge/noarch/webcolors-25.10.0-pyhd8ed1ab_0.conda
+  sha256: 21f6c8a20fe050d09bfda3fb0a9c3493936ce7d6e1b3b5f8b01319ee46d6c6f6
+  md5: 6639b6b0d8b5a284f027a2003669aa65
+  depends:
+  - python >=3.10
   license: BSD-3-Clause
   license_family: BSD
   purls:
   - pkg:pypi/webcolors?source=hash-mapping
-  size: 18431
-  timestamp: 1733359823938
+  size: 18987
+  timestamp: 1761899393153
 - conda: https://conda.anaconda.org/conda-forge/noarch/webencodings-0.5.1-pyhd8ed1ab_3.conda
   sha256: 19ff205e138bb056a46f9e3839935a2e60bd1cf01c8241a5e172a422fed4f9c6
   md5: 2841eb5bfc75ce15e9a0054b98dcd64d
@@ -5849,43 +7438,40 @@ packages:
   - pkg:pypi/websocket-client?source=hash-mapping
   size: 61391
   timestamp: 1759928175142
-- conda: https://conda.anaconda.org/conda-forge/noarch/werkzeug-3.1.3-pyhd8ed1ab_1.conda
-  sha256: cd9a603beae0b237be7d9dfae8ae0b36ad62666ac4bb073969bce7da6f55157c
-  md5: 0a9b57c159d56b508613cc39022c1b9e
-  depends:
-  - markupsafe >=2.1.1
-  - python >=3.9
-  license: BSD-3-Clause
-  license_family: BSD
-  purls:
-  - pkg:pypi/werkzeug?source=hash-mapping
-  size: 243546
-  timestamp: 1733160561258
-- conda: https://conda.anaconda.org/conda-forge/noarch/wheel-0.45.1-pyhd8ed1ab_1.conda
-  sha256: 1b34021e815ff89a4d902d879c3bd2040bc1bd6169b32e9427497fa05c55f1ce
-  md5: 75cb7132eb58d97896e173ef12ac9986
+- conda: https://conda.anaconda.org/conda-forge/noarch/wheel-0.46.3-pyhd8ed1ab_0.conda
+  sha256: d6cf2f0ebd5e09120c28ecba450556ce553752652d91795442f0e70f837126ae
+  md5: bdbd7385b4a67025ac2dba4ef8cb6a8f
   depends:
-  - python >=3.9
+  - packaging >=24.0
+  - python >=3.10
   license: MIT
   license_family: MIT
   purls:
   - pkg:pypi/wheel?source=hash-mapping
-  size: 62931
-  timestamp: 1733130309598
-- conda: https://conda.anaconda.org/conda-forge/linux-64/wrapt-2.0.0-py312h4c3975b_0.conda
-  sha256: d9383170b211cf881713c55b64e409c0323e616681f833e24119db55cbe520ce
-  md5: 0b8259ce223bc4062835fdf6879d0193
+  size: 31858
+  timestamp: 1769139207397
+- conda: https://conda.anaconda.org/conda-forge/noarch/widgetsnbextension-4.0.15-pyhd8ed1ab_0.conda
+  sha256: 826af5e2c09e5e45361fa19168f46ff524e7a766022615678c3a670c45895d9a
+  md5: dc257b7e7cad9b79c1dfba194e92297b
   depends:
-  - __glibc >=2.17,<3.0.a0
-  - libgcc >=14
-  - python >=3.12,<3.13.0a0
-  - python_abi 3.12.* *_cp312
-  license: BSD-2-Clause
+  - python >=3.10
+  license: BSD-3-Clause
   license_family: BSD
   purls:
-  - pkg:pypi/wrapt?source=hash-mapping
-  size: 85817
-  timestamp: 1760964243832
+  - pkg:pypi/widgetsnbextension?source=hash-mapping
+  size: 889195
+  timestamp: 1762040404362
+- conda: https://conda.anaconda.org/conda-forge/linux-64/xorg-libxau-1.0.12-hb03c661_1.conda
+  sha256: 6bc6ab7a90a5d8ac94c7e300cc10beb0500eeba4b99822768ca2f2ef356f731b
+  md5: b2895afaf55bf96a8c8282a2e47a5de0
+  depends:
+  - __glibc >=2.17,<3.0.a0
+  - libgcc >=14
+  license: MIT
+  license_family: MIT
+  purls: []
+  size: 15321
+  timestamp: 1762976464266
 - conda: https://conda.anaconda.org/conda-forge/linux-64/xorg-libxau-1.0.12-hb9d3cd8_0.conda
   sha256: ed10c9283974d311855ae08a16dfd7e56241fac632aec3b92e3cfe73cff31038
   md5: f6ebe2cb3f82ba6c057dde5d9debe4f7
@@ -5897,6 +7483,17 @@ packages:
   purls: []
   size: 14780
   timestamp: 1734229004433
+- conda: https://conda.anaconda.org/conda-forge/linux-64/xorg-libxdmcp-1.1.5-hb03c661_1.conda
+  sha256: 25d255fb2eef929d21ff660a0c687d38a6d2ccfbcbf0cc6aa738b12af6e9d142
+  md5: 1dafce8548e38671bea82e3f5c6ce22f
+  depends:
+  - __glibc >=2.17,<3.0.a0
+  - libgcc >=14
+  license: MIT
+  license_family: MIT
+  purls: []
+  size: 20591
+  timestamp: 1762976546182
 - conda: https://conda.anaconda.org/conda-forge/linux-64/xorg-libxdmcp-1.1.5-hb9d3cd8_0.conda
   sha256: 6b250f3e59db07c2514057944a3ea2044d6a8cdde8a47b6497c254520fade1ee
   md5: 8035c64cb77ed555e3f150b7b3972480
@@ -5919,17 +7516,17 @@ packages:
   purls: []
   size: 108219
   timestamp: 1746457673761
-- conda: https://conda.anaconda.org/conda-forge/noarch/xyzservices-2025.4.0-pyhd8ed1ab_0.conda
-  sha256: ac6d4d4133b1e0f69075158cdf00fccad20e29fc6cc45faa480cec37a84af6ae
-  md5: 5663fa346821cd06dc1ece2c2600be2c
+- conda: https://conda.anaconda.org/conda-forge/noarch/xyzservices-2025.11.0-pyhd8ed1ab_0.conda
+  sha256: b194a1fbc38f29c563b102ece9d006f7a165bf9074cdfe50563d3bce8cae9f84
+  md5: 16933322051fa260285f1a44aae91dd6
   depends:
   - python >=3.8
   license: BSD-3-Clause
   license_family: BSD
   purls:
   - pkg:pypi/xyzservices?source=hash-mapping
-  size: 49477
-  timestamp: 1745598150265
+  size: 51128
+  timestamp: 1763813786075
 - conda: https://conda.anaconda.org/conda-forge/linux-64/yaml-0.2.5-h280c20c_3.conda
   sha256: 6d9ea2f731e284e9316d95fa61869fe7bbba33df7929f82693c121022810f4ad
   md5: a77f85f77be52ff59391544bfe73390a
@@ -5984,6 +7581,18 @@ packages:
   - pkg:pypi/zict?source=hash-mapping
   size: 36341
   timestamp: 1733261642963
+- conda: https://conda.anaconda.org/conda-forge/noarch/zipp-3.23.0-pyhcf101f3_1.conda
+  sha256: b4533f7d9efc976511a73ef7d4a2473406d7f4c750884be8e8620b0ce70f4dae
+  md5: 30cd29cb87d819caead4d55184c1d115
+  depends:
+  - python >=3.10
+  - python
+  license: MIT
+  license_family: MIT
+  purls:
+  - pkg:pypi/zipp?source=compressed-mapping
+  size: 24194
+  timestamp: 1764460141901
 - conda: https://conda.anaconda.org/conda-forge/noarch/zipp-3.23.0-pyhd8ed1ab_0.conda
   sha256: 7560d21e1b021fd40b65bfb72f67945a3fcb83d78ad7ccf37b8b3165ec3b68ad
   md5: df5e78d904988eb55042c0c97446079f
@@ -5995,9 +7604,33 @@ packages:
   - pkg:pypi/zipp?source=hash-mapping
   size: 22963
   timestamp: 1749421737203
-- conda: https://conda.anaconda.org/conda-forge/linux-64/zlib-ng-2.2.5-hde8ca8f_0.conda
-  sha256: 3a8e7798deafd0722b6b5da50c36b7f361a80b30165d600f7760d569a162ff95
-  md5: 1920c3502e7f6688d650ab81cd3775fd
+- conda: https://conda.anaconda.org/conda-forge/linux-64/zlib-1.3.1-hb9d3cd8_2.conda
+  sha256: 5d7c0e5f0005f74112a34a7425179f4eb6e73c92f5d109e6af4ddeca407c92ab
+  md5: c9f075ab2f33b3bbee9e62d4ad0a6cd8
+  depends:
+  - __glibc >=2.17,<3.0.a0
+  - libgcc >=13
+  - libzlib 1.3.1 hb9d3cd8_2
+  license: Zlib
+  license_family: Other
+  purls: []
+  size: 92286
+  timestamp: 1727963153079
+- conda: https://conda.anaconda.org/conda-forge/linux-64/zlib-ng-2.2.5-hde8ca8f_1.conda
+  sha256: 84ea17cb646d8a916d9335415f57c9e5dd001de158972322c714ebe1b72670b0
+  md5: c860578a89dc9b6003d600181612287c
+  depends:
+  - __glibc >=2.17,<3.0.a0
+  - libgcc >=14
+  - libstdcxx >=14
+  license: Zlib
+  license_family: Other
+  purls: []
+  size: 110969
+  timestamp: 1764162891322
+- conda: https://conda.anaconda.org/conda-forge/linux-64/zlib-ng-2.3.3-hceb46e0_1.conda
+  sha256: ea4e50c465d70236408cb0bfe0115609fd14db1adcd8bd30d8918e0291f8a75f
+  md5: 2aadb0d17215603a82a2a6b0afd9a4cb
   depends:
   - __glibc >=2.17,<3.0.a0
   - libgcc >=14
@@ -6005,25 +7638,36 @@ packages:
   license: Zlib
   license_family: Other
   purls: []
-  size: 110843
-  timestamp: 1754587144298
-- conda: https://conda.anaconda.org/conda-forge/linux-64/zstandard-0.25.0-py312h5253ce2_0.conda
-  sha256: 1a3beda8068b55639edb92da8e0dc2d487e2a11aba627f709aab1d3cd5dd271c
-  md5: 05d73100768745631ab3de9dc1e08da2
+  size: 122618
+  timestamp: 1770167931827
+- conda: https://conda.anaconda.org/conda-forge/linux-64/zstandard-0.25.0-py312h5253ce2_1.conda
+  sha256: c2bcb8aa930d6ea3c9c7a64fc4fab58ad7bcac483a9a45de294f67d2f447f413
+  md5: 02738ff9855946075cbd1b5274399a41
   depends:
   - python
   - cffi >=1.11
   - zstd >=1.5.7,<1.5.8.0a0
-  - __glibc >=2.17,<3.0.a0
   - libgcc >=14
+  - __glibc >=2.17,<3.0.a0
   - zstd >=1.5.7,<1.6.0a0
   - python_abi 3.12.* *_cp312
   license: BSD-3-Clause
   license_family: BSD
   purls:
-  - pkg:pypi/zstandard?source=hash-mapping
-  size: 466871
-  timestamp: 1757930116600
+  - pkg:pypi/zstandard?source=compressed-mapping
+  size: 467133
+  timestamp: 1762512686069
+- conda: https://conda.anaconda.org/conda-forge/linux-64/zstd-1.5.7-hb78ec9c_6.conda
+  sha256: 68f0206ca6e98fea941e5717cec780ed2873ffabc0e1ed34428c061e2c6268c7
+  md5: 4a13eeac0b5c8e5b8ab496e6c4ddd829
+  depends:
+  - __glibc >=2.17,<3.0.a0
+  - libzlib >=1.3.1,<2.0a0
+  license: BSD-3-Clause
+  license_family: BSD
+  purls: []
+  size: 601375
+  timestamp: 1764777111296
 - conda: https://conda.anaconda.org/conda-forge/linux-64/zstd-1.5.7-hb8e6e7a_2.conda
   sha256: a4166e3d8ff4e35932510aaff7aa90772f84b4d07e9f6f83c614cba7ceefe0eb
   md5: 6432cb5d4ac0046c3ac0a8a0f95842f9
diff --git a/pixi.toml b/pixi.toml
index f8c4861a..7a3d42f6 100644
--- a/pixi.toml
+++ b/pixi.toml
@@ -1,7 +1,7 @@
 [workspace]
 channels = ["conda-forge"]
 name = "NSBI-workflow-tutorial"
-platforms = ["linux-64"]
+platforms = ["linux-64", "osx-arm64"]
 version = "0.1.0"
 
 [tasks]
@@ -9,15 +9,9 @@ version = "0.1.0"
 [dependencies]
 cabinetry = ">=0.6.0,<0.7"
 ipykernel = ">=6.30.1,<7"
-
-[feature.gpu.system-requirements]
-cuda = "12.5"
-
-[feature.gpu.dependencies]
-python = ">=3.12.11,<3.13"
+python = ">=3.11,<3.13"
 pip = ">=25.2,<26"
 jax = ">=0.4.31,<0.5"
-tf2onnx = ">=1.16.1,<2"
 scikit-learn = ">=1.7.1,<2"
 pytables = ">=3.10.2,<4"
 matplotlib-base = ">=3.10.5,<4"
@@ -27,17 +21,37 @@ scipy = ">=1.16.1,<2"
 pandas = ">=2.3.2,<3"
 awkward = ">=2.8.7,<3"
 pixi-kernel = ">=0.6.6,<0.7"
-tensorflow-gpu = "2.17.*"
-cuda-version = "12.5.*"
-cudnn = "8.9.*"
-onnxruntime = "1.18.*"
 uproot = ">=5.6.4,<6"
 coffea = ">=2025.7.3,<2026"
 awkward-pandas = ">=2023.8.0,<2024"
 
-[feature.gpu.pypi-dependencies]
+[target.linux-64.dependencies]
+python = ">=3.12.11,<3.13"
+
+[target.osx-arm64.dependencies]
+python = ">=3.11,<3.13"
+
+[pypi-dependencies]
 nsbi-common-utils = { path = ".", editable = true }
 higgsml = "==0.1.3"
+torch = ">=2.10.0,<3"
+lightning = ">=2.6.1,<3"
+ml-dtypes = "*"
+onnx = ">=1.20.1,<2"
+onnxscript = ">=0.6.0,<0.7"
+onnxruntime = ">=1.23.2,<2"
+
+[feature.gpu.pypi-dependencies]
+onnxruntime-gpu = { version = ">=1.24.1,<2", env-markers = "sys_platform == 'linux' and platform_machine == 'x86_64'" }
+
+[feature.gpu.system-requirements]
+cuda = "12"
+
+[feature.gpu.dependencies]
+
+[feature.cpu.dependencies]
 
 [environments]
-nsbi-env-gpu = { features = ["gpu"], no-default-feature = true }
+nsbi-env = { features = ["cpu"] }
+nsbi-env-gpu = { features = ["gpu"] }
+torch-mac-mps = { features = ["cpu"] }
\ No newline at end of file
diff --git a/src/nsbi_common_utils/__init__.py b/src/nsbi_common_utils/__init__.py
index d3096806..e070c60d 100644
--- a/src/nsbi_common_utils/__init__.py
+++ b/src/nsbi_common_utils/__init__.py
@@ -1,3 +1,21 @@
 # SPDX-FileCopyrightText: 2025-present jsandesa <jay.ajitbhai.sandesara@cern.ch>
 #
 # SPDX-License-Identifier: MIT
+
+from nsbi_common_utils import configuration
+from nsbi_common_utils import datasets
+from nsbi_common_utils import training
+from nsbi_common_utils import plotting
+from nsbi_common_utils import inference
+from nsbi_common_utils import model
+from nsbi_common_utils import workspace_builder
+
+__all__ = [
+    'configuration',
+    'datasets',
+    'training',
+    'plotting',
+    'inference',
+    'workspace_builder',
+    'model'
+]
\ No newline at end of file
diff --git a/src/nsbi_common_utils/configuration.py b/src/nsbi_common_utils/configuration.py
index 948f7b55..17981a11 100644
--- a/src/nsbi_common_utils/configuration.py
+++ b/src/nsbi_common_utils/configuration.py
@@ -246,6 +246,17 @@ def get_channel_filters(self, channel_name: str) -> str:
         filter_string = self.config["Regions"][idx]["Filter"]
 
         return filter_string
+    
+    def get_channel_asimov_weight_path(self, channel_name: str) -> str:
+        
+        idx = self._index_of_region(channel_name = channel_name)
+
+        if idx is None:
+            log.info(f"Region {channel_name} not found in the config.")
+
+        asimov_weight_path = self.config["Regions"][idx]["AsimovWeights"]
+
+        return asimov_weight_path
 
     def _index_of_region(self, channel_name: str) -> Optional[int]:
         regions: list[dict[str, Any]] = self.config["Regions"]
diff --git a/src/nsbi_common_utils/datasets.py b/src/nsbi_common_utils/datasets.py
index de0b3865..61951ea4 100644
--- a/src/nsbi_common_utils/datasets.py
+++ b/src/nsbi_common_utils/datasets.py
@@ -1,194 +1,304 @@
 import os
-import pandas as pd
-import numpy as np
+from collections import defaultdict
+from typing import Dict, List, Optional, Union
 import uproot
-import copy
-import pathlib
-from typing import Any, Dict, List, Literal, Optional, Union
-
-from nsbi_common_utils.configuration import ConfigManager
+import numpy as np
+import awkward as ak
+import pandas as pd
+import nsbi_common_utils
 
 class datasets:
+    """
+    Utility class for loading and saving HiggsML datasets from/to ROOT files.
+    
+    Handles:
+    - Loading nominal samples and systematic variations from ROOT files
+    - Applying feature engineering and adding new branches
+    - Saving modified DataFrames back to ROOT files without losing trees
+    """
 
-    """Lightweight helper for reading ROOT TTrees into pandas DataFrames (via uproot),
-    applying region filters from a config, merging/labeling for ML training, and
-    writing updated trees back to ROOT files."""
-
-    def __init__(self, 
-                config_path: Union[pathlib.Path, str], 
-                branches_to_load: List):
-        """Load analysis config and set the base list of branches to read.
+    def __init__(self, config_path: str, branches_to_load: List[str]):
+        """
+        Initialize the datasets helper.
 
         Args:
-            config_path: Path to a YAML/JSON config consumed by ConfigManager.
-            branches_to_load: Required list of TTree branches to import.
-        Raises:
-            Exception: If branches_to_load is empty.
+            config_path: Path to YAML config defining samples and systematics.
+            branches_to_load: List of branch/column names to read from ROOT files.
         """
-        self.config              = ConfigManager(file_path_string = config_path)
-        
-        if len(branches_to_load) == 0:
-            raise Exception(f"Empty branch list.")
-        self.branches_to_load           = list(branches_to_load)
-        self.branches_all               = list(self.branches_to_load)
-
-    def load_datasets_from_config(self,
-                                load_systematics = False):
-        """Read datasets defined in config into nested dictionaries of DataFrames.
-
-        Structure:
-            {
-              "Nominal": {sample_name: pd.DataFrame, ...},
-              "<Syst>_Up": {...}, "<Syst>_Dn": {...}  # if requested
-            }
-
-        Notes:
-            - Adds a 'sample_name' column and ensures a 'weights' column
-              (renaming per config 'Weight' when present, else defaults to 1.0).
-        Args:
-            load_systematics: If True, also load 'NormPlusShape' systematics.
-        Returns:
-            Dict[str, Dict[str, pd.DataFrame]]: datasets by type (nominal vs systematics) then sample.
+        self.config_path = config_path
+        self.config_helper              = nsbi_common_utils.configuration.ConfigManager(file_path_string = config_path)
+
+        self.branches_to_load           = branches_to_load
+        self.branches_all               = branches_to_load.copy()
+
+    def add_appended_branches(self, new_branches: List[str]) -> None:
         """
-        dict_datasets = {}
-        dict_datasets["Nominal"] = {}
+        Register additional branches (e.g., engineered features) to be saved.
 
-        for dict_sample in self.config.config["Samples"]:
+        Args:
+            new_branches: List of new column names created during preprocessing.
+        """
+        for branch in new_branches:
+            if branch not in self.branches_all:
+                self.branches_all.append(branch)
 
-            weight_branch       = [dict_sample["Weight"]] if "Weight" in dict_sample.keys() else []
+    def load_datasets_from_config(self, load_systematics: bool = False) -> Dict:
+        """
+        Load datasets according to the config structure.
 
-            path_to_root_file   = dict_sample["SamplePath"]
-            tree_name           = dict_sample["Tree"]
-            sample_name         = dict_sample["Name"]
-            branches_to_load    = list(self.branches_to_load)
-            if weight_branch[0] not in branches_to_load:
-                branches_to_load += weight_branch
-                
-            dict_datasets["Nominal"][sample_name] = load_dataframe_from_root(path_to_root_file, 
-                                                                            tree_name, 
-                                                                            branches_to_load)
+        Returns:
+            Nested dict: {region: {sample_name: DataFrame}}
+            - region: "Nominal" or systematic variation names (e.g., "JES_Up")
+            - sample_name: process name (e.g., "htautau", "ztautau")
 
-            dict_datasets["Nominal"][sample_name]["sample_name"] = sample_name
+        Args:
+            load_systematics: If True, also load systematic variation samples.
+        """
+        dict_datasets = {}
 
-            if "Weight" in dict_sample.keys():
-                dict_datasets["Nominal"][sample_name] = dict_datasets["Nominal"][sample_name].rename(columns={dict_sample['Weight']: "weights"})
+        # 1. Load nominal samples
+        dict_datasets["Nominal"] = {}
+        for sample_dict in self.config_helper.config["Samples"]:
+
+            # Extract metadata for the "sample" making up the model
+            sample_name = sample_dict["Name"]
+            file_path = sample_dict["SamplePath"]
+            tree_name = sample_dict["Tree"]
+            
+            # Determine which branches to load (include weight branch if specified)
+            weight_branch = sample_dict.get("Weight")
+            branches = self.branches_to_load.copy()
+            if weight_branch and weight_branch not in branches:
+                branches.append(weight_branch)
+
+            df = self._load_dataframe_from_root(file_path, tree_name, branches)
+            
+            df["sample_name"] = str(sample_name)
+            if weight_branch:
+                df = df.rename(columns={weight_branch: "weights"})
             else:
-                dict_datasets["Nominal"][sample_name]["weights"] = 1.0
+                df["weights"] = 1.0
+            
+            dict_datasets["Nominal"][sample_name] = df
 
+        # 2. Load systematic variations for constrained systematics (if load_systematics=True)
         if load_systematics:
-            systematics_dict_list = self.config.config.get("Systematics", [{}])
-            for dict_syst in systematics_dict_list:
-                syst_name = dict_syst["Name"]
-                syst_type = dict_syst["Type"]
+            systematics_list = self.config_helper.config.get("Systematics", [])
+            for syst_dict in systematics_list:
+
+                # Extract systematic uncertainty metadata
+                syst_name = syst_dict["Name"]
+                syst_type = syst_dict["Type"]
+
+                # Different treatment for different types of constrained uncertainty
+                # Currently only supporting NormPlusShape, no MCstat
                 if syst_type == "NormPlusShape":
                     for direction in ["Up", "Dn"]:
-                        syst_name_var        = syst_name + "_" + direction
-                        dict_datasets[syst_name_var] = {}
-                        for dict_sample in dict_syst[direction]:
-                            path_to_root_file   = dict_sample["Path"]
-                            sample_name         = dict_sample["SampleName"]
-                            tree_name           = dict_sample["Tree"]
-                            weight_branch       = [dict_sample["Weight"]] if "Weight" in dict_sample.keys() else []
-                            branches_to_load    = list(self.branches_to_load)
-                            if weight_branch[0] not in branches_to_load:
-                                branches_to_load += weight_branch
-                            dict_datasets[syst_name_var][sample_name] = load_dataframe_from_root(path_to_root_file, 
-                                                                                                tree_name, 
-                                                                                                branches_to_load)
-
-                            dict_datasets[syst_name_var][sample_name]["sample_name"] = sample_name
-
-                            if "Weight" in dict_sample.keys():
-                                dict_datasets[syst_name_var][sample_name] = dict_datasets[syst_name_var][sample_name].rename(columns={dict_sample['Weight']: "weights"})
+                        region_key = f"{syst_name}_{direction}"
+                        dict_datasets[region_key] = {}
+
+                        for sample_dict in syst_dict.get(direction, []):
+
+                            # Extract sample metadata
+                            sample_name = sample_dict["SampleName"]
+                            file_path = sample_dict["Path"]
+                            tree_name = sample_dict["Tree"]
+                            
+                            # Include weight branch if specified
+                            weight_branch = sample_dict.get("Weight")
+                            branches = self.branches_to_load.copy()
+                            if weight_branch and weight_branch not in branches:
+                                branches.append(weight_branch)
+
+                            df = self._load_dataframe_from_root(file_path, tree_name, branches)
+                            
+                            df["sample_name"] = str(sample_name)
+                            if weight_branch:
+                                df = df.rename(columns={weight_branch: "weights"})
                             else:
-                                dict_datasets[syst_name_var][sample_name]["weights"] = 1.0
+                                df["weights"] = 1.0
+                            
+                            dict_datasets[region_key][sample_name] = df
 
         return dict_datasets
 
-    def add_appended_branches(self, 
-                              branches: List):
+    def _load_dataframe_from_root(
+        self, 
+        file_path: str, 
+        tree_name: str, 
+        branches: List[str]
+    ) -> pd.DataFrame:
         """
-        Declare additional, derived branches to carry through on save.
+        Load a TTree from a ROOT file into a pandas DataFrame.
 
         Args:
-            branches: New branch names to append to the saved schema.
-        """
-        self.branches_all           = self.branches_to_load + branches
+            file_path: Path to the ROOT file.
+            tree_name: Name of the TTree to read.
+            branches: List of branch names to load.
 
-    def save_datasets(self,
-                    dict_datasets,
-                    save_systematics = False):
+        Returns:
+            DataFrame with the requested branches as columns.
+
+        Raises:
+            FileNotFoundError: If the ROOT file doesn't exist.
+            KeyError: If the tree is not found in the file.
         """
-        Write DataFrames back into their ROOT files, preserving other TTrees.
+        if not os.path.exists(file_path):
+            raise FileNotFoundError(f"ROOT file not found: {file_path}")
+
+        try:
+            with uproot.open(f"{file_path}:{tree_name}") as tree:
+                try:
+                    arrays = tree.arrays(branches, library="pd")
+                    if isinstance(arrays, pd.DataFrame):
+                        return arrays
+                except (ValueError, TypeError):
+                    pass  
+                
+                arrays_dict = tree.arrays(branches, library="np")
+                return pd.DataFrame(arrays_dict)
+                
+        except uproot.exceptions.KeyInFileError as e:
+            # Provide helpful error message with available trees
+            with uproot.open(file_path) as f:
+                available = [k.split(";")[0] for k in f.keys() if "TTree" in str(f.classname_of(k))]
+            raise KeyError(
+                f"Tree '{tree_name}' not found in {file_path}. "
+                f"Available trees: {available}"
+            ) from e
+        except Exception as e:
+            # If we can't convert to numpy (jagged branches), that's a data structure problem
+            raise ValueError(
+                f"Could not load tree '{tree_name}' from {file_path} as a flat DataFrame. "
+                f"The tree may contain jagged (variable-length) branches. "
+                f"Original error: {e}"
+            ) from e
+
+    def save_dataset_to_ntuple(
+        self, 
+        dict_datasets: Dict, 
+        save_systematics: bool = False
+    ) -> None:
+        """
+        Write DataFrames back to ROOT files, preserving other existing trees.
 
         Args:
             dict_datasets: Nested dict from load_datasets_from_config().
-            save_systematics: If True, also write available syst variations.
+            save_systematics: If True, also save systematic variation samples.
         """
-        for dict_sample in self.config.config["Samples"]:
-
-            path_to_root_file   = dict_sample["SamplePath"]
-            tree_name           = dict_sample["Tree"]
-            sample_name         = dict_sample["Name"]
-            self._save_dataset_to_ntuple(dict_datasets["Nominal"][sample_name], 
-                                path_to_root_file, 
-                                tree_name)
-
+        # Ensure "weights" and "sample_name" are in branches_all
+        # These columns are added during load, so they must be saved
+        for col in ["weights", "sample_name"]:
+            if col not in self.branches_all:
+                self.branches_all.append(col)
+
+        # 1. Save nominal samples
+        self._save_region_datasets(
+            dict_datasets["Nominal"], 
+            self.config_helper.config["Samples"]
+        )
+
+        # 2. Save systematic variations (if requested)
         if save_systematics:
-            systematics_dict_list = self.config.config.get("Systematics", [{}])
-            for dict_syst in systematics_dict_list:
+            systematics_list = self.config_helper.config.get("Systematics", [])
+            for syst_dict in systematics_list:
+                syst_name = syst_dict["Name"]
+                syst_type = syst_dict["Type"]
 
-                syst_name = dict_syst["Name"]
-                syst_type = dict_syst["Type"]
                 if syst_type == "NormPlusShape":
                     for direction in ["Up", "Dn"]:
-                        syst_name_var        = syst_name + "_" + direction
-                        if syst_name_var not in dict_datasets.keys(): continue
-                        for dict_sample in dict_syst[direction]:
-                            path_to_root_file   = dict_sample["Path"]
-                            sample_name         = dict_sample["SampleName"]
-
-                            if sample_name not in dict_datasets[syst_name_var].keys(): continue
-
-                            tree_name           = dict_sample["Tree"]
-                            self._save_dataset_to_ntuple(dict_datasets[syst_name_var][sample_name],
-                                                        path_to_root_file, 
-                                                        tree_name)
-
-    def _save_dataset_to_ntuple(self,
-                                dataset, 
-                                path_to_root_file: str, 
-                                tree_name: str):
+                        region_key = f"{syst_name}_{direction}"
+                        if region_key not in dict_datasets:
+                            continue
+
+                        sample_config = syst_dict.get(direction, [])
+                        self._save_region_datasets(
+                            dict_datasets[region_key], 
+                            sample_config
+                        )
+
+    def _save_region_datasets(
+        self, 
+        region_data: Dict[str, pd.DataFrame], 
+        sample_config_list: List[dict]
+    ) -> None:
         """
-        Replace a specific TTree with DataFrame contents.
+        Save all samples in a region (nominal or a systematic variation).
 
-        Behavior:
-            - Keeps other trees intact by copying them over.
-            - Ensures 'weights' exists in the saved branch list.
+        Groups samples by their target file path to avoid overwrites.
 
         Args:
-            dataset: DataFrame to write (columns = branches).
-            path_to_root_file: Destination ROOT file.
-            tree_name: Name of the tree to overwrite.
+            region_data: Dict mapping sample_name -> DataFrame.
+            sample_config_list: List of sample config dicts with keys:
+                - 'Name' or 'SampleName': process name
+                - 'SamplePath' or 'Path': ROOT file path
+                - 'Tree': tree name
         """
-        if "weights" not in self.branches_all:
-            self.branches_all =  self.branches_all + ["weights"]
-        dataset = dataset[self.branches_all]
-
-        tmp_path = path_to_root_file + ".tmp"
-
-        with uproot.open(path_to_root_file) as fin, uproot.recreate(tmp_path) as fout:
-            for _tree_name, classname in fin.classnames().items():
-                _tree_name = _tree_name.split(";")[0]
-                if _tree_name == tree_name:
-                    continue
-                if classname == "TTree":
-                    arrs = fin[_tree_name].arrays(library="ak")
-                    fout[_tree_name] = arrs
+        # Group samples by file path
+        file_to_trees: Dict[str, Dict[str, pd.DataFrame]] = defaultdict(dict)
+
+        for sample_dict in sample_config_list:
+            # Handle both Nominal config (Name, SamplePath) and Syst config (SampleName, Path)
+            sample_name = sample_dict.get("Name") or sample_dict.get("SampleName")
+            file_path = sample_dict.get("SamplePath") or sample_dict.get("Path")
+            tree_name = sample_dict["Tree"]
+
+            if sample_name not in region_data:
+                continue
+
+            # Filter to only the branches we want to save
+            df = region_data[sample_name]
+            available_branches = [b for b in self.branches_all if b in df.columns]
+            df_filtered = df[available_branches]
+
+            file_to_trees[file_path][tree_name] = df_filtered
+
+        print(f"DEBUG: ")
+        # Write each file atomically with all its trees
+        for file_path, trees_dict in file_to_trees.items():
+            self._write_file_with_trees(file_path, trees_dict)
+
+    def _write_file_with_trees(
+        self, 
+        file_path: str, 
+        new_trees_dict: Dict[str, pd.DataFrame]
+    ) -> None:
+        """
+        Write/update a ROOT file with multiple trees in one atomic operation.
 
-            fout[tree_name] = dataset
+        Preserves any existing trees not in new_trees_dict, and overwrites
+        trees whose names match keys in new_trees_dict.
 
-        os.replace(tmp_path, path_to_root_file)
+        Args:
+            file_path: Path to the ROOT file (created if it doesn't exist).
+            new_trees_dict: Dict mapping tree_name -> DataFrame.
+        """
+        tmp_path = file_path + ".tmp"
+        trees_to_write = {}
+
+        # 1. If file exists, copy over all trees except those being replaced
+        if os.path.exists(file_path):
+            try:
+                with uproot.open(file_path) as fin:
+                    for key, classname in fin.classnames().items():
+                        tree_name = key.split(";")[0]  
+                        if tree_name in new_trees_dict:
+                            continue  # will be replaced by new version
+                        if classname == "TTree":
+                            trees_to_write[tree_name] = fin[tree_name].arrays(library="ak")
+            except Exception as e:
+                print(f"Warning: Could not read existing trees from {file_path}: {e}")
+
+        # 2. Add the new/updated trees
+        trees_to_write.update(new_trees_dict)
+
+        # 3. Write everything to a temp file, then atomically replace
+        with uproot.recreate(tmp_path) as fout:
+            for tree_name, data in trees_to_write.items():
+                fout[tree_name] = data
+
+        os.replace(tmp_path, file_path)
+        print(f"Saved {len(new_trees_dict)} tree(s) to {file_path}: {list(new_trees_dict.keys())}")
 
     def filter_region_by_type(self,
                              dataset: Dict[str, Dict[str, pd.DataFrame]],
@@ -219,15 +329,17 @@ def filter_region_dataset(self,
         Returns:
             New dict with filtered DataFrames (copy).
         """
-        region_filters = self.config.get_channel_filters(channel_name = region)
+        region_filters = self.config_helper.get_channel_filters(channel_name = region)
         for sample_name, sample_dataframe in dataset.items():
             dataset[sample_name] = sample_dataframe.query(region_filters).copy()
         return dataset
 
+
     def merge_dataframe_dict_for_training(self, 
                                         dataset_dict, 
                                         label_sample_dict: Union[dict[str, int], None] = None,
-                                        samples_to_merge = []):
+                                        samples_to_merge = [],
+                                        isreferencehypothesis = False):
         """
         Concatenate selected samples; optionally add normalized weights + labels. 
         The returned sample is ready for training.
@@ -246,6 +358,9 @@ def merge_dataframe_dict_for_training(self,
         list_dataframes = []
         for sample_name, dataset in dataset_dict.items():
             if sample_name not in samples_to_merge: continue
+            if isreferencehypothesis:
+                weight_arr = dataset["weights"].to_numpy()
+                dataset["weights"] = weight_arr/np.sum(weight_arr)
             list_dataframes.append(dataset)
 
         dataset = pd.concat(list_dataframes)
@@ -294,25 +409,33 @@ def _add_normalised_weights_and_train_label_class(self,
 
         return dataset
     
-    def prepare_basis_training_dataset(self, dataset_numerator, processes_numerator, dataset_denominator, processes_denominator):
+    def prepare_basis_training_dataset(self, 
+                                       dataset_numerator, 
+                                       processes_numerator, 
+                                       dataset_denominator, 
+                                       processes_denominator, 
+                                       denominatorisreferencehypothesis = False):
 
         ref_train_label_sample_dict = {**{ref: 0 for ref in processes_denominator}}
 
         dataset_ref     = self.merge_dataframe_dict_for_training(dataset_denominator, 
                                                                   ref_train_label_sample_dict, 
-                                                                  samples_to_merge = processes_denominator)
+                                                                  samples_to_merge = processes_denominator,
+                                                                  isreferencehypothesis = denominatorisreferencehypothesis)
         
         numerator_train_label_sample_dict = {**{numerator: 1 for numerator in processes_numerator}}
         
         dataset_num = self.merge_dataframe_dict_for_training(dataset_numerator, 
                                                             numerator_train_label_sample_dict, 
-                                                            samples_to_merge = processes_numerator)
+                                                            samples_to_merge = processes_numerator,
+                                                            isreferencehypothesis = False)
         
         dataset_mix_model = pd.concat([dataset_num, dataset_ref])
 
         return dataset_mix_model
 
 
+
 def save_dataframe_as_root(dataset        : pd.DataFrame,
                            path_to_save   : str,
                            tree_name      : str) -> None:
@@ -324,15 +447,10 @@ def save_dataframe_as_root(dataset        : pd.DataFrame,
         path_to_save: Target ROOT file path.
         tree_name: Name of the TTree to create.
     """
-    with uproot.recreate(f"{path_to_save}") as ntuple:
-
-        arrays = {col: dataset[col].to_numpy() for col in dataset.columns}
 
-        ntuple[tree_name] = arrays
-        
+    with uproot.recreate(f"{path_to_save}") as ntuple:
+        ntuple[tree_name] = ak.Array({col: dataset[col].values for col in dataset.columns})
 
-import uproot
-import pandas as pd
 
 def load_dataframe_from_root(path_to_load: str,
                              tree_name: str,
@@ -354,5 +472,3 @@ def load_dataframe_from_root(path_to_load: str,
         dataframe = tree.arrays(branches_to_load, library="pd")
 
     return dataframe
-
-
diff --git a/src/nsbi_common_utils/inference.py b/src/nsbi_common_utils/inference.py
index 280ca09c..5e65fb05 100644
--- a/src/nsbi_common_utils/inference.py
+++ b/src/nsbi_common_utils/inference.py
@@ -4,9 +4,6 @@
 jax.config.update("jax_enable_x64", True)
 
 from sklearn.model_selection import train_test_split
-import tensorflow as tf
-tf.get_logger().setLevel('ERROR')
-from tensorflow.keras.optimizers import Nadam
 import matplotlib.pyplot as plt
 from iminuit import Minuit
 
diff --git a/src/nsbi_common_utils/lightning_tools/__init__.py b/src/nsbi_common_utils/lightning_tools/__init__.py
new file mode 100644
index 00000000..dd4b3119
--- /dev/null
+++ b/src/nsbi_common_utils/lightning_tools/__init__.py
@@ -0,0 +1,14 @@
+"""PyTorch Lightning tools for NSBI."""
+
+from nsbi_common_utils.lightning_tools.multiclass_training import MultiClassLightning
+from nsbi_common_utils.lightning_tools.density_ratio_training import DensityRatioLightning
+from nsbi_common_utils.lightning_tools.callbacks import PrintEpochMetrics, LossHistory
+from nsbi_common_utils.lightning_tools.datasets import WeightedTensorDataset
+
+__all__ = [
+    'MultiClassLightning',
+    'DensityRatioLightning',
+    'PrintEpochMetrics',
+    'WeightedTensorDataset',
+    'LossHistory'
+]
\ No newline at end of file
diff --git a/src/nsbi_common_utils/lightning_tools/callbacks.py b/src/nsbi_common_utils/lightning_tools/callbacks.py
new file mode 100644
index 00000000..624f719c
--- /dev/null
+++ b/src/nsbi_common_utils/lightning_tools/callbacks.py
@@ -0,0 +1,46 @@
+import torch
+torch.set_float32_matmul_precision("high")
+import torch.nn as nn
+import pytorch_lightning as pl
+import torch.nn.functional as F
+from torch.utils.data import Dataset
+from torch.utils.data import DataLoader
+from pytorch_lightning import Trainer
+from pytorch_lightning.callbacks import EarlyStopping, LearningRateMonitor
+
+class PrintEpochMetrics(pl.Callback):
+    def on_validation_epoch_end(self, trainer, pl_module):
+        m = trainer.callback_metrics
+        
+        output = f"Epoch {trainer.current_epoch:4d} | "
+
+        lr = trainer.optimizers[0].param_groups[0]["lr"]
+        
+        output += f"lr = {lr:.3e} | "
+
+        if "train_loss" in m:
+            output += f"train_loss = {m['train_loss'].item():.6f} | "
+        if "train_acc" in m:
+            output += f"train_acc = {m['train_acc'].item():.4f} | "
+        if "val_loss" in m:
+            output += f"val_loss = {m['val_loss'].item():.6f} | "
+        if "val_acc" in m:
+            output += f"val_acc = {m['val_acc'].item():.4f}"
+        
+        print(output)
+
+class LossHistory(pl.Callback):
+
+    def __init__(self):
+        self.train_loss = []
+        self.val_loss = []
+
+    def on_train_epoch_end(self, trainer, pl_module):
+        v = trainer.callback_metrics.get("train_loss")
+        if v is not None:
+            self.train_loss.append(v.cpu().item())
+
+    def on_validation_epoch_end(self, trainer, pl_module):
+        v = trainer.callback_metrics.get("val_loss")
+        if v is not None:
+            self.val_loss.append(v.cpu().item())
\ No newline at end of file
diff --git a/src/nsbi_common_utils/lightning_tools/datasets.py b/src/nsbi_common_utils/lightning_tools/datasets.py
new file mode 100644
index 00000000..6bcd1714
--- /dev/null
+++ b/src/nsbi_common_utils/lightning_tools/datasets.py
@@ -0,0 +1,23 @@
+import numpy as np
+import torch
+torch.set_float32_matmul_precision("high")
+import torch.nn as nn
+import pytorch_lightning as pl
+import torch.nn.functional as F
+from torch.utils.data import Dataset
+from torch.utils.data import DataLoader
+from pytorch_lightning import Trainer
+from pytorch_lightning.callbacks import EarlyStopping, LearningRateMonitor
+
+class WeightedTensorDataset(Dataset):
+
+    def __init__(self, x, y, w):
+        self.x = torch.as_tensor(np.asarray(x), dtype=torch.float32)
+        self.y = torch.as_tensor(np.asarray(y), dtype=torch.long)
+        self.w = torch.as_tensor(np.asarray(w), dtype=torch.float32)
+
+    def __len__(self):
+        return len(self.x)
+
+    def __getitem__(self, i):
+        return self.x[i], self.y[i], self.w[i]
diff --git a/src/nsbi_common_utils/lightning_tools/density_ratio_training.py b/src/nsbi_common_utils/lightning_tools/density_ratio_training.py
new file mode 100644
index 00000000..4d6a597f
--- /dev/null
+++ b/src/nsbi_common_utils/lightning_tools/density_ratio_training.py
@@ -0,0 +1,115 @@
+import torch
+torch.set_float32_matmul_precision("high")
+import torch.nn as nn
+import pytorch_lightning as pl
+import torch.nn.functional as F
+from torch.utils.data import Dataset
+from torch.utils.data import DataLoader
+from pytorch_lightning import Trainer
+from pytorch_lightning.callbacks import EarlyStopping, LearningRateMonitor
+
+class DensityRatioLightning(pl.LightningModule):
+    '''
+    Pytorch-lighning module for estimation of density ratios
+    '''
+    def __init__(self,
+                n_hidden        = 4,
+                n_neurons       = 1000,
+                input_dim       = 11,
+                learning_rate   = 0.1,
+                use_log_loss    = False,
+                activation      = "swish", 
+                callback_factor = 0.01, 
+                callback_patience = 30):
+        
+        super().__init__()
+
+        self.save_hyperparameters()
+
+        self.lr = learning_rate
+        self.use_log_loss = use_log_loss
+
+        # Get activation function
+        activations = {
+            "swish": nn.SiLU,
+            "relu": nn.ReLU,
+            "tanh": nn.Tanh,
+        }
+        activation_choice = activations.get(activation, nn.SiLU)
+
+        # Build architecture - feed forward MLP
+        layers = []
+        input_dim_ = input_dim
+        for _ in range(n_hidden):
+            layers.append(nn.Linear(input_dim_, n_neurons))
+            layers.append(activation_choice())
+            input_dim_ = n_neurons
+        
+        self.mlp = nn.Sequential(*layers)
+
+        if use_log_loss:
+            self.out = nn.Linear(input_dim_, 1)
+            self.from_logits = True
+        else:
+            self.out = nn.Linear(input_dim_, 1)
+            self.from_logits = False
+
+    def forward(self, x):
+
+        x = self.mlp(x)
+        x = self.out(x)
+        if not self.use_log_loss:
+            x = torch.sigmoid(x)
+        return x
+    
+    def training_step(self, batch, batch_idx):
+        x, y, w = batch
+        y = y.float().view(-1, 1)
+        w = w.float().view(-1, 1)
+
+        s_hat = self(x)
+        if self.use_log_loss:
+            loss = F.binary_cross_entropy_with_logits(s_hat, y, reduction="none")
+        else:
+            loss = F.binary_cross_entropy(s_hat, y, reduction="none")
+
+        weighted_loss = (loss * w).sum() / w.sum()
+    
+        self.log("train_loss", weighted_loss, prog_bar=True)
+        return weighted_loss
+    
+    
+    def validation_step(self, batch, batch_idx):
+        x, y, w = batch
+        y = y.float().view(-1, 1)
+        w = w.float().view(-1, 1)
+
+        s_hat = self(x)
+
+        if self.use_log_loss:
+            loss = F.binary_cross_entropy_with_logits(s_hat, y, reduction="none")
+        else:
+            loss = F.binary_cross_entropy(s_hat, y, reduction="none")
+
+        loss = (loss * w).sum() / w.sum()
+
+        self.log("val_loss", loss, prog_bar=True)
+
+    def configure_optimizers(self):
+
+        optimizer = torch.optim.NAdam(self.parameters(), lr=self.lr)
+
+        scheduler = torch.optim.lr_scheduler.StepLR(
+            optimizer,
+            step_size=self.hparams.callback_patience,   
+            gamma=self.hparams.callback_factor        
+        )
+
+        return {
+            "optimizer": optimizer,
+            "lr_scheduler": {
+                "scheduler": scheduler,
+                "interval": "epoch",
+                "frequency": 1
+            }
+        }
diff --git a/src/nsbi_common_utils/lightning_tools/multiclass_training.py b/src/nsbi_common_utils/lightning_tools/multiclass_training.py
new file mode 100644
index 00000000..160bce51
--- /dev/null
+++ b/src/nsbi_common_utils/lightning_tools/multiclass_training.py
@@ -0,0 +1,109 @@
+import torch
+torch.set_float32_matmul_precision("high")
+import torch.nn as nn
+import pytorch_lightning as pl
+import torch.nn.functional as F
+from torch.utils.data import Dataset
+from torch.utils.data import DataLoader
+from pytorch_lightning import Trainer
+from pytorch_lightning.callbacks import EarlyStopping, LearningRateMonitor
+
+class MultiClassLightning(pl.LightningModule):
+    def __init__(self,
+                n_hidden        = 4,
+                n_neurons       = 1000,
+                input_dim       = 11,
+                learning_rate   = 0.1,
+                activation      = "swish",
+                num_classes     = 3,
+                callback_factor = 0.1,
+                callback_patience = 30
+                ):
+        
+        super().__init__()
+
+        self.save_hyperparameters()
+        
+        self.lr = learning_rate
+
+        # Get activation function
+        activations = {
+            "swish": nn.SiLU,
+            "relu": nn.ReLU,
+            "tanh": nn.Tanh,
+        }
+        activation_choice = activations.get(activation, nn.SiLU)
+
+        # Build architecture - feed forward MLP
+        layers = []
+        input_dim_ = input_dim
+        for _ in range(n_hidden):
+            layers.append(nn.Linear(input_dim_, n_neurons))
+            layers.append(activation_choice())
+            input_dim_ = n_neurons
+        
+        self.mlp = nn.Sequential(*layers)
+        self.out = nn.Linear(input_dim_, num_classes)
+
+    def forward(self,x):
+        x       = self.mlp(x)
+        logits  = self.out(x)
+        return logits
+
+    def training_step(self, batch, batch_idx):
+        x, y, w = batch
+        y = y.long()
+        y_hat = self(x)
+        loss = F.cross_entropy(y_hat, y, reduction='none')
+
+        weighted_loss = (loss * w).sum() / w.sum()
+
+        preds = torch.argmax(y_hat, dim=1)
+        correct = (preds == y).float()
+        weighted_acc = (correct * w).sum() / w.sum()
+
+        self.log("train_loss", weighted_loss, prog_bar=True)
+        self.log("train_acc", weighted_acc, prog_bar=True)
+        
+        return weighted_loss
+    
+    def validation_step(self, batch, batch_idx):
+        x, y, w = batch
+        y = y.long()
+        y_hat = self(x)
+        loss = F.cross_entropy(y_hat, y, reduction='none')
+
+        weighted_loss = (loss * w).sum() / w.sum()
+
+        preds = torch.argmax(y_hat, dim=1)
+        correct = (preds == y).float()
+        weighted_acc =  (correct * w).sum() / w.sum()
+
+        self.log("val_loss", weighted_loss, prog_bar=True)
+        self.log("val_acc", weighted_acc, prog_bar=True)
+
+    def predict_step(self, batch, batch_idx, dataloader_idx=0):
+        logits = self(batch)
+        return F.softmax(logits, dim=1)
+
+    def configure_optimizers(self):
+
+        optimizer = torch.optim.NAdam(self.parameters(), lr=self.lr)
+        scheduler = torch.optim.lr_scheduler.ReduceLROnPlateau(
+            optimizer,
+            mode="min",
+            factor=self.hparams.callback_factor,
+            patience=self.hparams.callback_patience,
+            min_lr=1e-9
+        )
+
+        return {
+            "optimizer": optimizer,
+            "lr_scheduler": {
+                "scheduler": scheduler,
+                "monitor": "val_loss",   
+                "interval": "epoch",
+                "frequency": 1
+            }
+        }
+    
\ No newline at end of file
diff --git a/src/nsbi_common_utils/plotting.py b/src/nsbi_common_utils/plotting.py
index 0499cd15..62757567 100644
--- a/src/nsbi_common_utils/plotting.py
+++ b/src/nsbi_common_utils/plotting.py
@@ -7,7 +7,8 @@
 import math
 from scipy import stats
 
-hep.set_style("ATLAS")
+# hep.set_style("ATLAS")
+hep.style.use("ATLAS")
 
 # method for plotting lines
 def abline(slope, intercept):
@@ -36,25 +37,18 @@ def fill_histograms_wError(data, weights, edges, histrange, normalize=True):
     return h, h_err
 
 # Diagnostics for training loss and accuracy 
-def plot_loss(history, path_to_figures=""):
-
-    plt.plot(history.history['loss'])
-    plt.plot(history.history['val_loss'])
-    
-    plt.title('model loss', size=12)
-    plt.ylabel('loss', size=12)
-    plt.xlabel('epoch', size=12)
-    plt.legend(['train', 'validation'], loc='upper left')
-    plt.show()
-    plt.savefig(f'{path_to_figures}/loss_plot.png', bbox_inches='tight')
-    plt.clf()
-    
-    plt.plot(history.history['binary_accuracy'])
-    plt.plot(history.history['val_binary_accuracy'])
-    plt.show()
-    plt.savefig(f'{path_to_figures}/accuracy_plot.png', bbox_inches='tight')
+def plot_loss(loss_history, path_to_figures=""):
+
+    plt.plot(loss_history.train_loss)
+    plt.plot(loss_history.val_loss)
+    plt.title("model loss", size=12)
+    plt.ylabel("loss", size=12)
+    plt.xlabel("epoch", size=12)
+    plt.legend(["train", "validation"], loc="upper left")
+    plt.savefig(f"{path_to_figures}/loss_plot.png", bbox_inches="tight")
     plt.clf()
 
+
 def plot_calibration_curve(data_den, weight_den, data_num, weight_num, 
                            data_den_holdout, weight_den_holdout, data_num_holdout, weight_num_holdout, 
                            path_to_figures="", nbins=100, epsilon=1.0e-20, 
diff --git a/src/nsbi_common_utils/training/__init__.py b/src/nsbi_common_utils/training/__init__.py
new file mode 100644
index 00000000..80ae23d7
--- /dev/null
+++ b/src/nsbi_common_utils/training/__init__.py
@@ -0,0 +1,22 @@
+from nsbi_common_utils.training.utils import (
+    save_model,
+    predict_with_onnx,
+    convert_torch_to_onnx,
+    convert_logLR_to_score,
+    load_trained_model,
+    convert_score_to_ratio
+)
+
+from nsbi_common_utils.training.neural_ratio_estimation import density_ratio_trainer
+from nsbi_common_utils.training.preselection_training import preselection_network_trainer
+
+__all__ = [
+    "density_ratio_trainer",
+    "preselection_network_trainer",
+    "save_model",
+    "predict_with_onnx",
+    "convert_torch_to_onnx",
+    "convert_logLR_to_score",
+    "load_trained_model",
+    "convert_score_to_ratio"
+]
\ No newline at end of file
diff --git a/src/nsbi_common_utils/training.py b/src/nsbi_common_utils/training/neural_ratio_estimation.py
similarity index 62%
rename from src/nsbi_common_utils/training.py
rename to src/nsbi_common_utils/training/neural_ratio_estimation.py
index c40ffa30..8761b2ac 100644
--- a/src/nsbi_common_utils/training.py
+++ b/src/nsbi_common_utils/training/neural_ratio_estimation.py
@@ -2,55 +2,52 @@
 import os, importlib, sys, shutil
 import numpy as np
 import pandas as pd
-import math
 pd.options.mode.chained_assignment = None 
+import math
+import pickle 
 
 import warnings
 from sklearn.exceptions import InconsistentVersionWarning
 warnings.filterwarnings("ignore", category=InconsistentVersionWarning)
 
-import pickle 
+import torch
+torch.set_float32_matmul_precision("high")
+import torch.nn as nn
+import pytorch_lightning as pl
+import torch.nn.functional as F
+from torch.utils.data import Dataset
+from torch.utils.data import DataLoader
+from pytorch_lightning import Trainer
+from pytorch_lightning.callbacks import EarlyStopping, LearningRateMonitor
+from torch.utils.data import Subset
 
-import nsbi_common_utils
+from nsbi_common_utils.lightning_tools import MultiClassLightning, DensityRatioLightning, PrintEpochMetrics, LossHistory, WeightedTensorDataset
+from nsbi_common_utils.training.utils import save_model, predict_with_onnx, convert_torch_to_onnx, convert_logLR_to_score, load_trained_model
 
 from pathlib import Path
-
 from typing import Union, Dict
-import tensorflow as tf
-from tensorflow import keras
-from tensorflow.keras.callbacks import EarlyStopping
-import tensorflow.keras.backend as K
-from tensorflow.keras import layers
-from tensorflow.keras.layers import Dense
-
-import tf2onnx
+from joblib import dump, load
+
 import onnx
 import onnxruntime as rt
 
-
 from sklearn.model_selection import train_test_split
 from sklearn.preprocessing import StandardScaler, MinMaxScaler, PowerTransformer
 from sklearn.compose import ColumnTransformer
-from joblib import dump, load
-
-
-import pickle 
 
 from nsbi_common_utils.calibration import HistogramCalibrator, IsotonicCalibrator
-
-from nsbi_common_utils.plotting import plot_loss, plot_all_features, plot_all_features, plot_reweighted, plot_calibration_curve, plot_calibration_curve_ratio
-
-from joblib import dump, load
+from nsbi_common_utils.plotting import plot_loss, plot_all_features, plot_all_features, plot_reweighted, plot_calibration_curve, plot_calibration_curve_ratio, plot_overfit_side_by_side
 
 import logging
 _LOG_LEVELS = {
-    0: logging.WARNING,  # only warnings/errors
-    1: logging.INFO,     # info + warnings/errors
-    2: logging.DEBUG,    # debug + info + warnings/errors
+    0: logging.WARNING,  
+    1: logging.INFO,    
+    2: logging.DEBUG,   
 }
 
 logger = logging.getLogger("Training Logs")
-logger.propagate = True  # let the application decide handlers/formatters
+logger.propagate = True  
+
 
 def configure_logging(verbose_level: int = 1):
     """
@@ -69,218 +66,7 @@ def configure_logging(verbose_level: int = 1):
         h.setFormatter(fmt)
         logger.addHandler(h)
 
-def save_model(onnx_model_instance, 
-                    path_to_save_model: Union[str, Path], 
-                    scaler_instance, 
-                    path_to_save_scaler: Union[str, Path]) -> None:
-
-        # Save ONNX model
-        onnx.save_model(onnx_model_instance, str(path_to_save_model))
-
-        # Save the standardization scaler
-        dump(scaler_instance, str(path_to_save_scaler), compress=True)
-
-
-def load_trained_model(path_to_saved_model: Union[Path, str], 
-                        path_to_saved_scaler: Union[Path, str]):
-
-    # Load scaler
-    scaler          = load(str(path_to_saved_scaler))
-
-    # Load ONNX model
-    model           = onnx.load(str(path_to_saved_model))
-
-    return scaler, model
-
-
-def predict_with_onnx(dataset, scaler, model, batch_size = 10_000):
-
-    sess_opts = rt.SessionOptions()
-    sess_opts.intra_op_num_threads = 1
-    sess_opts.inter_op_num_threads = 1
-
-    if isinstance(model, onnx.ModelProto):
-        model = rt.InferenceSession(model.SerializeToString(), 
-                                    sess_options = sess_opts,
-                                    providers=["CUDAExecutionProvider", "CPUExecutionProvider"])
-    
-    elif isinstance(model, rt.InferenceSession):
-        model = model
-    else:
-        raise TypeError(f"Unsupported model type: {type(model)}")
-
-    scaled_dataset              = scaler.transform(dataset)
-
-    # Get model input/output names
-    input_name                  = model.get_inputs()[0].name
-    output_name                 = model.get_outputs()[0].name
-
-    preds = []
-    for i in range(0, len(scaled_dataset), batch_size):
-        batch       = scaled_dataset[i:i+batch_size]
-        pred        = model.run([output_name], {input_name: batch})[0]
-        preds.append(pred)
-
-    final_pred = np.concatenate(preds, axis=0)
-
-    return final_pred
-
-def convert_tf_to_onnx(model, opset=17):
-
-    model.output_names      = [t.name.split(":")[0] for t in model.outputs]
-
-    # Build a TensorSpec for every model input
-    sig = []
-    for i, inp in enumerate(model.inputs):
-        shape = [d if d is not None else None for d in inp.shape]  # keep None for dynamic batch
-        dtype = inp.dtype
-        name = inp.name.split(":")[0] or f"input_{i}"
-        sig.append(tf.TensorSpec(shape=shape, dtype=dtype, name=name))
-
-    # Convert using that signature
-    model_onnx, _ = tf2onnx.convert.from_keras(
-        model,
-        input_signature=tuple(sig),
-        opset=opset
-    )
-    return model_onnx
-
-    # model.output_names      = [t.name.split(":")[0] for t in model.outputs]
-
-    # input_dim               = model.input_shape[1]
-
-    # spec                    = (tf.TensorSpec([None, input_dim], tf.float32, name="input"),)
-
-    # model_onnx, _ = tf2onnx.convert.from_keras(model, input_signature=spec, opset=17)
-
-    # return model_onnx
-
-class preselection_network_trainer:
-    '''
-    A class for training the multi-class classification neural network for preselecting phase space for SBI
-    '''
-    def __init__(self, dataset, features, features_scaling, 
-                    train_labels_column = 'train_labels',
-                    weights_normed_column = 'weights_normed'):
-        '''
-        dataset: dataframe with the multiple classes for training
-        num_classes: number of classes corresponding to the number of output nodes of softmax layer
-        features: input features to use for training
-        features_scaling: subset of input features to standardize before training
-        '''
-        self.dataset                            = dataset
-        self.data_features_training             = dataset[features].copy()
-        self.features                           = features
-        self.features_scaling                   = features_scaling
-        self.num_classes                        = len(np.unique(dataset.train_labels))
-
-        self.train_labels_column                = train_labels_column
-        self.weights_normed_column              = weights_normed_column
-
-    # Defining a simple NN training for preselection - no need for "flexibility" here
-    def train(self, test_size=0.15, 
-                    random_state=42, 
-                    path_to_save='', 
-                    epochs=20, 
-                    batch_size=1024, 
-                    verbose=2, 
-                    learning_rate=0.1):
-
-        '''
-        The function will train the preselection NN, assign it to self.model variable, and save the model to user-provided path_to_save directory.
-
-        test_size: the fraction of dataset to set aside for diagnostics, not used in training and validation of the loss vs epoch curves
-        random_state: random state to use for splitting the train/test dataset before training NN
-        epochs: the number of epochs to train the NNs
-        batch_size: the size of each batch used during gradient optimization
-        learning_rate: the initial learning rate to pass to the optimizer
-        '''
-
-        # Split data into training and validation sets (including weights)
-        X_train, X_val, y_train, y_val, weight_train, weight_val = train_test_split(self.data_features_training, 
-                                                                                    self.dataset[self.train_labels_column], 
-                                                                                    self.dataset[self.weights_normed_column], 
-                                                                                    test_size=test_size, 
-                                                                                    random_state=random_state, 
-                                                                                    stratify=self.dataset[self.train_labels_column])
-
-        # Standardize the input features
-        self.scaler = ColumnTransformer([("scaler", StandardScaler(), self.features_scaling)],remainder='passthrough')
-        X_train = self.scaler.fit_transform(X_train)  # Fit & transform training data
-        X_val = self.scaler.transform(X_val)
         
-        # Define the neural network model
-        self.model = tf.keras.Sequential([
-            layers.Input(shape=(self.data_features_training.shape[1],)),  # Input layer
-            layers.Dense(1000, activation='swish'),
-            layers.Dense(1000, activation='swish'),
-            layers.Dense(1000, activation='swish'),
-            layers.Dense(1000, activation='swish'),
-            layers.Dense(self.num_classes, activation='softmax')  # Output layer for num_class classes
-        ])
-
-        # Using the Nadam optimizer by default
-        optimizer = tf.keras.optimizers.Nadam(learning_rate=learning_rate)
-
-        # Compile the model
-        self.model.compile(optimizer=optimizer,
-                      loss='sparse_categorical_crossentropy',
-                      weighted_metrics=["accuracy"])
-        
-        # setup the callbacks
-        callback_factor = 0.01
-        callback_patience = 30
-        reduce_lr = keras.callbacks.ReduceLROnPlateau(monitor='val_loss', factor=callback_factor,
-                                        patience=callback_patience, min_lr=0.000000001)
-        
-        # Train the model with sample weights
-        self.model.fit(X_train, y_train, sample_weight=weight_train, 
-                  validation_data=(X_val, y_val, weight_val), callbacks=[reduce_lr], epochs=epochs, batch_size=batch_size, verbose=verbose)
-
-        K.clear_session()
-
-        # Convert Keras model to ONNX
-        self.model                  = convert_tf_to_onnx(self.model)
-
-        # Save the trained model if user provides with a path
-        if path_to_save!='':
-
-            path_to_save      = Path(path_to_save)
-            path_to_save.mkdir(parents=True, exist_ok=True)
-
-            path_to_model           = path_to_save / 'model_preselection.onnx'
-            path_to_scaler          = path_to_save / 'model_scaler_presel.bin'
-
-            save_model(self.model, path_to_model, self.scaler, path_to_scaler)
-
-
-    def assign_trained_model(self, 
-                         path_to_models: str) -> None:
-        '''
-        Method to load the trained model
-
-        path_to_models: path to the directory with saved model and scaler files
-        '''
-
-        path_to_saved_scaler        = path_to_models + '/model_scaler_presel.bin'
-        path_to_saved_models        = path_to_models + '/model_preselection.onnx'
-
-        self.scaler, self.model     = load_trained_model(path_to_saved_models, path_to_saved_scaler)
-
-    def predict(self, dataset):
-        '''
-        Method that evaluates density ratios on provided dataset, using self.model
-
-        dataset: the dataset to evaluate trained model on
-        '''
-        pred                        = predict_with_onnx(dataset[self.features], 
-                                                        self.scaler, 
-                                                        self.model)
-        
-        return pred
-
-        
-
 class density_ratio_trainer:
     '''
     A class for training the density ratio neural networks for SBI analysis
@@ -291,7 +77,6 @@ def __init__(self, dataset,
                       features, 
                       features_scaling, 
                       sample_name, 
-                      output_dir, 
                       output_name, 
                       path_to_figures='',
                       path_to_models='', 
@@ -313,7 +98,6 @@ def __init__(self, dataset,
         self.features = features
         self.features_scaling = features_scaling
         self.sample_name = sample_name
-        self.output_dir = output_dir
         self.output_name = output_name
         self.use_log_loss = use_log_loss
         self.split_using_fold = split_using_fold
@@ -366,7 +150,8 @@ def train_ensemble(self, hidden_layers,
                             load_trained_models = False,
                             recalibrate_output=False,
                             summarize_model: bool = False,
-                            num_ensemble_members=1):
+                            num_ensemble_members=1,
+                            num_workers = 0):
         '''
         Train an ensemble of NNs
         '''
@@ -420,9 +205,8 @@ def train_ensemble(self, hidden_layers,
                         plot_scaled_features    = plot_scaled_features, 
                         load_trained_models     = load_trained_models_ensemble_member,
                         recalibrate_output      = recalibrate_output,
-                        summarize_model         = summarize_model)
+                        num_workers             = num_workers)
             
-            summarize_model = False
         
     def train(self, hidden_layers, 
                     neurons, 
@@ -445,7 +229,7 @@ def train(self, hidden_layers,
                     plot_scaled_features=False, 
                     load_trained_models = False,
                     recalibrate_output=False,
-                    summarize_model: bool = False):
+                    num_workers=0):
         '''
         Method that trains the density ratio NNs
 
@@ -508,11 +292,6 @@ def train(self, hidden_layers,
         # dataset to be used for training
         data_train, data_holdout = data_train_full[self.features], data_holdout_full[self.features]
 
-        # Setup callbacks
-        reduce_lr = keras.callbacks.ReduceLROnPlateau(monitor='val_loss', factor=callback_factor,
-                                        patience=callback_patience, min_lr=0.000000001)
-        es = EarlyStopping(monitor='val_loss', mode='min', verbose=2, patience=300)
-
         # Load pre-trained models and scaling
         if load_trained_models:
 
@@ -536,7 +315,7 @@ def train(self, hidden_layers,
 
 
         scaled_data_train = self.scaler[ensemble_index].fit_transform(data_train)
-        scaled_data_train= pd.DataFrame(scaled_data_train, columns=self.features)
+        scaled_data_train = pd.DataFrame(scaled_data_train, columns=self.features)
 
         if plot_scaled_features:
             plot_all_features(scaled_data_train, weight_train, label_train)
@@ -552,51 +331,95 @@ def train(self, hidden_layers,
             logger.info(f"Sum of weights of class 1: {np.sum(weight_train[label_train==1])}")
     
             logger.info(f"Using {activation} activation function")
-    
-            self.model_NN[ensemble_index] = build_model(n_hidden=hidden_layers, n_neurons=neurons, 
-                                        learning_rate=learning_rate, 
-                                        input_shape=[len(self.features)], 
-                                        use_log_loss=self.use_log_loss,
-                                        activation=activation)
-    
+
+            train_ds = WeightedTensorDataset(
+                scaled_data_train.values,
+                label_train,
+                weight_train
+            )
+
+            val_size = int(len(train_ds) * validation_split)
+            train_size = len(train_ds) - val_size
+
+            train_ds, val_ds = torch.utils.data.random_split(train_ds, [train_size, val_size])
+
+            train_loader = DataLoader(train_ds, 
+                                        batch_size=batch_size, 
+                                        shuffle=True,
+                                        num_workers=num_workers,  
+                                        pin_memory=False,  
+                                        persistent_workers=False)
+            
+            val_loader   = DataLoader(val_ds, 
+                                      batch_size=batch_size, 
+                                      shuffle=False,
+                                        num_workers=num_workers,  
+                                        pin_memory=False,  
+                                        persistent_workers=False)
+
+            model = DensityRatioLightning(
+                n_hidden=hidden_layers,
+                n_neurons=neurons,
+                input_dim=len(self.features),
+                learning_rate=learning_rate,
+                use_log_loss=self.use_log_loss,
+                activation=activation,
+                callback_factor=callback_factor,
+                callback_patience=callback_patience
+            )
+
+            loss_history = LossHistory()
+
             if callback:
-    
-                logger.info("Using Callbacks")
-    
-                self.history = self.model_NN[ensemble_index].fit(scaled_data_train, label_train, callbacks=[reduce_lr, es], 
-                                                                epochs=number_of_epochs, batch_size=batch_size, 
-                                                                validation_split=validation_split, sample_weight=weight_train, 
-                                                                verbose=self.verbose)
-    
+                trainer = Trainer(
+                    accelerator="auto",
+                    devices="auto",
+                    max_epochs=number_of_epochs,
+                    callbacks=[
+                        EarlyStopping(monitor="val_loss", patience=callback_patience),
+                        LearningRateMonitor(),
+                        loss_history,
+                        PrintEpochMetrics()
+                    ],
+                    logger=True,
+                    enable_checkpointing=False,
+                    enable_progress_bar=True,
+                )
             else:
-                logger.info("Not Using Callbacks")
-    
-                self.history = self.model_NN[ensemble_index].fit(scaled_data_train, label_train, 
-                                                                epochs=number_of_epochs, batch_size=batch_size, 
-                                                                validation_split=validation_split, sample_weight=weight_train, 
-                                                                verbose=self.verbose)
-            
-            K.clear_session()
+                trainer = Trainer(
+                    accelerator="auto",
+                    devices="auto",
+                    max_epochs=number_of_epochs,
+                    callbacks=[loss_history],
+                    logger=True,
+                    enable_checkpointing=False
+                )
+
+            trainer.fit(model, train_loader, val_loader)
+
+            self.model_NN[ensemble_index] = model
         
             logger.info("Finished Training")
-
-            if summarize_model:
-                logging.info(self.model_NN[ensemble_index].summary())
                 
-            # Convert Keras model to ONNX
-            self.model_NN[ensemble_index]                   = convert_tf_to_onnx(self.model_NN[ensemble_index])
-
             path_to_saved_scaler        = f"{self.path_to_models}model_scaler{ensemble_index}.bin"
             path_to_saved_model         = f"{self.path_to_models}model{ensemble_index}.onnx"
 
-            save_model(self.model_NN[ensemble_index], path_to_saved_model,
-                        self.scaler[ensemble_index], path_to_saved_scaler)
-    
+            save_model(self.model_NN[ensemble_index], 
+                        torch.randn((1, len(self.features))), 
+                        path_to_saved_model,
+                        self.scaler[ensemble_index], 
+                        path_to_saved_scaler,
+                        softmax_output = False)
+            
+            # Reassign the model to be in ONNX format
+            self.scaler[ensemble_index], self.model_NN[ensemble_index] = load_trained_model(path_to_saved_model, 
+                                                                                            path_to_saved_scaler)
+
             # Save metadata
             np.save(f"{self.path_to_models}num_events_random_state_train_holdout_split{ensemble_index}.npy", 
                     np.array([holdout_num, rnd_seed]))
     
-            plot_loss(self.history, path_to_figures=self.path_to_figures)
+            plot_loss(loss_history, path_to_figures=self.path_to_figures)
 
         
         # Do a first prediction without calibration layers
@@ -605,14 +428,10 @@ def train(self, hidden_layers,
                                                         use_log_loss = self.use_log_loss)
 
         calibration_method = 'direct'
-        # calibration_method = ''
         
         # If calibrating, use the train_data_prediction for building histogram
         if self.calibration:
 
-            importlib.reload(sys.modules['nsbi_common_utils.calibration'])
-            from nsbi_common_utils.calibration import HistogramCalibrator, IsotonicCalibrator
-
             self.calibration_switch = True
             path_to_calibrated_object = f"{self.path_to_models}model_calibrated_hist{ensemble_index}.obj"
 
@@ -701,10 +520,10 @@ def train(self, hidden_layers,
         for name, training_holdout_label, min_val, max_val in min_max_values:
             
             if min_val == 0:
-                raise Warning(f"WARNING: {name} {training_holdout_label} data has min score = 0 for ensemble member {ensemble_index}, which may indicate numerical instability!")
+                logger.warning(f"{name} {training_holdout_label} data has min score = 0 for ensemble member {ensemble_index}, which may indicate numerical instability!")
             
             if max_val == 1:
-                raise Warning(f"WARNING: {name} {training_holdout_label} data has max score = 1 for ensemble member {ensemble_index}, which may indicate numerical instability!")            
+                logger.warning(f"{name} {training_holdout_label} data has max score = 1 for ensemble member {ensemble_index}, which may indicate numerical instability!")            
 
 
     
@@ -718,35 +537,24 @@ def predict_with_model(self, data, ensemble_index = 0, use_log_loss=False):
         pred = predict_with_onnx(data[self.features], 
                                 self.scaler[ensemble_index],
                                 self.model_NN[ensemble_index])
-        
-        pred = pred.reshape(pred.shape[0],)
 
         if use_log_loss:
 
-            pred = convert_to_score(pred)
+            pred = convert_logLR_to_score(pred)
 
         if (self.calibration) & (self.calibration_switch):
     
             pred = self.histogram_calibrator[ensemble_index].cali_pred(pred)
             pred = pred.reshape(pred.shape[0],)
-            pred = np.clip(pred, 1e-25, 0.9999)
+            pred = np.clip(pred, 1e-8, 1.0 - 1e-8)
 
-        K.clear_session()
         return pred
     
-    def print_architecture(self, ensemble_index=0):
-        """
-        Print a concise architecture summary for the given ensemble member.
-        Works after reload because it reads the saved JSON summary.
-        """
-        logger.info(f"Model summary \n\n {onnx.helper.printable_graph(self.model_NN[ensemble_index].graph)}") 
     
     def make_overfit_plots(self, ensemble_index=0):
         '''
         Plot predictions for training and holdout to test compatibility
         '''
-        importlib.reload(sys.modules['nsbi_common_utils.plotting'])
-        from nsbi_common_utils.plotting import plot_overfit_side_by_side
 
         plot_overfit_side_by_side(
             self.score_den_training, self.score_den_holdout,
@@ -914,107 +722,4 @@ def evaluate_and_save_ratios(self, dataset, aggregation_type = 'mean_ratio'):
         np.save(saved_ratio_path, ratio_ensemble)
 
         return saved_ratio_path
-    
-    def evaluate_ratios(self, dataset, aggregation_type = 'mean_ratio'):
-        '''
-        Evaluate with self.model on the input dataset, and save to self.path_to_ratios
 
-        aggregation_type: choose an option on how to aggregate the ensemble models - 'median_ratio', 'mean_ratio', 'median_score', 'mean_score'
-        '''
-
-        logger.info(f"Evaluating density ratios")
-        score_pred = np.ones((self.num_ensemble_members, dataset.shape[0]))
-        ratio_pred = np.ones((self.num_ensemble_members, dataset.shape[0]))
-        log_ratio_pred = np.ones((self.num_ensemble_members, dataset.shape[0]))
-
-        for ensemble_index in range(self.num_ensemble_members):
-            score_pred[ensemble_index] = self.predict_with_model(dataset[self.features], 
-                                                                 use_log_loss=self.use_log_loss, 
-                                                                 ensemble_index=ensemble_index)
-            
-            ratio_pred[ensemble_index] = score_pred[ensemble_index] / (1.0 - score_pred[ensemble_index])
-            log_ratio_pred[ensemble_index] = np.log(score_pred[ensemble_index] / (1.0 - score_pred[ensemble_index]))
-
-        if aggregation_type == 'median_ratio':
-            ratio_ensemble = np.median(ratio_pred, axis=0)
-            
-        elif aggregation_type == 'mean_ratio':
-            ratio_ensemble = np.mean(ratio_pred, axis=0)
-            
-        elif aggregation_type == 'median_score':
-            score_aggregate = np.median(score_pred, axis=0)
-            ratio_ensemble = score_aggregate / (1.0 - score_aggregate)
-            
-        elif aggregation_type == 'mean_score':
-            score_aggregate = np.mean(score_pred, axis=0)
-            ratio_ensemble = score_aggregate / (1.0 - score_aggregate)
-
-        else:
-            raise Exception("aggregation_type not recognized, please choose between median_ratio, mean_ratio, median_score or mean_score")
-
-        return ratio_ensemble
-        
-
-def build_model(n_hidden=4, 
-                n_neurons=1000, 
-                learning_rate=0.1, 
-                input_shape=[11], 
-                use_log_loss=False, 
-                optimizer_choice='Nadam', 
-                activation='swish'):
-    '''
-    Method that builds the NN model used in density ratio training
-
-    activation: string with any activation function supported by keras. Option to use 'mish' too
-    optimizer_choice: Two options to choose from - 'Nadam' or 'Adam'
-    use_log_loss: option to use modified BCE loss function that regresses to log p_A/p_B
-    '''
-    model = tf.keras.models.Sequential()
-    options = {"input_shape":input_shape}
-    for layer in range(n_hidden):
-
-        if activation=='mish':
-            def mish(inputs):
-                x = tf.nn.softplus(inputs)
-                x = tf.nn.tanh(x)
-                x = tf.multiply(x, inputs)
-                return x
-
-            model.add(Dense(n_neurons, 
-                            activation=mish, 
-                            **options))
-        else:
-            model.add(Dense(n_neurons, 
-                            activation=activation, 
-                            **options))
-        options={}
-
-    if not use_log_loss:
-        model.add(Dense(1,activation='sigmoid',**options))
-    else:
-        model.add(Dense(1,activation='linear',**options))
-
-    if optimizer_choice=='Nadam':
-        optimizer = tf.keras.optimizers.Nadam(learning_rate=learning_rate) 
-    elif optimizer_choice=='Adam':
-        optimizer = tf.keras.optimizers.Adam(learning_rate=learning_rate) 
-    else:
-        raise Exception("Optimizer choice not recognized - please choose between 'Nadam' or 'Adam'")
-
-    if use_log_loss:
-        # Use the modified BCE loss that regresses to the log p_A/p_B instead of p_A/p_A+p_B
-        model.compile(loss=tf.keras.losses.BinaryCrossentropy(from_logits=True), 
-                      optimizer=optimizer, 
-                      weighted_metrics=['binary_accuracy'])
-    else:
-        model.compile(loss=tf.keras.losses.BinaryCrossentropy(), 
-                      optimizer=optimizer, 
-                      weighted_metrics=['binary_accuracy'])
-    return model
-
-
-def convert_to_score(logLR):
-    '''
-    Convert regressed logLR into relative probabilities for compatibility with other methods
-    '''
-    return 1.0/(1.0+np.exp(-logLR))
diff --git a/src/nsbi_common_utils/training/preselection_training.py b/src/nsbi_common_utils/training/preselection_training.py
new file mode 100644
index 00000000..3f2849c4
--- /dev/null
+++ b/src/nsbi_common_utils/training/preselection_training.py
@@ -0,0 +1,250 @@
+#import libraries
+import os, importlib, sys, shutil
+import numpy as np
+import pandas as pd
+pd.options.mode.chained_assignment = None 
+import math
+import pickle 
+
+import warnings
+from sklearn.exceptions import InconsistentVersionWarning
+warnings.filterwarnings("ignore", category=InconsistentVersionWarning)
+
+import torch
+torch.set_float32_matmul_precision("high")
+import torch.nn as nn
+import pytorch_lightning as pl
+import torch.nn.functional as F
+from torch.utils.data import Dataset
+from torch.utils.data import DataLoader
+from pytorch_lightning import Trainer
+from pytorch_lightning.callbacks import EarlyStopping, LearningRateMonitor
+from torch.utils.data import Subset
+
+from nsbi_common_utils.lightning_tools import MultiClassLightning, DensityRatioLightning, PrintEpochMetrics, LossHistory, WeightedTensorDataset
+from nsbi_common_utils.training.utils import save_model, predict_with_onnx, convert_torch_to_onnx, convert_logLR_to_score, load_trained_model
+
+
+from pathlib import Path
+from typing import Union, Dict
+from joblib import dump, load
+
+import onnx
+import onnxruntime as rt
+
+from sklearn.model_selection import train_test_split
+from sklearn.preprocessing import StandardScaler, MinMaxScaler, PowerTransformer
+from sklearn.compose import ColumnTransformer
+
+from nsbi_common_utils.calibration import HistogramCalibrator, IsotonicCalibrator
+from nsbi_common_utils.plotting import plot_loss, plot_all_features, plot_all_features, plot_reweighted, plot_calibration_curve, plot_calibration_curve_ratio, plot_overfit_side_by_side
+
+import logging
+_LOG_LEVELS = {
+    0: logging.WARNING,  
+    1: logging.INFO,    
+    2: logging.DEBUG,   
+}
+
+logger = logging.getLogger("Training Logs")
+logger.propagate = True  
+
+
+def configure_logging(verbose_level: int = 1):
+    """
+    Configure the logger
+    """
+    level = _LOG_LEVELS.get(verbose_level, logging.INFO)
+    logger.setLevel(level)
+
+    if not logger.handlers:
+        h = logging.StreamHandler()
+        h.setLevel(level)
+        fmt = logging.Formatter(
+            fmt="%(asctime)s | %(levelname)s | %(name)s | %(message)s",
+            datefmt="%Y-%m-%d %H:%M:%S",
+        )
+        h.setFormatter(fmt)
+        logger.addHandler(h)
+
+
+class preselection_network_trainer:
+    '''
+    A class for training the multi-class classification neural network 
+    used to preselect phase space for SBI
+    '''
+    def __init__(self, dataset, features, features_scaling, 
+                    train_labels_column = 'train_labels',
+                    weights_normed_column = 'weights_normed'):
+        '''
+        dataset: dataframe with the multiple classes for training
+        num_classes: number of classes corresponding to the number of output nodes of softmax layer
+        features: input features to use for training
+        features_scaling: subset of input features to standardize before training
+        '''
+        self.dataset                            = dataset
+        self.data_features_training             = dataset[features].copy()
+        self.features                           = features
+        self.features_scaling                   = features_scaling
+        self.num_classes                        = len(np.unique(dataset.train_labels))
+
+        self.train_labels_column                = train_labels_column
+        self.weights_normed_column              = weights_normed_column
+
+    # Defining a simple NN training for preselection - no need for "flexibility" here
+    def train(self, test_size=0.15, 
+                    random_state=42, 
+                    path_to_save='', 
+                    epochs=20, 
+                    batch_size=1024, 
+                    hidden_layers=4,
+                    neurons=1000,
+                    verbose=2, 
+                    learning_rate=0.1,
+                    validation_split=0.1,
+                    activation='swish',
+                    num_workers=4):
+
+        '''
+        The function will train the preselection NN, assign it to self.model variable, and save the model to user-provided path_to_save directory.
+
+        test_size:      the fraction of dataset to set aside for diagnostics, not used in training and validation of the loss vs epoch curves
+        random_state:   random state to use for splitting the train/test dataset before training NN
+        epochs:         the number of epochs to train the NNs
+        batch_size:     the size of each batch used during gradient optimization
+        learning_rate:  the initial learning rate to pass to the optimizer
+        '''
+
+        # Split data into training and validation sets (including weights)
+        data_train, data_holdout, y_train, y_holdout, weight_train, weight_holdout = train_test_split(self.data_features_training, 
+                                                                                                    self.dataset[self.train_labels_column], 
+                                                                                                    self.dataset[self.weights_normed_column], 
+                                                                                                    test_size=test_size, 
+                                                                                                    random_state=random_state, 
+                                                                                                    stratify=self.dataset[self.train_labels_column])
+
+        # Standardize the input features
+        self.scaler = ColumnTransformer([("scaler", StandardScaler(), self.features_scaling)],remainder='passthrough')
+        data_train_scaled = self.scaler.fit_transform(data_train)  # Fit & transform training data
+        data_holdout = self.scaler.transform(data_holdout)
+
+        train_ds = WeightedTensorDataset(
+                data_train_scaled,
+                y_train,
+                weight_train
+            )
+
+        val_size = int(len(train_ds) * validation_split)
+        train_size = len(train_ds) - val_size
+        
+        train_ds, val_ds = torch.utils.data.random_split(train_ds, [train_size, val_size])
+
+        use_mps = torch.backends.mps.is_available()
+        accelerator = 'cpu'
+        devices = 'auto'
+        pinMemory = True
+        if use_mps:
+            accelerator = "mps"
+            devices = 1 
+            pinMemory = False
+        print("selected accelerator:", accelerator)
+
+        train_loader = DataLoader(train_ds, 
+                                  batch_size=batch_size, 
+                                  shuffle=True,
+                                  num_workers=num_workers,  
+                                pin_memory=pinMemory,  
+                                persistent_workers=False  
+                                )
+        
+        val_loader   = DataLoader(val_ds, 
+                                  batch_size=batch_size, 
+                                  shuffle=False, 
+                                  num_workers=num_workers,  
+                                pin_memory=pinMemory,  
+                                persistent_workers=False  
+                                )
+        
+        self.model = MultiClassLightning(
+                n_hidden=hidden_layers,
+                n_neurons=neurons,
+                input_dim=len(self.features),
+                learning_rate=learning_rate,
+                activation=activation,
+                num_classes=self.num_classes
+            )
+
+        loss_history = LossHistory()  
+        
+        self.trainer = Trainer(
+            accelerator=accelerator,
+            devices=devices,
+            max_epochs=epochs,
+            callbacks=[
+                EarlyStopping(monitor="val_loss", patience=30),
+                LearningRateMonitor(),
+                loss_history,
+                PrintEpochMetrics()
+            ],
+            logger=True,
+            enable_checkpointing=False,
+            enable_progress_bar=True,
+            # precision='32-true'
+        )
+
+        self.trainer.fit(self.model, train_loader, val_loader)
+
+        ###### extra block to check if appleSilicon is working on GPUs
+        print("trainer root device:", self.trainer.strategy.root_device)
+        print("model device:", next(self.model.parameters()).device)
+        ########
+
+        
+        # Save the trained model if user provides with a path
+        if path_to_save=='': 
+            path_to_save='./'
+        else:
+            path_to_save      = Path(path_to_save)
+            path_to_save.mkdir(parents=True, exist_ok=True)
+
+        path_to_model           = path_to_save / 'model_preselection.onnx'
+        path_to_scaler          = path_to_save / 'model_scaler_presel.bin'
+
+        # Save lightning module as ONNX
+        save_model(self.model, 
+                   torch.randn((1, len(self.features))), 
+                   path_to_model, 
+                   self.scaler, 
+                   path_to_scaler,
+                   softmax_output = True)
+
+        # Reassign the model to be in ONNX format
+        self.scaler, self.model = load_trained_model(path_to_model, path_to_scaler)
+
+
+    def assign_trained_model(self, 
+                         path_to_models: str) -> None:
+        '''
+        Method to load the trained model
+
+        path_to_models: path to the directory with saved model and scaler files
+        '''
+
+        path_to_saved_scaler        = path_to_models + '/model_scaler_presel.bin'
+        path_to_saved_models        = path_to_models + '/model_preselection.onnx'
+
+        self.scaler, self.model     = load_trained_model(path_to_saved_models, path_to_saved_scaler)
+
+    def predict(self, dataset):
+        '''
+        Method that evaluates density ratios on provided dataset, using self.model
+
+        dataset: the dataset to evaluate trained model on
+        '''
+        pred                        = predict_with_onnx(dataset[self.features], 
+                                                        self.scaler, 
+                                                        self.model)
+        
+        return pred
+
+    
\ No newline at end of file
diff --git a/src/nsbi_common_utils/training/utils.py b/src/nsbi_common_utils/training/utils.py
new file mode 100644
index 00000000..a4036f14
--- /dev/null
+++ b/src/nsbi_common_utils/training/utils.py
@@ -0,0 +1,174 @@
+#import libraries
+import os, importlib, sys, shutil
+import numpy as np
+import pandas as pd
+pd.options.mode.chained_assignment = None 
+import math
+import pickle 
+
+import warnings
+from sklearn.exceptions import InconsistentVersionWarning
+warnings.filterwarnings("ignore", category=InconsistentVersionWarning)
+
+import torch
+torch.set_float32_matmul_precision("high")
+import torch.nn as nn
+import pytorch_lightning as pl
+import torch.nn.functional as F
+from torch.utils.data import Dataset
+from torch.utils.data import DataLoader
+from pytorch_lightning import Trainer
+from pytorch_lightning.callbacks import EarlyStopping, LearningRateMonitor
+from torch.utils.data import Subset
+
+from nsbi_common_utils.lightning_tools import MultiClassLightning, DensityRatioLightning, PrintEpochMetrics, LossHistory, WeightedTensorDataset
+
+from pathlib import Path
+from typing import Union, Dict
+from joblib import dump, load
+
+import onnx
+import onnxruntime as rt
+
+from sklearn.model_selection import train_test_split
+from sklearn.preprocessing import StandardScaler, MinMaxScaler, PowerTransformer
+from sklearn.compose import ColumnTransformer
+
+from nsbi_common_utils.calibration import HistogramCalibrator, IsotonicCalibrator
+from nsbi_common_utils.plotting import plot_loss, plot_all_features, plot_all_features, plot_reweighted, plot_calibration_curve, plot_calibration_curve_ratio, plot_overfit_side_by_side
+ 
+def save_model(lightning_model, 
+               input_sample,
+               path_to_save_model: Union[str, Path], 
+               scaler_instance, 
+               path_to_save_scaler: Union[str, Path],
+               softmax_output: bool = False) -> None:
+    
+    lightning_model.eval()
+
+    class ModelWithSoftmax(torch.nn.Module):
+        def __init__(self, model):
+            super().__init__()
+            self.model = model
+        
+        def forward(self, x):
+            # Get logits from the model
+            x = self.model.mlp(x)
+            logits = self.model.out(x)
+            # Apply softmax output
+            return F.softmax(logits, dim=1)
+        
+    if softmax_output:
+        lightning_model_export = ModelWithSoftmax(lightning_model)
+        print("Exporting ONNX model with softmax output (probabilities)")
+    else:
+        lightning_model_export = lightning_model
+    
+    torch.onnx.export(
+        lightning_model_export,
+        input_sample,
+        str(path_to_save_model),
+        export_params=True,
+        opset_version=17,
+        input_names=['features'], 
+        output_names=['output'],   
+        dynamic_axes={
+            'features': {0: 'batch_size'},
+            'output': {0: 'batch_size'}
+        }
+    )
+    
+    dump(scaler_instance, str(path_to_save_scaler), compress=True)
+
+def load_trained_model(path_to_saved_model: Union[Path, str], 
+                        path_to_saved_scaler: Union[Path, str]):
+
+    # Load scaler
+    scaler          = load(str(path_to_saved_scaler))
+
+    # Load ONNX model
+    model           = onnx.load(str(path_to_saved_model))
+
+    return scaler, model
+
+
+def predict_with_onnx(dataset, scaler, model, calibration_model = None, batch_size = 10_000):
+
+    sess_opts = rt.SessionOptions()
+    sess_opts.intra_op_num_threads = 1
+    sess_opts.inter_op_num_threads = 1
+
+    if isinstance(model, onnx.ModelProto):
+        model = rt.InferenceSession(model.SerializeToString(), 
+                                    sess_options = sess_opts,
+                                    providers=["CUDAExecutionProvider", "CPUExecutionProvider"])
+    
+    elif isinstance(model, rt.InferenceSession):
+        model = model
+    else:
+        raise TypeError(f"Unsupported model type: {type(model)}")
+
+    scaled_dataset  = scaler.transform(dataset)
+    n_samples       = len(scaled_dataset)
+    
+    input_name      = model.get_inputs()[0].name
+    output_name     = model.get_outputs()[0].name
+    
+    first_batch     = scaled_dataset[:min(batch_size, n_samples)]
+    first_pred      = model.run([output_name], {input_name: first_batch})[0]
+    
+    if len(first_pred.shape) > 1:
+        output_shape = (n_samples, first_pred.shape[1])
+    else:
+        output_shape = (n_samples,)
+    
+    preds = np.empty(output_shape, dtype=np.float32)
+    preds[:len(first_batch)] = first_pred
+    
+    # Process remaining batches
+    for i in range(batch_size, n_samples, batch_size):
+        end_idx = min(i + batch_size, n_samples)
+        batch = scaled_dataset[i:end_idx]
+        preds[i:end_idx] = model.run([output_name], {input_name: batch})[0]
+
+    preds = preds.reshape(preds.shape[0],)
+
+    if calibration_model is not None:
+        preds = calibration_model.cali_pred(preds)
+        preds = preds.reshape(preds.shape[0],)
+    
+    return preds
+
+def convert_torch_to_onnx(lightning_model, input_dim, opset=17):
+
+    lightning_model.eval()
+
+    dummy = torch.randn(1, input_dim, device=next(lightning_model.parameters()).device)
+
+    onnx_path = "__tmp_model.onnx"
+
+    torch.onnx.export(
+        lightning_model,
+        dummy,
+        onnx_path,
+        input_names=["input"],
+        output_names=["output"],
+        dynamic_axes={
+            "input": {0: "batch"},
+            "output": {0: "batch"}
+        },
+        opset_version=opset
+    )
+
+    return onnx.load(onnx_path)
+
+
+def convert_logLR_to_score(logLR):
+    '''
+    Convert regressed logLR into relative probabilities for compatibility with other methods
+    '''
+    return 1.0/(1.0+np.exp(-logLR))
+
+def convert_score_to_ratio(score):
+    return score / (1.0 - score)
+