diff --git a/notebooks/example.ipynb b/notebooks/example.ipynb
index 83494a1..3aea6e1 100644
--- a/notebooks/example.ipynb
+++ b/notebooks/example.ipynb
@@ -2,12 +2,13 @@
  "cells": [
   {
    "cell_type": "code",
-   "execution_count": 2,
+   "execution_count": 1,
    "id": "11ad68b2-7502-4dc4-8b78-d0a4fb53255d",
    "metadata": {},
    "outputs": [],
    "source": [
     "from pathlib import Path\n",
+    "import dask\n",
     "import xarray as xr\n",
     "import torch\n",
     "import torch.nn.functional\n",
@@ -27,61 +28,1759 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 2,
-   "id": "13a3b0c8-1d92-460d-84a4-a3a59ca081af",
+   "execution_count": null,
+   "id": "5b1b1129",
    "metadata": {},
    "outputs": [],
    "source": [
-    "data_folder = Path(\"../../data/output/\")\n",
+    "# # Normal reading: load full dataset in memory\n",
     "\n",
-    "file_names = [data_folder / \"202001_day_ERA5_masked_ts.nc\", data_folder / \"202002_day_ERA5_masked_ts.nc\"]\n",
-    "daily_data = xr.open_mfdataset(file_names)\n",
+    "# # Data folder\n",
+    "# data_folder = Path(\"../../data/output/\")\n",
     "\n",
-    "file_names = [data_folder / \"202001_mon_ERA5_full_ts.nc\", data_folder / \"202002_mon_ERA5_full_ts.nc\"]\n",
-    "monthly_data = xr.open_mfdataset(file_names)\n",
+    "# # Training patch size\n",
+    "# patch_size_training = 80\n",
     "\n",
-    "file_name = data_folder / \"era5_lsm_bool.nc\"  # downloded from era5 and regridded using the function `regrid_to_boundary_centered_grid`\n",
-    "lsm_mask = xr.open_dataset(file_name)\n",
-    "lsm_mask = lsm_mask.rename({'latitude': 'lat', 'longitude': 'lon'})"
+    "# # Get all files\n",
+    "# daily_files = sorted(data_folder.rglob(\"20*_day_ERA5_masked_ts.nc\"))\n",
+    "# monthly_files = sorted(data_folder.rglob(\"20*_mon_ERA5_full_ts.nc\"))\n",
+    "\n",
+    "# daily_data = xr.open_mfdataset(daily_files)\n",
+    "# monthly_data = xr.open_mfdataset(monthly_files)\n",
+    " \n",
+    "# lsm_mask = xr.open_dataset(data_folder / \"era5_lsm_bool.nc\" ) # downloaded from ERA5 and regridded\n",
+    "# lsm_mask = lsm_mask.rename({\"latitude\": \"lat\", \"longitude\": \"lon\"})[[\"lsm\"]]"
    ]
   },
   {
-   "cell_type": "markdown",
-   "id": "b4be672b-a2c1-4a6f-89a1-2c22cc6cde02",
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "13a3b0c8-1d92-460d-84a4-a3a59ca081af",
    "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/tmp/ipykernel_31814/1980159491.py:20: UserWarning: The specified chunks separate the stored chunks along dimension \"lat\" starting at index 40. This could degrade performance. Instead, consider rechunking after loading.\n",
+      "  daily_data = xr.open_mfdataset(\n",
+      "/tmp/ipykernel_31814/1980159491.py:20: UserWarning: The specified chunks separate the stored chunks along dimension \"lon\" starting at index 40. This could degrade performance. Instead, consider rechunking after loading.\n",
+      "  daily_data = xr.open_mfdataset(\n",
+      "/tmp/ipykernel_31814/1980159491.py:20: UserWarning: The specified chunks separate the stored chunks along dimension \"lat\" starting at index 40. This could degrade performance. Instead, consider rechunking after loading.\n",
+      "  daily_data = xr.open_mfdataset(\n",
+      "/tmp/ipykernel_31814/1980159491.py:20: UserWarning: The specified chunks separate the stored chunks along dimension \"lon\" starting at index 40. This could degrade performance. Instead, consider rechunking after loading.\n",
+      "  daily_data = xr.open_mfdataset(\n",
+      "/tmp/ipykernel_31814/1980159491.py:20: UserWarning: The specified chunks separate the stored chunks along dimension \"lat\" starting at index 40. This could degrade performance. Instead, consider rechunking after loading.\n",
+      "  daily_data = xr.open_mfdataset(\n",
+      "/tmp/ipykernel_31814/1980159491.py:20: UserWarning: The specified chunks separate the stored chunks along dimension \"lon\" starting at index 40. This could degrade performance. Instead, consider rechunking after loading.\n",
+      "  daily_data = xr.open_mfdataset(\n",
+      "/tmp/ipykernel_31814/1980159491.py:20: UserWarning: The specified chunks separate the stored chunks along dimension \"lat\" starting at index 40. This could degrade performance. Instead, consider rechunking after loading.\n",
+      "  daily_data = xr.open_mfdataset(\n",
+      "/tmp/ipykernel_31814/1980159491.py:20: UserWarning: The specified chunks separate the stored chunks along dimension \"lon\" starting at index 40. This could degrade performance. Instead, consider rechunking after loading.\n",
+      "  daily_data = xr.open_mfdataset(\n",
+      "/tmp/ipykernel_31814/1980159491.py:20: UserWarning: The specified chunks separate the stored chunks along dimension \"lat\" starting at index 40. This could degrade performance. Instead, consider rechunking after loading.\n",
+      "  daily_data = xr.open_mfdataset(\n",
+      "/tmp/ipykernel_31814/1980159491.py:20: UserWarning: The specified chunks separate the stored chunks along dimension \"lon\" starting at index 40. This could degrade performance. Instead, consider rechunking after loading.\n",
+      "  daily_data = xr.open_mfdataset(\n",
+      "/tmp/ipykernel_31814/1980159491.py:20: UserWarning: The specified chunks separate the stored chunks along dimension \"lat\" starting at index 40. This could degrade performance. Instead, consider rechunking after loading.\n",
+      "  daily_data = xr.open_mfdataset(\n",
+      "/tmp/ipykernel_31814/1980159491.py:20: UserWarning: The specified chunks separate the stored chunks along dimension \"lon\" starting at index 40. This could degrade performance. Instead, consider rechunking after loading.\n",
+      "  daily_data = xr.open_mfdataset(\n",
+      "/tmp/ipykernel_31814/1980159491.py:20: UserWarning: The specified chunks separate the stored chunks along dimension \"lat\" starting at index 40. This could degrade performance. Instead, consider rechunking after loading.\n",
+      "  daily_data = xr.open_mfdataset(\n",
+      "/tmp/ipykernel_31814/1980159491.py:20: UserWarning: The specified chunks separate the stored chunks along dimension \"lon\" starting at index 40. This could degrade performance. Instead, consider rechunking after loading.\n",
+      "  daily_data = xr.open_mfdataset(\n",
+      "/tmp/ipykernel_31814/1980159491.py:20: UserWarning: The specified chunks separate the stored chunks along dimension \"lat\" starting at index 40. This could degrade performance. Instead, consider rechunking after loading.\n",
+      "  daily_data = xr.open_mfdataset(\n",
+      "/tmp/ipykernel_31814/1980159491.py:20: UserWarning: The specified chunks separate the stored chunks along dimension \"lon\" starting at index 40. This could degrade performance. Instead, consider rechunking after loading.\n",
+      "  daily_data = xr.open_mfdataset(\n",
+      "/tmp/ipykernel_31814/1980159491.py:20: UserWarning: The specified chunks separate the stored chunks along dimension \"lat\" starting at index 40. This could degrade performance. Instead, consider rechunking after loading.\n",
+      "  daily_data = xr.open_mfdataset(\n",
+      "/tmp/ipykernel_31814/1980159491.py:20: UserWarning: The specified chunks separate the stored chunks along dimension \"lon\" starting at index 40. This could degrade performance. Instead, consider rechunking after loading.\n",
+      "  daily_data = xr.open_mfdataset(\n",
+      "/tmp/ipykernel_31814/1980159491.py:20: UserWarning: The specified chunks separate the stored chunks along dimension \"lat\" starting at index 40. This could degrade performance. Instead, consider rechunking after loading.\n",
+      "  daily_data = xr.open_mfdataset(\n",
+      "/tmp/ipykernel_31814/1980159491.py:20: UserWarning: The specified chunks separate the stored chunks along dimension \"lon\" starting at index 40. This could degrade performance. Instead, consider rechunking after loading.\n",
+      "  daily_data = xr.open_mfdataset(\n",
+      "/tmp/ipykernel_31814/1980159491.py:20: UserWarning: The specified chunks separate the stored chunks along dimension \"lat\" starting at index 40. This could degrade performance. Instead, consider rechunking after loading.\n",
+      "  daily_data = xr.open_mfdataset(\n",
+      "/tmp/ipykernel_31814/1980159491.py:20: UserWarning: The specified chunks separate the stored chunks along dimension \"lon\" starting at index 40. This could degrade performance. Instead, consider rechunking after loading.\n",
+      "  daily_data = xr.open_mfdataset(\n",
+      "/tmp/ipykernel_31814/1980159491.py:20: UserWarning: The specified chunks separate the stored chunks along dimension \"lat\" starting at index 40. This could degrade performance. Instead, consider rechunking after loading.\n",
+      "  daily_data = xr.open_mfdataset(\n",
+      "/tmp/ipykernel_31814/1980159491.py:20: UserWarning: The specified chunks separate the stored chunks along dimension \"lon\" starting at index 40. This could degrade performance. Instead, consider rechunking after loading.\n",
+      "  daily_data = xr.open_mfdataset(\n",
+      "/tmp/ipykernel_31814/1980159491.py:20: UserWarning: The specified chunks separate the stored chunks along dimension \"lat\" starting at index 40. This could degrade performance. Instead, consider rechunking after loading.\n",
+      "  daily_data = xr.open_mfdataset(\n",
+      "/tmp/ipykernel_31814/1980159491.py:20: UserWarning: The specified chunks separate the stored chunks along dimension \"lon\" starting at index 40. This could degrade performance. Instead, consider rechunking after loading.\n",
+      "  daily_data = xr.open_mfdataset(\n",
+      "/tmp/ipykernel_31814/1980159491.py:20: UserWarning: The specified chunks separate the stored chunks along dimension \"lat\" starting at index 40. This could degrade performance. Instead, consider rechunking after loading.\n",
+      "  daily_data = xr.open_mfdataset(\n",
+      "/tmp/ipykernel_31814/1980159491.py:20: UserWarning: The specified chunks separate the stored chunks along dimension \"lon\" starting at index 40. This could degrade performance. Instead, consider rechunking after loading.\n",
+      "  daily_data = xr.open_mfdataset(\n",
+      "/tmp/ipykernel_31814/1980159491.py:20: UserWarning: The specified chunks separate the stored chunks along dimension \"lat\" starting at index 40. This could degrade performance. Instead, consider rechunking after loading.\n",
+      "  daily_data = xr.open_mfdataset(\n",
+      "/tmp/ipykernel_31814/1980159491.py:20: UserWarning: The specified chunks separate the stored chunks along dimension \"lon\" starting at index 40. This could degrade performance. Instead, consider rechunking after loading.\n",
+      "  daily_data = xr.open_mfdataset(\n",
+      "/tmp/ipykernel_31814/1980159491.py:20: UserWarning: The specified chunks separate the stored chunks along dimension \"lat\" starting at index 40. This could degrade performance. Instead, consider rechunking after loading.\n",
+      "  daily_data = xr.open_mfdataset(\n",
+      "/tmp/ipykernel_31814/1980159491.py:20: UserWarning: The specified chunks separate the stored chunks along dimension \"lon\" starting at index 40. This could degrade performance. Instead, consider rechunking after loading.\n",
+      "  daily_data = xr.open_mfdataset(\n",
+      "/tmp/ipykernel_31814/1980159491.py:20: UserWarning: The specified chunks separate the stored chunks along dimension \"lat\" starting at index 40. This could degrade performance. Instead, consider rechunking after loading.\n",
+      "  daily_data = xr.open_mfdataset(\n",
+      "/tmp/ipykernel_31814/1980159491.py:20: UserWarning: The specified chunks separate the stored chunks along dimension \"lon\" starting at index 40. This could degrade performance. Instead, consider rechunking after loading.\n",
+      "  daily_data = xr.open_mfdataset(\n",
+      "/tmp/ipykernel_31814/1980159491.py:20: UserWarning: The specified chunks separate the stored chunks along dimension \"lat\" starting at index 40. This could degrade performance. Instead, consider rechunking after loading.\n",
+      "  daily_data = xr.open_mfdataset(\n",
+      "/tmp/ipykernel_31814/1980159491.py:20: UserWarning: The specified chunks separate the stored chunks along dimension \"lon\" starting at index 40. This could degrade performance. Instead, consider rechunking after loading.\n",
+      "  daily_data = xr.open_mfdataset(\n",
+      "/tmp/ipykernel_31814/1980159491.py:20: UserWarning: The specified chunks separate the stored chunks along dimension \"lat\" starting at index 40. This could degrade performance. Instead, consider rechunking after loading.\n",
+      "  daily_data = xr.open_mfdataset(\n",
+      "/tmp/ipykernel_31814/1980159491.py:20: UserWarning: The specified chunks separate the stored chunks along dimension \"lon\" starting at index 40. This could degrade performance. Instead, consider rechunking after loading.\n",
+      "  daily_data = xr.open_mfdataset(\n",
+      "/tmp/ipykernel_31814/1980159491.py:20: UserWarning: The specified chunks separate the stored chunks along dimension \"lat\" starting at index 40. This could degrade performance. Instead, consider rechunking after loading.\n",
+      "  daily_data = xr.open_mfdataset(\n",
+      "/tmp/ipykernel_31814/1980159491.py:20: UserWarning: The specified chunks separate the stored chunks along dimension \"lon\" starting at index 40. This could degrade performance. Instead, consider rechunking after loading.\n",
+      "  daily_data = xr.open_mfdataset(\n",
+      "/tmp/ipykernel_31814/1980159491.py:20: UserWarning: The specified chunks separate the stored chunks along dimension \"lat\" starting at index 40. This could degrade performance. Instead, consider rechunking after loading.\n",
+      "  daily_data = xr.open_mfdataset(\n",
+      "/tmp/ipykernel_31814/1980159491.py:20: UserWarning: The specified chunks separate the stored chunks along dimension \"lon\" starting at index 40. This could degrade performance. Instead, consider rechunking after loading.\n",
+      "  daily_data = xr.open_mfdataset(\n",
+      "/tmp/ipykernel_31814/1980159491.py:20: UserWarning: The specified chunks separate the stored chunks along dimension \"lat\" starting at index 40. This could degrade performance. Instead, consider rechunking after loading.\n",
+      "  daily_data = xr.open_mfdataset(\n",
+      "/tmp/ipykernel_31814/1980159491.py:20: UserWarning: The specified chunks separate the stored chunks along dimension \"lon\" starting at index 40. This could degrade performance. Instead, consider rechunking after loading.\n",
+      "  daily_data = xr.open_mfdataset(\n",
+      "/tmp/ipykernel_31814/1980159491.py:20: UserWarning: The specified chunks separate the stored chunks along dimension \"lat\" starting at index 40. This could degrade performance. Instead, consider rechunking after loading.\n",
+      "  daily_data = xr.open_mfdataset(\n",
+      "/tmp/ipykernel_31814/1980159491.py:20: UserWarning: The specified chunks separate the stored chunks along dimension \"lon\" starting at index 40. This could degrade performance. Instead, consider rechunking after loading.\n",
+      "  daily_data = xr.open_mfdataset(\n",
+      "/tmp/ipykernel_31814/1980159491.py:20: UserWarning: The specified chunks separate the stored chunks along dimension \"lat\" starting at index 40. This could degrade performance. Instead, consider rechunking after loading.\n",
+      "  daily_data = xr.open_mfdataset(\n",
+      "/tmp/ipykernel_31814/1980159491.py:20: UserWarning: The specified chunks separate the stored chunks along dimension \"lon\" starting at index 40. This could degrade performance. Instead, consider rechunking after loading.\n",
+      "  daily_data = xr.open_mfdataset(\n",
+      "/tmp/ipykernel_31814/1980159491.py:31: UserWarning: The specified chunks separate the stored chunks along dimension \"lat\" starting at index 40. This could degrade performance. Instead, consider rechunking after loading.\n",
+      "  monthly_data = xr.open_mfdataset(\n",
+      "/tmp/ipykernel_31814/1980159491.py:31: UserWarning: The specified chunks separate the stored chunks along dimension \"lon\" starting at index 40. This could degrade performance. Instead, consider rechunking after loading.\n",
+      "  monthly_data = xr.open_mfdataset(\n",
+      "/tmp/ipykernel_31814/1980159491.py:31: UserWarning: The specified chunks separate the stored chunks along dimension \"lat\" starting at index 40. This could degrade performance. Instead, consider rechunking after loading.\n",
+      "  monthly_data = xr.open_mfdataset(\n",
+      "/tmp/ipykernel_31814/1980159491.py:31: UserWarning: The specified chunks separate the stored chunks along dimension \"lon\" starting at index 40. This could degrade performance. Instead, consider rechunking after loading.\n",
+      "  monthly_data = xr.open_mfdataset(\n",
+      "/tmp/ipykernel_31814/1980159491.py:31: UserWarning: The specified chunks separate the stored chunks along dimension \"lat\" starting at index 40. This could degrade performance. Instead, consider rechunking after loading.\n",
+      "  monthly_data = xr.open_mfdataset(\n",
+      "/tmp/ipykernel_31814/1980159491.py:31: UserWarning: The specified chunks separate the stored chunks along dimension \"lon\" starting at index 40. This could degrade performance. Instead, consider rechunking after loading.\n",
+      "  monthly_data = xr.open_mfdataset(\n",
+      "/tmp/ipykernel_31814/1980159491.py:31: UserWarning: The specified chunks separate the stored chunks along dimension \"lat\" starting at index 40. This could degrade performance. Instead, consider rechunking after loading.\n",
+      "  monthly_data = xr.open_mfdataset(\n",
+      "/tmp/ipykernel_31814/1980159491.py:31: UserWarning: The specified chunks separate the stored chunks along dimension \"lon\" starting at index 40. This could degrade performance. Instead, consider rechunking after loading.\n",
+      "  monthly_data = xr.open_mfdataset(\n",
+      "/tmp/ipykernel_31814/1980159491.py:31: UserWarning: The specified chunks separate the stored chunks along dimension \"lat\" starting at index 40. This could degrade performance. Instead, consider rechunking after loading.\n",
+      "  monthly_data = xr.open_mfdataset(\n",
+      "/tmp/ipykernel_31814/1980159491.py:31: UserWarning: The specified chunks separate the stored chunks along dimension \"lon\" starting at index 40. This could degrade performance. Instead, consider rechunking after loading.\n",
+      "  monthly_data = xr.open_mfdataset(\n",
+      "/tmp/ipykernel_31814/1980159491.py:31: UserWarning: The specified chunks separate the stored chunks along dimension \"lat\" starting at index 40. This could degrade performance. Instead, consider rechunking after loading.\n",
+      "  monthly_data = xr.open_mfdataset(\n",
+      "/tmp/ipykernel_31814/1980159491.py:31: UserWarning: The specified chunks separate the stored chunks along dimension \"lon\" starting at index 40. This could degrade performance. Instead, consider rechunking after loading.\n",
+      "  monthly_data = xr.open_mfdataset(\n",
+      "/tmp/ipykernel_31814/1980159491.py:31: UserWarning: The specified chunks separate the stored chunks along dimension \"lat\" starting at index 40. This could degrade performance. Instead, consider rechunking after loading.\n",
+      "  monthly_data = xr.open_mfdataset(\n",
+      "/tmp/ipykernel_31814/1980159491.py:31: UserWarning: The specified chunks separate the stored chunks along dimension \"lon\" starting at index 40. This could degrade performance. Instead, consider rechunking after loading.\n",
+      "  monthly_data = xr.open_mfdataset(\n",
+      "/tmp/ipykernel_31814/1980159491.py:31: UserWarning: The specified chunks separate the stored chunks along dimension \"lat\" starting at index 40. This could degrade performance. Instead, consider rechunking after loading.\n",
+      "  monthly_data = xr.open_mfdataset(\n",
+      "/tmp/ipykernel_31814/1980159491.py:31: UserWarning: The specified chunks separate the stored chunks along dimension \"lon\" starting at index 40. This could degrade performance. Instead, consider rechunking after loading.\n",
+      "  monthly_data = xr.open_mfdataset(\n",
+      "/tmp/ipykernel_31814/1980159491.py:31: UserWarning: The specified chunks separate the stored chunks along dimension \"lat\" starting at index 40. This could degrade performance. Instead, consider rechunking after loading.\n",
+      "  monthly_data = xr.open_mfdataset(\n",
+      "/tmp/ipykernel_31814/1980159491.py:31: UserWarning: The specified chunks separate the stored chunks along dimension \"lon\" starting at index 40. This could degrade performance. Instead, consider rechunking after loading.\n",
+      "  monthly_data = xr.open_mfdataset(\n",
+      "/tmp/ipykernel_31814/1980159491.py:31: UserWarning: The specified chunks separate the stored chunks along dimension \"lat\" starting at index 40. This could degrade performance. Instead, consider rechunking after loading.\n",
+      "  monthly_data = xr.open_mfdataset(\n",
+      "/tmp/ipykernel_31814/1980159491.py:31: UserWarning: The specified chunks separate the stored chunks along dimension \"lon\" starting at index 40. This could degrade performance. Instead, consider rechunking after loading.\n",
+      "  monthly_data = xr.open_mfdataset(\n",
+      "/tmp/ipykernel_31814/1980159491.py:31: UserWarning: The specified chunks separate the stored chunks along dimension \"lat\" starting at index 40. This could degrade performance. Instead, consider rechunking after loading.\n",
+      "  monthly_data = xr.open_mfdataset(\n",
+      "/tmp/ipykernel_31814/1980159491.py:31: UserWarning: The specified chunks separate the stored chunks along dimension \"lon\" starting at index 40. This could degrade performance. Instead, consider rechunking after loading.\n",
+      "  monthly_data = xr.open_mfdataset(\n",
+      "/tmp/ipykernel_31814/1980159491.py:31: UserWarning: The specified chunks separate the stored chunks along dimension \"lat\" starting at index 40. This could degrade performance. Instead, consider rechunking after loading.\n",
+      "  monthly_data = xr.open_mfdataset(\n",
+      "/tmp/ipykernel_31814/1980159491.py:31: UserWarning: The specified chunks separate the stored chunks along dimension \"lon\" starting at index 40. This could degrade performance. Instead, consider rechunking after loading.\n",
+      "  monthly_data = xr.open_mfdataset(\n",
+      "/tmp/ipykernel_31814/1980159491.py:31: UserWarning: The specified chunks separate the stored chunks along dimension \"lat\" starting at index 40. This could degrade performance. Instead, consider rechunking after loading.\n",
+      "  monthly_data = xr.open_mfdataset(\n",
+      "/tmp/ipykernel_31814/1980159491.py:31: UserWarning: The specified chunks separate the stored chunks along dimension \"lon\" starting at index 40. This could degrade performance. Instead, consider rechunking after loading.\n",
+      "  monthly_data = xr.open_mfdataset(\n",
+      "/tmp/ipykernel_31814/1980159491.py:31: UserWarning: The specified chunks separate the stored chunks along dimension \"lat\" starting at index 40. This could degrade performance. Instead, consider rechunking after loading.\n",
+      "  monthly_data = xr.open_mfdataset(\n",
+      "/tmp/ipykernel_31814/1980159491.py:31: UserWarning: The specified chunks separate the stored chunks along dimension \"lon\" starting at index 40. This could degrade performance. Instead, consider rechunking after loading.\n",
+      "  monthly_data = xr.open_mfdataset(\n",
+      "/tmp/ipykernel_31814/1980159491.py:31: UserWarning: The specified chunks separate the stored chunks along dimension \"lat\" starting at index 40. This could degrade performance. Instead, consider rechunking after loading.\n",
+      "  monthly_data = xr.open_mfdataset(\n",
+      "/tmp/ipykernel_31814/1980159491.py:31: UserWarning: The specified chunks separate the stored chunks along dimension \"lon\" starting at index 40. This could degrade performance. Instead, consider rechunking after loading.\n",
+      "  monthly_data = xr.open_mfdataset(\n",
+      "/tmp/ipykernel_31814/1980159491.py:31: UserWarning: The specified chunks separate the stored chunks along dimension \"lat\" starting at index 40. This could degrade performance. Instead, consider rechunking after loading.\n",
+      "  monthly_data = xr.open_mfdataset(\n",
+      "/tmp/ipykernel_31814/1980159491.py:31: UserWarning: The specified chunks separate the stored chunks along dimension \"lon\" starting at index 40. This could degrade performance. Instead, consider rechunking after loading.\n",
+      "  monthly_data = xr.open_mfdataset(\n",
+      "/tmp/ipykernel_31814/1980159491.py:31: UserWarning: The specified chunks separate the stored chunks along dimension \"lat\" starting at index 40. This could degrade performance. Instead, consider rechunking after loading.\n",
+      "  monthly_data = xr.open_mfdataset(\n",
+      "/tmp/ipykernel_31814/1980159491.py:31: UserWarning: The specified chunks separate the stored chunks along dimension \"lon\" starting at index 40. This could degrade performance. Instead, consider rechunking after loading.\n",
+      "  monthly_data = xr.open_mfdataset(\n",
+      "/tmp/ipykernel_31814/1980159491.py:31: UserWarning: The specified chunks separate the stored chunks along dimension \"lat\" starting at index 40. This could degrade performance. Instead, consider rechunking after loading.\n",
+      "  monthly_data = xr.open_mfdataset(\n",
+      "/tmp/ipykernel_31814/1980159491.py:31: UserWarning: The specified chunks separate the stored chunks along dimension \"lon\" starting at index 40. This could degrade performance. Instead, consider rechunking after loading.\n",
+      "  monthly_data = xr.open_mfdataset(\n",
+      "/tmp/ipykernel_31814/1980159491.py:31: UserWarning: The specified chunks separate the stored chunks along dimension \"lat\" starting at index 40. This could degrade performance. Instead, consider rechunking after loading.\n",
+      "  monthly_data = xr.open_mfdataset(\n",
+      "/tmp/ipykernel_31814/1980159491.py:31: UserWarning: The specified chunks separate the stored chunks along dimension \"lon\" starting at index 40. This could degrade performance. Instead, consider rechunking after loading.\n",
+      "  monthly_data = xr.open_mfdataset(\n",
+      "/tmp/ipykernel_31814/1980159491.py:31: UserWarning: The specified chunks separate the stored chunks along dimension \"lat\" starting at index 40. This could degrade performance. Instead, consider rechunking after loading.\n",
+      "  monthly_data = xr.open_mfdataset(\n",
+      "/tmp/ipykernel_31814/1980159491.py:31: UserWarning: The specified chunks separate the stored chunks along dimension \"lon\" starting at index 40. This could degrade performance. Instead, consider rechunking after loading.\n",
+      "  monthly_data = xr.open_mfdataset(\n",
+      "/tmp/ipykernel_31814/1980159491.py:31: UserWarning: The specified chunks separate the stored chunks along dimension \"lat\" starting at index 40. This could degrade performance. Instead, consider rechunking after loading.\n",
+      "  monthly_data = xr.open_mfdataset(\n",
+      "/tmp/ipykernel_31814/1980159491.py:31: UserWarning: The specified chunks separate the stored chunks along dimension \"lon\" starting at index 40. This could degrade performance. Instead, consider rechunking after loading.\n",
+      "  monthly_data = xr.open_mfdataset(\n",
+      "/tmp/ipykernel_31814/1980159491.py:31: UserWarning: The specified chunks separate the stored chunks along dimension \"lat\" starting at index 40. This could degrade performance. Instead, consider rechunking after loading.\n",
+      "  monthly_data = xr.open_mfdataset(\n",
+      "/tmp/ipykernel_31814/1980159491.py:31: UserWarning: The specified chunks separate the stored chunks along dimension \"lon\" starting at index 40. This could degrade performance. Instead, consider rechunking after loading.\n",
+      "  monthly_data = xr.open_mfdataset(\n",
+      "/tmp/ipykernel_31814/1980159491.py:31: UserWarning: The specified chunks separate the stored chunks along dimension \"lat\" starting at index 40. This could degrade performance. Instead, consider rechunking after loading.\n",
+      "  monthly_data = xr.open_mfdataset(\n",
+      "/tmp/ipykernel_31814/1980159491.py:31: UserWarning: The specified chunks separate the stored chunks along dimension \"lon\" starting at index 40. This could degrade performance. Instead, consider rechunking after loading.\n",
+      "  monthly_data = xr.open_mfdataset(\n",
+      "/tmp/ipykernel_31814/1980159491.py:31: UserWarning: The specified chunks separate the stored chunks along dimension \"lat\" starting at index 40. This could degrade performance. Instead, consider rechunking after loading.\n",
+      "  monthly_data = xr.open_mfdataset(\n",
+      "/tmp/ipykernel_31814/1980159491.py:31: UserWarning: The specified chunks separate the stored chunks along dimension \"lon\" starting at index 40. This could degrade performance. Instead, consider rechunking after loading.\n",
+      "  monthly_data = xr.open_mfdataset(\n"
+     ]
+    }
+   ],
    "source": [
-    "## Subset data (for fast example)"
+    "# For debug, load part of the two year dataset\n",
+    "\n",
+    "# Data folder\n",
+    "data_folder = Path(\"../../data/output/\")\n",
+    "\n",
+    "# (Only for local debug) Subset the data while loading\n",
+    "# Define ROI once so subsetting happens during file open (not after)\n",
+    "lon_subset = slice(-10, 10)\n",
+    "lat_subset = slice(-5, 5)\n",
+    "\n",
+    "# Training patch size\n",
+    "patch_size_training = 20\n",
+    "\n",
+    "# Keep only required variable + spatial subset while reading each file\n",
+    "def _preprocess_roi(ds):\n",
+    "    return ds[[\"ts\"]].sel(lon=lon_subset, lat=lat_subset)\n",
+    "\n",
+    "daily_files = sorted(data_folder.rglob(\"20*_day_ERA5_masked_ts.nc\"))\n",
+    "monthly_files = sorted(data_folder.rglob(\"20*_mon_ERA5_full_ts.nc\"))\n",
+    "\n",
+    "# Use smaller spatial chunks to reduce peak memory per task\n",
+    "daily_data = xr.open_mfdataset(\n",
+    "    daily_files,\n",
+    "    combine=\"by_coords\",\n",
+    "    preprocess=_preprocess_roi,\n",
+    "    chunks={\"time\": 1, \"lat\": patch_size_training*2, \"lon\": patch_size_training*2},\n",
+    "    data_vars=\"minimal\",\n",
+    "    coords=\"minimal\",\n",
+    "    compat=\"override\",\n",
+    "    parallel=False,\n",
+    ")\n",
+    "\n",
+    "monthly_data = xr.open_mfdataset(\n",
+    "    monthly_files,\n",
+    "    combine=\"by_coords\",\n",
+    "    preprocess=_preprocess_roi,\n",
+    "    chunks={\"time\": 1, \"lat\": patch_size_training*2, \"lon\": patch_size_training*2},\n",
+    "    data_vars=\"minimal\",\n",
+    "    coords=\"minimal\",\n",
+    "    compat=\"override\",\n",
+    "    parallel=False,\n",
+    ")\n",
+    "\n",
+    "file_name = data_folder / \"era5_lsm_bool.nc\"  # downloaded from ERA5 and regridded\n",
+    "lsm_mask = xr.open_dataset(file_name)\n",
+    "lsm_mask = lsm_mask.rename({\"latitude\": \"lat\", \"longitude\": \"lon\"})[[\"lsm\"]].sel(lon=lon_subset, lat=lat_subset)"
    ]
   },
   {
    "cell_type": "code",
    "execution_count": 3,
-   "id": "82bc2879-b788-492e-a0c0-53a35896a9d8",
+   "id": "7d13e24a",
    "metadata": {},
    "outputs": [
     {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "(60, 160, 400) (2, 160, 400)\n"
-     ]
+     "data": {
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+       ".xr-var-list > li:nth-child(odd) > .xr-var-name span {\n",
+       "  background-color: var(--xr-background-color-row-odd);\n",
+       "  border-color: var(--xr-background-color-row-even);\n",
+       "}\n",
+       "\n",
+       ".xr-var-name {\n",
+       "  grid-column: 1;\n",
+       "}\n",
+       "\n",
+       ".xr-var-dims {\n",
+       "  grid-column: 2;\n",
+       "}\n",
+       "\n",
+       ".xr-var-dtype {\n",
+       "  grid-column: 3;\n",
+       "  text-align: right;\n",
+       "  color: var(--xr-font-color2);\n",
+       "}\n",
+       "\n",
+       ".xr-var-preview {\n",
+       "  grid-column: 4;\n",
+       "}\n",
+       "\n",
+       ".xr-index-preview {\n",
+       "  grid-column: 2 / 5;\n",
+       "  color: var(--xr-font-color2);\n",
+       "}\n",
+       "\n",
+       ".xr-var-name,\n",
+       ".xr-var-dims,\n",
+       ".xr-var-dtype,\n",
+       ".xr-preview,\n",
+       ".xr-attrs dt {\n",
+       "  white-space: nowrap;\n",
+       "  overflow: hidden;\n",
+       "  text-overflow: ellipsis;\n",
+       "  padding-right: 10px;\n",
+       "}\n",
+       "\n",
+       ".xr-var-name:hover,\n",
+       ".xr-var-dims:hover,\n",
+       ".xr-var-dtype:hover,\n",
+       ".xr-attrs dt:hover {\n",
+       "  overflow: visible;\n",
+       "  width: auto;\n",
+       "  z-index: 1;\n",
+       "}\n",
+       "\n",
+       ".xr-var-attrs,\n",
+       ".xr-var-data,\n",
+       ".xr-index-data {\n",
+       "  display: none;\n",
+       "  border-top: 2px dotted var(--xr-background-color);\n",
+       "  padding-bottom: 20px !important;\n",
+       "  padding-top: 10px !important;\n",
+       "}\n",
+       "\n",
+       ".xr-var-attrs-in + label,\n",
+       ".xr-var-data-in + label,\n",
+       ".xr-index-data-in + label {\n",
+       "  padding: 0 1px;\n",
+       "}\n",
+       "\n",
+       ".xr-var-attrs-in:checked ~ .xr-var-attrs,\n",
+       ".xr-var-data-in:checked ~ .xr-var-data,\n",
+       ".xr-index-data-in:checked ~ .xr-index-data {\n",
+       "  display: block;\n",
+       "}\n",
+       "\n",
+       ".xr-var-data > table {\n",
+       "  float: right;\n",
+       "}\n",
+       "\n",
+       ".xr-var-data > pre,\n",
+       ".xr-index-data > pre,\n",
+       ".xr-var-data > table > tbody > tr {\n",
+       "  background-color: transparent !important;\n",
+       "}\n",
+       "\n",
+       ".xr-var-name span,\n",
+       ".xr-var-data,\n",
+       ".xr-index-name div,\n",
+       ".xr-index-data,\n",
+       ".xr-attrs {\n",
+       "  padding-left: 25px !important;\n",
+       "}\n",
+       "\n",
+       ".xr-attrs,\n",
+       ".xr-var-attrs,\n",
+       ".xr-var-data,\n",
+       ".xr-index-data {\n",
+       "  grid-column: 1 / -1;\n",
+       "}\n",
+       "\n",
+       "dl.xr-attrs {\n",
+       "  padding: 0;\n",
+       "  margin: 0;\n",
+       "  display: grid;\n",
+       "  grid-template-columns: 125px auto;\n",
+       "}\n",
+       "\n",
+       ".xr-attrs dt,\n",
+       ".xr-attrs dd {\n",
+       "  padding: 0;\n",
+       "  margin: 0;\n",
+       "  float: left;\n",
+       "  padding-right: 10px;\n",
+       "  width: auto;\n",
+       "}\n",
+       "\n",
+       ".xr-attrs dt {\n",
+       "  font-weight: normal;\n",
+       "  grid-column: 1;\n",
+       "}\n",
+       "\n",
+       ".xr-attrs dt:hover span {\n",
+       "  display: inline-block;\n",
+       "  background: var(--xr-background-color);\n",
+       "  padding-right: 10px;\n",
+       "}\n",
+       "\n",
+       ".xr-attrs dd {\n",
+       "  grid-column: 2;\n",
+       "  white-space: pre-wrap;\n",
+       "  word-break: break-all;\n",
+       "}\n",
+       "\n",
+       ".xr-icon-database,\n",
+       ".xr-icon-file-text2,\n",
+       ".xr-no-icon {\n",
+       "  display: inline-block;\n",
+       "  vertical-align: middle;\n",
+       "  width: 1em;\n",
+       "  height: 1.5em !important;\n",
+       "  stroke-width: 0;\n",
+       "  stroke: currentColor;\n",
+       "  fill: currentColor;\n",
+       "}\n",
+       "\n",
+       ".xr-var-attrs-in:checked + label > .xr-icon-file-text2,\n",
+       ".xr-var-data-in:checked + label > .xr-icon-database,\n",
+       ".xr-index-data-in:checked + label > .xr-icon-database {\n",
+       "  color: var(--xr-font-color0);\n",
+       "  filter: drop-shadow(1px 1px 5px var(--xr-font-color2));\n",
+       "  stroke-width: 0.8px;\n",
+       "}\n",
+       "</style><pre class='xr-text-repr-fallback'>&lt;xarray.Dataset&gt; Size: 9MB\n",
+       "Dimensions:  (time: 731, lat: 40, lon: 80)\n",
+       "Coordinates:\n",
+       "  * time     (time) datetime64[ns] 6kB 2020-01-01T11:30:00 ... 2021-12-31T11:...\n",
+       "  * lat      (lat) float32 160B -4.875 -4.625 -4.375 ... 4.375 4.625 4.875\n",
+       "  * lon      (lon) float32 320B -9.875 -9.625 -9.375 ... 9.375 9.625 9.875\n",
+       "Data variables:\n",
+       "    ts       (time, lat, lon) float32 9MB dask.array&lt;chunksize=(1, 20, 40), meta=np.ndarray&gt;\n",
+       "Attributes:\n",
+       "    CDI:          Climate Data Interface version 2.2.4 (https://mpimet.mpg.de...\n",
+       "    Conventions:  CF-1.6\n",
+       "    history:      Tue Feb 03 08:53:20 2026: cdo daymean /work/bd0854/b380103/...\n",
+       "    frequency:    day\n",
+       "    CDO:          Climate Data Operators version 2.2.2 (https://mpimet.mpg.de...</pre><div class='xr-wrap' style='display:none'><div class='xr-header'><div class='xr-obj-type'>xarray.Dataset</div></div><ul class='xr-sections'><li class='xr-section-item'><input id='section-e4577d01-18b8-4399-9564-69cdef79c18a' class='xr-section-summary-in' type='checkbox' disabled /><label for='section-e4577d01-18b8-4399-9564-69cdef79c18a' class='xr-section-summary'>Dimensions:</label><div class='xr-section-inline-details'><ul class='xr-dim-list'><li><span class='xr-has-index'>time</span>: 731</li><li><span class='xr-has-index'>lat</span>: 40</li><li><span class='xr-has-index'>lon</span>: 80</li></ul></div></li><li class='xr-section-item'><input id='section-19ec7f82-163b-4586-98d1-87e6b8ce80f6' class='xr-section-summary-in' type='checkbox' checked /><label for='section-19ec7f82-163b-4586-98d1-87e6b8ce80f6' class='xr-section-summary' title='Expand/collapse section'>Coordinates: <span>(3)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>time</span></div><div class='xr-var-dims'>(time)</div><div class='xr-var-dtype'>datetime64[ns]</div><div class='xr-var-preview xr-preview'>2020-01-01T11:30:00 ... 2021-12-...</div><input id='attrs-b781b883-0734-4c92-9b03-6b910431a55e' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-b781b883-0734-4c92-9b03-6b910431a55e' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-30a2e245-f4ed-40ac-80c1-7eea715724b3' class='xr-var-data-in' type='checkbox'><label for='data-30a2e245-f4ed-40ac-80c1-7eea715724b3' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>standard_name :</span></dt><dd>time</dd><dt><span>long_name :</span></dt><dd>time</dd><dt><span>bounds :</span></dt><dd>time_bnds</dd><dt><span>axis :</span></dt><dd>T</dd></dl></div><div class='xr-var-data'><pre>array([&#x27;2020-01-01T11:30:00.000000000&#x27;, &#x27;2020-01-02T11:30:00.000000000&#x27;,\n",
+       "       &#x27;2020-01-03T11:30:00.000000000&#x27;, ..., &#x27;2021-12-29T11:30:00.000000000&#x27;,\n",
+       "       &#x27;2021-12-30T11:30:00.000000000&#x27;, &#x27;2021-12-31T11:30:00.000000000&#x27;],\n",
+       "      shape=(731,), dtype=&#x27;datetime64[ns]&#x27;)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>lat</span></div><div class='xr-var-dims'>(lat)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>-4.875 -4.625 ... 4.625 4.875</div><input id='attrs-7b2b1f6a-c113-4612-9ff7-2257d798ff32' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-7b2b1f6a-c113-4612-9ff7-2257d798ff32' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-b7b6de0e-d503-4d43-9664-a2f1e87c8f51' class='xr-var-data-in' type='checkbox'><label for='data-b7b6de0e-d503-4d43-9664-a2f1e87c8f51' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>standard_name :</span></dt><dd>latitude</dd><dt><span>long_name :</span></dt><dd>latitude</dd><dt><span>units :</span></dt><dd>degrees_north</dd><dt><span>axis :</span></dt><dd>Y</dd></dl></div><div class='xr-var-data'><pre>array([-4.875, -4.625, -4.375, -4.125, -3.875, -3.625, -3.375, -3.125, -2.875,\n",
+       "       -2.625, -2.375, -2.125, -1.875, -1.625, -1.375, -1.125, -0.875, -0.625,\n",
+       "       -0.375, -0.125,  0.125,  0.375,  0.625,  0.875,  1.125,  1.375,  1.625,\n",
+       "        1.875,  2.125,  2.375,  2.625,  2.875,  3.125,  3.375,  3.625,  3.875,\n",
+       "        4.125,  4.375,  4.625,  4.875], dtype=float32)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>lon</span></div><div class='xr-var-dims'>(lon)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>-9.875 -9.625 ... 9.625 9.875</div><input id='attrs-285d22db-612b-48cb-b63b-ea329022598b' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-285d22db-612b-48cb-b63b-ea329022598b' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-9838e08a-5499-42c3-8f2d-2147274c1230' class='xr-var-data-in' type='checkbox'><label for='data-9838e08a-5499-42c3-8f2d-2147274c1230' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>standard_name :</span></dt><dd>longitude</dd><dt><span>long_name :</span></dt><dd>longitude</dd><dt><span>units :</span></dt><dd>degrees_east</dd><dt><span>axis :</span></dt><dd>X</dd></dl></div><div class='xr-var-data'><pre>array([-9.875, -9.625, -9.375, -9.125, -8.875, -8.625, -8.375, -8.125, -7.875,\n",
+       "       -7.625, -7.375, -7.125, -6.875, -6.625, -6.375, -6.125, -5.875, -5.625,\n",
+       "       -5.375, -5.125, -4.875, -4.625, -4.375, -4.125, -3.875, -3.625, -3.375,\n",
+       "       -3.125, -2.875, -2.625, -2.375, -2.125, -1.875, -1.625, -1.375, -1.125,\n",
+       "       -0.875, -0.625, -0.375, -0.125,  0.125,  0.375,  0.625,  0.875,  1.125,\n",
+       "        1.375,  1.625,  1.875,  2.125,  2.375,  2.625,  2.875,  3.125,  3.375,\n",
+       "        3.625,  3.875,  4.125,  4.375,  4.625,  4.875,  5.125,  5.375,  5.625,\n",
+       "        5.875,  6.125,  6.375,  6.625,  6.875,  7.125,  7.375,  7.625,  7.875,\n",
+       "        8.125,  8.375,  8.625,  8.875,  9.125,  9.375,  9.625,  9.875],\n",
+       "      dtype=float32)</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-8b640276-a29c-487d-82bc-36d7d6431451' class='xr-section-summary-in' type='checkbox' checked /><label for='section-8b640276-a29c-487d-82bc-36d7d6431451' class='xr-section-summary' title='Expand/collapse section'>Data variables: <span>(1)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span>ts</span></div><div class='xr-var-dims'>(time, lat, lon)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1, 20, 40), meta=np.ndarray&gt;</div><input id='attrs-2ff68781-5ff2-4096-908d-86136f82ea73' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-2ff68781-5ff2-4096-908d-86136f82ea73' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-9f0f15d7-86fd-4325-9363-c2e66f0aff9b' class='xr-var-data-in' type='checkbox'><label for='data-9f0f15d7-86fd-4325-9363-c2e66f0aff9b' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>standard_name :</span></dt><dd>unknown</dd><dt><span>long_name :</span></dt><dd>Skin temperature</dd><dt><span>units :</span></dt><dd>K</dd><dt><span>cell_methods :</span></dt><dd>time: mean</dd><dt><span>GRIB_paramId :</span></dt><dd>235</dd><dt><span>GRIB_dataType :</span></dt><dd>an</dd><dt><span>GRIB_numberOfPoints :</span></dt><dd>1038240</dd><dt><span>GRIB_typeOfLevel :</span></dt><dd>surface</dd><dt><span>GRIB_stepUnits :</span></dt><dd>1</dd><dt><span>GRIB_stepType :</span></dt><dd>instant</dd><dt><span>GRIB_gridType :</span></dt><dd>regular_ll</dd><dt><span>GRIB_uvRelativeToGrid :</span></dt><dd>0</dd><dt><span>GRIB_NV :</span></dt><dd>0</dd><dt><span>GRIB_Nx :</span></dt><dd>1440</dd><dt><span>GRIB_Ny :</span></dt><dd>721</dd><dt><span>GRIB_cfName :</span></dt><dd>unknown</dd><dt><span>GRIB_cfVarName :</span></dt><dd>skt</dd><dt><span>GRIB_gridDefinitionDescription :</span></dt><dd>Latitude/Longitude Grid</dd><dt><span>GRIB_iDirectionIncrementInDegrees :</span></dt><dd>0.25</dd><dt><span>GRIB_iScansNegatively :</span></dt><dd>0</dd><dt><span>GRIB_jDirectionIncrementInDegrees :</span></dt><dd>0.25</dd><dt><span>GRIB_jPointsAreConsecutive :</span></dt><dd>0</dd><dt><span>GRIB_jScansPositively :</span></dt><dd>0</dd><dt><span>GRIB_latitudeOfFirstGridPointInDegrees :</span></dt><dd>90.0</dd><dt><span>GRIB_latitudeOfLastGridPointInDegrees :</span></dt><dd>-90.0</dd><dt><span>GRIB_longitudeOfFirstGridPointInDegrees :</span></dt><dd>0.0</dd><dt><span>GRIB_longitudeOfLastGridPointInDegrees :</span></dt><dd>359.75</dd><dt><span>GRIB_missingValue :</span></dt><dd>3.4028234663852886e+38</dd><dt><span>GRIB_name :</span></dt><dd>Skin temperature</dd><dt><span>GRIB_shortName :</span></dt><dd>skt</dd><dt><span>GRIB_totalNumber :</span></dt><dd>0</dd><dt><span>GRIB_units :</span></dt><dd>K</dd><dt><span>GRIB_surface :</span></dt><dd>0.0</dd></dl></div><div class='xr-var-data'><table>\n",
+       "    <tr>\n",
+       "        <td>\n",
+       "            <table style=\"border-collapse: collapse;\">\n",
+       "                <thead>\n",
+       "                    <tr>\n",
+       "                        <td> </td>\n",
+       "                        <th> Array </th>\n",
+       "                        <th> Chunk </th>\n",
+       "                    </tr>\n",
+       "                </thead>\n",
+       "                <tbody>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Bytes </th>\n",
+       "                        <td> 8.92 MiB </td>\n",
+       "                        <td> 3.12 kiB </td>\n",
+       "                    </tr>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Shape </th>\n",
+       "                        <td> (731, 40, 80) </td>\n",
+       "                        <td> (1, 20, 40) </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Dask graph </th>\n",
+       "                        <td colspan=\"2\"> 2924 chunks in 73 graph layers </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Data type </th>\n",
+       "                        <td colspan=\"2\"> float32 numpy.ndarray </td>\n",
+       "                    </tr>\n",
+       "                </tbody>\n",
+       "            </table>\n",
+       "        </td>\n",
+       "        <td>\n",
+       "        <svg width=\"169\" height=\"155\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\n",
+       "\n",
+       "  <!-- Horizontal lines -->\n",
+       "  <line x1=\"10\" y1=\"0\" x2=\"80\" y2=\"70\" style=\"stroke-width:2\" />\n",
+       "  <line x1=\"10\" y1=\"17\" x2=\"80\" y2=\"88\" />\n",
+       "  <line x1=\"10\" y1=\"35\" x2=\"80\" y2=\"105\" style=\"stroke-width:2\" />\n",
+       "\n",
+       "  <!-- Vertical lines -->\n",
+       "  <line x1=\"10\" y1=\"0\" x2=\"10\" y2=\"35\" style=\"stroke-width:2\" />\n",
+       "  <line x1=\"13\" y1=\"3\" x2=\"13\" y2=\"38\" />\n",
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+       "\n",
+       "  <!-- Horizontal lines -->\n",
+       "  <line x1=\"10\" y1=\"0\" x2=\"49\" y2=\"0\" style=\"stroke-width:2\" />\n",
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+       "</svg>\n",
+       "        </td>\n",
+       "    </tr>\n",
+       "</table></div></li></ul></div></li><li class='xr-section-item'><input id='section-31662ee2-f2f2-4ded-af7c-50e9cf755e7e' class='xr-section-summary-in' type='checkbox' checked /><label for='section-31662ee2-f2f2-4ded-af7c-50e9cf755e7e' class='xr-section-summary' title='Expand/collapse section'>Attributes: <span>(5)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><dl class='xr-attrs'><dt><span>CDI :</span></dt><dd>Climate Data Interface version 2.2.4 (https://mpimet.mpg.de/cdi)</dd><dt><span>Conventions :</span></dt><dd>CF-1.6</dd><dt><span>history :</span></dt><dd>Tue Feb 03 08:53:20 2026: cdo daymean /work/bd0854/b380103/eso4clima/output/concatenated/202001_hr_ERA5_masked_ts.nc /work/bd0854/b380103/eso4clima/output/concatenated/202001_day_ERA5_masked_ts.nc\n",
+       "Tue Feb 03 08:53:10 2026: cdo cat /work/bd0854/b380103/eso4clima/output/20200101_ERA5_masked_ts.nc /work/bd0854/b380103/eso4clima/output/20200102_ERA5_masked_ts.nc /work/bd0854/b380103/eso4clima/output/20200103_ERA5_masked_ts.nc /work/bd0854/b380103/eso4clima/output/20200104_ERA5_masked_ts.nc /work/bd0854/b380103/eso4clima/output/20200105_ERA5_masked_ts.nc /work/bd0854/b380103/eso4clima/output/20200106_ERA5_masked_ts.nc /work/bd0854/b380103/eso4clima/output/20200107_ERA5_masked_ts.nc /work/bd0854/b380103/eso4clima/output/20200108_ERA5_masked_ts.nc /work/bd0854/b380103/eso4clima/output/20200109_ERA5_masked_ts.nc /work/bd0854/b380103/eso4clima/output/20200110_ERA5_masked_ts.nc /work/bd0854/b380103/eso4clima/output/20200111_ERA5_masked_ts.nc /work/bd0854/b380103/eso4clima/output/20200112_ERA5_masked_ts.nc /work/bd0854/b380103/eso4clima/output/20200113_ERA5_masked_ts.nc /work/bd0854/b380103/eso4clima/output/20200114_ERA5_masked_ts.nc /work/bd0854/b380103/eso4clima/output/20200115_ERA5_masked_ts.nc /work/bd0854/b380103/eso4clima/output/20200116_ERA5_masked_ts.nc /work/bd0854/b380103/eso4clima/output/20200117_ERA5_masked_ts.nc /work/bd0854/b380103/eso4clima/output/20200118_ERA5_masked_ts.nc /work/bd0854/b380103/eso4clima/output/20200119_ERA5_masked_ts.nc /work/bd0854/b380103/eso4clima/output/20200120_ERA5_masked_ts.nc /work/bd0854/b380103/eso4clima/output/20200121_ERA5_masked_ts.nc /work/bd0854/b380103/eso4clima/output/20200122_ERA5_masked_ts.nc /work/bd0854/b380103/eso4clima/output/20200123_ERA5_masked_ts.nc /work/bd0854/b380103/eso4clima/output/20200124_ERA5_masked_ts.nc /work/bd0854/b380103/eso4clima/output/20200125_ERA5_masked_ts.nc /work/bd0854/b380103/eso4clima/output/20200126_ERA5_masked_ts.nc /work/bd0854/b380103/eso4clima/output/20200127_ERA5_masked_ts.nc /work/bd0854/b380103/eso4clima/output/20200128_ERA5_masked_ts.nc /work/bd0854/b380103/eso4clima/output/20200129_ERA5_masked_ts.nc /work/bd0854/b380103/eso4clima/output/20200130_ERA5_masked_ts.nc /work/bd0854/b380103/eso4clima/output/20200131_ERA5_masked_ts.nc /work/bd0854/b380103/eso4clima/output/concatenated/202001_hr_ERA5_masked_ts.nc</dd><dt><span>frequency :</span></dt><dd>day</dd><dt><span>CDO :</span></dt><dd>Climate Data Operators version 2.2.2 (https://mpimet.mpg.de/cdo)</dd></dl></div></li></ul></div></div>"
+      ],
+      "text/plain": [
+       "<xarray.Dataset> Size: 9MB\n",
+       "Dimensions:  (time: 731, lat: 40, lon: 80)\n",
+       "Coordinates:\n",
+       "  * time     (time) datetime64[ns] 6kB 2020-01-01T11:30:00 ... 2021-12-31T11:...\n",
+       "  * lat      (lat) float32 160B -4.875 -4.625 -4.375 ... 4.375 4.625 4.875\n",
+       "  * lon      (lon) float32 320B -9.875 -9.625 -9.375 ... 9.375 9.625 9.875\n",
+       "Data variables:\n",
+       "    ts       (time, lat, lon) float32 9MB dask.array<chunksize=(1, 20, 40), meta=np.ndarray>\n",
+       "Attributes:\n",
+       "    CDI:          Climate Data Interface version 2.2.4 (https://mpimet.mpg.de...\n",
+       "    Conventions:  CF-1.6\n",
+       "    history:      Tue Feb 03 08:53:20 2026: cdo daymean /work/bd0854/b380103/...\n",
+       "    frequency:    day\n",
+       "    CDO:          Climate Data Operators version 2.2.2 (https://mpimet.mpg.de..."
+      ]
+     },
+     "execution_count": 3,
+     "metadata": {},
+     "output_type": "execute_result"
     }
    ],
    "source": [
-    "# coordinates of subset\n",
-    "lon_subset = slice(-50, 50)\n",
-    "lat_subset = slice(-20, 20)\n",
-    "\n",
-    "daily_subset = daily_data.sel(lon=lon_subset, lat=lat_subset)\n",
-    "monthly_subset = monthly_data.sel(lon=lon_subset, lat=lat_subset)\n",
-    "lsm_subset = lsm_mask.sel(lon=lon_subset, lat=lat_subset)  # True=Land\n",
-    "\n",
-    "print(daily_subset[\"ts\"].shape, monthly_subset[\"ts\"].shape)"
+    "daily_data"
    ]
   },
   {
    "cell_type": "code",
    "execution_count": 4,
+   "id": "db9e7465",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
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+       "\n",
+       ".xr-header {\n",
+       "  border-bottom: solid 1px var(--xr-border-color);\n",
+       "  margin-bottom: 4px;\n",
+       "}\n",
+       "\n",
+       ".xr-header > div,\n",
+       ".xr-header > ul {\n",
+       "  display: inline;\n",
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+       "\n",
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+       "}\n",
+       "\n",
+       ".xr-obj-type,\n",
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+       "  color: var(--xr-font-color2);\n",
+       "  display: block;\n",
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+       "\n",
+       ".xr-sections {\n",
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+       ".xr-group-box-contents > input,\n",
+       ".xr-array-wrap > input {\n",
+       "  display: block;\n",
+       "  opacity: 0;\n",
+       "  height: 0;\n",
+       "  margin: 0;\n",
+       "}\n",
+       "\n",
+       ".xr-section-item > input + label,\n",
+       ".xr-var-item > input + label {\n",
+       "  color: var(--xr-disabled-color);\n",
+       "}\n",
+       "\n",
+       ".xr-section-item > input:enabled + label,\n",
+       ".xr-var-item > input:enabled + label,\n",
+       ".xr-array-wrap > input:enabled + label,\n",
+       ".xr-group-box-contents > input:enabled + label {\n",
+       "  cursor: pointer;\n",
+       "  color: var(--xr-font-color2);\n",
+       "}\n",
+       "\n",
+       ".xr-section-item > input:focus-visible + label,\n",
+       ".xr-var-item > input:focus-visible + label,\n",
+       ".xr-array-wrap > input:focus-visible + label,\n",
+       ".xr-group-box-contents > input:focus-visible + label {\n",
+       "  outline: auto;\n",
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+       "\n",
+       ".xr-section-item > input:enabled + label:hover,\n",
+       ".xr-var-item > input:enabled + label:hover,\n",
+       ".xr-array-wrap > input:enabled + label:hover,\n",
+       ".xr-group-box-contents > input:enabled + label:hover {\n",
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+       "}\n",
+       "\n",
+       ".xr-group-box-vline {\n",
+       "  grid-column-start: 1;\n",
+       "  border-right: 0.2em solid;\n",
+       "  border-color: var(--xr-border-color);\n",
+       "  width: 0px;\n",
+       "}\n",
+       "\n",
+       ".xr-group-box-hline {\n",
+       "  grid-column-start: 2;\n",
+       "  grid-row-start: 1;\n",
+       "  height: 1em;\n",
+       "  width: 26px;\n",
+       "  border-bottom: 0.2em solid;\n",
+       "  border-color: var(--xr-border-color);\n",
+       "}\n",
+       "\n",
+       ".xr-group-box-contents {\n",
+       "  grid-column-start: 3;\n",
+       "  padding-bottom: 4px;\n",
+       "}\n",
+       "\n",
+       ".xr-group-box-contents > label::before {\n",
+       "  content: \"📂\";\n",
+       "  padding-right: 0.3em;\n",
+       "}\n",
+       "\n",
+       ".xr-group-box-contents > input:checked + label::before {\n",
+       "  content: \"📁\";\n",
+       "}\n",
+       "\n",
+       ".xr-group-box-contents > input:checked + label {\n",
+       "  padding-bottom: 0px;\n",
+       "}\n",
+       "\n",
+       ".xr-group-box-contents > input:checked ~ .xr-sections {\n",
+       "  display: none;\n",
+       "}\n",
+       "\n",
+       ".xr-group-box-contents > input + label > span {\n",
+       "  display: none;\n",
+       "}\n",
+       "\n",
+       ".xr-group-box-ellipsis {\n",
+       "  font-size: 1.4em;\n",
+       "  font-weight: 900;\n",
+       "  color: var(--xr-font-color2);\n",
+       "  letter-spacing: 0.15em;\n",
+       "  cursor: default;\n",
+       "}\n",
+       "\n",
+       ".xr-array-wrap {\n",
+       "  grid-column: 1 / -1;\n",
+       "  display: grid;\n",
+       "  grid-template-columns: 20px auto;\n",
+       "}\n",
+       "\n",
+       ".xr-array-wrap > label {\n",
+       "  grid-column: 1;\n",
+       "  vertical-align: top;\n",
+       "}\n",
+       "\n",
+       ".xr-preview {\n",
+       "  color: var(--xr-font-color3);\n",
+       "}\n",
+       "\n",
+       ".xr-array-preview,\n",
+       ".xr-array-data {\n",
+       "  padding: 0 5px !important;\n",
+       "  grid-column: 2;\n",
+       "}\n",
+       "\n",
+       ".xr-array-data,\n",
+       ".xr-array-in:checked ~ .xr-array-preview {\n",
+       "  display: none;\n",
+       "}\n",
+       "\n",
+       ".xr-array-in:checked ~ .xr-array-data,\n",
+       ".xr-array-preview {\n",
+       "  display: inline-block;\n",
+       "}\n",
+       "\n",
+       ".xr-dim-list {\n",
+       "  display: inline-block !important;\n",
+       "  list-style: none;\n",
+       "  padding: 0 !important;\n",
+       "  margin: 0;\n",
+       "}\n",
+       "\n",
+       ".xr-dim-list li {\n",
+       "  display: inline-block;\n",
+       "  padding: 0;\n",
+       "  margin: 0;\n",
+       "}\n",
+       "\n",
+       ".xr-dim-list:before {\n",
+       "  content: \"(\";\n",
+       "}\n",
+       "\n",
+       ".xr-dim-list:after {\n",
+       "  content: \")\";\n",
+       "}\n",
+       "\n",
+       ".xr-dim-list li:not(:last-child):after {\n",
+       "  content: \",\";\n",
+       "  padding-right: 5px;\n",
+       "}\n",
+       "\n",
+       ".xr-has-index {\n",
+       "  font-weight: bold;\n",
+       "}\n",
+       "\n",
+       ".xr-var-list,\n",
+       ".xr-var-item {\n",
+       "  display: contents;\n",
+       "}\n",
+       "\n",
+       ".xr-var-item > div,\n",
+       ".xr-var-item label,\n",
+       ".xr-var-item > .xr-var-name span {\n",
+       "  background-color: var(--xr-background-color-row-even);\n",
+       "  border-color: var(--xr-background-color-row-odd);\n",
+       "  margin-bottom: 0;\n",
+       "  padding-top: 2px;\n",
+       "}\n",
+       "\n",
+       ".xr-var-item > .xr-var-name:hover span {\n",
+       "  padding-right: 5px;\n",
+       "}\n",
+       "\n",
+       ".xr-var-list > li:nth-child(odd) > div,\n",
+       ".xr-var-list > li:nth-child(odd) > label,\n",
+       ".xr-var-list > li:nth-child(odd) > .xr-var-name span {\n",
+       "  background-color: var(--xr-background-color-row-odd);\n",
+       "  border-color: var(--xr-background-color-row-even);\n",
+       "}\n",
+       "\n",
+       ".xr-var-name {\n",
+       "  grid-column: 1;\n",
+       "}\n",
+       "\n",
+       ".xr-var-dims {\n",
+       "  grid-column: 2;\n",
+       "}\n",
+       "\n",
+       ".xr-var-dtype {\n",
+       "  grid-column: 3;\n",
+       "  text-align: right;\n",
+       "  color: var(--xr-font-color2);\n",
+       "}\n",
+       "\n",
+       ".xr-var-preview {\n",
+       "  grid-column: 4;\n",
+       "}\n",
+       "\n",
+       ".xr-index-preview {\n",
+       "  grid-column: 2 / 5;\n",
+       "  color: var(--xr-font-color2);\n",
+       "}\n",
+       "\n",
+       ".xr-var-name,\n",
+       ".xr-var-dims,\n",
+       ".xr-var-dtype,\n",
+       ".xr-preview,\n",
+       ".xr-attrs dt {\n",
+       "  white-space: nowrap;\n",
+       "  overflow: hidden;\n",
+       "  text-overflow: ellipsis;\n",
+       "  padding-right: 10px;\n",
+       "}\n",
+       "\n",
+       ".xr-var-name:hover,\n",
+       ".xr-var-dims:hover,\n",
+       ".xr-var-dtype:hover,\n",
+       ".xr-attrs dt:hover {\n",
+       "  overflow: visible;\n",
+       "  width: auto;\n",
+       "  z-index: 1;\n",
+       "}\n",
+       "\n",
+       ".xr-var-attrs,\n",
+       ".xr-var-data,\n",
+       ".xr-index-data {\n",
+       "  display: none;\n",
+       "  border-top: 2px dotted var(--xr-background-color);\n",
+       "  padding-bottom: 20px !important;\n",
+       "  padding-top: 10px !important;\n",
+       "}\n",
+       "\n",
+       ".xr-var-attrs-in + label,\n",
+       ".xr-var-data-in + label,\n",
+       ".xr-index-data-in + label {\n",
+       "  padding: 0 1px;\n",
+       "}\n",
+       "\n",
+       ".xr-var-attrs-in:checked ~ .xr-var-attrs,\n",
+       ".xr-var-data-in:checked ~ .xr-var-data,\n",
+       ".xr-index-data-in:checked ~ .xr-index-data {\n",
+       "  display: block;\n",
+       "}\n",
+       "\n",
+       ".xr-var-data > table {\n",
+       "  float: right;\n",
+       "}\n",
+       "\n",
+       ".xr-var-data > pre,\n",
+       ".xr-index-data > pre,\n",
+       ".xr-var-data > table > tbody > tr {\n",
+       "  background-color: transparent !important;\n",
+       "}\n",
+       "\n",
+       ".xr-var-name span,\n",
+       ".xr-var-data,\n",
+       ".xr-index-name div,\n",
+       ".xr-index-data,\n",
+       ".xr-attrs {\n",
+       "  padding-left: 25px !important;\n",
+       "}\n",
+       "\n",
+       ".xr-attrs,\n",
+       ".xr-var-attrs,\n",
+       ".xr-var-data,\n",
+       ".xr-index-data {\n",
+       "  grid-column: 1 / -1;\n",
+       "}\n",
+       "\n",
+       "dl.xr-attrs {\n",
+       "  padding: 0;\n",
+       "  margin: 0;\n",
+       "  display: grid;\n",
+       "  grid-template-columns: 125px auto;\n",
+       "}\n",
+       "\n",
+       ".xr-attrs dt,\n",
+       ".xr-attrs dd {\n",
+       "  padding: 0;\n",
+       "  margin: 0;\n",
+       "  float: left;\n",
+       "  padding-right: 10px;\n",
+       "  width: auto;\n",
+       "}\n",
+       "\n",
+       ".xr-attrs dt {\n",
+       "  font-weight: normal;\n",
+       "  grid-column: 1;\n",
+       "}\n",
+       "\n",
+       ".xr-attrs dt:hover span {\n",
+       "  display: inline-block;\n",
+       "  background: var(--xr-background-color);\n",
+       "  padding-right: 10px;\n",
+       "}\n",
+       "\n",
+       ".xr-attrs dd {\n",
+       "  grid-column: 2;\n",
+       "  white-space: pre-wrap;\n",
+       "  word-break: break-all;\n",
+       "}\n",
+       "\n",
+       ".xr-icon-database,\n",
+       ".xr-icon-file-text2,\n",
+       ".xr-no-icon {\n",
+       "  display: inline-block;\n",
+       "  vertical-align: middle;\n",
+       "  width: 1em;\n",
+       "  height: 1.5em !important;\n",
+       "  stroke-width: 0;\n",
+       "  stroke: currentColor;\n",
+       "  fill: currentColor;\n",
+       "}\n",
+       "\n",
+       ".xr-var-attrs-in:checked + label > .xr-icon-file-text2,\n",
+       ".xr-var-data-in:checked + label > .xr-icon-database,\n",
+       ".xr-index-data-in:checked + label > .xr-icon-database {\n",
+       "  color: var(--xr-font-color0);\n",
+       "  filter: drop-shadow(1px 1px 5px var(--xr-font-color2));\n",
+       "  stroke-width: 0.8px;\n",
+       "}\n",
+       "</style><pre class='xr-text-repr-fallback'>&lt;xarray.Dataset&gt; Size: 308kB\n",
+       "Dimensions:  (time: 24, lat: 40, lon: 80)\n",
+       "Coordinates:\n",
+       "  * time     (time) datetime64[ns] 192B 2020-01-16T11:30:00 ... 2021-12-16T11...\n",
+       "  * lat      (lat) float32 160B -4.875 -4.625 -4.375 ... 4.375 4.625 4.875\n",
+       "  * lon      (lon) float32 320B -9.875 -9.625 -9.375 ... 9.375 9.625 9.875\n",
+       "Data variables:\n",
+       "    ts       (time, lat, lon) float32 307kB dask.array&lt;chunksize=(1, 20, 40), meta=np.ndarray&gt;\n",
+       "Attributes:\n",
+       "    CDI:          Climate Data Interface version 2.2.4 (https://mpimet.mpg.de...\n",
+       "    Conventions:  CF-1.6\n",
+       "    history:      Tue Feb 03 08:53:10 2026: cdo monmean /work/bd0854/b380103/...\n",
+       "    frequency:    mon\n",
+       "    CDO:          Climate Data Operators version 2.2.2 (https://mpimet.mpg.de...</pre><div class='xr-wrap' style='display:none'><div class='xr-header'><div class='xr-obj-type'>xarray.Dataset</div></div><ul class='xr-sections'><li class='xr-section-item'><input id='section-e4c85917-ab1f-46d5-98a7-d053e07eb36a' class='xr-section-summary-in' type='checkbox' disabled /><label for='section-e4c85917-ab1f-46d5-98a7-d053e07eb36a' class='xr-section-summary'>Dimensions:</label><div class='xr-section-inline-details'><ul class='xr-dim-list'><li><span class='xr-has-index'>time</span>: 24</li><li><span class='xr-has-index'>lat</span>: 40</li><li><span class='xr-has-index'>lon</span>: 80</li></ul></div></li><li class='xr-section-item'><input id='section-f6b7cc58-748f-456b-bb41-3e7f586e3fa0' class='xr-section-summary-in' type='checkbox' checked /><label for='section-f6b7cc58-748f-456b-bb41-3e7f586e3fa0' class='xr-section-summary' title='Expand/collapse section'>Coordinates: <span>(3)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>time</span></div><div class='xr-var-dims'>(time)</div><div class='xr-var-dtype'>datetime64[ns]</div><div class='xr-var-preview xr-preview'>2020-01-16T11:30:00 ... 2021-12-...</div><input id='attrs-f1780ba7-fa5a-439c-86b2-cf0f3bbe560f' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-f1780ba7-fa5a-439c-86b2-cf0f3bbe560f' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-f7069b95-f949-43ed-b59a-d8521a833702' class='xr-var-data-in' type='checkbox'><label for='data-f7069b95-f949-43ed-b59a-d8521a833702' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>standard_name :</span></dt><dd>time</dd><dt><span>long_name :</span></dt><dd>time</dd><dt><span>bounds :</span></dt><dd>time_bnds</dd><dt><span>axis :</span></dt><dd>T</dd></dl></div><div class='xr-var-data'><pre>array([&#x27;2020-01-16T11:30:00.000000000&#x27;, &#x27;2020-02-15T11:30:00.000000000&#x27;,\n",
+       "       &#x27;2020-03-16T11:30:00.000000000&#x27;, &#x27;2020-04-15T23:30:00.000000000&#x27;,\n",
+       "       &#x27;2020-05-16T11:30:00.000000000&#x27;, &#x27;2020-06-15T23:30:00.000000000&#x27;,\n",
+       "       &#x27;2020-07-16T11:30:00.000000000&#x27;, &#x27;2020-08-16T11:30:00.000000000&#x27;,\n",
+       "       &#x27;2020-09-15T23:30:00.000000000&#x27;, &#x27;2020-10-16T11:30:00.000000000&#x27;,\n",
+       "       &#x27;2020-11-15T23:30:00.000000000&#x27;, &#x27;2020-12-16T11:30:00.000000000&#x27;,\n",
+       "       &#x27;2021-01-16T11:30:00.000000000&#x27;, &#x27;2021-02-14T23:30:00.000000000&#x27;,\n",
+       "       &#x27;2021-03-16T11:30:00.000000000&#x27;, &#x27;2021-04-15T23:30:00.000000000&#x27;,\n",
+       "       &#x27;2021-05-16T11:30:00.000000000&#x27;, &#x27;2021-06-15T23:30:00.000000000&#x27;,\n",
+       "       &#x27;2021-07-16T11:30:00.000000000&#x27;, &#x27;2021-08-16T11:30:00.000000000&#x27;,\n",
+       "       &#x27;2021-09-15T23:30:00.000000000&#x27;, &#x27;2021-10-16T11:30:00.000000000&#x27;,\n",
+       "       &#x27;2021-11-15T23:30:00.000000000&#x27;, &#x27;2021-12-16T11:30:00.000000000&#x27;],\n",
+       "      dtype=&#x27;datetime64[ns]&#x27;)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>lat</span></div><div class='xr-var-dims'>(lat)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>-4.875 -4.625 ... 4.625 4.875</div><input id='attrs-cd4e0df4-4e77-47e2-baae-dc20f5ba63ee' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-cd4e0df4-4e77-47e2-baae-dc20f5ba63ee' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-28d8beb1-7a2e-48ae-a81e-04e946b99d01' class='xr-var-data-in' type='checkbox'><label for='data-28d8beb1-7a2e-48ae-a81e-04e946b99d01' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>standard_name :</span></dt><dd>latitude</dd><dt><span>long_name :</span></dt><dd>latitude</dd><dt><span>units :</span></dt><dd>degrees_north</dd><dt><span>axis :</span></dt><dd>Y</dd></dl></div><div class='xr-var-data'><pre>array([-4.875, -4.625, -4.375, -4.125, -3.875, -3.625, -3.375, -3.125, -2.875,\n",
+       "       -2.625, -2.375, -2.125, -1.875, -1.625, -1.375, -1.125, -0.875, -0.625,\n",
+       "       -0.375, -0.125,  0.125,  0.375,  0.625,  0.875,  1.125,  1.375,  1.625,\n",
+       "        1.875,  2.125,  2.375,  2.625,  2.875,  3.125,  3.375,  3.625,  3.875,\n",
+       "        4.125,  4.375,  4.625,  4.875], dtype=float32)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>lon</span></div><div class='xr-var-dims'>(lon)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>-9.875 -9.625 ... 9.625 9.875</div><input id='attrs-551e6a27-a2dd-437e-ab3f-f952b7b05f1d' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-551e6a27-a2dd-437e-ab3f-f952b7b05f1d' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-03cdacb5-c1cc-4558-9bfe-9d29ddeda654' class='xr-var-data-in' type='checkbox'><label for='data-03cdacb5-c1cc-4558-9bfe-9d29ddeda654' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>standard_name :</span></dt><dd>longitude</dd><dt><span>long_name :</span></dt><dd>longitude</dd><dt><span>units :</span></dt><dd>degrees_east</dd><dt><span>axis :</span></dt><dd>X</dd></dl></div><div class='xr-var-data'><pre>array([-9.875, -9.625, -9.375, -9.125, -8.875, -8.625, -8.375, -8.125, -7.875,\n",
+       "       -7.625, -7.375, -7.125, -6.875, -6.625, -6.375, -6.125, -5.875, -5.625,\n",
+       "       -5.375, -5.125, -4.875, -4.625, -4.375, -4.125, -3.875, -3.625, -3.375,\n",
+       "       -3.125, -2.875, -2.625, -2.375, -2.125, -1.875, -1.625, -1.375, -1.125,\n",
+       "       -0.875, -0.625, -0.375, -0.125,  0.125,  0.375,  0.625,  0.875,  1.125,\n",
+       "        1.375,  1.625,  1.875,  2.125,  2.375,  2.625,  2.875,  3.125,  3.375,\n",
+       "        3.625,  3.875,  4.125,  4.375,  4.625,  4.875,  5.125,  5.375,  5.625,\n",
+       "        5.875,  6.125,  6.375,  6.625,  6.875,  7.125,  7.375,  7.625,  7.875,\n",
+       "        8.125,  8.375,  8.625,  8.875,  9.125,  9.375,  9.625,  9.875],\n",
+       "      dtype=float32)</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-1d1ccb06-083c-44cb-aa84-90034b6433e4' class='xr-section-summary-in' type='checkbox' checked /><label for='section-1d1ccb06-083c-44cb-aa84-90034b6433e4' class='xr-section-summary' title='Expand/collapse section'>Data variables: <span>(1)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span>ts</span></div><div class='xr-var-dims'>(time, lat, lon)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1, 20, 40), meta=np.ndarray&gt;</div><input id='attrs-1852258f-6ad1-4870-9fe7-8ec4494862ef' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-1852258f-6ad1-4870-9fe7-8ec4494862ef' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-f2346f32-332e-4709-86b7-6aeb1e400e00' class='xr-var-data-in' type='checkbox'><label for='data-f2346f32-332e-4709-86b7-6aeb1e400e00' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>standard_name :</span></dt><dd>unknown</dd><dt><span>long_name :</span></dt><dd>Skin temperature</dd><dt><span>units :</span></dt><dd>K</dd><dt><span>cell_methods :</span></dt><dd>time: mean</dd><dt><span>GRIB_paramId :</span></dt><dd>235</dd><dt><span>GRIB_dataType :</span></dt><dd>an</dd><dt><span>GRIB_numberOfPoints :</span></dt><dd>1038240</dd><dt><span>GRIB_typeOfLevel :</span></dt><dd>surface</dd><dt><span>GRIB_stepUnits :</span></dt><dd>1</dd><dt><span>GRIB_stepType :</span></dt><dd>instant</dd><dt><span>GRIB_gridType :</span></dt><dd>regular_ll</dd><dt><span>GRIB_uvRelativeToGrid :</span></dt><dd>0</dd><dt><span>GRIB_NV :</span></dt><dd>0</dd><dt><span>GRIB_Nx :</span></dt><dd>1440</dd><dt><span>GRIB_Ny :</span></dt><dd>721</dd><dt><span>GRIB_cfName :</span></dt><dd>unknown</dd><dt><span>GRIB_cfVarName :</span></dt><dd>skt</dd><dt><span>GRIB_gridDefinitionDescription :</span></dt><dd>Latitude/Longitude Grid</dd><dt><span>GRIB_iDirectionIncrementInDegrees :</span></dt><dd>0.25</dd><dt><span>GRIB_iScansNegatively :</span></dt><dd>0</dd><dt><span>GRIB_jDirectionIncrementInDegrees :</span></dt><dd>0.25</dd><dt><span>GRIB_jPointsAreConsecutive :</span></dt><dd>0</dd><dt><span>GRIB_jScansPositively :</span></dt><dd>0</dd><dt><span>GRIB_latitudeOfFirstGridPointInDegrees :</span></dt><dd>90.0</dd><dt><span>GRIB_latitudeOfLastGridPointInDegrees :</span></dt><dd>-90.0</dd><dt><span>GRIB_longitudeOfFirstGridPointInDegrees :</span></dt><dd>0.0</dd><dt><span>GRIB_longitudeOfLastGridPointInDegrees :</span></dt><dd>359.75</dd><dt><span>GRIB_missingValue :</span></dt><dd>3.4028234663852886e+38</dd><dt><span>GRIB_name :</span></dt><dd>Skin temperature</dd><dt><span>GRIB_shortName :</span></dt><dd>skt</dd><dt><span>GRIB_totalNumber :</span></dt><dd>0</dd><dt><span>GRIB_units :</span></dt><dd>K</dd><dt><span>GRIB_surface :</span></dt><dd>0.0</dd></dl></div><div class='xr-var-data'><table>\n",
+       "    <tr>\n",
+       "        <td>\n",
+       "            <table style=\"border-collapse: collapse;\">\n",
+       "                <thead>\n",
+       "                    <tr>\n",
+       "                        <td> </td>\n",
+       "                        <th> Array </th>\n",
+       "                        <th> Chunk </th>\n",
+       "                    </tr>\n",
+       "                </thead>\n",
+       "                <tbody>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Bytes </th>\n",
+       "                        <td> 300.00 kiB </td>\n",
+       "                        <td> 3.12 kiB </td>\n",
+       "                    </tr>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Shape </th>\n",
+       "                        <td> (24, 40, 80) </td>\n",
+       "                        <td> (1, 20, 40) </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Dask graph </th>\n",
+       "                        <td colspan=\"2\"> 96 chunks in 73 graph layers </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Data type </th>\n",
+       "                        <td colspan=\"2\"> float32 numpy.ndarray </td>\n",
+       "                    </tr>\n",
+       "                </tbody>\n",
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+       "</table></div></li></ul></div></li><li class='xr-section-item'><input id='section-77f5ff37-b39b-4bb0-868d-772d481352ee' class='xr-section-summary-in' type='checkbox' checked /><label for='section-77f5ff37-b39b-4bb0-868d-772d481352ee' class='xr-section-summary' title='Expand/collapse section'>Attributes: <span>(5)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><dl class='xr-attrs'><dt><span>CDI :</span></dt><dd>Climate Data Interface version 2.2.4 (https://mpimet.mpg.de/cdi)</dd><dt><span>Conventions :</span></dt><dd>CF-1.6</dd><dt><span>history :</span></dt><dd>Tue Feb 03 08:53:10 2026: cdo monmean /work/bd0854/b380103/eso4clima/output/concatenated/202001_day_ERA5_full_ts.nc /work/bd0854/b380103/eso4clima/output/concatenated/202001_mon_ERA5_full_ts.nc\n",
+       "Tue Feb 03 08:53:05 2026: cdo daymean /work/bd0854/b380103/eso4clima/output/concatenated/202001_hr_ERA5_full_ts.nc /work/bd0854/b380103/eso4clima/output/concatenated/202001_day_ERA5_full_ts.nc\n",
+       "Tue Feb 03 08:52:58 2026: cdo cat /work/bd0854/b380103/eso4clima/output/20200101_ERA5_full_ts.nc /work/bd0854/b380103/eso4clima/output/20200102_ERA5_full_ts.nc /work/bd0854/b380103/eso4clima/output/20200103_ERA5_full_ts.nc /work/bd0854/b380103/eso4clima/output/20200104_ERA5_full_ts.nc /work/bd0854/b380103/eso4clima/output/20200105_ERA5_full_ts.nc /work/bd0854/b380103/eso4clima/output/20200106_ERA5_full_ts.nc /work/bd0854/b380103/eso4clima/output/20200107_ERA5_full_ts.nc /work/bd0854/b380103/eso4clima/output/20200108_ERA5_full_ts.nc /work/bd0854/b380103/eso4clima/output/20200109_ERA5_full_ts.nc /work/bd0854/b380103/eso4clima/output/20200110_ERA5_full_ts.nc /work/bd0854/b380103/eso4clima/output/20200111_ERA5_full_ts.nc /work/bd0854/b380103/eso4clima/output/20200112_ERA5_full_ts.nc /work/bd0854/b380103/eso4clima/output/20200113_ERA5_full_ts.nc /work/bd0854/b380103/eso4clima/output/20200114_ERA5_full_ts.nc /work/bd0854/b380103/eso4clima/output/20200115_ERA5_full_ts.nc /work/bd0854/b380103/eso4clima/output/20200116_ERA5_full_ts.nc /work/bd0854/b380103/eso4clima/output/20200117_ERA5_full_ts.nc /work/bd0854/b380103/eso4clima/output/20200118_ERA5_full_ts.nc /work/bd0854/b380103/eso4clima/output/20200119_ERA5_full_ts.nc /work/bd0854/b380103/eso4clima/output/20200120_ERA5_full_ts.nc /work/bd0854/b380103/eso4clima/output/20200121_ERA5_full_ts.nc /work/bd0854/b380103/eso4clima/output/20200122_ERA5_full_ts.nc /work/bd0854/b380103/eso4clima/output/20200123_ERA5_full_ts.nc /work/bd0854/b380103/eso4clima/output/20200124_ERA5_full_ts.nc /work/bd0854/b380103/eso4clima/output/20200125_ERA5_full_ts.nc /work/bd0854/b380103/eso4clima/output/20200126_ERA5_full_ts.nc /work/bd0854/b380103/eso4clima/output/20200127_ERA5_full_ts.nc /work/bd0854/b380103/eso4clima/output/20200128_ERA5_full_ts.nc /work/bd0854/b380103/eso4clima/output/20200129_ERA5_full_ts.nc /work/bd0854/b380103/eso4clima/output/20200130_ERA5_full_ts.nc /work/bd0854/b380103/eso4clima/output/20200131_ERA5_full_ts.nc /work/bd0854/b380103/eso4clima/output/concatenated/202001_hr_ERA5_full_ts.nc</dd><dt><span>frequency :</span></dt><dd>mon</dd><dt><span>CDO :</span></dt><dd>Climate Data Operators version 2.2.2 (https://mpimet.mpg.de/cdo)</dd></dl></div></li></ul></div></div>"
+      ],
+      "text/plain": [
+       "<xarray.Dataset> Size: 308kB\n",
+       "Dimensions:  (time: 24, lat: 40, lon: 80)\n",
+       "Coordinates:\n",
+       "  * time     (time) datetime64[ns] 192B 2020-01-16T11:30:00 ... 2021-12-16T11...\n",
+       "  * lat      (lat) float32 160B -4.875 -4.625 -4.375 ... 4.375 4.625 4.875\n",
+       "  * lon      (lon) float32 320B -9.875 -9.625 -9.375 ... 9.375 9.625 9.875\n",
+       "Data variables:\n",
+       "    ts       (time, lat, lon) float32 307kB dask.array<chunksize=(1, 20, 40), meta=np.ndarray>\n",
+       "Attributes:\n",
+       "    CDI:          Climate Data Interface version 2.2.4 (https://mpimet.mpg.de...\n",
+       "    Conventions:  CF-1.6\n",
+       "    history:      Tue Feb 03 08:53:10 2026: cdo monmean /work/bd0854/b380103/...\n",
+       "    frequency:    mon\n",
+       "    CDO:          Climate Data Operators version 2.2.2 (https://mpimet.mpg.de..."
+      ]
+     },
+     "execution_count": 4,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "monthly_data"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "b4be672b-a2c1-4a6f-89a1-2c22cc6cde02",
+   "metadata": {},
+   "source": [
+    "## (Only for local debug) Subset the data"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
    "id": "d70ba708-843c-42c2-9d44-3bd60bb3b13b",
    "metadata": {},
    "outputs": [
@@ -89,16 +1788,23 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "mean: [299.8475  300.13306], std: [1.9400501 2.0374947]\n"
+      "mean: [301.63776 301.94257 302.13834 302.134   301.60904 299.11246 298.08694\n",
+      " 297.7312  297.88882 299.01984 299.9284  300.27228 300.79147 301.4554\n",
+      " 301.62222 301.97076 301.54922 300.37915 298.88687 298.65665 298.91058\n",
+      " 299.715   300.3904  300.72144], std: [0.3829351  0.46096218 0.48185134 0.40551484 0.7647605  1.5153652\n",
+      " 1.2766156  1.2587669  1.2139583  1.0638267  0.9895063  1.064636\n",
+      " 0.84402895 0.59721696 0.47286218 0.36653116 0.62383425 1.0240722\n",
+      " 1.1819738  1.0563525  1.0531347  1.0451572  0.94474596 0.9479079 ]\n"
      ]
     }
    ],
    "source": [
-    "# create monthly mean to predict mean and std per month\n",
-    "daily_subset_averaged = daily_subset[\"ts\"].resample(time=\"MS\").mean(skipna=True)\n",
-    "mean = daily_subset_averaged.mean(dim=[\"lat\", \"lon\"], skipna=True).values\n",
-    "std = daily_subset_averaged.std(dim=[\"lat\", \"lon\"], skipna=True).values\n",
-    "print(f'mean: {mean}, std: {std}')"
+    "# Compute monthly climatology stats without persisting the full (time, lat, lon) monthly field\n",
+    "monthly_ts = daily_data[\"ts\"].resample(time=\"MS\").mean(skipna=True)\n",
+    "mean = monthly_ts.mean(dim=[\"lat\", \"lon\"], skipna=True).compute().values\n",
+    "std = monthly_ts.std(dim=[\"lat\", \"lon\"], skipna=True).compute().values\n",
+    "\n",
+    "print(f\"mean: {mean}, std: {std}\")"
    ]
   },
   {
@@ -117,10 +1823,10 @@
    "outputs": [],
    "source": [
     "dataset = STDataset(\n",
-    "    daily_da=daily_subset[\"ts\"],\n",
-    "    monthly_da=monthly_subset[\"ts\"],\n",
-    "    land_mask=lsm_subset[\"lsm\"],\n",
-    "    patch_size=(80, 80),\n",
+    "    daily_da=daily_data[\"ts\"],\n",
+    "    monthly_da=monthly_data[\"ts\"],\n",
+    "    land_mask=lsm_mask[\"lsm\"],\n",
+    "    patch_size=(patch_size_training, patch_size_training),\n",
     ")"
    ]
   },
@@ -141,7 +1847,7 @@
    "source": [
     "# create the model\n",
     "device = \"cuda\" if torch.cuda.is_available() else \"cpu\"\n",
-    "model = SpatioTemporalModel(embed_dim=128, patch_size=(1, 2, 2), overlap=2, max_months=2).to(device)\n",
+    "model = SpatioTemporalModel(embed_dim=128, patch_size=(1, 2, 2), overlap=2, max_months=monthly_data.sizes[\"time\"]).to(device)\n",
     "optimizer = torch.optim.Adam(model.parameters(), lr=1e-3)\n",
     "decoder = model.decoder\n",
     "\n",
@@ -184,11 +1890,11 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "The loss is 1.3227499723434448 at epoch 0\n",
-      "The loss is 0.25537195801734924 at epoch 20\n",
-      "No improvement for 10 epochs, stopping early at epoch 21.\n",
+      "The loss is 0.8419706225395203 at epoch 0\n",
+      "The loss is 0.5756334662437439 at epoch 20\n",
+      "No improvement for 10 epochs, stopping early at epoch 36.\n",
       "training done!\n",
-      "1.358122706413269\n"
+      "0.5638297200202942\n"
      ]
     }
    ],
@@ -263,16 +1969,16 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "torch.Size([1, 1, 2, 31, 160, 400]) torch.Size([1, 1, 2, 31, 160, 400]) torch.Size([1, 2, 160, 400]) torch.Size([160, 400]) torch.Size([1, 2, 31])\n"
+      "torch.Size([1, 1, 24, 31, 40, 80]) torch.Size([1, 1, 24, 31, 40, 80]) torch.Size([1, 24, 40, 80]) torch.Size([40, 80]) torch.Size([1, 24, 31])\n"
      ]
     }
    ],
    "source": [
     "dataset_pred = STDataset(\n",
-    "    daily_da=daily_subset[\"ts\"],\n",
-    "    monthly_da=monthly_subset[\"ts\"],\n",
-    "    land_mask=lsm_subset[\"lsm\"],\n",
-    "    patch_size=(daily_subset.sizes[\"lat\"], daily_subset.sizes[\"lon\"]),\n",
+    "    daily_da=daily_data[\"ts\"],\n",
+    "    monthly_da=monthly_data[\"ts\"],\n",
+    "    land_mask=lsm_mask[\"lsm\"],\n",
+    "    patch_size=(daily_data.sizes[\"lat\"], daily_data.sizes[\"lon\"]),\n",
     ")\n",
     "dataloader_pred = DataLoader(\n",
     "    dataset_pred,\n",
@@ -298,7 +2004,7 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "(2, 160, 400)\n"
+      "(24, 40, 80)\n"
      ]
     }
    ],
@@ -318,7 +2024,7 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "monthly_subset[\"ts_pred\"] = (\n",
+    "monthly_data[\"ts_pred\"] = (\n",
     "    (\"time\", \"lat\", \"lon\"),\n",
     "    monthly_prediction,  # remove batch dimensions\n",
     ")"
@@ -333,7 +2039,7 @@
     {
      "data": {
       "text/plain": [
-       "<matplotlib.collections.QuadMesh at 0x74da0e4ee050>"
+       "<matplotlib.collections.QuadMesh at 0x7d7f06142900>"
       ]
      },
      "execution_count": 13,
@@ -342,7 +2048,7 @@
     },
     {
      "data": {
-      "image/png": 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",
+      "image/png": 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OnYtnn30WXbt2xcsvv4zjx4/jv//9L4YMGWKwJMELL7yA1q1bY9CgQTh58iSqVKmCTz75BFqtFtOmTSvVMSWi/2eXpTCpXHr//fdF9erVhZOTk8Fq3qGhoWLAgAH6vFWrVgkA4tChQwbvnzJligAgrl27ZhAfMGCAcHd3l7a3fPlyERERIVxdXYWnp6do1KiRmDRpkrh69arN960k7du3FwBKfDzo+PHjomvXrsLNzU14e3uLfv36idTUVIOcAQMGmGzzwVXSk5OTxQsvvCA0Go3w8PAQzzzzjDh79qzZ+6DVasWsWbNEaGioUCqVokGDBuLrr7+W8nbt2lVin9q3b2/18Xpw5e20tDQxYMAA4ePjI1QqlWjVqpXYsmVLiW0/bAXvu987Y4/7v5+W9PPuMbl/he9r166JYcOGiXr16gl3d3ehVCpFnTp1xJgxY6TvthDmH/+7/V+1apX02qZNm0TTpk2FSqUSQUFB4p133jF6i6GbN2+KwYMHC19fX+Hm5ibat28v/RwSkeUUQpRioRkiIiKiCoJzloiIiIhMYLFEREREZAKLJSIiIiITWCwRERERmcBiiYiIiMgEFktEREREJlSoRSl1Oh2uXr0KT0/PR3r7DiIiKn+EEMjOzkZgYKDBLWRsKT8/H4WFhTZpS6lUQq1W26QtMlShiqWrV69KN6MkIiIyJTk5GUFBQTZvNz8/HzVCPZCarn14shn8/f2RlJTEgqkMVKhi6e5NR0P//S6c7vsyaVXyupyNml6QYmtbf1RmfSsPuu1+R4oFeWRKsT6+8o1fPZ3ypZiXkZgW8oifM+TPx5o8YzJ18i+XI7drSLEx9b43q73SelyOMfPM++45yveOjMvKykJwcLD+/w5bKywsRGq6FklxodB4WjdylZWtQ42IiygsLGSxVAYqVLF099Sbk1ptUCwJtfyLzsVdKcU0Gk3Zda4cqOQu34jT2HFy83SWYu5OcszDyLC2vYqlYp3cP7Wz/ONR1t+Bx+UYM8+8756jfO/ItLKetqHxdLK6WKKyVaGKJSIiIkejFTporbzxmFbobNMZMorFEhERkR3pIKAzMjppaRtUdjjuR0RERGQCR5aIiIjsSAcdrD2JZn0LZAqLJSIiIjvSCgGtsO40mrXvJ9N4Go6IiIjIhAo5suSSqYBz/r1LQXVK+bLQI+flxSsbXJ8qxQoSvKRY4qSx1nXwEYsYukCK5YTIeQXV/KTY7RAXKfZJwVNSrIFXqhRLy5fXLglzuyHFLuT52jTvfF4VKVZdnSHF9qbWlmKbLk+WYunHq0mxc+PGSbEH1Z47X4oVe8lt3Q4uf8eYeXKeud+73Sl1pNjG5Bgptj9qthSj8okTvB1fhSyWiIiIHIUOAloWSw6Np+GIiIiITODIEhERkR3xNJzjY7FERERkR7wazvGxWCIiIrIj3f8/rG2Dyg7nLBERERGZwJElIiIiO9La4Go4a99PprFYIiIisiOtuPOwtg0qOxWyWCpukgPhVqx/XnRbPgxvNd8rxXp5/i3Fvq7ZSop9dfZbKfbRmS5SzNWlyOB5bNc5xjtshqYj5YUls2rKPz2iaoEUC38tRYrV90qTYsEqeTG+1q7npFihcJZiTZSFUux4kXzcmyi1UuzvQmPtlT7veKF89vlJlbzw407NcSnWXJUtxeb6t5Zi/z39sxTbl2G4yOUr3a5JOVVc5PbbuCVKsXydsWPnOMeYeXKeud+73V5HpVgL5W0p9sp++efxm9bLpZgjaTpC/j0FeU1gaOXDgmPzy9div/R4qZDFEhERkaPgBG/Hx2KJiIjIjnRQQGtsiM3CNqjs8Go4IiIiIhM4skRERGRHOnHnYW0bVHZYLBEREdmR1gan4ax9P5nG03BEREREJnBkiYiIyI44suT4WCwRERHZkU4ooBNWXg1n5fvJNBZLREREdsSRJcdXIYsl3UV3QK2+F1DLlxH8fqO2FPNyzpNil277SDE3Z3mVbJ1O/iIXaw2njD0xS17d1tgfC+5X5VienxwzNiNN7S6v8nwz302KaTXyhuOzg6WYp1O+FDuXX03un+dpKfZHTl0plush5+3NqVfmeYWex6TY7uyGUixPnJdiaQWeUszTWT4uOUUqg+fG/hJ8nI9xRc8zlpPveUKKbctqJMUy3OWV8lNyNVIsas8YKba1/UIpZkzLAfOlWJGH/B11vSYvf5hfWf5l41wk/14tCDLSnnyzAKOimk2RYluPTDPvzURWqpDFEhERkaPQwglaK6+3km/CQ7bEYomIiMiOhA3mLAnOWSpTXDqAiIiIyASOLBEREdkRJ3g7vnI7sjRnzhwoFAqMGTPG3l0hIiIqNa1wssmDyk65PLqHDh3Cp59+isaNG9u7K0RERPSYK3fFUk5ODvr164fPPvsMlStXtnd3iIiIrKKDAjo4WfngabiyVO6KpejoaPTo0QOdO3d+aG5BQQGysrIMHkRERI7k7pwlax9UdsrVBO9169bhr7/+wqFDh8zKnz17NqZNkxctcwrJhbPbvVUptOmuUk5dTboUa6ROlmI+lXKkWJTrNSlWs74ca+96xeD5BwHtpJzqqltSbN9NecHMup7yym5eleRFNJu7XpBiuTqVFAtzuSHF4vNDpFgdZaoUy9appZgxxhbvLIJzmecZWzDSGGPHvp6L/L3oUvmkFHvSyHelhsrwO1BPmSLllMdjbOwv2keR9yi+K7bMM/a9c4K8wOOD3xMAaK2Wf75vBh+QYp3dEqXYyL/k9hq6X5ZilV9vJsXqaeTtHs8IkGJ13DOlWF6xUoo19ZJ/LjanhEuxSgr5uGz9eJ4UI3pUys3IUnJyMkaPHo01a9ZArTbvP4qYmBhkZmbqH8nJ8g8qERGRPXGCt+MrNyNLcXFxSE9PR/PmzfUxrVaLvXv34r///S8KCgrg7Gz4l5xKpYJKJY+aEBEROYo7c5asvJEuT8OVqXJTLHXq1AnHjhnev2vQoEGoV68e3n77balQIiIiKg90NrjdiQ7yvfjIdspNseTp6YmGDQ1vbOru7g5fX18pTkRERGQr5aZYIiIiehzZYs6RVnBkqSyV62Jp9+7d9u4CERGRVe6ulWRdGyyWyhKnzxMRERGZUK5HloiIiMo7rVBAK6y8ka6V7yfTKmSxJC54QHffWk1OKnn48piRhdcauflLsVO3A6VYNedsKXYkL1SKqZ2KDJ7fLHSXciobWViyUGvelX+nc+R9qFpJ7tuZ2/J+uXhqpdjJ29WNtCevin7+dlUpVlMpL+Z4NV++XU1DtbxYnq3zrhR4S7FsV3ntrrO3/aRYLaW8SF98rryQpK+R78CDed7OuVLO43KMmSfnmf29y5d/Hk+r5IVJTxv53WPs5zurWN5GptZNijkp5N+D5sZcnQul2NU8jbzdYnkB4GtZHlJMpy39SY/uIWOkmPCUf7duOTGz1NuwNa0NrobT8jRcmeJpOCIiIiITKuTIEhERkaPQCSforLwaTser4coUiyUiIiI74mk4x8fTcEREREQmsFgiIiKyIx3uXRFX2ofOwm0uXboUjRs3hkajgUajQWRkJDZv3qx/PT8/H9HR0fD19YWHhwf69OmDtDTDC1xGjRqFiIgIqFQqNG3a9KHbvHDhAhQKhdHH+vXr9XnGXl+3bp2Fe2hbLJaIiIjs6O6ilNY+LBEUFIQ5c+YgLi4Ohw8fRseOHdGrVy+cOHECADB27Fj89NNPWL9+Pfbs2YOrV6/i+eefl9p5/fXX0bdvX7O2GRwcjJSUFIPHtGnT4OHhge7duxvkrlq1yiCvd+/eFu2frXHOEhERkR3Z5nYnlr2/Z8+eBs9nzpyJpUuXYv/+/QgKCsKKFSuwdu1adOzYEcCd4qV+/frYv38/WrduDQBYvHgxAODatWs4evToQ7fp7OwMf3/DpTE2bdqEl156CR4ehktIeHt7S7n2xJElIiKix0RWVpbBo6Cg4KHv0Wq1WLduHXJzcxEZGYm4uDgUFRWhc+fO+px69eohJCQEsbGxNutrXFwc4uPjMXjwYOm16OhoVKlSBS1btsTKlSsh7Hy1X4UcWVKE5kLhdt+iiynyQmnB7hlSzEUhL9ToU0leWNDJyNnjENUNKVbPxXARwSif41JOa/UlKVZbLS+MWE+ZIsXi8+XFEuso5cXtcnUqKaZUFEsxXxd5wTtnhbyv/qpMs/IC1bfKPM/Yfhjtn5HPrJpSXgzSmMou8nfA2JUtxvIe9LgcY2vy7LUfZZ1n7DMzppqL/L1zgfy7p6ZaXoT0wd8pAPC0j/wX/z9dr0ixhq7JUqyNkT7/5S0vaBlu5Hv7t4+3FGuhkvdN5SR/L95vtEmKmetKH3kBYJ2D/0+ngwI6WLcC9933BwcHG8SnTJmCqVOnGn3PsWPHEBkZifz8fHh4eGDTpk0IDw9HfHw8lEolvL29DfL9/PyQmir/H1JaK1asQP369dGmTRuD+PTp09GxY0e4ublh27ZteOutt5CTk4NRo0bZbNuWcvCvEBER0ePNlqfhkpOTodHcWz1dpZL/GL6rbt26iI+PR2ZmJjZs2IABAwZgz549VvXDXLdv38batWvx7rvvSq/dH2vWrBlyc3Mxd+5cuxZLPA1HRET0mLh7ddvdh6liSalUonbt2oiIiMDs2bPRpEkTLFq0CP7+/igsLERGRoZBflpams3mEW3YsAF5eXno37//Q3NbtWqFy5cvm3VKsaywWCIiIrKju4tSWvuwlk6nQ0FBASIiIuDi4oKdO3fqX0tISMClS5cQGRlp9XaAO6fgnn32WVStKt/n8kHx8fGoXLmyycKvrPE0HBERkR3phAI6YeWcJQvfHxMTg+7duyMkJATZ2dlYu3Ytdu/eja1bt8LLywuDBw/GuHHj4OPjA41Gg5EjRyIyMlJ/JRwAJCYmIicnB6mpqbh9+zbi4+MBAOHh4VAqlbhy5Qo6deqE1atXo2XLlgbv27t3L3799VepXz/99BPS0tLQunVrqNVqbN++HbNmzcKECRNKd2BshMUSERFRBZOeno7+/fsjJSUFXl5eaNy4MbZu3YouXboAABYsWAAnJyf06dMHBQUFiIqKwieffGLQxpAhQwzmODVr1gwAkJSUhLCwMBQVFSEhIQF5eXkG71u5ciWCgoLQtWtXqV8uLi5YsmQJxo4dCyEEateujfnz52Po0KG2PgQWYbFERERkRzobnEazdFHKFStWmHxdrVZjyZIlWLJkSYk5u3fvNtlGWFiY0Uv+Z82ahVmzZhl9T7du3dCtWzeT7doDiyUiIiI70gkn6Ky8Gs7a95NpPLpEREREJnBkiYiIyI60UEBr5aKU1r6fTKuQxZK46A6hVt8LKOVzqknZPlIsT6OUYlcKvKVYtqtaip297SfFaikNV+KOz5VX3PZ1llfGNZbn7SyvDn3ydnUpVrWSvILu+dvypZs1lfJKwFfzK0uxhurLzCtlniP3jXm2zzO3LVv+TgGAuNwwKWbs98W+7CekmAtOSbG9OfWkWJFHghTbmdVAikFzQgqdzA6QYm/EvSbFlkV8JcXqrJ8hxdyL3eXtGtG9ziQptvnsh2a919Z4Gs7xVchiiYiIyFFoYf3IkHxDHLIllqJEREREJnBkiYiIyI54Gs7xsVgiIiKyI1veSJfKBo8uERERkQkcWSIiIrIjAQV0Vk7wFlw6oEyxWCIiIrIjnoZzfDy6RERERCZUyJElRWguFG73rUqR4irl1PC8KcXcnAqlmL8q06xtVlPKi0E+eOPEyi7yQnHGmJvn6yIvaOms0EkxY/tgLC9QfUuKKRXFZuWZ256t88ztn63zzOmfvY6JvY6do+9vWeeZ25Ytf6cAxn9fGMvzqJQvxYrgLMXcnAvMas9Y/6o650ixFt4XpFh799NSbMLffaXYjGbnpdgsVyM3YRXyKaq8RHkxXnvRCQV0RvpoaRtUdipksUREROQotHAyWnBa2gaVHR5dIiIiIhM4skRERGRHPA3n+FgsERER2ZEOTtBZeaLH2veTaSyWiIiI7EgrFNBaOTJk7fvJNJaiRERERCZwZImIiMiOOGfJ8bFYIiIisiMhnKCzcgVuwRW8y1SFLJbERXcItfpeQCmknKRsHymWp1FKsav5laVYQ/XlUuXZsi3mOX6eI/eNebbPc+S+lUVecr78O/SG2l2KpRZ4ye9V+sqxPHm71VyqSLHMa55SDAVyIRGQat7ivkRABS2WiIiIHIUWCmitvBGute8n01gsERER2ZFOWD/nSCefICEb4klOIiIiIhM4skRERGRHOhtM8Lb2/WQaiyUiIiI70kEBnZVzjqx9P5nGYomIiMiOuIK34+O4HREREZEJHFkiIiKyI85ZcnwVslhShOZC4aa9F0hxlXJqeN6UYm5OhVIsUH1LijkrdKXKM5ajVBSb1dbjklfaY/eo+mfL/bD1vjr6sXP0/S3rPEfuW1nkVVdlSDG1okiK1XFNk2LBLjekWGvv81Is3MhimGqvfClWXCwXEsLZcU5b6WCD251wzlKZYilKREREZEKFHFkiIiJyFMIGV8MJjiyVKRZLREREdqQTNjgNx6vhyhRPwxERERGZwJElIiIiO+LVcI6PxRIREZEd8TSc42MpSkRERGQCR5aIiIjsiPeGc3wsloiIiOyIp+EcX4UslsRFdwi1+l5AKaScpGwfKZanUUqxq/mVpVhDI6vKmpNny7aY5/h5jtw35tk+z5H7VhZ5Vwq8pVi2q1qKnb3tJ8UCXeRVwnffeEKKpXh6SbGiJA8p5lQoFxLb9o+VYvbCYsnxmVUsNW/e3KJGFQoFfvzxR1SvXr1UnSIiIiJyFGYVS/Hx8Rg/fjw8POSK/UFCCMyZMwcFBQVWd46IiOhxx5Elx2f2abiJEyeiWrVqZuV+9NFHpe4QERFRRcJiyfGZtXRAUlISqlatanajJ0+eRGhoaKk7RURERGVn6dKlaNy4MTQaDTQaDSIjI7F582b96/n5+YiOjoavry88PDzQp08fpKWlGbQxatQoREREQKVSoWnTpg/d5oULF6BQKIw+1q9fr8+7dOkSevToATc3N1SrVg0TJ05EcXGxzfa9NMwqlkJDQ6FQmF+1BgcHw9nZudSdMmb27Nl48skn4enpiWrVqqF3795ISEiw6TaIiIgeNYF7yweU9iFfpmRaUFAQ5syZg7i4OBw+fBgdO3ZEr169cOLECQDA2LFj8dNPP2H9+vXYs2cPrl69iueff15q5/XXX0ffvn3N2mZwcDBSUlIMHtOmTYOHhwe6d+8OANBqtejRowcKCwvx559/4ssvv8QXX3yB9957z8I9tK1SXQ2XkZGBgwcPIj09HTqdzuC1/v3726RjD9qzZw+io6Px5JNPori4GP/+97/RtWtXnDx5Eu7u7mWyTSIiorJmj9NwPXv2NHg+c+ZMLF26FPv370dQUBBWrFiBtWvXomPHjgCAVatWoX79+ti/fz9at24NAFi8eDEA4Nq1azh69OhDt+ns7Ax/f3+D2KZNm/DSSy/p50Rv27YNJ0+exI4dO+Dn54emTZvi/fffx9tvv42pU6dCqZSvSn8ULC6WfvrpJ/Tr1w85OTnQaDQGI04KhaLMiqUtW7YYPP/iiy9QrVo1xMXFoV27dmWyTSIiovIkKyvL4LlKpYJKpTL5Hq1Wi/Xr1yM3NxeRkZGIi4tDUVEROnfurM+pV68eQkJCEBsbqy+WrBUXF4f4+HgsWbJEH4uNjUWjRo3g53dvSYmoqCi8+eabOHHiBJo1a2aTbVvK4tudjB8/Hq+//jpycnKQkZGBW7du6R83b94siz4alZmZCQDw8ZHXQ7qroKAAWVlZBg8iIiJHcndkydoHcOdUl5eXl/4xe/bsErd77NgxeHh4QKVS4Y033sCmTZsQHh6O1NRUKJVKeHt7G+T7+fkhNTXVZvu9YsUK1K9fH23atNHHUlNTDQqlu9u9+5q9WDyydOXKFYwaNQpubm5l0R+z6HQ6jBkzBm3btkXDhg1LzJs9ezamTZsmxRWhuVC4ae8FUlylnBqecuHn5lQoxQLV8uJpzgpdqfKM5SgV8qS2xzmvtMfuUfXPlvth631lnmPnOXLfyiLPX5UpxYyprsowK89beVuKZRXLi1wqM+TTUU5FZm3Cbmx5Gi45ORkajUYfNzWqVLduXcTHxyMzMxMbNmzAgAEDsGfPHqv6Ya7bt29j7dq1ePfddx/J9qxl8chSVFQUDh8+XBZ9MVt0dDSOHz+OdevWmcyLiYlBZmam/pGcnPyIekhERPTo3b267e7DVLGkVCpRu3ZtREREYPbs2WjSpAkWLVoEf39/FBYWIiMjwyA/LS1NmnNUWhs2bEBeXp40dcff31+66u7uc1ttuzTMGln68ccf9f/u0aMHJk6ciJMnT6JRo0ZwcXExyH322Wdt28MHjBgxAj///DP27t2LoKAgk7nmnKslIiKyJ0dZZ0mn06GgoAARERFwcXHBzp070adPHwBAQkICLl26hMjISKu3A9w5Bffss89KyxJFRkZi5syZSE9P16/tuH37dmg0GoSHh9tk26VhVrHUu3dvKTZ9+nQpplAooNVqpbgtCCEwcuRIbNq0Cbt370aNGjXKZDtERESPkhAKCCuLHUvfHxMTg+7duyMkJATZ2dlYu3Ytdu/eja1bt8LLywuDBw/GuHHj4OPjA41Gg5EjRyIyMtJgcndiYiJycnKQmpqK27dvIz4+HgAQHh4OpVKJK1euoFOnTli9ejVatmxp8L69e/fi119/lfrVtWtXhIeH47XXXsOHH36I1NRUvPPOO4iOjrbr4IdZxdKDywPYQ3R0NNauXYsffvgBnp6e+oleXl5ecHWV5xwRERGVB3fXSrK2DUukp6ejf//+SElJgZeXFxo3boytW7eiS5cuAIAFCxbAyckJffr0QUFBAaKiovDJJ58YtDFkyBCDOU53r1RLSkpCWFgYioqKkJCQgLy8PIP3rVy5EkFBQejatavUL2dnZ/z888948803ERkZCXd3dwwYMMDoAM2jZPEE79WrV6Nv375ShVdYWIh169aV2dIBS5cuBQB06NDBIL5q1SoMHDiwTLZJRET0OFqxYoXJ19VqNZYsWWJwWf+Ddu/ebbKNsLAwCCEvlzlr1izMmjWrxPeFhoYaHXWyJ4sneA8aNEh/2f79srOzMWjQIJt0yhghhNEHCyUiIirPbLl0AJUNi0eWhBBGb31y+fJleHl52aRTREREFYU95iyRZcwulpo1a6a/4V2nTp1QqdK9t2q1WiQlJaFbt25l0kkiIiIiezG7WLp7RVx8fDyioqL093EB7qzVEBYWpr/E0NGJi+4Q6vsWM1PK51STsuWVwfM08j1pruZXlmIN1ZdLlWfLtpjn+HmO3Dfm2T7PkftmbV5MuDy/ZPNf/cxqLzGvmhQLUsqLAsenB0oxN6W82qT7Vfn3ucLSu8w+Yo6ydACVzOxiacqUKdBqtQgLC0PXrl0REBBQlv0iIiKqEHgazvFZNMHb2dkZw4cPR35+fln1h4iIiMihWHw1XMOGDXH+/Pmy6AsREVGFI2xwJRxHlsqWxcXSjBkzMGHCBPz8889ISUlBVlaWwYOIiIjMJwAIYeXD3jvxmLN46YCnn34awJ17wN2/hMDdJQXK6nYnRERERPZgcbG0a9eusugHERFRhaSDAopHfLsTsozFxVL79u3Loh9EREQVEq+Gc3wWF0sAkJGRgRUrVuDUqVMAgAYNGuD111/nCt5EREQW0gkFFFxnyaFZXCwdPnwYUVFRcHV1RcuWLQEA8+fPx8yZM7Ft2zY0b97c5p20NUVoLhRu982tSnGVcmp4youiuTkVSrFA9S0pplQUm5XnrNBZnGPtNu2VZ+5+2DrPkY+LvY4J8+yTZ01bOiPX4rg6y7+PEgv8pdj5HF8p9l1RSymWcEteHPJ6obsUy9e6SLF5p6KkmHslTylmzBPuqVLM0+m2FKvsKscKtPJ/Ya435Hmzv/9voll9ISqJxcXS2LFj8eyzz+Kzzz7T3/KkuLgYQ4YMwZgxY7B3716bd5KIiOhxdfeKNmvboLJTqpGl+wslAKhUqRImTZqEFi1a2LRzREREjzvOWXJ8Fq+zpNFocOnSJSmenJwMT0/zhl2JiIiIyguLi6W+ffti8ODB+Pbbb5GcnIzk5GSsW7cOQ4YMwSuvvFIWfSQiInps3R1ZsvZBZcfi03Dz5s2DQqFA//79UVx8ZxKri4sL3nzzTcyZM8fmHSQiInqc8Wo4x2dxsaRUKrFo0SLMnj0b586dAwDUqlULbm5uNu8cERERkb2Vap0lAHBzc0OjRo1s2RciIqIKh1fDOT6Li6Xc3FzMmTMHO3fuRHp6OnQ6w3VCzp8/b7POERERPe7uFEvWXg1no86QURYXS0OGDMGePXvw2muvISAgwOBmukRERESPG4uLpc2bN+OXX35B27Zty6I/j4S46A6hVt8LKOWSPCnbR4rlaZRS7Gp+ZSnWUH25VHm2bIt5jp/nyH1jnu3zzG0rOV/+3VNTeU2KncqSV+su8nSWYmfS5JW5fTW5UiztqrcUc1LIvxvzCuUVvOt4pEuxlHz59lf57vJ7T+cGSLGqlbKl2MVUeSVyoZP/WK9zrUCKOTqus+T4LC6WKleuDB8f+YeZiIiILCf+/2FtG1R2LF5n6f3338d7772HvLy8sugPERFRhcJ1lhyfxSNLH330Ec6dOwc/Pz+EhYXBxcVwWPWvv/6yWeeIiIiI7M3iYql3795l0A0iIqIKiufhHJ7FxdKUKVPMyvvmm2/w7LPPwt3d3eJOERERVRi2OI3G03BlyuI5S+YaPnw40tLSyqp5IiIiokei1Ct4P4zgCllEREQPxRW8HV+ZFUtERET0cFxnyfFVyGJJEZoLhZv2XiDFVcqp4XlTirk5FUqxQPUtKaZUFJuV56zQWZxjbZ65fbN1nq33w9H315w8Rz92zLNtnrlthalvSDFP59tSrLpbhhQzpui2/Gs+NUdeIFN1VV4wMvV2FSnm7Csv+ngxT14wsppKXljSGfL+NnC/KsXCXK5Lsaq+cnuFWnkRTkBlJEZknQpZLBERETkMobB+gjZHlsoUiyUiIiI74pwlx1dmV8OFhoZKC1YSERERlTcWjywlJydDoVAgKCgIAHDw4EGsXbsW4eHhGDZsmD7v+PHjtuslERHR44qLUjo8i0eWXn31VezatQsAkJqaii5duuDgwYP4z3/+g+nTp9u8g0RERI8z3hvO8VlcLB0/fhwtW7YEAHz33Xdo2LAh/vzzT6xZswZffPGFrftHRET0+BNWPqhMWVwsFRUVQaW6c2nmjh078OyzzwIA6tWrh5SUFNv2joiIiMjOLC6WGjRogGXLluH333/H9u3b0a1bNwDA1atX4esrr7VBREREJeNpOMdn8QTvDz74AM899xzmzp2LAQMGoEmTJgCAH3/8UX96ztGJi+4QavW9gFIew0zK9pFieRqlFLuaLy/u1lB9uVR5tmyLeY6f58h9Y57t88xt60K+/EdnbVWqFEvKkfMC3TKlGPKM/Jp31UohpyI5TajlPG2hsYUgZekFnlKsSMh9OZUbIMUCXeQFPK9dl9vTGemLX16uWf1zKJzg7fAsLpY6dOiA69evIysrC5Ur3/vhHzZsGNzc3GzaOSIiIiJ7K9U6S0IIxMXF4dNPP0V29p0l6JVKJYslIiIiiyls9KCyYvHI0sWLF9GtWzdcunQJBQUF6NKlCzw9PfHBBx+goKAAy5YtK4t+EhERPZ54Gs7hWTyyNHr0aLRo0QK3bt2Cq+u9G9A+99xz2Llzp007R0RERLa3dOlSNG7cGBqNBhqNBpGRkdi8ebP+9fz8fERHR8PX1xceHh7o06cP0tLSDNoYNWoUIiIioFKp0LRpU7O3HRsbi44dO8Ld3R0ajQbt2rXD7dv3bhYdFhYGhUJh8JgzZ47V+2wNi0eWfv/9d/z5559QKg0nO4eFheHKlSs26xgREVGFYIeRpaCgIMyZMwd16tSBEAJffvklevXqhSNHjqBBgwYYO3YsfvnlF6xfvx5eXl4YMWIEnn/+eezbt8+gnddffx0HDhzA0aNHzdpubGwsunXrhpiYGHz88ceoVKkS/v77bzg5GY7dTJ8+HUOHDtU/9/SUJ/jfLysry8w9BzQajdm5d1lcLOl0Omi18hUSly9ffujOEBER0QOE4s7D2jYs0LNnT4PnM2fOxNKlS7F//34EBQVhxYoVWLt2LTp27AgAWLVqFerXr4/9+/ejdevWAIDFixcDAK5du2Z2sTR27FiMGjUKkydP1sfq1q0r5Xl6esLf39/s/fH29oZCYd4xMFbDPIzFp+G6du2KhQsX6p8rFArk5ORgypQpePrppy3uABEREdmPVqvFunXrkJubi8jISMTFxaGoqAidO3fW59SrVw8hISGIjY0t9XbS09Nx4MABVKtWDW3atIGfnx/at2+PP/74Q8qdM2cOfH190axZM8ydOxfFxcUm2961axd+++03/Pbbb1i5ciWqVauGSZMmYdOmTdi0aRMmTZoEPz8/rFy5slR9t3hk6aOPPkJUVBTCw8ORn5+PV199FWfPnkWVKlXwzTfflKoTREREFZUQdx7WtgHIp6NUKpX+rhsPOnbsGCIjI5Gfnw8PDw9s2rQJ4eHhiI+Ph1KphLe3t0G+n58fUlPlNb/Mdf78eQDA1KlTMW/ePDRt2hSrV69Gp06dcPz4cdSpUwfAnblQzZs3h4+PD/7880/ExMQgJSUF8+fPL7Ht9u3b6/89ffp0zJ8/H6+88oo+9uyzz6JRo0ZYvnw5BgwYYHHfLS6WgoKC8Pfff2PdunU4evQocnJyMHjwYPTr189gwrcjU4TmQuF23zBcitzvGp43pZibU6EUC1TLi6cpFXIFbCzPWaGzOMfaPHP7VtHy7HGc7dU35tknz9y2wtQ3pJinU74UC3WX2/NRGlmQ0cgClBcGvC3nmaneRvNumB7sJvfPmEB1hhRzMnJcKqnk/dBVkisMhbVVhz3YcM5ScHCwQXjKlCmYOnWq0bfUrVsX8fHxyMzMxIYNGzBgwADs2bPHyo6UTKe787kOHz4cgwYNAgA0a9YMO3fuxMqVKzF79mwAwLhx4/Tvady4MZRKJYYPH47Zs2eXWPjdLzY21uiV+S1atMCQIUNK1XeLiyUAqFSpEv71r3+VaoNERER0HxvOWUpOTjaYwGyquFAqlahduzYAICIiAocOHcKiRYvQt29fFBYWIiMjw2B0KS0tzaJ5RA8KCLizWnt4eLhBvH79+rh06VKJ72vVqhWKi4tx4cIFo/ObHhQcHIzPPvsMH374oUH8888/l4pJc5WqWPrqq6/w6aef4vz584iNjUVoaCgWLFiAmjVrolevXqXqCBEREVnn7lIApaHT6VBQUICIiAi4uLhg586d6NOnDwAgISEBly5dQmRkZKn7FhYWhsDAQCQkJBjEz5w5g+7du5f4vvj4eDg5OaFatWpmbWfBggXo06cPNm/ejFatWgEADh48iLNnz+L7778vVd8tnuC9dOlSjBs3Dt27d8etW7f0s8orV65sMPGbiIiIHk4hbPOwRExMDPbu3YsLFy7g2LFjiImJwe7du9GvXz94eXlh8ODBGDduHHbt2oW4uDgMGjQIkZGR+ivhACAxMRHx8fFITU3F7du3ER8fj/j4eBQW3pmycuXKFdSrVw8HDx68s58KBSZOnIjFixdjw4YNSExMxLvvvovTp09j8ODBAO6cQlu4cCH+/vtvnD9/HmvWrMHYsWPxr3/9y+AWa6Y8/fTTOHPmDHr27ImbN2/i5s2b6NmzJ86cOVPqC9EsHln6+OOP8dlnn6F3794Gi0S1aNECEyZMKFUniIiIKiw7rLOUnp6O/v37IyUlBV5eXmjcuDG2bt2KLl26ALgzOuPk5IQ+ffqgoKAAUVFR+OSTTwzaGDJkiMEcp2bNmgEAkpKSEBYWhqKiIiQkJCAvL0+fM2bMGOTn52Ps2LG4efMmmjRpgu3bt6NWrVoA7pw2XLduHaZOnYqCggLUqFEDY8eONZjHZI7g4GDMmjXLsoNigsXFUlJSkv6A3E+lUiE3txze7ZmIiKiCWbFihcnX1Wo1lixZgiVLlpSYs3v3bpNthIWFQRiZcD958mSDdZbu17x5c+zfv99ku+b4/fff9dOF1q9fj+rVq+Orr75CjRo18I9//MPi9iw+DVejRg3Ex8dL8S1btqB+/foWd4CIiKhCuzvB29oHAQC+//57REVFwdXVFX/99RcKCgoAAJmZmaUebbK4WBo3bhyio6Px7bffQgiBgwcPYubMmYiJicGkSZNK1QkiIqIKS9joQQCAGTNmYNmyZfjss8/g4uKij7dt2xZ//fVXqdq0+DTckCFD4OrqinfeeQd5eXl49dVXERgYiEWLFuHll18uVSeIiIiIbCEhIQHt2rWT4l5eXsjIyChVmxYVS8XFxVi7di2ioqLQr18/5OXlIScnx+zL+YiIiOgBdpjg/Tjz9/dHYmIiwsLCDOJ//PEHatasWao2LSqWKlWqhDfeeAOnTp0CALi5ucHNza1UG7YncdEdQq2+F1DK37KkbB8plqdRSrGr+fKljA3Vl0uVZ8u2mOf4eY7cN+bZPs/cti7k+0qx2ir5FhPJed5SzKjbzublmakgz0WKVXKSV9dOzpP3t5Gb/N5DN0OlWI6XWorpLsr/1zgXyvN0FBnXpJjDY7FkU0OHDsXo0aOxcuVKKBQKXL16FbGxsZgwYQLefffdUrVp8Wm4li1b4siRIwgNlb/gRERERPY0efJk6HQ6dOrUCXl5eWjXrh1UKhUmTJiAkSNHlqpNi4ult956C+PHj8fly5cREREBd3d3g9cbN25cqo4QERFVSDa83UlFp9VqsW/fPkRHR2PixIlITExETk4OwsPD4eHhUep2LS6W7k7iHjVqlD6mUCgghIBCodCv6E1EREQPV5oVuI21QYCzszO6du2KU6dOwdvbW7oPXWmValFKIiIishHOWbKphg0b4vz586hRo4bN2rR4naXQ0FCTj7K2ZMkShIWFQa1Wo1WrVvp7zhARERHNmDEDEyZMwM8//4yUlBRkZWUZPErD4pGlH3/80WhcoVBArVajdu3aNq3m7vftt99i3LhxWLZsGVq1aoWFCxciKioKCQkJXL6AiIiI9DfLffbZZ6FQ3JvLZc10IYuLpd69e+vnKN3v/nlL//jHP/C///3P7DsEm2v+/PkYOnQoBg0aBABYtmwZfvnlF6xcubLE+8wQERE5MgVsMGfJJj15POzatcvmbVpcLG3fvh3/+c9/MHPmTLRs2RIAcPDgQbz77rt455134OXlheHDh2PChAkPvVGfJQoLCxEXF4eYmBh9zMnJCZ07d0ZsbKzR9xQUFOjvCQOg1MNvREREVD60b9/e5m1aXCyNHj0ay5cvR5s2bfSxTp06Qa1WY9iwYThx4gQWLlyI119/3aYdvX79OrRaLfz8/Azifn5+OH36tNH3zJ49G9OmTZPiitBcKNzuG4ZLcZVyanjelGJuToVSLFB9S4opFcVm5TkrdBbnWJtnbt8qWp6tj7M5efbYJvPsl2duW2HqG1KsqnO2FGvunSzFItzlC3CS6skL7L52YIgUO3Y9QIoFazKkWOvaBVKsheaiFMsX8n8vnk63pVgVda4Uy9PKi1cqs+SxE6ciKQTddfl3t8Pj0gE2d+vWLaxYsUK/iHZ4eDgGDRoEHx/558EcFk/wPnfuHDQajRTXaDQ4f/48AKBOnTq4fv16qTpkSzExMcjMzNQ/kpPlXy5ERER2xRvp2tTevXsRFhaGxYsX49atW7h16xYWL16MGjVqYO/evaVq0+KRpYiICEycOBGrV69G1apVAQDXrl3DpEmT8OSTTwIAzp49i+Dg4FJ1qCRVqlSBs7Mz0tLSDOJpaWnw9/c3+h6VSgWVSmXTfhAREZHjio6ORt++fbF06VI4O9+53Y9Wq8Vbb72F6OhoHDt2zOI2LR5ZWrFiBZKSkhAUFITatWujdu3aCAoKwoULF/D5558DAHJycvDOO+9Y3BlTlEolIiIisHPnTn1Mp9Nh586diIyMtOm2iIiIHhmOLNlUYmIixo8fry+UgDuLVY4bNw6JiYmlatPikaW6devi5MmT2LZtG86cOaOPdenSBU5Od2qv3r17l6ozDzNu3DgMGDAALVq0QMuWLbFw4ULk5ubqr44jIiIqb7iCt201b94cp06dQt26dQ3ip06dQpMmTUrVpsXFEnDnKrRu3bqhQ4cOUKlUBusYlKW+ffvi2rVreO+995CamoqmTZtiy5Yt0qRvIiIiqphGjRqF0aNHIzExEa1btwYA7N+/H0uWLMGcOXNw9OhRfa6597O1uFjS6XSYOXMmli1bhrS0NJw5cwY1a9bEu+++i7CwMAwePNjSJi0yYsQIjBgxoky3QURE9Mjwdic29corrwAAJk2aZPS10tzP1uJiacaMGfjyyy/x4YcfYujQofp4w4YNsXDhwjIvloiIiB4rLJZsqizuYWtxsbR69WosX74cnTp1whtvvKGPN2nSpMT1joiIiMg4zlmyLXPvU9ujRw98/vnnCAiQ1xh7kMXF0pUrV1C7dm0prtPpUFRkZIUwByQuukOo1fcCSvlblpQtL1yVp1FKsav58i1dGqovlyrPlm0xz/HzHLlvzLN9nrltXSnwlmJ5rvLvnlvFblKsyMhCkB4u8mK6Ia7ywo0X1XL/antek2KX8+Q8lZHVIU9ly/8BBbnI241PD5Ribkq5Pfer8u9pI2t6QpeXJweJjNi7dy9u35YXSjXG4qUDwsPD8fvvv0vxDRs2oFmzZpY2R0REVLHdXcHb2geVGYtHlt577z0MGDAAV65cgU6nw8aNG5GQkIDVq1fj559/Los+EhERPb44Z8nhWTyy1KtXL/z000/YsWMH3N3d8d577+HUqVP46aef0KVLl7LoIxEREZHdlGqdpX/+85/Yvn27rftCRERU4XCCt+MrVbFERERENsLTcA7PrGKpcuXKZq/SffOmfLUDERERkSP597//DR8f+cp3Y8wqlhYuXKj/940bNzBjxgxERUXpb2AbGxuLrVu34t1337W8t0RERBWZDU7DcWTpni+//BJVqlRBjx49ANxZyXv58uUIDw/HN998o1+HKSYmxuw2zSqWBgwYoP93nz59MH36dINbjowaNQr//e9/sWPHDowdO9bsjRMREVV4PA1nU7NmzcLSpUsB3BnMWbJkCRYsWICff/4ZY8eOxcaNGy1u0+I5S1u3bsUHH3wgxbt164bJkydb3AF7UITmQuF23/1gUlylnBqe8ulENyd5cbdA9S0pplQUm5Xn/MCKaubkWLtN5tn2M7Mmz5ZtMc/x88xtq7oqQ4q5KeTfPQ3dr0ixpio5pvU7LMWeVCdLsQZu8nvrKVOkWLxbiBSro0yVYsb4V8qUYmHe8nFxMjLMsunzOWZtAyvNS6PHV3Jysn7x7P/973/o06cPhg0bhrZt26JDhw6latPipQN8fX3xww8/SPEffvgBvr6+peoEERFRhSVs9CAAgIeHB27cuAEA2LZtm35ZI7VabfaK3Q+yeGRp2rRpGDJkCHbv3o1WrVoBAA4cOIAtW7bgs88+K1UniIiIKiouHWBbXbp0wZAhQ9CsWTOcOXMGTz/9NADgxIkTZt837kEWjywNHDgQ+/btg0ajwcaNG7Fx40ZoNBr88ccfGDhwYKk6QURERGQLEydORKtWrXDt2jV8//33+rNecXFx6NevX6naLNU6S61atcKaNWtKtUEiIiKishIREYGUlBRUq1bNID5y5Ej4+fnh3//+t8VtmjWylJWVZVGj2dnZFneEiIioQuKcJZsSQhhdGzI3NxdqtbpUbZq9KKWxKq0k1atXR3x8PGrWrFmqThEREVUUnLNkG+PGjQMAKBQKvPvuu3Bzc9O/ptVqceDAATRt2rRUbZtVLAkh8Pnnn8PDw8OsRouKikrVGSIiIqLSOHLkCIA7NcuxY8egVCr1rymVSjRp0gQTJkwoVdtmFUshISEWXenm7+8PFxeXUnWIiIiowuHIkNV27doFABg0aBAWLVoEjUZjs7bNKpYuXLhgsw0SERHRfbiCt02tWrXK5m2W6mq48k5cdIe4f5KXUv6WJWXLN9fL0yil2NX8ylKsofpyqfJs2RbzHD/PkfvGPNvnmdvWlQJvKZbtKk9KPXvbT4rVUqZJsfhcecVtX2f5Ihxjed7OuVLs5O3qUqxqJfkioOO55uUl3qgixVwqaaUYkT1VyGKJiIjIUXCCt+NjsURERGRPPA3n8CxewZuIiIioImGxREREZEd3T8NZ+7DE0qVL0bhxY2g0Gmg0GkRGRmLz5s361/Pz8xEdHQ1fX194eHigT58+SEsznBM3atQoREREQKVSWbR+UWxsLDp27Ah3d3doNBq0a9fO4Aa3N2/eRL9+/aDRaODt7Y3BgwcjJyfHsh20sVIVS7///jv+9a9/ITIyEleuXAEAfPXVV/jjjz9s2jkiIqLHnh1W8A4KCsKcOXMQFxeHw4cPo2PHjujVqxdOnDgBABg7dix++uknrF+/Hnv27MHVq1fx/PPPS+28/vrr6Nu3r9nbjY2NRbdu3dC1a1ccPHgQhw4dwogRI+DkdK8c6devH06cOIHt27fj559/xt69ezFs2DDLdtDGLJ6z9P333+O1115Dv379cOTIERQUFAAAMjMzMWvWLPz666827yQREdFjyw5zlnr27GnwfObMmVi6dCn279+PoKAgrFixAmvXrkXHjh0B3Lkcv379+ti/fz9at24NAFi8eDEA4Nq1azh69KhZ2x07dixGjRqFyZMn62N169bV//vUqVPYsmULDh06hBYtWgAAPv74Yzz99NOYN28eAgMDLdtRG7F4ZGnGjBlYtmwZPvvsM4OFJ9u2bYu//vrLpp0jIiIi82VlZRk87g5omKLVarFu3Trk5uYiMjIScXFxKCoqQufOnfU59erVQ0hICGJjY0vdt/T0dBw4cADVqlVDmzZt4Ofnh/bt2xuclYqNjYW3t7e+UAKAzp07w8nJCQcOHCj1tq1lcbGUkJCAdu3aSXEvLy9kZGTYok9EREQVhi3nLAUHB8PLy0v/mD17donbPXbsGDw8PKBSqfDGG29g06ZNCA8PR2pqKpRKJby9vQ3y/fz8kJqaWur9PH/+PABg6tSpGDp0KLZs2YLmzZujU6dOOHv2LAAgNTVVug9tpUqV4OPjY9W2rWXxaTh/f38kJiYiLCzMIP7HH3+UmxvnKkJzoXC7b9GzFFcpp4bnTSnm5lQoxQLVt6SYUlFcqjxbtvW45zkrdHZpz5Z59tgm8+yXZ25b/qpMKWZMNaW8wKPWyN+/lV3khSXNzTPG10Ve0NLYftR0vSbFQirJv1dbBCTLea5yXvg7C6TYyRljS+xnuWLD03DJyckGt/lQqVQlvqVu3bqIj49HZmYmNmzYgAEDBmDPnj1WdqRkOt2d78nw4cMxaNAgAECzZs2wc+dOrFy50mRhZ28WF0tDhw7F6NGjsXLlSigUCly9ehWxsbGYMGEC3n333bLoIxEREZnh7tVt5lAqlahduzYAICIiAocOHcKiRYvQt29fFBYWIiMjw2B0KS0tDf7+/qXuW0BAAAAgPDzcIF6/fn1cunQJwJ0BmfT0dIPXi4uLcfPmTau2bS2LT8NNnjwZr776Kjp16oScnBy0a9cOQ4YMwfDhwzFy5Miy6CMREdHjyw5Xwxmj0+lQUFCAiIgIuLi4YOfOnfrXEhIScOnSJURGRpa6/bCwMAQGBiIhIcEgfubMGYSGhgIAIiMjkZGRgbi4OP3rv/32G3Q6HVq1alXqbVvL4pElhUKB//znP5g4cSISExORk5OD8PBweHh4lEX/iIiIHmv2uN1JTEwMunfvjpCQEGRnZ2Pt2rXYvXs3tm7dCi8vLwwePBjjxo2Dj48PNBoNRo4cicjISP2VcAD0NUBqaipu376N+Ph4AHdGjpRKJa5cuYJOnTph9erVaNmyJRQKBSZOnIgpU6agSZMmaNq0Kb788kucPn0aGzZsAHBnlKlbt24YOnQoli1bhqKiIowYMQIvv/yy3a6EA6y43YlSqZSG0oiIiMjxpaeno3///khJSYGXlxcaN26MrVu3okuXLgCABQsWwMnJCX369EFBQQGioqLwySefGLQxZMgQgzlOzZo1AwAkJSUhLCwMRUVFSEhIQF5enj5nzJgxyM/Px9ixY3Hz5k00adIE27dvR61atfQ5a9aswYgRI9CpUyd9H+4uU2AvZhVLxhaiKsnGjRtL3RkiIqIKxw7rLK1YscLk62q1GkuWLMGSJUtKzNm9e7fJNsLCwiCE3LHJkycbrLP0IB8fH6xdu9Zk24+aWcWSl5eX/t9CCGzatAleXl76dRDi4uKQkZFhUVFFRERE9jkNR5Yxq1hatWqV/t9vv/02XnrpJSxbtgzOzs4A7ixo9dZbb5k9A5+IiIiovLD4ariVK1diwoQJ+kIJAJydnTFu3DisXLnSpp0jIiJ67DnI1XBUMosneBcXF+P06dMG93IBgNOnT+sXnHJ04qI7hFp9L6CUv2VJ2T5SLE+jlGJX8ytLsYbqy6XKs2VbzHP8PEfuG/Nsn2frbcaEy/fh/Ol841K3Z+u8i7erSLFw9RW5vTz5jIROKKSYKNVt38sJO8xZIstYXCwNGjQIgwcPxrlz59CyZUsAwIEDBzBnzhz9ipxERERkHsX/P6xtg8qOxcXSvHnz4O/vj48++ggpKSkA7qzKOXHiRIwfP97mHSQiIiKyJ4uLJScnJ0yaNAmTJk1CVtadexNxYjcREVEp8TScwyv1opQAiyQiIiJrcekAx2dxsVSjRg0oFCWfHT1//rxVHSIiIiJyJBYXS2PGjDF4XlRUhCNHjmDLli2YOHGirfpFRERUMfA0nMOzuFgaPXq00fiSJUtw+PBhqztERERU4bDYcWg2W7mie/fu+P77723VHBEREZFDsGqC9/02bNgAHx95IUdHpAjNhcJNey+Q4irl1PC8KcXcnAqlWKD6lhRTKopLlWfLth5VnrNCXoiUeeblOXLfmGf7PFtvc/bJp6VYE1fH2FcACHW9LsX8nbOkWJTfSSn2tMdxKTbyKfmCouf2RUuxTW1LvvGro+IEb8dncbHUrFkzgwneQgikpqbi2rVr+OSTT2zaOSIiosce5yw5PIuLpV69ehkUS05OTqhatSo6dOiAevXq2bRzRERERPZmcbE0derUMugGERFRxcTTcI7P4gnezs7OSE9Pl+I3btyAs7OzTTpFRERUYQgbPajMWDyyJITxT6SgoABKpdLqDhEREVUkHFlyfGYXS4sXLwYAKBQKfP755/Dw8NC/ptVqsXfvXs5ZIiIioseO2cXSggULANwZWVq2bJnBKTelUomwsDAsW7bM9j0kIiJ6nPFqOIdndrGUlJQEAHjqqaewceNGVK5cucw6RUREVGGwWHJ4Fs9Z2rVrV1n045ESF90h1Op7AaX8LUvKlhfYzNPIc7Ku5stFY0P15VLl2bIt5jl+niP3jXm2z3PkvpVFXnK+/Dv0htrdrLxjLoFSLLtALcWKdLyoiB4Ns4qlcePG4f3334e7uzvGjRtnMnf+/Pk26RgREVFFwAnejs+sYunIkSMoKioCAPz1118Gi1ISERGRFXgazuGZVSzdf+pt9+7dZdUXIiIiIodj8aKUr7/+OrKzs6V4bm4uXn/9dZt0ioiIqKJQCGGTB5Udi4ulL7/8Erdv35bit2/fxurVq23SKSIiogqDK3g7PLOLpaysLGRmZkIIgezsbGRlZekft27dwq+//opq1aqVSScvXLiAwYMHo0aNGnB1dUWtWrUwZcoUFBYWlsn2iIiIiO4ye+kAb29vKBQKKBQKPPHEE9LrCoUC06ZNs2nn7jp9+jR0Oh0+/fRT1K5dG8ePH8fQoUORm5uLefPmlck2iYiIHgVeDef4zC6Wdu3aBSEEOnbsiO+//x4+PvfWxlAqlQgNDUVgoLw2hi1069YN3bp10z+vWbMmEhISsHTpUhZLRERUvvFqOIdndrHUvn17AHdW8g4ODoaTk8XTnWwqMzPToGAzpqCgAAUFBfrnWVlZZd0tIiIii3BkyfFZvIJ3aGgoACAvLw+XLl2S5g01btzYNj0zITExER9//PFDR5Vmz55t9NSgIjQXCjftvUCKq5RTw/OmFHNzkudIBapvSTFnha5UecZylIpis9qydV5p94F55uc5ct+YZ/s8R+5bWeRVV2VIMbWiSIqFu12VYv90vSLF8mvtlmId3ZKkWMRm+U4Lcd1nSjEiS1g8PHTt2jU888wz8PT0RIMGDdCsWTODhyUmT56snwdV0uP06dMG77ly5Qq6deuGF198EUOHDjXZfkxMDDIzM/WP5ORkS3eXiIiobPFqOIdn8cjSmDFjkJGRgQMHDqBDhw7YtGkT0tLSMGPGDHz00UcWtTV+/HgMHDjQZE7NmjX1/7569SqeeuoptGnTBsuXL39o+yqVCiqVyqI+ERERPUo8Def4LC6WfvvtN/zwww9o0aIFnJycEBoaii5dukCj0WD27Nno0aOH2W1VrVoVVatWNSv3ypUreOqppxAREYFVq1bZfc4UERERVQwWVxy5ubn69ZQqV66Ma9euAQAaNWqEv/76y7a9+39XrlxBhw4dEBISgnnz5uHatWtITU1FampqmWyPiIjokeFpOIdn8chS3bp1kZCQgLCwMDRp0gSffvopwsLCsGzZMgQEBJRFH7F9+3YkJiYiMTERQUFBBq8JLvFORETlHE+jOTaLR5ZGjx6NlJQUAMCUKVOwefNmhISEYPHixZg1a5bNOwgAAwcOhBDC6IOIiIioLFk8svSvf/1L/++IiAhcvHgRp0+fRkhICKpUqWLTzhERET32hLjzsLYNKjMWF0sPcnNzQ/PmzW3RFyIiogqHV8M5PrOKpXHjxpnd4Pz580vdmUdFXHSHUKvvBZTytywpW14dPE8jL3Z2Nb+yFGuovlyqPFu2xTzHz3PkvjHP9nmO3LeyyLtS4C3Fsl3VUuxsvr8UO62SL975KydUirkotFIsK8fIIsNfz5ZiSf+KkWJEJTGrWDpy5IhZjSkUCqs6Q0REVOHw3nAOz6xiadeuXWXdDyIiogpJobvzsLYNKjtWz1kiIiIiK3BkyeFxGWwiIqIKZunSpWjcuDE0Gg00Gg0iIyOxefNm/ev5+fmIjo6Gr68vPDw80KdPH6SlpRm0MWrUKEREREClUqFp06ZmbbdDhw7SPWDfeOMNgxxj94ldt26d1ftsDY4sERER2ZE9roYLCgrCnDlzUKdOHQgh8OWXX6JXr144cuQIGjRogLFjx+KXX37B+vXr4eXlhREjRuD555/Hvn37DNp5/fXXceDAARw9etTsbQ8dOhTTp0/XP3dzc5NyVq1ahW7duumfe3t7W7aDNsZiiYiIyJ7ssM5Sz549DZ7PnDkTS5cuxf79+xEUFIQVK1Zg7dq16NixI4A7xUv9+vWxf/9+tG7dGgCwePFiAMC1a9csKpbc3Nzg7y9fBXk/b2/vh+Y8SjwNR0REVIFptVqsW7cOubm5iIyMRFxcHIqKitC5c2d9Tr169RASEoLY2Firt7dmzRpUqVIFDRs2RExMDPLy8qSc6OhoVKlSBS1btsTKlSvtfscOjiwRERHZkS1Pw2VlZRnEVSoVVCqV0fccO3YMkZGRyM/Ph4eHBzZt2oTw8HDEx8dDqVRKp778/PysvoH9q6++itDQUAQGBuLo0aN4++23kZCQgI0bN+pzpk+fjo4dO8LNzQ3btm3DW2+9hZycHIwaNcqqbVujQhZLitBcKNzuW8wsxcgiZp43pZibU6EUC1TfkmLORq7hNCfPmraUiuIy7Zuj5Zm7v458XBz9GDPPcX6+y2OevypTihlTzSVLirlAXmyykbu88GUXNzl2tG68FAtR3TCyZQdalNKGV8MFBwcbhKdMmYKpU6cafUvdunURHx+PzMxMbNiwAQMGDMCePXus7Ihpw4YN0/+7UaNGCAgIQKdOnXDu3DnUqlULAPDuu+/qc5o1a4bc3FzMnTuXxRIRERFZLzk5GRqNRv+8pFElAFAqlahduzaAO/d6PXToEBYtWoS+ffuisLAQGRkZBqNLaWlpNp9H1KpVKwBAYmKivlgylvP++++joKDA5P6UJc5ZIiIisqO7p+GsfQDQLwVw92FJcaHT6VBQUICIiAi4uLhg586d+tcSEhJw6dIlREZG2nTf4+PjAQABAQEmcypXrmy3QgngyBIREZF92eFquJiYGHTv3h0hISHIzs7G2rVrsXv3bmzduhVeXl4YPHgwxo0bBx8fH2g0GowcORKRkZH6K+GAO6NBOTk5SE1Nxe3bt/WFT3h4OJRKJa5cuYJOnTph9erVaNmyJc6dO4e1a9fi6aefhq+vL44ePYqxY8eiXbt2aNy4MQDgp59+QlpaGlq3bg21Wo3t27dj1qxZmDBhgnXHx0osloiIiCqY9PR09O/fHykpKfDy8kLjxo2xdetWdOnSBQCwYMECODk5oU+fPigoKEBUVBQ++eQTgzaGDBliMMepWbNmAICkpCSEhYWhqKgICQkJ+qvdlEolduzYgYULFyI3NxfBwcHo06cP3nnnHX0bLi4uWLJkCcaOHQshBGrXro358+dj6NChZX1ITGKxREREZEf2WJRyxYoVJl9Xq9VYsmQJlixZUmLO7t27TbYRFhZmcMl/cHDwQyeQd+vWzWAxSkfBYomIiMieeG84h8diiYiIyI7sMbJEluHVcEREREQmVMiRJXHRHUKtvhdQyiV5UraPFMvTKKXY1fzKUqyhWl4ozZw8W7bFPMfPc+S+Mc/2eY7cN3vmXSnwlmLZrmopdiKvuhQLdpEXm/w7Q867opK38emYBVJM+8CveG1BvpRTJnTizsPaNqjMVMhiiYiIyGFwzpLD42k4IiIiIhM4skRERGRHCthggrdNekIlYbFERERkT3ZYwZssw9NwRERERCZwZImIiMiOuM6S42OxREREZE+8Gs7h8TQcERERkQkcWSIiIrIjhRBQWDlB29r3k2kVslhShOZC4aa9F0hxlXJqeN6UYm5OhVIsUH1LijkrdKXKs2Vbj3ueUlFs0zx77IejH2Pm8ef7UeT5qzKlmDHVVXJ7VZ1zpFjXqqekWEd3OfZ8ZI2HblOX96hW8P7/h7VtUJmpkMUSERGRo+DIkuPjnCUiIiIiEziyREREZE+8Gs7hsVgiIiKyJ67g7fB4Go6IiIjIBI4sERER2RFX8HZ8LJaIiIjsiafhHB5PwxERERGZUCFHlsRFdwi1+l5AKVfkSdk+UixPo5RiV/MrS7GG6sulyrNlW8xz/DxH7hvzbJ/nyH0rD3nJ+fLv5BtqdymWkOcnxXwqyYtXaq+rpRicDf8v0N1+NKM1Ct2dh7VtUNmpkMUSERGRw+BpOIfH03BEREREJnBkiYiIyJ64KKXDY7FERERkR7w3nONjsURERGRPnLPk8DhniYiIiMgEjiwRERHZkwBg7aX/HFgqUyyWiIiI7IhzlhxfhSyWFKG5ULhp7wVSXKWcGp43pZibU6EUC1TfkmLORlYHMyfPlm2V1zylotih82y5v47+WTCPP9+OlFddlSHF1IoiKdbSM0mKdXSTY183kBe+rORkuN3i3AIkS1lUEVXIYomIiMhhCNhggrdNekIlYLFERERkT7wazuHxajgiIiIiEziyREREZE86AAobtEFlhsUSERGRHfFqOMfHYomIiMieOGfJ4XHOEhEREZEJHFkiIiKyJ44sObwKWSyJi+4QavW9gFL+kiVl+0ixPI1Sil3NryzFGqrlxc7MybNlW8xz/DxH7hvzbJ/nyH0rD3lXCrylWLarWoodzgmTYr6VcqTY2Yv+UszJRWvwXJeXL+WUCRZLDo+n4YiIiIhMqJAjS0RERA6DSwc4PBZLREREdsSlAxwfT8MRERERmVDuiqWCggI0bdoUCoUC8fHx9u4OERGRde5O8Lb2YYGlS5eicePG0Gg00Gg0iIyMxObNm/Wv5+fnIzo6Gr6+vvDw8ECfPn2QlpZm0MaoUaMQEREBlUqFpk2bmrXdDh06QKFQGDzeeOMNg5xLly6hR48ecHNzQ7Vq1TBx4kQUFxdbtH+2Vu6KpUmTJiEwMNDe3SAiIrINnbDNwwJBQUGYM2cO4uLicPjwYXTs2BG9evXCiRMnAABjx47FTz/9hPXr12PPnj24evUqnn/+eamd119/HX379rVo20OHDkVKSor+8eGHH+pf02q16NGjBwoLC/Hnn3/iyy+/xBdffIH33nvPom3YWrmas7R582Zs27YN33//vUEFTERERObr2bOnwfOZM2di6dKl2L9/P4KCgrBixQqsXbsWHTt2BACsWrUK9evXx/79+9G6dWsAwOLFiwEA165dw9GjR83etpubG/z95aUbAGDbtm04efIkduzYAT8/PzRt2hTvv/8+3n77bUydOhVKpbyEz6NQbkaW0tLSMHToUHz11Vdwc3Ozd3eIiIhsw4an4bKysgweBQUFD928VqvFunXrkJubi8jISMTFxaGoqAidO3fW59SrVw8hISGIjY21enfXrFmDKlWqoGHDhoiJiUFeXp7+tdjYWDRq1Ah+fn76WFRUFLKysvSjXvZQLkaWhBAYOHAg3njjDbRo0QIXLlww630FBQUGX5SsrKwy6iEREVFp2WBRStx5f3BwsEF0ypQpmDp1qtF3HDt2DJGRkcjPz4eHhwc2bdqE8PBwxMfHQ6lUwtvb2yDfz88PqampVvXy1VdfRWhoKAIDA3H06FG8/fbbSEhIwMaNGwEAqampBoXS3e3efc1e7FosTZ48GR988IHJnFOnTmHbtm3Izs5GTEyMRe3Pnj0b06ZNk+LFHlo4ud5bqVVz1lnKuRDgK8WWF/xTiuUWyEOCf9+S51TdyHF/aJ45OdbmHbsVIMWu5Xg4dN6N3PK3H+Z8Hrb+bJnn2HmO3LfymnfkZpAUy7wtr+qdlq+RYsiR//tzqlpkGKj0iBYvsuEK3snJydBo7u2vSqUq8S1169ZFfHw8MjMzsWHDBgwYMAB79uyxrh8PMWzYMP2/GzVqhICAAHTq1Annzp1DrVq1ynTb1rBrsTR+/HgMHDjQZE7NmjXx22+/ITY2VvrQW7RogX79+uHLL780+t6YmBiMGzdO/zwrK0uquomIiB4Xd69uM4dSqUTt2rUBABERETh06BAWLVqEvn37orCwEBkZGQajS2lpaSXONSqtVq1aAQASExNRq1Yt+Pv74+DBgwY5d6/Cs/W2LWHXYqlq1aqoWrXqQ/MWL16MGTNm6J9fvXoVUVFR+Pbbb/UH2hiVSmWyqiYiIrI7ncDd02jWtWFlEzodCgoKEBERARcXF+zcuRN9+vQBACQkJODSpUuIjIy0ejv3u7sEUEDAnVH7yMhIzJw5E+np6ahWrRoAYPv27dBoNAgPD7fpti1RLuYshYSEGDz38Lhz2qNWrVoICpKHYYmIiMoNobvzsLYNC8TExKB79+4ICQlBdnY21q5di927d2Pr1q3w8vLC4MGDMW7cOPj4+ECj0WDkyJGIjIzUXwkH3BkNysnJQWpqKm7fvq0vfMLDw6FUKnHlyhV06tQJq1evRsuWLXHu3DmsXbsWTz/9NHx9fXH06FGMHTsW7dq1Q+PGjQEAXbt2RXh4OF577TV8+OGHSE1NxTvvvIPo6Gi7Dn6Ui2KJiIiIbCc9PR39+/dHSkoKvLy80LhxY2zduhVdunQBACxYsABOTk7o06cPCgoKEBUVhU8++cSgjSFDhhjMcWrWrBkAICkpCWFhYSgqKkJCQoL+ajelUokdO3Zg4cKFyM3NRXBwMPr06YN33nlH34azszN+/vlnvPnmm4iMjIS7uzsGDBiA6dOnl/UhMalcFkthYWEQvA8OERE9Dmw4wdtcK1asMPm6Wq3GkiVLsGTJkhJzdu/ebbKNB/+vDg4ONmsCeWhoKH799deH5j1K5bJYIiIiemw4yJwlKlm5WZSSiIiIyB44skRERGRPdjgNR5apkMWSV4IznJX3FqKslCd/yQrOyLdUSQ+WD5fzTRcpdtlfXqhSkSovlPZgnjk51uZdekzycv3lqyIcqX/mfB62/myZ59h5jty3xyrvqpwXHyzHKp+UT6zkBhgufCny5QWLy4SADYolm/SESsDTcEREREQmVMiRJSIiIofB03AOj8USERGRPel0AKxclFL3iO5jV0GxWCIiIrInjiw5PM5ZIiIiIjKBI0tERET2xJElh8diiYiIyJ64grfD42k4IiIiIhMq5MiS+oYOlVzuXTng9esJKadKjSApprhdKDdmbOhToTCSl/nwPHNymPf45Dly35hn+zxH7tvjlOeULYWKfT2kWKUryVLsVlvD3/vFRY9mtEYIHYSw7mo2a99PplXIYomIiMhhCGH9aTTOWSpTPA1HREREZAJHloiIiOxJ2GCCN0eWyhSLJSIiInvS6QCFlXOOOGepTPE0HBEREZEJHFkiIiKyJ56Gc3gsloiIiOxI6HQQVp6G49IBZYvFEhERkT1xZMnhVchiyT2lAJUq3beYWVh1KUeRVyDFiqt4SrFKKTflvEAfOe/qw/PMyWHe45PnyH1jnu3zHLlvj1VeSoYU06qc5TxXtRRT3So2eO5cVCzlUMVUIYslIiIih6ETgIIjS46MxRIREZE9CQHA2qUDWCyVJS4dQERERGQCR5aIiIjsSOgEhJWn4QRHlsoUiyUiIiJ7EjpYfxqOSweUJZ6GIyIiIjKBI0tERER2xNNwjo/FEhERkT3xNJzDq1DF0t3Ku7j4gQUntfIClAptoRQrLnaRG9UZWbyyOL90ebZsi3kOn+fIfWNeGeQ5ct8qYJ6zkd/7xUWGeXffV9ajNsUosnoB72IU2aYzZJRCVKCxu/Pnz6NWrVr27gYREZUj586dQ82aNW3ebn5+PmrUqIHU1FSbtOfv74+kpCSo1fLq5GSdClUsZWRkoHLlyrh06RK8vLzs3Z1SycrKQnBwMJKTk6HRaOzdnVLjfjiOx2EfgMdjPx6HfQAen/3IzMxESEgIbt26BW9v7zLZRn5+PgoL5TMZpaFUKlkolZEKdRrOyenOxX9eXl7l+gcYADQaTbnfB4D74Ugeh30AHo/9eBz2AXh89uPu/x1lQa1Ws8ApB7h0ABEREZEJLJaIiIiITKhQxZJKpcKUKVOgUqns3ZVSexz2AeB+OJLHYR+Ax2M/Hod9ALgf9PipUBO8iYiIiCxVoUaWiIiIiCzFYomIiIjIBBZLRERERCawWCIiIiIy4bEqlmbOnIk2bdrAzc2txNVWL126hB49esDNzQ3VqlXDxIkTUVxcbLLdmzdvol+/ftBoNPD29sbgwYORk5NTBnsg2717NxQKhdHHoUOHSnxfhw4dpPw33njjkfS5JGFhYVKf5syZY/I9+fn5iI6Ohq+vLzw8PNCnTx+kpaU9oh4bunDhAgYPHowaNWrA1dUVtWrVwpQpUx66+q4jfBZLlixBWFgY1Go1WrVqhYMHD5rMX79+PerVqwe1Wo1GjRrh119/fUQ9NW727Nl48skn4enpiWrVqqF3795ISEgw+Z4vvvhCOu72Xvxv6tSpUp/q1atn8j2O9lkY+zlWKBSIjo42mu8on8PevXvRs2dPBAYGQqFQ4H//+5/B60IIvPfeewgICICrqys6d+6Ms2fPPrRdS3+2qHx6rIqlwsJCvPjii3jzzTeNvq7VatGjRw8UFhbizz//xJdffokvvvgC7733nsl2+/XrhxMnTmD79u34+eefsXfvXgwbNqwsdkHSpk0bpKSkGDyGDBmCGjVqoEWLFibfO3ToUIP3ffjhh4+kz6ZMnz7doE8jR440mT927Fj89NNPWL9+Pfbs2YOrV6/i+eeff0S9NXT69GnodDp8+umnOHHiBBYsWIBly5bh3//+90Pfa8/P4ttvv8W4ceMwZcoU/PXXX2jSpAmioqKQnp5uNP/PP//EK6+8gsGDB+PIkSPo3bs3evfujePHjz+yPj9oz549iI6Oxv79+7F9+3YUFRWha9euyM3NNfk+jUZjcNwvXrz4iHpcsgYNGhj06Y8//igx1xE/i0OHDhn0f/v27QCAF198scT3OMLnkJubiyZNmmDJkiVGX//www+xePFiLFu2DAcOHIC7uzuioqKQn2/kZr3/z9KfLSrHxGNo1apVwsvLS4r/+uuvwsnJSaSmpupjS5cuFRqNRhQUFBht6+TJkwKAOHTokD62efNmoVAoxJUrV2ze94cpLCwUVatWFdOnTzeZ1759ezF69OhH0ykzhYaGigULFpidn5GRIVxcXMT69ev1sVOnTgkAIjY2tgx6aLkPP/xQ1KhRw2SOvT+Lli1biujoaP1zrVYrAgMDxezZs43mv/TSS6JHjx4GsVatWonhw4eXaT8tkZ6eLgCIPXv2lJhT0u8Be5oyZYpo0qSJ2fnl4bMYPXq0qFWrltDpdEZfd8TPAYDYtGmT/rlOpxP+/v5i7ty5+lhGRoZQqVTim2++KbEdS3+2qPx6rEaWHiY2NhaNGjWCn5+fPhYVFYWsrCycOHGixPd4e3sbjOJ07twZTk5OOHDgQJn3+UE//vgjbty4gUGDBj00d82aNahSpQoaNmyImJgY5OXlPYIemjZnzhz4+vqiWbNmmDt3rslToHFxcSgqKkLnzp31sXr16iEkJASxsbGPorsPlZmZCR8fn4fm2euzKCwsRFxcnMExdHJyQufOnUs8hrGxsQb5wJ2fE0c55sCd4w7gocc+JycHoaGhCA4ORq9evUr8OX+Uzp49i8DAQNSsWRP9+vXDpUuXSsx19M+isLAQX3/9NV5//XUoFIoS8xzxc7hfUlISUlNTDY61l5cXWrVqVeKxLs3PFpVfFepGuqmpqQaFEgD989TU1BLfU61aNYNYpUqV4OPjU+J7ytKKFSsQFRWFoKAgk3mvvvoqQkNDERgYiKNHj+Ltt99GQkICNm7c+Ih6Khs1ahSaN28OHx8f/Pnnn4iJiUFKSgrmz59vND81NRVKpVKaf+bn52eXY/+gxMREfPzxx5g3b57JPHt+FtevX4dWqzX6vT99+rTR95T0c+IIxxwAdDodxowZg7Zt26Jhw4Yl5tWtWxcrV65E48aNkZmZiXnz5qFNmzY4ceLEQ39+ykqrVq3wxRdfoG7dukhJScG0adPwz3/+E8ePH4enp6eU7+ifxf/+9z9kZGRg4MCBJeY44ufwoLvH05JjXZqfLSq/HL5Ymjx5Mj744AOTOadOnXroJElHU5r9unz5MrZu3Yrvvvvuoe3fP6eqUaNGCAgIQKdOnXDu3DnUqlWr9B1/gCX7MW7cOH2scePGUCqVGD58OGbPnm3X2wmU5rO4cuUKunXrhhdffBFDhw41+d5H9VlUFNHR0Th+/LjJuT4AEBkZicjISP3zNm3aoH79+vj000/x/vvvl3U3jerevbv+340bN0arVq0QGhqK7777DoMHD7ZLn6yxYsUKdO/eHYGBgSXmOOLnQGQphy+Wxo8fb/KvFgCoWbOmWW35+/tLVyrcvbLK39+/xPc8OFmvuLgYN2/eLPE95ijNfq1atQq+vr549tlnLd5eq1atANwZDbHlf9DWfD6tWrVCcXExLly4gLp160qv+/v7o7CwEBkZGQajS2lpaVYd+wdZug9Xr17FU089hTZt2mD58uUWb6+sPgtjqlSpAmdnZ+kKQlPH0N/f36L8R2nEiBH6iywsHZVwcXFBs2bNkJiYWEa9s5y3tzeeeOKJEvvkyJ/FxYsXsWPHDotHSB3xc7h7PNPS0hAQEKCPp6WloWnTpkbfU5qfLSrH7D1pqiw8bIJ3WlqaPvbpp58KjUYj8vPzjbZ1d4L34cOH9bGtW7c+8gneOp1O1KhRQ4wfP75U7//jjz8EAPH333/buGel9/XXXwsnJydx8+ZNo6/fneC9YcMGfez06dN2neB9+fJlUadOHfHyyy+L4uLiUrXxqD+Lli1bihEjRuifa7VaUb16dZMTvJ955hmDWGRkpF0nFet0OhEdHS0CAwPFmTNnStVGcXGxqFu3rhg7dqyNe1d62dnZonLlymLRokVGX3fEz+KuKVOmCH9/f1FUVGTR+xzhc0AJE7znzZunj2VmZpo1wduSny0qvx6rYunixYviyJEjYtq0acLDw0McOXJEHDlyRGRnZwsh7vyQNmzYUHTt2lXEx8eLLVu2iKpVq4qYmBh9GwcOHBB169YVly9f1se6desmmjVrJg4cOCD++OMPUadOHfHKK6880n3bsWOHACBOnTolvXb58mVRt25dceDAASGEEImJiWL69Oni8OHDIikpSfzwww+iZs2aol27do+0z/f7888/xYIFC0R8fLw4d+6c+Prrr0XVqlVF//799TkP7ocQQrzxxhsiJCRE/Pbbb+Lw4cMiMjJSREZG2mMXxOXLl0Xt2rVFp06dxOXLl0VKSor+cX+Oo30W69atEyqVSnzxxRfi5MmTYtiwYcLb21t/Vehrr70mJk+erM/ft2+fqFSpkpg3b544deqUmDJlinBxcRHHjh17ZH1+0Jtvvim8vLzE7t27DY57Xl6ePufB/Zg2bZrYunWrOHfunIiLixMvv/yyUKvV4sSJE/bYBSGEEOPHjxe7d+8WSUlJYt++faJz586iSpUqIj09XQhRPj4LIe4UBSEhIeLtt9+WXnPUzyE7O1v/fwIAMX/+fHHkyBFx8eJFIYQQc+bMEd7e3uKHH34QR48eFb169RI1atQQt2/f1rfRsWNH8fHHH+ufP+xnix4fj1WxNGDAAAFAeuzatUufc+HCBdG9e3fh6uoqqlSpIsaPH2/wl9GuXbsEAJGUlKSP3bhxQ7zyyivCw8NDaDQaMWjQIH0B9qi88sorok2bNkZfS0pKMtjPS5cuiXbt2gkfHx+hUqlE7dq1xcSJE0VmZuYj7LGhuLg40apVK+Hl5SXUarWoX7++mDVrlsGI3oP7IYQQt2/fFm+99ZaoXLmycHNzE88995xBcfIorVq1yuj36/4BWkf9LD7++GMREhIilEqlaNmypdi/f7/+tfbt24sBAwYY5H/33XfiiSeeEEqlUjRo0ED88ssvj7S/DyrpuK9atUqf8+B+jBkzRr/Pfn5+4umnnxZ//fXXo+/8ffr27SsCAgKEUqkU1atXF3379hWJiYn618vDZyHEndF1ACIhIUF6zVE/h7u/2x983O2rTqcT7777rvDz8xMqlUp06tRJ2r/Q0FAxZcoUg5ipny16fCiEEKKsT/URERERlVcVap0lIiIiIkuxWCIiIiIygcUSERERkQksloiIiIhMYLFEREREZAKLJSIiIiITWCwRERERmcBiicqVDh06YMyYMY/VdgcOHIjevXtb1UZYWBgUCgUUCgUyMjJKzPviiy8M7rNHtjVw4ED95/C///3P3t0hIhthsURkho0bNxrcIT0sLAwLFy60X4eMmD59OlJSUuDl5WXvrjz2du/ebbQwXbRoEVJSUuzTKSIqM5Xs3QGi8sDHx8feXXgoT09Ph7nbeVFREVxcXOzdjUfOy8uLxSrRY4gjS1Su3bp1C/3790flypXh5uaG7t274+zZs/rX75522rp1K+rXrw8PDw9069bN4K//4uJijBo1Ct7e3vD19cXbb7+NAQMGGJwau/80XIcOHXDx4kWMHTtWf8oFAKZOnYqmTZsa9G/hwoUICwvTP9dqtRg3bpx+W5MmTcKDdxzS6XSYPXs2atSoAVdXVzRp0gQbNmwo1fH54osvEBISAjc3Nzz33HO4ceOGlPPDDz+gefPmUKvVqFmzJqZNm4bi4mL966dPn8Y//vEPqNVqhIeHY8eOHQanmS5cuACFQoFvv/0W7du3h1qtxpo1awAAn3/+OerXrw+1Wo169erhk08+Mdh2cnIyXnrpJXh7e8PHxwe9evXChQsX9K/v3r0bLVu2hLu7O7y9vdG2bVtcvHjRrH1/2H7Nnz8fjRo1gru7O4KDg/HWW28hJydH//rFixfRs2dPVK5cGe7u7mjQoAF+/fVXXLhwAU899RQAoHLlylAoFBg4cKBZfSKi8onFEpVrAwcOxOHDh/Hjjz8iNjYWQgg8/fTTKCoq0ufk5eVh3rx5+Oqrr7B3715cunQJEyZM0L/+wQcfYM2aNVi1ahX27duHrKwsk/NNNm7ciKCgIP1pL0tOu3z00Uf44osvsHLlSvzxxx+4efMmNm3aZJAze/ZsrF69GsuWLcOJEycwduxY/Otf/8KePXvMPzAADhw4gMGDB2PEiBGIj4/HU089hRkzZhjk/P777+jfvz9Gjx6NkydP4tNPP8UXX3yBmTNnArhT3PXu3Rtubm44cOAAli9fjv/85z9Gtzd58mSMHj0ap06dQlRUFNasWYP33nsPM2fOxKlTpzBr1iy8++67+PLLLwHcGX2KioqCp6cnfv/9d+zbt09fzBYWFqK4uBi9e/dG+/btcfToUcTGxmLYsGH64tSUh+0XADg5OWHx4sU4ceIEvvzyS/z222+YNGmS/vXo6GgUFBRg7969OHbsGD744AN4eHggODgY33//PQAgISEBKSkpWLRokUWfDRGVM/a9jy+RZdq3by9Gjx4thBDizJkzAoDYt2+f/vXr168LV1dX8d133wkhhFi1apUAYHBn9yVLlgg/Pz/9cz8/PzF37lz98+LiYhESEiJ69epldLtC3Ln7+IIFCwz6NmXKFNGkSROD2IIFC0RoaKj+eUBAgPjwww/1z4uKikRQUJB+W/n5+cLNzU38+eefBu0MHjxYvPLKKyUeF2P9eeWVV8TTTz9tEOvbt6/w8vLSP+/UqZOYNWuWQc5XX30lAgIChBBCbN68WVSqVEmkpKToX9++fbsAIDZt2iSEECIpKUkAEAsXLjRop1atWmLt2rUGsffff19ERkbqt1O3bl2h0+n0rxcUFAhXV1exdetWcePGDQFA7N69u8T9LsnD9suY9evXC19fX/3zRo0aialTpxrNvXsH+1u3bhl9/f7jQ0TlH+csUbl16tQpVKpUCa1atdLHfH19UbduXZw6dUofc3NzQ61atfTPAwICkJ6eDgDIzMxEWloaWrZsqX/d2dkZERER0Ol0Nu1vZmYmUlJSDPpbqVIltGjRQn8qLjExEXl5eejSpYvBewsLC9GsWTOLtnfq1Ck899xzBrHIyEhs2bJF//zvv//Gvn37DEZctFot8vPzkZeXh4SEBAQHBxvMhbr/WN2vRYsW+n/n5ubi3LlzGDx4MIYOHaqPFxcX6+f0/P3330hMTISnp6dBO/n5+Th37hy6du2KgQMHIioqCl26dEHnzp3x0ksvISAg4KH7/rD9cnNzw44dOzB79mycPn0aWVlZKC4uNnh91KhRePPNN7Ft2zZ07twZffr0QePGjR+6bSJ6/LBYosfegxONFQqFNE/IFpycnKR27z8daI67c2Z++eUXVK9e3eA1lUplXQdL2N60adPw/PPPS6+p1WqL2nJ3dzdoFwA+++wzg+IQuFOM3s2JiIjQz2+6X9WqVQEAq1atwqhRo7BlyxZ8++23eOedd7B9+3a0bt3aqv26cOECnnnmGbz55puYOXMmfHx88Mcff2Dw4MEoLCyEm5sbhgwZgqioKPzyyy/Ytm0bZs+ejY8++ggjR4606LgQUfnHYonKrfr166O4uBgHDhxAmzZtAAA3btxAQkICwsPDzWrDy8sLfn5+OHToENq1awfgzgjEX3/9JU3Wvp9SqYRWqzWIVa1aFampqRBC6OfVxMfHG2wrICAABw4c0G+ruLgYcXFxaN68OQAgPDwcKpUKly5dQvv27c3ah5LUr18fBw4cMIjt37/f4Hnz5s2RkJCA2rVrG22jbt26SE5ORlpaGvz8/AAAhw4deui2/fz8EBgYiPPnz6Nfv35Gc5o3b45vv/0W1apVg0ajKbGtZs2aoVmzZoiJiUFkZCTWrl370GLpYfsVFxcHnU6Hjz76CE5Od6Zufvfdd1JecHAw3njjDbzxxhuIiYnBZ599hpEjR0KpVAKA9B0goscTiyUqt+rUqYNevXph6NCh+PTTT+Hp6YnJkyejevXq6NWrl9ntjBw5ErNnz0bt2rVRr149fPzxx7h165bJicRhYWHYu3cvXn75ZahUKlSpUgUdOnTAtWvX8OGHH+KFF17Ali1bsHnzZoNCYPTo0ZgzZw7q1KmDevXqYf78+QZr9Xh6emLChAkYO3YsdDod/vGPfyAzMxP79u2DRqPBgAEDzN6vUaNGoW3btpg3bx569eqFrVu3GpyCA4D33nsPzzzzDEJCQvDCCy/AyckJf//9N44fP44ZM2agS5cuqFWrFgYMGIAPP/wQ2dnZeOeddwDgoROtp02bhlGjRsHLywvdunVDQUEBDh8+jFu3bmHcuHHo168f5s6di169emH69OkICgrCxYsXsXHjRkyaNAlFRUVYvnw5nn32WQQGBiIhIQFnz55F//79H7rvD9uv2rVro6ioCB9//DF69uyJffv2YdmyZQZtjBkzBt27d8cTTzyBW7duYdeuXahfvz4AIDQ0FAqFAj///DOefvppuLq6wsPDw+zPhojKGftOmSKyzIMTrW/evClee+014eXlJVxdXUVUVJQ4c+aM/vVVq1YZTGgWQohNmzaJ+7/6RUVFYsSIEUKj0YjKlSuLt99+W7z44ovi5ZdfLnG7sbGxonHjxkKlUhm0tXTpUhEcHCzc3d1F//79xcyZMw0meBcVFYnRo0cLjUYjvL29xbhx40T//v0NJpPrdDqxcOFCUbduXeHi4iKqVq0qoqKixJ49e0o8LsYmeAshxIoVK0RQUJBwdXUVPXv2FPPmzZOOx5YtW0SbNm2Eq6ur0Gg0omXLlmL58uX610+dOiXatm0rlEqlqFevnvjpp58EALFlyxYhxL0J3keOHJG2v2bNGtG0aVOhVCpF5cqVRbt27cTGjRv1r6ekpIj+/fuLKlWqCJVKJWrWrCmGDh0qMjMzRWpqqujdu7cICAgQSqVShIaGivfee09otdoSj4Ml+zV//nwREBCg/96sXr3aYNL2iBEjRK1atYRKpRJVq1YVr732mrh+/br+/dOnTxf+/v5CoVCIAQMGGGwbnOBN9FhRCFEGkzeIyjGdTof69evjpZdeMli125GFhYVhzJgxj+RWMPv27cM//vEPJCYmGkycp3sUCgU2bdpk9W1siMgxcJ0lqvAuXryIzz77DGfOnMGxY8fw5ptvIikpCa+++qq9u2aRt99+Gx4eHsjMzLRpu5s2bcL27dtx4cIF7NixA8OGDUPbtm1ZKBnxxhtv8HQc0WOII0tU4SUnJ+Pll1/G8ePHIYRAw4YNMWfOHP0k7PLg4sWL+ivvatasqZ+0bAurV6/GjBkzcOnSJVSpUgWdO3fGRx99BF9fX5ttw1INGjQocSXvTz/9tMRJ5WUtPT0dWVlZAO4sUXH/FYJEVH6xWCKicuf+4vBBfn5+0tpNRETWYLFEREREZALnLBERERGZwGKJiIiIyAQWS0REREQmsFgiIiIiMoHFEhEREZEJLJaIiIiITGCxRERERGQCiyUiIiIiE/4PEt8I6aVV+JwAAAAASUVORK5CYII=",
       "text/plain": [
        "<Figure size 640x480 with 2 Axes>"
       ]
@@ -352,7 +2058,7 @@
     }
    ],
    "source": [
-    "monthly_subset[\"ts_pred\"].isel(time=0).plot()"
+    "monthly_data[\"ts_pred\"].isel(time=0).plot()"
    ]
   },
   {
@@ -364,7 +2070,7 @@
     {
      "data": {
       "text/plain": [
-       "<matplotlib.collections.QuadMesh at 0x74da0e19e1d0>"
+       "<matplotlib.collections.QuadMesh at 0x7d7f04ed6350>"
       ]
      },
      "execution_count": 14,
@@ -373,7 +2079,7 @@
     },
     {
      "data": {
-      "image/png": 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",
+      "image/png": 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",
       "text/plain": [
        "<Figure size 640x480 with 2 Axes>"
       ]
@@ -384,7 +2090,7 @@
    ],
    "source": [
     "# original target\n",
-    "monthly_subset[\"ts\"].where(~lsm_subset[\"lsm\"].values).isel(time=0).plot()"
+    "monthly_data[\"ts\"].where(~lsm_mask[\"lsm\"].values).isel(time=0).plot()"
    ]
   },
   {
@@ -395,8 +2101,8 @@
    "outputs": [],
    "source": [
     "# Error\n",
-    "target = monthly_subset[\"ts\"].where(~lsm_subset[\"lsm\"].values)\n",
-    "err = target - monthly_subset[\"ts_pred\"]"
+    "target = monthly_data[\"ts\"].where(~lsm_mask[\"lsm\"].values)\n",
+    "err = target - monthly_data[\"ts_pred\"]"
    ]
   },
   {
@@ -408,7 +2114,7 @@
     {
      "data": {
       "text/plain": [
-       "<matplotlib.collections.QuadMesh at 0x74da0e101b50>"
+       "<matplotlib.collections.QuadMesh at 0x7d7f047ece10>"
       ]
      },
      "execution_count": 16,
@@ -417,7 +2123,7 @@
     },
     {
      "data": {
-      "image/png": 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",
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",
       "text/plain": [
        "<Figure size 640x480 with 2 Axes>"
       ]
@@ -439,7 +2145,7 @@
     {
      "data": {
       "text/plain": [
-       "<matplotlib.collections.QuadMesh at 0x74da0d6898d0>"
+       "<matplotlib.collections.QuadMesh at 0x7d7f04756fd0>"
       ]
      },
      "execution_count": 17,
@@ -448,7 +2154,7 @@
     },
     {
      "data": {
-      "image/png": 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uTJw4ERdddBHS6TR++9vfok+fPrjwwgvrHP+AAQNw/vnn4+abb0Y2m8Vee+2Fp59+Gq+//joeeughs5S5fv16jBs3DmvXrsW0adPwf//3f5F2tt1224ITQs1f/vIXfP3112bS8Nprr+Haa68FAJx00kkYPHgwAGDw4ME49thjMXLkSJSWluKNN97Ao48+il133RVnnnlmnf0k8eGHH+LZZ58FAHz++eeorKw0fe+yyy44/PDDix7nu+++iwMOOADTp083qUA+++wzHHzwwTj22GMxbNgwcM4xd+5cPPjggxgyZEhERF7f+w8ol/wpU6bEliNvvvlmHHHEETj00ENx3HHH4eOPP8Yf/vAHnHbaaRFX/x/96EfYZ599MGXKFMyfP99EtPZ9H1dddVWD7ilBdDhaIWAk0UG55ppr5NZbby0555Ho1oMHD5aTJ0829e69914JQP773/+OnJ8vivHkyZNlp06dYv3deeedco899pBlZWWyoqJCjhw5Ul588cXym2++afJry8f+++8vAeR9uXz88cfy0EMPleXl5bJbt27yxBNPlMuXL4/UmTx5csE23ajhixcvlj/60Y9kly5dZOfOneVhhx0mP/vss3pfg+/78vrrr5eDBw+WmUxGjhgxQj744IOROgsXLiw4Jvvzbej9siNRn3baaXL48OGyoqJCptNpud1228lf/OIXsqqqKm/bdUW01s9dfcZf33G+/PLLsYjXq1atkmeccYYcNmyY7NSpk8xkMnL77beX559/fmKE7vrcf3v89957b+zYU089JXfddVdZUlIiBwwYIC+77DITvdtmzZo18tRTT5U9e/aU5eXlcv/994/9HRLElgyTsglCxRIEQRAEQbRzSFNEEARBEAQBmhQRBEEQBEEAoEkRQRAEQRAEgHY0KZoxYwb22msvVFRUoHfv3jjyyCPx6aefRups3rwZZ599Nnr27InOnTvj6KOPbnMJJAmCIAiCaJu0m0nRq6++irPPPhtvv/02Zs+ejWw2i0MPPRQbNmwwdS644AI899xzmDVrFl599VV88803BWO8EARBEARBaNqt99mqVavQu3dvvPrqq9hvv/1QWVmJXr164eGHHzYxahYsWIAdd9wRc+bMwT777NPKIyYIgiAIoi3TboM36qiuPXr0AAC89957yGazJgEjAAwbNgyDBg0qOCmqqamJJEkUQmDNmjXo2bNni6auIAiCINoXUkqsX78e/fv3j6ROaWo2b96M2traJmkrk8mgtLS0SdrqiLTLSZEQAueffz723XdfkyRz+fLlyGQy6NatW6Runz59Ylm7bWbMmEHRXAmCIIgGs3jxYgwYMKBZ2t68eTN6lnXGRvh1V64Hffv2xcKFC2lilId2OSk6++yz8fHHH+ONN95odFuXXnoppk6darYrKysxaNAgzHh2Djp16gwfgAcgG6wy+lLCYwx+sJ3NCbVfSHicoTYnzbZdAkDWF/AYQ60v4HFmjtXkBFKcISdkpKwJ2s76ItJWre5TqlIICc5ZpLTHFPYTni+ENPv1Cqp7vt4vRXhcbavr8YNxSSEBIeELASkkfF9CClVfBi/GWazU6PZ9X0DKaH0pJfycgMjVwq/dDCnUFwPjnnkvcrVBOwJC+Grb9yGlb/ar0ocUIih9+NlscJ0+GPPAPA7pC+RqqgEAXqYMZd16oe+Ou+Kd69pOvi2C6Cg8Om8phJTggVW+LM2R9jjSwfcDYwweZzhg263w2aoqbN+rS2sON0ZVVRUGDhxoEi83B7W1tdgIH5OwNTKNlAHXQuCB5UtRW1tLk6I8tLtJ0c9//nM8//zzeO211yIz8759+6K2thbr1q2LWItWrFiBvn375m2vpKQkMRFiprwTMp0qIIKJAZPOJEf/I/fUP1z9qHLPmigEpZ6YpIKJSCoN5KzJUiql3usPI2Xt96WEpycwwTleSvXhBeMx2ZGC42YsUP2woB73pCmlUO2K4LiUYTteMG49CWJQEyF9XE+WmDXpEXqcOQEvpSZFseOMRUpuLU8KKcGDyRqCiZFgEsKvhYSElAxgHpiVC0q/58wL7rUPCB9gHJIH7834fXDuQeRqzcSIpXyIoA7nHngqA5GrBUtl4NduApgHP+fjk9//GARBND2dK6qwMav/Bhk8jyOdUhMjzhg8BqQ9jv+szqKitDO6dGlbkyJNS0gtMuDIsEYu0bVLBXHL0m68z6SU+PnPf46nnnoK//rXvzB06NDI8T322APpdBovvfSS2ffpp59i0aJF9UpUSRAEQRBtFY+xJnkRhWk3lqKzzz4bDz/8MJ555hlUVFQYnVDXrl1RVlaGrl274tRTT8XUqVPRo0cPdOnSBeeccw5Gjx5NnmcEQRBEu4YzwGvknIYDZC2qg3YzKbrjjjsAAGPHjo3sv/fee3HyyScDAG655RZwznH00UejpqYG48aNw+23396g/jhjSHsMQiizbtbXT1KgC0KwhMVZRLeTSSnjW6gxCvVD9nsv0AylgrVzXbpaowznSkPkwWiRanPM9JFJMdTmfHicByWLaYlywbZu32iSdH85Ac/j8H0BxhiklOAeByDA9PFUqDWCUG1xDkAfB4MPAS/Fla4IAhwsuEuAB6UR0ktmnDEwzox2yBwH4PuB5khIox1i3APj4TKZvV+jl8iktU/DuFp64+mMquv7RpNkt8NTafPer92E2g2V9XxiCIIolr4VJajcnEONr5bOU8Gymf09wRngccAXwEfLKjGyX9dWHjXRkWk3k6L6hFMqLS3FH//4R/zxj39sgRERBEEQRMvQFMtfHmj5rC7azaSotbC9sYTlgQZEPcxsLzN7v2d5WWkrjS+kEVrbgmv7XF0/aTtecvhCRKxBfp52a3NRTzZfb2tPMu1xJkMPsIhHWmD6sT3WTD3G4ENtM8EALo21yPYo07geaam0h1zWBxMMwhfgnMEXgaeZH7UO2VYeQHmgGc8zKEsQ8zxTAkAqU6Y+01TGtCNytZH2/FwtvJIyY1nKbqhEl7G/QNUrN4IgiKZl6y4lqMiksGZTFjW+MN9rQkrjgQYoK1GWCSDXbmSwTY7XBMtncRs64bLlPmEEQRAEQRAWZCnKgwcgrSOUciArBICotijjcdQ6MYRcS45GW5O0tSgDGI0RgJiVR5f6ldPWKEtbZNfLpFKOZglGY2SPIWZlSnGjKUqKd8Q8pTHytJWHB9YhMOWmHxwPtUVcud0H2iLBJBgHpFDHebDfS+l7y0wpAmsRoCx0vi+QyqTh5zxj1ZG+sgYZ93nLesQB8EwZ/OC4FD54SmmIvFTG6JJ4KmOsR/b5IluLNJTVKZUpM3qizZWrmuahIggiwg69Qxf7fy9ai41ZH1nLmqzExQweV5Z6ISX+u6wKI/q1Tdf85oSWz1oGmhQRBEEQRBuHls9aBlo+IwiCIAiCAFmKCiIcj7dCguuktB7ue7dekvt8Uv2cc67dtiugrs35icf1+bpeLhemCAFg3PHdMinVB+cMfk4vi0XvjUYLqDkAIQAJtWSmhdn2Ep2XCpbiUgy5WqnCA0AtxfkFBNeAneIjzAvkBUtmTJfci5bB0pmXKQO3wgIIvSTnhe7/IlcLka3FVoffgNXPXQKCIJqepWs3oKLEQ1YI+FIi60ukLbOI3t6cE5H9WxK0fNYy0KSIIAiCINo4DI1f2qEpUd3QpCgPnDPjEmqCNyYIrj0Wip+B/O747jHX8pN0jmsF0sEea4JgjsnCa88IrbWbfm1OoCTFUZMTKMt4qr0UN/0pK5DuN0jEyLnZ7wquASCVCawrkIBg8DwVvJFxBgTtq3YYmJDwUsr9n3u29UlZhxhjCNKXIZXh8IPEtYJLEzyS8ajgGgBEtjZy/yJBGNNRK5Fbn6cy8FIcnseNpUhZpUqQq60BT2fgiTJkKnpACoHSLr0SnhKCIJqCrbt3imz/63Pl3OBZQRx9oYI4Zn2J+curMLzvliW2JktRy0CaIoIgCIIgCJClKC/2jDzrK1dQX6rM8dorX+/LZwHSpWsxslODJGmMNK6uCFDWInMssW+R2F7OGZMeRy4nIkEVtdbH3gYQ0RbFgzACwtdao8Ba5Mfra/d/rVXSCZ/tMAB+0I7kDFwAggOpjAcRnCd8Bi+Vgp/LRYIw2qXe76VSwfj0uMJgjYx7xkrkedxYpXJZ37j7eyVlkMKHX9rJWJ4Igmhe3vrqW2T9MIij+jpR3ydZASAF1PpbnsWDvM9aBpoUEQRBEEQbx2NoguUzoi5oUpQHzhhSngpEyAFkfbVPMIk09C8XpS1KB4HFfJ7shWZbhjJeuGKpf/2EnmNh6SaTzVht1lj7faYsRmF9zySKdYM4up5svpDwMqo+eP4/NuWpFtUWMa23suopjVAYDFIKCXjq154X3MtUxjNWJm1FYpwhxVS90KqjtEU8qMeD41KodrnH1ThYBtwae5g6JG3aB4BU2oOf8yAzafWZeBzc0hRxQFmjOIPnCQidciSwLmXKo5oHgiCahzFDeuLtr9egJqc80fTXk5CIpP74fNV6bNerohVHSnREaFJEEARBEG0cWj5rGWhSRBAEQRBtHPI+axloUlSAnC9iomrACniYILhOCrKol888ztRSV/Bge4zFAkDme7lC6SShtV2qJTMeccu3x6OW2LiTL43FtnV7+QTXgFqyggiCOfpqWy9F+b4wS19cB4UMlrh44LYfOY4wbxpjEsK386oxyCAEgt2O54Xu/1JIsLTqx/N4pJ4WbOt+U2kvCN6olv54xlNhA4Jxcs5QYwTc0fAJBEE0H6VBCBH9ZxfmnFRsZurv9LOV67F9b1pCI5oOmhQRBEEQRBuHN8HyGcXgqRuaFOWBMyDlcXBjHQLSYMo6FAirkwTXWZEcxFELrO2AkEJIpKFMonbwRzdoIwBjtUlxhpxt1fGYEVpHgz/ywBqUQm1OoCzDUZvzUR4Iq1MZDzkhURZs65fe1u3Y1qOkYI6Mq+CL2rpj7l9gddFWHG22TQVWF5bmUSF0ECwyxT34voAUEh9cNwG7/ervKmhkKvxzFpaVyfQXtKMtWh54zJrFGQNPe0bc7aWYEVoDgaDbYxC+BMsp9/xUJo2a4HyCIFqGTmkPWV9iY9ZH1rJa23+GWV+CMYGFq9ejfyaLki49Wm/ALQAtn7UMNHEkCIIgCIIATYryIqTSFGV9lZzQl0BWqCCOWSGQ9fVLmvVuIcOkqzFtkQx1SL5EpM1aX8Q0RHYbvpCoyQnkgrI2ePkyPFfXd/sOy6imCABSljUIADJW6g/XWpUUzJFZv1oYY6FVhjGT+BVArDRaopSjBdLWnpzAe9eMBwB8cN2EsI/gafVSDDyw9AChlYhx9Z47/dnjVxYiDi/FkEp75uV5XJUpjlSamxQgfk7Az9Xi89uPyv+wEATRpGzfuwLpwAruC4lsYElX35vqezArBEb264qhW1VgcU26lUfc/Gjvs8a+iMLQ8hlBEARBtHHIJb9loEkRQRAEQbRxSFPUMtCkqAA8iHQMqGUj5UKvXMizEPAYA5fScq0PHjgvXC4D1MPMGYPHEIm+rNsEOHweLlu5S2i2m7wWWqc4U3nQPKDWD93u9RJYJliaCkXcPOKun0l58IVABmqJTAuv7fPtEpaQWwuu1TXYIucworUSTiP8aVJHKXlwvziD70dXdedd/33s9qu/q+rpYGksrdz0P7pJHTP3NWiPB0JwT4RtMY5IvjOe4pHPQ7n9qzxo6SAMAOcMJZ07toCTINoinTMeNmZ9AAJZX+1LB6YSHcZDQ5GtiaaCJkUEQRAE0cbx0ATLZxRurU5oUpQHIZWAWj+EbqBGISQEAvExosEc7aCKOmCjOhfgMtoHgJg7fqHgjRo7mKO2BtkCaSOYtvKihft5THhdY7n/A4hYpyLtiTA/mRY1m2CMQZZ7bS3SQREBJczmHoMUMPsjpa8E2lJKlJSmsc+1LwIA3r7sYACh4HqPy1+I9GfupbXNUxwsGKct4DbhAbTQO3C/B5SVSAZCeiAUZvu5HDpvRZYigmhptutVge16AS9+tgrjduiNvy9YgZqcCvKa9X1jNdpS4E2wfMYbef6WAHmfEQRBEARBgCxFBbHXr90gjlkmI+vavlQZ3U2QR2u6af+g0UEbOWdmnbw05SkLFA/d4F0LjX5v77O3M4InnmOCOjopRQCd4kO565dnPKUtCqxGOoij0RRZ+EIClvu+plZrcoSVqiMVpOjgwQ3h8UCI2uojhAQLtErpEiUO+t7NL0MKIJf14fsCqbS6V3YbH1w3AXtO/0fo8s8YWDoMymhc9Bkzbv2R68kJEzBSE4YR8PDJLUfETyIIokU4ePteAIAJw/rgxc9Wme+jrC/x4merkOYM+2+7VWsOsUVoEu+zIs8fMmQIvv7669j+s846C3/84x9j+++77z5MmTIlsq+kpASbN28uruNWhCZFBEEQBNHGaRLvsyLP//e//w3f9832xx9/jEMOOQTHHHNM3nO6dOmCTz/91GyzdrZkR5MigiAIgiBi9OrVK7J9ww03YNttt8X++++f9xzGGPr27dvcQ2s2aFJUAB29GTx878tQIK3znNl1bUF2iOX2HVTIWsJme1nLftnEI1RHSwAJ+c+sNmW0Xds9397OFVqCC8qMs3RmhwMwAu2cAAtstVp4zZ1lMy2s5h6H8IWppzH1OQB45riHIIu99QvEFlTrSNlaWK2XzOylSz8XXG+tDyElRNCvXvozwutcLQiCaBv4QprMAmpHmE+yo9OUy2dVVVWR/SUlJSgpKSl4bm1tLR588EFMnTq1oPWnuroagwcPhhACu+++O66//nqMGDGicQNvQUhoTRAEQRBtHL181tgXAAwcOBBdu3Y1rxkzZtTZ/9NPP41169bh5JNPzltnhx12wD333INnnnkGDz74IIQQGDNmDJYsWdJUt6HZYVJKWXe1LYeqqip07doVf3p1Prp16wpAza5dC5B213f3G/d7x2XcJp9VKSqERsSN3t12hdZuaR+vcdsJQgCE7YnEdnSQyCTs9m3rkvvyOEPOssK4uG719uOof41Iy01eCAkp1L7Q6qOE0nZgRpMHzYvP+4UvVEgFX0ZE1ra1CQA2VNUglfbw0U0TE+8BQRCtw/OfrEA2CEMCAOVpzwiyWxL9/6KyshJdunRp1j4e6T0M5bxxiTo2Ch/Hr1yAxYsXR8ZbH0vRuHHjkMlk8Nxzz9W7v2w2ix133BHHH388rrnmmgaPuyWh5TOCIAiCaOPwIDNCY9sAlBi6mEnc119/jRdffBFPPvlkUf2l02nstttu+Pzzz4s6rzWhSVEBQl1Q6HovhDQWnqyIW4fUtjruW8EfbbQuydUSmeMJFhX3WK2tSUo4Vwd8tDVFdl/5NEUabSWyrUWuZslNPeJqjMzxFEcu0BRp645tSdNBFm3tkV3P8zh8XygrjpAm2KO2EmlX+tCNPrQS6eCS5v7khNIwCV9pmhLd8BlytT6kkOjUpfCvJ4IgWp6sL5AVEjW+wHG7bN3aw2kRmMdi4UyKbqOBk6p7770XvXv3xsSJxVnNfd/HRx99hO9///sN6rc1oEkRQRAEQbRxuMfySjLq3UYDJkVCCNx7772YPHkyUqnolGHSpEnYeuutjSbp6quvxj777IPtttsO69atw80334yvv/4ap512WqPG3ZLQpIggCIIgiERefPFFLFq0CKecckrs2KJFi8B5qNtcu3YtTj/9dCxfvhzdu3fHHnvsgbfeegvDhw9vySE3inYltH7ttddw880347333sOyZcvw1FNP4cgjjzTHTz75ZNx///2Rc8aNG4cXXnih3n3YQuue3ZXQOhq5WtXL+eEyj95vL4nZpIOHxhVUZ30ZW0Zz3d9tQbOOdF2Xe759nruMBiihdbSduIA7qQQQRqYOznPzqAEJwu5cspBbtRcNH2Dv033X1vqQUoIxhpfP3w8A8N0bX8YbvzgA+97wL/MZcM7AGIOXUktnXorD48y8zP0IxNV+TiCXDUXWGiElcrU+qtdtxmd//GHs8yQIonV58bNVWF+TMxKGNGf44U79WnwcLSm0fmLISHRqpNB6g/Dxo68+atbxtnfalaVow4YN2GWXXXDKKafgqKOOSqwzfvx43HvvvWa7LkU9QRAEQbR1GGcm9luD20Djzt8SaFeTogkTJmDChAkF65SUlDRZNE39K0QbM1yxtQ7EaFuAktzOsxAmPoRtJcqK0GJjW4SAqFUlSXidT5ztWmfyueMXayVSQRs9Yx0CYKxEYcki+dNcwXXStj1+d78OBil8gVcvHIv9f/OKqf/GLw4AoISDnseMJUkjpbL+hAHqLWF4ys7PBkguTdBI3xcQOYFsjU9WIoJogyxcvR4lHsd6hN+9P9ypP57/ZAUO27FP6w6OaPe0q0lRfXjllVfQu3dvdO/eHQceeCCuvfZa9OzZM2/9mpoa1NTUmG030idBEARBtDbcY+CNtBRxshTVSYeaFI0fPx5HHXUUhg4dii+++AK//OUvMWHCBMyZMweel7wWO2PGDFx11VWx/ZyF4eO1pkhlmg8eTieNhxASaTBkmTT1tdLffo7TYMoC5QEAh8ckfK7ahhf1DjAWKJ7sTm9bhZKsO7U5gZIUN+7yvpAo4x5qfWGl6vACKxAzpcbdBgDPWdPW27qdskyoMcoJlRJEU5sTKMvo/rS1KRqE0tYTeZzhbz8dY85/9cKxkb7H3vIqvGB8VsYV9YnowI+BtchuF0BgJZJARtXhHoMM2sjV+vj414eBIIi2x9CtKiCxHh4HKjfnsDkn8NTHywAA//h0Jcbt0LuVR9g8MM7BeOOSULD2IyFuNTrUpOi4444z70eOHImdd94Z2267LV555RUcdNBBiedceumlmDp1qtmuqqrCwIEDm32sBEEQBEG0LTrUpMhlm222wVZbbYXPP/8876SoUHhzrSkCLM+kJH2Ps8tOBqveM/Ne646yIkzSCgA+ohob1W7UKpTvfb7jOWefsSIxBh8yksAViCd+TUoAW1epg0ECMEEd3f51e3ZpJ5S1r+vY+95FTkhUb87B4ww1tb5JB8IYC7P3WcEeGWdBGhDErkvjcRaKxbwwLYuXIvMyQbR1ttmqAttsBbz99Rqs3ZTF5uB7rMYXdZ/cTqHls5ahQ0+KlixZgm+//Rb9+rW8qyZBEARBNBXMI++zlqBdTYqqq6sjOVQWLlyIefPmoUePHujRoweuuuoqHH300ejbty+++OILXHzxxdhuu+0wbty4Vhw1QRAEQRDtgXY1KZo7dy4OOOAAs621QJMnT8Ydd9yBDz/8EPfffz/WrVuH/v3749BDD8U111zToFhFKY+jxOORoI0eA3wWLoVpQXWK2bnR4OTughXkUSX0y0IgDQAC8DwGPxBxA0rcbS/HmeOOIFmXSS79Zp+1jOYKjc3SlcfD86wgkvbSln1eoVK77Wvscbn50UqCdu3SXYpLcYZMisMTEqnyNDbW+kilwvHqtnwR5kfT+7TQWgdwTLoOX0h4GS/aVsYD4wyjr38Jc36ZvORKEETbYGCXEpSnPazeWGuC4f53WRVG9GvewIT/+HQlNmZ9ZDdtaNZ+bJSlqJFCa3Tc5cWmol1NisaOHYtCAbj/8Y9/tOBoCIIgCKJlIE1Ry9CuJkUtjZvaQ6fyAJQwVwQCbF2aesEbu7RF1r4Esk5qDC209n3t+h9PiaG3Cwmt3f06y32NJajWVhvXXd9DUHoMtX48yKIeV97SCUCpwwIkjUP3D8AEhcykvIhQO0nwnbQfUO7+jDP4OWGSJqZM+3ZqkmShN6CsVTVBKpVcrSBrEUG0cbbu3glpvgE1OYHKmhzGbd8y7viVNbkW6ceGMQbWyISwTNCkqC4aZ4sjCIIgCILoIJClKA8eA9LB+m3a2p/VaTIStEUciCSI9VhYard8VUkCXOmVbE2RL6VJHqvbTKd4pA94oat+UtBGIGo10lodbRVKOZqejKXR0efa1qNMOjpv9oLrSCqj1pdoMEetbyp3NDxag5RJpYL6KcfahMCKpLRFJY42SF+nduf3reP6Zddzg0Pa7WVSXpiEtsSD59OvKoJo6/Tu2gm9u3bCvxetbfa+dCJaHdi3JQ0v3OPgjdQUcUl2kLqgSRFBEARBtHGaxCVf0g+9uqBpI0EQBEEQBMhSlBfOoqJhGy241ktlAjLiRm/E2ME+HRlbi6y16Bqw8pcJLYQWxj0/2lboNp8UvTpJeG2XOrJ0TZAPzRYYu0toniWIrm8ka1P6Sfu5ui7Hbd8ND6CF2W6EbI/zoIyfr/cjJ5DKeJElwqS8avZ1ppylNDUOhvKMxKaMB8DDxc/9FzcdPiLxOSAIou2w16DuzdLus/OXI+uLUMKAMEelm82gOSFLUctAkyKCIAiCaOOQpqhloElRHjzOjNDaRkgJLiSEVGJrk+fMyr2lRNPhOeYXBg901gLgkkWF1lwHbwyEv1qXHQkEKZEGQ5ZJ+DwUWtvWFW11sQXI+awjdpnktm7nIdPn2UJtW8it3fwziLq7h1YfL9avbRHTYQDs89S41HlagJ1JRcet93tO9mg7z5ltZdIWs5SxDnnGSpSxPu/OpQIpzshKRBDtjNe//Bbf26Zno9r4aFklNtT6WL0xC19I87/Ac+L8kId7x4MmRQRBEATR1mmC5TPQ8lmd0KSoAMKJnq0tLkoTFA3kCABZP67/sQMxak2RrsMZiwRx9LjaVhaUYM3aCQSp9UjaimNbc1zNTNJ+rbnJFwwRCDU+ZRmvoDXKtQbZ7dbmBDJpboJAAmHwR51axLZi1frh9bj92Nehx2uPO26VivZXlvawKesH7fvGqqStQ+WZ0Fq0qdaHLzz4pemI9YwgiPZBsVaiZ+cvN+mVhKP11CSlFhJSGr1oS8AZM4FpG9MGURj61icIgiAIggBZigiCIAiizcM83viEsILsIHVBk6I8cMZiGeu5F4ijwcCFynqfFRLplCp1RGsttDZRqYPo1aHQWgn3hAiyu0sgzRHUVebNJDOpbhsCKEt7qPUFyjJeotu55y5lOQJmN7KzHf3ZPW6fnwmyzseyzQeljlhtC7JzCSZmHUlaC7Q9wUz+N7d/u8w4XwqeF4w9zUyfSedViFRsCTGT4pEXAHN9mRSPtEcQRMfivcXrsHZzFiWe+puHL8x3dZoH3+3B/wCdnSDNbeealh1vkySEJWV4ndCkiCAIgiDaOE0Sp4gmRXVCk6IC2KJowBJDW4G89C8IzhhygVg4K2Qk0JcWUwOIuNeHLvxhG3a9fGPRARz1mOyxFbIW2YJpIOq2bvpJEiwnnFdXEMf61tMC7dqE+vZ4gNBK5AqrSyzrjn1d+YTZ7v6ytLJupS3rUfXmXOL9IQiiY7B6Y23oLBNzkgm/j+15SNb6XsqK8HuY6DjQpIggCIIg2jikKWoZaFKUB4+F68d6ndlohKxAXr6Q4Ez9wvBSHFkhUeIEcbTdPXWwxrTHo2XgPm70R4Bx5QcQ0StxyeAxaYI+JrnTA8lWpKSUHq6+yG7PrmfXSXKDT7LCuP256CCT5RkPG2v9vNqnjMcjOh+71ONx9VGRF2PgjMFjoV5Lh0jQ+zzGUJLyUOrxiFaLIIj2ydtfr8HGIBwHgEiIFI8zpBmDkAi+w8MyDQRBG6PHAf29qlIXiRa0JnMPTaApaqLBdGBo2kgQBEEQBAGyFBEEQRBEm4dxBtZIy1Rjz98SIEtRAUKhtEyMdmpHuNb1gFAkrcXRvlRia19KZLUYOyh11GtAmXbtekJIZP3w5ctoVmZzXl1u7MHSUpkVudnen2+pzN3O126+yNj5ltSSluJMe4wZQbW9dOZxFlkq0+SC5Tcd2dtt1+MMac5ViAVP5bMrT6slspKUF1lOS3NVp3NJCuVpDxUlKdwzdxEIgmif2FkJ7O9ZTdZX3x9hNoLwXP39U2ge0ZJL7JxzkxS2wS9O//Lrgu4QQRAEQRAEaPksL4wpq0JESB0Ip/VUUltCwiCNQdDGIMijFkcrES835wopkU7xyDl2cLCsCAXZdhtAKBRU7YU519J6TEE7Hgt+wXjKGqWFw3ZeM02+YIxAKITuXJoy+5Pq28EdARS0HiWdr/OsmWCO+ngQlFFbffSYbHd+HRxSB5nU/ac5j4iojbWIM3AvuP8eM5a9lMdRGpyvQyxwzvDUx8vww536Ne6BIgiixUlzHvkvZ76rwZD1JUo814mGmfO04wsAeBwIjPsQUmJzTqC6NoeaFlyOapI4RY1NKLsFQJMigiAIgmjjNIlLfiPP3xKgSVEepKX/cTPbuwEG7YBfAEwQR20ByvoysrYNKI2R0hBF17J1mXXa0NYi31iswrI0xSPbHEBNTo1V96Oz0AP5gyra1h7Xbd+2AiUFdSwUnBFAbBtApD9t5XHb0/0khQvI148uSz1lJdK/9tKeshaVpJSVzGPRYGylKW4CtmX90N02TV8kBNEuqSjxsKGWRQLoaiLBcu3wJ0x/T/BAx6j2SwkwBnAwdM5wVNb46N+/rNmvgWhZaFJEEARBEG0cxjlYI4XSjT1/S4AmRQRBEATRxtEeZI1tgygMTYoKkE9onQ7Eea7QWoucU1q8F7SjShZx1XcF1roEYKJm+1Yb2o1Um3zTwdJOCecQQkbKrC/hpVWONgQRsjMeR60vEpfKPB66wWsBuBZ6x9zzWXTbXXKz3eWBwm75SZG43SU295ykiNvukpy+nrTHlaiah9Gs9b60x2LXwpkylW/OCZSnPWzM+kqcTbE9CKJd0rs8hTXcR60fOqbY39mcKcG1ZznPeIyhe6mHzhkVIsQTWWRZCp3Lo0tl/QBUVVW13MU0gaYINCmqE7pDBEEQBEEQIEtRQVyxc1boXxZRMXDWF4GoOSq8doM4Ro5pQXWQz8zk1QksUtnAHdwVWBuxt4RxH9WWqZTlZl6TC84LOtdC60Qhs2V9sq1YtlA7KS+bx/ILrG3yCa5dt/0koXVdVibbSmVbnbRYUgmtQ8G0sgYFVrYgWKTJhs2DYG4cyDKJkpRnLIIEQbQ/elSUw5cbUV3rIyfC71H1J63zHarvJg5lKS7xOPp162S1UoqS1hi8A+NN4H1GmqI6oTtEEARBEG0cLbRu7KsYrrzySjDGIq9hw4YVPGfWrFkYNmwYSktLMXLkSPztb39rzGW3OGQpykM0nYYqjSXF0hQBofZI64K05cEN4qjaYlHtkPOQhkHEAiuKF9US6bHYOhk1Nm11UWNJc4aanIDHdGgBpSly3ep1gEPdXxrKCpS2gkQCAJe6r8IpQOygkLbWx32flDLELpP6cMmnYUpzFYQx7TGUBClC9L0K76P6laiCOXJoeZHHpQpzIDm8YMycAe8tXoe+ndPwpcSgHp2xbN0G59ckQRBtkV5dylG6cRNqfWms+WnOkPEYKsrJpb4uRowYgRdffNFsp1L5pw1vvfUWjj/+eMyYMQOHHXYYHn74YRx55JF4//33sdNOO7XEcBsNTYoIgiAIoo2jgjd6dVcs2IZf9DmpVAp9+/atV93f/e53GD9+PKZNmwYAuOaaazB79mz84Q9/wMyZM4vuuzWgSVEBso6Xgl0CiGmKXO2Rrp/zRcQ6BCD0hHASFiYFZ7S1RNnACiSEBPfCPm3rkrZkccbA9diyftwDLaivU4iE6Uq48bYTCFKMiFCjpPpDZNzg4fjNfkv7o7VCOkhjfYNDutonX8a1STHNkbamWR57EWuYY3liTFmDGAMgGMrT6osnLaUJ7b8x6+Ob9UqvtXFFFTIew5I11RjQo3NdjxFBEK1MRXkZatcuh8x0AqRASeeurT2komnKiNau11xJSQlKSpKVU5999hn69++P0tJSjB49GjNmzMCgQYMS686ZMwdTp06N7Bs3bhyefvrpRo27JSFNEUEQBEFsQQwcOBBdu3Y1rxkzZiTWGzVqFO677z688MILuOOOO7Bw4UJ873vfw/r16xPrL1++HH369Ins69OnD5YvX97k19BckKWIIAiCINo4nHPwRnqP6fMXL16MLl26mP35rEQTJkww73feeWeMGjUKgwcPxuOPP45TTz21UWNpq7QrS9Frr72Gww8/HP379wdjLGaSk1LiiiuuQL9+/VBWVoaDDz4Yn332WYP7s5dfdCmc5SH7uJ3pHgjLlMcjSzoqIKByB7dLvV+dG5b6HN0m58y0meZhPq80V0s/pSmOzhkP5WkPXUtS6Jzx0LU0hfK0h4qSFNKco1Na1S8NssmXaGFyKmw3zVW7SpCsxlma4uBc1UtzhlIvWurr0a9McF5Z2oPHGcoyqjRBFq0y43GUpT2Upjx0SntmvOXBe91PqcdRmvKQ8bg5rzTlKYG1FbRRY39mJV54neHnEQqvS1MMJSmGbqUpdC1Jo1tZCl1LU6go8YwoW9dljGF55YYGP18EQbQcme59UdKpol0unQHh8lljXwDQpUuXyCvfpMilW7du+M53voPPP/888Xjfvn2xYsWKyL4VK1bUW5PUFmhXk6INGzZgl112wR//+MfE4zfddBNuu+02zJw5E++88w46deqEcePGYfPmzS08UoIgCIJoOppyUtRQqqur8cUXX6Bfv36Jx0ePHo2XXnopsm/27NkYPXp0o/ptSdrV8tmECRMi5jwbKSVuvfVWXHbZZfjBD34AAHjggQfQp08fPP300zjuuOOK6ktIGRNU58sAbwdUTErb4Qqtk0TbdpBEzwqamBXCCJ/TPG65AhARWAM6dIDK+q5F315g+anJCaR5OA51XmiFygaCamEJoc1+V4jtRQXhAOD7+cXp7nla+J3RKU0KCKJ9CXhemNUevro/EIhdRyz7dSCWLkkhCM7mRa5fo7vLcAYJgHlATiixuS+YOW6fJiXw7fqN6FlRnudJIgiCaJ9cdNFFOPzwwzF48GB88803mD59OjzPw/HHHw8AmDRpErbeemujSTrvvPOw//774ze/+Q0mTpyIRx99FHPnzsWdd97ZmpdRFO3KUlSIhQsXYvny5Tj44IPNvq5du2LUqFGYM2dO3vNqampQVVUVeREEQRBEW4KxJgjeyIr7l79kyRIcf/zx2GGHHfDjH/8YPXv2xNtvv41evXoBABYtWoRly5aZ+mPGjMHDDz+MO++8E7vssgueeOIJPP300+0mRhHQzixFhdDq9mKV7zNmzMBVV12VeMwNwqiDANrbOrkgYGmJPL0/sFR4HMa3G/lTamg3eFtDpK0nHgsTm6aDxKi2lkmlsLC0SJ49FhirUdYXyAoJX8iI1ka3VRJcm6/7D+qkg3qe6TNoH9Hxw1OBKk3yXCeViH2eug+eCQmgr91j8eCUmpSEFZ5AmHFpdDDKlGMmLvEC7ZTnBfdGNcyC8AKeVQJAKkgMmeYSAurzlAhDV3pMfawc6rw16zfC4wxdO1EwOIIgmp6mdMmvL48++mjB46+88kps3zHHHINjjjmmqH7aEh3GUtRQLr30UlRWVprX4sWLW3tIBEEQBEG0Ah3GUqTV7StWrIiIwFasWIFdd90173mFglYRBEEQRFugNSxFWyId5g4NHToUffv2jSjfq6qq8M477zRI+e6Kdd1tO/O6x1mkjO13loCSxNihWDsUb+cbkx252RYmmyUqHq2f9gK3fU+53mu3/ZKUp9zX9fkJIQb0Ng9CAQCIlbbA2QvCA3Aeuu/rPtIej7Sjt9M8GhYgpbPZu2EMrDAEnrVUaCOkNO2r/GccFSUplAbX7QWRt1kQwVrfZl9KSAnkRNgWZ+qeZLwgPEHg6p8JPlvtlq/HKRM+M4IgiKaAe7xJXkRh2pWlqLq6OhIfYeHChZg3bx569OiBQYMG4fzzz8e1116L7bffHkOHDsXll1+O/v3748gjj2y9QRMEQRAE0S5oV5OiuXPn4oADDjDbOsfK5MmTcd999+Hiiy/Ghg0bcMYZZ2DdunX47ne/ixdeeAGlpaVF96UtEtFM9ixSlngscb9baquSL5VwWQmRw360UFnV5UZYDUTzkuks7/lc17VFyJxrWbc8prK/pz0PWV8aN3stuDaCb0dEnub6/OhxLWTmwYWUpKLmsHLGjRVMI4Jr9yUADnNvk4TVkbFb96Ak5VlhDCSAcJsHWe9LPG6sRPq9CkrJwjxnkikrUWAtUgYiJRAPhgdmfQ5eKn5PGUI3foIgiOaEcQbWyIjWjL6w6qRdTYrGjh1bcImCMYarr74aV199dQuOiiAIgiCaF9IUtQztalLU0iQGIPRUhve0xyAkArf75FKT9QP9jJCo8ePBEV2NkT6esrLdAzBBGEOLEcx+u8xHmnPlxu4xNSYOE1JAW4uyIiw9ziB87f4uI2lM7PEai1pwb7Rlx3aXV4EuGWogACEjwSA9pq6Xe44bvxUgU98nVTL4ggWWOgEgvF92qpPydJiagwUu9FIqCxAHABZGStBWIwEJD0zHe4SUEqkgmKPunzEGKWWk1Kyr3ggA6NaZgjkSBEG0N2hSRBAEQRBtHLIUtQw0KSIIgiCINo6OaN3YNojC0KQoDyxwAVdLZYHLOtSSlRYXh9GaYcrQbT5sK80RLFmppaXNORELhqA3U1pwnXKiPPOowNqOXu3x0B1e1Y1fjwcV2VrnH4MH1OTCZUAP0SWrNA+WxvQ94HqpK7xWfQ88zpAO/tjSliDZXvoTXBrhdZZJcC6DJcRQUG6HOjDjtq5b3wdfqLxuvoyKsNNBGIDytIeSFFdRqTnAwcJ8ZcxZKuNquTP8HJgSUFvPgb5eFixZMnUAACAZgz1iyVikPYIgiKaAeR645zW6DaIw9ZoU7b777kU1yhjDs88+i6233rpBgyIIgiAIgmhp6jUpmjdvHi688EJ07ty5zrpSStxwww2oqalp9OBaGy0atktuCaztDPEmA3yQ5iwMgMjAmYTHuXFv54xhY9YHkEfMzaPWFo0tsPYsi1Cah1nm49cQTb2m27ODRboicS2w1vnA0h5H1hexcboCb9ud3hVeC19bm5ixlnFPKZ21sJwboXdwvYi2awTcWige5JhLe8pKpoXVKrhiaMljgMlb5sswaCMHg4/w+pk1fo2U4QFX2G7fR3O/g/tJEATRlJCmqGWo9/LZtGnT0Lt373rV/c1vftPgAREEQRAEEYUmRS1DvSZFCxcuRK9everd6Pz589G/f/8GD6otoFJ0MGM50fuAeMA+N1iitlzofbauxuPA5pyAx2Cy1ttteFZ91RaPtK3TS4RBHK33TnoPAOAeYhYsYyUKghWqfqNWjrBfGWiQAktYkDHe1jnp823NT5juhEXaK+deJAyAH2iJ3JuaDlJ9RLdhLHOlKdWeHZwxxVmgcdKBFS0tEQLrEFP3Q2r3+iCII2MSHOE9ZIyBW585sz4fxgBmAkYCyg4VnOeEESAIgiDaD/WaFA0ePLioRgcOHNigwRAEQRAEEYfxJvA+a+T5WwIN8j5bt24d3n33XaxcuRJCiMixSZMmNcnACIIgCIJQ0PJZy1D0pOi5557DiSeeiOrqanTp0iUSzZcx1mEmRWlLXKtFyFk/KrQW0l52YmZ5SVgCbHu5Si0lcfC0WvbKComNQX96GU1jlri0UDmSg01hlr6sZTNXSK23teA664fHPQ5kc+FY7RLW0lqaqXACQsJEunYjYftafO5E+7axhdwKhiwLx6PGKa3jwWdhLZ0BYVgBnc+sNK0iWaeCaN9mmVP3EkSeVm74qh0p9T0CwNQymna39xLGbd9vvXQGKQDGI6UEubwSBEG0V4qeNl544YU45ZRTUF1djXXr1mHt2rXmtWbNmuYYI0EQBEFs0TDOjLWowS9yja2Toi1FS5cuxbnnnovy8o6d20kFA+SRgIXaCBC6Zge7HaG17Y6vt3WAxZJUaEnKCgHOgJqcAMAjAl0tWjbvrT611UmXxpUcocCbWWOTEgAPM8lrq1NWCGN9MmMUoVg8ei1SWb58GXFDjwWw5OF+N0SAvpe+JawO85vpBsK6ahyhlcgOjJlmHGnOkUkxYyVKB5YiHWQxgiW6llJZpLSVU0o7r5k63+Na1G6J2yEBKcCkTpgmnD44GPcgJbBx02aUl5WCIAiiKSBNUctQ9B0aN24c5s6d2xxjIQiCIAiCaDXqZSl69tlnzfuJEydi2rRpmD9/PkaOHIl0Oh2pe8QRRzTtCFuJtLEKWS7sMq6/cbEzv+dDWXgAzlQnXhDMkTMW0xbpgJEq5UbU5d9YcxBah0ysQS17sba1G75tLfKFiAac5E5wxmBbB0sEBIRkJhqkb3RVoaZIp0HR7WotEw+sY+F90MEhozotVYaaLjudCRBupz1mrGPKmhRaefR126X5BaD89eEhSPHBEMl2n+ZBO7o97Ybv55R1yNYUmTY5lGJJQVYigiCaEsY9MN7INB+NPH9LoF6ToiOPPDK27+qrr47tY4zB9/1GD4ogCIIgCAvuqVdj22jnTJ06tehzLrvsMvTo0aNedes1KXLd7rcEuGWJ0VIfj/NICg9XM2NbMmz9iw4iyK3UoTr9B+CBM3V/s0Iaq4vW1EQ1OqptjzHjecVYqG2KDCfQBknoxKdRrZCQyoqjgyBypqxbdhDJSJkKgyaqxLJBSg6EHmeqHTVGY9UKzkt7gdWIhalFPEsr5SaCte9pkkZLW8tSnqqT4sxYiThgJEqhx5m6l7Y8TBhrWqhp0hYirSfijIFLX1mJhA8mcqGFSJeMmxcHkEqVYM165VfYo6Jja+8IYktm+boNLdcZ5+rV2DbaObfeeitGjx6NTCZTr/pvvPEGfv7znzftpMjmgQcewLHHHouSkpLI/traWjz66KMdxiWfIAiCIIi2x1NPPVXvtGMVFRVFtV30tHHKlCmorKyM7V+/fj2mTJlSbHMEQRAEQdQB87wmebV37r33XnTt2rXe9f/0pz+hT58+9a5ftKXIFqTaLFmypKiBtnXs/GUaO4eYWyqxcFxgnZS5XuMxFrigcwjPyjovJLIiFDCrfF7MLEtpN3y9dGa75Gskk2Z5iINBQJqs8Hopj3tA1pKAJS0HuteY9VUW+6wQ1rYd9DFcRrRF6SY4Y0JgS5XtPh6EMnT759Ft+/q10DroV+crs5fG7PxuQBjMMaWDUPJosEm9zJZiAGSwZCZ8ML9WueT7uaDxIGgjAOmpPyUGNYbSVAl8KbF+4yYICXTtVJb3OSAIon2yIduC0hLSFAEAJk+ejFwuV2e9+fPnY/jw4TjhhBOKar/ek6LddtsNjCndxkEHHYRUKjzV930sXLgQ48ePL6pzgiAIgiCIYjjxxBPx2GOP5T0+f/58HHjggVi+fHnRbdd7UqQ90ObNm4dx48ahc+fO5lgmk8GQIUNw9NFHFz2AtoottNYBDYUMAhgGmedtMTEQdx23s7TbFiRtkRAS8KCtRR6yTAVxFFwibbn7a/d7YyniXGWCDywlxlLktMuY1J7zqh+rNEVKe5jnEToHT4ix/nAVDDItWSC4Di1A9nWaewNdT7usR4NFegiCUKbs+2MJ0o3QOgw7oK9ZBVhUFqCUF7rmM6h9Up8Ay12fRduLp/QIrFBShhYikQP8WiO2BmC54/sqaGNgNZIAmBTwAHiMQzIOMBXMUUiJzuVkMSKIjsCCFVUt2yHnTWApav9CawCYM2cOfvrTn2LmzJmxY5988gkOPPBAjBkzpkFt13tSNH36dPi+jyFDhuDQQw9Fv379GtQhQRAEQRDFQRGtQ/7xj39gv/32Q48ePXD99deb/QsWLMCBBx6IffbZB7NmzWpQ20VpijzPw5lnnolPPvmkQZ21JxizLBaBNUT4iKTH8IMAiLbVyNXDaEwQRQvOdPBDBsEllBVFAFBaHTdYYUmKGwuJ7eKflHjVl+q4NAlXg6CIzni4VKYVX8po8EfEg0J6PAx2qLVO2kIkLMuWfQ+0DkvfOz0CO7SBmybF1Ra549JaKh1CwOOBpsiymAmV/xVWApHQxd5EthTgWhPEGJiUkAxg2hrkZ5UFyK8FhDDWISZykIF1SOqEsMFnx/wcpBCqT6buoGQcHuPgXhrVGzeRtYgg2jmfr1oPIVW4E6Ll2XHHHfG3v/0NBx10EHr06IGLLroICxYswAEHHIC99toLTzzxBLwGisqLFlrvtNNO+PLLLzF06NAGdUgQBEEQRJGwJhBas/YvtNbstddeePrpp3HYYYehuroad911F/bYYw888cQTEc1zsRRtS7v22mtx0UUX4fnnn8eyZctQVVUVeREEQRAE0cRo77PGvjoQBx54IB5++GFcd9112HXXXfHUU0/VO6hjPoqeTn3/+98HoHKc2a752lW/o6T50K7eAtJkmk974VEgFAungyUYV1TtiqDdZSAgXJLygojVOmp2mofHtfjXMxGbnUjWelSW67tpF/oa9NhUaecEAwAmo425UbK5JUwWkKFwWyp3dimtiwrugb53emxAKNAWXEaWy2xRuh1awCwTOiEIdDTsFLfK4B6q69GRqpXAWt1bBqaF0zbWMhfXy2S6nshFcp7pc02p3fL1UhqTYExA6i44D5fSRA7pVAk2btqMzTlB0a4Jop2io/LzPA4qRPPSvXv3WGig119/PRaPaM2aNUW3XfSk6OWXXy66E4IgCIIgGg4JrUNuvfXWZmu76EnR/vvv3xzjaHPYVgthBTxUO20RsbIiacuIHSgRCEW/9e1TB1XUbQNW/rHASqStQFpIbFuIbGzBtT22JGyxdpLQ2rQX5C/T3QmmrFBm28p27yEI0ggGEQi+w+uz+2aR/vT1cWZZsKwxcoSWn0jJ7PYCy6W0gjZKqSw7dpZ77pkgjMaCJHxVT+QiAmttDWIiZ6xDdslkcHel6gvcA3I5SJ4CkzlITyh3/TRQlk4nfg7tldo130BmOsH3VPofW8yu7xGTAiy7CZlu9QvPTxBtFR3qpEWh4I2GyZMnN1vbDVIjrVu3Dn/+85+NF9qIESNwyimndKiI1gRBEATRZqA4RS1C0Xdo7ty52HbbbXHLLbdgzZo1WLNmDX77299i2223xfvvv98cY2wVuOX6rtJIMOMCrjPVpzk3+3U9W/tjXOdZaAXSbdlWmIh7eayPQLfEmWlXByfkjMU0O5ypYIg6PKF9DfYY3GuK98+C7PNBhnkl6wm3TeDE+Hk6bABnCLeD4ymuXmnOzUvv9zhU1nvdvzUO3Zepx8P2PGMtUroiZmmMdIqUFEMkGCMTucAa5IcvKcH8rErnkd0M5qt6kNJYj5i2GhmrkpXyQ4qwXSkAP2vahBRguVogVwuW3QSe3YzNGzdg0+bN2LR5c4s9103N5k2bsHnjBshMJ0BKeNmN8LIbwbKb1CtXY0r4WUgvg5qq4tf5CaItwbWmkyRFLU6PHj2wevXqetcfNGgQvv7663rXL9pSdMEFF+CII47AXXfdZdzecrkcTjvtNJx//vl47bXXim2SIAiCIIgCNEVC146QEHbdunX4+9//Xu+VqW+//bYoB7CiJ0Vz586NTIgAIJVK4eKLL8aee+5ZbHMEQRAEQdQF541f/iri/BkzZuDJJ5/EggULUFZWhjFjxuDGG2/EDjvskPec++67D1OmTInsKykpweYmtoa3KU1Rly5dsGjRIgwbNiyyf/HixaioqGiygbUV7EzxtojYLYHkqNUuruDZRJY2xy2hspVtHohHrgYQiRydL5p2ktjbFoNrbEE1EHf1t0tfSuu4M+5AYO0F9TweapvDzuLXoq/THoduz9wns5ypwhaopcnoeUz4Jns9A5RYGgiWyCyFpBRmGBLB8ppe5tL1g3rmFCvPmerAElybm5sDY1wJrIUSWiMQXjMpIKVQS3SAWlpjDDXVNSjp3L40eZs2bwb3a4MlwuCe5bs3dugCL42aDetR0qnjfV8QWw7Mcu7oiLz66qs4++yzsddeeyGXy+GXv/wlDj30UMyfPx+dOnXKe16XLl3w6aefmm3Xdb6xCNG8KveiJ0XHHnssTj31VPz61782CdfefPNNTJs2Dccff3yTD5AgCIIgtnha2PvshRdeiGzfd9996N27N9577z3st99+ec9jjKFv374NHmJrU/Sk6Ne//jUYY5g0aRJyOSUyTafT+NnPfoYbbrihyQdYDFdeeSWuuuqqyL4ddtgBCxYsKLotLQ4WkEjz5ACIxgXcnBSeq9Hu6Cazu3bVd4IlIqFtO0CjXdW2BNmBHXV/kfAB5lwZZI0PrC+RNpxrt/pwA1Ka0sm7ZtzeAwuYOT+4TunclyR0hnsbE2yShePyGJDymJX1PhCSS2lcv7XwWZ0cBHEUueh+IMxnFrxnfs5YlJiM/yIx+xIsILG6geu+zpWmEr55YLlaSC+l2uIp00ZN1RqUdOlR+Ca1Eps3bYoGvZQCXAez1OELAGWV4zz53oicul7hq+iaBNEO+WzleuP0kmtiK0ghGPfAGjkp0ue72SdKSkpQUlJS8NzKykoASuhciOrqagwePBhCCOy+++64/vrrMWLEiEaMumUpeoEyk8ngd7/7HdauXYt58+Zh3rx5WLNmDW655ZY6b2pLMGLECCxbtsy83njjjdYeEkEQBEG0GQYOHIiuXbua14wZMwrWF0Lg/PPPx7777ouddtopb70ddtgB99xzD5555hk8+OCDEEJgzJgxWLJkSVNfQrPR4Kxp5eXlGDlyZFOOpUlIpVJNYrqzAwq6QRldPZCNTp2hrUw2JqUGi9ZNKtUYgnrWmOwgjbq+OxYpkagtYgiCOVp6KL3fzibvWol0bnsenM9YstVHq3gYA3yh+5dBCo3oONz+7PG69wMIg1lyICwtKxZjCLVDjoXIDr4YwVgwAmuR7V6vq+igjFpLZC42lWwtYjy0mriBHRlXVhIOMD8H6aUAv9acwxhHbeVqZLpuFb+5rcTGTUog6RmLkO8EurSsR4xHf2bZOiv3HgGoqa5sdzoqYsvm62+r4UuJVJLAs7lhTSC0Dv72Fi9ejC5dupjddRk0zj77bHz88cd1GhlGjx6N0aNHm+0xY8Zgxx13xJ/+9Cdcc801jRh4y1H0pGjDhg244YYb8NJLL2HlypUx0dOXX37ZZINrCJ999hn69++P0tJSjB49GjNmzMCgQYNadUwEQRAE0RiacvmsS5cukUlRIX7+85/j+eefx2uvvYYBAwYU1V86ncZuu+2Gzz//vOixthZFT4pOO+00vPrqqzjppJPQr1+/JleWN4ZRo0bhvvvuww477IBly5bhqquuwve+9z18/PHHeT3jampqUFNTY7bdtVaCIAiC2NKQUuKcc87BU089hVdeeQVDhw4tug3f9/HRRx+ZRPLNwRdffIF7770XX3zxBX73u9+hd+/e+Pvf/45BgwY1SMtU9KTo73//O/7v//4P++67b9GdNTcTJkww73feeWeMGjUKgwcPxuOPP45TTz018ZwZM2bExNlAGNEaCMXCWhxtRMwMJi+ajZ07jDF1niua1k1oV3ZXpB1b+nIEz6p9qx2Wv15kzMGSno6I7WKHB2CMmUzzPNhO1TEH1se1lVctI0aJrLCYMcTV3lJKS4DOwmz3TC2heUE0a8aUG364/GUJroHo8pkU4TIbkFBHRvYZAXa+pTcgLia2+9LHATC9nCRFkA/NceUHANSids03yPToj2KpWb8uKjS3l/FsgnABYBwlFd1i7ShXexWF22Mc8LNhLjgb+x4IAdifNEdiqAIjPOcpNEDSSBCtigiWztyMAi1CC6f5OPvss/Hwww/jmWeeQUVFBZYvXw4A6Nq1K8rKygAAkyZNwtZbb200SVdffTX22WcfbLfddli3bh1uvvlmfP311zjttNMaN+48vPrqq5gwYQL23XdfvPbaa7juuuvQu3dv/Oc//8Gf//xnPPHEE0W3WfS3Uvfu3etUn7cVunXrhu985zsFTXeXXnopKisrzWvx4sUtOEKCIAiCqAc6eGNjX/XkjjvuQGVlJcaOHYt+/fqZ12OPPWbqLFq0CMuWLTPba9euxemnn44dd9wR3//+91FVVYW33noLw4cPb9Jbobnkkktw7bXXYvbs2chkMmb/gQceiLfffrtBbRZtKbrmmmtwxRVX4P7770d5eXmDOm0pqqur8cUXX+Ckk07KWyefK6Jrv9AZ5yPi33oIr11sgbNu1xUee5wVFGBH2kPyzLbQefkE2/nGbluM9Jjd67DbMRvB9aZZNHikEXgn9Ku3VcBMFunHCKs5C9zzg5AEttocyC+odvYnWYnMKYwHAnE3uAEKC6wtEbLUYuyEc421RGTVrz874KQUyK78CjJTXq+M8jXVlWC1G8CEUMEgC40RUGEBAMhUBrXrVkJ6GWMx2rxpk7GOQUqw3CZ1TnBtph0/B3CuBOOmXR665AP5+w+uUTJOYmui3fDl6vXwhQ6V0vKykZZO82F/5+fjlVdeiWzfcsstuOWWW4odVoP56KOP8PDDD8f29+7du6j8aDZFT4p+85vf4IsvvkCfPn0wZMgQpNPpyPHWTAp70UUX4fDDD8fgwYPxzTffYPr06fA8j4JKEgRBEEQHo1u3bli2bFlM7/TBBx9g6623blCbRU+KjjzyyAZ11BIsWbIExx9/PL799lv06tUL3/3ud/H222+jV69eRbcVzUCvXcLVMf3bP0ljpImkqkBoEbLDwie5oqv2rLbySG5UfQZuaX5sRKAN0mWoWWJGHxTRDkEafU9U+xSadewfRx5LtlC543QDXdpjB0KjgrEkGU2SjJyn9UQMgUs+A1JcWYmYSAi4GAneGLreM3u/xvlFJB2LhmkjX0BC2/Ve5Iy1xARtjI0jsChpd3xfxPU3ANimSmRrNkB6aSBdGnHVr9mwXmWezyr9TyQwpaNlChvUN1uXfvCztxa1uc0AT6nP1G1HhyyQjqYoCN5qPlpfRO4dfF0/sB4591Jb4uoTtLK2crXRNGW6t99ouUT7ZNGaajAAKR4GzOVITr3UbLRwROv2wHHHHYdf/OIXmDVrFhhjEELgzTffxEUXXYRJkyY1qM2iJ0XTp0+vV71HHnkERxxxRMEcKU3No48+2mJ9EQRBEESLQZOiGNdffz3OPvtsDBw4EL7vY/jw4fB9HyeccAIuu+yyBrXZ4OCNdXHmmWdi1KhR2GabbZqrC4IgCIIgtkCklFi+fDluu+02XHHFFfjoo49QXV2N3XbbDdtvv32D2222SVF9RFptGYbQPV1FgY6Kk009R3jtMRZZjXGz09uCaqBuwZ4WUrslEL3HSceLySXsxpuKRLy2lvXs9rUAuz6lutb8kbRtwbkaO4vVV+73oTssE35kOSwmnHavMcmNngVXFclfxgHGwlWoQpnftZiap4BAPM2EyhgvrXPMUpq5wbl4WzZ6icmvVe9zNcjWbjTtcpFDJDu9G1k633KftR3JZabHbkXvTiztcQOBMt4ar1TuB0ZwrcMfCAF4ThRwPQaeikTyrl29BLLUiSsWhBBgUqBm/brEUAIE0VzU5JR8wjjetMIYGOdgjYxo3djz2xJSSmy33Xb473//i+233x4DBw5sknY7zh0iCIIgiI4K88IltIa+WMdZPuOcY/vtt8e3337bpO02m6WovaNya6nfBakgkCAzVqDABd8KwAiozPF2IEZE3ybm9tJ9qf1xqxEP+nXL+tQzY7Dqe1aZ5BqvS88Zm27HPs+cbJXGwmPuBYvcA53V3h2Xa1nUViF3H2cMHBKQQbBGO1CjK6J2hMKRfflK+4bY5HNxTzhfeqn4uUgKAaDKiAXJPa6tRYCxBoUu83lsga5lyy1j7WcDa5AM2lcCbOaMmwk/OM+P9i2ib5gWWPuWlcmzwhMYAXY0f1pt5Wp1LzLlSgSedGk8RVYiosVYtm4DNmSFEVh7zndXG0rosEVyww03YNq0abjjjjsKJqotBpoUEQRBEERbh7G4R2lD2uhATJo0CRs3bsQuu+yCTCZjIm1r1qxZU3SbNCnKg6uDYYH1IEj8Ds4Y/ISAhkBUB+NqiIC4ESJ6LK7FsY/ZZdK5+Y4ntavHG2qUouckpfvQ6Gt0S3OuoxHS+/K1p13u82mOgCBoo3bBB4w1IWYlSkyfoTty9DuJQRgDV3V9BxIsL0Z/Y+twtN7GTq+hrUFJfQERa0zip1pAExS5hnzXYo3d1QiZLhKz2eeJHmoFmQxLy7ImJYzpyLYS2ePTWqM896OQpslPlZqh1q5bCYhcg1KiEER9qKr1zYoBc6xELQ7jTTAp6liKmVtvvbXJ22y2SdHgwYNjgR0JgiAIgiCagsmTJzd5m0VPihYvXgzGGAYMGAAAePfdd/Hwww9j+PDhOOOMM0y9jz/+uOlG2UpwFupr1A/hMOCggNIQqb0yrI94gEZNUloLHUgxrBNqcmAdy/frpJC3l30drvbHppB3Wex8RK9Ra4SMNYc5miO3neD8pHFwxsAQD2bJtG5IBznU20nJV41+qIDexry3rBoxvY9M1icBxgKixpagF7J/jbkpLhJ+qalkqyJ2yFhwkqxCSW3VoZcyY03q36pnLFYyT+n2HUuzEvoQqlQmTvBG4dyrBMteJICmpaUqLyu1KhXjX0kQxfHl6vUo9XjwPyD6HaZpSbuLZDyuP2xAGx2JRYsWFTw+aNCgotsselJ0wgkn4IwzzsBJJ52E5cuX45BDDsGIESPw0EMPYfny5bjiiiuKHgRBEARBEAWg5bMYQ4YMKbic6fvJDhuFKHpS9PHHH2PvvfcGADz++OPYaaed8Oabb+Kf//wnfvrTn9KkiCAIgiCaGsYaL5TuYELrDz74ILKdzWbxwQcf4Le//S2uu+66BrVZ9KQom82arPIvvvgijjjiCADAsGHDsGzZsgYNoq2jl7z0HNuXypzq62UzaOFyIHRGKBLW+7gT1DGeHT4qgC4mKGJduAEkNTzSv8qTVuj8SH2EyzD28Xx9Jfcf7VDd4zDHGrOF1EAorLaXzewSiGZq1znIrEzuWhgdNGh1nhBAMTK4BLGzxhJcR+q5AR4jYm4lypZ6aYhHhdBmp92OXSYt0bljq6fgOp8AO9a+KRME2G5pj98NGGcHdQTU/YH6bJLurV3qwI0169cp13/OUbt6CTJbDUj+3AiiAXy+aj3SnOVdOkuK2kG0PLvsskts35577on+/fvj5ptvxlFHHVV0m0Xb0kaMGIGZM2fi9ddfx+zZszF+/HgAwDfffIOePXsWPQCCIAiCIOqA86Z5bQHssMMO+Pe//92gc4u2FN1444344Q9/iJtvvhmTJ082M7Vnn33WLKt1BLTImgPGJKItKZ75yRAqh23LjUn7IeOiYSDaZpKQ2g1+GGmgHsES60skeCSLj0FdgyOQ1uOLpAEJz0sUWpvzEgTUgGU9UfJyZgtxLcFzRBCcKK62+rQCCNrn2mlBDFpwbY0nYjFJssI4bcSsV/mCJbrtaOuN7qpQ0EW7jF1wgmC5ju28YnG7Wet+hQJs/ZnwcDtpf+AukNcAJwTgheO276GxWjkvJgVq1y4H83PKuiUQTVdCEI3k62+rkfGYCuDL49/fGuZ8ZzY3JLSOU1VVFdmWUmLZsmW48sorG5z/rOhJ0dixY7F69WpUVVWhe/fuZv8ZZ5yB8vLyBg2CIAiCIAiiGLp165YYz2/gwIF49NFHG9Rmg+IUSSnx3nvv4YsvvsAJJ5yAiooKZDKZDjcpkoFwxv7tq7E1RkIGvygCQY0wFpjww7IlF8Ky0CRphYTRKNVnjGF9fU5SUMV8QRaT2mOMReq5mqKkIJX6eH01RdrN3ra8xNNgJKTasEsbR0tUUDfjtpdARF+Tz0qTL1CitqzoPq0Aj7odnSA2MfWHa0UqcC3Rm5qsCdLXbPoLxpi3Peta9CcQ00tF+rUfKG5pixK0RM547f7chLSx+2HfJ2tcMpWJj4kgGsDC1euNdSjJSmTrS1s8IAR5n8V4+eWXI9ucc/Tq1QvbbbcdUqmGhWEs+qyvv/4a48ePx6JFi1BTU4NDDjkEFRUVuPHGG1FTU4OZM2c2aCAEQRAEQeSBJkUxGGMYM2ZMbAKUy+Xw2muvYb/99iu6zaLv0HnnnYc999wTa9eujeQZ+eEPf4iXXnqp6AEQBEEQBEEUywEHHJCY36yyshIHHHBAg9os2lL0+uuv46233kImEzVZDxkyBEuXLm3QINoqthDZzjwvHAG2yZzsKbd7pnWnVghgvQQnGGKZ592yPstcgJWTzUKfYwvF7az3ntW/m9/NzmqvXe7tHGYMcNxSo66qDGEmey0y1yVjlrBaOKJnd0nGdcXPV9oCaxEsv/lRgXRBMbEt9Ea4NBRbgkqIuByhgLu+HQog0qS1NKbbYNa57tKYW78YwWQsz5mzROWWoaA6fl4isWVO+76GQvvY6Rywc8Uxvza+BOgu6bnPDmBc+gmiMSxduyFww2dRRxsby7nEQ93f0U0KWYpi5AtN8+2336JTp04NarPobxMhRGKUyCVLlqCioqJBgyAIgiAIIj+SsSbwPmvJWVzzoeMPMcZw8sknm9iJgIpi/eGHH2LMmDENarvoSdGhhx6KW2+9FXfeeacZVHV1NaZPn47vf//7DRpEW0QFUARQ4BlyBdiha7va9oVl9XGzzKOw0NoVUNvYgmZ3pqxE38mBxQqJA93AjC7aSmRbsGxRtt2GDlgpnEFIxkJrUawDyxJRyCphfykk6J4BxAXVdoVIPyZYQKTt2AidLyK7fTvgYEz8bKwcLNw2Y2CBlcQ3wmTJvagiP1/pXosrjM4riLbGI/xw7NY1Rcapx5N473h0XG4/hYTZdvBGKcLgloAJalmwHc6VRTCol+nWO94HQRTBkjXV5r2xtiPIcZkQNLe+1nyieejatSsA9T+ooqIiIuXJZDLYZ599cPrppzeo7aInRb/5zW8wbtw4DB8+HJs3b8YJJ5yAzz77DFtttRUeeeSRBg2CIAiCIIgC0PKZ4d577wWgZDsXXXRRg5fKkih6UjRgwAD85z//waOPPooPP/wQ1dXVOPXUU3HiiSdGZmvtHZlg0TAamuDXgmcdi2httOWEy+A8QEoWPeZYeJK0RYUwVhkdtBFR7ZBxG3U0RPY6uNtDXa77jDHTvj2GeDuOzglSmV9czY4bINHRooT7naCJtqbIdsW3x+q2kfjezbVSPyfbfLqemJXITXdhv7etNsyy4RkhmweTUkPvt59JN1hjXcEdIwONu8tHxp4vJILbflLemkggzOjnnFdbZAdfFEKdx53x2CfZVqYO8iVPtA7frt8IX0hkPBax/OvvX8/ZNnojqO/algzeSLnP4kyfPr3J22yQQjGVSuEnP/lJU4+FIAiCIIgkyFKUyBNPPIHHH38cixYtQm1tbeTY+++/X3R7DbpDf/nLX/Dd734X/fv3x9dffw0AuOWWW/DMM880pDmCIAiCIIiiuO222zBlyhT06dMHH3zwAfbee2/07NkTX375JSZMmNCgNoueFN1xxx2YOnUqJkyYgLVr1xpPtO7du+PWW29t0CDaKkKqBQBtuHeX1BKX2KCWlbRp1eMMnnZJZ+oYt8yuuo1iSztydF0W0WJMvLZLf9L59jiEDATpSBZnm3MK9W8v++ibZP8i0u+TXl4qXnopSPeVyqgXTylxMeOq5J4pC74Yz1/q8dr7g5fuT3oplbcooQQL8hkFYzJjBMxYw2zznnWPmNpWH1C4bb83Y+SI3F9dr65rjNS3lwOdMvFztR8gHl4nEC6N1bXkxzgy3fvG91vnS4/c8YniWbp2A5ZXbkDW+iK1l87c71sgKhfg1nd5S6FznzX21ZG4/fbbceedd+L3v/89MpkMLr74YsyePRvnnnsuKisrG9Rm0Xfo97//Pe666y786le/ikSR3HPPPfHRRx81aBAEQRAEQRSAcSRmvS/m1cEmRYsWLTKu92VlZVi/fj0A4KSTTmqw41fRP7MWLlyI3XbbLba/pKQEGzZsaNAg2iLKyhNu266YSb+UjXO3I0QW0g5oqCpKKeExbYHSFqPkX+HaLd8I/zhLDsaY8IslIqwOgjK6Y7TbdnOXRepZ42II3OsBE9RR1WcRN3xzir0vKTCiXdqHgj9gMwxXXJwv91isoag7t4rV6JRIuIW6PX3ALaH7c+oBoQVDW2l40J5+qPQlJIm2OYcEj+Rwi7ml227zNkmCacD6MPIItO1r0fUjY0oQftslYAnlZfyYrsIcd3y7dIJxAjnUrl0eD9roh+dlevSP9UEQ+Vi/cRM2ZAXSxs8h+F5l0e8y+/m2reC2o0mLiqyJRPr27Ys1a9Zg8ODBGDRoEN5++23ssssuWLhwYeJKTn0oeto4dOhQzJs3L7b/hRdewI477tigQRAEQRAEUYBCUoJiXh2IAw88EM8++ywAYMqUKbjgggtwyCGH4Nhjj8UPf/jDBrVZtKVo6tSpOPvss7F582ZIKfHuu+/ikUcewYwZM3D33Xc3aBBtETsAopDQkRahf0vYVpR87utSSnicwRfSuHCq4HrKauTB0ghpF/rgfDsbsxu7T9ez6xdL0piTMtwnuegnzb8lEoI1mhTrgbULPJIuQlmcopnag16DUulYpIxahvJlis+XSd60ywNrSJ5S5rNa5bNIFQyQGH4BRVJ5iJwakw5SyEX+PjxVaqtR7AvNSyWHJbDvsZ+L6wj0eV4q/7W4YQ5EeO8Tx2vc760wB9b1xyyESf14KcDPRYM7Btv2Zx2xnhFEEVTXChW7FIgEz/WD7zf9DZ+vBKKa0Ba1FTXFpKaD/c3ceeedEMF3xdlnn42ePXvirbfewhFHHIEzzzyzQW0WPSk67bTTUFZWhssuuwwbN27ECSecgP79++N3v/sdjjvuuAYNgiAIgiAIor7kcjlcf/31OOWUUzBgwAAAwHHHHdfoeUhR08ZcLocHHngABx98MD777DNUV1dj+fLlWLJkCU499dRGDYQgCIIgiDzQ8lmEVCqFm266Cblcru7KxbRb7CB++tOf4pNPPgEAlJeXo7y8vEkH1FYw8lNLO2qCDbOoSdVLWDrTJWcA91hsaSp0rbfc3aGW1IRtqzVLVkEEbC2wRrTU40oSSrsC66TI1PbyWNK1RevXz2icJLR2l2kSl1XMchNCca8VhVm3pl3ZTd4vZ7lKHY8vv0Xzd+VfDsu75JPnWuyxS/sLyA5p4KUjY4QU0aUgt9TRnL08Qmu9BGYLu63jMpWJjlcfN4Jv5zzTvrNMxhEXfhe6D9Z+8yhb9Yzg2o1c7S7ZWaU+n7kRrgmiDlZVbTQOLkAob7CF1kCyi32SoDoMXdJyC2iUEDbOQQcdhFdffRVDhgxpsjaLXj7be++98cEHH2Dw4MFNNgiCIAiCIIhimDBhAi655BJ89NFH2GOPPWI50I444oii2yx6UnTWWWfhwgsvxJIlSxIHsfPOOxc9iLaIQDQTvf1r37aquNYZXd/jLGa1YYAR9GlXe+7IlrULvy3wU+NhieJu18ITZqovfH121mc9FjfbvdFJIyo01IJq7YKfVIb9IBRaOzm3IhndWZ4M78YyAcuiwyLHTcZ5IFGA7VJIkG2LgmWC+FnyVDx3m5vJPhiXdKw+krHQamZZPUQ+ATMACN/UixxPutZ8YnBbeG1fq85Ozz3LnV5EBeF2vxyAyEGyMKu9FnLXJX4HEBWEu8JwuwQABCZxXc93xfgEUT9WVW2EL2XEQUVvu0JrP/j+FVKFP5FSJn7PalrU8EJC6xhnnXUWAOC3v/1t7BhjzASXLoaiJ0VaxHTuuedGOjcPVwMGQRAEQRBEASghbAwhmv5HUoOCN24J9OnaCTVg8G03/OCYtvpoPGch2rYisch+ZXPhjMGEBNPpPoI6LI/2KKZb0n2zZNf6xGz2drDJQO/kWqDstu1xuxYgPXSPMRUmwASAjNfJSz4djfNrxrZYJR13rTJ1rbsnao8Syvh5+rOKBkyMaKacuoAVmkACUgfjDIIumrZkQp9SgGkLFbxEi5RkaeckV22mgy6q8yW3SgDgwfkMkfpG+yRV/ywYD7jlxo+6711k29EIxaxFjCPTdStTvXbNN5H6ea1pBJGHyg2bAi0Ri5VA3Pqjv8vTPLC8B89nNGij9V60oBGALEUF2bx5M0pLSxvdTtF3aPDgwQVfbYE//vGPGDJkCEpLSzFq1Ci8++67rT0kgiAIgmh3FPv/dNasWRg2bBhKS0sxcuRI/O1vf2u2sfm+j2uuuQZbb701OnfujC+//BIAcPnll+PPf/5zg9os2lKko0e6MMZQWlqK7bbbDkOHDm3QYJqCxx57DFOnTsXMmTMxatQo3HrrrRg3bhw+/fRT9O7du6i2enUpx5r1G5ELxDYCoXeYCcSIUEMERPU3EnEri1tqfU4kMBjiWiJXI8QhI8uWdmmvgSfpnVwtEZBfo2QHZbS1RGF7UeOFazSJ7HetSFKG1hdbjhPsNyUsr7MEzZFtuRBgsfEUGl94/cnaKLuElBEFmPtZR7RXQd3EY8HFJAeGs/oEh+d55p6YU517lq9076U6yQ26iOi91qXwjbYI8CCFbzzlJE+FwSeTPhO3n8iNzq8pilnoWKACsVOx2OPmlAyWyM+a9RtRK2SihkiX9ne5x6Lf5ea7GjL2N6JpSY1bUyR0Lfb8Yv+fvvXWWzj++OMxY8YMHHbYYXj44Ydx5JFH4v3338dOO+3UqLEncd111+H+++/HTTfdhNNPP93s32mnnXDrrbc2KFQQk0UmCOGcR7IIm4asB+273/0unn76aXTv3r3oATWWUaNGYa+99sIf/vAHAGrNceDAgTjnnHNwySWX1Hl+VVUVunbtisrKSnTp0iU+KXJy5RiX9TyTIi/P0pMmFgXaOlcdr3ty45buefbymVsfQGwSleSab6Ovpa5JR75/1OZ4fSdFSf/QTSMtOCmyPhf3/iRNcApOivLsd/s0z1WrToqgBN96UsSsiNy6bMykKJjcSJ5CSZcepmrt2uWxCNeuW39mqwEgiCTsSZEWWAOIff/l+y43S2wFJkXr161B7/4DzP+L5kD/T1qxfHmj+6iqqkKfvn3rPd5i/58ee+yx2LBhA55//nmzb5999sGuu+6KmTNnNmrsSWy33Xb405/+hIMOOggVFRX4z3/+g2222QYLFizA6NGjsXbt2qLbLHraOXv2bOy1116YPXs2KisrUVlZidmzZ2PUqFF4/vnn8dprr+Hbb7/FRRddVPRgGkttbS3ee+89HHzwwWYf5xwHH3ww5syZk3hOTU0NqqqqIi+CIAiC6Ki4//NqampidRry/3TOnDmR+gAwbty4vPUby9KlS7HddtvF9gshkM1mG9Rm0fbn8847D3feeSfGjBlj9h100EEoLS3FGWecgf/+97+49dZbccoppzRoQI1h9erV8H0fffr0iezv06cPFixYkHjOjBkzcNVVV+Vts0dFNDjlmvUbjUUlZcys6pgR60UEzdFtIGohcY+5lgzmCrPthmEJevVyHMJfP7q0LU1uYElNkji8UGAyp9tIaYwSMl7f3i/B4F6WfU2xQGO26dd5b+rKeP9J4wGiVjoJBKJ6VTLruN5fV5iDyDVYv0Q1bm4l1Xb0uDsGX6jPX1oWMEhr7M61GaG+hDkvSeAN5oXnBUubUlqfiRFi66UyBli560xogqRgk+YmWO9FnvdWPSZyqK1cHdTJwQRzTBBa09IZUYh11RuNpR4I3Q4iVnMgEswR0N+X6m+NSWmsowzKMsmFExqiBYXWKnhj/u/k+rYBAAMHDozsnz59Oq688srIvob8P12+fHli/eXLlzdq3PkYPnw4Xn/99Zie+YknnsBuu+3WoDaL/mb54osvEs1uXbp0MSKn7bffHqtXr27QgFqaSy+9FFOnTjXbVVVVsQeGIAiCIFoTKeM/7BrSBgAsXrw48n+8pKSkcQ23EldccQUmT56MpUuXQgiBJ598Ep9++ikeeOCByBJeMRQ9Kdpjjz0wbdo0PPDAA+jVqxcAYNWqVbj44oux1157AQA+++yzVplYbLXVVvA8DytWrIjsX7FiBfr27Zt4TklJSVEPhM5670sglxPwuEqZYf/ScLUgMvgVbowZ9bDA6PpchwNwzknSuoQWnrqDOtpC66TQ9roP3be+prp0LPqPLqLJiQi6k8ctAXjamuG251rawBN1NbF+69AGJWm3fJGstdL7XS2dOS6TxNRR0bsv47ouu42sSNaB+bodER+Tfh6TdGJZkRB4TubROrn7g8vkjIF5aTDhh+k1mAcI3wiuIxojFztNiPkA40ElzbbWMNnHA2tRJOgk55CZTrHuiObH//o/kJlyQArwzesh1q5Aao+JrT0sw7frN8K3zLqusNq22Jpt/f0G6+vGeTb1M250dnaddkaXLl3q1BQ15P9p3759i6rfWH7wgx/gueeew9VXX41OnTrhiiuuwO67747nnnsOhxxySIPaLFpT9Oc//xkLFy7EgAEDsN1222G77bbDgAED8NVXX+Huu+8GAFRXV+Oyyy5r0IAaQyaTwR577IGXXnrJ7BNC4KWXXsLo0aNbfDwEQRAE0RQIKZvkVV8a8v909OjRkfqA0iE35//f733ve5g9ezZWrlyJjRs34o033sChhx7a4PaKthTtsMMOmD9/Pv75z3/if//7n9l3yCGHmEBXRx55ZIMH1FimTp2KyZMnY88998Tee++NW2+9FRs2bMCUKVOapP2uncryHtu4aTMAhOvRgFqT1raSfM9jQkoGFbTP8mSyvB8A2zPCNfOoTtIJ5h8RWBzMIPVbp16SlshYvhztUD5NkRt6QB/nYJE/TGaVtpee227kCh3Nlv13ni/0gV2KQK9jfxzaoqLTs7hpXOoKf2AP07YWFfL+c/t2rToAYr9w9Q9gPaZIKhoLvV9bkfKlcClURm59kIZFPcdW2pOkBLEuSZ5pSdtuoteEX+Hkcda65Jb8F7KkM2RJJ7BcLYQUYJ2ax+uqIdRWrgZ4ecRiCgBgLGIxT7IGq+MsoiWKpNthLCxNnUauZxWBlv81to1iqOv/6aRJk7D11ltjxowZAJTmeP/998dvfvMbTJw4EY8++ijmzp2LO++8s5EjL8zcuXNNovrhw4djjz32aHBbDVIrcs4xfvx4jB07FiUlJTFBaWty7LHHYtWqVbjiiiuwfPly7LrrrnjhhRdi4i+CIAiCIPJT1//TRYsWGWMIAIwZMwYPP/wwLrvsMvzyl7/E9ttvj6effrpZYhQBwJIlS3D88cfjzTffRLdu3QAA69atw5gxY/Doo49iwIDif0AVHadICIHrrrsOM2fOxIoVK/C///0P22yzDS6//HIMGTKkQcGS2hJunKJiyGcpqjO0er4knnYVx1Jk9ufxqEqy9ggpE38p1MdSlGixkfn35ztf2p5TCWjPj2Io9sdaUswhtT/ZWmNbY/JpgsxYgtK+hLosRYX61ufpdpI84Oy23B8orhdcPv1YEuaXs36e7V/PwTYQjR0UsfrYZWIHeWJOJWF5rJGlqPXILfkvZKrUWIpY7Qaw9avhDfteaw8NgLIUreflMcsogHpbijgSLEVJ4kkpsL6yEr0GbdMicYoWfdM0cYoG9a9/nKK2zvjx47Fu3Trcf//92GGHHQAAn376KaZMmYIuXbrghRdeKLrNojVF1157Le677z7cdNNNyGQyZv9OO+1kNEVbKuVlpSgvK1V/L477pOtmHpnc5Js0Wf8g9CSLSRl5D8SXmvItQfHAhGwvWdlwxiKTEsbCV2xojsDZLd0o2NpzQi+R5Vu2sesXU9r92eN3x+FOiOzlqKRI4LYQs5BI2p6rmCVPpw8t/tSl2S8R2xbSKp3zdN++kEowb50vpTTHk0q7PWG1b5fRcUjVD2PQeeUkTwVeAJ7ax1NhtN18Lxt7n1sn6TzGldhav4gWx5//CgBAlHaFqOgNUVIBUdYVMl0GlLWdf66VrNz8HQDxHxH2Urje5s73Zkz8n2dCFKnbAui/7ca+OhKvvvoq7rjjDjMhApSc5/e//z1ee+21BrVZ9DfMAw88gDvvvBMnnniiSUEAALvsskve2AUEQRAEQTQc/YOlsa+OxMCBAxODNPq+j/79+zeozaInRc0RQbKjUV5WirJS9WJSoLSsLJZNWS9H2C8mBZjIOWZbkfjSViIOZTnSpceV+dfdr/d5nAVhBFSZ0tt5zrNfZtywhdNxaxVjSrBsl64Vy9NWKSBSJgm66yrd/iLjyyO41hYzN2ill1ACql3OoiWzxquDZOrSfa/biJwTlKmgTfs8WH3blip1bdHzOYNplzEWq5dU2l+O9jXp9qJlglXTWHO0ObFIoXV9xNf234AIX7VrvsnfF9E8dOoOf/FHQCoTflf5WUBKeIN3ae3Roaa6EuuqN4Z/Q/qZtv6OPc6Q9tT+VBBKJTzOghAowfeZnd5Gl1JE0t2wpOeYaFFuvvlmnHPOOZg7d67ZN3fuXJx33nn49a9/3aA2ixZaN0cESYIgCIIgCtPBDD2N5uSTT8bGjRsxatQopFJqOpPL5ZBKpXDKKadEMmusWbOmXm0WPSlqjgiSHZmSThXqjRFQB+kVGFMB8ixhtRHp5vn1oZNxGqSA5F5iokJ3O9aW/ePfEnAnnqeDl9n1guV1AQZu9V/fhKz5kuQWOq8+pXs9Gjv4pB3MUafTkIjuTwry6Ja2G78bQNHWJbli52KT+RbSNdnnJaUWcevpsdqhB/KV9r3jUClDOCQk9wAB6EB2YfBGL57NPsnRwNmf6dYbtetWRp61yLNnnyoF/ThvBbzBuyC39BNkuvVGzfp1gMgCIod0n6GtPTQAQJVIm/d20FoAwd+9jO23/941kjFwYT+7+sslIeBoC9MUy18dbfns1ltvbfI2i54UNUcESYIgCIIgiGKYPHlyk7fZoDhFOoIkQRAEQRDNT1N4j3U07zPNypUrsXLlSggn2fTOO+9cdFuUarqFYEHWb70EFmYzD8Wk9rJZLPaLjnRt7VPLFn5QH4llbBxIXpqo6zx7CU21A9gRt23ht9nvlDrTu6oXCp8ZCyN0KwF68vluOyxY+rKPh9cTLOkZ0XJUgB1mzw5N53oceolNoyNg2+fp/WmuTPApHi4/6vvjsbDhyPKbDkkQCJ49Y9cPtoOlMm3212Uh4TRn1sW5OOr0iEAbUUG3XdqtmeVHvTdY4pI8FV3yYrLuZQZnf+26lXHHAtOxEwsp+BuoXb2E4hW1IOKLd8EB5JZ+YjLOp7besTWHZKhdtxLc6wzAXhoL/z7034b9t+t+DwCW8wtgldLZduMXtdxSmghejW2jI/Hee+9h8uTJ+OSTT2ITPsYYfN/Pc2Z+6jUp6t69e72jVtdXzEQQBEEQBNFQTjnlFHznO9/Bn//8Z/Tp06dJsmvUa1Jki5m+/fZbXHvttRg3bpxJ8jZnzhz84x//wOWXX97oAXVUIhnFbcG084va5JICEkWq9vmJ7ST8Qk8SaNvtJrbnnqdzXTnC7kgp/Pg4tEXBEWIDUeG1lMrKlE8wnlTaxyPX5QjDkwTgkftjjcPdtoXXrjDTFWx7Vume67F4zrV8AmdXtJ1PqK3GEOZeKyQKt/uDsz9yLwq044v4PWAMynlAcnM+4EFarstJn03is1pAYJ1Yh2gxRJX6sZvadu9WHkmcmkwFRE6aIKq25dP+29AU/L8Ze0bbjtBayuj3VEPb6Eh8+eWX+Otf/5oYJqih1GtSZIuZjj76aFx99dX4+c9/bvade+65+MMf/oAXX3wRF1xwQZMNjiAIgiAI8j5L4qCDDsJ//vOfJp0UFZ37rHPnzpg3b15sEJ9//jl23XVXVFdXN9ngWoPG5D6ri9o13wBeBpmuW6GmujK/yzIQX9M25hVtebEsSgnbsbYa+Qs7YmnS74v95WRZjSK7k0IA2P3Y++tjTbD7a8S12+EHTJN53P6TQgMktmlZoADnFywKW3tiv3gT2s+X866h6H512/lCKWiNV73yo0U6cDREjp4odl7E+shJV7SFU7lhk0lbA4RaIq2XU/viWqK84UtimqGopihi+QRQtW4teg0d1iK5zz756htUNLKP9VVV2HFI/w6T+2z16tWYPHky9t57b+y0005Ip9OR40cccUTRbRYttO7ZsyeeeeYZXHjhhZH9zzzzDHr27Fn0AAiCIAiCKAx5n8WZM2cO3nzzTfz973+PHWtWobXNVVddhdNOOw2vvPIKRo0aBQB455138MILL+Cuu+4qegAEQRAEQRSGvM/inHPOOfjJT36Cyy+/HH369GmSNoueFJ188snYcccdcdttt+HJJ58EAOy444544403zCSJSCbTI0xQV9K5q3m/edOmAqLUqNCvrmWzJCG2vdjhHi94XkK9ekUpzlciWTht7y/Ynhms7QIeF0AagXiBfmPtJGC79SdFzLYF3FpM7Z4LOOcGbemlqEJLZHZZ15KaFnIDcXE48rTjUmgcDDDRv/2EcXtgAFNR2sNnTkCnV3R/n0aeTcYBkYt/xpxHImS7zzKg3LEz3XonXA3RkanZsB6bpAdf2n8D6hhPeLh1SAkAsb//wo4izvdv4DDTGkg0gdC6SUbSdvj2229xwQUXNNmECGhgnKJRo0bhoYcearJBEARBEARBFMNRRx2Fl19+Gdtuu22TtVmvSVFVVVVRoqz169ejoqKiwYPa0igtK4vtq61crQLjAQAP8qXpXymW9aZepR2kEYAJApkQFLJQO9KyxhQqmRThLxLnuM73ZoJA2mWS4DZfsLSglNY1Sue+6JxcknFw4QTNdPsBEq1RbrBKsz/4VckhIRFkqLeCSdrXBscKxqAsV8aekiBeTgoF4OWpo7FDBehSOP3aZeQ2WGUhqxSsbTj7ow06hnpbnA9HuG8fL0Dis5zdXOd5RMeids038Mt6AL4IA6QGMMYSBdaJViKrNMFrtRUoj9C6NRPvCSkb5TSh2+hIfOc738Gll16KN954AyNHjowJrc8999yi26x38MZly5ahd+/6mam33nprzJs3D9tss03RAyIIgiAIIopE45e/OtaUCLj77rvRuXNnvPrqq3j11VcjxxhjzTcpklKazutDNpsteiBElEzXrQoe1zqkSKbyfJog92TGo/utX+mF2jF169AOJf7h1aExylvPGVdd2+54TWmno6iH5qngfiASxBIIflCy6K/RQoEozeUmRFHMFzAyn1aoUD2gfpok1U4QPDJP6dZz6yvtlPWcqNowN4gxAFyVgaVNBnXz6jSiBqZImhu9M7vyK8h0KTLd+ya3QXQINm/cgM2CAZlugK8+e/tZ9JLERAEmZIbWDgH5vy+TNEXcizyzrms+0TosXLiwydus16Ro0KBBRXmW9e3bN2bGIgiCIAiiYVDwxvzU1tZi4cKF2HbbbZFKNS6la73O/uqrrxrVCUEQBEEQjaAJ0nx0tPWzjRs34pxzzsH9998PAPjf//6HbbbZBueccw623nprXHLJJUW3SYmE2imlZWUoLe8Elo268+crAYRLDlKo5QprW1PwfN1GUGrha1LpvhKF0lIoU3YBIbU7rkjf9jiD5RdT5jkeaT/B3T9RiKnHae0PTfBSvSzLvXSW0ZJK+xyPK7d+/dLLAG6ZJKTOd9xdOoNTJgmo9epDvpIFL9f12RzXDcU+Syfct7UMGannbic9A+YlIy9Wuwm1q5eA6LhI7sHjzLwYi5acqWeTm/2IvqQEE37kO4n5WUD4qrS/F4UfPl+AWTrLG22faBUuvfRS/Oc//8Err7yC0tJSs//ggw/GY4891qA2G2dnIgiCIAii2RGQEI009TT2/LbG008/jcceewz77LNPJGn2iBEj8MUXXzSoTZrutnNKuvQAhA8ZiKe1G78rNAagft3YQlj75RA7P6GMiZkdwba7LxYEMqlMOC7dvhL6DgXQzvXrbVN66nwT5iAorSCSBcdnCa0BQIBBMhZ67sqo0DqxbQf7XNOGY1gBEAnOaJc695O7vz7BIAEllFbnFS6190tSfSElfBGIyGP3zLUSWaalpOdPb/Pobzb92asXQ9Q8J8ByNWQt6sDo59wL3O5ti6W0nnkgzzKTY/2FtvYG1iFlLbKsQ4Wsyq0gsHaMow1+dSRWrVqV6BW/YcOGyCSpGGhSRBAEQRBEu2PPPffE//3f/5ltPRG6++67MXr06Aa1SctnHYCSLj0AADVVa5TrqBvcMcHFva5fOpEgj/q9FMYd1f7BkeQK3+AgkO74CmmL8mxHrGPOteugi4X0PnnHYf1a1PU4JCD1/UIk/EFdbbuBIe1SBulDAMsrOI+mKF8JhOlH7NLVFHmBu319NEWw6utf6uEv9rDfeHoYy+QVK/M8i3q/cFLgFIDlagoeJ9onmzdtQkq71EuprEOMgQdWypTjjm+n5AFgvr8QaBjN82RrERkH82tDa3Bd31MtbC0i77M4119/PSZMmID58+cjl8vhd7/7HebPn4+33norFreovjTIUvT666/jJz/5CUaPHo2lS5cCAP7yl7/gjTfeaNAgCIIgCILIDy2fxfnud7+LefPmIZfLYeTIkfjnP/+J3r17Y86cOdhjjz0a1GbRk6K//vWvGDduHMrKyvDBBx+gpkb9MqusrMT111/foEEQTYO2GAFxbY2hgHbHlFqzY+l5Sss7xXQ9ur5MOoen4logezwNKO127e287bvaIXs7j94nUur6BTRIRlOUoBuqq4+66pnjdWiK6lv6ebRG9dEUudokextwPN7MdSRoi8xFCevnfFQ3Zs4x2iLnuYs9w2G7TApkVywE0TEx6YACj1DbW1NbKiNGScYsi2NgJTIeZoFHapJnKhD1ZHW1kfm8c5sRLbRu7Kujse222+Kuu+7Cu+++i/nz5+PBBx/EyJEjG9xe0ZOia6+9FjNnzsRdd90VCdC477774v3332/wQAiCIAiCIOqL53lYuXJlbP+3334Lz/Ma1GbRmqJPP/0U++23X2x/165dsW7dugYNgiAIgiCI/DTF8ldHWz6TeS6opqYGmUymQW0WPSnq27cvPv/8cwwZMiSy/4033qAEsG2Aks5dUbtupXJn5l5coKoD32mkH5ba1OyIZJkU2LxxQ1TEnE+AWEAgHRNA11Xayy8F6sWWCK3z6yuoNvtNUEbEzrdzl7lCa7cd01aBMcT6dvowx4PlAI+zegmu3XoAjMAaQCyIY10Ca533LBRax11dGQtcpe3rT3rm7OdCB8SEswzhLkkIZ6nCbc/5XmQih+zyL5Duu21snET7w1/4PthW20SXXwOMQJpxAFw9s/pZl4gIrJnI5V0qy+tQodt3c/NJoZ7Lllw+k7LRWe4be35b4bbbbgOgvM3cvKy+7+O1117DsGHDGtR20ZOi008/Heeddx7uueceMMbwzTffYM6cObjoootw+eWXN2gQBEEQBEEQ9eGWW24BoCxFM2fOjCyVZTIZDBkyBDNnzmxQ20VPii655BIIIXDQQQdh48aN2G+//VBSUoKLLroI55xzToMGQTQtmW69UbtupbKgcE9ZU3jK/OoBHBf9SEA9HtZ3fgXFhLBuULM8ZeS3SX3OA2JWGck9Y3lR4/NiVhsZGb+X1/JihiKlyW4vpbL65DtPBmJjARU4LkkM7boBu20JMNWHe21OX6YPGRWN+kIaQXOh0hZmJ6X80J+Hm+3eBGmUEowxSCnhcWaOKzf8wL0/sEbZ1xyxbiWFWGDJGcltkbwragUA2NkVzHERHtTtRz5bQdaiDoLovBUigT/tlBuMA9IHY+rz199bkZAQtpVIW3g4Ty69VN3fTyIXholoQcuLL9SrsW10BBYuVM4UBxxwAJ588kl07969ydouelLEGMOvfvUrTJs2DZ9//jmqq6sxfPjwiPmKIAiCIIimg5bP4rz88stN3maDgzdmMhkMHz68KcdCNCGZbr2RXfkVZCoDeJnw15X+hR37Ja/NEvmDLkaqB/vMn1g9tUUF90fcXgvocPJofZJShsQtGJb1J0gXAASXDhZrF9Zxu3Tbi3iba9fgpDZk8rUlaYiAqLVIW2fyla5Lspsg1tUS5QvaqEN2MsYiwd4YQuuT7se4SAPgIkEXFNFY5UKNB0LXZmYH/Ex6Lqx2Y0Eh7b4ctHt+us/QxONbOp+ecRR2uPPJ1h5GQbIrFkJmykzCVoOrOUTwbGgdH8IEzkwKwK9V1h1tLdcmE6eUvtYOCRMKwvSnS+3Wb+8nOgz1mhQdddRR9W7wySdb749syJAh+PrrryP7ZsyYgUsuuaSVRkQQBEEQjUdIaWKNNaYNojD1mhR17drVvJdS4qmnnkLXrl2x5557AgDee+89rFu3rqjJU3Nx9dVX4/TTTzfbFRUVrTgagiAIgmg8OvFyY9sgClOvSdG9995r3v/iF7/Aj3/844ji2/d9nHXWWejSpUvzjLIIKioq0Ldv39YeRpsg3XsIACC7ahGQLlWiZ56CyU5ui1YdQWGSGDbiVq8F1HUIpl0SRcbgya7o9agXEUCz6HKTFlDbouUkEXNEw2m1YyvEbdGyLTLO1x4QPabHYsbk9q3rOX1xxozAWpd1CbBj2zKe76yQ4BqIC6/1kprHwnxq6qDluqy3zQeTHOKhTmwnACAmhpVeCkwvc0hfORMEyybudz4TOWRXLUK616D697+F4G/OtvYQ6kQvfdZWro4KnYG4+3zC9xeA+NJZomNHNF+k9Kx/jXZ9QD179h860aEoOqL1Pffcg4suuijiAud5HqZOnYp77rmnSQfXEG644Qb07NkTu+22G26++WbkcrmC9WtqalBVVRV5EQRBEERbQnufNfbVHHz11Vc49dRTMXToUJSVlWHbbbfF9OnTUVtbW/C8sWPHgjEWef30pz8tqu9169bhn//8Jx588EE88MADkVdDKFponcvlsGDBAuywww6R/QsWLIAQzXTH68m5556L3XffHT169MBbb72FSy+9FMuWLcNvf/vbvOfMmDEDV111VQuOsuXJ9yu5Zv06ZTJICI5Yl9C6qOCLFoUCGObLCeaeV0iU7JauQNouC50HxC02ACIu70nWIbvdQm0X6jNfGclCb/XhCqoLCazdsq7gjUIqwXV0fygkB5BsJQJMcEaTa6pQgEb7eanLquR+z5ibKCLPqbZqslwNatd8g0yP/oXb3cLIdClr7SEUxF/wOrxh31MbIgf4Vi6yABV2xLH+IHzG4edCAb+UgMgG1qagDILXapd+bW1ifi54fnRfUecA1XnLrkW1Ze8z/f//T3/6E7bbbjt8/PHHOP3007Fhwwb8+te/Lnju6aefjquvvtpsl5eX17vf5557DieeeCKqq6vRpUsX810FqO+tSZMmFX0tRU+KpkyZglNPPRVffPEF9t57bwDAO++8gxtuuAFTpkwpegB1cckll+DGG28sWOeTTz7BsGHDMHXqVLNv5513RiaTwZlnnokZM2agpKQk8dxLL700cl5VVRUGDhzYNIMnCIIgiCbAbwKhdWPPz8f48eMxfvx4s73NNtvg008/xR133FHnpKi8vLzBkpcLL7wQp5xyCq6//vqiJlOFKHpS9Otf/xp9+/bFb37zGyxbtgwA0K9fP0ybNg0XXnhhkwzK5sILL8TJJ59csE6+9CKjRo1CLpfDV199FbNsaUpKSvJOmDo6JRXdwvQddnBH55eYPl7fYI35gifmC4qYpP3RuhjAOu7ob2yrjBuoEECivqY+JRDqbrQex/4yCV3a43ofu52wjdDK4pKk9dH1RcQaBGSFNG70zOyPj12P2XyGefrJpylyS1+oYI6+0MEclaZIfZaWHsM8A/qDUlqjyHETQBTRZ43xvDo0daH2e8ul2jwjajlfCkSexdBaVNiMvyWy3R8eb+0hFKZTt8im0TTGgikGejM/F3Wj1xZF13qdWHrqmQ2C3eYLNOriWrzbC65MpDn+D1ZWVqJHjx511nvooYfw4IMPom/fvjj88MNx+eWX13uCs3TpUpx77rlNNiECGjAp4pzj4osvxsUXX2xubHMKrHv16oVevXo16Nx58+aBc47evXs38agIgiAIouUQaLz3mJ7Wuash06dPx5VXXtm4xi0+//xz/P73v6/TSnTCCSdg8ODB6N+/Pz788EP84he/wKefflrv0D7jxo3D3LlzmzTvaoODNwLNOxkqljlz5uCdd97BAQccgIqKCsyZMwcXXHABfvKTnzRpCHCCIAiCaGl8IeE3clakz1+8eHHk/3c+K1Ex8hXN0qVLMX78eBxzzDGR8DhJnHHGGeb9yJEj0a9fPxx00EH44osvsO22dafomThxIqZNm4b58+dj5MiRSKfTkeNHHHFEnW24MCmLs/8NHTo0ImZy+fLLL4seRFPw/vvv46yzzsKCBQtQU1ODoUOH4qSTTsLUqVOLMgtWVVWha9euqKysbFOTvuaiZsP66A7bRGy5q7r7TF3Xfdqtk4ekXGRAw7SL+Za+iqGYHGGw2nfFzXrlSC+3ud9hdl4xV8Acvaawvj0GxljBMeRbDjT91+Ne2Nfpjp8z1a/Hw+jfrjjfiFq18DkiTnWer4TIxKaeu8RhRbdmbnuFHhzGIFMZElon8O0fpqHnz29u7WHkpXbNN1aeMRH9nM3SacL3jX4utNi/DgcQAJFwI6Z9V/zvPPPrq6rQc4fdm/X/hf6f9Ne5n6NT58bF3dtQvR5H77ldvce7atUqfPvttwXrbLPNNshkMgCAb775BmPHjsU+++yD++67D5zX/b8gMr4NG9C5c2e88MILGDduXJ31C7XPGIPv+0X1DzTAUnT++edHtrPZLD744AO88MILmDZtWtEDaCp23313vP32263WP0EQBEE0F7IJvM+KtIEUJV9ZunQpDjjgAOyxxx649957i54QAUryAiidcn1oDo/3oidF5513XuL+P/7xj5g7d26jB0S0LEZADThi2WhQM13PDaZoKJDl3lRxBNa2O3shQbSmLkE0YImD6xAPu1YfO8AhoM5X41RvsjoOHKzzgsCIqr9A3GyE33Erj7G6WH74SkCtLC/mMzGC6bAtve0xCR8635kSPtti8OQyKpjOZ63S49TBGt16MviwZHAJMgh0CeaBCd8RXPtxS5CLLd4H4mLW2C93DkC5ZRt9tqls/ZJ34yLUJeLegpGtHEalTrwMIDar966VyHzWCZ89Y/mtRNqxJCk4LU+F7WsSrEQR0XcL4cvwO6ExbTQHS5cuxdixYzF48GD8+te/xqpVq8wx7Vm2dOlSHHTQQXjggQew995744svvsDDDz+M73//++jZsyc+/PBDXHDBBdhvv/2w8847N89A60GjNEU2EyZMwKWXXhqJfk0QBEEQRMdm9uzZ+Pzzz/H5559jwIABkWPmh2U2i08//RQbN24EoJLKv/jii7j11luxYcMGDBw4EEcffTQuu+yygn3ddtttOOOMM1BaWorbbrutYN1zzz236GspWlOUj5tuugm33347vvrqq6ZortXY4jRFVWsA7plfTknk1RQBiRqk8Ly4tSix/TxPYD5Tsav5yYfr0p60bet5CrmkJ7UHhBYeV9/jWprq6sPV6ek+7XZ0Hc8JsphvDDrFhzv2JPJpiDT6OrSmiQX9GFlHoCFS77X+x49uA/k1RJHOCvz6rq+2yH0uuUepPtohtWuXq3AKwo9aaDSO5ifmPq9DQLgpPuxznbYi2iI3KKTzbFZVVaHnsD1bRFP0yDv/Q3kjNUUbq9fj+FHfadf/34YOHYq5c+eiZ8+eGDp0aN56jLEGaZyLthTttttukS9wKSWWL1+OVatW4fbbby96AARBEARBFKYpvc/aMwsXLkx831QUPSn6wQ9+EJkUcc7Rq1cvjB07NuKWRxAEQRAE0Vx8/PHH2GmnnRKPPf300zjyyCOLbrPoSVFTBngiWp+SLj2UW76VYdwVIKqdOvt0NDJ1krCaBUJdhsJZ6YF4frEkt/gk9/i6xMOuyDkrostVeluXGr1cpUstsHb7N+35MlF4DSQLre1lPF+GEaw9yMi1AdayXdC3DCJa5yQCgbU6X/jSLKHZom+9dGYv/9ljte+VQNT6ax9XIvjgfKilQS3w9mAajAuug+z1kvGwdSvKdFI4h5jYP1/peErHBddO+1KgdvUSZLaK6h2Itk2me19kVy0CGAMTCcJoxJfM4rn4rCUzR9yf17GkQD7ISJstKLRuy7nPWotx48bhjTfeiC2j/fWvf8WkSZOwYcOGotss2iXD8zysXLkytv/bb7+F53lFD4AgCIIgiMJo77PGvjoSp512Gg4++GAsX77c7HvssccwadIk3HfffQ1qs+hJUT5ddk1NjQngRLQvWK4m/HUFxK1EQPhryWQ+96P7bXf7QIBreZ4nlvkywgNR65D9xEUsLSJqFXJL1zJjl+5+EXxh6Pd2PSAUN3uOQNsVO9siaMZYJNhiUigAbQmzx6S3jbu8vieWlccEd0ToRm/fOzUWZsbGGDNlPrd8c7617YrM46XVr2U5dEsZWIiSnqnw/DyifVs0axME9Ys5CNjj0OdLGXnGiZC1d1zS2kPIS3bVoqhoX+SU4Dr4LJltxbFfdj2rhG1FsrfddkUufEkB5tdG2jVttCDaUtTYV0fiqquuwve//30cfPDBWLNmDR5++GFMmTIFDzzwAI455pgGtVnv5TPt+sYYw913343OnTubY77v47XXXiNNEUEQBEEQLcbvf/97nHjiidhnn32wdOlSPPLII/jBD37Q4PbqPSm65ZZbAKhfpDNnzowslWUyGQwZMgQzZ85s8ECI1kUGAe5CbVA9dB1SQHIvoiHKpxWygyPaAQWFRCTzu6vdcTO121YgINC/AHkzwbtaIlsTZM632gW0NiiuPdL1ASuwYmAJ0tqinGXhsbU4cK7dvka3r1BzpC1f4eckGMCDMdvnM13R0Ra598pGj0+Xpo8E7VPks9AaKLDAcibBwSDBwCHVM+FbQRwDbZF5drgXF5clBNILP5C4a7S6SA74znmAFloFpjVumdg46YoS6P6zG1D90NXofOIVrT2UGOlegwJNkYxaCpO+jzSOez7guNnnO1+X2n3fbi9ffy2pKRISopHeY409vy3w7LPPxvYdddRReP3113H88ceDMWbqNCT3Wb0nRdr17YADDsCTTz5JSVYJgiAIooXQS/yNbaO9U8ij7J577sE999wDAC2X++zll18uuhOijZPdDMZT6he8nRKhHtaiMOVHcArTBgvLHyhPCg2TViLYX0g7BCBuJUL+UveXpClyrU6+Y0FxLTH2eIyHGZSfk4fQ2qWu3wpX4Z5rjTGfbskes6ctbJbXXmScljZJb+vUH3q/3Z6rJbJThdj7XetW0nhdC5+6dqjUH4GlUXmS6cHbz5QlLrN+aUf0IUBcu+GWwvE6iliXnJtlUbvmG0oO69AWrUQG4ef3KLODMwb7mQ7uyTmYn1MWRSDuUZbgYVanXsi1VhEtTnPkO7Op16Ro6tSpuOaaa9CpUydMnTq1YN3f/va3TTIwgiAIgiAU5JLfMtRrUvTBBx8gm80CAN5///2YtwpBEARBEM2HL2XMqt2QNjoCc+bMwbfffovDDjvM7HvggQcwffp0bNiwAUceeSR+//vfo6SkpOi26zUpspfMXnnllaI7Ido2ma0GoKa6UrlMB8JpI7jW23Yp/Eg9IK6btYMwqu3oUpYtWHYztdvHk5Zx6sr4rs+PCLOda5aOq3nkfK4uyAsCF0aX2oK1fd0Os5e3GDiTZjkpn3jcDspo92GP3R6zvqe+ADyu9nMpE+9dIVF65N5YJYIls5iI3BJ8m+VCW+AdSR4nAanc/9Wzg2Ths/ugWILVmNA637ItT6llDC8F+DlILwXm56ICbbO0kSDQpmWPRLL/fhbpvYoXpjYnuWWfATylPlt36UrkosEYjXu9WjpzSyYFpF0/XxDHQvv1+S3sjk9EufrqqzF27FgzKfroo49w6qmn4uSTT8aOO+6Im2++Gf37929QsOmivx1OOeUUrF+/PrZ/w4YNOOWUU4oeAEEQBEEQhdHeZ419dQTmzZuHgw46yGw/+uijGDVqFO666y5MnToVt912Gx5//PEGtV30pOj+++/Hpk2bYvs3bdqEBx54oEGDIFqX7KpFRsxogjMGv+TdYI0scK1mjhm2rhVVN9ihTZKbfWQ7qJcklC4UgDCfxUU6lhBd3x6fHfhQHxcFrlOfbx9mTmkLnnUf9tjtQJCMsVjQRs/SKCeNOSmFh70fiFqvPKc/U9r1Y/cw7tav2zXj0s9ORNQaaTTc7/zqTgzumCSONb/cA0uA3Z99jtMeEzmwXC1qqtaAiNLWrEQAkOq3PdJ9hkaF1glBF2GCLObU8+X7YQmEz0iSYNt+BuuyHrnPVQtaHX00QUTrFhtt87J27Vr06dPHbL/66quYMGGC2d5rr72wePHiBrVd70+0qqoKlZWVkFJi/fr1qKqqMq+1a9fib3/7G3r37t2gQRAEQRAEQdSHPn36mDBBtbW1eP/997HPPvuY4+vXr0c6nW5Q2/V2ye/WrZtJEfCd73wndpwxhquuuqpBgyBaGSkAL3iA6gjSWEhLxHQgP8YS01eo+qG7O2C5dFuu4HZAwqTEr0Coc9GlSV5q9qvx+E5ARCmi9QQQ0cv4QiLlBclOg20Ex6VU47DVBNro0bdHJyxbtwE+U+3VS99kma70mBGUuj8hrfO19kgCUomC4Ol7ZYcQ0P04oQxsdH3PCt6YpCUC1D00cN2+tnYBOaHGwRgDl8otnwcJhpn7C1t1HrrpR6w+YVBHKRBYJBM0RlpTZMrkII6xoI7qAVBDoJQf7QrJUyrVRlJYEKH0ZEZD5Dv6NSB0y0/SFLnPFqxnh6dCLRJgNEWx1DItAHmfhXz/+9/HJZdcghtvvBFPP/00ysvL8b3vfc8c//DDD7Httts2qO16T4pefvllSClx4IEH4q9//St69OhhjmUyGQwePBj9+1PsD4IgCIJoasj7LOSaa67BUUcdhf333x+dO3fG/fffH8m9es899+DQQw9tUNv1nhTtv//+AFRk64EDB4Jz8uAgCIIgiJZACGks141poyOw1VZb4bXXXkNlZSU6d+4cSTsGALNmzYrkZy2GoiNaDx48GACwceNGLFq0CLW1tZHjO++8c4MGQrQiVmRhY4p2RK75BNb6dF3qfGdJEaWBUARsR3nWuJni9fn6PFMPiCx9RdpFuDyll30S+w+27XbCZa5g6c1pz4ikrf16XEvXbgBnUZFevkjSSWO36wPK5V4EN8FeeguX4FiYh8yKZJ0v9IG7Aqb7A0IxuVm+hAzaZcg5S3C+YIHgO4iEjXCZ1AvumcfsTxVx9+WIWtzNScUi27GnLU8Oq3yRrfMKt2n5rF3hCqT195TJau9b7vhAwrp+4Jaf5/stlj8NiOdNs+qZ55IX/S+UaEK6du2auN9eySqWoj/RVatWYcqUKfj73/+eeLwhuUYIgiAIgsiP3wSWosaevyVQ9BrY+eefj3Xr1uGdd95BWVkZXnjhBdx///3YfvvtE7PXEm0fUd5dCQkZT84mbZW2wFoJgbX7vBIB+lJCIi7alVJCyvCPWh/Xf6K6tMXG+nwtNpYyFDm7ouRcIIDOCmUh8kXQn/XS28I6z23Hl1IFZxTq+rJCC6zVGHJCqu3g+nPC6kNI1X8w3qxw+wz71m2ol6oTqx/06QeBGrPCLlV9EYzRl0Ctr/bX+mqctb4aT3hPYI1djVcivFdZX+3TASpzQfvRcUv4IrhXzmfpW/UlY5DcCwKCqmdLvZjjnu8EVdT1uRfU5ea5jPxyd9sFIL1QJCsZN+JaW2SrX0T7wF/8EbLLvwg/M0d8b76vnM86FiiUO1Yfu2RcWXyCZ0o9O0EeSLvknnJI0c8l9yB5dNmmOdGTosa+iMIU/e3wr3/9C8888wz23HNPcM4xePBgHHLIIejSpQtmzJiBiRMnNsc4CYIgCIIgmpWiLUUbNmww8Yi6d++OVatWAQBGjhyJ999/v2lHRzQ7tZWrAQQ6jMDVVL8HENODmBQgJmAgi6X0UPuTU2i4mqF8pRvk0ZxvtqMet+YHoZWuwg3aKBLOt4/rUntouPocIAx4qA0eXqAj8oJwFfa4TQDIIBCj8WZHtA1b32SXHocJ4siYrf1hiWONXKtELKgjt8au9VAiob7drq2/8qx23H6Mxc+cFwRxdHUZebRDejuvjkO/tY5H3KKDII6R87S+JCm4I6X6aBdIxsH82rimyLe0REKEn7WWb7h/1O4z4AZpDPqK7tcPvBepF7EgtWAeUG0Bb9yrxYbbbin6m2GHHXbAp59+CgDYZZdd8Kc//QlLly7FzJkz0a9fvyYfIEEQBEFs6dDyWctQ9PLZeeedh2XLlgEApk+fjvHjx+Ohhx5CJpPBfffd19TjIwiCIAiCaBGKnhT95Cc/Me/32GMPfP3111iwYAEGDRqErbbaqkkHRzQ/Il0WEy7GMpQjKrBm0FGQVeRnveQFxJfMTLRkIaNLakF9E7HaKfO5lLvLNm7Eat9avlH1gnHq8SK63176Y8EbzhhyUgZu+Yi45RtxeVBPt8egXe3VEpoIKkupSi0WT3KN10uDPqJLWPocP+jTvsc8uOlegbHq7WxQ6sjXCM7P6YjYwZizIuzPjtxtvJqDMftCueP7EvC0+36wrQfIgjU/jiAKuvBVdGCRcyJYh8+Y8MKw/NH6HhCEhIg+kzy6rOKlAD+XmCFdClgi3C176Wzdnb9EtzOub+1h1IvUgBHILfvMLKPFct65pSuwzie01pGtdQRrI+R2QkK4ka29VLhUx3mLPkvkfdYyNNoNo7y8HLvvvntTjIUgCIIgiAQoeGPLUK9J0dSpU+vd4G9/+9sGD4ZoeWyBNYDoL3k3mJmlXbSDNAKIBWvUAQc9ziJWIl3qnF1eINLVpS0KLvT3myQi9oUbbDG0BnGdxT744ehxlhhskgXnGVEzosEilTDaEpoHfen6ru7StXjlQ1p92m3rHG4czFiLjGDaukYZ3Et7rLDO12UYeDK0aPHAOqTrCxnmXnPHo7bDcQooa5Ut1GZB7jsgtDDGgizqX+ImFASL9qetQDHxa55f5nZAPlGg5KpepseWm5KovViJNCy3GfCCFA51fcZJ3hN28Ebtxu9YHFVHcdG0HRDU5FcDlFXSPpfoMNRrUvTBBx/UqzGW8FARBEEQBNE4dCy0xrZBFKZek6KXX365ucdBtAI1G9bHM4gXCNYIWBodx7riaoFsLREQzXqvSwTna+2Mmw6DMRbTEvkiuZ5JQ2Fpi1hCKQMdjh6Xq4UCAr2NCM4L9sXitFuaIqZMNkavo/s3+iOrH5vwJ4R1jwIXfRbcYxVsMdntHkHfEtKk2uCMIWv1qe5ttD5ngQaKMSvEADOaJ53aQ1v0YLXrcQYpZGScksmIBc4PzEYi0Cype8oA5imNkfVLXerPIrCCMQSWOCiNUZheRlsE/IjuQ+pM6X5toA+x0j3YGiLH8lm7biUy3XonfCpEW0MHAI3ox5I0Rb6frCmCCuyZlO1eBwIF95DuNQgAkF35VUxjabREXioWpqSlIE1Ry0ChXQmCIAiijUOTopaBFkQJgiAIgiDQjiZF1113HcaMGYPy8nJ069Ytsc6iRYswceJElJeXo3fv3pg2bRpyOcqGncTmjRsAOBFeA+ws0sxZg84nsI5GO45mbId13C5ZcD63ojW7pf2Aum73dtRlvdRmVlac8eltzxVeI74sZUfctq/bLj2uxNYpzuAxBo8r4bK+XqPztNpgTqmPS7desJTFrfqRe5mQQkyPzQivdfsSEAjzs+WCqLY5oZb2dD61Wl8gK6TJd2ZHqpbWvVbHwxxpOlecfe/0ZwIE+dJgLSPaS1/B0pkvwzxrum4uaFMGkYNjOamMa3UQbdjKjQU775m5qU6E7FZaAiGKJzVghBJbB59ZJN9ZXULroF4kT1qSI4kUyK78yhy36xuBNefR58ZyUGkJcsHfZ2NfRGHazfJZbW0tjjnmGIwePRp//vOfY8d938fEiRPRt29fvPXWW1i2bBkmTZqEdDqN669vX94WBEEQBGFDy2ctQ7uZFF111VUAkDdq9j//+U/Mnz8fL774Ivr06YNdd90V11xzDX7xi1/gyiuvRCaTacHRtgMYN9YEO1ijm6NK/7J3Bda2sFoHXQQsC4ElsM5XaoG1GVJgOdClXd8Nfui6mmvxb5KA2g4KmdUCcMvCISBDTaZUQt+cjFpiYtapQIgtWTgOjZQIMt6rtjUcUdOOPqb79qGsQD7CUAK2WNq9h+H5LBiDNNeoxyolgqz24TVmIeExhqwJfCnh8bg+NfxMw/3hZ6XusscYOJNIcSU6T3nqvDRnEEIHh9TBHUPhtX5GpJRmHCqvnvUsJAm1AwuTtmAykQM8Din86C93NwCpXerjRLvBiKQZB0ROWX+TBNdAssA6lnvPsRwyL/KAG2G1blcLrPUzRM9Ph6XdLJ/VxZw5czBy5Ej06dPH7Bs3bhyqqqrw3//+N+95NTU1qKqqirwIgiAIoi2hgzc25kXBG+umw0yKli9fHpkQATDby5cvz3vejBkz0LVrV/MaOHBgs46ztaldvQQ11ZXmV7WbOdzVGOlf5CawIOJBFu1SZ163tUQMUQ2RbkdriRhjJsZVGMAwDBBoEwYQDNuPlNY47XG59YB4fDd1XI8j+frswIn2OLj1qitcV3hO/Br1d5ate7Kz2xc637bcSRk/blt7ckJpjLRFK+eHFildz1iZJFDrKy1SVmuSfATaJBmWEsj52jqlrEA5oaxdOWtba4h8oaxE+rq1VUzIuKXR1SSFpRV4z9IeuaXSMAV1pcqqXrvmm8IfFFEUG2fd1GxtewNHItV/B0Dk0YgK+3tM/0E4WiJAnW9ZeiKhHgBkVywM29O6NN42LIxaF9jYF1GYVp0UXXLJJeYfYr7XggULmnUMl156KSorK81r8eLFzdofQRAEQRBtk1bVFF144YU4+eSTC9bZZptt6tVW37598e6770b2rVixwhzLR0lJCUpKSurVR0fA77yVCYhna4qk63HBvUDjEfwid7ypbC8jO3hi6CEWLbMJP7BsPZFOT6GTlxrtkIjWdZOU2toi1X/UwmBbgSL3IdDbhGPR7QT9iPh1SMsry2fKEqN1PG4XOuii7ZnFWFjX9tYKNUVa28MiYzHaJH1NlnVFJ3a170HOyosppKWHCqw65p4ba5RE2mParqSkEyx6HgDUmqCOQgV+5Errw5hEWqpAkBmPwfeVxkg/G1mhrisLZYmTUoAxpgJkBuP0zGcZWsd04lkJ7YEnE61rOgCp5NrS6VkXqQL6SYHQM42FT1jN+nUoqegWez6I4ik/5uJm7yO19Y7ILfsMACCDBLEMuSBhrJMMGEAsDYfjgWYSvZqgjtqbMY+WyNaltTAktG4ZWtVS1KtXLwwbNqzgq74C6dGjR+Ojjz7CypUrzb7Zs2ejS5cuGD58eHNdAkEQBEE0O43VEzXFpKoQQ4YMia303HDDDQXP2bx5M84++2z07NkTnTt3xtFHH22MGa1Fu9EULVq0CPPmzcOiRYvg+z7mzZuHefPmobq6GgBw6KGHYvjw4TjppJPwn//8B//4xz9w2WWX4eyzz96iLEEEQRAE0RpcffXVWLZsmXmdc845BetfcMEFeO655zBr1iy8+uqr+Oabb3DUUUe10GiTaTcu+VdccQXuv/9+s73bbrsBUHnZxo4dC8/z8Pzzz+NnP/sZRo8ejU6dOmHy5Mm4+uqrW2vIbYrsyq/gd+4VzXOm38ayRfPI0hkAa5lDBwgMghxaS2dAKPY1yyEIgzvqZRCzBAe9NBdmt7czw0sZzafmZquHU6r6Yc4tXyrBtHDa0SJkHzImWlb9SLM0ZouThVWqjPQSnnWeWkrSy2H6mLoJ9hg8FhVo/397Zx5lVXHt/2/Vufd20zIPMsgMPiDGCX0oahQjEeKL0cSYZ2KCvEcwGhSEZCn8koiaGJyH5zMR8wxoFi41iSYxg4pxSgj6jAmKBtrgk0FlUEGmpu9wav/+qFN1qs49t5uh6b7d2Z+17jp96tSpqlP3cqm767v3dq8TxRb+eEsJkJGvugJ5c+M+uxmbbUeYXz1mm08/s64Xu/3HcxbVQ5yx3hyLoa5XVMp+BgLE98XvuT43W2OI3ttSCJsnDUDsBWO2I6P7zZab+SxR9CDms2M/j0TWWYAcEXVyu0RIVNz20NstBTDtDFWKt0FdIbUbsDPa/07unpEpVCUg2jrTF6LPiAoBGUCEJS+IbTXQHrbPunTp0qRcxWX79u2477778OCDD+KTn/wkAGDRokUYM2YMXnzxRZx44okHc6gVaTeWosWLF4OIyl4TJkywdYYMGYLf/e53aGhowPvvv49bbrkFmUy7WfcxDMMwTCohKYTqAF/RIi8Zhiafz7fIGG+44Qb06tULxx57LG6++eYmM0q88sorKBaLmDhxoi0bPXo0Bg8ejOXLl7fIePYHXjH8E5Df+RGocx+bWsEIrcsEhIANjKeiTOU6271vUQAcQbUTLFE4xzA2BOh6iR8ornDaCIIBLRpOoyxYY9JV2+nfFTebjPGu8Nr7sWQsFU6QRUXxONwAiwYhAAojq5AwGeO14NhYxIzQ2gROdMcQhqmPqANMRk+hnzG2DiWF2aZcQpQFgTRCa2P9MteLIUWpPcp//WZJohhqwbUJHlmMBmzq22OohdMh6RQnoQJyGUTaZQFl5sGds2iyAylSg0Raa5PUD0IEKKHF2yokG2oB0C7+QgodcM8IqAHfsul8LiCkDvIoAq3cT7poM+2LKMyCCAs6vYuxHBmHEQnrOJIKKX2fm+rDfA/KwBdgw7nuHpNpQloB1QKWImOhTYaemT9/Pq655poDanvmzJkYO3YsevbsiT//+c+YN28eNm7ciNtuuy21/qZNm5DL5crSdvXt27fJMDoHG14UMQzDMMw/ERs2bEDXrl3teSXd7dy5c3HjjTc22daqVaswevRozJkzx5YdddRRyOVy+PrXv44FCxa0K10vL4o6OI179gCZGhuwTCDxSyfFSkRCWLdx10oEwKb6MC7TVlsEeLqPwNXwwNcUaa2PTuUhASgR6VFUXG60SuaoNTy+hcGMz6Qe8fqXur2kJcWQ1OOkpbEw+hpXW5SmZdJWIkQJYSPrjNEUSYIgkWrdcd3L09q2Y0WsBXLbcY/JZ9CWFrL3q8jtPl/yLX7mPS0qhayUVjtkwgMUQ5MSRP/q1u8RyqBo8o3OqmTCBjjPqd+ThAkxeg9LRgemtKu/AiCJEELYzxqAKK1KpFkCIEVg58szMoo4FYg/SEQWAWnjPeR3bEVN157lD8VUJ6oEkbTYJIM02roJHRn0e0+OdQkyA6GMhkh/RoS5blN9lHzLUFN9HiRCRZAtpCnq2rWrtyiqxIGEzTnhhBNQKpWwdu1ajBo1qux6v379UCgU8NFHH3nWos2bN++1LulgwIsihmEYhqlySgoQB7goKlXYUaxEnz590KdPn/3qa8WKFZBS4tBDD029ftxxxyGbzeIPf/gDzjvvPABAfX091q9fj/Hjx+9Xny0BL4oYhmEYhtlvli9fjpdeegmnn346unTpguXLl2P27Nn4yle+gh49egAA3n33XZxxxhl44IEHMG7cOHTr1g3Tpk3DnDlz0LNnT3Tt2hWXX345xo8f32aeZwAvijo8esOM/C0yxJnGk5GrFfQ+V1LA7J4Dabmp4j6Ni3WyHIgF1iHFImtTj4icCNJRfyrpFq+3d0o2urJp1+/HCKxdYbWJDK23xOK6UVBnL6u9aS+EFieD4ujagTTbOACU8CJRmy0rEzHbRJQmZ07tVlisHdbjMKeRiDt0BdYwwddQtvWV5l5vtvECqSNRB/FuERQl2zFC7NCLVK2iARaV8uorKxQnW99EkTZbYYDTH1Tk7g9kZLw3GD+GEW4DGQlQGG2jAhAgL0K3zl0XC68zTvgCFwn9BgnXwVaISIirxbkU6KzrvHXWfii9Vw8KdEBfYUTyiZALJCQyA4+w9UVhDxAEfj4zc18kuLYRrQ2utMBEtgasALtsO60VaMnts5ampqYGDz30EK655hrk83kMGzYMs2fP9nRGxWIR9fX1aGhosGW33347pJQ477zzkM/nMWnSJPzwhz88KGPcW3hRxDAMwzBVTjUvisaOHYsXX3yxyTpDhw71YtoBQG1tLe6++27cfffdB2Vc+0O7iVPE7DuNDbshKbQiax3sLhISphxdsa+xNLjBFs05oMWuRmCdtBKZcnPuHk1gPttedBpIbR0IpA4PHwj9Gz8Qfj13XKbdNJGyK2KW0UsI7fothO7PHE075joQ1xOReNpYRLJB9LcQkJEIWCbE1qZ/kfg7kKYdYQM9ui93TsixXLlWoqJSXrZr113eFcMbrPu8I5DOSolsIFCTkZBC2GdK3m+OWSm9Z3ePSdF1nOU+sm5FnyuTB86cm2czz1II9fOVFEFBf3mbdkJlrutjISQ0luK/Q6UtjAT/s0rkuOlX+lWftBAwB8SK8yYd3A5UCSJ6udnuy7LeR1CQ09fCMPrQKUApP2hjdJ9w24vMobavUPdnBNkU5OIAkkyHgr8RGIZhGKbKqWZLUUeCF0UdlIY9jRCRXsh3W3Xc7p2jSbnhBmMEnKzw5pjQ8sRBEWOtkBAitRzws9y77SBRv7ye/4851jaZcRgNUqJdpw2XkNKPcaBF85y+PsamyZCRbkcZi5A2VRjDRKjgWEjI0/MkdT/lkLXAGC2Pq+sxqTRcdJuh16YZe1EZt3u/F3s9EVmzGGqrUj6KMpnUMakQXnBHAMhKQjFMBGV0NFDZQEQaKhMc0tUckQ07kIF+77LGBOiMs4TYchl4FjYBSRQ9p27TWiAjC53Opg5ARMEfo1/5JCTyu3ei5pAuFd4LZl8QafEaWpDMwCNQ3PSW7iuMUrQ04Zaf7TsM4drtoEzW6odEyUntkmLpEaqkNUZRyg8Q6UCOCoDQ1qJc90OR3/lRq1qKWjJ4I1MZtv0xDMMwDMOALUUMwzAMU/WEig44ThFvnzUPL4o6KEIAQoWeoJCEtHnPPIF1SgRrd5uC4AulzRaXKVcUl1c6GkyWe3suEhnkZZQDC3orTdltEOHlYbMRrE1ka5PnS5DNIG/bTZkfcz15HpgM8SY6c9Sv69JuROFmnqWAJ6oGABJaMCxMmTT7c76IObktaK5Vihxt58LZQssGsSDalLvbbSZCtannisml0A+bdPN3+4EkO27z/ICeK29ccUBgTxiuC4BsEOdl83PRkc23pkIgFwBQvpt9YpcVIREy0TxJSQij8AF2R88MJHpjA6nzpAmhog+OtHnPeOusZXj9wn/D0Y/8/qD3I1IE1m42e0EKpXfegCCFYNCRCIYe490frns1crOX3n22bSCOcG3yRKow+uwoUK7OuaH1NluICHSAi5qk9xdTDi+KGIZhGKbKUYoOWBPEmqLm4UVRB8Z1T7UCxESeM4XYSgSkBGl0BNAispq4103QRQVtBTICZdOgG3xRROJaI9h2rUjlgufYimD6EwCKTr41ID0/GRBbdYoVfhkltMXeuRdo0RE3mzKphHVTRxS0MX7GSBSsYoG1ud+dX1d4bZ7DEBi3dxVbk4qhGQMiy5URawMIjXUpyuVFqqyunjPtSq9k3JcrojYWIiMmz0efn3js+r1zBdvG6iQFISwRslLa5zVCbCO0LirfmuVatVz3fyKgJpOcV//9Me3mAh3sMYM4uKP7XoaKkJECWQCBCCBlJNYmqXNe5XcB4OCNLUGpsdQq/VCUqwxu/rLEUQdYrJzTgoKU//qcPJCA/v6kSJyNKC9a9tDB9npNl+7Yue3DFn02pu3hRRHDMAzDVDlEdMDbX7x91jy8KOqA5HfvjN0Ko185wrirOr+GjJYoDaPZSWqG3OCM1k2fYq2QqxlKDapogzUKqyFyrUa2H2gZSyCEZxlwNU6K4gzwbj+hlxZClGWQbypTPRBbSsKUH5pu+oyyOUsEYJTW/TxllhMaozDxZRUq7Q1s+oyzYxCcE4vr6q/PK4/dDQlgtTyJ/pMWLFf3Zd/DlCCRxo3fWImKKj5KATRGKT/MOPLWmmmsSQqBlCiFAplAhzmo9B4G0J8dckRKyrE+KgAh4s+jCABhJlVI1HbqBHTpXj5RzH5xzC+ebJV+bOBGwDv6wRd1TIxw7Yq4TEQRW6WTBuSdN7wUIUmrk9EWWY1Rglz3/UuWuj+QagFNEW+fNQu75DMMwzAMw4AtRQzDMAxT9bDQunXgRVEHo7Fht976MibfKKuzMf1StH3gRh2WkejVurnDjRgdC6zNVpnZOjPXgXiLwgqlzdaaKne794TSKiE0pjgzvG6nXNjtRkM2Amu3H/N8frtxuesCHpqtN3KuOa7krljY1I9GErnr68aT22nx9lEkVna24lzhtXkmby4lIld56d1TjCLaJt3ni1HW+FApZ9vL709vkZk8aeljdXH7cC8bIbNxlzdbb9lARoLv8ucpKmryPiMGLypCNhKQ1wRAlgQyUoBE/B4B8XtbMuEkRBy6Qfcfz4HtD/FWLyDRra5T2TMz7QOSmTiaNeB/vwkJEaYIvu2WWFChPP3ofn8m86q1Nu7u4IG0wTQNb58xDMMwDMOALUUdivyu7ToooxBRkEbh/7qJgjaSiF2dXZGtMQi4dgNXYA3EloIyATa0tSgQ8ZEoDugnvV/yle/XfUbjcMZnXLjd3FY231mKFcpcTwqplYjL9PMIL/eWsQZZl/EoaGM2KA+oGJIvonavl+U3k67pJhH0MDrNythN3WShN20UQyBr+lau9cdcV5HFL27PtcrEbvle157Y3RxdYssf2fbdcmvxSlOlO/WM4DpuPpqb6D4VxnMlhUAeIQIZRPcLSCSE1tFRCIIkgdD251q4CETCWoiKCghJoV+3Q1LHyrQPXKG1qHC0H5QyQTZZV/1ww8r4myC6rj9uvtAaQiLbb8RBfKK9g73PWgdeFDEMwzBMlcOaotaBF0UdgMaG3QAQZQE3JqDAzxodHZPBGo2WqJLmxw3aGGsy/Ou6XdOefx2Jdtz2zLkJ1OiOJ/lP11gaXJdsrQGCLXcMDzbbvbIDKA/KGEjnHH6gRTfwIgAUS1GgwZJJr2EsNMZGEVmJotOmgjN685NAOhawYmju9/VEsfVFW4fco74vut+OKw4EacfqnJZbc3ztUpoWyLUwmushwWqCKtVL9m80RdlA2ufOQ6EGEo0lhdqMBAnf/d+8p0QmwCOVfXaN7iwT6PkKVRzIkmnfkMzo77qwEGuIpNQWICnjLwOicq0QoF31172q/3bvc1N/yOi/RiGhsqw/+2eCF0UMwzAMU+VwnKLWgRdFDMMwDFPttMCiqKKJmrHwoqgDIEr5OGePMRUnhNZGQChBIAjrju9Grnax5yKOPE1E+yy0jgcZt+Pe56JSwgKYo4lk7W61mXEq11pudg8TEauBWGStqNyFPm5PWAG1G+3ZzSxv6rkYQbbNIC997XEgkts25V9ORtxt8ocZzLZSIPXkGrfzIBI+JwXXSeF0nPXe3+pyBdluuRt6wBVWByIWXLtbbQBQCs0xmkcp7Fy69WKiziOBuLvVlk3MlRDONqiZPWcbVVK5AFzBjzAeUtoYmPZImtDa5jkz29lEvv95UnBtHK+jyNfmfuver0p6C40Ucr0HHsSn2XsUkXakOcA2mKZhl3yGYRiGYRiwpajj4AqsASe/mUyc65/Pxt3dCJuN9ceQJrg25W7wxuaE1mkCa6++EVDDv8/gWn/0fb7A2rjdG6uQW889uoEZdT+xADpIWDRcgXVlgbQv7jUBEd3AiCZLfVMkAzEC8MTbRljd1C+8pAA7byNbmjFFlibn/dV5x/yQAsVEf0rFf2elQAlAJpDlgm6lA1kWQwUpBVQknAa08NqO03424n6zgbYFZklbloKM1HnUBKEYIjr64/SsSoFv0bOWvahfSdqqFQIohIQj+nepOI9M+8Bkrrd5yRALqLVZMYwt52lBGQH/SyXKh2atTc59mcPGtN6DNQNRC2iK2FLULLwoYhiGYZgqh4XWrQNvn3UARKkRIizqX0IqjMwiYZw5GnD24PU/CmH1JEYjgyaPIqWeFLpcCKEDDsLRqVhtjfCOth2Ydnz9SHm/8Q9AfZ/WQ5n2jSUiiAJSuvXcoxAmiKHfrw3SmNAKWUuGlAii53OfO+kC7mqO3LqBFKkvMydSxC8XRb71x5+jeIxxmpT4Ps+qElmR3LZcN3n36NZx45mYoI1SOvdFqTvcOQS09SgkoLGkEFIUSoD88QHauqRIW4GMRcp9DhMWobGkUFQK+TC0L3Nu+1Plx0JJhxXQoQyAxlChc46/7joC2b7DtK4IKE+9Ydzw7XlSS+TUc1GxNklEViivHeafBrYUMQzDMEyVoxQgDjh4YwsNpgPDi6KOhpDaLJKmKSLVZEJYoHIQx2RCWHM9qSmyQRhVfB/gewV59Zx+/XGZ+uaCf59K9JPUEpnzYuIXotHBJLVEjaUo3YQN3mju8L+ErCYqoQUy3l5xcMj04IfuXOQrpMZI9pNmLXJx9T9mzG7SWVUkSCls+pBApLeT1C6FjlUoG0jkS+XaHlNPRVYZcx5EqUkQlM91HFxSa4KKAGoBKBJRu8L+bS16CY+6bCBQFOSlX0kepRKozUiE0BanHjWJZKBMu8UmagViTZG9qMo1RTJIBHU0JuWElkg635tVlj2V03y0DmwfZBiGYRiGAVuKGIZhGKbqIXXgxqsqM35VJe3GUnT99dfjpJNOQl1dHbp3755ax4h+3ddDDz3UugNtZYrvr4co5oGwAKFKUWAzghvczAoHAQgVQhBBRttCVgAdvZLZ7AFtcrVBEhEHezRBGIH4gxQ495v23ffDFWQLEQmko3Mp4AU/1O0JSAgrmDaC6kDG9c1LCF9YrcfhipLTBdZuMMdYYC08UbU5d7fITH3XBdytl3RLd4MouuduW+ZoBNlNbXcB/nZXWj2lyLrJm/pG+Oy+isrfcjNzBGiBtRuywLzcuTQUQoVQxfnj3O1KMx63HSO0Lir9dz5UaCwp7CyE2FkIsatQQkMxREMxtNfMfaEjxDbHhqI+FhN7jvmSanYbkmk/mO+6OHhj6K8akm747paZ++8uKbJROnijqDJ3fAA2IeyBvpimaTeWokKhgPPPPx/jx4/HfffdV7HeokWLMHnyZHteaQHFMAzDMO0FdslvHdrNoujaa68FACxevLjJet27d0e/fv1aYURVgooi9Ampg5o1I7ROC+LoNUcmw3gcbNEVWJNTT98f1aM41Yd7vSz4I8y5uR4JoxP3hSo9aGPoCJ9dcbZbx23XaJmN4Nq1eKRmlldGDOyny3Azx/uCZGMJUTpNRpSN3lhe3Dq2bkKE7ZJMuZEUUScF20nrDhBbY8w9xZJOpVEshqkpP9z2dJqWhDg+mqs8KWSl8O6TUti5s1YiJxt9LiOhSNj79NwoOw+5QEbvoxG6Sy/ViTteKYW1umWDqN1AWlF63kkZUhNZ6YxFsKgIQ3p1BtMxIJkBjKXIuOGXWYeUL7QGYmG1e4TzPQkAQiIYeEQbPBVTDbSb7bO9ZcaMGejduzfGjRuHn/zkJ82q7fP5PHbs2OG9GIZhGKaaMJaiA30xTdNuLEV7w3XXXYdPfvKTqKurw1NPPYVvfOMb2LVrF2bOnFnxngULFlgrVHsk23cYShv/AQpLEMZa5NhsrCHAJDqMfj0JIlCkzyFytDEiSsQapeaIgxOWa4p0B8IGL7SJYUWcENZae0w9RFYjYRLHRtYYq/ExyWujfmV0hChLoSpE7H6vSKd0UIL854lctLOBtmgYC4TpTydRddJGSJ0WIhvo8ZqjjARXAUy8gERaCUdDZSwXbpBE9zyQAqqCwMW1EiVxrTMmeKKZe9dalA20tQWRhclYaVwrT1kCVYoTvaa53ZtUHo2hinRYkU4rjC1v7rMXStrSVigp5DISSNFzAdq6FCe6FQhL2uJWRMKyJwQkxRopKfQ4pfL7zZfiFCnm/kAI9K7Lps430/4IN6yECHLaSqQcDVGTx1hTREHkzh/E//3ZRLBA1WmJDJwQtnVoU0vR3LlzU8XR7mv16tV73d53v/tdnHzyyTj22GNx1VVX4corr8TNN9/c5D3z5s3D9u3b7WvDhg0H+lgMwzAMw7RD2tRS9M1vfhNTp05tss7w4cP3u/0TTjgB3/ve95DP51FTU5Nap6ampuI1hmEYhqkGWGjdOrTpoqhPnz7o06fPQWt/xYoV6NGjR4df9FCmBsjkKgqtXdNwWmRroFzwbIXRRnidIrROuu0D5ULqsgjUpp+kILqC4FpRtD2W2DxLtmvq2AjaxlPX2SIDYpFvmRDblvvtFkMjtEaq0LqYiEodkvJE2brM37Jy84ftjZu42boyW1bFUtyniSbt9lOMtvOsaDzanrTn5l47V+SJytHM9VCR3hKDH2m6UNJC60I0PrN1FipCp1xgt7IKYXzd4LZXBJAlI5SO5i2KjA0pEYaErDShAQAV5VtrKIYohoSiUqgNJBoDidqMRDFUOG1E7+YnmmkXqGxd7IovJRCGsaAa8VaY/70nAGnkBbBHfRJ9uwQ5ZPof3noPso8QtcCiiLfPmqXdaIrWr1+PrVu3Yv369QjDECtWrAAAjBw5Ep07d8bjjz+OzZs348QTT0RtbS2WLl2KH/zgB/jWt77VtgNnGIZhGKZd0G4WRVdffTXuv/9+e37ssccCAJ599llMmDAB2WwWd999N2bPng0iwsiRI3Hbbbdh+vTpbTXkVsMIBk0uIEACFAJC6F9U0a8mXdn8ikoXWhtrkBFaBzI6VhBau0EcrdAafoohcqxKRpAN4xIuY6G1K1zORgEDBYAQQDYKvhgLt7VlKHDOoYQNNCiDSPwLYYXWJnijK7SG1FnUYwG2n6srm5H2XD+zsTpFouQgHjdgxNACIeIy/exkrR5SCJQcC1PSiiSF36YZm7kOxGMEfOG1254RjbuBKo1FzrUCAbHVJnR+iSatQ8n6laxKyfu9+hJl9dz+TdBKQCErZTwvkcDaFYQDCqHS9RtLylqJiiGhGIbIBgp7igKdc5zzrCOhA9TGWe0BxFYiE6TWnMPkMSN73bjzQ0hAZkDZTsj2Gdyqz7A/UAsEX+Tts+ZpNy75ixcvtgnx3NeECRMAAJMnT8bf/vY37Ny5E7t27cKKFSvw9a9/HVK2m0dkGIZhmFTS/v/bn9fB4LnnnqvoLPXyyy9XvG/ChAll9S+55JKDMsa9pd1YipjKqNpuAOJfSfqkGU1RInijq3+RwgnKqMjTDpnryR8cSU2RNYQ0ozlKZp1PaozCSFMUu5AnNUlxe5WCNxq3fKCypqi8X/9YTDxwcrxGW1REeWZ7V0Nknslvw3+25DHtPhNM0bUqff2EIQCAH7241huztdY4KQ1MkEX3eiVtEVBuNUo7Jq1NRnvkWoGMxiitfwD2uj6GsYYpis3gWhKlFJ71S6fyIK+/UBEuOm4EmI5BcctaeJ9MoylC9P2XCNroaYqgvw8pyCHbjz8TLclJJ52EjRs3emXf/e538Yc//AHHH398k/dOnz4d1113nT2vq6s7KGPcW3hRxDAMwzBVTjV7n+VyOS+TRLFYxK9+9StcfvnlNj9mJerq6qoqCwXvLTEMwzBMldOSCWGTWRzy+XyLjvXXv/41PvzwQ/zHf/xHs3WXLFmC3r174+Mf/zjmzZuHhoaGFh3LvsKWog5AEVILgaWzhaYioXVUR7gRrYGKQuukcNoIrd3I1mm/NYzbuiTSkaqlv8VlrqdFtE7iboEFAiDhtANhhdUhUdyPAAQJIHqeQAgtxDbXywTVTQuyXZIC7KTgOhaGx/X9RqK5dQTRboRpc09WSut+7wqpg0DoXGyOUNvdvqsUOdu2l7LVZd4zI4BuaivNbGk157bvHk37leqn3e+OJzmuXaVS6v1NjSOXkdjVWEqtz7Q/Su/V6+8zk/NMRXnPSMXfe24ka1cyoEIEQ45ui2G3GKRCkAqbr9hMGwAwaNAgr3z+/Pm45pprDqhtl/vuuw+TJk3CwIEDm6z35S9/GUOGDMGAAQPw2muv4aqrrkJ9fT0effTRFhvLvsKLIoZhGIb5J2LDhg3o2rWrPa8Uy2/u3Lm48cYbm2xr1apVGD16tD1/55138OSTT+KRRx5pdhwXX3yx/fvII49E//79ccYZZ+Ctt97CiBFto/viRVE7Z8v23TZoX01GIhvUQFJoXVI9gTWgrUPGSkQEFdmSkqJfI4xOE1qb68aaZATZQogyobV7HdDWoTgzW2wVcnEDSbpCan0/eRYoUin3O7asWNCtj8YSU1TKs7DY64loiiFR/OyKUEzYyVxRtRu00Q3wWEko7Yq3QyIbiNGMPy1bvalrxhNIHQwxF0j89/K3PSuTm43eFTa7QROT5a51KGm1ac5t372/khDbWJ3ccST7TYYBMPX2FELvPJ8cl2MBzAX68759TxH/87/r8LVxQ8C0X6zAmlS6lcgVWKP8e88e2zEtaSnq2rWrtyiqxP5knVi0aBF69eqFz372s/s8vhNOOAEAsGbNGl4UMQzDMAyTDinVAouilF+RTbCvWSeICIsWLcKUKVOQze57EmYTlLl///77fG9LwYuido4JlAjoX/BZo99IBG0kIDrXv77NUUJbjeJs9aauiDVECU1RGGmOdLX46AZvjLU+wtMUZaN2zBHCd9eXTptk+4s0SJGmKIxDVNpylwBOuIEoyGM2MFYhafU2oaPHMdezQnrWI+lYXGQgvH7MeNxM7UpqK1G+FEZWC8QBHoFIU1Setd6aziTKgi0m77MBKIPYKuJpkBL3JzVErrUmrdytnzxW0iZV0iQl73OtTUm3fNdaZO51rUWhIu12b+4lQiFvzlVZv6EibNmRx9fGjQXTvhGlPERY8C1EpFI1RCRkbBky1zuApag98Mwzz+Dtt9/G1772tbJr7777Ls444ww88MADGDduHN566y08+OCDOOuss9CrVy+89tprmD17Nk499VQcddRRbTB6DS+KGIZhGKbKoTAEhQdoKTrA+5vjvvvuw0knneRpjAzFYhH19fXWuyyXy+Hpp5/GHXfcgd27d2PQoEE477zz8J3vfOegjrE5eFHEMAzDMFUOUQtoiujgLooefPDBiteGDh3qRdQeNGgQnn/++YM6nv2BF0XtnFKohcwmm0lREXKBzvUkSOk8P6RAUpcRxVtmRnBty53owGWCa0fcbMqFs03jCq9lSv3k0RVeuyRFxUmxtREfe2Jrp9xA5ORJQyygToqV44jU2n0/rV6aF7i7/aSIoMJ4ntzo4K7YOqHhtkJrRX62e/MsxYQKPU1oDQAhYsG1S0Glu9sbwXLyWVwBdiUBtamXJqx286GllVc6pgmzzbhCRdhTCBEqQkM07j3FWHAdKmXLk/c17Cli946Wjb/CtC7FTW/pbbOEuNpiIlfLTPx9BzjJFyWEKkFl072rGCYJL4oYhmEYpsppSe8zpjK8KOoASBGLk6XNTi/L3VIRibIJECqMfkVFRwAKwmarN4JrhSiIYzJ4oxVipwuuAxuc0bfaxD/gdBDGAL7VyFwPousAIIx1SMAGWTQCats+xfV1uQ7eaNozQmpzvxUrR9aubCY+FpUqqxfPtRus0Vh6hBWiZ2HyoElI5bjzC4EMfMuREU4bjHA6KbAG/KCNroC6uXxkaUdXaN1UeaXjvvRXyZ2/knu/OSYF1g2RtcjgWolCRbZeqAhhSaFUDNHYUMTOrXvAtD+KW9ZqcbUqQYQlgMjP7egKq42VyMt7Frvrk8wgM/CINnqSloMXRa0DS/IZhmEYhmHAlqJ2T1GRtbAYt3ohI/d2J4O6UvoIQDusJ4KdJYM4asuE4y5vdD1Rv0ltUVkwR6MJcvRAgJP9IqFZioM+Jjryq8fapCiIo0q2T9o6lNQMhUqHCTD9l+lzjLbIunY3Xc/VGYWKYH5/mcCNoSJHMxRbh3Qb6W27miJfm+Rba9x+gXKNT1NBFk1993qalqc5rU9av5U0RA2FEBkpUFJkjzUJ93/TbjK4Y6HkWINCX9tkyo3mqFQMEZYUivkQpWKI3Tvy2L11G5j2Q+GjLUCpoEOG2ECN0b8u1/3eaiX1f2FGO+l+r5lwJMGwgxOSoWFPIxr3tJ4lki1FrQMvihiGYRimymmL4I3/jPCiqJ1jrD9EsXXHBAQ0ViJjRbIQyn5VSeiUHzKhKTK/zG2i2Oh2c10IYTVGiI5Ga+QmfjVjNEEd4+GISBMEq5Nxna6SWiQTzFGQsOOw6TAUkBFxwliFKBijkxg2LQFsMaQyvY6pZ4I5Zo2WCZUSxMbBHBEqKCkQSJPItTyIYzHUuqSiojhJbRBb2Exky73REKUlbE1aXeC8l24Kj721CiWtVKa9tKNbPx9ZpUrReckpr8nIsvuTHmReCg/nOgBkHKsYReU6np+2GhXzJeR3bS0bN1NdFN9fD4osOwIAwiJEWIAoFQAVai2RazGSARCl9vA9zhztpLknLB60cdd1qsWu7a1niVQqjOfgQNpgmoQ1RQzDMAzDMGBLEcMwDMNUPawpah14UdTOKSq9lSNhtpYEMmbry9kS8res0KzQ2hU2u1nuo50pez157gqwdbvmXB+1m3t5cEVTP949igXXMhq/IrLBHE3ARrcds8VnkBBlwR4B383e3RJLirOBpoI+OvepZB29/WNE1+aaK7RWUVDI0O03cT0kSt2+qiR43ht3+qaOpp18SZUJo5sSSie33Eop7adt7Zl+KrWTTxGEu8dAiniLMCN1mIOMQKno56hjqpfCtk3w3i2T9Z6U8+Xg6GCiILRenjPne0x/yelwJCIsxPUPArsa9tiguK0BL4paB94+YxiGYRiGAVuK2i1bdzYgH2orUSCFDZ4YiCiYY1TPiFglqMzNPf61JbS1h2LhNmCE2toq4mhobRDHSuVGeK2Fx/AE1ybRvF2Nm3JHya3r+dYtiThAZKgIJHTARhhrUyKYoxlPNhoH3HKUC6QBQKpkWAIqE2a7QvRyoXX0h/H/hwnkGPURCat1UMaoT4r7DAIjzNYTo98LLdQuoNwalBRYJ8/T6jUnsAZgrUCZ6Ny1EpnzJEkBdKmJ9t3zpADbbc8+g2O585838IXjGalDQkT/HjLZAEEmh37n3YlNv5hVNmam7ShufhuAE/TVWINUGLviG6tRGsbtPhJc6+8c6ZuEhQTCIkrvvHFQgjdKUf7v4KAShiB5gJaeg5wQtiPAiyKGYRiGqXKIDtz77GAnhO0I8KKonbKrqKxlx1hd7K8ue55IABul9ACQmhAWiN34VUI7Y9qnyHoEwLMiWS0R4N2XTBSrE2D4R7d+UoNktUi2Xqwr8sYN8jREseanvJ7RJwHlQRoBP/BjpSCOpu1KmqJkudURJXRDXn8qPWhjIXRSWDhviJvAVT9LbA1yy5Ju7pX0SZU0RHlHa2SsNDUZ6bnXN5Xiw2qWQoVcIMvG6QanrBRE0h+rQiBldIytT6QIpAiqpECk030IGWDHxrfAVBeU66Rd7oFYE5T4D18kwoZoc3FC8eGmMiKlNUSRiZmEhAiyEKWD45YfEnmGKaZjwIsihmEYhqlySKkDtxRx8MZm4UURwzAMw1Q51ALBG9n7rHl4UdROCUSsppZCb5XJSHRtolgD0RYa9FaZF/HV2Trz2o0iSsciQvKE02aLzmyZGeF04DQVKvIE13Duz5r7oLfW4mjNAJGAIF+YbTBbbUaAbSJ2w4QfgBF2iyjSdmzXtkLs6HqIOExBMnK1FMJzw89KnQ8tGdk6kLG7flJoLZV2t89KYV3yteBaQEldUIzU31407UBAUnp5KGO3eTvHCcFzU5GsCyVlBcnJiNa2vWhrLSmwNltl7lZVKbEVl3YsE1aLcrf7Qui73ftbf/Hf8XaZTJSF9r4wVChEec/Ckm4v06kzOvXoi95n34APHp8Lpu0pfPCO/sN1tY/+NgJruBaN5BaZW+ZusQH6Hzspz6lElBoPwlPor9/W1lozBx9eFDEMwzBMlaO3zw5s+4u3z5qHF0XtlDASLgOwP1cEtDUk4+QXM7/MFURZHjHjhi8iq4ubd8xYTUy7Jq+aShxDFQupgXTBdaogOyEmjoM66vbM85l/wqHzy8+ML9mPCegI+G753jnIe8Zke+4xJCorN8Jre4yCMOqycmF1WrntU/l9mbKk2LqSULpSPrJKguykNSZNhA2UC6eT58bK5Fp5yoIzBtKGLEgezX1mHIVSbO2pnN/NjN+3JIWKsKcQIp8vIb+nBFVSNohjriYDKbsAOAwNH77L1qIqoPDRFr/AFVJTqMXRACAlSOlvOE9wLTO+hSkpvE60SzJT2a3/ANjVsAchoVWF1rx91jrwoohhGIZhqhxeFLUOvChqp5hghlZPBG01cVNoAHGKDKDczd09uvV0e8Leb4IVmvaBOEhjICMtjzkKeFYjW8+532iTjKaIiCCltgFkgljT5GqPkhqjQCStWpHmCCI9mGNCU6TnMBYEuNoAa9lKBHMEgKz0U31IFYcvkE4fCFVkHQFkIGwASKnie0OhNURKOUEcBdkyY1UKAqO7Ep61KM193c6PFF55c0EerfUnyl6f1BAZXC1QU5qignm3nB/ygShvJ2m9Mqk9ksEcy8eh+28ohCgUQpQK+j6ZkajN5ECKUFNLKBVDyIwexO4t6zF61q+w+s5zytpmDi7FLWttNntBKVs4yWCNyg/O6NVL3pp23WiUDoKVCDCaS2JNUQeEF0UMwzAMU+UoFepYcwcAW4qahxdFDMMwDFPlUKgAcYCLopCF1s3Bi6J2SlFpd3EFod3Tod9Ms2UVqtil241Q7QqTzZaWQHwEouzyCQVhUjjtCq+BpgXXIu1+s31ixuUIwL37TXtmHAmRtGnXRp6OIluHRF577nUz3tQHdNrzBNBuxGsnyrW7lRaqWJhdVG79dAF2pejWxZBs25WiTzfnDu8/S2U1aDKidbLME1inuM/rLbfQc5l3XeeTW3BuJGtXYA3EW2duv2GFsAOBFNjZWMKeQggVKggJBJB2pyXISIQlBZmRUIqgSl1Q2tMT+T0HJ7oxU5ni++tB2Vq7pUUKgJQQYSkWVgOAIC2MJgUKMvq6I6y29YzwGrBbcrbcRLSOzg+W0LqoyEoVmI4FL4oYhmEYpsrh3GetQwV/xupi7dq1mDZtGoYNG4ZOnTphxIgRmD9/PgqFglfvtddewyc+8QnU1tZi0KBBuOmmm9poxAeXd7buQlaKKFCjLjNvpBS+O31aJmdr3XF+5QjverqVKHm/6Q9w8q4JYQXgUsRBHUX0skLtKJN5EOVus/WcQJRaRB61Fz1jEF23QSqRuC8qCex4TH+J69H9yZcpD6Seu8CM0auDlOtxm2bezd+BdNoVOqijFAKZQEJKXea+T+Y8iMbjvtz2kuX2/kRZpXpNWYnS6iTLY6uNtIEWw0QclL21arnC7uR9oSLkSwol59hQCBEqnetMSKHnMSMQZCQy2UB/tjISmaxEriaDXKcscl16Yvf7WmzNtA7FzW9rIbQJyGg+H0r5AmkXIQEhQUEm+odt8jXGQRwpeulzEd+X+O4y7vzqzWUt+lyJf0qtAqmwRV5M07QLS9Hq1auhlMLChQsxcuRIvP7665g+fTp2796NW265BQCwY8cOnHnmmZg4cSLuuecerFy5Ev/5n/+J7t274+KLL27jJ2AYhmEYptppF4uiyZMnY/LkyfZ8+PDhqK+vx49+9CO7KFqyZAkKhQJ+8pOfIJfL4YgjjsCKFStw2223dchFkRDpP1XsL3giTytEznXA1waZX+huPYOp14QspcnrTd2fLDfBGiUA5VjATB2jWXKDOlbCaIsUfG2RENr9PVlurplzqSsCytdXJYM4us/pXk/+HT9z3J4issEaK2GCHtpzR5Pj1UvNJl/ujm/KK2mIXJrKXu8fVdn9Jv1GbP3xwwCEStnnqDQO18KU1CaFisrmQakooKmMPiuKtIEieg4hA5AKsXX9W2Xzx7QspffqQTIDytQATmoW72+loiCN+lQIBYhAf2eRAlTJaosgtFaI3GCPABAEkft+usCHhNRtZWoO3sO2EqTCAxdas6WoWdrF9lka27dvR8+ePe358uXLceqppyKXy9mySZMmob6+Htu2bWuLITIMwzBMi8DbZ61Du7AUJVmzZg3uuusuayUCgE2bNmHYsGFevb59+9prPXr0SG0rn88jn8/b8+3btwPQ23HVys4du6xuB4iDNxqNUdLy05SlCNh/S5G7r17mzEV+P01Zksi1rgCeJYgoruN5t0VjVUTR0dxg2oktRW479u9EOhCg/MemIrJeaMp5HsD3QAOAYnQsGQ8tKrcUpXmhKUWelSk01iOnbTcFCOCm6fDbte07nn3JNBumXpqlSCXepL22FEU/9ZP3A7DeY4GIvdFCRQhJoWgtRf5zmHK3PRW1oxxLUamkQEQIQwVSet6kFBCRXk2FCmGJEDYWEeZLUIU9UMVGCBlU9b/vjkBp5y6QDECZQnnyVkMimWscuDHUCaxVSQd0DKNyx6uszFLkXnfbRxwsUrbge76zYQ8AoGHXrqjrVnBDC4tl38/70wbTNG26KJo7dy5uvPHGJuusWrUKo0ePtufvvvsuJk+ejPPPPx/Tp08/4DEsWLAA1157bVn5oEGDDrhthmGqk27dvt3WQ2A6EDt37kS3bt0OStu5XA79+vXDpr8/0iLt9evXz9tRYXwEtcoSN533338fH374YZN1hg8fbt/A9957DxMmTMCJJ56IxYsXQzr701OmTMGOHTvwy1/+0pY9++yz+OQnP4mtW7futaVIKYWtW7eiV69eFXU7bcWOHTswaNAgbNiwAV27dm3r4bQ7eP72H567/Yfn7sCo5vkjIuzcuRMDBgzw/j9qaRobG8u8rfeXXC6H2traFmmrI9KmlqI+ffqgT58+e1X33Xffxemnn47jjjsOixYtKvsAjh8/Ht/+9rdRLBaRzWYBAEuXLsWoUaMqLogAoKamBjU1vgive/fu+/YgrUzXrl2r7suhPcHzt//w3O0/PHcHRrXO38GyELnU1tbyQqaVaBdC63fffRcTJkzA4MGDccstt+D999/Hpk2bsGnTJlvny1/+MnK5HKZNm4Y33ngDDz/8MO68807MmTOnDUfOMAzDMEx7oV0IrZcuXYo1a9ZgzZo1GDhwoHfN7P5169YNTz31FGbMmIHjjjsOvXv3xtVXX90h3fEZhmEYhml52sWiaOrUqZg6dWqz9Y466ij88Y9/PPgDaiNqamowf/78su0+Zu/g+dt/eO72H567A4Pnj2lN2lRozTAMwzAMUy20C00RwzAMwzDMwYYXRQzDMAzDMOBFEcMwDMMwDABeFDEMwzAMwwDgRVG7I5/P45hjjoEQAitWrPCuvfbaa/jEJz6B2tpaDBo0CDfddFPbDLLKWLt2LaZNm4Zhw4ahU6dOGDFiBObPn18WIZbnrzJ33303hg4ditraWpxwwgn43//937YeUtWxYMEC/Ou//iu6dOmCQw89FOeeey7q6+u9Oo2NjZgxYwZ69eqFzp0747zzzsPmzZvbaMTVyw033AAhBK644gpbxnPHtAa8KGpnXHnllRgwYEBZ+Y4dO3DmmWdiyJAheOWVV3DzzTfjmmuuwb333tsGo6wuVq9eDaUUFi5ciDfeeAO333477rnnHvy///f/bB2ev8o8/PDDmDNnDubPn4+//vWvOProozFp0iRs2bKlrYdWVTz//POYMWMGXnzxRSxduhTFYhFnnnkmdu/ebevMnj0bjz/+OH72s5/h+eefx3vvvYfPf/7zbTjq6uPll1/GwoULcdRRR3nlPHdMq0BMu+F3v/sdjR49mt544w0CQH/729/stR/+8IfUo0cPyufztuyqq66iUaNGtcFIq5+bbrqJhg0bZs95/iozbtw4mjFjhj0Pw5AGDBhACxYsaMNRVT9btmwhAPT8888TEdFHH31E2WyWfvazn9k6q1atIgC0fPnythpmVbFz5046/PDDaenSpXTaaafRrFmziIjnjmk92FLUTti8eTOmT5+On/70p6irqyu7vnz5cpx66qle9uNJkyahvr4e27Zta82htgu2b9+Onj172nOev3QKhQJeeeUVTJw40ZZJKTFx4kQsX768DUdW/Wzfvh0A7OfslVdeQbFY9OZy9OjRGDx4MM9lxIwZM/Bv//Zv3hwBPHdM68GLonYAEWHq1Km45JJLcPzxx6fW2bRpE/r27euVmXM3RxwDrFmzBnfddRe+/vWv2zKev3Q++OADhGGYOjf/zPPSHEopXHHFFTj55JPx8Y9/HID+HOVyubKE0zyXmoceegh//etfsWDBgrJrPHdMa8GLojZk7ty5EEI0+Vq9ejXuuusu7Ny5E/PmzWvrIVcVezt/Lu+++y4mT56M888/H9OnT2+jkTMdnRkzZuD111/HQw891NZDaRds2LABs2bNwpIlSzgbPNOmtIvcZx2Vb37zm83mdBs+fDieeeYZLF++vCz3z/HHH48LL7wQ999/P/r161fmiWHO+/Xr16Ljrhb2dv4M7733Hk4//XScdNJJZQLqf8b52xt69+6NIAhS5+afeV6a4rLLLsNvfvMbvPDCC14C6379+qFQKOCjjz7yLB48l3p7bMuWLRg7dqwtC8MQL7zwAv77v/8bTz75JM8d0zq0taiJaZ5169bRypUr7evJJ58kAPTzn/+cNmzYQESxULhQKNj75s2bx0LhiHfeeYcOP/xwuuCCC6hUKpVd5/mrzLhx4+iyyy6z52EY0mGHHcZC6wRKKZoxYwYNGDCA3nzzzbLrRiz885//3JatXr2axcJEtGPHDu87buXKlXT88cfTV77yFVq5ciXPHdNq8KKoHfL222+XeZ999NFH1LdvX/rqV79Kr7/+Oj300ENUV1dHCxcubLuBVgnvvPMOjRw5ks444wx65513aOPGjfZl4PmrzEMPPUQ1NTW0ePFi+vvf/04XX3wxde/enTZt2tTWQ6sqLr30UurWrRs999xz3mesoaHB1rnkkkto8ODB9Mwzz9Bf/vIXGj9+PI0fP74NR129uN5nRDx3TOvAi6J2SNqiiIjo1VdfpVNOOYVqamrosMMOoxtuuKFtBlhlLFq0iACkvlx4/ipz11130eDBgymXy9G4cePoxRdfbOshVR2VPmOLFi2ydfbs2UPf+MY3qEePHlRXV0ef+9znvMU5E5NcFPHcMa2BICJq9T07hmEYhmGYKoO9zxiGYRiGYcCLIoZhGIZhGAC8KGIYhmEYhgHAiyKGYRiGYRgAvChiGIZhGIYBwIsihmEYhmEYALwoYhiGYRiGAcCLIqYDMmHCBFxxxRUdqt+pU6fi3HPPPaA2hg4dahPlfvTRRxXrLV68uCwbOdNyTJ061b4Pv/zlL9t6OAzDOPCiiGFaiEcffRTf+9737PnQoUNxxx13tN2AUrjuuuuwceNGdOvWra2H0uF57rnnUhegd955JzZu3Ng2g2IYpkkybT0Ahuko9OzZs62H0CxdunSpmqzixWIR2Wy2rYfR6nTr1o0XpQxTpbCliOnwbNu2DVOmTEGPHj1QV1eHT3/60/jHP/5hr5vtoieffBJjxoxB586dMXnyZO/XfKlUwsyZM9G9e3f06tULV111FS666CJvS8vdPpswYQLWrVuH2bNn260SALjmmmtwzDHHeOO74447MHToUHsehiHmzJlj+7ryyiuRzMajlMKCBQswbNgwdOrUCUcffTR+/vOf79f8LF68GIMHD0ZdXR0+97nP4cMPPyyr86tf/Qpjx45FbW0thg8fjmuvvRalUsleX716NU455RTU1tbiYx/7GJ5++mlve2jt2rUQQuDhhx/GaaedhtraWixZsgQA8D//8z8YM2YMamtrMXr0aPzwhz/0+t6wYQO++MUvonv37ujZsyfOOeccrF271l5/7rnnMG7cOBxyyCHo3r07Tj75ZKxbt26vnr2557rttttw5JFH4pBDDsGgQYPwjW98A7t27bLX161bh7PPPhs9evTAIYccgiOOOAK/+93vsHbtWpx++ukAgB49ekAIgalTp+7VmBiGaTt4UcR0eKZOnYq//OUv+PWvf43ly5eDiHDWWWehWCzaOg0NDbjlllvw05/+FC+88ALWr1+Pb33rW/b6jTfeiCVLlmDRokVYtmwZduzY0aQe5NFHH8XAgQPtdtW+bJfceuutWLx4MX7yk5/gT3/6E7Zu3YrHHnvMq7NgwQI88MADuOeee/DGG29g9uzZ+MpXvoLnn39+7ycGwEsvvYRp06bhsssuw4oVK3D66afj+9//vlfnj3/8I6ZMmYJZs2bh73//OxYuXIjFixfj+uuvB6AXceeeey7q6urw0ksv4d5778W3v/3t1P7mzp2LWbNmYdWqVZg0aRKWLFmCq6++Gtdffz1WrVqFH/zgB/jud7+L+++/H4C2Jk2aNAldunTBH//4RyxbtswuWguFAkqlEs4991ycdtppeO2117B8+XJcfPHFdhHaFM09FwBIKfFf//VfeOONN3D//ffjmWeewZVXXmmvz5gxA/l8Hi+88AJWrlyJG2+8EZ07d8agQYPwi1/8AgBQX1+PjRs34s4779yn94ZhmDagbfPRMkzL42bXfvPNNwkALVu2zF7/4IMPqFOnTvTII48QEdGiRYsIAK1Zs8bWufvuu6lv3772vG/fvnTzzTfb81KpRIMHD6ZzzjkntV8ioiFDhtDtt9/ujW3+/Pl09NFHe2W33347DRkyxJ7379+fbrrpJnteLBZp4MCBtq/Gxkaqq6ujP//5z14706ZNoy996UsV5yVtPF/60pforLPO8sr+/d//nbp162bPzzjjDPrBD37g1fnpT39K/fv3JyKi3//+95TJZLyM5UuXLiUA9NhjjxER0dtvv00A6I477vDaGTFiBD344INe2fe+9z0aP3687WfUqFGklLLX8/k8derUiZ588kn68MMPCQA999xzFZ+7Es09Vxo/+9nPqFevXvb8yCOPpGuuuSa17rPPPksAaNu2banX3flhGKY6YE0R06FZtWoVMpkMTjjhBFvWq1cvjBo1CqtWrbJldXV1GDFihD3v378/tmzZAgDYvn07Nm/ejHHjxtnrQRDguOOOg1KqRce7fft2bNy40RtvJpPB8ccfb7fQ1qxZg4aGBnzqU5/y7i0UCjj22GP3qb9Vq1bhc5/7nFc2fvx4PPHEE/b81VdfxbJlyzwLShiGaGxsRENDA+rr6zFo0CBPq+TOlcvxxx9v/969ezfeeustTJs2DdOnT7flpVLJam5effVVrFmzBl26dPHaaWxsxFtvvYUzzzwTU6dOxaRJk/CpT30KEydOxBe/+EX079+/2Wdv7rnq6urw9NNPY8GCBVi9ejV27NiBUqnkXZ85cyYuvfRSPPXUU5g4cSLOO+88HHXUUc32zTBMdcKLIoYBygS/QogyHU9LIKUsa9fdxtsbjKblt7/9LQ477DDvWk1NzYENsEJ/1157LT7/+c+XXautrd2ntg455BCvXQD48Y9/7C0CAb3oNHWOO+44qz9y6dOnDwBg0aJFmDlzJp544gk8/PDD+M53voOlS5fixBNPPKDnWrt2LT7zmc/g0ksvxfXXX4+ePXviT3/6E6ZNm4ZCoYC6ujp87Wtfw6RJk/Db3/4WTz31FBYsWIBbb70Vl19++T7NC8Mw1QEvipgOzZgxY1AqlfDSSy/hpJNOAgB8+OGHqK+vx8c+9rG9aqNbt27o27cvXn75ZZx66qkAtEXhr3/9a5lo2iWXyyEMQ6+sT58+2LRpE4jI6l5WrFjh9dW/f3+89NJLtq9SqYRXXnkFY8eOBQB87GMfQ01NDdavX4/TTjttr56hEmPGjMFLL73klb344ove+dixY1FfX4+RI0emtjFq1Chs2LABmzdvRt++fQEAL7/8crN99+3bFwMGDMD//d//4cILL0ytM3bsWDz88MM49NBD0bVr14ptHXvssTj22GMxb948jB8/Hg8++GCzi6LmnuuVV16BUgq33norpNTyy0ceeaSs3qBBg3DJJZfgkksuwbx58/DjH/8Yl19+OXK5HACUfQYYhqleeFHEdGgOP/xwnHPOOZg+fToWLlyILl26YO7cuTjssMNwzjnn7HU7l19+ORYsWICRI0di9OjRuOuuu7Bt27YmBb1Dhw7FCy+8gAsuuAA1NTXo3bs3JkyYgPfffx833XQTvvCFL+CJJ57A73//e+8//FmzZuGGG27A4YcfjtGjR+O2227zYt106dIF3/rWtzB79mwopXDKKadg+/btWLZsGbp27YqLLrpor59r5syZOPnkk3HLLbfgnHPOwZNPPultnQHA1Vdfjc985jMYPHgwvvCFL0BKiVdffRWvv/46vv/97+NTn/oURowYgYsuugg33XQTdu7cie985zsA0Kzg+dprr8XMmTPRrVs3TJ48Gfl8Hn/5y1+wbds2zJkzBxdeeCFuvvlmnHPOObjuuuswcOBArFu3Do8++iiuvPJKFItF3HvvvfjsZz+LAQMGoL6+Hv/4xz8wZcqUZp+9uecaOXIkisUi7rrrLpx99tlYtmwZ7rnnHq+NK664Ap/+9KfxL//yL9i2bRueffZZjBkzBgAwZMgQCCHwm9/8BmeddRY6deqEzp077/V7wzBMG9C2kiaGaXmSguetW7fSV7/6VerWrRt16tSJJk2aRG+++aa9vmjRIk9YTET02GOPkfvPo1gs0mWXXUZdu3alHj160FVXXUXnn38+XXDBBRX7Xb58OR111FFUU1PjtfWjH/2IBg0aRIcccghNmTKFrr/+ek9oXSwWadasWdS1a1fq3r07zZkzh6ZMmeKJupVSdMcdd9CoUaMom81Snz59aNKkSfT8889XnJc0oTUR0X333UcDBw6kTp060dlnn0233HJL2Xw88cQTdNJJJ1GnTp2oa9euNG7cOLr33nvt9VWrVtHJJ59MuVyORo8eTY8//jgBoCeeeIKIYqH13/72t7L+lyxZQscccwzlcjnq0aMHnXrqqfToo4/a6xs3bqQpU6ZQ7969qaamhoYPH07Tp0+n7du306ZNm+jcc8+l/v37Uy6XoyFDhtDVV19NYRhWnId9ea7bbruN+vfvbz83DzzwgCeevuyyy2jEiBFUU1NDffr0oa9+9av0wQcf2Puvu+466tevHwkh6KKLLvL6BgutGabqEEQHQTjBMB0cpRTGjBmDL37xi14U62pm6NChuOKKK1olBcqyZctwyimnYM2aNZ6AnYkRQuCxxx474PQtDMO0HByniGH2gnXr1uHHP/4x3nzzTaxcuRKXXnop3n77bXz5y19u66HtE1dddRU6d+6M7du3t2i7jz32GJYuXYq1a9fi6aefxsUXX4yTTz6ZF0QpXHLJJbyNxjBVCluKGGYv2LBhAy644AK8/vrrICJ8/OMfxw033GDF0O2BdevWWU+34cOHW/FwS/DAAw/g+9//PtavX4/evXtj4sSJuPXWW9GrV68W62NfOeKIIypGtl64cGFFcffBZsuWLdixYwcAHfrB9chjGKZt4UURwzAdEncRmKRv375lsY8YhmF4UcQwDMMwDAPWFDEMwzAMwwDgRRHDMAzDMAwAXhQxDMMwDMMA4EURwzAMwzAMAF4UMQzDMAzDAOBFEcMwDMMwDABeFDEMwzAMwwDgRRHDMAzDMAwA4P8Dq+9hwJ3y/VEAAAAASUVORK5CYII=",
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",
       "text/plain": [
        "<Figure size 640x480 with 2 Axes>"
       ]
@@ -461,18 +2167,44 @@
     "err.isel(time=1).plot()"
    ]
   },
+  {
+   "cell_type": "markdown",
+   "id": "add2659d",
+   "metadata": {},
+   "source": [
+    "## Output model and prediction"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 18,
+   "id": "2cfec2ef",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Save the trained model\n",
+    "model_save_path = Path(\"./models/spatio_temporal_model.pth\")\n",
+    "model_save_path.parent.mkdir(parents=True, exist_ok=True)\n",
+    "torch.save(model.state_dict(), model_save_path)"
+   ]
+  },
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "bf3c8bea-c519-4a8f-a8a0-e25af79bac39",
+   "id": "088313a6",
    "metadata": {},
    "outputs": [],
-   "source": []
+   "source": [
+    "# Save the xr.Dataset with predictions\n",
+    "predictions_save_path = Path(\"./predicted_data/predictions.nc\")\n",
+    "predictions_save_path.parent.mkdir(parents=True, exist_ok=True)\n",
+    "monthly_data.to_netcdf(predictions_save_path)"
+   ]
   }
  ],
  "metadata": {
   "kernelspec": {
-   "display_name": "Python 3 (ipykernel)",
+   "display_name": "climanet",
    "language": "python",
    "name": "python3"
   },
@@ -486,7 +2218,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.11.14"
+   "version": "3.14.0"
   }
  },
  "nbformat": 4,
diff --git a/scripts/README.md b/scripts/README.md
new file mode 100644
index 0000000..ca1016c
--- /dev/null
+++ b/scripts/README.md
@@ -0,0 +1,42 @@
+# Execute training tasks on SLURM
+
+1. Make a working directory
+
+```sh
+mkdir training
+cd training
+```
+
+2. Clone this repo
+```sh
+git clone git@github.com:ESMValGroup/ClimaNet.git
+```
+
+3. Install uv for dependency management. Se [uv doc](https://docs.astral.sh/uv/getting-started/installation/).
+
+4. Create a venv and install Python dependencies using uv
+```sh
+cd ClimaNet
+```
+
+```
+uv sync
+```
+
+A `.venv` dir will appear
+
+5. Copy the python script and slurm script into the working dir:
+
+```sh
+cp ClimaNet/scripts/example* .
+```
+
+6. Config `example.slurm`, in the `source ...` line, make sure the venv just created is activated.
+   Note that the account is the ESO4CLIMA project account, which is shared by multiple users.
+
+7. Config `example.py`, make sure the path of input data and land mask data is correct.
+
+8. Execute the SLURM job
+```sh
+sbatch example.slurm
+```
\ No newline at end of file
diff --git a/scripts/example.py b/scripts/example.py
new file mode 100644
index 0000000..d685815
--- /dev/null
+++ b/scripts/example.py
@@ -0,0 +1,219 @@
+#!/usr/bin/env python3
+from pathlib import Path
+from matplotlib import pyplot as plt
+import torch
+import torch.nn.functional
+import xarray as xr
+from torch.utils.data import DataLoader
+
+from climanet import STDataset
+from climanet.st_encoder_decoder import SpatioTemporalModel
+from climanet.utils import pred_to_numpy
+
+
+def _preprocess_roi(ds, lon_subset, lat_subset):
+    return ds[["ts"]].sel(lon=lon_subset, lat=lat_subset)
+
+
+def main():
+    # Data files
+    data_folder = Path("/work/bd0854/b380103/eso4clima/output/v1.0/concatenated/") # HPC
+    # data_folder = Path("../../data/output/") # local
+    daily_files = sorted(data_folder.rglob("20*_day_ERA5_masked_ts.nc"))
+    monthly_files = sorted(data_folder.rglob("20*_mon_ERA5_full_ts.nc"))
+    daily_files.sort()
+    monthly_files.sort()
+
+    # Land surface
+    lsm_file = "/home/b/b383704/eso4clima/train_twoyears/era5_lsm_bool.nc" # HPC
+    # lsm_file = data_folder / "era5_lsm_bool.nc" # local
+    
+
+    # # Load full dataset
+    # daily_files = sorted(data_folder.rglob("20*_day_ERA5_masked_ts.nc"))
+    # monthly_files = sorted(data_folder.rglob("20*_mon_ERA5_full_ts.nc"))
+    # patch_size_training = 80
+    # daily_data = xr.open_mfdataset(daily_files)
+    # monthly_data = xr.open_mfdataset(monthly_files)
+
+    # Uncomment following for a partial debugging
+    lon_subset = slice(-10, 10)
+    lat_subset = slice(-5, 5)
+    patch_size_training = 20
+    daily_data = xr.open_mfdataset(
+        daily_files,
+        combine="by_coords",
+        preprocess=lambda ds: _preprocess_roi(ds, lon_subset, lat_subset),
+        chunks={"time": 1, "lat": patch_size_training * 2, "lon": patch_size_training * 2},
+        data_vars="minimal",
+        coords="minimal",
+        compat="override",
+        parallel=False,
+    )
+
+    monthly_data = xr.open_mfdataset(
+        monthly_files,
+        combine="by_coords",
+        preprocess=lambda ds: _preprocess_roi(ds, lon_subset, lat_subset),
+        chunks={"time": 1, "lat": patch_size_training * 2, "lon": patch_size_training * 2},
+        data_vars="minimal",
+        coords="minimal",
+        compat="override",
+        parallel=False,
+    )
+    
+    lsm_mask = xr.open_dataset(lsm_file)
+    lsm_mask = lsm_mask.rename({"latitude": "lat", "longitude": "lon"})[["lsm"]].sel(
+        lon=lon_subset, lat=lat_subset
+    )
+
+    # Compute monthly climatology stats without persisting the full (time, lat, lon) monthly field
+    monthly_ts = daily_data["ts"].resample(time="MS").mean(skipna=True)
+    mean = monthly_ts.mean(dim=["lat", "lon"], skipna=True).compute().values
+    std = monthly_ts.std(dim=["lat", "lon"], skipna=True).compute().values
+    print(f"mean: {mean}, std: {std}")
+
+    # Make a dataset
+    dataset = STDataset(
+        daily_da=daily_data["ts"],
+        monthly_da=monthly_data["ts"],
+        land_mask=lsm_mask["lsm"],
+        patch_size=(patch_size_training, patch_size_training),
+    )
+
+    # Initialize training
+    device = "cuda" if torch.cuda.is_available() else "cpu"
+    model = SpatioTemporalModel(
+        embed_dim=128,
+        patch_size=(1, 2, 2),
+        overlap=2,
+        max_months=monthly_data.sizes["time"],
+    ).to(device)
+    optimizer = torch.optim.Adam(model.parameters(), lr=1e-3)
+    decoder = model.decoder
+    with torch.no_grad():
+        decoder.bias.copy_(torch.from_numpy(mean))
+        decoder.scale.copy_(torch.from_numpy(std) + 1e-6)
+
+    # Make a dataloader
+    dataloader = DataLoader(
+        dataset,
+        batch_size=1,
+        shuffle=True,
+        pin_memory=False,
+    )
+
+    # Training process
+    best_loss = float("inf")
+    patience = 10
+    counter = 0
+    model.train()
+    for epoch in range(101):
+        for batch in dataloader:
+            optimizer.zero_grad()
+
+            daily_batch = batch["daily_patch"]
+            daily_mask = batch["daily_mask_patch"]
+            monthly_target = batch["monthly_patch"]
+            land_mask_patch = batch["land_mask_patch"][0, ...]
+            padded_days_mask = batch["padded_days_mask"]
+
+            pred = model(daily_batch, daily_mask, land_mask_patch, padded_days_mask)
+
+            ocean = (~land_mask_patch).to(pred.device)
+            ocean = ocean[None, None, :, :]
+
+            loss = (
+                torch.nn.functional.l1_loss(pred, monthly_target, reduction="none")
+                * ocean
+            )
+            loss_per_month = loss.sum(dim=(-2, -1)) / ocean.sum(dim=(-2, -1))
+            loss = loss_per_month.mean()
+
+            loss.backward()
+            optimizer.step()
+
+        if loss.item() < best_loss:
+            best_loss = loss.item()
+            counter = 0
+
+        if epoch % 20 == 0:
+            print(f"The loss is {best_loss} at epoch {epoch}")
+        else:
+            counter += 1
+            if counter >= patience:
+                print(
+                    f"No improvement for {patience} epochs, stopping early at epoch {epoch}."
+                )
+                break
+
+    print("training done!")
+    print(f"Final loss: {loss.item()}")
+
+    # Calculate prediction and error
+    dataset_pred = STDataset(
+        daily_da=daily_data["ts"],
+        monthly_da=monthly_data["ts"],
+        land_mask=lsm_mask["lsm"],
+        patch_size=(daily_data.sizes["lat"], daily_data.sizes["lon"]),
+    )
+    dataloader_pred = DataLoader(
+        dataset_pred,
+        batch_size=len(dataset_pred),
+        pin_memory=False,
+    )
+    full_batch = next(iter(dataloader_pred))
+    daily_batch = full_batch["daily_patch"]
+    daily_mask = full_batch["daily_mask_patch"]
+    monthly_target = full_batch["monthly_patch"]
+    land_mask_patch = full_batch["land_mask_patch"][0, ...]
+    padded_days_mask = full_batch["padded_days_mask"]
+    model.eval()
+    with torch.no_grad():
+        pred = model(daily_batch, daily_mask, land_mask_patch, padded_days_mask)
+    monthly_prediction = pred_to_numpy(pred, land_mask=land_mask_patch)[0]
+    monthly_data["ts_pred"] = (("time", "lat", "lon"), monthly_prediction)
+    target = monthly_data["ts"].where(~lsm_mask["lsm"].values)
+    err = target - monthly_data["ts_pred"]
+
+    # Save the trained model
+    model_save_path = Path("./models/spatio_temporal_model.pth")
+    model_save_path.parent.mkdir(parents=True, exist_ok=True)
+    torch.save(model.state_dict(), model_save_path)
+    # Save the xr.Dataset with predictions
+    predictions_save_path = Path("./predicted_data/predictions.nc")
+    predictions_save_path.parent.mkdir(parents=True, exist_ok=True)
+    monthly_data.to_netcdf(predictions_save_path)
+    print(f"Saved model to: {model_save_path}")
+    print(f"Saved predictions to: {predictions_save_path}")
+
+    # Plot and save inspections
+    plot_path = Path("./figures/") # local
+    plot_path.mkdir(parents=True, exist_ok=True)
+    # 1) Prediction (t=0)
+    fig, ax = plt.subplots(figsize=(8, 4))
+    monthly_data["ts_pred"].isel(time=0).plot(ax=ax)
+    fig.savefig(plot_path / "ts_pred_t0.png", dpi=200, bbox_inches="tight")
+    plt.close(fig)
+
+    # 2) Target (t=0)
+    fig, ax = plt.subplots(figsize=(8, 4))
+    monthly_data["ts"].where(~lsm_mask["lsm"].values).isel(time=0).plot(ax=ax)
+    fig.savefig(plot_path / "ts_target_t0.png", dpi=200, bbox_inches="tight")
+    plt.close(fig)
+
+    # 3) Error (t=0)
+    fig, ax = plt.subplots(figsize=(8, 4))
+    err.isel(time=0).plot(ax=ax)
+    fig.savefig(plot_path / "err_t0.png", dpi=200, bbox_inches="tight")
+    plt.close(fig)
+
+    # 4) Error (t=1)
+    fig, ax = plt.subplots(figsize=(8, 4))
+    err.isel(time=1).plot(ax=ax)
+    fig.savefig(plot_path / "err_t1.png", dpi=200, bbox_inches="tight")
+    plt.close(fig)
+
+
+if __name__ == "__main__":
+    main()
diff --git a/scripts/example.slurm b/scripts/example.slurm
new file mode 100644
index 0000000..a41b132
--- /dev/null
+++ b/scripts/example.slurm
@@ -0,0 +1,12 @@
+#!/bin/bash
+#SBATCH --job-name=eso4clima
+#SBATCH --partition=compute
+#SBATCH --nodes=1
+#SBATCH --ntasks-per-node=128
+#SBATCH --time=01:00:00
+#SBATCH --account=bd0854
+
+source /home/b/b383704/eso4clima/ClimaNet/.venv/bin/activate
+
+python /home/b/b383704/eso4clima/train_twoyears/example.py
+