diff --git a/.skyignore b/.skyignore
new file mode 100644
index 0000000..86cfc06
--- /dev/null
+++ b/.skyignore
@@ -0,0 +1,6 @@
+/artifacts
+/data/raw
+/.venv
+/notebooks
+__pycache__
+*.pyc
diff --git a/configs/remote_mlp_tiny.toml b/configs/remote_mlp_tiny.toml
new file mode 100644
index 0000000..2d7eb9b
--- /dev/null
+++ b/configs/remote_mlp_tiny.toml
@@ -0,0 +1,32 @@
+[run]
+name = "remote_mlp_tiny"
+seed = 0
+artifact_dir = "artifacts/current_run/training_runs"
+
+[data]
+root = "data/processed/minimal"
+train_cases = 4
+val_cases = 1
+test_cases = 1
+points_per_case = 128
+batch_size = 128
+
+[model]
+type = "mlp"
+hidden_width = 128
+depth = 4
+activation = "gelu"
+
+[optim]
+lr = 0.001
+weight_decay = 0.0
+steps = 100
+log_interval = 25
+
+[device]
+type = "cuda"
+allow_cpu_fallback = false
+benchmark_kernels = true
+
+[loss]
+type = "normalized_mse"
diff --git a/configs/remote_smoke.toml b/configs/remote_smoke.toml
new file mode 100644
index 0000000..c05edf4
--- /dev/null
+++ b/configs/remote_smoke.toml
@@ -0,0 +1,73 @@
+[run]
+name = "airfrans-smoke"
+timeout_minutes = 45
+local_artifact_dir = "artifacts/remote_runs"
+max_attempts = 2
+
+[provider]
+kind = "vastai"
+disk_gb = 64
+max_price_per_hour = 0.60
+image = "vastai/base:0.0.2"
+
+[provider.gpu]
+name = "RTX 4090"
+count = 1
+min_vram_gb = 16
+
+[selection]
+min_reliability = 0.95
+min_down_mbps = 100
+min_up_mbps = 25
+require_verified = true
+blocked_geos = ["CN"]
+blacklist_hosts = [59017]
+drop_cheap_frac = 0.30
+image_size_gb = 5.0
+base_url = "https://cloud.vast.ai"
+
+[workspace]
+workdir = "."
+exclude = [
+  "/artifacts",
+  "/data/raw",
+  "/.venv",
+  "/notebooks",
+  "__pycache__",
+  "*.pyc",
+]
+
+[bootstrap]
+command = """
+uv sync --no-dev
+uv run --no-dev python -c "import torch; assert torch.cuda.is_available(); print(torch.cuda.get_device_name(0))"
+"""
+
+[data]
+validation_command = """
+uv run --no-dev python -c "from pathlib import Path; files=sorted(Path('data/processed/minimal').glob('*.npz')); assert len(files) >= 6; print(f'processed_minimal_cases={len(files)}')"
+"""
+
+[job]
+command = """
+uv run --no-dev remote-run smoke-train configs/remote_mlp_tiny.toml --artifact-dir artifacts/current_run --run-id "$AIRFRANS_REMOTE_RUN_ID"
+"""
+artifact_dir = "artifacts/current_run"
+heartbeat_file = "artifacts/current_run/heartbeat.json"
+metrics_file = "artifacts/current_run/metrics.jsonl"
+
+[artifacts]
+mode = "rsync"
+required = [
+  "final_metrics.json",
+  "metrics.jsonl",
+  "checkpoint.pt",
+  "run_manifest.json",
+  "environment_manifest.json",
+  "artifact_manifest.json",
+  "checksums.txt",
+]
+
+[cleanup]
+on_success = "sky_down"
+on_failure = "collect_then_keep"
diff --git a/notebooks/airfrans_simulation_deep_dive.ipynb b/notebooks/airfrans_simulation_deep_dive.ipynb
index aff1d3f..09be800 100644
--- a/notebooks/airfrans_simulation_deep_dive.ipynb
+++ b/notebooks/airfrans_simulation_deep_dive.ipynb
@@ -60,7 +60,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 1,
    "id": "1346d8f4",
    "metadata": {
     "execution": {
@@ -70,7 +70,16 @@
      "shell.execute_reply": "2026-07-20T19:11:12.518263Z"
     }
    },
-   "outputs": [],
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "repo: /home/aaron/data/airfrans\n",
+      "raw data: /home/aaron/data/airfrans/data/raw/OF_dataset\n"
+     ]
+    }
+   ],
    "source": [
     "from pathlib import Path\n",
     "import gzip\n",
@@ -135,7 +144,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 2,
    "id": "eb38682d",
    "metadata": {
     "execution": {
@@ -145,7 +154,19 @@
      "shell.execute_reply": "2026-07-20T19:11:12.523621Z"
     }
    },
-   "outputs": [],
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Re=2e6  U_inf= 31.20 m/s  Mach=0.090  q/rho=   486.7 m^2/s^2  q=   576.3 Pa\n",
+      "Re=3e6  U_inf= 46.80 m/s  Mach=0.135  q/rho=  1095.1 m^2/s^2  q=  1296.6 Pa\n",
+      "Re=4e6  U_inf= 62.40 m/s  Mach=0.180  q/rho=  1946.9 m^2/s^2  q=  2305.1 Pa\n",
+      "Re=5e6  U_inf= 78.00 m/s  Mach=0.225  q/rho=  3042.0 m^2/s^2  q=  3601.7 Pa\n",
+      "Re=6e6  U_inf= 93.60 m/s  Mach=0.270  q/rho=  4380.5 m^2/s^2  q=  5186.5 Pa\n"
+     ]
+    }
+   ],
    "source": [
     "NU_DATASET = 1.56e-5          # m^2/s, paper Table 6 / docs note\n",
     "RHO = 1.184                   # kg/m^3, paper Table 6\n",
@@ -226,7 +247,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 3,
    "id": "a7370288",
    "metadata": {
     "execution": {
@@ -236,7 +257,34 @@
      "shell.execute_reply": "2026-07-20T19:11:12.586915Z"
     }
    },
-   "outputs": [],
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "AirfRANS raw subset\n",
+      "status: ok\n",
+      "data_dir: /home/aaron/data/airfrans/data/raw/OF_dataset\n",
+      "manifest: /home/aaron/data/airfrans/data/raw/OF_dataset_subset_manifest.json\n",
+      "simulations: 50 / 50\n",
+      "files: 5820 / 5820\n",
+      "bytes: 7565631222 / 7565631222 (7.57 GB)\n",
+      "source_zip_bytes: 71310335730\n",
+      "sample_seed: 20260719\n",
+      "sample_method: uniform random sample without replacement from the 1000 top-level OF_dataset simulation directories, using sorted names as the sampling population\n",
+      "sample_simulations:\n",
+      "- airFoil2D_SST_32.137_12.122_4.854_5.202_9.247\n",
+      "- airFoil2D_SST_33.238_-0.071_0.667_2.065_6.479\n",
+      "- airFoil2D_SST_33.816_-1.984_6.59_0.0_7.983\n",
+      "- airFoil2D_SST_33.846_6.261_0.922_3.023_1.0_12.36\n",
+      "- airFoil2D_SST_36.155_8.69_3.296_7.636_1.0_8.571\n",
+      "- airFoil2D_SST_38.797_3.751_1.748_7.04_0.0_19.453\n",
+      "- airFoil2D_SST_40.175_10.442_1.962_3.054_0.0_6.878\n",
+      "- airFoil2D_SST_40.585_14.545_0.315_2.716_9.25\n",
+      "... 42 more\n"
+     ]
+    }
+   ],
    "source": [
     "report = inspect_raw_subset(DATA_DIR, MANIFEST_PATH)\n",
     "print(format_raw_inspection(report, sample_limit=8))\n",
@@ -286,7 +334,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 4,
    "id": "473b211e",
    "metadata": {
     "execution": {
@@ -296,7 +344,52 @@
      "shell.execute_reply": "2026-07-20T19:11:12.596532Z"
     }
    },
-   "outputs": [],
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "local simulations: 50\n",
+      "U_inf range: 32.137 .. 93.213 m/s\n",
+      "Re range from U/nu: 2.060e+06 .. 5.975e+06\n",
+      "angle range: -2.718 .. 14.794 deg\n",
+      "families: {'NACA-4-like': 26, 'NACA-5-like': 24}\n"
+     ]
+    },
+    {
+     "data": {
+      "text/plain": [
+       "[{'name': 'airFoil2D_SST_32.137_12.122_4.854_5.202_9.247',\n",
+       "  'turbulence': 'SST',\n",
+       "  'u_inf': 32.137,\n",
+       "  'alpha_deg': 12.122,\n",
+       "  'naca_params': [4.854, 5.202, 9.247],\n",
+       "  'family': 'NACA-4-like',\n",
+       "  'reynolds_from_name': 2060064.1025641027,\n",
+       "  'mach_from_name': 0.09285466628142156},\n",
+       " {'name': 'airFoil2D_SST_33.238_-0.071_0.667_2.065_6.479',\n",
+       "  'turbulence': 'SST',\n",
+       "  'u_inf': 33.238,\n",
+       "  'alpha_deg': -0.071,\n",
+       "  'naca_params': [0.667, 2.065, 6.479],\n",
+       "  'family': 'NACA-4-like',\n",
+       "  'reynolds_from_name': 2130641.0256410255,\n",
+       "  'mach_from_name': 0.09603582779543483},\n",
+       " {'name': 'airFoil2D_SST_33.816_-1.984_6.59_0.0_7.983',\n",
+       "  'turbulence': 'SST',\n",
+       "  'u_inf': 33.816,\n",
+       "  'alpha_deg': -1.984,\n",
+       "  'naca_params': [6.59, 0.0, 7.983],\n",
+       "  'family': 'NACA-4-like',\n",
+       "  'reynolds_from_name': 2167692.307692308,\n",
+       "  'mach_from_name': 0.09770586535683329}]"
+      ]
+     },
+     "execution_count": 4,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
    "source": [
     "SIM_RE = re.compile(\n",
     "    r\"^airFoil2D_(?P<turbulence>[^_]+)_\"\n",
@@ -339,7 +432,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 5,
    "id": "2f2e29bb",
    "metadata": {
     "execution": {
@@ -349,7 +442,18 @@
      "shell.execute_reply": "2026-07-20T19:11:12.852448Z"
     }
    },
-   "outputs": [],
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 1300x350 with 3 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
    "source": [
     "fig, axes = plt.subplots(1, 3, figsize=(13, 3.5))\n",
     "axes[0].hist(re_vals / 1e6, bins=12)\n",
@@ -389,7 +493,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 6,
    "id": "b1fa24ec",
    "metadata": {
     "execution": {
@@ -399,7 +503,29 @@
      "shell.execute_reply": "2026-07-20T19:11:12.856721Z"
     }
    },
-   "outputs": [],
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "airFoil2D_SST_32.137_12.122_4.854_5.202_9.247\n",
+      "{\n",
+      "  \"name\": \"airFoil2D_SST_32.137_12.122_4.854_5.202_9.247\",\n",
+      "  \"turbulence\": \"SST\",\n",
+      "  \"u_inf\": 32.137,\n",
+      "  \"alpha_deg\": 12.122,\n",
+      "  \"naca_params\": [\n",
+      "    4.854,\n",
+      "    5.202,\n",
+      "    9.247\n",
+      "  ],\n",
+      "  \"family\": \"NACA-4-like\",\n",
+      "  \"reynolds_from_name\": 2060064.1025641027,\n",
+      "  \"mach_from_name\": 0.09285466628142156\n",
+      "}\n"
+     ]
+    }
+   ],
    "source": [
     "SIM_INDEX = 0\n",
     "SIM_NAME = sim_names[SIM_INDEX]\n",
@@ -411,7 +537,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 7,
    "id": "e35d99c0",
    "metadata": {
     "execution": {
@@ -537,7 +663,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 8,
    "id": "9f350525",
    "metadata": {
     "execution": {
@@ -547,7 +673,25 @@
      "shell.execute_reply": "2026-07-20T19:11:12.876263Z"
     }
    },
-   "outputs": [],
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "solver application: simpleFoam\n",
+      "final solver iteration/endTime: 40000\n",
+      "writeInterval: $endTime\n",
+      "RAS turbulence model: kOmegaSST\n",
+      "Uinf from controlDict: 32.137 m/s\n",
+      "nu from transportProperties: 1.560e-05 m^2/s\n",
+      "Re = U*chord/nu: 2.060e+06\n",
+      "Mach = U/c: 0.093\n",
+      "dragDir: [0.97770268 0.20999399 0.        ]\n",
+      "liftDir: [-0.20999399  0.97770268  0.        ]\n",
+      "angle inferred from dragDir: 12.122 deg\n"
+     ]
+    }
+   ],
    "source": [
     "control_text = read_text(SIM_DIR / \"system\" / \"controlDict\")\n",
     "transport_text = read_text(SIM_DIR / \"constant\" / \"transportProperties\")\n",
@@ -603,7 +747,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 9,
    "id": "1e0fc222",
    "metadata": {
     "execution": {
@@ -613,7 +757,241 @@
      "shell.execute_reply": "2026-07-20T19:11:12.884089Z"
     }
    },
-   "outputs": [],
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\n",
+      "--- 0/U ---\n",
+      "/*--------------------------------*- C++ -*----------------------------------*\\\n",
+      "| =========                 |                                                 |\n",
+      "| \\\\      /  F ield         | OpenFOAM: The Open Source CFD Toolbox           |\n",
+      "|  \\\\    /   O peration     | Version:  v2112                                 |\n",
+      "|   \\\\  /    A nd           | Website:  www.openfoam.com                      |\n",
+      "|    \\\\/     M anipulation  |                                                 |\n",
+      "\\*---------------------------------------------------------------------------*/\n",
+      "FoamFile\n",
+      "{\n",
+      "    version     2.0;\n",
+      "    format      ascii;\n",
+      "    class       volVectorField;\n",
+      "    object      U;\n",
+      "}\n",
+      "// * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * //\n",
+      "\n",
+      "dimensions      [0 1 -1 0 0 0 0];\n",
+      "\n",
+      "field\t\t(31.42043092852577 6.748576832617464 0);\n",
+      "\n",
+      "internalField   uniform $field;\n",
+      "\n",
+      "boundaryField\n",
+      "{\n",
+      "    aerofoil\n",
+      "    {\n",
+      "        type           noSlip;\n",
+      "    }\n",
+      "\n",
+      "    freestream\n",
+      "    {\n",
+      "        type\t\t\tfreestreamVelocity;\n",
+      "        freestreamValue\tuniform $field;\n",
+      "    }\n",
+      "\n",
+      "    frontAndBack\n",
+      "    {\n",
+      "        type            empty;\n",
+      "    }\n",
+      "}\n",
+      "\n",
+      "\n",
+      "// ************************************************************************* //\n",
+      "\n",
+      "--- 0/p ---\n",
+      "/*--------------------------------*- C++ -*----------------------------------*\\\n",
+      "| =========                 |                                                 |\n",
+      "| \\\\      /  F ield         | OpenFOAM: The Open Source CFD Toolbox           |\n",
+      "|  \\\\    /   O peration     | Version:  v2112                                 |\n",
+      "|   \\\\  /    A nd           | Website:  www.openfoam.com                      |\n",
+      "|    \\\\/     M anipulation  |                                                 |\n",
+      "\\*---------------------------------------------------------------------------*/\n",
+      "FoamFile\n",
+      "{\n",
+      "    version     2.0;\n",
+      "    format      ascii;\n",
+      "    class       volScalarField;\n",
+      "    object      p;\n",
+      "}\n",
+      "// * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * //\n",
+      "\n",
+      "field           0;\n",
+      "\n",
+      "dimensions      [0 2 -2 0 0 0 0];\n",
+      "\n",
+      "internalField   uniform $field;\n",
+      "\n",
+      "boundaryField\n",
+      "{\n",
+      "    aerofoil\n",
+      "    {\n",
+      "        type           zeroGradient;\n",
+      "    }\n",
+      "\n",
+      "    freestream\n",
+      "    {\n",
+      "        type\t\t\tfreestreamPressure;\n",
+      "        freestreamValue\tuniform $field;\n",
+      "    }\n",
+      "\n",
+      "    frontAndBack\n",
+      "    {\n",
+      "        type            empty;\n",
+      "    }\n",
+      "}\n",
+      "\n",
+      "\n",
+      "// ************************************************************************* //\n",
+      "\n",
+      "--- 0/nut ---\n",
+      "/*--------------------------------*- C++ -*----------------------------------*\\\n",
+      "| =========                 |                                                 |\n",
+      "| \\\\      /  F ield         | OpenFOAM: The Open Source CFD Toolbox           |\n",
+      "|  \\\\    /   O peration     | Version:  v2112                                 |\n",
+      "|   \\\\  /    A nd           | Website:  www.openfoam.com                      |\n",
+      "|    \\\\/     M anipulation  |                                                 |\n",
+      "\\*---------------------------------------------------------------------------*/\n",
+      "FoamFile\n",
+      "{\n",
+      "    version     2.0;\n",
+      "    format      ascii;\n",
+      "    class       volScalarField;\n",
+      "    object      nut;\n",
+      "}\n",
+      "// * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * //\n",
+      "\n",
+      "dimensions      [0 2 -1 0 0 0 0];\n",
+      "\n",
+      "field\t\t1.56e-5;\n",
+      "\n",
+      "internalField   uniform $field;\n",
+      "\n",
+      "boundaryField\n",
+      "{\n",
+      "    aerofoil\n",
+      "    {\n",
+      "        type           nutLowReWallFunction;\n",
+      "        value\t\tuniform 0;\n",
+      "    }\n",
+      "\n",
+      "    freestream\n",
+      "    {\n",
+      "        type            freestream;\n",
+      "        freestreamValue uniform $field;\n",
+      "    }\n",
+      "\n",
+      "    frontAndBack\n",
+      "    {\n",
+      "        type            empty;\n",
+      "    }\n",
+      "}\n",
+      "\n",
+      "\n",
+      "// ************************************************************************* //\n",
+      "\n",
+      "--- 0/k ---\n",
+      "/*--------------------------------*- C++ -*----------------------------------*\\\n",
+      "| =========                 |                                                 |\n",
+      "| \\\\      /  F ield         | OpenFOAM: The Open Source CFD Toolbox           |\n",
+      "|  \\\\    /   O peration     | Version:  v2112                                 |\n",
+      "|   \\\\  /    A nd           | Website:  www.openfoam.com                      |\n",
+      "|    \\\\/     M anipulation  |                                                 |\n",
+      "\\*---------------------------------------------------------------------------*/\n",
+      "FoamFile\n",
+      "{\n",
+      "    version     2.0;\n",
+      "    format      ascii;\n",
+      "    class       volScalarField;\n",
+      "    object      k;\n",
+      "}\n",
+      "// * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * //\n",
+      "\n",
+      "dimensions      [0 2 -2 0 0 0 0];\n",
+      "\n",
+      "field\t\t1.2533430000000003e-09;\n",
+      "\n",
+      "internalField\tuniform $field; \n",
+      "\n",
+      "boundaryField\n",
+      "{\n",
+      "\taerofoil\n",
+      "\t{\n",
+      "\t\ttype\t\tfixedValue;\n",
+      "\t\tvalue\t\tuniform 0;\n",
+      "\t}\t\n",
+      "\t\n",
+      "\tfreestream\n",
+      "\t{\n",
+      "\t\ttype\t\t\tfreestream;\n",
+      "\t\tfreestreamValue\tuniform $field;\n",
+      "\t}\n",
+      "\n",
+      "\tfrontAndBack\n",
+      "\t{\n",
+      "\t\ttype\t\tempty;\n",
+      "\t}\n",
+      "}\n",
+      "\n",
+      "\n",
+      "// ************************************************************************* //\n",
+      "\n",
+      "--- 0/omega ---\n",
+      "/*--------------------------------*- C++ -*----------------------------------*\\\n",
+      "| =========                 |                                                 |\n",
+      "| \\\\      /  F ield         | OpenFOAM: The Open Source CFD Toolbox           |\n",
+      "|  \\\\    /   O peration     | Version:  v2112                                 |\n",
+      "|   \\\\  /    A nd           | Website:  www.openfoam.com                      |\n",
+      "|    \\\\/     M anipulation  |                                                 |\n",
+      "\\*---------------------------------------------------------------------------*/\n",
+      "FoamFile\n",
+      "{\n",
+      "    version     2.0;\n",
+      "    format      ascii;\n",
+      "    class       volScalarField;\n",
+      "    object      omega;\n",
+      "}\n",
+      "// * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * //\n",
+      "\n",
+      "dimensions      [0 0 -1 0 0 0 0];\n",
+      "\n",
+      "field\t\t0.40171250000000003;\n",
+      "\n",
+      "internalField   uniform $field; \n",
+      "\n",
+      "boundaryField\n",
+      "{\n",
+      "\taerofoil\n",
+      "\t{\n",
+      "\t\ttype\t\tomegaWallFunction;\n",
+      "\t\tvalue\t\tuniform 312000000.0;\n",
+      "\t}\n",
+      "\t\n",
+      "\tfreestream\n",
+      "\t{\n",
+      "\t\ttype\t\t\tfreestream;\n",
+      "\t\tfreestreamValue\tuniform $field;\n",
+      "\t}\n",
+      "\n",
+      "\tfrontAndBack\n",
+      "\t{\n",
+      "\t\ttype\t\tempty;\n",
+      "\t}\n",
+      "}\n",
+      "\n",
+      "// ************************************************************************* //\n"
+     ]
+    }
+   ],
    "source": [
     "for field in [\"U\", \"p\", \"nut\", \"k\", \"omega\"]:\n",
     "    candidate = SIM_DIR / \"0\" / field\n",
@@ -652,7 +1030,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 10,
    "id": "dc969024",
    "metadata": {
     "execution": {
@@ -662,7 +1040,19 @@
      "shell.execute_reply": "2026-07-20T19:11:14.071808Z"
     }
    },
-   "outputs": [],
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "mesh vertices/points: (520500, 3)\n",
+      "patches:\n",
+      "  aerofoil     type=wall       nFaces=     908 startFace=516702\n",
+      "  freestream   type=patch      nFaces=    1624 startFace=517610\n",
+      "  frontAndBack type=empty      nFaces=  517968 startFace=519234\n"
+     ]
+    }
+   ],
    "source": [
     "points = parse_foam_list(SIM_DIR / \"constant\" / \"polyMesh\" / \"points.gz\", columns=3)\n",
     "patches = parse_boundary(SIM_DIR / \"constant\" / \"polyMesh\" / \"boundary\")\n",
@@ -674,7 +1064,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 11,
    "id": "04c89f1f",
    "metadata": {
     "execution": {
@@ -684,7 +1074,18 @@
      "shell.execute_reply": "2026-07-20T19:11:14.346335Z"
     }
    },
-   "outputs": [],
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 1200x400 with 2 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
    "source": [
     "rng = np.random.default_rng(20260720)\n",
     "sample_count = min(80_000, len(points))\n",
@@ -736,7 +1137,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 12,
    "id": "ba58a7d4",
    "metadata": {
     "execution": {
@@ -746,7 +1147,22 @@
      "shell.execute_reply": "2026-07-20T19:11:14.383778Z"
     }
    },
-   "outputs": [],
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "['Time', 'Cd', 'Cs', 'Cl', 'CmRoll', 'CmPitch', 'CmYaw', 'Cd(f)', 'Cd(r)', 'Cs(f)', 'Cs(r)', 'Cl(f)', 'Cl(r)']\n",
+      "rows: 40000\n",
+      "final iteration: 40000\n",
+      "final Cd: 0.024150\n",
+      "final Cl: 1.675002\n",
+      "final CmPitch: -0.520471\n",
+      "last 500 Cd std: 3.358e-08\n",
+      "last 500 Cl std: 1.529e-06\n"
+     ]
+    }
+   ],
    "source": [
     "coeff_path = SIM_DIR / \"postProcessing\" / \"forceCoeffs1\" / \"0\" / \"coefficient.dat\"\n",
     "coeff_columns, coeff_data = load_force_coefficients(coeff_path)\n",
@@ -768,7 +1184,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 13,
    "id": "51cb325f",
    "metadata": {
     "execution": {
@@ -778,7 +1194,18 @@
      "shell.execute_reply": "2026-07-20T19:11:14.681608Z"
     }
    },
-   "outputs": [],
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 1200x400 with 2 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
    "source": [
     "fig, axes = plt.subplots(1, 2, figsize=(12, 4))\n",
     "axes[0].plot(time, cd, label=\"Cd\")\n",
@@ -815,7 +1242,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 14,
    "id": "6d3ea796",
    "metadata": {
     "execution": {
@@ -825,7 +1252,19 @@
      "shell.execute_reply": "2026-07-20T19:11:14.776955Z"
     }
    },
-   "outputs": [],
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "aerofoil faces: 908\n",
+      "surface_force rows: (908, 3)\n",
+      "wall_shear rows: (908, 3)\n",
+      "yPlus rows: (908,)\n",
+      "yPlus min/median/max: 0.0025 / 0.1003 / 0.3785\n"
+     ]
+    }
+   ],
    "source": [
     "aerofoil_patch = patches[\"aerofoil\"]\n",
     "aerofoil_faces = parse_faces(\n",
@@ -851,7 +1290,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 15,
    "id": "29982f97",
    "metadata": {
     "execution": {
@@ -861,7 +1300,18 @@
      "shell.execute_reply": "2026-07-20T19:11:15.101306Z"
     }
    },
-   "outputs": [],
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 1400x400 with 6 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
    "source": [
     "fig, axes = plt.subplots(1, 3, figsize=(14, 4))\n",
     "for ax, values, title in [\n",
@@ -879,7 +1329,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 16,
    "id": "a9582f0a",
    "metadata": {
     "execution": {
@@ -889,7 +1339,18 @@
      "shell.execute_reply": "2026-07-20T19:11:15.282063Z"
     }
    },
-   "outputs": [],
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 1000x800 with 3 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
    "source": [
     "idx = np.arange(len(y_plus))\n",
     "fig, axes = plt.subplots(3, 1, figsize=(10, 8), sharex=True)\n",
@@ -937,7 +1398,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 17,
    "id": "187f7fea",
    "metadata": {
     "execution": {
@@ -947,7 +1408,66 @@
      "shell.execute_reply": "2026-07-20T19:11:17.039369Z"
     }
    },
-   "outputs": [],
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "[{'name': '|U_xy|',\n",
+       "  'count': 258984,\n",
+       "  'min': 9.274197929486949e-05,\n",
+       "  'p01': 0.21615380686796398,\n",
+       "  'mean': 32.09927286785441,\n",
+       "  'p99': 97.0217835728182,\n",
+       "  'max': 101.66421787590755},\n",
+       " {'name': 'Ux',\n",
+       "  'count': 258984,\n",
+       "  'min': -40.1626,\n",
+       "  'p01': -26.663384999999998,\n",
+       "  'mean': 24.143595454931,\n",
+       "  'p99': 57.23976799999999,\n",
+       "  'max': 64.0673},\n",
+       " {'name': 'Uy',\n",
+       "  'count': 258984,\n",
+       "  'min': -4.48142,\n",
+       "  'p01': -2.5176868000000003,\n",
+       "  'mean': 12.237414191923309,\n",
+       "  'p99': 95.408068,\n",
+       "  'max': 100.078},\n",
+       " {'name': 'p',\n",
+       "  'count': 258984,\n",
+       "  'min': -4783.75,\n",
+       "  'p01': -4551.2704,\n",
+       "  'mean': -371.1175586042671,\n",
+       "  'p99': 486.94471999999973,\n",
+       "  'max': 516.845},\n",
+       " {'name': 'nut',\n",
+       "  'count': 258984,\n",
+       "  'min': 6.05286e-19,\n",
+       "  'p01': 3.6575001e-14,\n",
+       "  'mean': 0.0006963185103278898,\n",
+       "  'p99': 0.015897779999999934,\n",
+       "  'max': 0.0205544},\n",
+       " {'name': 'k',\n",
+       "  'count': 258984,\n",
+       "  'min': 1e-15,\n",
+       "  'p01': 7.4761248e-10,\n",
+       "  'mean': 1.8431521013304757,\n",
+       "  'p99': 29.00798499999999,\n",
+       "  'max': 70.9575},\n",
+       " {'name': 'omega',\n",
+       "  'count': 258984,\n",
+       "  'min': 0.282306,\n",
+       "  'p01': 0.31310847,\n",
+       "  'mean': 5880015.620109469,\n",
+       "  'p99': 56715574.99999955,\n",
+       "  'max': 1938740000.0}]"
+      ]
+     },
+     "execution_count": 17,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
    "source": [
     "U = parse_foam_list(SIM_DIR / \"40000\" / \"U.gz\", columns=3)\n",
     "p = parse_foam_list(SIM_DIR / \"40000\" / \"p.gz\", columns=1)\n",
@@ -980,7 +1500,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 18,
    "id": "2eccca27",
    "metadata": {
     "execution": {
@@ -990,7 +1510,18 @@
      "shell.execute_reply": "2026-07-20T19:11:17.671231Z"
     }
    },
-   "outputs": [],
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 1200x800 with 4 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
    "source": [
     "plots = [(speed, \"speed |U_xy|\"), (p, \"reduced pressure p\"), (nut, \"turbulent viscosity nut\"), (k, \"turbulent kinetic energy k\")]\n",
     "fig, axes = plt.subplots(2, 2, figsize=(12, 8))\n",
@@ -1028,7 +1559,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 19,
    "id": "2fdb418b",
    "metadata": {
     "execution": {
@@ -1038,7 +1569,18 @@
      "shell.execute_reply": "2026-07-20T19:11:17.776578Z"
     }
    },
-   "outputs": [],
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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gx97UVmjvmjr6vjTl97//PY8++igrVqxg6tSpLbp5dietrblfv35ceumlPPjggwQCAfLz8/nkk0+YMmVKwryUHbl2IvbmZyoQCAStIYSeQCAQ7GGmTJmS8GH3iiuuYMmSJdTV1elR99atW8fEiRP53e9+1+L1+vfvzzXXXBOX3NzlcvGvf/2LVatWMXfuXP773/+iqip9+vTh1Vdf5YwzzsBkMrFp0yamTp3KlClTWrz+WWedxaJFi1i7dm2HhN7AgQM79GBvMpn44osv+Pvf/86iRYsoKyvjjjvuYOjQoSxZsiTuQTozM5OpU6fG7UE0m818+eWXvPnmm3z33Xf6/OheqNh9UonmW61W5s+fz+zZs/nss88Ih8MkJSW1+ADfkfuBFmH1oosu4sMPP2TRokW4XC6GDRvGsmXL9PfIaDTy73//m08++YSvvvqK+fPn069fP1asWMHo0aP1a+Xl5TF16lQyMzPj7pGbm5vwPc/JyWHq1Km622JH1tTa+9LSOmLJz8/n1FNPZe7cue0OwpLo82npXiaTialTpzJs2DC9r63P8q9//StHHXUUixcvpqKigocffpj8/HwWLFgQ50rZ0Z+TROO7+pkmen0CgUDQGSS16e5kgUAgEAj2Y4qKijj//PP53//+x/jx4w+4++0PDBgwgGAwyNatW1uMLCsQCASCPYvYoycQCASC/ZbKysq4Y1mWeeGFF8jPz0+YAmB/u9/+yNKlS9myZQtXX321EHkCgUDQgwjXTYFAIBDst8yYMYOUlBTGjRuH1+vlX//6FytWrODjjz9OGGxmf7vf/sTatWtZsWIFTz31FPn5+dx44409vSSBQCA4qBGumwKBQCDYbwkGg7z33nssW7aM2tpaBgwYwPTp0/dYwIu9fb/9iffff5+ioiJ69+7NddddR//+/Xt6SQKBQHBQI4SeQCAQCAQCgUAgEBxgiD16AoFAIBAIBAKBQHCAIYSeQCAQCAQCgUAgEBxgHNw7x7sRRVHYvXs3LperzSS9AoFAIBAIBAKBQNBRVFWlvr6e/Px8DIbWbXZC6HUTu3fvpqCgoKeXIRAIBAKBQCAQCA5wduzYQe/evVsdI4ReN+FyuQDtTU9OTu7h1QgEAoFAIBAIBIIDDbfbTUFBga49WkMIvW4i6q6ZnJwshJ5AIBAIBAKBQCDYY7Rnq5gIxiIQCAQCgUAgEAgEBxhC6AkEAoFAIBAIBALBAYZw3RQIBIIuICsyATmAX/YTCGu1P+yP6/PJPgLhgNYX9hNSQoSVMGE1TFgJE5JDejushAkpocYxShhVVYn+p6gKKira/82PVVQkJAySQS9GyYgkSRglY7N+g2TAbDBjMpiwGC1YDBatNlowG8xxfWaDGbPRHDfGarTiMDmwm+w4zFptMog/LQKBQCAQ9DTir7FAIDhoCcpB3EE3dYE6vbiDbhpCDXhCHr00PY4tvrCvp1/GPofZYNZFn91k14VgrBh0mBw4zU6SLEkkmZNwWVxxdZJFaztMDpGyRiDYx5FlmVAo1NPLEAgOCMxmM0ajsVuuJYSeQCA4IAgpIWr9tVT5q6j2VVPlr6LKV0V1oBp3ICLmgvGCrrtFmsVgwWqyYjPasBqt2Ew2rW2KaRutugXNZDBpVrKY42hf7LEBA5IkISE1qwG9bZAMSEgoqoKsyqioyEqkVmUURUFB0c7H9IfkEEElqFkS5RBBOUhQCRKUY/oix0ElGDcmEA7gC/vwhr3Iqqx/FtH3uasYJANOsxOX2RUvCmPaKZYUUqwxxZJCqi2VFEsKZqO5y2sQCASJUVWV0tJSamtre3opAsEBRWpqKrm5uV3+olMIPYFAsM+iqir1oXrKPeWUecso95ZT7i2n2q8JuWp/tSbm/NXUBmo7dQ+DZCDZkqwLBJc1YlEyJ+EwO0gyJ+E0O5uV6Hmn2akLOKOhe76B2x9RVZWQEtJEX8iLL+zTBaBeh2KOQ168YS/1wXrqg/U0hBpoCDbEtcNqGEVV9DF4Or4uu8lOqjU1TgSmWFP0vmRLMqnWVFJtqaRZ00i3p+Myu4QVUSBoB1GRl52djcMhrO8CQVdRVRWv10t5eTkAeXl5XbqeEHoCgaBHUFWVan81JZ4SyrxllHkahVxU1JV5yzpkdTNIBv1hPcOWQbotnXRbesIH/WSrJu6SzEkYJBGXqqtIkqTv20uxpnT5eqqq4pf9mvgL1dMQbIhvhxpFYazrbdRq6w66UVRFF5wlnpJ239tkMOk/O9GSZksj3db4cxU9Trel4zA7uvx6BYL9DVmWdZGXkZHR08sRCA4Y7HY7AOXl5WRnZ3fJjVMIPYFAsEdQVZW6QB27GnbFld0Nu/XaL/vbda1kSzLZjmxynDlk27PJsGfoD9wZ9sY6xZJyUFvVDiQkSdL39WWR1eH5UUtgrACsDdQ2E4W1gVrcATc1/hpqAjV4Qh7CSlj/0qE92Iw2XfRl2DPItGeSac8ky55FpkOrs+xZZNozhSup4IAhuifP4RBfdAgE3U309yoUCh1cQq+uro6PPvqIsrIyRo4cyZQpU9p0FQgGg3z00UesX7+eyy67jMLCwm65rkBwsKOoCmWeMrbVb2Nb3Ta2ureys34nOxt2srthN96wt9X5EhJZ9ixynDnkOHLIdmTrgi722G6y76VXJDhQMEgG3YrbEfxhPzX+GqoD1VT7qqn2V2vHEXfhaDtaotFVd3t2s9uzu83rp1hTdNEXKwRjxWGWIwun2dnZly4Q7FXEs5JA0P101+/VfiX0tm/fzrHHHkuvXr044ogjeOGFFzjyyCP56KOPWnxD3nnnHe644w5GjhzJF198waRJk5oJvc5cVyA4WIi6WG5zb4sv9dvY7t5OQA60Oj/LnkV+Uj69knrRK6lXXDvXmYvFaNlLr0QgaBubyUZeUh55SW3vi1BVFV/Yp+8XjQYBqvBVUOmt1GpfYx1Wwro1cVPtplav7TQ7yXFoX3hEv/jQ60hJsaaIv1ECgeCAYdWqVXzzzTfccsstPb2UA4b9Sujdcccd5OXl8d1332Eymbj++usZPnw4RUVFnH/++QnnDBgwgBUrVhAMBikoKOi26woEBxqqqlLhq2BT7SY21Wxic91mNtVuori2mPpQfYvzTAYTvZN6U5hcSN/kvvRJ7qMLuvykfKxG6158FQLB3kOSJBxmBw6zgwJX4r8vUaKuzBW+ikYB6G0UghXeCk0keivwhr14Qh621G1hS92WFq9pNVqbCcCoRTzXkUuOM4d0W7rYgyoQJGDhwoUsXbqU1NRUzjnnHLKzszs1ZvXq1Xz66acMGjSICy64oNn5qqoqvvrqK3bt2sWAAQM444wzMJsPHhfuFStWsGjRIm666aY2x65atYpnn31WCL1uRFJVVe3pRbQHWZZxuVw8+eSTXHfddXr/CSecQFZWFv/6179anb9z504KCgpYsGABkyZN6rbrRnG73aSkpFBXV0dycnLHXpxAsJep9lezqWYTm2o3sblWE3SbajfhDroTjpeQyHPm0Te5b1wpTC4kLylPJMgWCLoRT8ijByMq85TF1dH+an91u65lMVjIdeaSl5RHvjNfr/OT8sl15pLryBX7BgWdwu/3U1xcTL9+/bDZbD29nA7xhz/8gffee49p06axceNGVqxYwcKFCxk1alS7x1RUVHDOOedQV1eH1+tlzJgxFBUVxd3nb3/7Gw899BDjx4+nV69ezJs3j1AoxKJFi8jJydmrr7mneP311/nzn//M1q1b2xwrLHqNtPb71RHNsd88nW3fvh2fz8egQYPi+gcNGsTSpUv3+nUDgQCBQKPLmtud+AFZIOhJFFVhm3sb66vXs656Heuq1rGxZmOLD4kGyUAfVx8Gpg5kQOoABqYNZEDKAPok9xGWOYFgL+E0O+mX0o9+Kf1aHBOQA5roiwrBqAiMEYYVvgqCSpDt9dvZXr894XUkJLIcWXEiMM+ZFycIRVRRwYHE4sWLeemll1i8eDFHH300qqpyxhlncP311/Ptt9+2e4zRaOTxxx/n2GOP5Te/+U3Ce40YMYJffvkFp1Pbc/vnP/+ZkSNH8vDDDzNr1qwW17hixQr++9//YjKZOPHEExkyZAgVFRW8+OKLXHfddWRmZupjt27dyt///nduu+021qxZw7Jly7jgggv4+uuv2bVrF8cccwzHHHMMZWVlfPLJJ3g8Hk455RRGjBihX2PZsmWdmgeat8L8+fNZsWIFGRkZnHzyyfTt2xeA9evXM2fOHGpra3nggQcAOOmkk7BYLCxbtoyLLrqIjz76iJKSEv74xz+2+H588803/PDDD+Tk5PCb3/yGlJSuR3Y+WNhvhF5DQwNAsw83NTVVP7c3r/vYY4/x4IMPdvq+AkF3E5JDbK7bzLqqdbqw21C9IWFAFAmJ3q7emphLHaiXwpRCIegEgv0Aq9FKgaugVZfRkBKizFNGiaeEEk8Juxt2x9UlDSUElaAeYXRVxaqE10m1ptI7qTe9XL3ondSb3q7e9ErqRW9Xb3KduZgNwiIo2H/48MMPGTJkCEcffTSguWBfeeWVTJ06VQ9n354x6enpHHvssa3e68gjj4w7tlqtHHrooRQXF7c4Z9asWdx7771cdNFFWCwW/vrXv3LnnXcybdo0/vGPf5CSksLNN9+sj3/ppZf4z3/+w/3338+yZcu47777eOaZZ5gyZQoNDQ3cfffd3HzzzXz00UdMmTKF6upq7rnnHhYuXMhRRx0F0Ol5wWCQs846ix07dnDqqaeydu1abrvtNt566y3OPPPMFl9j9H7/93//x/HHH09+fj7Q3HXT7/dz9tln89NPP3HOOeewbt06nnnmGRYsWCBSerST/UboJSUlAVp0zFhqa2v1c3vzunfddVecadntdre4B1Ag6G7CSphNtZtYXbGatVVrWVu1lk21mwgpoWZjbUYbg9MHMyx9GMPShzE0fSj9U/uLSJYCwQGO2WCmt0sTZolQVZUqfxUlDSXs9uxurCMicLdnN/XBemoDtdQGavml6pdm1zBKRnKduc0EYFQYplnTRMCYgwRVVfGF5B65t91sbPfP2YYNGxJ6cQFs3LiR7Ozsdo3pDJWVlcyfP59bb721xTFRV8fodqJwOMyGDRuQJIkrrriC2bNn60IvHA7z1ltvce+99+rza2tr+eqrrzjiiCMAUBSFZ599lpUrV+pup+eccw4vv/yyLtg6O+/pp5+msrKSVatW6fsO33zzTa655hp2797N0KFDOeuss1i9erVu0QNN0NXU1PDBBx9w4okntvhePPHEE6xcuZJffvlFf8937NjRpXQDBxv7jdArKCjAbrfz66+/csopp+j9v/76K0OGDNnr17VarVitwvIh2DuUekr5ufJnfq74mdWVmrhLlEjcZXYxLEMTc8MyNGFXmFwocssJBIJmSJKkp3UYmTUy4Zj6YD27G3azs2Enu+p3sbNhJzvrd+p5MQNyQG8vLW2+3cFhcugCsI+rD32StdLX1ZccZ44IFHMA4QvJDL/vyx6599qHTsFhad8jbUNDQ7Mv5lNTU/Vz7R3TUUKhEBdeeCG5ubmtBibp3bs3c+fO5ZRTTmHQoEGYTCbdXfLyyy/ngQceYPny5YwdO5bPP/+c2tpaLr74Yn1+fn6+LtZAcx/t169f3P7DESNGsHjx4rj7dmZeUVERqampPPHEE6iqiqqq1NTUUFpayvbt23UXzkRkZGS0KvIAPvroIy699NI4YS2MKh1jvxF6JpOJs88+m7feeosZM2ZgMpnYuHEjixYt4t1339XHzZkzh507d3Lttdd263UFgr1FSAmxrmodK8tXsrJ8JT9X/Ey5r3ni5iRzEodkHsLIzJEMzxjO0PSh9ErqJb49FwgE3YbL4mJI+hCGpDf/4lNRFSp9lexq2KXlz4zk0IzW0QiiG2s2srFmY7P5FoOFAleBJv6ECBTsJZKSkhJ6cUXPtXdMRwiHw1x44YVs3ryZb7/9ttVrvPrqq9x1110cddRRuFwuzjrrLP70pz+RnZ1Nr169OO2005g9ezZjx45l9uzZ/OY3vyEtLS3u9cViMpkS9kUT3ndlXklJCTk5OYTDYb0vJSWF+++/v01jSOw+w5YoLy+nd+/EHgmC9rHfCD3QTLjHHHMMxx13HEcccQQfffQRZ555ZlwKhDlz5rBkyRJd6K1atYpPPvlED5by5ptvsnDhQiZNmqRH32zPdQWCPYUn5OGnip9YUbaCleUrWV2xGr/sjxtjlIwMShvEyMyRjMwcyaFZh1KYUigehAQCQY9hkAxkO7LJdmQzJntMs/MBOcDuht3satjFjvodbHdvZ0f9Dra5t7GzYSdBJcjmus1srtvcbG6sCOyb3JcCV4GWvsXVR4jAfRS72cjah05pe+Aeund7GTJkCJ9//nlc36+//grA4MGD2z2mvYTDYS666CJ+/PFHFi5cSJ8+fVod36tXL/7xj3+gKAorVqzgxhtvZPr06Xz5pWYtvfrqq7n00ku58847+eyzz5g3b16H1tOdZGdnM2DAgDi3zKZ05cvn7Oxsdu7c2en5gv1M6PXp04dffvmFDz/8kLKyMl555RVOP/30uB+is846i8MOO6zZ3OTkZO6///5OX1cg6C7qAnX8UPYDP5T+wI9lP7KhZgOKqsSNSbGmMCZ7DGOyx3Bo1qEMSx8mIt8JBIL9CqvR2mL00LASptRTyna3FhF0m3ubFh3Uvb1NERgNRNPH1YfClEL9HoXJhaRYRTS+nkKSpHa7T/YkU6dO5bnnnuN///ufHlHzjTfeYMKECbqLYHvGtAdZlvntb3/L8uXLWbhwIYWFhW3Oid7TYDBwxBFHcO655/Lqq6/q508//XQcDgfTpk2jV69ebbo/7kmmTp3KM888w+233x7npvn9998zfvx4QLPwddbdNbon8I477iArKwvQ9ui5XC7dlVbQOvv+b2QTUlJSuOKKK1o8f9ZZZ8Udjx49mtGjR3f5ugJBZ/GGvKwoX8GykmUsLV3Kuqp1qMSnr+yV1Isx2WM4LOcwDss+jH4p/cQ31gKB4IDFZDDpgWKO5ui4c2ElTImnhB3uHWyr36aLwe3u7eys30lADui5P9kRf910W3qc8Iu28535Yq+yAIBjjjmGa6+9lrPOOkvPkffjjz+yYMGCDo0BePTRRwkGg6xfvx6DwcADDzyAw+HQUwXcfffdvP/++/zud7/jzTff1Ofl5uYyY8aMhOt78sknKS8v56ijjsLn8/HPf/6TRx55RD9vNBq57LLLeOyxx7j//vt71Chxxx13sGzZMkaPHs15552Hw+Fg6dKlFBYW6kJv/PjxeDweLrnkEgYOHMhJJ53U7uvfeeedfPfdd4waNYpzzjkHv9/PDz/80OxzELTMfif0BIJ9nZAcYmX5SpaWLmV56XJ+rviZsBqOG9MvpR9H5h7J4TmHMyZ7DLnO3B5arUAgEOxbmAwmPXVESyJwu3s7W91b2Vq3lWJ3McV1xZR7y6n2V1Ptr+bHsh/j5lkMFvok94kTgf1T+lOYUojT7NybL0+wD/Diiy9y3nnnsXTpUkaOHMk777zTzFLXnjFRLrzwwoT9Y8eObdGbrCU++eQTlixZwvfff4/FYuGGG25g+PDhcWMmTZrE448/zuWXXx7Xf+SRR+rROqMcffTRzfbaTZo0Kc6FtLPzrFYrc+fO5bvvvmPp0qXYbDamT58eF9SlV69e/PTTT3z55ZdUV1e3eD/QjDOxEe1tNhvz58/nq6++YtWqVeTn5/P888+3mSRc0Iikqqra9jBBW3QkS73gwGNH/Q4W71rM4l2LWVq6tFlEzF5JvTgy90iOzDuSI3OPJNvRudDMAoFAIEiMJ+Rhq3srxXWa8IuKwG112wgqwRbnZduzNfGXoom/gakDGZA6gAy7yNPVGn6/n+LiYvr164fNZuvp5RxUXHbZZZSXlzfbRyg4cGjt96sjmkNY9ASCTuAL+1heulwTd7sXs829Le58hi2DcfnjOCr3KMbmjm0xj5VAIBAIugen2cmIjBGMyBgR1y8rMiWeEl0ARi2AW+u2UuWvotxXTrmvvFl6iDRrGv1TG4VftE63pe/NlyUQ6Hz++ed89tln/POf/2TRokU9vRzBfoAQegJBOyn1lLJgxwIW7ljID6U/xH1DbJJMHJp9KMf2OpZj8o9hSPoQscdOIBAI9gGMBqO+H3BC7wlx5+oCdXFWwC21W9hUu4ldDbuoCdTwY9mPzdxA06xpDEgdECf+hAAU7C169erFt99+G5fsXCBoCeG62U0I180DD1VV2VCzgQXbF7BgxwLWVa+LO5/nzOOYXsdwbP6xHJl3JC6Lq4dWKhAIBILuxBf2saVuiy78Ntdu1gVgS6Tb0jXRlzJAF3+DUgeRakvdewvfiwjXTYFgzyFcNwWCPUBIDvFD2Q+65a7EU6Kfk5AYnT2a4wuO57jex9E/pb9IwSEQCAQHIHaTPaEbqDfkpdhdrAu/zbWb2Vy7mV0Nu7RAMKXVLC9dHjcny57F4LTBDE4bzKC0QQxOG0y/lH5YjJa9+ZIEAsFBiBB6goOekBJiaclSvtz6Jf/Z/h/qg/X6OZvRxvj88bq4E5vzBQKB4ODFYXa0LADrihvFX12jAKzwVVDhq2Dx7sX6eJNkojClME78DU4bTI4jR3yBKBAIug0h9AQHJSElxPKS5Xy5TRN3dYE6/Vy6LZ1JBZM4vuB4xuWNw2YSLikHPIoMIS+EfBAOgByEsL9JOwhyQOsLByLtyDk5CHIIlHCkyKDK8cdKzLHa9DjqQR/jSR/nVd9SPyAZwGAEyRipY49NMW1DzJgmYw1mMFrAZNFqowVMVjBawWhO0LY01rFtk00rBrE/VXBw4TA7GJE5ghGZ8QLQE/Lwa82vbKzZGFfXh+obcwEWN45PtiTHCb/BaYMZmDoQh9mxl1+RQCA4EBBCT3DQEFbCLC9drlvuagO1+rkMWwYn9T2JUwpP4bDsw0Ri3X0ZVdVEmd8NAXekrms8DtRD0KONCXojdeyxp3l/2N/Tr+rAwmQHsx3Mjkgd226lz+KItB1gTQKLCyzO+LbJCsLiIdhPcJqdjM4ezejs0XqfqqqUekr5tVYTfhurN7KxZiNb3VtxB90JA8AUuAp04TckbQhDM4aS78wX1j+BQNAqQugJDng2VG9gzuY5fLblM6r8VXp/ui2dk/ueLMRdT6CqmsjyVoGvGrzV4KuJ1JFjf11iIed3axaxPYXR2sRCZYn0RY5jrV2mmLFGs2YZM0SsZQaTVqQmx4amlrZIHUV/cJPa7lNVUJVGC6FeK40Ww7hzLYyVQxGrZKTolsxA5FygSTti3ZSDjW1VaVxb2KcVX3X3fz4GE1iSwBoRfpakiBBMimk7NWEYbVtdYE0GW4pWom2zsNYL9j6SJJGXlEdeUh7H9T5O7w/IAYrrinXxFxWClb5KdtTvYEf9Dv6z/T/6eJfFxdD0oQxNH8qw9GEMSR9C/5T+mAzi0U4gEGiIfw0EBySVvko+3/I5czbPYUPNBr0/1ZrKSX1P4tTCUzk853DxB7G7UFVNqHkqoKEMGsq12lMZEW5V4K2JEXXVmkjoCpIh8sCerNV6OyIAzM5GC5HFGakdMf0JzpvtwlrUWeSwZhkN+RrdYPU6UV9L5yKW12A9BBog2NBoeQVNvPprtdJVjJYYAZgc37alNv5M6eIwpm1P02rhpiroJqxGqy7cGNDYX+Wr4tfaX/m15lc2VG9gQ80GNtVuoj5Yz/LS5XHBXywGC4PSBunXGZo+lMFpg4Xrp6BdlJWVUVpayqGHHtrTS4lj8+bNSJJE//79e3op+x3iKVdwwBCQAyzYsYBPN3/K4l2LkSNWH7PBzKSCSZzZ/0yO7X0sZoO5h1e6HxH0Qn1JcwHXUAYNMX2e8s4JN6MVHOlgT9fq2Hbsw7U19kE8UlucQpTtSxhNYIxY1PYEiqyJvkBE+DUVgoH6mHZDzHlPjGU4pkbVfma9lVrpDJJBE4T2NLBH6zaKLVUbaxT/DgnaR4Y9gwx7BuPyxul9ITnEptpNrK9er5cNNRvwhDysqVrDmqo1+lgJib7JfRmWPoyhGY0C8GDP+1ddXc3u3bvp27cvLlfr6ZFWr16Nz+dLmLtOlmVWrVpFcnIygwYN6vK9epIPPviAWbNmsX79+p5eShz3338/NpuN119/vaeXst8hhJ5gv+fXml/5YOMHzN0yNy5i5qisUZzV/yxO7XcqKdaUHlzhPkrQC+7d4N4ZqXdB3a5IO9Lvq+nYNW0pkJSjFWeWVhzp4MjQHnLjRF2GZkUTYk3QHgzGRtfLrqIomhD0u2NchGPdg2tj2nVNxkXaYZ/mruqr7pyLqsUVLxCjvxOODHBkasfOzJi+DM1NWCAAzEYzwzKGMSxjmN6nqAo763eyrnodG6o3sK56Heur11Ppq2Sreytb3VuZt3WePj7bka2Jv/ShWiTRzBFkO7J74uXsVZYtW8Ydd9zB6tWryc/PZ/Pmzfzud7/jhRdewGRq/lj8xRdfcMYZZwAQDof1fq/Xy7PPPssbb7xBZWUlp5xyCkVFRV26l0DQ3YifMsF+SUAO8PW2r/lgwwesKF+h9+c58zij/xmcNeAsClMKe26B+wL+OqjdDjXbtLo2Wm+Hup3td30z2cEVEW9J2RERl93YjvY7s8SeJ8H+gcEQIxoLOneNkF/7HfLVJCit9Ecj/AbrtVK3vf33tLjAGSsGM5oIwkjtjAhFa4pwLT2IMEgG+iT3oU9yH04pPEXvr/RVxln+1levZ5t7G+Xecsq95Xy781t9bKY9kxEZIxieMVwXf5n2zJ54OXuMtWvX8sADDzBx4kQA1q1bx9FHH03fvn25884748aWlpZyzTXXcO211/LSSy/FnausrMTv97Nw4UJuvPHGLt+rKYFAgA0bNpCTk0NOTo7eX1dXx7p16wCw2+0MHDgQp9MZNzfWBbOqqopdu3YxePBgPfH2tm3b8Hg8DB48uEXBWVlZya5duxg2bBgWS/Ocjz6fj40bN5KRkUHv3r1bvP+OHTsoKSnh8MMPx2jU9qOXl5eze/du+vXrR0pK8y/vVFVl3bp1uFwuCgo6+W+0AABJVZvG6hZ0ho5kqRd0nm3ubRRtLOKTTZ/oUTONkpET+pzAeYPPY1zeOAzSQfJgI4c0EVe9OSLmIiUq7Noj5CxJkNwLkvMhpVdjOzmmbUsRVjeBoLuQw5plsKkI9FZp+1e9lVrbUxXpi5TOBCCSjI3WwKQs7QsaZ1ZjO/oFTbQWFsODBk/Iw8aajayrWse66nWsqVrD5trNKLFBlSJk27MZnjlcF4DDM4aTac/E7/dTXFxMv379dAGxv3Leeefh8/n47LPP9D5FUZg8eTJTpkwhOTmZGTNmxFn0YvnNb36DyWRqZtFr772a8uyzz/LAAw+QkZFBIBBgwoQJvPnmm9jtdpYsWcJNN90EgMfjYdOmTdxyyy088sgj+vxZs2bxxBNPMHjwYLZu3Yrf7yccDvPOO+/w6KOPsmPHDhoaGkhJSeGbb74hPz8/bt7o0aNZtWoViqKgKAofffQR48eP16//3HPP8eCDD9KrVy9qamrIz8/nvffeY+DAgXHXOeyww1i5ciX5+fnMnz8fgCuuuIKvvvqKPn36UFxczLRp03j55ZcxmzV39l27djFlyhS2b99OZmYmDocDl8vF0KFDDyrXzdZ+vzqiOYRFT7DPE1bCLNyxkH9t+BdLSpbo/bnOXM4bdB7nDDrnwHU3UWRNtFVvhqotkXoTVG3W+tt6+HNkQGpfSOsLqX20dmrfRlFnE19KCAR7FaOpcT9qe1EUzRIYJ/6igrAyIhCr4kVisF7798ET2UNbsa7t+9hSEgjA7MTC0Gzv/Hsg6HGcZidjsscwJnuM3ucL+9hQvYE1VWtYW7WWtVVr2VK3hXJfOeU7ylm4Y6E+NseRw/is8ZyZdiaeoAeTxYRJMjYGTdrbdGEbgCzLrF69msmTJ8f1P/7440iSxM0338wbb7zRHats8V6x/Otf/+LOO+/k008/5ZRTNMvs+++/T01NDXa7nXHjxrFkSeOz0IYNGxg/fjyTJk3i5JNP1vt37tzJHXfcwXXXXUc4HOaEE05g8uTJvPbaa1x55ZUEg0GOPvponn32WZ5++um4eeeffz6ffvopqqoyc+ZMLr30UtatW4fJZOKjjz7iiSeeYOnSpQwZMgRFUbj++uu55JJL4ta1c+dOZsyYwb///W+979JLL8Xn87Fz506SkpKoqqpi4sSJPPPMM7qF84YbbiAtLY3vv/8eh8PBW2+9xaWXXsrQoUO7/gEchLRb6P33v/9t90WPPfbYTi1GIIilIdjAR79+xLvr32VXwy5A21Q+ofcELhh8Acf2OvbASYkQqIeKjVCxXnsgq9wUsdRtbT3IidkB6f0hrTBG0EVFXZ89FxhDIBDsPQyGxmAuDGzfnHCg0ULoqYwEVIqIvoYK7Vhvl0eimdZpperXtq9vccULQFeuVpJyNVdvV57WdqQLj4D9BLvJ3iznnzfkZUPNBtZUrtEFYHFdMWXeMpaWLGWScxK7PbspDZZikcMMevm4lm+wJ7l7txagqxPcd999lJSU6FYygP/973/85S9/YcWKFd2aqzDRvZry0ksvcdFFF+kiD+CCCy5oNq60tJTdu3cTDAYZNWoUCxcujBN6aWlpXHfddQCYTCZOPvlkNm3axJVXXgmAxWLhxBNPZNWqVXHXNZvNPPjgg4CWCuSRRx7hr3/9K4sWLeKEE05g1qxZTJ48mfr6epYvX46qqkycOJGXXnqJyspKMjM1V1+n08kdd9yhX7empoZ33nmHv/71r6xfvx5VVVFVlUmTJjF37lzuvPNOamtr+fjjj/nqq69wOLRIsdOnT+exxx7rwLssiKXdQm/ChAntvqjwBhV0hV0Nu3hn3Tt89OtHeEIeQEuLcN7g8zhv8Hn0SurVwyvsAn43VG6E8nURUbceKjZA3Y6W5xitkN4P0gdARv9IPRAyBmgPU+IhSiAQNMVkheQ8rbRFXHqU8ngB2FAe0x+p5YBmMayuh+otrV/bYI4IwJwmYjA3pj9P8z4Q+wn3ORxmRzPLnyfkYV3VOjZVbMIetmM2mJGRCSmhHlxp55g1axZPP/00H3/8se52CHDxxRdz2WWXsX37drZv386WLdrP+ZIlSygsLCQ3N7fb7tWUX3/9lbPPPrvF89u3b+eCCy5g7dq19O3bF6fTyebNm5ulHsjOjvd0stvtCfs8Hk9cX15eXlxk0IyMDDIzM9myZQsnnHACGzZsYOvWrWzcuDFu3lFHHUVtba0u9PLz8+P2/23atAlFUXjppZd0N80oUdfR4uJiVFVlyJAhceeFNa/ztFvohUKNv8BvvfUWTz/9NM888wxjx44FYPny5dx6663cfvvt3b9KwUHB+ur1vP7z63y97Wt9n0D/lP5cMvwSzux/JjbTfrQHQA5rLpalP0Ppaihbo4k6966W5yTlQNYQyBoGmYM0IZc+AFJ6axEHBQKBYE8gSY3upFlDWh+rqtoew1gh2FCupWFpKNPq+jJoKNXcSJWQ9kVWa19mARhMmnXQldtcECbnNxZbqvhyq4dxmp0ckXsEh6QeQnFxMYUphZgtZvxhH5U3/4xf9uEL+wnJzYWfJEnYTDbsRht2kwO7yYbZYO661awTeQJffvllbr31Vj744AOmTJkSdy43N5eFCxeycOFCACoqKpBlmZtuuombb76ZadOmddu9muJwOHC73S2ev/3228nKyqKiogKrVdtXe+aZZ6IozfdXdoamwi/aF90LZrPZuPDCC+P2BCbC0OSLm+g+s1deeUXXDk2JCsyma/B4PKSnH9zpQDpLu4VerCp/+umnef/99xkxYoTed+qpp1JQUMAFF1zAZZdd1q2LFBzYrCxfyWurX+O7Xd/pfePzxnPpiEs5Ov/ofT+4StCjCbnS1RFh97N2HPYnHp+Uqz1MZQ9rFHZZQzq2Z0cgEAh6AklqjFia2YYbaTgYEX+lmvCrL23SjghCT4XmOlq/WyutYbLHCL9eEatlJHCUK9J2Zgnr4F7GaDDitCThtDRuFwgrYXxhX2MJ+ZBVGS/glf0g+yGgzbWb7HHFZNizISReffVVbrrpJt5//33OOuusZudj95oBvP7668yYMaNZf3fcqynHHXccc+bM4f7779cFsKIohMNhLBYLmzdvZtq0abrIc7vdfP/993oKiK5SVVXFqlWrGD16NACLFy/G5/MxZoxm1Z00aRJFRUU88MADcZY5j8fTLPpnLMOGDSM7O5t33323mdCLzi0sLCQzM5P58+frVjy3282SJUtE9M1O0qnfpC1btsSFeo2SnZ2tm7cFgtZQVZX/7f4fr/38Gj+W/QhooaFPKTyFKw+5kiHpbXyr3FP462D3Sti1olHUVW0CErgrm52QewjkjoScEZA9HDIHC0EnEAgODkwWSC3QSmvIoYhlMEYMRgWhu0SzErp3a/kKwz5t/3L15pavZzBFRF+MINSPI+LQlSeS1u9hTAYTLosLl0Wz0qiqSlAJ4gs1ij+/7EdWZBqCDTQEG/S5FqMlTvjZTLZu+9L3rbfeYubMmdx5553k5OTo4s3pdDJy5MgOXWv58uXIskxNTQ1Go5ElS5ZgNps5/PDDO32v++67jyOPPJKzzz6bq666Cr/fz0svvcRbb71FQUEBxx9/PC+++CIDBgzAZDLxxBNPUF9fn/BancFisTBt2jQeeeQRFEXhj3/8I5dccomeDP6BBx7gqKOO4uSTT+b666/H4XCwdOlSvvzyS77//vsWr2symfjLX/7C9OnTURSF008/nZqaGj7//HN69erFo48+islk4t577+Xuu+/GaDTSt29fnn76aYLBVmIVCFqlU0LvkEMO4a677uKFF17QTbF+v5+77767w78kgoMLVVVZUrKEWStnsbpyNQBmg5mzBpzFFYdcQZ/kPj28whjCASj7RRN1u37USuXGxGOTcjVBp5dR2r464XLZIqqqEpJV/GGZQEjBH5IJhBvrQNPjsExIVgnLCmFF1YqsaH2KQlhWG9uRc2FZJRQzTlYUZJXIJnBQVDVSmvZFjokcK1qtqqCi1ZKkBQeSJM0dSSLSJ4EhckykNsSMMWgT9fEGSbuG0WDAZJAwGiTMRinuuLHfEHes1QZMxiZ9RgNmg4TFZNCKsUndWr/RgMkorCGCvYjRrEUCTmlj/3XIpwm+qPBz79KEoHtXY399qWYdbNNdVNKCyCT30tzjUwoide/GY2emcBPtRiRJwmq0YjVaSSUV0JK8+8P+OMtfUA7qpS6SdzLq8ukwObCb7DhMDsydFOobN27kyCOP5JtvvuGbb77R+wcPHsw//vGPhHOys7MZN25cs/477rgDr7cx0uhNN91EamoqX3zxRafvVVhYyIoVK3jmmWd47rnnyM/P55FHHtEtWg8//DAul4sXX3wRq9XKb3/7W0466aQ4z7vc3FzdIhclPz+fUaNGxfX17t07zjsvNzeXc845hyuuuIK///3v7N69m8suuywu519BQQErV67kL3/5Cy+//DI2m41x48Yxb968Vu8PMG3aNPr168err77KI488Qn5+PmeccQYXXXSRPubGG2/E4XDw/vvv43K5+O1vf8txxx2nWzAFHaNTefRWrFjB6aefTjAY5JBDDkFVVdasWYPNZuOzzz5L+OEe6Ig8em2zomwFL6x8gR/KfgDAZrRxwZALuHT4peQ4m1uI9zp1O2H7EtixVBN1pT8njniZ2hd6HQZ5hzaKuqQDNL0DICsq9f4Q9f4wnmAYT0DGG1sHZbyB+NoTCMeN8TUTcQr+sIyI27RvYpDAYjJgNhqwxghBm9kYKQbseruxz2Y2RvoN8edMBuwWY9x5q8mo99lMQlwKugk5pLmLxgnA3RFhGFPaEzjEaI0I0BaEYHIvsHR8b9iBwp7KoxdWwvjDfrxhry7+ZKV5KiGzwYzD3Cj8rCbrvr/VQyBoJ92VR6/TCdN9Ph/vvfcea9euBWD48OFceOGF2O0HZ24dIfRa5pfKX5i1chaLdy8GtH+cpw2ZxpUjryTTntkzi1JkKF+rCbuouEv07a89HXodHlMO077l3Y9QVRVvUKbaE6TGG6TOp4k2ty+E2x/C7Qvj9rfc1xBInCC2u7FGhITVZMBqNmAzGeNqi1ETHmZj1IKlWbxMRs3SpbUNmKPnjJLeNhu1c1GLV6MlTcIQY4GLO25SR9tRS17Ushe18sVaAVFjLYCNbYhaDRstg2rkM5IVlbCsWSrliFVSVlTdEtnSsW69bHIcklVCskIgrBCMFlkhJMcchxUCkeOexmIy4LQYcVhMOCxGHFYTDrMRp1Xrc1qN2M0m/dhhMeKwGHFao21Tk2Otz2gQFhlBExRFCxTj3qWVup0RC+DOxlJfSkKX/KY4MiJWwQRCMLVACzBzgO4X3FsJ01VVJaSE8Ia8uvjzJ9gDL0mSLvrsJjsOs2OP7/UTCPYUPZow/bLLLuPNN9/k8ssv78z0LlNcXExZWRlDhgwhLS2ty3M2btzI7t3xG8CdTmeLUYEE7aOkoYTnVzzP58WfA2CSTJwz6ByuGXUNuc6OhybuEnJI21tX/C1s+x52Ltcix8UiGbU9dX3GQ++xmrBLK9znXHeCYYVqT7CxeIPURNo13tg6pPV7g93yIG8zG0iympo9UDstJhxWrXZaTdrDekztMEctN5oVJ1pbo7VJsxp1Z64iQcdQVU0gxgrCYFgTiaGYtj8kayWs4A/K+MPasS+o6G2taGN9TY6j7Wh/IObnMnrvGm/3hmiP/twmWU0k2SK11YzLFt+nH0f6XFZz3DnxM3oAYTBoOQCTsiB/dOIx4aDmCqqLvyZCsG6nlmIimsC+dHXi6xitkX2Kkdymep7TSJ2Uvc/9jdnXkCQJi9GCxWjRXT5lRdatfd6wtzHQS8iLNyZpe3Svn8PkwG62YzPaxO+x4KCiUxY9h8NBdXX1Hv0GJxE+n48LL7yQ//znP/Tr149Nmzbx6KOPcvPNN3dpzowZM/jwww/j/JT79evH3/72t3avTVj0GvGEPLzx8xv8Y+0/CMgBJCTO6H8GM0fPpMC1l6ImKbL2h7f4OyheBNu/h5iN3gBYkjRB12c89DkKeh3RYwnGVVXF7QtTXu+noj5ARUOAcne09lPREKCiPkB5fYDaTj4IW0wG0h0WUh1mkm1mku0mkm3aA2+yPb4v2R7pj2mbhWudoJtRFFUXkN5QjAtwMIw3IOMJhvEF5Ti3YF+wcYwnIEfOh/E2mad0s1uwySDFCMUYYWgzxx0n20yk6L9j8b9XDotRPGQeSPjrWheC7l2gtvElm8mmWf/S+saIwRgh6MzaZ4Xg3rLotQdVVQnKQd3i5w17CYQDzcYZJIMW4MVsx2lyYjfZMYq99IJ9kB616E2YMIF58+ZxzjnndGZ6p3nggQdYuXIlmzdvJicnh08//ZSzzjqL8ePHJ9wk25E5EydOpKioaG+9lAMSWZH5eNPHzFo5iyp/FQBjc8dy2xG3MTxj+J69uapC5a+w+RvY+p1W/HXxY+xpUDgBCo+FPuMgewQY97xbh6qq1HhDlNT5KKn1U1LnY3edn9I6P7trfZTU+Sl1+ztkdTMZJNKcFtIdFtKcZtKdFtIclvi6yXm7WTxkCvYtDAYJu0Wz+LbPN6N9qKomIL2R/aKeYJgGf5j6gFY3BJoeh2gIhHVX5dgxDcEwqgphRaXWG+r0Fy2g/d5q4q/5lyspdnOb54RVcR8jmmYiZ0Ti83JI2w9Yux1qt0XqmOLepaXhqfpVK4kw2ZsIwEhJ66uJQUfGPisE9yaSJGE1WbGarKRF/jWJWv28Yc3K5wv7UFQFT8iDJ+ShkkoAPciLw+zoUpAXgWBfpFNPuaNHj+a3v/0tl156KcOHD8discSdnzFjRrcsrilvvvkmf/jDH/TUDmeeeSajRo3ib3/7W4tCr71zfD4fy5YtIyUlRQ9ZK2g/v1T+wkPfP8S66nUA9E3uyy2H38LxBcfvuQeToEez1v36NWz6WvvDGYs1GfoeDf2O00r2iD2yV0JVVSoaAuyo9rKj2sf2ai/bq726iCup8+EPtU/EpdjNZLusZEVKfNumtZOspDq6IcGsQHCAIkmSHggm3Wlpe0IrKIqKNyTrgrA+oVAM4wmE4/a3avtdtX2vdb6QHik26nLdGSxGQ6P4c5hJc1hItWvtVLtmrdeK1p8a6XfZTBjEXsW9j9GsCbK0vsCE5uflkGb5ayoAo6LQvVtLJ1G5QSuJMDsiwq+fttUgPVKn9dP6zT1raetJjAYjSZYkkiK5/VRVJSAHdFdPT9hDSA7hD/vxh/1U+6sBMBvNmrUvYvWzGC3i761gv6VTaubDDz8kPz+f+fPnM3/+/Gbn94TQ27VrF+Xl5XpukiiHH344K1eu7PKc+fPnU1JSQmlpKZIk8corr3DmmWe2uJ5AIEAg0OgW4Ha7Wxx7IFMfrOeFlS/w3vr3UFFxWVxce+i1TBsyrfu/FVNVLb1BVNht+198VEyjRRN2/SdB4XFaVMxustj5Q7Im4Kq8upDbUe1lR43Wbo+Qy0yykJdiJy/FRn6qndwUW2M72UZ2shWrSbiQCAT7EgaDpLtrQucemlVVxReSm4jARlFY540/jrbr9HEhFBWCskJlQ5DKho4JRYOkfYmU6rBEanNECFri2ikx7TSHGZfNLILZ7EmMZk2YpfdLfD4cBHdECNYksAjWl0DICxXrtdIMScsdqIvAwkg7cuxIP6isgdEUDTaTTf9VDskh3eLnDXvxh/2E5BC1ci21gVpAE4yxFr/uzOknEOxpOvUUvGnTpu5eR5vU1NQAkJ4en2w6MzNTP9fZOVOmTOGRRx4hIyMDRVG49957mTZtGj/99JOeILIpjz32GA8++GCnX8/+jqqqfLXtK55Y9gQVvgoAzuh/BrcdcRsZ9ozuu5Eia1Ex130KGz5rbrVL7QuDToaBJ0O/CWBxdvpWUcvc5nIPWyob2FzuYXNFA1sqG9hZ42s1FYBBgrwUO33SHfRJd1CQbqdXmp28FDv5KXZyUoSIEwgOViRJigQxMpGb0nGxqKoqnqCsC8Rar2YlrPOGqPUFqfFG+4K6e2mtN0itL4Q3KKOoUOMNdTjQjSRBsq1RGDa6g2vu4bGu4unOiIXRYRHisLswWSC9v1YSEQ5oFsGaYqjZqpXqYk0U1hRr+9KjkUW3/bf5fGtyxOKYwBqYUrBXtjb0NGajmRRjCinWFKDR3dMT8uj7/WRFpj5YT31QS0ouSVKc8BP7/AT7MvvNb3HUPdTn88X1e73eZq6jHZ1z1lln6W2DwcDDDz/Myy+/zKeffsott9yS8Np33XVX3Dm3260nszzQKfOU8eD3D/Ldru8AzU3z3nH3Mi4vsftshwkHNJfMdXNg/efgrWw8Z7RC4TGasBt0MmQM7PA3koqisr3ay/rSejZXNESKhy0VDdT7W04l4LKZ6JsRFXKORlGX5iA/1Y7FJL7hEwgE3Y8kNVoV8+lYCqNAWI4RhVok3toYkagLw5h2nU/bs6iqaHN9Iba1e62a9TAqCDUh2EQkxopFh4Vku0m4xnUGkxUyBmilKaqqRQOtjorA4hghuFXLLRhwa/liS39uPl8yatFCo8IvKgSjwrMLX6ruyzR194wmdI+1+smKrO/zi2Iz2XCYHThNTpHWQbBP0emfRLfbzezZs1m3bh2qqjJ8+HCuuOKKPRZxsnfv3hgMBnbt2hXXv2vXLvr27dttcwCMRiPp6enNUi7EYrVasVqtHXgFBwZfFH/Bw0sexh10YzaYuWrkVVw58kqsxi6+F4EGzR1z3VzY+KUWtjqKLRWGTIFhZ0D/4zuUoLbWG2R9aT3rS9xaXVrPhtJ6fKHmyVdBs8wVpDvon+lkQFYSA7KTtHZ2EhlO4acvEAj2L6wmI9kuI9mujlkSg2ElIvI0AahZBCPpW5qlcwlR7dFydKoqjUFrKj1t3wgwGqRGQdgkmFRGknacmWQlI8lChtNKmsOMSUQBbh1J0nK+OjOhIEGqqJBP85BpSQjKgUYrIQubz3flQd5RMOQa8LhATdKEp9F6QOUNNEgGzXJndoA9fp9fVPjF7fND2+dnNVlxmpw4zUL4CXqWTv3krVq1ismTJ2MwGBg9ejSSJDFnzhweffRRvvrqK0aPHt3Ny9RSOhxzzDHMmTOH6dOnA9DQ0MD8+fN56KGH9HEbNmzA7XYzduzYds1RVRWv14vT6Yy7xtatWxk5cmS3v479FXfQzaNLH+WzLZ8BcEjGITwy4RH6p7TgUtIewkEtSubP72uWu3CM5TUpVxN2w86EvsdoexnaoLzez88761i9s46fd9WxrsRNSV3zpKqgJecelJPEoGwXA7I0Udc/K4m+GQ5sZuGCIRAIDm4sJoMeCKq9hGVFtxrG5fP0BqlqaJrnUxOMnqCMrKhUNgSobGgeDj8RkgRpDgsZTk0IZiRZtbZTE4OZsX1JVpJtwmLYDLMdsoZopSmKAg2l8cKvplhrV28GX422P1D9Efo1gKccAhWN8w1mTfRFi9F6wIjA2H1+6TZtW1B0n1/UyheUgwTCAQLhgB7gxWqy4jA5dOFnNnQshsFrr73Ga6+9xrJly1oc8+677/LII4+wZs2abrvmgcKAAQN46qmnOPfcc3t6KTrFxcWMGTOGtWvXkp+fv8fu0ymhd9NNN/Gb3/yGF154QbdqBQIBrr/+em6++WYWLFjQrYuM8sgjj3DiiSdy++23M378eGbNmkVubi5XX321Puapp55iyZIl/PLLL+2aEwqFGDt2LJdddhkjRoxg+/btPP744xx55JFcdNFFe+R17G8sK1nGPYvvodRTikEycM2oa7hm1DUd/ocK0P6A7FgCP38Aaz7W/mBESSuEYWdppdfhrf5BqGoI8POuOk3YRepSd2JR1zvNztDcZIbluRiS62JobjKFGQ7xjbBAIBB0IyajgcwkK5lJ7ReH/pBMbcQi2FQIVnuCVHmCVDUEqGrQ2jXeIKqKfv7X8rbvYTZKZDitpEeEYWaMCNSFobPRYmi3HORf9hkMWhCX5HwtwFlTvNVQvQWqdoCSAtYUMIS1bReqDEoIgqHmuWshgQi0aXsRTVbogQAnxcXFHHvssQQCASorKzs85ocffuDll19mzpw5TJkyhb///e+ElBDekCb8Vq9ZzQWTL2h2zVffe5WJEyZqwq9JSoe33nqLp556itWrV+t9gUCgzaB/wWCQurq6VsfE0p5rdpXTTjuNiy++WDe29BR1dXUEg52LdrynkGWZuro6FKX9qbU6Q6eE3tKlSykqKopzXbRarTzyyCP06dOn2xbXlAkTJrBo0SJeeuklXn31VQ477DDee+89kpIak1wPGTIEWZbbPcdisbBgwQJefPFFXnnlFdLS0rj33nu5/PLLD/oUC2ElzKyVs5j9y2xUVApcBTw24TEOzTq04xer2wWr3oVVb0dcQSIk5cAhU2HkeZB/WML9dqqqsrmigeVba1i+tZoft9WwrcrbbJwkwcCsJEb1TmVU7xSG5yczJNdFsk3kxBEIBIJ9EZvZSG6Ksd1BasKyQo03RJWnUfw1CsEAlQ2RY0+Q6oYg9YEwIVml1O1v8cvApjgsRl30ZSY1prjJSrKQ5Yrvc1gOwucER7pWMg+B4mJI6QU2m7YvUJE1t89wTJH9mgdPWyLQaGm0/pmsWjJ5k1Xr3wMW2VAoxEUXXcSwYcNYuHBhh8ds3LiRGTNm8Pvf/54dO3bg8WiuymaDmRSrFuCl2llNvbueH3/+EUeqA6+sJXJ3OB3U+Guo8WtfdluMFl30eX3eTgmwiy++eJ+yWNXU1DB//nxef/31nl7KQU2n/oWy2+1UVFSQmZkZ119RUYHD0f79U51h3LhxLebMA7j99ts7PCcnJyfO/VMAlb5K/rjojywvXQ7A1EFT+ePYP2p+6u0lHISN82DFW7D5P6BGvrWwuGD4WTDyfC2/XZNoVYGwzC+76vhhaw3Lt9bw47bqhNHi+mc6Gdk7hZG9UhjVO5UR+ck4rQfhH12BQCA4SDAZO+ZS6g/JmmWwIUhlVBxGhGBUHEb7Kj1BgmEFb1DGW+1jR7Wvzes7LMZG8RcRgJl6bdHXmplkPfC3BUiSFqnTaGoerEVVQYlY/ZoJwYD2fCAHtRK7R1+7cHPxF627sPftnnvuYcCAARx//PEtCr3WxgwaNIgffvgBgM8++6zVe+Vn5pObmwtoX6J7Q148YQ/ekJbSISgHCcpBPvnqE2696VYC/gApqVok0Pvuvw+rWft5j7pblpSUMG7cOF544QX9ukVFRc1cN3/88UcefPBBfvjhB3Jycrjhhhu4/PLLE66xoqKCs88+m4EDBzJ79mwAHn30Ud577z3q6uoYMWIEDz30UNzz9IABA7j++utZuHAhS5cu5bLLLuOxxx4DYN68eYwcOZJevXoBmmh+/PHHef/996mpqWHChAk888wzutviySefzPLly5Ekiby8PE455RQeeughXC5X3P2uvPJKfvjhB1asWIHT6eSee+7hsMMO49Zbb2XFihUUFBTwzDPPMGFCfO7Kbdu2MXXqVH744QeSk5O55557uPDCC/Xz4XC40693x44d3HPPPSxYsACr1crEiRN5/PHHycrK0ucuXbqUW265ha1btzJ06FAuu+yyVn9muotO/Yacc845XHzxxbzwwguMHatt8l22bBnXXXcdv/nNb7pzfYIe4OeKn7lpwU2U+8pxmBw8eMyDnFp4avsvULcLfngDfvx7fMTMvsfAmOkw/Oy4gCqyovLzrjoWb6pk8aZKftxWQyAcb8q2mgyMLkhlbGE6RxSmMaZPGil2YakTCAQCQcvYzEbyU+3kp7YdrVRVVRoCYd1SGN03WFHfWGvtIBX1AXwhGW9QZluVN6GXSVNcNlO8RTCmznRZyEqykenSLIn7YxRnVVXxhdsQxwYDGOzaHsHGiZq1TxeBQQj7G49Bswom2sIpGcFkw25NRjLbYqyA1latgF9++SUffPABq1at4oMPPujUmI7s+zz66KMJBoMMGTKE2267jdNOO41kqxa8MKyE9ZQOJ5x8Ajf/6Wb+9uLf+OjbjwCw2Wx88s4nbNiwgU8//5TZb87GZDBx6aWXcv311+tra+q6uWrVKo499lhmzpzJU089hd/v5//+7/847rjjGDAgPlLr9u3bmTx5MkcddRRvvPEGJpOJ6dOns3XrVt5880169erFnDlzOPHEE1m1apWeeqyuro57772XV199lVdffTUuIOPcuXM544wz9ONLL72UpUuX8uKLL3LIIYfwv//9j7/85S88/vjjAHz88ceEw2FUVWXLli1cf/31XH/99bz55pv6Nerq6njyySeZPXs2L7zwAq+88gq/+93vGDhwIE888QSHHXYYzz33HOeffz5bt27FZmv0FLjvvvuYNWsWzz33HJ9++inTp0+nT58+HH205p58+eWXd+r11tTUcOyxx3LeeeexcOFCVFXlT3/6E1OmTGHp0qUYDAYqKyuZPHkyV1xxBW+//Tbr1q3jd7/7Xbt/frpCp4Tec889x1VXXcWECRP0H3RVVTn//PN57rnnunWBgr3LvOJ5/GnxnwjIAfqn9Oe5Sc/RP7UdAVdUVct3t/QVLeedGnGfTcqF0RdpAi8mBPT2Ki8LN5bz318rWbKlCneTtAYZTguH903Thd2I/JT98g+fQCAQCPYPJEnCZdMSxRdmtp4+IJrbsLI+QEVDQK/jRGFDUOuvDxCUFer9Yer9YbZUtB2NNM1hJjPJSnaylWyXrbF2WclJ1urs5H3LddQX9nHUu0f1yL2XnvAajrjo31L8XkCTLeIWaqOssorLL7+cf/3rX6SkpCS8XllZWZtj2oMkSfzhD3/gqquuIjk5mffee4/TTz+dDz74gKlTpwJgMphwWVy4LC5ynbkUZhRiMVnol9sPT8iDP+wnrISx2qz86fk/ITklzCYzV1x7Bffcdg+yIifM4/fII48wfvx4nn32Wb0vaqmLZd26dUyePJnzzz+fZ555BkmSWL9+Pe+88w47duzQLXIzZ85k3rx5vPHGG7o4A7jmmmv47W9/G3dNWZb58ssvuemmmwD45ZdfeO+991i0aJFuaZs2bRrTpk3T58Ruwzr88MN57rnnOO6445g9ezaGmJgNN9xwg+6ies899/DII48wffp0PVXaPffcw7PPPsv69evjgkNedNFFenyOP/zhD3z77bc8+eSTfPLJJ116vf/3f/9HTk4OzzzzTNz7nJqayrJlyxg3bhyvvPIK2dnZPPvss0iSRL9+/fjTn/7EjTfe2Ozz6G469S9EcnIy77//PsXFxaxduxZJkhg2bBj9+vXr7vUJ9hKqqvLKT6/w0k8vATCx90SeOO4JnOY2cuWEg1pglaUvx+fiKZwAR16jpUUwmgjJCj9srmLBhnL+s66MzU3+0LlsJsb3z+DYQZkcPSCDAVlJIkqaQCAQCPZJYnMbtkcUuv3hGItgU0HY2K5qCBJWVD3B/a/lCfayxZBkNUVEoCYEc5oKw8i5JOsBHnnUmQ1IEXdQP6BGrILN92VOv+gPXD7tbCaM7AMNZVqqCdC+sI68R9OnT+fyyy9v5v7XUUaMGMGsWbP047vvvpv169fz5z//WRd6TTEYDEhI5DobXT3TbGnk98onNTmVoBzEH/ZjcpmorqpmQ80G7CY77oAbFRVFVTBIBpYtW8Y111zT6vrKy8uZMGECM2fO5OGHH9b7ly5dCsChh2oxGVRV1b7c8HiapRaLjoll8eLFmM1m3etv+fLlWK1WjjnmmBbXsmTJEh555BF++eUXamtrkWWZYDBIaWlpXFTKoUOH6m2bzYbD4YjrS0/XoqFWVVXFXX/8+PHNjl988cUuv96lS5eyevVqMjMz9XmqqhIKhdi8eTPjxo3j559/Zty4cXG/g03Xs6fo0ldB/fr1E+LuAEBWZB5Z+ggfbNTM/5ePuJwbD7sx4TdEOiEfrHwb/vs8uHdqfSY7jLpAE3i5h+ANhlmwpoJ5v5Tw7caKuGTkRoPEEX3TOG5wFscMzOSQ/GQRBVMgEAgEBxySJJFiN5NiNzMwO6nVsYqiUusL6aKwvN5PeX2AcneAsno/FW6tr8ytuY42BMI0VLRtJXRYjLoYzEq2khMjAnULocvWpeT1dpOdpRcv7dTcrmI32RtdNVVV2+sXFX2xtRLi60Xfs2TFal6c/TYAwWAYWZZJTU3msXtuYebVl/P111+zZMkSXQgEg8HImFQee+wxZs6c2em1HnHEERQVFbV7vMlgwmayYTFZGJQ2iJAcwhPy6EndVVXFG/JSH6xHVmTWV6/HaXYSlsNg0M639Jm6XC4KCgr45ptv+OMf/6jvhwuFQlitVjZu3BhnTQMwm+O3zSTKKT137lymTJmi31dRFAwGQ7NrRSktLWXy5MncdNNNPP3002RmZrJx40bd5TWWRNdI1Keqatyx0Rj/TGsymQiHw11+vaFQiClTpiS0lkbjlsiynPD+e4NO3+U///kPixcvprq6utm5559/vitrEuxFgnKQO7+7k6+3fY2ExL3j7uWCIc1DAesE6uGH2fC/WVruHNAiZ467Fg67FI8xmW/Wl/P5/B9ZsKEcf6hxr12608KkwVmcMCybCYOyxB47gUAgEAhiMBgk0p1akvghua4Wx0X3E0ZFYHm9v7GuD1Dm1uoKd4D6QBhvUGZrlZetbewltJoMujUwah3MiojBnGQruck2clJsuBIEPpMkqWMB2/YUUozbJsnx5xSZmrJdcYFg3vrn+9x83xNsXfIZdpsVfDXUrP029oK89fEX2pjV32N3pULQq7mDdiIv4Pr168nOzm7xvMlkajXkvtloJtWYSqZdC4g4KG0QDaEG7JF9j6qq0hBsYMDQAXzz3284t/pcnGYteXuSOQmL0aILMLvdzhdffMGpp57KaaedxhdffEFSUhKjRo3C7/ezbt26Vq1wLTF37lz+/Oc/68cjR47E5/Px008/JbQA/vjjjyiKEhcYcdOmTR2+b2usWrUq7njlypUMGaLlkezK6x01ahT/+Mc/sNvtCUUvwODBg/n888+b3X9v0Cmh99BDD/HQQw9x1FFHkZaW1t1rEuwlQnKImxfezKKdizAZTDwx4QkmF05uYbAPlrwE/3uhMfddSgEcexPhURezqLieoo+L+WZ9vLgrSLczZWQek4fnMrogFaPhAHYdEQgEAoFgLxC7n3BAVutWQm8wHBGBjdbA8oh1sEwXiAHqfCECYYUd7Yg46rAYGZnr4A9HJGOr9WGzq5iNBswGCZPRoLWN0r7nLmowkpodn5zanq1FF08ddCSEAhD2kWpPb3T9VBU9t2KqyQ++Uq2AlvrBZIOQHyQLBD1x0UCfeuophg4dygknnIDZbKaoqIg33nij1UjvBQUFlJeXU1ZWRk5OTpsvyWK0kG5MJ92WjslgYkDqADwhDzNvnMn0c6bz9mtvc+5vz2VXcBfvvv4u5154LoMHDNYD5yQlJTFv3jxOPfVUTj31VL744guOPPJITjrpJK6++mrefvttxowZQ3l5Oe+++y59+/ZtNY1DcXExmzdvZvLkxufJI488kkmTJnHVVVfx9ttvM2jQIJYvX853333HbbfdRmFhIV6vl88++4zTTz+dlStXcu+997b52jvCm2++ydlnn82JJ57IvHnzePfdd/VANl15vTNnzuSVV17hyiuv5NlnnyUzM5MNGzbw9NNP89RTT5Gens7vf/97nnnmGZ577jmuu+46Nm3aFOcquyfplNB7+eWX+fzzz+M+RMH+RUgJcfui21m0cxFWo5W/nPAXjs5PkBhVUeDn9+E/D4F7l9aXPgAm3MKm3NP5YFUpHz/9P8rrG8Nh9c1wMGVkHlMOyeOQXsn73j/0AoFAIBAcJDgsJgoz295L6A/Jje6i7karYNRCWOb2U1rnx+3XLIQ7a7wEwkm4/SHqw4n/zpsi4i8q/MxGQ0QINvYZpH1EEJoiUTuJCb6iqiCHwPlf7diRoYm/kB9Umd6jT6DB68Xj9SNJkJqVS0ZaKpuXzAOTjXNPHs89f36G6dOn4/f76dOnD3/5y19a3Ts3adIkTj/9dPr27YvNZuOBBx7okJufzWTDZrJx0ZkXYfvAxl133cUT9z5Baloq0y6fRnpOOrX+Wmr9tQTlIJtrN5NkTqJoThFTz5zKaaedxrx58/joo4+44447OPHEE/F4PGRkZHDJJZe0mJ4hyqeffsrEiRPjgquAFlXzxhtv5LDDDkNRFMaMGcMrr7wCaHsZn3rqKS6++GL8fj/Z2dlcccUV3Zr6bMaMGdxxxx2sW7cOs9nM/fffz5lnnqmf7+zrLSgo4Ntvv+WWW26hV69emM1mBgwYwK233qobw/r27cs///lPrrvuOu644w569+7NNddcw913391tr68lJLWpE2s7yMjIYMeOHXs8Z97+hNvtJiUlhbq6urjwsvsiiqpw53d3Mq94HmaDmVknzOLoXglEXvF38NU9UPKTdpzcm+Cke/i3fDTvLt/Fyu21+tB0p4WzR+cz9bDejMgX4k4gEAgEggMRX1DWEtBXu7H4Ksnt3QfJaCEkK4RklZCsEJZVVNr3eGmQGkVfnAiMsQ6aIoJwTxAMBvH5fK1G12w2JpIXsK6qDDUUvw/QgEyyK17kqKqKLMuYLHZNTEZTQZgjxwliIiiKQn19PTabDUmSCAQCcTnlwuEwDQ0NpKam6mv0+/0Jn0H9fj82mw1FVfCGvDSEGqj2VNPgbcCZ1PgFgCzLEICs1CzSHGlYjVYkScLn82G3N09R4na7sdvtcfvYJk+ezBlnnMENN9yQ8L2MBiqxWCwJz3u9XhwOB4qi4Ha7SU5O1vfNJbpfXV0dDocjrq+2tpakpCRdIMfOC4VCGI3GFvcLAh16vbHIsowsyy2+NoBAIIDVatVfX0pKSsJnZr/fT3FxMf369YtLExFdR3s1R6eE3llnncXvf/97Tj/99I5OPWDZn4Tecz8+x+xfZmOSTDx//PNMLJgYP6C6GL64S0t2DmBx4TnqRt6UT+Vvy0qpbNA2xhoNEpMGZ3H+Eb05YWiOSH8gEAgEAsFBQmsPoqqqElZUwjHir7FWCCtaW1ba/wgaZx00xVoFG9t7Sgx2iGhi+JC/0f0z7NcCxLRE1AXUbNMC20Wti53YA9gRQkoIT9CDJ+ShIdRAWIlPdWUymPS9fU6zE7Ox9dgKDQ0NZGZmsnbtWvr3b0dqLkGLdJfQa7ctOGpeBRg+fLiej2LgwIHNlOiMGTPae1nBXubfm/7N7F+0yEAPH/twvMiTw/D9LFj4OIR9IBmpHXEJs5TzeGuBh0B4OwB5KTamj+/LeYf1JjvZlug2AoFAIBAIDlIkSdLFV2up6uVYMagoujUwkXUwLCuEZfCF5BavZzLECMCIGLTEiEHT3hCDBhNYTGBp4iqryBG3T1+j+2fYD0pIE4FyEALu+DlGa6P4i00IL3WPADQbzKTaUkm1paKqKgE5oIs+b8hLWAlTF6ijLqAlYrearLrwc5gczaKzOxwOysrKupR7UNC9tNuiN3DgwHZftLsj5ewP7A8WvVXlq7jiyysIKSGuHnk1NxwWY1YvXwcfXQOlqwHw5B/Nk8ar+PuvjUJuZK8UrprQjykj8zCLVAgCgUAgEBy0tGZx6E6i1sF4EagJwaCsEJYVgrLaLJx+S5iMBiwJrIF7VQzGIocjVj9fo/gL+UBtSdBGo4ra411AjZbGFBPdgKIq+MI+GoINNIQa8DfJSShJEg6TgyRLEknmJN3NU9A97HWL3sEo3g4kGoIN/HHRHwkpIU7qcxLXjblOO6GqsPRV+Po+kAPItjT+mXoNfyoehapKSBKcPCyHqyb0Z2xhmvglFggEAoFAsNeItQ62hKqqyEq8JTAYEYbBGGGoqo2WQWjZMtiSCIxtd9vzkNEExiSwxuzri+wB1K1/sZZAVWnsi9NeBk346RZAu1YbO5dJzSAZ9LQMOeQQVsJ4QhE3z2CD5vYZOS6jDLPBrIs+p9nZei5mwV5j72TrE/Q4Ty5/khJPCb2SevHnY/+MQTJoaRI+ugZ+/QqAzalHM73iUnbXat8OnD4yj5tOGsSgnJZz+QgEAoFAIBD0JJIkYYpY41pyFY3dNxhsYhkMNRGD0eMW7wdxaSQsJq1tiRGERkMXxKAkgdGsldhcgHoyeH+89S/sBxQIebUSi8Gsib44Adhx90+TwUSKNYUUawqqqhKUgzSENGufJ+QhpISo8ddQ469BQsJutpNkTsJlcQlrXw/SKaH3yy+/8M477/DYY4/F9d95551Mnz6dESNGdMviBN3Dwh0L+XjTx0hIPHLsIzjNTqjYAP+8CKo3IxusPMN0Xio9HpCYMCiTO08byoh84WMtEAgEAoGgZToR069HaM++wVg30TgBGI5pK+0TgwYpsj8wdq+gqVEMdspFNDYZvK1JGoho5M+QL+IG6tNEoRKCQKjJ/j+p0eUzVgC2EWylcRkSVpMVq8lKhj0DRVX0vX0NwQaCchBvyIs35KXcW47JYIqz9pkMws7UFt31e9Wpd/qGG27gvvvua9Z/yimncOONNzJ//vwuL0zQPXhDXh78/kEALh1+KYfnHA6/zocPLoNgPdWmHC7x3MhatZC+GQ7uPX04Jw3LFt+8CAQCgUAgaJFoiHmv15swFP3+SHvdRBvFoEIwRgQGI6IwrCgoqoo/LOMPJ3YRjVoF9WAxppjAMSZtH6GxvVE3JanRbdOe2tivyDHCL0YEqopWh33gi7mOwdQo+mKDwLRh/TNIBlwWFy6LC5wQlIPUB+t1186wEtZz9wGN1j6zC5vJJp45E+D1apbZllI5tJdOCb0lS5ZwxBFHNOs/4ogjWLJkSZcWJOheijYWUemrpFdSL64/7HpYN1cTeUqIHxnO1Q03UGdI4bpJA7j+xIFYTcKnWiAQCAQCQesYjUZSU1MpLy8HtIiLB9MDuwGwSmA1A2YJMEYKKNFooopmGQwrKqGwQliJBJRRVBRVJRiGVpIuYJAkPXiMySBhMjXmF4xGGG37PTeB0aUVG41J4MN+kAMQilgClSAQgmAISBD902gDszUS+dOiuYS2cm+n5MRpcaKYFXwhH96wF2/YS1AOagIQbW+fUTLiMDu0YnIc9NY+VVXxer2Ul5eTmpqK0di15/JOvZuZmZmsWLGC4447Lq7/xx9/1LPAC3qegBzgzTVvAnD1yKuxrp+H+uGVSEqYufJ4bg7NpDA7lTcvOJRRvVN7dK0CgUAgEAj2L3JzcwF0sSdoHyYVlEgAGVnRhKDWVvQ+uR2eexJaTuNoMSVot098G0G1ahFAlaAmBOVI2ge1BfdUyRjZR2iJFLNmEWzjfpIiEZAD+GU/ATnQzEXRYrRgNVqxmWyYDV2zZu3PpKam6r9fXaFTQu+SSy7hiiuu4KWXXmLChAkALFq0iJkzZ3LJJZd0eVGC7uHjXz+mwldBrjOXsxQ7atElSKrMx/Ix3Baawdlj+vDouSOxmYUVTyAQCAQCQceQJIm8vDyys7MJhUI9vZwDCl8wTHl9gHJ3gLJ6PxWRutztp6w+QEV9oO2E8xJkOq3kJNvITbaSm2InJ9lKbrKd3BQrWS4bFlMrbpmqCg1lULkJqn7VSsWvULsNSCAATQ7IGAiZgyF7iFanD9QsgQkIKSE2VG3gx/If+aHsB7bVbYs7n+XI4oicIxibO5aRmSOxmhJf50DDbDZ32ZIXpd159GIJBoNcfvnlvPvuu0iSpKvxiy++mL/97W9YLJZuWdz+xL6WRy8khzj949Mp8ZRw98gZXPjVk0j+Wj6Uj+WO8AzunDKCK4/td1C5WQgEAoFAIBAcCMiKSmVDgF21PnbV+PR6Z42XnTU+dtR48YdaDhYDmvEtx2Wjd5o9UhxxdX6qPbEQDHq1/MulP0Hpz1CyGsrWaHv+mt3ECFlDIHcU5I6EvEhtb+4BWNJQwqKdi/h257csK11GQA7o52xGG+PyxjGh9wSO630cuc6uW7v2VzqiOTol9KJs2bKFlStXIkkSY8aMoV+/fp291H7Pvib0fqr4iUs+v4QUSwrz603Ydq9klTKAi+UHmDX9KE4YmtPTSxQIBAKBQCAQ7AFUVaXKE2RnjPhrrLV2twpBRYaqTZroK42UktXgq0588ZQ+jaIvd5TWTu6lu376wj6WlSzThV+Ztyxu+pC0IRzX+ziO630cIzNHHlR5+/aa0BM0sq8Jvfnb5nPzwps51JTK27+uplZ1ckbwUe64cDJnHprf08sTCAQCgUAgEPQQe1IIFqQ7KEhzkJdixewpbRR9UQFYuz3xBe3pMVa/SMkchCoZ2FizURd9qytWo9IoX9KsaRzb61iOKziOo/OPJtnS88/hexIh9HqAfU3ovbf+PR5Z+ggnerw8X17JFcHbOP6sS5k+rm9PL00gEAgEAoFAsA/THULQaJDIT7XRJ91Bn3SHLgD7OUP0DW0hqWYNUtkvmgisWA9qglQUZqcm/vJHQ95oyB9NdVImi0uWsGjnIhbvWkx9qL7xnpKRw3IOY2LviRxfcDx9kvt07xuzDyCEXg+wrwm9WStn8erqV5nmrufsihT+dsjfefaC0T29LIFAIBAIBALBfk5rQnB7tVYHw60LQZfVpIm/dDv9Uo2MNJcwUNlCru9XXDVrMZT9AiFv84lmR8TyN5pQ3khW2ewsqi/m293fUVxXHDd0QMoAju9zPMcXHM8hmYdgaCMn4P6AEHo9wL4m9B743wN8+OuH/KGmFiqP4/CrXmBsYXpPL0sgEAgEAoFAcICjKCoVDQG2V3vZXuVle7WXHdWRusZLmTvQ6nxJgnyXmaOSqxhr3c4wdQsF/o2kutdhTBT0xWSH3JHsyBnMtzYLCwOl/Fi9nrAa1odk2jOZ2HsiJ/Q5gaPyjsJq3D+jeO4xoffcc89xxhlnMGjQoC4v8kBjXxN61/3nOr7d+S33V1bxffAWnr7zVhFhUyAQCAQCgUDQ4/hDMjtrvDFCMGoJ1Pq8wQRunIABhf7Sbg43b+Mo23YOkYopDG3GojQXf3VmB//NHcACp53/hqrxKI3p6e0mO8fkH8Okgkkc1/s40mz7Tx7wPSb0pkyZwoIFC+jTpw9nnHEGZ5xxBhMmTMBk2ntZ7J966imef/55ysvLOeSQQ3j++eeZOHFil+d05rqx7GtC78J/n8Oa2k08V1rFukM/5/pTR/f0kgQCgUAgEAgEglaJuoXqVsAqzQqoHfvYXecjVr0YUOgnlTBSKmakoZhDDMUcYtiKE78+Jggst9tY4HSxIMlJudToVmqQDIzJHsPxBcdzQsEJFCQX7MVX23H2qOum1+vl66+/Zu7cuXz++ed4PB5OOeUUzjjjDE477TQyMzO7tPjW+Otf/8ott9xCUVER48eP58knn+T5559nzZo1FBYWdnpOZ67blH1N6J34znjKww3cu9PKUb//lsJMZ08vSSAQCAQCgUAg6BLBsMKuWp8uBKMuoVHrYH0gjIRCf6mEQyLib6ShmOHSNlySDxVYazGzwOFggcPORmt8/u8BSQUcXziZ4/ucsE/u69tre/RUVWXFihXMnTuXuXPnsmrVKo488kjd2jdy5MjOXjohQ4YM4dRTT+X//u//9Pv36dOH3/72tzz++OOdntOZ6zZlXxJ6iqpw+N8PJSzB9RVjuea22T26HoFAIBAIBAKBYE+jqiq13hDbqr1sq/Kwvcrb2K5swOnZxiFSMaMMWzQBKBVTYw6z0GFngcPBDzYrcsxWp1QsHOYczqQBZ3L26PP2CdHXEc3RJZ9LSZI4/PDDOfzww7n//vspLS3ls88+Y+7cuTz66KPU19e3fZF2Ul1dzcaNG3nsscfi7j9p0iT+97//dXpOZ667r1NZX0k48jPae9gpPbsYgUAgEAgEAoFgLyBJEmlOC2lOC6MLUpud9wbDbK/2sq3Ky+oqL3Or3IRL15Nc8wuTatZxuXEzlY5KvnNa+K/DTq0hyDeeVaxZ8RO/GX3+3n9BXaRbN9fl5uZy5ZVXcuWVVxIMBtue0AFKS0sByMrKiuvPzs5m2bJlnZ7TmesCBAIBAoHGiEFut7s9L2Ov8PInNwEgqSpjxh7Ts4sRCAQCgUAgEAj2ARwWE0NzkxmaG2sJGw1AKBRiZ8ku5N2bmbRtKePKf2JneAu/WupxSen7ZVDDPRZFxWKxtD2oG1AUpcNvfHvmtDXmscce48EHH+zQffcW4/qfxqe/riZgkHhn0f3cdt4bPb0kgUAgEAgEAoGg06iqSlAJ4g/78YV9+MI+va33yT58QQ9+fw1+fw0+fx2+oBtfsB5/0IM/Ok8O4FVC+JQQflXBj4JfgoAhxjVT14JmzvQl9cRL7jJ7L1xmF8nLywOgvLw8rr+iooLc3NxOz+nMdQHuuusubrnlFv3Y7XZTULBvROk55djpfL9+Ph8aVvCxeym/91XjsoscegKBQCAQCASCPYOsyBER5ccX0kRXMyGmCy1/876wX5sbHR/yaiXs1cbLQRS6Of23oVlDx6qARZWwYMS2j0fibIn9RuilpaUxbNgwFixYwNSpUwFN2S9YsIDf/e53+jhFUVBVFaPR2K457b1uU6xWK1brvpto8YyJD/PDwlPZZjHyxjd3ctPpf+3pJQkEAoFAIBAIehBFVfCH/XjDXl1geUPeOMHlDXs1oRZ7HHu+hfFBpXu3bbWGSVWxKyp2VcGuqtgUFZuqHdsUFbuqYlSMSIoJVDOqYkFRrYQUOyHFRlBx4FOcYEzB7swgxZVDRlo+edm96Z+dzcDMVHKS7fulu2YsnRJ6t9xyC1dccQWHHHJId6+nVW6//Xb+8Ic/cPLJJzN+/HieeOIJ3G43M2fO1Mdcc801LFmyhF9++aXdc9ozZn/j8IEFDJ0zim2Za3i34nsu8ZST6czu6WUJBAKBQCAQCFpBVVUCcqBFcdWa8GpLpPnCzROLdzeSCjbArqrYFQW7ImOLiDG7GhFkihLTVrGpSkS4qTHCTcGmgs3sxGxMQjG6CKlJeJUkqsMOdgbs7PLbqFac1JJEnepkN0nUqEm4cSJjBCA32UafDAeFGQ4GZDjpk+6gMMNJnwwHKXbzHn8/epJOCb3Fixfz3HPPMXbsWK644gouuugiUlJSunttzbj88supr6/n5ptvpqysjJEjR/LVV1/FuUwajca4BO7tmdOeMfsbkiTRd9gfGbbjt6yzmXh90d3cedrrPb0sgUAgEAgEggOGqLuiN+zFE/LgDXvxhiIl3Fi3dK4lQaaoSts37yJ2gwW7wYxdMmGXDDiQsKtoIkwO45BD2ENB7GE/jpAfuyLjiIqxiFBzxFjVokLNqqo0s4NJBrClgj2tWQlZU6kliYqwnd0BO9u9Voo9Zja4zayvNeCua/29sJgMFKTZ6ZPuYGS6g4J0B30znPTNcNAn3YHNbNxTb+E+T6fz6K1Zs4bZs2fz9ttvU19fz9SpU7niiiuYNGnSfm/m7Az7Uh69KNuqPLzwyu/4svcGzCrMPXce+cm9e3pZAoFAIBAIBHsdVVV1UeUNNRFfTeqWznlCHr29NyxkFoMFh9mB3WTXi8Nk08QZBuwYcIBuObOHQzjCIexhP/agD0fQiz3gwe53Y/e7cYSDuiWtUxnhTHawp8aItkhtS23sd6Q39tvTUG1pVISsbK/xs6PGy/aqxmTn26u9lNX7aUuN5CRb6RMRcX3SHRSkOegTEXJZSVYMhoNHe+y1hOmghSKdO3cus2fPZt68efTt25fLL7+c3//+981SFhzI7ItCD+B3L36JZLyBH+0Wzs4YzZ/PeKunlyQQCAQCgUDQJmElrImroAdPyENDqEETYWFPu8RYnKUtcqx2dzCPCAbJgNPkxG624zQ7cZgcOMwOvc9hcmj9ZofWNto065kcxqHI2MNB7KGAZj0LeLEHGrAFGjD568BXA/5a8NVq7a6KS4MpXpzp7bTmIq5p22xLeEm3P8TOah87a7zsqPGxI0bI7ajx4g+1bpVzWowUxAi5aClId9A7zX5QW+WastcSpoMW/CSaU05VVfLy8njrrbd49NFHef3117n44ou7egtBF5h8xFDWf3UIP/bZyKeVK7l8+SsMGDujp5clEAgEAoHgACSshPGEPK2WqGBrCDXoIizaji1+2b9H1igh6YKraR0rxuwme7w4ixFwDrMDh8GGQ5FxyiEsgQYkf11EkNVoosxfC54a8JXEiLWIcAvWd/lVYEtJLMgSiriYtsUJHfS+c/tD7KzwsbOmlp01vkjx6rXbH251vkGCvBR7o4jL0ARc9DjdaTkoPQL3NJ226K1YsYLZs2fz7rvvYjKZuPTSS7nqqqsYOnQoqqry97//nT/+8Y/N0hYcqOyrFj1vMMy0FxfitNzJmqQAo/wBXhoxk5Rjb+7ppQkEAoFAINgHCCkh3ULWVIS1JdSaztsT4sxsMOM0O+OKLtCaiLPYOk6YxdQ2kw2DFHFcVFUINoC/rtFi1lSstdT210FX99JZXBERltq6OGsq6KzJYOiU82VCYi1ynRFyAOlOC73T7PROszezzOWn2jEbu2+9BzN73HXz0EMP5ZdffuGkk07iqquu4uyzz26WIF1VVQwGA130DN1v2FeFHkC528/Uv36EL/0JAkaFgcEgL2efQO5pT2vf6ggEAoFAINjvCMkh6kP1eIIe6kP1NAQb9Loh1EB9sF4XYa1Z2AJyoNvXZjFYmokzp9lJkjlJF2HRdpI5SRdr0XZssRgtrd9MkTXRpVvUahuFWKK2r7ZxrL8OlLZFTKsk2rfWUjtuP1sKGPdO1MfuFnK90xxx7V6pdpzW/SZr237NHnfdPPvss5kzZw59+/ZtcYwkSYRCoc5cXtDNZCfbePvysznntRD2rL+wyQLTKxfy6qvj6X/Gi9BvQk8vUSAQCASCg4Zo+PyoGIsKs4ZQgybWgvWN5yIiLXZctL+7BZrVaE0ozvRicuK0aHWSpYlIMzlIsiRpY8xOzB0VMOFgo/Dy1IJ/e0Sc1TQXZ3HirQ4Cbujq3juDqWPuj7HtFvat7U3c/hC7Egi4qKir87X9TC6E3IFHl4OxCDT2ZYtelLW73Uz/+zykjL8QsLpJkWUerajiuGHT4Ph7wJXT00sUCAQCgWCfJhq5sakY08VaAqtaovPhrlqRYoiKLJfZhdPixGV2kWRJIsmchMviamZNS2RBc5gdmA1dsC6pKoS8LVvO2rKshbxdfRvA7NCsZLEWs9hokLaUxG17qjZ3H90jJisq5fV+dtf62FXrZ1eNj921vsixVuqFRe6gYa9G3RRo7A9CD2B3rY8r317IDtPzSPbdAJxb38Dt7gBJ4/4AR18Ptn13/QKBQCAQdBZFVTRXxkRiLNKnW8+aWNeiYz0hD7Iqd8t6JCSSzEmaKIsItSSLJryibZfFpY+JFXB6bU7CaOimiISKolnH2ivOmraVbvDksqZERFhrgi01gWBLAZO16/fvATyBsC7adtf64wTc7lofpXV+wkrbj+tCyB0cCKHXA+wvQg/AF5S5/cMf+LrkTSzp/wUJ8sJh7q+s5hjVBkdeDUf+HpIOnvQYAoFAINi3CSvh5mIsgdWs6Z61pi6Q3YVRMsZZzVoTY9HzseOi1jU9KEh3IYfA744RYjXtt7IF3F0PLiIZ27amNTuf0jimu0TrPoKiqFQ2BNhZ22iF213rZ2fUKlfno9bbtkA2GiRyk230SrPTK9VOfqqN/FSt3SvVTl6qnSQh5A4KhNDrAfYnoQea68n7P+zg4flzUTPfw2CpAeAYr49bqmsZrBrh0AvhiCsg79AeXq1AIBAI9meCcrBVMZbIuuYJxYu17kxMbTFY4sRYU1fHWGtZ3PmYts1o2zPh4BVFC72vBxdppSQSb8GGrq/BZGtdkLUm3ixJ+6wL5J7AF5TZXRdxo6zxNbpX1nrZXeunpM5HSG77UdtlM+miLV8vNnqnae1slw3jQZQUXNAyQuj1APub0Iuyq9bHbUXLWOH+J+b0JUiSjEGFsxoauKrWTd9wGPIPg8MuheFngyO9p5csEAgEgr2Eqqr4ZX+8GGsizBK5OMadDzYQVILdtia7yR4nwJxmZ4suji3tWWszimNX6IhQi40Uqbe7IbAINIbtb681LXbsPhBcZF8gEJYpqwuwu05zn9TriIArqfNT7Wn7Z9sgQW6yTRdwvSLirVdqY1+ybe9E3xTs/wih1wPsr0IPtD/kc37azSNffofbMQdz8s8ASCqc5PVxZW0dI4JBLSJV/0kw4lwYerr2R0EgEAgE+ySKqui5zpq6MLY3gIgn6CGsdl/QkGhAkKaiLJEY089HgolEx3UpYEh72FeEmtEaI8QSFGtyC+ItkmPNKNz4WiMYVihz+ympaxRtJbU+dtf5KY30VTa07wsKp8Woi7dYd8qoVS432YZJ5JATdBNC6PUA+7PQi+IJhHl54WbeWL4AUv+DybVePzdKMXJBVTmneLzYVBWMFhhwIoz4jVaL/XwCgUDQbYSUUJwIi6sTCLRmAUYi59XuEByAQTLEuTI2dXGMFWMtWdccJkf3BQ1pDUVpTIDdIYEWKd2xTw20v5NRC1mbJcE4YVXrNCE5VsRpAi5O0NX5qWwI0J4nYKvJQF6KjbwUO3mptsZ2pO6VaifZbtozbrwCQQKE0OsBDgShF6Xc7eelhZv558plSKkLMKX8hCRpf/RckoXTggqnV+xidCCA/v1U3qEw8CRN9BUcudcSgAoEAsG+hKqqeMPe5m6NkT1pTV0do/vQoueifX7Z321rMhlMrQYIaU8AEbvJvvceZEP+SORHNwRirGR6X2ydQLwJoXZAE5YVyuoDlNb54lwoS2r9lLg1UVfRThFn0UVcrHiLFXV20hxmIeIE+xRC6PUAB5LQi7Kr1sfs/xbzrxVrCNqXYk5dpgdtAcg3OpgckJlYsZ3R/gC6k4jFBYXHQt+joc94TQSa9uB+CIFAIOgGQnKomZVMF2NRV8c2LGyekAelO0RGhNj9aE0tarF71BIGEIkINavRunceVFUVwv4mYqwNkZaoX+6m/XxGS+tCTAi1fY56f4gyt5/SugClbn+k7Y9rVzYEaEemASxGA7kpNnJTbOSn2MhN0dwoYwVdutMiRJxgv0MIvR7gQBR6Udz+EP9atoPZizdTHl6DOXklJtcaJGNAH5NstHGs5GRS5S6OrqsgJfZfYZMdeh+hib6CoyB/DDgzeuCVCASCA4looBBPyIM35MUT8mjtcGM7ei4qwrwhL56wJ25O9FxADrR903YSG3q/6V60RH2JLGpOsxOTYS/ts2pTpCUSbAmEWnfkUYtiTdaKrZW6NRFnsh1U0R/3ZcKyQmVDkNKIWCtzR8RbRMRF255g+/ITmo0SOck28lPs5KbYyEttbEfrDKcFg4hSKTgAEUKvBziQhV6UsKww75dSXv9vMT/trMCUtB6Tay3mpA1g9OrjjJKBQ63ZHBGUOaJyG4e6q3A0/TFL7aMJvrzRkfpQEdFTIDgICCmhOFHWVHw1FWD62HATMRfy4g17uy1xdSytRXXUg4LEWNFi96ZF5+2x0PtNUVUI+SBQHylubX+aftxSSWBl6zaRJoHV1bZIsyY3BhWxNWlbXGAQwSv2B9qywpW5/VTUt88KB1qagdxkzRKXk6wFMslJ0WqtbSXTaRUiTnDQIoReD3AwCL1Y1pW4+eCHnXyyahfVHj9G+3aMSetISttIyFgSN9YkGRluy+ZwWeLw2nJGVGwlU0ng2pSUA9nDIGuYVmcPh6wh2h99gUDQIyiqgi/saybCEomvqDWtIdiQUJh5Qp5uDbMfi8PkwGl24jQ7cZgjbVNMO9pvcjYb1yNWNDmUWIAF2xJo9TFCzq3V3egqqom09gi0ZLCmJO4XIu2AIBhWqGgIUO72U+YOdNkKZzJIZLusZEcEmy7kUqy6oMtNseGwiGihAkFrCKHXAxxsQi9KMKzwzfpyPvhhBws3ViArKpK5CpNzMxmZO5FsW2iQK5vNy7akMsKcwohgiOE1JQyv2kFGIvEHkFKgCb+MQZDeD9L7ayWlQISPFgiaoKoqQSXYLmEWay1rZmGLtH1hX7dFbozFYrDEi7JWhFhTwda02E12DNIeFhZyGEIeCHog0KCJrWDkOJjgOFCvjYuzoMWItHD3BVvRiFrRYoolqdGyZk1qcs6V2LpmSRIi7QDHH5KpqA9QXq8JuHK3n/L6AOX1Ad36Vl4faFd+uCjtscJlOK0i4bdA0A0IodcDHKxCL5aK+gBf/FLC5z+XsrS4SnfTkMzVFOSVkpG5A6+0hRLv9oQPjulmFwMsqQxQjQz0eRhYU8KAulJSWxKABhOk9m0Ufun9IbUAUnprItCeJvZnCPYLZEVObB1rwaUx0T602DHdmfcsikEytGohayrEYi1sTa1nDpMD856MzBsVZYGGGCHWiihLKN48mnUt2u52YRbBZG8u0Doi1qJts0MItIMcbzBMubtRsJVHxJzW59fP1fna76JrNkpku2xkuazCCicQ7CMIodcDCKEXT0V9gC/XlDLvlxK+31wV55uf6lQYPdBLTmYFIdN2NtWtZ2vd1hatBi6Tg96mJAowURAM0tvrpqCulIKgj5ywTItZmcwOSO4VEX6RktwLXLmQlK25ijoyhVVQ0GEUVcEf9icM+tGiS2MLQUC8IW+3htKPxW6yt8ulsS3R5jQ7u2/Pmapqboshb6T4NCEV8sX3hbwQbHLcbLxPE3QhX2TsHhZloH3BZEmKFKcmvizOxuNoO6FQc8WLNYtL/PsjaBVVVWkIhOOtbe4mQi7S1xBo/xc8VpOB7GQr2S4bOZE6y6WJN829UusTqQUEgn0PIfR6ACH0WqbaE+TbjeV8s76CbzeU4/Y3/jGSJBiel8y4AS4K8zzYnRVsr9/CptpNbK7dzG7P7lavbZKM5Jtd5GAiKxwmJxgg2+cmy1tLjiyTLctkhWVath1I4MzURF9U/Ol1DjgytCAx9jSwp2sPceKP3n5FWAnjDXv14B2+kC/uuKXaF/a1em5PYJJMOC0tCLFEgq0V0eYwOdq/1ywqvsI+LYdZOFJCvo7XrYmxaP8eCKCSkKaiLFqsrnhR1lSk6QKuaX+SSBUj6Bb8IZnKhgCVDUEq6gNUNgSoqA/Etxs0AecLtf/3xW426sItO6bW+1xaLRJ8CwT7L0Lo9QBC6LWPsKzw47YavtlQzsL1FWwoq487bzEaGNMnlSP7pXNEYTrD863UhcrZUb+DHfU72NmwU6vrd7KzYSdhpX3fYKYarKRhJE1RSZPDpAb9pAU8pMoy6YpMqqyQoigkKQpORcWlKNhVlWZ/Bo2WRtGnC8BIcaQ3CQfuavJNvkskkm+CqqqElBC+sA9/2E9ADmht2Y8/rBWf7NPbsccBOaAdh32aKGtBxO2p4B8AEhJ2k72Z9SvWctbufWhmJxbJjKSEIBzQcomF/Vo7HAA5AOFInxzsnAgL+yNCzte87taAHu19A42aiDLbNQu82aG1LTFtc/S8vYWxMX1NxZrRIr6YEew1QrJCVVPhFlNXxtSxX3i2B5fVRFaylexYq1sTMZftspJkFQJOIDjQEUKvBxBCr3OU1/v5fnMVizdV8t9fK9ldF+9yZZBgaG4yYwvTOLwwnbGFaeSl2AFtX1O5t5xdDbso95ZrxVfe2I6UUCdDhhtUcAJJiopTDpMUEYJRMWhXFayqilVVsakqViVSq411bDGqYDRaMVmcGC1JGM1OjGY7JpMdo8WB0ezAZHZgNDsxmO1IFmfMw26T2mTTRKPJqtVGq/ZQa7JotdEKBgOqqhJWw8iKjKzKhJUwsirHHSuqEjdGVmT9OKSECMgBQnKIoBIkKAe1YyVEUE5wHBmjFyVISNauEVSCBMIB/LJfF3Z+2d+tyaVbwySZIpYuu1aMNhwmGw6jFbvRisNgiRSzVktGHAYTDsmEAyMOyYgdAw4kHIAdCZuiYlDCmvDShVmMGAsHYtqRc3KgUbw1E3Ddl8etS5jsWrLoFmtb489h0zqRGDM7YsRbTJ+wjgn2cWRFpcoToLI+GCfWYsVctK7xduxvjdkokZVkJctlJbNJHW1H3SjFHjiBQBBFCL0eQAi9rqOqKlurvHy/uYoftlbzw7Yatld7m43rlWrn8L5pjOqdwqjeqYzIT8ZpTfxHUFVVagO1VPgqqAvUUeOv0UqghtpAbdxxfbCehlADDcGGPZKbq6OYVBWDClLM3sVYG6Pa5Evbpr/ICqDsR9/sGgE7BmxIWJE0IaVK2JCwAXYVbNGCik0Bm6piUxVsqopTVnCoKg5FiRQZhyzjCIdwyEHMcrBnrFZdISraTZaIuI/U0ePWBFeLdWsCzq59ebAf/dwIBB1BVVU8QZmqiNtkVUOAKk+w8TjSrvYEqYzU7c3/BmA0SGQ4LQlFm1ZbyHZZyUoS7pMCgaBzCKHXAwiht2coc/v5YWsNP2yr5oetNazZXdfsj64kwcCsJEb2TmFUrxRGFaQyPC8Zm7nFMC2toqoqftlPQ7CBhlADnpCH+mC9Hjwj2q+7D8p+AuEAATnQ7Di2rSjheMuZKhPuIeFhUFWMaGLSCBhbODarKhZVxaKCRVUxR6yTlkgxR/qtqoqZxn5rzLnYYosWRcUeFWkRS2jPOLVKEYuoRdvPZbRox7FtoxkMkTFGU3zbaIkcR6yrTcWY0Rrpjz0XOY49F9eOEXUiiqJA0CaBsEy1J0hVgybOqhqCVHkCkePGdlTUBcId+3dXkiDDaWludUuykumykJVki9RW0hwWkchbIBDsUQ5oobdo0SJefPFFysrKGDlyJHfffTd5eXldmvPkk0/y0Ucfxc0ZMGAA77zzTrvXJYTe3qEhEGbV9lpW7ahh9c46ft5VR0kTd0/QvlUdmJXE0DwXQ3OTGZrnYlhuMjnJ1n3qG1RVVVFUpZlbZawgbIoUY9WLey2qCkooElQjiFGVMSoyRlXBJIf1tlFVMaiKFhBDkTUrlyLHHEfqRH3Rsd39HkoGMBi1PVtxtaGFc4YEY6P9Tc5FxVqsKNOPO/dlgEAg2HOEZIVab0gTb01EWlMrXFVDkPoORJuMYjcbyUiykJFkJdNp0dsZ0bbTSrpTs76lOy2YjOJLF4FAsG9wwAq9BQsWMHnyZO644w7Gjx/PCy+8wMaNG/npp59wuVydnjNjxgw2btzIo48+qs9zOByMGjWq3WsTQq/nKHf7+XlXnS78Vu+spbIhcQCOFLuZobkuhuUlMzTXxcDsJAZkJZHmFHuFBAKBoLtRFBW3P0SVJ0iNJ0i1J0iNN0i1JxSpI/3exvMdDVQCYDJIukDLSNKsb+kR0ZYZ6YsVcmLPm0Ag2F85YIXeMcccQ0FBAe+99x4AXq+XvLw87rvvPm699dZOz5kxYwaVlZUUFRV1em1C6O07qKpKSZ2f9aVu1pXUs760nvUlbrZUepBb2GyR7rTQP9PJgKwkBmQ76Z+ZxIDsJArS7OKbXIFAIKAxp1uNJxQnzHTBpgu3xvM13o7tcYsiSZBqN+viLDPJ2kTIacIt3akJObHfTSAQHCwckELP6/Xicrn4xz/+wW9/+1u9f+rUqXi9XubNm9fpOTNmzGDevHkMGDCAlJQUJkyYwHXXXYfF0n4rjxB6+z7+kMzmigbWl9SzvtTN+tJ6tlR42FXbck40s1Gib4aTAVlO+mcl0SfdoZe8FJsQgQKBYL9DVVV8IZkab4hab5A6b4haX4hab4han3Zc4w1GjkNxx0G5c/uKXTYT6c7/b+/Og6Mu7z+Av/e+SDbhklw/AomEoJyROlCEyiVOYbBDtR0oWGtRDrEClquDSG2HolbKGIcqzFCwIPcRAkSgBQ3lKBCMQVKQ+wgmgYVcu9ns8fn9sZtNNiQhkGuzeb9mMt/9Pt/n2X028xC+732++3y1CDdqK201CDdp0daoRbhJi3YmrW8/1KCBit91IyK6z8NkjhZz7cL169fhdrsRGRnpVx4ZGYl//etf9WpjNBrxq1/9CkOGDMHNmzfxpz/9CVu3bsXXX38Nlar67/DY7XbY7RVLoRcWFj7qW6Mmoteo8ESkGU9Emv3KrWVOXMovwaXbJbiYV4yL+cXe/WKUOty4kFeMC3nFAHL92qmUCkSG6X3BL6Z8G+7Zhhk1/ISZiBpN+QqSd0vKUFApqN2zOrz7Zd4w50CBzT+4PWpgAwC9Rol2Jh3CTZoqwU1bKbhp0Nb7OMyohVbND8WIiJpaswa99957D7t37661zpYtWxAdHQ2Hw3N/Gp1O53fcYDD4jlVV1zZLlizxq/PMM8+ge/fu2Lx5M375y19W+9xLlizB4sWLa+07tQxGrRpPRpnxZJR/AHS7BTkFNlzKL8HF/GJcvl2C6xYrrlmsuH7XhjKnG9ctNly32PAf3LnveU1aFTqZ9YgMM6BTqB4RYQZEmvW+sgizHiF63kCdqDVzuNwotDlQWOr0bh0otDlRWOoJa1XLCm2e8vJg53yU6yK9NCoFwoxahBk0CDNqYDZoEWbUIMzgmWkze8vDvOXlIc6g5SJGREQtQbMGvfHjx2PEiBG11unQoQMAoG3btgAAi8Xid/zOnTto165dtW3r2qZqEIyPj0dsbCwyMzNrDHrz58/HrFmzfPuFhYWIiYmp9b1Qy6JUKhAdbkR0uBGDu3XwO+Z2C/KK7Lh+14prd7zhz2L17FusyC20o6TMhYv5JbiYX1Lja7TRqRFh9oTAjiEVS3Z3DC3f6tEhRAeTVsXZQaIAZHe6UFzqRLHdiaJSZ63hrLowZy2r/z07tSqlJ5AZNX7BLcxYEdbCveXmSnWM/LtCRBTUmjXoxcXFIS4urk51IyMj0alTJ5w4cQKjR4/2lR8/fhzPPPNMg7UBAKfTidu3b8NkMtVYR6fT3RcQqfVQKhXo5J2d6x/b9r7jpQ4XbhWU4tY9G3K821uF3m1BKXLu2VDoPTn8Pq8Y3+cV1/p6Bo3KF/7Kb8Bb06VS4UbtI99DkKg1KP+OWnGpE0V2p19QK7Y7UVzq8OxXOla1bnlZfS6BrKyNTo1QvRqhBg1C9RqEGtTerfenyrEwg+fffJhBC71GycBGRET3aTHf0QOA3/zmN1i1ahVee+01REVFYevWrTh79ixWr17tq7NkyRKcOXPGdw+8B7UpKyvDsmXLMHPmTGi1WjidTsydOxdWqxXjxo1rlvdJLZ9eo0KX9iZ0aV/zhwUldqcnDBZ4wl9+kd3vJ6/IU1ZS5oLN4cLVO1ZcvWOt0+sbtaoqQbDisiuz0f9EMqTSY37CT4FGRGB3umEtc6HE7oTN4dn677tgLfOWlTlhtXu2tjIXSrz1KsKcJ8TV44rHapm0KrTRe/4dmWsIZ2bf46r/BtVc2ImIiBpciwp677zzDr7//nvEx8cjJiYGN27cQHJyMvr37++rc/HiRWRmZta5jUajQXFxMSIjIxEREYFbt24hLCwMO3fuRGJiYpO/R2o9TDo14ju2QXzHNrXWK7E7PeGv2I68Qjvyi0pxu7hiKfOq96VyuQXWMhesZbZaVxStjkqp8J2chnhPWstPSEP0Gpi0Khh1api0Kph0ahi1aph0Kt/WpFXD6D2mU3OWIdi53IJSh+eDiFKHC6UOt3freVxR7n/cVv7YWXHMWuaqEtA8gc3qcNV4W5T6Uio8M2kheg3a6NRoo1f7tiE6tV9ZiF6NNjpNlX3PcZNWzRUiiYgo4LSY2ytUlpOTg9zcXMTHx993o/RLly6huLj4vpud19YG8Mzsff/99wgPD0dERMRDn6Dy9goUCEQEhaXO+25AXB4ELSV2FNqcKLL7f3+osNTZ4CfTKqXCE/q0aug1Sug1KujUSui8W9++WgW9xn+r0yih99b1lamVUKuU0CgVUKuUUKsUUCsVUCuV0Ki8ZUoFNN5jGqW3jvexsgWdiLvdAqdb4HILHG43XK5K+y43XO6a951uN5wuT7nD5Ybd6UaZ040yl2fr8G7LnG7YKz32lXu39srl3jJblTDncDXtfx86tdL7AYPK+1PxQYNvv/yY9wMJg/b+YBbi3Ro0nMEmIqKWJSjvoxfoGPSoJSv/zpJ/+HOgyLd4hHfhCLv/pXHll895ZhCdKLF7ZmsCkVIBqFVKqBQKKBSAstJWqQAUVbeotK/01odnCwUAAdwiEABS/tj711RE4BZAIN5jALyPBRV1pVJ7X0hzVzxPS1Ie3vUaJQwalSfIa1TQq5UwaFXQe4O8QavyhnqVt56nXflMsEFbMTNs9M0ce0IcZ82IiKi1C8r76BFR41EoFN5ZETU6mfX1ei7PpaMVIbDE7oLd6ZkJqm5rd3pmiCpv7d7L+uzldbxbh6vSjJVv5krgqlJW3ZLzbgHKnA2zcEZz0agUUHlnMctnNCvvex4roPLOcmpVSmjV3h+VEhq1EroqZVq1EhrvVle5buV63na6KuHMF+jULWvGlIiIqDVg0COiBqVSKhDiXeSluYh4wp5f+HO54XAL3O7ymTXPrJtnds07A+fdr5hxq9h3V5qBc7sFSqVnhk/hnfXzPK6Y9Su/IrDy7GHFLCEAVJpZhOf35glvnktQVZWCXEu79JSIiIiaH4MeEQUdhUIBjUoBjQowgLeaICIiotaH6zkTEREREREFGQY9IiIiIiKiIMNLNxtI+eKlhYWFzdwTIiIiIiIKRuVZoy43TmDQayBFRUUAgJiYmGbuCRERERERBbOioiKYzeZa6/A+eg3E7XYjJycHISEhAXED3sLCQsTExOD69eu8rx89Eo4hqi+OIaovjiGqL44hqq9AG0MigqKiIkRGRkKprP1beJzRayBKpRLR0dHN3Y37hIaGBsSgpJaLY4jqi2OI6otjiOqLY4jqK5DG0INm8spxMRYiIiIiIqIgw6BHREREREQUZBj0gpROp8OiRYug0+mauyvUQnEMUX1xDFF9cQxRfXEMUX215DHExViIiIiIiIiCDGf0iIiIiIiIggyDHhERERERUZBh0CMiIiIiIgoyDHot2NWrV3Hy5EkUFRU1ahsKXpcvX8apU6dQUlJSp/oOhwNnzpzBpUuX4HK5Grl31BJcuHABp06dgs1me6h2ubm5OHz4MG7dutVIPaOW4ty5c8jIyIDdbq9zG5vNhtOnTyM3N7cRe0YtRXZ2Nk6fPg2Hw1Gn+na7HdnZ2cjMzOT5EAEAzp49iyNHjtS5vojgu+++Q2ZmJpxOZyP2rJ6EWpySkhIZPXq0GI1G6d69uxiNRvn0008bvA0Fr4KCAhk+fLi0adNGunXrJm3atJG1a9fWWN9ut8uCBQukffv28sQTT0hUVJTExcXJoUOHmrDXFEjy8/Nl4MCBYjabJT4+Xsxms2zbtq1ObW02m/Tu3VsUCoV8/PHHjdxTClQ5OTmSlJQk4eHh0rVrV2nXrp3s2bPnge3ef/99adOmjTz55JPStWtXefXVV8XhcDRBjynQXLlyRXr16iXt27eX2NhYeeyxx+TgwYO1ttm4caN06NBB4uLipFevXmI0GuWdd95pmg5TwFm3bp30799fwsPDRafT1anNuXPnpHv37tKxY0eJiYmRqKgoOXLkSCP39NEw6LVAb731lsTGxkpubq6IiHzxxReiUCjk9OnTDdqGgtdvf/tbSUhIEIvFIiIiK1euFLVaLefOnau2vsVikT//+c9SWFgoIiIul0tmzJgh4eHhUlRU1GT9psDx85//XPr16yfFxcUiIvLBBx+IwWCQGzduPLDt1KlTZfr06WIymRj0WrFRo0bJwIEDxWaziYjIokWLJDQ0VPLz82tsk5ycLEajUdLT031l//jHP3zjkFqXQYMGybBhw6SsrExERGbPni3t2rWTgoKCauvbbDbR6XSyaNEiX1lqaqoAkKNHjzZFlynALFy4UI4dOyYrV66sc9Dr27evjBkzRpxOp4iIvP766xIZGen7WxZIGPRaGJfLJeHh4fKXv/zFr/zxxx+X3/3udw3WhoJXaWmpGI1GSU5O9pW53W6JjIyUP/zhD3V+njNnzggAOX78eGN0kwKYxWIRlUol//znP31ldrtdzGazfPDBB7W23bZtmyQmJorVamXQa8Vu3rwpCoVCduzY4SsrLi4Wg8EgK1asqLaNw+GQjh07ypw5c5qqmxTAzp8/LwDkwIEDvrLbt2+LWq2Wzz//vNo2P/zwgwCQtLQ0X9ndu3cFgKSkpDR6nylw1TXoZWRkCAA5duyYr+z69euiUChk+/btjdjDR8Pv6LUwV65cwd27d5GUlORX3r9/f5w+fbrB2lDwOnfuHKxWq994UCgUeOqppx5qPJw4cQJKpRJdunRpjG5SAMvKyoLL5fIbQ1qtFr179651DF27dg1Tp07FunXrYDAYmqKrFKC++eYbiIjfGDKZTEhMTKxxDGVlZSEvLw9jxoxBXl4eMjIycPfu3abqMgWY8nFSeQy1a9cOXbt2rXEMPfbYY5g5cybmzZuHbdu2Ye/evZg0aRKGDRuGUaNGNUm/qWUrH1v9+vXzlUVHRyMiIiIgz6kZ9FoYi8UCwPPHrLJ27dr5jjVEGwpeDTEerl27hjlz5mDKlCno0KFDg/eRAtujjCGXy4Xx48fj7bffRt++fRu9jxTYHmUM5eTkAAC2bNmCXr164dVXX0VkZCQmT57MxaFaIYvFApVKBbPZ7Ff+oP/LJk6cCIVCgbfffhtz5szBqVOnMGPGDGg0msbuMgUBi8WC0NDQ+8ZLoJ5TM+i1MOUDq7S01K/cZrNBq9U2WBsKXvUdD7m5uRg5ciT69u2Ljz76qFH6SIHtUcbQ8uXLcevWLTz99NM4fPgwDh8+DLfbjUuXLuHkyZON3mcKLPX5v+zMmTO4fPkyTp8+jYyMDGzYsAHJycmN22EKOBqNBi6X676VNmsbQz/88AMGDx6Ml156CZcuXUJWVhbWrl2LcePG4eDBg03RbWrhNBrNfX+3gMA9p2bQa2E6d+4MALh586Zf+c2bN/F///d/DdaGgld9xkNeXh6GDh2KmJgY7Ny5EzqdrtH6SYHrUcaQWq1GREQE5s+fj3nz5mHevHmw2+3YuXMnli5d2uh9psDyKGMoNjYWAPDyyy/7Lv1NTEzE4MGDkZ6e3nidpYBUPobKZ3rL5eTk1DiGvvrqKxQXF2P69Om+smHDhiEhIQG7du1qvM5S0OjcuTPKyspw+/ZtX5nL5UJubm5AnlMz6LUwYWFheOqpp5CSkuIrKygowKFDhzBixAhf2fnz55GZmflQbah1iI6ORvfu3f3GQ15eHo4ePeo3HrKzs5GVleXbz8/Px9ChQxEREYGUlBTo9fom7TcFjh49eiAyMtJvDJV/Ol55DGVlZSE7OxsA8Oabb/pm8sp/DAYDZs6cic2bNzf5e6DmlZSUhLZt2/qNoaysLFy+fNlvDGVmZuL8+fMAgG7duqFLly7VhkNeQt76DBgwACaTyW8MHT16FHl5eX5jKCMjAxcvXgQA3zi5ceOG73hZWRny8vI4hqhGJ06cwJUrVwAAgwcPhlar9Rt3//73v1FUVBSY59TNvRoMPbx9+/aJWq2WhQsXyo4dO2TIkCGSkJAgVqvVV2fChAmSlJT0UG2o9di+fbuoVCp57733ZPv27TJgwADp3bu3b4lqEZGxY8fKkCFDRESkqKhIevbsKZ07d5Z9+/ZJenq67+fOnTvN9C6oOa1Zs0Y0Go18+OGHsnXrVunTp48MGjRIXC6Xr86QIUNk7NixNT4HV91s3T755BPR6/WyfPly2bRpkyQmJsrIkSP96iQlJcmECRN8+1u2bBGz2Swff/yxpKWlyeTJk8VoNMp3333X1N2nALB06VIxmUyyYsUK2bBhg8TFxcnPfvYzvzoJCQny+uuvi4hndeC+fftKz549ZePGjZKamipjxoyRsLAwuXbtWnO8BWpm//vf/yQ9PV3mzp0rWq3Wd25TfjspEZGoqCiZPXu2b3/hwoUSFhYmK1eulHXr1kl0dLRMmjSpObr/QOrmDpr08EaMGIEDBw5gxYoV+M9//oOkpCTMmzfPbxW7hIQEvxmXurSh1uOFF17A3r17sXLlShw6dAiDBw/G3Llz/b5c3KNHDxQVFQGo+PJxaGgoFi9e7PdcS5YswTPPPNOk/afmN2nSJISFhWHNmjVIS0vDmDFj8Pvf/x5KZcWFIj179kRISEiNzzFw4EBERkY2RXcpAE2bNg0dOnTAunXrYLVaMX78eMyaNcuvTp8+fXyX6AHAuHHjYDabsWrVKqSkpKBbt27IzMxEfHx8U3efAsCcOXMQFRWFjRs3wm63Y/LkyXjrrbf86iQlJfnGh1arxaFDh5CcnIz169ejrKwMiYmJSE5ORkxMTDO8A2puX3zxBQ4cOADAsxr9vHnzAACfffYZevToAQD40Y9+5LfC+OLFi9GlSxds3boVTqcTs2bNwhtvvNH0na8DhYhIc3eCiIiIiIiIGg6/o0dERERERBRkGPSIiIiIiIiCDIMeERERERFRkGHQIyIiIiIiCjIMekREREREREGGQY+IiIiIiCjIMOgREREREREFGQY9IiKiZpCTk4PNmzc36WueO3cO+/bta9LXJCKi5sGgR0RE9IicTic2bNgAm8320G0zMjIwceLERuhVzXbt2oUFCxY06WsSEVHzYNAjIiJ6ROnp6Zg1axb0en1zd4WIiMgPgx4REbV6+/fvR3p6ul/Z2bNnsXnzZrjd7hrbpaam4qc//SkUCkWNdfLz87Fnzx58/fXXsNvt1dY5c+YMUlNTcfHixWqPX7hwAdu3b8dXX32FsrIyv2Pll4CKCI4fP44tW7bg3r17ADwzjgcPHsT+/fuRn59fYx+JiCj4qJu7A0RERM3NarVi9OjROHLkCJKSkmCxWPDcc89h4sSJePHFF2tsl5qaivfff7/G40uXLsXixYvRt29faLVa3Lt3DykpKYiJiQEAuN1uPP/887h79y7CwsJw8OBB/O1vf8PUqVN9zzFjxgysXr0aP/7xj3Hp0iW43W58+eWXiI+PB+C5BHTChAlYtWoVLBYL4uLi0L9/f4gIhg4divz8fPTs2RPffvstHn/88Qb6jRERUaBj0CMiolZv7NixeOWVVzB+/HhkZGTgtddeQ6dOnbB48eIa21y4cAFXr17F8OHDqz2+Z88eLFiwAGlpaRgxYgQAIDs72+/7fA6HA8OHD8fs2bMBAJ988gnmz5+PKVOmQKFQYPfu3fj000/x3//+F3369IHD4cDo0aMxbdo0v0VVHA4HBgwYgHfffddXNmvWLDidTmRnZyMkJASXL19G79690a1bt/r8qoiIqIXgpZtEREQAPvroIyiVSgwYMABpaWlYv349NBpNjfV37dqFZ599FiaTqdrja9aswciRI30hDwASExP9gpZCocCUKVN8+z/5yU9QUFCA3NxcAMCGDRvw/PPPo0+fPgAAjUaDOXPmYP/+/bBYLH6v9+abb/rtb9q0CVOmTEFISAgAoEuXLrXOThIRUXBh0CMiIgJgNBqxYMECZGVlYfr06Q+8zDE1NRWjR4+u8fi1a9ceOHum1Wr9gqJOpwMAlJaWAgCuXr2Krl27+rWJi4vzHav8PG3btvXtO51O5OTkIDY21q9tly5dau0PEREFDwY9IiIiAEVFRXj33XfRo0cPrFq1Cjdv3qyxbmFhIdLT02sNemFhYbhz5069+tS+ffv7Zu7K99u3b+8rq7oYjFqthtlsxt27d/3Kq+4TEVHwYtAjIiIC8MYbb8BkMuHkyZPo1asXfv3rX0NEqq375ZdfIiEhAZ07d67x+UaOHIndu3f7hT2n0/lQYWvQoEFIS0uD1Wr1lW3evBmxsbGIjo5+YNsdO3b4vXZKSkqdX5uIiFo2Bj0iImr1Nm3ahI0bN2L9+vUwGAz4/PPPcerUKSxbtqza+g+6bBMApk6dih49euDpp5/Ghx9+iOXLl2PQoEG4fPlynfs1bdo0tG/fHs8++yz+/ve/Y+bMmfjrX/+KZcuW1XpLBwD44x//iN27d+Pll1/GZ599hlGjRvluu0BERMGPQY+IiFo1p9OJQ4cOYeXKlXjyyScBANHR0Vi7di2+/fZbFBcX+9V3u93Yu3fvA4OeXq/HwYMHMX/+fGRnZ+P69etYsWIF+vXrBwCIiorCSy+95NcmJCQEv/jFL3zf29Pr9Th69ChefPFFHD16FCKCw4cP44UXXvC1qe55AKBv3744duwYQkND8c033+CVV17B6tWr8dxzzz3074iIiFoehdR0XQoRERHd5+jRoxgzZgxyc3OhUqmauztERETV4oweERHRQ8jPz8f8+fMZ8oiIKKBxRo+IiIiIiCjIcEaPiIiIiIgoyDDoERERERERBRkGPSIiIiIioiDDoEdERERERBRkGPSIiIiIiIiCDIMeERERERFRkGHQIyIiIiIiCjIMekREREREREGGQY+IiIiIiCjIMOgREREREREFmf8HcsJ0ibovxHQAAAAASUVORK5CYII=",
+      "text/plain": [
+       "<Figure size 900x300 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
    "source": [
     "def naca4(M=2.0, P=4.0, XX=12.0, n=250):\n",
     "    x = np.linspace(0, 1, n)\n",
@@ -1139,7 +1681,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 20,
    "id": "336f680c",
    "metadata": {
     "execution": {
@@ -1149,7 +1691,218 @@
      "shell.execute_reply": "2026-07-20T19:11:17.780315Z"
     }
    },
-   "outputs": [],
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "--- system/controlDict preview ---\n",
+      "/*--------------------------------*- C++ -*----------------------------------*\\\n",
+      "| =========                 |                                                 |\n",
+      "| \\\\      /  F ield         | OpenFOAM: The Open Source CFD Toolbox           |\n",
+      "|  \\\\    /   O peration     | Version:  v2112                                 |\n",
+      "|   \\\\  /    A nd           | Website:  www.openfoam.com                      |\n",
+      "|    \\\\/     M anipulation  |                                                 |\n",
+      "\\*---------------------------------------------------------------------------*/\n",
+      "FoamFile\n",
+      "{\n",
+      "    version     2.0;\n",
+      "    format      ascii;\n",
+      "    class       dictionary;\n",
+      "    object      controlDict;\n",
+      "}\n",
+      "// * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * //\n",
+      "Uinf\t32.137;\n",
+      "\n",
+      "application     simpleFoam;\n",
+      "\n",
+      "startFrom       startTime;\n",
+      "\n",
+      "startTime       0;\n",
+      "\n",
+      "stopAt          endTime;\n",
+      "\n",
+      "endTime\t40000;\n",
+      "\n",
+      "deltaT          1;\n",
+      "\n",
+      "writeControl    timeStep;\n",
+      "\n",
+      "writeInterval   $endTime;\n",
+      "\n",
+      "purgeWrite      0;\n",
+      "\n",
+      "writeFormat     ascii;\n",
+      "\n",
+      "writePrecision  6;\n",
+      "\n",
+      "writeCompression on;\n",
+      "\n",
+      "timeFormat      general;\n",
+      "\n",
+      "timePrecision   6;\n",
+      "\n",
+      "runTimeModifiable true;\n",
+      "\n",
+      "functions\n",
+      "{\n",
+      "\tforces_object\n",
+      "\t{\n",
+      "\t    type forces;\n",
+      "\t    libs (\"libforces.so\");\n",
+      "\n",
+      "\t    enabled true;\n",
+      "\n",
+      "\t    writeControl   timeStep;\n",
+      "\t    writeInterval  $endTime;\n",
+      "\n",
+      "\t    patches (\"aerofoil\");\n",
+      "\n",
+      "\t    p\t\tp;\n",
+      "\t    U\t\tU;\n",
+      "\t    rho\trhoInf;\n",
+      "\n",
+      "\t    //// Density only for incompressible flows\n",
+      "\t    rhoInf 1.204;\n",
+      "\t    \n",
+      "\t    //// Centre of rotation\n",
+      "\t    CofR (0 0 0);\n",
+      "\t}\n",
+      "\t\n",
+      "\tforceCoeffs1\n",
+      "\t{\n",
+      "\t    // Mandatory entries\n",
+      "\t    type            forceCoeffs;\n",
+      "\t    libs            (\"libforces.so\");\n",
+      "\t    patches         (\"aerofoil\");\n",
+      "\n",
+      "\n",
+      "\t    // Optional entries\n",
+      "\n",
+      "\t    // Field names\n",
+      "\t    p\t\tp;\n",
+      "\t    U\t\tU;\n",
+      "\t    rho\trhoInf;\n",
+      "\t    \n",
+      "\t    ////Density only for incompressible flows\n",
+      "\t    rhoInf 1.204;\n",
+      "\n",
+      "\t    // Reference pressure [Pa]\n",
+      "\t    pRef            0;\n",
+      "\n",
+      "\t    // Include porosity effects?\n",
+      "\t    porosity        no;\n",
+      "\n",
+      "\t    // Store and write volume field representations of forces and moments\n",
+      "\t    writeFields     yes;\n",
+      "\t    writeControl    timeStep;\n",
+      "\t    writeInterval   $endTime;\n",
+      "\n",
+      "\t    // Centre of rotation for moment calculations\n",
+      "\t    CofR            (0 0 0);\n",
+      "\n",
+      "\t    // Lift direction\n",
+      "\t    liftDir\t     (-0.20999398925280716 0.9777026769308202 0);\n",
+      "\n",
+      "\t    // Drag direction\n",
+      "\t    dragDir\t     (0.9777026769308202 0.20999398925280716 0);\n",
+      "\n",
+      "\t    // Pitch axis\n",
+      "\t    pitchAxis       (0 0 1);\n",
+      "\n",
+      "\t    // Freestream velocity magnitude [m/s]\n",
+      "\t    magUInf         $Uinf;\n",
+      "\n",
+      "\t    // Reference length [m]\n",
+      "\t    lRef            1;\n",
+      "\n",
+      "\t    // Reference area [m2]\n",
+      "\t    Aref            1;\n",
+      "\n",
+      "\t    // Spatial data binning\n",
+      "\t    // - extents given by the bounds of the input geometry\n",
+      "\t    /*binData\n",
+      "\t    {\n",
+      "\t\tnBin        20;\n",
+      "\t\tdirection   (1 0 0);\n",
+      "\t\tcumulative  yes;\n",
+      "\t    }*/\n",
+      "\t}\n",
+      "\n",
+      "    momErr\n",
+      "    {\n",
+      "        type            momentumError;\n",
+      "        libs            (fieldFunctionObjects);\n",
+      "        executeControl  writeTime;\n",
+      "        writeControl    writeTime;\n",
+      "    }\n",
+      "\n",
+      "    contErr\n",
+      "    {\n",
+      "        type            div;\n",
+      "        libs            (fieldFunctionObjects);\n",
+      "        field           phi;\n",
+      "        executeControl  writeTime;\n",
+      "        writeControl    writeTime;\n",
+      "    }\n",
+      "\n",
+      "\n",
+      "    turbulenceFields1\n",
+      "    {\n",
+      "        type            turbulenceFields;\n",
+      "        libs            (fieldFunctionObjects);\n",
+      "        fields\n",
+      "        (\n",
+      "            R\n",
+      "            I\n",
+      "            L\n",
+      "            k\n",
+      "            epsilon\n",
+      "            omega\n",
+      "            nut\n",
+      "            nuEff\n",
+      "            devReff\n",
+      "        );\n",
+      "\n",
+      "        executeControl  writeTime;\n",
+      "        writeControl    writeTime;\n",
+      "    }\n",
+      "\n",
+      "   yplus\n",
+      "   {\n",
+      "\ttype\t\tyPlus;\n",
+      "\tlibs\t\t(fieldFunctionObjects);\n",
+      "\n",
+      "\tenabled\ttrue;\n",
+      "\texecuteControl\twriteTime;\n",
+      "\twriteControl\twriteTime;\n",
+      "   }\n",
+      "   \n",
+      "   wallshearstress\n",
+      "   {\n",
+      "   \ttype\t\twallShearStress;\n",
+      "   \tlibs\t\t(fieldFunctionObjects);\n",
+      "   \t\n",
+      "   \texecuteControl\twriteTime;\n",
+      "   \twriteControl\twriteTime;\n",
+      "   }\n",
+      "   \n",
+      "   mach\n",
+      "   {\n",
+      "   \ttype\t\tMachNo;\n",
+      "   \tlibs\t\t(fieldFunctionObjects);\n",
+      "   \t\n",
+      "   \texecuteControl\twriteTime;\n",
+      "   \twriteControl\twriteTime;\n",
+      "   }\n",
+      "}\n",
+      "\n",
+      "\n",
+      "// ************************************************************************* //\n",
+      "\n"
+     ]
+    }
+   ],
    "source": [
     "print(\"--- system/controlDict preview ---\")\n",
     "print(read_text(SIM_DIR / \"system\" / \"controlDict\", max_bytes=6_000))\n"
@@ -1157,7 +1910,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 21,
    "id": "940c0923",
    "metadata": {
     "execution": {
@@ -1167,7 +1920,70 @@
      "shell.execute_reply": "2026-07-20T19:11:17.784234Z"
     }
    },
-   "outputs": [],
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "--- constant/turbulenceProperties preview ---\n",
+      "/*--------------------------------*- C++ -*----------------------------------*\\\n",
+      "| =========                 |                                                 |\n",
+      "| \\\\      /  F ield         | OpenFOAM: The Open Source CFD Toolbox           |\n",
+      "|  \\\\    /   O peration     | Version:  v2112                                 |\n",
+      "|   \\\\  /    A nd           | Website:  www.openfoam.com                      |\n",
+      "|    \\\\/     M anipulation  |                                                 |\n",
+      "\\*---------------------------------------------------------------------------*/\n",
+      "FoamFile\n",
+      "{\n",
+      "    version     2.0;\n",
+      "    format      ascii;\n",
+      "    class       dictionary;\n",
+      "    object      turbulenceProperties;\n",
+      "}\n",
+      "// * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * //\n",
+      "\n",
+      "simulationType          RAS;\n",
+      "\n",
+      "RAS\n",
+      "{\n",
+      "\tRASModel\tkOmegaSST;\n",
+      "\n",
+      "    turbulence          on;\n",
+      "\n",
+      "    printCoeffs         on;\n",
+      "}\n",
+      "\n",
+      "\n",
+      "// ************************************************************************* //\n",
+      "\n",
+      "\n",
+      "--- constant/transportProperties preview ---\n",
+      "/*--------------------------------*- C++ -*----------------------------------*\\\n",
+      "| =========                 |                                                 |\n",
+      "| \\\\      /  F ield         | OpenFOAM: The Open Source CFD Toolbox           |\n",
+      "|  \\\\    /   O peration     | Version:  v2112                                 |\n",
+      "|   \\\\  /    A nd           | Website:  www.openfoam.com                      |\n",
+      "|    \\\\/     M anipulation  |                                                 |\n",
+      "\\*---------------------------------------------------------------------------*/\n",
+      "FoamFile\n",
+      "{\n",
+      "    version     2.0;\n",
+      "    format      ascii;\n",
+      "    class       dictionary;\n",
+      "    object      transportProperties;\n",
+      "}\n",
+      "// * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * //\n",
+      "\n",
+      "transportModel  Newtonian;\n",
+      "\n",
+      "nu              1.56e-5;\n",
+      "\n",
+      "\n",
+      "// ************************************************************************* //\n",
+      "\n"
+     ]
+    }
+   ],
    "source": [
     "print(\"--- constant/turbulenceProperties preview ---\")\n",
     "print(read_text(SIM_DIR / \"constant\" / \"turbulenceProperties\", max_bytes=3_000))\n",
@@ -1177,7 +1993,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 22,
    "id": "3992406a",
    "metadata": {
     "execution": {
@@ -1187,7 +2003,147 @@
      "shell.execute_reply": "2026-07-20T19:11:17.787486Z"
     }
    },
-   "outputs": [],
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "--- final U field header and first values ---\n",
+      "/*--------------------------------*- C++ -*----------------------------------*\\\n",
+      "| =========                 |                                                 |\n",
+      "| \\\\      /  F ield         | OpenFOAM: The Open Source CFD Toolbox           |\n",
+      "|  \\\\    /   O peration     | Version:  2112                                  |\n",
+      "|   \\\\  /    A nd           | Website:  www.openfoam.com                      |\n",
+      "|    \\\\/     M anipulation  |                                                 |\n",
+      "\\*---------------------------------------------------------------------------*/\n",
+      "FoamFile\n",
+      "{\n",
+      "    version     2.0;\n",
+      "    format      ascii;\n",
+      "    arch        \"LSB;label=32;scalar=64\";\n",
+      "    class       volVectorField;\n",
+      "    location    \"40000\";\n",
+      "    object      U;\n",
+      "}\n",
+      "// * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * //\n",
+      "\n",
+      "dimensions      [0 1 -1 0 0 0 0];\n",
+      "\n",
+      "internalField   nonuniform List<vector> \n",
+      "258984\n",
+      "(\n",
+      "(30.9771 6.60589 7.13234e-22)\n",
+      "(30.9449 6.59872 0)\n",
+      "(30.9331 6.59577 -4.37143e-32)\n",
+      "(30.924 6.59278 2.99688e-21)\n",
+      "(30.9226 6.5912 -2.39008e-21)\n",
+      "(30.9275 6.59069 -2.40176e-32)\n",
+      "(30.9387 6.59126 2.71343e-22)\n",
+      "(30.9558 6.59283 -4.89239e-22)\n",
+      "(30.9785 6.59534 -1.07139e-21)\n",
+      "(31.0064 6.59871 -2.02935e-21)\n",
+      "(31.0395 6.60292 2.80758e-21)\n",
+      "(31.0775 6.60792 -3.88218e-21)\n",
+      "(31.1205 6.61368 -2.44941e-21)\n",
+      "(31.1682 6.62018 3.2547e-21)\n",
+      "(31.2205 6.62735 -1.00218e-31)\n",
+      "(31.2768 6.63505 6.6084e-21)\n",
+      "(31.3352 6.64288 1.06892e-20)\n",
+      "(31.3911 6.64978 2.97873e-31)\n",
+      "(31.4321 6.65311 -1.39049e-29)\n",
+      "(31.4453 6.65005 0)\n",
+      "(31.4446 6.64357 4.23594e-28)\n",
+      "(31.446 6.63704 6.78282e-28)\n",
+      "(31.4474 6.63002 8.20923e-19)\n",
+      "(31.4489 6.62246 9.06432e-19)\n",
+      "(31.4506 6.61435 1.24796e-22)\n",
+      "(31.4523 6.60564 -2.22734e-18)\n",
+      "(31.4542 6.5963 6.06896e-19)\n",
+      "(31.4563 6.58629 6.63523e-19)\n",
+      "(31.4585 6.57555 0)\n",
+      "(31.4608 6.56405 -1.32388e-22)\n",
+      "(31.4633 6.55171 0)\n",
+      "(31.4659 6.5385 5.05547e-19)\n",
+      "(31.4687 6.52436 0)\n",
+      "(31.4717 6.50922 0)\n",
+      "(31.4749 6.49301 -7.48502e-19)\n",
+      "(31.4782 6.47567 -1.71535e-27)\n",
+      "(31.4818 6.45714 -9.07347e-23)\n",
+      "(31.4855 6.43732 0)\n",
+      "(31.4895 6.41614 -1.20768e-22)\n",
+      "(31.4937 6.39353 8.10999e-19)\n",
+      "(31.498 6.36938 -9.57919e-19)\n",
+      "(31.5026 6.34362 -4.55391e-27)\n",
+      "(31.5074 6.31615 -1.34617e-18)\n",
+      "(31.5124 6.28687 -7.9968e-19)\n",
+      "(31.5176 6.25568 9.51028e-19)\n",
+      "(31.523 6.22247 4.54929e-27)\n",
+      "(31.5286 6.18715 -6.73109e-19)\n",
+      "(31.5343 6.14959 -8.00407e-19)\n",
+      "(31.5402 6.1097 -7.64645e-27)\n",
+      "(31.5463 6.06735 -1.13053e-18)\n",
+      "(31.5524 6.02244 -1.07685e-26)\n",
+      "(31.5587 5.97485 1.11139e-26)\n",
+      "(31.565 5.92447 9.41686e-19)\n",
+      "(31.5712 5.87119 -1.55796e-26)\n",
+      "(31.5775 5.81489 2.10475e-26)\n",
+      "(31.5836 5.75548 -7.75313e-19)\n",
+      "(31.5896 5.69284 -9.12881e-19)\n",
+      "(31.5953 5.62689 -1.07297e-18)\n",
+      "(31.6007 5.55753 0)\n",
+      "(31.6057 5.48468 1.47569e-18)\n",
+      "(31.6103 5.40826 8.63075e-19)\n",
+      "(31.6142 5.32822 1.00778e-18)\n",
+      "(31.6174 5.2445 0)\n",
+      "(31.6198 5.15706 -1.36747e-18)\n",
+      "(31.6213 5.06586 -7.94401e-19)\n",
+      "(31.6216 4.9709 -5.20441e-26)\n",
+      "(31.6208 4.87218 0)\n",
+      "(31.6185 4.7697 -1.23312e-18)\n",
+      "(31.6146 4.66349 0)\n",
+      "(31.6091 4.5536 0)\n",
+      "(31.6018 4.44011 -1.88511e-18)\n",
+      "(31.5928 4.3231 -1.08226e-18)\n",
+      "(31.5821 4.20267 -1.24049e-18)\n",
+      "(31.5694 4.0789 -8.31105e-27)\n",
+      "(31.554 3.95181 9.2397e-26)\n",
+      "(31.5342 3.8213 0)\n",
+      "(31.5075 3.68715 -1.05318e-18)\n",
+      "(31.4716 3.54914 -6.7894e-23)\n",
+      "(31.4283 3.40744 2.7224e-18)\n",
+      "(31.3918 3.26374 -1.76631e-25)\n",
+      "(31.3952 3.1223 0)\n",
+      "(31.4677 2.98758 0)\n",
+      "(31.5514 2.85369 -2.75739e-25)\n",
+      "(31.4104 2.69109 2.94594e-26)\n",
+      "(30.7887 2.4651 -1.11872e-20)\n",
+      "(29.8417 2.2057 5.89645e-21)\n",
+      "(28.7783 1.94455 0)\n",
+      "(27.654 1.68842 -8.75387e-28)\n",
+      "(26.4758 1.43935 -8.61442e-26)\n",
+      "(25.2452 1.19962 -7.57069e-28)\n",
+      "(23.9674 0.971889 1.52416e-21)\n",
+      "(22.6519 0.758733 7.17327e-28)\n",
+      "(21.3102 0.562214 1.57519e-21)\n",
+      "(19.9537 0.383784 0)\n",
+      "(18.5935 0.224254 0)\n",
+      "(17.2397 0.0838885 0)\n",
+      "(15.9016 -0.0376447 -9.34507e-22)\n",
+      "(14.5875 -0.141296 0)\n",
+      "(13.3044 -0.228505 -5.15522e-22)\n",
+      "(12.0583 -0.301162 5.3938e-22)\n",
+      "(10.8529 -0.361165 5.28847e-28)\n",
+      "(9.69116 -0.410397 -5.71385e-28)\n",
+      "(8.57561 -0.450505 6.06921e-22)\n",
+      "(7.50773 -0.482642 3.10611e-22)\n",
+      "(6.48864 -0.50726 -3.15733e-22)\n",
+      "(5.52035 -0.524054 6.37224e-28)\n",
+      "(4.60703 -0.532391 -5.47e-28)\n",
+      "(3.75636 -0.531905 2.98549e-22)\n",
+      "(2.9803 -\n"
+     ]
+    }
+   ],
    "source": [
     "print(\"--- final U field header and first values ---\")\n",
     "print(read_text(SIM_DIR / \"40000\" / \"U.gz\", max_bytes=4_000))\n"
@@ -1215,4 +2171,4 @@
  },
  "nbformat": 4,
  "nbformat_minor": 5
-}
\ No newline at end of file
+}
diff --git a/notebooks/explore_test_run.ipynb b/notebooks/explore_test_run.ipynb
new file mode 100644
index 0000000..227c444
--- /dev/null
+++ b/notebooks/explore_test_run.ipynb
@@ -0,0 +1,1152 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "id": "be7a6c7e",
+   "metadata": {},
+   "source": [
+    "# Explore a completed training run\n",
+    "\n",
+    "Use this notebook to sanity-check the artifacts written by `airfrans-frontier train`: final metrics, training curves, split manifest, normalization stats, checkpoint contents, and reloaded-model predictions. It reads existing artifacts only.\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "23b531cc",
+   "metadata": {},
+   "source": [
+    "## Setup\n",
+    "\n",
+    "Run from the repository root or from `notebooks/`. Select the repository `.venv` kernel. If imports fail, run `uv sync --dev` from the repository root and restart the kernel.\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "id": "18ae45d0",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "python: /home/aaron/data/airfrans/.venv/bin/python\n",
+      "repo: /home/aaron/data/airfrans\n"
+     ]
+    }
+   ],
+   "source": [
+    "from __future__ import annotations\n",
+    "\n",
+    "import json\n",
+    "import os\n",
+    "import sys\n",
+    "from pathlib import Path\n",
+    "\n",
+    "for path in list(sys.path):\n",
+    "    if \"swactor-mvp/pydeps\" in path:\n",
+    "        sys.path.remove(path)\n",
+    "if \"PYTHONPATH\" in os.environ:\n",
+    "    os.environ[\"PYTHONPATH\"] = os.pathsep.join(\n",
+    "        entry for entry in os.environ[\"PYTHONPATH\"].split(os.pathsep) if \"swactor-mvp/pydeps\" not in entry\n",
+    "    )\n",
+    "\n",
+    "print(f\"python: {sys.executable}\")\n",
+    "\n",
+    "try:\n",
+    "    import matplotlib.pyplot as plt\n",
+    "    import numpy as np\n",
+    "    import torch\n",
+    "except (ImportError, ModuleNotFoundError) as exc:\n",
+    "    raise ImportError(\n",
+    "        f\"Required notebook dependencies are not importable in this kernel: {sys.executable}. \"\n",
+    "        \"From the repository root, run `uv sync --dev`, then restart the notebook kernel.\"\n",
+    "    ) from exc\n",
+    "\n",
+    "\n",
+    "def find_repo_root(start: Path) -> Path:\n",
+    "    for candidate in (start, *start.parents):\n",
+    "        if (candidate / \"pyproject.toml\").exists() and (candidate / \"src\" / \"airfrans_frontier\").exists():\n",
+    "            return candidate\n",
+    "    raise RuntimeError(f\"Could not find repository root from {start}\")\n",
+    "\n",
+    "\n",
+    "REPO_ROOT = find_repo_root(Path.cwd())\n",
+    "SRC_DIR = REPO_ROOT / \"src\"\n",
+    "if str(SRC_DIR) not in sys.path:\n",
+    "    sys.path.insert(0, str(SRC_DIR))\n",
+    "\n",
+    "from airfrans_frontier.models.mlp import PointwiseMLP\n",
+    "\n",
+    "print(f\"repo: {REPO_ROOT}\")\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "1388d69e",
+   "metadata": {},
+   "source": [
+    "## Select a run\n",
+    "\n",
+    "By default this picks the newest directory under `artifacts/runs`. Override `RUN_DIR` manually if you want an older run.\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "id": "fd3c05a7",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "selected run: artifacts/runs/20260721T082353Z_mlp_tiny\n",
+      "available runs:\n",
+      "  artifacts/runs/20260721T082151Z_mlp_tiny\n",
+      "* artifacts/runs/20260721T082353Z_mlp_tiny\n"
+     ]
+    }
+   ],
+   "source": [
+    "RUNS_DIR = REPO_ROOT / \"artifacts\" / \"runs\"\n",
+    "run_dirs = sorted([path for path in RUNS_DIR.glob(\"*\") if path.is_dir()], key=lambda path: path.stat().st_mtime)\n",
+    "if not run_dirs:\n",
+    "    raise FileNotFoundError(f\"No training runs found under {RUNS_DIR}\")\n",
+    "\n",
+    "RUN_DIR = run_dirs[-1]\n",
+    "print(f\"selected run: {RUN_DIR.relative_to(REPO_ROOT)}\")\n",
+    "print(\"available runs:\")\n",
+    "for path in run_dirs:\n",
+    "    marker = \"*\" if path == RUN_DIR else \" \"\n",
+    "    print(f\"{marker} {path.relative_to(REPO_ROOT)}\")\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "id": "e7e15a27",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "config.toml                 438 bytes\n",
+      "split_manifest.json         153 bytes\n",
+      "normalization.json          733 bytes\n",
+      "metrics.jsonl               725 bytes\n",
+      "final_metrics.json        1,074 bytes\n",
+      "checkpoint.pt           623,285 bytes\n"
+     ]
+    }
+   ],
+   "source": [
+    "expected_files = [\n",
+    "    \"config.toml\",\n",
+    "    \"split_manifest.json\",\n",
+    "    \"normalization.json\",\n",
+    "    \"metrics.jsonl\",\n",
+    "    \"final_metrics.json\",\n",
+    "    \"checkpoint.pt\",\n",
+    "]\n",
+    "missing = [name for name in expected_files if not (RUN_DIR / name).exists()]\n",
+    "if missing:\n",
+    "    raise FileNotFoundError(f\"Run is missing expected artifacts: {missing}\")\n",
+    "\n",
+    "for name in expected_files:\n",
+    "    path = RUN_DIR / name\n",
+    "    print(f\"{name:20} {path.stat().st_size:>10,} bytes\")\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "da64dacb",
+   "metadata": {},
+   "source": [
+    "## Final metrics\n",
+    "\n",
+    "This is the end-of-run summary written by the training loop. The key checks are: train loss dropped from the initial loss, validation/test losses are finite, and device/GPU fields match the intended hardware.\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "id": "cb352094",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "{'device': 'cuda:0',\n",
+       " 'elapsed_seconds': 1.7905638560005173,\n",
+       " 'gpu_memory_peak_allocated_mb': 17,\n",
+       " 'gpu_memory_total_mb': 3717,\n",
+       " 'gpu_name': 'NVIDIA T550 Laptop GPU',\n",
+       " 'initial_train_loss': 1.0026862990200476,\n",
+       " 'parameter_count': 50820,\n",
+       " 'points_per_case': 128,\n",
+       " 'steps': 500,\n",
+       " 'test_cases': 1,\n",
+       " 'test_loss': 0.00022942002189996227,\n",
+       " 'test_mse_per_channel': {'pressure': 0.000408856492614153,\n",
+       "  'turbulent_viscosity': 0.00035512470154015644,\n",
+       "  'velocity_x': 5.778009158681787e-05,\n",
+       "  'velocity_y': 9.591880185872174e-05},\n",
+       " 'train_cases': 4,\n",
+       " 'train_loss': 0.0001373240501380092,\n",
+       " 'train_mse_per_channel': {'pressure': 0.00023612300953799542,\n",
+       "  'turbulent_viscosity': 0.00018737132594797248,\n",
+       "  'velocity_x': 5.6441453127828415e-05,\n",
+       "  'velocity_y': 6.936041193824046e-05},\n",
+       " 'val_cases': 1,\n",
+       " 'val_loss': 0.0007143189445776568,\n",
+       " 'val_mse_per_channel': {'pressure': 0.0012492708046041544,\n",
+       "  'turbulent_viscosity': 0.0010587173853462636,\n",
+       "  'velocity_x': 0.00028163288487499037,\n",
+       "  'velocity_y': 0.0002676547034852188}}"
+      ]
+     },
+     "execution_count": 4,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "final_metrics = json.loads((RUN_DIR / \"final_metrics.json\").read_text())\n",
+    "final_metrics\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "id": "42010827",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "initial_train_loss: 1.00269\n",
+      "train_loss:         0.000137324\n",
+      "val_loss:           0.000714319\n",
+      "test_loss:          0.00022942\n",
+      "loss improvement:   7301.6x\n",
+      "device:             cuda:0\n",
+      "gpu:                NVIDIA T550 Laptop GPU\n",
+      "parameters:         50,820\n",
+      "basic metric checks: ok\n"
+     ]
+    }
+   ],
+   "source": [
+    "initial = final_metrics[\"initial_train_loss\"]\n",
+    "train = final_metrics[\"train_loss\"]\n",
+    "val = final_metrics.get(\"val_loss\")\n",
+    "test = final_metrics.get(\"test_loss\")\n",
+    "\n",
+    "print(f\"initial_train_loss: {initial:.6g}\")\n",
+    "print(f\"train_loss:         {train:.6g}\")\n",
+    "print(f\"val_loss:           {val:.6g}\" if val is not None else \"val_loss:           None\")\n",
+    "print(f\"test_loss:          {test:.6g}\" if test is not None else \"test_loss:          None\")\n",
+    "print(f\"loss improvement:   {initial / train:.1f}x\")\n",
+    "print(f\"device:             {final_metrics.get('device')}\")\n",
+    "print(f\"gpu:                {final_metrics.get('gpu_name')}\")\n",
+    "print(f\"parameters:         {final_metrics.get('parameter_count'):,}\")\n",
+    "\n",
+    "assert np.isfinite(train), \"train loss is not finite\"\n",
+    "assert train < initial, \"train loss did not improve\"\n",
+    "if val is not None:\n",
+    "    assert np.isfinite(val), \"val loss is not finite\"\n",
+    "if test is not None:\n",
+    "    assert np.isfinite(test), \"test loss is not finite\"\n",
+    "print(\"basic metric checks: ok\")\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "e4cf0126",
+   "metadata": {},
+   "source": [
+    "## Training curves\n",
+    "\n",
+    "`metrics.jsonl` has one JSON row per log interval. A sanity run should show train loss falling; validation loss should be finite and usually trend down on this tiny baseline.\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "id": "b4d36a9d",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "{'elapsed_seconds': 0.0, 'step': 0, 'train_loss': 1.0026862990200476, 'val_loss': 0.8999216645838499}\n",
+      "{'elapsed_seconds': 0.6329330740009027, 'step': 100, 'train_loss': 0.005394324883408844, 'val_loss': 0.008841235591800927}\n",
+      "{'elapsed_seconds': 0.9306512370003475, 'step': 200, 'train_loss': 0.0008050906900035648, 'val_loss': 0.002610322449175867}\n",
+      "{'elapsed_seconds': 1.130966939001155, 'step': 300, 'train_loss': 0.00029600711922912065, 'val_loss': 0.0015054571447265593}\n",
+      "{'elapsed_seconds': 1.4691060960012692, 'step': 400, 'train_loss': 0.00019286412112246845, 'val_loss': 0.0012647405838277085}\n",
+      "{'elapsed_seconds': 1.7875807079999504, 'step': 500, 'train_loss': 0.0001373240501380092, 'val_loss': 0.0007143189445776568}\n"
+     ]
+    }
+   ],
+   "source": [
+    "metric_rows = [json.loads(line) for line in (RUN_DIR / \"metrics.jsonl\").read_text().splitlines() if line.strip()]\n",
+    "steps = np.array([row[\"step\"] for row in metric_rows])\n",
+    "train_loss = np.array([row[\"train_loss\"] for row in metric_rows], dtype=float)\n",
+    "val_loss = np.array([row[\"val_loss\"] if row[\"val_loss\"] is not None else np.nan for row in metric_rows], dtype=float)\n",
+    "\n",
+    "for row in metric_rows:\n",
+    "    print(row)\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "id": "c678379b",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 800x450 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "fig, ax = plt.subplots(figsize=(8, 4.5))\n",
+    "ax.plot(steps, train_loss, marker=\"o\", label=\"train\")\n",
+    "if np.isfinite(val_loss).any():\n",
+    "    ax.plot(steps, val_loss, marker=\"o\", label=\"val\")\n",
+    "ax.set_yscale(\"log\")\n",
+    "ax.set_xlabel(\"optimization step\")\n",
+    "ax.set_ylabel(\"normalized MSE\")\n",
+    "ax.set_title(RUN_DIR.name)\n",
+    "ax.grid(True, which=\"both\", alpha=0.25)\n",
+    "ax.legend()\n",
+    "fig.tight_layout()\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "804c8844",
+   "metadata": {},
+   "source": [
+    "## Split and normalization\n",
+    "\n",
+    "The split manifest tells you exactly which `.npz` cases produced train/validation/test metrics. Normalization is computed from the train split and stored so the checkpoint can be used later.\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "id": "d993f11f",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[run]\n",
+      "name = \"mlp_tiny\"\n",
+      "seed = 0\n",
+      "artifact_dir = \"artifacts/runs\"\n",
+      "\n",
+      "[data]\n",
+      "root = \"data/processed/minimal\"\n",
+      "train_cases = 4\n",
+      "val_cases = 1\n",
+      "test_cases = 1\n",
+      "points_per_case = 128\n",
+      "batch_size = 128\n",
+      "\n",
+      "[model]\n",
+      "type = \"mlp\"\n",
+      "hidden_width = 128\n",
+      "depth = 4\n",
+      "activation = \"gelu\"\n",
+      "\n",
+      "[optim]\n",
+      "lr = 0.001\n",
+      "weight_decay = 0.0\n",
+      "steps = 500\n",
+      "log_interval = 100\n",
+      "\n",
+      "[device]\n",
+      "type = \"cuda\"\n",
+      "allow_cpu_fallback = false\n",
+      "benchmark_kernels = true\n",
+      "\n",
+      "[loss]\n",
+      "type = \"normalized_mse\"\n",
+      "\n",
+      "split manifest:\n",
+      "{\n",
+      "  \"test_ids\": [\n",
+      "    \"case_03\"\n",
+      "  ],\n",
+      "  \"train_ids\": [\n",
+      "    \"case_04\",\n",
+      "    \"case_02\",\n",
+      "    \"case_01\",\n",
+      "    \"case_00\"\n",
+      "  ],\n",
+      "  \"val_ids\": [\n",
+      "    \"case_05\"\n",
+      "  ]\n",
+      "}\n",
+      "normalization target names: ['velocity_x', 'velocity_y', 'pressure', 'turbulent_viscosity']\n"
+     ]
+    }
+   ],
+   "source": [
+    "split_manifest = json.loads((RUN_DIR / \"split_manifest.json\").read_text())\n",
+    "normalization = json.loads((RUN_DIR / \"normalization.json\").read_text())\n",
+    "config_text = (RUN_DIR / \"config.toml\").read_text()\n",
+    "\n",
+    "print(config_text)\n",
+    "print(\"split manifest:\")\n",
+    "print(json.dumps(split_manifest, indent=2))\n",
+    "print(\"normalization target names:\", normalization[\"target_names\"])\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "id": "b16e05bc",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 1200x400 with 2 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "feature_mean = np.array(normalization[\"feature_mean\"], dtype=np.float32)\n",
+    "feature_std = np.array(normalization[\"feature_std\"], dtype=np.float32)\n",
+    "target_mean = np.array(normalization[\"target_mean\"], dtype=np.float32)\n",
+    "target_std = np.array(normalization[\"target_std\"], dtype=np.float32)\n",
+    "\n",
+    "fig, axes = plt.subplots(1, 2, figsize=(12, 4))\n",
+    "axes[0].bar(normalization[\"feature_names\"], feature_mean)\n",
+    "axes[0].set_title(\"feature means\")\n",
+    "axes[0].tick_params(axis=\"x\", rotation=45)\n",
+    "axes[0].grid(axis=\"y\", alpha=0.25)\n",
+    "\n",
+    "axes[1].bar(normalization[\"target_names\"], target_std)\n",
+    "axes[1].set_title(\"target stds\")\n",
+    "axes[1].tick_params(axis=\"x\", rotation=45)\n",
+    "axes[1].grid(axis=\"y\", alpha=0.25)\n",
+    "fig.tight_layout()\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "32f03cf3",
+   "metadata": {},
+   "source": [
+    "## What the input features mean\n",
+    "\n",
+    "Each row is one sampled point from one case. The MLP sees five scalar inputs for that point:\n",
+    "\n",
+    "| feature | meaning | varies per |\n",
+    "|---|---|---|\n",
+    "| `re_norm` | normalized Reynolds-number-like case condition; constant across all points in one case | case |\n",
+    "| `aoa_norm` | normalized angle-of-attack-like case condition; constant across all points in one case | case |\n",
+    "| `x` | point x-coordinate in the local 2D domain | point |\n",
+    "| `y` | point y-coordinate in the local 2D domain | point |\n",
+    "| `sdf` | signed-distance-like geometry feature; here computed as radial distance from a radius-0.5 body proxy, so negative means inside/near the proxy and positive means farther away | point |\n",
+    "\n",
+    "For the current `data/processed/minimal` sanity dataset, these are deliberately compact synthetic features from the training-framework spec, not the full AirfRANS graph feature set. The target formulas used by the synthetic sanity data are simple functions of these columns, which is why the tiny MLP should learn them quickly."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "id": "c9b3b2fe",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "re_norm  - case-level normalized Reynolds-number-like condition\n",
+      "aoa_norm - case-level normalized angle-of-attack-like condition\n",
+      "x        - sampled point x-coordinate\n",
+      "y        - sampled point y-coordinate\n",
+      "sdf      - signed-distance-like geometry signal; negative near/inside radius-0.5 proxy\n"
+     ]
+    }
+   ],
+   "source": [
+    "feature_descriptions = {\n",
+    "    \"re_norm\": \"case-level normalized Reynolds-number-like condition\",\n",
+    "    \"aoa_norm\": \"case-level normalized angle-of-attack-like condition\",\n",
+    "    \"x\": \"sampled point x-coordinate\",\n",
+    "    \"y\": \"sampled point y-coordinate\",\n",
+    "    \"sdf\": \"signed-distance-like geometry signal; negative near/inside radius-0.5 proxy\",\n",
+    "}\n",
+    "\n",
+    "for name in normalization[\"feature_names\"]:\n",
+    "    print(f\"{name:8} - {feature_descriptions.get(name, 'no description recorded')}\")\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "id": "2a31f98e",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "[{'split': 'test',\n",
+       "  'case_id': 'case_03',\n",
+       "  'points': 256,\n",
+       "  're_norm_min': 0.10000000149011612,\n",
+       "  're_norm_mean': 0.10000001639127731,\n",
+       "  're_norm_max': 0.10000000149011612,\n",
+       "  'aoa_norm_min': 0.10000000149011612,\n",
+       "  'aoa_norm_mean': 0.10000001639127731,\n",
+       "  'aoa_norm_max': 0.10000000149011612,\n",
+       "  'x_min': -0.992418110370636,\n",
+       "  'x_mean': -0.028246253728866577,\n",
+       "  'x_max': 0.9915499091148376,\n",
+       "  'y_min': -0.999594509601593,\n",
+       "  'y_mean': 0.00812380388379097,\n",
+       "  'y_max': 0.9907281994819641,\n",
+       "  'sdf_min': -0.483142614364624,\n",
+       "  'sdf_mean': 0.24713799357414246,\n",
+       "  'sdf_max': 0.855535626411438},\n",
+       " {'split': 'train',\n",
+       "  'case_id': 'case_04',\n",
+       "  'points': 256,\n",
+       "  're_norm_min': 0.30000001192092896,\n",
+       "  're_norm_mean': 0.30000001192092896,\n",
+       "  're_norm_max': 0.30000001192092896,\n",
+       "  'aoa_norm_min': 0.20000000298023224,\n",
+       "  'aoa_norm_mean': 0.20000003278255463,\n",
+       "  'aoa_norm_max': 0.20000000298023224,\n",
+       "  'x_min': -0.9973810315132141,\n",
+       "  'x_mean': 0.016297580674290657,\n",
+       "  'x_max': 0.9969028830528259,\n",
+       "  'y_min': -0.9971365332603455,\n",
+       "  'y_mean': -0.03903472423553467,\n",
+       "  'y_max': 0.988767683506012,\n",
+       "  'sdf_min': -0.4517866373062134,\n",
+       "  'sdf_mean': 0.29825884103775024,\n",
+       "  'sdf_max': 0.8830513954162598},\n",
+       " {'split': 'train',\n",
+       "  'case_id': 'case_02',\n",
+       "  'points': 256,\n",
+       "  're_norm_min': -0.10000000149011612,\n",
+       "  're_norm_mean': -0.10000001639127731,\n",
+       "  're_norm_max': -0.10000000149011612,\n",
+       "  'aoa_norm_min': 0.0,\n",
+       "  'aoa_norm_mean': 0.0,\n",
+       "  'aoa_norm_max': 0.0,\n",
+       "  'x_min': -0.9979289770126343,\n",
+       "  'x_mean': 0.03891447186470032,\n",
+       "  'x_max': 0.9994633793830872,\n",
+       "  'y_min': -0.9955872893333435,\n",
+       "  'y_mean': -0.018903596326708794,\n",
+       "  'y_max': 0.9769426584243774,\n",
+       "  'sdf_min': -0.4469470679759979,\n",
+       "  'sdf_mean': 0.2548476755619049,\n",
+       "  'sdf_max': 0.8520128726959229},\n",
+       " {'split': 'train',\n",
+       "  'case_id': 'case_01',\n",
+       "  'points': 256,\n",
+       "  're_norm_min': -0.30000001192092896,\n",
+       "  're_norm_mean': -0.30000001192092896,\n",
+       "  're_norm_max': -0.30000001192092896,\n",
+       "  'aoa_norm_min': -0.10000000149011612,\n",
+       "  'aoa_norm_mean': -0.10000001639127731,\n",
+       "  'aoa_norm_max': -0.10000000149011612,\n",
+       "  'x_min': -0.9986386895179749,\n",
+       "  'x_mean': -0.039472322911024094,\n",
+       "  'x_max': 0.991798460483551,\n",
+       "  'y_min': -0.9973899126052856,\n",
+       "  'y_mean': 0.029526807367801666,\n",
+       "  'y_max': 0.998464822769165,\n",
+       "  'sdf_min': -0.4342630207538605,\n",
+       "  'sdf_mean': 0.2738206684589386,\n",
+       "  'sdf_max': 0.9004930257797241},\n",
+       " {'split': 'train',\n",
+       "  'case_id': 'case_00',\n",
+       "  'points': 256,\n",
+       "  're_norm_min': -0.5,\n",
+       "  're_norm_mean': -0.5,\n",
+       "  're_norm_max': -0.5,\n",
+       "  'aoa_norm_min': -0.20000000298023224,\n",
+       "  'aoa_norm_mean': -0.20000003278255463,\n",
+       "  'aoa_norm_max': -0.20000000298023224,\n",
+       "  'x_min': -0.9898697733879089,\n",
+       "  'x_mean': -0.004540023393929005,\n",
+       "  'x_max': 0.9964226484298706,\n",
+       "  'y_min': -0.9919538497924805,\n",
+       "  'y_mean': -0.0564127042889595,\n",
+       "  'y_max': 0.9963796734809875,\n",
+       "  'sdf_min': -0.40864098072052,\n",
+       "  'sdf_mean': 0.25872567296028137,\n",
+       "  'sdf_max': 0.8634771108627319},\n",
+       " {'split': 'val',\n",
+       "  'case_id': 'case_05',\n",
+       "  'points': 256,\n",
+       "  're_norm_min': 0.5,\n",
+       "  're_norm_mean': 0.5,\n",
+       "  're_norm_max': 0.5,\n",
+       "  'aoa_norm_min': 0.30000001192092896,\n",
+       "  'aoa_norm_mean': 0.30000001192092896,\n",
+       "  'aoa_norm_max': 0.30000001192092896,\n",
+       "  'x_min': -0.986579179763794,\n",
+       "  'x_mean': -0.012095458805561066,\n",
+       "  'x_max': 0.9900404810905457,\n",
+       "  'y_min': -0.9959282875061035,\n",
+       "  'y_mean': 0.022574514150619507,\n",
+       "  'y_max': 0.9964964985847473,\n",
+       "  'sdf_min': -0.4551549255847931,\n",
+       "  'sdf_mean': 0.25610190629959106,\n",
+       "  'sdf_max': 0.8619978427886963}]"
+      ]
+     },
+     "execution_count": 11,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "import tomllib\n",
+    "\n",
+    "config = tomllib.loads(config_text)\n",
+    "DATA_ROOT = REPO_ROOT / config[\"data\"][\"root\"]\n",
+    "\n",
+    "\n",
+    "def load_case(case_id: str) -> tuple[np.ndarray, np.ndarray]:\n",
+    "    data = np.load(DATA_ROOT / f\"{case_id}.npz\")\n",
+    "    return data[\"features\"].astype(np.float32), data[\"targets\"].astype(np.float32)\n",
+    "\n",
+    "\n",
+    "feature_rows = []\n",
+    "for split_name, ids in split_manifest.items():\n",
+    "    for case_id in ids:\n",
+    "        features, _ = load_case(case_id)\n",
+    "        row = {\"split\": split_name.replace(\"_ids\", \"\"), \"case_id\": case_id, \"points\": len(features)}\n",
+    "        for index, name in enumerate(normalization[\"feature_names\"]):\n",
+    "            values = features[:, index]\n",
+    "            row[f\"{name}_min\"] = float(values.min())\n",
+    "            row[f\"{name}_mean\"] = float(values.mean())\n",
+    "            row[f\"{name}_max\"] = float(values.max())\n",
+    "        feature_rows.append(row)\n",
+    "\n",
+    "feature_rows\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "id": "ccab3feb",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "case-level conditions for case_05:\n",
+      "re_norm  = 0.500\n",
+      "aoa_norm = 0.300\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 1150x430 with 3 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# Visualize one case's point-level features. re_norm and aoa_norm are constant for the case;\n",
+    "# x, y, and sdf vary over sampled points.\n",
+    "case_id = (split_manifest.get(\"val_ids\") or split_manifest.get(\"test_ids\") or split_manifest.get(\"train_ids\"))[0]\n",
+    "features, _ = load_case(case_id)\n",
+    "feature_index = {name: idx for idx, name in enumerate(normalization[\"feature_names\"])}\n",
+    "\n",
+    "x_values = features[:, feature_index[\"x\"]]\n",
+    "y_values = features[:, feature_index[\"y\"]]\n",
+    "sdf_values = features[:, feature_index[\"sdf\"]]\n",
+    "\n",
+    "fig, axes = plt.subplots(1, 2, figsize=(11.5, 4.3))\n",
+    "scatter = axes[0].scatter(x_values, y_values, c=sdf_values, s=18, cmap=\"coolwarm\")\n",
+    "axes[0].set_title(f\"{case_id}: sampled points colored by sdf\")\n",
+    "axes[0].set_xlabel(\"x\")\n",
+    "axes[0].set_ylabel(\"y\")\n",
+    "axes[0].set_aspect(\"equal\", adjustable=\"box\")\n",
+    "axes[0].grid(alpha=0.25)\n",
+    "fig.colorbar(scatter, ax=axes[0], label=\"sdf\")\n",
+    "\n",
+    "axes[1].hist(sdf_values, bins=30, edgecolor=\"white\")\n",
+    "axes[1].set_title(\"sdf distribution for selected case\")\n",
+    "axes[1].set_xlabel(\"sdf\")\n",
+    "axes[1].set_ylabel(\"sampled points\")\n",
+    "axes[1].grid(alpha=0.25)\n",
+    "fig.tight_layout()\n",
+    "\n",
+    "print(f\"case-level conditions for {case_id}:\")\n",
+    "print(f\"re_norm  = {features[0, feature_index['re_norm']]:.3f}\")\n",
+    "print(f\"aoa_norm = {features[0, feature_index['aoa_norm']]:.3f}\")\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "3c22f84b",
+   "metadata": {},
+   "source": [
+    "## Checkpoint contents\n",
+    "\n",
+    "The checkpoint is saved with CPU-compatible tensors. This section verifies it can be loaded, shows metadata, and reconstructs the model.\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 13,
+   "id": "ed0720ec",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "checkpoint keys:\n",
+      "  step: int\n",
+      "  model_type: str\n",
+      "  input_dim: int\n",
+      "  output_dim: int\n",
+      "  model_config: dict\n",
+      "  model_state_dict: 10 tensors\n",
+      "  optimizer_state_dict: 2 tensors\n",
+      "  normalization: dict\n",
+      "  target_names: tuple\n",
+      "  feature_names: tuple\n",
+      "  final_metrics: dict\n",
+      "\n",
+      "step: 500\n",
+      "model_type: mlp\n",
+      "input_dim: 5\n",
+      "output_dim: 4\n",
+      "feature_names: ('re_norm', 'aoa_norm', 'x', 'y', 'sdf')\n",
+      "target_names: ('velocity_x', 'velocity_y', 'pressure', 'turbulent_viscosity')\n",
+      "model_config: {'type': 'mlp', 'hidden_width': 128, 'depth': 4, 'activation': 'gelu'}\n",
+      "\n",
+      "first model tensors:\n",
+      "network.0.weight (128, 5) torch.float32\n",
+      "network.0.bias (128,) torch.float32\n",
+      "network.2.weight (128, 128) torch.float32\n",
+      "network.2.bias (128,) torch.float32\n",
+      "network.4.weight (128, 128) torch.float32\n",
+      "network.4.bias (128,) torch.float32\n",
+      "network.6.weight (128, 128) torch.float32\n",
+      "network.6.bias (128,) torch.float32\n"
+     ]
+    }
+   ],
+   "source": [
+    "checkpoint = torch.load(RUN_DIR / \"checkpoint.pt\", map_location=\"cpu\")\n",
+    "\n",
+    "print(\"checkpoint keys:\")\n",
+    "for key, value in checkpoint.items():\n",
+    "    if key.endswith(\"state_dict\"):\n",
+    "        print(f\"  {key}: {len(value)} tensors\")\n",
+    "    else:\n",
+    "        print(f\"  {key}: {type(value).__name__}\")\n",
+    "\n",
+    "print()\n",
+    "print(\"step:\", checkpoint[\"step\"])\n",
+    "print(\"model_type:\", checkpoint[\"model_type\"])\n",
+    "print(\"input_dim:\", checkpoint[\"input_dim\"])\n",
+    "print(\"output_dim:\", checkpoint[\"output_dim\"])\n",
+    "print(\"feature_names:\", checkpoint[\"feature_names\"])\n",
+    "print(\"target_names:\", checkpoint[\"target_names\"])\n",
+    "print(\"model_config:\", checkpoint[\"model_config\"])\n",
+    "\n",
+    "print()\n",
+    "print(\"first model tensors:\")\n",
+    "for name, tensor in list(checkpoint[\"model_state_dict\"].items())[:8]:\n",
+    "    print(name, tuple(tensor.shape), tensor.dtype)\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "94b4e5d8",
+   "metadata": {},
+   "source": [
+    "## MLP architecture\n",
+    "\n",
+    "The training loop derives `input_dim` from normalized feature columns and `output_dim` from normalized target columns. This cell expands that into the concrete network shape, layer table, and parameter count so the abstraction is visible at a glance."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 14,
+   "id": "e48801e5",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "MLP shape at a glance\n",
+      "----------------------\n",
+      "inputs (5):  ['re_norm', 'aoa_norm', 'x', 'y', 'sdf']\n",
+      "outputs (4): ['velocity_x', 'velocity_y', 'pressure', 'turbulent_viscosity']\n",
+      "architecture: 5 -> 128 -> 128 -> 128 -> 128 -> 4\n",
+      "activation after hidden layers: gelu\n",
+      "hidden layers: 4\n",
+      "hidden width: 128\n",
+      "\n",
+      "layer table:\n",
+      " 0: Linear      5 -> 128  params=768\n",
+      " 1: GELU          ->      params=0\n",
+      " 2: Linear    128 -> 128  params=16512\n",
+      " 3: GELU          ->      params=0\n",
+      " 4: Linear    128 -> 128  params=16512\n",
+      " 5: GELU          ->      params=0\n",
+      " 6: Linear    128 -> 128  params=16512\n",
+      " 7: GELU          ->      params=0\n",
+      " 8: Linear    128 -> 4    params=516\n",
+      "\n",
+      "total parameters: 50,820\n"
+     ]
+    }
+   ],
+   "source": [
+    "input_names = list(checkpoint[\"feature_names\"])\n",
+    "target_names = list(checkpoint[\"target_names\"])\n",
+    "model_config = checkpoint[\"model_config\"]\n",
+    "\n",
+    "print(\"MLP shape at a glance\")\n",
+    "print(\"----------------------\")\n",
+    "print(f\"inputs ({len(input_names)}):  {input_names}\")\n",
+    "print(f\"outputs ({len(target_names)}): {target_names}\")\n",
+    "print(\n",
+    "    \"architecture: \"\n",
+    "    f\"{checkpoint['input_dim']} -> \"\n",
+    "    + \" -> \".join([str(model_config[\"hidden_width\"])] * model_config[\"depth\"])\n",
+    "    + f\" -> {checkpoint['output_dim']}\"\n",
+    ")\n",
+    "print(f\"activation after hidden layers: {model_config['activation']}\")\n",
+    "print(f\"hidden layers: {model_config['depth']}\")\n",
+    "print(f\"hidden width: {model_config['hidden_width']}\")\n",
+    "print()\n",
+    "\n",
+    "model = PointwiseMLP(\n",
+    "    input_dim=checkpoint[\"input_dim\"],\n",
+    "    output_dim=checkpoint[\"output_dim\"],\n",
+    "    hidden_width=model_config[\"hidden_width\"],\n",
+    "    depth=model_config[\"depth\"],\n",
+    "    activation=model_config[\"activation\"],\n",
+    ")\n",
+    "model.load_state_dict(checkpoint[\"model_state_dict\"])\n",
+    "model.eval()\n",
+    "\n",
+    "layer_rows = []\n",
+    "for index, layer in enumerate(model.network):\n",
+    "    row = {\"index\": index, \"type\": layer.__class__.__name__}\n",
+    "    if isinstance(layer, torch.nn.Linear):\n",
+    "        row[\"in_features\"] = layer.in_features\n",
+    "        row[\"out_features\"] = layer.out_features\n",
+    "        row[\"parameters\"] = layer.weight.numel() + layer.bias.numel()\n",
+    "    else:\n",
+    "        row[\"in_features\"] = \"\"\n",
+    "        row[\"out_features\"] = \"\"\n",
+    "        row[\"parameters\"] = 0\n",
+    "    layer_rows.append(row)\n",
+    "\n",
+    "print(\"layer table:\")\n",
+    "for row in layer_rows:\n",
+    "    print(\n",
+    "        f\"{row['index']:>2}: {row['type']:<8} \"\n",
+    "        f\"{str(row['in_features']):>4} -> {str(row['out_features']):<4} \"\n",
+    "        f\"params={row['parameters']}\"\n",
+    "    )\n",
+    "\n",
+    "parameter_count = sum(parameters.numel() for parameters in model.parameters())\n",
+    "print()\n",
+    "print(f\"total parameters: {parameter_count:,}\")\n",
+    "assert parameter_count == final_metrics[\"parameter_count\"]\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "6f638448",
+   "metadata": {},
+   "source": [
+    "## Reloaded-model predictions\n",
+    "\n",
+    "This section runs the checkpoint on real `.npz` cases from the split. Predictions are de-normalized back to the target scale before comparison.\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 15,
+   "id": "09e67ecb",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "/home/aaron/data/airfrans/data/processed/minimal\n",
+      "['case_00.npz', 'case_01.npz', 'case_02.npz', 'case_03.npz', 'case_04.npz', 'case_05.npz']\n"
+     ]
+    }
+   ],
+   "source": [
+    "def config_value(config: str, section: str, key: str) -> str:\n",
+    "    current_section = None\n",
+    "    for raw_line in config.splitlines():\n",
+    "        line = raw_line.strip()\n",
+    "        if not line or line.startswith(\"#\"):\n",
+    "            continue\n",
+    "        if line.startswith(\"[\") and line.endswith(\"]\"):\n",
+    "            current_section = line[1:-1]\n",
+    "            continue\n",
+    "        if current_section == section and line.startswith(f\"{key} =\"):\n",
+    "            return line.split(\"=\", 1)[1].strip().strip('\\\"')\n",
+    "    raise KeyError(f\"{section}.{key}\")\n",
+    "\n",
+    "\n",
+    "DATA_ROOT = REPO_ROOT / config_value(config_text, \"data\", \"root\")\n",
+    "print(DATA_ROOT)\n",
+    "print(sorted(path.name for path in DATA_ROOT.glob(\"*.npz\")))\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 16,
+   "id": "924e2cef",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "[{'split': 'test',\n",
+       "  'case_id': 'case_03',\n",
+       "  'rows': 256,\n",
+       "  'normalized_mse': 0.00025704235304147005,\n",
+       "  'mse_velocity_x': 5.847604370501358e-06,\n",
+       "  'mse_velocity_y': 6.842037691967562e-06,\n",
+       "  'mse_pressure': 4.433627691469155e-05,\n",
+       "  'mse_turbulent_viscosity': 1.3116708032612223e-05},\n",
+       " {'split': 'train',\n",
+       "  'case_id': 'case_04',\n",
+       "  'rows': 256,\n",
+       "  'normalized_mse': 0.00018982301116921008,\n",
+       "  'mse_velocity_x': 6.853222657809965e-06,\n",
+       "  'mse_velocity_y': 6.716706138831796e-06,\n",
+       "  'mse_pressure': 2.8528093025670387e-05,\n",
+       "  'mse_turbulent_viscosity': 9.420269634574652e-06},\n",
+       " {'split': 'train',\n",
+       "  'case_id': 'case_02',\n",
+       "  'rows': 256,\n",
+       "  'normalized_mse': 0.0001731793163344264,\n",
+       "  'mse_velocity_x': 5.711051926482469e-06,\n",
+       "  'mse_velocity_y': 4.464081939659081e-06,\n",
+       "  'mse_pressure': 2.741067874012515e-05,\n",
+       "  'mse_turbulent_viscosity': 9.072812645172235e-06},\n",
+       " {'split': 'train',\n",
+       "  'case_id': 'case_01',\n",
+       "  'rows': 256,\n",
+       "  'normalized_mse': 0.00011508660099934787,\n",
+       "  'mse_velocity_x': 3.957362423534505e-06,\n",
+       "  'mse_velocity_y': 2.683263573999284e-06,\n",
+       "  'mse_pressure': 2.2211312170838937e-05,\n",
+       "  'mse_turbulent_viscosity': 5.00482428833493e-06},\n",
+       " {'split': 'train',\n",
+       "  'case_id': 'case_00',\n",
+       "  'rows': 256,\n",
+       "  'normalized_mse': 0.00016892868734430522,\n",
+       "  'mse_velocity_x': 8.186969353118911e-06,\n",
+       "  'mse_velocity_y': 7.417508186335908e-06,\n",
+       "  'mse_pressure': 3.0236758902901784e-05,\n",
+       "  'mse_turbulent_viscosity': 5.846786280017113e-06},\n",
+       " {'split': 'val',\n",
+       "  'case_id': 'case_05',\n",
+       "  'rows': 256,\n",
+       "  'normalized_mse': 0.0007373533444479108,\n",
+       "  'mse_velocity_x': 2.7578798835747875e-05,\n",
+       "  'mse_velocity_y': 1.9889514078386128e-05,\n",
+       "  'mse_pressure': 0.00013202131958678365,\n",
+       "  'mse_turbulent_viscosity': 3.3118620194727555e-05}]"
+      ]
+     },
+     "execution_count": 16,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "def load_case(case_id: str) -> tuple[np.ndarray, np.ndarray]:\n",
+    "    data = np.load(DATA_ROOT / f\"{case_id}.npz\")\n",
+    "    return data[\"features\"].astype(np.float32), data[\"targets\"].astype(np.float32)\n",
+    "\n",
+    "\n",
+    "def predict_targets(features: np.ndarray, batch_size: int = 4096) -> np.ndarray:\n",
+    "    features_norm = (features - feature_mean) / feature_std\n",
+    "    outputs = []\n",
+    "    with torch.no_grad():\n",
+    "        for start in range(0, len(features_norm), batch_size):\n",
+    "            batch = torch.from_numpy(features_norm[start : start + batch_size])\n",
+    "            pred_norm = model(batch).numpy()\n",
+    "            outputs.append(pred_norm)\n",
+    "    pred_norm_all = np.concatenate(outputs, axis=0)\n",
+    "    return pred_norm_all * target_std + target_mean\n",
+    "\n",
+    "\n",
+    "def normalized_mse(predictions: np.ndarray, targets: np.ndarray) -> float:\n",
+    "    pred_norm = (predictions - target_mean) / target_std\n",
+    "    target_norm = (targets - target_mean) / target_std\n",
+    "    return float(np.mean((pred_norm - target_norm) ** 2))\n",
+    "\n",
+    "\n",
+    "case_rows = []\n",
+    "for split_name, ids in split_manifest.items():\n",
+    "    for case_id in ids:\n",
+    "        features, targets = load_case(case_id)\n",
+    "        predictions = predict_targets(features)\n",
+    "        per_channel_mse = np.mean((predictions - targets) ** 2, axis=0)\n",
+    "        case_rows.append(\n",
+    "            {\n",
+    "                \"split\": split_name.replace(\"_ids\", \"\"),\n",
+    "                \"case_id\": case_id,\n",
+    "                \"rows\": len(features),\n",
+    "                \"normalized_mse\": normalized_mse(predictions, targets),\n",
+    "                **{f\"mse_{name}\": float(value) for name, value in zip(checkpoint[\"target_names\"], per_channel_mse)},\n",
+    "            }\n",
+    "        )\n",
+    "\n",
+    "case_rows\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 17,
+   "id": "4fe67c98",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "case: case_05\n",
+      "target names: ('velocity_x', 'velocity_y', 'pressure', 'turbulent_viscosity')\n",
+      "first 5 predictions:\n",
+      "[[ 0.44094524 -0.02950683  0.19028291  0.0982319 ]\n",
+      " [ 0.02009592  0.14751981 -0.21558036  0.03777217]\n",
+      " [ 0.45918038 -0.00707006  0.2556273   0.12591958]\n",
+      " [ 0.31692797  0.19948506 -0.60716456  0.3552236 ]\n",
+      " [ 0.23262833  0.05736347 -0.33526948  0.07037625]]\n",
+      "first 5 actual targets:\n",
+      "[[ 0.44482228 -0.03109705  0.18664566  0.09674599]\n",
+      " [ 0.01824543  0.1526474  -0.20674932  0.03895786]\n",
+      " [ 0.4640298  -0.00911091  0.25503194  0.12455965]\n",
+      " [ 0.32461593  0.1904991  -0.6266293   0.35012734]\n",
+      " [ 0.23149356  0.05860274 -0.33879325  0.06851949]]\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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Ta8nLONyovPQhS1BQULafs85/6dKlXMd/6NChVKtWzd7Otc51LVnH53eT6WvJGvfc2swq++fv5p+/h6z+/P33MGXKFHr16sWSJUtYtmwZb7/9NiEhIXzzzTe0bdv2lsf4z99RbvI6ngMHDsTPz49Dhw5lSxbUq1cv29/G9fqc17+TvAoLCyMiIiJHedaeI3lJVNSoUQOAU6dOKbEhIiIichspsSEiIiJSwJycnJg2bRoVK1ZkxIgR9o1yq1Spwu+//84HH3yQ6wPpa6lQoQIrV64kPj4ef39/e3lSUhLnz5+nffv2AOzcuZOQkJBsSQ24sqH4P9tbvXo1ly9fxsvLy15+9OhRe/IjS17jzk+bualatSq//fYbkZGROWZ93Ii8xp2f8z7wwAM89dRTdOvWjU6dOrFkyRK8vb1z1MttE+LDhw/j5uZmf1ia19/pv5WXpFJu8tKHqylatCj+/v6UL1+eqVOnXrVeZmYmPj4+Vz3X9RQtWpSAgIDrzm7JzxgEBwfj6+ub6/mzNu7+5+bUeXXfffdx3333AXDmzBnatGnDiBEj8r1p9a2KMS/jabVa2bt3L6NHj86W1EhJSSE8PDzHDKRr9Tmvfyd51aRJEyZOnJjjv+Vt27YB5PhezM2ZM2eAK2MsIiIiIrePuaADEBEREREIDQ3l2WefZf369fz6668ADBkyBCcnJ1555ZUc9dPS0ggPD79qe/3798cwDN5///1s5e+//z6ZmZn2ZZFKlixJXFwcUVFR9jpHjhxh6dKl2Y7r168fVquVzz77LFv55MmTcXJyylaW17jz02ZuBg0ahLOzM2PHjs2RCDl+/Ph1j/+nvMad3/Ped999rFq1ij179nDfffcRFxeXo87PP/+c7c3xc+fO8cMPP9CrVy/7LI+8/k7/reDgYEwmU65xXkte+nA1ZrOZYcOGsXjxYrZv357j81OnTpGUlISTkxO9e/dm0aJFnDx50v55TEwMP/3003VjNJvNPPHEE/z222+sXbs222cJCQnExMQA2Gfn5GUMnJyc6NOnD8uXL2f//v328suXLzNp0iQqVKhAo0aNrtvO3yUlJXHq1KlsZaVKlaJq1apcvnw5X23dqhghb+Pp7OxMaGioPYGS5YMPPsj2309e+pzXv5O8Gjx4ME5OTnz++ef2MqvVypQpU6hZsyZNmjSxl+e29N/JkyeZPXs299xzT56WphMRERGRm0czNkREREQKieeff54vvviCl19+mY4dO1K9enW+++47Bg4cyPbt22nfvj1eXl6Eh4fz+++/8+abb1KxYsVc22rcuDFvv/0248aNIzw8nIYNG7Jz505+/PFH3njjDVq0aAHAyJEjmTlzJq1ateKxxx4jOjqaVatW8fzzz/Piiy/a22vatCkvvfQSY8eO5eDBg9SqVYu1a9fywAMP4Oyc/Z+UeY07P23mpkaNGsyePZvBgwdz+PBhOnbsSGZmJr/99hvdunVj9OjR+Rr/vMZ9I+dt2LAh69evp3379rRo0YKVK1fa91WAK0mVHj160LZtWwzDYNasWYSFhfHxxx/n+3f6b7m6utKhQwe+/PJL3Nzc8PPzo0aNGjRr1uyax+WlD9fy5ptvcvbsWZo3b07fvn2pWbMmly5dYu/evRw6dIiNGzfi4+PDu+++y5YtW2jcuDGDBg3CxcWF3377jSeeeIIxY8Zc9zxvvPEGR48epV27djzyyCNUr16d06dP89tvv7F06VKCgoJo164d7u7uDBs2jPvvvx8XFxd69ux51bfy33vvPfbs2UPz5s0ZMmQIvr6+zJ07l9jYWH799dc8Jer+LjU1lfbt21OlShXq1KmDv78/O3bsYMWKFXz55Zf5autWxZglL+P5+uuvM3z4cPr27Uu9evXYsmUL3t7eNGnSxJ4My2uf8/p3khfVqlXj448/ZtSoUURERFC9enUWLlzIuXPnWLVqVbaZO1OnTmXz5s20bNmSsLAwTpw4wddff01oaCjz58+/obETERERkRunGRsiIiIit1FYWBhDhw6176Pxd35+fkyZMoUGDRpw4sQJAB566CFOnTrF448/TlJSErGxsTRs2JAdO3bY39D38PBg6NChOTYkHjt2LHv37qV27dqcOXOGGjVqsGfPHl599VV7nRIlSnDw4EGGDBlCREQEpUqVYvXq1bRu3ZqhQ4dmW2d/woQJrFu3juLFixMTE8Po0aMZPnw4Q4YMybFkS17izm+buenfvz/Hjx+nd+/exMTE4OLiwmeffWZPLvj5+TF06FCqVq2a49hHHnnEvuRNfuO+kfNWr16djRs30qJFC7799ttsb6sXL16cJUuW4OPjQ2JiIq+88gq7d+/OsY9KXn6nuf095Hcc5s2bx9ixYzl//jx79uzh/PnzV/8l5KMP14rD2dmZOXPmsGXLFipXrszp06dxc3Nj6NChHD582N5OYGAgO3fuZPz48SQnJ+Ph4cGiRYto27Ztjr/Z2rVrM3To0GwzRlxdXVm4cCFr1qyhZMmSnDt3jqpVq7J582YqV64MQJkyZdi2bRsNGjTg0KFD7Nmzh9TU1Ku2WaRIETZv3szMmTOBKzNInn76acLDw+2bcwM0aNCAoUOHYjZnvw3L+l4ICwsDrswYOXz4MMOGDcPJyYmzZ89Sv359Tpw4wcCBA6/5ewgJCWHo0KGUL18+W/m/jfFq8jKeQ4cOZdOmTVSsWJGIiAgeeeQRvvrqKzp37sxDDz2Urz7n9e8kr55++ml27dpFWFgYJ0+e5OGHH+bQoUM5vk8/++wzvv32W0qWLMmpU6fw8fHh66+/Zv/+/VSqVClf5xQRERGRf89k5GUBYxERERERuemOHTtGxYoVmT59OoMHDy7ocG7IndAHERERERFxLJqxISIiIiIiIiIiIiIiDkN7bIiIiIiIiMgdISIigsWLF1+zTtWqVWnZsuXtCUhEREREbgklNkRERERECsi19pxwFHdCH+TOcfnyZfbs2XPNOt7e3rcnGBERERG5ZbTHhoiIiIiIiIiIiIiIOAztsSEiIiIiIiIiIiIiIg5DiQ0REREREREREREREXEYSmyIiIiIiIiIiIiIiIjDUGJDREREREREREREREQchhIbIiIiIiIiIiIiIiLiMJTYEBERERERERERERERh6HEhoiIiIiIiIiIiIiIOAwlNkRERERERERERERExGEosSEiIiIiIiIiIiIiIg5DiQ0REREREREREREREXEYSmyIiIiIiIiIiIiIiIjDUGJDREREREREREREREQchhIbIiIiIiIiIiIiIiLiMJTYEBERERERERERERERh6HEhoiIiIiIiIiIiIiIOAwlNkRERERERERERERExGEosSEiIiIiIiIiIiIiIg5DiQ0REREREREREREREXEYSmyIiIiIiIiIiIiIiIjDUGJDREREREREREREREQchhIbIiIiIiIiIiIiIiLiMJTYEBERERERERERERERh6HEhoiIiIiIiIiIiIiIOAwlNkRERERERERERERExGEosSEiIiIiIiIiIiIiIg5DiQ0REREREREREREREXEYSmyIiIiIiIiIiIiIiIjDUGJDREREREREREREREQchhIbIiIiIiIiIiIiIiLiMJTYEBERERERERERERERh6HEhoiIiIiIiIiIiIiIOAwlNkRERERERERERERExGEosSEiIiIiIiIiIiIiIg5DiQ0REREREREREREREXEYSmyIiIiIiIiIiIiIiIjDUGJDREREREREREREREQchhIbIiIiIiIiIiIiIiLiMJTYEBERERERERERERERh6HEhoiIiIiIiIiIiIiIOAwlNkRERERERERERERExGEosSFyA1q1akWzZs0ctn0RESkY7u7uPP/88w7bvoiIiIiIXNu7776LyWQiISHhprY7depUTCYT586du6ntFhRH6Y+jxHk3UmJDxEE0atSIVq1aFXQYIiLiYEwmEy+99FJBhyEiIiIiUuA++eQTTCYTERERBR1KoaUxuj6NUeHgXNABiEhOa9euLegQRETEAaWlpRV0CCIiIiIiItc1bNgwhg0bVtBhXJejxHk30owNERERERERERERERFxGEpsyB3voYceIjAwkPT09ByfDRw4EF9fXy5fvmwvW7FiBW3atMHX1xcPDw8aNWrE8uXL83SuGTNmULt2bdzd3SlSpAidOnVi165dOeqtWLGCtm3bUqRIEXx9fWnTpk22WRr/3GMjNDSUbdu2sW7dOkwmEyaTidDQUAD69u1LQEAAqampOc7z2GOP5ejftezYsQM3NzcGDRqUrXzTpk24uLjw5JNP5qkdERFHZ7PZKFOmDG3bts318+rVq9O0aVP7z4ZhMGXKFOrUqYOHhwd+fn488MAD7N+//7rnSktLY9y4cZQvXx43NzdCQ0P5z3/+w4ULF7LVMwyDqVOnUr9+fby8vAgJCaF3796Eh4fb6/x9j41Tp05hMpkAeO+99+zXj27dumGz2ShfvvxVlzisUqUKTZo0uW7swE1tS0TkTjZjxgxMJhOHDx9m9OjRFC1aFC8vL+6//34OHz6ca90jR47wyiuvUKJECcxmM8nJyQCsW7eODh064Ofnh7u7O3Xr1mXBggXZ2tixYwedOnWiaNGi+Pj4ULduXaZNm0ZmZma+6lSoUIFevXrl6M+wYcMoUqRItrJWrVrRqFEjTp06xYMPPoivry/dunUDIDMzkw8//JAaNWrg7u6Ov78/vXr14vjx4/9mWEVE8uzZZ5/lv//9LwDFihWz//s463nMyJEj8fb2znHc4sWLMZlMbN261V6WtY9GZGQkzz33HCEhIXh4eGQ7Li0tjSeffJLAwEB8fHzo0aMHp0+fzlYnP+e8mjNnzvD4449TvHhxXF1dKVu2LK+99hoZGRk54o2JieGFF14gODgYLy8vunTpku2+43pjdD0LFy7EZDKxbNmyHJ+tXLkSk8nEjz/+COS+d0V0dDRDhw6lVKlSeHh4ULlyZZ5//nni4uJy9Hnw4MGULFkSd3d3qlevzscff5ytz7t37+aBBx6gSJEiuLu7c8899zBt2rRs7eTlfP+M81pjtGTJEkwmE4sXL87R/19//RWTycT8+fPzNJZyfUpsyB1v8ODBxMXF5fhSSU5O5scff6RPnz54eXkBV24gOnXqRIMGDThw4ADnzp2jR48edOnShZ9++uma53n11VcZOnQo/fr149y5c+zYsQOz2UyzZs3Yvn27vd7UqVPp1KkT1apVY+fOnZw/f55XX32VTz/99KptR0RE0LBhQ1q2bIlhGBiGYV/H78knnyQ+Pp65c+dmOyY2Npb58+fTu3dve/+up379+kycOJFZs2Yxc+ZMAKKionj44YepU6cOEydOzFM7IiKOzmw28/jjj7N69WpOnjyZ7bOtW7dy8ODBbEngJ554ghdeeIERI0Zw7tw59u/fT0BAAE2aNOHo0aNXPY9hGHTr1o3PP/+c9957j+joaJYsWcK2bdto3LgxMTEx9roDBw7kueeeY8CAARw9epQDBw7w0EMP8fnnn+fadpkyZTAMA4DRo0fbrx+LFy/GbDYzfPhw1q1bx8GDB7Md98cff3DkyJEcSe5rjdXNaktE5G7w2muvUbZsWY4cOcK2bduIj4+nRYsWORLaWXWLFy/OX3/9xZw5c+wPRNq0aUPFihXZu3cvFy9e5PHHH6dv377Mnj0buHIv0L59ewIDA9m1axdRUVHMmjWLP//8kx07duS5zo1ITU1lxIgRjBkzhpMnT/Lwww9jGAYPPfQQ77zzDmPHjiUiIoJdu3aRkZFBkyZNcu27iMjN9sknn9ifa1y8eNH+7+N/s5/p888/T506dTh8+DCfffZZjs+aNGnCiRMnWLt2LeHh4bRs2fKmbip+8uRJ6tWrR3h4OD///DNxcXFMnz6dGTNm8Mgjj+SoP2bMGGrXrs2xY8dYvXo1u3btYuDAgfbP/+0YdenShaJFi9qfKf3drFmzCAwMpGvXrlc9vl+/fmzatImlS5eSkJDAr7/+SlhYGF9//bW9zvHjx6lbty579uzhhx9+ICYmhoULF3L+/Hk2bNgAwK5du2jWrBlWq5Xt27dz/vx5HnvsMUaMGMHYsWPzdb5/utYYPfDAA5QqVYrJkyfnOG7y5MkEBgbaE/5yExgid7jMzEyjVKlSRrt27bKVz5gxwwCMrVu3GoZhGAkJCYaPj4/Ru3fvHG306tXLKF++vP3nli1bGk2bNrX/HBERYbi4uBiPPvpotuMuX75sFC1a1GjZsqVhGIYRGxtreHl5GT169LhmzP9s3zAMo2HDhvZ2/qlWrVpGvXr1spV98MEHBmBs2bLlmufKTb9+/Qx3d3djx44dRps2bYyAgADj1KlT+W5HRMSRnTlzxjCbzcYrr7ySrXzw4MGGt7e3kZSUZBiGYWzatMkAjIkTJ2arZ7FYjHLlyhn9+/e3l7m5uRmjRo2y//zLL78YgDF58uRsx/7111+GyWQyRo8ebRiGYaxdu9YAjPfff/+aMf+zfcMwDMDezt/FxsYa7u7uxsiRI7OV9+zZM1v/8uJmtiUicqeaPn26ARjPPPNMtvIzZ84YLi4uxtNPP52j7lNPPZWtbmpqqhEcHGx06NAhR/uDBw82QkJCDKvVaqxatcoAjE2bNl01nrzUMQzDKF++vNGzZ88c5UOHDjX8/PyylbVs2dIAjG3btmUrX7JkiQEYc+bMyVaelJRkBAUF5RgTEZFbZeLEiQZgXLx4McdnTz75pOHl5ZWjfNGiRTmer0yYMMEAjDfeeCNH/azPxo8fn6183759hslkMt58880bOueUKVMMwDh79qy97KGHHjICAgKMmJiYbMcvWLDAAIwdO3Zki+mf9xMfffSRARinT5+2l11rjPLi+eefN5ydnY3IyEh7WWxsrOHm5mY8++yzV+2PzWYzXFxcctx//VO3bt0MX19fIzo6+qp12rdvbwQEBOS4Dxk0aJDh7OxsnD17Ns/ny23crzVGb7/9tmEymYzw8HB72enTpw2z2azr3U2mGRtyx8t66/aPP/7gzJkz9vJZs2ZRvXp1GjZsCFzZsDspKYmHHnooRxtt27bl+PHjnD9/PtdzbNiwgYyMDHr06JGt3NPTk/vvv5+NGzeSnp7OunXruHz5Mv369buJPYQRI0awc+dO+5tVhmHw5ZdfUq1aNRo1apTv9qZNm0a5cuVo3rw5a9as4dtvv6V06dI3NWYRkcKuZMmStGvXjtmzZ2Oz2QC4fPky8+bNo3fv3vYp4z///DNAjuuHi4sLLVq0YN26dVc9xx9//AGQ4/pRs2ZNKlWqZP/8l19+Abip14+AgAD69OnDN998Y1+y8MKFCyxZsoSHH3441ynxt6MtEZE7XZcuXbL9XLJkSerWrcvq1auvW3fbtm1ER0df9Z4lMjKSw4cPU7lyZdzc3Hj22WdZsmQJiYmJOernpc6NCAkJoUGDBtnKfv75Z5ycnOjevXu2cm9vbxo1anTNa6WISGH2z+/pa31Wo0YNypcvn+v3/Y2w2WwsX76cNm3aEBgYmO2zrCV1//n92rlz5xwxAZw4ceKmxAQwaNAgrFYr33zzjb3su+++Iz09/ZozuU0mE7Vq1WLq1KlMmTKFs2fP5qhjs9lYsWIF7du3JygoKNd2MjMzWbt2Le3bt89xH9KrVy+sVqt9qffrne9GDB48GBcXF6ZMmWIvmzp1KjabTTPZbzIlNuSukDWt7quvvgLgyJEjbN68OdsXStbSTr1798bZ2RknJyfMZjNms5lhw4YBV6Zr5yarPGvfi78LDQ0lMzOThIQEoqKiAChRosRN6tkVAwYMwNfX1/6luXLlSo4dO3bDX5heXl706dOHtLQ0ateuTceOHW9muCIiDmPw4MGcPXuW33//HYAff/yRpKSkXK8fpUuXznH9mD179lWvHXDl+mE2mylatGiOz0JDQ+1LUUVFRWEymShWrNjN7B5PPvkkiYmJfPfddwBMnz4dq9V6Q9ePm9mWiMidLCQkJNeyvy8/mOWf9w1Z15wnnnjCfs3Juu706dMHuHJtCQsL45dffsHd3Z2ePXvi7+9PgwYN7A9WgDzVuRbj/5c7vF7MWXFnZmbi5+eX41q5bNmya14rRUQK2tW+7+Daz3fy832fn3NmSUpKIiUlhYULF+a4JmTtgfTP79d/3k/4+voC3NTlsapUqULTpk2zLUc1a9YsGjRoYE+kXM3ChQu57777GD16NKVKlaJ8+fK88MILxMfHA1f6nJaWds1xT05OxmKxXPUZHWD/HVzvfDeiaNGi9OrVi9mzZ5OWlobFYmHmzJnUr1+fmjVr3nC7kpMSG3JXKFWqFG3btmX27NkYhsHMmTNxdXXNtt5gVqZ3+fLlWK1WMjMzsdls2Gw2+3p5tWrVyrX9gIAAACIjI3N8FhkZiZOTE0WKFCE4OBjgqjM/bpSXlxePPfYYP/zwA/Hx8UyePBkXF5dc11PMi+3bt/PWW2/RsGFD/vzzT95///2bGq+IiKPo0qULwcHBzJo1C7jyD/KqVavSuHFje52goCD7Rny5XT9SUlKu2n5AQAA2m43o6Ogcn0VGRtqvTcHBwRiGwcWLF29q/+rVq0f9+vWZPHkyVquV6dOn3/Bm3zezLRGRO9nV7hn++bYtXJn993dZ14Xvv//efs3Juu5k3bO0aNECgDZt2rB+/XoSEhJYsWIFlSpVYvjw4Xz88cf29vJSx8/Pj6SkpByxXe2e5p8xZ8Xt4eFBWlpartfKv8+sFxEpKH5+fqSmppKZmZmt/FrPcHL7zsuSl+/7GzlnFm9vb9zc3Hjssceuek145513sh1jMpmu2+7NMGjQIA4fPsyWLVvYvXs3e/bsydMLT6VLl2bu3LnExcWxc+dOHn30UT7//HP7TEUfHx/c3d2vOT7e3t64urpedfzhf9fT653vRj355JPExcXxww8/sHDhQqKiovTC1y2gxIbcNQYPHsypU6dYsWIF33zzDV26dMk2ba1169Z4eXkxb968fLfdvHlzXFxcWLRoUbby1NRUfv31V5o1a4abm5v9HN9//32+z+Hl5UV6evpVPx8xYgSpqam8+eabLF++3P4wLr9iY2N56KGHqFq1KmvXruWpp57i5ZdfZv369fluS0TE0WUlwRcvXsyWLVvYsGFDjn+QPvjggxiGwfz58/Pd/n333QeQ4/px4MABjhw5Yv/8gQceALih64enp+d1rx979+5l9OjRnD9//l/9g/tmtiUicqfKWsIwy7lz59i9e7f9O/9amjRpQmBgYL7uWby9vWnXrh3ffvstoaGhuf67/lp1ypcvz8GDB7PN4khISGDTpk15juHBBx8kNTWVpUuX5vkYEZFbwcvLCyDXfx+XL18em83GwYMHs5UvW7bshs71z+/7AwcOcPz48Wzf9//mnE5OTnTq1InffvuNS5cu3VCMubnWGOXVww8/jI+PDzNnzmTmzJl4enraZxbmhbOzM3Xr1uW1116jd+/e9k3BzWYz999/PytXrrzqzBcnJydatmzJypUr7cvkZlm4cKH987yc72quN0ZNmjShdu3aTJ48mcmTJ+Pp6Unfvn3z1HfJOyU25K7RtWtXgoKCGDp0KJGRkTketgQEBPDJJ5/w9ddfM2rUKI4dO0ZaWhrh4eHMmDEjx/rnfxcaGsoLL7zAnDlz+Oijj4iNjeXYsWP07t2bhIQE3n33XQD8/f358MMPWbRoEU8//TTHjx8nKSmJdevW5Vhv9p9q1KjBwYMHCQ8Pz3VKYpUqVWjTpg2ffPIJmZmZN/QwyTAMBgwYwKVLl1i4cCHu7u589NFH1KtXjz59+uSa7RYRudMNGjQIi8VC7969c50N16JFC4YMGcKoUaOYNGkS586d4/Lly+zbt4+3336b0aNHX7Xt+++/n7Zt2/LSSy/x008/kZiYyI4dO+jduzfFixdn1KhR9nP85z//4bXXXuOzzz7jwoULxMbGsmDBAp555plrxl+jRg02btyY66wQgD59+hAYGMjHH3+Mi4sLjz76aD5H6Na0JSJyp7pw4QLTp08nISGBAwcO2B/+vPjii9c91sPDg8mTJ7N06VKGDRvG4cOHSU1N5fjx48yZM4f7778fgHnz5vHEE0+wefNmEhISSEpK4ptvviEqKorWrVvnuQ7AkCFDOHfuHG+88QYJCQkcOnSIQYMGZZu9eD3du3enR48eDBkyhFmzZnHx4kWSk5PZs2cP48aNy/FGsYjIrZK1FNLy5cvJyMjI9lmvXr3w9/fnueee49y5c0RERDB27Fj8/f1v6FyHDh1i7ty5XLp0id27d9O3b1/CwsIYOXLkTTvnhx9+iM1mo3PnzmzcuJGkpCQiIiJYuXIl3bt3Z9++ffmO+1pjlFdZS5zPmzeP77//noceesi+7NXVREZG0q5dO5YuXcqFCxdIT09n69atrF69mlatWtnrvf/++7i6unL//fezefNmkpOTOXz4MM8//7x9/5K3336b5ORkHn74YcLDw4mLi+OTTz5h1qxZjBo1irCwsDyf70bHaMSIEezYsYONGzfSq1ev6/ZfbsBt365cpAD997//NQCjZMmSRmZmZq51Vq9ebXTq1MkICAgw3NzcjMqVKxtDhw41Dh06ZK/TsmVLo2nTpjmOnTp1qlGrVi3D1dXV8PX1NTp27Ghs3749R73ly5cbrVq1Mry9vQ0/Pz+jTZs2xtq1a6/ZfkREhNGxY0fDx8fHAIyQkJAc7S5cuNAAjLCwsKv271reeOMNAzAWL16crfzMmTNGYGCg0bp1a8Nqtea7XRERR9e4cWMDMHr06HHVOl999ZXRuHFjw9vb2/D29jbuuece47XXXjMiIyPtddzc3IxRo0ZlOy4lJcUYM2aMUbZsWcPFxcUIDg42HnnkEePs2bPZ6tlsNuPzzz83ateubbi7uxshISFG7969jaNHj16z/e3btxv16tUz3N3dDcDo2rVrjthfeOEFAzC6d++en2HJ1c1sS0TkTjJ9+nQDMA4dOmSMGjXKCAoKMjw8PIz27dsbBw4cyLVueHh4rm1t2bLF6Nq1qxEUFGS4uroaFSpUMB5//HFj7969hmEYRmpqqjFjxgyjWbNmRpEiRQw/Pz+jfv36xrRp0wybzZbnOlk+/fRTo1SpUoabm5vRqFEjY8eOHcbQoUMNPz+/bPVatmxpNGzYMNeYMzMzjc8//9yoV6+e4enpafj6+hp169Y1JkyYYMTFxd3IkIqI3JCxY8caxYoVM8xmswEYa9assX+2bt0649577zVcXV2NcuXKGbNmzTIWLVpkAMaWLVvs9SZMmGAARnx8fI72sz67ePGiMXToUMPf39/w8vIyunbtapw4cSJH/byec8qUKQaQ4z7hwoULxogRI4wyZcoYLi4uRvHixY3777/fWLx4sf3Z0NXi3bJliwEYixYtyvMY5dW2bdsMwACM9evX5/g8t/6sWrXK6N69u1G8eHHDw8PDqFixovHSSy8ZCQkJ2Y49deqU8dhjjxmhoaGGm5ubUaNGDePjjz82LBaLvc6OHTuMTp06Gb6+voarq6tRo0YNY/Lkydnaycv5rjbu1xujy5cvG0WKFDEAY926dfkeP7k+k2HkYTcaEXEIq1atol27dowbN47x48cXdDgiIuIg3nrrLV555RWWLVtG586dC01bIiJ3khkzZjBkyBDCw8OpUKFCQYcjIiIit5DVaiUsLAxfX1+OHj1a0OHckbQUlcgdZN68eZjNZgYOHFjQoYiIiAOZN28eYWFhdOzYsVC1JSIiIiIi4ojWrVtHZGQkgwcPLuhQ7ljOBR2AiNwcu3fvZu7cufTr14+yZcsWdDgiIuIAbDYb3333Hfv37+fzzz/HycmpULQlIiIiIiLiqBISEnjrrbcIDAxk6NChBR3OHUszNkQcnNVqxWQy0axZM1q3bs2nn36ao85LL72EyWS65v8WL158+4MXEZECs3HjRpycnHj66ad56qmnGD58eI46rVq1uu71IyIiIk9tiYiIiIiIY9qzZ8917wv69OlT0GEWCr169SIoKIioqCh+/PFH/Pz8CjqkO5b22BAREREREREREREREYehGRsiIiIiIiIiIiIiIuIwlNgQERERERERERERERGHoc3D88Bms3HhwgV8fHwwmUwFHY6IyA0xDIOkpCSKFy+O2ay89q2ma4eI3Al07bj9dP0QkTuBrh+3n64fInInyM/1Q4mNPLhw4QIlS5Ys6DBERG6Ks2fPEhYWVtBh3PF07RCRO4muHbePrh8icifR9eP20fVDRO4kebl+KLGRBz4+PsCVAfX19S3gaK5k4ePj4/H397+j3nxQvxzLndivO7FP8L9+OTk5Ubp0aft3mtxahe3acS2O/rev+Aueo/dB8V9dYmIiJUuW1LXjNnKk60dh4uj/HRc0jd+/o/HLSdeP268wXD/030LeaJzyRuOUN3faOOXn+qHERh5kTeHz9fUtFDcXNpsNq9WKr6/vHfEHm0X9cix3Yr/uxD7B//rl7HzlK1/Tkm+PwnbtuBZH/9tX/AXP0fug+K9P147bx5GuH4WJo/93XNA0fv+Oxu/qdP24fQrD9UP/LeSNxilvNE55c6eOU16uH3dOb0VERERERERERERE5I6nxIaIiIiIiIiIiIiIiDgMJTZERERERERERERERMRhKLEhIiIiIiIiIiIiIiIOQ5uHi4iIiIiIiIiI3ESXL18mPDyc4OBgSpQocc26J06cICoqKke5k5MT9evXv1Uhiog4NCU2REREREREREREbpKZM2fyzDPPUKxYMc6dO0fHjh35/vvv8fDwyLX+vHnzWLJkSbaygwcPEhgYyMmTJ29HyCIiDkeJDRERB2Gx2lh7JIro5HSCvd1oVbkors5aUVBERERERG4+3X/cmL179/LEE0/w3Xff0adPHyIiImjQoAGvvPIKH374Ya7HjBkzhjFjxth/Tk5OplixYjz22GO3K2wRkZsmOc3K5LXHOJ+QSokiHoxoVQFv95ufhlBiQ0TEAVisNt5afpDNx2PJtGbg5OzCxmMxjOtcTTcXIiJyTVkPpi4mJFOsiLceTImIyHUlp1kZOXc3By4kYrJZ8fF01/1HHn311VdUqFCBPn36ABAaGsoTTzzBxIkTef/99zGbrz9+8+fPJyUlhYEDB97qcEVEbqrkNCs9p2zieHQy2DIxOTnzx6FIFg5vetOTG0psiIgUYlkPo9YdjWb14Sj8M2LZ8uVLVH9wCJtpwtojUbSvHlrQYYqISCGVlRj/+ZffOPzjh9Qe8h7tmtyrB1MiInJVFquNkXN3szE8mqRdS7m0dxU1hk5k0zF0/5EHu3fvzrEvRsOGDYmLi+PMmTOUKVPmum3MmDGDDh06UKpUqavWSU9PJz093f5zYmIiADabDZvNdmPB/0s2mw3DMArs/I5C45Q3Gqe8KWzjNHlNOCci4olfOQXDmk7JHqM5FZPM5DXhPN+h8nWPz08/lNgQESmk/j5LIyHFwsWDO9i5+F08ff0JLVOReMMgOjn9+g2JiMhda83hSObNnkb40skUr96A4sVC2Xw8Vg+mRETkqtYeiWL/mVhifv2MS3tWEtykFymZTjinW3X/kQdxcXHUrl07W1lgYCAAsbGx101sHDp0iC1btvDTTz9ds96ECRN44403cpTHx8djtVrzFfPNYrPZSEpKwjCMPM1MuVtpnPJG45Q3hW2cos6dIGbeWBLPhVPn4Wco7WfissXgclICcXFx1z0+KSkpz+dSYkNEpJBaeySKzcdjCfJyIXLLYi4s/AzPsnVo8sR4XPyLYEq2EOztVtBhiohIIWWxWHhv3CiOLplL9Q79qP/wU5jNTiTEXtaDKRERuapjZ85z9KsXST53mFLdnye4TnuSLVYyDXT/kQcuLi7ZZlIApKam2j+7npkzZxIaGsqDDz54zXpjxozhueees/+cmJhIyZIl8ff3x9fX9wYi//dsNhsmkwl/f/9C8YC1sNI45Y3GKW8K0zjt3P0nX44ZTGpaGqUeeQ/K1eB4oo3UDBNtfIoQEBBw3TacnfOerlBiQ0SkkIpOTiczw8KeuZ9yZN0iijXtiWfzx4jJcCYj2UKT8oG0qly0oMN0KDExMaSmplKyZMl8HZOenk6xYsUK/B8JIiJ5FR0dTc+ePdm6dRtVe4+mZpuumM1OpFismEwmPZgSEZFc7d27lzcHdyM96TJVBn8ERStdSWrYDKoX99X9Rx6ULl2aCxcuZCvL+vlaS0sBZGRkMGfOHAYOHHjdh3tubm64ueW8npvN5gK9bzGZTAUegyPQOOWNxilvCsM4zf9xAQMeeRS3oDDC+r6L4RVEXEoGTmYT5YK8GdG6Yp7iy08f9FchIlJIuWYks2faC4Rv/Jlmj4+j+SPPEeTrSfvqobzYobLWR8+H6Oho2rZtS1hYGDVr1qRixYps3779msesWrWKihUrUqlSJRo0aEBgYCATJ068TRGLiNy4v/76i/r163PkyBF+X/UHvfoOIDrZwqnYy0QrMS4iIlfx44KFNGrcBHdvP7q89jXFKtbC190ZX3cXmlYI4vO+9+r+Iw/uu+8+1qxZQ3Jysr1syZIl1K1blyJFigBXZlds3brVPpMjy88//0x0dDSDBw++nSGLiNwwwzAYP348vR9+CP8qjej40pe0rFOF4n7ueLk607ZqyC3ZOBw0Y0NEpNDI2ig8Ojmd5AsnmPDfgWQkJFJn+ERcy9Qg9nIG7auFKKFxAx5//HESExOJiorCy8uLkSNH0rVrV44dO4aXl1eO+larlZ49e/Loo4/y6aefYjab+eGHH+jbty9NmzalQYMGBdALEZHrW7x4MQMGDKBixYqsX7+eUqVK0fhv15dgbzdaVS6q64iIiNgZhsGb49/i9ddepeg9rSjz8IvE4UHJAE/aVC5KqJ+7rh35MGjQICZNmkT37t159tln2blzJz/88APLly+319m+fTvt2rVj37591KhRw14+c+ZM2rRpQ/ny5QsidBGRfElJSeE///kPP/74I72eGEVsxc74ensDULWYL6diL9OsYtAtSWqAEhsiIoXC3zcKj9q3gYNz3yGoWCl2bN/B2QxPPYz6F86dO8fy5ctZsmSJfa3Z8ePHM336dBYvXkz//v1zHBMfH09iYiKdOnWyT4Ps0qULAGfOnFFiQ0QKDYvVxprDkcTFxvDbwg/5etJ79OrVi9mzZ9sTt67OZm0ULiIiuUpJSWHgwIHMmzePsh0ep0H3wXi5uZBisXIuPpVQP3ddQ/LJ09OTDRs28M477/D+++8THBzMypUradOmjb2On58fDRs2xNPT016WlJTEpUuXGD16dEGELSKSL2fPnqVr164cOXKEhQsX4l25Ce//doQUixVPV+fbsgSuEhsiIoXA2iNRbDoWQ/SGH9i3dBphdVpSoddozmZ46kbiX9q5cycAjRo1spcFBQVRsWJFdu3alWtiIzg4mIEDBzJ+/Hg8PT3x8vLiiy++oE6dOnTq1Om2xS4ici3JaVZGzt3N/lORRP38EWf/XEeLPiOY8/WnuLvqn/kiInJt586do1u3bhw6dIin35nCftcqeLld2dza09UZw0gnOjn9Oq1IbkJCQvj000+v+nn9+vXZunVrtjIfHx82btx4q0MTEfnXtmzZQvfu3XFzc2Pz5s3cc889WKw2Nh6LYfPxWAwjHZPJdMuXwNUdj4hIIXA+5hIHvhtP5J+rqd11MHW6DOZ0fKpuJG6C2NhYAAIDA7OVBwUFERMTc9Xjxo4dS+/evenatSseHh7YbDa+++67bG9V/V16ejrp6f/7fSUmJgJgs9mw2Wz/thu3lM1mwzCMQh/n1Sj+gufofXDE+C1WG0/N3cX6Pw9zccFbZMScpWLfcbjUbcO6o9G0qxZyU87jSGNyIwzD4Pjx47i5uVGyZMlr1o2Li+PMmTO5flalShXc3d0xDIO9e/fm+LxUqVIEBATclJhFRG6GrVu30r17d1xdXdm0aRNRLqEcuM1v2oqIiOP5+uuveeKJJ2jQoAELFy6kaNEriQtXZzPjOle7rUvgKrEhIlLAzp8/z7tP9ib66GGaPPEWVRq3143ETeTk5ARARkaG/f/DlUSEs3Pul8GEhASaNGnCgAED2LZtG05OTvz000/cf//9rF27lqZNm+Y4ZsKECbzxxhs5yuPj47FarTepN7eGzWYjKSkJwzDsS285EsVf8By9D44Y/46TcZz+aysXZr+B2cmZ7qM+IM2/HB5OFuLjYomLc7kp50lKSrop7RRG27dvp0+fPiQnJ5Oamkq1atVYuHAhYWFhudZft25dju/5ixcvEh0dzblz5yhevDjp6enUqVOHChUqZNvD6bXXXqN79+63tD8iInk1Z84chgwZQr169fjpp58oWrRogbxpKyIijiMzM5OXXnqJDz/8kEGDBjF58mRcXV2z1bndS+AqsSEicpv9fZPw2JMHefe5QTg5OTHw3W84SQinYi/rRuImynoD9+LFi5QtW9ZeHhERQceOHXM9ZvXq1URFRTF27Fh7MqRHjx5UqVKF+fPn55rYGDNmDM8995z958TEREqWLIm/v799b4/CymazYTKZ8Pf3d5iHun+n+Aueo/fBkeK3WG2sOxrNp7Pns/bLN3EvVoGKfV7BCC7CyXgbXu5m/AMCb9rsgKslgB1dSkoK3bt3p2vXrnzxxRekp6fTvn17HnnkEdasWZPrMd27d8+RnGjWrBm1atWiePHi2cq/+uormjVrdsviFxG5EZmZmYwZM4YPPviAgQMHMnnyZNzcrrxIVRBv2oqIiGO4dOkS/fr1Y8WKFXzyySc8/fTTmEymgg5LiQ0Rkdvp75uEX9y1ksPzP6B4+WpsWvULoaGhupG4BRo2bIiHhwcrVqxg+PDhAOzbt4+zZ8/SqlUre72TJ0/i4eFBaGgoRYoUAa4sY5W1hJXNZiM+Pt7+2T+5ubnZbwz/zmw2F/oHpQAmk8lhYs2N4i94jt4HR4jfYrXx1rIDfPv5e5xeMxefWm0p1ukpMl1cSLHayLBB1eJ+tK4SctP6UZjH499YtmwZERERvP7665hMJtzd3Xn55Zfp2LEj4eHhVKxY8bptHDlyhE2bNjFv3rwcn8XFxXHkyBHKlCmT67VBROR2S0xMpF+/fvz6669MnDiRZ555JsdDqdv9pq2IiBR+x44do0uXLly4cIFff/2V9u3bF3RIdg6X2IiPj2fBggVERkZSs2ZNunTpkqcM0a5du1izZg2enp707NmTkJCbs+6wiEh+rD0SxabwaC7+8RWHVsyhTONOlO32LIcvmSkVphuJW8Hb25sXX3yRsWPHEhgYSFBQEKNGjaJVq1a0adPGXu/BBx+kUaNGzJgxgyZNmlC1alUeffRR3n77bXx8fJgyZQrx8fH07du3AHsjInez5buOM3XcMOIOb6d+n2ehRifiLlvwcjLh6epMkwpFmNT3XiXF82DXrl2UKVPGviYwQOPGjQHYvXt3nhIbM2fOJCgoiG7duuX4rF+/fhQtWpRz587Rv39/Pv3000I/e09E7lwHDx/l/s4PEBMVwfgp3zJiYO9C8aatiIgUbn/88QcPPfQQXn4BjJu+CEpUxWK1FZr7DYdKbJw+fZqmTZtStmxZ6tWrx1NPPcXMmTNZvHjxVd8mMwyDESNGMHfuXPr374+npyft2rVjwYIFVKpU6Tb3QETudqcjY/jrq3HEHtpK/d7PUKNDP07HpWiT8FvstddeIzAwkE8//ZTU1FTatGnDa6+9lq1OuXLlKFasGADu7u6sXbuW9957j9dff520tDSqVq3K5s2bqVKlSkF0QUTucsePH+epvp1JuHiBdv+dSFjNxtgMg0MXE2lULoAO5b1pfU853F0d6p/3BebvM/Ky+Pr64uLiQkxMzHWPz8jI4JtvvuGxxx7Ltraw2Wxm0qRJDBs2DGdnZw4dOkTHjh0ZOXIk33zzTa5tpaenk57+v38HJCYmAldmCt7pm7ffTDabDcMwNGY3SOP37xTm8Vv5+x9079ULs7svtUZMYk1yMdKWH2Ds/VVv6YOpwjgWIiKSN4Zh8MUXX/Dss89SplYjSvR8iV/PmFhx9ggbj8UwrnO1QpHccKg7nxdffJGSJUuyZs0anJ2dGTlypH298z59+uR6zJdffsns2bPZtWsX1apVA2Ds2LGkpaXdztBFRDhx4gRvD+1F/LlzNB/5ARXuba5Nwm8Tk8nEU089xVNPPXXVOkuXLs32c9GiRfnoo49udWgiIrmKT7YwdvE+LiSkYlw4wMapY/H0LUL9pycTUP7KbIK0jEy83V24v0Yx6oa6FIqbC0fh4uKSLZkAV9aet1qtuLhcf+P1ZcuWERkZyeDBg7OVu7q6MnLkSPvPVatWZezYsfYXsnJre8KECTk2JYcrM9WtVmteu3TXs9lsJCUlYRjGHbuE2q2k8ft3Ctv4ZWTa2HMmgR+//4avP3uXkEr38OCTb+BXpAjpGZmcPh/Jmr1u1C97c/Zjyk1SUtIta1tERG4di8XCU089xbRp0+jx6BPE13iYED9PPF2dSbFY2Xw8lrVHogrFiiMOk9iwWq38/PPPfPjhh/ZNDMuXL0+LFi346aefrprY+Oyzz+jXr589qQHg7+9/W2IWEcmydu1aevXqRRF/f4Z88D3hFj9tEi4iIrmKT7bQ+qM1JKRaSfrzF+JWfYl3mVqsXr2cb/fEsvl4LIaRbr+GtKwUTHJiQkGH7VBKlizJxYsXs5VdvHgRwzAoWbLkdY+fOXMmzZs3z9MsvhIlSpCRkUFkZCRhYWE5Ph8zZgzPPfec/efExERKliyJv7+/lq/KB5vNhslkwt/fv1A8WHY0Gr9/pzCNn8Vq4/Ule/nqw9eJ2r6MoIbdCGo3iAyvYOKsJjDB+VQbcZmuBATcusRG1nMbERFxHDExMfTq1YvNmzczc+ZM3Krfx6yNJ/H8/1nhnq7OGEZ6oVl1xGGuNGfOnCE1NZUKFSpkK69YsSJbtmzJ9ZikpCQOHTrE6NGjWbFiBbt27aJ48eJ06dIlx9Tzvyvs08EL8zTXf0P9cix3Yr9uVZ+mTp3KM888Q8uWLfnhhx/w9i3CuqPRxCSnE+TtRstKwTibb9107TvxdyUicicbu3gf8clpJPwxjcQ/f8G37oMUaTOYiRsu8GmfOqw9EkV0cjrB3m60qlwUTdTIvzZt2vDyyy+zc+dO6tWrB1yZheHm5kbTpk2BK9fPv/76i1KlSmV7+HfhwgVWrFjBV199laPd9PT0HJuFr1mzBj8/P0JDc3+rzc3NLdcNxgv7ZvaFkclk0rj9Cxq/f6ewjN+iLQf5/IXHuXz2EMUeeBbvWu1IyjA4F59KyQAvUixWMJkJ9nG/pbEW9DiIiEj+7Nu3jy5dunD58mVWr15Ns2bNWHkgApPJRIrFap+xUZhWHXGYxMbly5cBcry15OfnZ//sny5dugTApEmT8PLyomnTpnz77beMGjWKVatWce+99+Z6XGGfDl7YprneLOqXY7kT+3Wz+5SRkcHLL7/MV199xeDBgxk/fjwAyYkJ1A11AVzsP99KWf0SERHHcOp8BJHzXyX93EGCOz2NX+0OZGQaXEhIxdXZnGPatxLX+deoUSMeeOABHnnkET744AMuXbrEmDFjePHFF+33GykpKdSpU4fp06dnW3Jq9uzZ+Pj40KtXrxztTps2jT///JOuXbsSEBDAr7/+ymeffcbHH3+st5dF5Jbbv38/I3p3JC0pmQqPvYdf2ZpYbQaJaRlEJqaTaaAZ4yIiksOSJUsYMGAA5cuXZ+3atZQuXRqAVpWLsvFYTI4Z44XlGuIw/7r29vYG/pesyJKQkGD/7GrHeHp6sm7dOnt5u3bteOmll1i5cmWuxxX26eCFaZrrzaR+OZY7sV83s0+xsbH079+fDRs2MHXqVIYMGXKTosy/rH45OTkVWAwiIpI3Bw4cYPPHQ8lISqJY37fwKl0Tm80AoHgRjwKO7s4yb9483nvvPSZMmICbmxvvvPMOw4YNs3/u5OTEPffck2Om9+7du3n22Wfx8Mj5+xg5ciTz58/nm2++ITIyknLlyrFmzRqaNWt2y/sjIne3pUuX0r9/fzwDixPW6y28ixYHwGQCJ7OJ2qWK0L56qH22n/ZlEhERwzB49913efnll+nevTtff/11tufsrs5mxnWulmPGeGG5hjhMYqNUqVJ4enpy9OhROnToYC8/evToVde2LVKkCKGhoTRq1ChbeePGjZkzZ85Vz+UI08ELyzTXm039cix3Yr9utE/JaVYmrT7Kn2cSSIs+w47pL2FNvcwff/xBixYtblG0eZfVLxERKbyWLVtGv379KFmqNEbft0lxDyIj80pSo4iHM+90q1nAEd5ZPD09eeONN3KdqQ3g4eHBnj17cpQvWLDgqm2aTCZ69+5N7969b1aYIiLZWKy2bA+YWlYKZuJHHzB27Fi6detG/9Hv8+aKE6RmZGI2mbAZBp6uzvSuX4pONYsVdPgiIlJIpKamMnjwYL7//nteffVVXnvttVyfG+U2Y7ywcJjEhpOTE127duWbb75h2LBhuLi4cOTIETZs2MAPP/xgr7d48WLOnj3LU089BUDv3r3ZuHEjhmFgMpkA2LBhA9WrVy+QfojInSc5zUqPKZs4FpVM8rEdxCx9Hxe/EB5/42saNdEbmiIicm2GYfD+++8zZswYunbtypw5c8jAlbGL93EhIZXiRTx4p1tN/L1dCzpUEREpQBarjbeWH/z/JUEMbFYLo379jP3rlvPKK6/w+uuvY7XB7guprD4chTXThrOTmTZVitK2akhBhy8iIoXE+fPn6d69O/v372f+/Pk89NBDBR3SDXGYxAbAe++9R/PmzWnatCn16tVj8eLFdOvWjZ49e9rrLFu2jK1bt9oTG6+99hqtWrWicePGNGnShG3btnHy5EnWrFlTUN0QkTuIxWrjhQV7CY9MImHbTySsnY1nxYYEdX6ObTHOrD0SVWgz2yIiUvDS0tIYMmQI3377LePGjeONN96wvyk1ZUDdAo5OREQKk7VHoth8PJZgb1e4HM/Kqc+TcP4EYz/8kjdHPQGAqxlee7A6LSsFF8plQ0REpGBt376dbt264eTkxMaNG6+6B7UjcKjERsmSJdm3bx+LFi0iMjKSWbNm0aFDB/tMDIDu3bvToEED+8/+/v5s376dpUuXcvr0aZ555hk6d+6Ml5dXQXRBRO4gWW9MrTt0gehlE7l8YA1+TXoT1HIAmTYTGZk2opPTCzpMEREppC5evEi3bt3466+/+OGHH7R8kYiIXFN0cjqGYXD53FH+mPQCJpOJe5/8lGrNOmSrV5iXDRERkYLz/fffM3DgQO69915++uknQkMd+1rhUIkNAB8fHx599NGrft65c+ccZW5ubg47pUZECieL1cYnq46yZMsBTn/3OqkRJwh68AW8qrXEZrtSx8XJTLB3zv16REREdu7cSbdu3QDYuHEjdetqdoaIiFxbsLcbkX+u5vD89wgoVYkmQyeQ7OSjew4REbkmm83Gyy+/zLvvvstjjz3Gl19+iZubW459mxxthp/DJTZERAqSxWpj1aFIvtlyil07d3Hyh9cxAcX6v4tLaCUAbICrk4kO1UNpVblogcYrIiKFzw8//MDjjz9OrVq1WLx4McWKaTNXERG5NpvNxupvP+PAdxMIrdueyr1GkezkRpPygbrnEBGRq0pKSqJ///4sW7aMDz/8kOeeew6TyZRj3yaTycTGYzGM61zNYZIbSmyIiORR1pf+7wcjObn9d6KWfYJrcGnK9HkVs3cgbs4mUi02yhf1YmiL8nSsUcxhLgYiInJrWaw2Vh+KYOrHE1gy+3P69evPzJkzcHd3L+jQRESkkPn7G7T+Hi6kpFzm3dFPsnvDKsa//S71uzxKzGWLQ75dKyIit8+JEyfo0qULZ8+eZdmyZXTq1Mn+2d/3bfJ0dSbFYmXz8ViH2itWiQ0RkTxaeySKTeHRXPzjayL/mEORmm3w7zASm5s7mZk2vNxcebh+CYfKbouIyK2VNdNv5pqDrPnyNeIPbaZC56GUe+hJzM6uBR2eiIgUMn9/g9ZqtXL69Gku/Pgm1kvR1Hz8bSzVO9O6SojuN0RE5JrWrl1Lr169KFKkCFu3bqVq1arZPs/at8nT9Up6wNPVGcNId6i9YpXYEBHJo7NRcez7+jWiD2wkuM3jFG/RG0umgY+bMwbQq24Yz7atpJsMEREB/vdw6udNezk4exzWS5FUeWQ8tZq0ZsuJOId6G0pERG6PrDdo/T2d2bppGxcWvo3ZzYvQRz7EUqwMm47F6PrhAFJTU/nyyy/Zs2cPRYsWZeDAgVSpUuW6x+3Zs4e5c+cSFxdH27Zt6d27922IVkTuNF9++SUjR46kZcuWzJ8/n4CAgBx1gr3dMJlMpFis9hkbJpPJofZt0tM3EZGrsFhtrDwQwTebT/HS138w5vHuxB7dSZNh71Kp/QDSrDYyMm2YzCY61ghVUkNERLJZeySK5StXc2DykxjWNCoO/gRz6bqkWmwYhuFQb0OJiMitk3Xf8d2206w7Go3NZmPv7z9x7vuxuBYtQ+ijH+EWXIq0DBuxyRZdPwo5q9VKmzZt+Oqrr6hfvz5RUVHce++97Nix45rHTZ48mSZNmmC1WmnWrBm//vorr7/++u0JWkTuCBkZGQwf8STDhg2jdbf+PPfhV3j7Fsm1bqvKRWlSPpDoZAunYi8TnWxxuH2bNGNDROQfLFYbKw9G8PmacCIupZFw8i/O//gWTu6elB80keSQsgQ4mfH3dKVUoCePNSpN22qhSmqIiEg287+dzZ9TxxFQ/h4CuozGw7cI6VYbyekZmM1mh3obSkREbo1/bt56OdXCsaVfELtjKT73dsa/zRBMTs6YTCZshoHVZuj6Uch9++237N69m7Nnz1K06JUHhLGxsbz44ousWbMm12MOHz7MM888w4wZM3jssccAeOyxx4iOjr5tcYuIY4uLi+Ohh3uzfv16qvQahbVhZ9789SjfbD/Ho43L0LZq9mUMXZ3NjOtczb6nkyPu26TEhojI36RaMnnumx1sPhGP1WaQtPc34lZOwT2sKiUfepkyxUNITM+kSflAWlYKdrgvfRERuXWyNnuNSLjMT5MnsOT7mZRo0o3G/f/LxSQrMcnpZNoMUjJstK8W7FBvQ4mIyK2RtfRUoJcLl+ITOPL1KySc2Evo/SNxq9URABNg2AwAygR66vpRyP3yyy+0bNnSntQA6N27N4899hjJycl4e3vnOGb27NkEBgbyyCOPZCsPDg6+5fGKiOM7cuTIlWRobBy1h35IhVr1ORefyqUUC7uS0zkdm8LWE7E59oR1dTY79NKGSmyIiPw/i9XGxFVH2HQsEavNRvzqmSTtWop37fsJbDsUk5srJpMZL1eoVtzXob/8RUTk5rFYbSzbe4EPVx4mOjaOiEXvcvnUX3R44mXqduzNtpNxuGqmn4iI5CI6OZ3MTBv7Dhzi2LevkpmWTPG+b1GlTiOSUjOIvWwBwACK+7ozZ2BDXT8KuePHj1OnTp1sZaVLl8Zms3Hq1Clq1KiR45g9e/bQsGFDNmzYwPfff4+bmxstW7akZ8+eVz1Peno66en/W5YsMTERAJvNhs1mu0m9yR+b7cpymwV1fkehccobjVPeLF++nH79+lG6dGlen7mE5SetpFkySUyx4OlsJj3ThoeLmS3HY1hzOJJ21UIKOuRrys/vW4kNERGuPJT67I9wDl9MwpqWTNSS90k7vZeAdsPxubczAIZhAIbDbaYkIiK3jsVq47Ul+/lx11lSo88StfBNbKnJhPV7i4xKjWhaIYiWlYIddnq3iIjcOharjTOxlzmycz0Ri9/H2TeI4o99jGuRUC6nZzLugeocjkjkfEIqJYp4MKJVBbzd9RinsEtLS8sxKyPr57S0tFyPSU5OZv/+/Tz//PMMHjyY+Ph4hg0bxm+//ca0adNyPWbChAm88cYbOcrj4+OxWq3/shc3xmazkZSUhGEYmM36987VaJzyRuN0bYZh8MUXX/Dmm2/Spk0bpk2bxpFYK3sizmAzrJTyNnB1smHJNCjhC2lWK/FxscTFuRR06NeUlJSU57q6IorIXS9rXdvf9l/EKeYMF755C1tqEkV7j8ej9D32ek5mE1abQdMKQZr+LSIiWKw2Pll1lCV7LpAUvpPope/j7BNE6KMfY/YvRmyyhfgUC/0bli7oUEVEpJCxWG2MX3aAr7/8nIsrpuNRoT7BDzyPk7snJpMJA4Ok9Axe7FiloEOVfPLz8yMuLi5bWWxsLABFihS56jExMTH89ddfBAQEAFCxYkV69erFq6++SlhYWI5jxowZw3PPPWf/OTExkZIlS+Lv74+vr+9N6k3+2Gw2TCYT/v7+ehB9DRqnvNE4XV1aWhpDhw7l22+/5aWXXuLZZ58lMDCQ4mGw5UI6vx+MJC7ZwMkMgd5u+BiuxKZl4B8QaP+OKaycnfOerlBiQ0Tueiv2R/Dz3gtcOrKDsz9NwOQVQOijH+PiX8xep2QRdx5vVpYwf0+9bSsiIiSnWRk5dzc7TsYRsWkB8Wu/wqNcXYIefAGzmyeANngVERG7rH2YsmbwpaSkMPPtF7i48zf8mzyMb7MBYDLj5mzGMAwMA11DHFStWrXYvn17trJ9+/bh7e1NmTJlcj3mnnvuyZbUAKhS5UpS68KFC7kmNtzc3HBzy/k3YjabC/QhsMlkKvAYHIHGKW80TjldvHiRHj16sGfPHr777jv69OlDXFwcZrMZd2cz4zpXp1HZQL7ecpozcSm4ODkRc9lKk/JBtK4SUujHMj/xKbEhIne15DQrby07wOl1PxK/ZhaBle7F+/4XwM0LE+DsZKJR2QCmDKinad8iIkJympVJq8NZtPs8UQnJxK78guR9q/Bt2IsiLR7BZHay19UGryIiAv+bIb75eCyGYWBJiuPQN68Sf/YodR55hbTSjcm0GVgyDTIyDUymK3v66RrimPr378+XX37JmjVraN26NcnJyUyfPp0+ffrY30Tev38/b731Fh9++CFhYWEMGDCADz74gK1bt9KoUSMAFixYgK+vL9WqVSvI7ohIIbJr1y66du2KYRisX7+e+vXr59iTwtXZTKdaxWlbLTRbQv1OfElXT+lE5K5lsdp4bu4ODs1/n+R9qyjSsCd1HhzA8SRnnJ3NNC4XQO/6pWhbNeSO+/IXEZH8S06z0mPKJo5FJZORHE/0ondIjzhG4AOj8K7eOlvd4n5u2uBVREQAWHskis3HYwn2diXlwjF+/+x5rNZMqg6ZSMV76nAuPpXY5HTMZvB0deLe0v583vdeXUMcVPPmzXnllVd44IEHaNKkCUeOHCE4OJh3333XXiciIoJ58+Yxbtw4wsLCqFatGp9++int27enadOmJCQkcPToUebMmZNjvw4RuTvNmzePxx9/nBo1arB48WKKFy9+zfquzmbaVw+9TdEVDCU2ROSuZLHaeOm79Xz76pOkXgwnsPNz+NZoDSYDgBJ+7kx/tL5uJkRExO6TVUcJj0wmPfI4UQvfAiOT0H7v4l68MgbgZIJywV60qRLCU20qaqafiMhdLmv5qeX7LpKUlkH8vnXs+e4dfIqVpfajb1KqZBgxyRbcnM0U8XKjTKAnjzYuoxer7gBvvvkm//nPf9i7dy/BwcE0btwYJ6f/zeqsWbMmc+fOpWTJkvayESNG0L17d3bs2IGvry/33ntvge2VISKFh81m4/XXX2f8+PH069ePGTNm4OHhUdBhFQq62xKRu9LMJauZ/Gx/bJlWivV7F/cSlTEZBk4mE05maF89VDcTIiJiZ7HaWLr3AsmHNxL7y0RcAksR3ONlnH2CMP6/Tvmi3ix7qrmuHyIikm35qcTL6YT/NpuETXPxrd6Sog88y2VnT/o1KI27i/mOXibkblauXDnKlSuX62chISH06dMnR3mxYsXo0qXLrQ5NRBxEcnIyjz76KIsXL2bChAmMHj0ak8lU0GEVGkpsiMhdwWK1sWL/RRbsOsexbavY/vVbeBQtRYdnPuRQogsplkwAMjEoH+zDU20qFnDEIiJSWFisNj5eeZhjK74ifuP3eFZtQeD9z2B2ubJhpwnwcHViZOsKeiAlIiLAleWnNh2LwbCkcnTu21w6tJkiLR4lqOnDODmZwTBw/v8XqkRERP7p9OnTdOnShRMnTrBkyRIefPDBgg6p0FFiQ0TuaBarjVWHIpm18SR/nokldsMPXNr0PZ5VmhPy4LOYvAOpF+BMeFQSyakZNCjjw+iu9bV8iIjIXS4rIT5/51kOnY0mfP57JB3eRJEWj+Lb6KFsb0r5ujvzwD3F6VijWAFGLCIihcnFxFTOnznNyR/eICMhguAe4/Cs2BAns4nywd6kWKzEp2YUdJgiIlIIbdy4kR49euDt7c2WLVuoUaNGQYdUKOnJnYjcsbKmf/9+MJKLMQlEL59IypFNBLR4BO9GD2M1mTgWlUyAlyuers60rVqU4Y1ClNQQEbnLWaw2Xl2yn592nyMlPpKohW9hjb9A0R7j8KncCKvtyiwNgHJFvRjVrrLWQ3dQFouFv/76Czc3N2rUqHHNqf02m43169fnKK9cuTLFimVPauWnXRG5M23bvIXwGc9gdnGnxIAPcAouA4DVBhmZNsxmM8HebgUbpIiIFDozZ85k+PDhNG3alB9//JGgoKCCDqnQ0tM7Ebljrdgfwc97L3Ap+gIXf3yLjLjzBHcfi0+VJhgGGAZUDPGma+0SBHu70aJiEMmJCQUdtoiIFLC1R6L47UAESacPELXoHUwuboQ+8iGuwWUAE05mAw8XZ+qV8efzvvcqIe6g1qxZQ+/evfH19SU5ORl/f3+WLVtG+fLlc61vsVho3bo1tWvXxs/Pz17+wgsv0Llz5xtuV0TuPLNmzWLaS8NwK16FoK4v4ezld+X+A7AZBnEpGdxfI5RWlYsWdKgiIlJIWK1Wnn/+eT799FOGDRvGZ599houLS0GHVajpLkxE7kjJaVbeWn6Qi0f/InrR25icXQkd8AGuRcvakxpms4l6pQPo37A0cOVNTLlzpaSksH79elJTU2ncuDGhoXlbz/jkyZPs2bOHsLAw6tevf4ujFJGCZLHaWHngApPWHOPctl+IWfEF7iWqENRtDE6eVx5ke7iYcXd15qG6YTzbtpJmaTiopKQkHn74YQYOHMh7772H1Wqlc+fO9O/fn61bt17z2EmTJtGsWbOb3q6IOCaL1caaw5HEx8Xi65vKiq8+YtJnn9LpoUeIrtGP+HQbzmYzZhNYMm14ujjpGiIiItnEx8fTu3dvVq9ezRdffMGIESMKOiSHoMSGiNxRktOsTF57jN8PRnBi83Jif/sct2KVCe4+1v5QymaA2QTlg70Y0apCAUcst8O+ffvo0KEDwcHBBAQEMGDAAKZNm0b//v2veozVamX48OH88MMPtG7dmsTERLy8vPj5558xm3UTKnKnyci08frPB1i48wyRq2aStHMJ3vd0IKDdMExO/3tTytnJzP01QvVAysH9/PPPJCQkMHr0aACcnZ156aWXaNOmDYcOHaJq1apXPfbMmTNs27aNcuXKERwcfNPaFRHHY7HaeOPnA6w+HEmQkcCG2e+RePxPPvl0EsOGD+fNZQdZ9tdFUixWADxdnHjgnuK6hoiIiN2RI0fo0qUL0dHRrFy5kjZt2hR0SA5DiQ0RuWMkp1npOWUTx6MSif7jKxJ3LMK7VnsC2g/P9lCqdIAHHWuE8lSbSlo+5C7x2GOP0aRJE3788UdMJhMTJ05kyJAhtG3blpCQkFyPGTduHEuXLuWvv/6ibNmyAKxcuZLMzEwlNkTuMBarjQW7zrFk6zHOLXyPtFN7CGg7FO97H7DvjWACgn3cGNe5Gh1rhOqBlIP7888/KVOmDAEBAfayunXr2j+7VgJi1KhRFCtWjIMHD3L//fczY8YMAgMDb7jd9PR00tPT7T8nJiYCV2aSajZp3tlsNgzD0JjdII3fjfn9wEWW7z1PUsRp/lo4nrTkS5TtP54KLbvj4mTilc5VaVI+kM3HYwBoUi6Q+6qG4Gy+82eL3+n9ExH5tyxWGx/Omsfbo4YSVDSUTVu2UbVyxYIOy6HoiZ6IODyL1caqgxF8vuYYh89EErv0fS6f/BP/+57Ap+6DmE0mTIANKOLhzMudq9G+et6WIRLHd+jQIf78808+/fRT+wPKoUOHMm7cOBYtWsSwYcNyHJOcnMykSZN444037EkNgPbt29+2uEXk9khOs/LU3F2cOvQXx2e9QeblBEJ7v4l3uTpYMw0MINDLhV51w5QQv4PEx8dnSz4A+Pr64uLiQnx8fK7HODk5MWfOHAYMGABcmbnRtm1bhg0bxo8//njD7U6YMIE33ngj1xitVmu++3a3stlsJCUlYRiGXkC4ARq//MnItLHnTAK//nUB8+ntnPnhfbx8/bl3xIe4BxXnr+PnaFjiysbgDYu70rB4cfuxd8uefklJSQUdgohIoZWekcmDw8bw+1cfEVilAWUHvMLcw+mMK2/TC1T54JB3ZjabjZSUFLy9vfN1nNVqJSEhAQ8PD7y8vG5RdCJyO1msNl5dsp+le85zKfIsUQvHY7scT4k+b+JaqjY2rmzSZwCuTiY61iimTfruMvv27QOgRo0a9jJPT0/KlStn/+yfdu3aRUpKCu3bt2fHjh2cPXuWChUqUKtWrauex5HfuHX0tzQVf8FztD5YrDbWHY3mXEIK32w6xbE/NxG15H1Mnv4Uf/RjXAKKg82GGXAym+hdryTPd6gMFM43UG/l+BfG/t4Mrq6upKamZiuzWq1YrVZcXV1zPcbFxcWe1AAoVaoUL730EkOHDsViseDq6npD7Y4ZM4bnnnvO/nNiYiIlS5bE398fX1/fG+3iXcdms2EymfD399eD+Rug8cs7i9XG+OUH+eNQJCfW/Ej0HzPxLHsv9R4ZxTmLN5ZLUCLByJHkvNs4Ozvk4yYRkVsuPT2dLn3+w++Lf6BK+/406j2SNKvB5uOxrD0SpRdx88GhrjSGYTBu3DgmTZpEWloaJUqU4PPPP6dz5855Ov7JJ59k2rRpPPPMM3zyySe3NlgRuS1WHYxgyd4LxIfvJnrJu5g9ixD66Mc4B5TA1dmMJdNGiK8bFYv60KtuGB1rFFP2+y5z6dIlAIoUKZKtPCAgwP7ZP0VHRwPw5ptvcuzYMcqUKcOGDRto2LAhixYtws3NLccxjvzGraO/pan4C54j9SEj08acrafZf/4SMUlpRGxYysVfZ1K8Um0qPvQCTu5eXEmHg8kEJYq40/eeAOLi4go28Gu4leN/p75xW7p0aebPn49hGPbZfOfPn8cwDEqXLp3ndoKDg7FarURFRREWFnZD7bq5ueV6XTGbzYX+v6fCxmQyadz+BY1f3qw+HMHSXac5u/xzLu1ZiW+DHgS0fBSTq5mMNMBkopif510/jnd7/0VEchMZGUnPnj3Ztn0HVfuOoXH77gB4uoJhpBOdnH6dFuTvHCqx8emnn/LFF1+wcuVK6tWrx8cff0yPHj3Yt28flSpVuuaxCxYsYMeOHdqwT+QOkLX01PrwGNYciSRqy2Li/piOZ9k6BD74Amb3K7O5LJk2Khb1ZuHwplo65C7m4eEBwOXLl7PN9EtKSrJ/9k/u7u7AlZkde/bsAa48mKpevTqTJk3i+eefz3GMI79x6+hvaSr+gudIffj9YCQrjyVzOSWDgwsnkbh3JX71u1G1y6OcueyMzWrCjInSQZ60rhzCk60rFPpryK0c/zv1jdt27drx0ksvsXnzZpo2bQrA4sWL8fT0pEmTJgBkZmayYcMGKleuTLFixUhKSsLHxydbO7/99huBgYEUK1Ysz+2KiGOyWG2sPRLF5BW7Cf/6JSwXjxLywH9xr34fJgwwGbg4mXB1caZ5xaCCDldERAqZPXv20KVLFzIyMvhw9k/8HOFNisWKp6szKRYrJpOJYO+cL7vI1TnUncqkSZMYPHgwjRo1AuDFF19kypQpfPnll3z00UdXPe7UqVM8/fTT/PHHH/Tt2/d2hSsit0DW0lNL/jxPSno6cb9PJXnvb/jU70ZAq8dxdnbGajNwNptoVy2ED3rdU+gfSMmtVa5cOQBOnz5N9erVgSsPAc+ePctDDz2U6zEVKlQAoGvXrvayEiVKUL9+fXbt2pXrMY7+xq2jv6Wp+Aueo/QhIimNCxFRnJw3nrQLRwjs9F98a7YBs4HVMGFgommlYKY9Us+hZvjdqvEv7L/PG3XvvffSp08fBgwYwPjx47l06RIvv/wyr776qj0JnpqaSuvWrZk+fTqDBw/m22+/5ffff6dr164EBATw66+/Mn36dKZNm4aTk1Oe2xURx5OcZmXE97vYvH03p+a+hpGZQUjfCXiEVcW4MskPF7MZf2832lQJoW01LSMiInI3y0qGRyenE+ztRvyBDQx8/D9UqVKFJUuWUDS0OLHLD7L5eCyGkY7JZKJJ+UAtnZ5PDvO0Lzo6mhMnTtCsWbNs5S1atGDbtm1XPc5qtdKvXz/GjRun2Roid4AV+y+yaPc5UpISiF48gfTzhwm8/xm8a7UDwGozMJugSflAPu1Tx6EeSsmt0aBBA4KDg5k/f759qag//viDmJiYbEsZLlu2jKCgIBo1akSVKlWoVKkSBw8epGfPngBkZGQQHh5O3bp1C6QfIvLvZN1cLFy5ifDpozCsFor3nYBLiapkLT1lNkHlYr583vdeXT/uAt988w2ff/45c+fOxc3NjenTp2d7CcrJyYmWLVvaZ2MMHz6ccuXKMX/+fCIjIylXrhy7du3Ksf/S9doVEcdisdoY8f0uVixbSsyyj3AJCCO4xzicfYOx/X9Sw83ZTPOKgdxbqRStq4ToGiIichezWG289f9JC5vNxqlVczj521f0eughvp49G09PTwDGda6WLfnRqnJRXT/yyaESG3BlHdu/Cw4OZuvWrVc9bty4cfj7+zNixIg8n6uwbwDraBt05pX65VgKol/JaVbe+/UQly8eJ/Knt7BlWCjW923cw6oBBibAzcVMg7IBfNanDs7m/G16qt/VncnZ2ZmJEyfy+OOPk5qaSlBQEB999BFPPPFEtodRL730Eo0aNbLPCpw0aRK9evUiOTmZcuXKsWDBAjIyMnj22WcLqCcicqOybi4WL17Mge/fxrlIcUL6jcPFryg2A0yAu4sTzSoFMqlvXc30u0u4uLjw3//+l//+97+5fu7h4cHatWuzlXXo0IEOHTr8q3ZFxLGsORzJyjlfEL12Dl6VmxHQ6VnMrleWLTWbrsyYuyfMj/80LUtIcNAdO9NNRETyZu2RKDYfj8XfJZOdc97h1I4/KNdxIINffcue1ABwdTZro/B/yWHu2rI238vMzMxWbrVar/oPhw0bNjBt2jQ2btxITEyM/fi0tDRiYmIICsp93cvCvgGsI23QmR/ql2O53f3KyLTx0cojJB9cQ8T8j/EIKMY9T0zAvUgwWUkND1cn2lULpVfdMCwpicSl5O8cd/rv6m7Wv39/ypYty7x58zh27BiffPIJffr0yVbnwQcftC9BBdC+fXs2bdrEnDlz2L17N506dWLBggX4+fnd7vBF5F9acziS76ZO5MSKWRSr3Qrv9k+DsxuB3q6YMJGRmUn76kEMaVsLd1eH+eexiIjcIll7+q09eI65H44hctdqijTrj3/TPtgw2et5uTrh7e7Co03K4uJ059w/iIhI/mXNEF++7yKxEef5c96bJEaeoc3I96BMA2IuWwo6xDuOw9y5FS9eHLiye/zfRUZG2qeH/9PRo0cxm820aNHCXpaQkMCxY8dYsGABkZGR9vVw/66wbwDrSBt05of65VhuV78sVhvrjkaz7mgU3301g4g13+BZqQmBnf/LBbM7XJlQhYeLmW5VwxjSttoNT927039XuX3f3U2aNGlyzY1bJ0yYkKOsZs2avP/++7cyLBG5hSxWGyv2nOL5p4dzYstKancdwj0PDuRYTAqRiWlkZBoU8XShSfmi9KqrpUNEROT/99P4bheb9h7h3Pw3yYg7R1C3MXhXbvr/CxdeYQK83JxpVy2E+6oUJTkxoYAiFhGRgvb35acuHt3LkW9fw8nFjU5jpuFZrDzRyRZtDH4LOExiw8/Pj1q1arFq1Sr7Zq+ZmZmsXr2aYcOG2eslJydjtVopUqQIgwYNYtCgQdnaqV27Nq1ateKTTz656rkcYQNYR9mgM7/UL8dyq/tlsdp459fDbDh8gb++m0DcvrUUadqXoBZ9sdrMZC2sVMzPjTH3V6VjjWL/+qHUnf67EhG5W1isNl78ejWzXn+Sy5FnCO0xFte6rTGZzZQo4k6mzaBNlaK0rBRMi4pBeiAlIiIkp1npM20Lu3ZuJ+qntzA5uRDa/wNcQ8phcGXpKTPg7GSmVgk/Hm9ahrbVQlFeXETk7pa1/FTyvlUc/vY9PEtUJqjbGCJdiuKdbNHG4LeIwyQ2AF5++WUGDBhAkyZNaNy4MR988AFWq5Xhw4fb6zz77LNs3bqV/fv3F2CkIvJvZE3fW3c0mhXbDnLyh9e5dOEkxXuMwb1yUzCZcHaCTJtBtWK+/PBEY62HLiIi9utHdHI6W7ZsZfqrI3B2dqHzy9OJdAkl7rKFQxcT8XZ3oV21EMZ1vjLL727dg0hERK5ITrMyafVRFv15npNbfiVmxSTcQisR3H0sTl5F7ItPlQ/2okHZQJpWCKJt1f/N9NN1RETk7hZ5KYWjSydzdt18KjXvQsNHXuRodCoNywXSuWYxbQx+izjUk8CHH36YtLQ0PvnkE8aOHUvNmjVZvXo1oaH/22jFx8cHf3//q7bh7++Pt7f37QhXRG7A36fvnT/6F+HfvY6TkzP3j5lOgmcJIhPT8HBxwt3FiWrFffm8771KaoiISPbp3ztXcujHD/AoVpEHn/sQryJBBBoGhy4m6uZCRESySU6z0mPKJsIjLhG39msSt/+EV812BLYfgcnZxV7PbIYGZQN5u3vNAoxWHE1CQgLe3t44O1/7ntVisRAXF5ejPCgo6LrHikjBunTpEpNeGsLZjWu5t/ez3NOhL6kZmXi7u9C5ZjFtEH4LOdy346OPPsqjjz561c8nTpx4zePXrFlzs0MSkZsg6y3bNYej+PVABJZDazmy4EPcQspT4qFXMAeVooSrOdvSIXooJSIiWdYeiWJTeBQXfp/F4ZXfUbzB/Xi0Gkq84YkXkKabCxERycWk1Uc5eiaKqKXvk3pyN/5thuBTrwsm0/82CTeZwMvVmablAwswUnEkK1asYPjw4URERGAymRg6dCgfffTRVZcH3rx5M61btyYkJCRb+bp166hcufLtCFlEbkB4eDhdunQhIiKCfq9OIdK3MqfjUjCZTFp+6jZwuMSGiNx5ktOsjJy7mwPnLxF/OY3oNd+QuG0BfrXaUqzzU2TgxMVLqfh5umZbOkRERO5uf1966s9jF9g762XiDm+nQZ9nqdquD7vPJBCfkoHVdlk3FyIikqu1O/ZxYc4LWJPjKNrrNTzK1c32uZMJgrxdaVstlLbVlBiX6ztx4gTdunXjjTfeYNSoUezfv5/77ruP4OBgxo4de81jz507pxkaIg5ixcrfeeihh/EuEsBH3y7j4fsasvl4DNHJ6QR7u+ll3NtA35YiUqAsVhsj5+5m07EYbOkpXFz8PqkndhHQZhAhTXpiA7xdnGhfPVSzNERExO7vS09djj7H3lkvk34phmYjP6Divc1JsVgJ9nGjQ/VQSvh76OZCRESAK9ePVYci2XQshhN/bWXVu89h9vSj2CMf4RIYZq/nZDJRtZgP3euWoGQRT11DJM+mT59O0aJFGT16NAC1a9fmiSee4IsvvrhuYiM9PZ309HS8vLxuR6gicgMMw+DTzz7nuef+i3/Fe6k44FW+P2LllPWwXsS9zZTYEJECY7Ha+GjlEbYejyU97iKRC8eTcSmaoj1fxaN8PTJsBgBVQn147cHqujiIiIjd2iNRbDoWw6Xjf/LX16/j4uVLhcGfYClWllOxV2ZoNK0QxLNtK+n6ISIiwJX7jzd+PsDPey9wcctiYn6fhnvpewjqOhon9+x7cTYqH8BX/2mga4jk2/bt22natGm2shYtWvDuu+9y7tw5wsLCrnIkBAcHk5mZSbFixRg3bhyDBw++1eGKSD5YLBaeeuoppk2bRskWD9G4z1N4e7iTYrGy+Xgsa49Eadnb20iJDREpEFk3FQt3nyPh+B6iF0/A7OFNsUc+wjWoJE5mE/6erjiZTTzauIxuKEREBLiyfOHktcfYEB7Nvt9/JHrlVLzK3kOJHmNw8/KldaVgaoT5aYaGiIjksPZIFCv3n+fUkk9J2rMCn3pd8W89EJPZKVs9N2cT/RuW1jVEbkh0dDTVqlXLVhYcHAxAVFRUrokNHx8fvvrqK3r37o2rqytz5sxh0KBBeHp60q9fv1zPkzW7I0tiYiIANpsNm812s7qTLzabDcMwCuz8jkLjlDeFZZwsVhvrjkZz8txFJo97ksN/7WLI2Pc4EVAfHw83wMDL1Ylow0Z0Utptj7ewjNPNkp9+KLEhIgVi1cEIlu+7SOz2ZcSs+hL3kjWvvCnl4YMBuDiZ8HF3pmmFINpWDblueyIicueLT7bQedIGLsYnE/v7lyTt+RWful0o/8BwMjGRmpGJv5cr/RuWLuhQRUSkkLBYbaw6GMGm47HsDT/D/ukvkXb+MIH3P413rfbZ6jqZAJOJRmUDdQ8iN8xkMmG1WrOVZf3s5OSU2yHUrVuXunX/t7/Lf/7zH3777TemTJly1cTGhAkTeOONN3KUx8fH5zj/7WKz2UhKSsIwjKtulC4ap7wqyHHKyLSx50wCcSnp/HkmgSOHD7Pmy1fJtKQx+M3JdGrdjB93ncPLSMXNxYn0jExKeGQS4GQhLi7utsZ6p/09JSUl5bmuEhsicttk3VSsOxrNL3vPcXr5FJL+XI5P3QfxbzMYk9kJE1DMz53BzcsS5q+1bEVE5AqL1cYjs7ZxNiKKmMUTSDt3iICOT+FzTweSLDbcnM14uDgR4udW0KGKiEghkTVLfNneC8SdP0bEj+OxZaQR0vdt3MOqZ6trNoGHqzP1yvjzed97dQ8iN6xEiRJERkZmK8v6uXjx4nlup0KFCmzYsOGqn48ZM4bnnnvO/nNiYiIlS5bE398fX1/ffEZ9c9hsNkwmE/7+/nfEA9ZbReOUNwU1TharjXd+PcSW47Ekp2Vw5s/1RCz9iCIhYTR7/gvCXQMwefhSukQIW47HYhgZmEwmGpcPofU95W779eNO+3tyds57ukKJDRG5LeKTLfSfuZWjkcmkX75E9OJ3ST93gIAOI/Gp3RETV24mfD1cGPdANTrVLFbQIYuISCGQtcnrvB1n+GvffiJ+fAObJZVifd/CNawGAB6uThT3c8dqMyjm61HAEYuISGGx9kgUfxyOIvbgJi4s+RDnIqEU6zsBZ7+i2eq5mKGIpysP1SupvZnkX2vRogXvv/8+GRkZuLi4APDbb79RpUoV+5JUFsuVt7qDgoJwdnbGMAxMJlO2drZv307ZsmWveh43Nzfc3HK+0GE2mwv04abJZCrwGByBxilvCmKc1odHsfl4HEFerpz64zsu/DITnypNaDjoNYKCinAq9jIJqRmM61ydtUeiiE5OL/BlcO+kv6f89EGJDRG55eKTLbSZuJb4yxlYYs4QvfBNbOkphPR5G/eSVx5KGYCLs5lONYtp2reIiABX9tMY8d0utp+KI+HQFqJ+/hBnvxCK9X0HD/9QMmwGAE4mE5kGNK0QRKvKRa/TqoiI3C2iktI4u/o7LqyajWelRgR1fg6Ta84EuLe7C/fXLKakhtwUQ4YMYeLEiQwaNIjRo0ezc+dOZsyYwVdffWWvs379etq1a8e+ffuoUaMGw4cPp0aNGrRs2RKAmTNnsmrVKn7++eeC6obIXSs6OR2rJY0d89/hxLaVBLXoT0DzvmQ6u5FisWIymQj2dsPV2ayNwguYEhsicsskp1mZtDqcH7af4VKalZRj24n5+YMrD6X6vI2z3/8SGCagXJAXY+6vqpsJEREhOc1Kn2lbOHDhEglbF5Kw7ms8KjYk6IFRmF097EkNf08XnmpTkTB/Dy1fKCIidqmpqcx66zkurFqEf9O++DXri9lkJtP4Xx0zEOzjxsudq9GxRqiuIXJTBAYGsm7dOkaPHk2nTp0IDg5m2rRp9O/f317Hzc2NkJAQ+4yOd955h/fee49HHnkEi8VCtWrV2Lp1K/Xr1y+obojctUyX49g9+RlSIk7Ravg7pITVJ+6yhUspFjJtBk3KB+plqkJCiQ0RuSWS06z0mLyRY9GXybQZJG5fSMLa/38o1fk5zG6e2er7eTiTbrWx+XiMMt4iInc5i9XGyLm7OXA2huhfPuPywbX4Ne5Nkeb9wWTGBLg6m6lY1Js5Axvi7+1a0CGLiEghcuHCBbp168b+/fvpNuoDjnhUJyXDRqZxZYNwNxczZpOJ5hWD+aDXPXi769GI3FxVqlRhyZIlV/28efPmRERE2H8OCAjgvffe47333rsd4YnIVWzfvp3nB3TDZLFx78hJmEtUxBuoWsyXNpWLEurnrpepChFdvUXklvho5RGORl3GsFqIXTGJywfW4Pv/D6VMpv9dAExcedu2Zpgf5+JTiU5OL7igRUSkwFmsNj5ZdZQt+45x8Yc3sESfIqjLi3hVbWHfj8nLzZkBjUpryRAREcFitbH2SBQRl9KISEzj8vnDfDF2OB6uzmzcuJEatWqzYt8FPl97nIhLaXi4OuHt5kzTCkGM61xN1xERkbtY1jUkOjmdfWuX8clro7j33nv5Yf4CDl8yF4r9M+TqlNgQkZvKYrWxbO95vtlyCmtyHNE/vU1G9EmCHnwBr2ot7fWczSY8XJ0o5utGCX9P0jIy7esUiojI3clitfHW8oMs/G0dx+e8Cpgo1v99XEIrAFf2Y3JzNvPgPcWV1BAREft1Y9OxGKKT0on88w+il3+CV/EK9H/9C2rUqo2rs5kudcLoWLN4odnkVURECp79GhIezfFfZ3B69ffUat2F337+AR8vD0qFFXSEcj1KbIjITWOx2njj5wP8uOscKRfCif7pLcAgpN97uBWraK9Xyt+DBcOa8PnaY2w+HsuZuBRMJpPWKRQRucutPRLFTz/O5/C893ANKUtoz3Hg4Y/ZDIYBob7uvNSxMh1rFtfDKBERYe2RKDYfj8WEwdmVs0jYMh+vGm0o2ukp1p7NYNWhSDrVLAagTV5FRCSbtUeiWH/gDOHzJnBx32Zq9xpJQKOebDl1ifbVPQo6PMkDJTZE5F/Lmrq37mg0v+y7SML+tcQs/wSX4DIE9xiHs3eAva6fhzODm5ejqJ874zpX01tTIiJ3MYvVxop9F1iw+zyp6RkcXj6DA798TfnG91O009PEpxtYMm14uDhTr4w/n/e9V+ugi4jcxf6+ZEiwtxtnE1K4dOkSR+dNIPHINgJbD8SnQXfcXJxIsVjZdCzGntgQERH5uwNHwtn52ZNYEqNp++zHlKzVhFOxl7VEugPRnaGI/CvJaVZG/vAne84kkJZhJXLNHBK3zMOremsCOz6Fyfl/G7p6uJgJ8nYj1M8d0FtTIiJ3M4vVxqtL9vPT7nOkpVwmZvnHpIZvI7DNQGr3ehwfd1cuJKQSn5JBr7phWnpKROQul7VkyObjsRiGAUB85DkOzB5HRmI0RXu9imf5+pgAk8nElQUMRUREclq7di2vD+qB1cmTOk9+jnupCiSnZ2iJdAejxIaI3LCMTBujftjNhqOx2CypRC/7iNTwbRRp9R98G/T8/xuK/3E2m2hUTstNiYjIlanfK/Zf5HLsRaIXjseaGGV/KHU8OoUALysmk4mONUKV1JBbJjExke3bt+Pm5kbDhg1xdXW97jEnTpzg+PHjhIWFUbVq1Wyf2Ww2li5dmuOYOnXqULp06ZsWt8jdxmK18cmqo6zYH4G/pwvF/T3Zv3MLe796FWd3L0r952MoEoYBmEyQabPh6eJE0/KBBR26iIgUMlOnTuWpp54irFo9PDuOIs7Zk7iIRDxdnHjgnuJ6ZuVAlNgQkRuSnGbl89XhbDmWQMalSKIXjifjUiTBPV/Bs0KDbHXdnc0U9XXFkglNywfq4ZSIyF3q70uI7D9/ieijfxKxaAJmNy9CH/kIt6BSmExQsag3XeuU0DKFckstW7aM/v37U7FiRRITE0lLS+OXX36hRo0audbfs2cPI0aMIDIyknLlyvHXX39RoUIFFi1aRNGiV26ALRYL3bt3p3nz5gQE/G8pTm9vbyU2RG5Q1kyNX/dHkJBiITndyvENizmxZBLupWrQcNB4vPyKcC4ulcS0DDxczPh4uHJflaK0rabZ4SIickVGRgbPPvsskydPpmu/QSTW7kuwryeplkyS0q2kZWTStEKQ7j0ciBIbIpJvyWlWHv5yM5lpSSSdPkDUogmY3TwJe+wjnANLARDk7Upaho1SAZ4E+7hhNpk4FXuZ+NSMAo5eREQKwj+XEDm6bjEXln+OW1h1gru9hLOH75U3bYF6ZQLo31APgeXWSUhIYMCAAYwaNYpXX30VwzDo0aMHjzzyCH/++Weux8TGxvLxxx/TqFEjAJKTk2nSpAnPPPMMc+fOzVb3nXfeoVmzZre8HyJ3uqyZGr/uu4jVMCDTysWV00nYuYzQxl3xajEQVy9fQnzc8XJ14kxcKm2qFKVlpWAlxkVE7nJ/f6nKzXqZL8aNZOPGDUybNg3PWu2ZtfEk3m7OeLs5E+zjduWZVYqloMOWfFBiQ0TyJTnNyiMztxIemYzbkZVELJ2KW1hVgruNwezhi80AT1cnutcJ44/DUfi4O2M2mUixWLVWofwfe/cdH0W1NnD8N1uy6b2T0DuEroCA9N4RpIqigIINQXxRLCAICmJHsaDYsdCLiFSlSlda6D29b5Kts+8fMQu5AQwxIYXnez/3XjJzZvaZ2U3OnvPMOUcIcQfbcDSW9UfjcNU4OL/2Iy79vhSvJj3x6zAGRatzzoQe6GlgfLvqJRqrKP9WrlxJVlYWTz/9NJAzH/+kSZNo06YNf//9N1FRUfmO6dixY56fPT096dWrFz///HO+skePHiUzM5OqVatSo0aN4rkIIcq53IT42r9jSM60YM3KIHHFbEwXjxDQ9XEqtO5LgIcLNtXBuaRMFEWhc90QXuxZVxIaQghxh7PYVKavOsLG4/FkxJzl9LevoLFls279b3Rs3471R2JR/umrcnfRSZ9VGSWJDSFEgVhsKqsPXWb6qqOkZplJ3fQZ6ftW4dW4O34dH0XR5vw5cXfR0rdhOE92qEG21f7Pk7lmFEXhnmqyvoYQQtxJcp+SupiaxcLfzxAbl0jCijfIuvA3kb2fwqV+F/zc9dgcYLM50GrgxR618XSVr6iieB06dIjKlSvj4+Pj3NaoUSMA/vrrr+smNq5n69at+dbZAHjrrbcIDw9n3759NG3alG+//ZawsLDrnsNsNmM2m50/p6enAznrdaiqWtBLuuOpqorD4ZB7Vkil8f5tPh7HztOJuGgVLAkXiFsyA9VsJHLoDFwio7i7sh+vD2jAzjNJJBrNBHoaaFszCJ2G234dpfH+lTS5F0KIkrThaCyrD10h6dhOYpbPQecTTPjQmWT65jxA1a5WMNtOJUqfVRknrUYhxL9KMVoYvnAXx2MysJqMJK54A9P5Q9Tq8xiWOj1RyVkkvF64N4+3r06nOiG46DS82LOuc9ifzJMuhBB3ltwnbX+PjuNCsglz4kXil76Kmm0kfMgM3Ks2QqMoaLVavF20zsZEt6jwkg5d3AHS0tLw8/PLs83T0xMXFxdSU1MLdI65c+eyd+9edu7c6dym1WpZtWoVvXr1AiAhIYEOHTowZswYVq9efd3zzJ49m+nTp+fbnpKSgs1mK+AVCVVVycjIwOFwoNHI981bVZruX7bFzvKDlzl8OQ0Ph4n0U38S++0buPoG0/CRebgHhOKm19Aq0oAlK52moXpAD4AxPbVEYi5N96+0yMjIKOkQhBB3sG2nErn0+2KSN3+Je43mBPWaiM3FnQ+2nKZbVLj0WZUTktgQQtxUitFCh7e3kJJpxZp0kfglM1CzMwgdPIOIqCjO5DxUSIiXgR/GtszzlK2LTkOXerJgnxBC3GksNpV566NZuu8iRotK9um9JKycg847kNAH30bvG4rFptKskh8P3VOZlGyrNCbEbeXi4kJWVlaebVarFYvFgsHw71MQLFq0iKlTp/Ltt9/SpEkT53a9Xu9MagAEBQUxefJkHn74YUwmE66urvnO9fzzzzNx4kTnz+np6URGRuLn54e3t3dhLu+OpKoqiqLg5+cnHcuFUFrun9FkY/jXOzibkInNrpL25zKStyzCvfpdBPeaRKzOHW0G+HvqCQwIxN/fv8RivVZpuX+liU4n3U1CiJJhMplY9d5Ukreuxqfl/QS0HYFG0WJVHcSmmdgSHU+XeqHSZ1UOSE0jhLgho8lG3/nbSMm0kn1mX06nlGcAoSPfwuAXCv/MiG7QaZjas65MHSKEEMI5n+3P+y5hstrJ2LOMlC2LcKvWjMBez6IxuAM5dcdDrarQI+r60/MIUZyqVq3K4sWLUVXV2Ql58eJFAKpUqXLTY7/66ivGjh3LV199xaBBg/71tXx9fbHb7SQlJVGhQoV8+w0Gw3WTKRqNRjpIb5GiKHLf/oOSvH+5Uxd+vfM8J+IyUW0Wkn79gMzDm/BuMQjfex9AVTRoFVCByoGetK8dUqrea/n85SX3QQhREmJiYujfvz8nDh4irN9zGGrdiwOwOxzoNApuLloSjOZ/PY8oG6QXUgiRj9Fk4/1NJ1l24DJx6SYy9iwnZcsXuFVtSmDvyf90SuUkNbQa6N+4At3qS5ZbCCEEbImOZ+OxOCxmM0nrPiDz8Ea8WwzEt80DKBqts1ykvzud6oSUYKTiTtatWzcmT57M5s2bnYuC//TTT3h7e3PPPfcAYLfbWbVqFY0bN6ZSpUoAfPPNN4wZM4Yvv/ySIUOG5DtvYmIigYGBebatWLGC0NDQG66xIcSdLnfqwh2nk7iYlInNmEL8splY4s4Q2PtZPOq2QwG0GgU/dz06rYaRLSvLCD8hhLjD5SbFc6eS8jZeZOB9/XE4HGzevIUFR2HH6SQUcqYJ9HXT4WnQyQLh5UiBExtTpkwp8Elff/31QgVTEKdPn+bzzz8nLi6OqKgoxo4di5ub2w3L22w2lixZwo4dO9DpdLRu3Zp+/fqhKEqxxShEWWY02Rjw4TZOJWRit1pJ+nU+mYc34N38PnzvHenslFKAAA8XxnepQ69GEdKwENd18eJF5s+fX6CyFStWZPz48cUWy9GjR1m6dCnZ2dm0b9+eTp06FfjYH3/8kR07dvDggw/SuHHjYotRiPIgwWgmLTmBmO9fxRx3moBek/Cs1z5PGY0CT7SvIXXHHWr79u2sWrWqQGVbt26dZ2qnolK/fn1Gjx7NAw88wAsvvEBaWhozZsxg3rx5zrZFdnY2/fv359NPP2X06NGsXr2ahx56iMGDB+Pm5sby5cuBnOlWcmNctWoV33zzDX369MHf359ffvmFpUuX8tVXX8nTy0LcwJboeHacTsLPXcexv08St2QmOFRCh7+BIayms5yi5PxP57ohkhgXReb777/n0KFDBSo7bNgwGjRoUMwRCSEKwmiy8cT3+zl6JR2NAsZjvxP941waN2zA8uXLCA8Pp0HjvGU8DDpaVQ+UBcLLkQInNg4ePOj8t9lsZsuWLfj4+FCnTh0gp8MoPT2ddu3aFXWMTn/99RetW7emZ8+etGjRgs8//5xvv/2Wbdu24eLikq+8qqrUrVuXpk2b0rJlS7Kyshg3bhyLFy/mhx9+KLY4hSirLDaVST8e5ER8JvbMFBKWzcIceypfp5S7i4aZferTMtJASFCgNNTFDWVmZuapP+Li4jh48CChoaHUqFGDzMxMDh8+jEaj4bHHHiu2OFatWsXAgQMZNmwYgYGBDBw4kHHjxjF79ux/PXb//v1MmjSJS5cu0aJFC0lsCPEv0i6e4PSnT6GqKuHDXkcfXitfmf6NZKTfnSwhISFP3RAdHc25c+eoXr06ERERxMfHc/ToUUJDQ/91Wqj/4uOPP+bLL79kw4YNGAwGli1bRvfu3Z37dTodffv2pXLlykDO2he9evUiMzOTRYsWOcu5ubk5ExujRo2ibt26/Pjjj+zZs4fq1atz9OhRqlatWmzXIURZk/uEbWyaidh0E+cTM0lIz+b49t+JWfkW+qCKBPV/EZ1XgPMYL1ctrnodA5tGMKFTTUmMiyJz/vz5PHXSn3/+SVpaGvXr18fPz49z585x/vx5atWqlaeOEEKUHItN5Ynv97P9VCIaxUHS1m9J2vY9IU068dJHnxAeHg6Ap6uOTx5oJguEl2MFTmysW7fO+e8JEyZQqVIl5s+fj4eHB5DTefX444/j5+dX9FH+Y8qUKbRs2ZLvv/8egKFDh1KpUiW+/PJLxowZk6+8oiisX7/e2RgBaNWqFffeey//93//l2ehPyHuZBabyvL9F5i97gQpWVYscaeJXzITVBuhw17H8E+nlAJE+rmx4vHW+LjrSE5OLtnARalXu3btPPVHixYteOutt3jqqafQanNG/1y5coV+/frRvHnzYolBVVXGjx/Pk08+yZtvvgnk1AX33XcfDz30ELVq5e90zWU0Ghk6dCjz58+nb9++xRKfEOXJTz/9xLOjRuEdUhm3Hv+Hzivwn4kLc+qQCn6uTOxUk14NK0iD4g7Wr18/+vXrB+QkOe666y5+/fVXunTp4iyzf/9++vfvT9u2bYstDo1Gw6hRoxg1atR197u6ujpHZUDOk7rDhg371/M2b9682Oo0Ico6o8nG+O/2ceBCKiaLHavqQHGopGz7jrQdi3Gv25aAbk+h0eedJsTdRU+XeiGS1BBFbsqUKc4ZSrZv385DDz3Erl27qFnz6mihxYsX88orr3D33XeXVJhCiGtsiY7nyJV0FJuJ+FVvkXp8B0EdRlGpw1DSbXnrCFkgvHwr1DeCJUuWMHfuXGdSA8DDw4M5c+awZMmSIgvuWhaLhQ0bNnD//fc7twUHB9OhQwdWr1593WMURcmT1ACcT0slJiYWS5xClDUWm8rzS/7iuaVHSMmyknl8G7HfPofWw4fQkW87kxp6jULbWkGsffpe/Dzzj5AS4t9cvnyZhIQEnnnmGWdSAyA8PJwXX3yRpUuXFsvr7tu3j0uXLjFixAjntt69e+Pj48PKlStveuz48ePp0aNHns42IUR+qqryxhtvMGTIEPr27cux/btp37Q23q46vF11hHobGNa8IpsmtWdA00jplBJOv//+O61atcr3d7ZJkyYMHz6ctWvXllBkQoiiZrGpjP9uH3+cSCTDZMOqOlAt2cQtn03ajh/wbfsggb2ezZPUUIAawR683LsuL/asK/WHKFbLly/nqaeeypPUABgyZAgVKlTgwIEDJRSZEOJaCUYzttRYLix6lvTT+6k2dBr+9wzCgSLrZ9xhCrV4eHJyMomJiQQFBeXZnpiYSFJSUpEE9r8uXLiA1Wp1LtyXq1KlSvzxxx8FPs+HH36Il5fXTTPtZrMZs9ns/Dk9PR3IabSrqnqLkRc9VVVxOBylIpaiJNd1+1lsKm//Fs2Kg5dQHCqp2xeTuv17POrcS2D3J9HoXQEHWg082LISz3atjYtO4/xdKK3XVVjl8Zqg9FxXcnIy6enpWK1W9Hp9nn3FWX+cOHECgGrVqjm3abVaKlWqxMmTJ2943JdffsmBAwfYu3cvDofjhuVylfa642ZKy2eksCT+28tiU9l4LI4/TiVyOSULm8XEga9fI3rXRqZNf5UXp76Aoih8MqIpW08kkGg0E+hpoG3NIHQaSuV1lrX34H8VZ/zFfU9y2xXXk5iYKOviCVFOWGwq72w4wZ9nkp0j+Wxp8cQveRVbWhxBA17EvcbVkU7af37164R5s3hsSzxdC9V1IcQtSU5Oxt3d/br7kpKSiq29IoS4NbHRBzjy0RNoDO5UHvU2uqCK2FUHdcO9Zf2MO0yhvh307t2bIUOG8Pbbb9OsWTMA9u7dyzPPPFNsU3VkZ2cD4OnpmWe7l5eXc9+/Wb58OW+88QZffvklvr6+Nyw3e/Zspk+fnm97SkoKNput4EEXE1VVycjIwOFwlKu1DeS6bh+rXWXf+RTWH4nldIKRSEM2R35+h9QjO6jaaQSV2w36pyPBgateS6tqATx0dzDG9FTnOUrjdf1X5fGa4Op1lbS6devi4+PDoEGDmDZtGrVq1SIzM5MNGzbw3HPP8fLLLxfL62ZlZQH56w9vb2/nvv914sQJJk2axObNmzEYDJhMpn99ndJed9xMWf/sS/y3j9WusvCPM2w7lYhNdZCdEs9f37xGdnIMncdOxRDVhfjEJPTanOtoGqoHchKZ19YhpU1Zeg+upzjjL+76o3Pnzjz55JNMnjyZcePGUaFCBRISEvjuu+/44osvbukBJiFE6ZRitPDA57s5GW/EbMtJlpouHSFh2SwUvSuhI+biElTZWV4hZ4HXJpX8+GBoE0lqiNumd+/eDB06lLCwMPr16+dcY2Pu3LlcvHiRNm3aFPhcDoeDFStWcPDgQYKDgxk0aFC+h4NvxGQyMWvWLAwGA1OnTi3s5QhRLljtKr8djSMx00KQp4Ez21by/JNPEF6rEZGDpmLVeWB3QL1wbz4Y2kRG9t1hCvUN4eOPP+bxxx+nc+fOzqe4NBoNw4YNY/78+UUaYC4fHx8gp4PoWsnJyc59N7Nu3TqGDBnCvHnzGD58+E3LPv/880ycONH5c3p6OpGRkfj5+eHt7V2I6IuWqqooioKfn1+ZbHzfiFzX7WGxqcxYc5R1f8eQbrJiSY0ndslrWFOuENz/BRw1W3L2nz4MP3c9r/aKomPt/IsrlbbrKgrl8Zrg6nVdO/1TSdBqtaxevZqxY8fmWYDbw8ODZ599lieffLJYXtfLywuAtLS0PEntlJQU6tate91jXn/9dYKCgli4cCEAdrsdgK+++oorV67kqSNylfa642bK+mdf4r99Vh28zJf7ErE7wHTpGHHLZqHRGwgbMQdtlcqsOp5O4xp2OtcNLOlQb0lZeg+upzjj1+mKt0OxcuXKrFixgieeeMK5DhLkTFP41Vdf0aJFi2J9fSFE8bHYVFYfvMSLK4+QZbk6+ivj0HqS13+IoUJtgvo9j9Y9b3s+yMvAtD716FQnRDqoxG3Vr18/5s6dy9SpUxk3bpxze8OGDfnll19u+oDstRwOBwMGDGDv3r0MGTKEJUuWMG3aNLZt25ZvmqvrmThxIj/88AN6vV4SG+KOZrGpfL3rPL+dzsRms3Nq1Ydc2raU9v2GM+GlWeh0OlKyrbIo+B2sUC0VHx8fvvnmG958802io6OBnAViQ0JCijS4a0VGRuLr68vhw4fp3r27c/vff/9NVFTUTY/99ddf6d+/P7Nnz+bpp5/+19cyGAwYDPnnZNNoNKWmsasoSqmKp6jIdRUvo8nGpB8PsPF4AqrqIOvScRKWvYaic8l5Uiq4CrlNDoNW4Zen2xLs43rD85WW6ypK5fGa4Op1lbTatWvz+++/c/r0ac6cOYO3tzd16tQp1o7/3OTF8ePHnR1kZrOZs2fPMmbMmOseM3jwYBo0aOD8OXfERVBQEGFhYdc9pizUHTdT1j/7Ev/tseTAFawOBeNf60n69WqnlN7dGwdgtTtIzLSU+uu4nrLyHtxIccV/O+5H165dOX78OMeOHePy5cuEhIRQp06d6/5NFUKUDRabyssrDvPzvkvY1JzJpxyqnZRNC8nYtxLPRt3w7/Qoijbv9KQ6DbzYozY9oq7/fUuI4vbEE0/w8MMPc+zYMZKSkqhYsSK1atW6pakRly1bxqpVqzh27Bg1atRAVVXatWvH5MmTWbFixb8eu23bNp566ik++uij/3o5QpRpW08kcORyGj6KjZ1fvcKVY3sJ6jqOrLsG8N6Ws9xTLUDWX7rD/adHsEJDQwkNvT0ryyuKwpAhQ1i4cCGPPfYYXl5e7Nixgz///JNXX33VWW7hwoWcOnWK2bNnA/Dbb7/Rr18/Zs2axTPPPHNbYhWiNDKabAz4aDsn44w4AOPfG0j69QMMYbUI6v8COncfHIBGgQq+bix57J6bJjWE+C+qVauWZ82L4tSgQQNq167NggULnImNr7/+GovFQr9+/Zzl3njjDSpXrszgwYPp2rUrXbt2de4zmUxMnjyZ7t27M2TIkNsStxClUZbJTPLGT8nYuwLPht3w75zbKZXTaaXTamTBPlEoWq2W+vXrU79+/ZIORQhRBNb9fYWlBy47kxp2k5HEFW9gOn8I/86P4dukJwa9lizr1ZEcOg30bxxBt6jwkgpbCADc3d1p2rRpoY9funQp99xzDzVq1AByHhIYOXIk48ePx2Qy4ep6/Xb2hQsXePzxx/n1119Zs2ZNoV9fiPIi0WgmNfYCWz6ZRnZGGpWGvYZr5Yb4uLvg765nx+kktkTH06Xe7embFqVPoRMbv/32G59//jlnzpxh9+7dALz33nuMHDmywMPzbtWsWbPo0qUL9evXp169evzxxx8888wzdOnSxVlm586d7Nq1i9mzZ5ORkUHfvn3x9vZm3759jBgxwllu9OjRtGvXrljiFKK0sdhUJv14gJNxRlTVTsqWL8jYsxzPBl3w7zIORavHVa+hQaQvD7aoRKe6oZLxFsXC4XCwcOFCVq9ejb+/P59//jmZmZksWLCASZMmFdvrfv7553Tv3p0OHToQGBjI6tWrmTdvHpGRkc4yX3/9NS1atGDw4MHFFocQZVlqaip/fT6VjAM78Ov0KF5NeuV5etFFp6F9tWBZsE/csqysLN588022b99O165dmThxIidOnGD//v2STBaiDLHYVNYdjuWnvRfZcy4Zyz/raViTLhG/dAZqVhrB97+KW+VGeLvr+fXpe1m4/SwHLqTgqtcysEkFukWFSztElKgrV64we/Zs/vrrL6ZOnUqXLl3YtGkTLi4utG7dukDnOHHiRL5EffXq1bFarZw9e5Y6derkO8ZutzNs2DCee+45oqKiCpTYMJvNmM1m58/p6elAzhSVudPG326qquJwOErs9csKuU8Fc/bAdn558yncfINoMeEjEjW+aBRw1Sp4uGhJcKgkZJju+PtY3j5Pt3IdhUps/PDDD4wZM4aHHnqIxYsXO7dbLBbmzJnDrFmzCnPaf+Xn58euXbvYvn07cXFxvPXWW9SuXTtPmdGjRzufwHVxceGTTz657rmu7cwSojyz2FRmrjnK1hOJ2E1GElbOxXTugLNTSqMoGPQaRraszKQutaQhIYrVxIkTWbZsGa1bt+bo0aNAzhobO3fuZOXKlfTp06dYXrdly5acPHmSdevWkZ2dzauvvpqv/pgyZcoNRyHq9XrefvttmjRpUizxCVHanThxgj59+pAYH0+TR+eQ6lcbh5ozTsPVRUPP+mG0q+xGp0bVpB4Rt0RVVbp06YLFYsHHx4cLFy4AOWtv9OvXj1atWsn3diHKAItN5aVlf7PkmlEaANln9pGwcg46T39CR76F3i8crQK+bjoOXkrl+R75O3iFKCmJiYk0btyYFi1akJSURHJyMgBVqlShd+/eHDx4sEBrT2VmZuabajd3bdjMzMzrHvPyyy/j7u5eoOnTc82ePZvp06fn256SkuKcSvd2U1WVjIwMHA5HmZ3e83aQ+3RzDoeDBQsWMG3aNOo0aU6DIf+HTeuKu8mKl6ueyr5gsWVTwc2Ov9bi/F29U5W3z1NGRkaByxYqsfHaa6/x448/0q1bN95//33n9v79+9OuXbtiS2xAzjD1e++994b7r11g0GAw5BmlIcSdaEt0PNtPJWJKvkzMD9NRM1MIHjQdtyo5izdrNXBfkwhJaohil5GRwWeffUZ0dDQXL17M86W9f//+fP3118WW2ICc9TEeeOCBG+6/WX2h1WqZMGFCMUQlROllsalsiY5n08bfmP/Sk1QID2P37t2ERVbhwy2nuJyaTQVfN8a3q467i4bk5GSpR8Qt27JlC6mpqRw8eJD58+dz9uxZIOcBpY4dO/Ljjz8W64g+IUTRWH3oCj/tu+Rcr8/hcJCxdyUpmxfiVqUJgX0mozF4AFAz1BOLzUGC0XzjEwpRAj755BO6d+/OokWLGDp0qHN7lSpVcHNzY/fu3bRq1epfz+Pp6UlaWlqebampqc59/ystLY3XX3+dESNG8PzzzwOwa9cujEYjU6ZMYciQITRq1Cjfcc8//zwTJ050/pyenk5kZCR+fn7FuobhzaiqiqIo+Pn5lYsO1uIi9+nGzGYz48aN48svv+TZZ5/lqQnPcCQZ4jJMbIlO4GJyFinxNhRFoWW1ENo3rHrHt0HK2+epIAlkZ9nCvMDJkydp27YtQJ4pCIKDg4mPjy/MKYUQRSi3MyrBaObw5TTOHNzJxZ9eQ+Phl/OklH8FZ9nmVQN4pXe9O74iEMXvzJkzVK1alfDwcC5dupRnn9QfQpQuKUYLIxbuYv8v3xO7/hP8azWjz0vvUKlKzoiM57rlHfFUXoY9i9vv5MmTtGrVCp1Ol29hVqkbhCj9jCYb72w4wRfbz15NatisJK3/kMy/f8P77gH4tn0QRaMFwEWrYLaqaDSyJpMofW7U1wW3VifVqVOH6OjoPNuio6MxGAxUrVo1X3kXFxdee+21PNvc3NxQFAVfX1/0ev11X8dgMGAw5P890mg0Jdq5qShKicdQFsh9yi8uLo4BAwawb98+vvrqK4YPH05ycjJd6vmj0WgYendlZ19XkKeBdrWCpS/rH+Xp83Qr11CoxEZQUBCnTp0iKioqzx/7TZs2UalSpcKcUghRRHKnntp+KhGjycq535cQ/9uneFRpjH/vyWhcc54QUQBPg5bhd1eUikDcFkFBQVy6dAmz2ZyvoSD1hxClh9Fko/s7mzjy09sY/1qP990DCOo0ir1XTLI4nyhyue0KyN+JtGnTJgYNGlQSYQkhCiA+zUTX934nJdPq3GbPTCVh2SzMsScI6PkMnvU7AqBRwOEAm+ogy6rSpW6QrMkkSp0b1UkZGRns3buXGTNmFOg8gwYNok+fPvz9999ERUVhtVr54osv6NOnDy4uLgCcOnWKzz77jGeeeYaQkBCmTJmS7zwHDhy47nYhyqM9e/fTs3dvLFYrc75YyuBB3fKVcdFppC0i8ihUb+aoUaMYO3YsBw8eRFEU4uLi+PLLLxkzZgyjR48u6hiFEAVksam8s+EEa/+OISY5g+M/zyN+/cd4NetD0MCX0btfHfbqZdDSp1EFOtWVSkHcHuHh4TRr1oyxY8dy4cIFHA4HZ8+e5bXXXuOtt97ikUceKekQhRDAnOW7OfTxJIxHNhHc6xmCOjyM2a6QnGmRaUNEkevatSvHjx/njTfecM4JfvjwYUaNGsX+/fslsSFEKWSxqSzdd5E2czblSWpY4s8Q89UzWFOvEDr0dWdSA0CrKGg1CtWCPXm5Zx1e7FlXHq4Spc4DDzzAggUL+PHHHzGZTGRnZ7Nt2zZ69uxJREQEjRs3LtB5evbsyYgRI+jYsSNjxoyhVatWxMTEMHfuXGeZc+fO8cYbb5CQkFBclyNEmfHt9z9yT6tWGBUPwka+xXdnXZi+6ggWm4wKFzdXqBEbL730EomJiTRr1gy73U5oaCgajYZx48bx7LPPFnWMQogCMJpsPPbNPnafScJk/OdJqZhoAntMwCOqE4oCIV6uAFjsKvc3i2RCp5rSoBC31bfffsuIESMYOHAgAFWrVsXT05N3333XOexbCFH8rp2yMMjTwD3VAtlxOpG9Bw4w7+mHsZpNhA2bjUdE3ZwhfnYHNhWZNkQUOQ8PD9auXcvQoUM5duwYAPPnz6dChQqsWLGCoKCgEo5QCHEto8nG+O/2sf1kIvara4STFb2DxDXz0PtHEDTgRXTeV393NQoY9FqaVfbjg6FN8HQtVDeEEMUuKiqKTz/9lMcee4yEhASWLl0KQNOmTVmyZEm+kYU38+WXX7J582YOHjxI27Zt6du3L15eXs79NWrUYPbs2YSGXv9Bw3bt2l13PQ4hyhNVVZk2/VVmvDodjzpt8O/+NCa9K5YMM6sOXeGeagE0D3cp6TBFKVaobxSpqanMnz+fadOmcfDgQVRVpWHDhoSGhpKYmEhgYGBRxymEuAGLTWXd4RhmrT1GbLoZS/xZ4pfMwGGzEDJkNm4Rdchtc1jsKr7uLnSqFiBJDXHb2Ww29Ho969at48yZM0RHR+Pq6kqzZs1wc3MjLS0NHx+fkg5TiHIvd8rCHaeTcDgcOBwOTDaV+L9+5+ySORj8KxA+ZDYGnyDsDgcOFRxA5QB3mTZEFDmTyUT16tU5cuQIf/31F5cuXSIgIICmTZtit9vJzMzEw8OjpMMUQpCT1BjyyU6OXEl3ti8cDgdpOxaTtu1b3Gu3IaDH02j0OQ9TKYCXqxYXnVYeqhJlQkZGBr169eLixYvs37+f5ORkIiMjadCgARkZGVgsFudUUgXRvn172rdvf919lSpVuuk0Uy1atKBFixa3fA1ClAUWm8q6A+d4bcqT/LlpLcHtR+J21yD02pw6wu5wkGmxs+N0Is3Dw0s4WlGaFXqNDYfDQVBQEJ07d77uPiFE8TOabDz+7T52nEnCaneQdWIniavnofMLJ/i+F9F5B+MAtAq4u2jpWi+UtjWDZIElUSL27t3LhAkT2LVrF1WrVs2zcN6uXbuc+4QQxWtLdDw7TicR5OmCq17LvvPJXNj0LWl/fINXnTbUGPgs6XYdqsOBouR0TIX4uPL1w82l7hBFbsGCBZw7d4533nmHhg0b0rBhQ+e++fPnO/cJIUqWxabyxPf7ORZzNamhWk0krXmHrOht+LQZgU/LwSiKggLoNAp+7nq83PS0qh4oSQ1RJowZM4Z+/foxZMgQWrZsecN9QojCs9hUJi/ayBfTHicr4SLVh70Cle/GanfgwIGiKDhUUJC+ZfHvinQMaHZ2Nq6urkV5SiHEDRhNNu7/eAfHYjJQHQ7Sd/5I6h9f416rFQE9nkHjcvVJKUWBppX9eaV3PWlQiFIpOzsbNze3kg5DiDtCgtGMw+HA3UVHTFIaZ3+cjfHY7wS1HUF4+xFk2x34uWupHeqNn4cLFXzdGN+uukwdIm47qRuEKB1y1/Hbey7Fuc2WnkDC0plYky8R1O8F3GvdA4CLVmFUqyo0jPQlJctCkKdBHqoS5YLUSUIUjfmL1/DRMw+iNxjo/eJCsjwrcCrBiAZQHcA/D8u76LXcUzWgRGMVpd8ttVBnzpx53X9Dzrxoe/fupUGDBkUTmRDihiw2lce/3cexmAzsVhNJv7xH1rHf8Wk1DJ9WQ1CUqw0HV72G5lUD+GBoE2lQiBJx+fJlvvjiCy5dusTly5fz1R9Wq5U1a9bQqlWrEopQiDtLkKcBRVFIjLvC9g/+j8y4cwT1e56gqHvRaTSYbDZAYUSLSnSpd/15n4X4r/7880/Wr1/Pzp07SUlJyVc3ZGZm8s033zBnzpwSilAIYbGprD54iTnro4nPsOR0OAHmy8eIX/YailZP6Ii5uARfHYVbOdCDSV1qSbtDlCnLli3jyJEjHD16FIfDwalTp/LsT0xMZOPGjcybN6+EIhSifFi0aBHPjX0Uj4ja9JgwFzdvP3wdDmLSTFhsKlZVxeEAN72GPg3D6VgnBGN6akmHLUqxW0psrF69+rr/BtDr9VSuXJnZs2cXTWRCiOuy2FTmrY/OmX4qIzHnSamkiwT2nYJH7dZ5ygZ5uvBSzzp0iwqXxoUoMampqaxevRqj0ej897Xc3Nxo2bIlL730UglFKMSdIWdNplh+2nuRKycOcfLbaWi0eio8MBfP8Oo4AKPFhl11UDfcW9bTEMXq7NmzrF69mtjYWMxmc766wdvbm7Fjx3L//feXUIRC3LmMJhvvbIhm6f5LJGfZ8u77eyNJv76PIawmQf1eQOvh69zn7qLlifY1pN0hypz9+/fz22+/ceXKFbKzszl//rxzn6IoBAQEsGDBAqpXr16CUQpRdtntdv7v//6PefPm0e2+YZjvHoXDNWcNNZPVTpCXgU51QjCac+qcVtUC6FQ3FKlOxL+5pcRG7tznvXr1ytf4EEIUP4tNZfqqIyzZfwnjxeMkLJ0JGh2hw+fiEnL1SSlXnYYRLSoyoVMtmTpElLh69eqxa9cuoqOj+fTTT3nzzTdLOiQh7hgWm8qW6HgupWTx095LnIjLIO3vTSStex9DaA1qDX+FBjUqcTk1iyyLHbsD6oV7yyg/UewGDx7M4MGDWblyJXFxcYwZM6akQxJCkJPU6P/Rdk4nZOXZ7lDtpG79kvQ/l+LZoAv+XcahaPVAztS3vu56ekSF0a2+jPQTZc+MGTOYMWMGc+bMoWXLlrRp06akQxKizMptfyQYzQR5GmgcamDkiGGsX7+ed999l0fHPc5ra4+x43QSDocZRVFoVT3wuqP9VFUtoasQZUWhejwlqSFEydhwLI61f8eQfHAjCb+8hyGkGkEDpqL18MtTrl/jCrzYq14JRSnE9dWqVUuSGkLcRhabysw1R9l+KpErqdlkma3OTimPqE4EdHmcdEVPrVAvHmhZydn4kLnQxe3Up0+fkg5BCPEPq11l8g/78yU1VHMmiSvnkn12P34dx+DVtA9ajYJOA3YHVAvyZEKnmnSqEyL1hyjTnnvuuZIOQYgyLbf9kZO0cJCVcIljX72E1ZjMzAXf8dioQbjoNLzYs26e5Ie0P0RhFepTc+DAASZOnJhv+8SJEzlw4MB/DkoIcZXRZGPOuuM8+d1+Zqz8m7NrPyFh9Tw86rQlZOjsfEkNN72Ge2sEllC0QtzcN998w8KFC/NsM5lMDBs2jNTU1JIJSohyakt0PDtOJ6HTKFiyM0lYOpP0Pcvx6zCagO5Po+j0OIC/LqXSpV4ow5vnrKkhjQpxu73wwgvs3Lkzz7bTp08zduxYHP8sICmEKF4Wm8rCP86wNToxz3ZryhViv34W0+VjBA98Be9mfVEUBS+DDh93A21qBLF0XCt6RIVJ/SHKPKvVyogRI0hISMizffXq1bK+hhAFkNv+CPJ0QRPzN3veHUeW2UbFB99mQ3oIM9ccxWJTcdFppP0hikShRmw89dRT111LY8CAATzzzDNs2bLlv8YlhABSjBZ6fvAHcWkm7OYs4lfOJfvMPvzaP4LP3f1woOQp76bX0LdRBTrVlSHgovQxmUxMnDiRM2fO5Nnu6upKkyZNeP3113n99ddLKDohyp+Y9GyMJivZSZe5+OVL2IzJBA98BbeqTfOUc9VrSyhCIWDfvn389ttvzJo1K8/2atWqkZyczIoVK+jXr1+xvX5sbCzbtm3DYDDQtm1bvL29i+SYwpxXiJK08Xg8204mwDXti+xzB0lc8Toadx/CHpiHPiACyJn7vEv9EMK83eQpW1GufPLJJ/j6+hIUFJRne/fu3Xn66acZPHgwERERJRSdEKVfgtGMqqqc3bqE3YvfxrVSI4L6PIfdwxuj2cb2U4lsiY6nSz3psxJFo1CJjb1799KoUaN82xs2bMiePXv+a0xC3PGMJhvvbzrB939eJN1kw5oSQ8KSGdgyEgm+72XcqzVzltVrFSoHuNO0kj/31gySIeCi1IqOjiY4OBhPT898+xo2bChJDSGKkMWmsulYPBcO7yFu2Sw0bt55OqVy6TUwsEmFEopSiJx2RcOGDa+7L7dtUVyJjcWLF/PII4/QsmVL0tLSOH/+PGvWrOGuu+76T8cU5rxClLTNx+Ow/TNAyuFwkLF/NSkbP8W1UkMC+/4fWtec72/9GoYzZ1BDaW+Icmnv3r20atUq33atVkvt2rU5cOCAJDaEuAlPnYPDP7xJwt61eN/VD992o9BotOi1ChkmGwo5yQ8hikqhEhuhoaHs3r2bjh075tm+a9cuAgNlChwh/ovcURoxaSYcDjCd/4uE5bPRuHkSNnIe+oBIvAxa3Fx0ZFvtDL274nUXWRKitAkJCeHMmTMkJibmqyuk/hCiaOQu1rf5eDy//vQ1cb9+lKdTSgEcgEYBT4OWHlHhdIsKL+mwxR0sNDSUPXv2oKoqGk3e7zK7du2iU6dOxfK6iYmJjBkzhldffZVJkyYBMGzYMB588EGOHj1a6GMKc14hSpLFprLhaCy/Ho4hxBUcdivJv32C8dA6vJr1xa/9wyianJF9ni4aejSQKadE+ZXb1zV69Og8200mE4cOHZL2ihD/49qFwtXMVJ597CESTh8moPvTeDbo7Cxn0GnIstpRHRDkaSjBiEV5U6jExkMPPcSoUaN45513uPfee3E4HPz+++9MmDCBhx9+uKhjFOKOkWK00PntrSRmWgDIOLCW5A0f4xoZRWC/Kc4npewOB15uerrWD5WkhigzQkNDadu2LX369GHOnDk0aNCA9PR0li1bxqxZs1i6dGlJhyhEmWY02Xj8233sP5fIhTUfkn5gLb539aVC17HYUTDbVBpH+tK8agAhPgaZQkSUCp07d2b8+PE88MADPP/881SuXJmYmBg++OADtm7dyscff1wsr7ty5UqsViuPPvqoc9vTTz9NixYtOHDgAI0bNy7UMYU5rxAlwWJTWX3oCnN/PU5chhkcDiz2dGJ/eB3T5eMEdH8KzwZd8hxTN9yHdrWCSyhiIYrf8OHDadq0KZGRkYwcOZKAgABOnDjBSy+9hI+PD3fffXdJhyhEqXHtQuHpl05y8PMXsVvNhAydhVtEXXJXSXMAGWYbAHXDvaUeEUWqUImNqVOncuXKFQYNGoSqqgBoNBpGjx7NSy+9VKQBCnGnSDFa6PD2FlIyrTjsNpI3foLxwFq8mvbGr8No55NSCtC2VjD9GlWQDilR5nz55ZcMHTqUNm3aOLd5eHjwxhtv0L179xKMTIiyzWJTGfv1Xv74+wwJy2djvnwc/25P4dWwC4pGi6tGwaY6qB3mzXPdapd0uEI4ubu7s3r1agYPHkxUVJRze3h4OMuXLy+2KT8OHz5MpUqV8kyPWL9+fee+6yUgCnJMYc5rNpsxm69Oy5Ceng6AqqrOtpb4d6qq4nA45J4VgNFkY/y3+9h5OsnZ8WRNOMueZTOxmM2EDZ2Ja0Q9+GevQQs+7i482LISOg1yj69DPn/5lcV7Ub9+fb7++mvGjRvHK6+84tzeuHFjVqxYgVYr65IJkSt3oXDzqV3sXzgdrW8oYcNm4+ITjEZRsKsOHICigEGnpVllPz4Y2kT6sESRKlRiQ6fT8fHHHzN9+nQOHTqEoig0aNCA0FBZ/EWIW5WznsZJFv95gTSTDXt2OgnLX8d86Sj+XZ/Aq1G3POUDPF3o16iCLLYkyqTg4GA2btzIyZMnOXnyJO7u7jRt2hQvL6+SDk2IMstosvHMDwfYsmsfCUtmoFpNhAyZiWtkTmdqttWOXqvgrtfSqlpACUcrRH6NGzfm+PHjHDx4kJiYGAIDA2nSpAl6vb7YXjMtLQ1fX9882zw8PHBxcSEtLa3QxxTmvLNnz2b69On5tqekpGCz2Qp2QQJVVcnIyMDhcOSb1kxclZFtZcrSv0jKtFDlnzXtE47u4shPb+MdGErTR2bh6hdMblJDr1EI8nalfgUfmoTqSU5OLrngSzH5/OWXkZFR0iEUyv3330+fPn3Yu3cvaWlpVKxYMU/iXQiRIz7DxNnfvubMuoUEN2iLV9ensWlcUB2gVXLKaBXwNOgY3qISEzrVlKSGKHKFSmzkCg0NlWSGEP+B0WTjvo+2cyrBiF0FS8J5EpbOQDVn5emUyqXVQP0KMgRclH01atSgRo0aJR2GEGWa0WTjnQ0n+GHPReIObydx9ZvofEIIGzobnc/VekKrUfBxd6Fj7WA61ZXvbaJ00mg0NGnS5La9npubG5mZmXm2WSwWLBYLbm5uhT6mMOd9/vnnmThxovPn9PR0IiMj8fPzw9vb+5av7U6lqiqKouDn5ycdyzeQkGai96dbyLKogILD4SBt18+k/P41HjVb0HDI01w0u6Om5/RI6TUKA5pG0K5WMG1rBkmH1E3I5y8/ne4/dTeVKFdXV1q3bl3SYQhRaiWkZDBtwqOc2b2e4LbDqdHlQRKyrDjsOUlx2z+jNVz0Wno1DJekhig2Ba5ppk2b5vz/3H//W1khxM19uOUUZxIz0aCQcWo3iavm5nRKDXkNnU+Is5wGcNFruLuyvwzdE2XKpUuX+Oyzz4iIiKBbt2589tlnNywbERGRb6E+IUR+OfOiX2baqiOkZdtI3/0zqVu/wq1mCwJ7TkTjcrXzVAH6N65Ah9rBMn2hKDV27drFunXraNGiBb6+vqxbt+6GZVu0aEG3bt1uuL+wqlWrxjfffIPdbndOLXLu3DnnvsIeU5jzGgwGDIb8C2lqNBrpIL1FiqLIfbsOi01lxYHLPL/sb2yqA1BQrWaSfnmPrGNb8blnKH6th6A1KKhmBRUFBWheLZDpfaOk7igg+fzlVVbuw88//8zhw4cZOHAgx48f5/DhwzcsO3DgQOf0gkLcKSw2lQ1HY9l+OgmAyq4mJo0dgTH2PIF9p+BWuzWX0i3oNODuosXNRYfJaifEx5Un21WjW1S41COi2BQ4sbFhwwYgJ2mR++8bkcSGEDdnsalsiY7nj5MJWG0qabuXkLr1S9xqNCew1yRnp5SHi5Zu9UNx1WtpVT2QTnVCpEIQZUp6ejobNmygXr163HPPPTetP+rVqyeJDSFuILfeiE0zseFYHDtOJWKxmEla9x5ZR7fic88QfFoPQ1Hy1hG1wzx5tW99qTtEqXLx4kU2bNiAv78/YWFhN60b/P39iyWx0aNHDyZNmsS6devo2bMnAN9//z3+/v7cc889ANhsNr755htatWpFjRo1CnRMQcoIcTulGC0M+2wXx2KvTgtky0gkYelrWBMvENh3Ch61W6PgIHf6Ka0C91QL5MPhTaX+EOXe4cOH2bBhA23atHH++0batGkjiQ1xR7HYVKavOsLqQ1fIstoxXT5OzM8zQaMjdMQc3MOqo6oOVMCg0zCseSUqBrgT5GmQh6rEbVHgxMa2bduu+28hxK3JrRg2HY8nIc1I4pp3yTyyGe+Wg/FtMxxF0aDTKNQO9eLrh5vj5+lS0iELUWh169aV+kOI/8hosvHE9/s5ciUdq81OerYNS0YSCctmYk24QGCf5/Coc2++48J9XPnukZbSoBClzqBBgxg0aFCen2+3WrVqMWHCBEaOHMkzzzxDWloa7777Lp9++ikuLjnfvUwmE6NGjeLTTz+lRo0aBTqmIGWEuF1SjBbav7WZ1Kyra7WYr0STsOw1UDSEDH8DQ2h15z5XnZbW1f0Z2Kwi3eqHSv0h7gjXzkrSsWNHeVBXiGtsiY5n4/F4LHYVy7GtXF71Ni4h1QjqPxWdpx+KAlqtgsPuQFEUKga4M7x5pZIOW9xByu6kh0KUMUaTjflbTrPxWBznkrKwG5OIWTITS/w5AntPxqNuWwA0CgxoWoGZMuxbCCHueBabyhPf72f7qUS0GgVVdZAdc4KEpTMBJV+nFICXQcfguyKZ0Kkmnq7yVU+IG3nrrbdo06YNGzZswGAwsGXLljyjKvR6PQ8++CA1a9Ys8DEFLSNEcbPYVIYt3JknqZF5dAuJa9/FJaQqwf1fROvp59znbdDyWNsq9LirZpmZQkgIIUTxMJpsfLjlFNtPJZJqzCZhy1ck7/iJgMZd8Ok0HrtGh+OfgX4OR85oP71WQ5Bn/qk1hShOBW7tTpkypcAnff311wsVTEHFx8cTHx9P1apVcXd3L7ZjhCgq2RY7w7/ewcn4TFQHmGNOkrB0BigKYcPfQB9aA1edBn8PF1z1GjrVlimnRPlw8eJF5s+fX6CyFStWZPz48cUckRBly5boeI5eSUerKOi1GuL/2kDC2vfQB1UhaMBUdJ7+ecp3rhPM24MbS0JDlGrbt29n1apVBSrbunVrevXqVWyx9O/fn/79+193n8FgYNGiRbd0zK2UEaI4bTgaS3SMEQCHQyX1969I3/UzHvU7END1CRTd1RFErjoNPRtVoGll/xudTohy6/vvv+fQoUMFKjts2DAaNGhQzBEJUbKMJhv3fbSdM4mZWLMziV85l+wz+wjtMoaQe+4j22oH1YGqglXNSWq4aBW61gulXa3gEo5e3GkK3Oo9ePCg899ms5ktW7bg4+NDnTp1ADh69Cjp6em0a9euqGN0slgsjBo1iiVLlhAWFkZCQgJvv/02Y8aMKdJjhChKFpvK/M0nORlnREUh8+hWkn55F31QZYIHvIjB2x+bCnqdBneDjnuqBUhlIMqNzMzMPPVHXFwcBw8eJDQ0lBo1apCZmcnhw4fRaDQ89thjJReoEKVUgtGMAphtNmI3fk3arp/wqNeegG5P5umUAmhVLYD5Mh+6KAMSEhLy1A3R0dGcO3eO6tWrExERQXx8PEePHiU0NJQqVaqUXKBClEG5azIt2nEOFVDNWSSufpPsU3vwa/8wXnf1R1EUZ3lPg5ZZ/aPoUjcEY3pqicUtREk5f/58njrpzz//JC0tjfr16+Pn58e5c+c4f/48tWrVonv37gU+7/79+5k4cSIHDx4kODiYxx9/nKeffvqG5c1mM59++imLFi3izJkzhIeHM2LECJ577jkZRSVuqw+3nMpJaiTHcOXnV7GlJxJ838sYqjUj02LH3UVH17oheLvp+OtSGq56LQObRtCtfpi0Q8RtV+DExrp165z/njBhApUqVWL+/Pl4eHgAOZ1Xjz/+OH5+fjc6xX82c+ZMNm/ezMmTJ4mMjOSHH35g6NChNGnShKZNmxbZMUIUlRSjhRELd2LOTMfhcJDyx7ek7/wxT6eUoii4u2joGRVGh9rBssCSKFdq166dp/5o0aIFb731Fk899RRarRaAK1eu0K9fP5o3b15SYQpRavm5u5Calk7csjlkn/oT33aj8L57gLNTSqPkLPVaN8yLjx9oJvWHKBP69etHv379gJwkx1133cWvv/5Kly5dnGX2799P//79adu2bQlFKUTZYrGprDscy/ubTnAxOQuL3YE1NZaEJf90Sg18GbdqdznLaxRQFBjevBJ9GlVAVdUSjF6IkjNlyhTnDCXbt2/noYceYteuXXmmIVy8eDGvvPIKd999d4HOGRcXR6dOnRg6dCjffvste/bsYdiwYbi7u9/wIduff/6Zy5cvs3DhQipWrMiuXbsYPnw4FouFl19++b9fqBAFdDk1m8xzh4hbOhuNmydhD8xDHxiJArSoEsDQ5hXpVEdmGRGlQ6E+hUuWLGHu3LnOpAaAh4cHc+bMYcmSJUUW3P/67LPPGD16NJGRkQAMHjyYOnXqsHDhwiI9Roj/ymJTWbrvIq3mbORYTAY2cxbxy2aRvvMnfNs9REDPic4nbV20Gvo3rsCrfevTpZ4s0ifKr8uXL5OQkMAzzzzjTGoAhIeH8+KLL7J06dISjE6I0in24nnOLZqI6cJfhAx8Gd/m96EoCgrg7aojwNNA25pB/DD2Hpl+SpRJv//+O61atcqT1ABo0qQJw4cPZ+3atSUUmRBlh8Wm8sqKwzz780FOxmdisjnIOv8XsV9NxGGzEvbAvDxJDQCNolA9yJMnO9QooaiFKH2WL1/OU089lSepATBkyBAqVKjAgQMHCnSeTz75BJ1Ox3vvvUeFChXo168fY8eO5Y033rjhMcOHD2f27Nk0bNgQPz8/unfvTrdu3fjjjz/+0zUJURBGk43Za48x6KMdLP/2c2IWv4RLSDXCHngLfWBOf6qiQMUAd3pEycgMUXoUqgWcnJxMYmIiQUFBebYnJiaSlJRUJIH9r5iYGGJiYrjrrrxfyJo3b87+/fuL7Bgh/guLTWXDsTgW7TjL/vMp2FSwp8ayb/lMslPiCbrvJdyrX33Kw0UDfRqG80rvelIxiHIvOTmZ9PR0rFYrer0+z77irD+EKEtypxBJMJqJObaP6RNGo2jdiHzobTyCK6LVaLDY7Bj0Wno3DKdtzSAZ6SfKtNx2xfUkJibmmTJHCHF96w7HsvTAZSy2nLnOMw6sJXnDx7hG1iew7xS0bl55ykf4utKzQThPdqghSXEhrpGcnHzDNVmTkpIK3F7Ztm0b9957b56HuTp27Mi7775LbGwsoaGhNz3e4XCwf/9+Nm/ezNSpUwt+AUIU0LVtDi+Djvc2neR0bBqJGz7BeGAtXk1749dhNIom5zOsUXL+K0RpU6hvMb1792bIkCG8/fbbNGvWDIC9e/fyzDPP0Ldv3yINMFduBRIQEJBne2Bg4A0rl8IcAzlzG5rNZufP6enpAKiqWiqG6KqqisPhKBWxFKWyfl0Wm8qMNUdZ93cMqVlWHID54mHil83G4OZOxIi56IIqkTNpCPi4agnwcqVdrSB0GsrcdZf19+t6yuM1Qem5rrp16+Lj48OgQYOYNm0atWrVIjMzkw0bNvDcc88V+xDrb775hsWLF5OdnU379u2ZNGkSbm5uNyx/4sQJPvnkEw4dOoS/vz99+vRh2LBh0skmipTFpvLb0Vj+Pn2JZPslDl9JJz7dTPLetZxb9T4htRpTY8iLmHUeZJhsWFUVFWhSyU+S4qJc6Ny5M08++SSTJ09m3LhxVKhQgYSEBL777ju++OILeVJViH9hsal8sPkkZpuKw24jeeOnGA+swatJr5xOKW1Ok9+gUwjyNPBUh2r0a1JR6g8hrqN3794MHTqUsLAw+vXr51xjY+7cuVy8eJE2bdoU6DwxMTFUq1Ytz7bg4Jx1NP8tsREaGkpiYiJ2u52JEyfyxBNP3LBsaey7Ki1tz9KuJO+TxaYy65dj7DydlDNleqaFpKQk4pa/genSEQK6PoF3o67/lHagUUCvVXDV56wJeztjls9TwZS3+3Qr11GoxMbHH3/M448/TufOnZ0vptFoGDZsGPPnzy/MKf9V7tO91/7RBsjOzs735O9/OQZg9uzZTJ8+Pd/2lJQUbDbbLcVdHFRVJSMjA4fDUa4WkSrr17XleDx7j1/AT+fAzxsu7/mVcysX4Fu5Lp0ffo5kxQfHP0kNRYFQbx1REZ40CNKSnJxcwtHfurL+fl1PebwmuHpdJU2r1bJ69WrGjh1L48aNnds9PDx49tlnefLJJ4vttefOncv06dOZN28egYGBTJ06lV27drF69errlt+/fz8jRoxg9OjRdO3alTNnzvD000+zZ88e3nnnnWKLU9xZLDaV6auOsPavKwS5WDiVpmC320nd9Bnp+1cT3LwPtfo/QeVgHy4mZ6EAqgPqhnvzwdAm0iklyoXKlSuzYsUKnnjiCd58803n9vDwcL766itatGhRgtEJUTpZbCobjsay/XQSl1KyuJCUiT07ncQVr2O6eAT/rk/g1aibs7y7Xss7QxrRpd7NnxIX4k7Xr18/5s6dy9SpUxk3bpxze8OGDfnll1/w9fUt8Ln+tz2Z+7PD4bjpcefPnycrK4tt27bx8MMP4+nped3+KSidfVfltU1d1EryPu05m8z5y3HU9tNhtkL05TMc++ZV7KYsmjw8A/8q9fP0XbnqtLi7aGlc0Y8mIbrb2n8ln6eCKW/36Vb6rwqV2PDx8eGbb77hzTffJDo6GshZIDYkJKQwpyuQiIgIFEXhypUrebZfuXKFihUrFtkxAM8//zwTJ050/pyenk5kZCR+fn54e3v/h6soGqqqoigKfn5+5eIDm6usXpfFprL2rytMWXMG1QEOVSV502ek71uNV+Me+HUcTbKi5Ww6qCi46zX0aBROx1ohtK0ZVGY7psrq+3Uz5fGa4Op1XTsUuqTUrl2b33//ndOnT3PmzBm8vb2pU6dOsf5tzc7O5tVXX2XmzJk8+uijAFSqVIm77rqL7du306pVq3zH1KlTh8OHD+f5HCiKwhNPPMEbb7yBwWAotnjFncFiU3lnwwlWHbqC2WJFcQF7dgbxK97AdPEwgd0eJ+CuXpjsCu1rBxHm7UaC0UyQp0GmnhLlTteuXTl+/DjHjh3j8uXLhISEUKdOHflbK8R1WGwqU5f9zYpDV7DaVByAJfECCUtmoJozCRk8E9eKUXmOqeDnSrtawSUTsBBlzBNPPMHDDz/MsWPHSEpKomLFitSqVeuWRm2HhISQkJCQZ1t8fLxz380YDAYMBgO9e/dm8uTJzJo164aJjdLYd1Ve29RFrSTvU0J0BtEpKpYEMwlHd3Jl+Ry0PiGEjJxJik8IKTkDf9Ao4OOmp1vtMO6tEVQi/VfyeSqY8nafdLqCpyv+04SaoaGh/zo3YFHx8PCgRYsWrFmzhuHDhwM5nVUbN27MM+fg+fPnMRqN1KtXr8DH/K/ciuR/aTSaUvMBURSlVMVTVMradRlNNp74fj87TiVicyjYszNIXPEGpot/499lPF6Ne+D45z86rYYwXzfa1AzmxZ51y0WnVFl7vwqiPF4TXL2u0qJatWr5hmcXl927d2M0Gundu7dzW7NmzQgPD2fDhg3XTWxcb4oqT09P7HY7NptNOtvEf2Kxqcxcc5RfDsdiNNvAAelxF7ny9UxUUyYhg2fgVrEBNruK3QFh3m7ylK0o97RaLfXr16d+/folHYoQpdq6v6+wdP8l7P889J11eg+JK+eg8w4mdPAM9L556wsXrcKT7auXi7aHELeLu7s7TZs2LfTxLVu25LPPPsPhcDgTIps3b6ZixYqEh4cX+Dz/NrVMae27Kq9t6qJWEvfJYlPZdCKBeKOF1J1LSNn6JW41mhPYcyIagzvXfto8XLR0iyr5NWHl81Qw5ek+3co1FDqx8dtvv/H5559z5swZdu/eDcB7773HyJEjb2l43q2YMWMG3bp1o1atWrRs2ZJ3330XX19f5xO4uWV27drF4cOHC3yMEIVhNNkY8slOjsakozrAmnSR+CWvomYbCbl/Bq6VGjjLVgpw54F2VQjzcZcnbcUdzeFwsHDhQlavXo2/vz+ff/45mZmZLFiwgEmTJhXLa54/fx4gXyMiPDzcue/fWCwW3nzzTTp37oyHh8d1y5TGOW4LqqzPyVnW4t98PI4dpxLQYQeHg+zTe9iz6k00XoGEj5z3T6eUA4fqoH64F/fWCCz111bW3oP/JfHf/NzFLSsrizfffJPt27fTtWtXJk6cyIkTJ9i/fz9Dhgwp9tcXoiz5ef9l7I6c71Tpfy4ldcsi3KrfTWCvSWgMeRc91mkUBjSJoFtUwTtShbjTXblyhdmzZ/PXX38xdepUunTpwqZNm3BxcaF169YFOseYMWN46623mDp1Ki+88AL79u3jk08+4dVXX3WW2bx5M927d2f//v3UrVuXKVOm0LZtW1q1aoXBYGDr1q3MmzePBx54oLguVdwhrl0o/EJSFkfOJ5C4+i0yDm/Gu+VgfNsMR1E0KOQkwx0KNKvkz4gWlehUJ0T6r0SpVqjExg8//MCYMWN46KGHWLx4sXO7xWJhzpw5zJo1q8gCvFbHjh359ddf+eCDD1i/fj1RUVF8/PHHeYbYVa5cGaPReEvHCHErcue0fWfjSU7GGXEA2af3krByDjqvQEJHvoXeL8xZ3k2voX/jCHo0q1wuMqdC/BcTJ05k2bJltG7dmqNHjwI5I/J27tzJypUr6dOnT5G/ptVqBcDFxSXPdjc3N+e+m3E4HDz88MPExMSwYsWKG5YrjXPcFlRZn5OzrMRvtascvJDKttMJaC1G3HGgPbSc2F8XEVG3GdUHTELr6g44UIDKAR683LUyxvTUEo7835WV9+BGJP4bK+41mlRVpUuXLlgsFnx8fLhw4QKQ852+X79+tGrVisjIyGKNQYiyJMNkw2GzkLTufTKPbMa75f34thmBouT93Xd30TCzbxS9GoZLp5QQBZSYmEjjxo1p0aIFSUlJzrUEqlSpQu/evTl48GCBpkiJjIzkl19+cU5j6+/vz8SJE3n66aedZex2O2az2fkAwUMPPcTLL7/MiBEjyMrKolKlSkyePJkJEyYUy7WKO0PuLCNHrqSjwUFifDyXf3oVS8I5AntPxqNuWwByJ1oL9XFDp1Xo2SCMHlFhNz6xEKVEoRIbr732Gj/++CPdunXj/fffd27v378/7dq1K7bEBkCHDh3o0KHDDfe/+OKLt3yMEAWVu9Drmr+ukJptw+FwkLFnGSlbFuFWtSmBvSfneVJKr4HejSrQtJJfCUYtROmQkZHBZ599RnR0NBcvXszzxb5///58/fXXxZLY8Pf3B3ISDIGBgc7tSUlJNGnS5KbHOhwOxo4dy2+//cbmzZtv2rlWGue4LaiyPidnWYjfYlOZ9csxdpxK5FJyFiazlaRf52M8vAnfFgOp1XM454xaVEtOsyLIy4V5D7TG0/U/zRp625SF9+BmJP4bu5U5bgtjy5YtpKamcvDgQebPn8/Zs2eBnGR0x44d+fHHH4ttRJ8QZY3FpnLm/AViv5uONeEsgb2fxaNuO+d+vVbBoNNSOcCdrx9ujp+ny41PJoTI55NPPqF79+4sWrSIoUOHOrdXqVIFNzc3du/efd1pbK+nTZs2HDp0CFVVr1s3d+jQgezsbOdUUrVr1+bHH38EyDOFlRCFZbGpPPH9frafSkSrUci+fILLP88Ah4MKI+agDamep7xGk7NYuKIoBHnK1MuibChUS+XkyZO0bftPVu+aP7bBwcHOBZGEKG8sNpV566NZsu8SZpuKw5bTKZV5eAPezQfie+8DKJqcxZl1GoVK/u483akGXeqGlImnbYUobmfOnKFq1aqEh4dz6dKlPPuKs/7ITV7s2bOH7t27AzlJjpMnT/Lcc8/d8DiHw8Fjjz3GypUr2bRpE3Xr1r3p65TWOW4LqqzPyVma47fYVN7bdIq1f8diV1Uy01KIXzYTS9wZAnpNwrteO9A4UFFQUdAo0K9xBN7uZatDqjS/BwUh8V9fcd+PkydP0qpVK3Q6Xb5OHGlbCJHXp0t/4+iCJ3E4HIQMex1DWM08+++pFsDw5pVk6lshCulGfV1Q+DrpRvWoRqPB1dX1uvskqSGKwpboeI5eSUerUbBE/8HFZfPQB1Um5L4XcfEKwKbmLNakVUCjUXA4HJhtKvfWDKJdreASjl6IgilUYiMoKIhTp04RFRWV5w/upk2bqFSpUpEFJ0RpkbvQ67IDlzHZVOzGFBKWvYY57jQBvSbhWa89kJPQ6FovhL6NKjgbFGV1vm4hilpQUBCXLl3CbDbn+7JenPVH5cqV6dChA6+//jodOnTAYDAwa9YsvLy86Nevn7Pc8OHDqV+/Ps8//zwA48ePZ/ny5WzatIl69eoVS2yi/MutP9b+HUOS0YIp9hQJS2eCQyV02OsYwmsBjjzHVA/25MkONa9/QiHKmdx2BeTvyNm0aRODBg0qibCEKFFGk40Pt5zicmo2FXzdGN+uOmtWLGHigw+hD6iIf7+p6LwC8hzj765n/rCmZWaknxCl0Y3qpIyMDPbu3cuMGTNKKjQhCix3TY01f8dgtlhJ3PwlSdt/wCeqAz5dnkDv4kK4rxspmRayrXb83fVotBoqB7gzsmVlWVdDlCmF+tYzatQoxo4dy0cffYSiKMTFxbFu3TomT57Ms88+W9QxClFiciuErScS2HgsDqvNjiXuNPFLZvxPp1TOWhphPq70bVSBLvVCSzhyIUqf8PBwmjVrxtixY+nTpw8Oh4OzZ8/y3Xff8dZbb7F+/fpie+0vv/ySvn37EhoaipeXFxaLhZ9//hkfHx9nmUOHDuHm5gbA+vXrWbBgAZGRkTzyyCN5zvXdd99RtWrVYotVlC8bjsWx/kgc6dlWjMf+IGntO+gDKxI0YCo6r8A8ZV31GgbfVYlJXWpJx5S4Y3Tt2pUnn3ySN954A7PZjM1m4/Dhw8ybN4/9+/c7p+UQ4k5hNNkY8NF2ziRk4nA4cODgvTkzubz5Wxq164VX5/EkZEO29erDUx4GLeuevlfqDiH+owceeIB27drRsGFDTCYT2dnZbNu2jRdeeIGIiAgaN25c0iEKcVO5D1XtOJ1EWlo6pxfPJvPELoI6jMK3xX1Y7eCi06LTKAR7G4j0d6d97SDCvN1ktJ8okwr1zeell14iMTGRZs2aYbfbCQ0NRaPRMG7cOElsiHIjdz2NjcfjMZqsZFnsGI9tI3HN2/k6pTQK+LjpaV1DhuwJcTPffvstI0aMYODAgQBUrVoVT09P3n33Xeew7+IQERHBvn37OHnyJNnZ2dSpUwe9Xp+nzHfffYenpycAd999Nzt37rzuucLCZBE1UTAWm8qi7WeJS88i5Y/vSNuxGPc6bQno/hQa/dVpy7wMOnpGBfJIxwZlbvopIf4rDw8P1q5dy9ChQzl27BgA8+fPp0KFCqxYsYKgoKASjlCI2+v9TSc5FW/E4QCHNZv41W+RfWIXoR1H4dftAfRaDaF6OylZVmwqsp6GEEUoKiqKTz/9lMcee4yEhASWLl0KQNOmTVmyZIlMESVKvS3R8ew4nYSbKYn9n/8f2fGXCRv0MsH1WuKi01I7zIuBTSLJMFsJ8jRIMkOUeYVKbOh0OubPn8+0adM4ePAgqqrSsGFDQkPlKXVRPuSsp3GcJfsuoTocOBwqyX98R9r27/Gscy9+3Z92dkrpNQqNK/ryUKsqMmRPiH8RGBjIunXrOHPmDNHR0bi6utKsWTO8vLxuy+vXqFHjhvsaNGjg/Levry8tWrS4HSGJciq3Htl3Opb4lfPIOrED33tH4t1ikLNRrFHA203Pa33r0byCQZ60FXeshg0bcuTIEf766y8uXbpEQEAATZs2zZeAFuJOcOBCCqoDyEgg5udXsabGEnTfi4REtSLE25X4DDPdo8Kp4OcmnVJCFIMBAwbQs2dP9u/fT3JyMpGRkXnaCUKURtdOP3X56F5OLZ6B1uBOo8c/QBsQSctqgfSMCpM6Q5Q7hWpBu7i4YLFYCAoKonPnzkUdkxAlKnfo3vJ/1tPAaiJp7dsYj2/Ht80D+LS8HxedBo1GQa/VMOzuikzqUksqByH+xZ9//snEiRPZtm0bVatWlemcRLkVn2bivgXbOXfuAnFLZ2BLjSVowIu418ibLHPVa+kZFUbHOiEY01NLJlghStj777/PuXPnmDdvHg0bNqRhw4YlHZIQJcpVr8V06QgJy2ahcXEldMRcXIIqoygK7i46wEwFPzeGN5e1LYUoaiNGjKBPnz7cf//9tGzZsqTDEaJAjCYbT3y/nyNX0onZvZrLa+ZjiKhDcL/nSXbxRp9lpWPtYJkyXZRLhUps+Pn5kZCQIEPDRbly7Xoam47H46rXkBIfT/ySmVhTrhAx6CV0VZtj0GnwddfjYdDRqnqgJDWEKKCgoCBiYmJKOgwhilWK0UK7eZtJPnOYhGWvoeivdkrl0ilQNdiTJ9pXp1v9MKQKEXeywMBAdu3aVdJhCHHb5bY9YtKziUszE+rtSqiPK57nfifu+6kYKtQmqN/zaN1z1gML83Ely2JDURSCPA3/cnYhRGEEBAQQFxdX0mEIcVO59UeC0Yyfm54f9lxk+6l4kjZ+RuqelXg27oF/x7FodTocDrCpDo5cSaNPowolHboQRa5QiY1HHnmEV155hXfeeQcXF5nLU5RtFpvKusOxvLfpBJdTsrHa1Zzh37HHiflxJorOhbARczGEVsXdRcvQuyOpGOAhQ7+FuEVVqlShUqVKfPbZZ4wePbqkwxGiSFlsKhuOxTF77THi964j6dcP83VKaRRw0Wl4sGXlPElxVVVvdmohyrVu3brxyiuvsGvXLpkCUNwxnE/XXk7DaLZhUx24aiF1y+dc+mMJDTsNRG35EHa0WFUHBg2YrHYsdgf3VAuQNf2EKCYjR47koYceon///kRERJR0OELkc+3i4KqqkpRpIS01lbjlb2A6f4jALuPxaNwDAJ1Wg0GnwWSxE5tuLuHIhSgehUpsbNy4kT///JPFixdTvXr1fMmNbdu2FUlwQhS33AXClx24TJbF7txu/Os3kn6dj1uFWoQOmIri5o2Xq44eUWFM6lJbkhlCFMLRo0eJjY1lzJgxTJs2jYoVK6LRXP1dqlevHh9//HEJRihE4VhsKq+sOMzy/Re5vP5TMvauwLNhN/w7P4qivbpGgEGnYUCTCBnpJ8Q1NmzYQHZ2Ni1btqRGjRoEB+ftsL3//vt56qmnSig6IYqexabyxPf72X4qEQCb3YHDbOTKijfIOn+Imv2fZta059BpNTlP47q7gMNBSrYs9CpEcVu1ahUXL16kWrVq1KxZEx8fnzz7p0+fTseOHUsoOiGuLg4e4KHnYnIWiZfOEr9kBmp2BiGDZ+Ja6ep6MAadBgVAgQq+biUWsxDFqVCJje7du9O9e/eijkWI225LdDybjsdjtuYkNRyqnZTNn//TKdWVwM6P4ePlTs1QLx5sUYlOdUOlISFEIXl7e3P//fffcL88FSXKqg3H4li6+wQXfp6N6dxB/Do9ileTXs5FwiFntMbI/xmpIYSAyMhIHnnkkRvur1mz5m2MRojiZTTZmPzzIXacTsRmdwBgSbpE/NIZqFlpVBg8E0PNJiQaLYy8p3LJBivEHah+/fpMmDDhhvtDQkJuXzBCXEdsmgmjyUqWxca5gzuIXTEHnWcAoSPfQu8XlqesyWIHBaoGejC+XfUSiliI4lWoxMa0adOKOAwhbq/cOQnX/B1DptkGCqjZRhJWzsnTKWXQa+kRFcYrvetJR5QQ/1FERITUH6LcyJ16avupRDb/eZAzC59HzUoj+P5XcavcKF/5/o0qSFJDiOto0aJFiU5Bdfz4cbZs2YLBYKBbt26EhYXdtLzFYmHLli2cPn2aiIgIunTpgsFwdb0Du93O22+/ne+4rl27EhUVVeTxi7LDaLJx30fbORVv5J+cBtln9pGwcg46T39CR76FZ1AFsq12YtNNJRusEHeogQMHMnDgwJIOQ4h8LDaVDUdj+XrXeRIyzKTtWU7Sps9xq9qUwN6T0RjcnWW1CrSoFkCgp4EKvm6Mb1cdT9dCdf8KUer9p0/2mTNnOH78OAB16tShSpUqRRKUEMXp2jkJczLddkyJl3OG72WmODulNAr4uutpWzNIOqKEKGJ//fUXZ86cwdvbm6ioKIKCgko6JCH+1bULvW46Gs+Bi6kkRe8hZtnraD38CH1gHi7+FXBcc4yfu46XetalV8MKUpcIcRNms5mDBw9y+fJlQkJCaNiwIZ6ensX6mgsWLGDixIn06dOH1NRUnnrqKVauXEn79u2vW37z5s2MGTOGatWqUbVqVT7++GOeeOIJNmzYQI0aNQCwWq1MnjyZIUOGUKHC1UU6s7Ozi/VaROn34ZZTnEnMxO4Ah8NBxt4VpGz+HLcqTQjsMxmNwQObCu4uWkJ8ZHFwIUpSamoqf/31F4mJiVSqVIlGjRqh1WpLOixxh8rtw1p/NI6ElAzif5mP8fAGvJvfh++9I1E0eT+b7i5aHmxZmS71QksoYiFun0IlNpKSknjkkUdYsWKFc350VVXp378/n332Gf7+/kUapBBFIbdDatPxeNYdicXHVYeLVoPtwgFif56NxsMvZ/ief04j1MNFS8c6IbI4nxBF6NChQzz88MPs378fnU6HzWbDYDDwxBNP8MYbb0iDQZRa/5sUj88wk75nBUmbFuJZtQl+vXM6pa5NarjpFF7rF0WPBuElFrcQZcHy5ct58sknuXTpkrNuCAgI4PXXX2f06NHF8poxMTFMmDCB9957j7FjxwLw6KOP8sgjj3Dq1Kk8a0Dl8vHxYevWrc6EhaqqtG3blgkTJrBmzZo8ZR9//HFat25dLLGLsulCSlbOeho2K0nr55P59wa87x6Ab9sHnZ1SbnoNQV4GwrxlLnQhSsrrr7/OrFmzyMjIcNZJNWvW5JNPPqFt27YlHZ64w1hsKvPWH2f5gctYM5K5/MMMzLGnCOg1Cc96+R/E0CjQpKKf9GOJO0ahHh0cM2YMly5dYufOnZhMJkwmEzt37uTChQvOhoEQpUnuIuEvrzjMz/sukZpl5VxSFkd++4Hz372MS3htIh58i+CISvi46fB21TGseUWZgkqIImSz2ejTpw/Vq1cnOjoas9lMVlYWP/zwA19//fV1p+4QorRYffASS/ZdIiY1izRjFglr3yNp46f4N+9P9QdexcXNAwCFnC9XrnoNfRtH0KmuPCklxM2cPn2awYMHM2rUKGJjY7FarSQnJ/PKK68wfvx4tm3bViyvu3LlSjQaDQ888IBz22OPPcbZs2fZu3fvdY9p0qRJnlEYGo2GNm3acPLkyXxl169fz/z58/nll18wm81FfwGizDCabMxee5RtJxKwZaYQt3gqmUe3ENDzGfzaP5znSVutVkOr6oHSISVECfnxxx+ZMWMG77//Punp6VgsFi5cuEDnzp3p06cPSUlJJR2iuINYbCovrzjM59vPkXg+mpOfPo0tNZbQYa/jXb89Lv9UH3pNTmLc21VH6+qBzB/eVPqxxB2jUCM21q1bx6FDh5xDriFnftzvv/+ehg0bFllwQhSFnAx3ND/uuYBVzdnmsFtJXv8Rxr/W431Xf3zbPYSLi55IPw+CvAycS8qkYoCHVAZCFKGjR49iMpn47rvvnCMz3Nzc6Nu3L+np6XzxxRc8++yzJRylEPmlGC1MXXGEbKuKPTOVhOWzMMecIKDHM3hFdSTT6kCn1eBp0NAw0pcIP3daVQugU91QqUeE+BcbN26kU6dOvPrqq85tfn5+PPnkkxw/fpy1a9cWy8iHY8eOUbFiRdzcrj4ZX7t2bee+u++++1/PYbPZWLlyZb6yer2e/fv3Ex4ezjvvvAPAqlWrnOf/X2azOU/yIz09HcgZEaKq6i1d151MVVUcDkepumcJaSZ6vP8HKVlWzHFniFsyA1QbYUNn41qhNlwzzs/HVce0XnXoWj8MnYbbfh2l8f6VJXL/8iuL92LdunVMmTKFBx980LktMjKSDz74gJ07d7J9+3b69OlTghGKO8mGo7GsPHSF1CN/kLT2bfQBkQT1fxGddyCqAxRFg7erhh5RYURF+BDkaaBdrWBpf4g7SqESG0FBQXh7e+fb7u3tTXCwPF0iSo/cqUOW7r2a1LBnpZGwbBbmmGgCekzAM6oTGiXnKVuzTSXLYkNRFII8ZW5bIYpSUFAQ7u7u151uSuoPUVoZTTYGLNhOtlXFEn+G+CUzcNithA6djaFCHRyAxebA3UVDzwbhMtJPiFt0o3YF3FrdYDabef/9929aplq1avTv3x+AjIwMfH198+x3c3PDxcWFjIyMAr3mM888w+XLl1m1apVzm06nY9++fc6Fwi0WC126dGHUqFHs3LnzuueZPXs206dPz7c9JSUFm81WoFhETidqRkYGDofjulOJ3U5Wu8qfZ5L45I8z+OlUrBd2cP7nt/EIiqDBiKm4+gSSm9RQgIoB7rzYoy5ebnqM6aklEnNpun9lkdy//Ar6t7Q0Kao6SYj/ymiy8cGmE1zZ9BVp27/Ho869+Hd/Co3eFcgZoRHh54bqgA61g2U9DXHHKlRiY8iQIUyYMIEFCxbg4+MD5Cyu9PTTTzNkyJAiDVCIwrLYVOasO8ZPey+Sbc1pOFjiz+Z0StkshAyZjWtEHQw6Da56DVkWO2nZFuwOB/dUC5Ah4EIUsbCwMCpWrMisWbOYPHkyer0egHPnzjFt2jRefvnlEo5QiLxSjBZ6vP87MWlmsk7sIHH1PPT+EQQNeBGddxAaBUK9Xckw2+gZFSZJDSEKoW3btkycOJEVK1bQt29f5/YdO3bw3XffsXHjxgKdx+FwEBsbe9My164D6O7u7hwZkctsNmOxWPDw8PjX13v55ZdZtGgR69ato0qVKs7tOp3OmdQAcHFxYdy4cQwdOhSj0XjdBdGff/55Jk6c6Pw5PT2dyMhI/Pz8btjBJvJTVRVFUfDz8yvRjmWjycbkHw7w55kksiwqqTsWk7rtOzxqt8a/x9NcUVwhPSehodMoPNCyEs92rV3i9UdpuX9lldy//HS6QnU3laiBAwcyZMgQOnTo4PxbrqoqX331FfHx8TRq1KhkAxTlUu56sAlGM0GeBu6q5M/ADzaxa9EMsqJ34NvmAbxb3o9Wo+SM1AC83fQ4UGhVXfquxJ2tUDXNli1b+PPPP1mxYgU1atTA4XBw6tQpsrOzufvuu/MMFy+ueXGFuBmjycZj3+xl+6kk5wDvnE6pt9D5hRN834vovHP++Bt0GgI9DUT6u9O+dhBh3m4yfE+IYnD06FESEhKYOnUqb775JlWqVCEzM5NTp06h0+mYN28e8+bNA6BevXp8/PHHJRyxuFNZbCobjsXx6srDxKSbSduxmLRt3+JeqzUBPSagccl5Usrfw4VKAe4kGC10qC31hhCFsXHjRmw2G/369SM8PJzw8HASEhI4f/48QUFBPPTQQ86y999/P0899dR1z+Pq6sqbb75Z4NetVasWixYtwmq1OhPtp06dAsgz3e71TJ8+nbfffpu1a9fSqlWrf30tnU6Hw+EgIyPjuokNg8GAwZB/pLBGo5EO0lukKEqJ3jeLTeWpHw6y/VQSVlM2CWveISt6Gz5tRuDTcjAoCir/dEq56+kZFcZz3euWmvqjpO9fWSf3L6+yeB/WrFlDUlISDRo0oFq1avj5+XHhwgXi4+OpWrUqnTp1cpadPn06HTt2LMFoRXlgsanM+uU4O04noaoOMs02UuIvc27xq9hSrhDUfyruNVsCoDpyFgevHebFkGYVCfVxlb4rcccrVGKje/fudO/evahjEeI/y+2MemfDCU7GGXGQ8wRf2s4fSPvjG9xrtSKgxzPOTqlADz1PdawpFYIQt4G3tzf3339/gcpGREQUczRCXF/uFIa/Ho4hJjmdpLXvknX8D3xaD8fnniEoiuIs62nQkWC0yCg/If6DyMhIHnnkkQKVrVmzZpG9bq9evZgwYQJLly5l8ODBACxatIjQ0FBatGgBgNVq5d1336Vr167OJ3dnzJjBm2++ydq1a2nTpk2+8548eZJKlSrh4uIC5HwP/fLLL6levTphYWFFFr8ofSw2lXc2nGDfuRSs6QnE/jQDS/Jlgvq/gHvNe5zlFKBpJV8eblVF1mISopSpX78+EyZMKFDZkJCQ4g1GlHtWu8p7G0+y9u8Y9FoNmSYL8aePkLDsNRSdC6Ej5uISfHVUqF6rcE/VAOYPb4qna9kbESVEcSjUb8K0adOKOAwh/juLTWX6qiOs+esKqdk58xGrVtPVTqlWw/BpNQRFyWk8aIAXetRlQFPpQBXidoiIiJD6Q5R66w7HsurQFVLirxD380ysyZcI7Pc8HrWuPpWtUeDBlpWpHuIpi/QJ8R+1aNHCmUi4nSpXrswrr7zC6NGj2blzJ2lpaXz77bf88MMPzulTzGYzkydPxtfXl6ioKL766itefvllBgwYwO7du9m9ezeQM91U7kiSw4cPM2DAANq1a4e/vz+//vorZ8+e5aeffrrt1yiKX+70ITHp2Ww+nsCRy2kkn/2b2KWvodG5EDZiDvrgqs7y7i5apvWuQ7/GkVJvCFEKDRw4kIEDBxbZ+Q4ePMjBgwcJDg6mY8eO1x2dd63MzEx27NhBfHw8tWvXpmnTpkUWiyhdLDaVr3ed5+e/U0jOtKI6wPj3BpJ+/QBDWC2C+r+A1j1n6n+dJme0Rqc6Ibw7pLHUH0JcQ1J8otxY9/cVlu6/RPY/q4Tb0hNJWDYTa9LFPJ1SGgXc9Fp6RoXRq2F4SYYshBCiFEkxWnhp+d/Enz5M/LKZKFo9ocPn4hJSNU+5phX9eL5HHWlUCFHGvfTSS7Rr144NGzYQHBzMwYMHqVu3rnO/i4sLkyZNokGDBgAEBwczadIkgDzreVzbUdW/f3/uuusuVq1aRVxcHOPHj6dfv36yVkY5lDvCb8fpJIwmK0mZFsxHNxOz6p2cTql+z6P39MUBBHoa6N+4Ak92qCFP2Qpxh5gwYQJffPEFXbp04fDhwwBs3ryZ0NDrL/L8xRdfMH36dGrWrElQUBDPPPMMjRs3ZsWKFbi6ut7O0MVtsPVETjJcr9Og2u0kb/mCjD3L8WzQBf8u41C0emdZuwo1QzyZO7ChtD+E+B/yrUqUCxabygdbTjuTGubLx0lY9hpodHk6pcJ9XBndpioRfrKOhhBCiJw1mT7ccorziZn8fjKBmH3rSVr3PobQmjlPSnn45inv4aJlVOsqUn8IUU60adPmulNKQU5i49p1O7p160a3bt3+9ZwRERGMGzeuyGIUpdOW6Hh2nE4iyNMFjUPlxKqFpOxaSkCTbgR1G4/FocFdr6NpZT8+GNpEEhpC3EE2bNjAe++9x65du7j77rsxmUy0aNGCyZMn8/XXX1/3mNDQUPbv34+/vz+Qk0CvV68eb7/9Ns8///ztDF/cBolGM6rDgTUrk7ifZ2I6dwC/To/i1aRXnqlvARpX9OWrh5tLPSLEdchvhSizcod+JxjNXE7JJjbNBIDx8KZ/OqWq/9Mp5QdAnVAvfnrsHqkMhBBCABCfZqLru1tJybLhUO2kbv2S9D+X4hHVmYAu41F0+jzl9VqF3g3D6VRH5lQWQog7XYLRjMPhQGczcejzF0g59ifBXR6lQdchaBSFlCwrA5tGMKFTTUmGC3GHWbx4Mc2aNePuu+8GwNXVldGjR/Pcc8/x+eefo9fr8x3zv+vYhoaG0rBhQ44dO3ZbYha3j8WmciEpiwvnz3Jw4avYM1MJHjQdtyqN85UN9jJIUkOIm5DfDFEm5a6nsel4PDa7itmmYrFaSdn8xT+dUp0I6PK4s1NKAZpU8pPKQAghBJAz7VT7eVvItNhRzZkkrpxL9tn9+HUYg1ezPnmelPJ21dIo0o+BTSPoVj9MOqiEEOIOdO1DVUGeBvzc9GQnXmbloqmY0pOp9eBr2MMbkGGy4emqp1v9UElqCHGHOnLkSJ6pDQHq1q1LdnY2Z8+epWbNmv96jvj4ePbs2UPv3r1vWMZsNmM2m50/p6enA6CqKqqqFjL6/0ZVVRwOR4m9fmlnNNl4+ocD/LFpA+d/no3i7kv4yHno/SsADmc5g07BVa9lavdauLto7tj7KZ+ngilv9+lWrqPM9fLu2LGD+fPnExcXR1RUFFOmTCEk5MZPTqakpPDJJ5+wY8cOdDodrVu3Zty4cTJHYRm34Vgcqw5dIdtqB8BmyiRuxVyyz+zDr8NovJr1zdMp5arX0KpaQEmFK4QQohSx2FSGf76LTIsda8oVEpbMwGZMJnjgK7hVzbtIowaY2S+KPo0qlEywQgghSlzuehrbTyWSabahOsAn+RgHPnkRjZsPTZ6cj3tQJJH+7nSoFUyoj6tMeyvEHSw9PR1fX9882/z8/Jz7/o3VamXYsGFEREQwduzYG5abPXs206dPz7c9JSUFm812a0EXEVVVycjIwOFwoNHI38BrWe0qc9YdY+fSbzmzdiFhNRtRa9CzaFw90Slg/yexoVUUvN10NKvsz90VDCQnJ5dw5CVHPk8FU97uU0ZGRoHLlqnExu+//06nTp2YOHEi999/Px988AGtWrXi4MGDeHp65itvt9tp3LgxQ4cO5ZFHHiErK4vp06ezfPlyNm7ciE5Xpi5fXOP3EwlkmGwoClhTrhD3c06nVKWhr+JWtYlzrQ0AN72Gvo0q0Knu9RfpEkIIceew2FTmrT/OsSsZZJ87SOKK19G4+xD2wDz0ARH5yreoFkC3+mElEKkQQojSYkt0vDOpkZZtJXH3ChI3fEpgrWa8+8kXOFw8CPI0SDJDCAGAm5tbvo653ISGu7v7TY+12+0MHz6ckydPsnXrVjw8PG5Y9vnnn2fixIl5XiMyMhI/Pz+8vb3/wxUUnqqqKIqCn59fuehgLSoWm8rctYf5/O1ZpB9aj+9dfanT50HOGXVgVdAqCigKngYdNUK9eKB5JTrWCbnj6xT5PBVMebtPt9JfX6Z69qdOnUq/fv14/fXXAejUqRNhYWF88sknef6Y59Jqtfz99994eXk5t9WuXZvGjRuzZ88eWrZsedtiF/9d7vDvi6lZbDgaiwPIPneIhOWvo3XzIuyBedStX4chd1Xkcmo2adlWtBqFVtUC6FQ39I6vEIQQ4k4Xn2Zi4IIdXEzJIn3/GpI3fIJrpYYE9v0/tK55H5BQgFY1AlgwvJnUH0IIcYdLMJpzkhqZ2VxZ+yHpB9fh3awvnh0eZv0pI588UFvqCiGEU40aNTh79myebWfPnkWr1VK5cuUbHpeb1Ni1axdbtmy5aVkAg8GAwWDIt12j0ZRo56aiKCUeQ2lisalMXbyDBS89Ttbl4wT0mIB3VEfQOFBRcDgUvNz01Az14sEWlaT/6n/I56lgytN9upVrKDOJjaysLHbs2MGXX37p3Obh4UGnTp347bffrpvYAPIkNQB8fHwAMJlMxResKFIWm8qGY3Es2n6W6Nh0Mkx2VCBj/xqSN3zs7JTSu3nSMMKXkfdULumQhRBClCIWm8rqQ1eYsvQvzBYLyRs+xnhwHV7N+uLX/mEUjTZPeb0GXusfRb/GEdKoEEIIQZCnAbMxlXPfTMd0+ThB3Z/Cq2EXNBqFo1fS2RIdT5d6MjpcCJGjT58+PPjgg1y8eJHIyEgAvv32Wzp16uQcsXH58mVWrVrF4MGD8fPzw263M2LECHbs2MHmzZupWrVqSV6C+I+MJhsfbjnF5dRsLp86zqp5z2C3mgkZMhvXiDpcu54GQL1wHxaMaCrrwgpxi8rMb8zFixdRVZUKFfLOcV2hQgU2bdpU4PPMnj2bkJAQmjdvfsMypXEBpmuVt0Vhcl3vuiw2lVm/HGP9kVgS0s04AIfdRvKGT8g4+AveTfvg3+FhtFotngYtrWsElrr7cie9X2VdebwmKL/XJURBXE7Oou/8bSRmWrFnpZGwfDbmy8cJ6P4Ung265CvfONKHzx+8Gz9PlxKIVgghRGkUaInj1KdPYc3KJnToLNwj66LTKGg0CholZ0SHEELkGjx4MAsXLqRjx448+OCD7N27lz///JNt27Y5yxw7doxx48bRunVr/Pz8mDRpEosXL2by5Mn89ttv/PbbbwBERETQq1evkroUUQjxaSa6vvs7KVlWsk7sJHH1PHR+4UQMfx08g/KV1yjw9+U0fj+ZQI8omQJXiFtRoomNWbNmsXbt2puW+eGHH6hQoQJWqxUg36Lfbm5uWCyWAr3ehx9+yKJFi1izZs1N5zUsjQswXau8LQqT63rXtedsMucuxxGgs+Dp7cCSmc7fP76B8cIxavd7ggp35XRKuWgV7q3pT5MQXalbWOlOer/KuvJ4TXD1uoS408SnmWgzZzMqYEk4R8KSGahWEyFDX8M1ol6eshoF2tQI5NORd8koDSGEuMPlToGbYDRz8s/NzHvhSapUrUpQ/5c4a3bLSWhoFLzd9HgYdAR55p8KRghx59JoNPzyyy988803HDx4kKZNm/Luu+9SsWJFZ5mIiAgeffRR/P39AahSpQqPPvoo6enpHDx40Fnu2oduRelnNNno9u7vJGdaSNv5A2l/fIN7rVYE9HgGjcEVnUbBYr86WsOgVTC46Mi02Nh+KlESG0LcohJNbAwaNIh77733pmUCAgIAnH/sk5KS8uxPSkpy7ruZhQsX8swzz/D999/TuXPnm5YtjQswXau8LQqT63rXlRCdweEEO+kmB6b4C8QtmYFqySZ08EzMkfU4/09fbed6IUzo2ahUdkbdSe9XWVcerwmuXpdWq/33wkKUE0aTjV7v/4EKZJ3cTeLqN9H5hhI2dDY6n2BnOQXwctXRKNKX+cOalsp6RAghxO1jsanMXHOU7acSObfxG878spDaLTuz9Zcl6A1uPPH9fo5eSUejgIdBR6vqgbSrFfzvJxZC3FH0ej2jRo264f7atWuzYMEC589PP/307QhLFLP3N50gKS2DxF/eI+vY7/i0GoZPqyEoigbVASgKyj/TUBl0GjwMOuwOpWSDFqIMK9HERo0aNahRo0aByoaHhxMSEsK+ffvyDMP7888/adWq1U2P/eKLLxg/fjzffPMN991337++VmldgOla5WlRmGvlXpdNhQ1HY/l69wVSTTaMJ/8kcdWb6HxCCBs6C51PCCqgKFA9yJM5Axvh6lJ6Z1Yr7+9Xebqu8nhNcPW6hCjvLDaV1QcvMX31MVKzraTv+onU37/GrWYLAntOROPi5iyrVWByl9pUDfagXa1gSWoIIYRgS3Q8fxy/wpklb3Jhz2/U6/kwIe1GsOdSJl3qefHJA82cozmCPA1SfwghhMhZG/ZoLN9tOkDsd9OwJl4ksO8UPGq3dpbRKOBt0GLTa7Bac2aDMdlU7A5w12tpVS2gpMIXoswqvT3B1zFq1Cg+++wzxo4dS1hYGMuXL+fw4cN89tlnzjJvvPEGR44c4auvvgLgq6++4rHHHuObb75h0KBBJRW6uAUWm8qMNcdY83cMqZkW0nb/TOrWr3Cr0ZzAXpOcnVLBXi70b1yBJzvUlAWWhBBCYLGpTFt1lJ/2XsRqMZO07j2yjm7F554h+LQehqJc7Xgy6DTM7h/FgKYRJRixEEKIkmaxqWw4Fsf2U4kApCXEsu+Dp8iKP0f78bOoclcnziVlOtfRcNFpZKFwIYQQTrkj/Zb+uoXjX70MGh2hI+bgElItTzmtRkGn09K1djB2VeXMpVjcLDkzK3SsHUynulK3CHGrylRv8CuvvEJ0dDTVq1enUqVKnDt3jnfffTfPQuAnT55k//79AKSmpjJq1Ch8fX159913effdd53lpkyZIgswlVIbj8Wx+tAVjFlZJK55j8yjW/BpORi/e4ejKBrc9VqaVfHng6FNJKEhhCgQs9nM//3f/7F48WKys7Np374977//PpGRkUV6jCg52RY7U77bx4bjidgyEklY9hrWhAsE9vk/POq0yVNWp4GBTSPo1TC8hKIVQghRGlhsKtNXHWH1XzFkWWyYrkQT+/NMFI2GDs9+RGSN+mRZbCiKIutoCCGEyCN3PaatJxL44fvvOLNsHq6h1Qnq/wKKu5+znAIEexmoHOTBgy0q0aluKKqqsvmQG8l2F4K8XGX0nxCFVKZ6hV1dXVm6dCkXLlwgLi6OmjVr4uPjk6fMlClTnIvkenp6snXr1uueq6BTYInbw2JT2Xw8jpTkJH45aSQ9OZ7YJTMxx58jpO9zuNW+F29XHS56LYOaRjChU035oy+EKLCnnnqKX3/9lTVr1hAYGMijjz5K9+7dOXjwIDrd9avCwhwjSkaq0cKzPx1kT4wV85UTJCx7DRQNIcPfwBBaPU9ZvUbhwXsq8Vy3OlKPCCHEHW5LdDybjsdjttmxRP/O5eVvYQitRsT9L5HhGcK5pEwUReGeagGyjoYQQgggp/9q3eEYPth8ipiUTGI3LiJxx0/4N+5CUNfHMRgMZFrsuGhBUTT0ahBGxzoheZIXqgp3VfHH399fpowW4j8okz0zFStWpGLFitfdV7361Q4MnU5H69atr1tOlB65T0ptPh5HuKuNA4ePc/HHGWgUDRVHzkUTXA3VAQ6ge/1QSWoIIW5JcnIyn3/+OV988QVNmzYFYMGCBVSpUoW1a9fSp0+fIjlGlAyLTWXkot2YMi0Yj24lYe17uIRUJbj/i2g9/fKUddNrGNAkQpIaQgghAEgwmrFarSRu+oKkHT8R0Kgzgd2fwMPNlQ61g6kb7i3raAghhHCy2FReWXGYJQcuY8o0krhqLtln9uHf4RECWw7A3aAj22JHUcDH3UDnuiG82LOu1CFCFJMymdgQ5cu6w7EsO3AZu81O5l9bOfvzu+iDqhA+6EXcvAOwqg4MGoWhd1dkUpdaUiEIIW7J7t27sdlstG/f3rmtcuXKVKtWjR07dlw3SVGYY8TtlTv0e8PxOI5dScO+8ysSfl+CR/0OBHR9AkXnkqe8ToGRLStLPSKEEHcwo8nGh1tOcTk1mwq+blT0hNPfTyc1+k/Cu4whqOV9mO0qep2WtjWDZC0NIYQQeWw4FseKQ1fITLhMwpIZ2DISCb7vZTyqN8OmggbwcXehcoA7I1tWplOdEGl7CFGMJLEhSpTFpvLB5pNkmq2k//E1qTt/wqt+e/y6PombuxseLlp0Wg0dagdLZ5QQolBiYmIACAoKyrM9KCiI2NjYIjvGbDZjNpudP6enpwOgqiqqqhYu+NtEVVUcDkepjzOX0WTj8e/2s+9cElmZmSSsnkfW6T0EtBuF1939URSFnHF+Odx0Gvo0rsAznWqg01DqrrOs3f/rKevXIPHf/NxClAfxaSa6vfs7KVlWFAXUtFgSl83EmpZI5WGvoqvUmEyrHXd9ziKuMvWUEEKIXLkPVS3afpaUk/uJX/46GjdPwkbOQx+Qswajh4uWrvXDaFszSEb6CXGbSGJDlKgt0fFciU8mYdkcsk/upma3h7A1GIBDo6FZJT+61g+V4d9CiCKR09l9lUajweFw3KD0rR8ze/Zspk+fnm97SkoKNpvtFqO9vVRVJSMjA4fDUerneM222Jm28jAxyVn4mWK58N1MzGmJdBj9IprKd/2Tzrj6HlX0c2dA0wiaVvLDmJ5aQlHfXFm6/zdS1q9B4r+x3LXrhCjLjCYbXd/LSWoAZJ/7i4Tls9G6efH4W9/RuVUztp9KBKBVtQA61Q2VtocQQgjg6vTpG4/Hc3brUuLXL8C1YgMC+01B6+rpLNekkh+v9K4n9YcQt5EkNsRtlZvlTjCaCfI08NfxE5z5YiKmlDhCB75IxSZ3cSZNwUWnYchdkfRoEF7SIQshyriQkBAAEhISCA+/+jclPj6eVq1aFdkxzz//PBMnTnT+nJ6eTmRkJH5+fnh7e//n6yhOqqqiKAp+fn6ltlPXYlNZefAyM1YfJdNiJ/vCYeKXz0Zj8CR8xBw0lSM5mw4qV5NR1YM9eOfBVni6lu6vO2Xh/v+bsn4NEv+N6XSl+/dHiIL4cMspUjNzkhoZB9aSvOFjXCPrE9xvCqpvBD2iwugRFVbCUQohhChNcvuvNh2PZ8X+C8Ss+4i0/Wvxatobvw6jUTRaZ9naYV58MLSJJDWEuM2kpSJuG4tNZeaao+w4nYTD4SD1zCGOfPUKios79R59F6t3BTSoaDRwdxV/OtWVOW2FEP9d8+bN0Wq1bN26laFDhwJw8eJFTp8+zT333FNkxxgMBgwGQ77tGo2mTHSUKopSamO12FReWnGEn/ZdAiDj4DqSf1uAa2Q9AvtOQefmhQMHKgoqCkGeegY0ieTJDjVKfVIjV2m+/wVV1q9B4r++sno/hMhlsansPZeMw24jaeOnGA+syemUav8IGq2OCr5uJR2iEEKIUsZiU3l5xWF+PRJLanISMctmY750jMg+E3Ct3xmL3YFeo+DuoqVhRV8+HNa0zLQ7hChP5LdOFDujycY7G06w5q8rxBvNuOs1EL2JIz+9jU+VBnR9+g0SLHqyzFa83FRah/ry/tCmkukWQhSJwMBARo4cyUsvvUTDhg0JCAjg8ccfp2bNmvTs2dNZrlGjRjRv3pyPP/64wMeI22Pd4RiWHbiMw24jZdOnZOxfg1eTnvh1GIOi1ZE79ZQC1A/3ZvHYltKwEEII4Xyw6ui5K8T+OAPzpSP4d30Cr0bdAPBz1zO+XfUSjlIIIURps+5wDEv3XyIz9hzxS15FtWQTMmQm3tUbotNosJttNK7oy+g2VWXqdCFKkLT6RbFKMVro8f7vxKTlLKjrUO2c/3UBGftXE9K8D7X7P0mPptUJ9XElIcOEv9ZC+4ZVcXWRj6YQoujMnz+fCRMm0LJlS0wmE+3ateOXX35Br9c7y9hsNux2+y0dI4qXxaay+tAVXlj2N+bMdBJXzMZ08Qj+XR/Hq1H3fOXvqR7IRyOaSVJDCCEEkLOe3/rt+zj3xQvYMo2EDJ6Ja8UoFMDXQ8+6p+6VOkMIIQSQd+r0b3edJzV6F4mr3kTnE0LY0FnofEIwmu246h14GnQ81KoKXerJTCNClCT5FieKjcWm8sDnu51JDXt2BokrXsd08TD+Xcbj0aQHZlUh1MeVLvVCUVWV5ORkyXQLIYqcm5sbH3/8MR9//PENyxw6dCjPYuEFOUYUH6PJxrhv9rH9dCLmhIs5T0qZMwkZPAPXig3ylHXVKfSMCmVc10aSGBdC3JI9e/awceNGDAYDvXv3pnr1Gz+9b7PZmDZtWr7t/fv3p2nTpoU+ryg6VrvKb0fjSMy0EORp4Jdf1rD3vafwDgyjy7MfkKT4kphppmlFPz4cLtOGCCGEyOm72nA0ls+3n+VYTAZWm53EHT+TsvVL3Go0J7DXJDQuV6ctdNNr6REVRqc6ISUYtRACQHqQRbEwmmw8vfgAx2PTAbAmXiT264lY4s4Qcv8MvBr3QHVAxQB32tUKLuFohRACtFqtzCVfShhNNgZ/vIM/TiWSeWoPMV9PRNG5EDryrXxJjSoBbgy8qyJD7q4oiXEhxC2ZO3cu7dq149y5c+zevZv69euzZs2aG5a32Wy89tprxMfH4+rq6vyvVqvNU+5WzyuKhsWm8uWOc0xbdYS31kcz+tmXeee5MfhVb0yH5xbgFxJBuK8rYT5ujGhRSZIaQgghsNhUpq86wvNL/2bv+VSMWdlcWTmPlK2L8G55P0H9X8iT1HDVaegRFcYrvetJ20OIUkC+zYkiF59mosmNFZEAAGpZSURBVNu7v5OcZf3/9u47PIqqbQP4vS276b0nJARCAqH33jsISO8oFhRUQKUJ6guIIIiIwMuHiCjlBZFelKqUUJQuNaGHQHrvm9053x8xK2sIhJBks+H+XZfX5c6cM/OcYTNnZ56ZcwAAWbfOIG7nfCjtXOAxahFUDnmv6llZKDCqqR87AyIiMtDqJLyz4RyuRqUi5Y9tSD68GpZVG8Gl54eQq60M5WzVCrzXIRB+ztZoHeiC9NRk0wVNRGYnIiIC06dPx+rVqzFs2DAAwMSJEzFmzBjcu3evQLLiUSNHjkTLli1LfLtUPOnZOvz38E2cvpOAtNRkJKZoEfPLMqRcOgTXloPRZdR7iErVIikhAzKZDM2rOPPBKiIiAvDPXH5ZWj106YmI2/oZcuPuwuWlSbCu0caorEYlh5e9Bm2qufI+FlE5wcQGlaj0bB26LD6KpMxcCCGQdnobkg7/AMsqDY1uSillwEt1vNCxBscjJCKif8a0/e16LE6GRyF+zzdIv/wb7JoOgEPrEZDJ/rl4UMiBOS/XQq+63gAASZJMFTYRmaldu3bBwsICAwYMMCwbPXo0vv76a/zxxx9o3rx5oXW3bt2KI0eOICAgAD179oStrW2JbJeeXXq2Dv2WH8ft+AwISYKnSMC9tXOhjb0Dv35ToQpqBR9Ha4xoVhlx6TlwtVFzklciIoJWJ2H7+UjM2HYJWgnIibqBuK2zAcjgPvQLqD0DjcpbquTwtNegZaArk+NE5QgTG/TcHp1gKfRGfF5SQ5eLhH1LkXH5EOya9odDqxGQyRWQAfB3tsTEjtXQtZYXLyqIiAhanYTP9lzF8ZvxuHf/Ae7/PBvamNtweelDWNdoa1RWIQP61fNB15qepgmWiCqEsLAwVKpUCRYWFoZl1apVM6wrLAFhZWWF2NhYKJVKzJ8/Hx9++CF2796NevXqFXu7OTk5yMnJMXxOTc0bylWSJCZun+K/v9/A3fh0WKsUSLt/A6c3z4ZOEvAb+QWc/KojPUcHuRzoWN34JhSPa0GSJEEIwWNTTDx+BfFYUHml1Un4eNsl/HQ2EgCQcfUIEn5dDJWrP1z7zoDSxslQViED7CxV6F7LE22quTI5TlTOMLFBzyV/PMJD12Oh00tIz9ZBn56EuG1zkBNzC849P4BNSDsAgLVaAQ87DaZ0DUbnEL6pQUREef3Iwv1h2H7+AbKibuDuhpmAkOAx7AuoPasZyskBBLha4b0O1dC1picvKIjISHZ2Nj777LMnlgkODsbw4cMBABkZGbCzszNar1arYWFhgYyMjMfWV6lUuHTpEgICAgDk3bTr0aMHXnvtNZw7d67Y2507dy5mzpxZYHlSUhJ0Ot0T2/Siy0hLRmVbgaQrR3D5p0Vw9KyE6kM+gsbeGUqFDj7WcjTzViMxMdHUoZZ7kiQhLS0NQgjOOVYMPH4FpaWlmToEIiP5D+UeCY/D9osPIYSE5GPrkHpyE6xD2sG567uQKS2M6lirlZxTg6gcY2KDnsvBq9HYffEhtHoJMpkMGQ9vImrzrLybUkPnQe0VZChrqZSjRVUXvrZHREQA8oYQGbv+LE7cSkDKlaNI+OVrqFwrwaPvDMhsnKGQA5IAXG0sMKNHDSY0iKhQMpkMGo3miWVUKpXh/21sbJCSkmK0Pjs7G1qtFjY2No+tr1AoDEkNAJDL5XjttdcwYMAApKSkwN7evljbnTZtGt5//33D59TUVPj6+sLR0bFAkoSMWVrb4fTuxUg8/hOcardDvX5jcTtTDXWmAvZWFmgX6IaOdauw7ygCScq7nnN0dOSN+WLg8StIqeTtJio/Hn1DPDYtB1kZ6Yjf8xWybvwBh7avwK5xP8hkMgCAWiFDFTcb1PK2R5sgN3Ss7s5+hKicYk9DxabVSdh4+j7Sc3TQqBTICjuOB1sXQOVSCa59p0Np6wIAkMsAB0slBjaqhAkdq7FDICIipGfrMPjbk7jyMBlJx/6HlBMbYVWjDZy7vgeZSg2VXAYnaxXkcjk+7lkD3Wtx6CkiKpxarcaMGTOKXD44OBjfffcdsrOzDQmRsLAww7qiyh9qJTs7G/b29sXarlqthlqtLrBcLpfzBum/PDoEro1ch+MrpiPxxE44t3sFTk37QW4BuFto8FrLAPg4WnHIkGckk8n4vXsOPH7GXvTjEBERgU8++QQXLlyAm5sbxo4diz59+jyxTnh4OFasWIG9e/diwIAB+M9//lMmsVZ0Wp2Erw+GY89fD5GZKyE19gHitn4GXUoMXPt9DKuqjQ1lFTJgwYC66FXXy4QRE1FRvdg9DRVberYOb649gz/vJEInSXj42xrc2fQZ7IObwWf4F3B194SNWgkHSyX8na3Qs443kxpERATgn6TGpbsxiNk2FyknfoJDm1Fw6fkh5Kq8m3sKhQw2GhU61XBHx+ruJo6YiCqal156CTqdDhs3bjQsW7lyJSpVqoTGjfNucGi1WsyYMQNnz54FAFy6dMloOCmdToeVK1eiRo0acHd3L/J26dlpdRJ++eshRqz6A5/uvIKlO05ieO8u2H/gAP63cTMmT56C7rW90KO2F359rzVGtwxA5xAPXnsQkUmkpqaiZcuWSElJwbJly9C9e3f0798f27dvL7TO6dOn8dJLL8HDwwMqlQrR0dFlF3AFpdVJ2HnhIXp8cxQrj91GQkYukm5dRPSa9yFys+Ex/EujpIYMQN/63uhak0OnE5kLvrFBz0Srk3DwWgy+PhiOW3HpENpsxO/6CpnhJ+DQeiScWwyEtVqFmb1qQqOSIy49B642aj4tRUREAPL6kXc2nMNf128ievOsvCel+s6AVWATAHkXFFYWCvSq44V2wW7sP4ioVPj4+GD+/PkYO3YsDh8+jJSUFOzbtw/bt283PGWs1WoxZ84c+Pv7o0GDBoiMjMTAgQPRsGFDODk54eDBg8jMzMTPP//8TNulZ5M/p98vl6KQmpULbeQVRG/7HGpLa9R7dxmcQ5pjSIgHJElCYmIibDS8xCUi0/ruu++QkpKCDRs2QKPRoEWLFrh06RI+/fTTQt/aqFOnDq5fvw6ZTIYdO3aUbcAVUFK6FsO+O4VrMWkQIm9Z2oW9SDywHGqfELj2mQqF5T/DPXrYqTG5SzB61vHitQeRGeGvPiqyRy8qUjJzoU2JRdzW2chNioJr3xmwqdYUVhZK9Kztia41+YQUERH949HJ+o4dO4aHP82GTKWB5/AFULn6G8pZquToU8+bE/QRUakbP3482rZti99++w1qtRqLFy9GpUqVDOvVajVmz56Nhg0bAgC6deuGJk2aYN++fYiJicHcuXPRpUuXAsNIPW27VDT5/cbvYbHYceEhtDoJKRf2IWH/cmh8qqP5W3Ogt7BFXHqOqUMlIjLy+++/o23btkZzP/Xo0QPff/89EhMT4eTkVKCOhYVFgWX07LQ6CbsvPsSMHZeQqc0bLlJIeiT99h3Szu6CTb0ecOrwBmSKf26H2mmUmNW7JjqH8E0NInPDxAYV2cFrMdj9VxQyc3TIjLyKuG1zIFNpEPDqQshc/KFWKjC8qR+HnCIiIiPp2Tq8s+EcrjxMxcM/9uDhnqXQ+FSHV99pEBo7SCLvTQ1Pew2mdg1C11p8UoqIykadOnVQp06dx65TqVQF5u1wcnLCkCFDnmu79HT5k7yeuJWA2NRsZGRrkfL7KqSc2Qm7et3g2GEMUiQNbGUyuNoUnJ+EiMiU7t+/j6ZNmxot8/DIu2keGRn52MRGceTk5CAn55/kbmpqKoC8+Z/y54Aqa5IkQQhhkv1rdRJm77mKLWciodVLkAPQZ6Uhdsd8ZN+/BOfOb8OuXve/S+e9xiEDUK+SPVoHupRpzKY8TuaEx6loKtpxepZ2MLFBRXb8ZjwytTpkXD6AmD1LofYOhmufaZCs7aEA0NDfkUkNIiIykj/0VGh4DBJ/W4WkP3fAtm5XOHcaA6WFGkII6CWB6p522PhmMw4hQkT0Anv07b5D12Ph52SJ1OQk3Ph5FrLvXYRrl7dhU68HJAFk5UroUtMZbYPcTB02EZERvV5f4A2M/Lf7dDpdie1n7ty5mDlzZoHlSUlJJbqfZyFJEtLS0iCEKPMhGE/dTsDV2w/gY60HAGTEReLi+tnQZaaj3qsz4RRQG/kJDQBQymWo4WWHiR0rIz01uUxjNeVxMic8TkVT0Y5TWlpakcvy7gE9VXq2Dv89fBOhYTGIPfAtUk/vgH29bnDo8CagUMFCIUfTKs5YOqQ+kxpERGS4MRWXnoMHSVm4eDMSUZvmIPPuBXh3Hwd17W4QANRKOdQqBUK87LB0SH0mNYiIXmCPvqWRkqlFclYucuIiEL7uE2jTUuA+aDZsK9eFTAbIZTIMaeyLDzoH8/qDiModZ2dnxMfHGy3L/+zs7Fxi+5k2bRref/99w+fU1FT4+vrC0dERdnZ2T6hZeiRJgkwmg6OjY5ncYNXqJBy6HovQm3H47VoM4tL0AGTIvHUGsTsXQGnrDI8RC5Hs6InkvBdaYG+pRNeanmgZ6IoOwaaZz6+sj5O54nEqmop2nJTKot8X4B0EeqL0bB36LT+OG5ExiN72BbLuXoBTxzFwbdILkgDkchlGNfPHB52DeFFBRERGw04pZEBGTN5NKV1mCqqM+BwOVeojPUcHC6UcL9XxQptqrpwgnIiIcDgsFsdvxkMpl8FCKUfGrTO4vX0+rBxcEPL2EmRoXGBpoYCVWokOwW5MahBRudWoUSNs3brVaNmJEyfg5uZWonMuqdXqAvM8AYBcLjfpzU2ZTFYmMWh1Ej7ecQU7LjyAVp/3JoYQQNrpbUg6/AMsAxrA5aVJkKutkD+wjUYlx7sdqmF0y4BSja0oyuo4mTsep6KpSMfpWdrAxAYV8OiTtqE34nHtehhit8yGLiMZnoNmwcKvLmQyGZysLf6+qGBSg4iI8vqPcf87i+M3EwAA2XfOIWrbPChsHBH4xjeQO3giXauDXgjU93PkBOFERGQQlZqFuLQc6PQSEv7YhtiDq2AZ0ABVBk2Ds5Mj6jtZoX2QGzzsNUyIE1G5Nnr0aHz99ddYsWIFxowZg9u3b2PFihV48803IZPJAAAnT57Eq6++il27diEwMNDEEZsfrU7CnN1X8PPZSMMyoctFwr5lyLh8EHZN+sOh9QjI5ArDemsLOTzsLeHjaGWKkImoFJhdYkMIgTNnziAmJgY1a9aEv79/ketGR0cjNDQUgYGBnMyvEP9+0jbqyh94sGUuVLZOCB6zBGonLyRn56Kauy3GtavKiwoiIgKQd3GxcH8YTtxKQK5eQvrZnUj8bRWsAurD++UpsLG3h1IugySAGn8PPcX+g4joxfTog1SuNmq0DXJDTEoOMrOykbh/GZIuHIBL8wGwazUCtQPcMLypH687iMhsVK9eHevXr8fYsWMxY8YMpKSkYOjQofjkk08MZTIyMhAWFmaY/Fur1aJ27doAgIiICFy5cgWHDx9GQEAAfvnlF5O0o7yKTclG3+WhiEz+Z+J0fXoS4rbNQU7MLTj3/AA2Ie0K1LNUKdCiqgvnZiKqQMwqsZGamoru3bvj1q1bCA4Oxp9//omJEyfis88+e2pdvV6PAQMG4I8//sDYsWPx9ddfl37AZiQ9W4evD4bj5zP3kZqtg1wmkHFuN2IOrITGvx58+02Fxs4eWr0EuUyGFlVd0DnEw9RhExFROZCercPb687gxO0E6LS5SNj/X2RcOgD7Jn3h0HoUNFZqDGzoC29HS8MNLN6cIiJ6MT06l4YkScjMleDvfAcu8mxEbfgIWQ/D4dX7Q9jX7gClXIaO1d153UFEZmfgwIHo27cv7t+/DycnJ9jb2xutb968Oa5du4aAgLwhkVQqFbZv315gO48baupFpdVJ2Hz2HmZsvwpJPLI85hZit8wGJD08hs6D2ivIqJ5SDthbqjCwUSVM6FiN1yFEFYhZJTZmzJiBmJgYXLt2DQ4ODjhy5Ajatm2L9u3bo3379k+s+5///Ac+Pj7PNLP6iyI9W4c+/w3FzdgMAIDQ5yJ+/3Kk/7UfTk37wqHNKOhkCqRk5wIAAlysMbZtVVOGTERE5YRWJ+HNtWdw4lYC9BnJiNv2OXKiw+HcfSJsanUAALjba3gRQUREAPLm0jhxKwHO1ipEJmUhJVOLEzevInrLbAi9Dg3HLYZj5ZqAENBJAh72GlOHTERULEqlEpUrV37sOisrKwQHBxs+y2Qyo89kLD1bh7fXn8GxGwlGyzOuhyJhzyKoXHzh+vIMKO1cjNZbquTwtNegZaArr0eIKiCzSWwIIbBu3TpMnjwZDg4OAIA2bdqgUaNGWLdu3RMTG7///jvWrVuH8+fPo23btmUTsBlZ8ls4bv2d1NBnJCNu++fIicq7KeVUtyPsLFVo5O8EC6Uc3g6WGNu2Kmw0ZvPVISKiUpA/jMjvYbE4dTsB2pjbiN06G0KfC48hc6H2rg4ZAI2FAu+2rcKLCCIiAgDEpedACIEsrYTkzFxkh59A5LYFsHarBJ+Bn0Dh6gmdXoLs77fEOWQIEdGLTauT8Nb6Mwh9JKkhhISU4xuQcnwDrKq3hnO38ZCrjN9uCXK3xbAmlTg3E1EFZjZ3pyMjI5GUlGQYczBf7dq1ceHChULrxcfHY+TIkVi7dq0hIfI0OTk5hnEOgbwhsABAkiRIkvTMsZc0SZIghHjuWLQ6CUfC47D/SjRkENDG3kHMltmAPheegz+Hxqc6hCShlrcdFg2sY9QJlMZxKKl2lTdsl/moiG0CKm67yHS0Ogkzd13BoeuxSM7QIv36CcTvWQiVkw9c+86A0s4VACCXAX3reaNrLS8TR0xEROWFq40aMpkMaVk5iD2yHvFH18GuRmu0GP0xsoUCzaq4oIaXHYcuJCIiaHUS5v5yzSipIWmzkbDnK2SGn4BDqxGwazbQMCk7AMgB2GiUGN8xEN1reZogaiIqKyZNbJw5cwZ37959Yplu3brB2toaKSkpAABHR0ej9c7OzkhOTn5sXSEERo0aheHDhz/Tmxpz587FzJkzCyxPSkqCTqcr8nZKiyRJSEtLgxACcnnxfujn6iWsPXUPVx6kwErkwOb+CURsXgQrZy/UHj4dGgdXAAKVna3xSRd/pKcml2gbHqck2lUesV3moyK2CfinXUQlIT1bh4k/ncdv12MhCYHk4xuRHLoeVsGt4Nx9POSqf4YMqeZug09fCuFNKSIiMrzpF52SDXcrYO+ymUi8fAwubUagWtdRsLWxhDYjF22quXJODSIiQnq2Dm+tO4vQm/GGZbrUWMRu+Qy6pIdwfXk6rKo1AwAoZIBGpYClhQIqhRztg93Qsbq7qUInojJi0sTGH3/8gd9///2JZVq2bAlra2vDhEmZmZlG69PT06HRPH7c1U2bNuH48eMYPnw4Nm/eDABISUnBzZs3sXnzZvTr188oq5tv2rRpeP/99w2fU1NT4evrC0dHR9jZ2T1TG0uDJOW9mu3o6Fism69anYRFB8Lx0/kEWCjliDmyCREH18A6qAWcuk/AQ7kGSAWCPWywcETzMht26nnbVV6xXeajIrYJ+KddCoXC1KGQGdPqJOy+EImZu68hJVsHKTcbCXu+RmZYKOxbDoN988EFnpR6uw2HoCIiIuMJw7MSo/HX6hnITniA5mM+h+TXGGqlAgkZuWhexZlDTxERveDSs3VYuD8MG09HICv3n1EHsiOvIm7b55ApLeAxfAEs3PLmL1HIgBaBLhjcqBKSMrV844/oBWLSxMa4ceMwbty4IpX19fWFUqlERESE0fKIiAgEBAQ8to69vT06duyILVu2GJYlJSUhLCwMGzduRN++fR+b2FCr1YZEyqPkcnm5udkpk8mKFY9WJ2H2nmvYei4SGZmZSPhlMTKvH4NL62GwaToYCoUcKoUMDf0csXx4wzKfS6O47Srv2C7zURHbBPzTLqLiyB966ucz96HVC+hS4xC39TPkJkbCtc9HsApqblTeQiFD77ocgoqIiPLkTxguiwnDuf+bBrnKAvXfWYrpI7tCqZAjLj2HN6KIiAgPEjPRcdFhZOUKo+Xpfx1Awr5lUHsFwfXlj6CwsgcA2GuU0KgU6FDdjcNOEb2AzGaODY1Gg3bt2mHz5s149dVXAQAJCQk4dOgQFi5caCh39uxZJCYmolOnTujatSu6du1qtJ26deuibdu2+Prrr8sy/HJBq5Pw9cFw7Lr4EJmJsYjZMhu5iZFw6zMNttVbwFajQvdanmhTzZUXFUREBMC478jVC+Q8uIbYbXMgU6j+flIq7+EClUKGJpWd4edshRZVXdCxujv7ESIiApA3YfjDP39F2Jav4BoQgvbj5iEmV42krFwMa+Jn6vCIiMjE0rN1mL/3Otacume0XEh6JP3+PdLO7IBNnS5w6vQWZAoVAMDXUQMvB0skZOTC087SFGETkYmZTWIDAObNm4dWrVrh1VdfRbNmzbBy5UpUq1YNo0ePNpRZvnw5Tp06hcuXL5sw0vInPVuHdzacw9m7SUi4cxkxWz+DTKGC5983pSQBVPOw5VjoRERk8GjfkZ6jQ9qlQ0jYtwRqz2pw7fMRFNYOAAAZgOYBzlg5qhH7ECIiMqLX67F1+Vxc+2kFAlq+hFajpiJHkkGmyxsuhIiIXmzp2Tq8/N9Q3IjNMFouZacjbud8ZN+9AMeOY2Bbv6dh1BWFDMjKlTiMIdELzqwSG/Xr18fZs2fx7bff4siRI+jXrx/GjRtnNGxUw4YNnzgPRqdOnRASElIW4ZYLWp2Eg1ejsejgDdyJz0DapUOI/fUbqD0C4d73I2jsnCAJwFqtxKimfrwhRUREAPL6j3HrzyL0VgKEpEPibz8g9fQ2WNfqBOfOYyFT5j0pJZMBLaq4YNmwBuxDiIheQPmTgj9uOKmUlBQMHjwY+/fvR5fXpyI3uAvup2ghk8l4I4qIiAAAXx8ML5DUyE18gNgtsyFlJMFt4CxY+tc1rFPKASsLJbqEeHDEEaIXnFklNgAgODgYX331VaHr33rrrSfWX7BgQUmHVG7lT9K3/2oMYpIzkHj4R6T+uRV2tTvBoVPeTSmdXsBGrUSPWp7oWMPD1CETEVE5cfBaDP64m4jcrHTE75yPrDvn4djhDdg26JU3Z4sMcLdTY1KnauhZ14cXE0REL6BHJwUXQkAmkyH0Zjxm9KiBe3du4aWXXkJMTAz27t2LNu06FJoAISKiF49WJ+HgtRhs/NN4+KmsO+cQv+MLyK0d4THyK6icvP9VU4b6fo4ccYSIzC+xQUWTPyb6r5ejIbIzELtlDjJvn4Vj+9fh2aIvtDoJCrkcjSs7YXAjX3Ss4cEOgYiIDE/e/nD8DjJi7yNmy2zo05PgOfA/sPCvD5VchuZVnTGoUSXOo0FE9ILLnxTc1cYCVhZKZGp1OHErAQtX/4z5k9+Cu7s7/vjjD1SrVg0A0DmED1IREVHeJOG9lx1HYoYW0t/LhBBIO7sTSb+tgsa/Hlx7T4ZcbW1UT6WQoVmAM5YOqc/rECJiYqMiyn9yau/laMRG3kX05lnQpSXCa9B/YOFXHzm6vKepmlZxxrcjGrIzIKIXRmRkJLKyslClShXI5U8/90mShDt37sDJyQmOjo5lEKHp5Ool/HopCmtORSAiMRPR107j4c9zoLCyh+fIhVA5+UAAqOxqjZUjOZcGERHlTQouhICVRd5lpaVKgYijmzFj93J06tgRGzduhIODg2mDJCKiciU2JRutv/wdeumfZUKfi8T9y5H+137YNXoZDm1fgUyugAyAi40KOToBD3sN3mkXiK41+WAuEeVhYqMCyZ9PY+Pp+7hwPxn6yL8QuX4WlFb28B61EGpnH0gArCwUqO/nyAw3Eb0woqOj0b9/f1y4cAHW1tbQaDT43//+hxYtWjy2fGpqKmbPno3vvvsOTk5OiIuLQ506dbB69WpUrVq1jKMvfVqdhLWn7mHb1RTEpeUg7dwexB5YAUu/OnDpNQUWVraQyQCVQo532lVl30FERAAAVxs1ZDIZMrU6qOUCx378ArdDd6LvqDH46bulUCp5uUlERP/Q6iQMXnnKKKmhz0hG3PbPkRMVDufuE2BTqyMAoEOwK3rX9UFaTi6HMCSix+IvzQpCq5Mwc9cV7L74EGnZuUg5tweJB7+FdeU68Hp5KiQLa1haKFHDyw4jm/lz+BAieqG88sorkCQJsbGxsLS0xIQJE/Dyyy/j1q1bsLW1LVD+3r17cHd3R0REBGxtbZGeno7+/fsbkiMViVYn4ZtDN/DnnUTotXok7FuG5HO/wqlJH1Tq8iZScyWoVXLYalToEOyGrjU9TR0yERGZWP6whVGpWfBxtMStiIe49OOnSI24ipfenYUNX02HktcaRET0iKR0LUZ8/wdux/8zUbg29jZit3wGodPCffBcaHyqAwCquFlj+XCOMEJET8bERgVxOCwWh67HIkerRfKB/0PSuV9g26AXPDq/ARdbS+gkgf4NfDChYzV2DET0QomIiMC+ffuwe/duWFlZAQA++eQT/Pe//8X27dsxYsSIAnVq1aqFWrVqGT7b2Njgrbfewssvv4ykpKQKMyzVP0MXRsEmOx6X18xDVuR1+PR6H7Z1OsHeRg2NTkL7YDe0qebKp6SIiF5w+W+I/3jyHiISM2GpUiAj6hau/PgxFJIWC1dvwbghPdlXEBGRkQeJmWi38Hdo9f8syww/gfjdX0Hp6AW3fjOgtHODHIBaJcd4viVOREXAxIYZ0+okHL0Ri7j0HFx9mIqslETc3zgbWZHX4N5jPCxrdoJOALmSQLeaHkxqENEL6ezZswCAxo0bG5Y5OzsjMDAQ586de2xi43H++usv2NvbV5ixwrU6CV8fDMevl6ORG3sXp9Z+DG1ONryGzoFlpZrI1UvI0UnoVMMdM3rUYP9BRBXSwYMHcfDgQajVarz88suoW7duoWVPnjyJ9evXP3bd9OnT4enpCZ1OhwkTJhRYP2zYMDRr1qyEojaN/GT4/qsxSEjPgUIuQ/K907i5aR40zl5Y9uNODO/Y0NRhEhFROZB/vyoqNQu3YzLw46l7hnVCCKSc/Akpx9bBKqgFnLtPhNxCAxkAG40SL9XxQtdaXqYLnojMBhMbZipLq8ek9Wdx5WEa5DIgM+YOrq/5BJI2C1VGzoO1Xy1kaHWwVCkwgG9qEFEFc/36dWRnZxe6XqlUombNmgCAhIQEAICTk5NRGWdnZ8THxxdpf3/99Rfmz5+P6dOnQyaTPbZMTk4OcnJyDJ9TU1MB5E1ALknSY+uUNa1OwpHwOESnZuFwWByuPEjBw4vHEL1zIWycPVBp2FxItq6wVskR7GWH4U390SHYDUo5yk0bHkeSJAghynWMT2Lu8QPm3wbG/+RtV1Qff/wxvvnmG7z99tuIiYlB48aNsW7dOgwcOPCx5e3t7REcHGy07H//+x+uXbuGBQsWAAB0Oh2WLVuGiRMnIiAgwKiuuTscFosTtxJgpZIjRS5D8omfEP3bj/Cs0wbVBk6BzNbV1CESEVE5kKuX8Pmv13D8ZgLiUnOQmqMzrJNys5Hwy2JkXj8G+xZDYd9iMOQyOZytlJArFBxphIieCRMbZig9W4dZu64g9G4mZHI5sm7+iYfb58PC0QN+w+cCdm7I0OpgZaFEz9qe7BSIqMKZNm0a7ty5U+h6R0dH/P777wAAlUoFANBqtbC0tDSUycnJMax7klu3bqF79+7o3bs3pkyZUmi5uXPnYubMmQWWJyUlQafTPaZG2crS6rH4UDjuJmRCp9cjS6tH+okteLh3DTxDmqHTqAnIUdlBJ4A21VzRv4EPVAo50lOTTR36U0mShLS0NAghIJebX39n7vED5t8Gxl+4tLS0Et1eeXH79m3MnTsXmzZtQt++fQHkJcDfffddvPzyy4/tH2rUqIEaNWoYPut0Onz++ecYPny4Uf8CAH379kXLli1LtxFlLC49B0IIaGR6PNz2BVKvHIFL62Go1mM0lEoFXG3Upg6RiIjKgQsRyTh5KwFymQyZuf9cB+lS4xG37TPkJtyHS59psA5qAQBQKmRQqZToVMOd96+I6JkwsWFGtDoJuy8+xJw9V+Co1EInASknf0LSkbWwDWqGaoOmolNtP8jleU8Tt6jqwknCiahC2rZtW5HL+vr6AgCioqKMnp6NiopCt27dnlj39u3baNeuHVq2bIk1a9Y88YbhtGnT8P777xs+p6amwtfXF46OjrCzsytyvKVBq5Mwaf1ZnLiZCoVchtycbETvWYz0q0fh2XYYnFsORqxOIDFHhi41PfFGx+pm1XdIkgSZTAZHR0ezvSltzvED5t8Gxl84pbJiXi7kz7vUq1cvw7IRI0Zg7ty5OHXqFFq1avXUbfzyyy+IiorC66+/XmDdmjVrsGPHDgQEBKB///5wdTX/txlcbdTISY3H+R9mID3qLrz6ToNVcEvk6AVaBzmjbZCbqUMkIiITyZ+D6fiteKQkJSI1KxdZWh10f7/4mfPgOuK2zQHkSngMWwAL97zrMmsLBWr52GNkM3/evyKiZ1Yxr1QqIK1Owqc7LuPns5GQJAl2ljmI270UGVePwKH5YDi3GQa5hQbtgt3QOcTD1OESEZUbTZo0gZWVFX755Re88847AIALFy4gMjISHTp0MJQLCwuDlZWVIRFy584dtGvXDs2aNcO6deugUCieuB+1Wg21uuDTqnK53KQ3StOzdZi0+SJO3EqEXsigyk5G5Pr/ICcuAh4vT0XNVl0hh4C1LAftgnwxoVOQWV5QyGQykx/r52Hu8QPm3wbG/3jmejye5saNG/D19TVK3OQnv2/cuFGkxMaqVavQuHFj1KlTx2i5o6MjLCws4OjoiI0bN2L69OnYs2dPoXNsmMNQhgCgSbqFi0veRq6Qo8G4xVC4VYG/s1W5GbbQ3IeUMzUev+fD41cQjwUQFxeHK1euwM3NzeiNv5KuY2panYSZu65g18WHyNLmwt9GIC5NBgl5D92mX/4NCXuXQO1RFa4vfwSFtSMAQCkH5vatja41Pczy+oOITI+JDTNx8FoMdv71EDpJQEpLwLl1c5AZcw8uvSbDunprCAHU8LLjk1JERP9ibW2Njz76CNOnT4ednR1cXFwwefJkdOrUCa1btzaU69evH5o2bYrvvvsOUVFRaN++Pdzc3DB58mRcvnzZUC44OBgajcYUTXlmSela9Fh6DFHJ2RAAch6G4e62OZDL5fAa/gXUHlWRmpULW40SjQOc8F6HQF5UEJFZysrKwqRJk55YpmbNmnjrrbcM5W1tbY3Wq9VqWFhYIDMz86n7i46Oxi+//ILly5cbLVepVLhy5Qo8PT0BAB999BH69euH119/HVeuXHnstsr7UIYA8PPPP2PixImoWasW3p+9GHIrO9hbWqBuJYdyM2yhuQ8pZ2o8fs+Hx6+gijqUYVEtWrQIH330EWrWrInbt2+jdu3a2LlzZ4G+53nrlAd7Lz3EtvORyNRKkAMQfy8Xkh7JR35E6p9bYV2rI5w7j4NM+c9Qj5/3CUGvupwknIiKj4kNM6DVSVhz8i4yc/TIiQpH3NbPoJTL4DnsC6g8AgEA1T3tsHRIfd6QIiJ6jOnTp8PNzQ0//vgjsrKy0Lt3b0ybNs2oTHBwMCpVqgQAuHnzJuzt7ZGbm4vXXnvNqNyWLVtQpUqVMou9OLQ6CQevxeCz3VfxMCVvkvWMq4cR/8tiWLgHwLPfDChtHBHsYYeBDX3hbqdGbVcF+xAiMlsKhaLAxN7/lv9GHgDY2NggOTnZaH1WVha0Wm2Rhg/88ccfodFoMHjw4AJx5Cc18g0bNgz9+vVDUlISHB0dC2yrPA1lqNVJOBIeh/j0HLjYqNGyihNm/ecTzJ8/H6NGjcLy5csf+3ZieWDuQ8qZGo/f8+HxK6iiDmVYFKdPn8b777+PXbt2oWfPnkhMTESjRo3w0UcfYcmSJSVWx5S0OgmHw2IRmZSJxQdvIFNr/IaOlJOJuF1fIuv2WTi2fx22DXtDJpMZ1ldxs0af+pXKOmwiqmBe3J7GjBwOi8XdhExkXjuCuF8Ww8K1MhqNnIYHcIYEoJKjJTa+2Qw2Gv5zEhEV5o033sAbb7xR6PrNmzcb/r9Vq1a4cOFCGURVsvLHtv3x5D2Ex6QhOTMXQkhIOboGKac2w7pmezh3eQdqjQbNqjhj6ZD6sNEoIUkSEhMTTR0+EVGxWVhYGIYbLIqQkBCsWLECmZmZsLKyAgBcvXoVAIo09Mf333+PIUOGwMbG5qll84eZKuzti/IylKFWJ+HzX6/jxK0ECCGgz8nEw23zcevcMSxcuBATJ040uilVHpn7kHKmxuP3fHj8jL3Ix+HHH39ESEgIevbsCQBwcnLCmDFj8Pnnn+Prr79+7BC3xaljKlqdhM/2XMXxm/GITslCxr+SGpkJD/Fw7Rzo0hPh1v9TWAY0MFpvZaHA+PacJJyInh/vhJdT+dnvuPQcXIlMRtSBVYj9fQOsQ9rBpes7UNupgFTA016NHeNaMqlBRPSCi03JRv//O4GHKdnQSwJy2d9PSu3+Elk3T8Op/WuwadgHcpkM7YPdsHhwPV5MENEL66WXXsL48eOxevVqjBs3DgCwbNkyVK1aFQ0a5N2A0Wq1eP/99zFs2DCj+TGOHj2K8PBwrFu3rsB2z5w5g8qVK8PZ2RlA3lsgS5cuRb169cr9BOKHw2Jx4lYCXG0soEuOwf6l7yMjKRazlq3B+28NM3V4RERm48KFC6hXr57Rsnr16iElJQX37t0zzOn0vHVMNUfT79djcOJmHDJydNDq9Hj0iiL73gWc2TEf0NjCa8SXsHD2wT+DUwEOlip0q+2JzjXcXuh5WDgvT9HwOBVNRTtOz9IO3g0vZ9Kzdfj6YBh2XoxCWnYurKDF7c1fIPn6KdR6eSx82gxEVFIWLBR6dK7hhi8H1mNSg4joBZeUrkXbhYeRqdUblmUnRSNuyyzoUuPh1v8TWFZpBADwdNBgQf86TGoQ0QvNw8MDS5cuxTvvvIMDBw4gJSUF58+fx+7duw1vJWi1Wixbtgx169Y1SmysWrUKderUQaNGjQpsNzU1FS1btkTVqlXh5OSEw4cPw9bWFj/99FOZta244tJzIIRAyq0L+G3ZVKit7dHw3WXwr9fS1KEREZmV5ORkODk5GS3LT3gnJSWVWB1TzdEUnxAPG2RBKdPDzhaQ/s5bRJ76BXf3fAuPqrVQbeAUKC1tkJ/UsFDIEeBqja41PdHAz7FczM1kSpyXp2h4nIqmoh2nZ5mjiXfEy5H0bB36Lj+OGzHpEAByk6Nxa8ts6NPi4DfkP7Cq1QI5OgEXOw3aVrHGe93qQWPBf0IioheZVifhtTWnjZMaEX8hbvs8yNXW8B6xECpXX1go5KjqZoO1o5swIU5EBGD06NFo27Ytjhw5ArVajc6dO8PFxcWwXq1WY8mSJWjevLlRvbZt2xomIf+39u3b48yZMzh69ChiYmLw2muvoUWLFmU+hMijb3+72qjRNsjtqQltVxs1HpzcifBt38AzuAGavD4LqUIDV5vyOacGEVF5ZWFhgaysLKNlmZmZhnUlVccUczRpdRIO3r6FS3ESBPIeBBB6HRIOfou0C7/CvsFLqN77VdzLUELKzVtvq1ZiWFM/vNchkA9X/Y3z8hQNj1PRVLTj9CxzNPHORjmh1UmYtPkibsbmJTWyIy4hbvvcvJtSIxfC3a8qOgS7oYaXHVysLTjJKxERIT1bh3c2nMNfkSmGZWnnf0HiwRXQ+NaES++pcHF2QjUPW4xq6oeONTzYdxARPSIgIOCxw3sAgEqleuy8Ha+++uoTt2ltbY1u3bqVSHzFkT/2ef5cGTKZDKE34zGjR41C+4Dc3FxsWTITYVv+Dz4t+6LqS2ORKpRoXsUZbYPcyrgFRETmzd/fH/fv3zdaFhkZCZlMBj8/vxKrU1ZzNOXP43f8VgIikzIRFp0GC6UcWToBfWYK4nbMQ07kNTh1fReOdTtDrhCQIIMEGWzVCvSs640JnYJ4HfIvnJenaHiciqYiHadnaQMTG+VA/sVH6I14SAJIu7AXiQeWQ+MTApc+U6G0soMEoE01V3QO8eAkr0REBK1Owtj/ncXxm/GQpLwnpRIPrUT6+T2wrd8Tju1fh5OtJea8XAsdq7vzQoKI6AXx6FwZVhZKZGp1OHErAYfDYtE5xKNA+YSEBAwYMADHjh3Df5f/H6q06v1Mb3oQEZGxrl27YsKECUhKSoKjoyMAYMuWLWjWrJnhTYqEhAScPn0aLVu2hI2NTZHqmIJWJ2HmrivYffEhMnP10EsCQuQNMKWNu4e4LbMgabPgPvgzuFati+61POAgz4ZXkoCngxVaV3PltQgRlRomNsqB/IsPaxVwb/f/Ie3cbtjW7wHH9m9Apsj7JwrxsuPTUkREZHDwajT+vJMISQKk7FTEbp+H7PtX4NRlHGzrdoOVhQL7xreGm73G1KESEVEpe3ToqasPUyFJAlZ/D1lrZaGEEDmIS88pUO/KlSvo1asXUlNTcejQIbRu3bqsQyciqnBGjRqFZcuWoUePHhg3bhzOnDmDHTt24ODBg4Yy58+fR7du3XDp0iXUrFmzSHVM4eDVaOz+6yHSc3RQyP8eegpA5s0/EL/rSyjt3eE55HMo7d2hVMjQPtgNDTxUcHJyqhBPjhNR+cbERjkQl54DbUYK7m2YjbSw83DqPBa29boDABQyoHlVZywdUp8ZbiIiMjh+KwG5egm58RGI3TIb+pwMuA/6DFZ+tVDX1wGrRjaCo83jx+MlIqKK499DT2Vo9cjS6uFqawEbtQqZWh1kMlmBuTJ2796NoUOHwt/fHwcPHkTlypVN1AIioopFrVbjyJEjWLRoETZt2gRXV1ccP34cjRo1MpRxcXFBly5dYGtrW+Q6ZU2rk7D6xF2kZOVNRK7XCwghkPrHZiQfWQPLwCZw6fkB5BaWAAAHSxXaVHN94ScGJ6Kyw8RGOZAVcw9nvhkLfVYaOn/wDVIdqyEpIxcBrtZ4q00VdK3pyaQGEREVkHXrNGK2z4fC3g1eg2dDYe+Bqm422PhmM/YbREQviH8PPZWek4vrUWmISMyClYUWMpnMaK4MIQTmz5+PadOmoVevXli7dq3hxhoREZUMBwcHzJw5s9D1devWxd69e5+pTlk7HBaL8Og0w2cpNweJe5cg4+ph2DcbBPtWwyCXyaGQyyAg0DmE8/kRUdliYqOMPfqauKuNGlm3z+CDEcNg4+CGwDcXQOfkCWuZDJ1qeDxxgj8iInpxCSEQfWwTojbNgU21JvDo9SGgsoRKKcc77QLZdxARvUDi0nMgxD9DT9moVXC2sUCzKi6o4WVnNFdGdnY23njjDaxbtw7Tp0/HrFmzOFQIEREZ5N+zik7Jxr6r0UjJzntbQ5eWgLhtnyE37h5cek2GdfW8oQsFAAGBqq42eLd9oAkjJ6IXERMbZSg9W4d3NpzDlYepkEMg8dRW3NyzAj169MAPP67FuahsTtRHRERG/p0Qb+pnh3fGvoW1a9eied/XoK8/CHoBKBVytA92Q9eaBSeGJSKiisvVRg2ZTIZMrc4wWbhcLkebaq5Gk4VHRUWhT58++Ouvv/C///0PQ4YMMWHURERU3uQPbRh6Iw5RKdnIypUAADlR4Yjb+hkAGdyHzYfaoypkACxVcrjbadA5xAPvtg+EjUYJSZJM2gYierEwsVFGtDoJ72w4h+M34yGTchH7yxKk/HUIfu2HYtyc+XBxckBnJ1NHSURE5cm/x03XpiXizoaZSIgIx/r169F/4GCjpAeT4kREL562QW4IvRn/d1+RU2DoKQA4c+YM+vTpAyEEjh49atIx24mIqHw6eC0G+69EIzkrF9l/JzUyrh5Bwq+LoXKtDNe+06G0cYJSLoOjlQoDGvpiQsdqvP4gIpMxu8TGpk2bsHjxYsTExKBWrVr4/PPPUb169SfWiYmJwZw5c/Dbb7/BysoKb775Jl5//fUyijjvxtTXB8Nx5m4S9OmJiN36OTKjbsK7zyR4NOqMxL8nYiIiInrUo+OmZz68gQOLP4RekvDl6m0YOrgrABg9jUtERC8eC6UcM3rUKDTRvXHjRrz66quoXbs2tm3bBi8vLxNHTERE5YlWJ+HgtRjM33sdMak5ecNLCQnJx9Yh9eQmWIe0g3PXdyFTWgAAbNQKdKvlyaQGEZmcWSU2tm3bhmHDhmHJkiVo1qwZFi5ciDZt2uDKlStwdXV9bJ3Y2Fg0adIENWvWxOrVq2FlZYWlS5ciODgYLVu2LPWY85+2/fVyNJLvX0f05s8AIaHaawsBt6rQi7zXx4mIiP497FR0SnbefBrnf8ex72fB0bsKgobPhFPlJyf0iYjoxWKhlBdIdEuShE8++QRz5szBsGHDsHLlSlhaWpooQiIiKo+0Ogkzd13Bnr8eIvnvh26lnEzE7/kKWTf+gEPbV2DXuB9kMhkAwN1Wjek9aqBrTU4UTkSmZ1aJjdmzZ2PUqFF46623AADff/89PD09sXz5cnzyySePrTNjxgwolUps3boVFhZ52eXly5eX2bh/+U/basND8fB/82DhWgmuL0+H3sEVkARCvOyMXhMnIqIX07+HnZLJZPC2V+P23u9x9+BaBDTtgvrDpiJJK2NCnIiInig9PR0jRozAjh07MG/ePEyePNlwU4qIiCjf7osP8PPZSGh1effIcpOjEbdlNnSpsXDt9zGsqjYGAMhlQKfq7lg4sC5sNGZ1K5GIKjCzORulpqbi/PnzmDJlimGZUqlEhw4dcPTo0cfWEUJg8+bNePfddw1JjXxyeelmlvOfut198QGu716JB7+vh0uddnDuNh65MiXUSgUa+jti6ZD6zHITEZHRsFNWFkqkpKbi168/RtyVUFTp/gYqtRuCJG3BcdOJiIgedffuXfTq1Qt37tzBjh078NJLL5k6JCIiKofSs3WYufuqIamRHXEJcdvnQq62gsfwL2Hh6gcAsFDI0Le+D2b1rsn7V0RUrphNYuPBgwcAAA8P41es3d3dcfHixcfWiYuLQ1JSEjw8PDB48GCcPXsWXl5eGDVqFF599dVCn1rKyclBTk6O4XNqaiqAvNe5i/Kmh1Yn4fNfr+HY1Uj8tf5zJF4JhU/nV9F+4BuISc1BYmYu+tf3wXsdAmGhlD/z2yOSJEEIUWZvnZQVtsu8VMR2VcQ2ARW3XRVNXHoOhBCwslAiLf4hfvvmQ6TGPsD4L75F9+49OUE4ERE91dGjR9GvXz/Y2dnh1KlTCAkJMXVIRERUTv338E2kZecNP5V2YS8SDyyH2icErn2mQmFpBxkAjUqOkc388UHnIF6DEFG5Y9LExvvvv49NmzY9sUxoaCj8/f0NN+SUSuOQVSoV9Hr9Y+vm5uYCAKZMmYKvv/4as2bNwh9//IExY8YgLS0N48ePf2y9uXPnYubMmQWWJyUlQad7+kTfp+8k4urly7i84mOkxT1E89GfwjawIbLSUuGuVqB9gANG1HdGemryU7f1OJIkIS0tDUKIUn/zpCyxXealIrarIrYJ+KddVL652qghk8kQce08Qv87BQqNFRq+uwzdu3fjBOFERPRU33//PcaMGYOWLVti8+bNcHZ2NnVIRERUjj1IzgIgIfHAt0g7txs29XrAqcMbkCmUkAGw1SjxUh0vJjWIqNwyaWLj448/xvvvv//EMp6engBgmBw8Pj7eaH18fHyhE4c7OztDLpdjwIABGD16NACgWrVqOHnyJH744YdCExvTpk0ziis1NRW+vr5wdHSEnZ3dU9uVeDMdccIGantX9HxjFhx8quBaVCqCPZzRraYn2lRzfa5OQZIkyGQyODo6Vribr2yX+aiI7aqIbQL+aZdCoTB1KPQEbYPcEHozHvseKmHtWx01Bk9B2zpVOOwUEREViUajwRtvvIHFixdDpVKZOhwiIirnvB0soZAroFCq4NxlLGzqdgcAOFop0SXEE60DXdCxBicJJ6Lyy6SJDUdHRzg6OhaprJubG/z8/HD8+HH07t3bsDw0NLTQcWM1Gg3q1q0LGxsbo+W2trbIysoqdF9qtRpqdcGJWeVyeZFudrraaqCyskOrd7+ClYUSmVodrDUW6F7Lq8SeupXJZEWOx5ywXealIrarIrYJ+KddVH5ZKOWY0aMGWlZ1QdyADhx2ioiInsnQoUMxdOhQU4dBRERmYmzbqjh0LQbo+DokIQABuNtrsOedVnC0sXj6BoiITMys7paMGzcOq1atwsWLFyFJEr755htERETgzTffNJSZPHkyOnbsaPg8fvx4bNiwAVevXgUAhIeHY82aNUbJkZLWNsgNzas4Iy5di7sJGYhL13KyVyIieioLpRydQzwwrIkfOofw6SgiIiIiIiodNholtrzdAm+0CkDP2l4Y06YK9k9ow6QGEZkNs5k8HAA++OADREVFoWnTplAoFLCzs8PGjRtRvXp1Q5nExERER0cbPo8cORIPHjxA8+bNIZfLodVqMXr0aMyaNavU4sx/6vZwWCwneyUiIiIiIiIionLHRqPE5K7Bpg6DiKhYzCqxIZfL8dVXX2HevHlISUmBi4sLZDKZUZkFCxZAq9UaLZs2bRomTZqE5ORkODs7F6hTGvKfuiUiIiIiIiIiIiIiopJjVomNfBYWFoVOGF7YnB1KpRIuLi6lGRYREREREREREREREZUyjo1ERERERERERERERERmg4kNIiIiIiIiIiIiIiIyG0xsEBERERERERERERGR2WBig4iIiIiIiIiIiIiIzIZZTh5e1oQQAIDU1FQTR5JHkiSkpaVBqVRCLq84uSm2y7xUxHZVxDYB/7RLoVAA+OecRqWrvPUdT2Lu333Gb3rm3gbGX7j8cxj7jrJjTv1HeWLuf8emxuP3fHj8CmL/UfbKQ//Bv4Wi4XEqGh6noqlox+lZ+g8mNoogLS0NAODr62viSIiInl9aWhrs7e1NHUaFx76DiCoS9h1lh/0HEVUk7D/KDvsPIqpIitJ/yATT508lSRIePnwIW1tbyGQyU4eD1NRU+Pr64v79+7CzszN1OCWG7TIvFbFdFbFNwD/tioiIgEwmg5eXV4XI4pd35a3veBJz/+4zftMz9zYw/sIJIZCWlsa+owyZU/9Rnpj737Gp8fg9Hx6/gth/lL3y0H/wb6FoeJyKhsepaCracXqW/oNvbBSBXC6Hj4+PqcMowM7OrkJ8Yf+N7TIvFbFdFbFNAGBvb18h21Velde+40nM/bvP+E3P3NvA+B+PT9qWLXPsP8oTc/87NjUev+fD42eM/UfZKk/9B/8WiobHqWh4nIqmIh2novYfTJsTEREREREREREREZHZYGKDiIiIiIiIiIiIiIjMBhMbZkitVuPTTz+FWq02dSgliu0yLxWxXRWxTUDFbReVHHP/jjB+0zP3NjB+IvPHv4Pnw+P3fHj8iPLwb6FoeJyKhsepaF7k48TJw4mIiIiIiIiIiIiIyGzwjQ0iIiIiIiIiIiIiIjIbTGwQEREREREREREREZHZYGKDiIiIiIiIiIiIiIjMhtLUAdCTPXz4EA8ePEDVqlXh6Oj4xLL37t3D/fv3jZap1Wo0atToubZbGsLCwpCRkYGaNWvCwsLiqeVzcnIQFhYGBwcH+Pr6QiaTGa2/ePEi0tLSjJa5u7sjMDCwROMGAK1Wi8uXL8Pa2hpBQUElVqc42y1JSUlJuHnzJry9veHl5VWkOhEREUhOTkbVqlVhZWVltC4+Ph7Xr18vUKdp06ZQKsvu1HPv3j3ExcUhKCgItra2Tyx748YNxMTEGC2ztbVFnTp1nmu7JU0IgatXr0Kn06FmzZpQKBSFlk1JScGlS5ceu65GjRpwcnICAJw+fRo5OTlG6319feHn51dygVO5dPv2bSQlJSE4OBjW1tZPLZ+bm4uwsDBYWVnBz8/vid+/khQXF4e7d+/Cz88Pbm5upVantGRlZeHq1auwt7dH1apVi1QnLi4OkZGRqFy5MhwcHEo3wKd4lvPOv925cwcPHjxArVq1YG9vX4pRPllxzts6nQ7Xrl2Dra0t/P39SzfAp0hMTMStW7fg4+MDT0/PItWJiorCgwcP4OrqyvM5VWjP8ju2vPxGNYXY2Fjcu3cP/v7+cHV1LbU6FVVkZCSio6NRtWrVp/bL+X3foywtLdGgQYNSjJCobNy8eRMpKSmoUaMGLC0tS62Oucs/ZwQGBhbpN7Ber8eNGzcgl8sREBBQ4fukfPn9zLNes2m1Wvz5559wdHRESEhIKUZYPqSkpODGjRvw8PCAj49PkevdvXsXqampqFGjRsX8Tgkql3Jzc8WIESOERqMRNWrUEGq1WsyZM+eJdaZMmSLs7e1FixYtDP+9/PLLz73dkhQZGSnq1asnnJycROXKlYWLi4vYt29foeWTkpLE2LFjhYODg6hdu7ZwdXUVdevWFZcuXTIq16BBA+Hn52fU9tJo1759+4SLi4uoXLmycHJyEvXq1RORkZHPXac42y1Jc+bMEWq1WtSoUUNoNBoxYsQIkZubW2j5LVu2iOrVqwtfX19Rs2ZNYW1tLT777DOjMhs2bBBKpdLo36RFixYiOTm5tJsjhBAiIyND9OzZU1hZWYng4GBhZWUlVqxY8cQ6o0aNEq6urkbxvvbaa8+93ZJ07do1Ua1aNeHu7i58fHyEj4+P+OOPPwotf+7cuQL/BlWqVBEAxPHjxw3lvL29RWBgoFG55cuXl0WTyESSk5NFu3bthK2trahWrZqwtbUV69evL7R8dna2mDp1qnB2dhYhISHCy8tLVK1aVRw9erTUY/3www8N5yi1Wi3effddIUlSidcpLT///LOwt7cXgYGBws7OTrRs2VIkJCQUWv7UqVOidevWhj7P0tJSjB49Wmi12jKM+h/Pet55VFRUlPDw8BAAxIEDB0o50scr7nl748aNwtXVVVSpUkWEhISIbt26icTExDKIuKCZM2cafZ9feeUVodPpCi2fnJwsOnfuLGxtbUWDBg2Es7OzqFu3rrh161YZRk1UNp71d6ypf6OagiRJ4t133zU6j3zwwQclXqeiysnJEYMGDRKWlpaievXqQqPRiAULFjyxzvjx44Wjo6PRd2zQoEFlFDFR6UhISBAtW7YUdnZ2IjAwUNjb24uff/65xOuYu+zsbNG/f3+jc8aiRYueWGfOnDnCw8NDBAcHi8qVKwsvLy+xffv2sgnYhCZMmGDUz0yYMKHIdSdOnCjkcrno0qVLKUZYPixatEhoNBpRvXp1YWlpKfr37y+ys7OfWCc8PFw0adJEODs7i4YNG4pq1aoZ3QOqKJjYKKfmzZsnXFxcxO3bt4UQQhw6dEjI5fInJgGmTJkiOnToUOLbLUkdO3YUrVq1MvwBTp8+Xdjb2xd6g+f69eti2bJlhvLZ2dmib9++IjAw0KhcgwYNxNy5c0s19oSEBGFvby8++eQTQywtWrQQnTp1eq46xdluSdq7d69QKBTi999/F0IIcevWLeHk5CS++OKLQut89dVX4tq1a4bPBw4cEAqFQuzYscOwbMOGDcLZ2bnU4n6aCRMmCH9/fxETE2OIRyaTifPnzxdaZ9SoUWLYsGElvt2SIkmSqF27tujTp4/Q6/VCCCFee+014evr+9RO7VFDhw4t8Dfk7e0tVq9eXZLhUjn3yiuviBo1ahhu5CxfvlyoVCpx8+bNx5aPi4sTc+fOFampqUIIIfR6vXj77beFs7OzyMzMLLU4161bJywtLcXZs2eFEEJcvHhRWFlZiVWrVpVondJy7949oVarxTfffCOEECI1NVXUrFlTDBkypNA6a9euNUoY3bhxQzg7O4uZM2eWerz/9jznHb1eLzp27CimTJli0sRGcc7b+/fvF3K5XKxbt86w7MCBAyIsLKy0wy1g9+7dQqlUGr4T4eHhwsHBQSxcuLDQOpMnTxZeXl4iPj5eCCFEZmamaNSokejdu3dZhExUZorzO9bUv1FN4bvvvhM2Njbir7/+EkIIcfr0aaFWq5/4QENx6lRU//nPf4SHh4eIiIgQQgjxyy+/CJlMJo4cOVJonfHjx4sePXqUVYhEZWLw4MGiTp06Ii0tTQiRd7NVrVaLe/fulWgdczdjxgzh5eVleFh1165dBR4s/LePPvpIxMXFGT7Pnj1baDSaMn3gtaz98MMPwsrKSly4cEEIkfdQpqWlpfjxxx+fWnfPnj2ievXqolevXhU+sREaGipkMpnYs2ePEEKI+/fvCw8PD/Hxxx8XWiclJUVUqlRJDB482HDN9uDBA7Ft27ayCLlMMbFRTlWrVq1AprJly5ZPfMpjypQpolWrVuLcuXMiPDz8sU/yFWe7JSUiIkIAELt37zYsS0lJEWq1WqxcubLI29m9e7cAYLhBIUReYmPq1Knizz//FA8ePCjRuPN9++23QqPRGDpkIYTYvn27AGD4kVucOsXZbkkaOHCgaNu2rdGyd955RwQFBT3TdoKDg8WkSZMMnzds2CCcnJzElStXxKVLl57pxvvz0uv1wtHRUcybN89oeWBgoBg/fnyh9UaNGiX69u0rzpw5I27fvm24ife82y0pf/75pwAgzpw5Y1h29+5dAUDs2rWrSNtISkoSGo1GzJ8/32i5t7e3WLBggTh9+rSIjo4u0bip/MnMzBQajUb83//9n2GZXq8X7u7u4tNPPy3ydi5cuCAAGBIIpaF9+/aif//+RssGDx4sWrRoUaJ1Ssvnn38unJ2djfrk7777TqhUKpGSklLk7QwePLjMEt6Pep7zzpw5c0TXrl3F/fv3TZbYKO55u1mzZqJXr16lHF3R9O3bV3Ts2NFo2VtvvSVCQkIKrfPqq68W6NvHjBkjmjdvXioxEplKcX7HmvI3qqk0b95cDB8+3GhZnz59nvhQXHHqVFSVKlUSU6dONVrWsGFDMWrUqELrjB8/XnTq1EmcPXtW3Lhx44lv2RGZg5SUFKFSqcQPP/xgWJabmyucnJwKfci0OHUqAi8vLzFjxgyjZXXr1i0wGsSTxMfHCwBGD49WNK1btxaDBw82Wta/f3/Rpk2bJ9Z78OCB8PLyEufOnRODBg2q8ImN0aNHi4YNGxotmzp1qvD29i60zsKFC4WVlVWFfhs1HycPL4cyMjIQHh5eYPzNxo0b4/z580+se+LECYwYMQKtW7eGt7c3Nm/eXCLbLQn5+3h0/3Z2dggKCnqm/Z8+fRr29vZwcXExWv7NN9/gjTfeQHBwMBo0aIDLly+XTOB/O3/+PIKCgmBjY2NY1rhxYwDAhQsXil2nONstSefPn3/sdyI8PByZmZlF2kZcXBzu3btXYNz4xMRE9O7dG71794aTkxO++OKLEov7Se7evYukpKQC7WrUqNFTv2u7du3C6NGj0bhxYwQEBODAgQMlst2ScP78ecjlctSrV8+wLH8cyqLuf/369dDr9Rg1alSBdbNmzcLrr7+OgIAAtG3bFnfu3Cmx2Kl8uXbtGrKzs42+y3K5HA0aNHjm87FCoSjVuQcKO0c9Kc7i1Ckt58+fR926dY3mpGjcuDFyc3Nx5cqVIm1Dr9fj/PnzRZ6boyQV97xz8uRJLF26FD/88EMZRFm44py309PT8ccff+Cll15Camoqzp49i+jo6LII97EK+z5fu3atwNxI+SZOnIjw8HB89tlnOHToEJYtW4bt27fj008/LYuQicpMcX/Hmuo3qqmYe19qSomJiYiIiCjWsfjtt98watQotGzZEr6+vtixY0dphkpUqi5fvozc3FyjvwWlUom6desW+rdQnDrmLjY2Fg8fPnzu8+fp06cBAFWqVCnR+MqT4vQzkiRh+PDhGD9+vNH1SUVW2HF68OAB4uLiHlvn0KFDaNWqFWxsbHDx4kXcvHkTer2+LMItcxVw1pDy6XGTh/1b3bp1YWNjg6SkJACAs7Oz0XpnZ2ckJiYWWr9Vq1Z499134e3tDSEEPv/8cwwdOhTVqlVD7dq1i73dJzl//jwyMjIKXa9UKtG0aVMAMOzjefZ/7tw5zJ8/HzNnzoRc/k9e7r333sOAAQNgaWmJtLQ0DBkyBH369MGlS5dKbGKqxMTEx8aev664dYqz3ZJU2P6FEEhKSiowKfi/SZKE1157DZ6enhg2bJhheeXKlXHhwgXDxNs7duxA3759UalSJQwZMqTkG/KIJ33X/vrrr0LrvfTSS1i4cCGcnZ2h0+nwwQcfoF+/frh8+TIqVapU7O2WlMTERDg4OBh99/P3X9TvyqpVq9C7d+8Ck3LNmjULI0eOhFKpRHx8PHr37o1Bgwbh1KlTBfZH5dOVK1cM5/nCNG/eHHK5/Inf5aImtO7evYtp06Zh3LhxhknoS5oQAsnJyY+NMzMzEzk5OVCr1c9dpzSVxDn+008/RWRkJCZOnFji8T1Ncc47ycnJGDp0KFasWAF3d3dERkaWRaiPVZzzdkxMDCRJwtmzZ/Hxxx/D09MTN27cQOvWrbF+/foyn8i9sO+QJElITk6Gu7t7gTrBwcEYNWoUvvzySwQEBODevXt46aWXDL/JiMqr2NhYhIeHP7FMYGCg4XtfnN+xpvyNagrZ2dnIysp67HFKSkqCEAIymey561RUxb2Gbd++PaZOnQoPDw9IkoRPP/0UgwYNwvnz51G9evVSjZmoNBTnb6Ek7gGZm5Joc0JCAsaNG4e+fftW2EmxdTod0tLSHnucUlNTodfrjR4MyzdnzhwIIfDhhx+WVagm97TrSVdX1wJ1Hj58CHd3d0PyJzExERYWFli7di1atGhR+kGXISY2ysiuXbuwadOmJ5ZZtWoVgoKCoFKpAOT9oHxUVlYWLCwsCq3fo0cPw//LZDJ89NFH+L//+z9s2bIFtWvXLvZ2n2TRokW4fft2oettbW3x66+/AoDR/vP//1n2HxYWhu7du2PQoEEFTmIjR4402ueXX36J6tWr48yZM2jVqtUztakwKpXqsccOQKHxF6VOcbZbkp5n/0IIvPXWWzh16hQOHz4Ma2trw7omTZoYle3duze6deuGjRs3lvpFY3G/6/369TP8v1KpxJdffomVK1diz549ePvtt0vlb+hZPO7f6ln2f/78eZw/fx7z5s0rsG706NGG/3dxccGcOXPQrl073Lp1C4GBgc8XOJWJ1atX49SpU08sc+DAAVhaWj73dzkqKgqdO3dGo0aNsGDBguIH/RQymQxKpbLQc9Sjfcnz1ClNz3uOX7p0KRYsWICtW7ea5G+xOOedyZMnw8/PD46OjggNDTU8SXT58mV4enqW6QVacb7r+XUOHTqEy5cvw9nZGbGxsWjSpAmmTZuG5cuXl27Qj4nnWb9D77//Pvbu3YsbN27A1dUVmZmZ6NmzJwYOHIi9e/eWesxExXX27FnMmTPniWWmTp2Knj17Aije34cpf6OawpPOg0ql8rEJiuLUqaiK+5upV69ehv+Xy+WYOXMmVqxYge3btzOxQWbpeX5Tmer62RSet82pqano3r07nJ2dsXr16lKJsTxQKBSQy+WPPU5yufyxSY1r165hzpw5WL16NU6cOAEAiI+PR3p6OkJDQ1G/fv2nPphrjop7L/LAgQPYv38/OnToAL1ejzfeeAMDBgxAREQElMqKkw6oOC0p59577z289957RSrr6uoKKyurAm94PHjwAJUqVSryPmUyGdzc3AzbKantPmrNmjVFLuvn52fYX3BwsNH+O3bs+MS64eHhaN++Pbp06YJVq1Y99cd0/lNcT3tL5ln4+fnh2LFjRsvyt1/Y8StKneJstyT5+fk99jthZWVVICv8KCEExo4di23btuG3335DjRo1nrovd3d3XLx48bljfppHv2uPetbvukqlgqOjo2E7JbXd4vLz80NmZiaSk5MNTwzrdDrExsYWaf+rVq2Cv7//U//eAOO/ISY2zMOXX35Z5LKPfpcfvbh+8OABgoKCnlg3Ojoa7du3R0BAALZt21bqFyWVKlV67N+cj49PoW8TFadOafHz88OZM2cKxAI8/Ry/fPlyfPDBB/j555+NHl4oS8U579jZ2UGn02Hq1KkAAK1WCyDvHBQZGflM39XnVZzztqenJywsLDBgwABDP+jm5oa+ffti3759pRvwYxTWT9va2sLR0fGxdXbv3o2hQ4canuCysrLCq6++ilGjRiE7OxsajabU4yYqjm7duqFbt25FLl/c37H/Vla/UU1BoVDA29v7sccp/xxZEnUqKi8vL6hUquf+/S+Xy+Hq6lqi16dEZenR31SVK1c2LH/w4EGBhPHz1DF33t7eUCgUxTpnpKWloWvXrtDr9Th48CDs7OxKM1STkslk8PX1fabjlJGRgYYNG2LZsmWGZWFhYYbrjrVr1xp9zyqKwn7rKBQKeHl5PbaOv78/UlJS0KFDBwB5/fobb7yB1atX4+bNm0b3ZM0dxxcph+RyOdq3b4+dO3calmm1Wvz666/o1KmTYdndu3cN4+4BKDAkVGRkJK5du4aaNWs+03ZLS6NGjWBvb2+0//Pnz+P+/ftG+79w4QJu3Lhh+Hzjxg20a9cOHTp0wOrVqwvclMrKyoIQwmjZ/v37AcDQ9pLQqVMn3Lt3z2joih07dsDBwQGNGjUCAOTm5iI0NBTx8fFFrlOUMqWpU6dO2Lt3L3Jzc43236FDB8OxjouLQ2hoKHQ6naHMO++8g82bN+PQoUOoVatWge3++/uYk5ODo0ePlui/SWEcHBzQsGFDo+9aSkoKDh8+bPRdCw8PN1zE6vX6AlnwK1eu4OHDh4aYi7rd0tK2bVuoVCqj/R84cACZmZlGyYo///wT9+7dM6qbnZ2N//3vf3jttdcK/A09bji5/fv3Q6FQ8ImyCsrf3x9Vq1Y1+i5FRUXhzz//NPouX7161Wi+opiYGLRv3x6VKlXC9u3by2RIp06dOmH37t2G87wQAjt37jSK8+HDhwgNDX2mOmWlU6dOOH/+vNFwTDt27EClSpVQrVo1AEBmZiZCQ0ORkpJiKLNixQpMmDABmzZtMnrqs6wV57zz5ZdfIjQ01PDf1q1bAeS95VmWSQ2geP2BSqVC+/btC1xAREZGPvZV79LWqVMn/Prrr0Z98I4dO4yOf2xsLEJDQw1j57q6uhYYAuz+/fuwtbVlUoMqlOL8jjXlb1RT6dSpE3bt2mXoFyVJwq5du4zOg5GRkYYnYIta50WgUqnQpk0bo34kOzsb+/btMzoWt2/fxtmzZw2f//09u3v3Lm7cuFGhv2dUsQUFBcHX19fob+HevXu4cOGC0d/C5cuXcfXq1WeqU5FoNBq0atXKqM1ZWVk4cOCAUZtv3bqFc+fOGT7nJzW0Wi0OHDhQ5kOfmsLjrtn+3c88ePAAx48fBwA0bNjQ6BojNDQUHTp0QJMmTRAaGlohkxpA3nE6cOCA0f2qHTt2oHXr1obr8cTERISGhhrm3+vSpQvi4+ON5uPLvzb493zFZq+MJyunIjp79qzQaDTivffeEzt37hS9evUSXl5eIi4uzlDmgw8+EN7e3obPtWrVEvPmzRO//vqrWL16tQgKChIhISEiNTX1mbZbmhYvXiwsLS3FkiVLxKZNm0RQUJDo3r27UZk6deqIUaNGCSGEiIyMFD4+PqJevXriyJEj4tixY4b/0tPThRBCnDt3TjRt2lSsWLFC7Nu3T8ybN0/Y2dmJV199tcTj79atmwgODhabNm0SS5YsERqNRnzzzTeG9VFRUQKA2LBhQ5HrFLVMaYmLixNeXl6id+/eYufOneKdd94RGo1GnD171lBm7dq1AoDhezJp0iShUCjEf//7X6N/k7CwMEOdPn36iKlTp4qdO3eKTZs2iTZt2ghnZ2cRHh5eJu3av3+/UCqV4uOPPxbbt28Xbdq0EUFBQSIzM9NQZtiwYaJBgwZCCCHS0tJESEiIWLRokdi7d6/4v//7P+Hr6yuaN28utFrtM223NE2bNk04OjqKVatWiXXr1glvb28xevRoozLu7u5iypQpRsvWrVsnFAqFiIyMLLDNvXv3ivbt24tVq1aJvXv3ik8++URoNBoxbdq0Um0LmdbmzZuFUqkUc+bMEVu3bhWNGzcW9evXF7m5uYYyPXr0EB06dBBCCJGSkiJCQkJE5cqVxYEDB4z+9hMTE0stzjt37ghHR0cxbNgwsXPnTjFq1ChhZ2cnbty4YSizaNEi8ehPmqLUKSs6nU40bdpUNGjQQGzZskV88cUXQqlUivXr1xvKXLp0SQAQBw4cEELknXNlMpmYNGmS0XE+d+5cmccvRPHPO/nu379v1L6y9qz9gRBCnDlzRtjY2IhZs2aJffv2iU8++UQoFArxyy+/lHn80dHRwsPDQ/Tt21fs3LlTvP3228LS0lJcuHDBUGb16tUCgEhKShJC5H2HFAqF+OSTT8T+/fvFokWLhK2trZg+fXqZx09UmorzO9bUv1FNITw8XNjZ2YlXXnlF7Ny5UwwdOlQ4OjqKu3fvGsrMnTtXqNXqZ6rzojh58qSwsLAQH3zwgdixY4fo1q2bqFSpkuGcK4QQ48aNE1WqVDF8Dg4OFgsWLBC//vqrWLVqlahataqoW7euyMjIMEELiErGunXrhFKpFPPnzxdbtmwR9evXF02bNhV6vd5QpkOHDqJHjx7PVKeiCQ0NFSqVSkyaNEns2LFDdO7cWfj7+4uUlBRDmTFjxoigoCAhhBC5ubmiVatWwsXFRezatcvo939UVJSpmlHqbt26JRwcHMTIkSPFzp07xYgRI4SDg4O4deuWocyCBQuEQqEodBuDBg0SXbp0KYtwTSY5OVn4+/uLrl27ih07dogPP/xQqFQqERoaaiizbds2AUDcuXNHCCFETk6OqFu3rujVq5fYs2eP+OGHH4S3t7d47bXXTNSK0sOhqMqp+vXrIzQ0FIsXL8bXX3+N4OBgLFmyxCizVrlyZTRu3Njw+bfffsOSJUvwzTffwNbWFm+99Rbefvttoydqi7Ld0vTee+/B3d0dGzZsQGZmJkaMGIH333/fqEy9evUMQ99ERkYaXl/86KOPjMr98MMPqFq1KurVq4dvv/0WK1aswJYtW+Dl5YUff/wRffr0KfH4t2zZgq+++gorV66ElZUVfvjhBwwaNMiw3sLCAi1atDB6ovNpdYpaprS4uLjg5MmT+OKLL/D111/D29vbMD5hPjc3N7Ro0cIwXmR0dDSaNm2K9evXG22rS5cu+PjjjwEAGzZswIoVK/D9998DANq0aYMtW7Y807AAz6NTp044ePAgli9fjuPHj6NBgwaYOnWq0WTyQUFBhidXbWxssHfvXixduhR79+6Fs7MzZsyYgdGjRxuNP1iU7ZamOXPmICAgAFu3boVOp8OHH36Id955x6hMkyZN4O/vb7Ts0qVLGDduHLy9vQtss0uXLrC3t8f333+Pe/fuwdfXF7t37za8tkgVU79+/bB792589913+O2339C+fXtMmTLF6PseEhJiGEYoISEBDg4OcHBwwH/+8x+jbc2fPx/NmzcvlTj9/f1x6tQpLFiwAIsWLULlypVx6tQpVK1a1VDG29vbaBK0otQpKwqFAvv27cOCBQvw3//+F/b29ti2bZthfHgAsLa2RosWLQxPZt25cwfNmzfHiRMnjJ6eDQgIeKYhIEtKcc87+dRqtVH7ytqz9gcA0KBBAxw7dgyLFy/GkSNH4OfnhxMnThj97ior7u7uOHXqlKGf9vHxwfHjxw0TH+eXadGiheHvd/jw4fDy8sLatWsxf/58uLm5YeXKlRg4cGCZx09UmorzO9bUv1FNITAwECdPnsTChQuxaNEiVKlSBadOnTIaVsrX19eoLy1KnRdF06ZNcfToUSxZsgSLFy9GSEgIVq5cadSvValSBQ0bNjR8PnLkiOHa3N7eHuPHj8ebb75ZYecVoBfDsGHDYG9vj9WrV2Pfvn3o3r07Jk2aZDQiQK1atYy+50WpU9G0aNECR44cwdKlS7F48WLUrFkTP/zwg9HQUlWrVkVaWhqAvLfAJElCUFBQgfkwJ02ahN69e5dp/GUlICAAJ0+exJdffolFixYZPgcEBBjK+Pj4oGXLloVuIzg4GOnp6WURrsnY29vjxIkTmDdvHhYvXgwPDw8cOXIEzZo1M5RxdnZGixYtDNczFhYW+P333w3H1tHREbNmzcIrr7xiolaUHpkQ/xrDh4iIiIiIiIiIiIiIqJyquClSIiIiIiIiIiIiIiKqcJjYICIiIiIiIiIiIiIis8HEBhERERERERERERERmQ0mNoiIiIiIiIiIiIiIyGwwsUFERERERERERERERGaDiQ0iIiIiIiIiIiIiIjIbTGwQEREREREREREREZHZYGKDqAK6cOECjhw5YuowiIjIzBw6dAiXL182dRhERGRmNm3ahOjoaFOHQUREZiQpKQkbN25Ebm6uqUMhM8XEBpGJnD9/HseOHSuVba9btw5z584tlW0TEZFpHTx4EFevXi2VbX/66afYvHlzqWybiIhMa+PGjYiJiSmVbQ8dOhQXLlwolW0TEZHpJCQkYOPGjdDpdCW+7Vu3bmHIkCHIyMgo8W3Ti4GJDSIT+fHHH/HFF1+YOgwiIjIzM2bMwNatW00dBhERmZkhQ4bg0qVLpg6DiIjMyI0bNzBkyBBkZ2ebOhSiApSmDoDIHIWFheH8+fMAADs7O9SqVQu+vr6PLXv9+nWEhYUhICAAtWrVAgBcvXoV4eHhiI6OxsaNGwEArVu3xp07d6BWq9GwYUND/UuXLiEuLg7t27d/5n0TEVH5cvz4cdy/fx8ymQyurq6oW7cunJycCpSTJAlnzpxBdHQ06tati0qVKgEAjh07hoSEBFy6dMnQf/Tt2xd79+5FrVq1ULlyZcM2Dh8+DCcnJ9SuXfuZ9k1EROXPzp07kZmZCblcDh8fH9SrVw+WlpYFymVlZeHUqVPIzs5GkyZNDOf5bdu2AcjrG+Lj42Fra4v27dtjx44d6NGjB2xtbQ3b2LJlC5o2bQpvb+9n2jcREZUvWVlZ2LFjBwBApVIhICAAdevWhUwmK1A2MTERf/zxBzQaDZo1awaNRoP09HQcPHgQQF7foFar4e/vDx8fH/z555/o27evoX5KSgp+/fVX9OnTBxqN5pn2TVRcTGwQFcONGzewfft2AEBycjJCQ0MxZcoUfPzxx4YyGRkZGDp0KI4cOYKmTZsiKioK1atXx8aNGxEWFoZbt24hPT3dsJ2goCAsWrQILi4uRomNn376CaGhoYbERlH2TURE5dPp06dx6tQpAEBkZCQuXbqEH3/8EX369DGUuX37Nvr06YPExETUqVMH169fx9ixY/HBBx/g5MmTSEpKwrVr1wwXBT169MDYsWPx2WefGSU2PvvsMzRs2NCQ2CjKvomIqHzat28fEhISoNfrce3aNWRkZGD37t0ICQkxlDl06BCGDh0KZ2dn+Pn5ISwsDMuXL0eXLl3w66+/AshLct+8eRMeHh6oU6cOhgwZgmvXriE4ONiwnVGjRmHdunWGxEZR9k1EROVPVlaW4f5RTk4Ozpw5g0qVKmHv3r1GCe0lS5Zg6tSpqF27NiwtLREXF4dt27bB1tbWMH/rrl27oFQq0apVK0RGRuL11183Smzcv38fQ4YMQVRUFDw8PIq8b6LnwcQGUTH07NkTPXv2NHy+fPkyGjZsiP79+6N69eoAgEmTJuHy5cu4evUqvLy8AMCQrX755Zdx5MgR3Lx50/DEbUnum4iIyqcJEyYYfV6zZg3GjBmDHj16QKVSAQAGDBhgeApKo9FAr9cbbkhNnjwZW7duRc+ePTFjxowS3zcREZVPy5YtM/o8duxYfPjhh4b+IT4+Hn379sW4cePw+eefA8h7ejb/Te9vv/0WK1euxPTp09GxY0cAeUnuktg3ERGVT05OTkb3nLRaLVq3bo2vvvoKn376KQDgyJEjGD9+PLZu3Wp44OnWrVvIzs5G1apVMXv2bBw8eBA//PADbGxsAKBIc/IVZd9Ez4uJDaJiSk9Px7lz5xATEwO9Xg9nZ2ecOXMG1atXhyRJhgm885MaANC7d+9S3zcREZVv0dHRuHz5MhISEqDT6RAbG4u7d+8iMDAQly5dwrlz53DmzBloNBoAgEKhMEpol9a+iYiofLt58ybCw8ORmpoKe3t7/Pnnn4Z127ZtgyRJRjeL7O3t0bZt21LfNxERlW/nz59HREQEsrKyDA9Q5VuzZg1atGhh9BZ3lSpVymTfRM+LiQ2iYti1axdGjhwJf39/+Pv7Q61WIzMzE7GxsQCApKQkpKWloVq1amW+byIiKr8+//xzzJkzB/Xr14e7uzvkcjkAIDY2FoGBgYiIiACAUuk/nrZvIiIqn/R6PYYMGYJ9+/ahcePGcHR0RHx8PBITE6HX66FQKBAREYFKlSpBrVaX+b6JiKh8iouLQ6dOnRAXF4c6derAzs4ON27cgIWFhaFMREREqVx7FGXfRM+LiQ2iYhg/fjw++ugjTJo0ybDMx8cHQggAgI2NDRQKBRISEp5pu3K5HJIkGS3Lzs5+pn0TEVH5FBMTg+nTp+Po0aNo1aoVgLxhQH7++WfDOdzBwQEAkJCQ8Exjzz6t/yjKvomIqHzas2cP9u/fj5s3b8LV1RVA3jAgv//+u1H/UZxrDwBG/YckSdBqtc+0byIiKp8WLVoEtVqNe/fuQanMuwX84Ycf4vDhw4Yyxe0/nnbvqij7JnpeclMHQGSOYmJiEBQUZPh86tQpPHjwwPBZrVajTZs2WLNmjVG9uLg4w//b2NgUOPF7e3vj5s2bhs9CCMNETUXdNxERlU8xMTEAYHQO//f4tA0aNICTk9Nz9x8JCQm4dOnSM+2biIjKp+joaDg5ORkSC0DBc3inTp0QExOD/fv3Gy1/tP+wtrY26j/c3NygVCqN+o/jx48jNzf3mfZNRETlU3R0NKpUqWJILOTm5mLnzp1GZTp37oyDBw8iKirKsEySJCQmJgKAYV6NR/sPb29vpKWlGa4xAOD3339/5n0TPS++sUFUDL1798YHH3yAhw8fIikpCYsXLzac7PMtXrwYbdu2Rbdu3dC7d29ERUVh9+7dOHv2LACgYcOGWLx4MZYtWwZnZ2e0bt0aw4YNw5IlSzBx4kSEhIRg+/btuHfvHmrUqPFM+yYiovKnevXqqFatGgYPHozhw4fjypUrWLt2rVEZjUaD5cuXY8SIEbh//z6aNGmCCxcuICUlxVC2YcOG2LRpE6pUqQJLS0v07dsXI0eOxOTJk2FnZwc7OzusWrXK8CRuUfdNRETlU6dOnTBx4kSMHj0aLVq0wIEDB7Bv3z6jMrVr18bkyZPRt29fjB8/Hn5+fti/fz9atGiBiRMnAsjrP7755hskJyfD0dERPXr0wNChQzFhwgQ8ePAAqamp+OGHH4yGlyrKvomIqHzq3bs3BgwYgMDAQHh6emLNmjWIi4uDnZ2docwrr7yCn376CU2bNsW4ceNgaWmJn376CXPnzkWrVq0QEBAAR0dHzJgxA23atEHlypXRqFEj1KhRAwMGDMCoUaMQFhZmNFF4UfdN9Lz4xgZRMXz//fcYN24cTp06hdjYWOzatQvjxo1DcHCwoUzNmjVx6dIltGjRAqdOnYKFhYXRE1S9e/fGsmXLcPHiRWzfvh0xMTFo2LAhjh07Br1ej7/++gtjxozB0qVL0b59+2fad7169UpsokAiIioZKpUKR48eRcuWLXH48GFYWVnh5MmTGDRokNGTsAMHDsTp06fh6OiIEydOICQkBN9//71h/ccff4wxY8bg6NGj2L59O3JycvD2229j1apVuHPnDiIiIrBixQp8+OGHqFOnzjPtu2PHjqhZs2bZHRQiInqqypUr49SpU7C3t8exY8fQqFEj7N27F4MGDTJKYn/xxRfYunUrUlJScO7cOQwbNsyQ1ACA9evXo3Hjxti3bx8OHDgAAFi5ciUmTJiAM2fOICcnBwcOHMDw4cPh4+PzTPseNGgQPD09y+iIEBFRUfTu3Rs7duxATEwMzpw5g7feegvff/89unTpYiijUqmwd+9ezJ49G+Hh4bhz5w6++uorw/C1VlZWOHToECwtLbFr1y6cPXsWCoUCR44cQfv27XHy5El4eXnhwIEDGDRoECwtLYu8bycnJwwaNIjzblCxyQQHxiQiIiIiIiIiIiIiIjPBNzaIiIiIiIiIiIiIiMhsMLFBRERERERERERERERmg4kNIiIiIiIiIiIiIiIyG0xsEBERERERERERERGR2WBig4iIiIiIiIiIiIiIzAYTG0REREREREREREREZDaY2CAiIiIiIiIiIiIiIrPBxAYREREREREREREREZkNJjaIiIiIiIiIiIiIiMhsMLFBRERERERERERERERmg4kNIiIiIiIiIiIiIiIyG0xsEBERERERERERERGR2fh/ixe/Wqcr31UAAAAASUVORK5CYII=",
+      "text/plain": [
+       "<Figure size 1600x380 with 4 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# Pick a validation case if present, otherwise test, otherwise train.\n",
+    "case_id = (split_manifest.get(\"val_ids\") or split_manifest.get(\"test_ids\") or split_manifest.get(\"train_ids\"))[0]\n",
+    "features, targets = load_case(case_id)\n",
+    "predictions = predict_targets(features)\n",
+    "\n",
+    "print(f\"case: {case_id}\")\n",
+    "print(\"target names:\", checkpoint[\"target_names\"])\n",
+    "print(\"first 5 predictions:\")\n",
+    "print(predictions[:5])\n",
+    "print(\"first 5 actual targets:\")\n",
+    "print(targets[:5])\n",
+    "\n",
+    "fig, axes = plt.subplots(1, len(checkpoint[\"target_names\"]), figsize=(4 * len(checkpoint[\"target_names\"]), 3.8))\n",
+    "if len(checkpoint[\"target_names\"]) == 1:\n",
+    "    axes = [axes]\n",
+    "for index, (ax, name) in enumerate(zip(axes, checkpoint[\"target_names\"])):\n",
+    "    ax.scatter(targets[:, index], predictions[:, index], s=12, alpha=0.7)\n",
+    "    low = float(min(targets[:, index].min(), predictions[:, index].min()))\n",
+    "    high = float(max(targets[:, index].max(), predictions[:, index].max()))\n",
+    "    ax.plot([low, high], [low, high], color=\"black\", linewidth=1)\n",
+    "    ax.set_title(name)\n",
+    "    ax.set_xlabel(\"actual\")\n",
+    "    ax.set_ylabel(\"predicted\")\n",
+    "    ax.grid(alpha=0.25)\n",
+    "fig.suptitle(f\"Reloaded checkpoint predictions for {case_id}\", y=1.03)\n",
+    "fig.tight_layout()\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "5901d5d7",
+   "metadata": {},
+   "source": [
+    "## What good looks like\n",
+    "\n",
+    "For this tiny baseline sanity run:\n",
+    "\n",
+    "- `train_loss` is finite and much lower than `initial_train_loss`.\n",
+    "- `val_loss` and `test_loss` are finite.\n",
+    "- The loss curve falls on a log-scale plot rather than staying flat or exploding.\n",
+    "- `checkpoint.pt` loads on CPU and reconstructs `PointwiseMLP` without missing/unexpected keys.\n",
+    "- Reloaded predictions have the expected target channels and roughly follow the identity line in predicted-vs-actual plots.\n",
+    "\n",
+    "This does not prove the model is scientifically useful. It proves the training framework can run end-to-end, write readable artifacts, reload the model, and produce numerically sensible outputs on the processed mini dataset.\n"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": ".venv",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.12.12"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}
diff --git a/pyproject.toml b/pyproject.toml
index 409b4f3..fd545a1 100644
--- a/pyproject.toml
+++ b/pyproject.toml
@@ -10,6 +10,7 @@ dependencies = [
 
 [project.scripts]
 airfrans-frontier = "airfrans_frontier.cli:main"
+remote-run = "airfrans_frontier.remote.cli:main"
 
 [tool.hatch.build.targets.wheel]
 packages = ["src/airfrans_frontier"]
@@ -25,4 +26,5 @@ dev = [
     "numpy>=2.4.0",
     "nbclient>=0.10.2",
     "nbformat>=5.10.4",
+    "skypilot[vast]>=0.12.3.post1",
 ]
diff --git a/src/airfrans_frontier/remote/__init__.py b/src/airfrans_frontier/remote/__init__.py
new file mode 100644
index 0000000..7f54a6b
--- /dev/null
+++ b/src/airfrans_frontier/remote/__init__.py
@@ -0,0 +1 @@
+"""Remote GPU smoke-run orchestration helpers."""
diff --git a/src/airfrans_frontier/remote/artifacts.py b/src/airfrans_frontier/remote/artifacts.py
new file mode 100644
index 0000000..b4a1108
--- /dev/null
+++ b/src/airfrans_frontier/remote/artifacts.py
@@ -0,0 +1,71 @@
+from __future__ import annotations
+
+import hashlib
+import json
+from pathlib import Path
+from typing import Any, Iterable
+
+
+DEFAULT_REQUIRED = ("final_metrics.json", "metrics.jsonl", "checkpoint.pt", "run_manifest.json")
+
+
+def verify_artifacts(artifact_dir: str | Path, required: Iterable[str] = DEFAULT_REQUIRED) -> dict[str, Any]:
+    root = Path(artifact_dir)
+    if not root.exists():
+        raise FileNotFoundError(f"Artifact directory not found: {root}")
+    if not root.is_dir():
+        raise ValueError(f"Artifact path is not a directory: {root}")
+
+    missing = [name for name in required if not (root / name).is_file()]
+    if missing:
+        raise ValueError(f"Artifact directory missing required files: {', '.join(missing)}")
+
+    _validate_json(root / "final_metrics.json")
+    _validate_json(root / "run_manifest.json")
+    _validate_jsonl(root / "metrics.jsonl")
+
+    files = sorted(path for path in root.rglob("*") if path.is_file())
+    manifest = {
+        "artifact_dir": str(root),
+        "file_count": len(files),
+        "files": [
+            {
+                "path": str(path.relative_to(root)),
+                "bytes": path.stat().st_size,
+                "sha256": sha256_file(path),
+            }
+            for path in files
+        ],
+    }
+    (root / "artifact_manifest.json").write_text(json.dumps(manifest, indent=2, sort_keys=True) + "\n")
+    (root / "checksums.txt").write_text(
+        "".join(f"{item['sha256']}  {item['path']}\n" for item in manifest["files"])
+    )
+    return manifest
+
+
+def sha256_file(path: Path) -> str:
+    digest = hashlib.sha256()
+    with path.open("rb") as file:
+        for chunk in iter(lambda: file.read(1024 * 1024), b""):
+            digest.update(chunk)
+    return digest.hexdigest()
+
+
+def _validate_json(path: Path) -> None:
+    try:
+        json.loads(path.read_text())
+    except json.JSONDecodeError as exc:
+        raise ValueError(f"Invalid JSON artifact {path}: {exc}") from exc
+
+
+def _validate_jsonl(path: Path) -> None:
+    with path.open() as file:
+        for line_number, line in enumerate(file, start=1):
+            stripped = line.strip()
+            if not stripped:
+                continue
+            try:
+                json.loads(stripped)
+            except json.JSONDecodeError as exc:
+                raise ValueError(f"Invalid JSONL artifact {path}:{line_number}: {exc}") from exc
diff --git a/src/airfrans_frontier/remote/cli.py b/src/airfrans_frontier/remote/cli.py
new file mode 100644
index 0000000..b1d7188
--- /dev/null
+++ b/src/airfrans_frontier/remote/cli.py
@@ -0,0 +1,241 @@
+from __future__ import annotations
+
+import argparse
+import json
+import os
+import shutil
+import subprocess
+import sys
+import time
+from pathlib import Path
+from typing import Any
+
+from airfrans_frontier.remote.artifacts import verify_artifacts
+from airfrans_frontier.remote.config import RemoteRunConfig, load_remote_run_config
+from airfrans_frontier.remote.skypilot import render_skypilot_yaml, write_skyignore
+from airfrans_frontier.remote.skypilot_patch import apply_patch, patch_status, require_patch
+from airfrans_frontier.remote.smoke import run_smoke_training
+from airfrans_frontier.remote.vast import SelectionResult, select_offer
+
+
+def build_parser() -> argparse.ArgumentParser:
+    parser = argparse.ArgumentParser(prog="remote-run")
+    subparsers = parser.add_subparsers(required=True)
+
+    doctor = subparsers.add_parser("doctor", help="check local remote-run and SkyPilot readiness")
+    doctor.add_argument("--apply-skypilot-patch", action="store_true")
+    doctor.set_defaults(command="doctor")
+
+    select = subparsers.add_parser("select", help="select a Vast.ai offer from a remote config")
+    select.add_argument("config")
+    select.add_argument("--out")
+    select.set_defaults(command="select")
+
+    render = subparsers.add_parser("render", help="render patched SkyPilot YAML")
+    render.add_argument("config")
+    render.add_argument("--selection", required=True)
+    render.add_argument("--run-id", required=True)
+    render.add_argument("--out")
+    render.set_defaults(command="render")
+
+    verify = subparsers.add_parser("verify-artifacts", help="verify a collected artifact directory")
+    verify.add_argument("artifact_dir")
+    verify.set_defaults(command="verify-artifacts")
+
+    smoke = subparsers.add_parser("smoke-train", help="run the configured smoke training job and flatten artifacts")
+    smoke.add_argument("training_config")
+    smoke.add_argument("--artifact-dir", required=True)
+    smoke.add_argument("--run-id", required=True)
+    smoke.set_defaults(command="smoke-train")
+
+    run = subparsers.add_parser("run", help="select a Vast offer and execute a SkyPilot run")
+    run.add_argument("config")
+    run.add_argument("--dry-run", action="store_true")
+    run.add_argument("--skip-down", action="store_true", help="leave the SkyPilot cluster running after collection")
+    run.set_defaults(command="run")
+
+    return parser
+
+
+def main(argv: list[str] | None = None) -> int:
+    parser = build_parser()
+    args = parser.parse_args(argv)
+    try:
+        if args.command == "doctor":
+            return _doctor(apply=args.apply_skypilot_patch)
+        if args.command == "select":
+            config = load_remote_run_config(args.config)
+            result = select_offer(config)
+            _emit_json(result.to_manifest(), args.out)
+            return 0
+        if args.command == "render":
+            config = load_remote_run_config(args.config)
+            selection = _selection_from_manifest(Path(args.selection))
+            text = render_skypilot_yaml(config, selection, run_id=args.run_id)
+            if args.out:
+                Path(args.out).write_text(text)
+            else:
+                print(text)
+            return 0
+        if args.command == "verify-artifacts":
+            manifest = verify_artifacts(args.artifact_dir)
+            print(json.dumps({"status": "ok", "file_count": manifest["file_count"]}, sort_keys=True))
+            return 0
+        if args.command == "smoke-train":
+            output = run_smoke_training(args.training_config, artifact_dir=args.artifact_dir, run_id=args.run_id)
+            print(f"artifact_dir: {output}")
+            return 0
+        if args.command == "run":
+            return _run(args.config, dry_run=args.dry_run, skip_down=args.skip_down)
+    except (FileNotFoundError, NotADirectoryError, ValueError, RuntimeError, subprocess.CalledProcessError) as exc:
+        print(f"error: {exc}", file=sys.stderr)
+        return 1
+    parser.error(f"unknown command: {args.command}")
+    return 2
+
+
+def _doctor(*, apply: bool) -> int:
+    problems: list[str] = []
+    if not os.environ.get("VAST_API_KEY"):
+        problems.append("VAST_API_KEY is not set")
+    sky = shutil.which("sky")
+    if not sky:
+        problems.append("sky executable not found on PATH")
+    if apply:
+        status = apply_patch()
+    else:
+        status = patch_status()
+    if not status.installed:
+        problems.append(status.message)
+    elif not status.patched:
+        problems.append(status.message)
+    payload = {
+        "vast_api_key_present": bool(os.environ.get("VAST_API_KEY")),
+        "sky_executable": sky,
+        "skypilot_patch": {
+            "path": str(status.path) if status.path else None,
+            "installed": status.installed,
+            "patched": status.patched,
+            "message": status.message,
+        },
+        "problems": problems,
+    }
+    print(json.dumps(payload, indent=2, sort_keys=True))
+    return 1 if problems else 0
+
+
+def _run(config_path: str | Path, *, dry_run: bool, skip_down: bool) -> int:
+    config = load_remote_run_config(config_path)
+    if not dry_run:
+        require_patch()
+    run_id = f"{config.run.name}-{time.strftime('%Y%m%dT%H%M%SZ', time.gmtime())}"
+    local_run_dir = config.run.local_artifact_dir / run_id
+    local_run_dir.mkdir(parents=True, exist_ok=False)
+    state_path = local_run_dir / "orchestrator_state.json"
+
+    def state(phase: str, **extra: Any) -> None:
+        payload = {
+            "run_id": run_id,
+            "phase": phase,
+            "updated_at": time.time(),
+            "config_path": str(config.path),
+            **extra,
+        }
+        state_path.write_text(json.dumps(payload, indent=2, sort_keys=True) + "\n")
+
+    state("SELECTING_OFFER")
+    selection = select_offer(config)
+    selection_path = local_run_dir / "selection_manifest.json"
+    selection_path.write_text(json.dumps(selection.to_manifest(), indent=2, sort_keys=True) + "\n")
+
+    state("RENDERING_SKYPILOT_CONFIG", selected_offer_id=selection.selected_offer_id)
+    sky_yaml = render_skypilot_yaml(config, selection, run_id=run_id)
+    sky_yaml_path = local_run_dir / "sky.yaml"
+    sky_yaml_path.write_text(sky_yaml)
+    (local_run_dir / "config.toml").write_text(config.raw_text)
+    write_skyignore(config)
+
+    if dry_run:
+        state("DRY_RUN", selected_offer_id=selection.selected_offer_id, sky_yaml=str(sky_yaml_path))
+        print(f"run_id: {run_id}")
+        print(f"selection: {selection_path}")
+        print(f"sky_yaml: {sky_yaml_path}")
+        return 0
+
+    env = _subprocess_env()
+    try:
+        state("PROVISIONING", selected_offer_id=selection.selected_offer_id)
+        _run_checked(["sky", "launch", "-c", run_id, str(sky_yaml_path), "-y"], env=env, timeout=config.run.timeout_minutes * 60)
+
+        state("COLLECTING", selected_offer_id=selection.selected_offer_id)
+        _collect_with_rsync(cluster=run_id, remote_dir=config.job.artifact_dir, local_dir=local_run_dir, env=env)
+
+        state("VERIFYING_ARTIFACTS", selected_offer_id=selection.selected_offer_id)
+        verify_artifacts(local_run_dir, required=config.artifacts.required)
+    except Exception as exc:
+        state("FAILED", selected_offer_id=selection.selected_offer_id, error=str(exc))
+        if config.cleanup.on_failure == "sky_down" and not skip_down:
+            _run_best_effort(["sky", "down", run_id, "-y"], env=env)
+        raise
+    else:
+        if config.cleanup.on_success == "sky_down" and not skip_down:
+            state("CLEANING_UP", selected_offer_id=selection.selected_offer_id)
+            _run_checked(["sky", "down", run_id, "-y"], env=env, timeout=300)
+        state("SUCCEEDED", selected_offer_id=selection.selected_offer_id)
+        print(f"run_id: {run_id}")
+        print(f"artifacts: {local_run_dir}")
+        return 0
+
+
+def _collect_with_rsync(*, cluster: str, remote_dir: Path, local_dir: Path, env: dict[str, str]) -> None:
+    local_dir.mkdir(parents=True, exist_ok=True)
+    source = f"{cluster}:~/sky_workdir/{remote_dir}/"
+    _run_checked(["rsync", "-Pavz", source, f"{local_dir}/"], env=env, timeout=600)
+
+
+def _run_checked(argv: list[str], *, env: dict[str, str], timeout: int) -> None:
+    subprocess.run(argv, check=True, env=env, timeout=timeout)
+
+
+def _run_best_effort(argv: list[str], *, env: dict[str, str]) -> None:
+    try:
+        subprocess.run(argv, check=False, env=env, timeout=300)
+    except Exception:
+        pass
+
+
+def _subprocess_env() -> dict[str, str]:
+    env = dict(os.environ)
+    env.pop("PYTHONPATH", None)
+    return env
+
+
+def _emit_json(data: dict[str, Any], out: str | None) -> None:
+    text = json.dumps(data, indent=2, sort_keys=True) + "\n"
+    if out:
+        Path(out).write_text(text)
+    else:
+        print(text, end="")
+
+
+def _selection_from_manifest(path: Path) -> SelectionResult:
+    from airfrans_frontier.remote.vast import VastOffer, effective_price
+
+    data = json.loads(path.read_text())
+    raw_offer = data.get("selected_offer")
+    if not isinstance(raw_offer, dict):
+        raise ValueError(f"Selection manifest missing selected_offer object: {path}")
+    offer = VastOffer.from_mapping({**raw_offer, "id": data.get("selected_offer_id", raw_offer.get("id"))})
+    # Preserve manifest values by building a minimal SelectionResult. Effective price is already stored.
+    return SelectionResult(
+        selected_offer=offer,
+        candidate_count=int(data.get("candidate_count", 0)),
+        survivor_count=int(data.get("survivor_count", 0)),
+        effective_price=float(raw_offer.get("effective_price", data.get("effective_price", 0.0))) if raw_offer else 0.0,
+        query=data.get("query", {}) if isinstance(data.get("query"), dict) else {},
+        policy=data.get("policy", {}) if isinstance(data.get("policy"), dict) else {},
+    )
+
+
+if __name__ == "__main__":
+    raise SystemExit(main())
diff --git a/src/airfrans_frontier/remote/config.py b/src/airfrans_frontier/remote/config.py
new file mode 100644
index 0000000..69cc305
--- /dev/null
+++ b/src/airfrans_frontier/remote/config.py
@@ -0,0 +1,331 @@
+from __future__ import annotations
+
+import tomllib
+from dataclasses import dataclass
+from pathlib import Path
+from typing import Any
+
+
+@dataclass(frozen=True)
+class RunConfig:
+    name: str
+    timeout_minutes: int
+    local_artifact_dir: Path
+    max_attempts: int
+
+
+@dataclass(frozen=True)
+class GpuConfig:
+    name: str | None
+    count: int
+    min_vram_gb: int | None
+
+
+@dataclass(frozen=True)
+class ProviderConfig:
+    kind: str
+    gpu: GpuConfig
+    disk_gb: int
+    max_price_per_hour: float | None
+    image: str | None
+
+
+@dataclass(frozen=True)
+class SelectionConfig:
+    min_reliability: float
+    min_down_mbps: float
+    min_up_mbps: float | None
+    require_verified: bool
+    blocked_geos: tuple[str, ...]
+    blacklist_hosts: tuple[int, ...]
+    drop_cheap_frac: float
+    image_size_gb: float | None
+    base_url: str
+
+
+@dataclass(frozen=True)
+class WorkspaceConfig:
+    workdir: Path
+    exclude: tuple[str, ...]
+
+
+@dataclass(frozen=True)
+class BootstrapConfig:
+    command: str
+
+
+@dataclass(frozen=True)
+class DataConfig:
+    validation_command: str | None
+
+
+@dataclass(frozen=True)
+class JobConfig:
+    command: str
+    artifact_dir: Path
+    heartbeat_file: Path
+    metrics_file: Path
+
+
+@dataclass(frozen=True)
+class ArtifactConfig:
+    mode: str
+    required: tuple[str, ...]
+
+
+@dataclass(frozen=True)
+class CleanupConfig:
+    on_success: str
+    on_failure: str
+
+
+@dataclass(frozen=True)
+class RemoteRunConfig:
+    path: Path
+    raw_text: str
+    run: RunConfig
+    provider: ProviderConfig
+    selection: SelectionConfig
+    workspace: WorkspaceConfig
+    bootstrap: BootstrapConfig
+    data: DataConfig
+    job: JobConfig
+    artifacts: ArtifactConfig
+    cleanup: CleanupConfig
+
+
+_REQUIRED_SECTIONS = ("run", "provider", "selection", "workspace", "bootstrap", "job", "artifacts")
+
+
+def load_remote_run_config(path: str | Path) -> RemoteRunConfig:
+    config_path = Path(path).expanduser()
+    if not config_path.exists():
+        raise FileNotFoundError(f"Remote run config not found: {config_path}")
+    if not config_path.is_file():
+        raise ValueError(f"Remote run config is not a file: {config_path}")
+
+    text = config_path.read_text()
+    try:
+        raw = tomllib.loads(text)
+    except tomllib.TOMLDecodeError as exc:
+        raise ValueError(f"Invalid TOML config {config_path}: {exc}") from exc
+
+    for section in _REQUIRED_SECTIONS:
+        if section not in raw or not isinstance(raw[section], dict):
+            raise ValueError(f"Remote run config missing [{section}] section")
+
+    run_raw = raw["run"]
+    provider_raw = raw["provider"]
+    gpu_raw = _table(provider_raw, "gpu")
+    selection_raw = raw["selection"]
+    workspace_raw = raw["workspace"]
+    bootstrap_raw = raw["bootstrap"]
+    data_raw = raw.get("data", {})
+    if data_raw is None:
+        data_raw = {}
+    if not isinstance(data_raw, dict):
+        raise ValueError("Remote run [data] section must be a table")
+    job_raw = raw["job"]
+    artifacts_raw = raw["artifacts"]
+    cleanup_raw = raw.get("cleanup", {})
+    if cleanup_raw is None:
+        cleanup_raw = {}
+    if not isinstance(cleanup_raw, dict):
+        raise ValueError("Remote run [cleanup] section must be a table")
+
+    gpu = GpuConfig(
+        name=_optional_string(gpu_raw, "name"),
+        count=_integer(gpu_raw, "count", minimum=1),
+        min_vram_gb=_optional_integer(gpu_raw, "min_vram_gb", minimum=1),
+    )
+    provider = ProviderConfig(
+        kind=_choice(_string(provider_raw, "kind"), {"vastai"}, "provider.kind"),
+        gpu=gpu,
+        disk_gb=_integer(provider_raw, "disk_gb", minimum=16),
+        max_price_per_hour=_optional_number(provider_raw, "max_price_per_hour", minimum=0.0, exclusive_minimum=True),
+        image=_optional_string(provider_raw, "image"),
+    )
+
+    drop_cheap_frac = _number(selection_raw, "drop_cheap_frac", default=0.30, minimum=0.0)
+    if drop_cheap_frac >= 1.0:
+        raise ValueError("selection.drop_cheap_frac must be less than 1.0")
+    selection = SelectionConfig(
+        min_reliability=_number(selection_raw, "min_reliability", default=0.95, minimum=0.0),
+        min_down_mbps=_number(selection_raw, "min_down_mbps", default=100.0, minimum=0.0),
+        min_up_mbps=_optional_number(selection_raw, "min_up_mbps", minimum=0.0, exclusive_minimum=True),
+        require_verified=_boolean(selection_raw, "require_verified", default=False),
+        blocked_geos=_string_tuple(selection_raw, "blocked_geos", default=("CN",)),
+        blacklist_hosts=tuple(int(value) for value in _integer_list(selection_raw, "blacklist_hosts", default=(59017,))),
+        drop_cheap_frac=drop_cheap_frac,
+        image_size_gb=_optional_number(selection_raw, "image_size_gb", minimum=0.0, exclusive_minimum=True),
+        base_url=_string(selection_raw, "base_url", default="https://cloud.vast.ai"),
+    )
+
+    workspace = WorkspaceConfig(
+        workdir=_path(workspace_raw, "workdir", default="."),
+        exclude=_string_tuple(workspace_raw, "exclude", default=()),
+    )
+    bootstrap = BootstrapConfig(command=_string(bootstrap_raw, "command"))
+    data = DataConfig(validation_command=_optional_string(data_raw, "validation_command"))
+    job = JobConfig(
+        command=_string(job_raw, "command"),
+        artifact_dir=Path(_string(job_raw, "artifact_dir")),
+        heartbeat_file=Path(_string(job_raw, "heartbeat_file", default="artifacts/current_run/heartbeat.json")),
+        metrics_file=Path(_string(job_raw, "metrics_file", default="artifacts/current_run/metrics.jsonl")),
+    )
+    artifacts = ArtifactConfig(
+        mode=_choice(_string(artifacts_raw, "mode", default="rsync"), {"rsync", "object_store_upload"}, "artifacts.mode"),
+        required=_string_tuple(artifacts_raw, "required", default=("final_metrics.json", "metrics.jsonl", "checkpoint.pt", "run_manifest.json")),
+    )
+    cleanup = CleanupConfig(
+        on_success=_choice(_string(cleanup_raw, "on_success", default="sky_down"), {"sky_down", "keep"}, "cleanup.on_success"),
+        on_failure=_choice(_string(cleanup_raw, "on_failure", default="collect_then_keep"), {"collect_then_keep", "sky_down"}, "cleanup.on_failure"),
+    )
+
+    return RemoteRunConfig(
+        path=config_path,
+        raw_text=text,
+        run=RunConfig(
+            name=_string(run_raw, "name"),
+            timeout_minutes=_integer(run_raw, "timeout_minutes", minimum=1, default=60),
+            local_artifact_dir=_path(run_raw, "local_artifact_dir", default="artifacts/remote_runs"),
+            max_attempts=_integer(run_raw, "max_attempts", minimum=1, default=2),
+        ),
+        provider=provider,
+        selection=selection,
+        workspace=workspace,
+        bootstrap=bootstrap,
+        data=data,
+        job=job,
+        artifacts=artifacts,
+        cleanup=cleanup,
+    )
+
+
+def _table(section: dict[str, Any], key: str) -> dict[str, Any]:
+    value = section.get(key)
+    if not isinstance(value, dict):
+        raise ValueError(f"Remote run config missing table: {key}")
+    return value
+
+
+def _path(section: dict[str, Any], key: str, *, default: str | None = None) -> Path:
+    return Path(_string(section, key, default=default)).expanduser()
+
+
+def _string(section: dict[str, Any], key: str, *, default: str | None = None) -> str:
+    if key not in section:
+        if default is not None:
+            return default
+        raise ValueError(f"Remote run config missing key: {key}")
+    value = section[key]
+    if not isinstance(value, str) or not value:
+        raise ValueError(f"Remote run config key must be a non-empty string: {key}")
+    return value
+
+
+def _optional_string(section: dict[str, Any], key: str) -> str | None:
+    if key not in section:
+        return None
+    return _string(section, key)
+
+
+def _integer(section: dict[str, Any], key: str, *, minimum: int | None = None, default: int | None = None) -> int:
+    if key not in section:
+        if default is not None:
+            return default
+        raise ValueError(f"Remote run config missing key: {key}")
+    value = section[key]
+    if not isinstance(value, int):
+        raise ValueError(f"Remote run config key must be an integer: {key}")
+    if minimum is not None and value < minimum:
+        raise ValueError(f"Remote run config key must be >= {minimum}: {key}")
+    return value
+
+
+def _optional_integer(section: dict[str, Any], key: str, *, minimum: int | None = None) -> int | None:
+    if key not in section:
+        return None
+    return _integer(section, key, minimum=minimum)
+
+
+def _integer_list(section: dict[str, Any], key: str, *, default: tuple[int, ...]) -> tuple[int, ...]:
+    if key not in section:
+        return default
+    value = section[key]
+    if not isinstance(value, list):
+        raise ValueError(f"Remote run config key must be a list of integers: {key}")
+    result: list[int] = []
+    for item in value:
+        if not isinstance(item, int):
+            raise ValueError(f"Remote run config key must be a list of integers: {key}")
+        result.append(item)
+    return tuple(result)
+
+
+def _number(
+    section: dict[str, Any],
+    key: str,
+    *,
+    default: float | None = None,
+    minimum: float | None = None,
+    exclusive_minimum: bool = False,
+) -> float:
+    if key not in section:
+        if default is not None:
+            return default
+        raise ValueError(f"Remote run config missing key: {key}")
+    value = section[key]
+    if not isinstance(value, (int, float)):
+        raise ValueError(f"Remote run config key must be a number: {key}")
+    result = float(value)
+    if minimum is not None:
+        invalid = result <= minimum if exclusive_minimum else result < minimum
+        if invalid:
+            comparator = ">" if exclusive_minimum else ">="
+            raise ValueError(f"Remote run config key must be {comparator} {minimum}: {key}")
+    return result
+
+
+def _optional_number(
+    section: dict[str, Any],
+    key: str,
+    *,
+    minimum: float | None = None,
+    exclusive_minimum: bool = False,
+) -> float | None:
+    if key not in section:
+        return None
+    return _number(section, key, minimum=minimum, exclusive_minimum=exclusive_minimum)
+
+
+def _boolean(section: dict[str, Any], key: str, *, default: bool | None = None) -> bool:
+    if key not in section:
+        if default is not None:
+            return default
+        raise ValueError(f"Remote run config missing key: {key}")
+    value = section[key]
+    if not isinstance(value, bool):
+        raise ValueError(f"Remote run config key must be a boolean: {key}")
+    return value
+
+
+def _string_tuple(section: dict[str, Any], key: str, *, default: tuple[str, ...]) -> tuple[str, ...]:
+    if key not in section:
+        return default
+    value = section[key]
+    if not isinstance(value, list):
+        raise ValueError(f"Remote run config key must be a list of strings: {key}")
+    result: list[str] = []
+    for item in value:
+        if not isinstance(item, str):
+            raise ValueError(f"Remote run config key must be a list of strings: {key}")
+        result.append(item)
+    return tuple(result)
+
+
+def _choice(value: str, allowed: set[str], key: str) -> str:
+    if value not in allowed:
+        allowed_values = ", ".join(sorted(allowed))
+        raise ValueError(f"Unsupported {key}: {value}; expected one of {allowed_values}")
+    return value
diff --git a/src/airfrans_frontier/remote/skypilot.py b/src/airfrans_frontier/remote/skypilot.py
new file mode 100644
index 0000000..4e9c9b6
--- /dev/null
+++ b/src/airfrans_frontier/remote/skypilot.py
@@ -0,0 +1,105 @@
+from __future__ import annotations
+
+from pathlib import Path
+
+from airfrans_frontier.remote.config import RemoteRunConfig
+from airfrans_frontier.remote.vast import SelectionResult
+
+
+def render_skypilot_yaml(config: RemoteRunConfig, selection: SelectionResult, *, run_id: str) -> str:
+    setup = _compose_setup(config)
+    run = _compose_run(config, run_id=run_id)
+    accelerator = _accelerator(config)
+    lines = [
+        f"name: {run_id}",
+        "",
+        "resources:",
+        "  infra: vast",
+        f"  accelerators: {accelerator}",
+        f"  disk_size: {config.provider.disk_gb}",
+    ]
+    if config.provider.max_price_per_hour is not None:
+        lines.append(f"  max_hourly_cost: {config.provider.max_price_per_hour}")
+    if config.provider.image:
+        image = config.provider.image
+        if not image.startswith("docker:"):
+            image = f"docker:{image}"
+        lines.append(f"  image_id: {image}")
+    lines.extend(
+        [
+            "",
+            f"workdir: {_yaml_scalar(str(config.workspace.workdir))}",
+            "",
+            "envs:",
+            f"  AIRFRANS_REMOTE_RUN_ID: {run_id}",
+            "",
+            "setup: |",
+            *_indent_block(setup),
+            "",
+            "run: |",
+            *_indent_block(run),
+            "",
+            "config:",
+            "  vast:",
+            "    create_instance_kwargs:",
+            f"      selected_offer_id: {selection.selected_offer_id}",
+            "",
+        ]
+    )
+    return "\n".join(lines)
+
+
+def write_skyignore(config: RemoteRunConfig, path: str | Path = ".skyignore") -> Path:
+    skyignore_path = Path(path)
+    entries = list(config.workspace.exclude)
+    content = "\n".join(entries).rstrip() + "\n"
+    skyignore_path.write_text(content)
+    return skyignore_path
+
+
+def _compose_setup(config: RemoteRunConfig) -> str:
+    return "\n".join(
+        [
+            "set -euo pipefail",
+            "export PATH=\"$HOME/.local/bin:$PATH\"",
+            "if ! command -v uv >/dev/null 2>&1; then curl -LsSf https://astral.sh/uv/install.sh | sh; fi",
+            "export PATH=\"$HOME/.local/bin:$PATH\"",
+            config.bootstrap.command.strip(),
+        ]
+    )
+
+
+def _compose_run(config: RemoteRunConfig, *, run_id: str) -> str:
+    lines = [
+        "set -euo pipefail",
+        "export PATH=\"$HOME/.local/bin:$PATH\"",
+        f"mkdir -p {_sh_quote(str(config.job.artifact_dir))}",
+        f"nvidia-smi | tee {_sh_quote(str(config.job.artifact_dir / 'nvidia_smi.txt'))}",
+    ]
+    if config.data.validation_command:
+        lines.append(config.data.validation_command.strip())
+    lines.append(config.job.command.strip())
+    lines.append(f"uv run --no-dev remote-run verify-artifacts {_sh_quote(str(config.job.artifact_dir))}")
+    lines.append(f"echo 'remote run {run_id} complete'")
+    return "\n".join(lines)
+
+
+def _accelerator(config: RemoteRunConfig) -> str:
+    name = config.provider.gpu.name or "T4"
+    # SkyPilot accelerator names omit spaces for common Vast names.
+    sky_name = name.replace(" ", "")
+    return f"{sky_name}:{config.provider.gpu.count}"
+
+
+def _indent_block(text: str) -> list[str]:
+    return [f"  {line}" if line else "" for line in text.splitlines()]
+
+
+def _yaml_scalar(value: str) -> str:
+    if value and all(ch.isalnum() or ch in "./_-" for ch in value):
+        return value
+    return repr(value)
+
+
+def _sh_quote(value: str) -> str:
+    return "'" + value.replace("'", "'\\''") + "'"
diff --git a/src/airfrans_frontier/remote/skypilot_patch.py b/src/airfrans_frontier/remote/skypilot_patch.py
new file mode 100644
index 0000000..d6fccd5
--- /dev/null
+++ b/src/airfrans_frontier/remote/skypilot_patch.py
@@ -0,0 +1,83 @@
+from __future__ import annotations
+
+import importlib
+from dataclasses import dataclass
+from pathlib import Path
+
+
+PATCH_MARKER = "AIRFRANS_SELECTED_OFFER_ID_PATCH"
+SDK_API_KEY_MARKER = "AIRFRANS_VAST_SDK_API_KEY_PATCH"
+
+
+@dataclass(frozen=True)
+class PatchStatus:
+    path: Path | None
+    installed: bool
+    patched: bool
+    message: str
+
+
+def locate_vast_utils() -> Path:
+    try:
+        module = importlib.import_module("sky.provision.vast.utils")
+    except ModuleNotFoundError as exc:
+        raise RuntimeError("SkyPilot is not importable in this Python environment") from exc
+    path = getattr(module, "__file__", None)
+    if not path:
+        raise RuntimeError("Could not locate sky.provision.vast.utils source file")
+    return Path(path)
+
+
+def patch_status() -> PatchStatus:
+    try:
+        path = locate_vast_utils()
+    except RuntimeError as exc:
+        return PatchStatus(path=None, installed=False, patched=False, message=str(exc))
+    text = path.read_text()
+    patched = PATCH_MARKER in text and SDK_API_KEY_MARKER in text
+    if patched:
+        return PatchStatus(path=path, installed=True, patched=True, message="SkyPilot Vast selected-offer patch is installed")
+    return PatchStatus(path=path, installed=True, patched=False, message="SkyPilot Vast selected-offer patch is missing")
+
+
+def apply_patch() -> PatchStatus:
+    path = locate_vast_utils()
+    text = path.read_text()
+    already_selected = PATCH_MARKER in text
+    already_api_key = SDK_API_KEY_MARKER in text
+    if already_selected and already_api_key:
+        return PatchStatus(path=path, installed=True, patched=True, message="SkyPilot Vast selected-offer patch already installed")
+
+    if not already_selected:
+        old = '''    instance_list = vast.vast().search_offers(query=query_str)\n\n    if isinstance(instance_list, int) or len(instance_list) == 0:\n        raise RuntimeError('Failed to create instances, could not find an '\n                           'offer that satisfies the requirements '\n                           f'"{query_str}".')\n\n    instance_touse = instance_list[0]\n\n    # Start with user-provided kwargs as the base\n    launch_params: Dict[str, Any] = dict(create_instance_kwargs or {})\n    # Remove None values to avoid overriding defaults\n    launch_params = {k: v for k, v in launch_params.items() if v is not None}\n'''
+        new = f'''    # {PATCH_MARKER}: allow callers to bypass SkyPilot's Vast offer\n    # search after selecting a vetted Vast offer themselves. This keeps\n    # SkyPilot's create/wait/bootstrap/run lifecycle while preserving\n    # external marketplace-quality filtering.\n    launch_params: Dict[str, Any] = dict(create_instance_kwargs or {{}})\n    launch_params = {{k: v for k, v in launch_params.items() if v is not None}}\n    selected_offer_id = launch_params.pop('selected_offer_id', None)\n\n    if selected_offer_id is not None:\n        logger.info(f'Using externally selected Vast offer {{selected_offer_id}}.')\n        try:\n            instance_touse = {{'id': int(selected_offer_id)}}\n        except (TypeError, ValueError) as e:\n            raise RuntimeError(\n                f'Invalid selected_offer_id for Vast: {{selected_offer_id!r}}'\n            ) from e\n    else:\n        instance_list = vast.vast().search_offers(query=query_str)\n\n        if isinstance(instance_list, int) or len(instance_list) == 0:\n            raise RuntimeError('Failed to create instances, could not find an '\n                               'offer that satisfies the requirements '\n                               f'"{{query_str}}".')\n\n        instance_touse = instance_list[0]\n\n'''
+        if old not in text:
+            raise RuntimeError(
+                f"Could not apply SkyPilot Vast selected-offer patch; expected source block not found in {path}"
+            )
+        text = text.replace(old, new, 1)
+
+    if not already_api_key:
+        old_key = "        f'echo \"{vast.vast().client.api_key}\" > ~/.vast_api_key',"
+        new_key = (
+            f"        # {SDK_API_KEY_MARKER}: vastai-sdk exposes api_key directly in current releases.\n"
+            "        f'echo \"{vast.vast().api_key}\" > ~/.vast_api_key',"
+        )
+        if old_key not in text:
+            raise RuntimeError(
+                f"Could not apply SkyPilot Vast API-key patch; expected source line not found in {path}"
+            )
+        text = text.replace(old_key, new_key, 1)
+
+    path.write_text(text)
+    return PatchStatus(path=path, installed=True, patched=True, message="SkyPilot Vast selected-offer patch installed")
+
+
+def require_patch() -> Path:
+    status = patch_status()
+    if not status.installed:
+        raise RuntimeError(status.message)
+    if not status.patched:
+        raise RuntimeError(f"{status.message}; run `remote-run doctor --apply-skypilot-patch`")
+    assert status.path is not None
+    return status.path
diff --git a/src/airfrans_frontier/remote/smoke.py b/src/airfrans_frontier/remote/smoke.py
new file mode 100644
index 0000000..c9b5370
--- /dev/null
+++ b/src/airfrans_frontier/remote/smoke.py
@@ -0,0 +1,127 @@
+from __future__ import annotations
+
+import json
+import platform
+import shutil
+import subprocess
+import sys
+import time
+from pathlib import Path
+from typing import Any
+
+from airfrans_frontier.remote.artifacts import verify_artifacts
+from airfrans_frontier.runtime import remove_pythonpath_entries
+
+
+def run_smoke_training(config_path: str | Path, *, artifact_dir: str | Path, run_id: str) -> Path:
+    remove_pythonpath_entries()
+    from airfrans_frontier.training.loop import train_from_config_path
+
+    output_dir = Path(artifact_dir)
+    output_dir.mkdir(parents=True, exist_ok=True)
+    heartbeat_path = output_dir / "heartbeat.json"
+    _write_json(
+        heartbeat_path,
+        {
+            "run_id": run_id,
+            "phase": "starting",
+            "timestamp": time.time(),
+        },
+    )
+    _write_json(output_dir / "environment_manifest.json", environment_manifest())
+
+    started = time.time()
+    _write_json(
+        heartbeat_path,
+        {
+            "run_id": run_id,
+            "phase": "training",
+            "started_at": started,
+            "timestamp": time.time(),
+        },
+    )
+    result = train_from_config_path(config_path)
+    finished = time.time()
+
+    training_dir = result.run_dir
+    required_from_training = [
+        "final_metrics.json",
+        "metrics.jsonl",
+        "checkpoint.pt",
+        "config.toml",
+        "normalization.json",
+        "split_manifest.json",
+    ]
+    for name in required_from_training:
+        source = training_dir / name
+        if source.is_file():
+            shutil.copy2(source, output_dir / name)
+
+    run_manifest: dict[str, Any] = {
+        "run_id": run_id,
+        "command": f"remote-run smoke-train {config_path}",
+        "started_at": started,
+        "finished_at": finished,
+        "elapsed_seconds": finished - started,
+        "exit_code": 0,
+        "training_run_dir": str(training_dir),
+        "artifact_dir": str(output_dir),
+        "final_metrics_path": str(output_dir / "final_metrics.json"),
+        "checkpoint_path": str(output_dir / "checkpoint.pt"),
+    }
+    _write_json(output_dir / "run_manifest.json", run_manifest)
+    _write_json(
+        heartbeat_path,
+        {
+            "run_id": run_id,
+            "phase": "completed",
+            "started_at": started,
+            "finished_at": finished,
+            "timestamp": time.time(),
+        },
+    )
+    verify_artifacts(output_dir)
+    return output_dir
+
+
+def environment_manifest() -> dict[str, Any]:
+    manifest: dict[str, Any] = {
+        "python": sys.version,
+        "platform": platform.platform(),
+        "executable": sys.executable,
+    }
+    try:
+        import torch
+
+        manifest.update(
+            {
+                "torch_version": torch.__version__,
+                "cuda_available": torch.cuda.is_available(),
+                "cuda_version": torch.version.cuda,
+                "gpu_name": torch.cuda.get_device_name(0) if torch.cuda.is_available() else None,
+            }
+        )
+    except Exception as exc:  # pragma: no cover - environment reporting should not hide root run errors.
+        manifest["torch_error"] = repr(exc)
+
+    try:
+        result = subprocess.run(
+            ["nvidia-smi", "--query-gpu=name,memory.total,driver_version", "--format=csv,noheader"],
+            text=True,
+            stdout=subprocess.PIPE,
+            stderr=subprocess.PIPE,
+            timeout=15,
+            check=False,
+        )
+        manifest["nvidia_smi"] = {
+            "returncode": result.returncode,
+            "stdout": result.stdout.strip(),
+            "stderr": result.stderr.strip(),
+        }
+    except OSError as exc:
+        manifest["nvidia_smi"] = {"error": str(exc)}
+    return manifest
+
+
+def _write_json(path: Path, data: Any) -> None:
+    path.write_text(json.dumps(data, indent=2, sort_keys=True) + "\n")
diff --git a/src/airfrans_frontier/remote/vast.py b/src/airfrans_frontier/remote/vast.py
new file mode 100644
index 0000000..389440d
--- /dev/null
+++ b/src/airfrans_frontier/remote/vast.py
@@ -0,0 +1,258 @@
+from __future__ import annotations
+
+import json
+import math
+import os
+import urllib.parse
+import urllib.request
+from dataclasses import asdict, dataclass
+from typing import Any, Mapping
+
+from airfrans_frontier.remote.config import RemoteRunConfig, SelectionConfig
+
+
+@dataclass(frozen=True)
+class VastOffer:
+    id: int
+    gpu_name: str
+    dph_total: float
+    gpu_ram: float | None
+    geolocation: str | None
+    inet_down_cost_per_tb: float
+    inet_up_cost_per_tb: float
+    host_id: int | None
+    verification: str | None
+    reliability2: float | None
+    cuda_max_good: float | None
+    direct_port_count: int | None
+    inet_down: float | None
+    inet_up: float | None
+    verified: bool | None
+
+    @classmethod
+    def from_mapping(cls, data: Mapping[str, Any]) -> VastOffer:
+        return cls(
+            id=_int(data, "id"),
+            gpu_name=_string(data, "gpu_name"),
+            dph_total=_float(data, "dph_total"),
+            gpu_ram=_optional_float(data, "gpu_ram"),
+            geolocation=_optional_string(data, "geolocation"),
+            inet_down_cost_per_tb=_optional_float(data, "internet_down_cost_per_tb") or 0.0,
+            inet_up_cost_per_tb=_optional_float(data, "internet_up_cost_per_tb") or 0.0,
+            host_id=_optional_int(data, "host_id"),
+            verification=_optional_string(data, "verification"),
+            reliability2=_optional_float(data, "reliability2"),
+            cuda_max_good=_optional_float(data, "cuda_max_good"),
+            direct_port_count=_optional_int(data, "direct_port_count"),
+            inet_down=_optional_float(data, "inet_down"),
+            inet_up=_optional_float(data, "inet_up"),
+            verified=_optional_bool(data, "verified"),
+        )
+
+
+@dataclass(frozen=True)
+class SelectionResult:
+    selected_offer: VastOffer
+    candidate_count: int
+    survivor_count: int
+    effective_price: float
+    query: dict[str, Any]
+    policy: dict[str, Any]
+
+    @property
+    def selected_offer_id(self) -> int:
+        return self.selected_offer.id
+
+    def to_manifest(self) -> dict[str, Any]:
+        offer = asdict(self.selected_offer)
+        offer["effective_price"] = self.effective_price
+        return {
+            "selected_offer_id": self.selected_offer_id,
+            "selected_offer": offer,
+            "candidate_count": self.candidate_count,
+            "survivor_count": self.survivor_count,
+            "query": self.query,
+            "policy": self.policy,
+        }
+
+
+def select_offer(config: RemoteRunConfig, *, api_key: str | None = None) -> SelectionResult:
+    if config.provider.kind != "vastai":
+        raise ValueError(f"Unsupported provider: {config.provider.kind}")
+    resolved_key = api_key or os.environ.get("VAST_API_KEY")
+    if not resolved_key:
+        raise RuntimeError("VAST_API_KEY is required for Vast.ai offer selection")
+
+    query = build_query(config)
+    offers = search_offers(
+        base_url=config.selection.base_url,
+        api_key=resolved_key,
+        query=query,
+    )
+    return choose_offer(offers, config, query=query)
+
+
+def build_query(config: RemoteRunConfig) -> dict[str, Any]:
+    selection = config.selection
+    provider = config.provider
+    query: dict[str, Any] = {
+        "rentable": {"eq": True},
+        "rented": {"eq": False},
+        "reliability2": {"gte": selection.min_reliability},
+        "cuda_max_good": {"gte": 12.6},
+        "direct_port_count": {"gte": 1},
+        "num_gpus": {"eq": provider.gpu.count},
+        "inet_down": {"gte": selection.min_down_mbps},
+        "limit": 5000,
+    }
+    if selection.min_up_mbps is not None:
+        query["inet_up"] = {"gte": selection.min_up_mbps}
+    if selection.require_verified:
+        query["verified"] = {"eq": True}
+    if provider.gpu.min_vram_gb is not None:
+        query["gpu_ram"] = {"gte": provider.gpu.min_vram_gb * 1024}
+    if provider.gpu.name:
+        query["gpu_name"] = {"eq": provider.gpu.name}
+    return query
+
+
+def search_offers(*, base_url: str, api_key: str, query: Mapping[str, Any]) -> list[VastOffer]:
+    encoded = urllib.parse.quote(json.dumps(query, separators=(",", ":")))
+    url = f"{base_url.rstrip('/')}/api/v0/bundles/?q={encoded}"
+    request = urllib.request.Request(url, headers={"Authorization": f"Bearer {api_key}"})
+    try:
+        with urllib.request.urlopen(request, timeout=45) as response:
+            payload = json.loads(response.read().decode("utf-8"))
+    except urllib.error.HTTPError as exc:
+        body = exc.read().decode("utf-8", errors="replace")
+        raise RuntimeError(f"Vast offer search HTTP {exc.code}: {body}") from exc
+    except OSError as exc:
+        raise RuntimeError(f"Vast offer search failed: {exc}") from exc
+
+    raw_offers = payload.get("offers")
+    if not isinstance(raw_offers, list):
+        raise RuntimeError("Vast offer search response missing offers list")
+    return [VastOffer.from_mapping(item) for item in raw_offers if isinstance(item, Mapping)]
+
+
+def choose_offer(offers: list[VastOffer], config: RemoteRunConfig, *, query: Mapping[str, Any]) -> SelectionResult:
+    survivors = reachable_offers(offers, config.selection)
+    ranked = rank_survivors(survivors, config.selection)
+    if config.provider.max_price_per_hour is not None:
+        ranked = [offer for offer in ranked if effective_price(offer, config.selection) <= config.provider.max_price_per_hour]
+    if not ranked:
+        raise RuntimeError("No Vast offers survived quality filters and price cap")
+    selected = ranked[0]
+    return SelectionResult(
+        selected_offer=selected,
+        candidate_count=len(offers),
+        survivor_count=len(ranked),
+        effective_price=effective_price(selected, config.selection),
+        query=dict(query),
+        policy=selection_policy_manifest(config),
+    )
+
+
+def reachable_offers(offers: list[VastOffer], selection: SelectionConfig) -> list[VastOffer]:
+    blacklist = set(selection.blacklist_hosts)
+    blocked = tuple(item.upper() for item in selection.blocked_geos)
+    result: list[VastOffer] = []
+    for offer in offers:
+        geo = (offer.geolocation or "").upper()
+        if blocked and any(token and token in geo for token in blocked):
+            continue
+        if offer.host_id is not None and offer.host_id in blacklist:
+            continue
+        if offer.verification == "deverified":
+            continue
+        result.append(offer)
+    return result
+
+
+def rank_survivors(offers: list[VastOffer], selection: SelectionConfig) -> list[VastOffer]:
+    by_model: dict[str, list[VastOffer]] = {}
+    for offer in offers:
+        by_model.setdefault(offer.gpu_name, []).append(offer)
+
+    survivors: list[VastOffer] = []
+    for group in by_model.values():
+        group.sort(key=lambda offer: effective_price(offer, selection))
+        drop = math.floor(selection.drop_cheap_frac * len(group))
+        survivors.extend(group[drop:])
+    survivors.sort(key=lambda offer: effective_price(offer, selection))
+    return survivors
+
+
+def effective_price(offer: VastOffer, selection: SelectionConfig) -> float:
+    image_pull = 0.0
+    if selection.image_size_gb is not None:
+        image_pull = selection.image_size_gb * offer.inet_down_cost_per_tb / 1000.0
+    return offer.dph_total + image_pull
+
+
+def selection_policy_manifest(config: RemoteRunConfig) -> dict[str, Any]:
+    return {
+        "gpu_name": config.provider.gpu.name,
+        "gpu_count": config.provider.gpu.count,
+        "min_vram_gb": config.provider.gpu.min_vram_gb,
+        "max_price_per_hour": config.provider.max_price_per_hour,
+        "min_reliability": config.selection.min_reliability,
+        "min_down_mbps": config.selection.min_down_mbps,
+        "min_up_mbps": config.selection.min_up_mbps,
+        "require_verified": config.selection.require_verified,
+        "blocked_geos": list(config.selection.blocked_geos),
+        "blacklist_hosts": list(config.selection.blacklist_hosts),
+        "drop_cheap_frac": config.selection.drop_cheap_frac,
+        "image_size_gb": config.selection.image_size_gb,
+    }
+
+
+def _string(data: Mapping[str, Any], key: str) -> str:
+    value = data.get(key)
+    if not isinstance(value, str):
+        raise ValueError(f"Vast offer missing string field: {key}")
+    return value
+
+
+def _optional_string(data: Mapping[str, Any], key: str) -> str | None:
+    value = data.get(key)
+    return value if isinstance(value, str) else None
+
+
+def _int(data: Mapping[str, Any], key: str) -> int:
+    value = data.get(key)
+    if isinstance(value, bool) or not isinstance(value, int):
+        raise ValueError(f"Vast offer missing integer field: {key}")
+    return value
+
+
+def _optional_int(data: Mapping[str, Any], key: str) -> int | None:
+    value = data.get(key)
+    if isinstance(value, bool):
+        return None
+    if isinstance(value, int):
+        return value
+    if isinstance(value, float) and value.is_integer():
+        return int(value)
+    return None
+
+
+def _float(data: Mapping[str, Any], key: str) -> float:
+    value = data.get(key)
+    if isinstance(value, bool) or not isinstance(value, (int, float)):
+        raise ValueError(f"Vast offer missing numeric field: {key}")
+    return float(value)
+
+
+def _optional_float(data: Mapping[str, Any], key: str) -> float | None:
+    value = data.get(key)
+    if isinstance(value, bool):
+        return None
+    if isinstance(value, (int, float)):
+        return float(value)
+    return None
+
+
+def _optional_bool(data: Mapping[str, Any], key: str) -> bool | None:
+    value = data.get(key)
+    return value if isinstance(value, bool) else None
diff --git a/tests/test_remote_run.py b/tests/test_remote_run.py
new file mode 100644
index 0000000..53fdd8e
--- /dev/null
+++ b/tests/test_remote_run.py
@@ -0,0 +1,99 @@
+from __future__ import annotations
+
+import json
+import tempfile
+import unittest
+from pathlib import Path
+
+from airfrans_frontier.remote.artifacts import verify_artifacts
+from airfrans_frontier.remote.config import load_remote_run_config
+from airfrans_frontier.remote.skypilot import render_skypilot_yaml
+from airfrans_frontier.remote.vast import VastOffer, choose_offer
+
+
+class RemoteRunConfigTests(unittest.TestCase):
+    def test_loads_remote_smoke_config(self) -> None:
+        config = load_remote_run_config("configs/remote_smoke.toml")
+
+        self.assertEqual(config.provider.kind, "vastai")
+        self.assertEqual(config.provider.gpu.name, "RTX 4090")
+        self.assertEqual(config.job.artifact_dir.as_posix(), "artifacts/current_run")
+        self.assertIn("checkpoint.pt", config.artifacts.required)
+
+
+class VastSelectionTests(unittest.TestCase):
+    def test_selection_filters_bad_hosts_and_drops_suspiciously_cheap_tail(self) -> None:
+        config = load_remote_run_config("configs/remote_smoke.toml")
+        offers = [
+            offer(1, price=0.10, host=1),
+            offer(2, price=0.20, host=2),
+            offer(3, price=0.30, host=3),
+            offer(4, price=0.40, host=4),
+            offer(5, price=0.50, host=59017),
+            offer(6, price=0.25, host=6, geo="CN"),
+            offer(7, price=0.26, host=7, verification="deverified"),
+        ]
+
+        result = choose_offer(offers, config, query={"test": True})
+
+        # Four reachable RTX 4090 offers remain; drop_cheap_frac=0.30 drops floor(1.2)=1 cheapest.
+        self.assertEqual(result.selected_offer_id, 2)
+        self.assertEqual(result.candidate_count, 7)
+        self.assertEqual(result.survivor_count, 3)
+
+    def test_rendered_yaml_injects_selected_offer(self) -> None:
+        config = load_remote_run_config("configs/remote_smoke.toml")
+        result = choose_offer([offer(123, price=0.30, host=22), offer(124, price=0.40, host=23)], config, query={})
+
+        yaml = render_skypilot_yaml(config, result, run_id="airfrans-test")
+
+        self.assertIn("selected_offer_id: 123", yaml)
+        self.assertNotIn("sky launch", yaml)
+        self.assertIn("remote-run smoke-train", yaml)
+
+
+class ArtifactVerificationTests(unittest.TestCase):
+    def test_verify_artifacts_requires_contract_files_and_writes_manifest(self) -> None:
+        with tempfile.TemporaryDirectory() as tmp:
+            root = Path(tmp)
+            (root / "final_metrics.json").write_text(json.dumps({"loss": 1.0}) + "\n")
+            (root / "metrics.jsonl").write_text(json.dumps({"step": 0}) + "\n")
+            (root / "checkpoint.pt").write_bytes(b"weights")
+            (root / "run_manifest.json").write_text(json.dumps({"exit_code": 0}) + "\n")
+
+            manifest = verify_artifacts(root)
+
+            self.assertEqual(manifest["file_count"], 4)
+            self.assertTrue((root / "artifact_manifest.json").is_file())
+            self.assertTrue((root / "checksums.txt").is_file())
+
+
+def offer(
+    offer_id: int,
+    *,
+    price: float,
+    host: int,
+    geo: str = "US",
+    verification: str = "verified",
+) -> VastOffer:
+    return VastOffer(
+        id=offer_id,
+        gpu_name="RTX 4090",
+        dph_total=price,
+        gpu_ram=24_000,
+        geolocation=geo,
+        inet_down_cost_per_tb=0.0,
+        inet_up_cost_per_tb=0.0,
+        host_id=host,
+        verification=verification,
+        reliability2=0.99,
+        cuda_max_good=12.8,
+        direct_port_count=1,
+        inet_down=500.0,
+        inet_up=100.0,
+        verified=True,
+    )
+
+
+if __name__ == "__main__":
+    unittest.main()
diff --git a/tools/vastai/Cargo.toml b/tools/vastai/Cargo.toml
new file mode 100644
index 0000000..49e6bf9
--- /dev/null
+++ b/tools/vastai/Cargo.toml
@@ -0,0 +1,15 @@
+[package]
+name = "swactor-vastai"
+version = "0.1.0"
+edition = "2024"
+publish = false
+
+[dependencies]
+reqwest = { version = "0.12", features = ["json"] }
+serde = { version = "1", features = ["derive"] }
+serde_json = "1"
+tokio = { version = "1", features = ["time"] }
+urlencoding = "2"
+
+[dev-dependencies]
+wiremock = "0.6"
diff --git a/tools/vastai/src/client.rs b/tools/vastai/src/client.rs
new file mode 100644
index 0000000..52d670d
--- /dev/null
+++ b/tools/vastai/src/client.rs
@@ -0,0 +1,76 @@
+use crate::types::{
+    LabeledInstance, LifecyclePolicy, Offer, ProvisionRequest, ProvisionedFleet, RunningInstance,
+};
+
+/// Small convenience wrapper around a reqwest client + vast.ai endpoint.
+#[derive(Clone)]
+pub struct VastClient {
+    http: reqwest::Client,
+    base_url: String,
+    api_key: String,
+}
+
+impl VastClient {
+    pub fn new(api_key: impl Into<String>) -> Self {
+        Self::with_base_url("https://cloud.vast.ai", api_key)
+    }
+
+    pub fn with_base_url(base_url: impl Into<String>, api_key: impl Into<String>) -> Self {
+        Self {
+            http: reqwest::Client::new(),
+            base_url: base_url.into(),
+            api_key: api_key.into(),
+        }
+    }
+
+    pub fn http(&self) -> &reqwest::Client {
+        &self.http
+    }
+
+    pub fn base_url(&self) -> &str {
+        &self.base_url
+    }
+
+    pub fn api_key(&self) -> &str {
+        &self.api_key
+    }
+
+    pub async fn search_offers(
+        &self,
+        policy: &crate::types::SelectionPolicy,
+        target_count: u32,
+    ) -> Result<Vec<Offer>, String> {
+        crate::search::select_offer_pool_with_policy(
+            &self.http,
+            &self.base_url,
+            &self.api_key,
+            policy,
+            target_count,
+        )
+        .await
+    }
+
+    pub async fn provision(&self, req: ProvisionRequest) -> Result<ProvisionedFleet, String> {
+        crate::lease::provision_fleet(&self.http, &self.base_url, &self.api_key, req).await
+    }
+
+    pub async fn wait_for_running(
+        &self,
+        contract_id: u64,
+        policy: &LifecyclePolicy,
+    ) -> Result<RunningInstance, String> {
+        crate::monitor::wait_for_running_with_policy(
+            &self.http,
+            &self.base_url,
+            &self.api_key,
+            contract_id,
+            policy,
+        )
+        .await
+    }
+
+    pub async fn list_by_label(&self, label: &str) -> Result<Vec<LabeledInstance>, String> {
+        crate::teardown::list_instances_by_label(&self.http, &self.base_url, &self.api_key, label)
+            .await
+    }
+}
diff --git a/tools/vastai/src/config.rs b/tools/vastai/src/config.rs
new file mode 100644
index 0000000..e333c8c
--- /dev/null
+++ b/tools/vastai/src/config.rs
@@ -0,0 +1,87 @@
+use std::time::Duration;
+
+use crate::types::{LifecyclePolicy, SelectionPolicy};
+
+pub const ENV_IMAGE_SIZE_GB: &str = "PP_IMAGE_SIZE_GB";
+pub const ENV_GPU_MIN_RAM_MB: &str = "PP_GPU_MIN_RAM_MB";
+pub const ENV_MIN_INET_DOWN_MBPS: &str = "PP_MIN_INET_DOWN_MBPS";
+pub const ENV_MIN_INET_UP_MBPS: &str = "PP_MIN_INET_UP_MBPS";
+pub const ENV_MIN_RELIABILITY: &str = "PP_MIN_RELIABILITY";
+pub const ENV_REQUIRE_VERIFIED: &str = "PP_REQUIRE_VERIFIED";
+pub const ENV_DROP_CHEAP_FRAC: &str = "PP_DROP_CHEAP_FRAC";
+pub const ENV_LEASE_PACE_MS: &str = "PP_LEASE_PACE_MS";
+pub const ENV_BLACKLIST_HOSTS: &str = "PP_BLACKLIST_HOSTS";
+pub const ENV_ASSUME_YES: &str = "PP_ASSUME_YES";
+
+pub fn truthy_env(name: &str) -> bool {
+    std::env::var(name)
+        .ok()
+        .map(|s| matches!(s.trim().to_ascii_lowercase().as_str(), "1" | "true" | "yes"))
+        .unwrap_or(false)
+}
+
+fn env_positive_u64(name: &str) -> Option<u64> {
+    std::env::var(name)
+        .ok()
+        .and_then(|s| s.trim().parse::<u64>().ok())
+        .filter(|&n| n > 0)
+}
+
+fn env_nonnegative_f64(name: &str, default: f64) -> f64 {
+    std::env::var(name)
+        .ok()
+        .and_then(|s| s.trim().parse::<f64>().ok())
+        .filter(|&v| v >= 0.0)
+        .unwrap_or(default)
+}
+
+fn env_optional_positive_f64(name: &str) -> Option<f64> {
+    std::env::var(name)
+        .ok()
+        .and_then(|s| s.trim().parse::<f64>().ok())
+        .filter(|&v| v > 0.0)
+}
+
+impl SelectionPolicy {
+    /// Build selection policy from the historical `PP_*` environment knobs.
+    pub fn from_env() -> Self {
+        let mut policy = Self::default();
+        policy.min_gpu_ram_mb = env_positive_u64(ENV_GPU_MIN_RAM_MB);
+        policy.min_down_mbps = env_nonnegative_f64(ENV_MIN_INET_DOWN_MBPS, 100.0);
+        policy.min_reliability = std::env::var(ENV_MIN_RELIABILITY)
+            .ok()
+            .and_then(|s| s.trim().parse::<f64>().ok())
+            .filter(|&v| (0.0..=1.0).contains(&v))
+            .unwrap_or(0.95);
+        policy.require_verified = truthy_env(ENV_REQUIRE_VERIFIED);
+        policy.min_up_mbps = env_optional_positive_f64(ENV_MIN_INET_UP_MBPS);
+        policy.drop_cheap_frac = std::env::var(ENV_DROP_CHEAP_FRAC)
+            .ok()
+            .and_then(|s| s.trim().parse::<f64>().ok())
+            .filter(|v| v.is_finite())
+            .map(|v| v.clamp(0.0, 0.99))
+            .unwrap_or(0.30);
+        policy.image_size_gb = env_optional_positive_f64(ENV_IMAGE_SIZE_GB);
+        if let Ok(raw) = std::env::var(ENV_BLACKLIST_HOSTS) {
+            policy
+                .blacklist_hosts
+                .extend(raw.split(',').filter_map(|s| s.trim().parse::<u64>().ok()));
+        }
+        policy
+    }
+}
+
+impl LifecyclePolicy {
+    /// Build lifecycle policy from environment, using caller-provided poll cadence.
+    pub fn from_env(poll_interval: Duration) -> Self {
+        let mut policy = Self::default();
+        policy.lease_pace = Duration::from_millis(
+            std::env::var(ENV_LEASE_PACE_MS)
+                .ok()
+                .and_then(|s| s.trim().parse::<u64>().ok())
+                .unwrap_or(600),
+        );
+        policy.poll_interval = poll_interval;
+        policy
+    }
+}
diff --git a/tools/vastai/src/filters.rs b/tools/vastai/src/filters.rs
new file mode 100644
index 0000000..b246788
--- /dev/null
+++ b/tools/vastai/src/filters.rs
@@ -0,0 +1,17 @@
+use std::collections::HashSet;
+
+use crate::types::{Offer, SelectionPolicy};
+
+pub(crate) fn reachable_offers(offers: Vec<Offer>, policy: &SelectionPolicy) -> Vec<Offer> {
+    let blacklist: HashSet<u64> = policy.blacklist_hosts.iter().copied().collect();
+    offers
+        .into_iter()
+        .filter(|o| {
+            o.geolocation
+                .as_deref()
+                .map_or(false, |g| !g.to_uppercase().contains("CN"))
+        })
+        .filter(|o| o.host_id.map_or(true, |h| !blacklist.contains(&h)))
+        .filter(|o| o.verification.as_deref() != Some("deverified"))
+        .collect()
+}
diff --git a/tools/vastai/src/lease.rs b/tools/vastai/src/lease.rs
new file mode 100644
index 0000000..8f14dde
--- /dev/null
+++ b/tools/vastai/src/lease.rs
@@ -0,0 +1,288 @@
+use std::collections::{BTreeMap, HashSet};
+use std::io::{IsTerminal, Write};
+use std::time::Duration;
+
+use crate::config::{ENV_ASSUME_YES, truthy_env};
+use crate::monitor::wait_for_running_with_policy;
+use crate::pricing::{CostModel, plan_picks};
+use crate::provision::create_instance;
+use crate::search::select_offer_pool_with_policy;
+use crate::teardown::{destroy_instance_with_retry, rollback};
+use crate::types::{
+    CreateInstanceRequest, InstanceInfo, Offer, ProvisionRequest, ProvisionedFleet,
+    ProvisionedInstance,
+};
+
+/// Print the planned lease + hourly cost and, on TTY, require y/N confirmation.
+pub fn confirm_lease(pool: &[Offer], num_instances: u32, cost: &CostModel) -> Result<(), String> {
+    let picks = plan_picks(pool, num_instances);
+    let total_dph: f64 = picks.iter().map(|o| o.dph_total).sum();
+    let total_eff: f64 = picks.iter().map(|o| cost.effective_price(o)).sum();
+
+    eprintln!("vastai: lease plan — {num_instances} instance(s), cheapest on distinct hosts:");
+    for (i, o) in picks.iter().enumerate() {
+        eprintln!(
+            "  node {i}  {:<14} {:>8}  ${:.3}/hr  [{}]  host {}",
+            o.gpu_name,
+            o.gpu_ram
+                .map(|r| format!("{:.0}MB", r))
+                .unwrap_or_else(|| "?MB".into()),
+            o.dph_total,
+            o.geolocation.as_deref().unwrap_or("?"),
+            o.host_id
+                .map(|h| h.to_string())
+                .unwrap_or_else(|| "?".into()),
+        );
+    }
+    if picks.len() < num_instances as usize {
+        eprintln!(
+            "  WARNING: only {} distinct-host offer(s) available for {num_instances} instance(s)",
+            picks.len(),
+        );
+    }
+    let eff_note = if (total_eff - total_dph).abs() > 1e-6 {
+        format!("   (image-pull priced in: ${total_eff:.3}/hr eff)")
+    } else {
+        String::new()
+    };
+    eprintln!(
+        "  TOTAL  ${total_dph:.3}/hr  (~${:.2}/day){eff_note}",
+        total_dph * 24.0,
+    );
+
+    if truthy_env(ENV_ASSUME_YES) {
+        eprintln!("vastai: {ENV_ASSUME_YES} set — proceeding without confirmation");
+        return Ok(());
+    }
+    if !std::io::stdin().is_terminal() {
+        eprintln!(
+            "vastai: stdin is not a TTY — proceeding without interactive confirmation \
+             (set {ENV_ASSUME_YES}=1 to silence this)"
+        );
+        return Ok(());
+    }
+
+    eprint!("Proceed with renting these {num_instances} instance(s)? [y/N]: ");
+    let _ = std::io::stderr().flush();
+    let mut line = String::new();
+    std::io::stdin()
+        .read_line(&mut line)
+        .map_err(|e| format!("failed to read lease confirmation: {e}"))?;
+    let ans = line.trim().to_ascii_lowercase();
+    if ans == "y" || ans == "yes" {
+        Ok(())
+    } else {
+        Err("operator declined the lease (cost not confirmed); no instances were created".into())
+    }
+}
+
+fn next_eligible_offer<'a>(
+    pool: &'a [Offer],
+    tried_offer_ids: &[u64],
+    used_host_ids: &HashSet<u64>,
+) -> Option<&'a Offer> {
+    pool.iter().find(|o| {
+        !tried_offer_ids.contains(&o.id) && o.host_id.map_or(true, |h| !used_host_ids.contains(&h))
+    })
+}
+
+fn env_for_index(req: &ProvisionRequest, index: u32) -> BTreeMap<String, String> {
+    let mut env = req.env.clone();
+    if let Some(extra) = req.per_instance_env.get(index as usize) {
+        env.extend(extra.clone());
+    }
+    env
+}
+
+async fn provision_one(
+    client: &reqwest::Client,
+    base_url: &str,
+    api_key: &str,
+    req: &ProvisionRequest,
+    pool: &[Offer],
+    index: u32,
+    tried_offer_ids: &mut Vec<u64>,
+    used_host_ids: &mut HashSet<u64>,
+) -> Result<ProvisionedInstance, String> {
+    let mut attempt = 1_u64;
+    loop {
+        let offer = match next_eligible_offer(pool, tried_offer_ids, used_host_ids) {
+            Some(o) => o.clone(),
+            None => {
+                return Err(format!(
+                    "pool exhausted for index {index} (no untried offer on an unused host)"
+                ));
+            }
+        };
+        tried_offer_ids.push(offer.id);
+        let cost = CostModel::from_policy(&req.selection);
+        eprintln!(
+            "lease_chain: index {index} → offer {} — {} {} @ ${:.3}/hr [{}] host {} eff ${:.3}/hr",
+            offer.id,
+            offer.gpu_name,
+            offer
+                .gpu_ram
+                .map(|r| format!("{:.0}MB", r))
+                .unwrap_or_else(|| "?MB".into()),
+            offer.dph_total,
+            offer.geolocation.as_deref().unwrap_or("?"),
+            offer
+                .host_id
+                .map(|h| h.to_string())
+                .unwrap_or_else(|| "?".into()),
+            cost.effective_price(&offer),
+        );
+
+        let create = CreateInstanceRequest {
+            offer_id: offer.id,
+            image: req.image.clone(),
+            disk_gb: req.disk_gb,
+            label: req.label.clone(),
+            env: env_for_index(req, index),
+            onstart: req.onstart.clone(),
+        };
+        match create_instance(client, base_url, api_key, &create).await {
+            Ok(info) => {
+                if let Some(h) = offer.host_id {
+                    used_host_ids.insert(h);
+                }
+                return Ok(ProvisionedInstance {
+                    index,
+                    contract_id: info.contract_id,
+                    offer_id: offer.id,
+                    host_id: offer.host_id,
+                    gpu_name: offer.gpu_name,
+                    gpu_ram: offer.gpu_ram,
+                    dph_total: offer.dph_total,
+                });
+            }
+            Err(e) => {
+                eprintln!(
+                    "lease_chain: index {index} create on offer {} failed (attempt {attempt}): {e}",
+                    offer.id,
+                );
+                let is_429 = e.contains("429") || e.contains("Too Many Requests");
+                let backoff = if is_429 {
+                    std::cmp::min(
+                        Duration::from_millis(2_000_u64.saturating_mul(attempt)),
+                        Duration::from_secs(30),
+                    )
+                } else {
+                    Duration::from_millis(400)
+                };
+                tokio::time::sleep(backoff).await;
+                attempt = attempt.saturating_add(1);
+            }
+        }
+    }
+}
+
+fn as_instance_infos(instances: &[ProvisionedInstance]) -> Vec<InstanceInfo> {
+    instances
+        .iter()
+        .map(|i| InstanceInfo {
+            contract_id: i.contract_id,
+        })
+        .collect()
+}
+
+/// Rent, monitor, replace, and roll back an N-instance fleet.
+pub async fn provision_fleet(
+    client: &reqwest::Client,
+    base_url: &str,
+    api_key: &str,
+    req: ProvisionRequest,
+) -> Result<ProvisionedFleet, String> {
+    let pool = select_offer_pool_with_policy(client, base_url, api_key, &req.selection, req.count)
+        .await
+        .map_err(|e| format!("lease_chain: {e}"))?;
+
+    if req.confirm_lease {
+        confirm_lease(&pool, req.count, &CostModel::from_policy(&req.selection))?;
+    }
+
+    let mut tried_offer_ids = Vec::new();
+    let mut created: Vec<ProvisionedInstance> = Vec::with_capacity(req.count as usize);
+    let mut used_host_ids = HashSet::new();
+
+    for index in 0..req.count {
+        match provision_one(
+            client,
+            base_url,
+            api_key,
+            &req,
+            &pool,
+            index,
+            &mut tried_offer_ids,
+            &mut used_host_ids,
+        )
+        .await
+        {
+            Ok(info) => created.push(info),
+            Err(e) => {
+                rollback(client, base_url, api_key, &as_instance_infos(&created)).await;
+                return Err(format!("lease_chain: {e}"));
+            }
+        }
+        if index + 1 < req.count {
+            tokio::time::sleep(req.lifecycle.lease_pace).await;
+        }
+    }
+
+    for index in 0..req.count {
+        let idx = index as usize;
+        loop {
+            let cid = created[idx].contract_id;
+            match wait_for_running_with_policy(client, base_url, api_key, cid, &req.lifecycle).await
+            {
+                Ok(_) => break,
+                Err(e) => {
+                    eprintln!(
+                        "lease_chain: index {index} contract {cid} did not reach running: {e}"
+                    );
+                    if let Err(de) =
+                        destroy_instance_with_retry(client, base_url, api_key, cid).await
+                    {
+                        eprintln!(
+                            "lease_chain: WARNING could not destroy dead contract {cid}: {de}"
+                        );
+                    }
+                    eprintln!("lease_chain: replacing index {index}");
+                    match provision_one(
+                        client,
+                        base_url,
+                        api_key,
+                        &req,
+                        &pool,
+                        index,
+                        &mut tried_offer_ids,
+                        &mut used_host_ids,
+                    )
+                    .await
+                    {
+                        Ok(info) => created[idx] = info,
+                        Err(pe) => {
+                            let survivors: Vec<InstanceInfo> = created
+                                .iter()
+                                .enumerate()
+                                .filter(|(i, _)| *i != idx)
+                                .map(|(_, c)| InstanceInfo {
+                                    contract_id: c.contract_id,
+                                })
+                                .collect();
+                            rollback(client, base_url, api_key, &survivors).await;
+                            return Err(format!(
+                                "lease_chain: index {index} replacement could not be provisioned: {pe}"
+                            ));
+                        }
+                    }
+                }
+            }
+        }
+    }
+
+    Ok(ProvisionedFleet {
+        label: req.label,
+        instances: created,
+    })
+}
diff --git a/tools/vastai/src/lib.rs b/tools/vastai/src/lib.rs
new file mode 100644
index 0000000..7c7ec4b
--- /dev/null
+++ b/tools/vastai/src/lib.rs
@@ -0,0 +1,34 @@
+//! Peripheral vast.ai provisioning utility.
+//!
+//! This crate is intentionally outside `crates/`: swactor itself stays a
+//! general-purpose actor runtime, while this utility rents, monitors, and tears
+//! down vast.ai machines for apps that choose to use it.
+
+pub mod client;
+pub mod config;
+pub mod filters;
+pub mod lease;
+pub mod logs;
+pub mod monitor;
+pub mod pricing;
+pub mod provision;
+pub mod search;
+pub mod state;
+pub mod teardown;
+pub mod types;
+
+pub use client::VastClient;
+pub use lease::{confirm_lease, provision_fleet};
+pub use logs::{fetch_logs, request_logs};
+pub use monitor::{wait_for_running, wait_for_running_with_policy};
+pub use pricing::CostModel;
+pub use provision::create_instance;
+pub use search::{select_offer_pool, select_offer_pool_with_policy};
+pub use teardown::{
+    destroy_all_instances, destroy_instance, destroy_instance_with_retry, list_instances_by_label,
+};
+pub use types::{
+    ContractRef, CreateInstanceRequest, FleetState, InstanceInfo, LabeledInstance, LifecyclePolicy,
+    Offer, ProvisionRequest, ProvisionedFleet, ProvisionedInstance, RunningInstance,
+    SelectionPolicy,
+};
diff --git a/tools/vastai/src/logs.rs b/tools/vastai/src/logs.rs
new file mode 100644
index 0000000..647681c
--- /dev/null
+++ b/tools/vastai/src/logs.rs
@@ -0,0 +1,36 @@
+use std::time::Duration;
+
+pub async fn request_logs(
+    client: &reqwest::Client,
+    base_url: &str,
+    api_key: &str,
+    contract_id: u64,
+) -> Result<String, String> {
+    let url = format!("{base_url}/api/v0/instances/request_logs/{contract_id}/");
+    let resp = client
+        .put(&url)
+        .header("Authorization", format!("Bearer {api_key}"))
+        .send()
+        .await
+        .map_err(|e| format!("request_logs failed: {e}"))?;
+    let body: serde_json::Value = resp
+        .json()
+        .await
+        .map_err(|e| format!("request_logs parse failed: {e}"))?;
+    body["result_url"]
+        .as_str()
+        .map(|s| s.to_string())
+        .ok_or_else(|| "no result_url in log response".to_string())
+}
+
+pub async fn fetch_logs(client: &reqwest::Client, log_url: &str) -> Result<String, String> {
+    tokio::time::sleep(Duration::from_secs(5)).await;
+    let resp = client
+        .get(log_url)
+        .send()
+        .await
+        .map_err(|e| format!("fetch_logs failed: {e}"))?;
+    resp.text()
+        .await
+        .map_err(|e| format!("fetch_logs read failed: {e}"))
+}
diff --git a/tools/vastai/src/monitor.rs b/tools/vastai/src/monitor.rs
new file mode 100644
index 0000000..a06343a
--- /dev/null
+++ b/tools/vastai/src/monitor.rs
@@ -0,0 +1,114 @@
+use crate::types::{InstanceResponse, LifecyclePolicy, RunningInstance};
+
+/// Historical env-backed polling wrapper.
+pub async fn wait_for_running(
+    client: &reqwest::Client,
+    base_url: &str,
+    api_key: &str,
+    contract_id: u64,
+    poll_interval: std::time::Duration,
+) -> Result<RunningInstance, String> {
+    let policy = LifecyclePolicy::from_env(poll_interval);
+    wait_for_running_with_policy(client, base_url, api_key, contract_id, &policy).await
+}
+
+/// Poll vast.ai until an instance reaches `running`, or fail on terminal provider state.
+pub async fn wait_for_running_with_policy(
+    client: &reqwest::Client,
+    base_url: &str,
+    api_key: &str,
+    contract_id: u64,
+    policy: &LifecyclePolicy,
+) -> Result<RunningInstance, String> {
+    let url = format!("{base_url}/api/v0/instances/{contract_id}/");
+    let mut state_since = std::time::Instant::now();
+    let mut last_state: Option<String> = None;
+
+    let mut poll = 0_u64;
+    loop {
+        poll += 1;
+        let resp = match client
+            .get(&url)
+            .header("Authorization", format!("Bearer {api_key}"))
+            .send()
+            .await
+        {
+            Ok(r) => r,
+            Err(e) => {
+                eprintln!("  contract {contract_id} poll {poll}: request error: {e} (retrying)");
+                tokio::time::sleep(policy.poll_interval).await;
+                continue;
+            }
+        };
+
+        if !resp.status().is_success() {
+            let status = resp.status();
+            let body = resp.text().await.unwrap_or_default();
+            eprintln!(
+                "  contract {contract_id} poll {poll}: HTTP {status} (retrying): {}",
+                body.chars().take(80).collect::<String>(),
+            );
+            tokio::time::sleep(policy.poll_interval).await;
+            continue;
+        }
+
+        let wrapper: InstanceResponse = resp
+            .json()
+            .await
+            .map_err(|e| format!("wait_for_running parse failed: {e}"))?;
+        let status = wrapper.instances;
+
+        let actual = status.actual_status.as_deref().unwrap_or("unknown");
+        let intended = status.intended_status.as_deref().unwrap_or("unknown");
+
+        let msg = status.status_msg.clone();
+        let disk = status.disk_usage;
+
+        if last_state.as_deref() != Some(actual) {
+            state_since = std::time::Instant::now();
+        }
+        last_state = Some(actual.to_string());
+        let in_state = state_since.elapsed().as_secs();
+        let msg_disp = match msg.as_deref() {
+            Some(m) if !m.is_empty() => format!(" msg=\"{m}\""),
+            _ => String::new(),
+        };
+        let disk_disp = match disk {
+            Some(d) if d >= 0.0 => format!(" disk={d:.2}GB"),
+            _ => String::new(),
+        };
+        eprintln!(
+            "  contract {contract_id} poll {poll}: status={actual} in-state={in_state}s{msg_disp}{disk_disp}",
+        );
+
+        if let Some(m) = &msg {
+            if m.contains("Error") || m.contains("failed") {
+                return Err(format!("instance {contract_id} error: {m}"));
+            }
+        }
+        if intended == "stopped" && actual != "running" {
+            return Err(format!(
+                "instance {contract_id} stopped: {}",
+                msg.unwrap_or_default()
+            ));
+        }
+
+        match actual {
+            "running" => {
+                let ip = status
+                    .public_ipaddr
+                    .unwrap_or_else(|| "unknown".to_string());
+                let port = status.ssh_port.unwrap_or(0);
+                return Ok(RunningInstance { ip, port });
+            }
+            "exited" | "error" => {
+                return Err(format!(
+                    "instance {contract_id} reached terminal status: {actual}"
+                ));
+            }
+            _ => {
+                tokio::time::sleep(policy.poll_interval).await;
+            }
+        }
+    }
+}
diff --git a/tools/vastai/src/pricing.rs b/tools/vastai/src/pricing.rs
new file mode 100644
index 0000000..ebbabd6
--- /dev/null
+++ b/tools/vastai/src/pricing.rs
@@ -0,0 +1,67 @@
+use crate::types::{Offer, SelectionPolicy};
+
+/// Cost model for ranking offers on true lease cost rather than listed $/hr.
+#[derive(Debug, Clone, Default)]
+pub struct CostModel {
+    /// Deploy-image size in GB; `None` → image pull is not priced in.
+    pub image_gb: Option<f64>,
+}
+
+impl CostModel {
+    pub fn from_policy(policy: &SelectionPolicy) -> Self {
+        Self {
+            image_gb: policy.image_size_gb,
+        }
+    }
+
+    /// One-time cost of pulling the deploy image to this offer's host.
+    pub fn pull_cost(&self, o: &Offer) -> f64 {
+        self.image_gb
+            .map_or(0.0, |gb| gb * o.inet_down_cost_per_tb / 1000.0)
+    }
+
+    /// Effective price used for ranking.
+    pub fn effective_price(&self, o: &Offer) -> f64 {
+        o.dph_total + self.pull_cost(o)
+    }
+}
+
+/// Drop the suspiciously-cheap tail within each GPU model, then merge by price.
+pub(crate) fn rank_survivors(offers: Vec<Offer>, cost: &CostModel, drop_frac: f64) -> Vec<Offer> {
+    let mut by_model: std::collections::HashMap<String, Vec<Offer>> =
+        std::collections::HashMap::new();
+    for o in offers {
+        by_model.entry(o.gpu_name.clone()).or_default().push(o);
+    }
+    let price = |o: &Offer| cost.effective_price(o);
+    let by_price = |a: &Offer, b: &Offer| {
+        price(a)
+            .partial_cmp(&price(b))
+            .unwrap_or(std::cmp::Ordering::Equal)
+    };
+    let mut survivors = Vec::new();
+    for (_model, mut group) in by_model {
+        group.sort_by(&by_price);
+        let drop = (drop_frac * group.len() as f64).floor() as usize;
+        survivors.extend(group.into_iter().skip(drop));
+    }
+    survivors.sort_by(&by_price);
+    survivors
+}
+
+pub(crate) fn plan_picks(pool: &[Offer], num_instances: u32) -> Vec<&Offer> {
+    let mut picks = Vec::with_capacity(num_instances as usize);
+    let mut used = std::collections::HashSet::new();
+    for o in pool {
+        if picks.len() == num_instances as usize {
+            break;
+        }
+        if let Some(h) = o.host_id {
+            if !used.insert(h) {
+                continue;
+            }
+        }
+        picks.push(o);
+    }
+    picks
+}
diff --git a/tools/vastai/src/provision.rs b/tools/vastai/src/provision.rs
new file mode 100644
index 0000000..d61d901
--- /dev/null
+++ b/tools/vastai/src/provision.rs
@@ -0,0 +1,52 @@
+use serde_json::Value;
+
+use crate::types::{CreateInstanceRequest, CreateResponse, InstanceInfo};
+
+/// Create one vast.ai instance from a fully generic payload.
+pub async fn create_instance(
+    client: &reqwest::Client,
+    base_url: &str,
+    api_key: &str,
+    req: &CreateInstanceRequest,
+) -> Result<InstanceInfo, String> {
+    let url = format!("{base_url}/api/v0/asks/{}/", req.offer_id);
+    let env = req
+        .env
+        .iter()
+        .map(|(k, v)| (k.clone(), Value::String(v.clone())))
+        .collect::<serde_json::Map<String, Value>>();
+
+    let mut body = serde_json::json!({
+        "image": req.image,
+        "env": env,
+        "disk": req.disk_gb,
+    });
+    if let Some(onstart) = req.onstart.as_deref() {
+        body["onstart"] = Value::String(onstart.to_string());
+    }
+    if let Some(label) = req.label.as_deref() {
+        body["label"] = Value::String(label.to_string());
+    }
+
+    let resp = client
+        .put(&url)
+        .header("Authorization", format!("Bearer {api_key}"))
+        .json(&body)
+        .send()
+        .await
+        .map_err(|e| format!("create_instance request failed: {e}"))?;
+
+    if !resp.status().is_success() {
+        let status = resp.status();
+        let body = resp.text().await.unwrap_or_default();
+        return Err(format!("create_instance HTTP {status}: {body}"));
+    }
+
+    let parsed: CreateResponse = resp
+        .json()
+        .await
+        .map_err(|e| format!("create_instance parse failed: {e}"))?;
+    Ok(InstanceInfo {
+        contract_id: parsed.new_contract,
+    })
+}
diff --git a/tools/vastai/src/search.rs b/tools/vastai/src/search.rs
new file mode 100644
index 0000000..7e98622
--- /dev/null
+++ b/tools/vastai/src/search.rs
@@ -0,0 +1,94 @@
+use crate::filters::reachable_offers;
+use crate::pricing::{CostModel, rank_survivors};
+use crate::types::{Offer, SearchResponse, SelectionPolicy};
+
+/// Historical env-backed offer search wrapper.
+pub async fn select_offer_pool(
+    client: &reqwest::Client,
+    base_url: &str,
+    api_key: &str,
+    gpu_name: &str,
+    target_count: u32,
+) -> Result<Vec<Offer>, String> {
+    let mut policy = SelectionPolicy::from_env();
+    if !gpu_name.is_empty() {
+        policy.gpu_name = Some(gpu_name.to_string());
+    }
+    select_offer_pool_with_policy(client, base_url, api_key, &policy, target_count).await
+}
+
+/// Search vast.ai offers, apply quality filters, and rank survivors.
+pub async fn select_offer_pool_with_policy(
+    client: &reqwest::Client,
+    base_url: &str,
+    api_key: &str,
+    policy: &SelectionPolicy,
+    target_count: u32,
+) -> Result<Vec<Offer>, String> {
+    // Hard gates expressed server-side. Network speed cannot be probed before
+    // renting, so this trusts vast.ai's measured inet figures.
+    let mut query = serde_json::json!({
+        "rentable": {"eq": true},
+        "rented": {"eq": false},
+        "reliability2": {"gte": policy.min_reliability},
+        "cuda_max_good": {"gte": 12.6},
+        "direct_port_count": {"gte": 1},
+        "num_gpus": {"eq": 1},
+        "inet_down": {"gte": policy.min_down_mbps},
+        // vast.ai treats `limit` as a scan budget, not a simple result cap.
+        "limit": 5000,
+    });
+    if let Some(up) = policy.min_up_mbps {
+        query["inet_up"] = serde_json::json!({"gte": up});
+    }
+    if policy.require_verified {
+        query["verified"] = serde_json::json!({"eq": true});
+    }
+    if let Some(min_ram) = policy.min_gpu_ram_mb {
+        query["gpu_ram"] = serde_json::json!({"gte": min_ram});
+    }
+    if let Some(gpu_name) = policy.gpu_name.as_deref().filter(|s| !s.is_empty()) {
+        query["gpu_name"] = serde_json::json!({"eq": gpu_name});
+    }
+
+    let url = format!(
+        "{base_url}/api/v0/bundles/?q={}",
+        urlencoding::encode(&query.to_string())
+    );
+    let resp = client
+        .get(&url)
+        .header("Authorization", format!("Bearer {api_key}"))
+        .send()
+        .await
+        .map_err(|e| format!("select_offer_pool request failed: {e}"))?;
+
+    if !resp.status().is_success() {
+        let status = resp.status();
+        let body = resp.text().await.unwrap_or_default();
+        return Err(format!("select_offer_pool HTTP {status}: {body}"));
+    }
+
+    let body: SearchResponse = resp
+        .json()
+        .await
+        .map_err(|e| format!("select_offer_pool parse failed: {e}"))?;
+
+    let reachable = reachable_offers(body.offers, policy);
+    let cost = CostModel::from_policy(policy);
+    let pool = rank_survivors(reachable, &cost, policy.drop_cheap_frac);
+
+    if pool.is_empty() {
+        return Err(
+            "no offers available (after quality/geo/host-blacklist filters and cheap-tail drop)"
+                .to_string(),
+        );
+    }
+    eprintln!(
+        "select_offer_pool: {} survivor(s) for {target_count} instance(s) after \
+         per-model {:.0}% cheap-drop (cheapest ${:.3}/hr eff)",
+        pool.len(),
+        policy.drop_cheap_frac * 100.0,
+        cost.effective_price(&pool[0]),
+    );
+    Ok(pool)
+}
diff --git a/tools/vastai/src/state.rs b/tools/vastai/src/state.rs
new file mode 100644
index 0000000..338412e
--- /dev/null
+++ b/tools/vastai/src/state.rs
@@ -0,0 +1,19 @@
+use std::path::Path;
+
+use crate::types::FleetState;
+
+impl FleetState {
+    pub fn load(path: &Path) -> Result<Self, String> {
+        let raw = std::fs::read_to_string(path)
+            .map_err(|e| format!("cannot read fleet state {}: {e}", path.display()))?;
+        serde_json::from_str(&raw)
+            .map_err(|e| format!("cannot parse fleet state {}: {e}", path.display()))
+    }
+
+    pub fn save(&self, path: &Path) -> Result<(), String> {
+        let raw = serde_json::to_string_pretty(self)
+            .map_err(|e| format!("cannot serialize fleet state: {e}"))?;
+        std::fs::write(path, raw)
+            .map_err(|e| format!("cannot write fleet state {}: {e}", path.display()))
+    }
+}
diff --git a/tools/vastai/src/teardown.rs b/tools/vastai/src/teardown.rs
new file mode 100644
index 0000000..dd5258e
--- /dev/null
+++ b/tools/vastai/src/teardown.rs
@@ -0,0 +1,115 @@
+use std::time::Duration;
+
+use crate::types::{InstanceInfo, InstanceListResponse, LabeledInstance};
+
+/// Destroy one vast.ai instance by contract id.
+pub async fn destroy_instance(
+    client: &reqwest::Client,
+    base_url: &str,
+    api_key: &str,
+    contract_id: u64,
+) -> Result<(), String> {
+    let url = format!("{base_url}/api/v0/instances/{contract_id}/");
+    let resp = client
+        .delete(&url)
+        .header("Authorization", format!("Bearer {api_key}"))
+        .send()
+        .await
+        .map_err(|e| format!("destroy_instance request failed: {e}"))?;
+    if !resp.status().is_success() {
+        let status = resp.status();
+        let body = resp.text().await.unwrap_or_default();
+        return Err(format!(
+            "destroy_instance {contract_id} HTTP {status}: {body}"
+        ));
+    }
+    Ok(())
+}
+
+/// Destroy every contract and return per-id results in the same order.
+pub async fn destroy_all_instances(
+    client: &reqwest::Client,
+    base_url: &str,
+    api_key: &str,
+    contract_ids: &[u64],
+) -> Vec<Result<(), String>> {
+    let mut results = Vec::with_capacity(contract_ids.len());
+    for &id in contract_ids {
+        results.push(destroy_instance(client, base_url, api_key, id).await);
+    }
+    results
+}
+
+/// Destroy one contract, retrying transient failures so rollback does not strand billing instances.
+pub async fn destroy_instance_with_retry(
+    client: &reqwest::Client,
+    base_url: &str,
+    api_key: &str,
+    contract_id: u64,
+) -> Result<(), String> {
+    let mut attempt = 1_u64;
+    loop {
+        match destroy_instance(client, base_url, api_key, contract_id).await {
+            Ok(()) => return Ok(()),
+            Err(_) => {
+                let backoff = std::cmp::min(
+                    Duration::from_millis(500_u64.saturating_mul(attempt)),
+                    Duration::from_secs(30),
+                );
+                tokio::time::sleep(backoff).await;
+                attempt = attempt.saturating_add(1);
+            }
+        }
+    }
+}
+
+pub(crate) async fn rollback(
+    client: &reqwest::Client,
+    base_url: &str,
+    api_key: &str,
+    created: &[InstanceInfo],
+) {
+    for info in created {
+        if let Err(e) =
+            destroy_instance_with_retry(client, base_url, api_key, info.contract_id).await
+        {
+            eprintln!(
+                "lease_chain: WARNING rollback could not destroy {}: {e}",
+                info.contract_id
+            );
+        }
+    }
+}
+
+/// List every instance on the account tagged with `label`, sorted by contract id.
+pub async fn list_instances_by_label(
+    client: &reqwest::Client,
+    base_url: &str,
+    api_key: &str,
+    label: &str,
+) -> Result<Vec<LabeledInstance>, String> {
+    let url = format!("{base_url}/api/v0/instances/");
+    let resp = client
+        .get(&url)
+        .header("Authorization", format!("Bearer {api_key}"))
+        .send()
+        .await
+        .map_err(|e| format!("list_instances request failed: {e}"))?;
+    if !resp.status().is_success() {
+        let status = resp.status();
+        let body = resp.text().await.unwrap_or_default();
+        return Err(format!("list_instances HTTP {status}: {body}"));
+    }
+    let body: InstanceListResponse = resp
+        .json()
+        .await
+        .map_err(|e| format!("list_instances parse failed: {e}"))?;
+    let mut out: Vec<LabeledInstance> = body
+        .instances
+        .into_iter()
+        .filter(|e| e.label.as_deref() == Some(label))
+        .map(Into::into)
+        .collect();
+    out.sort_by_key(|i| i.contract_id);
+    Ok(out)
+}
diff --git a/tools/vastai/src/types.rs b/tools/vastai/src/types.rs
new file mode 100644
index 0000000..5f6568a
--- /dev/null
+++ b/tools/vastai/src/types.rs
@@ -0,0 +1,226 @@
+use std::collections::BTreeMap;
+use std::time::Duration;
+
+use serde::{Deserialize, Serialize};
+
+/// Identifier for a rented vast.ai instance.
+#[derive(Debug, Clone, PartialEq, Eq, Serialize, Deserialize)]
+pub struct InstanceInfo {
+    pub contract_id: u64,
+}
+
+/// A vast.ai offer (GPU rental option) returned by the bundle search endpoint.
+#[derive(Debug, Clone, Deserialize, Serialize)]
+pub struct Offer {
+    pub id: u64,
+    pub gpu_name: String,
+    pub dph_total: f64,
+    #[serde(default)]
+    pub gpu_ram: Option<f64>,
+    #[serde(default)]
+    pub geolocation: Option<String>,
+    /// Inbound bandwidth price ($/TB). vast.ai bills Docker image pulls here.
+    #[serde(default, rename = "internet_down_cost_per_tb")]
+    pub inet_down_cost_per_tb: f64,
+    /// Outbound bandwidth price ($/TB), surfaced so callers can account for it.
+    #[serde(default, rename = "internet_up_cost_per_tb")]
+    pub inet_up_cost_per_tb: f64,
+    /// Marketplace host that owns the machine. Used for host-level blacklist and
+    /// distinct-host provisioning.
+    #[serde(default)]
+    pub host_id: Option<u64>,
+    /// vast.ai host verification state: `verified`, `unverified`, or
+    /// `deverified`.
+    #[serde(default)]
+    pub verification: Option<String>,
+}
+
+/// Connection details for a running instance.
+#[derive(Debug, Clone, PartialEq, Eq)]
+pub struct RunningInstance {
+    pub ip: String,
+    pub port: u16,
+}
+
+/// SSH endpoint + identity of a held instance, discovered by label.
+#[derive(Debug, Clone, PartialEq, Eq)]
+pub struct LabeledInstance {
+    pub contract_id: u64,
+    /// vast.ai SSH proxy host (e.g. `ssh5.vast.ai`); empty if not yet assigned.
+    pub ssh_host: String,
+    pub ssh_port: u16,
+    pub public_ipaddr: String,
+    pub actual_status: String,
+}
+
+/// Offer selection knobs. Apps choose policy; this crate applies it.
+#[derive(Debug, Clone)]
+pub struct SelectionPolicy {
+    pub gpu_name: Option<String>,
+    pub min_gpu_ram_mb: Option<u64>,
+    pub min_reliability: f64,
+    pub require_verified: bool,
+    pub min_down_mbps: f64,
+    pub min_up_mbps: Option<f64>,
+    pub blacklist_hosts: Vec<u64>,
+    pub drop_cheap_frac: f64,
+    pub image_size_gb: Option<f64>,
+}
+
+impl Default for SelectionPolicy {
+    fn default() -> Self {
+        Self {
+            gpu_name: None,
+            min_gpu_ram_mb: None,
+            min_reliability: 0.95,
+            require_verified: false,
+            min_down_mbps: 100.0,
+            min_up_mbps: None,
+            blacklist_hosts: vec![59017],
+            drop_cheap_frac: 0.30,
+            image_size_gb: None,
+        }
+    }
+}
+
+/// Provisioning and monitoring retry/timing knobs.
+#[derive(Debug, Clone)]
+pub struct LifecyclePolicy {
+    pub lease_pace: Duration,
+    pub poll_interval: Duration,
+}
+
+impl Default for LifecyclePolicy {
+    fn default() -> Self {
+        Self {
+            lease_pace: Duration::from_millis(600),
+            poll_interval: Duration::from_secs(10),
+        }
+    }
+}
+
+/// Generic vast.ai create-instance payload.
+#[derive(Debug, Clone)]
+pub struct CreateInstanceRequest {
+    pub offer_id: u64,
+    pub image: String,
+    pub disk_gb: u32,
+    pub label: Option<String>,
+    pub env: BTreeMap<String, String>,
+    /// Command passed to vast.ai's `onstart`; `None` omits the field.
+    pub onstart: Option<String>,
+}
+
+/// Generic N-instance provision request. Apps decide what env each machine runs.
+#[derive(Debug, Clone)]
+pub struct ProvisionRequest {
+    pub count: u32,
+    pub image: String,
+    pub label: Option<String>,
+    pub disk_gb: u32,
+    /// Env applied to every instance.
+    pub env: BTreeMap<String, String>,
+    /// Per-index env overlays, merged after `env`.
+    pub per_instance_env: Vec<BTreeMap<String, String>>,
+    pub onstart: Option<String>,
+    pub selection: SelectionPolicy,
+    pub lifecycle: LifecyclePolicy,
+    /// Whether to print the lease plan and ask on TTY before spending money.
+    pub confirm_lease: bool,
+}
+
+/// A successfully-provisioned instance with the offer facts used to pick it.
+#[derive(Debug, Clone, PartialEq)]
+pub struct ProvisionedInstance {
+    pub index: u32,
+    pub contract_id: u64,
+    pub offer_id: u64,
+    pub host_id: Option<u64>,
+    pub gpu_name: String,
+    pub gpu_ram: Option<f64>,
+    pub dph_total: f64,
+}
+
+#[derive(Debug, Clone, PartialEq)]
+pub struct ProvisionedFleet {
+    pub label: Option<String>,
+    pub instances: Vec<ProvisionedInstance>,
+}
+
+/// Generic held-fleet handle file.
+#[derive(Debug, Clone, Serialize, Deserialize, PartialEq, Eq)]
+pub struct FleetState {
+    pub label: String,
+    pub image: String,
+    pub contracts: Vec<ContractRef>,
+    pub created_at: u64,
+    #[serde(default)]
+    pub metadata: BTreeMap<String, String>,
+}
+
+#[derive(Debug, Clone, Serialize, Deserialize, PartialEq, Eq)]
+pub struct ContractRef {
+    pub id: u64,
+    pub index: u32,
+}
+
+#[derive(Debug, Deserialize)]
+pub(crate) struct SearchResponse {
+    pub offers: Vec<Offer>,
+}
+
+#[derive(Debug, Deserialize)]
+pub(crate) struct CreateResponse {
+    pub new_contract: u64,
+}
+
+#[derive(Debug, Deserialize)]
+pub(crate) struct InstanceResponse {
+    pub instances: InstanceStatus,
+}
+
+#[derive(Debug, Deserialize)]
+pub(crate) struct InstanceStatus {
+    pub actual_status: Option<String>,
+    pub intended_status: Option<String>,
+    #[serde(default)]
+    pub status_msg: Option<String>,
+    #[serde(default)]
+    pub public_ipaddr: Option<String>,
+    #[serde(default)]
+    pub ssh_port: Option<u16>,
+    #[serde(default)]
+    pub disk_usage: Option<f64>,
+}
+
+#[derive(Debug, Deserialize)]
+pub(crate) struct InstanceListResponse {
+    pub instances: Vec<InstanceListEntry>,
+}
+
+#[derive(Debug, Deserialize)]
+pub(crate) struct InstanceListEntry {
+    pub id: u64,
+    #[serde(default)]
+    pub label: Option<String>,
+    #[serde(default)]
+    pub actual_status: Option<String>,
+    #[serde(default)]
+    pub ssh_host: Option<String>,
+    #[serde(default)]
+    pub ssh_port: Option<u16>,
+    #[serde(default)]
+    pub public_ipaddr: Option<String>,
+}
+
+impl From<InstanceListEntry> for LabeledInstance {
+    fn from(e: InstanceListEntry) -> Self {
+        Self {
+            contract_id: e.id,
+            ssh_host: e.ssh_host.unwrap_or_default(),
+            ssh_port: e.ssh_port.unwrap_or(0),
+            public_ipaddr: e.public_ipaddr.unwrap_or_default(),
+            actual_status: e.actual_status.unwrap_or_else(|| "unknown".to_string()),
+        }
+    }
+}
diff --git a/uv.lock b/uv.lock
index 0ba29f0..994bb1d 100644
--- a/uv.lock
+++ b/uv.lock
@@ -2,8 +2,173 @@ version = 1
 revision = 3
 requires-python = ">=3.11"
 resolution-markers = [
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-    "python_full_version < '3.12'",
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+    "python_full_version >= '3.14' and sys_platform == 'emscripten'",
+    "python_full_version >= '3.14' and sys_platform != 'emscripten' and sys_platform != 'win32'",
+    "python_full_version >= '3.12' and python_full_version < '3.14' and sys_platform == 'win32'",
+    "python_full_version >= '3.12' and python_full_version < '3.14' and sys_platform == 'emscripten'",
+    "python_full_version >= '3.12' and python_full_version < '3.14' and sys_platform != 'emscripten' and sys_platform != 'win32'",
+    "python_full_version < '3.12' and sys_platform == 'win32'",
+    "python_full_version < '3.12' and sys_platform == 'emscripten'",
+    "python_full_version < '3.12' and sys_platform != 'emscripten' and sys_platform != 'win32'",
+]
+
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 dependencies = [
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+    { name = "nvidia-nvtx", marker = "(platform_machine == 'aarch64' and sys_platform == 'linux') or (platform_machine == 'x86_64' and sys_platform == 'linux')" },
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+    { name = "pydantic" },
+    { name = "starlette" },
+    { name = "typing-extensions" },
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 version = "2.21.2"
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