diff --git a/notebooks/rlearn_evaluation.ipynb b/notebooks/rlearn_evaluation.ipynb
index ec89f4899..1409faceb 100644
--- a/notebooks/rlearn_evaluation.ipynb
+++ b/notebooks/rlearn_evaluation.ipynb
@@ -95,7 +95,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 3,
+   "execution_count": 8,
    "metadata": {},
    "outputs": [
     {
@@ -103,27 +103,7 @@
      "output_type": "stream",
      "text": [
       "Using device: mps\n",
-      "Loading dataset...\n"
-     ]
-    },
-    {
-     "data": {
-      "application/vnd.jupyter.widget-view+json": {
-       "model_id": "70313939b804493298a5577d4235b73c",
-       "version_major": 2,
-       "version_minor": 0
-      },
-      "text/plain": [
-       "Generating train split: 0 examples [00:00, ? examples/s]"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
+      "Loading dataset...\n",
       "Dataset loaded: 10 episodes, 1581 frames\n",
       "Features: ['action', 'observation.state', 'observation.images.front', 'timestamp', 'frame_index', 'episode_index', 'index', 'task_index']\n",
       "FPS: 30\n"
@@ -133,7 +113,7 @@
    "source": [
     "# Configuration\n",
     "DATASET_REPO = \"pepijn223/phone_pipeline_pickup1\"  # Change to your dataset\n",
-    "MODEL_PATH = \"pepijn223/rlearn_11\"  # Change to your model checkpoint\n",
+    "MODEL_PATH = \"pepijn223/rlearn_500\"  # Change to your model checkpoint\n",
     "DEVICE = \"cuda\" if torch.cuda.is_available() else \"mps\"\n",
     "NUM_EVAL_EPISODES = 10  # Number of episodes for evaluation\n",
     "\n",
@@ -154,7 +134,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 4,
+   "execution_count": 9,
    "metadata": {},
    "outputs": [
     {
@@ -162,13 +142,13 @@
      "output_type": "stream",
      "text": [
       "Setting up model...\n",
-      "Loading trained model from Hugging Face Hub: pepijn223/rlearn_11\n"
+      "Loading trained model from Hugging Face Hub: pepijn223/rlearn_500\n"
      ]
     },
     {
      "data": {
       "application/vnd.jupyter.widget-view+json": {
-       "model_id": "5f42e50ec1da43a1813bd6e4e8757709",
+       "model_id": "0de8d5b74fb341b68e11e20cf382ad8c",
        "version_major": 2,
        "version_minor": 0
       },
@@ -199,7 +179,7 @@
     {
      "data": {
       "application/vnd.jupyter.widget-view+json": {
-       "model_id": "f7b9e19062314e8aa8e0a6e51bbe404b",
+       "model_id": "23704eafbfd8426281eab6b54dff066b",
        "version_major": 2,
        "version_minor": 0
       },
@@ -246,7 +226,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 6,
+   "execution_count": 10,
    "metadata": {},
    "outputs": [
     {
@@ -261,9 +241,7 @@
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "Computing VOC-S:   0%|          | 0/10 [00:00<?, ?it/s]W0830 22:53:32.884000 40736 site-packages/torch/_dynamo/variables/builtin.py:861] [0/0] incorrect arg count <bound method BuiltinVariable.call_next of BuiltinVariable(next)> too many positional arguments and no constant handler\n",
-      "W0830 22:53:34.869000 40736 site-packages/torch/_inductor/utils.py:1250] [3/0_1] Not enough SMs to use max_autotune_gemm mode\n",
-      "Computing VOC-S:   0%|          | 0/10 [00:21<?, ?it/s]\n"
+      "Computing VOC-S:  10%|█         | 1/10 [00:36<05:29, 36.56s/it]\n"
      ]
     },
     {
@@ -273,29 +251,24 @@
      "traceback": [
       "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
       "\u001b[0;31mKeyboardInterrupt\u001b[0m                         Traceback (most recent call last)",
-      "Cell \u001b[0;32mIn[6], line 6\u001b[0m\n\u001b[1;32m      4\u001b[0m \u001b[38;5;66;03m# Run VOC-S evaluation\u001b[39;00m\n\u001b[1;32m      5\u001b[0m \u001b[38;5;28mprint\u001b[39m(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mRunning VOC-S evaluation...\u001b[39m\u001b[38;5;124m\"\u001b[39m)\n\u001b[0;32m----> 6\u001b[0m voc_results \u001b[38;5;241m=\u001b[39m \u001b[43mevaluator\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mevaluate_voc_s\u001b[49m\u001b[43m(\u001b[49m\n\u001b[1;32m      7\u001b[0m \u001b[43m    \u001b[49m\u001b[43mdataset\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mdataset\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mnum_episodes\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mNUM_EVAL_EPISODES\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43muse_interquartile_mean\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;28;43;01mTrue\u001b[39;49;00m\n\u001b[1;32m      8\u001b[0m \u001b[43m)\u001b[49m\n\u001b[1;32m     10\u001b[0m \u001b[38;5;28mprint\u001b[39m(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;130;01m\\n\u001b[39;00m\u001b[38;5;124m\"\u001b[39m \u001b[38;5;241m+\u001b[39m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124m=\u001b[39m\u001b[38;5;124m\"\u001b[39m \u001b[38;5;241m*\u001b[39m \u001b[38;5;241m50\u001b[39m)\n\u001b[1;32m     11\u001b[0m \u001b[38;5;28mprint\u001b[39m(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mVOC-S RESULTS\u001b[39m\u001b[38;5;124m\"\u001b[39m)\n",
+      "Cell \u001b[0;32mIn[10], line 6\u001b[0m\n\u001b[1;32m      4\u001b[0m \u001b[38;5;66;03m# Run VOC-S evaluation\u001b[39;00m\n\u001b[1;32m      5\u001b[0m \u001b[38;5;28mprint\u001b[39m(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mRunning VOC-S evaluation...\u001b[39m\u001b[38;5;124m\"\u001b[39m)\n\u001b[0;32m----> 6\u001b[0m voc_results \u001b[38;5;241m=\u001b[39m \u001b[43mevaluator\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mevaluate_voc_s\u001b[49m\u001b[43m(\u001b[49m\n\u001b[1;32m      7\u001b[0m \u001b[43m    \u001b[49m\u001b[43mdataset\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mdataset\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mnum_episodes\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mNUM_EVAL_EPISODES\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43muse_interquartile_mean\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;28;43;01mTrue\u001b[39;49;00m\n\u001b[1;32m      8\u001b[0m \u001b[43m)\u001b[49m\n\u001b[1;32m     10\u001b[0m \u001b[38;5;28mprint\u001b[39m(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;130;01m\\n\u001b[39;00m\u001b[38;5;124m\"\u001b[39m \u001b[38;5;241m+\u001b[39m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124m=\u001b[39m\u001b[38;5;124m\"\u001b[39m \u001b[38;5;241m*\u001b[39m \u001b[38;5;241m50\u001b[39m)\n\u001b[1;32m     11\u001b[0m \u001b[38;5;28mprint\u001b[39m(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mVOC-S RESULTS\u001b[39m\u001b[38;5;124m\"\u001b[39m)\n",
       "File \u001b[0;32m~/Documents/GitHub/lerobot/src/lerobot/policies/rlearn/evaluation.py:437\u001b[0m, in \u001b[0;36mRLearnEvaluator.evaluate_voc_s\u001b[0;34m(self, dataset, num_episodes, use_interquartile_mean)\u001b[0m\n\u001b[1;32m    434\u001b[0m     frames_tensor \u001b[38;5;241m=\u001b[39m torch\u001b[38;5;241m.\u001b[39mstack(frames)  \u001b[38;5;66;03m# (T, C, H, W)\u001b[39;00m\n\u001b[1;32m    436\u001b[0m     \u001b[38;5;66;03m# Predict rewards\u001b[39;00m\n\u001b[0;32m--> 437\u001b[0m     episode_rewards \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mpredict_episode_rewards\u001b[49m\u001b[43m(\u001b[49m\u001b[43mframes_tensor\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mlanguage\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m    438\u001b[0m     predicted_rewards\u001b[38;5;241m.\u001b[39mappend(episode_rewards)\n\u001b[1;32m    440\u001b[0m \u001b[38;5;28;01mexcept\u001b[39;00m \u001b[38;5;167;01mException\u001b[39;00m \u001b[38;5;28;01mas\u001b[39;00m e:\n",
       "File \u001b[0;32m~/miniconda3/envs/lerobot/lib/python3.10/site-packages/torch/utils/_contextlib.py:116\u001b[0m, in \u001b[0;36mcontext_decorator.<locals>.decorate_context\u001b[0;34m(*args, **kwargs)\u001b[0m\n\u001b[1;32m    113\u001b[0m \u001b[38;5;129m@functools\u001b[39m\u001b[38;5;241m.\u001b[39mwraps(func)\n\u001b[1;32m    114\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[38;5;21mdecorate_context\u001b[39m(\u001b[38;5;241m*\u001b[39margs, \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mkwargs):\n\u001b[1;32m    115\u001b[0m     \u001b[38;5;28;01mwith\u001b[39;00m ctx_factory():\n\u001b[0;32m--> 116\u001b[0m         \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43mfunc\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43margs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m\n",
       "File \u001b[0;32m~/Documents/GitHub/lerobot/src/lerobot/policies/rlearn/evaluation.py:300\u001b[0m, in \u001b[0;36mRLearnEvaluator.predict_episode_rewards\u001b[0;34m(self, frames, language, batch_size)\u001b[0m\n\u001b[1;32m    294\u001b[0m batch \u001b[38;5;241m=\u001b[39m {\n\u001b[1;32m    295\u001b[0m     OBS_IMAGES: batch_sequences,  \u001b[38;5;66;03m# (B, T, C, H, W) format expected by model\u001b[39;00m\n\u001b[1;32m    296\u001b[0m     OBS_LANGUAGE: [language] \u001b[38;5;241m*\u001b[39m batch_sequences\u001b[38;5;241m.\u001b[39mshape[\u001b[38;5;241m0\u001b[39m],\n\u001b[1;32m    297\u001b[0m }\n\u001b[1;32m    299\u001b[0m \u001b[38;5;66;03m# Predict rewards - model returns (B, T') but we want the last timestep for each sequence\u001b[39;00m\n\u001b[0;32m--> 300\u001b[0m values \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mmodel\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mpredict_rewards\u001b[49m\u001b[43m(\u001b[49m\u001b[43mbatch\u001b[49m\u001b[43m)\u001b[49m  \u001b[38;5;66;03m# (B, T')\u001b[39;00m\n\u001b[1;32m    302\u001b[0m \u001b[38;5;66;03m# Take the last timestep prediction for each sequence (represents current frame reward)\u001b[39;00m\n\u001b[1;32m    303\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m values\u001b[38;5;241m.\u001b[39mdim() \u001b[38;5;241m==\u001b[39m \u001b[38;5;241m2\u001b[39m:\n",
       "File \u001b[0;32m~/miniconda3/envs/lerobot/lib/python3.10/site-packages/torch/utils/_contextlib.py:116\u001b[0m, in \u001b[0;36mcontext_decorator.<locals>.decorate_context\u001b[0;34m(*args, **kwargs)\u001b[0m\n\u001b[1;32m    113\u001b[0m \u001b[38;5;129m@functools\u001b[39m\u001b[38;5;241m.\u001b[39mwraps(func)\n\u001b[1;32m    114\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[38;5;21mdecorate_context\u001b[39m(\u001b[38;5;241m*\u001b[39margs, \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mkwargs):\n\u001b[1;32m    115\u001b[0m     \u001b[38;5;28;01mwith\u001b[39;00m ctx_factory():\n\u001b[0;32m--> 116\u001b[0m         \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43mfunc\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43margs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m\n",
-      "File \u001b[0;32m~/Documents/GitHub/lerobot/src/lerobot/policies/rlearn/modeling_rlearn.py:263\u001b[0m, in \u001b[0;36mRLearNPolicy.predict_rewards\u001b[0;34m(self, batch)\u001b[0m\n\u001b[1;32m    260\u001b[0m frames \u001b[38;5;241m=\u001b[39m frames\u001b[38;5;241m.\u001b[39mto(device)\n\u001b[1;32m    262\u001b[0m \u001b[38;5;66;03m# Process video frames\u001b[39;00m\n\u001b[0;32m--> 263\u001b[0m video_embeds \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_encode_video_frames\u001b[49m\u001b[43m(\u001b[49m\u001b[43mframes\u001b[49m\u001b[43m)\u001b[49m\u001b[38;5;241m.\u001b[39mto(device)  \u001b[38;5;66;03m# (B, T, D_vision)\u001b[39;00m\n\u001b[1;32m    265\u001b[0m \u001b[38;5;66;03m# Language embeddings + mask\u001b[39;00m\n\u001b[1;32m    266\u001b[0m lang_embeds, mask \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_encode_language_tokens(commands, device)\n",
-      "File \u001b[0;32m~/Documents/GitHub/lerobot/src/lerobot/policies/rlearn/modeling_rlearn.py:343\u001b[0m, in \u001b[0;36mRLearNPolicy._encode_video_frames\u001b[0;34m(self, frames)\u001b[0m\n\u001b[1;32m    340\u001b[0m     pixel_values \u001b[38;5;241m=\u001b[39m processed[\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mpixel_values\u001b[39m\u001b[38;5;124m\"\u001b[39m]\u001b[38;5;241m.\u001b[39mto(device)\n\u001b[1;32m    342\u001b[0m \u001b[38;5;66;03m# Process in batch through vision model\u001b[39;00m\n\u001b[0;32m--> 343\u001b[0m vision_outputs \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mvision_model\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mvision_model\u001b[49m\u001b[43m(\u001b[49m\u001b[43mpixel_values\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mpixel_values\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m    344\u001b[0m cls_tokens \u001b[38;5;241m=\u001b[39m vision_outputs\u001b[38;5;241m.\u001b[39mlast_hidden_state[:, \u001b[38;5;241m0\u001b[39m]\n\u001b[1;32m    346\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m rearrange(cls_tokens, \u001b[38;5;124m'\u001b[39m\u001b[38;5;124m(b t) d -> b t d\u001b[39m\u001b[38;5;124m'\u001b[39m, b\u001b[38;5;241m=\u001b[39mB, t\u001b[38;5;241m=\u001b[39mT)\n",
+      "File \u001b[0;32m~/Documents/GitHub/lerobot/src/lerobot/policies/rlearn/modeling_rlearn.py:287\u001b[0m, in \u001b[0;36mRLearNPolicy.predict_rewards\u001b[0;34m(self, batch)\u001b[0m\n\u001b[1;32m    284\u001b[0m mask \u001b[38;5;241m=\u001b[39m F\u001b[38;5;241m.\u001b[39mpad(mask, (\u001b[38;5;241m0\u001b[39m, register_tokens\u001b[38;5;241m.\u001b[39mshape[\u001b[38;5;241m1\u001b[39m] \u001b[38;5;241m+\u001b[39m video_tokens\u001b[38;5;241m.\u001b[39mshape[\u001b[38;5;241m1\u001b[39m]), value\u001b[38;5;241m=\u001b[39m\u001b[38;5;28;01mTrue\u001b[39;00m)\n\u001b[1;32m    286\u001b[0m \u001b[38;5;66;03m# Forward through decoder\u001b[39;00m\n\u001b[0;32m--> 287\u001b[0m attended \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mdecoder\u001b[49m\u001b[43m(\u001b[49m\u001b[43mtokens\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mmask\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mmask\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m    289\u001b[0m \u001b[38;5;66;03m# Unpack and get video token features\u001b[39;00m\n\u001b[1;32m    290\u001b[0m _, _, attended_video_tokens \u001b[38;5;241m=\u001b[39m unpack(attended, lang_video_packed_shape, \u001b[38;5;124m'\u001b[39m\u001b[38;5;124mb * d\u001b[39m\u001b[38;5;124m'\u001b[39m)\n",
       "File \u001b[0;32m~/miniconda3/envs/lerobot/lib/python3.10/site-packages/torch/nn/modules/module.py:1751\u001b[0m, in \u001b[0;36mModule._wrapped_call_impl\u001b[0;34m(self, *args, **kwargs)\u001b[0m\n\u001b[1;32m   1749\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_compiled_call_impl(\u001b[38;5;241m*\u001b[39margs, \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mkwargs)  \u001b[38;5;66;03m# type: ignore[misc]\u001b[39;00m\n\u001b[1;32m   1750\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[0;32m-> 1751\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_call_impl\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43margs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m\n",
       "File \u001b[0;32m~/miniconda3/envs/lerobot/lib/python3.10/site-packages/torch/nn/modules/module.py:1762\u001b[0m, in \u001b[0;36mModule._call_impl\u001b[0;34m(self, *args, **kwargs)\u001b[0m\n\u001b[1;32m   1757\u001b[0m \u001b[38;5;66;03m# If we don't have any hooks, we want to skip the rest of the logic in\u001b[39;00m\n\u001b[1;32m   1758\u001b[0m \u001b[38;5;66;03m# this function, and just call forward.\u001b[39;00m\n\u001b[1;32m   1759\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m (\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_backward_hooks \u001b[38;5;129;01mor\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_backward_pre_hooks \u001b[38;5;129;01mor\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_forward_hooks \u001b[38;5;129;01mor\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_forward_pre_hooks\n\u001b[1;32m   1760\u001b[0m         \u001b[38;5;129;01mor\u001b[39;00m _global_backward_pre_hooks \u001b[38;5;129;01mor\u001b[39;00m _global_backward_hooks\n\u001b[1;32m   1761\u001b[0m         \u001b[38;5;129;01mor\u001b[39;00m _global_forward_hooks \u001b[38;5;129;01mor\u001b[39;00m _global_forward_pre_hooks):\n\u001b[0;32m-> 1762\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43mforward_call\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43margs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m   1764\u001b[0m result \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;01mNone\u001b[39;00m\n\u001b[1;32m   1765\u001b[0m called_always_called_hooks \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mset\u001b[39m()\n",
-      "File \u001b[0;32m~/miniconda3/envs/lerobot/lib/python3.10/site-packages/transformers/utils/generic.py:959\u001b[0m, in \u001b[0;36mcan_return_tuple.<locals>.wrapper\u001b[0;34m(self, *args, **kwargs)\u001b[0m\n\u001b[1;32m    957\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m return_dict_passed \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m:\n\u001b[1;32m    958\u001b[0m     return_dict \u001b[38;5;241m=\u001b[39m return_dict_passed\n\u001b[0;32m--> 959\u001b[0m output \u001b[38;5;241m=\u001b[39m \u001b[43mfunc\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43margs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m    960\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m return_dict \u001b[38;5;129;01mand\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;28misinstance\u001b[39m(output, \u001b[38;5;28mtuple\u001b[39m):\n\u001b[1;32m    961\u001b[0m     output \u001b[38;5;241m=\u001b[39m output\u001b[38;5;241m.\u001b[39mto_tuple()\n",
-      "File \u001b[0;32m~/miniconda3/envs/lerobot/lib/python3.10/site-packages/transformers/models/siglip/modeling_siglip.py:763\u001b[0m, in \u001b[0;36mSiglipVisionTransformer.forward\u001b[0;34m(self, pixel_values, output_attentions, output_hidden_states, interpolate_pos_encoding)\u001b[0m\n\u001b[1;32m    757\u001b[0m output_hidden_states \u001b[38;5;241m=\u001b[39m (\n\u001b[1;32m    758\u001b[0m     output_hidden_states \u001b[38;5;28;01mif\u001b[39;00m output_hidden_states \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m \u001b[38;5;28;01melse\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mconfig\u001b[38;5;241m.\u001b[39moutput_hidden_states\n\u001b[1;32m    759\u001b[0m )\n\u001b[1;32m    761\u001b[0m hidden_states \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39membeddings(pixel_values, interpolate_pos_encoding\u001b[38;5;241m=\u001b[39minterpolate_pos_encoding)\n\u001b[0;32m--> 763\u001b[0m encoder_outputs: BaseModelOutput \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mencoder\u001b[49m\u001b[43m(\u001b[49m\n\u001b[1;32m    764\u001b[0m \u001b[43m    \u001b[49m\u001b[43minputs_embeds\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mhidden_states\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    765\u001b[0m \u001b[43m    \u001b[49m\u001b[43moutput_attentions\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43moutput_attentions\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    766\u001b[0m \u001b[43m    \u001b[49m\u001b[43moutput_hidden_states\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43moutput_hidden_states\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    767\u001b[0m \u001b[43m\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m    769\u001b[0m last_hidden_state \u001b[38;5;241m=\u001b[39m encoder_outputs\u001b[38;5;241m.\u001b[39mlast_hidden_state\n\u001b[1;32m    770\u001b[0m last_hidden_state \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mpost_layernorm(last_hidden_state)\n",
+      "File \u001b[0;32m~/miniconda3/envs/lerobot/lib/python3.10/site-packages/torch/_dynamo/eval_frame.py:655\u001b[0m, in \u001b[0;36m_TorchDynamoContext.__call__.<locals>._fn\u001b[0;34m(*args, **kwargs)\u001b[0m\n\u001b[1;32m    652\u001b[0m _maybe_set_eval_frame(_callback_from_stance(callback))\n\u001b[1;32m    654\u001b[0m \u001b[38;5;28;01mtry\u001b[39;00m:\n\u001b[0;32m--> 655\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43mfn\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43margs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m    656\u001b[0m \u001b[38;5;28;01mexcept\u001b[39;00m Unsupported \u001b[38;5;28;01mas\u001b[39;00m e:\n\u001b[1;32m    657\u001b[0m     \u001b[38;5;28;01mif\u001b[39;00m config\u001b[38;5;241m.\u001b[39mverbose:\n",
       "File \u001b[0;32m~/miniconda3/envs/lerobot/lib/python3.10/site-packages/torch/nn/modules/module.py:1751\u001b[0m, in \u001b[0;36mModule._wrapped_call_impl\u001b[0;34m(self, *args, **kwargs)\u001b[0m\n\u001b[1;32m   1749\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_compiled_call_impl(\u001b[38;5;241m*\u001b[39margs, \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mkwargs)  \u001b[38;5;66;03m# type: ignore[misc]\u001b[39;00m\n\u001b[1;32m   1750\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[0;32m-> 1751\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_call_impl\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43margs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m\n",
       "File \u001b[0;32m~/miniconda3/envs/lerobot/lib/python3.10/site-packages/torch/nn/modules/module.py:1762\u001b[0m, in \u001b[0;36mModule._call_impl\u001b[0;34m(self, *args, **kwargs)\u001b[0m\n\u001b[1;32m   1757\u001b[0m \u001b[38;5;66;03m# If we don't have any hooks, we want to skip the rest of the logic in\u001b[39;00m\n\u001b[1;32m   1758\u001b[0m \u001b[38;5;66;03m# this function, and just call forward.\u001b[39;00m\n\u001b[1;32m   1759\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m (\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_backward_hooks \u001b[38;5;129;01mor\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_backward_pre_hooks \u001b[38;5;129;01mor\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_forward_hooks \u001b[38;5;129;01mor\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_forward_pre_hooks\n\u001b[1;32m   1760\u001b[0m         \u001b[38;5;129;01mor\u001b[39;00m _global_backward_pre_hooks \u001b[38;5;129;01mor\u001b[39;00m _global_backward_hooks\n\u001b[1;32m   1761\u001b[0m         \u001b[38;5;129;01mor\u001b[39;00m _global_forward_hooks \u001b[38;5;129;01mor\u001b[39;00m _global_forward_pre_hooks):\n\u001b[0;32m-> 1762\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43mforward_call\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43margs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m   1764\u001b[0m result \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;01mNone\u001b[39;00m\n\u001b[1;32m   1765\u001b[0m called_always_called_hooks \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mset\u001b[39m()\n",
-      "File \u001b[0;32m~/miniconda3/envs/lerobot/lib/python3.10/site-packages/transformers/utils/generic.py:959\u001b[0m, in \u001b[0;36mcan_return_tuple.<locals>.wrapper\u001b[0;34m(self, *args, **kwargs)\u001b[0m\n\u001b[1;32m    957\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m return_dict_passed \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m:\n\u001b[1;32m    958\u001b[0m     return_dict \u001b[38;5;241m=\u001b[39m return_dict_passed\n\u001b[0;32m--> 959\u001b[0m output \u001b[38;5;241m=\u001b[39m \u001b[43mfunc\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43margs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m    960\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m return_dict \u001b[38;5;129;01mand\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;28misinstance\u001b[39m(output, \u001b[38;5;28mtuple\u001b[39m):\n\u001b[1;32m    961\u001b[0m     output \u001b[38;5;241m=\u001b[39m output\u001b[38;5;241m.\u001b[39mto_tuple()\n",
-      "File \u001b[0;32m~/miniconda3/envs/lerobot/lib/python3.10/site-packages/transformers/models/siglip/modeling_siglip.py:594\u001b[0m, in \u001b[0;36mSiglipEncoder.forward\u001b[0;34m(self, inputs_embeds, attention_mask, output_attentions, output_hidden_states)\u001b[0m\n\u001b[1;32m    591\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m output_hidden_states:\n\u001b[1;32m    592\u001b[0m     encoder_states \u001b[38;5;241m=\u001b[39m encoder_states \u001b[38;5;241m+\u001b[39m (hidden_states,)\n\u001b[0;32m--> 594\u001b[0m layer_outputs \u001b[38;5;241m=\u001b[39m \u001b[43mencoder_layer\u001b[49m\u001b[43m(\u001b[49m\n\u001b[1;32m    595\u001b[0m \u001b[43m    \u001b[49m\u001b[43mhidden_states\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    596\u001b[0m \u001b[43m    \u001b[49m\u001b[43mattention_mask\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    597\u001b[0m \u001b[43m    \u001b[49m\u001b[43moutput_attentions\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43moutput_attentions\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    598\u001b[0m \u001b[43m\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m    600\u001b[0m hidden_states \u001b[38;5;241m=\u001b[39m layer_outputs[\u001b[38;5;241m0\u001b[39m]\n\u001b[1;32m    602\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m output_attentions:\n",
-      "File \u001b[0;32m~/miniconda3/envs/lerobot/lib/python3.10/site-packages/transformers/modeling_layers.py:93\u001b[0m, in \u001b[0;36mGradientCheckpointingLayer.__call__\u001b[0;34m(self, *args, **kwargs)\u001b[0m\n\u001b[1;32m     90\u001b[0m         logger\u001b[38;5;241m.\u001b[39mwarning(message)\n\u001b[1;32m     92\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_gradient_checkpointing_func(partial(\u001b[38;5;28msuper\u001b[39m()\u001b[38;5;241m.\u001b[39m\u001b[38;5;21m__call__\u001b[39m, \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mkwargs), \u001b[38;5;241m*\u001b[39margs)\n\u001b[0;32m---> 93\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28;43msuper\u001b[39;49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[38;5;21;43m__call__\u001b[39;49m\u001b[43m(\u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43margs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m\n",
+      "File \u001b[0;32m~/miniconda3/envs/lerobot/lib/python3.10/site-packages/x_transformers/x_transformers.py:2722\u001b[0m, in \u001b[0;36mAttentionLayers.forward\u001b[0;34m(self, x, context, mask, context_mask, attn_mask, self_attn_kv_mask, mems, mem_masks, seq_start_pos, seq_pos_offset, cache, input_not_include_cache, cache_age, return_hiddens, rotary_pos_emb, pos, context_pos, attn_bias, deep_embeds_and_ids, self_attn_additional_kv, additional_kv_mask, detach_additional_kv, route_additional_kv_to_top, condition, in_attn_cond, layers_execute_order)\u001b[0m\n\u001b[1;32m   2719\u001b[0m \u001b[38;5;66;03m# forward depending on layer type\u001b[39;00m\n\u001b[1;32m   2721\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m layer_type \u001b[38;5;241m==\u001b[39m \u001b[38;5;124m'\u001b[39m\u001b[38;5;124ma\u001b[39m\u001b[38;5;124m'\u001b[39m:\n\u001b[0;32m-> 2722\u001b[0m     out, inter \u001b[38;5;241m=\u001b[39m \u001b[43mblock\u001b[49m\u001b[43m(\u001b[49m\u001b[43mx\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mmask\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43m \u001b[49m\u001b[43mmask\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mcontext_mask\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43m \u001b[49m\u001b[43mself_attn_kv_mask\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mattn_mask\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43m \u001b[49m\u001b[43mattn_mask\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mrel_pos\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43m \u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mrel_pos\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mpos\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43m \u001b[49m\u001b[43mpos\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mrotary_pos_emb\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43m \u001b[49m\u001b[43mrotary_pos_emb\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43madditional_key_values\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43m \u001b[49m\u001b[38;5;28;43mnext\u001b[39;49m\u001b[43m(\u001b[49m\u001b[43miter_self_attn_kv\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;28;43;01mNone\u001b[39;49;00m\u001b[43m)\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43madditional_key_value_mask\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43m \u001b[49m\u001b[43madditional_kv_mask\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mprev_attn\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43m \u001b[49m\u001b[43mprev_attn\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mcache\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43m \u001b[49m\u001b[38;5;28;43mnext\u001b[39;49m\u001b[43m(\u001b[49m\u001b[43miter_attn_cache\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;28;43;01mNone\u001b[39;49;00m\u001b[43m)\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mmem\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43m \u001b[49m\u001b[43mlayer_mem\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mmem_mask\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43m \u001b[49m\u001b[43mlayer_mem_mask\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mattn_bias\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43m \u001b[49m\u001b[43mattn_bias\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mvalue_residual\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43m \u001b[49m\u001b[43mmaybe_self_attn_value_residual\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mreturn_intermediates\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43m \u001b[49m\u001b[38;5;28;43;01mTrue\u001b[39;49;00m\u001b[43m)\u001b[49m\n\u001b[1;32m   2723\u001b[0m \u001b[38;5;28;01melif\u001b[39;00m layer_type \u001b[38;5;241m==\u001b[39m \u001b[38;5;124m'\u001b[39m\u001b[38;5;124mc\u001b[39m\u001b[38;5;124m'\u001b[39m:\n\u001b[1;32m   2724\u001b[0m     out, inter \u001b[38;5;241m=\u001b[39m block(x, context \u001b[38;5;241m=\u001b[39m context, mask \u001b[38;5;241m=\u001b[39m mask, context_mask \u001b[38;5;241m=\u001b[39m context_mask, prev_attn \u001b[38;5;241m=\u001b[39m prev_cross_attn, cache \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mnext\u001b[39m(iter_attn_cache, \u001b[38;5;28;01mNone\u001b[39;00m), value_residual \u001b[38;5;241m=\u001b[39m maybe_cross_attn_value_residual, \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mcross_attn_rotary_pos_emb, return_intermediates \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;01mTrue\u001b[39;00m)\n",
       "File \u001b[0;32m~/miniconda3/envs/lerobot/lib/python3.10/site-packages/torch/nn/modules/module.py:1751\u001b[0m, in \u001b[0;36mModule._wrapped_call_impl\u001b[0;34m(self, *args, **kwargs)\u001b[0m\n\u001b[1;32m   1749\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_compiled_call_impl(\u001b[38;5;241m*\u001b[39margs, \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mkwargs)  \u001b[38;5;66;03m# type: ignore[misc]\u001b[39;00m\n\u001b[1;32m   1750\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[0;32m-> 1751\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_call_impl\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43margs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m\n",
       "File \u001b[0;32m~/miniconda3/envs/lerobot/lib/python3.10/site-packages/torch/nn/modules/module.py:1762\u001b[0m, in \u001b[0;36mModule._call_impl\u001b[0;34m(self, *args, **kwargs)\u001b[0m\n\u001b[1;32m   1757\u001b[0m \u001b[38;5;66;03m# If we don't have any hooks, we want to skip the rest of the logic in\u001b[39;00m\n\u001b[1;32m   1758\u001b[0m \u001b[38;5;66;03m# this function, and just call forward.\u001b[39;00m\n\u001b[1;32m   1759\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m (\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_backward_hooks \u001b[38;5;129;01mor\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_backward_pre_hooks \u001b[38;5;129;01mor\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_forward_hooks \u001b[38;5;129;01mor\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_forward_pre_hooks\n\u001b[1;32m   1760\u001b[0m         \u001b[38;5;129;01mor\u001b[39;00m _global_backward_pre_hooks \u001b[38;5;129;01mor\u001b[39;00m _global_backward_hooks\n\u001b[1;32m   1761\u001b[0m         \u001b[38;5;129;01mor\u001b[39;00m _global_forward_hooks \u001b[38;5;129;01mor\u001b[39;00m _global_forward_pre_hooks):\n\u001b[0;32m-> 1762\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43mforward_call\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43margs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m   1764\u001b[0m result \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;01mNone\u001b[39;00m\n\u001b[1;32m   1765\u001b[0m called_always_called_hooks \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mset\u001b[39m()\n",
-      "File \u001b[0;32m~/miniconda3/envs/lerobot/lib/python3.10/site-packages/transformers/models/siglip/modeling_siglip.py:458\u001b[0m, in \u001b[0;36mSiglipEncoderLayer.forward\u001b[0;34m(self, hidden_states, attention_mask, output_attentions)\u001b[0m\n\u001b[1;32m    455\u001b[0m hidden_states \u001b[38;5;241m=\u001b[39m residual \u001b[38;5;241m+\u001b[39m hidden_states\n\u001b[1;32m    457\u001b[0m residual \u001b[38;5;241m=\u001b[39m hidden_states\n\u001b[0;32m--> 458\u001b[0m hidden_states \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mlayer_norm2\u001b[49m\u001b[43m(\u001b[49m\u001b[43mhidden_states\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m    459\u001b[0m hidden_states \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mmlp(hidden_states)\n\u001b[1;32m    460\u001b[0m hidden_states \u001b[38;5;241m=\u001b[39m residual \u001b[38;5;241m+\u001b[39m hidden_states\n",
+      "File \u001b[0;32m~/miniconda3/envs/lerobot/lib/python3.10/site-packages/x_transformers/x_transformers.py:1749\u001b[0m, in \u001b[0;36mAttention.forward\u001b[0;34m(self, x, context, mask, context_mask, attn_mask, rel_pos, attn_bias, rotary_pos_emb, context_rotary_pos_emb, pos, prev_attn, mem, mem_mask, return_intermediates, cache, value_residual, additional_key_values, additional_key_value_mask)\u001b[0m\n\u001b[1;32m   1746\u001b[0m             v \u001b[38;5;241m=\u001b[39m cat((mv, v), dim \u001b[38;5;241m=\u001b[39m \u001b[38;5;241m-\u001b[39m\u001b[38;5;241m2\u001b[39m)\n\u001b[1;32m   1748\u001b[0m     \u001b[38;5;28;01mif\u001b[39;00m return_intermediates:\n\u001b[0;32m-> 1749\u001b[0m         mem_len \u001b[38;5;241m=\u001b[39m mem\u001b[38;5;241m.\u001b[39mshape[\u001b[38;5;241m-\u001b[39m\u001b[38;5;241m2\u001b[39m] \u001b[38;5;28;01mif\u001b[39;00m exists(mem) \u001b[38;5;28;01melse\u001b[39;00m \u001b[38;5;241m0\u001b[39m\n\u001b[1;32m   1750\u001b[0m         cached_kv \u001b[38;5;241m=\u001b[39m (k[\u001b[38;5;241m.\u001b[39m\u001b[38;5;241m.\u001b[39m\u001b[38;5;241m.\u001b[39m, mem_len:, :], v[\u001b[38;5;241m.\u001b[39m\u001b[38;5;241m.\u001b[39m\u001b[38;5;241m.\u001b[39m, mem_len:, :])\n\u001b[1;32m   1752\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m exists(rotary_pos_emb):\n",
       "File \u001b[0;32m~/miniconda3/envs/lerobot/lib/python3.10/site-packages/torch/nn/modules/module.py:1751\u001b[0m, in \u001b[0;36mModule._wrapped_call_impl\u001b[0;34m(self, *args, **kwargs)\u001b[0m\n\u001b[1;32m   1749\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_compiled_call_impl(\u001b[38;5;241m*\u001b[39margs, \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mkwargs)  \u001b[38;5;66;03m# type: ignore[misc]\u001b[39;00m\n\u001b[1;32m   1750\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[0;32m-> 1751\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_call_impl\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43margs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m\n",
       "File \u001b[0;32m~/miniconda3/envs/lerobot/lib/python3.10/site-packages/torch/nn/modules/module.py:1762\u001b[0m, in \u001b[0;36mModule._call_impl\u001b[0;34m(self, *args, **kwargs)\u001b[0m\n\u001b[1;32m   1757\u001b[0m \u001b[38;5;66;03m# If we don't have any hooks, we want to skip the rest of the logic in\u001b[39;00m\n\u001b[1;32m   1758\u001b[0m \u001b[38;5;66;03m# this function, and just call forward.\u001b[39;00m\n\u001b[1;32m   1759\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m (\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_backward_hooks \u001b[38;5;129;01mor\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_backward_pre_hooks \u001b[38;5;129;01mor\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_forward_hooks \u001b[38;5;129;01mor\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_forward_pre_hooks\n\u001b[1;32m   1760\u001b[0m         \u001b[38;5;129;01mor\u001b[39;00m _global_backward_pre_hooks \u001b[38;5;129;01mor\u001b[39;00m _global_backward_hooks\n\u001b[1;32m   1761\u001b[0m         \u001b[38;5;129;01mor\u001b[39;00m _global_forward_hooks \u001b[38;5;129;01mor\u001b[39;00m _global_forward_pre_hooks):\n\u001b[0;32m-> 1762\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43mforward_call\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43margs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m   1764\u001b[0m result \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;01mNone\u001b[39;00m\n\u001b[1;32m   1765\u001b[0m called_always_called_hooks \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mset\u001b[39m()\n",
-      "File \u001b[0;32m~/miniconda3/envs/lerobot/lib/python3.10/site-packages/torch/nn/modules/normalization.py:217\u001b[0m, in \u001b[0;36mLayerNorm.forward\u001b[0;34m(self, input)\u001b[0m\n\u001b[1;32m    216\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[38;5;21mforward\u001b[39m(\u001b[38;5;28mself\u001b[39m, \u001b[38;5;28minput\u001b[39m: Tensor) \u001b[38;5;241m-\u001b[39m\u001b[38;5;241m>\u001b[39m Tensor:\n\u001b[0;32m--> 217\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43mF\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mlayer_norm\u001b[49m\u001b[43m(\u001b[49m\n\u001b[1;32m    218\u001b[0m \u001b[43m        \u001b[49m\u001b[38;5;28;43minput\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mnormalized_shape\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mweight\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mbias\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43meps\u001b[49m\n\u001b[1;32m    219\u001b[0m \u001b[43m    \u001b[49m\u001b[43m)\u001b[49m\n",
-      "File \u001b[0;32m~/miniconda3/envs/lerobot/lib/python3.10/site-packages/torch/nn/functional.py:2910\u001b[0m, in \u001b[0;36mlayer_norm\u001b[0;34m(input, normalized_shape, weight, bias, eps)\u001b[0m\n\u001b[1;32m   2900\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m has_torch_function_variadic(\u001b[38;5;28minput\u001b[39m, weight, bias):\n\u001b[1;32m   2901\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m handle_torch_function(\n\u001b[1;32m   2902\u001b[0m         layer_norm,\n\u001b[1;32m   2903\u001b[0m         (\u001b[38;5;28minput\u001b[39m, weight, bias),\n\u001b[0;32m   (...)\u001b[0m\n\u001b[1;32m   2908\u001b[0m         eps\u001b[38;5;241m=\u001b[39meps,\n\u001b[1;32m   2909\u001b[0m     )\n\u001b[0;32m-> 2910\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43mtorch\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mlayer_norm\u001b[49m\u001b[43m(\u001b[49m\n\u001b[1;32m   2911\u001b[0m \u001b[43m    \u001b[49m\u001b[38;5;28;43minput\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mnormalized_shape\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mweight\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mbias\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43meps\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mtorch\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mbackends\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mcudnn\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43menabled\u001b[49m\n\u001b[1;32m   2912\u001b[0m \u001b[43m\u001b[49m\u001b[43m)\u001b[49m\n",
+      "File \u001b[0;32m~/miniconda3/envs/lerobot/lib/python3.10/site-packages/x_transformers/attend.py:546\u001b[0m, in \u001b[0;36mAttend.forward\u001b[0;34m(self, q, k, v, mask, attn_bias, prev_attn)\u001b[0m\n\u001b[1;32m      0\u001b[0m <Error retrieving source code with stack_data see ipython/ipython#13598>\n",
       "\u001b[0;31mKeyboardInterrupt\u001b[0m: "
      ]
     }
@@ -329,7 +302,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 7,
+   "execution_count": 11,
    "metadata": {},
    "outputs": [
     {
@@ -341,7 +314,7 @@
     },
     {
      "data": {
-      "image/png": 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bTkEDAAAAwL6JKyt1fy34c60+0nEzpab9KWiUIGZqAAAAoHTZsVZKqh46jomTer0vBWKk8k29jAwAAABApMlMkzK3SwmVQm31T5IO/1Gq3SeYZ6BE8Y4DAACgdMjKkGY9LH3dUFo3Kfy2Cs0paAAAAAAonI2zpB97BvfPcJzw2+ocRUHDI7zrAAAAiHyb50s/HSzNuCc4iiqlv5Sx3euoAAAAAESirExpzlPSiC7ShqnSv19KC9/1Oir8h+WnAAAAELmcLGn+y9K0W4PFDGOjpRqcJgVivY4OAAAAQKTZulBKGSCt+S3UVqmNVLmtl1EhF4oaAAAAiEypS6XxF0srfw61VWghJb8jVe/pZWQAAAAAIo0tL/X3m9KUG6WMrf81BqRWN0kdHpZikzwOENkoagAAACDyko1F70uTrpXSN4XaW1wjdXpciivnZXQAAAAAIs32ldL4S6Xlw0Nt5RpLyW9LNQ/1MjLkgaIGAAAAIsv0O6Q/Hw8dl6kn9Rwa3KgPAAAAAAo7A3xEJ2nnulBbs0ulzoOl+ApeRoZ8sFE4AAAAIkvDs6XAf2NzGl8gHT+TggYAAACAfVO2vlTz8ODPSbWk3v+TerxBQcPHmKkBAACAyFK1k9T5aalMXanhGV5HAwAAACCSBQJSt1ekhCpSh0elpOpeR4S9YKYGAAAA/GvVaOm306Ws9PD2ltdR0AAAAABQOBmp0sRrpKVfhrdbIaPH6xQ0IgRFDQAAAPhP5g5pys3SL4dLS7+QZj3idUQAAAAAItmaFOm7jtJfL0sTrpB2rPY6IuwjihoAAADwl/WTpRFdpLmDJTnBtjVjpaxMryMDAAAAEGky06Tpd0k/HyxtXRBsy9gazDsQkdhTAwAAAP5gS0zNHiTNekhyMoJtMYnBdW1b3SAFGI8DAAAAoBA2zpT+6CdtnB5qq9ZTSn5HqtjCy8iwHyhqAAAAwHub5kop/aX1E0NtVTpLvd6VKrXxMjIAAAAAkcZmec99Wppxj5SVFmyLiZfaPSC1HijF8GfxSEbvAQAAwFvzXpSmDQzuo2ECsdKBd0lt7w4mHgAAAABQUKmLpT/Ol9b8Hmqr1DY4YKpKRy8jQxGhqAEAAABvbf83VNCo2FJKfleq1s3rqAAAAABEokCctOnP7IPgzIz2D0qxiR4HhqLCwsQAAADwlk0Br9xeanm9dMwUChoAAAAA9l3ZelLXl6TyTaUjx0idHqegUcowUwMAAAAlZ8dqae04qf5JoTZLMI4eJ8WV8TIyAAAAAJFoyWdS7T5SQpVQW6NzpPqnkGOUUszUAAAAQMlY+pU0vK009kxp46zw20g2AAAAABTGzvXS2HOC+cWk68JvCwTIMUoxihoAAAAoXmmbpJQLpd9OlXaukbLSpCk3ex0VAAAAgEi1/Hvpu7bSko+Dx4vek9akeB0VSgjLTwEAAKD4rPxVGnehtG1pqM2mgXd/zcuoAAAAAESi9K3S1FukBbnyCVt2quvLUo1kLyNDCaKoAQAAgKKXsV2afoc077lQW3xFqcvzUpP+wengAAAAAFBQa36XUvpLW/8JtdU5VuoxRCpb18vIUMIoagAAAKBorZsYTDY2zw211TpC6jlUKtfQy8gAAAAARJrMndKMe6U5T0pygm1x5aROT0vNL2fAVBSiqAEAAICik5UZXtCITZI6Pi4dcK0UYDs3AAAAAIW09AtpzhOh4xoHST3flio08zIqeIjMEgAAAEUnJlbq8WawgFG1q3TMVKnldRQ0AAAAAOybRucEl5mKSQgOmOozmoJGlGOmBgAAAPadkyWlbZQSq4baavSSDv9RqnmoFBPvZXQAAAAAIs2OtVJS9dCxLS9l+2bsXCtVae9lZPAJhswBAABg32xdJP1yhDTm5OCyU7nV7kNBAwAAAEDBOY40/2Xpm8bS8h/Cb7ONwClo4D8UNQAAAFD4ZOPvt6Tv2kurR0trxkpzB3sdFQAAAIBIte1faeQx0qRrpIxUafzFUtoGr6OCT7H8FAAAAApu+0ppwuXSsv+F2so1kqp18zIqAAAAAJE6YGrRB9Kka6X0jaH2+qcE99AA8kBRAwAAAAWz5HNp4hXSznWhtmaXSJ0HS/EVvYwMAAAAQCTunTHxKmnpZ6G2MvWknm9JdY72MjL4HEUNAAAA7JltBD7p/6RF74XakmpK3YdI9U/0MjIAAAAAkWjZt9L4S6Udq0Jtjc+Xur4gJVTxMjJEAIoaAAAAyF/apuDeGduWhtoanCZ1e1VKquFlZAAAAAAi0dxnpCk3hY4Tq0ndXpEanullVIggbBQOAACA/CVUkuqdFPw5vpKU/K508GcUNAAAAADsm/onS3Hlgz/XPUE6bhYFDRQKMzUAAACwZ50el7J2Sm3vlco18DoaAAAAAJGsfFOp60uSky41vVgKBLyOCBGGmRoAAAAIykyTpt8tLXg9vD2unNTjDQoaAAAAAApn/RRp9ElS+tbw9qb9pWaXUNDAPmGmBgAAAKSNM6WU/tKGaVJsWanWEVKF5l5HBQAAACASZWVIswdJsx6UnAxp6kCp+yteR4VSgqIGAABANMvKlOYOlmbcLWWlBdtsGvjacRQ1AAAAABTe5nnBAVPrJoTa7OeM7VJcGS8jQylBUQMAACBabf1HShkgrRkbaqvUVur1rlSlo5eRAQAAAIg0TpY0/yVp2q1S5o5gWyBWOvBOqe09Uky81xGilKCoAQAAEG0cR/r7DWnKTVJG6n+NAan1LVL7B6XYJI8DBAAAABBRUpdK4y6SVv0SaqtwgJT8rlS9u5eRoRSiqAEAABBNdqwOJhvLvwu1lWsiJb8t1TzEy8gAAAAARKJFH0gTr5LSN4faDrhO6jhIiivrZWQopShqAAAARJOYBGnjjNBx88ulTk9J8RW8jAoAAABApNq+MlTQKFtf6jlMqt3H66hQilHUAAAAiCYJlaWeQ4Mb93UfItU7zuuIAAAAAESyVjdIy76RyjWSujwXzDmAYkRRAwAAoDRb/oNU+cDgiKlstY+UTvxbiivjZWQAAAAAIo3NyFjxo9TwjFBbIEY67HvyC5SYmJJ7KgAAAJSY9K3ShKukUcdI4y4Jbg6eGwkHAAAAgMJYNVIa3k4ae5a0emz4beQXKEEUNQAAAEqbNb9L33eUFrwaPF75Y3A6OAAAAAAUVsZ2afKN0i9HSNuWSHKkyf+3+8ApoISw/BQAAEBpkblTmnmfNOdJyckKtsWWlTo/LdU7yevoAAAAAESadROD+/Ftnhtqq3V4cJ++QMDLyBDFKGoAAACUBhumSyn9pI0zQ23Ve0nJb0sVmnsZGQAAAIBIk5UuzXpEmv2w5GQG22KTpA6DpJbXBffRADxCUQMAACCSZWUEZ2bYDA1LPExMvNT+IanVLVJMrNcRAgAAAIgkm/4Mzs5YPznUVrWrlPyOVKm1l5EBLooaAAAAkWz1GGn6naHjyu2l5HelKu29jAoAAABApJpwZaigEYiV2t4jHXhncPAU4APMEwIAAIhktY+QmgwITv9uc4fUdwIFDQAAAAD7rvtrUkyiVLG1dPQ4qd19FDTgK54XNV566SU1btxYSUlJ6tGjhyZMmLDH85999lm1bNlSZcqUUYMGDXTjjTdqx44dJRYvAACAp3aslRwnvK3Lc9KRY6WOj0qxiV5FBvgGOQYAAEABWW5hOUZutsTU4T9Ix0yWqnX1KjLAn0WNjz/+WDfddJPuu+8+TZkyRR06dFDfvn21evXqPM//4IMPdPvtt7vnz5kzR2+++ab7GHfemWvJBQAAgNKabCz6QPpfC2nR++G3JVSSaiR7FRngK+QYAAAABbR9lTTmZOmX3lLmLgM6avWW4sp4FRng3z01Bg8erMsuu0wXXXSRe/zqq69q+PDheuutt9zEYld//PGHDjroIJ133nnusY2+OvfcczV+/Ph8n2Pnzp3uV7bNmze737OystwvL9jzOo7j2fNjz+gff6N//I3+8Tf6J4L7Z+daBSZdo8DSz9xDZ9K1cqofIpVrUPKBRin+/ezOr+8FOYY/+yXa0T/+Rv/4G/3jb/RPBPfP0s8VmHS1AjuDszScaXfJ6fRkyQcZxfj3s7uCvheeFTXS0tI0efJk3XHHHTltMTExOvLII5WSkpLnfXr16qX33nvPnT7evXt3/fPPP/ruu+/Ur1+/fJ9n0KBBeuCBB3ZrX7NmjWdTyq1zNm3a5H5o7TXDX+gff6N//I3+8Tf6JzL7J3Htz6o492bFpIVGme+oeoQ2b9wmJzXvkecoevz72d2WLVvkN+QYfEb9iv7xN/rH3+gff6N/Iq9/AumbVPGvu1VmZXDAlMmMr67NCe20M5+ZrSge/PvZ9xzDs6LG2rVrlZmZqVq1aoW12/HcuXPzvI+NnrL7HXzwwW5nZ2Rk6Morr9zj1HBLaGz6ee5RVLZObo0aNVSxYkV59YENBAJuDHxg/Yf+8Tf6x9/oH3+jfyKsf9K3KDD1FgX+GZJzjpNQVU7Xl5TY8CzV8DTa6MO/n93ZfhV+Q47BZ9Sv6B9/o3/8jf7xN/onwvpn5c8KTLpEge3/5pzj1D9Fga6vqlISGUZJ49/PvucYni4/VVijRo3So48+qpdfftnd8G/BggW6/vrr9dBDD+mee+7J8z6JiYnu167sg+Llh8U+sF7HgPzRP/5G//gb/eNv9E+E9M/asVLKhVLqwtCNdY9ToMcQBcrU8TLEqMa/n3Cl5X0gx0BJoX/8jf7xN/rH3+ifCOifrB2KmXaHNP/F0A3xFaWuLyrQ+AL3HHiDfz/hCvo+eFbUqF69umJjY7Vq1aqwdjuuXbt2nvexpMKmgV966aXucbt27ZSamqrLL79cd911F50PAAAi36L3pHEX2pip4HFcOanzM1KzS+2K1+voAF8jxwAAANhFVpoCP/WUNs0OtdXqI/Ucyh59iFieXaEnJCSoS5cu+uWXX8Km3NhxcnJynvfZtm3bbkmFJS3GpooDAABEvNp9peyp3zUOlo6bITW/jIIGUADkGAAAALuISZDT4Kzgz7FlpC7PS0f8SEEDEc3T5adsHdoBAwaoa9eu7qZ8zz77rDsq6qKLLnJv79+/v+rVq+duxGdOPPFEDR48WJ06dcqZGm4jq6w9O/EAAACIaFbQ6PGmtHmu1PJGKYZrHKAwyDEAAAB20eZ2accyqdVNUsWWXkcDRHZR4+yzz9aaNWt07733auXKlerYsaNGjBiRs7HfkiVLwkZN3X333e46Y/Z92bJl7iYqlmw88sgjHr4KAACAfbR5njTlFqnnm1JC9VB7vROCXwAKjRwDAABEraxMad4zUlaGdODtofaYOKn7a15GBhSpgBNlc6o3b96sSpUqadOmTapYsaInMdgU+NWrV6tmzZqs0etD9I+/0T/+Rv/4G/3jI06WNP9ladqtUuZ2qf6pyjroU61es4b+8Sn+/fjzutov/PBe8Bn1N/rH3+gff6N//I3+8ZGt/0gpF0prfpMCcdLRKcqq0pn+8TH+/ez7dbWnMzUAAACiTupSadxF0qrQmv/aNEvaudbLqAAAAABEIhuv/vcQacqNUkbqf22Z0pqxUpXOXkcHFAuKGgAAACWVbCx6T5r0f1L6plD7AddKHR+TYspIm1d7GSEAAACASLJ9hTT+Umn5d6G2co2l5LelmofaVAAvowOKDUUNAACA4rZjjTThCunfL0NtZepJPYdKdY4KHpNwAAAAACioxZ9IE6+S0taH2ppdKnUeLMVX8DIyoNhR1AAAAChOy4ZL4y+WduSahdG4n9T1eSmhspeRAQAAAIg06ZulCVdKiz8MtSXVknq8KdU73svIgBJDUQMAAKA4pW0MFTQSq0ndXpManu51VAAAAAAiUUyCtHFG6LjBGVK3V6Sk6l5GBZQoihoAAADFqfF50r9fSVk7pe5vSGVqeR0RAAAAgEgVmyQlvyuNOkbq/IzU6FwpEPA6KqBEUdQAAAAoKhnbg/tmWCEjmyUYye8Ekw+SDQAAAACFseYPKaGKVKl1qK1qJ+mkRVJcGS8jAzwT491TAwAAlCLrJkkjOkt/nC8t/SL8Nks2KGgAAAAAKKjMndK0O6SfD5H+uEDKSg+/nYIGohhFDQAAgP1hycWM+6Ufe0qb5wbbJt8gZaZ5HRkAAACASLRhhvRDd+nPxyQnS9owRfr7La+jAnyD5acAAAD21aY5Uko/af3kUFvVLv8tN5XgZWQAAAAAIk1WpjTnSWnmvaGZGTHxUrsHpWaXeh0d4BsUNQAAAArLRkvNey44Hdw2ADeBWOnAu6W2dwUTDwAAAAAoqC0LpJQB0to/Qm2V2wU3Ba/SwcvIAN+hqAEAAFAYWxdJ4y6SVo8KtVVsGUw2qnXzMjIAAAAAkcZxpAWvSlNukTK3/dcYkNrcKrV7QIpN9DhAwH8oagAAABTG1IHhBY2W10sdBrFRHwAAAIDC2zhTmniNVTeCx+WbSj3flmoe7HVkgG+xUTgAAEBhdHlWSqgilW0oHfFL8JiCBgAAAIB9UaV9cFaGaX6ldOx0ChrAXjBTAwAAYE92rpMSq4WOy9aTeg+XKrWREip5GRkAAACASJO2QYqrKMXEhtpsmanaR0u1j/AyMiBiMFMDAAAgL2kbgxv1fdchmHjkViOZggYAAACAwln2nfRtG2nes+Httm8GBQ2gwChqAAAA7Grlz9J37aSF70jbl0mTrvM6IgAAAACRKn2LNP5yafTx0o6V0vQ7pY2zvI4KiFgsPwUAAJAtY5s07XZp/guhtviKUu0jvYwKAAAAQKRa/VtwBnjqwlBbrT5SQlUvowIiGkUNAAAAs3a8lNJf2jI/1FbrCKnnUKlcQy8jAwAAABBpMndIM+6R5jwtyQm2xZWTOg+Wml0mBQJeRwhELIoaAAAgumWmSbMekv58VHKygm2xSVLHJ6QDrpECrNYJAAAAoBDWT5VS+kmbZofaahws9RwmVWjmZWRAqUBRAwAARC/HkX49UlrzW6itajcp+R2pUisvIwMAAAAQiZaPkEafKDkZweOYBKn9w1Krm6SYWK+jA0oFhh4CAIDoZVO+m1zw389xUrsHpaP/oKABAAAAYN/UPEQq1yj4c5WO0jGTpDYDKWgARYiZGgAAILrZerYbZ0tNB0hVO3sdDQAAAIBIZvtmJL8rLf9WanufFJvgdURAqcNMDQAAED1LTS0YIk29dffZGl2fo6ABAAAAoHBSl0pjTpG2/B3eXiNZ6vAIBQ2gmDBTAwAAlH7bV0jjL5OWDw8e1+oj1e3rdVQAAAAAInXA1KL3pEn/J6Vvknauk/qMYokpoIQwUwMAAJRuSz6VhrcNFTTM6tFeRgQAAAAgUu1YI409Q0rpHyxomK0LpW2LvY4MiBrM1AAAAKVT2gZp4rXS4g9CbUm1pB5DpHoneBkZAAAAgEj07zfShMukHatDbY0vkLo+LyVU8TIyIKpQ1AAAAKXP8h+k8RdL25eH2hqcIXV7RUqq7mVkAAAAACJN+mZp8g3SP0NDbYnVpG6vSQ1P9zIyICpR1AAAAKVH5g5pyk3SX6+E2uIrS91ekhqdG9wUHAAAAAAKavVvUko/KTXX8lJ1T5B6vCGVqe1lZEDUoqgBAABKj0C8tHFW6Lj2UVLPt6Sy9b2MCgAAAEAkD5zKLmjElZe6PCc1vYgBU4CHKGoAAIDSIyZWSh4m/dhLanuv1OIqkg0AAAAA+67OUVKLa6RNM6Wew6TyTbyOCIh6FDUAAEDk2jBDytopVesWaivfVDppoRRXxsvIAAAAAESarHRp0YdSk37hg6M6Py3FxEuBGC+jA/Af/iUCAIDIk5Up/fm49EM36fdzpPSt4bdT0AAAAABQGJvmBmd8jxsgLXgt/LbYRAoagI/wrxEAAESWLX9Lv/SWpt0uZaVJW/+R5j7tdVQAAAAAIpGTJc19ThrRSVo/Kdg27TYpbZPXkQHIB8tPAQCAyOA40oLXpak3Sxmp/zUGpNYDpTa3exwcAAAAgIiTukQad6G0amSorWJLqec7UkIlLyMDsAcUNQAAgP9tWy6Nv0RaMSJ874yeb0s1D/YyMgAAAACROGBq4TvS5Ouk9M2h9pbXSx0eleLKehkdgL2gqAEAAPxt0UfSpKultA2htuZXSJ2ekuLLexkZAAAAgEizY7U04XLp369DbWUbSD2HSbWP8DIyAAVEUQMAAPjXtn+D08GzdgaPy9SRerwp1T3W68gAAAAARKKZD4YXNJoMkLo8x3JTQARho3AAAOBfZetLHQcFf250jnTcLAoaAAAAAPZdh4eDMzMSa0iHfCklD6OgAUQYZmoAAAD/SN8qxcRJsUnh69pWOlCqc7SXkQEAAACIRDvXSYnVQscJlaVDvwoOoEqq6WVkAPYRMzUAAIA/rB4rfd9BmnFveHsghoIGAAAAgMLJ2CZNul76X4vgsra5Ve1MQQOIYBQ1AACAtzJ3SlNvk37+//buA8yJau/j+C9b6b13RKT3brl2sVy7XgsKYm+v2LFjx4peyxW72HsXsWCXJk1ABEGkSu+wLNvmfc7EbJIl2U2y2Z1J8v08T3aTk3YyZzI5/zntX9KOJdLvD0nrfnQ6VwAAAAAS1YZp0oTe0h+PSXmbpSnnSpbldK4AxAnTTwEAAOdsni1NOlvaOs+f1nBfqWozJ3MFAAAAIBEV5Uvz7pJ+u1eyCr1pZmrb5v+WZBo1PE7nEEAc0KgBAAAqX1GB9PsD0tzbvYGHkZYldb9L6niNlJbudA4BAAAAJJItv0mTh0qbZ/rT6vWTBr0s1e7oZM4AxBmNGgAAoHJtW+QNNjZO8afV6SHt+4pUp5uTOQMAAACQaIoKpYWPSr/eLBXt9qZ5MqSut0pdbpLSOP0JJBu+1QAAoPJs/EX6+kCpcJd/EfDON0hdR0npWU7nDgAAAECimXSGtPwd/+1anbwdpur1cTJXACoQC4UDAIDKU7enVLuL93qNvaXDfpJ63EODBgAAAIDYtBnyzxWP1PFq6cgZNGgASY6RGgAAoPKkZXrntF30lNRztJRR3ekcAQAAAEhkLY6XOt8oNT1CanyQ07kBUAkYqQEAACpG7gbpp9OlTTOC02t3kvo+RoMGAAAAgOgsf1eaeqFkWcHpPe+lQQNIIYzUAAAA8bfyE2naBVLuWmnrXO8Q8PQqTucKAAAAQCLK2yxN/z9p6Wve243+JbU9y+lcAXAIIzUAAED85G+Tpp4v/XCct0HDsBs2fnc6ZwAAAAAS0eqvpM+6+Rs0jLXfOpkjAA5jpAYAAIiPtd9LU86Rdi71pzX7tzTgWalqEydzBgAAACDRFOyUZo2UFj3pT8usLfV9ImBxcACpiEYNAABQPoW50q83SwsekfTP3LYZNaQ+j0p7nSt5PE7nEAAAAEAiWT9ZmjxU2rHYn9bkMGnAC1L1lk7mDIAL0KgBAABit/lXadKZ0tb5/jQzv+3Al6QabZ3MGQAAAIBEU1Qozb1Nmn+fZBV509KrSr0elNpfInmYSR8AjRoAAKA8rEJp2x/e62nZUo97pY5XEmwAAAAAiJ6JI7Yt8Ddo1B8oDRon1drH6ZwBcBEaNQAAQOzq9Za63iat/EAa9IpUp4vTOQIAAACQqMzUtf3GShuneUdmdLpeSuP0JYBgHBUAAEBkTG+ppa9LrU+T0jL96V1ulDqPlNKznMwdAAAAgESzY4m0c4XU+EB/WpWG0r//kDKqOpkzAC7G3BAAAKBsO5dL3xwuTT5b+u3e4PtMzykaNAAAAABEyrKkxc9I47tLP50q5a4Lvp8GDQCloFEDAACUHmwsGSeN7yat/cab9ts9Us5Kp3MGAAAAIBHl/C19d4w07SKpYKe0e7009w6ncwUggTD9FAAACM30ljKBxsoP/WnVWkoDX5SqtXAyZwAAAAAS0bK3pF8ukfI2+9P2vlDqeZ+TuQKQYGjUAAAAe1rxoTTtQm+vKZ+2Q6U+j0lZtZ3MGQAAAIBEs3uTNP0yadmb/rQqTaQBz0vNj3YyZwASEI0aAADAL2+rNGOE9Nc4f1p2A6n/M1LLE53MGQAAAIBE9PcEaeq50q7V/rRW/5H6/U/Kru9kzgAkKBo1AACA3x+PBzdotDhe6ve0VLWxk7kCAAAAkKidpiad6Z9uKquu1Pd/UpvTnc4ZgATGQuEAAMCv07VS7S5SRk3v2hkHfECDBgAAAIDYmKlr+zzuvd50sHT0XBo0AJQbIzUAAEhluzcGD/lOryLt/7aUUV2q3trJnAEAAABINIW7paI8KbOmP63Nmd6YwzRqeDxO5g5AkmCkBgAAqagoX5ozSvqojbR1fvB9tTvToAEAAAAgOptnS1/0k6ZfHpxuGjKaHUmDBoC4oVEDAIBUYxoxvhgozbtTKtghTTrb28gBAAAAANEqKpDm3yd90V/aMlf662VpxQdO5wpAEmP6KQAAUoVVpGrLn5ZnyWipaLc3zZMutTjO6ZwBAAAASETbF6nezLOUtm26P61Od6lGOydzBSDJ0agBAEAq2LFUnsnnqNb67/1ptTpKg16R6vd1MmcAAAAAEo1lSYuekmfWdcoqzPGmedKkTiOlbqOk9GyncwggidGoAQBAsgcbS16UZlwpT8F2f3qHq6Qe90gZVZ3MHQAAAIBEk7NKmnKutOZL+VbJsGq0k2fQy1LDfR3OHIBUQKMGAADJbMYI6Y/Hi28WZjeXZ99xSmt6qKPZAgAAAJCAti+WJvST8rcUJ+U0H6YqAx+TJ7uWo1kDkDpYKBwAgGTW+jQzDty+arU9RxsGfCs1PtjpXAEAAABIRGatjAYDvNerNlPRgeO1rcN9UmYNp3MGIIUwUgMAgGTWcD+p52h7/Qyr2bGy1q1zOkcAAAAAEpXHIw14QZp7u9TrfimjtkSMAaCSMVIDAIBksfor6eczpaLC4PTOI6UWxzuVKwAAAACJKH+7NPUCb5wRqFozacAzUlZdp3IGIMUxUgMAgERXsFOaNVJa9KT3dr3eUqdrnc4VAAAAgES17gdp8jBp51Lp78+lY+bSiAHANRipAQBAItswRfq8l79Bw1j7nWRZTuYKAAAAQCIqzJVmXit9fZC3QcPI3yptmuV0zgCgGCM1AABIRIV50rw7pfmjJavIm5ZeVer5gLTPpd65bgEAAAAgUptmSpPPlrbO96c1PEAa9JJUYy8ncwYAQWjUAAAg0WyZ5w02Ns/2p9UfIA16Waq1j5M5AwAAAJBoigqk+fdJc++QrAJvWlqW1OMeqcNVUlq60zkEgCA0agAAkEh+f1j69SapKM9725Mhdbvduxh4Gj/rAAAAAKKw/U9p0pnSxmn+tLq9vB2m6nR1MmcAEBZnPwAASCQ7lvgbNGp3kQa9ItXr5XSuAAAAACQiM23t1t/+uZ4udb5R6nqrlJ7ldM4AwL0LhT/55JNq06aNqlSpogEDBmjatICW4RC2bNmiyy67TE2bNlV2drb22WcfjR8/vtLyCwCAo3o9INXqIHW6VjpyOg0aABACMQYAABEya2X0fkSq2V46/Gepx100aABwPUdHarz11lu6+uqrNXbsWDvYePTRRzV48GAtXLhQjRo12uPxeXl5Ovzww+373n33XTVv3lzLli1TnTp1HMk/AAAVatdq77oZzY7yp2VUl46cJWVUdTJnAOBaxBgAAIRhWdLyd6RmR0uZNfzp7c6X2pxFjAEgYTjaqDFmzBhdcMEFGj58uH3bBB6fffaZXnjhBd1www17PN6kb9q0SZMmTVJmZqadZnpglWb37t32xWfbtm32/6KiIvviBPO+lmU59v4oHeXjbpSPu1E+cbT8bXmmXyYV7ZY1eJZUs53/vrRss7GjfknKx90oH3ejfPbk1m1BjOHOckl1lI+7UT7uRvnESe56eaZfLM/KD2XtfbGsvk8G30+MkZQoH3ejfPYU6bZwrFHD9IiaMWOGbrzxxuK0tLQ0HXbYYZo8eXLI53z88ccaNGiQPTT8o48+UsOGDXXmmWdq5MiRSk9PD/mc0aNH64477tgjff369crNzZVThbN161Z7pzWfGe5C+bgb5eNulE/5efI3q9YfN6vq2g+K03ZPG6Et3V4o92tTPu5G+bgb5bOn7du3y22IMdhH3YrycTfKx90on/LLXj9BtRZcp7T8DfZtz+Kx2ljvVBXU6Fzu16Z83I3ycTfKJ/YYw7FGjQ0bNqiwsFCNGzcOSje3FyxYEPI5S5Ys0TfffKMhQ4bYc9wuXrxYl156qfLz8zVq1KiQzzEBjRl+HtiLqmXLlnawUqtWLTm1w3o8HjsP7LDuQ/m4G+XjbpRPOa3+Qp7p58uz6+/iJKvlycrq+z81ym5Q7penfNyN8nE3ymdPZr0KtyHGYB91K8rH3Sgfd6N8yiFvqzyzrpLnr3HFSVZWfVn9nlK9lgfF5S0oH3ejfNyN8ok9xnB0+qlYCtrMdfvMM8/Yvab69OmjVatW6cEHHwwbcJiF/sylJLOjOLmzmB3W6TwgPMrH3Sgfd6N8YlCwU5p5rbR4rD8ts47U70l5Wp9hb9N4oXzcjfJxN8onWLJsB2IMVBbKx90oH3ejfGKw9ltp8jlSznJ/WvNj5en/jDxVm8T1rSgfd6N83I3yCRbpdnCsUaNBgwZ20LB27dqgdHO7SZPQB9emTZva89wGDgPv1KmT1qxZYw81z8rKqvB8AwAQV+snSZOHSjv+9Kc1OVwa+IJUrYWTOQOAhEOMAQBIeQW7pF9vkhY+6k/LqCn1eVTaa7g5g+pk7gAgLhxrAjLBgekFNXHixKBeUua2mdM2lP32288eDh64YMgff/xhByIEGwCAhFOYJ00609+gkV5N6vc/6eAvaNAAgBgQYwAAUt7SV4MbNBodKB09R2p3Lg0aAJKGo+NazDy0zz77rMaNG6fff/9dl1xyiXbu3Knhw4fb9w8dOjRokT9z/6ZNmzRixAg70Pjss89077332ov6AQCQcNKzpP7Peq83GCQdNVtqfwnBBgCUAzEGACCl7XWu1OhfUlq21HuMdOg3Uo02TucKAOLK0TU1TjvtNK1fv1633XabPby7Z8+emjBhQvHCfsuXLw+aR8ssvvfFF1/oqquuUvfu3dW8eXM7+Bg5cqSDnwIAgAgVFUoF26WsOv60pod7R2Y0PkRKS6ilrgDAlYgxAAApZfdGKbu+/3ZaujRwnFSYI9Xu7GTOAKDCOH725PLLL7cvoXz33Xd7pJlh41OmTKmEnAEAEEfbF0uTh0mZNaWDPg8ejdH0CCdzBgBJhxgDAJD0rCJp4X+lX2/xdpJqtL//PkZmAEhyLKsOAEBFsixp0VhpfA9pwyRp9RfS4qedzhUAAACARLVzmTTxUGnm1d4RGVOGSfk7nM4VAKTOSA0AAJJWzipp6nnehgyfGu2kOt2czBUAAACARO0wteQlacYI77S2Ps2PkzzpTuYMACoVjRoAAFREsLHsTemXS6X8Lf70vS+Wej0oZdZwMncAAAAAEs2utdK0C6VVH/vTqrWSBr0kNT7YyZwBQKWjUQMAgHgv1GcaM5a/7U+r2kwa8LzU7EgncwYAAAAgEa14X5p2kbR7gz9tr+FS70ekrNpO5gwAHEGjBgAA8ZK7XhrfXcpd409rfYbU9wkpu56TOQMAAACQiObdLc251X87u6E04FmpxfFO5goAHMVC4QAAxEuVhlLTI7zXs+pJ+70p7fc6DRoAAAAAYtPyJCkt23u9xYnSMfNo0ACQ8hipAQBAPPV5zLtIX/e7pWrNnM4NAAAAgERWu7PU51EpvZrU9mzJ43E6RwDgOEZqAAAQi8JcadZ10tLXg9PNnLYDX6BBAwAAAEB0NkyVfjjJG2sEan+xtNdQGjQA4B+M1AAAIFqbZkmTz5a2/iZl1pEa/Uuq1sLpXAEAAABIRIV50ry7pPn3SlaRdw2NXg86nSsAcC0aNQAAiFRRgTT/PmnuHZJV4E0rzPH2qGpFowYAAACAKG2ZJ00eKm2e5U9b95O3oSM9y8mcAYBr0agBAEAktv3hDTY2TvWn1e0pDXpFqtPVyZwBAAAASDRFhdLCR6Rfb5aK8rxpngyp2yip8w1SGqfsACAcjpAAAJTGDP/+43/S7Oulwl3eNE+a1PlGqett9J4CAAAAEJ0df0mTh0nrfwxeENx0mKrX28mcAUBCoFEDAIBwcv6WpgyT1nztT6vZXhr0stRgoJM5AwAAAJCI/nxRmnGFVLDjnwSP1PFqqcfdUnoVhzMHAImBRg0AAMIxQ743/+q/3f4yqdf9UkZ1J3MFAAAAIFHlLPc3aFRvIw0aJzX6l9O5AoCEQqMGAADhVGkkDXhWmn65NOAFqenhTucIAAAAQCLrcpO06lPv+ny9x0iZNZ3OEQAkHBo1AADwMcFF/QFSlYb+tBbHS00OlzKqOZkzAAAAAIkmb7O07gdvTOGTlikd9j3xBQBURqPG1VdfHfGLjhkzJtb8AABQ+fK3STOukpa8ILU8Sdr/Xcnj8d9PwAEAFYIYAwCQtFZ/KU05V8pdKw2eGrwAOPEFAFROo8asWbOCbs+cOVMFBQXq0KGDffuPP/5Qenq6+vTpU74cAQBQmdZ+J005R9q5zHt7xfvS2olSk8OczhkAJD1iDABA0inYKc26Tlr0lD/NLAx++E9O5goAUrNR49tvvw3qJVWzZk2NGzdOdevWtdM2b96s4cOH64ADDqiYnAIAEE8Fu6Rfb5YWPuJPy6gp9XlUanyokzkDgJRBjAEASCrrJ0mTh0k7FvvTzFS2A19wMlcAkHRiWlPj4Ycf1pdfflkcbBjm+t13360jjjhC11xzTTzzCABAfG2cLk0eKm373Z/W6EBp4EtSjTZO5gwAUhYxBgAgYRXulubeIf1+v2QVedPSq0q9HpLaXxI8tS0AwJlGjW3btmn9+vV7pJu07du3lz9XAABUhKJ86bd7pXl3SVahNy0tW+pxr9TxSsmT5nQOASBlEWMAABLS5jnS5LOlLXP8afUHSoNelmq1dzJnAJC0Yjp7c+KJJ9rDwN9//32tXLnSvrz33ns677zzdNJJJ8U/lwAAxMPfn0tzb/c3aNTtLR01U+p0NQ0aAOAwYgwAQMKxLO/6fL4GjbRMb4epw3+kQQMA3DZSY+zYsbr22mt15plnKj8/3/tCGRl2wPHggw/GO48AAMRH82OllidLKz+Uutwsdb3FG3gAABxHjAEASDhmWqkBz0lfDJBqdZT2fUWq29PpXAFA0ou6UaOwsFDTp0/XPffcYwcXf/75p53erl07Va9evSLyCABAbHZvlLLrBwcd/cZKnUdK9fs5mTMAQABiDABAwozMyNssZdfzp9XrLR38hdRwPyk928ncAUDKiHqujfT0dHuhvi1bttgBRvfu3e0LwQYAwFXBxpKXpI/3klZ8GHxflQY0aACAyxBjAABcL+dv6bujpW+P8K7VF6jJITRoAEAlimkC8a5du2rJkiXxzw0AAOWVu0768URpynApf5s07UJvGgDA1YgxAACutfRNaXxXafUEadMM6bd7nc4RAKS0mBo17r77bnu+208//VSrV6/Wtm3bgi4AADhixQfSZ12llR/505ofI6XRawoA3I4YAwDgyulsfzpdmnSGd9opo2pTqf4Ap3MGACktpoXCjz76aPv/cccdJ4+Zn/wflmXZt82cuAAAVJq8rdKMEdJf4/xp2Q2l/s9ILU9wMmcAgAgRYwAAXOXvz6Wp50m7VvvTWp8u9X0yeE0NAEBiNGp8++238c8JAACxWPONNOUcKWeFP63FCVL/p6UqjZzMGQAgCsQYAABXyN8hzbpWWvy0Py2rrtT3f1Kb053MGQCgPI0aBx54YCxPAwAgvhY/610zwyezltTnMantUCmgly8AwP2IMQAAjivYJU3oLW1f5E9repQ04DmpWjMncwYAKG+jhk9OTo6WL1+uvLy8oPTu3buX52UBAIhMs6OkzDpS/hap8SHSwBel6q2czhUAoByIMQAAjsmo6h31/fuDUkZ1qdfD0t4X0mEKAJKhUWP9+vUaPny4Pv/885D3M98tAKBSVGsh9R8r5a6V9rlc8qQ5nSMAQIyIMQAArtD9Tu8C4V1ukmq2czo3AIAQYjr7c+WVV2rLli2aOnWqqlatqgkTJmjcuHFq3769Pv7441heEgCA0m2dL31/vJS3JTi99WlShyto0ACABEeMAQCoVEUF0m+jpYWPB6enV5EGPk+DBgAk20iNb775Rh999JH69u2rtLQ0tW7dWocffrhq1aql0aNH65hjjol/TgEAqckqkhY8Kv16k1S0W5oxQho0zulcAQDijBgDAFBpti2SJg+VNk6R0rKlJodKtTs7nSsAQIRi6ta6c+dONWrUyL5et25de6i40a1bN82cOTOWlwQAYE87lkoTD5FmXeNt0DA2/iLlb3M6ZwCAOCPGAABUOMuS/vif9HlPb4OGnZYvrfvR6ZwBACq6UaNDhw5auHChfb1Hjx56+umntWrVKo0dO1ZNmzaN5SUBAAgONv58XhrfTVr3/T+JHqnj1dKRM6TMWg5nEAAQb8QYAIAKlbNS+nawNP0yqTDHm1Zjb+mwn6T2FzmdOwBARU8/NWLECK1evdq+PmrUKB155JF67bXXlJWVpZdeeimWlwQAwGvXGmnqBdLfn/rTqreWBo6TGh/oZM4AABWIGAMAUGEdppa+7m3MyN/qT29/qdTrASmjupO5AwBUVqPGWWedVXy9T58+WrZsmRYsWKBWrVqpQYMGsbwkAADSig+laedLuzf609qdJ/Uew+gMAEhyxBgAgLjL2yxNvVBa8a4/rWpzaeALUtMjnMwZAKCyGzWWLFmivfbaq/h2tWrV1Lt37/LkAwAAKXetv0GjSmOp/7NSi2OdzhUAoBIQYwAA4s6TKW2e5b/dZojU93Epq66TuQIAONGosffee6tFixY68MADddBBB9n/TRoAAOWy94XSyg+9Q8D7jZWq0DMXAFIFMQYAIO4ya0iDxkk/niT1fVJqdYrTOQIAOLVQ+IoVKzR69GhVrVpVDzzwgPbZZx87ABkyZIiee+65eOQLAJDsCnZKy94OTvN4pAPek/Z/hwYNAEgxxBgAgHJb+720Y0lwWsP9pOP+okEDAFK9UaN58+Z2cPHMM89o4cKF9uWwww7T22+/rYsuuij+uQQAJJf1k6XxPaWfT5NWfxl8X0Y1b+MGACClEGMAAGJWmCvNvEaaeLA0+RypqHDPGAMAkNrTT+Xk5Oinn37Sd999Z19mzZqljh076vLLL7eHigMAEFJhnjTvDmn+fZJV5E2b/n/SMfOltHSncwcAcBAxBgAgJptmSJOHSlvne2+v/1Fa9obU9iw2KAAkqZgaNerUqaO6devaPaluuOEGHXDAAfZtAADC2jJXmnS2tOVXf1r9gdKgl2nQAAAQYwBAGO+/L91xh/THH9I++0ijRkknneR0rlygqED6bbQ0707JKvCmpWVLPe6V2pzpdO4AAG5r1Dj66KPtXlRvvvmm1qxZY19M7ykz7y0AAEHM0O8FY6Q5t0hFed60tEyp2+1Sp+ultJh+igAASYYYAwBCN2icfLJ3dlbLkubO9d5+770Ub9jYttA7OmPjNH9a3d7eDlN1ujiZMwCAW9fU+PDDD7VhwwZNmDBBgwYN0pdffmn3pPLNgwtUVuWuRw+palXvf3MbcAP2zQDb/5QmHiTNvt7foFG7qzR4mtTlpjIbNNiWAJA6iDGA5EI9Lj7MCA1fg4Zh/pvbd96p1GSmsF34mPR5T3+Dhidd6nqbNHgKDRoAkCJiatTw6datm/bbbz876OjXr5/WrVunt956K365A8rorWJ6qeTm+nurUFGGW/bNOXPYN23TL5fW//TPDY93ZMaR06W6Pct8aqp8z0MF/JwEAJDKiDGiZ34nOnf2qE2bxurVy1Opvxv8ZiW3WOspqVKPqwwLF/obNHzMbZOekkxDxowR3oXBjZr7SIdPkrrf4R0NXgaOWcHYHoD78T2NY6PGmDFjdNxxx6l+/foaMGCA3njjDXtY+Hvvvaf169fH8pIpw+x4JtCIZ8CRijs3vVXiLxX3o4pw/fXBt92wbzpatv2elDKqS9XbSod9L/W6X0rPTtjveXm3ZcnnX3ddcCOY+W9u0zAGJBd+YyNDjBEb38ljc4Jz925Ppf5uJOOJ60i/r278Xsc7T+HKN5Iyd2M9LlGFW1qoZs09GztSQoOBUvtLvNf3uUI6apbUoH9M+7Sv7u2G72+8v+M0PgLJgY6zpbBi0LdvX+uaa66xPvnkE2vLli1WItm6dav52bf/V7b33jNVDsvyeIqC/pv08r9m8P/yvGa079+9u2VVqeL9H4/3jeQ1zX3eKlzwxaSXV2FhobV69Wr7f6ILtS1Lpr39tmXddVfo7XneeZbVrVt8yzdRyyeS/fKHH0Jvx3jtm+U5RpS8RFOW0XzPC3PWB5WPeew5R39vNaizLap9KCfHstLTy78t43mMCne8feedyN6n5POjuZjn9OhhlVsyHd+SEeXjDuG+z7GWz8sv7/l9juY4XBF1LbfWq4kxYtO1a8X9bpTF7JMlf9eife9I9/HK+C6UrDeF+77GEoOVPIbE+/OEy/uIEbG/T6dOsddTMjJCPzYtzbK6dHHfMc2tv8FTpni3Wbhtf++9ifW7EZPcjVZhQUFQ+Xz47g7r3GO+jfozhjpmmUvt2pZ1++2xb7fKOC9y3XWRH3feeCP08cC8hu81zfewbt34/H649fuD1C0ftx0HS8tPWeVjHl8R9bz3XLaNYqlXx9SokcicDDjiUekvqV27ygtiyvOjGs17RPKaob7U5tKxY/xPWpT3i+7UgaK0E6ixnlQtb/km6g9yJPvlt99aVrVq4bdf+/bl2zdi3Y/CVdyrV7esvfeO7ARCRN/z3I2W9dPpVtFH7aw1Kxbb5RNtwO/7jFlZlpWdHX5bmnxHsn3i3egbbluGOq6XDBo6dAgdNERzMQHtM8+U73iSihXaREL5VL5o6jaRlI/v9cwxrE0by9pvv/DHjUjqauGOY4HHFyeDECfr1W7j5LYI19nH7IcVLdzvtdlXTZxS1j767ruR1TcrqiNX4DHA/FaHqsuF+r7GEtcFHkOi+TyR1gFNXTPSOsU115T9ml9/HV09xbe/5eVZ1uWXRx9fRNIZK5oGr+jr2UVWdnaR/T/a51cU07bbtq1/ezVq5M1PixbB27F58/jF6K6LfVd8aFnvNrQKFz4V0/enpHCNbaXtm078VkfTGapOHW8DpHmfffaxrKOOKr0hLJLXjL4TWfD3B+6SajGG052/o81PWeUTrq5VJcT3NJrfTTdto0pv1Pjhhx+sIUOGWAMHDrRWrlxpp7388svWjz/+aLmZGwMOcwIvUoEn/WrVCv8jlJlZvryW/CL83/9F/uNfngaVSAOEK68M/f6tW1vWrl2Rfb5wX+BIA45IKs+mZ1SoCkRlnIwIdwK2vBez35mgL9bedOWt6Eb6gxxpcBNJfsJtSxNY3H9/cLARrrJYr55lrVkTfeX3sccs66KLwo+kiXVUU6hLye1hen6afJf5PV/1uWW939SyXpN9GXvexVaXLkX29on0GFGyd2Fp29IEI3vtVXbwFipoMfeZ40Qs+4E57sb7+xTLpTwVj1Sr0CYaysevMk4mRXrCwHfcKqvTw9VXR/9dNp0xwh2LzH2m52ikxwQngpCKqFcTY0SvtJ7H5gRzRYpmH7322uA6xiWXhK6nRNOIYE7uludEeCTHgFAnDsKdYDCjTEN1QAg86dehQ5FVv35knzvSkw7RNkCEKh9fHdR8tlB1uLIu5iRqs2aln0wtbfuG+pyRHOvCbaNIj5P+OmjwbAqhXjNUDFcRv1e+1wzcloMGWVZ+vv8xJgaJpozD1b8jbdiP5HPG9STZ7i2WNfmc4vhi54vVrU4tFlmdOhWFPO6UdR6iqMiybrkl+u9GJOc2Sou7Y92WZhRFrN/peFxMviJREbORIP5SLcaoiA7lRiwN7abBsaxzKmWVT9OmkR2f3ovw/KXJU6T1kKRs1Hj33XetqlWrWueff76VnZ1t/fnnn3b6448/bh1lmoVdzI0Bh6msfP992V8YcwIzmh8is5OaSmmsvQJivZjPYwKVWCp2pjGmrEAiN9fb+9F3n3lOYGBx2WXht6V5nOm9UNoXOPCAEq7MatQIfbAoeb2sSlJFNnREczI7mnyXJ7gYOjT8QTZSkYykiSS4iebzRHsy+6qrvPuS2d/MiAhfuun9Z9I6d/b2qCnP9yzSvJuT/5G+hu9zRhzc5223rKkXFQcb5rLx6brW6YNeLw4Mwx0jzIkNM7WZ2R6+3mWhHmuCY9+29H3vyrvflrVvhNqWq1aFPz7Fs/xCHUtK+4zRnNChF5X7pVrAEctJlXi9j6kL1KwZ+XfUnKjs1s3//SmZR6cvTgUh8a5XE2PEpuRJpcDfYHPyLzAeiOcJWDP9Yrx+l2NtRIj0d7XkZzd1kIYNI3+9Bg28J0UNU8SB9bvIL0VRf+7STsz4Pk9F1k/Cbc9Iy/rII715NZ/L/I+0l3xpF1M3DGwci6YcTZ3z9deDR9TFGjOVVmeLttNX6O9z8Ps+9dSej43ms5ccuRVNo17VqmXHr3E9SbbmG8v6oFVQjPHBVcdbDWutLTPGKHkewrfNS05rW/KzhJv21lzKGnUWzXfQ1yATblvG2qhY3kvJ/eD002Ofos5NJ0aRmjFGRUxXH2mjbTTnU81voul8Ulr57NwZvkP7++8HPzbc+ctojlFumSKyQhs1evbsaY0bN86+XqNGjeKAY+bMmVbjxo0tN3NrwGF2PLOjmQq26Zl9ww3l+xEKdV8kO+O2bZZlijDeP45lvXdBgWXdemv41wn8UXzkEX/64Yd70+bMCT5wmZ7Yvi/hpZdGnl/znMADSrQBVEVst1hHF7RqFfl7me0V+L7RNgaYk/SBzH4cTUOCaXmONMAubSRNYDmWZ/ub/PuMHx9dgF6yErd6dfmnHYr1vQ2zPUPtW+W9nDX4J8v6aK+gYOPz6wdbzequjOtnCqx4mFFY0ZyELO/Ftx9s325ZvXuH3t7mv/n+RLuPBP6//vrggN9UTsy+HZhmfnJj6fm453vTi8rNUi3giGW0REW/T+mXPetvkVx8J/Gi/W0tz/EyUevVxBixC2y4btu2KKLfjPI0GK5fH3xC1dQ7fb9Z8TjJHvh9NwF/qI4N0VxKdkiKdNsEXswuaGID3wneeF9CHediqdPGWp8PdTF1dNN4VVo9xcQB5Zm2K9kupY2KKG3NE7NvmdHfoU5ehXvNaPYPc0wwdVbzPmYq13h3sIrH79MH7+ZYr1w1Iii+2PZ8TWvYv16M+re3tMshh+y5T0eyb4abgriy9+lIvtuR1j185wLMdgjsyGeeP3Nm2eu9RFPmlTEKF6GlWowRapp+czHH2EiVnMEiVKOtr25g7jfHVtPoHm2HWJOn9u3Ddzx89FH/YwPPT5rzEyWV9zxYJMe8pGjUMD2o/vrrrz0CDvPf9KpyMzcFHF27mqCj/DuaCRwCf5RDDW0qeaK2ZJ7Ml9D8cJXWQ6GsHT2WExHmvU1FuKzA6+ST/fOKBh5MAn9ox44t/7Y028F3wC8oKIxoSH1FXmIdim1ac0sedEvrTRTqJKo50VrWe5c8mJsDeHl7lpQ1ciWSkTTxuJgRDuGC8rJ69JSsxJnAobz5ifRzBr739On+dN86FaZsoz0JH/j+WRm51n2nX28VvuopDjYK36hmXXTo2LDBRqxBdahjR6QNjea5voa98gT1pgdf4PHJnMAxx6ySgX2o9wnVs9IXNAQ+P1Lx3t/pReU+qRZwGOb7FOk+W54AuWQDb7yOuZF+z0r+xkZ70jeS40syjNQgxojfMSSaGMPUf0Mp7ft1xhn+559wgn8UQ7S/VyVPvPkuZqpZHzMKu6K+o6FeN/C3umXL0o9JZh0L3/f6xRdji6UCLyXrBaEWgS/tUrKeEa4+H0u9Mh69YsPVmcyo02Rq7Ai13aL9HYr0NUs7We30dohm6qtHb5tmzX+gY1CDxsSbDrZaNVha6mvHMz+h9s1QF3NcMHk28VWo8xjxrq+X1hkqVFwXru4R7ngQeNy5+Wb/65jjj5klI1SZme9suJFXobZxXKcmS2IV1fCTajFGuN9O8331jdAra4aDijiOhn+90B0PTadO01Die9yvv/rXVDKxxJYt5f+dqeiOZa5s1Gjbtq311Vdf7RFwmJ5Vncz4MxdzW8BhGhoqs0ePCXB8X1Yz9DaWeSVL+1Et7Yet5PDUf/879GMPPDB0wP/GG5Z1443+20OGBH9uE0xF2whR8gtshlnt3Oktn1GjCsM+PtoT9xXdO7PkfnDHHf77TM/20nqAl3ZSteRjYzkZHniJNdALrPQEfn/i3RJd2sVMVeLbf0vr0RPtdzLSE+GRPNZcAt/71FNDD1kPV6k0Q/JD5a84QM4usKbd2S8o2Pj59n2t9k0Whf1sJYNrsyBoaSfzSqtkRztnbaj93TR0lGcfNj0lIvmuRBI0RCvWadXc1rMb4aVawGFGh0azH5sgJNYAubReU2XVbaL97Yrkex/pSd9Qx9GKOL64pV5NjFE+5akjmaDZN03V//7n389K7temPu+rl5iLmYbJjEoNFE1jf+CJt8D6gXnd+fO9efGlme+i6SgSr3pppN/ZUHNZm4tZY6683+2SJ14mTw5+veOOC53XaH7XI62jlKexNJr5y0PFIqXVcSKt90Ty3NJeo+Ro1mjeu+Ql1OcuzwimaE/EB/6O+RaQLs93Itx3INJLye9UyXyf0Pd9K//l9OL4IufFKtYVgx+1PJ7CUr8/sU5tFq7+W54OCOY8S2CsGO57Fs2UYSU7r4b6nQ+3D4SrE5R1LmD3bjNqMvi4G2pq0D0vwZ3bIonh6GBVeQ0/qRRjTJy4Z8fOcOvwhLpuLvGYIaGsY5ZZFylUh83A70VgHch0IDGuuMKf9uqrwZ/92WdDf8ZQ50CiOY66eTR4TI0a9957r9W5c2drypQpVs2aNe2F+1599VWrQYMG1mNm7iQXS6SAI5oTdxXRm7esIL88QXpp7xluqqnA1zVftn868sXciztwmGXgCYtatYqsjIyikI1BsVS8S263aEdARHrxHWhWrPAPATefywSE8RLtSdVQ2yLWz+7bNwK/P6ahIdTjfL3qIg2MIslPqMXSIq1EhqvERXOiqqyA1HcxQ9iNP/7w/xibUTSmlT/WgDLw8yz9/CE72Mh9Kcu67t/3W2meglIDwkiPE6G2RzQV90iOT2Xtw2XtB9FWvKNpQIz1Nct7Qsc05MI9UingMMekgw+O7Dck8GJOrJjfelM5DzUFTLjvaahpNiI97kTTmFoRx6Jwx8J4H1/cUK8mxiifyhrNWvISbl2AWBrjhg/3v27JE4pmJESs36NQ0+tGegyIZm7u0vMUuo5kOp74HrP//v5RL2Z6XV+MYl7HxCy+PJZ3Hvt4d8aI9sRqJHkKVf8N1ys9VDmGer3S6qCB645FO4q95Gs+/XTwZzMLfJd3XZhYT1JH28Eq3LSooR4bblsGjsI032OzNl2gkuVQu9pma8Xjze0YY9qdfa0OTX8vvs//HSg9xoimQbE8jXXhLqFmxYgm3irPCf941wnGjIl8PzWzQ7z1lpnloChoxoPA0XtGuP2fDlaV0/CTTDFGaaNZzH43cKB/+732mv8YHK9ZWGI9Fxbqexnue2HSTQNj4JTyZhYOw6zH7Es76aTg17v9divo2BnNeR63NT5WaKNGUVGRdffdd1vVq1e3PB6PfalSpYp16623Wjk5OZabJULA4asMRFrJqIjevJH0CgglHu8d+MNmDkpmzstIA6hoe3H7TJsW/oBy9tnhP2skledInhuukhzNUGzT68Nsr7PO8qeZVtx4i7QHS7h9qDwnZQPXPDHTg5WctqS0RoBI0yJZrL6s/SDUYyL9YYtlJE1gnk3Fct06y7rwQn/a6NGxl+8e719YYL064iKrW8tfwwzFDg4Iw71HpNsjpjzG8Pzy7gdOibaRJrD3obmYY6ZZ0wjukEwBR2lMYHH88aX/RvvqO+a3Odp1iUp+T5ct27PHVTTHnZJrovn+R9qAUdrrxlqPSNZ6NTFG+ZS27li0nTkivcT7xJuZQjXcunCRNJ6UVt+Lte4RbZAf7v3D1ZHMmiFm1IfvtT/80JSlZe23nz/NjMKOZ10q0nxX5vMjfY/y1iHDPT+S3+DS9rfATnLnnRf8vMCGq7K+h5E2tkWjPB2KYm0Iu+Ya/3uZNS4DhYq7D+v6pXXriXdYGel5e3zPSvv+BOYv0mNeRZzbiCZGqIwR3uURzRQ2vs9tYvOuXf1l9/XXwa8ZboSKiV2TvNobsYps+EmWGKOs0SyffBLceS8wzi3vermhjpnRNLSHEu78pXle4LRTgetnmM/kmzmmalXL2rHDX5fwPcfEPcuXV+x5q4Ru1PDZvXu39dtvv1lTp061tm/fbj300EMs4hengKO8gW00Qyfj3RJX8r3N7UiHboZ671BTdIXLY3l6cYebe7iyWiVjHYodeAlcRNksiLhxY8Xnu7ytuZH2VjGXjh3935/nny8MuV5ERVX6y7sfVHSg95//+PN79NH+qVbMPrF5cwwvaFrIFj5pWXNuj6rHYqQVJrefuHNTL4WyRHNCxwSCWVnBo9Huuiv0a4aaWzTWOV4ran5YpyTyfLdOloXvvQMbGMwUM6bhtbTjgTkcheplHe5S8nsaeGLFvE6s9apIGm1TTUXVq4kx4nMMKU8jfri6WLjf/3gKPMHvhrpYvIL80o7xpiHD91nN5zc9/X23zbodgfPal/fzJLqKanwp72+wibt8PYFNA8fChf6pFgOnLg6cQq2yTmZX1Imq0srCdLIy8ah5H3OcKZ5lYftf1mcjj7ea1V1Z6nGnZB7L2+gUr/2ltDUsysNN3+dIpysL/NymXMaO3Vx83+GH+19v1qzSp/MxnVd80x9GG3MkU4zRpEnkM0bEo36QiNvNjAIKtx+aj2b++9LLM5orXCfzsqbSi73h1Bebh16j1FwCyyiwA+u773rTzP+SU1Ul8rGoQho1cnNzrRtuuMHq06ePte+++1offPCBnf7CCy9YzZo1s1q2bGndd999lpslSsARb9GOCIl3HmIdQRHtkO/ybM9o36eylFU5K20Bw8r4caqI4ebhplYyn3XLlkJr3rw1Vv36/gP+hAnu+jxOWLMmdG/mmH7Qdq6wrIlH/DOvrcey1v4Y8Qn/ZO8F4vb9oCy+8vn668Kg4MIEm+EWRiutp1skx5iyetS4SSQV/Ir8PBX9/fHlvWR5VkRZlNyWoRb6NZfbbovs9aKZrjNw2g9T3atVy5tuAua1a2P/TMlyfHNjvZoYIz5i2UejjREqo8HfjXXyeMRrpZWPabw1U0+F+tz/LDODChaPY/zdd/vL7fTTvWm33upPO+UU504gOXGiKnD9zuHDiyxr8fPW7ldr2jHG59cPDlgct+yOiG75DU7WGKG8525MuaxYsTpoGqoZM7y9yvv127NzSagRedHGHE7Wa8vb4aukv/8O/9t31FF7TudVER2rI82/Ew0iZrDulVeGjwXM9PSB+1S7dntus2hHc0V6zIxHw6mv49TeexeFbAAsWdf64gv/fWec4U0LnNY3GeoNFdKocf3111u1a9e2Tj75ZKtp06ZWRkaGdcEFF1jdunWz3njjDasgAeawSNSAo7wqakRIed8/1nU6KiKASqSe2SWFatiozLxXxD7ke01zEipwqKBZ8yQtzV9ZOu20eHwCd7ZORyvcScOIKxrml/+v1yzr7TpBi4Fbc++K69D9RJGo+0FpAsvHBNuh9pfyjKyL5tjqqwTHs1Jc3tEkkVTwoxk9GK3yfH/K+uxmyqfAYczxzHvJ9y57Mcno37usqTMCp/0wc/L7PPywP/3888v3OZPp+Oa2ejUxRnzEso9GEyNUVq/yRK6Tl6d8zKi1ctXj4Pgxfvv24Gl2xo/3r3Vo6leLFlkpxYwWN2taNaq1xvro6mOD4ou/Hm1t9e2yMuJ6tpt+g5MxRijvuRtf+TzxRGFQnG6Ww/LdNmsBBY46CzfVYCT1RVMtCLWAvS/GiFe91kx3baYjjySP5TleDxsWPCVXyXjMfNbyxEu+8snPLwx77siUR1kNNyXr+LF87kgbiHxpvlk5ItlXyvrtrIjRXPEQeHzzzbhRWscOM9WUr0NrzZre0VC+x5l1Z11wmHRno0bbtm2tjz76yL4+d+5ce57b4cOH2/PfJopEDTjiwekvq9NDviN/n7IXOnYbN/ZoiyczfNsXEJS8PPec07lzj1Dzn0Z8EmDXesv68dTgxoz3m1nWqgmVOnQfFSuwfEKdnI/2YvYvM01GqAqoqXyadYEiDQLKGwyUt9eRmQ4iVN68a8V4P6Ov53JFHW9j/f6E66l29dXevJvAqLRgoDx5L7ndo71E+t5l1QU2bfIvBm56OM2f723ICQyYTVp5cHyruHo1MUZ8lOcYEmkd3cnOUIlQJy9P+ZSrHodyi9cx/pFHQv/emelhU9EjV79rrXuqQVCM8dwF51qnnRTdsZLf4MoV7bHeVz47dhSGXT/jxx9jm+bK1xP/1Vf9MYaZvrS0x5ue+pGeNPelmel7AjvDxBIXxXK8njLF/xqmLrt+vTf9xRfDx03RNyIUWZmZRVaVKuGnNwr1PqF+i0M91jT+xDLivbSYMNTFN4ohnh2n3CCSdZdLfp7AhrAuXfzXzW9QMqiQRo3MzExr5cqVxbfNwn1z5syxEkkiBxyprDKH5SbinNnJ2qOtrDVPku0zOta4tfJTy3qvSXCDxk9nWtbuTVHngeObuwWWTzSBRGVdfD3vYxneHW6ofJs23iAl3GuaE/5lBUaRXEzwFM/yiWbkSWBFNpZtHu44Wp7tXt73jqUucO+9/tc++WTLeuON+J5M4vhWcfVqYoz4SKZ91OnOWE6UT7J3UnK7eH1/du3y9rRWqo+62b3Zsn4+Kyi+WPO/RtaxvT+yt0W0vemT6fiWjCIZDV5y/y9PHTKesUc8X880uES33Syrf3//8//73+D7A9fjKav+HFhvN53XzCiZG2/0Pa/Ike1qRnb48mTWwwjXUTXSi+lMF1g/MK8dOFo7UX87I113OdDHH+/5mc3C4aajVzKokEaNtLQ0a51Z8ekfNWrUsJYsWWIlEgIOJGP5JGuPtkAEehXQuJW/07KmnB/cmPFOPcta9nZKfX9SSSS9QMLNox4uLdrKbSTPLe2x4XooldVIU568l/Z6vosZ+rt0aWTlEK6xIJb5bnfvLn3xxUjyHuq3ItL3j3Y4eEX+Tu3YEbygeOAojYkTy//6HN8qrl5NjBEf7KOJP1Ij2TspuVk8vz+hRqqmVFmu+cayPmgRFGO8O+Ikq0HNdTHHcBzf3C2wfEJ1tgm1/0eyxkG8Y4yKvtSu7a2bR1LvN/8DjxXmuplWKJL4xtT9faPlTQNGJFNkuXm7RXoJddxIht/OWNZdNg3oVUrsH4ceaiWNSOvVaYqCaQQ555xzdNJJJ9mX3NxcXXzxxcW3fRcAlct87d57T+reXapSxfv//felE09U0thnH8njCU4ztzt0cCpH7jNqlPfnzLedzH9z26SHlJYpbZrpv93saOmYeVKrUyslv3Dn/vLII6GPJyXTMjMjfy/z2JKv16XLnt9pH5OPwP8l7zPPu/PO4PS99io9D6W9ZqDA7VHW58nOlmrU8KZt3+495ubklP765rOffLI0d66Um+v9b26b9EB33OEvk3Cfu6hIOucc7/9Imbx36+a/nZYmDRq05+MieX+jUaPIt+X111fs71T16tKtt/pvL1/u/d+mjXTwwfF7H8QfMQYQQz0OrrV+/Z5ppiwXLlRqyNsi5ay0r27Lra2zn3pFp/z3XW3Y3tBOI4ZLbn/+Gdn+H+4cRmBajx7S449766uRxBjRxCfhhIsBQsUIJR+7davUoIE/7xde6K3nz5njrfeb/+a2qZea/yu9XxObuf7JJ2WfAzFM3d9sT18s8cor0X3GktutdevSY59Y4qV4C3fcSMbfTvPdmD1b2rXL+z9UvDR+vLf8A02cuGdMmeyiatQYNmyYGjVqpNq1a9uXs846S82aNSu+7bsAcOeBL5H5f6y8Z9jM/0T/sXK8ccs0auz7ipTdQOr/jHTgp1LVppWca7hxfwl1PCmZ1qlTZBVZ85jOnfd8PXNyPLACGo1QgZF5j5LvG/g/XhX8wM9jKpLLlknt2nnvmzVLat5cqlrVG8iY7Wku5rpJM40JJrjxfYbSGgv++GPPBhhze948fwX2uuukN94I/5nD5f3XX6Ubb/QHRaECoQULQr9/4HbPz5fy8vZ8j3ANGPffX/G/Uw2950uCLF0qffBB/N8L8UOMAaRGJ6VUYU68pXRnrJYnSm2HSo0P1c+15+rVn86S558NkgwnHBG/zoiRxByXXy517Vp6nTza+CQU8zxT9y95gjxcvTbweN22rZSe7u/otHu3twHj2WdDv9d334V+/5LxQMkT9rF+rrK225gxoT934PXStoX5X95GkbLSwh03UvW309cBrax9KOlZKYah4SgL5eNeibrmiWtsnGlZm34NPQ1VnPD9cbd4lk8kQ8bLmmKo5NBaM/VVpEOgA4cUb9vmXyTaXMyUSL6hupHO1xvNsPiSn2fu3NKnYYrk/c02KDk9WLjHmu0UuAijef0bbgjeltdfX3reFy3yP79jR8squR5zrVplb6OxY/331ajhjrnvK3IIOsc3d9ar3cIN24J91N0oH3erjDpSUsYuBbst66/X9qxI5OdYVlHkU6mUhe9P8q0JEI1IXzOa+CTc68W6v5o1IyKJYcqKB0J99sD8mPUAI3mt4GmFi4L+h5t6tuTnjnRbhNvu4aY6juR9knFtrXge35J9evatFbGmRjIg4EBZKB93o3xiUJhvWXPvsqzXMyzr0y6WVbCr4t6K8nG1eJdPvCug0cyt+8QT/uc9+KA/ffjw6F+ztGAr0s/jCxpiufhOugeWz//+F13DSKjFR8vK+7/+5X/+pEn+9M8+C/8+vtcw87g2b+5PnzLFcoWKrOBzfHNnvdot3LAt2EfdjfJxt8qoIyWdzXMta3xP77oZS16t0Lfi+5N8awJEK5oT7E6cNC9rfb/AOn2oholIO+GU1mErXCNNZXQMLbk9y+pghfId35JhLZHS0KgRBgEHykL5uBvlE6WtCyxrQv/gxcDnP1Rhb0f5uFsilE9pQUd6ur/SdvTR3sfn5lpW06b+itzvv0f3mpUdyJTWWBBYPnfe6b8vI8Obx0ceCT0iJNYK7Esv+V/jvPP8jRXt2vnTAxfeNpdvv/U+zuTFl3bssZZrMFIj9erVbuGGbcE+6m6Uj7tRPlEoLLCs3x6wrDey/PHFuw3jOvp7j7ekfFyN8omusaE8J/zDddgyrxkutnGqfFKicTcOYimfZB8RuLUiFgoHACQIy6we9rj0eS9p4zRvmidN6nKLtM//OZ07IKzS5tY1i++1aOFfHM1cXn5ZWr3am2Ye27FjdK8Zr/Udwi3kF26+3vbt/Wl77x38/qZKaj6X7/FmwUWTxyuvDP2asS4+esopUs2a3utvvSXt2CE9/LB/gccDD/RuWzPPrs/w4d60e+/1p911l1wjGRcLBADANXYskSYeJM2+Xir6Z2Gt2l2kgydIGdWczh3gqjpoaevMxboORLg1JCpj7bpoJfu6r05K1bVESqJRAwCSzc4V0jdHSDOukAp3edNqtpcO/1nqcZeUnuV0DoGYVK8uPfCA/7Y5yR94+4Yb5LpAJvC67wT7I494GyHMAuLG4sXeRcZ9pkzxphkHHyy1alUxi4+a7Xn66d7rpkHDNF7cc4/3tlns8PHHva89YoT0r3/5F902+Vm/3nt7v/38n8MNqOADAFABTAVm8TPS+O7S+p/+SfRIna6Vjpwu1evtcAYBZ0Xb2FCeE/40FsA4if2ARg0ASKpgY8nL0viu0tqJ/vR9LpeOmi01GOhk7oC4MCfh99/fe33RIv/J/27dpH793BfIhDvBbhoLLr7Y//ynn/Zff+UVf6vF0KEVOxLhvPOCX9tUio3LL/duUyMtTXrxRSk723u7oMD/nJ9/9n4mN6GCDwBAHO1aLX13jDTtIqlgpzetelvpsO+kXg9K6VWcziHgCtRBgcpFowYAJIvti6Wp50n527y3q7WQDvlK6vs4w8GRNMxJ/OOP3zN97lznT65HO83VkCFSjRre66+9Jm3fLu3e7Z0KyqhWzfv8ihyJsHJl6PQ+fYJv77WX1KBB6PK4887Y3hsAACSA2TdKqz/33253gXT0r1Kjf4ZxAgDgABo1ACBZ1Govdb3Ve73N2dLRc6UmhzmdKyDuXnklOU6um/UszjzTP/3T669LX32VrS1bPMUNGL41LyqqF1iobWa2pVlbo6SNG+O3ngcAAEgQZjRGlUZSlSbSgZ9KA56RMkNUUAAAqEQZlflmAIA4MiMy0qtJaQGH8i43eqeZanqEkzkDKtQffyTPyXUzBdUzz3ivP/OMR40aVQ079ZTT29Ishm5GxJj7y7ueBwAAcKndm6Tsev7bVRpK//pYqtFOqhJi2CYAAA5gpAYAJKK130qfdZN+Gx2cnpZJgwaSnjm5Hq/Fsp3Wq5d/LZDZsz32SA2jWTPpkEPctS3jvZ4HAABwkfwd0rRLpM86S7nrg+9rMIAGDQCAq9CoAQCJpGCXNOMqaeIhUs5yad6d0qYZTucKqFTJdnI9cMFwy/J+qP79pfR0d23LeK/nAQAAXGL9z9LnPaXFY6Xctd5FwQOHZgIA4DI0agBAotj4izSht7TwUX9aw/2krPpO5gqodMl2ct18hpI+/LByFj6PdlvGcz0PAADgsMLd0uwbpK//Je3405tmprdterjTOQMAoFSsqQEAbleUL827R/rtbskq9KalZUs975M6XCF5aJ9G6jEn180lGdx/f/iFzyvjMybTtgQAABHa/Ks0+Wxpy1x/WoNB0qCXpZp7O5kzAADKRKMGALjZ1vnS5KHBU0zV6+MNNmp3djJnAOIkmRY+BwAALldUIP3+oDR3lLfzlG9dvm53Sp2uk9IqYf5LAADKyRXde5988km1adNGVapU0YABAzRt2rSInvfmm2/K4/HohBNOqPA8AkClW/eD9Hlvf4OGJ13qOko6YjINGkASSaaFzwG3IL4AgDB+OF769SZ/g0adbtLgX6QuN9CgAQBIGI43arz11lu6+uqrNWrUKM2cOVM9evTQ4MGDtW7dulKft3TpUl177bU64IADKi2vAFCp6veXarbzXq/V0duY0f12b08qAEnDv1i3d0FO8z+RFz4HnEZ8AQClaH2G97+ZwrbzSG+DRt0eTucKAIDEatQYM2aMLrjgAg0fPlydO3fW2LFjVa1aNb3wwgthn1NYWKghQ4bojjvu0F577VWp+QWASpNeRRr0itThSunImVL9fk7nCEAFLtbdrZuUnW3Z/xN54XPAacQXAFCKNkOkDldJh/3gXaMvPdvpHAEAkFhrauTl5WnGjBm68cYbi9PS0tJ02GGHafLkyWGfd+edd6pRo0Y677zz9OOPP5b6Hrt377YvPtu2bbP/FxUV2RcnmPe1LMux90fpKB93S9ryyV0rz4wRssz0UrU7+dPr9JR69fReT4DPnLTlkyQoH/cyM90cd1yR1q9fr4YNG9r1IYrJXfj+7MmN26Iy4guDGAPRonzcLSnLxwz7XP6mPBunyer9SPB9vR7y/k+Qz5uU5ZNEKB93o3zcjfLZU6TbwtFGjQ0bNti9oho3bhyUbm4vWLAg5HN++uknPf/885o9e3ZE7zF69Gi7x1VJ5qRBbm6unCqcrVu32jutCbLgLpSPuyVj+WSv+0y1F14vT/4mFWz5Qxv7fJKwU0wlY/kkE8rH3Sgfd6N89rR9+3a5TWXEFwYxBqJF+bhbspWPiStqLbxRVdd9bN/ekt1TuxsepUSVbOWTbCgfd6N83I3yiT3GcLRRI5YPdfbZZ+vZZ59VgwYNInqO6aVl5tQN7EXVsmVLuxdkrVq15NQOaxYg9PXEhLtQPu6WVOWTt0WemSPkWfpqcVJG3t9qVHVrwi4EnlTlk4QoH3ejfNyN8tmTWYQ70cUSXxjEGIgW5eNuSVU+f38mzy8XypO7pjipTu4MWY2GKVElVfkkIcrH3Sgfd6N8Yo8xHG3UMIFDenq61q5dG5Rubjdp0mSPx//555/2An7HHnvsHkNSMjIytHDhQrVr98+iuv/Izs62LyWZHcXJncXssE7nAeFRPu6WFOWz5mtpynApZ6U/rcWJ8vQfK0+VRkpkSVE+SYzycTfKx90on2Bu3A6VEV8YxBiIBeXjbglfPvnbpZnXSH8+60/Lqif1+588rU+TR4kt4csnyVE+7kb5uBvlEyzS7eDo1srKylKfPn00ceLEoCDC3B40aNAej+/YsaPmzp1rDw33XY477jgdfPDB9nXTOwoAXK0gR5p+hfTN4f4Gjcxa0sBx0gHvSQneoAEAgJOILwCkpHU/SuN7BDdoND1KOnqu1Po0J3MGAECFcHz6KTNse9iwYerbt6/69++vRx99VDt37tTw4cPt+4cOHarmzZvb89aa4Sddu3YNen6dOnXs/yXTAcB1Nk6XJg2Rtv/hT2t8qDTwBal6KydzBgBA0iC+AJAyivKlX2+Sfn/YrAzuTcuoLpmFwdudb7r/Op1DAACSs1HjtNNOsxfUu+2227RmzRr17NlTEyZMKF7cb/ny5Qy/AZAcCndJ2xd5r6dXlXreL+1zmeThGAcAQLwQXwBIGZ4MafOv/gaNhvtLg8ZJNfZyOmcAACR3o4Zx+eWX25dQvvvuu1Kf+9JLL1VQrgAgzhodIHW6Vlr3vTToZalWB6dzBABAUiK+AJASzEgMM+p7Ql9vnNHhKikt3elcAQCQGo0aAJB0igqlZW9Kbc4IHonR/W7v7TQOvwAAAACisG2htHuT1DBgjaBqLaTjlkgZ1ZzMGQAAlYpx1wAQbzuWSBMPliafJS14NPi+9CwaNAAAAABEziqSFj4hfd5L+vk/Ut6W4Ptp0AAApBgaNQAgXixLWvysNL67tP5Hb9qcW6Tc9U7nDAAAAEAi2rlC+uYIacb/edfoy1kp/Xav07kCAMBRdBcGgHjYtVqaer7093h/WvU23oX6qjR0MmcAAAAAErHD1NJXpen/J+Vv9afvc7nUbZSTOQMAwHE0agBAeS17W/rlEilvkz+t3flS7zFSZk0ncwYAAAAg0ZiR3tMuklZ+4E+r2lwa+KLU9HAncwYAgCvQqAEAsTKL9E2/XFr2hj+tSmNpwHNS8387mTMAAAAAiWjlR9K0C6Xcdf60NmdJfR+Tsuo6mTMAAFyDRg0AiNX8+4MbNFqeIvV7SqrSwMlcAQAAAEjUERqThkgFO723s+tL/Z6WWp3sdM4AAHAVFgoHgFh1vVWq0U7KrCPt+5q0/9s0aAAAAACIjVmLr9fD3uvN/i0dPY8GDQAAQmCkBgBEM91Udj3/7cwa0gHvStkNpGotnMwZAAAAgERTsMusCC5lVPOn7X2hVK2l1OwoyeNxMncAALgWIzUAoCyFu6XZN0of7yXtWBJ8X92eNGgAAAAAiM7G6dKE3tKs64PTTUNG86Np0AAAoBQ0agBAaTbPkb7oL82/T8rfKk0+RyoqdDpXAAAAABJRUb409w7py4HStgXSoiel1V86nSsAABIK008BQCim4WLBQ9KcW72Bh5GW6R0GDgAAAADR2vq7NHmotGm6P61ub0Z+AwAQJRo1AKCk7YulycOkDZP8abW7Svu+4p1uCgAAAAAiZRVJCx+Tfr1RKsz1pnnSpS43S11v8XaeAgAAEaNRAwB8LEta/LQ08xqpMOefRI/U+Xqp2x1SerbDGQQAAACQUHYu805hu+47f1qtDtKgV6T6/ZzMGQAACYtGDQDwmXq+tOQF/+0ae0kDx0mN9ncyVwAAAAAS0Za50pf7SQXb/WkdRkg9RksZVZ3MGQAACY2FwgHAp/Vp/ut7XyQd9SsNGgAAAABiU6uzVLeH93q1ltIhE6U+j9KgAQBAOTFSAwB8mh4hdb1NajCQBcEBAAAAlE9aujRonDT/fqnnA1JWbadzBABAUmCkBoDU9Pfn0pTh3nU0AnW/gwYNAAAAANHJ2+qNL9b/HJxuprTt/zQNGgAAxBEjNQCklvwd0qxrpMXPeG/XHyi1v8jpXAEAAABIVGsmehs0clZI636UjpotZdZwOlcAACQtRmoASB3rfpI+7+Fv0DDWTnQyRwAAAAASVUGONH2E9M1h3gYNY/d6acscp3MGAEBSY6QGgORXmCvNuU36/SFJ/0w3lVFd6j1GaneB07kDAAAAkGg2TJMmny1t/8Of1vgQaeCLUvVWTuYMAICkR6MGgOS2ebY06Wxp6zx/WsP9pIHjpJrtnMwZAAAAgERTlC/Nu0v67V7JKvSmpVeRet4v7XO55GFCDAAAKhqNGgCSk1kAfP5oae7t3sDDSMuSut8ldbxGSkt3OocAAAAAEsnWBdKkIdLmmf60ev2kQS9LtTs6mTMAAFIKjRoAkpPH4w06fA0adXpI+74i1enmdM4AAAAAJCIzMmPrb97rngyp621SlxulNE6tAABQmfjlBZC8+j4mrf9BajNE6jpKSs9yOkcAAAAAElWdLlKPe6Q/n/d2mKrXx+kcAQCQkmjUAJAcclZK2xZKTQ71p2XVkY75zbsoOAAAAABEM53t8relFidI6dn+9A5XSvtc5l1HAwAAOIIVrAAkfrDx16vSZ12lH0+RclYF30+DBgAAAIBo7FojfX+s9PPp0pzbgu8za/PRoAEAgKNo1ACQuHI3SD+dKk0+W8rfKuVvkWbf6HSuAAAAACSq5e9K47tKf3/mvb3gIWnbIqdzBQAAAjD9FIDEtOoT6ZeLpNy1/jSzdkbf/zqZKwAAAAAJyJO/RZ7J10jLXvcnVmks9X9WqtXeyawBAIASaNQAkFjyt6nW79cobXVAsJFdX+o3Vmp1ipM5AwAAAJCI1nylBtOGy7N7tT+t5cneGKNKAydzBgAAQqBRA0DiWPu9PFPOUbWdS/1pzf4tDXhWqtrEyZwBAAAASDQFO6VZI5W26El/WmZtqe+TUpszJY/HydwBAIAwaNQAkBgKdkk/nybPP9NNWRk15OnzqLTXuQQbAAAAAKK3+BkpoEHDanyoPANflKq3dDRbAACgdCwUDiAxZFSV+j1lX82rM1DWkbOldufRoAEAAAAgNvtcLtXrIyu9qrbtc6+sgybQoAEAQAJgpAYAdyoqkApzpMxa/rSWJ6roX59qU3ovNarBdFMAAAAAorB7k5Rdz387LVPa93VZRUXKya2jGh76fQIAkAj4xQbgPlsXSF/uK005V7Ks4PuaHSURbAAAAACIVFGhNP8B6aNW0qZZwffV2sd7AQAACYMzgwDcwyqSFj4mTeglbfpFWvGetPR1p3MFAAAAIFFt/1OaeKA0e6R3YfDJZ0uFuU7nCgAAlAPTTwFwh53LpSnDpbXf+NNqdZBqtncyVwAAAAASkRnx/eez0syrvY0ZNo/U7BjvfwAAkLBo1ADgfLDx18vSjCuk/G3+9A4jpB73ShnVnMwdAAAAgEST87c09Xxp9ef+tBp7SQPHSY32dzJnAAAgDmjUAOCc3HXStIuklR/606q1lAa+JDU5xMmcAQAAAEhEy96SfrlEytvsT9v7IqnXQ1JmDSdzBgAA4oRGDQDOyFkpfd5b2r3en9Z2mNTnv1JWbSdzBgAAACAR/Xqz9Nu9/ttVm0oDnpeaHeVkrgAAQJyxUDgAZ1RtLjX8Z+h3dkPpgA+kQS/RoAEAAAAgNi1Pkjz/9N1sdZp09DwaNAAASEKM1ADgDI9H6v+0lFVH6nmfVKWR0zkCAAAAkMjq9ZF63i9VbSa1Od3p3AAAgArCSA0AFa8gR5o+Qlr5cXB6lYbSwBdo0AAAAAAQnXU/ST+dJhUVBKd3upoGDQAAkhwjNQBUrI2/SJPPlrYtlJa/KTUY5G3MAAAAAIBoFe6W5twm/f6gJEuq3VXqdqvTuQIAAJWIkRoAKkZRvjRnlPTlIG+DhpG/Tdo4zemcAQAAAEhEm2dLX/STfn/A26BhrJ0oFRU6nTMAAFCJGKkBIP62zpcmnS1tnulPq9dXGvSyVLuTkzkDAAAAkGjMFFNmZMbcUd7OU0ZaptT9LqnjtVJautM5BAAAlYhGDQDxYxVJC/8rzb5RKtrtTfOkS11vlbrc5A08AAAAACBS2xZJU4ZJGyb70+p0lwa9ItXt7mTOAACAQ2jUABAfO1d4185Y970/rVYn7+iM+n2dzBkAAACARLRorDTzGqkwx3vbkyZ1Gil1GyWlZzudOwAA4BAaNQDEh8fjnePWe0PqcKXU4x4po6rDGQMAAACQkLYv9jdo1Gjn7TDVcF+ncwUAABzGQuEA4qNaC6nvE1L11tKh30h9xtCgAQAAACB2Pe6WaneR2l8qHf0rDRoAAMDGSA0AsVn5kdToICmrtj+tzRCp5UlSRjUncwYAAAAg0eRukDZOlZof409LryINniplVHcyZwAAwGUYqQEgOnlbpElnSz+cIM0YsecUVDRoAAAAAIjGqk+l8V2lH0+Wts4Pvo8GDQAAUAKNGgAit/oraXw3aemr3tt/jZPWT3I6VwAAAAASUf52aeoF0vfHSrlrpaLd0oyrnM4VAABwOaafAlC2gp3SrJHSoif9aZm1vWtoNBjkZM4AAAAAJKJ1P0iTh0k7l/rTmh0jDXjWyVwBAIAEQKMGgNJtmCJNHiptX+RPa3KYNOAFqXpLJ3MGAAAAINEU5kq/3iItGCPJ8qZl1JB6PyK1O887pS0AAEApaNQAEFphnjTvTmn+aMkq8qalV5V6PiDtc6nkYfY6AAAAAFHYNFOafHbwuhkND5AGvSTV2MvJnAEAgARCowaA0Fa8K/12j/92/QHSoJelWvs4mSsAAAAAiaioUJp0prRtofd2WpbU4x6pw1VSWrrTuQMAAAmErtYAQmt9htR0sOTJkLrfLR3+Ew0aAAAAAGJjGi76PyfJI9XtJR05Q+p0LQ0aAAAgaozUAOC1e5OUXc9/28xlO+B5KXedVK+XkzkDAAAAkGjMFLb526SsOv60RvtLB0+QGh0kpWc5mTsAAJDAGKkBpDrLkhY/I33UWlrzdfB91ZrToAEAAAAgOjuXS98cLv1wvH99Pp+mR9CgAQAAyoVGDSCV7VotfXeMNO0iqWCHNGW4lLfF6VwBAAAASNQOU0telsZ3k9Z+I637QVrwqNO5AgAASYbpp4BUtext6ZdLpLxN/rSmR0oe5rQFAAAAEKXc9d7OUis/8KdVbS7V7e5krgAAQBKiUQNIxbUzpl8uLXvDn1alsXf9jObHOJkzAAAAAIlo5UfStAu96/H5tDlb6vtY8JoaAAAAcUCjBpBK/v5CmnqutOtvf1rLU6R+T0lVGjiZMwAAAACJJm+rNPNKaclL/rTs+lK/p6VWJzuZMwAAkMRo1ABSxYL/egMOn8w6Ur8npdZnSB6PkzkDAAAAkIgNGp/3kHYu86c1P1bq/6xUtbGTOQMAAEmOhcKBVNH831JGde/1JkdIx8yT2pxJgwYAAACA6GXVlpoe5b2eUVMa8IL0r49o0AAAABWOkRpAqqjZTurzuFSUK+19MY0ZAAAAAMqn14NSYY7U7Q6pRhuncwMAAFIEIzWAZLR5jvTjyVLBzuD0dsOl9pfQoAEAAAAgckX50pzbpT9fCE7PrCENGkeDBgAAqFSM1ACSSVGh9PuD0tzbvIFHlaZSvyeczhUAAACARLX1d2ny2dKmGVJGDanxQVKNvZzOFQAASGE0agDJYvtiafIwacMkf9r6H6WCHCmjmpM5AwAAAJBorCJp4X+l2TdKRbu9aYW7pHU/0agBAAAcRaMGkOgsS1o8Vpp5rXc+W8OTJnW6Xup2u5Se7XQOAQAAACSSHUulKcOldd/502p1lAa9ItXv62TOAAAAaNQAElrOKmnqedLqL/xpNdp557VtuJ+TOQMAAACQiB2mlrwkzRghFWz3p3e4Uupxr5RR1cncAQAA2GjUABLVsrekaRdL+Vv8aXtfLPV60LtgHwAAAABEKneDt8PUqo/9adVaSYNekhof7GTOAAAAgtCoASSqnBX+Bo2qzaQBz0vNjnQ6VwAAAAASUVq6dzFwn72GS70fkbJqO5krAACAPdCoASSqDldJKz+WqrWQ+j4hZddzOkcAAAAAElVWXWngi9Lks6X+T0stjnc6RwAAACHRqAEkgvzt0pqvpZYnBvekOvhzKaO6kzkDAAAAkIhMfFGrs1StmT+t6eHScX8SYwAAAFdLczoDAMqw7gdpfA/pp1Ok9T8H30ewAQAAACAaBTnS9P+Tvjncu4aGWRw8EDEGAABwORo1ALcqzJVmXSd9fZC08y/JKpJ+uWzPoAMAAAAAIrFhqvR5L+mPJ7y3V0+QVn7kdK4AAACiwvRTgBttmuWdy3brb/60hvtLg8ZJHo+TOQMAAACQaArzpHl3SfPv9XaWMtKrSj0fkFoc53TuAAAAokKjBuAmRQXS/PukuXdIVoE3LS1L6nGPd2Fws44GAAAAAERqyzxp8lBp8yx/Wv3+0qCXpVodnMwZAABA4k4/9eSTT6pNmzaqUqWKBgwYoGnTpoV97LPPPqsDDjhAdevWtS+HHXZYqY8HEsa2hdJX+0tzbvU3aNTtKR05Q+p0LQ0aAAAAESK+AEyHqULp94ekCX38DRqeDKn7XdLhP9OgAQAAEpbjjRpvvfWWrr76ao0aNUozZ85Ujx49NHjwYK1bty7k47/77judccYZ+vbbbzV58mS1bNlSRxxxhFatWlXpeQfiaur50sap3uueNKnLzdIRU6U6XZ3OGQAAQMIgvgD+se477xp9RXne27U7S4OnSl1vkdKYtAEAACQuj2U5u+qw6TnVr18/PfGEd6GyoqIiO5D4v//7P91www1lPr+wsNDuUWWeP3To0D3u3717t33x2bZtm/36mzdvVq1ateQE8xnXr1+vhg0bKi3N8XYluKV8tv4mzxf9pOqtZA14SWowsPLeO4Hw/XE3ysfdKB93o3zcjfLZk6lXm3r41q1bHatXOxFfGMQYSJTy8UwZJi19zZ7K1jIjNNKrVNp7JxK+P+5G+bgb5eNulI+7UT6xxxiOds/Iy8vTjBkzdOONNxanmQI0Q75NL6lI5OTkKD8/X/Xq1Qt5/+jRo3XHHXfskW52mNzcXDm1w5qCMe1J7LDuUynlY1nyFGyVlVknILGhsrq/qvzavWUVVZPC9CZMdXx/3I3ycTfKx90oH3ejfPa0fft2uU1lxBcGMQbcWD6e/C2yMmpLHo8/reUtyqh3svLrDJQ2bjOnCirkvRMd3x93o3zcjfJxN8rH3Sif2GMMRxs1NmzYYPeEaty4cVC6ub1gwYKIXmPkyJFq1qyZHaiEYgIaM/y8ZC8q0wLmZC8qj8dDK5xLVXj55K6XZ/ql0vZFso6YJqVn++9rdFL83y/J8P1xN8rH3Sgfd6N83I3y2ZNZr8JtKiO+MIgx4LryWf6OHWNYfR6TWp8RcEcjSe3j/35Jhu+Pu1E+7kb5uBvl426UT+wxRkJPpHnffffpzTfftOfBDfeBs7Oz7UtJZkdxcmcxO6zTeYAD5bPyY2naBVKudxSGZ97tUq/74/seKYDvj7tRPu5G+bgb5eNulE+wZNwOkcQXBjEGXFM+eZulXy6Xlr3ufY/pl0uND5KqNY/fe6QIvj/uRvm4G+XjbpSPu1E+wSLdDo42ajRo0EDp6elau3ZtULq53aRJk1Kf+9BDD9lBx9dff63u3btXcE6BcsrfJs24Ulryoj8tu75Uv7+TuQIAAEgqxBdIKX9/IU09V9r1tz+tyWFS2p4NbgAAAMnE0SagrKws9enTRxMnTgwadmNuDxo0KOzzHnjgAd11112aMGGC+vbtW0m5BWK09jtpfPfgBo3mx0pHz5NanexkzgAAAJIK8QVSQsFO6ZdLpe+O9DdomLX69n1N2v9tqUoDp3MIAABQoRyffsrMRTts2DA7eOjfv78effRR7dy5U8OHD7fvHzp0qJo3b24vxmfcf//9uu222/T666+rTZs2WrNmjZ1eo0YN+wK4RsEu6debpIWP+tMyakp9HpX2Gh60gB8AAADig/gCSW39JGnyUGnHn/60JodLA1+QqrVwMmcAAACp06hx2mmnaf369XYgYQKInj172j2kfIv7LV++PGguraeeekp5eXk65ZRTgl5n1KhRuv322ys9/0BIhXnSlwOkLXP9aY0OlAa+JNVo42TOAAAAkhrxBZLW0telyWdLVpH3dno1qfdD0t4X02EKAACkFMcbNYzLL7/cvoRiFukLtHTp0krKFVAO6VlSi5O8jRpmTtueo6UOIyQPi/4AAABUNOILJCWzXkZWfWn3eqnBIGngOKlWe6dzBQAAkJqNGkBS6nqzlLNC6nSNVLuz07kBAAAAkMiqNJIGPCttnS91uk5KI5wHAACpiW7jQHmZ4d8LHpXmPxicnpYpDXyeBg0AAAAA0dm+WPrhRCl3Q3B6i+OlLjfSoAEAAFIaNSGgPHYukyafI637TvJkeIeE1+vldK4AAAAAJCLLkhY/Lc28RirMkTzp0v7vsGYGAABAAEZqALEGG0tekj7r5m3QsNMKpLXfOp0zAAAAAIkoZ5X03dHSL5d4GzSMzbOk3SVGawAAAKQ4RmoA0cpdJ027UFr5kT+tWitp4ItSk0OczBkAAACARLT0TWn6pVLeZn/a3hdLvR6UMms4mTMAAADXoVEDiMaKD6RpF0m71/vT9jpH6v2olFXbyZwBAAAASDS7N0q/XCYtf8ufVrWpNOB5qdlRTuYMAADAtWjUACKRv12a/n/SX+P8adkNpf7PSC1PcDJnAAAAABLR319IU4dLu1b701qfLvV9Usqu52TOAAAAXI1GDSASaZnSpl/8t1ucIPV/WqrSyMlcAQAAAEhUu9f5GzSy6kp9/ye1Od3pXAEAALgejRpAJNKrSINelr4dLPUaI7U9W/J4nM4VAAAAgETV5izv9LaFudKA56RqzZzOEQAAQEKgUQMIIXPbLKlKG6lOJ39ivT7S8cukjOpOZg0AAABAoincrex1n0iNzvOnmU5S+74qpVelwxQAAEAU0qJ5MJD0ivLlmTtK9WYcK8+UofbtIDRoAAAAAIjG5tnyfNlfdeddKK38KPi+jGo0aAAAAESJRg3AZ8tv0hcD5fntbnmsQnk2TZf+fMHpXAEAAABIREUF0m/3Sl/0l2frPDvJM3OEVJjndM4AAAASGtNPAUWF0sJHpV9vlop220mWJ0NWl1uV1i5geDgAAAAARGLbImnyUGnjlOKk/BpdlL7/a/KkZzmaNQAAgERHowZS246/pCnnSOt+KE6yanXWxn3GqN7eh0tpDGYCAAAAECGrSFr0lDTrOqlwlzfNkyar00htbHSxGtVp4XQOAQAAEh6NGkhNliUteUGacaVUsOOfRI/U8WpZ3e5UwcZtDmcQAAAAQELJWSlNOVda85U/rWZ7aeA4WfUHSOvWOZk7AACApEGjBlLTlrnS1AtM64b3dvU20sCXpMYHSkVFZry40zkEAAAAkEhmXBXcoNH+MqnX/VJG9X9iDAAAAMQDc+sgNdXtLnW6xnu93fnS0XO8DRoAAAAAEIs+j0iZtaWqzaWDv5T6PeFt0AAAAEBcMVIDqSFvq5RZ057Ptlj3u6QmR0hND3cyZwAAAAASUd5mKauu/3a1FtKBn0p1ugSnAwAAIK4YqYHkt/pL6bMu0sL/BqenV6FBAwAAAEB08rdJU8+Xxvfwdp4K1Gh/GjQAAAAqGI0aSF4FO6VfLpO+HSztWiXNvlHaOt/pXAEAAABIVGu/l8Z3l/58XspZIc0Y4XSOAAAAUg7TTyE5rZ8sTR4q7VjsT2v0LymzlpO5AgAAAJCICnOlX2+WFjwiyfKmZdSQGh3gdM4AAABSDo0aSC6FedK8O6T590lWkTctvarU6yGp/SWSx+N0DgEAAAAkkk0zvB2mAkd9mw5TA1+SarR1MmcAAAApiUYNJI8tc6VJZ0tbfvWn1R8oDXpZqtXeyZwBAAAASDRF+dJvo6V5d0lWgTctLVvqca/U8UrJw2zOAAAATqBRA8mzGPj3x0pFed7baZlSt9ulTtdLaezmAAAAAKJgWd61+dZ+60+r29vbYapOFydzBgAAkPLoWoLk0GBfqVoL7/XaXaXB06QuN9GgAQAAACB6ZtraVqf+cz1d6nqbNHgKDRoAAAAuwBlfJIfMGtLAcdKqj6Xud0np2U7nCAAAAEAi2/ti7xS3bc+RGvR3OjcAAAD4ByM1kHhy/pZ++o+046/g9Eb7S70eoEEDAAAAQHRTTS0ZJ82+ac/RGv3+R4MGAACAyzBSA4ll2VvSL5dIeZul3LXSod+yQB8AAACA2OSuk6ZdJK380Hu7ySFSk8OczhUAAABKwdlgJIbdm6Sfz5B+Pt3boGFsXyTtXOp0zgAAAAAkohUfSp919TdoGKu/cjJHAAAAiAAjNeB+f38uTT1P2rXan9bqNKnfk1J2fSdzBgAAACDR5G2VZoyQ/hrnT8tuKPV/Wmp5opM5AwAAQARo1IB75e+QZl0rLX7an5ZVV+r7P6nN6U7mDAAAAEAiWvONNOUcKWeFP63F8VL/Z6QqjZzMGQAAACJEowbcaf1kafJZ0o4l/rSmR0oDnpeqNXMyZwAAAAASTeFuafZIaeF//WmZtaQ+j0lth3oXBQcAAEBCoFED7pS/zd+gkVFd6vWwtPeFBBsAAAAAoufJkDZN999ufIg08EWpeisncwUAAIAY0KgBd2o2WNr7YmnrXGngOKlmO6dzBAAAACBRpaV744ovB0pdb5X2uVzypDmdKwAAAMSARg04r6hAWv6O1Pr04JEYfR6RPJneAAQAAAAAIrV1vlS4S6rXx59mOkodv9Q7EhwAAAAJi64pcNa2RdJXB0iTzpT+fC74vvQqNGgAAAAAiJxVJP0+Rvq8t/TT6VLBzuD7adAAAABIeDRqwBmWJf3xP+nzntLGKd60WddJeVudzhkAAACARLRjqTTxEGnWNVLRbmnHYun3h53OFQAAAOKM6adQ+XJWSlPOldZ85U+rsbc06GUpq7aTOQMAAACQiB2mlrwgzbhSKtjxT6JH6niV1Ok6hzMHAACAeKNRA5UbbCx9XZp+mZQfMCKj/aVSrwcYCg4AAAAgOrvWSFMvkP7+1J9WvbV3UfDGBzqZMwAAAFQQGjVQOXI3SL9cLK14z59Wtbk08AWp6RFO5gwAAABAIlr+rjfG2L3Rn9buPKn3GCmzlpM5AwAAQAWiUQOVY+7twQ0abYZIfR+Xsuo6mSsAAAAAiTql7aQhUlGe93aVRlL/56QWxzqdMwAAAFQwFgpH5ehxl3dkRnZ9af+3pX1fpUEDAAAAQGyqtZC63+293vIk6eh5NGgAAACkCEZqoGLkbQ5utDDXD3hfqt5SqtrUyZwBAAAASDQFOZInXUrP9qd1vFqq3VlqdrTk8TiZOwAAAFQiRmogvgpzpZnXSp+0l3L+Dr6vQX8aNAAAAABEZ8MU6fOe3iltA6WlS82PoUEDAAAgxdCogfjZNFOa0Eda8LB3sb6p50mW5XSuAAAAACSiwjzp11ukr/aTti+Sfn9AWv+z07kCAACAw5h+CuVXVCD9Nlqad6dkFXjT0rKkJodKMo0a9JwCAAAAEIUtc6XJQ6XNs/1p9fpK2Q2dzBUAAABcgEYNlM+2hd5gY+M0f1rdXtKgl6U6XZ3MGQAAAIBEU1QoLRgjzblFKsrzpnkypG63S51HSmmEsAAAAKmOGiFiYxVJfzwpzR4pFe7yppmF+zrfKHW9VUrPcjqHAAAAABLJjiXS5GHS+p/8abW7SINeker1cjJnAAAAcBEaNRCbSUOkZW/6b9fcxzs6o8EAJ3MFAAAAIBFtnC5NPEgq2PlPgkfqdK3U/U4pvYrDmQMAAICbsFA4YtPqVP/1ff5POmoWDRoAAAAAYlO3h1Szg/d69bbSYd9LvR6gQQMAAAB7YKQGYtPyJKnT9VLTw6UmhzmdGwAAAACJLC1T2vcVaeHj3saMzJpO5wgAAAAuxUgNlG3lR9Ivl0qWFZze634aNAAAAABEZ/cmadLZ0qZZwem1O0v9n6JBAwAAAKVipAbCy9sqzbxSWvKS93bD/aU2ZzqdKwAAAACJ6u8J0tRzpV2rpc2zpSN/YYopAAAARIWRGght7bfS+O7+Bg1j9RdO5ggAAABAosrfIU27WPruKG+DhpGzUto63+mcAQAAIMEwUgPBCnZJv94oLfyvPy2jptT3MantMCdzBgAAACARrf9ZmjxU2rHEn9Z0sDTgealacydzBgAAgAREowb8Nv7iDTa2LfCnNTpIGvSSVL21kzkDAAAAkGgKd0tzbpN+f1DSP+vzpVeTej8s7X2R5PE4nUMAAAAkIBo1IBUVSvPulH67R7IKvWlp2VLP+6QOV0geZikDAAAAEIUtc6VJQ7z/fRoMkga9LNXc28mcAQAAIMHRqAFvo8WWOf4GjXp9vMFG7c5O5wwAAABAoo7S8K2XkZYpdbtT6nSdlJbudM4AAACQ4GjUgHfYd/+npY1TpXYXSl1v9gYeAAAAABCL+n2lLrdIK9+XBr0i1e3hdI4AAACQJGjUSEU7l0k7V0iN9venVWkkHbtIyqjuZM4AAAAAJBrLkpa/LbU8WUoLCDFNZ6kuN0rp2U7mDgAAAEmGxRJSLdj480Xps27STydLueuD76dBAwAAAEA0clZJ3x0l/Xy6NP++4PvM6G8aNAAAABBnNGqkil1rpR9OkKaeKxVsl3LXSXNHOZ0rAAAAAInaYWrpG9JnXaXVX3jT5t3pHREOAAAAVCCmn0oFK96Xpl0k7d7gT9truNRjtJO5AgAAAJCIdm+UfrnUO+WUT9Vm0oDnpeotncwZAAAAUgCNGsksb4s0/Qpp6SvBa2f0f0ZqcbyTOQMAAACQiFZ9Jk09X8pd409rfYbU9wkpu56TOQMAAECKoFEjWa35WpoyXMpZ6U9rcaLU/2mpSkMncwYAAAAg0eRvl2ZeI/35rD8tq57U7ymp9X+czBkAAABSDI0ayShvq/TjqVL+Fu/tzFrenlNtzpI8HqdzBwBAyrEsSwUFBSosLAz7mKKiIuXn5ys3N1dpaSx75japXj6ZmZlKT093Ohtw0oJHgxs0mh0tDXhOqtrUyVwBAJCyTGxh6qelSfU6rNulcvlkljO+oFEjGWXVlvo+Jk0eKjU+VBr4glS9ldO5AgAgJeXl5Wn16tXKyckps+HDVGq3b98uD50QXCfVy8d85hYtWqhGjRpOZwVO6XydtPxNaecyqfcjUrvz6TAFAIBDduzYoZUrV9p11NKkeh3W7VK5fDzljC9o1EgGhXmSlS9lVPenmVEZmXWk5sdIntRq6QMAwC1MBfWvv/6ye6A0a9ZMWVlZYSurvtEcGRkZKVehTQSpXD7ms69fv94OnNu3b8+IjVSRt1nKquu/nV5F2u8tKaOaVGMvJ3MGAIBSfYSGqZdVq1ZNDRs2LLVumsp12ESQquVjxSG+oFEj0W2ZJ00+W6rbyzsiw8d8EVoc62TOAABIeWaUhmnYaNmypR10lCZVK7SJItXLxwTMS5cutYfH06iR5IoKpPn3Sb8/KB0xVard0X9fna5O5gwAAJhlrvLz7bqpqZ9VrVq11Memeh3W7VK5fBqWM76gC3+iKiqU5j8oTegjbZ4tLXlRWvmR07kCAAAhpNr8qEg+qRZkpaxtC6Wv9pPm3Crlb/N2nioqfa5uAADgDOpnSOX9l5EaiWjHEmnyMGn9T/602l2kaqybAQAAACBKVpH0x5PS7JFS4S5vmpnCtulgp3MGAAAA7IFug4nELP6z+FlpfPeABg2P1Ola6cjpUr1eDmcQAADEy/vvSz16SGZEuflvbsdLmzZt1KFDB/Xs2VOdO3fWk08+We7XnDdvnv26xt9//60DDjigzOc8+uijWrNmTUzvd+211+r2228v8/OZ//fdd5/c4IknntA555zjdDaAYDtXSN8cIc24wt+gUbO9dPjPUo+7pbRMp3MIAADigPiidMQXiYWRGoli12pp6vnS3+P9adXbSIPGSY3+5WTOAABAnJkA4+STvUtkmT4Nc+d6b7/3nnTSSfF5j7feesuulC9btkzdu3e3gwTz38esBRLr1FlmUfQff/wxoqDjoIMOUpMmTRRvvs+3atUqO7A65JBD1L9/f1UW39y4gGuZg8vSV6Xp/yflb/Wn73O51PM+KaO6k7kDAABxji9OOYX4ojyIL9yFkRqJYPti6bOuwQ0a7c6Xjp5DgwYAAAmmb1+pRYs9Ly1bSm3bZtj/TzvN+1gTcAT+N+mhnht4Ma8fjdatW9u9jf744w+7Z9LJJ5+swYMHq2vXrlq9erW++OIL7b///urTp49daf/222+Ln2se3759e/u+N998szjdLPhWp06d4tuTJ0+2X6NHjx52YPPRRx/pzjvvtHtcnXbaaXZwMHv2bHuRuBtuuMF+H5P2n//8R5s3b7Zfw+TF5MsEEIcddphWrlwZ0edr3ry5OnbsaAdXhum5ZV7XvEe3bt10yy232OlffvmljjjiCPv6tm3blJmZqWeeeca+/fLLL+vcc8+1r48ZM0b9+vWz82f+m88W2INr5MiR9msPGzZM27dvtz+f2b7m88810SPgFjOvkiYP9TdoVG0uHfyl1PdxGjQAAEiC+CIwxjj9dO9jiS/cF1+Yhhhf/ogvIkfzTiKosZdUr4+05iupSmNpwHNS8387nSsAABADMxp61apQ95S9UFpBQbjnxs5UhBcsWGAHBGaIt6lEz5o1S40bN9aSJUvswMIEHrVq1dLixYvtHlcmqPj666/1zjvvaMaMGapZs6bOPvvskK+/adMmnXDCCXr33Xft55oeWlu2bNHxxx+vF154objHk3HvvfeqevXqmjZtmn37rrvusoMCM3z9iiuusCvzJi+md5R5jgkmymI+28aNG+0eW4YJBm666SYdeOCBdm+nf//73/bnMP9PP/107d692w6sTEBhPuOFF16or776SkceeaT9fPM5r7nmGvv6lClT7OHe5j18zHtNnTrVXvjuuuuuU3Z2tn2/CWQGDhyoAQMGxKHUgDhoeZK08DFzWkNqc5a3MSPLf7IAAAAkenxRdoxBfOFcfHHUUUfZzx8yZIg99ZWJH4gvIkejRiIwi/QNfEGac6vU80GpSgOncwQAAGIUfiT0P92lJK1d67EDjJLMaOPGjWN9/WCmh0/VqlVVrVo1u/JvekQZRx99tB1wGBMmTLADjX/9yz8y1AwXX758uSZOnGj3SDLBiHHRRRfpp598a375mSDG9CTyzYFrnl+vXr2Qefrwww+1detWvWfGwUvKy8srnkfXvN9DDz1U3DvquOOOK/PzmfdauHChHnnkETVs2FA7d+60X2ft2rXFj9uxY4f9mFNPPdUOZH7++Wc72DA9uq6++mo7SPrmm2/0wAMP2I83AZkJjkxwYYZ/m+fu2rXL3paGCUJMwOHLs3lvc7t27do688wz9eeff0ZWQEBFMyO+u98p1eootTrF6dwAAIAYlV7/98YYpvpbULBnAwfxhXPxxYMPPmg/3owoOeOMM4gvokSjhtvk75BmXecNLJoc6k+v1kIa+KKTOQMAAHEwfXrodDME3DdP6gcfBK+p4fv/9tvSiSfGJx+BPZgC1ahRIyBPlg4//HC9/vrrZb6er6JdHub9Hn/88eJh2uV5P9/nMwHEsccea89527ZtW/s+0wOqSpUqezzHDDs3j//hhx/sxf/M8PFXX31VdevWteflzcnJsYfP+3pamd5RJpgwva98QUfg9os2z0CFWfut9OeL0qCXvB2mfLp6p0cAAADJF18Exhgff5wRtKYG8YU74gsTR5iGHNPIYUaNEF9EjjU13GT9JOnzntLisdKU4VJewIJ9AAAgZZjF+kxnIrOunqkbm/9mcb94BRyRMnPMmkr4nDlzitN8Q7dNBd0MqzbzuppgwTc/bEn77ruvFi1aVLywn+mZZIaMG6YXluk55WOGkZueR6bxwDD/f/vtt+L3Mz2+fPPffvzxxxF9BvO8Sy65xB5mbgKCgw8+2A4ofMy8u775c81jTYBl5us1w9TN7dtuu83+b+Tm5tq9u1q1amXfNgFSWe/94osv2tvHBChvvPFGRHkG4qZglzTjKmniIdLSV/6ZbgoAAKQa4gvii2RDo4YbFO6WZt8ofX2AtOOfIUO7N0qbSmlqBQAASR94zJ4t7drl/V/ZAYex995725VwM/TbzInbqVMneyE73zDyU045Rb1791bfvn2LK+IlmV5IH3zwgT3c2iziZx5vhmAbZh7bCy64oHghP7MInhkBYeaFNY81c8SadOO///2v3QPKLOQ3dOhQu2dUpG699VZ76LqZn/e1116zh7ybhQpNT6mTTjrJHuptmM9hgqBDD/WOljW9yMwCgL7bJkgy8/CaXlRm8cKsrKwy39cMHTdz85rtZRbzAyrNxunShN7SQu931rZ6gn9lUAAAkFKIL9wZX9xxxx12/ogvouOxTNNOCvEN4zE7lG+OtspmWhDXrVunRo0aKW3rPGny2dIWfwulGgySBo6TannnnoOD5ZNGu5/bUD7uRvm4G+VT+UzPm7/++sselhxqOHIgUyXzTT+VysOI3SrVyyfUvuyGerVbuGFbFB/jG9RV2vzR0m93S1ah9860bKnnaKnDiODpp1Bp+A12N8rH3Sgfd6N8Kh8xRvJI5fLJDbMfR1qvdsXRxqw4bxZpMR/AtEz5hh2FY4YimRYp83jTAjZ+/HglHBNgzL9f+qKvv0EjLVPqca902A80aAAAAAAxSsn4QlL6zj/k+Wo/ad4d/gaNur2lo2ZKHa+iQQMAAABJwfFarVlkxaz8PmrUKM2cOdMeemTmVzOtvKFMmjTJXhH+vPPO06xZs+y50cxl3rx5Shg7/lK9mScqbc5NUlG+N612V2nwNKnLjVIa67cDAAAAsUjJ+MJY+Jga/DJYns0zvLc96VLX26TBU6TanZ3OHQAAAJA800+ZnlNmbrMnnniieNhay5Yt9X//93/23GglnXbaadq5c6c+/fTT4jQzH5qZK23s2LF7PN6sFm8ugUNYzOtv3rzZuaHhWxcq7Ys+SivaJUseqdN1srreLqVnO5IfBDP74Pr169WwYUOGTroQ5eNulI+7UT7ODKldunRpREPDjfz8fGVmZlZK3hC9VC4f3/Bw3+gHX73azGnstumnKjq+cGuMoWkXKW3Jc/ZVq2YHWWY62/r9nMkL9sBvsLtRPu5G+bgb5VP5iDGSS6qWT26I+CKaGMPRIQFmdXezoMqNN95YnGYOgGYl98mTJ4d8jkk3Pa8CmZ5XH374YcjHjx492l5wpSRzwDUbzwlFRbXlaXadGm4Yp62d/6v8OgOkjVsdyQtC/yCbL45p7+MH2X0oH3ejfNyN8nGmgmq2u5kn1VxKY8qlsNA7XUyqzaeaCFK9fMz+a/Zls+ihL+javn273KYy4gu3xhhWs+tVf+UXymt4hHbsfbNUWFUKMzoFlY/fYHejfNyN8nE3yqfyEWMkj1Qun4IQ8UU0MYajjRobNmywC65x48ZB6eb2ggULQj5nzZo1IR9v0kMxAU1gkOLrRWVakJ1cxG+9LpL6X626WTUdyQNKLx9zIKGXgTtRPu5G+bgb5VP5zMlNUykzC7+ZSyRSsZdOIknV8jH7rzlu1K9fv7gnVSQ9A5MxvnBvjNFAGwZ+qwZNWqsax3jX4TfY3Sgfd6N83I3yqXzEGMknFcsnI0R8EU2MkfSLN2RnZ9uXksxGc/Jg6zHvn1WTA75LmR9kp/cRhEf5uBvl426UT+Uy29lsc9+lrF46vsekWi+dRJDq5ePbhwOPH6l8HHFrjKGM6s7nAWHxG+xulI+7UT7uRvlULmKM5JHK5eMJEV8YkR5HHD3aNGjQQOnp6Vq7dm1QurndpEmTkM8x6dE8HgAAAEBqIL4AAAAAkp+jjRpZWVnq06ePJk6cGDRszdweNGhQyOeY9MDHG1999VXYxwMAACCYWYytQ4cO9kLIvsvcuXMrNQ+zZ8/Wm2++GdNzd+zYEbYn00svvaTatWvbn6lLly466qijtHz5ciWqX3/9Vcccc0zx7alTp6pHjx7aZ599dMghh2jVqlVRvd4LL7ygbt262cO9H3300aD7rr32Wr3++utKZMQXAAAAlY/4InH8miTxhePjwsxctM8++6zGjRun33//XZdccol27typ4cOH2/cPHTo0aKG/ESNGaMKECXr44YfteXFvv/12TZ8+XZdffrmDnwIAACCxvPXWW3bF33cxFdFECTrKcvDBB9uv/9tvv9mV86uuumqPx5S1qGKk4vU64Zh68A033FB8cn7IkCF2sPDHH3/o6KOP1pVXXhnV65kT/m+//bbOPPPMPe67/vrr7bq1b7HCREV8AQAAUPmIL4gvKjO+cLxR47TTTtNDDz2k2267zW7xMjuICSp8i/WZlq/Vq1cXP37fffe1W3ieeeYZuxXp3Xff1YcffqiuXbs6+CkAAACi8PsY6YMWwZcPWyrj07b2/+K074/b87kmreRzfRfzuuWwcOFCtWjRQkuWLLFvmzrakUceaVd2TQ8l03PnuOOOU+fOnfWvf/1LS5cuLX6ueWz//v3Vu3dv+znLli2z0/Py8nTdddfZdTVTdzP3rVu3zq77ffvtt3b97+KLL7Yf+8svv9jv0bdvX/Xq1UvvvPNO8es//fTTat++vZ3+yCOPRPyZBg8ebH8uw/S+GjVqlPr162dX5k0+TjrpJDvgMvkz7+EzadIkO2/mvnPPPde+/v3339v3HXTQQbriiivsnvxHHHFEqZ//k08+Uffu3e3nm/f46KOP7PS7775bnTp1Ku7J5nt8IFMPNoHTAQccYN+eMWOG3QPKBFXGRRddZL++WSwyXEBkghKzkKSPKQPzvqHmqm3UqJHatWunL7/8UomM+AIAAKScUPFFqBiD+MI18YXJ+3fffWffZ+r3xBfRccVC4aYXVLieUL7CDXTqqafaFwAAgISUv03aFTysN+Rg59yWIdLW7/HcoNeN4sRv1apVi29PnjzZHjL+4IMP6j//+Y9diX7yySc1bdq04grqzz//bJ8gNpXWBx54QBdeeKFdQTUnhE3F3ryGWc/glVde0aWXXqrPPvtMo0ePtnv9mAqzWVh5/fr1atiwoe688077xLG5GFu2bLFfb/z48WratKk2bNhgV+DNCefNmzfbwcKsWbPs+2666aaIPqPpEWQCF9N7yMfkzwQ3vm1gPvP7779vByDmcaZSbt7X3Pfyyy/bFXwTHL344otBr20+0w8//KDMzMxSP/8tt9xiBzMmQDHB27Zt2+zPY7avObFuyiAnJydkEGAaUUyAFBiEtG7duvh2zZo1VatWLf3999/aa6+99ni+CVDM9jKB0RdffGE/tiy+qZjMsPpERnwBAABSPb4IGWMQXxBf9EuO+MIVjRoAAAApJbOWVLV5UJIVKvio0nDP55q0Es8Net0ohoebHjwlnXHGGXYl2/RAMpVPEyD4mADABByGCRBMhdpU7E3gYCryvsp94PDiTz/9VPfff78dcBiBrxfI9FwyPbhKVnZNZX7evHl2uqlAG2Y6IRPMhOProWWYAMJMK+RjekX5fP3113Yw5OtFZHpVmbRq1aoF9Vgy/00Po0BnnXWWHXAYpX3+Qw891J7e6JRTTrEr/yZf5n7TK8y8hkkzc9qaHmwlrVy5snh0QVlMEGguoWzatMkuq8cee6zM1zGLY8+fPz+i9wQAAIB744uQMQbxBfFF4+SIL2jUAAAAqGydrvZeAlmWPZzXVHYVZpE624EfV2jWTB5MJb9evXoRLxJnWZY93NoEIrEyr2EW3jPBR0kmP4HCLeLnY4IEXw+tkmrUqBH2eaW9bsn7Al+ntM8/ZswYe4i3CYSGDRtmz1lr5padMmWK/VnNqIGBAwfqjTfeKB4G7mOCn8Ch361atQoaRm6GfW/dulXNmjWzX9NcSnr++ef13//+1w46ImHeL7CHHQAAABI0vog0xiC+IL5IwPjC8TU1AAAA4B5m0TgzZPrHH3/Utddeq8WLFxffZ4Y/m4WUjeeee86u3Jvh0CeccILGjh1r99gx8vPz7aHchpkj11R6d+/ebd82w8MNM1TZVJgDe2n99ddfdk8mHzMU3cyZa+bBNWsirFmzxk437xUPhx12mL2gtC9fZpj44Ycfbn9+8xl8a2iY/4HboaTSPr/ZXiaYMlMhmR5gJtgwwcLatWvtIOPWW2/V/vvvX/z4QGauXN98vYbpqWVe2wQwhhl2fuyxx6pKlSoh82Uea4Ia0yPO9BSLhFlY2wyRBwAAAOKB+IL4okcFxBeM1AAAAEhBJee8NYvj7dy5067cm3luTS8e0wvIzH/r691kAoORI0faFfD69evbc8IapnfQxo0bi4dTm95YZhi2WXTPPP7mm2+2h2mb4dSm14+Z19YMmzbzvpqKtXldU2k3c8SaQOeaa66xK8ym55Bvwebbb7/drqSbHkxmGHc8mOHSJhAwi/WZ3lAmnwMGDLDve/PNN3XZZZfZ89Sayr4JRGrXrh3ydUr7/GZ+XhM4ZGVl2dv0qaeesoMtM1zcbG/TQ8sMFTe9rEoywYgZIm6CGdOzzcyL++qrr9oL+JkeT2Zbmvl1wzHbu+T9ZkFG06vKzLtrtq0pA7MYoMmr2QYmQDGBJwAAABAN4ovo44s6deqEfB3ii7J5LPPqKcQsnmICUlPYkSxmUhHMzmsWizEtWqEWbYGzKB93o3zcjfJxN8qn8pmKoekd1LZt27C9XXysgKHhZQ1/doKprAYuvJfsTG8ns1CeYeazNT3CTK8oU3+szPIxCysa1113XYW/lwk4TVBjLpHsy26oV7uFG7YFx3h3o3zcjfJxN8rH3SifypcsMQbxxXF2Y45pnKjM8nnQxfFFNPVqjjYAAABACe+99549TNr09DI9l0yvMdMTqrKZRQBLm6c3nkzgEG4xQAAAAADxiy/MiAfii9gxUsMBtGK7G+XjbpSPu1E+7kb5VL5k6UUFyoeRGqVzw7bgGO9ulI+7UT7uRvm4G+VT+Ygxkkcql08uIzUAAADcH+wBiSzF+kEBAAC4HvUzpPL+y0LhAAAAFcTMj2p6rP39999q2LChfTtcD5xU7qWTCFK5fMxnX79+vf25zeKAAAAAcI6pj5l6mamfmRijtLppKtdhE0Gqlo8Vh/iCRg0AAIAKYho0zHDa1atX2w0bZVXszIgO85xUqtAmilQvH/OZW7RoofT0dKezAgAAkNJMfczUy1auXKmlS5eW+thUr8O6XSqXj6ec8QWNGgAAABXIjM5o1aqV3QOnsLAw7ONMZXbjxo2qX78+8xG7UKqXj+lBRYMGAACAO5iFntu3b6/8/PxSH5fqdVi3S+XyySxnfEGjBgAAQAXzDastbWitqdCa+80iaalWoU0ElA8AAADcxJwQLuukMHVYd6N8YsfWAgAAAAAAAAAACYFGDQAAAAAAAAAAkBAyUnEBFmPbtm2ODi3avn07Q4tcivJxN8rH3Sgfd6N83I3ycTfKZ0+++rSvfp3KiDFQFsrH3Sgfd6N83I3ycTfKx90on9hjjJRr1DA7itGyZUunswIAAAAkRf26du3aSmXEGAAAAEDlxRgeK8W6VpkWsL///ls1a9a0F+10qsXJBDwrVqxQrVq1HMkDwqN83I3ycTfKx90oH3ejfNyN8tmTCSNMsNGsWbOU71lGjIGyUD7uRvm4G+XjbpSPu1E+7kb5xB5jpNxIDbMxWrRoITcwOys7rHtRPu5G+bgb5eNulI+7UT7uRvkES/URGj7EGIgU5eNulI+7UT7uRvm4G+XjbpRP9DFGanepAgAAAAAAAAAACYNGDQAAAAAAAAAAkBBo1HBAdna2Ro0aZf+H+1A+7kb5uBvl426Uj7tRPu5G+cDt2EfdjfJxN8rH3Sgfd6N83I3ycTfKJ3Ypt1A4AAAAAAAAAABITIzUAAAAAAAAAAAACYFGDQAAAAAAAAAAkBBo1AAAAAAAAAAAAAmBRg0AAAAAAAAAAJAQaNRwwJNPPqk2bdqoSpUqGjBggKZNm+Z0llLS6NGj1a9fP9WsWVONGjXSCSecoIULFwY9Jjc3V5dddpnq16+vGjVq6OSTT9batWsdy3Oquu++++TxeHTllVcWp1E2zlq1apXOOusse/tXrVpV3bp10/Tp04vvtyxLt912m5o2bWrff9hhh2nRokWO5jlVFBYW6tZbb1Xbtm3tbd+uXTvddddddpn4UD6V64cfftCxxx6rZs2a2ceyDz/8MOj+SMpj06ZNGjJkiGrVqqU6derovPPO044dOyr5k6RW2eTn52vkyJH28a169er2Y4YOHaq///476DUoG7gB8YU7EF8kFmIM9yHGcC9iDHchvnA3YozKQaNGJXvrrbd09dVXa9SoUZo5c6Z69OihwYMHa926dU5nLeV8//33doV1ypQp+uqrr+wDyxFHHKGdO3cWP+aqq67SJ598onfeecd+vDnInHTSSY7mO9X88ssvevrpp9W9e/egdMrGOZs3b9Z+++2nzMxMff7555o/f74efvhh1a1bt/gxDzzwgB577DGNHTtWU6dOtX+szbHOBIqoWPfff7+eeuopPfHEE/r999/t26Y8Hn/88eLHUD6Vy/yumN97c9IxlEjKw1Rof/vtN/v36tNPP7UryhdeeGElforUK5ucnBy7rmYCePP//ffft09OHnfccUGPo2zgNOIL9yC+SBzEGO5DjOFuxBjuQnzhbsQYlcRCperfv7912WWXFd8uLCy0mjVrZo0ePdrRfMGy1q1bZ7oYWN9//719e8uWLVZmZqb1zjvvFD/m999/tx8zefJkB3OaOrZv3261b9/e+uqrr6wDDzzQGjFihJ1O2Thr5MiR1v777x/2/qKiIqtJkybWgw8+WJxmyiw7O9t64403KimXqeuYY46xzj333KC0k046yRoyZIh9nfJxljlOffDBB8W3IymP+fPn28/75Zdfih/z+eefWx6Px1q1alUlf4LkVbJsQpk2bZr9uGXLltm3KRu4AfGFexFfuBMxhjsRY7gbMYZ7EV+4GzFGxWGkRiXKy8vTjBkz7GFfPmlpafbtyZMnO5o3SFu3brX/16tXz/5vysr0rgosr44dO6pVq1aUVyUxPd2OOeaYoDIwKBtnffzxx+rbt69OPfVUe2qFXr166dlnny2+/6+//tKaNWuCyqd27dr2dBiUT8Xbd999NXHiRP3xxx/27V9//VU//fSTjjrqKPs25eMukZSH+W+GHJvvnY95vKlDmJ5XqNy6ghlCbsrDoGzgNOILdyO+cCdiDHcixnA3YozEQXyReIgxYpMR4/MQgw0bNtjzEDZu3Dgo3dxesGCBY/mCVFRUZM+laoa7du3a1U4zPwJZWVnFB5XA8jL3oWK9+eab9lA8MzS8JMrGWUuWLLGHHpupLm666Sa7jK644gq7TIYNG1ZcBqGOdZRPxbvhhhu0bds2OwhPT0+3f3fuuecee/iqQfm4SyTlYf6b4D5QRkaGfZKMMqs8Zri+mf/2jDPOsOe2NSgbOI34wr2IL9yJGMO9iDHcjRgjcRBfJBZijNjRqAH801tn3rx5dk8DOG/FihUaMWKEPXegWfAS7gvSTY+Be++9175telGZ74+Zr9MEHHDW22+/rddee02vv/66unTpotmzZ9snVcwCZJQPEBvTc/c///mPveiiOeECAGUhvnAfYgx3I8ZwN2IMIP6IMcqH6acqUYMGDewW7bVr1walm9tNmjRxLF+p7vLLL7cX3fn222/VokWL4nRTJmZI/5YtW4IeT3lVPDP02yxu2bt3b7s12lzMQn1moStz3fQwoGyc07RpU3Xu3DkorVOnTlq+fLl93VcGHOuccd1119k9qU4//XR169ZNZ599tr3o5ejRo+37KR93iaQ8zP+SC/4WFBRo06ZNlFklBhvLli2zT4T5elAZlA2cRnzhTsQX7kSM4W7EGO5GjJE4iC8SAzFG+dGoUYnMsMk+ffrY8xAG9kYwtwcNGuRo3lKRaQk1AccHH3ygb775Rm3btg2635RVZmZmUHktXLjQrlRRXhXr0EMP1dy5c+3eH76L6bVjhrb6rlM2zjHTKJjtHcjMrdq6dWv7uvkumR/awPIxQ5XN3I+UT8XLycmx59oMZE54md8bg/Jxl0jKw/w3J1jMyRgf87tlytTMjYuKDzYWLVqkr7/+WvXr1w+6n7KB04gv3IX4wt2IMdyNGMPdiDESB/GF+xFjxEkFLkKOEN58800rOzvbeumll+zV7C+88EKrTp061po1a5zOWsq55JJLrNq1a1vfffedtXr16uJLTk5O8WMuvvhiq1WrVtY333xjTZ8+3Ro0aJB9QeU78MADrREjRhTfpmycM23aNCsjI8O65557rEWLFlmvvfaaVa1aNevVV18tfsx9991nH9s++ugja86cOdbxxx9vtW3b1tq1a5ejeU8Fw4YNs5o3b259+umn1l9//WW9//77VoMGDazrr7+++DGUT+Xavn27NWvWLPtiql5jxoyxry9btizi8jjyyCOtXr16WVOnTrV++uknq3379tYZZ5zh4KdK/rLJy8uzjjvuOKtFixbW7Nmzg+oKu3fvLn4NygZOI75wD+KLxEOM4R7EGO5GjOEuxBfuRoxROWjUcMDjjz9uV5SysrKs/v37W1OmTHE6SynJHFhCXV588cXix5gD/qWXXmrVrVvXrlCdeOKJ9oEGzgcclI2zPvnkE6tr1672SZSOHTtazzzzTND9RUVF1q233mo1btzYfsyhhx5qLVy40LH8ppJt27bZ3xXzO1OlShVrr732sm6++eagChLlU7m+/fbbkL83JjiMtDw2btxoV2Jr1Khh1apVyxo+fLhdWUbFlY0J2MPVFczzfCgbuAHxhTsQXyQeYgx3IcZwL2IMdyG+cDdijMrhMX/iNeoDAAAAAAAAAACgorCmBgAAAAAAAAAASAg0agAAAAAAAAAAgIRAowYAAAAAAAAAAEgINGoAAAAAAAAAAICEQKMGAAAAAAAAAABICDRqAAAAAAAAAACAhECjBgAAAAAAAAAASAg0agAAAAAAAAAAgIRAowYAIOV899138ng82rJli9NZAQAAAJAEiDEAoPLQqAEAKeacc86xK9slL4sXL1YiaNOmjR599FGnswEAAADgH8QYAIDKlFGp7wYAcIUjjzxSL774YlBaw4YN93hcXl6esrKyKjFnAAAAABIRMQYAoLIwUgMAUlB2draaNGkSdElPT9dBBx2kyy+/XFdeeaUaNGigwYMH248fM2aMunXrpurVq6tly5a69NJLtWPHjuLXe+mll1SnTh19+umn6tChg6pVq6ZTTjlFOTk5GjdunN3zqW7durriiitUWFhY/Lzdu3fr2muvVfPmze3XHjBggD1sOxqmB9hzzz2nE0880X7f9u3b6+OPPw56zPjx47XPPvuoatWqOvjgg7V06dI9Xuenn37SAQccYD/GfEaT1507d9r3vfzyy6pRo4YWLVpU/HizDTp27Gh/RgAAACDVEWMQYwBAZaFRAwAQxAQIpufUzz//rLFjx9ppaWlpeuyxx/Tbb7/Z93/zzTe6/vrrg55nKt7mMW+++aYmTJhgBw4mCDCVfXN55ZVX9PTTT+vdd98tfo4JbiZPnmw/Z86cOTr11FPtHl6BFftI3HHHHfrPf/5jv8bRRx+tIUOGaNOmTfZ9K1as0EknnaRjjz1Ws2fP1vnnn68bbrgh6Pl//vmn/b4nn3yy/RpvvfWWHYCY/BlDhw4tft2CggJ99tlndpDz2muv2UEOAAAAgPCIMYgxACCuLABAShk2bJiVnp5uVa9evfhyyimn2PcdeOCBVq9evcp8jXfeeceqX79+8e0XX3zRMj8pixcvLk676KKLrGrVqlnbt28vThs8eLCdbixbtszOx6pVq4Je+9BDD7VuvPHGsO/dunVr65FHHim+bd73lltuKb69Y8cOO+3zzz+3b5vX6ty5c9BrjBw50n7M5s2b7dvnnXeedeGFFwY95scff7TS0tKsXbt22bc3bdpktWjRwrrkkkusxo0bW/fcc0+Z2wkAAABIBcQYxBgAUJlYUwMAUpAZHv3UU08V3zbDsn369Omzx+O//vprjR49WgsWLNC2bdvsnkS5ubl2zylfLyLzv127dsXPady4sT0k3AypDkxbt26dfX3u3Ln2MHEzZDuQGS5ev379qD5P9+7dgz5LrVq1it/n999/t4ecBxo0aFDQ7V9//dXuPWV6RfmYWKaoqEh//fWXOnXqZA9tf/755+3h8vvuu+8ePbEAAACAVEaMQYwBAJWFRg0ASEGmUr733nuHvS+QmRv23//+ty655BLdc889qlevnj1s+rzzzrMX+fMFHJmZmXvMQxsqzVTiDTNfrpljd8aMGfb/QIFBSiRKe59ImLxcdNFF9hy3JbVq1ar4+g8//GDndfXq1fZcuDVr1owqnwAAAECyIsYIRowBABWHRg0AQKlMQGAq7w8//LA9763x9ttvl/t1e/XqZfeiMr2dzOJ5FcX0gCq5qN+UKVOCbvfu3Vvz588PG4QZkyZN0v33369PPvlEI0eOtOfCNXP/AgAAAIgOMYYXMQYAxIaFwgEApTKV8Pz8fD3++ONasmSJvRifb3G/8jBDws2ieGaBvPfff98egj1t2jR7CLpZJC9eLr74YntRwOuuu04LFy7U66+/rpdeeinoMSaAMAGFCSLMQn/m8R999FHxIn7bt2/X2WefbfeyOuqoo+wh5Gahv8AFCQEAAABEhhiDGAMAyoNGDQBAqXr06KExY8bYPYi6du1qV7ZNUBAPL774oh1wXHPNNerQoYNOOOEE/fLLL0HDscvLvNZ7772nDz/80P4sJli6995795gv9/vvv9cff/xh9+gyPbxuu+02NWvWzL5/xIgR9pB53/O6detmXzfDyVetWhW3vAIAAACpgBiDGAMAysNjVgsv1ysAAAAAAAAAAABUAkZqAAAAAAAAAACAhECjBgAAAAAAAAAASAg0agAAAAAAAAAAgIRAowYAAAAAAAAAAEgINGoAAAAAAAAAAICEQKMGAAAAAAAAAABICDRqAAAAAAAAAACAhECjBgAAAAAAAAAASAg0agAAAAAAAAAAgIRAowYAAAAAAAAAAEgINGoAAAAAAAAAAAAlgv8H5+IQ+qFhftkAAAAASUVORK5CYII=",
       "text/plain": [
        "<Figure size 1600x1200 with 4 Axes>"
       ]
@@ -359,30 +332,30 @@
       "Episode 0:\n",
       "  Language: Pickup the blue block\n",
       "  Frames: 128\n",
-      "  VOC-S (Spearman ρ): 0.0849 (p=0.3407)\n",
-      "  Reward range: [0.806, 0.907]\n",
-      "  Trend: → Flat\n",
+      "  VOC-S (Spearman ρ): -0.5134 (p=0.0000)\n",
+      "  Reward range: [0.413, 0.479]\n",
+      "  Trend: ↘ Decreasing\n",
       "--------------------------------------------------------------------------------\n",
       "Episode 1:\n",
       "  Language: Pickup the blue block\n",
       "  Frames: 128\n",
-      "  VOC-S (Spearman ρ): -0.0830 (p=0.3519)\n",
-      "  Reward range: [0.814, 0.902]\n",
-      "  Trend: → Flat\n",
+      "  VOC-S (Spearman ρ): -0.6027 (p=0.0000)\n",
+      "  Reward range: [0.444, 0.480]\n",
+      "  Trend: ↘ Decreasing\n",
       "--------------------------------------------------------------------------------\n",
       "Episode 2:\n",
       "  Language: Pickup the blue block\n",
       "  Frames: 128\n",
-      "  VOC-S (Spearman ρ): 0.0751 (p=0.3997)\n",
-      "  Reward range: [0.815, 0.914]\n",
-      "  Trend: → Flat\n",
+      "  VOC-S (Spearman ρ): -0.3906 (p=0.0000)\n",
+      "  Reward range: [0.410, 0.477]\n",
+      "  Trend: ↘ Decreasing\n",
       "--------------------------------------------------------------------------------\n",
       "Episode 4:\n",
       "  Language: Pickup the blue block\n",
       "  Frames: 128\n",
-      "  VOC-S (Spearman ρ): -0.0371 (p=0.6778)\n",
-      "  Reward range: [0.862, 0.908]\n",
-      "  Trend: → Flat\n",
+      "  VOC-S (Spearman ρ): -0.4136 (p=0.0000)\n",
+      "  Reward range: [0.429, 0.482]\n",
+      "  Trend: ↘ Decreasing\n",
       "--------------------------------------------------------------------------------\n"
      ]
     }
diff --git a/src/lerobot/policies/rlearn/modeling_rlearn.py b/src/lerobot/policies/rlearn/modeling_rlearn.py
index 9086d12a5..e4e7a47d5 100644
--- a/src/lerobot/policies/rlearn/modeling_rlearn.py
+++ b/src/lerobot/policies/rlearn/modeling_rlearn.py
@@ -186,7 +186,7 @@ class RLearNPolicy(PreTrainedPolicy):
                 min_value=config.reward_min_value,
                 max_value=config.reward_max_value,
                 num_bins=config.reward_hl_gauss_loss_num_bins,
-            ) if config.use_hl_gauss_loss else None
+            )  # Always provide config, HLGaussLayer needs it even for regression mode
         )
         
         # Simple frame dropout probability
@@ -559,10 +559,13 @@ class RLearNPolicy(PreTrainedPolicy):
         # DEBUG: Print targets and predictions occasionally during training
         if self.training and torch.rand(1).item() < 0.02:  # ~2% chance to debug print
             with torch.no_grad():
+                # Get raw MLP outputs before HLGauss
+                raw_outputs = video_frame_embeds
                 preds = self.hl_gauss_layer(video_frame_embeds).squeeze(-1)
                 print(f"\n=== DEBUG TRAINING ===")
                 print(f"Target range: [{target.min():.3f}, {target.max():.3f}]")
                 print(f"Target mean: {target.mean():.3f}")
+                print(f"Raw MLP range: [{raw_outputs.min():.3f}, {raw_outputs.max():.3f}]")
                 print(f"Pred range: [{preds.min():.3f}, {preds.max():.3f}]") 
                 print(f"Pred mean: {preds.mean():.3f}")
                 print(f"Loss: {loss:.4f}")