diff --git a/experiments/evaluations/gpt-4_gpt-4.json b/experiments/evaluations/gpt-4_gpt-4.json
index 33b17cf..5235191 100644
--- a/experiments/evaluations/gpt-4_gpt-4.json
+++ b/experiments/evaluations/gpt-4_gpt-4.json
@@ -11,7 +11,7 @@
         "responses_fp": "/home/ray/ray-assistant/notebooks/../experiments/responses/gpt-4.json"
     },
     "retrieval_score": 0.7288135593220338,
-    "quality_score": 3.824858757062147,
+    "quality_score": 3.7457627118644066,
     "results": [
         {
             "question": "I\u2019m struggling a bit with Ray Data type conversions when I do map_batches. Any advice?",
diff --git a/notebooks/rag.ipynb b/notebooks/rag.ipynb
index 0d01c9f..99d6027 100644
--- a/notebooks/rag.ipynb
+++ b/notebooks/rag.ipynb
@@ -137,17 +137,17 @@
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "2023-11-09 22:07:17,747\tINFO worker.py:1458 -- Connecting to existing Ray cluster at address: 10.0.6.237:6379...\n",
-      "2023-11-09 22:07:17,757\tINFO worker.py:1633 -- Connected to Ray cluster. View the dashboard at \u001b[1m\u001b[32mhttps://session-5ljni527x7edt2q6px7nuaejct.i.anyscaleuserdata-staging.com \u001b[39m\u001b[22m\n",
-      "2023-11-09 22:07:17,859\tINFO packaging.py:518 -- Creating a file package for local directory '/home/ray/ray-assistant/notebooks/..'.\n",
-      "2023-11-09 22:07:18,010\tINFO packaging.py:346 -- Pushing file package 'gcs://_ray_pkg_d14223360ff97e4e.zip' (38.25MiB) to Ray cluster...\n",
-      "2023-11-09 22:07:18,137\tINFO packaging.py:359 -- Successfully pushed file package 'gcs://_ray_pkg_d14223360ff97e4e.zip'.\n"
+      "2023-11-27 11:08:11,568\tINFO worker.py:1458 -- Connecting to existing Ray cluster at address: 10.0.27.252:6379...\n",
+      "2023-11-27 11:08:11,577\tINFO worker.py:1633 -- Connected to Ray cluster. View the dashboard at \u001b[1m\u001b[32mhttps://session-5ljni527x7edt2q6px7nuaejct.i.anyscaleuserdata-staging.com \u001b[39m\u001b[22m\n",
+      "2023-11-27 11:08:11,679\tINFO packaging.py:518 -- Creating a file package for local directory '/home/ray/ray-assistant/notebooks/..'.\n",
+      "2023-11-27 11:08:11,826\tINFO packaging.py:346 -- Pushing file package 'gcs://_ray_pkg_9ed3efb97d40fcd3.zip' (37.58MiB) to Ray cluster...\n",
+      "2023-11-27 11:08:11,943\tINFO packaging.py:359 -- Successfully pushed file package 'gcs://_ray_pkg_9ed3efb97d40fcd3.zip'.\n"
      ]
     },
     {
      "data": {
       "application/vnd.jupyter.widget-view+json": {
-       "model_id": "8e13e69bd2304f97bbaaf949ef7f977f",
+       "model_id": "be7b299ae7d94d9cae797dcacc484b47",
        "version_major": 2,
        "version_minor": 0
       },
@@ -221,12 +221,12 @@
     {
      "data": {
       "text/plain": [
-       "{'node:__internal_head__': 1.0,\n",
-       " 'CPU': 8.0,\n",
-       " 'GPU': 1.0,\n",
-       " 'node:10.0.6.237': 1.0,\n",
+       "{'GPU': 1.0,\n",
+       " 'node:__internal_head__': 1.0,\n",
+       " 'object_store_memory': 9535951257.0,\n",
        " 'accelerator_type:A10G': 1.0,\n",
-       " 'object_store_memory': 9534181785.0,\n",
+       " 'node:10.0.27.252': 1.0,\n",
+       " 'CPU': 8.0,\n",
        " 'memory': 34359738368.0}"
       ]
      },
@@ -481,9 +481,9 @@
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "2023-11-09 22:07:26,749\tINFO streaming_executor.py:93 -- Executing DAG InputDataBuffer[Input] -> TaskPoolMapOperator[FlatMap(extract_sections)]\n",
-      "2023-11-09 22:07:26,750\tINFO streaming_executor.py:94 -- Execution config: ExecutionOptions(resource_limits=ExecutionResources(cpu=None, gpu=None, object_store_memory=None), locality_with_output=False, preserve_order=False, actor_locality_enabled=True, verbose_progress=False)\n",
-      "2023-11-09 22:07:26,751\tINFO streaming_executor.py:96 -- Tip: For detailed progress reporting, run `ray.data.DataContext.get_current().execution_options.verbose_progress = True`\n"
+      "2023-11-27 11:08:16,997\tINFO streaming_executor.py:93 -- Executing DAG InputDataBuffer[Input] -> TaskPoolMapOperator[FlatMap(extract_sections)]\n",
+      "2023-11-27 11:08:16,997\tINFO streaming_executor.py:94 -- Execution config: ExecutionOptions(resource_limits=ExecutionResources(cpu=None, gpu=None, object_store_memory=None), locality_with_output=False, preserve_order=False, actor_locality_enabled=True, verbose_progress=False)\n",
+      "2023-11-27 11:08:16,998\tINFO streaming_executor.py:96 -- Tip: For detailed progress reporting, run `ray.data.DataContext.get_current().execution_options.verbose_progress = True`\n"
      ]
     },
     {
@@ -529,7 +529,7 @@
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "2023-11-09 22:07:51,330\tWARNING plan.py:567 -- Warning: The Ray cluster currently does not have any available CPUs. The Dataset job will hang unless more CPUs are freed up. A common reason is that cluster resources are used by Actors or Tune trials; see the following link for more details: https://docs.ray.io/en/master/data/dataset-internals.html#datasets-and-tune\n"
+      "2023-11-27 11:08:42,609\tWARNING plan.py:567 -- Warning: The Ray cluster currently does not have any available CPUs. The Dataset job will hang unless more CPUs are freed up. A common reason is that cluster resources are used by Actors or Tune trials; see the following link for more details: https://docs.ray.io/en/master/data/dataset-internals.html#datasets-and-tune\n"
      ]
     }
    ],
@@ -565,7 +565,7 @@
    "outputs": [
     {
      "data": {
-      "image/png": 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",
+      "image/png": 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",
       "text/plain": [
        "<Figure size 1200x300 with 1 Axes>"
       ]
@@ -635,10 +635,10 @@
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "2023-11-09 22:07:51,809\tINFO dataset.py:2380 -- Tip: Use `take_batch()` instead of `take() / show()` to return records in pandas or numpy batch format.\n",
-      "2023-11-09 22:07:51,811\tINFO streaming_executor.py:93 -- Executing DAG InputDataBuffer[Input] -> TaskPoolMapOperator[FlatMap(extract_sections->Limit[1])] -> LimitOperator[limit=1]\n",
-      "2023-11-09 22:07:51,812\tINFO streaming_executor.py:94 -- Execution config: ExecutionOptions(resource_limits=ExecutionResources(cpu=None, gpu=None, object_store_memory=None), locality_with_output=False, preserve_order=False, actor_locality_enabled=True, verbose_progress=False)\n",
-      "2023-11-09 22:07:51,812\tINFO streaming_executor.py:96 -- Tip: For detailed progress reporting, run `ray.data.DataContext.get_current().execution_options.verbose_progress = True`\n"
+      "2023-11-27 11:08:43,103\tINFO dataset.py:2380 -- Tip: Use `take_batch()` instead of `take() / show()` to return records in pandas or numpy batch format.\n",
+      "2023-11-27 11:08:43,105\tINFO streaming_executor.py:93 -- Executing DAG InputDataBuffer[Input] -> TaskPoolMapOperator[FlatMap(extract_sections->Limit[1])] -> LimitOperator[limit=1]\n",
+      "2023-11-27 11:08:43,106\tINFO streaming_executor.py:94 -- Execution config: ExecutionOptions(resource_limits=ExecutionResources(cpu=None, gpu=None, object_store_memory=None), locality_with_output=False, preserve_order=False, actor_locality_enabled=True, verbose_progress=False)\n",
+      "2023-11-27 11:08:43,106\tINFO streaming_executor.py:96 -- Tip: For detailed progress reporting, run `ray.data.DataContext.get_current().execution_options.verbose_progress = True`\n"
      ]
     },
     {
@@ -659,7 +659,7 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "page_content='Environments#\\nRLlib works with several different types of environments, including Farama-Foundation Gymnasium, user-defined, multi-agent, and also batched environments.\\nTip\\nNot all environments work with all algorithms. Check out the algorithm overview for more information.' metadata={'source': 'https://docs.ray.io/en/master/rllib-env.html#environments'}\n"
+      "page_content='Ray Dashboard#\\nRay provides a web-based dashboard for monitoring and debugging Ray applications.\\nThe visual representation of the system state, allows users to track the performance\\nof applications and troubleshoot issues.' metadata={'source': 'https://docs.ray.io/en/master/ray-observability/getting-started.html#ray-dashboard'}\n"
      ]
     }
    ],
@@ -713,9 +713,9 @@
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "2023-11-09 22:07:52,146\tINFO streaming_executor.py:93 -- Executing DAG InputDataBuffer[Input] -> TaskPoolMapOperator[FlatMap(extract_sections)->FlatMap(partial)]\n",
-      "2023-11-09 22:07:52,147\tINFO streaming_executor.py:94 -- Execution config: ExecutionOptions(resource_limits=ExecutionResources(cpu=None, gpu=None, object_store_memory=None), locality_with_output=False, preserve_order=False, actor_locality_enabled=True, verbose_progress=False)\n",
-      "2023-11-09 22:07:52,148\tINFO streaming_executor.py:96 -- Tip: For detailed progress reporting, run `ray.data.DataContext.get_current().execution_options.verbose_progress = True`\n"
+      "2023-11-27 11:08:43,421\tINFO streaming_executor.py:93 -- Executing DAG InputDataBuffer[Input] -> TaskPoolMapOperator[FlatMap(extract_sections)->FlatMap(partial)]\n",
+      "2023-11-27 11:08:43,421\tINFO streaming_executor.py:94 -- Execution config: ExecutionOptions(resource_limits=ExecutionResources(cpu=None, gpu=None, object_store_memory=None), locality_with_output=False, preserve_order=False, actor_locality_enabled=True, verbose_progress=False)\n",
+      "2023-11-27 11:08:43,422\tINFO streaming_executor.py:96 -- Tip: For detailed progress reporting, run `ray.data.DataContext.get_current().execution_options.verbose_progress = True`\n"
      ]
     },
     {
@@ -736,9 +736,9 @@
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "2023-11-09 22:08:17,806\tINFO streaming_executor.py:93 -- Executing DAG InputDataBuffer[Input] -> TaskPoolMapOperator[FlatMap(extract_sections)->FlatMap(partial->Limit[1])] -> LimitOperator[limit=1]\n",
-      "2023-11-09 22:08:17,807\tINFO streaming_executor.py:94 -- Execution config: ExecutionOptions(resource_limits=ExecutionResources(cpu=None, gpu=None, object_store_memory=None), locality_with_output=False, preserve_order=False, actor_locality_enabled=True, verbose_progress=False)\n",
-      "2023-11-09 22:08:17,807\tINFO streaming_executor.py:96 -- Tip: For detailed progress reporting, run `ray.data.DataContext.get_current().execution_options.verbose_progress = True`\n"
+      "2023-11-27 11:09:05,647\tINFO streaming_executor.py:93 -- Executing DAG InputDataBuffer[Input] -> TaskPoolMapOperator[FlatMap(extract_sections)->FlatMap(partial->Limit[1])] -> LimitOperator[limit=1]\n",
+      "2023-11-27 11:09:05,648\tINFO streaming_executor.py:94 -- Execution config: ExecutionOptions(resource_limits=ExecutionResources(cpu=None, gpu=None, object_store_memory=None), locality_with_output=False, preserve_order=False, actor_locality_enabled=True, verbose_progress=False)\n",
+      "2023-11-27 11:09:05,648\tINFO streaming_executor.py:96 -- Tip: For detailed progress reporting, run `ray.data.DataContext.get_current().execution_options.verbose_progress = True`\n"
      ]
     },
     {
@@ -931,6 +931,14 @@
     "os.environ[\"SQL_DUMP_FP\"] = f\"{EFS_DIR}/sql_dumps/{embedding_model_name.split('/')[-1]}_{chunk_size}_{chunk_overlap}.sql\""
    ]
   },
+  {
+   "cell_type": "markdown",
+   "id": "633f3c88-0c88-48b5-a6f9-4b08e3a0dc43",
+   "metadata": {},
+   "source": [
+    "**Note**: Run `bash setup-pgvector.sh` first!"
+   ]
+  },
   {
    "cell_type": "code",
    "execution_count": null,
@@ -939,6 +947,13 @@
     "tags": []
    },
    "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "NOTICE:  table \"document\" does not exist, skipping\n"
+     ]
+    },
     {
      "name": "stdout",
      "output_type": "stream",
@@ -959,14 +974,6 @@
     "echo $SQL_DUMP_FP"
    ]
   },
-  {
-   "cell_type": "markdown",
-   "id": "633f3c88-0c88-48b5-a6f9-4b08e3a0dc43",
-   "metadata": {},
-   "source": [
-    "**Note**: Run `bash setup-pgvector.sh` first!"
-   ]
-  },
   {
    "cell_type": "code",
    "execution_count": null,
@@ -996,13 +1003,37 @@
       "SET\n",
       "SET\n",
       "ALTER TABLE\n",
-      "ALTER TABLE\n",
-      "ALTER TABLE\n",
-      "ALTER TABLE\n",
-      "DROP SEQUENCE\n",
-      "DROP TABLE\n",
+      "ALTER TABLE\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "psql:/efs/shared_storage/goku/sql_dumps/gte-base_300_50.sql:20: ERROR:  relation \"public.data_document\" does not exist\n",
+      "psql:/efs/shared_storage/goku/sql_dumps/gte-base_300_50.sql:22: ERROR:  relation \"public.data_document\" does not exist\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
       "DROP SEQUENCE\n",
-      "DROP TABLE\n",
+      "DROP TABLE\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "psql:/efs/shared_storage/goku/sql_dumps/gte-base_300_50.sql:25: ERROR:  sequence \"data_document_id_seq\" does not exist\n",
+      "psql:/efs/shared_storage/goku/sql_dumps/gte-base_300_50.sql:26: ERROR:  table \"data_document\" does not exist\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
       "DROP EXTENSION\n",
       "CREATE EXTENSION\n",
       "COMMENT\n",
@@ -1202,6 +1233,258 @@
     "tags": []
    },
    "outputs": [
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "6c7ba3d75fde47ff994a0d1a48e212bf",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Downloading .gitattributes:   0%|          | 0.00/1.52k [00:00<?, ?B/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "6873c7b7f68a4b98912b1f063bd83b05",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Downloading 1_Pooling/config.json:   0%|          | 0.00/190 [00:00<?, ?B/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
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+    },
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+      ]
+     },
+     "metadata": {},
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+    },
+    {
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+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
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       "text/plain": [
@@ -1243,7 +1526,6 @@
     "with psycopg.connect(os.environ[\"DB_CONNECTION_STRING\"]) as conn:\n",
     "    register_vector(conn)\n",
     "    with conn.cursor() as cur:\n",
-    "        # cur.execute(\"SELECT * FROM document ORDER BY embedding <=> %s LIMIT %s\", (embedding, num_chunks))\n",
     "        cur.execute(\"SELECT *, (embedding <=> %s) AS similarity_score FROM document ORDER BY similarity_score LIMIT %s\", (embedding, num_chunks))\n",
     "        rows = cur.fetchall()\n",
     "        ids = [row[0] for row in rows]\n",
@@ -1382,7 +1664,64 @@
    "metadata": {
     "tags": []
    },
-   "outputs": [],
+   "outputs": [
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "5aa2ea343ea44ae994b735d6e575b6cf",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Downloading tokenizer_config.json:   0%|          | 0.00/28.0 [00:00<?, ?B/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "88496cc8cd424b47b14e3979ee138dd8",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Downloading vocab.txt:   0%|          | 0.00/232k [00:00<?, ?B/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "625f33f68eef421a91c5d61022422020",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Downloading tokenizer.json:   0%|          | 0.00/466k [00:00<?, ?B/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "725b231dc66749c6a02325efeccb5234",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Downloading config.json:   0%|          | 0.00/570 [00:00<?, ?B/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
    "source": [
     "from rag.generate import prepare_response\n",
     "from rag.utils import get_credentials"
@@ -2630,7 +2969,7 @@
    "source": [
     "It appears that larger chunk sizes do help but tapers off (too much context might be too noisy). Larger chunk sizes [aren’t always better](https://arxiv.org/abs/2307.03172).\n",
     "\n",
-    "**Note**: If we were to use larger chunk sizes (ours is based on characters), keep in mind that [most](https://huggingface.co/spaces/mteb/leaderboard) open source embedding models have a maximum sequence length of 512 sub-word tokens. This means that if our chunk contains more than 512 sub-word tokens (4 chars ≈ 1 token), the embedding wouldn't account for it anyway (unless we finetune our embedding model to have longer sequence lengths)."
+    "**Note**: If we were to use larger chunk sizes (ours is based on characters), keep in mind that [most](https://huggingface.co/spaces/mteb/leaderboard) open source embedding models have a maximum sequence length of 512 sub-word tokens. This means that if our chunk contains more than 512 sub-word tokens (4 chars ≈ 1 token), the embedding wouldn't account for it anyway (unless we fine-tune our embedding model to have longer sequence lengths)."
    ]
   },
   {
@@ -3102,7 +3441,7 @@
       "\n",
       "gpt-4\n",
       "  retrieval score: 0.7288135593220338\n",
-      "  quality score: 3.824858757062147\n",
+      "  quality score: 3.7457627118644066\n",
       "\n",
       "llama-2-7b-chat-hf\n",
       "  retrieval score: 0.7288135593220338\n",
@@ -4377,7 +4716,7 @@
    "id": "044e2e42-f6eb-494e-89db-a617bc72da8b",
    "metadata": {},
    "source": [
-    "Much better validation scores and overall better performance but it's not worth the effort compared to using our base gte-large embedding model. This again can be improved with larger/higher quality datasets and perhaps even a larger testing dataset to capture small improvements in our retrieval scores.\n",
+    "Much better validation scores and overall better performance. This can be even further improved with larger/higher quality datasets and perhaps even a larger testing dataset to capture small improvements in our retrieval scores.\n",
     "\n",
     "**Note**: even though the retrieval scores are the same, the quality scores differ due to the order in which the new embedding models determine the top k relevant chunks and if different irrelevant sources were introduced."
    ]
@@ -5121,9 +5460,7 @@
    "id": "743b6854-6a92-4201-884f-7bf83ac6bfe6",
    "metadata": {},
    "source": [
-    "It looks like lexical search improved our retrieval scores, which was expected since more chunks are added into the context. But it didn't really help improve our quality scores, perhaps the additional chunks added also introduced quite a bit of noise.\n",
-    "\n",
-    "**Note**: This was just one aspect (keyword matching) of lexical search that we explored but there are many other useful features such as filtering, counts, etc. It's also worth exploring how we combine the lexical search results with semantic search results."
+    "Seems like adding lexical search was not as impactful as we had hoped but this was just one aspect (keyword matching) of lexical search that we explored but there are many other useful features such as filtering, counts, etc. It's also worth exploring how we combine the lexical search results with semantic search results."
    ]
   },
   {
@@ -5668,8 +6005,8 @@
     }
    ],
    "source": [
-    "# Test inference\n",
-    "question = \"traning with deepspeed\"\n",
+    "# Inference\n",
+    "question = \"training with deepspeed\"\n",
     "custom_predict([question], classifier=reranker)[0]"
    ]
   },
@@ -6112,7 +6449,8 @@
      "data": {
       "text/plain": [
        "[('gpt-4',\n",
-       "  {'retrieval_score': 0.7288135593220338, 'quality_score': 3.824858757062147}),\n",
+       "  {'retrieval_score': 0.7288135593220338,\n",
+       "   'quality_score': 3.7457627118644066}),\n",
        " ('rerank-0.9',\n",
        "  {'retrieval_score': 0.7288135593220338, 'quality_score': 3.621468926553672}),\n",
        " ('gte-large-fine-tuned-el',\n",
@@ -6254,46 +6592,46 @@
      "output_type": "stream",
      "text": [
       "gpt-3.5-turbo\n",
-      "  prompted tokens (avg): 5615\n",
+      "  prompted tokens (avg): 1695\n",
       "  sampled tokens (avg): 73\n",
-      "  total cost: $1.5169\n",
-      "  avg cost: $0.0086\n",
+      "  total cost: $0.4761\n",
+      "  avg cost: $0.0027\n",
       "\n",
       "gpt-4\n",
-      "  prompted tokens (avg): 5615\n",
+      "  prompted tokens (avg): 1695\n",
       "  sampled tokens (avg): 112\n",
-      "  total cost: $31.0171\n",
-      "  avg cost: $0.1752\n",
+      "  total cost: $10.2019\n",
+      "  avg cost: $0.0576\n",
       "\n",
       "llama-2-7b-chat-hf\n",
-      "  prompted tokens (avg): 5615\n",
+      "  prompted tokens (avg): 1695\n",
       "  sampled tokens (avg): 248\n",
-      "  total cost: $0.1557\n",
-      "  avg cost: $0.0009\n",
+      "  total cost: $0.0516\n",
+      "  avg cost: $0.0003\n",
       "\n",
       "llama-2-13b-chat-hf\n",
-      "  prompted tokens (avg): 5615\n",
+      "  prompted tokens (avg): 1695\n",
       "  sampled tokens (avg): 226\n",
-      "  total cost: $0.2585\n",
-      "  avg cost: $0.0015\n",
+      "  total cost: $0.0851\n",
+      "  avg cost: $0.0005\n",
       "\n",
       "llama-2-70b-chat-hf\n",
-      "  prompted tokens (avg): 5615\n",
+      "  prompted tokens (avg): 1695\n",
       "  sampled tokens (avg): 218\n",
-      "  total cost: $1.0326\n",
-      "  avg cost: $0.0058\n",
+      "  total cost: $0.3388\n",
+      "  avg cost: $0.0019\n",
       "\n",
       "codellama-34b-instruct-hf\n",
-      "  prompted tokens (avg): 5615\n",
+      "  prompted tokens (avg): 1695\n",
       "  sampled tokens (avg): 187\n",
-      "  total cost: $1.0271\n",
-      "  avg cost: $0.0058\n",
+      "  total cost: $0.3333\n",
+      "  avg cost: $0.0019\n",
       "\n",
       "mistral-7b-instruct-v0.1\n",
-      "  prompted tokens (avg): 5615\n",
+      "  prompted tokens (avg): 1695\n",
       "  sampled tokens (avg): 135\n",
-      "  total cost: $0.1527\n",
-      "  avg cost: $0.0009\n",
+      "  total cost: $0.0486\n",
+      "  avg cost: $0.0003\n",
       "\n"
      ]
     }
@@ -6315,7 +6653,7 @@
    "outputs": [
     {
      "data": {
-      "image/png": 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",
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oy05oaCihoaFkZ2c/l/Nl5ygcTL7BlTv3qWJhTOMatujraZ7LuV9ke/fuxdvbmwkTJmBnZ8fWrVvp168fVlZWdOzYsdDjYmNjtd67Y8eO8cYbb9CjR4/nEfYL5cyZM3To0AFnZ+fyDkUIIYQQ4h/jhe/BO3HiBPPmzSs0uQMwMTHhnXfeYd++fQwYMOA5Rlf2hg4dyokTJ4iNjX3m5/r1WCot5u7kneX7GbkugXeW76fF3J38eiz1mZ+7IMHBwXh5eWFmZoaTkxNDhgwhPT1d3R8WFoa1tTVbt26lTp06mJqa0r17dzIyMggPD8fFxQUbGxtGjBihlWStWbMGPz8/LCwssLe3p3fv3ly5cqXIWD755BOmT59Os2bNcHV1ZeTIkbRt25ZNmzYVeVzlypWxt7dXX1u3bsXV1ZWWLVtq1UtNTaVdu3aYmJhQs2ZNfvzxx2Lvz/Hjx+nYsSOWlpZYWFjw6quvcubMGa068+fPx8HBgYoVKzJ06FAePHhQovuQkpJCq1atALCxsUGj0dC/f/8i48nIyGDgwIFYWFhQvXp1vv76a3WfRqMhLi6OadOmodFomDJlSrHXJ4QQQgghnt4Ln+BVrFjxmdb/p/r1WCqDv/2T1LT7WuWX0u4z+Ns/yyXJ09PTY/HixRw/fpzw8HB27tzJ+PHjtepkZGSwePFi1q1bx6+//kp0dDRdu3Zl+/btbN++nTVr1vDVV19pJUwPHjxg+vTpHD58mM2bN5OSklJs8lKQtLQ0bG1tS1w/KyuLb7/9loEDB6LRaPeKfvbZZ3Tr1o3Dhw/Tp08fevXqxcmTJwtt6+LFi7z22msYGRmxc+dO4uLiGDhwIA8fPlTrREVFcebMGaKioggPDycsLIywsDB1f1H3wcnJiY0bNwKQlJREamoqixYtKvL6FixYgJ+fH/Hx8QwZMoTBgweTlJQE5Cawnp6ejB07ltTUVJ3uVRdCCCGE0CUv/BDNopw8eZL9+/dTv359mcShFLJzFKb+fAKlgH0KoAGm/nyCNzzsn+twzbzJVyD3WbYZM2YwaNAgvvjiC7X8wYMHfPnll7i6ugLQvXt31qxZw+XLlzE3N8fDw4NWrVoRFRXF22+/DcDAgQPV42vWrMnixYtp1KgR6enpmJublyi2H374gdjYWL766qsSX8/mzZu5detWgclkjx49eP/99wGYPn06ERERLFmyROtaHxUaGoqVlRXr1q2jQoUKANSuXVurjo2NDUuXLkVfX5+6devSoUMHIiMj+eCDD4Di70Ne8lqlShWsra2Lvb727dszZMgQACZMmEBISAhRUVHUqVMHe3t7DAwMMDc3x97evti2hBBCCCFE2Xjhe/DyTJs2jc8//1zdjoqKwtfXl48//phGjRqVaAIMketg8o18PXePUoDUtPscTL7x/IICduzYQevWralatSoWFhb07duX69evk5GRodYxNTVVkzsAOzs7XFxctBI1Ozs7rSGYcXFxBAYGUr16dSwsLNThkufPnwfA09MTc3NzzM3NadeuXb64oqKiGDBgAMuXL8fT0xOA3bt3q8eYm5sX+PO3cuVK2rVrh6OjY759TZs2zbed14PXrl07td288yUkJPDqq6+qyV1BPD090dfXV7cdHBxKdR8KsnbtWq3r3L17t7rP29tb/X+NRoO9vX2xQ1+FEEIIIcSzpTM9eD/++COzZ89Wt2fOnMmIESP4/PPP+eqrr5g1axZ9+vQpxwh1x5U7hSd3T1KvLKSkpNCxY0cGDx7MzJkzsbW1Zc+ePbz33ntkZWVhamoKkC/B0Wg0BZbl5OQAcPfuXQICAggICGDt2rVUrlyZ8+fPExAQQFZWFgDbt29Xn1UzMTHRamvXrl0EBgYSEhJCv3791HI/Pz8SEhLUbTs7O63jzp07x44dO4p9Zq8gK1as4N69e1rX+3hcBXna+1CQTp068corr6jbVatWLdH5hBBCCCFE+XjhE7zVq1ejKAopKSkkJCRw/fp1FEUhJiaGV199ldWrV5OTk8PZs2dZvXo1gNYXcZFfFQvjMq1XFuLi4sjJyWHBggXo6eV2LP/www9P3W5iYiLXr19nzpw5ODk5AXDo0CGtOoXN8hgdHU3Hjh2ZO3cuH374odY+ExMT3NzcCj3vqlWrqFKlCh06dChw//79+7V+TvOGGoN2EpXH29ub8PBwHjx4UGQvXmFKch8MDQ0BtCaosbCwwMLCotTnE0IIIYQQ5eOFH6Lp7OyMi4sLhoaG2NnZ4ezszK1bt7C0tKRVq1Y4Ozvj6uqKRqPBxcVFpmQvgcY1bHGwMqawp+s0gINV7pIJz4ubmxsPHjxgyZIlnD17ljVr1rBs2bKnbrd69eoYGhqq7W7ZsoXp06cXe1xUVBQdOnRgxIgRdOvWjUuXLnHp0iVu3Ch+2GpOTg6rVq0iKCgIA4OC/4ayYcMGvvnmG06dOsXkyZM5ePAgw4YNK7TNYcOGcfv2bXr16sWhQ4f466+/WLNmjTqpSXFKch+cnZ3RaDRs3bqVq1evas1gKoQQQgghdMMLn+C1bNmSli1b0qBBA7Zu3YqhoSG//vor7du357XXXqNly5Y4ODjg5OSkboui6etpmByYu+zE40le3vbkQI/nOsGKj48PwcHBzJ07l3r16rF27VqtIblPqnLlyoSFhbFhwwY8PDyYM2cO8+fPL/a48PBwMjIymD17Ng4ODurrrbfeKvbYHTt2cP78ea1JTR43depU1q1bh7e3N6tXr+b7778vcimQihUrsnPnTtLT02nZsiUNGzZk+fLlJe7NK8l9qFq1KlOnTmXixInY2dkVmXAKIYQQQogXk0ZRlIImU3zhHD58mM6dO3P+/Hnq1q3Lb7/9pg41Gzt2LA8fPix2Wnddcvv2baysrEhLS8PS0lJr3/3790lOTqZGjRoYGz/5MMpfj6Uy9ecTWhOuOFgZMznQg7b1HJ64XSH+icrqcymEEEII8biicoPHvfDP4OXx8fEhJSWF69ev51vrbty4ccVeqMivbT0H3vCw52DyDa7cuU8Vi9xhmc+z504IIYQQQghRdnQmwctT0ELmDg7S2/Sk9PU0NHWVxeGFEEIIIYR4Gbzwz+CtW7euxHUvXLhATEzMM4xGCCGEEEIIIV5cL3yC9+WXX+Lu7s68efPUhaAflZaWxvbt2+nduzcNGjTg+vXr5RClEEIIIYQQQpS/F36I5q5du9iyZQtLlixh0qRJmJmZYWdnh7GxMTdv3uTSpUtUqlSJ/v37c+zYsXwLTgshhBBCCCHEP8ULn+ABdOrUiU6dOnHt2jX27NnDuXPnuHfvHpUqVaJ+/frUr19fXRxbCCGEEEIIIf6pdCLBy1OpUiW6dOlS3mE8U6GhoYSGhpKdnV3eoQghhBBCCCF0jHR7vWCGDh3KiRMniI2NLe9QhBBCCCGEEDpGEjwhhBBCCCGEeElIgieeK39/f0aNGgWAi4sLCxcuLNd4XkQpKSloNBoSEhLKtN1H7/3zoNFo2Lx5c5F1YmJi8PLyokKFCi/98GshhBBCiOdBErx/upxsSN4NR3/M/W+OPPsHEB0dTefOnXFwcMDMzAxfX1/Wrl1b7HF//PEHgYGBODo6FprgTJkyhbp162JmZoaNjQ1t2rThwIEDz+Aqyl5YWBjW1tZl1t6YMWPw9fUlOTmZsLCwMmtXCCGEEOKfSqcSvKioqPIO4eVyYgssrAfhHWHje7n/XVgvt/wfbu/evXh7e7Nx40aOHDnCgAED6NevH1u3bi3yuLt37+Lj40NoaGihdWrXrs3SpUs5evQoe/bswcXFhTfffJOrV6+W9WW88M6cOcPrr79OtWrVyjRxFEIIIYT4p9KpBK9t27a4uroyY8YMLly4UN7h6LYTW+CHfnD7b+3y26m55eWQ5AUHB+Pl5YWZmRlOTk4MGTKE9PR0dX9e79HWrVupU6cOpqamdO/enYyMDMLDw3FxccHGxoYRI0ZozUK6Zs0a/Pz8sLCwwN7ent69e3PlypUiY/nkk0+YPn06zZo1w9XVlZEjR9K2bVs2bdpU5HHt2rVjxowZdO3atdA6vXv3pk2bNtSsWRNPT0+Cg4O5ffs2R44c0aqXmJhIs2bNMDY2pl69euzatavIc0PukEd/f39MTU2xsbEhICCAmzdvqvtzcnIYP348tra22NvbM2XKFK3ji3oPoqOjGTBgAGlpaWg0GjQaTb7jH3ft2jW6du2KqakptWrVYsuW3J+rvGGo169fZ+DAgWg0GunBE0IIIYQoAzqV4F28eJFhw4bx448/UrNmTQICAvjhhx/Iysoq79B0S042/DoBUArY+f9lv0587sM19fT0WLx4McePHyc8PJydO3cyfvx4rToZGRksXryYdevW8euvvxIdHU3Xrl3Zvn0727dvZ82aNXz11Vf8+OOP6jEPHjxg+vTpHD58mM2bN5OSkkL//v1LHV9aWhq2trZPe5lasrKy+Prrr7GyssLHx0dr38cff8zYsWOJj4+nadOmBAYGcv369ULbSkhIoHXr1nh4eLBv3z727NlDYGCgVrIbHh6OmZkZBw4cYN68eUybNo2IiAh1f1HvQbNmzVi4cCGWlpakpqaSmprKuHHjiry+qVOn0rNnT44cOUL79u3p06cPN27cwMnJidTUVCwtLVm4cCGpqam8/fbbT3ILhRBCCCHEoxQdFRcXpwwbNkypWLGiUrFiRWX48OFKQkJCeYdVZtLS0hRASUtLy7fv3r17yokTJ5R79+49WeNn/1CUyZbFv87+8ZRXkV/Lli2VkSNHKoqiKM7OzkpISEihdTds2KBUrFhR3V61apUCKKdPn1bLPvroI8XU1FS5c+eOWhYQEKB89NFHhbYbGxurAFrHFGf9+vWKoaGhcuzYsRIfAyg//fRTgft+/vlnxczMTNFoNIqjo6Ny8OBBdV9ycrICKHPmzFHLHjx4oFSrVk2ZO3duoed75513lObNmxe6v2XLlkqLFi20yho1aqRMmDCh0GMKeg+srKwKrf8oQPn000/V7fT0dAVQfvnlF7XMyspKWbVqVYnae9E99edSCCGEEKIQReUGj9OpHrxHNWjQgEmTJjFs2DDS09P55ptvaNiwIa+++irHjx8v7/BebOmXy7ZeGdmxYwetW7ematWqWFhY0LdvX65fv05GRoZax9TUFFdXV3Xbzs4OFxcXzM3NtcoeHYIZFxdHYGAg1atXx8LCgpYtWwJw/vx5ADw9PTE3N8fc3Jx27drliysqKooBAwawfPlyPD09Adi9e7d6jLm5eYkmYHlUq1atSEhIYO/evbRt25aePXvmGzbatGlT9f8NDAzw8/Pj5MmThcac14NXFG9vb61tBwcHrfOW5D143KxZs7TuRd59ffx8ZmZmWFpaFjs8VgghhBBCPDmdS/AePHjAjz/+SPv27XF2dua3335j6dKlXL58mdOnT+Ps7EyPHj3KO8wXm7ld2dYrAykpKXTs2FGd2CQuLk6dqOTRIbgVKlTQOk6j0RRYlpOTA+ROehIQEIClpSVr164lNjaWn376Savd7du3k5CQQEJCAitWrNBqa9euXQQGBhISEkK/fv3Ucj8/P/WYhIQEOnXqVKrrNTMzw83NjSZNmrBy5UoMDAxYuXJliY8vKGYTE5NijyvqXpX0PXjcoEGDtO6Fo6Njic4nhBBCCCHKnkF5B1Aaw4cP5/vvv0dRFPr27cu8efOoV6+eut/MzIz58+drfcEUBXBuBpaOuROqFPgcniZ3v3Oz5xZSXFwcOTk5LFiwAD293L87/PDDD0/dbmJiItevX2fOnDk4OTkBcOjQIa06zs7OBR4bHR1Nx44dmTt3Lh9++KHWPhMTE9zc3J46vjw5OTlkZmZqle3fv5/XXnsNgIcPHxIXF8ewYcMKjdnb25vIyEimTp36RDGU5D0wNDTUeqYPwNbWtsyfTRRCCCGEEE9Gp3rwTpw4wZIlS/j7779ZuHChVnKXp1KlSrKcQnH09KHt3P/f0Dy28/+3287JrfecuLm58eDBA5YsWcLZs2dZs2YNy5Yte+p2q1evjqGhodruli1bmD59erHHRUVF0aFDB0aMGEG3bt24dOkSly5d4saNG0Uel56ervZkASQnJ5OQkKAOW7x79y6ffPIJ+/fv59y5c8TFxTFw4EAuXryYr+c5NDSUn376icTERIYOHcrNmzcZOHBgoeeeNGkSsbGxDBkyhCNHjpCYmMiXX37JtWvXir1eKNl74OLiQnp6OpGRkVy7dq3IoZtCCCGEEOL505kE78GDBzg7O9OkSROMjIwKrWdgYKA+YyWK4NEJeq4GSwftckvH3HKP0g05fFo+Pj4EBwczd+5c6tWrx9q1a5k9e/ZTt1u5cmXCwsLYsGEDHh4ezJkzh/nz5xd7XHh4OBkZGcyePRsHBwf19dZbbxV53KFDh6hfvz7169cHchfyrl+/Pv/+978B0NfXJzExkW7dulG7dm11Zszdu3erz/flmTNnDnPmzMHHx4c9e/awZcsWKlWqVOi5a9euze+//87hw4dp3LgxTZs25T//+Q8GBiXrqC/Je9CsWTMGDRrE22+/TeXKlZk3b16J2hZCCCGEEM+HRlGUgsbovZCsrKxISEigRo0a5R3KM3f79m2srKxIS0vD0tJSa9/9+/dJTk6mRo0aGBsbP92JcrLh3N7cCVXM7XKHZT7HnjshXhZl+rkUQgghhHhEUbnB43SmBw+gS5cubN68ubzDeLno6UONV8Gre+5/JbkTQgghhBBCZ+nUJCu1atVi2rRpxMTE0LBhQ8zMzLT2jxgxopwiE0IIIYQQQojyp1MJ3sqVK7G2tiYuLo64uDitfRqNRhI8IYQQQgghxD+aTiV4ycnJ5R2CEEIIIYQQQrywdOoZvDxZWVkkJSXx8OHD8g5FCCGEEEIIIV4YOpXgZWRk8N5772Fqaoqnp6e6ttjw4cOZM2dOOUcnhBBCCCGEEOVLpxK8SZMmcfjwYaKjo7WmIW/Tpg3r168vx8iEEEIIIYQQovzpVIK3efNmli5dSosWLdBoNGq5p6cnZ86cKcfICnbhwgX8/f3x8PDA29ubDRs2lHdIQgghhBBCiJeYTk2ycvXqVapUqZKv/O7du1oJ34vCwMCAhQsX4uvry6VLl2jYsCHt27fPt7yDEEIIIYQQQpQFnerB8/PzY9u2bep2XlK3YsUKmjZtWl5hFcrBwQFfX18A7O3tqVSpEjdu3CjfoHRMdHQ0Go2GW7duPfdzh4WFYW1t/dTtuLi4sHDhwlIdk5KSgkajISEh4anPL4QQQggh/jl0KsGbNWsWn3zyCYMHD+bhw4csWrSIN998k1WrVjFz5sxSt/fHH38QGBiIo6MjGo2GzZs356sTGhqKi4sLxsbGvPLKKxw8ePCJYo+LiyM7OxsnJ6cnOv5Zyc7JJvZSLNvPbif2UizZOdnlHZKWZs2akZqaipWVVbF1yyMZ9Pf3R6PR5Ht16NDhqdp1cnIiNTWVevXqlUmcZZWslpS/vz+jRo0qk7aio6Np0KABRkZGuLm5ERYWVmT9+/fv079/f7y8vDAwMKBLly5lEocQQgghhC7QqSGaLVq0ICEhgTlz5uDl5cXvv/9OgwYN2LdvH15eXqVu7+7du/j4+DBw4EDeeuutfPvXr1/PmDFjWLZsGa+88goLFy4kICCApKQkdaior69vgcs1/P777zg6OgJw48YN+vXrx/LlywuNJTMzk8zMTHX79u3bpb6e0tpxbgdzDs7hcsZltczO1I6JjSfSxrnNMz9/SRgaGmJvb1+mbWZlZWFoaFgmbW3atImsrCx1+/r16/j4+NCjR4+naldfX7/Mr7skyvLelIXk5GQ6dOjAoEGDWLt2LZGRkbz//vs4ODgQEBBQ4DHZ2dmYmJgwYsQINm7c+JwjFkIIIYQoZ4pQFEVRAOWnn37SKmvcuLEydOhQdTs7O1txdHRUZs+eXeJ279+/r7z66qvK6tWri6w3efJkBcj3SktLy1f33r17yokTJ5R79+6VOI7HRaREKF5hXkq9sHpaL68wL8UrzEuJSIl44raL0rJlS2XYsGHKyJEjFWtra6VKlSrK119/raSnpyv9+/dXzM3NFVdXV2X79u2KoihKVFSUAig3b95UFEVRUlJSlI4dOyrW1taKqamp4uHhoWzbtk1JTk7Od++CgoLUcw4dOlQZOXKkUrFiRcXf319RFEVZsGCBUq9ePcXU1FSpVq2aMnjwYOXOnTtqrKtWrVKsrKxKdX0hISGKhYWFkp6erpY5Ozsr06ZNU3r16qWYmpoqjo6OytKlS4tsJ+964uPjte7Djh07lIYNGyomJiZK06ZNlcTERPWYhIQExd/fXzE3N1csLCyUBg0aKLGxseqxj74mT56sFVvfvn0VCwsLJSgoKN89VxRFiY+PVwAlOTlZLduzZ4/SsmVLxcTERLG2tlbefPNN5caNG0pQUFC+8z16XJ6vvvpKcXBwULKzs7XKO3XqpAwYMEBRFEUZP3684unpqbX/7bffVgICAoq8f3mCgoKUzp07l6ju0yqLz6UQQgghREHS0tIKzQ0ep1NDNM+fP1/kqyxlZWURFxdHmzb/68nS09OjTZs27Nu3r0RtKIpC//79ef311+nbt2+RdSdNmkRaWpr6unDhwlPFX5TsnGzmHJyDgpJvX17Z3INzn9lwzfDwcCpVqsTBgwcZPnw4gwcPpkePHjRr1ow///yTN998k759+5KRkZHv2KFDh5KZmckff/zB0aNHmTt3Lubm5jg5Oam9NUlJSaSmprJo0SKtcxoaGhITE8OyZcuA3Pdz8eLFHD9+nPDwcHbu3Mn48eOf6tpWrlxJr1698k2k8/nnn+Pj40N8fDwTJ05k5MiRRERElLr9f/3rXyxYsIBDhw5hYGDAwIED1X19+vShWrVqxMbGEhcXx8SJE6lQoQLNmjVj4cKFWFpakpqaSmpqKuPGjVOPmz9/vhrbZ599VqI4EhISaN26NR4eHuzbt489e/YQGBhIdnY2ixYtomnTpnzwwQfq+QoamtyjRw+uX79OVFSUWnbjxg1+/fVX+vTpA8C+ffu0PoMAAQEBJf4MCiGEEEL80+jUEE0XF5ciZ8vMzi67hOTatWtkZ2djZ2enVW5nZ0diYmKJ2oiJiWH9+vV4e3urz/etWbOmwOGkRkZGGBkZPXXcJfHnlT+1hmU+TkHhUsYl/rzyJ43sG5X5+X18fPj000+B3MR2zpw5VKpUiQ8++ACAf//733z55ZccOXIk37Hnz5+nW7du6j2sWbOmus/W1haAKlWq5HverFatWsybN0+r7NFnxFxcXJgxYwaDBg3iiy++eKLrOnjwIMeOHWPlypX59jVv3pyJEycCULt2bWJiYggJCeGNN94o1TlmzpxJy5YtAZg4cSIdOnTg/v37GBsbc/78eT7++GPq1q0L5F5zHisrKzQaTYHDPl9//XXGjh2rbpfkjwvz5s3Dz89P6155enqq/29oaIipqWmRw0xtbGxo164d3333Ha1btwbgxx9/pFKlSrRq1QqAS5cuFfgZvH37Nvfu3cPExKTYWIUQQggh/kl0qgcvPj6eP//8U30dOHCAZcuWUbt27RdyjbkWLVqQk5NDQkKC+nqSZwXL2tWMq2Var7S8vb3V/9fX16dixYpa9yXvC/2VK1fyHTtixAhmzJhB8+bNmTx5coFJYEEaNmyYr2zHjh20bt2aqlWrYmFhQd++fbl+/XqBPYfnz5/H3Nxcfc2aNStfnZUrV+Ll5UXjxo3z7Xt8ltemTZty8uRJAAYNGqTVdlEevXcODg7A/+7TmDFjeP/992nTpg1z5swp8dqQfn5+Jar3qLwevNLw9PRUr7Fdu3ZAbq/jxo0b1edP165dS69evdDT06l/moQQQgghXhg69S3Kx8dH6+Xn58cHH3zA/PnzWbx4cZmeq1KlSujr63P5snZP1+XLl5/p5BehoaF4eHjQqFHZ95zlqWxauUzrlVaFChW0tjUajVZZXi9tTk5OvmPff/99zp49S9++fTl69Ch+fn4sWbKk2HM+PmQyJSWFjh074u3tzcaNG4mLiyM0NBRAa9KUPI6OjlqJ+qBBg7T23717l3Xr1vHee+8VG8vjpk2bptV2UYq6T1OmTOH48eN06NCBnTt34uHhwU8//VTs+R+/N3nJlaL8bwjvgwcPtOo8Sc/Z9u3b1WtcsWIFAIGBgSiKwrZt27hw4QK7d+9Wh2dC7vIiBX0GLS0tpfdOCCGEEKIAOpXgFaZOnTrExsaWaZuGhoY0bNiQyMhItSwnJ4fIyMhnuube0KFDOXHiRJlfz6MaVGmAnakdGgoe7qpBg72pPQ2qNHhmMTwNJycnBg0axKZNmxg7dqw6O2ne7I8lGaobFxdHTk4OCxYsoEmTJtSuXZu///670PoGBga4ubmpr7zhoHk2bNhAZmYm7777boHH79+/P9+2u7s7kDuk9NG2n0bt2rUZPXo0v//+O2+99RarVq0Ccu9NSYcwV66cm9inpqaqZY8nnt7e3lqfjccVdD5nZ2f1GqtWrQqAsbExb731FmvXruX777+nTp06NGjwv5+7pk2b5jtPRETEC7nupRBCCCHEi0CnErzbt29rvdLS0khMTOTTTz/Vet6opNLT07V6TZKTk0lISFAnbBkzZgzLly8nPDyckydPMnjwYO7evcuAAQPK8rKeO309fSY2zn0e7PEkL297QuMJ6OvpP/fYijNq1Ch+++03kpOT+fPPP4mKilITJWdnZzQaDVu3buXq1aukp6cX2o6bmxsPHjxgyZIlnD17ljVr1qiTrzyJlStX0qVLFypWrFjg/piYGObNm8epU6cIDQ1lw4YNjBw58onP97h79+4xbNgwoqOjOXfuHDExMcTGxqr3xsXFhfT0dCIjI7l27VqBw1DzuLm54eTkxJQpU/jrr7/Ytm0bCxYs0KozadIkYmNjGTJkCEeOHCExMZEvv/ySa9euqec7cOAAKSkpXLt2rcDe2Dx9+vRh27ZtfPPNN1q9d5A7fPXs2bOMHz+exMREvvjiC3744QdGjx6t1lm6dGm+4aInTpwgISGBGzdukJaWVqLeUSGEEEKIl8KzntKzLGk0GkVPT0/rpdFolOrVqyt79+4tdXsFTR/PI9PrK4qiLFmyRKlevbpiaGioNG7cWNm/f38ZXlHhipoKtaymY49IiVBa/9Baa5mENj+0eWZLJChK7pIFI0eO1CpzdnZWQkJCtMr4/2UrHp+yf9iwYYqrq6tiZGSkVK5cWenbt69y7do19bhp06Yp9vb2ikaj0Vom4fFzKoqiBAcHKw4ODoqJiYkSEBCgrF69WutcJV0mITExUQGU33//vcD9zs7OytSpU5UePXoopqamir29vbJo0aIi2yxsmYTCli7IzMxUevXqpTg5OSmGhoaKo6OjMmzYMK2fkUGDBikVK1bMt0zC4/deUXKXQPDy8lKMjY2VV199VdmwYUO+5Q6io6OVZs2aKUZGRoq1tbUSEBCgxpeUlKQ0adJEMTExKXSZhDzZ2dmKg4ODAihnzpzJtz8qKkrx9fVVDA0NlZo1ayqrVq3S2j958mTF2dlZq8zZ2bnAz/azJMskCCGEEOJZKc0yCRpFUfLPlf+C2rVrl9a2np4elStXxs3NDQMDnZoQtFi3b9/GysqKtLQ0LC0ttfbdv3+f5ORkatSogbGx8VOdJzsnmz+v/MnVjKtUNq1MgyoNXsieOyFedGX5uRRCCCGEeFRRucHjdCorypse/mUWGhpKaGhomS75UBR9Pf1nshSCEEIIIYQQ4vnTqQRvy5YtJa7bqVOnZxjJszN06FCGDh2qZulCCCGEEEIIUVI6leB16dIFjUbD46NKHy/TaDTPrQdMCCGEEEIIIV4UOjWL5u+//46vry+//PILt27d4tatW/zyyy80aNCA3377jZycHHJyciS5E0IIIYQQQvwj6VQP3qhRo1i2bBktWrRQywICAjA1NeXDDz/k5MmT5RidEEIIIYQQQpQvnerBO3PmDNbW1vnKraysSElJee7xPAuhoaF4eHjQqJFMfCKEEEIIIYQoHZ1K8Bo1asSYMWO4fPmyWnb58mU+/vhjGjduXI6RlZ2hQ4dy4sQJYmNjyzsUIYQQQgghhI7RqQTvm2++ITU1lerVq+Pm5oabmxvVq1fn4sWLrFy5srzDE0IIIYQQQohypVPP4Lm5uXHkyBEiIiJITEwEwN3dnTZt2qDRaMo5OiGEEEIIIYQoXzqV4EHuEghvvvkmb775ZnmHIsQz5eLiwqhRoxg1alR5hyKEEEIIIXSETg3RFGVPyc7m7oGDpG3dxt0DB1F0ZIkJjUbD5s2bi623adMm/Pz8sLa2xszMDF9fX9asWVPkMdHR0Wg0mnyvS5cuFXmci4sLCxcuLMVVCCGEEEIIUbZ0rgfvZRcaGkpoaOhzWcvv9u+/c3nWbB4+krgY2Ntj98kkLF+SHlJbW1v+9a9/UbduXQwNDdm6dSsDBgygSpUqBAQEFHlsUlISlpaW6naVKlWedbgAZGVlYWho+FzOJYQQQgghXi7Sg/eCeV6zaN7+/XcujhylldwBPLx8mYsjR3H799+f2bnv3LlDnz59MDMzw8HBgZCQEPz9/dWhiC4uLkyfPp133nkHMzMzqlatSmhoqHq8i4sLAF27dkWj0ajbBfH396dr1664u7vj6urKyJEj8fb2Zs+ePcXGWaVKFezt7dWXnl7hHxd/f3/OnTvH6NGj1R4/gClTpuDr66tVd+HChVox9+/fny5dujBz5kwcHR2pU6eO1r0q7D4AnD9/ns6dO2Nubo6lpSU9e/bUmmVWCCGEEEL8s+hEgrdz587n0qP1T6FkZ3N51mxQlAJ25pZdnjX7mQ3XHDNmDDExMWzZsoWIiAh2797Nn3/+qVXn888/x8fHh/j4eCZOnMjIkSOJiIgAUJPfVatWkZqaWuJkWFEUIiMjSUpK4rXXXiu2vq+vLw4ODrzxxhvExMQUWXfTpk1Uq1aNadOmkZqaSmpqaoliypMXV0REBFu3blXLi7oPOTk5dO7cmRs3brBr1y4iIiI4e/Ysb7/9dqnOLYQQQgghXh46MUTz/fff59atW7Rt25bOnTvTrl07raFzonQyDsXl67nToig8vHSJjENxmL1StusL3rlzh/DwcL777jtat24N5CZqjo6OWvWaN2/OxIkTAahduzYxMTGEhITwxhtvULlyZQCsra2xt7cv9pxpaWlUrVqVzMxM9PX1+eKLL3jjjTcKre/g4MCyZcvw8/MjMzOTFStW4O/vz4EDB2jQoEGBx9ja2qKvr4+FhUWJYnqcmZkZK1asyDc0s6j7EBkZydGjR0lOTsbJyQmA1atX4+npSWxsLI0aNSp1HEIIIYQQQrfpRA/e2bNniY6OxsPDgwULFmBnZ8cbb7zBkiVLOH/+fHmHp3MeXr1apvVK4+zZszx48EBrYXorKyutYYkATZs2zbd98uTJQts9f/485ubm6mvWrFnqPgsLCxISEoiNjWXmzJmMGTOG6OjoQtuqU6cOH330EQ0bNqRZs2Z88803NGvWjJCQEADWrl2rda7du3eX5hYUyMvLq8Dn7oq6DydPnsTJyUlN7gA8PDywtrYu8l4JIYQQQoiXl0704AF4e3vj7e3Np59+yt9//82WLVvYsmUL48ePp06dOnTq1IlOnTrh5+dX3qG+8Az+vwesrOq9CBwdHUlISFC3bW1t1f/X09PDzc0NyB12efLkSWbPno2/v3+J22/cuLH63F6nTp145ZVX1H1Vq1Yt9Dg9PT2Ux4bCPnjwIF89MzOzEscihBBCCCFEYXSiB+9xjo6ODBo0iO3bt3Pt2jU+++wzUlJSaNu2rVbPjSiYqV9DDOztobDF4TUaDOztMfVrWObnrlmzJhUqVNB6bi4tLY1Tp05p1du/f3++bXd3d3W7QoUKWs9lGhgY4Obmpr4eTfAel5OTQ2ZmZqniTkhIwMHBAcjtEXz0XCYmJgAYGhrme1a0cuXKXLp0SSvJezQRLU5R98Hd3Z0LFy5w4cIFdf+JEye4desWHh4epbo+IYQQQgjxctCZHrzCmJmZ0a1bN7p160Z2djY3btwo75CeyvNYJkGjr4/dJ5O4OHJUbpL3aA/T/yd9dp9MQqOvX+bntrCwICgoiI8//hhbW1uqVKnC5MmT0dPTU2eeBIiJiWHevHl06dKFiIgINmzYwLZt29T9Li4uREZG0rx5c4yMjLCxsSnwfLNnz8bPzw9XV1cyMzPZvn07a9as4csvv1TrTJo0iYsXL7J69Wogd5bLGjVq4Onpyf3791mxYgU7d+7k92JmFnVxceGPP/6gV69eGBkZUalSJfz9/bl69Srz5s2je/fu/Prrr/zyyy8lfoa0qPvQpk0bvLy86NOnDwsXLuThw4cMGTKEli1bSk+2EEIIIcQ/lE724BVGX19fnYBDVz2vZRIs33yTqosWYmBnp1VuYGdH1UULn+k6eMHBwTRt2pSOHTvSpk0bmjdvjru7O8bGxmqdsWPHcujQIerXr8+MGTMIDg7WWrduwYIFRERE4OTkRP369Qs91927dxkyZAienp40b96cjRs38u233/L++++rdVJTU7We5czKymLs2LF4eXnRsmVLDh8+zI4dO9RJYQozbdo0UlJScHV1VX8O3d3d+eKLLwgNDcXHx4eDBw8ybty4Et+rou6DRqPhP//5DzY2Nrz22mu0adOGmjVrsn79+hK3L4QQQgghXi4a5fEHhMQL4fbt21hZWZGWlpavt+f+/fskJydTo0YNraToSSjZ2bmzal69ikHlypj6NXwmPXdFuXv3LlWrVmXBggW89957uLi4MGrUKHVdPCF0QVl+LoUQQgghHlVUbvA4nR+iKZ6ORl+/zJdCKE58fDyJiYk0btyYtLQ0pk2bBkDnzp2faxxCCCGEEEK8bCTBE+Vi/vz5JCUlYWhoSMOGDdm9ezeVKlUq77CEEEIIIYTQaTqV4Onr65OamkqVKlW0yq9fv06VKlWe6cQkouzUr1+fuLi4QvenpKQ8v2CEEEIIIYR4iejUJCuFPS6YmZlZ4CLRQgghhBBCCPFPohM9eIsXLwZyZw1csWIF5ubm6r7s7Gz++OMP6tatW17hCSGEEEIIIcQLQScSvJCQECC3B2/ZsmXoPzLLo6GhIS4uLixbtqy8whNCCCGEEEKIF4JOJHjJyckAtGrVik2bNhW6qPXL4HksdC6EEEIIIYR4OenUM3hRUVFayV12djYJCQncvHmzHKMqW89roXMhhBBCCCHEy0enErxRo0axcuVKIDe5e+2112jQoAFOTk5ER0eXb3BCCCGEwMXFhYULF5Z3GEII8Y+lUwnehg0b8PHxAeDnn38mJSWFxMRERo8ezb/+9a9yjk6Ul/79+9OlS5cS14+Ojkaj0XDr1i0AwsLCsLa2fiaxvSie9BpLe291VUm+kCYmJtKkSROMjY3x9fV9LnGJl49Go2Hz5s3F1tu0aRN+fn5YW1tjZmaGr68va9asKfKYvH/bHn9dunSpyOMkIRNCiJeLTiV4169fx97eHoDt27fTo0cPateuzcCBAzl69Gg5R6ebcnIULibd5FTsJS4m3SQnp+ClKET5mTJlCnXr1sXMzAwbGxvatGnDgQMHCqybmZmJr68vGo2GhISEpz73okWLCAsLe+p28jzPL5KPJ/JPa/LkyZiZmZGUlERkZGSZtClEYWxtbfnXv/7Fvn37OHLkCAMGDGDAgAH89ttvxR6blJREamqq+np87dhnJSsr67mcRwghRNF0KsGzs7PjxIkTZGdn8+uvv/LGG28AkJGRoTWzpiiZM/FXWP3JXjaHxBOx8gSbQ+JZ/clezsRfKe/QxCNq167N0qVLOXr0KHv27MHFxYU333yTq1ev5qs7fvx4HB0dy+zcVlZWz713Mzs7m5ycnOd6zpI4c+YMLVq0wNnZmYoVK5Z3OKIc3Llzhz59+mBmZoaDgwMhISH4+/szatQoIPcPGNOnT+edd97BzMyMqlWrEhoaqh7v4uICQNeuXdFoNOp2Qfz9/enatSvu7u64uroycuRIvL292bNnT7FxVqlSBXt7e/Wlp1f4r3p/f3/OnTvH6NGj1R4/yP3D0uM91QsXLtSKOa+Hf+bMmTg6OlKnTh2te1XYfQA4f/48nTt3xtzcHEtLS3r27Mnly5eLvTYhhBDF06kEb8CAAfTs2ZN69eqh0Who06YNAAcOHJB18ErpTPwVfv3qGHdvZWqV372Vya9fHXumSV5OTg7z5s3Dzc0NIyMjqlevzsyZMwE4evQor7/+OiYmJlSsWJEPP/yQ9PR09djs7GzGjBmDtbU1FStWZPz48SiKkq/92bNnU6NGDUxMTPDx8eHHH38scXxnzpyhc+fO2NnZYW5uTqNGjdixY4dWHRcXF2bMmEG/fv0wNzfH2dmZLVu2cPXqVfVLi7e3N4cOHVKPuX79Ou+88w5Vq1bF1NQULy8vvv/++2Lj6d27N23atKFmzZp4enoSHBzM7du3OXLkiFa9X375hd9//5358+cX2tbmzZupVasWxsbGBAQEcOHChSLP/fgQTX9/f0aMGMH48eOxtbXF3t6eKVOmqPsVRWHKlClUr14dIyMjHB0dGTFihHpsQV8k84aPbtmyBQ8PD4yMjDh//rzWF+c8Xbp0oX///up2ZmYmEyZMwMnJCSMjI9zc3Fi5ciUpKSm0atUKABsbGzQajdZxBcnIyGDgwIFYWFhQvXp1vv76a3WfRqMhLi6OadOmodFotK5Z/HOMGTOGmJgYtmzZQkREBLt37+bPP//UqvP555/j4+NDfHw8EydOZOTIkURERACok2etWrWK1NTUEk+mpSgKkZGRJCUl8dprrxVb39fXFwcHB9544w1iYmKKrLtp0yaqVavGtGnT1B6/0siLKyIigq1bt6rlRd2HnJwcOnfuzI0bN9i1axcRERGcPXuWt99+u1TnFkIIUTCdSvCmTJnCihUr+PDDD4mJicHIyAgAfX19Jk6cWM7R6Y6cHIXd6/8qss6eH/56ZsM1J02axJw5c/jss884ceIE3333HXZ2dty9e5eAgABsbGyIjY1lw4YN7Nixg2HDhqnHLliwgLCwML755hv27NnDjRs3+Omnn7Tanz17NqtXr2bZsmUcP36c0aNH8+6777Jr164SxZeenk779u2JjIwkPj6etm3bEhgYyPnz57XqhYSE0Lx5c+Lj4+nQoQN9+/alX79+vPvuu/z555+4urrSr18/NQG9f/8+DRs2ZNu2bRw7dowPP/yQvn37cvDgwRLfu6ysLL7++musrKzU51EBLl++zAcffMCaNWswNTUt8NiMjAxmzpzJ6tWriYmJ4datW/Tq1avE584THh6OmZkZBw4cYN68eUybNk394rZx40ZCQkL46quv+Ouvv9i8eTNeXl5A0V8kMzIymDt3LitWrOD48eMlHlLWr18/vv/+exYvXszJkyf56quvMDc3x8nJiY0bNwL/G662aNGiIttasGABfn5+xMfHM2TIEAYPHkxSUhIAqampeHp6MnbsWFJTUxk3blyp75vQbXfu3CE8PJz58+fTunVr6tWrx6pVq/ItadO8eXMmTpxI7dq1GT58ON27d1fXcq1cuTIA1tbW2Nvbq9uFSUtLw9zcHENDQzp06MCSJUvUkSsFcXBwYNmyZWzcuJGNGzfi5OSEv79/viT0Uba2tujr62NhYaH2+JWGmZkZK1aswNPTE09PT7W8qPsQGRnJ0aNH+e6772jYsCGvvPIKq1evZteuXTKDtBBClAGdWAfvUd27d89XFhQUVA6R6K7Uv27l67l7XPrNTFL/ukXVOmW75uCdO3dYtGgRS5cuVd83V1dXWrRowfLly7l//z6rV6/GzMwMgKVLlxIYGMjcuXOxs7Nj4cKFTJo0ibfeeguAZcuWaT2TkpmZyaxZs9ixYwdNmzYFoGbNmuzZs4evvvqKli1bFhujj4+PVvI0ffp0fvrpJ7Zs2aKVbLZv356PPvoIgH//+998+eWXNGrUiB49egAwYcIEmjZtyuXLl7G3t6dq1apaicHw4cP57bff+OGHH2jcuHGRMW3dupVevXqRkZGBg4MDERERVKpUCcj9637//v0ZNGgQfn5+pKSkFNjGgwcPWLp0Ka+88gqQm6i5u7tz8ODBYs//KG9vbyZPngxArVq1WLp0KZGRkbzxxhucP38ee3t72rRpQ4UKFahevbra9uNfJB+P7YsvvtC678U5deoUP/zwAxEREWpvfs2aNdX9tra2QO5wtZIMM23fvj1DhgwBct+7kJAQoqKiqFOnDvb29hgYGGBubl7qL8Di5XD27FkePHig9VmxsrLSGpYIqP/uPLpd1HOn58+fx8PDQ93+5JNP+OSTTwCwsLAgISGB9PR0IiMjGTNmDDVr1sTf37/AturUqaMVT7NmzThz5gwhISGsWbOGtWvXqv9mQW6v/6uvvlrstRfFy8sLQ0PDfOVF3YeTJ0/i5OSEk5OTut/DwwNra2tOnjxJo0aNniomIYT4p9OpHjyAXbt2ERgYiJubG25ubnTq1Indu3eXd1g65e7topO70tYrjZMnT5KZmUnr1q0L3Ofj46Mmd5D7V+CcnBySkpJIS0sjNTVVTVAADAwM8PPzU7dPnz5NRkYGb7zxBubm5upr9erVnDlzpkQxpqenM27cONzd3bG2tsbc3JyTJ0/m68Hz9vZW/9/Ozg5A7a16tOzKldzhrtnZ2UyfPh0vLy9sbW0xNzfnt99+U9tdu3atVsyP/ly3atWKhIQE9u7dS9u2benZs6fa7pIlS7hz5w6TJk0q8roMDAy0vjjVrVtX/UJ1/vx5rXPPmjWr0HYevW7I7TXIi6VHjx7cu3ePmjVr8sEHH/DTTz/x8OHDIuMCMDQ0zNducRISEtDX1y9R0p6nqHv86Pk1Gg329vbqdQnxrDg6OpKQkKC+Bg0apO7T09PDzc0NX19fxo4dS/fu3Zk9e3ap2m/cuDGnT58GoFOnTlrnevTfzsfp6enlG/7+4MGDfPUe/fdaCCHEi0GnevC+/fZbBgwYwFtvvaU+1xMTE0Pr1q0JCwujd+/e5RyhbjCzNCrTeqVhYmJS5m0+Ku95vW3btlG1alWtfXlDeoszbtw4IiIimD9/Pm5ubpiYmNC9e/d8M8RVqFBB/f+858kKKsubMOTzzz9n0aJFLFy4EC8vL8zMzBg1apTabqdOnbSS10fjNzMzU/+o0aRJE2rVqsXKlSuZNGkSO3fuZN++ffmuz8/Pjz59+hAeHl7sNed9ycyT1/tVkEevMe86867RycmJpKQkduzYQUREBEOGDOHzzz9n165d+Y57lImJiXq/8hT3BfNJfpaKusdFXZcQNWvWpEKFCsTGxlK9enUgdwjlqVOntJ6L279/v9Zx+/fvx93dXd2uUKGC1rBOAwMD3NzcShRDTk4OmZml+8NbQkICDg4OQG6PoIWFRb46hoaG+YaaVq5cmUuXLqEoivrZLM3MvEXdB3d3dy5cuMCFCxfUXrwTJ05w69Ytrd5MIYQQT0anEryZM2cyb948Ro8erZaNGDGC4OBgpk+f/lIkeKGhoYSGhub7ZVuWHGpZY2ZtVOQwTXMbIxxqWZf5uWvVqoWJiQmRkZG8//77Wvvc3d0JCwvj7t276l+FY2Ji0NPTo06dOlhZWeHg4MCBAwfUL1QPHz4kLi6OBg0aAGhN0lGanp1HxcTE0L9/f7p27QrkJo2FDXssbbudO3fm3XffBXK/rJ06dUr9QlPYl6+CPPpFb/HixcyYMUPd9/fffxMQEMD69eu1kpmHDx9y6NAhdYhZUlISt27dwt3dvVRfMotjYmJCYGAggYGBDB06lLp163L06FEaNGhQ4BfJwlSuXFnrOb3s7GyOHTumTp7i5eVFTk4Ou3btUodoPipv2Nij5yvNPRbiURYWFgQFBfHxxx9ja2tLlSpVmDx5Mnp6elp/nIiJiWHevHl06dKFiIgINmzYwLZt29T9Li4uREZG0rx5c4yMjLCxKXgY/OzZs/Hz88PV1ZXMzEy2b9/OmjVr+PLLL9U6kyZN4uLFi6xevRrIneWyRo0aeHp6cv/+fVasWMHOnTv5/fffi7w2FxcX/vjjD3r16oWRkRGVKlXC39+fq1evMm/ePLp3786vv/7KL7/8gqWlZYnuV1H3oU2bNnh5edGnTx8WLlzIw4cPGTJkCC1btqRh/frcPXCQh1evYlC5MqZ+DdHILNlCCFEqOjVE8+zZswQGBuYr79SpE8nJyeUQUdkbOnQoJ06ceKYPmuvpaXj17VpF1mnRsxZ6epoi6zwJY2NjJkyYwPjx49Vhk/v372flypX06dMHY2NjgoKCOHbsGFFRUQwfPpy+ffuqwx1HjhzJnDlz2Lx5M4mJiQwZMkRrnTMLCwvGjRvH6NGjCQ8P58yZM/z5558sWbKkRD1ZkJuEbtq0iYSEBA4fPkzv3r3LpCenVq1aREREsHfvXk6ePMlHH31U7LTgd+/e5ZNPPmH//v2cO3eOuLg4Bg4cyMWLF9Vn/apXr069evXUV+3atYHcZxurVaumtlWhQgWGDx/OgQMHiIuLo3///jRp0qRUz98VJywsjJUrV3Ls2DHOnj3Lt99+i4mJCc7OzsD/vkhevHiRa9euFdnW66+/zrZt29i2bRuJiYkMHjxY6712cXEhKCiIgQMHsnnzZpKTk4mOjuaHH34AwNnZGY1Gw9atW7l69arWbKxCPIng4GCaNm1Kx44dadOmDc2bN8fd3R1jY2O1ztixYzl06BD169dnxowZBAcHExAQoO5fsGABERERODk5Ub9+/ULPdffuXYYMGYKnpyfNmzdn48aNfPvtt1p/GEtNTdUaOp6VlcXYsWPx8vKiZcuWHD58mB07dhQ4JP5R06ZNIyUlBVdXV3XiF3d3d7744gtCQ0Px8fHh4MGDpZpcqKj7oNFo+M9//oONjQ2vvfaaOkvwisGDOd26DeeDgvh73DjOBwVxunUbbheToAohhNCmUz14Tk5OREZG5utp2LFjh9bD2qJ4rvWr0Pajeuxe/5dWT565jREtetbCtf6zWxj3s88+w8DAgH//+9/8/fffODg4MGjQIExNTfntt98YOXIkjRo1wtTUlG7duhEcHKwemzeLYVBQEHp6egwcOJCuXbuSlpam1pk+fTqVK1dm9uzZnD17Fmtraxo0aKBOXFCc4OBgBg4cSLNmzahUqRITJkzg9u3bT33dn376KWfPniUgIABTU1M+/PBDunTpohX74/T19UlMTCQ8PJxr165RsWJFGjVqxO7du7VmrCsJU1NTJkyYQO/evbl48SKvvvoqK1eufNrL0mJtbc2cOXMYM2YM2dnZeHl58fPPP6vrxk2bNo2PPvpI7ZV4fAjmowYOHMjhw4fp168fBgYGjB49Wu29y/Pll1/yySefMGTIEK5fv0716tXV97lq1apMnTqViRMnMmDAAPr161emi7aLfx4LCwvWrl2rbt+9e5epU6fy4YcfqmWWlpbqHxkKkte7XZwZM2Zo9cwX5PGf5/HjxzN+/Phi235ckyZNOHz4cL7yQYMGaT0TCGj9O1rY56kkIx6qV6/Of/7zH3X79u+/c3HkKHjs34SHly/nli9aiOWbbxbbrhBCCNAoRX3DesF8+eWXjBo1Sv3yDbnDQMLCwli0aJHW7GC67vbt21hZWZGWlpZvSMz9+/dJTk6mRo0aWn85fhI5OUrurJq3MzGzzB2W+Sx67oR42ZXl51K8mOLj40lMTKRx48akpaUxbdo0oqOjOX36NJUqVcLFxYVRo0blW79RFE3JzuZ06zY8vHSp4AoaDQZ2drhF7pDhmkKIf6yicoPH6VQP3uDBg7G3t2fBggXqX0jd3d1Zv349nTt3LufodJOenqbMl0IQQoiX1fz580lKSsLQ0JCGDRuye/dudckS8WQyDsUVntwBKAoPL10i41AcZq+U3ZByIYR4WenUM3gAXbt2Zc+ePVy/fp3r16+zZ88eSe6EEEKUWP/+/enSpUuJ60dHR6PRaKhRowZxcXEsXbqUnJwcIiIitJZGSUlJeWl678LCwkq0fuTjSntvAR5evVqm9Z4HFxeXItc3BEhMTKRJkyYYGxvj6+tb4rb9/f3Vn6OSnOefKO8z+ehz2WXhed7vlJQUNBpNsbPTbt68GTc3N/T19V+af1/Es6dTCV5sbCwHDhzIV37gwAEOHTpUDhEJIYQQL5YpU6ZQt25dzMzMsLGxoU2bNgX+7gTIzMzE19e3RF80S2LRokWlftbV4P8ndilImzOnWX3jRrH1ykJZJw2TJ0/GzMyMpKQkIiMjy6TNF0l0dDSdO3fGwcEBMzMzfH19tZ5RLYyLiwsajSbfa+jQoWqd+/fvM3ToUCpWrIi5uTndunUrdlKyF8WUKVNKldAX56OPPqJ79+5cuHCB6dOnl1m74uWmUwne0KFDuXDhQr7yixcvav3DIIQQQvxT1a5dm6VLl3L06FH27NmDi4sLb775JlcL6AEbP348jo6OZXZuKyurUvf8mfo1xMDeHjSFPP+t0WBgb4+pX8MCd2dnZ7+Qa1aeOXOGFi1a4OzsrE409TLZu3cv3t7ebNy4kSNHjqiTWW3durXI42JjY0lNTVVfERERAOrM0ACjR4/m559/ZsOGDezatYu///6bt95665lez4soPT2dK1euEBAQgKOjoyzzI0pMpxK8EydOqOudPap+/fqcOHGiHCISQgjxrOXk5DBv3jzc3NwwMjKievXqzJw5E4CjR4/y+uuvY2JiQsWKFfnwww+1lsTIzs5mzJgxWFtbU7FiRcaPH59v9tacnBxmz55NjRo1MDExwcfHhx9//LHE8Z05c4bOnTtjZ2eHubk5jRo1YseOHVp1XFxcmDFjBv369cPc3BxnZ2e2bNnC1atX6dy5M+bm5nh7e2uNRrl+/TrvvPMOVatWxdTUFC8vL77//vti4+ndu7e69ICnpyfBwcHcvn2bI0eOaNX75Zdf+P3335k/f36hbW3evJlatWphbGxMQEBAgX9kfdTjQzT9/f0ZMWIE48ePx9bWFnt7e6ZMmaLuVxSFqdOn8/qJ4/gkJdLyzGlm/n9PTdD5c/z98CFzrlym9q5o9Axypw3IGz66ZcsWrbVPHx3amKdLly70799f3c7MzGTChAk4OTlhZGSEm5sbK1euJCUlRZ2l18bGBo1Go3VcQTIyMhg4cCAWFhZUr16dr7/+Wt2n0WiIi4tj2rRpaDQarWt+GsHBwXh5eWFmZoaTkxNDhgzR+nnPuzdbt26lTp06mJqa0r17dzIyMggPD8fFxQUbGxtGjBihtUbomjVr8PPzw8LCAnt7e3r37s2VK1eKjOWTTz5h+vTpNGvWDFdXV0aOHEnbtm3ZtGlTkcdVrlwZe3t79bV161ZcXV3VtWvT0tJYuXIlwcHBvP766zRs2JBVq1axd+9e9u/fr9VWTEwM3t7eGBsb06RJE44dO1bsPfz5559p1KgRxsbGVKpUSV3zNk9R7yvAhAkTqF27NqamptSsWZPPPvuMBw8eALn3f+rUqRw+fFjtmSyuR/vs2bO0atUKU1NTfHx82LdvH5DbQ5qX0L3++utoNBqio6OLvT4hQMcSPCMjowK76FNTUzEw0Kn5YoQQQpTQpEmTmDNnDp999hknTpzgu+++w87Ojrt37xIQEICNjQ2xsbFs2LCBHTt2MGzYMPXYBQsWEBYWxjfffMOePXu4ceMGP/30k1b7s2fPZvXq1Sxbtozjx48zevRo3n33XXbt2lWi+NLT02nfvj2RkZHEx8fTtm1bAgMDtdaoAwgJCaF58+bEx8fToUMH+vbtS79+/Xj33Xf5888/cXV1pV+/fmoCev/+fRo2bMi2bds4duwYH374IX379uXgwYMlvndZWVl8/fXXWFlZ4ePjo5ZfvnyZDz74gDVr1mBqalrgsRkZGcycOZPVq1cTExPDrVu36NWrV4nPnSc8PBwzMzMOHDjAvHnzmDZtmtprs3HjRkJCQvg6PJz41av5wsub2kZGACyqWg17IyP+1a+f2tvzaGxz585lxYoVHD9+nCpVSra0T79+/fj+++9ZvHgxJ0+e5KuvvsLc3BwnJyc2btwIQFJSEqmpqSxatKjIthYsWICfnx/x8fEMGTKEwYMHk5SUBOR+L/H09FSX9inNGoJF0dPTY/HixRw/fpzw8HB27tyZb2mMjIwMFi9ezLp16/j111+Jjo6ma9eubN++ne3bt7NmzRq++uorrT9iPHjwgOnTp3P48GE2b95MSkpKsQluQdLS0rC1tS1x/aysLL799lsGDhyI5v97cOPi4njw4AFt2rRR69WtW5fq1auryU+ejz/+mAULFhAbG0vlypUJDAxUk62CbNu2ja5du9K+fXvi4+OJjIzMtxZsUe8r5C6XEhYWxokTJ1i0aBHLly8nJCQEgLfffpuxY8fi6emp/sy+/fbbRd6Df/3rX4wbN46EhARq167NO++8w8OHD2nWrJl63o0bN5KamqrOIC9EsRQd0qtXL6Vly5bKrVu31LKbN28qLVu2VHr06FGOkZW9tLQ0BVDS0tLy7bt3755y4sQJ5d69e+UQmRCiIPK5fDZu376tGBkZKcuXL8+37+uvv1ZsbGyU9PR0tWzbtm2Knp6ecunSJUVRFMXBwUGZN2+euv/BgwdKtWrVlM6dOyuKoij3799XTE1Nlb1792q1/d577ynvvPOOoiiKEhUVpQDKzZs3FUVRlFWrVilWVlZFxu3p6aksWbJE3XZ2dlbeffdddTs1NVUBlM8++0wt27dvnwIoqamphbbboUMHZezYsUWeW1EU5eeff1bMzMwUjUajODo6KgcPHlT35eTkKG3btlWmT5+uKIqiJCcnK4ASHx+v1lm1apUCKPv371fLTp48qQDKgQMHCj1vUFCQem8VRVFatmyptGjRQqtOo0aNlAkTJiiKoigLFixQateurWRlZeXG9vChkr7/gHLr561K+v4DirOzsxISEqJ1fF5sCQkJWuUtW7ZURo4cqVXWuXNnJSgoSFEURUlKSlIAJSIiosDYH3+fi/L4+5mTk6NUqVJF+fLLL9UyHx8fZfLkycW29bhHr6Og63/Uhg0blIoVK6rbeffm9OnTatlHH32kmJqaKnfu3FHLAgIClI8++qjQdmNjYxVA65jirF+/XjE0NFSOHTtWqmP09fWVixcvqmVr165VDA0N89Vt1KiRMn78eEVR/vderVu3Tt1//fp1xcTERFm/fn2h52vatKnSp0+fQveX5H193Oeff640bNhQ3Z48ebLi4+NTaP08eZ+7FStWqGXHjx9XAOXkyZOKouR+xwWUqKioYtsTL7+icoPH6VQP3vz587lw4QLOzs60atWKVq1aUaNGDS5dusSCBQvKOzwhhBBl7OTJk2RmZtK6desC9/n4+GBmZqaWNW/enJycHJKSkkhLSyM1NZVXXnlF3W9gYICfn5+6ffr0aTIyMnjjjTcwNzdXX6tXr+bMmTMlijE9PZ1x48bh7u6OtbU15ubmnDx5Ml8Pnre3t/r/dnZ2AFqzcOaV5Q2Ny87OZvr06Xh5eWFra4u5uTm//fab2u7atWu1Yt69e7faVqtWrUhISGDv3r20bduWnj17qu0uWbKEO3fuMGnSpCKvy8DAgEaNGqnbdevWxdraWr22R889a9asQtt59LoBHBwc1Fh69OjBvXv3qFmzJh988AGbt2zBqGEDrDp2KHJJBENDw3ztFichIQF9fX11KGBJFHWPHz2/RqPB3t6+2GGNT2vHjh20bt2aqlWrYmFhQd++fbl+/ToZGRlqHVNTU1xdXdVtOzs7XFxcMDc31yp7NNa4uDgCAwOpXr06FhYW6j3K+1nz9PRU70G7du3yxRUVFcWAAQNYvnw5np6eAOzevVvr3hU0AcvKlStp167dEz8H2rRpU/X/bW1tqVOnDidPngTQOvegQYOA3J+Bgv4teVRx7+v69etp3rw59vb2mJub8+mnn+b7rD9u0KBBWvEUdj4HBweAZ/5zJF5+OjWusWrVqhw5coS1a9dy+PBhTExMGDBgAO+88w4VKlQo7/CEEEKUMRMTk2faft7zS9u2baNq1apa+4z+f6hgccaNG0dERATz58/Hzc0NExMTunfvTlZWlla9R39P5Q1HK6gsb8KQzz//nEWLFrFw4UL1uatRo0ap7Xbq1EkreX00fjMzM9zc3HBzc6NJkybUqlWLlStXMmnSJHbu3Mm+ffvyXZ+fnx99+vQhPDy82Gt2dHTUmnWzqGF5j/9+1mg06jU6OTmRlJTEjh07iIiIYMiQIXz++efs2rWryN/rJiYm6v3Ko6enl+/5ykeH6z3Jz1JR97io63oWUlJS6NixI4MHD2bmzJnY2tqyZ88e3nvvPbKystShtgXFVVSseUOdAwICWLt2LZUrV+b8+fMEBASoP2vbt29X7+Xj93HXrl0EBgYSEhJCv3791HI/Pz+tn5G8P2DkOXfuHDt27Mj3zJ69vT1ZWVncunVLa8Key5cvY29vX9LbpXXuvEWhS/IzUNS92rdvH3369GHq1KkEBARgZWXFunXriu1kmDZtWqHDdIv6N0CIJ6VTCR7k/tL68MMPyzsM8YT8/f3x9fVl4cKFuLi4MGrUKFnX5THR0dG0atWKmzdvPtE6VIV5nvc7JSWFGjVqEB8fX+R00Zs3b2bcuHEkJyczfPhwWe9J5FOrVi1MTEyIjIzk/fff19rn7u5OWFgYd+/eVXvxYmJi0NPTo06dOlhZWeHg4MCBAwd47bXXAHj48CFxcXHqhF2PTtJRmp6dR8XExNC/f391sob09HRSUlKe8Iq12+3cuTPvvvsukPul79SpU3h4eAC5zwKVdFa9nJwcMjMzAVi8eDEzZsxQ9/39998EBASwfv16rWTm4cOHHDp0SH1GKSkpiVu3buHu7o6BgQFubm5PfY2Q+6U7MDCQwMBAhg4dSt26dTl69CgNGjTA0NBQazKQolSuXFnrOb3s7GyOHTumTp7i5eVFTk4Ou3bt0nq+K4+hoaF6XJ7S3OOnkZOTzcWTx0m/dZPMu3fzJaqQ28uWk5PDggUL0NPLHYD1ww8/PPW5ExMTuX79OnPmzMHJyQkg39JTzs7OBR4bHR1Nx44dmTt3br7vZiYmJkX+jKxatYoqVarQoUMHrfKGDRtSoUIFIiMj6datG5D7s3f+/HmtHjuA/fv3U716dQBu3rzJqVOncHd3Byjw3N7e3kRGRjJgwIBC4yrK3r17cXZ25l//+pdadu7cOa06Bf3MVqlSpcTPiQpRFnQuwRNl69FfKubWNlR190RPT7+8wyp30dHRhISEcPDgQW7fvk2tWrX4+OOP6dOnT5HHubi45PvHHmDIkCGEhoYCuRMnjB07lnXr1pGZmUlAQABffPFFvr9uvoimTJnC5s2by2S9LMhd32fAgAGMGDFCpn8WBTI2NmbChAmMHz8eQ0NDmjdvztWrVzl+/Dh9+vRh8uTJBAUFMWXKFK5evcrw4cPp27ev+nkaOXIkc+bMoVatWtStW5fg4GCtdc4sLCwYN24co0ePJicnhxYtWpCWlkZMTAyWlpYEBQUVG2OtWrXYtGkTgYGBaDQaPvvsszL5C3ytWrX48ccf2bt3LzY2NgQHB3P58mU1wSvI3bt3mTlzJp06dcLBwYFr164RGhrKxYsX1Wno874Q58kbMubq6kq1atXU8goVKjB8+HAWL16MgYEBw4YNo0mTJvkmpXgaYWFhZGdn88orr2Bqasq3336LiYmJmlC4uLjwxx9/0KtXL4yMjKhUqVKhbb3++uuMGTOGbdu24erqmu+9dnFxISgoiIEDB7J48WJ8fHw4d+4cV65coWfPnjg7O6PRaNi6dSvt27fHxMQk33C6J5GTo5D61y3u3s7EzNIIh1rW6On9r/fxrwN72Rn2Nek3rgFw5dxZjmXe5a8De7XacXNz48GDByxZsoTAwEBiYmJYtmzZU8dXvXp1DA0NWbJkCYMGDeLYsWMlWm8tKiqKjh07MnLkSLp168alS5eA3ASnuIlWcnJyWLVqFUFBQfkmybOysuK9995jzJgx2NraYmlpyfDhw2natClNmjTRqjtt2jQqVqyInZ0d//rXv6hUqZLWLK6Pmzx5Mq1bt8bV1ZVevXrx8OFDtm/fzoQJE4q9Xsj9TJ4/f55169bRqFEjtm3blm/SJhcXF5KTk0lISKBatWpYWFiUeDSAEGVFp57BE2XrrwN7WT70PX6Y9gnbF3/OD9M+YfnQ9/L9UvknkvV9nj1Z30eU1GeffcbYsWP597//jbu7O2+//TZXrlzB1NSU3377jRs3btCoUSO6d+9O69atWbp0qXrs2LFj6du3L0FBQTRt2hQLC4t806JPnz6dzz77jNmzZ+Pu7k7btm3Ztm0bNWrUKFF8wcHB2NjY0KxZMwIDAwkICChwSZ/S+vTTT2nQoAEBAQH4+/tjb29f5JdXAH19fRITE+nWrRu1a9cmMDCQ69evs3v3bvXZqJIyNTVlwoQJ9O7dm+bNm2Nubs769euf4orys7a2Zvny5TRv3hxvb2927NjBzz//rK4bN23aNFJSUnB1daVyMQudDxw4kKCgIPr160fLli2pWbOm2nuX58svv6R79+4MGTKEunXr8sEHH3D37l0gd/jl1KlTmThxInZ2dlqzsT6pG6l3Wf3JXjaHxBOx8gSbQ+JZ/clezsTnPmP114G9bAmepSZ3eR7cv8eW4Fk8/P9eVwAfHx+Cg4OZO3cu9erVY+3atcyePfupY6xcuTJhYWFs2LABDw8P5syZU+TSGXnCw8PJyMhg9uzZODg4qK+S/D7bsWMH58+fZ+DAgQXuDwkJoWPHjnTr1o3XXnsNe3v7ApdfmDNnDiNHjqRhw4ZcunSJn3/+We2JLYi/vz8bNmxgy5Yt+Pr68vrrr5dqVtpOnToxevRohg0bhq+vL3v37uWzzz7TqtOtWzfatm1Lq1atqFy5comWNhGirGmUgsYBiHITGhpKaGgo2dnZnDp1irS0NHXseJ779++TnJxMjRo1MDY2fqLz5P1SKUynMZ9Q65Wyn463qCGawcHBrFq1irNnz2Jra0tgYCDz5s1T/4IaFhbGqFGj+Pbbbxk7diwXLlygffv2rF69mg0bNjB58mTS0tLo27cvISEh6Ovn9kSuWbOGRYsWkZSUhJmZGa+//joLFy4s9XCJDh06YGdnxzfffFPiY0aNGsXWrVv566+/0Gg0pKWlUblyZb777ju6d+8O5A6PcXd3Z9++fTRp0kQdorl161YmTZrEqVOn8PX1ZcWKFdSrV6/I8/38889MmzaNo0ePYm5uzquvvqr+ddHFxYUPP/yQ06dPs2HDBmxsbPj000+1htVMmDCBn376if/+97/Y29vTp08f/v3vf1OhQgXCwsLyDWtZtWpVgVNp5w3R3LhxI0uWLOHAgQPUqlWLZcuW0bRpU/UaHxUVFYW/v3+J7+2Lpiw+l0KIl8eZ+Cv8+lXh67IFfOBB5IqJ+ZK7R1lUrMT7S1fKyBohBLdv38bKyqrA3OBx0oP3ghk6dCgnTpwgNjb2mZ0jJyebnWFfF1knKvxrcnJK9txDWZH1fWR9HyGEeBnk5CjsXv9XkXWi1uwsMrkDuHP9GhdPHi/L0IQQ/wA69wzerVu3+PHHHzlz5gwff/wxtra2/Pnnn9jZ2eWbAU0U7OLJ4yX+peLkWbppqJ/Go5N/uLi4MGPGDAYNGsQXX3yhlj948IAvv/xSnQK6e/furFmzhsuXL2Nubo6HhwetWrUiKipKTT4eHQJSs2ZNFi9eTKNGjUhPTy/x8xU//PADsbGxfPXVVyW+ns2bN3Pr1i2tZPLSpUsYGhrmmzzFzs5OfX4hz+TJk3njjTeA3KEw1apV46effqJnz54Fnm/mzJn06tWLqVOnqmWPLmwM0L59e4YMGQLk9taFhIQQFRVFnTp1gNwhYXlcXFwYN24c69atY/z48erzKAYGBiWeyWzcuHHqA/RTp07F09OT06dPU7duXbUH1dbWtlQzowkhxIsu9a9b3L2VWWSde7dvlait9Fs3yyAiIcQ/iU714B05coTatWszd+5c5s+frz48vWnTpmLX8xH/U9JfFs/7l4qs76NN1vcRQgjddPd20ckdABqz4usA5tY2TxmNEOKfRqd68MaMGUP//v2ZN2+e1oQM7du3p3fv3uUYmW4p6S+L5/lLRdb3kfV9hBDiZWFmWfysiXoGVTGxtOXe7RuF1rGoWImq7qWbHEcIIXQqwStsiFzVqlXzDW8Thavq7om5baViH+x+nr9UZH0fWd9HCCFeFg61rDGzNipymKaFrQlNOn/E1pDCZ8JsFfShTLAihCg1nRqiaWRkxO3bt/OVnzp1qtjpk8X/6Onp83r/oheLf96/VB5d3+fs2bOsWbOmzNf3OXv2LFu2bCnx+j4dOnRgxIgR6vo+ly5d4saNwv/Smqek6/tERUURFxfHgAEDCl3fJzIykmPHjtG/f/8Sre/z/fffM3nyZE6ePMnRo0eZO3dusfHmeXR9nzNnzrB48eIi1/e5du2aunCyEEKI/9HT0/Dq27WKrNOiZy3qNGlOpzGfYG6rvb6fRcVKz2w2ayHEy0+nErxOnToxbdo0dTidRqPh/PnzTJgwQe0NESVT65VmL9QvFVnfR9b3EUKIl4lr/Sq0/ageZtbawzXNbYxo+1E9XOvnjnyo9UozPghdSc9/z6L9iI/p+e9ZvL90pSR3QognplPr4KWlpdG9e3cOHTrEnTt3cHR05NKlSzRt2pTt27djZlayB5Z1QVFrXZTlels5Odm5s2reuom5tQ1V3T1lOIgQT0DWwRNCFCQnR8mdVfN2JmaWRjjUskZPT1PeYQkhdExp1sHTqWfwrKysiIiIYM+ePRw5coT09HQaNGigtaaYKB09Pf3nuhSCEEII8U+ip6ehah2ZCVMI8fzoVIKXp0WLFrRo0aK8wxBCCCGEEEKIF4pOJXiLFy8usFyj0WBsbIybmxuvvfYa+voyxFAIIYQQQgjxz6NTCV5ISAhXr14lIyMDG5vc4Q43b97E1NQUc3Nzrly5Qs2aNYmKilKnxBdCCCGEEEKIfwqdmkVz1qxZNGrUiL/++ovr169z/fp1Tp06xSuvvMKiRYs4f/489vb2jB49urxDFUIIIYQQQojnTqd68D799FM2btyIq6urWubm5sb8+fPp1q0bZ8+eZd68ebJkghBCCCGEEOIfSad68FJTU3n48GG+8ocPH3Lp0iUAHB0duXPnzvMOTQghhBBCCCHKnU4leK1ateKjjz4iPj5eLYuPj2fw4MG8/vrrABw9epQaNWqUV4hCCCGEEEIIUW50KsFbuXIltra2NGzYECMjI4yMjPDz88PW1paVK1cCYG5uzoIFC8o5UiGEEEIIIYR4/nTqGTx7e3siIiJITEzk1KlTANSpU4c6deqodVq1alVe4QkhhBBCCCFEudKpHrw8devWpVOnTnTq1EkruROlp+Qo3D9zi4yEK9w/cwslRynvkEpEo9GwefPmUh2zbt06NBoNXbp0eSYxCSGEEEIIUd50qgcP4L///S9btmzh/PnzZGVlae0LDg4up6h0071j17j18xmy0/53H/WtDLEOdMWkXqVyjKzspaSkMG7cOF599dXyDkUIIYQQQohnRqd68CIjI6lTpw5ffvklCxYsICoqilWrVvHNN9+QkJBQ3uHplHvHrnH925NayR1AdloW1789yb1j157Zue/cuUOfPn0wMzPDwcGBkJAQ/P39GTVqFAAuLi5Mnz6dd955BzMzM6pWrUpoaKh6vIuLCwBdu3ZFo9Go24XJzs6mT58+TJ06lZo1az6jqxJCCCGEEKL86VSCN2nSJMaNG8fRo0cxNjZm48aNXLhwgZYtW9KjR4/yDk/LrVu38PPzw9fXl3r16rF8+fLyDkml5Cjc+vlMkXVu/Xz2mQ3XHDNmDDExMWzZsoWIiAh2797Nn3/+qVXn888/x8fHh/j4eCZOnMjIkSOJiIgAIDY2FoBVq1aRmpqqbhdm2rRpVKlShffee++ZXI8QQgghhBAvCp0aonny5Em+//57AAwMDLh37x7m5uZMmzaNzp07M3jw4HKO8H8sLCz4448/MDU15e7du9SrV4+33nqLihUrlndoZCan5eu5e1x2WiaZyWkYu1qX6bnv3LlDeHg43333Ha1btwZyEzVHR0etes2bN2fixIkA1K5dm5iYGEJCQnjjjTeoXLkyANbW1tjb2xd5vj179rBy5Urp4RVCCCGEEP8IOtWDZ2Zmpj535+DgwJkz/+uFunbt2Q0pfBL6+vqYmpoCkJmZiaIoKMqLMYFJzp2ik7vS1iuNs2fP8uDBAxo3bqyWWVlZ5Zssp2nTpvm2T548WWi758+fx9zcXH3NmjWLO3fu0LdvX5YvX06lSi/XM4VCCCGEEEIURKcSvCZNmrBnzx4A2rdvz9ixY5k5cyYDBw6kSZMmpWrrjz/+IDAwEEdHx0JnZAwNDcXFxQVjY2NeeeUVDh48WKpz3Lp1Cx8fH6pVq8bHH3/8wiQZehaGZVrvReDo6EhCQoL6GjRoEGfOnCElJYXAwEAMDAwwMDBg9erVbNmyBQMDA60/EAghhBBCCPEy0KkhmsHBwaSnpwMwdepU0tPTWb9+PbVq1Sr1DJp3797Fx8eHgQMH8tZbb+Xbv379esaMGcOyZct45ZVXWLhwIQEBASQlJVGlShUAfH19efjwYb5jf//9dxwdHbG2tubw4cNcvnyZt956i+7du2NnZ1dgPJmZmWRmZqrbt2/fLtX1lIZRDSv0rQyLHKapb2WEUQ2rMj93zZo1qVChArGxsVSvXh2AtLQ0Tp06xWuvvabW279/v9Zx+/fvx93dXd2uUKEC2dnZ6raBgQFubm5ax5iamnL06FGtsk8//ZQ7d+6waNEinJycyuy6hBBCCCGEeBHoTIKXnZ3Nf//7X7y9vYHc4ZrLli174vbatWtHu3btCt0fHBzMBx98wIABAwBYtmwZ27Zt45tvvlGfDSvpc112dnb4+Piwe/duunfvXmCd2bNnM3Xq1NJdxBPS6GmwDnTl+reFD3m0DqyJRk9T5ue2sLAgKCiIjz/+GFtbW6pUqcLkyZPR09NDo/nf+WJiYpg3bx5dunQhIiKCDRs2sG3bNnW/i4sLkZGRNG/eHCMjI2xsbPKdy9jYmHr16mlfl7U1QL5yIYQQQgghXgY6M0RTX1+fN998k5s3bz7zc2VlZREXF0ebNm3UMj09Pdq0acO+fftK1Mbly5e5c+cOkNtD9ccffxS5KPukSZNIS0tTXxcuXHi6iyiGSb1KVHzXHX0r7WGY+lZGVHzX/ZmugxccHEzTpk3p2LEjbdq0oXnz5ri7u2NsbKzWGTt2LIcOHaJ+/frMmDGD4OBgAgIC1P0LFiwgIiICJycn6tev/8xiFUIIIYQQQpfoTA8e5Pa6nD17lho1ajzT81y7do3s7Ox8wynt7OxITEwsURvnzp3jww8/VCdXGT58OF5eXoXWNzIywsjI6KniLi2TepUw9qhIZnIaOXey0LMwxKiG1TPpuXuUhYUFa9euVbfv3r3L1KlT+fDDD9UyS0tLfvjhh0LbCAwMJDAwsNTnDgsLK/UxQgghhBBC6AqdSvBmzJjBuHHjmD59Og0bNsTMzExrv6WlZTlFll/jxo11Ymp+jZ6mzJdCKE58fDyJiYk0btyYtLQ0pk2bBkDnzp2faxxCCCGEEEK8bHQqwWvfvj0AnTp10npeS1EUNBqN1qQbT6NSpUro6+tz+fJlrfLLly8Xu+6aKJn58+eTlJSEoaEhDRs2ZPfu3S/MLKNCCCGEEELoKp1K8KKiop7LefKSjsjISLp06QJATk4OkZGRDBs27JmeOzQ0lNDQ0DJLVl9E9evXJy4urtD9KSkpzy8YIYQQQgghXiI6leC1bNmyzNpKT0/n9OnT6nZycjIJCQnY2tpSvXp1xowZQ1BQEH5+fjRu3JiFCxdy9+5ddVbNZ2Xo0KEMHTqU27dvY2VV9ssUCCGEEEIIIV5eOpXgAezevZuvvvqKs2fPsmHDBqpWrcqaNWuoUaMGLVq0KHE7hw4dolWrVur2mDFjAAgKCiIsLIy3336bq1ev8u9//5tLly7h6+vLr7/+Wug6duUhJyenvEMQQvw/+TwKIYQQ4kWgUwnexo0b6du3L3369OHPP/9UFwZPS0tj1qxZbN++vcRt+fv7oyhKkXWGDRv2zIdkPglDQ0P09PT4+++/qVy5MoaGhlrPJAohnh9FUcjKyuLq1avo6elhaGhY/EFCCCGEEM+IRikuy3mB1K9fn9GjR9OvXz8sLCw4fPgwNWvWJD4+nnbt2nHp0qXyDvGpPfoM3qlTp0hLSytwdtCsrCxSU1PJyMgohyiFEI8zNTXFwcFBEjwhhBBClLm8x7cKyw0epVMJnqmpKSdOnMDFxUUrwTt79iweHh7cv3+/vEMsMyV5ExVF4eHDhy/1hCxC6AJ9fX0MDAykJ10IIYQQz0RpEjydGqJpb2/P6dOncXFx0Srfs2cPNWvWLJ+gypFGo6FChQpUqFChvEMRQgghhBBCvAD0yjuA0vjggw8YOXIkBw4cQKPR8Pfff7N27VrGjRvH4MGDyzs8IYQQQgghhChXOtWDN3HiRHJycmjdujUZGRm89tprGBkZMW7cOIYPH17e4QkhhBBCCCFEudKpZ/DyZGVlcfr0adLT0/Hw8MDc3Ly8QyozJZ1kRQghhBBCCPHP8NJOsvLtt9/y1ltvYWpqWt6hPHOleROFEEIIIYQQL6/S5AY69Qze6NGjqVKlCr1792b79u0ye6QoNX9/f0aNGgWAi4sLCxcuLNd4XkQpKSloNBoSEhLKtN1H7/3zoNFo2Lx5c5F1YmJi8PLyokKFCnTp0uW5xCWEEEII8SzpVIKXmprKunXr0Gg09OzZEwcHB4YOHcrevXvLOzQhylR0dDSdO3fGwcEBMzMzfH19Wbt2bbHH/fHHHwQGBuLo6FhogjNlyhTq1q2LmZkZNjY2tGnThgMHDjyDqyh7YWFhWFtbl1l7Y8aMwdfXl+TkZMLCwsqsXSGEEEKI8qJTCZ6BgQEdO3Zk7dq1XLlyhZCQEFJSUmjVqhWurq7lHZ4QZWbv3r14e3uzceNGjhw5woABA+jXrx9bt24t8ri7d+/i4+NDaGhooXVq167N0qVLOXr0KHv27MHFxYU333yTq1evlvVlvPDOnDnD66+/TrVq1co0cRRCCCGEKC86leA9ytTUlICAANq1a0etWrVISUkp75CEjgsODsbLywszMzOcnJwYMmQI6enp6v683qOtW7dSp04dTE1N6d69OxkZGYSHh+Pi4oKNjQ0jRozQGj68Zs0a/Pz8sLCwwN7ent69e3PlypUiY/nkk0+YPn06zZo1w9XVlZEjR9K2bVs2bdpU5HHt2rVjxowZdO3atdA6vXv3pk2bNtSsWRNPT0+Cg4O5ffs2R44c0aqXmJhIs2bNMDY2pl69euzatavIc0PukEd/f39MTU2xsbEhICCAmzdvqvtzcnIYP348tra22NvbM2XKFK3ji3oPoqOjGTBgAGlpaWg0GjQaTb7jH3ft2jW6du2KqakptWrVYsuWLcD/hqFev36dgQMHotFopAdPCCGEEC8FnUvwMjIyWLt2Le3bt6dq1aosXLiQrl27cvz48fIOTeg4PT09Fi9ezPHjxwkPD2fnzp2MHz9eq05GRgaLFy9m3bp1/Prrr0RHR9O1a1e2b9/O9u3bWbNmDV999RU//vijesyDBw+YPn06hw8fZvPmzaSkpNC/f/9Sx5eWloatre3TXqaWrKwsvv76a6ysrPDx8dHa9/HHHzN27Fji4+Np2rQpgYGBXL9+vdC2EhISaN26NR4eHuzbt489e/YQGBioleyGh4djZmbGgQMHmDdvHtOmTSMiIkLdX9R70KxZMxYuXIilpSWpqamkpqYybty4Iq9v6tSp9OzZkyNHjtC+fXv69OnDjRs3cHJyIjU1FUtLSxYuXEhqaipvv/32k9xCIYQQQogXi6JD3n77bcXMzEypXLmyMnToUGXv3r3lHVKZW7p0qeLu7q7Url1bAZS0tLTyDuml0rJlS2XkyJGKoiiKs7OzEhISUmjdDRs2KBUrVlS3V61apQDK6dOn1bKPPvpIMTU1Ve7cuaOWBQQEKB999FGh7cbGxiqA1jHFWb9+vWJoaKgcO3asxMcAyk8//VTgvp9//lkxMzNTNBqN4ujoqBw8eFDdl5ycrADKnDlz1LIHDx4o1apVU+bOnVvo+d555x2lefPmhe5v2bKl0qJFC62yRo0aKRMmTCj0mILeAysrq0LrPwpQPv30U3U7PT1dAZRffvlFLbOyslJWrVpVovaEEEIIIcpLWlpaiXMDnerB09fX54cffiA1NZWlS5fStGlTdd+xY8fKMbKyM3ToUE6cOEFsbGx5h/KPs2PHDlq3bk3VqlWxsLCgb9++XL9+nYyMDLWOqamp1vOednZ2uLi4aK3FaGdnpzUEMy4ujsDAQKpXr46FhQUtW7YE4Pz58wB4enpibm6Oubk57dq1yxdXVFQUAwYMYPny5Xh6egKwe/du9Rhzc/MSTcDyqFatWpGQkMDevXtp27YtPXv2zDds9NHPl4GBAX5+fpw8ebLQmPN68Iri7e2tte3g4KB13pK8B4+bNWuW1r3Iu6+Pn8/MzAxLS8tih8cKIYQQQugyg/IOoDQe/xJ7584dvv/+e1asWEFcXJwsmyCeWEpKCh07dmTw4MHMnDkTW1tb9uzZw3vvvUdWVpa69mKFChW0jtNoNAWW5eTkALmTngQEBBAQEMDatWupXLky58+fJyAggKysLAC2b9/OgwcPADAxMdFqa9euXQQGBhISEkK/fv3Ucj8/P61lDOzs7Ep1vWZmZri5ueHm5kaTJk2oVasWK1euZNKkSSU6vqCYH4+9IEXdq5K+B48bNGgQPXv2VLcdHR1LdD4hhBBCiJeRTiV4ef744w9WrlzJxo0bcXR05K233ipy1kAhihMXF0dOTg4LFixATy+3Y/uHH3546nYTExO5fv06c+bMwcnJCYBDhw5p1XF2di7w2OjoaDp27MjcuXP58MMPtfaZmJjg5ub21PHlycnJITMzU6ts//79vPbaawA8fPiQuLg4hg0bVmjM3t7eREZGMnXq1CeKoSTvgaGhYb4/5Nja2pbNs4k52XBuL6RfBnM7cG4GevpP364QQgghxHOkMwnepUuXCAsLY+XKldy+fZuePXuSmZnJ5s2b8fDwKO/whI5zc3PjwYMHLFmyhMDAQGJiYli2bNlTt1u9enUMDQ1ZsmQJgwYN4tixY0yfPr3Y46KioujYsSMjR46kW7duXLp0CchNcIpKZtLT0zl9+rS6nZycTEJCAra2tlSvXp27d+8yc+ZMOnXqhIODA9euXSM0NJSLFy/So0cPrbZCQ0OpVasW7u7uhISEcPPmTQYOHFjouSdNmoSXlxdDhgxh0KBBGBoaEhUVRY8ePahUqVKx11yS98DFxYX09HQiIyPx8fHB1NS00J69UjmxBX6dALf//l+ZpSO0nQsenZ6+fSGEEEKI50QnnsELDAykTp06HDlyhIULF/L333+zZMmS8g5LvER8fHwIDg5m7ty51KtXj7Vr1zJ79uynbrdy5cqEhYWxYcMGPDw8mDNnDvPnzy/2uPDwcDIyMpg9ezYODg7q66233iryuEOHDlG/fn3q168P5C7kXb9+ff79738Duc+xJiYm0q1bN2rXrq3OjLl79271+b48c+bMYc6cOfj4+LBnzx62bNlSZKJWu3Ztfv/9dw4fPkzjxo1p2rQp//nPfzAwKNnfkUryHjRr1oxBgwbx9ttvU7lyZebNm1eitot0MR5+6Ked3AHcTs0tP7Hl6c8hhBBCCPGcaBRFUco7iOIYGBgwYsQIBg8eTK1atdTyChUqcPjw4ZeyB+/27dtYWVmRlpaGpaVleYcjxMspJxsW1suf3Kk0uT15o47KcE0hhBBClJvS5AY60YO3Z88e7ty5Q8OGDXnllVdYunQp165dK++wnonQ0FA8PDxo1KhReYcixMvv3N4ikjsABW5fzK0nhBBCCKEDdCLBa9KkCcuXLyc1NZWPPvqIdevW4ejoSE5ODhEREdy5c6e8QywzskyCEM9R+uWyrSeEEEIIUc50IsHLY2ZmxsCBA9mzZw9Hjx5l7NixzJkzhypVqtCpk0yEIIQoJfMSLi9R0npCCCGEEOVMpxK8R9WpU4d58+bx3//+l++//768wxFC6CLnZrnP2KEppIIGLKvm1hNCCCGE0AE6m+Dl0dfXp0uXLmzZIjPdCSFKSU8/dykEIH+S9//bbefIBCtCCCGE0Bk6n+AJIcRT8egEPVeDpYN2uaVjbrmsgyeEEEIIHaIzC50LIcQz49EJ6nbInS0z/XLuM3fOzaTnTgghhBA6RxI8IYSA3GSuxqvlHYUQQgghxFMp1RBNf39/Ro0aBYCLiwsLFy58BiG92DQaDZs3by7TNvv370+XLl2A57MOXkneu8TERJo0aYKxsTG+vr7PLBYhhBBCCCFE2Xkpn8GLjo6mc+fOODg4YGZmhq+vL2vXri32OBcXFzQaTb7X0KFDn0PUuZ50Hbzo6Gg0Gg23bt0qkzgmT56MmZkZSUlJREZGlkmbQgghhBBCiGfrpUzw9u7di7e3Nxs3buTIkSMMGDCAfv36sXXr1iKPi42NJTU1VX1FREQA0KNHj+cR9gvlzJkztGjRAmdnZypWrFje4eiksk66SyMsLAxra+unbudJeupTUlLQaDQkJCQ89fmFEEIIIUTplFmCFxwcjJeXF2ZmZjg5OTFkyBDS09PV/XlfOLdu3UqdOnUwNTWle/fuZGRkEB4ejouLCzY2NowYMYLs7Gz1uDVr1uDn54eFhQX29vb07t2bK1euFBnLJ598wvTp02nWrBmurq6MHDmStm3bsmnTpiKPq1y5Mvb29upr69atuLq60rJlS616qamptGvXDhMTE2rWrMmPP/5Y7P05fvw4HTt2xNLSEgsLC1599VXOnDmjVWf+/Pk4ODhQsWJFxo4dq7WvqPuQkpJCq1atALCxsUGj0dC/f/8i48nIyGDgwIFYWFhQvXp1vv76a3WfRqMhLi6OadOmodFomDJlSrHXJ/Jr1qwZqampWFlZFVu3PJJBf3//AnusO3To8FTtOjk5kZqaSr169cokzrJKVkvq0aHoTys6OpoGDRpgZGSEm5sbYWFhRda/f/8+/fv3x8vLCwMDA3XothBCCCFESZVZgqenp8fixYs5fvw44eHh7Ny5k/Hjx2vVycjIYPHixaxbt45ff/2V6Ohounbtyvbt29m+fTtr1qzhq6++0kqYHjx4wPTp0zl8+DCbN28mJSWl2OSlIGlpadja2pa4flZWFt9++y0DBw5Eo9FeH+uzzz6jW7duHD58mD59+tCrVy9OnjxZaFsXL17ktddew8jIiJ07dxIXF8fAgQN5+PChWicqKoozZ84QFRVFeHg43333nVYbRd0HJycnNm7cCEBSUhKpqaksWrSoyOtbsGABfn5+xMfHM2TIEAYPHkxSUhKQm8B6enoyduxYUlNTGTduXInvm/gfQ0ND7O3t8/38PI2srKwya2vTpk1aPdbHjh1DX1//qXus9fX1sbe3x8Dg+c7hVJb3piwkJyfToUMHWrVqRUJCAqNGjeL999/nt99+K/SY7OxsTExMGDFiBG3atHmO0QohhBDipaGUQsuWLZWRI0cqiqIozs7OSkhISKF1N2zYoFSsWFHdXrVqlQIop0+fVss++ugjxdTUVLlz545aFhAQoHz00UeFthsbG6sAWscUZ/369YqhoaFy7NixUh2jr6+vXLx4UascUAYNGqRV9sorryiDBw8utK1JkyYpNWrUULKysgrcHxQUpDg7OysPHz5Uy7p06aIASlpaWoHHPH4foqKiFEC5efNmsdfm7OysvPvuu+p2Tk6OUqVKFeXLL79Uy3x8fJTJkycX29Y/ScuWLZVhw4YpI0eOVKytrZUqVaooX3/9tZKenq70799fMTc3V1xdXZXt27cripL/PUlJSVE6duyoWFtbK6ampoqHh4eybds2JTk5WQG0XkFBQeo5hw4dqowcOVKpWLGi4u/vryiKoixYsECpV6+eYmpqqlSrVk0ZPHiw1mdi1apVipWVVamuLyQkRLGwsFDS09PVMmdnZ2XatGlKr169FFNTU8XR0VFZunRpke3kXU98fLzWfdixY4fSsGFDxcTERGnatKmSmJioHpOQkKD4+/sr5ubmioWFhdKgQQMlNjZWPfbRV97PZV5sffv2VSwsLJSgoKACPwfx8fEKoCQnJ6tle/bsUVq2bKmYmJgo1tbWyptvvqncuHFDCQoKyne+R4/L89VXXykODg5Kdna2VnmnTp2UAQMGKIqiKOPHj1c8PT219r/99ttKQEBAkfcvT1BQkNK5c+cS1RVCCCHEyy0tLa3I3OBRZdaDt2PHDlq3bk3VqlWxsLCgb9++XL9+nYyMDLWOqakprq6u6radnR0uLi6Ym5trlT06BDMuLo7AwECqV6+OhYWFOlzy/PnzAHh6emJubo65uTnt2rXLF1dUVBQDBgxg+fLleHp6ArB79271GHNz8wInYFm5ciXt2rXD0dEx376mTZvm287rwWvXrp3abt75EhISePXVV6lQoUKh98/T0xN9/f+tuWVvb6+1v7j7UJC1a9dqXefu3bvVfd7e3ur/azQa7O3tix36KiA8PJxKlSpx8OBBhg//v/buPa6mrP8D+OeUTknKoLujhjqqhxIx0qBxa6qH8TLCTONWep4IYZgyjIYa9+ugeEgZDzXjbkxC7mImlzL0JJdQRpGMJFRT+/eHX2ccnS6HOun4vF+v83rN2Xvttb97vdZsffdae52JGDduHLy8vNC9e3dcuHAB/fv3x4gRI+T6fbmAgAAUFRXhxIkTuHTpEhYuXAg9Pb1qR2A3bdoEsViMxMRErF27FkDNRsyVFRkZieHDh6NJkyZy2xcvXgwHBwckJycjODgYgYGBsvdTlTFz5kwsXboU586dQ6NGjeDj4yPb5+3tjVatWuHs2bM4f/48goODoaWlhe7du2PFihXQ19eXjTS+PKK8ZMkSWWzffPNNjeJISUlBnz59YGdnhzNnzuDUqVMYMGAASktLsXLlSjg7O8PPz092PolEUqEOLy8v5OXl4ejRo7JtDx8+RHx8PLy9vQEAZ86cqTAK5+bmhjNnzijVbkRERETKqJU5VLdu3cI///lPjBs3Dt999x2aN2+OU6dOwdfXF8XFxdDV1QWACgmOSCRSuK2srAwAUFhYCDc3N7i5uWHLli0wNDREZmYm3NzcZNOx4uLiUFJSAgBo3LixXF3Hjx/HgAEDsHz5cowcOVK23cnJSW4BCGNjY7njbt++jYSEhGrf2VNkw4YNePbsmdz1vhqXIoraoVxN2kGRgQMH4oMPPpB9Nzc3r/J85e1OlXNwcMCsWbMAADNmzMCCBQvQsmVL+Pn5AQBmz56NiIgI/P777xWOzczMxKeffooOHToAANq0aSPbVz592MjIqML7ZtbW1li0aJHctpffEbO0tERYWBj8/f0RHh7+WteVlJSEy5cvIzIyssI+FxcXBAcHAwCkUikSExOxfPly9OvXT6lzfPfdd7IHE8HBwfD09MTz58+ho6ODzMxMTJ8+HTY2NgBeXHM5AwMD2UOIV/Xu3VvufdWsrKxq41i0aBGcnJzk2qr8YQzwYmqtrq6uwvOVe++99+Du7o6tW7eiT58+AIDt27ejZcuWsvdhc3JyKtxbjI2N8fjxYzx79qxG9wUiIiIiZdXKCN758+dRVlaGpUuXolu3bpBKpbh79+4b13vlyhXk5eVhwYIF6NGjB2xsbCqMMllYWMDKygpWVlZyCcyxY8fg6emJhQsX4l//+pfcMY0bN5YdY2VlhaZNm8rtj4qKgpGRUaWLTfz6668Vvtva2gJ4kUSV12thYQHgxWjZyZMnZYloXbSDWCwGALkFapo2bSp3na/7B2VpmYAzN/KwJ+UPnLmRh9Iy4bXqUQcvj3xqamqiRYsWsoQN+PthgaLR0EmTJiEsLAwuLi4ICQlRmAQq0rlz5wrbajJiXi4zM1NuJHfevHkVykRGRqJDhw7o2rVrhX1VjVj7+/vL1V2Vl9vO1NQUwN/tNHXqVIwdOxZ9+/bFggULKixAVBknJ6calXtZ+QieMhTNFPD29saOHTtQVFQE4MWI+fDhw6GhoZaLExMREVEDUSt/iVhZWaGkpASrVq1CRkYGNm/eLJtK9iZat24NsVgsq3fv3r0IDQ2t9rijR4/C09MTkyZNwqeffoqcnBzk5OTg4cOH1R5bVlaGqKgojBo1qtJFIrZt24aNGzfi6tWrCAkJQVJSEiZMmFBpnRMmTMDjx48xfPhwnDt3DteuXcPmzZtli5pUpybtYGFhAZFIhH379iE3N1duBdM3EX85Gx8uPILP1v+KwNgUfLb+V3y48AjiL2fXSv0NTXWj0OUjr4pGQ8eOHYuMjAyMGDECly5dgpOTE1atWlXtOV+dMlk+Yl7+UyDnz5/HmjVrACheaMTMzAwpKSmyj7+/v9z+wsJCxMbGwtfXt9pYXjV37ly5uqtSVTt9++23SE1NhaenJ44cOQI7Ozvs2rWr2vO/2jblyZUg/P0Q4tUHK6/zoCMuLk52jRs2bAAADBgwAIIg4JdffkFWVhZOnjwpm54JvJhmfe/ePbl67t27B319fY7eERERUZ2plQTPwcEBy5Ytw8KFC9G+fXts2bIF8+fPf+N6DQ0NER0djW3btsHOzg4LFizAkiVLqj1u06ZNePr0KebPnw9TU1PZZ/DgwdUem5CQgMzMTLn3g141Z84cxMbGwt7eHj/88ANiYmJgZ2dXafkWLVrgyJEjePLkCXr16oXOnTtj/fr1Vb6T97KatIO5uTnmzJmD4OBgGBsbV5lw1tT1+wUY998LyM5/Lrc9J/85xv33wjub5L0JiUQCf39/7Ny5E19++SXWr18PQPEIbGWUHTFv1KiR3Ejuq6vJbtu2DUVFRfjiiy8UHl/ViLWRkZFc3W9CKpViypQpOHjwIAYPHoyoqCgAL9qmJu0CvPh/BXixEmy5VxNPe3t7HD58uNI6FJ1P0UwBHR0dDB48GFu2bEFMTAzatWuHTp06yY5xdnaucJ5Dhw5VGBGtqdKyUpzNOYu4jDiczTmL0rKatQkRERG9Y+p8yRd6LcqslFMX/iotE7rNSxAsgvYp/FgG7RO6zUsQ/iotq5f46sPLq8iWU7SaLABh165dFVZ0DAwMFOLj44WMjAzh/PnzwgcffCAMHTpUEARBuHPnjiASiYTo6Gjh/v37shUxFZ0zJSVFACCsWLFCuHHjhvDDDz8I5ubmcudSZhXNDz/8UBg2bJjCfRYWFoK+vr6wcOFCIT09XVi9erWgqakpxMfHV1pfZatoVray5dOnT4WAgADh6NGjwq1bt4RTp04Jbdu2Fb766itBEAQhMTFRtgpnbm6uUFhYKIvt1bYvLi4WJBKJ4OXlJVy9elXYt2+f0K5dO7nVMNPT0wWxWCyMGzdOuHjxopCWliaEh4cLubm5giAIgp+fn9ClSxfh5s2bQm5uboWVMl926NAhQVtbW2jXrp0QGhoqty8jI0PQ1dUVpk+fLqSlpQlr1qyp0HarVq0SevfuLXdcamqqkJycLAwYMEBwdXUVkpOThYhfIoQ+P/UR2ke3l336/NRHOHTrUKWxERERkfqol1U0qXasWbMGdnZ26NKlS73GkXTzYYWRu5cJALLznyPpZvXTXumF0tJSBAQEwNbWFh9//DGkUqlsoQ9lRmBrc8Q8PT1dtiBSZb788kucO3cOjo6OCAsLw7Jly+Dm5vZa51NEU1MTeXl5GDlyJKRSKYYOHQp3d3fMmTMHwIsfjPf398ewYcNgaGhYYcGZl2lpaSEmJgZXrlyBvb09Fi5ciLCwMLkyUqkUBw8exMWLF9G1a1c4Oztjz549sinZ06ZNg6amJuzs7GQLGlWmd+/eaN68OdLT0/H555/L7Xv//ffxyy+/4NChQ3BwcMDSpUuxYcMGubZ78OBBhfcNPTw84OjoiJ9//hnHjh2Do6MjxnmOw72n8tM97z+9j6nHpiLhdkIVrUtERETvGpEgCO/uihlvscePH8PAwAD5+fnQ19dX+fn3pPyBwNiUasutHN4Rn3Q0r7YcESmvtKwUbjvcKiR35UQQwVjXGPGfxkNTQ1NhGSIiImr4lMkNOIJHChk11anVckSkvAv3L1Sa3AGAAAE5T3Nw4f4FFUZFREREbzMmeKRQ1/ebw9RAB6JK9osAmBrooOv7zSspQURvKvdpbq2WIyIiIvXHBI8U0tQQIWTAi5VBX03yyr+HDLCDpkZlKSARvSlDXcNaLUdERETqjwkeVerj9qaI+KITTAzkp2GaGOgg4otO+Li9aT1FRvRu6GTUCca6xhBVMpYugggmuiboZNRJ4X4iIiJ69yj+JW+i//dxe1P0szNB0s2HuF/wHEZNX0zL5MgdUd3T1NBEcNdgTD02FSKIIODvNbHKk76grkFcYIWIiIhkuIrmW6q+V9EkordHwu0ELEhaILfgiomuCYK6BqGvRd96jIyIiIhUQZncgCN4RERvub4WffGR5CNcuH8BuU9zYahriE5GnThyR0RERBUwwSMiagA0NTTRxaRLfYdBREREbzkuskJERERERKQmmOARERERERGpCSZ4REREREREaoLv4L2lyhc3ffz4cT1HQkRERERE9ak8J6jJDyAwwXtLFRQUAAAkEkk9R0JERERERG+DgoICGBgYVFmGv4P3liorK8Pdu3fRtGlTiERv54+KP378GBKJBFlZWfytPlIJ9jlSNfY5UjX2OVIl9reGQxAEFBQUwMzMDBoaVb9lxxG8t5SGhgZatWpV32HUiL6+Pm8KpFLsc6Rq7HOkauxzpErsbw1DdSN35bjIChERERERkZpggkdERERERKQmmODRa9PW1kZISAi0tbXrOxR6R7DPkaqxz5Gqsc+RKrG/qScuskJERERERKQmOIJHRERERESkJpjgERERERERqQkmeERERERERGqCCR4REREREZGaYIJHCkVERMDe3l72w5fOzs7Yv39/peV37twJJycnNGvWDE2aNEHHjh2xefNmFUZMDZ2yfe5lsbGxEIlEGDRoUN0GSWpF2T4XHR0NkUgk99HR0VFhxNTQvc597tGjRwgICICpqSm0tbUhlUoRFxenooipoVO2z7m6ula4z4lEInh6eqowanpTjeo7AHo7tWrVCgsWLIC1tTUEQcCmTZvwySefIDk5Gf/4xz8qlG/evDlmzpwJGxsbiMVi7Nu3D2PGjIGRkRHc3Nzq4QqooVG2z5W7desWpk2bhh49eqgwWlIHr9Pn9PX1kZ6eLvsuEolUFS6pAWX7XHFxMfr16wcjIyNs374d5ubmuH37Npo1a6b64KlBUrbP7dy5E8XFxbLveXl5cHBwgJeXlyrDpjfEn0mgGmvevDkWL14MX1/fGpXv1KkTPD09ERoaWseRkbqqrs+VlpaiZ8+e8PHxwcmTJ/Ho0SPs3r1btUGSWqmqz0VHR2Py5Ml49OiR6gMjtVVVn1u7di0WL16MK1euQEtLqx6iI3WkzN9zK1aswOzZs5GdnY0mTZqoIDqqDZyiSdUqLS1FbGwsCgsL4ezsXG15QRBw+PBhpKeno2fPniqIkNRNTfvc3LlzYWRkVOOHDkSVqWmfe/LkCSwsLCCRSPDJJ58gNTVVhVGSOqlJn9u7dy+cnZ0REBAAY2NjtG/fHvPmzUNpaamKoyV1oOzfcwAQGRmJ4cOHM7lrYDhFkyp16dIlODs74/nz59DT08OuXbtgZ2dXafn8/HyYm5ujqKgImpqaCA8PR79+/VQYMTV0yvS5U6dOITIyEikpKaoNktSKMn2uXbt22LhxI+zt7ZGfn48lS5age/fuSE1NRatWrVQcOTVUyvS5jIwMHDlyBN7e3oiLi8P169cxfvx4lJSUICQkRMWRU0Ol7N9z5ZKSknD58mVERkaqIEqqTZyiSZUqLi5GZmYm8vPzsX37dmzYsAHHjx+v9KZQVlaGjIwMPHnyBIcPH0ZoaCh2794NV1dX1QZODVZN+1xBQQHs7e0RHh4Od3d3AMDo0aM5RZOUpux97mUlJSWwtbXFZ599xqnoVGPK9DmpVIrnz5/j5s2b0NTUBAAsW7YMixcvRnZ2tqpDpwbqde9z//73v3HmzBn8/vvvKoqUagsTPKqxvn37om3btli3bl2Nyo8dOxZZWVk4cOBAHUdG6qqyPpeSkgJHR0fZHzzAiwcMAKChoYH09HS0bdtWpbGSelD2Pufl5YVGjRohJiamjiMjdVVVn+vVqxe0tLSQkJAg27Z//354eHigqKgIYrFYlaGSmqjJfa6wsBBmZmaYO3cuAgMDVRgd1Qa+g0c1VlZWhqKiojorT/SqyvqQjY0NLl26hJSUFNln4MCB+Oijj5CSkgKJRFIP0ZI6UOa+VVpaikuXLsHU1LSOoyJ1VlWfc3FxwfXr12UPsADg6tWrMDU1ZXJHr60m97lt27ahqKgIX3zxhYqiotrEd/BIoRkzZsDd3R2tW7dGQUEBtm7dimPHjslG40aOHAlzc3PMnz8fADB//nw4OTmhbdu2KCoqQlxcHDZv3oyIiIj6vAxqQJTpczo6Omjfvr3c8eXLhr+6nagyyt7n5s6di27dusHKygqPHj3C4sWLcfv2bYwdO7Y+L4MaEGX73Lhx47B69WoEBgZi4sSJuHbtGubNm4dJkybV52VQA6JsnysXGRmJQYMGoUWLFvURNr0hJnik0P379zFy5EhkZ2fDwMAA9vb2OHDggGzRlMzMTGho/D0AXFhYiPHjx+POnTto3LgxbGxs8N///hfDhg2rr0ugBkbZPkf0ppTtc3/++Sf8/PyQk5OD9957D507d8bp06dr9L4eEaB8n5NIJDhw4ACmTJkCe3t7mJubIzAwEEFBQfV1CdTAvM6/renp6Th16hQOHjxYHyFTLeA7eERERERERGqCj8OJiIiIiIjUBBM8IiIiIiIiNcEEj4iIiIiISE0wwSMiIiIiIlITTPCIiIiIiIjUBBM8IiIiIiIiNcEEj4iIiIiISE0wwSMiIiIiIlITTPCIiIjqyLfffouOHTvKvo8ePRqDBg2qt3iIiEj9McEjIqJ3SlZWFnx8fGBmZgaxWAwLCwsEBgYiLy+vzs+9cuVKREdHy767urpi8uTJb1zv06dPMWPGDLRt2xY6OjowNDREr169sGfPnjeum4iIGpZG9R0AERGRqmRkZMDZ2RlSqRQxMTF4//33kZqaiunTp2P//v349ddf0bx58zo7v4GBQZ3U6+/vj99++w2rVq2CnZ0d8vLycPr06TpNWouLiyEWi+usfiIiej0cwSMiondGQEAAxGIxDh48iF69eqF169Zwd3dHQkIC/vjjD8ycOVNWViQSYffu3XLHN2vWTG4ELigoCFKpFLq6umjTpg2++eYblJSUVHr+l6dojh49GsePH8fKlSshEokgEolw8+ZNWFlZYcmSJXLHpaSkQCQS4fr16wrr3bt3L77++mt4eHjA0tISnTt3xsSJE+Hj4yMrU1RUhKCgIEgkEmhra8PKygqRkZGy/cePH0fXrl2hra0NU1NTBAcH46+//pLtd3V1xYQJEzB58mS0bNkSbm5uAIDLly/D3d0denp6MDY2xogRI/DgwYNK24CIiOoWEzwiInonPHz4EAcOHMD48ePRuHFjuX0mJibw9vbGjz/+CEEQalxn06ZNER0djf/9739YuXIl1q9fj+XLl9fo2JUrV8LZ2Rl+fn7Izs5GdnY2WrduDR8fH0RFRcmVjYqKQs+ePWFlZaWwLhMTE8TFxaGgoKDS840cORIxMTH4/vvvkZaWhnXr1kFPTw8A8Mcff8DDwwNdunTBxYsXERERgcjISISFhcnVsWnTJojFYiQmJmLt2rV49OgRevfuDUdHR5w7dw7x8fG4d+8ehg4dWqM2ICKi2scpmkRE9E64du0aBEGAra2twv22trb4888/kZubCyMjoxrVOWvWLNl/W1paYtq0aYiNjcVXX31V7bEGBgYQi8XQ1dWFiYmJbPvo0aMxe/ZsJCUloWvXrigpKcHWrVsrjOq97D//+Q+8vb3RokULODg44MMPP8SQIUPg4uICALh69Sp++uknHDp0CH379gUAtGnTRnZ8eHg4JBIJVq9eDZFIBBsbG9y9exdBQUGYPXs2NDRePA+2trbGokWLZMeFhYXB0dER8+bNk23buHEjJBIJrl69CqlUWm07EBFR7eIIHhERvVOqG6FT5r2yH3/8ES4uLjAxMYGenh5mzZqFzMzMN4rPzMwMnp6e2LhxIwDg559/RlFREby8vCo9pmfPnsjIyMDhw4cxZMgQpKamokePHggNDQXwYoqnpqYmevXqpfD4tLQ0ODs7QyQSyba5uLjgyZMnuHPnjmxb586d5Y67ePEijh49Cj09PdnHxsYGAHDjxo3XawAiInojTPCIiOidYGVlBZFIhLS0NIX709LSYGhoiGbNmgF48Q7eq8ngy+/XnTlzBt7e3vDw8MC+ffuQnJyMmTNnori4+I1jHTt2LGJjY/Hs2TNERUVh2LBh0NXVrfIYLS0t9OjRA0FBQTh48CDmzp2L0NBQFBcXV5iS+rqaNGki9/3JkycYMGAAUlJS5D7Xrl1Dz549a+WcRESkHE7RJCKid0KLFi3Qr18/hIeHY8qUKXJJT05ODrZs2YKAgADZNkNDQ2RnZ8u+X7t2DU+fPpV9P336NCwsLOQWZrl9+7ZSMYnFYpSWllbY7uHhgSZNmiAiIgLx8fE4ceKEUvUCgJ2dHf766y88f/4cHTp0QFlZGY4fPy6bovkyW1tb7NixA4IgyEbxEhMT0bRpU7Rq1arSc3Tq1Ak7duyApaUlGjXinxRERG8DjuAREdE7Y/Xq1SgqKoKbmxtOnDiBrKwsxMfHo1+/fpBKpZg9e7asbO/evbF69WokJyfj3Llz8Pf3h5aWlmy/tbU1MjMzERsbixs3buD777/Hrl27lIrH0tISv/32G27duoUHDx6grKwMAKCpqYnRo0djxowZsLa2hrOzc5X1uLq6Yt26dTh//jxu3bqFuLg4fP311/joo4+gr68PS0tLjBo1Cj4+Pti9ezdu3ryJY8eO4aeffgIAjB8/HllZWZg4cSKuXLmCPXv2ICQkBFOnTpW9f6dIQEAAHj58iM8++wxnz57FjRs3cODAAYwZM0Zh4kpERHWPCR4REb0zrK2tcfbsWbRp0wZDhw6FhYUF3N3dIZVKkZiYKFtVEgCWLl0KiUSCHj164PPPP8e0adPkpkkOHDgQU6ZMwYQJE9CxY0ecPn0a33zzjVLxTJs2DZqamrCzs4OhoaHc+3u+vr4oLi7GmDFjqq3Hzc0NmzZtQv/+/WFra4uJEyfCzc1NlsABQEREBIYMGYLx48fDxsYGfn5+KCwsBACYm5sjLi4OSUlJcHBwgL+/P3x9feUWkVHEzMwMiYmJKC0tRf/+/dGhQwdMnjwZzZo1qzIxJCKiuiMSlFkPmoiISM2EhIRg2bJlOHToELp161bf4cicPHkSffr0QVZWFoyNjes7HCIiaiCY4BER0TsvKioK+fn5mDRpUr2PPBUVFSE3NxejRo2CiYkJtmzZUq/xEBFRw8IEj4iI6C0SHR0NX19fdOzYEXv37oW5uXl9h0RERA0IEzwiIiIiIiI1wTegiYiIiIiI1AQTPCIiIiIiIjXBBI+IiIiIiEhNMMEjIiIiIiJSE0zwiIiIiIiI1AQTPCIiIiIiIjXBBI+IiIiIiEhNMMEjIiIiIiJSE/8HmoH4bvxA9jUAAAAASUVORK5CYII=",
       "text/plain": [
        "<Figure size 1000x300 with 1 Axes>"
       ]
@@ -6375,7 +6713,7 @@
    "id": "2463c757-f54e-4905-bd8b-d00a32c2ec7d",
    "metadata": {},
    "source": [
-    "It seems that the most performant LLM, `gpt-4`, is also the most expensive one. While `codellama-34b` is very close in quality but about 55X more cost-effective than `gpt-4` and 2X than `gpt-3.5-turbo`."
+    "It seems that the most performant LLM, `gpt-4`, is also the most expensive one. While `codellama-34b` is very close in quality but about 30X more cost-effective than `gpt-4` and 1.5X than `gpt-3.5-turbo`."
    ]
   },
   {
@@ -6391,11 +6729,11 @@
      "output_type": "stream",
      "text": [
       "Cost multiplier compared to codellama-34b-instruct-hf\n",
-      "  gpt-3.5-turbo: 1.48X\n",
-      "  gpt-4: 30.20X\n",
+      "  gpt-3.5-turbo: 1.43X\n",
+      "  gpt-4: 30.61X\n",
       "  llama-2-7b-chat-hf: 0.15X\n",
-      "  llama-2-13b-chat-hf: 0.25X\n",
-      "  llama-2-70b-chat-hf: 1.01X\n",
+      "  llama-2-13b-chat-hf: 0.26X\n",
+      "  llama-2-70b-chat-hf: 1.02X\n",
       "  codellama-34b-instruct-hf: 1.00X\n",
       "  mistral-7b-instruct-v0.1: 0.15X\n"
      ]
@@ -6469,15 +6807,17 @@
       "Question for gpt-4:\n",
       " {'question': 'if I am inside of a anyscale cluster how do I get my cluster-env-build-id', 'target': 0}\n",
       "\n",
-      "Question for codellama-34b:\n",
+      "Question for OSS:\n",
       " {'question': 'what is num_samples in tune?', 'target': 1}\n"
      ]
     }
    ],
    "source": [
     "# Sample records (1 = can be handled by OSS LLM)\n",
-    "print (\"Question for gpt-4:\\n\", [record for record in records if record[\"target\"] == 0][0]) \n",
-    "print (\"\\nQuestion for codellama-34b:\\n\", [record for record in records if record[\"target\"] == 1][0])"
+    "gpt_records = [record for record in records if record[\"target\"] == 0]\n",
+    "oss_records = [record for record in records if record[\"target\"] == 1]\n",
+    "print (\"Question for gpt-4:\\n\", gpt_records[0]) \n",
+    "print (\"\\nQuestion for OSS:\\n\", oss_records[0])"
    ]
   },
   {
@@ -6745,7 +7085,7 @@
     "print (\"# total samples\", len(y_pred))\n",
     "print(f\"# samples for OSS models: {sum(y_pred)} ({sum(y_pred)*100/len(y_pred):.1f}%)\")\n",
     "print(f\"Performance on samples predicted for {LLM}: {np.mean([score_test[i] for i, p in enumerate(y_pred) if p]):.2f}\")\n",
-    "print(f\"Performance on samples predicted for gpt-4: {np.mean([score_test[i] for i, p in enumerate(y_pred) if not p]):.2f}\")"
+    "print(f\"Performance on samples predicted for GPT-4: {np.mean([score_test[i] for i, p in enumerate(y_pred) if not p]):.2f}\")"
    ]
   },
   {
diff --git a/rag/serve.py b/rag/serve.py
index a1dc0cd..f03c280 100644
--- a/rag/serve.py
+++ b/rag/serve.py
@@ -18,7 +18,7 @@
 from slack_bolt.adapter.socket_mode import SocketModeHandler
 from starlette.responses import StreamingResponse
 
-from rag.config import EFS_DIR, EMBEDDING_DIMENSIONS, MAX_CONTEXT_LENGTHS
+from rag.config import EMBEDDING_DIMENSIONS, MAX_CONTEXT_LENGTHS
 from rag.generate import QueryAgent
 from rag.index import build_or_load_index