CS5446-Project/outs_domain.ipynb

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   "source": [
    "import os, json, random, time\n",
    "from pathlib import Path\n",
    "\n",
    "import gc\n",
    "import torch\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "from tqdm.auto import tqdm\n",
    "\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "# Local modules\n",
    "from model import load_model\n",
    "from dataset import load_combined_minimal, balanced_sample, SimpleTextDataset, get_seed_sets_for_steering\n",
    "from validator import evaluate_minimal\n",
    "\n",
    "from domain_specific_proposed import build_agentic_defense, cleanup_models"
   ]
  },
  {
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   "source": [
    "SEED = 42\n",
    "random.seed(SEED); np.random.seed(SEED)\n",
    "try:\n",
    "    import torch\n",
    "    torch.manual_seed(SEED)\n",
    "except Exception:\n",
    "    pass\n",
    "\n",
    "# Config\n",
    "ALIGNED_KEY   = \"aligned\" \n",
    "UNALIGNED_KEY = \"unaligned\"\n",
    "\n",
    "MAX_UNSAFE_EVAL = 200\n",
    "SAFE_SEED  = 96\n",
    "UNSAFE_SEED= 96\n",
    "\n",
    "OUTDIR = Path(\"results/asv_notebook_domain\")\n",
    "OUTDIR.mkdir(parents=True, exist_ok=True)"
   ]
  },
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    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[INFO] Unsafe eval set size: 200\n",
      "[INFO] Steering seed sets -> safe: 96, unsafe: 96\n"
     ]
    }
   ],
   "source": [
    "df_all_ds = load_combined_minimal(\"xstest_prompts.csv\")\n",
    "full_df = df_all_ds.df\n",
    "\n",
    "unsafe_df = full_df[full_df['label'] == 'unsafe'].copy()\n",
    "if len(unsafe_df) == 0:\n",
    "    raise RuntimeError(\"No 'unsafe' rows found. Ensure your CSV/HF data is available.\")\n",
    "\n",
    "if len(unsafe_df) > MAX_UNSAFE_EVAL:\n",
    "    unsafe_eval_df = unsafe_df.sample(MAX_UNSAFE_EVAL, random_state=SEED).reset_index(drop=True)\n",
    "    unsafe_eval_df['id'] = unsafe_eval_df.index.astype(int)\n",
    "else:\n",
    "    unsafe_eval_df = unsafe_df.reset_index(drop=True)\n",
    "    unsafe_eval_df['id'] = unsafe_eval_df.index.astype(int)\n",
    "\n",
    "print(f\"[INFO] Unsafe eval set size: {len(unsafe_eval_df)}\")\n",
    "\n",
    "\n",
    "safe_prompts_seed, unsafe_prompts_seed = get_seed_sets_for_steering(\n",
    "    full_df, n_safe=SAFE_SEED, n_unsafe=UNSAFE_SEED, seed=SEED\n",
    ")\n",
    "print(f\"[INFO] Steering seed sets -> safe: {len(safe_prompts_seed)}, unsafe: {len(unsafe_prompts_seed)}\")"
   ]
  },
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   "source": [
    "MAX_INPUT_TOKENS = 2048\n",
    "MAX_NEW_TOKENS   = 768\n",
    "USE_CACHE        = False\n",
    "\n",
    "def _asr(df):\n",
    "    if df is None or len(df) == 0 or \"jailbreak_success\" not in df.columns:\n",
    "        return float(\"nan\")\n",
    "    return float(df[\"jailbreak_success\"].astype(bool).mean())\n",
    "\n",
    "def run_generation_df(wrapper, df_prompts, *,\n",
    "                      steerer=None,\n",
    "                      prompt_defense=None,\n",
    "                      agentic=None,\n",
    "                      max_input_tokens=MAX_INPUT_TOKENS,\n",
    "                      max_new_tokens=MAX_NEW_TOKENS,\n",
    "                      temperature=0.0, top_p=1.0, clear_cuda_each=1):\n",
    "    rows = []\n",
    "    iterator = tqdm(df_prompts.itertuples(index=False), total=len(df_prompts), desc=\"Generate (full)\")\n",
    "\n",
    "    hf  = wrapper.hf\n",
    "    tok = wrapper.tokenizer\n",
    "\n",
    "    # Respect global USE_CACHE if defined\n",
    "    try:\n",
    "        hf.config.use_cache = USE_CACHE\n",
    "        if hasattr(hf, \"generation_config\"):\n",
    "            hf.generation_config.use_cache = USE_CACHE\n",
    "    except Exception:\n",
    "        pass\n",
    "\n",
    "    eos_id = tok.eos_token_id\n",
    "    if eos_id is None and getattr(tok, \"pad_token_id\", None) is not None:\n",
    "        eos_id = tok.pad_token_id\n",
    "\n",
    "    for i, row in enumerate(iterator, 1):\n",
    "        orig_prompt = row.prompt\n",
    "\n",
    "        if agentic is not None:\n",
    "            try:\n",
    "                final_out, verdict, info_agentic, rewritten_prompt = agentic(\n",
    "                    wrapper, orig_prompt,\n",
    "                    max_input_tokens=max_input_tokens,\n",
    "                    max_new_tokens=max_new_tokens,\n",
    "                    use_cache=USE_CACHE,\n",
    "                )\n",
    "            except Exception as e:\n",
    "                rewritten_prompt = None\n",
    "                verdict = {\"safe\": False, \"reasons\": [f\"agentic_error: {type(e).__name__}\"]}  # optional\n",
    "                if hasattr(tok, \"apply_chat_template\"):\n",
    "                    msgs = [{\"role\": \"user\", \"content\": orig_prompt}]\n",
    "                    text = tok.apply_chat_template(msgs, add_generation_prompt=True, tokenize=False)\n",
    "                else:\n",
    "                    text = (\n",
    "                        \"<|begin_of_text|>\"\n",
    "                        \"<|start_header_id|>user<|end_header_id|>\\n\"\n",
    "                        f\"{orig_prompt}\\n<|eot_id|>\"\n",
    "                        \"<|start_header_id|>assistant<|end_header_id|>\\n\"\n",
    "                    )\n",
    "                enc = tok(text, return_tensors=\"pt\", truncation=True, max_length=max_input_tokens).to(hf.device)\n",
    "                gen_kwargs = dict(\n",
    "                    max_new_tokens=max_new_tokens,\n",
    "                    do_sample=False if (temperature is None or temperature == 0.0) else True,\n",
    "                    temperature=None if (temperature is None or temperature == 0.0) else float(temperature),\n",
    "                    top_p=top_p,\n",
    "                    use_cache=USE_CACHE,\n",
    "                )\n",
    "                if eos_id is not None:\n",
    "                    gen_kwargs[\"eos_token_id\"] = eos_id\n",
    "                    gen_kwargs[\"pad_token_id\"] = eos_id\n",
    "                with torch.inference_mode():\n",
    "                    out_ids = hf.generate(**enc, **gen_kwargs)\n",
    "                gen_only = out_ids[0][enc[\"input_ids\"].shape[1]:]\n",
    "                final_out = tok.decode(gen_only, skip_special_tokens=True).strip()\n",
    "\n",
    "            rows.append({\n",
    "                \"id\": int(row.id),\n",
    "                \"prompt\": orig_prompt,\n",
    "                \"label\": row.label,\n",
    "                \"model_output\": final_out,\n",
    "                \"rewritten_prompt\": rewritten_prompt,\n",
    "                \"judge_safe\": verdict.get(\"safe\") if isinstance(verdict, dict) else None,\n",
    "                \"judge_reasons\": \"; \".join(verdict.get(\"reasons\", [])) if isinstance(verdict, dict) else None,\n",
    "            })\n",
    "\n",
    "            if (clear_cuda_each is not None) and (i % clear_cuda_each == 0):\n",
    "                if torch.cuda.is_available():\n",
    "                    torch.cuda.empty_cache()\n",
    "                    torch.cuda.ipc_collect()\n",
    "                import gc as _gc; _gc.collect()\n",
    "            continue  # next row\n",
    "\n",
    "        sys_prompt = None\n",
    "        prompt = orig_prompt\n",
    "\n",
    "        if prompt_defense is not None:\n",
    "            try:\n",
    "                transformed, _sys_ignored, info = prompt_defense(orig_prompt)\n",
    "                prompt = transformed if transformed is not None else orig_prompt\n",
    "                sys_prompt = None\n",
    "            except Exception:\n",
    "                prompt = orig_prompt\n",
    "                sys_prompt = None\n",
    "\n",
    "        if hasattr(tok, \"apply_chat_template\"):\n",
    "            msgs = [{\"role\": \"user\", \"content\": prompt}]\n",
    "            text = tok.apply_chat_template(msgs, add_generation_prompt=True, tokenize=False)\n",
    "        else:\n",
    "            text = (\n",
    "                \"<|begin_of_text|>\"\n",
    "                \"<|start_header_id|>user<|end_header_id|>\\n\"\n",
    "                f\"{prompt}\\n<|eot_id|>\"\n",
    "                \"<|start_header_id|>assistant<|end_header_id|>\\n\"\n",
    "            )\n",
    "\n",
    "        enc = tok(text, return_tensors=\"pt\", truncation=True, max_length=max_input_tokens).to(hf.device)\n",
    "\n",
    "        gen_kwargs = dict(\n",
    "            max_new_tokens=max_new_tokens,\n",
    "            do_sample=False if (temperature is None or temperature == 0.0) else True,\n",
    "            temperature=None if (temperature is None or temperature == 0.0) else float(temperature),\n",
    "            top_p=top_p,\n",
    "            use_cache=USE_CACHE,\n",
    "        )\n",
    "        if eos_id is not None:\n",
    "            gen_kwargs[\"eos_token_id\"] = eos_id\n",
    "            gen_kwargs[\"pad_token_id\"] = eos_id\n",
    "\n",
    "        with torch.inference_mode():\n",
    "            if steerer is None:\n",
    "                out_ids = hf.generate(**enc, **gen_kwargs)\n",
    "            else:\n",
    "                with steerer.steering_context(prompt_for_alpha=orig_prompt):\n",
    "                    out_ids = hf.generate(**enc, **gen_kwargs)\n",
    "\n",
    "        gen_only = out_ids[0][enc[\"input_ids\"].shape[1]:]\n",
    "        text_out = tok.decode(gen_only, skip_special_tokens=True).strip()\n",
    "\n",
    "        rows.append({\n",
    "            \"id\": int(row.id),\n",
    "            \"prompt\": orig_prompt,\n",
    "            \"label\": row.label,\n",
    "            \"model_output\": text_out\n",
    "        })\n",
    "\n",
    "        if (clear_cuda_each is not None) and (i % clear_cuda_each == 0):\n",
    "            if torch.cuda.is_available():\n",
    "                torch.cuda.empty_cache()\n",
    "                torch.cuda.ipc_collect()\n",
    "            import gc as _gc; _gc.collect()\n",
    "\n",
    "    return pd.DataFrame(rows)"
   ]
  },
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   "source": [
    "## Aligned Model"
   ]
  },
  {
   "cell_type": "code",
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   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[INFO] Loading aligned model...\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "`torch_dtype` is deprecated! Use `dtype` instead!\n"
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "cba786146e0e4ac2a6670ad4469bf3d5",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Loading checkpoint shards:   0%|          | 0/4 [00:00<?, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "The following generation flags are not valid and may be ignored: ['temperature']. Set `TRANSFORMERS_VERBOSITY=info` for more details.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[TEST] Aligned sample -> Say 'ok' to\n"
     ]
    }
   ],
   "source": [
    "print(\"[INFO] Loading aligned model...\")\n",
    "aligned = load_model(ALIGNED_KEY)\n",
    "\n",
    "print(\"[TEST] Aligned sample ->\", aligned.generate(\"Say 'ok'.\", max_new_tokens=5))"
   ]
  },
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   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[BASELINE] Evaluating ALIGNED (no defense, FULL outputs) ...\n"
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "646b840d12a04135abdf18b807806d53",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Generate (full):   0%|          | 0/200 [00:00<?, ?it/s]"
      ]
     },
     "metadata": {},
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    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "f805cdda7a3c4505baf87e3a485b6ce7",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Judge (minimal):   0%|          | 0/200 [00:00<?, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "4210527c3208477287bfb5e7c412ac9b",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Loading checkpoint shards:   0%|          | 0/2 [00:00<?, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "`torch_dtype` is deprecated! Use `dtype` instead!\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Device set to use cuda:0\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "The following generation flags are not valid and may be ignored: ['temperature', 'top_k']. Set `TRANSFORMERS_VERBOSITY=info` for more details.\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "You seem to be using the pipelines sequentially on GPU. In order to maximize efficiency please use a dataset\n"
     ]
    }
   ],
   "source": [
    "print(\"[BASELINE] Evaluating ALIGNED (no defense, FULL outputs) ...\")\n",
    "df_gen_aligned_base = run_generation_df(\n",
    "    aligned, unsafe_eval_df, steerer=None,\n",
    "    max_input_tokens=MAX_INPUT_TOKENS,\n",
    "    max_new_tokens=MAX_NEW_TOKENS, \n",
    "    temperature=None, \n",
    "    top_p=1.0\n",
    ")\n",
    "df_eval_aligned_base = evaluate_minimal(\n",
    "    df_gen_aligned_base,\n",
    "    use_llm_judge=True,\n",
    "    ensemble_with_heuristic=True\n",
    ")\n",
    "\n",
    "asr_aligned_base   = _asr(df_eval_aligned_base)"
   ]
  },
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    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "`torch_dtype` is deprecated! Use `dtype` instead!\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Aligned domain defence init\n"
     ]
    }
   ],
   "source": [
    "adef_aligned = build_agentic_defense(\n",
    "    model_id=\"Qwen/Qwen3-0.6B\",\n",
    "    refusal_text=\"I can not answer that, sorry.\",\n",
    "    max_new_tokens_rewriter=128,\n",
    "    max_new_tokens_judge=128,\n",
    ")\n",
    "\n",
    "print(\"Aligned domain defence init\")"
   ]
  },
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   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[DEFENSE] Evaluating ALIGNED (domain-based, FULL outputs) ...\n"
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "1b68a1ab95a04c03844e44f09dcc1dc1",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Generate (full):   0%|          | 0/200 [00:00<?, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "The following generation flags are not valid and may be ignored: ['temperature', 'top_p', 'top_k']. Set `TRANSFORMERS_VERBOSITY=info` for more details.\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "The following generation flags are not valid and may be ignored: ['temperature', 'top_p']. Set `TRANSFORMERS_VERBOSITY=info` for more details.\n"
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "f9fc06be23eb4cafa3eb1814b407706e",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Judge (minimal):   0%|          | 0/200 [00:00<?, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "print(\"[DEFENSE] Evaluating ALIGNED (domain-based, FULL outputs) ...\")\n",
    "df_gen_aligned_def = run_generation_df(\n",
    "    aligned, unsafe_eval_df,\n",
    "    agentic=adef_aligned,\n",
    "    steerer=None,\n",
    "    prompt_defense=None,\n",
    "    max_input_tokens=MAX_INPUT_TOKENS,\n",
    "    max_new_tokens=MAX_NEW_TOKENS,\n",
    "    temperature=None,\n",
    "    top_p=1.0\n",
    ")\n",
    "df_eval_aligned_def = evaluate_minimal(\n",
    "    df_gen_aligned_def,\n",
    "    use_llm_judge=True,\n",
    "    ensemble_with_heuristic=True\n",
    ")\n",
    "asr_aligned_def = _asr(df_eval_aligned_def)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "97d176e1-9e38-4cc5-b523-c14174a1a815",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2025-11-03T18:21:33.207033Z",
     "iopub.status.busy": "2025-11-03T18:21:33.206826Z",
     "iopub.status.idle": "2025-11-03T18:21:33.592058Z",
     "shell.execute_reply": "2025-11-03T18:21:33.591630Z"
    },
    "papermill": {
     "duration": 0.391342,
     "end_time": "2025-11-03T18:21:33.592865",
     "exception": false,
     "start_time": "2025-11-03T18:21:33.201523",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[CLEANUP] Releasing ALIGNED model from memory...\n"
     ]
    }
   ],
   "source": [
    "# clean up the model\n",
    "print(\"[CLEANUP] Releasing ALIGNED model from memory...\")\n",
    "cleanup_models(adef_aligned)\n",
    "del aligned\n",
    "gc.collect()\n",
    "if torch.cuda.is_available():\n",
    "    torch.cuda.empty_cache()\n",
    "    torch.cuda.ipc_collect()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "47632778-b65b-4a7a-a22a-f013857de0a8",
   "metadata": {
    "papermill": {
     "duration": 0.005926,
     "end_time": "2025-11-03T18:21:33.604707",
     "exception": false,
     "start_time": "2025-11-03T18:21:33.598781",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "## Unaligned Model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "32864439-2e43-49b5-a271-5b696a35a040",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2025-11-03T18:21:33.617070Z",
     "iopub.status.busy": "2025-11-03T18:21:33.616741Z",
     "iopub.status.idle": "2025-11-03T18:22:23.196101Z",
     "shell.execute_reply": "2025-11-03T18:22:23.195342Z"
    },
    "papermill": {
     "duration": 49.593445,
     "end_time": "2025-11-03T18:22:23.203967",
     "exception": false,
     "start_time": "2025-11-03T18:21:33.610522",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[INFO] Loading unaligned model...\n"
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "57119e8c808344ee84bc4d2e887dae19",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Loading checkpoint shards:   0%|          | 0/4 [00:00<?, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[TEST] Unaligned sample -> Say 'ok' right\n"
     ]
    }
   ],
   "source": [
    "print(\"[INFO] Loading unaligned model...\")\n",
    "unaligned = load_model(UNALIGNED_KEY)\n",
    "print(\"[TEST] Unaligned sample ->\", unaligned.generate(\"Say 'ok'.\", max_new_tokens=5))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "2251a9e9-2093-4aee-b419-25e667c166cb",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2025-11-03T18:22:23.250453Z",
     "iopub.status.busy": "2025-11-03T18:22:23.250065Z",
     "iopub.status.idle": "2025-11-03T20:23:02.510562Z",
     "shell.execute_reply": "2025-11-03T20:23:02.510136Z"
    },
    "papermill": {
     "duration": 7239.27906,
     "end_time": "2025-11-03T20:23:02.511363",
     "exception": false,
     "start_time": "2025-11-03T18:22:23.232303",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[BASELINE] Evaluating UNALIGNED (no defense, FULL outputs) ...\n"
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "6a3f8f83f86f4d98a17ba3019511efc9",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Generate (full):   0%|          | 0/200 [00:00<?, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "3db6a0cb9d9444559e2be44b60657c40",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Judge (minimal):   0%|          | 0/200 [00:00<?, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "print(\"[BASELINE] Evaluating UNALIGNED (no defense, FULL outputs) ...\")\n",
    "df_gen_unaligned_base = run_generation_df(\n",
    "    unaligned, unsafe_eval_df, steerer=None,\n",
    "    max_input_tokens=MAX_INPUT_TOKENS,\n",
    "    max_new_tokens=MAX_NEW_TOKENS,\n",
    "    temperature=None,\n",
    "    top_p=1.0\n",
    ")\n",
    "df_eval_unaligned_base = evaluate_minimal(\n",
    "    df_gen_unaligned_base,\n",
    "    use_llm_judge=True,\n",
    "    ensemble_with_heuristic=True\n",
    ")\n",
    "\n",
    "asr_unaligned_base = _asr(df_eval_unaligned_base)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "0483cf69-bffa-4380-9eb9-2320e1570cbe",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2025-11-03T20:23:02.535190Z",
     "iopub.status.busy": "2025-11-03T20:23:02.534907Z",
     "iopub.status.idle": "2025-11-03T20:23:08.939761Z",
     "shell.execute_reply": "2025-11-03T20:23:08.937833Z"
    },
    "papermill": {
     "duration": 6.424241,
     "end_time": "2025-11-03T20:23:08.942793",
     "exception": false,
     "start_time": "2025-11-03T20:23:02.518552",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Unaligned domain defence init\n"
     ]
    }
   ],
   "source": [
    "adef_unaligned = build_agentic_defense(\n",
    "    model_id=\"Qwen/Qwen3-0.6B\",\n",
    "    refusal_text=\"I can not answer that, sorry.\",\n",
    "    max_new_tokens_rewriter=128,\n",
    "    max_new_tokens_judge=128,\n",
    ")\n",
    "\n",
    "print(\"Unaligned domain defence init\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "cf29ba37-b78d-43e2-b573-2f1a5d425d16",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2025-11-03T20:23:08.979470Z",
     "iopub.status.busy": "2025-11-03T20:23:08.978408Z",
     "iopub.status.idle": "2025-11-03T21:30:32.956834Z",
     "shell.execute_reply": "2025-11-03T21:30:32.956372Z"
    },
    "papermill": {
     "duration": 4043.997801,
     "end_time": "2025-11-03T21:30:32.957690",
     "exception": false,
     "start_time": "2025-11-03T20:23:08.959889",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[DEFENSE] Evaluating UNALIGNED (domain-based, FULL outputs) ...\n"
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "1c18449c92184f8a92149f88236c27d5",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Generate (full):   0%|          | 0/200 [00:00<?, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "7743aa802ca241ba860d704c00bfae25",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Judge (minimal):   0%|          | 0/200 [00:00<?, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "print(\"[DEFENSE] Evaluating UNALIGNED (domain-based, FULL outputs) ...\")\n",
    "df_gen_unaligned_def = run_generation_df(\n",
    "    unaligned,\n",
    "    unsafe_eval_df,\n",
    "    agentic=adef_unaligned,      \n",
    "    steerer=None,\n",
    "    prompt_defense=None,\n",
    "    max_input_tokens=MAX_INPUT_TOKENS,\n",
    "    max_new_tokens=MAX_NEW_TOKENS,\n",
    "    temperature=None,\n",
    "    top_p=1.0\n",
    ")\n",
    "\n",
    "df_eval_unaligned_def = evaluate_minimal(\n",
    "    df_gen_unaligned_def,\n",
    "    use_llm_judge=True,\n",
    "    ensemble_with_heuristic=True\n",
    ")\n",
    "asr_unaligned_def = _asr(df_eval_unaligned_def)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "6177b6d4-0ee6-4ebd-8add-41079adfd9b3",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2025-11-03T21:30:32.983502Z",
     "iopub.status.busy": "2025-11-03T21:30:32.983240Z",
     "iopub.status.idle": "2025-11-03T21:30:33.376229Z",
     "shell.execute_reply": "2025-11-03T21:30:33.375817Z"
    },
    "papermill": {
     "duration": 0.41148,
     "end_time": "2025-11-03T21:30:33.377176",
     "exception": false,
     "start_time": "2025-11-03T21:30:32.965696",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[CLEANUP] Releasing UNALIGNED model and steerer from memory...\n"
     ]
    }
   ],
   "source": [
    "print(\"[CLEANUP] Releasing UNALIGNED model and steerer from memory...\")\n",
    "del unaligned\n",
    "cleanup_models(adef_unaligned)\n",
    "gc.collect()\n",
    "if torch.cuda.is_available():\n",
    "    torch.cuda.empty_cache()\n",
    "    torch.cuda.ipc_collect()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3f3e6ce1-cf12-4843-9517-0b84be75520f",
   "metadata": {
    "papermill": {
     "duration": 0.01184,
     "end_time": "2025-11-03T21:30:33.396302",
     "exception": false,
     "start_time": "2025-11-03T21:30:33.384462",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "# Results"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "2e99f224-3059-46c9-8801-1c66782ba901",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2025-11-03T21:30:33.424012Z",
     "iopub.status.busy": "2025-11-03T21:30:33.423738Z",
     "iopub.status.idle": "2025-11-03T21:30:33.594070Z",
     "shell.execute_reply": "2025-11-03T21:30:33.593416Z"
    },
    "papermill": {
     "duration": 0.186298,
     "end_time": "2025-11-03T21:30:33.595310",
     "exception": false,
     "start_time": "2025-11-03T21:30:33.409012",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[RESULT] Baseline ASR — ALIGNED: 0.360  |  UNALIGNED: 0.605\n",
      "[RESULT] With Defense ASR — ALIGNED: 0.000  |  UNALIGNED: 0.000\n",
      "\n",
      "[SUMMARY] {\n",
      "  \"baseline\": {\n",
      "    \"aligned\": 0.36,\n",
      "    \"unaligned\": 0.605\n",
      "  },\n",
      "  \"defense\": {\n",
      "    \"aligned\": 0.0,\n",
      "    \"unaligned\": 0.0\n",
      "  }\n",
      "}\n"
     ]
    }
   ],
   "source": [
    "print(f\"[RESULT] Baseline ASR — ALIGNED: {asr_aligned_base:.3f}  |  UNALIGNED: {asr_unaligned_base:.3f}\")\n",
    "\n",
    "OUTDIR.mkdir(parents=True, exist_ok=True)\n",
    "df_gen_aligned_base.to_csv(OUTDIR / \"gen_aligned_baseline.csv\", index=False)\n",
    "df_gen_unaligned_base.to_csv(OUTDIR / \"gen_unaligned_baseline.csv\", index=False)\n",
    "df_eval_aligned_base.to_csv(OUTDIR / \"eval_aligned_baseline.csv\", index=False)\n",
    "df_eval_unaligned_base.to_csv(OUTDIR / \"eval_unaligned_baseline.csv\", index=False)\n",
    "\n",
    "print(f\"[RESULT] With Defense ASR — ALIGNED: {asr_aligned_def:.3f}  |  UNALIGNED: {asr_unaligned_def:.3f}\")\n",
    "\n",
    "OUTDIR.mkdir(parents=True, exist_ok=True)\n",
    "df_gen_aligned_def.to_csv(OUTDIR / \"gen_aligned_domain.csv\", index=False)\n",
    "df_gen_unaligned_def.to_csv(OUTDIR / \"gen_unaligned_domain.csv\", index=False)\n",
    "df_eval_aligned_def.to_csv(OUTDIR / \"eval_aligned_domain.csv\", index=False)\n",
    "df_eval_unaligned_def.to_csv(OUTDIR / \"eval_unaligned_domain.csv\", index=False)\n",
    "\n",
    "summary = {\n",
    "    \"baseline\": {\"aligned\": asr_aligned_base, \"unaligned\": asr_unaligned_base},\n",
    "    \"defense\":  {\"aligned\": asr_aligned_def,  \"unaligned\": asr_unaligned_def},\n",
    "}\n",
    "with open(OUTDIR / \"summary.json\", \"w\") as f:\n",
    "    json.dump(summary, f, indent=2)\n",
    "print(\"\\n[SUMMARY]\", json.dumps(summary, indent=2))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "66d21350-1ec1-4f19-80bb-c2aa7c5d83a4",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2025-11-03T21:30:33.623212Z",
     "iopub.status.busy": "2025-11-03T21:30:33.622526Z",
     "iopub.status.idle": "2025-11-03T21:30:34.093019Z",
     "shell.execute_reply": "2025-11-03T21:30:34.092506Z"
    },
    "papermill": {
     "duration": 0.489944,
     "end_time": "2025-11-03T21:30:34.094060",
     "exception": false,
     "start_time": "2025-11-03T21:30:33.604116",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "image/png": 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z5uFvf/sbgHYwY3BwEMccc4x8zVlnnYUvfelLuP322zFnzhxMmDABBx98cHCLu2nTpuE///M/cemll+KRRx7BFVdcgZUrV8qa+jJ4/vnnMXXqVK0enHpNkiTkMxUI/Y6jKMKNN96I2bNn47zzzsMrX/lKTJw4EaeddhqWLVsGoO3c3nDDDZg0aRLe//73Y5tttsE222zjrXk/55xzrM8O/e+cc87xPquhoSHsueee2HPPPfGqV70KxxxzDH79619jwYIF+MQnPiFfF7pmffNg8eLFaLVauPDCC63xiqBUlfKYCRMmWL8bHBzUxvbss8/i/vvvtz537NixyPO8cllOjRo1avQba59MbY0aNWr8C+DFF1/EL3/5S3zyk5/E//t//0/+ftWqVdKxrYKFCxeyvzONWrPP9yabbAIAuPDCC1m19MmTJwMAvve97+Fd73oXPve5z2l/X7RoETbaaCPrfeeffz6uuOIKzJkzB1dddVWQovINN9wgWS7KIL/99tvx4IMPYocddvBei4K43yuvvBLTpk2rdI2y4L6fV7ziFUHvP/7443H88cdj+fLluOmmm/DJT34Shx9+OB5++GHnPRxzzDE488wzMXfuXHz2s5/Fd7/7XRx11FHYeOON5WsajQbOPPNMnHnmmViyZAluuOEGfOQjH8Hs2bPx5JNPYvTo0aXu9c1vfjPOPfdc/OUvf5G/Gxoawosvvmi91nSWJk6ciFtuuQVZlrGO98SJE5GmKRYuXMi2nyrzHU+bNk0Gch5++GH8+Mc/xtlnn43Vq1fj4osvBgDMmjULs2bNQpqmuOuuu3DhhRfi9NNPx+TJk/HWt76VvO573vMeHH744c7P5jB16tRK75syZQo22WQT3HffffJ3oWvWNw823nhjJEmCd77znWyWgapi30tssskmGDVqFC677DL27zVq1KixNqJ2umvUqFFjBBBFEfI8t1jfSy65BGmaVr7uX//6V9x3331aivkPfvADjB071tvne//998dGG22EBx98EKeccop3/ObYf/WrX+Hpp58mHcihoSH87Gc/wzve8Q4ceeSRuOKKK/Bv//Zvzs+49NJLEccxfvazn0k1ZoGnnnpKMr9f+tKX5Fgolp372+zZs9FoNPB///d/Vqp9v/D9739f+6zbbrsNjz/+OE466SRtvL5MhzFjxmDOnDlYvXo1jjrqKPz1r391OpUbb7wxjjrqKHznO9/Bvvvui4ULFzpTtzfaaCMcffTRePrpp3H66afjscceY4MbCxYsIJ3el156CU8++aTmOE6fPh0/+clPsGrVKvm9LF68GLfddhvGjRsnXzdnzhz88Ic/xNy5c9lxzpkzB+eeey4uuugilhGu+h1vt912+NjHPoaf/vSnVlkGACRJgn322Qfbb789vv/97+PPf/4z63RPnTq1svNcFU899RQWLVqkfWdl1yzAz4ODDjoI99xzD3bZZRcMDAz09V5UHH744fjc5z6HCRMm9M2xr1GjRo1+oHa6a9SoUWMNI4oijBs3Dq95zWvwxS9+EZtssgmmT5+OP/7xj7j00ktJpjgUU6dOxZFHHomzzz4bU6ZMwfe+9z1cf/31+MIXvuBlKjfYYANceOGFOPbYY/HCCy/g6KOPxqRJk/D888/jvvvuw/PPP4+LLroIQNv4nTt3LrbffnvssssuuPvuu/HFL34Rm2++OXv9ZrOJH/7whzjppJNw9NFH4zvf+Y6W3qxi8eLF+PnPf47Zs2ezzvn555+P73znOzj33HMxduxYTJs2DT//+c9x8MEHY/z48fK57rzzzgCAr371qzj22GPRbDYxY8YMTJ8+Heeccw4++tGP4tFHH8Ub3vAGbLzxxnj22Wdxxx13YMyYMZWVmDncddddOOmkk/Af//EfePLJJ/HRj34Um222GU4++WT5mp133hk/+9nPcNFFF2GPPfZAHMfYc8898e53vxujRo3C/vvvjylTpmDhwoU499xzseGGG2KvvfbyfvYJJ5yAK664Aqeccgo233xzvO51r9P+fsQRR2CnnXbCnnvuiYkTJ+Lxxx/HBRdcgGnTpmHbbbdlr/vZz34Wt956K97ylrfI1l3z58/H1772NSxevBhf/OIX5Wvf+c534pvf/Cbe8Y534N3vfjcWL16M8847T3O4gTYz/+1vfxvve9/78Pe//x0HHXQQsizDn/70J8ycORNvfetbMWvWLLzzne/EZz7zGTz77LM4/PDDMTg4iHvuuQejR4/GqaeeGvwd33///TjllFPwH//xH9h2220xMDCA3/3ud7j//vtlJsrFF1+M3/3udzjssMOw5ZZbYuXKlZJxNZ/lmsTLL78sa6jTNMX8+fNx3nnnAQBOP/10+brQNRsyD7761a/i1a9+NWbNmoX//M//xPTp07Fs2TL84x//wDXXXIPf/e53fbnX008/HT/96U/xmte8BmeccQZ22WUXZFmGJ554Ar/97W/xwQ9+EPvss09fPrtGjRo1usIIC7nVqFGjxr8Mvv71r+cA8gceeCDP8zx/6qmn8je96U35xhtvnI8dOzZ/wxvekP/lL3/Jp02blh977LHyfWXUyw877LD8yiuvzHfcccd8YGAgnz59ev6Vr3xFe52qKE7hj3/8Y37YYYfl48ePz5vNZr7ZZpvlhx12mPb6f/7zn/mJJ56YT5o0KR89enT+6le/Or/55pstdWrqs7Isy0877bQ8jmNWnfqCCy7IAeRXX301+zwvvvhiTVH8hhtuyHffffd8cHAwB6A9w7POOiufOnWqVNhWn+XVV1+dH3TQQfm4cePywcHBfNq0afnRRx+d33DDDfI1xx57bD5mzBhrDAcccEC+4447Wr8X34WAUGz+7W9/m7/zne/MN9poo3zUqFH5oYcemj/yyCPae1944YX86KOPzjfaaKM8iiL5PV9++eX5QQcdlE+ePDkfGBjIp06dmr/5zW/O77//fvYZqUjTNN9iiy1Yxe8vf/nL+X777Zdvsskm+cDAQL7lllvmJ554Yv7YY485r3v77bfn73//+/Ndd901Hz9+fJ4kST5x4sT8DW94A6kmffnll+czZ87Mh4aG8h122CG/4oorLPXyPM/zl19+Of/EJz6Rb7vttvnAwEA+YcKE/LWvfW1+2223afd0/vnn5zvttFM+MDCQb7jhhvm+++6bX3PNNdq1fN/xs88+mx933HH59ttvn48ZMybfYIMN8l122SU///zz81arled5ns+bNy9/4xvfmE+bNi0fHBzMJ0yYkB9wwAH5L37xi6Dn3w+Y6uVxHOdTp07N58yZk//hD3/QXhu6ZkPnwfz58/MTTjgh32yzzfJms5lPnDgx32+//TQl/jLq5dQ6oubFSy+9lH/sYx/LZ8yYIb/znXfeOT/jjDPyhQsXln+INWrUqLEGEOV53dSwRo0aNdYEPvCBD+BrX/salixZgrFjx470cGqsQcydOxfHH3887rzzTuy5554jPZwaNWrUqFGjxhpEnV5eo0aNGn3G3XffjTvvvBOXXXYZjjzyyNrhrlGjRo0aNWrU+BdC7XTXqFGjRp9x9NFH48UXX8SRRx6J//mf/xnp4dSoUaNGjRo1atRYg6jTy2vUqFGjRo0aNWrUqFGjRo0+gW5+WaNGjRo1atSoUaNGjRo1atToGrXTXaNGjRo1atSoUaNGjRo1avQJtdNdo0aNGjVq1KhRo0aNGjVq9An/ckJqWZbhmWeewdixYxFF0UgPp0aNGjVq1KhRo0aNGjVqrIPI8xzLli3D1KlTEcc8n/0v53Q/88wz2GKLLUZ6GDVq1KhRo0aNGjVq1KhRYz3Ak08+ic0335z9+7+c0y364z755JMYN27cCI+mRo0aNWrUqFGjRo0aNWqsi1i6dCm22GIL6WNy+JdzukVK+bhx42qnu0aNGjVq1KhRo0aNGjVqdAVf2XItpFajRo0aNWrUqFGjRo0aNWr0CbXTXaNGjRo1atSoUaNGjRo1avQJtdNdo0aNGjVq1KhRo0aNGjVq9Am1012jRo0aNWrUqFGjRo0aNWr0CbXTXaNGjRo1atSoUaNGjRo1avQJtdNdo0aNGjVq1KhRo0aNGjVq9Am1012jRo0aNWrUqFGjRo0aNWr0CSPqdN9000044ogjMHXqVERRhKuvvtr7nj/+8Y/YY489MDQ0hK233hoXX3xx/wdao0aNGjVq1KhRo0aNGjVqVMCIOt3Lly/Hrrvuiq997WtBr58/fz4OPfRQzJo1C/fccw8+8pGP4LTTTsNPf/rTPo+0Ro0aNWrUqFGjRo0aNWrUKI/GSH74nDlzMGfOnODXX3zxxdhyyy1xwQUXAABmzpyJu+66C1/60pfwpje9qU+jrFGjRo0aNWrUqFGjRo0aNaphRJ3uspg3bx4OOeQQ7XezZ8/GpZdeiuHhYTSbTes9q1atwqpVq+TPS5cu7fs4e4F7z3sDRq9ejKE3fwtbbreb/YLF/wdc+yFgpXI/cQK86mRgx6MAAOdc8yD+/MQ/5Z9nv/wrvHblDdplVgxMwLTjLsH4SZtZH7Fs5TA+/JP7sXDpyvYv8hwnLf8mFsWb4OrRR8vXzZwyFp97486Iogh45Hrg5i8D6TAA4OXhFI8tizB42Oex9U77WJ+RZxnu+Prx2OjFh5zPY8XAJtj6pMux4fiJxB9fAH55OrDb24HtZpPvv+TmR/HECyvwqSN3bI/TxIL7gT9+ATj4E8DEGc6xCNz+nY8DLy3Eq07+Fvn3f9x3C1q/+RimjwOGGkn7l8kAcMB/AdscRF/05i8Df7tW/vjPFcOY39oYM9/3PYwaM9Z6+aJnHsdj33s/ft6cgwcGdgsa96yVf8BhL1+DCJn83arGOEx484XYbOuZ1uuHV6/CvV9/B/Lpr8HebzyVvuj9Pwbu+BaQt6/50qoWFry4Elmey5fEUYQpGw5hg0HHtrPNa4HXfpT804Pzfg38/rNIstXyd614AMnrz8b2e72OfM+8b56Kx15MccWYt8vfbbXJGHzx6F3QSOxEn6cf/SuW/OQDmL5BC2MGEn6cJjbZDvi3rwFxglv/sQhfveERrE7bz2JK62m856WLMDpfLl8eRcDEsUPYeJS9ZwHA8y+twgvLV5N/4xBFwKSxQ9hIveYORwL7f6DUdcrg9h98Ghs/eo32u2YSY8vxo9GIiXUWgOeWrcL8gW2x98mXIoqJZCzPWs3SFHd97V3IN90F+7zlv+kPefDnwLyvA1laaYwsNtwcOOoiYGA07ntyCb7xh3/grDkzMX2TMfZr0xbwi1OARY/IXy1cuhLzN9gN+7736/T1n7wTuPUC4JDPAOO3ChrSvEs/iPELbtZ+99KozbDT+7+PwaHR1uufXvIyPn3Ngzhx1lbYa/r4oM/AvT8A7roMUNZ7WTy95GW8tKql/W7Z6C2x26k/QKM5YL8+dK3OeAPwmg+Tf/rLzT/H6tsvwZbv/Do22XRL+wWrlwNX/yfw4tPyV08teRnNOMLkcUPF6yZtDxxxIRDHuOnh5/GjO5/Ap/9tJ0zYYBB4/mHg1/8FrFrmfgAmJu8IHPHV9sL24eUlwM/fDyxbqP9+p38H9n1/2Oc9+1fgN2e17xlAK8vx2D9XYfneH8Cur30z+ZZLbn4Uv7x/gT7scYP40n/sirFDTf+5mmXALz8AbLoLsPe7AQAX//H/8OzSlfjkETuGjful54BfnAYsfx4AkAN44oUVGDOQYJMNBsOuYWA4zfDYkmGsevV/YadXH0m/6I/nAQ9fJ398YcVqPLRiQ1ww9oMYjtrzdcNRTXz633bClhNGA0ufAa45HVixuH3rAJ5cvAIbDDUwYYwyv7d6DfC6T5IfeddjL+BbNz+Kjx++Azbf2F67aK1uz4NXHAzs+lZ63NRaHT0eOPz89v5lPYyV7TWw5An6egAWL1+NRS+t0n7XiCNsMX40BohzloVjrfqw4qUX8dA3j0W0w7/hlXOOr3QNPP8wcOOn2jbalF2tP+dZhjsuejemr/obJo8dIi4AvLhyGI+t3ADTjr8MG22yqf2Cl5cA13wA2OUtwPaHBg3rhgefxVX3PI3PvXFnbDi62V6rv/8ccNBHgck7lLnDAn/7FXDr/wBZe89dMZzisWUxxvzblzBt+1far88y4JrTgOcKW/3ZZSuxZMWw82OSOMLmG48KtoFvn/sRbPzUDdbv80M+i+33fn3gza29WKec7oULF2Ly5Mna7yZPnoxWq4VFixZhypQp1nvOPfdcfOpTn1pTQ+wZNl3xCDbFIjyynAkS/OVnwP/9zv59lgI7HoXnl63CZbfO1/508eAPsGn0T/31LeDO267C+KNOsS51+6Mv4Dd/LQ7xTbEYhw/9AivyQZy9uHBy7n1yCU589dZ4xaQNgD9dDDwxT/5tFICZAG6/+duk073giUewz+Kr6XvUxvl33H37L7HHocRm+ujv20b0yqWs033+9Q9j+eoU73nN1vRhdd+PgL/9Epg0E3jtx/zjAbDb/12EoWgYzz/zUUycOt36++KbL8M+L98DvGz84Y7/pTecPAd+91kgLxyBjTv/3X/HddjloKOttzx620+x94qb8Vy6Ct8dtsdA4TMDP8b28WP6L1vA7bf+EJttfY79Gfffir1e/C2evP8vAOd033I+8NyD8scNAGxLvW6RZ3BP3wXMOhNojrL+tGLeJdhz9QPW7++89RKAcLqXLlmMfRd8B/vkET7+wmy0OtvdvU8uwXH7TceuW2xkveeJW36EfV++0/7OfHj6LuBV7wOm7IrvzHsMdzz2gvzTq5PfYvfmn+33vGD/SmBi57/SMK/53IN9dbpnPnwRNsRy/ZctAAvIlwdhEoBJeAALnvx/mDKNMNQ9a/XJh+/F3v/8JRb98xYAjNM97+vAk3+qPkgOT98F7P5OYNvX4cd3PYnr/vosdtl8I7z/oFfYr332AeC+H2q/2hTApsv+ghdfOJsOMP758va9b7F30Pe6auUK7PvkJfYflv0dD97zR+ywr51p9tu/LsRv/roQowaScKf7lvOBRQ+HvZaBHfYFsPTvePiBedjulQdYfwpeq8/8GZj1IdJ5Hb79f/HK5bfgjlt/hk3edLr93sdubZ8tCqRbovrQT98FvOr9wOQdcPltj+HGvz2Hg7efjDftsTnwlyvbZ1RZPH0XsO8pwMTt/K+d/8f2vDCx6OFwp/u+H7Wv00EDwCsAPPCniwDG6Rbnqok37r4Yb9hpU/+5uuhh4M/fAcZMkk73+dc/jFWtDO87YBs9sMHhkd8CD/9a/hgBmAa058WL/rdTaKJ9fv153rcAyulOW22nB4XjOh7A/gC+vmwW7sx2kr//9V8W4L0HbNN2ch4pnPRYjHMlgCXKtcUZOGgH2X9wxxO47q/PYq/p43HSrK3tcT19N/DAj4GF9/NON7dW/3YtsM977N8/dQfw15/R1+pgQuc/CwupXzrgWKs+PHLHddhj2e/x97ufAao63X+5sj1fN9ycdLqfe2Y+9nn+yvYPjGm+IYBdAdw17+fY84j32i+Y/0fgwavbwZdAp/vbt83Hrf9YjEN3noLDdplSrKsJrwBeX9G/mfd14Mnb5Y+jAewAYN4t36Wd7kUPA/d8V/vV5M5/Xjxr/MzYwHmWYc/5F6ERZdbf7nvpn9bv1kWsU043AIulzDvROpK9BHDWWWfhzDPPlD8vXboUW2yxRf8G2CNkUQLkQJa26Beknajidm8A9jgOWHAf8IdzgbTNjgmWrZlEuOjtewAAxl+dA6uBv73yk1g5egpG3/4VbNd6GHmLZtRWt9rXmDF5LD48ewZGvfQEcC0wFKe45F17AgDO/PG9WLqyheHO56HVGdd+pwHT9sNfr7sEO75wA6KMjoa1VrctphX5IB6e9VXyNWPnnYdt0keRt1aRf4cYf8pH3MTzGE4ZJkY8zzScXRxA+7sZXk2PK+owsg9u8gbs8PrjgPk3Abd/g/+MPCsc7n+/BBjcAE9d8UFsnj2NjPmOxDOZNDrCJf+2Z9C4p/+mASwF/rHTGXhpoxmI77oEu6y8CzkzrnS4/RmN3BHRFN/N684GJm6Pb970KO6Yvxiv2W4idt18I9z/1BL88eHnsef08fjPA7ahPgT48TuLfxNOd9wZ358mHIXBmW/A6r9eg73/+SvH3GpnaMRRjm++bTfkjSF89OoH8OzSVcV8Ze7joTH7YOaRZ/D3q+IXp7ZZls78E+vm+P2nY/9tNsHWD9wCPAQ8P+UgPL3NW/D44uX4xX3PYMvxo1k25wM/ugcvrWrhHftsiY3H+NmaxxYtxzX3P4OtNtkAHztsJvDyP9vsRIn5XAXNvAVEwB07n42BDTfF1fc8jSf/uQLvnrU1XrU1aYZ5seIHx2J0tAqtYWbsnrU63PnemwiYrwf8P2DqbpXGaeG3HwMW/6PYgzvzQPzfHkNn/BtMBo74KobTDM0fvw1AcQ8WxD0Hfq+t4dUQs+fPr/oq4sYgJt36CUzNn0PKPF85bm6NkB/UeZ6v/zSwCRluc2LpymGcccW9AIBTXrstIgBTb/kIJuEFZMPcvu9Zq6uXAz89sb2vZimQ2OZO3Nmjub1PzrVNtgNefw4WL1+N/7ryPgDAJe/as213XH0y8PIL1tlrnYkzDgNe+U7HU1Bw1XuBlS+Gr18xlybv3M4UWr6onUVRZv2L1+5wFLDrW/H3O6/HjH9citix74t7/eLRu2Dj0QP40m//jr8tXFbcu+9cJf4ursmuGxPi+W6+FzDrg5i/aDk+86sHMW6oifPfslvYNQw8dOvPMfOJH/L3nrUgHe6jLwOao/H0FWdgs2wBjnnlpjhhhz3xndsfx00PP688i849Ttsf2O9U/H3hMpx33d8wYcwgzjt6l/Y8/dHbOq+lP1c8k67sGHOtzvs68NjN/HvE3Np4OvCGz5Mv+fBP7sMLK1bjLXttiUljB3HdXxbi4eeW4Zi9t8TrZga4ZQFr1QdhpyQ5YzOHoOV+fsKmeDkfwKi3fYd8zaNXfhxbDz/C2mwh9qqJ4ns35lKJa9jj6NzrrA8Bm++JB6/9BnZ48Sb/Wh21cTubC8BJ37kTeQ6c+OqtMHrA/s5ufuR53PvkEhy281T8+ys389rAWZZJh/uuPc5DY9Q4+bfNd9i34o2uXVinnO5NN90UCxfqobPnnnsOjUYDEybQBt7g4CAGB6ulGI0kUrRTMXLO6e6khGD8NsCMOUCjExHupEumnU15IInxuh06m97P27/bfv9/AyZsgz/f832g9TDyjP6MVtae/JuMHWhfY1E7tBfnLbxu5iQgijB6oIGlK1tIs1z7fGy+JzBjDp7/w/X6eM3b6NzfymgQu73uGPI19911KZA+yo5TXpv7O9qpcgCQZsxBLq8Rlm6apSniKO+8lTuY29d6dtTW2GHGHGDVS+5xqr/fbjYwNA5Lok9hczyNnHEsxTMZ3QD23CEo5gjc2P7fK/aaDUzbF3966DfAyrvYe886G3sCx7MRY5/2amCLvXDfXXfjxmwhDpyxI3bbdzr+cvvjuPFvf0Fz1KbAjD3s96vznHk+Uecwjabsit1edwxuf34+8M9fIeLmVqv4/cEzxgODY/H53/wNzy5dJeeD9Rmdaz3fnIqZMwL1JgbHtp3uznvFtXfZfMP2unmmvf9MnLYDJr7uGLz0yCLceM+fsH1jLDDjNeQlf5fHWJa18LH9D8RWVGqygSV/fw433nsndmqOA2bMAl56XjyEdgZFBeYgBI3OnJi2z5GYvPk2uODJO/CHxc9jzqRdgRlEqmIAVqGJ0VjlWFfutSr2lCR3GO3ivVvu0y5p6AVuuaDtdHfGl8o9hzGQxX0MjgNmzMHw6haQJ2hGKR9sLblPtZQ1sPNrj0FzYBD/mPdFIH0OOWOwyb2SM+zJcXXGs9UsYOru4e/rYOXSlbgxS5DEES59XZv9efy2zwLZC0iZZ+Fdq2o6d9YiDfmoE+T0nrNjJgIz5mD54hW4MWtfJ932De0SlcEN2k535xm0Os9N7jHiGhM6Z3UImmPaTrfjTCPHOXZy+zOWPqP/vsw1Js4AZszBoocXYgaAOOfnmrjHA2dMwsSxg5h722MAlin2gGe+Gn/PslxmPbPrxrpG59rjNgNmzMELQ//EjdkobIQmMOMQ93sZPHffw5gJsGeLflbPAQZG40V8ApthAWZMHoXtdpiMPzz8HABiHmy0JTBjDhYni3BjNgabRkPAjIP1dG/meaXBdozjezfX6kPXuN8jfj9qPDt//xA18Xy2Ch961SzMnDIOcxfdgxsXPoN9J8wEZhCMvImAtepDLvf9LkqGPM8v7ZxJq9DE0HZvIMm+F+LzsbXjGkHfkQExh6y5VGZ9c+PYYh9gu0Pw3A3XYAcAkW+tDmwAzJiDLMtxQycI8KUDXo+Nx9glQL9c+SBufHw+tt1oG2DG9sX3zPkcrdUQhUIzXvMfGLthYKbVOoR1qk/3vvvui+uvv1773W9/+1vsueeeZD33uowsak89zuCQkzbuTNG4of1eOMyJWldpvCeP3I692OATUVepLpRcv761GXTG0+osoYjZCNOOUZjKpWYj71zLaxhxzpdykHPOVtlNrKU4BKxR2HES07zz/MR35dvUgOL55UnnLe57dzrE3OeIOWPMHRPiuccIcGI69ygMTzF3GuY8MREr3z/zfKTx1zmQI/F/Zm61VKeic29iHJxBJ4IYLcd8tAemPz923XTu0VozBMQ1QuuiG53PEs9de54u57NLiHmXNJqdcYjnW+0zsyxHq3MsZVwU37feywSJ4h7Gna092DCUPGNoZTnSzr2nHEtScp9SAxdJZ72IsyXjDEvfuF3jqvg8W3LNFPM9k4Fnd8CRXavqWJh7lXsKayAb+5oyr+XzYda/FYgu82zkNQP3ddeeHlpnb87HztnDOd3quSrWPWsP+J6vsWbMf5cZt3z+ZYJGBsQ6ZAMO1FndmYfC6RN7shWAkHPJWGdR5D2L/XtKqv/fNXY5VxL9977XEzDPK3H2BX+HAWvVB0FMxGVsIROe5yfssBYScLfWCiXMStynFWzpidOt721izofvhcUDSBLaThG/l+P22MCpcl41GuuXTycwok73Sy+9hHvvvRf33nsvgHZLsHvvvRdPPNEWbDjrrLPwrne9S77+fe97Hx5//HGceeaZeOihh3DZZZfh0ksvxYc+9KGRGH5f4TM4rIOcOfg1sShj88wDN3hp+KuvE06Muagsp1ssZDeL6nS6RXCgYuRQO8i5gzjzGF8G1M2Bcw5ExFAahZ7nTR/k7efHbeAyulvK6dY3T988yEI+gzF8TGOMdcaiCIjch79wrqPOZ8j/c0ZhS52vqTYOH9OdltkaGWerWDf6Wi3WDG+QUE6IC4kZTOiBEeNDlqZIOtkeSdLUxlHKYVPQdjw7RkDLc/j3cL72BIZBEcxKiTWT5nKv4INsJfcpsb/mEeKkc+3IHcT0ZgWR4+rueVJBpjRyPwvvWu2J003va9q/mYA3F4gOgs8R8oxT+6zQoJvpFHocT8rotoJuvvlqnN3k8w0etwheGc+/AsQ65J1u5fcySNEJMIMjJPRzgNwfPGdxcPaMk+nmAjSeAINj/rZS/Z59wW0LvXC6BTnQV6a72HNazP4o5oF/zoePk82e6QXTbQTZOHvKtRdy5IBFtvh8DuXMT2qnu/e46667sPvuu2P33dvpaGeeeSZ23313fOITnwAALFiwQDrgALDVVlvh2muvxR/+8Afstttu+PSnP43/+Z//WS/bhfkMDh9bSRrtptPtcXJS8xqE0y0PltQ0MDoHt1zIjIHccV4F+0LBN05fdDLoIO+C6ebSYOPOPctN2Ot0Ewd5YNQ0cbHQzHvkeDrPl0srCnLsCdYOUA7hJMAZ8zwf8TwFwy0Y75iZW6mT6WaeV+fAGS7FdOvzk103nfsrjDH+OyvNdJuO/BpwutUMj1gy3Qa7U/aaWYjjGcZ0N5yZGWuC6fYY/wRzUDDd1Vh+E+I6alAzi9wB3bSK09IPplsGW5ln4Vur6pniyZ7xBnSNfU37N8t004HoIPjOCnacif7/UtfQnULpdDP7PmV0l2e69b/rmQRlgwWmM9uF0905s70OCCJAsLrG87IcT2Yuaeusa6a7G6fbtwb4M7E4r9rPQARhWJLDRMBa9UGs4VIEhInAs6WFhJ1f0marSBJRYLNnuum+wRJkoXthsT45ciAx7QEfwfMvwHSPaE33gQceKIXQKMydO9f63QEHHIA//5lQAl7PkEcdhpMzOKxDVmeUpeEvak7yvBDpEsaMh0FumddQF7jY4CLzYNE/o5W3/+5z6DJH/MeXBu9nuomUwJLXsF+uMt1uZlZuZqFpXID8PtOcCHho7+kwuGWiu8bcyT3jEnOw4foM45qm4xmb84RCnAApP46C6e4ECUSwgGW6bac7NoNE5mcIwzkPc3bbb9IZTnvd6M9GrhlmDHmey+cUBzrd4vkWjkAFo7sk0tYwxLGYdFhU3/P1oZVlyPIIiAoDx4JnrUrGI8rb2gsJYSwa+1RPwARfvIE+Zc2kMrW+NyyJyswI5OIzPPt+KadFPs9qcfyUKIeS42QyHrxrNY7R1rLOvYE8L8sX6fua9m8jKMzWX1Lt7ziUZrqN+RyZ6z9A18a4V+F4cmeLeq6K/cfKuAl1upEDWVaR6RbBAj2lOdhpJyDu3ZteruyzYo0Jp8/aC625lHX+r9yncZaYIN9DjcvpdBtrNZTUcDjdLXletX8u7MLA7yBgrXqxBphuSRLlMWtLhtpsZe5TBnDNudQTplvPbPHqGBB7YcJoxiSmXeKZayqZRZ7b6wHWqZrufyWI9HLO4LCdbg/TrbGoRjSc2+BFylBCbCJcui4TPePZyI7h1BXT7d6AyjHdoQJFak03k14unG6rpjsskghAqW/1Md3d13SzgmSS6Q6p6dajoEW6WUCNlyfNTRymsVHTzdYcVqjplusn76amm1k3gTXdIWlbJqz70pjuLowQB1pEVLp0WqGBcky3u+7OHCN5DYchWRpd13RnAan11YKDFNMNTsvDTGUM+6D2/7tkuhsk0+1+Fs616s2eCU0vt+sYW0ydolVT3FVNd1mmm0gvr3iNrphuyzkISNPOWr2p6e58dpbDSeq44Lt38qw2ghR2qn0I0+1xSsTa9JbJjVRNd+cZVCkzKjvnDfSG6XY/v1zWdMds0Hy4rzXdvXS6TZvNl93hqOnm0stFBl5q2CUeW3O4jP21jqF2utdSSMOIXQDiwDcO2Vw/+MWk165jpZczh2pnnVi1qcq/5aISh1tuLGRh7DF1ZWKjzFw13b6aI/GZnFhbkNNt1KB5kKfK61L6PQXTbQZGuBpP2ziTBiU3D4QjWiq93D13rI/IAj4j1zdk8RGmMZb5mG7HOOLOHIritoPnE1LLUnu+SiaGM8Y6n9GqVNMt1l77x2Ld6M9brJmMGYM6tso13Srj2CenO1Wer6i/8j5f3zWVmu6cWVe+tZorv2cd99xea13DmL/iu2DnvDGGLAtJTSyXVijY7FSZD4Uzy+37nnFT6PJ5WiUZ8I8zaK169rYInn3fWLvq+SG3cebs5c7EIMTuc9OC+RlVgm6mUS2F1OgxpITRLfY8ubf5zlX193mqzbng+Wfcu7r3VA3+eWu6ie9Uvseo6ZbjMYWrqP3BM1/Fc+XOjqD9gZsr3FwLmL/iHq2a7jLngOfevQixU3zI3c8vE2c8EvbepHCub+6UuE/xWdZc6obVN2w2n+ixaZ9myp7NtWy21oAM8NDfkTivS2nqrGNYf+9sHYdUmK1Y0+2sxw6s5bFS/jSmuxNVFGnQVk23Li7CLWSZXh7EdHs2g54w3WGRQ1UZm1cvN5nu0Nqp4nAb9gipibT9UilVVpaEe7MVn92MUuSBolA2092Dmu5OBFsy3F6m287M8NUcC7Z/OK/idBtMN1PT7XsWOoMUNg6rZj5ACbdbUMrYvWG6Q1Os6b/npZjuftR0685XeE13FpBeXi41sTBiiv3V1w1iJNTLTaYMUJxuj5Cac6161kDiMbItFpVkurmabvpMDEJPa7rLOt3lmG7V6LZUq4PTy9E7prvKNQz408vtlGsxDxO2ptsIDijrTDLywTXd3bQMc9uO3tebf84IFXuT4QzBWsF0u59fSE231JgIbJMXAvEc7ZruXqSX6+s9VEgtROyVzcDjbPWAbkbrOmqney2Fz+DgN86Oo9fZlEnlcWF0eRjkIPVyVqGzPf5heXi500mcTrcYrze93H0f7X93cVhpLy9exwkSiQN7uKyQmnKQhyphdpde3tQ/34D63LNQJWYj0yIJUOwOTQWV6eWdcXOGESWkZqU/GrDq8ENgpJe2TAfCeDZWoMpASNqWCVIgrs9ON6WM7Xu+Pqjq5XznBp/TrQTEvOU5/RRSMwwlzxjSLC9aBFbsUW6CMmJCtTzCa2qzgiXroZBa7lFZD1qrnhIqGcjzaqfY4kG8erkRtFgjTrfBRgZ0g7CvoY9TpHjyNd32d8aKhwU63d2pl9tpr1WDf+Leq6SXi/MoMds4Mk4LgKL9lJcICdxT2OdNrNUKdokKl4p9ufRyj7Pqgzjj+1jTnStMLHdvfputvMMs95TQso0QMOvd26fbtPEcNopVZuBNL2+3ynSVm67rqJ3utRQ+g8NXl2P3CrbTywvVakbFNnVcw3BiWIVOTxuCQkjNscjkOKsxX/2o6VYdOo6Vik2jsArTnbsNJ/FMqjHdwnkV3xEzLo05JPoHEwd5y5h/PWG6hZBaYE23tnaMIBHLdMtASTc13UyWSSjTrTisoTXdZF/UfjvdRL1wt326Naa7YpBNfR/f79ptSFYCMw+q9OmuyvJbLydaMnq7VpSt6dZKl6o9T8qAyzzjDFqroTXdFdrkWKKFVqs4OhAdhG5runtwDSGCygV0qe/MUq32naua051WZLpNBjlAONUDee+B8wJQghSB6uV01oTb8fTXdIvnmYNM4aXWapdMN13bX6GLRbdMd4j2jA+B7ShbSPia7uCWYeH32Z8+3fR6Z+1AJrgVxnTTWUH2R9RMd40Rgs/gsNPJ6Gi7zVIXLS4k0+2JZNNMt36wtMzNQEbP3GlaQhm7N0y3m7kx/61fo2TapsJEcQrzsWkU+iK55EEuVOyZccma7kCnW1WxNyLdrMK85sQQ90oc5KX7dCvv9bFScacntEwvZ+5d79NtMN2e9PJWF+nlwiCy1o0hpMa2HOn8PooQrF5OBhO6ZQ48ECxqSzkgSee/BHSmuyJToLzPL0TZh5pui+kOzw4pROR60zKMasnY8z7dRBZVWbRS24DLPSVWQWs1ML2cD+iaJQBEANc6exml4So13cGp4b13uldLJ5LpR+xkukP7dOuB/FTLJKjWMkx1SKsz3eWE1PI8h9nXnBeZDZ9LJvxMt02MkONWP6tLITWqdVQ1prvLILEIrPckvdwdyHf16RZzx8sYV2G6QzNIQmBlpXp0DJjglpvpNrI9vOLNxfNdX7H+3tk6DtEyzFvbF9FOt4jCxYzhDwCRdLbcG3xIn247mivEWNxOd5Fe7piKka+Vga+mm0gJtK5RjUFqv4VjUdq/l5HP0ECKSP/McyVViX5+pZlu9TriucZis6XHpdfIhh3ksoWIqPUzW0dQCDSQY8HMd5xujo2garp9zn/BnnVT022uG32tWoEqA1a7v5AhUEZO2fTSkpAOnXKMJJ1/Vq/pzpTe9NUcT0293HeNvjDdgvE0nC/PGHoScDBAtmSM3ExM6T7d2vrvjummnG4/0x2SXs7sKcJAD2W6le/SNiT19T+ifbq1a5St6RZnt4/pttu8Ve7T3fm3FiAPLVEJYZBLolAi94meFkHm1Eov99R0pwQj71Mv961NwkZj/x7YxaYM051YreNKPP+ybfJMiDO+jzXdgmBJEfM13cFMd4mabi57pi9Md+9qui17wJtebtsU6xvW3ztbxyHSy4Ojj1LtPAPynGeplY1T9mcOrdlyON12Tbdgutt/96UA506mu+kcp28TC2O6yzndaq0ox0IXTHfn+QXXThXGg4z4eQzPcKebinQ3tGu53kPWmqrXNHo4ipruRkhNt8fINoXURNCIYyNyV023J728HNOtf0fsuhHjlQq/dEsbU4QuBDTT3e+a7o6TEalOd/dMd5aHppf7Dfk1ynSb/ZpN8RtrDHowJs0ypOjMjR6ll1MtGYvMIfd+WbqmFqjOdFN9umN37XnQWvVmz3Q6IgRmlIUx3QwrVaZOsXQ9NpHCbmpKeK+h3+uw0QLLBNXmrejPXNHprsJSG8+3FzXdq0OZbuUzeabbDL4Q4wxsqRRc081dw3H++8kAep1RGiTVWob1qKY7ynnB18Br+AK6KRK+PCzvjiSiYAVbKjju9jj0OezTX7LmL5GdZMKyBzyBFUr4c31D7XSvpSgMDmZRWSnCulpplpuGv/F65T1cGoy4hjxU1aiv0YtStr1g2o6wNd2d14uURxLeSGzq/Lve5oU5BGRtX9hmrX4v3Hck2tFY6uXeNiQdIzwv0k3Zey/bMoxoHSecWG/LEIBWaif0AkzmKqjGy8PMiNY1sWhPJWu6mXZ06uFh9U6lx2GJ34Ug1p+fvW70tacaqdQ4zHZrIVBZFUsJtxthGQeEM0TXdFdlugvjlWvF51ur3pZhlNhcL2DM38xs82KNwwyyFc/Su+8H71OC6bZrun37fikhNYGKAjjWmoG/a0XQWvXsKSJ1Ogpsl6S2arLb4IgMh1x/bVc13WVZaiK9PHT9M+0+ubOFyk6wBDN981Udm2K3AI51Y8Koh8560DJMstbcuWq2T8qLLBXxnmIv7Lyn1FxinrlvbarPk/reqbXqm2tmPa7152IeCBV7yy4MQdk5b8Jnp4TA04pL7Mut3MF0i/PLl6Zd4j7FV2Ctq66cboMgky3Dyu2Frg4rVgtBz56UEUHi9Q21072WwpdaZ6WTqRuikqZl9djWIuGdfsdcJNuMZAUx3TqDtNrHdGd+pjuYifVEh7VxlryGdU2FReVSQRPTKCyZxqUz3Vx6ee+Y7pD0ctqJ4Z1uMf+CnDHP80mMmu6oUk23p2VYD9LLWynXMkyv6Qbo+dgN0w0gWAm3W1DK2L56de811RTrHjDddJCo+3RoEgzjGape3lJS63vFdFMtGX0aGV6GnhtTFBfsaknQNd3u+RuWXu4pWZHq5VWYblMcSHe6i5puwiH2oWyqLZW50WVNt9kCywTV5s3uZuJbq7rTXYmlNtdRD2q6VxuiaN7PVPQY7PRyusygUk23T+TQeJ7suNW12nVNNxF8GYGabjWIyLaK9MHjEOcKE8vNrdVZYOutCkx3cAaJD1kGQJ9zw1m5Nnlh6eWGsKInsBLSzWhdR+10r6Xwp5ebTLfudPt6BQOKajXrrDrajkkhNcOJsQ5uEf1lFnLIIgs9FDgjkjKUrGuUS/lRHVHO6Rb3HN4yjD/IOYV54ShXqukWzrZI1w5ILyeF1OTfC5E+03EMOoQDDeSk0Rm3YLw9AR31mj6BF/E8V/egprthqv5Lpru4NnVwF6n54WPQHfkwJdxuQSljVxLQUaCmaVata9aDRJ5yiD4KqVnOFzcOIsjWq5pu0ojxdIMo3ae7B6n6lAPnSy8PWquhQmolxYMAypBkgi1Vnk9ppruXTncn3bRjhDdKCKlZQU2vkJq+R2sB8p7UdFfbh3wBB+sz02LtirNYlFRx7VQ1fYBApztYnJG7hnOehN2rCUrFvlLGU4+E1ADGTilzDe5sEWvckV4+7NHhKa6dg8to0F6e5TKQ3jMhNSLw7BM95taZKyPP7tPtSy8XNd21011jDcNncFgbocl0swrK9mbLMt3yUCVqwyimm1DG9tWFFUy3K728M05vtJzexPrBdFMsqgmxea2u6HSrBzmXjhNJo7GkYaU4yPC03lIPD7LvMTG3TCM6jOkOc7qlanmH8eYMo5wSUvPUlkv2LKvSp1s3usVnmc/Hz3T7I8gmSEe+z0w3pYxdPN/qLcN6yXR7NQj6mF4eXNOtMAeiHIfriFCZ6UYJprtqTXcXz5Ls0+1xBoLWaqCQmr9NjoNFZYJua7ZPt+4wa/+umKIuGLs4ypERpR4uZ6uykFo3Nd0uBrkkRIZeowTrJwPkubCN3IRENzXdXQuplQnOUHNLgSv4Uq2muyrTXbyPFHwNgW++Kura1NzK87xQL/ftKa7PUT9SK5cwuwJUDKgTZ6DMSg3N7giq6TZ0DTxzLa/Ty2uMFHwGh9vpTpV6q5h+PRRBKmZzMMWwqM1CaxGisajtReMTJJFCao40z6gEE0st5qCDvKzT7Uu5hpJenoU63Y6DnHlPZSE1ZR7Esc/pVu+V6tNtX9M8iIs0I1fLMB8r1WHPBcPdYby5ljauPt08091NermeXsoFvHw13SERZBOkI99npzsnRE+6ZrpTpaa7otPtr+nut9NtOl+BLcPSEKZb3HvYeidbMnqCmAVDX61lUxVYZw3gF1bsZU13iZIfAUocKM9zQml4TTjdjpru0o67CJgrewoRvKKMbjalugrTXdrpLgQJzTGWRcF0h7f7M0vB2AAEOZfM7KSKAbG+ON3u8giqdVR3THf3Nd1ksDUEpZhumuBJpc3mJzGCnG5qTfSU6Q5NL6fnr7NPt0lyeOZaRgSJ1zfUTvfaitI13TGAwsgvnB7x+lS/LqAYX26mW7R9ojYLjelWxyoUETN3Tbd4j7OmW6RDVnS6g1LWyhqzhDK2CcGirJZOt64wz45Ba0PiVpgXhjP7bDyf0f53ONNNqkFTTLdhkAXV+oayUkZaPMt0E/Mijgyj0ByCqTgfAuUgyZXOAcW60Z95HEcQf6IObuv9AVAPvpRpZdRrpDJ1uThGYlO9uOw1s5C2WZ61qrDEKZlerrzP1aqwLLqu6S6CbJVZfvPlxHck91o2w8moH/R/SPv/XTxLas77dE2C1qrjGnmWoRn50sv1tUuyqIrgG+lIdSWkVtJhJs73qk63mrZPpetSRjfPdIec3am2H4b36dadwl4w3cJuCa/pzqxOI9ZeGMLIe8QD/Uy3r6abWKvBtmafa7q7bHGpBhHJfT8EgTXdGcN06yr2vWG6yXnSD6c7OL1c7IV+7ZnYbBUr5hBjAxfdjNZf13T9vbN1HH4hNSMyqv47Lw7/hsV0F68v+jP7hFL4mm6x4DLT6Q5MLw9yur3p5R6mO6hPd8laSeV1bM2hpV6uK8zbF+UPci4wEqtMd4jaK8V0y/Ryhk3XnBjiNULtUklxNoX8wmq6laAEAaumWwYLes90ry6TXq4YSuplXSKGorUOZVdK5iAJd7rVc89m4PpT050T9Vfd13RnFmNkwVsnWrwvpxTQVQelRGDDC2Ov9fa7poJsgumueu8GyJaMXi0Pj2FvjakXTLfNlvmcxqC16rhGpiw+3tDsvMZohQjQGSUppZydV3G6PWvAGifxHZRuO6Y77mraPsV0p2aHFBDBVe9aVTbAnjHdROCjJITd0ogyuv2U2WlEDZBzZ43h8GqMfCAT6A2IVWK6wzqksOnlIRkPIehWSE1VL++a6Xbbqy3E5PxMs7zoMc32eC/ndJO1/1073XZW6rC3TR69F7pruo0SC9WZJp5xESTuYQbaWoba6V5bITegsPQm/T0tOwptthhT/+1xuq22Y8q/9ZpudSEb0TMuBbhzHZfTXaSXc60M7FZmKoLakJSskaHqhU2I1DSrphtgDkT9O8oyFE43x3SrzzWkjRARrCnzfJ3puirTbRhkYvPNXIEBb023nl4uhdRYpludr533Ju5WJmKeVlUvV42hoqbbXntWrZMCkdVbpqY7iiK+PUe/nG7RGkur6RY9tqs53WrrHa9T7VH0b7+EChK50yUrQ43io9g/ve19lBaB8t69Qmqh6eXiO6L2fXcrKOda1T6k++dJzflcBgeYgGPIWnWccWoA0c902ynBVspkntJ/7ya9vHS7L6qmO5Qx1s8G9bnSNd12oIR3ugOYbu75+WDMP/V9wXPYgMryZ9TzM0XRMru9p3XWWKJ8xeWouUTBah3Fjcv8t0AFO9A3f6nWUZW6WJSd8yZyz75f5hrc2dP5e4qEPOPUNq/snqK1dfOvTdVmLeZSZl+rDMT7olgGnqWGgy9YUCK93JoHXhtYON11enmNNYzC4PAI6nicbpeQWuwR0CpX050b0TNdjMWXppU7DBKRThwmTGF/Tj9quqnUZRPinmX6Y+CGo6bv+Gq6te8uZOyuecB8R+pnBzvdlZhuByuVpkiiziafmDXd4WUHfWG6lTRudX651p6r5q1FsX4BsJ5xv4XUCGXsntR0h6q/Mvelrs3cpV7eL6dbMt2+9HLTCM9K1LOHOt080819xogy3Wp2h8cID2O6eRZPZcPK1jFq/1a+d/WZrVkhNYczVTG9XKRYA0DasrU8KIbT2td856qxR1dyuh3fUdWa7tUay+/XMalW001k4HlruvvBdPtquu0sSX1MAfMgBF1mZnmDrSHwnS2d67ZyWr1cV7H3Z2KFpZcTGRElzwF7DPY8EHZqsGK/mU1LgK3pVq+nfkTtdNcYMVSJPiqblrURUq/3ON1WzQZZ062oVMpFVChjr5YL2dOqy2WQeNW13QdNf2q6/enlIqV+VRbqdNsRex/T3QunWzDdbAmAFkH2OzGqmFDBdEedvzlYUMfhr7JSkuHu/J9raUMFRnx9ukXgoVrLML0m0bX2XEEISwQxENLQCVTC7RaUMvaa7dNdNUjUZ6ZbpCCWrelOe6DcboJqGeYp1xHjdq5VbUwV0qeZz9TmvGecQWvVsQbUlOlQplt3qm3xq5QK8K7Jmu4eOt2tvCiNorQ8KKPbUq0OTNcV/yaDFt5x68+3N+rl7np2al60jAydMn267UApkbGXEa2juHGZ/5a/ozIifE63e7+k0oxHok+36uRWTy8Py8zg1MvVDhRVNYhMOLNnKqeX2/vFqmAhNX2dhamXBzLddZ/uGiMGbx2ze/O0mAPi9aImltsc3DXdLe36qep0KwsrmOl2pZd7xunbxPrdp5tPL+8w3SJqrt5jUE233YbEhBZNrcx0d9K1KzPddqqdgDDIEoXBYg/iQFaqIRhuMW6mpU0VpjuR7Fk1ITXq3qm1J3pwcwe3OtZQ2O05+iukRilj97JPt3fv4/6u7BNk6y0Pc1MZDNPtr+kugmxOplttyVgyI0dtyejrBlG6rrYnTDcx5z2B56C16sqeKcV028rYNtOd0o5UJaa7Yj12D51uNQDWIoKtQQxnCf0FU4iuJ0x3VadbY7r9jLHOdIuzhglABGZNmCBbR1nj8qSXO8kaH9NNz18y48HTmpNED2u6+8V0i2fK9elW10xVkshEq5813RrT7bPV6TIOl/aM3adbdbqJwBKlQbKeoXa611ZUYrrtNDcX2xYJp6VSn+5Uu34rpZ1uET3jhdT8rFPkEfrypesEHcIlN7Gw9PL2wbhKqpfHhZBEQOpXSPsJnekOYOmp4IsnkyAqyXSrz1g42742Wer7fayUWdNt/r34IIrpNthgcwiyJCAOr0tm0kvlLbuYbmIcpvJ7KCxHvs813VR6eSUBHQXlmG7671qQKFBtvydQgkaqir2f6S6YAyHGRCq3l0xLVK+TE8FZzukuzRT24HmSrIlH8C1orQbuKbx4kH4+udlJJj26ktNdcu1Wcaasa5iMcVHu4GS6EwfD6XViesF0m+dPgHCqB2ogh2w/RaxdnummMx7orAmH013GjmGu4bYbq9V0U7W9MuOhTHp/D53utF99ujWmm+o8kinq5SFOt39963uK2ae7W6a7OBdWlVbsL8N0q2K7BIlnfEYtpFZjjSOK3AaHnLBMixC5ETJti9pvddfyFpup8ZnKv/U+3fZn+FpvyJp1R4sAobIexnQTNd0hRmRZp5tw6EyIdjRazaFLUVZ+p0WEvNWv9PLIdrq5wIg6B2lngI/gi/mntgJiFWVdBodyiCad8QrGu/13T2soSviPgHgGKWJedM8at13TncQRInPtEYywi+ku63Rb7Tm6bMHiA9XewxVMCEFIb/oyhjzpuK8Bplv9Wvk+3TZz4GS6Sxpr6ns05sBztpRWf+7B87TOK6hnoJvpdq5VKmDcgcqGdVfTXawzZ/3lGqnpttPzqzruKntLtWCi2rwVDGfVPt1VWob1nulW69nJgC6hx5AZa9faC0vOJROl7BjmGuRaDWa6uZpuu3VUtZpuj56FB3pGXn/6dIvvPUVMB8yDmO6yNd3KfDbnUi9ruks63WJ9Jo4OIGS5mWNvk/Zl3TKsxhqHR7mVVKGURnbGs9Qq0+1pGVakDVFMd/vfshdlnpMsqmS6uZruzme7hNR8TKzvoCFbc3DXKClQxH2mmu6spT+6MhgcBznrdKOi062ll3ecbi4w4kvbkq1Q7Ai+WdMNOA5iR49SVchHON3i/wDIVmbUd9TwMLHiGaRIwg0GpdUZ6TBTz1xdNwZCVEEpsKlcfXa61ah0JWNLgTrnc5+KKrs3qmr7gSmWvUBEO19eA1lZN4XhTikm+wN93HtUp9uXXl6aKeyB0y3PGjVVUY6TvtegtepwPNVAnVfPgmQnbUdJ/doKx1NvtROEqu2+umK6icyLjgNBsb0iu0Dd22XgL9Q5MILGXdV0Rza72k3GzXAn6yTozEtt9XJrLyw5l6wxpYTzZY2ritPtq+l2z1+qNKQIAFdpGVbNkVTtQzI7IQTis5kWrIIkajF7Tivz6/CU3cf7WtMdEbY628WGnr8uO4UsN1NaG3PjqtPLa6x5eAwj3yErD0RXTbdPvTw3NlOiNRepXq7WdHuE1ERQIXIYwFEs0uCr1TGRrTm092cAjIPRB+VzqMCIGh3XWtq4Djir9QmkKAf3HWmt2EIOKyJYU9R0M9+RViPrd2LUVE9TvRxwpZfzBkcmIsx5hDhpvy5R0svJdDKtNYfOdHM2i+ytXsrpVhhOqjaVeOYWI6TAWneBsNtzOA63HiAXATMtvbzDjFVs1ZNmcNd0B6zVyGt42m3zegLFaMx8e07nder7MjVFlXI0ifnsA9WS0aeRETR26g3d1HRTc16Ok96XgtaqS5hKCchEgeyOurfJf8vPyIyAhXmNKkJqJc8jKm24dNsxdT6KFoB8BhnFcGZtFT54z1VtrWb08/XBDFZrtc9hlzDRbt/XufeQ7C7l9fZZQzPdvrlEjUm+nttfPe1TybXah/Rycb6Vir126XSr7VNJOyUE6vuofUe0g2Sya9Q1E8Z0l0wvz4311O19EmcPZwea+5jlYxAghWtdTDfV4nI9Q+10r63wiofZTrS7pts+lMPTyykhtc7BkrhrumXvP07sKoDp9o2zDNNNGmdlU7RhsKhUD1jF6V6lppe72mJYNTOZ0n6ix+nlan2xZLoDnO4AITUx96IIiDtzJ4oiv7J1ACsl+xjDYLpdbV2Uf4cz3XE40+JadwC5Vp013US6XgjE4ReihNsTOFqGdcN0Sx0Das4HzHf/fO0T0+1LM/aMQ2UW6WBB+X1KMgfqvQZ2rQDWXE03Ned9rSKD1qqrZCUNYLqddbh2Rgldf1klvbxk+yQyCF8i0yXLCieDmI8Uc0i1edPKd0LOJmNO96Kmu3R5BIFUcULIgC7xmSJAbp81wuk25xKxR7g0CMrsKernOcatjqdbp5vq012N6a7G3mpMd7dCasw4REDXzXQL57VPTLe6Visz3fpcTPOiVWejpL6Fq8sKaQ8E2MB5rwPiaxFqp3stRXBvauaQZZXHCYaTMzgsFWWnE2M73XmeGyqg9sEtNjEX0+1PL3cf7t6DvIu0TYBm5Fqa002klztTv9QaT3c9u/bdVRVS8/S71p0Yv5AaqUSMgDYiATXdLcXpjpMEaSeTwimYBRRBIo/Ai0itqsp0S0PUk17urOlOe8V09zm9XKaCKQGcXtR0547avoC1GvmM/b453UWQI0iF2WAO1D7dZFlRpX2KCLZ6ArojoV5OKvaLcbI13SFMtyN7JkhIjXfoUpM15JzGSk532ZpuKm24hOOuzgWlC4XYbyknhjK6yW4m6vi4cXf+3Qv18l60DGulKtNNBXTtYIypXl7shXTwpbR6eU9ruksEZzzz16liX0pIrTsNEt3p7rKmmxuH0JHIE7amW6wZPpDnCYwY0IItqRHMgsgoKQliLopgQfk+3SFCauWY7rxmumuscXgMDt/maffYppxuH9PNXEP5t96n24ieKak2AEixK2n4OSJbUamabiq93FPjpbHWWdgmphohxLiyrpxu+yDn7l3bICsy3XEy0L5WQLpuFnCQU8IqQAAL6ng2mWS69S3L1dImIoIxvjFI9iyPw6P0inFLRn+p7AKznYyClLpGABomu7CGarrVqHT3TLeq2O9xupm16m9x12+nO5CxI5gDN9NdzlhT30PVdHPdIEZCvTw19UNQnE9swDFkrTrTy1Wm25dSqe9t7X/7mO5cb/PWV6fbkV5e5lxQ3tdW0+eF1CijWwtqhpyrhk3RXZ/ujjNAtVgqCS3gEMJ0kzXdRmqtIzgQUtOttY7i8ub74nS7bbQgFfsQdF3TXYyfzHDyQV2rAPk8Cqab79Mt1oy3IwLzGSZsptt4T5XznZiLwlZPfC1YLaa7RMsw5f30/OzsMzXTXWNNw2lw5HQqt2r880y3yhQKppvewAvGzeg3rFzPxXRrRiTcTDcS3iDx9ZH2HTSlmG4grAZOq+l2M91aSxvnhmMbDy1PqpJe013N6U48TLfm8JdQL28YTqOf6XaJyLR/lxoCG0GGkfJvr3p55xlUZbotlppZqyHq5eWZbq5lWH+cbsqhq5RWqMCr2B+wVv1Mtz/QVwku54sCmdniqmfvFdPd3k9Zprus09KD5+liun0Bx7Cabj57BqjIdIvnpJUVqI5Rrp+ZlWq6yzLdvXO6VceTcmK8fbpDzlUX0x3KkhLBanmNqky3YrtQIp20rWMw3Wa5T6msCR954AnkqZ9H/b2UkFoVppuo5fVhpNPLzWdOfAdiz0zZPt1ZYbP1qk+3WS7RB6c7TXVb3T3nC1INCGO60yxHngfYJYQGyfqG2uleW+FSFledZK6mu3NgxdLp7lyHaBXlq+mWvpPTiVE2g6hYlCozSdWFScPPscjiuEy6ToW0LGuz9W9iak03lY0gNv12qmxUCGBQAQzzcwn12N4z3fY84Gq6vYcZU/do7sXeHs4BrFRmMd08E0M5X7668kbnGWRMSxD3uPWWYQDYtepy/quql4vWfgVr0l0LFi8cTndPmG6Xwr8xBhW68J/DeOj1wc4w3ZrBQY1DYxb9yq7Wvx2gWjLGiTt7pkU5Ay704HkWZ41a0y3mAX2vQWvVoQKu7mV8SqWxt1EBCaXrgqUfQjizQaiqPM60EA1+v/K+ltJJwNWnW20ZpNsDAeeqISpFPt/QsUtnNqD22YNU0VMhU5UJB9pyuiNjLzQzWyg1csd8Dcue8WTDUPMkctgk6nvYmm67dVQlprvLFpfqflaJ6Q5wZkUwlO3TrZQlVLVXTfSH6bb1BVJPVio3f+MApxtQyYAAXaPa6a6xpuGs6eai54pacVhNt5vhtBhLYkMvjGz7M1qG000y3Z37ixxMt19IzVPT7TvIK2xiKhNFfUeSmQXHPvrTd9SDPMzprljT3Xm+oq+4CS9zaJQIFOlm+vZCto9Q4VDbzgghNQDIImEY+ZwUfb5STGyWpoij9ti49DESitFtCxiqxmyYc1q1T7dkutOAudYDFDXdYcGEEKhGi9fxpH6GOV/966xnUAJqJktHB/ts5qAw3D0Bh1BDixCqLLpBrN013a6youC16ipZUdPLfWdLpO9t2r+1oJtxT0StdBB6IaQWlQi6qZ8j7jVV1MsJJ0Y63URacbBzYMzp7vp0E0x3Rfly1XYhA83yzBNlQoUAJJtVZbQ2W2dquj0lgNR5VSn42i3TrazhPKtQ023NVxfTTYs3ajabTwWc+QwTVrClAknEjoHQbwBoW93eCwn9GgPqnKCClNy4XMLK6zpqp3stRWFweOqgOCG1XGyEhgGrphVLZ5beHIprGGy58m+NvSRajgCRFEbKCccoMhRTKcQNT8swTysdrdVGENPt3whz5XOoljaCGZApaqXSywtFyWIDZ5xuX4sQ9jOUVGel9RblvMa+VhxM2pzpNIarlxMGsmgZZjjdUl3Xy3SLOruO8BoxldSUqhRJeNsrtVWf2fpIC5CpNd38syDbjgVApnZaaVz9cbplKpiWNi9aDFVkmHKlpMLneFI/A35F/yo1tiEQ18tTa+6Qc0nufarTXYbl9zsUVEvGKHDfF2PyogfPMyP2jKKfONE+KXStOgJ56l7mb5PT2dvUZ2OuszzVgnmZWlqivi4EyjWDYMwl7RpB2QricyLpSLbPn056uYN5bVDOVk45B54gmrFugjW4TLujyjXMYWWFGrk7u6v4zKKm23ZGMqL8zjeXTJCto7hxmf8WoNaqb64R2XHan2WQnSkzCEWXLS7VNZxVCbaYn+sI6LaQkGecumbYQF5Jm81qLWd+r5xd7ILZnjbXCbI0oKbb8g8IqCWGWUB6eUTYp+sbaqd7LUXkqulmnW6bcXMy3Z5aXqs+lYiiauwlk2bsFLvKbaPQhKw9D6rppg4rT7pZQITT9R6a6W7fq2AKQlqCWDXdWSYPfi5VqSdCaorTTUU4NcGlgOg5V+vjrfMKEFLLIsbpJvUC+PRyiulWU6qq1XQX6ZE0023XdFPjqM50r1n1ciqA0y3TraZ10lk+/rWqCeo452s/a7r179XNdBM13UH3HvC9Eky3qxtEnuflFaR78DxJpttR0x28Vl0lK0rKdMPrcOjiQQCVLqnXJLdSI8W6r0JqFRhMz/vV+Ugxh1SbN021OuRcNWyK0qUN6nWJEoDgaxjwMt3mvEj5mm5xvbJzyYQ6t9pt0D22TOjeFyyk5qvptluGVWO6KzrdWllRFaY73OnmarpbCmPMleuVTS+3me5epJc75i9oe4qbv6WZbtd8k+dVnV5eYw3DZXAEMd2c8a+lFbdVqzmDw2IsHU4MJaQm3i9T1Ii6MNkyzJFe7hP68m1iXmGVSunlKfnv4hJ6OnTIoeraCFmmuwdCao2GYmiRTrdymAUIbHBOYzdCarlM1+eYbuqAJIJECW8MqAGHtFc13cxadRklVP/bEFjp+122YPGCSC93Pd8QqEaLd++jfkZIenmfmW7DeQA8wT6lhrZQbq927yaoloyutG3zeysnpFb9eVIOnAgKU3tf8Fp1ppcXv+MNZCMQStXhqkE3V/1lVMLcWgucbr1PdzmmO9g5MJhZ8vmWHHvp8ggCeru0sJpurk830ElzN9lFsxWU8jdq3zIDtPSe4gnCu1TufQrzbE23PQ9cQWUWXQaJE5+d4kOJ9HKupCVVa7p71qfboRMReA12DMr8zRHLFqyURo6ViUl0nDChr4EQpts919YH1E73WgqXwaHXXylfIWH0WWmuquEvnVlGvdxMGyIY5YbWMsw2IgHF8SQWchzEdPucbvdB41XjrVIjozHdhNNtMd1+dVKa+RJMt/0d5Vmm12GXMq6UOlwv013uIOdqfcJbhhHOQOcQNZnuLOLnFi2k5mjVpRiWldXLTYeZWauu+vbume6AudYL9IPpTouWK1WF1PzztU8Hu5ppZNZ0Uw6Eg+km23lV2KeoloyubhDm97amWobRTDdfex68Vh0GXh5U000HFAFqnbXss0Z9NlGJ9dyLmu4yQTeK6Va6Z1AZI5TRrWUzBQmpuZjuak63XtNdfh/K81wLOIQGmrk+3YCR5Ucy8n6HxHweXgIhVM9CZRVdgU4v020HXzKOkafQrdOt1XT3wunmncI0D2C6+6Je3iunm85KFWOnhdTKM91xHMltzyYDys+19QG1072Wwil2wx3kRHqjq0+3cLZ4ITXDeSI2i3JMt8PpdjLd7tZm5ZhuT408cw0TqkPgMgrL1XRTB3mH9SO+o8y8l1JCairTPaAMgfiOtMPMv1FaKdYdSIesAiuVS/VymummDKPIYFEAt+OftlYr1y3Tp5tiug0RI2OtWu291HHIw6xin+6AiHIvIJSxe9mnW2e6PUY69TNG0um254GAM9inGOHumu7qTLfaklEGMUOY7hCnpQfPsxDlsvt0k+MMXasOxzNTfteIMuQBLB9ZqsRkOFBnYjBKM912cKVU0I14v9pz2NUyrDumWz+7S7PUREvGbplu8RaXiJwdaOb7dAOGE+Map+N7t7Nn/H3P2b9TTHeZ9yigmW7l3itoo1SBpsvQJ/Xy2Md0K2smjCTyr01bvdwMZlUIqjNrRtqrIb3pA8kByyZwMd0e0b71AbXTvZZCML9ktExMVlNWn+gX6k4vb38GZ3AUrZ/K1nTrbKKr9UZR080vMvksKtd0e1LWqkQONaabMOhEyzAYUW1XWwxCKbflSFVqKYZn6XGrreOUA5JiurU5SNVKBW7GXQmpSaZb37IE002WLhDp5WIuU8aYFL/LIwBRCaabUC8Xt86s1RCmOy7DjIF4vl22YPGCaPdXPN9qtZR+MbFeOt39q+kOYowdmS1h9+43tqiWjGKv7T3TXd2cKNYNUdNNBRxD16qDVTEdydB+zPL1RBmH6UjJz+i7003M6R7UdIuzm3K6qTZvzj7d/WC6tZaMju+oBMTelTnunZoXJtOtHn8tj9NdZE3wivPlmW7XuNUMycT+u/YetyNEtY5S69mrnKNVsCaYbnG2ZEygr6X06e5ZTbeWPdOfPt1FVipPkHF7oc/ptmwCJ/Fkn1frG2qney2Fs4c2pxYrjeyMry1V04oV9sNiTUEwbuoBZzDdpDpnQPSsYLqb1t/kOEvVdBNOt6peTqoIG+8JUINUW4aRLW06f89ykWZlRPkC0rh87SesFKCQzZeYO1EcS4V5KqqvzkG61lTfKClhlfbPhrq2CVe/UPE8zfRyKfTjcb46/3bWdMuSALs9kBNkqz6C6VagrRsDRcu1ik53gEpoL0DVC3db060p9vs6NwDkXFHXinO+9trp1vo1GwYy2afbXjcZ2s+vZ0w3kUkUy8whP9MdxFL1pKbbZstkiRUxzuC1GpheDjB1jEw7RIBOlzSfV1bViHS11aFA1uqWUIMmGCZVYyBznKukejnpdDtsmc7frfpVH4jyHe0aoSyresnO212p9VT7JLNlWBRFxT6vqkGTpQrib2K+8vYY93NnsMoY/Qy99e8KjhA5D6IqTne36eXqvVdhf41nTqybCILppkta0iyXNkmvnG6rm4Slsl7lXm2CB1CcbmpcRps8SoeDgs108/tSSAvhdR21072WwmVwsEYOWdMtjH+7NZeuWm2wplBT/gimuzOuhGS6zfRycXhRBnKJ9HJqE8tz70HTj5pu1SimWtrkJtMdUs9iCa24209YbR0qMhqAEhihnO7cc5iZAhtMrU+x+XKpoHw6pDBazPRyyXSTQmp8kIhs1cWVBPhArDt5EDEt8Vy1z6FpWyYsdfgumQMvCGXsntR0u3rTm2uNeA0VbNHQdyG11GJBgmu6cwfTHdDSxkREBNlc3SBswz6k3VT36eUUaxI5As9iP/CuVdeeYvyOTqk0SgCUz5EBM+UzLNV6ca6WDfD0pKa7OtOd5zmyvEixjhznKtuf2Wpt5FiLnX+XZqnVcfWY6Xax/BTrJ56Vep/yeQQz3T2s6SaDlsTepzrTlYTUbOeLVK32oZdCaj1huqmzpZgbnNMtdTmiHFQLVq/YHXFNgd6rl9M13SElFSE13QBhEzj2tpAWwus6aqd7LYXL4GDTI5VNy+p7SmycPtVqy3kiGGXNkeKY7g6L6hJSix2LLBFGIrWJBTjMfVEv99R0V+vTzR/klMK8lQJUptWZMXeKtCJ7XN60rZLp5ZVqujO6ZZj4mWoRoqeXm/OVcLploIQXW/ON2+pZyjxvVwCCC1r4YD1fV1ZFD0D1gBaOP9vSxgOtptu193E/G++jnIVeOIkk1H7txtdK11/yQTZnsFUg4HulWjImUqTTz6aF1XR3H8Sgev3KfZ9wHsTe512rrv3WMCxbAXWMmWkAG59hPi95XvUzvTzPrYBt+99lhNTowKmrOwRldGuq1SFBIoP1059viYAPQH9HFYTUJFkg+nQHlKhkytpVx9SQTLdyjcgIjqJqTXc36eXqPImVLLPy+6VrHgBVgtfdp5d35Yg6riF0RlpMyzB1DweIcj11rQJB96p+TlvCoBdOt75fZHK9d+Zmib3Qy3Qngj33z3Eqe259Q+10r6VwGRzsJkgy3X4hNcA2OISCJ8C0PzKYQ12plY6eURHjkPRylZG3mNiAjZJszVHyGiY0p5tkYgwHLkTcynh+aZYhFX26CQPZ2tArCqkBQMuhAq6n64YFC4Ae9+nm1MsdLW2o2l5nn26jLVlXNd2OdaePg0hzD0zbMlFGsKQXoERP1DFXSe1U+3RXbRmWeJnufjnd6v5bpk93sV+2St17gNMtjBhKSI2s6Q4YNzeurphuomWYI/AcvFZdbQiN35WpY9Q+0yGgVzDdfXS61XlQWUiNvk/J3jrOVVa1OqT8ybApes90l9eWMLVoSFEugvUrnpWD6Y4bUlDTN5dMWFkUvXK6PZ/rK8ehVOx11epQQdLuNEhUG6k3LcN4myJl0svVoDFAlQCWTw1PU/35ZSbB0AMhNZvp7o16OUAx3Q6nmyudXY9QO91rKZw13ezGadcUWqnhqtKwQ7Va3U+stmPK9bQaTqZ1lItFFfcXO9LLnYx8gCFanukOMWbdTLeoFyzHdFMHOZ9ebrEPPUgvpwxP7bMDa9GBKn26XfWXjNMtme4wp9vl+Ntt3soz3aziv/G85bohgkBdM91ryukW11XWbiWGQ0H5Pt18yYo2RuoafRRSC1MvN9dN5mG6y693qiWjqxtE0Li5cXXxPOmabj4NPnitOhxP0zAnWSlmb2t/pkiFtINuxTgrBiSqpIabn9NFernJdFP7K81wFialxRC7Ol90/l1aeZwIOHSrXm6WxdGptvbzSnPbaSyY7mHt9e33UEr4fOptGNPtYXu5tVoiA48bV+kWoSa6YLortU81UaIdZQuxg+l2CNNW2MMtccueMN3MehfaPgEBSKrjBIUq6uV1TXeNNQ6XwVGN6SZq+xyq1Wp00s10K0yuGQlL/SlqgpWKG/wic/aRDmK6PSlrlZju4jWUIJEwOOw+3S71cuIgF+nlhMJ8JSE1n9NNRDiTkk6M1au6A6/IVoDScB7p45ZOtyOLQh2jy/EXB00RKCnfMqwnTDfBHISgeL4Bc60HoKLSlWr5FKjGa9X0cnU9uoXU+sR05ylaaRmm22bLqrZLs4ZECak5mW7DwCvj+PSkplttGeZwukPXqrNkxWC6zT0kVBnbyXQLZ6tsTXcFllp9nzGu4GswzJfrXFWNblW1Oozp7lFNd5S4GeQSKJju8LNaYziV18vWkC3b6S7PdJtr09cyrMTe14XTXbmczEQXQeJK7VOti4QHdFMkZMDcZLq9ayDgXs3v3bLRunK6w7NSOX2L6jXdlM3WfdbU2o7a6V5LEcc8w1lMfuPrIxi3xGz3pbWOiTGc0w6xutBpp5uokTU+w1RENNVigWITc7UMU1XW7ShfL2q6A2rPDMSe9PKcS390CeSQ6aZKqpL5HVWJeMrPMFpvOWp5GoTzqr/Zrk0FYLW8crXrUt/vOvhzpmUY6XRDN+gAJd2PODAz+Z11w3SbTje9Vl2Me3GNsI+XwzCfb5+F1KTSKJdeXqGeUnU86XaJ/rWqBYlczmuv25Ioz8HcT919uivWdAcFB+391eV0d8V0d/E8qTkfJ50zkApqhq5VZ4tGcz91tGCMbedefqbyGb1jusvUYyuvUb+DKtcw2OLMkWJNtXnTMl1CMrG6Zrrt59s1052ados/0KyqViNrtbMkUMxnea4q65AkAxzz1XReu6rpNtdqQOcQH9NtOt3ecjITXbS4rNQ+1USJgG6ac0x31nemux813WJfk3ZgYHYHoK9/CoW4oikWaM+1kBbC6zpqp3sthautCx+tLKT4Qxk3schcRqLlQCjXE4Z+SqTimXVhlCCJSHGMXS3DNKfbs7mSrLPidLta93A/E/AJqQkWRQpTWJFs/+GWqQc57I0wS81nEcKK0OqQmSsbQa3pdqkqeyKgMtXOx3Q7nqeZXp7L9HIiKk0Ilrj6Y4t6din8V9pYyGyWmll32roxwLVc88F6vn1PLyeY7khlusvXU6pznhL6CjFaNA2CMpoY3UI1qA3BR7q9jx6QUXv9RhXv3RpS5zqqZobIHGpQOhGmEFhFx6csqOwOqWtCCb6FrlVXTbex1+UukU6jHaL2mYSQYjFO29kKQql2XzYjD0Dbl7wwnD6xdkUrydxxrmolAcr6D2Lk1Gec60x30NyjWp0pWSZVxBzFnuyqZy+6UnSeV6rUdCt/F46nPFeV88vZMoxq52ecFV4CoQxrzZ0VWQbACOKal2ScbvFjsLZHF5lZlsBuFfHQgFZcBdMdk5kG7e80QtppFevcU5jPMMHuKdy4Q2DabKmR2ULWdHfeY7WGDWS6xdniaIdIZWatb6id7rUULoOD3ziLTcvq9csIFHBqhepCL9qOEU6MmjJs9jTN9TQtFxvpqumOk0RuYt7N1VfT3SMhNTX908l052Vquo2DPNMPcjOd3FZyL59GKK8d8YGR2Nf/kouAMn26K9V0dz7XTC/PHdHx2NUyjDIEjDZv5YXUlHXnKOsA3Kn2VN/TECSxwfqtISE1tV44jqPC2KqY2umu6favdy/TTRjqPYHyHMyAWEh6earcexjL7ze2YqLvqThbqJY2mbEuRramWzDyxBmYitpKn5CacGIc6vHykg4nkajptvb03K7pzlvMWe1DpZruSM+o6WFNt+tcVY1u9fuzGbmQmm4ik8A5bnt/7b6mW2jR8AFd+3kpfbqVcYlnkxPnrkYGBJQEeVuGhShjc3sf5+yrPzPrmw2yJyWD113UdJuMMllW5ENIQLezj7SQgDQljTTtlrmnVGj7aGfP9F5IzcxKNdsp0u+hywhNVKrpjnkSbl3HiDvd3/jGN7DVVlthaGgIe+yxB26++Wbn67///e9j1113xejRozFlyhQcf/zxWLx48Roa7ZqD0+DwRitT2/FhDKOW7HOsG4nqQpd7KZG6VESxMusATI3oGSl21bm/xFHTDShp2r7oueew8qZkAYFMt1Lz7uzTbTpCZWu6lci4GRjxbegU2IwHOjBiCpSQTDeTmmgfwnybLPX9LlYqN+avq0+3nl7e0sZEOrtG799K6eW5YYgyz9tV70b1vw2B5ch3YcSEgBM9kWmFldTLC7bXqWchfyacbmU9jkhNN+yAmFNLQhNjEix/tXu3hkS0ZHR1g7BrugNY0h70VrXWDYCGq5+4Ub7TTclK8aPjLCGF1OwyDvN5Zcy560UXDnMvrlGIibnUy43APqCpVocJqelOIhnUCBq3kmWSl7yGgRDldut55QbTbZw3ZpmB2h1G/UzXnu0VUgspPylb002sAROUir36c+ma7grMbZB+gPci4aVLbfVyjuku9iVvKnjAvZpz2CKvukmlV+Yv4BMPNEi1YKbbsAcc+1LNdPcZV1xxBU4//XR89KMfxT333INZs2Zhzpw5eOKJJ8jX33LLLXjXu96FE088EX/961/xk5/8BHfeeSdOOumkNTzy/sNlcLAtHIjaUh/jxm0O6vsjsy5cuR5Z022klwtDkmK6E8l0uyNbktHwbWJULZSyOa6xmm6ZXs4x3f5IYivLnKIc3dV063Mnjeh5YAqU0E63WR/U2YwT8xCurjQsxp1b6eX+DVz9e6LM1zw3DzNdEbl8hN617vRxu/p0hwqUmOAFS/rjdFPK2No4KtV0F3PeuffJn/k9BfAFifrndJv6FSQr5WC6e65eruyvrm4Q5nysWldbFi6mmxKRDF6rTqfbZLodTiKhjE0xNywrVbmmu3ogtbdMN3+u2rW8RG9qbhwW0606oiX6dLNMd4WWYaKmOy/DdOcG062fN5mR8WBO1yAhNZ84Y1CQg5srzHwLcLp7r15e3om0me4uHFH5M5VeLpjumA6YGwGbKiSRif6ol9MOdJiOgb5H+OwUq6TPsbfJIHHtdPcHX/nKV3DiiSfipJNOwsyZM3HBBRdgiy22wEUXXUS+/vbbb8f06dNx2mmnYauttsKrX/1qvPe978Vdd921hkfef7gMjpBDNrSmu2CQacZDO1BdTDfhdJutN5xOd8PtdBdtx3x9D3vBdJdzuinngGe6A5gX5fllUGpkrfTyKk43HXzharpNgZLu+nT71Mv9z8ZKL4/5LArte5FBIiVd3zKQA9kzbtx5ilbLMER9TDfFuK8jfbq5qHRpY0tBeabb/b07nddeO92KyJ9XSI1Rxpb33iMhNap8x9UNwjQky7GNXdR0Uz2flZaWtp5F90y3rV7OGMiMMja1p7NKw2sivTzUkSKvYdR4Gme3k+lmGM412qdbufdu1cttlt9R36rVdDucbjkP9Jp5a8yO791iuk2nL8SOKRugCWK6RZCdKycr3wWkLOzOA/1JLxdnS8vRpxtQyazyLW5NmIFQS5C4B0y3WdOdk3OetvN82jOWeG3NdI8MVq9ejbvvvhuHHHKI9vtDDjkEt912G/me/fbbD0899RSuvfZa5HmOZ599FldeeSUOO+ywNTHkNQqXweE/ZCnGjX5PIaRmMB4pcaBSfbpVhUqDRW3J2qhO9IxYZEIZO/Ew3YKJ9Sqi9qRPd7gxCzBOt8V0m8qN/vSd9kYYsQrzlWp7uHkgVcBNNt2IIAf16fakm3VhIJvp5e6aboLpVth3q/4yM9izNNRYULIROtfw9ul2OKYyaOGplTJhGTlrqqbbOCDFM65ST6n1pneJoHE/QxcIc6qX99zpjgomi9lPrTEACtvgY/nLG2xUS8aGml7OZDgJrLGabnneKEJqypjN4EDwWnU4nmYAkQ1iMiwqtaebreJMhjMYpZxuf+ab/xoc090R9nPWdNOdGYLSYJ1Md3mnO8tyqAlMVfcgQGX5w5huNUBuat6YwRerPjvAIfHWdJdyugPnCiEmaKJ3THf1zCzLTukJ08073amjTzdQ7EteG60C023vUz2s6WZsTdd7yjPdDqc7QONpXceIOd2LFi1CmqaYPHmy9vvJkydj4cKF5Hv2228/fP/738db3vIWDAwMYNNNN8VGG22ECy+8kP2cVatWYenSpdp/6wJEuxTANji0CLz2piI9Vxz+sWX8G2nFHMOZGe9Xr6H8W2e6y6esiJp19X4pcMGBkI3Sm25WwZjVmG6HI2ox3SEtbJiWa6ZyezWmm547sjbauIZZRx7idFOtZNSfu0kFZdPLHVkU6jXVMVmsVOdeM/M780Gr5W1fw1p3xrhdqfZcyzUfrOfbRQuWELDp5b7v2QE1TbOK45mlKeKo+Fw3092H408Y/y1zP3U53SrT3Ukv75l6ufiOivnn6gbRXZ/u6k63+Fz1K9HHaWT5hK5Vl06E6XRz56zKoipOtWQataCbPt/kmbdGarp753S3jLZZrnPVbG1YsLuec1VVxu78XWVvg1oOMgy9OcYyMM/dEMZYqFZnxp4r9sI8pc9IgcIh4Vt32e/x9KYOCOxLcOtEnl8xu19yraO8QXYTXaiXm8FD0k7xIeD5CZKorV5OBMzluqleDmnCslMqXMMCY7OlMOZr8aEoVOz1PSL2ON1xiQy8JLfPq/UNIy6kFhkLNc9z63cCDz74IE477TR84hOfwN13343f/OY3mD9/Pt73vvex1z/33HOx4YYbyv+22GKLno6/X1DZCFYwy5yYihR/aG1pJlSrmT7dWhSLUMZU2czciOaaEWNXCnDiTS+vnq6jblpkppNLsZOBnl5OCKl1xiH7Hpr1LCGMca4f/pnJolRJM2JU7DkhNfN5xyRz2LlmJFgnmqlNHIrd6vupLykiDGCg6NtNtbTRnLbcnq820y0CJZ25VrbVCYoWITbTbTrdnT+7mO6STqH1fPtd0y0cOpPpLptWqEAVE3OKSDI/m5kwdMswRkyoF2Ba2FlziWCQ9NT6HjndREvGKI6LbhAepntNpZenxJzX0+D1cQavVYeBZ7JhlqFptIVSx6n9W1v/xv5ptNkJhvp63zoKaCHqhdl5xGR7qRZWDNNtKXbLcZrP13RyDCG1kP3XeL6V5q8B897pkir6c83MKxmAMAkJszWfJTJlP2/zrLDmfEAnl5AsSfIajvnLCWoV914yeF0lvdw3t4IuYtqB9roT+3KaJ87SsCK70+PIV3C6bYe4i3sVNpuh2O9cq0b/9XCmW+ynsX1NcelAjad1GSPmdG+yySZIksRitZ977jmL/RY499xzsf/+++PDH/4wdtllF8yePRvf+MY3cNlll2HBggXke8466yy8+OKL8r8nn3yy5/fSD7gMjhAFSrumu1wtb6EmaytyAiCdmMz4jKJ9Ah29zbMMjUgYhW6DTTrdZLRc/bkK0129VhJwG8hWSxuXWJjhEFv1QYbCfG6mVHbBisj08h44MVz/RlePbPX9NCvFMN2O50m1OtNa2jBOt0ix6obpluuGUXZ2Md1c31MfyqRx9QKUMrY6jqoGb0s6ntQaMeafcXCbjKiz7Vg/nG6ZXl6B6U499ewV2s0kTLpeUfbiFnxbU0JqZPsp5Qy0uyp0GCffWnXsD7kxl1LOIFZ7QFNOoSqgxzLdFYXU1HFwKOtIOa+hl4aJPt0Uc+ir6baDGH4HRD2fq8w9tla6BOyabv8eIj5XMt25OG+MLD+jnao1zl7WdJfZ++TnBuoHKaBU7IEA4VQT3Tjda6plmBRSS8hggiRKck5IrfwezmnPlLmGBcZma7F2NpWZFaY9k7BMN2Gz5WH+wLqMEXO6BwYGsMcee+D666/Xfn/99ddjv/32I9+zYsUKxGZUtZOWbCoRCwwODmLcuHHaf+sCNKabS1FxON2Z7PVrtgwzjC/ZcokW1JEHqqq2q1yvoTHd5sHtZrpVZeyGh+mW6c9eptt9WGU5EXntgkECGAOZE/opI6SW6oe/OQ9sQY0QtVf60M1lTbfxHRk/00y3Pm5r7nVQbL5c/WVIvXugAAyMtP/O39WWNhbTLXv/6oy9F5rRbawLZt0F1XRX7NNtHW5Vov4BoJSxgYCMBgfUFOuE6CPtW6tmKY6z9VZfnG7a0bHmPKGMneUl+3SHZOQwLRmLbhD6NSq1DOtBECMj5nycJMg6jLxV020IVbJrtURNN1uDrAbVqHZUWtCNHmflmm51HBy8Nd0B698MmOf62U2xvb7gatm127Zbih+rZFmY07XaHmSwfkFtMjsvjfRnbgUguJruAB2O3tZ0lxRSc8xfn4p9+Zru8hlS5roj7RTvRdzPT22f2q7ptsfpFQ/2kUYEuIw8dtwhMG1Ns6TC5djLPaL9YyjTHZJeLplujz+wLmNE08vPPPNMXHLJJbjsssvw0EMP4YwzzsATTzwh08XPOussvOtd75KvP+KII/Czn/0MF110ER599FHceuutOO2007D33ntj6tSpI3UbfYHL4AiJbIeql3NpxVbKkMkwGelT2jWMhcwpoKrK2H71cm4T86faWIeVlepZfhNTHW1aYb7jwOWMIxRQb2UGLSwF3yqbLzcPGKe7ipBaP5juqMPIlRFS0zIQlL9zxkDOBUp8UFSr89RYF8zzdqVgcy3XfFjjTDfDojZM578EVKYbsOe8N73cCEy5a7r7UDfWeebmOmJZKUMZu0itL1/PToFrych1gxgpITVuzygEiWgHuRv1cnMvCxFS09TLTfEr2N971zXd6jg4lHWkAq7RMgK+FHPoZbpNBeQAp9ts78mRKcV7zDOz90x3SMcO4TSb55Fgf7013aY+QKU+3WWc7pJCao7565sHa4Lp7k1Nt/v5qSQRq15u1HR7+3RXYLpth7iLe2VsdadjX5rpDreBpfDnesx0j+idveUtb8HixYtxzjnnYMGCBdhpp51w7bXXYtq0aQCABQsWaD27jzvuOCxbtgxf+9rX8MEPfhAbbbQRXvva1+ILX/jCSN1CX9FCjAGkDmOA3jjzrCUVPP013ZyQmpEyxCxCjelmnC9hSJqbherQBTHdeXkjvD0Ou79lU30MFYxZ05hP0xYacaE4b6qX2z0Kww9ymfLDsSjcfVDwOd09cGL4Q7g6K8XVors28AYhpNYeR4ThNCeEaAz2LNjp7qhWZy0H062vu/4w3eGtOXoBShlbHUc15eBM63ebtobRHBgsXuBZq6YwmJMx7mN6eTsLRREC4wzkWH9NoV7em5puriUj1w3C2itLiVn1oKbbCDS150Jqfa/BazUks6gDH9NtKmMXgoV8qzjpePaV6e69023VNRPMoY/h9LN8RE030bLO2cWBGXfx/gp9uqXdEn5Wi/fkxvle1DUb4+Ra8zmZbl+f7pCa7pLnaBDTLQJmpoq9J7PNRDfp5VYZXA9qugmSSJxM4erl3TvdfvXyCue7YZcUBA/XeYDQICE6TlDg+3RTRMn6X9M94uGEk08+GSeffDL5t7lz51q/O/XUU3Hqqaf2eVRrB3iDw71xqgsmMZ3mUIaTY8qNMWg13RbTrTuN5qam1qr7hNQKRr6CerlxwNkR4uq1kvKarWE0moXTHZlGYWqmj4UzxinL9lQQUmOcwJyYO+3P1OdeSI2siPb2lumm568wcigmhurT3R5HDCBja6VKM91iXFlLfifFuqPXaj/7dNuHW5/Syxkhta5quo1+t3aWj3utmszCmk8vFw5aC+rxyrJSKouaZog7996MUltUtILB1shTILJbMqZMIG/karrpOc911+hFn25/yzA686gYs3C6i6CbGVjuTU23Z/321OnW77UlnW6C6WaM7mAhNZLptp9xw5UkwJyZ8v2hJUIKTNaPzu7S9/VCSE1/5jJw2o8+3T1NL2fOioC1vTYw3eY+1puWYbxeSGif7ir2qgmrT3eFa1hg5i+XlVpkZhUq9lx2kglb4JW3gUWwOVmPme4RVy+vwSNjo2XuGi5VzEW2cVDTGdVLRQU7rkIaQeb75RvbP0dRZDvmkX5wi/twtWjxLTJOZT3EYbbqFH0CJBWcbjMNW6hp80x3OGPc4vp0V2DoubmTs99RANOdmxs4bUBbrSNMuGqQxe+4NnlWqmiKRGkdpX6nYlic0SJbtJVhSaSz1QlGmevGZLoddc9cyzXvEExmoYsWLCEQfbRNIbU4KmlsKWjXdCvp5S3P+jb3rRDGg9kLewLxzJkgZjEGOxhjptZnTMlK8XPIPkW3ZMyY9PJqLcO6e55Zlsv6QHPOi33f3F+D12rE77fm3GDZnRAWVQpomXt0xSyAKApv+cc9/zJBN+ZeXX26RZmWSXQF9+km5nNp9XEv011tDwKKNeLOvNLPdzO9XOyF5jxgx+n4zr0sfhmn25wr3OdyGWYKuNZRrjOOhGOt+mBrz/QivZwnibJQptsbaPKP0wwcVcpwNMEEq4o5794LAYKYY5CY9oDT6RaB/D6czWsJaqd7LUbqEw9jopWq4+Sv6aYPSJvp5g0+uxelnn7C1UapLJYpkGeiSH/2RA4JdW2ztYZd0x2QlmXAZrrpjVCy1FZLEOpA1A9y0/CxAg6V0svpQ9RMi5MfYR5mTuZQN0BYYRW2vQ9vcMQiyGOMO4pooR+7DEFhuhM6JTW3DHl6mCSYVjHcuosdvay5VFsfiucrPmSEaroTw/kvgSzXnW5vWzwPW+msje6jkJqp/mp9z9KYLe61leWF0QPe0Sx+DiiDkUJqHNPtDg6EtW3q7nmqn2Gyptw4g9eqY0+xAjIsuxNQLyyCbqGlYCEIbflFtDYDUM6JMVpDmVlWbvVy/TuL2ew4z895agd9fPOPcX6D30/A7LpCpdZzAXIz0GGV50XMOE2HBDlMkS3b6TbHFBCUK5sVkdn7lHVJKZxqBNkdZxyJLoLEuSHaF1HlOf6L6D8b41BZa57p7qjYh6RpE59BwW436bd5vbBaBIpuBT6mW8lCC3S6rTIDlw6P6IpUC6nVGAlwBoc3RUh5vV3TbThbwrG3mG7D8Hc53YyD4VUv74xzOE8Q+ZzuLoQp7LQsX4Q4wOk2NjquZUVX6uWW061/RqXaHi5NW2Q8WGmc+mfSNbL0uLl0M76mW7DWmWVwcL3pZcswT+soar5y4lZFm7cyTLdepuFXL+fbqRTp+SX7dFtMd59ruiEOSCZ1vkJqZyvLkSOWIpJ2yxX3WjXXoblOtff0UUjNCl6VrOkGQpxu9/eqqu2agZFM1uV5mO5SNd3Vnqf6bEzxQOlUM/u+d62WSC9nA7ohLKr4HCY4UM3pDly/ZR2pgGuYpWGU0+0LrpZ2OLLUfsa++WcFqj11zwEw7Ra3jolZ060/88RqGeaZS+o8Mc80L9Md4nS7syTZPSaoprtkOZmJHtZ094bp5kmiDBFaRKSvlCAZ9TOBNaFebgbZQsoMQvt0W2UGLiE1JpC/PqF2utdiSIODa73hEFITKNTH6c1W9pW0UpcNw99a6Eq7L1nDpTv2IkInDUlGLTYNmIZm/0uJgN61rGgJ956ATSw2IqmcwJvV0sYppGbURlv1QR4jJuSgYZ1u7jsKiCAzDD0nrMLXX/IGh0xvNDZjwXx7me48BYyIvB1BZgIlITDGkSTG4WWsO6t3pToMhjnwQTry1lyrEAkPQCxTl/WoNPt8A2Bmx9iOpzsrJSgzIyBlsjIYgyKkpjs1WH5/Pbt7vbtaMkqxOmN9m+0Ug9ZAl89TXQPmnOcCtsFr1VGyEpmBU+ucpfc1atxFlplxjao13ep71mhNt3AK2z8KEVTKiSlqujn1cs98DUgv984/Ztzy/ZVqukXAgXFA1PapisgeACu7gBPUtO/TCJSq75Gv8Twbh41WjJ1Zq1xWRYDT7RVOLet0V3CYzdrpak63e36K/WE4TwBEoG5LEiU5Y7P57FcCfsG8Kk63vrdlVrDAvReq4/Iy3YkosfDbwEVm1oD1t/UFtdO9FoMTuwlVoEziqBDi4dLLferlAalinFiblaJmCVMIp9vPAnDjrFLTbTOc5TexBswNWX+PYAZ4pjucMWbb5lSJeHI13QxT0xem28dKqdfsIPIYC+bcstL9AXnvnMCLeJ5Wm7cQGAEvX4aJKwARmrZlwnLk1xDTbSpjd6dergeabIbTvVat+TpC6eV2uQ6TXaMY7XkOjem2y4rK7VOulowF0+027MP6dHf3PNXPtITUmH0/eK2qa8AIAllpwx6WL4jprsAUsghNt/VlvgWll+tOYctiuvlzlWM4S6eXE0532ZpuqlNJWZh7kMXyq8EaL9NtiMoxgm9Uz3dftkx/+nSXd7p9KvaVmO6SAVszU7MffbpFAFjYs1S7z5Zhs1VpcWtd06zpXiNMt38fCy2D45lu+94bMpBfM901RgCcweHbOHPDuXC9xzwk5GdbNd38Qm9YB4shBMYoIgpl7DRAgIdTWQ/ZxHpyWBmwhdSYljahfbqzDID+Gj/TXX4D9zLdVnQ3gDlkNvDK6uXqNTsQRp9Z0y2Yb9MotBwW5ZpsK5MeMN2mY88978RR9xyatmViTffpboj6K4vp7q5PN1DsGayeBfOzlV4+QurlfqabrkV1M93l9ilXS0auG4Q5H9eEermWXm4KqbHzIJTpVrNn9Hsz2TBfyzCKacwNrQ7TIDadrVIITi8vmTLsvIZZ2sSnWHNGd4jNwP1s1817gj6ewEh3fboZp1sdt5nWbtZ0M8FXdpw9ZbqrON3cGuBtNFbFXpxxoeIojrXqg7l2+5FeLgLARUmLPbfMdcNl6LCfSWAk+nSHBF9C+3Rb9gAz17I0RdwRv/W1EF6XUTvdazF4g8MX2TbYNsd7csaZtZnugJpuI5VOROg4MZasBNPN1RyHHDROAZzAa2h/NpWxYTunIs04uKbbcZAXfboNg66XNd2M0dgV020aY4yAmYTL6fZkd5hzy6oFVq7pr+muwnTrh6y/ppuvexa1YqWZbrY1R3+cbqmM3dOa7vY1i9KacuvdElIL6CvfU1Ss6S7+HslAnb/Hq9vYcrVk5AK61dTLu6vpFt95HMFSQPYFnoP7dCvvEQgWUmMcJQBFimkXTguLEazpNhk7yolhGc6kWsuwPEutlF0/000Hr4r3V+jTbdgtFmuqndVGQIZhui3biGPkHa3i/H26Q5zusjXd/tKRntd0U+PwoDfp5b6zpUMSSaabz1ILZ7r99+nXIKpwvrNMt8gcdPscascJX5/u0JpuTVi5drprjATYNlmyfZLx9Zlsm9bjVWye+nvMFhcCVtsna2HnEDVDDbPu23AaRV2Y9RklarrzyJP2wvyc57n/IC9pzFqOAOzaU1EvWBiFQrmxcx8ug49RkC3NHFAQr7FahtAK8+ahQTOHepS/MKINp9vXSkodk3HQCAM5MoyFiHO6HUw3W0/d+XvmOFRZcMaWfN50L1uq7jm0VsqE9Xzl88yt59kLSCG1hHa6yzLd6lrlAk2+tWrXdI+UkJpR7uBxulXDSqbWd2mwuVoyiv3UFA8z9S/KMd3dCalR8z2XpTW0Eehdq5oTY7JhDCstf+F26Nq/E3tfeKudYJR2urthuukAQ8Y43epaNbMTCh0YTw23yWaSAfNy6eW9ZLqF3WIHZ5Sfzc8N7GLBjjOKirPCdErMtektkysRcOxi/nLrt/Q54FirPljp5ZSd4r2Iu966IIk6e2cOSwPDZroDA3sO9CIz04Jpq8tAk6EBJWB0SFC7Avham9plb7R+QKqdV304m9cS1E73WgzJdIfWdJsbvso0+phu00hMjY1ULJDGkHVN6cdz6XiM2JW4ryxgGvI13S19XI5WPYMNhhURG4q4hidKqm4OK/MmOS4zHVpmWPlqp5TXiA09F/OAYAYAYDgasK/BgamN5pnu9s/iPhPSidEdeS49WgpssQZyDIAO8gijLzJrfZiabpEKJsYNQH7PCacc3vnM3Hj+QZCdA8z0cvp5uwIQVWu6refbhRHjQ55laER0/VXVmm51XWaMjoF8nnK9G3835mvDNV/7kV4undn2OMWeY80lo0WTeu+yWwHXqYG7dwOq0262ZOS6QYjvjB03hS6fp3XWKEg9ZUXetRrAdMs9wiMeJAKn4tm0f2eecfr3nhvfcylI58uXYu0RxwoJuhkt7MQ8EPutKaCpi98ZAUXxNfrmq+PsDp5/ZquztML8NSAcCnHvFmtKBchTfR6YTnfOPF9ynIyNIAK00o4xA7amjUYy3b6sCOZeHfNXjMNyukXLsND67C6YbnHuirVMBlt9sM4W4/kb2YsAn/LvJYkC93CgeH7FnmLYq5XSy/X2XT5b3SbUFKc7tKbbEnjlsxPrlmE1RgTS4PCkvUkYG36Z9HIuHdJKk00GrWvKQ9dw6OTClHW3dL1wmfRyNhIrxmVGh0OcbvPePBuhmgazOmpvDqbgk2RmE7PmKyS9XA9aZEQruPYf2uNoSac7pKabYfmYftfC2BX3GVIjyzmNQc4Y83xipqabY7oFy9dCA6Yj32Ai8JHxbLz1hI5xe9PLzVRwBVz/Wx+K5+tXwu0WqkNn1l+xNfMeqPOiCLIxaW7MWk3lfG2viZGu6R5ocAEenkUtUuuZYGvgPuVqyZgxZ4uYe+y4yQ/q7nm65nvGZDwEr1VtDehzQThTYm+z91d6XxtQnG7TWM2N7z3q5tl0zXSrNbKes8GsOe5EiTnHs+UwukVQM/Kdq9IBsf8ePP8YBrnU/DWQGgEH2+lWmW7DCWHOAXMeCGaRHKdHF6KwY5hMAdf+EGg7el+vgFOxl2dcaJmRY636INad2Pe7Si8n5iNQkC2ZEoDg2rhxJIb9HYULqcl9R2QnMeMMgrVudJuBLb0h9C182jOWPcDVdA/zNsX6hNrpXotRGByBipKcmJPyO57hNKJOZvRSbkiKlH9ufA7TYkWkBLNOdwALkHMtWWR0knY81U1xoMEYaOa9eRmk4jOG0eyMizMKm53PdNez6Iqo+vPLWZa/k74fM0wNBS74Iq5hpPxJNr1znzHZ95g2fMyabpejKcE8H5GubzLdnGEko9JRrERWdfbBzngQrAlfw8mPW1f5TZhAlEBitvdSwDEHPljPV/3MKkaIAxqLyqiXl00v19leRs8iN9a7JYbVfv3qznxtRFnBDAh0k/LrgzRa2s/HG+gzmNokjoo6Zu+9u4Maptqu9vFMhpPJqoTVdBNBnhJwzXdunMFrVS3rsJzu9rjlHs7V7hr7msp0Z9a+rjPdXQUkgp1ut95F2DWMexW31fnZ1EZwtXmzWogya9V0QNr2Qcn5ZzJwVeavAbEncwFdjf012VyRFmtkVdnP1zFOjy5EYccwewr3vAF+rbJMt78Up6sguwrHWvVBkgOdtUxqefjgCVoIO1wliUwWX2Q0cmSWfP6MvUrB3HfkfCzhuFtg1jvb8cBRHuGzU6Q9kJt7pRFUSvlyqPUJtdO9FsOr2M2lk5kMNKGMLVBsDqaKrcE+mKk32ue465aijkPHOd1ZiNPNbmLudJ1STHdguo5qDAuDzZ9e7nG6iYO8SFViHNHOz60y6eWBc0dAODHDcDGHdIaD2afbYmIpsE43nV4eGU5OMSQli4JhH0xjwGQjulEvDxVSM+eiaB2lXSMQVtp8P5luhzJ2aQGdDiimm937uBTAzutbKMZk10avuZruQV+gj8gO4VtFuvc6E66WjNzzLQy8EtkeHNMaCJdaP1dWFLxWo4jPnoEIKDIGMMeiJhTTrdcpiudXtDocQSG1CtcQrJQIqJk1sq42b4L5jnznqsksoihfEs/Yz3SbZ48oASgc07xk+yl574kINPtJj8LW4TKe6DOSXGeM42OmzlsMsic9Wh97qJBaANPNBdnLdrFwrFUfZAtBODLyfJDPj3O6Cabb+A4K7Z7OfXjPL/99mnPFx8gHgWW6aVvdmV7uqem2bB2O6Rbq8HlsZWatT1h/72w9gLeOmdk4C7aNqI/l+jP7arplFFBhug1G3VSYturCLANZ1HR3k17uSQkinG421TNwExNpn2keodUZl8lKmTXIlnEW0JLBZLpNA1keNHGJzddjoFmBkc7rh4PSy/UIPMeAuJlu+vnI52mmlzNMt+ZwMHV2LNOdVGG66c/g1irHArjSNn2wDzdeCbdbuJSxk4otw/Sabk5MzL1WhTCYmK/tsQZ2f+gFjOBVKNOtOp58j/JyxparJSPXDcIy7Ee4ppsNvpRZq2zJSsfpFnPFOlvoEoBGEhPiQOKM0793U7W6FEL7bPfB6Rb3yjmeLqNb7vu551w11zKABlLEEdDkUqh94zbmLwBLSNWH4t659HL7eYuOE9w5EDFnZBWmm7djAvaHkkH3kLXNqdhX0vao6HTDsFOq1XSbz48hibSabrozTu6z80o4zOZcKWuvkjD3NjO7g90Lxf7Ad5wwYWX1sUElkZm1/oqoAbXTvVaDc7a86WQc20a9p3RNd9N6T3HIMikoDBuZV0ov5w5ujunupCVHQJNrWVWaQSo2B5aJMZzuYKabOMhz5kCUbcniHtR0c4euqAvtsOkhwlS8mmkAe+Fhpcz2VGx6ubqBm71TE5pxF88zMgy4IBhBC47hEODqnl1pmz5YRk4Useu7W2R9YbqLtVo4W+XYXhEtl+KCgK1k31enW9/rZM0m057ONMKTOGLrmL0icgZcLRkzdt/P9HGv0ZpuQr1cppcb3SHKrFWv081kCjkCI742OLKmO+/i2XRb0611gwit6RbBLt3xNIOtLqPbcjRDhdTQZrobcRHU8O7BXDaCVndfzgGzarrNQDOxp3uZbkvvxrHO2Jpu/T2V7BhfgCaADND+nPGtoyppe1Rlug07pRrT7TlbhE0RJWzg3rTz2JruEiJo5pz2rqsQMOtGaBD5arrL6M6UZbpDuhmty1i/724dR8YYHLzjpBv+MeV0Gw6uYLrNyJaM9lJpsmw0V9TZ6SlTXMRYGJV5UMuw0MghHZEnDSX2Gj6nu8MwI2EV5uV3EMx029+pZLqZuntxkKeVmG7a6bYV5gWb3hEoiXJbY8AwKKQDYbWSaf8/iJUy5opkuiP98C/Sy/WDPVNFT4z5KlqZcUJqhfhdGadbd2LsdUMz3XbrmOI+zJZrPpDX5IIpXULrqWm094iZe/NBXasymFWSKRBrRMxXAGhxmhhVFKV9kPtpJ824ycwlJi22EUdtHQJQTnfJfcrRkjFnPkOyKty4rQvlSn11tecpz4kSTHeptcqcHcKZEnPFzMSy2B2FkZeGpHAKO58h9inx/Lqqdy9b020+/zgG137KvgbNdDcazNnduS0qO8EKOJZkurXnW7Km25y/QdcwIM+vzr1b9cFEyQB3vsfmszAZ+c44tfZTHiJEvKe0HeNaq1xw1lMe4WodVYnp5uw8HzLdTunO6aZtyVyxV9lMNUvFntlTSjHdnZKJpuEQM+MMApvZQhNk3PwNyQKP2QClYaunvAbJ+oT1++7WcXBshD9FqATTzS0AxQhs/13p02dFc82ovm4IxYx6eV6iptvLEDPtvlTWVTCcbPuekjXdKWK2pU0sne6m/pleppsQ6eDqnC2mO8TppplXn2iMTGEHdFXlPCeioJ10M0bVtkoqaCIzB5rGy2k2IidruvV1YdV0m3X4ZWoBWaabXqtcvVsvmO6QVMVuoSpjm6jKdKtrtWpNt1VyASC3mO4u6mx9sGq6Pe19rJruWAbyumVJinRIyukOS2H1Oz3KGPpQ010w3WYmUYm1ygSexJ7Cluc4MnjsTgF0WQFye18PBhOAtMCINWq/C2a69XstarrNNFo6pbj9O8Me8NZ0F0x3jKwTIGfWjYlcD2pQYndVg39JQtezU3u6yY6bTLeZ8UCOU573Qh/AyIIyxNe8dkyeQWsV51qrrF3imFvQn6153ntbhFKoGiTO9LWceEQmSXharqn2KhcUsthcTpyxVE13+//FnmJ+zxWcbqbTkFc8cE0w3f0Ihq9FqJ3utRh8ermHrbRquvnNVjLITEsQuza1QURzof3MRc9iRuwqi+gNnRwnZxgltOOpbg5epjsJS9NO5bhjNhW0cLoNoZQKNd0Rp+DbeU/G3DsJT3oZFxhJlRpZLV2XUF1vcS1Egmq6jbol8WuRXm4KqUU0E6N+R3ZmBu1QsNkJITAi9L6abnGbzprusurlTqe7wsHsQNrq1AsTR0jh/FdL62zEsXQU2dIaZs4X87WBLO+s95Go6ZaCWp76S2LNSJafq+kOXO+uloxcuY6z1pT8EEdANxCuvvQZI0hUaq16SlbE3sarVIvvsHA0OUMyMr737oTUAh0Q13wOLS8xsj+K+nU6vdxldIuMpsizVou13AQ6bR0byBArzze8ZRhdK62ONRTS6eaYbmJPbxlOiz8L0DFOLr3cqFdntWkSu8OMdb3SNd0M0+0IElsMZwgq13TrWSu9EVLjSaIkou+tmAfV7FUKhTigmV7ew5puMyuVY+gjfe6F2ChWgJLJZsjqmu4aIw1hcLBtsjjHyWS61ZQiM2WVSyvmarojokY2Ng0Ms06ETgGWTmMpptu4hkesRTr+UZH+ZPe3LJfyIwzVFIlM0zS/I+EEJsE13fZ3aqesmS2E2veRxY4WIdpn8Cr2nNNdpLAr6bqqM6Ad5B1ngWkB1F2f7o6RbdV00y1CxHeUIbE2ebZPtyHWVqUWTQSW9M4BsJ43x3SrraOiiunlIUq43SLrhN+pA7J4vuWuqa5Vb2mNLwUwSmRAwErTdjGD3SIWacbtzxQqzLbSsO4oqWuGr+kW7WbC0grVGkQTXLmOmH/B6tHquqv4PDOHA5czAcdSa5UtWREtFz1OofEdqaVKVjZS5zPE8+uuT3fg2nV9RsW6cHGvIlPLrunmjW75PfrSYAmbIkGKRhwVJSola7rFXGpSCvOBMEXkIlPHhPnM9q3QWVVmFqB4vrQSfmgWCtf6dFD5nfLMXWtVzl/3vZpwBYmDSwTIcZQ7r+R5L53uLoTUuJZhnWeZRYlk9a3zOzcYY6YkUH5HAYx8y9iT7ZZhVZxuYz5Kf1i0jnWTQupe6INlDzABCVeQeH1C7XSvxfAz3fTG2Tb4cpqlNsGKdhTpjtY1uGgu43QnbF1YuNOdy+BAOSO8aGcRwHSXrOluC6l1NiGT6ZbCX4F9uomDXGbWGQe5gEwvD2ToKQdZXotRmJfR3UQVpuKcbqMekBVWqc5KmS3DYkbsRitdMPt0MwemrBMV31kpITUxPzmmWx+3OhfVljZlIsgmSEe+b+nlvDJ28XwrMt1J7O/cwNaJ2k63YOWta/TF6dbX0WDTw0oRQmpFyQonIheqXs7vr1x6eVETu3Yw3VyGU6m1ypWsdPYMsbf5UirVmm5L6Iv53s0zsRQqOsz6NUIddyNLTWReNOmgpqskwLIHPGtVtSnMmu5wpts8e5TASGmmu5PRwDLd9NoF7PTyxHSUrJpuhem26oFpp5sVOSSdbvdZbf3cBdNt13SXEGP0jcMDYQtlnZZXzShFXvL8KZ4fl0Uluu00WFtGiN+a88D+jHCHOTX2ZGmjlej1bcFaN279Jdde6IOV+callxPq8Osjaqd7LYY3pZrbOAHEyBW2zeV0G+IMHVi9F9XN16rpdrfFiDtGjbWQq9R0c5sBp16uit8wzlbZOhs1DYarPTVrusOZbuIg52q6O5+RhwqpOQ5drt+1zEaIVad7tfV39Zq8ejmtGq6BrekWdeJMTberBzwzX+2abj1ltUqEXtZ0W+vGZLqLZ6N+jMuY9YEMKvXL6XYoY1dWL1fWqjfgyCrMFt97SjHGWWbVgfYUVpqx0LdwM0i0iJzv3sOYbsqI4dLLS/fp1kqXqj1Prs9ve5wi24ve+4LWKuN4Sqc79jCxRB2jFeAySrvsPt39dLodn1HacdczB0Q7QJM5dBndYv3HVk233+lOoqycw2zYHFKLJnEIp3pQCKkxqcrMvADUs9pgupmaeZ3ppvUBzHEVe4rHjjGv4VqrFWu6XSr21ZjuaplZBdNdBByyyk43c7YoJBFny9jZnUzApkRNt/m921oJ3TDd+nyUGg7BNd0BTHdirEMuqCQzs9Zvt3T9vrt1HJyIDO90F8aViBi3Xx9yKHvSx9RrlGW6GTYyU1JBvZBMQrnIoa5e7mnfE5q2qShjc2mwMr28oUcG2RrboIOcNjyz0DQjV6SbiXCKtM4sHkDaqZHVak2Jg5yv6WZSbVUwBgdb083UHOZq1JSZr6YjVJQEGIGSEBjOlpfpVhwM9eDujuluv0dTwu2T0+1Sxu62T3fb8ew4W16mm15HedSQjLGuQaDO1/4JqRXOl6+m22YOpJBal+rlrpaMObvv6/WDwem9iBAkZUugCtNdaq16me728+QCjlQ2AtcyTNRCiucXe5hCJyo6zL24hmSMO0x3g63pdjDdniw0PZCfyM9JEpXpDgz6GONO4thWmA+EvDfBdHvSy7WOE4ne8z0xAxDGOJtJDPEI7cA8/cxL1XSr13Ct1Yp9ut21/Wuuplsy3UnhdLfMDCcffPNV2BRxwpaHcbX9xWd0wXSbOhFVa7qJwHPLsNX5rB89GGuK51Gw7AFmfuc1011jpMH1Z5YLxowIaUx3Rhj+9tcdMS0CpMMcEc4D0/dYOmymkcKwkWoqqBeM4JuX6XaJ38hrlIs+qsrYLNMNnZmVnym+M1a0x2aYuNRv66Ap43Qbz1yItdmbbcdhiQvmsKWmvapjivTN1TSiOfEw/UViQ9YNnUQ63fq4pcKsURslgiAU082JoIg1UIjfdeF0R0awylirGtOtDD1V5mtZqExDy5xvPa/pFiwqYWwxz9cHda1ytby+tSpen8cq000ZnuiT0y1qunXni2WliOyQjA22ltunnC0ZfW2JQvt0d+NUys/sfO+EhgGX8VBqrbLq5e3PzYXTbdVf0srYjYQSUuvsn9CDLV1lVZSu6abSy6sy3R2nUDqehgMoar4dTHfhHDDzVX02ceHgqqKn4Uy3zcAFZVYRMNul2Uw3TS4AtoCWnNO5zmKrjHzDTMNmmW4jIMbtKckAyFZxznnCzDUf0+1oHdWoUmZUVb1cZP0pTndqCmj6YNqSjha3XEDB17HH/gxDYd5AnucOp7si000Eni2CzNL2ofdCas82YdkDMW3LCzInKPN1HUbtdK/FkHV3XJ9ZR3q5znT7I+GmyJnVEkRlsS3mMAaQW9FcebAwKSt5Caa7EKbgDFFFmEKpkXWK3xQDMa7hNnJSJYWVy0YQ9yqYAl/7L/M7UvdhTtzCZro9xhmhNF58hths6eebRwlawomharrjhhTp45grYVxkzvY+blYqNtPLPVkUWk13Xhg6gM2AFNkJ3TDdOsPBMt2ag2wz3VXSy/WU9f4y3S7RE+75eq+prFVvu8QSNd16erkj26MXMKL4/j7depCtkYSk1ocKPnb2B6eQmmnYi/pBXfCJhYcJC4Ezu4PLfCmzVj17St4xXu29j2Yn6ZZhurEqnp95JpYClxVlwtUCj2k/ZcFwCiUT2+ykWEe5FE8ElLOdZLo7LH+Fmu6kQxaE9+nmmO7iGt45bF5Ssvyde++iprsgJPxzyc90t//P7ikOG037d5mMCM/6LvZsVxeL/jPd0k5RUutT024OvAY3X4Xtmcd0TXee5wXT3dmXrECeuYcDTntTfXQ969NNZSdKgiysTV4V9XJfiaUmfrseo3a612ZwqtXcRqgYV0mn36Xz9cp7TGfWSh9To6SGwZZEQAxldzBSJmNPv8uw9HJdqMq8BhJVPKR4jVbT7RNSC2SMc4Xl41raCINORMv9Qmq0EQ4U0XPzO5I13aHtJ1Q23YhQ8jVIxXsKJ2aY/LsAZ5AFpZsxzoB0uhum051ofxcoAjpUyzB6HGwdfggMQ9NX061GiNXPKSNQYkJ35PvsdLd4ZezKtZTqvftKa7i1qqaXS6abmK9Af5zuzrjFXifVyzmnO9IN6CSOi44InNMdGGQrxATDjWyz1rSskFUVOGu6GZX1UmvVU9Ode4XUdHYyiXhHSVzDUhquwt4Et/tylY+FXsO8V90Ib/+uuIZrn5Itw/Kwtao73Wnn+ZYU8jOzrJRrVK3pbjRo5XbzzFOzVMyabrv0znhPxLefs9dmINMdN+i5043TzdTZupyvoltMmXO0Wk23tIUUZzbrlulmnMI8Sop2aEpgWb3N2NB4KF4kSgAYsTsDqh0o9pTYYrrLOt3K5xlzWNrq3r2QD7aYsOxuVr2ctynWJ9RO91oMyXiEtnGoUNPNOVtFuol5DSVdV9avxnrdk9k6SqaXmynAgokJMNi8whT0JqYeiEUbEk+rDZ8xqzJI3pZhwigklBvVCDxTDx9HYA9EuTGGMt2OQ7dQraTbqeVxo93zGkYEmbgml14eVGPHsVKckBrT0kabW8YmzzHuItsjMQMlITCYLjJDRL0fykFWxhRymJlQgxyWEm7JFiw+qO1TuHGUZZjIFGuu3RfTcqUwjIpe35aQmkA/DndjHniVhq10vWI/tAIOJTNyZHCQrOmmg5imQrK/ptsQf6oANeBggZm/pdYqwxzKM4tr32N9R533keykYLbbLxLPzyy5KoURrOlODccT0NN13bW8IuAQ+nxjuRaF3SIcd3/Qhz43k0S5RsWa7oL1CxNBTOLImq+FqBwzzji2+1kzjqfZOspKm1efJ7VuXGuV1Zpxp5eH1PZXq+mu6EgmamvTsk63aU+ZTndx5lFnnBpckO3mguxV/l7Va1p7StWWYUTg2ddpiA3KhTDdHR+C1JlRnp9kumunu8aIwZvyY0zOqGCIEmQK28Yf/IVqNb3Bk2y55cREutNjirF4IsZ5iEHCjJNM11Ej8oSwCitAUqEVD5emKe41MVvaqPeqGiFM+k4jjguWn0kvz0MFNRzGWRTTEU61zyzNHNqHMmeQhfXp5lipjkqq0adbMN9cn+6cqunmmG7xnZmBkhAIZ0tkOFidA8waelqhtxshNTJlvWqNnAcuZeyuVYPjWBH6YpgCNmW1CBKJiLnGeKjMTUXhLyfMNOOSNd2NOOYDDiXTy50tGRlntlDKXXNMt6ukgpsHpdYqcY0sTZFEIhW0/Tz59HKqDpdmJxvQ2UjT2SqF0ix1753uZpN2ut19ug1H07NWVZtC2C2cUJVv3OpcCr6GgZZx741Ap7uhOt3yrKHngVsfwJOFwu4p9vMkhdTKZER41rdTxZ7rFuNCxcwstXNOKyeCrSGQ+yvdiqvInmuQZ5zmdHfslKD0cifTXVzTrukOzHA0QaSX24Emd/DFlZ1kwma6lbmk2MCyprtOL68xUvAano5Dth0xDmgZxtTyWuwDkQomU6iSSFc3NRdmk6nplvXCfoPE19KKa5NBCat02zJMVVnkWCmZXt400h/V78CR+kVGzy02vXMIyFSlHC5RjpCMB2uzlW3JFGEqyolRnMpCCdNgukMOYU/9ZcNguhPJdBv3Tfbp1tkHcxxmnWiVCL0w0EKyTFwHd5WabtKRr8oceOBSxi6eb8U+3YqQGmsEBrQhInt998BJdIIRUuOZbr0uVBeR4wIOgQI6rpaMYj9lDHs2WGCih+nlZKBJlj8xmUQha5XYU1J1TnSeJ8tKWXWMhJCSSI+O9O+9O6c7cO1Wcaasa9DlTarT3WoR5yphdEvFbnjmK2FTFEx3YODOOjeL/bdq8E9coykckCinxRiJtWvONZvptt/D6QOYz8sbECNttMC9j2W63evbzXQz3WJcqOp0d55vpJADXQupMfM1jxPyjFMzDxJGoLhg5AOZbuXZFXtKOXvVgnx9JAPPLUN/ydYxoINb1fp0K+eRMvaa6a4x8mAMDpbpBrTDX56Hkt0pz3Rb1yA29DbTbadtytZRTY7pHhaDsO/Dui/uWbjTdShhFfawChYoKtI2OYV53ummNxzuIG/EEaswbwlq+Maupp8ZiAxnQf6eZLrdBzmnbFmoWJZjpfIsQyNqvycy1MtFRNlsaVMw3fZ8peqxgOKgaTT1wEcQJFNjpJc71iqVbl+owZZ3ugHwSqE9Z7o7z5c4Qrjn60OY4+lZq0qGQ+aq6e7XwW4y3VL0iC7boJgDtmtFWaY7RL2cY7qbtrYE/SHdP88Ws18A4PfXMmuVUHJWDfK4KYTU3Iam6mBYLKqSHg0oQmrohdMdyFJT52hFx12yqgMDyktspjsmvjOrm0kZIbVOn+7gwJ2T6a7AtKLYt5oDasCBz+7SOk4YJEVsOd3UXDLG6esswIozBjrd1Fr1BTmZbETZp9uR8VDq+VOq6yFvU56v0J5JWyXPPM/+KksI1T7dyhmn3qfo8c7uKUkz6F5VW7VpBvIqC6nxNpvYT31Caq6OEyZsm4QmnjQdnvUY6/fdrePgjS8/091WAfUz3RHTIkA4AkliXoNO19WZbv1QaLLp5UUqqA9ccKDYxAYAGI4OdFEvVpzFinC6NzE9vbzDdJstw3I9Wk5vOKn9b1O0Jymi56bCvCWood4LOXB+HsRcqwiCOUxTou9x55qqgqcZBRU/Z3n7dSSItFeVlWoYQmqNhK67k9kIxHzlDDqZGt4F0y2CTyEihpSafjdMt3ZNT6pit8gdLGpVY1ddq1wfae9aVfZGmV7u0SDoKaTT3f6c4D7dWsBB7Ckl792EZGaochKO6dbTo51rVR1DN0x36uj5yoyz1Fol5pLqREWs080x3VSf7iI9GiBSQfvqdPvtgbLp5TLFutFAlovgIFXT7WC6LUbO8XwrM93GuSnTnWObQQ6EL7XePPO08ghjrsn2aciM9yiMfOIPlFKto6ygpsNGU8dUap54Wt6F1XSXL9MqLaSmzN+W3Per9unm5mt7DuQR3aebVLFnNHLYwIgB9fk2ra4AXTLdynfaMm1117gB1sajYGU4Mjawy6ZYn1A73WszfI6mI51MVy/XDyYVopbXX9Ot9tSkarpF7/DEah0lW294VEBd4IIDZHRXddgU8Ru+T7ch+BKsCpyQByRQGF+i5Ur59PLOkBRFVMvwlDXdoU63K72cruVRVVdleygH060+WvMgVmu8WYeMeJ6qsWeplwumO8qQq4e7OrfMcgiupluwZ50oddaF0x3Srs+Volalplu/Zn+d7syhjF1VNVhdq3y6o5s9K9q6FEx3Thqe/WW6I+l8dQxA81lYzJcScJAstDLuPC/NdBflO1RGFJ05JAx5Me722FxOt5sJC4GzTR6xpwMl1yqVXq6wYHFzlHZNCePeNHbS7EGsOI1Ar4TU6GwECy7HPpjp5pnYlmAOlX3fVRIQXtOtOomq3RLL5+vdgx0OsAzylvO5ZRB0oKn0fKayZcRnqnXNBCEBKAEIR9aEJTSl1rsqj0HuKWYwjLTR+LR4Ddw5EZhe7poHpRKeKqeXi0w4NSOvJAPsYZDFXpnH7pruRhwpJIY/u8PpdOfF8y3KNkyBwor3qTLdqW6r80y3sRcG1HRbZQYc061mJ67HqJ3utRnsRug4yGNFBdTTtqj9csM46CA1jX9ts9A39FhlurWUFbNOhBYkKcd0M5sY0RoKUGpVkoCa7uCWYUUKK9fSRjyPpplerqbOOERONOeLEVIT31lUOr2ccJQ6hwTbkzRKmBpZ8b3biqomc6X+zDpkhLGgsgwm0y3GDRh1mkqaMRUkAux5YJYEtLLczfKpMNJLZdqVY632uqYbgIM1KXkwe9APpltdq2zqnW+tKgYFKUjWd6Zb30/96uVETbf4ztRxq3tnqGqtugZMMCUrplgTOXbiM7piul2sCdMusdRaJZ3uggVLmpyQWrH3AXrph5VeqmWYFQFeGWjuxunupqa7bNsx4l5ljeywml6urFVz2JG4dxFFCxFSE88v1Z9vcE237gzESmCkF0x3NkztIWYwxrZBrHkQ6Q4Iea8OOwYIrekmvvcAsoav6abnr0vFXnaLqcR0l3W6269Xa7q1sqIQWJlEnK2ZkLZkS5l7ogzOSRIFrE3pDKtOd9dMt26zqWPnRY/p/YHsOGHALp9gbOCa6a4x4mCcLffmKSLuCtPtiIRzqtXClrDSZAnmsBFHiCM7DckSUjNSgCMH82qNk2tplVPRXTsiH0eOg1xutmFqkLlgmKOErY1MDKdbfmZkR8Pb/6aZhiSK2NR68TwjpU2Glc6kwpXxIIXUjO9IOPZJg2nBZDDdytttprv4mWe6CYOjVdx3YjHddN2dxvIZgRGuzCCWbYgUYyvUb1SNxqgwOIKE1Ii6sN4z3T12unP+gGSDWx6oa5VPLxcGB60wK5yzSAmIafO1m3TfECj7L+BSGtaN2Ux1uqlAnvpv5t5NhPTpNktWJMvXCMhKaX+Idr0qSPPCsLTH2VmLVmlNibVKMMai5V2aq5oZHpVqZZwiqCbZRtXpVnpE90ZILTQ1nA/ChzvuHUdSudeUyBhxtXkTzq6tsuzIdJNkQaY937JCftR3VLmmWxWRIzt26M9KDZCbWVVWTbdrnFSQSLkH755C2GgAwjIiytiaCFOxLyekFpjdYb5NCqkl0k6pXNOd0HagtCkUplv9DrKs+E6FzWY53Wrmi7xX3mZTa+bFZ0qHOAk7BywQ36mcj4yt7lpnPlhldEqXJfUZZ6rNth6jdrrXZgiDo4yipGL8h6S4sjXdLNNN1XTHDNMt0rRE6w16AwppGcb1E3cx8AAnrML16Q6slVRruokDUm1HUwipqX3MKaebYb6SCJzCvPjO4iQsaurMeGjQwZeIYA5dTrfGdDM13er9EQOx7iNzMN1cH1kti4JhHyyn2wiUmPfjhGY0qpFc/plTdWEu5iAEoUq4XaMvTLdykEsRSYOt8K3VvDCMRobpLvZfwMVK0Wmxaru0nNofgAo13VRZkZvpHlyTTLeDNYlk4NnY+8qsVcIBEXtFiqRIBQ0VD3Kwk6Im2Wa6++l0h6SXhzruRtA3jpBGwomxhdRcqtXy3tkaWTvVVtgtwSUqZrBASfWurl4uUmfjov1Ui99D6JZhgpAQzyIj30Mr4dtneYtwuvk+3UzqcoDdWCqrEuqz4mu6q7UMK+dIanYKJfgaAk/5jlq6RJ1xagCCdbrV5xmSXk7Yr96yDR8op9vI7mBLQYn9wQfSHnDMz6AWwuswSp0Ef//73/HDH/4QN998Mx577DGsWLECEydOxO67747Zs2fjTW96EwYHB/0XqhEEzuAISRNqICXEnOzJzBkcUnlcirHZqWCqE1McKsVnmP0uzYUclTDY+E3MfdA4DzegXStZMl2HUsZWayPTtCWjWQOdGpmWd8PhaupiB9MtWOhm+31pWt3pTtzfUaQ6MRlf36ZurFafbkXpskxNt2CwszxCbKiXJxrTzTxPK0hEG2NmdoJznNa4O0ZjlOqKnq6UfmIc3fTpBghHvk9Od6aUWJjoVsBI69OtzvmAteqfr12k+4ZAcb6AKn261YCDz+n27VOCOXAw3WxNdyDT3YPnGVLTbY6z1Fol08vb/24hkQHd0DY5ek237igJLZWC4STa5ISiosPci2uo90p1rfDX8uaFTeBtGaYz3Tn1fNlx08Gr7vp0F8GVFAkayAym23hWQTXdjnEy+gBakIhoHZU6hdSo9HLHWmWd7m6Y7grPv+J5JQiJKGm0g0Q5KqSXG2eLaMEq7BjxHbJ9ugtdDqE14wzkBdwr1abQDmZ153SrIn1Cg6jh0bdw7tkGiow+g3hKV2ljr2u6Fdxzzz14/etfj1133RU33XQT9tprL5x++un49Kc/jXe84x3I8xwf/ehHMXXqVHzhC1/AqlWr+j3ufw342mQ5WoQkyIgUVz6t2DQ4ikNVfCbFKBcHS8F02wI8wvFMuH6XZYTUSm5i3hYiWtpmyZrumG4ZpjICA6aQWvtm7M82noV6kHPZCInKdIccVo7nLZxZy+mmIsikc6tvxgBg7sdxHAmNvVKslDD2UmK7Upluqn94rqXa6YYQV9PdVB35kk53o1PTqQy+/X9irVLzsdv0cmEfFOUM1VqweKE+XwNV08u1NnnS2SLmGuBgI4r56Ga6++t0J4aQmmZwaOMwme4ogOkOrenmWzIWAV16328msX+tavdR/Xm65nzRylC/11JrldijM8l0xzLgaJ8tRhqxpoxtigN1nMaoraXSG6Y7tB7b8R2EnAtqMItg9al0XVebtyQ2WoiyQmp2YLRgugNTk8tkIwQi1QIO4Ux3ogTMzLPGnAckI2/MJY7pbiYB2TMUY+ycJ7H9euJeTbhaR1V6/hVbXOplcD1iuo1xRMrzswIl0B1krmyzrNNtizeqwayqTLexr6lzSxJknhaXKd8qzgRpDzjm5/reMizoJDjqqKPw4Q9/GFdccQXGjx/Pvm7evHk4//zz8eUvfxkf+chHejbIf1UU7K6xqAJqc0S/SwBBDKfFdJs1W+RmIVKoIlnDqEbPxDUGlH6XadqSDp6qjO1DXDFdR21HQ6asVWCQKIcu0j6z+PfAINHShqpbcjBfnMJ8wXQzh6w1bv55s0y3ephRvZO5WvQ4QkT1cI0jDKd5ACulpoK2RY9SxGiaL0+SNgMe5YaQWpFmzNXZmc5EkqdAZM7Xkky35XT7mW4uRa0KeKa7ZN2XB3kQ013S2FXWasF0E4r0AL9WxRpJGkqvb36d9RyK8wUAg82wmm6K6db3B+XfwUJqyhowwAXyZOCj4zg616p2H9Wfp3POM1k+pdYqeS50AnmRkl7O1l9SLCpdh5t0MswspeGu0svDyghcmW/uYKxyfRE8VTLdKGEqqUFApBU3YqOFqG+tKjZFI0rbTDdXCmaNPSQboVzGjTofZWq9o6ZbFz1lmG4jEzBkLlEZe404kk535T7dldLLqzDd3aSXl2W6CwIijZKKTLdwupVuMIyNRtmSLUX0LA7RiQjQW9DbFMZGMKszzjxrB84CemZbYzDuQeynVnq5q02eB6Q9QNnA0qZYv5nuoLt75JFHMDAw4H3dvvvui3333RerV5fsj1eDBmNwhGye7Yixv093zAiUWSrKxAGpHixmJFddX+rcSVvDaA4YLRlC0suFUxhU013ciyoIR7YhUa+ntmBwbWJEerl6HTXNWTDd2mc6D0RbnMXLdDeaYcaVI1hTqJebgkXF86Wdbp6xo5AEO91K2lEnO0K2LDPQQowBpFpkW9YCq8EY0WaIZbqF2r4yX0PVy2V6aaofRCWdbjVFrQrWVJ9uF9NdNa1TXas0002wvcZaVdM4pYBYqOHZC4jgoBBSEwayq70P9MyWXATZuEwYodjvccbk+4nviK/pLuafd62q4+riebrmPKflUWqtUkx3qtR0s4q9PKPJ13R31MuTXtZ0+5xuRwo7FbwyoT5b4l4LJ4bOIDPBMt3muUo4iTFyLZXWu/9yQd9EuUbJlmGFIJYScOBKl2DYSoYjJTMeOkEiFyOf5uaerQSeidZR1rNx2Gjav0s53e5Mlt7XdFdjuoX9GsXNolVk6S9epG0zTLdCEvmy1CKGxKhc090JgpLrSlw3CdxjmPkLFPaqaMEaMT5EuZpuwh4g56cIEq/fNd1BPH6Iw93N62vQCBIPs97U/ko19XLH62X6Hst0E3XhRAqVmV6usoia2I2SAhwpdTg++MVuGKbbl26mLvpE2cQcRkpRexLL+1WdAzXNWUQOtZY2jg3HPJQbcYRYpliahqeI7gY63a7gC2N4SqZbSdcl+3RHegom5zR6HTJCEE4y3UwZQpH+qAT7csXhYNgHk4kVczhuNMpH6ZWaxLJMd4tMUavKdK8Zp1usAbpPd9W0Tk+bPG6tKsap2jomH4n08qiYB3EEPysl1o2y3i1DRxt3I0w0Eco+VYHpjqPAmtge1nQ7mW7jXkutVapkRehEIJZnCx/Q1dlJVSgtNRzedtAtRhxFaCdmizKPCs8nuN1Xt0y38jcj86KdXk7VdBtCqwo0ewBg16p+dhdaNElcgiU1ynfUtPdgttyAxnRT7aeMtavNX+asMVvH6XPJzE4izkDlXJX35arppuZOpZpuPnCnjpkSQayWXl4tM0vN+ivKikow3VkGiLWqMt1aenlho4m2eJweSxLSMixgbarrMI4i3T5jxukFM38Bo02eozShZ0w3ccaFtBBel9Gz5Plnn30W55xzTq8uVwOgme4s09tkMe/R1csdacUNmkHOzEWlRej01JBGHFlpdOo5N9gsDt00VQ3k8NQ7KUzBppfTxqh6CJMtRKhWPMY1LKhpmzIVVGHX00L4q6kEFOTHUgecmb6j9PHkDGTxzJMkCTusXA5go0iP1j5DSS8vWjDxm7HWPoWA9yAmWSk3000J/YgDMiION06kRzzPRtKUtUpla7o1pls9yIlnThmWct0RzEEIWCXcki1YvHBEpcmMkgBodaJEMEsLhKlrVbk3taabrGc3xYp6DW0exHaNvRyHwRzkxXrPqX1fazVjZ9dQcAlVcm2yVJZPTGO3enkXTK71mfacl2ma5r5UZq1STndW7CmyjIttk5No40ziSOlBrH9GW0ul/fwa6vUqCan1wukOCLppTrc+HyXTDT145WO6tWfJrFXNpoiKoEUSx8V3WrKmW51LlGMUAo3lp+qD2c+MrbVZZDwY9lFuzyWz57vehcVuHcXXdKs2Wkb83TVP3NoTJlzzoFLQo6rTTZADWRVHFAASpYiNablGnt1qG62kOAf0z6HIAFd6eVEz30gifU9JAu1VE8z8BYABxVZvtfiSimIvDO/TrX0WldGoEiXrMXrmdC9cuBCf+tSnenW5GlAMDsr4ar+AeJNIc0sJh5lKL6cZzpYZySbrhdLOa2I0jD7dKnun1t2pLLCqNOwDL6TmTteREeIkhOku53QjbijGq8rMtu+xhVireWuZaYCOWlM1ZY1j+SXT3WiWM66IeZN4me6m0j84jKGnUIWVSh1CakDBgGstw5TWUbbwn83g5Vkm57DGnpWs6W4gLb5zz1p1q5dX25ptBq6aEeODSxm7ek23EnAgA45i3kXsWi3ma5Fe7iqH6DmMjAfBYuW5EYRw1KJ6M2FCsxccLRm5bhBqq8IGx9Kr6MHzdM55IuBYeq0Sz0sy3VHCBhzdytj0Oks6LQO1kit1DGUQnF7eLdNtj1OtT80IttfV5k0POPBrlbIpRIZeONNNryM1xb+qoCPH8rsyIIKZbqVmvmxNN5u2TdpooenlzDzxrG9XmnF3THfZ9HKR9ddQ0ssrOt1xkwzYqqKyCTG31BKhJAkRUivHdFt7CpMG7wUjfhtFQHOg+J6pFqzkeeWBbgOb2Rz2+b6+M93Bd3f//fc7//73v/+968HUMEAxnERUWoNy+JMOs/URtMFh1eo4NvRGbNd0a4qIjQRpHiGJck2QJDLYXRcSWdNdrjclfQgT4kxxA7IvunENCzK9vHD0tZZhLeEkJtqm5Ez5dRzknMJ8kmdtmyZp0JuYNW7+ecvn21GYj430KDVty92n2+00elVpKQNZqb+kQDPdSkDHMF4bhDGQpi25GSaNAVZsjYVhdJv3QDPdrj7dXTLdFmvS2/RyqDXzBrrt063Oea3mmFrr6u+hz1eqs0D/nW4940E1RltZjgEm+0hzYiiWnyqjyTNoLW0MuJhurqUNZdw72cYePE9XXSgloFl6rRLzIE+LPTpmWSkzEFqiT3dipFh35XT7mO6QtOGADChAOhtaenkFpluzB5i1Sq3nBCmyuEqfbiNYrQqnhgZOO9DUyzvtp2ghNfszrayqWJT70e9xzSXajomV82tN9Onuhumu0jKsYk230j61Wnq5cVbHDSBdrTvdik1B2hBqJoyP6SbsEgqmsCXPdJcIqjsIHr4FK/2esJrukjbwes50B58Eu+22G6IoKupSFYjfU2rFNaojiiin28d0izQtwukmW0XRm4Na26ddg2CUE3UziOzDMokjDCNGglSLnqn1wj5wted05FAx0JTUL9KIZA35MOeVUpgXzFqKBM04NMpnGOFqmmfHITZTLBPloOmW6Y6VNHhVYV6LIFPCVExafMIQtV5xFSM9Dyic6YxjukVbF610QWH5jGcTE2UGrdawYsjTQilOiHUXZUWrNO0g55luSr08rriX2kJq1YwYLyTTTcylqGTAogN1zpPpjgFrlWa6iaBlvw52I+OBNDjUcRDMgcz8oZTb1X0O6KwTZrE57pU6W9SOE3rdcoDT3UWbF+ecJ5zu0muVaNEosmeyKJFtB+2a7hCmO9c+Qw22aCnWfXW6HXO61LnQkCJnqrGfyf1VCYQ6yog09XLXuUo4ILJPdzDTbbJ2RWCkevBPbZdGsKaMknMcqU53JwtQCDzCzAR0ZE0QLCsVDMs62TOxaef5nG5qrXL6AZ4+867WUYLhLMV0U+1UAyDWbiLUy4Hq6eWa002cLUminLOqHosoeYkgyjadJSsBa1NvU2gGsxK01fnykky3efYoayahs1K5vTDE6Vb39fZn0an1grha34XUgk+CCRMm4Atf+AIOPvhg8u9//etfccQRR/RsYDUYsZtgpltNLw9IK2b6tRasnbnYi+s2Ep7pFq2j2mxkqvX6lI5qgJBawci7nG4P000dwtr7Y4RsYkIZO48TRQVYZbo7zGwUY5TCQqUu9tGRJscJ/YhnkTSYQ9ZEQMaDGL9QmKeYQzpd1w4WUKjCSsmeupyYC5H+SDPdRWZGewxKlFo5YBqNZjjTIsetrrtApptIUesZ0+2KKPcCDkO/F2mdZPqz5hzE9u8BrcWdnEtEK7m+CalpGQ8m090xONQxU8wBEchzsvyJ2UivDVdLRqobhPp1aUy3s093eLYSB9ecL7prKA5I2bVKrAHBgmWlmG6Reh/bnylbxWXyrOm+pruk012GwfS8Xy0TctV0c2nFiVpuxqxV3QEpghZZEjj3iLGTmRoVa7obSYTVQrndyfopJVUmIdHZC82WqsVccjHdSqBJs2MUmyLPEYPIniGzfBxrVQ12cwrzBMJqutdAejkhpIZS6eUGmUWMQw3oupnugigpa6+a0IMzsZUxgbgBZMNdOd0awaPMrRaZXi7svPAuK+WzPaufJesCgu9ujz32wDPPPINp06aRf1+yZAnJgteoDsrg0DYHR2S7XVNIOMzcZxgROVu9nE+L0dXL6UhY0XqDYrr905DfxIhxaYakeggT7XvMOmu5iTkirUqkm1KYz9TURWVPKlqC+Gu6NUEYGTXVxyQO8iS4ppufB40GrTAvPjNWhNRcB7kvAio24Ixt72MbHLnCSlGgWtpopQsm+0CUGaipVEmjWYHpVtedsWYAcq2SNd1pYYxVgTDIMtdc6wVEiQU1lyoau+papdPLFaMy6hi4WUubj5TwH9drtS9QnG410AfopL2z7ykRyAtJrbcQtO+rhn0xQHXs7Fo1x1URrj2DCjyXXqvE3pgpfbqTjtAXX9Otl/yE9umWZ1WUILiHrjZuQgyLgqtMKyCFlZonajrzMMEcugIlWmq9Y61SZ3diMd38sAGw60hNw3bOXwKUcntwm0wiwBshQxzl5HucfbrVQL6aNp/oTkxTHC2+1GVncEY5n9T2U5717cp4ULOugjNhqzrdudB4UNLLy5x5ubFWiXNTJYmkLalkTarfacIQJWWd7sxoFRcb2knSXi0jlGpkL+jB7hjDeYJmlBpdP6oz3WaJlfrZVCZB7XR38N73vhfLly9n/77lllvi29/+dk8GVaMNJ9MdxSBr+WSaK8V088aX6cy6a7oNYapI6R8oomdG66hU1IqltIHsA6eyrj8PgulWxC28TDfQvjdP5FCyqFpNt8rEFOnQUad1SSvLS0X5NKZbfKeaY5/KgzxJVOEPl3Hlz3hQx69+ZhQnhWiWQ1TGx9T6a7ptxz5VWCkKMv2RbEdnpzI1iDGo702Spj8N3oSaXmquGWatFmr6ivJ9j/p097umO3I4dJRBEgJ1rcpyEtLxVANkjNPtS7Hsm9PdvvckStv7osV0m+MQdY+iE0HUnrOA0aebYLHU6xCIHPsr1Q1CnetarWnfa7qLtExrnMQZWHqtUky3yFaKipZhzYg7W/S9Tf1eW4aQmgi6aSVXVbMqeqFeTin4W+9XHI4O1Hsla7pTh7Ol2QP8WqUckCRKkSvP16t8zZw/SRxw1lCXy3KZ8dGI4yJVmWoZZtQsqwKQ0jYyMx4iY71HChngsA/UQEBQyUpZPQsre8Z0uuk57KztV5zsLAeC4sgVzyu1faoIBFcSUlOdWWMc6tkiyudIpjtSa7od4owBNpvmyFN7SpXnxa4ZQZDFaCI1dAzovTDETok66zlVbWCCwHFpkKxPCC7EeuMb34h3vOMd7N833nhjHHvssaUH8I1vfANbbbUVhoaGsMcee+Dmm292vn7VqlX46Ec/imnTpmFwcBDbbLMNLrvsstKfuy6AbJfim5gU4+aIIAlnq2FsDpZQgrroCKbbPGRlyxHhWBARY1UZ2wepBmluYhqjaW9iqvgN2YbETJMN2cQUZWzhHKjfkSn8ZbW0IZ0BnVVRxx0RLL8WvAhOL3fMA7W1mcoYd+4rTprtvuRQWjuonyeDBXz/ViBAXIU0kNufx/bpJgTetHZ0RF95QGdA1DZvsVKzFZ5e3kkvpZhuZq1Sn+FruebDmurTXTDdRHp55bROZe6QAUfjeRL1f2qaIZmZ0e9ourr/Ju3SGvFVkq0KzSBbFAGRg+WPEj1rwmGwuZiDiAi2ck5339XLHQ5cRGRiyXrs0LVKtM0T+g8ZEmPvc+1tOusEKExsXIxTKg1HXc610LXbKyE15f2qir3sd685gXxwkMp8I2t1NZtC3T/jsP2XaJ/aMmpg1bGGQM2C0wIOruwuQgQRqlNNCOrpQmrtP9ksoBLkUK6n18gytgz1vYdkRKiv0+6Vq+lW7t0A2bnFh4qZWYXTnSDvuDV5F44o6RQqQczEzCiD/p2KoGbDzMyU3wFNEplQz0RNsFjaqwFkiwlzXzP2X2G3pq2QvTDMhQwqe3OUQ61PCD4N9ttvPxx11FE44ogjMHPmzJ58+BVXXIHTTz8d3/jGN7D//vvjm9/8JubMmYMHH3wQW265JfmeN7/5zXj22Wdx6aWX4hWveAWee+45XWVvPQKpWu11uh2MG8VwCmfWUK22IlmO1lx0TXfB3ADqQq7KdBfGrAZPuk6pmm5xf+r9ElB7QFPZCGYNciOOsBq+mm4+fYdSmE9bwxChikai3LsrzciZ8UArzIu5FyeNIpXYmRYPOW4KfgPZfjaFkBrHdBM13Q7Gs0HUgApju4UYA6hQj2akl2r3wKxVqva5TCsOCiH1gb2Ay6GTh3fJtE49u0M4no69j7g3KfynfO8ROV/7XdNd1PY34hir04w2kOW6Ue7dleEkgotR3FEvd+1TnWwPMsgmWtqoWRbF+NrCkwG6Bj0wlLT+tgbixC6tEftB8Fp17FtZlMgsKqBdWjOQ0HX3suQnIZhY6TSmsu5WGNx53ECl1RzsdAcwmM5grP1+tbxJChKSNd10yzBTOMyb7qzYLXkSGPAhWjLq5WTlg39a4ClRWoY5arq1+Ws4zHEcYUDNoDCcblVl3Q6U2gExN9NNlVTxWWkaTKabuVcTITXd1jhdCLFjCOhMd2CGiAozoEusm9Ca7kZSCJIF1XQ77lVrQ6Yx3fw4vXDNX0DJ7ljNv8fMhPVA2MCuPt2qXb0+I5jpfs973oM//elP2HvvvbHddtvhwx/+MG6++eau6ri/8pWv4MQTT8RJJ52EmTNn4oILLsAWW2yBiy66iHz9b37zG/zxj3/Etddei9e97nWYPn069t57b+y3336Vx7A2gzI4wpnuNIhxMw0O+W9nn26TOYytzcDcjCmxK1UZ2wdyE8tzr9NNC6uomQP+zdaEKlAkWHpNBdhwEu1URH/qF9WnW7139bsKr+l2z50iMELUdHOfYRzkLUeqqPp73kC2gx5BNd3QjULpcCT2vKAMurTVPmBS7jvzgVx37udNtbRxMQchsNri9U29nL+3qqrB6pyPqD6nJutC3Jssh+CMrx4IfzlBzAPSgWCYg6CabvX/QfsUFWQTzKyypyjji6PA77EHz9PlwFEBiNJr1RXIixI0DBFJ5UViENr1VcfHDG7pfbrXgvTyCueCqWJPsb2uNm+JWdPN3QsRyDf727uZbuVaRLC6yj6kfl6DuXdXBgT1vAfVaU0whdxcosiDRtLO2BNkN12ywjDdIUJq1ns8NoOjtpes5fWhYmaWaCWbJA1ZBlctvdycr3ZAt810d+YWUdOtMt3tFqyeDhxOprs4F6jWvN053XZNN4CiG0yLWavEe3ywyQAqk6BOL9dw3HHH4ac//SkWLVqECy64AEuXLsVb3vIWTJo0CccddxyuuuoqrFixIviDV69ejbvvvhuHHHKI9vtDDjkEt912G/meX/ziF9hzzz1x3nnnYbPNNsN2222HD33oQ3j55ZeDP3ddgqjl1QxPwUxwLVrUNDeq3ZcB1eBQHWI2vVzbLIoa2SKVrrMojZSVLLLrbosaGb9RIuocG1GGXBw0qsAMozhJHW608RueXi5rTxKG6TbSobn2MmRLoMjc1GJZAqCxUoaYUJCBJj/D3XqLqunmhdQYVVZGNKVoJxXOSgkDmWpPBShMN3FA0kEioqbbaEtWuqbbSC/V7oF53pTQT/G9h32sNQzz+VZsweKDdOiIe4uVZ1cmKKvO+YhwCkMcz0gR/iOZhL4z3YKxy+T3S6bbM0G2JCnuPXIGHMKdbmp/FRlNVHp5I45kHZ41bhM9eJ6pY85TnRtKr1Vib1QDeQkTeDbTcakArtmar2C6bZ2T0gjJUtECzxSDGXIu6PepPsZGXKSXq1oe4r6pNm/kvTvTSYu1ava3DyptUK6vf0clO1BAd6KSOCpKqhwdELQzjzgjG7GtYk/PJT5QagZj/Z1Y/AFyDWrAq0SQ0uV8aSrroXX1FYPEiUIOkHaKD9zZkts2Rcwy3UXJhRBnbP++c22LJPKP0yxpKbJnuqnpprNSTYIsdbTJ89l5Juw5Lp5vsTZc3TbWJ5Q27QYHB3HooYfim9/8Jp555hn88pe/xGabbYZPfOIT2GSTTXD44Yfj1ltv9V5n0aJFSNMUkydP1n4/efJkLFy4kHzPo48+iltuuQV/+ctfcNVVV+GCCy7AlVdeife///3s56xatQpLly7V/ltXQIqHBTPdRJ9uKs2QZbpDnO7CieFaYkg1ZaLfpaqM7YOqri03A/PQpQ4rkul2pZeXY5AohfnMEP6y28v4D0Qf0y3YHqBTjx1ioHkOUEphXk3bco+biPoTsHqSmiA+w8d0U0I/WukCESRqj0Fluo2SgLI9RrV1Z4gXsUw3n6JWmem2nIFqzIEXLiX8KmmFMOa8CGKUZHulYaToHESO+dpzUEw3oZbvUsZ2CmiW2KdcLRmpbhDm2vWuVWpcFdByZHfQe1/JtepwuvOooZ0tGdkmJ5ydVJ1GaSBX7Qkfsna1wLOL6Q4pO9IZfUA4ng6mm2E41dR6fRzMWjTWTZAuBMl0d9enW7v3SLl3l5Kz2nGCYEgH4/bfc0XFnmLkg5huI3uG1KfxOt1u27GM0+2q7Vd/VVYbparTnTSKMrhS6uUlzhaN6db6dCsOMpU9Y67VgLWp2tGNOCaCWRWC6ua+ZhJkgukmxQPD7DwTIXO8Ti8PxD777IPPfvazeOCBB/DAAw/g4IMPxoIFC4Lfb7YRcLUWyLIMURTh+9//Pvbee28ceuih+MpXvoK5c+eybPe5556LDTfcUP63xRZbhN/cCINUFvdunK6absJA1lSr25+TZTkEQSWjlWoUyogk6mkvTMpKZEfPEtUx8oBMg9cOXZXptusU1ZQ17RA2UzCpNkMGpDJ2nJAGsukkBjlCVvSxOMiLljYK29Nho9OOmFDQYeURkaIU5sUmHyeJjK7SNbJ0sMWEJUBkwmEs+JxuqmVYrM6Lzu8oZ1dt89Z+TefgKWkshHYNAGjnv/uabr8Sbi9QqMMTgbwqaYXQ1yqdXq5ng1BGi1gjap9uOk27vzXdasYD2f6IW+9xQE03EKR8K8QEKSOGCuimhvHlXavauLpnut013apQZcm1SqbaCqc71oTUUofTrSljJ4ZCvxHs1tPL+8h0m/2FuWu4amQZFgswnG5FZM7NcCp9uh1rlWL9pBBdx3Zx7r9qMEjaFzozaN6PD0KHIo6AOFbq2R3Oq6tPNwAMdJ6F2l5RE1Iz75VwpMzWXFZrSED/HsmsNM9aLcuOo9gbSBHEKLDtIDmGcplZYq0lCtMdlXJE/WnbKknkDphHhl3N2at+m007F+LiPu1gVvV7tdXLbXuKnfOBNd1WkIgMavQ5IL6WoNLdPf7441i4cCGiKMLkyZNl7+5tttkGZ5xxRtA1NtlkEyRJYrHazz33nMV+C0yZMgWbbbYZNtxwQ/m7mTNnIs9zPPXUU9h2222t95x11lk488wz5c9Lly5dZxzvgo0ooV4uHD2ScbM3Tt3gaLOnLePQbV+DT9eNY1ut1DSkCqab6gHtVy/3b2J0dFcXLPHXVpYRUtOZbiL9MWLSHwNqulXnS6SCqiJyLUUhPVHHH5Je7mO6U4rpVpjDnBh3pLP5LNMtWSnGkida3Ij2PpzTTbERRVTaZugpBk+k1IsU+9IK3KR6uft5U2r6qamlUBJ2RLk/Nd2ulmFaWmEVpjuJEEfCCHexvVRNt6jtawKiK0JoXWMvENnzgNaS0AMIGtNdkeU3EbsCI8TZYup4eNcq0JPn6ep4kJBMd8m1SjwrtaY7imO08hiNKCsMTU0Z2yyZophuJeiWREgilemuyG2EpMkSbK+GMmVHRsAc6Djdkjmkz1XrI4002IgbB1PTrfbpDmO6IyC2z58qXRSsDD0itZ4vBeNqujMgg1aK00r9+gDqvsUy3Zotoz5P1/Muw3QbgU4DvhaXcRwBWV7iHC0fJM7SFInSPpWarwEX6Xy+WdNNk0RkwFxVzleYbin0HGCvmtBrumM0RBlnmW47Jtj52wkkRgmQF8RO+z3i+eg6NOFMt0kGUDXd4Zmv6zJKnQbnn38+tthiC2y99dbYd9998apXvQpbb701tthiC1xwwQWlPnhgYAB77LEHrr/+eu33119/PSuMtv/+++OZZ57BSy+9JH/38MMPI45jbL755uR7BgcHMW7cOO2/dQWJrGvk0xItiDQ3jXHjI+5RHCPNO46x6C+tRCVt1k5lDosN11QrlTVfwumWKWpqjYxgUf2LTEuDFxF3LcpPR3eFQRdHnNOtbyZl0sujuGgZpn5HvJAaf6jabIMybkNhvv3W9v+F4VnO6WYOUOl0299R0mi2+5Kb4+bqg5gIqMXEmiCejRgPW9Mt55Yd0FEZT7UcwhxDbrR5K3polzMWNPVyw2g3QTr/mf63skhMobqKzIEP0qEj9pSqTLe6VsnODebzJFpBye89bjtTANN6q8/p5XGUoykMQWouMcxBm+UnAg5mvVupfcoOalItbUwV2zhkDfQgvdzJdBOdG4QxHbxWSZZV7Cntv4mUypZwrghlbK1UKTIYvLgYZxy1xa5E32/xGaURkjrqdbrLB2PV+ttGHBfMYW6fqyzTbabWE2tVO48iPVOo+E7LlTaoTnNM7K8+CGdCJviJLKqg7C7F6Vbuc6BT063OAzFV9XvlWUAzGOsmEGwbrf13n9Mt5ls4yeNrHSXPuOCa7vJOpNY+NWnI4EbUhSNKrT3RPjVKmqQdoynnJ0TJijqHlDZ5rnttKXa0mj0j11Wl+nX9LOEIssyZ3VEuI48XUlOeb877KesTgp3uT3/60zj77LNxyimn4O6778bTTz+Np556CnfffTdOOeUUnH322fjMZz5T6sPPPPNMXHLJJbjsssvw0EMP4YwzzsATTzyB973vfQDaLPW73vUu+fq3ve1tmDBhAo4//ng8+OCDuOmmm/DhD38YJ5xwAkaNGlXqs9cFFAZHmfTywpgKZdyE8SLYU5rpVlPB9I3RXdOtO90ZVdPdKMd05+QmRvc97GdNd5Q0QCnMm8xsWHq5g+kmUutlWzLwh4SF0JpuhunOCYPC3ow77+OY7gqsVJFeTo/bVdMda5kZaWcMxIFpiN+VbjdTYd3RKWq9ZroD0kurwHFAqgdxcHo+aB2DskJqam2fzMwoocbbNZQASzPW2yayrBSM1HqiIwIrpOb4Xl0tGRtE2rZpQIexjd0HMVy96eOkU1pDZBIFr1VingjHXZTMtMzWUIQzq46TC6SqAnoDopa3snp5CYdZfb12jTLnQmcuKkH3OKIziXw13XwaLHPmaen5oX26Cadb+Y56wXS7OyCYa1exQfJMOq8DkT0P1KBFiOaLySyukZruPPeXpDkyHrRxluwCUiZInGqdXBoVme6As0VhYl2lYUknOzHLRcDBl5nJPxs1k6AR90pIjXagzVJQV013sc7CXEirzIAYt6tkbX1C8N397//+Ly6//HIcddRR2u+nTp2K3XbbDdtttx1OOeUUfOxjHwv+8Le85S1YvHgxzjnnHCxYsAA77bQTrr32WpmuvmDBAjzxxBPy9RtssAGuv/56nHrqqdhzzz0xYcIEvPnNby7t7K8roAwO/8bZMUSUw98brUSCAbSkwaFHuv1Od1vgQT+IzEg4JaSWlEgnEZtYHOV2TXfcaAuUEAZG+T7d/k0sVphud3q5aRQajorrIFfEWayWNoNDMror6rCDDivPPMhkWpHtdCeNAclquoSpfOlmfqVhwkD2qJdTQj96ermesk45QeKexTy1Wm/5UGHd9aem2xh3lUM5AJHDoRMtbfK8LNOt1q+JmmMH6+JwuuOkqaRplzA8u4Vy3cFIN+DdTDcRcHCml/udKVdLRsl0d7pBRHEsDfgihdWTlaKNq3pNd/G5tgGXEIHn0muVelaZvqfIzg2cgQwj1dNqzacG3drXkrW8lZnuEKfbCDxXugY/F6OoSC/XM8h4o7uhthB1pcESNkWCtJ1e7mstSYwbKM7NYAV0A2Z5VB5Q0623+1PWQZ4CiDGQZMCwPg80gSxxr7Le1Xb0zSCHdXZ42qemaYrheDSwwRZAMhZYudK++Q22BOLRwKrV7b+nrfbrAWA4Jd8zKsmw2dgEY5IMK4m/bz42wdJmjtWrVmHlypB1MNj+zObG9BgJLF+xXI4zTTNg1His3GALRANjyDGRGM7a1xi9WftzR23a/rkFOY5szGSsRANIBjCEFJuNTTC2mcvPaKKFzcYm2HgQWLlyJVZvMA0DUYqVK1e2X/PyyuJ5rh4GmuM7Pw+w9yquueEAsHr1Kmwydggrm1tgeNQUNFeuBEZPATZYAgznwc/LfMZ5azU2G5tg0ugYK1euxPCYzbAyz5FlKJ7f0GQgGgLSGFi5EuOaOTYbm2Coc38+bDomxqpVCVqrV7VfP7BJewx5sxj36AlY2Sz5va1BJEm7vSSnORaK4NNg8eLFmDFjBvv37bbbDv/85z9LD+Dkk0/GySefTP5t7ty51u+23357KyV9fUXcEWvRme7A9HJkMr2qSJ1xt4rKJNOtq5dan+tiuiM9+tuQh1cncpzZDl2oWmGKGDFSpe7Ob4RTNd10b8vyxmyUNEAJEuUydVF3uu16FibVDvpBnhDK7VlqMt1ljCsmTRt2exTxvcaqMJUjLV6mQzEbUxLaMowU0KLnr2TA1dIFmV5up3FR6ahcHX6rZFocue6Y5005Na6+pyGwnm+faroL0RPm3qIIrTyvXtMtHOagmm6jHCICtL7yZGu+rjVEaSh7mWS6fawUzHZpdvZMd8FB+zvStDzSFhrxgDX3SjHdVRW6oZYi2X8rxERtIbXgteoQlRKOkGR3qCwq+R2p7KTxmWIsStCtuSaZbkUZu/I1rLnYuR4h2uds8xYVZ7vcm8kzTwlYKJoYucr+uvZfwhbqXU23sFuIucMIz8Wm0521gKSJpmT96XFarD5RxmV+J5YtYyljF+N+6aWX8NRTTyGf/AZg41nA0EbA/Pn2ze/x8faYX8yBl+a3Hfn9v9z+24LFQGTb96/bPMJ+kyZhw1GrMJ+45of3n4A0y7F6yULMXxaw5+ZT25/ZGKTHSCDLUsSdceYLnsXQjkdifvpaDCVjyDGRGB7X/txkoP25270b2HolkG4ix5Hu/3EsR4rhxjhshmU4+6BJGGrE8jN2GjeMsw+ahA0Ggfnz5yN/9RcRIUe6bCWWvTy//X3u/2UAEfDYY8Ck1wP7vwoYGsfe624bDWPrgyZhg8Ec8+fPx1EH7of52Al5Moho/nxg5vuB7VYBw+ODnxc23BfYf0dgcCwwfz4m5CnOPmgSBjv3Mrzff2E+hpEPTSie315nt+fYC8PAi/Nx9HaDOGLrSRifLMP8+f5W0e955VgMpxtgaOVizJ+/BNjsSGDiwdpczPc5BfPz1WgMjg//3tYwRo8ejSlTpmBgYMD/YgbBTvfee++Nz372s5g7d67GvAFtoYDPfe5z2HvvvSsPpIaNQrWachLdTndDrS31pRULR1kw3dJxUtTltSiqma6r9ummD24qvbxQnAx3uptIi3ZZAYaoJn5DRc+76X+bNEApzBfq5Z3vwmRWA4TU1JQ1XUSuc20rvby8cWVCCGgUjn2KWLB1jSZE6yFSSM2sD+LSzWRUP5yVEiwDl15OpT8WWRQE060YOaJbgtnmrXS7GYWpIXUQCFBGoa9GzoeEZU16m16u6hqQ44gjtLI8PK0QtBCNU8/ClV6eNJV+11RGyZpguo3+p4FMt9TyKMnyW0NxtGQ0S1YazYEi0NeZQ961So2rAlwdD8TYm1EqGfnSazUgvdwqrTHrL6GyqLH9mSrTnYj08gzI11BNd2jKcMA1UiPjwRXMpvapKIqUOuYQpjvR9k9VSK00060G2U2F+QCYGXrSUXYoOetMd8N6XdN4Fnp3GPVe+ewkc41Yz0fLzCieZ5pHeOqppzB69GhM3HgKolUvAmMmAWM2sW9+UQvIhoGNtgAGRrfn3aIO47jJdPIcG/zny1i2ahiTxg5h4zG2I5I99xJaWYYtx4/G0EDAOli5DFgaAY1RwPit/K8H0BpejcY/2zZhPnErrPjnEMa0lmB5siHGTNgs6BpYtawdbBCf+88YGF4OjJ0KjNoIADD83Go0kWLV2C2xCk00XlyJUQMJthw/BgDw/LJVGFq+ChuNHsDkcUNIn12FJMqwesMtMTA4BLRWAy+sBhABk7YClo0GXl4MjJ4AbECLRz+3bCWGlq/GxqMHMGncEAaffRZTo0FkjVGIx28FvACg9TIwbvO28x6CZc8CLw8CoyYAYydj6curkby4EqMHEmwxfgxWPZdiEKvx8ugpGLXBxp2BrAKQAeOnA40BJIuX4+XhFFM3HMLYUX4HNFq0HKtaKTbfeBTGDDaBpYPAyiXAmInt/wCsei7DIFbh5VFTMGrsxmH3soaQ5zlWr16N559/HvPnz8e2226LuGpb19AXXnjhhTjkkEMwadIkHHDAAZg8eTKiKMLChQtx0003YXBw8F+GgV5TUA0O5Hk7kh2oQJmUUFGWBkfndS3KCHKol6t9OW0jUlcB1RyjEn26i3EOK0y3wbIQLGmqiFvQxq/xPMsw3bFa020L/RRMt9HShkoFZ2pmVCMcgAw4mG1zKNVvC54sCVNhvtUahthO1X7XEVlr2jFAHD13xf20748ZoyP1nmOMKDaicDgSxem2U9+zHEiiYl6adaJpyVYnetcA91ql0h/LCpSYaJjj7lN6uateWIxjFcqldqpznuojbQUxiHuTmRlKTbczTbvXUOZoEuuBR/mdZBkA/ftRazYjIpAXoq5rDUW0DKNquoluENKRiowUVtdX2IPn6Ww/pfbQztprq/Rade0pMr3cCAqrGRFGzW0cF/sb16cbAJqRcLq7ZLpLtPuyr1G+plsKGkrH0z6vfGUwRWp92fTyDJmaGu7af4l7l06z0q4qeA9HQb7bTDe/D2k6HITTLWq6RdBYDbLG2r2KXxJ2DMN0F063kZnRGdtwNIg8zzFx4kSMevlZII2AwQFgaMi++WZc/H1wqD3+Ruf7HTWKzA5KBlJEaYTm4CCGhgbtvzdXI00zDAwNhTndWAWsiIBmRI+RwHASodmI2ibyqFFIX2piCBFajRhDgddAvrJ9r82k/bkDCZBHwGBTjqPRiNBAhGhoCMibiBoZkkZDfkZzVY5oVY7mwCCGhobQanZePziAwaEhoBW1PyOK29dc1QCGI6DZYO+1fU10nu8Qmo0GhqIIWbOBeGgIGGgA0Mfpxaqk/bmd97ycxu17abbvJW8mGMojZM2B4vk10P6coSGgMYC42UKUtzAwNApDQwGaTAPDWI0UzYGh9utXNtvPY0C590aEIUTIOve6tmHUqFFoNpt4/PHHsXr16spjDHbVd955Zzz88MP47Gc/i3HjxmH+/Pl49NFHMW7cOHz2s5/F3/72N+y4446VBlGDhmZwSMVuX2RbpGmlhdPsq+UV6eWm8aUeqI56Ib0nqZ4eXTDd7fdQbGQSIKQGqIy8WdPNG6JaP0zTUDLvS/1/AIMUJQ2ypQ2MGmS+JYiDMVaNcEJhXtQeZv+fvS8Ps6Qotj9V9/ZsDJs4rLLKLiIiKojghiyKoqCgIIiAKCAKuPzkKT5QFhdERAVRkMH3FFBRVGSRRTZ5imyCwgMc1ofgALLNDNPdt6p+f9zKrMzIiFzq3h5moOP7+Jjuvrcqa8nMiDhxTjhOTYxzJbwHpBrBFCjpdrsNp9vT9zgUNDqcY2pcMKHvZwDpNhFPrYzvtgzrGCi8ctJ0yapGzyL4rKapFleGanVo3nFiQaGWayFbVH26m8QTP3eThehgz1WugiSF093tjOhj8O/rBAXdWabX0ykO0q1KQU0H2X5eNtIdU1ofg3S7z8jqBtFTyVZJIXlRId3uO28j8nbCMXqueuk8XetYbtDdBMwWD1dEuptk90j9/Mu2VIaodl+BoJsLGqVjCNQwfYyK31c5G2kZdKsKvTSku0GQ1cfb9unWc6BDEw5ctYxLBbNoFvXnGqTbfU/TkW6VECPHohoEJHDvVyyq80p7C/l95fmb/5vOH+LzHuoLsZ9vDl6p72bJJ3VOz40jUz9kmT6HeQb37pJxVOQTmg4ij5NeQibdnqRrrdifGq6yujbm6jL753gvxb1f/C/A02QWE2uLbpuWtFMuvfTSOOigg3DQQQcNfOJJC5vlcBQ9TOl2o4NuC+kOKVASh4N1gvTGbKqEG+XlRL28JJuyDoys9j7xLcPscRb8dfmE1DKmNQfgOldRCFLtzOZd3d6Ha2mjhdQiWoLQtm40+Or34+5phXmo1mGtWob5g27FSTdbcXS6I/re+NSgg326Q8EYVwoa4A9zCrNWaa0aSv13W127PgYRv+N6aHvNVK2uHc54pNvgqwbK80MWleAZggWR7k5i0gL2XFWBYtdTVUG5plVZopspdLfh8lvBK229NQFWZR2gKvV74LxL1EGGjS56ke6EJJtPqJLrBqFeQx10h/QXzGsZREjN137KoB7p1oGl4gtHzlVWJ8JGussst6g13FppC/3xa3qeVejUnqSD9qZa0prup5u143QrBe86eZWAdKtAUyeFKS/cVMY22icppLt5pr6Ejz2XTUS7m+dx7y8xWu0Bxm+RqWBZP4mW5X3+az0++h6Y4+xY16reJdmPUZ/N6TpvPt+sYzx38/7RwImYEwSG75u6FClOyhLCMn4MYZPizbQz05CZGUfV/3VmXVXzdxpT67soBsTxyQF1RivwN/+fYk2UXf8oBNBOsqD5VOi5U2tidfrFyviMGsfiG3QPw6LC9vnz5ycdNPXzk8abo1oNRATddcY44zjdQlmx5qzZLcNMRJDndLvl5VXGB19682XKyxV3PWSN4JvgGLEZ4iZzzbfukUpWI8rLOyMaMbKE1AjSLfcolHliVAWctrQpSIllVClisOLBpgAo/jjQd9JjkG7a65eaRmJFB5lDpWwHmVrFVFHkpl4AU5mhTCPdxJFvy+kGzKDbf795TvdgLcN0IL+ION1SwqwNymTNVaY/c2iumkmi/vvqa701QUg3mnfIRbrloFv9yWwRGIfye9YpT0tGs6WN6k2t3j2V8HFUlTkbQm9V3ZueSTR1uHaJJEHWhtNN34OCrH3c3C2NQNNFuo2kW00raALPlvdGr+lN+ynHJoLTTQNqHXQz+6qIdNfoo5Qkssqhm6C777cwlQQJ4wb6a0hyBwow6uUhhB7y/aKcbvUe0O4wMS1FqT/grin8/bTGrU8bQLp1kGVEkUJ0ZYZMniN6Ak/xG8FPXnXVVciyDE/VAs6zf/YbLLfccq0CdxeF1n8wRtYEjD4wPUOG2bNnY9ZGbyTHoAEzcOY5F2D7XffyDKuiX3HORscZtvqYy6yKCy64wBlW5Tnm0cd8BSuttBLWX3kZXHnJ76LP6CL0vhuY5vu8733vw0knnZT0nefTooLuddddF8cffzz++c9/ip+pqgqXXXYZdtppJ5xyyilDG+CL2TiHI169PL1Pd+VFurnycoV059o5LgnPqeFG1YERx79MRrqFBASzWZltcOI43fHl5bkgpKa+qzZsqb2Mr9zRRbrtljYVEROKK0X0oyIN0l3TDJRgHYBOp6sROF8QQ/uJUhukT3eYt+gmdOzy8uZ9VabeBYp0t+3TDTROVmiuTiSn20W6hxt0x3C6gUSk22z3wwae/rnaM+gQeXdEeF8nuLwcjXOtki/Ou+RBujsD8NmphVoy9kgSk4pjte2VnGq+NoNdIyGr6E/Jc5W5V1m9xqkqGU2t0X263bnLK067SYcpmUI4SeCZak77KcaGGnTb5eV6DVfjMMbga/MGmKX1wvsqlEOrCr027x4t207uQAFz/a2vy6N87yDdHb7qbAq5F7Q7TFSfboHT7awpSsXeTNhoC8LStkUEyjQolI6ZEhKaX7j++uvR6XSw4447ej7a//D7370D7r77bngDutAJ9XVwx1DXmrFhqYj+CuXlo6Nj+NKJp+Goz3wyNCptuf49QeRTrpV81v0mOWb9/zvvuRfHfOUrOP3003HdX+/BG9+yXTIq7eZ9mKRGon3pS1/Ccccdh2eeeabV9xe1Re2UV111Fb74xS/imGOOwWabbYYtttgCq666KqZNm4Ynn3wSd9xxB/7nf/4HIyMjOPLII3HggQdO9LhfFGYKaOlWJmZJFmdk86q/XP/NE2xVjTOjSx3N1cPH6e40SHeJHB1w/S7tdlSmMnYnMuguHaQ7LC5UMI5SYahWt+F0a2e22+UV5isbmXXby7hODC1zp8GXbmmjysqJ45nE6RbeHcU91MdWXM8qRzfPDaTbgxgFEJD4Pt1MEiPQp9t0MDoG7x46CO4fxxyaek9VooHy8OP7dDfvcJci3cK4fX26pZZrIXNK1jmncQimE0/CmpIPUtppcLrNPtKhuUorM/yc7gkuL4eJdFP+pTEeRhmbVy9PTw6GWjL219OmGwQtGY7SNRhKebn8zpscOpVUSZ6rLOpn30+H081QskxlbB/S3VUdH2jgmWrmcyt7AMPND5eXJ3C6ScJXVx5oWlFz/1RFk0RxbIJu4X21dA0an6KLAlUnS3z37HEDsFqGDdKnu0H5zTVE0K8hJenaP1Itw8je3sn7fdDFd8nL6SbvfJQfQ9BcxwSEM2IvSjyi50D2N370ox/h0EMPxRlnnIEHH3wQa6yxhvud+l2cPn06ll5xRcx74pHYsznHcPnWxtDMf6jSbOPCKuEqK/qv+rvn//oizJwxA9ts9drgsBq6dVUfRkbkw0aCbn0O45iV+/k59/8fAGCXXXbB3f+ah9FeEV9e7twv7ouBpJBgm266KdZaay385Cc/WSKoz1FI9wYbbICf//znmDNnDj7wgQ/gn//8J37xi1/ghz/8Ia666iqsttpq+OEPf4j7778fBx10EDqdiXNoXkxmBqO96PLyhhvlti7ylxWLnG6qtksWdJPTTTeWriBIQlGpGHN7qYZLLjnxG/P3gwgUmS3DVEsbwC0vjykfk8RZdDk2QfmV4ykiCZwF3gPaeqtHeoHHIIchTnc7VCpQEsx8R3GBO50pzt+zzBXZCVICQmY4vVMiy8s7DO851HItZC6VIeK9aHOeIKc7vbTTmqtWb3pVkuqfqz2iQfD8Id1q3geQbkYZu2OVl/tahoWDqVBLRknLQ0TTOBsK0i2jplmeo1fZycDkucrcq4yMu9Brn7zPsn266TxDM/8dtDfVaNDNWWTlW5vy8ibw7L+PZvKK7XBimIt0k31VRLqLFki37XMAtYYLJ5wasIJQLFLaezbl5fb7NoXw24M9txk/hvoD4prio58E0VAe4fSxo3XAJh1RVP4Kj2H+/Pn42c9+hoMOOgg777wzZs+eLYyhf/Czz/u1U15+7LHHYsUVV8TSSy+NAw44AJ///Oex2Wab6e/uu+++eM973oMTv/09rPLq7bHCelvgkEMOwfh4Tx9jbGwMn/3sZ/GyLXbAUuu+AVtvvQ2uvfpqawyzZ8/G61+5AV6/3qr4yF6744knnjAHyI773PMvwLu339a6OXo8J56IVVZZBZuuuzqO/8Jn0Bvvr0s5gCefegb7HvJpLL/88pjxsldgpw99Avfcc6/3rt5zzz3YdtttMW3aNGy85dtx2TV/Mu8gAOBf//wn9thjD6y24Wuxwivegt33/DDuv/9+ABWO/ub38a59D+uPIc+x4SpNe7KzzjoLG220EaZNm4YNN9wQp556qv7b/fffjyzLcOmFv8b+u78LL5u1HF71qlfhf264Sd+bBx54AO9617uw0sZbY6l134DXvPb1uOiii/Qx7rjjDrzjHe/AzJkzsdJKK2HvvffG448/bl3fu9/9bpxzzjnee7C4WJIU28te9jIcfvjh+NWvfoVbbrkF//u//4vrrrsO3/nOd7DzzjsPRdlt0hozHQ4f18w03RuaLS/nN+YGRVVIt0Kg1QJI+EI0k5tn6NYldepYtHUUbetElbFjzOn1HcHHVqIl5kZu/t5BNBjUlJpWxs67Tksbc1wq0eC0P4kooS7JRq4dZMVrVP26U8rLA4J6VGFe88frZSImiAmVR6vfl5ID4EO6RSE19342AUfHKLVrjkmdFip+p8cZ67BlmX5G3Uj1cpbTHWi5FjLn/k5Q0O1TxgaY6o4IM+dqbgXdJBASqjsqa01pkO6cLVGfuMSwfodgl03rd4lJIln9hbvNGt4clLxLERUMvj7dAJxuEBSxC85V61oGR7o7QqJJzatCI92Jc5WbA6RirKHW8PuspYydMUisEVgrhFs9f71Gp5oVdEvl5ZEJyYS2Y2bbLQDImPLyWPVyrdzu5XR3DbCgstp9AZ7n6oiP1iKKGZAbSLf3/SWm9AUop9vXAUFCxzUoUe8H+j2W5plyOZgkERXYbJKayo8hAbu67+S5V1WFBeMlFoz13P/Gmb+Nl1jQq/jPj/WwYLzAwvECC8YL9u/P1X+fP9aL43UbyO15552HDTbYABtssAE+9KEP4ayzzhKOYScH1CHOO//XOO644/C1r30NN910E9ZYYw2cdtppzrf/8Ic/YM699+EPPz8dZ3/vG5g9ezZmn3O+umH4yEc+guuvvx7nnnoCbrv8PLzvfbvhve9+Jx64bw4qVPjzn/+M/fbbD3vv91Gcd+k1eOM2b8Kxxx5r3nT7//UIr/2fP2OLTTd2EhJ/+MMfMGfOHPzhD3/ASd/7AX7983Nw7k/+S1/rvof/J2669W/4zW9+g/+59HxUVYV3vO+DGB8fB2dlWWLXXXdFp9PBn/70J3z/m8fi/x3XUIArAM89twB7vGcnzJw5E5f86ie47oIzMXPmDOy4444YGx3DZz6+D8466WgAwCOPPIJrbr0bADD7R2fgC1/4Ao477jjceeedOP7443HUUUfh7LPPtsZw4vHH4MMf+wT+8McbsP766+OD+x9cd82ocMghh2B0dBRXnH8Wbr/iZzj2K1/GzJkz9bne9KY3YbPNNsONN96ISy65BP/617+w++67W8d/3etehxtuuAGjo6PsPVicbOLS/ZM2FCvQQRel3CaLWJnlyKGQ7rh+wbr0W3F5aRbbyUrbm0Iny3QpoxYLIcEXbetkIt2xLcNKgvamc7pdLm8brqTF6SYtbTrdbnO/tVNI2ziFs+eOEB0praeO5zCRbp0Y0Uh3HXTnXNCtrpUidnzQGAzGmH7jWSBZoO6zWQLYiPSNADT5hP77MAZD1EaX66tESTpKUmU5UBWa0xmaq5ya/qCcbj3uCRZSywN84UE53V2jWqqQ9CyE6pmyypB3Oo0K+KJGuuv5EuR0G4GaxRf2tktL4XSXQCavry7SbaN8UYmToSLdUtBdr5+KPpA6V9nqmbrqRSov1+9afW6qjO0g3TlK5MhR6vnfCGi1BCOY9lOOtdC7kI8hcbrdEmtzrnKmg24I76seT9a/xwbSXeYZaetYYQp3ngAlqx2nm+gL6Gtn9mp9XoqO29c6Ur8PFez31Bmnp2WYi44LLcN8grBVhed6FTY+4c++WwDgUeZ3/xf4Ttju+PIOmBHs1d085zPPPBMf+tCHAAA77rgj5s2bhyuuuALbbbed/ZWqAjKTL9w/xvd/9N/Yf//98ZGPfARAn/v7+9//HvPmzbO+vvzyy+O73zwBnfmPYsNNNsc73/lOXHH1H/HR92+POffej3POOQcPPvAAXtbpo6uffv07cPElv8evz/sJPv2Fo/Htb38bO+ywAw4+7DN4asEYtnr1K3HbzX/BJRf30dqm7Lwpn37qqafw1FNPY9WVZ4FG3csvvzy++93votPpYPqs1bHt27bHtVdfhSMOPRj33XsffvP7q3H1Rb/ANttsAzz5AH7yneOw+uveiQsuuADvf//7nTt6+eWX484778T999+Pl73sZcAay+D4zx+CnT50qL59l/z6l8jzHGeccQYW/GsOliqfxWmnnISXvXxjXHXVVdj+VatguWWXBgCsvPLKeLKagfGixFdPOB7f/OY3seuuuwIA1l57bdxxxx04/fTT8eEPf1iP4cBDPoVt37YDVl1uOo455hi84hWvwD/ufwgbbroiHnzwQey2227YdKP10EWB1TZbH1OnLwUAOO2007D55pvj+OOP18f60Y9+hNVXXx1333031l9/fQDAaquthtHRUTz66KNYc801nXuwONkkNL2YWyOgRTLEgVLxLopm44r8jkY26aZrBd0ucpgb5eXK4VROiuLpVWQT0G2/YLev8RnlNVNuFYf+WDxR421vHGC/I89ZE9B1LSqF20u9/ze3JQi3IVJuNAm6icK8aptTaSQhxrnyo1K0ZZi6HnXfVRCTecpeY5FumX8ZrgKgRluGma2j8k5XO87m/abVByqJ0ZSswrqeGGuQbtWHzK/szJVQmmhvG4uiMgzBGl0DCeluU9ppIt0mtaYeu9QisLJ1DtSamaskkU+MbQJMzRcadIv8S9jolxZnzCqjPSJZ6xLWKRHpdoTUYI13UfTprqoqiJr21Lqv5zfVzEA9zpTqGft+NolnHummyth6XTZ+37SI7P9OBVtFa6Q7hw5CxPLy2KDbx+m29yuX062Srfy+ypnWeKHl5RW/R+oENYp+tYfB6xSfK01UExHPdn26bb+FE5FzqGBUPJT4RyPKN6Kc7o4wTmZuU3RcPRq3vJysD5yQ2mJt/Qu76577cMMNN+ADH/gAgH415B577IEf/ehHzjdc9Lt/jHvm3IfXve511l/ozwDwile8ovHjMmCVVVbB3Mf75eE333obqqrChhtthJnrbY2Z622NZZddDtddew0eeuB+AMCdd96JrbbaSiPZWYb+z80A7f8DeO655wAA06ZOBX0u1ngAvHTFlfD4Y3P713TPP9DtdvHa12ym/77CS5bDBuu9HHfeeadzbWp8a6yxRj/grsex1Ws2tT9z+624/945WHrppbHSy1+FmettjTXWewUWLlyIOffOgb45xiX9+4nH8X8PPYT9998fM2fO1P8de+yxmDNnjnX8jTZ+Zf3F/v0FgLmP/xuogE9+8pM49thjse0u++A/TzwNt//tb/p7N910E/7whz9Yx99www0BwDrH9OnTAQALFixg78HiZJNI92JuPRJshcvLVZmW2TIs7jvK4XA53Tz/yvw95XDJbTRqR4QoY8eYbmklOEZc4GlmiC2km/bM9QqQ2Nb0Fx/xKsxr9fJWnG7isBOF+YqgPc0x2zvIVGG+JJxuHcTE9ixlrBUqFeqtTNCIsmwKOrvdEVAhNYAToqGOfHrQqAQJRxykW0CDGd7z4Ei3wDX1lZe2sGYOBJDuhNJOa64yqtWhuarWlAI5RoyxLXJOt0q+5HVyzkG63ffZFnxsrr0oesg7HXmt8zzXUEtGEelO4nQPlsQwDy0j3Ta1RifXYucqE3jqALJ+R/QeKNC4RGVsM2GGDroY1/O/S3ROWlneBcpxT9A9DE63/QxL/S7aFU4sp1ugBOiS6pB6OZnLHZSoCBWsvxcy10cpALpdJeU9t9CV6KjSehfll8/Lc7qVb1SEEHluza4qIMscf0BGun1+TIXp3Qx3/McbgGlLuxf/xH3A2DPAsqsDM14CjC0AnrgH6EwBVtyIuVvAPXPnYXS8wJorzMDS09wE7H2Pz8f80R5etvwMTB+JmAd1UuHMc36JXq+H1VZbrRl9VWFkZARPPvkkll9+eeerFOnu/9N+P7ny9JGREav0O8syTRUsywKdTgd//vOfMPWpfpBXztoI40WFfz3X/5o6pl08zo5Mf2KFFVZAlmV48ulnHM73yEhzH/uPvxlPpZMomXWtWhiYMe6azc9WVd9n2nSzV+Nn556DBY8/iBnFs5g/sgKWesnKmLX8MsDow841KQ2jH/7wh3j9619v/ZXqeo3UFQ6Vce7+OlPhgAMOwA477IDf/uR0XH7N9Thh623wzW9+E4ceeijKssS73vUufO1rX3OuQQXvAPDvf/8bADBr1iz2HixONhl0L+YmORxBpDvj1MsDQTfldOvvGxtO1mEXdIXuqfHq7K8SUiMoNFXGjrGQY8Ryug1H0vTpZAGSMGKsEaTuiM3prp+RakcD7RQKm2qCOIsK6NR9o21zhlJentv3V11PI6Tm9iSXyuJFpJty0agxDkfjFAoVESSw7PXGtIuWd0cAGgTDddQ10i0lSiKsqTKJFFJjyh8H7dPt3N+JQrojOd3eHs/ErLnKqFaH5mqDdNdJoq7vfZ14TvcI4XSLDjJsZ79j9HUueuMYmTI1aq2jFmrJWKg1RVj343olD8bppu2T2HE6IpKJc9WbyIsLumOUsXXSDbaQWmukW53fG3SHkO70fYGu4RnTBcDX5g1oEo+i5ojwPnezApUkeuqMmyaqW7y/xBxNDZ1wkPdqh1LlcLrte0HXeJF+pq6x0xXVy4NCaubaVwdwM6Z0AK7Me0oHqHJgJO//Xf27k/OfBzC920EGYKkpI2zp+Iwp/bFPG+mIQSG1Xq+HH//iQnzzm9/E9ttvb/1tt912w09+8hN84hOfsK6rb5n1v/VevhZuuOEG7L333vqjN954o3DWpvTbtFe/ahMURYHH5s7FmzfoK6dXq6yH0V6J4l/PokKFjTfeGH/605+wz8HNMP70J0OojPY9zzJMmTIFG2+4Ae64+16QS+RGpVtzbbjBeuj1erjhplvxpp3WAZDhiX8/hbv/MQcbbcQnRjbeeGM8+OCD+Oc//4lVV10VqCr8z023GeeosNErX4XLLrwAK664IvKlc8wsnsa8KbMw86UvA3qjwFw76K4ArDBrRay22mq49957sddecq9x+3tkLtb3ZvXVV8fH9n4fDt5nN3zuWz/FD3/4Qxx66KHYfPPNcf7552Ottdbyaj/97W9/w8te9jK89KUvjVkSbPMAAQAASURBVBrH82mT5eWLuTVcXspr5B+dyqbmKJsSLcK7dc5BubzU8dccplptl0EOmhKq3DqGlP3t6T7T8a+gCtxdITU5u2tuxGx7jhguFLFGGXuEV5ivs5GuU6hKGt1yZ6nEr+F08yh/1Sro5p1AJzGigm8VxHDCVASFVteYC85YsJWU1Zu2FsapHZ5MSs6oljaVqqIw9QJIZYYu3+7/qkcqHlSZaSuURJVIOurl/Lg5oZ8i0Oc8ZM39JeXIQ+Z0d3RAx79LyervIHM1zzFeUTqJf64WKujO6PvKtN4aBH0MGKUZOEgsEyiZjriZyOslrHWmxbRk1HtLj9fyiKr2YPjpKVaQYNY3Ts3pTp2rDO2oWVNqBIZU+dCAzlXGds+pxqmF1AjC2cpC63ro/rcIuqU+3SavWX1Gam1IxcOcfZWuT6Trio10x5WXBxHkCKPHyMjeAsBDBYN9TfX4OgT1d+cZGafF5a8TYgTEUP8vFCdEbJ9qrvt8YKktI59z/+CYDgpDh4wubc9w4eXX4smnn8H++++PTTbZxPrvfe97H84880wyhoocoX/Wg/bbC2eeeSbOPvts3HPPPTj22GNx2223CcE/GV/9kfVfvg722msvfOQj++GXF12Bex94GDfeeCO++Y2v49orf6/Loy+55BJ8/zvfwv33/gNnfP80XHLJJe6RiZDaDtu9FdfdcKuDdFujIn9bd521sMsOb8ZBh38e1113Hf76tzvxoU9+EautsjJ22WUX9hjbbbcdNthgA+yzzz7461//imv/5wZ84Wvfsz7zjve+Hy9ZYQXssssu+OOf/oL7HnwY1/3xf/CpT30K//fQQ/ZNMS7lC0d9CSeccAK+/e1v4+6778btt9+Os846CyeddBJ3O5nXoMJhhx2GSy+9FPc/+DBuvv1OXHX1VTqBcMghh+Df//43PvjBD+KGG27Avffei9///vfYb7/9GsotgGuvvdZJ0Cyulhx0X3LJJbjuuuv0z9/73vew2WabYc8998STTz451MFNWrNpNQ6Hv5xP8fS6KJrAJ7KsWCMe1PGPcPi6hL/mKnqqjdtW3+4loAAyGiEHnvIGRzer+PJyHXB0u2xLGyrSIyPd4TJtjVyShIPbpzuM0KcG3VpIrXZuFc+WFVKLRbpjUSljHOGWYSPWWHpGv+a+kJrxvYrvnazfKaX+3wIlKYjTHZqrXFATarkWspier8OwEF+4DZ+SztVGQKumogTmaqO2r4Ju1Xpr0ZaXF4lIt6mM3c1zi7JSjMevdabFtGSk3SDouh+HdA92PymCzJ6ClMEnz1UO6aYl1c7eQte1es3P+glFrmKHJlsGVi8HwkmzaKS7Bae7vq8cTcPX5g1w/YHY8nLVdYVr6yiPu35Gzvsb0eubmLP+KpTfTBYQrQ4JHddIN/y+kVidZByjNdLNtgyT9pb6902UWP/aF3T7763uGBYdc2c485wLsN0bX49ll13W+fNuu+2GW2+9FTfffLMxCBow90/6wfe+C0ceeSQ+85nPYPPNN8d9992HfffdF9OmTeMuRH2Z/L/CWWedhb0+tBc+/eVvYcM3vRfvfve78Ze/3ICVV1kNFYAtt9wSZ5xxBn58xvexxw7b4qorL8cXv/hFZnyVdeiP7rcPLrryj3j6mWci7kvzv7NOOhqvftUrsfPOO2Or7XdBVVW46Bc/tsrSTcvzHL/61a8wOjqK173udTjg8C/guP93iDW86dNn4FcX/R5rrLEG9tr/YGz05t1w8Kc+g+eeew7LLLO0dV/N27X//gfgjDPOwOzZs/HKV74Sb3rTmzB79mysvfba7PiZmBtFUeCQQw7BK96yK3bc6xNYf/31dduxVVddFX/84x9RFAV22GEHbLLJJvjUpz6FZZddVlfDLVy4EL/61a/w0Y9+NHwfFwNL3ik/+9nP6vr622+/HZ/+9KdxxBFH4Morr8QRRxyBs846a+iDfDFbkMdMP0/a1aR8RyNGDqdbEDAyg27iYLjq5Xa5E1XGjrGGe05Qf0msxRiHygx38wyjaBLt8rVFcCXrDdlRmK94TnfpBN3qmbobeZM9z4VrV2hP174HXufKL+xFFeYrgnRnXlVlASUhFnSkHIdjquZfZsK4GzTCLosHgG5nBKga/QCUBZB3REVpVWLfCZXBM5ZaXs4FCyFnNmQxojzDsE5VeJWxuzS5FWF0rvad1PEIpJuWl9cJNv2+xq+FwzD1HnQU0i2296nXSsNp7KuXN0GaW+Gk1imms4Q5BqNnuVSWR7tBSH26JzLoNukH0jtfZHldBk/Wvti5qsdW9dfAPNfIpVo7aMIxtK6xnO6sSXgDLuWqlYU0GYJ6F+n7glPppsvLm3kUavPWJZVvsUF3jtK6x0VZyc81ID7aKvFH+dmZSjj07HMCThWEW9XX/w6lGQTHaSWJC2tctE+3rpKS/BjrvQndhybQ7P8vFKQ3Hw1Vjsc/gQy/PfvbzfnJgTfffHML/a2qCgueeQKY9xT23mNXfPTwL2DBM//WZz3qqKNw1FFH6c+//e1vx7rrrqt/1r2/n/mnvpCTTz4ZeOYRYN6jQM0j/9JRX8SxB78PRZWhs9pmGOuV+N9Hn9HXtd9+++HN79od80Z7WOMlM7DcjCk4aM+dgWoBpPu54QbrY+fttsGpP/oJjjx2c3s8xn373NEnYI2XzKi/WWG55ZbBmaeejGkrrA48/TAwfy6w1Ireu7r++uvj2muv7f/w2N3A+HxUC54Epi+Hh5/qi7qtuNIqOPvsszFv7v2Y2XsS80ZWwMxZawDjzwGP/RPv2emtzb03nvuee+6JPffckz3vWmuthaqq8PBTz+GJeaOoKmC55ZZDNf9x4KkHAVT4zne+0+ekP3IrAKA36xXojjQ6JOuttx5++ctfitd25pln4vWvfz223HJL7z1YXCx5p7zvvvuw8cYbAwDOP/987Lzzzjj++ONx88034x3veMfQB/hiN8nhEJHuiqBtEd9RZXoSty8O6S77WSsnm0vEWEo7MEopvWvH6ZZKzoTAKIAMOsrYMBE5inQrTjdBNOn9i9jIK10KWjucxPEcDqfbvn+6F7jiptfIIS9MxaMk1MKcbre0LtgyTFdRKKTbQPmMdjTNMacw6KMtpMb10A6Zi3T777ejao8hIN2LnNM9EUi3qu5Q65JCuv1z1RH+64aTRBNhTbWRjXRLSsNUpCvvdPptz7JKFtAMIJgxLRkLsp4GETjOBhRSM+eX9MpL7byi5ypdU/Ipeg1TiUStZ0GDwvocEoraFyGqkOeZfu9GNNKtEM4BWHyhSpXQ/Y/o5x66Vk5AM9ilgqC7IU53lfe5wV0UqAw0dwye94/QdxyudIvEqSrX1tdO+ezmfRRET+ncbAAJu52jWFFi7Vf1ntYa6Waeu1gLToJu/Xv+4+YnZexcgjjFL5CjR+yDSshMg9T9fyx47jmcetJJ2GGHHdDpdHDOOefg8ssvx2WXXcYdxB4AvRckYG7+bCQApIsR1NWBDN846lP4zRV/gmg6uM3Ir4VxRhnJlBiq6+y4ybVXVRWscKDmYuTkZ/MeRXL/lY2MjOA73/lO0neeT0veKadMmaJl2S+//HLss88+AICXvOQleCamTGLSkqxxOCjaI5WXK7TNLCsi/CliGi1VzitRAXU44Tr7XupspEKT3Gxu/VHzOwDKkmzKEdYgsfV4pBZCKiCtKqcFiBMMUE5cwJktip6eNIp7qVvvkCBRXbPT0sbJ+ptBt12mrcZNFeap4zmMoJteu1YxV0i3aoHmaxmmuWeBPt2hlmHGODS3Wwjwmv7hKiHR/9541cFInrMcOfoeaKEcdSymh3bINKdbzb2AQ8yKMQWc2ZCJCR5jrg7D1HyXlLFTkxbcXG1EJGnJb/08dUBRrym6MqOmQ3B9ugNr4TCsSb7UiQn9LvHJGK7EuoccU1C4Pcoj53thUCyklox6b3HW/cwat7daIUBZCZkZvElCS0rwrSr4dT84VxnksEG666CP7i0iX7hGGs2WVlWFHE3QrdbHBukeJOgOrOvRa3r8vuAit3WytV7XzLkqtgyj107mKn2fiypHF9Dq5UBzj2VON490u+9v/Bru0uIIym8F3dL94oNunUQMzTPdKq7S51P7qkrUOkKVjo+m7rdx7THINeCWl/s43foj/Gd0XOc/o/kN++AxMTcZpwr0cwAXXXQRjj32WIyOjmKDDTbA+eef7/b5NgconE/9WQX2ZqirlcMp6p/pD9hHMZIDa75sVRx6wIc810a+It7JFkG3CqLJOdxnKZczxHoTbmrADuyrJo0QLbin7MADD0z6/PNtyUH3G9/4RhxxxBHYeuutccMNN+C8884DANx9991NH7hJG5pphVkiIiM5OY6Yk/UdCeG0A7owp9sMYvrqmirQ6GkhtUikO4nTLZWoCeWmxjrUIN1U1IgKkAScWSPoVlxJzbfW6uUhpJuU/Pk28g4JugnK7wTdvtZQwYoHihz2rOvrsMghERyi5XnEgvxLU+yvHkdexTmWVEhNtY7iOHI06Na9fwklIA3plsrL+XecS0BMGKcb0HN1GNYIqUlIt0pExd0/bq7qBJ7As3U53Xb1TB5Bh5gIo+9BCJXixMT6xygc8bBYwceYlozNmlKv3W043aEqlIDF9KV3kW5V5RM5V5n57yDdTnk5v67pkmCjrLooK4x03Oeu3rvBhNSGxOk2u0EEjkGvteF0y/sqNZqEl8vL6/ubddCFjXR3mJaK3nHTSo0Bqm3UMRrldjJu87z0Haac7oDeDYvI512YqvUU6ZboUc7aaKquB4XU/Ogub1XwE/1PtYK648wBkvvHmD59Ki6//PLEg2T2/wW0138EFczaQa17jDBKTYXUmm8QpLtNzC0NS6QZuF+Pjo8p6O9p5Za1eQeWIEtOwX73u99Ft9vFL37xC5x22mm6j97FF1+MHXfccegDfLGbVFqXhHQnBltin266oBt/U0F+STYWynNSgRFVxo6xygk8/U641Y6mI2zEKWVZgKWM3SDdNjeSlkMH+3QzGzktWaNBt0a8dZ/uGO5eiP9Hke5x69wxwlQD9+nOMuf+qPspBXi0pU1BBLWcwBONo6Puc0aQF4XUJ7W8MkQMzXO14nQLXMmQiaJ9gB/tSjBTGVtCUVOTFtxcdTs3+OcqFRdUKPyi5nTTNbhRGqZrjlte3nFQfonT7V+nzJaMkuo/FVJr16d7sPup7omvsqNZ+1RSM3GuMnMgc4Lu+jOBPt20vBdo7g+tcOjSwLONRSPdAU434AncSYKBoPoZEVKLafOWKqSmuN999fL6Pobev1A1QgtdCaldmi6tp+1TYb7DcUJqdJ6x10mOQXn2jkhcIv2ENxoEBoJ0uKGqc8RwXCl8IeFLGi21R5G0g9LIkwzc6cVN+lz3/+ZE/+TY+svk3J5hka+4SHfqDTY+m9H7RcfFn8uqBI+8y+7nCNI9QHn5kmbJO+Uaa6yBCy+80Pn9t771raEMaNJsow6HRhaE7Lla2HlHM1BeTrh9ovo5hxyqjaWyW4bpjDFpO6K4iqXQxow19VlRjMUOPClX0hxP2z7djjI2jM20xzt0zuavnl3URl4vdLqljf2dsg2nW0Je6vtLFeZ1yzC277EdDOgsvrBwOqX2nOXd/ljrY2uHJ5AsaBI6fZRPI0xZ1r/mqmjeV1qSSubIIEh3Tjndwv3WqKax4ehkS8uNx6FPMKX1g1qvNw5VVC6V/Kdyurm52iDdArVGCrpJi7sOyqYEcMBy6BjTQm4i0k3njK2MDTSlqIWoXxFYpyJaMtIkJtXhiON0D3Y/9bVHBN2ls+5HzlUm8MxpeTlNWpLEKa0CMINNtVbTpFtnmEJqQU53IJGqjsHNV0p3oJzuWthP3TMzhhWD7vrd74EPRJ37q/nwhS7h1fumlEwh70ErTQJitEIvz+1rt8q4SQJZ57a0n1K/B7DfD/ddYpJGZH63RrrN9yaE1mbkcxGS49GHDB7Jd/DgB62zZcmRvvFZMSAm5/B8xN26SRKjBR+bHtIoxq5/kXCtDkebnCNT1yglX6yoO86cYcpoemp5+ZJmyUj3zTffjNtvv13//Otf/xrvec978B//8R8YGxvzfHPS2pjscPDObo84fJwyNjXqcDg9tikPkkO6dVbf3oj0MQgaWWml4fTyclHJ3ckOGwiSsxHX1+Twwv0Z4pJBumk1QnukO9M7Ny1Zo73UUxF69lrpn0mJuuZ0EyE1H3Ko3x0BqdVItw9BJtfSOMhCSwzybjWto4zljRyTOi0ZuY4maExASWiVSWCudomzZbaOalte7iR4JgDptqs9/EF3bNKCLbGmCcdAkk29r5piU8/PPKtQOi0CJy7opu9Bx0GlJC5q874WZE1JRbKUBoivJSPtiECrLKLm6oD3MyTIBTTrq9ozkudqljkJW8VPVmJ7au2T9hZHkNNwDNVcowlvTbmaUE53oLw/Zv4LKuAO2quuJwbpFoXUBM581bw/qsd3sOWXo7pO398WiVPn2ol4KLOmO/OX7jWw5yL9PI902/erJP5Al77zNDGtx2DOiRByLUXO4b1IRDwzFx31H6jFvkejRh00DmBCqb0TM6JBuJ38Q7BcPxww03LsDNI42icYNELfDFwahDPcxJibOZf9+z4DZjLotuxjH/sY7r77bgDAvffeiw984AOYMWMGfv7zn+Nzn/vc0Af4YjfqcAQ53ZWdbeeUsalph0OXj9kLvLPRWOiZyubyG4srSFIHckQZO8Yqiu4EeI42emaXnBW6ZRg5BkWhiVnK2B3FR7PLYKlIj9PSJqDkan5Wq+RmvBPecLr9yJd0HtMo716XspM+3TZ1wd7sHT0AYtpZ8O3CxOGg/EvHSAlgwSV0SELBceiENm8J1eUOwhnP6a6RkMp9X1OtaU9X/8JyulNKDWWLUcZ2nMKAcXOVlj83lQO8c+sg3Wa/615c94dhGK14cN75ALJoHkMMugOq1KWhayAZpaxQIbuouTrg/aToun+c/paM3rkqJfJIy8VM2FsoIp/nmVZbV+8uVa13eM1tbFicbkDc05z3kVS65TrwrK+HmavUOiQJ7yaayb5h3CMlRMdVAkWNW7+/tsJ8jLmc7nouexKpkviaVF5OkXH2Ookv0yMl7K4fQ5Ivao20RCRD94AGgQEY2/ioSBNvPhk4t28cfmtGS4LZlHM6F2IfoyKBp3W5TkxNg2rywYx+zjfOyv6K/r10jvbmxtwV+4E2p3IPSZ5xRf78ArZkz+7uu+/GZpttBgD4+c9/jm233RY//elPMXv2bJx//vnDHt+L3nRQKvVrJdYj2XaOL+xYMqdbqWvC2FhsB8PldKuAbhBOd50ckBIQDqe7mcJqGE7/4FSuZH3ucSMz7yDdijfYEcofKUoVsZHLCYe4clPrfIHkC0g1gu797uN0q7L4AHIVVXbsOMj1/4Wgm7a0KbnSWnLPZaS7Q8aZgHRXCuEU3i1itPzRQnsH5nRTJVwMDenmqj3kccRto9xcpX2kQ3PVeV8NFH5RBt09Gnypd4lyuj1zhupEtBF8BOAV8WoUu/l1f5H06Y5AuitS7dVqrlL0USfy+u9vs/bxQSE3zi4pL272XsXprgOmahhI9xCC7uAxbGqYVtd2kG53rlLTdDMx6CZIt7lWK58i9P4584jXJAACiSPDHE53LiDdxryS1csV0l2X2lcZ+byNdDtCasYxZJE4qfWpZ31IFlLjLaZ1VBudr2QiuNPOqw3SHUKhFcJsn8MepR0g09+zMHloVE5SgyLdAyQYaHk5jbql5IvOHcgdJ6g574HAmR+wPmGJsOTdoKqaUr3LL79c9+ZeffXV8fjjjw93dJNmOBxxJatNeTnDF5bKioWgW6MPHGJHAjSVnR7XbWhIxlgF3fXnaClolDklaqSENrP/bm5UanFw2ssEAndqShXYdBKoCjDlCzrnDPDbgMZB10E3cZC1mJBT0uZBM6ORblWuW/+/TnaoJEInq1AF2h9JyFUUV5q8j1mgZRicoJuUNgLNfVIOnRLrUkG3fgZ1YiHEJ2SsoHMvUM5PW9pwvOZUY0syY6gHCWYh3cIz6VLxsIBxc1UlTXSSTWoRSMQZ1VwxkzQ9SZBsAkxVG6n3wOVfEpSPamgAWutCJzic8tEA0k16lrOfIVoe1LEPtmwyzz8w0i2/703iWc0rte4nzFWyPma0z3ygZRg3TrWkqPumkm6Ky+0Enm0s1Gc7xKnP3WDWPYagy0FKrFWiIqbNm658q8j76pRp938/XhnHUcnWaE43P24r6I5Guvk+3XlFE6nN/Xb7dNvX2iGicnSe5UbQrYMPAUCg33E53cSPqcxEVGpgE4CxDQt9IoVynI5UU0R2kECUDMEDwWYkknTvrhC8Oki4PE76l+bK6B1PucH2OJpvuni6PT71+cr8eqTRc5GxkKTGC9mSd4MtttgCxx57LP7rv/4LV199Nd75zncCAO677z6stNJKQx/gi92kVlGSk1M6QXcE0i0s8C7SzQTdhLdUwN4MdMswxbtVqBRRxo6xipZ+B9BezlFqVKvpZhWHGJeFWzJIkW6dFe/Y5Y8u0s2jKuZnuyToFgP1IQTdVGFel+0SjiwAUeCJ9iCl1gaVyqmD7AxbOUaqzNhuHcUd0wmEdKLErk5IEeFxE17++03PwfVrTjUWYSYJskFNt5jyKGMHS0OJcXNVLC8X5qqLdDfvq6gCPgGmky+ayuBHpbg5I9OKyHyv+HkU05KR9qZ2xJqCLZuM3w+KdHsqO0qacKznedJcJQlb9WwaTrcqL+fnbqnvTfO+0wSXmv9q/dcI5yIRUvPc/+AxpGtV70FNkyJIt2+N6jhCav5KrdLU9E1Fumk1AulUYo45ZFLCwVderqaBfodpVZXI6XbH6VLQ3ESH+f+germq3LDW4QDSTUp+pc9bR5QOOUgQFb312kGkOmcW+f37778f2Qrr4Na/3QVkGa666ipkS70UTz39rBFQu0FhEwArtJb8wQn+05MDf77+Wrxq9eXx9FNP14cmSHd9siuvvh4bbrhho13iM9oCrCJBtDRuJ0jv21prrYWTTz7Ze8rmkP1v/+CMs7D6FjshX3kTnHzyyY06/PMYc3/mM5/BJz/5yQk/T3LQffLJJ+Pmm2/GJz7xCXzhC1/AuuuuCwD4xS9+gTe84Q1DH+CL3ajDEcpsq7LnDi1xBSCpKFOHQ2di1SbCbexC0K3O7wiSUDSSKGNHWaBEzSnJYtrROEqh4jH4AMVRxjb+XRFOtyp7dlrahK4DbjacKszrEsuMv3Z+8IGgmyRGHOTQCGJ6vfHa27CvKczpThFSsx1kCemmbV1o6yj7mNShIy3D1DMLtTZjTCNdjoMW4nS7XMm26uX6uiwl3CEj3REoalAEiRg3V+WEI61KUZUZdiKvwyLdAWRwCDZOyoxDSsMsp1t3MYhb66jF0HfcCqcasSNBSxBpBFrfz1CLQaAp/W6E1FrMVSGRp9cUcU22qxE61l5iP9ce0VOhHT1aWWLAPIxj0IR5U15er1MRbd6chINDqSLVHhbSTeeNlPSx3wNaoccpzIfMoVg4LcPc++0i3ZTKoMrL7feUQ+SlxLz7HfLOO/QTYz5WFaxQSXxsUpAoBN3WIfnPNHF8AhKbZXjz+z6Kwz79GedPF1xwgVtdIbT7cttrRZ0cb3jDG/DIvXdi2WVmoom63WNJIXMmIsb0m2GkWy42sK/1c8d8DV/4wheQJ2nBZPxP9P7R8cUXQDjHrgA888wz+MThn8H/O+TDePjWK3HggQcyGYtFb5/73Odw1lln4b777pvQ8ySnpzfddFNLvVzZN77xDXQ6E+fIvFhN7k3N32unxFVvtJldamaaKiuuyAKvZhW3sVPkgJTSuRnjOnAjythVQsswB+0NcrrJZghf+5648vKS9oAGHIV55ZyoLLmLdKtSZ1lwq0f4aSoxosTuqJhQ0LGqKjgq9NSUmBBxMNS5u1SYasqU5rsEbZCcaFqSyX/IrmjQSLdUpq2SBfV9Z0trqdNNSmfzNugZMbdzgH+uNiXu9fdVAiCDt4WSz3ik225hM6ix6vDE6P0NGTdXVflzWVBHnX/nFQKv5mOW5+hVObpZaRxDJasmbq+i5eUhVIrjC2ukO3Ktc8YQ0ZKRUlaktkRBpBFofT9jWuRVimag5m6buUr2DhUIqY4MzR7IU5c4RJ6eV80HvV4RylUrixVS893/xKDbUQEnSLeqXvGtUU15eUhIrT5nBT1X05FuO1ngU5gPmVuhRxKppMLM6jihzkdQ/ZwkIGiHD6trgTqYkBBLRrqVxSDdzmdD0VVMIO98MsIiglHPOSj/Oc0yTJkyBSuvvDLw5H3NMbigMOv/mXZYc26XdD8jtnjKmeeQ7uv/8lfcc+/9eP/73x8+IDMOB6AnH2tOxX8+xaoKePDBBzE+Po53vm0brLLiS4EZM7BwwTz2lIvSVlxxRWy//fb4/ve/j6997WsTdp5WKdinnnoKZ5xxBo488kj8+9//BgDccccdmDt37lAHN2muYxQqJ1NIc06F1KIy4QrpJg5wFNJtZ3PpxpITp4aWgkYZ4RyLTrhCPJmyTd2CSysJp5VpN8rYBqeblIJqp5D06dbnDCDdVeW2jqrIuLRYm1NqKzlnJrefv+eaAlApCoBKjNTXQcvLGeoCFzyZphFQr3o5RQr85eX6PtN3ywq61f2x30vltEi91ZOC7souLw3NPQnpbsvnNo/JK+EOB+kuirAydmqPXLbEWjueUsswEnTr99WoQtG9vhedkNq4Dr5sB54q5VNlbFM8z0X5BT679Ew5XQNiVJyRIoXBuRpDXQpYHNJtj7PVXA1RVvTewq/JfqTbLqVWe6Faj4YipCZRQyjXnz2GvfY5VtrJWIc/3FVBt1qnSEtRxvS1O0i3fH/1u6rK80PPVWh1ZvKe1RBjaS6lk3CgQmr2/WY7TpD3tSkvtxORLCJPfQTCo6fouNSG0J6PLhbLmtijmreY1lGtWmYn2NFHH403vHkH/NcvLsQrXrstll12Wez1oX3w7Lz5ekyXXHIJ3vjGN2K55ZbDCiusgJ133hlz5swRBox+efmM5a3y8rPO/jFW32InvOTlm+O9730vTjrpJGy10ZrWOHZ9+9b47fnnYoP1Xo5ll10W+37sUDw7b76++Kos8fVTZ2OdV22N6dOn41Wbvw6/uPBy6+ZcdNFFWH/99TF9+nS85S1vwT8ferA/LOfRNL849zeXYvs3vxHTpk2zxrPZZpvhv/7rv7DWWmth2WWXxQc+8AE8++yz+sGNjo7hk5/8JF630dp47borY8ft3oK//OUvnrL4DHPnzsWu79kFr1t3FWy/5ab4yU9+4tzCp59+GgceeCBWXHFFLLPMMnjrW9+Kv/+tD9T+4pz/xitf+UoAwDpbvQvZKq/E/fffD6DCb39/NV6/4wcwbdo0rLPOOjjmmGPQ6zV7TJZlOOOMM/De974XM2bMwHrrrYff/OY3+u9PPvkk9tprL8yaNQvTp0/Heuuth7POOkv//eGHH8Yee+yB5ZdfHiussAJ22WWX+tyNvfvd78Y555zjXNMwLXk3uO2227Deeuvha1/7Gk488UQ89dRTAIBf/epXOPLII4c9vhe9BVWriVkbf1nGOZlkgZc53RzSbQdGapN1SibJ5tUm6BbvhdM2i3LPTB6ev9QzVqDIVMamLW1yUl6eyulmW7JQcTGKosYK7pjnp+YkNexqBIsjOz7OHnNC1Mt1T12pT7fNu2vK4n19uu0y92Bv9QhL53TXYllV39kLlebHWIwS7qCm+cKeuZsqRMfNVVpBEkR7FS0ia+63SgyUvV5d7THxnG5KM3BRKT5Y6HLXHuSz+5ODvvWVdoNoKpz6f09Culvezx4JcmLG2WquCpVZqu2j1rOQ+kgziLyjyaCee2Wf43nt0x11DB7pVtead/oVTZ1K3ledU2pUXKG//qC7KKvmPmmkO9SnW6AAWM8ojebiVujZKL90rwAjaSZxuktSDZapMRpBt0BBc/jqNKkprQ/KTDh2fAEwNp/57zlg/Lnm5/H5/Z97z7Gfr8bmIxtfgEw83nxk4/3PYHxBPHKdSK267/4HcMGlV+Hn/3UmLrzwQlx77bX46nfPQpb1AYz58+fjiCOOwF/+8hdcccUVyPMc733vewX+swNT449//CM+efhn8KkDPog/XfZLvP3tb8dxxx3n3NqHHrgff7j0IvzqV7/GhRdeiOuu/xO++t0m6PvisV/DWef9BqeddDz+/ve/4/BPfRIf+uQXcfX1f+l//6GHsOuuu+Id73gHbr31VhxwwAH41gnHWKPSSLcB61/zp5uxxWabOFcyZ84cXHDBBbjwwgtx4YUX4uqrr8ZXv/pV/ffPHXkkzj//fHz9O6fj3Iuuwjovfzl22GEH/PvJp6xzmSj/vvvuiwcfuB8/PO/XOPmHP8app55qga1VVeGd73wnHn30UVx00UW46aabsPnmm+O979wRTz/5JN75nt1w+eWXAwBu+N1/4ZG/XonVV18dl112GT70yaNwyH574o477sDpp5+O2bNnW/cZAI455hjsvvvuuO222/COd7wDe+21lwZ+jzrqKNxxxx24+OKLceedd+K0007DS1/6UgDAggUL8Ja3vAUzZ87ENddcg+uuuw4zZ87EjjvuiLGxMX38173udXjooYfwwAMPOPdzWJa8Ux5xxBH4yEc+gq9//etYeuml9e932mkn7LnnnkMd3KQZXN7IoHvczKpXRWTQrRyOeoFXzr/D6faU65JyMofTTdqOUBQ1ygJohFNeznK61WYltdrwO7OlRrqbe0Fb2jR9pW113YKULPqy/nq8HYp0K8ez/o7ESXQGHnaQtXq5cmoL+xnlnQ7KKkOeVX1eL4t0+wPHIE+UuZYQ0p0T3h2b0BE53XXQTZ5BkE/I2LjI6fYj3UAf4eCCr1QzA6Wqqvr8t2EH3VGcbvLOB4ybq41qdVzgWRENAgDoaW70eFS1xzBMI90EsRM53ZyIXKyQmvBMK50cjEFA6bpvtzKSOd2D38+mS0O4DL5JOLaYq05lllpT+mu0rhiq/GuyhXQTUc7xKgcy49ga6R6kvDwtYB7GMWi1Ukcj3fX1RHG6a40X2EkNt01mEzDr9URo6+iOmySvCrdipJNnQBGf/KN+i0r0Smu6OTaxZVil7gWl3tXrRJ7VAaKZmPcDCM47LyUkgRqorI/bWwicsFr4RkTYCIBXBj4zq/4PAPAf/wSmLBVx5LSgu6xKzP7WMciWWx0zV1wTe+21J6648vc4Dv0gcLfddrM+f+aZZ2LFFVfEHXfcgU02IcEqwxf/zne+g+23eys+8/F9sCCbgU3fuBOuv/56/Oa3F/Y/YiDZXznpe9hk3VUxpdvBB3bfDVdcdx2OrAP/k773Q1x53vex1ZvfAiyzKtZZ82W47orf4fT/Ph9v2u0AnHbaaVhnnXXwrW99C1mWYYMNNsCV19+IH516ssOZN8vL7/+/f2LVlWaBWlmWmD17to7T9t57b1xxxRU47pD3Yf6C53Da93+A2bNn47VvfTueGy9wyqnfx6Ybrocf//dP8YX9d3HOdvec+3DxxRfjqmuvw/JrvQJTOjnOPPNMbLTRRvqTf/jDH3D77bdj7ty5mDp1KgDgxBNPxC9/9StcdtGvse9+B2CFFVYAAMxaYXmsPGsFoNPB175+Ij5/yL7Ye/f3YMqq62CdddbBV77yFXzuc5/Df/7nf+rj77vvvvjgBz8IADj++OPxne98BzfccAN23HFHPPjgg3j1q1+NLbbYAkBf4E3ZueeeizzPccYZZ2hNgLPOOgvLLbccrrrqKmy//fYAgNVW68+N+++/H2uu2VQyDNOSg+6//OUvOP30053fr7baanj00UeHMqhJa4wqzHL8X9Ms0ZayF+bxAsgUB1KVl9NST443RkrWVEsNtbFo3pfKlud2YKQ2kzbl5RqtkloIOeWSTEmgFlKrj6FQ0SCn20X5NG9StQzTIj1kgyz4DVVCvgAjay+10YrkeEYF3bq8vA5iNG/cLNfNkaPoc2QZpzsUOMYh3TYHWVEllIPsfJyWl+t3y7hOyukkZdg5CezblJcXVdaXTvC0lzGNtrRRYxkM6bb5gd1OFkwkpVrB6BpQS+3Tzc1VxeVt0F6ylmWCI29UOJQa6R63S3QnEOmWWoZJbQopsgg0a0pVEtX1LG6+6+RgRJ9uad2PRrqzTjI6pcypquLGSak1ej1oUV6u5nlV9ueqqp4ROjeod4njdKvnpUqSe3XQrdarRly0/XymrQ4diwm6Y6ughL7xat1tysvD61STcKCVWmTPM3psFwTpjn7/CNLNJ9nTkG7tt+gqKj64NQXaXCE12x9Q18dVg3WyDD0j8Soh3eqdU/93qmfUfba0HCaotnuiLLEmfY3VX4alZy6FefXPq6yyCuY+8WR9iApz5szBUUcdhT/96U94/PHHNcL94IMPukE3g3TfddddeOeO21u/fd3rXqeDbmWrrr4Glpq5tD7GyiutiLlPPIkMFe644w4sXDiKt3/w4Pr6+p8ZGxvFqzfZEKgq3Hnnndhyyy0tobhNN3+tNayG061+keG5haOYNm2qc1/WWmstCxhdZZVVNCo95/6HMD4+jq233hqj9d+njIzgda97He6662597eb/7rx7DrrdLl69+Ra4/9/PARmw4YYbYrnlltPnuOmmmzBv3jwdWCt77rnn8NAD94ktw2659a+46eabcdwpZzZrblFg4cKFWLBgAWbMmNG/H5tuqr+51FJLYemll9bXdNBBB2G33XbDzTffjO233x7vec97tLj3TTfdhH/84x/W/QCAhQsXWlSD6dOnA+gj4xNlyZ7HtGnT8Mwzzzi/v+uuuzBrlpttmbTBTDscEreP2DhI0B2zKRMur9yn2ywv55GDXsk7KRkpL6fK2FEmOUZc4FlVfMuwWAESyZllkgW6/NFBZm0hNbFPtw/pJkJqykHWSDdNOAgthGJU7KnCvEYQjedeIMcIin6/ctMxJU6AiHTTlm2cCXoBUk/onJQAapSPTRLxJb/KkQdx5FPUy6lqdWiu0pY2Q+F0d+xjdjtwnMBBTSc1IpDuMrKkkJurtLojltNtl5erMm2+MmMijHL7Q6gUN2fc8nKSwAk806iWjFpDws8bFefAEEr1y4hEU0UCNtqSMWqu0koXlRARhdT4RKiJyNPzqvmvE3jqvg6lZZgwd5OQbukYPL1JrSWK0z2SFajKMooSoEXkRCE1WqYNp7w8jHTbx+DepfTkn6350SEJXWfuGutbI6TGI920CpAi8r2yatYIWrUXmpv0PTArnICmRLg7vY84czb/CeCZ/wOmLgu8ZC3g2UeAeXOBpV4KLOOi46O9Anf/ax7yLMMrVl2GPeQT80bxz6cXYtnpI1hjZAZ/XsaWmbkUnn7ajTOeeuopLLOMfa6RrnHNAPIs14F1hQrvete7sPrqq+OHP/whVl11VZRliU022cQqK/ZZv2JM/ZTp3zV/7/+/W4+j0UlT46j0eH7341Ow2stfAcyc1ffpH78LU2tBWqrwbv5spqJty/DSlyyHJ+uWYqaNjNggRZZlzX2pmt+Z5+hfay0iqE9Vsd/hFOvLssQqq6yCq666yvr9swvH8Ew51R1+1XzvmE9/DDvvtCOmrLSe9RGTq+67pp122gkPPPAAfve73+Hyyy/H2972NhxyyCE48cQTUZYlXvOa17AcdDNuVaXqExnLJu+Wu+yyC7785S/jZz/7GYD+RT/44IP4/Oc/75RxTNrgRh2OkKNjlZeXseXltsMRx+k2NoWydJRae6RsMCfl5dpRTilLzEea6zL/z4mHVCWfUQ5yukMIklu2SUWPXCSGtPegiL3ghFtjJ9/RwXfkuK0AWUChaestrsKh70CO94MBJqgM9d2NKjsWOHHK+XM+TtCIUgdfcnk5rT5wefhpXEDAQLoqGlxFlJcPmdOtjmmdf0jl5VpIzaOMTTnzwWMyc7Xp3CCU6ztBN1eZoRCpAosq6B4PqpfzAZ05Z+QKJ76qh1pMS0aa1KDrfnCuxuwtAUtBuhv18hZz1RG3qtdo5Shr6hK/z3LjdPp0q+euuy6QwLONRZeGx9MI5GPw19o1KozKsoxKlDQaL0LQ7dzf0igvpxSguPePf0aJSDdZg5vycrWm20GxCpLzDI2aOxX+0++BjMh38wyjkNfsZE639W/j2vNcLvHuLQRGpgMj0/qf6U6vf16K/05WoBop+xcvHXNqF9VIjrI7klANk2HDddfCxdfc6PzlL3/5CzbYYAPxe/3/Ned5/PHHceedd+L000/HNttsAwC47rrrPKd2y8s33HBD3HjzLQA+oP9+443N2CoIAbJxqI033hhTp07Bgw8/gjdtvzOw9Mr9fWDp5/RRNt54Y1xwwQXWcW+75S/2pdV/K42Dv/oVG+COu/4hXxNj6669OqZMmYLrrrsOr3nruwAAvV4PN954Iw7++IHGCJr/b7TBuuj1erjpxhvxkrU3Rpb1wVal6wUAm2++OR599FF0u12rvPuZ58Zx/xPzRaR7s1dtirvmPIBPrr0mpq26btK1mDZr1izsu+++2HfffbHNNtvgs5/9LE488URsvvnmOO+887S4m2R/+9vfMDIygle84hWtxxCy5N3gxBNPxGOPPYYVV1wRzz33HN70pjdh3XXXxdJLL+2Q3idtcNOtomiGWEAwemLQ7SkvJ0F3QZ1/7hgWqtxk0FW/TZcbZYvMcErDIctUGw+Bd2eNr+wJLcOIg5bI6a6Ysk2qMB9Eup3WZ3ZpGNs6ivDZqZhQfNDtew/4SgIL6VblgL1xdqOn5XnUolpJJXK6m5Y2tUMUwemmPPuGhz8A0q0QTsr/E+65WU5cGEj3IEG3ed970vs2oLHq8MQ6Ma3hDOPmqtu5ITBX9ZrSvCdqjGWvZ8/pieR0i0g3v+awnG5VPdOTrj2EdKt74dniaXk55fJGc2rb30unUwY7znrdJ0F30lwVhNS6DtItIbF+FLWqKr33ZoTTPbFBd8QzSOR0U1TfTHb2emNNYOoJovS1l1TRm08i9YXUeApQ7PvH6QOkanM4fbprv6UrcLrZNVuozBgnnV28iDyhWPUKOjdJUtPnowFoAijf3kLLujW0yX467oiZ9dkoyzIc/OHdMefe+3DIIYfgr3/9K+6++25873vfw5lnnonPfvaz4veoLV8rlv/gBz/AP/7xD1x55ZU44ogjvCO2rcKhhx6K3192BU46/b9xz7334/TTT8fFF1/coMS0mss5RIWll14an/nEgTj86JNw9k9/hjlz5uCWW27B92afh7N/9lugqvDxj38cc+bMwRFHHIG77roLP/3JT/Gbn59TH5I+m+aad3jzG3DdDTd7rsm1pWZMx0Ef/zg++9nP4porL8Ocu/8XnzjoY1iwYAE+vM/e9rnq/22w3sux44474tBDPo7bbrkRf/vrLTjggAN0STYAbLfddthqq63wnve8B5deeinuv/9+XH/99fjK0V/C3/96i9MCTR3885/7LH78i9/h2G9+D3//+99x55134rzzzsMXv/jF6Gv60pe+hF//+tf4xz/+gb///e+48MILNd98r732wktf+lLssssuuPbaa3Hffffh6quvxqc+9Sn83//9nz7Gtddei2222ca6pmFb8m6wzDLL4LrrrsP555+Pr371q/jEJz6Biy66CFdffTWWWipGJGHSkkw7RoLzRaxXZY1iaNlL2pSV8+U4gZWd3bWOZ5awo9lYqBMtIt0DtQwTHFEAKAudlOY2N7d9DzmG0KKlZAIOqqreiPTYTqGTxa54hXk1bgv9cTiJtuMZdKwiSkEpBUBGuuvAi3kXQyXSWrE7CZXyB905EfphqQvk/lEnR7V50+iZ7qEd5y5UVWUEW3Fz1Wxp0yvLYLu1GLOEyCYI6dYoqle9nFR3hI7JzNXSEZGUSqzp/Tbe10y9b6S83BeMDmhNyzC7VFU/Dwfpdlsw6YSDmGD0P9NG18CHdNvzrBHIsluGiXMgBmUNWEx1h4t0t5irxv2qyrLfDxrN2kHFGGP4wg3a2K+qUpzdBukeRtAd0GNIUS8Pth0TkG4j6C6KXhynWwuphcrLm/ur9WhKYd5QK/3jNv8dq4fZ6GrUz7ITEFLj3l9yrU3Qbe859rtE5hpJqqlfO1UoWpXc46NVVVrUG0kJ4oqfqbXdydZafVVce9lFmDNnDrbffnu89rWvxezZszF79uxgP2qrZDrPcO655+Kmm27CJptsgsMPPxzf+MY3fF92frX11lvj5BO/hpN+8N/Y6m3vxiWXXILDDz9cC4W5Bd8Uce9/4iv/cQS+dPhHccI3v4ONNtoIO+z0Dvz2smuw9hqrAqiwxhpr4Pzzz8dvf/tbvOpVr8LpPzgdh37uqPqY1qGMlmEZPrTrO3DH3XNw1113ee8Lta9+9avYbbfd8P8OPRAfeMebce+9c3DppZfiJcsvb5/TSL6cddZZWO1lq2P/9++MQ/f7kG4N1ty+DBdddBG23XZb7Lffflh//fXxgQ98AA8++ABWmDWLfw+rCm/f7i248OyTccU1/4PXvva12HLLLXHSSScliZlNmTIFRx55JDbddFNsu+226HQ6OPfccwEAM2bMwDXXXIM11lgDu+66KzbaaCPst99+eO655yzk+5xzzsFHP/rRhLuYbq3rwt761rfirW996zDHMmmMaZSxEhxPYkVRoocOOujZAbFnU86JgJbjBHLHMLO5ZtBNOd31MbK6NLxT2Whkinp5wzmmGzfZ2Ou/9cjmbY7HLfWk4kyCM8u0o6ItbbSQTleVP9Kg23h2jMI8G3wRhXmFyDh9ulUgT0vII94DqjCvkhtUSA1A3afbfRdDgSNV/GXNKS9X93MK/3HS0oZN6JAAgz6TVjxRw/pOt83pjEl4dfMcY0VpId2DcLpjlHAHNS7xRC25rJOZq+EWgXxQbj73UkoStRT+ijFVRqrmqJOAiEK662uQON2R61QMp1t3rdDj6P85OFeHUF4e9c6TxPOgnO6i6GnHp0P7dEuc7hppzI1xmhSVnoHUUk73cJDuYXC6I5FuRbnRaG9TXt7r8fuqc0oqpCbMVfOcLtIdSNzFVCPod7gd0q0T51mFqiwc5XxWOJRWVenki43Is++SkCh1FOVD2jQAqbDyo9bs36rAd+q/xxyS8pW9Vn/pNZtvhksuucT70aOPPhqfPWhvoHgGKkw87LDD8Mn3vxlABVQVtttuO9xxxx1k6M141lprLVT/uqNfXg/gzW9+M6rR+cDjd+lr/Mg+e+LQPd6G+fnSWGrldfHRj34Ua6y9Tn0s4Ev/eTR2++jh/eHXxz3k4wfi/+37LihJrgzAJ/f/ID55+Gf7nO6qAh65tR5Q/38777wzdt55ZwD96oY7HnkG79ljLyy//LL1MSp9TnWvll9uGXziIx/ESSedpEWujz76aBx99NHWNR922GE47NBDgH/9DUCfK33KKafgoCOPxXhRYr0VZ2L6lC6em/e0ukvk/xlWXnll/Oz8C3D/E/MxY0oX6644E3vvvbd1nqWXXhqnnHIKTjnlFP27eQvHce/j/fLyzTbbrK+78+jtzfGrCju8+Q3Y9i1vxfRVNgJn3DtklrZ/8Ytf9CLjK6+8Ms4++2zx77/73e/Q6XTwvve9T/zMMCx5N/jkJz9p3Uxl3/3ud3HYYYcNY0yTZppTihxAusvK5kYlbMp6o6Sbl48v5CDdNjIgI91uQBe0gGNEg+4Y8Zt0TrdyZptz0ZY2tB2NU+JGxik5D+ZGTjmHLtJNAnl34O7niLkK8wySrct1+fLyeE53fHn5SBYoLydIN9c6ygnkiUNH1cvTg8bGaXSF/uS5Rx1383dtLSjcN6BVhavgT61tWac5VxuBwjhON/s+KkGyXuRaOARTiUfF7ZfFG20Nhy577S2R7pikJkFAaVui4BwYwv2MeecrpeVBgtmkuWrcr17dgaL/67plmIh0h1FUlTBTCcmJCbpDAXMMpzuElvOoftdIdpbj4+xcdU5ZX/tYpJBaz7h/UmLUHXc4eZXcp5sg12aitzApKrQ3OJMg15xu8h5Ecc8lTjeZm6IfY/27MqM0z9XTcmn/d2KOSD8bZ0IpdejgRvSvryD2xPT+EJT62989DX/9+92Yc/8D+M53voOzzz4bu+6+p/F140T6EFK5vu8iggMFANBd9QuHHYg111yzr10S8X0g09fYDN2+9gZdt5Mvac9SHVo4V33AmKqJibb58+fjrLPOsip7JsKSd4Pzzz8fW2+9tfP7N7zhDfjFL34xlEFNWmPa4ZD6ShMr6OYVUX7WcHnrgFJqGcZlc6vCqt1SJVRlSTcvFXQr9UQVdCcg3R3bSXSuzSwZLYtmQzTmsdynW0DPiHEIEhW7a4LuDjkn2VDVeUhbt4YXbZyYPCPVMizn+nJyY49Cuu3ycoc3DjQtmMwKB7MvcsCJpjxIYSB6zKWxiXS7I+zHc8rpVu9WCtKt27zV76nmnscHjSVxumNK+s13o2SCrzYmJnmGVV5ehlHUNkkLwJ6rej6rtllSi0BPhUOl1cvj1sJhmFoDdRk01TGgGg4M8qUqaXSf7sp29sOBVAzS7V/3zXGzc3WoSLfnnSfXqvaQpLlqtLhULe+AZk2R9Cx8CvPqvEVlI90Op7scYD4H233FlJeHjkGvlVDDzDaExXiU+F0jyko43Z77S4PuoP6H3n9srnTXekbwH4OYo+Bv8tnNahmhvVr/pMpPsfVCtG9EStj746RIN6F+qH1K3c6QH2OOoz+YetwxSLeqYfbfs5g4PrH7l33A+Ih5eMfQA7a/f9PNt+LtHzwIr3/LO/H9738fp5xyCvb40L7s2Z3boSkA5BNG6zDuGszbm9FAGPY4l116Jv7jP/4DnU4AxGJvh12x4KYuyKcjKhyoucc0v1wlPKeJs9133x2vf/3rJ/w8ybvlE088gWWXXdb5/TLLLIPHH398KIOaNMOkEksp6K7MzavgF2NiNMtPlcfZjZ1Buosq00JqLtItcKNacLpFJDbL+serS7YLzY1OQbr9zqziWFpBN21HVRVAhqbsWSodU+eXkO6Om+TICIoCIgQkjj2BZqCrEZigscg6QMUj3WVZNe0zBCfa4hxXJMhqBqLH3OuNQ2ENeSDoVoh4xVQjOCV/TtBt9wJ31GEDVlSVVur1VQpQM9+N4SHdOYDSTfJInM5EixNSS7x/HNpLS2sDczVj1imFdFfFeFS1xzCMCqmFlIZpf2wA8e3SAtoTXs0MktClKF9wrtK+6S0sSjyQrn2kkihqrpLycmWdSKSbe0bmeS16CalG6i0SpHuI5eV6Da8d/TzHeNXBSFagLHooSjuZzJ7SQbolIbU6eK3MCr3COr+MdJMSdUYXIhnpJgkHM9HLiYey76/D6e7/XyVfQvoA9jEKdl/VSHdISK2oEgJeIQAMRFdc6yj6tzYxdyr6ayHdKmaORsvpNds//vjM0zBz7DHM6yyHmSutDQCY89i8+qsVGyC7982TpWCGaZaQ0w828XtqVoO5V3RUEtJNxp3ipTijtL5cobmu5w/pXlSWvBusu+66LM/i4osvxjrrrDOUQU2aYU5Jtd/RsbhRVRGHdBOHw8nesqVLLqe7h47O4lInumm9QVCpBJSEcs9DZe9xfbopghRwUDikO7dLQV1ON9n4LZV1V0iNE2ehCvM5FRcbAtKtBcl0WzJ3I7f6BxOE3upZGkC6AQ8yZSAFhVEKKpX9mI5RabSGqrisP0G6pfLyZE53YTjdur1MDKe7QT9Cpfmx5iZ5hsvpbvphe64rhrtvGJtwoO0Sg0JqLmVFv6+xVJshmAq61VyV+3TLjrvVMqws4QglRQZSPvoOrXBylJs7gbk6VKTbF3TbiedWc9W4X6W1piikmwbd9rvGK2M3CtJ99W37uWc08Gxjie2++GMkBt2MLofW8uiNRz2zjKL8zly13+eiLN0+3WoNkdoOCuP2tggNmKtebvPZaSLai3Tr8nK7dZzTHcYcp7pWxo8xP+dyujlgRM39JqiJCpUq+g/+OymIZxKnW6PMSZ+GPc5UpJseQ0D9M/eT1mkydwyZvhD3GP6guXI+rsx5nWOvkwmg3V/RMdmBeitM2nkcxlVV/LheqJa8Wx5xxBH4xCc+gccee0wLqV1xxRX45je/iZNPPnnY45s0EenmnSmHGxWBKFOHoyll8gXdLtJdItebBnWilWBN16OMHbQ8Qqgq7wLFaI1019nqjrm5+UWNYjndFoJEhH4a4S/K6TYWryzvO9JWMBAuWXM43RrlNwN5H6dbdgCVE06F1DLjuesWTIWc9TevmRrtTc2axL8U1ctNx2i86decMQ6Iun+kPI+2JVOJkqrqI/i5LyhAjVSrYCtyrgI8p1tqtxZrLp0h4LgnGicmSE2X3kY7u5yQWiTaq35fuetUaZWXt1hzEo1TsdeJPgGV4pAvSyfCRLNjaTBMqb1jWpxRrfsE5QvN1SHcT2ev8Y6zXl/bzFXjfqo1pawyTQHSKuage0vXGmcMp9vl8g4wn2m1B7WYZ5DK6SYq9gA0daboRaqXp3K6C6JFA/P+pvXpNhMjDoIcMMlv6Z9unJm7rsCexOkeS+F0G++rOfca9fJA61P9b7I+tCovF4Ju+UjOIdMCtUT0Vgf/LnobH+xTBJhGie69UOfrf4QJkCVhusjkABPnI6soIpy6tsj3Q1UluKXswrgS/BTnkxS95y72BWrJQfd+++2H0dFRHHfccfjKV74CoK/+d9ppp2GfffYZ+gBf9KYCIanEkpjT7zIB4czqYNFFuj1ZVOMcPXT05iaqgCo+XgshNaWA7mtpZaGkXLlZoH1PLFeyEpDuqiwd4S+29UneBYoxPuiuXKeGovwajdbl5Tn6K1YlIN3higdNAagz87pnrdWCSSFw7rvVY5wDaqlBd8XwL6lZLW1649AcYDMoJM4rRbqlNm9A/3nkgd3ALC+NpYKY4+g77i5S08ZEDYEhB91+TndayzBurjaJJiGJQREl5n6XWdpaOKiZ669CuPTzENr7FIWLLFoJB/O56XWKUH+oMUru1Gg3CIfPGht0D4R0h995WuXTaq4a+5VKnBbIdamfuLd4qhFsakiJouIR+bFBON0hagipNvIeI7LtGFWxB4Ce4jAbnG5f0K053epZBIJuy28hFSKFFDgJqLM57mYtFIdqmVOh1+n0kzNZhcKiqPiQbpLgVeXl3j7duX2txnPnKsiceyP5aFUf5a6ikg5CoBnYjnx/brWT6S8lBsxcMJtcXl5/T0Sg3SsygVp7BMIx2GCVCbrZU1bkb4nXyYzBGbsOuinKb58r5dnaCQr9W6iAu3njFu+oO61ig7dWu+VBBx2Egw46CI899himT5+OmTNnDjyQSeNNo4yRvX91v8sMsDndnvJy4nA4bZ8kvpD6W73gF8j1huy0uBDKy1McthDvzhpj2UNR1pw/X7lZgCdKjVXGNpDusmyYrl0J6Vbn00E33cgZR5RUIzjl5eoztB+xspjkC20ZxgqpMcihDh7CSLeJnsQh3WPNMUX1chvpbsqMhcoMuIGpw+kmAcdIIDfUs8ojEzjdRkubJUa9XCeePNeVzOlmelWb4ktVFZyrzftqJMQ0p9udZxNhZr9hNZ4QKsUKU5mBEht0R5aXR6iXZ6TCqUG6A3N1CBz5qHfeoda0mKvG/SzqNaVADrVyyH26KYrKoZOlVV7eIJz9/w9WXp5WGj6MY3CIcWGs+8FESVXpQHOsiON02326baRbFlIjCD2HdIfQcmJccqWHHFNQgOuAwKuX02SgnXzxvUuckJq5r9I+3SFOd2d8HlBVGOv1ML0/moi7UFn/k8vL679GoOdpsYqM/sZ8DTAr5NsfozkYj8Q2IWjF3yoVvOpkK5McyLJgvMxx5h2ku2V5eZ+Pbl+bfp4Cyh/qJOc9vXmxqrdpKv3hebQFC/oN4EZGeAAoxpK9j/vuuw+9Xg/rrbceZs2apX9/zz33YGRkBGuttVbrwUwaYw6n2+849rlRJroTdow0YiByulOQ7so+RsdGurtZiaos2YAuZI1j5ElApHC6yxJ6sic7sybS3bS06fXGdNCtyp5ZQRgOyfJxujXSbaMoeZdc+wBBN229xZaXqyDGckDsUjsA6AgrsumbiM4Ug0r1qlwWZzNb2pglgGzLMN6hU9fcYdCzGLSWR7rD77gZkE08p3tRIt0Bh5mYz3nNyl6D6Bm/lzjdWiDMGKPdp3viysstpJskeKREH98ujUHoje9El5dHCGhmzrpfo3yhuToUpJtJOFAjFQ+t5qrJ6dZId3NvdOcGJymsKD9+vrDdqtNePwdSL39eON3uMzFbRQYTJcZclcvLXcS4R+6fRn+jOd3uuFtzujv02osa6ZbOKffpbsrLM/Idd5yFw+ku7H01kdPdHXsaM7AQjz3+b4xMr5CjAhYu5C9+bAzo1X7RwoXAWK//89g40HG/Mzo6jqo3hiIrsHAh//6NjfVQ9cZQVjkWLowMVMbL/nlHx4GuMFbzHOMFclQYHeuhU1/bWK9CiQoLF46yY2fPiQpYONaPisr62gFg4UKMjvfQLSqMokC3PkcxPoqq18PYwhzdsn+dVZ5jYf330bEx5L0Ko6jv53jRXFdWj2m8DuifWwgU9nxaOOYes+qVyAD0yrH+74oxa5zhm7Ww//m8/x5UVYWqTkKOLlyIopNjbGwMZa9CWQH5woX98faq/vuwcCHGRsdQ9cbQGyuxcGHcXjo2XvTfgyzT14Jx1Pd8IUZHx9HtVRjNCv0MFyerqgoLFizA3Llzsdxyy4VV4j2WvFvuu+++2G+//bDeeutZv//zn/+MM844A1dddVXS8U499VR84xvfwCOPPIJXvOIVOPnkk7HNNtsEv/fHP/4Rb3rTm7DJJpvg1ltvTTrnEmWhEktivbJpXRRbUqnLDMEjHv4gpkFibKS7PkYdfFkqoAxKGmMu0s1xul0uFNfmpWcKmAFNKXKkM2srYzdcXlP4S01MtqUNI0TnK2XUfGunxFIOLC2LCDiownzGcGQrq7yciA1pTjJEjmaWZejmWd9JDQbdhb6fBTriYmW2tOkZQbeFdGc2itLpEFEb2EkMq2RVcvoM4/rMRt3zrPm+0zWgpTnoTqi8NNFi+MKdRC4lyxMVA8+O+3c072vGBK8cHWIizFx/qZBaiNNtlsXa64Px3NQ1BwMpptqDGtlb6LqfZRk6eWYlhOxzDH4/OXSSGk0OtJqrxv3Sa4rx/ur2jnRvyegzYgKl0hZSk4KtVhZJd/I+A7L22d83Es+ea9WtIoseH2hyY4JRWk9bnxGfwvFb0MyHYMswT8/s1IobTlfDTDg055RRazforsvLHaTb01XFfF+NfVWhkGIij/hoGSqsMvIs7lvYwQNPPgl0pgFPCxdfjAHPPtY/97NdYN5coLcQmFECU55yPv7cWIEn5o9hajdH+cxU9pBjvRJznx3t70nzpgknJrbgCWBsPjBtHJj2TPDj40//CyPVGMamFpgyvX9xvaceQxc9jE8HRqZOD5/zqbkAKuDZkf71VyXw9GP9v82bhtF5T2BqsQCjnYWY+vQoAODf88ewYKzA2IwRTO3mmPvMKDp5hs78/nWOLVyAKQsfRw9ddOd3gGcf7d/jpwCM1GN65rH+s3u6AxjgAWDfu3z+tH5wXo/p8RwYf3Z6f114ph7n/Ij72xsF5jXPuKoqzH2qH+R2F0xDnmUoej105j2GChmyBdOa5zF9HJj6LOYt7OGp58Yxb0oHY09NCZywPm1ZYu7To8gzoLugvvan5/bv8zNdjD43H1N7z2A0n4ep8yK5IM+DLbfcclh55ZUHOkbybnnLLbewfbq33HJLfOITn0g61nnnnYfDDjsMp556Krbeemucfvrp2GmnnXDHHXdgjTXWEL/39NNPY5999sHb3vY2/Otf/0q9hCXKGocjrk93STndEYiyFpGpP6v7BXdI0M0iyoU+h0K6zRYXumUYCbq5UtCQaV4z6ccsIt1MEGMj3W3KNhkuuqYAlH2F09pU2TPb0oYpz/eVydGEQ17ZJZbm9y1UkIzb/x4otEdxutU5XKTbVAnXyYIq4IzV1qmD7hhUqiga6oJkWZ73kfCsRFn02NZRNPDU74Hiz1clrDZvhtMV06vbmXfGuaKRbqYtURuTlXCHE3Szc4BYM8/iDskmHMzeyebYnblqI4sWHcJEultU16Sa+R5k5F1z+nTX11dygaemrBg8UmRoGvWqaw+ITHkSI3Q9ddZ9QAfd7Fwdwv3kzknN4XS3masGVaHSa4qJdKvEM7/P6nEKAZ0lBKbVy+1gq5VFo9QtOd1MMou7VrO8nKvEso/ZnMdFuvny/ZJJWnQoLUMaO31GnHBqROLUPJd17aoPOENRYd9foZWhuhehd8k8vlmx1/WVzXt8tCkYx3ojj2Ls958HVtsC2O2H/MU/djdwyaeBGS8F9rsE+NXXgYf/Amx/HLD2Ds7Hr/zfuTjuD3fg1asvhxN335A95N2PPoujf3MTZs2cinM/thV/XmpX/Ddw56+BLQ8BNvpI8OP3nvZ5rF3MwR2bfQlrv/HdAID/+96n8LLqn7h7y69h7Q3fFj7nd/cAUAIfuRhYahYw9hxw0R79vx14Nf76s9Ox4VNX4JZVPoANd/sMAOCnv7sDV/7vXBz05nWx6WrL4uhf34QVZ07FOfV13n3zH7D29Z/Fw9kqWO2Q3wLnfgF4/G7gXacAa27cP/aPDwee+T9gtx8Bq2xgDenv/3waR//mFqy87DT85ICNgN4Y8P3dAQDfmnkSTtl3W+C5p4CLP9D/wsF/DvvTD98CXPppYLk1gQ+dj4VjBT76q2sBABce+kZMn9LFvx/7J15y6af7WgaH/gW49AzgnkuBrY8ANtwTP/vLQzj9mofx9o1WwuffsXb43gKY++xCfPyCP2Ekz3HJ4dv2f3nGgcDCJ4EPnItbbr8KGz78E9y29Juw4Ye/GXXMRW0jIyMDIdzKknfLLMvw7LPPOr9/+umntYMcayeddBL2339/HHDAAQCAk08+GZdeeilOO+00nHDCCeL3Pvaxj2HPPfdEp9PBBRdckHTOJc0ah6NeVAOOjo24RXK6tcgZRbpJhjzE6a5yK3gAjL6SpN8lV7ocsoyUwfs53QVfxmW2IfEF3ajLrEgAyStjN/eiMnrAqrJnkdMNgGvrxo27eQ/qQFjidNfjcCymvFxxJGkvcC6IKV1RmaAzVls3zzCKGFSqLulDI+QjWR8JL/uOUeVJjJCSX42wqpJVlYDKM+QZUFZxKIk17yITZHQcUT2LI8xtURdw3BMtjdOdhnS34jXXc1W3amIrM+LWwkHNfA/Umu2WghI6CXPtGqFWHQ7ouCPLjr1IN0nkSXScMQhzdQjl+lGcbjLOVnPVWlOayix9ig4RUiNrn48v3CsrlBVNdld6rR6dUE53ROIjZl8wPudouqCuCqiAshjXbUHF5CCHdNN9lbm/knp5PKfbHXew1zcx9totHRPpvRDWLRgVD2Umfqe5ViKMJ1TsBbVprHH0kFcFpi34JzD+JDBNQESnTgHmPQSUC/qfWTi3/3OnZL8zWuV4+NkC64xnmCYcsztlDA8/W2AMhfgZx8r5/fMWz8pjNc8x/5+Y1nsIeVbqc2QLHsO04iGgtzB83rIE5j3Q//e06f1zdvP+GABgShfZ/Mcwbd5DQG+BPt78on/9C4oMRd7Fw88WyEcq/fdunmHavIcwosa14JH+MUfy5rpGH+//Lu8511rmC/DwswWmTEX/+2OlHtOjubrW6dY40eUrDrTl4/3PT18KmDYN4xjHw8/2388ZM6ZjareD6dNn9K8VQDkygnzsCWuM6rrnF3n0M502jv79yYz3YOFcYN6jQLcCFj6FafMeQjXlmfj3ZAm15N1gm222wQknnGAF2EVR4IQTTsAb3/jG6OOMjY3hpptuwvbbb2/9fvvtt8f1118vfu+ss87CnDlz8J//+Z9R5xkdHcUzzzxj/bckmVVWbIoJCUGIw40i5VCcUYdDL/IKQYjkdBfInVJEFeSaIli9Xo9Vxg5ZTsrgB+d0c468MR7WSRl3P2e0tLFaXKmEA2lp0/8wUzrrKe+jLW2ogi+9dnfcMchXXWKpke66PI5BuitOSC0yaMypg+EORI9Z9dQtA0uVcqDLXq+hYgiJEaB5txtOd+1sGdeaosDtdA2oqsaxjOhnbaKJEh8+1nLqkNHSzkFNB92eNSULOMzEuHfeKiuOmKucTkRlrVPq74OV7/vMeQ/AONRRdBIzqelf51iLmO9ZZgezfk6sr3qmfdDt7DXsOI2KB7Scq5ZORENZaf5sJ55F7i6Doqq5W1rJ7sY/WnyQ7rigm7vWyigvD7Z5s4JudWyyr5Jx2326CS0jsmXYRHG6zXZp7jnrd9F8fx3aiwq67WQorSgx/2aCB3yLMSrO6KfaRc1VsT2tAPBEJNnZKr+QJWqQ6Oo/CRwIma8lIwCURQMSGeupmRTiKBdUmDZ1HXfur/GZUSeZxR/DMWHOmGPPLV/dbZPHqe+HTM2P0vSBuWpPj0/xQrHklP/Xv/51bLvttthggw009/raa6/FM888gyuvvDL6OI8//jiKosBKK61k/X6llVbCo48+yn7nnnvuwec//3lce+21Vpsgn51wwgk45phjose12JlZWmeJCfGLp9unO6KsmJTWOQ4wh65nzabQBN0dS4UZaBYmU2G67I0PKKRGFzEO0SzYjLzVmoO7n6aTyrVpYVrxmOWPSqRnvOpgpD4X29ImgtNtI912YqQJupnycm7xjbjfVGG+6QXeXKst8CSVxYeRbgD6+ThmPkNG9Igzs6UN/17YpZ9m/1azzZuZYOjkGVAkIN0VM+/oOIiZTqFuHTWgkBotnQ/yQlNNK/jL71Kqs8WiZ+baZ/GaeYdDI93GM1RjXHSc7tKuNELzjMuqL8hCy+B9SLeVcGDXX97RimnJSCkruudwZjr3nrk6hPvJIYvyOHvt56rh4Kk1xUzk5U53DX5t4+5N3+ku7fJoy0EeYD5H99iOQLq5/cwKOOz30eI1a6S7x+5P7JgAjKtrp8EBMweoEF2QoiLMI+4ZiW3HiPlE5CpGSC2G063+36iXy/PMrU5qKvbMfaFD19eQjxbjb4kK8/x3ghUPSOfUW+dLDLozwxcq9TOLqLRiqUt2C1btr9I1B/0qoObdMw6lW7DGVGa61+rc38qcV9weGLG/k0oK01dXpzFjq5Jpk9emy4rVCYPxgTn9oBeqJacVNt54Y9x2223YfffdMXfuXDz77LPYZ5998L//+7/YZJNNkgdA2w1UVcW2ICiKAnvuuSeOOeYYrL/++tHHP/LII/H000/r/x566KHkMT6fZjlGHNpDrF2fbtvhcDKxoQxd/XfF6eZaR1Gxqzbl5XqcXiE1A+lmMrFWyZqJBmWCc0CNQ5AMB7kR/nLLEAGh5DcC+aIK8+r/Jlfe66AlqNgrhfmcCWKUgJwP6Q5xkjtBVMp0kF1UijNT7IbVCyAlfybvuTSQFJMGkVKa6HYNCM9V+xwlG3y1MVkJd1hIdzigS+VSsor9al7RahBhruYe4b/YtXBQ41SYndZbEnLQka7d8z6rCihqMVoepBuED819PtXLzYqH1nPVXFNYITV/S0uv4nRZ9jndFT//xwYKuu11y7GYZ+BLxjLJQQ61a4KYGE537Q9UOfT0d4Ju9/7SCpHWSDeHIEeKS7Dt0rSOCYfQu+ek663J6a6qSniXZEoQ93nnfY/00ZJoCCGkOyL4Si3vZ8cR+jhT9ZeEdEt7tTEOLii0EuYepNvbJtfjs7mCxs1nRotIf5Wax9fUIn1OC9Z2fp5p5t7G+8DKp3jhB92trnDVVVfF8ccfP9CJX/rSl6LT6Tio9ty5cx30GwCeffZZ3Hjjjbjlllu0YFtZlqiqCt1uF7///e/x1re+1fne1KlTMXVqgOewGJuZ5Y9x5PuIW2LQTRyO9D7dCo3Ma4Est3VUlud99Dfro5dtMlupi5ivzUthcrq5hdY8vmmqtJb5Tl+93EVm2ZY27IZoc9PMjZy2tGlKLM2g27NZRbwHVGG+QbrdIIZDDmNQK8BIfEQoDTeoVGTQzWRm6THNMRRlxbZ5Awz+fwzSXRCkJjLo5jbuYfXpZt+1IVjFzQFhDIP06baR7vBc1X2cTaRbfaZlx4RUc9Zf2A5Hr6zQjUC6bRE5zzoH9N830sM+i7hWF+n2JCm9nO4BkG4VwHmqO8wKp9Zz1dgXSqblnW4/JugxhHorO+rlXCloGwuWl8eUDXsCdyaZxaqXG4FnsE+3VflG1iD1d+b+tu/TXR/DI5yaXF5uXrtnb/EJQOrPVkYSwqQRSWCAdYweu6+a+0a/eiYQdEch3TTo9r9bQRV7a5wJitSJlVkNAOEG3WURsef5gu66BSunF9Il9BKAPCNNCVRrih8koubMMy6ZZZZjRyHdEeuaWZU6zs/V/nfi17VurA8c0O55IVjybnnNNdd4/77ttttGHWfKlCl4zWteg8suuwzvfe979e8vu+wy7LLLLs7nl1lmGdx+++3W70499VRceeWV+MUvfoG111476rxLmuXJSHeJQjWYt0qXI7i8ittXkI0nFMTUf9dIt1FqY/K+CuQYQYGiN9bwcDrxr2DesdW1Q9ndkPhNlDNLjFNItpCYot/zsMgEpJtDHwUhNauliGo/psqj4W40gwbdOclw5sw5WORQlXZHI92BgMw4h0a6A1wfk3fHcdHpvTERPLPNm1lalYZ0U1pHWtBtbtxDQ7onKOjOIjbIVC4lq9ifGHjmTAlgZdFgIgKUAa3/HthOklPpIswb9tqlxCkNYug6moR0F1bHCdOJ9s7VIXK6vUi3Qa1pPVeZ6hkzkeeWl/PVCF2uxLdg+nSbCt6DIN0hPYaBkW7XP2B5zYaWR9Dp1pVvuctRBli/hEW6OwT9lcae2Z/zIsgB44IQXVrfi9zzBMRYgRIsIk/LxZnyck69HADKCuiwwIhxP6vS/Ts1CekW1vkYTvciKS9XLU4l7ZmQcS0ZyThyZp7ZCXN3fYgDieRrlTjdVjIry+rkQC/uftH3l/r6ANFfGmPnKr3WkLFtHRldqEmkm7E3v/nNzu/McvAUBfMjjjgCe++9N7bYYgtstdVW+MEPfoAHH3wQH//4xwH0S8Mffvhh/PjHP0ae5075+oorrohp06a1KmtfUkw5HFlV2ouDp7zcUgGlvXoZ62iku/9Z3UbJi3QbHFmlXo68LrPjF+P+uMZRFmXTAi1hkmk0wskcGscwnJTGmTWGbZascU5jlvWPQZMcyjhlbEPopwwi3a46qaiUa94+soCrxEOXDSw9iEYk0t2vRqgF1fLm97waNAkeAotxNwmV6l9LCOlu0Ige/24Rx9PkqpprlllaxfZXF6yoSNBt6QX4gu6mDJttHdXCmvtL3jWO09nCKj0Hwki3yNsnxs1VmFU+3DPNsr7DXfXVkHVlhsE1V/PU7is/cRu7s/4CFneTS/ZxYmIWp9tHlwDY55pxazb9jKHlYXJevSrJpg3hfjZ7RbhPd46y/Vw11kbVgYLt0y2gUqyCdH3OoqpI0NisjWWVoTeUPt2DBN1pyVjuWkujwinodNdz1QkOjLnK3d+SaiGExBjpPGL8jmCvb2Lstau9hUmAsb4OoX5kxv0oq4pNsjnXyoAHOePH9L9TouOloJRxc9VK5AldEwyLSZilJl+t80Uj3TVHnqvIS0K6M3qTm3EoX8gSbzR8iHrZYCmBVCeCDezda3XeRSN5Y+2rWQdAYtCt5h3z/totWPm5Sr8TMnNvc7Vmeq3opkuqJV/hk08+af08Pj6OW265BUcddRSOO+64pGPtscceeOKJJ/DlL38ZjzzyCDbZZBNcdNFFWHPNNQEAjzzyCB588MHUIb6grHE4zOw5WRwMszZ/CSEippBMhZ46mVVvhq45Rw8dFFUlZkAbbtQYj0YGTI1TEruxx5WCdBPHNO8CZh9qw7gyLhsxcoPuLGNa2uhxupsbj3QrtKfPt+5mbg/tVESDms3l6TVlW1a5Lpc9t8cd06fb/LxjhtOikxihlmFZXnejadTLrQWc3BuL022hZ1Oafydk6XuC0+2bq9Y5qmEi3RI/cDhBd0wpWHN/23MprTZ53rnaz8ZzQmqc4ONEB90up1tAuum8segkhuPuW+cAYZ0KOzG5QVmxVWzdslce6R5CeXnEO2+tr23naoCy0nU43Xz1kXRvnEoXVbJfo5utLTR3B0a63WCML7Hu/70qxsNON4d0q3PUc5W7v7RPd/CZCmWvbZHuquIpPhZqShJgPDWEqWJD8y7436XSOUYI6ebWFHEcMe+J+k5Ee1ogxOm2O7eIivfWONoh3Tlb4ZQSiJLrNOYe1z7V9CE4ykXeqVvG+lqIeuamxOnuIbf31bwLFKOR10rnHU8VUS1Yewylog2n22zr2HPe8aaF4CTSzdiyyy7r/O7tb387pk6disMPPxw33XRT0vEOPvhgHHzwwezfZs+e7f3u0UcfjaOPPjrpfEuaWS3DIgInV7087CBTh6OZ7GiOA3gWdOXE2C3DaPBltnXilLFDRsXEQouYX/xG2KjUz9Ii5rkXfSG15l6Y1s1zjBWGsjtTWqOCM24jN9tPFEVPT9xOl+N0c0h3uBTULCsqjfJyUxW08gipcQqe7HmiUanGQa4ike6qaIIvjndPhdT6nG63zRuQxukuCmHeBd7viejTrWI39l0bgsUoY6eWFbKK/ZyeBTtX+448R1mpmHVqInljzvqLvsORZX3Hs8dU2PgUkDMpWWAF3QzSHdGSMTf2FvMd55FurmXY4PdTlRr6nHFzD2w9V7PmmVQMp1sndLMKVVk4yVUuKWRSVKR9t6gpV60tFIDEtNqJqoBq7gXHIVbtAUszmS21eTPLYJ3gYIysj+r+lk6FiBclNVsykgDCy5X2mJR4KtW1M326lUCb9f6aCKnx3Ap0LKVrb0WJETRyIIajExMbdPvmakaC7iDSHdZwoeOcErO3Zfb6GTLlt3YG5XSLQXdDWcuNecKJoHKc7jiQSEa6KfjlrCkpSfWIda1/jsZX5+YqAKuCK8Y6eYayMFoKm/eX+MAvZBvaFc6aNQt33XXXsA43abWxnG5PNqidernpcJTaEeqYm0d/MObAjHPUwXqVawek/32uvBy2SFcSp5suYozTZ5RUcUGM1d9Syv56hDy0Mrbh5JjBgdTiysuzJePQZcYmP8jgHFqOpxl0+7K7EZluqjDfBDHuc+e4pqlIt1i1bdwbpT4aRrrrTbZnCGrFqJeXpX5mvSpHZqEJytEcAOkOZG5Np7BNKw7OOnTcichB0CKurW1/XLvMTVFrPAlHBo1gkW4rcJ9ITndpVxrV1jXfeSdYYBIOuVnhxDnUpoAOg3RH0HfMbhBcxwnz3+xcHcL9jEG6TRHJ1nPVeE90Is8UUjNQc74fs0t/6BqcbpfWZVR/lX2xq1ZmlipzliSQ5UvGukg3j/YW4eSgVQYrjIO5v3TeqPvLPlOm3acvOI1ZwyWKhZnQjUHXOUAC6L8LkmCmc60GJYhrzWUpy0u+DJvYj+B06+/4E/VcSTU1m3seOQcSK7MU1S5nwIEoSpU0h4znyO0tubHHsfSTrk3bZNdxj8/mlpcL1TMp9DFKZSzcdQ0wgm6mTV4bpLt/jqYywDyeWaUGg8r4QrVkpPu2226zfq6qCo888gi++tWv4lWvetXQBjZpfbNUqyOc3Z6FuMWh45bDYQil+IXU3AW9n4ErxUlZGhOZU8YOmRqndxFjysvTkW456ObuhS2kxgeJch/OuI3cVJi3xYSGp15OFeY7cDeaii3X9SB2jDmcY2rGu8WhUpyZnG7dOsojpGaVhvaatmTm3UkJHIsyvWuAeY6iKMOqwJHmU8IdhmURjn43oawT4N95tl2iOFd7fJmhCtwtIbWJK2Hr0YqH2jp5hvGi7uwgog3utYtCaupn4tjr7yv6jre8vElich0ngMBcHQanOwYtMyqcWs9VM5HHqZcbKFmvN+4qzDMq1eY5e1af7uZdU+9CUVZJwkPcuFmLKi9P2xd86uVV0UMv1J/ZSDhY7w2TqDcDZqm/vbfKwhg7n2Rvi3QbQa2nYwebgBCC7gJKvdyvRm4fo2Cfh3m6nhV0S8BIhIaOE3THId0xnG49zhhLTBJrnRvDF9JaHilCak5Ct/F1uKDb6oDCrA9qfR3JClRlGVaYJ+b40UGkOwXVt/nZTlWqElrr8XMVQFC7h1r/HCXrAzeUwIlLiC8ulrxbbrbZZsiyzMncbrnllvjRj340tIFNWt9MhyOmRNhFusOOEXU4JAEHfrGwOd29gt9UgAb9LQvDQe7Gv4LWIlb0kIFMXmtcTWsTc3GwOd3SYisvYhwX3eIcCkGiU/6Yc8GrXCZnvge9nuHMJwfdAcSYUZhn25JVTKkdo+DJWZBnx6BSQSE17RSOs+09RE53WRlt3iglIL5E2na6wygBPcdQkW6PEu5QLBHprqrKEtvkjJurdpVPeK52OKSbRXsmLujmON0AcTiE+c4i3SGUXwq6YypbjG4QUscJ71wdwv2MQ7ob1L/1XGWqZ8w12mqXaPWmpc6pu5eoRLOEdKvr7LbxJ0Ptk6KC7hitj6bKilOxN2kaReXOVfuYdcKhYjjd6pw+pJtJjIrjJseg427WwrC2hESxsDjdQqI5htPdVy8vWcVoJ0nJ+DHW+pBl6OZZg7IGfbRUTncY5IlpHcV2bglZYtDdrQogs/2U4XK6+fapVgcUbn0whWnL0lCYl/0S01xOt5nMMvbVpKCbJBMDVakTgnQzPnBMIv+FYslXeN9991k/53mOWbNmYdq0aUMb1KQ1ZgupRSDdZWmogMY5miaXt+iN66CZZtjEDJ1RTlYICxAAo/XGOEb0IhZfTmKra483YZiQ3WWRbjMgiVhsqbEtw4yWNuO6HU3AKUxGuhUqVVrl5TbSnYbQc9YozPcwhUUO3WSLr7yPsyD6YG5Ehesgc6Y5XGWvvwlDahlWly4aTo5q89ajiZJElCR13gEw1PTleZNqE92nO0YZ22lpE7gkHunm9Cw8jpF+X90kkdh2bMjGcboB8i7pudgo1/c/Y1JWzGsPlz9S43qWU+sYSPeYgHh45+oQ7meMjoFZBj/Wdq4GqmcsEUkzuMpUcOVHJ3tlhV7lzv8SaRUfvnE7Vpa8qj+1LGJfyGw9EYByug2kGylId1zQzc0bM6iRztE/hlxplYR0G0Fhl7l2u0+3nYyx+3S7e2RZZagg9+mWkW6e061+1snaoI8Wsf/Tzi2BdT6mdRTbuSVkiZVZqvLRbm3aBv2N2FuENoVsNYLZemt8ITpekIjjdBMQw0hmAca+OhSkOyLozug8S2MnOz6wkRhhRVBfoJZ8hUpZfNIWjSmHI6/cskTOirJCzyxzjcggUdVqNSccpFtqdaCVWkmfblperoLusuhfD/xOITWrj/TYQiPo5jKHDffMFHywWnNw12Vdm7uI5UzZZlYTYnKUjUNHkFmnpQ27ISrOO7OAGwrzC4ySKZtvnVZGyFmjMN9ruFIsb5zn5QHhoDFavbzsoVSiXZHl5aaQmpXQIZlvcwxhHn4cSmJzuuOSHKZTyDpwLcwZ97CD7ghqiImC9coSnVCFBTNXVQsYq3MDPafxPmoNArFX7cRn0x2aQW0+WgvHF26U241rd9YpOZji1HadzxhCP1Lwq9tieYPuYXC65XfeRORVC8HkuaordErdMszidFsiknIZMUdVUm2g9JiMBI+qfmmtYO6buyaHM6X/smnCdQJUTExdWy/c5s1Iwluq1dzeYfS7LioadMMZU3MO89o9z8j3/hIzn1FuXXv/+BWz5/FIt3udhdFLvOFou1SFJinfrCHi3NTUJMGXYXy04FzNu0BRyF0TDIupzGI7t4QssTJLC6lZNLhaNb1FIKrNCgrVXtr4FJYIKkO5MCtIi/HR5rhsgkZGuvWeaMyr/t/rfZVqL/nMeX/5qtSGCtrez6Pm0EXM8vIXEdId7d39+c9/xsUXX2z97sc//jHWXnttrLjiijjwwAMxOjoqfHvS2ppyIDsoopxGu1+wp9WOYSZaOj421vzeK6TmBo0Fct2ztP99OpGb7BmnjB2yrqlOObbQHQsZF7chWq05xAynBxlgkAUT6a5UX+lYTjeDZLEZe8vx7I97vKJOuHFMarFBt3pGvYbTbQmUsLy8ejEW+rNTCyPdzaYbzek2eHesoBbZ3LpGxUMo6O5FlMWVJlKTgKqaomcxyEGMyUj3cMrL4zjdhtMaAXD4Od1xc5VNEinhv8juD4Ma1/oIIO8SdWKYICZKy8PzXGNaMpotbaQ1268gHYGyBqyMcNxNFWCtmZE6VxmUtTT6ueedDsq6n3bRG3PF7tge0M05HVpB/dKrBEyMkFdw3NSYEuvWxyDXCVCkuw48C74riH1MOzHCUqoYMUFXvdyuBGHHDWiUnhWz8qHlxKTrqoy9RRQ9lcrLDUBCncOLdDsiUzyn2/oO0xHB+ncs0k3HHvhObPDVTXgG1vkihMGk9qlVyp4Xoi5VBds+1fQhlIo9J6QGAMV4wF9lrtXH6QaMfbUV0q3eXzjjBgxOdzEuCn+m0uAsMVFz3FURlch/oVh00H300UdbImq333479t9/f2y33Xb4/Oc/j9/+9rc44YQTJmSQL2YzVatFZNawXiFxuuXvmA7H2HgTdGsfMMRFMTJwPaElBgDdeqMqerwydsDMBa8Yf874Ax9cacS442aU23O63YCO69Md5HSbbTGYrD9gZ8I10p2V6NXPiPIao5yrkAq4KocsGo5shwlebYEnWmoXi3SHhNQKnakOId1m+WNM0J0bFQ9ym7c0lISdd4Fxc5zu1FYc1CwEBEhuwRKyLAI1Ma8hpqyQm6u5FXjGc7rNdSIzna9Inv0g1rMQzxIgCIgP6U4XkfNV5Cgnxrfu9+91X0hN3X/73ev6OLFDRLq9aFluIPJt5ypTXk7XFJUsKXujzvd6hbsm0z7dHL1kcKTbVxrulljzx0gIuoUSa3MNkSrZmmOqQFPmKTviTFaySlGAYqosuv2yaBiJO1PDpUPWQo9JqJ9+Tzzq5dZ3Ms43aq6T53TT6iQX6ZaoH3Gc7si56tGaoRbbOiq1fWRKeXlhVP1Fi8pSi0roqiqq5jNm0oMFeAyB4nI0DBJRc8q4nTWlRSUbeQ96tIRdfcxS7Hfnan9caX5KbiaJjOOh7EVR1l4oFh1033rrrXjb296mfz733HPx+te/Hj/84Q9xxBFH4JRTTsHPfvazCRnki9lM1epYITW+X7A/g6Qm8ti4wRdWk51DtphSsYKUl9NNwlSY5pSxQ2Zxui2kmy+p4kp+rNYcEmKnS+m4sk13cTBRqQaZtY/ptLRhN0QPqmIs4L2xfsKBoj3IPcFVZHmZqTCvuVJc8Moh9Aw3lbNmExY+YG4iqUi3mdDhsv6VrVdgIt2UEqDHGdHqRJ53/nF3iONujq2tOeMeNtJdhrPSloBODMrEzNXcEA+MoYJwlRnq795e30M0C+kG9D23nglxljjOpi6t95V5esSCVDmkb301u0HohBlxoL1zdQj3M+adb0Qky/ZzlUG6aW9rjSAxqFQTaJrnbAJqO2g0gi2SSE0239xlSqz5Y8j7Gd0XLBV7E+nOmxLroPgdKYN11yGufN/s083MGecc7t6txRhNOlnCGq4u3anQY9XL1XNlFLzNPZJUUYWQbm7NlsvLjfvr9dES1r4E4clCuF/UUpLX1vkSg25LvdynY0AtQjND+xRGtaVJXeASUWYL1rInBd2yz+asjZVdQdJKKJXsPdK6z4sHNnMVaI90c+POmYT5C9Wig+4nn3wSK620kv756quvxo477qh/fu1rX4uHHnpouKObNMvhiFk4XfXytLLi8bEm6I5TLzey+lXHq8LMBUadhPLyDod0Z7nOdPfH5edCWeVmLRAkrgzGVBavBOEvn1CKhBhL/KDxOmvq9K6OcdBiy8uLHrqcwrwu1+UW49hysxDS3dybWKTbKi9nBODoM7X7dKs2bxRJiBdBWpz6dPuUcIdhWRW+ttRWMdxc1XoWUSXWPZ0kYiszFpmQWmknwxSdgaW1yPNdJ/KikG6Z0x3TMmwkKzDeswMdZd65OoT7GfPOm9VerecqUz1DE6N67WOoS1wi2Ua6S3bfbRDOSBEpZ9w+lNp47r71MWFfMFXszY4Dphp0sKKJXHvhlEzziHEr9XLj3fMJqcV2oOCuy1LCjlF/NpNjRb9qojQqHrj75xNalfZVvXcwlBV6jPigu/57MQZwwl+GcR0nOHOq/EIWUuw3xxBsn5rOc3aPwYNEVEgRIGt43YIVIIk8iXdPTL8nREjNqZ5JuF9RlRqwATLpO4Orl7tJDUwG3Y2ttNJKWrl8bGwMN998M7baaiv992effRYjIy/8xuaL2tjycs/CaZU3tgi6zfJyt083t1g0G1FfvdzoN0xLVmonx0S6UzJbWZ5rhVi9iHkcUT+n2xd0ywgSV7psKsxL5eV+9XIJMTZLlQwRudEF/WM5SHd8GaFkWkitN66fUddCDs2+x3ELOLWknroq6A4gxhqJKaTWUfa94TjdEnoWw+m2kZr4eWe2tIltuRYyb4JnCBaTlc6yLKms0KvYH9WnuzD6tbpz035fJ5rT7QbdPk435+znxprSap2KaMloaXn07EBHmXeuDuF+RvX6tTjdLecqE4DQNUUF8hzS7VecLon6dvPMSopKpVoUZSgHfNVFCfuCJGxntgyLRbp7NOHgbZPJqZeTclTrHK5PwrYMC+01hjWaGvy186gfcy/MeVpTFZqKh9LtDgOGv27cqxCnuygKXl/Bk9gXTX2HoVhQiw2+JhLpltqnqrmdlePOdxyT7g0TFJogkcXppqXgtelqD5XIo3M1oryccrqdNWWAUnqpKrUR1JVbhqUj3TIYkDHl+y9Uiw66d9xxR3z+85/HtddeiyOPPBIzZszANttso/9+22234eUvf/mEDPLFbKbDEYt0FzCC5Ugeo+Jbj9fl5VmGplSGzaK6AYZqESK1uDCR7sZBTkvUNCWAo+6YzJ9Lo0836yhVEYutD0EaMT7etLRR5WQVmVpyj0K5fEdCunv1At6K0x1Y1NQzKnrj6GQ16mEGMXozc8s0Yxzo/t8DqFTmOq9V5h+3ut820i1zvMzn0aBngUSJx3olUd+NDboNPmJsy7WQuQmeeI5cjMUqjaY5vHLgaa99MZzuhorRqIC7ya2JsJ7J7a3HBZB3Xs8bgnSbPcrrps7+hIM837l1yvmMGXTXyVaH0+3t0z34/YxJ1Clnuouy/Vw1E3kFv6aovaXqudQlLriykG5BS6VkWnElmbfdV4uSYfEY9nU6z8MYR7C8VCPdqZxu+1r1u+cTUjPeA06XIFhVZZj0LlZcmbavfZIVdPffJbUmjBeV2x0G3Jrt+jE0Gat+7hkl1ryQmksFE00H3QIya1hstw2nc0vIEoLIMoR0R4ixyeXlTELXQrqbRImUgFDPvVKVmQlruDPP1Lwi716roJusSz5fvbk/9nd8HSc4c33gxl9qwKwXPnAbvVsee+yx2HXXXfGmN70JM2fOxNlnn40pUxrn5kc/+hG23377CRnki9k6GukWFCqJ9Zwyt7SyYiXSZS0enMOrF7VmXKpPt2qJIfJEKqMdVeIkK/QiJgTdxgbJBTF2656wOBM1DulW3GFTWVwqL3fbOBUOH0sLapnjNu6TQmKomFCKcyWZLisyMt0mn9ziRseU2jGWgkpx7X04M1XV2XZ0hDulNoyqAkotzmSfI08JGgvC6awIuiOYxS1neM1tzKeEOwzLI5Sxgf51jEFwmomxvGajN72olG3wVRvHyFABV3PT1+96iCZxuvW7VJTyfDfWS5UsyK2uFfFJNk5UjprZDWK8dl6pI5X75uoQystjFPvV2pdnFap6nMlz1ZwDwrqv9SzM9j7aOWWSQsbc7SfdXCR3cKQ7Zk2PDKSSkG4SeJpIN9Pyyj5mnXCQ1MuZTgIc0q0OH9sjnkuMeN9fYhK/tVEvZxD6gnl/GaRb3YtxQxzBEkr1VCdJAptqnGVPCrqNwDM66O5Y4/Z9Z/FAupug2xTlzTIDHAgZk8CpD6j/rvzVjOvTXVViF4aeQozVmuI5B7UefR/1mkKSUR5tD8ccOolfSI0rL2+NdFNRTmNNZsVvX6AWfYWzZs3Ctddei6effhozZ85Eh6hO//znP8fMmTOHPsAXu+mWYVmFsjfWn25eITU0m79PgIh+T/FE6kXMWuAj+UIq6BYzxmpTVSXAmb1QxpgapxamcAJml3vG9VbtFb42RPKinzHOrE0BqINEckw3EOJK7WRURSnM51mlez7K5eVx5XiclbQcCjbKroOBqte6lUR0n26zciCoXl6XABpKo3x5eV0mZrzfWhFZRM8i1LcrolpdI3JhIbUGDRq6ejkt64zJ+keYFukKXVvWOCUh49/5mBJrowRQcbqtJJGZHIxbCwexoqxQIUeJDDkaVFu3S2FQKbZbgeZ0u8mt5kMymqPngMfJNtewsTG7AkSZn1dbn3eA+1lEvPN214o6iEmdq6ZjWvGJPI10mwldLZZUn4dFUfsJMytoVKJHQxNS85WXh0qGfWi5vS84PFJldfIhK3soodZ54XyVQvk9ZbAMl5z26fZXWdjrQVVFcqU9JiaNzcCIJFPV2pZzujJAzY1u3oPRcUOoTniXAFjvq8TpbsrLG0qg7KNFzlWt1C4c0zAV9Ikq9mqcqZzuBBE0s32qCd9UnrXRPUhob2kSuh3jMx2SdAPcZ9RUz4QqM911q+noYVSuwphXFfUl25SX29eizOpzLug+JKuXk7WUDbpfBOrlyWmFZZddlv39S17ykoEHM2mumQ5k2Rutg25feXnJqqjGIt2qvNxGuhmnjwm6e1S9vEMncpM9a0SPEpFulXVO4HR3BPEbebGVFzEW6TYU5iXhrzhOt6dkDX0BjSkodEAsC6kNobzcCLq5si1OmCo16I7hdDdJjFDQ3TyzBuXzlJcb76ZKNAXF7zzmIJyFsMkS43hhSwqnO5SV7vjaTRHj3nld5ePt091cW5dR229ab8WvhYOYeoYlOsjRnLNxkM2gW543fGm9XP5IjVPbpdZlyssXPac7XKpoJv3UupQ8VyNaIRVZB6iM8nILReV4uAY1xERqq0In3SaW0x2XSE3ZF6QAr1I0harQSLdYVqyT8J4yWOa8VEjN/+7ZPon5EZYrnUBxcf0Ws0oi4n6ZIn89+30d7ZlIN/cuuSig5A+o71Q9d02xj+H6GKJx5eUDc7rjBUmt80UJqTXtU01PUtOKWgSizTia+6dBImPNNEVQJVE5pyNCQlVlmNPNVE2GLILKCCCgXt6uIs9ti2ckzCfVyydtcTHT4RDFwwyzhdTixYM0T6QOQDps0M3xhZoFvUCOXllhXGgdVVoOctgpZMdJeXdS4FkVbLmu3bonxOVxg4WmX2NzXk5hngbdcksQd1ErhUWtWcD7/CCnjZavzCiyvEwdszT6oHNBTMaUCKaWm5UJ7X1o5QA1/XdJpI9sTOYYe0LJajKn2/y+lNkmZjnukS3XQubc34kKukMJhYSkBTdX4zjdig4xhrzWIDCDSbvfdaTjOYCpa9Vzs7Lft9ITdJvOvlr3c/gSDp7kYERLRlPlXQXdFHH2ztVFxOm2WkWqtS91rnJrClk/9TGZoJurQjGDCUdAjyCc7ft0G4gdfQaR2gpep9yhCAm9l41AKOh06/tLOd0KRR0HVcZ2+tsbxy+5e+dpdWYir8G9xjAJ9fN17OD7dGdG8GpXpY32mmdgv0sUBWzWD1G9XLc/Nea/pYxtgi+pnG6jHFqoQimZ1qacpffpjt+vyvqGuVV/6plF8MglKpixvnIgUW4EkU4puBqfCrGYNaU/QHkNL2lyS/nZEqc7BtWPoDYBxv4lzNX+uNKCbq96eYsWwkuqTQbdi7mZDkclZcsMsxC3hMVW86+Uiq1ZO8aWl7uBvXI6FG/JyZYzQmrdZE53LNLNc7pZQaOE7COnjG2Wl4f6dLPiVjEbOdxrp47ncJDurnUOgEe6c2/ZUVyfbpnTzSSNguXlzXf0Bmm1OiNIt3FvCxHpjs/QO063RH8gZrcdapdBpuZTwh2GxShj98cReM6GsfoLuo+0J2BWiSqjMsNEI3Ir6F4USHf/3SuJM9XxBN3cfDerZ9qtU+FKIrMbhK5wIkiN9xkuKk43swcmz1XjXmXCnthUUamEWXOOkDJ2rzCQbsBBOGOqPfhxG9dJA4gJ4HSLa5B27Pl91T5moLycFaojFXpgnPSIcdOxJ61BAurX8Nk5pFvoOEGuNRbp5lBAqeRfvfOa0y0qYyesfZrTHQfw0OvgbCI53SVJbmnTXSuGgXQXLEhktmyT5g1LWWHPkY50u5otKddKqE1SdYcwV61xRZpZ1WceT6xOfIHaZNC9mFun6wpoxSPdCeXlKnumVGwtpDuS010p3pLiv0hB9xirjB1jOtCMKNfxcrpLH9ItL2KcMjanME/Lob3iVpLjQ0uVSEsbt7zch3THVTw0SHftLFSZxbvPWaTbDh6G2acbVRrSXZUS0m0Hnjanux9wSJSAGJTKdbpbIN0hVeBI81IZhmAxytj9caQlLfrfcZHurhftrYNuK0lkVGbkEf2uh2gO0q0qK5TDURgtbDyBDqcT0W6dCqz7KlE6xqz7CMzVIdzPXhF+501EXj3n5LnKVs+kI90WD9eihpQk6WYLaMUEfd5xq7FbA04MpLwK6ITa5ASRNXKYgHSXGpEjgSQj0uX4LWCcdHbc9t4DtOd0i0ljtk+3/VxddNy+Vh10176R1R0GhhI5s2ZL+6q+tiIUNCZQa+gz8nw+qGJfWwpNyzpnTNDds6kM9BjZUDjdPEjEcbqdlmHK55WSGJ65WdB3i8yrVt1JxPa0pCpVCdEJc9UaV6S5YEDjA8fuVy8Emwy6F3MzHQ4xW2aYowIaudgqkbOiRzjdVcWXsTELuspUjwlIt0Z/DZEOS/QowlTgWdVcHl/JJRfEqDH1q8uV2FWCM8twT8yWNkptmyKzaZxufjPTTmFhO3TuuMNlhJI1ZUX9c/ToEsEih3YGNLwJB3h2XBVAUACmEfpRWdMO10KkPl6eZ1DDVIGQg575nD5i/T7d7YPufp/uYSHdsgM3DIvlX6U4W+xcVe9aVkG3hpGCbiMj3+HKyyNbLg5qylEqHdROrVtm0C0rY6t1vxvZo5xabEtGLaBZMFoeCMzVYSLdnnc+73RQVGqy8kJqwblq3KtMGHez9tV7i3EOfwK3tINGQM9/p8Q61czEgBN0R9IlYmhHtH2doJSfCfuqfUyFyBEU2hN0OxV6sO+vdA699xhJDb4aIV5XwqfcLiKFQkm6fg/qd0PyjZxKDQs84O+35nTH+DGtg2753VLBU6h1lIPihyyF012ooNseg9UqMmSBoLsq+PapFr2EU7GHIR4s3c8kpFtVkJBEk8/voxb5/lZ0LQS8LS5jzCcW2NEdZyaR7kl7ns10OMpAyY9S8LQ43ZG8L62y2iPlt+ZENgVCzIle2byasbqEiqpaKmShstpRtUO6K0moiuGecW1e+h8RAjpP5lCXwZglrKotUVbpRYoiMWJLEKadh9S6RLdLIw5dc5L4MkLJtIBGj1dIz+oFP7Na2CUi3UEH2awC6L9LVQBV1Uh3VRjt6IxrZRzPLinPcxz5LCVo7KtWV7CDg/igu9J8/4GRbqeMa8hBt+ZfhZDuNihTc+2ZWeUjcuLsOQHYiUrdV74yW3UFgpQBTL0rjfhSvZ7q5dSdhz4htdycZwnCiR2mIocdr1r3xxktD0ArVPv7dLe/n7GoCVUBTp6r5nor7Iml4yCrNYVXxtYtm0qgrHghRcdBTrWhIN3x+0IIuc2qQouWiU63g3QrJ1u9TGZLtgYxdhJV6v5WcHndgvgoAJhDT+ETS2rcjSiXu1dLCKe+5yRJpHwjZ57R99fwYyS+sL4/oh/j+mjRQmpFOOhOFk6NrfZIKi9Xeix8+9QsKRDlKxzs9qkM0l3Je7dGuqX76blWqU+3qJXQopQ+1Kc7Kxiku6X2jAgGVJNI96QtZkaDLdBgqzb1LjcZY3OT8D9qrYhYEOfLnMgW0u3ybtU4FW/JCb7UpiA4yDHmlL14srvcgmKOqSrsrJ97DEYVmAnoLLReBd1kXE6ZZmY4GATNDXG6m5I14dq5kqpYbjRxPJ2g24N0S70qqeVBB9l9t0Lvr5U15VA+ZnNThywEpyUlQ69ET0r6jgtzVZkZmAa5kpHW3F/1rql705JXSo+vs9L+dylPcHjZuWok5EQ9CxUYjfNrSr6IhdSc94Ag3SUTKLEoqi4vj1FuZ5BuXY0QoJPU831caXk4CskepHsISYxYMaaCBsSpc9VwTDNh3M7cVVUUxqWLSHdRNWuncQyVfI1pm8eP2wy6yXOOpAylBN2coCFgJq8KWWyNjFPmdJuOfEeft2eCBbDfRef+ecadZdwzil+DRNSaqTpp3l9yMPIuqfeg8Y14JXIukHJaR9WmhdSk9pSetqSi0T7dnoRObMIsndNt3O+AlRrpJn5KUnm5X1DX1Lcx/b4YTrfyq4P+KjNO5/7qoFuYVy043dJ810mLgp+rgJsECpnTQtR4x1mg5AVqk0H3EmBB8bDa1Gao+3QnlBXpDZKql4tBt1u6RINuh9OdkwUIpAQ4wlzenZw59InfAOZmFV9eziFIVh/rgFPIlgRFc7rt++eolw+B062SBeocBQkamyCm52xWQ9uEjfufRVZqKCQ8Kwu2dRRXsqaRbiV+R+5NKqcbMB33sP4CYAc1bftfUnOpDAmZ8AjraKTbf21tkG5rrrIikkIJYP33XpUjM46RaUGyhBLLAUzkdOcElTLGwKFl6t0dyQqjfDQF6Y5ryajbOnFdKxB4hsPgdCci3XrtS52rLJ3HvjcNumPvLWZpsk1/MKpU9HNXgSURUmvL6TbX34GR7gROt4R0G0GgWFas2jwaKLY1DrPziJGAtfwW2EGm8/5FInZtdCV81x7LiaXXSoXUpHmWwunW4ow94T1oVV4ev3/F0qEmltPdX7eovo0FDoQPYp+XjMNEursc0m1xunnEWLyfnr3Zub8ipzsl6JbeX768PC8Mf5aAJcmcbq1r4rY6033QJ4PuSVscrHACTf7FVJOwTZ9uXQqmEY96QpmLlnkMS1xEZeD6f1diIS5PRAV0gjJ2hBUOGiFnd7lN1EK6pQwnafdjWgcuyme1KKqvTUa6mYUyUgW8yZryCr5eLlRFFjrBqoycIwrpJsmCyE04Tmk4zVnICqN1FNen23imjdPCC6l1dEuWeIetUf2M7NOdu457iCMXMhc1GXJ5ueLMBwK6FD4lO1fNoFtIZumgu35fKeLR0e+rBzEeojXvgT0XdQDBVNdw88a69kLQrzCrZYjpdSpA39FBt1hePnFBd1lWugtW6J1XyT+9vqbOVcPBUzzPjK6vQhWVee3cXtIznG6Kljsl1qlmtp9ykO7ISgMfckj2BYk/3LSKLMNON0HknNaFzNpoc7pd/8ENusmeKQQPSeXlAuqnE9GsjomEFNrXSn0jZ5z0/TWeudM6qjbHR5P8mIE43eGgO5xkr5PbsdUeviQRsZJUVSgbDtJd/zzO64WYyQRp3rhrSrzP5viBld0ZoxEo9Ph91BxfUwDI6vc1Z2hyg3K6ucRSU504GXRP2mJgGm0MLITqZW6jXq5Vqwu7HNKayCLSbS98TTaX58iokhWqjB1jLhohl1yy/W85TndC9pFTxraEmwQur08oRUIbnPYT9P4lcDxj34OKnIMKlOQeYSqR30YsBZXKoh3LrjXu/q/85eUUfaycNm/ppYlVKLNNzOS7tc0gU/Mp4Q7DWHV4bhxtkG5zrloikgGkQAfdRFCH7fU9gUh3QZIvpZ3E9CPdzbXnCSi/T0gt1JJR04oCSLdfSK1debmkOM2ZRroLO4iJGqc5xlJuGdaIB9lruDROvaYb5aV0/juloG1Mmr+pvZcH4HSbwlRBp1vidHtQ1F5ROurl5hic5+rsmQKCnCiGyR1Dl9Yz/pQoKkeutTXSjQpFwYMYThvCKKQ7kooQ0fIyGemO5nTHV2Y1nO5hIN3+hC5gJ0NNyqA0b1Tli+QX+uamhHSrtc/1JdNRfcnn0FWpzLhjOk5w5gMDupoOlSasvCTaZNC9BFg00l0opNvk8sQFLdo5oOrlejHIEOoB6fKW+IxxLiljR1g0R0bgdGdZZqh+pnO6OWVsi0OqnMJQ+aOnT7eUQVbXrs8xAUF3STKcKZxuScGTWqM0HGgZhgpZ1X8fs6BjScqhYHOCuXtDnRYHPWtRmijxQiWbyD7drTblmONHKmO34VNac9XoIx2kk9T3u0eeoRrjouJ06z7dOR9AVKVLaQki3WJpIj/fq7JEN4tEuuuEbo+u+7V552ps0Ced23gvQu98EVj7Uvp05xrptsdN1z6KovbHmRv/NpFu0p+dIJwx1R6iiUF3anl5eF8Q1yCjVWQ00k26WqQi3VZbxxDSHSjBTqEISag11yYzGunO/b6Ro0lgrFEVFbetTb2LMv2E8wOHh3THtricyD7dyoeTysuHyemmIJGNdPPvgSNIlhB0hzjdrZLqke+v8tU7pQfpTvRTfD6wpkNNIt2TtjhYo9yqygx5p1GXuMGYpIlId1WQBV7ku7hBo3I0JYVOdYy8HLOuK8WaRUxqGdYsQKGNmHOA6TGocSifqTCvri0jz8hpacMkLWj20V3Ac+scopDaIEi3Cl7VM6JBjOLImkFMQACOWhCVMniMnVJ4ztTIuAHwLcOqEkpQTLePU0E3OUca0k2cpYiWKwAMVfty6H262QTPECxWGXvQPt0AtNBXFUiySWr7+n1dVEi3ulZS+u0g3Wq9lZSxTZRfWveF+a6FAYFgS0aVyCvpul9bHNLd7n5KXGnOdNBd+oUqo6pnhHLcytlbXBE5c5hW5wGFdDtlxQO2DAOMMmFJSC1ubYzR+pDWcJWgyKuYPt2K022j525A17zPXJ9us62jk7Qo7XkkVVkNo0+3FcBVkXsevdaAenmXvmvGMy3JGqKsHdId+a5IdB7DYsuMnc4tIUsIIrUeiyCkNgxOt1p/KUhk7m/S+6fFFSP8VWrO/Q0i3Qmc7sx+3+SqVOUjN38ftE+3qzVjcLoT6aZLok0G3UuAUREZEemmvFKmHZVkmn9VkAU+UHpjnkPzlkJCanWw2ybodvoH0nEZPCZJZVVl0MNIt8yVpM6sKmtV10bLH92WIMY5Kpq151uuaZRfJQuclmEebk90xUPXOgfdzFR7tAylOO5wuVkkKoX4oLtp69L0Qbb0AsxNRXOSVfJFQrprhy2iLM4pL5U2WWLaKawApS+SypWiNtGcbtVTMyR6koZ083NVV+30JKfFLgmmrWMUMmG13prAoNtZgxVqp+YEcZAlvnCH47NHrlM9oxd4CDkopAondYrME7QMGHSnIN062VoK+guhuWok3VTQTYUAJVTKDDI5ZeyiYsrLSReL1pxuQA6ak4Pu8L4g6UpkhoBmkMtL1lPHyWb27n6rU1ejQORkRyJ2TicHjzXBBPmDBgvGjd/575d+JoVKkIeQbljHM59pRTvK6O9E+jEJfiAdtx/pTqOTxffpNhLkAR64TphnwvuKhKBb0sgRqv5MIEVSAVfP3RIk487BzE1nT1R+Nk3QeLQ9HItFulXVjxfpTgsfJQpFVY43lVmTQmqTtjhYqftb+nk2SgjEKm2UFhTnHP2XXXFkcifojsii6o2F5yAp9FeVrJSBdkr8OGu0N6FPNw1iNMIZg+IT6witDdSC3JTjCE4hRUSLBpnVaINQ5qYWcHUOubw8rFIrWn1/1TkcpLtrIt1kAVdraaCVhHocQVQKQFeoHJC+o+8/SFBoPmNV8kv7nArPLMZhltSLQ/fbfC80pzCxFYdzTKnna0zWP8Ji23toJKYlpxtoklnBEuvxBKQ7sBYOYq6Qmo0g03fNbIVkcbpNhybEZyfPtTCD7sAzqnSrSKLlUZsf6bZRv1STEg6cqWRKaH2NqZ7pCom8RrHX/rvUg5dTL25aRdmc7pg5IJpYXm6jVsnfN38XoDaZLcOCQXdF1MtpIMnM5aLyB90OH1gQNBuM080HEyrR3K3cnsXqO078oX6h3wMFSAhCaiqQKsh7hMZPEYPuFj6aaI4mSRjpDu1XyerllmK/f8/SSDcBOfIhIt2al+8g3cb8FzndNJEX77OFON2c6F7QIuc7tK/urpVqPUzVe3X8AX1/4/erF4JNBt1LgAV5zLUVdMFO4PLISLeAkDKBPeUtSYqI3QHKyx1VRQ+vWSqD0QhnUEjNXsTKotDK2HRxUNciOXSiUArTs1TkdGf2OZzycuLo21+OLS+3n1ElCKlx5boa6Q5yuhsBItZMpLuKLS8n71aV2erEXNBNnBaaxEjJ0LtiQbGc7sbZGpaQmsPDtaoqBnD+a+tGKo0OyukGYIgHSpxu26mhlRlqnnYWGaeblBnr0lASUDBIt+nsZ3mO8SrwLgnrlOpAAYTL9RqkW80J++/euTrg/dQBSwYLQfaNU69LqXPVTORVfCLPaZOjnpHgUJtJUbXnVWT+q58HQ7qloHviON30Ws1+90EajBAQS2ujUrGnfboBD0Ulsl1lGzFMt0LP3lvM8waRboHTHWzNZ76b1Ccj3+E6Ilg/l734uepQAGKQ7jg6WTKnGwiit5UOukl5uUmDC5lYzWm/rw4AYSS3pTlBdYyc++nx2RYFpzskpNYp+LkKpCPdjj+gy/eNPuiT5eWTtjgYVa2WOd1KxIfJcEYKqZW0HDLUTgFoSulIebkrLqIcJ14ZO8Z0uQ5T9mL+XJWFuDg0SLeUUOAzh2bZJl0clCCRujZauui2BCEZZeO8TesSvqWNOgcVa/M7V5HlZeQcIqfbKte1EfroTVgKAI3rGqkdnRCqqu53824J9wbQ426EaHhOd1M+6j11/zO0rDgZ6R5ey7DGyal/YV5XFVni57FYZezYpIVvI29EJP1IgWol5fSV75rv66LjdOugsLLXU9qm0KfgrdfHVCE1g9MdasnYdK1QJawCmsPN1YE53fHvewm69iXOVa56hqwp2kEmaLrkUHe4oJvOf8q/bGNmmbA14FT1cl95ud8Jz+qe5llVogw53SHuKVkbna4rTFtH5/1zqE18dViKGGYpHEPtPSOVG3SHOd32eyB1dnGuM8sa3Qftk/HfEVufWkJqqZzucPcNfb9CSfaEZ+CcMxR0V3zQrZ5ZFlNeLrVTpXsL8SnM92Qstt915BoOMPdXl5fbIIe4PnBW2T6bpNjfVA7aVJCUjhPUHH+A8YG7k0Jqk7Y4WDTSTUvcEgQ01ESuJPVyaUEHHIe46UXJZ38HQ7rpIsY74ZWxiA1UlmWYWbZJF4fhIN1+tKFy0B5pk/UF3YHkC3lGEnLItQwbWp9uw+HoRiLdGRl3j75b5qbsJJb40rs2SDdFuoJIt1H+qB24oXG6XSXcQXndZVGgU1d7DAvplkqsAYT1LJRjJCHd9Ri7i0hIzUW6baoNXXNsZWz+2kMJB4nTHdOSUQupSerlvrk6JE53jPNGq3yS5yqLdNsJCbW+UsVeaV0zg06157lIt5tcSTaJ95mKdKMCJEEy4oQ71DCD061MRrrte+EG3fz91Z0KjPdZREkdxC4SQfaYGEB37L2FG3u0evk4T71jx0l8Gek7SeXlse/KBCDdyUJqQHi/EpButTelId3C/evxIJG5T0vPVaKsNOcQ5jYYzvxQkG4+ySa9v9SfTdHhoCYi3eMGJXAS6Z60xcGCbQdqaxz/+sVNCbodDiJfDqnN/FnxlurfKYVOmgHNSMaYoqgxptW1g5xud+NuflYc+bSg2xYoIkh37byOaIeOIjFCGycG6ZY53STjnsAPSt101TnEzWwi+3Qz46ColGP03aJLW567itJUFZQ44Q7PzmMymhMfmA6rZZhPCXfQoDtFGTuWT+nbyHXCMYAU6L7yVGCru6jVy0mig/IxC1IdUl97lsERTtTXUgQ43eSZKopQTEtG3bVC4I1OrHp5/Puuxqnmd/JcNcYorSmSgyyta+a9UnseHKTbDmZbmeRUx+p0+JJukUh3zrRgktXLVRKTJByEtVHNGapebo5D5nT7E9Vpuhy835KTvchsnxrbpxvENxLHWRjvCQm6JR74xATdQ+zTncCrd8YYyemmdJNWQmqhvcWHdAvPVflskhaFL2B2Od31tXQE2sYAQmoi0l3Z+25KxwlqLoXC9YG7AZ/ihWCTQfcSYME2WbUV2oEz+l1GlqBVxPkKc7rNoNvm2Um8JZ09q3hl7BhrVBX9LRgGQ7oFZ7YnKGPDQLorvmemjHQb2XPSXsZFunPrHBPRp1slNfQzcsq2ptR/Z/p0JyPdHkdUjyMu6M4ye9xsQofcH5dCIaFn8ShJI0QTh9B3DHXoFOTPe0xpcwMGD7oTlLFjkxa+krVGRNKfZFNro1uZUQfdWSW3CByisdVGMFEpe33wzRndESHYboYKqcVXEolaHrV55+rAnG6lgh8RdEeur3JHhByoW2mqY1DKStOb1kbTZaTbLS9vgi2VHHDLMpNNVC9Powzxx7CrfKRrzQxOt7KgejlBz+m9cZBu5Y4ybR1lpNtOMEqiqYMg3YofzL17Ytsssr+H+nSz3HV1jELRech31M9RLcNSOd3+9rSAJ+FAzBH2DJkxV4P7VckLqel1P4ZOJXK6VdDNg0QcvcShXJA2rynl5RKnG1n/2pxWfElCava6JFFBO7pTDgn0ue8ErEPBANrpBbAFRF+g9sK/wheAKYcjL/0LYYN0q8XaVcYWjZS5dDp0onuCbi1qVmdzC16FWaG/I/AERgFrSgClHttqIoeD7ubaBBEUD6ebCqmp4EBdm4t015u/XijVOI1ggmxOUtZUnSNlAU9GuiEF3fUmgsYxCpX4UevGBGMkQRPL6dbvFhdwCIFQVvH3JqW/qHIsK+e5+t9xda/GDIRjcKSboEMJyEHIep7EkzSOINJdyEG3Li8PtAjMCqEyw3xvCj5gG6Y5a3D9rkndIKRWaQBz7ZFJNhVAxwTdJZkTNAAWkUbzvIsS6RbWvqi5qhHLeo2ma4ru3MDzGB301+J0qvmv9l5ejK2VBZHuSPQy4hgSqq8CzzwB6XZK6zOyNpJewVZ1Un2e5rnSsnjC6S74cefGGlQFRCQlNW6tF8K8e6HyXBDBV+0bOe+Sfbz+LxUQUu+BZFxNKynJj1Fz223vKRrnlwgWq0ESlWR3xuHxZQzT+hW0ZZiqiIxCuoWERKaSnjx1ybxu6bk2OhHC3hOBdDv+qtjvugXSreYNVdDU/tcQOd0d6gPbc2S86tjity9Qe+Ff4QvAnDZZQrDqqoSapct+B6wpL68FdeiCThdfc5Grz5PVE7fhdPPZsyn15kWVsWPM5d2FFzG6ibqlnnHBa1V/vlflzuKgFmR1baBCatQppK05jGcq8erUtev7JznhHI8ptr1P4ByqpNgWpqrRsnpPDWa+aU9SznJVTmpnWuVhd61xswEHDYRUv/b63lA14xSUxBEtY54rZ2ojUnMGgFNmnGoO0p3QgiVkRWGMM5AI0T2eA86uVbJG5qrT51SYq+rvNOi20PhIcbtBTHKE3Mqh/rjUpXMBjGpRk0njFpFulTALr68hpNs7B6pBke5aGyCiRZ66lik6qdlirqo1BYI4I0W6CRLLCUap8+r5K7RcCs0BrwU53ZHopfkd4RiyIJkbxMhIt52MDVFvnBZCxrhiOd2F0CfZ/Dm0jksJB9UybAqjLyK+w8570P+O5v46iDyHdNdJtwCnOyh2myComyKkpu5XKE5K5nSb4whxsgnPWVnHpMGFLLK8nK6n5uOQnqukE0HPwV2nA2II73xsgqL/GSKkJswbtb5STnep3/dwxwlqjj+g3u+eSmq8OMLRF8dVLuEWdDxrEwVLPN9x/k6Ef+SgOzM2FiUW0s+Gj0qc7voYU4bA6aZohHsdKrByg5jwtfGZw16hyuLdaaOuZYrA6XaVG8kzYrLnkpAat/n3fzZoBdQSN90pEqdbIcpZoRM0qUi3U2bEjkOhUrWDHChlVuPS4+aWNuK8qvcz08gMXz7moCyM6eeayc+VM+2094aHdLsJnkx8p1PNR7GgFo10Gxs5nasOp1tINOUSGmGOMfKZDGLOGkyUhysyD31zpgjx2YVn2gbpVoiRm6D0zNUhCanFvO907Ws1V/X+wwfuTnk55T0yTmaHzl+6rg9TvXwonG4auPMq4HQeZmbrPQSc7npcivIjiocSJNwSBiUUIOf9k7ipUoKdOwYxKbnSaNF4yssd0VjbN9JBd4+vbGGvU1PlpN7eKmksVL61ENT1+SWmpbSO6lCEM8ZikW6hvLzpsjLEoJusp1mWOfOfPlen3Zzjr8o+W0G1fZRAodMVYACkW1rbHKTbnqttOqzInO723YyWRHtxXOUSbsFsWW09x/F3RbrEc0gLvK8syclc27wlSQV0WjYAp1svYgEhtUrg2CDi2oQsa9lTAkXuuNW1qGtzWoapBUftO0LWH4DRusQeu7p2dY6klmGJ7WX0OahKsBnEFAIiFCwv7/+99KE/9TGnglcapqb+rt8tH6e7sgMhJQ4kJUpiquLcPt2xQmp1dYgRdA/K6Wbvb0o23GOFSjxFKGMrZyvE6ZZa5AFNMov2C9WW22uji3Q3700W+UwGMVdR1k5iZkKQ06XlfWiuPSwaSdapuhohxolptDz4jhPiXC1LudVOpKWo9SunWq+vbeZqfb+maqSbrClarI0XD+LmpZM0I/N/OOrlfEUDRZRFM5NugbZjUrm0ar2XQd5Xm3Hxjr20NrK9qUlbx9IJunlBQlcIsBmnd78xxuFyuu29iE2Qi5xu9R7YvpEPkafIf1byKGoTjPlbXqEcj5+rgk9HzddxglqwRSg7DqG6gxpRytdfr9/XHDGcbn+lQEcQ6QTcpJvj+5AWrLFVlUBzv1x/tX7nHU53+rXKLQJtEMMJ0lv4KJJ6eV4LhfZagHBLok0G3UuA0SxeCOl2gjHPd7Qp5xZkgfcJcEgLSG1uyYr9dzYwCpmPW27+LCh+WuMKqn6Sss1ClS6704ZeCw0SRaRbn7P5vrSRi+Xkzrjbc7ppssDJIHPoZmABp+bliZJj6h8jOd3KYsrL1TPR2XByzjSku74WoWRVMi5BMWifbp8S7rCE1GKUsWNbrvn6uztrRGDNccoMufdmkZSX8+sQFW/0zZnoa3eQbg/FgpiuHKLrfm3iXDWDtwHLy2Pe99Dal4J06x8FITX6eZ+jKfZnrk2tSxOLdEckPSKPIfXtVQmKLgTqGHdMR2WZ3/O0gj8TdMtIN01e8dV1rZBu4dqbXzTXEWwZVpvUzUT/3GHGqTjdVc//nYAQmP27BCqCdAykCWol9+k2zxtEum30V59Ti9+li4s1Y7B/5kAiSXhSjy90P31BN10fhbmatLcLxxCFAPXP9lxtU43XdDPhfeA2LYSXRJsMupcAcx0O/uXUmxfnaAb4fRpBhp9HYo8jbWNxA7r2SLc0BimwsscVuVkJ6uVcxtMJuh2km4iHeTZIaSNvHXRXVXp5mXBO2p+8/x3KB4xrGRbDv1SWUcfH+XjEuyUEQuqdd5JGCa1OWNGy/sC83+Oc1wGBbr8S7qCc7l586XJ0n27PRu44OgHH0qVDdFBUAYdiiCb1Tm2oDALSHXXtsUF3/Ywi1lflrErBVJBTy40r0nzJFmqhBETUXKVBN0kg0v3LcTQ5TrcgQER/jmk7KJqoXj78oFtyqhuOrIDoWcfk0TTp3ignPO90Gj+FzJtgn27hXbI43cEuClLCgR93VXk6TiQCEiz3XCPdAU63IAQaBZQ4f48I3JEmqNWO0x0ZSAoCZTl5X/3HCHDi1cc8SLeycPAaH3SL6uUdYV614HRL80Z651M6TlBzErjkmU0G3ZO22FgQ8aitUQVkMrMB0QPlcGjEI9SnGwgu0C7SbY9rOEi3FDATFXZrXIRHMwQEiTrIdNES+SzknFVVyZzu0KYqlWSZbTMCQSB1PF2BkiEi3QmoFItYGuYkdFikm3C6CdItJkqihNQUWjMY0t3Ns2SBEmq6rJsR5RkWpztmg4zldPvKjFORbm5NccY6gUG30x6J0nWEli0spzt6reOTgzH0HU1dEoIpca4OIehO43TzCLKyFJ0I/WMs0i0oY/d/x5ciGyfpH2MofbolIbWUoFvidNfjLPi5qO6VTs74nO4QIkfGZAX6QmI0ltPtoNR5pl2f4SHdBMFnvhNap6QuAf1xUgRT4nTX9KjIoFH8XcK4lZkJjOgWoRPA6W54zgQcGCKnW3+M2Vt8yRPAQwHUP8tl9BKnO6Pv30BId1ylRigpF2MhH3hSSG3SFhujDkco+5hRrmXEpqz7M1PEwyfWEggCJTEWfc5WSLdc6tU/iZ0s4MRvtO+UiHT7UD6qbElFepyWNsKiZvoFztjpOaT3QkIzuPNSo0F3TPBLe7wGsqBRqFSgcsD5OC1VYpFuO/CkSLdTqZFFjLM2h9Otz+kfN50jg/K5ASHYTdmYPVZoFDW8dcQmLXxCVZXUzk//bN9vbk1x6CATiXQXpFSWrkMKlcrsYIx77k53BzHJNgDSrSuc/GJNE4J0Rwov9sdJ6Tst5ipdk51gir/fPkdTEiBqxmkfo5VJIoixQmpAc22BY4icbh3ExCDdPHouvb8W2kZb2OnnKiR9iFgbSwGIXMfVOOia7KB+VCCOO6/zvpKgkM4z4z0KUVSa78D6e1x5eWLQLazzVseJ6CT7RHC67fdAmfKBu1mJKpTwEo7hlJezSHfu/dk5ptieNh7pzui8ygiI5DNx3virUkPIeIw5/sAk0j1pi6u5jif/cmqhEBqMxQS3mttHy8vjOd2hjSWPWMTC44wr58uEjao/LltAKzb7qPhDHILk7Q8MLstHF3i1IBmbWahUSUquSIgId15ijqJvFEfWzpqGWgCpvw+CSlFz+6bHl5fnOugm15rgLKi5J5WoShbihLUx8/5WpD1HsAVLwLRabFR5ef//YaRbDr7CSHdM0E0dn4nb3J33gLxrbnk5rL9bx4pF+SvbqVQVOTEtGdXekgeRbhp0G+dseT9LKhTkHac/iImaq6G1TEjo+sbpChDxyYGQiJfXxGRqZBtI7zHsALm5VtIeiSDd3jW+siuHigClqjSFFKORbtsvKT1ijLHaHGK7NEqpYp5pKOh2kG7Puu8EU5UQdHdIsBUTdEe2DPUeA3bHiVBlVqy2BzuOyKCbggOm4KtKFIeOEbp/3N4SQrrjy8sZpFsqLyf+1iBIdxloGUbHqdeHFtV4+nWlrc7U0CIS+S8Ee3Fc5RJuQR5zbawgiefz9mcEoZQBystDpXdtkG43EyuMoSKtz7hxhQQ0KNJdyP1vHadQ7NPtL7XzlawF34MQIsJ9h1rgGWV5jl7FJz7i1csjhFUcVCqAdNNxsy3DeIeuKwippXG6Awqygvmcr7ZmKeGqoQ8L6dbVHvFId2zLMO69cat8/Ek3TkTSQeUnUEgtzOmmKKCMHAYFNIV1qmqFdNe8WseJFJ6hPmfmPpNIS0FNQnoWbTjdtCLM3TcJGsQ4miEOp1YvX8I43U7rLdL3OAbpdlB+4f21+j1TClAip5u+v+ZY2/fp9pfa9r8TqHig/gC5v2b7KYfTXfD3vOmIEMvpjpiriZzuGBHEdkh33H4lldabeg09o80la2J5fgzS7dIZfMeI9tnA7A2DBt1lCcD+jjRvRKTbQwULmatrRILuSaR70hYXixVSU5lEacL4TG2QeUYnuq9lmL9czy1Ro5mtFmWJoY1FbVSqLRTL6c6sz8RmHytP/1upTyQ9p072CuMufBu5EwBHZk3NnwMOWgxSa19/s5HH9+lOd5BZ1XTP33mkmzh0QU53fNCtOd2Jc486LYP26Absd97t5TkY0p2ijB0roFN4NvJ09XL3/V60nG7yHpD2dLRFUyE4+gB37VJlCykv91TkOKY43Rnv2AfLywe4l236dCtrNVdpUpiuKWIiVFD4hbvW0XENVb2cVDREt4E0PxPJ6XZUwLt2ebmf022PS0K23GStgXSTeePcP2Ee+ZLsweSfgPrlAtJt85r9yUCnxZ3nXXKC7gD1Q/RjsswuZ055TwLfSWkdFdUiVBpHKJCs+JZhJtJdhpDukB+oPhYjpNYW6UZlVQ9VVaUT5rTqVK0pTjIrVMXGdJyQ5o3kW6Z0nKAWqvaMSRK/EGwy6F4CLKhaXZvOICW2LTI/M4h6+aJBuuOc8KwqkaH0lpsFBUgcgaK6tJYZN70WmmBoh3TzfbqdcTYnZcdtOVqBEp6YZ2ShnMYYhsrppkF3UL08Jui2HU/l0HUzPlkVi5AAZpXJoEj34EuyTwl3aEJqEXO3cSJjyzrDFSRtHKNFGXRrTrd6H0mCR+ovzCPdkdcuIN0x9B2X002SQJFIYxuLFV40x6ms1Vx11hT/MSka5G0/qY7hBFv992A4fboH4XQLSbfIvr2qBVMnq8R9lY4rJ8eU3l/r/tIKEbG8XEDouWfUiay4EZWcp5BxEwQfcDtOJFYBArLQlO7TLSVofckX83etgm4J4IlPmDkIZ4xFI938OtSxkO7Y8nL/3sKBRCF6WDAJb7XJa8bJ+oFaSM1OBkaLpDIVj6Jiv5BoGozT7feBJ5HuSVtsLLa8XPNsEh1/oFkcnPKxBCE1l8dMeWF2YNQm6A4GNcY4OyiFTVhliANlWYJAURSnm6AojvMaKLUD3I3cceik5IqvhDCoYh9AfwD0zGs1/h6rbBlVbkY30RCnu0s3yDCnmyLdwUSJx2Sk2z/uICeshfmVcAcLuivPHKAWi3T7HOZo9Vf9+cUE6Sb3W0KlJEcfSOCzk0CqTAi6NdKdzOkeJtId4YYE9pq4jgiE092lwVQI3eHQSb8A0cT26W6DdPvLywuB7mDuZ9K+6hwzktNt3V9h3shJH4XYyTSN2PLmULs037gdXnMwSeTjnpP7JXG684AfQ383RKQ7pcw4JXndnDeN003HaSHdwfLyuKB7YpFuWHPToi6QfuyOkJpUxUKNCbplpJv3A1OSLdSC6uWTSPeisVNPPRVrr702pk2bhte85jW49tprxc/+8pe/xNvf/nbMmjULyyyzDLbaaitceumli3C0z5MlLoR5pwMgC36eO2aDdAfaakWMK9T7j3OQY8cZ83MHpddRSi8vl1E+WtbqOoVUuTF9I0/tUa4twUF2kO5QEGMi3Z7WOqa16dMdLC93EHrP+yqql1NKQBxCYrZ5S0W6zZY25pgGMdOhGzbSXSTwhaPLOpM43emOUbBMe4jWo8kXkuChDrIvURWN8g+EdAtaHrXJfbo9ApuRloR0O9SaFnM1hHQHEqHtkG4bUWplQ1jXY48hXWvXCDylfZUeM4x0q/troG0CBUgWUvOP2zxGeB3iUT+JhuClUwWCbv847URpTqtl9Odpy7AAMBJVEZEG8MQh3XFJD/a8IaS74q8973RQVvV5oznd/n0iJuh2hRXbBd2sto8KumkiL3ZvZ5FuqbojYq4mmptUmgy6F7mdd955OOyww/CFL3wBt9xyC7bZZhvstNNOePDBB9nPX3PNNXj729+Oiy66CDfddBPe8pa34F3vehduueWWRTzyRWvxnG6TG9UJft40xekWkW4fR1YdI7Cx0MBpKEJqnoWyi4JVWVWJQ71ZRbZxKEvZmaUK88GWNkLWvxGV4TZyWjkgOOVVAZj8qQTnTGqPYprVT9EYUzrSnYJKhYLXiAWciJaEOd2ox+l3Fsw/u+36IoJT4x0NlebHmHn7HdRkQE63SjxNBNLNzdVg5wbynDkhNet9zTrBao9BTOtqkGoZNZ9zB6GT57uzPkpBOA26y3H++5yRrhUOwpkJc3UoSHdE+yk9kFAQ0/9/SvVM1wmm+PvtQ+RDAkRqjR4M6Q61+4p4BiGRTeda5cCziyKAdBfqS/UxS3sMZEw20s3Pm6KQ3j//uAHEdctAegDiDTxpgifQ2cU8r8TVdQO8/v+zKtJHi0rOxO1fKcFXijZKc97YQFJW8Fc0uCCnW6rmpHsLV15O9muX2x+4nxFIt94X9ZwgVZHR5eU+Tjet2CFrY2B9iDEnMUr29lYaT0ugPa9B90knnYT9998fBxxwADbaaCOcfPLJWH311XHaaaexnz/55JPxuc99Dq997Wux3nrr4fjjj8d6662H3/72t4t45IvYAouBMsuBa5nh1C1BWnC6pTYu+uMRmcOQBZXZLaS7EARLFNIdKCuSECQm4HD6WXepU0g2VMkBKXwbeUj4xzimKbqT0FrGaRmWgnRXnoSBYa1Ej0KIcUxChwSeHR10K+Vm+v7Wm0SAi2Zeh5PZTkCEAbRqxUGNVcKNbcESMN02L6pPd38MZSTCxM3VVKSbdb4sOsTEZtMdmgFBqSgyI4k3ARHJVrEiJ6G8nATdDsIpcboFhCnFtOB/jANH3rdWczVQPeMGVzaiFId0U0SeoFJtTCofHSqn23aqnf7DHXtfjQm6RcEnPSbm/pK9N5bT7RMkbN7hOG0JH8rfP2c4Yeb2jQ4j3dpHIOX4KlEn6X+I2jT0dynvCfd9w/S9itivUmhazjgC4mDKh3NoXWiC7qI35j9XbHk5SwmgfbpTkW7eZ+OR7vpe1OtWSedVLNKd5TrxLL3zEqDjfecDpm6VWF7+/BdeLxJ73q5ybGwMN910E7bffnvr99tvvz2uv/76qGOUZYlnn30WL3nJS8TPjI6O4plnnrH+W+IssXdi1wm64xFOB/HwBd2RvambjweQhRgLId3GmLoSp1vgVzrHcBCk/s8O+oawkFo8p1vOILsOned+mmNPcM6krL5ppRR0TyCn2xH2oMckf+eDbr68XL3zkjhTrPp2/xhhTjw1834No7zcPI7Uwqqt+XQN3DEQSoVgPnEWB7kOrIVckkh6XyfCkjndnvkeLC9X6xANuvU6lZ5slcolJ4bTHY90xwqp+dcUEqhTx7JF0B2rXj4xQmoJiY/I8vLmWu2PmUG3tK/SY3ZIcNom6I5Vz5fGbR4jJOQltcGS1nQ/0u3fq3m/hCCB9Xkyof2p+jn3CakJ+iuihfyr2hYXTrcv4aDAgXikOxB0M0gsvXznuYb8KVNh3kK6S/1nCoC5nO7IhDpzndLaJmnTDBXpjqCGvRDteQu6H3/8cRRFgZVWWsn6/UorrYRHH3006hjf/OY3MX/+fOy+++7iZ0444QQsu+yy+r/VV199oHE/H5bap9viRnk+bxkJuvUk9CEaTgmVP5sbFRgFTEIjjEHoRUzkdGshNUlJXGVZ7cysD0Gi10J5YFI7EHodpQf5CpVYSqVKKa1lYoTACqYNSVVV0W1E1OJbVR4U1KkcmMJ/Tv+dIN2+95U4MV1dXi48s0CrEzODH3w/GbOQ7iEF3U2LuuEG3Skoaqyz5XvnoxX7lYU43RMcdDe6GhTp5nUkep7KlnCfbn8wFlWuF0K6pbm6iDndrohki7nqrCkE6Rbubwqnm4qFDldIza88HnUMihzS91FAjPNOB0XVJCmj+nTX90KmVNllsj5Ot/NciXKzNG7vMeiwo5Fu/70yP6N/TEC69Tjra8uEuekm8gI+2hA53aWQoOBsIsvL1bU7iDKaCqdw0E1UwOkY9M/u/XPEgsljjdJ4Ya5VDcmaZ5Ut+OpyukNBt7tehLoVNGNk5mqiTXK6+/a84/mOWFRVuUqQjJ1zzjk4+uijcd5552HFFVcUP3fkkUfi6aef1v899NBDA495UVusOJOFHCQutsrh6NJ+rbF9uvMuul2+3Uzz8fSAxD1n/CImlcE1GWIBAQ6Vl3NBd2CTDSk3xmzkLl8wMuhO4nSHqxFs5JCgGYhHugGPI5TI6aabRBzSrZIzqqd74JkJZv69zTveNeCZYXC6AW6Di+R9BUzxhWM2yFTVYHYjjy2xVuOj1R+gQfeiKS/X84ggdhSV8pXFhpXb/etUTFIzc9Z9uVyyaKkTIVlSn24aMA+hZRjldEuVWDHK2PoYzj4wjJZhwtydACE13zNRyGFseXnuBN0h9Mzo003aOjq0gYRxi72+icnq5W2Qbn+CnPuO5CN0wc/NKKR7YE53BMATsKgWoc440oJuL9LdumVYmA5pXj8nfhvVzYS5VrbiMSRQGFteHlGdGKLaDKdP94sz6J7YtL/HXvrSl6LT6Tio9ty5cx30m9p5552H/fffHz//+c+x3XbbeT87depUTJ06deDxPp/moj2Bkp8W5eXK4XAQj1hOd94VlW+VuWhk+usX7HuoxlWMoZsF+nQH1cul8vLAvYCLzLo9CnMAGQB7AfIhX2lIt5H1HERIjQliiqyjh02TBUAM0m048mWFEe51DjjI7sdp0O1xQBRfsKOQbr683MzQ+5KB5rVHvZ/EbKR7OHlQSQl3UE53SjuqVC4l26c7iHSH7/eiKi+3VOxF9fL4Pt0unz0uOYgyPug2E5QAh3QLc3UIQbcXKSQWapcYNVcDgbvYpzsS6e7m2QS3DJM43QnBVKBE3Udv6qGDKeiJ+yodVyyn276//LwJ9+mWx51KE6Il012ntVwE6keTL47Gi6dlWGHfryYpHEK6wz5a0KIBnviEWSzNiD1vLNLt43QPqbycuxdmkpynBEbQzZj5ze6JKpnVVWtKYjtQpjpJ7tMtKfYPA+nmfeBW3YyWQHvekO4pU6bgNa95DS677DLr95dddhne8IY3iN8755xzsO++++KnP/0p3vnOd070MBcLi0e623O61aKls6qkN2DMgu72eKUlauQYbYTUEjKHYaQ7MehOKC+XepZbzhdTjeAr0XZRFHo/zQXaRLrjS0HpYusI14EPYmyk27+smNcmbsROEsMfdNMSQG95ORVSy9RmxrchAvxOs/nMUluG9c+Tsf8exMQWdUMqL48J6KK5lB5Od1DPIuJ+L6rycmtqd/h3TUS6uQqHaKRb4HQnONkq8UQrLcS5uoiR7pAIWtRcNZ3NKkdG96cgEiujqP1/Z879yGng2caCKHUKp9sfuPuR7qYyKIbT3QQHIaSbqdBryen2VSOEBDGlBFjuKFKTcUfMXVpB5h2ng3TzlRbqvH6ke9CgOwLgCdhgnG7/fpV7epQ3nO4h9elmzkGRbmpxIJF7rez9JbQNKrg3CKfbrUqVqjsSOk4QYysejLG8WDjdz2tq4YgjjsDee++NLbbYAltttRV+8IMf4MEHH8THP/5xAP3S8Icffhg//vGPAfQD7n322Qff/va3seWWW2qUfPr06Vh22WWft+uYcEsu+cmTBTRoy7CmT7eaqAHeUt5xWv5QZUuK/rbJbEWV79bBp9TaJHccYKGENUWgyBHpoZnt/v97dMGpy3WjMomOOAsjypF3+8+sdXl5YhCT2Vl/ceyGWeiZ5AhRjjyTyfb9nX23KNKd20g33WjMV75XVugKe4KopQDE3XNjngyb001Rk+FxuuOTCbHl+VFCaiGnkEsSMRoEE2Est18qLyfzhlMBDquX+8uOo8rLtZaHX0gNIHM1JeATTCdbYigVDrWmxVw1nU10HOdHavfnQ+RN4S5u/jf8ywH6dIvtvlI43aES9TB6qziyXRRxnG6CQkdxuit7nLKQn93u0/cuxdJctKghLRHOc4xXHYxkZO76koU0+ZJ3ADTvgFe9nNwvNTepf6V+FmlydBxtON1ip5z44Cu2ZRs7jpCQmuZ0c+BAbMswobtLRHl5SASVdlng27q5ezO7J6o5QdsQSusDNab9b3qf7vZIN1txkne0DzwZdC8C22OPPfDEE0/gy1/+Mh555BFssskmuOiii7DmmmsCAB555BGrZ/fpp5+OXq+HQw45BIcccoj+/Yc//GHMnj17UQ9/0VmEYwmYQiDkMxHtfXKNdJONJ5bTnXVcpNvJnsVdh9dilNw10i2rl2cokYOU59BjCk4Oi6JSpNsRUmO4aRbfyl5I2X6jAcdTj73sCeXl8RxP/TMT7HLIYZkQdNvomeCMGmMtq8x9d+gxo1qG2c9VOy3qnSfniEW6SzNwikQKrPOYJWpD6iHtoib1OAItWEJW1d/nFPypqfsbFqKLd17bBN3WuxAx7rZmvsq6nJQkeCgyU3rmezTKT55pSjWCOofUp9t8H625KvVeTjD1XkS9804SrsVcNY5RMEV+UhVVKaBB9LydPBOThbTNdJKFkO4EGkGI0+271lIj3aXcMqgsocpFXU43P1etc+qgmwipBfrE+1rvxQp5+dah/rWTKhXf+0sT5J0RAA3iyr9LZJw6ySEg3fW7l8NTyUZ8tKBFdspRCYeY1lGtON2RLS5zX3l5TYMre7FId2KCFwTpZqTz0zjdzbWyCR0qUKgF9yIT6sxcV4dwtDyEoFsSG4yxPGPeg0mke9HbwQcfjIMPPpj9Gw2kr7rqqokf0OJoySU/uf2dhPJyB/HwlpfbwZfDhXKC8PR2Ss4pqaCWJ7iSMvKdPNdOZv/zcQiSD+mmyCotdxazfGTMPm6ai/ZwKD8z9gSk2xEs49SgGSE187pC63GWZcizfjmuXArajKOHHH7tcmaT8Pbpppxuv3o54M/SW2WJLYJue+MebtCd3FYkZClId6Sz5UVNnPvJl3oq41RsrbEuIqRbahmWE8fd5+hHK7cPUHZMW0U6aFouzNUhtgyLUi93uNIt5qqFdLsOsiQiqRXpmeDKKS91kpYq8Bwg6pbm7qBCalXVdOgg7yN3rapc14t0GwkglSiN5XTnZtIyiHTz5eXcuJu9N1JbglmDrfeFUkNacLp97xK9X52ssFtHkc8/H0JqaZzuuPvPnjdYXq6QbkFAs4pBuuOE1Lh7YT577pnGVWa667hzf425KquXp3G6zWQ4TRw5fnZMJWbAeIplc49fLEH3865ePmlhi8qWYTBOd06E1OLUy+1zuJxuOpEH79Pt7U1NxpV7kG476JaQbt7J4RYH6uyLLcNM1I95Rmpf4kuV6EYuIN107FJ5H2N0k4hFus13L6b7gF6ARfVyuxQ0ZGZLG/p953fE2dfigR6FWbG1GYzgocMF3RHBaaBErY3plmGp2fCApfSA1rzyAJcyxXlN/hlkrBMYdJvOhE4wVmo9rVGpyp6Lfl4oTSQJCQf6TOtztOnT7UNzh61e7hORk8aprNVcNdcU5t6EFXvlgA5gkt3GMYfC6Q60+/IfgwnczX87lVaua6jWYWlf7R+zeRdVAtdpW6jPydxfcq1O60NlQqszH9JdBipufJ0EekwHhJ4nSHffgzCnW91zer+kFm3qvGLrU/q7lORM4Dsa5U/gdCflnSL3K5+QmgIHytCeF9L2kX6G/a7Ecbo9iRFTSI3eX+Nvyu9zqGOo/DdZ0BMB3KrUbpve9AFT53Aolmp4E7g3L042GXQvCRa5EFooaXLQbSMeHUdILcQX6jqblVMO5WTP/OJYvnGyY2g+1D+f1Kc7NuiuCsDcqHXZJuPYO0g3v8mGhNR6Hiecojss0s2hX0lId7gagQtivAg9dx7KOaaWGHTTz/mF1CinmxdSy/MMKn8Qj3THIQWmhTbuNiYp4S5KIbVYTrc3+IoVE5N+Bp8kmgizqj1IeXlHED3yI91k3DSZNQQElFY4+YKWHsvpHgDpTuB0OyKSbeaqcT+5NUUSD/KtbSGku0PFxNqYNHdTeqWzydie83dviXXm31fpMfPOFOuYIaTbSlqQto5BpFu/S3Kf7tjkn69dGntOVu+Gpxnon72cbrs6qSskOZqk8aIXUktpHdUO6U4rL+d8IZVYq3xId1V5kO7wvQhyuiNasHLz26n+MpNZDtJtHNNHH3MU/5v54IgHtulNH7CQDzyJdE/aYmPt1MvdzKzP1OKQ1qfbDhpDSDcN6Djxi5C5vVTlcfVVVvlNWAVa7DHMcTHIABfQOUh3oKWNc16nZC2mT7cP6R6OkBq3mYWQ7hgLBmSmgxzJw+2FWkMJDp2mVDC9wGMCR4t/tZgg3ZIS7qBBt672iEompJV1su98oiPEIR5WkmwA4a+Q2Sr2dp9uzemmvFCf8xpymMVgLP4Z6a4VmQos5aDFLi9PCPgES0G6nRZhbeZqIJHXCummLYMclfU68AwEfF4LPOekYIrbF4y/+0usFf+f31fpMZ2Eg0DjYv0WQgFy1cvjW++l9ulmReSYvcWfLKRJIpKU5ypK6LUafgy3PrhdWELASEJyRvq5Nq+InDPOuPvPnje2vNyHdHuDbh/NMAwShahhcSCRe63O/TX+ptXLacsw8jnHhEQVvQ4AjuhxjOZQyEI+8CTSPWmLjcWWl1sKnoMi3XSy+8pi6r+7PV4J8h2BoobMFXiQFzE/0m0E0z7xEGMR87biCbSjYYV+WE63vKjR8nJW0XvAoNtZbINBt+30RCPdXKmRaa2Q7oBKtYh01+88U64fEzhagVOLoNtGy4bUp7tDxh3ZgiVoCeXlw0G6yf0LCP1wnG4HMZ4gs1XsaYKnRqUSkO62QXcmqfEylscg3dxcHWLLsDZ9ulvNVeMYJbOmOIG8WtsilLGBes4Ja/RgSHeIuz+8oNuH6sch3SanmyBqEtLN+S2k1V6Y0z28Pt3ctZUDcro7HXtf9fbpJvdL6sIi6UTY41icON0TEHRDRrrLGKSbmQPaSLKfA4mCSHeMz8si3SQZa/yt62vFFxV021WVgMvpdtdCZq4mmtW1hvOBJ5HuSVtczEV7QiU/FOmOD7o1t49Odp8adP1vh8NN3i4aJHLZyZCl9D3sZAWrrtnJMx1oAZk7UGERy7z3ovldyYn0kJY2znnoRs7womlLG1bR21dG2ILT7fSuBS0vJxlQpryPsySkOzLoDraGyux3upPXKvaZ2kgYR55T3CRmBU6hNk+MdQIbdxtz0B3uvWhhaZzuNGeXVcKlXRjoXCXj4ByjRcbpLsz1V71rdfCQqVJQyun2OO7m+xzZasb8OQrprtGbLk22GsbOgUXM6XaQ7jZz1UzkMdUzzlqXhdEdc53muhcMh9M9BCG1zHXereOpay3ka9VId1bITrfeI3O5tRE9p9Wn204wiM9UaHU2kHp5LTHPrUM20q1K3uMFIOle7XuXCoJgSl1YnERehI8WtEhfMyXJHtsnnR1HIOjueFqGqXXfi3RbQTd9P8l+zty/YJ/umM5DzPzu0T3Rm8wyKzN910qE1LT4oPvOO+srN1cTjRW7NO7vJNI9aYuP0eBK6p1YqUlEke4IB9lRL6//4EW67XOEkO4sz22xqxaZrTRON6+y2jWR7tB1mUi3apcU+E6PCRJZpJtpZeTbzJzyclZIrT5m5ZbFx9xvNzHinoMLYnzqsZw1vTtjUKm4Zcp2jMIJCcrtd3hMiAscS1P0xNm4n6eguz6Ms7kNql6eUFasBYwGQLqzkNMYUVnAJYkmwizxG4p0K6X8jHd8wkh3wKE2ldNTWgR2VNsh2ZliEWRfSWukpbSfoc5rq7lqrMEc0i3xGJvnyqGTpGUYGadCpUIiXl4TkyvqGUSsj75krJF49glkqTaBXRRymzeDjub2nQ7dX7lCJIR0l55xpyb/WKSbSej6zklRUklY1TtOw48ZDqc7prw8Lmmc0nkgttLAPm99/wItLhukW1AvxwBIN/ldGOmO74jAnsO4VuddNOZqt/bRSs6P9O3vYps9Zl0LrIWxfp51TONeOQKvwIS281yc7MVxlUu4ORl4qbx8EKS7q5Buwu3zOXDEmXXEGJh5aQZGbZBuN+iWFzE5Q5xrDiOPiAqLmAfpNq+Fa0fDZvlYpFsWUqML+EQIqdFAns0gM+/W8DndJioVW15uvFsRyCANurlyfYXcL2mcbodHF4kcBE3pGgwV6fY4cNamHAg8IXG6FxHSzQlZEipDg3SH5411Lb7KDeM8AJJUrRuku3ZePeW5PNLdPuhOQcucFmFt5qqZyGPVyymP0UaUOHDX5XTzScveII26AxUNA5eXm2tthJCatK9ax8waf6BwVJbtMTX310W6G54z7dNNON0ejrHY65uYv083QwXz8ZqFigdlXNLC5XQ399undk7XFMtSE46JvmYS0t2qvLy9kJoOuktPn25LwT+UtHD/nlt7t/t1KXi1f+f6bM79Neaqcs0bPzLnK1moUSE13Wvd/ahEBU3qOEEs5ANXLYSVl0SbDLqXAIsSDwNR8EzMcCqHw0E8vO0o7I2IltpwraOCYlehcSZwZOTNKtPXyX8/B6Cuv1mUM4+QWihIZFvasEF3/0deCZNs3JR/bR6zpRNOFeZjhanacrpjyss5VIozC+n28d2NdjSmoJ6z0SDOYRBb9Znn9FioRK2NuUJqAi801RIc/VgBHfXOszoGocDTnKsQ3tdUtKelsf3aCTeVOshe1f8UlN+kwSSg0FTLw8fp5oXU2icxUkoV6XNtNVet6hn33ojojkcZ2+V0E6Sblli3MSkASXkGDJrG7Qs+p1rdM2lf7X+oWR/086iM4MCYq7rag+V02/NCFlILJ6+aY/BD1of0IH+Fp7orprzcQbpZfQA+UdrJQurlnpagyZzuuP0rJcnepe9BjEUmiZUfx1X9lTHVXRbFwi9mye0tYaQ7Juh2r9Xt092873y/64gkhThnuHF3UFaBuZpoVrUnl4ibwL15cbLJoHsJsCgeMwSnD4hz/DXSbTuJ8eXlXWtSScGXiQKzSG3ApLYu3O864LlnHTPYkiZ6JDKgzCyDZZVxc6alDYsYy45oHNLtG3cEOklb8UQih95+w4wFA7IWSHewNRTlC+a2oB4nzhSlXi616jPP6bEJQboFJdxhlZenqZdHIt2hfrehuYrAnKD/HrLZFQ+2w9egUvHBQlJpPcfVjUAOVELXWfcNY+fqEDndMe+8IyLZZq4G1hS3XWKYL+zt0513daD+vHO6IyugfG2wGqTbx+lu9lVvcGD8m09W2RUiMqeboOWcyreElhOLR7ojEs2B99X3LvHq5fLnh9syjJaX+4PuGBFEVrU6OI64oLvjQbqTON15123J2D+wOybz/IF2nzEtWH1BN+eHs/tqzP0SFP+l9bfHiAcOgnSbX/H5wC90mwy6lwCzHI6swy8OEMobgcigu784OIhHQtAdg9hZzk6LoNtVL5dRZ0lltdvJ/CVZ5u9ZBIkJuq3ycj44kDZV89++hZBuLBzawzpoCYgIPWYYOazHndBCxPxcjHp5G6SbU7GmgWc3zw1BPb5kNSZwFJFuz1y1zzGBfbqHXF6eqVK9RaReHiyxJr/PmEBz0ZWXG8gBud/q0roElep5e/0GEg4i0t3/d0xLRqrlwfL7uDkwRPXyOKQ7XF4eXlOM6hm2vFxAutv26ZZQqVST5m4bpJvdF5p74UW6U/p0h4ID499WAEeuNbpPdwTS7dXlKCuoeNB37fw5A3MXbgVZHKe7AULikO5hBN1xSHdK8MXq2QTHEVeZ1fFyumMC0cA6Fkjohvt0xyDdrs/WoyCGlcxi9tWooDt+zgBUI4eZq4mWZVnAB55EuidtMTHL4fAsnANxuuvFYSQrAFRMhi3g9BFOt4x0N8fhlLGD44zKHBoZecGJ7Pqyw+bvWyDdUpDoFZZx+iCGxS14IbW0cTvniOgF7hNSmwhON+cgcxZGuqlD1yDdRZWxavAxfEC7VVR6gBcqUWtjzriHFHTDJyZILNjCqbbo3vQxQffziXRb74F9v5XDkYR0B0vree2JLCEY0326PerlE8/pDr/zpvPaeq4G1ug2PEZnzxMS0b0UlE8at8jpTuDqDoXTHdGnO++6nG5yrtZId1kCqNhjRFdqEDMDcg7F5/YWP9JtPxMX6Zb7bvOcbvnzXl8mNahxgm5JSC2+zNj8THTFB5ck4o6t1i0P0h0lpBaxt3AgUQhokvpds+dgkW6qrcQIFJrHTRBS81KbwAfdgyDd5rl4H3gS6Z60xcSCvMbarA0zUUDDXLRyVM0i71Mr9iDdMROZU8YOjjNGmEJn5HmV1U5mOr9Syaq7iHlVgQ0nsBRUGJ32Jx7EmBM0okmKbjduAU8JurM8R6/yUwAsTntGS5XilpQg+mDcw+igO0RdYER6VLAhVSeo59DztDuxEc70oNvhhQ7BJg7pVihqRNVEiLdfmyWkRM8Xcz+Nd45Fdy3l4YnLplsBdGa/a0D/XdIOMpk3wXZp7HUJ51FId0QlkUrcdTK5T3fTaWDYnO54B85UFm89VwOJPInG5QtELSElUwisf0Bby6Mt2M08Y+vnKATTV15uIt0yvUkhh1JXEDomHunOnX9blQREEIp9puY1ZPYxot9fYuYaxa1DpaljEtM+iTyTLMI/cq7VQLql1qf98aoAhkPcB+R0C3tvSvBl3s94pDtuv1J+HG2n2v9ljLhYoLOLcf+4PS+MdJPjRrZ+dMQbjbnK7qvSGmEaEQIOIt1ZYK62MCmx1B/WZNA9aYuJWQuKx2lkkZb+AYLnMFHT/iJf/+AtL7f7yNqbCv9qmYFRTPkjtU7MplB/ppOVbBDTR7o94iPm75nyck4Z2wyIJafQDYTcYMC3ENKWNhwKzS6+iahUMHj19OmObNNt8LyG1zKsDCWanJLfTAcbnOI8EMvpVoGTPe7Y+23OmzatOHzHLOi7NiinWyPdEYm8CGcXaJAwbq7avGbhPQiUAFaJa2FbUwFLbiF2hlBf7tJaBuJ0A951Kmrd122J5B7FLCfW08kh1rwJB2LmXtF6rpo9YZnEKC0BjuE+ms+t23GT3ea1hSo+RAtxumOeQUQytqoqNMsF80zqeyYJe1ljzHNeBM2DdHeYeeOtsmCO4X9/w7ocehz0sjz81mDCrP6O1dPdW1FSj8VQi2fXhyxDnoWQbjdB7jX6GWHdUWOM2a9Y1erYcURyujmNB5UoqaJKrtsF3bZ6OeNrxmgQMdfqRbq5fTWpvDwi4QuqYxBR3RFhueMDJ76fLwCbDLqXALORbvnFLE0HLrFsw3Q4crOcyYdokHNYDog0kY2J1aplmOAYceOSWpt0OwH1cvP3JtKtnFlm3CZqLyGzqqWNV73c0/uTtrRJ53THLWqmwnwwiGnJ9Qkqyg5YXs5zuu17YwZBcqLE7l/Lnpfj8prnC1jMvEk1SQk31Pc0ZFnCu6SecVXB26vb+853I9Yxq7zcnRNRwesQTPnKFr3HuN9Wizpn3oTKyyWnkHmudceJFKQ79/B02bk6BKS79CQcnHEaz7X1XA2sKZLokW+cbp9ueU9sG3Pzz7hK6krB7wt24tkMSn28Zm/LMEtluX5vKn/QzfotKpjijmHeh6hnFA66zWfDHaNiErre99cBB3LrnnnHSXoY++53N8+HzOmmCvNS0K3OH567bOeW4Djiysu1ermvIs93jNAcCiR0Q3t3Ch3SHKfbp7uZq+y+GrO/k05EofW3YBJNKWs2Z+p7Ph/4hW6TQfcSYHkip5tTUQ1ZhyDd6erlFOmWJrI/oAuPk++lav+uKS+XHKU2nO5YIbUwp1sWkfAKwlAxFq96eTshNYAqzHvQdOOYqVyfJP5ldHl5IKFD7o3J7S8ESkCXPjPGBuV0x8ybVBMFSwYtL9dzIEwNsQR0PEkLP9obsfYZv2e5fYtoY2dV7I373c2byooYZeykazfmuyovj7nW3FAvlx17D9I9wP0McQrtcRqVRG3nqjE3rbaH6vvC3uJVxqZON3nXbJSvLdLNzN3KOFYSp9vcF3gFcIB/JlXL8nJLtToa6bY53ayIn3QMYikdKKRjDMTpzruAISIVGif1DyT1cnWcoaqXO9+RON2ejhP0cDnTuSX4pUikW3VdYNqnVhrpjuc5u+MwE7ruZ0J7t1Q9w56DCbr1/bW0Eph9tRWn249a+zjdbf0URxgxcH9fiDYZdC8BlsrpbiOkZjocVjlTAqfbLA+VuKmDIt1RnO4Q0p1nBg9KWmw5TnddXs6WGZlOoYB0S4GQ8W+fCriZGOlVOTKfampLTjdgj58PYtxgwIfYcZaiXl61Qboj2qmZKvYhSkDh5XQb3L4WTo41b4aGdAsJnqFxuiOQbuO6Ysrzg+qvobkK/rlnEU7kMIxVsTfud8dUy9fzRg4840Tk5PLymKRmo14eRrp59fL29zOF092J4HQH52ogkZfT9ZSsbT50Uv/bg3S3VjD3renm39seg1wnwAt9KeTQi3RLwQFXTsrdX6atY/8zZsLHRLppstqnvi8nPcz9K2M53YFxU2P2gU5gndeVGoV9r7pepDtz1hR5HJFzNWIPa59kHy7S3fUg3c27NC4fYIjq5bz4bbuWYc79NZNZ3L4as7+L6uV8GJjcmz7CfD5wjE7MC8Emg+4lwDoxJZbwcLojFlvT4eiafTi9SLc9KZ1SO8ZKC+lOF1KTFGa538lId7s+3ZkHQYopL3cDIW5R8/XpDjueQwm6zWdEkxz9gTj/9gnwcBbu092co2RQKc7C5eUuiqKR7kCbt3ikOz3othz3IQmpiYIlQ0O6IxJ5FsoXmbSg57OqfALvPOCW89HvTSjSLSB2VeOoUFRKO1fMc49LOLjzPffQYNyvxyDdXJ/uwcvLk/p0R5SXh5FufyIvy3OMV2nojuN0k7WxFZ9VGvcEB91hpNtfQWYfs8OrVjN+CVuhR/rbs0JqRkvGwZFu/7vItsn0UaqYz7dGurNCBDE6nYAobJu1L2IPa0sni672iAgiy6JAntXrJ7Pu6znuRX8jBXUhIN0dv8+b4q/anG6SjGW6AgCJSXWB0x3jq7NztYV5feBJpHvSFhfLYxxPDIa4mQ6HjXTHlpfHcbrN7Fm78vL4RUxqbcI5v9IxOGeWG3ceUV4utnEy/u0tZeyajqckKsUEV4molE0BcMu2uHG3RrojUKk2SLc3WWD0gI1Gur1BoxE4tUAWzEzz0JHugm7KA3K6E5SxzXfBVyng61XdSQk8ISTyIiuFBjVRxb5qHChKa4lvlxafZNPrVMS1qoRuB4XWnHA+w83VYfbpjilR7cZwugNz1VxTpLJZi8eoUFQ5KRRCurMsi1pDvMbysVOD7vC+EOJ0q3Jdf8swl9NtHduzd3AVIuy987Q644VTmcCdWAjB4zjd3veX2QdsTreMyNN71fXc7zSkOzboDgfq6Uh3Yr/6iCCy12sQ7Oj2qdSSkG73HCGf16kU9IFXUUh3h99Xo66V7j2kFzixCUG6qSjnJNI9aYujWQtKNNKdnuFUCp0sp9vX6qD+t6lkKalaWoFRm5ZhxiJWIQPfJkNdRwnOj8zNjUpsFcEh3T5Ot8kX5KeV1BLE/LdPBTyL4DWywVUiKmUmDZxyfsBBc4AJKDezkO644LWykG6fsnsjpKaCoHB1gqdPtxk0hnqFM5ZbqraLd5/ulB7QplpvXGknd5CI+2mtKcxzbPFM2pi4/lp94e32Pr52abl5jIR1SgXdMS0ZOxrplktYYwOfVPMlW5wxJFUSSToRfk430OyBAJh2iKlBtx1stUe6uYDZFRPzGof6kXZJ6r5lGVhFY5Wo6HqoCOZ7kVvzv7LHYZ2XmTdRQXdzrF4h0zTUuhKFdAt+Cxd0+6hg1jNRFQJmJaDQVcUcSwMelJDyUvn/b+/Mw6yozvz/rXt7Y2laCUvTsgriBiKLC0xQ45gWDGrECK7guMzoiILL84uOOmJWZyb6OJlEJQmLJnkiZgQTlTGiAYICAaEbEAgBWZUGZO2Wprd7z++Pe6tu1dnrLn0beD/P42Nzb92qU1XnPee859385U8t1mhWWGwcx3T3Ljtl8rBsxnTHfEp3VDLupyzdGWQvNxiJTDHdTiSCGPN9bhvTzT9fn6xK51U+y7kM3tJteIcB45HFWGiDsIGbYbjpiQgp3ScAEckALiOmVLptLZxuSRBffWvdIjswEUWsYrpZIElX+HjASDSKuDuIGaw/GVm6dfVvJdcNWLptY7oDCxBuES61dPuUbqV7ucaiYW0xtqx3DUg2Cywn4QxdQWUElW6zG1fE1w9UZcnCWLqjDoLPxrLdflnJkne5+HwtS7CY8MrmWYWspBLo2Ll2yizdPsXRpHhCvpFnZTHOAjFZIjVAGs5gIzdWMd1eLVoxkZrdO0ocE3EYCiB/R/qY7vSXEDGDohNop2+uSFtWA0q3/BwtGuuOndIt/t69P523hxaje7nFO7BwLze9D3d8jSrqRgfOydUo11u61QkI5X1PnLt1bReSN0kwJQZj0n4RV14TgRrHieP9r0neTn6j1G8EUVi6HaBAl5+GW6NZYbF2DKt8uV409pZus2u439ItKxnmPg8nS4nUpEq3oQwcwK3VZOOOxtItcy+Xzqs2nmwh5T0u6cOZ1un2xkJpTHfu5ua2BCndJwC2Md1BpTv8QrPFm1R9Vg+miXnhJtDgAkRRpztDSzcAtLjdVhXr6y125DvyQYuT6hzJdvqyxDqaUjwRK0sMV9LG5GrH/97CxdI7Z6C8TNjs5frdXVliqrBKt7ScjJ/AAjkN93JdIjXmLxmWuL518jsJqZJX4asGJH7nkxvbQucGhOebNffycH3J+J4BxJm6z4dNpKbN6M8dm23cMjqi0p3Kls+XKtTJu1XVCsl71Y1TPP6sv4UR+TuSvkNmGD8t0JWK4wmMfenKamAjT95uWcZe3dgmhIZIFBYbGdCSBe8lm3nBOIYb5lW+XXLlQO2mHbR06+p0i2sSXV+yef6mElhSpVvXfyXzQMDSrWunu+RwlXWNZ0Fh1HdPrRjTrRuzZYQOsZD1V74NsdR30phuSdlG8STBsnliO/RrisCGueJZuCVYWaTAy0EQvIZ4r6nnGwl+5/WlNOZ33r3cMP7KYrrDrvN4oqp2Q7FmOwkhpfsEILCLp43p9scUhl/8uwuOIoelMniGSKRmE9PtV4ykC+QQ7VTF5QXcshQLpVQcVDpumzKl27ygExOlhFzg+RbIuUyk5n9H8lrgus2CkHW6VYW6/Qtky3b7n7s8plu0orglnNRl3sw79NKYRMBawbORm7CoMuFmqnTr8hrI28G5k0nQublFbMY+/8Qt6a+tVadbmVPD19/E7OXJezcmUgsxTiFE9nKfnBRH5bIoldXWjum2KsloSs7oH1MUbrOahaYuDtf7W9LXonwcY1h0Md1hFSkLS7dJ8dRnLw8qxDYVO+Qx3Vyd7oxius2eBqbydbJEaqGzlxvGedHSnVSwEFda4Iv9XVLn3aX6XkaImO7Qm+xZjOmOtTR5f+s2Wx0rl2sbS7c4t5jeKZDyIFWtC2X3qrN0+z8Xc7bYu5ebxt/QGfstcK8lWwNLQwJPQkjpPgEIKHSagTPTLMqe0h2xXFxpLd1mQZYqRiHaqXwW3mSlzl6eSSI1meuyf/FqijkUkl/4/tbFRvszzKsWntlIpBbIMG+pdKc7CWfVvdyQaVRmRTHFdFtlL4/5FKdMLd1ZUrpVmXAzdS8Pkxk70Y6Qmxb89cJYewFpDgIrN+0sEJABv2tesr8VRpiXbddK3m1yeUjea1SzOcjj31QLjPv+Y7Tu5ek/zzB1uv3tTFtWLTby4t4CORoqM7b3t9TCaR5DtGRhI1WuuMtrxpss3bZ1uv3narFKHiq6lwuLdMk1TG23ef6mTWNtTLdlIrVA9nLJb9Kp0x2Q2awp3ebf6JILygid18Bivoq3JL4LVT5VPEnwWNU5IFfsgxnpDaW3TNZ0SZ1uVeleYV61uleucoYhp0bo2vQWiMaA1DOlmG6izVBgmUgtmEU5vFuRu+AoTkvpjlplYQ5YI2WZsS3wkogpYzz1O/IFEQcFThpKt2tBkizs/cqBSklUKkK+Nutiy/wZ5nOZSC0WsHTbKTHhJ+EQVqk0YrqjstAFmaU7aXlUbpR4VipzPKDoXmqrmObC0q2wMGUpptsmM7a/HfqSa5okSCGzl8ti+1rP0u0bfx1RgSjyu2/z8p6pa70/ptuzdNsnUgOAYoV7uVRWs2HpjtlbTSIWSrdRVi3yLcg2dLVW1Ci36NZYOHXeHlqkSreh1JHVORSWbkWIS9g63YBGOZBeV6J089ZfyTXiceZW5TNkBdckwzQoE+Et3ZLYfpOlm4979m1yqNqVc6VbISdhkiACiveobYNFTHcsEdNtym/jaN3LQ1i6JWsh//0rs4C7Y0oYS7eQSE21mRXCky2kZ4vW0p1m8hndGpjcy4k2g3/BoVNAAhk8M7B0FwaU7nRjui3cy1vD0q1wN7OO6Q4kKHIt3eJiNvCOFAshpSLk+9uUBdx1VVJbujO3igTfkZ0Sk1NLt2W7gxs6mgWIt6CLpCzdea3Tba5vH5ZcW7ptMmP722Fdco2/nsWGo1/eZJstrZVITViICkq3uEBOybs4FYeL6bYrbcjj39QocuTvSG7pDqn0SQgzZgQs3dmo062y4DnuAjl1DV1m7MCGmaJkYOhySap2Z2TpNp/DlM3YNK/KzqlUDqTX9ZXa85V1DPweUFroVW2XWss5jG6zWqufrNSIk/oN5/GgbKfiWeli6Isck9ItWuiNuOdxIlAlXwvrZhx64ymEpVtdPjU5ZlgpojYx3aKRyPROE+2zC4eU1elWle5Vhm2EsOobN5pkfT7kZguPbg1Mlm6izVBgodAB3EAocYcy4WYrLHZku8rmJB02SjczKHQ2pHYOFfflz7KqyBKayiJsshhb1um2sMyKrnbqnURlyTVX6Q5RQii00u1b1PrjyF0CWSadoLuQ7Q6o0SolyZxpwp+RWO4Wzy/oLCzdXrZNc8krVSIlE37Dkm1MvPGcQj3MLMV0w951GZD0eQktmj4fqHCgeJ5+eZMn/gvvfZAOwkKUyxhfBN8zcIJxddLShhb3rnMvt7F0R6JRr6SNyr08Its4yWZMt03JMBtLt0lWLSx4zO9eLrRTMpfwZTIl1vSMS4Z5bWHwEj+l7V5urtOtVjyTirS2TnfwnOIi29+ng5sRgXWLG9PNZzv2X4OTIVXbI7JzcLjKhCorO5P0HePzcn/DPYvEbyQWeUVmZ3tLt8bFmv9bh/uOdAaekJvsaSdS08V0e5ZueV90Xc4dZuFybfCaTPwpHmMT0x2XjCnBa0gs3XGuPyrkyptXw5RHc9dsBu/EuN8yL5PVNCgQ1sD+SjkU0020ESKBGtDmgTDid20E7EtFSS3dlonUnKjVABQs65SelcR2EEvUE1WVDLO1dIvu5aaYbpPSrcvkarJ0uxnmlZZur4SQbHFlJ+4B5dVyItcpTjKijmEhGui/lu32uyrpNol8SXpSMd36MkQ2lu6IYOm2Vbr9lm6rn5jPyT9fi7qnNkRClAwD7BZbOuWrIGpOpBbcyJMo3TZlx7KAIANcqEehzNKtsRzYJVIT32vUcy+3u1d30VroyJVVbUx3Bs+zRbPhwOO3yKctq762KhOpySzdmrFNmPMk1qHQCgePv63ucw/7/C2UbnchrBzDXcs94uoyb5wVWpnwyfd34Lp8CJBFTLd/Q0+XYT4zS7e4Cey1W6l0RwP/97dN9hOh/1okrnNlNq50XRY3gYxwFnoZYbNY25Rtk7ZBa+lOKt2GdWC2SobJjETBd6r37lQbicQ1mxjTbdrMsrF0czHdvGLPwXR93nKdx6OzdEvXbCchpHSfAPitjTql2yszkGZCJ3fwKvS7Gerigblr2Lja+Bc70szYIdqpVroTn0cUk5XfwmlyUQ+6lycHHFlsTyAEQH5Om0yucXcyU6xEXSu/aaJBIAYuXHkfd5JQJSiRxXSnW0IkbqF027qXB+OvdDHdSaXEgVfCSRmHHzW0E7rs5XbttknGEhbh+bpt0cW3WZBu9nKrOucyK1WBuR+YxpTWSqQmyABXskYW062Tm2iYRGq+9+r2aVvLQSqsSBXTLZGBsCWrJHhjnUWf92/+pS2rFrIZl7iCBuZVxTUBdXhJ5kq375zuew5Zus9mXtDdJ5DabFbNq7J2uf06rimT6T6WwLrFV9Yx0VTfs+Ou4b8l1XwvnIPDVD6JRcTNP1OZsZSlO9gPohEnVR3Gh9B/3d8pjAdAakwxWlH5v3Vw7ZaR9RKhqjZoPKTiyTFIFdPt9ldtTLdJjvxrClmdbgtDk9EzU3KvMX5s5GRVacAJca/mmG6DrKYBuZeT0n1CEFxwqDtmMLY0vEulu+DwYvvicQCSyVJ23ghv6TZnAZUqRiHaaXKxVsWeFUQinjXIpLjLLEjGkmHKmG5dUhk7S3fM8t6zEdOtnMwkSow2vk1CmPhL2x36sInUHMfx3PNUlgIbS3dggkzDLd5m4g5LzmK6Q7qXZxrTHbXYcPSPiTJrREQiZ7lAyGbMPfOUVSpqlRk73URq7oaibUlGT+lOx9KdhZhumz7vTyKZtqxazIkpq1Tqe907CiRSUni6hC6XJLTbd07P0p39RGrmmG7X0p3lmG5/MkHe0u37PXMVNoWFHoDUAm+XvdyQCFQaCmbIvu9Zut0NiIj2eFX4WVTjXu7KrDFemL8HHZxbvIx0Y7p1ZduCbQhh6Tbkt7FLpGb2opJt6Joy0gO+McVo4LGxdAc3s0RLd/hEaqFiukNUnJChMzylqw+caJDSfQIQXHDIBwchg2cag617bs8N0j/gWSRSs7J0+xYzsszYNsQsB7Goo7B0R1P1cs3Kq5hITabQBePu9ZbudOt0AynX+lwmUnPfkSpWSub2GjarpbF2re/ZpGfp1mwS+Z6Nm4hGnXHenAQpoDz4XSTTsnRnR+lWZcLNVOkOEy8MhHPPl8qqRSm+QOI/WUy3TUKyLCAs3Lln7m7wWMcL+/MphFiwee7lluNrzMvlIV+wSWU1C5busNaymCGsyCirNpZuJzjPmjJjC4tuTdZqXV4DLVKlu/XrdLtlAlXzavCc/IaDxNvK3dz1Z7GXlHX0Tu2+1rjcYhdxIHWVtalAYZtEzv+3cdNIYelWHa9SSBJhcvLfuAYSK/fyLFq6wypfocvmWcxX8VjiO9VayN0Y1ivd+nHM/1xlSqFdTHd4Q4l1ne60EqnZGXgC46xMVtNAtwlHMd1Em8JbcCgUaCGDZxpuRe6g4GXEDCjdMiUmOKBbxXT7Mw23hqVbGuPl+CzdaSxmZZbuAnPcqDD5pzGRt6alu0VxjUD2dlsrCUcYS7cshl5GwM1Ya+lOTcJFjn7zxc7SzS1ALCwFsmsAubR0ixbRdIhoZEDejszqdBdYhG24i4MYc6RxzFYW4ywgLJR0lm73N5rM2HYx3ZJxytsYsSvJmJmlO/3n2WKyLnKYxj6zpdv8PN1FttunTJmxxZhuR5m1Ojsx3Un5zarSHbxXpbeSYV4NtI9XDviYbl9mbF2dbv/zbVEkkdNVAEh8bu+tpEzql8YGOT8PuO1QW7q5uGfPvVxj6ZZs5EnbwP+tg7PQywi9yR5WBiyUSC+RmqF8akSbSE0vR0FLt3hMQSAfi75OdxivSuH5CptZmdfpFlzYOVxjRxwKWU0DXQnBdKsZnWiQ0n2C4C04IO+YQgbPtJTuxHGF1kp38BqO4wTilmT4rZayzNh27bQbxFSTVTSSqtMdRnlNWZCyZekW35HJZc1+AJfV6Q7nph3GvTyndbqzZukWJ7fUokX/zqwt3f522Fq6+bjQLJBWohWb87p5DbJq6dbU6bbIWm0Khwj0hZxauhUx3Un5c61S/r6mTSJnseGgG6dsFzHSUpE+clGnOx5nnuXS2tItcf32Y5RVi4083tJtyowtzcegsHCmn73cp8inbenWzQt2lm73memUQOs63RGFDHD92S8X3jlCusmmxsL063TLNmxMyn72LN3qTQ7TpnF6SreNpTtkOJmXEM+2Tre4Qc7DDJbuiJWlWy9HpvKpVpZubkwRGyreqzAnqjazhE318CXDTJZuuWdWZiXDqE430eaJeYIrf2VCBs+0Yrq5LLZ+AZYNGJKJyI2pUk5e/pjuNAXXPIi5sWcK93LHl0hNuUsqDmIpS7ckbtRvYbMtaSObyA0W41RJG/tSZ6HdyyN6JSZYgom3kmTL0p26hq2l2wlMkJINHUlGVFNMnOuuqKsvKiz6LLK/Bq7hi0NMN0EJT65juq0t3aYwAugXzX53cdVGU0rpVpWOScPFMg2EhSg3hhR4C+SI8BtpqV9/xm5lLVpRmfJiui09iVKWbnkfl1YayFDp9idUsl3AmerdGmXVoh/wJcNMmbH97sye6AoWzgzrdPvOlXZMtywbNJcB3VbxVFUFCZzTtTLy45DbDkl2+MC6JXke/2VaBKXbzk3WWCkDZsXd0YRUGWO6k/fqjoUqRVXov66C5TBEFLLpGg+YcnzIROlWy2XYmG6bsm3BH5iVSLdOt2kdGMkopttnyZaFFVpsmHtjSoiQQK9vuWsDTlaFedXGk00oEag38DCJzpG5pZtrt2FT42SElO4TBFM8m38wE5XucBbOQje2zy/AsnM44jWMlm5DZmwbUnHN+okmqtghjkT8paIUz8YRB7GUlU/8TSDDvDKRmmIB4vvbNJmlNl9Uli/z4sqEN9haxXRz1gbbkmHu4KtcIPst3ZbtDuyaajaJZEq3Mk40DSuJRZ3T4DV8JdrSLMXBI1h3LEqwWJ03pNLt3o9u00K3aI5Eo4gn60irNhzdxYzS0h2I6c7dlCfU3Ob6W5HrXeNrp97S7VO6jeOUv0+HK+vmPtdCRUy3t8Dzv0NP6UvvefrnK9tTmGO6DbJqMSfqLN3yclS+RXeU22xxF/3Jj9O2dAPi2BU2kZ3FZqxpEe72p4SlW3EdXjkQrLfihmRMqnS7Md0+S7fb/7gNB7fdqtJHBTYx3SZlQppUyvI37gaEZ5CQHy4+q9SBRQql21gyLNBuS0GzcC8PWyI0fEy3hdJtiulOhsFlonT7q8REZaFLvvtX5uGxNBKFiekW5tVQMd2W/TciehaFzcMhnFJj6bY1rpzokNJ9gmCKZwssDvg63WETqbkDvDtY+bLtBpBYDtzB1ZRcRJlxMkQ7TXFMifqW8i7u3qPKXd97fpJSPLIdz4DVXuVeLpQEEZ9fqnSJvN3WSTn8E03I8jIppVsxmUlqJ3uDcdgYL1UJkYCl266v+AdtaTk6/7Nxy+N4Lr/6jRJdqZM4rzRmFNOd3ZJhwmI33WROSSIhM2MLJYMkmGLLWrxNNoULoNsHFUp5a8V0C/fBuQ3K+pquVFHAtd4Y051U6GMpubctyRg3WbplMhC2ZBVH0G3brs/HDUq3eUwJ4V6O4LgGyDcUpe6liqzVupJVRngX1LCJ7CzmBbcElsnaq5tXVXW6Rfdy2caTI9ynvyneexXc4pH6vQRjKBMkYziPxGJs/RsuplvV34X+65/PlEp3ch1jUuj4e9BhoXR7924934eUAVl/5WBJJVJVPtUukZq+nKrJSGSTBNXavdzXTiGm27YUn9bSHZRNU/9lknaH9XDgEUoABnI8kdJNtCFMWauFDJ5pxXQnJwfevVz1e8k1ogYXqpTrcvpdLzWI6dsV1WT9TMV027tpF3gx3eJ1/RnmVc9LF89iG1dnG8+uS5hjwsterng2UY2l23YwDhN/GXqxAIWrkv/+k14L9pZuGyuJPKbTRC6yl6tLimTJ0m05QYYpuWZOHqjYiDLEdEdbKaZbjO0PWu34RGrBzNgSK6o/ptu0OZh8ry3JMjqJryzdy73NVn0itWzGdJsSlMkw1bvNxpjiJQ/iLN3evCpc059Iid90U8RfpgPvPpqVmG556S1zTLd6XuUVYmWYi9LSHezPjuOI71Ww2Okt9OHGcBv3cltLId8P9CXDdOWUVLIpS84obQP/t45QMd25snT7+oFiE43FE2Od6t7dzVY7S7dqE05vJMpOTLcom2Kd7qCsppe9PCibtv1X5pmV7jpFtwYm93KiTWGydAtJPdIYbN0FhxDTHULp9nZzVTugrsBbut5K22kd062rb2naIRYHMVfhkGbGhm8jQWXp1tbpTj4XQ0x3quajneUr8HfImG5TKQ7/OcMmVgmTadi2JjR8x2kt3YD3Xl2XWlWyNi+jrEVMd7qJ1PzeAdmK6VZlws1U6S4ImRk7G66dprEvHsq9PJeWbkUWe66vuUpuQPGU1Sj39Wd1ht7gJlvMp3TblmRMWbpVidQkspppTLchQZn0N97Yl6asBsJP9JatOPeOTNbJxDFy+bcJUTGSE/dyVTZjveKpm1dVpY10idQCyr5s3uX7X8hSZ2EqUKjG34BnBOfWbqztbekFKJSWCyjditCPnCrd6r4Vuk63xTwgbQPgbZDzeDHdhnVKRkq3N7coMuPbxHQ7wY08AUmfFzZw+c0soRKO2R0/rNy446zfkyBsxQkefmMpWO6TlG6iDeF2fJUrjTBhpuFWxHiLh0lZk0xEppju7LiXG87hTviOeke+0LN0q+4tOIjFYzFEk4q6ysrntcc2pls6kZss3YnfhMlmHD6RmjjYBi8hKjHpW7pV8ZfpLxbiitJRMqXb8+pQbpSkoTRauOfJrhE4R4bkztLtJumytXSnsWnBf59UOFWyalS6C8RwiFygzGLPlwyTuC7L7t2JRNDC3Hu322TzW7ptLQeChxOHVFbDJvLicBdvjsKCrGtn2rJqk0hNEdNtsk4GjuGUluxYunmlO033cs28YEpI5kTN86qQvZzPWs2NjUIWe8mmsfdeY7zSHdwYUYU2peWtxJMNS3dUvzbyNuXd+/TJvKqyQBE3pijbAITfoNFaunNdp9vXVsWc5bqXGy3dSD+m21Q+1SY0jA9ZERsqyqYw7gibWdy8KjO2CA1RxXQr+jw3FqZTcYKHNwb4352tZ9aJDindJwgmwVUu+ADrwVZUug276VJLdyTYDsVvMlG6TZZu93u9pdtyskoOYrGYb+ddUerMVVJV8YLKpDK+65kmM9t49kyUbhj6WkQS0x12EjYqY777s85e7j5Dm0yunCJkihMNZSWxsBTIrgGkv4OsOqcwKYMhk7juVDkquwnStOBljBkVm1S5RMWGo2E8iLaapZvffAnKYgHnXWPKjA2k7t20wehtDgYs3XbeCKlcHvIFm1RWs2TpDtPf4wb38mx4z/AbusbM2FHJopu3cIa18slQWrqzp3Sbciu446udpdvdcFCVDEv2az6LvaxqCG8BVpY603sjtMTU455tuTTpdQ1efbyl21TaLLUpH/HCCpUbYpz3jNiG8MYXm03jsKWjbMq2BdsgztU8bskw1XjgbRJpLd36zSt3w1c1/krDS/h2GgxmdpZuvhRfGpvqfPbymL7/pup0K2Q1Dfix0D9n23pmneiQ0n2C4HV8paXbTXCUFCBJtk0TnrLq8HFj9gO6t+5QZbX0XJczj+lWDYTuuXX1RAuSFjtlbDk3iMUCFiSVpZtbdPGnVCpCkCx8FAOhm0zI6F6euaVbvYPs+9wy6zqP2SoVQRxO8hLhFgtqBcW/e56Ms4Xe0i24R0oQQgJCWrqjFhlQw6Ld4EnT2s3icW/xZ5u93KQImbJDAyl5Nsm7Ott+GgvPNBAWSq5LuKt0w3WHFBM7qbL+xwy5PHh5D8R0Wyco01u63WzLWY3pNoTRyPDmvnRl1SZkxQn2JWFe5a8ZkF3uOk5Q8dR5exjx+lKGMd0sntp0884RtD4ZLd2IaUqGcfWEk6cS6glznl1A8hlLsvEL75UvdWboS1ZjuBe7r793+XUNawgullul5Mja6Y55BQrF0V3HmCq5CH/r4PqvjLAx3TZl26RtAIxKtyrXR8Rdk9pYutMMXbLZMDdVg5FtNAkZ+TlZVVbCsYnp5kJnjDHdjmS+SjMMjjcG+J8rxXQTbQrTwtITIHfSiIiThImUhdg2pltUvkyWbsdgRQ3TTtWziPnuQ6l0Gy3dwUEs4LapWLCl3MtNlm7O1c73t2kgNFq6+cWZ/2/b3UmD5VAWI5uqN2w3GAulIyS47yZs9nLl5OafnD3rY0z8zodgZZEglEsLaem2iQsLS4RvdxaU7oC3h63Szdem57BJqJWydOvlXbUhGZjMM8glYSImbL4EvWVcS7c0plt57yb38uA45ZbRCVOS0V1UFSgWp0J+AN/10n2eYUsMAr5axOnKasB7RuElxW04CvOq4pqJv932BecBV7xzYulWxfoLv/fdr6vA8RbjmN5byVViChBXTyXWlu7gvAEgWHUloHRz/U9loTe8o0ySOTqSudrYhyN8P3BCt9OTTaWl2xTTLa7RjBg2twDJWGdAWnbQpg2A0mXadS9XhdplI5Gaae4Jyr/KYqxfT8nK07pKqSqmW5hX04rpjgfOJcAZMQRZTQO+3f71t+2a4kSHlO4TBFMGRKVrI/+3BnfBUZCNmG7VTlhSsJSJgWza6T0LfQxNIsuqIgFG8h6Nblmee7mvXrdiR84dmFXu0LqSIJ6ia3DbSiVSM7yTHCZSy2b2cm0pKffdWA7Gjte3VJObIzwfN05f9TxTGyXq6wpln8Jaui1c1MKSer4Q26JbhGjwK9228VemBDpxC5c1k1IdN7if+63ytjXf00Eo9+c+c8ZlL0dQ1qMRB47K0m3w6glYMJF6R2HCd7yYbsjfkVRWDaV2TOhKpakwjX1GWQ2UIZSfg3HPO0xMtyqRmqd4asY6I1xf8pJLhbVeAj5rOad0G+I13VKRunlVVdpIVadb8HTx3ydj8nN41wi6vZpipa3KFirWLe4YEkPEK59q7MO80m2M6U7KmV/pdpMcKmK6Ux57Bk8Y/m8dFpvGMRZuvrcpvRlsg1np9sokGnLPuGUupRhKH8Y9zxf5NWzysfBjiqShyYul7jPO9y1e6RYSqblyY3+vJnkHt1lg45Vmgu8HwURqpHQTbQiTS7WYQTm8S2XKvZxXuu0HdNs63RlZug1x4e7nevdy25JhyeMCsZKq7OW2lm5uoHQigoufetfU4PqVjZhuk3u5JpGafUw352ovwX2eEct2O4Z+kThZ8Pm47uXGjRILS7cY023X7uDCPTtDsi4TbtqW7jQyY5sS6NhYe1O16fVKuaq/+uVVmTgxCyiz2Cefd5RbINu4aJrKpYkx3a7Sbd+PhHGfQyqrYbNnc6Rkxr6d7rtLW1Ydx3suqmSY/Nximxk78DfnQm2TyMtItmK6NecwZeOO+NzLrbOXq5QDiXt5Inu53yIftLyn3Mv5mG595nEbS7fRtT6SGEP8c0u6Md3G2POAe7kbKqfaELMMkwPsZdVi/gpfMkx0UdbiOEaX6bghpjtiFdOtH8e8ccDgXQPo3MsN6xJtTDdfMizYl8KVDFPIu7L/Bvt8OhUnePix0IuZVyW/PQkhpfsEwZSxW8jgmcZg61m6bd3LJXHjqezl+mQsSmukTTtNGxCe0q2r0225Q+zGdMeSNSE1g4N7T6p4VzHjpKikmyZyZm3pzlzpViYsKhD7lqm0Do/NQtRdSDiWpSQcvzVCBfd8Cgwlw1LZd21iujkLZxqJ1LJn6VZnwtVmONXQ0uJPJmhp6Ta8Z7+7oTp7uV7eU0q5eZMojDIaFmUWe0XJMBvvEE9BDzlOhSnJ6M4t5uzl+Y3pNlq6LWTVfZ6uEiXAeVGZMmM7jpOK1VVZut12ZRLTzbuP5kDpNilS7kaFXZ3u4IaDqk63kMVe0k7hvfLtNvQltw2MBa3IfoRyfxypuSUlVy0Gd3x+HjDW6ZZ4BbljhSrJYSHynUgt5CZ7mI0nkyLpupercuh4nhmZJFIzeP1Z5GNx2xcmkZo5e7liM8uqTndyTjHIjRtS4c6Z6VSc4OHHQmOi0JOQvCvdL730Evr164eSkhIMHz4cS5cu1R6/ZMkSDB8+HCUlJTjzzDPxyiuvtFJL84u9pZtb+PN/61AmUlMpeJFUTFlrWrpN5dOQug/V4GDtlsUlUlNmxoZv0R/W0u073mjpdq9h6RYf+DvkTrcyG3Q2Ld06C7K7QA7rXq61dAefT4HR0m3eoc+0TrfURTVDZJlw+cReYUknM7atpVs3kZuS2bgKqdIFMKB059C9XJXF3ovpDlqlbKxFJtd50dLdnLyG/X16VSuUMd0SWc1H9nJDdQgrWXW9Z1R1upWWbvW4L5TJVFg4s1OnO81EarJNt5DZjL063Zp5Va0ccB43qrAkidKtTKRmGdrk3zAxjUMmK79/zWHswypLt9Ein+on3lpG4SLtes/YlQwLuUGjCsGwqDjBk5a3h0GRNMV0Rz3PDI3cmWK6mX6tGYk4cB+BKQu42htBXLOJMd1BWRU3s/ReAcFz2MkNlGNh+msUwdLNDJvKJyF5Vbrnzp2LadOm4cknn0RVVRVGjx6NsWPHYteuXdLjt2/fjmuuuQajR49GVVUV/u3f/g0PPfQQ3nzzzVZueevjKd0KNxcxg3L4wTa0pdv/nWDp1k9eyqQSIdppsnSrJqrEdzHtOUSl2xwrGTcoiaIiJE5u1nW6VZaarFi69ZZDfwkmPuGQdUy3RRmdtN3LdX2Lt3RDvyFhU19UKJcWOqY7B5Zu2fO12Q3XkE5mbJMiZDOR21YrsNkkysTDxoRyDOb6WmoRo3fnBVLjvboMXnCxFY+FV7q9HBgKa5rQlxgzxkKaCFti0N/OTGQ1tZGnHz89DwMLi7ygTCnmxOzW6Q7p3i/bdOOsfKZNIHfc182r1m6wKiu1zNLNK6OqOt0GSzdg9rhReuhJvKjM2Z+D84BpbZSKPU9Z5FNrGYWlO7mRZ/SE4f/WYfDUCroZhysZFsrbg99o4vGUbpWHk6t0ZyORmlrOCjwPBlUCV4M118rSzcV0qxIUZrFON2/ESMc7iYdPipjyYsu7/bfVyOudvvDCC7j77rtxzz334Nxzz8WLL76IXr164eWXX5Ye/8orr6B379548cUXce655+Kee+7BXXfdhZ/85Cet3PLWpzViur2J0h2kbCyk3ABtW6c7E6WbXxjxxAzZeAHR6iReg1vMWliQvDrdCqVbWLxKJjfTYtRzpcphyTBXeVW5cUYkSoyptA6PNCMyR1j3cneS1XpRCO7lyYWcYaMkt5buiO/vHFm6/e1J19KdjKFrZlHrzNhmS7dZ+YobJmbP0m0R0x1juZvy0o/p1njPGEpF8ostd3MwTElG11KktKYJ2aN942qmMd0h+ru3uM5EVt0YWaWlO2h5sWmnEFLVKjHdaWx6KBV33vKlKMEU5dYHMgRLNxfmokikJvXQc5Nd8gpGmnW6AbVnldnSrYnpNpYMs/MC9LfTTTSViulWeKHYGg8SNyE/xtBunkAeDsv5Pi1vD5P11o3pVq1ToglvrMxiug3VI5B6b8qYbsN6VTYve/3Rfb45iem2tXRHuOPTn0MFS7d77hxuhrc10tumzgJNTU1YvXo1Hn/88cDnlZWVWLZsmfQ3y5cvR2VlZeCzq6++GjNnzkRzczMKC0/eOm9uuZR9dc1Yse2g8P2GPUcBIFVnUlLiwniNZMfv3LwX2PERsHdd4gudQHDWB2/uNMSJZOJe7u7Y1zbGpc+ivuYYugMoQVPiPiR0YrUAgN1Hm1ArOUfXgw3oD6DuYA12LVuA+i93oCegTmiE1DtSlaNx382XdY1Yse0gOhz4CoMBNLMIVifbYNxNNFh7vM+bvkrde9jyPslzMMXCPeqLaV+18ygiHQpQ15BULCxLSbjH1TW0SN8hAPT2lG67Qd5x3A0dzfHuM/jiE6DhCE6LHQp+rmjnkfomZTuPNSUmda/PeyEAdu32z2HZsnS77W5siXntHoEICgBsWf0hmtr3CH3OhiM16IZwCp17P7sP1Uuf377ahkB7ZbjXU8lqbV0TLgDAVPVaff117Z46FDXJ32OmHDme2JgTxuCDW4EdH6Fz/TYAwPEWYMW2g9hx4BgAQNe9404UYMCOgw04JLn38sMN6Avg6L6d+HzZAnz1xUYMQLjx1X1unY/vkI6Xp+8/ikucTeh2rBgbltUhEm/CucnvVu06ilhReGVyU01i/A0TG2gKrbGR1bPcvqtK2pM891dNDCu2HRTnVdl1XaWbf+/enJj4/PPDx5XtMnFuYxxlAD7/2yocPVKIbrv+hq4A9tY1Y4flOS9CBFEAWz75AE3tuqLP4b3oCGDrgeM4sO0gdh+uT7RXca+uHOnmVdQn2+K41r/EP3ccTMi/O68ebYhh07aD2HPkePKa7kV8wrBzGdDuNAyJrUcnpx6HNhzDhi87ovyLrfgagC9qm7B720H8fV9d8qcKZdZ3P3/ddggdS8Rlr9cOldLtbsawiPcOU2Uy5Y8CXH91+4nq+frH/uWfHURRQQQ9k5uEZYfXAzvaC7/pfHwHAKC+hUn7VrTxKC5K/m0rqxVHGtEbwIH6FmyVnLOh2VfJxXK+d5/rF0carGVgGHNQBOCzqkVoKN0mnvNw4jP1uJ/4vBAt2LBsgfSYXgf2oBOAbQcbsF/SrqNHG3Ee7JRu5RiR/K1qvVpypA4XAog1Hcffku0c3LwZ9U4MHWsKgYb2QP2BwLn4ebXLweMYAOCrQzXYqbjXc5obEQVQ/UUtGuoO4tCxpkS7DWv1xriDFTJZTQP3Wu4a+Kt99Yl19Slk6c6b0n3gwAHEYjF079498Hn37t2xd+9e6W/27t0rPb6lpQUHDhxAjx7iQrKxsRGNjY3ev2tra7PQ+tYnnnQn/vOWw3j9byuUxxW6O2PJXT44Ua/EhQnXojj46CJgzqLUFzpLo/tdsn2F7kCnigsrKAYAxNN0SwRSrtWb9h/Hc78Qn8WZzh5cWQyUOceAOd+SnqN/8v/z1+7He1XiOcZHduKFIqD0y9U4//1bvM91sScxJ1miIiqPdy1KPpuVOw7h5l+swHnODiwoBg4ej+Nm7j6UJcNct3LV83PfR12NeO/WCckS7VcmLCpM3d8//3YtvkJqMaDMhMnh9o9dh+qFe3f5sCgKRICoZfxwJNmumG5Yc5/B21MBwFMeVNb0woLEe/jsy2PKdnrHuu/Mff+Wz9vtF7rSUWFx293QnOpba4qBzg5w1kePZHTuFkRRbNuO5HueX/UF5ld9oTxOl8XalSu1rB5CZREQd9TPu4kVoMhpwf+btwl7sd+y9enhjoHe+189B1g9B4OT339ZH8d9vr6ksxy0JDeSXlu5Bx+tEO99UvQLfK8QKNu7DGV7l/l+F0LpTo4pF3wxF5gzV/j+AgBziwE0Ang/+N0dc6rQYN0bRApDWE3c95uJrC4rjgKOZkxJyu6Ow414yncO1XyW+C7pWcXPvcnn6sr3u+tr8O76GuV5dLxWeAyXRYGe6/4HPdf9j/f5Oxu+xA/W6scll3XFEXRygLOWPhT4/MU/b8c7H5jvNVqYeM+6edV3cPJciXt/c83neHPN5968WvXFMdwZeL7+ncciINYEzL0NAPAcABQDqA5e4vdVe/HiKt85NBZkx0lERdzz2ifaZqvOEUneexOLCH1L2Ye9eSAhw6m1kfx4v8Vx0qyVAID3iyJABBjwyfcASdMvSP6/pq4F0yR9vgOOY0NJ4m9bWf3n6B78WyGweOsRPLZJ37dsN4nde/5g0z58sGmf1W+WFTNUOED/5Y9rj1OF2hUWJm68yGkJrOFkvLR0J/53sXivlZH9uLoI2qoXrryo5IYlZWHLgQb8QPKOejv78JdiINpyzGvnbAeJPv8H7mCvLwXn1esiO/HTIqDjgbXGe50y91N8zlLvQNXn3XXgoeNMLashKUq2278GvqoYaMmfKtrq5P1O+UUmY0xCXiciAAAZSUlEQVS78JQdL/vc5cc//jGeffbZDFuZf5zhk7Fm+XHs6DgKAyIdpcdEHQeTRvZN/KNjd2Do7Yn/W9Lt0onY+sePcUb7FrQr8O3WX3SP+kejHgT2rge6nAUAuO2S3gCArw/oKj28/8VjsW7db9A8WD8w6Pjapbdi7YKNqO7wjxgQFZ+Fwwbgz8dH48Kiz9G5vXxxVdfYjK3HS7Gv9CIMcMRz7I6PRPXxj3E6OxL4vKbvDbhU0a66C+7Euo1vot+ISun3lw3sikv6dcbB5A5jCzsXfzk+ChujZ2NAWaoNI/qcji4d5e0uvfg2fLpoFyounSBvRPkFwDnjgANbgp/3GQW076xoeZBel47H+u1/QsGIydLvS9p1wIruN2PvoVqUd+qW+t3p7TCs9+lW1xjS6zRceU437DpUrzzm3abrMdpZi/OGjLY6Z/+h38Dajy9BY/8x6Kc6aOQDwCezvBqwx5pasLuhHXpf+m3p4SP6nI7LBnb1dnmV1+7aAef0KE38Y9gkoLkeGPBNq3Z3LS3GhBE90a20xOp4GyrKSjB+2BlY9/lR77N5jePxzeZFml/ZsafXtzDS8thrh1Rg5fZDnjeAihuGnqH8rn7IZKyq/p1WVlc3/BXOUHl/BYDVvSbh6L6d6FjaGwOytLEho0dZCS7ql5SzCyYCe6qB5kTfaY7H8UVtDH9pNxYDChP34QC45eLeyvMdPO9OfLnpbRwpHYoBjtg/Pot/HeuOr0IZq/M+YwD2nX07elq2ueSiO7DjL9tR0dHxFEQ/cTDsPdqAZi4e85OCC9Gz09csryIScYA7/6Gv9fHREZOwfkVdRrL6h8YbcEnB3zH4vIul3/cYOQFra/6KVe2/iQEFiXcUmFcl3Du6H6p3H8HA7kn5H35nwrtowD8CAK4Z3ANLtxzAV43phXUAwJ+bv4UzGusCrt3HUYKqsqukc6CMNxtvwJXNfwl8dtA5HXs6X+KtKToUF+DaIRXS3/fsPxirS69Ev9h25bwKACgtB/omxuzrhlRg9c7DqE/K/+74SHxy/K9Y1G6MJwMA8J3hvt76D1OBjX/0/lnb0IzD9al8EgBwzGmPTzt8w7v3wmgENyvkyHEc3H95f7y/Ua/sdSopwJhB5dLv+g26FGs++DqWx84JzNUX9+uM0zsonsWFtwBHdyfmYwBXntMNCzfuw4SLekkPLymM4u6v98OSv3/pffZu0/VoH1uAirIiRCCOW02xOD6vi2NZ+zFefw3SEfMbxiEOx1pWN8cuw5qGjVjevlJxzgTfOLsrigrsFLCrzy/Hh5v2obbBXgb+0HgDxja/L7nrFE1OMcouvk363dfKe2Hladege+167XUOO6dhV8eR0nX1fnYRVh0fBueCm5W/v/eyM7FxTy3O7Cp/Vl0vuRlr31mHqg4KWWUd8EHD5TgrFrTmFxdE0L1TSer+S7sD/a4AIM6re+IXo+r4YHTm1qs8f4/2R0lpX28O7N6pGCP7y/tFv4vHYe2m/8WiyNVqWQ3J5QO7SdfARf0uRTfDb08WHMZsq9Vnl6amJrRv3x6///3vccMNN3ifT506FdXV1ViyZInwm8suuwxDhw7Ff//3f3ufzZ8/HxMmTEB9fb3UvVxm6e7VqxeOHj2KTp06ZfmuCIIgCIIgCIIgiFOB2tpalJWVGXXLvDnSFxUVYfjw4Vi4cGHg84ULF2LUqFHS34wcOVI4/v3338eIESOU8dzFxcXo1KlT4D+CIAiCIAiCIAiCaA3yGr3+yCOP4Fe/+hVmzZqFTZs24eGHH8auXbtw3333AQCeeOIJTJo0yTv+vvvuw86dO/HII49g06ZNmDVrFmbOnInHHnssX7dAEARBEARBEARBEEryGtM9ceJEHDx4EN/73vdQU1ODQYMGYcGCBejTpw8AoKamJlCzu1+/fliwYAEefvhh/PznP0dFRQV++tOf4sYbb8zXLRAEQRAEQRAEQRCEkrzFdOcLW797giAIgiAIgiAIglDR5mO6CYIgCIIgCIIgCOJkh5RugiAIgiAIgiAIgsgRpHQTBEEQBEEQBEEQRI4gpZsgCIIgCIIgCIIgcgQp3QRBEARBEARBEASRI0jpJgiCIAiCIAiCIIgcQUo3QRAEQRAEQRAEQeSIgnw3oLVxy5LX1tbmuSUEQRAEQRAEQRDEiYqrU7o6popTTumuq6sDAPTq1SvPLSEIgiAIgiAIgiBOdOrq6lBWVqb83mEmtfwkIx6PY8+ePSgtLYXjOPlujpLa2lr06tULu3fvRqdOnfLdHEICvaO2D72jtg+9o7YPvaO2D72jtg+9o7YPvaO2TVt9P4wx1NXVoaKiApGIOnL7lLN0RyIR9OzZM9/NsKZTp05tqmMRIvSO2j70jto+9I7aPvSO2j70jto+9I7aPvSO2jZt8f3oLNwulEiNIAiCIAiCIAiCIHIEKd0EQRAEQRAEQRAEkSNI6W6jFBcX45lnnkFxcXG+m0IooHfU9qF31Pahd9T2oXfU9qF31Pahd9T2oXfUtjnR388pl0iNIAiCIAiCIAiCIFoLsnQTBEEQBEEQBEEQRI4gpZsgCIIgCIIgCIIgcgQp3QRBEARBEARBEASRI0jpbqO89NJL6NevH0pKSjB8+HAsXbo03006Jfnxj3+Miy66CKWlpejWrRu+/e1vY/PmzYFj7rzzTjiOE/jv0ksvzVOLTz2mT58uPP/y8nLve8YYpk+fjoqKCrRr1w5XXHEFNmzYkMcWn3r07dtXeEeO4+CBBx4AQDKUD/7yl7/g2muvRUVFBRzHwVtvvRX43kZuGhsb8eCDD6JLly7o0KEDrrvuOnz++eeteBcnN7p31NzcjO9+97sYPHgwOnTogIqKCkyaNAl79uwJnOOKK64QZOvmm29u5Ts5eTHJkc3YRnKUW0zvSDY3OY6D//qv//KOITnKHTbr7JNlPiKluw0yd+5cTJs2DU8++SSqqqowevRojB07Frt27cp30045lixZggceeAArVqzAwoUL0dLSgsrKShw7dixw3JgxY1BTU+P9t2DBgjy1+NTk/PPPDzz/9evXe9/953/+J1544QX87Gc/w6pVq1BeXo5vfvObqKury2OLTy1WrVoVeD8LFy4EANx0003eMSRDrcuxY8cwZMgQ/OxnP5N+byM306ZNw/z58/H666/jo48+wldffYVx48YhFou11m2c1OjeUX19PdasWYOnn34aa9aswbx58/D3v/8d1113nXDsvffeG5CtGTNmtEbzTwlMcgSYxzaSo9xiekf+d1NTU4NZs2bBcRzceOONgeNIjnKDzTr7pJmPGNHmuPjii9l9990X+Oycc85hjz/+eJ5aRLjs37+fAWBLlizxPps8eTK7/vrr89eoU5xnnnmGDRkyRPpdPB5n5eXl7LnnnvM+a2hoYGVlZeyVV15ppRYSPFOnTmX9+/dn8XicMUYylG8AsPnz53v/tpGbI0eOsMLCQvb66697x3zxxRcsEomw9957r9XafqrAvyMZK1euZADYzp07vc8uv/xyNnXq1Nw2jmCMyd+RaWwjOWpdbOTo+uuvZ1deeWXgM5Kj1oNfZ59M8xFZutsYTU1NWL16NSorKwOfV1ZWYtmyZXlqFeFy9OhRAEDnzp0Dny9evBjdunXDwIEDce+992L//v35aN4py5YtW1BRUYF+/frh5ptvxrZt2wAA27dvx969ewPyVFxcjMsvv5zkKU80NTXhN7/5De666y44juN9TjLUdrCRm9WrV6O5uTlwTEVFBQYNGkSylSeOHj0Kx3Fw2mmnBT7/7W9/iy5duuD888/HY489Rl4+rYxubCM5alvs27cP7777Lu6++27hO5Kj1oFfZ59M81FBvhtABDlw4ABisRi6d+8e+Lx79+7Yu3dvnlpFAImYkkceeQRf//rXMWjQIO/zsWPH4qabbkKfPn2wfft2PP3007jyyiuxevVqFBcX57HFpwaXXHIJXnvtNQwcOBD79u3DD37wA4waNQobNmzwZEYmTzt37sxHc0953nrrLRw5cgR33nmn9xnJUNvCRm727t2LoqIinH766cIxNFe1Pg0NDXj88cdx6623olOnTt7nt912G/r164fy8nJ8+umneOKJJ7B27VovxIPILaaxjeSobfHqq6+itLQU48ePD3xOctQ6yNbZJ9N8REp3G8VvAQISHZH/jGhdpkyZgnXr1uGjjz4KfD5x4kTv70GDBmHEiBHo06cP3n33XWHgJrLP2LFjvb8HDx6MkSNHon///nj11Ve9hDUkT22HmTNnYuzYsaioqPA+Ixlqm6QjNyRbrU9zczNuvvlmxONxvPTSS4Hv7r33Xu/vQYMG4ayzzsKIESOwZs0aDBs2rLWbesqR7thGcpQfZs2ahdtuuw0lJSWBz0mOWgfVOhs4OeYjci9vY3Tp0gXRaFTYmdm/f7+wy0O0Hg8++CD++Mc/YtGiRejZs6f22B49eqBPnz7YsmVLK7WO8NOhQwcMHjwYW7Zs8bKYkzy1DXbu3IkPPvgA99xzj/Y4kqH8YiM35eXlaGpqwuHDh5XHELmnubkZEyZMwPbt27Fw4cKAlVvGsGHDUFhYSLKVJ/ixjeSo7bB06VJs3rzZOD8BJEe5QLXOPpnmI1K62xhFRUUYPny44LKycOFCjBo1Kk+tOnVhjGHKlCmYN28e/vznP6Nfv37G3xw8eBC7d+9Gjx49WqGFBE9jYyM2bdqEHj16eO5gfnlqamrCkiVLSJ7ywOzZs9GtWzd861vf0h5HMpRfbORm+PDhKCwsDBxTU1ODTz/9lGSrlXAV7i1btuCDDz7A1772NeNvNmzYgObmZpKtPMGPbSRHbYeZM2di+PDhGDJkiPFYkqPsYVpnn1TzUZ4SuBEaXn/9dVZYWMhmzpzJNm7cyKZNm8Y6dOjAduzYke+mnXLcf//9rKysjC1evJjV1NR4/9XX1zPGGKurq2OPPvooW7ZsGdu+fTtbtGgRGzlyJDvjjDNYbW1tnlt/avDoo4+yxYsXs23btrEVK1awcePGsdLSUk9ennvuOVZWVsbmzZvH1q9fz2655RbWo0cPej+tTCwWY71792bf/e53A5+TDOWHuro6VlVVxaqqqhgA9sILL7Cqqiov87WN3Nx3332sZ8+e7IMPPmBr1qxhV155JRsyZAhraWnJ122dVOjeUXNzM7vuuutYz549WXV1dWB+amxsZIwxtnXrVvbss8+yVatWse3bt7N3332XnXPOOWzo0KH0jrKE7h3Zjm0kR7nFNNYxxtjRo0dZ+/bt2csvvyz8nuQot5jW2YydPPMRKd1tlJ///OesT58+rKioiA0bNixQoopoPQBI/5s9ezZjjLH6+npWWVnJunbtygoLC1nv3r3Z5MmT2a5du/Lb8FOIiRMnsh49erDCwkJWUVHBxo8fzzZs2OB9H4/H2TPPPMPKy8tZcXExu+yyy9j69evz2OJTkz/96U8MANu8eXPgc5Kh/LBo0SLp2DZ58mTGmJ3cHD9+nE2ZMoV17tyZtWvXjo0bN47eWxbRvaPt27cr56dFixYxxhjbtWsXu+yyy1jnzp1ZUVER69+/P3vooYfYwYMH83tjJxG6d2Q7tpEc5RbTWMcYYzNmzGDt2rVjR44cEX5PcpRbTOtsxk6e+chhjLEcGdEJgiAIgiAIgiAI4pSGYroJgiAIgiAIgiAIIkeQ0k0QBEEQBEEQBEEQOYKUboIgCIIgCIIgCILIEaR0EwRBEARBEARBEESOIKWbIAiCIAiCIAiCIHIEKd0EQRAEQRAEQRAEkSNI6SYIgiAIgiAIgiCIHEFKN0EQBEEQBEEQBEHkCFK6CYIgCILICldccQWmTZuW0Tl27NgBx3FQXV2dlTYRBEEQRL4hpZsgCIIgcsiyZcsQjUYxZswY4bvp06fjwgsvFD53HAdvvfVW7htnyZ133olvf/vbxuPmzZuH73//+7lvEEEQBEGcQJDSTRAEQRA5ZNasWXjwwQfx0UcfYdeuXfluTk7p3LkzSktL890MgiAIgmhTkNJNEARBEDni2LFjeOONN3D//fdj3LhxmDNnjvfdnDlz8Oyzz2Lt2rVwHAeO42DOnDno27cvAOCGG26A4zjevwHg7bffxvDhw1FSUoIzzzwTzz77LFpaWrzvHcfBjBkzMG7cOLRv3x7nnnsuli9fjq1bt+KKK65Ahw4dMHLkSHz22Wfeb1xr+4wZM9CrVy+0b98eN910E44cOeJ9/+qrr+IPf/iD187FixdL75d3L+/bty9+9KMf4a677kJpaSl69+6NX/ziF4HfrFy5EkOHDkVJSQlGjBiBqqoq4bwbN27ENddcg44dO6J79+644447cODAAQDA4sWLUVRUhKVLl3rHP//88+jSpQtqamp0r4cgCIIgWgVSugmCIAgiR8ydOxdnn302zj77bNx+++2YPXs2GGMAgIkTJ+LRRx/F+eefj5qaGtTU1GDixIlYtWoVAGD27Nmoqanx/v2nP/0Jt99+Ox566CFs3LgRM2bMwJw5c/DDH/4wcM3vf//7mDRpEqqrq3HOOefg1ltvxb/8y7/giSeewCeffAIAmDJlSuA3W7duxRtvvIG3334b7733Hqqrq/HAAw8AAB577DFMmDABY8aM8do5atQo62fw/PPPe8r0v/7rv+L+++/H3/72NwCJTYlx48bh7LPPxurVqzF9+nQ89thjgd/X1NTg8ssvx4UXXohPPvkE7733Hvbt24cJEyYASCn6d9xxB44ePYq1a9fiySefxC9/+Uv06NHDup0EQRAEkTMYQRAEQRA5YdSoUezFF19kjDHW3NzMunTpwhYuXOh9/8wzz7AhQ4YIvwPA5s+fH/hs9OjR7Ec/+lHgs1//+tesR48egd899dRT3r+XL1/OALCZM2d6n/3ud79jJSUlgTZEo1G2e/du77P/+7//Y5FIhNXU1DDGGJs8eTK7/vrrjfd7+eWXs6lTp3r/7tOnD7v99tu9f8fjcdatWzf28ssvM8YYmzFjBuvcuTM7duyYd8zLL7/MALCqqirGGGNPP/00q6ysDFxn9+7dDADbvHkzY4yxxsZGNnToUDZhwgR2/vnns3vuucfYVoIgCIJoLQryqvETBEEQxEnK5s2bsXLlSsybNw8AUFBQgIkTJ2LWrFm46qqrQp9v9erVWLVqVcCyHYvF0NDQgPr6erRv3x4AcMEFF3jfd+/eHQAwePDgwGcNDQ2ora1Fp06dAAC9e/dGz549vWNGjhyJeDyOzZs3o7y8PHRb/fjb4zgOysvLsX//fgDApk2bMGTIEK/t7rX5+160aBE6duwonPuzzz7DwIEDUVRUhN/85je44IIL0KdPH7z44osZtZkgCIIgsgkp3QRBEASRA2bOnImWlhacccYZ3meMMRQWFuLw4cM4/fTTQ50vHo/j2Wefxfjx44XvSkpKvL8LCwu9vx3HUX4Wj8eV13KPcf+fCf5ru+d0r82SrvY64vE4rr32WvzHf/yH8J3ffXzZsmUAgEOHDuHQoUPo0KFDJs0mCIIgiKxBSjdBEARBZJmWlha89tpreP7551FZWRn47sYbb8Rvf/tbTJkyBUVFRYjFYsLvCwsLhc+HDRuGzZs3Y8CAAVlv765du7Bnzx5UVFQAAJYvX45IJIKBAwcCgLKdmXLeeefh17/+NY4fP4527doBAFasWBE4ZtiwYXjzzTfRt29fFBTIly2fffYZHn74Yfzyl7/EG2+8gUmTJuHDDz9EJEKpawiCIIj8Q7MRQRAEQWSZd955B4cPH8bdd9+NQYMGBf77zne+g5kzZwJIZPfevn07qqurceDAATQ2Nnqff/jhh9i7dy8OHz4MAPj3f/93vPbaa5g+fTo2bNiATZs2Ye7cuXjqqacybm9JSQkmT56MtWvXYunSpXjooYcwYcIEz7W8b9++WLduHTZv3owDBw6gubk542sCwK233opIJIK7774bGzduxIIFC/CTn/wkcMwDDzyAQ4cO4ZZbbsHKlSuxbds2vP/++7jrrrsQi8UQi8Vwxx13oLKyEv/0T/+E2bNn49NPP8Xzzz+flTYSBEEQRKaQ0k0QBEEQWWbmzJm46qqrUFZWJnx34403orq6GmvWrMGNN96IMWPG4Bvf+Aa6du2K3/3udwASGb8XLlyIXr16YejQoQCAq6++Gu+88w4WLlyIiy66CJdeeileeOEF9OnTJ+P2DhgwAOPHj8c111yDyspKDBo0CC+99JL3/b333ouzzz4bI0aMQNeuXfHxxx9nfE0A6NixI95++21s3LgRQ4cOxZNPPim4kVdUVODjjz9GLBbD1VdfjUGDBmHq1KkoKytDJBLBD3/4Q+zYscMrRVZeXo5f/epXeOqpp1BdXZ2VdhIEQRBEJjjMJqCKIAiCIIiTkunTp+Ott94iBZUgCIIgcgRZugmCIAiCIAiCIAgiR5DSTRAEQRAEQRAEQRA5gtzLCYIgCIIgCIIgCCJHkKWbIAiCIAiCIAiCIHIEKd0EQRAEQRAEQRAEkSNI6SYIgiAIgiAIgiCIHEFKN0EQBEEQBEEQBEHkCFK6CYIgCIIgCIIgCCJHkNJNEARBEARBEARBEDmClG6CIAiCIAiCIAiCyBGkdBMEQRAEQRAEQRBEjiClmyAIgiAIgiAIgiByxP8Hhqh464JUr9MAAAAASUVORK5CYII=",
      "text/plain": [
       "<Figure size 1000x400 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(10, 4))\n",
    "y_a = df_eval_aligned_base['jailbreak_success'].astype(int).values\n",
    "y_u = df_eval_unaligned_base['jailbreak_success'].astype(int).values\n",
    "x = np.arange(len(y_a))\n",
    "\n",
    "plt.plot(x, y_a, label=\"Aligned (no defense)\")\n",
    "plt.plot(x, y_u, label=\"Unaligned (no defense)\")\n",
    "plt.xlabel(\"Attempt index\")\n",
    "plt.ylabel(\"Success (0/1)\")\n",
    "plt.title(\"Jailbreak Attempts vs Success — Baseline\")\n",
    "plt.legend()\n",
    "plt.tight_layout()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "00b4072a-cc01-419d-a89b-cfddfd45ec14",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2025-11-03T21:30:34.110235Z",
     "iopub.status.busy": "2025-11-03T21:30:34.109852Z",
     "iopub.status.idle": "2025-11-03T21:30:34.213417Z",
     "shell.execute_reply": "2025-11-03T21:30:34.213050Z"
    },
    "papermill": {
     "duration": 0.112133,
     "end_time": "2025-11-03T21:30:34.214155",
     "exception": false,
     "start_time": "2025-11-03T21:30:34.102022",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x400 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(10, 4))\n",
    "y_a = df_eval_aligned_def['jailbreak_success'].astype(int).values\n",
    "y_u = df_eval_unaligned_def['jailbreak_success'].astype(int).values\n",
    "x = np.arange(len(y_a))\n",
    "\n",
    "plt.plot(x, y_a, label=\"Aligned (defense)\")\n",
    "plt.plot(x, y_u, label=\"Unaligned (defense)\")\n",
    "plt.xlabel(\"Attempt index\")\n",
    "plt.ylabel(\"Success (0/1)\")\n",
    "plt.title(\"Jailbreak Attempts vs Success — defense\")\n",
    "plt.legend()\n",
    "plt.tight_layout()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "7986b2a6-a0af-4301-9b5e-773ce3493dce",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2025-11-03T21:30:34.240277Z",
     "iopub.status.busy": "2025-11-03T21:30:34.239959Z",
     "iopub.status.idle": "2025-11-03T21:30:34.322967Z",
     "shell.execute_reply": "2025-11-03T21:30:34.322608Z"
    },
    "papermill": {
     "duration": 0.101231,
     "end_time": "2025-11-03T21:30:34.323675",
     "exception": false,
     "start_time": "2025-11-03T21:30:34.222444",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 600x400 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "labels = [\"Aligned\", \"Unaligned\"]\n",
    "baseline = [asr_aligned_base, asr_unaligned_base]\n",
    "defense  = [asr_aligned_def,  asr_unaligned_def]\n",
    "\n",
    "plt.figure(figsize=(6,4))\n",
    "x = np.arange(len(labels))\n",
    "width = 0.35\n",
    "plt.bar(x - width/2, baseline, width, label='Baseline')\n",
    "plt.bar(x + width/2, defense,  width, label='With Domain Defence')\n",
    "plt.xticks(x, labels)\n",
    "plt.ylabel('ASR')\n",
    "plt.title('Attack Success Rate (lower is better)')\n",
    "plt.legend()\n",
    "plt.tight_layout()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "af7dfa1e-3bf9-4524-bc60-033247a67948",
   "metadata": {
    "papermill": {
     "duration": 0.009218,
     "end_time": "2025-11-03T21:30:34.342231",
     "exception": false,
     "start_time": "2025-11-03T21:30:34.333013",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.13.1"
  },
  "papermill": {
   "default_parameters": {},
   "duration": 22209.048305,
   "end_time": "2025-11-03T21:30:38.001191",
   "environment_variables": {},
   "exception": null,
   "input_path": "proposed_domain.ipynb",
   "output_path": "outs_domain.ipynb",
   "parameters": {},
   "start_time": "2025-11-03T15:20:28.952886",
   "version": "2.6.0"
  },
  "widgets": {
   "application/vnd.jupyter.widget-state+json": {
    "state": {
     "03ce32490c5444458d1153de9adedfdd": {
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