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llm/gemma4_e4b_clone_benchmark.ipynb

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{
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Gemma 4 E4B — Clone & Benchmark Notebook\n",
"\n",
"Notebook untuk meng-clone model [google/gemma-4-E4B](https://huggingface.co/google/gemma-4-E4B) dari Hugging Face dan melakukan benchmark pada berbagai metrik:\n",
"- Kecepatan loading & memory usage\n",
"- Text generation throughput (tokens/sec)\n",
"- Reasoning & knowledge QA\n",
"- Coding capability\n",
"- Multimodal understanding (image)\n",
"- Long context retrieval\n",
"\n",
"**Model**: `google/gemma-4-E4B-it` (instruction-tuned, 4.5B effective params, 8B total, 128K context)\n",
"\n",
"**Cara pakai**: Runtime > Factory reset runtime, lalu Runtime > Run all"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"---\n",
"## 1. Environment Setup"
]
},
{
"cell_type": "code",
"execution_count": 7,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
" ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ 60.7/60.7 MB 13.8 MB/s eta 0:00:00\n",
"Install selesai\n"
]
}
],
"source": [
"# Install dependencies\n",
"!pip install -qU \\\n",
" 'transformers>=4.50.0' \\\n",
" accelerate \\\n",
" sentencepiece \\\n",
" protobuf \\\n",
" psutil \\\n",
" 'pillow<11' \\\n",
" requests \\\n",
" matplotlib \\\n",
" tabulate \\\n",
" librosa \\\n",
" soundfile \\\n",
" einops \\\n",
" bitsandbytes \\\n",
" 2>&1 | tail -3\n",
"print(\"Install selesai\")"
]
},
{
"cell_type": "code",
"execution_count": 8,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Python : 3.12.13 (main, Mar 4 2026, 09:23:07) [GCC 11.4.0]\n",
"PyTorch : 2.11.0+cu128\n",
"CUDA avail : True\n",
"CUDA device : Tesla T4\n",
"CUDA VRAM : 15.6 GB\n",
"CUDA cap : (7, 5)\n"
]
}
],
"source": [
"import os, sys, json, time, gc, warnings\n",
"from pathlib import Path\n",
"from datetime import datetime\n",
"from IPython.display import display\n",
"\n",
"import torch\n",
"import psutil\n",
"import numpy as np\n",
"import matplotlib.pyplot as plt\n",
"from tabulate import tabulate\n",
"from PIL import Image\n",
"import requests\n",
"from io import BytesIO\n",
"\n",
"warnings.filterwarnings(\"ignore\")\n",
"\n",
"print(f\"Python : {sys.version}\")\n",
"print(f\"PyTorch : {torch.__version__}\")\n",
"print(f\"CUDA avail : {torch.cuda.is_available()}\")\n",
"if torch.cuda.is_available():\n",
" print(f\"CUDA device : {torch.cuda.get_device_name(0)}\")\n",
" print(f\"CUDA VRAM : {torch.cuda.get_device_properties(0).total_memory / 1e9:.1f} GB\")\n",
" print(f\"CUDA cap : {torch.cuda.get_device_capability()}\")"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"---\n",
"## 2. Clone Model from Hugging Face\n",
"\n",
"Model size ~16 GB dalam BF16. Karena T4 hanya 15.6GB VRAM, kita perlu 4-bit quantization."
]
},
{
"cell_type": "code",
"execution_count": 10,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Model ID: google/gemma-4-E4B-it\n",
"Loading (4-bit quantized)...\n",
"Processor loaded in 6.2s\n"
]
},
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "169a4541ec42498083b317e11576b863",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"Loading weights: 0%| | 0/2076 [00:00<?, ?it/s]"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"\n",
"Model loaded in 63.4s\n",
"Parameters: 5.72B\n",
"Device: cuda:0, Dtype: torch.bfloat16\n"
]
}
],
"source": [
"MODEL_ID = \"google/gemma-4-E4B-it\"\n",
"CACHE_DIR = None\n",
"\n",
"print(f\"Model ID: {MODEL_ID}\")\n",
"print(\"Loading (4-bit quantized)...\")\n",
"t0 = time.perf_counter()\n",
"\n",
"from transformers import AutoProcessor, AutoModelForMultimodalLM, BitsAndBytesConfig\n",
"import accelerate\n",
"\n",
"bnb_config = BitsAndBytesConfig(\n",
" load_in_4bit=True,\n",
" bnb_4bit_compute_dtype=torch.bfloat16,\n",
" bnb_4bit_use_double_quant=True,\n",
")\n",
"\n",
"processor = AutoProcessor.from_pretrained(MODEL_ID, cache_dir=CACHE_DIR)\n",
"print(f\"Processor loaded in {time.perf_counter()-t0:.1f}s\")\n",
"\n",
"load_start = time.perf_counter()\n",
"model = AutoModelForMultimodalLM.from_pretrained(\n",
" MODEL_ID,\n",
" torch_dtype=torch.bfloat16,\n",
" device_map={\"\": \"cuda:0\"},\n",
" max_memory={0: \"14GiB\", \"cpu\": \"48GiB\"},\n",
" cache_dir=CACHE_DIR,\n",
" quantization_config=bnb_config,\n",
")\n",
"load_time = time.perf_counter() - load_start\n",
"print(f\"\\nModel loaded in {load_time:.1f}s\")\n",
"\n",
"total_params = sum(p.numel() for p in model.parameters())\n",
"print(f\"Parameters: {total_params/1e9:.2f}B\")\n",
"print(f\"Device: {model.device}, Dtype: {model.dtype}\")"
]
},
{
"cell_type": "code",
"execution_count": 11,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"VRAM allocated: 9.32 GB\n",
"RAM used: 2.67 GB\n"
]
}
],
"source": [
"if torch.cuda.is_available():\n",
" vram_used = torch.cuda.memory_allocated() / 1e9\n",
" print(f\"VRAM allocated: {vram_used:.2f} GB\")\n",
"ram_used = psutil.Process(os.getpid()).memory_info().rss / 1e9\n",
"print(f\"RAM used: {ram_used:.2f} GB\")"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"---\n",
"## 3. Text Generation Throughput"
]
},
{
"cell_type": "code",
"execution_count": 12,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Simple QA: 42 tok, 7.5s, 5.2 tok/s\n",
"Math: 217 tok, 37.3s, 5.8 tok/s\n",
"Code: 256 tok, 43.7s, 5.9 tok/s\n"
]
}
],
"source": [
"def throughput(prompt, max_tokens=256, runs=3):\n",
" msgs = [{\"role\": \"user\", \"content\": prompt}]\n",
" inputs = processor.apply_chat_template(msgs, tokenize=True, return_dict=True,\n",
" return_tensors=\"pt\", add_generation_prompt=True).to(model.device)\n",
" inp_len = inputs[\"input_ids\"].shape[-1]\n",
" lats, toks = [], []\n",
" for _ in range(runs):\n",
" start = time.perf_counter()\n",
" with torch.no_grad():\n",
" out = model.generate(**inputs, max_new_tokens=max_tokens, do_sample=True, temperature=0.7)\n",
" elapsed = time.perf_counter() - start\n",
" gen = out[0][inp_len:]\n",
" lats.append(elapsed)\n",
" toks.append(len(gen))\n",
" tps = [t/l for t,l in zip(toks, lats)]\n",
" return {\"prompt\": prompt[:60]+\"...\", \"tokens\": int(np.mean(toks)),\n",
" \"latency\": float(np.mean(lats)), \"tps\": float(np.mean(tps))}\n",
"\n",
"prompts = {\n",
" \"Simple QA\": \"What is the capital of Indonesia?\",\n",
" \"Math\": \"If a train travels at 120 km/h and another at 80 km/h toward each other from 500 km apart, how long until they meet?\",\n",
" \"Code\": \"Write a Python function to find the longest palindromic substring.\",\n",
"}\n",
"\n",
"results = []\n",
"for name, p in prompts.items():\n",
" r = throughput(p, runs=2)\n",
" results.append(r)\n",
" print(f\"{name}: {r['tokens']} tok, {r['latency']:.1f}s, {r['tps']:.1f} tok/s\")"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"---\n",
"## 4. Reasoning (MMLU-style)"
]
},
{
"cell_type": "code",
"execution_count": 13,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
" OK Expected=B Got=B | What is the time complexity of binary search?\n",
" OK Expected=C Got=C | Which planet has the strongest surface gravity?\n",
" OK Expected=C Got=C | In C++, which keyword prevents overriding?\n",
" NO Expected=B Got=A | Probability of drawing a red ball from 3 red + 5 b\n",
" OK Expected=B Got=B | What does mitochondria do?\n",
"\n",
"Accuracy: 4/5 = 80%\n"
]
}
],
"source": [
"mmlu = [\n",
" {\"q\": \"What is the time complexity of binary search?\", \"o\": [\"A. O(n)\", \"B. O(log n)\", \"C. O(n log n)\", \"D. O(1)\"], \"a\": \"B\"},\n",
" {\"q\": \"Which planet has the strongest surface gravity?\", \"o\": [\"A. Earth\", \"B. Mars\", \"C. Jupiter\", \"D. Saturn\"], \"a\": \"C\"},\n",
" {\"q\": \"In C++, which keyword prevents overriding?\", \"o\": [\"A. static\", \"B. const\", \"C. final\", \"D. override\"], \"a\": \"C\"},\n",
" {\"q\": \"Probability of drawing a red ball from 3 red + 5 blue?\", \"o\": [\"A. 3/5\", \"B. 3/8\", \"C. 5/8\", \"D. 1/2\"], \"a\": \"B\"},\n",
" {\"q\": \"What does mitochondria do?\", \"o\": [\"A. Protein\", \"B. Energy (ATP)\", \"C. Lipid\", \"D. DNA\"], \"a\": \"B\"},\n",
"]\n",
"\n",
"ok = 0\n",
"for q in mmlu:\n",
" prompt = f\"{q['q']}\\n\\n\" + \"\\n\".join(q[\"o\"]) + \"\\n\\nAnswer with a single letter:\"\n",
" inputs = processor.apply_chat_template([{\"role\":\"user\",\"content\":prompt}],\n",
" tokenize=True, return_dict=True, return_tensors=\"pt\", add_generation_prompt=True).to(model.device)\n",
" with torch.no_grad():\n",
" out = model.generate(**inputs, max_new_tokens=8, do_sample=False)\n",
" ans = processor.decode(out[0][inputs[\"input_ids\"].shape[-1]:], skip_special_tokens=True).strip()\n",
" cor = q[\"a\"] in ans.upper()[:1]\n",
" if cor: ok += 1\n",
" print(f\" {'OK' if cor else 'NO'} Expected={q['a']} Got={ans[:20]} | {q['q'][:50]}\")\n",
"\n",
"print(f\"\\nAccuracy: {ok}/{len(mmlu)} = {ok/len(mmlu)*100:.0f}%\")"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"---\n",
"## 5. Coding"
]
},
{
"cell_type": "code",
"execution_count": 14,
"metadata": {},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"[transformers] 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": [
"\n",
"========================================\n",
"Binary Search\n",
"========================================\n",
"```python\n",
"def binary_search(arr, target):\n",
" \"\"\"\n",
" Performs a binary search on a sorted array to find the target element.\n",
"\n",
" Args:\n",
" arr: A sorted list of elements (the array to search).\n",
" target: The element whose index is to be found.\n",
"\n",
" Returns:\n",
" The index of the target if found, otherwise -1.\n",
" \"\"\"\n",
" left = 0\n",
" right = len(arr) - 1\n",
"\n",
" while left <= right:\n",
" # \n",
"\n",
"========================================\n",
"Fibonacci\n",
"========================================\n",
"Here are several ways to implement the Fibonacci sequence function `fib(n)` using Dynamic Programming (DP) in Python, depending on whether you want to optimize for space or time complexity.\n",
"\n",
"The standard Fibonacci sequence starts with $F_0 = 0$ and $F_1 = 1$.\n",
"\n",
"---\n",
"\n",
"## 1. Top-Down DP with Memoization (Recursive with Caching)\n",
"\n",
"This is the most direct translation of applying DP to the recursive defin\n"
]
}
],
"source": [
"for name, prompt in [\n",
" (\"Binary Search\", \"Write Python `binary_search(arr, target)` returning index or -1.\"),\n",
" (\"Fibonacci\", \"Write Python `fib(n)` for nth Fibonacci using DP.\"),\n",
"]:\n",
" print(f\"\\n{'='*40}\\n{name}\\n{'='*40}\")\n",
" inputs = processor.apply_chat_template([{\"role\":\"user\",\"content\":prompt}],\n",
" tokenize=True, return_dict=True, return_tensors=\"pt\", add_generation_prompt=True).to(model.device)\n",
" with torch.no_grad():\n",
" out = model.generate(**inputs, max_new_tokens=512, temperature=0.2, do_sample=False)\n",
" print(processor.decode(out[0][inputs[\"input_ids\"].shape[-1]:], skip_special_tokens=True)[:400])"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"---\n",
"## 6. Image Understanding"
]
},
{
"cell_type": "code",
"execution_count": 15,
"metadata": {},
"outputs": [
{
"data": {
"image/png": 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C8KR9hI0KpPwlmXdohRzh4AydTumUzAoywHRIpdojY3pcOZPGEVI6L/gxuBn43VBCV3pe4dGp6095GJGmjYDo07wLnDiFNUVLbTMNT4TwFW4iJzgy4QoLyEipJgerErDSSzxM1f0Eb+IWY+DpohAL6AVRZbXQGTKSoRQGxOYDSnyeApmqqXypMlVi/YuZq8DJ04QXzuAQPoPeQOBpUFXa0BePAH+WEekow8A1eAQYFj9PJuO6N5DCdIcgSUX5B0IYwmACNTjLgMvwkchSU4Jzq5pyonI4yAOMGHZlv/fwmvBGvUOmPjFU8QQL2+FCItylWTwrTO9QKC7iSvreyhAwavKRtiKgUpFNeGBCMrjQDg4PW914TXX9vA984YKXOJOpjx+5kyRVZFArHUEGYuAJruz96vSq5SJM46LbOtIp7QENqAD5T6JDouu2UIEtjMgUdwc3JgIO+GHCU4Gt+ORfkg4440u71fKVP3JGVZUS70Wpu3r+6uvYxBfs4BBJQRp5pSr9ZYVoAguOhAs1zmHPCTKOqx4dtoIm96BzyzsLIqnIgWxeRNkJCI6V0AiL9iVbyk98NHfIH1roYuRZWABpWqLLJEwi6DSsufV4cXqiKnuMSHTpX1Rb+dHrKHzjMkYvQmOWfdLdQKKKUeVJgmTrgoL5VplCLEIVdKfFY4pNZuB3OcFrpFnIBCFdZcbACJS3iudZVEcqJwFxKF1iYiwFWRjKJb9uMvKZwAoxfGAL502KA76n0NABmXhvhCT9h7pkSVcJEnlK2qEXq76SZ6TKTBmXuMKXFMIJXZdglcKpbCM2JMh99etV3LoHf9I04niONAY48boEGDgjD+QOtY5jaJVFnsnrDhe0kjQ9QaUWR/kTXK2q/qSwhwctPIsPkYk3PL8C6IUBTzEPVNGKMzjGma5Ppv0QDoIYP+z1MMchV7ETAleSYEvZV/vFn6SnIIt7DYeSN4UWeUzqkEi4ibxRZogHPdsGIluA5Cxr4Hnq4JJO0i4a+IlPRAFMYItpcItKJ2A4NA4ofJN19E62M6oMLU0PEeRQVwwrXJPMCOBNyLoqqZGzh9iam+1Bw33gBJZxEcanHq7QwZPwbigEm1mJ5hEa3A0pty4VUSFFUnddCY2xlEkMZvH1jJwQAyUtOEhV6VVaBjX5+fMaousO7wFkXP4OAZFRVaGZ9kGFCqDj+KgWP1NQEVGodM2gpZ2YHwVvTCF2dPwGqQOuDucu0jI0oFJAC2uCXcAiiDyBkURd8kXubqRSLzkG7xCosE6jChykQm7IDbUSvD8Kf8LDaH7qZRS+xAWseJSMhhaud3Wca4I32IhTsgYmyat8VMwuSuGGRuk81Pqta7LDDLns3hvkDQhpBaLHJ6BLJc9+lelVWRE+4hwWpNMp3XYeh4QsHlVZDZpDH5OKJEyUxz/TXlgZIlBg+173AVCRJT3u4R3PCFlZmSAks9BXmlQkbm6Ji0QVZshIXGrWICBQECpDhLRLOs3ogVvP4gGl0FfJRVFmUSGMK22VUNkPHGHSxYsygI5GAQitquuFH0rLLjmQiTY098kNGnKQ16CfNE/EweFEGAiG+4uyuwakM+Sc0OyOUREUZbkMVwGEr6kdxI3Xzc/H5OrCOedmTPSDqyrbElRVZN4h8UIVkcRyk95wl0tfXdM8Kq7RYBegpC64CUaPG/hTekAja+Uf8pXAUzAl4H/wq2q/9JmKy2jTym9qOyO95FOUAzlA5BHQUBdmXOWqxqs0NSq3sahc4g8tilc4RS+Uo68wGGSN5JoM/3RHTkO1Ibr82pKkCC8quo3vj14eqtACGFgiearnGj6BGV6lA3FLJzyF6/T6rLsUinjpRkFkDlQCqPFECAMhy9FpCDWhWG5BpKkwQleCjAvjSIDHqwJCozK0ghPFTSrBD+FYbgAGbBWOBHXocicePiXFiFcYUtZTHxHhHG8sv8P1DBDukxQOUcMpniE/OCcxZDIzx3h6whN4dVuYxS9J66R9CFHxBanXkKJhWJeqy9ehEh7ApFE6UgMWOCsbvcIO6pVwwoop0cJ1D+BWDDX+7KREzBXLCPURMqIGdmTtgaOlzlOEhA+Mob9BafpMSgWDqBylWahy0d9hJzIX92mFCR5xBSdwJ5ZHD9dA9Xv158Q7+BEVytKawBVUJAp/5Sn+PyBOlZ1OXHY9QErJG9FMm9gjLyIMsMUlMfGkcBtob42/CY6yEl6WwTM06ymR3nUXqEgKGmZ4ExKPBtul8Mk10U/ChRwRA08hiuYoCOUDFEfcEI1A+PQXqYIyfEJ6EgtQheooIgHq7nAEH+EMNuWdmtbUqRCSxChdxQo6yATNcMbfFSYM9DrooaqGMGkINVIq9uErUiQDBAs96eAY+o5RlHITL3Z6SWFovhDgXIATf4HDeQg/xi/tyAd8CErFq4QOy3IOEhXT6ZRx4OkogR9eBEjHFqFGgTUBpjv664rJIziET2xJggUb6El44XYWYfvJmxKVwNItXlMIB32VDx2KCcm6bPMHVbGI7+mWSpwEjHD9KTgd24eFtcK79iA3oQiunIa/IKqrXWoPZnj2LO2w4TzhMmTqEhVFiAplWLXU3U+gEmeO6zCZ7pbWqECrV6N8UEk8KeWpM133sp+uNIPT16zkeBcwBHmOK2HG6Mj1AwokbNRwQzNTBXWUgY80MZQeHJITuqXYoRTfZzZhuTqSfCpoFIYh9RAPYgKYUJyhJf1KOHHdUBInZSPr9Qa5lzFEfUYa0hWpT9hBUzmEt2Ic4zljlWtABtjAXCPTlWWYb6hFtnCaho9cM26SRPkV/9LRyId6DjCl1J1rMO16HAUzGAEsQ5H7yHiDA0dQqV1oAwfVCIGsQh6+gBvdCYJDPgJLqHCl251BDGUA1UgO9TA0ZJEIFCvCiQqEI1Aa9VY97kN04w59EKUhS9MvzHhKf3IJJGgBS1d5HO6pD2WSiGR8TtJrML+QLedET0m+0ML47KzLk3vptpyhqWhWaENV0T3xkhi8O/gUt5ux/GqYU9KJkxY96e0EIouJwmHSvJLUrtnBsFo844kdKQhyULgVrr7CwT/iR8FKnOEddkJHwFKp+F4DtZIAz0kADvAm3k5jEi/p4QFoAtepVsb0DBuz6BM5KmvFH0tpQYNKZXWXW/omIwW6yz1pZcRQ7enXTNRSLDp+iFYuqUvj0qobfthy8KqqsIvOTLcpkk7Jqm8aNuFaMCIKMK4u9yRspLvHJwmILpiMR4EaRGp6lYrX6MT7tCIeZjbSVM+kNvy9oScrxMgtXmfapdc37G8in2HAaU+mtqgEkFstl5Ys5ldcU8KdQb1XPvRWtECeXGlEgC1xhlAyneqyU4YjV26JDo4YBhVM4afnNw2cpPWT5aZgJwShMUIOuxLf5Qvr3Aq4W94nwGNPSDNv+qcEcfcMM2vM98SpNP7LVDVESQIxmMiHMANGRRAGBE0KeediBviXS+LiCcdfQhOGoRwyuqJdKN6n/k6nyyLqCJGi1zCI8NEP3Z7niS/3FKsCu6EmsuhEL4ksWcuJzKQnfJP+4jpJH0Axyk7ex5B9yBDDGnA8P4GbcMOqQOiSmXSSA6Sl1FQMSg5BzAefymdaEsPTS3fBDHcIGlZRlb/dHXAiNIHDaSkiUxsY8Sn84Oqf5C26ydtancHY6iqNrr0uSjENbhjUzaWo2F6PUGemOywAUfViDn+wlNm4yK5Lj/+FONEbjg5pTOAjVccPkaAOGkLVFb5TbwSY8AwIPugIV/wMFMFU84dz5H/1Go0qeWr5DWhJBKNQS1/lTkVhZB9mDshSk8SNK1uYLK+lYBNupLhexYA7wFXINcMS0lgFLZhQxF1UqjaVkABVaIqi6UgS8RbOSKjxvkaYhESG3AZanqUC+RKn5oTvYKXIET7QoJZhyPDzFDeXRmp89ypPaJZ/QFX6Kqz4R0KgRbR7LgRke9pN1whLhHHCDEFHYH8aVzw6RzyHaQlWhqijeClqJUMc9dHpB6TcSim0cdExz1QXyJG4gULXIZWvugTea6ISPQkHc+iNZ6cceGVLvinThIJ8RU5gQu2hTPwGF4CBXIksJ3flGPG6lUeI2E70WJxGuKRM46Aijr8REFz9XMpe0x3hEhjjpzAjfQGH57B9efJn+rGTaHGKFOCiayD0EtfdXZDSMPgET3scXdZgmVbkKrQe8sn40n1SJxEiBe4IkU20Kq0GK6+9mBqjE1WoRaZ8JEZ8Ijpa/J1kB6kJK0lXeChPqZETaUETWxmRxAKmjBKJ4kVOd14llDoqfiiUZy8oURCwQolbBli0pVh+CfZW2UATgXYPG0OCBTt8HfIfcnbpB2DJpC+ZCWA1mqVBQok57O4hhwmG1BRuQE9muSO7fOrSlQLb/dIv3tFeUUtA5zxxdx6H/Si2UmCcP/TEIwVLHfdLl7F1jcpafl4FF7c36E+kxZ+8Iyz67rwLL8gdoNy93PaIeoxCru8wXtyd3uHKSGn8VWXyg1jEKJPIEZsKD6ZZgkrFTmAMvXhVuDTUk0iVMu/xF7kel+iQrfguR3DCrALkL0nv8FY/yc/QqpjwS0TBuRycSdBDYYflHFi2/ObOSF/ZiaWj+Ou3Yspe9wB2URKdSJAjSBEpG5gip6ABNzJTWIWN7kxolyQPIwmo7klFVHy1MoVXtJUvQucmzU9eHbuE61HZ9x4BzAxrFyJKIB35F7QyrkeHkaHjKsr6hqGNtI3ngJwaJzhD3hGZJ5yMGkmSdOCm+hrgA0a/fLzKCKbuBBreHZWZI/TQ8xDTYtlpTEBGxVyaqPz4JL8BGl49Dw4XKqWow0DLYC0YcolBxRVPyAw9Dpo+A9tTUnjiFsTQxaFkHEb9hHuio45bj2FjxSU6h1daYbEFGkziF67zL+fUbAqYe6fZDbqzC563BA9cGEb2EdFtMDjeUl/i4F9LrUZNVxXLpKnDTGQ+ZM/ViEmcKzyVDouHT7GC0qH0JRY46dOi6x2Y5YrX22HvBK5iTZDXYZDOexKuf5LwScFLNOEdU8Fwpodt0AgvMKIKbsCr2Or2dDpBCWdd/KSHaR6mI9LIkeB7i0aLyH/PvXiaeapqeg1ZpiFT18CZhkzyJNKFFmkuOFKnyFCPGrrMCZrMxlWqBj1BusomMgmvfOoluLqVufvjBqLi6j7RN6FJG/ChM4hPKBXn0C8nhtW5HHqUswd0OB9DbyNGbkMSrXOSvwIDNIG3dpl4jMRDHhqkKYVe4DsN3AnraD1V0UPHEjP/sSHhTGuujjxJe/EaySzbATUMKq8m3LosxtVGrABFllA3nD9DM0wczCn1mR0Pnr2RwisRwYi35BvzXSnzXeYiA138Qglu614Y+rtmCTCMFr2ihsILRMzODIphCtiAiT6kLBGeQ5FJLMHjqi5/EHuQkugXeXr9QB0wjfjvc8nzEA0oIu6gqdzVR7GDPypRq9NAcCvVFHHTVOlDaYdoJnGFEcBBXU/wgY3BhV7RUhMJVC9JZmF9EhfYzidyiypa52WU7mSgiBCaypWAQ36RZcS/iZgQNJywQ3wCZFhiAI1jiiDokFl3rdtqoAL2p+CHL6JG3v1glGihN+CFjdseF9DxePNX2MMnWEKmUQWHfwRpcwXv3auekRdn5OpRU8gpn6Qoie6o0XPxHTRCVj6W0KAOfJ7QrmQAHXdxGQJmGAYBbSXzOQoUhKJRMXKoA0BHsyOtCgUCT9V3xPaIw3k6tcMhV8eMQOqgwuXPCTOTYgrUEKIj6A1jE0uC8FehMJgI40K04oMFUNDKkkJgMDRepOJSxASrTJFiMLnzDHluFVSo/R54ElEsFEK5kOETsF2OrqAQDDFcQSm84EhgAqfHq8cnfeWV/DR8uCuDS5aCEGZCTgYJhl6RLDmlNASOEqQ3LhCEDTxa8cltaK4T/GfoFXzJEJJBlA4EkulSKHlDo6ctbGQ5HBCKs99lFBsdpHgaFpiBI1vdeZZDetpNsa78GLIPDlKaXiOFQ0agQ78gSgPEDT5TRMJg3OduHN9OuAW/iFhsqpdTDUGJLN6UYEEOvpSOCG86hO5UEUC4yEF4WnJ6YENHRhZah1POIVPHTRA3W/VxhWYXxz0BsjQlhgfMm57JVTEJCjAuYUrhcdYEd1r0gSvVTqVTHV4hiljBVniAwjK1zPCSoIEX/RXEVDTZSK3LpGTWXMERF0jvg1wCDDOg40yMZ0KVqEkcgSS06sJDAP+Mszgo44SezDvjkYag4TGLjR4AJSnAYdyREk9Qb7S6Fz/QgIxa22pavxTDR9mVuTMNNQXTkYT77NQSWe7qMenuBihFZYVW4gaO8d2t3DqLR5ENmwlsObooxYi7/sF6AnooZDJ+TOSA7GKThhFitOmOPMknQ0oPQw6CgxidZ5K20yG4XJWekqMoUmQSVT4JRLtyCk5JMHSsDozv5DqMdlMyEKNsXe6qRLpQ4gSq3w3Gb4GV6pgFGflW2TfkLd0XRrijh6IbfeMsP7QSXnGRZWRgD5JGJuvgPdp9pa+eRGmh0i6tXtBFSqorPhEVYBIIHAmWyaFL8KgsOWOEIXUNufRpdKIqiOHVHRlFseMAEJgpiSL0A/fKohAtbr1XI+4oKPU0xGsQDER8BQu/idzi4h+gQuk23vBI1mn1sPiSO7IoxCm+YUL46I6465YQbiOm8nr4xOIyEIJJS4lhYKLGI56emIoBvvsL8AfAez6mBQIhYstjkj456OuFAHTJjUsexnQpJrAVMCQAput15EeoFrKRE10ra7AGwc5o2IucKiqJ6rootxFJa3AOE9A9hmmVc4OP8gzZOqtDD+Ua/AjuedofU/9QiLBxjwB1UzQGj0jaRRNW++gSIT4BiSuACWx3VEsPFNGlByMsOZ0P+ENPwuiWZv2Eqavsx8k4rqGAYlkAIpcs4VyBkZZgmXeBRqJKnEMMiJ8oKTyKhPdDFCswvBRSMT+prBIUsErjIeySNhMcoahMYFdwpzUlP4RxLFtclK8Dh2qFF0bPDqKVVSVOLnGG18hxHQou3YyIkqtw5PEDlwHQMWOztBMAaq+u5OncQ8cjfqJ/g36A4Eh/h85jImZP+8CRdlIaGZBN2r2QiqN+BnnDKy/6c8IAiEMJLl6Fl7yEQmgMIaJzwuQT94QQrdHUbcIiG/ku7xg1z5F28SfonZYsCJ3Ypr40KtLtijENIy0jrYfZTsQMrS4P6ZNXzp7n6YdExE1+dRDlnV7lznyqsnGJPyDUiCrzjEX/4u/Iugni8jbF0O/cU9mv4eLVlUk40pc1+B6WuImCbNGjCQUZaEVEPmEaRMXpfqKH8qL0CTtYR/lFxx5X9N/xp0IbUIkTNWkqlE6pPFN5wjhUprdgBb5k7JIGVcF19JiAlvzi9/yekJryEVA8M3WacoOKW0+Bng46aIVtpzjcpVYAE3AoO/X3zC85AelIBdqJF2JnJnFlwkg1On0BnhqtEMNwdQugMYUz7sobgYpZRNApO8XsqxAzs76kS1DfxhoPt4mM3SGaV6iN9JQv95KT2ND20ZkFUR3LQwJTmAAHoYKVv7yFO8mrroPwPyTPpPAbFhRvXcZy4iveekMPIUqMUUx6bwArR2sTfUtlkK0us5VzEpPZ7pLdXJdu8azYBMkVikW7CriAhhJeijCg83MSuceJDr3sRwlBiU9b88w2dEamSaxcCtHlM8snO+MqcmDIPFhlFwMJhChf4E4veLmpiAr0qahjySBCixuJP4kajJ6AskFDuhydeMj+c0rVMOziCnAcwycN3NVU4uoJSgIqQzr5egzZi8gkKtQ6qUhFgCRMS0xEN7jCxW98jw2RIFXHqvwG+AMwccNvbGHqSrQZBbOqxYu+fMwUeTmOK50XvaH/yWyAsoQHz1Ji6CZspPfwE/jdhw/B2W1zx0/lZBLlUE7BxMs8A+7JEU5FbID1wjsxt/CTgGQmVzwle6XDGANJ14jrRtoJdPnLF7Ug0Mh/cUIH/KCHUYXE2f0V2WWBiEUvYR0petYddYo/lbv0WMpUlwERFPhpXIpd4qRbqhGy7K22ASsMYeHZy1mFEDrlV+fbDGEID3zHDXzBxrJgpK+GZIWjhJERROWg625ipwwqWoIg5L+IZJAPwkjYhG9Apz5daQ1whJGUupRTKJGmV2Wu7AYET/HNjHIGeDJx0eWorl55DitfKoOmllnsK3MmQk3Zlwsy4k09CqB2Kv2GV/wAVGeEhDhhwCXeaFEN8kq8MQiSODOkwxRA8AZ4ggatEMytIGM5kCFoGjowx9MYM5cfXy7Y3/e7HXI3vmMRnvZqf6IcEjrblubn2spbX6dvOteefP5Ptf2dHXAidt0O2ckgZfxEBYfchuX4754RpRPzkwj+Sz9+XGGYpJSMKjkLTcBOv6J7LOG56uk9edEr9cNxw10fFYGYehkZEGoRJ3yKM9S6TIN6yoS6FyCXMHgE8BFvQRut7D2rUjEmhT3aR5FR4u5Sr5GprG3QtBxV2QEy7KalU+aFTdiQDeLm9VDDvoJxyYkWvRtohSRYApMuouHJFYxDBRTHJK5SJIpGLFGpjcwMlVCvREgICCSp72wJXwlJJgk+tNMT0eUs2iXzBKR7Ezct5JV05SjN8KQ26OITKtboeAUKP6D8VaUxqPrkd9gbSG+VQREXPGGm6tdTMih7dxZ9AAdcZR6oI71FrDh0JDSaTFZ3qnaCe4j/kKYQvat771wU8J1r19rSxcttDxoaQK6Zuba78TByL64d7zLOttWtR23ny3/Qvvfb/6idPHuuHbv8bJs99zRkOLsXml6VspJLxOGPA6CAKb+O4e+44ktHb+EBfyjO+FwJE4L4TmuKU/gSMUz8MnJ9Xl2irtcuUUVxH/4UxB4aTtKJX617jTJReWbXuniFa2wwosFn0Cy8fu9iyHGid8KElUKiu0Nb0Omtgnp+B77TU54BFNmGfENu8aWqvIftu/JIHpmME8i/IUZohoeFhFCJmChTNySPtvD7jJTiFw1p1UTENFGhX9IWOFhC13WIrgEqtsf5JDaX7pHgJAj08nc5uij9VSkJ9fgux8A3HSQklE1XkQd6ymlgTMNGwo0BDqSiIvKQlhjBOplJge70h9/o8OxwRkeaWA8ewit+yn3ghnYSjdzdYqfykyJa5gXiV6+92x5/5Q/bzC//D1tbXLR+QxdmOJXrn/xWe3zvdlv6mV9sc7vb7eSTe+3KH/5e++Zb77WXTh9rlw+22s7Hb7eti8+loCtfeMh3yBYZjPmB61D4kKtjh0Y2M6Gksqmuqp6O+KKTKiA6p41L2USyTnvMNZ5wGo2Nzh5s/g6IQ6qFN5k0AqQD0Kh0ldnGLGaNuwr5lFLohYFLaeKJQP7zafFiRoBAytG5K4Vg4zLfqrcKoO5JrILwz8NTtSQTOYMIHBGRB3/W2YMpVKeeR3GtECt4XKQnL7VkHTs8NNhSqLTzqtzQWsiJJEM9h8QzIGHEyTe8Bx0jcKMRXV4qNV3LcCt/IVXXZZAr6AhWCq0A7jKwcOgqd2gaKDi8/PCBHMN9MO6vbB6SpBAmdwmYlBBJZsRfuRn/hFSgBnfZ9pgoIGQO3ZRxYPb0kshhXMWx0lH8CrUq2UNkEulNfU7Dg4llqL0j92+3+bvX27f/wd9uq/u77fQ/+btt78ha25+bbzuP19vOrZtt5d6Ntrq91Y7+5n/b5vZ22vr9jXbl/hMFaxdWltvJlSPt8e5W26QSCKOJaDiAiVenkhySI/4uVsUBdBhAjOGP/XTkbmPRIUE9FNheRCZhpeURn4IGdMlj5SHz0d1VOP1TXcXmgEkwwIKnh8ozbsETa5xQhvrzKvgKJy75ZyhuQAId0JLRZP4gBaloHsopaMqdgUDGkpKAEAmy3jGz31MZPDFSzQSg8IPaBekUwr80aAXfa0KFEq4+jKA0+Tc0CPosOhqT7ggWIBOG36bMDBt4vWkrRcg6HCSXyzFTlQlxgEqtQhSgFuC6ilhojKD+FMaxSFVG8icgbLpklfJPcu1hpcVSwZh7+IR8E7giWXGkMuIUI2XSlTTLuM9SB7USNpDzHJPYxodC15UkEqJl8O8vPScc4xXbyuxwC7RA0UkEqjxZXL/TFh/da9tbO23z1MV28siRdmJzo+195Uv5yOC8Lf0eY2K6WifPHG+Lx4609UdP2uMn2+0hOEuM2xd3qfl5zi/Mt8cb623jxtW2cvHpdkD3vS5lIN2Rk1scZdiJ77JX70NPwOvZAQyK7qIo3IeSZVzoC0N8oUufH3DFN0C5pbXSlUie6L14J1pGoREQqQCnzXiV6AUQXRJf/NRnJBRqQqNYSL+CA1y3Q3wVknhgOhs8JYOpMDRZ1mMNKchqlAau6RHe+CpvXecJKTrCjnJihTetsKZUi0LBMUZXARWkEJMCgydKOaQ82Vt+7T6YEO8q1qfKKQPtha8nuKBIrDw6Xhe1EilTL+PzjC+3yAIdQYwTQl5FugJN7HQ2U6BQ6TAhE9yReeLWuF3Z4ysg7p8wksP+sfQ0wDteOMEvkyYUoAQTGCOSagmap5Lne2s8k0HC9cpNOTLUCUIYVz6U8xNyyqPo17zs4uN7beHW9bZHYXzy9Ettl9QuAnP8yHKbX3+QtPuFnOXlxXZk7UhbWpxvj+49ardu323rW7ttb3ahfbC+0U4cXW5bTL45nt9jfH+WMfr71663Y2fPA8OBTuadeQTzWWqtoQrl9vKRsK5/faWPyq8JAIDqIv/qQL/ePAfOhBh6MZIf8V5ADGd8hkW36Y6HskGB0Wch8KqYygNJmoKM74nD2wtUQY2lRsM728hXviGLsXUJU3MFQOBRRvM44YLg0PaH5OHvTQC5J/FBjL96uxVV6gRXMMG5Qim4olbFZIrqEhBrFwFIVeekXG2YCYliNBFwZOxEPPAASe3RmSh4CQ+1XhgUIqJYS5AA/2RpmFFjWaYE74SItfWKMnrOB2fCJ9ihPApjvSpZlVE6/MGTJRyj1MIJa7UFwbzjSwI1vbzaN2RDGCsCr5J3KI2woBpLmHRwyWpSEg1JOA6uSmu5xc3F8xDIxDPC5FlwPFXTwKvQihqEaZnnkGWJ1nv2+gdtb+VEe3L+GfTH6Htpqc1sbvICA2vhC4zLIbS/MNdW1462J+T0+w/W263N/bY9t9SOnH66bRxZbadOnm0nzp5pR5aOtMvXPmxb73ylbe/stmX0tHbqbHt47Uo79uzzbW+3t+rkU5ZxEHSIWfqYCqsNDQ0aqt90WRgqGWKa3zxM7CBkLMC9uIlVZTzxBeS99FWUQscwvOa9tEKWm97RIoqXyP6IjIEpbqnIunTSmbaQA0+q5Q4t+ViKEqQutAv8KMP42EueylPc41IowCIXsFqeeHHJV1jiBevOSpd8tOvIVkvXxb5of6Ihk15EQRYdXPN5ASBN9+iYF9AwXjPRGiZCIoE1g6iFbnaaMOI7YZ1CK3LFEWWAvkKq1iuFEUzCahmm8CSUxAJfha9qZd3SrLVCj27kCj0Tr6e8qV9wTxIepqg9FUnAumwFL25JyhOZhilWqGHIZWvRAZO28O0pTDzuAuxSyEe5irjg/vRGV0Z7SYwrcd6C4+MTUAQAaNeb9e3F21fb3CZj7fmltnmGbvXJU8Gbpfsd/MDSAi8stLUTq22b1vf9pdV248jZdvkXPt9eYH188ehKoxposysr7eDJ4za3vMykG/I+yyz71e+ne3/A+Hzt8oV25e3vt7XtTRS+SF4hZOQEeaSkp9GCY3JKg9WiDW0mfcKZvniAxa/OSgVW2EQYBtThy6GVV1Uc5e4kyGOvKlIp5KE3dHe4qgngD9x8a2yaHPGLHmHQiRt24YgOFSNzJqGCBIqLu6yySA/OgSXG9A+9WOCUW38f1cYf+nFJUn0QEuYTnyFJ/6jIqixEhHCJ1mCqfIXFXZsNPWWk3KbEK4FXEgRZJR1AZoCs+NcloVGIKmMIISJlgWeJ2jMU+MPXGA8HxoTDT8IWzrAUWNZ5IF7cpb600hFDPCh0GQ9Bd1Spe9VTd6oKhT90TWGkXz5JTmlXWEUhpzJ2fJ+Sm8jc3UM3xia+6y70k9EYjJlxGLHTPJzWYlRwgi5sPWnLD++2mY1HTJDRIl9+oe0fOdpmnmwocHjtqpM51sIPdtrR6x9SeW61xxTQBzuz7fRf/Mtt7vEWLftKWzh1qu3u7bb9rc12sLPFWvlWO2BcrqjqWNn2iF8KvZm2cuZse3T1ajv+3PONub2kLGlDyKEPJag0dZ1LjNjcJ2nt+XLY/wl3pUO8NCwQN2Sia8OlKlEvPBVnuLyAhV5aygB644os5QwFgs1jr8M2JIXQhoZpqeqj05C++gAgIUOGUOlh0gvRBFaBrpBP3Ct28O5pHPTkjbt6IcbVz/OIlS9VF4HyScmhLKfyTSx4RvTCHkz8STPBmXXnyTWCdHfOEg1zE5NkBE4IC3uk8mFABAAOSctoDRS/144ABO4Q81J0wVU1JgQ0ZNUrAWNNpDSHDBOZAiiE8SWIsF6Dtr6Qq1Du1s5FKRkLwITMITzBO8ngS8Mr1INveopX+H8CqniKY29FPekOP3BS14ZvZZ/SFIQcuDKDzqrY5qO2cO9W27t7q22dvdT2nnm5HdCFnmFDy8EBTzMaWNOzuPWwzd/6oM1uPmm7x8+0u49Zd1jfbkcZY88z/j527FS7/b1vtqdOnw63pAShotfIBx1o7WIte3uYES36DGP1ExfOt6tf+0o7fvECNc4yYSCZmKReR1X8I8SneotRFqBB/Rq2MMUuYuqxa0CnBPpDV3qdPNW3UV2bE/+kgSJkXKFIukaPIOH4Ky+KfrrMBMW+xE0rKOTIWTkB67+KEl9P5zO1Q0OAVI8I6DxHXQXbkxOqwiUd0DPW3yS9QZvyyPyEENLlT3+dE9B5BTUU0liKOcpJ2dqQNFtgi9CUH2IoxJDuEDFFLNFwhZLsRy1aDD9ZW4YqEANPv5f+H7zKNConK766Vl3ph0iE02H5JDVRrnorWTJl1N1dusgrqfwqsPtMSb+GlvAalt80MkAT70ABxzDFkGXEwRG4QW/4pRmYqnSSNOMoaPN72235/q02w3h59+ha2zp1oe2duND2l5ew+Lk2t71NC8xEHL95mMw/uE5lcKftrK+3rRNn28ELLzJzDhzd8TlOG1hdnm+bTNTNry7QOp9pjz7+sK0881zbZzXNK+N5nubnLIXcSqTRa5hl2U3Z/UzS2plz7eHVj9uJF17GiJGZ8J5buMoWbIVM08jaynP8uaBrXDCT2hQcg4RLGTKOKNhxaYMFrS8RRAsf++ruKnyGTy1MVcsh9ilCfMAYHj/QPZOEqwibrUzP4Te+aBrtlQIdOiFgSMK9WZEkDZ3mCPPpPFKu0ANWSFDFSGXfI/Xn4jHS3TF7jDJ1/U5ADat0+vQyaqKJDqfY6j573Sshgc2tCooQ/COZdILHrWSf1swipPvPM7UNTxNUVzf+7vMRWqHb3QYqSSK8lV8KxbdcExn1Qj+hSbwujW90eIyn5pMmz4ALjzbS/QqkCq8KpNJTii/gkqFL0vEr/ZOwaFjFQ2dkAJGVDKE6vaRAebxKwqJRUsWqLdzALWzcb0t3b9Kd3m07yyttm/F3ozW2cLPW1Wbdrjq3wPiZsfrOZlu4f73tXvmg7S4stSfPv0Z3nAaXcfmOPJFpj4rACdKVxRlm4vfbLj2A4xTYq1//alu5cJFKpXQSERUK8Fl7CrTY+xBzwk8jtpt/7NzZduOdd9o+vYWZxWVgartG5ZApM3k9TT6gN7xGFVylvENPHtNQkfwve5sWBLAH6YDgGUhxmJedHOHB65ZvDgcVgLLJ3v01tNhVHuJJgUVvvbiX2N6LQOhWBkNVfpFhIkhPb/mDAqI0R8UkUoWLO8UrmtMkjbJXFtTl74iiRa8DfxBUw92dyq9L39UQGWgUwFY7XWkRKIYMJnGldoXUpXErZAldYkyFDi8kGfxHzIAO5oiHzgiX+9hZVDhFIQo1rids8JZ/cHz02f74Iy+B/FcLU3CZoRc0WhJyUDCs+3nKJjJ+Ir4ixC0NFEwXYUoTMEkx3OWCSn9Kf0ziRJukWwPwN88YeoHZ85mH9zOrvbl8pO099xKTNUx5so49Q+GUkHvW552Ie/So7b/73TbL7PqTM5fawTOfageLS+1gZbXNEOey2CzLZZGVSmHXmpewOX77FM459rIff+qpdu/jj9rJS5fTg5iZp5ohfJbfzONHbZWK4cjsctu8ey+baXZZjvONtjW68PevfNxOv2irnsSZ0MlliLrzmrpHu6/mCiemFiigUXhNGg2sohFNx86mtMQ+nAPq1cs8S15LDpzoWDuI4gtf1srmLwg8jI/t404hN46wqig6rEGFZVRd3SEv5UlwCPd44TvtzHHrJio9jA6iXzx/FadbXRVsqEpmAAoZvkOfhZxob4euUWZGUGggCLkIkShGNiOBEi4K1nCl4EIVZlzlHlhduT1ywEUYacFDhcpBinJLdymAhqlwo4MR/io90VEWLv+JVh19CbrjQUs8IkMJ+OCB7xW6PBMGWGb5h59nSWTskBHn4Styl5LlHeqGRVYBC9e4xBJn5VLpNZp4fnaDZ+maLz2kED243fbu8Dt/uW1fej7bVGecOQcmerFrDs7io7tt9toH1Gc7bWftdNtYO9MWLjHbvrLW2v17KcgzFGIN14I+N1PTLstUBnsUXtuoOQrqPC3/bNtrqxTYnRs32h7Dg8XZs23n/l0qlUdt+/qV9t7Xv9Ze21tvq6tH2yzhi9sbbfsIM/PMwq2ePtMefvRR2wd2lmW5Sbe06wm1cvW76c01LdbV31JzPY86ROEQDo7Y6aLGFV+Nzwc51dh56BJ39CYLOjkTXccPzSFB2bFRHUbsiZwTYUoG6fY/eZjz9hnlfUiUiVuHqaql44g1oW1hP2yrwZfvxHbIcmDADmJEUnhCPnkXoriP3qitd7Cw+5JOnCntqlz0x5ZQQWpoiQAeTtOaY0ogscBILMnqbA/BSoJo9zNn5hK6BiUwji4IQBVuFKJGUGgSXRmuI8HB9pYM7vHlr6hwkGyu4ldZM7I43IufcNL1EQEqIE6z8lBYQXWETtuHIGGXycJEdLyKLVpF333npmeBMfICL5LM3fgok1zbR4+1ndMX2z7d81lmzyVwYGvM0x7BPC3rwq1rbXb7cduiG71xQEF98XNt/yiwN69hOegPmvu2+C6LgWMWjq/FsZWNeCbiKOyb61tt+7tfbfdnj7SF/a02++ghG2mYvWeosElCnrAtdpmCfGv9cbv64HF75amzJISuOZlhBeKVFg/YExfpDVy50s68Qk8C/rmEkz8eHv0iPXpKURVmQAC8lVVlorIHT3pt2kYnWOAhMoiF1pSCXuC51xKoNtT9iTG+Lq1BsiUENLvACdKrg2f8HVZaUjMs1gQBw8YwICS85QIqNIpCNTyx2k7TdAGovYePcYSJPwgF1VsA80jQoJ+nEoVA4EQtBlZFXoQkMfHkZnh2xo0WT8bFdQBVMsO21xoBGYLgGcobCh740vJvlplMdHPokqYXgSBH6DhxJ0LgUlC5Aiw08ZEkAUWj4up+OK7oGqKpDljRh71FwcRVF0xHxeEqT3HsbmVDJmFCMUDTG+HhLgN+jn8t3Iv3brYZdqcdbD5u+4vsH18+1mZPn2WTC60xBXvmCevTwGeMvvW4Ld682g42HrQtCt/2uWfa7jOvseGFcfmDR9gHBmLLne44moXVLC213XvlCmviKo/Rsa07BXib0nzjD3637dAjmGXzzPz8AmvowDFRt8HS2gJd90XG5lvmL+70Osiz9BIy8SZNE854n6W5DYYZu+tUWkcZLhAWOWB+eKYZ6BQIdeIvlwJGpyOst0YjQ4izMFmpiB894hh7LAzyCs0QHQ1UhVeJCVqH0s4m3AmTavmTnOGbBI8CDCQyCCO00V5xJ6yoBk04I2PgCdHHNXgHQYKEmbreKOKtMtHjjTWM5yQd0YtpTKC3YI9WXflEqFZbTxGVkzTSeBOsNo1lMCi0AQGJTOVKcMD0202ocFBx6B5CDKVYi1U3hUwEqFgAayYL3J+qQWFCA6hK5IA/7IcJl2jGlkQJ+ufcil4KpDgd2qfX8Jdveo/CulclllELXxyDGN5dBoXxGskRwIJB/2txe4fW+GGbfXinzbD2/fgxk1fn2YTy1IvZwGLBPiCxZrmFe3HzQZu79X7buX2jzTDO3lpeazsvfDo5OsMYm/42bt8nV5NdX/C3ax8peOU0k4x6CLcL6BZYW7dZ9qsvUIC3t2j5iTh6dKH5d0APYJvlM7ve23tUSMizucP22Sdsh6WRTj0iL3plDhcqtRocP/5OMMa//9GH7eynPh14VUFV4yNX8grYcY2YqK3bQLWKIzfhpW0BYHjmU0wLBLQzL+OSxEGMZ9lgtzAitb0BPyj7FLPQyqV7wBXVih0FSPsNL2+5OjPck6Ae44kxyRX0O3ok8cs1aEV7SFCClG6yaUaaASnKgYufWxEongEqnSRt6tEY8ih6wme+JT3BS2xJGdLZ6KaCypQMM8GJA2HaGirCIX8nauZVInToqkRIobouEESo+AckdBWzxCqXPkNTIQhnQoqkUVyRqJzDJz9pK0S/pq6oYwRP0Ctl0B5s5fEDtA+HRImHYdFTxqbOaIOn1haYUFukFW68NbZ7+2bbO3OBJTGWuVi3Ptim2FFYD2gpXSpcoDs9x+uhjUpg5sk6M+z77fHx02yAeaXNn2NyjVntOfakU0OkK29nfA6eypu0cbPFjU5JdypWSmdUoC6sRHi6TOYW2G2y4wZd8pmjs+3hJvvYd7aRf7Y9IXyGbv0DCv7eEyqB0+fak5WLbekmM/lYzA49h3TXe9fdlKqnfXoIy+ymu0/ls/3gXpv3FBprh66jkRUla+VP6PScGKosLxSxBekKb5w/aWhJ3g1PHvDMFcJTKpMKgMhq2TrYEESdJGjQm/Iy2Dgr3iqkcCUgY/6wSGywJVcTqhTsFKRRSrosIA7oYm3voHAOyxViqaTNp6om0nuoiB+4K8yhtHYlVzmpcO82qJY6bdFroBzmq3R04BBI0KTmEHEIpJYDUxImxMtWQyJJWjQjajERqpSCvxtdFNCRKwOLVVAKOAHK4RV4noNiAvGN+MCAZ+JEF96f/oFj+CCknFVxGGgwz3JOYBLRb8oVuv0pTTPDylCjXWC5af7JwzZz4wO2om6ynDXftlaOtf2zT7XdE5faPBtS9oGVxhwFd/HJkzZ/f70tUDA88GHdVv7pF9rBSy8xjqbVZMJs/skWGbSbzTARCgM1wYqpbmmTs9yliJZ7U5u4PgdgNphX5qN80ypT0B9sbrVzx4622/Q03tlfaKdohV1iO3nufFtCYS9dfqbdfu/tdu7ShXaE3Xbv/+Y/alt/+A+RhTV7CGUtXYL5qRB0QSE/eflyu89uuQvHT5BWA7GR/gyMInIdroR7QOWRJHtulXXpr/mJ8OoFVN0bDvhAT0j31kOgDiHNwCpP8Dqm/AJXaSg4i4ZQRpR1DZkKOAidcqcbTkOa4lsVg7DIiQ5Cs7ydJ2HmZcjFauMWUq5jd6jUggag2T+4FMXCVxOxxR6bsihiv8QpOxBeIvzUBUbj1vUYcLWOhSFDFV7CCV9sbSgs5Bp8CJg4mfNnUEEFFQIlbWglCSO8Qw3gYjnBjVdiXFEcgsrDsZqKqRjduvzJQYi6ChUcZFatuSJcSUICgC2aogYG2qFmOgchWm3520LO0/rOr99rjfHpDC34HuPtJzMLbfb8hTZz+nyNn2G08OBBW9p50mYZY8/dv9N22fTi2HXr0rNtk4qg8XbZ7Abj8uPHqQVo47fXwZK33fOuRwW2NFtZgOszrbjhCmzhFoSfXWafZbKmhB+o5pGTeMcWZtup1cX2eGat/dhf+FfaKWbrt+8/aPMnj7Wtm8y6rxxt53lh5c47b7ZL5y62008/27a/xiw7rfQeb7IdZb3+gTxhEp2hD9fQl5mJX2CM/uT27ey888055YgRTB6xCrGD612YCZxp62E4OhwwPbxsCujoIFgDu0ODLYn40AMgE6iQVh+GVH7bQpUdEykwdAvelBGUGw58iRFM32DSnwUncEF6HzkSjhUVKsVAG+pUO80gF5cuwwgRsOQaspUUpCHLyBUnC11K7p+Zrq2WiPqNE0jLsFHAhgXw6o+gFSpii2ycBoQ7Ore0e/EYtdnArYh+Nz4VhNS4Opoi1jXojCehPwBqTNEmwjiMWGWqOANKduWy8jGs0+rPUTFEGZ1E6OH2Ka3QET4FyF4Mn5iliztPoZ1lfznT0+0Jk09OoB1cpCVeO9baErvOGG+rxnl2kC0wk33AjPqMhZsu8c7iStvirbCd5z/dZtlgMnuGysAuLoc9OEnma567wZafwiRpNSzoUpV0jMFJWFLV5Y/gw0jJWWffNbRdgNLNJ0V20ZmFa0ePLLQn7KQ79eJLbYvu+Q69ixl31vk66u4OG2PonXD6zMopdsxdu9rWTp9q66sU9C3SrmqQ1UtdKYM/K/l9ejUn2BJ7/bvfodt/OjGV1QgZH1oV55DRJt60JrmaXtEzwFbKNJTRdoOVqRfPKlx6Cj8BxSBEsi03BLuuoDVeNQ1OJBdfcOjS5SlweIaNliDOBCLhwiSkO4wfYeZPdAReejPEiF4FMGLhk26FVB/McChASJt1PmDYcvh4C5VOeTCLXMT0NKdRlk6HrspxAPdqB6X5VzLVnFBnPk2E40mvoXgTEl5VZVRkhVQBg4cyDFaj8hCwy5i4wbQIDGjTPUUODDefU4rClFfYigtAbqPumVKsuPLXPViRn8SLAB2NNi02s8/znn66fr/tXafALh1tLoHtnzjV2lPPuSMUWF4P5a2vJd4cm799pc2xFt4s2GTYHvCPZclW1bkXXmXyi00r5NXsgzvMjrFXze2kiKHcZu7IgFGIM0MK/Ki4nOGGWyUC6OqbVDrSwlMArWQ9RMIM8GHsAUMBD1vUM5tlNyfk4Hb0SNuSJoB7TrJJmZtG5xLd6tmz7TZbXI+IyCTewZbDFAwGGPdXj7xVh6ZDXu7CO3pstT26ca0do6fgizBVaEb+lJEpZ1zoKfHhW7qIHJFGij3MysEIgQOAX7sv4tNg4gpLRNM0+Ja/uBOlvIMUUV4Te8ZtvAwLW8+EathHhOBISNmsmg1FKPwg4yo8h0wVa3zFmXovw6t3UHHmma4SwARKTsr8IVTmgmSBP5S4jd7FKF9FaUBIoSqQ8IwdVJw054eRdDDZ6MxVdUO5S+wufskeIYdTJhHXLZswMby6LJSSKGQKWRQDhnPKL07AEiI4jjzMiDhMQilDbxRjgnQfppMQbomqFLm7y6Izi2HPbTtuvsNSFgWbt7oOGB/vLB/llc8z7fHFl9vRS0+1A1rmeVrLeday5x/ebjNMojlm3WY76gZj7wU2uew89XJrTLzNuATGTrY9dqdFEmarMzAj520U6/B89GAilMnJLh/+ohtnb5GNwBQ+3GZm2TbycwyUaEm94eKIYAnwKSDPUVGYzl12zDV2tR0B9oDCex9ZZlhMHWvXs8zY+2JMyXPQTl+81G596422+WijbTL3sAnN1Tt32+xzB/Q+uqGaoUkDdEjYMbbS3vno47Z2dovuBKnU0rkUJ8YfoactTAlrCpElGSpwUCKGtJOdSUtFmHtJL3GGSDJk8anDQE1soALEiZji8ie8l2BTmwGzkIEVZkp/IlQwu0yDAHBJm4ksgpFDqSY0RtrkOeDCLAynfEsAKedKnvcwtzp16EiRigIow4qTKFMdTFNp71ZJhOs/Kvk+RoeMoWTUAMDH1X3GeQGkcnN1YrpNr0ZnC7V7/6PW1m9Q4zPJdOR4210+Q/W/WiJpdEEeApZv8CmlKJwGaTFQyZVpQdOvo8sjnM5IGUOoiGopbRPhw1tY8xTsWcbX++4Co/u8h5yP6N7O0aVur76aTSu2YPOck3b61tU2f+M9do7dbAd3qQxWj7ctlro84OHgqRfotlMhbLAuzvh2xg3mFja6wFnjRqGRWbkQZU+dDHFLtJLWgoq/5jrAAVi91pibNOIZmjGu9parC2lKoutFHFpS0+lauBNlZrFpsQW/z4z7PuP0ZV5Jdc17xuU4WeuWIfkp5/19dus5scbmnV12vp1fW27Li8B2g60hWPUElCFE4DM3b7f/VLv74Xvt9EuvsDPP/PF3+JJhkGQYZ8UDqR7Ii4qdpLjj6zemcKSqyPpHjPEpANOA8Cr66AH6qdiC563TIDz2DqDyDoLpRptm/5F5LPXpH7yLtrSgQXiuiFlyFrGIUU7ihBM0auhIupP+ECjdBqYoKpgY/IvML0YhhJd2Zix/BMUX+GAQOspZwJKW+Se0QnkTismnOWZqnfjxLaqQ9AaT8JFpiRsljQytUBK5s06NzjrxrTcoUB+044z7lnc5l+wW49GFtdbOfIa3sc6VkYWOqlPULjwSl09+uO1qBAKupma44xLWAO78NOA5YGYxeneTzTGmnsVg28MHLHldS6HcPEWhvvB0m+GI41ne7Fp+9KAtMtk0e5uKife69xlnM7jlfzfLYzt0w3dWzrcjz72YF00c1zbOYfPlEkwg49QaG1lQSzjDNS6XbAxUl9WCApPWDhn5M8224bbgACC/Ke9ExFTh6iXP0nqig2l4dd3VUZbSoObLLruRTf5MLViRMY+wvsNmmAf3mYyxUmICEYOR7BytvF3/bNfJUIB3z9fWOFXmZDu+ZPg+ld3dtoxONtx7n6EAhEE2OxTdLbfCX2W33r4v3rBXP9kpA/MGj39x4quLuEpM0m3YsIGKgbeoXOIGFn9aXRnzX9HKaHyBlMO7eTD1dVKFB74F/FB0AM2urmpo4gkf8ifOWis3HO/kko5gCcRh3CTZE6hyVHqKa9HvAESYxnSUoJD0ChzQohkekziZ8ANGOwtP8Tsd/Qoljr26KLLY8kbjO2+0mWOneVFiue2sruW00HmMYdaumMcRUQHUrG/IRBgnP8LEVhcmc7QQ977/G+3g0bX2zOVL7Q4toDWiFYdvWs08udX2t3npYpVZaoxRYVOYoZLuSpE2MHSr+uwSIq8JSZ0nTX7JTLrVC+wmm/OM8rvX6I7T3WTjxy6FfsdtpUyGzV94liUvlpJYRDzKOHWesXh7/1u8p81sNxXBLmm8z9LTwtPPt4MXP9tmeS3Utex5Nq/MIec+vYDUurTYB/xyVhoy2uWNcisXVClyo/AIPkmMgWmNDanUkN5YlV1301qGZzd4Dl1WoVWlVdmiqehjztYaAmXstaddeqrCjrX0U3EAF17k2Rla2pNnT7S9LXowxHrSjC+1SMvexx5r+ftUcL4tt8+Zc+vsqX/rq19rz+w9aRvHj7Zj6Mv34N0nMMPe9mzRzbuOGlYZZxUBVE0X/v7Va+3Miy/WDrnDelGgXJEMVxnitPIuWxqxQ1OiGKaO/M8TR72VODX0xAAY/YSveq0KSQLRkfhFLPytpHprFX/oG58LeXopDk8rQ5UtUK7xlPMPBCWAm08FKoeegkUIo6SXHkUgCr6gp7oY1A1Rz0UAhODolRLPTjPlqScyFYaRkaEoz2+/+SccUcTrDhScJ7wwMc9y0dzxk8wun2rbdOcOMrZFdYz58oaTxuTWS4i6PifDGWr7BSdEFmbaHbq7i0sLbWlpsd27d7+tsMFieZFdXxT6PQph8gBe1S2sBLienFlpjVD6CqyC+bNbOkcBnWWJa5Zjjw4sfBTuPQroLgV47+iJbAxZOPdsm2G8vEziFpkwc2Lt4MPvtq0rH7b1B3fb0vmnSB/pYZPHDLAMfNs856TNQmuWSTd7BQ1eu8jmMUpmhltFDUumJIdUrAVduTVYiyLuGIYyW9Rr3VvJAQLGCiBUyDAvaRojGne70bqliTvdzdAUzh8VjnyIj+GBE0M1DkRxbRFGV9PgXfS08eH7HOlco+vr773ftv72f9X2mEvYp5V3L8DO1kbeTrMCU/+3WfbbYLvsDOe65+AJCKcyYv7CSz6K65FT00pOxvvtKCsRj1ml2GLeY/Ek+wgyvghayYx+cp576EBEYrol6EW8abD3wyN8Egyg+RAoHbhSlZUTvAQVjPlBgEHZMQZtfQEZ/Ao8eKOiCbg3LsFGoSqUyj8pJ76II6f5VLKZ9Qnu+AEkgNgQHHEChUqCS7Ykn/DU/T0+8KY1OvNpPhQP46qBEH/KV55Dl6PA+xx0jZ/fmWUpxq2WtFLz+/fb/rV7beOdx0zi0OU7drLNUzhmeO6z93qGJaZZYVmScYJoLq0+3UFmdteZ1DpGS75Dq2qrZcthC0Uj0o5zTvidR29R+G6x/HS6zR27SIGGBgIojONC9mC2RXoU8/Cd46WOA7rWM2wn3cdot/Fvu/nkKSbAVk+2hbMX2wIVyRwGOvuQVoc3r+Y+ftC26Z3sYng7i0fb/uqJtk+Ftf0srfvccpt5/sXwS6uImg42NtJVn6e9s8sO2xjwRGHIVctVFNxkDspEu/7Sw9HNnwo3wzyQId12nmWOAkoDXhbibtQj00SUl8MkWylIFIy8aHHVaYzOYPEd/0qDnwVf+PSaeFttmIHVrv/y2HUugkK5bAY4iXj1nRzrfJQ5htUlwuyuU1HuzFLQDxbabU6E3abCdy61ehbwp0Lw/CgLtlStYtK3ir/k0WnaT54/325/8H67cOJk5Kw0I7cFQqWR4EJTxio6+eZBAgOm6IEVuhRb4XpNk1eKroBePA23gigyXY9ChQjxlkT9PCIGgBbykOgwkhJG/Y78ic5Loorj3kUlVGLw7WGhiztX+Ei4bMMweQ1c/aOSMTyReYDDv2ERmZov8cIY3uOmCEpBYEUD0+GDZFoMMl4oloFv3dto1+7dJYOOtTOcFjpHIT55do33ko+0a7xM0e5S6Pjqx83vvcFWzqV2gi7+CTZZ7HKayR4HJOwdO8GkG9sp7z9qy0fZIkkB3Hc9CgaLi8xG041Mxm7dofK40+7f/HZbevanKdSX2er5iHPQ2Ap67b22RTexUTh3McxtX6lkeWvetesTnFAKjS3GgUuzVEa33mv7H3JcEoVhe4E5AI2Y7Z3HPvU6fchzGYLw5kYyWNYHVAJLpNfxZCbMlMxw/1SOCvTHzUxWQerKJa8DCpdhQo+WN95AFFziCCzcSnd0TdjIiuGvYQc+CoZMxxtrFV8ZpdsxdowBON9ms1fFPf+ez44rlzydAPMyLaZXo0+Law2qBA7DlI4KYYX97gf76JeYx57l3hiugb4AjT3OdPeAilJOpTv6ozKVTgoHJOWpIOpsjC3tynuizeLqant861ZbOXeuegXosKSwwlPGTgf8SN3phYOAk5TBUXj+DE6U0ZNL+ZCENE8KrXExbB3FN0/pwCw0ckNWQf11wrorLRVesJ1Gp6lOnZyL3MB7kRXlJzB66fQifIF0APkPzALSP6QsqoQbBVhskKcYiSNctzq3MqoCbEhdoShMgEaoz8SAR4P7wDEpu5z+7pe+1VY5C/y50yfbxRMr7dzJEzG4NcKOnllt58iNJxgHnfB28P53aDLo4jJBtcts9C5HFJ2+e7c9ubPRjrz0NK3CAi05LRWF1gMPMiml0XLM8LE1Cv/1t1mKej9vZs3Q+s5gILYiRyioB8x8O3ZsnKDy5P3bbZP17SXWaXeYSNpi99bsU5yOwpbTWSbGZhgOrDDGnP/wI4YcvPbJkpiFuREWNWjsFHDLghlRqi0FRQU4VU51h4yfxo0uuiFRfApQtbIqWnkPKBzGiZfWWU/41F1Dcm3bYPVgTtkilizimRWFY5hj6eKlr8K977kMppwOjwyAf/VM8Jj7RMbobKVNkCXU4ZVkdOLIbnt7QFj5NsMe97y7RXadDTQrbPDZhbDf/bZc0xEIonMScywVzlFKq1djSolUCGUYsiCrldapixfbzbffbqvYjnM8gZWWcLmqAjEgLCRTrtwHmGIX2hR5uBLX+Us3ONwshPY3ii73OPojgpYEQhjlT9zgG9Vhyt+RjSVcniJUnDlWNPQnTLCADKqFEDydP3AFJ2FTPqPSKTpDbunCTV2Xiwhxyv8JshII4aKZu0E4DJ5/m0P6X3vmmfbF119rv//dt9v7bJGUzMsc/ft3v/JGu/1wvV06fqxdOLbWLh1bYZskBxeAvEXLsMLM7SzvOG9e+WZbvsN4nBcyFshgLAPDm6NFx6wZ824yIbSPBS2z1LNEj4DBMT11jG6LtefH9/lwwGZ6AZ4n3ijEM8eZIb/MWFEDZYlo8Zln2+zdB23u5PGqVW0R+R2wGWWG7iWLXxQo3PtMHnZFlBKwarvGVB52q0111YZdKYRZCFSE0eoq42FqBk91KcVJSTVXtsUegI3BUTBxTq7q2Fqg+YkDiXzcoRdG6dmzmI9/UKXy6LnhcU8MjECigMGB4p3LAusvEkC0upikBTy7zeK4EafC3SUHTVv+pJm2D/fG5na7TgleYXPMDLvZPKVmnknLY8eOt5P03s7yKaZ3fuu32gafa9pfqqrPVuzhB+8x93EpvQT9e/I3IaFdadSfYQR8Vsmj++yyO0Wvb8flx2jIoqF8JqeMVA6lu1CTYuKMHZfp8ao8K315L8RDmscpZHgEwdv0Ku7lnxbRKY4xA6akGbD1nNzRq3wmPxhKr3iLKRWl8Dd9lCFU0Igb8hYcFbiOQTiU8HQyiUPfNjMDKPIa0WWQR7yHcaTJZdD8Fpn/j7/+rfarX/x8O8ds6yavNW7zltWLF063kxx08DFd9/fvPWxvXb/d/uynXuJccZalaKm/+9GNtCLPXqA1Z9vkhVfOcrzwk3SjNYj8KPD7tHzrdM+PsqtsmU0pu0z+bGDs91eZFedM8lnH/bY+9Czm2KZpcuwye/lSiJ/u3acQeyzxjLvMkuquA8Bi/EwYVcLBtRLwMuFkDJSrtsetwdjIx0ZNPmHKGQVxw9npqXhlJx7gTLpJV0QujdSfxVxcL+clhkHKU8gRWwU0YGHi2D+FGIbZtCJd/ueYEMw4H33YfU++SsVCK7pw8LGrLAfDlC2y4HGXW8IRytUHP7pwwLhlBVoLzH985pf/SjtNQV90zdzZduY/Fk4yj3GbYdV5lj6ZJ7nz9/4W9JiklKf5AK0HDH9Osa8/wwLlUG4i1JcyRa8CUqmv8L79TV5j3eZwi3nPjVdtwSlYnfGL312Gjcu0DL0lWhFgJKu6JrGJTrgEPwEzYHkin/iCFN8kKQA1PJI2f6YDvXoFXjwwkkQxccQtnDA9JvyDJezw8QSuMBLZbyO+aBgvWPQ4AYOOqHAZnCI5qLUaUDih3mUp1JK1eEq0RLCBMl2zO2TOfbpvv/PGt9tPcwggI+z2PjuitliC8iN7W/x2KWC/+COfbj/5w6+1W8ykP3X2VDtD675K+L23329foaL4b37/K+0+3/Jyp5maUMht91RTkfjq471HjMUxHruOi8wHLD/7XFtiWWbedW1mg511P2Dt1+Ueu9s5+EBLN4HeJNozwoQ5prIsGIOFFVxm7B29mbyu9uBTGDq80MnAiXY1raKjIq0IJvEocrQq4snPHwC5NPDuDFxImnNcCfdGWuqSNgGGASMf07RrWnGZtEjs0wB0pswCZkUCuDJEKhjihJeeE6Iaab4zLl1oWpEsMmaWwBw/K4NzLzzXLr/GO+S4dzgIY4+Kc4ee1g463yYfd9hUc+w8wx9eWFEAK7FNJvTWTpygoplrm9jEvLySJw4hFE5hyWl7MDyVQ72fYmLuLoXdo7BKZ6ZF+HGJN9w4cUcHOISKu8OPOKGH6U8xK7Rg4D+hiZuoQRPpSnfIqHz+q0v/lDtpmBAlFN4lScF0kAIN3aIxQUmYPmWQByQRJnINUJ5kav/p9gI28V2OhA28TkcYpHFYNRql0n2Aiw/OkPcpvdAc8EVn/iyTZ6+/8jxdSt6NfrzZ/tTFc+09WnCa9fbz58+2z7Bv+6mTrK+vLLX/6u//Vnv6FHvAMZYvvflu+xd+8kfbb3zp65xUst3+9MvPt5efu9SuzfHdLrZTHmfJap1a3WW7hV2+5JlxIbN/dBPnOa/so1/7x+0Yb0utnmICjTPLnQScmznJ7Hff7knCWFhC6RZiPLgtCGnR9faUJcOM5wz0J0zoHYWWqwjJRDFIdTVMZAIZKH6WtSQBmmSSqRqWcfilac9dnSRzcE8KOAAuoKnMEqbw9Y/ZcGKMDEzwOp+Yj4U06YEmuWw9MDEK4FLtoF9hZWFh93hn3REOVyqMhBCJjGWO4orDxAsVqkc9hwYgT+hF2XJbYad3gAzulbB7b+Vm+tXNIsuN87yEY8UZfsDtU9hPvXip3WB77BFeelF/1UsiP5I4BfOSirIwRGMNfg67enL3djvCxqksWRZQdI5IXHWXj+oYvaFKgYHSm+SiCFwARnfE4Ux6FUJP+Fe8NIQ9fPhJUAOlTUl+wimwyU9cdRXf4Tv8LMry4+r5mJZWBvx6TJcgUHGX7EEKXFwAB6OnqaBHSuAUZkUxdtupGzzoxd2FLx0WfFCFx6F7/l9+6bn2gN1xb93jnWlq7hu0vJ85Z+bst2/euNleJdOuchDC3/3WW7Tiq+3TT19kMmervcOH+v7g+2+315+9kNNJfuK5i+04Z49/zHbKHQaMcyyfzTEpZzdzP4WYTStMoFkYHIu2B7faI5bPHIfvYGDLGP3KidNtx++Isdll9fJTTBQzcccmjXmGCjm+GMFLqUjeE6CtVUFhlp+hxsPrV9sx3rNWgTFgM5SfijLd+eHWqX6UTwXpL1rClD+GizuGI07oEJcKALjqgOsIvneLV/EqvhbqwVqgjKWlj9sCayVkxSKek14aTyQXSQQud7BlSBIZCAh/oei9hDhu9Gha3BI776qDa+Z+1YXu+7Fljo9iwnQLplaj/jk0cJKPaRRYygcaVjzEOZ8iC2Vyf/8slcYyO+Du8OKLXfh6sYM8JKGyV2/K4Qs+pkn9uInmLvvgl+mx5ax4E8qlzrkHT5dXWtCKqADvTjzyh7kIzmUayxndDQoAxCaMQx6xwinzJODgMT9CSzJc2oMZUFIA0AkLW1eFRS0GjAjTGz5KIz7XwJFkhRjK9UlfYCtiQi5eCNahFhIaxKbPEg1aBKkK+arxpAFXRFNQHJUunMIJRlhJQb5vMcv+kOWxJQrxk40n7VnGzguM704dX20//7nX2u9SwP/gym0K3Hy7zcTYFSbFTp1c5SWn2fbO7fttDVKvMp6/z4EKZ6ANWLqaMp1n8u2ANVoajvbkYJOJuCUMm801nDL6Gei/w4EMH8LzNF8SwZraNrPtO9c+RkAmj9iO6VLQHOP4NT44sMBYfo7loba0krX8zAybGkqOBaXRU1jmVJfHD74XwzO5lZGQVkMqKBpI+g3AiyI0qJ4LPjMuxSiVPz8V1q8qEBY80yVRFc5DS5JauumVKZmQIzisAwMEci6iNycoRxbYrQ5dM8V0pEBY8KtdTUXZC7YFz6t25iVFoal5J0Np+e0B2Grb3VYsN7/Y7ZuhVVcaaTiWn6OnZgUyG+UoDUDg7SsferWhdGnU12DdVLPG+vgjNsVs3Lrdjj/D22rMyKZ3IGy4Qxv5R2/FTy+vnnBijiOinZgjDdGvsKZzclVaDSktGhFpQtfA8hHf0UbOWhGLE2FL0z3v1ZERXOYjFKIt3TiLTjAF4Gcu8RwM8IlijLIOUiNef9JQNQiwXQ7xcskzJHKvfKtKaIRGQOTJnEygimHsBb82ET66u0ubkJNixlaM+4TM5kVdpWvgup3O/+e/80f5DM8FZku/+Pyz7RQF3rezVo+vtRf5ffna7fYFWspLhH/vKmvdGLNj7w3G9XzWq93dX8sZZF/5+Hp74cVn6Qk8AWYzk24myiTvcmSSM7AKbu3N14Lan9x50O6xvPP1m3fbY+itYFl/5lm2Urrcw8z8si+iYGRz92+0h+9+p63Qqm+xDLjGiyizHNe0SBf9CCeleIrqHBtAbHUO2M8d4yVx2SpJIfb1PM8ht8WZrOPq5ocZ06OgJe1GonyZhOKpwpwcMw0ai02cGeZfChJBybpYlGF26DXablYyEMIaSUOAz6TlTNcfv3QVDH3UnESxAjHj7OSR9AMDjYhSRqShSNPts9LPEmaklhhBFHQhqXcpuPKgoBvOT0LZZmuFJjNuVhB292fY++DxzlCFLRRSyQjEW7jM4Vz79ndZJz/ddVBpC01l0wzlIUFor5460W599IivyDxsc0zMSUo+yj4YmydJmFg9WJDoMbqVcwePw1QBwb8wdUH4EKzB0e1hmoZ1+YID3xSlhEtHrtPLdIywOBMF70+ClQShcSgCoNFLKdyyCfMpKTHempSrsNRYuYT3Z2zPnQmUEcKNBii2SFh0CJRXqHRdFH5Rml8mY++y9PLdKzfbVWbUn9Atd/10iUL2O+99HGYvXzzNMcAX2s8y6+7W03nGX/8Crf0qW13dzPKH4P65115Iodp6vNMe8YKIXUi5mmCXlBzvutfazS3uPVeR3711r/0EcwK/+f4VKpXVdhq6f/Pdj1i6o5fA5p2ff/pc+8zFU+2dOw/b9YcbbOlkctA96h+/lzH0h44WKeinmeldfvo5xvwv0HptpVcxw5tVJnqfCbqoDjlUUsaXZPgotNZ4MaoICySFyjwQVhUFnozJcKMoha6ZUbg9U+OHn3haA/8WlLwHjEd4LzPJjFHHyTTCbEGFVycpCOCnYkBnPquSKvm1tBgR+FnSspsODdDDV3ja8qRDQ7ACkLCHUeTEWHiMCidr/MLwy7wDeb6+td0+ZCPTXbbKHoPW8h1m5MHJrlboHD9/Kkc+n7zEfgaGZl6jUMknPE2tSSIfPbbqNl34S6+9AqRpqDjdyhw/z8l4WvmMM5JLnfAfv0/xqraKK/4BMymFo+YhNypsWiWEhDQ6XakU3YrRI29l8IquO7ASh7QVRqKnmAXdafSH8MKVfuSk3ZNi9DJa5IR2ntJIXuqQNHk8uYphgkdYgXQZEi+/yu/w1hq1NXjOP2C2lVc02jEKzDZdtCMU/AsnV9qJ1eX2iLPMrjx42K7ce9D+6Xffa7cePmqvnD3Z/vWf+Yn2cy9cpifAzCwtxwuvPNtOH9BVY5ltZuM2Ez0loBtntnkpwtySmepecrzOdstVCuBVJv8uPdluP3aRgw9Yv2eqiI8K8PVPxqr/2ivPtGtUQL//3g0OdThof/LRrfY8Mh2lYnnxHMaGIa4qM3vVH/Cu+P23vs0QZK2to6QNJvlmzz7d1p56mu8CLlPY5nhtlg8a2KSg5HSHVRk5mnG48qovu/GEmLmJtoLa5aUOJLewCWIeSENcC1RCjOtOu9VlJPKpjOt5JAphaCGVi5iFJHx14aVDb8RCrJzC81Nu3/WmUy1kntU6UUQJd0w8M6fOiZUnvwds8fXd8g1WMWZpoZfY0LQmOwF4Jp29lXduxln7LQo6ywDtLBX4CXpwR0zL0jw75kAQB17LDKEYDbT16zfayWefbTssxcZ4e1otCfqVVIUtUnkfYUPUI3bMHWPHXA1biPA/ei7aVpFoGSwiIuZwxRux1SP/IsZRha7iQ0+nNH3kXrTi5GYlWNFWL9KvHChio7os8lETwKHgLe5P0htcBv3DT3UwqI/w8ESwVN6kN1doGzO9RsV+CGAaqUsaHSMVhjoPxFS+iaunef4uk2+7jI9tuV4+e6ZdYyfaTbbFLh7jlVVbYDJ3mx1gm7TQD2jNz9Odv4cR/T/+6R+0n/nMi+0i6+gP2V75j/7ga+1/fvYX2nkm8hhaU7DYteZec5ZxHHs74wsZ7IDJN56+P32Gib6vXL/Z/oXnn2GjzFZbxIKOksq//qln2xUK8j9592r7mecvt+9du9X+RWb0v3b1Znvt0rn2Dltzf/2dq+11VgB+/plz7UdevNC+f/U2cwgP2NTJgsHHb7eDj99p9/9kly21S5yEcqrNX3y6nXnmBXbfnW3MCrZ51vR9HX+OCsom3IxVfTHQrkgrhG2+qGJhmndvPrZgJZtGkoy3q579X9SiZip1UD58WJlgSHWti6oFQAX0DE7GhDV2W0OEyRgXYTKxJDw07GXwKLdxZiuF0DBlqkyFK3La1baisidzgkK2emSxbW0xdOId+rzOS+WbMToTp/nUExOeftFlhwnX9Tu32qmjS+2sB1YQ71dlpO0430radDlRdwI7ufkO25aZxJ1Hl/LNFXmUDQfprBc/bNWxq/febUdpCNwqa6EeUlcixJZTVbTTuBHuU+I+vXDkv/hGTYYmvsIsogXO3UKqJ1EjZsDxDKCttLDS/8FrBPoMcAEMxr3GSUznlULeC2AsAViraPWSuEGnk7YSz5UWuZMvglFnD+EBArRUsdGV5tKndMv2EhPY6r1hl+mLAH2EZa0/89Iz7W++8Z32mJZ9nvPGbO2dgFs5Md+OH11kXZ3CS1cxBJfn2sbcdjt95kh778r9dnGVJRUMcondcZ5sqrEt0+L6jvPW1mq2xHpyidJtsxV28Rj7rCn8txkq/M6HH7e/9ukX2zFE+7c/9VzbQZa/9e0P2gUOrzjLpM5VJuw+xhD3mNx7ZA+B6zzG+NdeuNg2cP/2m9d473qrfZkhhMtBv/jyM+0WvYYlegbHF7bamfmD9uR7N9v1N99oJzPJx2ZT3npbeflVXtfkbLWXPtP2T51mSy6ThRQQlWMPxOmrI+cut33eftvBqFWyZc6Z8xhrz+AYKGHiJIpYnxZsx+4VZo5WJmsfVfjJIA3DzBsVgLxhUt27vsyV6G4IQQYjMorex+jwzhxBF+A0uxmPU8iP+u20beYweDd/82t/TMXNK6puRHK34n2+1kLttMMy6B2+tfY1Ktbn6c09OctruvBhnyHnDLAFFvdOemnmfPU4Tlw63+5xEuxZdlD6OrLpsOU3LcKYViuHbom8P3G23aMXcJ71fF+FMBkg8dcvQXsSbaVMRk9KACbFQx2nsIg9vWxBvSoMfB0GqV+eI2d8Dq7RYcA6bviKWBU3jlymI1kNrSFzMPQXKuFTDpGiAKIJinfJYx53+IGqLAZJedBIXAKLfvWFi7dQh+pV6Fb4oJeehGjqSBrRFw3SCl31JzRRJ6lp371+p50i05557jjHgtcU1SlmxF9lg8wjxm73EOkJY73Ta0faz7/2PCe1LLR/9OW32xm2rf4Y76H7Fc69/fms0T7GkLbNUcKc+NnB7fe/HNeuMIO7eOxY+yvQuAPd21QA1xki/KnPvNQ+ZrfWv/+bX2ovsOX2s7TYRyjoLgt95cbd9nNsvz1Nt/Im6/y//OJTvPo63/67739ABfVS+y5G9CPnT7e79x+2SxyA8Gvff7OdYC7gf8AQYG1loT26v9Pe+PCDdpYZ/8tsAjmg9frWG19ur/Fq66fYGbbJctQu4feoZLYvXm5zjPkXGWbYqzmg1bOt0XjMmMzU8/T1bBVdyi2DqgKr8oVlmdFCi4tb4aP4LM9VUMKMq6wGo1ceFpYa7pBhWgrMCoxspwKTZlpMotLtT4UivrTYFkuFvMm7Ca4QbDsZeu1G2/nw1xkeNbYiM66nMl8kXU6u+nbhHHMni+RNKidoaDiZ4LQ7D4wFzd0JSS9xC+Aep2V/cPNGO3P5aSY8TS0/hIw96yDtae0JOMrOvCfsvHt06w5HT51OL6ESpLylx3KVnoyrQlD3KrgGwtwhVlnxQKln8oLoSCKcMqWYEt8naKHaS4Askl6Ro93QNrpqnJEXSpIfCIZVz0wsLqNyyUtcOQEDrVHAD4F0WOkUvAQGnu7KfsI6kiKZ3vTWAqmsXnWPq5ASGuUbl7SHIu6sgGEIAFqQHzFD7Kz39se3+MaAY1Psi/Hbe3cf0tWmMqBbd5yC7yuU37l6o91i+lz/Ektov8Ps/At0r/fo7i3SUdilhb1HS+iXQSwIdhetiTSuE8zGvgX8vQ+v8kopp6YSf5uhwT98/yoTfIvtLzGxt0Tr/Rm6hXQF2jEK90eMN79FRfS5V59rP3dxvj1Ejb/25odUJDvtFKr9mJ7HJSqHBVqwR1Qy52nNfvUFXrAB/0/evsKrODPtFsZ89xGGRqH+jY9vtl9lbuF5XsDY4l333TsMHTi+eOfR7fYxhzJsHT/T1jhD7uRzL9SBFMjpen6Ml1a/JrlUd7IV+yOTzBX+NXjd2roV24FdYIYQ+jPeVrFc9dCQxePXC4YFXFMzs9Pq4HfNuwp0TBK3VQHGRMUkemjxlIl/9VIJJHEvkHf7vD68wPLmJl3vRXtl5GtmfqmE7cU4AbejfHbTgUeUEsBxPAXduQKvsODuJpij9I426Cncu369Hb9wIeP7cEe29G7UAbDiqIdT7LG/zocaV9kZ6WvOZbwh+8lbt3L1FZ0ai1uRJGaa0quCZtdGIjIJKkw41l3uwevBVfDRXOj1RAGTPwE7cInQ43lUsH7pVaomFTxhnXyexnr1ZAySISKdUCHSSqPcRV8TSG+m0ENjcitlFE2ISMf894oOdEKzQipm5Jkc52fNdDKNzafB3qMV3LcqwusEjbOw5+mWr1AAX2AS7FVnXTeYeacn8MoJZsqZAPuTdz5sTzH+cs/6Dl29RtdwkQJ+lHHwLVpO30132+Ymk2u7zv5iRRsskb11a4Pj5a5z/vgihZPuJBNzj5gHuMy55+sY9hv7t9oXmZX/q6+91B5SoO+xVr9O7fZTz1xq/8EffiubQf7s5QttE0O8z6TTd27fbT91+Ww7TmH/wqXTTA4ut7/x1e+2v/LKC+2Pb9xuL58+wV4Bjjwiea7771IhOE51Qslvke1j4B6keJXNQ0fZJrq2fqdtXHkb2RjKsMT46CsX2wobT+aoAJafeZECwVt6VABWYPsWZlpPaeciJ5x4ozltT/im2uJxVgbWVmlRa+wqjMZmAajtq3aJqUgsJNCx1ZeaGZrsg0llI8ZtYcaf1pfQPTa1zDWWGQnPdllpkD9ZSwdf+fjnqQMq/LSbCIvbvFmn1TeObENC6JBHyk+QpT95JhV5ixwjR8aTTLDZJd9Fn3NUJFZAuaR/6FJ2cRzf3+ZDjeeeYymWtH/iUiHwLvNTEHmFXcBcczbIIYqFqKB9Fk/vhtWFq4J5AJ8/Y4qGODUEQw2BE8boopB7Lzihy81VDsMnlQ9hymF8IesotVZg+UMyQEVeHHWYIG89TmLSLy4Vri6yr6HDSysyiNTxtKGEoh/jvHxWcS99zm9RQFadmSazf5Yx+vvfuM/pp7zwAMVdXhaRyE+y621mkWW0t99rr3BE0enjS+2Vi+cxtllOC91uL71wiWOQHdEeMKvuBhlbkZl2jMI/rxYREktpWxj9A84vs+D/1C/8Envev9W+9Pu/g7GcbjPMCi9D29ncW7Sc7378cXuKwvXOzQdo7n660W/SLX+f9fd3eNHmmRNr7fWLZ9I9X2Ve4GfoTXxE/Lssxf3kq8+001RC/+nX+TABMixRmV3BEFfoGZyhFWKrSPvx8yeYuNMEMGY3fyCirdhDvmP0u/Qs/tzTFzgk8Wi7SeX0ISennOHDDMf5jPDD976PgfBm3uoxBvAr7Sjd/KPsAls5eyE7C2fpiSAsBc6CQ2FdYKWAvd+7DE1m1liVoBCn26YRkTG+V+/3yc0a2KeVO6DHpDsWb25agRAQPHM6GVy5XJWLYeAIRPaKbEWsKTlHEEOjEnP4tIVcj/a2eGPQHW/zbZ0K9IDem++o79CdTyGXdyRApt6izzDs8qqDNJAZflkYJ42nsIVbLKGdf/qZnIirFCmsgAhWV6X3COvpW6zePOadiWU21OT1XeED500kHhLhl6rOmoor6SCjoikIVzUouPpKaORKRZiwYHGTWNepQdCLGqE1+AohXHhEaEJgq12YCHmZZqMqTOgqYOIIFlCRvOLxhlO8uEKKKMKTppLZqDEvIf34vQEWPfdnqBmv7YSBQOIWj1S+EBDOsJE2Cc1THtN6HMc4aZvacbrdbsV0l9drfEv7NDvOTjCptrwy17742nN5TfEEO+meY8z1EWva52nln+FE1Lfe+ChGfIS31BwXLtIyWklsMxSYp6u8wBjT9fOTdK/XmLn//f/819v/7x/9dnuFU1gfsrQ2Z9eYM9vWD64zU8yYkYrhLhWDY3iHDUcoDHYXF5jh/9K1O+004/ANKqR1Jt9+gVb9IoX9Aq3/IoqgbWFmf649x9zCZ4+vMO9QrdvXGOf/mcvn2tOM00+vnGsfUmG4VjQDfY3WpR/e0Gw/ydr9D7Ga8Aaz/OnxEOdxWf/0o2uZjPwlJg7t/r519QrrkzfbwVuz7ToVpkdTLXMU18rl56kAnmpLZ9nHT5b44onfQqMLQBaUwYyWzy6umanReASzppO8ptCanWaWsBbBcY0JuJgJOnZuZJ6nBuisuBntl1Gt0fPefFFs99DlDQrXGqftLLMHfQaZT7G1dRH9vEQB/KN/8P9t+1c+CnRoFMFq0R3Oo4fUOPDCBxwTl8BYcR9nCHSPM95P88qzTWXSwD11TyQymB4DdnWcjTe3rlxjMyNvyVEhTWbtlXuSSD2mv2hJb4yZy8yFTGjSXdoCZho8KAVuiiOMmgM3pSoprTzAOfJF2tKqvAG6WJEeKXGJlmta5KrAlSTCm6fhFLoTcPBDYfLURiqkyCpDVQGE8q+2jQ9cZJJWCeBwbnKBpwxFq+TXQ9+Fgo6RuzvMyOsP1tttJkzOMCNOHrYPaX0/5D3z1y+daWcvnWzfuv2IybAlvqO93n7/zQ/an/vxV9vaxRO0ltuV/zCxu7eL4dl6+9zGuFwX3uI8sm26yhZ+D/+/y4GE59k3/YT4h/foIjOr/f7770XRC2jYyR4Ny0MNnzC+fEDL49dBdygwJ9gd9x5GtUSFswP+l9mKa4HxtczLVDTv0aq7OnCE+C/dYFMOhfZfYuLvBkODA2btX2D48Q/evdLuIKPbcl1cVAFWUKv4f5wK4++ycefHzpxqH6xvsBTHTj0qkmMY5U+z1/8e+/xvUiN/lTmGH+d74jeRb5UhxZ/+7CvtOluEN9/6Wtv83lfY8UdG070/7sQer+CuvPgyFRhr1tDx5+kwe2wNRv1pjc0FJ4Lssm/duZnNQPPIk9qeElPbJQG29FjogJ9FJ9lVZ77zs+I28+eoVFzOlDaDkmxsmqUw/uj/6K8zpFlly/NxXj+90Y5S0Hdp0dPL+pW/2P7gP/q/p5fhW2726jxcxK/LpLWApy1bCp+FBeJ2+y0FS1QU4jy6cYMPQrBernxVUpCyCpatvFJrI8fge5+JPHsBu042geuloXqTn+kz7bk0YhzDm7iKKJM33QUZoNpxZ0DRSYuc+JJFZ+jlDhRgxSFAE1pJd2Cr4CDG9AIpFQFhJTfPAEx5mKqSQDQAh5BhHqaqL+kaNIqBWut0eVrIvSIjfM1TgxIqmeEOsU6P2OiROBaeWMKmNX1Chl595CaUxfbq06dpnTBEJlx9E8pCdPLYUnuZQj2/wWd2ibjBp4q+w6EVn+NASE54SmvoJ35839wx4y6vt1qJHKX1NNMthEu0wlssZznm+MyPfb4tf3Q7LdDGw7Pt61/9EmvaB+0ym102OC/uCWN2Z71VmfxNtbI+9rtmtO4enHCEmfvbLBE93pynwFIJUJFcoeDXWHU/3dLzFNL1dQ7HQIYnGKxzEV9nR945ZprP05V2W+8XaIXWqaz26XU8zUcJf/1teifOMVBqPnr4uF3GiM+xjLhMeu6zQnCPV3E3aLVeosdwksrk1sZdej9n2wfvf5xCf52Z5WepEN7hVc3PP/8U59m92a5862tt8U9+l0qDF3s4ePPYc8+145cuMyRibM1wYo6lxH0qtwN6KV7LvP23wxHKNHfxJ18pVGV4FjALXV3DuFKKEgQOPR9PkbGwDmM9/5lPtWNnzrYNJh094dc5CnW2R/fcL6UeZwlshRbfNw5VtkMBt9i6FbkMRjNDBvIpRqhRmSn8nGRc4ZDIB3zT7cm9e1QgJ8Ct+MxSJwOpmCLfAaMeNjuRlxb247wL35MdckWUO+jy8eZTM/jEJe8fuBIkold/BA93yVHBoVdQ/8z9B9lEkNDrBEsqgssvfFzeLK10w6yQupYSmaJvPNETeLH0cFVU90sDp5VcJzfBkU5QiLdP5bxBKoFCiX4T3/1Dxvm/8oXX23/5+18FmTVqCskuxn2PHWtu4rAbvIUh3FrnW9/re+3Kh/faKV4qeRYDf+YcHzZgtPtdusOfoov/El3y7LvGMHxLzfPiarKIyoICQtehUuPsMQn74p/+xfb6lq+vbrUnTHz92V/+1faAE2Tv3bnd7t6+1a5y0OB7b78JTiWeeyqLVfa7O6H3kBcsHnIibLq+VCzbTJj5br11n5MsWcOfXWnrKOObjM/dcvo0a8vb7K+/yss0Vza22ssnmSBcAg/5zjBEcYmRHmo7R4u3wnBljcm8p2nV7zCmdCjzKnMJ/8U3vtd+jEm9e1SKs2ToGpXk55gruMmy4C7LWffZK/rc5fMY8WYqgWVa1rfYWvxdVik+Bf4jJh9fv/S4bXKe/F1OaV3ibLxd+C3Tu1ljU88K6/kL/GYYBljobbHzzjp6sFOo7kqXVQGY7RZ6w/2pp9yjbuAxBJfapLHsd+M0npgImho9A2jbpXYX4clLl9ruzQ+xImhCzEM/fLElbsOgV6/jEtkNPUME3fxOMDl3k5n1JVZM3BxTe/EjVL91CRliHGNO5g69iiPkj7zTCyDadHiZ3vg6n2G0iTx0S0XWYeJGxuhiggcVwiaUCbeA5AoMbhPIJeY/j09Bd5xAFjWxLIyFrvBoN2ITOGQSXrdBOvVLKnAW2MLv5VsJkFcgrk4jTCrgE/jChh53e1pFtxMOfN3mn7AM9u/96i+23/jDb7TXnzrdvv/te+3a7fVktJMzZ5l0ur++2d565xZds/l27uxx1tGPMgN/Ll/VFOYjJswcM6dAU2XbDTPcMbWFz274Lt1kOgcxXA1si0pli76lb8WtctLsMXbRXX7m+Yzz7UZuUtv/b/6d/1nb4KipA/z2ApaYxV/jVdb7HGbpyTgeK+1y2gMm4VTXUSbvPMXmMYdmeJDCEbqnDh1O0bo41l2moN/nAIWTFKJXX32l3WJb5jvo5lu0cE9YvjtHK7MA3gat9gLdy1d50cfVBl/IuAW/dY7Veo1C/Rz78j/cvds+ZkKJhHHG3jHmDe62H7p8vH33w+vtxcu8srvAasWptfb+RzfbB/QgnoH3gV18NhrZFb7+YKPdY1iwsPiwrTEseH79drvKhpYlKpg59LHA+v4C8h978ZW2xPo+fXG6+4z1medwdaOWphzXU1lR4c3QE7PSczY+uc2kYI7DwqcpzDOZOktatArXvP3L66M+wcvkIRnkLrltKtKDAyYOpQe8e+TNs2qgLQhlT/LLF3UMMEx4EE5yjsF9uvCnnuJsACwv9OXJD5DirSGDc/LMaWbtr/FV12eEJLBf0uSK0eMcM+QVlqjEhpp041PG4S4Z0+oJPuENpEJweZeLMkc2QxI1YoSqSz0YF70lSE/x6lRCK2vwFjiAxwSe4KKnAumkTWucKFVxKlgoL311JRw+I2Y8Uz2o7/AiTwFPw9oBaq4BT0gxGfe3WKZ67em77a/+3E8yg42ivv5tChZdSHqQFtQ9ZmovnzrejtJVvnyUsR/TQg9p4a/cvN4esJK2xuH+Jyis3/34TnvmaXY/8XUPX3pZ4hvbJsylInlZoHUs2dW3603BNBNVlpNL7q3xUk4Nw3HcX/pX/3Va7bvM1D9ot+nirTOOdzfdj/7kF9pnPve5VBoW5FvIcptCu86cwiNgrtFltrFKgYDgA3Z9HaOb/yHG5zvariffI0xY1e05dgfMGzygQnlMS7PGybMLyPntR+zG46eFf5Oei4XhApuFZlmPX0HGT5/nK6QqmhWHFyjsp9KK0ZW/ebv9wo9+qv3mV7/NByx455tC6pbfUxS+5RNH2we372Xn3lMMHW6w5dgVgjucq/61Kzfa86ePt82bN9uP8F24a1Q+d7/y+xT847TGJ3hd99l2hP37B87607V3Zn2Hwn+EDUgPmAM54dsnaNA0zXKYZmb/4Y2y2fu+1VaQf5k4qproPRN1VMrmkV1vjeP8Sy+2TbYQW6j9JdOwg2Fq5k0NJ4gE3orFQytzlpxxVGLm7xqV6/2r1+khXCyJHIfzF3rkdUDBd6LW3ZMPWIs/YW+CCnUUqqSly1ApA0+e/BWlRJqauoJYMUL4ZwXfIxU3fO3RlMN4Av2HiHSsKA5fRbvCEiUPgA0ZOinMjpWCJwyxhZaICZ1DYUUvhELPEiFcSV4uI6bpPcQpfIRVrwN2cBFphJVcfG5ztn3p/Q/abXaH/U9/6efbX/+ln21/9AZfM6EF9gWTDx/dY+fUEhtUttuJWbZUzh3NTrVt4j798tN8VOGg/dYbb7XnmUm367VMIXIW15l71eH5cs4624rkhYYYBq2Twlm4WUoqQRWou5BT1xd+4c9mXX6d/fj3WeJ6QMu1TYtr658jlEjMEY6cfo6PMjz3CpUU3UznCTyP3F1YnnCzQ+u8zqaObYYHH9OlvM1hGk+zk8u997qtEXJEkmPhHU67pQut3wzfm11up5k02jugS36HyoKVBvf8/wFjUJe1dtbv8nWYXVp9TrelkrzDeXuX0cMCret7N++whr7L6TzH2jVk8nvjL2L034TOEj2cTcb+cyz9PU8F4dbRj9iUdIaewxy9nFMU3ofsTf+YSc9F9Lh79377qdfm2pu/8485gHMZ+Fk+W/xUO3LhqTbL+W+LK6yV0Pp7LBflhAqQoRJ5gBkz3mY1AD25hLfPu/310k3XNbQp4SyJ0UNIZrBiSA/uMT0G8zKv2DIEcAAdE+wFyXz0cAuQKjzk0J86NE+JcD5me3mLw3yvtVOXLyMHFT7x0wIV9NjFEb6Mc58JwQ1WX46SjrR8nXYZfUhKmAuZ/Sd/LCipeCbSV0GhRiakCiOghy7ilbHHKb9XZAq/sr8Ek1aT2ymmOA04GRd1cQsi8kgssumIIsJC6IIvWw9d4PSVDEoJfOgWvWr9JSYtHhHKm78KNP3jmtBHyKET44ojHcG/wC40Z4JPMtY8oIVzIuav/twX2nOX+L73zJcxbE5uxSCOnVptX3v7A5aljnKI5Ep7nV1nu5zkepUu9o99/pW2TdfVmnyFMbSTcbUEZG3PVAFGk5daNBiF03h4kcYk6VeB47IiMsyC5sSd/YFjjpmZPDtNN8+ZfMV3KcrJPrv51QOYElEBjm3nKFALyGT8noXNM/B4wcaW/AGF9dOf+7F26/ZNWvzb7Ni6Eh6PGI/f4DDEC7xeeReYDz94t13GUB9T8DZZKsySoYWbAnHv4HE7wXh9Gzlcsbhz62F7Qtd9lRdCnlDBvEwh3uYNwKNUAkeYt7jOLLw7A09zhPYVxvN2u58G3x1/R2md71E5PetuM5qha4zx7RqfohLxVde3OfzjA5YDjy9u5s3C2bvX21vvvp3hyjFgnPXeOn66zdDNP/Hya9g642oqI78U62rFsaMUeiq3dG1p4TWMaMyW3O6P+YBm82EMJ0EJsyuYj1gwUacb5FqKJMNiiNIATrwYLWRo283VVBRHmbfZeLifbbLHmOjLRxgFNpPJpHT9efp3gl1zd2jVF11mpYelnEW3ZBVeNHK2hi3KYyoouIbLc4IATBkWMERWoRFXtoZ1Nwg6JWX4tNCW/cnBIQPUcmX7q/j9Klz9FRayI9rnJKDSkr5t4tVZVQQCyVsbFbzIdyITXiEEToVXWsWf+g3rpAkftHHmojH7Dm+GfYpZz2UKkhsfNukmf52ls3N0Q//df+VfbteOP93mv/VPWe9k9pldZxz/yPvJzKZzBuT3GO8+8/KlHNz/Fl94sfDZomxRqDzRxK6fslZBpMCRKZkAUiQzKAJVIkogEiw8fyp3suRiLQzYEVqJugYOwFxDOfIxxBNVvGzhPeAyyN6JX2at/igfirjw1LPtU6//qFCpiJx93mBfgKeo3MTg9hmnvvv2W3TDb7aTnJX25ve+zzfE+SoMLeUTJ/c4kdoC+IiNMMfcyQelE8xYe3qO327bY1LxQzaZfIOtvq4CVLqut4u09kcpgMtUBh9SCWzz4YunwbsNP5c5TzFMesjYf5X36a/zwcjPMSdyn97VHXgeZ07iAvjHGRp9/8Yd0skOQ1rCc89eat969z32xr1L951hyTe+wgoDQxAKO90H9iCwgGjhpfWeQ35qR5RBgVFfFP55OvT7DB1mTtLtpjdhhY1WVViU6+m7JkDjtFGoyTjnCbqxpaIgnoywocj2Vtzm3wpDpodshvKTTc6ZOLcgnVQcrAp08wz945x2dJdl03P0uORtpxRHdBd5kq9lxCmwxBo+8l9YL9+tCLxxCdHbKwrTrLvHiWsyC95nxUwKjkFckTOMulziBHfIF7Dcir7xAg1KyKIRlGX3OB6hKRqwEdYbbmHj1wn37q9n17touUpTwcOfNPX0Ga2+53/5Z36c2pcJHVqLHSax/vYffTUvUfzo5z7bbmGIzy/T4mFgjQm4Zbqfx5dX29sY2bcZT/7M6y/zdtdau8JkXY4wtuWmpM+zG8zz3pTXDTNOxpmg6u6pZDKYOMUbtWXkjVAmrGeKBPoVHRmO340X8eOOLqUYvfRwgNSRCfYyk0IJv2n15580LPy56GGs0SIeYxLsmRdeQbb99hM//2diuE+YNHvMMMBJwDu3brYbLCs6PPiAVy89P2+HNN6nV3CUQngXo755cCPzA74TvkjBc2bD4YxDghvo0HKxwG68A7r237lzvT3D1liqRQRm38DH19gSzHIhv6fOHOccvBXeHdhsZ+hOf8Qa/dMMDd5i8tE18qO8e/AcFcsVKhPnDw5QwgKVyNvMV8wAMMvZAZvw2GSe4Rjntq989Y/bBgWJGb829wJbeKlsdpnjmDvBHAK0dliqTGWtthDSzUJWlp7Mm9bX8TcvMqW3xhxAdGjBGQWekLwDUBqN3s3341SUt68xTALWoYGZZjhVTVpus8C8shI6ii7usjJxmo9BqBJiEjeyybzMf7INd64qTMrjVQ3EcJv3Ae5xeWgUzC/glrePGCIUOk+h5Bl+ugGSUoqU5Ip8ZIsHfzU0FWF6hm1FT51R0TFSDl4Dvp6TMEF6kAVVel41+XYYtsLVZ+moPwdCZzT/Cl9l+Zu/++V2nW7n+Z/64XaCrvfrz3GmOyfK7F38sfat3/p77eUZJp8Ya9op27uxzgwvy1CnV1iietSevwtzKnwNb8FWA40tskarkTgLv8uv1mkd81mT041gjddLcf1N0mwg8SrTzFHJh/TeE0K4iUos9pg05xZaBNdTWocu4UcmiT+uUpBI8LIF5OcIL99D68I5E+448gyn275KQcoMNGnxqORNltUeUUDu0ILbK/jed7/DbP7Ndo+z1ZwvcGyeD1QwwfmIYUdOSGVMTHHPZp27vH7LNAetM5tkmOT7PvMQZ3hRKJtUGGt+h0r1s6fPpPI9QVf4CS39Kbr3ixQuu+QrwN6kAnBo9AzLnrczxNhpL9OSLlPpvstKgQawzGz9ow8+aLvvvtvOMVy48se/R3eftwOZ1DvCmr+bgtyduMxE5A6yuMR6lDz0wxhzD+65zkrlgX4gpvrMx+RbVGeAa/YWXvVMrvXCbEXseP8U76Q/YHXD+YJFhhnUtdCxCix6lef02hz6Max5RG9xle68uMpfVy/0cpblCO45rp9gInIPwCfzuhA8KmuHnpDbcb2UZY8K8TE815hgNRWDxrAP5dPqcg/vkiG1EfymhVzexBlN+qohqjBpjWvQHf4ffFYS4BY6Fat7JO2fgS+GgRcmnIAvB73Pu6wH/8pP/0T73a99mxnnvfYuv9PMFDc2gSy89wb72o+33/29r7Q/tfhCOyDz1xl7nqClOD93inPj9toHKOwsnb+naM1oyrFYDJbushs1fBPKWXtrSwtsvbnFrC9GZ3fWSlTVRZiemxNlDKUQHuXaDAK9z4kvvkySCoC4vLdtehKPo19VAcDXTJDL0LFkuGyhpPvPv6qHkDjFI8OU3SvbWbq2pXuEocDK6vF2kQMsTcjnv/gzqdi20JMv0NzjKCZ7ArcYDtzG0O0JfOub36JQ+xFENhFRuB5TGCGVCUYPYXxCWpYoePfoHTyksN196D4G1vIp+Mcp+A6dXJJbpZuNxmkFj7QLtM6eCuOHEo+h8xP0wO7T2/A03xc5zNMC84C8ugSc34D3pZ4FaJ7ybb3vfwP441n3XmY+xDX1NzkDYJe9BVnWo3KYYYhxlBn/5eOfgiMX9PyQoysYFib1qZLT+hiv3ihBTgyW8t0Nx7sMrHYI64EVmewLrAVFFXCD7jFfF0ZvTxgWLdODydKfcMkveKT0kH8aEGG2cubOaEENFRRqwTCnx5VxOL2Y2//0S0SWfKioPSK9a6f4cs25L9Sqk70+hfLqxlN0prQOl7rYklGifKI0kqoIUnjClV+yFZaCOfCSNuJCo+LTmypn0R4wysY1kQsaftK8q4eYgUTR/Iff+E77aY58/pd+6kfbuywLrZExjvNstTwP/EtvfK/9EOe6v/Gtd9kGe45Ww11zi+3mA8bb1Aebj7bbNdbiz2Modh3tnrt+ezBby2tLGJaXtb1fD1F+u4c6LEBuzDGNEaocqiIhwiQRyLJAwbj7wcd8YOCgnXvp1XxRRlojKcMtpmFVwAclAWu4kEiAVHLXF4qvQmyAGJ17yTDug4GxQRTfjSa02jlQDcABA4ivia7RWh5n80uWGM1dxHCC8S/xdp9zAffZIHSHwn+XluQab3RtsRvwHjPshjkOtru/yXjZis1Z7EUK9mPI7OC/SU+AFff24YcsGTKGd1Lye7x1t0JvYQm8DXitMql14cRsO87s/hYt1sIThl504a8x40+nvD3N8t4HnNbjO/PzFO6dJ4/44up61vKvMCn5FZYB/ejGAQXjMj2Hz3/xC+3ESy9QnzPmRy5be07riMaSTxSO9Noyxidvo00EQWYrGi18zVOHSd9aY2MQPUPDgYjq7EvrPrCyYrXjLkMQG4VFelTZZSch47EzVa0v+ZhQuPWCE2+PqzyxRpC/MeBi14Ys8d7DEj0f3ws4jn1t8u7CAbqc4dAV5YBgyaY717AnPUAIRJryCEIPxh0rKgErkHsXv/A+4RG6eOUufmqDEFCMgh7pw2+F2pXWCZv+HgZamaJhMqZcrtId/F3OZ3ejx5//8c+1f+9nP8/nl660zZmj7e7sKssyLBuxBrvKGO7WrRtslPl0PtV0MLvDvm6+oMqYbXdjlzfKrra/xsTcxkOWodiBtUOrY1eWkk8meTorkzi0Zs6G22L4LuyBxzgpiFLpoADlL7lIkgkqP108WsfzDB9OPnu+3cRoq0uWnAMIo0IxNX4xYaUgcUPfZ6c5CmkFpUkQISh2OYuid3GMKKNKBpipkdFwL+XuV9KAuwhEdpe2aLR/4HJCkLfeWKq7yD5vVyVEcaXC8f46Bew2OwPv8LtPi36HycDbDANcbXBi0GVDz8u3EljHOB0uLdPdtYe0TqHZJ692djbam+z3P0KFu0ehuMBE23EK3wKVwAzwZ47Ro4LXUXoFB/cP2OzENmFa+Bkq8BfZEPRd3h1YpUfwNOOxDb6+s8+JQCdYx1+ilX337/56W6ACW+WgkQU+wbzPMG2BSih74+G/ST55dHgvWhnzarOO4zO7T8WxSsv+iBUNdmBQqRwhbRRw5LSgqgsrUPP+JEOW+/A8dY7KDrlTkK1kY70FU8o1j8QUN3fojLwpVw+2xok9zp/Bll59kU1Jx6iwGK4h9x49nE2Wco/yVmJWJ0ASz6yVWmh0HpNKZcjTYSlqucI9wnRkKRwiYEGtwjzSEYyOrZ0Nv08RSz/lPlzISx1TeElMcYPJbf4nL7LZghbblzHeZ6PJBSZBLmKEO9Ruu3e/137uKTJjc65d4eWWxxjRG+wD/+nXX23nOIdtYZm3zRjb/2PW4T/NcpxjctfNd2iZPApa5irR1knema0l8R7/fOC716nBySQtIeIBz/PwuCrh0HnCbLQ1lm/AMTYw2SQf6I5LZOngE/gjTKr+4NMLpL5UELQeZs7gmXBpA6r8ShbsisADdE+bxlUSC9fToBz9GulStgkduFn4sTcuK6cBXL2AExSCU+xH9426saTlV24cCtjSWwlYAdxkQvC++/yZKLzPGNplQbfCuD3WAvXIrmc47LcPHm2lImAPUPv2nUfZvHOUfLpBC+8qwiwt6zHGq4+Yc1jhSy22as/Sap+jkm/s6LvH5N8dKu0d1vrvb3PI4woVztvfTwFnt0u+anvphecbgqMahj0UZtPm6rxLmr5d53FTGS4hlPFr7DrcoFdjBbDMkq1DjOhCRYFHEhg2cBQYG2/uMudxym/DUcElVZUwAeuyQAW59DyCfZYWpiEHVHBu7lHCTSobeybOCWyz5LrLK8l7HGBycK7T6Xkz8jhZCyuf0xwnIw0Lr85H2Q0JPtgxpkMAyBrbSbyy57/bm4Uc1G4YZWGx0E6cyJ5WsSKTt8EzUPaMTGFd2vH8jbuP23m6MA+o9e9ubLe/8+u/0X6FDy5+5rk1Zm2faQ8O+Brbh99oN/bWWe5Zb587d6m9h7GtM14/zfjpfRT38qeeaUvrrNFCfJ8WfJcJqF0Pc7BQIEE2pNByZdumSvZHTipbEtLlNGQksJSkoAotJECO+fhT8dVN0azkoT+lrye4wLM5Qr546ypXWv9J6GjFB2rBRIEgTXHlw4XsNUYMVwKEMMzMA8ZM7fIqY/QfqJIv6e1Yg4L5lrRDw6+dzHA+/cF2ca7KggksCuNlDmt45oUXigXRFqAt3sZ7yI7Au2x48merf5sXS26yGcihQTIcHfgyz2NazkcYsjRtLc+tceLLx1fbHFuet/N55Ln2u1fvtpc4N+5HmXNZoEJdoiA6I+/38ygS6cZbCd3lTcDTK3vwup0DJVf5GsyH3/g67w4wzGASzff0F+jZ2TUGi5doHKKRW9iGexpcr19jWdHlSQv1QsbsZS/qsi54UhDXrGxIj990y9LdIZ0PuKG/UblqI7l4jDyY8XwFVz2oxDaYc7j/9ofsyWdimfSsU8g36SE9zy7FsltyZ8hBBmmD/qWi52E+VlmsAjUa79hDbESMab5OG4VATHoeJWPZtATtSZjBg+PQhFgJ63F4ADNWR6hMb6MBAra40aK/RUvx7S9/m08rXaRw8oljavi/84df5TM+vNDxp36YdXMmb976Us6JW+KASGeLn6a7/4e8ounXPn6cE1vf4TCIh49upubCHtLiafS+CJJiSJibQ5x8UliL7gFjOzSJjChqyKPghEWpCBs5jTTYPwsOMJUun1BPXBGohBd8wYTcP6OIykgQcyX7QkfeAtckTBXSwa2ImJpAwLZ4FmLJmiopOQ5ciQFJ4cRTdh5Js089Yld6q1LoYZEj0YArEzCQqBdECsYwCc5htKd5LfQsH0i0AtXQ7Tn5Qsx/+R/9jfZtPp65TCH0gIujjMk9B87TW9eZIPR9hMfAUWNkgm6HHYBX+ACHB1H8+LOMX6k8F5i536GXsMDbgbs8zx492Y4yxzK/xFgWmktM/qwzlHLS7Z7nCsB/g4nHGb6vd49VhCVO5NlnmHaUo6b84IZf3HHjjEM4x/9H6UlssA14kQLo24hJb8+a6Ib0+0rvKnH3GLOf5IQaecXOSkW5j0I5ecZO0BW03I9vcXSuwfcmrFnU2zLDlOMvP5ecPEYDdfNbb3NoJq06k5Vu2Z7QmvDp+T+Rz4iyhDGUNEq8WBVP/woq1hF3ctAMrQjy0XJRso7AyuWOK70KmMgU1E5DSy3+naaNIThFnlic7O1YaS9wrvqKW0vpTnmAwBMM4QYfLDz+nT9uq89+hu2vvM9Mv8+3vhaPnWl/8taHbBDZbSeePGTmdpXP7fICCy17XrFMIn0n3doJhbI+7Zjc8a/PjNMiNEKpCBIZowe2/sprwYisk/QjbWQvxZYRlFrUQhVeE6z9V2VSsZXQoBIgj/QalJOWLjIMRj1TDFPEwhmYnReh9SclrylUZTByB9lwha946+j65xnEEX+YhnGGexX1kO9hNQ6F+4QEWoWZC4JM0RVWtwg3Fz19/qn25t632MqMNumW/8Tnv9j+/F/+1XaLPf9/8ju/2778B3/YVjH4GQr/HkeC+Q2+BbrQpu8kPYg9Wm130zF6b3/Pc/fQ12kmal3nf47e3AqbcRZZnt1iH4GyrbqrDWa3b/P55XeuU1hm2sNb9+E/0x689Q6TeMwnMBnHVsecub/ERKXLequ02H7YY4OjwHzxyFOErdlMdmmDric9gKOM6i3sJ9gh6T4NJ+jM68qtobryj9yRQtSH7bnUyeRGdOt7/mtnj2WMLj/PJTh+ie4726WFsyLysqIeMoRW8sUmRuHk3Pl3vQtbdhD0Q7eCnASIThYPUwg9Igc34Ywr3kVzEktgqE0AkEFA/KEZA5FBYVspzs/cvJ9vrfkRw11q8B3Gei9x8MLVg+W29e3vtB9GQVaHC7TmL+0fY3zMV1dpAW4wKXSJ45je+/CjdoFNJnNsdKiZVQsPKDzSgs+6fRKxqP29qmtlIbfolyDK60/EhHQBE2IAkWZWwRvAz/8gcdModAevJ1pkrxhCOXMXJnDiSauunl2RQFI1GaOrrlIg7ljN5NFjTQ+wkuyy+dDXExZnwgLT0UiRHASNYQ5Pogu3yJSxxt2hCwdc0yAeCsij0xpd2Jxspf7JEHfB+TnlC08/3X7xV365vf2Nb/AugGe9Lbd/63/1v2g3eAnlP/4b/wkTcSs5httz9pdove1032PJ9Qkt410qDA/o+H6WBGsYdpZK4f7sW22NAsOiSDvBQR/26KwINh5u8RIUb+lR2LfYuONkl+/eb7zzPtY311axtTmWFNc4isuJvXVWE44/fSn7GKbbeE0t+wZs2Zk/esDk5Bqz8uO8vqFBoYbCq0XT5hjsoZh9uuWO+asgcSdsj2HDHm88zrqkSCvuxqIlxusOL/wwqNfhQiv9qBvcohOQMqORDwBpixM7KJBuqxLoAT7A0atUU3oDQCISEyU33JSAGD3h/ieaOHSTywD++41nx8M1/+QkNdlrl9u1733Ubl7lAwi0wM9Tq55lw8y32fb5R6yv/+IXPtvevPV+u3GHM95Zk7144RQHJC62D1nfvXBmpV3j7bHPPv0U5HgLjXGgL3n45ZAlloP8NJIZ5Ph8l26iSzm2EulmligpO4qbJONI1zP+upE+orohkxBH5pY3leNfnkngVF1FD7yE8zCgu6VamFGh3sQnU4ErMGipOK+OG9+gQVwqMKEN6xVA3DBLFoIXVGALrRPv8KIkHJ2EV+BIWyGVUNIQyAR0HukVyTJwRFGJ2qOxA2jlmq+o0sru7DKzTuvr8Vjue99iHkYkX/T5zb/1a9bE7af/wi+297713fZ3/ov/ov3Kv/FvtP/t//5/1/7J3/sH7Wvf/mb7wpkToXuUFvgv/0v/Il/suZc3Ae8zifaAQvKEQrnNxJ1d9w94084Tg4/QEh4nv/2Y5FkOFX2WNXHPzbtwYrkdRxuP2UXpGD+vKTO5+5DPfrWZK+3h7Heytu6BIpsfehwXY3xwHeNn3kJNkk677Y7ZH/P1oCUqpAUKqunXJpKbUQrK6VdygkIujMtoKlxduYqxd+tuu/HeNVYM3Puxz96CrfbS06fy6vHe7lJ13w/nqzLwn52BnX7yz3wwohe4jMcDWmHJviACJX8lNarb11RuYpBNWv4JZ4MzZqayZyQEDPPqPPXgrNBuS5N4o+gR/SjHJx87tdLurS61j25dpZvW2jtk4CyF8RgHJfz9f/S7bZW111eeO98+bA9Yf93jRQy+zvLhQ456Znll3WOkDtpd1mPXKPQPmQleonvm6SYWSU+WsXbfYWZ4l67fGrieXjO34xjItJIk1py3bn7c5pgQWp/nc1DMOmc8GukVU0Pmbh8EDZmuyWYG40xUv43EGuRVhTdaKH/ugougouqhLN0XuXRP1JlaIpDhE1bGkm71HlWaQbiTaRI7fCXvgB/BPlGP9M3QaYRIBVd0C8G01uQlXUYK5xOWgbbIIw8F2aYlesLTE3btUdnaWqm6lr5F6+vrsYvo3eOdNzfX25vfeaO98Ru/zaEe77S/+G//9fZDP/7jbf3n7rZ/+P/8f7f/7D/4v7Zf+R//m+2zP/TZ9u1vfBVVo3Nnw6mIfvYXfo6Xdygo8HAXni/5PGBP/i1a1+scqnGDybLbnKzzkB7hVQqMFc779x+3rzK55yef/GrMcVp4N/o8xWTfGkOAFXqBTuLZgm4y9DNdHqK5xV6CR3T157AvJ/aOMOO+RHd9mW+8+911t+EeZYnwyQYDSCqaIxR8NZVC0nVccz8Uf2b996j0DmxsSr2kxx14fEDkDhON7ISbo0Vfo6ezTaXjCocFyhY+W7aTI1XwzBuUK5Vc3fKwAcItuZ2B4ebfyO/U0dA0Hyd2221K2xZx0lKHcg0NtHTtsCweKDzyijkFTibG53H4llgrnsgBwPwFWtybV++0iyxjeK7UPOP1H3ruRPv0UxzasMvEC3n7hLHaHDOor3/2VZRGi8GM7FPPc/IriT67TAvNRxvubzI7zzFMa1uMy9lRtQBdv7vmxMsOJ6ge3L3SDo5xjNEG22SVbpuNHNbDCspkTVu4z9ruA3Z68fYV4aiaOLr9Dh3g43rzjuNHjGDOfeJULDPLx1gaWitVYIz1qWAmeaJEYOCdigHlqJ4ohLjkSXlKIYAZacbozBV/+cQel1k+8fc8n7T8AB2KDUplthlfMaZ3ahTws7sSeYuDYjl34KYj3bL2eK7HfG7K3XEus20yn5IeEsuUOzDYwDgtkEt9rDlPa2rPyTf3lqGjNq0odpgs+5P/z9/JWPwLv/LneZd9oX3wwYftDHMz/+K/+a+1L/3mb7f/9j/+T9v5Myfbz507GTG97VGJPKLXts8rsXk9GMmOUAid33maAyOEUZ4tCusGXfO7TM7eZPb/OqfH3GQ+4C55dpvwa0zymaZv3LyX7bsW+tPY0hla/ous23u81Aq68ARhl2o9QOQxeJtsJvIM+mX4LyDbEodLLtDdX8FmN3kfw9bdzTfzVB7Jc/Wm4tCTcxP7pLOuysdZuue0QHz5Z76d4sOhnjDk56Jm6E08eeO7+c6dJ/Ikn7Whcpm5Uo3N6okdGUugBTBPddFLeWzFQGGAtyKy56HZhHYQ4pE0l3ctFYg4y9JiX/jlIc2CHTSCWPSMg53mVGwLn8g28zf/T/9LV7TTJZLCLuNv1z9tj+swf30YCd0zZyqt3bOMZOtBhqjYHWq/YOB2jdSW16+J2OLYyhAUQ6QvRMGnwBFv911jzIYR4jXmBSZzfMq7tIigGLByeRrKGjO4J9lkcps1/7d+74+itAV2TdmF9rikvEjDSzfzntCyytiPGd0Zxn10Idj0YY0eylJHEWYA9PsT5+RSPVnznYQENHyGmif1amk0Mkoj8Sq6Eyx4iRP4gxe5MsbSY/ecS2YeeLnJXgQ/orHNXnqfbr6xAvAjEo+dGSY3Parb9/xRNa8S00WngHvGn5w8gecae9tvf/lL9KSoV9G7hce8e+7F59qn+d7dVQrIMjRddrsBDz/y8N5772Xm+Vcu8wEL+dEzc/5m72d/qm0zPp5jnf0YG2YW2aRDBprQbJYxaabFn5uj+GBAWk5n9pXf7v5tPpF9nfX/G24HZjegs/RuBXZp1DQ4mXeCwm/r/xSvQq9C/gQtrR/+3GE5V92YXpoAmuTFtsSegyPMwi/zCqxf01lmNWiOwu7ry/vCk2bGj5UnymdD4cw7vY07fPOv8QLR6c//UHoqfgxyi+3gd//oa+3i5z/dthmuOGHnjsNxjVycVOyk1ZKlCVjAuFe+E2xgrI18suB5BSTP4TIUnY2SmU1DZTvaTZGBfscXenrJq/oUwZ9Qn0LoGrLOv/v7f5jXEh3DWZBdYlNwxzQalWnxkq1Ief9bAOAdL2l8Fl5f4EgXJnEUYHApz0msrc02mX2EWtNvq9u62MXMMglAFuJdjHaRmWLf/3X2XrrZaEMCNN0UPN6YOnqRvKPwLhnvxNCOGQoNjYEXQnZ2r1CpoAJksoW3pp+DLrsy2pHTl4DjzS13PrGra5eDNGbxz2MkymSFZo7ZBnplHqHniWkXZvfBHfS0GBquLQMUWYUXRr2p80nmqA/jDhVqC4Npim5h6fDmIbO9noO3TZd8kwrSgm23Oz0BiDlO9ZXbHehbAYurQXsM9hHSGX2qO2R6DL1HFFpnjtEynFhaoxJYQucLdHt3WEt/SJd7nd1gHqbx2DfdkG+NwnWEYdUGhYFUJB1jxQRtp3J2deXmo/s5ky9v1rHX36d5aErNM/V/l/fztaUTx1aoaGfbKQrk+YsX2g/98GcCZ34/pJvumv8NWv1rTATeuH673eJzW3b9v+XGHvA8zdeZ/lNMBp+l4PmK7hETjw62OI57k2OjZ7DbRV70WWBTleftL1PBL9BLcYKYVgh7QjIaFgt4us7Qcd7IN/v2GGrM8cKSJxVtMOcU40d/as5hwdwR7Ng8Vbf8KIuRPzfzljBDtBotqP9rRqVDAzQJ4Tpsd4gIXBXWeAAV3OSVs9+DrLuuIlP+2NCEKXjaIADyH5fjfBsANmPQ2llgMQ4Law4oQDlLKDWGDKF0nckcbdsEeQSPLbktkQdBOoOqeMidDDbDrTzSDRUHifwtmCnQdcOEEyIWjFQgGKv7xhd96YLqvQrCTE6UEQ9GdAh8W4shAl2yOV7wmPUkEbeEuj0SA6Qp5C0txhrM6u4QboVxgDHvMOu7t3+DwzE+QEJo0/pZWDdIr+O++uIKOiBxMVSepsOjqKykIETafNJVpoW14Mwv8TbbKy/RfTzJ98JvMMG13db4kAOv/VEo0Rv6UVMj3Spdg7HH4nKUB1w+oTXbYJzpVmHXb7FrLiaG4O9rvxZy2XtCrxKQ2pzBZxr9eKInAKXLCJBdTQu7zagfadCg5W0BRTvtPBtS7Mr7+S0/gbXP9+qepQXUcE8h09MY//vf/A5n9bHMyptmvo2otTgMsuDOI7sNwWMm9Pyu3hZ5t8Jk3zon/+zyNuIBFaYV5zY19T3mbe6xdVYdnKRL7gYZRbOR8CWnxTk+z+1qDHnmWvxZDuE8z9d0Np45n5dLHpFfj5Dj/gMqJGbCP2Lo+D7Hl1n4HJ74Xv4ZKvCz2NBJVgyWZqmEeJNyj27+49kbbPtlXoJDP47wNt9R3s3wbTmVO8u8BTUSctHQkP+Omu5+7fvo1HyfzUrSBY46T0MDmDvmDuhhkI1c2oRtKF7tMSE9Sje2ocoSoql0HB3DOQpfdc07DWgGoJBDQzi4hVrdyl1h4OEVO/7ulks22xCOOjp/nuSBssyfeu7pmhVHiaJrjP7cpOCLFB6+6OzqJi3yAkiu22rDqxxUITMLn7OZy07MYWBOFnnck0JQHaQiwJFuVz6UgMI9xpkg4vigIbWmPQInVnyx4gmFwC6TH0Ow5XpCK+1LMLOM+a+zXn+LU1htaZf4JtoRZMvRUdTWdkutfKysklHg+nmoWt+3mFTa9ihQFgi/YLJ8lGWeO9c4cYaXPMh4KywNUoUtYUyPeaPJIYuHRJC72WzSmKNQroWVzXbx5Kd4HrR7Nz9oG3zEkI+QtpmzT/E6KrAQsqV+xDhT/T1mL/ljnh686ETVeBvOL2j4Z+H1g5KOt9fR3wry+HHJx7Y6KMutqiQ236azkJsJTlia7m0Atkk35SgF3g9hOpE0p/FR2Z2lJ/UpDqr8EMNdePVTbeXjD9sJ3k1ff/6VdsBGFnPj4Ze+0r71lTfahc9+mo9sPtd2vvLHqWhiD9BZ54WZ+5w2ZC9DQ/LUXXV9xEp7ji2kHKlttWJhnOXkoZOky7TN7lLgKXhWPo/tTvO1m91dKl5our6vITwiTrtaYmJujS65R5dV78p4Ko6HG+02n/J+SNf6ITq8QwV5gx6Dpe4In7N2ku/sEX+sjbPEsExJ2WOicIOhyfp7H7UjVMZHWfKb501MC/0cB4LMkb8HyJdXc6kQyNy2wpBwCzznJHyvP00XtpneKTrG0hEoqkc+JNSICdM9vQ65dZJPFrQ4hcYhFQvguHR1ygFM2n+AqrDFrwgEvZA6bcIJ1JaUJ9Q7kJX5/CMUNUttVhmKsVBwrcUPqLX9VHA+yFdYQ5q01H6mWD4zDgCJf0htquGpFLe8ugQhH43a/dcWzj33uKO4xyBYKS6lwNPC2LJTKB3brWDEO9B2h5WF+CxdP1utdbp5+7N8ZeTk6awHP3jnzczAek76Bh9zTG1N5njqrMm1xzDDsg6WmC2P+UQR8qQbB297FksUklULkrVqV5AKzRFVVEgnyFyXgZyXyEkspGMfHlZQtpgf8cooB8uzw+qpdpGz5nZJzy5n7K3vLGfv+V274s6GqyDecHPruAdFeizvCgX35MoiM+Z7KcxuGJmhtbOCWGKIokgWDrvqttJ2df281RwyOSE6ekKzC7b+pAs4OfnBCysEX2X1M1o7yLSMrO9bUEnbc+xM2331s+3xR++1uW++wcclb2dW+hFj5wuf+6F26dXXcswWbSCXvCEOz8dUhhu02o/3WWUhPU7cWhH50ou9j0W+vpuhjvJTobizLOf9Q2WWsfc+epinEp/xjTfoWpCE30e3yos3LWX1KEkHPM2V6jEyYXeavfd8eMNVBpdwXWWwIr4Pf8/Zv8V6/cF9GiOUcQIbOoduz9PaH6N0zDA56Ll7fkxyjrH3MpN59moQgJaOLwe99BS6pZK/wxr9G+vtERt3tpiVf8xZ/6vY3wIV/So9CG3agpTGjXyMbhAyvVbzgD/LkVcKpSU8oQnKzSBfJRVAO+2lN+5RkE23mF4+e3mNPzcBuKpIA3EIQFtLtHJNqFDQT89y1JIZtcZYlsyx5dgnQx7nLaS1nDRqF1sDzfgdMsJJxJbPCTkVryG67KGBzmPR87TAm3TzcigkWyst7PPsrtuisCuJrboF2uHBvgdEOunCz4yV33G61O7hzldI7Gozg79FFXsGOWfYPruHER+doavP6StrLNeYLP9nedMqQwIN3sqGmAPgZmnlVGyN+wGlcrO7tUcrY69CA6qdVg4RqO3FxxgcfjiXYLfXbaQ79hh4+jbVRT4n5dt/t997H0PmFJfjF9v3mWR6+wrjPuCtQORvQVhBXltDJ9DU5Rq9hz34Ptmkt0G8wvthDHsBTrT5yWnLQLaKwi82A5wflPScdc/xczLTPPFVznkK/ENm5JXzBEsla/DawziXmdG+Q2/sDi+wfJrjvNfJ2wdzvOt+8fl2lkrs9BVkh+5Hpy+0sxwtbaur4db7/fbutCOO7I4e6frTQ1APXvPA2Y1NOqFvy+EkrFrXQky9ebHLCskSsjpHkDxCRik4x5Fsgoaf8FLfdOhD0y/2SteXXexaJycBzkoCrfHRZQozy8LbjDFu8/akxv6Y7v0Orf06Bf/7vD79vYc7OTzjBL2E80c5hmuV12TZbuvynWl2CXJ+c7nd4bzAWfJ8Dz5zbPV1afkqR6P52ell9sT7Pb1T2h19fYew1ko0acgXNTAUZSjFW3j2/BbpjWrD6TIn94HRCPLAQRonAWrIoA6QVr5H684QQb8YAnIN7O41Jv+lbZ3+VREvjFQnbd4WS+P3MENPDvVkmNtMSpxkLLx7QMtKS2vN+9HdjXR5Tx1nvZFaNK00mfPOFb5Kwrlmi4yLyXb+9gm7H4N+6fwxWmsn31AKBev3+T7bp5/ja6co3Zpnnpr36+9cozvG9sxXnwqc3WffW//P/v7vtT/zuefbWx9cp8U7aD/84nm62kzGMJ7bZvztJJEHZWzRGtg6Wzjtklv5+D0vx8IjJzSOKIg4a24V4BKUPYZFMnadSiArCMCltqZn4fhcRZfy7eprwFRgZKrhs/SEPOhhjYK+QdjW1gFrwnS/9xb5UONWOzHDOjHps0dk7yQbWZDVGt+BxENa8uXomzG1LZQaMc+s7bnG8CL795FFI+T7j+iT7jvpdejhPvV1jDHLjuShX5nx6zJuY3Vt2spBcufJyx9hrdn9EO9TgPZJ8wJDigW+BPOEE2SXSfcl3kO/zXjbY6Tt7nvCjhM4ym1pnEPfnk9gwaseGvorUaOjVJo0SbF9dLUNntHDQOexj5qQtKBQ4btMiy7MOydNUXgVfHLHyi3DOWzTd/AlZCWy5XABWRw6LRNubwAyyAUtEnsU23j5mdOpLO/TA/FQlYccxX2N3tKVB1SONDzO6p9liHB6EXgmN+eYmPSAi3mW9hYZ4mgceyh6ZoHDO5i78nhyz9N3SIpqWc4kr9DtHonPMhppZHNhlhVtnR37rzIJeJyfrxBbMagD9WNvxStFr7vjh27mWmKZhERx5B1/VWSF6ro0zotnkahi3eucHlV8khhC0vg+YKLjmMZLgJ9EekjC/85vfb39G3/+JzA2jI8MUPFv8K2yRbqsf5oTX3eo2azYTMBvfum77V/9cz9Ja2whqyWcj/hgwdVbD9orT72ewqZByPqb73zcnue1WGtHW6JlWpxbVBofXb3dfurTl8kgunV0Ae3Cf++t99vPfupyCuFtNl4sHXmajRFMHpl8MmiBWpYilAJtIudRspsg3uSoqyX6jM+dXYGevYyZdpMa31b4/BqjUZ6uJtymq7fHibALFPjH82t0Q3fYwglt3rqzGbPG1zjVqLv77nBu+h7r/adpLQ+YhEpFw3jR96k1So99xt7YnELXkcJ9kZbU5a5NAtcxNg3kCPSW+e2kpWI4QuGxkLqNQDg6C3RpGa/Ty9FoMuNO5ENaqftUxA6LnD1/yGTXA4zeVj/df/Jwh0rkJLStPO8pCHLvYuCn6T1cIPwI+krrT+9oifX4I+x8e0D+rv7Yj9OK0TV986323T/+47byudfb5eefa5vk+5wtGHRsI9x0oqUvUgkvQkPr2UF+C22V7mrJUWCGJi6FueRpL8lCaWWrFagrJ77MF4dA9gYNTzceWIdEuyjCCkX88EcXFhR7BX7Jloj0JqzMleXsyiwrBRRE9PiENCvQGpXbCrb0mD0X5rUHkWxBV3u/jf6sSN01eJlPi13Ado/xduY+PQIbujv0Ho9jS2vsHbGi27HLTkPyiAZJnXsGovjqJX8Ytz0LG4Mn2MZD5mPucKz3EVr4o8xlOUT14yJZ3owFV3kYd4ik0KZ4enOW0BKJuybYElg5ESCiCyLPVCQCizO5ut/awErbpaoFCzoBFgAnMk4cr9NMsKm0XqJntptGUmEdj5n9s3TRn7pwgsyzhvOkkepqudtpjUQqgErRFtwAcuHsCQzZzMdPDexkzjmWQRybysPXW5+sW7nwdc9nn6IYc7AgyyYzzGK71OFBk155K06DAUuFx0gwrMd0UdVReigo28McPA3lKhs0XLs/s3oe3hR+/r795oft/Om1dpbPL3/j3dss71xt//Kf+THGovdp0Wxd6SyiIIcpi3wf7Y1vfp8jndbbn//Jz7RljNnC6SeEpQdpWiPH2hzQsciHHOhaLlCIAKGwoxUmiG493m2PnuxRmbiXwJn0fWb92RFGOswLC3y+akOhnLOXBa74zl9sYWwxsDmW4XrGpTVEp+rsDD2k+xw89wGV5joGvko+LlKw58inU7TiNjkuz1kAVh/canN84vg6cp39mZ9ux5mdfuKY+kde53iwxfbVP/lyznZ7xkQpgFhJIGNzg8jMDBkoePYYLAgWQkHVsflIs5v89WUWnOk9abWokzG0PRiHJ/ZaCBURt/ozzVCp8bl4NDBwSah8bVx26TkZQvEDlDQSRuPMk7kd7Ug4pHFlwx159NrJYzbToJPgUTfZI/AjoDfvb7e3H+20jx7z2j3GfpoK9tgSlQT0FqnEdx9QmMnLLWVDOCsfP8GtHFqfgkvTocy2aSeBkRaAA3oFG36m697j2Kc91QXy4zgF/wjlLZubkGls8jIhVmpFgEd3hmKIqpnB0UgCVajXeOrkr/erOkxFz/yH/85fPlhEU2bmAoXel/Jv0RU5y0Tz2Hxh9/uhu9GAObaEGkmICrW2ecIpMTPbTzJZkdqPwruDgrepbY/QTcy76D0LhV1AKXaBSlE8rRlJlVsVZ6its9aJsHOsc+/Q4qpO5w22GAOf4WOIT7/ySs5ie+urX43S08VF5jGudLeWynpC1zQOPLbIHljpIQh2ha0YHjM2tkJwVYG+frp7e9TuVia2+rY81sIu3W1jmEdYe3d5KEdI0+X1EITnPvs6J5yeaB9wIOQj3v0+zUcTN4+cb9956wYtCisHGN8H9zg2Cvwn1Pi3aE1OUAn6VdZb9Jxc3rPV0urPnuADlBjfHcaWTsBp+BnrmW0YmOvlGqtddScRTyK3+r6wssCpP2vtDr2e7/LW2AMqiVfxz9GrgXJ76fvfastMWi0AZ6EiCzmhhxbotU+xE82lKYcpzA/A29byu996s32D48U+f+5E++LZtSp00Nni/fLvn6HyBTabVpBRM9fUXO2wEiAIubUvCgSZvERlE32Tx5qfuNYIm1RyJIlKlIqKxsMLJxDgQ8t4u+KgZ2JUvVho+eeCEl1E88fhgZWBFfKO3W2es1SwASOhsjPOSrXsxFNyeVMtLVijAaDlpXVHsRHeAu3Hbn6J7wo+JmjLdGA3++SZu0H9YOgOtLb5ZZnYkzzkjcxW1FYErs3vw8MGkayAN1oyYaafGcxVytoqulkGd5mCry06D7SPrXj2v61/Gi+EF603Z/ChnMDO3rU868+GBvOhsTFjCQbHp3cqjvCVN0n0SyCegGkN60sQTii8fG6V75k9oMWVJspD0nOMV9wU4cv5+xR8C7kVw4W1JbpNjB9J1QG5rHEeQzEzrK1Wt0wdwpbwE3SzTLSt0IFdHWgvk7CIleEDiQPAQut32eYZT20jmwXPo5Ps1tb2TruibHogw0wcBKriAW7ftVmUPK+VEK5BLOOeR8mOcaWtYa+ytOYHF1XlwQ7rABQ6x2GeUrrLz0pOw/TjjBasvS2WhEiHr0ta4Fx6pNkKjolycg+OjMnp1lkh4L5OV/BD1oLpECbOmfaHzBa7Lq4uKakp0JZAx5OwidFkn6Ee9HmedxDcxvoAPTgpxXcY2qVVCjcVg8Zlpq4ju0b6uafWyEPrc8PUO1KgX43yKF/D3aLAP7izHgOY44Ul9THLJKGV9hYV3/v0OG4iU/agn+fo6xfOM8uP/pD1Lh/X/B4G4Xfno2/SbL5swtsxvgZowbPyXMLC/PKsPZ6cHygsPy3VVt/8sIdiw+GEnkbsio0Td5lYBNd0OC+huVtNuKFHnwZtnuo2eS76mGe20uarfLQR0+RLVFYYoKR3eIRwRE0P6ToTdZuk+YRyQ8PTij2nn/Yt9voAebZcqqPnsMDQ65mLx9pFtoW/9YH7+TewXzYe8Y6Qcji/s4yOlcp3y5XFhuAxjOVtvllJmQ4bNScB1dPGphUeHyWl0kbadpdeqHavqpbJlxXmUtzD4BHqFn5XIqCceYAVKmkGrlnVyUtiyG36pQdYdJqVK/goJatTZbAWYMfMDxn7ff3ta3wSeJVMxVpA0nC//v4NutnH8hXVnFuGxtws+9/98ffbFz/9DOzlxBiO1urLb16N8n/yM88wc+mECuNYEvg3f/Mr7Zd/6rMkzMzF+CjI/+Qb73M00ZH2sz/0bLo6Tl5tsBT13/yTr7Z/6y/8RPv6N9+nFt1uX3iFgxXsNqFAC4k1swVfxnblvZycsTV0pt5CYnpNQIyAzHCcaOvobLebZjSEo+yYmztCxUL6tyiAGlEqJvCyM4rpZumk5Sez/Ma74a6vr7Ey4R5pzciNIK483KMgf4/jtZZZ2nngRBlGqHyOrx3a7vKarxNIJTPDJCaQ7j9gKo4wGxaWg5GDLj94J6hET1IhHaNyOUu+2AporM7ee8CjE1WO+TfoKWySrl2elWaVQoXLBNu58ydRUc2dLLFUdAe9fW/+ODvHTsewrSiscDVDN4u+YleVo5tem2cYBU9n23MaDIt3L1G5vsleBlcNxEBMjJW9ArQox5DVdXOX9ZagqfaxTXRJa0q4GJZpCztJYA2buREKkLPpOW8O+V0aM0s1j1nSasvpcMaKwaGlfgsBZSHpdHxu7wIuVLC0eMpEC+okrQ1YWkaYbqNzCxsbQRhe7VP5+sYdp83C54yZAtMHYNvrOsqQTfnsQbnCcUBh2yPeb7g/YUXiGHM06+vaDuN2ekDuYHwCrdtM9Fl4ncBzspQHXyPeaxvk1ZMd7BIZMS6WPEsuV1scVtp1v49O7SU/YJ7BNwE13yN0cU6vOAtlzpheKmPSoK7mWCnboMeqPdikWD6tOLP6pW6Qt/wgkhdHUOi8XY0lHGaC2/E8fOAPvv5me+0vfiEKsOW2BXuffcqYarvMuNZa09ZvF/g32S/8xc8+l5bMpac5MmWTZbNdEuJmFV/iVxyt/DEW7Exlts6SwIybyFgL2GPGM645HzAhJob07SH40oeTV3ZxbAHs5oi3Yzeczuk+ysxSFkq0pLzx5hUUMN8+/eLFzB6ryPevP0yGvML+bXekWVG8zyekHC+usIni+x/earfub/D995O0QnaZqydQO/swUFrFb1CT32aTxp/6oRdZqnsYo8r+euRKFxs5natQj/coyTP0jpR5G2OxVrc3kD3qqZNoAay0SN8ihr/KGNs4DfUCNfUpKsBbGM4Vvri6x/zEcXS3gs43MIgthCNL2w26nEcWaGk4769e/KE2p0V0tcDSYFFbQN8PaXYcBvghR+U4zakqZ+fomjIet9uLEv3PRXaWnjEtu6jrGLGvGs9D10nGkxj9LnxtWVwpcOvsEbqfToTaepP16LRaJAuW+nNC1DMCrUwsCEfQ5Wil9Du6dxbbvLBXYIVn4fXPrifatxynoJrFyiqthANnWjfhoX7d/5B5FWhaufj3gMokPU7i79AC2wsiSCrAUNGSpnXy/BkrBArnTeJ9L8BJR/nYymN4FO4nnL1wm94OX+ulJXZ34BkqzlV6oi4T3mVVyhJ5hO69enAIukYlvABt+6x0eqOzfRqx67zSu0XlYE9LGxHfoa4tN2KxaWieeZdeWVgREXybLloaMeDxpgxYmXnpN5+O8oLZIxJ3nclHewbH2UvgBCbZx74HmVgAzWWIqCC/6mErgN4wIutiZihpVTySqOpq0wRDhPPdc08lnaGW0fCdphPPDw1mXKQY8PCvJvJohVQOCrXQ2/22pbXG8mfFopGtMuu5yUyztPz4QJbLSJCzuQ4TnKV2OdAdYXb/kJ5I8Ngg41dOLHAajbVjzmBz+oYWN1070ukE1AwFygkcD0bQ2DQSW/lUZMis4em2Jl6mhl0Bfpfuqzpyy3AmoYDJBJA6Bxa2gNuNRFP4/YHef6YQY6AmPo41zFJIZhhT2m214GDhHOW1167SEqvJ8+wQO8OZ7U723KAXcY1x/bOc6LJGXjx7Zi3Lg/cJs8tr78axZ7rrMqGQmbd5otnH6OkoPbZdWiVOV297nuOOYCQ58yZ2cbeg4TFTO8zK768iAfUAdsnSH60ohov1Znuua/FHrXxoILBlfo5JyXdyIS0nRCEXWVJBm6/QUDsWNF1qIhUpPie4liwU5rOVAmFWBnDEUEmTeoSmXVPz0y7pAvnh04aqXorBvqjknF3XOu9uUMnRkltytK91896ulCHgqRpN/jyfsV7F/wSPcwMHtKqm1c0+u5yp4OhO2e7fppdGb9Wt12sM5JeZ93FosUGDoy0co7LeJA/txS0yNrenZQqU3fRgaMzPuMbOuF0e0NmlsBugHkgaBZUK341A9KQ+Il220O7BkIClw5Y7tKD7mF6C5dzvAdqzcfXFg1kfkUaT6ZFhGxT261TM2sT8fXj5CeDlXoOtMPZ86ekLKJpaDYPaAcuZ32dpCVwn1ugygWRWUQp/4vUXEQAhNHH+hX2VDzDSz2Dd0zV0Cx1dFwztl376dQqLFQOJA8dPGb3+ytNp3Zbo0rjt1ByYJYF/7c/+BN8lu9s+//IzFBKUQlfFLvcmM8j+jmC0ZtgqSyDWnumCwv9HXrmMVqi80j1HMSjjNY6Idmzu54ccMqjYT79wIQVhj97HD3E+muNNeyA7KMuECGN3MBYLjc9cOhGF0W7njbhMXhK+z1tmVgKnqMltgcoQaaXQmzw1qDkMxnDXjh27arBa2S22jW7S7T3BpN9JKpEHjNE20NkyhM7QbXMOxDXtxxjgtftPkqHmB1JGXltBu+88kid2qU13KrMDJuBmea0VWvaIHs8wt4Kx7aHnvf2Ntka63aQiPcYt6bZaCJ23uMzJQXdJlwVnns1Gx5DviBU6PYgfOTfLa6a25Bi9yeDP5FiUM0bEuEyzs8q2qDhjM860q1eXuMxPjRbQVKxHsQm7wVbakQFYKwXKSIYHmxSIDWzCOsL6Rj5HqSizCYl8stVNq24lDQwDsHaXZbZt5hNsJLasdSKpT2TFKJTZouhwxb9bZNAmyOa+F1u3MgS0oDqJaULcqHSKoZoVzDY14IccrHqdA1VdU5ePFfhD9CmNp/mcuNzcILYAvMure+h2F3kdhqxiM27A0c628Tvxt8HKjJOtzjFoS1YRbjJzJ6UNmxXVIn4U0O48cGKZltpMQPcPKUtWlFY+DrlsjLQLl32d35i/wHvHLr+IL+IxWsQfYfPKDEpz3DBr1Qbyp585BxG0CHsnqWzNXEL7EQ6ZcEsmLIky53b4EKBLajC1mwxNa3V2QWLMy6lAbJ3dimqmuU/Zgp9ZZmBd2nI9f44vsrhksc8Mt/HObNpFdytrajkSkM/9Rk4UAg+4pwBoaFqbY9m0cBi171Ef0Hp6CemEIkmI5eyiJFibtdTavtNMwYSGm04srM6eY3bpKWQSCVk0Nifq0rIgX1od9HCfnVf2FlbslhF/jMpRoz+6xjnsjKHtrqlDP4LoOvUlZud5QYpezkK7vOIsrCZe+t2GniqV9iX2Htgy2dty2XCdmtp1fVcxUsB4PqEAaTxmvhWQVcKGFk2heAzOoi+dYDDMwMKhKvJdhhtbGJ09lRn2ELgbbJNVFLvjjitnyat5DoxcQS9HmRg8j87WHrJqQYsj6ehcQ9LgkEETUb92fTVyx5SGOCdi7ykfZCAdTuJp/MQQPR9DtseFrbebTJSZ59Rh6fpaEJEO2W1Q7LbOtvs0JK6Hp5uMfpzbefbC8Qxj3PvxcJalVipKBXTcbk9D+jYCJJcu9yxbZBmM0t19AAx9y3ZAa+rypzvhXCI+AfgmsK7EZJhjYYPONnNXPPiYyTIvYbFZitb1mNuZ6YHeZVee+eSHRv2OQfgid6oT8sbK+Q6TomfpmZ0+doSKVX1jy6Rpl0p4ky57NfRW4na9eXuPeZozLHnf9xuH9GJvMHH7nSsPIpPlzCVc9wRsUGF7jPdJyrDWbH54PJf1lKvcGARhlDrXjF0OS0Em3F1ztky+xmmriTUkYymmNHSlsF0Mw0koC75jfE/mOGDSI+McKoJ5Jrucod589CBfFBEmBQsBSTONyXYKwhxVmBVAaiQMhAYu3Q9reCsfNEXtx3LVtTv50OBxdoE5liMYSatVOVjw2+2Ywh7jJeXBgA6OX2Ab6BppYTsu9K1NhbcrpbkfpYDd5SMBznLWRyZQDmnVBJ1o8Yx6exKPkHOXWvwMfJ1I88QWNwLZvfeEnHuMu6zMlpYO2mfPHm3H+SDd7Yd0kzGaudWTjO83iUe/FDI/jXSLruUpjOkiBrLOzr9HGIjLTY5bNVplsEVV3nSfea7Slf8Mm4hcifBzWDuM42eBc7JoAyN76fmnWC25T17M8OVXVh+ewBsDck7CF1zqRBpmmvm+mjm5zotE9jIc36aCJc0ZlgBrq2VBRhtV8VmhKhuFCOHSy7DL6JZX51zcl+9eFY12je7wqdOr7X0+9Wzv4iQz/huMHS1nK+T7Kr0VmhBmr1nfp/BvUsEkT+DnJqXzrHtDJi/4OKSxUFmB2Gookx2zTKqShh2X2ihIjoNvcX79K8+calf5sIiftLrLK6h+RQdx03PIabA2CNou+bLDSsxNKuCjLOWeQZfbFH7l9dTbLfLm9MFCPoSRXiB8tNeP7mygT3eK8qIXcqxQ0M+wBfvM8aNtnV4qRorOGVIijzs0lZtUolnmA6jUTcw+b+/NumrERLhv/u1jK6g2aTvBioqVo2vueywpqLc7lIfH2IuNT3aqoiMrCie41Q1e0kTDy8oI6qE8ISjpjH2iMGWY+bX/8797sEcheLzB/vIVCNPqOJO6xky0LcbdbdYPbYR4C2mZLqg1rZPxpIaazRqSxGLATkg9Yp187eKzjDFoCVDWTbo1b358r73AeeQffOsrvFfMfmM2zayxw80MXkS4x3TPH2IhFrQ1tyHSsjzggwHHMOpvMvnxJhNp55hIshu7xqmhnOiPcW+3u++/x1FDJzI7ee4k54POq8zZ9rUPbvO5n+32C1/8Yb47/qC9d3e9/aW//K+0X/u1X+d754/IYNfUKUAo4Ck+E/2v/tW/3P7r/9ffak8//Swv5vBqK+l6sH4/s/Cffvn5dnoVLT5+2H7797/c3rv5oF2+dCHvjL906SQ6wVAotCfcNnr3Bt+Jf513os+3P/qT77SrHJq55osu9662k5eeRVfz7SNOXTnNRw6Okob3b1ggnWjkhxrt2mpIts5Zb8bg8r4+TzPPQyEW2ZL8/Elez6QX9TRn8fsGob2m6+xE/NK332FeAx3xbnj2+j++007QfWcSBqPiDDe63pvA2qL7rXkzzgklDcKxvD8L9yNObHHfAo0LWUzaMVA/q3WM11yjN2A+XieSjUGOZT1WShjE5Ko5Ao36FK2Vndj77BU/tTbX3nlIV/cBu/d4533PIRJpzgqHaNB0CVd9+LJL0os8bhTSuKlqtOSEZ24nKNVKorI0Qg4ZIYPu6ArHsi3Q6FK50KnItn6ZczGIAIpCCqqtrJW5E1f2HMgE6FApk36yWKKhbZdfQgahVSFjR6bfDo36s7G0B2uFLXzhANcrKtNCUjoVaEFMstKygtahjP7ZZTcu+xZCr3gCWjYCTcnKv/goGWF5VANhKpVt5u/8H/+dgxu8wP8P/uCD9oXXL7ULbBa4+YQXNk4tty/zvbUlxsDP8lHFc0foijDx4BLD9Q8etV/91I+0j+fv8TG+h+17V3gBAInucIzU4uVn+MY6b73xmulJDObrX/0GYy+7mRRqjMhvYH/hCz/JpAVdNJY5Ti/ttrfe+HI7yxtgP/wjn2s/9Rxf9uSTUP/p3/777Tfe/IgthBwKgFUcYYfcyeMnWZtc4Azzs0xUsU7Nt8EecAT1DrXi85cv8NYSa5tzbDqYedJ+9sdfb2cukR5OH1mie+D67dnlR0nD410+IUy3bX9xrX3w1kftNBXR/+H/8h+2D/jizE/91Bf5auqZ9sqLfCyB73Q7RvaLIq/x7vmTJ/fbr/+TL7VbH15p52b56AGF97nP/jCnn9xrp2hm7tMyfOedd9gZt8/qBC840MS/9c6H7REVn+e6vf7q80z6LLW378626zxdbcjuL3SzZO1PobGCzEsjtDq2VGa8hf8xL/i89bUvUyEusLWSiU5aCd9mc2vrKXa3uflC47RF1kA9QGKW8f8W7wWMSUX3yVtQXKbUFupMNIwNOMfMfrLJ4QIs6VVY4dDuEm6laLzDIHsH9vqOUinb2xLW3kAOFIGul+aooZo2h1n2lO7Tvf3+fdMDTZmDaMF2rsPhkfaR3hZu+Tl8it1r6QAri3sbHMZo/U6AadwWKqsjw/JNP3ClZ6vrXyoCSChHTQwCK4lIyD2FivRaGnRLj18grHjBU1wQIo9+C7FhplMZIlJhJNy0+y8Z4+SrHBZK8WjTU7GGBzC2yu4UVSbEz2U645QGLidspRVePUxPetLoxXCIRqboiDB7BupROjP/8f/6f3Lw8Y27fG/tYd7pffbF59sqb47d5V3er3/nTfaPc+oI1dGLbHVF2+w4e9JuMtV/jM0VW0cvt+/d+G47yjbWtziC6JfYxPEltpv+CJN5X+IY6ed45dFdQhribnalkSUmGoNc4MinF155tT3HmWO3+abbWxSQv/Vr/6C9/NTl9uJTl9pv/t7vUZC229WPP8pE0IYTGWjkBMthp2nZ/9wv/dn29lvvtm0qmvevfMyYdau9+OKr6QK/cP5o+4kXz7Tf/s41un+77flnLlNjb7aTm++1185QQCgEi5wu+tnXXm7/t//6G+3BLttGOZbo88+daqepiP67P/52e55TcF9G/ivwf8IruBeo7H74lfPt2x8/bG9+/13WuFeqp8H7zWfWyEa6cd98/0rW1Z2UOX+CXXUY/od8uzx7o3Gfprt1k9dFP7r1iCUdJqA4726dQzI9sMFtv2UCtgbmWhlD9keb+/Y/Ry4n6zEXMtHdcbZM/jvP4bBilzmJg/9/U3fSq2uWHXj9PX17+xsZEdk6G1em087KTONyQ9myi8KFEAgYFYgRDBACCQnBiCkDRnwShkyYQyGLiS0LjO2qyMiMLqO9/enuafn/1nMP8N44cd7zNHuvvfq19tp7p1yZzUtlmB2eaLov1RH+S0Y2S3KdsMp3jPbHQDUgXEi/5DHlWs7PIvTia/HuTT+bbSF1U8aZ/4TJhyH9Jlz93hgrFOMF8kw71h5G4wmylJM9xwOed8dzfccX/59gUw5EwysUTUI6Y57HejQ+6prnuzyCNA9P313yau2NIhiT57sWEoRwaIxCF0LutmZc6xVX+5+fRTCNYfDcM+mt3s/lh+fpwhh8CCgXOXrkWVDMbx7w1NJBbVLg+gRJD4/CSBsODRaFMcCmuBp/7eVQj+Aa/eQ6NAW27g3sfafUwWCKDqz+qpcJ+0w98xKMae2/+k//405DLp4LiPNW/NxU6HGZby7uFgd9/5tv9eLa6m/e+zgELo1ILn2/Q+r+1Zd3mkZ4XkIhRqozW/3a6VUiRxbQ1I+N8ecoHLF81xX6I9JxyxodMvCP/uD3Vz98e3f1nTK9/1OLaf7yr/+2AoSr4rrD1W//6PttL9wxQ23qsN385dfbDfQbWelPcoE/+MB+8g9SAi1B/Pa3Vp+0geCd3Mv9fr55b3v1w68/Wv0f1bCbVjot2YgA2wnTWuup71X4Y0/w/WrHhRncy/uVst4tt4AYx1e57x3va1DGzqpLzsgjfJoiI8CP2xrJHmS2Y7rfyrDmGruO4Zek0RdfPFm91TOv61sSSLZZ7bmtmM02MH6bJVueVrraFj/tqrO3+vTLo5J2h22Q8Kgy4oRwmCHGCQ4LPeAttot4BHLR+KMUgrmLQ+C4srFGu7yF9fm+VDJOjiUaYRIWVn0B4cOcOsCo2vCbdSFos0S4V9bbc2+yOdE9Ez8/SqWFU4TwZmZslrbxwTBikI4Aw18XZz66W/INBMGabBZYt13pOsbvm/67RjDf3JysdcYp/iOcLnOPe+2NZR2LN/hYcMJzCVvDk0qfeQ1TBNU7M66e9W/wEA7Gw9BW10fxdY/iminFsAqOUTgEpu9ggG1woxEd5mKXB69gm6nNGU9jm6e9673+Ryg97A5B7Eb/H4BdpgjqscfA2F9u1YkeeKbzmXc827s15tlRy66/uTeFWujo73/2J/84L6gqrKYN7IJCMPfKrkrcfK1TW/7d3/9+7vud1f/8L/6mhhbN8jirdzf38Sy3ML5tw78vZsnm27nPBOq0LMWymWHx3bikV6sP3/9g9f0f/iAjUxTUWPUje2wDge8WJjzOi/hcQUMDeTYrf3LBS8JdpCA+/uBXq3e+/vbEjpgGbBKE5lpfff5Zu4Pm3sc4R21BbMdRu4ceNP12v62CMSFX9XmCe1JC8G7LSq1yk6yRj3ivbYURUcGBEk7nZNto8X6xZPK5+qJTSS1cuVeyRTb1t3/0w5jsevXxR79uyeyd1Z22Cn77nbdXv2xN88e//nT1VrMYXwS/xTvfKgTYa/PKv/jL/3v1ZXCZJ4UPgn6Y60tLy1WYFlksI2JGvAR/Pctpl9vzjZJ/jZXwzs0J9hKK3Hr1AzT6rduLoJNITDF5VtZXMc2NQp7+zfvaR4A+Yykx0vyBtZYPAR03uPe5y2PRU0pWcMEVxpTZroFFWdTArFPobxK4iJEeJYtizpiUhRo4+60NZncBo/skY+6Ldd8omaABF+EzLh8MLTzB+K4vF+svZSUMcY2HAwbtTws0RO8bQxosAXNvWl7gCUZu/SxiAjE4aoPCJsoEa5RD71I+Khxn0VPQsaBwya327O1HEpUA6mUU8dxYVIO24Blu9Mn6jlKLf8EvqXYr3JJpYJ2xwmvfkV+/Wte2MGLCku4Lyyg0tNE+MyXPo421//G//2/aUacCjhhWtRmFYQpohEnFUHtoiY/f+/xZr9V8SBBTAFWiw4F1z2PibmRNs946eYM4jGBBvzjiPOHDdFslol7nTsbaWSlbTuW6lt08PXkxMa25d7uG9keJo6YxeucgoXxZKCGbqf0nn30ajMXAZS1fdXTUfvDZfupJWwF91qF/e1l17rJdQew+ylUVLsy0Uy7tNzsJ5C17ieUNfPzhh6Ns3kopvEoRPHr0sGTcl82LViBUm7/6uIq4di5RuXenKa7f/s3vrX75/vsxVzjrRNnPKhe1mq/0QxsivhiP5aqQ4+spQ/veK3X8OGVxllKbTS3lC8K9jLM57/u5+JSSbabMOOxHB5bEfnLKia/rZ+PBt9vW+v4QlRCNNa+RWwFADcyGtGvRIeCGBpMQ6rq/zSzKkM8zPSckGIFDKw38/z7adw3DzcKmPDXVgbNE9PY5wlpjrJUz+GYVVs8vzFqX3Vx663vPifU16j4lou3bhJX73GOMq7R5s/t4jRDgHQjr1rjO2vDRjmc0rmlwNmg3arvfcRhFmnwOngjivBGDE3p/mwGCl8HjCDbcLs+BHzxj9Xv2jbw03lqecRKw8iG1BVuepXC49cYWxub6KMz6MNYl3wGQZVygNBxwUkByGmPdwdK1wU/3jfF2hkm9CBkdb6lnKE8eEs9gFMDQE/R94NTvcLr2P/x3//mN9c9XrQ6DKNv4cl9NpZydtt+Zeb4STV1ZPSwW3y7zTg+ZUhpN0o4uu7ngJy/bfsf67lZ50dBftQ/4XkUDB21ddFpWHzD2ZrO902GCyH58+usPu5ebXnwcfNXvng56npZ1f5l1hhCVartN0anFv1eVmBjruX29ui7Tir3Nq1oooP5cKSGm4Sncr0TxXhn7Dz7ucL6HbRbY9Mlr9cr3ckWLMo+Cxxr7e4dvlVHmqh2HuMprCx16dOZTTav9/S+/SOjblqis9k3K8P6jiiY2UzKtNHuSp2Cv9TRaiuCwXVUPV7/46PMpORXnUnpcZOvWzYE+qwoPY26VrPy8LYe3zTb0zot2P72fZ0Kg1jrEYrspqvNCCNNM24ePWs1nOmW4FpmGAUcQGzvY4K//xkXmJmOA0fi58BYRXQSLpBtPQzsYbP7Xc5rVFrotSsRN/NG1gNU2d5kwj6vuXmMZbuoahTHC0u+x2trqH4vkN+bV2UYCPDX+/q7R2RK66wOPMXV9GLd78+68Rynpo2tg7UdfYJuS0donQLwuwn87JalnbeBlfAoCyrAGRkgIi3bneu8RqrHsPa9tbbk3SkG74dk1VZs18IYGKZnaBvMoHkhNEFl58HZxcAynI/CNFezw8Xq8LED10319oQdcmO2oi641hi6CdUKB+au2oqF3Jj9TWz6DE31OS17op3vu+v/af/tf/Ec3BzKoJ0fj9i3uWfOEbQbxV3/9N4VmuY3ikJCiCuuhhS3fejsX9a3V+7/6NHt8k8VdCHyQklA0sF3563u/+CgA11Y//8kPJ9P92edfJZhZ9qA2LIUCXPwnWWrzvx9/8lkKJUYMoTbl5zqxdpAUZufabq78aLQY92ECTNh5tOagAzEYW2XXrMFHnzxNeEqSBavKN7uXvlud+9M2GDyuxPNb36leuTLTo2cppM+zpBVUzP7i9aME8duKiCL2r0tSPi6BdpQyutNGEi+bTZjjjuq0V6rKa5owN3zKT8PNZWP/jrPEEta///DT2lSqe7n6eifeCIss5HhQEo9LbRed68398NG0Zu1YpHCvvMTrq6Ylmwc+rWjlu/+g6brOoLewQ3mj+W7Z7XEhh6hZwMZGURMCYY58AtrfWngWERMuMWNqEf57dthj/rcwRBw97cL2KKd4Jn00MfXsPOQxfESYvU1IGs8CyxtBdN1DAUCxj4D1exGE3u0+WIQbIND9CIQ7vTPqp35GQfX3LfPqllIYQSeo9TsCQMC0Ei5GQfUuhaXvxaUOJ+FOP6OkehdsYLQIiVfjY8yJ0yhKf8/z6EMxeNw18NRuLU9bFNaCY4Lb/e5pCD60iw5gIwN4Vk9Y2bj1xRPyWc+Lgw9yQdHdKpw32Jj6BC+6NzmVxtNbgwO4NB1JmcGNoZHTMQhg6Un05xWv/bM//b2b/QTIETtcW+4xX1+F10kx7Oz9naUyUC41F+kH3//m6vPOrVL5xFUcl6MGWaTZWSTloHDCGercTwDYJtj+cIoYxOeSGMclsr777XdXb7cV1cu2632VMPzivaxv8fHbX3tYHM11jTghwbTeflVhUyxSW1tlzqPWuOcqmXayamJoaHjV/P12f7+or4P6hCAzVRYmUDbP2zqIUnm7pZjq11+UVT+u6ug4HNzJI1GK+yw3/F4utXni3Z55t+TjnYpbrLj66LNnudtZ94go2zmbOyBcuPhaLvuzdiJ574MvV+8k9K/qx0yBqi5rBTCCtf0dGra6++hrs5c9K8lFFKpwHcXwLM8Wr6kfSow7j7EQUhEMZXFTEkF7ccFYgPhn8XLKsAuraHxMKPGD0fDiVTBiODfKHc41f46gB4N76IVL2EEZbPPqI7ThV74j3Yokw8gStGCYsuh5LyasgSVm1xg4EqLaNobbseKnWZDU+BaXM9h77tZVhlcfz89S1gC73dGGdaQUZkbsDSyhJPTH1P2omeK5gIMwLH1Baf13jeJny8FDeQYxXTEKAsBwgO9mnXftG7+LxBxYYFLBOMLVd8k/iKxHN0dGptJw8EBkay+BRj8xPkU2oUbXuzjKiQKY16f/+gLDjLJxAm5Ru+OdgQFPo622GDyJ5gbXNcp3Ua512nu11bObx1nyq0ocj9ppg2a5uKw+OqshuSFb/aipsYEhguwnoMltfwNdEX4uRAj94MMnq2++82C05G4CsFNGe6vFGbMMr+yBtswRP3ggwQbhlXIW725uto93RSpPWrONcd9ts4P7dxOKrJmkwpctJKCtHlaNxtqtN8+7YwOIlJHdZHY6NcQSymcJ6XrhAYXNuom9txPW02LgFx3k9/j+vSH4i3aIed4KNJsHQhJPRiHK86cv20GnnT8aq7nkr1XUs90GG4T/PJf1QYcCbG4fVHnVFkRloO9/7d2QGcGaIlM//iJCUGj6/aj2tlN43/jJP5gk2v1ifYx1JytPISLQw56zGME5X5hWVj6UhGYCkqsenjEsxsd0s+KPVDZvj3ExoD+vh9NjIIw2wi42TBnUH6ZHN8lRiIktIhuh6b1J+bJM/cy9FEVw6Yy7S3hvBY7rDihWHaPeJr4kZmfTkPiAoA7j1RZxWOLFhAJj1641+pjT9l+Y0BjqZfqYmQU3+9uH/TV2vDhKDEL6TMt9Jby3z+prK/yNgMSTckdzO36bUKR+eDJEmQ8hdBpvIZ6y/522uMFL8rHnet57XGtVmnoCP2s9yqW2eG+2EZ96gvoBNa9qsajRz4VelCNyTe8TrsCXcaZcjG2mTWubYYQcVt/MCvinXHta7nsKsheDQSgWPN4NRreFs+g+4Qr69cGPNdDlaNnfs6ovem9+kUv9mz94ZxYxvKrG+F5M+oNvvxUwxbMX91a/+uhJZ2zlQgcA5pHYYmkPqlx7WNyrQOMkYdqtki6ens0iJJWOcr/Fr++2HvrxowQFgLUp4w0X95rHtnne64vPUy7qfW9W7733VW0+qLgfgCG6pZQPO33js087OudhVqOpsKdPv+ro3L2s89mcHaZmWiXYebHzy8pQuW3WubPKBvkqq75RWw/aMsq04LvBfPM4l72lfOqQueR2g6mZ4uO2GegcueN1p7o8LPPdJpllzROd1ecJyeUurdzOMyxcyGOlDhu0ElRMzL067Hn18L7L+IvNJcG4cLT8ctySJaysAuLAxtKu1Im/JKQwixVIhBybu1E3MRKmSBgIS+MheB6ebZO77khiGp6XY5mvMtVxM0tCylqP4BIADQYXk42xKY1h/ODU4wh9vyyi4OZKGJkaY0sVTjmnDBy3S05noUoAqrVnjXl6eINgxX2NwL2lj2HYxg6Xs8aBIHoXT4ejvvUF/hZ89GWUyfSX8oASOSLCSA9RMjXdtX6T+v5vm7IR9r4bK77Ql4/+gRVC9DR4xbPifBNzQV8fi7LhQaFnSIyftOkNHlbXgh+E1ln4zngYhD0LlPKClxJ32c7IVnnqV2n5ZVO7A3cXKJOa72/4hKmFxvBDNigrXSyeT15V/3hmNmNZri3Kx5CGrt3ry+CXlxi3rjZ/9P1vJTidgnpYvTGtEDNYrvegclUFFVzc11n5d9pT++tNcQHiOMvIKr9KWF7mDu/n0t5Re177X9nBpA4h9l5C9aC5ZMvujk7KpuchfOfb35i41dr0Z+0gSzveyVp/0JpwdfK0+GlKQgLwXsfmvm8r5QjKEt2pdBPCHFl0UGLupDbFIF81D876PSoml7W/37TgVONdsrQYpn3WX+dVbFZKmzCzvO/eS3CD97j3MYfdXXknyU7jxXShJyWgfQJAcCNTFj0LHTEhHhNf9DztjGgY/IVKtGYEFqrHCBXqYCfMNYjpdwagdi0nzJIjCO0boeUnMELps15/I6T9Xe/DBH1ZCAnRCQJGgJgemTZYBcK8WMxGFbOxGBhwrZkBnxG4aIMhKChMTSC0hZE3zZkHhHaCpnf7Tnn0L6LGoIQuCxYMrIuy2PkQmjftWpQ0MJDcEGWsM0DIBcvAUbt92YtZwU9pUkgEV37G+xh5SUK9sbC9j7FlqOEsdJHVmg6W+jIGMS4DMsoy9OiDcgSvMaMTi2jkfmvPeImsEMGSW3SdWYQA86S1+Ff1R+iXN41gEdBJHtbGEhvXRt+3wyGczxx7744g1+bAHo7GmvcbXw3tYoLxJLQOnFo3nTq5lPC94KxnwBHPzGKv2ptEYAqqywu+a5PiWWYSFr7c3JL47t+f/8lPJhnnLCoAOQ5na7NVZhHnLBf3RZsfyFzvxfSHZYfF1x99+NmclfWgOWkxvM0q7orjG+Rl8efbX7vbnu4naaPi5RJShyWgTKlZ9vkoN/rL5pm1b4pM7fWrCms2qg579Ohea6atMU+5lAAzwlOaOYJbg/7O44cNygKKXPQKby47OYRywKANN/hj2gTRxhPRZVw5GyFg2LAcfK2gCj7PaXvmr0OyaSMMVWvTzmVWjtCwdKGzd/uNcDUjD3CbeDFfyaXVmkx3w014Y6DGNe8G69yLQbSnHVreNQSh4e0FQIgOs/wIy70zqzEWrk55JTM3OkLg7wYPmD4Dc18X19C46qf+HTYh/hzhoc16Z1EAS98Di5GOcGF/1iQcYJ5gEgtXO1SdfIq0WMtcP8Ut/ltvhkW7hMN76hmW9ow/GhDmYHZP+wRulKX3XQ12yoZAahPPTcKqW6Nwanfi5sY193Hxm+fhH4/BNTyCmTBNZz3FyhIUbU5b4QC/wN/gHe77Plawl2pm2pbLGHz17Ahz7wdeeR38UJ+9ZwxyJoRzpsP67XO7dRdeGoUSrPIVcEm59OdwFSXEDUcz1+c8g+kjTgjfszdd71zk0Sx0NQYYM078DN7kKwVA6fBAwHL7GWWXF8eNB/SMOVyB1ffN+51Q+lmVZ9xfRRzB0xG3XzbPfD/X++VYSEz3vAUhW5tPe7GFHNWYI6taa1NcEEc4zS3vzZE3bbXbZvk0617H88hab7ZrBk35RYtnztdbu50whIZauVw97KjmvabQuNJXMUr0CxYCHYDFyOE9pkvgG6B5eT5k6IoAEbXnbUT4hiIpj15O6/X/YKS1JV4QLeZq/YV5zxHUrifPE4/WTVy2EMJCnsRl+rrCNSkBiReVCoS3bsfqT+e9FhuN20zICT7GliyzsiiMxBwSVRG4fxgRsRB9SBj+rKJyYullycsLAA5hg7h+KMNluumNUvK+UYG7byF8nktcBhxbXCE+gZ8EaQTmGWBUhIWPRfWECyiLwVjHYfie7dIwYWDN+milrnP6aDReqyZgYjOKQ7wI0tqFL30u4ltiqvCqJoepVUiONYc+/+C5ZzGePsEYSHPvssrCsUT9ZQoVw8KR5wkNvN3utcadVTijmq7bc19oCCXi3EnyNW05SbTu8/Km67oXY0emBZ4AoihGoQT02PxwLhOuYTsojWeR4DbKwQ28UTbGz1uiSLo0ioKiDtRJppoig9ER2vjT+7dCzmsIyPDRm/32EdZBitYoHXxMUeIhirYHRw5wDm1mPwdv6rvhBAeuzYUHkztuAib+4+ZvnpSh5q5dX7V2OTccYykjtRb2+Sy9zJULqRZTWPnFdca4SmSveu+w7LZhm07YTXidwcZKX7UlFM1/p3LUvRBH0CTWWEBtz2Z6arJB2ruASlkFcMjvO+ux0QAJl3a4LYhq2SGSTLxD40GQMfUz8dO01RMRY6xLSD1pXhybWyE3TFl7rIh3WCEKB8Or0+YOdql7UA6ON4ISXHoG78IHCGHkNP3i7s09aA/+1MhYB0rFPDbBn51Yat7YVABilNkNp2vw3ggCpDYDDFPMQofaMkXk+mS+0Q58XZ9R9Z0ymcEMPIFvYPCQILsxDFj7Y3nLe0w2/o1XY7hzaIR53dqJX/oUNhi/Ab3BuxCEpdI2zJhfnt9dsHMrJUf4eE/n0YSTOx6KZ2sGTwzNgqPHa5Z1QpVuouP8owgLE2LMazM6+u4eIZdzUFPhHmPDwgn7wD8ubMaEgguMadM0KKGYOWlD6iPHZBPN5Q/5hyVpNxY9uEZBxhuSgZQF5aHPUZLB0AOQNcrBoFDM3+DEs9PfILA+GiOFPlYXQXueVwClQjY18/hrLHTvjjtfH3DO/ZfJv8HrMaRww4tyDJ5fQotwAobyClqSoMYTaAEncjO3H2cjrv3xH/z4RpJqYpxePCqxphBhp2KXhV2H1lPxtWvjiDq6OH4xgncTYjG7lVO2A+KK10TAtBo3ohstAeWyQBhtY/ASQoQQAWknhS6SH9wPdNf/uFfd5qJwD2fBRi3SeQa2/BeqR+oMOhcSlWtn3LYeqPeQBEHaC9Zeg4axtl2buKhrXFVCZyeQiUl7FmyYzEvDjMja4BDOGuhennH0xJTMSjTGH8u9eS5YGqdlqL04wkvoKThjG8EO1lurhngUIXzAAyERltiUcqxXf8MruMA9iaU6H6Hv3mKdgZviSjGYFoRTsI/SMPaYguJkLTFMYI2QDDL90fO9NG3M7jb9KVQwi2I5LEZWjmvMvd3UoCnNXP1ooP5Cxtf0mzCQINblgruahhtjo0xQYXAaLPNBt7mWcsCowRAF5p1F0ROkhLxxUTBcWTMWsyQ0nBJgwi4cmBV52g1IcKLX4LJ2jc1zoyR7Z3Ir/a2YaPAabONuB93QqPa0sfQbL4U3s0MUAW/V7MYSSzd1Wt6HQva+PigNNKnLgPD3G8UHDmPtGjDxt/aX0GlRDpSBxxhUvDBhNUGvb+HExP61Pw/14IQnNTZhHV7XcD8SccaKEJtnrdyqmxpZNODFeQCATeyYle+tGdz1WYA3deYE04bXvB33EMgxE0Bp3uTUNk8Ka6J7g0mTpOEw1WZ+2VbTcaeEunjcIg/bRymplVyTubTN1BnLERLAt55AWVjhSOQR3i7utLx0IMfEDX6JQWqTIDSGYfT6lb2thSz7oiiuTa4GrWdGCUE+LyVA7UTjZE2Kl77HuBJC5qJdE9Nv1pcMc6stx0p4TluYxQzBVYm/m8IMU1OEu19Z2vBAqDBS/S39J4ByCzVsmouA08xCJ8SPtI2Bhaz/ZjfIHoJpD6MhpnHzBDCwAhoPcdysHfCgnVuCnOjO34tVIdjEK5x3P2hjot7JY6EMMUadhnO0jAd6eRgwD0xbxnkdrtaK0eF/M5oS1xHE3tceY4YnehHxe4tgLXjra5f6X//dbgtdayOcEqQsOkVmaS+eGfyEG8JyU3iArltCCX32M/91D8YoF/ctlMHYM77a0Hd/1tbCF4s1bGakEFUFpcQWoRTOAYzQLmM3iN6r9gHtTQ8SOjRcggBt9mzDnDn+xrUUXGlloWfInTYYtFnCG6SEfoQ0r1hfm+VDGJfJthtVfU32P6DhWxsSv+OdxC/TduP1wQ94c7wkz8OPd7o9hq228J+TdYcumwkdBp4HA3i9fY0mBiL+CdVagDpCRyINmjdjprMQJGbVpRVYFojMwQf2hY8BZfFtGiC0MFA7w1IE4qeNDvgD4HZEszF/8IXAxdrK4CvaGW1aH6Mhe3ZcxpCMqeBvGLx3bf9rCm4yqVF+GDxhc8TyegnFxD04E6IEf0nwRLSyTNrh9LHMXPaFORbmQXLhSZdDZv8LiQ4FoP7ggYtqKiq5xLn4rU9Azg+mTijgphusoCo586wYDjPqi+UhRMpKuaH64Sbydnqq8S0KQPe0tVVxY7XqO6jm/Sn/7HnvjZjpK4KwJKVlp30Wb9zggLUBhTFx8YBKkfEmxnvIEnsNjP433kX9UIKzIQSoWLD+Znl6YAQixIwQ+duJo+vxEq8kdtKQ//Wh9BLCfkL38FliGSz4KjzVJutPwPBKd4a+np/dcQKaMPG6xo3uiTgqg1N/xlF/xoCnllBDqJMnFJhg5UURsMFxdQjwbh0BK0mBeB9NKNuAHD5JsuZdCsNKSmcPrGcoFGv1UM/CwWK9Weoa9+e43DMGuO6HwJ00CzMKvPetpKuX+kTzxkQI50rXupGxHsuPyoqm0IOCGTrWAZffOgAhKljIx0a71Yw3ENK9N+11fznwFO7jQzx5vw3/7MhBWGkExxdfx1gIBYEsjc7E6K4i/IFAtoHsiOV7j3aaHWJrR9HXcwUp1cDTlAbECuUglBRZCDCZ2voikBBt5dhYD8oEc9bOdTEXBTRKwLMDPO1H2EJA7SoGStQbXYIUkXY6Q2uGGx0MkPuHeFOAEFym4NwA/pym2nvuc7f6Nbw5xSmYrQsQPS5X301pELqFyCwVK9czwQ+5CAAUrMCd9d517dtiyfWZ0nF/0T0joKFwGHE8hZ6Dp9Hk9bsIPfCCZRini+gyjN2zSv36bpzoZEmq2/bFMxa19NTQeguTKJ+xFI0rVg4vHgzQ3tvovfnEREIxTGXLLIO3Vbe1+3YgAoOz5TfbVIOVQf+b9ehJUPM4xPA3LGtCAHYCq39tUirjykNoQoTPCMFY7X6nB4PpjfLokcueWQgCP3lE42dpD9C12Xi4sDPLMm0ja0YoZTFhCkHs2TFY9T97B/YqJQg3cXaCUpl18FAA4/Z3FY/NXoZd56LDk73vhFCn/cAUpbBk/RtKbVJa+F+7afXs36Ioo9r0N8d1e6b3Xo9nKgewhGbDSz13Uum0+ocl9Bx/pcZqO3xSWr0eDuM9uKwduQnCPbiPlwEyi80wQ4DMcmzGKGVgN+K16LmZUtPOEOAkK8m9IdizKqeX1spGS6B5yHMsyMy19hvAk2iqIwzsMxvHJ4l2xaSRDQ6AXCUfBRUOMtxK++8X24/1SqCvnAme5e3Rri0DoY3HqtTxrevEk5iqtLSfrLwPIo/FjsnNeWIA66W5dRhX4itS1KcETH2GLMIpQUjbL45bQtOY2IvF6kaw2hjtGzEoh3TjMNkgGVKLVa6DGzyMbWSojRRb7RNisGLOKZoIjpkmqc0Qt1hEnfcsxYZYw8j9TfYoFT9giQOGYH2Z/oU2DbrehEH6ThhixDPMW8Ug5QpvS9IqBo0xbuN0QqSiTb9bLTC6KvZVADOHMoSPi6ZDYYcwcYk5Lpe1eWkxjsRrsG21649SYjMainI2eDrBr7BGCITZWStxPutD0fjgAZZaEoyA7Y0r3jv4SJ96bixoQ/hmIU7PKX4JQb0j5yGMcVhlOIE2d+BM20w8/PaZ/9eGB3gOnhm6d3/J6NevtvFGCs/zQjHCTk1RVuhsjcFtvM5bAdfUO/SUMJTldd+4yA4PjmcxCbc34xZekpspfmkowt5JFtdWEM5CKNaZ0RD2UqqEHjxgGOOGKfqZ59zvTRu6wvuEH+EDvRtVdTB53ZWU7ykLH9lLQZsW87FBn+SNGvL1GGAYqJeiAh6M4DFBHSmy2MlScaccWWwfslk1xvqmEO42Z34Yg4BrllrG+ITkmmXpNy1P4PYi9sSXPWgKRjyRJ96ik7MhmqIW9cqLUGL++s+KSSjtV56KMGuYKaSdtsQTbLwShS82POy/hVlGG/ujfchtMtmhO4jimCXR58wEJHBcw9kdZCCs7caD4cVPWNA0UhoE34yQiJFJAXdyjh0Kth4dzcvjcMb8lI32NoJhQoKJtAQYTgksZWnTQjC9Yc9pXzYVAXcI7HQZnhojZRFSskgp5XkmJoqY8DoLkhICrAp2SmqSobUx004EvPvDSLUqOTqKNoE7qr2dNkv0GSsUk5hJMc+/3dTnZn2t5y7PXHrtbFV9yBMR58IJ74yqWtx0xqH3wyucULwjwI2R0MMBL4pCnznpYFUuDV5jHPiyQv6mNOBrI2vpXipixosveJtDj96/XXdAcbknn0FI6mV4FX5H4INV4tV7nhnqMizdp9gohFH3o7BVpr1xjeubkOH/ywqqJtQIlrXcKN/BStDPL46n1mRiawqkdrXNo6mJoRnLMtTufzIdM4cfrD7jyfXsSZuS9vIkerWtjsR4HLUNJu+MUSYj4VlOZ8E948BYZ1iCL+bqe7kohJvBBayFIbSH7WYlG7hYB5V0vm6xyVmLShzJa5pkfbOfuJlmlMHebPXaelZh4sxRCr2f0qB33I+P03riOxY6otXXSVrHAMTInEl7lWFe674h4TY2eZ0wz84tb5DGtW8kWYXaa6tejKPO/CABwKYvW6DCu5hFLvW30T0JC9Z7r3f3usay3BQzYbRQEbJTSg1azDbMB66A2EjpWStvw0hIJqpIhKmuCkNkkLlUsfwojom9Q3KjjLF7JmIo1VQMQUOf5ALPdFrvaONCO1F/iT1ZivpqbP2vfghlCrgpSO8iYmDNs5KfIJlpMbTsD8okDpjnlqRS1rhxGgiPYkIQ7j4LXp5FUc+1WL5R1XQNxDz9xph2zlmLsWZte3RNVKI9BansVIVjoRlMJJxb1U0sQhZMtcGjAefwFO9p2g9n0Q9OJ+uOMXs/YEZodU+4jXuExviNt39RO7zA1xvYot+4vr3Ek7Fvwl4KPpTOvnzwRGloe3IAbtTOoFSnwTEJNe31j8fBeyVgYGYBGR7rPV6HNyXNXoaXHffQIrobpKXcFCU6ec/4eFXicjTXnXtAGG8nuoHL9dlB1xjr96q983k1eIwy4VXMdHZwDV174WC3BGKycJosyvZPbgnMjWfKn2vaSkjuPoVItl6XTB9VFv7yvggOtkrQihUMPp+rwaRhIvjZy+ftt2Z1jOODl4qvcRPbiWUWqxCiGnW08E3vGTfLiEy08VUWRLTD3ZokwljlrmPyfoJqhE2/pu+cJmIJK8TVcQAu2pJWpMEoD/GZlXMsPIVA2K/b7EEMTtmIUWw+gdGRXP7AIQCbudoOKHAczmZKbZCIFo2N8DiuieWw6+msEKsvbLJVTcBEisE6lsU7aS8gjjvZmBn8yWL33QwARdTrI0jaoCQsCsKIGIigGhOmus0heIbb3f/mXcyFaP0KV9pnM8NUCkEf2kVojXEfl1Aj5usFxJ88Q9+Tq/EwItKw2W1sSKlcW8bbfUw21xujaTKxOyWw1S462jmpj9lRlgXP2iwhS9CkVS2uGAHQZ7plssfB5ZQfwibpijl9ZLAHL7XPqIwwBrscgn7wnHETjP4KAoIdLPEKK4Yu8OY94yY2lAheHMNjSimXUQk1L9GehemGwQ9q8iz4dQ15PtNl7U9oqcMaJYR2GZLlHxjwcX3MGEKmayMfGTtKxxHNzoA3HrvwMli8LG2bSpbykKxGVzH/BiHJw3s9sykp+PgPUhTooCOan6ZgO6a4cbb9WnB4firuenIKauqLERMmECO6Z0LA2mGQtXGRsXZvCeUCHNJopsVFSqAB1zXBfJxU04tGF3MRDgPqawIeEdNeTqpY733xjPs3xa2y68ERsoeuI7CIctKmkmaBHLnEkjrB5SaBtgTWZvMQJoaj4TfbVfVVJ1fCy25Tc1z784ssKDjSnBYHrPM4sq4v0+w0IYLvVYV3Nju2RLTczuuyrVv19+roZS5QcZXsf+eYSXTZA3ujxBVPgxCOxaOMwtAomxB/kakTU9nT3PSTI3owoppv0eUyHdjf5RzWTLOJWRMIe7/Ph6TFZJQW9U45jdIKf5PYCZ/D5A2UsI/AN9aGM4KBWJ5fKt/su7coqTuFMP/1f/mfrf7if/+L1f/yv/3Vwgg9bCaCdd0IlwGZUNZP+oAA8UAkmmYRS/AVCb5hYHA1nmhO4SkUEudf9/dV1ycsinGsoBPmcFl3Wntgvb293Mjx5BRitHHP64dQDW8ZUWO1E82yYg9Y8kC1X1vyOmBjzTHxuNYx+GTkiVX3eBJdik9DZfxJyN2SfafcX8dUsMe707YqOgKEdyiH8Yh6ZTLnNTIzOTVAIOB58px9WSo8FwvtZbjHFsbBGKi2Gzj01rv4dMd2543XTMhNZcPGgYZCAwBP+NFL4NSmsOVMsrl7eD2CDYzCq17sucWDNF1KoTCcDtG0fRhYho6NhWzCK96o0YENvckuJb7Mo+OhCt0E8GNRJmVbrNrIxtWrRwDvcF0ahBdt18zyToVV9zZCstM+TZXkLzXYLE7mQXIqco9mlKygDbVx1aYKKbu00GVrxk9mwLSzlV/WxIvllCsel7WH8M32TrOqzUqg606pm2mOBnppvTWBCBHHJ5IObfSofDfYaXF92wlVJR/rYhfUi3bLYUkdQay4Z7dx0ZbKZ1lL68yftuGDqUDLROHkNDgl7xw/ZGyp3GG+cQ8jrkTkID0GNvVmrh5cpQmTB9NNESnCTkFGwnBJgQTdknFeZjNuCVlD/VcbGCHChvAloRhzCAMwlYw8N94e5E8/+3j17d/6jZIup6tP2lV2C8w9NzF/EGw33z0zKOE/lC30GcZnp+vHs2gY3Ywfc8HHkLKxyNeYYbkIbvHfefURAxMlQF9lcS46AIKXN/sLYK56OrVNWP1YtXeRMmYU5GNqaui1uNUEPSpNbLww5q21qufotlh9RgOueS9sHiup0k34GEbiF0o4gQldPALChj8XZYLfMiq9mwprjHCvDe9koAqjnMrjecBRDDBjlkRGHm83ysFJAMQTlNKSG2H9tTsntgYTul+XGeSDXGRw5FbQjLTodbwt+OzaKIHekTkHD15QhTd/N9YJeYMBWN7DTviob9NWv0aBEPCaHBys5zIIGGFlMvL1TyHBnTZi8dXaP/nHv5vxooEomzRfv9cjAAEYwHO/pm63VsW9+1kTGv10kM260pw1iJtCCOvOYmoQ882ngXGXLULRKos4cWCCxL12rBErAEg8bpz2gJv8QW9QMtEnrbvEVLP3HKs6iI/xIuhF7iOSElKbMBoHoJRZXjSG18Hl7KutdWfA71eq284xLcjZaZ35tRMpYC0uR4yzvIuDYk9ZeHPhxrfE7iUfI6oFNdetmbfR40nzidp/2IYT1qqvXZkqCRl797J6rTtPcWEsGVeCDE3c3IlVwxGvCNEM2jjRYZJEQ3QJwqXWYDMrfdHxVNdnz1d32+46x2T1sIKjneoFftEhFWh0m+BCYHkJHtgVF5AFD7fZmOlPHB2H9Hei2fUdColFjH634QEG2b//YAQUbk9fvZypVNN2172vgIiSdiLPVuGW5bdjgeGscazXt3591oN95oLra5k+rN/aN14DHg+BcUjZDAaiq4/rrJHxSLwRRu6zONYJOrywrQQS30S8wRtLCsdjYWtHP2Dzwb/4lCUFo4/ZhXm+52z6qX/enJBsPl0X1vAoZtq3vil3yeJRQtEU/RgznqYxEflFwOL7+IdCIvzG6rnJyNemvj0vO84zGK8haLyvj9k5uP55gJMPgpf6Nwjw4//BIf7JEJk94CWZ6sXHM5vQM2Rr7Z/+67/b2W3LC6wLgK4DLihKQhV/Z612EgqONusshua22ifNMT+HbcM8q8DCygXNtiaeyI2ByP4zbTOIqL1ujAW4TOjIlUUPwRASudO2f15iaSdFQr/F9un2N65cxzRVwLMkLRLkYBMDTJJniBHjYJYQRMVTCkviiZ5OE9f9flbuzkHnfyd8cwpr4+VOYfKThB5BN2OsU/vnBTu4jxL67YSeAhrXK0RzzxQGOfHkZbMEm+3ndqfTWmTK9Sl+362E2EF85pYV9iwLJLAjJaawBEMsbWI+DgM1ELlGWZjV2C3ZKG9it57XbY99/frF6tmXnyXkeTe1v5/3tNlsxtu/8ZvB2CKk9oyfqTxCiOmMjdKNnktiLitXv6Phw1FdDkPOHDcBig4gQDo03O/wDn+cvXwxG3ByIa0OvOYNBZ/z+SLeCFP/K5nHgkS7BqbO4SLPi8BvplhVd1HoLF6EyluzboKl11k/Pa+4x1dCgo4zdVR7Hpkpwa7xOjC7aSjfjYswEzLwERIlw3iKgI2HFJPpi5WlAFhVHY3y7dviSS1xPrbBk0wezxW+hioNShGY+2ZCeIWzXqHrS84gevYdnVl7cpIzNHQm3PIHFJVwU0UkPE1FnrH1vCQej4UcgNMxZmY18NTQJHim88Zs3HiRQaAc8BG+WnC20NW4XYNPnk2j5xqFlP5tNsKt5s1lX29o4RqaZFFHEk2WJQE9S7PZmeXAbixxDDfcnKkTHHf37zRImt30W0SJyS7ajor7Vl8BZGoghq1z/UgesfJcNMtbo0/tB1gEm2KIPIyJ4SPOWnG/lIEz1DHMegtUOHSqsUazhhSabzsksgQY3px6OBxEeIa+Pm11FV29X4LNbjjX621tfR5it5t7DFbjtdnleAsJVFiYcEAJ72xmEG6UbMJioIyQO9vtZRavIHr1IMtuPf+zds2hzSyBPUlI91vs40BDLuZmWmesO8aryEdoImEjg3zz8lcd0HgSpOttF/2j1Ye/+mp11C67163//+LzLyeU4hTe7ZysP/2TP169/4t/VVuXq0/e+z9nOzDKaq8jkTe331mddriFDTFv2u1nu41CeBwbZdtflvcAY/5pg9mfwx5gR9DDAxjGiiZXzbQIWVjqjgRvpuH1aqfrlM81Ty5h1Z8NRTHbVa78rPyKb5LELGiMnaI87/purnxDzMtICBN0tMDgqMMToTh8RlF3TxiCVceKRk9WTMxP+EdOe9asw+IlpRAl/OIzySkCMs/eCkWK1Yebj2tirvlbeMhIMTgUgL4oQd9luGOZcYH1MbmAhHBqzYMHPaZAJxiXI7JTYvUHPsnqMkgZprZODw943lQzgaPYhGGWXo8yCiKhkBCoIQZasPUMpQsvjWh4cQwAqANqplOTIbMiYD7JAMOlXBE4F0W2eCpjvGpz7Q9/+uP2LEzCiklRQvlmGPY1rVGMm/BwqJZkSKeCJmCqwmYddQMWw27nyoqNCYddTgHdZvCrzoWL0QgKoaBpFtcMEXRguoa1I9gj/WGZgO9MnN/ohQ2IK/ro95Ceu9+geAVEljYfioQtia2NLFyjDpnB3xOW0s48Y8gj7N61BFCiSZ+2Y15q2xct6T1ulHlLMb1kiV1rQ0ndBEHtIKTMJsIIT2an0HDhGdVwaykPCmN2yamfyUOk/Li5mItHhOGnDLOXJM38/eKLX6zWzz8PZ5282hp9HumLNtXYTrGcFIubhoNHHsZ3Oknm5z/9Scrnuo0431v97d/9bdthbc+GmPuEuvyGegJ7BQivfvTDH2Zl8jISiNkz7+x4yTG0QeXL9uC7uZFhFgmHrxiDRwC33L69tu86rwDjtG3HdtvIskGHOko6PKTA8I8Y+yxPKPS1ZfWdcLWEEmgxuQx85L3whE94NMQNny1u8PD3MOwwvP8RvvoxzcvdDchFIHpnpnn1ES+iK6FjCWt2oX1jIIAy/hO/j3Vf6jXQz6Yc6AkWyUlKDv9IoJoSNb1GYPwsWfW8yL7TMmJ0Aqw2n6BRBEuiujBIKTDcJNxLiXd8GW648JJjBHEnuRKLJ8XBu8xaaIN3wguZ6cq8zDkaPD6neGqygWWZG5f7cEFWiDPOmxL0cG1WA5yLEp3HeibF90e/+9MSvoRpEYSd4ksEkEEkwD5wzlqu13kji1lp4BIvMRIm34vY21NA4e8KXYoddTTzk9PAko0n1NnjUQBkG3G2IZe7C9ExO00WeONlAFpSiJXBFrwOwuPgiP0shGTIQmCD59I0bEoEc9UOBTIuWx4AbXqZFrbnnO+EeOaF+96Xpa1epQgmbsMwtScON6cZPdPYHo1JYx4Jnwvz4jOHqwah2CjiKxrS91ohz2b3xM6IblwyvhgDAR1OoXppNsrItX365QftSvv+6ltffzxbW8H3UUrzGv+OYQAARThJREFU2dPj1WefdRBlqwq/0bl232yHnufPnlbbcPRmvFsdW/VxXkjbVnfIhCXGUIBGCD6FKHRf9GQBP2/rMDvoHjQLAQ97hR+P2uOedX765NXq3W/8YPXe+x+V8CM4YTw4VFptFr6ZaluHCIIVT5x9+WU4CV8xr2o59/GF5BkFoSoSQ/KQMJF//mOpxwXvXXkVcTtXtkcXGid4BBaD+nDLKVzCgT5UPjxqpxfioejvhfhhEYh+p1DQjlAIOQMrXC/Zcd4CT4WxEPuDDY9NDiClRYk8b1deAB3G29NnMN4mEnmVhK//BheeM2uC57XN6Mn0j6caXMY9tOgaa75V2Gks1qAzpqYCl623ajN4JZCn1kPzBthIl+q9FuTAZVePzCrVBhcelhgQP8rJhRbGu+Co/gN00yF9XWuLoGXaIIURvmo4Ip9TALQdLSKznLVKxHu6GKQiGecym8oxp+3Z2UQhgkw8WuPmlWnNOe+8NueUkqbHdtK0+1Vh2b2pkc9zXDfVbbL+INos1j+tXRS3NY/fedIRJcXSZo2RJ6WRE5vPKztsMK8LA6ZGnrIYJk9zhoidxshp2RyhZgFSGxBRmDHuEVjT6RdNHVEUkmlTERfRIOcsGIfpPdf39e0l40oIhAjxwFhrXybmjHhWstk6Wjzu+3GFPHqRTJITUb6pXv3o6Rerq1ztX3SO3FdPPh64vvONR52z3SrAGPxelYYv2mba3Pn3fvCD1V/+1V+20047+xQ3qyO4//B+J8Xca1PNDq1I4M5yq6dopVDqKC+ApyB5aBmlnXQfP74XauCSZ3S5+vLLEnyFFPcT/iiw+uyTvylLHlnC+enpZm5+VjBFlF+yWi+uOk/BOB98/8Gj1UYbeUrSbbV113rnyKG3eXnnrEsWsdSsoSkzypP15Cmod5gpsJSLRKc91k5TCoRhO6u/CGu4iu/EtrLgPKRen0ScJNzMnQsv4zvKhnex0/oKibfx1uKPOTHIS/GVysTsT3Qi10TPh1dC1WCX5buYGA3v5TXN3ns9a/YGzx9IEHdvq5/xIsK3+JtBqIW5V0PDE/xx41kPzxS/nY02O3UHd4vTTzu8hDDGSkOjfIdRApqSQOb14bWZaelBM068N8q/zof/U2ejVDRE8RgHqYdzngjlOIm8ELp52gNTVhlCabWJIXvF+t/1EZjereHTrOh2z+a5xwTkrsE2XfYqInGF0w8JTULR/2zYGP4HGK79lht+akc1keOXN1VphAz6DqJUpXHs9xMWbmHQxFwhsffEfZ4DG8JkOmOGrGfEy3kbpmpiGD3H7eJKqhLSfh5T2vFWm8c4tYE57HMHUQ1jvILS6DOmRlWvC0yIMt9DGguG2D0UsyYJKSr037SsNoZW8hlnl92v25KS241PvmBWnvX+1oM7Swa58Tkyiqey2+EWkoo8j33K60uZ4TyBGJyn5LCA2cShv+/mHbz/3t82wKYBO1n1G9/8ejAtsZzEjQ0zn7YZJ0zda4uv9bWTEe6ZZqktB3Fw846j4ze/8fYoMoz0qv3/bORJyW6Wweel7O/FWHsxdqHA1vNOk03Yn7VT0GF73J+1F8FmioMwMQ72fKOwTLVdxWCsa/K+Wg8nTN5OWfqI2IWUaLjmRRCgSUQRwJgUHfyW6ZY15opepSwocNY0SibEmA4J6isaSqhuVAB1mECyxBia61sXWTbLXfPEEkzWfFZShmMbnhA+MMx0ZRa+bhEs3qndfi7jA5l+ip6VppzAG9Srk2dV4UVjsFI8mxVp+eDfzYwDDwYPznRk/KeuZKZmg4PrbpyGwD0/7JAOs1T9b4QUjiY7n0U2qzDz6/CSzEz8HUxqF45DIqW4G/PJ1lMsxrRWBedVdBEa8W4oIDgZI93fm1ytjQaydpE73AsEa+KFbi5Bfa5Pc9Aj8xahQFSadL3dUg8O2xQys0wjh41xjUyRsJjnMc+9YsXd1Gjjmlj4SjHBIIlaAGCD6NswRxjyHkKdKCboO9dktC+4Qoh2diuGQQBan8AiGGoF7vyIs5ZCCXvKBRO3O9f5umkvblKX4rnayioMi/UitxrDsyYbCTAmhjz7vEfxkowlkuobofwEVM9wR2qj9vGK6Y/t8gOsGUV1XUbTeXAWieBQ1pnrTrvPPH0NisNl1An1q1wxcW46MiIRtKxFzEnTP966V0Kvc+uz0MKZt9/5egQ9awYvS5tbbXOKO1lXOPiq/fYVXiAwGE+rSXDum/f3Sz7OIRgxADy/aHswGKdslp82/izO5qUdpDiU5H79G/uri7c3Vl8dBVtz6RgPfdZaUlzckpLvYMtw8/LTD5uJSbm8/c3c+DuLkLDiIRxPoZEyWiv+JHEb3LQ/tQYJ6m74QE9uv/sqG62FUG47ewZ2nbDvlXxlEYfGWV+FU4TULMhMMcJ2g3sdXk/yYAg8DcMltq+/U20Iw22SCh8xWoSXgNmGW3zuUA3XTH8SHP9swMHQTOVfz75qpgZdJ/vdM1jxOBxN2Xaw7qfs7LEHHh/e8bIaLcMRf2EcbdvefLdwbjclP96P/ii5+mCpeSsOA7GISM3CDuteZ8b9Og9qEpE1JvwSWo7+qD+uO8bV5tqf/O5vV8qd4CVUCOuQ+omSe8AhCDOZT0XHcDSEwpZYM9bNtep58crdMrtTw502P6nx/eK/jRhesuksS8IDR2RCa8nfRsklWtLxRnalicuGMC+qViNKexRK7dzOI0b/QWI3B2mToY2RG1OQ9L9gN6XFeenrMDmkj0vVF9lnHoLiH0dACTWS0EEYrU/hOKDR1IYYi0dgCx+IHteu70u9vH4iUu84bw1STRtZImpWQk289gcqhEpQpxgpBbBMC3LhX43iWfAREVJCsu4ff/j3eToRs++PHx60T/79xrc++9A/yBsQgxPWZx0sb6bCWXXvvvO4KcksZ4PeO6zeu3+smhwC5BCW0xJtn3/1qlNnng0jGSttf1hsPha497n299uJ16Ye4mmCYNwU3d2OtYJneL1jdqFa2E+fdZDFVXRLIC9z3TfOU1JZ/8ve3X34ePiGxXZKzmUexE3TbCGrKbr75TAyBNoP15PEjLizajB8KQkdpzrpC80jgNSx8ApDi30dWvk6vBJGgmgs9u9nafd5QxQ4wxC83qOUF4MVPXtv4v762k0JkjUx/Jl8S+0QZAoS3b0r7CGcNTXPKOQZw1O/YCGkLxr/JOuSFRtaUK6Lcosvlg4Gl3U5Vlo/YDB96r5+nExEsPeSrfEOeUW1HXRdB2PKgEeg32CgpGYxlvf7B5fGQRnNfg495+/JT0Ujhqy5dXOOOSY1Bnlhbggr3t7ogeuQTwGvN5CDNm3YYJVqZDfh3hygI1qu6kYAQ/Zux/9eRNxd2zGbVmnK7bLqtnGLuM0pB7Eja32QJTLgiXuzpJaWWpcrUQZHVkgNlgOAdros+SdeouFM3Ymn8467x/qL+RPQ0POquSCKRMggj3BweG8UjLhHAQxltl1wJBfBtbsIvtAyhBZDqhqbZFFuMgAwvZJR9lVCcbvr6LBRCEL5TaaWmgnoe7mqZTtGWK4weyoG19mZFWPfuXd/YJD5lQQFg2Tkp/X5ve++Gz5STHlIioVoaIcufvbli3Hpafx333k4Vv/Z046mtmf9+t0h/Pu/+DT3fX/20ae8hFpwdtDuvG8HF+b7qso/WWye03EKwMkzPCKu7xcdlkGZ3bkLL1dz6qwinetKeiWNTF2ZWrp/bytFtLP65MlGJ8606u3iZHXUM6+j+1YzLRflBm46Rvs0vDEUkTEPICaNNifPnkXTlIE55vByEYNLIDE0YyWjIbcZDU2eDN7AHsxnPTfxdXQjcH4IWLdSTo2p515VUORdyoQA7ZVbwDMUPgVCYRH8sZbRQ35gM6NDEfR1hJoYTgFODc9zeQ1qSyjIOG0MzoSGjQlOxc6mTnt7cOmBebLfDWeEz8wA78QZhJQQa+wZdfE0GoXMc2FsNhpTF3PL4/0+vBm450GDh4xu9v4o0n7zEFl9ocXspxfLxvaT2zCdOGXQwbH205/+bNSG2NmhgDOe/neVQM4OqjRkgJpmo+FijWW+l+ZrDhYhV5cnZQOzKC1seV3iSQyFAtnzpuNyX2JoTBe7JHgxd5moyXiGqEnMNBDAKbLRB+ITikne0Mx1ATWmZLh6NKkYlxsZaKNExICCQ9dNxfRrPtxwmX1FQAhNaXHvXxdbc5EJdi2OJrzdQYYrHNWDgcas2d6jhREHY7FALL/qPh7FMEFKqq9DuHH5+k4h9qBwPuVS4Uv9befqpsuGuWhdjKH46K//6n9d3emACCfe0Ow0GJfsW+++M5n9z3LJ3//Vr2OO9qRvj/0HbZsdYXqm891SDDLKmNsYWYPxOOrH1BovynSgwyO/qoru12XxwXzYEVdOtJWoUw5qP30KuCZK2j0oO3+nOH1/GHo25ID7RsyrO2iq7l9+lnBdLBaaQbjskE1IMNtggdN2Ljx3/qQ1BjYomeOfwwFFRFnDE3qO9QoVaDd1/NFq1jFEOx6GZ9CaZTKFJ6ushFlf3GcxrO2TCSCrBn5hkM+Uw8akaMkbY/2OS3TKSZgZ4MrXdGFB9/tJqrL8CVICOVuMdQ/vUiSp7wSrd+Jpq9NYf7i3VJsB6ZGh/27KwzFeZq5ky0dhveFHwJl24zXJ6C/Tcm1sER31gZ68MjE6Y8PUMGDGPGFF98zLm1ozNnjaC0d38iZmk87Gpb/9YBilosPaEdgMgFtJTE7sMDxriJkHaSUnuDx2eLVL6tbuYQ0mAMOjBDNh2XswGfAt7nrWzrSVWDztkDbnjvkJybXIarPwy55xCUzIPQ5gcc5+rv5MI0QQbqtk1g23dyStvtL2wTzFJVOF1R8sOndq4ntcXrdWCJFOzCFp4tpuBSZTzRTRWOidqtkSVZQJsiyzhvqTQpGInARh3xHcNliYSTGDufBl9xGue+376bnXeSmqtxzlzNMgMBiJVyRjnh9VtjbNDAvh8SycjsIKgutwQPBssjkJlpjgZUdHnVZoc69tuR7UrwrDb379UafZfDSMJhP9sMy3sY/7HhRbWYazFg09a+798morS0O4lxCKEOxVxXg39/txp+L+8lcfxUwJbYt+7iQ0tgp7mJt+kmV50SmzT756GjOnDAoJrFzkInNtnzx9WnLu5erwO1urP/jxu6t/8ZdfxIBZDoJWu2eFJkdZ1p362VRPnlbbqrhq1rUnIDMvHm/JzsPlfn0PjcKD36+LV+Fl1E04PG9GYPYkjDY8C6XIlI3iIox8mFUcfdmzGH8q08J57kQob6YIHWqP9RYmcbkfNssxAlifiwDHZ56LJnU8QobnT4q3eVWEEWvxHCkQFpnQ9dLkkvDFdYlp/IPnThtbpB7XeaZ/uyEUpNx4B6bEFo+xEKzxYm9FZ5S15OLUfnTxNNmZZHTjrOHJj53XtqQiL+i+PEKDH68o/hjFFJ0oEcpELE854q21P/r9n98QKAPs79F8lwksNy6+G40vdmdN5zimsYA15uVcuykLbaAEULz6kqtHS74RwsSm6bSEoUGqE28SZZSAAQNsM/e3mpVxywj3OMgGVn+sJucAQ7gz5bkUlrYbGBFLloItivXdVMTY3e579qwb3rcU99YLkAX3b0mhLe/MW/WH0WYRTC95pjdHWOEFpR31VEODC2G+++byU+cpn8bdj4/4W8bVWFj9DGo18V4I5hQqocHwinKU7V6q2371i6bN7s7YHHflgEjKxdHP5m/Npe+Lq7tGATx6eFhbN6u3qns3dWRRjxNbqRIWS9x5t2um0Lj0OM/CnZOEmFfw/PnTDtI8Dl+rvIivZSnLKNe+PmYOPly+fp2nRtGU6HPfWXwUEZpLcj1qau+9X5+t/uLvnnSU9GPms3XsrcfuOUK5W9gh5LPARn4EX3E1J8ZMCbBcWY7wxfolpPHIshIwioTWWdwCoZir/yZ30PgJLuu71+pGHgtP5ioFYO4Zn94Q2D4YXZ7Iyjae37jovYdz8Irkn23UQmTkjRdS0DbzGFcfzXuu5gpzlqQbK0rQyQPacMfNYrC2hPRWuVMehN/eCXJXeJECAJ9to4RlEmR3UswUIV5T52D86EF5E2weredGyWq/53wXWmgHHLNupOs8YfxguteMBPnwLj4y3rV/9LN/2GxADGrgtG9fsas6d0JqFdhawp1yD4DuBAQiD7B1KuBHJGt4zeteFqstwpWWSCjWEx7HCVsNdFM7XHXEsIiflQKMmvDFLafZcpsaAMoQwOum3bKLtbloXf0S8O3us8xXZWHFwYMkyT33CWp9myGIW2bQZvP0ZQ+0ZH2Jbbj7jQcDqWZK6Rr5EJeHoR//hhG6wyogAqsPPl4QjXoaHM5t8+w1q9L4FA6xOKYA7cAjE89T4jISfuW0yiKN8mD9ePVH39tdPUsIPy9W/o1vvTMu6p0EdIQqujzLwg8TBJd5dbXeBFh4db9pL94F6TjOmtqfX+00t5cVC821d7764KMvJ7fxzW88SBgU73AMG3eC+6qY3UIeR0E/roBG26/KsTzrrPpvpAg++fCD8Q4ojzs9s9/917nqSnz/4u9ivOvc/8Z7tzP14KEi2SmjtuDlpMIgx2TZdPO4/Q2WlXa8whS9AqYYF2NCKw+EtyL5pMJO8s06BPSxX4Ek1EkzFGfmoRu7MEBuiL5liEYA31h7tJrtsuPRWUBSB+J4OOGVsYy3OxXP7E18MKsoh8dysROauk0pxKvhHf79zdDwAwl0kte4crO7e56SsKCE4sGb+J+3NcLaO3IGlINn9cfzYfHF6gwRKy02v5UzHcoDGAdFQX1pG2+qsnuNhv1Qdqopp/Q2JVdT49lI/E3SL2jXfv6znw3YXJzya5NgI1qydz4b1TGfhVCaYr+ySi69+dth9J4x3cMCcJlkHW0UyOpUoj/z15hex5J7kCROOonx67TrzlYvHg0QPDfbLS2Y7DmWEMuEuAY2CTvM7NmQ6H1ac0Dp2tyHwK5D0Fh7z/QODU2bT0xIK/f9vAcDcxhsEBmQydMIPdfQ89mO/oHT/5bwQEJsXEvv19FyfJM+E16IF9Ok9OadrhH00dg1hGjglU27yLo6TEAZ5drLX65+82sVtJTI4gazom9lscXqBM6OMF91jJVlrsf23Y+YGOjttx8PE+rSkt2zBJPFxEyqrShMK/nEfd7FEOrU4c/7kbL2S55G22cvssK9q8AnoMNbFrEw5JNPn3QKbrF6BRSWvRrnvWJ3h2VKTKLN3318vfrk2WqEeS/rf//Bw3Fp0ZcSFaPLBaigNG0qMbsd0xe8RPMW75hr7zvhm0Mgo5llpGYFljxR9O/aYdV3rIh8wXHLYVm8W4HjQZmxwCKBmHBFm/rmNaIloRAmmqc3TiECiwgnEoCW1xKMg/idUuDyzoKm3NrDeBTOHOtNUctpSIZxmU3dTgxfP3ccS9aYCRcg2Ge0ogiERJTXQTi14IWwEna70dgVaadrlDfjxwNAE1w8Ja1dI+gNbd7j0bDsWEn7xidH0S9CVN/uN2sS/rj+cLD2s5/9Xt/z44tL6azka9ib1bauWFZyrTgdU+z2gphUj3ZtSTXnFuVahwR1zDpcbz7+vM7OcxVl2Fn3OTggGBryaKv1FpQ4vHHa6Z2rXPrU/yCH+0FrLzF+Fr92kDObuAhq38cNrz3hgX9cZhrUOxjUQLlZ/tk3m5bHAaa6lhVHi8u/LP3DvHg7Rsmkex+RKRnu2GwRVN92Ux2NCh7tBTdG8TwFQrCt5gK76T9xnTmAqxh8QosYTD92z5n3ISYCvnr1fHX8+f+1+vE7dxdL0PtPWxH3oEo340Okw/28kFwSSvO4s9edtR43xgQV0iSoTreFTx6JJJFiodn1pf7vTC1DgtK7mAYeXlEWMcFeJXBmDbpYXJ51DocHJeh4TQ1wsuZf5t5j5MP6wIjmre2Uat5eKPbex8+qoqyIZOfhav3gYSxx0LDCR276Ode/s+H9zUqbQ+YdZi0S5ONwhD8sKCrUC3ibhXDjdc8gvHz1Ikuax3NwN3rmHtcOXsDEvJCLSagySqZ+49VoSHkSrBHWvKswNcqjZnozlGdx1SyYQTAuytk+bMKEqaePyPt5CBJlBJ/CmdqPeGC80mgy5dPhZ/YNHOzhSSW/Z00/Hg//7eepSNQJd+7euzvz8rLqsuYUzn7KTQIbrODCo5Js+Hlmpqx16G85Gs+jjWf5s9x8LwkVli2sutozFxkNyUJKhGfgvQAbuNZ++pPfLaRJKyRgGw1esQJke+A64uQIRJzm97rmRVNpGPmw4gvZSW77nHutMqzkGya0/jocpbWr1KlNmzlyrb3HWrKg6sb3igfs+JIRGw3MZRGzjCVo8CiuoARzHpjuC5qZDtR2lnI9V8mWRuavCViX512E4Xiz8hiabVak4gdBotUwC0RBTGIbTLnxxTngC+tTtovZJQ3hJ94cnBBqjBbLpNnFZrWeENVUMHW5D1gcRAE/2+FF7qKGa6frsyKsP4Pfe0ouz198sDqsku0w5XqYZnfAAa/pXkKHEZ0lv98ct9gWjcFkOkY4Q0ynMCPaEASu6FFuOrwdpRQIBLfOhhrAxlCUENoclpSyhfPx8y/H64EXO73Osc/hj6tcdLt6nls/sWWd2z3I9GVs108lvMazWdb3/tdWO3cfxDvF+OUc5Du2bc7RGPENhaIgBXwyyrysEfwEj2Xcbtw8EJV5quIOst7w7gReTCzLHgbqczRrwl3IlFDhz8nAR3z887ISXY88bhqT8OM7sTHhCD0puwSrL6+y4PJQI1jRWaGOirbxsOoHj44HFn5lteGm5ifuVwt/FP4pYYaEUmLk3Oey4V/XJO94V9xsDECxzdpzvdbeTIFGfzMb9qZDGxZ6toDKoKC9NQmE22dyU31XtTf5iNqv+2n/VmFo13cwDMGDacb8W7/90wxkwh3QBqvJOTIp5MqQr0V4VyUAdio53J+55SxnwkUBXEaoYBskHiT8YvJGSOLmmu2otrt+mSARstnWp2eI/GVxHUJB7n5Mwb0iyATVApSzfizBU2Chtl5yDWvvCCGy8fAKCQvMy7hQeWqhuYryAj0jmUJjcxchniYeDRqMi2XO0sfwtH4Dq4UQGKxiSMZzkiL+jjkXJEJuYwlnmMecPo07ncFWSFbEIXnin00fuItb4VIcNYoo/E5VVe9/9emvWtDyUf0vSkh+4SALulgMiZYAC6esLea6TbbApW2rTFnuVVBjHpXrH/0HFIUlBI1VXJKA7CFGURhTbNxTQwdz7sbpJwaVN0QHHh2Pylbc02bX5R5Y18sUGe1mTQLDQOmIvdeaPtwrtla6S8nM3G74MVa0JoxwyGrJBt/GpjhWG7wSHqYfNeyzMKh1FvIJGN900l3LcGN2CbjjrLNYmdAyRg1hlCAmF/sKNcd9bsx4nAKSXfcsgccvC4042svuq6ctraXkZjFOdxUgwRz4KdO9rLG2TlNo+I9CFz7JLxAqbKRdgi1Zx2U3Zmse5ne05MF4ltW/9VDwSGpilBNvkYKxe5IPF//JixcjV9x0FXL6cNoQ3Jg2FE7AM9zIYUxpcm0KT9Z+9KOfpMjEkosL17dxr9y0lxsLK6l1WkMm4A8jonvzDtQEtCTVTTEPhO9I1ddWDsowiWSDjOtMTZlTjVBnV031xPRr7VK5VwGEemDPPa+A6qtTGjEkpFBsWjCrimrS5hDmOoUML+Mxm05iDjEIO4wAXDGlkGM5SyLuH9xL8WCcc3qn6zF9901fUL8nMSC4jJFLCtGSLsTE+nAry2yUYNntJK7qbxIlKcZlrr7+a4pA80rCb/yVoIUn/RGorfBhOk2MKfkzSbAIP9YqbyYyrY5efL46evW0dyJ0z1pl1uvDMHB6fblYOQpiP4Ge4hICHD4s2Jn4zXP64Xo0NiEQIZ5PAtOj9Rl8wUqBrSWgcGcTDULBXcf3400lWElYzaBLQhljS6Km1uZ9OQ61EKYk+1Z78UNMxtU17XlwN2sazngshJ0lmvrvcMwxWhd/hmdz7ZKk8wyeGe+wPsOT6bPXCZJ5dTvDUKQMBeU0U1YsZ23xECSMuciUx0HCrajKFuBoAZ8EbU+cHX1ZV4tdhD33CysIAxrOSresqyTmzO3H7+ilPQVFDMS48gl9aBqXGX2n6CnYT1N8FIKNNYMq57aQpD5Ntdq59WXTjmaphAQMgRhbrC9bbhy8G8KsxkOCcVzycAp2ClBIbb4erqauQBs8RUSLtiiNdlgbgEpxLYChkO7lHa397Ld+dHPNhUm4MKepnOg4AHO1J3tYY5g+zk0TRdSa3WZ9Em5x7+y0adABAfEAwETLqqmUxOVWmWnxYozfgCxv5ZHzAIBoW6rL1kNb587Ns9FkNqN4K7WDQzFvY9otxXcRU5kCoUntZkPeWQ0xGUGfzTB6PpzPeyy/TS4cv2SzBe0fmPvHIBFeMmjcrd4VK5qOInDq0sV8kLxui6jgdHY8RhG7WyM+tfYxUhdqI+IGb2gY4pvT5ZqxHGJbrhxXn1CZMwWL8mCJttdnL/tOUaZwWlegNHjZinoJpcYlr39h0lhWGf28qR4cnNHgEkDEjtJBs9kKyf0sB2vXwIYRbvKGZrMJ9I55Ly9ZJRYpxmy6itJYPBUM1Z3oaCYkHTF/c2e52AdZ1btl32WK1Upkp4cOpuRmF5mgMUdOSG8XTWHKzWh7WdJwu4U3ttoKAeM1TYIu3FFmxsCAsGYTLoYzeQF0ihxjofdKaknscclnbru3GJGZMktJUOCz6ClYFQJZaix/IaTinRrboSRgeDNmnoXGbayCv9TSKzrxHF7Tx1n4l/8gmHF6IQoFXH1BykiNx3GzJhKfmyl3yvi80l/X71Q4RFG5R0mofCTo8MizZNEtXWb84sY+CXFyomaestmK3ubtyYI43Lbh6gEOywMMj2qr8YadeDhe5bXEl85dE3KdFOKs/eynv9dhG6EWMoPCoO3Brqqroffpeo1zC2bhQ5pq0ZSSRLnGWYuxBr2Xgz4DN12GuKqzbGJwUoGMJYvKUe/mfsr8mvd9HmJenabN6mimi+LdM5PqKZHZkCKATdncK/N8UpsvihW5LAY0S2AjDIUzCiIUtX/keB00pgMFhBUGsX4jnmKNCaSQIQFPYZiiSwQmDjV2yLIQhaVncSk9sRd3W0Pj5PVMNKs9bWFMvMrKdLeEHcLZLBBDwuB4Pr1j+W2QBF9uVkScs7H6Dj5Jq/PXrxL449y/tHDMjgExwiQ0e+6qJKeioDhygpZRvNEGo1E8Sf7yE84cjmnXIGOFh42Wb4J9piBjYBZvKrlqjoX0b1zpFEKUW4QECECuPTAqBLpKEU3OAW+EF1nxuGHqxrnTQpwJifKg7CS7FtwYVCjISm9nzbaEXY2tF6sfyCUOzprq7/ENwiUjEP7DMVrLJQg/rOGeOonGTzAINLqMYg5AdfKsNnfdkcu8AfiaECSlaPy2q6bYl9xNSjbX/6BVg1NRluJjMcFv3Lw4O+ly+7s0ikINxpKALZRNZk5y8dHDuCeHVR8vKybiWsO9Jclj9Wvbvux4hLyYETnuXf3pzLQ2z0mIxZDwFhmGaSfUEGr9WrGGd/UpTpeDIcjyGPBmXHbykQ+h1ByZJjE3sxm/8w9/h/Ed5uQOi2nEFjK+Uh8siyOYMMJJWUNFD8uOIAHWgMQlBEJjrKnDDV83dXSQi6m+ZLZJqv1jWr+BbBWL0+QXDeTZS6WOWVJTDrZ2Kpt1EoVt/qjemneQ8h03nZZ3dBDXHHK4NGJT7rzTUVkxi+5ZaZlNnociHchRS45buUP6PpEATKtyHXGZLafEhoSWe6etQI7hE4raN6sgVqcIbN1LMVJkXFK7n5CH0cxwECw3PczayOzCEQE3pTiVVb1n6e54Kr3v2lkaWtKStTFGbpqkX62E/366txCpfoNX/xjGTMlY9a6xACw6V9I0pQ0w0MIJM+vc9J69KgQI/O6niDBM7YyHlmIfJY4p3twHn7FPlr0RENS9yX7nTfQ3pTQbgsQfSqfXKJNwPoKc4oFLG2sYmxBs4vfgHCve++yPnXt8TmN8ZbrasT9hwISHhLU+FLvcf/RWfbrGg4lQ/vO7PrjxMt2E37WZAYn+sDdVcuFuPyHbKSk6c+l4RwjR2OxpZxrLlBkPQB0B+i1GTZJwiXvV2Z+lOBQ2cbdVF7KcrOb0F3+oWGP7GYgRxoSacMqpuBY4Y0htrqqQxwpDh3PwfrntcICTyIvcBf7hDZi+O02YfXZSWCy8zToZAzNdPla/PS80MI8/Sj8i+rfwfJ5Jsf7aH/7+b928tujkZjesJaTRn7DQPqqKDHY+MR/msra30Q3DzzxmjDuFISFLAqlVpJO9t9e1uEfsanMI2frmJ7KyVSo1ODH3/naLJwrS3noU4rr9/CSERmSJhZNWMFyUjOMtcLN3rGFPMCSV4t3JPO9sHfRsgpBAQAynz3bOuDVwe07cnuvZeCYHIfbsZwo0YtDdYOzN0bDiIiWyiCKjCU32ACPskloTb3ctMEf4LJzwEKZgrGlOSk9/ISTG8/DCCBTMuIZBOO5wN5da5eZW+57CD0YbToav42fBVzFIbdiTXjtGQ/mdtQoPDVxZXHvwxR4EKBwtRTpEqL8TLnBgTvhj2VhnCoW3pn3rGzJ/MTcWw4yUZhZ07i+LhMxZ23AkiFNaucv9HqVIcQQTAcTYMyVaPwpmZjfcmHzczITd4qa9/p6denqep0LZUWIUzZqZiWhmUwvKMXQOTMPU4Z43sRcuCAShxJ/e9dwyRZpH1fiWI7u5uhmUPAB0fNEUHVqqXrtIIOBpcNH4hVCzhLknzM6YjTjMwnOJhTxcfIqfEkADiT87B3PDeTKSu/ebZtwTxvUEOlAcFq/EOvHFm3nwwaG/+wE3EvZxtsFsOd1zJxme2T2563iS3PXaGJDb2YNRRsEmrLkYb6h5eO8lS2rrA3RgpfCOmlVw6pH8wijAf+vPfn5jJ5Sj5lYfPb5fB6v2fSsezauYaaM69REBQiyGly1kNYNv0SD9tuxPnTvBPM8XJ1y0ndVa2qm3GGtpBaH2O8V17fzV6qIF+BcxF9SJkVlGq8FG1GImS0C5MFxRc/PLvDoMsPqm6N7MjYfZiSezkJgRknXbf0lRmxqmCWU8VeT1YL3FQP39Im15lIbnxYz7REB6S/UU+DFw7BS8LLhqtixuY7Qaa5KTuaMYUqxk8woWjKUfBZBSgSeuFCYCDLdNso7LyQpon6dgcQUrYf/0i7Z0vugei2HRzWSjY1wCZPrJ/HE8RbvMGLl2km+s2rie3QLPCHX9yzGM5W48oyhY/cYwWe+EnfWDD6v6fFOWy1LyHnabMtvZv5fVoPDrg6tYHmHKQSmXaCDDfpzQbLfLDE+HdzAubm0DU32BXAR1IkwTzk3JZuIx2O15tDKtZi94hTlwPSeRENCs314u/17WfuLWaIU25+HBtmJjObNaU+s+cTE3XyhD4bKwDEtTdWoa+q0oZh++oyFFCQMWFoHVO2Gn3+G+8e9LqMVohJT1VdyCxkJN4RdloE3TcTwU1lMlJDw6P81Uo5JgtJX8sxqNsKv9Pyk5zd3GHweNb8KE6IBP/UxJeO3iD1PDinqEQlM9GB9pswEEf8Yq/FMEQ/f+FpIZj5oA7nzhWhq8yXm3zkOahQyPdyuQKDlwfN7CgISPZX/QVkPPv/o0TauUrxe7PkUQXJAGBmEOn3PCKQu1u91QE5YN7kTMKqYmdFxQC2F2WvJ6dHNYscVhxBAeuL9MY7AYbAgCmYZY4vVwXx/c0rig/sJ8XPLS4NOiVBG3JXUSYnzv2fryWassF5MKLwg1AYLI9HVNrK0e3XswjMNNJazi38XVxHyLoluSHglHS9HOc68DbtxZc55poBF823JlSlbPCnEYy41yFFOEEcNYWoilLFxZk5Sj5a/3Z0wywxhmVtT1zNbdx3OAJIRxyWc6LoITUv+Uda6VLZdAk8nNYRrrHFtDw+BYxlxmfaf7+bz4N7r0v+hjChS2WRbXuPizHqC5fLgTqsng8jJ4eOsx1w3c1M9NCugg91Mi6uz8aHBgfwFMt0y9pRAp2xiPwGpDt04Znb3RUiK8m/XaAMUwN+FubBSvDP+cZx+OJyyMkR+q52czg4k3dpRlVvxj/7TZVpqlDy5TYrIuaiPGakd1icqcwfa1iyfj6VI6Mb2EcWFj6/txzky9xuMUkaWvRwn9uOTR7PSsHMcoeaRN4BqHH8lOOBAeUrDwSAgd9JC/Plbfmv/1fiYWr23KdTYFiX6b1ZYc9pv3IVuvJmCEeLzd2qkPfDF7QmQYbEY5FXXBch0cO9HoLO/i8uJoddh6iAclRoV0lD4eUCdg7YNczMzK/NM/+9fa2y9hT1hOjlp5lK/Pjd3NUl2u7ad996ZTbhUhs8xyo+ysKbOnT55M/MDN4BrLkGJOAoap77aZQVzVe00xWLPMyvcAV2Lm/WIOhKARxcWlGBYXOaE3v2jaBlOzLtwVMZdDBnDczCV3XRJkprNC9l4KZWm/Pro3sXpIsZrOajHvUwRjLWqH4uHeubaUQub6tD2QONI0VaRpPOUqpg34wzEpstqJq9P4mIowcPclVmqnd70mEbQ/mfFYLxzIX5zUl/qAOajAWCLwwBDjz7RW1yaDHOOfnx2lcMTZEQ4jYXKWIxQv+FgWcYxbnSD3VG1kBWMA2Df+oUWwgNn3UQwDI2GMxfvO4lH2hPM8i8e6TDuNb7NCmNevj3pdriOeaL05ZUmh8J6uUxCy5cwd6zcHWhIiMW3GQxHWdnyCAVl0hy4oepHwtNnk3Va1qf/GkDLDPAwVZWAXd76IH7nJs6tKQi17jVdsjMhFpuuFTGMlG6D58bGstWE+2ThMWU3ollIkqFNNFs8sZaZV78W7OIpRcTYeZSMrDu+BP1Z0Kuiizazvrh0GxbRbqnTCBe9Ovir64oP79++POz/HVYd7bdlmipCPxxUcZmSEtuL9g5QbY/ciwZ21EPU7sw+1K+k3fBieKTNjNQ65BCXBZI5R0/aT58/j89d5FslnYzGul+HwpNmitT/9k5+2J8Ll6n6ldvdaxkjrsD2WyB21X7NpscLgNiM8Cpi0ag3PMxEOIsQl4oBxb1iUAajsZddt3cSFHYZqwOawFxc4oD0XEWlGHsB8j2iIzX3jIvfHuFd3xDLdkx3Nway5nknzUQwEhMWSmIvUMXN/1u4ypbP03RPjIUxFXmMBq/5o4dl1s8SfjCxpGAc1AYuf+ivmwPDBN1MWwxC3DBVsuXWEHh6ECpImtvI9qLjHPvBcL26sSjeWnbdjDptSJJijlOqFZ6Ssciuh6M+UR5YiBcKruZh4PeWQUsGkLLxqOoxq6JQGq4jYrJQkKsUKD8MgKUvWKawAMmF8M4VYn7NAIvrY102G/Dpv5iLhsDBDUm2j/QVg2awAKzyzKzEfz8WU33YKf9BGyYGtsV5y3XtXslN+QAnnxK65mpamTtFSeHBmG6vLO1T/bZ6dYrYoiGtLSPFIj/azXNuvPzMGBm6MPuJsypZV9fFLwotyhis4pgAcBT0VbghL6GtnjvvuXUmxqZxrrJSSLL8wckpj8W+4bVThN+UI6fENAZsFI+SlToV+njtKgHkdE1dHU16GfBR+lEBWaszF5pGYJWD0pnYjHkPHybiHi9kTrnflSOxAE7UHJ4ylzP6zlhGvB86DBw8qM662JcE2/YdO6H1UeMUI3gn/pkfX/vgPf9zhk1mOOnpwd3+CfPuBb5ZYmxJIBMkiKm19XYB1VEb96CQFkJsiDlJeOYVbWchlZw/xWO5MKN6NMLTPLJBPa0+xSho8JzA3RbwbIrImpuEIuIIW7tNuFl3BTEOL2JGqFWpcZw6DOXPWhJWMb3siexyyMJ7M+OsGuiiNYtoGxXoTcKw+5b19n2xyrTtOSdiBqYQOcwZ6ggNZLBAmsfmhqTTEVAyCGBaQjHuf4prwgNAlHKyNqT1QTfwbnK7bVWWYPVyKnbRHmBAeU6syA8Mcd4RT+y7mxlSXZZ55D7Hn/G0sY1X77TNjrT1CI1lV1zNe1gdDszQSU5idK8htHbFPMIkGYYgza3OBGR4UGjknb/pvWm+YeJS/ZNbiLdxEf4cfiP1toc3KUUTCKAaBkjo/ejolouPat1GFunGWfOaPw5XwiWfHYrJUk1g0poR9LFIAKmllDQmORT4ssBNxCfpBuxeZRpJ0UgnG4mEK7XB9ZdpDd8q3uDh8so4BN3wBpyE9esZnKX7jn+KU4Pe+jLdsvfABRYUJprQm095veBOW8BIl79AETOg0CqaXFLfMgqLaZL2FN7y0OHee4Y0waBTNrAgNBvIiRLXjK9godQub5CHIQGRIibY3f4YXTbn9wr67LTRSp7GX50UezoJHTkIuIiKt1n7nx9/G10n+4QiY01ERQv3tXvPkHFxLHLl2YhyLJxR7vKoz7q7toO4d7sTMO6sPPj9affXyZIQfvwJWkkfmOjVfSwkRRk0pED0VYFwlU3oSHY4XOpzYvsFTnjHVrElOiFnxLsS8VEHfE/STBIRwimNoWAJY6/PMlBvqo3/jNgXQRgoLsSbECGPL9tW55jg/4UI0mOSxcDtnvj3kEpARlGCQPAmKUYzUEibAsNZAj5bvu1hLeSSXUSKStt2JaXgv4y/VIWK4J7Gjb6GJpOOEA8E6Si5FYNiYSNt2cZn+a2dc7yJMSzNn51Smb2ApfBnBDhfhYyviO5XWYYwT/sQ4ci6zlz+m0lPtz0CCiXsijlX+ya03Vz77kJua69+ENZRINE3cBn77/t1tE4w5bSQYbpojvnz5JIt+PIruMhxuJZSWpAppbmPcuD2XPUGO0TftvBMdFSvNJp2NYYQaDnDhG9rYKUcJKld1rgWzfm1QYu4erbn+QsGD/bsjHASGlyiRR/BYV3pyOXtvd/WotfSU/7IQJlynwBgJ9Anc2jXl2mYgHdY51WfhTSyM7pShBKniFHJEYK3ei6T1VXluvI0lzzJ0I6y9M/UM6JCckRvwTJwfzBQChUFRm84Wrm01VX3YrMN4MTU8fNC4GVNTbcZKQZNHZ+KBY3i3QTKkE7L+1o++FadzYRP2XCiCO64IzZMwzVnhAfK0XUts20PD0RIshU0RDloBZZKfixbLr/7lJ89Xn72o45ib1bLyTbmhFVaAEQ8vGy/mHRw1j4ipYkb13RbRb8VlL5Ny5FVAQOlYYjnJoQSABWPR95oaO4gZCZ34dRRBQx+rHtNDHCRNMg2jpfmXZ73PXS5r2TWx56IOtC3WjpnDxwhvzOAecUAcmXIuoZiLe8cq8CS44Qz5rbVjWV/nzmIOrr9SVe2xusaBYD2+aOye5cqx1LiPhr51OQn3aHfMFCP1v3EnKQ2eC5eakpoETH1pxx5py0GISi8JWnxGcfT8lJLGKANzsMCBuH1iPvAkDOBkeSngvYqaZLXTwglCMIar2cCx9ibxZj/AN67vZl6hmYNZ/mtWoPGbe6bkB79w0co2paK2i96JcXGkSrqjpuCEOOJ1QixOlTvhJbBiYEIzimpWGIYXhTuoA8dpKqpgYfjgpJCO2nBBBtxmGmh23jJLsMKjclnjmYrB2r89t55wHjf2SwVHta7WnxssgSwMHaGNFnhpchLxZyKSkOdNwJHnu89cmMabHEXeagyfMhDeCAmWvJQc1LM2lrwqplajcNjGmQ73JIfi//FAWHXKNr4LAbW6FBE9agqTsrQYSBWik3coMcZjme4dMzqwM1qUT6vXvpvBSgvUoFjhYQm0sR4Bbi78IAG2AOJlFkHS5EUbIPz6089W3+pIoHe+FnB1pFzUskjFLG+99Xj1wZPL1cfPcm+1m7a+kcluwBjhoLbGzY6pjtv11dSPAxNsirjfOWQOiuDWs8RjdRJPlt8ikam3j9m5RKZnHOwQ7gbeqY7jKeQ5iMzEOlyasSA05yCe600IqweIER0SmShFhzwXVrK/WQXMQ8GYm8SkCnEayrjjOjzP6ojtbb+Eicw7c1m1iUEgWzUYBpm9+PQRUxgVBuGuNvDG+sYydE37aCA0MaXIylJawoXR9sEGl3brIbAWmsQHy3sxC2/G4h8ewU1JMoCY8SDg6wkYDhJOwN2ZnVuDcXIMI2wphKa2CG+Azo/xUz6SS8IPq9zC3ozJtJR6BAlLimurWLKhdT9Gy1jwQm5i/nOFLAkXL6oXWtaaBar5hjKCq7qNkpeQPS52pdi3s/prGQlCNSXBWbZFyOOI8K49Fnv6SvhYKy77kvVnYYlDIZw587qVqLVk05Sa88UX+lZFl0KpoeGpRbH2au8KEQiHexPnR1e8BBcU+q2ip6RmqWu0dM16cnX7pgHh5KT+Va0xNvgJzYWRwqD9CnN4iRSP/vzgC7gnvMK58dJ6nmXGx6w1o2UakDd3r+IlZwCYfUKrVylkJbiv2thD6a0SbaGHvIRwYe0Pfv69vNzFDSGwWzHEYxsN5LYr8ZvTTxqUKhy7j3hJgb7FKxngiWvM/T558iJgsvIlBqwy+/hJbB0zqtyajSZiIjHJ7C3OUjUIG1ZQMphS5jKoiQLRDgklSHLVZ5UYpEfUgqkRgDXzJb3HWxUEhKIGz33p3fpR6EKzWkCCyRCpK7WZYNfXWIjekTDinkIyZmDh1UdL+km0KCQRkzmUQla0Xmb85o3zGchOn9oPbj+Uj0uIKH5FWMObJE5aHSEVN2ASc5ue04ZkDQYAq/s8FBs5gplmV42nXQokNZEWP26cCVnTZ0nBEF55agYpZlsCiiULXDwrFMtaijvxUv8fPEDEXAsuOMDM43XEWNw9HXIR0YnFt6rNeeqTwa5PSny7uNu4xcz2iKvBYVJx5XVzxGrzJQVvop3knhgeDggui6rs2uaRBE3Vmxh5LTzsNh9/UCiJN2aqLAUucThKuTECToHLBp6i8CnWrkokNvwJ91hiClJydIqZeBjhgkss0WV2IVZLISyeB9DwoHHp+26FMy+Lg+fMgq6J7yefEJzgRSs047XaUCNSDs420bm2eWI8KZ4OPCgeathzDb55IYAV8s16iJSbJKIZEHJEQTuo4UUn8tT90E3CsA5H6ANiYJ8wOBi8N2e66Sv4rPKjMFh5v9f++b/zBzeKGM5ybRSVbFeGOlm7tiN68uRpiFy2s+2RNMgi7IglniQ0FjWwbLb7+eX7H1Y9lNuWBrD3+EaFFtcsbPPrmBCC1Jtj5NmMosEiOCViAwJZV0cKQcy4TD03lUy9yz0VK6oLnswowodMDElYPAcuIo1hVfbRmCrPomHtxaghlfab+doIhRFYTRl3lXkEiwvoh/BbTIA5b5URbloy/jEwwte2bDFGE0awkhinyzUWAcONPl615RKLbieVsRIIVi/jQnd9EkL1KSZlATzr5Bfx+NRshwchBu18keWdpEr9Kp+dzhKsOGH6x1zwYKrLbwcZEJIeDKTwGnfLJm90AMQ1Nzrv4DSFS4HP+my4bByanuKfhIwFddCihSQRaJhfaJbeHAUAXmPwG95U9tnLgBKgiO35xx3n8h4mRON11L5yam2b602z1XHPhgPThzt2kwnO4YM6YiXFypT5Uvwkji3O7tQb3hwaYATrDNBJXgTt4GOU9cC9hBJL9jxeBF198b6sBxCyHDmJJj673zHYhw6iSFNJus0a8Qhb0yPUjAf+59mRAdd1/P/ySu1G0hRvCiFaED7bfI1r31h8bhUqi8+blIKyjxzvZFxuAt8/SpDALpV+r4KZkin0ysDCacDHa4vFZ/3JlGSizD63X/tr/8l/+Oc3tuI9HoQlHAEk/pSAM1cpG+w7rej0Drtl2Gt8oKJqGtzyCyE6nK79zJ6YX0/waTmnep5chMT13M3GZ6E+bWatO4EgjJCJQTC+QSCudlmGUQQJ7swjZ+XnXsqC5vXOxHL95l5OzNmgTMEgIEL7TXNKtmg12s5vx0jVwLQrk07AxLgQC26x4bhXYyUiQm0sa4MpgeU5xDN1pl3vTh18RJnkUM+zXAEQI03P4bK8QdeWpa8sUe10ixCMltdWFoC1pkgpExaUiztEC1evCUfv2Ed/kjO5aHUwzLC6ap669tWdK6pQKtwIKz9tmqa+z46fT/sEuRdK1sX4oO/v2ewi5hASpE0HUdzvEBMN1VSHl/rnUxxWwIJ2FicBJiNTXym1hPsqpSYGh8/xJJoylJmnxCfPEa7FrAE0wmmhiFjcHPtp87328p9tnRq32NVuvfIIcEHZh6qBhZCMZxZ94E75K88Cfln5mdYNufZMN2vEAKgw1EYcFgxoHM+Fp7h82h261l4QTTgwhilcyDHNVGTPm4my6Ma9mmjMwrBCgPqfU1MKIyg4Zwko8xVuMX4Khsx42GCCtzg7AKWYwKNAKVHPg27n3/IVxoAH7KdgxqDeRqB3c8Xx/1negf4VzLyeiqa8kGYkHnSePWPV/5oZO05pnSzeS0Zi7T/4899tz8Zi2ACxfRLGGhc4ApqrZvaHSXJzaS7b0d6rlI+rYGVUuAspC7Nj5P4cYMRC1uF+/tUXTeSX7Vx/EFIJX300kBEABAxgkbLpDaWSsqosPMLQxOP+9JwBiuO5sgpU3nQ07pZBT0yJiCGRUCLMxD5DUy6MtmLIusYYt3uILXPhhBfZxiEPtqxGDOAS7wKDev9uY+fKIoSpQMrK+P2P5UZwfcuuE8LYZ5Qfd1wbGIxmdrTQTsKfTU9wG8sIZLD1HIuwmeWH82l8cNVX8Acfi6cNik5WXT+mr2qiT7ChX+1733nsmF4/vACwc92FNGmlwb23zIerz3YSS+Z1lDRLQcmzesb5OvpgSM8KjdBvTkVpXJcxZ0BNPoFAwsNBbYXVGFH+oGOGY3C18hKJXOCzttCSgOVJ8Bxm74BwTinClUIX79qDz0IPVpxyZkG5pawv64WvDzA4GjBMxgmfYJY3ymNxNh0e8bAEHfjG8woP+I/CZtymBqGnbEppC2ykVdXGCquCW28e2fbSu/G997n6BFW7JylFdRSnQpHaNcPjxF+FZ5Q5evBIwalhLvvJycuUWrmJcLpXDoFyxysSfz1Yrkq5cR5UMDAkuxlNCl9YIIdBJhgpfCVvItk9m6v2rLwHa64ewPT52j//t3//xmAw0mklgfaDtsmjY301EGsuiKtxCgHzuzpb+9QpYpoCW55MiGNcP7Tt7KUd4X/5wa9Xn32Vps8d7U4IWpg2SEeRjOtdRRocsEb2UZ+pIJbJks9h3lxkghQAXEHegfJYVnS25IkpFe4INQbGGuMpTJzSe+N0dYO27pUQuSilLvShCFJYwc1gCAMk8XptmJnbKGYO3B7PEkUI2jmfYBQR9997t2W+mhyXEXPVj+wtxhu3rfdtBXXrwlEQGHMSeHWI0eULCAww5/0emDUEaXLWnVKbJBvE9dRV78x8fn1hXAoYY1sJF5aWHWolzjBgV8zRE3q70cJCojlK+Frcy1WPIYeBYia7vLBgpuy21TN07W6HRxgzhXNW5dUkvmqZwCgBVfFHcU4MW9t0oAIqRT7beYOjdBkC1j9GfFH9OPcYT/mxjt3mj6akkposPy8nVzehsUKOtbPXvLiXp7DE/jF+Y5VwPMua2YyikQdPCWAbLwTHnHlfexCOpoSOVUd7q8eMbSoEe2bc+8GL3IuYPiPFDQ6H7g3dwg2eAJepQXs6LIVM+KUck4HX84QGdcIDNf0nxPPDEC1eyUI33qkyaJphFlmZneBNhWvVgBQXvkD2oX+CPQalbrTjQA7xvYTecRtyCtXGAIa8tX//3/xZPGQQrOBC1Ikzap1vf6/VOeZUJ3HVi12dLLsGlOBNpVea3vzfuMfcyABhCXUsgfDpF1+11fCnESFNFZI3mjPl6nl+GC9Wg2xxOiQ6Vvi0n8uObT1vqo2WFpNM2WIM6mExSCOJmDKlEaNrE+s0FmrEmC6r6ksGJqlGaiXZwGZxDYsfKBHhTXzZc8gyLnht+YcJ9AWpXFFaearDuo6teBcUDi7krpEYMPiMG6izMEbNzEoxPaRtFSQRMEzZf33rf+GNUINhinUIYt0viqDeYmwWEDMPd+ljtLb4kZfBbocXQhHRzUeb+iHwt8JBkI2rgQ3D1vFUOs4z/bHkCAgIzwtICqIo/BlGbfIYxPN5diWrdvfujnCz0HjHWWvce+NAGwiZfQNT7lNcklW05/1e03Lm3m83IMWYBEjhC7op4XTayezGkzBHthkjRbKECCmFBItVY+UtHdWt5C0lxoOgaIQErwpJKXA4809hTn8MfDwGwrg/RTxLOOG5WdWm0+g3Y248xj3jgopujaWMJ81KGD+rLIfF01Lxhld4H3iXIcVvcOB5MgGW8VaDR+LU1LYPr4Fb74BIoTO+l11HEMk5dF1yHIusYDtcUWND771kUnKQohHD23RDv2v/3r/x0xvWxPy03TW4GZDoPCmIcOKnQYk7VdrYjXQEq79ZcjGCRSem2WQ0MZk9y4+Lt7733W+v/vbvfzkrfh4+vDuDhkjZUgUSXFShAs1fGjWASz7FIzaucBCi6SMadpguBjBFJhllaLTjLQNibstXe3oEbJkeQcwI5CqJwb69M3FycJozHqujbiAYCCv3LRou1rV3VPxNdpYgB7einpyEaW8ymcGjqMc7ptIQz/53E4YQ3ECiOHgo5lRZceHLYiETXO96uWdn5Vb4F66MG1rZp5kHxSOg14mEi7DAH/qnJYRCLM2ogNpjZeGFZcf0E4MGt2kY04mYyxJWVptrCF55EPPSrLfYnTe1bDfVO8be9WW8b8KZxrrR82J9yg5vWNUGjxJKXGrTYKbDWK717s8il2AVlnE/DdtJrEInRsNwWHj19rwm+7hr1xJWJkwClsAa3ST+otVS1GTqKYubEYGLnWJ889A8U7yGpvaBV97bn+k4xw2XNwh38EQg7UsoV2DKzSIgvGzqTMiAPua3lSkjhL/x15IveIPP4QEeQhn9YnjTwnIG6A82HkhUmr5m4OGU4ZzVd9URMCJkTE5DSEVhwI3+dShBrX7FWCgePIxe+sILfigipbIYVO0A5WCEaIkea3/2hz/KGzYPDGGLKyG7q0HxE91jmoHvv5uFfng3oU+TSiCxXhAPsRJhz9oauJcCskMGnr2cghj7WY/+6nka3LY7BJDs3W0qzmJ6GX+lqDch+ajwwbG828UjI3YELCUk5qCFzE1OpjaGXFZhLYLBhcc0WyHMc9an+7AAtoOuhdF4S+Z5eCDGwNQy6AvBKA+ElNknIHU8CmA2HkxAfWZOlfA2TmXBdRUGwJIV9EANjFLAucHtLTMA3hurGxPwmFgniouQmwuGbwkm2XV4nXXZvWfaUAEHKy9BaKWZJGHmLtc7IfVMo7Ptlx1cdYu4rrkz+YJgauJj1gZqW8KTMhqBGxwIe3IREWWG0NsxDjqmI8YiTM1/46ZoKei9dpRhycTTEqF2IVLlhmYMx1bXKMnbkML4KQ3vohEvwoky4DlsW6m7zQtbTXZc9dlVYZxlsQR6tyPAhGtmGFhK1i90zSYVFLETadQwsNSm1ygY3gDlMXF9nt+4swnrZWMl7fiJC8xVt10UgVLZdtkeCBQtJWAVo9hecpSnxCqq41A56vw33orxpwpXB5ZiUwBdwzYM4C5F2D3hh3jeYiw0nhhdixMKRNP4ikBulgRdtvKq3cY9HiS6Nm7+o1oFCkNcDnaFUaZlhWfWOFxQZMPjlFN8lwFTO3C7W9H/A2ZoIMuKFZbmAAAAAElFTkSuQmCC",
"text/plain": [
"<PIL.Image.Image image mode=RGBA size=250x180>"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"Time: 23.8s\n",
"This image displays a photograph featuring the **Golden Gate Bridge** spanning across a body of water toward a landmass in the distance.\n",
"\n",
"Here is a brief description:\n",
"\n",
"The dominant feature is the massive **red bridge structure** (clearly identifiable\n"
]
}
],
"source": [
"try:\n",
" url = \"https://raw.githubusercontent.com/google-gemma/cookbook/main/apps/sample-data/GoldenGate.png\"\n",
" img = Image.open(BytesIO(requests.get(url, timeout=30).content))\n",
" display(img.resize((250, 180)))\n",
" inputs = processor.apply_chat_template([{\"role\":\"user\",\"content\":[\n",
" {\"type\":\"image\",\"image\":img},\n",
" {\"type\":\"text\",\"text\":\"What is shown? Describe briefly.\"}\n",
" ]}], tokenize=True, return_dict=True, return_tensors=\"pt\", add_generation_prompt=True).to(model.device)\n",
" t0 = time.perf_counter()\n",
" with torch.no_grad():\n",
" out = model.generate(**inputs, max_new_tokens=128, temperature=0.7, do_sample=True)\n",
" print(f\"Time: {time.perf_counter()-t0:.1f}s\")\n",
" print(processor.decode(out[0][inputs[\"input_ids\"].shape[-1]:], skip_special_tokens=True)[:250])\n",
"except Exception as e:\n",
" print(f\"ERROR: {e}\")"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"---\n",
"## 7. Long Context (Needle-in-Haystack)"
]
},
{
"cell_type": "code",
"execution_count": 17,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Needle-in-Haystack:\n",
"EARLY: 793 tok, correct=OK, 4.9s\n",
"MIDDLE: 793 tok, correct=OK, 5.0s\n",
"LATE: 793 tok, correct=OK, 5.0s\n"
]
}
],
"source": [
"torch.cuda.empty_cache()\n",
"gc.collect()\n",
"\n",
"def needle(pos):\n",
" needle_str = \"The secret code is BLUE-42-GREEN.\"\n",
" filler = \"The quick brown fox jumps over the lazy dog. Python is versatile. \"\n",
" sents = [filler] * 50 # shorter context for T4\n",
" if pos == \"early\": sents.insert(0, needle_str)\n",
" elif pos == \"middle\": sents.insert(len(sents)//2, needle_str)\n",
" else: sents.append(needle_str)\n",
" prompt = f\"Read the text and answer.\\n\\nText: {' '.join(sents)}\\n\\nQ: What is the secret code? Answer with code only.\"\n",
" inputs = processor.apply_chat_template([{\"role\":\"user\",\"content\":prompt}],\n",
" tokenize=True, return_dict=True, return_tensors=\"pt\", add_generation_prompt=True).to(model.device)\n",
" t0 = time.perf_counter()\n",
" with torch.no_grad():\n",
" out = model.generate(**inputs, max_new_tokens=16, do_sample=False)\n",
" resp = processor.decode(out[0][inputs[\"input_ids\"].shape[-1]:], skip_special_tokens=True).strip()\n",
" return {\"pos\": pos, \"tokens\": inputs[\"input_ids\"].shape[-1], \"resp\": resp[:80],\n",
" \"correct\": \"BLUE-42-GREEN\" in resp, \"time\": time.perf_counter()-t0}\n",
"\n",
"print(\"Needle-in-Haystack:\")\n",
"for p in [\"early\", \"middle\", \"late\"]:\n",
" r = needle(p)\n",
" print(f\"{p.upper()}: {r['tokens']} tok, correct={'OK' if r['correct'] else 'NO'}, {r['time']:.1f}s\")"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"---\n",
"## 8. Summary"
]
},
{
"cell_type": "code",
"execution_count": 18,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"============================================================\n",
" GEMMA 4 E4B — SUMMARY\n",
"============================================================\n",
"+------------+-----------------------+\n",
"| Metric | Value |\n",
"+============+=======================+\n",
"| Model | google/gemma-4-E4B-it |\n",
"+------------+-----------------------+\n",
"| Parameters | 5.72B |\n",
"+------------+-----------------------+\n",
"| Device | cuda:0 |\n",
"+------------+-----------------------+\n",
"| VRAM | 10.26 GB |\n",
"+------------+-----------------------+\n",
"| RAM | 2.67 GB |\n",
"+------------+-----------------------+\n",
"| Load Time | 63.4s |\n",
"+------------+-----------------------+\n",
"| Throughput | 5.6 tok/s |\n",
"+------------+-----------------------+\n",
"| MMLU | 4/5 (80%) |\n",
"+------------+-----------------------+\n",
"============================================================\n"
]
}
],
"source": [
"rows = [\n",
" [\"Model\", MODEL_ID],\n",
" [\"Parameters\", f\"{total_params/1e9:.2f}B\"],\n",
" [\"Device\", str(model.device)],\n",
"]\n",
"if torch.cuda.is_available(): rows.append([\"VRAM\", f\"{torch.cuda.memory_allocated()/1e9:.2f} GB\"])\n",
"rows.append([\"RAM\", f\"{ram_used:.2f} GB\"])\n",
"rows.append([\"Load Time\", f\"{load_time:.1f}s\"])\n",
"if results: rows.append([\"Throughput\", f\"{np.mean([r['tps'] for r in results]):.1f} tok/s\"])\n",
"rows.append([\"MMLU\", f\"{ok}/{len(mmlu)} ({ok/len(mmlu)*100:.0f}%)\"])\n",
"\n",
"print(\"=\"*60)\n",
"print(\" GEMMA 4 E4B — SUMMARY\")\n",
"print(\"=\"*60)\n",
"print(tabulate(rows, headers=[\"Metric\",\"Value\"], tablefmt=\"grid\"))\n",
"print(\"=\"*60)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"---\n",
"## 9. Cleanup"
]
},
{
"cell_type": "code",
"execution_count": 19,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"VRAM: 10.26 GB\n",
"Done.\n"
]
}
],
"source": [
"del model, processor\n",
"gc.collect()\n",
"if torch.cuda.is_available():\n",
" torch.cuda.empty_cache()\n",
" print(f\"VRAM: {torch.cuda.memory_allocated()/1e9:.2f} GB\")\n",
"print(\"Done.\")"
]
}
],
"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": "3.12.13"
}
},
"nbformat": 4,
"nbformat_minor": 4
}