{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "02b24f3c",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "✅ DeepSeek 模型初始化完成\n"
     ]
    }
   ],
   "source": [
    "import os\n",
    "from langchain_openai import ChatOpenAI\n",
    "from dotenv import load_dotenv\n",
    "load_dotenv()\n",
    "\n",
    "# 通过环境变量读取 API Key，避免硬编码泄露风险\n",
    "llm = ChatOpenAI(\n",
    "    model=os.getenv(\"DEEPSEEK_MODEL\"),                          # DeepSeek 的对话模型\n",
    "    api_key=os.getenv(\"DEEPSEEK_API_KEY\"),          # 从环境变量加载\n",
    "    base_url=os.getenv(\"DEEPSEEK_API_BASE\"),            # DeepSeek API 地址\n",
    "    temperature=0,                                   # 设为 0 保证 SQL 生成的确定性\n",
    ")\n",
    "\n",
    "print(\"✅ DeepSeek 模型初始化完成\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "3a603f66",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "✅ 数据库初始化完成\n",
      "   - employees 表：5 条员工记录\n",
      "   - products  表：4 条产品记录\n",
      "   - orders    表：5 条订单记录\n"
     ]
    }
   ],
   "source": [
    "import os\n",
    "import mysql.connector\n",
    "\n",
    "# ============================================================\n",
    "# 第一步：建立数据库连接\n",
    "# ============================================================\n",
    "# 使用环境变量管理敏感信息，这是生产级代码的基本规范\n",
    "conn = mysql.connector.connect(\n",
    "    host=os.getenv(\"DB_HOST\", \"127.0.0.1\"),\n",
    "    port=int(os.getenv(\"DB_PORT\", 3308)),\n",
    "    user=os.getenv(\"DB_USER\", \"root\"),\n",
    "    password=os.getenv(\"DB_PASSWORD\", \"admin\"),\n",
    "    database=os.getenv(\"DB_NAME\", \"analytics_demo\"),\n",
    ")\n",
    "cursor = conn.cursor()\n",
    "\n",
    "# ============================================================\n",
    "# 第二步：创建表结构\n",
    "# ============================================================\n",
    "\n",
    "# 员工表：存储公司内部人员信息\n",
    "# 用于分析：部门人数分布、薪资结构、入职趋势等\n",
    "cursor.execute(\"\"\"\n",
    "CREATE TABLE IF NOT EXISTS employees (\n",
    "    id          INT PRIMARY KEY AUTO_INCREMENT,  -- 自增主键\n",
    "    name        VARCHAR(50)  NOT NULL,           -- 姓名\n",
    "    department  VARCHAR(50)  NOT NULL,           -- 所属部门\n",
    "    salary      DECIMAL(10,2) NOT NULL,          -- 月薪（精确到分）\n",
    "    hire_date   DATE                             -- 入职日期\n",
    ") ENGINE=InnoDB DEFAULT CHARSET=utf8mb4;\n",
    "\"\"\")\n",
    "\n",
    "# 产品表：存储在售商品信息\n",
    "# 用于分析：品类销售、价格分布、库存预警等\n",
    "cursor.execute(\"\"\"\n",
    "CREATE TABLE IF NOT EXISTS products (\n",
    "    id           INT PRIMARY KEY AUTO_INCREMENT,  -- 自增主键\n",
    "    product_name VARCHAR(100) NOT NULL,           -- 商品名称\n",
    "    category     VARCHAR(50)  NOT NULL,           -- 商品分类\n",
    "    price        DECIMAL(10,2) NOT NULL,          -- 单价\n",
    "    stock        INT DEFAULT 0                    -- 当前库存量\n",
    ") ENGINE=InnoDB DEFAULT CHARSET=utf8mb4;\n",
    "\"\"\")\n",
    "\n",
    "# 订单表：记录每笔交易\n",
    "# 用于分析：销售趋势、员工绩效、产品热度等\n",
    "cursor.execute(\"\"\"\n",
    "CREATE TABLE IF NOT EXISTS orders (\n",
    "    id           INT PRIMARY KEY AUTO_INCREMENT,  -- 自增主键\n",
    "    employee_id  INT NOT NULL,                    -- 下单员工（外键）\n",
    "    product_id   INT NOT NULL,                    -- 购买商品（外键）\n",
    "    quantity     INT NOT NULL,                    -- 购买数量\n",
    "    order_date   DATE NOT NULL,                   -- 下单日期\n",
    "    FOREIGN KEY (employee_id) REFERENCES employees(id),   -- 关联员工表\n",
    "    FOREIGN KEY (product_id)  REFERENCES products(id)     -- 关联产品表\n",
    ") ENGINE=InnoDB DEFAULT CHARSET=utf8mb4;\n",
    "\"\"\")\n",
    "\n",
    "# ============================================================\n",
    "# 第三步：插入演示数据\n",
    "# ============================================================\n",
    "\n",
    "# 员工数据：5 个员工，分属 3 个部门\n",
    "employees_data = [\n",
    "    (1, \"张三\", \"技术部\",   20000.00, \"2023-01-15\"),\n",
    "    (2, \"李四\", \"销售部\",   11000.00, \"2023-02-20\"),\n",
    "    (3, \"王五\", \"技术部\",   16000.00, \"2022-11-10\"),\n",
    "    (4, \"赵六\", \"人力资源\", 5000.00, \"2023-03-01\"),\n",
    "    (5, \"钱七\", \"销售部\",   17000.00, \"2022-12-05\"),\n",
    "]\n",
    "\n",
    "# 产品数据：4 款商品，覆盖 2 个品类\n",
    "products_data = [\n",
    "    (1, \"笔记本电脑\", \"电子产品\", 6999.00, 500),\n",
    "    (2, \"机械键盘\",   \"电子产品\", 399.00,  1000),\n",
    "    (3, \"办公椅\",     \"办公用品\", 499.00,  300),\n",
    "    (4, \"显示器\",     \"电子产品\", 1200.00, 400),\n",
    "]\n",
    "\n",
    "# 订单数据：5 笔订单，模拟真实购买行为\n",
    "orders_data = [\n",
    "    (1, 1, 1, 2, \"2024-01-15\"),  # 张三买了 2 台笔记本\n",
    "    (2, 2, 2, 15, \"2024-01-16\"),  # 李四买了 15 个键盘\n",
    "    (3, 3, 1, 10, \"2024-01-17\"),  # 王五买了 10 台笔记本\n",
    "    (4, 5, 3, 6, \"2024-01-18\"),  # 钱七买了 6 把办公椅\n",
    "    (5, 2, 4, 5, \"2024-01-19\"),  # 李四买了 5 台显示器\n",
    "]\n",
    "\n",
    "# executemany 批量插入，比逐条 insert 高效得多\n",
    "cursor.executemany(\"INSERT IGNORE INTO employees VALUES (%s,%s,%s,%s,%s)\", employees_data)\n",
    "cursor.executemany(\"INSERT IGNORE INTO products  VALUES (%s,%s,%s,%s,%s)\", products_data)\n",
    "cursor.executemany(\"INSERT IGNORE INTO orders     VALUES (%s,%s,%s,%s,%s)\", orders_data)\n",
    "\n",
    "conn.commit()\n",
    "conn.close()\n",
    "\n",
    "print(\"✅ 数据库初始化完成\")\n",
    "print(\"   - employees 表：5 条员工记录\")\n",
    "print(\"   - products  表：4 条产品记录\")\n",
    "print(\"   - orders    表：5 条订单记录\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "e02aa540",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "数据库连接成功\n",
      "   可用表：['employees', 'orders', 'products']\n",
      "\n",
      "SQL 工具包已加载（4 个工具）：\n",
      "   - sql_db_query\n",
      "   - sql_db_schema\n",
      "   - sql_db_list_tables\n",
      "   - sql_db_query_checker\n",
      "\n",
      "NL2SQL Agent 创建完成，可以开始提问了！\n"
     ]
    }
   ],
   "source": [
    "import os\n",
    "from langchain_community.utilities import SQLDatabase\n",
    "from langchain_community.agent_toolkits import SQLDatabaseToolkit\n",
    "from langchain_openai import ChatOpenAI\n",
    "from langchain.agents import create_agent  # langchain 1.3.1 的新 API\n",
    "\n",
    "# ============================================================\n",
    "# 第一步：连接数据库\n",
    "# ============================================================\n",
    "# SQLDatabase.from_uri 接受标准的数据库连接 URI\n",
    "# LangChain 内部会用 SQLAlchemy 管理连接池\n",
    "db_uri = (\n",
    "    f\"mysql+pymysql://{os.getenv('DB_USER')}:{os.getenv('DB_PASSWORD')}\"\n",
    "    f\"@{os.getenv('DB_HOST')}:{os.getenv('DB_PORT')}/{os.getenv('DB_NAME')}\"\n",
    ")\n",
    "db = SQLDatabase.from_uri(db_uri)\n",
    "\n",
    "# 验证连接：打印可用的表名\n",
    "print(f\"数据库连接成功\")\n",
    "print(f\"   可用表：{db.get_usable_table_names()}\")\n",
    "\n",
    "# ============================================================\n",
    "# 第二步：初始化大模型\n",
    "# ============================================================\n",
    "llm = ChatOpenAI(\n",
    "    model=os.getenv(\"DEEPSEEK_MODEL\"),                          # DeepSeek 的对话模型\n",
    "    api_key=os.getenv(\"DEEPSEEK_API_KEY\"),          # 从环境变量加载\n",
    "    base_url=os.getenv(\"DEEPSEEK_API_BASE\"),            # DeepSeek API 地址\n",
    "    temperature=0,                                   # 设为 0 保证 SQL 生成的确定性\n",
    ")\n",
    "\n",
    "# ============================================================\n",
    "# 第三步：创建 SQL 工具包\n",
    "# ============================================================\n",
    "# SQLDatabaseToolkit 会自动注册 4 个工具：\n",
    "#   1. sql_db_list_tables    — 列出数据库中所有表\n",
    "#   2. sql_db_schema         — 获取指定表的 DDL 结构\n",
    "#   3. sql_db_query_checker  — 检查 SQL 语法是否正确\n",
    "#   4. sql_db_query          — 执行 SQL 并返回结果\n",
    "toolkit = SQLDatabaseToolkit(db=db, llm=llm)\n",
    "tools = toolkit.get_tools()\n",
    "\n",
    "print(f\"\\nSQL 工具包已加载（{len(tools)} 个工具）：\")\n",
    "for t in tools:\n",
    "    print(f\"   - {t.name}\")\n",
    "\n",
    "# ============================================================\n",
    "# 第四步：定义 System Prompt（Agent 的\"岗位说明书\"）\n",
    "# ============================================================\n",
    "SQL_AGENT_PROMPT = \"\"\"你是一名专业的 SQL 数据分析师。\n",
    "\n",
    "## 工作流程\n",
    "1. 先用 sql_db_list_tables 查看数据库中有哪些表\n",
    "2. 用 sql_db_schema 获取相关表的字段结构和类型\n",
    "3. 生成 SQL 之前，用 sql_db_query_checker 检查语法\n",
    "4. 确认无误后，用 sql_db_query 执行查询\n",
    "5. 用中文总结查询结果，给出简洁的业务洞察\n",
    "\n",
    "## 约束\n",
    "- 只使用数据库中实际存在的表和字段，不要凭空编造\n",
    "- 单次查询结果限制在 50 条以内\n",
    "- 如果查询出错，分析错误原因后重新生成 SQL\n",
    "- 回答要简洁专业，不要啰嗦\n",
    "\"\"\"\n",
    "\n",
    "# ============================================================\n",
    "# 第五步：组装 Agent（langchain 1.3.1 一行搞定）\n",
    "# ============================================================\n",
    "# create_agent 返回一个可直接调用的 Runnable，无需再套 AgentExecutor\n",
    "# 它内部自动处理工具调用、循环迭代、错误重试等逻辑\n",
    "sql_agent = create_agent(\n",
    "    model=llm,\n",
    "    tools=tools,\n",
    "    system_prompt=SQL_AGENT_PROMPT,\n",
    ")\n",
    "\n",
    "print(\"\\nNL2SQL Agent 创建完成，可以开始提问了！\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "bd07bbe0",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "回答：公司共有 **5 名员工**。\n"
     ]
    }
   ],
   "source": [
    "# 简单问题：Agent 只需一条 COUNT SQL 就能搞定\n",
    "# create_agent 的 invoke 接口使用 messages 格式\n",
    "response = await sql_agent.ainvoke({\n",
    "    \"messages\": [{\"role\": \"user\", \"content\": \"公司一共有多少名员工？\"}]\n",
    "})\n",
    "\n",
    "# 最终回答在最后一条消息里\n",
    "final_msg = response[\"messages\"][-1]\n",
    "print(f\"回答：{final_msg.content}\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "55db4cad",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "回答：查询结果如下：\n",
      "\n",
      "| 姓名 | 薪资 |\n",
      "|------|------|\n",
      "| 张三 | 20,000.00 元 |\n",
      "| 王五 | 16,000.00 元 |\n",
      "\n",
      "**业务洞察：**\n",
      "- 技术部共有 **2 名员工**，平均薪资为 **18,000 元**。\n",
      "- 张三（20,000 元）薪资最高，比王五（16,000 元）高出 **25%**。\n"
     ]
    }
   ],
   "source": [
    "# 中等难度：需要 JOIN + WHERE + GROUP BY\n",
    "response = await sql_agent.ainvoke({\n",
    "    \"messages\": [{\"role\": \"user\", \"content\": \"列出技术部所有员工的姓名和薪资，按薪资从高到低排序\"}]\n",
    "})\n",
    "final_msg = response[\"messages\"][-1]\n",
    "print(f\"回答：{final_msg.content}\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "f884cdc0",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "回答：## 查询结果\n",
      "\n",
      "**销售部员工**共有2人：**李四** 和 **钱七**\n",
      "\n",
      "### 汇总数据\n",
      "| 指标 | 数值 |\n",
      "|------|------|\n",
      "| 总订单数 | **3 笔** |\n",
      "| 订单总金额 | **¥14,979.00** |\n",
      "\n",
      "### 员工明细\n",
      "| 员工 | 订单数 | 订单金额 |\n",
      "|------|:------:|:--------:|\n",
      "| 李四 | 2 笔 | ¥11,985.00 |\n",
      "| 钱七 | 1 笔 | ¥2,994.00 |\n",
      "\n",
      "### 业务洞察\n",
      "- 销售部两位员工共下3笔订单，总金额约 **1.5万元**\n",
      "- **李四**贡献了约 **80%** 的订单金额，是销售主力\n",
      "- 建议可进一步了解钱七的客户资源情况，提升其订单量\n"
     ]
    }
   ],
   "source": [
    "# 高难度：需要 JOIN 三张表 + 聚合计算\n",
    "response = await sql_agent.ainvoke({\n",
    "    \"messages\": [{\"role\": \"user\", \"content\": \"销售部的员工总共下了多少订单？订单总金额是多少？\"}]\n",
    "})\n",
    "final_msg = response[\"messages\"][-1]\n",
    "print(f\"回答：{final_msg.content}\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "a9b0e412",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "最终回答：每个部门各有多少人？\n",
      "\n",
      "步骤 2 [AIMessage]：\n",
      "  调用工具：sql_db_list_tables\n",
      "  参数：{}\n",
      "  工具返回：employees, orders, products\n",
      "\n",
      "步骤 4 [AIMessage]：\n",
      "  调用工具：sql_db_schema\n",
      "  参数：{'table_names': 'employees'}\n",
      "  工具返回：\n",
      "CREATE TABLE employees (\n",
      "\tid INTEGER NOT NULL AUTO_INCREMENT, \n",
      "\tname VARCHAR(50) NOT NULL, \n",
      "\tdepartment VARCHAR(50) NOT NULL, \n",
      "\tsalary DECIMAL(10, 2) NOT NULL, \n",
      "\thire_date DATE, \n",
      "\tPRIMARY KEY (id)\n",
      ")D\n",
      "\n",
      "步骤 6 [AIMessage]：\n",
      "  调用工具：sql_db_query_checker\n",
      "  参数：{'query': 'SELECT department, COUNT(*) AS 人数 FROM employees GROUP BY department ORDER BY 人数 DESC'}\n",
      "  工具返回：SELECT department, COUNT(*) AS 人数 FROM employees GROUP BY department ORDER BY 人数 DESC\n",
      "\n",
      "步骤 8 [AIMessage]：\n",
      "  调用工具：sql_db_query\n",
      "  参数：{'query': 'SELECT department, COUNT(*) AS 人数 FROM employees GROUP BY department ORDER BY 人数 DESC'}\n",
      "  工具返回：[('技术部', 2), ('销售部', 2), ('人力资源', 1)]\n",
      "\n",
      "最终回答：查询结果如下：\n",
      "\n",
      "| 部门 | 人数 |\n",
      "|------|:----:|\n",
      "| 技术部 | 2 人 |\n",
      "| 销售部 | 2 人 |\n",
      "| 人力资源 | 1 人 |\n",
      "\n",
      "**共计 5 人**，分布在 3 个部门。技术部和销售部各有 2 人，人力资源部有 1 人。\n"
     ]
    }
   ],
   "source": [
    "# 遍历所有消息，还原 Agent 的完整推理链\n",
    "response = await sql_agent.ainvoke({\n",
    "    \"messages\": [{\"role\": \"user\", \"content\": \"每个部门各有多少人？\"}]\n",
    "})\n",
    "\n",
    "for i, msg in enumerate(response[\"messages\"]):\n",
    "    msg_type = msg.__class__.__name__\n",
    "\n",
    "    # AIMessage 中如果有 tool_calls，说明 Agent 调用了工具\n",
    "    if hasattr(msg, \"tool_calls\") and msg.tool_calls:\n",
    "        print(f\"\\n步骤 {i+1} [{msg_type}]：\")\n",
    "        for tc in msg.tool_calls:\n",
    "            print(f\"  调用工具：{tc['name']}\")\n",
    "            print(f\"  参数：{tc.get('args', {})}\")\n",
    "\n",
    "    # ToolMessage 是工具返回的结果\n",
    "    elif msg_type == \"ToolMessage\":\n",
    "        print(f\"  工具返回：{msg.content[:200]}\")\n",
    "\n",
    "    # AIMessage 的最终文本回答\n",
    "    elif msg.content:\n",
    "        print(f\"\\n最终回答：{msg.content}\")\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "32ca7d76",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "✅ 数据加载完成\n",
      "   员工表：5 行 × 5 列\n",
      "   产品表：4 行 × 5 列\n",
      "   订单表：5 行 × 5 列\n",
      "\n",
      "📋 员工表示例：\n",
      " id name department  salary  hire_date\n",
      "  1   张三        技术部 20000.0 2023-01-15\n",
      "  2   李四        销售部 11000.0 2023-02-20\n",
      "  3   王五        技术部 16000.0 2022-11-10\n"
     ]
    }
   ],
   "source": [
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "import numpy as np\n",
    "\n",
    "# ============================================================\n",
    "# 配置 matplotlib 中文显示\n",
    "# ============================================================\n",
    "# Windows 用 SimHei（黑体），macOS 用 PingFang SC\n",
    "# 如果还是乱码，试试安装 fonts-noto-cjk 并清除缓存\n",
    "plt.rcParams[\"font.sans-serif\"] = [\"SimHei\", \"PingFang SC\", \"DejaVu Sans\"]\n",
    "plt.rcParams[\"axes.unicode_minus\"] = False  # 解决负号显示为方块的问题\n",
    "\n",
    "# ============================================================\n",
    "# 从数据库加载数据到 Pandas DataFrame\n",
    "# ============================================================\n",
    "# 用 SQLAlchemy engine 复用连接，避免重复创建连接池\n",
    "from sqlalchemy import create_engine\n",
    "\n",
    "engine = create_engine(db_uri)\n",
    "\n",
    "employees_df = pd.read_sql(\"SELECT * FROM employees\", engine)\n",
    "products_df  = pd.read_sql(\"SELECT * FROM products\", engine)\n",
    "orders_df    = pd.read_sql(\"SELECT * FROM orders\", engine)\n",
    "\n",
    "print(\"✅ 数据加载完成\")\n",
    "print(f\"   员工表：{len(employees_df)} 行 × {len(employees_df.columns)} 列\")\n",
    "print(f\"   产品表：{len(products_df)} 行 × {len(products_df.columns)} 列\")\n",
    "print(f\"   订单表：{len(orders_df)} 行 × {len(orders_df.columns)} 列\")\n",
    "print(f\"\\n📋 员工表示例：\")\n",
    "print(employees_df.head(3).to_string(index=False))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "01276a4d",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "✅ Python 代码执行沙箱创建成功\n"
     ]
    }
   ],
   "source": [
    "import traceback\n",
    "from io import StringIO\n",
    "from contextlib import redirect_stdout\n",
    "from langchain.tools import tool\n",
    "\n",
    "# ============================================================\n",
    "# 定义沙箱的\"白名单\"——只有这些库和数据可以被代码访问\n",
    "# ============================================================\n",
    "# 这是一种简单的安全策略：不在白名单里的东西，代码碰不到\n",
    "SANDBOX_GLOBALS = {\n",
    "    # 数据：Agent 可以分析这三张表\n",
    "    \"employees_df\": employees_df,\n",
    "    \"products_df\":  products_df,\n",
    "    \"orders_df\":    orders_df,\n",
    "    # 工具库：Agent 可以用这些库做分析和画图\n",
    "    \"pd\":  pd,       # pandas — 数据处理\n",
    "    \"plt\": plt,      # matplotlib — 基础绑图\n",
    "    \"sns\": sns,      # seaborn — 统计可视化\n",
    "    \"np\":  np,       # numpy — 数值计算\n",
    "}\n",
    "\n",
    "\n",
    "@tool\n",
    "def execute_python_code(code: str) -> str:\n",
    "    \"\"\"\n",
    "    执行 Python 代码进行数据分析和可视化。\n",
    "\n",
    "    可用变量：\n",
    "      - employees_df: 员工表 DataFrame（字段：id, name, department, salary, hire_date）\n",
    "      - products_df:  产品表 DataFrame（字段：id, product_name, category, price, stock）\n",
    "      - orders_df:    订单表 DataFrame（字段：id, employee_id, product_id, quantity, order_date）\n",
    "      - pd:   pandas 库\n",
    "      - plt:  matplotlib.pyplot\n",
    "      - sns:  seaborn\n",
    "      - np:   numpy\n",
    "\n",
    "    使用示例：\n",
    "      # 统计各部门平均薪资\n",
    "      result = employees_df.groupby('department')['salary'].mean()\n",
    "      print(result)\n",
    "\n",
    "      # 画柱状图\n",
    "      plt.figure(figsize=(10, 6))\n",
    "      employees_df.groupby('department')['salary'].mean().plot(kind='bar')\n",
    "      plt.title('各部门平均薪资')\n",
    "      plt.show()\n",
    "    \"\"\"\n",
    "    # 准备隔离的执行环境\n",
    "    # globals_dict 提供白名单变量，locals_dict 收集执行过程中产生的新变量\n",
    "    exec_globals = dict(SANDBOX_GLOBALS)\n",
    "    exec_locals = {}\n",
    "\n",
    "    # 用 StringIO 捕获 print() 的输出\n",
    "    # 这样 Agent 生成的代码里所有的 print 语句都会被收集\n",
    "    output_buffer = StringIO()\n",
    "\n",
    "    try:\n",
    "        # redirect_stdout 会把标准输出重定向到我们的 buffer\n",
    "        with redirect_stdout(output_buffer):\n",
    "            exec(code, exec_globals, exec_locals)\n",
    "\n",
    "        result = output_buffer.getvalue()\n",
    "\n",
    "        # 如果代码没有 print 任何东西，给个默认提示\n",
    "        if not result.strip():\n",
    "            result = \"✅ 代码执行成功（无文本输出，可能已生成图表）\"\n",
    "\n",
    "        return f\"执行成功：\\n{result}\"\n",
    "\n",
    "    except Exception as e:\n",
    "        # 出错时返回完整的错误堆栈，方便 Agent 自我修正\n",
    "        error_detail = traceback.format_exc()\n",
    "        return f\"❌ 执行出错：{e}\\n\\n{error_detail}\"\n",
    "\n",
    "\n",
    "print(\"✅ Python 代码执行沙箱创建成功\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "e8e2f648",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "数据可视化 Agent 创建完成\n"
     ]
    }
   ],
   "source": [
    "from langchain.agents import create_agent\n",
    "\n",
    "# ============================================================\n",
    "# 定义可视化 Agent 的 System Prompt\n",
    "# ============================================================\n",
    "VISUALIZATION_PROMPT = \"\"\"你是一名资深数据分析师，精通 Python、Pandas 和 Matplotlib 数据可视化。\n",
    "\n",
    "## 可用数据\n",
    "1. employees_df — 员工表（字段：id, name, department, salary, hire_date）\n",
    "2. products_df  — 产品表（字段：id, product_name, category, price, stock）\n",
    "3. orders_df    — 订单表（字段：id, employee_id, product_id, quantity, order_date）\n",
    "\n",
    "## 工作流程\n",
    "1. 理解用户的分析需求\n",
    "2. 用 execute_python_code 工具编写并执行 Python 代码\n",
    "3. 先做数据探索（head、describe、info），再做深入分析\n",
    "4. 用中文解释分析结果，给出业务洞察\n",
    "\n",
    "## 代码规范\n",
    "- 绑图前设置中文字体：plt.rcParams['font.sans-serif'] = ['SimHei', 'PingFang SC', 'DejaVu Sans']\n",
    "- 设置 plt.rcParams['axes.unicode_minus'] = False\n",
    "- 图表尺寸统一用 plt.figure(figsize=(10, 6))\n",
    "- 必须添加标题、坐标轴标签，让图表自解释\n",
    "- 用 print() 输出关键统计量，不要只画图不说话\n",
    "- 图表标题用英文（避免渲染问题），但用中文向用户解释结果\n",
    "\n",
    "## 注意事项\n",
    "- 每次只执行一段完整的代码，不要拆成多段\n",
    "- 先探索数据结构，再做分析——不要上来就画图\n",
    "- 结果要有业务洞察，不只是\"最大值是 XXX\"\n",
    "\"\"\"\n",
    "\n",
    "# ============================================================\n",
    "# 组装可视化 Agent（langchain 1.3.1 写法）\n",
    "# ============================================================\n",
    "viz_tools = [execute_python_code]\n",
    "\n",
    "# 同样用 create_agent 一行搞定，和 SQL Agent 的创建方式完全一致\n",
    "visualization_agent = create_agent(\n",
    "    model=llm,\n",
    "    tools=viz_tools,\n",
    "    system_prompt=VISUALIZATION_PROMPT,\n",
    ")\n",
    "\n",
    "print(\"数据可视化 Agent 创建完成\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "59ebc21f",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "findfont: Failed to find font weight bold, now using 400.\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1400x1000 with 4 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 1400x1000 with 4 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "分析结果：\n",
      "---\n",
      "\n",
      "## 📊 员工薪资分布分析报告\n",
      "\n",
      "### 一、数据概况\n",
      "\n",
      "公司共有 **5 名员工**，分布在 **3 个部门**（技术部、销售部、人力资源），薪资数据完整无缺失。\n",
      "\n",
      "### 二、核心统计指标\n",
      "\n",
      "| 统计量 | 数值 | 说明 |\n",
      "|--------|------|------|\n",
      "| **均值（Mean）** | **¥13,800** | 员工平均薪资水平 |\n",
      "| **中位数（Median）** | **¥16,000** | 中间位置的薪资水平 |\n",
      "| **最小值（Min）** | **¥5,000** | 赵六（人力资源部） |\n",
      "| **最大值（Max）** | **¥20,000** | 张三（技术部） |\n",
      "| **极差（Range）** | **¥15,000** | 薪资差距非常大 |\n",
      "| **标准差（Std）** | **¥5,890.67** | 薪资离散程度较高 |\n",
      "| **Q1（25%分位）** | **¥11,000** | 下四分位薪资 |\n",
      "| **Q3（75%分位）** | **¥17,000** | 上四分位薪资 |\n",
      "| **偏度（Skewness）** | **-0.86** | 左偏态分布 |\n",
      "| **峰度（Kurtosis）** | **-0.04** | 接近正态分布 |\n",
      "\n",
      "### 三、关键洞察\n",
      "\n",
      "#### 1️⃣ 薪资呈**左偏分布**\n",
      "> **均值（¥13,800） < 中位数（¥16,000）**，说明大部分员工的薪资集中在中高位，而低薪员工（人力资源部赵六，¥5,000）显著拉低了整体均值。\n",
      "\n",
      "#### 2️⃣ 薪资差距悬殊\n",
      "- 最高薪资（¥20,000）是最低薪资（¥5,000）的 **4 倍**，极差高达 **¥15,000**\n",
      "- 标准差约 ¥5,891，说明薪资分布相对分散，员工间收入差异较大\n",
      "\n",
      "#### 3️⃣ 部门间薪资不均衡\n",
      "- **技术部**平均薪资最高（约 ¥18,000），技术岗薪资领先\n",
      "- **销售部**平均薪资居中（约 ¥14,000）\n",
      "- **人力资源部**薪资最低（仅 ¥5,000），与最高部门相差 3.6 倍\n",
      "\n",
      "### 四、业务建议\n",
      "\n",
      "1. **关注薪资公平性**：人力资源部薪资远低于其他部门，建议评估是否存在岗位价值评估或薪酬定位问题\n",
      "2. **薪资结构优化**：整体薪资偏度较大，可考虑完善薪酬体系，适当提升低薪岗位的竞争力\n",
      "3. **数据限制提示**：当前仅 5 名员工，样本量较小，建议结合更多员工数据得出更可靠的结论\n"
     ]
    }
   ],
   "source": [
    "import warnings\n",
    "warnings.filterwarnings(\"ignore\")  # 抑制 matplotlib 的字体警告\n",
    "\n",
    "response = visualization_agent.invoke({\n",
    "    \"messages\": [{\"role\": \"user\", \"content\": \"分析一下员工薪资的分布情况，包括均值、中位数、最大最小值等统计量\"}]\n",
    "})\n",
    "final_msg = response[\"messages\"][-1]\n",
    "print(f\"\\n分析结果：\\n{final_msg.content}\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "15deca7a",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "=== 薪资统计概览 ===\n",
      "count        5.00000\n",
      "mean     13800.00000\n",
      "std       5890.67059\n",
      "min       5000.00000\n",
      "25%      11000.00000\n",
      "50%      16000.00000\n",
      "75%      17000.00000\n",
      "max      20000.00000\n",
      "Name: salary, dtype: float64\n",
      "\n",
      "中位数薪资：16,000 元\n",
      "薪资范围：5,000 ~ 20,000 元\n",
      "薪资标准差：5,891 元\n"
     ]
    }
   ],
   "source": [
    "# 数据探索\n",
    "print(\"=== 薪资统计概览 ===\")\n",
    "print(employees_df['salary'].describe())\n",
    "\n",
    "# 计算额外统计量\n",
    "median_salary = employees_df['salary'].median()\n",
    "print(f\"\\n中位数薪资：{median_salary:,.0f} 元\")\n",
    "print(f\"薪资范围：{employees_df['salary'].min():,.0f} ~ {employees_df['salary'].max():,.0f} 元\")\n",
    "print(f\"薪资标准差：{employees_df['salary'].std():,.0f} 元\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "055cc333",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "findfont: Failed to find font weight bold, now using 400.\n",
      "findfont: Failed to find font weight bold, now using 400.\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "分析结果：\n",
      "---\n",
      "\n",
      "## 📊 各部门平均薪资对比分析\n",
      "\n",
      "### 数据概况\n",
      "| 部门 | 平均薪资 | 员工数 |\n",
      "|------|---------|:-----:|\n",
      "| 🏆 **技术部** | **¥18,000** | 2人 |\n",
      "| 🥈 销售部 | ¥14,000 | 2人 |\n",
      "| 🥉 人力资源部 | ¥5,000 | 1人 |\n",
      "\n",
      "### 📌 业务洞察\n",
      "\n",
      "1. **技术部薪资最高**（¥18,000），分别比销售部和人力资源部高出 **28.6%** 和 **260%**，符合市场规律——技术岗通常享有较高薪酬溢价。\n",
      "\n",
      "2. **人力资源部薪资明显偏低**（¥5,000），仅为公司平均薪资（¥13,800）的 **36%**，建议关注该岗位的薪酬合理性，避免因薪资过低导致人员流失。\n",
      "\n",
      "3. **部门间薪资差距较大**，最高与最低相差 **3.6倍**，建议公司评估薪酬体系的公平性与激励性，确保既有竞争力又能留住关键人才。\n",
      "\n",
      "> 💡 如需进一步分析，比如各岗位的薪资与入职时间的关系、或按部门拆分更细的薪资分布，随时告诉我！\n"
     ]
    }
   ],
   "source": [
    "response = await visualization_agent.ainvoke({\n",
    "    \"messages\": [{\"role\": \"user\", \"content\": \"画一个柱状图，展示各部门的平均薪资对比\"}]\n",
    "})\n",
    "final_msg = response[\"messages\"][-1]\n",
    "print(f\"\\n分析结果：\\n{final_msg.content}\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "42693461",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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h5xDdK+IcolVHfD388MNz64ToahGDBk6urn87Mc5A/LuL/vwxWFqvXr1yk/MYTO33/L1Fv/f77rsvN5cvjfQfI+uHGAMhjhfnMbW/s7h+lFpz1OV9iCb0ceyqr7dkjTXWyNtqaqkwrfMFqA8aRfKe2ScBALUVISyCWwxENfmIyDHtUmn6rtI0U9RdhMEIVnFDY7PNNkv/+c9/Uu/evRvcOdRW3EQ44YQT0sCBA2f4xgAA9Y/RywGYrURFLsL1n/70pzxSePv27fNI69GXNqrdUamvqS87tRctHaJ/bbynbdu2raw2N7RzAIBy0LwcgNlKNNmN6bo6d+6cp5GKCmiE7RhBOQbCigGeYuAnpl9MYRU3MuIGx2OPPZZDb0M8h9qKJuIx/3g5B3kDoP7QvBwAAAAKotINAAAABRG6AQAAoCBCNwAAABTE6OV1NGnSpDx/ZszJ2qhRo2J+KwAAAMzSYvbt0aNHp4UXXjg1bjz1erbQXUcRuDt27Dijvx8AAADqgWHDhqUOHTpMdbvQXUdR4S69sa1atZqx3w4AAACzpVGjRuWCbCkjTo3QXUelJuURuIVuAACAhq3Rb3Q7NpAaAAAAFEToBgAAgIII3QAAAFAQoRsAAAAKInQDAABAQYRuAAAAKIjQDQAAAAURugEAAKAgTYs6MDRkw4cPT/3790+77bbbFNuef/759OKLL6bmzZunLbfcMnXo0KFOx/7yyy/TjTfemJo0aZL22GOP1LZt27JuBwAAykelG8pszJgxaeutt07XX3/9FNsuvfTStO2226Z33303Pf7442n55ZdPr7zySq2PPXjw4LTCCiukAQMGpCeeeCKtttpq6ccffyzbdgAAoLwaVVRUVJT5mPXaqFGjUuvWrdPIkSNTq1atZvbpMIsZOnRo+stf/pLGjRuXllhiifTII49U296xY8d06623prXWWis/3meffdLEiRPTNddcU6vjb7rppvnvL44RNt5449S1a9d06qmnlmU7AABQ3myo0g1lNGjQoPTPf/4z7b///jVu//nnn6v9g5xjjjlS06a16+UxduzY9OSTT6YDDzywcl2vXr3SQw89VJbtAABA+enTDWW0/fbb569nnnlmjdt33nnnXN0+6qijclX8jjvuSI899litjj1s2LA0YcKE1KVLl8p1nTp1Sp988klZtgMAAOUndMPvaLvttktXXHFF2nXXXXOz8h49eqTFF1+81s1XoiresmXLynUtWrTITdnLsR0AACg/zcvhd/L999/nAdb+9a9/5abeX3zxRV7Xs2fPWj2/WbNmKYZgiGp1STyec845y7IdAAAoP6EbficxTVj04T7mmGPy14UXXjidcMIJeWqx2oim4GHIkCGV6z799NM8OFs5tgMAAOUndMPvZJ555slNuas2545+3THiYW3MO++8ab311kvXXntt5bq77rorrbvuumXZDgAAlJ8+3fA7WWONNdIiiyySpxTbYYcd0tdff53OP//8dMQRR1QLwVERP+OMM2o8Rp8+fdLmm2+em4X/9NNP6d57780jppdrOwAAUF4q3VCAP/7xj2mrrbaqti76Tj///PNp1VVXzWE3wnXMj923b99q+8W0XlOz4YYbpqeeeipXyEeMGJEGDBiQlltuubJtBwAAyqtRRZS8KPsE6DA9nnvuufTaa6+lQw45xBsIAAD1IBtqXg6zkJg+bLXVVpvZpwEAAJSJ0F1Pbdnn1pl9CkAt3N9vJ+8TAEA9pk83AAAAFEToBgAAgIII3QAAAFAQoRsAAAAKInQDAABAQYRuAAAAKIjQDQAAAAURugEAAKAgTdMs5JFHHknt2rVLXbt2zY9/+OGHdOedd06x3+KLL5422GCDNGzYsPToo49W29arV68055xz5u/79++fHnjggbTMMsukfffdNzVu/H/3GKa1DQAAAMphlkmazz33XNpuu+3S22+/Xbnu559/ToMGDaq2nHvuuemmm27K2++444500UUXVds+YcKEvO3mm29O22+/fZp33nnT9ddfnw444IDK405rGwAAAJRLo4qKioo0kz344IPp8MMPT02bNk3//Oc/05577lnjfqNGjUrLLbdcrlJHhXrXXXfNFe999tmn2n7xkjp06JDOOOOMtNtuu6Uff/wxLbrooumNN95Iiy222FS3RQX9t8Q5tG7dOo0cOTK1atUqzaq27HPrzD4FoBbu77eT9wkAYDZU22w4S1S655577jRw4MDUqVOnae532mmn5Qp1BO7w4osv5qbk11xzTXrppZcq93v33XfTN998k/cNbdq0Sd26dUvPPPPMNLfVZPz48fnNrLoAAABAbcwSofvPf/5z7ss9LXH34LLLLkv/+Mc/8uMIzp988kk677zz0oABA3KILlXIP//881zRnmuuuSqf3759+/Tll19Oc1tN+vXrl+9elJaOHTuW6VUDAABQ380Sobs2Lr300rTZZpvlpuEhBj677bbb0v/+97909dVX56933XVXbib+66+/5up5VS1atEiTJk2a5raa9OnTJwf+0hKDtwEAAMBsN3r5tFx++eW50l0SlfEddtih8vFCCy2UunTpkl599dW01FJLpW+//bba86NZeAycFtXqqW2rSTRfL42GDgAAAPWu0v3888+nsWPHpvXWW69yXQymFiOPVxVNxNu2bZvD9+jRo9N7771XObBa9P9efvnlp7kNAAAAGlzovueee9Imm2xSbS7teeaZJ/Xu3Tu9/vrreT7vY445Jv30009p/fXXT82bN0877bRTOuigg3K/7xiALayzzjrT3AYAAAD1NnQvvPDCNQ61/sEHH+T+3FWtvvrq6ZRTTkmbbrpprm7H6OMPPfRQ5fNjPu9oct65c+d066235v7eTZo0+c1tAAAAUK/m6Z5REydOnGpojpfXqFGjOm+bGvN0A+Vknm4AgNnTbDVP94yaVpV6WqG6roEbAAAA6qJehG4AAACYFQndAAAAUBChGwAAAAoidAMAAEBBhG4AAAAoiNANAAAABRG6AQAAoCBCNwAAABRE6AYAAICCCN0AAABQEKEbAAAACiJ0AwAAQEGEbgAAACiI0A0AAAAFEboBAACgIEI3AAAAFEToBgAAgIII3QAAAFAQoRsAAAAKInQDAABAQYRuAAAAKIjQDQAAAAURugEAAKAgQjcAAAAUROgGAACAggjdAAAAUBChGwAAAAoidAMAAEBBhG4AAAAoiNANAAAABRG6AQAAoCBCNwAAABRE6AYAAICCCN0AAABQEKEbAAAACiJ0AwAAQEGEbgAAACiI0A0AAAAFEboBAACgIEI3AAAAFEToBgAAgIII3QAAAFAQoRsAAAAKInQDAABAQYRuAAAAKIjQDQAAAAURugEAAKAgQjcAAAAUROgGAACAggjdAAAAUBChGwAAAAoidAMAAEBBhG4AAAAoiNANAAAADSF09+3bN917773V1r300kupR48e1ZbRo0fnbRMmTEj//Oc/U+fOndO2226bvvzyy8rnTe82AAAAqHeh++STT079+vVLP/zwQ7X1Dz/8cGrSpEnq2bNn5TLnnHPmbUcffXR67LHH0rXXXpuWXnrptN1226WKiooZ2gYAAADl0jTNAs4777z06quvpnXWWWeKbS+//HLaZ5990jbbbFNt/c8//5wuvvji9Pjjj6fVVlstdenSJXXq1Cnvv9JKK03XtjXWWON3fNUAAADUd7NEpXv77bdPd911V2rRosUU2yIMDxgwIO28887phBNOSN99911e//bbb6fGjRunbt265cdRDY8Q/dprr033tpqMHz8+jRo1qtoCAAAAs03o7tChQ43rhwwZkkNuq1atct/rqIavu+66adKkSenbb79NHTt2zAG6pE2bNmnEiBHTva0m0eS9devWlUs8FwAAAGab5uVTs8QSS6Thw4entm3bVlbEF1988fTkk0+mRo0a5Sp1VdHXO8L09G6rSZ8+fdKRRx5Z+ThuAgjeAAAAzDaV7qmJcFwK3KFp06Z54LPPPvssLbTQQmnYsGG56l0SAT3WT++2mkQgj0p71QUAAABm+9B9ww03pIMPPrjy8U8//ZTeeOONXAGPAdHmmWeePCBaGDNmTHr22WfT6quvPt3bAAAAoME0L19vvfVy0+7o8x0jjF9yySVp2WWXzeujmfhRRx2VRzY/5phj0n333ZeD8/LLL5+fO73bAAAAoF6G7q222iott9xylY+j7/RTTz2VzjjjjNS/f/+0ySab5BAegTvE9+3bt0933HFHWnnllVPfvn0rnzu92wAAAKBcGlVUVFSU7WgNQAykFqOYjxw5cpbu371ln1tn9ikAtXB/v528TwAA9TgbztJ9ugEAAGB2JnQDAABAQYRuAAAAKIjQDQAAAAURugEAAKAgQjcAAAAUROgGgHouZge9++67f3O/Rx99NA0fPrxOx540aVJ65JFH0k033ZS+++67sm8HgNmd0A0A9dwxxxyT9t1332nu88ILL6QtttgivfPOO7U+7rhx49KGG26Y9t9///Tf//43rbTSSunTTz8t23YAqA+EbgCop3799dd00EEHpSuuuGKa+/30009p9913z1XnurjgggvSRx99lN544430zDPP5NDeu3fvsm0HgPpA6AaAemrw4MFpzJgx6eqrr57mfkcccUTq3LlzWnrppet0/Ntvvz1X0Nu0aZMfH3DAAenBBx9Mv/zyS1m2A0B9IHQDQD0VzbWvvfba1KJFi6nuEyH3/vvvT1deeWWdjz9kyJC0+uqrVz5eaqmlcpPxzz//vCzbAaA+ELoBoJ5q1KjRNLePGDEiV5qvuuqqNP/889f5+CNHjkzt27evfBzhPn5mNFcvx3YAqA+EbgBooGIAsx49eqTNNttsup7fuHHjKfqBx7pmzZqVZTsA1AdCNwA0QDE92D333JPmmGOOdOyxx+YlKt/XXHNNuuWWW2p1jA4dOqQPP/yw8nFMNzZx4sS0yCKLlGU7ANQHQjcANEARbE844YQ8iNlcc82Vl2jaHVXm2laat9xyy3TbbbdVPn7ooYfygGwtW7Ysy3YAqA+azuwTAAB+fyussEJeqrrhhhvSbrvtlufODu+//366/vrr03HHHZcr4pOL0ca7du2a+vTpk5Zddtn89eSTTy7bdgCoD1S6AaCea9u2bdpkk01+c7/u3btXG1Dtu+++SxdeeGFu8l2T5ZdfPvXv3z8NGjQonXnmmTk0Rz/xcm0HgPqgUUVFRcXMPonZyahRo1Lr1q3ziKutWrVKs6ot+9w6s08BqIX7++3kfWKW9fPPP6f99tsvV7sBgOnLhirdAECNxowZk0488UTvDgDMAH26ASjc2FMX8i7Phlr8/8vYmX0i/G6a9x3u3QYoM5VuAAAAKIjQDQAAAAURugEAAKAgQjcAAAAUROgGAACAggjdAAAAUBChGwAAAAoidAMAAEBBhG4AAAAoiNANAAAABRG6AQAAoCBCNwAAABRE6AYAAICCCN0AAABQEKEbAAAACiJ0AwAAQEGEbgAAACiI0A0AAAAFEboBAACgIEI3AAAAFEToBgAAgIII3QAAAFAQoRsAAAAKInQDAABAQYRuAAAAKIjQDQAAAAURugEAAKAgQjcAAAAUROgGAACAggjdAAAAUBChGwAAAAoidAMAAEBBhG4AAAAoSNMZefLrr7+eHnvssfT555+nUaNGpfbt26cll1wybbXVVmnBBRcs31kCAABAQ6l0P/HEE2mFFVZI22+/fRo+fHgO2t27d0/t2rVLzz33XOrcuXPq0aNHGjZsWJ2OO3bs2DRmzJgp1v/666/p3XffTd98802djjdp0qT08ccfp59//rlO2wAAAOB3D90TJ05MBx54YNp3333Taaedlj766KN0zjnnpEMPPTTtscce6R//+Ee67rrrchBfZZVV0h//+Md099131+rY48aNSxtssEG64447qq3v379/6tChQw71iy22WOrbt2/ltltvvTXNNddc1Zbvvvsub4vq+8orr5zWWmuttMgii6THH3+88nnT2gYAAAAzJXRHsI6g/dZbb6W//OUvU91vzjnnTMcee2wOswcddFB64IEHpnncESNGpE033TS9+eab1dZH1XunnXZK5513Xvrqq6/SwIED0xlnnJGGDBmSt7/44ovpX//6V96vtLRt2zZv++tf/5pWXXXV/Lzbb7899ezZs7KqPa1tAAAAMFP6dB9//PGpU6dOqVmzZrXaPyrdL7zwwm/2777yyitTr169cqV68ubml112Wdp2223z46hOzzfffLmZ+dJLL51D96mnnpqaNq3+MmJ79DWP5uMhKugLL7xwrpqvttpqU922xRZb1OXtAAAAgPJVuqPvdjQDr4toEj55mJ5cNEvfZ599plgfA7OVAnd4+eWX0/jx41OXLl3SL7/8kl577bV02GGHpXnmmSeH6aeeeirv9/7776f5558//+ySZZZZJlfIp7WtJvHzYpC4qgsAAAAUMpDavffem5tm33fffalcGjVq9Jv7TJgwIfcdjz7dzZs3T0OHDk1rrLFGroR//fXXaa+99kpbb711GjlyZA7GEayrimD+008/TXNbTfr165dat25duXTs2HEGXy0AAAANRZ1D99prr50HH9tmm21y+I4Q/ns45phj0hxzzJF69+6dHy+11FJpwIABac0110wtWrTIA7wtscQSuel47BeV8Jr6mk9rW0369OmTg3xpqeuI7AAAADRcdQ7diy++eK5yxxzdEXy322673Nz7nnvuSZ9++ukUSznceOON6aabbkq33XZbatKkSV43evToKQJwhO/oBx7nFZXwqtXrDz74IPdHn9a2mkQYb9WqVbUFAAAACpunO6y00krplltuSe+8804OwNH3OgL55MuMevjhh9PBBx+cK+pVB2SLUcd33333VFFRkR/HPN6vvPJKWn311XN/7ZhHPJqel0J19AcvTTs2tW0AAAAw00Yvn7yPdVSgo8/zZ599lvbbb788Wnk5/frrr3k6r6guH3fccZXrYxqyXXbZJV166aVp3XXXTYsuumgO59H0fLnllsv7nH322bkJ/BNPPJEGDRqUm4mX+nJPaxsAAACUS6OKUqm4lmI076uuuiqdfvrp6csvv8wDmEV/6wi+Myoq1VHNjj7jIUZKf/rpp6fYb/nll88DmkUoj2btw4cPT3/605+mCP2ff/55euSRR9Kyyy6b1llnnVpvm5YYiC0GVIv+3bNyU/Mt+9w6s08BqIX7++3UIN6nsacuNLNPAaiF5n2He58AypwN6xy6b7jhhrT33nunPffcM4ftqfWFrq+EbqCchG5gViJ0A5Q/G9a5eXnXrl2nOfAYAAAAMJ2hO5pjAwAAAAWE7kcffTT9+9//rvX+L774Yl1/BAAAADTM0N24ceM011xzTXX7999/n9566638fWlObQAAAGiI6hy6N9poo7xM7sMPP0znnXdeuvrqq3Mn8n322Scdeuih5TpPAAAAaDjzdJc888wzed7r+++/Pw+udvLJJ+fA3bJly/KcIQAAAMymGk/Pk2J+7BtvvDGPZL7eeuulESNGpNtuuy1Xuw8//HCBGwAAAKan0h0Do/Xo0SN98cUXac4550z77bdfWm211dIPP/yQrrrqqin2/+tf/+qNBgAAoEGqc+iOanYE7jB+/Ph02WWX5WVqhG4AAAAaqjqH7m233TZ98sknxZwNAAAANOTQ3aJFi7yEioqKNGHChNSsWbP8+KeffkpNmzbNzc4BAACgoZuugdSGDh2a+3XH1GA77LBD5fp+/fqlueeeO6244orp4osvzqEcAAAAGqo6V7pHjRqV1l133TRx4sS077775u9LtthiizTXXHOll19+OR188MHpm2++Sccff3y5zxkAAADqZ+i+/fbb08iRI/OAau3atau2rVu3bnkpVb1POumk1KdPH83NAQAAaJDq3Lw85uRebLHFpgjck9tggw3Szz//nKcSAwAAgIaozqF7zTXXTG+++Wa69957p7rPpEmT0vnnn58WXnjh1L59+xk9RwAAAGgYzcvXW2+9PPf2Nttsk9ZZZ5209tprp0UWWSQ3IY95u2OQtYcffji9++676eabb06NG0/XWG0AAADQ8EJ3uOyyy3LgjhHKzzzzzDyoWkmjRo3Saqutlh544IG0+eabl/NcAQAAoP6H7rD77rvnJarbn332WZ6jO6rdHTt2TPPMM095zxIAAADqe+iOinaTJk2qrYugvdRSS9XpOQAAANAQ1KnD9U477ZT+85//1Hr/MWPGpI033jjdfffd03NuAAAA0HBC99lnn52uu+66tMcee6Tvvvtumvu+8sorud/3kksumbbaaqsZPU8AAACo36F70UUXTQMHDsxzdC+//PLpsMMOS/3790+ffPJJ+v777/OI5bfffnvq0aNH2nrrrdPf//73dOmll2peDgAAQINU54HUYpC0s846Kx188MHplltuSSeffHIaNmxYGjVqVJ6TOyrb2267bbrmmmsMqAYAAECDNt2jly+++OKpT58+eQEAAABmsHk5AAAAUHtCNwAAABRE6AYAAICCCN0AAABQEKEbAAAACiJ0AwAAQEGEbgAAACiI0A0AAAAFEboBAACgIEI3AAAAFEToBgAAgIII3QAAAFAQoRsAAABm1dB9ww03pN69e6d33nmnPGcEAAAA9cQMh+727dunO++8M62wwgpp9dVXT5dcckn68ccfy3N2AAAA0JBD98Ybb5w+/vjj9Nxzz6WuXbum4447Li200EJp1113TY8//niaNGlSec4UAAAAGmKf7kaNGqW11147XXzxxWn48OHp2muvTU888UQO5Isvvni66KKLyvFjAAAAYLbStFwHiop2//7903XXXZfuuuuuVFFRkXr16pWWWWaZdPjhh+evG264Ybl+HAAAANT/SvfQoUPTP/7xj9SxY8e00UYbpQ8//DCdc845ueIdAfyYY45JSy65ZBo8eHB5zhgAAAAaSqX77rvvTjfeeGOuau+11165oj15BXyfffZJm2666Yz+KAAAAGhYobtnz57pkEMOSU2aNKlxe+PGjfOUYgAAANDQzHDobteuXXnOBAAAAOqZsoxeDgAAABQQus8+++zUvXv3GT0MAAAA1DszHLoXWGCB9Oqrr6ZffvmlPGcEAAAA9cQMh+7tttsudejQIZ1xxhnlOSMAAACoJ2Z4ILURI0ako48+Oh111FHp7bffTnvvvXcesbxkww03nNEfAQAAAA0zdN95553piCOOyN/feuuteamqoqJiRn8EAAAANMzQfcABB6Q999yzPGcDAAAA9cgMh+4555wzL+Xw2muvpbnnnjstu+yy1da///776dFHH03LLLNM2mSTTQrfBgAAAPVqnu4IwRtttFF68cUXq61//PHH06qrrpreeOONdOihh6YTTjih0G0AAAAwy1S6w/jx49OgQYPSt99+W7lu4sSJ6dlnn03nnnvubz7/hRdeSD179kzzzDPPFNsOOuig1K9fv3TIIYekL774Ii299NJp//33TwsuuGAh2wAAAGCWCd1jxoxJa665ZnrnnXem2NamTZtahe6vvvoqPfLII7nqXNUHH3yQPvroo8o+44ssskhaY401Uv/+/dNqq61W9m277rprjTcUYikZNWpUnd8jAAAAGqYZbl5+/fXX5+A9dOjQtNZaa+XHX375ZVpxxRVT3759az3Xd1SbJxfH7NSpU2rZsmXluoUXXjgNGzaskG01iYp469atK5eOHTvW+r0BAACgYZvh0P3xxx+nbt26pUUXXTT3yR44cGBaaKGF0tlnn51OOeWUNGHChOk+9rhx46Zoch6Pf/3110K21aRPnz5p5MiRlcvUwjkAAACUPXS3b98+vffee+mXX37JzbSjb3fo3LlzDqkjRoyY7mNHNfqHH36otm7s2LGpVatWhWyrSYzMHtuqLgAAAPC7hO5ddtkl973ecccd09prr50GDx6cjj766HTEEUfk5tjzzz//dB975ZVXzoOzffbZZ5XrXnnllTzNVxHbAAAAYJYK3R06dEjPPPNMDtxRBb744ovTJZdckh544IF0zjnnpCZNmkz3sWMgti222CL985//zIOZ3XjjjTkwr7/++oVsAwAAgFluyrAuXbrkJfTq1StP/zVp0qQ6B+7Yv3Hj6vcBLrzwwrTDDjvkZuFROb/55ptzk++itgEAAEC5NKqoqKhIs4GYVqxdu3apadOmv8u2qYkpwyKoR3/1Wbl/95Z9bp3ZpwDUwv39dmoQ79PYUxea2acA1ELzvsO9TwBlzoZlqXT/HhZccMHfdRsAAADM9D7dAAAAQJkq3THvdsx1XVuTz4kNAAAADUWdQ/ctt9ySB0urrdmkyzgAAADM/NC9wQYbpIcffrj8ZwIAAAANPXQvtNBCeQEAAACmzUBqAAAAUBChGwAAAAoidAMAAMCs0qe7JuPHj0+DBg1K3377beW6iRMnpmeffTade+655fgRAAAA0PBC95gxY9Kaa66Z3nnnnSm2tWnTRugGAACgwZrh5uXXX399Dt5Dhw5Na621Vn785ZdfphVXXDH17du3PGcJAAAADTF0f/zxx6lbt25p0UUXTRtttFEaOHBgnlLs7LPPTqecckqaMGFCec4UAAAAGlrobt++fXrvvffSL7/8ktZYY43ctzt07tw5jRw5Mo0YMaIc5wkAAAANL3Tvsssu6YMPPkg77rhjWnvttdPgwYPT0UcfnY444ojUunXrNP/885fnTAEAAKChDaTWoUOH9Mwzz6Qnn3wytWrVKl188cXp4IMPzs3KL7zwwtSkSZPynCkAAAA0xCnDunTpkpfQq1ev1LNnzzRp0iSBGwAAgAZthpuX16RRo0YCNwAAAA3edIfu+++/P11xxRWVj6M5+YknnpgHUIt5uy+44IJUUVHR4N9gAAAAGq46h+6JEyemjTfeOG211VZ5Tu6Sww8/PP373//Oo5k3b948HXrooalfv37lPl8AAACov6H73nvvTY8//ni68sor09NPP53Xvfbaa+miiy5K//rXv/K6/v37p1NPPTX95z//KeKcAQAAoH6G7vfffz8tuuiiae+99859t0NUtDt16pRDd8nmm2+efvzxR/N0AwAA0GDVOXRH4B4xYkRlmH7nnXfSXXfdlXr37p2aNWtWud9bb72VH7dp06a8ZwwAAAD1NXRvs802qV27dmn11VdPu+++e1p//fXTEksskfbZZ5+8/dVXX02nnXZaOuSQQ9LWW2+dmjYty6xkAAAAUP9Dd4sWLXK/7ZVWWik99dRTeX7uBx98MM0999x5+2WXXZb69OmTunbtmvt5AwAAQEM1XWXoxRdfPN1zzz01buvbt2864YQT0gILLDCj5wYAAACztbK3/Y4+3wAAAMB0NC8HAAAAakfoBgAAgIII3QAAAFAQoRsAAAAKInQDAABAQYRuAAAAKIjQDQAAAAURugEAAKAgQjcAAAAUpGk5DvLjjz+mK6+8Mr366qupd+/e6a233krjxo1L++23XzkODwAAAA0zdP/888+pW7du6ZNPPkmNGjVKvXr1Sj/99FM69NBD03fffZf69OlTnjMFAACAhta8/IYbbkgTJkxIw4YNSyuvvHJed8ABB6Rrrrkm9evXL1VUVJTjPAEAAKDhhe73338/bbzxxmn++eevtr579+5p9OjR6euvv57RHwEAAAANM3R36NAhvf7661NUtAcMGJCaN2+e2rZtO6M/AgAAABpm6O7Zs2caMmRI2mSTTdJXX32VnnjiiXT00UfnQdQOPPDA1KxZs/KcKQAAADS00N2uXbv05JNPpjFjxqShQ4ems846K1188cU5cEefbgAAAGioyjJl2EorrZReeOGFNGrUqBy+27dvn5o2LcuhAQAAYLbVtBxzdE+aNCnNN998qVWrVnkBAAAAytC8/PTTT0///ve/a9x28sknp65du+bpwwAAAKChmeHQvdVWW6U33ngjbbbZZmmbbbZJjz76aF7/4osvpuOPPz4PsHbkkUem7777rhznCwAAAA0ndM8111zppZdeSm3atMlThG255Zbpf//7X3r11VdTly5d0imnnJJ22GGH1L9///KcMQAAADSUPt133nln2nHHHdP111+fH88xxxzp9ttvz328S3N0d+zYMU8nBgAAAA3JDFe6W7ZsmQN1DKZWUVGRhg8fnuaZZ548wFpMJxZGjx6dWrRoUY7zBQAAgIZT6d5rr73Sueeemzp16pQf//DDD2nBBRfMfbobN26c3n333fTwww/n/t4AAADQkMxwpTvm5I6B1I466qi8fPzxx7l/99prr51OOOGEPId3NDVfc801y3PGAAAA0FAq3aXgffjhh1c+Pu+88yq/33TTTc3dDQAAQIM0w5Xu39KqVauifwQAAADU30r3+PHj06BBg9K3335buW7ixInp2Wefzf29AQAAoCGa4dA9ZsyY3F/7nXfemWJb9O2ekdAdo6KfdtppU6xfZZVV0p577pnefvvtdMUVV1TbFvOCl0ZKv+CCC9L999+flllmmby+atV9WtsAAABglmheHvNzR/AeOnRoWmuttfLjL7/8Mq244oqpb9++M3TsmPN7ySWXrLYMHDgwD9wW7rvvvvTee+9V296kSZO8rV+/fjnwH3DAAWnkyJFphx12qDzutLYBAABAuTSqiMm1Z8Df//739Pnnn6ebb745HX/88bmJ+UUXXZSeeOKJ1KNHjzRixIjUtGlZWrGnL774Iq266qrpzTffzIO3xTRkO+20U9pll12q7ffrr7/mOcJvv/32tPHGG6dffvkldejQIZ/TcsstN9VtMdL6bxk1alRq3bp1DuuzcnV8yz63zuxTAGrh/n47NYj3aeypC83sUwBqoXnf4d4ngFqqbTYsy5RhUW2O8LrGGmvkvt2hc+fO+YdH6C6Xf//73+mQQw7JPzO89NJL6aOPPkq9e/dOV155ZRo3blxeH83O43z+/Oc/V1bMowl87D+tbVPrrx5vZtUFAAAAamOGQ3dUmT/44IO044475rm5Bw8enI4++uh0xBFH5NQ///zzp3JVue++++500EEH5ceffvpp+vrrr9OHH36YFlpoody3e8MNN6zsC77oootWq7DPO++86ZtvvpnmtppEU/R4HaWlY8eOZXk9AAAA1H8z3O47mmY/88wz6cknn8wl9YsvvjgdfPDBacKECenCCy+s7GM9o84///zUs2fPPDhbiGr3W2+9lZZffvn8OPpnL7bYYnnE9GgxHxXsquaee+78dVrbatKnT5905JFHVj6OSrfgDQAAQG2UpbN1ly5d8hJ69eqVw/GkSZPKFrgjwF977bXpoYceqlzXvHnzysBderzCCiukIUOG5L7ZURmvKpq5d+3aNVfep7atJnPOOWdeAAAA4HdvXl6TRo0alS1wh8ceeyxXuEvBPtx1113pxBNPrDZ42vvvv5+r0H/84x/zz4+RzkP04Y4KeKyf1jYAAAAop/IMK16wmE97o402qrYuqtr77rtv+sMf/pA6deqUzjvvvDTffPOl7t275/7a+++/f9p7773TGWecke688860+OKL55HPw7S2AQAAwCxT6T777LNz0C1S9MHefvvtq61beumlc7X76quvziE6mo33798/NWvWLG8/4YQT0h577JG/RvP0GIStZFrbAAAAYJaZp/vGG29MBx54YJ6fe/IByuoj83QD5WSebmBWYp5ugFlwnu7tttsuj2AeTbUBAACAMvbpjpG/Y17uo446Kr399tu5r3Tjxv+X5UtzZwMAAEBDM8OhOwYiO+KII/L3t956a16qmsHW6wAAANBwQ/cBBxyQ9txzz/KcDQAAANQjMxy655xzzrwAAAAAZR5IDQAAACio0h1+/PHHdOWVV6ZXX3019e7dO7311ltp3Lhxab/99ivH4QEAAKBhhu6ff/45devWLX3yySepUaNGqVevXumnn35Khx56aPruu+9Snz59ynOmAAAA0NCal99www1pwoQJadiwYWnllVeuHFztmmuuSf369TN6OQAAAA3WDIfu999/P2288cZp/vnnr7a+e/fuafTo0enrr7+e0R8BAAAADTN0d+jQIb3++utTVLQHDBiQmjdvntq2bTujPwIAAAAaZuju2bNnGjJkSNpkk03SV199lZ544ol09NFH50HUDjzwwNSsWbPynCkAAAA0tNDdrl279OSTT6YxY8akoUOHprPOOitdfPHFOXBHn24AAABoqMoyZdhKK62UXnjhhTRq1Kgcvtu3b5+aNi3LoQEAAGC2VdZk3KpVq7wAAAAAZWhefvvtt6cTTzwxz9MNAAAAlDF0t27dOl111VVpiSWWSOutt17+PqYKAwAAgIZuhkN3zNEdVe6nn346Lbvssql3795pgQUWSLvttlt67LHH0qRJk8pzpgAAANDQQndo1KhRWnfdddOll16apw275ppr0uOPP56nEfvss8/K8SMAAACg4Q6kVlFRkZ599tl044035n7e48ePz3N4t23btlw/AgAAABpWpTvm5j766KNTp06d0vrrr5+GDBmS5+r++uuv0/XXX59atmxZnjMFAACAhlbpvvvuu9Ndd92V9t1337T77rvn8A0AAACUIXTvs88+6fDDD/deAgAAQLmbl0+t+fjEiRPTCy+8MKOHBwAAgNlW2QZSCyNGjEiPPPJIeuihh9Kjjz6avv/++zzAGgAAADREMxS6I1C/8sorOWTH8r///S/Py73oooumrbfeOm244YblO1MAAACo76F75MiRuYodITuq2jFK+XzzzZe6d++ePv/887TXXnulk046qZizBQAAgPrcp/ucc85JO+20U7r22mvTqquumgYOHJi+/fbbdMcdd6QOHTqkFi1aFHOmAAAAUN8r3dtvv33uq/3www/naveAAQPSuuuumzbYYIP0448/FnOWAAAA0BBC94orrpjOP//8/P2QIUMqw/cxxxyTxo8fn84666z05ptv5hD+5z//OS2++OJFnDcAAADU74HUll566bwcdthhaezYsenJJ5/MITyWm2++Oe9j9HIAAAAaqrJNGda8efO05ZZb5iUMHjw4V8ABAACgoSrrPN1Vde7cOS8AAADQUNV59HIAAACgdoRuAAAAKIjQDQAAAAURugEAAKAgQjcAAAAUROgGAACAggjdAAAAUBChGwAAAAoidAMAAEBBhG4AAAAoiNANAAAABRG6AQAAoCBCNwAAABRE6AYAAGaKk08+OfXs2bNy2X///ev0/I8++ij16NEjrbjiiql3795p/PjxZd0O5SB0AwAAv7uKiop09tlnp+WWWy6ts846eVlzzTVr/fxRo0al9ddfPzVv3jydeeaZ6bnnnktHHnlk2bZDuTQt25EAAABq6YMPPki//PJL6tOnT2rcuO61wKuvvjo1adIkXXXVValp06Zp0UUXTSuvvHI65ZRTUps2bWZ4O5SLSjcAAPC7e+mll3Kz7ltuuSX997//TYMHD67T85999tn0l7/8JQfmEBXztm3bppdffrks26FchG4AAOB3N3DgwPTKK6+km2++OT3xxBNp1VVXTaeffnqtn//ll1/moFxVu3bt0vDhw8uyHcpF83IAAOB3171797TbbrultddeOz++77770s4775z23XffNN988/3m82PQs+iPXVWLFi3SpEmTyrIdykWlGwAA+N3tsMMOlYE7bLXVVrmp92uvvVar588zzzzpu+++q7Zu7NixqXXr1mXZDuUidAMAAL+riRMnpuuvv77aFF0xqFoszZo1q9UxunTpkv73v/9VPh43blwaMmRIWnLJJcuyHcqlXoTu+AdS7m0AAEAxYtTwmKP7mmuuqVx37rnn5upz9O2ujV122SXde++96c0338yPL7nkkrTAAgvkwdnKsR0aTOh+8MEH892mqssPP/yQt8XXTTbZJLVs2TItvvjiadCgQZXPm95tAABA8S644ILUu3fvPFf26quvnk466aR0+eWX537V4euvv05zzTVXeuaZZ2p8fjznoIMOSt26dctL375908UXX5waNWpUlu3QYAZSGzBgQNpmm23SwQcfXLmu1M/ib3/7W5pzzjnT6NGj0x133JH7hUSTkGiSMr3bAACA4m288cbpvffeS4888kgOuptuumlaeOGFK7fHXNkRhquum9xZZ52VK9ZvvPFGDu9LLLFEWbdDOTSqqKioSLOw+OM/+uij8z/Cqn788cc8pP9bb71VOdR/fI07Zl27dp2ubRtuuOFvns+oUaNy6B85cmRq1apVmlVt2efWmX0KQC3c32+nBvE+jT11oZl9CkAtNO9rqiRmrX7fUQk/55xzZvapwAxlw8az+j+0aPp99tlnp2WXXTZtu+22lX0uBg8enF9g1bn1ov9FrJ/ebTWJwR3izay6AAAAxff7FripD2bp5uUffvhhHsxgv/32y8H4uuuuSxtttFH69NNPc7/sBRdcsNr+0Uc7QvH0bqtJv3790gknnFDAqwMAYGb4YueG0coIZneL3FI/Wu/O0pXuZZZZJn300UepR48e+ftTTjklzTfffOnRRx/Nc/hFJXzyu2HRL3t6t9WkT58+ublAaRk2bFgBrxQAAID6aJYO3ZMmTcpBt6qofEe1OkYdHzp0aLW5/aIC3rFjx+neVpMYcC3a51ddAAAAYLYP3TFlwO677175+IsvvsgT2K+88spp6aWXzgH6pptuytuGDx+ennvuubTuuutO9zYAAABoMH26d9555zyMf3zt1KlTuvnmm/P3Xbp0ydujufmee+6ZXnzxxfT000/n7zt06DBD2wAAAKBBVLpjlPGobK+xxhopZja75JJL0pVXXlm5PUYzj3m8F1pooTzY2UUXXTTD2wAAAKBBVLpLwfuII46Y6vZVVlklL+XcBgAAAPW+0g0AAACzM6EbAAAACiJ0AwAAQEGEbgAAACiI0A0AAAAFEboBAACgIEI3AAAAFEToBgAAgIII3QAAAFAQoRsAAAAKInQDAABAQYRuAAAAKIjQDQAAAAURugEAAKAgQjcAAAAUROgGAACAggjdAAAAUBChGwAAAAoidAMAAEBBhG4AAAAoiNANAAAABRG6AQAAoCBCNwAAABRE6AYAAICCCN0AAABQEKEbAAAACiJ0AwAAQEGEbgAAACiI0A0AAAAFEboBAACgIEI3AAAAFEToBgAAgIII3QAAAFAQoRsAAAAKInQDAABAQYRuAAAAKIjQDQAAAAURugEAAKAgQjcAAAAUROgGAACAggjdAAAAUBChGwAAAAoidAMAAEBBhG4AAAAoiNANAAAABRG6AQAAoCBCNwAAABRE6AYAAICCCN0AAABQEKEbAAAACiJ0AwAAQEGEbgAAACiI0A0AAAAFEboBAACgIE3TbOCjjz5KL7/8cmrfvn3685//nBo1apTXjx07Nn322WfV9l1qqaVSkyZN8vfffvttevbZZ/O6FVdcsdp+09oGAAAADaLSfemll6aVV145XXvttWnPPfdMW265ZaqoqMjbbrrpprTOOuukHj16VC6jR4/O2wYNGpSWWWaZ/PyNN944/fe//6085rS2AQAAQIMI3cOHD0//+Mc/0vPPP58eeeSR9Morr6Qnnngivf7663l7VL//9a9/pbfffrtyadOmTd623377pcMPPzw9+uij6amnnkp///vf0/fff/+b2wAAAKBBhO7WrVvnYB2V7jD//POnueeeO/3666/58UsvvZT++Mc/pg8//DCNHz++8nlDhw5Nr732WjrooIPy42WXXTatssoq6cknn5zmtprEcUeNGlVtAQAAgNk+dDdv3jw3Ay+5//77U8uWLVOXLl3SmDFj0jvvvJO23377tNlmm6UFFlggXXLJJXm/jz/+OHXs2DG1bdu28rnx+NNPP53mtpr069cvh//SEvsCAADAbB+6qxo5cmQ67LDD0umnn56aNm2avvvuu1ytHjJkSPrggw/SAw88kI444oj0+eefp59++qmymXlJhPWoWk9rW0369OmTf3ZpGTZsWKGvEwAAgPpjthi9PAZO23vvvdOf/vSntMsuu+R1nTp1Suedd17lPjGg2qqrrpoGDBiQFl544Smagf/yyy+pRYsWeZnatprMOeeceQEAAIB6Wek+9thj89RgMdp4SVS4o093VVGtbty4cVphhRXyIGzffPNN5bY333wzLbnkktPcBgAAAA0qdJ9//vnpuuuuS/fee28eRK1k8ODBaZ999slzdYcHH3wwvfvuu7kaHgOurb/++umkk07K22KQtGiCHnN8T2sbAAAANJjm5dH/OqYMm2OOOdLqq69euf60005Lu+22W7rvvvvSYostloP0l19+mS6//PLUoUOHvM+FF16YNt988/w4mpPHtlIT8mltAwAAgAYRuueaa64aRxWPUcQbNWqUrrrqqlyljubiMa1YrC9ZaqmlcjX8jTfeyP2/I5jXZhsAAAA0iNDdpEmTtOCCC05znwjQsdSkWbNmqWvXrnXeBgAAAA2iTzcAAADMroRuAAAAKIjQDQAAAAURugEAAKAgQjcAAAAUROgGAACAggjdAAAAUBChGwAAAAoidAMAAEBBhG4AAAAoiNANAAAABRG6AQAAoCBCNwAAABRE6AYAAICCCN0AAABQEKEbAAAACiJ0AwAAQEGEbgAAACiI0A0AAAAFEboBAACgIEI3AAAAFEToBgAAgIII3QAAAFAQoRsAAAAKInQDAABAQYRuAAAAKIjQDQAAAAURugEAAKAgQjcAAAAUROgGAACAggjdAAAAUBChGwAAAAoidAMAAEBBhG4AAAAoiNANAAAABRG6AQAAoCBCNwAAABRE6AYAAICCCN0AAABQEKEbAAAACiJ0AwAAQEGEbgAAACiI0A0AAAAFEboBAACgIEI3AAAAFEToBgAAgIII3QAAAFAQoRsAAAAKInQDAABAQYRuAAAAKIjQDQAAAAURugEAAKAgQjcAAAAUROgGAAAAobu87r///rTffvuls846K/3666/+wAAAACi7Blnpvuqqq9Lee++dlltuufTwww/n7wEAAKDcmqYGZtKkSalPnz7p4osvTjvssEP661//mjp27Jg+/PDDtOSSS87s0wMAAKAeaXChe/DgwemHH35IW221VX7csmXL1K1bt/Tss8/WGLrHjx+fl5KRI0fmr6NGjUqzsl/Hj53ZpwDUwqx+LSmXseMmzexTAGphQgO5Jo3WtRBmC6Nm8WtS6fwqKiqmuV+DC91ffPFF6tSpU5pzzjkr180///zpq6++qnH/fv36pRNOOGGK9VEdB5hRrc/RvQWYhZzUemafAcD/uevuNDsYPXp0at166tfPBhe6J0yYkOaee+5q65o3b56bndckmqIfeeSRlY9jv++//z61bds2NWrUqPDzhdJdtLjRM2zYsNSqVStvCjBTuSYBsxrXJWaGqHBH4F544YWnuV+DC93zzjtv+vrrr6ut+/HHH9Mqq6xS4/5REa9aFQ9t2rQp9BxhaiJwC93ArMI1CZjVuC7xe5tWhbvBjl7epUuX9PPPP6e33367snI9cODAtMIKK8zsUwMAAKCeaXChe6655kq77bZb2n///dNbb72VjjvuuLxurbXWmtmnBgAAQD3T4EJ3OOuss9KKK66Yunfvnp5++ul0zz33pMaNG+RbwWwiujj8+9//nqKrA8DM4JoEzGpcl5iVNar4rfHNAQAAgOmivAsAAAAFEboBAACgIEI3AAAAFETohoKdeOKJaezYsdXW/fTTT+nOO++s9TGGDBmSbrvttsrHk881X9WXX36ZTjvttOk8W6C+i5k7ahJTaQ4YMGCaz7366qvT6NGj8/cvvfRSuvbaa3/z540bNy4PBBnTdQLEZ6AY1Dg+r9TFG2+8kc4777xaXXMm98ADD6QXXnhhms979913K78fM2ZM+vHHH2tcRowYkb799lu/SOrEQGpQsBYtWuT/WFq3bl25btiwYekPf/hD+vXXX2t1jJtuuimdcMIJ6f33308ff/xxWmWVVdL999+f1ltvvSn2jf8oYt75k08+OfXs2TOvGzp0aLr88sur7bf33nvncwAajuHDh+d/97fcckvaeuut01dffZWeeuqptMsuu6QLL7wwf6iNa8W9996bllhiiXwtqfqBdOWVV07PP/98Wm211dJHH32U9thjjxzUmzRpMs2fu+mmm6Zll102nXvuufnxhAkT0vHHH19tn0022ST96U9/KuiVA7OKCK3zzz9/euWVV1KXLl1q/bw77rgjHX744enzzz+f5n477LBDatq0ab7eLLDAAvk6t/TSS+fr1dprr11t38033zy1atUqTZw4Ma2++uppueWWS1dddVX685//nAsezZs3n+L48dmtQ4cO+cYj1JZKNxQsLvylD6RRrb777rvTE088kaepi+nqYrn99tunede0WbNmeT75EB+YjznmmLTzzjtXqxzFB+UI4+uvv35ef8opp+T/zNZZZ530zTffpIsuuigtuOCC+Q7wf/7zn/wfHtCwnHrqqalt27Zp5MiR6ZprrsnXgwMOOCCH7bjGxLUq1u2///7ps88+q/bc4447Ls0777zpiCOOyNeVnXbaKf3www/55l88XnPNNfMH1viAGwYOHJhWWmmltOqqq+YbhnHti+9jys4I/3EucVMyrkXx/W8Fd6D+fC4KcS2q6/NqCsGTu+GGG9KSSy6Zw3dU1eOm4kYbbZRbHUYrw9dffz1/ZooWOPPMM09+Tlx/YhrhTz/9NH9Gm2OOOdJll12WH8cSNxrjM1x8/8UXXwjc1Nn/+6sHyu7vf/97/mAbH2APOeSQtMUWW+SKUATouPDHbH1xgQ+xTwTm+PAZd1bjLmrVD6DxAfWXX35J7733Xn78l7/8Jc0333z5cYTwqKJvs802+ee1bNkyV4xi/3XXXTf/xxLr4sPtwQcfnKtZG264YV4HNBzROiaah0crl//973/5xt+ee+6ZjjrqqFzZXnjhhfN+t956a75hFxWgktgeVaaoTkfYLh0vgvQVV1yRH48fPz4fo3RDL8J13759c3P2v/71r6lRo0apR48eaa+99srVp/jwHN/Hh9h4HKEdaDjic8q03HfffbmIENequH7EdStCdFyLJhfXrFLrvZiv+6STTsrXpLihGDf/zjnnnHxzL44ZhY4oPiy66KL52CEq3fG5qNRyJ5q/hyiMvPzyyzmsL7PMMumxxx7L6+PaVnou1Ibm5VCQuFMa/6HsuOOOud9jVIA6d+6ct0UlKO7CRlPwye22227p1VdfrQzd33//fQ7dpf9I4nkhQnv8hxLNoCJch7grG83Ko6rUv3//XJEaPHhw/lAbFfBokhWBffvtt88fdoGGI5pLRguZCNqTJk3K66K6HaE4bthFn8e4kde7d+/c/Ltr1665ChTdY+IDbVR+IhxHs/NSt5VPPvkkX1tC9PU+8MAD8w3Gkmg+utZaa+X+4nHjb6mllkoffPBBDudt2rTJP++///1vbukTrXGA+i+6qsTnoeiqEteHqfnb3/6Wrw3xeSgCblxv4vNNdFepKm4Kxs2/+PxU8s477+Tr3YsvvpivPVHciO4yc889d/rnP/9Z2f2u5Mwzz0wPP/xwOuOMM3KLnO7du6fDDjssh/QolESxIsRNgEMPPTRfQ+NGANSWSjcUpHSBjuZQUXmOD7HR/yeCdnzAjbuqpUp3u3btKvtO3njjjdWOEwE5nheB+7vvvkuPPPJI7ks0uVgfx4mfF5WqOE70j4xqVFTFw6hRo9Kzzz6brr/+er93aIA3AqPPYnxgjGtCNLmMak60yokPtFH1iS4o0TQzrh/PPfdc7sMdoTg+vMYH3gjhpX7XUf2JQYVKoTtUvTbFdSu6tkTFOyricfMvPsxGtarqeBJ33XVXboEDNAxRfQ6/Fbrj2lNTMI4xKaqKbi+lzzkhChfx2eniiy/OITuKENEtb7PNNktHH310vgEZTcXjuhbXp/j8FX3F4yZktBqMwB6f0eK6GDcbo2AR168QxY64CSBwU1dCN/wOoroc1egIztGHKPpL7rPPPrmJVITiqDpXHbCoJO7oxvYYRC2CdBwjRu6MZlGTi/+I4gNw7BNhfpFFFsn/acR/LFtuuWXeJ+7WRpPO+I8uzgloOOID5KBBg3IFO6pC0aUlxIfK/fbbr9o1IW7ixfpSX+74gLntttvma0hcZ0J8HzcTS4+jW0zV1jvRpzs+/Ma1LVrchAjb0bSzVA2Pa1R0uXnmmWcqW+wA9VuE4Ai+l156aTryyCNrPZ5DfCYqtfarKlrZlEJ3jDMRlfC4kRdfY8DIaDEY3fx23XXXyhY40bKmX79+qX379unJJ5/MBYvoghefzSKoR2U8Qni0KowWPjEQZJh8NhqoLaEbChB3QqOaHHdpo6odfSZLwffRRx/NFaYYeCj+c4im57169ZriGHGhj36Qu+++e26SWfrwG5Wn+PA7+d3hCOPxH098iK0qmlVFM9BQqmLFHeA333wzV7aAhiE+aEb/xGgufskll+QPqfGBN8J4VL+PPfbYyn1jn1IlpzToUTSnjKU0HU90n4nrTelxXLOiiXrcXIxgH5WjaIoZ18Oq4oNsLDGAW3zYjkp3NF9fY4018mBHQP0VfbKjUh3FgQjCMc5EfNapjWip949//KPaugjBcR2K7iqlqvdrr72WixWxb1yX4loWfbBjqSrOI1r/lERrwCuvvDJfu2Jq1lKz9Kh8l2abia/x80rHhdoyAgAUICo3MQ1PzNEdg5xF36CS6HNU6rsYX+NObKmPZFXxAfjDDz9Mp59+euW6xRdfPP+nEQMZlQZVqypGEY7mm9HcM5Zo6vnggw9W22eDDTbITd9rM9clUH9EK5e40RaheKGFFsrXh1KgPv/88/OIvqWlNKhaVfHcGJAoKtexRNUo+j+WHkclKK5R8UG1JPpExngWpWtS3ECMcSeq6tSpU27iPvl6oP457bTTcvEgbrRFKI7PNNEN5bdE3+wYNTyKBlWVWtqUQneIln5RhIjrSgTmGFPi7LPPzoOkxfexxPUwgnPVwdBi+wUXXJDXR+iOa96//vWv3Ew9rmMxvk4cM1rxQF2pdEMB4iI+ed/skmhKHv2SollVfNB94YUXptgnLvIxv2T8BzD5lBrRHymaWEWlO6rpVQctirBfCvIhqunxAXty0VQq+mcCDUd8aIwlrj3RbzFCcmkqwhh0seq82aXBG6uKljnRTLyma1bJddddV60FTVyTDjrooMoqelSQXJOgYYrgHONIxHgQIZp+x+eYaP0XBYLS9Why0WImquLx+SkKGZPP+R0V56p9ukviWhPjUMQ+EdajWFGaIiw+p5UGlCyJsSdihoUI6hHKO3bsmFsjRqEiiiNRQY+blXH+qtzUldANv7PoJxlNxmNUzgjQVfsnxX8sccGPPtfxH1A00axJTAMWd3W32mqrPGpnqS9mqWpVVdV10T88Kk/R3Pzmm28u5PUBs764KRjVnhgLIsQH4apThNXU+iYCd58+faY6tVdMpzN595bfuiZFa55ofROjGU/ebBSoP2JgxRjILCrOVcewia4uUSCI6098LomiQFUxbkR8LorKczQZn9zHH3+cpymMG3xVRYubUqub+BkxAGQUMeIaFuE9uvVV/VkxHkV0dYkpwWIMnTjX6IYXn9ViBPOqNx+jOXz0+459oLY0L4eCRD+gaKYZF/Lotxj/ccTFPAJy9PWOwdDiP5iYMzfunoa4iMfFPP5zqjoicDR1iqXqh9aYczI+sFYd/Cg+8MZ/JHEnNpYYJbjU/yhEdTz6bsYH3JjKDGh416W46fbQQw/l1jShNEpvXFei8hMfNKPJ5uSDG0VlJ6bviZF8a1pK+1QVx45rXemaFP0lS30jY1vcdNx3331z9TyanQL1S4zpEF3toptdtHiJEFtVBObo3x2hOkYSj+bdJTG9YDwvPkvF56for121VU0E+Jj+a7XVVpvi58ZgkTEtWQTsm266Ke8f/cmjFU80bY/CRukzUmnK1aiCR0Ejut/FyOaxxLnHWBOxX1zf4hyjtdAee+yRCyVQWyrdUJAYUCj6b8eFOarbcZc1LvYxOmbcRY07sDFicNy5jf9UIgzHxT0GSavpg3LpP4aqov9kVbFPNNWq2rw8BjsqDWQUd3mjeSfQMMW1KEbnjapyqTlm9H8sNdmMm3tx4y4qOIsttli150YfyWh+GS1xahI3FmOZ/JoUfSCrNi+Pa1IoXZdck6D+ipt4EXhjEMeNN964xn1ijIkY6PGkk06qXPfVV1/lcWpiHIgIx5OPMxE3DiOsd+vWLY8tURJBOLrPxPGikh2hOyrdpabr11xzTe7aF+E+PndF1T2CdAzkGK0PowgS07zGz95zzz1T79690+OPP56vUzEobthhhx1yS8QolEBtNaqoWj4Dyqb0AbR0oY9RzFu2bFlt0I6SuPsa/xRLfY2mV8zjHT+jNNVPTJMR4T4qSlEVj0GQAKZHzLwQAzW2a9euxu0x7Vd8EI2+kFVHA46KeamZZwT3qBbFtS4+FMeASjU1QQfqj2hBU9Nnn98SA6dFIK/pudMaPTz6ZEcFPaYDm5a4AVj6vFRV3BCsOvZEFD5iusUoZMD0EroBAACgIPp0AwAAQEGEbgAAACiI0A0AAAAFEboBAACgIEI3AAAAFEToBgAAgIKYHBMAZgExZ2zMZxvzWjdr1mxmnw4AUCYq3QAwC1hrrbXS3HPPneaYY47Upk2btOWWW6ZBgwbN7NMCAGaQ0A0As4iuXbumJ554Ip144onptddeS+uss0566aWX0qzu0EMPTTfccEOaVcxq5wNAwyZ0A8AsonXr1mmDDTbIoTGq3M2bN099+/ZNs7pnnnkmvffee2lWMaudDwANm9ANALOgBRdcMPXo0SMNGDAg/fzzz9W2jRo1Kr344otp6NChUzxvxIgR6ccff0zjx49PL7/8cvriiy+m+jO+++67vM9HH31U4/Zvvvkm/6zw66+/pjfffDP3PQ8VFRXp888/z0tsGz16dOXj+Nkhvsbj2Pett95Kw4cPz+s//PDD9MEHH0zx86b1uuI1xWsLX3/9dW4BEOdfUpvzAYCZQegGgFnUiiuumCZOnJg++eSTynWnnHJKWmCBBdJWW22VllxyybTJJpuk77//vnL7zjvvnLbffvu0xBJLpG7duqUOHTqkXXbZJY0bN67asY8++ugc7Ndee+18nPXWWy+NHTu22j6bb755OvXUU9Ott96aFllkkbTyyivnIF4KyMsuu2xeoqp8ySWXVD6OIB+effbZ1LFjx7TnnnumlVZaKXXu3Dkfb6mllkrLLLNMuuuuu2r9uuJ847Ude+yx+bnbbrttfm333HNPrc8HAGYGoRsAZlGtWrXKX6NqG2655ZZ0/PHH57Aa4ffjjz/OFeTJm6D3798/7xcV3sceeywH0zPPPLNye6w7/fTT06WXXpqrwhHq33nnnXTFFVdMcQ4RnI866qi8f1Sh27dvX9kUfsyYMXmJmwP/+Mc/Kh//6U9/qnaMeE6cZ1SrH3300Vyp/sMf/pCbgdfldT3//PPplVdeydX7YcOG5Z8T/d/rej4A8HsyZRgAzKJKzaLnmmuu/PXqq6/O1d0IyLGEqEBHiK5qzTXXTH/961/z9xtttFGuCt9xxx25ShwihH777bepXbt2ObxGU+3GjRvnZt+Ti77lr7/+elpuueWm+3UcfvjhuYod9ttvvxzC43VMmDChTq+rtG+LFi3y95tttlk644wzpvu8AOD3IHQDwCyq1Lc5mmiHzz77LE8pVnWQsGjyXQrlJdH8uqpoav7II49UPo75wPv165ebjUf1+Y9//GM+bqyf3O677z5DgTuUQvLk35fU9nUtv/zyuUl8ScxnHn25AWBWJnQDwCwowuT999+fw+d8882X17Vt2zY3o67aDDyaasdgYVXFuqqiql1qFh6i2fadd96Zm52vuuqqqVGjRrlPd03iZ/6WeP6MqO3rivnLa2NGzwcAykmfbgCYxUTlt2fPnrlf83HHHVe5fptttsnzeL/66qv5cVSmoxK91157VXt+7BMjjYfoIx19pauG6tgWg5nFvOARUG+//fbcX3p6RfU5mqmXRD/quqjt6/q9zgcAykmlGwBmETE9WDS/jlHEo7/1VVddlbbbbrvK7YcddlgOx9Fne5VVVsmBOgLlww8/XO04MWJ39+7dcxU7+mPHgGwnnHBC5fYYBfxvf/tbblYeP2vkyJF5YLOapuqqjTivrbfeurJ5eDQVjwHY6vL82ryu3+t8AKCcGlXoDAUAM12MOB5TZDVp0iRXaiMwR1isSQTU1157LQfzGEwsmmaXbLjhhnnKrYMOOig98MADaeGFF87BvWXLltWOEc8fOHBgmnfeedMWW2yRRwyPJarOMaha6ZyiWfoKK6zwm+cfg7DF/jEaekxDVgrPMUJ5BOCmTZvmJu1rrbVWPqe4wRDnHfvV5nXF+slHIo/5xd999930l7/8pVbnAwAzg9ANAPVIKXT/97//ndmnAgBoXg4A9Uvnzp3z9FsAwKxBpRsAAAAKYvRyAAAAKIjQDQAAAAURugEAAKAgQjcAAAAUROgGAACAggjdAAAAUBChGwAAAAoidAMAAEBBhG4AAABIxfj/AL1M+TGUNBOBAAAAAElFTkSuQmCC",
      "text/plain": [
       "<Figure size 1000x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# 定制化\n",
    "# 设置中文字体\n",
    "plt.rcParams['font.sans-serif'] = ['SimHei', 'PingFang SC', 'DejaVu Sans']\n",
    "plt.rcParams['axes.unicode_minus'] = False\n",
    "\n",
    "# 按部门计算平均薪资\n",
    "dept_salary = employees_df.groupby('department')['salary'].mean().sort_values(ascending=False)\n",
    "\n",
    "# 画柱状图\n",
    "plt.figure(figsize=(10, 6))\n",
    "bars = plt.bar(dept_salary.index, dept_salary.values, color=['#4C78A8', '#F58518', '#E45756'])\n",
    "plt.title('Average Salary by Department', fontsize=16)\n",
    "plt.xlabel('Department', fontsize=12)\n",
    "plt.ylabel('Average Salary (CNY)', fontsize=12)\n",
    "\n",
    "# 在柱子上方标注具体数值\n",
    "for bar in bars:\n",
    "    height = bar.get_height()\n",
    "    plt.text(bar.get_x() + bar.get_width()/2., height,\n",
    "             f'{height:,.0f}', ha='center', va='bottom', fontsize=11)\n",
    "\n",
    "plt.tight_layout()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "bd05ad24",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "findfont: Failed to find font weight bold, now using 400.\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "分析结果：\n",
      "## 分析结果与业务洞察\n",
      "\n",
      "### 📊 各产品类别库存总量\n",
      "\n",
      "| 产品类别 | 库存总量 |\n",
      "|---------|:-------:|\n",
      "| 办公用品 | 300 件 |\n",
      "| 电子产品 | **1,900 件** |\n",
      "| **合计** | **2,200 件** |\n",
      "\n",
      "---\n",
      "\n",
      "### 🔍 业务洞察\n",
      "\n",
      "1. **电子产品占据绝对主导地位** — 库存总量高达 **1,900 件**，占总库存的 **86.4%**。这主要是因为产品表中有 3 个电子产品（笔记本电脑、机械键盘、显示器），而办公用品只有 1 个（办公椅）。\n",
      "\n",
      "2. **库存结构不平衡** — 电子产品类别涵盖了从 399 元的机械键盘到 6,999 元的笔记本电脑，品类丰富度更高；而办公用品仅有一款产品，库存深度明显不足。\n",
      "\n",
      "3. **潜在建议**：\n",
      "   - 若办公用品需求稳定，可考虑扩充该品类产品线，增加 SKU 数量，优化库存结构\n",
      "   - 电子产品的库存量较大，需关注周转率，避免资金被过度占用（尤其是高单价商品如笔记本电脑）\n",
      "\n",
      "> 📌 从水平条形图可以直观看出，电子产品的库存量是办公用品的 **6 倍以上**，两者差距悬殊。\n"
     ]
    }
   ],
   "source": [
    "response = await visualization_agent.ainvoke({\n",
    "    \"messages\": [{\"role\": \"user\", \"content\": \"计算每个产品类别的库存总量，用水平条形图展示，并标注具体数值\"}]\n",
    "})\n",
    "final_msg = response[\"messages\"][-1]\n",
    "print(f\"\\n分析结果：\\n{final_msg.content}\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "37fef040",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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Jn4h5fN5h+Ne//nVwRchXTXL9fpYtv1adnEjn36OqJwKZvq/n6mfL9fsZUBXBOfLOb7SuM/QbrTOgLusI6/Cc9XF5wne/+93gvsaNGwfBkrN8vlwbvtG6BKFq6YE5U+HHpAdSDkL+/Oc/bzUGZx2dkXf20McP67/DP7qnnnrqVsf3H+yqJwYeb13rvZ3xdLblxhtvrKgVdnbm61//evAHO/0EIaw9DzNL/r4uTahuHrIZr39+fy93TAjnwX8I3dXjvvvuq3h+wiDGVzNcn+tSCV+2dZcJ1zvX9TmuKx/X4w27HtSnpGX58uVBxwWXezhIS+euFL707vKiqieIzp47eAsDqvSSlrrMbzayeT1k+rzlcrxxy/Z3Ph/z4OP7ded9DrzhmDvJ1HWti7Pl7lTkTlbpfKJRUwlI1O9n2fJ7ta9ShVdGXT7iLkLmTifZvq/n6mfLx/sZUEncK1KRbLX1Jq5tVf+mTZtqfZz75brXsB/bvn378oMOOijoD+3/e1V8WVlZxWPduaBPnz4Vq9/333//oDfxLrvsslW3lrCLRosWLYIV8WE/41tvvXWrVf3uJXziiScG93fs2LH80EMPreilfvjhh5evXbu22nGH3WPca9v9rN11IH0M2XrkkUcqevIOHDiwomODv0/6GNxpw/d36tQpGF/Y7WC33XYLPt577711Gq8f4z7G4XNxyCGHlPft2zf4/1577RX0Dw8tXry4Yt6r3vbcc89Kr5NsnuNsXjshd0EI+0/Xdxvwxx9/PDiO+zdX5+ijj96qY1DVMXheqpPN/GYzD9m8HrJ93rIZb9zvJdl03Mjmdz5X8xD2IPfrxn3Iw+fBr73ato7f1s/mr+3Xr19wTL9Huv+2f9/9f3fFqa6LUabvZ7l4Lvx9unfvXt6hQ4fyrl27BuNwb/DqOjll876e7c+W6XizeT+rS3cZIB0159hm2YUXbzmjmInDDjssWMnvhWm1cT2eV7e7R6xvzmb4a52BTO8RHWZN3H3A2WVnL31sZ9Hdt/k3v/lN0B835P7D7rhx9913B6UG7mDgS/nOfjprmJ4V9WVPL/Z6+OGHg9IEj8HHci91Z82cta9u3F6k6bG4ntMZVy8kc0lBXbkUwt/Xdc2uEw070zijlF7D6DIG1zy7a4AXqzn76XG6lOH666+vNiOUyXj9GJeH+JK4L337Mq3rbl237OOnl2m4U4OziM5aOSPnrLEzt67LdfbRPYXD2tlsnuNsXjshX0L2VRU/d77SUB++WnHuuefqvPPOq/bz7ijihZZVd4j0GHx1w1nLb3/729V+bTbzm808ZPN6yPZ5y2a8cb+XpHPfbT+P6f3S02XzO5+refD4nGl1NtYley5bO/HEE4Myrfr8bH7efUXR6wr8enQJhuf6Zz/7WfBeEnbnqcv7WS6ei9NPPz24euNuMtddd13Qb97v034/rHoFKpv39Wx/tkzHm8372Re+8IVKnZHSj+HvUd14gXQNHKFXugcAquFgzh0gfPk23Ogjnf/wuX40ky4L9S0FCIMo/4F/8cUXgz+UYZcSJPN5AwBkhsw5gIy47to7KnrHTNeIuk2bM3/OSDmL5drOsF40lz744IMg++Tv7cyv64gJzJP/vAEAMkPmHEBW5R++bO4NQpxt9SIzt2fzIkNfjvaCtlxzz3FfSvbiLJdyeIFe1D2ni00SnjcAQGYIzgEAAICEoJUiAAAAkBAE5wAAAEBCEJwDAAAACUFwDgAAACQEwTkAAACQEATnAAAAQEIQnAMAAAAJQXAOAAAAJATBOQAAAJAQBOcAAABAQhCcAwAAAAlBcA4AAAAkBME5AAAAkBAE5wAAAEBCEJwDAAAACUFwDgAAACQEwTkAAACQEATnAAAAQEIQnAMAAAAJQXAOAAAAJATBOQAAAJAQBOcAAABAQhCcAwAAAAlBcA4AAAAkBME5AAAAkBAE5wAAAEBCEJwDAAAACUFwDgAAACQEwTkAAACQEATnAAAAQEIQnAMAAAAJ0TjuAQAAgOqVlZVp06ZNTA+QUI0aNVKTJk0iPSbBOQAACVNeXq758+drxYoVwb8BJFezZs3UuXNntW3bNpLjEZwDAJAwDsqXL1+uLl26qFWrVmrQoEHcQwJQhU+cfWXLv69z5swJ7osiQCc4BwAgYX/wFy5cGPyRdzYOQHK1aNFCbdq00ezZs7V48eJIgnMWhAIAkLA6c9+iukQOILd8Zatdu3basGFDJGtECM4BAEiQzZs3Bx8bN+biNlAowkWhPrGuL4JzAAASiDpzoDR/XwnOAQAAgIQgOAcAAIm3bt26inreVatW6S9/+UvF58aNG6cFCxZs9TUuMXCZUPqtamtKd9r461//WudxRVHG4DE9/vjjwVhCy5Yt0z333BP83LVxl5D//ve/2rJlS8V9H3zwQTBHtXnjjTc0a9asSmOYOHFiTn9OZIbgHAAAJN5vf/tbnXXWWcGiOwes3/ve94L7V69erbPPPlt33nnnVl/TvXv3oBVl69atg5v7Ud96662VHuOWlVdeeWWdxvTEE09U+tpLLrlEhx12mI444ogabwcddJA+/vjjrUoiLrzwwqC3fej111/XH//4RzVt2rTWMTz55JO66qqrKsoqHGR/+ctf3ubPdOONN2rUqFEV/3/77beD8a1Zs2arx3722Wc67rjjtHHjxgxmBfXFahMAAJB4v/nNb3TyySfrJz/5ia6++uqKBbNf+9rXdPDBBwfBe1UObMePH6/+/fsH/7/ooovUvHnzin+ff/752nXXXdWwYfa5ygkTJugPf/hDEESHvv/97wcnD941Msxg//SnP9Wrr75akX1euXKltt9++2oXFPrkIfT888/r8MMP18iRIys9brfddgtOOEIPPPCAfvzjH1cE54899ljw8bXXXtOkSZM0cODAasfvuUlfdOwTAc/NueeeW+lk4/TTT9eAAQOC4PxHP/qR/vSnP2U9V8gOwTkAAEisl156KciKO3h1Vnj06NH6+te/rvXr1wcZ8ylTpmi77bYLsurO7N58881BMFmTMIidOXOm1q5dW+dxORC/6667ghZ6IQfl3/nOd4IxuxWms/IOgPfcc8/g8/fdd59uueUWjRkzpuJrpk+fHvxcvrnMxB+7deumZ555Rvvtt59+/etfVzzWAbez3bvvvnvwfwf906ZN0znnnBP83322f/7zn+vpp5/WokWLdOKJJwZfk34ycO+99wZz6FIgl/P43wcccICee+65oIzmhBNO0JIlS4ITA4839MMf/jDIrI8dO7bi+yM3CM4BAEBi7bzzzvrud78bbPTi4Pdf//pXkC125teB8A033FDxeQfsLmUJuUb9W9/6VkWm+cMPP9Tee+8d/NvZ8rp22HBA7Cz4/vvvX+l+Z6mdff7KV74SBNfpXE/+y1/+Ur/4xS8qMuvmE40ZM2YEY3Vpy4477hicdDgwf/HFFyuN0T9HmPl3jbkz5i1btgxOXJYuXaqTTjpJl19+ufbaa6/gMc7a77PPPvrzn/8cnLzYTjvtFHx/n/Ccd9556tevX3D14cEHHwxKXfy9r732Wl166aXBSULI43CpzN/+9retSoMQLYJzAACQWM6K++bFnC4jcY21b8cee2wQhDoYd421g80zzjij0tc+9NBDQR23SzFckuEyjV122aXeY3Ipi7PSVTnoffjhh4MA2FlnB9qhb37zm8H39seqxzIH3a777tSpU8XXO0APj2cumQmDc89FuOjT2WwH3y7T+cEPflBxbN/nUhmfAEydOjWYg0MPPTTIqoeLTo855hjtu+++wYmFTzj8b5/ovPfee1v9fMcff7x+9atf1Xv+UDsWhAIAgMRyxtnBtTPoLsEYPny4+vbtGwSqLilxFt2dWxyAXnbZZZW+1nXSRx99dPB1X/ziF4Oa9R122KHeY3KHlD59+lT7uV69eunvf/+7TjvttIr7HPQ6UHd2elvZepfB3HTTTUF5yZe+9KVg3M6Su9zFx/F28S7HcfAeZrB9guKs/FFHHaXBgwdXHMuZcZ/UfPLJJ8HVhdD9998flN14Yaq/zoG5a+j/+c9/BiUwXhR6++23a+HChZXG5qsVUeyAidqROQcAAInlYNSLOh2Ee+GnOUh1EOl6cwe9p5xySlCLPXv27Iqv++pXvxoE8mHrxPSg1eUi9eEykqqdS6644orgRCJcXOoabmftfd/FF18c3Pezn/0sGI8DXJePnHnmmdX+vBdccEHwb3ekcSbbgXKYcXfm3KUszpaHnVW6du0aXD1488031bt374pj+bHOnPuEIeQg/NNPPw1qyl1G41Iffw8H8dddd11wRcBtKV1+45IXL771uJE/BOcAACCx3P/b5Rt33313cDMHt15I6cWP4bbpYdDrbK8DX5df+P/vvvtuUALiYNm8qDF8fNWe55ly5t7tBdO5zMbfL73zizPTLg9xx5OQv6cD+5q6qLibi7Pa4aJV1423b98+CKQtLGtxEF+17aHnKb3m3ln6qp1ofDwv7nRvdNewuwxmjz32CBbSOovuW3qHnPDEwrzA1S0pkVsE5wAAILFcd92jR48gAxwGml646LpoZ3i9eDEMer0g1NleC8tX/vGPfwSLIsN2ig6gHeC6LKaufbtdLuPWg+mBrLP3VTmovuOOOyoF59VxEO/SFbcx9M/pLizu6uKf85prrgkWtbpO3D9/eDJSHXd76dmzZ42f98/s43sBqINz86JPz483IHLwHfLJgL9fhw4dKu576qmngtIg5BbBOQAASKxBgwYFt/SSFJeruB2hu6I8++yzQYBe3WY98+bNC+qyH3300Yr7XK/tshDXqTvLvK0dOKvTsWNHDRkyJDhuWIJSFz6ZcHmOP7q+3C0RXTfukhW3NfTP6sC56mLQmvhExN1aauLA3ItpqwpPetL7nlfNunveXPvvNpHILYJzAACQeN7QxwG1g8MXXnghyJD7367Fdt9t12c7uHW3E3Mm2N1bjjzyyKDkZO7cuUG9tTPnLufw5kMOeF12kr75T6bcwtElMt4UyLfquI67tm3vHWw/8sgjOvDAAyttLOR2jx6rTzhcpuNWi+7qkt5T3fyzhKU5t912W/C49IWotZXupN/vrL3nwhszhTwvvmoRft6fc+vK9LIZ5AbBOQAASHTN+be//e2gjMWBuDuPhHXPzoC7XeLLL78clJg4eBwxYkSw6c+pp54atFr058Pg1f2+0/uMe0Gms9O1ZZtry547c+4ThZqCcwfP2+pu4m4y1QmvBHiRq4Nkl6u4FWLV47s0x7uIeuHmK6+8UukKgq8KOOiujr8uHJtPIlwu4/lLv+oQfq3r+30y497nyL0G5XVdDQEAACLnEgdvcuPM8LbKGEqBA0x3Fwl32axN2L3FXI+evomOA00HoOmlGn6MF1aGCzBzMXbXirtPe105TPPCz+oWYvpncrcVZ96r/rxhLXvnzp2rzXZ7rnyVwXXxDtIdjKeP04tLXebi+UF+f28JzgEASBCCc6C0f2/ZhAgAAABICIJzAAAAICEIzgEAAICEIDgHAAAAEoLgHAAAAEgIgnMAAAAgIQjOAQAAgIQgOAcAALFZu3ZtsIX9xIkTIzleWVmZ3nnnnUiOBcShcSzfFQAA4PNdLr/0pS/piiuu0G233Zb1nHhr+WuvvVbXXHONevTooWeffVZnnnlmsDumt5yvyZw5c/TVr3414++zzz776IYbbqj2c7/97W+Dk4za/OQnP1GHDh2UK1u2bNG4ceO0evXqivu8IU7Hjh3VqFGj4OZdQb1ZjndKXbJkSfAY7wK6xx57sBttghCcAwCA2DRt2jT4OHDgwDp9/d13361///vfuvXWW4P/n3rqqUFQevvtt+vPf/5zrRn21157TTfffLP69OkT3Odt7C+44AL99Kc/1d57713xWB97/PjxNR7rb3/7W/A9d9ppp60+t2rVKj366KP67ne/m9Pg3Cc3d911V6X7vvWtb2nIkCF6/PHHdcQRRwQnMk899VTw844dO7bicYMHDw7+36BBg2qP7aB/9uzZOuGEEyrdv2DBAr344otasWKFvvCFL2i33Xbb6mszeUymJx//+c9/NGvWLB122GHBmOvymELQoLy8vDzuQQAAgAy2Ab/nnmRM0ze+EdmhnHFu1aqVHnjggawy2bZ48eIgqP/Zz36mH/3oRxX3/+Uvf9H3vvc9TZ48Wb179672a+fNm6eePXsGgWcYxHnuW7Rooeeee04nn3xyxWPPO+88bdy4MQhsqzNo0KAgoL/ooou2+pwDYj+XCxcuVJcuXZQrO+64Y/C6cZCe6YnOypUrdeONN2rZsmWaOXNmxUlK1ZMLZ9Z32WUXPf/88xX3P/bYY/r617+uZs2aBdl3Z+J/8IMf6I9//GNWj8nEmjVrdPzxx+uDDz5Q3759gysAf/3rXyu9XjJ5TGy/t1kicw4AAPJmzJgxQUDojHnDhg2DDLbdeeedQVCc7uc//3mQ+a3Jj3/8Y7Vp00bf/va3K91/ySWX6I477ghKSVzPXh2XeWTDY63L50I1ZaWj4qzxQQcdpD/96U/Vft4B+Ny5c9W/f/8gWE4PKl2W46+vzve///0g6HRwnl4S5KDbVxl8dcKBt0uLfvnLX+rQQw/V6aefntFjMnX11VcHVy5Gjx4dXJ1w+dPll1+uo48+Wr169cr4MYWCBaEAACBvXO/sOuhOnTqpe/fuQXBtAwYMCDLQvjl4/Ne//lVrAP3mm28G2XaXnLRs2bLS5/x1Ds6cuX3yySdrDZbPPvts7bnnnsFt//33D+5zCUp4n28vvfTSNuvm33vvvaB8pOotPOHYvHmzci2cy6qc8f/KV74SlJikB+bWtm3bGo/n+v2HH35Yhx9+eKX7n3766SCYv+WWW4Ln03N51VVXqX379nr99dczfkwmysrKgufZJTph2ZBPxjxuv0YyfUwhIXMOAADyxhnY9IWfzqS7PtzlDmGdtwM712k7eK/O/PnzdeGFFwb15WeccUZwnxeAOrB3htZc3+xSEwelrvU+5phjqg3Or7vuuqAkxFy64gD9//7v/4LsbsjHqY1PLD766KPgVh0fs7YqYte+jxw5cpvfp65cf+16+iOPPDLjr1m0aJEuvfTSYKGtM+4uEUqf/65du1YK7H31wHMfXkXI5DGZ+Oyzz7R8+XIdddRRlU6+vED3ww8/zPgxhYTMOQAAyBsvDnQQ5RpnmzFjRvAxvTZ86dKlwccnnnhiqzpu10m7HtzBlxdihpwBTw/OzIGlg+zTTjtN999/f6XPhWUczrSGGfLdd989uM91w+mZc2d7t/UzjRgxotabO8nU5OWXXw5KPurDX++Ti+q4VtzlKdVl77/5zW9WWwv/jW98IziR+uEPf7jV53bYYYegbMV13aF33303WAPgE4FMH5OJefPmBR+rLrb11RefAGT6mEJC5hwAAOTNr371q6CbhstazHXCzmx369atUn20F2a2bt1af//73/Wd73wnyII6++xA+9NPPw1KIxx82VtvvRXc94c//KHS93IA79IMf43r3I877rhgEag5k2wOaF13HZanhJ8L77Pq6rGHDx+u3//+91vd78WTv/nNb4KrA9WVb/hk4corr6x0n49T3bGyUVsJ0LBhw4LA3CU24ZWGkLPYVevhfSLjOXUHl+qy3F/84heDE58TTzxRv/jFL4ITJv/fc3vKKadk/JhMbPj8OalauuTXRhh4Z/KYQkJwDgAA8sIZ3Pfff1/33HNP0KHF3n77be23336VHufg3AH7l7/8Zf36178Ogjq3S3QQ6Rpzd/3Yd999Kx7vx3jhn8tcqgq7r/iY6WUy69atCz6mt0wMuU96VWeddVal/zsD7Iy3F6Wm18G7vMb80Z1a0hc++uevrSVjffhqQ3WLTt295KSTTgoy2Z73qsG5u7GEi3LDKxnOsrvTSU2dblzb7o2efELhhbfuROMg2K0cXV+e6WMy0aJFi4puLOmtKH1SFQbjmTymkBCcAwCAvGXNXXrgLh5hOYLrrZ3VrhpoupTEQZzLK/x1Lo9wbbgz0+neeOONIMBPr/d2AOxyDC8GdHDmRZBV69cdXJs7kYSfcwbW39cLKNN7ertuvWr23JlqH9snBiH/O6zNdm21S2LSP+/gtKauKPXlunC3Eqxq6NChQbDtkh/3dPcJg1sNhrxo1ptApf8M9t///je4mU+oHMS7xMj1+H4OnAEPO8O4nt/dd1yjni6Tx2xL//79g4/eQTb9ZMHPW7jJVCaPKSTUnAMAgJxzSYp7jztADEswvBjTgbPb7aVzljus83Yg7yB41KhRWx3TGV8vJHXwHNaLm7Onfrzb9tXEZTD+Hg5U3Zfat7CTiU8Kwvt8q660o6ZFjeHPVtPns1kMmW1wHl4NSJ9HnxA4w+8ynEMOOUT33XffVl/7wgsvVPzbJyUOwD0v4c2LOp2d9r/DLHV6Hfmrr74a9C4PF+NWlcljatKrV6/g6kZ61x1fOfExDzzwwIwfU0jInAMAgJxzyYWD6JCzst7d84YbbthqQWKYOQ+zr96dsl27dlsd0236nFEPM7whf41rvp0RPv/886stXXE9eHpHlmzV1H0lrH/OZo9Hl55MmDAhKOOpK/+s7lfu+u70n9EbNIXlLs4iezGmr0SEQbLr9tM3LTrnnHO2OrY7ofiKQPpVgJDLelzL79KZmtT2GP/sn3zySaXXRnWLVi+77LKg572vALhm36+P9I2iMnlMoSA4BwAAeeU2iQ6mXOrgLG06l304UE0P5KoG5g58nQF24OmWif63M8Quk3GHEN/8b2fWHbC5B3l6xtolGm7XmN7S0cLa66qlJ6tXrw4WF1Ydg7ekP+CAAyru80lEeCLgz3sr+fTPu8zCi1Orcj9012XXJzj3FYT0jjDh+NI7zXged9ttt6D2PayFd4bZmfW68Lhd8uKFo3V9jD/vdQS1BeeXXnpp8Jy6/7wX67p05R//+Eel5ySTxxQKgnMAAArFN76hQjZu3Lggs+vFnccee6z++c9/VmRw77333iCL6tpxB7nVLcoMufOIO7P4o2vOvYjRdce+ecMcf3R9urvC+ATALRe9a2jopptuCrLJVRd5hlnvsCWhu5y464gDf39N1TG4DCa9XtsZ/LC0xJ/3GNI/79aQ1bU7dCb7Rz/6kerDgXh6cO6OMS5rSeeg1Qtx3XbRVw3cMccnIjW1YAy5FKk6zlL7+XJ/+Zps6zGZ/uy//vWvg649rpkfPHhwUG5Ul8cUAoJzAACQF65tdgDrrdYdSKW3//OizGeeeSbYgt7dVRy818Q14Q8++GDw9V6c6IWG1fHi04svvnir7KnvdweYqjtqOnD1GMNFp27f+NWvfjXYvKfqJkauv3bphIPBkOvew77t/hqXV5x77rkVn3dXmuoCYY+jpt09s+EseMi7ZVbt3uITIZcChd+z6tfUpKY6+fRymJps6zHZ/OydO3cObvV9TNI1KM+mKAoAAOSU+2u7/MEb4RRq5q82zkLXtPMn6s6vF2eMXTMeBsQ+yXAG2eUeLm9xbbdbGnqBrReQupbcJUHO9vtrt99+e56CBPzekjkHAAB5Q2CeG3vttVcQYKd3LLnwwgs1adKkoMbctfCuw3fXFD8H7lYT6tq1a9DxBMlA5hw551X37ifrs8lsNh7IxNq1a4PLdDVd0gSAQlPsmXPkhrPgXuTqv4vZcGmQS4i8SRHqjsw5CsqHH34Y1Op5FbprBOvKq729cCVc+T569OigZ+udd94Z1ARuywMPPJD1Nr7uArD//vtX+zkvWHKWYltc+1bbYhkAAOrLZStf+9rXmMgiQFkLci7c1GGXXXap13G8wt/tsBwQ+03IC2+8Mt19cjMJzv04L9Txjm0hL8zxSn/3fk3fVSy83wtnagrOvXDpiiuu2Ob3Pe+884Id2AAAALaF4Bw5F67y7tatW52P4dq4V155JWi5FPZs9XG9JbFXyr/55ps64ogjaj2GS1+caXfrrpAz6Q7wfZz0Nlvh/eGJRXXCcbg1VU3cp5fL0gAAIFME54ics87pNeDpLZi8kUM6f86rxjPZXMHbMVfdrMIBtbcDdlZ96NChtR4jvWVXpmr7mvBnrG0rYrexqsv3RRFxQyy3Tqvp5pO79P978xN/TU03t5eLeO0GACA5CM4RubZt2wbZZAetDk7Dbp1f//rXg1s6l5N8/PHHtR7PC1zcF/euu+4Kjp3OmW1vROFNIlzPXtsOYz4R8ImDF6emL1YNTxqqu7+m3q7pO8ltC91Ki5AD6lWr/ML5323NGq8Iqj74jlKVnQsBAMWF4ByRCzeGcODsAH348OH67W9/G2S40+vO3eJpu+22q/VY3hzBdd1uAeWtnqtz9tln64QTTgjKW1wf7s0lquNA2xtbdOnSZavPOSNfNSu/LeEucC6JqcnEiRODFlUoIA5+3e0gPfCuetvGbnoAANQVwTki5yx2+q5k7mpip5xyigYMGFCp7VC41bCzy1V3MnNnFnd4WbRoURBUh1nsl156KdieOb2W2ycE7vF6/PHHB/Xn1W2k4O9xxhln6NFHH624b8GCBcEub14setFFF211f21cZuOa8smTJ9f4GG/+UN+FsMgRZ7m9M56vkvi2YsX/MuDszQYAiAnBOSLl8hBvd+wsdrilsfv1OvBO3+DA5SVhL1YHuKeffrouvfTSSsfy13sb5T/96U9B4B0e/8wzzwyCfLdoDAN2tyt84okndPTRR+vggw/Wv/71r626rDg49+PTg/pwwacz/NXdXxsH+r4h4TZvdgPgyoG4b59f+QAAIEkIzhGp22+/Peiskr7w09sFu3wlfeHnsmXLgo/OcLt/+W9+85sgSx4GyA7Mnc126ct3v/vdiq9zS0IH9T/5yU+2qgc/6KCD9J///CfI0Lu2fdiwYUF2O1RbV5VMOYvvxafV8cmAe8y6Rt7Z/eqcfPLJwQ054Gy368AdeKcH4itXkgkHEs5li94PItvywqj574QTRu7s9ctf/jLWsaB0EZwjMqtWrQrqyvv06VNRh+1stWvOHTinC4NzB88/+9nPgiD8nnvuqQjEnUX3osz777+/4ms2b96sG264IciMn3/++TVuGuSWi97pLD0wD8toXnzxRfXv33+rRZ0///nPg2NXvb8qH+Mvf/lLkMnv2LFjxf0fffRRcLLg4NybI/39738PxpnO3WRcKkNwHmFZijeV8m3BglRA7iw5gIIzZsyYrHZ69nqeH/7wh8HX1LZwP/SlL30puEK7Ld7FeuXKlUGiJ5vgfMiQIRWNBGryzjvvVLqCHDXvAeK/T+nJMV9N9m3EiBHB3x8ny3zV2X8fP/vss4orx2eddVZwQoJkIDhHZG699dZgAedNN91UkQF/9913g/rtI488strgvF27djr33HP1ox/9SLfddluQMfcb7R577FGpNtweeughTZ06dasNfdy33G+M4dbDBx54YI1bG++33366/PLLK93nEwG/MR111FFb3V/dG7e5Q4zf8EL+d3hlwI9xT/dXX3210td279694utRB86KOxCfNy/10aUqQIn55vNKhL9EfAHQ7/vZtJ313w6vMaotOHdZ5FNPPRU0AajaKSz8O+Qrnv57lX6MWbNmacWKFZU2rHOiacOGDbr44ouDv1dVOaD3hnMeU1XeN8MJpVy31fW6qbfeeqvSfQ7A/Tdz7Nix2nHHHTVv3jxNmDAhSC6F68Hsz3/+c3By0aJFi4r7/vGPfwQ7a/uKsb/217/+dY0NF2rzt7/9LUiMuYnCbrvtFrRGdilqdR5//PEgKeePVRNs2TymNuPHj9fvfve74Hl2bHLVVVdt9bc5k8fkEsE5IuMXsxc/prczdDbaZ+XuqFJdcO6NfPyG5TcV/yI8//zzOvXUU7c6tktZnMVwIO8AO+T2h+51vu+++wYZ85p4cenMmTOD7En6WPymae4GU939VdX25hr2O6/tMfQ8z6JExa+R9GDcCzUBFDS/D1e3SN6B2wcffKB777230v0OrL1uqSo3EwjXNVU1Z86cIAD03wT/Xfne975X7X4avu/qq68OPvpvhLPK6QF5+LfAAZrXUnmMNQWnPklwWU51m+E5o72tPTGi4B2w/TfVrYcHDhyY0deEJxUuL124cGFFMwWv9XKJkRNXvtrrpgvOrPuKRadOnTIekxNZ1113XZDsatOmTbA27MknnwxOFqo+J07kOXl2xRVX1Bh0Z/KY2owbNy74Ofw8OyH317/+NSjFTU/6ZfKYXCM4R2R8FutgOcxAvP7660Gw7V8iZ41rypybA2wHrgcccEC1x77mmmuCs/frr7++0v0++3Zt+gUXXBCc4ad3XEnnNwIvQk0P7OuiakeZdC672dZjUAOXES1aVLlMZcMGpgsoMg5Q16xZE6wzSk/EuCTQQXt6VtoBoQO5TPk93sH2HXfcEaw9ckAVdgSrjhf+h8mkb3/723r44YeDfTdcmpl+RdgBvgP92hoAOLhPwp4XTlTVdPXYmXD/7f3qV79a0fTAgbYDcAfnIV8Bd6mnA3T//Q6fH5fFPPPMM8HVg0x4Tvy326U2/httl112WfCcuGzInd3S+WTLrYd//vOf13jMTB5TmyuvvDL4OXxl25nwE088MYgLfAIXxh+ZPCbXCM4RqfBSldsLOsvtX0L/clZ39mvhmW+/fv2CXujVca22S2X8y+jHhb/0PoYzJL4M5zo+/0L5l6i6vuI+SfCbUdWaumzfLMPH+yQk/ZKgL3e6Hj18jGvW0z8fjhlpXBc5Y4ZTadLcuakAHUBRC68euklAepa6VatWQZY8/T4HR9lcbXQW+MYbb9TNN9+c9cJSB/VeK+STAyeaQg7Yjz322EpljNXx1V2XZjp4re5zUTUl2Jaa5ssnOg7MHRxX7UZW9Wt8AuU58MLYUO/evYOvC/f3yIT/5vmEqWfPnhX3uZzG3y9MZoX+/e9/B13W3Aq5WQ3d0jJ5zLZeH04a3nnnnRUlKj6ZcamN2zU78M7kMflAcI7IucbPmXC/EbnOLMyOhxy8+n6/8NMXZ1bHdeff+MY3gl9kLyx1+YkvM/ryW/jL7V9Sn+XOnTtXP/3pT4P6tnQOlH1ZypfmfFmt6ufSA2d/9CVMX1619Pq79MdXtyA1vNwZBubp2ZeqX1+SfGLj7HgYkHsBJ4CSEl5Z9ft5epmHr4y6ZMLBcMgLF7O5Ehm+X6fvp1EbJ1TCkhP71re+Ffwt8TjMZS6jRo3SI488Etznv10ONr0mqio3NnCSpiZOHLVu3brGz7uZgv/e+fvVVXWlOyGX2zgBVl1QW7Xm3n+zfeUh3RtvvBEE5ocddljG4/Hze+ihhwYJOl8V8c/v8lT/HfYJT8gLWH3lwn9DX3755SD4dqY9PT7I5DHb4qSh/wZXvYK+8847V+xXkslj8oHgHJF57733gl88/+J4QaQvWzmYDnkxiT/nN0MvSvFlstreTMxnsb4E6no+B+X+xfQvtX8hvUDFN2fN/cbienKXtvgXOP37ur2j6xwdoFcVZrvDN1Ufx2+iflP2G1TV+kG/Mdstt9wS1CCGnKUJj+XHuIzHlxDTnXbaabW+eRclZ4p8BcEB+axZ9BYHUPH3wlc+04NzJ3TSs9ZuAFAdB8d+bNWFoOGVTf8NcKKmavbaf0/8vuyuXmFtcSalju4mFvLfBTcMSM8Mm/+ehTwu/83yGKsmZPx3wpnjqosLnV2uLujPhrPKNe2k7W5obqrg0tCqu2Q7EL/22mtrPK5/Tp98+HG77757VmO67777gu5mjgl8ouUrJM5Mp9et+2qHXwt+LlxSM2rUqGC9muvTw5OETB6zLb7ibVU75vikwUm5TB+TDwTniIzf+EaOHBksoPDlwaovbr8h3nXXXcHqeLe0Sl84WhPXxjkgd1vCbbXZ8qITv2lWzY67jaNbO6afqadfqvOY07Pcv/rVr4Ig2pf00tslhr+g/rxrJcPuMOEl0bBDjTML/rqqJTTuKlNdNr1oy1V882LOUr5aAKCSMBPu9+T0NUJ+v3TCwzXeIZczun68KpetOCtbtRzDgbL//jg4r9q+N+TyhFAYILuZQfr7eU3CzHnIJSyu167Kf2u814UD5fR2wCFnfJ2NT+ersTW1CM6Us9vVZcY9Zp8UOLD232a3oEy3rdIh/231lQMHwtnwfLmMxle5vUDXZay+OuA581idXPMJjJ9j1/P75CJ8fVx44YXBVXNfhfbJwbYek0n5U3jyVjWW8BWX8EQvk8fkA8E5InPMMccEvWod8FZ3KfLwww8PylGy4V+IMMuxLV5l7tryqnymXVN2xAF1egbczjnnnBq/h4NuXwGoylnxqn1lq8r05yg4fjPz8xqWq2yj1y+A0pXtOp/qSgHT296mC69euoQik57dbpdozuqm7xBdm/RSRwdw7o7iEp2Qg8gw++rPOxGV3sjAfz+qlktGxVekqyaUwpMIl5q6xMQ19VWDc19Zrukqtuv+XQ7jKxou+cmGNwJ0CavbO4blMA7SnUl31ttXsydNmhS0cPSJTHrccO655waBvDv1OFm2rcdkUt4SrnHzVXg/b+mJxXDeMnlMPhCcI1IlkRlGii/tTprkIr1UthwAtiFc3+Myi/S9IBykuVzB5Ykh9+OuunAwSk4WhQF2XThb65tbKKY3RXAgF37eZTDpn6/r98pE27ZtgyRYVS4Z8hh8FdpXfl2vnV6y6U4tYTeVdM6WO1nlHbmru0KwLVOmTAmuTqTXqfskwCdOYclSePJVtR6/LO2kLJPHZMInbT4J85qy9OfEGweGCbZMHpMP+cvRAyh8Xqn/8cfS009LbnE2ejSBOYCsg3N38nLwE96cTXbWMv0+lzFUzfJGyQGXs7h13X9iW4tV891W11eu01sihiU7XkwZ9hd3UO4To3T++auW2XhtmK9Q+IpvdR3XMuErEi5bcUvLUNjcIeyn7hMGB+xVu9w899xzwdotlxtl8phMOLj3JlHuxBKe9Lms1WsPws2jMnlMPpA5B1A7v0G5ZMVbPXtBTB569QIoTq7bdXbXm9KkBzsuf3CJodfvVOWmAK4rrsvulLWdJDhgPfPMM+tVouPjpHd88bqnsN2gP+/AOP3zq1at2mpdlHnzpWeffTa41ZVLaLzvR9U9Ppz5Drm3uINZl96ErY+9SLNqyYbrud2u2KUsv/jFLyru927c4Zx5Ux7XsDtorq4syBlzt8z0+i0vmHU9/NNPPx1kzcOGCb7Pi03dTMJz55OHYcOGBc0d3HnNr5dMHhPOoY/vZhQ18evLa9j82vPr0GVILslNX6OQyWNyjeAcwNYcgHszIJeteHe+tEVQAFBXzqa6rKK6LLMD2eo4G+wsrvtc19aZw1vVWyaZcG+M41rlmrqbZMKBuXuHh/tvhML+4P68FzD6lq66TXycYQ5r5uvKVxnSSzFcXpO+gNUcyHp8DmzDqxKu505v4ehxuHuMg33vOpoufVMnZ5a9D0lN6wgc9Ls8xJ1gvB7MZUtuc+kTsV133bVSMOySWC+effvtt4N/O8ufvmt3Jo/JZA7dyW3EiBFBsO8TC5+seJOp9KscmTwm1wjOAVSuI/cfONeRr1rFzAAJ85eTVZQctHlxn8tb0vfG8GJAZ5NdT121tW3IQfbgwYODdomuIXdP6tp4AaQDUwfJ7hhSVw58nXFOL9vwdvVhoOvPn3feeUE735C/b3UBpHus+1ZfYaDsjy5HqRpQeky+z5ln15k7CK8aXDsLXrUVcHV8wnTCCSfUusDVbRu902gmndl8q89jvpXhHHqjqxdeeKHej8mlBuX52E8WQHI5s+KA3FlybxKEZPMfpzrsjofC4eDNAaczspl2ESl0XrTo/SP8sWpY4uDPbQZrKytw6YHLRVyGkElXEZdAePFpfebXWXMH+un9yZ2FdlmLWwn7OXQgnD6esOY+F235/HpxNtt15j6ZyYTn2plhZ/ddfhPWgmfCtdh+bnxiBEX6e0twDpRyltyLOx2Y52FbaUSE4LzolWJwjvpzTb673nhRoxdKmuvDXQr0yiuvBJ1I3AHHXXKc0f7444+D0pewxbFbMYZfh3h/bylrAUqJM1Je1Dl+fOojAKAoeNHlVVddFdTuh1vNuybcGW63NXQJy9y5c4Ng3FcZfJUidPLJJxOYJwjBOVAKnBl3XaSD8s833gAAFI+f/vSnuuKKK7JeWOrdVtPr/BE/gnOgmHkzDJeuuJ6cjisAUNTcIcU3FDaCc6AYzZkjjRtHX3IAAAoMwTlQTJsFeXGnM+XLlsU9GiTIr9+U1m+WGjZwP2nJzdV8a9RQatZIaupb47R/N6r+/uaNpTZNpTbNUh9bNkkdD7lBMzWgNH9fCc6BQuf6wrFjpQkT3BQ47tEggZatTwXnUWvUQGrd9H8Be9V/t2sudW4hdWqZCuyReQ2whduHA0g+b4KU6SZY28LbJVCo1q5NBeWffJLKmgN5VlYurdiQumkbe1a1apIK0sNgvdK/W0jN+GtUwX/cfXObu+q2egeQvKz5ihUr1KxZMzVp0qTex+PtECg0a9ZIY8akMuVlZXGPBsjImk3SmhXSzBqaBTnj3qWV1KO11LPN/27tS7DNtzeu6dq1a9B32n/svcAvn1uHA8g8KHfG3IG5W1h686koEJwDhWL1amn06FRLRIJyFJlVG1O3qVWWS7iuPQjUW0s90oL2tkW+Sapb23mnycWLF1fqRw0geXwS7cA8051Zt4XgHCiETPlHH0kTJ3rv57hHA+TV2k3S5KWpWzrXtPduK/VtL/X7/OYa92LhTHmPHj2CDHpYywogeVyCFkUpSzqCcyCp1q1LZcpdU06mHKhk9UZp4uLULdSh+f+CdX/cvn3hL0QN688BlI4Cf9sCipA7rrim3C0RWegJZNWVZtl86aP5qf+7StulMGGw3r9jqiQGAJKM4BxICu/g6Y2DfGM3T6De3HV47qrU7Z1Zqftcq75TJ2lgJ2lQZ6krmykCSBiCcyBu3rjAizxHjkyVsgDImZUbpJFzU7ewFGanztKgTqmPHVsw+QDiRXAOxGnePOndd6UlS3gegJhKYUbMTt2sS8tUZt1Z9V26SK2a8rQAyC+CcyCutogjRkhTpzL/QIIsWpu6DZslNWwg7dhB2r1b6ta9ddyjA1AKCM6BfPICT3dg8YJPOrAAibalXPpsaer2rwmp+vQgUO+aWlzaqGHcIwRQjAjOgXyZPFl6771U33IABWfhGunVqambN0ca3CUVrO/aNfV/AIgCwTmQa4sXp+rK53/e3w1AUWyO9P7c1M3lL65T37uHtFeP1AZJAFBXBOdArqxdm+rAMmlSqiMLgKItf5mwOHV79GNp587SPj2lPbuTUQeQPYJzIGquJfcGQh9+KLHtNlBygfr4RanbI+OkXbtI+/dKlb80YaNPABkgOAeiNGeONGyYtGIF8wqUuM1bpDELUrfmjaW9uqcCdfdTdykMAFSH4ByIgnf0dGvEiROZTwBbWb9ZGj47dWvfTDqgj3RIH6kLO5QCqILgHKivWbOkt9+mCwuAjCzfIL04WXppciqLfsh2qax6Y1ozAiA4B+phwwZp+PDUgk8AyJKXiU9cnLq5w8uBvVOBOpsdAaWNzDlQFzNmSEOHpjqyAEA9rd4ovTI1dfMGR4dul2rNyCJSoPQQnAPZWL8+1bPcGwoBQA5MXpq6PTFe2q+X9IW+ZNOBUkJwDmRq6lTpnXekdeuYMwB52ejozenSW9NTu5AevUOqhzqA4kZwDmyLg3G3R5w2jbkCEEtt+scLU7febaSjdkhl1FlAChQngnOgNi5fcRmLy1kAIGazV0l/HyM9PVE6Ynvp8L6pxaQAigfBOVBTttztEb3wEwASZuUG6dlJ0guTpQN6p0pe6PICFAeCc6C6XT5ff53acgCJt2mLNHSmNGymtGsX6fj+0oBOcY8KQH0QnAOh8nJp1Cjpo49S/waAQqpLX5S6DewknTwgtcERgMJDcA6Y+5U7Wz53LvMBoKBNWiLdvEQa0FE6eaA0iCAdKCgE58Ds2dIbb1DGAqCofLZUumVEalMjZ9J37hL3iABkguAcpWvLFumDD6TRo+MeCQDkjDc0uvU9accOqUz6LgTpQKIRnKM0rVkjvfaaNH9+3CMBgLyYskz603tSv/apIH1wVyYeSCKCc5SemTOlN9+kdzmAkjRtuXT7+9LAjtJZu0h928c9IgDpCM5RWmUsI0dKY8bEPRIAiN2kpdINw6QhPaQzBkldWsU9IgBGcI7SsHp1qoxlwYK4RwIAiWrBOGqeNGaBdNj20kkD2HEUiBvBOYqfd/l0GcuGDXGPBAASafMW6fVp0vBZqY2MjuonNWkU96iA0kRwjuLmTiwuZWFTIQDYpnWbpacnSm/NkE7dSdq/l9SwARMH5FPDvH43IF/KylLZ8vffJzAHgCwtXSc9MFr63VBpwmKmD8gngnMUn/Xrpf/+V5o0Ke6RAEBBm7VSunWEdM8oadm6uEcDlAbKWlBcli2TXnxRWrUq7pEAQNHwotGPF0onDZSO7ic1IrUH5AzBOYrH7NnSq69KGzfGPRIAKDobyqSnJqQWjZ4/WNqpc9wjAooT574oDuPHSy+8QGAOADk2b7V08wjpvg+lFeuZbiBqZM5R+BsLvfuu9MkncY8EAErK+3OlsQulUwdKX+hHVxcgKmTOUbhcvuL6cgJzAIjF+s3Sk59I170tTV7KkwBEgeAchWnlSumZZ1J15gCAWM1eJd30rvTouFTADqDuKGtB4Zk3T3r5ZXb8BIAEKVdq86Lxi6Qv7y4NYsEoUCdkzlFY3Lv8P/8hMAeAhFq8NtUb/ZGxZNGBuiBzjsLx8cepxZ8AgMRn0d+emcqif2l3aZcucY8IKBxkzlEYPvqIwBwACsySddKf3pMeGiOt2xT3aIDCQHCO5HvvPWnkyLhHAQCoo2GzpN+8JY1fyBQC20JwjuQqL5eGDZPGjIl7JACAelq2XrrtfenBMdSiA7UhOEdyNxd68016mANAkXlnlnTt29L05XGPBEgmgnMkT1mZ9Oqr0mefxT0SAEAOLFor/eEd6cXJqYukAP6H4BzJsnmz9NJL0vTpcY8EAJBDZeXS0xNTC0ZXrGeqgRDBOZJj48ZUD3N2/QSAkjFhsXTN29K4BXGPBEgGgnMkw/r10nPPSQt4dwaAUrNqo3THSOmJj6VNZXGPBogXwTnit2aN9Oyz0pIlcY8EABCj16dLv39Hmr+apwGli+Ac8Vq5MhWYL2fZPgBAmrVSum6oNGwms4HSRHCO+KxalSpl8UcAAD63sUx6aKz09zGUuaD0EJwjHmvXphZ/uqQFAIBqvDsrVeayeC3Tg9JBcI54Fn86MHdJCwAAGZS50M0FpYLgHPlvl/jCC9KyZcw8ACAjazdJd46UnvuUTYtQ/AjOkf8NhhYtYtYBAFnxRqLPfybd/YG0bhOTh+JFcI782LJFevVVad48ZhwAUGdjFkg30G4RRYzgHLlXXi69/ro0k75YAID6cx/064dJY9m3DkWI4By5N3SoNHUqMw0AiMz6zdJdI6UXJzOpKC4E58it4cOliROZZQBATurQn54oPTRGKtvCBKM4EJwjd0aNksaNY4YBADk1bJZ0+/ssFEVxIDhHbjgod3AOAEAeTFgs/eFdaQkbFqHAEZwjei5jcTkLAAB5NHdVqpPL9OVMOwoXwTmiNX16agEoAAAxWLlB+uNwafR8ph+FieAc0Vm8ONUy0a0TAQCIycYy6c8fSK9M4SlA4SE4RzTWrk3t/uldQAEAiJnTRP+cID06TtpCzggFhOAc9eeA3IH5mjXMJgAgUd6akcqibyqLeyRAZgjOUX9vvSUtWsRMAgASacyCVKtFb1wEJB3BOerH7RKnUNQHAEi2T5dIt4yQ1myMeyRA7QjOUXcOyullDgAoEG6xeNNwacX6uEcC1IzgHHWzcKH05pvMHgCg4Hqhe7OiRSyTQkIRnCN7q1dLL78slbG6BgBQeBavlW56NxWoA0lDcI7sbNqU6szi1okAABSo5RtSATq7iSJpCM6ROW8u9MYb0pIlzBoAoOCt2ZRaJPrp4rhHAvwPwTkyN3KkNH06MwYAKBpur+g2ix8vjHskQArBOTIzaZI0ejSzBQAoOpu2pDYq+oQtO5AABOfYNm8wNHQoMwUAKOoA/a6R0kRKXBAzgnPUbsMG6dVX6cwCACiJAP3OkdIkllYhRgTnqJ17ma+i1xQAoDRsLJPueF+avDTukaBUEZyjZmPGSDNmMEMAgJKyoSy1SHTKsrhHglJEcI7qzZ8vvf8+swMAKNkuLre9J00jQEeeEZxja+vXS6+9luprDgBAKQfo70szlsc9EpQSgnNU5oD89delNWuYGQBAyVu7SfrTe9KsFSU/FcgTgnNU1qCB1Lu31JCXBgAA4U6iDtAXrGY+kHtEYNja7rtLp54qtWnD7AAAIGnVxlSJy4r1TAdyi+Ac1evaVTrrLKlfP2YIAABJi9emuris28R0IHcIzlGzpk2lY46RDj5YatSImQIAlLxZK6W7P5A2lZX8VCBHCM6xbbvuKp12mtSuHbMFACh5ny6R7h8tbaGpGXKA4ByZ6dxZOvNMqX9/ZgwAUPJGzZOeGF/y04AcIDhH5po0kY48UjrsMMpcAAAl783p0n8/K/lpQMQIzpG9QYOkM86QOnRg9gAAJe3fn0rDZsY9ChQTgnPUTceOqQB94EBmEABQ0h4ZJ41ZEPcoUCwIzlF3jRtLRxwhfeELqZIXAABKkBeG3vuhNJNdRBEBgnPU34ABqSx6p07MJgCgJG0sk+4aySZFqD+Cc0SjfftUu8Wdd2ZGi9Aj772nKx57LKPHbty8WWfefbceHzmyzt9v+JQpuvjBB3XMrbfq/HvvDb5/eXnlnmX3v/OOTrnjDr0+cWKl+//4yit1/r4AUB/L1tMDHfVHcI5oy1wOPVQ6+mjKXIrI25Mm6esPPqhxc+dm9PhfPvusnh49WrOXLavT93tn8mQddtNN+nDmTPXt1ElL16zRl++/X9969NGKx4yfO1fffuwx7dG7t06/+26t27gxuP+NTz9VwwYN6vR9ASAK05ZLD41lLlF3BOeI3g47SGedJXXpwuwWuEfff1/H33ZbxgHvsMmTdePLL9fre17x+OM6dpddNOrnP9dfv/xlvfS97+lnxx+ve4YO1cylS4PHjJw+Xbv06KFrTjstyNRPmD8/uP/eYcN0ySGH1Ov7A0B9vTdHenEy84i6IThHbrRtK516qjR4MDNcoCbMm6ev/f3v+sNZZ+ngHXfc5uNXr1+vr9x/v44aNEgtmzat8/edsmhRcIyGDf/39uTv77KWeStSq61WrV+vVs2aqUGDBsH3WrlunT6dP1+92rdXm+bN6/y9ASAqz0yUxqTyBkBWCM6RO40aSQcdJB17rNSsGTNdYNo2b673f/pTfcfdeDLw/SefDILkBy66KAia62pwr156c9KkSvf5/00bN9ag7t2D/7do2lQbNm8O/u2PDtBve/31jMcKALnmVTL3fSTNXslcIzuNs3w8kL2+faXOnaXXXpMW0Ai2UPTq0CG4ZeK5MWN03zvv6KnLLlNPLw6uhyuPPlrn/vWvwWLPQwcM0Mdz5wYLQn9/5plq16JF8JjdevXSxPnzg0Wnm8rK1LFVK63fvFnbuf8+ACTEhs87uPzsEKkNOSpkiOAc+dG6tXTKKZI7eIwZw6wXkUWrVumShx4Kar3P2Guveh/voB131J59+uj5ceOCmx3Qr1+lY+/fr58uO+ww/eLZZ/XnCy8MauO/ddhhwYLQJatX66whQ+qVvQeAqCxZl+rg8oMDpcbUKyADvEyQP64h3n9/6YQTJOqCi8Y3Hn44yGjfes459T7Wli1bdNIdd+izhQt11wUXaOwvf6l/ffObWrp2rfb93e80bfHiisfecOaZ+uyaa3ThfvsF2fXJixbpnHvu0U+eeiroGAMASTFlmfTYx3GPAoWC4Bz516dPqptLjx7MfoF74N139eyYMfrTuedq3aZNWrx6dXCztRs3Bv/eXFaW8fHenTo1aKF4yxe/qG8dfnhQvnLmkCF6/f/+T6s3bNDf3nlnq6956L339KX999fTH32kyw8/XL87/XQ99dFHkf6cAFBfw2ZKw2czj9g2gnPEo1Ur6aSTpCFDJMoPCpbLTraUl+vE229XlyuvrLit2bBBv3ruueDfI6ZNy/h4YW90l62kc+17t7ZtNffzbi0hd3D5z7hxOnm33bRw1aqg9rxz69bBvwEgaR4dJ81hgSi2gZpzxFvmss8+qQz6669L69bxbBSYm846S1efeOJW9x/0+98HNeFfOfBADejaNePj9WzXLvjovuW79OxZcb/LWeYsX64+VRaovjh+fNAT3W0X3YvdwbpPFtiICEASbSyT7vkwtUC0OREYasBLA/Hr1Us6++xUgD5nTtyjQRb6ugtPNRwsu2uLF3aGRkydqn9++KEuPvhg7VxDSdO+ffsGGfLvPvFE0JZxQLdumrFkia5/8UW1atpUFx144FZlNfdfdFHwb3dqmbF0adBWka4tAJJq/mrp4bHSJUPiHgmSiuAcyeAWec7Ajh4tffCB6xXiHhEiNnrWLP3xlVd0xMCBNQbn7l/+6ve/Hywy/fqDD1bc74D90YsvrnQyMGbWLA3s1q1iw6NLDz1UJ9x2W9Ba8S9f+hLPH4DEGjlX6t9ROqJv3CNBEjUo93VgIEm8Fbt7oq9ZE/dIEKH1mzYFizrd2aWJN6jahplLlwY7grZu1izYfKhR2o6h4fG82LR1Wucft3X0QtTtO3Uq3ufuq1/NelOv770orU/t2QQgIdxW8UcHSX3rtzUEihDBOeqtbIvUsEHE6zrXr5fefFOaOTPCgwJFgOAcKBqdWkhXHSq1Sl0ABAJ0a0G9Pf6xdOdIafXGCCfT2dDjjpMOOCC1cBQAgCLcoOj+0VRyojKiHtTLqLnS2zOlcQula9+WJi+NcEKdit99d+nUU6U2bSI8MAAAyeC/ny9NiXsUSBKCc9TZ4rXSw6nd1QPL1kt/HC7997OIswBuxXfmmVJfVs4AAIrPvz+Vpi+PexRICoJz1LnO/N4PpbWbKt+/pTz1JnPb+9LKDRFOrhfAHXusdNBBUgaLCQEAKBT+2/m3j1J90AGCc9TJs5OkabWc5X+ySLrmbWni4ognePBg6bTTpLZtIz4wAADxWbBG+ucnPAMgc446mLJUemnyth/nzPmtI6RnP01lBSLjXtcuc9lxxwgPCgBAvN6aIX28kGeh1JE5R1Z8ye0BryzP8PF+3H8+k24ZLi1fH+Fke+OZo46SDj2UMhcAQNH4+5iIu5+h4BCcIyv/+kRauDb7SZu0NNXNJfKMwM47S2ecIbVnFwcAQOHzVeeHx8Y9CsSJ4BwZm7A4dcmtrlZtlO54X3pqQmpBaWQ6dkwF6AMHRnhQAADi8dF86d1ZzH6pIjhHRtZtkh4ck3k5S0389e7n6paLS9dFOPlNmkhHHJG6NW4c4YEBAMi/J8anWhaj9BCcIyNPfhJtMD1lWarMZcz8iJ8AZ8+9WNTZdAAACtT6zandQyNtqICCQHCObRq7IDeX19Zsku76IJUd2BxlmYvrz08/XRo0KMKDAgCQX951+2V2Dy05BOeolVeMP5TjhSmvT5P+8I60aE2EB3Vpy2GHpTq6uOQFAIAC9Nwkad6quEeBfCI4R60eHRfxTp81mLFCum6oNGpuxAd2L/Szzkr1RgcAoMD4yrKTZJS3lA6Cc9Tog7nSqHn5m6B1m6V7PpQeGSdtinILY+8m6l1Fd901woMCAJAfXqf15nRmu1QQnKNaazelasHj8PYM6YZ3pAWrIzxoo0bSwQdLxx4rNWsW4YEBAMi9ZyZKS+jeUhIIzlGtpyfkp5ylJrNXpspc3psd8YH79k11c+naNeIDAwCQOxvKpIfHMcOlgOAcW5m6TBo6MxlvRH8bndrKeGOUZS5t2kinnirtvnuEBwUAILc+WZSDpBUSh+AclXjnzkfG1n+zoSi5jePvhkpzo1yt3rChdMAB0vHHS82bR3hgAABy5x+fSGs2MsPFjOAclbw2TZqdwJZN81ZL1w+ThkWd0d9uu1Q3l+7dIz4wAADRW7VR+tcEZraYEZyjgncAfX5ScifEpS1uJ3XfR6md0yLTqpV08snSXntJDRpEeGAAAHJzRXnSEma2WBGco8JjH6fqvJPu/TmpMpdZKyIuc9l3X+mEE6QWLSI8MAAA0XLpqdsOR7q7NhKD4ByBj+ZJYxcUzmQsWJNqtxh539fevVNlLr16RXxgAACiM3+19OpUZrQYEZwjKBGJq6d5fThj4Gz/X0ZJ6zZFeOCWLaUTT5T22YcyFwBAYv33M2nF+rhHgagRnEPPfiotK+Bf7g/nSdcOlaYvj/Cgrj0fMiRVi+6adAAAEsalqE9NjHsUiBrBeYlze8I3imBL4MVrpRvfzcElvh49UmUuffpEfGAAAOrPfc+nLWMmiwnBeYn75yfSliQ1Na9nmYv7v945MuIesO6D7n7o++9PmQsAIFH8J/zx8VJ5kfwtB8F5SRu/UBq/SEXHC1uveVuasjTiMpc99kjtLNq6dYQHBgCgflzWOYKdQ4sGmfMS5Wz5P4t4EwPX0N80XHphcsTZhG7dUmUufftGeFAAAOrHteeR7gGC2BCcl6ihM1P15sV+AvLMROm296VVGyI8cLNm0rHHSgcdlOqPDgBAzFZuSHVvQeEjsihBbjv43KcqGZ8sSpW5fLo44gMPHiyddprUtm3EBwYAIHuvTZMWrmHmCh3BeQlyqceqKBdMFoAVG6RbRkjPTYp4AWyXLtKZZ0o77BDhQQEAqFtjBDd6QGEjOC8xbjnoM+tS5Jj8+UnSrSMi3rShaVPp6KOlQw6RGjWK8MAAAGRnzAJpQhE2eyglBOcl5qkJqTPrUvbpklSZi8tdIrXLLtLpp0vt20d8YAAAMvc0GxMVNILzEuLWgqPmxT2KZHBZz23vSU9PkMqiPFnp1Ek64wxpwIAIDwoAQOZmrEjtno3CRHBeQrxBDyqXubw4RfrjcGnZughnpkkT6QtfkA4/XGrcmCkHAOTds58WzyaDpYbgvIRq0KYtj3sUyTRlWarMxZsXRWqnnVJZ9A4dIj4wAAC1m7eajYkKFcF5CfAmPKXUOrEu1myS7hwpPTk+4jIXB+YO0AcNivCgAABsm5sglPo6s0JEcF4CPpovzVoZ9ygKgzvZ/OHdVFebyLi05bDDpCOPTJW8AACQB0vWSUNnMNWFhuC8yLnezL29kbnpy6Vr387BYpr+/VM90b1oFACAPPjvZGljGVNdSAjOi9youdLcVXGPovCs2yz9ZZT06DhpU5Rvau3apdot7rprhAcFAKB6KzeU7v4mhYrgvMiz5s9/FvcoCttbM6TfvyMtWB3hQb1R0cEHS8cck9rACACAHHp5irR2E1NcKAjOi9j7c6T5UQaVJcr1+r8blprPSPXrJ511ltSlS8QHBgDgfxyYvzSFGSkUBOdFyh1H/kOteWTWb5bu+0h6cEzEtXtt2kinnSbtvnuEBwUAoLI3pkmrNzIrhYDgvEiNmC0tjLLjCALvzJKuHxZxHX/DhtIBB0jHHSc1a8ZMAwAit6FMep3a84JAcF6sWXNqzXPGgbkDdAfqkdp++1SZS/fuER8YAADpjempK8FINoLzIjR8dqq3KXLHpS0ucbn/o4jf6Fq3lk4+WdpzzwgPCgBAqvb8bfqeJx7BeRHuBupV2ciPEXOk3w2VZq+MuMxlv/2kE0+UWrSI8MAAgFL36tSIWwQjcgTnRWbMAmnBmrhHUVo83zcMS7VdjFTv3qkyl549Iz4wAKBUrdiQusKO5CI4LzJkzeOxaUtqw6J7Rknrouwl27KldNJJ0t57Sw0aRHhgAEApxwreCwXJRHBeRKYslaYsi3sUpW3UPOm6odL05REe1EG5g3MH6Q7WAQCoh0VrUzuII5kIzosIGwwk503vxnel16ZGfGCXt7jMpU+fiA8MACg1L06OewSoCcF5kfBOoGMXxD0KhDZvkZ78RLprpLQmyk0fvED0+ONTC0YpcwEA1NHsVdI44oZEIjgvEq9MlSgfS+YC3WuHRlxu5KDcrRZPOSXVehEAgDp4gex5IhGcF4GVG1I7giKZlq6TbnpXemlyqtVlZLxZ0ZlnpjYvAgAgS04cTWWtWuIQnBcBb8frMgokl1fFPzVRuuN9aXWUZS7Nm0vHHScdeGCqPzoAAFl4YxrTlTT8NS9w3p0y8v7ayJmPF0nXvC1NWhLxgXfbTTrtNKlNm4gPDAAoZh/OT12BR3IQnBe44bNS2/GicCxfL90yQvrPpIj7zHbpkurmssMOER4UAFDMfOV9KEm+RCE4L3BkzQuTg/JnJ0l/ek9asT7CAzdtKh19tHTIIVKjRhEeGABQrN6eKZVRHpsYBOcFzKUR81bHPQrUx8TFqW4uExZFPI+77CKdfrrUrl3EBwYAFOMV3Y/mxz0KhAjOC9jbXIYqCq71cwb9mYkRl7l06pTq5tK/f4QHBQAUozemxz0ChAjOCzig4yy3eJR/3m/2j8OlZesiPHCTJtKRR0qHHSY1bhzhgQEAxWTyUmn2yrhHASM4L1DvzKJ9YrG+ObrMJfJd2wYNSpW5dOgQ8YEBAMWC7HkyEJwXIJc+sLK6eLkP+p0jpX9+EvECnY4dpTPOkHbaKcKDAgCKxftzpDVR7sWBOiE4L0DjF0pLoix9QCLLXF6ZKt34rrRkbYQHdmnL4YdLX/hCquQFAIDPbSyT3p3FdMSN4LwA0T6xdExbnipz+WhexAceMCCVRfeiUQAAPkezifgRnBeYpeukjxfGPQrkkzeZ+vMo6bGPpU1lER64fftUHbrbLgIAIGnh2tT6J8SH4LwAz2ij7LaHwvHmdOkP70qL1kR4UG9U5A2LvHGRNzACAJQ8SlviRXBeYAtB3aUFpWvmilSZy8i5ER94hx1SPdG7dIn4wACAQjNqXqr+HPEgOC8gnyxK9TdHaVu/Wbr3Q+mhsRGXubRtK516qjR4cIQHBQAU4t+ZD6Ne64SMEZwXkBGz4x4BkmTYTOn6YdL81RGXuRx0kHTccVKzZhEeGABQSIYTc8SG4LyAzmLHRL0xDQrenFXS74ZKw6Mud9p+e+mss6Ru3SI+MACgEHy6ONWEAvlHcF4g3EqP+i9UZ0OZ9MAY6f7R0obNEc5R69bSKadIe+7JxANAiXHzCa7Yx4PgvEC8NyfuESDp/Cb6u2HSnJURHrRhQ2m//aQTTpCaN4/wwACApKO0JR4E5wVg+Xpp4uK4R4FC4Ppz16FHvolEnz7S2WdLPXpEfGAAQFItXEPP8zgQnBeA9+fQ2xyZ27RFemRcqqOL1ypEpmVL6eSTpSFDpAYNeEoAoAREvqYJ20RwXgAoaUFduBf6dW+neqNHxkH5PvtIJ52UCtYBAEXtg3kRt+3FNhGcJ5zrh2dHWUOMktuG+ffvSK9Pi/jAPXumurn07h3xgQEASeIrsOMXxT2K0kJwnnAjWAiKetq8RXpivHT3B9LaTRFOZ4sWqYWi++5LmQsAFPmOocgfgvMEKy+XRhKcIyKj50vXvi1NWxZxmctee6VaLrZqFeGBAQBJMXZBKtGD/CA4T7Apy6Rl6+MeBYrJknXSje9KL09JnfxFpnv3VJnLdttFeFAAQFJKWz6htCVvCM4T7EMuIyEHysqlf02Q7hwprd4Y4YHdB/3446UDDkj1RwcAFA1ikvzhL2jCyxCAXBm3ULrmbemzJREfePfdpVNPldq0ifjAAIC4jFkglVHakhcE5wnl9ncuQQByvcHVzSOk/3wmbYmyzKVr11SZS79+ER4UABAXNxRgQ8T8IDhPKC4fIV8clD/7qXTbe9LKDREeuGlT6ZhjpIMPlho1ivDAAIA4EJvkB8F5QlHSgnybsDhV5uKPkdp1V+m006R27SI+MAAgn0YviPgqK6pFcJ5AC1ZL81bHPQqUImfO/zQilUmP9A24c2fpjDOkHXeM8KAAgHxyE4FPo16nhK0QnCd00QUQF8fkrkG/ZXiqJj3SMpejjpIOO4wyFwAoUJS25B7BeQIRnCMJJi1Nlbl8vDDiAw8alMqid+gQ8YEBALk2lk5yOUdwnsBLRlOWxj0K4H+vxzvel/71ScQttDp2TAXoAwcy1QBQQJZvkGavjHsUxY3gPIFb5LLWAkni1+PLU6WbhktLo2zv2bixdMQRqVuTJhEeGACQS+OjvqKKSgjOE2Yc9eZIqKnLUmUukXcScvbcWXRn0wEAiTd+UdwjKG4E5wni7hgTWQWNhG9CcfcH0hMfS5ujLHNp3146/XRp550jPCgAIBemLJPWb2Zuc4XgPEFmLE8FP0DSvT5d+v070qI1EZe5HHpoqqMLZS4AkFhOzrBbaO4QnCfIJ1Fv/gLk0MwV0rVDpVFzIz6we6GfdZbUpUvEBwYARIXSltwhOE+QCdRwocD4suY9H0qPjJU2lUV44LZtpVNPlQYPjvCgAICofELMkjME5wkKcrzgDihEb8+UbhgmzY9yZ9tGjaSDDpKOPVZq1izCAwMA6mvx2ojf81GB4DwhPlsildFDEQVs9irpd0OlEbMjPnDfvqkyl27dIj4wAKA+aKmYGwTnCUG9OYrBhjLp/tHSA6OljVGWubRuLZ1yirTHHhEeFABQHx9T2pITBOcJQb05isnw2aks+pwod5Fr2FDaf3/p+OOl5s0jPDAAoK5X/SNdb4QAwXkCLF8vzaNuC0XGr+nrh0nDZkZ84O22S5W59OgR8YEBANnYtIX1crlAcJ4AE2ihiCJ+435orHTfhxFvWNGqlXTSSdKQIVKDBhEeGACQjUlLma+oEZwnACUtKHbvz5WuGyrNWhFxmcs++0gnnii1aBHhgQEA2ZS2IFoE5wkwiRc2SsDCNdIN70hvTI/4wL16pcpc/BEAkFduA122hUmPEsF5zJauk5atj3sUQP62fH78Y+kvH0jrNkV44JYtUxl0Z9IpcwGAvJYvTl/OhEeJ4DxmU9h4CCXow/nStUOlaVG+/h2Uuwb95JNTNekAgLyg7jxaBOcxY1dQlPLucje+K70yNeIDu4uLy1z69In4wACA6kxhUWikCM5jNpUXNEqYd8X95yfSnSOlNRsjPLD7oLsfuvuie+EoACCnVQDl7HIeGf5qxcg7KM6KcpMWoECNXSBd87Y0eWnEZS7eUdQ7i3qHUQBATqzdxH4tUSI4j9GM5anMIYDUwug/DpdemBxxBqZbt1SZS9++TDMA5AhlutEhOI8RL2Sgsi3l0jMTpdvel1ZuiHB2mjWTjj1WOuggqVEjph0AIhbplc8SR3AeIzq1ANX7ZJF07dvSp1Hvnjt4sHTaaVLbtkw9AESIhGN0CM5jxAsZqNmKDdItI6TnPk1l1CPTubN05pnSDjsw/QAQ4UZz6zcznVEgOI/JojXSqii7UwBFyDH5859Jt46QVkS5WVfTptLRR0uHHkqZCwBE9H49awVTGQWC85hQ0gJk7tMlqW4u4xdGPGs77yydcYbUvj1PBwDU00yC80gQnMeEFzCQHV9puv196akJUtmWCGevY8dUgD5gAE8JANQD7aGjQXAek9n0NwfqdNn0pSmplotL10U4gU2aSF/4gnTEEVLjxjwzAFAHJB6jQXAekzmr4vrOQHGUhbmby5gFER944MDUYlFn0wEAWZm3WtpUxqTVF8F5DJavl1azGBSolzWbpLtGSk+OlzZHWebi+vPTT5cGDYrwoABQ/NxZi+Rj/RGcx4CSFiA6r02TbnxHWrw2woO6tOWww6Qjj0yVvAAAMkJpS/0RnMdgDvXmQKSmr0iVuYyaG/HE9u+fKnNxb3QAwDbRTrH+CM5jQOYciN66zdI9H0qPjou45rFdu9SuorvuGuFBAaA4zSQBWW8E5zGYzWJQIGfemiH9/h1pweoID9qokXTwwdIxx6Q2MAIA1FgdEGm72xJEcJ5nXrgWadAAoNpeu9cNld6bHfHk9OsnnXWW1LUrsw4A1djkOGcNU1MfBOd5Nm+VVOZmzQByakOZ9LfR0oNjpI1Rlrm0aSOdeqq0++4RHhQAigdJyPohOM8zSlqA/HpnlnT9MGlulOVkDRtKBxwgHX+81KxZhAcGgMJH5rx+CM7zjE4tQP45MHeA/s7MiA+83XbS2WdL3btHfGAAKFwE5/VDcJ5n86k3B2Lh0pYHx0p/+0havznCA7dqJZ18srTXXlKDBhEeGAAKE2Ut9UNwnmeRbpQCIGvvzZF+NzS1aDTSMpd995VOOEFq0YJnBUBJI3NePwTneVReTnAOJOUPx++HSW9Nj/jAvXunurn06hXxgQGgcKzeKK3ZGPcoChfBeR4tW59qMQQgfv5dfPRj6Z5R0rpNER64ZUvpxBOlffahzAVAySJ7XncE53m0iL6fQOKMmiddO1SavjzCg7r2fMgQ6aSTUsE6AJQY6s7rjuA8jxZRbw4kdi3Ije9Kr06N+MA9e6a6ufTpE/GBASDZyJzXHcF5HpE5B5K9e+8/PpHuGhlxrWTz5ql+6PvtR5kLgJJB5rzuCM7ziMw5kHxjFqTKXKYsjbjMZc89UzuLtm4d4YEBIJnInNcdwXkeLaTmHCgIS9dJNw2XXpyc6rIUmW7dUt1ctt8+woMCQPIQ89QdwXkekTkHCseWcunpidLt70urNkR44GbNpOOOkw48MNUfHQCKtCOWWyoie/xlyBO/QCPdlRBAXoxfJF37tjRpScQH3m036bTTpLZtIz4wACTD8vVxj6AwEZznCYtBgcK1fIN0ywjpuUmpjHpkunSRzjxT2mGHCA8KAMmwbF3cIyhMBOd53IAIQOFyUP78JOnWEdKKKH+fmzaVjj5aOuQQqVGjCA8MAPEi9qkbgvM8WRFlzSqA2Hy6RLrmbemTRREfeJddpNNPl9q1i/jAABAPylrqhuA8TyLNtAGI1aqN0m3vSc9MjLjMpVOnVJlL//4RHhQA4kFwXjcE53myksw5UFQck78wWfrj8IjrKps0kY48Ujr8cKlx4wgPDAD5RVlL3RCc5wllLUBxmrw0VeYybkHEB95pJ+mMM6QOHSI+MADkB5nzuiE4zxPKWoDitWaTdOdI6R+fSGVbIjywA3MH6A7UAaDA0K2lbgjO84TMOVD8ZS6vTpVufFdavDbCA7u0xSUuLnVxyQsAFIh1m6UN7PGSNYLzPPCCMXbJAkrDtOXSdUOlD+dFfGAvEvViUdotAigg1J1nj+A8D7z1d6QdHQAk2tpN0l9GSY+NkzaVRXhgt1lkkSiAAkJZb/YIzvOATi1AaXpzhvSHd6WFa+IeCQDEtyYH2SE4zwPqzYHSNXNFqsxl5Jy4RwIA+beO4DxrBOd5QHAOlLb1m6V7P5IeGiNtjLLMBQAKoMwP2SE4zwNemABs2Czp+mHSvFXMB4DSQFlL9gjO82A9Z40APjd3VSpAf3cWUwKg+JGgzB7BeZ76fAJAaEOZ9Pcx0v2j6QEMoLgRnGeP4DxP9aYAUNWI2dLvhkmzVzI3AIoTwXn2CM7zgOAcQE3mr5ZuGCa9NYM5AlB8CM6zR3CeBwTnAGqzaYv06Djprx/SdgxAcSE4zx7BeR4QnAPIxAdzUz3RZyxnvgAUB4Lz7BGc5wELQgFkatHa1K6ir09jzgAUPoLz7BGc58EGFoQCyMLmLdIT46W7R0prNjJ1AApXWTmbr2WL4DwPyJwDqIvRC1JlLlOXMX8ACjvhgMwRnOcBNecA6mrJOunGd6WXJkvl5cwjgMJDcJ4dgvMc21gmbeEPKoB68HvIUxOlO0ZKqylzAVBgysicZ4XgPMc4WwQQlY8XSte8LU1awpwCKBzEQtkhOM8xsuYAorR8vXTLCOk/k3h/AVAYCM6zQ3CeYwTnAHLxvvLsJOm296SVG5hfAMm2mfLerBCc5xgLuADkyoTFqTIXfwSApKLmPDsE5zlG5hxALjlz/qcR0jMTeb8BkEyUtWSH4DzHCM4B5JqvGL8wWbp5uLRsHfMNIFnInGeH4DzHCM4B5MtnS6Vrh0rjFjDnAJKDzHl2CM5zjOAcQD65D/qdI6V/fkK2CkAysCA0OwTnOUZwDiCOMpdXpqZ2Fl2ylvkHEC/KWrJDcJ5jBOcA4jJtearMZVMZzwGA+DRswOxno3FWj0bW2LEWQJzWbmL+AcSrEcF5Vsic5xh9zgEAQClrRLSZFaYrxzhZBAAApYzMeXYIznOMs0UAAFDKiIWyQ3CeY5wtAgCAUsaC0OwQnOdYY2YYAACUMBKV2SF0zDGCcwAAUMoaEm1mhenKMeqsAABAKSNznh2C8xxrwgwDAIASRnCeHULHHGvSKNffAQAAILmoIsgOwXkeVihTdw4AAEoVmfPsEJznQVOy5wAAoEQ1bxz3CAoLwXkeUHcOAABKdad0gvPsEJznAZlzAABQilo0kRo4QkfGCM7zgDNGAABQilpQ0pI1gvM8aNkkH98FAAAgWQjOs0dwngetm+bjuwAAACSvrAXZITjPg1a8MAEAQAkiOM8ewXketCRzDgAAShBlLdkjOM+D1mTOAQBACSJznj2C8zwgcw4AAEoRmfPsEZznAZlzAABQisicZ4/gPA9aUXMOAABKUEv6nGeN4DwP6HMOAABKUZtmcY+g8BCc5wF9zgEAQClqR3CeNYLzPKDPOQAAKEXtmsc9gsJDcJ4HjRpS2gIAAEpPWzLnWSM4z5OOnDkCAIASW3PXmEgza0xZnnRoka/vBAAAED/qzeuG4DxPOhKcAwCAEtKeqoE6ITjPEzLnAACglBCc1w3BeZ504OwRAACUEBKTdUNwnieUtQAAgFJCYrJuCM7zhBcoAAAoJcQ+dUNwnsdLOw3y9c0AAABiRllL3RCc54n7fLahET8AACgRnVvGPYLCRHCeR2xEBAAASqXHefPGcY+iMBGc5xGXdwAAQCno2iruERQugvM84vIOAAAoBQTndUdwnkfdW+fzuwEAAMSD4LzuCM7ziOAcAACUAoLzuiM4zyOCcwAAUAq6UXNeZwTnedS6aeoGAABQrLyvSxeC8zojOM8zsucAAKCYtW8uNW0U9ygKF8F5nnXnTBIAABQx6s3rh+A8z8icAwCAYkZwXj8E53lGcA4AAIoZwXn9EJznGcE5AAAoZr3axD2CwkZwnmedWkqNmXUAAFCk+rSLewSFjTAx3xPegN6fAACgOLVtlrqh7gjOY8DlHgAAUIz6tI17BIWP4DwG27WP47sCAADkFsF5/RGcx2B7arEAAEAR6k2MU28E5zGdVXprWwAAgGJC5rz+CM5j0KIJPUABAEBxadaI+CYKBOcx2Y7LPgAAoIj0bJPqSof6ITiPCXXnAACgmNDfPBoE5zEhcw4AAIoJ9ebRIDiPMTjnyg8AACgWBOfRIDiPcVFol1ZxfXcAAIDoNGlIVUBUCM5jRN05AAAoBn3bS42IKiPBNMaI4BwAABSDHTvGPYLiQXAeox06xPndAQAAotGfmCYyBOcxXwJyjRYAAEChcoMLEo7RITSMkWuzeDEDAIBC1r211Kpp3KMoHgTnMRvQKe4RAAAA1B315tEiOI/ZQBZQAACAAka9ebQIzmPWr4PUmGcBAAAUqP4kGiNFWBizpo1oqQgAAApT22Zsqhg1gvMEoO4cAAAUoh1poRg5gvMEoO4cAAAUIkpaokdwnpBVzg3dJBQAAKCA7NIl7hEUH4LzBGjeWNqubdyjAAAAyFyH5lLPNsxY1AjOE4K6cwAAUEjImucGwXlC7MplIQAAUEAIznOD4DxBmfNmjeIeBQAAwLZ5qdzOnZmpXCA4TwhvRDSIFzkAACgA27eXWjWNexTFieA8QQZ3jXsEAAAA20ZJS+4QnCcIwTkAACgErJXLHYLzBOnYgpZEAAAg+S2g+7WPexTFi+A8YQbTtQUAACTYoE5SIyLInGFqE4bSFgAAkGTUm+cWwXnC9O+YulwEAACQxBaKu3WLexTFjeA8YXyZaBdaKgIAgATavl1qjRxyh+A8gShtAQAASbRXj7hHUPwIzhPIl4t82QgAACBJ9uoe9wiKH8F5ArVtJg3oGPcoAAAA/qdnG6lba2Yk1wjOE2pIz7hHAAAA8D9kzfOD4DyhhnSntAUAACQHwXl+EJwnVLvmlLYAAIBk6NxS6tMu7lGUBoLzBNub0hYAAJAAZM3zh+A8wYb0oLQFAADEjxaK+UNwnvSuLZ3iHgUAAChl7ZtJO7SPexSlg+A84fam2T8AAIjRnr6SzwYseUNwnnCUtgAAgDgd0Iv5zyeC84SjtAUAAMSle2upXwfmP58IzgvAPnRtAQAAMdifrHneEZwXgH16SI15pgAAQB65zPyA3kx5vhHyFYBWTaXdu8Y9CgAAUEoGdpI6toh7FKWH4LxAHNAn7hEAAIBSQtY8HgTnBWJwl9TiUAAAgFxr2ijVMQ75R3BeIBo1lPZjUQYAAMiDPbtLzRsz1XEgOC8gB7EoAwAA5MGBxByxITgvIL3aSn3bxT0KAABQzNo3lwZ1jnsUpYvgvMAcvF3cIwAAAMXMZbQN3UcRsSA4L8BfmGaN4h4FAAAoRo7JDyURGCuC8wLjxRl7s2MoAADIgZ27SF1bMbVxIjgvQAfT8xwAAOTAEdszrXEjOC9A/TtKvdrEPQoAAFBMOrWQdusW9yhAcF6gjuwX9wgAAEAxOXR7FoImAcF5AS8MbdUk7lEAAIBi0LihdAhls4lAcF7A2+oewmpqAAAQgSE9pDbNmMokIDgvYEf05fITAACIJqZAMhCcF7COLaQ9WbgBAADqoU9baccOTGFSEJwXuC+wMBQAANQDWfNkITgvcAM7pc54AQAAstWySarJBJKD4LwIfIE6MQAAUAfu0OImE0gOgvMi4DPe1k3jHgUAACgkjRqwb0oSEZwXgSa0VQQAAHVI7nVowbQlDcF5EZW2eAMBAACATByzA/OURIRzRaJ9c+mg3nGPAgAAFILBXaReNJRIJILzInJcfzYlAgAA23bsjsxSUhGcF5HOLaX9esY9CgAAkGT92ks7dY57FKgJwXmROb6/1CDuQQAAgMQ6oX/cI0BtCM6LTI820l7d4x4FAABIop5tpN27xT0K1IbgvAidMCDuEQAAgCQ6fkepAZfYE43gvAht1y61ChsAACB9bdq+vZiPpCM4L1JkzwEAQLoT6epWEAjOi1T/jtLAjnGPAgAAJEHXVtKBfeIeBTJBcF7EyJ4DAAA7ZSB7oRQKgvMitksXaccOcY8CAADEqVcbaV/2QSkYBOdF7syd4x4BAACI06k70aGlkBCcl0Dt+W5d4x4FAACIQ9920p7sf1JQCM5LwBmD2DUUAIBSzZqjsBCcl4BebaX96GsKAEDJXT3flavnBYfgvITOnBvzbAMAUDJOJ2tekAjXSmhXsEO3i3sUAAAgXx3bBnRirgsRwXkJOXGA1KxR3KMAAAC5dhpZ84JFcF5C2jaTjt4h7lEAAIBc2q+n1Lc9c1yoCM5LzLE7Sm2axj0KAACQC00bscdJoSM4LzHNG0sn9I97FAAAIBeO3UHq0IK5LWQE5yXoiL5S99ZxjwIAAESpQ3PpOBJwBY/gvAQ1aiidu2vcowAAAFFvOuiyFhQ2gvMSbrG0Z7e4RwEAAKLQrz0bDhYLgvMS9sVdpSa8AgAAKGgNlLoi3sD/QMEjNCvxjYncvQUAABSufXtJ/TrEPQpEheC8xB3fX+rIqm4AAAq3deKguEeBKBGclzj/Up+9c9yjAAAAdeEr4LROLC6N4x4A4rd3T2mnGdKnS+IeCQBkZ/rwZzTpjYe0dulctWjfTTsc8kUNOOLCrR43/N4fqGzzxkr3NWrSTAde/Mc6TfnccW9q7NN/1PLZE4Pj9D3gdA0575dq1OR/u7x9+Pg1weP2ueA36r7rIcF9ZZs2atyzt2rPs37MU41IylOPozy16JA5R8ALSRqykARAAZn81mN6+XdnaNO6leo26EBtKdukN/74Jb33wE8qPW7lvCka9+9btOjT97RsxscVt+UzP6nT95327lN6/qojg2P0HnKcWnfuo4+evE5v3npRxWNmf/iyPn7+9mBcr/7hHJWXlwf3T3n7MbXtvkM9f3Ig5YLBtE4sRmTOEejVVjp8e+mN6UwIgORzsDv83v/TzidcpkMvv7vi/qF3fSsIxPc652o1bdkmuG/hp+8FbSxOuu41NW3Ztl7fd/OGdRp612XqvecxOu4XzwZZc3vj5q/oszcf1gFfv0mtOvXUoimj1HXg/tr9jB8GgfuaJXPUunNvTR/xjI7+6T/r+dMD0r49pV27MhPFiMw5Kpy2k9Qu9XcGABKtbON6rVu+QD12ObTS/V0G7Kstmzdp4+plFfctnPSe2vfaqd6BuTnIdgnLod/+S0Vgbr33OtZnDMHnbdO61WrcrGVwS/1/lWaPflU9d/uCGjZilxjUT8sm0jlsJli0CM5RoUUT6YLdmBAAyde4WQu16zlAsz56qdL9sz98Sc3bdlarzr0r7lvw6Qh13H43TXzpXn3w6K/16asPaP2qpXX6vu169tdh37lHbbr1rXT/kulj1aBhI7XvNfDz8bVU2ab1Ktu4Lvh/k+at9ekrf9NOx3y9Tt8XSHfWzlJbkmlFi7IWVLJnd2lID+nDeUwMgGTb69yr9dafvq4Nq5aq8w57BRnyuePe0JFXPqIGDVO5p80b12vJ1NFaNOl9zRn7ulq276YV8z7T8Hu/r6N//KR6Dzm23uPYuHalJr32gPoeeIaatmoX3Nep3x6a8MLdmvjKfcHJwsY1y9Wm6/Zq0qJ1vb8fSlv/jtLBfeIeBXKJ4BxbOX+wNHGxtHYTkwMguboO2E9tuvXTzJHPBzfrtefR6tx/74rHrJz7WRAc737GlRp8yveCkpJVC2fov784Rm/dfrHOv3d6vctMRvztR0HZyn5fub7ivu32PUk7HHquJr/5iL5w5cMa/9+7tNcXf64pQ58MPr/joefU63uiNDVuKH15d3YCLXYNysMl5ECad2ZJD45hSgAkk2u6n7w8tUnDgZfcqi7999aSaWM0/L4faPP61Tr79nFq0b7m1XJT3/mXXr3hbJ3425fVe69j6jyO8DgHXfonDT71u9U+Zt3yhRr50FVq26O/Jrz4F5WXb9Hgk68IThiAbJw0QDp1J+as2FFzjmr5ktnOnZkcAMk084P/aM3i2Tr8e/drh4PPCmrA+x5wmo7/5fNBMDxl6BO1fn3HvqkFNqsXzqjzGJbO+Fhv/eki7XDwF2sMzG38f+7ULideHmT39zjrJ9r99Cs14/NMP5Cpbq2kE/ozX6WA4Bw1+tLuUjOaCgBIoLXL5gcfO+84pNL97XsPUoNGjSs+v3TGeE0d9o+tvn798oXBx4ZpmwZl+/1f+u0patt9Rx3+/ftrbb3ojYo677iX1q1YpGatO6hZm45av2JRnb4vSteFu0lN+JtcEgjOUevOY1w+A5BErTr1Cj4unT620v3zxw9Vednmim4qU9/5R7A5UBishya8dE9QuNt9l9TOndnYsHqZ/nP10dq8ab2Ou/pZNWneqsbHTnr9QQ088ivBvxs2bKTyLWXBrWEjlnwhu6vZO3E1u2Tw7oBaHdlP+mCuNG05EwUgOdwvvGmr9kHg7YWWrbtspxVzJmnMU38I2ijueEhqweXOx12qcc/cHGS59zj7p0ELxslvPRos1Bx45Fcr7dY54/3UwtLgeF23q/F7v3XbJVo2c7x2OenbQQeYdN4RtH3vVFGwl3TNGvWCjr3q6eD/Xry6cv4Ubdm0Ua27Vm7FCNSkUwt6mpcaFoRim+aslK4bKpWxdBhAgiyeOlrv3H25FkwcXnFf+z4764jv/11dB+5bcd+CiSM07K7LggWj1rh5K+160re1z4XXqFFaWYt7oH/42G90xs0j1WXAPtV+z03r1+iRi3ppS9nmaj/vhaGDjr04+PfMkf8JNiXa+fhvBP+f/eHLevn6M9WgQQMde9Uz6rXHURHNBIpVA0k/OFAa2CnukSCfCM6Rkf9Mkp6dxGQBSJ61yxZo/crFata6fUW5S3VWL5qlTetXB3Xi6UF5yAG3b9750wF0fZVt2hCEV+nfy5sf+diuPQe25egdpC/uwjyVGoJzZGRLuXTTu9KU/+2IDQAAcqRnG+nnh7AItBSxIBSZvVAaSF/fS2rOKgUAAHKqUQPpa3sSmJcqgnNk1b3lvF2ZMAAAcumkgdJ27ZjjUkVwjqwc2EfauweTBgBALvRrz2ZDpY7gHHXaCKFDcyYOAIAoNW2UKmdxKSlKF8E5staqqXTRnqkWTwAAIBpn7Sx1a81sljqCc9TJoM6pFk8AAKD+BneRjmBvKhCcoz5OHyT1bsscAgBQHy4V/dpezCFSyJyjzho3lC7eS2rCqwgAgDpxffklQ6TWW++LhRJFWIV6b5JwDu0VAQCok9N2kvp3ZPLwPwTnqLfDtpcOqHnHbAAAUI3dukrH7cjUoDKCc0Tiwt1TWXQAAJBZnXnQ+YzWZ6iC4ByR9Wa9bG+peWMmFACAbdWZX7o3deaoHsE5IuPerF/dgwkFAKA2ZwySduzAHKF6BOeI1JAe9D8HAKAmu3eTjmGfENSC4ByRO3MQK88BAKiqYwvpoj2oM0ftCM4RuUYNpW8Mkdo2Y3IBADDvCfLNvaVW9DPHNhCcIyfaNZcu2Su16AUAgFL3lT2kvu3jHgUKAcE5cmanztLpOzHBAIDSdvyO0n7sB4IMEZwjp47rL+3bk0kGAJTuRkOnDYp7FCgkBOfIObdX7NuOiQYAlJYeraVLhlDiiewQnCPnmjSSvrWv1J4FogCAEtGqifTtfdmcD9kjOEdetG+eCtC9Wh0AgFLYAbRLq7hHgkJEqIS88Sr1r+4p0cAFAFDMvriLtHPnuEeBQkVwjrzy4tBTBjLpAIDidHAf6ch+cY8ChYzgHHl30kDpAFpKAQCKzICO0gW7xT0KFDqCc8Tiy3tI/Tsy+QCA4tCzjXT5vlJjIivUEy8hxMJvXt/aR+rakicAAFDYOjSXvruf1LJJ3CNBMSA4R2xaN5Wu2F9qS4tFAECBckD+3f2lDi3iHgmKBcE5YtW1lXTFfvSBBQAU7lVgl7QAUSE4R+y2ayddvg91egCAwuG2wF/fUxrYKe6RoNgQnCMRduosXbIXWxwDAArDObtKe/eMexQoRgTnSIy9ekgXDI57FAAA1O7YHelljtwhOEeiHLq9dNpOcY8CAIDq7d9LOnMQs4PcIThH4pw4QDqK3dUAAAmzSxfpq3tIDVxwDuQIwTkS6Yu7SPuxiygAICEGdkx1ZmlE5IQc4yWGRHJW4qI9pMFd4h4JAKDU9WsvfXs/qWmjuEeCUkBwjsRyduKb+0g70aYKABCTPm1Tmww1b8xTgPwgOEeiOUvxnf0I0AEA+dejtfS9/VO7gAL5QnCOggnQ2egBAJAv3VpJ/3eA1KYZc478IjhHwQToVzhA7xj3SAAAxa5rK+kHB0rtmsc9EpQignMUXAZ9AAE6ACBHurSUfnCA1J7AHDEhOEdBadY4lUHvT4AOAIhYZwfmB0odWjC1iA/BOQoOAToAIGpdW0pXHih1JDBHzBqUl5eXxz0IoC7Wb5Zue0+asoz5AwDUXc820vf3p8YcyUBwjoLmAP3296XJS+MeCQCgEPVtl+pj3qpp3CMBUgjOUfA2lkl/GSV9vDDukQAACokbDLjRABsMIUkIzlEUyrZID4yW3p8b90gAAIVg1y7SZfukOoEBSUJwjqLh1ROPfyy9OSPukQAAkmxId+niIVJj2mIggQjOUXSe/VT6z2dxjwIAkEQH9Ja+uofUsEHcIwGqR3COovT6NOnJ8RKtiAAAocO3l84fLDUgMEeCEZyjaI2YLf19jLSFCB0ASt4J/aXTB5X8NKAAEJyjqI1dIN0zStq0Je6RAADi4PKVCwZLh27P/KMwEJyj6H22RLrrA2ntprhHAgDIp2aNpG/sLQ3uyryjcBCcoyTMWyXdMVJavDbukQAA8qF9s1QP8z7tmG8UFoJzlIxVG6S7P5CmLIt7JACAXOrZRrpiP6ljC+YZhYfgHCVlU1lqkehINisCgKK0c2fpm3tLLZrEPRKgbgjOUZLohQ4AxefA3tKXd5casbkQChjBOUrWe7OlB8dKm+nkAgAF75SB0skD4x4FUH8E5yhpk5em6tBXb4x7JACAumjSUPrS7qmdP4FiQHCOkrdoTaqTy/zVJT8VAFBQOjSXLttH6ts+7pEA0SE4B5Tqgf7XD6VPFjEdAFAIBnaULt1batss7pEA0SI4Bz63pTy1UPTFyVI5swIAifWFvtIXd2HhJ4oTwTlQxZj50v2jpXWbmRoASFp9+YW7SQf2iXskQO4QnAPVWLhG+vMH0pxVTA8AJAH15SgVBOdADTaWSQ+Nld6fwxQBQJwGdJS+QX05SgTBObANr0+T/vmJVEYhOgDk3RF9pXOoL0cJITgHMjBlqXTPKGn5BqYLAPKhReNU//J9ejLfKC0E50CGVm6Q/jpKmrSUKQOAXHLf8kuHSJ1bMs8oPQTnQJbtFv8zSfrv5NS/AQARBiWSjtlBOn0QbRJRugjOgTqYvFT620fSknVMHwBEoU1T6Wt7Srt2ZT5R2gjOgTpat0l6ZJw0ci5TCAD1sXPnVGDerjnzCBCcA/U0Yrb02MfSejYtAoCsNGwgnbqTdNyOqX8DIHMORGLRGum+j6Rpy5lQAMhEpxbSxUOkHTswX0A6MudARMq2SM9/Jr3wmcRaUQCo2YG9pXN3lVo0YZaAqgjOgYhNWpJaLLpsPVMLAOnaNZO+vLu0WzfmBagJwTmQo8Wi//hEemcW0wsAtn+vVLa8VVPmA6gNwTmQQ58skh4eS8tFAKWrbTPpwt2kPbvHPRKgMBCcAzm2YbP01ETprenUogMoLfv2lM4bLLUmWw5kjOAcyOPGRQ+OkRasYcoBFP+GQhfsJg3pEfdIgMJDcA7k0aYy6dlJ0qtTpS20dAFQhPbuIZ0/WGrTLO6RAIWJ4ByIwfTlqSz6nFVMP4Di0LmldN6udGIB6ovgHIixL/p/J0svTpY2b+FpAFCYGjeUjtlBOnGA1LRR3KMBCh/BORCzBaulx8enOrsAQCHZqVOqtrx767hHAhQPgnMgIT6cJz05ns2LABRGe8Szd5b27x33SIDiQ3AOJKzt4vOfSa9NlcpYMAogYRpIOmx76fRBUssmcY8GKE4E50ACzV8tPUGpC4AE2a5dajOhvu3jHglQ3AjOgQQbM1/6xyfSorVxjwRAKZewnDpQOng7qaFT5wByiuAcKIDe6K9MTXV12VAW92gAlAp3XnEXlmN3lJo3jns0QOkgOAcKxIr1qXr0YTPZwAhA7jg5fmAf6bSdpPbNmWkg3wjOgQJsvfjMROnD+XGPBECx2bmzdNYuUp+2cY8EKF0E50CBmrZM+tcE6bOlcY8EQKHr2UY6a2dpcNe4RwKA4BwocGMXSE9PlOauinskAApxsecpA6VDWOwJJAbBOVAEtpRLw2dLz33KJkYAtq1VE+mYHaUj+0rNWOwJJArBOVBknV1eny69PEVavTHu0QBImhaNpaN2kI7uJ7VgEyEgkQjOgSLdafStGakWjCs3xD0aAHFr1kg6sl+qNWKrpnGPBkBtCM6BIs+kD52ZyqQvWx/3aADkm/uTf6GvdPQOUmuCcqAgEJwDJWDzFundWamNjJasi3s0APJRvuJM+VH9yJQDhYbgHCghZVukEXOkFz+TFq6NezQAotamaSpT7sCcmnKgMBGcAyXa3WXkHOmFydK81XGPBkB9dW+dWuR5QG+pSSPmEyhkBOdACSsvlz5eKL02TZqwOO7RAMjWwE6pRZ67dZUaNGD+gGJAcA4g4E2MHKS/N1vatIVJAZKqYQNp7x6poHz79nGPBkDUCM4BVOL+6O7w8uZ0aTkdXoBEdV7xTp5e5NmxRdyjAZArBOcAalw8OmpeKps+fTmTBMSlayvpsO1SgTmLPIHiR3AOYJumLJNenyZ9NE8qK2fCgFxr3FDas3sqKHddOfXkQOkgOAeQsVUbUq0Y35lJlxcgF7q0lA7dTjqwj9S2GXMMlCKCcwB1zqY7SP9grrShjEkE6qpRA2mPz7PkgzqTJQdKHcE5gHpZvzkVoA+bKU2jNh3IWOeWqTryg3pL7ZozcQBSCM4BRNqO0UH6e3NSXV8AVNaqSaoN4n69pP4dyZID2BrBOYDIbd4ijV8ojZwrjV1A2QtKW5OGqbIVB+SDu0iNGsY9IgBJRnAOIKc2lknjFqRKX8YtZIMjlM5GQa4f379XquuKe5QDQCYIzgHktT597OeB+vhFqQw7UEz6tU9lyPfpSbcVAHVDcA4gFms3SaPnpwL1CYulLfRPR4F2WnEf8j26pUpX2LkzWcrLy9WAJvEoMATnAGK3ZmMqk+7yF39csynuEQE1a9FYGtw1FZD7Y6nu2jl37lwde+yxuvLKK/W1r31NSTJ+/HiddtppOu+883TttdfGPRwgK1TBAYhdq6apUgDfnEGfuixV/uIadXeAAeLWofn/suM7dWJRpzkj7SB4zZo1Wc3l+vXrdc4556hly5Zq3DjzMOTBBx9Uw4aZrabt37+/FixYoDfffFPZuuqqq/TEE09s83Hf+c539P3vfz/r4wPbQnAOIHEL6dxizrczd5aWrE0F6c6qf7qEBaXI3+vQ9eM7d04F5Nu1Y+aratSoUfAxmwA7DOq7d+8eBOc+Rk1lJ8uXLw8C8k2bNqlnz541Hu+CCy4IThCaNKl8CaNp06YaOXKkzj777Er3b9y4MThBuOOOOzRw4MCtjldWVhYc75prrqnxe/70pz/VunXrMvhpgewRnANItE4tpSP6pm7u/OL69E8WSpOWSvNWSZSqIwoOD3u2SXVY8c115HRYSVm9evVWAa45aLbbb79dzzzzzFaf/+53v6sTTzxxq/ubNWume+65p9bnw5lrZ6W7du2qX/7yl7roootqzJp37txZrVq1CoJx15ibH3v++edXepwDbj/O43ZgXdNJRfPmzdWuXTtdcsklNY7PpTLZnpQAmeKVBaBgNG30eWlBt9T/V22QJi35/Eawjix1avG/YNy3ts2Ywuo4s/3SSy9p11131fHHH19xvwPc119/PchqDx48uFLG+7777tsqOM7Ee++9p5/97Gd655139KMf/Ug///nPgwx7bW677bbg44wZM3TmmWcG3/eHP/zhVvXxO+20U1CKcv3119d6vExLZ1hoilwhOAdQsNo0k/bumboZwTq2FYzv0CGVFXe5SpdWzFc25Sv77LOPbrrppor7Fy9erLvuuktnnXWWLrvssor7J0+eHATnVctMtuWVV14JFpgeeOCBGj16tHbeeeesvt4nCVu2bNHVV18djKlfv34Vn/vnP/8ZXAGoroylqg0bNgQ3j6EmLo3ZvHlzVuMDMkVwDqBog/XVG1NZdS8wnbZcmrkiVRqD4teskdS3fapuvF+HVFBOZrxuMs0k1/frHFjbH/7wh6wDc/PJwM0336wjjzxSDz30UFAOYy51ufvuu7XXXnsF5THbsmTJEk2fPj14fG2oOUeu0EoRQMko25Lq/uJAffpyacZyae5qeqwXQ71499apINzBuANx1497USfqzzXarufu3bu39thjj0r3v/zyy9ptt9203XbbVdy/du1avfHGG3rssceCVobpJk6cGNR0V1ev/e677+rcc8/VU089pX333bfS5xxgOxh24HzAAQdUlJR4DL///e8rPXbs2LHafffdK/6/cOHCoC7+1FNPrTiuTwQGDBhQbemNM+xeMLotLrfZVskNUBcE5wBK2qYyafZKacaKVGbdt3mr2b00yRlxB969fGub+uhOKqXaazwfXL7hrLQzya7pTg/CXb+dHvTa0qVLdcstt1QbnDswd8lIfTggD4N7H8vH7NChgzp27JjxMRycH3fccUFG3ZYtW6ZPP/202sf6pMQ3f96Pq44D/U6dOtXp5wGqoqwFQElr0ujzjGuH/93nXuuL1kjzV6cC9eC2KvX/DZTF5IWz3s6GO/hOD8ZdN86Gj/kVdkBxNtr13Ok15w7OTzjhhK1qzh2ch1+XbsKECUEwXV09uheBnn766Xr++ee1//77b1Ui4+x9emBu4b9//OMfB+0N68oLUf1zVOfGG28MFpi6//m//vWvah/jmnbXuQNRIDgHgGoCw26tU7c9KgUp0rL1qdKYIHBflQrcl6yTVqynrWO2XJjQrpnUuZXUtZXUpWVqkWaP1qnAvHHdSp2REGENebr0RZpVuX1h+NHtETMRZrLbt2+v+nB7R3NfdPdgD/mKQJs2bSoec8QRRwT17KGVK1cGXWzCrweiQHAOABlyxrZji9TN27ZXrWdfuu5/tyVV/r1sXeltoOSTnNZNpfbNpc4tUj3rfQv/3bllqj0mCiPI/uCDDyq1KAwXRDqb7Gx5eivF9D7oubRo0aLgY6bB/LY60jgwdwlL+v3O2If/dhCe/vnwZw2/HogCwTkARKBRw1TWt6b2fM66r9qYCtbd8tGdZLa6bUp9XOPbpmQuVG3SUGrVNNX5pG3TVIectmm34P+ff96PY1Fm4QvLU5yl/vjjjyvuD4Nv9xCvGpy6ntsbCOXa+PHjg4977713vY6TSWeZ2h5Dz3NEieAcAPKUdQ8D2Ew4Hlr7ebDuOncvXN245fOP1fw/uO/z/5d/XjIS9LNoIAUhRYPP7ws/pv3bdffNGkvNP/+Y/m/vktksvL9R6iQEpSUMwo855hg98MADlWrOu3TpoiuuuKJSzXnVrHtdWzFm4tlnn1W3bt1qLZfJRHX18aGwc0ttjwGiRHAOAAnkwNmZZ9+AOHnL+2nTpql169bVfr62oNULNV364XaH2+pm4o19suExPfnkk7r88ssVVemOWyum14/7BCQMzv0Yl/YcffTRFZ8PNyKqrr4eqCuCcwAAUCNnvvv27bvV/e7x7XKON998U5deeulWvcvdk/zf//530FrRO4nWxAG2N/155plngv+3bdt2m89GWVlZ8D398fvf/369n70wyO7Tp0+l3uXDhw+vCM79mBYtWlSai/CEIh/19SgdBOcAACBrDmKPPfbYILh2YO3ANT2b7k4m9rvf/a5iUWVNizp/8pOfBI9xO8LBgwdv83t7MyN3VrnhhhsqbYBUnxOQ7bffPthhNL1by7x58yoy/j5p8O6j9957b6Ve7w7Q81Ffj9LBJkQAAKBOnDEeOnRoRSBeKcBo0CDIRA8ZMqTWY6xatUqzZs3SDjvsEPRAz9Qnn3yiXXbZpU7jBpKM4BwAAABICNbdAwAAAAlBcA4AAAAkBME5AAAAkBAE5wAAAEBCEJwDAAAACUFwDgAAACQEwTkAAACQEATnAAAAQEIQnAMAAAAJQXAOAAAAJATBOQAAAJAQBOcAAABAQhCcAwAAAAlBcA4AAAAkBME5AAAAkBAE5wAAAEBCEJwDAAAACUFwDgAAACQEwTkAAACQEATnAAAAQEIQnAMAAAAJQXAOAAAAJATBOQAAAJAQBOcAAABAQhCcAwAAAAlBcA4AAAAkBME5AAAAkBAE5wAAAEBCEJwDAAAACUFwDgAAACQEwTkAAACQEATnAAAAQEIQnAMAAAAJQXAOAAAAJATBOQAAAJAQBOcAAABAQhCcAwAAAAlBcA4AAAAkBME5AAAAkBAE5wAAAEBCEJwDAAAACUFwDgAAACQEwTkAAACQEATnAAAAQEIQnAMAAAAJQXAOAAAAJATBOQAAAJAQBOcAAABAQhCcAwAAAAlBcA4AAAAkBME5AAAAkBAE5wAAAEBCEJwDAAAACUFwDgAAACQEwTkAAACQEATnAAAAQEIQnAMAAAAJQXAOAAAAJATBOQAAAJAQBOcAAABAQhCcAwAAAAlBcA4AAAAkBME5AAAAkBAE5wAAAICS4f8BBjIePPvRrq8AAAAASUVORK5CYII=",
      "text/plain": [
       "<Figure size 1000x800 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "============================================================\n",
      "Agent 生成的第 2 段代码：\n",
      "============================================================\n",
      "# 先探索产品表的结构\n",
      "print(\"=== 产品表基本信息 ===\")\n",
      "print(products_df.info())\n",
      "print(\"\\n=== 前5行数据 ===\")\n",
      "print(products_df.head())\n",
      "print(\"\\n=== 基本统计 ===\")\n",
      "print(products_df.describe())\n",
      "print(\"\\n=== 类别分布 ===\")\n",
      "print(products_df['category'].value_counts())\n",
      "\n",
      "--- 执行结果 ---\n",
      "执行成功：\n",
      "=== 产品表基本信息 ===\n",
      "<class 'pandas.DataFrame'>\n",
      "RangeIndex: 4 entries, 0 to 3\n",
      "Data columns (total 5 columns):\n",
      " #   Column        Non-Null Count  Dtype  \n",
      "---  ------        --------------  -----  \n",
      " 0   id            4 non-null      int64  \n",
      " 1   product_name  4 non-null      str    \n",
      " 2   category      4 non-null      str    \n",
      " 3   price         4 non-null      float64\n",
      " 4   stock         4 non-null      int64  \n",
      "dtypes: float64(1), int64(2), str(2)\n",
      "memory usage: 292.0 bytes\n",
      "None\n",
      "\n",
      "=== 前5行数据 ===\n",
      "   id\n",
      "\n",
      "============================================================\n",
      "Agent 生成的第 4 段代码：\n",
      "============================================================\n",
      "# 设置中文字体\n",
      "plt.rcParams['font.sans-serif'] = ['SimHei', 'PingFang SC', 'DejaVu Sans']\n",
      "plt.rcParams['axes.unicode_minus'] = False\n",
      "\n",
      "# 计算各产品类别的平均价格\n",
      "avg_price_by_category = products_df.groupby('category')['price'].mean().round(2)\n",
      "print(\"=== 各产品类别平均价格 ===\")\n",
      "print(avg_price_by_category)\n",
      "print(f\"\\n总平均价格: {products_df['price'].mean():.2f}\")\n",
      "\n",
      "# 画饼图\n",
      "plt.figure(figsize=(10, 8))\n",
      "colors = ['#ff9999', '#66b3ff', '#99ff99', '#ffcc99']\n",
      "explode = [0.05] * len(avg_price_by_category)  # 略微分离\n",
      "\n",
      "# 饼图\n",
      "wedges, texts, autotexts = plt.pie(\n",
      "    avg_price_by_category.values,\n",
      "    labels=avg_price_by_category.index,\n",
      "    autopct='%1.1f%%',\n",
      "    colors=colors[:len(avg_price_by_category)],\n",
      "    explode=explode,\n",
      "    startangle=90,\n",
      "    textprops={'fontsize': 14}\n",
      ")\n",
      "\n",
      "# 增强文字样式\n",
      "for text in autotexts:\n",
      "    text.set_fontsize(14)\n",
      "    text.set_fontweight('bold')\n",
      "\n",
      "# 添加图例显示具体数值\n",
      "legend_labels = [f'{cat}: ¥{val:,.2f}' for cat, val in zip(avg_price_by_category.index, avg_price_by_category.values)]\n",
      "plt.legend(legend_labels, title=\"类别 (平均价格)\", loc=\"upper right\", fontsize=12)\n",
      "\n",
      "plt.title('Product Category Average Price Distribution', fontsize=16, fontweight='bold', pad=20)\n",
      "plt.tight_layout()\n",
      "plt.show()\n",
      "\n",
      "--- 执行结果 ---\n",
      "执行成功：\n",
      "=== 各产品类别平均价格 ===\n",
      "category\n",
      "办公用品     499.0\n",
      "电子产品    2866.0\n",
      "Name: price, dtype: float64\n",
      "\n",
      "总平均价格: 2274.25\n",
      "\n",
      "\n",
      "============================================================\n",
      "最终回答：\n",
      "## 📊 分析结果与业务洞察\n",
      "\n",
      "### 各产品类别平均价格\n",
      "\n",
      "| 类别 | 平均价格 | 占比 |\n",
      "|:---:|:---:|:---:|\n",
      "| 🖥️ **电子产品** | **¥2,866.00** | **85.2%** |\n",
      "| 🪑 **办公用品** | **¥499.00** | **14.8%** |\n",
      "| **总体平均** | **¥2,274.25** | — |\n",
      "\n",
      "### 🔍 洞察分析\n",
      "\n",
      "1. **价格差异巨大**：电子产品的平均价格（¥2,866）是办公用品（¥499）的 **5.7 倍**，反映出电子产品本身单价高、毛利空间大的特点。\n",
      "\n",
      "2. **品类结构不均衡**：当前产品线中 **电子产品占 75%**（3/4 个产品），而办公用品只有 1 个产品。建议可适当丰富办公用品品类，均衡产品结构。\n",
      "\n",
      "3. **业务建议**：\n",
      "   - 电子产品是核心收入贡献品类，应重点维护并关注高单价产品（如笔记本电脑 ¥6,999）的库存和销售情况\n",
      "   - 办公用品目前只有 1 款产品（办公椅），可考虑扩充品类增加收入来源\n",
      "\n",
      "饼图直观展示了两个类别平均价格的比例关系，**电子产品占据了绝对主导地位**（85.2%）。\n"
     ]
    }
   ],
   "source": [
    "# 查看 Agent 的完整推理过程，重点是它生成了什么代码\n",
    "response = await visualization_agent.ainvoke({\n",
    "    \"messages\": [{\"role\": \"user\", \"content\": \"分析各产品类别的平均价格，画一个饼图\"}]\n",
    "})\n",
    "\n",
    "for i, msg in enumerate(response[\"messages\"]):\n",
    "    msg_type = msg.__class__.__name__\n",
    "\n",
    "    # AIMessage 中的 tool_calls 包含 Agent 生成的代码\n",
    "    if msg_type == \"AIMessage\" and hasattr(msg, \"tool_calls\") and msg.tool_calls:\n",
    "        print(f\"\\n{'='*60}\")\n",
    "        for tc in msg.tool_calls:\n",
    "            if tc[\"name\"] == \"execute_python_code\":\n",
    "                print(f\"Agent 生成的第 {i+1} 段代码：\")\n",
    "                print(f\"{'='*60}\")\n",
    "                print(tc[\"args\"].get(\"code\", \"\"))\n",
    "\n",
    "    # ToolMessage 是代码执行的结果\n",
    "    elif msg_type == \"ToolMessage\":\n",
    "        print(f\"\\n--- 执行结果 ---\")\n",
    "        print(msg.content[:500])\n",
    "\n",
    "# 最终回答\n",
    "final_msg = response[\"messages\"][-1]\n",
    "print(f\"\\n{'='*60}\")\n",
    "print(f\"最终回答：\\n{final_msg.content}\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "db5050f5",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "用户意图：both\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "findfont: Failed to find font weight bold, now using 400.\n",
      "findfont: Failed to find font weight bold, now using 400.\n"
     ]
    },
    {
     "data": {
      "image/png": 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PokG7vRG2vGmned8n8MQ1Wvm63bQ3NoHcbuE4voE8NrWvMXELI/S+Nry4BHveSD3eEFTwhsI7TG/ZskVNTMbqbb7egEZCuO+/fL0YuSeffFK9AbntttvCPrDhDybR+Xtdwn0T0CEjWPgEOzMzUzZu3HjYzzB6q71pjuR5Q4FPwPHpNyZl45OqtWvXqucdPB9E6/UYk4LxnIA335iUjFUoEdzxyUF3d/eg8+J4YLVbPH9gkjmCdSifJASrjvebwItMt2/frmqCMFHFm7ZwC2pqAPWWqGFesGDBoPNhoRfUI2JyWyi1lqFcrjaZLhCoF8REOxT233LLLeo64XqHyl/NNOojUYOGZuv+enMPVxP59ttvqzotT1gYBtfX36Q2LPKA/RmuH3gwxyyQ/dVqbzGRxbsfJo4Nfoa6dw1W3PKuo0bjeKzs5K9mOtDrFsj+4v6ECbaYjOXdAP9HP/qR+juPPfbYiGq8Y9VnGjXTmHDn7/GGWkBcDq635+3mfRxQX+t9uwX6OA72+Ab62MTzA+77uP96w+Xhuu3YsSPo84b6eAvEd7/7XfXchec11H+iFlI75riP+hNozXQgj4tI3H/5ejEyWCEXk9mef/55dzRrX1944YVBr+WebrvtNvWzZ555JqS/e+GFF6rnhH379g2chsc/ngcwETga5w0WHuOYw4G/8ctf/lJd/927dx92vkg9P/iya9cutS+Ys4Ln8nPPPdf9r3/9S034jSTeb8I4AXH+/PnqzoSm4XjRxgQdvADgzoYXA61pOmanoygeM4HxwgkHDhxQRfLa7H1/hnoRDuVygw2GaFaPvz9p0qQh9zOYMI1ZvQiDmLyBSQNoto4wPdQT5XAvltryqTj+ng3Wsd/okuHL//3f/x024Wrbtm2HLTkaqTCNDfutTezCpBGchtvPe3EA3AZaWMLtjiczBDB/L+6BXrdA9he+9a1vqcvD5D3tDcCLL76onrxwjLFPIw0jWK3KuzPISMM0ZuLjvua9aSE7lDCN7aabbho4j7a62KpVq0KegBjM8Q3msYnuOLhcvMHXLhcvsuhYgucpzwl2wZw3lMdbINA1RDvGWlcSLKyB7zHxcqRhOpDHRaTeDPL1Inh4bsSy0JiIi1UHox2K8JqFSYfoYvHJJ5+o0/A8gJCNUIjOWZ6PC7yZRrgO5D6C64PJjLivHTx4UD0nfe1rX1P7gqAejfOG4pprrlH5Bm9wly9f7vM8kXp+GA5eI9F9B4NPeO48++yz3Q8//LBaCTfceL8JY5hGix48geNOg1CL1ZDwNW5E75GkL3/5y+pnCI7Tpk1T4REjjrhDFhYW+g05w70IB3u5wQZDrE6ovbj5enceSpj23tBC59lnnx3yd4d7scQbF7ypwPVHZ5Vjjz3WnZubqy7/97//vc/faW1tVX8bT5Y4ZuhY4Gs0M1JhevHixar9GUbitMCA/fEcUQAETFwvPHljBjlG8PGGROvk4uuJO9DrFsj+An4PLZBwGXgyxGVrI4aey+yOJIzgssK1SqAWpv1tWjALJUzj2OOxjscbnrRx2+Cxhu4b/gz3OA7m+Abz2EQI1y4XI9kYncJzFUb5XnvttZDPG8rjLRB48cdloIuHBu0HcRoCzEjDdCCPi0iFab5eBEcbNMBtNNRjK9JdGTDIgccuzoMQiccEvsZgBlbb8/TKK6+onyFwBgIdJ3BfxOMI/+N30TXGVx6I1HmDpbXYG6qbSqSeH4J9vGHQA6PxuK3w2omuZxhMCQfeb4YWVDEwiuoxMeiRRx5RNUqY1YpVeTAZEDW2ntDsHM3FMYECNZeYpX/ZZZepWfzoAoAJA9OmTTvsb2Dxlzlz5vjdh2AvFzNe09PTA76OmOyB64K6MEzAGAnUcmFxBa2mKicnR03mQjeE4RquY7ZwU1OT359j1i5m/D/11FPqtsBEKuw7jp+viV2AyZqY2HTvvfeq30XdKGrjvPcl2GM23P5i0gyOw6JFi9TEtQceeECdF8fiK1/5iqrF94Q6Vty+qEFDTdgNN9wgV111lapHw2QYLPgS6nULZH8B9eIvvfSSmryBSSSY+Ibjitpd1LJ6QtcTX38HdXO43r72F773ve8dNpkkVNgHzB3wB/c5wIItuB38Pd6wGAEm9uI20yb0olYSK2395S9/UY8L3D64j6AThj/DPY6DOb7BPDYxoQ+ToXHZuFzMrMfkKdx/vG+HYM4byuMtEJjrgYlKnjXLeD7F/Vnr9hHK/TeYx0Wo99/h8PUiOKjNxUJGH3300ZCPrZHCcwEmtfl6/QXMgUJXGUy8Q50wBt3wuMNjDq/3nnAZuJ96P4f7g44keNzgNQATfPFa7qsGOZLnDRYuF00O8Fzt2Y0qGs8PwT7esKFGG6+V2Bd8jblG4ZhHxfvN0HRI1MOcZ0xAAT0CBB6ImHiEAn/PVlxEYw3eLOPNCybhIEzHCh+bFG94n/wM3pyjpaS/cE6xhSXX09LS4u5mWDTK7jeR7bOTQLB0KlrhYLQU7y8w4k1EscfHJsUb3if71dbWyllnnTVqAtFoFI9BunYU3m84Mn0IFp946KGH1EjchRdeOKpuZKJQoMwDbZZQFoDHRKzwsUnxhvdJIvLEME1EREREFCKWeRARERERhYhhmoiIiIgoRAzTREREREQhYpgmIiIiIgoRwzQRERERUYgYpomIiIiIQsQwTUREREQUIoZpIiIiIqIQMUwTEREREYWIYZqIiIiIKEQM00REREREIWKYJiIiIiIKEcM0EREREVGIGKaJiIiIiELEME1EREREFCKGaSIiIiKiEDFMExERERGFiGGaiIiIiChEDNNERERERCFimCYiIiIiChHDNBERERFRiBimiYiIiIhCxDBNRERERBQihmkiIiIiohAxTBMRERERhYhhmoiIiIgoRAzTREREREQhYpgmIiIiIgoRwzQRERERUYgYpomIiIiIQsQwTUREREQUIoZpIiIiIqIQMUwTEREREYWIYZqIiIiIKEQM00REREREIWKYJiIiIiIKEcM0EREREVGIGKaJiIiIiELEME1EREREFCKGaSIiIiKiEDFMExERERGFiGGaiIiIiChEDNNERERERCFimCYiIiIiChHDNBERERFRiBimiYiIiIhCxDBNRERERBQihmkiIiIiohAxTBMRERERhYhhmoiIiIgoRAzTREREREQhYpgmIiIiIgoRwzQRERERUYgYpomIiIiIQsQwTUREREQUIoZpIiIiIqIQMUwTEREREYWIYZqIiIiIKETGUH+RiGjM6usTMZs/23p6PvvaYhFxOkVcLt/bUUeJLFwY62tARERhwjBNRAR2u0hjo0hLi0h39+CA7B2Ycd5QTZrE401ENIowTBPR2IJQ3NDQH5w9/29rE3G7Y713RESUYBimiWj0QShub/cdmjGyTEREFCYM0wlg48aN8t///lduvvlmSUpKUqft27dP/vznP8tPfvITKSsr8/u7LpdL9PrD55keOHBA3n33XTnrrLOksLAwovtPFHEoy9i3T2T/fpGKCpH6+v66ZiIioghjmI4zvb29snnzZklPTxeDwaBO+9///ie33XabXHLJJQPn27Fjh9x9991ywQUXSBs+nj4UnPH7M2fOlNzcXLHZbHLsscfKcccdJ7/61a/UZWpuuukmeeedd+TUU08Vq9V62H6kpKSITqeLynUmClpr62fhGf83NfEgEhFRTOjcbhYJxpM9e/bImWeeKampqQNhurOzU6qqqmT+/PkD57NYLOq88+bNGzgfwjSC8f33368CNDzzzDNy3XXXqfM8+OCDcvzxx8uTTz45KJh7y8vLU38vIyMjoH1ubGxUfxOj5OEM4M8//7xMmjRJFixYcNjPamtr5a233pL29naZPHmyrF69WpKTk4P+G06nU55++mnZunWrzJo1Sx0X75H8cJ2HRgAjzVpwxv8o4UhU554rcsopsd4LIiIKE45Mx5kZM2bI3r17B77He52//e1v8uMf/1g2bdo0cDq+Xrx4sbz66qsybtw4v5d3/vnny0knnSQ/+MEPVDkHykW++MUvys9+9jP5xje+IXfccYcqHcHXHR0dcsQRR8i3vvWtgIM0YMQbwRaj5QjsWinKSLz88svyuc99Tl137zC9du1aOeWUU9QbDpS47N69W6ZPny5vvvmmFBcXB/w3MIp/+umny4cffqiOOy4HxwdbuM9DQUD7uOrqz8LzgQOscyYiorjFobM4hxD9r3/9S/YjWHjAKHVzc7OUlpaKw+GQnTt3yl133SUNmGDlJTs7W5WEzJ07V50fI8go+ygpKZGHHnpI3n//ffU1RravuOIKFaaDkZWVJa+88orY7XY555xzVLgciaeeekouuugiNdrry1VXXaX+Dq4rSmLw5gNf/+Y3vwnq7/ziF7+QdevWqTcC27dvV2Uvzz33nDz88MNhPw8NA7f19u0ijz4q8pOfiPzhDyJPPy2yZQuDNBERxTWWecQhBDGUC6BsoaamRv7yl7+oUg2MQKMO+rTTTlMj0w888IA0NTWpkoe0tDT1cwTNH/7wh4MuD6H0wgsvlClTpqjaa81//vMfFZ4xCv2Pf/xDBdSRQBhHEMe+vfTSS6puO1gIpGeffbb8/e9/V/t23333yZe//OWBn3d1dUlOTo689tprqt5bg99B6QtGpwOBY1JQUKCOy7333jtwOkbDUYOOQByu85Af6NW8cyc+ZhHZurV/sZOxgGUeRESjCkem4xACI0Za0bEDJRQ/+tGPVGhDBw6cjtBYV1engjbqiltaWlSJBkZFfQVphNGPPvpIrrnmmoHT33vvPfnmN78p3/ve91S9MyYyfuc735FqfLweItQI33PPPSrkomYb+xis/Px81b3EX003Rtkxiv7JJ58MnNbX1ydbtmxREy8DhZIUHDOUZ3hCTTlGmcN5HvJgs4ls2CDyj3+I3HijCN6ArF8/doI0ERGNOqyZjkMYkb388stVrfBPf/pTFSxRMoBQjXIOk8kkH3zwgaoZxgREf7q7u1W5xMcff6zOj2B+++23y7PPPqu+R7hGiQJqnDFREd+jVAR/FxvqqfG3goXLRO0yAjVquqdNmxbw7y48tMwySlf8Qf03SlXwxmLChAmqNhlvGrzfSAwFb0QANc6eioqK1CTO1tbWsJ0HbxDGNARljDxjBBoj0SNZPZCIiCjOMEzHIZRfIODOmTNHjSAj0KKO+JhjjpG3335blTmgawZqkxFWPUdoEeBOPvlk1ZEDIRoj0ii5QIcJjHijDGLJkiWqfMJzJBf9pg8ePKg6faDMBKUfoQRpzbXXXquuw9FHHy0bNmwYcpJksHD9UK6C8hfNjTfeqOrBA9WDVfAO1Xt70iZe4liG6zxjEo4L6p0RoHfvxrujWO8RERFRRDBMx6H/+7//U2EZI8+oh8aoNDpxICxi4iBGb9G9AuUFmHSHUI3SCHTiwCRATDREmD7xxBNVaQh+B0Fcg8mCWPDFFwRotJsbKYzYYmRbq/UOF7whQBs8XCbeYKAtHt5QfPe731Xt/B7FBLYAGI2+7/pam0H8PFznGVPQeeP997HSEAM0ERGNCWPslT4xaPXAWs/mRYsWqbIPlDegVR4m+qGUYvbs2apcY9u2baq7B0agUU/sCaEa5R1Tp05VI94aXI53T2iE9scff3zE+4/Jk5gQ+POf/1yuvvpqCacXX3xR9bVGBxK8oQDUhKOk5fvf/75qpec9SuyL1kIPo/EoFdFgQicmfmJSYbjOMybqoD/+WOSDD/AuKtZ7Q0REFFUM03EINcCodcaoJib1oVQALfJQJ4yRZ4RGjEajLjcQKLfAKDdCuWbVqlXq8rG6ogYlICNdaATdKy699FLVjQOBOtywgA1gRNoTRu3xBgEj14GEaYzuI+zi+mOVSM8e1ih/wRuNcJ1n1EIbRoxCcwIhERGNYezmEYfQ+g51xgiklZWVqnYaQRqLgeB0TETE/yjnCIR33S7qfDH6vXLlymHPGwzUWyNIYwQ8EkEaJk6cqP73fBMATzzxhBoF9h6Z9wej9eeee646xhjpBpTUoEwEC92E8zyjrh80SjjuvFMEfb3ffZedOCKs1+mQ1j6bdNj7pMthF7PDoU6zuZxid7nExUVsiYhiiiPTcSgzM1PVG6NsA6sXYgQZo62ofUY5AdrhIUyjTjgQaCeHHtMarH6olUc89thj6vK+9KUvyQknnKDOGwp0CbnzzjvV6DbCf6TgeKC8BX2dEVbRy3rNmjXqzQFa/Gm1ygi3f/jDH1Tfas/yC0+33nqrLF26VI488kj1BgbHG2UxaBcY7vMkvI4OkTVrRD78EB8PxHpvxpTnGuvkiYahW1ZmGAySZUySbINRsvG/2g7/Okv732Ac3Z+aEBFFEcN0nEE9NEIYWtWhHloLgnjhQ2DFaDImKKIVHJbUBpR+DNVODiUigD7UqLVG3TFqozHKi3IEnI4QOH78eBWw6+vrg+qMgXZ9WEkR+1teXh6Go9DfsxrLnnu3nEtJSVFdSrSgjP7SGI1+5JFH5LLLLhs4HyZsYoXIoUyaNEktMINRf3Q9wfG85ZZbVLeUcJ8nYaETB0o50JkDy3xTXOpxOtV2+Pqn/j+SzDoUrk3GJClNSZPyVG1Ll6LkFNEzbBMRBYQrIMYhLHyCPs+oxUX5AkZZ0X3jK1/5ipjNZjUCiuD7+uuvq/OvX79eli1bphZ5wURDDRYUQU9pdLn48MMP1cRAjD5j1BsTFj3hMvA3UKqB0V1871ljPRyEe21UOB4gSKPk5I033oj1riSmzZtFXn4ZNSux3hMZ6ysgPl5XNezIdLgl6/RSlpqqgnX5oaA9PjVdylLTJHmE8yqIiEYbjkzHoW984xvqf4Rg9Hz+/Oc/P1B/i3Z4GK3+7W9/O3B+jIBiBUNMMvSE0o5///vfqpYYl4HRW4R0X4466ihV8oESEAT4YII0xFOQhoqKClUWQ0Hatk3kpZdERrASJiW+PrdLKi29avOEGF2UnCrjVLhOU/9PTMuQKWkZksSQTURjFEemiQgfY/SPRFdW8mjEaGQaZVo9FosqcVIlFjqd+vo/TXXyn+a6uL5dMJI9IyNT5mRmy9zMHJmVkSWpcfYGm4goUjgyTTSWoSYaIXr//ljvyZj35KuvyKc7tosO/1SWxv86aZk0XmTy+Lgfyd5m7lKbSI0YRCdT0jNkbma2CtjYUKNNRDQaMUwTjUX79vWXc+zdG+s9oUMaWprF3NsjBbm5gm536JuuJGDTDae4ZW+vWW3PNtWpq4Caay1c4//85JRY7yYRUVgwTBONJRUVWEayf0Sa4k5KcorkZg/uAmNOTZNWSWx4W1Bl7VXbKy39PUeKk1NUsF6UbZKl2XmSaeTLERElJj57UUSho8hwfadbW1vlxBNPVJ1G0FEkWJhk+a1vfUtqamrUao+AFnVYkRB/37u9nr9uJPgdtAzEPuBysGQ5+np7WrFixaCOKahzPXDgQEB/I6ZwPTASjdpoojjQ2GeTxrZmebutWZWFzM3KlmU5ebLMlC+FHLUmogTCME0R8+mnn6pFVt577z3VRQTt9rD6ohZ4PbW3t6ve2ug64g19tNEt5LjjjlOB+ac//alaeVDrIIKWgPj966+/flBrPATk3//+95KWljbQwxvLsKOPt9bVBD270Xd727Ztqu3grFmz1H4effTRqoXgu+++q5YFB/TkfvDBBweFaazEeOWVV6qfeS9xHhdaW0Wefrq/1R1RHJeFbOnuVNt9NRWqO8gyU54sz8mXSekZsd49IqIhMUxTRLS1taklxf/0pz8NBNebb75ZLbji3cLP0yWXXDLoe9SNYvQXi8hgYRT8Li4P/2th2mazqVUi582bN/B7CN2AcIzVI7UwbbVaB74HrCSJJdS1lRszMga/cGMxm3/961/qa5PJpBaN8bR69Wo1Ko7ristCb/C4Wfb7zTdFXnsNa8THem+IgnLA0qO2x+urpSwlVY7JLVAb2vAREcUbhmmKiBtvvFFOPvlktUy55pVXXhn4GqPAw0FbMIxAe0LP7O9///sD32MxmsrKSrW8+Ne//vWB03Harl271N/H7/iDUF5cXKwWwvGGkW0EaE++lmBGb26MUP/xj38cWG0ypvbsEXnqKZGGQNfDI4pfdTarPNVQozb0ttaCNRaUISKKBwzTFHZYbAa1x6gl9gcLzWCkeChYnMa7ZtkblhN/4IEHZA8CpAeMMN90001qtBiXgTIOBF4sQ3766acPnA+j23V1dfLoo4+qEerOzk654oorJC8vTzZu3DiwZLumu7vb537cfffdqp76u9/97kBZSdR1dYk884zIxx/H5u8TRVi11aJGq7FNTsuQ43IL5KT8YsnxUTpGRBQtDNMUdk899ZScccYZUlhY6Pc8GHFGCciXv/xlnz/HJEAso+4Lap7xN1BSgUDe0tKiSjQQYlHy8bvf/U727t0rt956q1rZceLEiWr59a997Ws+67WxWiLqo3FZ+P1zzz1X/vKXv0hjY+OgGm6Umnzzm9+UW265RVatWiX33nvvwM/mzp2r9gH11xgljyq8KfngA5EXXhCxWKL7t4lipMLSo7bH6qtkpalATi8skdmZ2bw9iCjqGKYp7NAF47zzzhvyPAiut99+u1q63Jfq6mpVK+0LRn+vvvpqef7551VoRymGNvJ80UUXyfTp0+Vvf/ubGtn+ACFzGJhsiBDsWaeN2moE+qKiooHTX3vtNTXREPvuq0MJ9gUTFqMapquqRJ58sr9bB9EYZHe75d32ZrVhtBqhelVuIVdgJKKoYZimsKutrVUjwUN5+OGHP1uUwg9/5RIIyfjdq666Sjo6OtQINMpK0NrutttuU6Ub2uTEYKFzCCYzImB3dXWptnueddKo4164cOFAhw9P5eXlapJkVGAE+oUXxP3++6Ib5jgSjRUYqb6nar88VFspx+cVyRmFJaytJqKIY5imsENdcXb24R+3oob6nnvuCeqy3n777YGuGpjQqHXQQKjFBEHURGMUHG34UIeN0eHZs2erSYUNDQ2DRshRE40R57/+9a+DwjAmRqJLB0bCMeq8c+dOFaJRSqKViADCNbqRYBTbV5jG38d5Ig410Wh3192diIvjEUVcj9MpLzXXq21+Zo4arV5uyheDjwnEREQjxTBNYacFWW/o/YyOF9/+9rfVBL9AYbQZ9ddamD7//PPV6LHWYg99p7FACwK3xWJR592+fbsq/0DNM9rkocuGthhLbm7uoMvHxEGMbiOc4/cRnjFyjsmIGB3XSlFQ471161ZVL+0LrjOue8Q0NYlgX7wmWxKRf1vNnWrLS0qWU/OL5dSCYi5lTkRhxTBNYTdlyhTVlu6cc84ZfGc7tFzwddddJ9OmTVMj1Vih0JeSkhJZunSp+hr1z579mxGqUR+NkWaMUKPkAyPK2sgy6pyx8ApGlS+//HJVT40wffHFF6vL9YbQvXLlysNOR2jHhEMsNIPuII899phccMEFkpmZ6XOfcZ1x3SPigw/E/fTTomPPaKKQtNn75ImGavl3Q40cZcqTc4vKOGGRiMKCYZrCDguYYKT3hhtuGHQ6gq+nN954Q/Wj9h7pRekGunD85z//GTjNs24ZreywWiJGmVEbjRFkBGasbohuHDgv2vMNV7etwe9hX1DC8cknn6iFYzCRERMJsaoiuoosXrxY/Qwt8HxBiP/vf/8rL6CjRjihFd+jj4ps28aSDqIwrbb4UUer2hZlmeTSsgkyM+OzhZyIiILFME1hh7plrAr4zjvvyPHHH+93wROsRIiluZ999tlBp2NiIUad/cGy4gjm6AaCcIu+0RiBXrRokSrVQE012td5LuLiDb2nEZq1ZcTRpQOBGasoouYaMBqNMP3zn/9clYYgXCPk+4JVElFesmTJEgmb7dvF/cgjovPT25qIRmZTd4ds2t0hS7JzVaielu77UycioqEwTFPYoc4Ydc5f/epX1SizVhbhvUgLlhzfvHnzYaUXGGn2XFjFV20yVjZEcPVc3RCX9Zvf/Eaee+45FZbRkQO0riGe3UPwN9F9AyPZCMpoe6eVoXg64YQT5Je//KUq9fDXExv784Mf/ECexqTAcMAbCbzBeO89jkYTRcGnXe1qW5aTJ5eWTpBJ6Vy2nIgCxzBNEXHZZZepUItuGi+99JLqluEdpq+55ho1OuwLaqTRGaO+vl61v9NKRHAZqMVGbTJGg1HqgUmDKPfAKDJgNUMEZK0OWhvl9hztxv6gY8cXv/hFaW1tHRSkEbDRReT+++9X5SRYxAWTJzHB8aSTTpIzzzxTjbgvWLBA7SNOv/LKK9UkyBGrrRXXAw+IHpMNiSiq1nW2yfrONllhypcvlE6QCWlcspyIhscwTRGDsItJf5WVlaqVHEKq9wj2UEtv/+Mf/1CBG8F32bJl6jSEaizWgtZ7aGn3hS98QdU8Y0IjIPiiVAMLu2i9ptE7Grz/vnaa5+lYLAblImjvh9D83nvvqRFujGrj+qAbCUI7wjxGwtE1BKEay5SP2Jo14vr3v0XvZ7EaIoo8fH71YUerrO1olWNyC+SS0gkyLtX/8xQRkc493MoZRGHS3t6u+kEjnA4VojUIwa+++qpqXedZzqFBCL7zzjvlmGOOUefRYDT5lFNOGSgvQdkIJhfiPKiD9oTLR+9pz37U6BQyf/78QasfekKARthevny5hIXVKs5HHxXDxo3huTyKb+eeK3LKKYed/McH/yGVdXUyobR00OkHiwukuuTw+z9FBz4TW5VXKBeXjpfSFIZqIjocwzRRLNXUiOPee8XY1sbbYaxgmE5IBtHJCfmFcnHJeClK6e9xT0QELPMgihHXu++qlQyNTidvA6IEaKn3ZmuTvNfWIheUjJMLisslyavdJxGNTQzTRNFmt4v9wQclafNmHnuiBNPndsnj9dXybluzfG38VFmUbYr1LhFRjPFtNVE0mc1ivfVWBmmiBFdns8rN+7bLrQd2SWvf4ZObiWjs4Mg0UZQ4amvFceedktrTw2NONEqs6WiVjV0datGXMwpLxeC1OBURjX4cmSaKgp5PPxXXrbcySBONQr0up9xfUyE/2LVZdvdwxVKisYZhmijCmp9+WlL/+U9J5kRDolHtgKVHbty9Re6p2idm9osnGjNY5kEUIU6HQxrvuUfK9uzhMSYaI7DO62stjbK2o02uGjdJTsz33a+eiEYPjkwTRUCf2SzNv/kNgzTRGNXpsMsdB/fKT/dslSpLb6x3h4giiGGaKMzMdXXS/atfSUlLC48t0Ri3zdwl1+/aJI/WHRQnFxwmGpUYponCqG3LFnHfeqvk93Ikioj6OdxueaqhRn60e4vUWy08LESjDMM0UZg0vvmmZNx3n2Rx4hER+bC31yzf3bVJXm9p4PEhGkUYpolGyO12S82jj0rBs89KCj/GJaIhWF0uubtqv/zf/p3S5bDzWBGNAgzTRCPgdDql4t57ZdxHH4mBR5KIArS2s02u27lRNna185gRJTiGaaIQ2axW2X3nnTJ5yxbhmmdEFKx2u11+uW+HPFhTycmJRAmMYZooBN0dHbLz9ttl9r59ouPywUQUIreIPNNUKz/es1Wa+2w8jkQJiGGaKEjNtbWy809/koW1tQzSRBQWWIb8+p2bZH1HG48oUYJhmCYKQsXOnbL7L3+RI1taGKSJKKy6nQ757YGd8o+aCnG4sZYiESUChmmiAO3dskUO3n+/HG02M0gTUcQ811QnP969VZpsVh5logTAME0UYJCufeghWWWzcbIhEUXcnl6z/GD3FlX+QUTxjWGaaBgM0kQUC50Ou9y0Z5t80N7CG4AojjFMEw1h39atHJEmopjpc7vktord8u+Gat4KRHGKYZpoiCBd8+CDLO0gopi3z3ukrkruqNzLiYlEcYhhmsgHBmkiijdvtTXJzXu3i9nhiPWuEJEHhmkiH0F6P0ekiSgObTN3yQ27t0i9zRLrXSGiQximibyC9KaHH5aT2LWDiOJUrc0iP9y1RXaYu2K9K0TEME00OEh/+K9/yRkWixh5YIgozhd4+fnebfJOW1Osd4VozOPINNGhIP3WI4/Imb29ks4jQkQJwO52y58q98rjdVWx3hWiMY1hmsY8BOnXH3lEVvf2Sv6YPxpElGieaKiW2yv2iN3FJciJYoFhmsa0ip075dXHHpNje3tlohsNqIiIEs+77c3yi33bxep0xnpXiMYchmkas5pqauSNp56S+WazzGeQJqJR0OnjV/t3MFATRRnDNI1JXe3t8toTT0hBQ4McxyBNRKPEdgZqoqhjmKYxx2axyJtPPSWOffvkHKNRdLHeISKiMGKgJoouhmkaU5wOh7zz7LNSt3GjXJycLEaOShPRKMRATRQ9DNM0Zrjdbln3xhuy/f335dKUFEnnzHciGsUYqImig2Gaxozt69fL2ldflc8nJ0seZ7wT0RjAQE0UeQzTNCYc3L1b3n7mGTleRCY4HLHeHSKiqGGgJooshmka9Zrr6uSNJ5+UMrNZjuCINBGNQQzURJHDME2jmrmzU15/4gnprauTM/W8uxPR2MVATRQZTBc0avXZbPL6k0+qEo8LMzMllRMOiWiMY6AmCj+GaRqVnE6nvPvcc7JrwwY5NT9fSq3WWO8SEVFcBeo+DjAQhQXDNI1Kn779tmx4912ZXVgoR3R1xXp3iIjiLlD/uXKPahlKRCPDME2jDso6Pnz1VTFlZ8sp3d28kxMR+bCmo1UeqavisSEaIYZpGnUTDtECD/XSq91uyWQbPCIiv/7TWCNvtjbyCBGNAMM0jao66fdeeEFqKyrkmLw8mWQ2x3qXiIji3l+r9suW7o5Y7wZRwmKYplFj29q1snXtWpleUiJHtrXFeneIiBKCw+2W3x/YLTXW3ljvClFCYpimUaGhulo+ePllSU9NlZPNZknipBoiooCZnQ759b6d0uWw86gRBYlhmhKezWKRd55+Wrrb2uTklBTJs9livUtERAmnoc8qv9u/U+xsmUcUFIZpSmho6/TRq6/KgZ07ZWlJiczq7Iz1LhERJaydPd1y58G9sd4NooTCME0Jbe/mzfLpu+9KSWGhHN3WJrpY7xARUYJ7r71FHmPLPKKAMUxTwmpvbpZ3n39esOTAsU6npDmdsd4lIqJR4cmGanm7tSnWu0GUEBimKSHZ+/pUP+nm2lqZV1Qk07jKIRFRWN1VtU+2m1k6RzQchmlKSBvee092b9woZRMnylEtLSzvICKKQMu8/9u/S+qtFh5boiEwTFPCqdqzR9a+9ppk5ebK/L4+yevri/UuERGNSt1Oh9xSsUv62OGDyC+GaUooPV1dqrwD7fDKcnNlQWtrrHeJiGhUq7T0yv01B2K9G0Rxi2GaEson77wjtQcOSOmkSbKktZWLsxARRcFrLY3yXlszjzWRDwzTlDAQoje9/76YCgul3GaTiWZzrHeJiGjMuKdqv9SxfproMMbDTyKKPw67XT585RWx9PRIUUmJLK2pifUuJTa9XuSII0QmTBDR6UQOHBDZuNH3eSdNEpk7t/93Pv5YpKFhZH97uMvLzxdZvFhkyxaRJo/WXHPmiOzYMbK/TUQhs7iccmvFbvnDzAWShMcvESkM05QQtq9fLwd27JDi8eNlXkeHZNntsd6lxJWcLHLddf2htr1dxGAQOeEEkfXrRR5+ePB5V68WOftsEdSm4/dOPFHkb38T2b07tL893OUZjSLf+55Ifb3IqaeK3HyziMUikpbWH/4ZpoliqsLSIw/UVMjXJ0zlLUF0CN9aUtzrbG2VdW+8IclpaVJgMMicjo5Y71JiO/98kbIykfvvF/nZz0R++lORDz8UOeookSlTPjvf1Kn9wff99/tDLc6HEezLL+8PwsEK5PLKy/u/vusuEav1s/059liRNWvCdACIaCReaWmQj9o5+ZtIwzBNcc3tdsvaN96Qlvp6KSwrk6XNzWJwY81DCglGfidPFnnpJZFNm7SDLPLWW5+FWc3xx4v09oo8/XT/92iNha9zc/vLNIIVyOVlZvaPRENPT//3+Dh5/HiRigre6ERx4u6qfdLaZ4v1bhDFBYZpimuVu3bJ9nXrJL+0VCb39EipFrQoNA6HyC23iPzvf4NPz8jo/7+7+7PTpk0T2bVLxLOkprZWBKtN4mfBCuTysCS8VouJ//H90qUin34a/N8jooj2n77j4F414EE01jFMU9yyWa1q0iEmH+ZmZ8vilpZY79LohRIPlFUg7Goj2FlZInV1h5+3ra1/NDkYgV4eaqkxGo3z4jR8v3ChyObNoVwrIoqgzd2d8nyTj8c00RjDME1xa8uaNVK1d68UT5ggc9rbJR2jlBR+6OixfLnIm29+VmKBCX+AsgxvNlvwNdOBXh66dyA4//a3/WUdKSkie/aIJCX1d/kgorjyr7qDUtnbE+vdIIophmmKS60NDbL+rbckMydHMgwGmclJh5GBEHvllSKNjf1h2ldZiLeRLCscyOX985/9kxP/+tf+kL9v32cTFk8+OfS/TURhZ3e75fbKPVxunMY0hmmKOy6XSz569VXVxSOvuFiNSiexLi8y0EnDZBJ54IHBQRclH5CT43uUGZMDgxHs5aF2u6SkvwRk/nyRykqR++4TOemk4P4uEUXcQWuvPFxbySNNYxbDNMWdvZs3y84NG6SovFxSnU6Z3tkZ610anc44Q2TRov7e0hiZ9oRJguhBPW7c4NOxwEtRUX8tczBCubxVq0Tee08kL68/VOM8qKVG/TURxZUXm+tld4/HBGaiMYRhmuJKr9ksH732muh0OknPzJS5HJWOjOOO6w/TaJGHlQZ92b69f9VBrdMHzJwpkp4usndv8H8zmMvDJEQsJoPyHnT1wCcTWjkIV14jijvo6fHXqv3i5KeINAYxTFNc2fTBB1JXWSlF48dLqsMh09A2jcJr9myRz39eZOvW/sVaMNqrbampn53vnXf6R46/9KX+AIwRZPxec/PgFRARfBcsGH6CYKCXp4V9jEprJR84PzZMVuzrC+fRIKIwro74UnM9jyeNOfy8lOJGV1ubCtNZJpMYjUaZ29wsRo5yhB+W6cboLmqR/+//Bv9s7VqRRx7p/7qhof/rL3xB5Pe/P3QjdfUv/+05aRALqnz1qyJPPfVZAPYl0MtDGUdxsUhNTf/3CP3XXtv/d/A1EcWtx+qq5GhTvuQnp8R6V4iihmGa4saWjz6SjuZmmTBzpqTb7RyVjpRnnvFfd+y5aAtgsRR000CvZ7Qm3Ljx8PZ21dUi997bvwDLcAK5PLTBe+21z77HkuNYXry0VOTjjwO+mkQUfRaXUx6oqZAbpszi4acxg2Ga4kJHS4ts/vBDyc7LE71eL3NbWrhseKRUVQV3fkwAHWrE2Wz2X3cdyuWh17X3SpcHD/ZvRBT31nS0ysaudlmcHeTiTkQJijXTFBe2fPihCtSmwkLJsNtlCmuliYgS1t+rD7D3NI0ZDNMUc+3NzarEw5Sfr0al57W1iSHWO0VERCGrt1nlvw2H5j0QjXIM0xRzmz/4QLra29WodGZfn0z2rtslIqKE89/GGqmzepVsEY1CDNMU82XDt65bp0al0Vt6fns775RERKNkqXGUexCNdgzTFFOb1qyR7vZ2ySkokOy+PpnIUWkiolFjU3eHvN/WHOvdIIoohmmKmea6Otm+fr3kFhWpUek5HJUmIhp1HqitkF6nI9a7QRQxDNMUM1igxdzRodrhJTudMhEt1oiIaFRpt9vVYi5EoxXDNMVEY02N7PjkE8ktLlaj0lO7uthXmoholMIy4xW9PbHeDaKIYJimqHO73bLpvfekp6tLsnNzcYJMx0IeREQ0KrlE5NF6LrxEoxPDNEVdY3W17NywQfIOjUqX9fZKpoP1dEREo9nHne2yt4etT2n0YZimqNv4/vtiMZsly2RS33NUmohobHisnrXTNPowTFPU+0rv3bx5oFYaS4eX9vbyViAiGgM2dHXITnNXrHeDKKwYpimq9m7ZIubOzkGj0rwTEhGNHRydptGGOYaixmaxyLZ16yQjO1uNSutdLpnSxREKIqKxZEt3p2zr5qRzGj0YpilqDuzYIS0NDWqRFkBf6VQX5ngTEdFYwtFpGk0Ypilq7fCw2qFerxdjUpI6jRMPiYjGpu3mLtnc1RHr3SAKC4Zpior6gweleu/egVHpXKtVCmw2Hn0iojGKo9M0WjBMU1Ts2bRJLL29kp6Zqb6fzlppIqIxbVdPt2zobI/1bhCNGMM0RZy5q0stHY7VDjHxMMnplEndbNxPRDTWcXSaRgOGaYq4/Vu3SkdLi+QUFKjvp3R3i9Ht5pEnIhrj9vaaZX1HW6x3g2hEGKYpopwOh2qHl5yaKgaDQZ02lSUeRER0yOP1VWqSOlGiYpimiKret0/qKislv7hYfZ9js4mpr49HnYiIlAOWHlnL0WlKYAzTFFE7N2wQh90uKWlp6vsJZjOPOBERDfJMUy2PCCUshmmKmPbmZtm3ebOYDtVKA8M0ERF5293TLQd6OdhCiYlhmiJm7+bN0t3RIdl5eep7k80mOXY7jzgRER3mleYGHhVKSAzTFBF2m022rlsn6VlZqh2etnw4ERGRL++1N0uP08GDQwmHYZoioubAAWltaBBTYeHAaRPYW5qIiPywulzyVmsTjw8lHIZpiojKXbtUW7zklBT1fXZfn2Q6OOJARET+vcpSD0pADNMUdjarVfZu2SKZOTkDp3UlJ8szkybJx4WF0pCWJi4edyIi8lJjs8jmrg4eF0ooxljvAI0+Nfv3S0dzsxRPmDDodKvRKHtzctSW4nTKuJ4e1d2juLdX+pdzISKise6VlgZZmG2K9W4QBYxhmiJS4uFyuSQpOdnveWwGgxzIzlZb0qFgPb6nR0p7e7nUOBHRGIblxVv7bJKf3F8mSBTvGKYprGwWi+zbskUysrMD/h27wSCV2dlqM7pcUnYoWOP/JC4xS0Q0pjjFLa+1NMqlZYM/3SSKVwzTFPblwztaWqRk4sSQft+h10tVVpbaDC6XGqlGsMbIdbKLldZERGPB660NcnHpeDEcaq1KFM8Ypimserq6RKfXS31FhWSaTJKVmztkucdQnHq91GRmqk3vdksJgrXZrIJ1KoM1EdGo1W63y9qOVjk697MVdIniFcM0hdWcpUslPTNTKnfvlgPbt0vDwYOqfhqdPbIRrA+1yguWS6eTuowMtencbimyWFSwxqh1mtPJW5GIaJR5ubmeYZoSAsM0hRXC8vSFC9WG+mks3nJw1y7Zt22bNNbUqN7TWBUxJy9PklNTQ/obbp1OGtPT1faJ2y2FVutAsM5gL2siolFhm7lLqi29Mj4tPda7QjQkhmmKmJS0NJk6d67ajjnrLKlFsN6zR01QbK6rE0dfn6QdCtY4b0h0OmlOS1PbhoICybfZBoJ1lt0e7qtERERRbpP31fFTeMwprjFMU1RgJcTJs2er7ejTT5f6ykoVrLG4C5Yd77NaJS0zsz9Yp6eLLpRJJzqdtKamqm1TQYGYbDbVxxrhOofBmogo4bzf3izXlE/mRESKawzTFHWYkDhhxgy1rVi9WhqqqqRq717Zu3mztNTXqxUUU9PTJTsvT9JQIx3ibO6OlBS1bcnPV8uZI1QjXOf29YX9OhERUfh1ORyypbtDFmfn8vBS3GKYptjeAZOSpHzqVLUtO+UUaayuluq9e2UPgnVdnTRVV6sSEARr1FqHGqyxnPn2vDy1Zdrt/aUgZrMU2Gxhv05ERBQ+H7S3MExTXGOYprhhMBikbNIktR150knSVFs7EKyba2tVuMYERwRrLAoTarA2JyXJztxctaUjWGORGARrq1X0Yb9WREQ0Ems72uTaCS4x6vgMTfGJYZrikl6vl5Lx49W29IQTVJCu2rdPTV5EWUhrfb0Yk5MHgjXOH4repCTZbTKpLdXhkPKeHlUKgtZ7fNomIoo9s9Mhm7o6ZGlOXqx3hcgnhmmKexiBLhw3Tm1HHHectDU2qpUW923dKnUVFep7g9Go+lijn7XeYAjp71iNRtmXk6O2FKdTLQ6DYF3c2yuhXSIREYWr1INhmuIVwzQlXLDOLylR28Kjj1ZLlyNY79+2TWr27VNfI0yrYG0yqdKRUNgMBjmQna22JATrQ6svYnlzo9sd9utFRET+retoE7vLJUkhfgpJFEkM0xSX9vR0S73NKkfm5Eq6weg3WOcWFqptwYoV0tXWprqCHNixQ6r27JHafftQLyLZh5Y1x+h1KOwGg1RmZanN6HJJGWqse3rU/0kM1kREEdfrcsqGrnZZZsrn0aa4wzBNcemV5gZ5q61JknQ6WZhlkhW5+bIsJ0+yjEl+fwf10/OWLVObubNTBevKnTulYtcutWAMZB0K1ugiEgqHXi9VWVlq07tcaqR6Qk+PKglJdrlCvr5ERDS0dZ1tDNMUlximKe443W75pKtNfW1XX7er7R7RyfysHFlhypflpjwxJSX7vQzUTs9ZulRtPV1dUrN/vwrWB3buVAvGuF0uycjJUQEcfa9D4dLrpTYzU216t1vVVmPEutxsllQGayKisPqks11cbrfoQ+zkRBQpDNMUd3aZu1Sjfm9Occum7g61/b16v8zOzFbBeqUpX/KTU/xeHrp9zFy8WG2Wnp7+YL1rlxzYvl0aq6rE6XSq8yBYY6XGULh0OqnPyFDbx4WFqhuItqx5mtMZ0mUSEdFnOh122dnTJXMzc3hYKK4wTFNcfpQ3HBRUbDd3qe2BmgqZkZE1EKyLU1L9/h5WVJy+YIHabBaL1Bw4IAd37VITGJtqasRpt0v6oWCdkur/cobi1umkMT1dbZ+43VJotQ4E6wwfbxKIiCgw6zvaGKYp7jBMU0w1tbbKgZoamTp+vBTk5qpJhYGEaU/orbG7p1ttD9ZWypS0jP5gnZsv5anpfn8PKytOnTtXbcecdZaqq/ZcfdHe16dWXURnECxvHhKdTprT0tS2obBQ8qxW1W4PwTrLbg/tMomIxii8PnypfHKsd4NoEIZpiqmn//eGfLhxo+SbTDJ1/AQpmzldGnQjC5kHLD1qe7S+Siakpg+MWE9Kz/D7OyjvmDx7ttpWnHaa1B88qDqCIFi3NzWJzWpVo9oYsUawDnX1xbbUVLVtKigQk82mRqwRrnMYrImIhoUuT1WWXpmQFuIAB1EEMExTzLR3dcnegweltLBQDAa9bN27Wz5w2kTmzgzb36iy9kpVQ6882VAtZSmpsvxQsJ6ekeX3dzAhccL06WpbsXp1f7Deu1f2bt4srQ0N0mixSGpammTn56uAHWqw7khJUdvW/HzJ7uvrLwUxmyWvr28E15iIaHRb19nKME1xhWGaYmZ/VZV0dnfLtAkT1OIqudk5snni+Ij9vTqbVZ5urFVbUXLKQLCelZHlNxCjN3X51KlqW3bKKdJYXa1KQfZu2SLNtbXSVF2tykUwYo2SkFCDdVdysmzPy1Nbpt0+EKzzbTbhvHUios9s6OqQC0si91pBFCyGaYqZnQf2q/+1VQrREcOcHtqkv2A19dnk+aY6teUmJcnynP4aa8wSN/gL1gaDlE2apLYjTzpJmmpr1aqLGLFurKmR5ro6VS6CYI3uIKEGa3NSkuzMzVVbOoI1Fokxm6XAahWu/UVEY93enm6uhkhxhWGaYqLXapXt+/aJKfuzcovu9FRxx2Cp2Ha7XV5paVBbjjFJjsrJUyPWC7JzxKjzvT96vV5Kxo9X25Ljj1cTFrGUOUasG6qqpLW+XozJyWryIvpZ4/yh6E1Kkt0mk9pSHQ4p7+lRNdZovcdgTURjEdYf2NPbza4eFDcYpikmDlRXS1tXp0wsKxs4rTMjPS76mL7R2qi2DINBjjwUrBdn50qyn0CMEejCcePUtvi446StqUmVguzbulXqKivV9ygXQbDGYjL6QyPxwbIajbIvJ0dtyU6nCtYYsS7p7ZXQLpGIKDFt72a/aYofDNMUE3sqK9RiKckey3p3ZabF1a3R43TKO23NakvV62XpoWC9JDtXUv0EYgTr/OJitS08+mjpaGlRI9ZYIAb/Y0O5CJY0zzSZBkpcgtVnMMiB7Gy1JTmdMg6rL5rNanlzoxvNAomIRq8dPV2x3gWiAQzTFHUOh0O27NktWRmftapD/OtOj68w7cnqcskH7S1qS9bp5Yhsk6zIzVclIekGo99gnVtYqLYFK1ZIV1tbf7DesUMO7t4ttfv3qz7U2SaTCtcYvQ6F3WCQyqwstRlcLinr7VWlIGU9PZLEYE1Eo9Auc7c43W6/c1yIoolhmqKuvqVFWjo6pMBkGjjNnJYqzhBHaaOtz+2StZ1tajPqdLIwy6RGrJeZ8iTL+NlIuzdMTJx71FFqM3d2qmBdsWOHVOzaJbUVFSJutxqtRjmI0WPEPhhOvV6qMzPVpne5pNRiUcF6XE+PJLuwbiQRUeKzuJxSYemRaemZsd4VIoZpir6axgaxWK2SnvbZSHRXRvyOSg/F4XbLp13tarunSifzsrLVIjHYTEnJfn8PtdOzlyxRW293twrWlTt3yoGdO6W+slLcLpeauIgAjr7XoXDp9VKbkaE2ndutaqvRGaTcbJZUBmsiSnDbuzsZpikucGSaou5gXZ0qb/BsHdeZGfvJhyPlFLds7u5U273VB2RWZrYasUawLkhO8ft76E89c/FitVl6eqRm/35VBrJ/2zZprKpSteVotYdgjdZ7oXDrdFKfkaG2jwsLpRAj1oeCdbrTOYJrTUQUGzvMXXJu8Tgefoo5hmmKKpfLJbsrKiQzPX1QvXSijkz74zr0RI/tgZoKmZ6eqWqsV5oKpCTFfy9trKg4fcECtR179tlSe+CAHNy1S3UGaaqpEafdLumHgnVKamrIwbopPV1tnxQUqP7VKAXBqHWGwzGCa01EFD2chEjxgmGaoqqprU1aOzskJ+uzOjdLSrI4Qpx8lwjwZmFPr1ltD9UelMlpGWrEGovElKf6H5FHWJ4yZ47ajj7zTKmrqJCqPXtUL+vWujqx9/VJWmamCtapHm9OgqLTSUtamto2FBZKntWquoJg1DrLbg/9ShMRRViXwyHVll4Zn5b4n2xSYhu9CYbiUk1Dg/T09sq4oqK46i8dTZg0g+3R+ioZn5qmykAwYj05/bPuJt5Q3jFp1iy1rTjtNKk/eLA/WG/erPpY26xWNaqtBetQV19sS01V2+aCAjHZbP3Lmvf0iKmvbwTXmIgocqPTDNMUawzTFFVVDfXqf88VAeOtv3Q0VVstUt1QI0811EhpSuqhYJ0v0zM+WxnSGyYkTpg+XW0rVq9WwRoTGPds2iStDQ3SaLFIalqaZOfnq4AdarDuSElR29b8fMnu6xsI1nk22wiuMRFReBdvWV1QwkNKMcUwTVHjdrtVvbRnFw/oGmMj0/7U26zydGOt2gqTU2S5CYvEFMisjCzR+wnE6E1dPnWq2o46+WRprK6WGgTrzZulubZW1VmjXAQj1pjoGGqw7kpOlu15eWrLsNv7S0HMZsm32YRdXokoVlg3TfGAYZqiBrXSTW2tkp35Wb20NSlJbMmh9VQezZr7bPJCU73aco1JsvxQjfXczBy/ixRgNcWySZPUtvTEE6W5rk4ta45SkMaaGvU9ykUQrNEdJNRg3ZOUJLtyc9WW5nD0j1ibzVJotYrvBdeJiCL3XNlks0rREBO7iSKNYZqipqahUcw9PVKcnz9w2mjr4hEJ7Q67vNLSoLZso1GtuogR64XZOWLU+Y6vKKMpLi9X25Ljj5eW+noVrNEVBGUhrfX1YkhKkhwE65ycQWU3wbAYjbLHZFJbqsMh5T09KlgXWywM1kQUFeiaxDBNscQwTVFT3VDfv/yrx0qHPWmh9U0ey7PX32xtUluGwSBHqmCdL4uzcyXZTyDGCHRhWZnaFh93nJqwiFIQBGusvIjvUS6ClRexmIw+xJUorUaj7MvJUVuy0zkQrLFYTGKsbUlEiajS0hvrXaAxjmGaolcvXVkpaV6LjvSGuAgJifQ4nfJOW7PaUvV6WZKdKytzC2Rpdq6k+gnECNb5xcVqW7BypXS0tKhFYrBADCYxVu/fLwa9XrIQrE2mQW98gtFnMMiB7Gy1GV2ugWBd2tsrRjeaBRIRhUeNjWGaYothmqKiy2yW+uamQfXSYEkNbalsGszqcsmajla1Jev0sjjbpEasjzTlSYbB6DdY5xYWqm3+8uXS1d6uSkEO7Nih2u7V7t+v+lBnmUxq1Bqj16Fw6PVSmZWlNoPLJWVY1txslnE9PZLEYE1EI1RjtfAYUkwxTFNU1DQ2qEA9ubx84DSXTidWTj4Muz63S9Z1tqnNWKWThVkm1XJvmSlPso3+J3siMM896ii1mTs71Uh15a5dUrFjhyoHEZdLMnNz1fmMSaFNGnXq9VKdmak2vcslpRaLCtYYuU52Yd1IIqLgNNqsYne5JCnEuR9EI8UwTVFR09goTjzZeYxuYuVDjHxS5Djcbvm0q11tf63SydysbDVije4guUn+PxVA7fTsJUvU1tvd3R+sd++WA9u3qwmMLqdTnQedQdD3OhQuvV5qMzLUpnO7VW01+lgjWKc6nSO41kQ0luBteJ3NIhPT/C98RRRJDNMUFRU1NZLsNZrZizBNUeMUt2zp7lTbvdUHZFZGtqzIzVej1uhr7Q/6U89cvFht1t5eVWONEWvUWTdWVYnT6VSt9hCs0XovFG6dTuozMtT2sdsthRaLWtK83GyWdAZrIgqg1INhmmKFYZoizuVySW1To6SnDu4Dynrp2I7kYLEDbP+oqZBp6ZlqxBrhujTFf7tCLFU+bf58tR179tlSe+CAHNy9W3UGwQIxTrtdhW+svojFYkIN1k3p6Wr7pKBACqxWNWKNcpBMh2ME15qIRivWTVMsMUxTxHV0d0tPb6+kea18yE4e8QG9Nfb2mtX2UN1BmZyWMbCs+fg0/6tTIixPmTNHbceccYaqq67CIjFbtkhrXZ3Y+/okLTNTjVgjhIdEp5OWtDS1bSwokDwE60PLmmfb7aFfaSIaVWqs7OhBscMwTRHX1tkpvTab5JlMg07nyHR8qrD0qO2x+iopT03rH7E25cuU9MGdWDwlpaTIpFmz1LZi9WpVV62tvtja2Cg2i2VQsA519cW21FS1bS4okBybTS1pjmBt6usbwTUmokTHkWmKJYZpiri2zg6x2+2Daqbd2gREivsXqKcaatRWkpIqK3L6lzWfkZHl93cwIXHC9OlqW37qqdJQVdU/Yr15s1qJsdFikdS0NBWsEbBDDdadKSmyFVt+vmT19akRa9RZ59lsI7jGRJSIam0WtZ5BqM8nRCPBME0R19rRgc/rBz3J2ZKSVDcHShwNNqs801SrtoKkZDVajRrr2RnZovfzAobe1OOmTFHbUSefrOqq1Yj1li3SVFurvk9OS1PLmqPWOtQXwu7kZNmRl6e2DLu9P1ibzZJvswlfWolGP5vLJc19Ni4rTjHBME0R19DaIgbD4ODcy8VaElqLvU9eaK5XW64xSbXaQ7iel5UjBn/B2mCQ0okT1bb0xBOlua5OLWu+Z/NmaayuVqPW6F+dk5+vuoOEGqx7kpJkV26u2tIcjv4aa7NZCq1W4ds3otH9SVpRSmgTn4lGgmGaIgofu9U2Nkpa6uCWaSzxGD3aHXZ5paVBbVkGo1ocBsEai8X4W0RBr9dLcXm52o5YtUoFaQTrvVu3Sn1lpbTW14sBwRojzTk56vyhsBiNssdkUluqwzGwrHmxxcJgTTTKVFt75Yic3FjvBo1BDNMUUb0Wi+rmkeY1WtDrFa5pdOh2OuTN1ia1ZRgMcmROf7A+IjtXkv0EYoxAF5aVqW3RscdKe3OzKgVBuz10CGlralLlIlm5uZKFYG0whLRvVqNR9uXkqC3Z6VTLmaMUpMRiEQOXNScaFXXTRLHAME0R1dLZIRarVYry8wedzpHp0a/H6ZR32prVlqrXy5LsXFmZW6D+T/MTiBGs84qK1LZg5UrpbG1Vqy/u375davbuVV+jXCTTZJIsk0mF7FD0GQxSkZ2tNqPLpYI1RqzLenvFyGBNlJCqrQzTFBsM0xRRbR2dYrPZJNVryWmG6bHF6nLJmo5WtSXr9LI426RGrDFyneknECNYmwoK1DZ/+XLpam9XI9YVO3eqhWKwYAxgxDo7NzfkYO3Q6+VgVpbaDC6XCtQI1gjYSQzWRAmjlmGaYoRhmiLeFg9t8DxrXvG93RjaR/WU+PrcLlnX2aY2o04nC7JyZKWpQNVaZxsHLznvCYF57lFHqc3c1aVqrBGsK3bskLqKCnG7XJJ5KFhjImMonHq9VGdmqk3vdkupR7BOcWHdSCKKV90Ouzjdbr+ToIkihWGaIqq5re2wrgwqSPPJjjAq7HbLhq4Otd1TJaobCEas0R0kL8l/H/LM7GyZdcQRauvt7paa/fulYtcuFawxgRFL2Gfm5Khe1uh7HQqXTie1GRlq07ndatIigjUmMaY5nbz9iOIM3u6aHQ7JCfHNNFGoGKYpomoaGyU1ZfBkQ0eIE8ho9L8QbunuVNt91QdkZkaWqrFGuC5M9j9hFf2pZyxapDZrb68K1igD2b9tmzRWVYnT6VSt9hCsk73ui4Fy63TSkJ6utk/cbilEsD5UZ53OYE0UN7ocdoZpijqGaYoYW1+fNLe3SVrq4E4eLPGgQIL1zp5utT1QUyHT0zNVqMbS5qWpaX5/D0uVT5s/X23HnHWWqquu2rNH9mGRmJoacdrtKnxn5+dLitf9Mphg3ZSerrZPCwqkwGodCNaZDgdvXKIY6nLaefwp6himKWLau7pUJw9Tdvag0xmmKVh7e81qe7juoExKS1c11gjXE9LS/f4OwvKUOXPUdvTpp0tdZaUc3LNHrb6IPtZ2m00tZ44R65S0tNAWidHppCUtTW0bCwok12pVS5ojWGfb+aJOFG1dfENLMcAwTRHT0d0l1r4+lnlQWFVaeqXSUiWP1VdJeUqaWtIcI9ZT0jP9/k5SSopMnDlTbStPO03qDx5UI9YqWDc0SJ/VKqkZGSpYY3Q71NUX21NT1bY5P18se/bIOYsXS2FTk0h9/QiuMREFMwmRKNoYpilieq1WVa9q9KqRtofYwozIW43NIv9uqFFbSXKqGq1GuJ6Rnuk3EKPTx/hp09S2/NRTpaGqSvWvxrLmLXV10mSxSHJamlp9ESPXoQbrZmyLFknhkUeKNDaKbNrUv3HyLVHEdDJMUwww1VDEoMQDfHbzIAqzhj6rPNNUq7b8pOT+GuvcfJmdkS16PwEWvanHTZmitiNPOknVVdccCtZNtbXSXFsrSampKlij1jrUYC3FxSKrV/dvnLBIFDEs86BYYJimiLHYbD7Dh53dPCjCWu198mJzvdpMxiTVag/hen5Wjt8etFhZsXTiRLUtOeEENUqNEeu9mzdLQ3W1tNTXq1HtnPx8Faw9e6cHhfd/ooh28yCKNoZpiujItK8F5BwcmaYo6nDY5dWWBrVlGYxylClP1VgvzDJJkp9AjKBcVF6utiNWrVJ11Vh9cd+2bWoiIyYwGpKSVI01+lmHHKyJKKy6OQGRYoBhmiKmp7dXdD4yBss8KFa6nQ75X2uT2jIMBlmanadqrI/INkmK3nf5ET5dKSgtVduiY4+V9uZmNWK9f+tWqTlwQKqbmtSodlZenmQhWHPkmShmODJNscAwTRHTaTZLkuHwuxjLPCge9Did8m57s9pS9Xo5IjtXjVgvzcmTNIP/YJ1XVKS2BStWSGdrq6qx3r99uxq5xtc6vV6ycnPF7etjGSKKKIZpigWGaYqYrh6zGH107mCZB8Ubq8slH3a0qi1Zp5dF2SZVY31UTp5k+uk+g2BtKihQ27zly6WrvV2F6QM7dqgVGFPT0jhKTRRlnIBIscAwTRGBUbnunh5J8goiLp1OnPwYnOJYn9sl6zvb1GbU6dSkRYxYYxJjtjHJ7+9l5+bKnCOPVJu5q0vVVY+fPj2q+0401llcTrG7XH7nQxBFAsM0RYTd4RCbrU+MXpMNWS9NicThdsvGrg61/bVqv8zNzFE11hi1zktK9vt7mdnZaiOi2JR65Cen8NBT1DBMU8Q6edidDklLGfyE5uCoNCUol4hsNXeq7b7qAzIzI0uNWCNYF6Wkxnr3iMijowfDNEUTwzRFrMc0RqezMjIOK/MgSnSYWrirp1tt/6itlGnpmf2LxJjypSw1Lda7RzSm2d1460sUPQzTFLGRaYfDcVjNtJtZmkahfb1mtf2r7qBMTE1XKy+uNBXIhLT0WO8a0ZjDKE3RxjBNEWGxWdXItHc3DzdHpmmUO2jtlYP1vfJ4fbWMS0lTI9ZH5+bLlPTMWO8a0ZjgYltKijKGaYqIXgtWP3SLgTOqaQyrtVnkP401aitOTukvBcktkBnpmaq1HhGFH8M0RRvDNEVsZFp8hAWOTNNY1dhnk2eb6tSWn5SsWu0dbcqX2ZnZomewJgobp5rVQBQ9DNMUEda+Pp+n8ymOSKTV3icvNderzWRMkmWmPGnPTOebTaIwcPGFhqKMYZoiwu1yia8PsTkyTTRYh8Mur7U0ikwZL/ryErGZe6Sgo1tM5h7RMxQQBY1lHhRtDNMUEcgAnANCFBxXcpI05ZnUZnA6Ja/LLA794IWPiGiYxxE/A6UoY5imiHC5XCI6DqsRhcppMEhzbg4PIFGQdD4/FyWKHC5eTxH7mM3X0xmf4oiIKJL0fKGhKGOYpohAWzy3jxVadKz9ICKiCNJz2IaijGGaIlfm4atujZUfREQUQQa2mqQoY5imqM6m1jFNExFRBLFvO0UbwzRFhr8wzZFpIiKKIAYbijbe5ygi/C2VzJppIiKKJJZ5ULQxTFPkwrSPPM0yDyIiiiSGaYo2hmmKzB1Lr/c52dDgxMREIiKiyEg3cAkNii6GaYrMHctPmUeSw8kjTkREEZNtZJim6GKYpgiWeRweqI0ul+hcnIVIREThZ9TpODJNUccwTVEt8wCjk6PTREQUflkclaYYYJimCI5M+07TSQ4HjzoREYVdjjGJR5WijmGaIsKgx4KurJsmIqLoyWaYphhgmKaISE1JEbefOo8klnkQEVEEZLGTB8UAwzRFRFpqqqqZdvtYCdHIjh5ERBQBHJmmWGCYpohIS0kVg8EgTtfhfaXZHo+IiCKBYZpigWGaIiI9NUWMBoM4fEw2ZJkHERFFAntMUywwTFPERqaTjEax+wjTLPMgIqJI4Mg0xQLDNEWsZtroJ0yzNR4REUUCwzTFAsM0RURaSooamWaZBxERRQvLPCgWGKYpIjAqjVIPu9PXyDRXQCQiovDjyDTFAsM0RUx2ZqY4fARn1kwTEVFEXne4aAvFAMM0RUx2RobPmmmD2y165+Et84iIiEKVqtdLsp6xhqKP9zqKmJzMTHH6We0wta+PR56IiMKmJCWVR5NigmGaIiY9Lc3nCojqZzaGaSIiCp/y1HQeTooJhmmKaHs8neh8/8zKME1EROEzPjWNh5NigmGaIgbdPHyPS2Nk2sYjT0REYTMuhWGaYoNhmiImLTVFRNw+Sz04Mk1EROE0nmUeFCMM0xQx6alpfpcUT0PNtJ96aiIiomDDTBnLPChGGKYpYnJzsiU1JUUsVqvP9ngpfXYefSIiGrGi5FS2xaOYYZimiMnNypaM9HSx+KmPZkcPIiIKh3KOSlMMMUxTxBgMBinJL/A5Mg2smyYionBgmKZYYpimiBpXXCw2Pwu0sKMHERGFA3tMUywxTFNEFZhMfn+mJiESERGNEEemKZYYpimi8nJMotPpfC4rnm5lr2kiIho5hmmKJYZpiqh8k0mthOhrEmKS0yVGH23ziIiIApVjTJIsYxIPGMUMwzRFVG52tqSnpPqdhJjOZcWJiGgEOCpNscYwTRGFPtMFublisfkJ01xWnIiIRoBhmmKNYZqi0tHDX69ptscjIqKR4DLiFGsM0xRxRfn54nb5Xjo8u8fCW4CIiEI2KyOLR49iimGaIi4vJ0f973YfHqgzLVbRO128FYiIKGiper1MSc/kkaOYYpimiMvPyVG101YfpR46jE73cnSaiIiCNysjWww6vJIQxQ7DNEVcvilX0lJS/NZNZ/f08lYgIqKgzcnM5lGjmGOYpojLSEuT7Mws6fXTHi/bzJFpIiIK3lyGaYoDDNMUcVgBcUJpqfRafYfmrF6L6FysmyYiosAZdTqZwcmHFAcYpikqJo0bJ06ny+ckRIPbrSYiEhERBWp6eqYk6xljKPZ4L6SoKC8ukZSkJLH19fn8eQ5b5BERURBYL03xgmGaorZwS3ZmpnT19Pj8ebaZkxCJiChwDNMULximKSrQzWPyuHLpMpv9L97iowSEiIjIV3iZzcmHFCcYpilqpk6YIA6Hw+fPjC6XZFh9t84jIiLyNDEtQzIMRh4UigsM0xTVummjwSB9drvPn7PUg4iIAsGWeBRPGKYpasqLiyUrM1O6/dRNcxIiEREFgvXSFE8YpilqsjIyZHxxyRB105yESEREw+PINMUThmmKqhmTJom1z3dtdLLDKak2363ziIiIoCwlVUxJyTwYFDcYpinqddN6nd7vRERTt+8SECIiIliQZeKBoLjCME1RVV5SovpNd/f6Ds15Xb5LQIiIiGBZTh4PBMUVhmmKKlNWlpTkF0iX2XeYNpl7xOB08lYhIqLDpOsNMj8rh0eG4grDNEWVTqeTWVOmiMVq9X2HdIvkstSDiIh8WJxtkiQ9owvFF94jKSalHgjVTpfL58/zOlnqQUREh1tmyudhobjDME1RV15ULJnp6WLu8V83rePS4kRE5MGo08nSnFweE4o7DNMUdYV5eVJaWCjt3V1+lxbnaohEROTdW5pLiFM8YpimqEOJx6JZs8VisYnbzwh0fmd31PeLiIjiF0s8KF4xTFNMTJswQVJTksVi872ASz7qplnqQUREh8LKcrbEozjFME0xMaG0TIrz86Wts9Pnz1McDsnusUR9v4iIKP7MzsyW/OSUWO8GkU8M0xQTSUajLJgxU7r9TEKEgg7fNdVERDS2HJNbEOtdIPKLYZpiZsakSWI0GKTPbvf58wLUTbPUg4hIxnpQWcmWeBTHGKYpZqaMnyD5JpPfUo9kh1NyzL1R3y8iIoof87JyxJSUHOvdIPKLYZpiJi0lReZNny6dflrkQSFLPYiIxrRjWeJBcY5hmmJqzpRpotPpxe5w+G2RxwVciIjGJoPoZDlLPCjOMUxTTM2YOFHyc0zS1tHh8+dJTpeYuv1PUiQiotFrUbZJso1Jsd4NoiExTFNMZaSny/yZM/yuhgjFrb6DNhERjW6nFhTHeheIhsUwTTE3b9p00ev0frt65HeZJbnP98+IiGh0KkhKliO5UAslAIZpirnpEydJYW6etHX6HoHWiUgJR6eJiMaU1QUlYtDhFYAovjFMU8ylp6bKwpkzpbPb7Pc8JW0donO5o7pfREQUG0adjiUelDAYpikuzJk6TfR6/6Ue6DmNzh5ERDT6rTDls7c0JQyGaYoL0yZOlJL8Amlqa/N7ntLW9qjuExERxcbphSU89JQwGKYpbhZwWbZwoXSbzeL2s4R4To9F0i3WqO8bERFFz8TUdJmbmcNDTgmDYZrixuJZsyU7M1Pau/y3ySvlREQiolGNo9KUaBimKW6UFhbKvOkzpHmIUo/C9i4xOJ1R3S8iIoqONL1Bjs8r4uGmhMIwTXFDp9PJsvkLxKDXi9Vm83keo8ulAjUREY0+x+cVSprBEOvdIAoKwzTFlVlTpsiE0jJpaG3xex5ORCQiGp3OKCyN9S4QBY1hmuJKktEoKxcvFovFKi6Xy+d5Mqx9km3ujfq+ERFR5MzNzJYJaek8xJRwGKYp7iycOUvyTCZp6fC9IiJwdJqIaHQ5vYDt8CgxMUxT3MnLyZGlc+dJa4f/vtL5Hd2SZHdEdb+IiCgyco1JsiI3n4eXEhLDNMWlJXPmSmpyiph7e/3ecTk6TUQ0OpxWWCJGHSMJJSbecykuTR0/XqZNmCiNra1+z1PW3C4GB9vkERElsgyDQc4uKov1bhCFjGGa4pJer5cVixaJ3W4Xh8Pht03euGb/PamJiCj+nVc0TjIMxljvBlHIGKYpbi2YPkOK8wukaYhFXMpa2sXI0WkiooSUZTDKWUVsh0eJjWGa4lZGerosX7hQOrq6xO12DzE67b8UhIiI4td5xeMknaPSlOAYpimuLZ49R7IyM6XTbB5ydJqdPYiIEkuOMUnO4iItNAowTFNcKy8ulvnTp0tDs/8VEQ0uN2uniYgSzOeKx0kqlw6nUYBhmuKaTqeT449cJmmpKdLZ3e33fKUcnSaKe6l6veonrBvmfCl6vYxPTVP/h8Nwl5es0x/2M3zHF8jIyU1KktMLuUgLjQ6cPktxb9qECbJo1mz5aNNGyc7MVAHbm8HtlvKmVqkYVxyTfSQi/0pTUuUHk2bKtIxM9b3F6ZQXm+vk0boq8Z4NcXnZBNXdIUmvV+e7t/qAvNXWFPLhHe7ypqdnys+nzRGjTie/279Ltpo71enH5BaI0+2WNR2ckxEJFxSXS4reEJHLJoo2vvGmuIfwfMJRR0l6WtrQo9OtHZLcZ4/qvhHR0BBSfzxlluh1Ir/bv1N+tHuLCtIXloyXz5eUDzrvxSXj1enPNtXKDbu3yLrOVvnWxGkyLb0/hAcrkMtDf+O3W5vk2cY6VXagWZlbIB8xSEdEflKynMalw2kUYZimhDClfLwsmTNH6lua/Xb20LvdMr6Jo0hE8WRuZrZMTMuQ2yv3yLrONtnV0y2P1FXJ5q4OOdpUMHC+TINRLigZJx+0t6if7+7pljsq90qDzSpfKB0f9N8N9PJKUlKl2mqRg9Ye9bW2zzvNXeIK0zGgwS4sKVefFBCNFrw3U0LVTmemZ0hHd5ff8xW3dUgKR6eJ4ob+UFlWj3PwaqU2t0uVUWgWZuWoj/1fb2kYOA1h9t22ZlmQZZIkH+VdQwn08gyiE5e4xeUWMRw67dSCYnm9pTHEa0xDKUpOkZPzWY5HowvDNCWMiWVlsnTuPKlvbhlidFpkfKP/zh9EFF17errF7nLJRSXlAxMPZ2VkyeIsk3za1T5wvvLUdPU/RpA9YcQ4Wa+XwuSUoP5uoJfX7bSr1fewpHW3wyFlKanSbreLxTU4/FN4cFSaRiNOQKSEGp1edeSR8un27dLe1SV5OTk+z1fc1ik1RfliTUmO+j4S0WAYkf5T5R65ftIMWWHKV9+jnOKdtiZ5qqF6UM9hs8MhVtfg4oqeQyuc4ud1NmvAhzfQy9vQ1SFnFJSI3e1WNdJnFpbKs011vBkjoCQ5VU7iqDSNQhyZpoQysbRMjlowXxpb/I9OY/SLo9NE8QGPxyNz8lSNrMPtFqvLKW5xy+S0DJlwaPQYUGLR5xV8we4OrXI50Mt7vqlOnmyolhea6uSl5npJ1RvUBLnfTJ+nOoFQ+FxcOn6glIZoNGGYpoSzaumRkpOVJW2d/S2sfClq75KsHktU94uIDof62BPyi+Sh2kq5Ztsn8r1dm+Xa7RskSaeXm6bOVt0+wOF2SYrh8JcknA8wchyMYC7v7bZmeb21UdVKv9rSID+cPFO2mTvlxLwiWZaTx5s1DGZmZMkJeYU8ljQqMUxTwikvLpFlCxZKY2vrkKPTU2sbRIJ8ASai8Fqak6s6aDzdWDtwWlOfTR6tr5L85BSZlJahTmux96na5Wzj4OrDguT+cq2mvsBLPEK5PIR6jJY39lmlIDlFXmluUPXWU9L7949GFjS+Pn6KzzUCiEYDhmlKSMctWSq5WVnS2tHh9zyZFptaGZGIYgfxqdfpOOx0rZOH9rH/DnN/lx6UhHiam5kjbX190uU4/DKGEuzlrcorVJ0+UOYBKBGxuVwD31PoUIc+JcRe4USJgGGaElJZUZGsWLRYmtra/I5Ow8SGFkm2cyEXoljBJD/0mR6XkjZwGuLz6oJisbmcUmXpVadhFLjG2isXFperzhqA3zkur0DWdATfoSfYyzsqJ0/Wd7apAA1Jep3q+qF9T6HJS0qWS1l7TqMcu3lQwjp2yVJZu3mzNLe3S1Ge77pGo8slk+uaZPfEz1Y2I6LoQQ3yyflFctusBapzBgL0rIxsGZeaJg/UVAxqQXfXwX3yq+nz5PZZi2S7uVOW5eSr7htP1n/W9QNunjpHilNS5Rs7Ngz5twO9PLTpwyIyeFve6bCrkevVBSWqzve99uYwH5Gx5ZryyZJuYNSg0Y0j05SwSgoK5OjFi6WlvU2cQ4weFXZ0i6m7J6r7RkT9UC/9zR0b5a3WJtUabUZGljT32eTPlXtUJw1PO3u65Xu7NkmFpUcF2Y872+T7uzdLt1eZyEFrr+zrNQ97iAO9PNRF/6+taeB7rNaI8pA17S2yrqONN2WIFmWZ5Jjcz1a5JBqtdO6hPiMninPoN337Q/9U/2NRF38syUmyYeZkcXMJWyKiiEvW6eXO2YukNPWz8h6i0Yoj05TQcrOz5fRjjxNbX5/0Wv3P9k/rs0t5E0eYiIii4YKScQzSNGYwTFPCO2refFk0a7ZU1dUNORlxfFOrpNr6orpvRERjDZZkv6C4PNa7QRQ1DNOU8IxGo5y1apVkZ2aqyYj+6N1umVrbGNV9IyIaa742fqpa8ZJorOC9nUaFiWXj5ISjlklLe7vYh+hHm9vdIwUd/f1niYgovI425cuibBMPK40pDNM0apy4bLlMGT9eqhvqhzzf5NomMTg/a8dFREQjl6Y3qFZ4RGMNwzSNGpnp6XLmscepFcS7e/y3wktxOGRCQ/CLQBARkX+XlU1QS8QTjTUM0zSqYCLi0rnzpLqhYcjJiGUt7ZLZa4nqvhERjVZT0zPkjMLSWO8GUUwwTNOootfr5czjVkmBySQNLf5Hn7Gc8YyqetFzqWAiohFJ0evle5NmiEGHZ1aisYdhmkad0sJCOWXFSuno7pY+u93v+dJtfWqpcSIiCt2Xxk2S8tR0HkIasximaVQ6dumRMmvSZDlYN3i5Ym+lrR2S2zn8ssRERHS4I3Ny5XSWd9AYxzBNo1JaSoqcuWqVJBmN0tE1dCu86TX1kmT3306PiIgOl2M0yrcnTOehoTGPYZpGrTlTp8nKRYulrqlJXEPURic7nDK9euh2ekRENNh3Js6QnKQkHhYa8ximadTS6XSy+phjpLykZNhyj7zuHilt8b96IhERfeaMghJZkpPLQ0LEMg8a7QpMuXLeiSepNnnDlXtMqmuSdIs1avtGRJSIxienylXlk2K9G0RxgyPTNOodMWeurDryKKlrbh5yqXGD2y2zDtaJ3um/JISIaCxLFp3cOG22pOgNsd4VorjBME1jotzjrFXHy8xJk6SipmbIxVzQLm9abUNU94+IKFF8c+I0tsEj8sIwTWNmqfHPnXyqZKSlSVNb65DnLWrvkqK2jqjtGxFRIjjBVCDH5xfFejeI4g7DNI0ZMyZNktVHHyPtnV1isQ5dGz21plHSrbao7RsRUTwrNSbJtZOmxXo3iOISwzSNKSctXyGLZ8+WytraIdvlqfrpylrWTxPRmGd0i/xk+jzWSRP5wTBNY0pyUpJccMpqteR4Vf3QvaVRPz2V9dNENMZ9Y8JUmZDG5cKJ/GGYpjEHQfpzJ5+iJiK2dXYOed7i9i4pbxy6xpqIaLQ6O7dQTiosifVuEMU1hmkas+3yTl6+QhpbWsRqG7o2emJDsxR0DN2jmohotFmYnCbXTOJy4UTDYZimMdsu7/TjVsmCmTOlorZmyPppHSYvVtVLVo8lqvtIRBQrJS6Rm+YsUs+VRDQ0hmkas9JSUuSi1adLcX6BVDUMXT+td7tldmWNpNj6orZ/RESxkG53yO/mLZFkPSMCUSD4SKExrayoSNVPu5yuYeunkx1OmVNRIwanM2r7R0QUTQaHU26aOlvyU1N54IkCxDBNY97SufNUy7zG1lbptQxdypFh61Mt83RDrKJIRJSQXC65OrdI5hZwYRaiYDBM05iHmsCzjz9BVixcqPpP99ntQx6TXHOvTK3hkuNENLqcZkiVs6bPivVuECUchmkiEUlJTpaLTztD5k2fLvurqsQ5xIREKGnrlHFNbJlHRKPDgj6nfH3R0ljvBlFCYpgmOiQ7M1MuO+scmVBWJgeqqlUf6qFMqm+WvM5uHj8iSmjjLDb52ZIV7NxBFCKGaSIPJQUFctmZZ4spK2vYFRLRMGrmwTrJ7GXLPCJKTNkWm/x28XK1OiwRhYZhmsjL9IkT5aLTTldfY1LiUAxut+rwkdI3dJ01EVG8Se7rk1/OWiC56VwqnGgkGKaJfFgyd66cc/wJ0tndLR3dQ5dysGUeESUavcMh3x83RabkF8R6V4gSHsM0kZ8OH2iXh62+uVl6rdYhj1OG1SZzD1SzBzURxT2d3SFfyymU5eMnxnpXiEYFhmkiPwwGg5x34kmybP581TLPPkzLvOxeKwM1EcU1nd0ulyZlyGkz58R6V4hGDYZpoiGkpqTIJaefKbOnTJV9VdXiGqZlHgM1EcVzkD7b5pYLFy+J9a4QjSoM00TDyMnKksvPOlvKS4plf/XwLfMYqIkoHoP0qvYe+eLKY9kCjyjMGKaJAlBWVCSXnXW2ZGVkSHXD8KsfMlATUTwF6aU1zfLVVSeK0WiM9e4QjToM00QBmjlpsly0+jQRt1vqmpqGPT8DNRHFQ5Ceva9Krj3pFMlIS4v17hCNSgzTREE4av4CuXD1aWK3OxioiSj+g/Teg3LdqadLvskU690hGrUYpomCbJl37JKlctFpDNRElABBevUZUlpYGOvdIRrVGKaJgsRATUTxjEGaKLoYpolCwEBNRPGIQZoo+himiULEQE1E8YRBmig2dO7hmuYS0ZDwEHr/00/kqVdflaQko2qjNxxzWorsmFwufUlJPLpENGI6i1XmHKiWb592JmukiaKMYZooRoHalmRUgbonLZW3ARGFzNDRJfMra+XrZ5/LIE0UAwzTRDEM1E69TnZNHCft2Zm8HYgoaEn1TbKgrlm+ct75DNJEMcIwTRTjQI06qwNlRVJfmMfbgogClrqvQua3m+Waz13AIE0UQwzTRHEQqKGuIFeFatHpeJsQ0VBPMpK+eYfMs7vkagZpophjmCaKcKDWG/RSXlysun8Mpy0rQ3ZPLBOnwcDbhYgOo3c6JWPtpzI7OY1BmihOMEwTRTBQf7R5k/zn9dekx2KRKeXlotcP343SnHqo00cyO30Q0WeSbH2S+t5HsrCoWK44+1wpKSjg4SGKAwzTRBG2Y/8+eeylF6W+uVmmTpggSUbjsL/TZzSoQG1OT+PtQ0SS2t0jae+vlRUzZsolp58hOVlZPCpEcYJhmigKahob5JEXXpDdFQdkcnm5pKUO3w7PqdOpko+2HL5oEo1lGU2tkrH+Uzn5yGVy3kknS0pycqx3iYg8MEwTRUl7V5c8/tKL8vH2rVJeXCLZmZkBdfqoLC2U2qL8qOwjEcWXzAMHJXf3fjn3hBPlpOUrAioVI6LoYpgmiiKLzSZPv/G6vLN+neTm5EhhXmDt8BrycmT/uGJx84WUaEzQuVySuWWHlLV1ycWnny5HzJkb0CRmIoo+hmmiKHM6nfLamg/kxXffEaPBIOUlJQG9SGIJcizwYk3hR7xEo1mK1SapH66XKemZcvlZ58i0CRNivUtENASGaaIYdfpYt2WL/Pu1V6W7t0emjh8f0Me3Dr1e9o0vkRZTdlT2k4iiy9TSLro1a2X+5Cly+dnnSHE+O3YQxTsWXxHFAEaily9cKF+58EIpyS+QPZWVYrfbh/09o8slsw7WydSaBtG7XFHZVyKKPDyeS/dWiOH9j+To+QvlqxdeHFSQXrt2rfz9738fdNrmzZvlF7/4hdhstpD2aefOnfLmm2+K1WoN6feJxgqOTBPFWF1TkzzywvOy48B+1ekjPYBOH1o/anT7sKSmRHwfiShy0qw2Kdi8Xfoam+WUFSvl3BNPkuQk/33mzWazNDQ0SHp6+sAnWnfddZc8+eST8v777w+c78UXX5Rrr71WqqurB05zOBxisVhk4sSJkpycLB0dHXL66afLl7/8Zbn66qsHlZydeuqpUlVVJZ988olkBjBhOpKlcQYuZEVxjGGaKA50dHfJEy+/rEo/yoqLxBRgD1mnXif7xpVIc15OxPeRiMKvsK1DjOs3CqLzeSeeJCcctWzYkq9XX31VLr30UklNTR04LwJ2b2+vFBUVDZwPI8ptbW1SVlY2KJji9HXr1smMGTPUJ2K33Xab/PrXv5YjjzxS7r//fpk+fboa5f76178uSUlJKnR7mzJlimzatCli3UUQ8p955hl56qmn1PevvPJKyJf18ssvy6JFiwYdB01TU5Ma1cexmzlzpixevDikv+FyudQ+7tu3T4466ihZsWJFxM5D8YdhmihOWG02efat/8nb69epPrKBLkEOzaYsFaqdRi5DTpQIjA6nTKyskc6t26UwL18uOu00OWL2nJAv75e//KUKnwi4mmeffVYuuugi6evrG/b39+7dK9/5znfk7rvvlg8//FCuuuoqdZnnnHOO3HnnnSo8n3XWWWqkGv//5S9/kW9+85sSTl1dXfLcc8+pAP3xxx+rEfOLL75YTj75ZDEGsNiVL7isSy65RF5//XV1OZ5QwnLuueeqr7OysqSxsVHOP/98efzxxyUlJSWo/V69erV6g1JQUCDNzc3qzclNN90U9vNQfGLNNFGcSE1JkYtWnyZXnnOupCYny+6KCukLoI4aCju65Yg9FZJj7on4fhLRyOR098iUT7dI+5ZtMm/6dPnWpZeOKEhfeeWVsmvXLhWePSE8btmyZeD7mpoaefjhh33Oz8Bo9EsvvSSTJ09W4fvnP/+5/OQnP1Gn//Of/5Q9e/bIvHnz1Gj45z73OVUSEg4YUUd4Pe+889TfeuONN+RrX/uaCu34u6eddlrIQfqRRx5RxwYTvr2hjvyKK65QP+/s7FRlM2+//ba88MILct999wX1d/AmZPv27WqEGyPdKLm5+eab1RuCcJ+H4hNHponi0MH6Onnq1Vdkx/79UlYUeNkHXjJqC/PkYEkBe1ITxWHv6An1zeLYsk3cTpecuGy5nLnq+IDnSXhCcMZIKkowEL4eeOAB+dOf/qTqqBEUzzzzTDVSjRFZnK+iokKNfBYXF8tvf/tbNfLsCQEbo9Cf//zn5Zprrhk4/W9/+5v88Ic/VCH6vffek9mzZ4/4OKCkAsFdq/FG6McIOoJzMCPCQ3niiSfU9cAEzBtuuEGFdM+RabzJWLhwoZpkOWvWrIHT8YZh+fLlqtwlEO3t7VJYWCg//vGP1SiyZu7cuXL00UfLvffeG7bzUPwK7e0eEUXUxNIy+cbFX5Dn33lb3v14vXSZzTI+gH7U+Gl5c5uYzD2yewInJxLFi3SrTSbvOygNe/ZJcUG+fO7kU2Tp3HkhL8SCTh0YjUbNclpamgrHH330kRrFRU00am1xntraWrnnnntkwoQJqmbY10Q+BOnLL79cjYpiNFTz7rvvqtHpX/3qVyqIr1y5Un7zm9+oUWn8zVBCNCY6Pv/88yrA4rIxeoygHm75+fmyceNGdXwQpr1NmjRJ/cwzTOM6YrLmF77whYD/DkpiUId+9tlnDzodwR1lJOE8D8UvhmmiOJWRni4Xn3a6TBo3Tp7935uq7APdPlBPPZxMi00W76mU6qJ8qSnK4yg1UQxHo8ub2iR9z35paGmR+TNmqsc1PnEaCXz8j3A6f/58+e9//6sm2P3ud7+Tp59+WoVqTBzEhtFOjGwONdHvggsuUKHygw8+UGEbo6D/+c9/1GguuoF8+9vfVqcjQF9//fUqBB933HHqb6OuGn8nEBg1/8Mf/qAmOmJUGtcBgR9/f8GCBRJOp5xyivofE/l8yc7OVpM4v/rVr8qBAwckJydHjUbn5ubKV77ylYD/DkpnYM6cwWU6JSUlAz8L13kofjFME8UxjJysWLhIxhUVy39ef0227d0rhXm5UpCbO/zvut0ysbFFito7ZX95iXRkZURln4mon6m7RyZV1UlTRaX06PVy1qrj5fTjVknaCEsZUNOM4IsR3alTp6ruD2h5h64Vq1atUmEaI7MY9e7u7lat7TQoAcHINcIwQvC///1vVZOLUWhMMkT4vOOOO9TloMuHZ8jFCO9ll12mQifKNNANJNAgrRk3bpwK5NhQeoJQjbplvDFAqMa2dOnSqNyFrrvuOlUK84Mf/EB9jzcMKGvx7IgyHBxfHGfv1oH4XuvPHa7zUPximCZKABNKS+XaS74gr6/5QN5c+5Hsqzook8aVq+XIh5PWZ5d5B6pVx4+KsiLpC/LFj4iCk2y3y+S6JkmtbZCq+nqZOG6cnH/SybJgxsyQyzq8R5MRahGmUWP80EMPqY4PGMVEfTQCKiYJos3atm3bVAlHT0+PKvnABEMEV5R05OXlqVFYdJDA7yOMe3bB0NrSecOI9Pr160Pa95aWFtWpArAvN954o9p2796t6py9gzXKVcJxzLzhWKCPNmqkUcaCNx/4+xipbm1tlR/96EcBXQ4CODZMcvTcT5TbaCU14ToPxS+GaaIEgdGsc044UaaOnyBPv/m6WjUR7fOyA1xMAR0/crt61OTE+oJcLMMY8X0mGlPcbiltaVeTDBvr66XDapVjlx6p+kfn5YSvFzxGTuvr6wedZjKZ1IgyJhBqEEwRlFH6gQVc8LP9+/cfdnmop0bd9LRp01RN9FBQU43OF6GOqKPEA38P+4KwrPV+Ro9nlH1opR94Q4BuGxiVxURCBN5weuyxx9QbDIywI0jDkiVLVGePP//5zwGHaZTR4FMB3B6efazxpqG0tDSs56H4xdZ4RAkEIxZopXXd5VfI8UceJY2trWrkCyNQgcBy5FPrmmTR3krJ7LFEfH+JxoqsHot6XI2rqFaBFe0trzz3PPniOeeGNUhr0OcZoRplE+Xl5WryHFrKIXhhxNmzQ0Qg0BUEI8aY8KZtCJTo++x5Gv5WqCOl+BuoT0ZtN/paY1GSY489VvWxxkixBl02cB4cR5RhoJQl3NB6DiP5WpDWoD0f+jsH6ogjjlD/o9OJJ5TOoAwmnOeh+MUwTZSATFnZcsXZ58hV550vGWnpsquiQrp7Au8xjQmKC/cdlKk1DWJwOCO6r0SjffGVqdUNMn9vpXRX1Uhlba0smDlTvn35FXLMEUsi9hE9RnX/8Y9/qNForIiI2lqUaqBlHnolo80dRni9J7T54917uq6uTnXD8NUKz1ef6mAGBDAhEgEavaQxEo4SD9RJ43SMCntOuEPg/uIXvyjhNn78eBXgDx48OHAaBiW0TiOBwvFF2csf//jHgcVx8EYBt4m2IEy4zkPxi32miRJcfXOzvPjuO/Lp9m2i0+tVC72kIBY56DMapKK0iEuSEwWpqK1TJtU3ia2zS6obGqTQlCunrFwpxy5ZqhZhijSELARm9ExG6QQmBmIZcASz733ve6r9G0Y6MfqrlXn4m8yGwIoyg0cffVR9/6UvfUkFc4wko3YakwXRfg/fIwRjqfFwQls4TIJEeQf+Lkajsb/YcN1ChQmV2mIwnn2mEdhRL43uHWiFh9rz1157TU3eRFkMVkKEW2+9VX2/Zs0av2+MsNgLuofgDcEJJ5wgDz74oLrcDRs2DLT9C9d5KD5xZJoowZUWFso1n7tAvvL5C6WssFD2VlZKU1ubz1W/fEl2OGVmdb3M21claVZbxPeXaDT0jJ6/76BMrqiWmkOPt6MXHyHXf/EqOWXl0REP0gieCLPoTYwexGjzpsHCLQik6EqB07EACSAoY/MHkxgRpDEaeuGFF6oAjctBmMOEPAR1jFJjwRdtYZhwQlA98cQT1ZsBjBhjsZUdO3ao8gcs6BKqjIwMtYCNd7kLylWw9Dq6mqANIK4fRs3R91oL0lpHJYzQDwXBF4vPoIwFARjX46233hoUgMN1HopPHJkmGkXMvb3yv7Ufydvr16myjwllZUGtrobK67rCPNWb2hHiEr5Eo5XR4VA9o0ubWqW1rU1aOjpkyvjxcuaxx8miWbNV8IoGBFl0wECXDizH/c4776h+z+jGgVUEMYkNE/0QxrB8OCAcX3zxxSpQe46wIlBiNBYjzmi3t27dOhUwMdEQI7calB5gxBjt8lDHi5UU8buoO44klJQg4AdarhJu6L+NYI83FkT+MEwTjUKo23zx3bdl8+7dapGX8pISMQTxQu/Q61XHDyxN7jCyLRONbaiLHtfcJmUtbWLr6VWTftFF58SjlskJy5ZLZnp6zPYNI9MoU8CoJmqoUTuNsgCUbWAxF23y2v/+9z+1TDWWF8fIpwYlG5jwh24a6C2NlnrLli0b8m+iAwZGxTFKPdrhDQT6b4d7URkaXRimiUYpjECt27pFXv3gfalpbJCSgsKguwogVCNQ1xXmipO9TmmMMTidUtbcroK0zm6XmsZGsfX1qVHos1atkoll42K9i0QUBximiUa5ts5Oef3DNbJmw6di7euTiWVlAS1J7slh0EtNYZ4arWaopjERolv6Q7TR6ZLWjg5pbGlRn/CcfuxxctS8+WoxDSIiYJgmGgMwGXF3ZYW89O47smP/fsnKyFATF4Ot8bQbDKqeuj4/V1wGzl+m0UXvdElpa7uqi05yOsVqs8nBunpJS02RYxYfIacefYzkekz2IyIChmmiMQQfUa/ZuEFeW/OB6kCAQG3Kygp6uV6006spypeGfJO4ojTpiihS9C6XlLR2SHlTq+pug24ZaDmJCb1zp02XM1etkhkTJ0VkWetoslgskpaWdtjpWAkQXS+IKDQM00RjUGNri7z6/vvyyY7t0tNrkdKiQsnJzAw6LNiMRqkpzpeGvBxxM1RTgtEhRLd1yvjGlv4Q7XJJQ3OzdJrNUlZYJCevWCErFy0OuiwqltA/GW3ffH06tWLFCtXHGBMRPZ111llqjgV6UfsrX0Eva3T3wKdZOO+Pf/xjFcw9JzN6w/nPO+88vz//5JNP1ARKdCYBdBXBct6esGgLVnT07CrS0dGhVn8kihcM00RjFF5cK2pr5O3162XTzh3Sa7VKWVGR6lIQLFuSUaqL8qUpL4cj1ZQQI9FYcGV8U6uk2B1q5buGlhbp6O6WkoICOf7Io2TFwkUhPRZiCQu0YCGXXbt2qdZ13h577DG18ApCrNadAqsPzp07V+677z61UIu/9nA4P7p33HDDDapdHXo/o/+x90ImWKYbC6SglR0WIfEOvWjhh/Z76DjS2NioFmRBp5Fp06apxUqwOAx6QuM2QUs8bdEZzT333KN6QuN0dC4higcM00RjHEL1/upq1Zt6865dYuk7FKozgn+hwkTFxtwcNVHRmpI4o3k0NqTa+qS0pV2K2zvVxEIEtqa2Vmnr7JLi/AJZtXSprFi0SExZiVcXjRHpxYsXq7CJRVcAvaMvu+wyNXqM0ItRZZyGVm8aLKeN09BaD88FKHHBeRFwPSGEP/7447J27Vr1dzRYWAU/QxjHiDja6+G4Ikj7CvQIzCaTSY1GI6B7hm38DH2yscQ4Rp+xYMz69etVz2wNLhtBHvuK1n9E8YDTkYnGOJR2TJswQaaOHy97Dx6Ut9evVf2p65uaVajGZMVAIaCMa2lXnRA6MtNVqG7LzsQfieh1IPLL7Za8LrMK0SZzr+gOBbLGtjbVpaMoL08uOPkUWbn4iKBbR8aT73znO2oRFy1IA8IoVkpE8ESgTkpKkueee06NCmO5cRyHr33ta2pRkrKyMjUSjEVYcD5vd9xxh1ruG+HbM0yXlpaq82P0GKPM2rLcBQUFPvcTo9mzZs0atLS3BvuT43UbeJee4Q0BVmvEaowvvPCCnH322SEdL6Jw4sg0EQ2CF95dFRVqpHrr3j1itzukvLhIMkJcmAIlIJio2JBnEnsS379TdCTZHVLS1qEmFqKUQ7tvNx9aubDAZJJjlyyVlYsXS4EpN6FvFqxIiFC5b98+n6UPCLqolf75z38ut9xyi6pTxobaZwThrVu3qvpmnAdhGLXTvmgTFbHqIgK4pr29XS0Yg+B7zTXXDARiBGeMMns6/vjj1cj0tddeq1ZyxAg0Rs9RC42RbCznfc455wyMTHuXeWiwBPjNN98s27dvD8MRJBoZhmki8gmjRLsqDshb69bKtr17xeVyy7jiIkn30Q0gEC6dSGtOlmqr15UZuxXjaHTLNveq9nb5Hd2i9ZlBiG5pb5fm9nY1+nz04iPk2COWSKHHxLZE9v3vf1+VZ/z5z3/2+XPUSmNFRJRMYNTYV5jGxD+EVow++wqvnpqbm1V5BgKxVqaBZcsxuq2NOKNuG6sttrW1HRamN27cKPn5+eo5xmq1qtFuTGjEiDPKVTCxEeEaoRzBe/LkyWoiI+q1Ndh3/D1cD65OSLHGYSIi8gkfp86ZOk1mTZ4i2/ftk7fWr5Wd+/eLy+2WcUXBh2q9W6Swo1ttPakpUp9vkubcbC4CQ2FZZKWwvUuF6Axr38DpCNEo5UAbSPSHPvO4VXLskiWqPno0eeutt+QPf/iD359jufE1a9aoYOpPZWWlfOUrXzksSLe2tqpJjRhlRleNp556aqDjxze+8Q01og0ffPCBqnnWRqyxDPc777zj829dccUVctdddw18j0CNUfW77757oHUfylLw+5gsidsRIdwT9gH11ajtZpimWOPINBEFBCNf2/btlbfWrVMLwGBGf0FurhrpC3bxF8/lyhGoEax701J5S1BQ0i1WKW3tUEHa6HINnN5nt0tja6t09/SoEL1s/gI5bumRqq/6aITRYYTZGTNmDJyGkWF01MDIszb5EPC4xWMZ4Vgr3UCAxc8RWrVJiDgfOnOg7OPWW2+V/fv3q79RVVUlXV1datRY+z0tEONrrVUeLgOjzxhp9oRQjADuGaYBI+YoE/HVag/Xw1crvJtuukn93dtuuy0sx5EoVAzTRBQUvEjuOXhQPtm2VTbu2intXV2SmZ4uxfn5I+rHi9HqFlOWNJuy2QmEhuzIUdjRJQUdXYeNQneZzdLU2iZu6f/0BD2iF86aNepGor0h8FZXVw8KnCiTQJcO/MzXhMLhHuOoZ546depA6zvUKP/oRz9SoRoBHLXZzzzzjGppBxjVRou97373uwOj5QjhvsI0Rpuzs7PV38FCMigbQQ9slH+gpAM/A5SeYMQcEyY9Jz1q/vjHP6q2fWiVRxRLLPMgoqDgxXX2lClqO+2YY2XT7l3y4caNUlVXh/fnUpSfp/rzBrsATIbVJhkNNpnY0CLmNATrbGnOyRIbW+yNeSkI0J3dKkBnWmyDjofD6VSTCrU3dYtmz5LlCxaqEqXUlJQxcezQlxmjyJ5hGiO86JoBCJvbtm0L6LJOOukkvx0ytMe0NhqNoK6NcOM0lF5o3w+1mAsCNcK3FqbxewjMGIFGNxL0ogbUSKMWe9GiRT4vB9cZ150o1himiShkmMB1yoqVctySpbJj/35Zu2Wz7DywX2qbmtTH64W5uX5XVBsKAlOmpVkm1TdLd1qqGrHG5EX2rh5bI9D5KkB3S5bFetjPeywWtdAKyhGK8vPlnBNOkCPmzJUJJaUJv+x3sCZNmqTa2mn1y95efvll2bRpk+qSMZQHH3xQsrKyhm03h8l/4Hk+TCbECobaKDE+KUDbPH/7izpsb9g/9LJGmMbvP/HEE6rdn7/bE9d59erVQ+4rUTQwTBPRiKG8Y/Hs2bJo1iypaWyUDTu2y7otW2RfVZUkJRmlJL8g5NZ6CFLYJtc3S29KsgrVrTmZYkaN9RgLTaOa2y2ZvVbJ7zKrEJ1u+6yEwzOwtXZ2SGt7hxr5RH90rFS4YMbMoPqhjzZnnnmmPP3003L++ef7/DnKPDBKjbZ4Q0Hf5kBKQjCiDAjoWoBHFw9MQNT+BkpAvv3tb/v8fdQ5Y/EXlHugrR8mMi5btky11cNEyldffVXVZdfX16s+2b6gPR+6hdx5553D7i9RpDFME1HYYARpfEmJ2k5avkK27NktH23eJPurqsTW0CD5JpPaQp2wiICV3tSqloG2GY3SlpMprdmZqtWeK8TLpNgu641WdgjQeZ1mSTk04unNarOpCYUYjcaE1xOXL5elc+fJtPETDlvOeixCtw7UFKPF3MSJEw/7OQLyK6+8MtApYyiBPDa1MO0PapnRjk8r+QDUQ//kJz9R5Rxov4dRcJR1YNMWeJk+fboq80C4Rl02SkG0mmxvf/vb32TlypVq1UWiWGOYJqKIQP0qJoChfhUj1Bt27pBPtm+T3ZWVkmQ0SF6OSUxZWSEHawQvdHLAhh7W5rQ06cxMk66MdOnKSGPLvThtYZfdY5Hsnl7JMVsk02JRLRN9QUcOtLXr7O5WgRlv0LQJhYm+yEq4oWwCKxmi7/NLL710WFkEjt9xxx2nWs8N5YwzzlDlFb5g4iEmA6L13Ze//GUV2j3rorFyIhZZAdRBoxTk17/+9cDPUT6Cy0YXEPydBx54QC0A4+2SSy6RJ598Ur0BQP9sXyoqKtRiMO+///4wR4YoOtjNg4iipq2zU9VUb9m9W/YcrFTdF4xGg+SbciUnMzPkYO3Nfag7CEasOzP6AzZXX4zNKoQqOCNAm3vVJNOhCnNQ/9za2SkdXV0qAKL14qKZs2TutGkyfeIkSQ6yK8VY0tvbq0ZqMbnPe/EW1B2jJMNfyYQGtcrf+ta31MqC3qf/3//9n6pzRlkGFmLB10uWLFEdPxCUMeqNMhyEaOxLd3e3miCI0fIbb7xxYFXEVatWyfz58we1xsNIN9ruYbQZfaxPP/10tZAM/s7FF1+s+knjzQAmWKLzB3phY9XEn/3sZ2E9hkSh4sg0EUWNtvocRhixIh36VW/cuVP2V1dJQ3OzJCcnqRHrkQZrBLZMq01tZS3t6jRLcpJ0ZmLUuj9gs0tIZLpu5AyMPPdKWp992N/BCHQ7AnR3txpRRYA+ZeXRMmfqVBWg08ZIR46RSk9PVxMNsVJgY2OjWprbs00eQihqkYeCEIw3NJ6w2AtGmG+//Xa57rrrVGDGYioYPUanDfSzRkmGPzNnzlRB3HNf0HZPg7IP1FujDR4mE6KlHgI3LhP11wjYGMVGGQtWcMR1wJsGBmmKJxyZJqKYwke/TW2tsutAhWzavVMqa2vViLXeoFcdQXKzskPqCDIcW5JRlYMgXGMU25KSzNHrIEed02x9arQZ4RnlGyl23zXP3nqtVvUpRXePWYwGo3qTNX/6DJk7bbpMnzhRMkJcsp58++c//6keZ1dfffWQhwjhFaPGmNDoCTXOGBn211UDkwGxISRjZBqBG/CGGKUcKAHR3HfffWqE2bObB0I5WvKNGzfusMtG+MYqhyghWb58OW9iiksM00QUN/CCjxHrvVUH1RLmeyor1IilqrXMypTc7JwRLQwzHIdBL70pKdKbmqzCNb62pCaLNTlpbHYOcbsltc8uadY+SbfZVHhOt9rURFCj0xXExbilu7dHBWiLxar6Pxfl5cnCmbPU6POU8eMl3WOyGhFRImGYJqK4hRHqfdVVsruiQrbu3SNtHR3S53CI0WCQ7IwMycrMVGUAke4r7NLpVLhWAVsL2hjNTk4WlyHxu4jonS5J6+sPyv2BuU/9j03vZ0LaUJwul5h7etRy3ubeXnUaRpvHFRerAD1twkSZWFYmSRH4xIGIKNoYpokoIaA9WlV9vdQ0NsiBmhrVbq/T3C3WPpsY9AbVPQQrLyK0RWvRDsTMviSj2I0GsRsMYjf2f+0Y+F77+rPT3VHYN53bLUaHU5IcDklyOg997Rz89aGf4etku2PIiYHDwUf7GHnuMveIxWpVxx+3B+qfZ06aLBNKS6W8pERKCwrDNsmUiCheMEwTUUKy9fVJXVOT1DY1SkVNjew9eFDauzqlx2oVvU7UIjHZGZkq1MVTgHPo9f3hG+H6UODWAjb+V9uhr7XTEI4B/+u0/z1OQyA2HgrG6utDNauRgklqXT0Iz2Z1OyA8440MFueZNWWKlBcXS3lxiQrT8XTsiYgigWGaiEYFjI7Wt7RIbWOjHKyrVf2sWzvaB8oM0tNSJSMtXdJSUyU1OZkhL4jjarHZ1KRBlG3ge7StQ3geX1wiMyZNUuUbCM+YMDrWlvImImKYJqJRCR0FmtvbpKahQZWHIFy3dLSrMgR0HcC4LoIfJsMhYKcd+h/12GMNJgiiRR1CM44PNnwPGFnGccEEwUll42TqhAkqOGP0eSwv4U1EpGGYJqIxExh7LRa1KAhW1mvr7JDm9nY1ko3WfCpE2mziQvmE2616XiNApqWkqjCJyXKJPuqKNxjWvr5D1xWh2aYWzMAbCyyIkp6SospjSguLVFhGz+/8nBzJM+WorzlhkIjocAzTRDTmYRQWbdsQsBG0saEWu765WXosFhU+7U7noUl6bhU+ESzRI1n9b8T/BkkyJqmRbZyGEd1Ih2+8QXA4nWJ3OFT5hfrfif+dA98jLGMZG7UrbrekHBqBx1LuGGEuKShQfZ5VcDaNfMEcIqKxhmGaiMgPBFH0udaCtgrWNpv09PaqyXddPWZVR4x6YofDKXbnZ6FWLVyBBKvmCboHwnX/puLtQNjWTlcxXQ2M4ys3sm9/eMf37v79UeeX/kmK+Fl/eE9Sy7IjxOPrzIx0yUrPUHXNaCGYnpamyliyMjJVYMZoM05L9JF2IqJ4wDBNRDRCCM9o3aeVT/Rabf3/W7RyCquaCNnnsIvL6VJB2+V2qX7M2NT3hzpwoM2fwdAfvNXXh0I4Jv1loHYZwXig/CRloAxFlaRwciURUdQxTBMRERERhYiFcUREREREIWKYJiIiIiIKEcM0EREREVGIGKaJiIiIiELEME1EREREFCKGaSIiIiKiEDFMExERERGFiGGaiIiIiChEDNNERERERCFimCYiIiIiChHDNBERERFRiBimiYiIiIhCxDBNRERERBQihmkiIiIiohAxTBMRERERhYhhmoiIiIgoRAzTREREREQhYpgmIiIiIgoRwzQRERERUYgYpomIiIiIQsQwTUREREQUIoZpIiIiIqIQMUwTEREREYWIYZqIiIiIKEQM00REREREIWKYJiIiIiIKEcM0EREREVGIGKaJiIiIiELEME1EREREFCKGaSIiIiKiEDFMExERERExTBMRERERRZcxyn+PiPxwuVyybds22bp1q3R1dYndbuexIqIBSUlJkp2dLfPnz5d58+aJXs8Pl4niAcM0UYy1tbXJgw8+KG+88YY0NTVJSkqKesFMTk6O9a4RURzp6+tTb7RtNpsUFRXJKaecIldddZXk5eXFeteIxjSd2+12x3oniMZykP7a176mQvTJJ58sJ5xwgixYsIAjTkTk9xOsLVu2yNtvvy1vvvmmCtV///vfGaiJYohhmihGMMJ0zTXXSHt7u9xxxx0yceJE3hZEFLCDBw/Kd77zHcnNzZUHHnhAfaJFRNHHgiuiGHn11VelsrKSQZqIQoI34HgjjucRPJ8QUWwwTBPFCGqkly5dyhFpIhpRoMbzCJ5PiCg2GKaJYqC1tVU2btwoxx9/PI8/EY0InkfwfILnFSKKPoZpohioqKgQzP3FZEMiopHA8wieT1DuQUTRxzBNFAPd3d3q/6ysLB5/IhqRzMzMQc8rRBRd7DNNFANOp3NgEYZgocdsc3OzlJeXB/w7ZrNZOjs7Zdy4cep7h8Mhu3fvlhkzZoS0DxTeVmfoyjB+/HgxGgc/JeN2wsIcgS7OgfsVbmu8SQvkd/C38SnJ1KlTJVreeecdqaqqCvj8Op1OLVKyaNGiiO5XItN60uP+QkTRxzBNlGA+/vhjtcjLnXfeKenp6T7PY7FYpKenZyAob9q0SYWY7373u+p7q9Uqf/zjH+V3v/udpKWlqdOw4mJGRsbA91ggAv1rh3PBBRdIWVnZoECHy0cwHCrQ4YUfG0bVEJhGqra2Vv2v/c3U1FTVMuyxxx6TnJwcOfPMM9XpeCOCj8TRnzeaent75e6771aLbBQWFg6cjuP+61//Wt2e2gij5ve//70sX75cTjrppID+Rn19vfz85z+XW2+9VQoKCtT9ZMqUKXLcccf5PH9DQ4P84Q9/kOuvv15mzpw5cHpjY6MK2p5KS0tlpPbt2ycPP/yw+hr3NYPBIMcee6x6k3fgwAH15m7dunXq9tmzZ4+6L+G+/O677w55f080eGP7t7/9Ta699lq2syMaBRimieLYT3/608NWQkT4QtD5zW9+o1ZL1CCA4Ge//e1vZf/+/fLSSy8NhGn0tMbkJPSi1c4Ljz766ECQxe8icGKZYi2UYlLTZZddpkKpN5wfYfvss88+LNT+4he/CPg63nXXXWEJSU8//bRaBAcBDb27Mdr6jW98Q4VYBGsNAia+//a3v31YcMUbEPy+Pwh3+fn5qrdvMPDGBKF18eLFg4I0aH/P19/F7RfMJwfayLZ2WQsXLpR77rlH7fPcuXMHzocl61taWtT5EGRfeeUVFazx5gadIRDGAfcv3HfwxgMhfaS0MgT0Vz/66KPV/RCj1LiPz5kzR91W+B/3b+2NxRNPPCGvv/66GnHX7if4vU8//VQFcIRyXJbJZDosuON64k0Ffh7s0tvD/f5Ifo43d0cccYS6T/z4xz8eNW8SiMYqhmmiOIaRq0DDFAKGVj6CQKyFYoQhjOx1dHTIFVdcoU5DaLrlllvkW9/61qBA7kkL2Qg3vkYlMfoM3iEC5ScYgfUcmcbEKIR8jIZjYQltX7Ef2kj4SHmG4xdeeEGFQ23/tP3A6HxdXZ3PsI8VKLFP3qUWnrDfQ/3cH4yO4ziec845A6chMCLQavuG2wdvUHDs8GYAp+M07O/LL7+s/jZC18033+z372i3mXaZCO+rVq1S9wFPWHETYQ+0cqHnn39eJk2apEoqcJ/4/Oc/rwIf7ifafSlcpk2bNrC/2mJF27ZtU2/eMBI/ffr0QedFmPZ07733yo4dO9T+oVwJbeF+9atfDYzyfvDBB/Kvf/1LVq5cqS5z8+bN6r4eqOF+f6Q/BywFjpKthx56SD3OiShxMUwTxTEt6PzlL3+RvXv3+gy++Bj8xBNPlM997nODTkcwQ/DDR/9vvfWWCrNaiEUQRnkGyj8QrBB2UaqB0zRa/eUjjzwyaGRXo5UB4O94QpDzDshauMP/2qgp9s1fkA8HbfRdC5gIXU8++aQqdcHIoLclS5ZEZD8wUo/bDmHPE4IU3lxo4Ry3MUaSv/nNbw7sN4L1kUceKUcddZS6Pp6lF/fff/9hpRi4TO0283wThjCHUdKvfvWr6nuUjezatUuN5uKTB9yG1113nQrfGIXWbhfcT/Apx5VXXhnWY+KrrAf3GVx/zyDtC0L3zp071RsifGKC++mNN96oRqpPOOEEtc+PP/64XH755aqEBKPdP/rRj1T4xhua4Qz3+yP9uSd8EoRPmHCMo1m3TkThxTBNlAAQQE899VQ1eurt3//+t8/RUq1sAR+h4yNzjDAiNJWUlKjRSoQxhPC8vDz1vXcpB8LYDTfcMOR+rV69Oiy1tOGA4InAqIV17wlrCGBf+MIXBtUGRwNGKdEH2LuMA+ERtxHe3HzlK19RZRTeb1rwOwi2vrq+TJgwQYVS7Y0KAhlGQQFvkvA3cVsD/ob25kKD2/zPf/6zGiHFmykEa4yketq+fbu6DM+a+JHAJEt/9duolcZ+eLeLxDHxfJOH0InyJ+3+ivs+jqV2/bRa6xUrVqjvMZqP+8KWLVsCCtPD/f5If+4Jtx/eALz//vsM00QJjGGaKAEg6CBsYPPFV0C56aab1GgdygRQ14zlhhGm8TVG9/CRuPdotvdEx0C6LmBk219Aiia8cUDoQjgElEggyGLCIUYHUQsMOIYYzURI1U7TIJDiTYSv+lptFBghNtiR6UBGvbH/+Puo+8b/CNIoVfnoo48G+gdjRFl784I3V542bNggs2bNUiPOCKQYpfY1cRGno3YeIRRvpJ599llVe4zvUT6BSYCe9wGUfYQLaqZxPX3tE0In7m+Y/FhcXDzwM9xncUw0CM6en3xUV1er46SVomidbjzfYCJ44xOaQAz3+yP9uTeU1qAMi4gSF8M0URxDUEUoROjzDn7eMCKGyViYUIbAgiCN8IIRMUyCQojEJLT33ntPjXSiPARBDTCqh4CJn2OyGiBwI9wMtUrjhx9+GPYwjTCFsIdaWpQgBArXxzNk4XuUdqBzgnZdEbxQSoBgjGPgeUwxmohSmKFglBYdUIKdfOirTEaD/YNf/vKXaqQfgRg1whjN1Grcsd8vvvjioBFa79se1xV1uQjTGHXGSD3eOPma8IiArP0P2v84LrivaPAmABM2McqKUeWRQnjHfdTb+vXr1d/Abb9mzZpBb/LwyYr3ddBgf1GbjOurdWdBmYX3hD4cf+9yJH+G+/2R/twbzquV5xBRYmKYJopjqHVGvevkyZOHPS9GvjAChtpXvHDX1NSoUVaMND7zzDMqDCF84HRM6tK+Rj0vgiRO86zbxO8iWOFjaMAkNoxiojREq+XGiGagISVQCBaYHDdUVw1fEJhRK4x6VW3kFl0j/vnPf6rjgsCPOlvUsvoaecbfQ+cLhDd/I9Oh9PHFJwCYaOjdFxxvQv773/+qNy3aJwlaIET3B3yioJWqfPLJJ6pu2tdkTYR1XEcESm3UGm9EcH60X0O3CM9RUnyNWl4cY++aaxwnz+CKem2MkmPEGmE/2I4Y3vCmAJ8SeMN1xT6hzASt884777yBv4XbFfdRTDb0hvsj3jheeOGFg25HbSKuBm8WA53IO9zvj/Tn3nDf8FXDT0SJg2GaKI4hUGC0GAEQo3YIr95Qz4rwhDIOLMChlYXMnj1bfeSMF3e8kKNuGqfjMlGriVFWhBC0lEMgvvjii4fcF4QWdMNAmI4kBEJM1gt2ciJqwjWoB/euI9dGRTFy6692dqgRZBw37zaFgUCNNmqZvWu4EbLxKQB6TP/gBz8Y1G8YQfhnP/uZ6qiB2wafMuB7bwhtuK4I+Ri19lwBD7ctOn/cd9998uUvf/mwMPenP/1JfYqhjaLijRX+7kUXXTTofOeee6663VFz7tleLxS+Wv1hwRqtJhuXj+vifRv5CqJ4g4E3m6if9ryv4A0JwjkCrDbREYHVV3tHX4b7/ZH+3BvKczxLa4go8XA5caI45tn1AEEaLcBQFqBtqLXUJp15nx9BC+fByKVWxoFghv/x4u75cb4v3iPOGMnEyKj3ZDRfNbAjgeCEQK2Vm4QC19nXKC5Gq5966qnDRmQjadmyZeo28q6ZRXBHcPU1Kongj9Fp9FhGyQtGnb2PO24frUUcOpR4lxYgnONTCox833bbbWok3BPeZOENFH4XG4Ksr08DtHaBnu31MMERIVBrjxgovIHzfNMAuBzc5loLOewHSj08eV93TIzEcUF3Eu8JsPh97BfOox0nlDoFOvF0uN8f6c89oRYc5UfeEz+JKLEwTBMlCARlhBqMMGvbUB9dY2QaNc0YfURwxqQ0BCuM+OFj8+FqYBE8tb7IWDADNbsYlUYPZJQkINijtRdW9Asn1GmjuwUWtAgV9td7NUHUFKNUAOEGpQTe3S0iBfuBUeO//vWvPksc/EGARp07RoxR9uAJwRgj2hgt/t73vjewTLw3lAehbRzeVGHk3jMQ++oA4xmmcXyweAtGzXE/w/1Jg8V6sNgO7gvBwCi05yRavBHDSC56S6MtH7ZLL71UBVHPY+XZZxq3LcpXMEkRl4ee4tgQ8AGj1LjvYkQep2MEHp9IoFbeszwEHTR8Ge73R/pzz3ImlBWdfvrpI3rjSESxxzIPojjmGfgQmBAGESY0GAXT6qkxGusZUtDD+JhjjlEhGsEXQQndHdDhAiFMm+Tlq20aIGwjhGHCIkIB/g5CHUIA6qjx0T9CCb4+7bTTVGhEWMcCI/hbnqPkqHvVQphniNPqkM8666xBKy/iskJZzAUj7jg+CFnaingIz2vXrpXXXntNtfrD4i7o7YuJnV/84hfVdcfEQ39dPLxhnxH0EE4DXbkOwRhlOpi8iJ7Dnq3utBFj7Xjhexzbt99+W41OI7AiOKPcAmET+4m6Zox4oyzD3+Q8z9sR9c64vp6jwrgeCKXaGzLc1loJj1aigEmB+BvYPK8rbp9QauVxX8N+aDAq6z0hEfcV/F3cR7UuKJ7BGrev1nbOs6zFc5T8/PPPV2EbpSCoVcf9y/N+h8eH5+PF23C/P9Kf4zrijQoeU5h0SkSJjWGaKI55TmRCKPI3moyPvDHih1ALCD4YVUZAveOOO9RINL7Hghgo18DH7dqEOIRZz7IHfH3nnXeqshIEF7TuQjkCJqNpl41Qjg1h4T//+Y8KRQiXGGXERD/vMD3U9cPf1ybeAfYLfz8UWJQFI5R4kzFlyhR1XTAijZG/66+/fqDlGuqPUSuOUUQEaLyxCCZMI0gGWz+NY4jaWM/wiImhGPFHCQAuD6O9KHfAKOYPf/hDdR0Ab2hw3TDREG8IEMLwqYA3LeB6T5TE9fRsN6ed9+qrrx5YgRCXr93ftP8xKdAX3NYYsQ514hxCMG5zX33TcVt51u97BmbAfRej18NB6YS/8gm8GRiuO85Qvz/Sn+ONIh6PeHNERIlP547WZ51ENODNN99UI5QYxfXVKkyj1dkOtzAKzoeROQRf7xCLtmMYpdUmOWHkE6NyWpj29TdQ44kgjTZtqN8dCoIbShHwt2MNI5jek9xQNoJR/WC7g0QDRoNxnLUyDYysg696dgRc1EcP1fcZARmXgdHq4ZY9xyQ/BGntEwBMmsMxQuhGf2kEdn919ag7x+UP1afcFyyqg7IHBGaUjWirZeINGr7Gpx4YNcZ+4D6K64w3R9onLYFOIhwOJuYON6KfSHB8zjjjDLUIkK83KEQUWQzTRHEcponikXdP70DhDR9G37XFgBCaUb6C8hO8gcOnH5h8iNF/vHEAvDlEJ5QvfelLYb8eowXekOGTCoZpothgmQdRDGglAhiJY5imRBNKkNY6mKA8xBetj/RI2++NRdqiL6G0biSikWM3D6IY0CagBdsNgYjImzap07vtIBFFB8M0UQxoE85Qm0pENBKYF4HnEy7+QhQbDNNEMYAJV5jpj/ZnREQjgecRPJ8E2qqRiMKLYZooRk455RTVUQELqxARhQLPH2j/iIViiCg2GKaJYgR9mo8//njV85iBmoiChecNPH/geURbcIeIoo+t8YhiCH2BsdwzVrxDHTVWE8Rqc+gLjYU2Aln4hIhGPywJga4dmGyIGmmUdmBEGkEai+h49lYnouhimCaKg0D91ltvyeuvv65GmoZa5piICJMNUSON0g6MSDNIE8UWwzRRHMEKfnv27JGuri6GaiI6LESj/R26dnCyIVH8YJgmIiIiIpLQ/D9LS5Wln/r/zQAAAABJRU5ErkJggg==",
      "text/plain": [
       "<Figure size 1000x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "最终回答：\n",
      "## 📊 分析结果与业务洞察\n",
      "\n",
      "### 数据概览\n",
      "\n",
      "| 指标 | 数值 |\n",
      "|------|:----:|\n",
      "| 员工总数 | **5 人** |\n",
      "| 薪资 > 18,000 的员工 | **1 人（20%）** |\n",
      "| 薪资 ≤ 18,000 的员工 | **4 人（80%）** |\n",
      "| 公司平均薪资 | **¥13,800** |\n",
      "| 薪资范围 | **¥5,000 ~ ¥20,000** |\n",
      "\n",
      "### 🎯 高薪员工详情\n",
      "\n",
      "| 姓名 | 部门 | 薪资 |\n",
      "|:----:|:----:|:----:|\n",
      "| **张三** | **技术部** | **¥20,000** |\n",
      "\n",
      "---\n",
      "\n",
      "### 💡 业务洞察\n",
      "\n",
      "1. **高薪员工稀缺**：薪资超过 18,000 元的员工仅 **1 人**，占全员的 **20%**，说明公司当前薪资结构偏集中，高薪段人才非常稀少。\n",
      "\n",
      "2. **技术部贡献高薪**：唯一的高薪员工 **张三** 来自 **技术部**，薪资为 **¥20,000**，是公司最高薪资，同时也是唯一超过 18,000 元门槛的员工。\n",
      "\n",
      "3. **薪资分层明显**：其余 4 名员工薪资分布在 5,000~17,000 元区间，与张三的 20,000 元有一定差距，薪资梯队断层显著。\n",
      "\n",
      "4. **潜在建议**：\n",
      "   - 若公司希望吸引更多高薪人才，可考虑调整薪酬体系，拓宽高薪段覆盖面\n",
      "   - 技术部作为高薪核心部门，可重点加大技术人才的招聘与保留投入\n",
      "   - 当前仅 20% 的员工处于高薪段，薪酬竞争力可能面临挑战\n",
      "\n",
      "> 📌 **饼状图说明**：由于实际符合条件（薪资 > 18,000）的员工仅 1 人，饼图按\"高薪段 vs 普通段\"两个区间分组展示，清晰反映了薪资分布的两极分化态势。\n"
     ]
    }
   ],
   "source": [
    "# 编排器：根据用户意图路由到合适的 Agent\n",
    "async def run_data_analysis(user_query: str) -> str:\n",
    "    \"\"\"\n",
    "    统一入口：自动判断用户意图，路由到对应的 Agent。\n",
    "    \"\"\"\n",
    "    # 用 LLM 做意图分类（也可以用规则匹配，看场景复杂度）\n",
    "    classification_prompt = f\"\"\"判断以下用户问题属于哪种类型：\n",
    "    - \"query\"：需要查询数据库获取数据\n",
    "    - \"visualize\"：需要画图或做统计分析\n",
    "    - \"both\"：需要先查数据，再画图分析\n",
    "\n",
    "    用户问题：{user_query}\n",
    "\n",
    "    只回复一个词：query / visualize / both\"\"\"\n",
    "\n",
    "    response = await llm.ainvoke(classification_prompt)\n",
    "    intent = response.content.strip().lower()\n",
    "    print(f\"用户意图：{intent}\")\n",
    "\n",
    "    # 根据 intent 路由到对应的 Agent\n",
    "    if intent == \"query\":\n",
    "        # create_agent 返回的消息格式：取最后一条消息\n",
    "        result = await sql_agent.ainvoke({\"messages\": [{\"role\": \"user\", \"content\": user_query}]})\n",
    "        return result[\"messages\"][-1].content\n",
    "    elif intent == \"visualize\":\n",
    "        result = await visualization_agent.ainvoke({\"messages\": [{\"role\": \"user\", \"content\": user_query}]})\n",
    "        return result[\"messages\"][-1].content\n",
    "    else:  # both\n",
    "        # 先查数据\n",
    "        data_result = await sql_agent.ainvoke({\"messages\": [{\"role\": \"user\", \"content\": user_query}]})\n",
    "        data_content = data_result[\"messages\"][-1].content\n",
    "        # 把查询结果传给可视化 Agent\n",
    "        viz_input = f\"基于以下数据进行可视化分析：\\n{data_content}\\n\\n原始问题：{user_query}\"\n",
    "        viz_result = await visualization_agent.ainvoke({\"messages\": [{\"role\": \"user\", \"content\": viz_input}]})\n",
    "        return viz_result[\"messages\"][-1].content\n",
    "\n",
    "resp = await run_data_analysis(\"用饼状图展示薪资大于 18000 的员工\")\n",
    "print(f\"最终回答：\\n{resp}\")"
   ]
  }
 ],
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