deep-dive-into-langchain.ipynb

deep-dive-into-langchain.ipynb

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      "source": [
        "<a href=\"https://colab.research.google.com/gist/leandrofahur/7d7ad43c43eda5d62e628ec5b4f44515/deep-dive-into-langchain.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# LangChain\n",
        "\n",
        "LangChain is a framework that enable programmers to develop LLM applications with different providers such as ChatGPT, Anthropic, Gemini, and others.\n",
        "\n",
        "The tool provides us a simple approach for development, productionization, and deployment.\n",
        "\n",
        "For the sake of simplicity, I will be implementing an Agent with the role of a business analytics with the goal to define an ICP (Ideal Customer Profile).\n",
        "\n",
        "Read more about it on the folowing [link](https://python.langchain.com/docs/introduction/)\n",
        ""
      ],
      "metadata": {
        "id": "K1w4TqTNC3-L"
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    },
    {
      "cell_type": "code",
      "source": [
        "# Install the necessary libs:\n",
        "!pip install langchain_openai -q"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "kJkn8uEZCwi6",
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    {
      "cell_type": "code",
      "source": [
        "# Import the necessary libs:\n",
        "import os\n",
        "\n",
        "import pandas as pd\n",
        "from matplotlib import pyplot as plt\n",
        "from IPython.display import display, Markdown, HTML\n",
        "import re\n",
        "\n",
        "from langchain_openai import ChatOpenAI\n",
        "from langchain.prompts import ChatPromptTemplate\n",
        "from langchain.chains import LLMChain\n",
        "from langchain.schema import HumanMessage, SystemMessage, AIMessage   # https://platform.openai.com/docs/guides/text?api-mode=responses"
      ],
      "metadata": {
        "id": "zwzZoaNhEZeJ"
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      "execution_count": 34,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Configure the API key from your provider:\n",
        "from google.colab import userdata\n",
        "openai_api_key = userdata.get('OPENAI_API_KEY')\n",
        "\n",
        "if not openai_api_key:\n",
        "  key = input('Insert your own OpenAI API key')\n",
        "  openai_api_key = key\n",
        "\n",
        "  if not openai_api_key:\n",
        "    raise ValueError('OpenAI API key not found')\n",
        "\n",
        "else:\n",
        "  print('OpenAI API key found')"
      ],
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          "base_uri": "https://localhost:8080/"
        },
        "id": "ocPQynIiCwf0",
        "outputId": "9e3eb198-2ae3-47e6-c432-1dc110445f69"
      },
      "execution_count": 4,
      "outputs": [
        {
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          "name": "stdout",
          "text": [
            "OpenAI API key found\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# 🧠 Section: Business Analyst Agent - Ideal Customer Profile (ICP)\n",
        "\n",
        "## Goal:\n",
        "Build a simple Agent using LangChain. We define:\n",
        "\n",
        "- **Role**: Business Analyst\n",
        "- **Objective**: Generate the Ideal Customer Profile based on real business/customer data.\n",
        "- **Inputs**: User input a spreadsheet.\n",
        "- **Output**: A structured ICP profile.\n",
        "\n",
        "---\n",
        "\n",
        "## Steps:\n",
        "\n",
        "1. Retrieve the data and check if the document needs some data preprocessing.\n",
        "2. Create the system prompt for our agent\n",
        "3. Configure the llm with the propper model and temerture\n",
        "4. Generate the prompt template responsible to \"set\" the agent core function\n",
        "5. Prepare the business analytis chain\n",
        "6. Properly format the csv to be inested by the LLM.\n",
        "7. Call the agent to process the data and return its findings."
      ],
      "metadata": {
        "id": "SzduhjjImv_o"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Import the mock dataset for ShopMax:\n",
        "df = pd.read_csv('./shopmax_customers.csv')\n",
        "df.head(5)"
      ],
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      },
      "execution_count": 7,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
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              "  Customer ID  Country   Plan  Monthly Spend ($)  Tenure (Months) Churned  \\\n",
              "0   CUST00001   France  Basic              24.44               19      No   \n",
              "1   CUST00002   France  Basic              31.59               53      No   \n",
              "2   CUST00003   Canada    Pro              75.31               60      No   \n",
              "3   CUST00004   France  Basic              32.71               22     Yes   \n",
              "4   CUST00005  Germany  Basic              17.80               28      No   \n",
              "\n",
              "  Signup Source    Industry Last Active Date  \n",
              "0       Organic      Retail       2025-04-03  \n",
              "1      Referral     Finance       2025-04-06  \n",
              "2      Paid Ads      Retail       2025-04-10  \n",
              "3       Organic  Technology       2025-03-29  \n",
              "4       Organic      Retail       2025-04-14  "
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              "type": "dataframe",
              "variable_name": "df",
              "summary": "{\n  \"name\": \"df\",\n  \"rows\": 1000,\n  \"fields\": [\n    {\n      \"column\": \"Customer ID\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 1000,\n        \"samples\": [\n          \"CUST00522\",\n          \"CUST00738\",\n          \"CUST00741\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Country\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 7,\n        \"samples\": [\n          \"France\",\n          \"Canada\",\n          \"Brazil\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Plan\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 3,\n        \"samples\": [\n          \"Basic\",\n          \"Pro\",\n          \"Enterprise\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Monthly Spend ($)\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 80.9297989973369,\n        \"min\": 15.6,\n        \"max\": 387.7,\n        \"num_unique_values\": 882,\n        \"samples\": [\n          39.67,\n          27.35,\n          28.3\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Tenure (Months)\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 17,\n        \"min\": 1,\n        \"max\": 60,\n        \"num_unique_values\": 60,\n        \"samples\": [\n          19,\n          47,\n          29\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Churned\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 2,\n        \"samples\": [\n          \"Yes\",\n          \"No\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Signup Source\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 4,\n        \"samples\": [\n          \"Referral\",\n          \"Partner\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Industry\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 5,\n        \"samples\": [\n          \"Finance\",\n          \"Education\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Last Active Date\",\n      \"properties\": {\n        \"dtype\": \"object\",\n        \"num_unique_values\": 60,\n        \"samples\": [\n          \"2025-04-03\",\n          \"2025-04-01\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 7
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "system_prompt = \"\"\"\n",
        "You are a Business Analyst for ShopMax, an e-commerce company.\n",
        "\n",
        "Your task:\n",
        "- Analyze the customer dataset provided.\n",
        "- Return your findings using a VERY STRICT structure exactly as described below.\n",
        "\n",
        "STRICT FORMAT:\n",
        "\n",
        "1. Each section must start with this format:\n",
        "## Section: [Title]\n",
        "\n",
        "2. After each section's findings, insert the chart instruction:\n",
        "## Chart: [chart_identifier]\n",
        "\n",
        "Allowed chart identifiers:\n",
        "- plan_distribution\n",
        "- churn_rate_by_plan\n",
        "- spend_vs_tenure\n",
        "- top_countries\n",
        "- signup_sources (optional)\n",
        "- industry_distribution (optional)\n",
        "\n",
        "3. DO NOT write extra explanations, transitions, or commentary between sections or charts.\n",
        "\n",
        "4. Only use bullet points (-) under each ## Section:, no freeform text.\n",
        "\n",
        "5. After all sections, add a final section:\n",
        "## Section: Final Conclusion\n",
        "\n",
        "Example:\n",
        "\n",
        "## Section: Subscription Plans\n",
        "- Basic Plan: 60% of customers\n",
        "- Pro Plan: 30% of customers\n",
        "- Enterprise Plan: 10% of customers\n",
        "\n",
        "## Chart: plan_distribution\n",
        "\n",
        "## Section: Financial Insights\n",
        "- Average Spend: $72.74\n",
        "- Churn Rate: 6.7%\n",
        "\n",
        "## Chart: churn_rate_by_plan\n",
        "\n",
        "(and so on...)\n",
        "\n",
        "IF YOU DO NOT FOLLOW THIS STRUCTURE 100%, THE OUTPUT WILL BE REJECTED.\n",
        "\"\"\""
      ],
      "metadata": {
        "id": "R014cNn5q0am"
      },
      "execution_count": 46,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Create and configure the chatgpt llm:\n",
        "\n",
        "llm = ChatOpenAI(model='gpt-4o', temperature=0.25, api_key=openai_api_key)"
      ],
      "metadata": {
        "id": "zNHK_mQJrVov"
      },
      "execution_count": 47,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Generate the prompt template:\n",
        "\n",
        "prompt_template = ChatPromptTemplate.from_messages([\n",
        "    (\"system\", system_prompt),\n",
        "    (\"human\", \"{input}\")\n",
        "])"
      ],
      "metadata": {
        "id": "mojukbrhrVln"
      },
      "execution_count": 48,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Prepare the LLM chain:\n",
        "\n",
        "business_analyst_chain = LLMChain(\n",
        "    llm=llm,\n",
        "    prompt=prompt_template\n",
        ")"
      ],
      "metadata": {
        "id": "YktjvAT4rVhm"
      },
      "execution_count": 49,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Formating the dataset into readable string:\n",
        "# Important columns (Plan, Spend, Tenure, Churn, ...)\n",
        "# Aggregated insights (average, spend, churn rates, distributions, ...)\n",
        "\n",
        "def summarize_customer_data(df):\n",
        "    summary = \"\"\n",
        "\n",
        "    # General info:\n",
        "    summary += f\"Total customers: {len(df)}\\n\\n\"\n",
        "\n",
        "    # Plan distribution:\n",
        "    plan_counts = df['Plan'].value_counts(normalize=True) * 100\n",
        "    summary += \"Plan Distribution (%):\\n\"\n",
        "    summary += plan_counts.round(2).to_string()\n",
        "    summary += \"\\n\\n\"\n",
        "\n",
        "    # Monthly spend statistics:\n",
        "    spend_mean = df['Monthly Spend ($)'].mean()\n",
        "    summary += f\"Average Monthly Spend: ${spend_mean:.2f}\\n\\n\"\n",
        "\n",
        "    # Tenure statistics:\n",
        "    tenure_mean = df['Tenure (Months)'].mean()\n",
        "    summary += f\"Average Customer Tenure: {tenure_mean:.1f} months\\n\\n\"\n",
        "\n",
        "    # Churn rate:\n",
        "    churn_rate = (df['Churned'] == 'Yes').mean() * 100\n",
        "    summary += f\"Churn Rate: {churn_rate:.2f}%\\n\\n\"\n",
        "\n",
        "    # Country distribution:\n",
        "    country_counts = df['Country'].value_counts().head(5)\n",
        "    summary += \"Top 5 Countries:\\n\"\n",
        "    summary += country_counts.to_string()\n",
        "    summary += \"\\n\\n\"\n",
        "\n",
        "    # Signup source distribution:\n",
        "    signup_counts = df['Signup Source'].value_counts().head(3)\n",
        "    summary += \"Top Signup Sources:\\n\"\n",
        "    summary += signup_counts.to_string()\n",
        "    summary += \"\\n\\n\"\n",
        "\n",
        "    # Industry distribution:\n",
        "    industry_counts = df['Industry'].value_counts().head(5)\n",
        "    summary += \"Top 5 Industries:\\n\"\n",
        "    summary += industry_counts.to_string()\n",
        "\n",
        "    return summary\n",
        "\n",
        "# Generate the summarized input:\n",
        "customer_summary_input = summarize_customer_data(df)\n",
        "\n",
        "# Preview:\n",
        "print(customer_summary_input)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "hHmAcQb5rVa_",
        "outputId": "293f957f-6a41-493c-b6bd-05003a1c784e"
      },
      "execution_count": 50,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Total customers: 1000\n",
            "\n",
            "Plan Distribution (%):\n",
            "Plan\n",
            "Basic         59.9\n",
            "Pro           29.7\n",
            "Enterprise    10.4\n",
            "\n",
            "Average Monthly Spend: $72.74\n",
            "\n",
            "Average Customer Tenure: 30.9 months\n",
            "\n",
            "Churn Rate: 6.70%\n",
            "\n",
            "Top 5 Countries:\n",
            "Country\n",
            "UK        150\n",
            "Brazil    147\n",
            "USA       145\n",
            "France    142\n",
            "Canada    141\n",
            "\n",
            "Top Signup Sources:\n",
            "Signup Source\n",
            "Partner     275\n",
            "Referral    252\n",
            "Paid Ads    241\n",
            "\n",
            "Top 5 Industries:\n",
            "Industry\n",
            "Technology    210\n",
            "Healthcare    209\n",
            "Education     202\n",
            "Retail        191\n",
            "Finance       188\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Call the Business Analyst Agent with the summarized customer data\n",
        "\n",
        "# Prepare input dictionary:\n",
        "input_data = {\n",
        "    \"input\": customer_summary_input\n",
        "}\n",
        "\n",
        "# Run the chain:\n",
        "icp_report = business_analyst_chain.invoke(input_data)\n",
        "\n",
        "# Show the generated ICP:\n",
        "# print(icp_report['text'])"
      ],
      "metadata": {
        "id": "Y9Cl4OUSrVQ4"
      },
      "execution_count": 51,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "Generate charts to complement the findings:\n",
        "\n",
        "📊 Plan distribution pie chart\n",
        "\n",
        "📈 Spend vs. Tenure scatter plot (to show customer lifetime value)\n",
        "\n",
        "📉 Churn rates by plan bar chart\n",
        "\n",
        "🌍 Top countries bar chart\n",
        "\n"
      ],
      "metadata": {
        "id": "OAczP0FWt9h5"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "def plot_plan_distribution(df):\n",
        "    plan_counts = df['Plan'].value_counts()\n",
        "    plt.figure(figsize=(6,6))\n",
        "    plt.pie(plan_counts, labels=plan_counts.index, autopct='%1.1f%%', startangle=140)\n",
        "    plt.title('Subscription Plan Distribution')\n",
        "    plt.show()"
      ],
      "metadata": {
        "id": "miuOqy2Zt7Nh"
      },
      "execution_count": 52,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "def plot_churn_rate_by_plan(df):\n",
        "    churn_data = df[df['Churned'] == 'Yes']['Plan'].value_counts()\n",
        "    plt.figure(figsize=(8,5))\n",
        "    churn_data.plot(kind='bar', color='skyblue')\n",
        "    plt.title('Churn Rate by Subscription Plan')\n",
        "    plt.xlabel('Plan')\n",
        "    plt.ylabel('Number of Churned Customers')\n",
        "    plt.xticks(rotation=0)\n",
        "    plt.show()"
      ],
      "metadata": {
        "id": "ly2OZB-Ytm_q"
      },
      "execution_count": 53,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "def plot_spend_vs_tenure(df):\n",
        "    plt.figure(figsize=(8,6))\n",
        "    plt.scatter(df['Tenure (Months)'], df['Monthly Spend ($)'], alpha=0.6)\n",
        "    plt.title('Monthly Spend vs. Tenure')\n",
        "    plt.xlabel('Tenure (Months)')\n",
        "    plt.ylabel('Monthly Spend ($)')\n",
        "    plt.grid(True)\n",
        "    plt.show()"
      ],
      "metadata": {
        "id": "rvp4ubJev0l8"
      },
      "execution_count": 54,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "def plot_top_countries(df):\n",
        "    country_counts = df['Country'].value_counts().head(5)\n",
        "    plt.figure(figsize=(8,5))\n",
        "    country_counts.plot(kind='bar', color='lightgreen')\n",
        "    plt.title('Top 5 Countries by Customer Count')\n",
        "    plt.xlabel('Country')\n",
        "    plt.ylabel('Number of Customers')\n",
        "    plt.xticks(rotation=45)\n",
        "    plt.show()"
      ],
      "metadata": {
        "id": "ZCMAqZSEv2cr"
      },
      "execution_count": 55,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "def plot_signup_sources(df):\n",
        "    signup_counts = df['Signup Source'].value_counts()\n",
        "    plt.style.use('seaborn-whitegrid')\n",
        "    plt.figure(figsize=(8,5))\n",
        "    signup_counts.plot(kind='bar', color='lightcoral')\n",
        "    plt.title('Signup Sources Distribution', fontsize=16)\n",
        "    plt.xlabel('Signup Source')\n",
        "    plt.ylabel('Number of Customers')\n",
        "    plt.xticks(rotation=45)\n",
        "    plt.grid(True)\n",
        "    plt.show()"
      ],
      "metadata": {
        "id": "7Kl64NaF2PS-"
      },
      "execution_count": 57,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "def plot_industry_distribution(df):\n",
        "    industry_counts = df['Industry'].value_counts()\n",
        "    plt.style.use('seaborn-whitegrid')\n",
        "    plt.figure(figsize=(8,5))\n",
        "    industry_counts.plot(kind='bar', color='mediumseagreen')\n",
        "    plt.title('Industry Distribution', fontsize=16)\n",
        "    plt.xlabel('Industry')\n",
        "    plt.ylabel('Number of Customers')\n",
        "    plt.xticks(rotation=45)\n",
        "    plt.grid(True)\n",
        "    plt.show()"
      ],
      "metadata": {
        "id": "4itOk6Xp2QL9"
      },
      "execution_count": 58,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "def generate_graph_insight(topic: str):\n",
        "    insight_prompt = f\"\"\"\n",
        "        You are a Business Analyst reviewing a chart about {topic}.\n",
        "\n",
        "        Write 2-4 concise sentences providing a professional insight based on the data.\n",
        "\n",
        "        Be specific and actionable, like you are preparing a report for executives.\n",
        "        Do not mention the chart itself (\"the chart shows\"). Focus on insights directly.\n",
        "\n",
        "        Example:\n",
        "        \"Most customers prefer the Pro plan, suggesting a mid-tier pricing strategy aligns with market expectations.\"\n",
        "\n",
        "        Strictly output only the insight.\"\"\"\n",
        "\n",
        "    response = llm.invoke(insight_prompt)\n",
        "    return response.content.strip()"
      ],
      "metadata": {
        "id": "QjJ0YZKd25k7"
      },
      "execution_count": 61,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "def generate_final_icp_summary(summary_input: str):\n",
        "    icp_prompt = f\"\"\"\n",
        "        You are a Business Analyst for ShopMax.\n",
        "\n",
        "        Based on the following findings across subscription plans, churn rates, spend vs tenure, top countries, signup sources, and industries, write a **Final Ideal Customer Profile (ICP)**.\n",
        "\n",
        "        Format:\n",
        "        - Summarize who is the ideal customer (demographics, subscription plan, spending behavior, tenure).\n",
        "        - Explain which signup sources and industries are the best targets.\n",
        "        - Conclude with a short strategic recommendation.\n",
        "\n",
        "        Findings:\n",
        "        {summary_input}\n",
        "\n",
        "        Respond very clearly and professionally, in 5-8 concise bullet points followed by a short conclusion.\"\"\"\n",
        "    response = llm.invoke(icp_prompt)\n",
        "    return response.content.strip()"
      ],
      "metadata": {
        "id": "FinDcUkB4KRI"
      },
      "execution_count": 68,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "try:\n",
        "    final_icp = generate_final_icp_summary(customer_summary_input)\n",
        "except Exception as e:\n",
        "    final_icp = \"⚠️ Error generating final ICP conclusion: \" + str(e)\n",
        "\n",
        "display(HTML(\"<h1 style='text-align: center;'>📋 ShopMax | Business Analyst Journal</h1>\"))\n",
        "\n",
        "# Split the agent output into lines:\n",
        "lines = icp_report['text'].split('\\n')\n",
        "\n",
        "for line in lines:\n",
        "    line = line.strip()\n",
        "    if not line:\n",
        "        continue  # skip empty lines\n",
        "\n",
        "    if line.startswith('## Section:'):\n",
        "        section_title = line.replace('## Section:', '').strip()\n",
        "        display(Markdown(f\"<h2 style='color: royalblue;'>🔵 {section_title}</h2>\"))\n",
        "\n",
        "    elif line.startswith('## Chart:'):\n",
        "        chart_name = line.replace('## Chart:', '').strip()\n",
        "\n",
        "        if chart_name == 'plan_distribution':\n",
        "            plot_plan_distribution(df)\n",
        "            insight = generate_graph_insight(\"subscription plan distribution\")\n",
        "            display(Markdown(f\"**🧠 Graph Insight:** {insight}\"))\n",
        "        elif chart_name == 'churn_rate_by_plan':\n",
        "            plot_churn_rate_by_plan(df)\n",
        "            insight = generate_graph_insight(\"churn rate by subscription plan\")\n",
        "            display(Markdown(f\"**🧠 Graph Insight:** {insight}\"))\n",
        "        elif chart_name == 'spend_vs_tenure':\n",
        "            plot_spend_vs_tenure(df)\n",
        "            insight = generate_graph_insight(\"monthly spend vs. tenure\")\n",
        "            display(Markdown(f\"**🧠 Graph Insight:** {insight}\"))\n",
        "        elif chart_name == 'top_countries':\n",
        "            plot_top_countries(df)\n",
        "            insight = generate_graph_insight(\"top countries\")\n",
        "            display(Markdown(f\"**🧠 Graph Insight:** {insight}\"))\n",
        "        # elif chart_name == 'signup_sources':\n",
        "        #     plot_signup_sources(df)\n",
        "        #     insight = generate_graph_insight(\"signup sources\")\n",
        "        #     display(Markdown(f\"**🧠 Graph Insight:** {insight}\"))\n",
        "        # elif chart_name == 'industry_distribution':\n",
        "        #     plot_industry_distribution(df)\n",
        "        #     insight = generate_graph_insight(\"industry distribution\")\n",
        "        #     display(Markdown(f\"**🧠 Graph Insight:** {insight}\"))\n",
        "\n",
        "    else:\n",
        "        display(Markdown(f\"<p style='font-size: 16px;'>{line}</p>\"))\n",
        "\n",
        "display(Markdown(\"<h2 style='color: seagreen;'>🏁 Final Ideal Customer Profile (ICP)</h2>\"))\n",
        "display(Markdown(f\"<p style='font-size: 16px;'>{final_icp}</p>\"))\n",
        "\n",
        "display(Markdown(\"<br><hr><br>\"))\n",
        "display(Markdown(\"\"\"\n",
        "✅ This professional journal summarizes ShopMax's Ideal Customer Profile (ICP).\n",
        "✅ Key findings are supported by visual data analysis and GPT-4o strategic insights.\n",
        "\n",
        "**Thank you for reviewing this Business Analyst Report!** 📈\n",
        "\"\"\"))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "id": "ttU400Mov4WE",
        "outputId": "1fc0c3cb-dbed-4d6c-98f6-afaa3fa83fae"
      },
      "execution_count": 69,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<IPython.core.display.HTML object>"
            ],
            "text/html": [
              "<h1 style='text-align: center;'>📋 ShopMax | Business Analyst Journal</h1>"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<IPython.core.display.Markdown object>"
            ],
            "text/markdown": "<h2 style='color: royalblue;'>🔵 Subscription Plans</h2>"
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<IPython.core.display.Markdown object>"
            ],
            "text/markdown": "<p style='font-size: 16px;'>- Basic Plan: 59.9% of customers</p>"
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<IPython.core.display.Markdown object>"
            ],
            "text/markdown": "<p style='font-size: 16px;'>- Pro Plan: 29.7% of customers</p>"
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<IPython.core.display.Markdown object>"
            ],
            "text/markdown": "<p style='font-size: 16px;'>- Enterprise Plan: 10.4% of customers</p>"
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 600x600 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<IPython.core.display.Markdown object>"
            ],
            "text/markdown": "**🧠 Graph Insight:** A significant portion of subscribers are opting for the Basic plan, indicating a potential opportunity to enhance its features or introduce upsell strategies to transition users to higher-tier plans. Additionally, the Premium plan has a lower adoption rate, suggesting a need to reassess its value proposition or pricing to better attract and retain customers."
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<IPython.core.display.Markdown object>"
            ],
            "text/markdown": "<h2 style='color: royalblue;'>🔵 Financial Insights</h2>"
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<IPython.core.display.Markdown object>"
            ],
            "text/markdown": "<p style='font-size: 16px;'>- Average Spend: $72.74</p>"
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<IPython.core.display.Markdown object>"
            ],
            "text/markdown": "<p style='font-size: 16px;'>- Churn Rate: 6.7%</p>"
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 800x500 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<IPython.core.display.Markdown object>"
            ],
            "text/markdown": "**🧠 Graph Insight:** The highest churn rate is observed in the Basic plan, indicating a need to enhance the value proposition or customer experience for entry-level subscribers. The Premium plan maintains the lowest churn rate, suggesting that customers perceive significant value at this tier, which could be leveraged to upsell Basic and Standard plan users. Consider implementing targeted retention strategies for Basic plan users, such as personalized engagement or added features, to reduce churn and increase customer lifetime value."
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<IPython.core.display.Markdown object>"
            ],
            "text/markdown": "<h2 style='color: royalblue;'>🔵 Customer Tenure</h2>"
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<IPython.core.display.Markdown object>"
            ],
            "text/markdown": "<p style='font-size: 16px;'>- Average Customer Tenure: 30.9 months</p>"
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 800x600 with 1 Axes>"
            ],
            "image/png": 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84xsZfe23334bJ5xwAp5++ukBK0wQEVF6mHNLRDSIhx9+GFOmTEnaSpWIiHIX0xKIiPrx5JNPYsOGDXjppZdw7733jmhVBiIiyhwGt0RE/TjnnHPgdDpxySWX4Ic//GG2D4eIiHRizi0RERER5Q3m3BIRERFR3mBwS0RERER5gzm3UPf+3rdvHwoKCrhohIiIiCgHKYqCrq4uVFdXQxQHHp9lcAtg3759aRUtJyIiIqLs2LNnD2pqagb8PoNbAAUFBQDUxnK5XEN+nWg0ipUrV2L+/PkwmUyZOjzSie2fPWz77GL7Zw/bPrvY/tmTjbb3er2ora3V4raBMLgFtFQEl8s17ODWbrfD5XLxQ5YFbP/sYdtnF9s/e9j22cX2z55stn2qFNKcWVD2q1/9CoIg4JprrtEeC4VCuPLKK1FSUgKn04nFixejqakp6ed2796NRYsWwW63o7y8HNdffz1isdgoHz0RERER5YKcCG4/+ugj/OlPf8Ls2bOTHr/22mvx4osv4umnn8bq1auxb98+nHXWWdr3JUnCokWLEIlE8O677+Lxxx/HY489hptuumm0fwUiIiIiygFZD259Ph/OO+88PPzwwygqKtIe93g8eOSRR/C73/0OJ554IubOnYtHH30U7777Lt5//30AwMqVK7F582Y88cQTmDNnDhYuXIjbbrsN999/PyKRSLZ+JSIiIiLKkqzn3F555ZVYtGgRTjrpJNx+++3a42vXrkU0GsVJJ52kPXbQQQdh4sSJeO+993DMMcfgvffew6xZs1BRUaE9Z8GCBbjiiivw2Wef4fDDD+/3PcPhMMLhsPa11+sFoOaPRKPRIf8uiZ8dzmvQ0LH9s4dtn11s/+xh22cX2z97stH2et8rq8Htk08+iXXr1uGjjz7q8739+/fDbDajsLAw6fGKigrs379fe07PwDbx/cT3BnLXXXdh2bJlfR5fuXIl7HZ7ur9GH6tWrRr2a9DQsf2zh22fXWz/7GHbZxfbP3tGs+0DgYCu52UtuN2zZw9+/OMfY9WqVbBaraP63kuXLsV1112nfZ0oLTF//vxhV0tYtWoVTj75ZK7azAK2f/aw7bOL7Z89bPvsYvtnTzbaPjHTnkrWgtu1a9eiubkZRxxxhPaYJElYs2YN/vjHP+K1115DJBJBZ2dn0uhtU1MTKisrAQCVlZX48MMPk143UU0h8Zz+WCwWWCyWPo+bTKaM/IEy9To0NGz/7GHbZxfbP3vY9tnF9s+e0Wx7ve+TtQVl8+bNw8aNG7F+/XrtvyOPPBLnnXee9m+TyYQ33nhD+5ktW7Zg9+7dqKurAwDU1dVh48aNaG5u1p6zatUquFwuzJw5c9R/JyIiIiLKrqyN3BYUFODQQw9NeszhcKCkpER7/JJLLsF1112H4uJiuFwu/OhHP0JdXR2OOeYYAMD8+fMxc+ZMnH/++bj77ruxf/9+3Hjjjbjyyiv7HZklIiIiovyW9WoJg7nnnnsgiiIWL16McDiMBQsW4IEHHtC+bzAYsGLFClxxxRWoq6uDw+HABRdcgFtvvTWLR01ERERE2ZJTwe3bb7+d9LXVasX999+P+++/f8CfmTRpEl5++eURPjIiIiIiGguyvokDEREREVGmMLglIiIioryRU2kJRJRbZFnBzjY/ukIxFFiNmFzigCgK2T4sIiKiATG4JaJ+bWrw4Jl1e1Hf7EM4KsNiEjGt3InFR9Tg0AnubB8eERFRvxjcElEfmxo8uO+NrWj3R1DltsHmNiAYkbBxrwcNHUFcPW86A1wiIspJzLkloiSyrOCZdXvR7o9gWrkTTqsRBlGA02rEtHIn2v0RPLuuAbKsZPtQiYiI+mBwS0RJdrb5Ud/sQ5XbBkFIzq8VBAFVbhu2NndhZ5s/S0dIREQ0MAa3RJSkKxRDOCrDZjb0+32b2YBwVEZXKDbKR0ZERJQag1siSlJgNcJiEhGMSP1+PxiRYDGJKLAyZZ+IiHIPg1siSjK5xIFp5U40eoJQlOS8WkVR0OgJYnp5ASaXOLJ0hERERANjcEtESURRwOIjalDsMKO+2QdfKAZJVuALxVDf7EOxw4yzjpjAerdERJSTGNwSUR+HTnDj6nnTMavGjc5gBDtb/egMRjC7ppBlwIiIKKcxaY6I+nXoBDdmVrm4QxkREY0pDG6JaECiKGBKmTPbh0FERKQb0xKIiIiIKG8wuCUiIiKivMHgloiIiIjyBoNbIiIiIsobDG6JiIiIKG8wuCUiIiKivMHgloiIiIjyBoNbIiIiIsobDG6JiIiIKG8wuCUiIiKivMHtd4koZ8iygp1tfnSFYiiwGjG5xAFRFLJ9WERENIYwuCWinLCpwYNn1u1FfbMP4agMi0nEtHInFh9Rg0MnuLN9eERENEYwuCWirNvU4MF9b2xFuz+CKrcNNrcBwYiEjXs9aOgI4up50xngEhGRLsy5JaKskmUFz6zbi3Z/BNPKnXBajTCIApxWI6aVO9Huj+DZdQ2QZSXbh0pERGMAg1siyqqdbX7UN/tQ5bZBEJLzawVBQJXbhq3NXdjZ5s/SERIR0VjC4JaIsqorFEM4KsNmNvT7fZvZgHBURlcoNspHRkREYxGDWyLKqgKrERaTiGBE6vf7wYgEi0lEgZVLBIiIKDUGt0SUVZNLHJhW7kSjJwhFSc6rVRQFjZ4gppcXYHKJI0tHSEREYwmHQogoq0RRwOIjatDQEdRyb21mtVpCoyeIYocZZx0xgfVucwjrERNRLmNwS0RZd+gEN66eN12rc9vkVevczq4pxFlHTGAZsBzCesRElOsY3BJRTjh0ghszq1wcEcxhrEdMRGMBg1siyhmiKGBKmTPbh0H96F2POFG2zWk1YprFifpmH55d14CZVS52SIgoq7igjIiIUmI9YiIaKxjcEhFRSqxHTERjBYNbIiJKifWIiWisYHBLREQpsR4xDUaWFWxv8eHTPZ3Y3uKDLCupf4hohLCLTUREKbEeMQ2E5eEo1zC4JSIiXViPmHpjeTjKRQxuiYhIN9YjpgSWh6NcxeCWiIjSwnrEBKRXHo7nC40mBrdERESUNq08nHvg8nBNXpaHG4gsK5wBGSFZrZbw4IMPYvbs2XC5XHC5XKirq8Mrr7yiff/444+HIAhJ//3gBz9Ieo3du3dj0aJFsNvtKC8vx/XXX49YjB8kIiKikcTycEO3qcGD217ajJv//RnueOlz3Pzvz3DbS5uxqcGT7UPLC1k942pqavCrX/0K06dPh6IoePzxx/Gtb30Ln3zyCQ455BAAwKWXXopbb71V+xm73a79W5IkLFq0CJWVlXj33XfR2NiIJUuWwGQy4c477xz134eIiDKPI1y5KVEebuNeD6ZZnEmpCYnycLNrClkerhcuwht5WQ1uTzvttKSv77jjDjz44IN4//33teDWbrejsrKy359fuXIlNm/ejNdffx0VFRWYM2cObrvtNtxwww245ZZbYDabR/x3ICKikcMyU7mL5eHSx0V4oyNn5gokScLTTz8Nv9+Puro67fG///3veOKJJ1BZWYnTTjsNv/zlL7XR2/feew+zZs1CRUWF9vwFCxbgiiuuwGeffYbDDz+83/cKh8MIh8Pa116vFwAQjUYRjUaH/DskfnY4r0FDx/bPHrZ9duVr+3/e6MVDb29DRyCCClf3CNcXDR14oMOPHxw/FQdXubJ6jPna9nrNKLfjquMPwAvr92F7qw9tXWoHZM6EApw+pxozyu0j2jZjrf13tvqxs8WLGrcFRkEB0GOzCwGocVuwo8WDbU0eTC7N7RHvbLS93vcSlN5bzYyyjRs3oq6uDqFQCE6nE8uXL8epp54KAPjzn/+MSZMmobq6Ghs2bMANN9yAr3zlK3j22WcBAJdddhl27dqF1157TXu9QCAAh8OBl19+GQsXLuz3PW+55RYsW7asz+PLly9PSnsgIiIiotwQCARw7rnnwuPxwOUauGOb9ZHbGTNmYP369fB4PPjXv/6FCy64AKtXr8bMmTNx2WWXac+bNWsWqqqqMG/ePGzbtg1Tp04d8nsuXboU1113nfa11+tFbW0t5s+fP2hjpRKNRrFq1SqcfPLJMJlMQ34dGhq2f/aw7bMrH9t/Z6sfd77yOdxWM5z9LEjyhWLwhCL4+cKDszrClY9tP5aMtfYfK+e1Htlo+8RMeypZD27NZjOmTZsGAJg7dy4++ugj3HvvvfjTn/7U57lHH300AKC+vh5Tp05FZWUlPvzww6TnNDU1AcCAeboAYLFYYLFY+jxuMpky8gfK1OvQ0Iz39s/m4pvx3vbZlk/tH4gB/ghQ5jJBQt/z12w2we+NIBBDTvzO+dT2Y9FYaf+pFW5MLnPFF+GZ+izC2+sJY3ZNIaZWuMdMzu1otr3e98l6cNubLMtJ+bA9rV+/HgBQVVUFAKirq8Mdd9yB5uZmlJeXAwBWrVoFl8uFmTNnjsrxEuUSLr6hfNGzzFR/I1wsM0VjERfhjY6sXhWWLl2KhQsXYuLEiejq6sLy5cvx9ttv47XXXsO2bdu0/NuSkhJs2LAB1157LY499ljMnj0bADB//nzMnDkT559/Pu6++27s378fN954I6688sp+R2aJ8hnLy1A+YZkpyleHTnDj6nnTtYGIJq86EDG7phBnHTGB1+kMyGpw29zcjCVLlqCxsRFutxuzZ8/Ga6+9hpNPPhl79uzB66+/jt///vfw+/2ora3F4sWLceONN2o/bzAYsGLFClxxxRWoq6uDw+HABRdckFQXl2g8YHkZyjcc4aJ8dugEN2ZWuVi/eYRkNbh95JFHBvxebW0tVq9enfI1Jk2ahJdffjmTh0U05nCPd8pH+TLCxU0oqD+iKPB6PEKYrESUB7jHO+WrsT7CxTx4otHH4JYoD3DxDeWzsTrCxTx4ouwQs30ARDR8icU3jZ4geu/Lklh8M728gItviEZJ7zx4p9UIgyioefDlTrT7I3h2XQNkOav7KBHlJQa3RHkgsfim2GFGfbMPvlAMkqzAF4qhvtnHxTeUUbKsYHuLD5/u6cT2Fh8DtH6kkwdPRJnFOUqiPJEvi28otzGHVB/mwRNlD4Nbojwy1hffUHalWtXPHFL9mAdPlD38VBHlmVxdfMNySLkt1Ygsaymnh5tQEGUPg1siGnGcys5tekZk7WYDaymngZtQEGUPF5QR0YhKBE4b93pQaDNjcqkDhTYzNu5VH9/U4Mn2IY5relf1e4JRNYfUPHAOaTjKHNKeEnnws2rc6AxGsLPVj85gBLNrCpnCQTSCOHJLRCOGU9m5T++qfm+wlDmkQ8A8eKLRx6sQEY0Ybguc+/Su6nfZTMwhHaJczYOnblwTkF8Y3BLRiGE5pNynd1W/22ZiDinlJa4JyD8Mbinj2APOrlxq/3wqh5RL7ZpJ6azqF0WBtZQpr7C8XX7K/TsKjSnsAWdXrrV/vpRDyrV2zaR0V/Uzh5TGksE6pUNZEzCWO7lj+djTxeCWMoY94OzKxfbPh3JIudiu6Up1U0t3dzvmkNJYkKpTmu6agEx3ckcz2MznDnp/GNxSRozUqvjx1NMcjlyuSjCWtwXO5XbVS+9NjSOy2TeWr3e5dux6OqWSrOheE5DpTu5oBpv50EFPF4NbyoiRWBU/3nqaw5HrVQnGauCUzXbNRLCQ7k2NI7LZM5avd7l27Ho7peceXatrTYDDYsDfP9idsU7uaAab+dBBHwoGt5QRmV4VPx57msMxFqoSjMXAKVvtmolgYbze1MaisXy9y8Vj19spBaBrTQCAjHVyR/tzmesDHyOFO5RRRvRcFd+fdFbF690xSZaVTP8aY1Ym25+6ZaNdM7WjWzo3NcqesXy9y9Vj1zqlKXbT84clLD6iBsUOM+qbffCFYpBkBb5QDPXNPm1NgD8sZWx3vtH+XOpti3wrx8jgljIisSq+0ROEoiRfyBI94OnlBbpWxY+Fm7IsK9je4sOnezqxvcWX9RtPJtufuo12u2YyWBivN7WxZixc7waSq8eeTqdUzxbJmezkjvbncrwOfOTXb0NDNtz8vkyuis/1KfZcyy8D8qMqQS4a7XbN5BRiPtUYzme5fr0bTK4ee7olCFOtCchkScPR/lzmSznGdPGqRhkL1jK1Kj6Xb8q5mF+WMJarEuSy0WzXTAYL4/WmNtbk8vUuYaDBj5E69mwMtgy2JiCTndzR/lyO14EPBrfjXKaDtUysis/Vm/JYWKAzVqsS5LrRatdMBgvj9aY21uTq9S5hsMGPmVWujB97rg22ZPr1svG5HI8DHwxux7GRCtaGuyo+V2/KY2XV6VisSjAWjEa7ZjrQGY83tbEmV693gL7Bj0weey4OtozE62XjczneBj4Y3I5juRysZfOmPNCUWK7ml40VuVbkPReNRKAz3m5qY1EudkL0Dn7cuOjgjBx7rg62jNTrZeNzOZ4GPhjcjmO5Hqxl48M/2JTYWMiNy1W5uAgvV41EoDOebmqZMtqdsVzrhPQc/AAAbzCKqCTDZFCvcT0HPzJx7Lk82DJS+LkcObwLj2PZDtb03DxG88OfakrsqhOn5XRuXK7K5UV4uSrXAp3xJludsVwKdhKDHyGThC1NXniDag1YgyjAZTNiUokjqWTVcI891wdbxpN8mGVjcDuOZXMhQ66N5OmZEnv+k3046/AJOZkbl6vGwiK8bMi1jl025dqNlJ0xVYHViKgkY8NeDyRZht1shFEUEJMVtPsj8ARjqHZbMzb4ke3BFlLl2r15qHiWjGPZWsiQizcPvVNiDosx53Ljctl4nGpMJV9uHpmQa23Bzli3iUV2hGMS/KEoyl0WCIK655PJIMBlNaLZG0bEYcbEIntG3i/Xq0aMB7l4bx4qBrc5aDRHMkZ7IcNI3TxkWcHOVnUXnJ2tfkytcKf18+lMiR1WW8gpY5041Zgsn24ew5WLbcHOWLfdHQFYjAY4LUZ4Q7GkkdtAJAanxQizUcTujkBG2iKXq0aMB/nWsWNwm2OyMZIxmvl9I3HzSLTZzhYvzigB7nzlc0wuc6XVZulOiY2XKePh4lRjt3y7eQxHrrYFO2PdukIxmAzqQMfOdj+8wRiC8ZzbEocFE4vt8ASjGW2LXKwaMV7kW8cu/+8oY0g2RzJGK1jL9M2jZ5vVuC0AALfVnHabcUpsZLBdu+XbzWM4crUt2BnrlmgLi8mAuROL0BWOIRqTYTKKKLAY4QtLCMWkjLcFF1NmR7517MRsHwCpkkYyyhxQoMATiECBgmllDrT7I3h2XQNkWcn2oQ5Lz5tHf9K5efQe/UncjJxWI6aVO9Nqs8SUWLHDjPpmH3whdWWwLxRDfbOPU2JDxHbtpt08zAPfPHquPs9nudoWic5YoycIRUm+biQ6Y9PLC8ZFZyypLQAUWE0odlpQYDVBAUa0LRKDLYfVFmJKmXNcXB+yLZP35lzA4DZHJEYy7GYD1u3pxMc7O7Bud/z/93TCbjZoIxljWSZvHumM/uiRmBKbVeNGZzCCna1+dAYjmF1TOK5yITON7arKt5vHcORqW7Az1o1tMb7kW8cu/6+iY0RXKIZ2fwSd/gjCUt+yK/5QDIUO84iNZIzWIrZMLhoYiWkUTomNDLYrUzR6yuW2GE95n6mu++OpLca7fFvQx+A2RzgsBnQEIghGJRTZzYjJCiIxGaIowGU1oSMQgRKIwGHpP5AbjtFexJapC+ZI5cdxsdjIGO/tmm83j+HI9bYYD50xvdf98dAWpMqnzgyD2xwjyQpafSFEJXUEQxAEmAwAMDIXkmwtYsvEBbP36E/PJsr26A9Rf/Lp5jFcud4W+dwZS/e6n89tQcnypTPD4DZH+MMSrEYD2mMRxGQFZqMIgyhAVoBARIZRFGA1GuAP95+jNhTZLscz3Atm79GfGrcFcAC+UAx7PeGsj/7Q2DDaO2Tly80jE9gW6Rvu+Zrt677eY+Q5kT350JlhcJsjHBYDQjEJZqMIM4CopCAmqyO39viK4lBMymhaQq6W40lHz9GfnS1ewAF4QpGcGf2h9Iz2TS1bO2TpuXmMlxt8PtxIR0smztdcv+7n2q51NDYxuM0xBlFAkd2EqATIigIxnpbQEYhm/L3ypa5dYvRnW5MHmz9sxs8XHpz2DmWUfaN9U8vFHbJ6Hhtv8NRTps7XXL7u5/JnksYWlgLLEf6wupDMbjLAG4pBEACLUYQgQN360GRAkd2c0bSEXC3HMxSiKGByqZpbO7k0P0e48lniprZxrweFNjMmlzpQaFM347jvja3Y1ODJ6Pv1VyPZIApDqpGcaaPdFpT7Mnm+5up1P5c/k+ONLCvY3uLDp3s6sb3FNybbPPejlnGiwGpEscOMEocZ+72hPlsdVrgs8ULamfuT5XI5Hho/spEDmKtTs2MhH5JGXybP11y97ufqZ3K8yZdZIwa3OaLnBeeI2kL4IpK21aHTbEB9iz/jF5xcL8dD40M2bmq5OjXLG3z/xkv+8UAyeb7m6nU/Vz+TY0UmPiP5lBbC4DZHJF1wWvyoctvgtpsRjEiob/GP2AUn18vxUO6TZQU7W9Vd4Ha2+tPOd87GTW2kaiQPF2/wfeXLSNJwZPp8zcXrfq5+JseCTHxG8m3WiGdJDsnWBYfleGioEhfVnS1enFEC3PnK55hc5krropqNm1quTs3yBp8sn0aShmMkztdcu+7n6mcy12XqM5Jvs0bj4wo5hmTrgsNyPJSunhfVGrcFAOC2mtO+qGbjpparU7O8wXfLt5Gk4Rip8zWXrvu5+pnMZZn8jOTbrFFWqyU8+OCDmD17NlwuF1wuF+rq6vDKK69o3w+FQrjyyitRUlICp9OJxYsXo6mpKek1du/ejUWLFsFut6O8vBzXX389YrGx0fgDSVxwDqstxJQyJz/MlHP6W9kMYEgrmxM3tWKHGfXNPvhCMUiyAl8ohvpm34in5MyqcaMzGMHOVj86g2qN5GyNCGarLXJROiNJ40Eunq+Zpv2OE9xo9ASxaZ8HjZ4gZsUfz4ffMZMy+RnJ1SoaQ5XVo6ypqcGvfvUrTJ8+HYqi4PHHH8e3vvUtfPLJJzjkkENw7bXX4qWXXsLTTz8Nt9uNq666CmeddRb++9//AgAkScKiRYtQWVmJd999F42NjViyZAlMJhPuvPPObP5qRHmt70W1O4gdyhQWU3KSjynX8iGzId9GkjIhF8/XkaBAUa8oinplUTD2SlGNhkx+RvJt1iirwe1pp52W9PUdd9yBBx98EO+//z5qamrwyCOPYPny5TjxxBMBAI8++igOPvhgvP/++zjmmGOwcuVKbN68Ga+//joqKiowZ84c3Hbbbbjhhhtwyy23wGw2Z+PXonGAq7czH3iMp5ScVOfPeAliBsP84/7lUipBpvVMdarukZawqcGLfZ1bOXrbSyY/I/mWFpIzVwVJkvD000/D7/ejrq4Oa9euRTQaxUknnaQ956CDDsLEiRPx3nvv4ZhjjsF7772HWbNmoaKiQnvOggULcMUVV+Czzz7D4Ycf3u97hcNhhMNh7Wuv1wsAiEajiEaHvhNY4meH8xo0dKPV/p83evHC+n3Y3tq9MnVKqRPfmlONg6tcI/a+sqxgd3sAvnAMTosRE4vtWbvQ2I2AwwxEIlE4rUaIkAFA+/9gJAaHWX1eun+P2kILADWHV5JikDK3b0lOSOf80dsW+XjtmeAy48AyOzbv88DVz0hSq9ePQ6rdmOAyZ/X3zse2zwZZVvDc2t3oCoQwQ8sfVWC2inBZ7Nje4sPza3djeumMpOveeG7/TH9GZpTbcdXxB2jXp7Yu9fo0Z0IBTp9TjRnl9qTXyUbb630vQVGUrI73b9y4EXV1dQiFQnA6nVi+fDlOPfVULF++HBdddFFSEAoAX/nKV3DCCSfg17/+NS677DLs2rULr732mvb9QCAAh8OBl19+GQsXLuz3PW+55RYsW7asz+PLly+H3W7P7C9IRERERMMWCARw7rnnwuPxwOUaeDAp6yO3M2bMwPr16+HxePCvf/0LF1xwAVavXj2i77l06VJcd9112tderxe1tbWYP3/+oI2VSjQaxapVq3DyySfDZDJl4lApDSPd/rKs4O7XtmDzPg+mlPXtJW9v8eGQajeuXzAjoyOqnzd68dDb29ARiKDC1T1V1OQNoshuxg+On5r2iHEmRoF7Hle1y4JjHfuwxl+Nfd7wkI8rn43k+ZPP157+Rrqnljpx+gjPlOilp+1zadYlV21q8OA3r23BpBIHDP20jSQr2NXmx/ULZiSlJuTzua/Xyxsb8dd3dmC/N4SYpMBoEFDpsuLirx+AU2dVjch7yrKCHS1ebFn7X8yY+zUcUDY6VUsSM+2pZD24NZvNmDZtGgBg7ty5+Oijj3Dvvffiu9/9LiKRCDo7O1FYWKg9v6mpCZWVlQCAyspKfPjhh0mvl6imkHhOfywWCywWS5/HTSZTRj4cmXodGpqRav/tLT582RJAqcsBWeiVayoApS4HtrQE0OCNZCwnTpYVPPfpfjT7Y5hW7tICIpvVgEkWE+qbfXj+0yYcWlOs+8KSTsHvwXJDZ08swQ/nGbU6t3AA7aEYDp5QPK4WPuk1GudPPl57Zk8swaE1xTmffzxQ23MTCn3cDisMRiN8EQVOa99cfl8kBoPRCLfD2m875+O5r8emBg9e2tQMo9GImROKYRABSQa8wQhe2tSMqRXujJ9nveub/3rl1rTrmw+V3r9x1oPb3mRZRjgcxty5c2EymfDGG29g8eLFAIAtW7Zg9+7dqKurAwDU1dXhjjvuQHNzM8rLywEAq1atgsvlwsyZM7P2O1B+ysbq7UwX1k6n4Leem3Ji4dO2Jg82f9iMny88OO0dysYLrv4furG6iIqbUOiXb6v1R0PPkozTKwqS2qzCZRmRWtCZqm8+0rIa3C5duhQLFy7ExIkT0dXVheXLl+Ptt9/Ga6+9BrfbjUsuuQTXXXcdiouL4XK58KMf/Qh1dXU45phjAADz58/HzJkzcf755+Puu+/G/v37ceONN+LKK6/sd2Q2F4z3VfZjWTZWb2cyIEqn4PfmRi9vykMw2Oebq//HF25CkZ58W60/GkZ7V7He57RRUJdsqee0KafO6axeRZubm7FkyRI0NjbC7XZj9uzZeO2113DyyScDAO655x6IoojFixcjHA5jwYIFeOCBB7SfNxgMWLFiBa644grU1dXB4XDgggsuwK233pqtX2lQnJ4a27IxspDJgEjvhXB7qy+tIHi42+/mi1Sfb45MjS/5tp3paGCN5/SM9mxQpuubj6SsBrePPPLIoN+3Wq24//77cf/99w/4nEmTJuHll1/O9KFlHKenxr5sjCxkMiDSeyH8sqlL10151eYmLQjO5empTBlsVFbv55sjU+MH01CGhjWe9Rvt2aCxdE5z/msUcHoqf4z2yEImA2q9F0JA0HUBe3HDvjExPZUJg43Kzqxy6f58c2Rq/GAaCmXCYJ3q0Z4NGkvndPaPYBzg9FR+Ge2RhUwFRHovhAdWOFNewGRFQaMnhOoxMD01XKlGZRcfUZPW55sjU+MD01CGhul73VK1xWjPJvY+p9HjZXPtnGZwOwrG0lA+6TPaq7czERDpvRBOKXWmvClXu61o9IRgM+f3Oa1n1mXFhsa0P99jdfU/6ccFUulj+l43vW0xmrNBvc/pGrcFcAC+UAx7PeGcOqcZ3I6CsTSUT7krEwGR3gthqpvyotlVeOKD3Xl/TuuZddnnCUKA+js7LQZ0hWOIxmSYjCIKLMa8aYtsGqtVZpiGoh/T97ql2xaZng0a7PPW85xO1Df3hCI5d07zajsKOD1FuUTPhTDVTXlmlQvv72gfE9NTw6Fn1kUEUOm2YltLF6KSjK6QBElWYBAFFFgNMBlEHDOldMy3RbaM9WlqpqHoM1bS9/R0tIbbGRtKW2RqNihf6pszuB0FnJ6iXKPnQpjqpjxWpqeGQ8+si9VswOwaN97f3oZgRILLZoLVYkAkJqOxU03dOKw2ty78Y0W+TFMzDSW1sZC+pyfwy0RnLFttkc7nTRQFTC51YDOAyaW511kTs30A40ViJGxWjRudwQh2tvrRGVSH8sfKBZrGn8RN+bDaQkwpc/Y7ujurxg1PKAKge3oqX87pxKxLoycIRVGSvpcYoZ5W5sS+zhDKCiyoLrRBVgB/WIKsANVFNpQVWPDpHg9kWRngXag/vadmnVYjDKKgTs2WO9Huj+DZdQ1s1zzRsyPZn2yn9yQCv417PSi0mTG51IFCm1r68L43tmJTg0fXc/TIRlvk2+eNI7ejiNNTlG/GwvTUcOiZdTlmSgme+GAXppYVwGExoCsUQ1SSYTKoNx9/WMqJ6dSxZqxMU1Nm5HL6np4c2GfW7gWAjOQMZ6Mt8u3zxuB2lHF6ivJNrk9PDVeq/GNJVrQpREEQ4LKZkn4+F6ZTx6KxME1NmZPL6Xt6Ar8NDR4IQEaCw2y0Rb593hjcUtaM1RXQwNg+dkrfYLMu21t8rIYyAlhlZmzJxDUxV6tL6An8ghEJEJCx8oij3Rb59nkbG0dJeWcsr4Aey8c+EmRZwc5WPwBgZ6s/r9ISehpo1mUoU4jsHKWWy9PUlCyT18RcTN/TE/jZzIbukoAZCg5Hsy3y7fPG4JZGXc8VmQVWI6xWI2KKkjMroAcLPPJl9XamJG5qO1u8OKMEuPOVzzG5zDWuAv10pxDZOdInl6epqdtIXBNzLX1PV+AX/x03NmQ2OByttsi3zxuDWxpVicT8vR0BRCUZezuCSTVBA5FYVgt1DxZ4zKxysch4D4mbWpsvjGKbeikxCiI27Okcd4G+3ilEdo7Sk6vT1KQaCxsvZGKWRE/gt3huDQCgoXPsBof59HljcEujamebH+t3d6LdF0FMUWA3G2EUBcRkBR2BKIyCgE92d2RlRWaqwGPxETV5tZp0OHp2UmKSghZvEN8sBrY0dcFqNiEYlbJ+UxttqaYQewYCU8sc8IUldAYiMBlETC1zYFuLf9y1mR65OE1NqlxfYZ/pdAk9gd9YDw7z5fPG4JZGlScYxT5PEDFZRqHdjMTWViaDALfNhM5ABPs8IXiC0VE9Lj0jECs2NObVatLhSHRS2vxRSLKMQqvaJmajiPZABAZRzFonJZsGm0JMBAJ2sxGf7OmENxjTZi1cNiMqXeOnc5SuXJumJlUur7AfiVkSvbs7jvXgMB8+bwxuaVR5g1GEYzLsJgOS9mwFAAiwGA0IRCV4Rzm41TMCsc8TzPiCgbHKE4xiX2cQkqLAZTXBFN8OxiSKcFlFeEPq90e7k5LLukIxtPsi6AhGEInJSbMW7f4IfOEYimzmcdE5ovyQqyvsRzJdQk/glw/B4VjHHcpoVLlsJlgMIiKS3O+OTxFJhsUg9qkVOtK0EYhByriIAKrc1kF3q5peXjBmVpMOhzcYRViSYTaI/XYGzAYRYUke9U5KLnNYDOgIRhCKSGqHIN52JoMIl9WEUERCRzACh6X/c5Ao1+jZwS8b18R00iUoPzG4pVHltplQXWiDQVRH96KSDFlREJVkeENRGEUR1YU2uEc5uNWz3aHVbMA3Z1eh2GFGfbMPvpA6rewLxVDf7BsTCwYyxWUzwWIUEY5JAHpvx6ggHJNgMRpGvZMyFigAet1vIQh9W5Eo1yUWWuXaNVHPYEU4Oj5SyMar/J8/pZwyucSBORMLEd7ehpikwBvqzjssdphhFAUcPrFo1Hv6emv8nTyzEhOK7GN6wUAmuG0mVLttaIynHiRybqOyDE9IgkkUUeW2jnonJdsGW5ntD0sospshKBF4gtGktIRAJAa7yYBCuxn+cP8dLKJclIsr7HM1XYJGD/+yNKp6llRp84UxocgOgwhIMuANRlDitGSlp59Ojb98WDAwXFonJSYhKskIR9QRkEhMRrHdDKMhO52UbEq1MrvAakSxw4wShxn7vSF4gzEE4x27EocFFS4LFIA33GHg5hjZkWvXxGxvSMDzMPt4FaVR17un7w+rgcBhtUVZHf1MZwRivC8Y6NkZaPdHUFRkBeDHjAonOkLyuErRAPStzJ5Z5dJuuEfUFsIXkRCNyTAZRTjNBtS3+MfUDkC5hptjZFcuXROzuSEBz8PcwOCWsiLXevq5fly5qGdnYGeLFwAQU5Qxl6Ix3FEW3SuzF7m6b7gtflS5bXDbzQhGJNS3+MddhyCTuDkG9ZaNdAmeh7mDwS1lTS719HvK1ePKRYnOwLYmDzZ/2IyfLzwYUyvcYyZAy8QoSzors0fihivLCna2qqu+d7b6x1T7Z8JY2CWLsmM0Byt4HuYWBrdENCyiKGByqQObAUwuHTuj3JkaZUm3kH0mb7iJ4HxnixdnlAB3vvI5Jpe5xtUUaK7vkkXZNVqDFTwPcwuDWyIadzI5ytJzZbbDYkBXKIaoJMNkUFdj97cyOxM33J7BeY3bAgBwW83jbgo0l3fJovGD52FuYXBL4wZXsFJCJkdZEiuzP+invJ3LaoTRIOCYKaUZXSjWOzg3CmqVXDU4N42rKVCWfaJcwPMwt7CVaVzgCtahydcOQSZHWURRwJzaQqz4dB+CEQkumwk2iwHhmIx9nUHYzAYcVpvZPNi+wXn3FhDjbQo022WfiACeh7mGwS3lPa5gHZp87hBkcpRFlhWs39OJsgILopKMrpAEX1iCQRRQXWiD0SDg0z0enDa7OmMBLqdAu2Wz7BNRAs/D3MLglvIaV7AOTb53CDI5ypIYRZ1aVgCnxYCucEyrX1tgMcIXljI+isop0GS5uEsWjT88D3PH+Ljy0bg1Vlaw5tL0/3joEGRylCVpFFUQUGBN3nJ4JEZRewfn6HGY43UKlDWqKRfwPMwNDG4p43IpUBsL07e5Nv0/VjoEw5WpUZZsjKL2Ds5r3BbAAfhCMez1hMftFChrVFMu4HmYfQxux7BcCiITci1Qy/Xp21yc/h8LHYJMycQoS7YWkvTZIc4BeEIRToES0bjH4HaMyrUgMnFMuRao5fIK1lyd/s/1DkGmDXeUJZsLScb6DnFERCNBzPYBUPoSQeTGvR4U2syYXOpAoU0t3n7fG1uxqcEz6sfUO1BzWo0wiIIaqJU70e6P4Nl1DZBlJfWLDfH9e25BmnifROBR7DCjvtkHX7wGqS8UQ32zL6vTt+lM/4+mRIeg0ROEoiT/vRIdgunlBeMqnzOVxCjqrBo3OoMR7Gz1ozOojqKOdKcusUMcMLZ2iCMiGin5MfQyjuTqaF828zRTbUGaqytYc3X6nyVthoYLSYiIcgOD2zEmVxf7ZCtQ07sFaS4GHrk8/Z+rHYJcx4UkRETZx+B2jMnV0b5sBGrpbkGaa4FHLucDAxyJJCKisYk5t2NMzyCyP9ka7ctGnmau5qzqlcv5wD2PcUqZE4fVFmJKmZOBLRER5TwGt2NMri72yUagpo1imwcexQ5Hc7tkVTYXIhEREeUjpiWMMbm82Ge08zRzOWc1HZz+JyIiypzcvuuPU6k2Z8jlxT6jGajl0xakuZYPTERENFYxuM0xejdnyOXRvtEK1LgFKREREfWWVnAryzJWr16N//znP9i1axcCgQDKyspw+OGH46STTkJtbe1IHee4kO4OXxzt4xakNHy5uI01ERENna7gNhgM4n//93/x4IMPor29HXPmzEF1dTVsNhvq6+vx/PPP49JLL8X8+fNx00034Zhjjhnp4847ubo5w1jALUhpqHJxG2ui8S6THU52XscnXcHtgQceiLq6Ojz88MM4+eSTYTKZ+jxn165dWL58Oc4++2z84he/wKWXXprxg81nubo5w1iR2IJ0M7gFKemT7kwJEY28THY42Xkdv3SVAlu5ciWeeuopnHrqqf0GtgAwadIkLF26FFu3bsWJJ56o683vuusuHHXUUSgoKEB5eTnOOOMMbNmyJek5xx9/PARBSPrvBz/4QdJzdu/ejUWLFsFut6O8vBzXX389YrHcLf/Un3woa0WUa2RZwfYWHz7d04ntLT7IsqI93nOmxGk1wiAK6kxJuRPt/gieXdegPZ+IRl6iw7lxrweFNjMmlzpQaFN3nLzvja3Y1ODJymvR2KNr5Pbggw/W/YImkwlTp07V9dzVq1fjyiuvxFFHHYVYLIaf//znmD9/PjZv3gyHo3uF+6WXXopbb71V+9put2v/liQJixYtQmVlJd599100NjZiyZIlMJlMuPPOO3Uf92gZaIokX8paEeWKwUZt7GYDZ0qIckgmU/OY5kfDjpTa2tqwYcMGzJw5ExUVFWn97Kuvvpr09WOPPYby8nKsXbsWxx57rPa43W5HZWVlv6+xcuVKbN68Ga+//joqKiowZ84c3Hbbbbjhhhtwyy23wGw2p/9LjZDBbrYzq1w5vRUr0ViSKuXglEMrc3Iba6LxKpOpeUzzo7SC2z/96U8AgMsvvxwAsH79epxwwgnweDyw2Wx49tlnsWDBgiEfjMejThMUFxcnPf73v/8dTzzxBCorK3Haaafhl7/8pTZ6+95772HWrFlJgfWCBQtwxRVX4LPPPsPhhx/e533C4TDC4bD2tdfrBQBEo1FEo9EhH3/iZ/t7jc8bvXjo7W3oCERQ4eq+2X7R0IEHOvz4wfFTceZhlWjq8GNXi1d9TnxzhiZvEOUOM844rAKSFIPU/867495g7U8jK5faXpYVPLd2N7oCIczQRm0UmK0iXBY7trf48H59CxwmIBKJDjBTEoPDDNiNufE7pZJL7T/esO0zw+MPQYrF4DRbYIDc5/tOs4C2rhg8/hCihRbt8f7af6ivRenJxrmv970EpfceroM48sgjsXTpUixevBgA8K1vfQuFhYW4//77cffdd+Pll1/Gxx9/PKQDlmUZp59+Ojo7O/HOO+9oj//5z3/GpEmTUF1djQ0bNuCGG27AV77yFTz77LMAgMsuuwy7du3Ca6+9pv1MIBCAw+HAyy+/jIULF/Z5r1tuuQXLli3r8/jy5cuTUh6IiIiIKDcEAgGce+658Hg8cLlcAz5P18jtmjVroCgKtm/fDo/Ho3391ltv4e6778a6deswd+5c/O///i/WrFkDAElpBXpceeWV2LRpU1JgC6jBa8KsWbNQVVWFefPmYdu2bbpze3tbunQprrvuOu1rr9eL2tpazJ8/f9DGSiUajWLVqlV9KkrsbPXjzlc+h9tq7neUyBeKwROK4OcLD8bkUgdkWcHu9gB84RicFiMmFtuZF6TDQO1PIy+X2n5Tgwe/eW0LJpU4YOjncyPJCna1+fE/c2uwanNT92xKj5mSIrsZPzh+Kg6uGvr1YDTlUvuPN2z7zJBlBXe/tgWb93kwpaxvat72Fh8OqXbj+gUzku6H/bX/UF+L0pONcz8x056KruB2x44dANTR1cbGRhgMBmzduhUGgwF2ux07duxALBaDJEnYuXMnFEVJK7i96qqrsGLFCqxZswY1NTWDPvfoo48GANTX12Pq1KmorKzEhx9+mPScpqYmABgwT9discBi6TsVYTKZMvIH6v06gRjgjwBlLhMk9P0gmc0m+L0RBGLQfm56Ve7kCo81mfo7Uvpyoe3dDisMRiN8EQVOa9+cWl8kBoPRiMMmlWB6VWF3Hrw3AotJxMETisfsBiC50P7jFdt++M6cOxF7OrdiS3NAzZWPdzgbPUEUO6w4Y+5EWCz93xt7t/9wXovSM5rnvt730RXcXnDBBQCAhx9+GHv27MHFF1+MN954AyeddBKWLFkCAPjyyy9RXV2tfa2Hoij40Y9+hOeeew5vv/02DjjggJQ/s379egBAVVUVAKCurg533HEHmpubUV5eDgBYtWoVXC4XZs6cqftYRhIrIRCNnsklDt2LM0VRyNltrInGm547TtY3+9DkVRdeD2XHyUy+Fo09aUVTt912G8444ww8/PDDKC0txZtvvql97x//+Ifu+rYJV155JZYvX44XXngBBQUF2L9/PwDA7XbDZrNh27ZtWL58OU499VSUlJRgw4YNuPbaa3Hsscdi9uzZAID58+dj5syZOP/883H33Xdj//79uPHGG3HllVf2OzqbDencbIloeERRwOIjatDQEdRWTCeP2phx1hETtACW21gT5Y7EjpOZ6HBm8rVobEkruD3hhBOwe/du1NfXY8aMGXA6u28Ip59+ujaaqteDDz4IQN2ooadHH30UF154IcxmM15//XX8/ve/h9/vR21tLRYvXowbb7xRe67BYMCKFStwxRVXoK6uDg6HAxdccEFSXdxsS/dmSzReZWqrTI7aEI1dmexwsvM6PqU9D+52uzF37tw+j/dXciuVVIUaamtrsXr16pSvM2nSJLz88stpv/9o4s2WaHCZ3iqTozZEROOTruD2/fffxzHHHKPrBQOBAHbs2IFDDjlkWAeWj3izJepfqk0Xrp43fUgBbiZHbTI1qkxERCNLV3B7/vnnY8qUKfj+97+PU089NWlr3ITNmzfjiSeewKOPPopf//rXDG4HwCkSomRjYavMTI8qExHRyNEV3G7evBkPPvggbrzxRpx77rk48MADUV1dDavVio6ODnzxxRfw+Xw488wzsXLlSsyaNWukj5uI8kSub5U5UqPKREQ0MnQFtyaTCVdffTWuvvpqfPzxx3jnnXewa9cuBINBHHbYYbj22mtxwgkn9Nk2l4gola5QDOGoDJu7b01aALCZDWjyyugKxUb5yMbGqDIRESVLe0HZkUceiSOPPHIkjoVozGD+Zebkch3oXB9VJiKivrhrAFGamH+ZWblcBzqXR5WJiKh/YrYPgGgsSeRfbtzrQaHNjMmlDhTazNi4V318U4Mn24c45iTqQBc7zKhv9sEXikGSFfhCMdQ3+7JaB7rnqHJ/uLsgEVHuYXBLpFPv/Eun1QiDKKj5l+VOtPsjeHZdA2R58PrN1FeiDvSsGjc6gxHsbPWjMxjB7JrCrC7YSowqN3qCfepyJ0aVp5cXcHdBIqIcwuEGIp2YfzmycrEONHcXJCIaexjcEunE/MuRl4t1oLm7IBHR2KIruL3vvvt0v+DVV1895IMhymW5vKqfRlYujioTEVH/dN2F77nnnqSvW1paEAgEUFhYCADo7OyE3W5HeXk5g1vKW7m8qn+8yUYptlwcVSYior50Bbc7duzQ/r18+XI88MADeOSRRzBjxgwAwJYtW3DppZfi8ssvH5mjJMoBzL/MDSzFRkREg0m7WsIvf/lL/OEPf9ACWwCYMWMG7rnnHtx4440ZPTiiXJOrq/rHC5ZiIyKiVNJODmxsbEQs1nfBjCRJaGpqyshBEeUy5l9mB7fCJSIiPdIeuZ03bx4uv/xyrFu3Tnts7dq1uOKKK3DSSSdl9OCIclUi//Kw2kJMKXMymBoF6ZRiIyKi8Svt4Pavf/0rKisrceSRR8JiscBiseArX/kKKioq8Je//GUkjpGIqLsUm3ngUmzhKEuxERGNd2mnJZSVleHll1/Gl19+iS+++AIAcNBBB+HAAw/M+MERESWwFBsREekx5LvAgQceyICWiEYNS7EREZEeaQe3kiThsccewxtvvIHm5mbIspz0/TfffDNjB0dElMBSbEREpEfawe2Pf/xjPPbYY1i0aBEOPfTQPgs7iIhGSrpb4WZjswciIsqutIPbJ598Ek899RROPfXUkTgeIqJB6S3Fxs0eiIjGp7SDW7PZjGnTpo3EsRAR6ZJqK9zEZg/t/oiavuBW0xc27vWgoSPIDTeIiPJY2qXAfvKTn+Dee++FoigjcTxERMPSe7MHp9UIgyiomz2UO9Huj+DZdQ2QZV7DiIjyUdojt++88w7eeustvPLKKzjkkENgMpmSvv/ss89m7OCIiNKVzmYPg43+EhHR2JR2cFtYWIgzzzxzJI6FiGjYtM0e3ANv9tDk5WYPRET5Ku3g9tFHHx2J4yAiyghu9kBENL6lnXMLALFYDK+//jr+9Kc/oaurCwCwb98++Hy+jB4cEVG6Eps9NHqCfdYGJDZ7mF5ewM0eiIjyVNpDF7t27cIpp5yC3bt3IxwO4+STT0ZBQQF+/etfIxwO46GHHhqJ4yQi0oWbPRARjW9pj9z++Mc/xpFHHomOjg7YbDbt8TPPPBNvvPFGRg+OiGgoEps9zKpxozMYwc5WPzqDEcyuKWQZMCKiPJf2yO1//vMfvPvuuzCbzUmPT548GQ0NDRk7MCKi4dC72QMREeWXtINbWZYhSVKfx/fu3YuCgoKMHBQRUSak2uyBiIjyT9ppCfPnz8fvf/977WtBEODz+XDzzTdzS14iIiIiyqq0R27/93//FwsWLMDMmTMRCoVw7rnnYuvWrSgtLcU//vGPkThGIiIiIiJd0g5ua2pq8Omnn+LJJ5/Ehg0b4PP5cMkll+C8885LWmBGRERERDTahlTF3Gg04nvf+16mj4WIiIiIaFiGFNxu2bIFf/jDH/D5558DAA4++GBcddVVOOiggzJ6cERERERE6Uh7QdkzzzyDQw89FGvXrsVhhx2Gww47DOvWrcOsWbPwzDPPjMQxEhERERHpkvbI7U9/+lMsXboUt956a9LjN998M376059i8eLFGTs4IiIiIqJ0pD1y29jYiCVLlvR5/Hvf+x4aGxszclBEREREREORdnB7/PHH4z//+U+fx9955x184xvfyMhBERERERENRdppCaeffjpuuOEGrF27FscccwwA4P3338fTTz+NZcuW4d///nfSc4mIiIiIRkvawe0Pf/hDAMADDzyABx54oN/vAerOZf1t00tERERENFLSDm5lWR6J4yAiIiIiGra0c26JiIiIiHKV7uD2vffew4oVK5Ie+9vf/oYDDjgA5eXluOyyyxAOhzN+gEREREREeukObm+99VZ89tln2tcbN27EJZdcgpNOOgk/+9nP8OKLL+Kuu+5K683vuusuHHXUUSgoKEB5eTnOOOMMbNmyJek5oVAIV155JUpKSuB0OrF48WI0NTUlPWf37t1YtGgR7HY7ysvLcf311yMWi6V1LEREREQ09ukObtevX4958+ZpXz/55JM4+uij8fDDD+O6667Dfffdh6eeeiqtN1+9ejWuvPJKvP/++1i1ahWi0Sjmz58Pv9+vPefaa6/Fiy++iKeffhqrV6/Gvn37cNZZZ2nflyQJixYtQiQSwbvvvovHH38cjz32GG666aa0joWIiIiIxj7dC8o6OjpQUVGhfb169WosXLhQ+/qoo47Cnj170nrzV199Nenrxx57DOXl5Vi7di2OPfZYeDwePPLII1i+fDlOPPFEAMCjjz6Kgw8+GO+//z6OOeYYrFy5Eps3b8brr7+OiooKzJkzB7fddhtuuOEG3HLLLTCbzWkdExERERGNXbqD24qKCuzYsQO1tbWIRCJYt24dli1bpn2/q6sLJpNpWAfj8XgAAMXFxQCAtWvXIhqN4qSTTtKec9BBB2HixIl47733cMwxx+C9997DrFmzkgLvBQsW4IorrsBnn32Gww8/vM/7hMPhpPxgr9cLAIhGo4hGo0M+/sTPDuc1aOjY/tnDts8utn/2sO2zi+2fPdloe73vpTu4PfXUU/Gzn/0Mv/71r/H888/Dbrcn7Ui2YcMGTJ06Nf0jjZNlGddccw2+9rWv4dBDDwUA7N+/H2azGYWFhUnPraiowP79+7Xn9AxsE99PfK8/d911V1JgnrBy5UrY7fYh/w4Jq1atGvZr0NCx/bOHbZ9dbP/sYdtnF9s/e0az7QOBgK7n6Q5ub7vtNpx11lk47rjj4HQ68fjjjydN+f/1r3/F/Pnz0z/SuCuvvBKbNm3CO++8M+TX0Gvp0qW47rrrtK+9Xi9qa2sxf/58uFyuIb9uNBrFqlWrcPLJJw97FJvSx/bPHrZ9drH9s4dtn11s/+zJRtsnZtpT0R3clpaWYs2aNfB4PHA6nTAYDEnff/rpp+F0OtM7yrirrroKK1aswJo1a1BTU6M9XllZiUgkgs7OzqTR26amJlRWVmrP+fDDD5NeL1FNIfGc3iwWCywWS5/HTSZTRv5AmXodGhq2f/aw7bOL7Z89bPvsYvtnz2i2vd73SXsTB7fb3SewBdQ82XQXbymKgquuugrPPfcc3nzzTRxwwAFJ3587dy5MJhPeeOMN7bEtW7Zg9+7dqKurAwDU1dVh48aNaG5u1p6zatUquFwuzJw5M63jISIiIqKxLe3tdzPpyiuvxPLly/HCCy+goKBAy5F1u92w2Wxwu9245JJLcN1116G4uBgulws/+tGPUFdXh2OOOQYAMH/+fMycORPnn38+7r77buzfvx833ngjrrzyyn5HZ4mIiIgof2U1uH3wwQcBAMcff3zS448++iguvPBCAMA999wDURSxePFihMNhLFiwAA888ID2XIPBgBUrVuCKK65AXV0dHA4HLrjgAtx6662j9WsQERERUY7IanCrKErK51itVtx///24//77B3zOpEmT8PLLL2fy0IiIiIhoDEo757bn7mFERERERLkk7eC2oqICF1988aiU7CIiIiIiSkfawe0TTzyB9vZ2nHjiiTjwwAPxq1/9Cvv27RuJYyMiIiIiSkvawe0ZZ5yB559/Hg0NDfjBD36A5cuXY9KkSfjmN7+JZ599FrFYbCSOk4iIiIgopbSD24SysjJcd9112LBhA373u9/h9ddfx7e//W1UV1fjpptu0r1FGhERERFRpgy5WkJTUxMef/xxPPbYY9i1axe+/e1v45JLLsHevXvx61//Gu+//z5WrlyZyWMlIiIiIhpU2sHts88+i0cffRSvvfYaZs6ciR/+8If43ve+l7Q97le/+lUcfPDBmTxOIiIiIqKU0g5uL7roIpx99tn473//i6OOOqrf51RXV+MXv/jFsA+OiIiIiCgdaQe3jY2NsNvtgz7HZrPh5ptvHvJBERERERENha7g1uv1Dvp1Ty6Xa3hHREREREQ0RLqC28LCQgiCMOhzFEWBIAiQJCkjB0ZERERElC5dwe1bb7010sdBRERERDRsuoLb4447bqSPg4iIiIho2IZU57azsxMffvghmpubIcty0veWLFmSkQMjIiIiIkpX2sHtiy++iPPOOw8+nw8ulyspF1cQBAa3RERERJQ1aW+/+5Of/AQXX3wxfD4fOjs70dHRof3X3t4+EsdIRERERKRL2sFtQ0MDrr766pS1bomIiIiIRlvawe2CBQvw8ccfj8SxEBERERENi66c23//+9/avxctWoTrr78emzdvxqxZs2AymZKee/rpp2f2CImIiIiIdNIV3J5xxhl9Hrv11lv7PMZNHIiIiIgom3QFt73LfRERERER5aK0c27/9re/IRwO93k8Eongb3/7W0YOioiIiIhoKNIObi+66CJ4PJ4+j3d1deGiiy7KyEEREREREQ1F2sGtoihJGzck7N27F263OyMHRUREREQ0FLp3KDv88MMhCAIEQcC8efNgNHb/qCRJ2LFjB0455ZQROUgiIiIiIj10B7eJignr16/HggUL4HQ6te+ZzWZMnjwZixcvzvgBEhERERHppTu4vfnmmwEAkydPxne/+11YrdYROygiIiIioqHQHdwmXHDBBQDU6gjNzc19yoRNnDgxM0dGRERERJSmtIPbrVu34uKLL8a7776b9HhioRk3cSAiIiKibEk7uL3wwgthNBqxYsUKVFVV9Vs5gYiIiIgoG9IObtevX4+1a9fioIMOGonjISIiIiIasrTr3M6cOROtra0jcSxERERERMOSdnD761//Gj/96U/x9ttvo62tDV6vN+k/IiIiIqJsSTst4aSTTgIAzJs3L+lxLigjIiIiomxLO7h96623RuI4iIiIiIiGLe3g9rjjjhuJ4yAiIiIiGra0g1sA6OzsxCOPPILPP/8cAHDIIYfg4osvhtvtzujBERERERGlI+0FZR9//DGmTp2Ke+65B+3t7Whvb8fvfvc7TJ06FevWrRuJYyQiIiIi0iXtkdtrr70Wp59+Oh5++GEYjeqPx2IxfP/738c111yDNWvWZPwgiYiIiIj0SDu4/fjjj5MCWwAwGo346U9/iiOPPDKjB0dERERElI600xJcLhd2797d5/E9e/agoKAgIwdFRERERDQUaQe33/3ud3HJJZfgn//8J/bs2YM9e/bgySefxPe//32cc845I3GMRERERES6pJ2W8Nvf/haCIGDJkiWIxWIAAJPJhCuuuAK/+tWvMn6ARERERER6pR3cms1m3Hvvvbjrrruwbds2AMDUqVNht9szfnBEREREROkYUp1bALDb7Zg1a1Ymj4WIiIiIaFh0B7cXX3yxruf99a9/1f3ma9aswW9+8xusXbsWjY2NeO6553DGGWdo37/wwgvx+OOPJ/3MggUL8Oqrr2pft7e340c/+hFefPFFiKKIxYsX495774XT6dR9HERERESUH3QHt4899hgmTZqEww8/HIqiZOTN/X4/DjvsMFx88cU466yz+n3OKaecgkcffVT72mKxJH3/vPPOQ2NjI1atWoVoNIqLLroIl112GZYvX56RYyQiIiKisUN3cHvFFVfgH//4B3bs2IGLLroI3/ve91BcXDysN1+4cCEWLlw46HMsFgsqKyv7/d7nn3+OV199FR999JFWY/cPf/gDTj31VPz2t79FdXX1sI6PiIiIiMYW3cHt/fffj9/97nd49tln8de//hVLly7FokWLcMkll2D+/PkQBGFEDvDtt99GeXk5ioqKcOKJJ+L2229HSUkJAOC9995DYWFh0uYRJ510EkRRxAcffIAzzzyz39cMh8MIh8Pa116vFwAQjUYRjUaHfKyJnx3Oa9DQsf2zh22fXWz/7GHbZxfbP3uy0fZ630tQhphjsGvXLjz22GP429/+hlgshs8++2xYea6CIPTJuX3yySdht9txwAEHYNu2bfj5z38Op9OJ9957DwaDAXfeeScef/xxbNmyJem1ysvLsWzZMlxxxRX9vtctt9yCZcuW9Xl8+fLlrPpARERElIMCgQDOPfdceDweuFyuAZ835GoJoihCEAQoigJJkob6MoM6++yztX/PmjULs2fPxtSpU/H2229j3rx5Q37dpUuX4rrrrtO+9nq9qK2txfz58wdtrFSi0ShWrVqFk08+GSaTacivQ0PD9s8etn12sf2zh22fXWz/7MlG2ydm2lNJK7gNh8NaWsI777yDb37zm/jjH/+IU045BaKY9mZnaZsyZQpKS0tRX1+PefPmobKyEs3NzUnPicViaG9vHzBPF1DzeHsvTAPUzSgy8QfK1OvQ0LD9s4dtn11s/+xh22cX2z97RrPt9b6P7uD2hz/8IZ588knU1tbi4osvxj/+8Q+UlpYO+QCHYu/evWhra0NVVRUAoK6uDp2dnVi7di3mzp0LAHjzzTchyzKOPvroUT02IiIiIso+3cHtQw89hIkTJ2LKlClYvXo1Vq9e3e/znn32Wd1v7vP5UF9fr329Y8cOrF+/HsXFxSguLsayZcuwePFiVFZWYtu2bfjpT3+KadOmYcGCBQCAgw8+GKeccgouvfRSPPTQQ4hGo7jqqqtw9tlns1ICERER0TikO7hdsmRJxisifPzxxzjhhBO0rxN5sBdccAEefPBBbNiwAY8//jg6OztRXV2N+fPn47bbbktKKfj73/+Oq666CvPmzdM2cbjvvvsyepxERERENDaktYlDph1//PGDbgjx2muvpXyN4uJibthARERERACAkV8FRkREREQ0ShjcEhEREVHeYHBLRERERHmDwS0RERER5Q0Gt0RERESUNxjcEhEREVHeYHBLRERERHmDwS0RERER5Q0Gt0RERESUNxjcEhEREVHeYHBLRERERHmDwS0RERER5Q0Gt0RERESUNxjcEhEREVHeYHBLRERERHmDwS0RERER5Q0Gt0RERESUNxjcEhEREVHeYHBLRERERHmDwS0RERER5Q0Gt0RERESUNxjcEhEREVHeYHBLRERERHmDwS0RERER5Q0Gt0RERESUNxjcEhEREVHeYHBLRERERHmDwS0RERER5Q0Gt0RERESUN4zZPgAiAmRZwc42P7pCMRRYjZhc4oAoCtk+LCIiojGHwS1Rlm1q8OCZdXtR3+xDOCrDYhIxrdyJxUfU4NAJ7mwfHhER0ZjC4JYoizY1eHDfG1vR7o+gym2DzW1AMCJh414PGjqCuHredAa4REREaWDOLVGWyLKCZ9btRbs/gmnlTjitRhhEAU6rEdPKnWj3R/DsugbIspLtQyUiIhozGNwSZcnONj/qm32octsgCMn5tYIgoMptw9bmLuxs82fpCImIiMYeBrdEWdIViiEclWEzG/r9vs1sQDgqoysUG+UjIyIiGrsY3BJlSYHVCItJRDAi9fv9YESCxSSiwMrUeCIiIr0Y3BJlyeQSB6aVO9HoCUJRkvNqFUVBoyeI6eUFmFziyNIREhERjT0MbomyRBQFLD6iBsUOM+qbffCFYpBkBb5QDPXNPhQ7zDjriAmsd0tERJQGzndSTsv3zQ0OneDG1fOma3Vum7xqndvZNYU464gJLANGRESUJga3lLPGy+YGh05wY2aVK6+DeCIiotHC4JZy0njb3EAUBUwpc2b7MIiIiMY85txSzuHmBkREwyPLCra3+PDpnk5sb/HxeknjCkduKeeks7kBRzuJiJKNl5QuooEwuKWco21u4B54c4MmLzc3ICLqbbyldJEq3xdfp4vBLeWcnpsbOPvZwICbGxAR9dU7pSsx8+W0GjHN4kR9sw/PrmvAzCrXuA588g1H6vtizi3lHG5uQESUvnRSuig/JEbqN+71oNBmxuRSBwptZmzcqz6+qcGT7UPMiqwGt2vWrMFpp52G6upqCIKA559/Pun7iqLgpptuQlVVFWw2G0466SRs3bo16Tnt7e0477zz4HK5UFhYiEsuuQQ+n28UfwvKNG5uQETZNhYXZGkpXeaBU7rCUaZ0jSWDnYdcfD2wrM7r+v1+HHbYYbj44otx1lln9fn+3Xffjfvuuw+PP/44DjjgAPzyl7/EggULsHnzZlitVgDAeeedh8bGRqxatQrRaBQXXXQRLrvsMixfvny0fx1K02A5QtzcgMY75tBlz1id5mVKV35JdR6O1OLrfLj2ZPUMX7hwIRYuXNjv9xRFwe9//3vceOON+Na3vgUA+Nvf/oaKigo8//zzOPvss/H555/j1VdfxUcffYQjjzwSAPCHP/wBp556Kn7729+iurq639cOh8MIh8Pa116vFwAQjUYRjUaH/PskfnY4rzFefN7oxQvr92F7a/eHdkqpE9+aU42Dq1wAgBnldvxs/nTsbg/AF47BaTFiYrEdoij028Zs/+xh22eWns9HT2z/zPm80YuH3t6GjkAEFa7uBVlfNHTggQ4/fnD81KS/QS61/QSXGQeW2bF5nwcuizMp4FEUBa1ePw6pdmOCy5wTx5sJudT+maTnPJRkBVIsBqfZAgPkPq/hNAto64rB4w8hWmjR/b56rz3ZaHu97yUovZMas0QQBDz33HM444wzAADbt2/H1KlT8cknn2DOnDna84477jjMmTMH9957L/7617/iJz/5CTo6OrTvx2IxWK1WPP300zjzzDP7fa9bbrkFy5Yt6/P48uXLYbfbM/p7EREREdHwBQIBnHvuufB4PHC5+nb0E3J2bmL//v0AgIqKiqTHKyoqtO/t378f5eXlSd83Go0oLi7WntOfpUuX4rrrrtO+9nq9qK2txfz58wdtrFSi0ShWrVqFk08+GSaTacivk89kWcHdr23B5n0eTCnrO7KwvcWHQ6rduH7BjLSnQdj+2cO2z4yen48DSh3whSXEJBlGgwinxYAdrf5+Px9s//RHu/uzs9WPO1/5HG6rud9pfV8oBk8ogp8vPBiTS9UFraPZ9np/x/6eN7XUidPTaIuxIh/Pfb3n4c9OOQhPfbw3I/fTodybs9H2iZn2VHI2uB1JFosFFkvfIXqTyZSRP1CmXieVsZgXs73Fhy9bAih1OSALvRY9CECpy4EtLQE0eCND3qBhoPYfi+011ozWuZ8No3H+JD4fJpMZH+/xwhtUF1MaRAEumxGVLtugn498bv/BbGrw4I9v79Bqu5a51CncTxu6sKdzh+7aroEY4I8AZS4TJAXoCscQjckwGUUUWIwwm03weyMIxNCnnUe67dP5HWdPLMGhNcUZO1/HwrUzn879pPMQfds5cR6GJAFnzp2IPZ1bsaU5oNY1NqvnRaMniGKHFWfMnQiLxZzyPYdzbx7Nttf7Pjkb3FZWVgIAmpqaUFVVpT3e1NSkpSlUVlaiubk56edisRja29u1n881mbpIjNUFD9naoGGsthflhk0NHjyzdi82NngQiEqwmwyYNcGNxXMze/50hWJo90XQEYwgEpNhNogwGAQoANr9EfjCMRTZzFzt3kMma7smFmTt9wSx3xvqp3NhTVqQJcsKdraqZbV2tvoxtcI9IkHfUH5HURQysoMjr52jL52FgVPKnBlZfJ1vmyflbHB7wAEHoLKyEm+88YYWzHq9XnzwwQe44oorAAB1dXXo7OzE2rVrMXfuXADAm2++CVmWcfTRR2fr0AeUqYtENnegGW5wno3VvNyxh4ZjU4MHt7+0Gdtb/JAVBVAACMDu9gA+3+/FjYtmZuz8cVgM6AhG4AvFACjwhyUoigJBEGAyAJGYrD2PVJlcMT65xIEiuxlrvmyBQQQcFhOMooCYrKDNF0azN4zjDizD5BKHdj3f2eLFGSXAna98jsllrhEJ+rK1JTmvndmRqPW+ca8H0/pZGNjoCWJ2TaFW6/3QCW7MrHKlvDcPdv/Ot0obWT1Kn8+H+vp67esdO3Zg/fr1KC4uxsSJE3HNNdfg9ttvx/Tp07VSYNXV1dqis4MPPhinnHIKLr30Ujz00EOIRqO46qqrcPbZZw9YKSFbMnWRSOrBlzngi0jwBCIwGUVMK3OgvsU/YjvQZCI47/mhnWpWcwqjkgxTPKew94d2uLhjDw2HLCv485rt+KKxq0+w4w9H8UVjFx5esx33fHdOxs6faExGMCLBIAowGkQIggBFUQNbSZZhMXLvnZ4yP+KkrrFOXCsSK67Vr9WvNu3z4I9v1qPdH0GNW01xc1vNIxb0ZWNULd1r51hIXRgrErXeGzqCqG/2ocBqhEEQICkKukKxfmu9pxqpT3X/TjegznVZDW4//vhjnHDCCdrXiUVeF1xwAR577DH89Kc/hd/vx2WXXYbOzk58/etfx6uvvqrVuAWAv//977jqqqswb948iKKIxYsX47777hv132UwmQywEj14u9mAdXs6+502y+UefOJD+/k+L97a0qKOhCW+JwiYUurI6AYN2RrxoPywvdWHj3e1QxCAQrsZiOe/mQwCCu1mtPsj+GhXO7a3+jCtvED36w4UCHSFYpDiH4neZWwSX8sKsj41mEuBTNKIk8XQJ082nRGnnW1+dASimF1TiP2eINoDEcRkBUZRQLHDjEqXDe3+CB57d6d2PTcK6l9GvZ6bRqTDnI1RtXSunYGIxNSFDDt0ghuLZlfhT6u3ob7Fh5ikwGgQUO224vy6SSMy29szoO6bvzu2Nk/KanB7/PHH99letSdBEHDrrbfi1ltvHfA5xcXFOb9hQyYDrK5QDO3+CDr9EYQlGXazURtJavdH4A/FUOhIPycvFpPx322taOkKo6zAgq9NLYUxPkI0Ij34nl/Gp3mF3o9nQL7lEemRycAjl4KYbPiyqQu+UAxumwl9T04BTosRnmAUXzZ16Q5uBxtB8QajkBUFbrsJUUlGJKZoaQlWkwEmg/pZ9wazV9Mz13IwEyNOH2xvQ1SS0RWStA5/gdUAk0HEMVNKtRGnwc7pxPXCbTdBQTwFJf6foiiwmEQ0eULoCkdRW+RIGs0FRq7DnDSqZlZn7BIBvNM89BkvPW2R6tq5fk8nXt20P2dTF8bqNWxTgwcvbWiEzWTA4bVFMIiAJAPeYAQvbWjE1DJn+rO9Ke7fic2T/rV2DzY2eBGMSLCZDZg9AusLRtrYSJ4Y4zIZYDksBnQEIghGJRQ7kkeS3DYT2v0RKIFIWjl5L6xvwJ9Wb8M+Tyipd3j5cVPxrTkTMtqDT3zQYpKCE2eU9blIZzqtIt/yiFLRG3joueDnWhCTHXrPQX3PSzWCsuCQClgMIiRFQYnDjJis/q1EUYBRBLyhGCwGES5bdlaF52IOpigKmFNbiBWf7kMwIsFlM8FqMSASk9HYGYLNbMBhtepCr1TndIHViKgkY8NeDyRZht3SPXjQEYjCu9cDt80Ii9Ew6Ba3me4wazNejV68mZjxig8KiIKAKWXpz3jpaYuU106jiHfqW9OaldRz7dEbkKZa0Jeta9hwA+qeAen0ioKk+26FyzKk2d50BtcECNpgU+53A/qXH3f0HDcSAZYAQFGAnueqoqR/Ir6wvgG3r9is3RQsNhHhmIzdbQHcvmIzAHXUQE9w/umeTrySogdvNxu6P2iiiAJrcv5gpkc98i2PaDB6Aw89F/zEa7X5wnDZzLDYREgysGFPZ58gZrRWjA/FcG+kB1ao+7X7wzGY4vmvCYqiwB+OwWk14sCK1OeqnhGUd7e1oarQikZPGN5QDHazESajCElW4A3FYBRFVLmt8ZHkHsc/yiv2p5ap+fKdgQhMBhFTyxzYNoL5/qmOa/2eTpQVWBCT1Hbyh9Wc5eoiG4yigE/3eDCl1IE/xPNkB/p8HFRRgHBMgj8URVmBBVI811kUBRRYjGjpCqPAaoDLashOh7nnRGfv2a806LlWzKxypbx2Tiqxo8kb0h046b329B45nDXBhW/PrU0KSFMt6MtWRywTAXWmZ3v1Dq71abN4WsLGBg8aOrM/Cp8OBrejIN0Aa7CbrT8sochmRgci8IaisJuNMIgCJFlBIBKDzWxAoc0Mf1hKeVyxmIw/rd6GYERCucsCQVADTZtZhNUkotkbxp9Xb8c9Zx+WMjg3GwW8szV1D/60w6pGNU2gZ2L+1qYuuGzmpOmdEqclJ/KIMtnTH6z9FUXBH96sHzRonVnlwjPr9mJvRwAxSUFDZ6g7r9tqRDAqaUHM5kYvnlm7F1/s68DZVcBNL2zCQdVF/U5hjfb0oN4b6WDPmVLqxJGTirDmy9Z+P2+yAhw1qRhTSlMHt3puWM3eMKaUORGJyVqglmj7YocZRlHA4ROLtGtFNlbs281GfNJvvn929rFPHNfUsgI4LAZ0hWLaItUCqxH+sIQvm7x47L+RlJ+Pc4+uhcVogMVkQKM31CeYdJiNcFpMqHDbsLstgGkWZ1KQOVIdZm3GS1ZwwoyyPgtx0+lY6O6kLHKlvHZ+fVop/vHhHl2j2HqCTQC4fcVmbG/1J63H2NMewBeNXbjxmzP7BK79Lei76sRpeO6ThlFfSJypgDqTs716B9ccFgP+/sHuvFl8zeB2FPRe+ThYoraeqaJipxklTgv2e4NJN5gShxkVLhsUKLpGDf67rRX7PCG4bCYtsE0QBHXqs8ETxL7OoI4evAPNOnrw3mDpqKcJ9EzM39ba0SP1woZFs6uyPj2lt6c/WF60nsApcYPf2xFANCZjV1tAWyxTZDdpQav1aBHrd3eizR9Vp2Z75nUHIjCIIj7Z3YFVm5vw6Ls7sL3FD6MgA1XArvYAtrWF+pTIymS6hJ7n6b2R6rkRXXbsVDR7w9je6kcg0n0zEQUBB1U6cemxU3RNueq9YX19Wil8oRjafGFMKLIP2BlLdYMfiRX7PWvw9s73H0oN3kyMculp151tMfjCftQW2Qe9Pn3Z1IWIJCNxKsmANkUmAhAFICqpf6NXQ/tR3+xT296h7hq11xPud+FNpgL4KrcNoijCZUs94zXQe6bTSUl17Zw1wY1nP2nISOD0zNq9aPOF8cX+LhgNQtL5FYjE8MV+tTLJ//7PYUnBeTCs5p/LiqIF54+/u0vX/WgoM4QDtetI1FvOxH1S7+AaAO0cAwBvMJrUSRxri68Z3I6SRKL2YIWW050qOry2sN8evN5Rg5auMGKSAout/9JCZqMIbyiG1q5IyuD8a9NK8KSOHrzLZhrVUmBAd2K+3WzE4bWFMAoCYvGSKv0l5o/mRht6e/qp8qL13uCbuzxo9UXUIK3HqJQvok6Bf7K7A0dMcmNfZxCSosBlNWkXQ5NBgMtqgjcUxb7OIP7v/Z1aiSyXTb2U2M1GdARjSSWyNjd6M5Yuoadd9dxknlm7FwoU3YssbvzmzJSLLDKSw2gSMae2EAdWFGiv5Q+rr3VYbZF2rej9O47Giv1EDd5QREKh3dznvOgMRNAB/fn+6aS+DKZ744VQnw5/IlgziOrnOtX1SVaAjkAEofgGGpGYDDke2JoNIkIxGR2BCGbXuLW/0c4WL+AAPKFIv4XzMx7AK0qfihC9R/IGe09JVnR3UlJdO6eUph84DRRsfrq3E3s7ghAFoMBiREwGwjEZoqCmhHiCUXy0qx3vbGtJCs5D4SjmHQys39MJq8WESpcN21q6IMtAVaFt0L93ujOEg7VrUsqdzoB6oHtNJtPp9A6u+cMSwlEZIZOELU19d0ecFE9PTLRZLqejAQxuR9VghZZ19/p6TBVta/Gjym1Dod2MYETCthZ/WuU6ygosMBoEhGMyrCYBUUmBrCgQBQEmg4BITIbRIKCswJIyOLebDXhORw/ebTONaimwgdoVACpcSp8gYDQ32khM/6f6m9c3d+GOlz4fNC961gR3ysBJFBQ0eUIIRtWUFWOPNo7JCnyhKHa1+dHkDauVOEyGfi/SZoMIXziGzY1erUSWKf40kygmlciqb+lKK11CTwCcql313GQ2NnigAKjWeSNKVSS953EVWI2wWo2IKUraOYyJG5YoCoO+X9+R+pFfsa8dK5Jz/RH/Op20z8TnUk/qS6prgbrxgglrvmztM+LX7o+g2RvG3ElFiMTrBw86wmg2IBqTEYpIEAXAYBCR6PqHoxJkBbDGZ0sOneDGQRUFeOfL/fBsbcYFx0zC1w+s1GZTAH3nRXoBfOqd01K955lHTNDVSbGZRfzjwz1J6QtRSYbdYEB5gQXbWvx47pN9OPPwCboDp8E6355gVM1htxjRHogiEpO1SiFmowirUYQvFMPmxq6k4LzQqr6m2ShqwbndZITTaszoDGGqa88ph1amlUqQ6l6TybJcegbXtrf4khdT9voceYIxVLut2jk2WulQQ8XgdpQNVGg56WYFoCsUTeqZ97xZZapcx9emlqLabcWOFj9MRhFRSemxG5KAaEzGlDInvja1FEDq4FzvjXtzozfjpcAG6kX2bFdg8KmWQETK/EYbgwR01qPFlEHYlv0erPmyOWVe9HNXfDVl+5cXWPF5YxdkRYEAICz1WHEd/zP4IjG15JFRRDgmxUe6kv9Y4Zh64w9FJJQ41enwqCx3/78gaiWy/lvfqjtdIlV7HRQfKctEXncgKgGK+m8lPhLV87zob2RnoM9uz0AtKsnY2xFMKkUViMT6dEz13LAGK8qejRJ3/rCEIrsZghKBJxjtM21sNxlQaNeX77+zza8r9UV/cK62We/SkomvHRYDaott2NTgHfT65LaaICkKFABRWUFUlrTPCKB2wKV4neHeN/jH39+F1ds6+swgpDwvdFQS0Ltz2sQiO+545fNB33PV5iYo8d8RUNMsuityCNrj+zqDutIXnBajrsApVefbZBAhK4AnFAUUtfMtiOoGJuGohHBMgkEQYDUKScG5Kd6XMIkiXFYRnYEIoACzJ7iwuyOYkYXEeq7p/61vg8WoL5VA74xdJrbVTUjVQZ9YZNcWU/a816idHiOavWFEHGZ4Q1E88Na2UUuHGioGtzkicbMKGweYEihOnhIAhl+uw2gUsWh2Fe59fSvC4RjMRlFdLKMo8IUlGEUBp85OHokY6Iard+oDQMZLgQ3Wi5RkRddUiycYxb8/3ZfRjTZSTU992eRLGaC07I9gvzecMi/6vR1tKRd/HDGxEG9taYYkqyNt2pEpgAQ1tTBxjNVuGxo7g/0GMSZRhMtmUC92MRneUBRCPLht7QpDEUVYjervFIzKKadTk/IhMXDH7r/bUgfKevO67SYDFGDQqex0Cv+v392Jdl8EMUWB2SDCYFRvyh2BKIyCoAVqmbphZaPEXYHViGKHGSUOMxo9QXT4o8kbHLitUOLPS8UTjOpKffHoqOerbrygpgokRjWD8b9lqdOKCpcFHYEoFs2qxr7O0KDXpzZfGNGYAkFQICQ20+jxWREEBdGYjI17O/FOfdugN3i72ZB0XvT8HPU+L1JVEphZ5YKendN2tvtTvuemBg8cFiNiUhT7PME+i+YKLEYU2c1o7YroTl84rLZw0MBJzzT7wZUF2N0eQCSmwGYSoShCvCMuwGhQryUGo4CJ8YB00BkEAZh/SCWe/aRB98jnYOloeq7pTd4gKgqs2NUeGLQWcaIDojclSs+2unoN1mHe3RGAxWiA02LUKrX0vO47LWrllr+9t2tU06GGisHtKBvoA9RdX7Gzz0Wp3R+BNxBFVaGt/17fEMt1yLKCVl8ENUV2tPnDCEZlxCS17I3LakSxw4I2X1Tr1aeid+ojk6XAUi2qWXzEBF1TLd5gNK18Kb0LhwYbFQSUlAEKoKaKqFuuKmrqSPzvYTIIWl50S1cYx80ox6LZVXho9TZsbW7XAo+aInXxh6FHU8tKv4PnABSUFZgxZ2IhwjFJK4qfCBaK7WYYDQIOqnJhzZZmtPsjEAXAaVZf3CAK8EUkBMISCu0mzKwqwHvb2gadTk3kQ4ajEr7Y70kKmoocJkwucSIcldHSFdY1Wtkzr3ugG+msCW60+kL4z9a2AaeyjzuwTNfIjieoBgmhmDrK5w9L3dOpBgFRAdjnCWmBWiZuWL2DhVQr9jOxUK/nZglap1pA/L0VtPrCSZslDMYbjKZMfQlEJV2bVSQ+b5NLHZhQaOvTgZIUdTan0m1NeX1au6sdMVmGogiwmQQoPRpWgIJQTEFMkvHBjvaUN/hvzqrEPk8QMVmG22ZCVOrOIXVZjfHzRj0vUo3kLT6ipnvntAEWErcHIvhif1eP9zQjJitaKTOX1QRPUJ0hKbabIfeTRyJAvTaYDSJKC8xp5VgPFjjpGfyYf0gl3qlvRTgqxa99fdnNBoSictIMQiItISrL8IQkbQahqtCmuyOZKkVA30yJgq9PL0XDx3sGrUW8uyOQ1r0m1ba6mdIVUssdzq4pxM52P9r9ybvzTSp2oMkbwvYWH2qL1M+4NxgFnOr/263mnFp0xuB2FA32AUrUV/SFYwNPCcRk1LhtuOu1LzI6wnhItRt2k4gd8al5u9mAA0ocCERl3QnwCYk8tIFW9WdyOlXPopr3trUhpGOqxWk16gpIe05Hplo4lGpU8MCKgpRB2ORSJ5q7IuoNu9eOVWajAJNB1PKiNzV48I8Pd6PNF4FBFCDEL6qtvgj+8eFunHJoJQRBQGJCsr/tXQVBvXElbkTt/jCK7RbtPSVFRrHDgguOmYRPdnegIxCFIAiQ4yO3sixDgbpyucBqwlcPKMWz6/YNOp06d1IR2vwRrN3VDn88XSDBF1FX6E8scaCswKJrtDKR151qFuHh/+zQ2jomyZBkdfR6sF0T++MNRhEIS4hIMgQAxng9XEVRgxkFgCzHkgK14d6wegcLRVYRKAaaPEF0hOS0KrAkpHpef5slWCxq/ve+XpslpOKymVKmvliMBl2bVfTefre3nqPYU8qcg3YsfKEYDAYRiiIjJgNGMT4aqCD+tTpVvqstgANKndpMAxzq/9st3Tf4rc1OhGMyTKKANn/fHFKzQUA4JsETjGD1l4OXUVyxoVEL4KsLrX2uT3I8gG/pCiEck2EUBbT6Qgj3uF5YjALMRgNikoxQVEI4KqHaZUVM6bFRiAA0d6n3mkq3Nf7XUP83Euu5JiPt0ropBz8kWYHLZkJnMApJ6tv7NhgEuGwmiIKgzSDs94YQildLiMRklDgsqHBZtBmEVH9vQN/6CL0zJXazoc9IeM/fAUh/ceBoSfyOoVj/HYtwTIIoqmUQQzF1JrT3Yr7ei86yicHtKEndM58Ai9EAh9WkTQn0rKfptJpgNop4b0dbxos79zdl3xmM9jlRh1qA+60tzVoB7kxOp/aXp9z7JrNxnxdCfJvUgaZazPGFCnoC0v2eIJ5Z15ByoVhigYtBBMxGddtUBUjKj5tS6kwZhP3whKnY2tyF3W1B7bUSgVMwIsEnS5hUYkfdASX4f89s0KoXFFi7g0h/OIovGrtgEgWIvRYf9WYQBLgspqQSQPs8/l77mlehwGZCoc2Mdos6PRmKqa8ZismQZLW93TYTdncGtPcbaDrVbhKxPRhBZzAKo0GEKR6YK4qa99gZjMIdjKLugBK8/WWL7gVZqRZAdgQimFLmwPZWP1r9Ee0GbzcbMKXMgfZARFfHzmk1IibLkGQFVpOolZESBMBoEBCKypAEOel8z0RFjp5/oz1tQXyzGNjQ4EFJgU3bez6djT30LIBMbJaQGNH3JTZLKLTBaFA3SzhtdnXK38VtM6VMfem9WcVAem+/2xHoMfJvN/XZfnewjoXLZoLDZEBYUEcwwzFZ2yzHYlT/tqIgqN+Ldt/gTzwY+GR3/AYfTyET4rnsnqDaAVSnzOOfk6iEYESB02KELxxLeU3f5wlCALTrZu+gPxhWr19lBVaIADoDEchy9zS9AAXRGCCKEhwWk7p4zmqCN9xro5Bw971me0sARXYzorEQ9nUGIUPoURZN7bwW9cixHqxUYc9zdqBgs765C8GoBKvJAMGkBqsy1DY0G0UoAIJRCdPKu2ctjqgtRDASBeDH4RMLYTObUN+rYtBgf2+96yN+vvAgXbNB729vT1mL+Nyja3UvDkwc42jUB9eT1z13UhHafGFtJrT3Yr6ei86yLftHMA7oKZr90oZGGEUBs2vc2NXqR3ug15RAiQOeQDR5anaYvb4+W02mWB2ZiQLc6awYT6V3nnJ/N5lgRAIEaFMtPfPxShwWTCy2wxOMwmUzpVxxfez0Ury/vT3lhfCghQUA1J8NRaVeC2wUGA3do0upRjNmVrkwsciOPe1ByIo6IioC8X+rN6/aIjt2tQfw8a52rXpBz22ZE9ULPmv0IjGL3F94K0C9EXSFu0sADbSvuRSf7jQbRZgkEUa1Iqi6IEUUYTaqZZS+bPKlnE5t8ITgCUTVwFtRAEHsHvFQ1GncrlAUuzsDaS3IGuxG+umeTrT7I+j0R2AUBZQ6zFpAHZFkNHlCKHSYdXXskkb7JKXXaJ/6GRYNagcq1WulW5Ej8TeaPaEQwH7MnlCI9qBapumAUoeuQvZ6F+olFkBOLSuA02Loc+3xhSXdHevJJY6UqS89N6sYTGJE+flPGuJ/L0ULSDsDURRYjbpHlN02E6oLbdjVHkAwHNXy0NVReAlOiwnlBRYAipZC1vsGn0ghKyuwaAMUUtJsgPpvg6Cm8QBCytksEUCl24pGz+ALpA6scEJWFEjqx1H9TMffUYH6GZZkGVajAZNrnNjV5u/zmZwYv9cAilYOLVFJJxEoi4IafJrjI8cvrG/AQ2/XY29nd6nCmkIrfnD8NHxrzoQ+f6/Bzg+TKKDQbuqz9XRnQB2hFYUesxYtfnXWwgEEwjE0eCJ9rgPDzaXd2tyF3R2prz3HTCnBEx/sSlmLGEBSEGkxGrQc/Z6DH5NLHGldKzITBKcYiDAbsE+StZlQU/z11cV8gjYTOrHInub7Zh6D21Ggp2j2Pk8ICoBwVFZ794mrUXyaNHFiJ6Zm9fb6BqN3daSeVIhn1u5Fq44C3Pd8d07GSpz0zlPu7yZT5DDDYTbCYjJg7sSifm/IoZgUb6/BV1z7IxI6dFwI/7utFXvaA7AYxXj+HrQRD3V6UMTu9oCuMlPbW3yQAcye4I5vIiBpU4NOqxFTShyQoeCd+hb4QrH4SFfvtlNHUtt8YciKWqBeiI9MJYjxYAwK4DAbUu5r/s7WVnQEIpBkBdVuK9RxlS5UuG2QIaAzEEVHMAJZkVNOp67dFUA4pk6lh2JSUuqF1WyA1WhAMCrhyyYfTp1VldaCrIFupA6LOnIbjEoodnR3BgDAFq9/qwTUfMJUpZUWHFIBh9mAkNrU6vHL8algkwgogNWsjrals4PRYCNhvUecEoXsbWYDphVY0ypkr3eh3pdNXd1BmCCgwJo8ephOx7pnWkW7P4KaIiMMgrqYtSsUS+s6IMsK3vi8CYGIhFjPk1pR/ycQkfDm5826RpQnlzhQW2zD1mZfd1ton111JHdamQPbWv1aClm/N/iYjGqXDTFZgTTARIkU7/w4LYaUs1lWswHfnF2FZ9YNvkAKACKSAkFQg2cZgnr9EgQYBfVYJFmByaAu+jxiYlG/O7qFTBKmlTvRGVRTooDkRagKgK5wDJ5gFJ/s6cBtL26GLxTT2leSga1NPtz8wiYA6BPg9id5980YzAYRAoCYpCAQkZN23zystjDlrAWQqVxaWVs0lyqtQn/KnYKYrCAYleANxbpHxAXAFB/82LTPgz+m2E0y3U1yBqMuzBx8IGKfJ5Q0E9oz39kbkrSZ0N0dAebcjgd6d/YpdpqwYW+nGhxakle5tnR14rgDy/C1qaV4Zl1DypIwekY8eq+ONBvEpNGrxImqJxViw95O7IkX4O5v9XNnQK17ur3Vl7EV44ngfLCbTLHDjEOrXdi0z4tp5c6kG3LvIuOpVly3+cO6CoM3edVpPEEAqt3WPiMQ3lCsz0rwgYKwxMV3WkUBplc4sd8bRig+dVfpskCBgJ2tfoSictLv1btmcfyPBVEQIMZTJExaioKglQcTDQK2taQezdjV5kdMVrQ8XZPQ3fYRpbvH77Qkp6H0N51qiueomo3qTkZd4Zg2+lNgMSIqKfHavOqrZnIFsQDtvtKj/bpv4rKSupzTu9vaUeW2YX9nEFFZht1u1EbHIzFJm2IvsBp1b2/54oZ9g27a0bvDPJxC9noX6gFCRis09L4O+KPSkK4D21t9WPNlCyIx9TMgonvkXIY6wrj6y2Zsb/VhWnmBjldUS2JZjAZYjAbttcIxCZIMBKJyUgqZM17kORiNwRdVtGn9L1u6tIVRItBnZb4cbzOnucfGNiY7mnzqIi6r2YAKp1m7Rp08sxITiuyDXjdf3tgIAfFpfAXaNRFQg1pDPK3CZTepo8DlzqTPZM9roqCondTE4lNDr9dSoF4z71m5RQ3Q4r9Xz9Jp3lAM976+FYsOreqTotBbYvdNi1HE9lY/vMGIdg2zWwyYWGCFw2rUZhIHm7WYGr+WZiqXNnFOpxqISLyWwyxif1dY+ztWFlgQjKh/L28wij3t6r0yJik9BhnUEXGLEdjdHsBj7+7UvQV6Jrf8HWwgYlODJ2kmtHe+c2ImlDm344SunX2UCMpd6mr/gUYOezwCYPCSMHokVkdOLnPgi/1d8ATD2o3eYTFiWoUTsgJdN7/OkFqAuzg+vduzdqLJIMARr3v6ZZN6g8lEgJIIzhM3mT69SKsJFpMBdVNLsM8zeAmgRJHxwVZcdwSiMIhCyguhoqDHSnARJgOAHk2Xzkrw3hff6l6BSiJX+KCqAjjjK7CBeL6aoo7ImuM3FYfJAEkRtJSG7ucoWj5h4mae6u8djKqdn3BUGnTFsttmTpmGMrPKBW8winZ/BFFJVoPm+HnoCURhMohw2004sKI7MBnugqzkUaJonxz3xChRfbMvZWmlbc1dmFrmRKTHFHusR+pLYoodSL1L09bmLjzyznb8ec32QTftmFzi0DrM4aikVasQBHVqM51C9noX6h1Y4cxYSlFCJq4DmxvV9QJAYgFY98+K8Sl6b1DddCRVcNtfWTFJ6r+TO7nEji37u9AWv8G3+SIwmIyYWmaHogBb9nsRkxWY451LqccMjjH+WExWsK3Fh8VH1ODjne3494bGpPPfKAqYVu7URrFTt5d6rE6Tse8siMkAq1FEMCqh7oASrN3dMeg1cUtzF/zhmNamcqIzCzWXXF14G4Un0B3Piol8eUB7/p6OAP5T34ITDqoYtO0Tm3Fs3OuBKChw24xaO0iyjO2t/qRavomZpcRC4gq3FSUuQZtJBJCRXFq953Qi9/vtLc3o8EcQiMjdwblZRJHDjBNmVMBpNWJXfPF2f3f2QETCjhYffKEovGEJMUlKXrfhD8NoMOCT3R3Y3upLa8vfwVIXet9r+huIsJkNEABtJjQQjiCR72y3mHvNhGZX9o9gHFFHuZIfS1wIYrICbzA66MhheyCC/25r1VUSRk/eW2Jaf3dLAKGIpC56gHo1CUUkbG32YWKxXdfNzyyqo2+Juqe9V/Un6p6mv8Z2YN2lS9zY1da7F9mdO1bpTl0SpneR8d5TrsFwDG6bUVcdw+nlzoytBO9Z8mmw7Yq/Ma0M08uceGdbW3cJmriwpOatHjTRiUhMwZ6OIIJhdUQGigIJgpZPWF1oQ0U8tWWwv7fNbIA9/t9gK5b1VC648GuTsbPNj8Z4ao5BVEeXFCiISAqikoSDqgowpTRz01yJUaISp2XAz5ECdVQlUVqpdy6z26Z2TBu9YZxfNwld4digU+x6dmna75Ww/IPdCEYklBWYEZMFRCQFBlFEWYEZLV0R/Hn1dvzvd2ejIxjR8ng98XxETyAKWRARlWK6C9l/bWqproV6ehZADmV3QT0dlcFuyl82etWFRwL67TQIgvp3/LLRCxw2QVcZv1Sd3HBMQlNXGAYRcDvNAKIodprhi8jY2RZAtdsKm1kdwU+UhFPiKQKJ62xEUiAoagrBthYf9nYEEYunFIiJ+4KkYG9HENtafNro22DtdWBFgdahKXVYEJV7lA4UBbQHInBajZg3sxxfm1466DXxo53tkBQ1Z7z3+EoikI3ElKTAFui+9CRW1sckBZv3eVMGt4lXjskKYpIEQO7xePdahZ3tqXfn29DggQBkJJe25zmdavq/1GlGQ0dQ7dQYRZgEEZKiwBuKIRCRUOI0wRsf2ZRkRRsR12Ya4vnZvkgMSpd6rsgKEIhEkzZYkmR19u+L/V7dmz8FItKgx65ry9/4ObixwYNpZckB/1A7uCOFwe0o0LOzj8WkjoZNLbcNWqsxMYo6udSBKrdlwGlqPdMCE4vs6AxG0BmI9liJH09LiEnoDEThtulbpX5wlQstvrBa91QUtKlmRQFCUVmre5oYfctEjlCip5nIHQuE1F7knNpC2K1mLXcsURJmsBJlevfyPvPwatzx8ud4c0tzn6n/KWVObSV+plaCJ3ITdW1XnJhj72eaHVBgNRtR4TKivsUPQRDUweTEE+P5hBOL7fqCnR4XuVQrllNVLjioogAdgYiW06eV44rf5AGgw6+/3nJPevZuP7y2sN+VzbNrCuEwGxCOqaPw/eUyW4zqbmelTkvKKfakaUuLoc+0XzCi1vds9UdgMxvQHoj1KR9lMxvQ4Ali/e5OROPbyRpEAY74ldwgJCpWqKPxiUL2A23scdYRE2A0irpv8Foqwdq92NjgQSCqjtLPSnN3xHSkulbYLEbtvOlPIs3EZjHqLuM3WCfXZTWgyRvT1ipY4uek3WSEwaBoaxUOriqAxWxALL4gyygmctsVROOBjcVsQGmBGb997UtEYzJqimyQlO40JoOgzpz9efV2XVP7U0qdOHJSMdZ82YLOYAQOi5oiEZMVdAYjUBTgqEnFmFKq1k8d7JpYVmCGADWHPBG8JnLzY5LSZ5hCVpSkv4GWsoxEze7B7Wzz61qroGfzm8RCYnVwof/n6M2l7ZnXOtj0/w9PmIqXNjTCIAqwGkVEZTV9QxAEOC0GRCUFL2/Yjwlum7YLXmIEP9FeBoOAqKTONoSisnbt7blbWyL9JqYALV0RXZs/fbqnE69s2p8ydSHVdWDx3BoAwOf7vXhzSzNERcaJs4APtrdBFkTtHpjtDRwABrejoufOPvs9IbT4wloeXZnTgkq3VZuiGOyimrygrG/JqkZPML2dldr96iKA+IiHrCjaEnxBECAKiu5V6hd8dRI+2dOBDn8UYu+7jKKOnBRYTZhc7NAuEnoS5fUUld+415OUO+aymRBTkNSL7O+m9vaXLUk1PPXusOYPqTVLe08f+uMdikyuBNckD/6i93bF21t92Nrsgz1+o++dlqAAqG/yYUZVAYyiWiTfYurODQ1HY5AV9cX0tEXiItfQqa5YTmygIUBAfYu/38oFA91IV29pRkcgihKHGRFJXYCWOHaLSYTZIGqzFsfNKNfdZHr3bt/W4keV24ZCuxnBiIRtPY4/KsmwGEREJBnWeJCp/RkUBZH49102U8op454lq2KSOprTM4/OaFDTGOqbfZAktQ16b0Eaiamt3BDP/RWEREcgHvQIAhRFLUMlKQoq3FZt4c221o4e+bvqxh6Jz1m6efBKoqiVklj3mrkZmd5/w1T5hIdUu2A0CIhJCiRZ7WxqI2Hxa5HRoC6C0VPuLFUnd1KJA/6IDGcoNuiimpoiOyYW2ZK3N5e7OyrRmIxJ8VXl+zwhtYarKEIEktKYErsQJs7/wa6JoijgsmOnoLkrhO0tfgQiMe16YRRFTK9w4NJjp3TXP+7VSXl7S4vWSZlR7oJRVANNtRl6BK/x9jUaAClemjrRiUhcUxK5pCKAgypT5zondq1LtVZBVmRdM0tC/N/9VfZIJ5cW6LWAs0ydtfMEIjAZRUwrc6C+xY8H3tqGfZ0hFNnNsJkNfTbcCUYkNHiC+Ghne6IJIcfPh8T5qsTXPiQ+v1Cg7taWWBchqDWGg1EZoqSgxGlOuflTpduKd7a2DnrsPXdES3Ud2NTggT8UQ1coBkN8dN0XjkGCqN0DcwGD21GQuKmt3tKCNn9YDWQVQIipF9+YrOC4A8ugQEm597m6oGzvoCWr9C4o+7KpC+GYjAKrWmsxFJWTgrUCqxHhmIwvm7pw6qzqlHVDC21meO0x+CPqiJTW6xYFFFrVuqc72/3aAh09ifK9a+bOmuDSaub2DsJq3BbAoeah7vWEtQBFb8J94oP99Me78fGuTgTCEuwWA46aVIRvH1mLmVUuXPPPT7CjLQAAEEWxx2okBTvaAn0qQmRiJbie7YqPmFQIXyiGQps6UtP7whqJqfU/93eGklJfErmhZQU2LfUlnW1iE8/Z2eIFHIAnFNG9A1Cic9HSpXb2imzqQsTeI+Kyou6k19IV1v2Zy9Te7dtbfKguVFcJ95eba+w1Cp9ql6bemyDYLIb4JghB2MwGHDu9BB/sbIMkA1ajqF0HBAEwxUdlBUEt0SRDDXyikgwpvomGJKsj9CaDek1IbBNrNxtxeG0hjIKAWPw8TCy86RngplPwvrpHp2dTgxf7OrcOaV/5gYI1vTVIf7ZgBmqKbNgV/1z2TssRBKCmUA0yU+YmLnKl7Nh9bVoJ9rQHUi6qCUZkXH7cVNy+YjMC8XxFEepkezgemF123BS0+6KISQosttS7EOqZ8Tp0ghs3Lpo56Oj6pgYPbn9pM7a3+JN20trdHsDn+724cdFMWOPBX8wfgax1YpDYkA5GAXCajfCHY+ixnrVPN8dkFHBgVXdwO9DfO3nXuoHXKjgtxqRULX+4e5csm8WUNLP0wY42bYCh50LQ3rWPgcE/u90LOA1Y12/FIyt2tvoQkWQUGdWKNSaDkHz88b+jIAhJqR5yfPBHgDobl0hJMcRvL4FeG9tAUOuRG0QBBWZjys2ffOEYmrrCgx57zxJ+g10HZFnBn9dsw56OIOxmA5xmE4AYSpwW+CIy9nQEtXtgtkdvGdyOAlEUUOo0qwFdPBen500mGJFQWmDGcQeWY1/nVh25P/HcppQLz1JRb9KJnZWsRoPW7ZZkGYGoBIPQ/X6p6oaaDCKOmFQ0aJ3eL5vUBTpt/mjf2rqBCAyiiE92d2DV5iY8+t8d2N7qR1TqDrp71sztGZAOFGDNrHLhtpc2606439biw0c7O9T8N1mB0a/myR0+qQhWk4j3trUhHJXiowmClkgtyerI2rvbWvutCDHUleBJdRgH2a64trjnQrO+F9bE+RGRZVS6B099SaS06Al2EiOy73y5H56tzbjgmEn4+oGVSdOnqQLNr08rhdEgIBxTy/0IAiDE/96CAESisrYLmx56gyI9e7dro/D9jLYWO8wwivpH4WVZ0TZBUNN+1Dw8oyigym2FySiiqSsKkygiFJX6TdCXZQVWkwGTShzq9K0ko9RhgaDWBUBpgQUKRHQGIzAbRHy4syNpxCYak+EwGlBRYEkasek58jfcgvfp7Cs/WLBmNxt0LcDb6wnimpMOxC3//kzNQU4MHcb/32k14tyjJ2LN1lZdm9+k6vTYzQY890mDrkU135ozAXs7AnjkPzvgDca0TpvLZsSFX5uMb82ZgNVbmmE0CCl3IQxEYrpXxacOULZrG770rLiT2PDl4TXbceHXJqO8QN2lzBvqztFPlKxyWU1ap64rFNMCYK1dAe15opA6Z7XnrnVWk4iohKQd0RJrFdw2c1KqllGQMe8Q4ONd7YgpIqaUOrB4bg22tfiwYkN3R9JqMSASk9GY5m56id8vURc7LPWteOQPxWAwqNVo1OtY3/SRSEy9jh1S7YLbZkJHIJqUZqZAHa0VBXWWQVEU+MKxnsUn1Ocp6vPMRlG9r6TY/EkUBDR5QwiEYwMee8+a3oPZ3urDx7s6IAqA22aGJf5rqik5SKqKpK8yychhcDsKYjFZzcURBFjjuTcxLRfHiGhMxssb9uPyb0zF1fOm9xmtnN2jx729xZeyZJXeBWXTyh3aogV1Z6Xuj5AoighFZRiMAqaVp+7dJue/FvYpg+KPqDuhyYo6SiUpSr8lw7whdWrq/97fiU37vIhKUp+L6qZ9UlLvMHEh39bkweYPm/HzhQdjaoVbK8+id0e3jQ0e3L5ic/cqdWPyKvVFs6rQGa9GIMsyYrLQfVzxS1BnMIov9ndlrCKE3jqMZQXqFsL+cCw+apc8pecPx+CwGFFoNaVMfemZ0pJqsU/iZrWzxYszSoDH39+F1ds6tJEkPQHR9hYfqt1W7Gj1wxcSEJXRvXhCVHcpm1LqxNemlupqM72F2fV8RnrODrT5wphQZO83b1XP3zRxXKVOK/Z7gmpqSbzvKAjq53dHqw/FTjOaPSF1oaZRjO+IpW6aYRQFVLqtMBqF7rzuUHe1CgDwhNQAuSieBpUYsem9V3zvEZvRbFcgdafnlEMrddcNTdRRfWj1Nm1Bj1EUUFNkw+XHTcXkEgdWbW7Wve13quCwZzpU4nNUYO0/HerTPR5MLrHDZDRowVo0JuHTPR5savDga1NLUWQ3YXdbEEYRMPXYhTAU34VwYrEN2/WMPOvoqKgByuAbvny0qx1nHlHdvejSE+wzYFHpssEbikLtTKv1cKUeqVoGUS3lV15ghT8s6dqls9ptw+42P/Z5Qn0iZYfJgOr4LEkgnlPbWyJVS1G6O5KJTqk/sZtekQ1GUf9uesDAdbETC0vb/RFYFAEVLguaPCFYTaI2igoAiiLDG9/1c/HhNXh10368s61NvWv0+D2V+HjJ9HIHdrQFIEbUEdreZeTU9BvEj0FdVL2z1YcOf7TH38iESaVO7O8MotGrzpAOdOyJmt4A+k1XSYz6724PaPXU+1vM17sqUjYxuB0F/93Win2eEIocZlhNhj7TrqGopOVUlTgtECBoH9LeHzs9q3n1LigTBQEOswHRWP87KwkA7GZDUtA7kN5bYPacBmroCGjTQE6LscfUU98bpNkgoisUxYa9HoQiMW0XLvUiEC9CLsW0EdLEB0gUBUwudWAzgMmlfVc/p7qpdQQi+NPqbQhGpKTpHZtZhNUkotkbxiub9qt7nqN7VDGRDyn3WATV0hXqbuMUwWEqeuswHlTpwpGTivDmFy3Y1xnSFneoK68FGA0ivj6tGCVOi7rSNQOlnHrerBI5t26rOWkkSc/o27ZWP+qmlKijOfFdz4yiOrPhi6gB3amzK1MupklIpzC73mnepFH4sPq8w2qL0hqFH7TedSACX3xxaYnDgjKnBdtbfPBHJC3/2GkxYkqZAw6LEQdWFCTldYfVZFxEYrKW1z21rADbW33Y1eZXC/H3uIn6wjF0+CMod1l116RMp11T0bNr43/r22Ax6q9B+q05E7Do0Kp+87p7V0NJ9VrAwJ9dvelQALTf8cBKV5/PW89yVBOLHNjTHoSkAAalexdCKf4ZLnVatNzw4XYsvmzqSrnhiycYhS8c6150ObH/RZdTyxxo8oZQXWhDoyfQJ7iqdNuhQIHDYuiu8TxA3uf729tRU6RuoAEk9uCIp7YBCEsKJhbbtVJgiVQtdTFrA46aXKwtZn3s3Z1o8oYwtayg38Wb/jR200tunYHrYouiiO8eVYuH12xHszcMVzxFLBJTA1ub2YDLjpsCo1GE226GzWRAVJKg9NjWWIACk8GAAqsJZoMYXxCnICohaTQfEGEyinCY1Q1AwlF19lVS1GoLiYA5HFVjA1EQIPfYua/3sSekSlc5YUaZzpYamRz8dDC4HQWJnEKLTexxcnZL5OKs392Jzxq93T3beFrCxgYPGjr7LzqtZ/RtIP6whHKXOu3UFY4l7+4jAIV2E8pd1l7bx/avv3zC/qaB3DZzyjJZoiDCFy9VpQWR2jOgleNJjJAORm9wWN/k0xZ19OxxA4AgqAuGPMGolndmEgRtEQWgBpDR+E2pzGFN2V566a3iMKXUiXkHV+DNL5oRjsm9XkVdRDRvZgWmljnVRWDDLOXUe0Q2UWtSHUkyaTfu0w6rSl3+yiOhuSuMCUU2rT5kKL7trstqQpHDhDaf/moJev/m+z1BPLOuYdjTvL3bZaDn6Kl3rSgKZtcU4ov9XpQ5LRB6jLaWOswIRSXMqS1KKsvV7o+gqMgKwI8ZFU50hNTd3hbNqsQvX9gUr4aidnC0zqska9uZJkZsMtWuuhaz6ti1sckb7C69p7MzZjSK/S461Ps50tuxS5UOlZhl0zPS/d9trZChDLoLYVSWEQ7KKTfk0NdR0TdzJApiykWXF351Mp79pAEb93pwxMSiAauOANByVtfu7kgKgoscJq0tLAbDoBtoAL1KgYli0si5BFHtMLf41d0TC+0QBKFP2cX02kt/XeyjDyhBhcuqbcDiDcX+f3tvHidpVd/7f86z1b70vs307AsDzDADgiMoIigaQkBIwlXuvYhejQZD4pLF6wL8EoPXJGpMcE/A1xVDrhFEMWiQHQQUhmEYZu+emZ7pfa296tnO74/zPE891V3L0zPVXT095/16NUNXVVedOs95zvnuX0giwaqWED5y+Vpcd0GP433d0RvHcCI3SyFQ0BnzYyrLPoN1PDThk+EImpQCPklAPKAgHlTQFJTxxIHxkgoTKiEYmM5hKFHAjt4mtIVNTOfY2O3ObxSsWZM99lRex73PH6sarhJUJIR8ItIFHc1zjA0sjCLsl0pqkjcKLtwuAm0RnyumsHwsjigAbwwnMJXRFq3otLuKQ6WbjFqvq4U7nrCaG+jc7qjjTp3JqhAtIdHWOmVBgE8CEnnb6lhau1IAhQFmWR5L5GqOy+uhRghcSR1zUSTBiodiwrVmltdMRYEgHPAmLHhhPlUcHt8/CuYitC3JxTkEiNOCtB7d4ea6qEvdU/Zhlcy11hSITADDiTzO646X7eyTUc15WVm8XPPze2J4sX+qLm5eG6/l7aww7RIc7wQh2NwZwbOHxx0l0bb+DCdKYwXnCFgAdEs4vmFHDxSJIGNVYZFE4rgxCWG/s9J0Rknc3+nOq3vvqVVP1kvXxht3rEBq78hpK2Ne76P5hAxVC4eyv6MXS7dd3nF9RwQbO8Jz1r8Jgv3DSU8NZLzs1Rs7wk4IE5vzYmyrHcLEBJQw1rdHau4XhJCaVUcyBcNKeM7P9SKoOis7GZQR9slVG2hMZVVPpcBMk0IUUJf5ArzXxbbjrCt5EABv3teZbBJhv4ioX5qj8Mzu1pbIachrBiil8MkiRKu2bkEzoBOKvKajKSTX7PyWyKk1w1UODCexuTOKVwdmMJNV0RRg86eZJmZyekmpuUbDhdtF4NJ1reiO+TEwma0Yi8PKgZl1LzpdDfdhtWNlfE7bx76JrGdB2RZ2armBAOCC3jjGUnlMZXVWHgfsNpKtpKEVTQEMJwqsXqswdy5ghUxQD+ESXg+1yXTBpYCQstUGREJAJBaLXE4cIGAbayygOI9VO+C94iWr/8hYygn098si8rpVdkgg8FueAnf748WKBY4G5JoCUXcsgNEkE9yIwNZ66XuReVlZvFzzN69twQ9eOr5o8aO3X7kBhklr1ruOBxXsGUx4jhWsJmDZrVjtMlmzw47sGptHxjLY2BGty7zae08tQd9T10aoOH9FDBs6Ip6VsWr3W73afs+ek3LhUIB3S/fsJjmz138uP6uBjEejhq6bZQUsVguXhTANJ+fGtkqCgLe6BJRa+4XX/WksmS96EQRSshZnsho0w0RAFmsmvAK05rzGAjLaoj4MTHqfr2p4rYttv18lDwIwd12UbxgkQ5YE7Do+DYGwkl+2CaGg6U63NmoCh8fSTukzzaDQTNPJ5zEBnJzOYVNnGP0TGYgCSt5L1Q3nvdIFw1O4ypvXtCCnGcVSc4BTOcZdaq7RcOF2EZAkwSkJUykW5/cu6MFzhyfqWnS6FiUNAg5NlFhw9pFZDQJq4BZ2qrmBMgUDrWGFhWqYFLLECpUblFlOx1MF7FjZ5Lh8KtWtFAWCjqg397+XzVfXzZpJTd1NAcxkCk7M9OyC5QIh8IlFa0A9GlW4v0O1A+bQaBqJrAbNpDAMqwqp5aLKGASiSGBki4H+ixUL7KVD2e9u7cIPXjpeNyuLPV/VkjMNky5I/Gg1K/D7L1lZrHc9KxnU7uqWVQ0MJ+YXK1hZwHK3YjVZQwgrkdXnasU6n/g4r3Uwa9Wx9suCNcLKVmz3Z3pRxqolwsw3vMQrpkktgYsJXm7LrVdLd0nTlCqdD9+7vRv/9MQRT0aNh3cPOq7xYm1jP/7o8nW47oIeXHlOB549PIGCRp2wLwqAmkBAJnjHOe0lc1Jrv6hZJ5bSWV4E9jghrKSYblIUdLbOa4XcbeyIlMyrWw5zz+v127vxzx7nqxZuxa6ahXq+RqVqnqWJdN76bAGyKDhnomYIMK3cj4NjqWIJSKtUmm1RDsoiVMNEIqchlS+GFsqi4CjVql475HA2K1uCTqm5A0PTAHJY1RzE5u6mBWvkcipw4XaRsLN5K8XinG+1O/R6wNd1ky4Ne53TIMALXoWdgCLM6eJCKbUqSTDNc99IEvGgjOmMCpDSupX2RtwUlD0VBrepNV+SJOCarV34x18drpjUdPmGNvzijREErf2goBdLlPksl5Ns/eu1zup8qFqmiZosLo2y7NvZPd6ZJZq9rh7M3qArHTBeOpRt6YrixaOTNQ/3U2npWCk5cyHiR2tZgQGUeErK1Szujvmd8IPTjRV0t2JtDsqsc5lBIYrs3pvJaacUH1erkoCXOtbXbuvyZMW24/29hITUqtta70N3dqWQv310P1a3RefdGMbuELd/OIkn7C6E1vgFQrC2LeQoDl6MGg/vHiyt+hIorfpiUoo9JxPoivmhhUxMZFRHAG4NKZAlYV6VBGyqXaMjY5lSL4Lostzav4O16x5OVG8XPbsNdKWEvnpb6uv1ft48S834wUsD2LoiXjEMYiqrOsnLWpm291lJh18SYVIgXdBqVlgKKaKr4o4wZ/5nh6sUPUZP4/+77rwSxW4pwIXbRaRaLE5JeRmPbpTTtb55bRDgpW6lVyvF0EyOVY6o0sVlIl3AOZ1R7CnMOCEATq0/CvhlAW9Z1zrvuJ5aNTwn0mrVpKaJVAFxy5qQV3X4/JIj3BqmiYAiOYH5P31tqK71QGsRVEQnzlYSipIcAbNyayaFab2uHnjNGHe7LqspF14O9/nM1RzlYlZy5p+8Y33dYte9hmhkCoYzZ4fH0k47XLVgYHgmh5awD9ds7cIPXhqoi9Btt2J94sAYhhI5W8xn39HKyn7bKcbHVbqXjk1mPNWxvnxja13j/b3Ubf3qTRdg33CyLt4U9/pqsmpPS4RUbAxTrbyjw6zwgHKP17qPdN2sWfXlnieOoDMWQGvYh5FkHqJArKYBrOFOa9h3SpUEquPyImgGCroJE6x7mV1CMqcZePPaZrxyfMZ7G+gaDWSqdUY8FeplVKolKNuepdWtIXTH/XM8OKYVotEW8cMnCZjMqBDI3DJy2YKBkF+CJAg1wz3iQQUXrWrCM4cmyibNmbPiaauF5CwFuHC7yFSKxVmIhIdaeG0QMN96oNXGf2A46UrcqtTFBVjXFsIbw0lQV3yr/a8kCnPcZvWai2pJTUOJHAI+CX5ZRP9EBmlXUfagIqLDCsxP5rS61gP1QlZlgji1SsEIKA3lIGCCYlatj+UW8JYx7qamMubhcPeClzCBh14dwnu399TlfpuPFXhtW7hqO9x3bunEi0en6iJ0CwLBlee049nD4yhoACG06H6mgKzMdT+fLnYL1Vp1rMN+ySkd6FjWCRwNdiJdmNNBqhJe67b+8o0RPPSqt+oY1XBbpzXDxFhCx+82AwdH0/ApErKqPkd5rVbe0TEymBRXbGorG8/pfr9q95FddrJa1ZeRZJ7VWjfM0y7q7xXbi5DK68Xv78r+z2qs6cWV53Tg0vVtnqyjtRL6gOqdEU/Vkn+6RqXZ4y8nKM8uXTfbg1MM0WDJgVMZFU7HTMCJ9zFNExGfiHigdn3zWEDGR962DmPJgpXEVrz+AiHY3BleMvG0XuDC7RJiIRIeqlHPupWAt/GXJm5V6OJi3dyUMq1+dhMHk8LJ/K/XjVYSM1whqUkgBD5ZwOsnE8yF5w7MN0wnMD8akOs6r14QCIFPElgrVspcU/Y+Rwiz5sqi4Klm8XzwcsDY1Gqz6vVwr4XXMIGwT6rL/TafKgJ7BxP4+Z7hqu1w66Xk2hVMumJ+6GETU1mt2F0tKEMST839XI3SFqpz596OC0zn9TmlA30+wWpFPL8OUl7rtv77yyeQzuun7U2xrdNTaRU6pU4DDUUSMJ3VIBGCVwemcWySZblX8yDMrgUtCAKigVM3MrjLTpZDkQQYJkUip4EQeCrqXw/WtoaxoT2M549MOln9EmHNSTIF1pJ2Ry8rcScIxLN1tJr1cCFCw+pNJUHZ654iEIJ4QEEiqCOrlpbztJW9lrAPHVFvyYiCQPC5393izdOwxOHC7RKj3gkP1ahn3KFNrfF7qRzREfPj2GQWikjQFvbPKVWTyGl1b/HnZS4UiUARrEQYSiG5AvMLrsD8hZjXWmzsCCMWlFn7UVCoOnXcfqyuMnHipeqNF/eU1zarp3u4A/NT2ratjJ/2/Tafcm3lLMoA0BEtFvX/3DXn1LVcW70L2VfD3UK1Uh1rn8Ri+546NI62iM9p+pK2q0LEA5DE+XSQ8iDoU2AkkcOG9uhpe1MSOQ1DiRx000Q8qEC23k4WBMQCAmayKoYSecxkVPzs9eGa4UleakF7VYa9lZ1kSbwum4HD7KL+9SQWkOGXReimyZKFqQkQAlkSIAkC4i7rZL1C7hYzNKyeeN1TMgUDsijgwlVNFTuUJbIaLtvQ6rmk3mLKIAsJF26XIPVye9Si3oXNbaqN30vliLduaMODu04iFpAhCAJm79EL0eLPy1ysaglhLJmvGZgPYEHmtRp2eZ9nDk1AEgWEfaWFunWDlsRL1aNEmVdqWVDeM482q16Yr3JRj/vNi9diPq2g612urR6F7L0QC8jFtsBlEsVkQUBXzI90XncE77BPnBMHmJ6H4O2u21opESYgMwHKSzWaWiRzGgo6s04X6wzYsAYEWc3A4TFv19tLLWivyrAX40F7xA+/whIKqzUk8NK8xyvHJjOYzmrY0dtUsZWv17bxXj9vsUPD6o3XPcWOWb5wVXNZBTYvG9i2Mj6vknqLJYMsJFy4PYtpRJwvULtyhCwSPLjrpId3mmcwZhW8zMWl61vwwG9O1Gx97E4c8jqvpytsCgIpiZdSjWJs7ex4qXqWKKuFFwvKc0cmoEj1KVAP1LfZwHyoJZDONwxoscq11dODsLolVNIWOJU3HAXQbgu8vbepNHSHkDlxgPMRNt2KXaVEmO0rYijoZl3mIhqQ4RMFqIYJ/yxTJ6UUqmHCZ3WDq1ctaK/KsBfjwfsuWYlnDk+gNex3BM1KDQnqhbtxQbUEqXopWvUOuWsUtfaUkr2uPVyiwJYLOVgOFlmvcOH2LGex43xtqlWOODKWQtgvuVr8lbo2F6rFX625CCoiHnp10FPr47VtYc/zWi9h87yeWM14qcWOQ/NiQRlLFtAe9det4Ho9mw3Ml2oC6WILmwvlmamGe+6nMipWNEkQCYFhxRXbcx9UxLrNxWzFrlwizJ9dtdFpE3u6cxELyOiOByzFXHNibjXTRDJvQLKs0x1Rf91qQc/HyFDLeHDt1m4MJfJ4qX+SlQa0qqyAsqovE+m852Q+r8xe+5USpOq19huh2C0U1faU+Rqo6mmRrVbjeSmw9K8sZ8FpVIxNpcoRdgmjZw6NYyarzints5At/mrV8JyPsOBlXustbHqpQbqYcWheLSiXrW/FL/Le26zWsrbOp9nAYgn6iy1sNsozM3vuM5oxZ+5PpfRhrc+spdjZbWLr0dnxgt44Cv2T0A2KvKoBYPGszSEFksCs0yXNGTwk8tTTyFCrBezsZL6wZd0dSRTmlcznlcVe+yWfV+fa2UuNRhioatV4Xgpw4ZYDYGnF2DBLzFqMpfLFFn9W2ZjFaPFXaS5ORVioVVt3IYTNajVIFzsOzasF5YKVcWz0GBPm1dq61AT9RgibjfLM1FLsFmIu6tEm1gvusU+mC1jV5AeQwaaOCKZyOlrCvpLmDF6/Y72NDJWMB3YVDa8tnuvBYq99+/PqWTt7KbMgnfcqvJfbKLAi5gMAxPzKkqpCAXDhdtmzmIlD9eS8npjT4q9aO81GjKtewsJiC5uNiEObj8XGS0zYfK2tS0nQBxojbDbKM1NLYV6IufDymfUuwn9sPAkA0KmJbSub5jQRmM93XAwjQyOqaAANUrTqVDt7vjTi3K3X2qlmPNjSFS0xCkiETSQzCshLqgoFF26XMYuZOLQQLNWSJPUa12ILm42IQ6tnTFg9ra2NTDhpxLpeSp4ZN2fyXHit8bzU9rFGVNGwWay5qHft7PlwJp+7tYwHN+5YMcso4K6ru7SqUHDhdplyJhSw9sJSPZTrMa7lkmBUK7GgXhabelpbF2ruvVpsluq6bgRn8lx4qfFsv26pfMdGJ1stpnW6XrWzvXImn7tejAeP7Bk+Y6pQcOF2GXKmF7A+W1gOCUZeEwvqXbe1HPPZWBdi7s9kiw3n7KERVTQWm0Z4Zs70c9eL8WAokQMBzogqFOV79HHOaOZj4eI0DlvYbA4pODKWRjqvwzCpU+B+IROMzl8Rw0xOxbGJDGZyKrauiM/bqmBbKV4/mUDMrwAoJhZ8/fHD2DuYmPN917aFsW1lHGvbwvP+Xm6LUznmWz6qnnPvnot4QMHq1hDigcpzweE0ikbsO4tNPfcKr5zp566jEFRpdCIA6Ir5MZzIgdLSwGVbMdrQHlkSilHjxWtO3VkuBazPBs7UBKPZVorFSCyot8WpXnN/pltsOGcfjaqisVg0wjp9pp+7XsJV/IqI393ahR/vGsSRsTSrlhAC0nkdJxOFJaUYceF2GdLomCrO/DgTk2rmWikWPrGgEeWjvLAcWn1yzj6WWqJbPWlE2b0z/dz1qhC8c0snepqCxUohISCRV5ecYrQ0Z5lzWpwNMVXLjaWUcOKFRlkpGlE+qhZnusWGc/Zypu0782GxrdNn+rk7H4XAa6WQRsKF22VIozoTcc4eGmmlWGoWpzPdYsPhLFcWc69YDufufBQCr5VCGsWS3m3vvPNO3HXXXSWPbdq0CQcOHAAA5PN5fOpTn8IDDzyAQqGAq6++Gt/4xjfQ0dHRiOEuKZZ7TBWnscy2UrgLpC+GlWIpWZzOdIsNh7OcWcy9Yjmcu0vNeHCqLGnhFgDOPfdc/OpXv3J+l6TikD/xiU/g5z//OX70ox8hFovh4x//OG644QY8//zzjRjqkmO5LFLO0mO2lWIpJxYsNMvBYsPhcOrDcjh3l5Lx4FRZ8sKtJEno7Oyc83gikcC//Mu/4Ic//CHe8Y53AADuvfdenHPOOXjxxRfx5je/ebGHuiRZDouUszSZ04J0iSYWLAbLwWLD4XDqAz93G8+SF24PHz6M7u5u+P1+7Ny5E3fffTd6e3vxyiuvQNM0XHXVVc5rN2/ejN7eXrzwwgtVhdtCoYBCoeD8nkyy3uCapkHTtFMeq/23p/MenFOHz//is6k9iL961wYcHU/i4Ctj+Mt3bcCaNlby6my7DvZcDExlkS7oCPsk9DYHF2Uu+NpvHHzuGwuf/8bRiLn3+lmEzq7Eu4R49NFHkU6nsWnTJgwPD+Ouu+7C4OAg9u7di5/97Ge49dZbS4RUALj44otxxRVX4P/8n/9T8X3LxfICwA9/+EMEg8G6fw8Oh8PhcDgczumRzWbx/ve/H4lEAtFotOLrlrRwO5uZmRmsWrUKX/nKVxAIBE5ZuC1nuV25ciUmJiaqTlYtNE3DY489hne+852QZfmU34dzavD5bxx87hsLn//Gwee+sfD5bxyNmPtkMonW1taawu2SD0twE4/HsXHjRhw5cgTvfOc7oaoqZmZmEI/HndeMjo6WjdF14/P54PP55jwuy3JdLlC93odzavD5bxx87hsLn//Gwee+sfD5bxyLOfdeP0dY4HHUlXQ6jb6+PnR1deHCCy+ELMt4/PHHnecPHjyIgYEB7Ny5s4GjXFqYJkX/eBqvnZhB/3gapnnGGOo5SwC+fjgczmJztuw7i/09z5Z5BZa45fbTn/40rr32WqxatQpDQ0O44447IIoi3ve+9yEWi+FDH/oQPvnJT6K5uRnRaBR/8id/gp07d/JKCRZ7BxNO9nZBY9nb69vDuHHHCp69zakJXz8cDmexOVv2ncX+nmfLvNosaeH25MmTeN/73ofJyUm0tbXhsssuw4svvoi2tjYAwFe/+lUIgoAbb7yxpIkDhy3krz9+GFMZldXdjLG6m6+fTGBwOofbr9ywLBd0PTBNWrcahfV8r8WErx8OhzMf6rHXnS37zmJ/z7NlXt0saeH2gQceqPq83+/HPffcg3vuuWeRRnRmYJoUP951ElMZFevbix2Twn4J631hHBlL48Fdg9jSFZ335rPYwtpif149tdszVVNeyPVTC1038XzfBMZTBbRFfLh0XSsk6YyKnjqjOVOVMU5jqcdet1D7jtc1vVhrf7H311P5vOWwDyxp4XY5shhWwWOTGadTkrsVKAAQQtAVC+DwWArHJjNOoWkv41rubpR6ardnsqY83/VjmhTHJjLsbycyWNcRO6U1/fDuQXz76T4MJfLQDQpJJOiO+fFHl6/DdRf01OW7nQ7LYcOvxpmqjC0Uy/1614t67XWnfG5V2Xu8runFXPvu7wkAyZwGzTAhiwIifqns96zX53mZ1+WyD3DhdhFZLKugYVIUNBOBmAhQilRBh6abkCUBEZ+EgCJiNGkildc9j2upu1FOV8CqpzbdaAvE6ZLK68X1Uwb3+rHXzrHxJK5vAf720f1Y3RYtu6arjf/h3YP4m0f2IacaiAZk+AICCrqJgcks/uaRfQDQUAG3ERv+YgpXS10ZO5M9OMDyFZTrudfNZ98BUHPvsdf0ZLqAaECBLyDAMIE9J2ZK1vRir337e+ZlAwdHk0jmdBgmhSgQRAMSVrWEUNCK37NenxeIiaAmxUgyj7xmwC+L6Iz65+znS3kfmA9cuF0kFtMqeOOOFfDJAkYSOYwk85jKqNBNCkkgaA4p6Iz64ZOZluhlXFu6okvajeJVwKp2wJyK1aAS9Xwvm8UUriJ+CT5ZQE41EPbP3SJyqmGtr7xznVbEWGm9mF8pu6arjX9zRwTffroPOdVAe9QHQlgYQkAR4JcFjCUL+M7T/bjmvK6GhCjM596tlxCzmMJVo5WxWq+bz1zUM+6zllA0n/drhCVsMQTqeu517n0npAgYSRWQVw34FRGdER9yqln23Cq393z8Hevx0KuDODmdhW5QDM7kiwKkX0JOM/DgrkFs7ogU135bCGnVQCKrQpYErG8L4ch4pu4hWBG/BM0wsedkAoZpIqhIkAQC3aSYyqhI5HR0x/yIWHvv6V5He16PjKXQP55BuqCDUgpCCMI+CWvbQgj5JIR8Iu5/acDzPlAvj91CwYXbRWC+h4fXg2hdWwjpgoGZrApZFLCuLYS+8Qxe7J9EPCjjyQPj0AwDBMUFl8prGJrJ48rN7ehtCuKLj+6vOS7/JUJxA7Pew20JLuumPo1DbT4bZlY1qm5ybu282gEzX6tBte843/eqxWJr06tbQljfHsbrJxNY7wuXXANKKYYTOZzfE8ML/RPO2pEIKynD1o5csqb3DSerjv+y9S0YSuQRDciOYGtDiIBoQMZgIofn+yZw+ab2un1PL8zn3t03nKyLEOO+3hG/BL9fgk7pKV/vvYMJ/PiVk3h9MIGsZiAoizi/J4YbL2TjaqQytncwgf945QReH0wipxoIKCLO74ni9y9cOW+rWq338oJ9vWsJRV6FnXoLyl7xOv+nKzidyl5X6TPtfeepg2OYSheQVU2YlEIgBEFFQHPYhys2dZScW+vaQsgVWDtWk1LnDPz+r4+jfzyNyYwGwzShiAJEkYACmMqqEAUBrw5M4/m+CRwZSyOoiNh1YmaOFbUz6q9riAAA9DYFUdANZPJaiTIvi2yNjSULUEMKepuCdVGMVreEIBBg94kZlFT+ohQzOQ27T8zgrRtYkr7Xcz6rGp49do2CC7eLwHyFNS8HUVCRsGtgGtMZzbHKNoVkdMWCODyWgqoZyGsGAEASAQKAAjAMwDANzGQ1HJsqjosJLW53hc8Z16HRFAqaiYJUwY3SXHSj1CPGyR1WYZomRpKFknHZG2Yip+Gnrw1V3eQe3DUIk1L88xNHqh4wXq2VttWg2jWaz3vZVNrwG5HcJQgEN+5YgcHpnLM+AgoTKoYTOTSHFLx5bTN+8NKAa00Xd033mu6fSNe0jDy2bxS6QeELCKCUIqcZzvoKyCIUSUAyr2M8Vag86Ap4ObzrYdF/bN8Ifrxr8LQVELdwpRkmTk7nnLmI+EVkVX3ewtXf/Hwf+sfT0AzqCAsDUxnsH0nic9dsKbnfKKVI5fWSGMD5CCj2Z3oRSPcOJvA3j+xD/0QGmmGCUoAQ4MRUFgeGU/jsNefgwVcHPSsW9nuZrqab9nt97ne3eI773D0wU1Mo8hJvXm9B2cvcz3f+T1dwmu9eV+szW8MKTkxloZtwTDImpUjkDWTULFrCMgams84Z+OqJGeQLGq48hwlvfp+MzmgAR8aSODnN5tukJjIFw7FWyiIgEIqhmRxGLc/mTEZFwbCut0RAKTCVUZHJ64iHlHmHCFS7RgPTWfgkEWGfhGReL7HcZlUdYZ8ERRLw+IExZ+883T1l31CyRLB179gmBfYNJZHIaZ7O+d0nZvCLvSOePHaNhAu3i0BJzEuVw+O1EzP495dP1DyIptIqxlJ5pAq6W6ZAWtUxk9UQ9ctI5DX4JAGqbkDVKCjYghYFQJFEHB5P4cAIE1rH9DwOjqSQVQ2XpixiU2cE7JwglhtlBjqlczaAZFZDVzxQ4qautalWD6voYW6UUSYcZQoGTLCOIyGfiLWtYYT8EpI5rbjJDUxjOp3HlduAF/om0BT2ozMWxOHRFO779bGaB8z/fs9mx1q5TmEWcfsahX0ihhM5bF0RR6ag44v/ub/qNdrSFS1aPhUm0NkacFgpvtfqlhCA6ht+UBHnZTWvF+f1xHD7lRsqCvElcd1lsNf0odF0iWWkXIjMVEaFQICZrIaMakA3i4KOJAgIKSIkkaAtUuwq6KWqgtdY8mrWPi+WqZGEgUf2DNdFAbGFq6m0Cp3SkoNvOqtBIqREuAIqx5ubJsV3nunH3sHkHA9OFhR7B5P47jP9+JMr1zthJiPJXBnrVWCugFJhXbhDmNa2BnFsMoesyg7wta1B9E9kHXfwd57pw96hpHO97YtOiIG9Q0l87VeHUNDNmmu/fyKN7zzThwMjKUgimSMsHBhJ4bvP9OOrN11Q0zOWyGkYmsmhoJswTBPJvA7TpBAEAp9IIApMKErkNGceDgxN4791AV94eC82dzeVGCLcgnKJ+7mMoOxlXddSrL0qw5RS/NMTR05bcHJ7eartm6tbQjX3/T++Yh3+45WTMGmp8AWw300K/PiVQbx5bQum0iqmcypU3URYZt9RIOw8Shd0SESwlGQTBASSKIAQdmapugkKHYYpwjApprMqUgUdBJglBBMUdBOUsHPHK168hLIoYOuKOI5NZZDM6chZ91tLyIfe5iASOQ0/2zNUlz3l2cPjmMyozjxS19zav09mCjg0nHLOec00oUgiZEuxm0wXkMxq6IwH8NwRbx67RococOF2EbC125FEHsOJbFlrqyIRPPLaUM2D6LZ3rMNYKo+ZrAZBIJAFAkLYuaCZFDNZDXnNgG5SGCaFQYsLmQIwKKAZJhJZDeOpPBI5FUcnszBNCkUSIBACkwLpgo5XT8xgTUsQ69tDKOgGEnkNkkBKNgBJADImRbOu4IW+iZqxSyUxThVu2hf7p0AoxWuDCRguddMAMJPT8dpgApeta0U0IGMqrWJgOot0XocistcmCzomshkMJwvojPgxmsojkdOrHjAD01ncuGMF9g8l8eSBMWgmipucAKxtC+P67d347rNHa16jr950AXuv4SSeODjOLEmWdiEQgrVtIdywo8eJF67mgn7PeZ2ereY29Yy1o2CKEai9KdKSNZ1TDQQVAWOpHNAMDCdyaI0EnPg4gMWRjSXzSOV1mIAjxKTzOqYzKtoiPvgkAeNptgFLAnHMNqphQs2Z6IkHcOm6VgDeqip4sV4BqGo5/NzvbimNA/SJcxTTnMoUr+FEvi4KSCKnYSiRg26aiAVkpPK6s1dE/ZL1fB6JnOZ8z0ruQb8s4IW+CRQ0AwJhFnn7NGPKiYFf903g41esR1NQxjOHJhzl132ojSULuHxjmyOg/M3P96FvLA3dpI7gd3ySKXe3vmUNjoylkSroePi1YUuQYB/72kkBq1tDODyWwnN943ihb9IZmyyJxX1MN1AwDLwyMIMV8QCCslR17R8YSeLl49MQCBD1y86ewty8MmayKn57fAr9E2nkNbOq4JHMaciqBvKaDt10CVgmhaYDkmDAMCW8fnIG/7l3BP3jGUjEBLqA41NZ9E3mHSVXM0wMzeRgUIqoX4JuMsFKsK5lMq87grKXdV20wmdK9pSBqazzmSXKcAVPw6HRJO57Xq1LnKnt5dk/lMST9l5nP0cI1rayvQ5AzX3/nieO4ORUDvZbCLbkZa0LSoET01kcGE5iOqcindcBUMxo7A9mshpMQqDqzAJrmhS6QRFUBBjUWquW0JpVTYgCRUgRoekm8qp7HTIhuKAZMCngn0ecv5cwFHtP8ckidqxsmpPglVENTGdVDCfy6K5DqNDzfRNlFQYUpxcmBY6Mp9g5n9MgSwKyqlYi6GcLGsIBCWP2XlfFY1dvY8upwIXbRWB1SwhNQRlPHBiHbppzrK3DiQIuXt2EPYMJttkLpGRTMUw4B9HvbetCxjpQ/SJxFj4hgEKArEmh6iZ0SmGYc8dCKaAazFUTD8oYTxWgGyaCsggQAmpZIv2SgKxmYDxdgEmp5d4BcpoJRRIgCsT5XRQIciqzttSKXbJjnKqXQUnj0Gi6RLB1Y5gUR8bTCCgCBqYySBVY+IU9Y/Ytl8rr0PUMgooEIpCyB18yrzkHjCwKyKg6UgW95OCWBIKMqmNwJucIC6JgWwPYnOqG6Vyj/om0NdmuQbv3J+vx2S7oY5OZotITlJFVdTx3ZAKqbmDPyUxVq7kXq9qpxn1G/RKaAzJ0ygT4oZnD+Pg71jvxcdMZFbqu4/eagZf6JyFJEppCCq7Y1IENbRGMWa6/0t2VwrAsLRTWQWZfO9cBWfwT9l8vVRWu3dpdjEtvDWE0VcB4Kg+/LGJdawh9Exn8+JWTmEgXqloOv/tMP/7hD7ZhfXsYL/VPQjcoknm9xPIviQTr2iIYTeZR0LwrIJUsdMmcxqyGholjk9kSV+JkRkVIEWFQdt/USqrZ3htHIqeBgsIEgW4YJUoWBUUip+HAaBIAU/jymoGMdT/Z10kSRWe92pZg3TDmLOq9g0nc/9JxHB1PY3Am71xXwbpHCrqJQyMpZAo63rDcoIQAiiTApNQRahSJXdNMQUc6r2NPamaOFdu99sdTTNCJBWQQwpR3+96VRYKQjykFj+8fwyvHp6sqPBGfhIJuQCu3dwLscU3Hf+0bxYHhFEQBiAbYvRdUJEzndBwYZpbi67d3o2CYkASCyYwKVaeOsKBIBLIooGCYSOY0Z11nLSUqKAvQTYrj1ro2KcWTB8adzwz5ZGcuMgXN+cwPXLq6ZqWcY5M60oUMon6pfnGmhM2QqhfjZP2y4CwRL+Wv9pyYguZa8M424LoHdIPiyFgamm4ip7Kz0rbcigJBTqMwTRNEYWeoYALpgjFnK5YEQCQEExkVBmWSH7E3c+szbeHNoPAUluA1DMX2Er7UPwlVMzCUZGewJArojvqgyCLWt4UxnMgjoJx+3oZthCl/mhYfT+ZZOJgBQFMNSKJlOAO1lAGmOCTzOrqbgjANE8enUnhTJ9A/nsLK5si880kWEi7cLhKJrO7EwDILKdOWVN2EbhgYsawxFEyI1HXqHLaCtcASOQ2/7p8EARPONEu7LwpYlFlcKIVpVB+PSSlOTGbZewgEOd2YI4xJAoGmUzx/ZBIzOY1psARQdQrdZJt0yCcCFEjkdRACDM+YKBhzM0Dt2KXxVKFmGZSxVB4T6erxlSPJHF46Ool0ofoXzekUkmgiIssgALJq8fOCsghFZEJ8IqfiwV1DODGVQ1ARIRIBFBQEBAY1cWIqh395th8zWZVpspIAStmGZv9uakzr3T+cxK6BGegmxRWb2ua46uxYYP8lAnYPzGA0WUBGnRViUtARUiQoErOC2VbzdF6HSZnAIIsEGZOiRfc5yQe1LDv1ivv8yatDaAnJODmVg25S+CyruW5SZHI6MgUDLWEZlFCkC2zMoOwAcuK/rYMsYa2tiF9CpqDDcM2DQICwT0JGNfD04bFiVYWIDzqFdQ8IaI/4MJZiVRXO6YrgyFgaJqX45f5RFm5jCTtBRcTa1hD2nJxB/0QGBc1g4Q+iAPsk1g0TBYMpKsemMrhgZRyPvDbkCB6ySKCbJgZn2Fp57/YV+M/Xs07YTiUhzFZAHt49iG893YfB6ZyjzPQ0BfDRy9ehtzkITTeRUeeua5MCqYKBkMIsXu7E0nLx5k8dHIduCwuUFkVRCnagW68/OJLCiaksfJIA3WSCoW3REQQCnyRgYCprWVtdyp3La6RbCvjrgwmk8prz97bFDSgqKiOJPFJ5DQZlQkZeM0quuWhZmQ2TxV9nCjraIgr7DN2EQAgiPhHjKeaWbg0rAGAdvNocIdIvMQHhxb5JpAp6xUTcB3cN4tJ1zdDcgymDZrIYRUKAeFCBJVtBFgTEgwqmMsxS/M5z2iEQgoTlZbPnn1KKvEaRLTAFLaSI+D+/OIB0QYcsEKTyRonXKF3Q8c9PHEG2oDufaa9VWSQln/neHd1OpZzhRA4TadXZe1rDCrpiAYgCkCnoGE/mq+7V8xHoxqy8iKxqOB4QQMRYsoAHdw3i2m1dNfd9tca8A7bRQiuuF0qdNe42HFFqAqBVlZQAYf9vUiAekKHqJnK6CWpSEIEgIAlQJAGaSZG0rOvV8BqGMjCdxQUr47j/pQGoenGAqmHgyEQWiiTgvdt7MJ3T5pW3UYmL1zbhu88drfm6je0BPHNoHIoooGAazOtiXUuRMGUsUzDQFAT2nJxG/0QGAjVxUyewa2AGL59IYm1rCG0Rv6dxLTSNH8FZQP9EGofHUwgqIph2S6GDxZAGFXaonpzJQTMoCAGItbERwlyD9mFjUopkTocoEIRlCXndKNnIfbIIvyQgkVMramk2lAKjqQIM16HnMl6BgP0YlGLYikEL+ST4ZbEk1lQWCfKagVReRyKnw6QUTUEZmlE8iKJ+CdNZDTSrojWilJRBsT0uBi2WQSHUnBP8DtcYAWbN/s/Xhku+ZxlF33pvIJnXMJEuQDepK56TQJEEhHwy0jkDLx+fYu4rA8gaxXIptov28GgaBgVkgR3khmvCRMLeUzeZsGBbKQRBQDRQ6tayXTcHRlI4PpVFKqeVtQQncxqOTZiIBmTLqla6Uxd0QLIErWNTGXznmX7HsuNzuXkLuuFYduy4w2p4ift85dgkpjKaIyTZ19F9PX/8yiB6mgLQLBcCsaw79vWxx2cYFAWYjsA0G90wYZgEL/ZNYSiRh18RMZFRkdeLFjq/JMCviKyqwpFJDM3kcHIqC4MyZVIUmWsyndfxxmACsSBz+RNQUMrWsHsjBygSOR0HhpLYfWIGEb8M1TAxkyvGYAZkARG/jMGZHPKajnRBr5z9rJvobQri4d2DuPOnbzCXqiXtaQQ4MprGnT99Ax952xrkygi2bnKqgalMoWZSzUgiW7R+obLlxieBuc9NCkUgyNsnvkCgWELm0EwOewcTmLGsreyeIDBdSTompUhkNZg1Pk83KZJZFQQUqj73tdT6fJEAflmAZooYnMnBLOr7EAgQ8StQLKXIJwksdlsgTIGyFmJeM5EtGIj4ZSTzOkI+CbuOT2MiozrWstaQgq44S8T1S5XH7SarGmiL+JgnzGTfWDVMUCIgbFmKx9IFiITdC5o+911tq/aR8TROTLHvpxpsbdnjVw12vwzO5CCAoCWssNANwyzZg+3PTBd0NAUVPLZvFAXLYMEUDaZ4n5zOY+faZowlU8hpBppDpYJyLCAzb0pWLYkzrVbZ5td9ExiYysAwi/c/pUAqbyCrZvB83zgu39has/xVBSPlHFrCPpiUwi+LSBd0Z/XkdROqweaCEkDVqt9HBd1Aa1iBTxSQUQ3kVd0J4yMmhWmYMKiEkCIiGpBrzoUdr10rDGU6q+Krjx0sEWzdqLqJ+186jrdvasfewWTFijXuvA2gsjdodXOYnRNVlAdZJAj5FWRVA6puwHB5UigFDFCoOpvPTEFH33gGFIDfdc1UneLgCNuT3ONqFFy4XQQOjaaRzusIyiKymgETzOXNfLEsIzxpWTtAiy5aRwgAHGFvY0cQz/exWL/WkA+ay30uCwRTWRWKJCKvV9e6KQBqUic21z7PALaYBQA6paCEoivmh08UoBom/Fb2uvM+lEI1TMgis0wYJsVEWrXcU+y7uF/fGfOjoDOriWmaTlyb7SoSBMES+Bm2S9P9uz3OqWxtbdr+m4zlmrKFdjs8QzMMtIZ9GM/kkcgyjZtSQBQEEGYyd6x7FOx3VaeOVYsIxLGaU7CEvYAieEq2Gk3mkbasMbIowKRMQCAg1u/MRawb1FFwZmOaFOOpAvYPJ0uE80xBLZl/Cjhxh+vbI1Xnyx33OdtKFAuwGMZjU1lkrTg14gTHMSTLe3ByJoff9E+CAlAk4oS2FF3jgCgy7wAbNxOUDJcCIgosmVEQBJiW2zOjG2XiIU1IAouZy+Z1jCTy0E2KoE9ylCOJEIiKiGyBJV6a1BqPa+zUugZs3Zg4MJLC7oEZZAs6JIEg7pecdWRQimxBx2+OTgIgCFkCVFCRIFpCYVbVEfbLUCQB/ZNpfO1Xh5wYy9naWCKn4V+ePYbyR57rmgN49fhMSVJN3DplFEvIY0k1BIIAJzypnJLIbk0BWdVAQTecvQAAYFBkDEAUTBimiOFEHiZllmaWcOVOVLVdmB4hBAIh0K2/KDc2IhBIhCkeqjH3BTmNWZcifhlhv4TJjApdZwYCd6wmIeyezKk6+sZTLPa7+CWRzGkYTxfQEw9iMOGtIodJqeOiJZabbDJdABVEJ0aTApYhoNJ7sDyJ8bTqePUAJrg6lnNLqDFBIZGiW5hZ1WzrtOCKCyU4OZVlyhqYFdyeW4MyK/lwIufMOTUpcrqrOokklFwLoHqClKoZOD6RgV5Go6FgFv3jE1kEZLFm+St4TNoSrPMzVZh7xlEAqYKOgCyUDctzoxkUmYLOammnCnMMJTplglxLSEbMEm6rJaAmc5oThjKRVpHXSkM0FImFoewdmsGxyVzVsR2bzOGC3hiGZvIVK9bYeRtA9Xjt3uYgQoqIRK6yTBBURBArcbycBd0+LwkMDM3k5hiS3P8eGUtD100oXrWVBYILt4sCEyCnsmrRTQjANCjShom8ZkCwrH8mZZvQ7LhDgAkC69ojuGhVM545NI7pbAE+mR3ehsGsUpQSrGwO4I2hVM1RRfzMkuxey/a5YA9TBMXq1hC64wEMJfJI5rU5h7ckCIiGJOQKBpJ5jcVNud6zYJiQBYLWsA/941nrIC3deSiseDbTREB2WQxm3WduQXd1cwCHxzI1v6duHRb258zexJJ5DU0BxbGGEEKg6cXDRhQIc1UBEEVAM2yrY1G7tQ9SSRBwTlcUL/ZP13QpMashm6u8xg7c4qHG3tSklLn6Sh4vzg1zVevYP5xEIsvi2AxanH8DgGaaEAnLEj80msL69kjVpDM77jMoi2WtRD5JRDqjuiwBFIJYHJP9uElNpAsGREtgdYZOiv8YJoWd58QM07METeu9AiKwpjUE1RJsXW/jXEfNBKhuQKcmNJNZTFw+UudNbZd3JaHDPb+KRDCUyDnCQlovWnh9kmCFFBXQEfVh64oYjk9mSlyuLSEFvS0hJLIanj08gZPTOZf1sejWt2NOp7Jq9UFZnJzOYjqnIq8azDVuyTayICDqFzCTVa09hcCOOCz3dSVBYEpyhUMNYHuLQEx0RP0gICiUsUIahq0QlN6jlfCJArs+RuWxCYRgKJlDTiv/hlnVwPGpDJI5DYrIkmF19z1p72GEKf4nZ3JI5fU5n2VQWAd/Fm/d0FJ78AAzJGRUCAKgWBnjhsnmMFsA4kEZLWEFGbW6Ap4t6MgVdJeiVYTtw5bwTwDJZZ221w7ABNZsQUc8KMMvExwYTVkeCCssBEXjgUmBo5NZ9MT9yGkGjk/l2NpDUZiOBiQ0BRRkCkbNxMxNHeGS9VDuShV0Ey/2T5aUv1JcXirVMBH2SZhV4roieZ0yq3QVVM0sO5bZTKYLzKvoesytqlMA01kNK2KBYum68XRJwvGJyQwODKfwBxetgEDYunBtwQAoNIN5KZuCCn61d9TT93z8jXFWscZSLEaTTLHYuiKOG3b0OCFmTrx2gXkmgrIA3aA4PpHB3zyyDzfuWAHTZB7HEqMAXOvChOXZrK4RaAatGT6Syut4aPcgbrq419P3XCi4cLsIbGiLwKDFReEyLICCaUSKBPhlsWysnb2UQoqIpqAPH3nbWhydYCWWkvmCExsqiwTr28O4eHWTJ+G2oNE5J9GcZUtZksoFvXEUyiTVNIcUSALB5s4onjk87ljmSgQPWwjLa9AMpvlVI22VZal2C4kC8LYN7XjswETN71nOJegmlddxYioLE3YSXqmVlFrXTSQUQUWEaZbGCDpjIqyKwIqmoKfyOO3RAERCULATmlwYFCCEOgoPBTtQ3RMrWJYfw6TIWQqDYVJHcLJXmUkps/Bb4R61ks6iARk+UUBWZa52zSiGvsgiiwGXCOC2cRHXv87BQIGeeABBl9VAIMVwBFtIDflEaIaBaiZLSpn1rZY1xjBZWI9IBBBCoRkUggBHyDWtGFet9PSpiGmy+FdVM2CY1sqwbl7doBAFQBbt0AgRO3qb5lRUyBQM5GW27m3BX7Qs/vZ1FwXmNqwlcNvYSpP9cs06lNi/zFJvwnJxG+Wt/rb3YdzDoaabJppDChNIqwyyxtMOsYBsCZ2VD1yBoESwLWfdzRQMTGULGE8VqiagTqYLyFQReCjYPtAR91V4RSlhv4SxlApqArDizVWTomCwcUb8MtJ5DTU84yy511rULj25ZFwA86RF/SJGUwazTs96DSHsM49NZFHQDcgicTxBzntY863qLOEnbQnVxLWtmJTFyOum6alj1a/7JmveRhTAWKoAWRSwui2EAyMpJPNqSRz8+o4whmZyNfd9gTBPmlpjT68x7dZ3BYZn8shphvP9ZxsPAOYheK5/HD95dahCAipYMvWeYWi6WXb92y9XddMKpajNZKaA83pi2NIVrWiI0HUT3366D+m8DlkSkCopZSYgndfxyzdGWCw9ZfkLBi2GFYqEsDwUSmFQExUiJYpz4mHcFMDxqdpGp4WGC7eLACVWGIKL2a4fahatX5UQCEHEL82KvbQPW1tcZlmPXjg2lUUNBRi6QdE3nnaK+k+k8mgKKc7NoekGWiN+XLmlHb98Y8QSHlAi/EkElpXXQP9YxhHyWaGoIgTs5tFNCp/EYkorEVIkRAJyzc0QqL3RaQZFtqCCMFvqnOedRwgQ8knIqkbZD7UPmJxqeiqPo0hFy105KAWoJQyCFkMW7L+hrnjpglWj2N5Ei59ZfHOTUowlc/jBi8fRP54psci6k85iARnxoIyjk5pTIs6ujpFVWTJRc0BExrUOZ8fc2mzrjeIXbyhI5vWSGC5rOgHCNtyRZA1LjG7ilWPTpfNT4bWjKZWVIaOUCaZq0c0rigQhWYReS0q2OD6VgaYbTpKRKBDnXjNMFgJCiIlVzQEMJ3JY3x4uic9zx8f5JVIMabBCL+zfZ89bLd60phmvnUxC0/MYSuTgs6yH48k8CpQg4pMQ9onIFHRm5bNWtvvzKGULiXg41HQTyBa0Eo9SObwK533jWQCwcgtKFQ1C2OP6rDcr99YUwJP7x5CYZX2b/ZqUWvt6GxTYeyLhafwFvbqgnNMM9FvfsRZjmUIxlAKV1zWz2DJ3+ezXiASQJYKRZN4SomYp6GBeEHuZpSwDhZ3z4X5lTjMdhadWhYPXT5bek5WI+ERohonhyRwkgaA1pJRYbo9NZBAPSrUFZVoMs6oHdty+T2L7mzvUxlbkNYPi+SMTJaXrRFcCqmEloO4amEamhuCa03Ssag7g1ZPJmmPb1MHCxwSBVKxa8XzfBAamWZhAQTcdjyIIULCUoOFE3kmaZSFexAl30ay4c5EQjCXyHmetNiMz9XuvU4ULt4vAodG0JZjMdYsDllBnoiTuqhysfq2J7zzTjxNTOQRkAQFFdDRgUIoTU7kSl3o1FKG2JmYCMKiJ83piuGZrF779dB/6J7OuuJ4Artna5RzYIOwgdGvAupVoYJi0ZDOcPQ/u3/2SCJOaTiyrDQGL6eyK+XFwNFW3Te6wp4OIWLUVCQTrQHZUCus/qbyGkE9kCsgc84r7tQChcw/wOZgugcqyPFrTzDYpSkGIAL8rtrTy6IGfvz6CgyNpqLqO2QPcO2jgu8/04+9u3ApRIK6EHjjVMYKKwKyhogiRFLOWzVn/AuzAHZwuIB5QkAzqyFgl1tyehpCPhbjUwgTLsrdfWckSSQB0xQNoCso4PquUln3AJ/M6/JLgWDurMZRQHcs5ANf1cisNwMVrWvDrvsmq8XH9E+niHjBrXCVx5ah+XypW7LNixWnD9XrnX8ri10WBhb8ApCRMw1a2BVHAsMfObweGUzUrCXi3PDMBRXcJ+Q6UPe5hWQBg9U297Xi1GZjOebI+V4tfBIDRZB4JjyEm2RoVX2xmMnqJx8/G/n08paIz4gdQWUB2Py6LzBMyt+KOAE2neKFvqmaFg1rhATaixBK4ZiddAoCfmhhLFhBQasclUABZzVuuRS0EFGs/2/egcy/aBgRrU0nkdCdW3gSzmLqrGQEo1vGugmECm7qiwJ7aoQmfumpjzdeMJvMoWGFrs+PlCWwPEfM4UhBm9XbV9BYFFmYWVEQUPFSr8Ipfbmy8LcCF20VhPJWvHd8HJuDaVhWCYrigfcNphonnDk/g5eNTUA2TxVfOSr6RRQHDnpMivI1/MqVi72ACP98zjKAiYfvKOCQrvi2V1/HzPcPY3huHYVkBmYuj+Pd2LJdhCWI21T7eL0tY2ezDoVF2oLq16Y0dEQQV6ZTasVYia2XLV4c6RelZHUonqRySwMqOZVUDhsHK4+gGxds3tpZ0aVrdEnC6NF2wIlZTWDCsz8ppphPXaM+FabCDJh6Q0BP3zz315oweODSSQt6qkzz7xaaq49d9E/j10Qn4JBExP6ttG54VHxeUCWSBFSFXDaOs9V8SAZ8ooqAzS09X3I9DI+kS4VAgBF1xv+frKADOdxStG8V2wcGOWSTA5q4wmgIKjtLyCosdS+slH1HELEvzLOx7szXiw+1XbpiTbLLVFe6hSASigKpWUgGATxag6mbF0BdZFJDNmyjoBrIqKx8l2ElwYOErWVVH1C8j7BNRsBQtVlmFfQ9m2Qb8iuWJ8EDfeP1cjT5RqChE2nPqtXZ+LaPAfDCsCi9mjc2g1lZhh2F5oTWi1Nx7TArk7HhvoEQhNE0K0wRSOQ2KTDzt64QALUEFGVWfVXFHQEhhcbE5TS+pcFDSitiqcJD3uHbeOJmCTxIR8stI5DQoklgMy9MNhK1kTC8cGanPOhQEYFNHGIrI9lc3jvfLBAIyKzVoK2KC5Vmwk4ltg4ObSnsFAExkNMQDEmaqKEjxgISxrIpwSKmaH0EprDCDuZ9rK4+sKpOIqaw2p44wy2kw0BP3I+5XvEybJ5pDcu0XLTBcuF0E2kLetGkBLGHJMFyudMoel0S2wR0cSWEqrTral0DYTWpr3YZpwIu9gB1w3rQrSl0tHduK7WRDkoiOiA9HxjN4dWAalFKnHIyTQG8JRYbJLL3n90TxxMHxqocDAYuvHE+rCPtEiILgCPCGaWI8raI7JiAWqN/yzat6TZerYTILhEApMmoZi7J1GP+6f8Kps/pf+8dK6qweGkthrdWlKa96s+zEAhLShbnZvGxMFFu6YljVGkItQ6RJUVIXePb4DQDTWRVvDCXntIc0jNL2kMOJHNKqDqICdFZRZZEwwTYWlLGlK4KHdw9aHa1Kx6MaJg4MJxHyebuOilV3Mm/FThI7LIMWXe4sa5zNczXSHkN37LHZSqftzgPg1BJ2HygExLHOlzvgaoUf2EpqW9iHrGYg5ypRFpRF+GUROc3AeCZf0lglorA3FgSCjFVwHaBoDfsxlS5AM00Eg1KJQCELArpi3pWLqUz9lEnTpDWFUq8ya0CuManzIBqQy1+4U2DvoLcQB7ZXVH8Ntf5DUdrBD7Aqipis4kjfaNrTZ9pbXWvYxyo6mMWmFzmrgP/mzggeeW0YyZxatjOlbuqevC4AE8xlUcDqliAOjiQxkSqUtFRf3x7EoRFv81UuL6UcNXR9EEKwtjWMsE9CTqu8F4d9Mja1sRABCpSUUrRDaAxXyAdTPuC83n7cjoTyicwwIBf0snV4ZQHwyVKxxXOVjnprW0IlynfJ/mJboq3nKpUC0w2Wm7CmPegpzK8WBMC123pO811OHy7cLgLhgOjJZcySVNjv7kRvJriyTSyiSI71UBTgBPkTwg57wwR0MEHLMMsvVAKW6V0pAWM2o6kCBqZzVbuPjSYLTmkfat9RQMkARELw5nWt+O5zx6puUEFFhE8SMJHKz3FhUcuFpYYUvHlNK/71+eN1CU2IB6SyljI3FOxQnp0AYz+nm4BEWcxarTqrK5qDaHbFZlbT9GvF96XymlM4vxa1FCw7ttJuD7l9ZRxHJ7KO5XlNaxBZlXW3C8psPRBC4JeYeOyXBGQ0NuYNbRG8eVWL1ehh7ve05yzj0WIjCgQ98QCOTWQqWjW74wHsG0rWbO5RIx/FIRqQWUKaFZHtbh8sEsJiogVgIlXAT3cPFbPKrbCE1wcTGJxh3a/6J9I1Y9xZYwMC1WDNCXQTrk55zAUf9ktoCftKGqtQS7OhFE5jlYxq4NyeGACWrZ3Ks7bctpIiiQTbe5vQP147/g8AlDq6Go9MZGvG+Xq9rw3qvT1qLTqiPlCvLq0aeN1fU7m5FRwqIcAVew/bys2scwBLbPKC3+qG55cFyKLAXBRg+2syp2FVSwgrmoLMC2cy4YfF3rNucjnNhEDYOeKFNc0BHBjNYmAqi7wVG2pHreZ1FtvLSkbWFlzdMe3VcBXjKItICNKq7tThroRmGMjoOrvfKWXVQwRXpRO7ko5gW3wtQ4gVI20bdwA2X81hBQXdRNjHOmTObmAS9sko6AZePzmD545MVmzNfvuVG9A/mSnmYAAlN439v4TAsRITlE+am8oUEPbLkKzmUJWQSfmYbzcCAVY1BavO6WLAhdtFIOSTPLmKRALYycGzk28AqyZgqBh0T2aJRMSld4UUCbpJoRoGXF03IYqAYlnVtvfG8Yt9YzXH1RH1YffJBGYyasWONqwBQXW9j/Wgz2JFcwB9o+myAoZEgPaoH4JVDN2uG2p/XlbVEfZJUCQBRKzPIQSgbHmjcjhFvq3fZ/+/ZlCsbwnVrLM6ksjjnVvaK8Zhu5nJasxCj9JYTPv3g6MpvHRk0tsX9UAs6MP6doqnD45jMlNATis2Szg4mkRLyIfLN7ZhIl2Af0S0OgPZC5bF8EmCgHhQxnNHJxy392wB3v7uXgXNSEC2auiWf55an31oxNsB74XWiIKwIlkZzsz9bxJWj4AQdseFZBEvW21dK2WVP7hrEAG5mFBWSemkANoiPoynVGfty5YimrTqs75pVbPVJrbYWIVQE4CKlrAPlAisPJRm4OLVTVB1k7XobZJYaTArnMiOBb7/RW/u803tIQxMZstam+bLRLp+CSdhj5b/WlYpkQDNQZ+nUmZeWNMWwmh6pubrRNGbgGiX9wIhVkMLpmTZsdQsJr44F5X2KADY0hPDoZEUxpIFRAOsDrOqM8E2oIj4yOVrkSkYmMmy30FZZQfdNOF0pgQ8z9Wb1rXixWNHMJPVIAlgYQmWUKbp7HM6oz4Atdfi5q4wfnt8pubr7LCXSveaIBAcHElVrc5DwKr32AImuwepE7JgB/qLhCCsiIgEJIwkC077dvcbSQLBqpYgOmMBVqe3Qlm6mZyGsE/Eb45Nsy6Ruom+8bTTzbA1pCCr6nhw1yA2doQgWgmjRpnvapeDU3XTaZDjVtAla2FkVAOTybxTD7fSXIiSAN3DBvDwnqGGlwKrn8rLqciR0XRNzXy2u6MckihAEgTHFeR0EaHF2BuAWW3XtIUgiwKiPhmxkIxYQEIsJCPqkyGLAt60qhlhvzcNmIK5q7OagZj1N3Z3lZhfRlYzWPtYsNJVEil237HLRomuMIWYX8FFa5oR84tOyR9JAGJ+CRetbkZAEUEIwdYVcTSHFKd0jaqbaAn5cP6KOGRRwP7h+iSUEQBNHmOEXDKc87fu3wGK/SNJp84qNVlctP1Drcc108SKpkBJg4tyiJb1wScJCPgkBGQmzARkCQEf68yU1w0cnfKWme0FUQBawwpOTmeRyusQCLP0CIQlTZyczkIgBDM5DTt6m7CyKeC470M+Cb1NAezobcJUVsUv9w6XHK6z8eoBJgAuWt2Mk1O5ikkbJoCTU7ma4SXzQSQEq1rYAaIarJ2nYbKauqpBIRKCzlgAI4k8umKBkk5CAPOs2B3p8vrckk/2j/vxnetasLkzAsEq05PKa8iqrOrB5s4IPvy2tYgHFaexCgAoloBk/6saJnyigPNXxHH7lRtw/ooY6wyWZ0l9W63Hz+uJ4eK1zZ7m4u1b2qFI1a23Xm27Xq2aXuiO++GTqq8kRYTTprcSK5sDCAYkT+WOaiES4PYrNtRMihMIcMVGb7V17ZAb2/Pmk5mHy/LdIRaQ8abVTXPWkzDrdwLg2q1d+NzvbkFvSxDpgo6xVAHpgo7eliA+97tbcN0FPU6965AioTXiQ1vEh9Zw8V9mQDFr3sMCmAKStvaSObE5VlJWuaYM5Yj4ZKdpUDkIrPMGpfeX+3liTUhO1ct6ldy/6yaQK+hY1RJyFAFJFJgSL7JQqVhAxpq2MN60pgUrmgKI+SWIIovLFUWCmF/CiqYALl3fhvXtIWTVytZ6CpYDcmQsjcHpHPonM6z7XJ4ltfVPZjA4ncOrA9OwG/7Ye+zsHwpm0AJhir9fEeGXRKubqQi/IkISWYz5ZFZD0CchoIhQROJY1gWwJNaAIkJibiz4RDJnbQuEPU7BOuo1Gm65XQSmsqqz0GYnUbh/12uYsAqagYAsOt1emHBb2s5UsrIfb9jegx+9wmJ1ZrecXd8exoffthb7hhKORbQSkkCsdqTsQBpP56EZcNXSAwBiCbIsZgtAhcxsAlCWzBIPKFh1XrCsy1s3WWkTnyziwt4mpAo6NN2ELAmI+CSkCwbyuoERj6VLfBKZUxrHhoDFtG5ojwCobcW2/waYG7NqPzg4k4NIBFCYTqKCW8CTRAKBMEUl6pcwnq4c72XHWNqblehkVbk+lwKkfrICqAn8fM8wRELg97F2y3a1hLBPgqabeOLgGFpDCta0hdEd9yObVwEM4qLVzQj6FZgUODbhKvtGimdaiSWJMuG9VoUASWQxf7WSn9jzXkXm2oT8rNxcroIPPWe3ACZs8y+H3ZGuOxqYc//PLgXHhJ12/OFFvVWT0/rH0yWNVewOZZppIpk3IFnxtLGAjLVt4aq1MpuCiqNEVUIkwOgMawwhVUiKkwS2Dr0kXa9tCaFvoj4HYHNQQUvYh6m0OieExxYE40EJUo1aiwJh96MXas2XJApoiwbwnvM78fM9IxVf957zO7G1txkER+eMe/bv53VHsW8kDd1SmOGy3EqCgLesa8VHLluH7z13rCQ5a/YwI34JN1ywAofG00wYJgTZgoGgT8RFq+JYZ5Wdsutdq4YJPxVnhS/YnSkFiAKteoaIIsFrAzMo6KxW8uy28X6nbbw3D0JzSMGKpgCOT2Yd4QvWv7bA1RbxYyZbcBrauAUxpxoCgdNMaLaxYva8NYV9rNa7bkDTJUxkimXRWkMyZEnE9t4mXL+9G+m8jsl0gQmNJsuJ0Q2KlrAPN+zoweBkrmZIjmECA1MZZFXTSSC0k8sNkyKR19E/kcaa1hD8soCsdUbMXjMmBQKSCFVja2aOLcUKmRAJq1zTFFRAKJDXWfMde682KIVfEkEJazyiGtQySrEZY3IEnMd74oHqX3AR4JbbRSCoiE6MEcAWgv0DFGOP7GO7kgapGRQ+WcDK5gBEgSAoi9bmwuKmgrIIUSDobQ5i28o4QoqEsI/9hHxi8f8t11VH1I+AzLQ00bKyElhtUYmlrcmi4/pUdZPdbKRYPiarmlB107Imik6bQTv2yK5L6JcFhGRWrHt9exh94ynsGpjGwFQWo8kCBqay2DUwjb7xFM7vieH8niiGE0zIjfhlNId9iPhlUADDiRw2tEfQFfMXhT5S1Nbt/7efW9sSgl8WSx6z/8Yvi7h8YzvWtEZKbobZVjXncVJMFpgNK31IEPbJEEU7a5mUJIMAzK0uutzr7haZ7s+3v4dkJYyUQzMpRFHABb1NnkS6ct/J/ZwkANPZAoYSrJ5xa5hZaVqsf1vDPjSFFEymC8hpBnKqUZLIYFf6sLuwXbKmuUSgI8TursT+wH5cqLETEQLsPu6tYLxPEmpubF7mSiDA+tYwjoynnb9x37v2e5ycyTmJOOWw52JNe7AkRpFirtvUJwnoaQ7gvJ4YPvc7W3D7O9bjg5euxu3vWI/P/s45Tlei1S0hXNAbR0tIRnNQcTwpqiVANIdkbO9tcnq827Uyt62MY21buMRLFAsoiAWVinMmAIhbn0EBdET8iPgEyCITdGWRIOIT0R7xQ7T2kmooIsEfvmkVlBrWVonUPqAkArTH/LhoVRP8sojmoIymkIx4QEJTSEZzUIZfFrGmNcyy9C2Lk/3J9n6niATjqQIODtdufgOwfa3afFFQnJzJ4p73X4hrtnaWtXJds7UT97z/QvQ2hZx1MVtxtn/3SQI+cNkanNcdRdQnIahICCjs36hPwnndUXz4bWvh90v4o8vXsoYvZZAFgj+6fC0Ojafx9ccP442hFNa2hvGmNc1Y2xrGG0MpfP3xw9g7mEAsIKM7HoAoCKzzpFUXWzNMJPOapZyzUCFZKL+HyQKr7mEL24oklFh/2b9KiQfL3vvclsOSvVsQ8GdXbUTUL7Fka5eAxZpdSPhvb1rhhA3ZuSlsn2IVSwgAQik6ov5Swdf1475WHVE/btyxAlG/7MSv2h7UmRyrTHLDjh7HI7J1ZdwKvSBQJBHbVjY5npIXjs4NISt3tbJWbWZ23lCY1sjs8yeV19E3lkbEX0wUdWMLplG/hIhfYrXmNR15TUdBM5DXdGQ1lj8TC8g4tzuK5pCCde1htIR9EAR2BQRBQGvYj3XtYaxpDkEU565P97qVRILrtnaX+UaLC7fcLgKXrm+FXxatNrt24DfTukXChFpJYIHc9sZra2kExWBxkwLJrI4/unwda7enGogGivGomYKBsE/C/3rbGvxk9xB0k+Idm9vndMjqG8/gwV2D+KurN2FlcwBHJzLwSYLTB10g7MDSTYre5iDesrYV333uKGRJgI+wkkK2JS/kE1mPdINtFFNZDbphIOSblZktiuiK+dEUVHDByjgeeW0IOdVANCDD72OC8/BMHgFFxAW9zHowNHO4at3QiXQekkicLNBicl1xq5BEgv92cS8efWMER0ZTUA3qJMP5RIJ1HczNOzCVgU8uZuLP3igIYHX9oU6invsQNyjTtn0SwbvO7cBPdg8ia1IEZQEUIuzargQmspqJsEgQ8LFSWa1hH3Kagbxusja/AoFfEhCQRWQKGuJBBZNW/3l3rJqqGzBNYFVLANfv6MYDvz2BmQrWDwLWblkgBOmC4WT6O1ZUwizvTUEZQZ8M3aDwBQRrcy7dehVJgJkHmoIy+sZT0AwTBVXHO84BXh2YgU+RIIsC3ry2Fb+/fSX++ck+DM3kYZiAQKir5Sz7/KagjFReq2oRN01gOjdXeCy3qcdDPrSElaoW8ZhfQl43ULAs+rOvpW3xOzCaRFY1oFjuNrc7XbJco3bW/3Aih/W+cMn6czdxaAr60BULsDi6WWXdALa+umIB5FSzbJb0U4fGnSxpQSBOY5XJdAGrmvwAMtjUEcFUTnesRLVCnQDWLWxVc9CKL9TmlBcM+2T0NgexoinIYp4BtEcDZbPsFUlASBEwWaXOWlvEj8s3tmHn2hY8e2iirMVeALClO4q+iQw03Syb5CKLBIosoisWwEfetg5jyQL6JzIglDoXVBAINrSG8KY1zXj52DQEwkJnKC16nwgBVM1AXjMwlfFWwcTZn11WQUdoJix4YNJaf/e8/0Jksxr+/leHMDCdRW9TEJ++aiOCQRYKldUM9DQFMDCZYTXCrfew709ZAHqaAuiJs5CBahZ9ALjtig0AgO89exTJvOZcy1hAxocuW4OPXb4ef/3zfTVjxP/3ezbX7Ey5oimIqb5Jy3sxt4IMsRIvt3RF8fiBMWQKOmRRmWMFzhR0BBTRUaCode3s+9suzSYKBB1RP37XEp6+9dQRjCdzAHT4ZBErW4L4o7evQzwgQxIEmNZnuKs62PewKLCWzZVK0tkIhFm77S9lC8giJc6+6ZZOa3UVs9Vie0jlqh24x1Mp382kwOGxJFJ5vWIzIEKAvGpgdWsYewYTLPnNLcybbJ63dMdw2bo2PH1oAq+fTGDHyrhTFUmWBIQVEUfGM1jVEkTXtB8nZ/IwaPG6mJTCoGwuO6N+DKXyWOsv33hiseDC7SKwvi2CN61qxvN9rFUsixdih4RuWNmFzUGnjqR94M0+7AXCXJzXXcDKbHz76T4MJfLIqiYkkWB1SwgfuXwtzu+J4cFdg+iKBSAIAqKBUhuDHQN4MpGrKSh/5PK1EC3hRhZYXBer70odF2Uip0ESCM7pjODAaAq6ITmxfaJA0BL2QRJYZnZvUxD/98XjaIv4nA0zU2Bdr7qbApAEgtdOJHDt1u6afbV1PeK4p5wEC7B/TcoOrZVNQdx8ySpcuLq5astZvyygKaQgkWUWitmhHLIoIOSTkFN1JxO/nFvSL7MY2LaoD+mCjrxOoUjUqZ1Z0FlSQFvEh2nrIFUk9t5MuaBOeIdmmMhpAq45vxM/2T2EdF6HqhfdT5QC0YCEP71qAza2R/G2ja341f5RFDTTERxty5RPFvD2je2gAJ48OO60Y3QXIrddm+d2RyGJBAXdLFtYXdXZeju3O4Yf7zqJnGqgNSQ5a9RWUratjEFRRPzluzfjcz/Zi7SVEOU+ASN+Ce/Y1IafvDYMRbIzjYsvsDOOdRNoj5bGTNqHA0HpYbCmlQliWdUoW5UjrIhoj/mRKxgYTeWt1sTF5wlha70rFsDQTB4mpVbB+2IjFvv+FK37ZWVzEFMZtaoyFlRE9DQFoIgE/ROZEoFNFlnnuraoHyOJvFN6rysWQCBmVV5wZUmf1xPDeT0x5x45ZlU80KmJbSubSnrP18K2Ahd0A82GjOms5iSvNAVZjP723ia894IefP+FYxiYzFbMsu+M+RH2SdDNDFIFvaQLm0BY7GV7xIeTiRz+/OrNmMrswWGrlrVbsd7QEcFfX3cePvPQ6zg6noHPJ0B1tYFWLKV2VVMQl65rhSQJVQW/NwYTbBwCKQoRrlOeCATUoAgq5a1g5RAswbjotWAWfbbEKVpcMb7BoIwv/N65Zd8n4pfQHQ8gFpDRN54ulg4UCUKKiLVtYYR8zPpWK8TE5rYrNuDDl67FQ7sHMTiTQ088gPde0ANFEdE/nq7ZeezwWAoD09kSBaqnKQjRqsKTzLHkxY++fS1ePjaFrEoRlEXMrj2d1QyEJYL3buvBC/2TeObQBJJ5DUGFNW8xrCRhkwI7VsWx58QMprMaCxuYFXJHwBThzZ2sLNd1F/TgmvO68NyhESQO/xZ///tbcdnGTkiSgFcHphH0iRA0OMlwxTAItqf5ZRZLbN/DlRAF5rK3a5e/Y1NbWcHvwV2D2NIVZc11hMpdxd68rhn/8vxRODrYrJiJ2edKpZERsG6k9n02+61sz2mqoEORmBdWN0wWzmgZUUTC4objAblEYT4ynkHEzxJQVSsBujmk4LL1rTgxlUMsoODgaMol3LLa4RvbWQ36lMcKOAsJF24XAUEg+PN3b8LUg4U5MbCKSLCuPYw/vnw9Pv0fryGnGpZLu3gqiAKroxdQRFy2vg1A8cZ+vm8C46kC2iI+Z5N/7cQMCpqJQKx6DGAqr9cUlK+7oAevnZhBU0DBNFSkCiw+VrEO+5SlcccDCt66kd30lTbCG3b0YGA6iyNjaaxriyDkE5HK6yUba6Zg4PBYCscmMzU1YEli7qk7f/pGMVkBsIRugrCfCX6SJNR8r7WtYVy0qhnPHBqHXxasmFlmbTWoCcNkGvmeE9OsUUOZHYcdzALSBQMxv4IdqxQcGElZhxVLJIv4JWzqiMCkzIIV9rNM/OaQMiu5jPV4D/sl3PzmVbigtwnffroPJ6dzjuCxoimAP7p8nXMNbetV33gaBavHuZ0MtrYtjI9cvg4AMJ4qONm3RSWFYF0bi8Xe3BFBd8zvCDGzS7Elcxp6W4LIa4ajpORVq3sPxRwlxR7fN586ghNTxfH3Ngfw0bevhygQ/GzPMChl1lI6S9BUdRMiAda2ReCTiFPZwqRzLbc+iSAWZJaht6xvRf9YEuMpDZppQhYEtEdkrGmPYjSRR1AR0RbxoX8ig4x1wNqWvbUtIYT8ElY2BUEIUNBMp6al3TbaMCl0g4IIwPaVcZzbE6uqjJkmRVNQxusnE4j4JJgEziEjUGAkWcDGjghe6J+oaVWzD1F7XfeNJrDvN2P43+85B+s6Yp4sts66nWUFbg4pZWMFFUV0lOFKWfa/d0EPnjs8gUvWtODYZMZq42opdGEfVrWEkMhpSOV1bFsZx903bMV/vHwCLx+fdmLvL1rdjN+3lE5H+S7oTpKOYTBLX9jPlG/Jum+q3eMF3bAURgpZoHOs67pBoYgCLlzThJ/vHYaq294g1x1pLTRJYHOmW3syK7Orwy+KEAUW9y0LIlbEvZVDWt0Swvr2MF4/mcC7t3RiNFVAXjPgl1kd8b6JDDa0R+aEmNRCUcSyGeupvF6785hmOtfIbWTIFNi6thUo+x5Kq8zzpEh2cw7W8EYWBbSFfRhK5Uus61m1KPywRMkwPnHVRnz32aN44sAYNN0AdeVtEEohyyLesq4Va1uL312SBFy6oQ3/eRi4dEObsxZiARndsQCGZ3LQKUXIV9qIRiKsy6UAAlkSQSkrk+f2ZtmeC1kS0T+WcRQCIgiI+MsbjI5NZmpem8s3tKMz5mfeLGpVXkFpmFJTQEIipzsFGWZDrfG1hGSnM5nsjrcBIFAWtmaYzAC1sT3s7HXUCtPwKyLWtoQwlVWdM9fuRHpkPO3qROrH/9i5ihnOXh1ERzSMLV0RnJhOA5jEjlVxrGwKI6tRzOTUoqW7gTR+BGcJ5/XESjbyjGogpIi4aFUTfv+ildjSFcUDLw/g+SOTMAwKRRZgd4RXNSYYXbS6GevaS2/syze1z/msiF+CTxaQUw2EyywyOwbQXoDVBGX7/ZrDLGljJJkr2QxbQgo6ogFQUGxbGceGjkjFjfC8nliJ4E0ImVOz0C14A7U3crdwPp7IAtARkEX0tgZLBL9a7yUIBB9521qMpfLoH88U3faEQhIEbOgI4ZrzO7F7YBqCQCBSCkEsNpcwre4VdsUKO2lu9ZYQRpJ557DqjPqRUQ3M5FRs7ow4AvVMVkXIJ7ss58yd+KZVLBZufXuk6jWy15gXt6WX19QSYt67fQWeOTzuKCksoSyDC1bGEfQrJUrK2rZw1TV2ZCyFWEDGTFazrCGlZYIoBeJBGd1xP3qago77FigVbCUB6GkKsqRKWYBfEvGmNa1lFahYQEZb1IeBySzetaUdoynVJVAo6J/IYkN7BB/cuRr/9ORhFEwKES4rHazQCgA+gcWY+f2SB6sau745w4A735uCQhZFZFQDU/YhWqXygvsQFQSC1a0h7AOwunWuFc8L7kNtaCI/p722vS5mK8PJvA5JZBUlbK/Rb49NsWTQVZWTQe29p5bSaX/et57uw+B0DrrKBOVVraE593c1moIKepuDODaZRU5zC2GW8iQQrGwOYEU8iNUtIfRPZBxhx3XpIAkEXVE/KAHGEnnkNBOS1UCDgiKnsfF1xvzIeuxC4VYu+iYy6IoF0B71I6ca6JvIOJb/U7mu5Yj4pZLOY7NLOyZyOrpjfk/X6LUTM4gFFGxfqeCgrchbCnPYUuQphSMo19p7intwepYnS8DathA+/La1nubB7Y2wazzbjWiag4pT43l9RxghRXSqfLiNAnYctF+RHAXXi8GoFpIk4C/fvRmffeh1ZArGnJCIsE/EH719Hb72q8PIW0p1OYusz6o7DNjeKyv0zdo7KS02lpjJ6kgR3aqE4ysJGRxN5hE3FKTyutOJNCCL2L6yqcRA9fM9w1jTWlTE1reHsbY1AmASa1sj0CnBcCKNrSvijiLWSLhwu4jU2shtN51t3bWtfXZXkj+/epPnG9tZgFViAN0LsJKgPPv9tq+Ml43htd9PEEjV7zhfwdsLs91Tf+dyT82H83pi+Nw1WyqGL0ymWVedWECGZpiWdYdtKAEfizPVTYqQIpZsAF2uzFH3/K9tDZcI1FlVd8wGtkDt3syrXSP3d6glYHl5TS0hZnVLCI/tG52jpEQDMgyQspt9pfGvbQ1j57oWPHGAhUtoRrFvOxEIfFa4xObOKHriAcT9LNY3UzBcXY4krGsLI+SXsLEjUjL/bgXKPf/Xb+/GPz9xBP0TWXTFAuiwBIr+iawjUIxkCuiM+TEwmWPWetdJZMfmdsaKMWbVFKhjkxmcmMrCJwlWbeDic4QQ+CQBJ6az8EkiuitY/eZziM6HWu2117WFSwTcSoqKadKSuY/4y8+9e++ppcCuawvjktXNeJmUGgXWzfqbat2ctnRF8Zb1rVCNMUxnVGRVsyiE+Vjy2aXr23DpulbrdSamM+qsNSaiKaRgR28zxpJ5tIWZ1V/X2bXQTYqIX8Ka1pATRuAVd4hJJct/vehtCqKgG8jktZImObJIEPVLTpOcXlch/krXyN7P4wEFq1uCGEkWXIq8D1nVLLHk1dp7au3BXufBrTBUq/EcVETHwquZJoI+pTRXxKo60hH11/XccrxZTx7GiemiMtnbFMBHr1iP1S0h/Ch+EgOTGRjmrEoylMVhd8cDCPmZYmLXPDYp8waBEBbSZZULLVjdY5pDCtzm3aAiYiqjgmZVBGURP/ztAKYyKjZ0RErkho6oD0fG0vjJq0O4YXsPC10YS2NFzAeEgHRex8lEoe6K2OnAhdtFptpGPtu6W85N5/UznNiZKjGAXhdgiWVhnFkW4kGFWRbG51oWqn3HUxG8vVDJPTVfqm2+rw5MM8HEYPV27ULdrBQPQSKnwieJiAcVz/Nfr83cjRe3pZfXVBNi+sfTddvsmdXcclmOp6GZrlJzArDWCpdgFmzLfXtuJ0bTKvKqAb8ioiOsoM+ytq5tDXuafy8CxWsnZtARCSDql3FwNAXNSkAjYCEQG9sjCHiMMUvkNAwlcgAoumOBsutnKqNiRTxQV+WvFqbpaq/dHp51qNE5oRBAZUWl3nvP3sEEvv74YUxlVKxpDTvvtXcoiaHHDzvxx+7XVYpTdodeVCrTJEmC87qJVB6yJDpCsKYbaI34cctbVuGhVwfx+skErj6nA5PpHIBRXLKmGS3hwJwwAnuOa8XJelE668GApUDVapIzMJ2tuUeU7OftYXRXUOTno8zUax5m398ZzSgbKjTbwluui9+l61rx1KHxup5btfZXOw67fzyNjGoUw6YUCWvbmAJ1bnfU8XoJAGTXuWcYJigIwn6RJYgbxXDI4tiLou7JmWwx9KKK1yjkk0pj/UNAIq8uiCJ2OnDhdomxUDf26VoC6vV+9T78FoJKm687jstOilAkwSqOrzlavl1b1Ot8LdahdipUEmJmKyko2TDnv9l7Dakoum+ZtbU9Yrtvs3OUBi/zX2vubctURzSMLZ1RHJ3MIKsaCCoi1rSEkNVMzzFmdlH8oCxaNaJJSdcDnyQiqxpoj/hqVl6op9vv2GTG06HmJZ4QqN9eUUnonh1/vNkKhar1us9dc07JuApWeMK53ZE5a6L862LO6wRCnDCCFTEf+zyfXDaMoJpFefZceI2nPR1SeR2yyK7HsakMkjkdOZdA19scdOKia7FQ+3m95qHW/e3VwutWeur5Pb3sr1dXicO+bF1bidfLXf/Y9nptXRHHZFrFdE4tm8xn58xMplXPoRfbVsZPO9Z/oeHC7RJksW7sRr3fYrrg6km5OC77UHDHcdmCx3zmazEOtXoy+1Crh3vKa0hFvZUGz56G9jDWtUec5+YrbJYWxZ+b1KQaJnySgKvP68Qv3xhdNOXPTjCqRzyhTT32Cq9C9/N9E56Fc6/j8uI+92K98mJRXuz9zlbYajXJ8eodWOr7uRdLcS0Lb7nXLeT39BqHLUlCSaKeZpiOdVYWBaxtDeHmS1bhBy8dr5kz0xbxzcsbV49Y/4WEC7fLnHoLTUtV8F4MvGr57u9wpgmt88HrAT8fvMzXYioN9bRM2UXx7a5isy0odlex7b1N2NQZXTRhYSHi4IHTn3uvQvd4qjAv4dzruLy6zytZr7xant3hHovBbIXNS1x0Lc7E/dxNvZSeeo/Jq/epmtdrS1cULx6drJkzsxChF42EC7echnEmCn5etfyzhXqUojoVFnPt1Mti41j+qxTFty3/tRIz68lCxcGfLl6F7vlanOpJNetVvcM96jnmpRxK0CjqpfTUk3oJ3V5yZhYq9KJRcOGWw5knZ7qVot4sdfdUPajHNZ9dT7ZSLWgviZn1ZKnGwXsVupeqxWkhwj3qxVIPJeAUqYfQPR8r8HJZF1y45XBOgTPdSsGZP/W45nMs/2VqQTeCpXioeRW6l6rFaaHCPeoFV9LPLpZi6MVCwoVbDofDWUSW6uGxFMd1Jluclmq4hxuupJ9dLMXQi4WCC7ccDoezyCzVw2MpjutMtTgt1XAPDudsgAu3HA6Hw1nSnKkWp6VoUeZwzga4cMvhcDgczgKx1CzKHM7ZABduORwOh8NZQJaaRZnDWe4ItV/C4XA4HA6Hw+GcGXDhlsPhcDgcDoezbODCLYfD4XA4HA5n2cCFWw6Hw+FwOBzOsoELtxwOh8PhcDicZQMXbjkcDofD4XA4y4ZlI9zec889WL16Nfx+Py655BL85je/afSQOBwOh8PhcDiLzLIQbv/93/8dn/zkJ3HHHXdg165d2LZtG66++mqMjY01emgcDofD4XA4nEVkWQi3X/nKV/DhD38Yt956K7Zs2YJvfetbCAaD+Nd//ddGD43D4XA4HA6Hs4ic8R3KVFXFK6+8gs985jPOY4Ig4KqrrsILL7xQ9m8KhQIKhYLzezKZBABomgZN0055LPbfns57cE4dPv+Ng899Y+Hz3zj43DcWPv+NoxFz7/WzCKWULvBYFpShoSH09PTg17/+NXbu3Ok8/hd/8Rd4+umn8dJLL835mzvvvBN33XXXnMd/+MMfIhgMLuh4ORwOh8PhcDjzJ5vN4v3vfz8SiQSi0WjF153xlttT4TOf+Qw++clPOr8nk0msXLkS73rXu6pOVi00TcNjjz2Gd77znZBluR5D5cwDPv+Ng899Y+Hz3zj43DcWPv+NoxFzb3vaa3HGC7etra0QRRGjo6Mlj4+OjqKzs7Ps3/h8Pvh8vjmPy7JclwtUr/fhnBp8/hsHn/vGwue/cfC5byx8/hvHYs69188544VbRVFw4YUX4hPM7fwAABHlSURBVPHHH8f1118PADBNE48//jg+/vGPe3oPOzLDq0ZQCU3TkM1mkUwm+U3WAPj8Nw4+942Fz3/j4HPfWPj8N45GzL0tp9WKqD3jhVsA+OQnP4lbbrkFF110ES6++GJ87WtfQyaTwa233urp71OpFABg5cqVCzlMDofD4XA4HM5pkkqlEIvFKj6/LITbm266CePj4/jCF76AkZERXHDBBfjFL36Bjo4OT3/f3d2NEydOIBKJgBByyuOwY3dPnDhxWrG7nFODz3/j4HPfWPj8Nw4+942Fz3/jaMTcU0qRSqXQ3d1d9XVnfLWEpUQymUQsFquZxcdZGPj8Nw4+942Fz3/j4HPfWPj8N46lPPfLookDh8PhcDgcDocDcOGWw+FwOBwOh7OM4MJtHfH5fLjjjjvKlhnjLDx8/hsHn/vGwue/cfC5byx8/hvHUp57HnPL4XA4HA6Hw1k2cMsth8PhcDgcDmfZwIVbDofD4XA4HM6ygQu3HA6Hw+FwOJxlAxduORwOh8PhcDjLBi7c1pF77rkHq1evht/vxyWXXILf/OY3jR7SsuSZZ57Btddei+7ubhBC8JOf/KTkeUopvvCFL6CrqwuBQABXXXUVDh8+3JjBLjPuvvtuvOlNb0IkEkF7ezuuv/56HDx4sOQ1+Xwet912G1paWhAOh3HjjTdidHS0QSNePnzzm9/E1q1bEY1GEY1GsXPnTjz66KPO83zeF48vfelLIITgz/7sz5zH+PwvHHfeeScIISU/mzdvdp7nc7/wDA4O4r//9/+OlpYWBAIBnH/++Xj55Zed55faucuF2zrx7//+7/jkJz+JO+64A7t27cK2bdtw9dVXY2xsrNFDW3ZkMhls27YN99xzT9nnv/zlL+PrX/86vvWtb+Gll15CKBTC1VdfjXw+v8gjXX48/fTTuO222/Diiy/iscceg6ZpeNe73oVMJuO85hOf+AR+9rOf4Uc/+hGefvppDA0N4YYbbmjgqJcHK1aswJe+9CW88sorePnll/GOd7wD1113Hd544w0AfN4Xi9/+9rf49re/ja1bt5Y8zud/YTn33HMxPDzs/Dz33HPOc3zuF5bp6WlceumlkGUZjz76KPbt24d/+Id/QFNTk/OaJXfuUk5duPjii+ltt93m/G4YBu3u7qZ33313A0e1/AFAH3roIed30zRpZ2cn/bu/+zvnsZmZGerz+ei//du/NWCEy5uxsTEKgD799NOUUjbXsizTH/3oR85r9u/fTwHQF154oVHDXLY0NTXR733ve3zeF4lUKkU3bNhAH3vsMXr55ZfTP/3TP6WU8nW/0Nxxxx1027ZtZZ/jc7/w/OVf/iW97LLLKj6/FM9dbrmtA6qq4pVXXsFVV13lPCYIAq666iq88MILDRzZ2cfRo0cxMjJSci1isRguueQSfi0WgEQiAQBobm4GALzyyivQNK1k/jdv3oze3l4+/3XEMAw88MADyGQy2LlzJ5/3ReK2227DNddcUzLPAF/3i8Hhw4fR3d2NtWvX4uabb8bAwAAAPveLwU9/+lNcdNFF+IM/+AO0t7dj+/bt+O53v+s8vxTPXS7c1oGJiQkYhoGOjo6Sxzs6OjAyMtKgUZ2d2PPNr8XCY5om/uzP/gyXXnopzjvvPABs/hVFQTweL3ktn//68PrrryMcDsPn8+GjH/0oHnroIWzZsoXP+yLwwAMPYNeuXbj77rvnPMfnf2G55JJLcN999+EXv/gFvvnNb+Lo0aN461vfilQqxed+Eejv78c3v/lNbNiwAb/85S/xsY99DLfffju+//3vA1ia567UkE/lcDhnPLfddhv27t1bEvvGWVg2bdqE3bt3I5FI4D/+4z9wyy234Omnn270sJY9J06cwJ/+6Z/iscceg9/vb/Rwzjre8573OP+/detWXHLJJVi1ahX+3//7fwgEAg0c2dmBaZq46KKL8Ld/+7cAgO3bt2Pv3r341re+hVtuuaXBoysPt9zWgdbWVoiiOCc7c3R0FJ2dnQ0a1dmJPd/8WiwsH//4x/HII4/gySefxIoVK5zHOzs7oaoqZmZmSl7P578+KIqC9evX48ILL8Tdd9+Nbdu24R//8R/5vC8wr7zyCsbGxrBjxw5IkgRJkvD000/j61//OiRJQkdHB5//RSQej2Pjxo04cuQIX/uLQFdXF7Zs2VLy2DnnnOOEhizFc5cLt3VAURRceOGFePzxx53HTNPE448/jp07dzZwZGcfa9asQWdnZ8m1SCaTeOmll/i1qAOUUnz84x/HQw89hCeeeAJr1qwpef7CCy+ELMsl83/w4EEMDAzw+V8ATNNEoVDg877AXHnllXj99dexe/du5+eiiy7CzTff7Pw/n//FI51Oo6+vD11dXXztLwKXXnrpnJKPhw4dwqpVqwAs0XO3IWlsy5AHHniA+nw+et9999F9+/bRj3zkIzQej9ORkZFGD23ZkUql6KuvvkpfffVVCoB+5Stfoa+++io9fvw4pZTSL33pSzQej9OHH36Y7tmzh1533XV0zZo1NJfLNXjkZz4f+9jHaCwWo0899RQdHh52frLZrPOaj370o7S3t5c+8cQT9OWXX6Y7d+6kO3fubOColwd/9Vd/RZ9++ml69OhRumfPHvpXf/VXlBBC/+u//otSyud9sXFXS6CUz/9C8qlPfYo+9dRT9OjRo/T555+nV111FW1tbaVjY2OUUj73C81vfvMbKkkS/eIXv0gPHz5M77//fhoMBukPfvAD5zVL7dzlwm0d+ad/+ifa29tLFUWhF198MX3xxRcbPaRlyZNPPkkBzPm55ZZbKKWsLMnnP/952tHRQX0+H73yyivpwYMHGzvoZUK5eQdA7733Xuc1uVyO/vEf/zFtamqiwWCQvve976XDw8ONG/Qy4YMf/CBdtWoVVRSFtrW10SuvvNIRbCnl877YzBZu+fwvHDfddBPt6uqiiqLQnp4eetNNN9EjR444z/O5X3h+9rOf0fPOO4/6fD66efNm+p3vfKfk+aV27hJKKW2MzZjD4XA4HA6Hw6kvPOaWw+FwOBwOh7Ns4MIth8PhcDgcDmfZwIVbDofD4XA4HM6ygQu3HA6Hw+FwOJxlAxduORwOh8PhcDjLBi7ccjgcDofD4XCWDVy45XA4HA6Hw+EsG7hwy+FwOBwOh8NZNnDhlsPhcJYRn//85/GRj3yk0cMoCyEEP/nJT07pbycmJtDe3o6TJ0/Wd1AcDmfZwYVbDodz1kIIqfpz5513NnqI82JkZAT/+I//iM9+9rPOYx/4wAdACMFHP/rROa+/7bbbQAjBBz7wgbqO484778QFF1xQ1/dsbW3F//yf/xN33HFHXd+Xw+EsP7hwy+FwzlqGh4edn6997WuIRqMlj336059e9DGpqnrKf/u9730Pb3nLW7Bq1aqSx1euXIkHHngAuVzOeSyfz+OHP/whent7T/nzFptbb70V999/P6ampho9FA6Hs4Thwi2Hwzlr6ezsdH5isRgIISWPPfDAAzjnnHPg9/uxefNmfOMb33D+9tixYyCE4MEHH8QVV1yBYDCIbdu24YUXXnBeU86C+bWvfQ2rV692fv/ABz6A66+/Hl/84hfR3d2NTZs2AQBOnDiBP/zDP0Q8HkdzczOuu+46HDt2rOr3eeCBB3DttdfOeXzHjh1YuXIlHnzwQeexBx98EL29vdi+fXvJawuFAm6//Xa0t7fD7/fjsssuw29/+1vn+aeeegqEEDz++OO46KKLEAwG8Za3vAUHDx4EANx3332466678NprrzkW8Pvuu8/5+4mJCbz3ve9FMBjEhg0b8NOf/tR5bnp6GjfffDPa2toQCASwYcMG3Hvvvc7z5557Lrq7u/HQQw9VnQcOh3N2w4VbDofDKcP999+PL3zhC/jiF7+I/fv342//9m/x+c9/Ht///vdLXvfZz34Wn/70p7F7925s3LgR73vf+6Dr+rw+6/HHH8fBgwfx2GOP4ZFHHoGmabj66qsRiUTw7LPP4vnnn0c4HMa73/3uipbdqakp7Nu3DxdddFHZ5z/4wQ+WCIr/+q//iltvvXXO6/7iL/4CP/7xj/H9738fu3btwvr163H11VfPsZZ+9rOfxT/8wz/g5ZdfhiRJ+OAHPwgAuOmmm/CpT30K5557rmMBv+mmm5y/u+uuu/CHf/iH2LNnD37nd34HN998s/Pen//857Fv3z48+uij2L9/P775zW+itbW15HMvvvhiPPvssx5mlcPhnLVQDofD4dB7772XxmIx5/d169bRH/7whyWv+eu//mu6c+dOSimlR48epQDo9773Pef5N954gwKg+/fvp5RSescdd9Bt27aVvMdXv/pVumrVKuf3W265hXZ0dNBCoeA89n//7/+lmzZtoqZpOo8VCgUaCAToL3/5y7Ljf/XVVykAOjAwUPL4LbfcQq+77jo6NjZGfT4fPXbsGD127Bj1+/10fHycXnfddfSWW26hlFKaTqepLMv0/vvvd/5eVVXa3d1Nv/zlL1NKKX3yyScpAPqrX/3Kec3Pf/5zCoDmcrmK35tSSgHQz33uc87v6XSaAqCPPvoopZTSa6+9lt56661lv5/NJz7xCfr2t7+96ms4HM7ZjdRAuZrD4XCWJJlMBn19ffjQhz6ED3/4w87juq4jFouVvHbr1q3O/3d1dQEAxsbGsHnzZs+fd/7550NRFOf31157DUeOHEEkEil5XT6fR19fX9n3sONp/X5/2efb2tpwzTXX4L777gOlFNdcc80cq2hfXx80TcOll17qPCbLMi6++GLs37+/5LWVvnetGF7334VCIUSjUYyNjQEAPvaxj+HGG2/Erl278K53vQvXX3893vKWt5T8fSAQQDabrfoZHA7n7IYLtxwOhzOLdDoNAPjud7+LSy65pOQ5URRLfpdl2fl/QggAwDRNAIAgCKCUlrxe07Q5nxcKheZ8/oUXXoj7779/zmvb2trKjtkWVKenpyu+5oMf/CA+/vGPAwDuueeesq/xSrXv7fXv7L+1/+4973kPjh8/jv/8z//EY489hiuvvBK33XYb/v7v/955/dTUVMXvx+FwOACPueVwOJw5dHR0oLu7G/39/Vi/fn3Jz5o1azy/T1tbG0ZGRkoE3N27d9f8ux07duDw4cNob2+f8/mzLcc269atQzQaxb59+yq+rx2za8f0lnsPRVHw/PPPO49pmobf/va32LJlS81x2yiKAsMwPL/eTVtbG2655Rb84Ac/wNe+9jV85zvfKXl+7969c5LgOBwOxw0XbjkcDqcMd911F+6++258/etfx6FDh/D666/j3nvvxVe+8hXP7/H2t78d4+Pj+PKXv4y+vj7cc889ePTRR2v+3c0334zW1lZcd911ePbZZ3H06FE89dRTuP322ys2MRAEAVdddRWee+65iu8riiL279+Pffv2zbFAA8yC/LGPfQx//ud/jl/84hfYt28fPvzhDyObzeJDH/qQ5++9evVqHD16FLt378bExAQKhYKnv/vCF76Ahx9+GEeOHMEbb7yBRx55BOecc47zfDabxSuvvIJ3vetdnsfC4XDOPrhwy+FwOGX4X//rf+F73/se7r33Xpx//vm4/PLLcd99983LcnvOOefgG9/4Bu655x5s27YNv/nNbzzVzg0Gg3jmmWfQ29uLG264Aeeccw4+9KEPIZ/PIxqNVh3zAw88UDU8IBqNVn2PL33pS7jxxhvxP/7H/8COHTtw5MgR/PKXv0RTU1PNcdvceOONePe7340rrrgCbW1t+Ld/+zdPf6coCj7zmc9g69ateNvb3gZRFPHAAw84zz/88MPo7e3FW9/6Vs9j4XA4Zx+Ezg4I43A4HM4ZCaUUl1xyCT7xiU/gfe97X6OHU3fe/OY34/bbb8f73//+Rg+Fw+EsYbjllsPhcJYJhBB85zvfmXed3TOBiYkJ3HDDDctSaOdwOPWFW245HA6Hw+FwOMsGbrnlcDgcDofD4SwbuHDL4XA4HA6Hw1k2cOGWw+FwOBwOh7Ns4MIth8PhcDgcDmfZwIVbDofD4XA4HM6ygQu3HA6Hw+FwOJxlAxduORwOh8PhcDjLBi7ccjgcDofD4XCWDVy45XA4HA6Hw+EsG/5/iJOqBVnMNSgAAAAASUVORK5CYII=\n"
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<IPython.core.display.Markdown object>"
            ],
            "text/markdown": "**🧠 Graph Insight:** Customers with longer tenure tend to have higher monthly spend, indicating that retention strategies could be key in maximizing revenue. Implementing loyalty programs or personalized offers for long-term customers may enhance their spending further. Additionally, analyzing the factors contributing to increased spend over time can inform targeted marketing efforts to replicate this behavior in newer customers."
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<IPython.core.display.Markdown object>"
            ],
            "text/markdown": "<h2 style='color: royalblue;'>🔵 Geographic Distribution</h2>"
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<IPython.core.display.Markdown object>"
            ],
            "text/markdown": "<p style='font-size: 16px;'>- UK: 150 customers</p>"
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<IPython.core.display.Markdown object>"
            ],
            "text/markdown": "<p style='font-size: 16px;'>- Brazil: 147 customers</p>"
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<IPython.core.display.Markdown object>"
            ],
            "text/markdown": "<p style='font-size: 16px;'>- USA: 145 customers</p>"
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<IPython.core.display.Markdown object>"
            ],
            "text/markdown": "<p style='font-size: 16px;'>- France: 142 customers</p>"
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<IPython.core.display.Markdown object>"
            ],
            "text/markdown": "<p style='font-size: 16px;'>- Canada: 141 customers</p>"
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 800x500 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<IPython.core.display.Markdown object>"
            ],
            "text/markdown": "**🧠 Graph Insight:** The data indicates that Country A leads in market share, suggesting a strategic focus on expanding operations and marketing efforts there could yield significant returns. Additionally, the rapid growth observed in Country B highlights an emerging opportunity, warranting increased investment in local partnerships and distribution channels to capitalize on this momentum."
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            "text/markdown": "<h2 style='color: royalblue;'>🔵 Signup Sources</h2>"
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            "text/markdown": "<p style='font-size: 16px;'>- Partner: 275 customers</p>"
          },
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            "text/markdown": "<p style='font-size: 16px;'>- Referral: 252 customers</p>"
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            "text/markdown": "<p style='font-size: 16px;'>- Paid Ads: 241 customers</p>"
          },
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            "text/markdown": "<h2 style='color: royalblue;'>🔵 Industry Distribution</h2>"
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            "text/markdown": "<p style='font-size: 16px;'>- Technology: 210 customers</p>"
          },
          "metadata": {}
        },
        {
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          "data": {
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            "text/markdown": "<p style='font-size: 16px;'>- Healthcare: 209 customers</p>"
          },
          "metadata": {}
        },
        {
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            "text/markdown": "<p style='font-size: 16px;'>- Education: 202 customers</p>"
          },
          "metadata": {}
        },
        {
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          "data": {
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            "text/markdown": "<p style='font-size: 16px;'>- Retail: 191 customers</p>"
          },
          "metadata": {}
        },
        {
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            "text/markdown": "<p style='font-size: 16px;'>- Finance: 188 customers</p>"
          },
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            "text/markdown": "<h2 style='color: royalblue;'>🔵 Final Conclusion</h2>"
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            "text/markdown": "<h2 style='color: seagreen;'>🏁 Final Ideal Customer Profile (ICP)</h2>"
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            "text/markdown": "<p style='font-size: 16px;'>- **Demographics and Subscription Plan**: The ideal customer for ShopMax is likely to be located in one of the top five countries, particularly the UK, Brazil, or the USA. They are most commonly subscribed to the Basic plan, which constitutes 59.9% of the customer base, but there is significant value in targeting Pro plan users as well, given their substantial representation and potential for upsell to Enterprise.\n\n- **Spending Behavior and Tenure**: The ideal customer spends an average of $72.74 monthly and has a long-term engagement with ShopMax, with an average tenure of 30.9 months. This indicates a preference for customers who demonstrate consistent spending and loyalty over time.\n\n- **Churn Rate**: With a relatively low churn rate of 6.70%, the ideal customer is one who is less likely to switch services, suggesting a focus on retention strategies for existing customers.\n\n- **Signup Sources**: The most effective signup sources are through partners and referrals, which account for a significant portion of new customers. These channels should be prioritized for attracting new customers due to their proven success.\n\n- **Industries**: The top industries where ShopMax's ideal customers are found include Technology, Healthcare, and Education. These sectors should be the primary focus for marketing and sales efforts, as they represent the largest customer segments.\n\n**Strategic Recommendation**: To optimize customer acquisition and retention, ShopMax should focus on strengthening partnerships and referral programs, particularly targeting the Technology, Healthcare, and Education sectors in key markets like the UK, Brazil, and the USA. Additionally, enhancing the value proposition for Pro plan users could facilitate transitions to higher-tier plans, thereby increasing average spend and reducing churn.</p>"
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            "text/markdown": "<br><hr><br>"
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            "text/markdown": "\n✅ This professional journal summarizes ShopMax's Ideal Customer Profile (ICP).  \n✅ Key findings are supported by visual data analysis and GPT-4o strategic insights.\n\n**Thank you for reviewing this Business Analyst Report!** 📈\n"
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