diff --git a/examples/rag/adaptive_rag.ipynb b/examples/rag/langgraph_adaptive_rag.ipynb
similarity index 99%
rename from examples/rag/adaptive_rag.ipynb
rename to examples/rag/langgraph_adaptive_rag.ipynb
index d39df157d..9c0bbdd8e 100644
--- a/examples/rag/adaptive_rag.ipynb
+++ b/examples/rag/langgraph_adaptive_rag.ipynb
@@ -22,7 +22,9 @@
     "* Single-shot RAG\n",
     "* Iterative RAG\n",
     "\n",
-    "Let's build on this to perform query analysis to route across some more interesting cases:\n",
+    "Let's build on this using LangGraph. \n",
+    "\n",
+    "In our implementation, we will route between:\n",
     "\n",
     "* Web search: for questions related to recent events\n",
     "* Self-corrective RAG: for questions related to our index\n",
@@ -45,7 +47,7 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "! pip install langchain_community tiktoken langchain-openai langchain-cohere langchainhub chromadb langchain langgraph"
+    "! pip install langchain_community tiktoken langchain-openai langchain-cohere langchainhub chromadb langchain langgraph  tavily-python"
    ]
   },
   {
@@ -58,7 +60,18 @@
     "### LLMs\n",
     "import os\n",
     "os.environ['OPENAI_API_KEY'] = <your-api-key>\n",
-    "os.environ['COHERE_API_KEY'] = <your-api-key>"
+    "os.environ['COHERE_API_KEY'] = <your-api-key>\n",
+    "os.environ['TAVILY_API_KEY'] = <your-api-key>"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "018c9e9f-8199-4f33-b5b4-7adfb66d6219",
+   "metadata": {},
+   "source": [
+    "### Tracing\n",
+    "\n",
+    "* Optionally, use [LangSmith](https://docs.smith.langchain.com/) for tracing (shown at bottom) by setting: "
    ]
   },
   {
@@ -157,7 +170,6 @@
     "from langchain_core.prompts import ChatPromptTemplate\n",
     "from langchain_core.pydantic_v1 import BaseModel, Field\n",
     "from langchain_openai import ChatOpenAI\n",
-    "### from langchain_cohere import ChatCohere\n",
     "\n",
     "# Data model\n",
     "class RouteQuery(BaseModel):\n",
@@ -218,6 +230,7 @@
     "# Prompt \n",
     "system = \"\"\"You are a grader assessing relevance of a retrieved document to a user question. \\n \n",
     "    If the document contains keyword(s) or semantic meaning related to the user question, grade it as relevant. \\n\n",
+    "    It does not need to be a stringent test. The goal is to filter out erroneous retrievals. \\n\n",
     "    Give a binary score 'yes' or 'no' score to indicate whether the document is relevant to the question.\"\"\"\n",
     "grade_prompt = ChatPromptTemplate.from_messages(\n",
     "    [\n",
@@ -440,7 +453,7 @@
     "from typing import List\n",
     "\n",
     "class GraphState(TypedDict):\n",
-    "    \"\"\"|\n",
+    "    \"\"\"\n",
     "    Represents the state of our graph.\n",
     "\n",
     "    Attributes:\n",
@@ -673,8 +686,6 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "from pprint import pprint\n",
-    "\n",
     "from langgraph.graph import END, StateGraph\n",
     "\n",
     "workflow = StateGraph(GraphState)\n",
@@ -751,6 +762,8 @@
     }
    ],
    "source": [
+    "from pprint import pprint\n",
+    "\n",
     "# Run \n",
     "inputs = {\"question\": \"What player at the Bears expected to draft first in the 2024 NFL draft?\"}\n",
     "for output in app.stream(inputs):\n",
diff --git a/examples/rag/langgraph_adaptive_rag_local.ipynb b/examples/rag/langgraph_adaptive_rag_local.ipynb
new file mode 100644
index 000000000..ff5a2aaa2
--- /dev/null
+++ b/examples/rag/langgraph_adaptive_rag_local.ipynb
@@ -0,0 +1,813 @@
+{
+ "cells": [
+  {
+   "attachments": {
+    "3755396d-c4a8-45bd-87d4-00cb56339fe5.png": {
+     "image/png": 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"
+    }
+   },
+   "cell_type": "markdown",
+   "id": "bb89d3f0-7ade-43a8-a527-4bec45971cf6",
+   "metadata": {},
+   "source": [
+    "# Adaptive RAG -- With local LLMs\n",
+    "\n",
+    "Adaptive RAG is a strategy for RAG that unites (1) [query analysis](https://blog.langchain.dev/query-construction/) with (2) [active / self-corrective RAG](https://blog.langchain.dev/agentic-rag-with-langgraph/).\n",
+    "\n",
+    "In the [paper](https://arxiv.org/abs/2403.14403), they report query analysis to route across:\n",
+    "\n",
+    "* No Retrieval\n",
+    "* Single-shot RAG\n",
+    "* Iterative RAG\n",
+    "\n",
+    "Let's build on this using LangGraph. \n",
+    "\n",
+    "In our implementation, we will route between:\n",
+    "\n",
+    "* Web search: for questions related to recent events\n",
+    "* Self-corrective RAG: for questions related to our index\n",
+    "\n",
+    "![Screenshot 2024-04-01 at 1.29.15 PM.png](attachment:3755396d-c4a8-45bd-87d4-00cb56339fe5.png)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "8cece98f-a3ed-417e-8b6a-1754e8f9c42a",
+   "metadata": {},
+   "source": [
+    "# Enviorment "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "88debf5c-6972-415c-b8fb-f65eab203b7a",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "! pip install -U langchain-nomic langchain_community tiktoken langchainhub chromadb langchain langgraph tavily-python"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "6a5d4a26-249b-4551-aa13-6c373429618e",
+   "metadata": {},
+   "source": [
+    "### LLMs\n",
+    "\n",
+    "#### Local Embeddings\n",
+    "\n",
+    "You can use `GPT4AllEmbeddings()` from Nomic, which can access use Nomic's recently released [v1](https://blog.nomic.ai/posts/nomic-embed-text-v1) and [v1.5](https://blog.nomic.ai/posts/nomic-embed-matryoshka) embeddings.\n",
+    "\n",
+    "Follow the documentation [here](https://docs.gpt4all.io/gpt4all_python_embedding.html#supported-embedding-models).\n",
+    "\n",
+    "#### Local LLM\n",
+    "\n",
+    "(1) Download [Ollama app](https://ollama.ai/).\n",
+    "\n",
+    "(2) Download a `Mistral` model from various Mistral versions [here](https://ollama.ai/library/mistral) and Mixtral versions [here](https://ollama.ai/library/mixtral) available. Also, try one of the [quantized command-R models](https://ollama.com/library/command-r).\n",
+    "\n",
+    "```\n",
+    "ollama pull mistral\n",
+    "ollama pull command-r:35b-v0.1-q2_K\n",
+    "```"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 64,
+   "id": "af8379bd-7eae-4ba6-b632-12e89eab9920",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Ollama model name\n",
+    "local_llm = \"mistral\"\n",
+    "local_llm = \"command-r:35b-v0.1-q2_K\""
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "5c104495-a8a4-4517-b6a6-9a2cbe1e82f2",
+   "metadata": {},
+   "source": [
+    "### Tracing\n",
+    "\n",
+    "Optionally, use [LangSmith](https://docs.smith.langchain.com/) for tracing (shown at bottom)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "f6cb8dce-f580-421d-a05f-9fc71de2b023",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "os.environ['LANGCHAIN_TRACING_V2'] = 'true'\n",
+    "os.environ['LANGCHAIN_ENDPOINT'] = 'https://api.smith.langchain.com'\n",
+    "os.environ['LANGCHAIN_API_KEY'] = <your-api-key>"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "04718a0c-7a48-4243-97a2-940a0239cc12",
+   "metadata": {},
+   "source": [
+    "## Index"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "id": "f9ff6b99-080d-4827-b2cb-f775543d76f5",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from langchain.text_splitter import RecursiveCharacterTextSplitter\n",
+    "from langchain_community.document_loaders import WebBaseLoader\n",
+    "from langchain_community.vectorstores import Chroma\n",
+    "from langchain_community.embeddings import GPT4AllEmbeddings\n",
+    "\n",
+    "urls = [\n",
+    "    \"https://lilianweng.github.io/posts/2023-06-23-agent/\",\n",
+    "    \"https://lilianweng.github.io/posts/2023-03-15-prompt-engineering/\",\n",
+    "    \"https://lilianweng.github.io/posts/2023-10-25-adv-attack-llm/\",\n",
+    "]\n",
+    "\n",
+    "docs = [WebBaseLoader(url).load() for url in urls]\n",
+    "docs_list = [item for sublist in docs for item in sublist]\n",
+    "\n",
+    "text_splitter = RecursiveCharacterTextSplitter.from_tiktoken_encoder(\n",
+    "    chunk_size=250, chunk_overlap=0\n",
+    ")\n",
+    "doc_splits = text_splitter.split_documents(docs_list)\n",
+    "\n",
+    "# Add to vectorDB\n",
+    "vectorstore = Chroma.from_documents(\n",
+    "    documents=doc_splits,\n",
+    "    collection_name=\"rag-chroma\",\n",
+    "    embedding=GPT4AllEmbeddings(),\n",
+    ")\n",
+    "retriever = vectorstore.as_retriever()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "2f3eb922-27a1-4a72-a727-85fbf5b3daf1",
+   "metadata": {},
+   "source": [
+    "## LLMs\n",
+    "\n",
+    "Note: tested cmd-R on Mac M2 32GB and [latency is ~52 sec for RAG generation](https://smith.langchain.com/public/3998fe48-efc2-4d18-9069-972643d0982d/r)."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 49,
+   "id": "7045e064-e666-4aea-9111-6e9d2007f27e",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "{'datasource': 'vectorstore'}\n"
+     ]
+    }
+   ],
+   "source": [
+    "### Router\n",
+    "\n",
+    "from langchain.prompts import PromptTemplate\n",
+    "from langchain_community.chat_models import ChatOllama\n",
+    "from langchain_core.output_parsers import JsonOutputParser\n",
+    "\n",
+    "# LLM\n",
+    "llm = ChatOllama(model=local_llm, format=\"json\", temperature=0)\n",
+    "\n",
+    "prompt = PromptTemplate(\n",
+    "    template=\"\"\"You are an expert at routing a user question to a vectorstore or web search. \\n\n",
+    "    Use the vectorstore for questions on LLM  agents, prompt engineering, and adversarial attacks. \\n\n",
+    "    You do not need to be stringent with the keywords in the question related to these topics. \\n\n",
+    "    Otherwise, use web-search. Give a binary choice 'web_search' or 'vectorstore' based on the question. \\n\n",
+    "    Return the a JSON with a single key 'datasource' and no premable or explaination. \\n\n",
+    "    Question to route: {question}\"\"\",\n",
+    "    input_variables=[\"question\"],\n",
+    ")\n",
+    "\n",
+    "question_router = prompt | llm | JsonOutputParser()\n",
+    "question = \"llm agent memory\"\n",
+    "docs = retriever.get_relevant_documents(question)\n",
+    "doc_txt = docs[1].page_content\n",
+    "print(question_router.invoke({\"question\": question}))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 53,
+   "id": "813cdcef-8b75-4214-a2ed-b89077b3d287",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "{'score': 'yes'}\n"
+     ]
+    }
+   ],
+   "source": [
+    "### Retrieval Grader \n",
+    "\n",
+    "from langchain.prompts import PromptTemplate\n",
+    "from langchain_community.chat_models import ChatOllama\n",
+    "from langchain_core.output_parsers import JsonOutputParser\n",
+    "\n",
+    "# LLM\n",
+    "llm = ChatOllama(model=local_llm, format=\"json\", temperature=0)\n",
+    "\n",
+    "prompt = PromptTemplate(\n",
+    "    template=\"\"\"You are a grader assessing relevance of a retrieved document to a user question. \\n \n",
+    "    Here is the retrieved document: \\n\\n {document} \\n\\n\n",
+    "    Here is the user question: {question} \\n\n",
+    "    If the document contains keywords related to the user question, grade it as relevant. \\n\n",
+    "    It does not need to be a stringent test. The goal is to filter out erroneous retrievals. \\n\n",
+    "    Give a binary score 'yes' or 'no' score to indicate whether the document is relevant to the question. \\n\n",
+    "    Provide the binary score as a JSON with a single key 'score' and no premable or explaination.\"\"\",\n",
+    "    input_variables=[\"question\", \"document\"],\n",
+    ")\n",
+    "\n",
+    "retrieval_grader = prompt | llm | JsonOutputParser()\n",
+    "question = \"agent memory\"\n",
+    "docs = retriever.get_relevant_documents(question)\n",
+    "doc_txt = docs[1].page_content\n",
+    "print(retrieval_grader.invoke({\"question\": question, \"document\": doc_txt}))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 65,
+   "id": "aeb8b373-0289-4dec-bd4b-8b2701200301",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "The agent's memory is supported by a memory stream, which functions as a long-term memory module that records the agents' experiences in natural language. This recorded information is then used to influence the agent's behavior and interaction with other agents.\n",
+      "Memory is also employed by the agent in reflection mechanisms, which create higher-level summaries of past events, and can be accessed for self-reflection.\n"
+     ]
+    }
+   ],
+   "source": [
+    "### Generate\n",
+    "\n",
+    "from langchain import hub\n",
+    "from langchain_community.chat_models import ChatOllama\n",
+    "from langchain_core.output_parsers import StrOutputParser\n",
+    "\n",
+    "# Prompt\n",
+    "prompt = hub.pull(\"rlm/rag-prompt\")\n",
+    "\n",
+    "# LLM\n",
+    "llm = ChatOllama(model=local_llm, temperature=0)\n",
+    "\n",
+    "# Post-processing\n",
+    "def format_docs(docs):\n",
+    "    return \"\\n\\n\".join(doc.page_content for doc in docs)\n",
+    "\n",
+    "# Chain\n",
+    "rag_chain = prompt | llm | StrOutputParser()\n",
+    "\n",
+    "# Run\n",
+    "question = \"agent memory\"\n",
+    "generation = rag_chain.invoke({\"context\": docs, \"question\": question})\n",
+    "print(generation)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 54,
+   "id": "38345cff-e2d0-436e-aa09-599522a61eed",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "{'score': 'yes'}"
+      ]
+     },
+     "execution_count": 54,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "### Hallucination Grader \n",
+    "\n",
+    "# LLM\n",
+    "llm = ChatOllama(model=local_llm, format=\"json\", temperature=0)\n",
+    "\n",
+    "# Prompt\n",
+    "prompt = PromptTemplate(\n",
+    "    template=\"\"\"You are a grader assessing whether an answer is grounded in / supported by a set of facts. \\n \n",
+    "    Here are the facts:\n",
+    "    \\n ------- \\n\n",
+    "    {documents} \n",
+    "    \\n ------- \\n\n",
+    "    Here is the answer: {generation}\n",
+    "    Give a binary score 'yes' or 'no' score to indicate whether the answer is grounded in / supported by a set of facts. \\n\n",
+    "    Provide the binary score as a JSON with a single key 'score' and no preamble or explanation.\"\"\",\n",
+    "    input_variables=[\"generation\", \"documents\"],\n",
+    ")\n",
+    "\n",
+    "hallucination_grader = prompt | llm | JsonOutputParser()\n",
+    "hallucination_grader.invoke({\"documents\": docs, \"generation\": generation})"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 55,
+   "id": "9771caa1-5542-47c3-8354-aeeafcf51964",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "{'score': 'yes'}"
+      ]
+     },
+     "execution_count": 55,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "### Answer Grader \n",
+    "\n",
+    "# LLM\n",
+    "llm = ChatOllama(model=local_llm, format=\"json\", temperature=0)\n",
+    "\n",
+    "# Prompt\n",
+    "prompt = PromptTemplate(\n",
+    "    template=\"\"\"You are a grader assessing whether an answer is useful to resolve a question. \\n \n",
+    "    Here is the answer:\n",
+    "    \\n ------- \\n\n",
+    "    {generation} \n",
+    "    \\n ------- \\n\n",
+    "    Here is the question: {question}\n",
+    "    Give a binary score 'yes' or 'no' to indicate whether the answer is useful to resolve a question. \\n\n",
+    "    Provide the binary score as a JSON with a single key 'score' and no preamble or explanation.\"\"\",\n",
+    "    input_variables=[\"generation\", \"question\"],\n",
+    ")\n",
+    "\n",
+    "answer_grader = prompt | llm | JsonOutputParser()\n",
+    "answer_grader.invoke({\"question\": question,\"generation\": generation})"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 56,
+   "id": "830ba5f7-9c8d-4c01-83b1-e4d51d40d48f",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "' What is agent memory and how can it be effectively utilized in vector database retrieval?'"
+      ]
+     },
+     "execution_count": 56,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "### Question Re-writer\n",
+    "\n",
+    "# LLM\n",
+    "llm = ChatOllama(model=local_llm, temperature=0)\n",
+    "\n",
+    "# Prompt \n",
+    "re_write_prompt = PromptTemplate(\n",
+    "    template=\"\"\"You a question re-writer that converts an input question to a better version that is optimized \\n \n",
+    "     for vectorstore retrieval. Look at the initial and formulate an improved question. \\n\n",
+    "     Here is the initial question: \\n\\n {question}. Improved question with no preamble: \\n \"\"\",\n",
+    "    input_variables=[\"generation\", \"question\"],\n",
+    ")\n",
+    "\n",
+    "question_rewriter = re_write_prompt | llm | StrOutputParser()\n",
+    "question_rewriter.invoke({\"question\": question})"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "686c9bb1-5069-45f9-8a7e-cba34fe07dd9",
+   "metadata": {},
+   "source": [
+    "## Web Search Tool"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 57,
+   "id": "6c3c1c70-ff84-41e8-bf72-738ed52f2dde",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "### Search\n",
+    "\n",
+    "from langchain_community.tools.tavily_search import TavilySearchResults\n",
+    "web_search_tool = TavilySearchResults(k=3)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "630d1751-a20b-4858-b3fd-0312de4f3ad7",
+   "metadata": {},
+   "source": [
+    "# Graph \n",
+    "\n",
+    "Capture the flow in as a graph.\n",
+    "\n",
+    "## Graph state"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 58,
+   "id": "6e09087e-b2a9-437a-abee-129e426df799",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from typing_extensions import TypedDict\n",
+    "from typing import List\n",
+    "\n",
+    "class GraphState(TypedDict):\n",
+    "    \"\"\"\n",
+    "    Represents the state of our graph.\n",
+    "\n",
+    "    Attributes:\n",
+    "        question: question\n",
+    "        generation: LLM generation\n",
+    "        documents: list of documents \n",
+    "    \"\"\"\n",
+    "    question : str\n",
+    "    generation : str\n",
+    "    documents : List[str]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 59,
+   "id": "7c5fa507-77ae-426a-a65f-f518b9525bd0",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "### Nodes\n",
+    "\n",
+    "from langchain.schema import Document\n",
+    "\n",
+    "def retrieve(state):\n",
+    "    \"\"\"\n",
+    "    Retrieve documents\n",
+    "\n",
+    "    Args:\n",
+    "        state (dict): The current graph state\n",
+    "\n",
+    "    Returns:\n",
+    "        state (dict): New key added to state, documents, that contains retrieved documents\n",
+    "    \"\"\"\n",
+    "    print(\"---RETRIEVE---\")\n",
+    "    question = state[\"question\"]\n",
+    "\n",
+    "    # Retrieval\n",
+    "    documents = retriever.get_relevant_documents(question)\n",
+    "    return {\"documents\": documents, \"question\": question}\n",
+    "\n",
+    "def generate(state):\n",
+    "    \"\"\"\n",
+    "    Generate answer\n",
+    "\n",
+    "    Args:\n",
+    "        state (dict): The current graph state\n",
+    "\n",
+    "    Returns:\n",
+    "        state (dict): New key added to state, generation, that contains LLM generation\n",
+    "    \"\"\"\n",
+    "    print(\"---GENERATE---\")\n",
+    "    question = state[\"question\"]\n",
+    "    documents = state[\"documents\"]\n",
+    "    \n",
+    "    # RAG generation\n",
+    "    generation = rag_chain.invoke({\"context\": documents, \"question\": question})\n",
+    "    return {\"documents\": documents, \"question\": question, \"generation\": generation}\n",
+    "\n",
+    "def grade_documents(state):\n",
+    "    \"\"\"\n",
+    "    Determines whether the retrieved documents are relevant to the question.\n",
+    "\n",
+    "    Args:\n",
+    "        state (dict): The current graph state\n",
+    "\n",
+    "    Returns:\n",
+    "        state (dict): Updates documents key with only filtered relevant documents\n",
+    "    \"\"\"\n",
+    "\n",
+    "    print(\"---CHECK DOCUMENT RELEVANCE TO QUESTION---\")\n",
+    "    question = state[\"question\"]\n",
+    "    documents = state[\"documents\"]\n",
+    "    \n",
+    "    # Score each doc\n",
+    "    filtered_docs = []\n",
+    "    for d in documents:\n",
+    "        score = retrieval_grader.invoke({\"question\": question, \"document\": d.page_content})\n",
+    "        grade = score['score']\n",
+    "        if grade == \"yes\":\n",
+    "            print(\"---GRADE: DOCUMENT RELEVANT---\")\n",
+    "            filtered_docs.append(d)\n",
+    "        else:\n",
+    "            print(\"---GRADE: DOCUMENT NOT RELEVANT---\")\n",
+    "            continue\n",
+    "    return {\"documents\": filtered_docs, \"question\": question}\n",
+    "\n",
+    "def transform_query(state):\n",
+    "    \"\"\"\n",
+    "    Transform the query to produce a better question.\n",
+    "\n",
+    "    Args:\n",
+    "        state (dict): The current graph state\n",
+    "\n",
+    "    Returns:\n",
+    "        state (dict): Updates question key with a re-phrased question\n",
+    "    \"\"\"\n",
+    "\n",
+    "    print(\"---TRANSFORM QUERY---\")\n",
+    "    question = state[\"question\"]\n",
+    "    documents = state[\"documents\"]\n",
+    "\n",
+    "    # Re-write question\n",
+    "    better_question = question_rewriter.invoke({\"question\": question})\n",
+    "    return {\"documents\": documents, \"question\": better_question}\n",
+    "\n",
+    "def web_search(state):\n",
+    "    \"\"\"\n",
+    "    Web search based on the re-phrased question.\n",
+    "\n",
+    "    Args:\n",
+    "        state (dict): The current graph state\n",
+    "\n",
+    "    Returns:\n",
+    "        state (dict): Updates documents key with appended web results\n",
+    "    \"\"\"\n",
+    "\n",
+    "    print(\"---WEB SEARCH---\")\n",
+    "    question = state[\"question\"]\n",
+    "\n",
+    "    # Web search\n",
+    "    docs = web_search_tool.invoke({\"query\": question})\n",
+    "    web_results = \"\\n\".join([d[\"content\"] for d in docs])\n",
+    "    web_results = Document(page_content=web_results)\n",
+    "\n",
+    "    return {\"documents\": web_results, \"question\": question}\n",
+    "\n",
+    "### Edges ###\n",
+    "\n",
+    "def route_question(state):\n",
+    "    \"\"\"\n",
+    "    Route question to web search or RAG.\n",
+    "\n",
+    "    Args:\n",
+    "        state (dict): The current graph state\n",
+    "\n",
+    "    Returns:\n",
+    "        str: Next node to call\n",
+    "    \"\"\"\n",
+    "\n",
+    "    print(\"---ROUTE QUESTION---\")\n",
+    "    question = state[\"question\"]\n",
+    "    print(question)\n",
+    "    source = question_router.invoke({\"question\": question})  \n",
+    "    print(source)\n",
+    "    print(source['datasource'])\n",
+    "    if source['datasource'] == 'web_search':\n",
+    "        print(\"---ROUTE QUESTION TO WEB SEARCH---\")\n",
+    "        return \"web_search\"\n",
+    "    elif source['datasource'] == 'vectorstore':\n",
+    "        print(\"---ROUTE QUESTION TO RAG---\")\n",
+    "        return \"vectorstore\"\n",
+    "\n",
+    "def decide_to_generate(state):\n",
+    "    \"\"\"\n",
+    "    Determines whether to generate an answer, or re-generate a question.\n",
+    "\n",
+    "    Args:\n",
+    "        state (dict): The current graph state\n",
+    "\n",
+    "    Returns:\n",
+    "        str: Binary decision for next node to call\n",
+    "    \"\"\"\n",
+    "\n",
+    "    print(\"---ASSESS GRADED DOCUMENTS---\")\n",
+    "    question = state[\"question\"]\n",
+    "    filtered_documents = state[\"documents\"]\n",
+    "\n",
+    "    if not filtered_documents:\n",
+    "        # All documents have been filtered check_relevance\n",
+    "        # We will re-generate a new query\n",
+    "        print(\"---DECISION: ALL DOCUMENTS ARE NOT RELEVANT TO QUESTION, TRANSFORM QUERY---\")\n",
+    "        return \"transform_query\"\n",
+    "    else:\n",
+    "        # We have relevant documents, so generate answer\n",
+    "        print(\"---DECISION: GENERATE---\")\n",
+    "        return \"generate\"\n",
+    "\n",
+    "def grade_generation_v_documents_and_question(state):\n",
+    "    \"\"\"\n",
+    "    Determines whether the generation is grounded in the document and answers question.\n",
+    "\n",
+    "    Args:\n",
+    "        state (dict): The current graph state\n",
+    "\n",
+    "    Returns:\n",
+    "        str: Decision for next node to call\n",
+    "    \"\"\"\n",
+    "\n",
+    "    print(\"---CHECK HALLUCINATIONS---\")\n",
+    "    question = state[\"question\"]\n",
+    "    documents = state[\"documents\"]\n",
+    "    generation = state[\"generation\"]\n",
+    "\n",
+    "    score = hallucination_grader.invoke({\"documents\": documents, \"generation\": generation})\n",
+    "    grade = score['score']\n",
+    "\n",
+    "    # Check hallucination\n",
+    "    if grade == \"yes\":\n",
+    "        print(\"---DECISION: GENERATION IS GROUNDED IN DOCUMENTS---\")\n",
+    "        # Check question-answering\n",
+    "        print(\"---GRADE GENERATION vs QUESTION---\")\n",
+    "        score = answer_grader.invoke({\"question\": question,\"generation\": generation})\n",
+    "        grade = score['score']\n",
+    "        if grade == \"yes\":\n",
+    "            print(\"---DECISION: GENERATION ADDRESSES QUESTION---\")\n",
+    "            return \"useful\"\n",
+    "        else:\n",
+    "            print(\"---DECISION: GENERATION DOES NOT ADDRESS QUESTION---\")\n",
+    "            return \"not useful\"\n",
+    "    else:\n",
+    "        pprint(\"---DECISION: GENERATION IS NOT GROUNDED IN DOCUMENTS, RE-TRY---\")\n",
+    "        return \"not supported\""
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "ed7d8eb6-31d7-4ab5-8a88-7081b64582bb",
+   "metadata": {},
+   "source": [
+    "## Build Graph"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 60,
+   "id": "450eb313-ca75-4a43-b57e-7034bd3f40bf",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from langgraph.graph import END, StateGraph\n",
+    "\n",
+    "workflow = StateGraph(GraphState)\n",
+    "\n",
+    "# Define the nodes\n",
+    "workflow.add_node(\"web_search\", web_search) # web search\n",
+    "workflow.add_node(\"retrieve\", retrieve) # retrieve\n",
+    "workflow.add_node(\"grade_documents\", grade_documents) # grade documents\n",
+    "workflow.add_node(\"generate\", generate) # generatae\n",
+    "workflow.add_node(\"transform_query\", transform_query) # transform_query\n",
+    "\n",
+    "# Build graph\n",
+    "workflow.set_conditional_entry_point(\n",
+    "    route_question,\n",
+    "    {\n",
+    "        \"web_search\": \"web_search\",\n",
+    "        \"vectorstore\": \"retrieve\",\n",
+    "    },\n",
+    ")\n",
+    "workflow.add_edge(\"web_search\", \"generate\")\n",
+    "workflow.add_edge(\"retrieve\", \"grade_documents\")\n",
+    "workflow.add_conditional_edges(\n",
+    "    \"grade_documents\",\n",
+    "    decide_to_generate,\n",
+    "    {\n",
+    "        \"transform_query\": \"transform_query\",\n",
+    "        \"generate\": \"generate\",\n",
+    "    },\n",
+    ")\n",
+    "workflow.add_edge(\"transform_query\", \"retrieve\")\n",
+    "workflow.add_conditional_edges(\n",
+    "    \"generate\",\n",
+    "    grade_generation_v_documents_and_question,\n",
+    "    {\n",
+    "        \"not supported\": \"generate\",\n",
+    "        \"useful\": END,\n",
+    "        \"not useful\": \"transform_query\",\n",
+    "    },\n",
+    ")\n",
+    "\n",
+    "# Compile\n",
+    "app = workflow.compile()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 63,
+   "id": "b095c1db-8bd1-4a34-937c-1a9b74ae74ff",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "---ROUTE QUESTION---\n",
+      "What is the AlphaCodium paper about?\n",
+      "{'datasource': 'web_search'}\n",
+      "web_search\n",
+      "---ROUTE QUESTION TO WEB SEARCH---\n",
+      "---WEB SEARCH---\n",
+      "\"Node 'web_search':\"\n",
+      "'\\n---\\n'\n",
+      "---GENERATE---\n",
+      "\"Node 'generate':\"\n",
+      "'\\n---\\n'\n",
+      "---CHECK HALLUCINATIONS---\n",
+      "---DECISION: GENERATION IS GROUNDED IN DOCUMENTS---\n",
+      "---GRADE GENERATION vs QUESTION---\n",
+      "---DECISION: GENERATION ADDRESSES QUESTION---\n",
+      "\"Node '__end__':\"\n",
+      "'\\n---\\n'\n",
+      "(' The AlphaCodium paper introduces a new approach called AlphaCodium for code '\n",
+      " 'generation by Large Language Models (LLMs). It is a test-based, multi-stage '\n",
+      " 'flow that improves LLM performances on code problems through an iterative '\n",
+      " 'process of generating and fixing code against input-output tests. The '\n",
+      " 'authors tested AlphaCodium on the CodeContests dataset and reported improved '\n",
+      " \"results compared to DeepMind's AlphaCode and AlphaCode2.\")\n"
+     ]
+    }
+   ],
+   "source": [
+    "from pprint import pprint\n",
+    "\n",
+    "# Run \n",
+    "inputs = {\"question\": \"What is the AlphaCodium paper about?\"}\n",
+    "for output in app.stream(inputs):\n",
+    "    for key, value in output.items():\n",
+    "        # Node\n",
+    "        pprint(f\"Node '{key}':\")\n",
+    "        # Optional: print full state at each node\n",
+    "        # pprint.pprint(value[\"keys\"], indent=2, width=80, depth=None)\n",
+    "    pprint(\"\\n---\\n\")\n",
+    "\n",
+    "# Final generation\n",
+    "pprint(value[\"generation\"])"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "644c7293-9cb5-4236-ba08-1e63b0309cb7",
+   "metadata": {},
+   "source": [
+    "Trace: \n",
+    "\n",
+    "https://smith.langchain.com/public/98f3b8d3-b26d-430d-a434-f25273345f56/r"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "4620ede9-b014-499f-8acf-80f80ce0d944",
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3 (ipykernel)",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.11.8"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}
diff --git a/examples/rag/langgraph_crag.ipynb b/examples/rag/langgraph_crag.ipynb
index 921fc1152..c9dbce965 100644
--- a/examples/rag/langgraph_crag.ipynb
+++ b/examples/rag/langgraph_crag.ipynb
@@ -1,19 +1,9 @@
 {
  "cells": [
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "459d0bcf-7c60-495e-91c3-85b0b8c67552",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "! pip install langchain_community tiktoken langchain-openai langchainhub chromadb langchain langgraph tavily-python"
-   ]
-  },
   {
    "attachments": {
-    "5bfa38a2-78a1-4e99-80a2-d98c8a440ea2.png": {
-     "image/png": 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"
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"
     }
    },
    "cell_type": "markdown",
@@ -22,42 +12,104 @@
    "source": [
     "# Corrective RAG (CRAG)\n",
     "\n",
-    "Self-reflection can enhance RAG, enabling correction of poor quality retrieval or generations.\n",
+    "Corrective-RAG (CRAG) is a strategy for RAG that incorperates self-reflection / self-grading on retrieved documents. \n",
     "\n",
-    "Several recent papers focus on this theme, but implementing the ideas can be tricky.\n",
-    "\n",
-    "Here we show how to implement ideas from the `Corrective RAG (CRAG)` paper [here](https://arxiv.org/pdf/2401.15884.pdf) using LangGraph.\n",
-    "\n",
-    "## Dependencies\n",
-    "\n",
-    "Set `OPENAI_API_KEY`\n",
-    "\n",
-    "Set `TAVILY_API_KEY` to enable web search [here](https://app.tavily.com/sign-in)\n",
-    "\n",
-    "## CRAG Detail\n",
-    "\n",
-    "Corrective-RAG (CRAG) is a recent paper that introduces an interesting approach for self-reflective RAG. \n",
-    "\n",
-    "The framework grades retrieved documents relative to the question:\n",
-    "\n",
-    "1. Correct documents -\n",
+    "In the paper [here](https://arxiv.org/pdf/2401.15884.pdf), a few steps are taken:\n",
     "\n",
     "* If at least one document exceeds the threshold for relevance, then it proceeds to generation\n",
     "* Before generation, it performns knowledge refinement\n",
     "* This paritions the document into \"knowledge strips\"\n",
-    "* It grades each strip, and filters our irrelevant ones                                           \n",
-    "\n",
-    "2. Ambiguous or incorrect documents -\n",
-    "\n",
+    "* It grades each strip, and filters our irrelevant ones\n",
     "* If all documents fall below the relevance threshold or if the grader is unsure, then the framework seeks an additional datasource\n",
     "* It will use web search to supplement retrieval\n",
-    "* The diagrams in the paper also suggest that query re-writing is used here \n",
+    " \n",
+    "We will implement some of these ideas from scratch using [LangGraph](https://python.langchain.com/docs/langgraph):\n",
     "\n",
-    "![Screenshot 2024-02-04 at 2.50.32 PM.png](attachment:5bfa38a2-78a1-4e99-80a2-d98c8a440ea2.png)\n",
+    "* Let's skip the knowledge refinement phase as a first pass. This can be added back as a node, if desired. \n",
+    "* If *any* documents are irrelevant, let's opt to supplement retrieval with web search. \n",
+    "* We'll use [Tavily Search](https://python.langchain.com/docs/integrations/tools/tavily_search) for web search.\n",
+    "* Let's use query re-writing to optimize the query for web search.\n",
     "\n",
-    "---\n",
+    "![Screenshot 2024-04-01 at 9.28.30 AM.png](attachment:683fae34-980f-43f0-a9c2-9894bebd9157.png)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "4931ac25-99f9-4f04-b3d1-4683f7853667",
+   "metadata": {},
+   "source": [
+    "# Enviorment "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "568c84d6-9df6-4b7b-b50d-476c0a64a04b",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "! pip install langchain_community tiktoken langchain-openai langchainhub chromadb langchain langgraph tavily-python"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "c9ca92ae-c84f-423e-bab5-2a8bd06bd4cf",
+   "metadata": {},
+   "source": [
+    "### LLMs"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "74710419-158d-4270-931c-de83db7b580d",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import os\n",
+    "os.environ['OPENAI_API_KEY'] = <your-api-key>"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "db34bf9b-fa1e-45d3-a83e-4f5eaf897bad",
+   "metadata": {},
+   "source": [
+    "### Search\n",
+    " \n",
+    "We'll use [Tavily Search](https://python.langchain.com/docs/integrations/tools/tavily_search) for web search."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "c3ac6e65-2d4e-48dd-9fff-40047373332d",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "os.environ['TAVILY_API_KEY'] = <your-api-key>"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "20d2644a-043e-419c-9ff8-45118b8e9f17",
+   "metadata": {},
+   "source": [
+    "### Tracing\n",
     "\n",
-    "Let's implement some of these ideas from scratch using [LangGraph](https://python.langchain.com/docs/langgraph)."
+    "Optionally, use [LangSmith](https://docs.smith.langchain.com/) for tracing (shown at bottom) by setting"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "e205f57e-5218-478b-ad8e-1723bdb0d45e",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "os.environ['LANGCHAIN_TRACING_V2'] = 'true'\n",
+    "os.environ['LANGCHAIN_ENDPOINT'] = 'https://api.smith.langchain.com'\n",
+    "os.environ['LANGCHAIN_API_KEY'] = <your-api-key>"
    ]
   },
   {
@@ -65,7 +117,7 @@
    "id": "a21f32d2-92ce-4995-b309-99347bafe3be",
    "metadata": {},
    "source": [
-    "## Retriever\n",
+    "## Index\n",
     " \n",
     "Let's index 3 blog posts."
    ]
@@ -107,95 +159,203 @@
   },
   {
    "cell_type": "markdown",
-   "id": "87194a1b-535a-4593-ab95-5736fae176d1",
+   "id": "6fca2db8-8d68-42b0-981d-4be5ccdbe293",
    "metadata": {},
    "source": [
-    "## State\n",
-    " \n",
-    "We will define a graph.\n",
-    "\n",
-    "Our state will be a `dict`.\n",
-    "\n",
-    "We can access this from any graph node as `state['keys']`."
+    "## LLMs"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 2,
+   "execution_count": 5,
+   "id": "7ece414c-2df5-4ffd-aa82-550a65775261",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "binary_score='yes'\n"
+     ]
+    }
+   ],
+   "source": [
+    "### Retrieval Grader \n",
+    "\n",
+    "from langchain_openai import ChatOpenAI\n",
+    "from langchain_core.prompts import ChatPromptTemplate\n",
+    "from langchain_core.pydantic_v1 import BaseModel, Field\n",
+    "\n",
+    "# Data model\n",
+    "class GradeDocuments(BaseModel):\n",
+    "    \"\"\"Binary score for relevance check on retrieved documents.\"\"\"\n",
+    "\n",
+    "    binary_score: str = Field(description=\"Documents are relevant to the question, 'yes' or 'no'\")\n",
+    "\n",
+    "# LLM with function call \n",
+    "llm = ChatOpenAI(model=\"gpt-3.5-turbo-0125\", temperature=0)\n",
+    "structured_llm_grader = llm.with_structured_output(GradeDocuments)\n",
+    "\n",
+    "# Prompt \n",
+    "system = \"\"\"You are a grader assessing relevance of a retrieved document to a user question. \\n \n",
+    "    If the document contains keyword(s) or semantic meaning related to the question, grade it as relevant. \\n\n",
+    "    Give a binary score 'yes' or 'no' score to indicate whether the document is relevant to the question.\"\"\"\n",
+    "grade_prompt = ChatPromptTemplate.from_messages(\n",
+    "    [\n",
+    "        (\"system\", system),\n",
+    "        (\"human\", \"Retrieved document: \\n\\n {document} \\n\\n User question: {question}\"),\n",
+    "    ]\n",
+    ")\n",
+    "\n",
+    "retrieval_grader = grade_prompt | structured_llm_grader\n",
+    "question = \"agent memory\"\n",
+    "docs = retriever.get_relevant_documents(question)\n",
+    "doc_txt = docs[1].page_content\n",
+    "print(retrieval_grader.invoke({\"question\": question, \"document\": doc_txt}))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "id": "a207c85f-e414-46b7-8999-4c0ead1493da",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "The design of generative agents combines LLM with memory, planning, and reflection mechanisms to enable agents to behave conditioned on past experience. Memory stream is a long-term memory module that records a comprehensive list of agents' experience in natural language. Short-term memory is utilized for in-context learning, while long-term memory allows agents to retain and recall information over extended periods.\n"
+     ]
+    }
+   ],
+   "source": [
+    "### Generate\n",
+    "\n",
+    "from langchain import hub\n",
+    "from langchain_core.output_parsers import StrOutputParser\n",
+    "\n",
+    "# Prompt\n",
+    "prompt = hub.pull(\"rlm/rag-prompt\")\n",
+    "\n",
+    "# LLM\n",
+    "llm = ChatOpenAI(model_name=\"gpt-3.5-turbo\", temperature=0)\n",
+    "\n",
+    "# Post-processing\n",
+    "def format_docs(docs):\n",
+    "    return \"\\n\\n\".join(doc.page_content for doc in docs)\n",
+    "\n",
+    "# Chain\n",
+    "rag_chain = prompt | llm | StrOutputParser()\n",
+    "\n",
+    "# Run\n",
+    "generation = rag_chain.invoke({\"context\": docs, \"question\": question})\n",
+    "print(generation)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "id": "30d0a69b-9087-4f85-af26-cab55b567872",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "'What is the role of memory in artificial intelligence agents?'"
+      ]
+     },
+     "execution_count": 7,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "### Question Re-writer\n",
+    "\n",
+    "# LLM \n",
+    "llm = ChatOpenAI(model=\"gpt-3.5-turbo-0125\", temperature=0)\n",
+    "\n",
+    "# Prompt \n",
+    "system = \"\"\"You a question re-writer that converts an input question to a better version that is optimized \\n \n",
+    "     for web search. Look at the input and try to reason about the underlying sematic intent / meaning.\"\"\"\n",
+    "re_write_prompt = ChatPromptTemplate.from_messages(\n",
+    "    [\n",
+    "        (\"system\", system),\n",
+    "        (\"human\", \"Here is the initial question: \\n\\n {question} \\n Formulate an improved question.\"),\n",
+    "    ]\n",
+    ")\n",
+    "\n",
+    "question_rewriter = re_write_prompt | llm | StrOutputParser()\n",
+    "question_rewriter.invoke({\"question\": question})"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "e4538467-4a15-4733-b93c-2b79d4d6bf25",
+   "metadata": {},
+   "source": [
+    "## Web Search Tool"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 38,
+   "id": "46d51b53-54a9-4e0a-9f14-e39998f5b340",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "### Search\n",
+    "\n",
+    "from langchain_community.tools.tavily_search import TavilySearchResults\n",
+    "web_search_tool = TavilySearchResults(k=3)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "87194a1b-535a-4593-ab95-5736fae176d1",
+   "metadata": {},
+   "source": [
+    "# Graph \n",
+    "\n",
+    "Capture the flow in as a graph.\n",
+    "\n",
+    "## Graph state"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 39,
    "id": "94b3945f-ef0f-458d-a443-f763903550b0",
    "metadata": {},
    "outputs": [],
    "source": [
-    "from typing import Dict, TypedDict\n",
-    "\n",
-    "from langchain_core.messages import BaseMessage\n",
-    "\n",
+    "from typing_extensions import TypedDict\n",
+    "from typing import List\n",
     "\n",
     "class GraphState(TypedDict):\n",
     "    \"\"\"\n",
     "    Represents the state of our graph.\n",
     "\n",
     "    Attributes:\n",
-    "        keys: A dictionary where each key is a string.\n",
+    "        question: question\n",
+    "        generation: LLM generation\n",
+    "        web_search: whether to add search\n",
+    "        documents: list of documents \n",
     "    \"\"\"\n",
-    "\n",
-    "    keys: Dict[str, any]"
-   ]
-  },
-  {
-   "attachments": {
-    "3b65f495-5fc4-497b-83e2-73844a97f6cc.png": {
-     "image/png": 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"
-    }
-   },
-   "cell_type": "markdown",
-   "id": "f81239f2-314d-41fe-9af9-d19b5b193b53",
-   "metadata": {},
-   "source": [
-    "## Nodes and Edges\n",
-    "\n",
-    "Each `node` will simply modify the `state`.\n",
-    "\n",
-    "Each `edge` will choose which `node` to call next.\n",
-    "\n",
-    "We can make some simplifications from the paper:\n",
-    "\n",
-    "* Let's skip the knowledge refinement phase as a first pass. This can be added back as a node, if desired. \n",
-    "* If *any* document is irrelevant, let's opt to supplement retrieval with web search. \n",
-    "* We'll use [Tavily Search](https://python.langchain.com/docs/integrations/tools/tavily_search) for web search.\n",
-    "* Let's use query re-writing to optimize the query for web search.\n",
-    "\n",
-    "Here is our graph flow:\n",
-    "\n",
-    "![Screenshot 2024-02-04 at 1.32.52 PM.png](attachment:3b65f495-5fc4-497b-83e2-73844a97f6cc.png)"
+    "    question : str\n",
+    "    generation : str\n",
+    "    web_search : str\n",
+    "    documents : List[str]"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 3,
+   "execution_count": 40,
    "id": "efd639c5-82e2-45e6-a94a-6a4039646ef5",
    "metadata": {},
    "outputs": [],
    "source": [
-    "import json\n",
-    "import operator\n",
-    "from typing import Annotated, Sequence, TypedDict\n",
-    "\n",
-    "from langchain import hub\n",
-    "from langchain.output_parsers.openai_tools import PydanticToolsParser\n",
-    "from langchain.prompts import PromptTemplate\n",
     "from langchain.schema import Document\n",
-    "from langchain_community.tools.tavily_search import TavilySearchResults\n",
-    "from langchain_community.vectorstores import Chroma\n",
-    "from langchain_core.messages import BaseMessage, FunctionMessage\n",
-    "from langchain_core.output_parsers import StrOutputParser\n",
-    "from langchain_core.pydantic_v1 import BaseModel, Field\n",
-    "from langchain_core.runnables import RunnablePassthrough\n",
-    "from langchain_core.utils.function_calling import convert_to_openai_tool\n",
-    "from langchain_openai import ChatOpenAI, OpenAIEmbeddings\n",
-    "\n",
-    "### Nodes ###\n",
-    "\n",
     "\n",
     "def retrieve(state):\n",
     "    \"\"\"\n",
@@ -208,11 +368,11 @@
     "        state (dict): New key added to state, documents, that contains retrieved documents\n",
     "    \"\"\"\n",
     "    print(\"---RETRIEVE---\")\n",
-    "    state_dict = state[\"keys\"]\n",
-    "    question = state_dict[\"question\"]\n",
-    "    documents = retriever.get_relevant_documents(question)\n",
-    "    return {\"keys\": {\"documents\": documents, \"question\": question}}\n",
+    "    question = state[\"question\"]\n",
     "\n",
+    "    # Retrieval\n",
+    "    documents = retriever.get_relevant_documents(question)\n",
+    "    return {\"documents\": documents, \"question\": question}\n",
     "\n",
     "def generate(state):\n",
     "    \"\"\"\n",
@@ -225,29 +385,12 @@
     "        state (dict): New key added to state, generation, that contains LLM generation\n",
     "    \"\"\"\n",
     "    print(\"---GENERATE---\")\n",
-    "    state_dict = state[\"keys\"]\n",
-    "    question = state_dict[\"question\"]\n",
-    "    documents = state_dict[\"documents\"]\n",
-    "\n",
-    "    # Prompt\n",
-    "    prompt = hub.pull(\"rlm/rag-prompt\")\n",
-    "\n",
-    "    # LLM\n",
-    "    llm = ChatOpenAI(model_name=\"gpt-3.5-turbo\", temperature=0, streaming=True)\n",
-    "\n",
-    "    # Post-processing\n",
-    "    def format_docs(docs):\n",
-    "        return \"\\n\\n\".join(doc.page_content for doc in docs)\n",
-    "\n",
-    "    # Chain\n",
-    "    rag_chain = prompt | llm | StrOutputParser()\n",
-    "\n",
-    "    # Run\n",
+    "    question = state[\"question\"]\n",
+    "    documents = state[\"documents\"]\n",
+    "    \n",
+    "    # RAG generation\n",
     "    generation = rag_chain.invoke({\"context\": documents, \"question\": question})\n",
-    "    return {\n",
-    "        \"keys\": {\"documents\": documents, \"question\": question, \"generation\": generation}\n",
-    "    }\n",
-    "\n",
+    "    return {\"documents\": documents, \"question\": question, \"generation\": generation}\n",
     "\n",
     "def grade_documents(state):\n",
     "    \"\"\"\n",
@@ -257,70 +400,27 @@
     "        state (dict): The current graph state\n",
     "\n",
     "    Returns:\n",
-    "        state (dict): Updates documents key with relevant documents\n",
+    "        state (dict): Updates documents key with only filtered relevant documents\n",
     "    \"\"\"\n",
     "\n",
-    "    print(\"---CHECK RELEVANCE---\")\n",
-    "    state_dict = state[\"keys\"]\n",
-    "    question = state_dict[\"question\"]\n",
-    "    documents = state_dict[\"documents\"]\n",
-    "\n",
-    "    # Data model\n",
-    "    class grade(BaseModel):\n",
-    "        \"\"\"Binary score for relevance check.\"\"\"\n",
-    "\n",
-    "        binary_score: str = Field(description=\"Relevance score 'yes' or 'no'\")\n",
-    "\n",
-    "    # LLM\n",
-    "    model = ChatOpenAI(temperature=0, model=\"gpt-4-0125-preview\", streaming=True)\n",
-    "\n",
-    "    # Tool\n",
-    "    grade_tool_oai = convert_to_openai_tool(grade)\n",
-    "\n",
-    "    # LLM with tool and enforce invocation\n",
-    "    llm_with_tool = model.bind(\n",
-    "        tools=[grade_tool_oai],\n",
-    "        tool_choice={\"type\": \"function\", \"function\": {\"name\": \"grade\"}},\n",
-    "    )\n",
-    "\n",
-    "    # Parser\n",
-    "    parser_tool = PydanticToolsParser(tools=[grade])\n",
-    "\n",
-    "    # Prompt\n",
-    "    prompt = PromptTemplate(\n",
-    "        template=\"\"\"You are a grader assessing relevance of a retrieved document to a user question. \\n \n",
-    "        Here is the retrieved document: \\n\\n {context} \\n\\n\n",
-    "        Here is the user question: {question} \\n\n",
-    "        If the document contains keyword(s) or semantic meaning related to the user question, grade it as relevant. \\n\n",
-    "        Give a binary score 'yes' or 'no' score to indicate whether the document is relevant to the question.\"\"\",\n",
-    "        input_variables=[\"context\", \"question\"],\n",
-    "    )\n",
-    "\n",
-    "    # Chain\n",
-    "    chain = prompt | llm_with_tool | parser_tool\n",
-    "\n",
-    "    # Score\n",
+    "    print(\"---CHECK DOCUMENT RELEVANCE TO QUESTION---\")\n",
+    "    question = state[\"question\"]\n",
+    "    documents = state[\"documents\"]\n",
+    "    \n",
+    "    # Score each doc\n",
     "    filtered_docs = []\n",
-    "    search = \"No\"  # Default do not opt for web search to supplement retrieval\n",
+    "    web_search = \"No\"\n",
     "    for d in documents:\n",
-    "        score = chain.invoke({\"question\": question, \"context\": d.page_content})\n",
-    "        grade = score[0].binary_score\n",
+    "        score = retrieval_grader.invoke({\"question\": question, \"document\": d.page_content})\n",
+    "        grade = score.binary_score\n",
     "        if grade == \"yes\":\n",
     "            print(\"---GRADE: DOCUMENT RELEVANT---\")\n",
     "            filtered_docs.append(d)\n",
     "        else:\n",
     "            print(\"---GRADE: DOCUMENT NOT RELEVANT---\")\n",
-    "            search = \"Yes\"  # Perform web search\n",
+    "            web_search = \"Yes\"\n",
     "            continue\n",
-    "\n",
-    "    return {\n",
-    "        \"keys\": {\n",
-    "            \"documents\": filtered_docs,\n",
-    "            \"question\": question,\n",
-    "            \"run_web_search\": search,\n",
-    "        }\n",
-    "    }\n",
-    "\n",
+    "    return {\"documents\": filtered_docs, \"question\": question, \"web_search\": web_search}\n",
     "\n",
     "def transform_query(state):\n",
     "    \"\"\"\n",
@@ -334,35 +434,16 @@
     "    \"\"\"\n",
     "\n",
     "    print(\"---TRANSFORM QUERY---\")\n",
-    "    state_dict = state[\"keys\"]\n",
-    "    question = state_dict[\"question\"]\n",
-    "    documents = state_dict[\"documents\"]\n",
-    "\n",
-    "    # Create a prompt template with format instructions and the query\n",
-    "    prompt = PromptTemplate(\n",
-    "        template=\"\"\"You are generating questions that is well optimized for retrieval. \\n \n",
-    "        Look at the input and try to reason about the underlying sematic intent / meaning. \\n \n",
-    "        Here is the initial question:\n",
-    "        \\n ------- \\n\n",
-    "        {question} \n",
-    "        \\n ------- \\n\n",
-    "        Formulate an improved question: \"\"\",\n",
-    "        input_variables=[\"question\"],\n",
-    "    )\n",
-    "\n",
-    "    # Grader\n",
-    "    model = ChatOpenAI(temperature=0, model=\"gpt-4-0125-preview\", streaming=True)\n",
-    "\n",
-    "    # Prompt\n",
-    "    chain = prompt | model | StrOutputParser()\n",
-    "    better_question = chain.invoke({\"question\": question})\n",
-    "\n",
-    "    return {\"keys\": {\"documents\": documents, \"question\": better_question}}\n",
-    "\n",
+    "    question = state[\"question\"]\n",
+    "    documents = state[\"documents\"]\n",
     "\n",
+    "    # Re-write question\n",
+    "    better_question = question_rewriter.invoke({\"question\": question})\n",
+    "    return {\"documents\": documents, \"question\": better_question}\n",
+    "    \n",
     "def web_search(state):\n",
     "    \"\"\"\n",
-    "    Web search based on the re-phrased question using Tavily API.\n",
+    "    Web search based on the re-phrased question.\n",
     "\n",
     "    Args:\n",
     "        state (dict): The current graph state\n",
@@ -372,43 +453,39 @@
     "    \"\"\"\n",
     "\n",
     "    print(\"---WEB SEARCH---\")\n",
-    "    state_dict = state[\"keys\"]\n",
-    "    question = state_dict[\"question\"]\n",
-    "    documents = state_dict[\"documents\"]\n",
+    "    question = state[\"question\"]\n",
+    "    documents = state[\"documents\"]\n",
     "\n",
-    "    tool = TavilySearchResults()\n",
-    "    docs = tool.invoke({\"query\": question})\n",
+    "    # Web search\n",
+    "    docs = web_search_tool.invoke({\"query\": question})\n",
     "    web_results = \"\\n\".join([d[\"content\"] for d in docs])\n",
     "    web_results = Document(page_content=web_results)\n",
     "    documents.append(web_results)\n",
     "\n",
-    "    return {\"keys\": {\"documents\": documents, \"question\": question}}\n",
-    "\n",
+    "    return {\"documents\": documents, \"question\": question}\n",
     "\n",
     "### Edges\n",
     "\n",
-    "\n",
     "def decide_to_generate(state):\n",
     "    \"\"\"\n",
-    "    Determines whether to generate an answer or re-generate a question for web search.\n",
+    "    Determines whether to generate an answer, or re-generate a question.\n",
     "\n",
     "    Args:\n",
-    "        state (dict): The current state of the agent, including all keys.\n",
+    "        state (dict): The current graph state\n",
     "\n",
     "    Returns:\n",
-    "        str: Next node to call\n",
+    "        str: Binary decision for next node to call\n",
     "    \"\"\"\n",
     "\n",
-    "    print(\"---DECIDE TO GENERATE---\")\n",
-    "    state_dict = state[\"keys\"]\n",
-    "    question = state_dict[\"question\"]\n",
-    "    filtered_documents = state_dict[\"documents\"]\n",
-    "    search = state_dict[\"run_web_search\"]\n",
+    "    print(\"---ASSESS GRADED DOCUMENTS---\")\n",
+    "    question = state[\"question\"]\n",
+    "    web_search = state[\"web_search\"]\n",
+    "    filtered_documents = state[\"documents\"]\n",
     "\n",
-    "    if search == \"Yes\":\n",
+    "    if web_search == \"Yes\":\n",
     "        # All documents have been filtered check_relevance\n",
     "        # We will re-generate a new query\n",
-    "        print(\"---DECISION: TRANSFORM QUERY and RUN WEB SEARCH---\")\n",
+    "        print(\"---DECISION: ALL DOCUMENTS ARE NOT RELEVANT TO QUESTION, TRANSFORM QUERY---\")\n",
     "        return \"transform_query\"\n",
     "    else:\n",
     "        # We have relevant documents, so generate answer\n",
@@ -428,13 +505,11 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 4,
+   "execution_count": 41,
    "id": "dedae17a-98c6-474d-90a7-9234b7c8cea0",
    "metadata": {},
    "outputs": [],
    "source": [
-    "import pprint\n",
-    "\n",
     "from langgraph.graph import END, StateGraph\n",
     "\n",
     "workflow = StateGraph(GraphState)\n",
@@ -444,7 +519,7 @@
     "workflow.add_node(\"grade_documents\", grade_documents)  # grade documents\n",
     "workflow.add_node(\"generate\", generate)  # generatae\n",
     "workflow.add_node(\"transform_query\", transform_query)  # transform_query\n",
-    "workflow.add_node(\"web_search\", web_search)  # web search\n",
+    "workflow.add_node(\"web_search_node\", web_search)  # web search\n",
     "\n",
     "# Build graph\n",
     "workflow.set_entry_point(\"retrieve\")\n",
@@ -457,8 +532,8 @@
     "        \"generate\": \"generate\",\n",
     "    },\n",
     ")\n",
-    "workflow.add_edge(\"transform_query\", \"web_search\")\n",
-    "workflow.add_edge(\"web_search\", \"generate\")\n",
+    "workflow.add_edge(\"transform_query\", \"web_search_node\")\n",
+    "workflow.add_edge(\"web_search_node\", \"generate\")\n",
     "workflow.add_edge(\"generate\", END)\n",
     "\n",
     "# Compile\n",
@@ -467,7 +542,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 5,
+   "execution_count": 42,
    "id": "f5b7c2fe-1fc7-4b76-bf93-ba701a40aa6b",
    "metadata": {},
    "outputs": [
@@ -478,46 +553,55 @@
       "---RETRIEVE---\n",
       "\"Node 'retrieve':\"\n",
       "'\\n---\\n'\n",
-      "---CHECK RELEVANCE---\n",
-      "---GRADE: DOCUMENT RELEVANT---\n",
-      "---GRADE: DOCUMENT RELEVANT---\n",
+      "---CHECK DOCUMENT RELEVANCE TO QUESTION---\n",
+      "---GRADE: DOCUMENT NOT RELEVANT---\n",
+      "---GRADE: DOCUMENT NOT RELEVANT---\n",
       "---GRADE: DOCUMENT RELEVANT---\n",
       "---GRADE: DOCUMENT RELEVANT---\n",
       "\"Node 'grade_documents':\"\n",
       "'\\n---\\n'\n",
-      "---DECIDE TO GENERATE---\n",
-      "---DECISION: GENERATE---\n",
+      "---ASSESS GRADED DOCUMENTS---\n",
+      "---DECISION: ALL DOCUMENTS ARE NOT RELEVANT TO QUESTION, TRANSFORM QUERY---\n",
+      "---TRANSFORM QUERY---\n",
+      "\"Node 'transform_query':\"\n",
+      "'\\n---\\n'\n",
+      "---WEB SEARCH---\n",
+      "\"Node 'web_search_node':\"\n",
+      "'\\n---\\n'\n",
       "---GENERATE---\n",
       "\"Node 'generate':\"\n",
       "'\\n---\\n'\n",
       "\"Node '__end__':\"\n",
       "'\\n---\\n'\n",
-      "('Short-term memory stores information needed for complex cognitive tasks and '\n",
-      " 'lasts for 20-30 seconds. Long-term memory can store information for a long '\n",
-      " 'time and has explicit and implicit subtypes. Sensory memory retains sensory '\n",
-      " 'impressions briefly after stimuli end.')\n"
+      "('Agents possess short-term memory, which is utilized for in-context learning, '\n",
+      " 'and long-term memory, allowing them to retain and recall vast amounts of '\n",
+      " 'information over extended periods. Some experts also classify working memory '\n",
+      " 'as a distinct type, although it can be considered a part of short-term '\n",
+      " 'memory in many cases.')\n"
      ]
     }
    ],
    "source": [
+    "from pprint import pprint\n",
+    "\n",
     "# Run\n",
-    "inputs = {\"keys\": {\"question\": \"Explain how the different types of agent memory work?\"}}\n",
+    "inputs = {\"question\": \"What are the types of agent memory?\"}\n",
     "for output in app.stream(inputs):\n",
     "    for key, value in output.items():\n",
     "        # Node\n",
-    "        pprint.pprint(f\"Node '{key}':\")\n",
+    "        pprint(f\"Node '{key}':\")\n",
     "        # Optional: print full state at each node\n",
     "        # pprint.pprint(value[\"keys\"], indent=2, width=80, depth=None)\n",
-    "    pprint.pprint(\"\\n---\\n\")\n",
+    "    pprint(\"\\n---\\n\")\n",
     "\n",
     "# Final generation\n",
-    "pprint.pprint(value[\"keys\"][\"generation\"])"
+    "pprint(value[\"generation\"])"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 11,
-   "id": "2bee03de-a32c-4bbe-b37a-a13bb825e4cb",
+   "execution_count": 43,
+   "id": "41ea1108-f385-4774-962d-db157922e231",
    "metadata": {},
    "outputs": [
     {
@@ -527,51 +611,51 @@
       "---RETRIEVE---\n",
       "\"Node 'retrieve':\"\n",
       "'\\n---\\n'\n",
-      "---CHECK RELEVANCE---\n",
-      "---GRADE: DOCUMENT NOT RELEVANT---\n",
+      "---CHECK DOCUMENT RELEVANCE TO QUESTION---\n",
       "---GRADE: DOCUMENT NOT RELEVANT---\n",
       "---GRADE: DOCUMENT NOT RELEVANT---\n",
       "---GRADE: DOCUMENT NOT RELEVANT---\n",
+      "---GRADE: DOCUMENT RELEVANT---\n",
       "\"Node 'grade_documents':\"\n",
       "'\\n---\\n'\n",
-      "---DECIDE TO GENERATE---\n",
-      "---DECISION: TRANSFORM QUERY and RUN WEB SEARCH---\n",
+      "---ASSESS GRADED DOCUMENTS---\n",
+      "---DECISION: ALL DOCUMENTS ARE NOT RELEVANT TO QUESTION, TRANSFORM QUERY---\n",
       "---TRANSFORM QUERY---\n",
       "\"Node 'transform_query':\"\n",
       "'\\n---\\n'\n",
       "---WEB SEARCH---\n",
-      "\"Node 'web_search':\"\n",
+      "\"Node 'web_search_node':\"\n",
       "'\\n---\\n'\n",
       "---GENERATE---\n",
       "\"Node 'generate':\"\n",
       "'\\n---\\n'\n",
       "\"Node '__end__':\"\n",
       "'\\n---\\n'\n",
-      "('The AlphaCodium paper uses a test-based, iterative approach for code '\n",
-      " 'generation. It employs a multi-stage, code-oriented flow that addresses the '\n",
-      " 'specific challenges of coding problems. Unlike traditional models, '\n",
-      " 'AlphaCodium actively engages in problem self-reflection, reasoning, and '\n",
-      " 'iterative code solution generation.')\n"
+      "('The AlphaCodium paper functions by proposing a code-oriented iterative flow '\n",
+      " 'that involves repeatedly running and fixing generated code against '\n",
+      " 'input-output tests. Its key mechanisms include generating additional data '\n",
+      " 'like problem reflection and test reasoning to aid the iterative process, as '\n",
+      " 'well as enriching the code generation process. AlphaCodium aims to improve '\n",
+      " 'the performance of Large Language Models on code problems by following a '\n",
+      " 'test-based, multi-stage approach.')\n"
      ]
     }
    ],
    "source": [
-    "# Correction for question not present in context\n",
-    "inputs = {\n",
-    "    \"keys\": {\n",
-    "        \"question\": \"What is the approach for code generation taken in the AlphaCodium paper?\"\n",
-    "    }\n",
-    "}\n",
+    "from pprint import pprint\n",
+    "\n",
+    "# Run\n",
+    "inputs = {\"question\": \"How does the AlphaCodium paper work?\"}\n",
     "for output in app.stream(inputs):\n",
     "    for key, value in output.items():\n",
     "        # Node\n",
-    "        pprint.pprint(f\"Node '{key}':\")\n",
-    "        # Optional: print full state\n",
+    "        pprint(f\"Node '{key}':\")\n",
+    "        # Optional: print full state at each node\n",
     "        # pprint.pprint(value[\"keys\"], indent=2, width=80, depth=None)\n",
-    "    pprint.pprint(\"\\n---\\n\")\n",
+    "    pprint(\"\\n---\\n\")\n",
     "\n",
     "# Final generation\n",
-    "pprint.pprint(value[\"keys\"][\"generation\"])"
+    "pprint(value[\"generation\"])"
    ]
   },
   {
@@ -581,10 +665,18 @@
    "source": [
     "LangSmith Traces - \n",
     " \n",
-    "* https://smith.langchain.com/public/7e0b9569-abfe-4337-b34b-842b1f93df63/r\n",
+    "* https://smith.langchain.com/public/f6b1716c-e842-4282-9112-1026b93e246b/r\n",
     "\n",
-    "*  https://smith.langchain.com/public/b40c5813-7caf-4cc8-b279-ee66060b2040/r"
+    "* https://smith.langchain.com/public/497c8ed9-d9e2-429e-8ada-e64de3ec26c9/r"
    ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "6ce65be5-fd12-4ffc-984c-34c132693e69",
+   "metadata": {},
+   "outputs": [],
+   "source": []
   }
  ],
  "metadata": {
@@ -603,7 +695,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.9.16"
+   "version": "3.11.8"
   }
  },
  "nbformat": 4,
diff --git a/examples/rag/langgraph_crag_local.ipynb b/examples/rag/langgraph_crag_local.ipynb
new file mode 100644
index 000000000..90d3b487d
--- /dev/null
+++ b/examples/rag/langgraph_crag_local.ipynb
@@ -0,0 +1,788 @@
+{
+ "cells": [
+  {
+   "attachments": {
+    "d3ff129f-c0ff-4951-993c-efafc7f29dce.png": {
+     "image/png": 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"
+    }
+   },
+   "cell_type": "markdown",
+   "id": "92ddc4f4-f7bf-4e0e-b5a5-5abd8a008b21",
+   "metadata": {},
+   "source": [
+    "# Corrective RAG (CRAG) -- With Local LLMs\n",
+    "\n",
+    "Corrective-RAG (CRAG) is a strategy for RAG that incorperates self-reflection / self-grading on retrieved documents. \n",
+    "\n",
+    "In the paper [here](https://arxiv.org/pdf/2401.15884.pdf), a few steps are taken:\n",
+    "\n",
+    "* If at least one document exceeds the threshold for relevance, then it proceeds to generation\n",
+    "* Before generation, it performns knowledge refinement\n",
+    "* This paritions the document into \"knowledge strips\"\n",
+    "* It grades each strip, and filters our irrelevant ones\n",
+    "* If all documents fall below the relevance threshold or if the grader is unsure, then the framework seeks an additional datasource\n",
+    "* It will use web search to supplement retrieval\n",
+    " \n",
+    "We will implement some of these ideas from scratch using [LangGraph](https://python.langchain.com/docs/langgraph):\n",
+    "\n",
+    "* Let's skip the knowledge refinement phase as a first pass. This can be added back as a node, if desired. \n",
+    "* If *any* documents are irrelevant, let's opt to supplement retrieval with web search. \n",
+    "* We'll use [Tavily Search](https://python.langchain.com/docs/integrations/tools/tavily_search) for web search.\n",
+    "* Let's use query re-writing to optimize the query for web search.\n",
+    "\n",
+    "![Screenshot 2024-04-01 at 12.36.05 PM.png](attachment:d3ff129f-c0ff-4951-993c-efafc7f29dce.png)\n",
+    "\n",
+    "## Running\n",
+    "\n",
+    "This notebook can be run three ways:\n",
+    "\n",
+    "(1) Mistral API\n",
+    "\n",
+    "(2) Locally \n",
+    "\n",
+    "(3) CoLab: [here](https://colab.research.google.com/drive/1U5OcwWjoXZSud30q4XOk1UlIJNjaD3kX?usp=sharing) is a link to a CoLab for this notebook. "
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "6ba4302f-09d9-4d2a-a18d-a6fd23704850",
+   "metadata": {},
+   "source": [
+    "# Enviorment "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "4a660963-bd3d-4c87-b2e4-b6e432055211",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "! pip install --quiet langchain_community tiktoken langchainhub chromadb langchain langgraph tavily-python langchain-mistralai gpt4all"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "728896ab-8aca-4cc3-a152-1491a76bc620",
+   "metadata": {},
+   "source": [
+    "### LLMs\n",
+    "\n",
+    "You can run this in two ways:\n",
+    "\n",
+    "(1) Use [Mistral API](https://auth.mistral.ai/ui/login?flow=cc5d3fa5-122b-4c87-bcd8-81e8151c6753).\n",
+    "\n",
+    "(2) Run locally, as shown below.\n",
+    "\n",
+    "#### Local Embeddings\n",
+    "\n",
+    "You can use `GPT4AllEmbeddings()` from Nomic, which can access use Nomic's recently released [v1](https://blog.nomic.ai/posts/nomic-embed-text-v1) and [v1.5](https://blog.nomic.ai/posts/nomic-embed-matryoshka) embeddings.\n",
+    "\n",
+    "\n",
+    "Follow the documentation [here](https://docs.gpt4all.io/gpt4all_python_embedding.html#supported-embedding-models).\n",
+    "\n",
+    "#### Local LLM\n",
+    "\n",
+    "(1) Download [Ollama app](https://ollama.ai/).\n",
+    "\n",
+    "(2) Download a `Mistral` model from various Mistral versions [here](https://ollama.ai/library/mistral) and Mixtral versions [here](https://ollama.ai/library/mixtral) available.\n",
+    "```\n",
+    "ollama pull mistral\n",
+    "```"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "d0cd7ff0-534d-4743-ba6d-e12024a2fd84",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# If using Mistral API\n",
+    "mistral_api_key =  <your-api-key>"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "83f74055-d137-466c-9555-4e2da9759ddb",
+   "metadata": {},
+   "source": [
+    "### Search\n",
+    " \n",
+    "We'll use [Tavily Search](https://python.langchain.com/docs/integrations/tools/tavily_search) for web search."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "28224481-4cb0-4bc6-bf88-2d2b383094df",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import os\n",
+    "os.environ['TAVILY_API_KEY'] = <your-api-key>"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "55ec4c0c-65cc-4816-86df-c40b55f9c2d5",
+   "metadata": {},
+   "source": [
+    "### Tracing\n",
+    "\n",
+    "Optionally, use [LangSmith](https://docs.smith.langchain.com/) for tracing (shown at bottom)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "68fed362-871a-46df-8ba0-579797ff2e9c",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "os.environ['LANGCHAIN_TRACING_V2'] = 'true'\n",
+    "os.environ['LANGCHAIN_ENDPOINT'] = 'https://api.smith.langchain.com'\n",
+    "os.environ['LANGCHAIN_API_KEY'] = <your-api-key>"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "c059c3a3-7f01-4d46-8289-fde4c1b4155f",
+   "metadata": {},
+   "source": [
+    "## Configuration\n",
+    "\n",
+    "Decide to run locally and select LLM to use with Ollama."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "id": "2f4db331-c4d0-4c7c-a9a5-0bebc8a89c6c",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "run_local = 'Yes'\n",
+    "local_llm = \"mistral:latest\""
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "6e2b6eed-3b3f-44b5-a34a-4ade1e94caf0",
+   "metadata": {},
+   "source": [
+    "## Index\n",
+    "\n",
+    "Let's index 3 blog posts."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "id": "bb8b789b-475b-4e1b-9c66-03504c837830",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from langchain_community.document_loaders import WebBaseLoader\n",
+    "from langchain_community.vectorstores import Chroma\n",
+    "from langchain_community.embeddings import GPT4AllEmbeddings\n",
+    "from langchain.text_splitter import RecursiveCharacterTextSplitter\n",
+    "from langchain_mistralai import MistralAIEmbeddings\n",
+    "\n",
+    "# Load\n",
+    "url = \"https://lilianweng.github.io/posts/2023-06-23-agent/\"\n",
+    "loader = WebBaseLoader(url)\n",
+    "docs = loader.load()\n",
+    "\n",
+    "# Split\n",
+    "text_splitter = RecursiveCharacterTextSplitter.from_tiktoken_encoder(\n",
+    "    chunk_size=500, chunk_overlap=100\n",
+    ")\n",
+    "all_splits = text_splitter.split_documents(docs)\n",
+    "\n",
+    "# Embed and index\n",
+    "if run_local == \"Yes\":\n",
+    "    embedding = GPT4AllEmbeddings()\n",
+    "else:\n",
+    "    embedding = MistralAIEmbeddings(mistral_api_key=mistral_api_key)\n",
+    "\n",
+    "# Index\n",
+    "vectorstore = Chroma.from_documents(\n",
+    "    documents=all_splits,\n",
+    "    collection_name=\"rag-chroma\",\n",
+    "    embedding=embedding,\n",
+    ")\n",
+    "retriever = vectorstore.as_retriever()"
+   ]
+  },
+  {
+   "attachments": {},
+   "cell_type": "markdown",
+   "id": "fe7fd10a-f64a-48de-a116-6d5890def1af",
+   "metadata": {},
+   "source": [
+    "## LLMs"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "id": "0e75c029-6c10-47c7-871c-1f4932b25309",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "{'score': 'yes'}\n"
+     ]
+    }
+   ],
+   "source": [
+    "### Retrieval Grader \n",
+    "\n",
+    "from langchain.prompts import PromptTemplate\n",
+    "from langchain_community.chat_models import ChatOllama\n",
+    "from langchain_mistralai.chat_models import ChatMistralAI\n",
+    "from langchain_core.output_parsers import JsonOutputParser\n",
+    "\n",
+    "# LLM\n",
+    "if run_local == \"Yes\":\n",
+    "    llm = ChatOllama(model=local_llm, format=\"json\", temperature=0)\n",
+    "else:\n",
+    "    llm = ChatMistralAI(\n",
+    "        model=\"mistral-medium\", temperature=0, mistral_api_key=mistral_api_key\n",
+    "    )\n",
+    "\n",
+    "prompt = PromptTemplate(\n",
+    "    template=\"\"\"You are a grader assessing relevance of a retrieved document to a user question. \\n \n",
+    "    Here is the retrieved document: \\n\\n {document} \\n\\n\n",
+    "    Here is the user question: {question} \\n\n",
+    "    If the document contains keywords related to the user question, grade it as relevant. \\n\n",
+    "    It does not need to be a stringent test. The goal is to filter out erroneous retrievals. \\n\n",
+    "    Give a binary score 'yes' or 'no' score to indicate whether the document is relevant to the question. \\n\n",
+    "    Provide the binary score as a JSON with a single key 'score' and no premable or explaination.\"\"\",\n",
+    "    input_variables=[\"question\", \"document\"],\n",
+    ")\n",
+    "\n",
+    "retrieval_grader = prompt | llm | JsonOutputParser()\n",
+    "question = \"agent memory\"\n",
+    "docs = retriever.get_relevant_documents(question)\n",
+    "doc_txt = docs[1].page_content\n",
+    "print(retrieval_grader.invoke({\"question\": question, \"document\": doc_txt}))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "id": "dad03302-bd93-43fc-949e-af51a3298cfa",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      " The given text discusses the concept of building autonomous agents using a large language model (LLM) as its core controller. The text highlights several key components of an LLM-powered agent system, including observation, retrieval, reflection, planning & reacting, and relationships between agents. It also mentions some challenges such as finite context length, long-term planning and task decomposition, and reliability of natural language interface. The text provides examples of proof-of-concept demos like AutoGPT and discusses their limitations. The architecture of the generative agent is also described, which results in emergent social behavior.\n"
+     ]
+    }
+   ],
+   "source": [
+    "### Generate\n",
+    "\n",
+    "from langchain import hub\n",
+    "from langchain_core.output_parsers import StrOutputParser\n",
+    "\n",
+    "# Prompt\n",
+    "prompt = hub.pull(\"rlm/rag-prompt\")\n",
+    "\n",
+    "# LLM\n",
+    "if run_local == \"Yes\":\n",
+    "    llm = ChatOllama(model=local_llm, temperature=0)\n",
+    "else:\n",
+    "    llm = ChatMistralAI(\n",
+    "        model=\"mistral-medium\", temperature=0, mistral_api_key=mistral_api_key\n",
+    "    )\n",
+    "\n",
+    "# Post-processing\n",
+    "def format_docs(docs):\n",
+    "    return \"\\n\\n\".join(doc.page_content for doc in docs)\n",
+    "\n",
+    "# Chain\n",
+    "rag_chain = prompt | llm | StrOutputParser()\n",
+    "\n",
+    "# Run\n",
+    "generation = rag_chain.invoke({\"context\": docs, \"question\": question})\n",
+    "print(generation)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "id": "f4b61211-70b5-4471-a714-feb9cc91e860",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "' What is agent memory and how can it be effectively utilized in vector database retrieval?'"
+      ]
+     },
+     "execution_count": 9,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "### Question Re-writer\n",
+    "\n",
+    "# LLM\n",
+    "if run_local == \"Yes\":\n",
+    "    llm = ChatOllama(model=local_llm, temperature=0)\n",
+    "else:\n",
+    "    llm = ChatMistralAI(\n",
+    "        model=\"mistral-medium\", temperature=0, mistral_api_key=mistral_api_key\n",
+    "    )\n",
+    "\n",
+    "# Prompt \n",
+    "re_write_prompt = PromptTemplate(\n",
+    "    template=\"\"\"You a question re-writer that converts an input question to a better version that is optimized \\n \n",
+    "     for vectorstore retrieval. Look at the initial and formulate an improved question. \\n\n",
+    "     Here is the initial question: \\n\\n {question}. Improved question with no preamble: \\n \"\"\",\n",
+    "    input_variables=[\"generation\", \"question\"],\n",
+    ")\n",
+    "\n",
+    "question_rewriter = re_write_prompt | llm | StrOutputParser()\n",
+    "question_rewriter.invoke({\"question\": question})"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "5d7fde29-e62e-4445-80f9-122eee0a3922",
+   "metadata": {},
+   "source": [
+    "## Web Search Tool"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "id": "b36a2f36-bc5f-408d-a5e8-3fa203c233f6",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "### Search\n",
+    "\n",
+    "from langchain_community.tools.tavily_search import TavilySearchResults\n",
+    "web_search_tool = TavilySearchResults(k=3)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "a3421cf0-9067-43fe-8681-0d3189d15dd3",
+   "metadata": {},
+   "source": [
+    "# Graph \n",
+    "\n",
+    "Capture the flow in as a graph.\n",
+    "\n",
+    "## Graph state"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "id": "10028794-2fbc-43f9-aa4c-7fe3abd69c1e",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from typing_extensions import TypedDict\n",
+    "from typing import List\n",
+    "\n",
+    "class GraphState(TypedDict):\n",
+    "    \"\"\"\n",
+    "    Represents the state of our graph.\n",
+    "\n",
+    "    Attributes:\n",
+    "        question: question\n",
+    "        generation: LLM generation\n",
+    "        web_search: whether to add search\n",
+    "        documents: list of documents \n",
+    "    \"\"\"\n",
+    "    question : str\n",
+    "    generation : str\n",
+    "    web_search : str\n",
+    "    documents : List[str]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 15,
+   "id": "447d1333-082d-479a-a6fa-0ac0df78bb9d",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from langchain.schema import Document\n",
+    "\n",
+    "def retrieve(state):\n",
+    "    \"\"\"\n",
+    "    Retrieve documents\n",
+    "\n",
+    "    Args:\n",
+    "        state (dict): The current graph state\n",
+    "\n",
+    "    Returns:ß\n",
+    "        state (dict): New key added to state, documents, that contains retrieved documents\n",
+    "    \"\"\"\n",
+    "    print(\"---RETRIEVE---\")\n",
+    "    question = state[\"question\"]\n",
+    "\n",
+    "    # Retrieval\n",
+    "    documents = retriever.get_relevant_documents(question)\n",
+    "    return {\"documents\": documents, \"question\": question}\n",
+    "\n",
+    "def generate(state):\n",
+    "    \"\"\"\n",
+    "    Generate answer\n",
+    "\n",
+    "    Args:\n",
+    "        state (dict): The current graph state\n",
+    "\n",
+    "    Returns:\n",
+    "        state (dict): New key added to state, generation, that contains LLM generation\n",
+    "    \"\"\"\n",
+    "    print(\"---GENERATE---\")\n",
+    "    question = state[\"question\"]\n",
+    "    documents = state[\"documents\"]\n",
+    "    \n",
+    "    # RAG generation\n",
+    "    generation = rag_chain.invoke({\"context\": documents, \"question\": question})\n",
+    "    return {\"documents\": documents, \"question\": question, \"generation\": generation}\n",
+    "\n",
+    "def grade_documents(state):\n",
+    "    \"\"\"\n",
+    "    Determines whether the retrieved documents are relevant to the question.\n",
+    "\n",
+    "    Args:\n",
+    "        state (dict): The current graph state\n",
+    "\n",
+    "    Returns:\n",
+    "        state (dict): Updates documents key with only filtered relevant documents\n",
+    "    \"\"\"\n",
+    "\n",
+    "    print(\"---CHECK DOCUMENT RELEVANCE TO QUESTION---\")\n",
+    "    question = state[\"question\"]\n",
+    "    documents = state[\"documents\"]\n",
+    "    \n",
+    "    # Score each doc\n",
+    "    filtered_docs = []\n",
+    "    web_search = \"No\"\n",
+    "    for d in documents:\n",
+    "        score = retrieval_grader.invoke({\"question\": question, \"document\": d.page_content})\n",
+    "        grade = score['score']\n",
+    "        if grade == \"yes\":\n",
+    "            print(\"---GRADE: DOCUMENT RELEVANT---\")\n",
+    "            filtered_docs.append(d)\n",
+    "        else:\n",
+    "            print(\"---GRADE: DOCUMENT NOT RELEVANT---\")\n",
+    "            web_search = \"Yes\"\n",
+    "            continue\n",
+    "    return {\"documents\": filtered_docs, \"question\": question, \"web_search\": web_search}\n",
+    "\n",
+    "def transform_query(state):\n",
+    "    \"\"\"\n",
+    "    Transform the query to produce a better question.\n",
+    "\n",
+    "    Args:\n",
+    "        state (dict): The current graph state\n",
+    "\n",
+    "    Returns:\n",
+    "        state (dict): Updates question key with a re-phrased question\n",
+    "    \"\"\"\n",
+    "\n",
+    "    print(\"---TRANSFORM QUERY---\")\n",
+    "    question = state[\"question\"]\n",
+    "    documents = state[\"documents\"]\n",
+    "\n",
+    "    # Re-write question\n",
+    "    better_question = question_rewriter.invoke({\"question\": question})\n",
+    "    return {\"documents\": documents, \"question\": better_question}\n",
+    "    \n",
+    "def web_search(state):\n",
+    "    \"\"\"\n",
+    "    Web search based on the re-phrased question.\n",
+    "\n",
+    "    Args:\n",
+    "        state (dict): The current graph state\n",
+    "\n",
+    "    Returns:\n",
+    "        state (dict): Updates documents key with appended web results\n",
+    "    \"\"\"\n",
+    "\n",
+    "    print(\"---WEB SEARCH---\")\n",
+    "    question = state[\"question\"]\n",
+    "    documents = state[\"documents\"]\n",
+    "\n",
+    "    # Web search\n",
+    "    docs = web_search_tool.invoke({\"query\": question})\n",
+    "    web_results = \"\\n\".join([d[\"content\"] for d in docs])\n",
+    "    web_results = Document(page_content=web_results)\n",
+    "    documents.append(web_results)\n",
+    "\n",
+    "    return {\"documents\": documents, \"question\": question}\n",
+    "\n",
+    "### Edges\n",
+    "\n",
+    "def decide_to_generate(state):\n",
+    "    \"\"\"\n",
+    "    Determines whether to generate an answer, or re-generate a question.\n",
+    "\n",
+    "    Args:\n",
+    "        state (dict): The current graph state\n",
+    "\n",
+    "    Returns:\n",
+    "        str: Binary decision for next node to call\n",
+    "    \"\"\"\n",
+    "\n",
+    "    print(\"---ASSESS GRADED DOCUMENTS---\")\n",
+    "    question = state[\"question\"]\n",
+    "    web_search = state[\"web_search\"]\n",
+    "    filtered_documents = state[\"documents\"]\n",
+    "\n",
+    "    if web_search == \"Yes\":\n",
+    "        # All documents have been filtered check_relevance\n",
+    "        # We will re-generate a new query\n",
+    "        print(\"---DECISION: ALL DOCUMENTS ARE NOT RELEVANT TO QUESTION, TRANSFORM QUERY---\")\n",
+    "        return \"transform_query\"\n",
+    "    else:\n",
+    "        # We have relevant documents, so generate answer\n",
+    "        print(\"---DECISION: GENERATE---\")\n",
+    "        return \"generate\""
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "6096626d-dfa5-48e0-8a24-3747b298bc67",
+   "metadata": {},
+   "source": [
+    "## Build Graph\n",
+    "\n",
+    "This just follows the flow we outlined in the figure above."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 16,
+   "id": "0a63776c-f9cd-46ce-b8cf-95c066dc5b06",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from langgraph.graph import END, StateGraph\n",
+    "\n",
+    "workflow = StateGraph(GraphState)\n",
+    "\n",
+    "# Define the nodes\n",
+    "workflow.add_node(\"retrieve\", retrieve)  # retrieve\n",
+    "workflow.add_node(\"grade_documents\", grade_documents)  # grade documents\n",
+    "workflow.add_node(\"generate\", generate)  # generatae\n",
+    "workflow.add_node(\"transform_query\", transform_query)  # transform_query\n",
+    "workflow.add_node(\"web_search_node\", web_search)  # web search\n",
+    "\n",
+    "# Build graph\n",
+    "workflow.set_entry_point(\"retrieve\")\n",
+    "workflow.add_edge(\"retrieve\", \"grade_documents\")\n",
+    "workflow.add_conditional_edges(\n",
+    "    \"grade_documents\",\n",
+    "    decide_to_generate,\n",
+    "    {\n",
+    "        \"transform_query\": \"transform_query\",\n",
+    "        \"generate\": \"generate\",\n",
+    "    },\n",
+    ")\n",
+    "workflow.add_edge(\"transform_query\", \"web_search_node\")\n",
+    "workflow.add_edge(\"web_search_node\", \"generate\")\n",
+    "workflow.add_edge(\"generate\", END)\n",
+    "\n",
+    "# Compile\n",
+    "app = workflow.compile()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 17,
+   "id": "3ab1d8df-a74e-4b48-a30b-e39bbfd5925a",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "---RETRIEVE---\n",
+      "\"Node 'retrieve':\"\n",
+      "'\\n---\\n'\n",
+      "---CHECK DOCUMENT RELEVANCE TO QUESTION---\n",
+      "---GRADE: DOCUMENT RELEVANT---\n",
+      "---GRADE: DOCUMENT RELEVANT---\n",
+      "---GRADE: DOCUMENT RELEVANT---\n",
+      "---GRADE: DOCUMENT RELEVANT---\n",
+      "\"Node 'grade_documents':\"\n",
+      "'\\n---\\n'\n",
+      "---ASSESS GRADED DOCUMENTS---\n",
+      "---DECISION: GENERATE---\n",
+      "---GENERATE---\n",
+      "\"Node 'generate':\"\n",
+      "'\\n---\\n'\n",
+      "\"Node '__end__':\"\n",
+      "'\\n---\\n'\n",
+      "(' The given text discusses the concept of building autonomous agents using '\n",
+      " 'large language models (LLMs) as their core controllers. LLMs have the '\n",
+      " 'potential to be powerful general problem solvers, extending beyond '\n",
+      " 'generating well-written copies, stories, essays, and programs. In an '\n",
+      " \"LLM-powered agent system, the model functions as the agent's brain, \"\n",
+      " 'complemented by several key components: planning, memory, and tool use.\\n'\n",
+      " '\\n'\n",
+      " '1. Planning: The agent breaks down large tasks into smaller subgoals for '\n",
+      " 'efficient handling of complex tasks and can do self-criticism and '\n",
+      " 'self-reflection to improve results.\\n'\n",
+      " '2. Memory: Short-term memory is utilized for in-context learning, while '\n",
+      " 'long-term memory provides the capability to retain and recall information '\n",
+      " 'over extended periods by leveraging an external vector store and fast '\n",
+      " 'retrieval.\\n'\n",
+      " '3. Tool use: The agent learns to call external APIs for missing information, '\n",
+      " 'including current information, code execution capability, access to '\n",
+      " 'proprietary information sources, and more.\\n'\n",
+      " '\\n'\n",
+      " 'The text also discusses the types of memory in human brains, including '\n",
+      " 'sensory memory, short-term memory (STM), and long-term memory (LTM). Sensory '\n",
+      " 'memory provides the ability to retain impressions of sensory information for '\n",
+      " 'a few seconds, while STM stores information needed for complex cognitive '\n",
+      " 'tasks and lasts for 20-30 seconds. LTM can store information for remarkably '\n",
+      " 'long periods with an essentially unlimited storage capacity and has two '\n",
+      " 'subtypes: explicit/declarative memory (memory of facts and events) and '\n",
+      " 'implicit/procedural memory (skills and routines).\\n'\n",
+      " '\\n'\n",
+      " 'The text also includes a figure comparing different methods, including AD, '\n",
+      " 'ED, source policies, and RL^2, on environments that require memory and '\n",
+      " 'exploration.')\n"
+     ]
+    }
+   ],
+   "source": [
+    "from pprint import pprint\n",
+    "\n",
+    "# Run\n",
+    "inputs = {\"question\": \"What are the types of agent memory?\"}\n",
+    "for output in app.stream(inputs):\n",
+    "    for key, value in output.items():\n",
+    "        # Node\n",
+    "        pprint(f\"Node '{key}':\")\n",
+    "        # Optional: print full state at each node\n",
+    "        # pprint.pprint(value[\"keys\"], indent=2, width=80, depth=None)\n",
+    "    pprint(\"\\n---\\n\")\n",
+    "\n",
+    "# Final generation\n",
+    "pprint(value[\"generation\"])"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "03ee2be9-2368-46ea-9edd-dc064a7c7c96",
+   "metadata": {},
+   "source": [
+    "Trace: \n",
+    "\n",
+    "https://smith.langchain.com/public/731df833-57de-4612-8fe8-07cb424bc9a6/r"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 18,
+   "id": "deb28175-27a1-4afc-9747-2983e87fc881",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "---RETRIEVE---\n",
+      "\"Node 'retrieve':\"\n",
+      "'\\n---\\n'\n",
+      "---CHECK DOCUMENT RELEVANCE TO QUESTION---\n",
+      "---GRADE: DOCUMENT NOT RELEVANT---\n",
+      "---GRADE: DOCUMENT NOT RELEVANT---\n",
+      "---GRADE: DOCUMENT NOT RELEVANT---\n",
+      "---GRADE: DOCUMENT NOT RELEVANT---\n",
+      "\"Node 'grade_documents':\"\n",
+      "'\\n---\\n'\n",
+      "---ASSESS GRADED DOCUMENTS---\n",
+      "---DECISION: ALL DOCUMENTS ARE NOT RELEVANT TO QUESTION, TRANSFORM QUERY---\n",
+      "---TRANSFORM QUERY---\n",
+      "\"Node 'transform_query':\"\n",
+      "'\\n---\\n'\n",
+      "---WEB SEARCH---\n",
+      "\"Node 'web_search_node':\"\n",
+      "'\\n---\\n'\n",
+      "---GENERATE---\n",
+      "\"Node 'generate':\"\n",
+      "'\\n---\\n'\n",
+      "\"Node '__end__':\"\n",
+      "'\\n---\\n'\n",
+      "(' AlphaCodium is a new approach to code generation by LLMs, proposed in a '\n",
+      " 'paper titled \"Code Generation with AlphaCodium: From Prompt Engineering to '\n",
+      " 'Flow Engineering.\" It\\'s described as a test-based, multi-stage flow that '\n",
+      " 'improves the performance of LLMs on code problems without requiring '\n",
+      " 'fine-tuning. The iterative process involves repeatedly running and fixing '\n",
+      " 'generated code against input-output tests, with two key elements being '\n",
+      " 'generating additional data for the process and enrichment.')\n"
+     ]
+    }
+   ],
+   "source": [
+    "from pprint import pprint\n",
+    "\n",
+    "# Run\n",
+    "inputs = {\"question\": \"How does the AlphaCodium paper work?\"}\n",
+    "for output in app.stream(inputs):\n",
+    "    for key, value in output.items():\n",
+    "        # Node\n",
+    "        pprint(f\"Node '{key}':\")\n",
+    "        # Optional: print full state at each node\n",
+    "        # pprint.pprint(value[\"keys\"], indent=2, width=80, depth=None)\n",
+    "    pprint(\"\\n---\\n\")\n",
+    "\n",
+    "# Final generation\n",
+    "pprint(value[\"generation\"])"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "598fe73e-f4e6-479f-8d9d-fc81680fff21",
+   "metadata": {},
+   "source": [
+    "Trace: \n",
+    "\n",
+    "https://smith.langchain.com/public/c8b75f1b-38b7-48f2-a399-7ebb969d34f6/r"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3 (ipykernel)",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.11.8"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}
diff --git a/examples/rag/langgraph_crag_mistral.ipynb b/examples/rag/langgraph_crag_mistral.ipynb
deleted file mode 100644
index 502498cfa..000000000
--- a/examples/rag/langgraph_crag_mistral.ipynb
+++ /dev/null
@@ -1,778 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "code",
-   "execution_count": 5,
-   "id": "19969669-b47f-47f3-b6d4-f7b155434840",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "! pip install --quiet langchain_community tiktoken langchain-openai langchainhub chromadb langchain langgraph tavily-python langchain-mistralai gpt4all llama-cpp-python"
-   ]
-  },
-  {
-   "attachments": {
-    "a65940f9-5c51-4d7c-9ca1-ae576e4bb51a.png": {
-     "image/png": 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"
-    }
-   },
-   "cell_type": "markdown",
-   "id": "92ddc4f4-f7bf-4e0e-b5a5-5abd8a008b21",
-   "metadata": {},
-   "source": [
-    "# Corrective RAG\n",
-    "\n",
-    "Self-reflection can enhance RAG, enabling correction of poor quality retrieval or generations.\n",
-    "\n",
-    "Several recent papers focus on this theme, but implementing the ideas can be tricky.\n",
-    "\n",
-    "Here we show how to implement self-reflective RAG using `Mistral` and `LangGraph`.\n",
-    "\n",
-    "We'll focus on ideas from one paper, `Corrective RAG (CRAG)` [here](https://arxiv.org/pdf/2401.15884.pdf).\n",
-    "\n",
-    "![Screenshot 2024-02-07 at 1.21.51 PM.png](attachment:a65940f9-5c51-4d7c-9ca1-ae576e4bb51a.png)\n",
-    "\n",
-    "## Setup\n",
-    "\n",
-    "### Using APIs \n",
-    "\n",
-    "* Set `MISTRAL_API_KEY` and set up Subscription to activate it.\n",
-    "* Set `TAVILY_API_KEY` to enable web search [here](https://app.tavily.com/sign-in).\n",
-    "\n",
-    "### Using CoLab \n",
-    "\n",
-    "* [Here](https://colab.research.google.com/drive/1U5OcwWjoXZSud30q4XOk1UlIJNjaD3kX?usp=sharing) is a link to a CoLab for this notebook. \n",
-    "\n",
-    "### Running Locally \n",
-    "\n",
-    "#### Embeddings\n",
-    "\n",
-    "There are several options for local embeddings.\n",
-    "\n",
-    "(1) You can use `GPT4AllEmbeddings()` from Nomic.\n",
-    "\n",
-    "(2) You can also use Nomic's recently released [v1](https://blog.nomic.ai/posts/nomic-embed-text-v1) and [v1.5](https://blog.nomic.ai/posts/nomic-embed-matryoshka) embeddings.\n",
-    "\n",
-    "For these, simply:\n",
-    "\n",
-    "Clone [`llama.cpp`](https://github.com/ggerganov/llama.cpp):\n",
-    "\n",
-    "```\n",
-    "git clone https://github.com/ggerganov/llama.cpp\n",
-    "```\n",
-    "\n",
-    "Download GGUF weights for Nomic's embedding model(s), allowing them to be run locally: \n",
-    "\n",
-    "* https://huggingface.co/nomic-ai/nomic-embed-text-v1-GGUF\n",
-    "* https://huggingface.co/nomic-ai/nomic-embed-text-v1.5-GGUF\n",
-    "\n",
-    "Add to `llama.cpp/model` directory.\n",
-    "\n",
-    "Build llama.cpp:\n",
-    "```\n",
-    "cd llama.cpp\n",
-    "make\n",
-    "```\n",
-    "\n",
-    "### LLM\n",
-    "\n",
-    "(1) Download [Ollama app](https://ollama.ai/).\n",
-    "\n",
-    "(2) Download a `Mistral` model from various Mistral versions [here](https://ollama.ai/library/mistral) and Mixtral versions [here](https://ollama.ai/library/mixtral) available.\n",
-    "```\n",
-    "ollama pull mistral:instruct\n",
-    "```\n",
-    "\n",
-    "### Tracing \n",
-    "\n",
-    "* Optionally, use [LangSmith](https://docs.smith.langchain.com/) for tracing (shown at bottom) by setting: \n",
-    "\n",
-    "```\n",
-    "export LANGCHAIN_TRACING_V2=true\n",
-    "export LANGCHAIN_ENDPOINT=https://api.smith.langchain.com\n",
-    "export LANGCHAIN_API_KEY=<your-api-key>\n",
-    "```"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 1,
-   "id": "abc064ab-7de1-4d03-a987-cd3078438d61",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# Check API keys\n",
-    "import os\n",
-    "\n",
-    "mistral_api_key = os.environ.get(\"MISTRAL_API_KEY\")\n",
-    "tavily_api_key = os.environ.get(\"TAVILY_API_KEY\")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 2,
-   "id": "9f644869-436e-4bf6-a267-b2465c7b5aef",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# Flags for running locally\n",
-    "\n",
-    "run_local = \"Yes\"\n",
-    "local_llm = \"mistral:instruct\""
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "6e2b6eed-3b3f-44b5-a34a-4ade1e94caf0",
-   "metadata": {},
-   "source": [
-    "## Indexing\n",
-    "\n",
-    "First, let's index a popular blog post on agents. \n",
-    "\n",
-    "We can use [Mistral embeddings](https://python.langchain.com/docs/integrations/text_embedding/mistralai).\n",
-    "\n",
-    "For local, we can use [GPT4All](https://python.langchain.com/docs/integrations/text_embedding/gpt4all), which is a CPU optimized SBERT model [here](https://docs.gpt4all.io/gpt4all_python_embedding.html).\n",
-    "\n",
-    "We'll use a local vectorstore, [Chroma](https://python.langchain.com/docs/integrations/vectorstores/chroma)."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 3,
-   "id": "254ae533-79e0-42f4-b200-1ec9160e1d3d",
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "bert_load_from_file: gguf version     = 2\n",
-      "bert_load_from_file: gguf alignment   = 32\n",
-      "bert_load_from_file: gguf data offset = 695552\n",
-      "bert_load_from_file: model name           = BERT\n",
-      "bert_load_from_file: model architecture   = bert\n",
-      "bert_load_from_file: model file type      = 1\n",
-      "bert_load_from_file: bert tokenizer vocab = 30522\n"
-     ]
-    }
-   ],
-   "source": [
-    "from langchain.text_splitter import RecursiveCharacterTextSplitter\n",
-    "from langchain_community.document_loaders import WebBaseLoader\n",
-    "from langchain_community.vectorstores import Chroma\n",
-    "from langchain_mistralai import MistralAIEmbeddings\n",
-    "from langchain_community.embeddings import GPT4AllEmbeddings\n",
-    "from langchain_community.embeddings import LlamaCppEmbeddings\n",
-    "\n",
-    "# Load\n",
-    "url = \"https://lilianweng.github.io/posts/2023-06-23-agent/\"\n",
-    "loader = WebBaseLoader(url)\n",
-    "docs = loader.load()\n",
-    "\n",
-    "# Split\n",
-    "text_splitter = RecursiveCharacterTextSplitter.from_tiktoken_encoder(\n",
-    "    chunk_size=500, chunk_overlap=100\n",
-    ")\n",
-    "all_splits = text_splitter.split_documents(docs)\n",
-    "\n",
-    "# Embed and index\n",
-    "if run_local == \"Yes\":\n",
-    "    # GPT4All\n",
-    "    embedding = GPT4AllEmbeddings()\n",
-    "    # Nomic v1 or v1.5\n",
-    "    # embd_model_path = \"/Users/rlm/Desktop/Code/llama.cpp/models/nomic-embd/nomic-embed-text-v1.Q4_K_S.gguf\"\n",
-    "    # embedding = LlamaCppEmbeddings(model_path=embd_model_path, n_batch=512)\n",
-    "else:\n",
-    "    embedding = MistralAIEmbeddings(mistral_api_key=mistral_api_key)\n",
-    "\n",
-    "# Index\n",
-    "vectorstore = Chroma.from_documents(\n",
-    "    documents=all_splits,\n",
-    "    collection_name=\"rag-chroma\",\n",
-    "    embedding=embedding,\n",
-    ")\n",
-    "retriever = vectorstore.as_retriever()"
-   ]
-  },
-  {
-   "attachments": {
-    "a2fac558-b18e-4610-bfa7-0d40c92e0ede.png": {
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"
-    }
-   },
-   "cell_type": "markdown",
-   "id": "fe7fd10a-f64a-48de-a116-6d5890def1af",
-   "metadata": {},
-   "source": [
-    "## Corrective RAG\n",
-    "\n",
-    "Let's implement self-reflective RAG with some ideas from the CRAG (Corrective RAG) [paper](https://arxiv.org/pdf/2401.15884.pdf):\n",
-    "\n",
-    "* Grade documents for relevance relative to the question.\n",
-    "* If any are irrelevant, then we will supplement the context used for generation with web search.\n",
-    "* For web search, we will re-phrase the question and use Tavily API.\n",
-    "* We will then pass retrieved documents and web results to an LLM for final answer generation.\n",
-    "\n",
-    "Here is a schematic of our graph in more detail:\n",
-    "\n",
-    "![crag.png](attachment:a2fac558-b18e-4610-bfa7-0d40c92e0ede.png)\n",
-    "\n",
-    "We will implement this using [LangGraph](https://python.langchain.com/docs/langgraph): \n",
-    "\n",
-    "* See video [here](https://www.youtube.com/watch?ref=blog.langchain.dev&v=pbAd8O1Lvm4&feature=youtu.be)\n",
-    "* See blog post [here](https://blog.langchain.dev/agentic-rag-with-langgraph/)\n",
-    "\n",
-    "---\n",
-    "\n",
-    "### State\n",
-    "\n",
-    "Every node in our graph will modify `state`, which is dict that contains values (`question`, `documents`, etc) relevant to RAG."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 4,
-   "id": "10028794-2fbc-43f9-aa4c-7fe3abd69c1e",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from typing import Annotated, Dict, TypedDict\n",
-    "\n",
-    "from langchain_core.messages import BaseMessage\n",
-    "\n",
-    "\n",
-    "class GraphState(TypedDict):\n",
-    "    \"\"\"\n",
-    "    Represents the state of our graph.\n",
-    "\n",
-    "    Attributes:\n",
-    "        keys: A dictionary where each key is a string.\n",
-    "    \"\"\"\n",
-    "\n",
-    "    keys: Dict[str, any]"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "0081ff31-4a91-4dbc-9977-8bcdc7dd0aeb",
-   "metadata": {},
-   "source": [
-    "### Nodes and Edges\n",
-    "\n",
-    "Every node in the graph we laid out above is a function.\n",
-    "\n",
-    "Each node will modify the state in some way.\n",
-    "\n",
-    "Each edge will choose which node to call next."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 5,
-   "id": "447d1333-082d-479a-a6fa-0ac0df78bb9d",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "import json\n",
-    "import operator\n",
-    "from typing import Annotated, Sequence, TypedDict\n",
-    "\n",
-    "from langchain import hub\n",
-    "from langchain_core.output_parsers import JsonOutputParser\n",
-    "from langchain.prompts import PromptTemplate\n",
-    "from langchain.schema import Document\n",
-    "from langchain_community.chat_models import ChatOllama\n",
-    "from langchain_community.tools.tavily_search import TavilySearchResults\n",
-    "from langchain_community.vectorstores import Chroma\n",
-    "from langchain_core.output_parsers import StrOutputParser\n",
-    "from langchain_core.runnables import RunnablePassthrough\n",
-    "from langchain_mistralai.chat_models import ChatMistralAI\n",
-    "\n",
-    "### Nodes ###\n",
-    "\n",
-    "\n",
-    "def retrieve(state):\n",
-    "    \"\"\"\n",
-    "    Retrieve documents\n",
-    "\n",
-    "    Args:\n",
-    "        state (dict): The current graph state\n",
-    "\n",
-    "    Returns:\n",
-    "        state (dict): New key added to state, documents, that contains retrieved documents\n",
-    "    \"\"\"\n",
-    "    print(\"---RETRIEVE---\")\n",
-    "    state_dict = state[\"keys\"]\n",
-    "    question = state_dict[\"question\"]\n",
-    "    local = state_dict[\"local\"]\n",
-    "    documents = retriever.get_relevant_documents(question)\n",
-    "    return {\"keys\": {\"documents\": documents, \"local\": local, \"question\": question}}\n",
-    "\n",
-    "\n",
-    "def generate(state):\n",
-    "    \"\"\"\n",
-    "    Generate answer\n",
-    "\n",
-    "    Args:\n",
-    "        state (dict): The current graph state\n",
-    "\n",
-    "    Returns:\n",
-    "        state (dict): New key added to state, generation, that contains generation\n",
-    "    \"\"\"\n",
-    "    print(\"---GENERATE---\")\n",
-    "    state_dict = state[\"keys\"]\n",
-    "    question = state_dict[\"question\"]\n",
-    "    documents = state_dict[\"documents\"]\n",
-    "    local = state_dict[\"local\"]\n",
-    "\n",
-    "    # Prompt\n",
-    "    prompt = hub.pull(\"rlm/rag-prompt\")\n",
-    "\n",
-    "    # LLM\n",
-    "    if local == \"Yes\":\n",
-    "        llm = ChatOllama(model=local_llm, temperature=0)\n",
-    "    else:\n",
-    "        llm = ChatMistralAI(\n",
-    "            model=\"mistral-medium\", temperature=0, mistral_api_key=mistral_api_key\n",
-    "        )\n",
-    "\n",
-    "    # Post-processing\n",
-    "    def format_docs(docs):\n",
-    "        return \"\\n\\n\".join(doc.page_content for doc in docs)\n",
-    "\n",
-    "    # Chain\n",
-    "    rag_chain = prompt | llm | StrOutputParser()\n",
-    "\n",
-    "    # Run\n",
-    "    generation = rag_chain.invoke({\"context\": documents, \"question\": question})\n",
-    "    return {\n",
-    "        \"keys\": {\"documents\": documents, \"question\": question, \"generation\": generation}\n",
-    "    }\n",
-    "\n",
-    "\n",
-    "def grade_documents(state):\n",
-    "    \"\"\"\n",
-    "    Determines whether the retrieved documents are relevant to the question.\n",
-    "\n",
-    "    Args:\n",
-    "        state (dict): The current graph state\n",
-    "\n",
-    "    Returns:\n",
-    "        state (dict): Updates documents key with relevant documents\n",
-    "    \"\"\"\n",
-    "\n",
-    "    print(\"---CHECK RELEVANCE---\")\n",
-    "    state_dict = state[\"keys\"]\n",
-    "    question = state_dict[\"question\"]\n",
-    "    documents = state_dict[\"documents\"]\n",
-    "    local = state_dict[\"local\"]\n",
-    "\n",
-    "    # LLM\n",
-    "    if local == \"Yes\":\n",
-    "        llm = ChatOllama(model=local_llm, format=\"json\", temperature=0)\n",
-    "    else:\n",
-    "        llm = ChatMistralAI(\n",
-    "            mistral_api_key=mistral_api_key, temperature=0, model=\"mistral-medium\"\n",
-    "        )\n",
-    "\n",
-    "    prompt = PromptTemplate(\n",
-    "        template=\"\"\"You are a grader assessing relevance of a retrieved document to a user question. \\n \n",
-    "        Here is the retrieved document: \\n\\n {context} \\n\\n\n",
-    "        Here is the user question: {question} \\n\n",
-    "        If the document contains keywords related to the user question, grade it as relevant. \\n\n",
-    "        It does not need to be a stringent test. The goal is to filter out erroneous retrievals. \\n\n",
-    "        Give a binary score 'yes' or 'no' score to indicate whether the document is relevant to the question. \\n\n",
-    "        Provide the binary score as a JSON with a single key 'score' and no premable or explaination.\"\"\",\n",
-    "        input_variables=[\"question\", \"context\"],\n",
-    "    )\n",
-    "\n",
-    "    chain = prompt | llm | JsonOutputParser()\n",
-    "\n",
-    "    # Score\n",
-    "    filtered_docs = []\n",
-    "    search = \"No\"  # Default do not opt for web search to supplement retrieval\n",
-    "    for d in documents:\n",
-    "        score = chain.invoke(\n",
-    "            {\n",
-    "                \"question\": question,\n",
-    "                \"context\": d.page_content,\n",
-    "            }\n",
-    "        )\n",
-    "        grade = score[\"score\"]\n",
-    "        if grade == \"yes\":\n",
-    "            print(\"---GRADE: DOCUMENT RELEVANT---\")\n",
-    "            filtered_docs.append(d)\n",
-    "        else:\n",
-    "            print(\"---GRADE: DOCUMENT NOT RELEVANT---\")\n",
-    "            search = \"Yes\"  # Perform web search\n",
-    "            continue\n",
-    "\n",
-    "    return {\n",
-    "        \"keys\": {\n",
-    "            \"documents\": filtered_docs,\n",
-    "            \"question\": question,\n",
-    "            \"local\": local,\n",
-    "            \"run_web_search\": search,\n",
-    "        }\n",
-    "    }\n",
-    "\n",
-    "\n",
-    "def transform_query(state):\n",
-    "    \"\"\"\n",
-    "    Transform the query to produce a better question.\n",
-    "\n",
-    "    Args:\n",
-    "        state (dict): The current graph state\n",
-    "\n",
-    "    Returns:\n",
-    "        state (dict): Updates question key with a re-phrased question\n",
-    "    \"\"\"\n",
-    "\n",
-    "    print(\"---TRANSFORM QUERY---\")\n",
-    "    state_dict = state[\"keys\"]\n",
-    "    question = state_dict[\"question\"]\n",
-    "    documents = state_dict[\"documents\"]\n",
-    "    local = state_dict[\"local\"]\n",
-    "\n",
-    "    # Create a prompt template with format instructions and the query\n",
-    "    prompt = PromptTemplate(\n",
-    "        template=\"\"\"You are generating questions that is well optimized for retrieval. \\n \n",
-    "        Look at the input and try to reason about the underlying sematic intent / meaning. \\n \n",
-    "        Here is the initial question:\n",
-    "        \\n ------- \\n\n",
-    "        {question} \n",
-    "        \\n ------- \\n\n",
-    "        Provide an improved question without any premable, only respond with the updated question: \"\"\",\n",
-    "        input_variables=[\"question\"],\n",
-    "    )\n",
-    "\n",
-    "    # Grader\n",
-    "    # LLM\n",
-    "    if local == \"Yes\":\n",
-    "        llm = ChatOllama(model=local_llm, temperature=0)\n",
-    "    else:\n",
-    "        llm = ChatMistralAI(\n",
-    "            mistral_api_key=mistral_api_key, temperature=0, model=\"mistral-medium\"\n",
-    "        )\n",
-    "\n",
-    "    # Prompt\n",
-    "    chain = prompt | llm | StrOutputParser()\n",
-    "    better_question = chain.invoke({\"question\": question})\n",
-    "\n",
-    "    return {\n",
-    "        \"keys\": {\"documents\": documents, \"question\": better_question, \"local\": local}\n",
-    "    }\n",
-    "\n",
-    "\n",
-    "def web_search(state):\n",
-    "    \"\"\"\n",
-    "    Web search based on the re-phrased question using Tavily API.\n",
-    "\n",
-    "    Args:\n",
-    "        state (dict): The current graph state\n",
-    "\n",
-    "    Returns:\n",
-    "        state (dict): Web results appended to documents.\n",
-    "    \"\"\"\n",
-    "\n",
-    "    print(\"---WEB SEARCH---\")\n",
-    "    state_dict = state[\"keys\"]\n",
-    "    question = state_dict[\"question\"]\n",
-    "    documents = state_dict[\"documents\"]\n",
-    "    local = state_dict[\"local\"]\n",
-    "\n",
-    "    tool = TavilySearchResults()\n",
-    "    docs = tool.invoke({\"query\": question})\n",
-    "    web_results = \"\\n\".join([d[\"content\"] for d in docs])\n",
-    "    web_results = Document(page_content=web_results)\n",
-    "    documents.append(web_results)\n",
-    "\n",
-    "    return {\"keys\": {\"documents\": documents, \"local\": local, \"question\": question}}\n",
-    "\n",
-    "\n",
-    "### Edges\n",
-    "\n",
-    "\n",
-    "def decide_to_generate(state):\n",
-    "    \"\"\"\n",
-    "    Determines whether to generate an answer or re-generate a question for web search.\n",
-    "\n",
-    "    Args:\n",
-    "        state (dict): The current state of the agent, including all keys.\n",
-    "\n",
-    "    Returns:\n",
-    "        str: Next node to call\n",
-    "    \"\"\"\n",
-    "\n",
-    "    print(\"---DECIDE TO GENERATE---\")\n",
-    "    state_dict = state[\"keys\"]\n",
-    "    question = state_dict[\"question\"]\n",
-    "    filtered_documents = state_dict[\"documents\"]\n",
-    "    search = state_dict[\"run_web_search\"]\n",
-    "\n",
-    "    if search == \"Yes\":\n",
-    "        # All documents have been filtered check_relevance\n",
-    "        # We will re-generate a new query\n",
-    "        print(\"---DECISION: TRANSFORM QUERY and RUN WEB SEARCH---\")\n",
-    "        return \"transform_query\"\n",
-    "    else:\n",
-    "        # We have relevant documents, so generate answer\n",
-    "        print(\"---DECISION: GENERATE---\")\n",
-    "        return \"generate\""
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "6096626d-dfa5-48e0-8a24-3747b298bc67",
-   "metadata": {},
-   "source": [
-    "## Build Graph\n",
-    "\n",
-    "This just follows the flow we outlined in the figure above."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 6,
-   "id": "0a63776c-f9cd-46ce-b8cf-95c066dc5b06",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "import pprint\n",
-    "\n",
-    "from langgraph.graph import END, StateGraph\n",
-    "\n",
-    "workflow = StateGraph(GraphState)\n",
-    "\n",
-    "# Define the nodes\n",
-    "workflow.add_node(\"retrieve\", retrieve)  # retrieve\n",
-    "workflow.add_node(\"grade_documents\", grade_documents)  # grade documents\n",
-    "workflow.add_node(\"generate\", generate)  # generatae\n",
-    "workflow.add_node(\"transform_query\", transform_query)  # transform_query\n",
-    "workflow.add_node(\"web_search\", web_search)  # web search\n",
-    "\n",
-    "# Build graph\n",
-    "workflow.set_entry_point(\"retrieve\")\n",
-    "workflow.add_edge(\"retrieve\", \"grade_documents\")\n",
-    "workflow.add_conditional_edges(\n",
-    "    \"grade_documents\",\n",
-    "    decide_to_generate,\n",
-    "    {\n",
-    "        \"transform_query\": \"transform_query\",\n",
-    "        \"generate\": \"generate\",\n",
-    "    },\n",
-    ")\n",
-    "workflow.add_edge(\"transform_query\", \"web_search\")\n",
-    "workflow.add_edge(\"web_search\", \"generate\")\n",
-    "workflow.add_edge(\"generate\", END)\n",
-    "\n",
-    "# Compile\n",
-    "app = workflow.compile()"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "ac0a868a-f0f9-4aa9-b955-a78da2359af8",
-   "metadata": {},
-   "source": [
-    "## Run\n",
-    "\n",
-    "`Mistral API -` \n",
-    "\n",
-    "Trace for below run: https://smith.langchain.com/public/0a5cbc97-a2f6-4697-856c-90a6302fd13e/r"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 8,
-   "id": "3ab1d8df-a74e-4b48-a30b-e39bbfd5925a",
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "---RETRIEVE---\n",
-      "\"Node 'retrieve':\"\n",
-      "'\\n---\\n'\n",
-      "---CHECK RELEVANCE---\n",
-      "---GRADE: DOCUMENT RELEVANT---\n",
-      "---GRADE: DOCUMENT RELEVANT---\n",
-      "---GRADE: DOCUMENT RELEVANT---\n",
-      "---GRADE: DOCUMENT RELEVANT---\n",
-      "\"Node 'grade_documents':\"\n",
-      "'\\n---\\n'\n",
-      "---DECIDE TO GENERATE---\n",
-      "---DECISION: GENERATE---\n",
-      "---GENERATE---\n",
-      "\"Node 'generate':\"\n",
-      "'\\n---\\n'\n",
-      "\"Node '__end__':\"\n",
-      "'\\n---\\n'\n",
-      "(' In an LLM (large language model)-powered autonomous agent system, LLM '\n",
-      " 'functions as the agent’s brain, complemented by several key components: '\n",
-      " 'planning and memory.\\n'\n",
-      " '\\n'\n",
-      " 'Planning involves breaking down large tasks into smaller subgoals for '\n",
-      " 'efficient handling of complex tasks and self-criticism and refinement to '\n",
-      " 'improve results.\\n'\n",
-      " '\\n'\n",
-      " 'Memory includes short-term memory, which utilizes in-context learning, and '\n",
-      " 'long-term memory, providing the agent with the capability to retain and '\n",
-      " 'recall information over extended periods using an external vector store and '\n",
-      " 'fast retrieval. The agent also learns to call external APIs for missing '\n",
-      " 'information.\\n'\n",
-      " '\\n'\n",
-      " 'Types of Memory:\\n'\n",
-      " '1. Sensory Memory: retains impressions of sensory information for a few '\n",
-      " 'seconds.\\n'\n",
-      " '2. Short-Term Memory (STM) or Working Memory: stores information needed for '\n",
-      " 'complex cognitive tasks and lasts for 20-30 seconds.\\n'\n",
-      " '3. Long-Term Memory (LTM): stores information for a remarkably long time, '\n",
-      " 'with two subtypes: explicit/declarative memory and implicit/procedural '\n",
-      " 'memory.\\n'\n",
-      " '\\n'\n",
-      " 'The agent uses LLM as its core controller, which can be extended beyond '\n",
-      " 'generating well-written copies, stories, essays, and programs to a powerful '\n",
-      " 'general problem solver.')\n"
-     ]
-    }
-   ],
-   "source": [
-    "# Run\n",
-    "inputs = {\n",
-    "    \"keys\": {\n",
-    "        \"question\": \"Explain how the different types of agent memory work?\",\n",
-    "        \"local\": run_local,\n",
-    "    }\n",
-    "}\n",
-    "for output in app.stream(inputs):\n",
-    "    for key, value in output.items():\n",
-    "        # Node\n",
-    "        pprint.pprint(f\"Node '{key}':\")\n",
-    "        # Optional: print full state at each node\n",
-    "        # pprint.pprint(value[\"keys\"], indent=2, width=80, depth=None)\n",
-    "    pprint.pprint(\"\\n---\\n\")\n",
-    "\n",
-    "# Final generation\n",
-    "pprint.pprint(value[\"keys\"][\"generation\"])"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "03ee2be9-2368-46ea-9edd-dc064a7c7c96",
-   "metadata": {},
-   "source": [
-    "`Local (Ollama) -` \n",
-    "\n",
-    "Trace for blow run: https://smith.langchain.com/public/3b23a1d4-720a-4b26-8f34-70d2f20f8832/r"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 11,
-   "id": "16ea2032-59c7-433d-aca4-2828a1239074",
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "---RETRIEVE---\n",
-      "\"Node 'retrieve':\"\n",
-      "'\\n---\\n'\n",
-      "---CHECK RELEVANCE---\n",
-      "---GRADE: DOCUMENT RELEVANT---\n",
-      "---GRADE: DOCUMENT RELEVANT---\n",
-      "---GRADE: DOCUMENT RELEVANT---\n",
-      "---GRADE: DOCUMENT RELEVANT---\n",
-      "\"Node 'grade_documents':\"\n",
-      "'\\n---\\n'\n",
-      "---DECIDE TO GENERATE---\n",
-      "---DECISION: GENERATE---\n",
-      "---GENERATE---\n",
-      "\"Node 'generate':\"\n",
-      "'\\n---\\n'\n",
-      "\"Node '__end__':\"\n",
-      "'\\n---\\n'\n",
-      "(' In an LLM (large language model)-powered autonomous agent system, LLM '\n",
-      " 'functions as the agent’s brain, complemented by several key components: '\n",
-      " 'planning and memory.\\n'\n",
-      " '\\n'\n",
-      " 'Planning involves breaking down large tasks into smaller subgoals for '\n",
-      " 'efficient handling of complex tasks and self-criticism and refinement to '\n",
-      " 'improve results.\\n'\n",
-      " '\\n'\n",
-      " 'Memory includes short-term memory, which utilizes in-context learning, and '\n",
-      " 'long-term memory, providing the agent with the capability to retain and '\n",
-      " 'recall information over extended periods using an external vector store and '\n",
-      " 'fast retrieval. The agent also learns to call external APIs for missing '\n",
-      " 'information.\\n'\n",
-      " '\\n'\n",
-      " 'Types of Memory:\\n'\n",
-      " '1. Sensory Memory: retains impressions of sensory information for a few '\n",
-      " 'seconds.\\n'\n",
-      " '2. Short-Term Memory (STM) or Working Memory: stores information needed for '\n",
-      " 'complex cognitive tasks and lasts for 20-30 seconds.\\n'\n",
-      " '3. Long-Term Memory (LTM): stores information for a remarkably long time, '\n",
-      " 'with two subtypes: explicit/declarative memory and implicit/procedural '\n",
-      " 'memory.\\n'\n",
-      " '\\n'\n",
-      " 'The agent uses LLM as its core controller, which can be extended beyond '\n",
-      " 'generating well-written copies, stories, essays, and programs to a powerful '\n",
-      " 'general problem solver.')\n"
-     ]
-    }
-   ],
-   "source": [
-    "# Run\n",
-    "inputs = {\n",
-    "    \"keys\": {\n",
-    "        \"question\": \"Explain how the different types of agent memory work?\",\n",
-    "        \"local\": run_local,\n",
-    "    }\n",
-    "}\n",
-    "for output in app.stream(inputs):\n",
-    "    for key, value in output.items():\n",
-    "        # Node\n",
-    "        pprint.pprint(f\"Node '{key}':\")\n",
-    "        # Optional: print full state at each node\n",
-    "        # pprint.pprint(value[\"keys\"], indent=2, width=80, depth=None)\n",
-    "    pprint.pprint(\"\\n---\\n\")\n",
-    "\n",
-    "# Final generation\n",
-    "pprint.pprint(value[\"keys\"][\"generation\"])"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "deb28175-27a1-4afc-9747-2983e87fc881",
-   "metadata": {},
-   "outputs": [],
-   "source": []
-  }
- ],
- "metadata": {
-  "kernelspec": {
-   "display_name": "Python 3 (ipykernel)",
-   "language": "python",
-   "name": "python3"
-  },
-  "language_info": {
-   "codemirror_mode": {
-    "name": "ipython",
-    "version": 3
-   },
-   "file_extension": ".py",
-   "mimetype": "text/x-python",
-   "name": "python",
-   "nbconvert_exporter": "python",
-   "pygments_lexer": "ipython3",
-   "version": "3.9.16"
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 5
-}
diff --git a/examples/rag/langgraph_self_rag.ipynb b/examples/rag/langgraph_self_rag.ipynb
index 661eb3979..b1bca4217 100644
--- a/examples/rag/langgraph_self_rag.ipynb
+++ b/examples/rag/langgraph_self_rag.ipynb
@@ -1,5 +1,58 @@
 {
  "cells": [
+  {
+   "attachments": {
+    "15cba0ab-a549-4909-8373-fb761e384eff.png": {
+     "image/png": 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"
+    }
+   },
+   "cell_type": "markdown",
+   "id": "919fe33c-0149-4f7d-b200-544a18986c9a",
+   "metadata": {},
+   "source": [
+    "# Self RAG\n",
+    "\n",
+    "Self-RAG is a strategy for RAG that incorperates self-reflection / self-grading on retrieved documents and generations. \n",
+    "\n",
+    "In the [paper](https://arxiv.org/abs/2310.11511), a few decisions are made:\n",
+    "\n",
+    "1. Should I retrieve from retriever, `R` -\n",
+    "\n",
+    "* Input: `x (question)` OR `x (question)`, `y (generation)`\n",
+    "* Decides when to retrieve `D` chunks with `R`\n",
+    "* Output: `yes, no, continue`\n",
+    "\n",
+    "2. Are the retrieved passages `D` relevant to the question `x` -\n",
+    "\n",
+    "* * Input: (`x (question)`, `d (chunk)`) for `d` in `D`\n",
+    "* `d` provides useful information to solve `x`\n",
+    "* Output: `relevant, irrelevant`\n",
+    "\n",
+    "3. Are the LLM generation from each chunk in `D` is relevant to the chunk (hallucinations, etc)  -\n",
+    "\n",
+    "* Input: `x (question)`, `d (chunk)`,  `y (generation)` for `d` in `D`\n",
+    "* All of the verification-worthy statements in `y (generation)` are supported by `d`\n",
+    "* Output: `{fully supported, partially supported, no support`\n",
+    "\n",
+    "4. The LLM generation from each chunk in `D` is a useful response to `x (question)` -\n",
+    "\n",
+    "* Input: `x (question)`, `y (generation)` for `d` in `D`\n",
+    "* `y (generation)` is a useful response to `x (question)`.\n",
+    "* Output: `{5, 4, 3, 2, 1}`\n",
+    "\n",
+    "We will implement some of these ideas from scratch using [LangGraph](https://python.langchain.com/docs/langgraph).\n",
+    "\n",
+    "![Screenshot 2024-04-01 at 12.41.50 PM.png](attachment:15cba0ab-a549-4909-8373-fb761e384eff.png)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "72f3ee57-68ab-4040-bd36-4014e2a23d96",
+   "metadata": {},
+   "source": [
+    "# Enviorment "
+   ]
+  },
   {
    "cell_type": "code",
    "execution_count": null,
@@ -11,69 +64,44 @@
    ]
   },
   {
-   "attachments": {
-    "ea6a57d2-f2ec-4061-840a-98deb3207248.png": {
-     "image/png": 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"
-    }
-   },
    "cell_type": "markdown",
-   "id": "919fe33c-0149-4f7d-b200-544a18986c9a",
+   "id": "15569b93-3c68-4aac-838c-37112d33987a",
    "metadata": {},
    "source": [
-    "# Self-RAG\n",
+    "### LLMs"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "f18b63c7-d0d3-41c1-ae6b-5a0f1b8ccf0f",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import os\n",
+    "os.environ['OPENAI_API_KEY'] = <your-api-key>"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "532d91fb-381e-4e11-b3b1-254321351773",
+   "metadata": {},
+   "source": [
+    "### Tracing\n",
     "\n",
-    "Self-reflection can enhance RAG, enabling correction of poor quality retrieval or generations.\n",
-    "\n",
-    "Several recent papers focus on this theme, but implementing the ideas can be tricky.\n",
-    "\n",
-    "Here we show how to implement ideas from the `Self RAG` paper [here](https://arxiv.org/abs/2310.11511) using LangGraph.\n",
-    "\n",
-    "## Dependencies\n",
-    "\n",
-    "Set `OPENAI_API_KEY`\n",
-    "\n",
-    "## Self-RAG Detail\n",
-    "\n",
-    "Self-RAG is a recent paper that introduces an interesting approach for self-reflective RAG. \n",
-    "\n",
-    "The framework trains an LLM (e.g., LLaMA2-7b or 13b) to generate tokens that govern the RAG process in a few ways:\n",
-    "\n",
-    "1. Should I retrieve from retriever, `R` -\n",
-    "\n",
-    "* Token: `Retrieve`\n",
-    "* Input: `x (question)` OR `x (question)`, `y (generation)`\n",
-    "* Decides when to retrieve `D` chunks with `R`\n",
-    "* Output: `yes, no, continue`\n",
-    "\n",
-    "2. Are the retrieved passages `D` relevant to the question `x` -\n",
-    "\n",
-    "* Token: `ISREL`\n",
-    "* * Input: (`x (question)`, `d (chunk)`) for `d` in `D`\n",
-    "* `d` provides useful information to solve `x`\n",
-    "* Output: `relevant, irrelevant`\n",
-    "\n",
-    "\n",
-    "3. Are the LLM generation from each chunk in `D` is relevant to the chunk (hallucinations, etc)  -\n",
-    "\n",
-    "* Token: `ISSUP`\n",
-    "* Input: `x (question)`, `d (chunk)`,  `y (generation)` for `d` in `D`\n",
-    "* All of the verification-worthy statements in `y (generation)` are supported by `d`\n",
-    "* Output: `{fully supported, partially supported, no support`\n",
-    "\n",
-    "4. The LLM generation from each chunk in `D` is a useful response to `x (question)` -\n",
-    "\n",
-    "* Token: `ISUSE`\n",
-    "* Input: `x (question)`, `y (generation)` for `d` in `D`\n",
-    "* `y (generation)` is a useful response to `x (question)`.\n",
-    "* Output: `{5, 4, 3, 2, 1}`\n",
-    "\n",
-    "We can represent this as a graph:\n",
-    "\n",
-    "![Screenshot 2024-02-02 at 1.36.44 PM.png](attachment:ea6a57d2-f2ec-4061-840a-98deb3207248.png)\n",
-    "\n",
-    "---\n",
-    "\n",
-    "Let's implement some of these ideas from scratch using [LangGraph](https://python.langchain.com/docs/langgraph)."
+    "Optionally, use [LangSmith](https://docs.smith.langchain.com/) for tracing (shown at bottom)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "ccc3dae5-1df6-48ca-af8a-50f0e6128876",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "os.environ['LANGCHAIN_TRACING_V2'] = 'true'\n",
+    "os.environ['LANGCHAIN_ENDPOINT'] = 'https://api.smith.langchain.com'\n",
+    "os.environ['LANGCHAIN_API_KEY'] = <your-api-key>"
    ]
   },
   {
@@ -88,7 +116,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 7,
+   "execution_count": 1,
    "id": "565a6d44-2c9f-4fff-b1ec-eea05df9350d",
    "metadata": {},
    "outputs": [],
@@ -123,88 +151,274 @@
   },
   {
    "cell_type": "markdown",
-   "id": "276001c5-c079-4e5b-9f42-81a06704d200",
+   "id": "29c12f74-53e2-43cc-896f-875d1c5d9d93",
    "metadata": {},
    "source": [
-    "## State\n",
-    " \n",
-    "We will define a graph.\n",
-    "\n",
-    "Our state will be a `dict`.\n",
-    "\n",
-    "We can access this from any graph node as `state['keys']`."
+    "## LLMs"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 2,
+   "execution_count": 19,
+   "id": "1fafad21-60cc-483e-92a3-6a7edb1838e3",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "binary_score='yes'\n"
+     ]
+    }
+   ],
+   "source": [
+    "### Retrieval Grader \n",
+    "\n",
+    "from typing import Literal\n",
+    "\n",
+    "from langchain_core.prompts import ChatPromptTemplate\n",
+    "from langchain_core.pydantic_v1 import BaseModel, Field\n",
+    "from langchain_openai import ChatOpenAI\n",
+    "\n",
+    "\n",
+    "# Data model\n",
+    "class GradeDocuments(BaseModel):\n",
+    "    \"\"\"Binary score for relevance check on retrieved documents.\"\"\"\n",
+    "\n",
+    "    binary_score: str = Field(description=\"Documents are relevant to the question, 'yes' or 'no'\")\n",
+    "\n",
+    "# LLM with function call \n",
+    "llm = ChatOpenAI(model=\"gpt-3.5-turbo-0125\", temperature=0)\n",
+    "structured_llm_grader = llm.with_structured_output(GradeDocuments)\n",
+    "\n",
+    "# Prompt \n",
+    "system = \"\"\"You are a grader assessing relevance of a retrieved document to a user question. \\n \n",
+    "    It does not need to be a stringent test. The goal is to filter out erroneous retrievals. \\n\n",
+    "    If the document contains keyword(s) or semantic meaning related to the user question, grade it as relevant. \\n\n",
+    "    Give a binary score 'yes' or 'no' score to indicate whether the document is relevant to the question.\"\"\"\n",
+    "grade_prompt = ChatPromptTemplate.from_messages(\n",
+    "    [\n",
+    "        (\"system\", system),\n",
+    "        (\"human\", \"Retrieved document: \\n\\n {document} \\n\\n User question: {question}\"),\n",
+    "    ]\n",
+    ")\n",
+    "\n",
+    "retrieval_grader = grade_prompt | structured_llm_grader\n",
+    "question = \"agent memory\"\n",
+    "docs = retriever.get_relevant_documents(question)\n",
+    "doc_txt = docs[1].page_content\n",
+    "print(retrieval_grader.invoke({\"question\": question, \"document\": doc_txt}))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "id": "dcd77cc1-4587-40ec-b633-5364eab9e1ec",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "The design of generative agents combines LLM with memory, planning, and reflection mechanisms to enable agents to behave conditioned on past experience. Long-term memory provides the agent with the capability to retain and recall infinite information over extended periods. Short-term memory is utilized for in-context learning.\n"
+     ]
+    }
+   ],
+   "source": [
+    "### Generate\n",
+    "\n",
+    "from langchain import hub\n",
+    "from langchain_core.output_parsers import StrOutputParser\n",
+    "\n",
+    "# Prompt\n",
+    "prompt = hub.pull(\"rlm/rag-prompt\")\n",
+    "\n",
+    "# LLM\n",
+    "llm = ChatOpenAI(model_name=\"gpt-3.5-turbo\", temperature=0)\n",
+    "\n",
+    "# Post-processing\n",
+    "def format_docs(docs):\n",
+    "    return \"\\n\\n\".join(doc.page_content for doc in docs)\n",
+    "\n",
+    "# Chain\n",
+    "rag_chain = prompt | llm | StrOutputParser()\n",
+    "\n",
+    "# Run\n",
+    "generation = rag_chain.invoke({\"context\": docs, \"question\": question})\n",
+    "print(generation)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "id": "e78931ec-940c-46ad-a0b2-f43f953f1fd7",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "GradeHallucinations(binary_score='yes')"
+      ]
+     },
+     "execution_count": 7,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "### Hallucination Grader \n",
+    "\n",
+    "# Data model\n",
+    "class GradeHallucinations(BaseModel):\n",
+    "    \"\"\"Binary score for hallucination present in generation answer.\"\"\"\n",
+    "\n",
+    "    binary_score: str = Field(description=\"Answer is grounded in the facts, 'yes' or 'no'\")\n",
+    "\n",
+    "# LLM with function call \n",
+    "llm = ChatOpenAI(model=\"gpt-3.5-turbo-0125\", temperature=0)\n",
+    "structured_llm_grader = llm.with_structured_output(GradeHallucinations)\n",
+    "\n",
+    "# Prompt \n",
+    "system = \"\"\"You are a grader assessing whether an LLM generation is grounded in / supported by a set of retrieved facts. \\n \n",
+    "     Give a binary score 'yes' or 'no'. 'Yes' means that the answer is grounded in / supported by the set of facts.\"\"\"\n",
+    "hallucination_prompt = ChatPromptTemplate.from_messages(\n",
+    "    [\n",
+    "        (\"system\", system),\n",
+    "        (\"human\", \"Set of facts: \\n\\n {documents} \\n\\n LLM generation: {generation}\"),\n",
+    "    ]\n",
+    ")\n",
+    "\n",
+    "hallucination_grader = hallucination_prompt | structured_llm_grader\n",
+    "hallucination_grader.invoke({\"documents\": docs, \"generation\": generation})"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "id": "bd62276f-bf26-40d0-8cff-e07b10e00321",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "GradeAnswer(binary_score='yes')"
+      ]
+     },
+     "execution_count": 8,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "### Answer Grader \n",
+    "\n",
+    "# Data model\n",
+    "class GradeAnswer(BaseModel):\n",
+    "    \"\"\"Binary score to assess answer addresses question.\"\"\"\n",
+    "\n",
+    "    binary_score: str = Field(description=\"Answer addresses the question, 'yes' or 'no'\")\n",
+    "\n",
+    "# LLM with function call \n",
+    "llm = ChatOpenAI(model=\"gpt-3.5-turbo-0125\", temperature=0)\n",
+    "structured_llm_grader = llm.with_structured_output(GradeAnswer)\n",
+    "\n",
+    "# Prompt \n",
+    "system = \"\"\"You are a grader assessing whether an answer addresses / resolves a question \\n \n",
+    "     Give a binary score 'yes' or 'no'. Yes' means that the answer resolves the question.\"\"\"\n",
+    "answer_prompt = ChatPromptTemplate.from_messages(\n",
+    "    [\n",
+    "        (\"system\", system),\n",
+    "        (\"human\", \"User question: \\n\\n {question} \\n\\n LLM generation: {generation}\"),\n",
+    "    ]\n",
+    ")\n",
+    "\n",
+    "answer_grader = answer_prompt | structured_llm_grader\n",
+    "answer_grader.invoke({\"question\": question,\"generation\": generation})"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "id": "c6f4c70e-1660-4149-82c0-837f19fc9fb5",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "\"What is the role of memory in an agent's functioning?\""
+      ]
+     },
+     "execution_count": 6,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "### Question Re-writer\n",
+    "\n",
+    "# LLM \n",
+    "llm = ChatOpenAI(model=\"gpt-3.5-turbo-0125\", temperature=0)\n",
+    "\n",
+    "# Prompt \n",
+    "system = \"\"\"You a question re-writer that converts an input question to a better version that is optimized \\n \n",
+    "     for vectorstore retrieval. Look at the input and try to reason about the underlying sematic intent / meaning.\"\"\"\n",
+    "re_write_prompt = ChatPromptTemplate.from_messages(\n",
+    "    [\n",
+    "        (\"system\", system),\n",
+    "        (\"human\", \"Here is the initial question: \\n\\n {question} \\n Formulate an improved question.\"),\n",
+    "    ]\n",
+    ")\n",
+    "\n",
+    "question_rewriter = re_write_prompt | llm | StrOutputParser()\n",
+    "question_rewriter.invoke({\"question\": question})"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "276001c5-c079-4e5b-9f42-81a06704d200",
+   "metadata": {},
+   "source": [
+    "# Graph \n",
+    "\n",
+    "Capture the flow in as a graph.\n",
+    "\n",
+    "## Graph state"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
    "id": "f1617e9e-66a8-4c1a-a1fe-cc936284c085",
    "metadata": {},
    "outputs": [],
    "source": [
-    "from typing import Dict, TypedDict\n",
-    "\n",
-    "from langchain_core.messages import BaseMessage\n",
-    "\n",
+    "from typing_extensions import TypedDict\n",
+    "from typing import List\n",
     "\n",
     "class GraphState(TypedDict):\n",
     "    \"\"\"\n",
     "    Represents the state of our graph.\n",
     "\n",
     "    Attributes:\n",
-    "        keys: A dictionary where each key is a string.\n",
+    "        question: question\n",
+    "        generation: LLM generation\n",
+    "        documents: list of documents \n",
     "    \"\"\"\n",
-    "\n",
-    "    keys: Dict[str, any]"
-   ]
-  },
-  {
-   "attachments": {
-    "e61fbd0c-e667-4160-a96c-82f95a560b44.png": {
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"
-    }
-   },
-   "cell_type": "markdown",
-   "id": "251feeea-c9a0-404a-8b55-bef3020bb5e2",
-   "metadata": {},
-   "source": [
-    "## Nodes and Edges\n",
-    "\n",
-    "Each `node` will simply modify the `state`.\n",
-    "\n",
-    "Each `edge` will choose which `node` to call next.\n",
-    "\n",
-    "We can lay out `self-RAG` as a graph.\n",
-    "\n",
-    "Here is our graph flow:\n",
-    "\n",
-    "![Screenshot 2024-02-02 at 9.01.01 PM.png](attachment:e61fbd0c-e667-4160-a96c-82f95a560b44.png)"
+    "    question : str\n",
+    "    generation : str\n",
+    "    documents : List[str]"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 4,
+   "execution_count": 10,
    "id": "add509d8-6682-4127-8d95-13dd37d79702",
    "metadata": {},
    "outputs": [],
    "source": [
-    "import json\n",
-    "import operator\n",
-    "from typing import Annotated, Sequence, TypedDict\n",
-    "\n",
-    "from langchain import hub\n",
-    "from langchain.output_parsers.openai_tools import PydanticToolsParser\n",
-    "from langchain.prompts import PromptTemplate\n",
-    "from langchain_community.vectorstores import Chroma\n",
-    "from langchain_core.messages import BaseMessage, FunctionMessage\n",
-    "from langchain_core.output_parsers import StrOutputParser\n",
-    "from langchain_core.pydantic_v1 import BaseModel, Field\n",
-    "from langchain_core.runnables import RunnablePassthrough\n",
-    "from langchain_core.utils.function_calling import convert_to_openai_tool\n",
-    "from langchain_openai import ChatOpenAI, OpenAIEmbeddings\n",
-    "\n",
-    "### Nodes ###\n",
+    "### Nodes\n",
     "\n",
+    "from langchain.schema import Document\n",
     "\n",
     "def retrieve(state):\n",
     "    \"\"\"\n",
@@ -217,11 +431,11 @@
     "        state (dict): New key added to state, documents, that contains retrieved documents\n",
     "    \"\"\"\n",
     "    print(\"---RETRIEVE---\")\n",
-    "    state_dict = state[\"keys\"]\n",
-    "    question = state_dict[\"question\"]\n",
-    "    documents = retriever.get_relevant_documents(question)\n",
-    "    return {\"keys\": {\"documents\": documents, \"question\": question}}\n",
+    "    question = state[\"question\"]\n",
     "\n",
+    "    # Retrieval\n",
+    "    documents = retriever.get_relevant_documents(question)\n",
+    "    return {\"documents\": documents, \"question\": question}\n",
     "\n",
     "def generate(state):\n",
     "    \"\"\"\n",
@@ -234,29 +448,12 @@
     "        state (dict): New key added to state, generation, that contains LLM generation\n",
     "    \"\"\"\n",
     "    print(\"---GENERATE---\")\n",
-    "    state_dict = state[\"keys\"]\n",
-    "    question = state_dict[\"question\"]\n",
-    "    documents = state_dict[\"documents\"]\n",
-    "\n",
-    "    # Prompt\n",
-    "    prompt = hub.pull(\"rlm/rag-prompt\")\n",
-    "\n",
-    "    # LLM\n",
-    "    llm = ChatOpenAI(model_name=\"gpt-3.5-turbo\", temperature=0)\n",
-    "\n",
-    "    # Post-processing\n",
-    "    def format_docs(docs):\n",
-    "        return \"\\n\\n\".join(doc.page_content for doc in docs)\n",
-    "\n",
-    "    # Chain\n",
-    "    rag_chain = prompt | llm | StrOutputParser()\n",
-    "\n",
-    "    # Run\n",
+    "    question = state[\"question\"]\n",
+    "    documents = state[\"documents\"]\n",
+    "    \n",
+    "    # RAG generation\n",
     "    generation = rag_chain.invoke({\"context\": documents, \"question\": question})\n",
-    "    return {\n",
-    "        \"keys\": {\"documents\": documents, \"question\": question, \"generation\": generation}\n",
-    "    }\n",
-    "\n",
+    "    return {\"documents\": documents, \"question\": question, \"generation\": generation}\n",
     "\n",
     "def grade_documents(state):\n",
     "    \"\"\"\n",
@@ -266,62 +463,25 @@
     "        state (dict): The current graph state\n",
     "\n",
     "    Returns:\n",
-    "        state (dict): Updates documents key with relevant documents\n",
+    "        state (dict): Updates documents key with only filtered relevant documents\n",
     "    \"\"\"\n",
     "\n",
-    "    print(\"---CHECK RELEVANCE---\")\n",
-    "    state_dict = state[\"keys\"]\n",
-    "    question = state_dict[\"question\"]\n",
-    "    documents = state_dict[\"documents\"]\n",
-    "\n",
-    "    # Data model\n",
-    "    class grade(BaseModel):\n",
-    "        \"\"\"Binary score for relevance check.\"\"\"\n",
-    "\n",
-    "        binary_score: str = Field(description=\"Relevance score 'yes' or 'no'\")\n",
-    "\n",
-    "    # LLM\n",
-    "    model = ChatOpenAI(temperature=0, model=\"gpt-4-0125-preview\", streaming=True)\n",
-    "\n",
-    "    # Tool\n",
-    "    grade_tool_oai = convert_to_openai_tool(grade)\n",
-    "\n",
-    "    # LLM with tool and enforce invocation\n",
-    "    llm_with_tool = model.bind(\n",
-    "        tools=[grade_tool_oai],\n",
-    "        tool_choice={\"type\": \"function\", \"function\": {\"name\": \"grade\"}},\n",
-    "    )\n",
-    "\n",
-    "    # Parser\n",
-    "    parser_tool = PydanticToolsParser(tools=[grade])\n",
-    "\n",
-    "    # Prompt\n",
-    "    prompt = PromptTemplate(\n",
-    "        template=\"\"\"You are a grader assessing relevance of a retrieved document to a user question. \\n \n",
-    "        Here is the retrieved document: \\n\\n {context} \\n\\n\n",
-    "        Here is the user question: {question} \\n\n",
-    "        If the document contains keyword(s) or semantic meaning related to the user question, grade it as relevant. \\n\n",
-    "        Give a binary score 'yes' or 'no' score to indicate whether the document is relevant to the question.\"\"\",\n",
-    "        input_variables=[\"context\", \"question\"],\n",
-    "    )\n",
-    "\n",
-    "    # Chain\n",
-    "    chain = prompt | llm_with_tool | parser_tool\n",
-    "\n",
-    "    # Score\n",
+    "    print(\"---CHECK DOCUMENT RELEVANCE TO QUESTION---\")\n",
+    "    question = state[\"question\"]\n",
+    "    documents = state[\"documents\"]\n",
+    "    \n",
+    "    # Score each doc\n",
     "    filtered_docs = []\n",
     "    for d in documents:\n",
-    "        score = chain.invoke({\"question\": question, \"context\": d.page_content})\n",
-    "        grade = score[0].binary_score\n",
+    "        score = retrieval_grader.invoke({\"question\": question, \"document\": d.page_content})\n",
+    "        grade = score.binary_score\n",
     "        if grade == \"yes\":\n",
     "            print(\"---GRADE: DOCUMENT RELEVANT---\")\n",
     "            filtered_docs.append(d)\n",
     "        else:\n",
     "            print(\"---GRADE: DOCUMENT NOT RELEVANT---\")\n",
     "            continue\n",
-    "\n",
-    "    return {\"keys\": {\"documents\": filtered_docs, \"question\": question}}\n",
-    "\n",
+    "    return {\"documents\": filtered_docs, \"question\": question}\n",
     "\n",
     "def transform_query(state):\n",
     "    \"\"\"\n",
@@ -335,210 +495,75 @@
     "    \"\"\"\n",
     "\n",
     "    print(\"---TRANSFORM QUERY---\")\n",
-    "    state_dict = state[\"keys\"]\n",
-    "    question = state_dict[\"question\"]\n",
-    "    documents = state_dict[\"documents\"]\n",
+    "    question = state[\"question\"]\n",
+    "    documents = state[\"documents\"]\n",
     "\n",
-    "    # Create a prompt template with format instructions and the query\n",
-    "    prompt = PromptTemplate(\n",
-    "        template=\"\"\"You are generating questions that is well optimized for retrieval. \\n \n",
-    "        Look at the input and try to reason about the underlying sematic intent / meaning. \\n \n",
-    "        Here is the initial question:\n",
-    "        \\n ------- \\n\n",
-    "        {question} \n",
-    "        \\n ------- \\n\n",
-    "        Formulate an improved question: \"\"\",\n",
-    "        input_variables=[\"question\"],\n",
-    "    )\n",
-    "\n",
-    "    # Grader\n",
-    "    model = ChatOpenAI(temperature=0, model=\"gpt-4-0125-preview\", streaming=True)\n",
-    "\n",
-    "    # Prompt\n",
-    "    chain = prompt | model | StrOutputParser()\n",
-    "    better_question = chain.invoke({\"question\": question})\n",
-    "\n",
-    "    return {\"keys\": {\"documents\": documents, \"question\": better_question}}\n",
-    "\n",
-    "\n",
-    "def prepare_for_final_grade(state):\n",
-    "    \"\"\"\n",
-    "    Passthrough state for final grade.\n",
-    "\n",
-    "    Args:\n",
-    "        state (dict): The current graph state\n",
-    "\n",
-    "    Returns:\n",
-    "        state (dict): The current graph state\n",
-    "    \"\"\"\n",
-    "\n",
-    "    print(\"---FINAL GRADE---\")\n",
-    "    state_dict = state[\"keys\"]\n",
-    "    question = state_dict[\"question\"]\n",
-    "    documents = state_dict[\"documents\"]\n",
-    "    generation = state_dict[\"generation\"]\n",
-    "\n",
-    "    return {\n",
-    "        \"keys\": {\"documents\": documents, \"question\": question, \"generation\": generation}\n",
-    "    }\n",
-    "\n",
-    "\n",
-    "### Edges ###\n",
+    "    # Re-write question\n",
+    "    better_question = question_rewriter.invoke({\"question\": question})\n",
+    "    return {\"documents\": documents, \"question\": better_question}\n",
     "\n",
+    "### Edges\n",
     "\n",
     "def decide_to_generate(state):\n",
     "    \"\"\"\n",
     "    Determines whether to generate an answer, or re-generate a question.\n",
     "\n",
     "    Args:\n",
-    "        state (dict): The current state of the agent, including all keys.\n",
+    "        state (dict): The current graph state\n",
     "\n",
     "    Returns:\n",
-    "        str: Next node to call\n",
+    "        str: Binary decision for next node to call\n",
     "    \"\"\"\n",
     "\n",
-    "    print(\"---DECIDE TO GENERATE---\")\n",
-    "    state_dict = state[\"keys\"]\n",
-    "    question = state_dict[\"question\"]\n",
-    "    filtered_documents = state_dict[\"documents\"]\n",
+    "    print(\"---ASSESS GRADED DOCUMENTS---\")\n",
+    "    question = state[\"question\"]\n",
+    "    filtered_documents = state[\"documents\"]\n",
     "\n",
     "    if not filtered_documents:\n",
     "        # All documents have been filtered check_relevance\n",
     "        # We will re-generate a new query\n",
-    "        print(\"---DECISION: TRANSFORM QUERY---\")\n",
+    "        print(\"---DECISION: ALL DOCUMENTS ARE NOT RELEVANT TO QUESTION, TRANSFORM QUERY---\")\n",
     "        return \"transform_query\"\n",
     "    else:\n",
     "        # We have relevant documents, so generate answer\n",
     "        print(\"---DECISION: GENERATE---\")\n",
     "        return \"generate\"\n",
     "\n",
-    "\n",
-    "def grade_generation_v_documents(state):\n",
+    "def grade_generation_v_documents_and_question(state):\n",
     "    \"\"\"\n",
-    "    Determines whether the generation is grounded in the document.\n",
+    "    Determines whether the generation is grounded in the document and answers question.\n",
     "\n",
     "    Args:\n",
-    "        state (dict): The current state of the agent, including all keys.\n",
+    "        state (dict): The current graph state\n",
     "\n",
     "    Returns:\n",
-    "        str: Binary decision\n",
+    "        str: Decision for next node to call\n",
     "    \"\"\"\n",
     "\n",
-    "    print(\"---GRADE GENERATION vs DOCUMENTS---\")\n",
-    "    state_dict = state[\"keys\"]\n",
-    "    question = state_dict[\"question\"]\n",
-    "    documents = state_dict[\"documents\"]\n",
-    "    generation = state_dict[\"generation\"]\n",
+    "    print(\"---CHECK HALLUCINATIONS---\")\n",
+    "    question = state[\"question\"]\n",
+    "    documents = state[\"documents\"]\n",
+    "    generation = state[\"generation\"]\n",
     "\n",
-    "    # Data model\n",
-    "    class grade(BaseModel):\n",
-    "        \"\"\"Binary score for relevance check.\"\"\"\n",
-    "\n",
-    "        binary_score: str = Field(description=\"Supported score 'yes' or 'no'\")\n",
-    "\n",
-    "    # LLM\n",
-    "    model = ChatOpenAI(temperature=0, model=\"gpt-4-0125-preview\", streaming=True)\n",
-    "\n",
-    "    # Tool\n",
-    "    grade_tool_oai = convert_to_openai_tool(grade)\n",
-    "\n",
-    "    # LLM with tool and enforce invocation\n",
-    "    llm_with_tool = model.bind(\n",
-    "        tools=[grade_tool_oai],\n",
-    "        tool_choice={\"type\": \"function\", \"function\": {\"name\": \"grade\"}},\n",
-    "    )\n",
-    "\n",
-    "    # Parser\n",
-    "    parser_tool = PydanticToolsParser(tools=[grade])\n",
-    "\n",
-    "    # Prompt\n",
-    "    prompt = PromptTemplate(\n",
-    "        template=\"\"\"You are a grader assessing whether an answer is grounded in / supported by a set of facts. \\n \n",
-    "        Here are the facts:\n",
-    "        \\n ------- \\n\n",
-    "        {documents} \n",
-    "        \\n ------- \\n\n",
-    "        Here is the answer: {generation}\n",
-    "        Give a binary score 'yes' or 'no' to indicate whether the answer is grounded in / supported by a set of facts.\"\"\",\n",
-    "        input_variables=[\"generation\", \"documents\"],\n",
-    "    )\n",
-    "\n",
-    "    # Chain\n",
-    "    chain = prompt | llm_with_tool | parser_tool\n",
-    "\n",
-    "    score = chain.invoke({\"generation\": generation, \"documents\": documents})\n",
-    "    grade = score[0].binary_score\n",
+    "    score = hallucination_grader.invoke({\"documents\": documents, \"generation\": generation})\n",
+    "    grade = score.binary_score\n",
     "\n",
+    "    # Check hallucination\n",
     "    if grade == \"yes\":\n",
-    "        print(\"---DECISION: SUPPORTED, MOVE TO FINAL GRADE---\")\n",
-    "        return \"supported\"\n",
+    "        print(\"---DECISION: GENERATION IS GROUNDED IN DOCUMENTS---\")\n",
+    "        # Check question-answering\n",
+    "        print(\"---GRADE GENERATION vs QUESTION---\")\n",
+    "        score = answer_grader.invoke({\"question\": question,\"generation\": generation})\n",
+    "        grade = score.binary_score\n",
+    "        if grade == \"yes\":\n",
+    "            print(\"---DECISION: GENERATION ADDRESSES QUESTION---\")\n",
+    "            return \"useful\"\n",
+    "        else:\n",
+    "            print(\"---DECISION: GENERATION DOES NOT ADDRESS QUESTION---\")\n",
+    "            return \"not useful\"\n",
     "    else:\n",
-    "        print(\"---DECISION: NOT SUPPORTED, GENERATE AGAIN---\")\n",
-    "        return \"not supported\"\n",
-    "\n",
-    "\n",
-    "def grade_generation_v_question(state):\n",
-    "    \"\"\"\n",
-    "    Determines whether the generation addresses the question.\n",
-    "\n",
-    "    Args:\n",
-    "        state (dict): The current state of the agent, including all keys.\n",
-    "\n",
-    "    Returns:\n",
-    "        str: Binary decision\n",
-    "    \"\"\"\n",
-    "\n",
-    "    print(\"---GRADE GENERATION vs QUESTION---\")\n",
-    "    state_dict = state[\"keys\"]\n",
-    "    question = state_dict[\"question\"]\n",
-    "    documents = state_dict[\"documents\"]\n",
-    "    generation = state_dict[\"generation\"]\n",
-    "\n",
-    "    # Data model\n",
-    "    class grade(BaseModel):\n",
-    "        \"\"\"Binary score for relevance check.\"\"\"\n",
-    "\n",
-    "        binary_score: str = Field(description=\"Useful score 'yes' or 'no'\")\n",
-    "\n",
-    "    # LLM\n",
-    "    model = ChatOpenAI(temperature=0, model=\"gpt-4-0125-preview\", streaming=True)\n",
-    "\n",
-    "    # Tool\n",
-    "    grade_tool_oai = convert_to_openai_tool(grade)\n",
-    "\n",
-    "    # LLM with tool and enforce invocation\n",
-    "    llm_with_tool = model.bind(\n",
-    "        tools=[grade_tool_oai],\n",
-    "        tool_choice={\"type\": \"function\", \"function\": {\"name\": \"grade\"}},\n",
-    "    )\n",
-    "\n",
-    "    # Parser\n",
-    "    parser_tool = PydanticToolsParser(tools=[grade])\n",
-    "\n",
-    "    # Prompt\n",
-    "    prompt = PromptTemplate(\n",
-    "        template=\"\"\"You are a grader assessing whether an answer is useful to resolve a question. \\n \n",
-    "        Here is the answer:\n",
-    "        \\n ------- \\n\n",
-    "        {generation} \n",
-    "        \\n ------- \\n\n",
-    "        Here is the question: {question}\n",
-    "        Give a binary score 'yes' or 'no' to indicate whether the answer is useful to resolve a question.\"\"\",\n",
-    "        input_variables=[\"generation\", \"question\"],\n",
-    "    )\n",
-    "\n",
-    "    # Prompt\n",
-    "    chain = prompt | llm_with_tool | parser_tool\n",
-    "\n",
-    "    score = chain.invoke({\"generation\": generation, \"question\": question})\n",
-    "    grade = score[0].binary_score\n",
-    "\n",
-    "    if grade == \"yes\":\n",
-    "        print(\"---DECISION: USEFUL---\")\n",
-    "        return \"useful\"\n",
-    "    else:\n",
-    "        print(\"---DECISION: NOT USEFUL---\")\n",
-    "        return \"not useful\""
+    "        pprint(\"---DECISION: GENERATION IS NOT GROUNDED IN DOCUMENTS, RE-TRY---\")\n",
+    "        return \"not supported\""
    ]
   },
   {
@@ -553,23 +578,20 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 8,
+   "execution_count": 13,
    "id": "0e09ca9f-e36d-4ef4-a0d5-79fdbada9fe0",
    "metadata": {},
    "outputs": [],
    "source": [
-    "import pprint\n",
-    "\n",
     "from langgraph.graph import END, StateGraph\n",
     "\n",
     "workflow = StateGraph(GraphState)\n",
     "\n",
     "# Define the nodes\n",
-    "workflow.add_node(\"retrieve\", retrieve)  # retrieve\n",
-    "workflow.add_node(\"grade_documents\", grade_documents)  # grade documents\n",
-    "workflow.add_node(\"generate\", generate)  # generatae\n",
-    "workflow.add_node(\"transform_query\", transform_query)  # transform_query\n",
-    "workflow.add_node(\"prepare_for_final_grade\", prepare_for_final_grade)  # passthrough\n",
+    "workflow.add_node(\"retrieve\", retrieve) # retrieve\n",
+    "workflow.add_node(\"grade_documents\", grade_documents) # grade documents\n",
+    "workflow.add_node(\"generate\", generate) # generatae\n",
+    "workflow.add_node(\"transform_query\", transform_query) # transform_query\n",
     "\n",
     "# Build graph\n",
     "workflow.set_entry_point(\"retrieve\")\n",
@@ -585,28 +607,21 @@
     "workflow.add_edge(\"transform_query\", \"retrieve\")\n",
     "workflow.add_conditional_edges(\n",
     "    \"generate\",\n",
-    "    grade_generation_v_documents,\n",
+    "    grade_generation_v_documents_and_question,\n",
     "    {\n",
-    "        \"supported\": \"prepare_for_final_grade\",\n",
     "        \"not supported\": \"generate\",\n",
-    "    },\n",
-    ")\n",
-    "workflow.add_conditional_edges(\n",
-    "    \"prepare_for_final_grade\",\n",
-    "    grade_generation_v_question,\n",
-    "    {\n",
     "        \"useful\": END,\n",
     "        \"not useful\": \"transform_query\",\n",
     "    },\n",
     ")\n",
-    "\n",
+    "ß\n",
     "# Compile\n",
     "app = workflow.compile()"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 9,
+   "execution_count": 20,
    "id": "fb69dbb9-91ee-4868-8c3c-93af3cd885be",
    "metadata": {},
    "outputs": [
@@ -617,54 +632,52 @@
       "---RETRIEVE---\n",
       "\"Node 'retrieve':\"\n",
       "'\\n---\\n'\n",
-      "---CHECK RELEVANCE---\n",
-      "---GRADE: DOCUMENT RELEVANT---\n",
-      "---GRADE: DOCUMENT RELEVANT---\n",
+      "---CHECK DOCUMENT RELEVANCE TO QUESTION---\n",
+      "---GRADE: DOCUMENT NOT RELEVANT---\n",
       "---GRADE: DOCUMENT RELEVANT---\n",
+      "---GRADE: DOCUMENT NOT RELEVANT---\n",
       "---GRADE: DOCUMENT RELEVANT---\n",
       "\"Node 'grade_documents':\"\n",
       "'\\n---\\n'\n",
-      "---DECIDE TO GENERATE---\n",
+      "---ASSESS GRADED DOCUMENTS---\n",
       "---DECISION: GENERATE---\n",
       "---GENERATE---\n",
       "\"Node 'generate':\"\n",
       "'\\n---\\n'\n",
-      "---GRADE GENERATION vs DOCUMENTS---\n",
-      "---DECISION: SUPPORTED, MOVE TO FINAL GRADE---\n",
-      "---FINAL GRADE---\n",
-      "\"Node 'prepare_for_final_grade':\"\n",
-      "'\\n---\\n'\n",
+      "---CHECK HALLUCINATIONS---\n",
+      "---DECISION: GENERATION IS GROUNDED IN DOCUMENTS---\n",
       "---GRADE GENERATION vs QUESTION---\n",
-      "---DECISION: USEFUL---\n",
+      "---DECISION: GENERATION ADDRESSES QUESTION---\n",
       "\"Node '__end__':\"\n",
       "'\\n---\\n'\n",
-      "('Short-term memory stores information needed for immediate cognitive tasks '\n",
-      " 'and lasts for about 20-30 seconds. Long-term memory can retain information '\n",
-      " 'for extended periods, with subtypes including explicit (facts and events) '\n",
-      " 'and implicit (skills and routines) memory. Sensory memory retains sensory '\n",
-      " 'impressions briefly after stimuli end, while long-term memory stores '\n",
-      " 'information for a long time.')\n"
+      "('Short-term memory is used for in-context learning and lasts for a short '\n",
+      " 'period, while long-term memory allows the agent to retain and recall '\n",
+      " 'information over extended periods. Agents can also utilize external tools '\n",
+      " 'like APIs to access additional information not stored in their model '\n",
+      " 'weights.')\n"
      ]
     }
    ],
    "source": [
+    "from pprint import pprint\n",
+    "\n",
     "# Run\n",
-    "inputs = {\"keys\": {\"question\": \"Explain how the different types of agent memory work?\"}}\n",
+    "inputs = {\"question\": \"Explain how the different types of agent memory work?\"}\n",
     "for output in app.stream(inputs):\n",
     "    for key, value in output.items():\n",
     "        # Node\n",
-    "        pprint.pprint(f\"Node '{key}':\")\n",
+    "        pprint(f\"Node '{key}':\")\n",
     "        # Optional: print full state at each node\n",
     "        # pprint.pprint(value[\"keys\"], indent=2, width=80, depth=None)\n",
-    "    pprint.pprint(\"\\n---\\n\")\n",
+    "    pprint(\"\\n---\\n\")\n",
     "\n",
     "# Final generation\n",
-    "pprint.pprint(value[\"keys\"][\"generation\"])"
+    "pprint(value[\"generation\"])"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 10,
+   "execution_count": 18,
    "id": "4138bc51-8c84-4b8a-8d24-f7f470721f6f",
    "metadata": {},
    "outputs": [
@@ -675,25 +688,22 @@
       "---RETRIEVE---\n",
       "\"Node 'retrieve':\"\n",
       "'\\n---\\n'\n",
-      "---CHECK RELEVANCE---\n",
+      "---CHECK DOCUMENT RELEVANCE TO QUESTION---\n",
       "---GRADE: DOCUMENT RELEVANT---\n",
       "---GRADE: DOCUMENT RELEVANT---\n",
       "---GRADE: DOCUMENT RELEVANT---\n",
       "---GRADE: DOCUMENT RELEVANT---\n",
       "\"Node 'grade_documents':\"\n",
       "'\\n---\\n'\n",
-      "---DECIDE TO GENERATE---\n",
+      "---ASSESS GRADED DOCUMENTS---\n",
       "---DECISION: GENERATE---\n",
       "---GENERATE---\n",
       "\"Node 'generate':\"\n",
       "'\\n---\\n'\n",
-      "---GRADE GENERATION vs DOCUMENTS---\n",
-      "---DECISION: SUPPORTED, MOVE TO FINAL GRADE---\n",
-      "---FINAL GRADE---\n",
-      "\"Node 'prepare_for_final_grade':\"\n",
-      "'\\n---\\n'\n",
+      "---CHECK HALLUCINATIONS---\n",
+      "---DECISION: GENERATION IS GROUNDED IN DOCUMENTS---\n",
       "---GRADE GENERATION vs QUESTION---\n",
-      "---DECISION: USEFUL---\n",
+      "---DECISION: GENERATION ADDRESSES QUESTION---\n",
       "\"Node '__end__':\"\n",
       "'\\n---\\n'\n",
       "('Chain of thought prompting works by providing a series of prompts or '\n",
@@ -701,22 +711,22 @@
       " 'involves iteratively constructing thought processes by asking follow-up '\n",
       " 'questions or exploring multiple reasoning possibilities at each step. '\n",
       " 'External search queries and relevant content from sources like Wikipedia can '\n",
-      " \"be integrated into the context to enhance the model's understanding.\")\n"
+      " 'be integrated into the context to enhance the chain of thought reasoning.')\n"
      ]
     }
    ],
    "source": [
-    "inputs = {\"keys\": {\"question\": \"Explain how chain of thought prompting works?\"}}\n",
+    "inputs = {\"question\": \"Explain how chain of thought prompting works?\"}\n",
     "for output in app.stream(inputs):\n",
     "    for key, value in output.items():\n",
     "        # Node\n",
-    "        pprint.pprint(f\"Node '{key}':\")\n",
+    "        pprint(f\"Node '{key}':\")\n",
     "        # Optional: print full state at each node\n",
     "        # pprint.pprint(value[\"keys\"], indent=2, width=80, depth=None)\n",
-    "    pprint.pprint(\"\\n---\\n\")\n",
+    "    pprint(\"\\n---\\n\")\n",
     "\n",
     "# Final generation\n",
-    "pprint.pprint(value[\"keys\"][\"generation\"])"
+    "pprint(value[\"generation\"])"
    ]
   },
   {
@@ -728,8 +738,16 @@
     " \n",
     "* https://smith.langchain.com/public/55d6180f-aab8-42bc-8799-dadce6247d9b/r\n",
     "\n",
-    "* https://smith.langchain.com/public/f85ebc95-81d9-47fc-91c6-b54e5b78f359/r"
+    "* https://smith.langchain.com/public/1c6bf654-61b2-4fc5-9889-054b020c78aa/r"
    ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "42369ab8-322d-434a-b5dd-2266e4cb2903",
+   "metadata": {},
+   "outputs": [],
+   "source": []
   }
  ],
  "metadata": {
@@ -748,7 +766,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.9.16"
+   "version": "3.11.8"
   }
  },
  "nbformat": 4,
diff --git a/examples/rag/langgraph_self_rag_local.ipynb b/examples/rag/langgraph_self_rag_local.ipynb
new file mode 100644
index 000000000..7a0e66082
--- /dev/null
+++ b/examples/rag/langgraph_self_rag_local.ipynb
@@ -0,0 +1,726 @@
+{
+ "cells": [
+  {
+   "attachments": {
+    "5fca0a3e-d13d-4bfa-95ea-58203640cc7a.png": {
+     "image/png": 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"
+    }
+   },
+   "cell_type": "markdown",
+   "id": "848ba742-7443-4123-8115-061da9823309",
+   "metadata": {},
+   "source": [
+    "# Self RAG -- With Local LLMs\n",
+    "\n",
+    "Self-RAG is a strategy for RAG that incorperates self-reflection / self-grading on retrieved documents and generations. \n",
+    "\n",
+    "In the [paper](https://arxiv.org/abs/2310.11511), a few decisions are made:\n",
+    "\n",
+    "1. Should I retrieve from retriever, `R` -\n",
+    "\n",
+    "* Input: `x (question)` OR `x (question)`, `y (generation)`\n",
+    "* Decides when to retrieve `D` chunks with `R`\n",
+    "* Output: `yes, no, continue`\n",
+    "\n",
+    "2. Are the retrieved passages `D` relevant to the question `x` -\n",
+    "\n",
+    "* * Input: (`x (question)`, `d (chunk)`) for `d` in `D`\n",
+    "* `d` provides useful information to solve `x`\n",
+    "* Output: `relevant, irrelevant`\n",
+    "\n",
+    "3. Are the LLM generation from each chunk in `D` is relevant to the chunk (hallucinations, etc)  -\n",
+    "\n",
+    "* Input: `x (question)`, `d (chunk)`,  `y (generation)` for `d` in `D`\n",
+    "* All of the verification-worthy statements in `y (generation)` are supported by `d`\n",
+    "* Output: `{fully supported, partially supported, no support`\n",
+    "\n",
+    "4. The LLM generation from each chunk in `D` is a useful response to `x (question)` -\n",
+    "\n",
+    "* Input: `x (question)`, `y (generation)` for `d` in `D`\n",
+    "* `y (generation)` is a useful response to `x (question)`.\n",
+    "* Output: `{5, 4, 3, 2, 1}`\n",
+    "\n",
+    "We will implement some of these ideas from scratch using [LangGraph](https://python.langchain.com/docs/langgraph).\n",
+    "\n",
+    "![Screenshot 2024-04-01 at 12.42.59 PM.png](attachment:5fca0a3e-d13d-4bfa-95ea-58203640cc7a.png)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "9ed0a85a-a33b-40a6-99fa-2444bf57a6cc",
+   "metadata": {},
+   "source": [
+    "# Enviorment "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "d7f9cc6d-a70c-433a-b0ad-ea47c5a0717e",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "! pip install -U langchain-nomic langchain_community tiktoken langchainhub chromadb langchain langgraph"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "ccc6b6bd-a2fa-4a43-83b7-704b8b6fb855",
+   "metadata": {},
+   "source": [
+    "### LLMs\n",
+    "\n",
+    "#### Local Embeddings\n",
+    "\n",
+    "You can use `GPT4AllEmbeddings()` from Nomic, which can access use Nomic's recently released [v1](https://blog.nomic.ai/posts/nomic-embed-text-v1) and [v1.5](https://blog.nomic.ai/posts/nomic-embed-matryoshka) embeddings.\n",
+    "\n",
+    "\n",
+    "Follow the documentation [here](https://docs.gpt4all.io/gpt4all_python_embedding.html#supported-embedding-models).\n",
+    "\n",
+    "#### Local LLM\n",
+    "\n",
+    "(1) Download [Ollama app](https://ollama.ai/).\n",
+    "\n",
+    "(2) Download a `Mistral` model from various Mistral versions [here](https://ollama.ai/library/mistral) and Mixtral versions [here](https://ollama.ai/library/mixtral) available.\n",
+    "```\n",
+    "ollama pull mistral\n",
+    "```"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "id": "bedffc73-6b10-42c8-8768-2085c8ed3398",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Ollama model name\n",
+    "local_llm = \"mistral\""
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "6306aa61-2e6a-47b6-b306-4987d0636f97",
+   "metadata": {},
+   "source": [
+    "### Tracing\n",
+    "\n",
+    "Optionally, use [LangSmith](https://docs.smith.langchain.com/) for tracing (shown at bottom)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "2208f342-8163-4af3-8dc0-aa70f5e06143",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "os.environ['LANGCHAIN_TRACING_V2'] = 'true'\n",
+    "os.environ['LANGCHAIN_ENDPOINT'] = 'https://api.smith.langchain.com'\n",
+    "os.environ['LANGCHAIN_API_KEY'] = <your-api-key>"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "ba68a46d-b617-4fdc-9113-fabdcf736feb",
+   "metadata": {},
+   "source": [
+    "## Index\n",
+    "\n",
+    "Let's index 3 blog posts."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "id": "c3bb9060-ad74-4470-9991-2ba167b6b8d8",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from langchain.text_splitter import RecursiveCharacterTextSplitter\n",
+    "from langchain_community.document_loaders import WebBaseLoader\n",
+    "from langchain_community.vectorstores import Chroma\n",
+    "from langchain_community.embeddings import GPT4AllEmbeddings\n",
+    "\n",
+    "urls = [\n",
+    "    \"https://lilianweng.github.io/posts/2023-06-23-agent/\",\n",
+    "    \"https://lilianweng.github.io/posts/2023-03-15-prompt-engineering/\",\n",
+    "    \"https://lilianweng.github.io/posts/2023-10-25-adv-attack-llm/\",\n",
+    "]\n",
+    "\n",
+    "docs = [WebBaseLoader(url).load() for url in urls]\n",
+    "docs_list = [item for sublist in docs for item in sublist]\n",
+    "\n",
+    "text_splitter = RecursiveCharacterTextSplitter.from_tiktoken_encoder(\n",
+    "    chunk_size=250, chunk_overlap=0\n",
+    ")\n",
+    "doc_splits = text_splitter.split_documents(docs_list)\n",
+    "\n",
+    "# Add to vectorDB\n",
+    "vectorstore = Chroma.from_documents(\n",
+    "    documents=doc_splits,\n",
+    "    collection_name=\"rag-chroma\",\n",
+    "    embedding=GPT4AllEmbeddings(),\n",
+    ")\n",
+    "retriever = vectorstore.as_retriever()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "cc60ff95-7e12-4004-b18e-a067a2dcc201",
+   "metadata": {},
+   "source": [
+    "## LLMs"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "id": "3aad0c60-3208-48fb-af82-0024630b4da1",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "{'score': 'yes'}\n"
+     ]
+    }
+   ],
+   "source": [
+    "### Retrieval Grader \n",
+    "\n",
+    "from langchain.prompts import PromptTemplate\n",
+    "from langchain_community.chat_models import ChatOllama\n",
+    "from langchain_core.output_parsers import JsonOutputParser\n",
+    "\n",
+    "# LLM\n",
+    "llm = ChatOllama(model=local_llm, format=\"json\", temperature=0)\n",
+    "\n",
+    "prompt = PromptTemplate(\n",
+    "    template=\"\"\"You are a grader assessing relevance of a retrieved document to a user question. \\n \n",
+    "    Here is the retrieved document: \\n\\n {document} \\n\\n\n",
+    "    Here is the user question: {question} \\n\n",
+    "    If the document contains keywords related to the user question, grade it as relevant. \\n\n",
+    "    It does not need to be a stringent test. The goal is to filter out erroneous retrievals. \\n\n",
+    "    Give a binary score 'yes' or 'no' score to indicate whether the document is relevant to the question. \\n\n",
+    "    Provide the binary score as a JSON with a single key 'score' and no premable or explaination.\"\"\",\n",
+    "    input_variables=[\"question\", \"document\"],\n",
+    ")\n",
+    "\n",
+    "retrieval_grader = prompt | llm | JsonOutputParser()\n",
+    "question = \"agent memory\"\n",
+    "docs = retriever.get_relevant_documents(question)\n",
+    "doc_txt = docs[1].page_content\n",
+    "print(retrieval_grader.invoke({\"question\": question, \"document\": doc_txt}))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "id": "e5e45953-248d-492f-af28-d5e80c664c95",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      " In an LLM-powered autonomous agent system, the Large Language Model (LLM) functions as the agent's brain. The agent has key components including memory, planning, and reflection mechanisms. The memory component is a long-term memory module that records a comprehensive list of agents’ experience in natural language. It includes a memory stream, which is an external database for storing past experiences. The reflection mechanism synthesizes memories into higher-level inferences over time and guides the agent's future behavior.\n"
+     ]
+    }
+   ],
+   "source": [
+    "### Generate\n",
+    "\n",
+    "from langchain import hub\n",
+    "from langchain_core.output_parsers import StrOutputParser\n",
+    "\n",
+    "# Prompt\n",
+    "prompt = hub.pull(\"rlm/rag-prompt\")\n",
+    "\n",
+    "# LLM\n",
+    "llm = ChatOllama(model=local_llm, temperature=0)\n",
+    "\n",
+    "# Post-processing\n",
+    "def format_docs(docs):\n",
+    "    return \"\\n\\n\".join(doc.page_content for doc in docs)\n",
+    "\n",
+    "# Chain\n",
+    "rag_chain = prompt | llm | StrOutputParser()\n",
+    "\n",
+    "# Run\n",
+    "generation = rag_chain.invoke({\"context\": docs, \"question\": question})\n",
+    "print(generation)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "id": "56297862-df87-42a7-ba9d-310926dfb328",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "{'score': 'yes'}"
+      ]
+     },
+     "execution_count": 8,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "### Hallucination Grader \n",
+    "\n",
+    "# LLM\n",
+    "llm = ChatOllama(model=local_llm, format=\"json\", temperature=0)\n",
+    "\n",
+    "# Prompt\n",
+    "prompt = PromptTemplate(\n",
+    "    template=\"\"\"You are a grader assessing whether an answer is grounded in / supported by a set of facts. \\n \n",
+    "    Here are the facts:\n",
+    "    \\n ------- \\n\n",
+    "    {documents} \n",
+    "    \\n ------- \\n\n",
+    "    Here is the answer: {generation}\n",
+    "    Give a binary score 'yes' or 'no' score to indicate whether the answer is grounded in / supported by a set of facts. \\n\n",
+    "    Provide the binary score as a JSON with a single key 'score' and no preamble or explanation.\"\"\",\n",
+    "    input_variables=[\"generation\", \"documents\"],\n",
+    ")\n",
+    "\n",
+    "hallucination_grader = prompt | llm | JsonOutputParser()\n",
+    "hallucination_grader.invoke({\"documents\": docs, \"generation\": generation})"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "id": "e1dd9174-2df6-45b1-8e69-13381f579c39",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "{'score': 'yes'}"
+      ]
+     },
+     "execution_count": 10,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "### Answer Grader \n",
+    "\n",
+    "# LLM\n",
+    "llm = ChatOllama(model=local_llm, format=\"json\", temperature=0)\n",
+    "\n",
+    "# Prompt\n",
+    "prompt = PromptTemplate(\n",
+    "    template=\"\"\"You are a grader assessing whether an answer is useful to resolve a question. \\n \n",
+    "    Here is the answer:\n",
+    "    \\n ------- \\n\n",
+    "    {generation} \n",
+    "    \\n ------- \\n\n",
+    "    Here is the question: {question}\n",
+    "    Give a binary score 'yes' or 'no' to indicate whether the answer is useful to resolve a question. \\n\n",
+    "    Provide the binary score as a JSON with a single key 'score' and no preamble or explanation.\"\"\",\n",
+    "    input_variables=[\"generation\", \"question\"],\n",
+    ")\n",
+    "\n",
+    "answer_grader = prompt | llm | JsonOutputParser()\n",
+    "answer_grader.invoke({\"question\": question,\"generation\": generation})"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "id": "5216d92b-1ca1-4bcf-a34f-c99ec2766b54",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "' What is agent memory and how can it be effectively utilized in vector database retrieval?'"
+      ]
+     },
+     "execution_count": 11,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "### Question Re-writer\n",
+    "\n",
+    "# LLM\n",
+    "llm = ChatOllama(model=local_llm, temperature=0)\n",
+    "\n",
+    "# Prompt \n",
+    "re_write_prompt = PromptTemplate(\n",
+    "    template=\"\"\"You a question re-writer that converts an input question to a better version that is optimized \\n \n",
+    "     for vectorstore retrieval. Look at the initial and formulate an improved question. \\n\n",
+    "     Here is the initial question: \\n\\n {question}. Improved question with no preamble: \\n \"\"\",\n",
+    "    input_variables=[\"generation\", \"question\"],\n",
+    ")\n",
+    "\n",
+    "question_rewriter = re_write_prompt | llm | StrOutputParser()\n",
+    "question_rewriter.invoke({\"question\": question})"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "3d3339b9-5f30-4d54-bfc9-9091c6035955",
+   "metadata": {},
+   "source": [
+    "# Graph \n",
+    "\n",
+    "Capture the flow in as a graph.\n",
+    "\n",
+    "## Graph state"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 13,
+   "id": "90fb1dc6-c482-483a-8441-39965c401beb",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from typing_extensions import TypedDict\n",
+    "from typing import List\n",
+    "\n",
+    "class GraphState(TypedDict):\n",
+    "    \"\"\"\n",
+    "    Represents the state of our graph.\n",
+    "\n",
+    "    Attributes:\n",
+    "        question: question\n",
+    "        generation: LLM generation\n",
+    "        documents: list of documents \n",
+    "    \"\"\"\n",
+    "    question : str\n",
+    "    generation : str\n",
+    "    documents : List[str]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 14,
+   "id": "5324ea49-5745-47b5-a0a5-bf58c8babe46",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "### Nodes\n",
+    "\n",
+    "from langchain.schema import Document\n",
+    "\n",
+    "def retrieve(state):\n",
+    "    \"\"\"\n",
+    "    Retrieve documents\n",
+    "\n",
+    "    Args:\n",
+    "        state (dict): The current graph state\n",
+    "\n",
+    "    Returns:\n",
+    "        state (dict): New key added to state, documents, that contains retrieved documents\n",
+    "    \"\"\"\n",
+    "    print(\"---RETRIEVE---\")\n",
+    "    question = state[\"question\"]\n",
+    "\n",
+    "    # Retrieval\n",
+    "    documents = retriever.get_relevant_documents(question)\n",
+    "    return {\"documents\": documents, \"question\": question}\n",
+    "\n",
+    "def generate(state):\n",
+    "    \"\"\"\n",
+    "    Generate answer\n",
+    "\n",
+    "    Args:\n",
+    "        state (dict): The current graph state\n",
+    "\n",
+    "    Returns:\n",
+    "        state (dict): New key added to state, generation, that contains LLM generation\n",
+    "    \"\"\"\n",
+    "    print(\"---GENERATE---\")\n",
+    "    question = state[\"question\"]\n",
+    "    documents = state[\"documents\"]\n",
+    "    \n",
+    "    # RAG generation\n",
+    "    generation = rag_chain.invoke({\"context\": documents, \"question\": question})\n",
+    "    return {\"documents\": documents, \"question\": question, \"generation\": generation}\n",
+    "\n",
+    "def grade_documents(state):\n",
+    "    \"\"\"\n",
+    "    Determines whether the retrieved documents are relevant to the question.\n",
+    "\n",
+    "    Args:\n",
+    "        state (dict): The current graph state\n",
+    "\n",
+    "    Returns:\n",
+    "        state (dict): Updates documents key with only filtered relevant documents\n",
+    "    \"\"\"\n",
+    "\n",
+    "    print(\"---CHECK DOCUMENT RELEVANCE TO QUESTION---\")\n",
+    "    question = state[\"question\"]\n",
+    "    documents = state[\"documents\"]\n",
+    "    \n",
+    "    # Score each doc\n",
+    "    filtered_docs = []\n",
+    "    for d in documents:\n",
+    "        score = retrieval_grader.invoke({\"question\": question, \"document\": d.page_content})\n",
+    "        grade = score['score']\n",
+    "        if grade == \"yes\":\n",
+    "            print(\"---GRADE: DOCUMENT RELEVANT---\")\n",
+    "            filtered_docs.append(d)\n",
+    "        else:\n",
+    "            print(\"---GRADE: DOCUMENT NOT RELEVANT---\")\n",
+    "            continue\n",
+    "    return {\"documents\": filtered_docs, \"question\": question}\n",
+    "\n",
+    "def transform_query(state):\n",
+    "    \"\"\"\n",
+    "    Transform the query to produce a better question.\n",
+    "\n",
+    "    Args:\n",
+    "        state (dict): The current graph state\n",
+    "\n",
+    "    Returns:\n",
+    "        state (dict): Updates question key with a re-phrased question\n",
+    "    \"\"\"\n",
+    "\n",
+    "    print(\"---TRANSFORM QUERY---\")\n",
+    "    question = state[\"question\"]\n",
+    "    documents = state[\"documents\"]\n",
+    "\n",
+    "    # Re-write question\n",
+    "    better_question = question_rewriter.invoke({\"question\": question})\n",
+    "    return {\"documents\": documents, \"question\": better_question}\n",
+    "\n",
+    "### Edges\n",
+    "\n",
+    "def decide_to_generate(state):\n",
+    "    \"\"\"\n",
+    "    Determines whether to generate an answer, or re-generate a question.\n",
+    "\n",
+    "    Args:\n",
+    "        state (dict): The current graph state\n",
+    "\n",
+    "    Returns:\n",
+    "        str: Binary decision for next node to call\n",
+    "    \"\"\"\n",
+    "\n",
+    "    print(\"---ASSESS GRADED DOCUMENTS---\")\n",
+    "    question = state[\"question\"]\n",
+    "    filtered_documents = state[\"documents\"]\n",
+    "\n",
+    "    if not filtered_documents:\n",
+    "        # All documents have been filtered check_relevance\n",
+    "        # We will re-generate a new query\n",
+    "        print(\"---DECISION: ALL DOCUMENTS ARE NOT RELEVANT TO QUESTION, TRANSFORM QUERY---\")\n",
+    "        return \"transform_query\"\n",
+    "    else:\n",
+    "        # We have relevant documents, so generate answer\n",
+    "        print(\"---DECISION: GENERATE---\")\n",
+    "        return \"generate\"\n",
+    "\n",
+    "def grade_generation_v_documents_and_question(state):\n",
+    "    \"\"\"\n",
+    "    Determines whether the generation is grounded in the document and answers question.\n",
+    "\n",
+    "    Args:\n",
+    "        state (dict): The current graph state\n",
+    "\n",
+    "    Returns:\n",
+    "        str: Decision for next node to call\n",
+    "    \"\"\"\n",
+    "\n",
+    "    print(\"---CHECK HALLUCINATIONS---\")\n",
+    "    question = state[\"question\"]\n",
+    "    documents = state[\"documents\"]\n",
+    "    generation = state[\"generation\"]\n",
+    "\n",
+    "    score = hallucination_grader.invoke({\"documents\": documents, \"generation\": generation})\n",
+    "    grade = score['score']\n",
+    "\n",
+    "    # Check hallucination\n",
+    "    if grade == \"yes\":\n",
+    "        print(\"---DECISION: GENERATION IS GROUNDED IN DOCUMENTS---\")\n",
+    "        # Check question-answering\n",
+    "        print(\"---GRADE GENERATION vs QUESTION---\")\n",
+    "        score = answer_grader.invoke({\"question\": question,\"generation\": generation})\n",
+    "        grade = score['score']\n",
+    "        if grade == \"yes\":\n",
+    "            print(\"---DECISION: GENERATION ADDRESSES QUESTION---\")\n",
+    "            return \"useful\"\n",
+    "        else:\n",
+    "            print(\"---DECISION: GENERATION DOES NOT ADDRESS QUESTION---\")\n",
+    "            return \"not useful\"\n",
+    "    else:\n",
+    "        pprint(\"---DECISION: GENERATION IS NOT GROUNDED IN DOCUMENTS, RE-TRY---\")\n",
+    "        return \"not supported\""
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "bdf3826c-b668-4f0b-bf83-81c40baaaf02",
+   "metadata": {},
+   "source": [
+    "## Build Graph\n",
+    "\n",
+    "This just follows the flow we outlined in the figure above."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 15,
+   "id": "5605dee4-b2df-46ae-a640-cc2ed90c21a6",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from langgraph.graph import END, StateGraph\n",
+    "\n",
+    "workflow = StateGraph(GraphState)\n",
+    "\n",
+    "# Define the nodes\n",
+    "workflow.add_node(\"retrieve\", retrieve) # retrieve\n",
+    "workflow.add_node(\"grade_documents\", grade_documents) # grade documents\n",
+    "workflow.add_node(\"generate\", generate) # generatae\n",
+    "workflow.add_node(\"transform_query\", transform_query) # transform_query\n",
+    "\n",
+    "# Build graph\n",
+    "workflow.set_entry_point(\"retrieve\")\n",
+    "workflow.add_edge(\"retrieve\", \"grade_documents\")\n",
+    "workflow.add_conditional_edges(\n",
+    "    \"grade_documents\",\n",
+    "    decide_to_generate,\n",
+    "    {\n",
+    "        \"transform_query\": \"transform_query\",\n",
+    "        \"generate\": \"generate\",\n",
+    "    },\n",
+    ")\n",
+    "workflow.add_edge(\"transform_query\", \"retrieve\")\n",
+    "workflow.add_conditional_edges(\n",
+    "    \"generate\",\n",
+    "    grade_generation_v_documents_and_question,\n",
+    "    {\n",
+    "        \"not supported\": \"generate\",\n",
+    "        \"useful\": END,\n",
+    "        \"not useful\": \"transform_query\",\n",
+    "    },\n",
+    ")\n",
+    "\n",
+    "# Compile\n",
+    "app = workflow.compile()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "105ae1b5-6963-4186-bb83-6d6cb96d095f",
+   "metadata": {},
+   "source": [
+    "## Run\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 16,
+   "id": "26a64f7d-0c14-4e31-a67f-63021dee626e",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "---RETRIEVE---\n",
+      "\"Node 'retrieve':\"\n",
+      "'\\n---\\n'\n",
+      "---CHECK DOCUMENT RELEVANCE TO QUESTION---\n",
+      "---GRADE: DOCUMENT RELEVANT---\n",
+      "---GRADE: DOCUMENT RELEVANT---\n",
+      "---GRADE: DOCUMENT RELEVANT---\n",
+      "---GRADE: DOCUMENT RELEVANT---\n",
+      "\"Node 'grade_documents':\"\n",
+      "'\\n---\\n'\n",
+      "---ASSESS GRADED DOCUMENTS---\n",
+      "---DECISION: GENERATE---\n",
+      "---GENERATE---\n",
+      "\"Node 'generate':\"\n",
+      "'\\n---\\n'\n",
+      "---CHECK HALLUCINATIONS---\n",
+      "---DECISION: GENERATION IS GROUNDED IN DOCUMENTS---\n",
+      "---GRADE GENERATION vs QUESTION---\n",
+      "---DECISION: GENERATION ADDRESSES QUESTION---\n",
+      "\"Node '__end__':\"\n",
+      "'\\n---\\n'\n",
+      "(' In a LLM-powered autonomous agent system, memory is a key component that '\n",
+      " 'enables agents to store and retrieve information. There are different types '\n",
+      " 'of memory in human brains, such as sensory memory which retains impressions '\n",
+      " 'of sensory information for a few seconds, and long-term memory which records '\n",
+      " \"experiences for extended periods (Lil'Log, 2023). In the context of LLM \"\n",
+      " 'agents, memory is often implemented as an external database or memory stream '\n",
+      " \"(Lil'Log, 2023). The agent can consult this memory to inform its behavior \"\n",
+      " 'based on relevance, recency, and importance. Additionally, reflection '\n",
+      " 'mechanisms synthesize memories into higher-level inferences over time and '\n",
+      " \"guide the agent's future behavior (Lil'Log, 2023).\")\n"
+     ]
+    }
+   ],
+   "source": [
+    "from pprint import pprint\n",
+    "\n",
+    "# Run\n",
+    "inputs = {\"question\": \"Explain how the different types of agent memory work?\"}\n",
+    "for output in app.stream(inputs):\n",
+    "    for key, value in output.items():\n",
+    "        # Node\n",
+    "        pprint(f\"Node '{key}':\")\n",
+    "        # Optional: print full state at each node\n",
+    "        # pprint.pprint(value[\"keys\"], indent=2, width=80, depth=None)\n",
+    "    pprint(\"\\n---\\n\")\n",
+    "\n",
+    "# Final generation\n",
+    "pprint(value[\"generation\"])"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "f9a27907-2611-4791-910b-0d66c59f5cf5",
+   "metadata": {},
+   "source": [
+    "Trace: \n",
+    "\n",
+    "https://smith.langchain.com/public/4163a342-5260-4852-8602-bda3f95177e7/r"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "953143c2-2f2a-4361-a36b-87db7cf21d63",
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3 (ipykernel)",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.11.8"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}
diff --git a/examples/rag/langgraph_self_rag_mistral_nomic.ipynb b/examples/rag/langgraph_self_rag_mistral_nomic.ipynb
deleted file mode 100644
index 9ac63ecad..000000000
--- a/examples/rag/langgraph_self_rag_mistral_nomic.ipynb
+++ /dev/null
@@ -1,712 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "a1908e8c-5970-4d9d-bc2b-d5fee7aa3baa",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "! pip install -U llama-cpp-python langchain-nomic langchain_community tiktoken langchainhub chromadb langchain langgraph"
-   ]
-  },
-  {
-   "attachments": {
-    "e3e60bc2-6033-4d66-af6d-1dfafce5cb9f.png": {
-     "image/png": 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tcG1ldGE+CmcX3IgAAEAASURBVHgB7L0HYCVluf//pPdkS3ZZ2EbvCigCgkhRuXpREdBrpygqVlDxWn6A+rehUq3YQJGLBa9iQUCliogICBcElrbL9pZsNv2knPyf72QnTObMSU6ySfbk5PPCycy889bPzLzn7Pud53mLBjwYAQIQgAAEIAABCEAAAhCAAAQgAAEIQAACEIAABCAAAQhAAAIQKFgCxQXbMzoGAQhAAAIQgAAEIAABCEAAAhCAAAQgAAEIQAACEIAABCAAAQgEBBAFuREgAAEIQAACEIAABCAAAQhAAAIQgAAEIAABCEAAAhCAAAQgUOAEEAUL/ALTPQhAAAIQgAAEIAABCEAAAhCAAAQgAAEIQAACEIAABCAAAQggCnIPQAACEIAABCAAAQhAAAIQgAAEIAABCEAAAhCAAAQgAAEIQKDACSAKFvgFpnsQgAAEIAABCEAAAhCAAAQgAAEIQAACEIAABCAAAQhAAAIQQBTkHoAABCAAAQhAAAIQgAAEIAABCEAAAhCAAAQgAAEIQAACEIBAgRNAFCzwC0z3IAABCEAAAhCAAAQgAAEIQAACEIAABCAAAQhAAAIQgAAEIIAoyD0AAQhAAAIQgAAEIAABCEAAAhCAAAQgAAEIQAACEIAABCAAgQIngChY4BeY7kEAAhCAAAQgAAEIQAACEIAABCAAAQhAAAIQgAAEIAABCEAAUZB7AAIQgAAEIAABCEAAAhCAAAQgAAEIQAACEIAABCAAAQhAAAIFTgBRsMAvMN2DAAQgAAEIQAACEIAABCAAAQhAAAIQgAAEIAABCEAAAhCAAKIg9wAEIAABCEAAAhCAAAQgAAEIQAACEIAABCAAAQhAAAIQgAAECpwAomCBX2C6BwEIQAACEIAABCAAAQhAAAIQgAAEIAABCEAAAhCAAAQgAAFEQe4BCEAAAhCAAAQgAAEIQAACEIAABCAAAQhAAAIQgAAEIAABCBQ4AUTBAr/AdA8CEIAABCAAAQhAAAIQgAAEIAABCEAAAhCAAAQgAAEIQAACiILcAxCAAAQgAAEIQAACEIAABCAAAQhAAAIQgAAEIAABCEAAAhAocAKIggV+gekeBCAAAQhAAAIQgAAEIAABCEAAAhCAAAQgAAEIQAACEIAABBAFuQcgAAEIQAACEIAABCAAAQhAAAIQgAAEIAABCEAAAhCAAAQgUOAEEAUL/ALTPQhAAAIQgAAEIAABCEAAAhCAAAQgAAEIQAACEIAABCAAAQggCnIPQAACEIAABCAAAQhAAAIQgAAEIAABCEAAAhCAAAQgAAEIQKDACSAKFvgFpnsQgAAEIAABCEAAAhCAAAQgAAEIQAACEIAABCAAAQhAAAIQQBTkHoAABCAAAQhAAAIQgAAEIAABCEAAAhCAAAQgAAEIQAACEIBAgRNAFCzwC0z3IAABCEAAAhCAAAQgAAEIQAACEIAABCAAAQhAAAIQgAAEIIAoyD0AAQhAAAIQgAAEIAABCEAAAhCAAAQgAAEIQAACEIAABCAAgQIngChY4BeY7kEAAhCAAAQgAAEIQAACEIAABCAAAQhAAAIQgAAEIAABCEAAUZB7AAIQgAAEIAABCEAAAhCAAAQgAAEIQAACEIAABCAAAQhAAAIFTgBRsMAvMN2DAAQgAAEIQAACEIAABCAAAQhAAAIQgAAEIAABCEAAAhCAAKIg9wAEIAABCEAAAhCAAAQgAAEIQAACEIAABCAAAQhAAAIQgAAECpwAomCBX2C6BwEIQAACEIAABCAAAQhAAAIQgAAEIAABCEAAAhCAAAQgAAFEQe4BCEAAAhCAAAQgAAEIQAACEIAABCAAAQhAAAIQgAAEIAABCBQ4AUTBAr/AdA8CEIAABCAAAQhAAAIQgAAEIAABCEAAAhCAAAQgAAEIQAACiILcAxCAAAQgAAEIQAACEIAABCAAAQhAAAIQgAAEIAABCEAAAhAocAKIggV+gekeBCAAAQhAAAIQgAAEIAABCEAAAhCAAAQgAAEIQAACEIAABBAFuQcgAAEIQAACEIAABCAAAQhAAAIQgAAEIAABCEAAAhCAAAQgUOAEEAUL/ALTPQhAAAIQgAAEIAABCEAAAhCAAAQgAAEIQAACEIAABCAAAQggCnIPQAACEIAABCAAAQhAAAIQgAAEIAABCEAAAhCAAAQgAAEIQKDACSAKFvgFpnsQgAAEIAABCEAAAhCAAAQgAAEIQAACEIAABCAAAQhAAAIQQBTkHoAABCAAAQhAAAIQgAAEIAABCEAAAhCAAAQgAAEIQAACEIBAgRNAFCzwC0z3IAABCEAAAhCAAAQgAAEIQAACEIAABCAAAQhAAAIQgAAEIIAoyD0AAQhAAAIQgAAEIAABCEAAAhCAAAQgAAEIQAACEIAABCAAgQIngChY4BeY7kEAAhCAAAQgAAEIQAACEIAABCAAAQhAAAIQgAAEIAABCEAAUZB7AAIQgAAEIAABCEAAAhCAAAQgAAEIQAACEIAABCAAAQhAAAIFTgBRsMAvMN2DAAQgAAEIQAACEIAABCAAAQhAAAIQgAAEIAABCEAAAhCAAKIg9wAEIAABCEAAAhCAAAQgAAEIQAACEIAABCAAAQhAAAIQgAAECpwAomCBX2C6BwEIQAACEIAABCAAAQhAAAIQgAAEIAABCEAAAhCAAAQgAAFEQe4BCEAAAhCAAAQgAAEIQAACEIAABCAAAQhAAAIQgAAEIAABCBQ4AUTBAr/AdA8CEIAABCAAAQhAAAIQgAAEIAABCEAAAhCAAAQgAAEIQAACiILcAxCAAAQgAAEIQAACEIAABCAAAQhAAAIQgAAEIAABCEAAAhAocAKIggV+gekeBCAAAQhAAAIQgAAEIAABCEAAAhCAAAQgAAEIQAACEIAABBAFuQcgAAEIQAACEIAABCAAAQhAAAIQgAAEIAABCEAAAhCAAAQgUOAEEAUL/ALTPQhAAAIQgAAEIAABCEAAAhCAAAQgAAEIQAACEIAABCAAAQiUggACEIAABCAAAQhAoDAIdHd32yOPPJJTZ3bbbTdrbGzMKS2JciPw7LPPWlNT06iJy8rK7OCDDx413fYkGBgYsI0bN9ry5cuDzzPPPGOtra02d+7c4LrPmzfPFi9ebIcccsj2VENeCORM4MYbb7Q///nP1tvba0ceeaS9+c1vttLS/PjnaNLYucsuu9jChQtz7h8JIQCBHUPgoYceCsaVsPba2lrbb7/9wkO2EIAABCAAAQhAAAIxAkU+YTAQi+MQAhCAAAQgAAEIQGAaEvjxj39sn/3sZ3Nq+eWXX24nn3xyTmnzLVFfX5+lUimrqanJq6YtXbo0p/bsvPPOdu+99+aUdjyJHnjgATvvvPNMIuVI4YgjjrBf/OIXIyXh3DgJdHZ2WnFxsVVWVo6zhMLK9t3vftcuuuiiYZ2SKPi1r31tWNyOOli5cqUdffTRw6r/2Mc+Zuecc86wOA4gsL0EGBu2l2Bm/he96EXDXsg57LDD7Prrr89MSAwEIAABCEAAAhCAQEAgP17N5GJAAAIQgAAEIFBQBFasWGFXXHGFRd89uvDCC23OnDkF1c9860xPT0++NWlC2iOR64YbbrDnnnsusHrTBH4YJLBpQvDYY4+1E044wWbNmhWemnFbTTZfcskl9sMf/jCnvstScCpDaCk2Wp2yHluyZIlJZNV2r732Mll+jDdkq1dWkxdccMF4ix3Kt2rVKrvjjjvstttus8cff9zWrVs3dE476sOhhx5qxx13nB1zzDHW0NAw7HyhH/T392cIguqzBGmJ1/Pnzy90BJPev+9///v22GOPDdUjwf8tb3nL0DE7O4YAY8OO4U6tEIAABCAAAQhAAAIjE0AUHJkPZyEAAQhAAAIQGAcBiTa//vWvh+X87//+72HHHEBgNAISWC6++GL7y1/+kjWpBBiJPvp87nOfs49+9KN2+umnW3l5edY8hXri/e9/fyBO5dq/RYsW5Zp0QtL9/ve/t5tuumnMZcki9FOf+pS97W1vG5e7yauuusruv//+xHrf85732IIFCxLPjRa5YcMGk8XtddddN2JSjYfhmLi9fRmxojw92dLSkrVl69evH7MoqJdNJHitXbt2qFy9EDARAu9QgdNs5w9/+IM9/PDDQ62uqqpCFByiMfU7jA1Tz5waIQABCEAAAhCAAARyJ4AomDsrUkIAAhCAAAQgAIG8JrDnnnvaKaecktFGrbczmivJjEw7OEJtPumkk8bUio6ODvviF79oN998s8lyRpZgUxne9KY3mayi4uGWW24xtW0yw9/+9resgqA4HHTQQcEagrIm1bqHElRkgTcdgthJ8Ln66qvtyiuvtH322SfnZm/ZsiWrIKhC/vrXv5qu21iD3L/K/eVYQ9gX5f/mN79pJSUlYy1i2qWXhbjE0KRnQFaUYw0SBePud5ubm8daDOkhMCkEGBsmBSuFQgACEIAABCAAAQhMIAFEwQmESVEQgAAEIAABCEBgRxI4/vjjTZ94kGAkK6vpEiRavetd7xp3c2UVduKJJwaCT1lZ2bjLGWtGWTUmhVNPPXVEYSopz1jiJER++ctfzsgiMfBb3/qWHXnkkRnnpmOEhO0PfvCD9sc//jFnS9B77rlnxK7efvvtYxYF5Sb0zDPPHLHc0U7KslVub88666zRkk7780VFRfalL33Jzj333GF90Zp9M9nd7zAYHBQEAcaGgriMdAICEIAABCAAAQgUPAFEwYK/xHQQAhCAAAQgAAEITC8C1157bWDNFm+1XAS+/e1vD9aa6+vrs0ceecS03qDSx4PSTaUgGK9/Ko8lbD366KPDqpRlliwU582bNyw+nw60HuQb3vCGjCZJ/JNbyag7xDDRU089Zd/+9rcDN7Fh3EhbsYmGuMWaxLne3t6c75U1a9ZkFQQlwr72ta+1Aw880Pbbbz+TGPbggw/a3XffHVyLaDtkIfef//mf0aiC3j/55JNt1113tbvuuivgcvDBB9vLX/7ygu4znZtZBBgbZtb1prcQgAAEIAABCEBgOhNAFJzOV4+2QwACEIAABCAAgQIkcOutt2b06rOf/WyG9aDcX8pdqtYQ/MhHPmJag1BB6wp++MMfziijUCOWLVuW0TWtL5jPgqAaLDegWiswW5AAqPPx9QC1jp8sSRsaGrJlDeJlQSmrwmg4//zz7dOf/nQ0KhCWjzjiiGFxSQdyWynrtqQgC93LLrssw/JNAuFpp51mv/3tb4N7VHklCP7yl780iaIzKRxyyCGmDwEChUaAsaHQrij9gQAEIAABCEAAAoVNoLiwu0fvIAABCEAAAhCAAASmE4HW1tYMCzGJfyO5E917773td7/7XSC6/Pd//3eGm8Lp1P/xtPW5557LyCYharoHXfef/OQniWtD5rJGpqwn4+vYvepVr7K4AHjnnXfmhOof//hHxlp2yigR+oc//GGGIBgtVOtj/vSnP7VDDz3Urr/++hknCEZZsA+BQiPA2FBoV5T+QAACEIAABCAAgcImgKVgYV9fegcBCEAAAhCYMQQkAMidpKzF/v3vf9vy5csDS6QDDjjA9t9/f5O7ut13333cPOSuUq4AJUY8/fTTJium0EJr8eLFtmDBAnvJS15ir3nNa3Ky0NK6ebKCkotBubksKSkJtnJvOHv27KDNL3zhC2eceNDc3JxxjeSKcbRQXl5uH//4x0dLlnhe11SuKnU9H3vsscAVp6y4VK+s2XT/aF0+Xat8DHFRUG4sR7Oiy8d+JLWptrY2sOw777zzhp3WMzia1ZlcVUaDrqesJ4855phh4t7NN99sn/zkJ6NJE/cl6sXDYYcdlrMILXeZO8Jl5h133GH/93//N9R0reMXFY3lqlWWi9Hwzne+MxiHwrgbbrjBVq5cGR4GrkBf//rXDx2HO9dcc421tLSEhyNu9913X5NL4JGC7m1ZWUaDrLLi4de//rXttttu8eiM4ze96U1jHlM3btxo//M//2P/+te/bNWqVbZ169bARazaf/jhh9srXvGKjHoKMeLvf/+7XXXVVcF3lb6v9NG4W19fHzxX+r6SZex4xh6V297ePoRNTPXd3dXVZbq2uofFXtdC11ljss7LUlxtGEvQ7wNdSwU9F/pe1/eOLHhf8IIX2H/9138FW53/2c9+Zps2bdJuEFTfokWLwsOh7WSODfqtcdNNNw3VFd85+uijh8bC7u7u4OUDudXW91ljY6NVV1db+Ky9+MUvjmcfOp7M3yTZrq9cN+uFHrm61vii66vvXP1e07XQ86Xvs/GGybh/xtsW8kEAAhCAAAQgAIF8IoAomE9Xg7ZAAAIQgAAEIDBmApr0+dKXvhRMhMUz33PPPaZPGGRFdvbZZweTmWFcLlsJjRINktY4U/5169YFxWhy64ILLjCtn/X5z39+xMnR1atX25/+9KdRq9eE2Ac+8IFgEn+sk5+jFp6HCaqqqjJa9c9//tMkBEy0KCehV+vTXXrppRl1aoI0uk6f3EN+9atftfnz52ek3dERmjSOBk1uF1KQ2BAPzzzzTDwq4/jPf/7zsLhjjz02OJbAGw0ShfU8Jk32h+kkWPzhD38ID4e22dyJDiXIgx2NW9F7XC8eREVBvUxxySWXDGupxM6oReUPfvCDYc+DrDiTRMGrr746eHFiWGFZDt785jePKgpKCIq3LUtxOaWTgDIWt636/njrW9+aUaWsS/X53ve+F6wj+cUvfnGYiJqRoQAiNB7m8p0l8V0vaMgqN9fwrW99a9g6srpHJWjpJRuNxdGg49Cl8I9//GOTO2GJXqMFuROWi99vfvObiUlVrsRCCdt6rs855xy74oorhr7flUnfT+95z3uG5Z/ssUFj3UjPgH4D6QWJJ5980t797ncPE+9Di2qJqldeeWVwXfR7orQ0cxpoMn+TJF1fjbdaUzZsYwg1+rtNv39+9KMfDYmeYZpcths2bJjQ+yeXOkkDAQhAAAIQgAAEpguB4unSUNoJAQhAAAIQgAAE4gRkLfS6170uURCMp9Xx1772tWD9Ob2NnkvQW/eajHv1q1+dVRBMKuc3v/mN/cd//MeQJWFSmlzboInKL3zhC4E1it7+L/SQJLpJdA0ngSeq/xIbJEpExZKRyr7ttttMopImV/MtxCfNd9lll3xr4na1R+JAPHR2dsajhh1v3rw545l92cteFqSRNZNEh2j461//Gj3M2I8KxOFJCWcvfelLw8O83cZFMLlUlYVOGOKWporX8xEN4YsPYdz2WF2HZeT7VtZjZ5111qjNlFgsC0S9ZFDIQSJLLkEv0YibxNRc88TL1fN77rnnZgiC8XSqS9+18fs1nk7CnV4IyiYIxtPre+Huu+8eJgjG04THO3ps0POre0/W1FFr3rB90a1+z/z+97+PRg3tT+VvEl1fvWgVFwSHGrNtR99tEg71m2qsYSLvn7HWTXoIQAACEIAABCCQ7wQQBfP9CtE+CEAAAhCAAAQSCWhS+x3veEfgxjMxQZZITf7nat1z0UUX2Te+8Y0sJY0crUn0D37wg5ZKpRITjnWyVJN9cukXF4ASC5/GkbIG1Lpr8fDGN77Rfv7zn8ejx3Usi5EzzzxzzEKjxBRZWbS1tY2r3qnKlGQFMlV1T0Y9cm8ZD3LXO1LQhH48hK7zxCe0GgzTSPQdKUTdb4bp5GZwOoQkVlE3vUlCQlQolOAQH3f22GOP6dD17Wqj7on4mpTZCpS17i9+8YtspwsiPi4Mj9YpWXxJmEmn06MlzTgvN85RK/+MBLEIfVePFPRCUC5WjtEytJ5pLmFHjw0S1tS3bJ4M4n2QdXySC96p/E0iq8aRXKLG26z7SAJwrkHfGRN5/+RaL+kgAAEIQAACEIDAdCGQ6TdiurScdkIAAhCAAAQgMKMJaAI2aZJSrhNlObB06dLAUu+Pf/xjxoS2hMF77713mHu8OEytOyRXeElBopU+qktrF8p6LO7CUfnCieKoq76wPK2HJHemEjclHPb09JgsE9euXWsrVqxIfINeE9TK88Mf/nDCXWmG7cqH7Yc//OHAojPeFlkW6Hp+5CMfSRQO4+mzHWuNsqTrddBBBwXuxmStKCFEruS0plQ06Bp8//vfH/f6hdGy2M+NgNw0xkPc+i1+Pi7yyW1k1DWt1va78cYbh7JpUl3PX2Vl5VBcdEdWyfEwkrvReNodebzTTjtlVC9RMIxPstbRuBaGuCCo+F133TU8PWyryfsk0VxC/IUXXjgsbS4HclMat+aVyBRfY1LuE/XSxEhBa7cmuaLNlicqlmr9V61xpvXznnjiCZMIFeciN9ayPi40UT7k85//+Z+Bm059Z+n7Sh9da7md1P2S9H0sYUbuH+MuN8Mys22jFtmyyJWVryygVYdchsbZy1rzQx/6ULAObLxMWcBlE/hk8XrGGWcE94XGdolVoavLXEXEyR4bZNkcfQY0HkbX2ZRYpnURw6B7ULy01rFejrj44ovDU8FW330SMvV9Fw1T+ZvkL3/5y1DV+r2me0u/2bZs2RKIeXJXHA/f/e53c35JK/rs6voeddRRQ/ePrEXjAupI90+8HRxDAAIQgAAEIACBQiCAKFgIV5E+QAACEIAABGYYAbkOjE6Shd3XxL/WeIq6BpS13tve9rYMkU2TunJJlbROnSad/9//+39hsUNblat88bW0zj///GAy8S1vecuwycrXvva1dsoppwzlj+5oElufbGHTpk32q1/9KqgvmkaTaX/729+CSb9ofCHty4pLn+jEcNi/cC0vibISD4855pjEaximj28l/Gj9r3jQ9X7Xu941bEJfk6tyC6gJ7egktKxH3/72t1uSBVa83Ik61iRpkjiWVL7uEVnRjhQkLkcFEvUv1/JHKjc8N2vWLNM6jNsbJMBoLcd4iLY9fk6CbnTSWeclAkZDdL28MF4uakMXo2FcuG1paQl3h7ajCZNDCXfwTtJ9Gr2fk0SN6JqNcvUXD9nWrTzppJPiSYNjCUjjEQUlXJ566qnDykwSBXfbbbeMdMMyjfNAopEEpWh/de/p3tY9FbUk1L4EsmyC6TibkDfZTjzxxBHbsmzZsuBFmviLFBpv9d1YV1c3Yv6kk8qrsSz6PX366acH4mvcckxuPLWeYTxIRIyHMN3//M//mNatC4Oef91v8iYQH0PCNPHtZI8Nevkg+gKC7rOoKKj23HLLLUGz9PskugamfmNIuNXvomhYs2ZNhii4I36TaM1GuQeNBj1baous8qNBfdZalRIPcw3XXXddIAiG6SWwHnfcccH3t14Mi4Zs9080DfsQgAAEIAABCECgUAggChbKlaQfEIAABCAAgRlEQG91Rye11XVN7MkioaKiYhgJTdxrYiguAsgKTG/Xhy4Fo5luv/32wEosGqd9WSfKWiQp7L333vbrX/86EAHVNk1mjma5klROGDdv3jx7//vfb3LTF7eykGu1bOJFmH+6b2UVoP4nCYPqmwQcTQ5rcldiw5FHHplTl2UdFr93NOn83ve+NzG/7g+t6RifoLzvvvsyxOHEAiYoUtZco617F1alSePR0mp9rWiQZcVHP/rRaNR278sapaGhYczldHV1mdxXaqI7SfyX5dBI7itlBRIVa9SA+P0h4UZjQ9S6Sfdatudq69atGf2YLqJg0jUInwFZKYcMNIaG8RJc5GJQYoxeUIgHWSHNhCALwaggGPZZ61zqhRO5pYyGQhYFo/1M2t9nn32Cl1hqa2stbukl4Xmkl2CSytMLGUnfobqf9SKOXsyIhqh1WBgvq0a5y4wHfb8oRAXBMI3K/8pXvpKzKJgPY4P6ru/LqCAY9kcvJ8VFwaRnOkyfbTvRv0nkEjwuCIZ1S4B+3/vel9FuCc56JnMJWtdSFoLxIEveT3ziExkvESTdP/G8HEMAAhCAAAQgAIFCIYAoWChXkn5AAAIQgAAEZhCBqBVL2G1ZecUFwfCcJu9lwRJ/u15CS5IoGLdAUDmaoMwmCIb1SGiQYNne3m4SCScinHDCCYHwFW2TLDIKPVRXVwduUr/+9a9nTAxG+y4umggVpwsuuCBxAj+aPu42VNafspobKci1mdwYRvOuWrVqpCyccwJJ61ZFwUiEG4vVR5hXk70jhbvuumvYaV3j0DIoeuJVr3qVXXPNNUNREiElNiQFPdPxkDTeyIpNIkRclIznLS4uHvW+i+fZnmNZvEXdhIbWf7IYCsP+++8fuO+TxYyC0kgIkPvFeEiyPoynme7H+t7QPZItyN1iPCAsWODGcyJEQYlc2ULc9aXSye12PCStR6oXBGRZOlKQC2mNM3LVPVrIl7FB1phJIelZTeKSlDcpbqJ+k8RftInXpTVb42LmWH77yG1otpD0fZB0/2TLTzwEIAABCEAAAhCY7gQQBaf7FaT9EIAABCAAgRlIILreVdj9bG+ch+flxjMuCsqqIykklf/ud787KWlGnNY92p4gIUXuUfU2eyg6yMIiKgpqvcOZELQG2Gc+85nA9ZzW6xrJnZvWf5Jb1Z/+9KeJQm/IKz5pf/jhh49qzSZrqZe85CXDREFZshGmnoBcxmoNqpFC6EovTPPKV77SSkpKwsOhrawCo6Kg7g1NDEvcj4ektQZl0Rhdp1B5JArG1/CKlxUejyZGh+kmYitrt6goGFoERp8HCYetra0WioIaHyUKbtiwYVgTlK5Q182LdlSin8TbbCFckzF6fjQxOJq2UPblrlcWp/q+0n0h18FxK9yxiDkhl5GEuyRXpEniXJL4lc2ld1hvuN13333D3RG3+TA26DdC0rilhs+ZMyewdo92IptFdDRNuD9Zv0mytTesd8899zS5+wzHI8WP5WWcqMvVsMxwG3UxH8Yl3T/hObYQgAAEIAABCECg0AggChbaFaU/EIAABCAAgRlAIDqRHXY36W348Jy2Se7usgk7UYuwsIzRJrDCdGPdPvDAA8HahuqTJryiE/dybSaXbHHxMslyZ6z1Tqf0EiHkGlZiqCw35KY1KWhCXhO+Wicq26RnlK/K0KR/XERKKjsuxCYJx0n5JipOa1zF3ciGZWuNpGjQBHGS281omvjzIoEj7mIzmn48+xMtHElEG826RCJAVEBXu7XWaFKQIBwPsjJMetbr6+vjSQOxLCltRsI8iIhPkIfuA6OT7HLJGnWFqDFJ91KYNuyGJutnQli4cOFM6OaY+igBUOOlXtDQ95Lun9D9rAqS+Kz7Ixqn+KR19xSfLWjMH0mQVT4JO6OJsPF2KF/SbwHFx0P8mYmfD4/zYWyIfweEbdNW4/Bpp50WjRpxfyp+k+j6Jr2oEW+YxteoKJj02yyeR8cqP7oOZVKaXO6fpHzEQQACEIAABCAAgUIggChYCFeRPkAAAhCAAARmEAG9tR6f9Jd4NpoAoTWg4iHJXZTK15pk0aDy4xZB0fPj2ZfrxMsvvzxx7cKwPFnz3HPPPeHhjN/Kcueyyy6z8847z37yk59kuBYLAen8nXfeOWRpGcYn3Tua3B7JAjHMG9+G7hfj8ZN1LIstfXIJmpjXpOhYgixctV5Tvob//d//tUMPPXTU5iWtpSiLo7i1W1iQnu3Qak5xt956a+IEepJlksqcLqJgfP3D8MWC6IsRcuUqS8EwhC9fxIWVsd5bYXnTbat7gzBIoL+/P3jZ4jvf+U6G4BdlpHsmvG+i8Rp7xxKSvq/Hkj9Mu3bt2nB3aJvrOCqLx1xCPowNE8FrKn+T5Nre+Msruh56cSFpndTotcq1/Gge9iEAAQhAAAIQgMBMIoAoOJOuNn2FAAQgAAEIFAABTU7GQ5IrqHiaJFEvyV1UUvnxsrb3+De/+Y2de+6521vMjM0vCx65FX3f+95nsqDTZGY0SMSQtaDWmYyGiby2oWvXaPnsj42A1gXTdYyHG264IUOgvOmmm3ISBW+//fZ4cXbqqadmxGWL0L0k971a0zIakiank1wTKs/JJ58czRrsS0ROEiwzEk5SRNytcSiSRi1eJSa3tbUNtSB8aSJMG54YzzqQYV6204+ABD2ttXnddddNu8YnWShO9NidD2PD9opg+fqbJMkKU9d0NFFw2t2oNBgCEIAABCAAAQhMMQFEwSkGTnUQgAAEIAABCGwfAVkExq17kiwT4rUkWXYluRFT+Zocj5YpSyK5KctFfIzXGz+WleNIgqDqnj17tknAktVOtB3xsmb6se6Dq666Klgv6eqrrx6G4957780QBZOu7bBMYziYiHthDNUVZFLd50cccURG32TZF7dalNtYuU9NmoAPC+jt7bUbb7wxPBz39r777rNjjz12WH5ZqcaDBMSTTjppWLTuMVkAx8M///nPHSoKxrmFloJRd7oaD6PuGJ955pmgG3H3xRqjCDOHgF6wyCYIahyU4FxbW2t6/vRCRtTydkdTShLLJPpPZMiHsUHrBo435PNvkubm5oxuYcGbgYQICEAAAhCAAAQgMGYCiIJjRkYGCEAAAhCAAAR2NAGtsxd3qymrP01MZgvxdbGULpvFy957750hxmntpH333Tdb8TnHa4I1Hl75ylfahz70ITv44IMz1sHp7u62z33ucxkiSbyMsR5rbahCCFqX6L3vfa/FRcFsaw/tt99+w66tJrWTRJzR2GzPxGRPT89oxc/o81rf8PTTTw9cxEZBXHnllcGzEI2L7mstrIkIsjaMi4L7779/RtFa2/Kcc86ZFi5E46KghBvdh6EoKKGvrKzM5DJRz4TEwSeffNI0TsRFnqSXKTLgTHFEKpWa4hpnRnXpdDpYzzXe27PPPtve+ta3Jt77cu/4hje8YejeiuedyuO4hazq1gtC+o6fqJAPY0OSJ4Rc+5cvv0mS2hu3UtbYNNLvvKQyiIMABCAAAQhAAAIQyCRQnBlFDAQgAAEIQAACEMhvAkli3v333z9io2X9Ew/ZLF722muveFL71a9+lRE3nggJCdFw2GGHBWvjHXLIIRmCoNJVVlZmTMpH8+eyH3eFqDzZXB/mUl6+pdHEb3zNtFDsiLc1fm0lfijuhBNOGNPnxS9+cbzorMfxSUyJLIUiymbt9HaeOOusszJKkPCbtEZYmPCuu+4Kd7dre8stt2Tkl/VikhD8gx/8ICNtPkZIaI2Hxx57bCgqKpJIOFfQsxFaCw4l9B25792Robi4OMNqO9vzviPbWQh16/rH2eoFlk9/+tOJgqD6LOEmLubsKBbx7wW14+mnn57Q5kz3sWFH/CaR2DxakNvaf//738OSiTUBAhCAAAQgAAEIQGD7CSAKbj9DSoAABCAAAQhAYIoJJIl5cUuxaJMkwPzoRz+KRgX7ixYtyohTxO67754Rr8n/bNZnGYmzRHR1dQ1zz6dkr3rVq0wuB7MFWcD86U9/ynY6p/gkQWDNmjU55Z0OieRqVW7roiEUNqJx2t9jjz3iUfblL395UkW6JGuVfJk0z4CRJxF6xk855ZSM1shaMFuIPydas1BuZEf7nHfeecOK1L0UFw70jMp6MR6uvfZae/jhh+PReXcsoUafaIi2O/pcRPcffPDBaJbAtfJI49WwxJN4sOeeew4rXS4QJ9ot5LAKZuhB6GY22n1Zto8UdF9F3dCOlHayzyWJgtdcc01O1T733HM5pZvOY8OO+k2i51Wi30hBLpfj7tPz0Up5pD5wDgIQgAAEIAABCOQrAUTBfL0ytAsCEIAABCAAgawEjjnmmIxzWt/rL3/5S0a8IrQeWVw0Uvyhhx6qTUY4+uijM+IU8eEPfzhwqZd4MofIoqKijFRJa+aEiTRpdtlll4WH494mraskASVpwnfclUxQRrmF/f73v2+5Tsiq2iT3Zy984QsTW6RrGxdHxOITn/jEpAmDcdeNathEWZ4mdrJAIuWiMB5+8pOfWJKgrXXv4qL9K17xisCCVMLASB8J8/GQZHV46qmnxpMFx69//etNQsNok9yJmacwMv4yRdR6OmqBE30pIppGTY1aFE5h0zOqigqX4clvfOMb4S7bCSKQ9J0l96DZgsTAiy++OON0UjkZiSYhYt68eRlW5Bonbr311uCTrUpZst1www3ZTmfET9exIem6TMVvEt0nf/vb3zI4hhF6ket73/teeDi0HYuF/lAmdiAAAQhAAAIQgAAEMgggCmYgIQICEIAABCAAgckg8Mgjj9i//vWvMX/0Rnk8HHDAAaaJ+Hh497vfHUzOh2KX1gH8yle+EnziaT/+8Y/bnDlz4tHBsQSECy64IOOc2iIB4YorrrAkyxRZrMkCTP2MWy2pMLkCjU64K+673/2uiU08aC3Bz372s8H5pHNyq6XJu1yEiPLy8gzXh3Jh+f73v9+WL18+VLzWGFO5v/zlL+3SSy8dip/KHYmCX/rSl+zlL395cI0l6D7xxBPW1taW0QwJvZqATrpWBx54YEZ6RWiSWFzjQS7UXvaylwXr2MXXUAvT6r6SSBLeX2H8aNskaxXxFWdd5zCoXInb3/zmN200d7hhnkLeau3Q//iP/8jo4ne+852MuLvvvjsj7qijjsqIS4pQPXHXoLfddltGUlkWa9xICroH9Tzp+snFafy5zAfLqbjbz7///e9DXdl1112H9qMC4Z133jkUr53ouWEnpvggPo6qeo2luj4SfeLuecVfbjAlHs+EoLFkPN+3yhMV/eIWmWL3xS9+MfjuiXPUur1vf/vbM9b7VTq1R9a3U/0caM3Zj370o/Gm2rve9a7g87vf/S7jWdULKZ/61KfspptuysiXLWK6jg076jeJOGot4N/85jfDvgMVr+96jaXxl7z0Mo/WsSRAAAIQgAAEIAABCGw/gSL/B+vIfhu2vw5KgAAEIAABCEBghhGQlc073/nOCem13EDefPPNGWVpgve4447LiM8lQpNLEnfia71F82pS+bWvfW0g/kXj4/uhmCBxJz7hKYEtXse5554bTITFy5FLNvVVk6dPPvlkMKEbptEEuNx8JVk7Ks1Xv/pVe8tb3hImT9xedNFFiQKjEouH+hF31aV2VFRUJJY3WZEf+chH7Le//W1i8WqnRAlN9GpyP5t4p75oXTgJgElBViCaXJRbyZGC1hpU/1tbW4exkdD8tre9baSsw86pnS960YuGxUUPwnso2p+Pfexjds4550STZd2Pr7F50kknWT5aTcnyLzrRfuyxxwYibNaO+QmJFG94wxsykkgEjLqSe8973pMhxEuEKCsry8ibFCEXotdff/2wU0nPr8YFuTWNut4clmnbge5VPc+9vb1Z79WxWMMm1THWuAsvvDArb4nxoWioFx5e/epXJxYvQShpbJfrzmyW12FB8fFR8eKUFG6//XZLcnscptU4qfsnqcwwTdJz9aY3vSnRkk1jX9xCfLRnMInT+eefb7oXpzroJZnR7slc2yRX21EXofvvv38iZ7HUizUtLS0mV4/RtQf1Usajjz6aWKWu+f/+7/8Gz0eYQONjdPzTWrvx5zFMG27j7Tr++OMtyY24XnY54ogjhpUflqGt2qN7Rd8tEvTjFsfRtCNd38kYG9Se6LVQW7Ld8/Fn6ZJLLrHXvOY10eYn7k/Fb5L49Y02RO2WBbK+r/Uil56rpKAxWt4akkK8/Im8f5LqIw4CEIAABCAAAQhMdwJYCk73K0j7IQABCEAAAjOUgISyXEWTOCJZosXFungarRP0ta99LeukdZheE5n6JE3UJbk5/OAHPxhmHbbVW/GyEPvFL34xTBBUIk34VlVVDUsfPUiyooue1/6ZZ54Zjxo6VtvjgqBOZhMhhzJOws6KFSuylqp2aqJZk9/RCeR4Bq3/mE0QVNri4mKTSJpkwRctS5PDqi/OJn4czZO0rwnns846K+lUEBfeQ9EEY60jmreQ9g855BA78sgjM7r07W9/eyhOgnzcMlcT6bkKgipIVqLxELWkC89pXPjpT38aiAxhXNJW96qsPUe7V5PyTlZc0tqWYV1RF7dxN6NhGm3jAnR4Tu+Zqs8jfcK00W229KO9tzp//nz7zGc+Ey0qYz/puYpaRmdkICKRwBe+8IXEeIl2cvGo76yoIKjEI413uuZaK3eqgizlk6zJw/rVHo23soqNCoKhqBymG207GWODvA/En5Fs7YinkxiaS9gRv0n0wk0Y1O7Qu0I2QVDf1aeddlqYhS0EIAABCEAAAhCAwHYSQBTcToBkhwAEIAABCEBgxxGQJceVV145qnAXtlATS3ITefLJJ4dRI261Lp2EgXe84x0jpst2MkkU1GTY5Zdfni1LRrwEEU3a1dXVZZwbS4Ssbj7/+c+PJUvi2m1jKmAcibds2TKOXINZdH3lWjKXdYdkFSJrJN1DYw3xCfBc8stVWnQidLQ8I4mjo+UttPNJ1iE/+9nPLGSU5Go1ad3RkbjIkige5Ao0KTQ0NARuij/0oQ8lnc7buGwiuO5LWd+GIbScCo+j25EEw2i6qdiXte9YLHbVJkTBsV8ZrZc3lu9ACYJJ63SOveaJy6HvfFkRxq3pstWgZyXp+3K0l4mm49iwI36TzJ4922ShmEuQBe+NN95oYkuAAAQgAAEIQAACEJgYAoiCE8ORUiAAAQhAAAIQiBDQm/lTFeQeS64i5eoz25v9msjW5LHckOYiGEXbrokoWRZKTJT1UbaJ9Wge7WvyUa4Dk4ImKCVIjSROKr/WNdKaeuI5kqVgUh1JcWeccYbdcMMNo4pTqluu+XbEJJy4yPJEawrlKkDIVZ2sLOVS8sQTT0zqemKcmMraVO5u5QovV9FuPFYuEmU1sZmLa0G1Q+7Pcg3xie7tFZBzrXeq0kkYP+iggzKq03pUCnLzGQ8vfelL41EjHstSLr5O3QMPPJA1j9zKfuITnwjcEGvNsqT2JWXWvSrRJMnNYVL6iYyLWgNGy5XrvniQa8akELoYjZ+T9e1EhlzGOwmZcuUrV5QnnHBCToKPrAfj6w2q3dXV1RnNjz9XGQnyKGKy3TzrO1BCvCx3swWNW1pvVy42tVbdWEK29X3HUsZoaeVe9I9//KNpq2ub7frqu+/aa69NvE+y3f/RuidybEi6L6N1jbQ/moAZzbsjfpNo3JQVarbfVBqPlebHP/5x1t92YR+m4v4J62ILAQhAAAIQgAAECoEAawoWwlWkDxCAAAQgAAEIDBGQpZnWwpNLKr2NrgnvbJN/Q5nGuKP1/bQemCwBNcGsNeo0EahPY2OjLVq0KOc6w7UCtUae3H1JAJSbPwliUXFVa+2E5xUf/YzFTWLY1fb2dnvmmWcCN5xqgxhJBAzbX1RUFCbdoVtdR13T5ubm4CN3anItKJFNLgQlBMtt20QFXc/169cH11frpIm5JrjFR5OXujbj4R1tn/qgtaLEX8Kx+lNfXx9Yg+6666453zvRMtnf8QS0tpqeY231fOm+1MS8nil99Hzly3O142lNfAv0XGkdSbk91rihMVLPswRGCeUSdORWOGoVOfGtKPwSt27dGoxf+v6TGByOjVrjM3p/L1u2LBgr9b2oMTPc6rpM5Jg9HuKhe1r14YknngjcmerekHvccC1LvWhy8cUXDyteL63EXx4YliDLwXQaGybjN8lIa/7pudVvtg0bNgRjp+6jffbZZ1QX71lQEw0BCEAAAhCAAAQgkAMBRMEcIJEEAhCAAAQgAAEIQAACEIAABCAAgZlBQG5T466JJXSO1QpyZtAauZcjiYIj5+QsBCAAAQhAAAIQgMBkEJhYPy+T0ULKhAAEIAABCEAAAhCAAAQgAAEIQAACU0DgtttuyxAEZZWOIDgF8KkCAhCAAAQgAAEIQGDSCSAKTjpiKoAABCAAAQhAAAIQgAAEIAABCEAg3wnce++9dt5552U087/+678y4oiAAAQgAAEIQAACEIDAdCQwcQuwTMfe02YIQAACEIAABCAAAQhAAAIQgAAEZiQBreu6YsUKe/zxx+3uu++2X/ziFxkctG7iBz7wgYx4IiAAAQhAAAIQgAAEIDAdCSAKTserRpshAAEIQAACEIAABCAAAQhAAAIQGJFAV1eXnX322dbe3m4dHR3Bp6mpKcij41zC+eefb/X19bkkJQ0EIAABCEAAAhCAAATyngCiYN5fIhoIAQhAAAIQgAAEIAABCEAAAhCAwFgJlJaW2h133DHWbEPpTznlFMN16BAOdiAAAQhAAAIQgAAECoAAomABXES6AAEIQAACEIAABCAAAQhAAAIQgMBwAmVlZcMjcjxasmSJXXTRRXbUUUflmINkEIAABCAAAQhAAAIQmB4EEAWnx3WilRCAAAQgAAEIQAACEIAABCAAAQiMkYDWBMzFVeiBBx5o++23nx100EGBdWBFRcUYayJ5EgHxD122Jp0nDgIQgAAEIAABCEBgagkUDXiY2iqpDQIQgAAEIAABCEAAAhCAAAQgAAEITD6B6667znp7e628vNwk9IVin7aVlZW2cOFCW7x4scnVKGHiCUgQTKVSQwXLenPevHlDx+xAAAIQgAAEIAABCEwtAUTBqeVNbRCAAAQgAAEIQAACEIAABCAAAQhAAAIQgAAEIAABCEAAAhCYcgLFU14jFUIAAhCAAAQgAAEIQAACEIAABCAAAQhAAAIQgAAEIAABCEAAAlNKAFFwSnFTGQQgAAEIQAACEIAABCAAAQhAAAIQgAAEIAABCEAAAhCAAASmngCi4NQzp0YIQAACEIAABCAAAQhAAAIQgAAEIAABCEAAAhCAAAQgAAEITCkBRMEpxU1lEIAABCAAAQhAAAIQgAAEIAABCEAAAhCAAAQgAAEIQAACEJh6AoiCU8+cGiEAAQhAAAIQgAAEIAABCEAAAhCAAAQgAAEIQAACEIAABCAwpQQQBacUN5VBAAIQgAAEIAABCEAAAhCAAAQgAAEIQAACEIAABCAAAQhAYOoJIApOPXNqhAAEIAABCEAAAhCAAAQgAAEIQAACEIAABCAAAQhAAAIQgMCUEkAUnFLcVAYBCEAAAhCAAAQgAAEIQAACEIAABCAAAQhAAAIQgAAEIACBqSeAKDj1zKkRAhCAAAQgAAEIQAACEIAABCAAAQhAAAIQgAAEIAABCEAAAlNKAFFwSnFTGQQgAAEIQAACEIAABCAAAQhAAAIQgAAEIAABCEAAAhCAAASmngCi4NQzp0YIQAACEIAABCAAAQhAAAIQgAAEIAABCEAAAhCAAAQgAAEITCkBRMEpxU1lEIAABCAAAQhAAAIQgAAEIAABCEAAAhCAAAQgAAEIQAACEJh6AoiCU8+cGiEAAQhAAAIQgAAEIAABCEAAAhCAAAQgAAEIQAACEIAABCAwpQQQBacUN5VBAAIQgAAEIAABCEAAAhCAAAQgAAEIQAACEIAABCAAAQhAYOoJIApOPXNqhAAEIAABCEAAAhCAAAQgAAEIQAACEIAABCAAAQhAAAIQgMCUEkAUnFLcVAYBCEAAAhCAAAQgAAEIQAACEIAABCAAAQhAAAIQgAAEIACBqSdQOvVVUiMEIAABCEAAAhCAwEgEBvxkkX+0HfwzGDHgEYofCuFBkHAwNpq3aNt55csWlCY8HxaXLW22+HjxYZnhNswXHmetJ6EtQX+UIazE97PmDytiCwEIQAACEIAABCAAAQhAAAIQgAAEIJBBoGjAQ0YsERCAAAQgAAEIQAACO4RAml9mY+IugVBiIwECEIAABCAAAQhAAAIQgAAEIAABCEBgZAJYCo7Mh7MQgAAEIAABCEBgUglIA+QVrfEjjvJDIBw/R3JCAAIQgAAEIAABCEAAAhCAAAQgUPgEEAUL/xrTQwhAAAIQgAAE8pCAhECMAif2woQCoSwHMR6cWLaUBgEIQAACEIAABCAAAQhAAAIQgMD0J4AoOP2vIT2AAAQgAAEIQGAaEQiFq2nU5GnX1MDycpsqiDg47S4fDYYABCAAAQhAAAIQgAAEIAABCEBgkggUT1K5FAsBCEAAAhCAAAQgECEgMVDrBeIqNAJlEndDzuF2EquiaAhAAAIznsCAD7btTVutr6fHVj77nPV0pOyph5fZ1s1brb+319LBF+AgJqUdNWz70ly/ep2l+9LWtG6z9ff3W3eq279LB6yvL+XfpwPW4/XtqKD6B9JpS6V6bPXGjbalo8v+cP999vSmTbZm02Zrb2sL2phTf3dUJ6gXAhCAAAQgAAEIQGDGESjyH6g5/CKfcVzoMAQgAAEIQAACEJhQApoPJewYArIWlEtRAgQgAAEITA6BdG+/9Vq/lRW7MyIXygZ8zJV4p1BWVjZYqQ63cyzu6uy0isrKoJii4vx5xzk9kLYnnltuG10YXDCrwTq6um2/RYut3PteqnZGvoQkZJaXlw8y4S8EIAABCEAAAhCAAASmmADuQ6cYONVBAAIQgAAEIDDzCCAI7thrHkxL+5/InOyObRC1QwACECgUAj629vf1+fhaZBUlg0LXQHFRINqVxAfd7RQEhayqujovyRUXDdjui3e1NY/+ny2Y3WgDs9Oujbo4qh8AJZkd17vZYkaAAAQgAAEIQAACEIDAVBPAUnCqiVMfBCAAAQhAAAIzigCCYH5dbp+rztuwIyaJQ6chTE7n7W1BwyCQ1wTkPlPmf0VTNLj29/dZSYksBCffSnCs46NLgLZ282arr66xmopydx3qXPw7R+NrOt3v7d72TraOe11ILS2xYoTBvL6/aRwEIAABCEAAAhAoRAKIgoV4VekTBCAAAQhAAAJ5QQBBMC8uw7BGBBO0w2Ly5yCcgA5bFBfqxiIaZisrHh/WFW7jdYbxSdtoWfF84bl4fFI5xEEAAvlJYLTnWOen/hkPbL8d2ChveCiZW++l+3vcjalb7fV2W6lbMhYVV7jlXskQcCUblBddqOvrcqGuwtJedteaR6xj3ZPWeNCrrbi8VkWNWmWvr3noEyxB8b3uTvV5F6FF1tPbYx3tLVZZVmwVVbOtONKGocawAwEIQAACEIAABCAAgSkggPvQKYBMFRCAAAQgAAEIzDwCCIL5ec21xNV0MMzY3on2seYPJ/+jV22sZeSaN1rX9tQRrY99CEBg4glkez71DMs1ZvEOWdNvFDEwgkFtlPVer7s37U2lrK91pXW0rLPG3Q8PUhW75V5pRY319w+m69r4nHU/dZsV182yvs5Wq9ztKD/nQp9b+fV7n0tDS79IHdHdMhf6wvGtvHxwqkXH+s5R3jqV291sPalOq6yui2ZlHwIQgAAEIAABCEAAAlNGAEvBKUNNRRCAAAQgAAEIzBQCEp5kVEDIXwJT5OluTAAGJ48zJ7zDSWZtw4n46GR8eF6VZZvEDxsSplW6cD88F26TylDakfIob1K+sMzoNlpvrnmi+dmHAAR2HAG57+zpSVlVVc12NSLtlnTuXdNK3IXmVIS0j2GdXe1uqVduKW+/QnlpuW1cv9kt94ptduM8621rtuKBLutqes5KqhtsoHyWlZZXWlFJtZVXen+LiifE3eegC1T1O3O8DxrGHwhAAAIQgAAEIAABCEwiAUTBSYRL0RCAAAQgAAEIzDwCEgMlChLym0A+uxENyUXFs2hcNiEtKV5lhIKezkfLjKbPFh+tV/thWWF8dBstLxrPPgQgAIGQQDiGpLpSwTqEZW5RV+RiWzgGTdY4Enw3u8Wf6gpf23Gvoh7S1tc34O48i6xz3aNmqU1WXD3fukurbWvTWpu9YA/raN1sdfVzrKe/1BpnL/C0k7+eYciLLQQgAAEIQAACEIAABCaaAKLgRBOlPAhAAAIQgAAEZjQBRMHpc/nz0VpQ9MLJ8VxIjjSBHi9HaeNx8Tqi5Y2UVueiaaP70TKjZWRLE03PPgQgULgEAnee7tJT6/O1NrdZdUONr/nXbxXVFW6BNzVCmywGVadCibv7XL9inVXXlFufWw8WpTuscnadtad6bdPmNfbk+kfMyipsweyFNquy0qrL6622YYHVVta71bZZZbmvT+hhtLEtHAdHSxcUxh8IQAACEIAABCAAAQhMMgFEwUlGB8XKAABAAElEQVQGTPEQgAAEIAABCMwsAqwlOH2ud75ZC4YTx1GC4SSyzkXPKz48F02f6368rHi+6Pn4ufB4pPqT8o+UPiyTLQQgUJgEfAQLDPQ62zrs6f97ylrXt1hPb58tPWB3W7j7Lu6is9/KK6oHxzV51dQbNh7CcWPAzfoGrfx8lcDgpYSxux0NxiX3Wbpxw8ag7LmNs23tU2ts85p1Vj+vwebMLvf4El9TsNpSA91en9dZWWZPLX/CKlxAbGlpsj133dv6eiuspnKOzZ8zx1K9vYFL0iKsBwOm/IEABCAAAQhAAAIQyH8CiIL5f41oIQQgAAEIQAAC04QAVoLT5EJFmplPwuBIQlr8XDhRHulKxm40T5g+Ghdm0DlZ8ChoX594ujBO8WGaMH/SNpo/zKstAQKFQCC8v7WNru9ZCH2bjD50d3b5un2VLqb1WfuWNuvt7LWnHnrC2rZ0WHF5ic3dZZbtdsBeNtuFud6ePncrOuBrDZZZe/NWKysrtrLKKl/bzwW7YAzRWDVOq0L/ku7a0uS632D+VE+/rXx8le132IHWu3WrpbY8bsU1cy2V6rcer3+gr9e2WpvNqplnA6VmtdX1NtDVb888/azts99B1tPX50JmmVsaVluJLB23jY8jM9QvhfGPhRJXfZQeuQrOQgACEIAABCAAAQhAYAQC/tOWAAEIQAACEIAABCAwEQR8PpAwzQjk69RqXECLH4+GORQt4vnix2E5Yby2Yd7wnLZhnM6HaaPnw32lC8+HW52L7odp2UJgOhEInwG1WfvRe3069WOq29rvrjrTvQPWXdRt/ak+e+6x5bZp3WYrKy212fNnWYULfhvXbLD6hg3W2t5qst4rcqu8qpJSq6ytsratG829e7r9Xvk2KWxw/cFxjSk+4JfPmmWp1q4AQ7FbH5aUlljrxi1Wku7ydnXbgn3rXYjst1R7hw2Uu9jnQmZ3V481zJltZT3F1trdZQt22cVSPd1WU1Pl57qtx9uqtRF9eULPOzjFEt4v0Xamenqtt9d5+P1TW+suU8cobvb393m7vc0lsmgkQAACEIAABCAAAQhAYHwEEAXHx41cEIAABCAAAQhAAAIFQEBCbqEYsIWT0NHLEo+LTlBH0yk+nlbnFadz2fKFaaJlKU9Y1kj5onnYh8B0IBDe12orFoI5XjEfDyq0Zl+fW9/19FhpVbm96KhDrSeVslXPrrTGxY02e+dZ1u9CXGtqo1X3VLho12mbqnusv9PrKE9bXUmjDbhv7gE3xivezgE73Z+2supBUa3UXYPOaWyw2roqW3X3fdbyzBNW1dZllXsudhFwvhW5cFmfqrLi+krr7e4N3IrOra7xtQdLLF1SZFs2bbKddtrJ+r2PxZIsIwaM8bGvo6PD/rVsva3f6C5I91hiPZ0ddtgL9rB+d1EaWBmOglNC4tbmDTZrzlxPuX3WhqNUxWkIQAACEIAABCAAgQInEPnZWuA9pXsQgAAEIAABCEBgEgn4fB1hGhLIp8sWim/xyeRcsEbFCqVPKiMpbrSyk/KEol98m63e0ergPATymYDuc7nXDZ8xPRMSBJOejXzux/a0TS4r9R0nYS7VMWhll2t5GmN7fN09FdDmYl+pr+m3ZWuztba1WGlZmTU1NdnjKx6z1v5m29zaZOu3rrayWVXW1d9tD634h82pnuvWdZ7fLeTSbnUYXofn688cxdNZFvdVP1rdJWm7r2uoT3lFhc1dvLM9ev99VjF/ntUedbTVH3WkbXYLwrVr11pbR6f1uyjZ29JupaXF9uTKp3wNwX7vR5s1rd9kVbX1lpZI6U0oNrUtbbKMTArVLoZWVwzYgfss9HS9tn7tervt1jvM+ty8MKO9mX3q93uwYc7OXp/e685XG/eknhMHAQhAAAIQgAAEIJBvBFhTMN+uCO2BAAQgAAEIQGBaEsiY05uWvZiZjdac7nSeYs2cJB9+HbOJF9F8YZqkOJUWjR9e+shHYbkjp+IsBPKbQNL9H8bNBIvBPl9b74lrf2yNB77QdnrBoW695y48JY6OctnaWtqsurba3Yb22poVa23O4nnW056y0qISX2Owwvqs15rclejmts3W3rfFOnva7YD9X2jNLU222a3wqqrckq+61BbucqC7EK20kvJaF2TLhmodcCHOByfbuulZmzVvr2AgT6d7ra+/2Mq3ufEcSuw7KbdULHchUtaCChqf+jq7bdWdd9nCg/e3/tJye/ihB23W/AWersT1OheD00UuXha5y84K27hqrdU1NNqeB+7h6w76eoOtW6x+TmPgDnRWXZ11dXVadXW1199vpe5S1E0KvRYJfCX+SduGjS024HXPXzDX29DnYmmPVVZWBmJnqdetoPsq+D56/k8Qzx8IQAACEIAABCAAAQhMFAFEwYkiSTkQgAAEIAABCMxoAoiC0/fya+5VwmAhhFCoGE2MU7pg8tk7Hk0b5heLaPxIbMaTZ6TyOAeBfCQQ3ufhVm2cCYJgb6onsIJr37zJaue5q0wXvMorq32AyLxKsqiUG003BvTxI22pTncX6uLamieXB+JZm7vQLHdXorUu8K18ZrX1uMXc7J0aPK7Clj/1tK1cvcLay5vt+MNfY/c99A/be8nutmThLlZe7i4+qxoCMa/Y19NT1bIeLHHhr3ntChcZl1l390abu/ANHufuPotlTZddsgzt8NavWWWz3U1ob3ePbV632urn+nqDqW63Ciy1zU3NNrC5ySo8rqax0dc73GKlfeU2b94C63NrwYraSkv19VhVTbXLfbJ+bLU5vgZiZVVNVhensmBUq9JuUdjuLGpq3BVpsWC5eOjQdHbDM7+zBXu82ssI1w1Ub3MJ3it1rFC+zHLpMmkgAAEIQAACEIAABMZFoORzHsaVk0wQgAAEIAABCEAAAkMEwknGoQh2pg0BTbnm8zxqKEJIpAv3BTcU7aJxIfTwXHisbTxdUppo+nA/Xm8Yn22ba7nZ8hMPgXwiED432uqj+1ufmSAI6jqUlJS45FVk5bMafL/MRbdtlnoZWtWAtbe3uzjXGYhenS4Irlu5wuob5gZuOivra62ktMT6026tV1k16JLVBbF5Cxtty5pmW7NstXX7en71dQ3WlNpgZaqrpNqKZdnX1mM1tQ3W1dZkLe2bXSSs8rJ8ncJuFxndvWeqc7Olu5+1VHeJtTUt83ULl7iwJ+u8cJwM1LLgOIhTvH9q6+qtqKTY1zpMuVJXFFg11tY3WMuWrS40pq10dr31daVdOGy0iopqq3N3ocVeVGd3t3W4+9FUV69VePv6PE6WgVVVlS7m+UGRS39+rwR1BZx87JYg6P3V2opFxSXebombKb+XvJ39rdbS5i5KBzZ4+W3W09VuZRVeV7GEwQzQg2Vv+9KSSCu3pbon129aY3U19cl5gtbwBwIQgAAEIAABCEAAAv4L0/9hM/hrFRoQgAAEIAABCEAAAuMmgKXguNHlRUYZa+RziAoSYTs1CRz/Ka+4bCGaVvtJaaP1REWPaN5s5Ss+qcyR0nMOAvlOIHwmwnZGn4swrlC3wUSBf7kF1mzRTgZTCD7WbBtuxKjf3WF2tLcFrjC7Uimr9PX6KmvrXFwrcgHQBcPOTmt3sa1hjlveVVX7cZs9+/jTPoYVW6cLbOmufuvu77LKuUXW7RZ4dY31tnzdM1ZXWmOvOPxEM9ciKyvrLN3rAp67EO3r67aqinJrXnOHVTXsYVXV812cKwtEu3Wrn7NFu+871OKMcWnbDIjaXOyip4S1dLrPSt3C8Mlly2zA2zZv54XWtHmLNcyqc8vAAaupqw361tmV8rpqbMCVQFn71brYOcutHft70y7kVXhZPS5olgeiZ7ELjp3eb1kEKqhaIZNFpewC+/q8zkBkTVtPd5sLlKXuzvQ56+3YbHULjvYU6UCIVd5oGBQBBy0hu7q63NWpVjRM20NP/9MO3edlPg7LXnPwRZCMvkcLYh8CEIAABCAAAQhAYEYSQBSckZedTkMAAhCAAAQgMNEEEAUnmujUlScdbdvc9tRVOoaaQlFCk7vRCd4koS56PlqF0urcaHl0Ppiw3lZXtvJUdrSskdJF28E+BCCQnwR6Up327MpHbNelL7BKt8QLR0UJZrIWjAZZvWnc1J9wHGhr77S//+NBq/c1AHfZeWc/mba5sxutzNcOHBjQ2n8l1tq00Tau3mgLlvp5N7nbsrHV1xasdledTdbR1WFzXFzrduu73iIXCV38a5hTbxXm1nqdXbbbXvt7fQMuorkYlpboVWQ9Lh5u2fKUWx7uarNne5vTpe5W1NcsdKFNL3o0b/ynzZ53gFsulrvlYGW0Cxn76mdvd8odeBZbx9YWK3K3pxW+pmFft7dz81Zbv26NNdbPtY2+3uHue+9ptQ311pNqt2K3WEz72oD1c+cGloA9LvitXrPe1jy+zDa5qHjMq19us+fO09vYQ+O3mOnT4ZaVNVpz0cVJWWGm0ykXF3uts/lBa9j5mCBNlH2Yr7W9xa0WGwIGEqklEmrcbuvxdm5abfsseoHXpfgEQTej50RAAAIQgAAEIAABCMw0AoiCM+2K018IQAACEIAABCacgN7+97k3wjQmkA+WgsEEbjDT/jxIxSmMRXQL84SlhGJgGB8tKzyXlFbnomnDNEnlhOfYQgAC05NAOhhrJN5J7XPRbdubEhLf9L9CIAZq6wPmtqhAeNKYsLG5zX75h7utprzfDn7BATZ3br3vu5tVL6g31eWi3TzfpoLiS8vdIq63K8jbvHWLrVm5xhoW1LgYWWkdnb42n9b5c1eibe6KdJeGOS541dms2bPcLajXbaXW2dVjFeXuKnTrJnfbOeBGhM0u/PVbzay9fFvmFnfl1rVludXM3t3r3GxllY2BSBZ0IvbH5bkgRl4/uzu6rKKyIhAH+932rset8FIu3FVWuqjoIlyfi3+yJlSe4rIKa2trtcrqaqvydsuSsag/bf0aN/u8n94OuUttcxGx1NPU1tQ+3wZV6QAHBvq8b07bhb3BMOgK1ItxoVAuSUuC9Q23nQw2QWv1R0Kr3JR6kKi4pb3Z12jstuqSOhcqPa+vSVhWVm7lzmKwrudFySATfyAAAQhAAAIQgAAEZiwBRMEZe+npOAQgAAEIQAACE0kAS8GJpDn1ZeWrKDgWEqFYl0ueuBiYLU9UFAzLD7fPT2Rny008BCAw3QgEVmcuOknM03963kP3obJGy3juPe3mlhYX81J2/6MrbW5tuc1315/NLe0usFW6W8xuq6sssr322StYb89Lc8u/Tne1WWRbWjqss6PJyordTWZNnZV5neVV9S7CVVsq1WJNTa1WXzPfqmuqrctdk9bNbrBVboW3ZMlCL7fTS0rbQG+rdbU+ad2tD1pRaYW7+dzXxcNFVla9R7CWYW9vtwtrssIbXB8xfj1kxbhlS5fXIdej/d6uKl8Xscu63E1ov6/tV+wuPsvd/Wd5ta8/6MphKiWXnyVW4usClrow2ZPq9TzlzkVuQWUBOFhPqqfHrQOLXLx0S0L/b9hLFp5I7lNrqus8vYt7AWltXEDtdatBFxN7e12W7Ot116PVwXm1W1pg0Gcvt89Vw/aOVuv0/pe7WpqSW1UXMrd2bLFqd7Pa0dPmwmCZ7b1wP3crer/Nmzvf5tcs9muybU1IFUiAAAQgAAEIQAACEJiRBMJX0mZk5+k0BCAAAQhAAAIQgAAE8oVAVIDb3jaFE9DxbViuJvpDcS+eJnocTR/us4UABKYnAYl6WuNupJDqdCFMgqB/XIEKBMG+/l537xnJJ3XKg8rrTnXbU8s3Wqtb9jVUV7p7za22bmOz7brLfBf0qqxxToPNmTff1q5ZbS2+dmC/r8+3cvlqt6jrseq6Civr3WK1sxp9fcAKX6tvjlWXpa1101pL9/lxzVzrL0656LjeXF3zGrV24YALdu5i1AWziopaV+DcwrB+b6use4kLc3PdXekiFwd3sTKJcRIxi0q8G4Mintrc1tamTSSUWENDlRdf5iJfpa/z1+vp+62iKG1zGudb96rVvsZft4t03b7+YKmlfL/b3ZkW+XqBUgDL3bJQVoY9Lhb2u4lf6O6zzIVD8XPbP+sJBLvnq2zv7LBHn7zPnn72EV+bsD8QWotcZJTLz2K3fCxx9sVef7VbGAaFbMvqxfm6hmm3ZNxqy5560JpaN1plWZU1b93oPFPWtHWDW1tW2/omb3OPu1FVe73di+fvalvdklDMCBCAAAQgAAEIQAACEMBSkHsAAhCAAAQgAAEIbCcBWQZsmyPdzpImLntHR4fdcsstPhG7xvbZdx875phjrLy8YuIqGENJK1eutNtvu8263PriiCNeagcddNDghPMYypjspPlgKTjePgaWPD6JHGyDQgYn85PuyagYqIl/pQnm/sPEmnX2oHs6lxAIB7kkHE8ab0PgyVB5g4YmF7KtycHJwWbLFmkwaC/cT85NLAQgMBIBCWutW5qsbtacIcEr5dZ/FS7i6Xnb4OvmrVy91q0DG621tc2t7FLWlSq2htKUzXZhsKio1K37NthODZVWWlni30N1btnmMl3fVkt1NVnNTnu5dWCNpbu6A7ek7gnTByVfGzDV5s9/qYtZbbbTbBf7Kkqtp6fPRbu0WxZaYAXY7esgevN87b8K62h60rfujrNkNy+gwurqXDDcFoaPU8mDiWIH0nKf2m+bNqw26y2xqspi6+po97UOFwfrDJbUedvlm9OFxr7+nqANGqTKXFBMybWoWyQG443/SQdrMZYGPHS+RK4+PV5tkTVmn1sS9hT1W3m6xF2R+vmhdRuDljhbtwl0GIGouc29qPKpkOVPrHArylW2xwH7uVvSOhdm26zPs5W4UNnUuikY00vdarK5udl2W7yH96HTuvq6bGGji6Ul7upUgz4BAhCAAAQgAAEIQGDGEkAUnLGXno5DAAIQgAAEIDCRBPLFfaisHC6//HL76kUXBROCYR+XLl1qF154oZ12+hmZ7t/CRBO8bWpqsnPP+Yj96le/8sncnqB0TYgeddRR9o1vftPFwYMnuMbxFzedRcF+LT7lc7wD7qKuZKDE56jdpZ7PHQ/4RHKxTFj8TCjyRQVEZQqmhgPfIe7ezie3VYasagbXFHt+4nj4pPr4OWfLqXZFw6DA6S7yvC824G74gjXOZPETCcqiOXJNn2uiXYdK730WB/VvwONlfTMsn7IQIACBUQkEz5S7zywudYszF+2uuvqqoTzvf//73TKu31rbW6zWXX6uW7verfvcleizT1prS6fNmreT/fqm31t5mbvELC63Y48+3BbNne3fP0XW41Z/9bVlVu9r9W1s7rBZ893dp1sArlm1xlIuti1atMiq6+usyy3ylj31lD300ANWXVVtJ7zylf64l/iag1U+vpV7XJVb8KUC4czSvdbuaw3W1DX4un964t2isLvDWra02W9/+9ug3ZXuzvTMM88c6kN0XJPgpv52bm1ycbHG++tWjQNy31nrwmORdXSkfO3ACrvlz3+y1WvX+XFHUNbsOW7N6GJiqY+jvW7FV+qWfx0bNljdbrtpBLIN6zfar3/za29O2hYvWWyve91JQf19zlVCoNogl6FlZS42eghf3Ii27Sln8Ne//tXdihbb4Qe92Brn72yNC2ZbrwN3J6ZBOztcPK2uqfchMW2bt2ywdVtW+/qCtbZ00W4eM+C/B9bbLo1LXcis8nYxIgaw+QMBCEAAAhCAAARmKAFEwRl64ek2BCAAAQhAAAITRyBfLAUH3GTiAx/8gH3/e99L7JzWgvrKV75iH/3YxyNWCYlJtztyzerVdvzxx9nTTz+dWFadW1zcdPMt9tKXvjTx/FRHTmdRUJPTjzzyiDVvaXZsPivv870VPkG9dOcu22muT3P7pHM0aNK51Neskns9hQEJaEWer2RnK656oRvB+PpYgZA2PF+0jIneV5u2bnWXeMuWBWK2y5iBS76ykrTtvbTbZtdJ+FRfhtdc4mJFaYlb52yLT7vVTVG5CwxV+3h/fO0sjx8UOWMZhxfDEQQgEBLwx2zAn7UBF7i0lmCfW6v1dHa7pVmvr0vXGKSSYKXnVeO43jhodcHs6QcesDq3smuoqbSnb7rZ0vMW2ImfPM/a2tuDPD//zW9dlJprxemUzW2otip3k5munWdtTZ02x4W8px95Vk+9jz9pq3C3nTffeqP9f5/7lJev//3PtrB40WL7+fXX24sPOdjHMVntpW1ryxZfK6/cXYBWW58LbBIxfaLD2tev8/X0nrXjX/GKwdze7n8+/JQ1NtQExzsvmBu4DfWkPoa4dOYvRvS49WGXj6l19Q3B+KHvzd5U2tc09HUH66ts/wMOtKefeVottYcefsj23/+A4EWbtAQ6dwHa62sRlvrYWlTsomRLqz3kY9pRRx0Z1PfGN77Rrv/l9UF/JKhqHFYYGOhzkXGrW/01WtoZFnmd/S4u/uEPf7C3vvWtbm3YHaTTH9XbMKvBfv+739uRR73U212sS+Cao1y5pqx3oMvXK/Q1G9WW/pQLtrXW3dVvfS6a9vg1XLLLUh8zB+sdKpQdCEAAAhCAAAQgAIEZRWDwdbQZ1WU6CwEIQAACEIAABAqTwLe+/a2sgqB6LDdwn/zkJ22vvfe2k056w6RB6HU3aqefflpWQVAVa12nd7z9bfbww/9ntZpYJoybgCbnL7n4ErvvH/f5ZL67z3OrOjcisXNPa7STj9/Jyx20pBuqYHAG3MLVpTTdrsn4dP2+Vrt4iR81+lFgPjiUZbJ3JAqudiH5sssus7/dc49Xp6nvtM1v6LYvfGxPO/IQt/6RMKDGRsKAixa+RFkweS/NsM8n/YsbD7OSqvd6/gVWLP+j6IERYuxCYPAx0mMRrLnnAtRQ8Geos7vTx+ctNqdhngtOEpN8bTsfo7vc8jsMel5vc5fQJ510UvBIbti40Rrmzrfy7lZ75p6/WWVtpTUe8RIXuGSlPPjQvvzQF1pr83KbVT/fGnbZ3TavXm+11bW28anNtmz5pkB4XLL/rm71NmAnvfEkW/7cM2F1w7arVq+yl7nIdv31v7LXv+51gcvM2XO8rcGDLkths/a2brvtz/fbwYurPO/zA4DGiL/c/Ac7+eQ3B2X2us/N0pL+wOJRLj7TbvFXVFJh9XOr3L1nrzWtbfZtn82eN8etAX1tQF838JlnBtsloVKWfv09Ltj5Cwgl5e5SNOXrEUp8W7XeihcutOrZs13c8xcuIkFutEvcXWixv8AhC0EJif297e5OdYNvXUws9bo9/qyz3mvXXHNNJOfgruptaWmxo19+dGD9//nPfz54KSLtLk1LXNjstXKb56Jr0G+/tD2dPS7Uem3Fabd0rPS+9gVEJHZGrREzKiICAhCAAAQgAAEIQKBgCUT+BVCwfaRjEIAABCAAAQhAYHIJPD/nOLn1jFC63IZqcjCXcMYZZwRWHrmkHU+aK664wm6//fZRs65YscLed/b7Rk1HgtEJaDJ7wCeZB9zKpFj7PpFf7JPrbkPiVn+aHpbst+0TPdYsuRuN6FxxkcuEshAc8PcGJaZNYdDktD6BgODtLlIf5M7P5/hLvC8SBNWPjE8QP9hP9UFvPBZ7enVLx/o7+JnCzlAVBPKcQH9XV/B0SBiKhq1Nm9195marLiqzv995h7Vt3mzPrvi3rVy/fEjcC9NrrViJinpud1+60NrT3Va1y0625xtPtYXHvcpafW3BqOi0fmOT7brfkVY9Z7G1b2216oZS63ALxBq38KuprbD5e+xiA5Wldud9dw0JgmrfCw96kb3zHafZa15zou2zzz6D44TX+8ZTT7EVK5YPNsetGoPxYsAt7OTC08e0Pef12/ylS2zr5k1DQ4AEtW9fcbH1p1oHPy6QdbR2u9W0xjy5Dm23MmV2AXDALfXK3c1pnVsHrnzy2WBc+ewnz/f6NK4MBrWv1NdWLCpxi8oet2ZO9fiY5eKh9yvt6/il3QLw+fcrNBb5gObiXKmv/ac1/7QGoPm4K7fN5u5Qt255zHq7N9mll17mguBPg0rE8PDDD7ezz35vYDW4t7/UE4YvfOEL7lr1oeAw1e1tryizhurqQUtAL7KsqNxWPLvc3bq6U1EXJ5evWul1uiDp43ykG2FxbCEAAQhAAAIQgAAEZgiB4f8KmCGdppsQgAAEIAABCECg0Ah8+tOfti3Nch/5fCh3F2Y/uuoqXxNp0FVaeKbVLcsuu/SS8HBCt7JC+M63v51R5oknnmhqYzzcfNNNvt5gKh7N8RgIaDI8LQEtyOMT2pqFdlUs7Z9A2wt1saStUvvkcXDKE6sMuQ6Mh9DaJx4/scdB7V6kGqTpe99ItIjOX8f7EG2AznkYtHKUjDjYn8FY/kIAAiGBUl+LT+5Bo0HPeK2v41fkLnibNm22BrcqG+gvtlq3HK72/+JjQPs2t6Cd7qqy2IWrF77gACtzl5tzd9rJanZfYgt2bgziB+sosp12XmL97U22ZcNytz50Aa6lN1j7s9PXDG2v7LfapXPtsY2r7cMf+uBQs97w+pPtp1ddY1+79Ot27c9+brfefqu9/GWDrjiV6IADDnALPrMnHn7CfnbRdS7wtQcCWG1Nlc3f72Crnr/AqmfJxelQkYElZMOsGnfBWePrGla6mNdtA70aP/utpr7G1ix3sdPXN5TV9ez5s61h3lzb58X7W82sWrvs+9+IFhUwkb7W4t+9Kbfea1m5wlo3rLNmdx26aWuLtbe0BWPrYO1qRLE9s/ZRW7PZhTp/8aHYxdfubrfcKy61kop5vh7i7h7X59+Vn/G0g42+4vLL7M477rArLrnIfnLV9+3ee26zPffaIyhS1+T0008PxNmqyhp3Y1pmLa0t7j61zAXNimDs7HXOm9Y0uxVhpe26cIlbJqYD68Vtw+Vg0/gLAQhAAAIQgAAEIDCjCPhPWAIEIAABCEAAAhCAwHQm0OLrKX3zG98Y1gWttfSnP//ZzjjjTLvkkkuHndPBtddea7IunOjwox/9yJ577rlhxS5ZssSuuvpqO/+CC+2www4bdk5u0L7+9a8Pi+NgrAQ0eRzOevs2ENS09WnfXGZ+w6RhEVmql1VQ+IkLBFmyjDN6UBAcbH8uHRheTfAPHO+7REECBCaTgJ6H6Ri0DmmxrNQiQU9McZleKhiw5k0bbNZO823Vug1WOafK6ubMtpS7hY6GH/zgB4HbzWq3TNOzquGm1i3V+nu7rbqmzupcBIwOQGWl/dbjvPqLO6yqssz6SlLW1LHS6hqKrGx2pT361BP2+H1/9+oHB6La2lq77tqrbJ8Dd7MKt8SrKPf1/nxtvb/cervXNfhs97n1W/PGTbZlXYu7LPUyu3qsqqrM2txKb8H8OUG6cq07GAnNTc222S0g9ZEl8qx5s6yozNcgdDG0uKTUFu62yN17+pqrpdUuDqYsleq2HresXLd5QzD+RYryEUbt8PZXV1hrZ6tV7jLfKuY32uJ99raGukqr8PiwP8qnlzd2m7uHr7O43tZtXO5r/bUHVo2l5bUu6NVaeVWDNbuQGAZZIp79vrNcSvR1Evt7rG3949a/pdPuvfeewfUUPaG+b5v1QpAzSbuV4uyGuUG7NIrq3F6H7mMLdl0QfN/rhYm0v/xR6q5SXX0Mq2ELAQhAAAIQgAAEIDDDCAz/l8AM6zzdhQAEIAABCEAAAhNCYBQxZULqGKGQZU8s87WJhgt8e+21lx155FFBrjPOPMNdsB00rITly5fbTTfdPCxuew8kFH3vyiszivn6xRdbY+M8q6ystB+6aCjBMhq+4e5GUymsBaNMRtsX6/AzWtqJOB9Owk+WGKhyNaEdF1k06T72qesd/EBOBHDKyFsC4XMXbvO2oSM0LG49HiR1kze53qyZ02i1i5ZYw4L5Vt9QY3Pqan3M9vXoKiqHlSj3zxrLJcwF44Msjf3RK6moCqyUuzta/XibaOovKtTU1lm7uxStqFlom1vX2bNNj7mYtdnm7rzQlrh14b4ujO1UN2uojkWLF9vGdRvdI2fKSl2k6+t0MdHFuyfv/edQmoGBElv5xHN+nHbRsMK6WztcaOty6/jawIJQ7Snytfbi4e677zJ9ZOVX5Gv6yWqyo73NZTQP7oI0sFZ2y8E2FxDlWbTbhcF/PfhAvBjP123/uOvntrH5aWtwq8QSdwFaU9Pg0luJyRrTSos8TuUNiodyi1xe6a5SG3az2lJf38+t9gaK3f2oc+rzsjTaNW1u8nYMjnpyRar1B8sq/RrU7WQNSw+1OXss9TaVWn19fdAefXfqGihojcMwSFDcbbfdfI3GtIuNale1rX1uRcZvhTA9WwhAAAIQgAAEIACBmUNAv1AJEIAABCAAAQhAAALTmMDNt2SKe+993/uGJgjLysrtvPPOy+jhT358dUbc9kTIdajExmgo8wnN449/xVDUnnvuZXPnzh061o6sBZvcaoOQvwQkgGjiXxPNkxUGrRB9Xa5IUJ2DRkFjlwYjxbALgQkjMChgD1rNTliheVJQqbudrPB19HZfutj0vbHr3nu6ZVmZC1uV/uxnPoNNTU2BMCgmPT09tmVzs/V29dqqp5+1u+/4c2AZF3TN1bb+znVuPTfLKt1CcZf5OwduSV+wdH/r9nX8Bja22ZL9drdNyx6WSjeYxV90Wbh0Vyt30a7Xha8yt26rb5hje7zowEHxzlOVuHC35rln3UKw312HpmzDs6v9e68yGKs2Ll9jHT1d1t2+NYPuOed81PTp6uoOzmlcq6mr9rUOfU3F9g53JzpgvS6m1c5qtJT7Oq2rn2X33/9gRjm9XSmbu/AAWzBnL9vw9L9t2ap/2caW9f5yQ48Vd6yz7qf+bq2t+m4L5Ebr883qdVusqqjOyqvrrbqi1i0Sy52TLPi0AqwYu7DnAmsQfON2fYG14UC6N+iX+TqBio2+qDHgYmNgXz1YjdfZOpjf/za4S9eeHq0XO2CL9tzdKsvcepEAAQhAAAIQgAAEIDCjCUzev+pnNFY6DwEIQAACEIAABKaKwID97re/HVZZha8ldMbpZwyLO2qb1WA08tFHH40ebvf+Qw89ZFt9vcJoOOmkk2zOnDlDUWrbsccdN3Ssnf7+fntu5cphcRzkRiCcGM4t9dhShZZQ2oYhFAYnr95tk+FhhWwhkCcEwudB9/7kPweT0+mwD9lLl7VfOhD6atx9pxalq6iWhaBbtD0/DATZVdallw66phaPktISX09wrv3/7H0HgCRHee43Oc9szru3l6NOOYMCoIgEmCiCBE/GJhmT/AAbMNmYZAx+YPxkCywBAlmYZxQAISEJ5XC6IJ1Op9u729vbnGd3dnbyvO+vnprtTXcn6QTcXpXU093VVX9VfdXdK9XX3/97fC40trbhste+gQTdjCo8EGtGIZUgyZZGV9ceVEeq4YtFlLKua9cB9HXsR7C+RRFg0oAoBPc9vR33/fZ+FEju5UiUbd/dgQRjGGpWsEDzLY31qGmO4mWvPQvtZ7QimUwgxfLecAwe7p38MEVINHvSbpCF1JQrGbpGzZL8E4Vh/76nsP/3d6EQnyCPloOPCnc65sRXv/qPdhPq2EXXqbWRKmQn+lFTWcRqxiB0Du1k6QwK3iB8ja2I0JWqlYp0Feqmar4GvSNDeHL3AXaLZJ4Clu5RqSBU2HtFATgDtjqSTorikR/eOFie+sGSTWYT+xxdtqaS48y3PAZoFaEqxPJC9DpJrvLONW5Dy8iZA4OAQcAgYBAwCBgEDALHLwKGFDx+596M3CBgEDAIGAQMAgaBJYDA5MQk5pJ7y1esQCwWmzW6Vsb1k9h+9tTf368IOXveQsdqPXKhC3PyJJ7g3PSxBRSKV7/j6lnFZFH00UcfmZV3PJ7oBXu9n4uBzpe9JFkMtu/VyYv5scypRWppQezP3V6M+UPV1e0cqoy5ZhD4YyJgf/70s/fH7M8Rta1eFSSeJPYh3xuFYh7TjH2n3yEL2XA6hcijao3l9TjFBac+tteRD0HGJkeEMiRfxbh34grTSXeXPjcGRgapmJtR/qap2HOS8JK/WV5HGE3NrZgaG8XeX/8CrZECgozf58qmFUknbXi9XtQsa8aJ565H14Hn0DfQjWUNlVQMWq4ypUwxX0TTxuWoX9WEisZqxidsQJKxAceHx5Ea2E4Xokk4xV01VXL2pOfypz/9qaLfXCTMRIk4MjSGUE072s49j7bz6NvfSbelSTz5+9+TeJsZi7Y1uu9Zjj0PfyBMt6lVmDwobkRXk+vLw+2vQLIYVeSmLi+v7hzH2chxNNfXYn/XIA7u72AcxmnkqbQcJUmZywvhZ7ldlX7mcxlM8GMbqTuZSLAfdBdKwtQ+h15/iH2IIcWYiwVFDM6Mt1AQ966zXYvq/pi9QcAgYBAwCBgEDAIGAYPA8YnAzKd7x+f4zagNAgYBg4BBwCBgEDAIHNMI9Pb1leMJ6YG0L1umFjr1uewl1tBrqNr7P//yL+XsBBcYO/bswdp168p5U1NT+B8qD++443Ze68Dk5ISKQSQLwgHGSBKVw5lnnoWr3noVNm06obxQLMqLO++8s2xHDoJUUWzcsHFWnpycfc7ZdE3nmRXb6IH7H8CHP/yReWWPpwz7IryM234ux/bzhRbojy5WsqhcYgmPruFjzprgfqgkc7FYGT1n9jI671A2F7u20Lwv1rbdhq53JGXt9RY7Fjvalezztan7sphtyRebsi1WdrH8Q/VlsTqH6of9mn282pbe28v9aR0LEZhDsvfXjJl3AE4SVb7oGchRse1WbjYX7+2RjG2a8ftiVMLd8djPsKJpLfygXUcA5NdQGa5R6jRpQd4kTm8InZ3PIDWZQEN9E/b37UEiwZiBLS4EYjlSWeMk1tz82yWli3TtmcaDv74LJ73sdKw94eXkyUiEIUfbQTGpknB94rbUwXiHXlqYmCxSgUhlY4jx+mJROF38O1N4hmXlW2iLaLNqWr8HDx5E98gwJqkKXNPWjErGvhVHnG7+fUp5GJuwrhrD/Bt74/+jGl8as70KRHUXrGtBMWcRrZlED6KtmzmmcYQ9fu4z8FEN6WCsQ52mpsbgohJTxhj2FdCf66br0xzyg0BttIbj9yviVpeXOShQIRmkgrGQyzKuY0DV9XD+nML0lZN8wOHk3+gIidA8RiaG+Pe3En6va5Za80jmtGzSHBgEDAIGAYOAQcAgYBAwCCxZBAwpuGSn1gzMIGAQMAgYBAwCBoHjAYEBqv3mpubm5rlZ6vyyyy6bRQrKAvqDDz6gSMHf//4+/Nu//Rt+/atfqRh/CxooZd5zzz3KldqGjRvx1re+Fe9+91/Q7VqIao4Ds6pFIoybRLXH3FQRq1BKxmFbHMGOjj0sJiuu9oXOuTWX9vlCC7Z2kkOuL1TmaKMii93yj0kWAjIHstnxF5e3ci5kuyS5LsS45NnJIz1/et6kjOTpelYLh//V7S9UUq7JlqNSS5RCcizPnWy6XXt7cl2SvraQzcXydFuyl/raxmI2db7e2+scqg3BSZK2L8diQzaNr+QtlPQ8SF3druztGCxU70jz7H060jp/tHJyP5KoCzVdBn+6F6mpXrh9vFcluJ3Let8KNguNSWMnfU8zpt9C6amnnsJznbvRP9SFqfQ41jStQ8gRIyFVTXecmRn8+T7JU+E2neQ9OlWg2s2J+lglHKkc1py4Ar33/l94Wk4nKSatWO8eiWFYIMmVyI0jOTDNj0j4fJHYq61qZX/lXpC/GA54/WHUV9dKC+gfGFZte0maFUgwTiXTeHrPAVVPRisftYjLUD02Ubd/7otfRKy6XikcCRfjJ9ISicZANIoc3XyGgyHseWanak/jZNWnim9qkuMJIJNOIUY8kwMdyPgDcIx7EYxUIj5JEtAhSy4yJsZd5Bj2dw+guoYUprhSTaYQn8qhpiqPzt4DzK+hen+aZa2k2qGL0XEqKkOBEFwkGN100zqdSPPv9b+SACTdyXiIdnehQn8GfSE8s/8ZVISrSdY2Lzi/ug2zNwgYBAwCBgGDgEHAIGAQOP4QMKTg8TfnZsQGAYOAQcAgYBAwCCwhBCYmJuaNpqGxcV6eZKxZvVotIOoFd8m74447cNvtt+PWX/5SkRqSdyRJFit3Mibhpz/1KXzrW9/Chz70oXn1RSnoLJEmdpuysBpmvCo7KTg4OMjF0MJRW7i3t3csHgtGgrFseiH6UONw00WeImC5WO6gssTrJmlV4BJ6VpbCbYmnsjwtNh0ua/FduNgiz5GhI7xEP4rOFK/PjjIgC/ROj3t2vhBcLrqppeLIKYv0tCz/SJJ+Z7PZGTWotMd/XMUk+8WYYPMSO5HqQ9SbQHUww6scP7eKANvleHJpIYnmjIU5TpdzZhwlmwW6D5QkPZEapVFK1gtKMpbrrrsOe6iq1c9OIODHxz/+cZLbFeX52bdvH37J56inp4ftWH09//wL8IpXvKK8aH/LLbfgkUeen6vczZs348/+7M+UDX0vSJ8kCcbPkLC47bbbSfA/CHmOpI/yfK1fvx6XXnoJXv7y81BdTcWS7Z667777cOuttyobz/dH7rU3vOENOOuss1QMUVEVP/LIo8q+7p+2KfektN3W1qqUxWvXruE7yFMiWKTUwrPTxRijv/jFL3DwYFepjDwHTrzuda/jeKgaY1ro2ZCx33///bj77rup1EqwTIHt1+B973ufIoRUxePwx+H0wh1oR8BThfjIbhJWGb5vnSSY6O5Tnv0FkujZ7nt0Ny44cx0m6UJzMj45r5QQbH3jHVjVuB4NdcuUC9F0IcX7Mg+fh0+8zbTEBfQGGBaPYe+mSboVkw60tq5AfGKA+bUIJA8iX5T4f9a97eK9kw0k6EcU2LXvCVy4+TVUrieRpQvU8tPN99KzT+/Enu1b0NwURcuaM3jNSbKswOegyPE6cNLadnW/Sedra2sVKSjHkpIc19jwCBpq66n4y2E6lSQ2UcZH9CA+Foc3SIUe+33vgw/xTuW7iO+bDes3QMhQ6aW/shKhimp4SQ4mJ6apNGyDZ2KM790EJofH4Am3YGpC4uxKaartfbz3nXQHOhmEh+D43WGsWt8OJz8yCAVymGDdaSoMdZK5GRkaJrHopKKxG/UN9cQ2g2isChe98iLiJsS/e/bfTbpCFSKytbYG+wZ6SKJWIRqYUVdq22ZvEDAIGAQMAgYBg4BBwCBw/CJgSMHjd+7NyA0CBgGDgEHAIGAQWAIIiPu2uamKC5ULpSjjDMqCvrhb00kW3l9sGqHi7+8/85l5ZiSu4WKqHlEW2pOQmxKT6mipeey2j5XjuYvzcj43b/5YLOJQyBBRr1nUHONWkQzMDFKZs3da5ZXrlVbpSzuVLYvdwjEVCqKG+RjzSCYqkq9cSznfkwVxa7neWuAuuoMInfYhBJtfrq5TasPrYs0iq/75n/8ZP/rRjVSwZUk6in0nrjzZg/dfXguKeMpJystWzUX8z1+eR+HSDWoNXWyJ+i3gdiC7f5qOA63WdUV1xor2e0y0RN4VeXgbRMXHcfG6pit1vee7F1XSQw89oMgm6Y8kIbsuuOB8XHTRJepcYqft2rWT471hFulQVVWJc889V5WRuZQYbEIMLuTKUBWa9WMhI8SFqHztcUJlvoV8/MEPrsePf/wjulqc/R4YHh6kq8Z9+PWv78DKlSvxl3/5HlxyyaWoqKhQLQjBecstN89q7chOHHT968Wpp56qSEFpd8uWLfj5z//rkPeqEHjy7jnhhBPwgQ98AC972cuo6Apz7mSMc5MomO/HT3/6E/TRdaMkqS/zPDkZJ57ncG5tN5CtupTZu3cvSdJflj86WEZ3yu961zUsVc1tofZsBpb4odMVRbSiRRHHuVxKkYLlIVuPdYlAI96EquPAEFa2N6AqEoAzNvv50/Uq3O1Y1taCwdEu5PgchDivkui1VBFzuhxvHMY2zKJtWQtdl7IfVJJPpRMIhmvhPfUNiFU1wb/vBk4R2+G/Hj73K5Y340B/Lwp+H3b1b8MpK89DekLIXsuqgwc1ITf8kQq0rDtDvQPZCJx81/B1hcxUHqP9QsrJvFvvym9+42v46Mf+d7lbf/Oxj+InN92EPN9fHXymGoIJknMJqt+j3ALopXvRlFJKOthvF+MBNipSUAw4yXImxhi/kH9Tw5lRTCQrEIo0w5OaolIyB3chg+y09RGEfOYwzQ8vRuMZ7Hx2O05c24jqRrr7JNA5ulR18ruHgDMIN6S/MymT4ccVZFLDoSCSjCXo471/8MB+Epw1HKobbi8/jODwNEkuI3X7vIg5q7Ca8SEnU3FE/EFVZsaqOTIIGAQMAgYBg4BBwCBgEDieEZj9CfDxjIQZu0HAIGAQMAgYBAwCBoFjEAFZCJybJB7SQsklCoIFlHsLlZU8ITFcXMgX1ZHEE5QF98OTVDPWKkhOLlReejx3UV9IjvkjmbF1PBzJXOrtcOPV5WT65VjIKiEFZStQcSmbky4CPfki/BTWlDeShX7m+zKyFdTm5d6bIZFG1ZmfKp8AGEuL+3B+Zgvy3J9NIpBJcj+tjj3ZMbiLk3CpBffZ/1sh94qo2CYnJzE5MUllTJzKrTFkMyQBCtOzbIfYToAu88JFuuBz5lDpLqDCy82XR4wqIT9XuT0kDHk6Z6ObTI7Hw/7rzZ0jLUiVUpk1OByQL+C64C04P/DAA6wtd22RJMIEdu/ejbGxsUNalLpHI4nK9utf/zquv/56SBxQsaueVz7f8owL/pLkudrD2KD/+I9fJWm5S52/2D4crr60LX2w+iGqTYvcFsyEFP3IRz5CEvHnxNAiWFVHbT+JxBSepvpLY6nfOzIWUVl2d/eo8dqqmMMjREDcbzo8tcjkSZ7bYvNZ1Qvo7e7C0ADj3NEdJuHG2SeJio3EPEmmxV7Qn/ybj8NPoritfg2W11GNCH4s4I0gzfeEvg/FfjaXxur2DVTIMc5fRoivCbhIInqoyovwb8U0FXnJNNWI5WfEiZa65Thr/XnY3HoaNrSdTPI7iQneH0J8SZKnKdLQhGVrT6OL0yzjGfLep5IwPZXCY796HEP9fYhUiErOeu7kHrz81a9V9bWNnt4+xAdHkE1OY3VDC6qobG1orkdtUxX7M413vP1qaYqpiO9/9/tUE6asU1qZToyglu66a5vakQ/w72QkhiKxSGRdSNN96mQmR/xE5Sy1HVQmZtDbM4Z1K1tRU1mBQC4AV86BqclxvuS8SCWoOJzm+HTnWC+TmkaEf3+z6Qyvy7MOEoQhqrS9VETyXSQfXZAIFQWhfjYl3qCb16f5Dh4ZGWBcQsozTTIIGAQMAgYBg4BBwCBgEDAIlBBY+BNLA49BwCBgEDAIGAQMAgYBg8AxgYB98VB3WKuZ9LneO6hIWIik09dlL4Ti1e+4Gh/84Afp2q0VIRKCEsNIFiIlrtT4+BhVUw/TbejfoaOjw1513rEoQRZLoqyyp8P1y172eD6WRV+98Ksx0wvvsuwtm3K8yQO5N9TacnmRXS4yR87VXp/rPZettRF7HV4uranLEW1aK9ZuMaPc/YlCUCqKTk+uWddVP5VByXHyH7qHZTlR98xNVo1yVVVG3KBKWVLR3EsXFqg3R2km5WT8knRp3V+VeRR+BHchqB577DFFBkboblDcdu7YsUPlL9aE4LFp0yZcccUVCn7poZCmYkfUgJJ8jIW2du1auttsYxkh9hw4+eSTVb5c1/N/7733Ug13G4k1kr8k4SSmmJRbtWqVUuSNj4/jAGN8Pvvss0oZ/PrXvx5r1qyhTaLDfqxYsQJXXvlaMVlOyWRClbfcn1rZ69atgyjtlGtalcWZ5McFLS0t5Xr2A7F/wgmb2ZdTWMfDd0YGQ0ND2Lr1SWI0oMhrcespZObpp58JsS91ZJMkfZP3yp49z6n4iJK3YcMG5brVev+MKzJWYpnqOlLm+E58MugquJ/uJRtqmkq4lJ+o2dAQZ3EtGQrF5uPH+62ppU3dw3JPyVz46O5SSQZphacLpt/edbcE4qPCzYlkyoUI63g9fkwxZl5pWlW9oN+DxNQoXxP8uCAgcQUr6S40qUjHbN5F0iuOFN1u6iR9CHn594fuiWsijPnHMcrfoszYkPWKkf7wHRGgm2oPY+153Tkk+BwN7aGysDCJ5tUBpPf+Dgx1qN4/jKRIko/qyKgDDY0Nqpm+vn48/dQOtptFiurAhqoYOvZ1wB8KI0Z1ntfvpVpVx+114GoqTm+86Se6i+w/n9GikPBpRFs3sw8hTPPZ81WGEHbX0KUoG1fqYpmPIiJBH848aQN2PrML++JDqK0OIp8KIkfFrccTZ7/j3FtvMD2DXo+PH1RMqedYSE2/308lYIBuSSepsgyTNEygoqqGJLyuYXVPPrOpYdzGSMiP7sEDWNawqtxvc2AQMAgYBAwCBgGDgEHAIHB8I2BIweN7/s3oDQIGAYOAQcAgYBA4xhGQBcK5SQiBBRMXURdZ11Wqnr8iEfihv/4QlrW3L1hd3P+J2883velNKsbZvffeg4999KNU9Ty9YHkhBBZKsnQp6iZ7ipBAfD4qRnvdpXI8l+SQRXl70tf13n5NH8sivFXNmmzLwuzF4nJ9vWKv6pTa4rHF482uo+2rfamo2rFwkZsqLQvkJZuz+i4X1eK5ZUVIukWJupJtWcYX159SVYhB6fOCdcrl2YQqzApOq0/W3S6ZRy/JuJqpDBLirJcKI7n3zzzzTAwMDCiloFqw53MibjVlAX9uuvzyy1WMQcttZlG5uhRXnpoUlOfryiuvxGtfSzVTiRQUm/J8SJL2hZAUUlCUmJLkuTzppJPwyU9+Eu3t7eo5EntCVD755JOM+zdBIvJK5X5U4Ugszz77bOXKUxko/XRTJfav//qvpZiIVuaFF16IN77xTSQdNcEvpCLVWYcg/M844wy8973vU2XkAwVRUT766CP43Oc+q9R/MobBwSE8/PBDihTUfbDGlleuGSWmoCRRKL/lLW+BuKIVUlDSb37zGwjJudC7TxU4jn4Es2RmCk/suR/RUATVldVwO30kqRa773W+7OXh0eczoOmPDHLTk2giaSUksCpJkk4n6x3C50w9fw6kkink6EJTiGAf4wN6SW5Xe5qsJlQlKnp9VfAk8whURBEf7meMw1HyZS66Eibx50/id1sfwOgUY46yvO7ZyMg46upqqER0Y2BwBE119aiqFzewOlElTbJwonOLIuCCjB+ZGOlCQ0sd1YJx+MMeTD50G+lKKupYJcB+tTP2YU1NrTIghJ8o/yYmJxgXsZYxAx1Yzdi7fYwzKETb737/MJV2JCGZJEZnMpFEelorBYGxoX6MjzXDT1JeFNfJVJpqwShGR4eQ9+aptHdhOK7dgToYZ9GNYiaFBro8rWN8wByf8aqQFwPxLrpfpUJ7OotggAp7hatqFlMk61tIYo6TBJS+TvJ5qqjx8Ll3sqyfCkyPUgtKHERJ8sx5+HGPS1STbj/fB164oj7L2JxfuX/Kfw/mXDOnBgGDgEHAIGAQMAgYBAwCSxcBQwou3bk1IzMIGAQMAgYBg4BB4DhAILzA4nx/WdkwG4Bbfn4L0mXXZzPX2qgEuv4/rseFr3jFTOZhjoSIeNWrLsLDdOcn8QRl0V4WGO1p67atVDgklPtRe76UmksK1tbWHvekoB2jxY71Aq5grfFeaGFXzQRX12WBnRzQrDTn1FqAZ6G8rETzojh8lNV+3dasyqWT2TNdssid5M+yr5g6IQ2pWyExWCwxfXP7NKuN2cYV2afszjI8q0b5xKpq0YdS3DpXtctlXsyBYLJ8+XL09/dTyTatyK7Nm0/Ac8/tVoo4UdAJWSVE39wkdStL8T7lWOZtfDxO0mWGbBFiPBqtQANdIkqScvak53x0lIorjk7IH8mT51HiBQqJJnV8VBJVkiBasWKVIielT5Kv7YlLYNlmUlGNZy7RFg5Hrui9IQAAQABJREFUSMrUlwhFKT27PzP1Z46kLwGSFdIXSUIgXn75Zbjuuv+rSEHpgxAXvb296rr9/p0kObN797PluIwtLa047bTT0djYTKJlVI11x45tVEF2UlG5TtU/nn/6Rw+io3831reeyLh/dcptsJ7jQ+FSzFGZypigc26v8nzw7YL89AimpzKoamil+8k8klMzKj55opa1LVNqVJm/Hdu3oZ1/R/y+aromzSiXmL/4+Z2cr5lepKfoGpSB83Lwck+3moyN53IESYZ1Y8qRxOlrzseerfHSM0tBISt76R4T03HkSRw21NcjTxehUySUtWH1jFNdmJ/oQTjWQtfB06gIJpE4+DAiq85H1t0Gb9MptGnFzlVvBpKI1157reqYuLJlZ/CWq96MbVu3krj00SXoJML0WTw+PoKtO3aq2IBSuL6+luQbSU2q/eQ54JMHT5AEXjBAt54SV9CJSFUVibss8nTPnM05Mcaxta1ZK9VVKhLHqoALo8UJHNg7ivraKuwfmkBluApNtU0YnRhFfjJdxkDGWR2LYlfHHvhdPgQjMmd0N0piMBKNKcJQ+pLPMLYg/Sv72X+dZG4FPhffL06Sj/bnTMrIRwvq3cMy+UKOIWH5HmJ7QgKbZBAwCBgEDAIGAYOAQcAgsLQRMKTg0p5fMzqDgEHAIGAQMAgYBJY4AvX1lhs0+zD37u2gi737sWXLFmylUujgwYNIUWXz2KOPKpWRvezKlStZ9kHUccH1haRgMIRvfPObWEOXh+9/3/vUwqO2M0j11DnnnK1cGwphIUqL0047TSkx4mX1hFVa3BkeyWK2tr0U97Jou1haCJuF8ubWl0Xzw9E4h7s+1+bzOy/14Eg6og1LWQ3FS9s53eIR7WV+ZKviwr+41u3s7MQ999yjlH2Plp6txsZGpaZ97rnnFrR5RHNWGvNiZSV/2bI2uvGVNXwrvuFDPPnYxz6Gq6++Bi972ctIxEXV8yRuPxezM7uDVqPzy1pE4vz82bXnn82eOKmvlY1SVggLSwkp+5my4u50586ny2XFdWg11V+veMWFJF4tV6iignzooQeVm1XWnt/0cZRTW1lPQqmahI64BWUcRxI7djwXgkLumVzRDe8i0AkhmJuaQNczD8HNeIIef0S50wzRTWc50UZrbYykoJUzMDSCZc0tVOdNIsr5GhvsRiQvbjxn1LJ5xr0Lk8ga6N4Db6gOk7kUKuja8vGHd+KU089GKEoVYKym3AQDGyI/0IexCQ9q156KTDqpVHaF8WmWkc7LS6LIeHyT8ETXIzU1QAVgBeKTLrStPJXkXj8SE2l4g1XKsbGUFeXegd1P4v3vfY9q56NUuks8vmf5vOboindqIsl6cYQZG3CaCr1vf+3zqpw0987/9S7s7R9EVimABSUHY/sFSVy6sG8kjdb6Cgx2H0RltAq+iBtP7diFkzaeSBXgzlJfrWcpybij1bWtSI4NqPwE4xbWNzSie2IIDXwWdnbtpWWhHJn4oYYobasrqii2dpD0C1KZOILW9hV0meolncn4sfyMQ0h48q0k9+TjC+vlKfeBxFgUQtdJ9668ym328k+B8ytOn4XszDAmYTDkoU1DCgr0JhkEDAIGAYOAQcAgYBBYygjM/q/CpTxSMzaDgEHAIGAQMAgYBAwCSxCBRroVk8U/vRAoQ/zd736ntsMNt55E4I6nnqKyyVL0HK784tcd+Mu/fI9yofiFz39+FvG40+Za9Fd33LGoiTPogtGkGQT0wr6eV73XJfR1OZdj+7ku83z2pXVkYZnE4Iu2t1DbNGut5S908RjKEzeW559/vlJJjYyMKPJdlIHi+lNci8r1Fzsfi8Gh5/o1r3kNbr/9NkUYyL0h7kqFGHz88cdVPMJLL70MF198kYoHKIpAt5vqLCZRBr1UfRP70pc8iQbBQFwYihpJVMG33vpLdHUdlCIqCVm5evUqVV73R8gZiScosRAlSf4ZZ5xZjpeo3FjSvti+66678Na3vo1kiI2oUrWOnx/B2kFCys94euR1hPPiBMvB4kmIWZmXmRiRC5Ql7m5+7FFk/D+XI6vizHokPuTEjMtnmZtVG07AA0/sUAbiw2OooaJ0bGQA/qlpPPjbO1BN95h6blUhzu8kCbFYNVWfJNeiVSGSUcAJp5xNBRxdjpK3SqfGWNQaTD6TR7RlJZWFRcbNm4CLf6ecfh/CJ59oEWalkjESbPK3LJdrxdhwD+rWroA3UglfSwMCdCG6J76FJgmOvNr4T9v6k+hh2CK+TjzxRGzbtk117557f4/NZ5yK3d10beocVK45+/r61DWpd9Wb30wXqXl4qObVSUjBTLILy0JRTHUPIB/wYGJ8FBMkQ1ta1mCcxGKSrlWtJO9VxlWsiiCQSaPCX8P58yBSXcF4ikCFx4vOjn7GCRQ3oMTAYcVoDTG+4TSyiISjKLB9F5W4Y8MDVPpG6HY1RHXoFIJUK3KyWKXAGIx0USr3BudI7Uny+b36b7zgINEGLZIwn08rd6jpdIHvrygS8RRiFe7Z86YHa/YGAYOAQcAgYBAwCBgEDAJLBgH5r0GTDAIGAYOAQcAgYBAwCBgEjlEEKioqIWq/F5J+8MMfHgVCcKblT3/6MzjllFNnMp7H0dlnnfU8Si/NorKIqzc9Qn0ui7uySZK8lyJZ1p+P5bn9mLEgi+hWEj0N+8xLshcPdUeUStXLFnlePj4iAy9NIcFelDuixhOCbZIqJSHhhTwQlaA8i+Ia0z5fR7Mn+n449dRT8Vd/9VeKhBSXo/qeENJn7969+N73vot3vetd+NSnPkXy8A4VX1DfPwv35+igK/2QOIs33XQTbrjhBnz/+9/Hxz/+cXzjG18ncUmXlUxSZtWqVcTw5epcYzVGwkiUgpZrVJIkVBe3t7fTFaoPmzZtKsc1FKJx9+7dikBUBo7wZ+745VziM+r29fERmvujFxMchSCSB0uNTZ4Z2RZJUmZyYvywzxFpY/7jwvLTLoEzO4HcxEEq6KaRnLZiOmrzF174qlJ7Rfzzv3yHhFWGcf3G0LlnKwnCOhLCJdWa6hPjDjI+4GR8CJNU4jW2MvahJwonuer6mmUkBF3I07VweposobwsmNx0PxuQOH8VAUzzvnaT/HTwXvd5hZSzysjvdCbLZ45qOHcAFXXLUd28Ht5qug31UT3n9iFE8qycWGHomd1I7ntOba/kc6TTA08+iic792GcCsZsNon/uvFH5Xfu6WechoOd+9G1fw9jGLKPCqMi4kNPwOlLIZkdQFWbFzUkKLt5n3sCXmSK0yTrfLRhtSDav2IhDTdV8n5HnmrBNsYyHEfQK/F0SRYSl0hjDXG0CHyHcr3MuIXpOEnTAIYPMo7pYC8ymSTxciJKt6KhYJiqTw9d/6bVvVx2Rcw2Zb7lHuG/qg+FYoaEZRdJ9R72nqpJEpTjdNebyIyR2MwqVec43X1LUveT1W3zaxAwCBgEDAIGAYOAQcAgsAQRONL/LV+CQzdDMggYBAwCBgGDgEHAIHDsI/DEE4+rWGbPdyTnUel08cWXPN9qhywvC5Df+973FFlyyIILXPzBD37AhU2tqFigwHGUJQuycxdl7QovIS9k02mmvKwEywpwaRWYBWQ9WhalD72JKzwuIEthXV7vD1eXi9tWbasNaZFUC7vAiuyGWpCWA/apwDwhHA7dF91X2pH+lPqk9ofpi1RQY2BbQkXqqqojMrAXmeT+liSk4ObNJ6GpqZnHk/j973+v1GttbW2M4bdCqfbmzt+LbHpedYn9J65C/+7v/g5XXHEl3Zm2KTWgha1gV1Tx+26//XZ89rOfxb+QtOnv71P5c41Z98/c3MOfz7rvSsUlTxSLX/vaV/HFL36BsUa/hbvvvkvFFpVrguHq1Wvwnve8l3ETLZWzVJVrohJ86qmny/e2YFlbW8P3iUO5bD3llFNKJAdjqlF9KLiLe0pRGB4u0TzLWe3M9FvuT2tOdX37NZ13LOznjkP3WcYjgeVSSZJ6jAkoKk7QnaQ9de3pQJFEtk5SJ8vznL8OLhJxE51PwOdJM4ZdSBdR+3dcc3UZv10kaZOMsdnU1s758mF0JIGa6lqrPJ9HgTnlSpLEDCrVWiFPIoy2p0gET072Ypjx+xihD75QFftnVXOQKHQ43EpNGKOtPOd6Ol1Ef5z9L82b7Hb2ddFF57hynerm6kaepFkhzRh5NDSdJanWT/Uh45mqxLHFYhWY7OhR2wl0Z63TzTf8CCfWN2E9XZBWhL3I0oWqTtdc/Q4ShaNoWdFIF6Y0qjpJRV5umKq9Djh8TnQPbEXHvq1Yu6yZLkaBoMePSir0kjnaUbeZvBk9KCZHEatv51xM0W1oA2J0ATsx3s1p8mOK7/VMiD2nSlCSeo1S1TjMeJrh1lpEqOarijFuI1WFLsYYdBCTyhhjC6ZTyoWsmm9pi5u4BBVNoMrjM5JJjVD9GIPHXUdyknEPqQKNkn8MMl7jeDGBhx99AiG2P8mYiparUY5S7h+TDAIGAYOAQcAgYBAwCBgElhwChhRcclNqBmQQMAgYBAwCBgGDwPGCwH/dfDMuu/RSzI3PZx+/LBbX1NSoGGgBKi8kraRK52tf+3p5Qdde/sUen8pF1o9/4hPKPZ20XV1drdoO2uNRLdDIT37yE7zmNVdC3DEe70lwm7vIrxdndb7e27FS687MEEJOLyrLdcUR8uLi+xKBpg1I2cPUk0VnqeUolKIROIRUYC0haNRCMglA2XNBu8Dx5BnbijIftcrN00P0RV+ThXGalE0lGZO+Vtoz3xqrXLOuK/eJgl95AKXqR2Gn50BcdUajUZx33vmKlBKCSlx0SsxMUbfJ9Zc+OejuL4zLL7+CxOCnSMB9Ce9///up1D1F9cXe/vj4GG6++Wf4+c9vsUgh+8VZxwLa4ZPgoLFYvLSaAF7We04l5+XVr341Pv/5LzBG4CvVueTJJuq/ffv2Yf/+fWWTorj89a9/hf/8zx/ixz/+UfmjAWlbML733ntIKAlxM9NGufK8A2tsc/uu25e9EO928n2eiWMyo4i+gR7ccdO/Ysu9v6TKTlxTWljo4bTRjatDFIelJFh4Wc5Pd5X+VecgQWqpbyzLR6v8MKrHMjPNjzi0Kc5JgZR/hvOyr4Mx9Eg4Hejax/vEIrfkVQBnAOOMCzidzGDggfsxTjLP7fGhb3AUESreHPkk3XbqXpDLJOGVofrQT3Vgnvtf3b2FLQAjJLd516iC5DvhY259VQW7Iu8e9sJfRbea/FvnZLxBko4TyRm3p1mSZ04qHv3rV6ntqquvVnMuxjp57x04sAeOnIuEYB7/dct/qzbkp721BTUVy0ighZQSWPLk/TdZDCNNojXjjKK25lSqEknQ8V5OZlJoIKE9FB9DbtwiXOX95WKdYbp87dj7HPFtRsdzfXiICsXG5WvQuKwVK1avJi6DanQCWYF1RrNjxGgIAxPMDzEWZNiPuppqdPd04WD3QRKGIwjQHW8hz3eu7R52c04FJ3WPEwu3m4Sriy5YSSbmqXZ05KapskxS5ZhipMEI1lKNO0EiNkJlpZCxaoy2OVcZ5scgYBAwCBgEDAIGAYOAQWBJIGD7z+4lMR4zCIOAQcAgYBAwCBgEDALHBQK333Yb/vzPr1VqoLkDFgLuda97HW6k+7OD3d3o6e3D/s4DGBwaRtfBg3TvtxOnn3763GpH7fzLX/4H9NKdYk9vr9pbbQ/h8S1b8Pd//1ms37BBLVTObfBuxgl7w+v/TKmA5l473s81eSE4zCYuSgSNWnXnCrKslZMRc3BxWJIsycvi8iE3XlTKOr1fqLy6ZtnhZWUvL/HLuHDvyDGGXj5DcQkVjFyUL1CJVOCisxw7ed1T4GI/UvByEVoqHlGfpA2WVUvvXE1X/Su1K9myyYK5RN2yrll70UAJOaFxUEQi845mEhedQi5dcsklXGgXkgXKdeiZjIsp8yRExkudpH1Ri8q90NTUhAsuuIDqu/fgW9/6Z0X4X8qPBYSolCRlJQ7fj3/8Yyr2LIJEk2Oyl2SpT61jlbHAj25T15m5J+Wmm0mbN2/GNddcg9e//vXqowBdXko0N7dAcFKEBbESG3J9cHAQ27dvx/j4eNnQrl27cN111+Gb3/ym2h555BF1TepIf3t6eiBlpH5pGOW61g1gO+Uhq80iTcTOi0lWu4fG7MXYP/K6RXQP7Z1VXPCRJGOsr2tAQ1MbRsbovjVH7Vh+kXFzKFletxJJUv7jCq1Aw9or4PfwHprjPrRrbD9ee8VrSuWBH/HDDtBd57pTTkf7uk2oX79RPaPyZEqKM75edWMdEqOM2dcWhs/hhc8fRX11HR/XAvrHSTK6Nd1nmXV5+CGBy4+Dg3RL2jWEm2/7Lfbs71dzrkrwURc68Nkd2zHY3cP7wo3xsXE817mbx4yNyNh94QBjLqq3jvWbq6nESG5KbQnGHBRXvOp9wZ/f3H4XsezFGsYGHeA9KUkwPHvTRqTHO0heA+FIVOXzTUf3nSeituE0RBwTGOwaQ11VDOnJJNLDEziwY6cgiJBf1JnEnFuSpGhtpA4RlxfbH30YK9e0Y826EzFKpaPHmaMKchDNVBFayXqH53IOfoQQQ5QxCMcTE/Dzg4SJiRESvF5U1dfCxZt/eJhuWUmQZ0hQijpQRiqE7DjHIkmeDyEC3Ww3lZliH7v4jUaeKsUmpKa9aG6sIrHoRCTgJ+kp9XMca5Ef6UiMR5MMAgYBg4BBwCBgEDAIGASWGgIznwQutZGZ8RgEDAIGAYOAQcAgYBBYogjs7diDt771qgXJs3PPPRf//h//gTVr1s4bvZCFh1Pszav0AjMqK6lKsKVAIIhTTj5FbeLO8Dt0Z/ilL35xnjLw/vvvx5ve+AbcxjhoQngcT8lOnsi4FyIt7GX0dcEpHIlQpRZTBJks+Ud9JOYoopruSotgZibJRTnXe7lCElEWkkV142K+UgQJEWcr5+BUuLho7BAikPmyxi2k3/CdNyLnuY1L3+JG1Ak386SA1D1/uhMrXyEL6GGW5fI4F9erYw7kejJI2dtXfVDVZvqlpH4kKUT9wrIu+ZFOS59KqSjqF7+byhenypYSjOYHRy2LkfiQPh7NpPHWhMsGkttr167FM8/sVK4wN27cqGLiicLtD5EsIq+gVLlyD0R4D0QiYbS1teLcc8/BP/3TP1Ed+HPlulP6MzAwoFTFomaUJPeSjEnbkb39/pI55OVyWTnQGFi5C/+effbZeN/73qf69UU+47/4xS+Uyk9s33jjjXx3vQ3Lli2b1ZYQfDt3PjMrT8jVQxGsQiDec889OOOMM8odkTasMdhulPJVazD2MR7JeMrVSwe6vuxfSP259l78uQP7evajoaqFpI8QYBb5Kc+zzJ+o46amEzjt5RfCH4lhcHwADTVNqpw89889sQ2Bhhj5fR+a6yop7g2ynjzHToSqm6j+m0IlVam9/d2qjv6pDzXirVdfhf936/+orD10/xqrbqBYj/HyGiSOX4LKP9HGCflbxIO7foOLg5cx1l8QuaCXLkOH4OfHBHWtK/iMe1CZ6cW+vTtZXt77bJ8Kwb07d9JmHd2RjmPT+nbUhh34+X37rIEJ/iwWI2HmchXR230AeSoPs9wnH3wAexub4W9upXqP5LhMPfHwk/RKZxNYtXKD6vNEfBRvf8Pr8MTjj/NyEXv3deDN0Tfiw3/zv3lmVbr8kovRP7wHVTVrkWY8wKmkjq3IdyY/dMjHo8gyLiCKAyTmeK2YIjF6Aka792GaRN2kihEo96OlRk0VohhJ9KK5tZ4xFb2KnEsMjKF7krEDq5pQEMzU7auQQ5pK5NEiFYdUUkaranGQH9xkJxNw+SJYQYVlhOpEf3ISHroULnLSM+kEfPxbPz05jYoaujvlfPDFDS9djg70P42Y38eYh372h/0mCdzYVI/u3lG0NVVBSFgnX5z5ohu/eWAbLjpnM8sV1H9rhEK22IwKPfNjEDAIGAQMAgYBg4BBwCBwrCJgSMFjdeZMvw0CBgGDgEHAIGAQOC4RkHhmp9FFp7gstCdZnBaF3ifoulOtBNsv/qkds69//dcfwpvf/BacdeYZOEj1oj395je/wQ9/+ANce+2f27OPu2NNPsjANfkwn4iwlFZyP4yPx9XCtoMEnNfHJe54AJ6JnHJZd0jwuAgsa9B5LigLIShEgiziz0olhk23r5bLWcSZexYRIQuFjCktvOsF+PU0sKGRccG4yF+kakcspqgmDLBP8+zPaownpfYcQgryVOLKOWaxm1KBNsdFGyhHpUSCsNBEJR/bKxcvX9SFXtxeMJCtsrISX/nKV9BNF34tLa2Me1etFvM1RnNbkXxNvMl4LDsWESfHOkkZif0mc67nXUYox7rcxERcEWwHDhxQimFR4Anh7yShILbluJlqJ3EZLPeGg6SAh6SAx8OYZmxL25ZjIRRl0+o93Q/Z6/Z03kx/dM78vdiSdoIkLK666q148smteO653argFOPaSazB7373u+xbUOXlclns3dvBMs+WjdXU1DKeYK2yU87kgbgXFrylHzKurVu3KrV0VVXNvL5KPem/Jj1VPD27MR5LXwV6uec1xocao9iTTeN3qLJzmnrJTtkjvGzzKxhDjmQ0hXU6OUtEvShbs3T16fP5EKfSrDpG1ryUcoyP175hJYmyPOPNhdDT3Umyqk3dK1LExa8EPD4Phqky9/st5anky93qpovRMGPc6ZSk61C3u4gIXVuOxvup0BM3lNbDJ/TWOaeQlPRXYGS0FxE+1A0NtXxnZVTsvo7nnkOslh82kLTUKc17JSuq28w0csk4qitr8PD2Du5D8uSrPshrYpzEW8DnQENrO7JjI4hnM2i76FK+bOIYfHYffHSfrZO42EyPxtGb6FBZz+7ci5NOPp0fFvCzBsoAH9/yJIlMHzr27+d1IUYdeMe1f43BVCW8iRQ8JOAKfDbLiUAcOHiARGEGNXU1cEwMkDxsxyhdnHb3xhFlvL8sYwtaibEdGY5w/84diAz3Ir66FZXnnIMJcZkdIaGXT2Oosxs5uSElkfEU3LKMPbhhw0kIkqxN0hWqyykfQviwrLEJPj7fXq7oZD1RzoePKsAsic8QUuMJeEMBunLdjRWr1iBHdaCQlZXhAJ+HKW5Rko9iJ8sWOBc1HrZjvYtSJArdTg82U8XYMzSEtvrKP9jHRGrc5scgYBAwCBgEDAIGAYOAQeAlR8CQgi85xKYBg4BBwCBgEDAIGAQMAkcPgW98/et0HSZxtGaSLGzfcMMNeAsX4Eur2zMX/4SPGhoasHXbdpx04mYu9M9WobyXrhAvvOBCLF+x4k94BC9d12Qx3U7I6GN7vl5wV3nsiiwlk57jYi+P6YpOiDrhCKxl+cX7qvg3kRUxiUtO+R+EeXVKJKG1xm8tyEuWIuykIivIeYkDsBmw7KqLLCQUoRCC8+yLDVtSMQJVVbYlRhesI+SMrRIPRTMom86Xsc0pMrvCCzyTZ85Llc/JVL/KppOQb5L0fOl8nbdjxw50dXXx1FqA76PqR+Lj6bkUN5/bt29jvEBRa1k9b2xsxKZNmxWpI3aTySQkBud3vvNtCOHzIFVRF154oXIJXFVlKXT3k9S49dZby+6FhWc466yzVcxD3Rd733Rbs/OsM2lzoev2svrYKif95p3I/m/adALjHr4avXQlLO4NJe/hhx9i336JN73pLYqUGyLx8OSTW9S4xI6Qk1dffQ3e+c53qv5KHemDbPfddx9J0P+lCBwh+/r7+4nXDlzAd4XVttUTGa8kyRN87777buXK1F7GKiFlnEq5uGHDRpJgvENZZ6Ek7UuS64uVWajeC82T1hbuyWyL7I1S8IrqbFZiDE+pH45WYt3mM+Ghq9tYrEr1Xc+p3MO8kSG0lbj8bV++ksQS3UzKk83xyocCLhJQFKBimq4v7YlXSYY3lu3dcccd6O7vRDhYgeGBUVStrGZx6QFHwgdxZf0GDB7oxtr1J6L/AOMJkrzCJDCVpUtNuhVN0e1muLKh3ISDpJWQw+npJFa2tzHuYRBXnBdlDL8EPlieXz6HARLQjLPnSFFJVxnj/ePCwCjj73Xw2YqP4OATj9KmPJck23nPuMLVrGMtg0TrImhvaUCMKttRKk+7DnQiRNXhno69MnwS137UVlSijsr3kdF+ROqqSaRZsyLvWmQSGKICV2xnqEjMEcvp3v3IpBJYQaViIr4Hkx0PlVBwEMcwGpsjqF15Evo6O7H3gQdQS4VxVawOORKzNeEsEgf6yrAJGBFPDEPdI5yfQboKbkV8YBBrGAdyiOpJDxWDOTKNTe3NSJIk91GFWeRHIalsGpnJFBoZC1G9a/j+LObixDBCErGW/RvkRxP1iPPjgkpi5nH7sfu5PcSvAVPpac7jGOcxgChxGR6dRE11JedZemOSQcAgYBAwCBgEDAIGAYPAUkDAkIJLYRbNGAwCBgGDgEHAIGAQOC4Q2Pn00/jGN74+b6wf/vCH8da3vX1e/rGQIWorIQZPYMwmWeDXSVQbn/70pxgH7SeyCq+zj5u9JiCOmwEfBwMVIvFnP/uZiu2niBJF4wr3MkO6CeF/0003KdJPk0+XXXY5Pv/5z6Ourl6V7SSZcP3115fJRCHUxS2n2A6FSLQwJRJUWZEw1KmpqVHF+RO1mCQ7qWUdl1gWXaG8fyEEmGVL7ArJ9sY3vlERl0888TiJjYLq9w9/+EOccsqpWLVqNYQYFbJUJ3knrFixXLlDFcwkiS3B6YQTTlAKQv2uGB4exqOPPoqXvezlikzUNuyvDCnzuc99btaYdTnZi4Ly7W9/B9VYG+eVWeg5tGNnt3N0jxmxLpMHBVvC1lGyRzeT/EcI4efrAlowb1m7gUpCqsIUnjP3m7oFS69XIeFkvD6vC/GeuxBrPIeqtIDCRNxuJlOMCWpLBcYSbV3WrpRrOarnJuITjKnXhMGRXt6rTdizc5etdBE9XR1UAgaQSk6jtqVFfdwi7m6FqEukxuH2uMmxxVnHun/E7aWXyt8qEoLDXb1UErqQyKbQNWqp/MS4uMt87JkHcO3r3o3efZ0IkMAbH6BKsa4RnjNrUJ3fiF1UUDro1lisippyvG8/aqk6lBSgqlCEf+s49w899CDdHBfxwU/8LcbpJlRSS8syxubkPcLnZuW6VYztFyndZ5ZWcSKRQSDoVx8iTPI+a1u7HHmSdJH6FkxOcR9ahuraHrYtIBdJ5I3C6Q5BoqzGVi4nKTqB+OAAFZcxeKkUTPGrjGR8rFSa9z3rhGIVjEWYQobz4yWBXUvl4/YtT+H00xgLkePpmx7EZC9JPvYjzTmI1tcwDKOPuLLfjPEYp7rQz3vc52/hOyFJi1TyBhp4W2VICFYhkyUALNtChejgcBy79g3j5aetQnffMDq7+9DetJ7N5NRHEAoU82MQMAgYBAwCBgGDgEHAIHDMI2D9X9YxPwwzAIOAQcAgYBAwCBgEDAJLH4Hvfe97ECWRPZ1N92Nf+cev2rOOuWNRN91ww43zFh2F5Ni2bdsxN56j0WEhHhbbhCjRZMnRaMvY+MMhMEMozZBwM3kz/dB5QtJYxNQMMV5XV4d3v/vdkBiGQrrpMhJ/b2xsTG1CCEq+3Cfr1q1T7nol9t5C95SUK5BcKQpxwuMXn+TenbHS1NRE0u3tirC02irQXehe9cxLf3fv3g1RNuq0cuUqxkVcpsiXuf2tISGyefPmcj+FJNu1a5eKl6j7rrHT9vReri+0CVkj/Z1bT5fV9XVf9PlLtWcv2U96v5zuQz7Ljrk8JIcsQJ8vISh9lJibHrr6FPJTzFjEoNX7aRJGWf03RVgkAiHqQH90FYkgKstK7cquUJwhmdlF5U50ZGSQ6mLLha9YfPrpp0hUOfDgw79EA13qziQHyTW6Bi161L2WI8kVCVUhwHiA4eoqLFuxCSGSwVMkDHVy0RWui/RZ955nMFkYw9M92/Fsz8N49uCWcr8EFSdtHejpRBMJ5hTjH44MjiKbostcpxeZXB5VrcsttbEyXESw6EJPT5/aWjdtQIKx/K77/v/RzeKOX91Rvr9WrV5BFWMTCc9h4d/QPzhkiZ5ZWoi+MIm4NqrZGRIR9VTvJUnkOkggTtNmmirCKUITIkEqpUWtGPI4MbprBxL9+1EY7EOIGLnplrQ43I8M3ZoGM2k4+EGMJDXj/MlP5uFJFRGm2jIYDikCcUV7O8fnRiKZQkDK0Ieol26DvSRZHVR6Fvn8B0nkilo8QkWmRxSwxCNXyPKdEOB7I8h5KHC6JdYgO08VqYc2BsfiWLGsnuRhSrklbaqvVgpEcQdskkHAIGAQMAgYBAwCBgGDwNJBwCgFl85cmpEYBAwCBgGDgEHAILCEEZicnMTNN988a4Sy4P+lL31plkJmVoFj6OSVr3oVrrjiCvz3f/93udeyKP+Tm37CmE8nl/PMgUHgD42AEDTLqIgSxVmexInEuptLIOk+iVJvxYqVpVPGOaOLXHGHqZO4At2wYQNPhSQ4stTW1lYmyOSZl/avueYaXHDB+djCGGi/+93d2LnzGZKBJEOo+pEy4vawvX05XvGKC5Vr0VWr1qj4gnJtbt8t8otxxNhPiUMoijmdhISTOoslD9VdQvpt3LhJ+CSV6uvrFNkgdnVbF198MRV9j9DV53ZFeEl+T083nnjiCYyOjhGzFaqu5J977jlUaDUv1iQuuujikrthq0GJmyjuSaUf0qYoDdesWUe12tiiNuwXvCTMGhoaVZbUtyfpjx6DPf+lPBYiLk8FWyhKDOgC9IUmIRcl/l2aRJOXce3cQgrOSkW60QwgN5VEgaRQIZ9iLDkSRiSbinTBiWKUZFqR8ejSxDVHV5P09VlORQyPjyEx1Y33fuAv8L1v/5ua109+8tP4r5t/hgo+L8/0Pi09YA3OE/9NjY7DFQnRbSXvUU8YHrqnFPLMQbeXY1SJR9asQmVgJm4hWSv0DvaTyKIJ9snlCmNd0wZsaj+L/fqUeoSEmDvtxHOokBvF1uEHsaptDdacdSrdaAaoIuR9y2FMxIXIEzRE8edF9XrG2HvWUhtmGHsv5HMjyudFnt25sXolRm86IePOK/LTm55CUeIcEhdJFORR4T7AZ7KSsQqHsGztCSikJrF/eJD9plovM4bBg9KWzGqRsQ/dCKzegKmRAxxTEMHhUeQ9LozSBalofLvoGtRLNaWo+cQNstyPQdbxV9ehmM4iOTZOZWOGRG8Ig1TYChEYoEvYsfg4YiQfB3q7McIYvSvokjtPspeOiUm+ct4lpiuDrAaDdWUy0EUVYZZMp4uxC+Xh7WcfWunGta6mkmpREpiMS8o7gWTizHMsYzbJIGAQMAgYBAwCBgGDgEHg2EeA/3085/98jv0xmREYBAwCBgGDgEHAIGAQ+IMiIP81NXsp+eg3/+/XXYf3vOcvZxm+9NLLcDvjOC2VtGXLEzjrzDPVoqUe07L2duzZ06EUUTrvpdhzbfWPnuz/Wf58iIheLgS//wMfUASL0DcFLvDW+bL465PCeM3qqFqMPtTgZGFd3PDl5SYmEEJhSdy/QyW5yjV7LiqLu0FplTn8l00vmKxnxEHlDstTdLJIsXJdsS91cuyUYEGxk3TtsClHAst1ysWIXvI3cAerlEKorHY6bO2FC4grW1HhyV6TRH6/RGGT8c4mjcTNnpTVcylKPlHZyF6UOXJNVHyHItrm9sJuY+416ZOoh1OpFGP2TSrXnFI+TJ+HQnIIYSZk3+HuJ+mb2JL+63HKGHTf7cSmvQ9SRtqXejpJWYlVZ8cmT3JKCEuxbU/SV7Eh7UuSY8FGtyvHkqevyV7wk/YkX7chbcomdvQYVKUj/NEYa3tS7XCYHaHpF1RMKKwXe9+KjdRYL6YmBlHRvJH4zCYFBccCXYqKUtAbCJIckueNbiL9jEsn9zozHEJNMdYgJ4Ux9cbourJWjUewiU+MIce6Y4lexvyzPtxobm7EPffeyTiBEj+ygPPOvJxuQhM8Bob6e+H2FTE1PgmH10elYQzjW59AfN8+LH/NlfAFo/jPG3+I977/g2yjiPblrXjogftIZMUUcZVPDsBJl6X++rUk/cLyulHvkS2PPYBlbS0k355CIbYcrdU1irOT18U0lYfbn+vAq151ker3mlUr8dCt/0Y2z1IxpjJZeCIeqg2dePt73oc777xLlZOfysoKPPHg/ciz82669nSFonBSLHnV267Cg0+IWhHY+sT9CPpFgefBsw/+Bg3taxANk1QLe5VCs3dgBE9vexLv+9+fE5O44Nyz8NUvfY62a4hdBt0dTyMWqMby1auw687bEV2xEvvZ3zd88tO8vy0l31MP3w9/jBgQswDdl3r4YcJwfAptrc0YHxxGjGrFoZEhxgOsw9RQPxqpmOzpGURVXRUpWQfndxoxEuWcQj4fEiNyhmjWz16G98JUYpxzHkJlNMT3NN9RRHiaqsOYBJQ0ySBgEDAIGAQMAgYBg4BBYEkhMPPZ6pIalhmMQcAgYBAwCBgEDAIGgaWFwC0/v2XegN7//vfPyzuWM0499TScc865eOCB+8vDOMD4adu2bsWpp51WzluqB39MEmKpYno0xiXElCYBNUG1mF1ZcNdldRk9r7IXsks2nafLHGp/qLLSN4kTKG1WVFSUzeh+HqpuuTAPpJz03U4Y6Ovalj6376WetC+bJvYkb267TrqC9JI8FlvSZ0narpTVx7KXzW5DX5M6ki/4Celoz9fH0n9tX8o/32Rv9/nWPZrlj5QQ1OOWtqXv9iRnnmAlqmINvOZU82PHRnAUijY3lUJhbJiuLvOK+CL3hBRdckqsPCHeMkIaUk0pajt7clDFGKT7z4kM4+RxThWxTNIp42AsPZJQvX0Hy32Sqm4qznIZxsgLhqhsm4S3yLh6rY2IbT6RHxb4sb9zD3JUSAohKEmch/pcjNfHcm4n4yu6A+i67zo0nf9X6rr+aWhehkhFFRV252Gcyr88+yWx+1wc3fB0mi4xhyz2kGaFe065GuGY2K6qZ/LDVMKtgzu0EstahSgU1Kz23/hnr+dHDxm4Sfp5SGCGYxFsefxhpIUkZTkhXcfGh+FqbEBX7160cZ/3OTCRHUP/nn6cvHodgoU04lMyJsuy1x9AU/tKxvHLYCSeACJR9rKIzv17EGlqQ5Hk3uxpJGFO8BpqaduRI/nopUKzj/E2o4hTuSy4Dw1PUHkYRzXjKAYrIujcsxcuKiK7SS56KGUMRytRjDGWJ+cxSOztSe4ZeWZ8tJPLiWtSuc5nWp4vjjPK59okg4BBwCBgEDAIGAQMAgaBpYeAIQWX3pyaERkEDAIGAYOAQcAgsMQQkGXKzv37543qlFNPnZd3rGe84Y1vmEUKynh++9vfHhek4LE+d0u1/3ayxX680Hjl+mJlJH8h0m0hO4fLW4gMsrdrJ38OZ0uuH6rfh6pv74e0ae+Dvd5C9u1l7cdi036u+yd7+zV7Gft47fn2Pix0LPbm2rSfL1TnTyFP+hiPT1AFRvKOhHA8mUZ1zM+4cF4qKQski0gIkmhz0EWkELIFpTqbTRrKOFwuN0ao1ht45CH4WhrQfNEVJM2otqWr0QJdTjpd4mI0hPgo4xvmZ9cXFWouk0JdZQvd6y5TcSHFxe7WXY8h605gcJRx8uiSVMn2yLMFw+IW08u4f0nEKtzIjY+jGArSfSfJQvaDLBTnfWZ5wk3SMlRZjex0HJOdB+HK9sHvjWFkz+O0Y5F3SslYdGI6xVi7jIPnoSpueKAXI2Q7R6kmbQj5SMCxrMXzUW3nQSaRhL9qvZpGb3I3pgb64Ai68efvvAb/fv0PeT9YMxymQs7LOIhpEnjiRnPgwH661vTTFatFagtxu3dPD+Jd+xBlXMRuYpRxT2GlpwaN7Y3o69vP8YThJzFoUYgOkp5ZkqxZxMf64KOy1dHYjsGRETT4XJgSUp59q2hfzj5YylnpuJsdenbbVtRzftxUcdbVNqGj41ksb27nvHPM7M+y1WswOjTMslQ1+kLwM85jdzfxp+vQ6DTvccJaXVWpVLTSexZj34RAt7AhX4sw51mUhE45YY8lZuGRktMWYubXIGAQMAgYBAwCBgGDgEHgWEFg5r+6j5Uem34aBAwCBgGDgEHAIGAQOM4QEKXA0BDVDra0avVq1NfX23KWxuGJJ544byDbd1iqjnkXTIZCQJEYPLKWyXlQWtSWi7LATS5q0WQVnfnVNg5bj6vKBboYLTclq8wzZ7PaU2Vsl4tqRbpcc3ZZZkt/pX3biJRlyRIzs9NMjly36s0usdTOZL51mnsshNjzIcW0nReyl7Z1+4u1q6+L/RfbL7GlbWi7i7V7uPHo+nP7pd2bHorgPJztP8T1bDZNMtCDTpJDAcZ+E1egmUQQlXSdKcc1tfK3gc8o3W06nXRvSfeyoEtPb5juP0kSirtgB5lDuZOiaxlvcuVy+EkCTTEenTO6noTdGLx1tMFnNZ1KYmBwhG5FA7OGlqVryTyJQ4/byTiU6xUpmKOL2IbKNrrPBDa0nYwvO79PG5ZSLpnMsK9UFIoasOhCmsrECm8I3fsP0E6GLjQZa/PBR8ptyBxkMtN0z+lD5yP3srwDwWqqG8f3KdeWJYez6KMSMDhEV6jdnfDVNyHauhKVVPft7e/DJN2HujlI/fopJOPIJzuQylSrdgKxSvijVC96YnA7pK0ApkhairLymmuuRmaKaj6668wylmCe/WmqXEfc6P+YSbBzpSZQV1+LfCqOuDeDocFdiHuieN3Gv0AuOoixgQOoaWlS5eXHTVIyPkQ3qMEwiUa6uiUWEs8ywRiE7StXItzehF3PdrJ9sS9UIuuQMK1tqEfYF8Q44xu6ifeq9rUY6OtFHWOUjpPUnGJsT8op2UcvaqqqMXKgB5W8tn+oF6srGDuQZKnTU81YlXQTTe+hOZ4nScpW1jUrN6fq9S3vDvlHGjfJIGAQMAgYBAwCBgGDgEFgSSNgSMElPb1mcAYBg4BBwCBgEDAI/EEQ0CuOL1FjCS4ETk1NzbJ+1plncbHXUizMujDnRBa5JdaYxBcTd3GHTVx4H6ZyQVx27t3bQbVDH5LJpFI4iXvCNipCNmzYwG3jkdljgwm2LwvQ0ofDJVGczE0jVJ+YdGgEhOSQBWQryRFvSrW2S2Jg5oIuYNtb9VQR/jjU6jCX27kwTJPzkuTpNWOtZZFYhEIQUlRUqj+7mjKj6jm4aF3qp2rHKqeuqyXw0rmVUTqRQbAvbIyCINsYma06YxWb+bX6beGhAJi5dAwf2Uks+zDmLuDPPbeXPVrH9r7o9vRet6HLyF6uzb2uy83d63pz8+XcbkMf6/3c8nY7uow9b255Obdfl2Ndb6Gyf+w8Ia3iE+PKZeco39fadezExAQaG+tVfDhxySl/IoqM4+nITqL3rm+j5dWfYtw4iXdn/S2Q+IsRklJJ/o3xMC9CZZx4x0wzniAjCyLP60O9PVTfDaChZdmsYbuoKHQUppGYSLGdmWft4YcfwV+881qESPi5SDTq5KQKcCqeVMrDabr5DJN09JK8q6tvRSDsQ4pytud6e3VxusokGcfYeVn+3Wq+4FJMxMcwMbgdroZ1KlaoPP9CnE0Nj2JZ+zK4SZxlUwz6xxdRH+OspqcTcDM2ns85yXLyt7JAxZwX6fFeKvpGVDuO3BrE3YwB6h2Biy5H3/Xmy/Hd/7yFbjSDjN3HeJm0Qe4TkaoYEiQfA1QEBkMReSWp90844EEhPUE3qMvgPziCWLiC5KIfz3Q+Br+7ivLIagRqrHeeovj4HvNRveilc1NQ9ecITDFeoheRmkqqMccRaqlCoIr2pQH1HiwiQeLQW2DcwP2daDjpRDVGJ/scoTK0SNLXFXIxfirnjm1N0RXswY5OTPCF2by8BfUeH6oamjjnBRzc14k2Eo9pxjL0UU3org+TJEwzlqy4NHaruKRzXR8rkMyPQcAgYBAwCBgEDAIGAYPAkkNg5r/Sl9zQzIAMAgYBg4BBwCBgEDAILA0E4vG4Un/YR7N23Vr76bxjWdR+9NFH8N73vgdP7XgKbW1t+PZ3voMrr3zNgovdabqhe4SLud/+9rfxy1/+z6wF8nnGmdHQ0IAPf/jDePNbrlKu4xYqI/3+0Ic+hJ/99CYuQrtw7bXX4qv/+FUuqi5ODsZisXmmpqen5+WZDI2AxaD5qaYJBiQGGAkYzn3Az9VnWdxP5NT6tS4ta9lq+Z7VRBUi3JwsWYtnwFwwgELzci6qyxVViqXFPt3eZdLo2L0f03RXp0lBIRBX+h2o9lt9cFA1VHTQBWC5rlRnTCwhheSQ+0w0jFx9M9VKskhvtWEtmXMBfmoaPV09JLFJgNOklBct08qACzWighEb3MqJJ04SE2Qeyi1KW84cF9zZNymrR1GucwwfaNJKk1X6/Eg+Djhaw5Y2ZZM+6H7Mta37JfkvRd8Wa1fas7ct55IWytM25Jq+fqgxWZb+hH55c/d290BcbNbU1GB6OkX3nnmM0GVnTXUt+aYIFWV0eqncUDrQt+UXaDjrzXwmSs9iae+hcm2cKvQIP/jI5LPwMhZdIVdABeP3yTNUIKmWmBxDRYREonoCZzDgLUB7PoSiAZxw4kbceuvt6uJ/fO+HeNfb3oVUQZ7YmeQn0ZZGBtOjAwhX1pJ4LKKHZGNtXTWFgySm+PXCxpaWcoX0RJwuMQ8iuLwRE3SvyUB6qKi9GMmJsfKc8e0CLwnSqWQCvmAVRsamkBodRDLPuJOpMWTIcCYn5aMS1mfZTDaPZGwdKulKU1JmYhQjPQPIRivgIcFWXxdS9OHff+bvqKZLw18RVu8gGX20qg4Dvf3EIy43lSq36uRTMN21A/tHqHZ0htHavALRYA3jNI6x3SG65Mwjwnekleg+mORpwBvkdboUzU2ADCn6kyNY2RBDFedgbLCXY5lSs6PfX1XRqCJ23ZUxunX1obd/UJF59fV1GBsZo/LPiWwigSipxqmxEXiijIGadqCzu1eRj3nO29T4FEdfQJq2i1QTZqgU9PgCyHIU4tI0m0pQxRlSuKp7Rt6jxNUkg4BBwCBgEDAIGAQMAgaBpYmAIQWX5ryaURkEDAIGAYOAQcAgsIQQ0IvW9iEdTvW3b98+nHfeeUrpIfW6urrwpje+EVu3bVMqP7stiVd4xRWvxq5du+zZhzzu7+/HJz/5Sfzt3/6t2j716c/ArjIoUOXx5je9CXfd9duyne9997t4/LHH8OBDDy8aW20hEmGh8ZeNHvcH1oJzajpNYiDNdXwuVnPBOkOVSz7ugcOfU8vhs2CS1eZSktpCIubcjC/VsgGN7/oCnL4ol4pnLwj3Uy30HZK6e/YcLJOC1Jng0xur8PIKEgk0JMSi2FPmaVOSoiCYKdyEcHX+NSeg7pq/Zxv0LzgrFbHz6Z24/sv/gMcee0JdISWBRk8GH1lfieqIX7jKeSwfzapMaU2Rm0ISTlAtxAuquFyQA1uS+0kIILlH7UkTRXpvv/bHOLbf97rP0g99/IcksQQrjcuh2tV91mWPBDddR+913bnnYktfW8iuLn8k5RYqK7Z1/qHaWajtP2Se9FEUfi2trSRdXRggsVZVU6fu84qKKF2KPgWnN4KWxhrh9eChu0uPj/EG6zZSMcY4cYwlZ08xuhwdGuul28kGevrM8T0ywWeJLkZZN0tV2cpVa5ErpjHaZ1dsFzHKmHVJEmedjG147pWvh+PLX1f4Hdh/EF63B7GofPxhvQekc5MkvNITCbrO5McH6SSm+KFBS1MjOvZ08mOA/Vh/4jqSnDN9C1RWwEf3ltv2dWM1P2px8GOSifFBNRZ5r6iPCdjHsVQe1R6qEnNOuKnCGzrYj9V0h1rtbUDfZC+e+f09ujRCbLu6rhFToz0KgiyVjhU8LyYyqF+xGedNk0h13oi1K9qRSKSRzaUQrm9AL110RquiSFKViZL6Ud4ee5/ZihXrV8CbJmU63g2k6BQ0O4a9vdO4YP0kxsZGGQ+Y5F8pEVWkR0hCkqx0+mPo29sFbyiAHk8ezmQBQbpRTU1T2SjvqBJ2Y4lxuhZdi/hUEr0H9lHV58PESBzPkaxtaW0jeUcCN5/gRxUjKoZjTUMzAokJVOS8lCV61Lu8tp5EZSaI+HgcMZKbkxNJVDImpNNJF6ZT3fCSnMwUXHSDmkeCH37UVDfMe2/qMZi9QcAgYBAwCBgEDAIGAYPAsY+AIQWP/Tk0IzAIGAQMAgYBg4BB4I+NgF73fIn6EQlbqg9ZCNZpz54Ofbjg/nOf+2yZENQFpP6Xv/xl/PjHP1FZsvD4ne98G5/8xCcYu8mK+6TLyr62rg4nMcZfNZUo4kauo6MDO3funFVWbHzlK1/BT3/6U2zbth2hsEX2HDhwYBYhqO1uIykpi9hNTTNxlvQ12Yur07nJTjbOvWbOLZJI5mEm8Zj/usjUuYUcs1+SQvNIMsYFo0qnyNhZTrcfDqqEHA66F7SVK3rCSFEVlCpyobmUHI4MnOKbVFbHuZbP9WUSkrqaVbncdCk/D9pmzC0H1TL2JP0vuENWG/Cw+3n224npPFWAaiMNQOO2LqnqlgfdcissK6SoDJoQSOFyf+ytcQ2dqiodP06uCBntopr1T5EM0nMre+mf3maP6Oif6Xbtlu34LHTdXvb5HOux6TpyPjfP3rYup/e6L4cqo8va97q8bs9+7U/1WPrsD/h4z5K0y+Wxas0qTNFtaCjsRyEfREVlJSYmeV5RjQyJo2KWT27LmYyjR8Ipzlhz9W18PizyrZDPKbeRNZV1JJdcfAZJrNGwL1ahXIqGoiQW+SxOjNJG0K7idmCc+eHaKpyzsh3D43QRXSJVpV9jA4OotpGC8ihGK2uQqqpCYrAbrnA9BrftI/nUjKaGWqwk6VfwULVmI+qdfP4bqyoQdlJtx/GmUxlEYySrSPzrZxt0fbmJqnl3YQIH+Dcx0tyKYFg+aSgiSFKxqphF47rTeGb97ZykonJAlJGRklo97UG9rxLPjT+GbF8ejcvaEaGi0cWYh26+0ERJhynGayxScU3CLB7v53ty5m/llIeEHkm0YNaJfqoqQ3yvjY11Y1msF90kBmsal8PLcUmSDzbSxTxcdPU5TreoUz1daG1fhRESi05nDd21phGrWY1+Ep+ikuYDwFoO1FZGMLB/D6ZJ8lXXxzBOBeUUXXqHqaqvoAvSRIb23REVN5LdRNATxD5iHKGyMkT3pul8Cl2792H16nX8mx5AkjETK2MsP3UQk3S9GuAcFKgSDbjDfPeDisHYvPesGoD5MQgYBAwCBgGDgEHAIGAQWDIIWP+FumSGYwZiEDAIGAQMAgYBg4BB4I+AgKx4voQpysW/YHA2ifLIww+pRfOFmhVVzwP337/QJSolLDuyCP73n/k0PvqRj8wi+U4++WTcettt6KZrOtl+/Zs7FYn405/djCe2bGG8wVFspzvSz3/hC2VloNgSZeLy5e2KOJSGxXXdQilLV2UdHXsWuqTyDnQemHetspKxmUxaFAG1GK+YOS4iW/8qUkxuS1k8V7H4yAFwnZvsFze5YN/kGrMKitETMlAKknxiZb0ppRhzpZxlVAyLEalXIgSFi7PbLR2rPJbR11S2zbZqQ9pT1mQAJfKPGUIwOIRtlICFPJYx6E2dW8XV+rlaQxcr0klu88hQZZ+XWFA2wU1vcknnl4r9yex0H2UvSe9fyg4KFvYkpKm0q/P13l7mSI91Xdnr44XGpPN0GW1fn+v6+lyX1+UW2us6+trcc8k/Eju6/h9+L5jl+c7O0rVnkXFawypGXIyuJZ0uxqhz+ki6h0hOuZFJpdUD5I40IFoRUzEDqxpaSWrlaaPAWIDjuOeuO5GamlRuQsVtpeR7InRX6cjDT5eaB3oHSZb76FazHk4qy+zJT3fFd+/YRoWwg+SUA5dffpm6LLfOnb+9m7G6a0kAAEAASURBVCSkuA/lQ8vEXvOPgg8+bnkq0p7Z8iCWtbViMj5O1aN8jOCii2wh2+TdY6UiCT1xD5zo34ru/U8hNx2H2+vCwc4dM8+2g39Pup6my9CncfKmM6j4G0BzbSWCVEYmGIfXT3eoHvaTs6qM+nxuBGN16OkeUJuf4807k3DHqKRLFag6nMZH6XI7N57mlkKG5Ju8Dle0raa6L48zXn4pXXjO2HOScFvb2s57ZpguVulmOTsAn4OkXboZCfdqHBzkhxMRkrCl91KBczLWQ7UfPWJ76Wq558n74I8F4KO6kJ9LYPjZX8Ez3lkan3ykQJKOKr7EU1sRDrr5PiQxSNI3wjiCyxlHMc2Xb0V1BdWJGcYVDKK5fSX2H+hEPJXEIN13h8IBFcOxbdly4p5DN68F6Go6lexnvMRBZHi/OAspuoCtte57NqjufwsuayLMr0HAIGAQMAgYBAwCBgGDwJJDwPqv9CU3LDMgg4BBwCBgEDAIGAQMAksHAVmQr6mtnTUgcfUpiruFkpQ/8aST5l0Sou6DH/ygyr/++v/AP/zDP5TL1NL+f95wAx597HEu7r4ajVTyzSf2HFyEDmHTpk34NN2FPkdlxtve9rayjRG6Vrvs0ksgrkWbm5vwyle+snxNH4jNluYWfTpvv3Xb1nl5J5ywaV6eyVgIASHTSNwIeSOL8ExCjMn6rtpbWSrf/qOvCXno4IlexrcqSmUhzArKpixSa2NyrDZtTEjFuYlZrFpKUrnEHCo7PNV7XUT6zcXyEo3AvsiI5J9SUblc2qRKuQ8sYPWtlCkXmaSePdkJJHlO7Jsshh8pISR2hOAWha3e5HyuS1J723Is6sS59XT9uXtdTiuEn0//5rb7fM41RlJnbpuL4aPL6f3h2rO3odtZrM5CNnV9fU32h0tz6yxU/kjsLFTvD5cn96gLPpJe/v/P3pvA2XXVZ4Lf2/f9vdpXlRbbkvGOV4gNATwYSIyTISs0E/rXIQxbdyfpTM90HAhJJiHbJCEEA0mACZA0SxgMCTYYg1mMjVdZ1lpaSrXX2/d9vv99dUq3nkpS2VLhKukc6b1777ln/c6550nnu9//7/cZ4+Pz+vl80iwoH9wSTU+CSjbxMTg/fQwOt5MmIdOo8352acbwH9giOSShwbkovysekoD0dkfTnvTBR1JMyMEW7MiValzvqQxefi66sYmHIthN85V5mg+104zp6153p1Gu4JxlXK3aIRmNSH6VSmU0WLZQhWX6vHOS5MrOzeDIj75FNVuNpFcQ9VpJJWd/7FicfBjZVA5uWwlzVL+l03n63outpBGl4FgihND2G1Es0t/f8WdpRrMCK8nBQIC4UMl3cD7D9J35USWZ6qAmL0AzovKxe5yYp0nTiHOY3STZupDG1VdfA4e3Y0DZlm9h9uAReBx2kn1tHJ46QmVlp41S4vDolSjtfxxe9tlFV4ORZhqxvh5sGx+iGrBMU6ARHD0y2WkvMzSpzPT0jCMw4IO3ksfQ6BBNptJsqD1Ik6BH2IY6SUgiZDSXCx1D9shB9O+5HMM7xthe+iSkAtPWzsNPX4MWSsGTc4tUXnoMpXM2k6MZ2B7EwwH002To1Ow0Hn/0EfpCPEZ/sAUMjIxSAUrzsC0Hy9kOl0GYclUWxbcOGgGNgEZAI6AR0AhoBDQClwwCa7/Cfcl0X3dUI6AR0AhoBDQCGgGNwAVA4Cewn7Z9YgKTR46sNFbIh+9//3t485vvWYkzn3z4w3+K73/vexCiToKYRvzVX30rrti9B9PTJ/E79AeowuDgIL729a+T7LtSRa3rKPk+9enP0OfUdnyQykEJohj8wz/8A/zlX/4/+MQnP4kbX/7yFfJSCJifvftujIyOrlm+9OnzNEPaHX76Na/pjtLXP2EEZKNfkSobVzVNiHIObEQwt/+FEElrtUXKElO6n/nMZ5Yx6SwAESp47rjjDlx11dUGWdOdV/Ldf//9ePxx8Zm43kXDQh+gV5Cof71hDrCbmOmu48Ved49tdz3nun+uelV+OSr8JY85XpWh0qjr7nQqv6RT+c1pzefqvsqjylLxct3dV3P+zXi+0nZOoWWxLgk0C00808xmIGSYEv3XT30M173ip3Agk8TQjj00qRk1zH9abCT5GESF126UDQW60/AzSHObNP3cIsntI5k2PZvC/Q8/gxuv3sGyM0hExXTo6jlrsTSwZ6gflXrJ8CfabFYNkl2e4TmSUS7+5sgLBsJvyesKTz3+A/QMDPMZr9P8KVVxfHkkOjKGwV07SFI1qK7Lw+WU9nVyNEhwJunvNjayDc1qA7NzSVw2RL+ICLG8Tlss1joyJTsWji6il/4Ur/rpt2L+5ElERiYwPXkEDV8AvREhETvpy5UGHqbPw+tJxkko5xfhoPKykjuGAv0BuqhY7KFp02qjCofdAitNhlp8JCcX5mBjWY5pKhsLQtqxPIKfPPBdDG2LIBGeQL4yQ9KPJo/pn7DmWMRwLEGfjBZEfUGjfslid1G5167A3aBicjCMxaVFjAXpA7acgp9mS1s0n71YkeKlhwYzCH88gmbvKBWNLpoxdSLodGN8fJjjnKI/wgSVfhmaiKVKkf4Rq1RHzk1XEOgJoZlL4vjhA2iynVaSrT6Hk8rHLOc71ZJUfS5Np+EPU4VI86iJkMvA48V8yXzcas/Qi+mnzqMR0AhoBDQCGgGNgEbgYkJAk4IX02jqvmgENAIaAY2ARkAjcNEi8OZ77sE3vvGNVf370w9/GG9845vgcNDkY1fYTqLuGE1x/vmf/xkOHDhI8vDNBiEnye69994VslDyPvDAg9h12WVdJdAvVDpN03RV9Pb2Gr7+xN9ff3+/sfGrEstm4O/+7r2YmZ7BJz7xcSP6o3/7t3jve96LbUJkTh6lqdHf46bqAu5mG+666w0q62nHb37zQRKd318VP0oC8cYbb1wVpy9+8ghw3/cnEkSReCGD0WzZYF/ugJkcOp96HnzwQXz6059mEbIh3ilJfHjZaO5wN4n3tZ5JSfXoo4/is5/9LPOsB1Ap2II777wTt99+u0EKdmq6sN8KGynVvLlvjpdz+bwY/Mx55cUACeayjQhTnLkOlVflMd9bbz5zHlWeOU6Vs7mPgj9xI/FmoQlO4bhkDonCy0qVnshxpW/PPfFD+hRMUL3aQMvixQT97TXKRSYmqUeFYXb6EIL9I6hUmwhF+xBJ9FNJSFOTR09g8okncflr76RZS2CoL4zbb9lDVWsDbkZkaGpUKVYVTtnkolGGi75Ai4UMrrnqWqMdcv+LX/pXfOC9v24QW2qm9/dGMD1zAiH6Kxzkur40Pwt3dYZtjSAai1PFOIdFmqc2OsdvC/2bemkGtTx3GI7EFbhs9wSJroOYXRBSjo2U+Ui/qbVqAcfmWxh9+QD+/eHHqOoDHDSnOUgfevV6FQHCYxWs2BAHCVDYyyjTh58EX2IEhaXjNPXppZnlChq0YGppUWlZKFJB6YU16KTqjvdIdFZyJC35e+nyLuPP/PHYZSg4qBJ0uBAJ99O0sR02ew1ZCjZFAdiiqVEbCVuWSizonZGKyHQhizrT0E4r+uIDVEnWqfQrUJnfRtIdJtl3lClpOlT8ILLNhUoR1elZVNI0URofpB9JKjCpLATNuraoTna6fTj83F6E+0ZYKc2N+ulHkGpA6fNQ7yBmZmhOdCmPeY8XLvonjsR7qWAsYd+T36VS0I+x3dcZCmabYNMVZE7Js3KusN505ypH39cIaAQ0AhoBjYBGQCOgEfjJIND5X9lPpi5di0ZAI6AR0AhoBDQCGoGLE4Fz75mdd7/f9rb/gHi8s5GpCvvhD3+I++77mLo87eilqc//TjOfnyJ5IQo9CdJUITRUePvb374mIViimbedO7ZjcKAfb3jDXehJxDEyPISPfORvVNaVo2wa/v6HPrSyeSibx/d9/D7jvpuKhD/8wz/Cxz/xybMSgrKp+N9++5R6URX+y7/8yyRaTt+sVPf18eJBQOYA/55fkAKWC5FNeOOcB5mjQkitZ4P7TA3otK9jNvRrX7ufhDl9ji2bDxXyPJVKYf/+A1ggAS5p1wpiPrSTp77KjKgyFarKMx/l3pnKW6uO9cap/qiyz4WN3D9Xmu66zWWrvCquO63cV6ShuieqM/VR46fardKofHJUZas4VZ7EKxWq3JPPVgqkZtCgOc6F579LW49NkkEVlFOzhsJOlHeysIvZ4EymzH7SH2BsAFa3+AB0IEDiiT2mv7syPDQtabGR2HKTrKIpZyGfyumioRy76S2/gABNglppPFTwGYt7cNlIFF6mDZPIE2JMBXm2enqHkM5XcejHD6BVqvDljevJvnVeUFlMpjH52I8Mc5kqz8jEZcbvSSLRQ7OmQQzz2ucfIInlRzqzhFS2CHtbTHMub1FQPegbvAb+/mvoly+KCl9KsbTp3zBBQmzlGbey7T686pYr6EexiRuuugzuWgVN9q1GpWEtX0Q2zTKXH0c3fQpeNzKGOfrLlU8mk6XpThuCLj9CrhD9BZL8Ax3+2SzwRn1wkWydp8nSCp/BfLYAb4Rx3o7aUvrlCFOBybKzOSoJc9No1xbgBMuiWc5GmUrJhoUqy4CCAA6nC9GozEUbf9dI3NIMaNtVgiuxgyZXC1RKFmkW1MfxFHPNxIHj4CCR57EVEAj3IMd6mvUsWnz5oFYmaUjys1aqY2B0guNJtWWhjgBNncapUvT5vXBTfRjkb/Dw4Ah6hkbROzCEAEljP8d555U3YuLK69FPxf9a5kPleTnTcyLrk3rW6vST2OK6poNGQCOgEdAIaAQ0AhoBjcDWQUCTgltnrHRLNQIaAY2ARkAjoBHYrAisvf9/QVvrpFm1d7zjHaeVKWZAH3/8sdPizxSxsDCP6ZMnjdsebhb+Cc2MrhWE4Mhm6SSJ4d9oWlSICQlTJ04Yx+6vHvqmUsSj3Pvxj3/cneSM17K5+L/+/M/h6aefWpVGNvTf/vb/bVWcvtAIrAsBeSYpDRIy5UKRQIpYEn+ee/c+a6gCXS4n/WcOGmSW3D906BD27dtnbJirTXPVXmmHEPtjY2P8jEJUsAP03SnzXMhCSS/nsVgM4+PjK+lEqSu+OBWppco7n6O5bVLnmTb/VR2SRqU7V1qVx1yH5JFr6YM5XqWV++ZyVRqpU0wfy0eCOY26NseZz6UM88coYI0yVPxmPorOzO70IrLjNqOZQuy5Iz08lznjWMHUP7gdjx3Owtl/FYo0KVmnYrBJM6FiXlQ4PaeHpjf5bFjpgLBVJ7lI4m32h4/AGRWftW2k5qcIspCFbTQqSRSSJ7F08hCfpYYx/gojIRkzhQoe/u7TcPfuRoUkVm5hySApJU2TdZSHJ2BVL3SQtGw0y4a/PX/ATZ+HKaT5W3R85ihJdCr16NfP62zBKo5Nxe+olCGCQKrgnMEeeFw+OCgBLNK/YOnkAeO+fLVZbyVLMo4EYIN+E70kQgdGB5Ca3ofsM1/F1A8/hXLuhKDUySPPWDmHG3ZtMz5o0schSdQszW7W6c/PQz+H/miQJjt9mKOPRVtI/DZakaOPxEAoALdhZbNDKAtGyRSVjq0s2gsHkIixnRYnVfEn4bR50Bvr5bylT8eq0VKj/hZfmGkjRmWgg+rIXgQTXpoUDSGzsJ8kZAx2t4MCwiDTSB1CDFLNyHF2RoZpPpQcYN1J34AkMYODCFKZXOOLCaV8mljV6K+xDBfNqQpx6XGJl0iOc7OFKNWg/TSnKuSrJxCmWVT6j+TYx+kPspdrFyeH8XzJs6LWIeOcE6ZK8k+FNsuSIG1y2jnn5Jx57E4H8/GeTCwdNAIaAY2ARkAjoBHQCGgEtgQCy/863hJt1Y3UCGgENAIaAY2ARkAjcEkj8N73vd8w5WkGoVAo4G6qAGUzbz1haurkSlohKALcKFwrDA0N4+Mf/wQ3H8UfUif8yq/8Cn57DTWfcZeb+m9729tUUqRoBq6zdboSdcaTv/7rv8KXv/zllY1tlfCDH/ygYYJUXV/sR0VgXOz93ND+cR6qIESIcXkqSt16UUdFOImZW1HDynhFo1G85S1vMXyzSaEnT05BSENR0nQHSf/+978f3/nOd4zPww8/jH/4h39YIRWlfHkmf+u3fgsPPfQQvv3th/Hww9/Bvffea8QLQXamoOaOOp4r3Znum+OlPepjjj/TudStSD85V3hJehWv8sp9FczpVJzqh6pfHc+Vrzu/Si/5Famp0mytIycxx9/hcpPEId0jc8HigJUmNmWhFeLKxriRwQRJpgCeP5GCyxeDlS9+LC7lUKtQ/UYMRE0oaQWPQiFtkH3x6643SKFmpY4Y1X/lag3zyTzJqCGEE4PwBaNo1qoko2gTcznI6HksNVyzm/74jj5FMrBmkHvveud/Uknw4b/4Kz4jHSJJGKSjU/TxR1OYycWjBn+UrzWpjHMxLo90cp6+8VqoWeX3qDPP7TTVaWFf8lWq9eaTJP5oNtNtRdsnivnlh5rltuhLMVcsY2FmFjZRTdKGpq93B2aLdkSvuguR3mGm7sw3C9tTWsqwvOPGx++kUpKsXTDoJyFHopS4NK2cux4XzfUGUaZPwWqlQiKNpDznbCaTp2ngjnJdSpw7dgC2YgqhPa9GMxQnMemkOi+Auo35WkmuC/QxSPOs0l5pMS1+opHjyzY0+dpoZKg+pDlYqhfbjSyKlSwyZY6Nw7/cu06eLMlJN0lgn9+HWH+UQ+/B7LEplKgcbVtb8AZ8KFHxePL4c6QR08jxhR4bzaqm5k4gOXsMLvox9PJ3PBiOU11Y4RywGSSencSeEMpCEFtlrWT7ZGDUvyVsjHeQjFfPkPJfKckaBVFfkhYVptkwYSukv5x2cJY0OmgENAIaAY2ARkAjoBHQCGxeBM78P8vN22bdMo2ARkAjoBHQCGgENAKXJAI9PT3467853XznzPQ0Duzfvy5M1AZfJ/HyxuoZcr6VJN9//I//ceXun/7Zn1PdEFu57j6RjeYXGoQsEN+IcjSHSCSCd/7Gu8xRF/25Ik5Wj9FF3+0L30GZh8ZUXN7ovkA1yLgICf91KmclCMkkz6T4vBwaGjLiisUSTYjux+zsrHFt/pLnQxRvipySa/XMqKMae7mWjXpJK3nMac1lyrnkUR+5VmXJuTlIGlWO+WhO80LOVZ3qKHnNdXfHqzpVOvO1SitHdV+O5njzPXM9RoblL5VexUk6hbeK26pHmdKkxFDIkswziD3BW/WG/eS5l1K23dtiTOUwzHGWSjX6/SM5SMWeTXwRCpnNT0uUXbQ+aSNLFewbQIskttXOMkgUOUnU7T00iy8/SPOfJNga1JwtHfoRWvkFVZlxTM0eha9tw/DuHVStRdE7vAOvecWe5TRtTNE8p6jpVEj09uH40hzyNH0ajg5QWReAvUbznxYXxscm6N8vQp9+p15uaVEFeODIswgHbejrCaHK1iSYz+OnqnG539J/K4JwkrEq55ZoXjNPxSHnTblAOVsEJfZT1JCqFfKiwM4rr0BftNf4OF0O+EnCOe1NYuFDi89bk+rfEutaIMGYIelm5/PnoBpOzLI6vW769BPisbO27Np9FXx912JuvoZ8hWWFQogHEgj5IwZJ5vPL/JM+qXntRjNANSL9Emap8LNbyxBT3W1vEF6SnYn+bYwvLOPW6WQ1X0Iyu0gCMYtSrkIfjzaEiFWTRGghXUCaJGCEZPDowA7EQzQFSj+KC0ePIsAyR8d2IcrxdXt9qFNN6KG6UNYVj9fDse4QfnXOB1H61UgGi6luUSUbzw1NlFqIh3rW5NkqkRiuUdVZzCZRo7nTRpnmapsNA43m8osSarz1USOgEdAIaAQ0AhoBjYBGYPMi0HnNbfO2T7dMI6AR0AhoBDQCGgGNgEbAhICYJ+wObvp8GqDvv/WEIfoFFJJB1ABLS4uGidAgNzLXCrIl+dRTT6/cSi4tnebXcOUmNwz/8R/+ceUyGuWm6MrVmU9kg/JaKlWmpmi2zhTSaSoeuAkaOkPbTEkvmlPZfFWfc3VKESQqnWzkyzzosARtmq+jP6gWN7hFsNbZW15OukwMEXfZ8JV7xvY2B8vScHEjWzQ1TKMKXj5KuwLcUA4GQ51NYibwtcuo0aRhgWXZOP4Up9DnFz9GZgs306lAcRgeyozyWiQy6Mysq+RTl6JgEjO5HsMXmrSC1Ab9blkaLIMm+NqnCl/ORFOAsnFN/18rgSQFWvJfHFPcys3zPxEV4FFuuAv+0tZrr70OwzTDd9lll+PAgQOMb1Ep+BzNiB7E2NjYGStkdoM0kKOE1eMp8+DUZnwnxZm/Vd6zzR1JI/fPN6i61ipnrTrWqlNIOgkqvSpTXa9VtpSj0q11X90zH+X8YiEEO30m0Xb0MPqHxvickdTjHJFnhEhyztPUJBVg8vSODvfgF++6juRSlWt8CtHwCJWrNI1J85tMiH2f+ScM3ngDguM7jPwt+qVz0Pdcg4tFkwSVhUq4V92wHVVyWeVqHeFInCq4V2Dfc3tXoJcRLNVTJN6iCHkHkU/Po0wyyh4fYrM6Y1WlKs0gH5lWpt7U5CQslQKsviBJpUWSlBa4uaY47H48f/x5nFywU/FXMdooFdXqDbhp7vTZ6Tk4SMj1R4Kolms0fVkx+tnpOZVtpRzrt9M37h5MPf0MYtu3kx1lAfRb2KC/PXbMQEnKbDZa9DGYgt3RefCcJMZCvXGcOPosmjb2k2tMJBLGycU0CkWqI+kvLxyJYqi3h2tpHX7iJOSXgTm/s9k8omOXIeaso9IqoZylCU+fqB+XqNADza9SsekWUlCePbaVeUt5miL10FejP8h7LrTog7RmIw4BJ9JLh2jilQpJzl1Z76SVwcEBkodc01iE0xPgmM6iv2+I+FTgpLrR4nCiRVOuHvoelOekl74D20tJEok5WHw2EqUZtslN4tOJNlWRVgfPnTThyr552EgbVYvynIifQqlRllljwKTFbIe8rGLcJ3ZF+qx0kTSsE582zZU2c0WqEjtmTi0h+lLctZO5dNAIaAQ0AhoBjYBGQCOgEdjsCGhScLOPkG6fRkAjoBHQCGgENAKbH4HOft+Gt3NxcQG/d++9q+qRTcBf/MVfMjYuV904w0Uf1Rqiajp+/Dj9OpXx3ve9F5/85N+vSRjI3uBrXvMaEoNPGuRcb1/fGUoFZmZm8MUvfmHl/vXX37ByfvYTCz796U9h+8QEzc+tVqLcd9/H8IEP/v7Zs19EdxVZst4urSJAuDErShah82j9jqRvA/UlkoI0fycb8qcFxgnxJIGexbgZzogYN9DphwxO4+aqLKKI+8AHfh8VmtKTdsrmsbVdR7+tBq+VjB39kFno06tDKcr+tRB23GwWlZs0SISgLRtakRiTUsqzZuhsQDdIWEtzpC9CXtfmSAR42CwxK8j4lSAb51TySD9krkqoWZh3rsrNbxXTib8w32088MC/k2ChyT82UEhYIQXD4QiP1+D++79q+N4Ugnty8ohhQtQlzEBXkHHjXwZzZ04lkrI799a+r8bdKIFp1zNvOmWequNCnEmZqi1y3l1Hp58cRaM/7NHyUepW+eSo4tVR3ZN0Kq77XK5V6K5HxZvzqritfbRgaNuO5VlD7KmAE8KGM4CqLz4DfMbcnG8ya3aN95GQowqtSBIqk6MyzE0VXo0qMAfC28dQXkrDM1aFy+KGgyqy4hxVfU4753SAZJ2DfvqS8NBsKOxtmh6tYt/hvYhHxO9gJ9BiJCr+MFo0S5orLyFfy5BkqtPsZogmdWNI8QWSSZq4rJMoNALTJ+hHL+mKIEDevpyeRpprTcDvgd3jR084ivl0DTWmk/bL42GlyUyHz4fJTBo9zJMuZVEj2WVtGG8IGKkkbYU+E/sdLjz/7w+hQqKx2TpMk6IFWIZ2IOSywR4+9dJLg5jlSEjGEstmsdsl5ApVtEjw26U+rhvlVBYx+g/MVbi2+eiHj/dOLs6hj0RaukYVX1tIPglcAzxh+EmStbnO1ScPsv9hlDMZOKI+uPhChJf+QBcyVC2yR9KnJhs8QPzz00dhp69EC7Fu+ntRZd8cbR8iPQEUGiTcpHSx18m/rSoJOyoLW2ybg+pFl9PDda+NqsNCH4I2xPiiRpWqwWwxY5gNtXvcKDXa8Pf3E1sbfLI2k4zMcEyFiKzl8zQlmuDLF5wTjBfTqPIMKYWg9K/dkBWcc4trvTxHFRK8DZKiNjo2JMLwDQ+ikS/CO9FP4pXEIMnNJrFtkMi1sbz1rEnsog4aAY2ARkAjoBHQCGgENAIvEQKaFHyJgNfVagQ0AhoBjYBGQCOgEXihCHzuc59Dtbq8ybqc+YYbbsDH7rtv3UXJZuNrX/ta3Lec5zOf/jTe8Y534NZbb1uzjP/6m7+J99EPmmzyicJwrSAbi29721tXbsnm4q+/850r1+c68XHj9Z//+V9wxx23r5AFkudPaFb0P/+X/2KQLucq41K9r8yu1up1g4QiFWdsDLdQ55hxc5Zm4WTjfCXwokNISYwwdeKBS1K0US800WxzA5pSEUdnW9q4L18uKvyuvPJK41qRLXVuHos2SfJKnIVMQZsqIQkdYod0BeNFfSN1Ubwo28y0Wbj2f0Ekj8ylOpVLHf9VVLTSnKCdpvucThIfRpukrE4wOCZhJ6Qfy9EGLVmkckVkixcwSNuyVN488MADRt/k2kGfZ1dffTUVWB5s2zZBhVEEi4uLBnH62GOPcT6/Gjt37lwhtjqYdBoleMm10YcX0E7Jo8pR4/ACsr/opKpOKUC13SCk2IEztUPiVVpzGlWWOqoy5ajiVD6JkyDx5nud2FPf5vIlVpES3fGncmytM+m7QeBwzqlgISGUPnYQ/p5B+qrzktyRp7bzIEi/LVYX/MGO+leu67UK6tUyYldehzZ/R2Q15ww0XiYgAwbnQB/Hi/WQBDy592sYuu5uqnAdhpIsThWZS/wXLgd5unqDvXhy6ft85utIU9Udjw7BmTuGRCIOUZVXWZcK0qr544dI8vkxb/HAEYqRwHRwzXJgceobcCZuRU/UhUoxqbKwv02kC3ncMDiM2dQ8UqUKElS5eWORlTTS3UohR+VhBtYwVW+ZFE2JhlluHY1MEgVnA1Z5kYEJZbVz079eor8XfvrnkzA/+ySCdioPw9uNNaNMMq3KFyk8JFv7w36aRY0gROWfvRlFPp+Bl6ZFLeLTkbgJ1vJeRTEzTfOiPgztuAr5Ik1qtqku9FdQzCVR54sac0vzRl2SR8Zh4fg0vJF+Kvy8VACGeczCHvIaCsi+vh3Yf+AhI72UL69teElq5mi22CLrLVWcNreDasF5rtUkNOss/+gJxFheMBiBj/4MyyTw2Agus1aD6CyWSsbzUKjRjGq9iHh4UB5ieaiMOSOnMreMOcML+Z0nj2jgIS9AiCJaFI0V4u8KuWmyNIMo63LST2GB1gZ8kRCsXKOtLF8TgstDrQ8aAY2ARkAjoBHQCGgENjkCndeDN3kjdfM0AhoBjYBGQCOgEdAIXOoIyCbsV/71X0+D4X3v/88rG+Cn3TxDxB/8wR+gb1n1Jxv7r6Ua8CN/8zfGJmF3FtkoFPLjTITgwsI88/80vvWtb61k/U0SiSMjIyvX6zl5xStfgVe84hWrkta4cf3QQ50N0lU3LvELGTP5CFEg4yPBOMgXN/XF7JyoTCwk+AwVEbeWV/4wztb1R1JJKbLNTUqQd4Uq6JRrFM4vRQrLPJBzMRHo5Lmdu+JW7iC3eWyQOJBdctk0NwhJkhbcYqbYSI52CgVFOSi1CUVw9mAV2ROJzRaVgKJ/lBzSppV+8Ez6117+iGcxso0G6dgmDsZHqpCM565OUp4xKKyffvqpFTWr4LBjxw4M0rSfjIEQIRNUu0qQa/EreOLEiZVxUqSWIrbU9VqVclhPC5Je2iFBylef0xL+BCJU2415wLacK0hb1wrmPqj+yVEFua/qkjipTz5rBZVPpTGXvVb6rRQndFad5EyNJNspdKQHFsTHdtJfnJ/PIO/Icy+YkSjP5XIyUfhRmPF5JaFm83jhoj85dzRMdSFVrCStyvRR6Nk2jgYJuBbz2kkCbbvuTZh76n6gmuGz1MTgrj1U7Z56IUXWmFI+idHoKPK5RZJbJKFQhpeKNpuod9cA2G1xwE0+rcYXFZ588jkUSmwzlXd2EmpuewlDwTLGRkWN3hlj8l9UJ8aRSmbhIcHZZv+b9CzIL4YOErKuRIfG+ftUodpuAaGxQcyfOMx+e6iCpHLRGSPhxfVyucwW55esTZm5SePjcdZQLGepxisRPvZdfC82KsgWkhggRmUSe3sPHqAKssJq6VuQfRCcJch6JOaZy8kC/S3WMJ+cRzr5CD0c7kU+9RgCJN+ikWGScGLLVFLzxQuuy4Hxm5Cr+VBuOlkPsQ/3IpsjYWcP0O9fCxESg0LOCoZSkyg8HVRMN0gAFnJUFHJdrYmisdKgD8MYRoZ2IRHvRYP2XoscyxoJxGY5B5v4Z6w2kOgZZp8d6IvRj2NomE3hOqqeJfZZnh1Zz+RlDEN1zWtRaMu1U8w9M70oBu0cEDFbG6Ia1cqPtMtJk6VCKFZovlTUglbONx00AhoBjYBGQCOgEdAIaAQ2PwL6X22bf4x0CzUCGgGNgEZAI6AR0AggR5NfP/rRj1YhIcTem970plVx67mIxuK47+MfN8yFSXoxCfme97wbt9xyM+772N9hiWon7hQam5KyMdn9kc3CH//4x3jPu/937NmzBw8//PBKtbfddht+87d+e+V6/ScWvOUtv3Ba8oe//e3T4i7lCEV+nEZ6yO6xsVctW89U/xibs7IRbRo/uZC7FpJL/MjR+BgZJa38IYFHou1cQfbFpRgjqRACvHZyw1tydlogrRAyTxLJfZbMxFYeyTKcq3hpJdN1FDkqtRyND7/UUciQlU8nl1GfbF4b9atM56zx7AnqVGJ+4xvf6Kgx2R8J4mNRSOsHH3wQzzyzl2rKU6ZCZ2dncfDgQRSLRWPTXdKrMZMxlHPxp9npidzdGkHNvzORc2frheRV+VU6wUGCwsR83Z1eXZuP3eWo6618lP6ZQ4uq16kDTximLIXEM4c2CS5JLqTOSjY+hAGquCpUrZEh7CTnUcgboecF4ypVXzWa+zz+1Dfhpp883iVJ6DHIIPGLycmJ/u23wub0G+RPKbO4PF9P1V4pJVElWdkk0d+weElEZg0/fG+45y6DlD+VsnOWpknKZDKDEM1q7u61IJvKkUykWdHIKPwksOrFlKFk67wCAKrv+DylaJrS5kIvzV3umNjJJ5w+Dvu2nSrf0oC9lUOE6r84iU3Q/6i3rxfuHduQGN1FpSTNeFI9Z6iJ+S0mLuWx87rCxqdFc5fR2Aji/UMoVOgzj2pAdziACZKtZT67bi5sfVQgurnGRYMOqiVpYtQwf9wZI3n5YHhiD/y9Awh7gwj6SJ7Sx6IteCMxCdInahD2AIk4Bln2RHG377nDJPZ8/P310WQoSdlcyVD/FYo57Hv+EDHprKJGJlnpSAIGaKKYB7iIdZPj1rJX4Qg6cWJxUihjqjJrJHzpi5AmYF1UM/a97GosHTmIuW9/C9OPPIQWfRs201RhctGucMxkjjW4pknxNSoLZU7IOwfKNKqQgg6Sw2KSWl5GWJhbYF/saOZLJI8zKC4skkglUc3xdHDd89Cf4Dp+Njpd0t8aAY2ARkAjoBHQCGgENAIvOQJr2+55yZulG6AR0AhoBDQCGgGNgEZg6yCwshm7gU1+9NEfokAFgDm86Wd+xjBdaI5b7/nrX38XPvf5f8Zbf/VXUKJ5MdkkfPTRR43Pb/zGb2D79u0YGBgwTIdJmUIceunfSczCHTt2zDCTaK5LNhWFEPzil75s+B8031vv+c+wP++jj0MhYFT4wQ9+oE4vuaOZHFBkiTqqeyvXREeIOiHThAyTMxXUJruKUvNVpeiQgVIAc3HHXMpokRg4q+qD420Qd/w2zHrKPnaniM7J8vdq/k/VKEdWto5gISEiKTt9Wu7VcjGGGrKrHKNUVsrmXdCQSqUgJkGFWJEg+AtJKOZEO31ZTXjJHH7ssR/h1a9+FU2I7jLyqC8ZM/mocVDxZzuqcVbHs6XdiHud9nbITPP5+dbVPY+7yzP3V52ro+RV5935tvK1kDBC8qlg4zM58bKbUSoX4RHllinUCiWSaQWqAO00yRmiSUliQvPNFpbh8QVx+OQkQlTuxvr7aHrSaagIpRxRBPp8HgxccQcalLvVSICJWUoxE1yW3wM+z75YH5VpOVT4Eogo9pKHnj1VM59BUkYoNoo0J1nG+MgwsslZ2P0hvO72V+BPPvgX8jrAShDl28hwHIVUnusUSajAIHpBQskTgYOmPos8L6bKaNHE6fJChiZVbvsfPYax6/tx0lKlIm4Iwb7tSKYXVBLWYUWd/clnj9PHXhyhOLc3/D1YWqCJTvoEdFLtt3hyWj2iVNMRn3YN5WrKaFsuv4Qc/S4G43ES/ln0+OIkFYdRzdLvntcLT6AflXyOmJRhK9VR99KksWGymNnZpxD9J1byM/QjmII9MQB3YIArYpN+BvlbOXsYFfKRc/uoXJRAzNr049vvo3lSRw2tuhs1Zx/VgVUq93eR2Esh1tOLvc8dMNaGzlrWhsfmRC6ZRIL+CTMH6KuU5loTNPX6+LGnWSjHeSJMso5qQrcTpRpNtrpI/tZz6Ln+OhRn51A8OYfsySlEJraxySRNaUJViN86x91qo4LUQz+UXI/Ex2Cb80ZUoGIdQOJkHqbpfzJCE6ZNmnOVMfVyHNpVUXGTryTJWeOa6CaBaBObozpoBDQCGgGNgEZAI6AR0AhsCQT0v9y2xDDpRmoENAIaAY2ARkAjsJkRkM27ZZ5iw5r5vUceOa3sV77ylafFvZCIu+++m/6LDuDn7rkHTzzxxArhIZvSonKSz3pCNBo1/BL+4R/93+tJfsY0/SQhd+3ahb17966kkTYIIenmhuVGBe59btqgSBPVQNmoXZMIMSbg2h3p0GqqhDWOxgTupJJT47MOUDpJVI41ymXUMle4fFPSvoAgLOfyk2UQaNL39WQ3JTKdrifnGdM8/vjj9OO1ZJCBkkiNS+cotaxeAST++ef3YXZ2xjAz2vFD1ilexk/ury6jc6/zfeqeOXbNcTcn2OBzc/3m8+5qVb8kfq10cl/Fy1GlV0d1T5XbfX2ueHV/qx7NhKDqQ6lSpsrMTSUfCUOR6C4/C04/fQk6LFj8yrfg2jkGR28CnlhMLOnyWRFVWBqjw7sxl8kjTvLH6fZgMZXGYF+cSrGSIaBzuf1UDhapBCQBND9PP3oenpPyo9nNEsnCnl4xOUnSyURIypMY73sZ3LVpmr204vjMEZJoPvgcLgT6+CywfRxY1XyeWpHNN6goS7D8YfS5W5g88Dia2TmKAUkJOoEd26/FsU/+zxXCTxRsQ+M9NIfpRsVJv4XpRQRoKrXaLBjElfHIsYpQT4KmTNOI9dJ0abCfpkZzCJNInSoxjqo4r8O/0g6Hk/gVaQqUhKMEl5jwrAZI1uXpt4/kF/GbPLwP/TEqB3MZ5BZPsD9BJPlCTphtd9Jkpn2ZsG3z5QMxoWxpezG053KUSR76E05DRTezuIRwcAjVhSWkn3yCCyFfnuCwicpwrHcCNp+T/gfpS7DpQqpuJSGZgy8YQ3aB6ssibZKyc+qFijpJQF/QT7OmTQQiCdbXwOHnD+C//7ffpxK5xPG34ctf+ALNlEYQ4Fim5xfgCsSQ4jj3TuygEpSKRJqKbdLnX7NBE7QkGW1UkotqVEypqiCmnWn7VV3ySGOtxClEn4HSHgcViJVCkeNBcjpEv430IymmSn2JIMfbnM9UhD7VCGgENAIaAY2ARkAjoBHYlAjof71tymHRjdIIaAQ0AhoBjYBGQCOwGgEh77rD+NhYd9QLvh4cHMIPfvgofsjPTTfdZCgP19qUNhcsm/SiJBDzib/zO/8HfadN4XwJQVX+Nddcq06No/jGmqUpxkslCDEiHyFm1bn03Xwu1zIGnY86l9gXH2QPXz5GMJ+/+CJfcM4Oh3CKSHjBBWxQBhkL8ZmZpwlfCYL7GJ+922+/Ha961atwxx2dzy233IahITEV2BmbeRIszzzzjJFP8kiQcRTTfHJUQd1T12qszWlUnEqzWY+qnea2S1tVvJyL6dHO3FUTTmI7uHbOOt9rpTHfv5TORd1lEd+dJGr2TT6Ox/d+z3iJo1Qu0fxkGYm7/xcEr9gJL31bgiYpjYeZarBdA7tpbpJKtEgQkycmkV3MwUmSaD6boWnQAJocghKVhj4SSm6a2UwM92OR8zY/v4gG+aJoOEb/eEtcj0ho+QdOQc75XMxOY3quhOdO7EO1XibpZUHEMoNGpoEbrrvuVFrjrIUjsxXMUFX45N59JNP8JK4iKIFmL/0NlGfTmDx0AAMksVSw0VxlqZrEkcWjhqnL7MIcvHYbFjJZgzg00pGMqpGMGxveSZWiF8mp/QiTxJohWTXgjmN2qYAizV0aBCIziA/CZOEIvLOPGx+HO4GAx42YK4Hdl18DS8WGkNWDhZmT9JHaRjC0Dc2iE8M921FtVbFQyJJQpBrPKAs4fPwo/ANUUR4/TgK2iPRiAQdP0Mcifx9Fgdds1OEgmSf1yxPvprLOGQ9RzelAD1WGHqoz/XRTGBGcMznU6fsvGCbJJuUzg/EaRJGmQukvsLyQgicSoMKwiVCbpkMnj2I/ycGDBw5TGWqhb8ccf5ediPaFqColMRqkv78ySVEqSMUHYJl+EqtTM0g98wOOZ51WAFxGPerL/Iwqtb6T5Ym9VfEpKG0JxaN01+jjizol2IlbZHyEZmftaHSZtVVl6qNGQCOgEdAIaAQ0AhoBjcDmRODUq2Gbs326VRoBjYBGQCOgEdAIaAQ2PQKy32/a49+Q9s7PzZ1WbiyeOC3uxUZcc+21eOR73+MGahFZbi6enJqi/6dTyihVrpNmwgZpPqyXZs7Eb5VcX8gwNjZ6WnEzMzMYHx8/Lf5CRcjYLXM2F6rIF1WOeVPWXIDEn5kgUcQKj+rUnHlLnQshuvkavLCwQNXf84Y/QWmdjMe73/1u3HXXXRwXIbhoQJVx4j/wU5/6FP76r/+KhE3dIG1EYfiGN7yJJnXDRsfEHO6RI0cMYkwi5uZm6YstZ4yvlFGimUIx4ys4qDEf4vN2HUkWj0eIoc05yNJ2c1Btlzi5Jx9znIpXx+5+dV9Luks5NMnKycsabasb24auhIuKPCFqvCSXLC7xCUjWjtIyIZxtJGnanH/Vcg1VqqxzTRJ1aS8GRsYQcHhhp4nOaqXJ5aJhHG1kBkvWGtIzc4h4/FS79SFAgi1Npdmzk08gFonAX/bBYxfFmAptLNRaSJUX4G2FMcTfA1s5hKcXPRilAvGmq3biUZrbNYcbrupDLtNE0+/G0/ufwe6JbXji+BFUqPwL0/9fzFKCpVpR/B395gFX33wD+koFPPP8MfxgModa00OibrCzYBsPSRMn6HcwMeqkSjFO86IBPkP0mUiitEJ1Y5mEqG+ZZJO21Oo0dZlPobasemw0ScJR4RjrG8XM9EmqCi144Jv3Y/SyHei1TSC0zY3jCxm05g8gQtOgdAWIxvJU56zGztFxqufKWKpkMDE2hnSmgKGRCJr2OKp8/dp/xS687D/9B1g//3nD9KaY2jx8eAlDcZoF7YmSpCW5RpJWzHZG4yRmvQEcnzpmwCbVyNMeGBxBleZDS+LXj7+3Fir2AgODBlErC4WQdc6An0rGgGEitgUv2lRIthrEsl1GIZuimnKQvhVdqKSOwd0/apiRFVPR5tVEnjl5AUKCvPQjz2ydfgPtrE+g5gSjqdmmQWxWcnXSuSR0SQaKYtLNF4R00AhoBDQCGgGNgEZAI6AR2DoIaFJw64yVbqlGQCOgEdAIaAQ0ApsUgc6G2cY2rkazZd1BlHoXMgi54WeZ8hkc5MbrSxBi9O3UHZLcEL1UgmzMKoJFHUVZJeHMRIncF2KKm7yWBn09cauXG/0sych32pd5J5g3xUVWmyYJmxx/sSfYdfu07Bc6QvqpNqNV2W0atxNze+yQsZkuG9JGOAsp1sGLrV8m6YzszNRBr5N9vd8Kezk+/PDDhg9N8cMmYyAE3y233MrNcSHpOqo3KVc20oXUjkYjECJR0oop3JmZaYyOjpDUseMrX/kK/umf/ompO0SZ5DN3qUjV1pe+9EXjwztyF69//esNE6Re+jjbjEFhpdqm5qmMqTqXo3xUWnVUeeRa7qu5ruL1cRmB5TXARoxOzJxAf3yAZjhl/Zdnp0YTnnZU0ll4giHDB17q0CF4/B7YqOoqWAoYi2/HUn4O6VYJzrYf8Xg/s9IfH0k4UQhW80UM9QnZxueO81TWEJfTg6t3Xk+zkV40CzksLHX88EmLhBCzk6Cq1avYOXwV1XD0qeezYaLcou9b/lat8aM4e2KG7YxTYW6jD0QHplMZ+K1N2K1RtItl1IJRKgJlznfWIRcPlVKWfgibuOXKUcRDaZrbpD9CMVnKP0YgcxhiX6okqyrpY6iyb0FPG/4o/SkeOwGv3wWrsRbKatiGy25BOruIHrZZwtJcCuErJvDc3kfhi4dxYt9RRAaH8fJX34V9e5/AwmIWLosbQSoRJ1lePOikMk69CGPBPP0bDgyE0b9jlOsB1XzJ56jA7IcvSn+DVP/RYCdqNM3aWVU5VhQNxsN2xKgorhTz6B0YMcyi5qo1RP0+WNiPgMu/6llpkphzxSIY4G9jI19AmWq/GlWJFpp77QSWy3G0W4I85qlSDJDUtVEtmIeP5kRdJAWb+XmaPaWJ2P5Bknx+zpE0vNHe09Z69fwJutSqo8J1LDA8zBcXOLdIXspLQPKs2h1WmklNwk1i08P5c1pByy3TB42ARkAjoBHQCGgENAIagc2JgCYFN+e46FZpBDQCGgGNgEZAI6ARWIVAN2kiN5Vvo1UJt/iFbBZ3h0a93h11UV4rYkSRJ3JUwXyu4sxH8T/VlvTc1Le0xM9VEw3an5ON3bMF2bsXzZDxpy3mCUnCsTDDWNyp6s9WxIu+Z57Tqn/ShU6LZQtfttKpkBLC0zhj97rJBlMbOxvZ0hw7N9W5YS6gsHcdB2umhJJkHUHGQ8h4Ue5l6T+rEyy44oor0NfXzzo6hKAaL1FyjY2NYdu2bQYpKDvlqVQKohbcs+dKkok040dypzPOQoKtoxGq1heSeP3FnldK6cfZwqkx7ZCBMt5qjks+hZs53dnKu2TvEWbx/yZBpvSusV18HhT2gq3VINBrJO3KNOVcWpoz1oLQ7lcyow29OTeJPRd6XUN45NlvIejzIkBiW2iycCSMBgklWScK6TysbhvNU3Ju2qkKc3uxME9VW38AyRwJQfqlU8HChpyYOwIfycNUkgpDJ33mUWVosXmxfc84Xlmq4C8/IeR3J8j6Ikq+Fn0COh1NDA+NYHJ6kgSYByU+W73+KAk7q6F269B3bDp/C3LVaQyR4BPZ4Bh9BrZsYXjEpKXqvrWBaCKGcIDKxipNcZLQrGdz6B3cjkCsASvJtkwpZ6wgshKIgtfiocI+PGw0zLpQQZptdXjD8Lt7MLEzZJhCnZ9dQu/wlVTynURicJwmVDMYpO/CQqPKidvxRyiNcIWiqPPZ9NJU69FZ+gWMDsLp3WEoOuenD/JFC/Zh2UegUSFJSV/UjaWFaSrtoyjlFgzpYYKmRIu5NE2yLsqywaJVB3mbpj75Fy2+uOEIUhU60otgW0y5dtJI0kazhmI6xXbOcZ2JwxeLw9VqoJQ+wXsFzh+aLKXK1OUO00zpEk2bBo21tMXfCVGcSqXm5UjWWZkXnt4ecrSd59cma5c8w/JhDg/NtLqobGwxrfxe6KAR0AhoBDQCGgGNgEZAI7B1ENCk4NYZK91SjYBGQCOgEdAIaAQuYQTUG/xmCMSk3MUWlC8jc7/sVMFcCkGRI9JXNd7nIl4ULlZuTFutQhVwc5Z8mGwgt0iONYTg696vXd5vlmg5bXJDmzvDBn0m/sg6hEN3JlXThTma+6XIIWNjmu2wyof9ILfJzXUaOWRfyFMsk2jmdjHSIP5OtUkITsFBtq2l30JvCiQvNmQyGYhC78orr1whtO68805jfKTdaszUUXwK3nrrbSQfhILohHK5YpCL0udhqm6uv/765TvLA6ESnnbsNFxIRnkGzJhJUnWt6pY487lcS1DpOldrp1H31nM0lyfna9Up5ZjjJZ0igc3xap6vp95LNo1p/hqn/DJTOELqzJO4FlLaSZKq/4ab0CahJuZsA1StunwkhGw0EUo/e1VLDodPnkCIBN1Qz5ChCnRSodakLzuv14Pc0aNcB0jGM18+kydxFcKP9j5Hs51VeKyn1uE2XzjwlawoUO3m8XNuknRMpWews3cnffHZMRihSpHPR5PjLkHWlHjUh6cOHKF/vhhsfDj9Hipfa3XDB+DxpVkEynbUqoXl9YfrEv3hhZoh2Fx9VPw54aIvvcX0EqouebYECXn+2yhTZZjmmjF9fD8mdt1I33klVAvsY4KkVTiAYqUsKY0gBFagZ4JkH8k4hnKdK0VyGr2JcXidXjhJ1LFxyNJcN0uG3U0fhPRp2xMN4fGDc7jysstIiimFngUeKi8dNgfqrM9dcSPZcGE4SvK0SRbPEUIvFX6uySmjLmlyi77/SqkKVYRe5KnsbBP3RC/JQfoNlKVs6uhxkqE0J9rJsZyPr0VQFWhnuxo0B2vj2mIRU7KmNC76h2zzfZrB7XuQnXoC+ZNzJP68KM9Nwt23i5LLFFqi+MzOgiI/+WXgOFup0rSgRhOrYnLWyvLl2ZRy21RFOzmmnTa3ECJ5bNzjb0W5TMUhbag6aM5YHB9K23TQCGgENAIaAY2ARkAjoBHYWgic+pf91mq3bq1GQCOgEdAIaAQ0AhqBTYMA99Fkb3JDg7FB11VDPk/zY729XbFb+3ItU6HRWGxDOyXjtxmCmSxR7VkrTt1TRx/Nut1zzz245dabuaHbmYieRgsT3Oyt07Tgqu4tz1PZAHZQcSP3aCyQm9N2OCa4eexQG7yScFVOVd2LPgoxZCaRFCEkcRJ6enrwcz/3czTNeYtRs/TFTx9cA+USuUqq9kj2mYO0zsrNcdnQFpJE0ssGteuqa2CVDW2jXKZ6kQMs7RJs3/72t6NKE34SJK6vr88gBY2Irq9oNMo+/Dxe/eqfXiHFXGxLkP43JbzxjW/Ebbfddsb8XcUZl5JXPp1N+Q6G4jtOzQ11XCuvxEmb5SPpzpdgV2VJuVKeuW5Vh7onRxVkrCXt2dKotPr4AhHgs+zfvp3PCJVx4mOQ/gbl+bbzWa7TXqWYfKzkSzQH6sb8zDwum7gSpUaWwr8Jg3y3kBjy0G+chQSPk78nbo+bhGIOg2En5pIZDCV64POQdDs5s9IwefZ644Mk9KI4cWIveoLbUaoUsEhzk5FoCzF/0JjjMk9VaHGNGR8dJillR55EXoumctlI+Olj0E1i0eZMYPvLrl15lqfmF9HIce46a5heSLH8OhztCspu+lNkAziljTnVPzqI4wcOIzHyMir3qIKMXI7U7DxKmTTaER/95JJ8W87QooK4nMmiZOuYAB0YHiE5ZkGdpKeo7eoF+uKjPz5PwIcUFYzVShEOYnhkMkmzpw489oNvG+Sr6lOb2GZKJfTFr0K+kabp0ioW6eNw8sQSMukc3vAamsNmeUZgexv05+ijv8ZSnuZSSYj62JeF1Dzjm1xr/LjscpKOlo7vUHlWJDi5xtUr9LfIcbFwrRNznXkSvuKLVIKsL3aHnePsxvT0FGKBXuRn99L0Zwo2YmGhzLCwSJOrNjdqxM9P86GI0HRsbhHBcA+cbIOqS8qTsbVw/hBh4y+nx0qQZ9gjilDOlYqYLJU3UHTQCGgENAIaAY2ARkAjoBHYcgjB2RTKAABAAElEQVRoUnDLDZlusEZAI6AR0AhoBDQClyICiQRNnnWFJaodtnMz+GIKx44dO607AwP0f7WBobO5vIEVbHDR4ltSyDTDPCPrMkxscj/Xyg15K5UdZuJG9nll11fihFCTIBvColKxcrPe6pQN99Vkj5HoRX6ZN5vV+ar2sFy5lnsyx++++83sB4kEaaN8kQy0ktiUq45+0NwQpuCOdac8Sb1MftHnl40b3bxhdHe5y+aM6zoXIsvv95/2jHW331yYkG4DAwOGT07VL3O/h4aGIB9VhrpnLuNs55Jv3759+MhHPrJShrFzf7ZMy/cmJrbjXe96l0EirCP5ShJzG6V+1XaVQN1XJK+KV0d1X45r5Vfpzvco5auPIiHPt8zNnr/FZ1x8gor/TQdVfaL6tZAMtFmdcBp+5YrwUAHo8LogBN3t192J3vAA6vRJ165kYfXRPCRpsQZNYtYrNItJMqicn+VzRZUZCXmPmwo9mhXOLy0iQvOeK4F1+kIROIoncd2eCZJ2bcTiY2A1yJGM89A86S+++WfwqX/54nIWKuSS8yTEonBHImjSrK5vaAfJyiS83JGY5rMWISm2Z9sw00s99BFI071/9f9+Cj97x88iRWJv4rod9HVoxTt/7V0rJJa8LHNw/xFEg354aDIzTf+zuUwOPbEEPGIitJ6DgypDY4GT9YF/GuFe1Et5o11Srt3hhpeKPUu7IcI3zM8dR8UR5PsRFYRdaSwWvMi1qSKs2ZFthMnxLZN8LKuYbWBsIIQTC7NU/qWQadgwny3h+ImTVPa58eCj++lHUCn6LcaLGPsn92HbyOXwUGFYoaIwm84gnBjGU/uPIZWvYttg1CAeG8Rf5vPffvTv8JZf+nlEac5VzJ/WSTL+2q+9A0pVPzIygnBYzMG2jJeEsgvH6FuQOnEfCcnKDKzBCZo0jcAWCCNCc5/Ntht2f4j+HUkGcq2VOWNnW04PxiIsq7ApdNZU4Xrd9Feog0ZAI6AR0AhoBDQCGgGNwNZEQJOCW3PcdKs1AhoBjYBGQCOgEbjEELj8sstP67EQaDfdfMtp8Vs3oo0nnnhiVfMDVEgNDAyuirvQF6s3PS906RtfnpgNFBOXEhQpIkchYMQUZ9eu7sY3qKsGaYuEtQgh1V65L/3wiEk6BolfRTIJGWXceWFfLyaP1NBNfJ2pVtU3831pt7lfqry1ylwrzlyWnHfXsUA11le/+lVTGxXp0J3TfG3FDTfcgHe+853myLOeq3rV8UxtPVO8FK7yyrnCRc43IkhdZr+FZ2vXRtT/UpQpxL68BOBORA1foG2qakUtWG9VUeR5hn7q/HUfIoGoQUiNJnZQEVeFk4RRs06ztuUCzUz6aeqzgZNHH0X/tuuRL1OVW0/CTgJJ/Hm6PXaEd2zHscMHV7rI1YV/SLA1mbeVRsK7wPr6YPEP0qedA5lsGne+5tWnSEGybS36tLN7rahkkoY5U1c7j6qnjKbNh7h3ED5vHGGq6H7zv74bf/zhvzDmzh9/5KPYe+goopEYLP/Wxnd++AgJtymjHaIK/uwX/h7xnl6qApN4+unvYXLOgauGEjR7SqItvg2Lx0mK5eb5QHMN4l+ZH6liEj6aCpVQ43sRHtafpCowEIoZc9Qd6EGBasJ4NICSJUxlcABuml5dTNJ3oJtmlluKHKXC0G5DppgimVdH0B9BciGD8QT9HyYupxqzTvUe8PxUyahL2iAvPARZ3+FDB0jyBRHqjaHUDuK733oWCyQHrSQnRdn4P/7P/wu/e+/vSpPxvve/H/d//Wu4bOdOQwH6ne88svI7KX5NP8B0IKEpJKH45I0PjGCJhKLg226UkT/xNHwTN9F3Y5Lll0kER2ga1N/hSTm+dq5XnSDriDpfjlrjIOs0/+qgEdAIaAQ0AhoBjYBGQCOwhRHQpOAWHjzddI2ARkAjoBHQCGgENg8CQj7IBt5Ghdtuu/W0oh/69rfxC7/4S6fFb9WI6elp7N+/f1Xzd3Ij1O12r4q70Bfkmy6KIKSIImFeaqWUaoccFTmjjupe93X3IKi8Kl33/Zf6WvVDtUO10xxvPlfpXsixO7+6FpOBp8K5Vp6Ous9BwmC9QdWz3vTmdOa8cq5wWcunoDnf+ZyrOoWwkKDqPJ8yt1ZeIekaNMNJ047847C4aPLTgh8f/D5u23MHNWQ0Lco4CXYqxGo0/ZgmgdU7NCDUHs1R5rGvOI/61H6qA6No0yWei2SRz+OCwx1AheYq0/Mk11ZCmyZthVjqQ7HE8uo0P2rzUhXHxdQbgqNmRdRD9R0XV2MOUIrsY3mVXB3uMMskYVmn8cl20w43FXXWoBgx5osBvih+/dfejq9//d/x7HP72bY27n/g31ZqVSeyZv/277wfsQRJNZrRrpCE7I/3YLzXh3DQh9rSApYwRwUcffG5IsJOq6wIUuGYL4v6mPPEWTWISLcli2YjR2LNiVA0TKKS6kj6GyzXMiim5+AFlYh8fIr0yWilj1MjL0m+xbmkYW51mC+uHNn7GF6283LM5SooVjMkFisI+B1o1pbrJgbSnrbDS5OsfpSzFUweXUSeSsJIiG1msdfuHiWeebzi+t14/sDz+OxnP2/g941//wbkYw6C7Qc/8AG8/g1vJKlrpe9F8clYoVLUSj+PYWLL+to+2APDqLIN8cGdBgEr3giblJe2+KEFaZLJfAHjIvkNNOOjzzUCGgGNgEZAI6AR0AhoBM6MgCYFz4yNvqMR0AhoBDQCGgGNgEZg3Qic2nJcd5YXlPCmm2+mTyP6EuIGqAr/+uUv40//9M8ME4cqbisfv/CFL9A8Gk3YmcKtt55OhppuX5BT2S/eysSgbLorUkQAkc3il4oUWW9bVDrVbnN7X8r2n2tCqfaq9q/VVolT6VS/1PFc5ZvvqzLMcXI+Pj6O973vfd3RZ7yWcqR+8T+qSLMzJl6+0d2HF9N+Kaq7nHPV+0LvmzFapSx9oQVt5fQ0/9jpu4koJskjpiGT+WkcnHkeE707SPw4OP6d//47XW5EqC4sU5VmpbnRXGEJe8auw9Sx/Yj52ogEI8gl07An+rgmFzG1/2ks0URmJ3QYJBqdhN8TwP4D+6hCtNCX3wBa9SYK9EfYLpGgtLrgpGqxSgUbJwIaXvrpWzpO/4P0K0iffqJEtNLkJj2HojrN37XmHKo9cRKNBXzmox/GV7/5PfzNRz+Jmbm5ldGR+fTK234Kv/rWX0C0x41qrkxVIv0fju2EvVSn8jFp+ENst6uw1y2oSHvqeerfrOI51ag3ERiExbVglHno2BEMToyhCprSbNTgofqw2ciD/BrVlR4Ucw3snTkOb6SMXmc/7FGfofZjh4y3gNrOIgm5MlInj2Cc/kRtVhKBJFLrJNxevmcYuSqpz1bZqEv88Fn5LA4O+ElkEiNHHSGaa62ly+xLD2LuNn0hJtE7EMVzR1L40P/4Xey58kr8/Sf/HkeOHFm1ptxyy434oz/4Y1x/9VXILtIsayTIOuv0DRkySEWX08fxtxGPLFWhEdjLRdQaJAkpBGxzDthoLtRG8lBG8tSz3RnXFbD1iUZAI6AR0AhoBDQCGgGNwEWLAK2NyDaIDhoBjYBGQCOgEdAIaAQ0AueDgPyDaqP/VXXn616LBx54YFUzP/ax+/Br73jHqriteNGmWbebb7kZj/3oR6ua/yUSn29608+sirvQF1tVJaH+GS/HUxu7HXS6ry80Zt3lmdsi91T96mhOL2lFNSb3VD6VTo7q3Jxns5xLe81t3ui2musSDJTaTs7PRYKpvJJWglz/JPA112s+v5B1m8uVvm30OEgdmzEY/5HnuApDJaYkV4c2stUkvvjQP+JNN/8qTUu6DRJPsGrSl10+u0QffyGD5CqWKzixdJiqPar4HH446JPQS/+YLvqNS2Xmsfc7D/CFlCxcwT76pyP5SHZp18h2tGkfs1bOUX0XoE/CGmrFBr0BlmAr1tEslnAkW4CLSrg6m3jz7a9EMjVJBaIDvlgEmZlJ2J0J+AM0ZZrJo5TKoGptIjw4SKVdEiWq+Xr6xkgeBvH0j59AZjaPgb4oYgN9JMH8mJ2bwtT0ETjpd9QRHsFwiD4VHS2E6E+UXhKphqujUM6gMbsXSet2zM8cgzvaj1fdcRO+9OiXDKjG4zuwrXcXLDT12ayXqH4M4xjbNdw/gvLiSfoMLOPoiSOYshUw4O8nkRZBNlfC0vwSXI42xke3IUGHjjUSq45agy/o0PdgfBi1dpbqyzpNqwJTcwuo0XSq30Eyrl7Fzt0jJAKXkC9MUxrZhrNNIq9uQ//AHjjaRSwtLtIfYh9Gd25HjgSrQfA1qpg8cZQmVkMY37YNjVbDMO3abLSQzyXpE7KFGNueSqe5LjQRjfYgn1kkKeulP0kfp0cTGZpAFbLXRsVwo9GkL0VKHzku6tmRZ0rWl/W+OLB6rukrjYBGQCOgEdAIaAQ0AhqBrYSAJgW30mjptmoENAIaAY2ARkAjsGkRkH1Z2ZrdyPCxv/voaT7BJiYm8ORTT8Hn829k1Rte9sf+7u/Yt19fVY/P58P0zAwVksFV8RfyoqOUuJAlbmxZigxRR6ntXOTQ+bbIXJfaQJYyzfHmc0mj0nXHq3zd8Sr9+bZ1o/Kr9m5kOy9EHaqMbhzO1u618pwtfXfZ6lqVo47mMszn6r7kM8erctY6mvOo++vNq9JflEeqBKsVKtF4tNmdJHxoxpPPnwoGbrxXLKWoYiNBRKKvQRUbXeEZfv8m6ScwTsVgvG8Ek5MHaXbSBqctRDOUDarIGhgf24FqMYsGzYcePvA04iTe2j6aBSUR11osovfaa1Ei+VXIppiPa1GtQEIrirljx7k+2OGzeQxyMUtyLpQIoeFyIJeagptEX6tNf4iVDE10WuCvlFABCUqvE8F4LyokrTy+AKaPTmJ0eALpTI6kXQvZ1BIqzSIKBZr9jPlhddqRPXoEuy67Bg1rA+1KEwjFqUikn79SEoGeCGqLR+Gin0BPwAo/TaEWWc4Th79rQHTH1XeiUajQb+0QSbwM/R7CINLajRKOf+ErCI0P4zj98FUcDUwuHoeL7Q44YhgK9SMSi2Lp5BwGWGbZ6UGA6sIGVZFWEm+FfJGKPJpx5cdqb1OH6IA31k8zp1n6HvSjUZohKTtLH4EcFyoJm2RN240eBJ2DcG/bgWy+TPUjFY0cyjpNsbZLi0hWGujp6ef40axpIWf4gqQQk7yijYRsBeVqGV7+O8BmpXlQ+fcIx73AqRGkAtEqyky3k6SfaCbbhnrT5XSt+u1QLxxs9O+Jmpv6qBHQCGgENAIaAY2ARkAj8NIh0P064UvXEl2zRkAjoBHQCGgENAIagS2MgGkfdsN6If4Dg8HVBJmYFbvx5S9fRdBsWAM2qOCvfe1+vOc97z6t9Ht/7/c2lBA8rcJNGiEb+4oUkaN589ZMAGxE81Xd5vpVnNQnbZFraYd8ZENZjuY05rySx5xO5ZP4zRZUHxTeG9U+VY8qX+Glrtdz7C5jPXm605xPGarN6qjK7h5fVYdKp44q/VrH7jSqzO74tfJe9HH0Uze571k89P/9C+Zmp9jdjr871W8DK0qhfb4YfQV6SVCRuWrSv6DEua3Yvn0Yx+b2YyE9hUDMRb95PhQaS9h3Yh9mk1Stzc/AKYRXvA/bRifQoDlMN5Vn8YGdGLjheir9vHiG9dbdPhJS9F/n8ZCActNkpYcEYoCkX5kEY9bwJ1itNpFfSiNfIS1FM9EZEnulQgrW0jymkyeYr2go3jw02+mwLmDm4DMIJCI4cOA4pg+fxKGnjyGbqSK5RB1gBVQ5hhF0edE/sgsZEpMBZwCtSD+OLFbx6CTNiLKeVKqC3sEdSC1m4GjRNy1J0x5/BFcMvMz4LM3OwEdy7KlHHkA1s4BKoURlnYe+Eal65DrWqpCAK5cR88cxGtyByy6/hgRfHWn6X9z/7JPwE5t0egaOzBIKNLdaWKCPQaoB3VWaIqV60EWT37WmA05ScbVKFh4vzanStGqN5KWtaYPX2g9aS0XlxBIyz+zDke88DEu1hZ6Bfqolq1R00kiry4YyVZ5i/tdONleU7cFIDNnkAkrZPMrE2OZxI0wyNru0CIeLZlkdLhRKJUTCPpqGLaPBcizUFtoM86gtzgWOE5Wl8gzVajXjKGu3JgTVk6OPGgGNgEZAI6AR0AhoBC5uBGz3MlzcXdS90whoBDQCGgGNgEZAI/CTQWCjlYIul8vYGPzKV76yqkNLS0uoU6Hwqle/elX8VrhIJpO46caXG+03t9dJs3T/+A//aGw0m+Mv9PlmNx2qiA85CjmlCBG1eSvXGxGkPvVRdZrrUvekbkUEqvtyT4KZTFNxEq/SqXIlbrMFc3ulbRvV1u56VF1mPCSNwswcf7Zzc7nnars5rar/hdRnzq/qUnNCtVHSdBPIqi6Vxnw0l6niVdnma3V+yR75/CcGBrF999WwkPBxujxrzBWuEcvrhOAqZJCFC5/V0kQ5v0Si0AmPrYG53BKCJOTqtRbG+iYQDThpktJPJV+V8FbgDkeoMJNxBP0F2lAtk9yib7ztO0Yxn6GakCZI81U7MnP7EPKG0WI5dGOHAM2NVnNF5ivCR7OWlVKVhJ2N/u8ClMG1qWZjOcUa4E/AF3LSPGmcRFuObaTCjsrCnt4+ONw2jF8+jqE925Hop6IuEkCiN4oW1XHOUJD++Gw4NjuLHzx7nCZK88gW6nC6axgfHoSvlsbo6BgtaNZRpQIyyUZFQmESdEESjAXyhFaMbbsCmVKBSsE6KnkSkoeegrNEMo2qvkQiDIutlypBL0m/OQRCdrQsXvhIDsZoqLTtCcJJIq9KP401R4BqyBZy9P3bpMnOJuuLxvpQapCAa5EQJPa1eg1u+vxjobBmK8icSCOZtsCVpi/H7TuQJAkYiUdRJynoYdmlStEw/eokAdqmylMI3TLNsjrpj1DUobVSBXQXaYyxl+1ttaqsn4pNpwPsDnx8kchOgrJRrsLGOGoIuTjzw/ziw9dBM6IyL17IM3/JPm+64xoBjYBGQCOgEdAIaAQuEgS0UvAiGUjdDY2ARkAjoBHQCGgEXnoENoifWdWxt7zlLejr61sVJxeHDh08LW4rRFRESUKlQnf47Oc+R7N2ie7oC3r9kxiv82mwbNTKR5FrQrSoz/mUe668ql5Jp+ozbxir9kic+J8y31N5JY3Ey7UE87kRscm/pL3q001wXaimK6ykPFWXHFUw31c4qnvmo9xT+VW8+fpseSW9SivH8+2rKku1w3xU98xH8/3uc3P/JY8OggBNQ7aa5HRaxrog5xxBw3S1P0izn7U6n7nVSDWpDBQsmySjmjRVmV84jiL9AzYot3M5/VQCWmhasw9eEmnp+SxG+8apnnMiTlOVoi8j+4fS1AGaEaWqjGU7mh7MnczA0m6QKLPTlKgd4zTx7MgmMd7nJcEWQ8vphj3RC4fHixLb5OJvlndwFypzWeMFkFadBKGvQVudbSrmHIjvvo1KQSvqhQaqc9MIB3rRLNcx9fgzqFO1Z6G/v0bpGKon99H34Cw8FDy6aCpzNpum6cwqTYVmsePyYbzu+nG84ad24pU3DJHMpBLP4sex6TRKi2nM0yehxR5Dbm4emaWa8RFffs8fWsCRI3tphrWKnvAA/Qimse2ym9Fz/R1U8VWQA4m++nMo5U4iSjWkuxWAnYpCK/tdoB/BNgm4PNtiJzHp9zRRIaE5MjoKW4kKvGYTCzNTGBsbRiTiRiwUw+jYKM2fVrBwnEo/mlgtL1VJHI4j/ro3wL3nOuzafQ3JPztfiAmiQNOtWZZvp6/EJhWIxSLJRpK0pBtROXkUtKuKcCLGMRIfg2K/1Y46fTH6qAR0k0Rs0degDGG7TnUoFZD8NTEI4UwmbXCDVpK7LfpetPL5kjVbre0yX+RFI2NirZ5O+kojoBHQCGgENAIaAY2ARuAiQEArBS+CQdRd0AhoBDQCGgGNgEZgcyAg29Zd+7EXvGEz9LH3oQ99aFW5oqr7p89+loqGnlXxW+HCS/XFt775TUxNiem7U4GUDN58zz0GYXEq9sKebWaVoCJE5CiEyPmSNetBTuqSoI6KvFFxKl61RY7me6qtEid5VXpVjjoamTbwy1zvBlbzooqWtpnbdyZMVBo5ni2NSqcao64ljwSVV11LnKRRnzOlkfgXElQ95jzmOiRezRtzW8zp1bm5D2uVq9JdikcReFXpE+/YyQWq3sQMpIvEfMdkr6yZYl5yeehX4BHcaWUUZSrfpul/Lzk/a8wLtPLwR7wozkzClXPAb49ieGy7QQxZ6INOTEy25IWNZoV++Y4gMDyKOlVppeIiFYQh1MnM2UkkOUgsWck92VlJpSEmMxNU+uVRy+Rhp7nRSP8gTVzmUM9VkbGUSabRPGYf/eo1PFTYkewrk+AqZdAg2dUbH0SaJjhLxTLJKyDu9xm+ApP5En0IUq1HossTDiGToS9CUQm63XCxfogpTpJoJ44coOLQiSR99u0a5MszxZMIhfxYmCvTb2KCOATw1OEUjswt4MQCTYq6fFRHhkExI3ZcdjUOPkeToJ4s3MEw6uILMCF+AJMk3nrQTDWRWiDhNzxEc50VBL3sK8uuUQHpzFRI1FbQoOnUfioZp6hajPcG4fZFCYwDDhKyLv5OV8sFZHJJuIidPxKnALMJH/0ZBkdIYlbq6JuYQCo9D68QvPUKTswfxVBvPzE5QMVfP31BWtGytVn+FPoGxrBEktBmc5EgJU4kf3NUbgaYt0ofgzaOh8XmQDE5j0qdqtDkEv99IrOE6kGaNa1U6YeQ6lAhjUVhqp5PmTjy3MkLH0bilZmkTzQCGgGNgEZAI6AR0AhoBC4WBLRS8GIZSd0PjYBGQCOgEdAIaAQ2BQLdG7IXulFf+9rXTivy5fQpeMUVu0+L3woRdvq4+p9f+CLc3Nw1h89//nOYn583R13w82UO7IKXez4FCiEiag1FjJg3as+n3LPlNdcp52YiRq7VR8pQ9+QoQe6pYE4ncd1pVR6V/kIfVf3mNl3oOl5seaptkv9cOJjbf6a0Ko3CWMo1x8m1CmuVofKtdU/lW+9xrTK627JWmu7yJY/KJ/dUG7vTXcrXoglsUB344CP76Zcub7yE0mzQhKX8obnKMwULlYVf/czf4vAzT5KsGoUv3Auvl6RZM4SQI4Z2ho7t3E6CbkWJBFyVfvQoRkMuu0hOMA/n2E2YO76EXJLqQE8PfQdSFVezGwqz9LFjyNAnYKWRR+74ARza+xR6hgbRO9QHZ08QomsT4s9DNWFbyKawF24SWTNH9sHWYp00bemsp+FMLSHN9mH2OBpHJ5Gwu5CxWTA4voMmUiNMW6Zpz8cxNfMYSTAPKmW2k0TbIs1l2r1N7F1cQpl9qBUz8BabuHzbAIJUKgZJbg315qlMtCOVOUYfvDvxUzfuMD6DvTGMDZG4o+rv8I+/jZC1Bq89ABdNnM5M04SpjT4BvX7Wa2Wf3WxqjOZDEwjS5GaSqkYPyTRbJIH+q8ex4/prYXNxhKieDAe8qNjDsDnaCHvYJk8L2VKeGARQpuLS6YmgRbIwPD6CYtuKIpV8/kQUBw49g1A8gmI5g6VUGj3RBHIkQJ3WBJ8HK/IkdsmzYtv4BBZyBfipUGzm00hN06+hk6q/Rg35dJokJH02ppMkTW0kLTlW7IMnEoGbvgkt/M3NpP9/9u4DPqoqfR/4m947vYXei6iIAhYQsKBSLKuiuOq6a++uDUFULLsq2Pgr9v5TFKyrKDZUVAQVkCa9lxBCeif/9zl6Lncmk5CElEnynM9nmNvvud8Z2DVP3nN0LkcdtjVr9TrJWLHKfO72u+P+O2i38Z0CFKAABShAAQpQoGEJMBRsWJ8nn4YCFKAABShAgToW+DMqqZlOlGhY9NSTT3pcHD84f/Chhzy21beVZs2aSe/evUt1e/HiRaW2VeeGmg5wK9tX+8NYG4bYQLAigUpl72WPdwcxuI+9p/f+8vbhGna/Pc+u2+thvbqb7bt9t9fHvWrifvb6lX1H/9ytrP55PwfO8T7W1zHe13afh/PtObYf9pp4r0iz59vhBe11fJ1rj/XeZ7f7Otfus+fY/tl1vh8QQDAUo8FXbEy45GnVm06qpyNGanUgSrp0jrrSzcSFsmrxDzqsZJ6069xVImKDJCY6WEecTJOS4EgJbdJOpGmExLRoIoEadoWEBGNUTx0GM03ioxNl7x9fSHR8E2nWtoMkNdWQTEOpgp17ZccvCyRl9Vqk0ZJXUqDVZpFaXRem76ESrMNqbtuxSwOtbMneuUNL04K0ym2/NI+I0irDBMnVIS5bNouRgIhcKc7eI0U6bGnulh167WKJ6dJLWg8eIrE9ekr7VomyMXWvhIU3kaCoJAmO6SMJAXnSqnmChMQlytq163XOwlhJ1/n3WmhoGKJzF2YuXyy9unTRIVK1ErJpEw3KdktQs66ybV+xLFqTLbu3bNAhP3VoT30F5++VzD3pOvdfoA7v2U4S47rIvnSteNwfKa1aRGn1Xq4kxuvwnCU6e2BJEznqxBNEMzytEOwgPXp00mrNXVphuVcrC5tIsD5/dLw+034d5lmDt2bx8VqxV2yGUI0KitfhR3OkRCsvY+PC9H4psjd9j+xI2S1RzbX6MamZ9jVe5xKM00x0o/6STJhkFKTos2mAqqFiZKzOn1iUr5+hVjcG6LyCGmLGJcRJRJxWFOoQpZF6r5RN2yRCJxfM35WilZfZkqRzMWbtSZXYaP2uZKTp8Kv5OoekVlhqNWGEBpI7Vy+TvN2b9VwNRf/6Nwp/Fyv670Lp7xq3UIACFKAABShAAQrUF4EA/T9+nv+VWl96zn5SgAIUoAAFKEABPxbAMG/V3ZYvXy79+vYxP+S31+7Tp6/8+ttv9f4HeZ9+8omMGnWqfSzzPmbMGHl39hyPbdW14o9DhyJ0qa1AxAYx3j8AxrrdB+vy+lPWf0Z4X7O6PjPv69h+4t32s7bu7d2Xqq67De1z4FoHew777GUdV9b2yvbT3b+K9MvX9d19dT+jr2Oreo+yrtVQt+O/4Au0GiwkWIeT1H83UHHtq+G4lO2bZcu6VTq8pVbdtWtnQq3omDhTMRasIVJ4ZLiGVLm6HfPI6TCvJTosplbFoSqtuLhQwrUCMGvnMgmM7ShBgZGSn6mVeXrf4IBiydT5+SL0/KLgTJ3bL1Ry09M14GquIaEGV+FRWlWXoMdl69CVORKTGC0BRYWSraVuMS0SJWXNColr0U5ydqeLXkLydQjQCA23cP6uTes0mEyVVRv2yGe/i3TomCjDB3aQ5s2aSmbqTokIiZQ9+j+ySzfqMJox0ZKQtlby92qYFtlG9m1N1xBR5zjsECsB+VkS1aKz7M7M0/kLm0hceIisWrFCAzwdL1RbM620C9DQLEKDzMjgAinakyYRWgVZpOfv00q9OB0+NL8gVVJ13r+CoGJp3aaDlBRlSGxUuIZprSRL77svdZM0b91Hj8+R9PRtGiy21CFP9Rcs9N/S0BCtEtR5C0sK9Pn1nEJNFPelZ2moqMFuWLRWYqbr33UtlgwL1Kq+eInRqsMcrYAsDsiSSLUOj9BhTPHZaLXf7rTdkpSYJDk6V2FSUrxkZezTaskCidNQL0tDv6AQfSYdgjVUh2ct0OcL0OV8/QxDiwMkMjFWgjRMLtRAsUT7EKpzPhboEKdbf/1ZWh57vMQ0aWb+zcF3Cb/IwUYBClCAAhSgAAUo0LAFfP/XQ8N+Zj4dBShAAQpQgAIUqHEB/KAPP5Ctzvb99995BIK49ogRww8aIFRnH2rqWiNGjJAWLVvKTv1BpW2ff/65ZGfpD3Wjo+2mQ37Xj8X8EPaQL1QDF6iNH8bakAfvvu7nDnAOFiwdbH8NEDmXtP3EBluN6Oys4wXbN+tj3w/WLffngWu4m72Gezu22XUsu9ex3Z7jvk5Flu017bFYd/fNbse7PdZ9L7vNfZw9332cez+XKyeA/30JC/3zP+Wtra8rFOtwkqgYS2rWQucUzJTd2zbocJzdpLgoRyKi4zVsKtSwUCsNg8Nlz9at0rR1OwnSYSiLNB3M35cmSS2bazWizldXEqJhWJGZfy5Kw73cvbskrGVb2a+T8YXq8JwlxQmSmZItUQkBEqdz5G3XCriQiBCtWNNhOwN0jr7EBJ1DcL+GaPskqUMbKcrYreGYzvPXRisDE3R4zpAYrYDUCrpireDLS5WSKJ0DUSv44iKL5OTj+0pKWrqkZIqet0j72EWvk60Bo34v9d5p6Tskfn+EtE3uLSk6519ScnsdwjNI4lq20kq/PO2fnrdng8R00OfVZ+jSramGglr9p22bzq/YTv/3JVurKIvCgiVE5zBM0zAwIkCfS4dkLS7O1cCsvYZ9myQwItDMsxiimVmBzsOXnq6BaHCsRGkQmZ2pw69qUBqjlXtp2WnSrkVbySkKNNWZ4ZH6MOqUsmefxKhJcnJbycraJVpMKYX7da7BgEIJCkYlZJRs3PK77EdQG6r3j9FqwaxMreqM0+P13MSWkq6VgiUZOq+jHhumoWJ4aJFk65yHsRoM5mSlS2iU3l+HfE3UQLFQh2VN0GrCLct+1wrLrpKTvk8SWrWWgtx8KdJAV8sQJXnYCAnTOQjx99I7ECzWCkQzr6CvLxa3UYACFKAABShAAQrUawGGgvX642PnKUABClCAAhTwVwGETxjRzetn+4fU3V9++aXU+UOHnVhqW33cEKSVLv369vUIBbOzs2X1H3/I4YcfXm2PhB+mN8aGoMaGNfgBsHeQYPdjn335qxN+eI3++mM/3X2DH/pYmWY/I/t89tyytruvb4+x51Tl3X0Nu+y+h/uadr97m3sZ+/3xM3L3sT4se4c1lelzsFaq2RYVkyCt24doABWi/8OkVWUYB1OCNPjRasOAYGneroMUpO2SgMRmWoEYKk3bttUquXwNDUskLDpWKwF1PkH9dzozY4+kF6RpZV6AVqy10Tns9kisBkuJnZpJzl4dihNVefq1D9ZQKkfnJgzX++qYmhKlQ2QGh8fp0KE5Okyp3qNZgQ4r2lrCE8IlOCdD9osGkXq/Eg3JYkIjZV9Ea50Lb7+EaUXg/jCd/zBPh95s0kp2bdig12wp27ZskaisVEmMSZKigm2yXLe30sq+vOwQidLwL6R1G624047osJvtm4dIVt4eCdAKwIyiAInQyjq0xOgw2asBZ75W4+mom9I6MUoSkxJl6+qlEtu0tQk1SyKitTIwWFJ0iNIcDfyC1UZKcqSphoV5WkkZH5EkBajQ279Xf4klUVo2CZcSTQ6LNGjcr88dHBqh4V+uzu1XKFlaubdf3ULCorSyL0Ci4yIkAUO0ah/SUrTCUZ8bn0czrUIM1mq+Ip0vsqAwVzJ1KNTI8CK9ZraGoi3VNU+DyHQJKSnU8LOdVngWmAAvWMd9bRoTL9u3bpHgqEjZr8Fek9atdAhXHUI1WoPC3CwdcbZQArQ0MzhS50MM0urF/ALtDyon1VnDzsCgP39ExEDQfEX4BwUoQAEKUIACFGiQAhwbokF+rHwoClCAAhSgAAX8QaByccDBe7xq5UqPg/BDuwEDjvTYVp9X+h12WKnur1u7ptS2qm6o7s+jqv2o7fMQztgAxx3S2O2+giy7r7b7Wt790Cf0FQ2hpn2WskKr8q5VE/vQP/TF3TfcB9vLa/Y57HH23Z6DdbsNx3qvV9fz23vY+9p+2XV7X+93ux/vdh+Wvc/HNrbKC3gH+JW/wp9nBOjfmZDwaA19QvU91lSWmakI9Y+gIP1lAH2V6BCUuVpRpjncn00DtR2rN5qAKjAsRIfR3KtVhUUS07STtGzTQ6sBdY7ApsmSuVcr9/YXSUBUhBTrMJUxWh2HCrfY2FgN+jT7i2oiBahwK9TwTIOoLL1O+xbtpUgrB7My0iU9I18ys4okK7dQCgKiJb8kXJq0bK8hZKTE5KVJ2J5NGg5maC4YKK069pGVm3dKctdu0rvPQMlds1SCIpOkuc5VmJ2ZrSHXbsnekyJbli7T4HKH5nd79HsZrvFnpg4rqlWRWqG4atsm89qhlel7S3K1ZK9EQnR+waCAIlny2xJJbN9LQpu31bkbm8jezeslLStXIjUsy9i9U6sRd+i8gC11TsC9kpKaKvv0l1c2bd9ugrm0lO2yRyv3MnVbceB+iWuSqMOIFmrgt1NaNG8qIUFamanhZGhgkeSkrZMgDQpTMzK08rBQ8vIKJUSDwEgNITN0GwK9zHTdp8PE4l+9vKIsDR81aNSgMFT7HBqucxdGxGjQmCnFal+Ymyl7tB+Z2Tk6t2GsBOtnl4mwMlzDQK2YDNH5KNPWb5JijBGrYWJAQKi+5M/g1Ax3qn9n/XFc7b++inyjAAUoQAEKUIACFKg+AYaC1WfJK1GAAhSgAAUoQIFSAtX5M7YtWhnhbm21kqNJk6buTfV6uVfPXqX6v2nz5lLbqroBPwBtTA0BDUI0G6TZkMYGN/bdmtj9dt3ux3tdNXtvG1yiH7afePfHZvtn+4Z1a2m32Xf7DPY57XZf7/Ya9vre13Vfw17X13Uqss19D/d9vO9h72P7hnf3uRW5F48pX6BEA58CBDnV0BAw2leUVpQFBuogmQiGEDxpQBWmIV6UDkWJtKhIq+eKCvdLy/atpLAAQVKAhmrFGmwVYKRMMydhfnam7NLhKaObNpHC4hKJjIuRQh3WNCYhSYozsnWevjCJRjCYlS1BOp9gcHCQbF31u0RpAJm+basONxovGVu3SbAGYbl6rxAN97Kz8nUOxD8kt0CjsPw8rTBMkh49u0jg1s2yYUuK7M3brxV9JbL1j58kXyvywkJjJH3xz4J5ErM18CsObyXpRZHSrEWsZl/7JG3DIsnXIC1HQ7xdGupl7c/Xyr8I8yrS4TwL9+XJ5n2bJDShpWzcukeH2myr8/ft0QrDAknRMC82obnEo5JOg7RY7UtAXolkpaZpiFqiYWe0PpcOMapDfcboUKIx4THSLDZGwnX4zlCNIfN0/sVcdWwW31Q9dZ5ADS6jE1tJRnaqhOu8iPkBERIakKfhYUdT8bhz6zqJ1rkY4xKbat0eAjsNBjNzJF7nIywuDhadElA2794lG3ela9XiDg12N2k1Y7bkaXiYl6+hqg75unblYv0sS7TyMVXnEYySLAwDqiFhWsouCda+hYWHS14axmLNk2KNGzFvpI7/ikRQz6uGLxkvQQEKUIACFKAABSjg9wIMBf3+I2IHKUABClCAAhSo7wLVEQwW6Q9U9+3b50HRRkPBQD8NRjw6WsGVTp07lzoydY/+cPYQG6Kj6vgMDrEbtX46whkEAKgoxbsNcGxoY8MB++7uoD3XHuveV5vLts+2j+7nqM1+VORe5VmVtw/Xtvvxbp/VbrPr9t1u93Wee19F+uw+xp6Ld++Gbd73dx9vl3GMr/O9r8f1igv8PP8LnddP59orORDwV/zsco7Uz9SEvBog5WhFXZBWwuktNAsr1IwoXzTjk6i4eK0qDJBUnWcP/44kNG2jn2+MaBGc5OqQlvuzNWBLaqIVg9Gig1BKkQ5pGRsbpeFemBQE7Zec1L1apZalc+KlS1pevk7GVyTxrZJNoBiglWz7Nuowl4HFsu7HBRKZsUVK9q6WhJim0r5dFynZuluCd2RJ3roU2a0VfXF6XlROuuTr/yZE6L2iWnbVEGyfBPbsI/lafbh9nd4rWPuxd50kJLeTPXq71NQNGlZmS1rqTu2fzmlYWCyRUYkSraEgXgmRiVISUSwJYU0kZccuddYQtHmMZBeUSHZxoGSlp+v/duRpsKaBpvrsSkvRYLPIVN9lZhZKRGCwBOpQoMF6/z/0WcK0em9fWpqE6kSGAYoZqmHifpRKmlo/nZdRw9dCnfMPcyEW52XI9p0aUubr0KY6DGpEZKR073GU7ovSKsESydBhWEODwvTvnVYa6ty6Gdm7dajVREmMKpGWzcIkLDBdhxfdJEVZa2XrprVKWyA5mXs1TAySlUt/lNSdWzWM1YpDHfYUA4IGaRVi/rZdkrV3nxRpoLk/RIcQ1ZA2Su+L3/vAdwF/f9koQAEKUIACFKAABRq+QID+nz/8EhobBShAAQpQgAIUoEANC5gfvFXxHnl5uTqHURPJyclxrjB27Fh5993ZpqLA2ViPF1avXiU9e/TweILrrr9eHn10mse2yqw0xjCwMj48lgIUoAAFKEABClCAAhSgAAUoQIHGI8BfBWs8nzWflAIUoAAFKECBOhZAAU7pGpw67pRf3b76dHCl6ruaXyGxMxSgAAUoQAEKUIACFKAABShAAQpQoEoCDAWrxMaTKEABClCAAhSgQNUEEAyieq2ygVVwcLCEYV4jV9uuQ6o1pLZzZ+nnCfd65oo8r/GFcWWRK3JxHkMBClCAAhSgAAUoQAEKUIACFKAABeqpAEPBevrBsdsUoAAFKEABCtRvASccrGBwFRwcIkk6d5O7rV2zRvJ0vqCG0pYtXVbqUVq0bFlqm68NtgqTw4X60uE2ClCAAhSgAAUoQAEKUIACFKAABSigv6hOBApQgAIUoAAFKECBuhNAJoggy7t60FeVW7du3Tw6mpqaKj///LPHtvq88tVXX5bqfudOnUttsxtssGrtfJnZY/lOAQpQgAIUoAAFKEABClCAAhSgAAUau0BwYwfg81OAAhSgAAUoQAF/EbDVbrY/3iHXhAkXSmJigt1t3tf8sVqOHTLEY1t9XCksLJQWLVrIhRde6NH9gQOPMkOtwqJE91SwsNLjGlyhAAUoQAEKUIACFKAABShAAQpQgAIU0J+rlGgjBAUoQAEKUIACFKAABShAAQpQgAIUoAAFKEABClCAAhSgAAUo0HAFOHxow/1s+WQUoAAFKEABClCAAhSgAAUoQAEKUIACFKAABShAAQpQgAIUMAIMBflFoAAFKEABClCAAhSgAAUoQAEKUIACFKAABShAAQpQgAIHEXjsscckOTlZhg8fLosWLTrI0dxNAf8T4PCh/veZsEcUoAAFKEABClCAAhSgAAUoQAEKUIACFKAABShAAQr4kcCCBQvkvPPOc3o0bNgwefHFF511LlCgPgiwUrA+fErsIwUoQAEKUIACFKAABShAAQpQgAIUoAAFKEABClCAAnUm4F0ZWFxcXGd94Y0pUFUBhoJVleN5FKAABShAAQpQgAIUoAAFKEABClCAAhSgAAUoQAEKNAqBefPmeTxnUlKSxzpXKFAfBBgK1odPiX2kAAUoQAEKUIACFKAABShAAQpQgAIUoAAFKEABClCgTgR27dolS5Ys8bh3YmKixzpXKFAfBBgK1odPiX2kAAUoQAEKUIACFKAABShAAQpQgAIUoAAFKEABCtSxwA8//CB79uyp417U/u3/7//+r9RN4+PjS23jBgr4u0Cwv3eQ/aMABShAAQpQgAIUoAAFKEABClCAAhSgAAUoQIHGI1BUVCTnnXeeZGZmymGHHSYPPvigz4dHOHXRRRcJ5nYbMWKE3HTTTT6P48bqEzj33HPNxY4++mgZPny4DBo0SHr16lV9N/DDK6Wnp8szzzxTqmdxcXGltnEDBfxdgJWC/v4JsX8UoAAFKEABClCAAhSgAAUoQAEKUIACFKAABRqRQHBwsAwcOFBWrlwpb775pixfvtzn07/44ovy+++/m+NOPvlkn8dwY/UKwPz000+XH3/8Ue677z459dRT5cwzz5Snn35a1q9fX70385OrPfXUU5KdnV2qNzExMaW2cQMF/F0goESbv3eS/aMABShAAQpQgAIUoAAFKEABClCAAhSgAAUoQIHGI7Bv3z5ThYYwZtSoUTJjxgyPh09LSzNVhNh42mmnCYIbttoTWLp0qcyfP1++++47wZCitqFic+TIkaaKsCHMuYfQGd8/X+2FF16QE0880dcubqOA3wowFPTbj4YdowAFKEABClCAAhSgAAUoQAEKUIACFKAABSjQeAWeeOIJefjhhw3AZ599Jt26dXMwHnvsMXn00UfN+hdffCGdO3d29nkvYBjSiIgIQQViRRtqabKysmT//v0SHR0tQUFBFT210R23YcMG+fzzz2Xu3LmyaNEi8/wJCQkybNgw5xUZGVnvXDCM7ejRo001qq/Oo4oVw6fWZMvNzZUFCxbIL7/8In/88YcsW7ZMMjIypGvXrnL11Veb8LUm72+vjSF6AwMDJSAgwG6q1nf8HUXQj7+jzZo1q9Tf1WrtSCO4GEPBRvAh8xEpQAEKUIACFKAABShAAQpQgAIUoAAFKEABCtQ3AYRyRx11lBm6EeHM448/bh4BoQjmtEMV4dlnn+0Eh+7n2759u0ydOtVUsaWmpppdvXv3liuvvLLMyq8vv/xS3nvvPVP9Zs+x14yKipJZs2Y1+Pnz7PNW9R3DiiIcxGvbtm3mMs2bN3fCQQSFlQlnq9qP6jjv2WefNUOk2muhAhLhtG2vv/66DBkyxK5W2zsCuG+//VZmz54t77//fpnXxXdyxYoVZe6vjh0IxR966CEzPCyuhwpR77kU0d+ff/5ZNm3aZP5O9u/fX/r06VOhz/m1116T559/vtTQs+3atTOB7IQJE0xIWB3Pwmv8KcBQkN8EClCAAhSgAAUoQAEKUIACFKAABShAAQpQgAIU8EsBdzDz1VdfSceOHc1Qoggq0BCeIEBwt19//VXGjx/vcx44HHfVVVfJv//9b/cp8uqrr8rEiRM9tnmvYJjMVq1aeW/mug+BnJwcEwx+8skn5t0egs8KwSCG3TzuuOPsZr97X7t2rcfQoF26dJHnnntOjj/+eKevb731lgmnnQ3lLKCC8oEHHpC9e/dK27ZtZdq0aZKUlORxBkJUzNn40UcfyY4dOzz2+VrB+agg9NWWLFkic+bMMZWu1157rTRp0sTXYeVuQ7Us/k4guLPtm2++kfbt29tV+frrr+XBBx8083o6G3UBgeUpp5wiU6ZMMZW27n12+aWXXpLJkyfbVZ/vuA7C/bFjx/rcz42VF2AoWHkznkEBClCAAhSgAAUoQAEKUIACFKAABShAAQpQgAK1IIDhEwcPHiyo3DvnnHPk7rvvlgEDBpjAD1VE9957r0cvUNk0ZswYQSiCds0115jgJj093VQUrl+/3my3ASNWUJHYq1cvsx1/4Jy+fftKbGysqXbKy8szQxsi5KgvVW7Ow/jBAswxvOi8efNk4cKFTo+6d+9ugrfhw4fL4Ycf7myv6wV8VzBP5ebNm52ufPjhh4KKR1Su2vbuu+/KkUceaVflf//7n9x8881mKFsEgPY7tXjxYhk3bpxzHBZQtYrQLjQ01GzH93zo0KHlhoEIyDBMLr6XeJ111lkmYPW4sK5gnkeE4rahz6hyrWx75ZVX5K677nJOw/2XL19uhhCFEcJ1hPLltX79+pnA3bu68IMPPjB/z8o7170PlcIwRR/YDk2g4oMoH9p9eDYFKEABClCAAhSgAAUoQAEKUIACFKAABShAAQpQoFICmAvwxhtvlDvvvFPefvttE6Jg2FA0DAXq3TB8pQ0EMecaQhrbevToYYIXrCPkue6668wuDHto2z/+8Q+Pc+z2mnhPTk6uicvWm2uuWrVK8HrqqadMnxFk4XOuy+AHoTK+b+5AEN8hhMR79uzxsPWeZxJBF76b+P698cYbpsJt9+7dcuGFF3qch5Xff/9dXnjhBbn88svNPgxd66s6EBb/+te/TNBd0e8LQkF3cwex7u3lLSPIdQeCOHbmzJkmEESIftFFFwkqcg/WYIG/t5dddplzKIYbvf/++511u4AqzBYtWpjqRjyD2wPDqGJORVwLgShb1QUCq34qz6QABShAAQpQgAIUoAAFKEABClCAAhSgAAUoQAEK1KwAKgRbtmxpbmKHMkTIYLe5775u3TpnFZVU7oahR/FC27hxo3nHH+6hFTFE5D333CPz58+XzMxM55iaWLj00ktr4rL19pqYow/VdRhetK4agjpUNNqGKrsrrrjCrAYEBNjN5j0kJMRjvVmzZs56SkqKoPoP1XQ2xHZ2/rWA4UdtK6sC9bbbbjPhd0UDQVwPQ7d6t6KiIu9NZa6jMhbVsu6G4XYxfyKe6eKLLy4VCCJgx/C633//vVx//fXuU+XTTz/1WMcQpO7AD8En/r6hMvE///mPqejFtRCyYu5Q21auXCmYK5Tt0ARYKXhofjybAhSgAAUoQAEKUIACFKAABShAAQpQgAIUoAAFalAAQyzedNNNHhV8tsLK+7buqj9UCno3O3zo1q1bnV0YFhLXe/rpp822559/XvBC69+/vwlBRo0aVe1Dh06aNEliYmJk+vTp5l4N+Q8EP9HR0RIZGWnesY7wCnPs7du3zwzhiuf3Dt5q0wRVbe7haNFHfDY2sPMO1rwrBVHVahuG10Rw7a7Sw/XcASG+iwixO3XqZIZRHTlypHz22Wf2EuYd1XqYVxPhIIavDQw8eJ2XHZLUXgjzONpnsNvKe8f3EpWMtqFfNhh95plnSj0T5kEcOHCgPdwEigj07N81zKfobu+884571cxJ6B164nuAoUcRnKJqEHMnolKTc3p60FVphaFgldh4EgUoQAEKUIACFKAABShAAQpQgAIUoAAFKEABCtSWwNixY2Xy5MkmVMHQhe7qvrL64A42vI/xDkluv/12OeOMM+Sll16Sjz/+2AlvMEQiXhg6EWGGO/jxvmZV1jGfXk01PCMCIlS04b2sF/b7etlz3dc51L7CEpViX3/9tSxdutS5HKrQTj/9dPNCeFbbDSEehul0N4TErVu3djbhGHdbtmyZ2W+Hs8RQobZhGFt3S0pKMt8fDEuK769tCLsQCsJ4xowZ8t///lcQvLkbzsFQuQjJ8D095phj3LtLLbdt29ZjG+ZHREOoie8w5hfcsGGDmaPvpJNO8jgWw3O6KxhRWfvoo4+aMBJVetOmTfM4HmF5SUmJuTaeAUN84hlsIIiD3dV+WEfFn224vu2f3eb9ju8GXmzVI8BQsHoceRUKUIACFKAABShAAQpQgAIUoAAFKEABClCAAhSoIQEEDghfUGmF6rqyGgIW26ZOnSqHHXaYXfV4R9Wad8PQlQg0MITh2rVr5aeffjIh4Zo1a0zlFOYhxFCm1dn69OkjeDXk5g4CsWwbhts8+eSTTSjkrjSz+2vrHfMI3nLLLR5DWmJuye7du5twC5//b7/9Zoa4dPcJ5+B1/vnnm4ANVY++GgJBBHEIwDp06GCGvbXDZ7qDRASzd9xxh4wePdqEf3ZuTHtNrJ977rkmZMMQnQcLB+15qDBEw/f31ltvtZvln//8p3muhIQEsw0VfXge2xDOogrQ/n175JFH7C7nHSEiXuW1M88802O3OzDs1q1bhaofPS7AlUMSYCh4SHw8mQIUoAAFKEABClCAAhSgAAUoQAEKUIACFKAABfxFoEuXLk5X3nzzTROiIFCsTMPQhbgOXu3bt5fx48eb091Dk1bmeo3x2LKCQFiceOKJJgxEWGUDp7o0evLJJ2Xu3LkeXcDcknhVpKWmpprDMLylr4bhP21Yje/WiBEjzPx5OBZzD3o3hNPvvfeeLFiwQF5++eVSQ4qiChHh4JFHHimYy+/YY4/1GHa1uLjY45L4HqOa79133/XYjpXFixcLqlWXL18uEyZM8NiP6lh8/9EQYnoP+2l2HOQPzA/pntuzoKDA4wwEoWy1K1C5fw1rt2+8GwUoQAEKUIACFKAABShAAQpQgAIUoAAFKEABClCgwgJHHHGECUm+/fZbU933t7/9zYQeGMIQ85GhmgvzCSKYsS0tLU0+//xzadOmjaCqC/PeIbzYsmWLme/MHteyZUu7yHcfAvn5+fL++++bF+aBc7cePXoIhqpEZSCW/aUhDPRVAVeR/uH7cOeddwqG0ESz1X/uc1H5h++ku7mf3z2UJb9BUQAAQABJREFUpvsYzB1oh83EsJ2oxvMeuhOVfRdeeKH5vmOoU1v9ispHd8OQty/psLj4O+HdEAaievHss892hszFMZhb0T1kp/dchxhiFM/70EMP+XxuXAPVlhju1D0PontORRxTl3NI4v6NsTEUbIyfOp+ZAhSgAAUoQAEKUIACFKAABShAAQpQgAIUoEADFEDIgGFDMQchKrgQnODl3RCG2BBl9erVHsMmeh+LdVRb4ZpspQUwHKQNAzFXnW2JiYkmCEQYOHToULvZb94xryGG0DxYa9eunfTt29cExqjcsw1z4WEeRLTc3FzzfbP78D5o0CC57LLL3JvMMkI421D1V1hYaOZ0tNu83xFmY7jQv//976ZyEHMOusM1hH0Iv1955RXTR8wF6W4I9DAfp6+G+QK9r4cwz7tq0D0/J4ZBtfME4vnnz58vmF8Rn32LFi1MVSQqBBGwezfvSsFdu3Z5H8L1GhZgKFjDwLw8BShAAQpQgAIUoAAFKEABClCAAhSgAAUoQAEK1J5AcnKyqYp64oknTIWVHd7R3YOdO3dK586dzaay5oLDTsyrNm7cOLniiivMsvsajX0ZQ0+igm327NmmstJ6IABEEIgXgkF/bJhf7+qrr/boGir/EFr169fPVMlhPsquXbt6DHGKAM7OieeeH9FXuHXfffd5VMnZm/Xs2dMumncE1Hbuy4ULF8qUKVOMHaoA7Xx/ODA+Pt4MF4pwEJV/CPRsQ2h38cUXm2FHmzdvbjebd1/BpPsAd8CIoURR3efdfFVB4hgMzYsAEK+KNO9QkEPyVkSteo9hKFi9nrwaBShAAQpQgAIUoAAFKEABClCAAhSgAAUoQAEK1IAAqqoq2hDm3XbbbeaF0AMhYFFRkakORGjinmfw1FNPlTVr1phjUPGF43A+AhnMeece/rCi92/Ix6EybNasWfLBBx84jzlw4EAzVyDmC7Rhq7PTzxZef/11wbCe7oagDdV0B/us4+LinNNQgYrvCr5LmLPP3VBtZ+cRdG/HMq6BIUTt0KFfffWVEwrOmTPHDHuLkA/BH4bnhKm7Xzgfcwmed955cvfdd8vHH39sbrFkyRLBsK2o1iurIfjEPH6bN28udQj69Pjjj3v83bAHxcbG2kUTiiIQ7d+/v7OtogveQ5sibEQo76/hcUWfqz4dx1CwPn1a7CsFKEABClCAAhSgAAUoQAEKUIACFKAABShAAQpUSgABX1kBjb0QhlzEMJFs5QtguE3Mw4eGireRI0ea0ArDa9aHhiFDvQPBq666ygwfe7D57RAAuqsD8byYn7J9+/ZmXr6JEycKqgPxfbvyyivL5bj00kvl5ptvNscsXbrUORb3sA0VrggXMVzn+eefb+bERCAYFBRkhg/FkJ3eQTnmwcSwpRi607tCFqEfhj/ds2ePIAh3N4SFCCHRd1+tV69eTviI/agmRDCM0LwyLSwsrNThGD6VrfYEAjTB9oywa+/evBMFKEABClCAAhSgAAUoQAEKUIACFKAABShAAQpQgAL1RABhFyrjzj33XBM+1ZNuO91Edd2CBQucdQR5Bxte0x6MKtLu3bvbVfP+3nvveVTMIXREpWSbNm08jvNeQSxz0UUXyTfffCPTp0935qvE8KFnn3229+EVXn/22WdNUIsqzmuuucY5b/To0fLAAw84od9vv/0mkyZNkoiICDnuuOPM0KORkZHO8d4LqLY99thjPYJGhI9PPfXUQav8EHSuXbvWhJEIHTGH4SeffGJu0bt3b4+w0fu+XK9+AYaC1W/KK1KAAhSgAAUoQAEKUIACFKAABShAAQpQgAIUoAAFKOBnAocffrgJtlAZh0DriCOOqFQPZ86cKVOnTnXOQQjYoUMHZ70yCwjL8AoPD/c4DUOHPvLII/Lll196bD/YCoI/zDNoh8b99NNPZdWqVaaSccyYMQc7/aD7MUypdwUkQj4M0ztixAiBKRoCT8yxiCpGPMO8efNMZaO9AeZlxDClmJNy2rRpztCpdj/fa1aAoWDN+vLqFKAABShAAQpQgAIUoAAFKEABClCAAhSgAAUoQAEK+IHADz/8IKtXrzbVeGUNlVleNxF4zZgxQ3AdDJ06YcKE8g4/pH0YqhT3+v777z1CNe+LHn/88abqEHMP1mTDsz/55JPy8MMP+7wNPDFf5/r1633utxsRFtoA0W7je+0JMBSsPWveiQIUoAAFKEABClCAAhSgAAUoQAEKUIACFKAABShAAQpUSiAlJUU2btwoGRkZEh0dLbGxsRIfH2/mDsR8mLXZPv/8czPXYVXu+dBDD5mhZ6tyLs+pHgGGgtXjyKtQgAIUoAAFKEABClCAAhSgAAUoQAEKUIACFKAABShAgQYvgJDytddeE8xhiPkGy2qoCERF5eDBg+Woo46ShISEsg7l9loSYChYS9C8DQUoQAEKUIACFKAABShAAQpQgAIUoAAFKEABClCAAhRoKAI5OTmyYsUK2bRpk2zZssXMkdisWTNp166ddOrUSdq2bdtQHrXBPAdDwQbzUfJBKEABClCAAhSgAAUoQAEKUIACFKAABShAAQpQgAIUoAAFKOBbIND3Zm6lAAUoQAEKUIACFKAABShAAQpQgAIUoAAFKEABClRN4OOPP5ZBgwZJcnKy3HXXXVW7CM+iAAUoQIFqFQiu1qvxYhSgAAUoQAEKUIACFKAABShAAQpQgAIUoAAFKNAoBVavXi0ffPCBvP/++2YoQYuA7WwUoAAFKFD3Ahw+tO4/A/aAAhSgAAUoQAEKUIACFKAABShAAQpQgAIUoEC9FVi2bJm88cYb5oWHiIqKkuzsbI/n+fHHH6Vly5Ye27hCAQpQgAK1K8BKwdr15t0oQAEKUIACFKAABShAAQpQgAIUoAAFKEABCjQIAe8wsEWLFjJ8+HB57bXXPJ5v+vTpDAQ9RLhCAQpQoG4EGArWjTvvSgEKUIACFKAABShAAQpQgAIUoAAFKEABClCgXgp4h4GYN/Bvf/ubCQRHjhzp8UxTp06VsWPHemzjCgUoQAEK1I0Ahw+tG3felQIUoAAFKEABClCAAhSgAAUoQAEKUIACFKBAvRJYv369vPjii/LKK6+Yfnft2tWEgeeee66Eh4dLp06dPJ5n0KBB8uabb3ps4woFKEABCtSdAEPBurPnnSlAAQpQgAIUoAAFKEABClCAAhSgAAUoQAEK+L1AZmamvPDCCzJz5kzJysqS3r17C4JAvEJCQkz/O3bsKMXFxR7PMnfuXOnevbvHNq5QgAIUoEDdCXD40Lqz550pQAEKUIACFKAABShAAQpQgAIUoAAFKEABCvi1wOuvvy5PP/20bN68WVq1aiXXX3+9XHzxxRIcfOBHy3379i0VCI4ePZqBoF9/suwcBSjQGAUO/MvdGJ+ez0wBClCAAhSgAAUoQAEKUIACFKAABShAAQpQgAKlBFDl99RTT8mSJUvM0KBXXHGFCQObN2/ucSyGCE1PT/fYhpUxY8aU2sYNFKAABShQtwIMBevWn3enAAUoQAEKUIACFKAABShAAQpQgAIUoAAFKOA3AgsXLjRh4Ndff236dM4555gwsGfPnqX6eNJJJ8m2bduc7cnJybJp0yYZMGCADBs2zNnOBQpQgAIU8A8BhoL+8TmwFxSgAAUoQAEKUIACFKAABShAAQpQgAIUoAAF6kzg+++/N/MGzps3z/RhxIgRJgwcPHiwzz6deeaZsmrVKmcfKgYXLFhg1lkl6LBwgQIUoIBfCTAU9KuPg52hAAUoQAEKUIACFKAABShAAQpQgAIUoAAFKFC7AjfffLPMmjXL3HT48OFy3nnnCd7LahMmTJBFixY5u0844QRBlSBCwY4dO8pZZ53l7OMCBShAAQr4jwBDQf/5LNgTClCAAhSgAAUoQAEKUIACFKAABShAAQpQgAK1LoBA8LDDDpNLLrlERo8eXe79//Wvf8k333zjHDNw4ECZMWOGnHjiiWYbAsHw8HBnPxcoQAEKUMB/BBgK+s9nwZ5QgAIUoAAFKEABClCAAhSgAAUoQAEKUIACFKh1AcwDWJF2ww03yKeffuoc2qdPH3n77bdlzpw5smPHDklKSmKVoKPDBQpQgAL+JxDof11ijyhAAQpQgAIUoAAFKEABClCAAhSgAAUoQAEKUMCfBCZOnCizZ892utSpUydnfe7cuWY7qgSbN2/uHMMFClCAAhTwLwGGgv71ebA3FKAABShAAQpQgAIUoAAFKEABClCAAhSgAAX8SmDq1Kny6quvOn1q1aqVvPvuuxIaGiobNmyQzz77TIKCglgl6AhxgQIUoIB/CjAU9M/Phb2iAAUoQAEKUIACFKAABShAAQpQgAIUoAAFKFDnAtOmTZOZM2c6/YiPj5d33nlHEhISzDYEgsXFxTJu3Djp2rWrcxwXKEABClDA/wQYCvrfZ8IeUYACFKAABShAAQpQgAIUoAAFKEABClCAAhSoc4GXXnpJpk+f7vQjJCREZs2aJa1bt3a22aFDR40a5WzjAgUoQAEK+KcAQ0H//FzYKwpQgAIUoAAFKEABClCAAhSgAAUoQAEKUIACdSbw/vvvy+TJkz3uj0DQXQ24cOFCWbx4sfTr10+GDh3qcSxXKEABClDA/wQYCvrfZ8IeUYACFKAABShAAQpQgAIUoAAFKEABClCAAhSoM4FvvvlGrr32Wo/7v/HGG9K/f3+PbRg6FI1Vgh4sXKEABSjgtwIMBf32o2HHKEABClCAAhSgAAUoQAEKUIACFKAABShAAQrUrsDSpUtlwoQJHjd99tlnZfDgwR7bUlNT5YMPPpDIyEiGgh4yXKEABSjgvwIMBf33s2HPKEABClCAAhSgAAUoQAEKUIACFKAABShAAQrUmsCWLVvk9NNP97jfY489JiNHjvTYhpVPP/1Udu3aJWeccYa0adOm1H5uoAAFKEAB/xNgKOh/nwl7RAEKUIACFKAABShAAQpQgAIUoAAFKEABClCgVgWysrJkyJAhHve8//77ZcyYMR7b7ApCQbSzzjrLbuI7BShAAQr4uQBDQT//gNg9ClCAAhSgAAUoQAEKUIACFKAABShAAQpQgAI1LdCrVy+PW0ycOFHGjx/vsc2uLFmyRObPny+nnnqqDBgwwG7mOwUoQAEK+LkAQ0E//4DYPQpQgAIUoAAFKEABClCAAhSgAAUoQAEKUIACNSnQs2dPj8vfcMMNctlll3lsc698+eWXZpVVgm4VLlOAAhTwf4GAEm3+3032kAIUoAAFKEABClCAAhSgAAUoQAEKUIACFKAABapb4Oijj5YdO3Y4l/3nP/8pd955p7POBQpQgAIUaDgCrBRsOJ8ln4QCFKAABShAAQpQgAIUoAAFKEABClCAAhSgQIUFRo4c6REIXnDBBQwEK6zHAylAAQrUPwGGgvXvM2OPKUABClCAAhSgAAUoQAEKUIACFKAABShAAQockgCG/ly9erVzjbFjx8rUqVOddS5QgAIUoEDDE2Ao2PA+Uz4RBShAAQpQgAIUoAAFKEABClCAAhSgAAUoQIEyBS655BL5+eefnf0nnXSSTJ8+3VnnAgUoQAEKNEwBhoIN83PlU1GAAhSgAAUoQAEKUIACFKAABShAAQpQgAIUKCVw/fXXyxdffOFsHzJkiMycOdNZ5wIFKEABCjRcAYaCDfez5ZNRgAIUoAAFKEABClCAAhSgAAUoQAEKUIACFHAEJk2aJHPmzHHWjzjiCHn99deddS5QgAIUoEDDFmAo2LA/Xz4dBShAAQpQgAIUoAAFKEABClCAAhSgAAUoQAF5+OGH5eWXX3YkevbsKbNnz3bWuUABClCAAg1fgKFgw/+M+YQUoAAFKEABClCAAhSgAAUoQAEKUIACFKBAIxZ45ZVX5IknnnAEOnToIJ988omzzgUKUIACFGgcAgEl2hrHo/IpKUABClCAAhSgAAUoQAEKUIACFKAABShAAQo0LoHPPvtMLrvsMuehW7VqJT/88IOzzgUKUIACFGg8AgwFG89nzSelAAUoQAEKUIACFKAABShAAQpQgAIUoAAFGpHAsmXL5LTTTnOeOCEhQX777TdnnQsUoAAFKNC4BBgKNq7Pm09LAQpQgAIUoAAFKEABClCAAhSgAAUoQAEKNAKBPXv2yBFHHOE8aUREhCAkDAkJcbZxgQIUoAAFGpcAQ8HG9XnzaSlAAQpQgAIUoAAFKEABClCAAhSgAAUoQIFGIJCcnOzxlAgEY2NjPbZxhQIUoAAFGpdAYON6XD4tBShAAQpQgAIUoAAFKEABClCAAhSgAAUoQIGGLXDYYYd5PODChQsZCHqIcIUCFKBA4xRgKNg4P3c+NQUoQAEKUIACFKAABShAAQpQgAIUoAAFKNAABU4++WRJS0tznmz+/PnSvHlzZ50LFKAABSjQeAUYCjbez55PTgEKUIACFKAABShAAQpQgAIUoAAFKEABCjQggfHjx8vKlSudJ5o7d654DyPq7OQCBShAAQo0OgGGgo3uI+cDU4ACFKAABShAAQpQgAIUoAAFKEABClCAAg1N4LLLLpPvvvvOeaz33ntPunfv7qxzgQIUoAAFKMBQkN8BClCAAhSgAAUoQAEKUIACFKAABShAAQpQgAL1WOC6666Tzz77zHmCt956S/r37++sc4ECFKAABSgAAYaC/B5QgAIUoAAFKEABClCAAhSgAAUoQAEKUIACFKiEwNatWyUjI6MSZ9TcobfffrugKtC2l156SY4++mi7yncKUIACFKCAI8BQ0KHgAgUoQAEKUIACFKAABShAAQpQgAIUoAAFKECBsgUwR9/gwYPNq0+fPnLKKafIjz/+WPYJNbzn3nvvlTfeeMO5y9NPPy1Dhw511rlAAQpQgAIUcAsElGhzb+AyBShAAQpQgAIUoAAFKEABClCAAhSgAAUoQAEKHBBAVeDNN98sCAV9tZNOOkkefvhhiY2N9bW7RrY98sgj8vjjjzvXnjZtmowbN85Z5wIFKEABClDAW4ChoLcI1ylAAQpQgAIUoAAFKEABClCAAhSgAAUoQAEK/CWwYsUKueyyywRDhqId1bevTDj5ZMnMyZE5X30pC5evMNt79uwpmMuvNoLBZ555Ru6//35zX/yB5fHjxzvrXKAABShAAQr4EmAo6EuF2yhAAQpQgAIUoAAFKEABClCAAhSgAAUoQIFGL4DKQFQIolIwJipKHrjqKhkx8CgPl5c++lgeePFFs602gsHXX39d7rjjDqcPkyZNkksvvdRZ5wIFKEABClCgLAGGgmXJcDsFKEABClCAAhSgAAUoQAEKUIACFKAABSjQaAVmzZplAkEAdGvfXl67Z4rEajDoq63cuFEuuGuSZGn1YE0Gg++//75ce+21ThduueUWufrqq511LlCAAhSgAAXKEwgsbyf3UYACFKAABShAAQpQgAIUoAAFKEABClCAAhRobALuQHDs0BPkg0d0vsAyAkHY9EBoeO89JjzEcKP33HNPtZN98cUXHoEgwkAGgtXOzAtSgAIUaNACrBRs0B8vH44CFKAABShAAQpQgAIUoAAFKEABClCAAhSojIA7ELz94ovl76eNqvDpGdnZcsGkybJaKwcvueQSmTx5coXPLe/AhQsXytlnn+0cgjkOJ06c6KxzgQIUoAAFKFARAYaCFVHiMRSgAAUoQAEKUIACFKAABShAAQpQgAIUoECDF7CBYHRkpMy47VYZ2KtXpZ/ZHQwiyHv44YcrfQ33CWvWrJHhw4c7my644AKZOnWqs84FClCAAhSgQEUFGApWVIrHUYACFKAABShAAQpQgAIUoAAFKEABClCAAg1WwB0IYihQDAla1YZgcMLku2Xlhg2mwq+qwWBKSooceeSRTjfOOusseeSRR5x1LlCAAhSgAAUqI8BQsDJaPJYCFKAABShAAQpQgAIUoAAFKEABClCAAhRocALVGQhaHBMM3j1FVq5fX6VgsKioSDp16mQvJ6NGjZIZM2Y461ygAAUoQAEKVFaAoWBlxXg8BShAAQpQgAIUoAAFKEABClCAAhSgAAUo0GAEaiIQtDgIBi+cco+sWreu0sFgly5dpKCgwFxq2LBh8txzz0lQUJC9NN8pQAEKUIAClRYIrPQZPIECFKAABShAAQpQgAIUoAAFKEABClCAAhSgQAMQsIFgTFSUHOqQob44YvW6r06eJN07dxZ7L1/HeW874YQTnEBw0KBB8uSTTzIQ9EbiOgUoQAEKVFqAlYKVJuMJFKAABShAAQpQgAIUoEBDEkjLLpBVW7Nk9fZMiQgNku6tY6Rrq2iz3JCek89CAQpQgAIUoICngA3pEAi+es+UQ5pD0PPKpdfMUKL33Csr1649aMXgmDFj5NdffzUX6d+/vzz//POSlJRU+qLcQgEKUIACFKikQHAlj+fhFKAABShAAQpQgAIUoAAF6r1AZm6R3PPWSlm4MlXy8ot9Pk9sdIicOrClXHFSJwkP5SArPpFqeeMjH6yRvII/P6+urWLk7EGta7kHvB0FKEABCjQUgXvuuceEbbURCMIMFYOvTLpLLtQ5BhFGoj388MPm3fsPVAkiFOzZs6epEGQg6C3EdQpQgAIUqKoAKwWrKsfzKEABClCAAhSgAAUoQIF6KfDL+n1y08wlkpNXVKH+J8aHyVu3Hi2xEfydygqB1eBBg278Uor3l5g79O4UL89fc0QN3o2XpgAFKECBhipw8803m2CutgJBt6OZY3Dy3bJqw4ZyKwZfeeUVOeaYYwTzCrJRgAIUoAAFqkuAv+5aXZK8DgUoQAEKUIACFKAABSjg9wKzFmyTKx5f7DMQjI4KkfCwoFLPsHdfvrz05cZS27mBAhSgAAUoQIH6J1CXgSC0zByDU+6W7u3blzvH4IQJExgI1r+vF3tMAQpQwO8F+Kuufv8RsYMUoAAFKEABClCAAhSgQHUIYMjQae+s9rhUSHCgTBzfQwZ3byIxf1UCbt2bK499sFbm/7bbHNu3c4JccXInj/O4QgEKUIACFKBA/ROwgWBsdLS8osFcDw3m6qKZYFDnMLxg0uSDDiVaF/3jPSlAAQpQoOEKsFKw4X62fDIKUIACFKAABShAAQpQwCXw1CfrnKEnsbll0wiZfdcgObl/CycQxPY2iRHy37/3kXOGtZMjeyTKjCv6S0hQAHaxUYACFKAABShQTwVsIIghQ+syELR8CAZf02Cw20EqBu3xfKcABShAAQpUhwDnFKwORV6DAhSgAAUoQAEKUIACFPBrgd3p+XL65O88+vje5MHSMiHcY1tlVzC93Zwft8nidfvkj62ZsnNPrkRFBkunVtHSo12s/H1oskfg6L5+hlYuTnpjubPputM6S3x0iDz3+UaZvzRF0jIKBJWMyS2jZEivJLl4WHsJCjx4OImKyP/7bous2JIhf2zJlLT0AmmaGC6d20TLEZ0S5NwhbaWsyzwwe7XsSstz+mQXbhzTVdolRci6ndny6Pt/yNptWZKZVWiu261tjNw0uos0jy9tee+sVfLrmjQJ1BviFR4aJIkxIdK3fZwc3jFe+iTHlflMJWp7wwtLbBfM+w/L9jjrGOq1f9cEZ929EBEaLFPO6yGh6ldW27ArW+Ys3C6r1GidPk9+frG0ahYp3drEyIjDmslxPZuUdSq3U4ACFKBAPROwgWBdVwj6YsMcg6gYXL1xY7lzDPo6l9soQAEKUIAClRVgKFhZMR5PAQpQgAIUoAAFKEABCtQ7gTe+3SKPvfuH0+9hR7SQBy7s5axXZWHXvjy57tmlsmFbZpmnI7iaqlWHQ3oklTrGO6i8Y3xPmfm/dbInLb/UsdiAysbnrztSkqJDfe7HxsXr0uRm7VNOXlGZx7RtESWPXtbPhHzeB42YOF8yNOzzblMu6iXN48Llcp2P0VdDWPncDUdKz7axHrtH37fABKUeG10r8bGhcue5PXwGcAhcj7nhC9fRlVv8+N4h0iQmzOdJ3t8HXwcd37+Z3H1uT4n0Mc+kr+O5jQIUoAAF/FPABoKoEHxVK/PqasjQ8nQYDJanw30UoAAFKFCdAmX/2mR13oXXogAFKEABClCAAhSgAAUoUIcCm1JyPO7+z5HtPdYru5KSkS9j71lQbiCIa+Zp9dlNz/wmSzamH/QWs77dWmYgiJN3pOTKf2cfCDa9L/j17yly5RO/lBsI4pwtWu13/gM/So72raINfpNeO1DV6H1esSZ403UeRu+Wus93wGmP26fVkLfMXCIfLtphN9X4+0Nz/vAIiMu64Te/7pYrn/61rN3cTgEKUIAC9UCgPgSCYPQeSnTKlCn1QJddpAAFKECB+igQXB87zT5TgAIUoAAFKEABClCAAhSojMCW3Z6hYHKzqMqcXurYR99f6zE/ISrlTh/SWpKbRkpmbqF89OMO2b33wDCcU99aJW/fOrDUddwb1mzOMKuo5DuuTxPZqkOR/rQi1QSL9rivftkl207rJK113kN3Qyj3wNur3JskVociPWNQa0mKCZWdOiTo7PlbpbBovzkG7zM+XS8367Cf7jbp/F6yec+fVo+7AsilG9Kd5xlzXBvpmxwr85bslgVLDwznuUSHCU3LLpCEqAOVjBeMSDbhY77er0BfqRoC7kzNk007sty3lQfeWClH6tCm7uFclVTuuqCnpOccqHp096lJQpicr8OzltWiw0JK7dq2N1dmf7PFY3sXHeZ1WL+mZqjRP7ZnydyfDgSUK/W5F6xKlUHdS1d6elyEKxSgAAUo4HcCNhCMjoz02wpBN5oNBjGU6AsvvCA9e/Y0w4m6j+EyBShAAQpQ4FAFGAoeqiDPpwAFKEABClCAAhSgAAX8XsAdCkZHhZQ5p15FHgRz0X25eKdzKIbAfOXGAR5z6l06vIPc/OJSsXPgIQSrSLh02uDWMvGs7hLw19SBGELz3P/+JJs0rLINcwV6h4Lv/LBNUHVn2xHdE2X6P/p5zKl30bBkueDhhbL3r+q9WV9tlitO7ihRruExj+2J8OvPAOyVeRuda/66Os1c+j867Ojxvf6ca2/UES0FcwZ+9P02e1vZpoGfOxS8/KSOzj73wt6sAnngndUy/7fdZjNCzZe+2iS3j+vmPkxOO7Klx/pT761xwtgWGoyOP66tx/6DrfxXqwTd7bLTOso/9LNyt3MGt5FLH/3Z2TRdA2CGgg4HFyhAAQrUCwF3IPjavff45ZChviDdwSCeAe3ss8/2dSi3UYACFKAABaokwOFDq8TGkyhAAQpQgAIUoAAFKECB+iTgHsYyKTasVNfX6ZCaCO3KeqHKzbYvlv0ZZNn1+yb09ggEsT1Yy9wm/a2HoILQtuUa5pXXQoIDTeWeDQRxLE4ffXQrj9M2elU9YudnWkFoG67zgPYpVN/dDXMR3nFOD/cmQcBZkYbQ7rjDmjmBoD3nqC4JdtG8704/UB3pscNrJVH78tBFfSQx/sBn8cfWA8Gn1+HVtrpweapzrU5tYkoFgtjZWysHxx1/IGzcWkEj58JcoAAFKECBOhWYNm2azJo1S1AhWJ8CQYtmg8Fu7duLDTftPr5TgAKlBZYuXSqjRo2SMWPGyJo1a0of4AdbiooOjHzhB91hFxq5ACsFG/kXgI9PAQpQgAIUoAAFKECBxiAQqOkagi203PzS/1H+tA6laavWfHnMnjTIqc7buCvXOQRVhwM6ewZjdieCr44aPNlhQTftPnCePcb93rNDnESEBrk3meXjezcR97CZe7MKSx2z1TVn4iAdejQusvTQmThpSA/PYTA363kIwSrSxh3TutRhPdrGCoYTta1r6xi76PO9sLhEhwMtkMjQYInUCsXD1W7eoj+rLje6qiF9nnyIG1GdaL8DuNQ5rn57X3p436bOMKM4B+fi82SjAAUoQAH/FkAYOH369HobCFpdGwxiKNG6qBh866235MUXX7Tdkeeee07atDnwv/fODi5QoBoEEJh9+OGH5krdu3eXHj08f4ntYLd4+umn5ffffzeHvfTSSzJ16tSDnVLj+9PS0uTtt9+WxYsXy6JFiyQ1NVWSkpLk+OOPl2uuuUY6dvQ9mkaNd4w3oIAKMBTk14ACFKAABShAAQpQgAIUaPACmH9uR8qfoVyaa5jNqjw4gjTbcnOLZOo7nnP52X14T9l3oHLOfZ77GLvcuonnPIF2e5hXxZ/dbt9LNOt0Dx26TOfBK69P9jy8b/pr/kD3trKWB3hVBeK4dkkRpYb8dJ+fk18sb363RT78cbvAPU/XbUNFo7vlFxzY595eXctbXJ8brvnp4l2y/K95HL3vsc8reMW5DAW9lbhOAQpQwL8EbFVdfa0Q9Na0weDom26u9WBw9uzZsnLlSqdL3377rZx33nnOOhcoUJ0C+fn5cv3115tL3njjjZUOBTt37ux0xx/CtuXLl8ull14qO3YcmKcaHUQwiL9bc+fOlU8++USSk5OdfnOBArUpwFCwNrV5LwpQgAIUoAAFKEABClCgTgQQuNlQsFCHAsVwoO7hNWO14q+ibdfeAxV/qCL74LttFTo17yChFyrnqtLSsg/MJYjzMWdgRfuUe5A+2f4gwMOQqJVpny/ZJZNfXu5Rnec+H59DbbZtew8EtLjvr6v36qtiPcgtrN2+VqxXPIoCFKAABayADQSx/uDVV9ebOQRt/8t6RzA447Zb5cJarBhMT0+XH3/80XQJlU0IMr788kuGgmV9SNxe5wJXXnmlIBgMDg6WESNG1Gl/MjIyzDyg2dl/DtF/1FFHydChQyU+Pl7ef/9983cL+2bOnOkXFY11isWb15mA569m1lk3eGMKUIACFKAABShAAQpQgAI1J9CmaaTHxZdtSvdYn3hWd/n+0WHO69VbBnrsd68kxByYB8+9/WDLVQ39DnbdsoYKPdh52B9VwSAyKrJyv0/6u1bgTXzxd5+BYLje07tKsCJ9PdRjmviYS7Ki16yoU0Wvx+MoQAEK1ITA3/72NxkwYIDcc8898ssvv9TELfzumvgB/CmnnGLmEETnTjxqgIwYeJTf9fNQOtRD5xZ89Z4pEqMBIT7bFStWHMrlDnruDz/84Bxz0003meXPPvtM8vI8f7nGOcjHAj6XyrQSHfYgMzNTEEgWF9fcyAEYphL3qWpD/yo6NxyO27dvn2RlZQmeryZaZfpzqPfPzc2t1HfA3q+wsFD27t0rOL+mWnh4uJxxxhly6qmnSkhIxX/RD35V+WzK+37HxsbKE088YR713nvvNf82IbQ8//zzTRBoDZYtW2YX+U6BWheo3H/Z1Xr3eEMKUIACFKAABShAAQpQgAKHLtBWh7l0txn/Wy/PX3OEsylAi+CC8cdfLSjowLLdZt87tYySDdsO/EBpykW9JKgCVXTN4sLtJar1HfeOjw11hhBNjA+TG8d2qdA9urWp2HyC4T7mOizvBs99vsFj9+hj28glJyZLi/gDBnkF++Xu/1shX/2yy+PYiq6g2rMyDZ+buw3omSSjB7Z0bypzuUPz6DL3cQcFKHBA4M477zTzJ2HLtddea35Ae2CvyAUXXCB79uyRG264QU466ST3Li5Xg8D48eMFc2s9//zz5nXMMcfIaaedZkIzVHw1xIYg1IZkrZo2NVWCDfE5bTA4RocSxTN///33gvChJhqqAtGOPvpoGT58uNxxxx1mHfOiDRkyxCzbPzCP2xVXXGFWMZ/j//t//88MjYhhE6M0xDzrrLPk1ltvNcv2HPuO+7z33nvy3XffmWpEux3vOBfX69Wrl+Tk5Dj/XkyePNn0Cd9xzB2HyjBcJ0D/P9xFF10k69evN/3+73//61wOoQ/mSHz11Vedeefw9wHVW3g299+NX3/91fzb5ZysC/PmzZMPPvhAcG9b/TVy5Ei5//77pal+52zDEJh4HsyNh1DeHmv34z6YXw59PZSGcO2BBx6Qjz76yLhZZ/y7imAMDWHUCSecYJY3b94s+LcB7bbbbpNRo0aZZfyBcMuuY567c845x9mHBdwLc3QiFIYtGobnHDNmjCDo8hXA7d6923x2//vf/xxvc+Jff4wePVoef/xxs4Z7ow8IDm175pln5J133rGr5h3zDD777LMe2/D5+Qpo77rrLsHnU1bbuHGjqdDD3yF8RvDr16+f/Pvf/5b+/ft7nFbV7/eJJ54oS5YsMdWB7gu6P3v39859DJcpUBsCDAVrQ5n3oAAFKEABClCAAhSgAAXqVGB4v2byxJw1Th9+X7dPVm/Lkm6tKx/2dG4VJfOcK4kJBEf0a+7aUvuLyc2jnFAQw4f27xgvTapY0VgdvV+yZp9zmb6dE+SOM7s563YhPDRQtqVW7rfGY6JDnOfcvqdy5yZFh5rPCkO+om3amS0n9m0uFchzbZf5TgEKHERg06ZNzjxkCKdQteFu9gflqM5gq34BBAJ4YYg6zFn18ccfC6q+Jk2a5ISDCHl8/SC/+ntT81fEkKE2EMTdHrzmasFwmw21IRh84Ibr5fZp000wiKCruoPB/fv3mxAMhgiVmjdvLl26dJE1a9bI119/XSoURKCD0AntueeeM0GdWdE/ELi8/PLLsnPnTo8KKexHQDdx4kR7aKl3nJuQkGC2owrM3mPbtm1mG8Iauw2/aIBw7ueffzb3RFjkbtddd535O+HehiFRETxhXjeEfq1atTK78W+Tva49HoEYvmvuhm0IeDAEJBrccAzCw7JaaGjoIQeCCB4RCiNwss064/q27+6qPARndjuOdTdcz+7zrqBElSNCXXz27oZw8NFHH5X58+ebsBXBrG1paWmmfzZAtNvd7wjhbMPn6N3QR+9+2u+C+9iy7oEQuay2cOFCM7Snez/utWDBAhN0IgAdO3ass7uq329cAMOFuhs+h4ceesjZNHDgQGeZCxSobYEDf2tr+868HwUoQAEKUIACFKAABShAgVoSQIXaqce0kv/9sN254+VPLJZH/9nPBGjOxgosHNnpzx9S2UMxb167m6KqFDDaaxzq+5FdE2TJmjTnMhdPWyRv/vtoiQ6v2jyFzoWquJDvmquwrHkLN+zOkT82VW54sVZNIp1QMCu7UH5emyYDNHSsaOvWPk5WrP8zsNytcwze9soy+c9FfSp6Oo+jAAUqIYAfWi9fvtxU+lTiNB5aDQKoxMELlV0ILz7//HMTiiAsbNmypakcRDg4ePDgarhb3VwCVWR4tW7dWhAUYdjQgVpV1tDbOK3UW71li7z0zrsybdo0U71Wnc+MkBWBGdqgQYPMO6qeEAwhaC4vyEMlFypT8d3D8ajmw7VwHtYRLqJhOE33dVCh1rdvXxNwImDCMKUIl5o1a2aODwwMlHbt2pnwKiUlxWxbt26deccfW7dulbi4OCdIaq/hqW2oIsT3Hq1Hjx6mChBB6rfffmuqahEIIeB6+OGHzTHox5tvvin4BQdU1aHhOfD3ZsqUKWZoU1TpIUjDcyE0wvVQ7WgDQRz7j3/8w8xxZwMw3AfPcagNz2IDQcxVhyASYSAqFL0r6Q71XvjFDhsIoqIPVYQIP19//XUTpOLflzlz5niEbAiGbViH/iFUxN/RiIgIU9WH4BE+tiHYxjXhAzM0nHPmmWfaQ8y7dXRvRKhrw098HqhUL6/hPvgFCdtQuY4AGcEk/i6h4Rr4tzEmJsYe5rxX9PvtnOBawL1vvPFG57uI7zMqW9koUFcCDAXrSp73pQAFKEABClCAAhSgAAVqVeCqUzvK3J92OPPc5eQVyeWPL5azh7aTIT2SpEebGMH8fJj2ZXd6fpl965McJ8cd1kzm/7bbHIPKs4sfWSgjBrSU83SYzORmkRLx13CbadkFsm5HtmD4zd7tamaYL3Ti4mHt5d1vtzqBGQKvUZO/lfHDk+Xkw1tISw1FQ4ICBEVyu/blyVrtU2cdTrNlwoHhPIt0Z1HRn1V0vh4ew33ahp9rhQaX/cOtponhsvOvSr41Or/gdytTZVD3JFOVhz78si5Nrn/6N3s58w7Hn9bslTY61CtCXF9DsqJK04Z6OOkGvcaVozvLCb2bSrO4MP3BUonszSqQ7fr8bZtESFOveQQnndtDzr3/wFxJ3/y6W0ZvWSD/OLmDHN010VRXYlSvwuIS2ZKSI+t2ZcnxvZqW+6weD8EVClDAQwA/XL/vvvs8tlVkBdU6+EG7e6i1ipzHYzwFjjzySMHr9ttvd8JBhIQvvPCCefXu3dv8AHzYsGFm+DzPs/17zQ4vmKmhTHRkZIMdNtTXp3CVVoO+++lc8xliGF4M81ld7ZtvvjGXQgiDoTvREB4jIELwsmHDBunQoYPZ7v3H8ccfL0899ZTZjOEbEaxgKEc0BEU2FETgZhuCIO8qPLvP/Z6cnGzuj6Ep0dauXWuG/UToiH6hotE2dyho+4N9CKAQHqJhGNRdu3aZUAvh8tSpUyUsLEwSExNNGOoOrjCk6NdaJWmfG89ihydFf/BvlQ3CcG3M+1je8JU4pqoNf3fR8Plg+FQblmHYSwSpNgCt6vXteQjpMBQsGobWnDFjht0lJ5xwgvl3BfZvv/22Ryj4xx9/OMchRPOulnN2/rVgv7u4n20IzGwgbbf5esf8qbatWrXKLpb5jmrAlStXmv1nn322+cyxghAQ1agIe9EPBJ0TJkwodZ2Kfr9LnagbHnvsMeezwWcFGwSlbBSoKwGGgnUlz/tSgAIUoAAFKEABClCAArUqgOE0Lz+jszz1nucwSLO+2ix4oSGIssNLlte5ied0l1G/75HCoj+DMpzz6U/bzcvXdfp1SZCZVx1e3iUPaR8Cv6kTestVT/7iXCcvv1ie/1jnTtQXmvez/ev0TjrPX3uzD388P2+DvPC/Dc66ewEB3/H//srZ1FqDz9l3HOOsey8cptWUn7qG97zpmd/M/RPiQiUtvcAx7poc61EteO1Tv5pL9egQJy9dd6T3ZU1/P16w3Tkf/o+9+4d5eR/8T32+S13Ph/0dtN8XntReXp270Tkcz3bfayucde+Fl28+SrprYMxGAQpUXAA/UMecYhgiEPOJ+aq68L5aZeZ58j6X6+ULIFxFeIQXfpCPYBAvVFGhSgZD5h1xxBGmagYBoQ2Dyr+qf+zN0Mqjq885u0EPG+otjSFSx500Ul5+d7apcELYVV0N3wk0VFDZYSERLNuGCjsbjtlt9h1Vgu7mnn/QVvhhf5MmTZzDUFmGOf8QNCEsKevfCju8J76/GNYS4Q2qyV555RUTCrqDwLZt2zrXtyEQqs9sIGh3IrhDAIS2ffv2Mp/r2GOP9diHv0foM5od1tI9tyCq0VavXm2CRwTv1TVcL6rN3M9jA0HTEf0D1XzVFQpu0WpU2y688EK7aN5R8YjPGkPDuis2sdMdpiJYw1CnGCazU6dOdf5LHu7g9rzzzvN4JlRBIhREQ+Dsq1X0++19Lqot8W8sGoJFzJnIQNBbieu1LRBY2zfk/ShAAQpQgAIUoAAFKEABCtSVwIQT2skz1x0hsTo3na9WkUAQ56Gi8PkbBkjbFgfmRXFfz/s6qDqr6XakDqN57997S2S479/99O7TRh2+s6baLWO7SnSUpzHuvyct3wn0cG8Emb4qAsvqFyobEfZVpG3enevzsMtP6igXaWVgRdvGlAO/vV7Rc3gcBRq7wIgRI8xwf3CwQ+qVZ4J5nvDDUgRVtmIE73aeJ/uD+/KuwX0VE0hKShL8QPzFF180w4recsstJoxZvHixGUIR80DiB/moDEOw4a/NXYk1ToPMxta6t/xzDrwff/yx2h597969ZshZXBDBH4ZlxQsVaAi30L744gvz7usPO9yn3YfKO18NVX2XX365swvDcyJ4wj3GjBljgi3Mv+ZuGIISDfMT2sAKlWIIxvALBZhX0LY2bdqYRRseYuWrr74yoRmCM/uyIRD2o2qwrHbYYYd57ELFI4Y8xQt/n9BQbWbDU/xCBIYjxbN07txZLrvsMsHfr0Nt+HxsQ+Wkd/P2995fmXUMyWobqgStmX23lboIaQsLC+2hcskllzjVixjm9I477hAMP4tfNEDVqPu6zkm1tICKUttQjehuqBC1Iau7ktV9jLdvWd9v9zlYtkOwYhnD5jIQhARbXQv4/q/Fuu4V708BClCAAhSgAAUoQAEKUKCGBA7rEC8fTBosj320Vhb9kSbbNRzzDszsrZvpMJhH6dCiCVGhdpPz3q11tMy69WiZ89M2eX7uBhN4OTu9FnJyPX+4hd0hwQFeR1VsNTyk7N/tHHlYczm2ZxN58n/r5LNFOyUj68AParyvnqbDbLpbeGj1/ech5jJ8898DZfqHa+UL7Yd3i48NlZvP7CrtdKjQwApWZ9pr/H1osnRvHSOPzlkjm3Zk2c2l3jFMqq8WrPe78uSOMu7oVnL/O6vNXIyoqiyrpWaWbVjWOdxOgcYugEoSzJd07733mvBp/PjxZZIc6jxPZV64mnf4+iF8Nd+iQpdD1R987TuWfa3bbe7j3Mve5+H5cnJyTCiLoAkvhCbR0dFmCEAMAxipw3TiPAz5d8MNN1SovzV1EIb/wzxgmKMsRvvV2NrC5b+bRy6rsq4qHt9//71z2hNPPCF4eTcMo4nvCb4L3s1WFnpvx7qtrLP7MKTtGToMKobA/Pjjj51fBsBQnXjNnDlTEDzZAMVWoCFws6EgvrOYJxDVanZYUYR09hx7L7wjvMKrrBYUVPYczO4qwLLOR0CEYUjnzZtnhinFu222MheVc/g3saqtoODA/2/zZV3eM5R1T3egV9Yx7go7X8fg3wTbULH5008/ybvvvisffvihEzLjlzxQ1YnX448/buadtOfU1rv7O+jus/f93ce59/kyt/vLOgf78/MPTEmAkJiNAv4gUH3/1ecPT8M+UIACFKAABShAAQpQgAIUqIAA5vy7bVw358g9mfmyYVeOGQ40SYcZbarDXCII1J+9ltuwf9zRrc3rz7kI82RTSq7kFRYLwqeYiGAzt128j1AR1/9p+onlXh87MS9eRY6zF8Kz3TKmq3lhnsAtOjwmArK8gmIzt2Gi3rdt0whn3kN73kVaRYlXdTXM8Xf/Bb2kQOfx26p9SFFjGLXSILBtYoRj+8btR4uOfmrm7QsLCZLQoEAJLSf4RP8w/9/btw6UAh0+dOXWTEnPKdT5CvUH5XqdqLAgcw8MF1tew7yFj/+jnzkkV202aTiM+QgxnyAMWySE6ZyLEWYuxvKuw30UoIBvgbFjx5ofgKNKAlUyGJ7SVzvUeZ58XbO6t6G6BUMbIqyo64YfPhcXl/2LDNXdv6ysLMHLXeHTsWPH6r5Npa+HedzOHjdOXtAhDB/QqscHrr660teorydsS0mROV99bbp/6aWXVttjIPCrSENl7wknnFCRQ8s9BtVjmJvvP//5jxmyEWESQkL8m4FhbREqYVhHtBYtWph3BHuY1xAN1V74LiJUtKFgt24H/r8dgmxUfyGQQqiIoUrLauV9p32FjL6ug6AJFax4IWzDM2C4VVTd2lDs4osvNn32df7BtrnDSXdl5MHOc+/3Dq/c1Yfu4xDu2Ybw9l//+pdd9XjHLxp4h5EIqv/+97+bF0J7/PuPkBTDSaNNmTKlVCjoDunsZ+lxo2pYcQ8ri3DZVnni0vg3Dp8RmncVodl4CH/gfwvxYqOAPwkwFPSnT4N9oQAFKEABClCAAhSgAAXqRAAB0sFCpIN1DAFhcw2a8PKXhmAS8+jhVVctNDhQOuowq3j5aqgWrGrDtfu1j6vq6c55CAE5b6DDwQUKVIsAfuA6TkOb2bNny5tvvllmKOiuQqnKPE/V0tmDXATPUd7wggc5vUHtxvxo7mEX6/LhLtVhGWdpNdlsDcgG9Oot44aeUJfdqbV7368hKBrCl+oKBTFc5yeffGKuiwDDVwiEOfwQnGAozuoIBc3N9A8ESxiSE6/2GkbZymL3MI4YctS2X375xYR9mM8PYR76tHz5crPbuxLr8MMPN8EcQiDMG+gedtZerybe8fcEv0iAFwJCVMehIVwvL4Asry+4pp2v9aOPPpIbb7zR2NlzFi1aZBc93t3VpKgGxfDAtn333Xd20ePdHYxhCGhUdtp5HT0OPMgK7o3vCl4ISfGLFQh28ZnZ4TpxCQSvNsDFcNEIDsurzDvIbX3udj8T7mGHxMXB7mGua6IqHPMU4vvqDiJ9dpIbKVBLAgwFawmat6EABShAAQpQgAIUoAAFKEABClCAAo1J4IILLjChIIbVu/POO30+ekXmecIPkN0Bgc8L1eDG66+/3i9CQVTToCoHPyz39e5rmz0W++x+u82+u/dhGeEJqsYwf9yKFSuM7MknnyynnXaanH766TUoXblLt2nbVi7W+REf1wowBGU9OrSXHq4Kp8pdrX4c/dJHH8u8nxaazqLyDRWT1dGWLVvmVEqdcsopZlhO7+sOHTpUEEbNnTvXhDbe+yu6jjkKP//8c8HcfwhJMBQphsbEsKAPPvigcxk7ZCg22EpBLP/www9O/zD3IZoNt7wDneuuu86EgjgGf49Hjx4tQ4YMEcwTiIpbfNdRgdepUyccIggPc3NzPSpj8QsB9pcX0CdflYMrV640c3CiGi0uLk4wnCj+3cJ2Gwji+u5wE+uVbfjFiUcffdT0B/PTXXHFFSZ0xPx9mMfQV2vSpIkTuL3//vtmfr9BgwaZINUd8GMOUYSrGJI1NDRU/v3vf5sqTlwTQSLmE8R5qMbEs+HfbjiiItM2/LuRl5dnwkt8N/FvVkZGhnz55ZdOpTXCP3cgaM/t16+fmUcW17777rvlrLPOMsE3vhv4DDDvLAJktBStlkUVom2Y+9I2fI/s54X7I2hGO+6440wVIPqNvzsI6fCdRliJ0NM2/BJIdTZ8/o888oi55HvvvWeC4uq8Pq9FgaoIMBSsihrPoQAFKEABClCAAhSgAAUoQAEKUIACFChXAEOGovoHwwGiYtBXcw9n5x5CzvtY93He+2p6/fzzz6/pW9T59fft22eCQIQ1qBhDYILA5ZprrjFhAIICf2w33XWX/KDDWf68dKnc9sST8uo9UyRWQ4eG2H7SwAZDpaIhAMLcjtXV5s+f71xq4MCBzrJ7AaEKQkEEZ/g7jb/bVWkIn2655ZZyT8W13UMuImizDaGRDfFsCIhtaHbdHjtgwAC58sorZcaMGSbIeuONNwQvd8P+W2+91WzC+zfffOPebaxt4Pb222+LLx/MG4iwrryGeTDdw5v+f/bOAz6K4ovjj5KQRgihJ6GFmtClg3QQlN5BKaKAoKCoCPKXJlJUEBBQigVBpIkUpfcmvfcOAUJCAgQSSqj+503Yze7eXXJJLuUuv/fx3JnZ2SnfvTtgf/fei6uvpXOcl3DmzJlyL/PmzSN+xWcspPXp00cVpsaOHatewqLi9OnTZX3RokUyH+Ix8Vli3r1795bfCRwuloU07qf0VQaYI8L31tWEkv3hhx+I+8dl7AVoztjzkcNJs3GoUSXcqNKXPUQVT7vJkydb3DvfK+V+8bXKD0r4RxAjRoxQvWs5dC2/tMZCqzKHtj0pZa0XIouj7D0KA4HUJpAxtReA+UEABEAABEAABEAABEAABEAABEAABEAABByTwNsitxTbbyJXmPLgXja8/J8xz5P2XHLmedLOk17LLASy5wp7U7EXDh9Z9GHvGX7ozgLhwIEDVa+stMrp1z/+IA/hbXbmyhWaumhxWl1mktZ1Wuztg2++lWMMHz6cWGCypSmiIHtraT2/tHNoRcg9e/ZoT8kyiy5aY69TxTj0pWKW8tjxefYg69q1K7HYZPQm04bdVMrakJB8vfb7hOtsLPSxmFepUqWYBsP/Q0NDDS2Wq8Y9Kj3Z+9GSsXchC16jR4+21MXqdvZuY2GpVq1aumt4b5y70JJxOFhjSFj2CP3www8tXUJ8zxYuXCiFMyNn5SJj/r+4ch2yGMbCqaX3Lgu4s2bNsig2J+Q+KeszvocaNmwov/OMP3Lge8Riax5SA6YAAEAASURBVC8RktiSGe+9pfe38XrlRyW8Fva4hoFAWiCQQfzSSqR6h4EACIAACIAACIAACIAACIAACIAACIAACIBA4glwuNAdO3YQC4GKNwiHjitTpoxuUPbO6NChg2zjB9w9evSQ5Z49e9Iw4fmlGHv0KGHd2IMjrge2yjU4xk1A8QjkvHBbRag/rrPxQ38WCThEaNmyZeMeJA2e5TCn7UXOu/sPH9I0EfawUdUqaXCViVtSpPCCa/XpQAoWIRM5pKISijBxo6WNqzgkJIs8HKqT8xmyYMKCF+egi8tjOKmrZw9YDkXJoSdZ9GKvMK0XYlLG5x8xcFjLx48fEz9u573wnozCVFLm0F7LYTrZg4+FUA5pyh5+SnhfFgj582w0hTvvW1kX5/hjwYtDhjITo/iljMHXspfoQ/EZ4/k4FKq5UKo8Hn+vcH82ZsBCs4uLizJUvEcWjpkleznyujjEq7LeeC+2sgPvg0VNXp+t3gOWpub3HL8fOFQuDATSAgH9TzjSwoqwBhAAARAAARAAARAAARAAARAAARAAARAAAYcgwHmlWCy0FOYutfI8OQRcKzfBD785PyC/WAxUhEB+2M4iQuPGjeWL6/ZqgYGBNHLUKOnZOESEMKxaupRDhBFlQbDr8BEOJQjye4zfa5a8z5LzPcjeXT4+PskyhYeHB/ErpYxFtuLFiydoOnPcrQ2Xydcaw7Oam5zHs3ZMc9dzm7e3t3xZOm+LdhbolHyDthgvrjGSmksyrrFxDgQSQwCiYGKo4RoQAAEQAAEQAAEQAAEQAAEQAAEQAAEQAAGrCHTu3NmiKMheKamR58mqhdt5J0UI5KM29B6H6VOEwNQQZpILqxKWkEOespBm7/kFFUGQw6I6iodgct17jAsCIAACIGA9AYiC1rNCTxAAARAAARAAARAAARAAARAAARAAARAAAQsEtDmWtF1Kly5NnKvs6NGj2ma1rOR54lChp0+fVts5z9PIkSORh0klYl2Bw67y6/jx4+oF7BHTqFEjKQayKOioxsIg57tbsmSJXQuDnEPw86nTZJ5ECIKO+m7FvkAABEAgdQggp2DqcMesIAACIAACIAACIAACIAACIAACIAACIAACBgIpmefJMLXdV1etWkXTp09XxUAOwdeiRQuqV68e1a1b1+73l5ANfPrpp1IYbFOvLo3r1y8hl6Z6XxYE2dMxSoQOhSCY6rfDbhbAP6ho0qSJXK+lnIJ2sxksFARAIFkJQBRMVrwYHARAAARAAARAAARAAARAAARAAARAAARAAASSn8CYMWNo1qxZ1Lp1a2rVqlW6EwKNhFkgYaHEnoRBrSAYEBBAixcvJs7LCQMBawhER0fTf//9R05OTsShmWEgAAIgYI4AREFzVNAGAiAAAiAAAiAAAiAAAiAAAiAAAiAAAiAAAiBgtwQiIyOpQ4cOdiMMQhC027caFg4CIAACdkUAoqBd3S4sFgRAAARAAARAAARAAARAwF4IPH3+H4VHPqbwe4/J1SkTFff1sJelp7l1Xr39iO7df0K5smWhHFmzkFOmDGlujVgQCIAACIBA2iPAwiB7DAYHB6dpj0GtIJg1a1batWsXPATT3tsJKwIBEAABhyAAUdAhbiM2AQIgAAIgAAIgAAIgAAIgkBYIsBC4aOc1WrkvlC4HR6lLKlEoG80dUEmto5AwAq3G7KKQ8EfqRcULelKLqj7UppoPZcoIgVAFgwIIgAAIgIAJgVOnTkmPwaioKOrXoT3179jRpE9qNhgFQQ4ZGhgYmJpLwtwgAAIgAAIOTACioAPfXGwNBEAABEAABEAABEAABEAg5QgcuBBBQ347TpH3n5pMWu+VPPR1t9Im7fbaEP3kBS3bd0Ndfun8WalMwWxq3daF3j8coqPnI0yGzZk9C33ToyyVLoB8SyZw0AACIAACIKAS0AqD4/r1k16D6slULEAQTEX4mBoEQAAE0imBjOl039g2CIAACIAACIAACIAACIAACNiMwK4zt+mDaYfMCoI8SeG8biZzsVfhxdAH6otDZFqyiAdPrOpn6Xpbt0dFP6XJS86qr9WHbtp6Ct14hfO56+pK5VbEY+o9+QAdvXxXacIRBEAgiQS2bt1KFSpUoPnz5ydxJFwOAmmHAHvejRgxQi5oyLRptHTL1lRfnFYQ5MV899138BBM9buCBYAACICA4xPI7PhbxA5BAARAAARAAARAAARAAARAIPkIXA57SB/POGIyQctaflSpiBe9Il45RR48oy3ZHSxFNW373yNrUh4vF22TLP++9Sr9sSFIlr08nWndqFomfRy5YUibEvRug0LE3ph7z0XQ2r2xXorPX/xHfaceohWCXS5PU86OzAV7AwFbEnj06BH9+OOPNGXKFDns+fPnbTk8xgKBVCfQvn17uYaBAwcSC4NsberVlceU/p9REJwwYQI1btw4pZeB+UAABEAABNIhAYiC6fCmY8sgAAIgAAIgAAIgAAIgAAK2I/Dj6ou6wTjH3Q/9XqEK/l66dmPl2fMXxib6Y8c1+qR5MZN2NBDlzpaF3qiYV74alMtFn806qmJhYfCnDVfof21LqG0ogAAIWEdgx44d9Pfff8tXdHS0elHXrl3VMgog4CgEWBjMnz8/9ezZM9WEQXOCoCJYOgpn7AMEQAAEQCDtEkD40LR7b7AyEAABEAABEAABEAABEACBNE4g9G40bT8Spq7SKXNGmjOwSryCoHqBobBiZzCxwAWLm0DtwJw066OKxAKsYiv/DabIR8+UKo4gAALxEFi8eDF16NCBunTpQlzWCoIsmPj7+8czAk6DgH0SqFatmnzPZ/XwkMLgNPH+TynTCoI8/6xZswiCYErRxzwgAAIgAAJMAKIg3gcgAAIgAAIgAAIgAAIgAAIgkEgCM9df1l3Zq1kRKubjoWtLSCX68XPaeCxWZEzItemtb7nCXtSubn512yymztkSE2JVbUQBBEDAhMCBAwekl9Rnn31Ge/fupdy5c5Orq6uuX6dOnXR1VEDA0QhwjsHFf/5JLMxNXbRY9RpMzn0aBUGeHyFDk5M4xgYBEAABEDBHAKKgOSpoAwEQAAEQAAEQAAEQAAEQAIF4CNx7+JRW74rNbcdea+2q+8ZzVfyn5225Gn8n9JAEutQpoCOxaPNVevLMNCyrrhMqIJBOCdy5c4c4l1rbtm1pw4YNVLx4cRo+fDi5uLgQ5xP09vaWZFq1akXFiiGMcTp9m6SrbbMwuGv3bgooUYKWbtmarMKgThB0d5eCJM+vtUmTJslqSEiIthllEAABEAABELApAeQUtClODAYCIAACIAACIAACIAACIJBeCJy+HqXbaoNKeck9SyZdW2Iq54Ii6ertR1Qgh95zx9qxwiMf08Kd1+n01Ug6L9b4KPo5+eVxp4CCWale6VzEoTetsW0nb9Em4bV47NJdCrsdTTm8stDrVfJRs8r5yNUpYb8vvXzzAS3bd4POXIuii8H36bHwiPTJ7UYl/LJSo/K5rV6Tcd2cZ7BiSW86eOaOPPVUCIJBYQ+T5K1pnAN1ELB3AkeOHJEhCletWiW3Ur58eerbty81adKEWrRoQVevXpUC4blz5+T5li1b2vuWsX4QsJqAp6cnLV6yhDqKULosDLKN69dPHm31v8gHD6jr8BEUJY4BQnBfvHQp8bxGW7BgAU2ePFk2T506VX4+jX1QBwEQAAEQAIGkEoAomFSCuB4EQAAEQAAEQAAEQAAEQCBdErhx55Fu362q5tPVE1LhXISFfT2IBUG2P7ZdpSFtSiRkCNl3+6lb9L9fjxOLY1q7HBxF/GLPxoZCvBzRKYCcxZzm7D+R0nD8inP019ZrutNhd6JpztrL8vXLJ5V15+KqzN9xjb7/K0Zs0PYLunGf+LV+XwjVqZCbRnYKJLdEiKqtqvmooiCPHxwRDVFQCxrldEtgt/CAWrRoES1btkwyCAgIoBEjRlD16tVlvWPHjnT06FEqWbIkVahQgVgUZKGwfv366ZYZNp4+CbBAt0jkFezVq5cUBk9dvkK/j/qSPIVHX1JNKwiWjEMQ5HnGjh1Ln3zyCd27d4/69+9Pz58/p9atWyd1CbgeBEAABEAABHQEzP8rUNcFFRAAARAAARAAARAAARAAARAAASOB68KbT2t+ifTsU8boWi82FOYqId49fS7UuQTYmkOh9NmsoyaCoHGIjQdCqdukA8ZmtT5q8WkTQVA9+bIwfc1FY5PZ+jfLzpkVBI2dtx0Oo/dnHDY2W1U3cg823BerBkEnEHAgAps2baLevXsT5wVkQTBv3rw0ZMgQWrt2rU4Q3LNnD/n7+9O3335LW7dulQS6devmQCSwFRCwnoAUBoWI3q5dOzpz5Yr07GNBLymmCII8Xlsh7v1pwUNQmaNhw4b066+/ys8ltw0YMID+FHkHYSAAAiAAAiBgSwIQBW1JE2OBAAiAAAiAAAiAAAiAAAikGwI3REhNreX0zKKtJrjcoGxucnnpKceefqsOhlg9xrMX/9EEgzcej9Wylh+9/XphKl5QH6aMvQY5NKjROPTo6t2xeRL5fLni2WlQx5I0omspalc3v7zkwOmYcJ3G67X1YOFJuXSb3tuwWAFPeq95Eerfuhg1NnhWnr58j3adua0dwqpynuwuun48LwwE0iOBFStWUJcuXeidd96hdevWUaZMmaTn0/Lly6lPnz4qEvYQZEHQx8eHfvzxRzp27BhxDjPOJVizZk21HwogkB4JfPfdd/IzxEJetxEjKbHCoFYQZKFxoggLai5kqJFxpUqVaPbs2VS3bl15ivOALly40NgNdRAAARAAARBINAGED000OlwIAiAAAiAAAiAAAiAAAiCQngloPdJYgMuUMUOScPD1rV71pYWbrspx5m+5Rq2q+Fg15iKRQ/D+g6dqXy9PZ5o3sArleilU9m3sT+y1pxXpJiw9R/XL5KYMmmX/tOGKOgYX6lfMS+OEGKjYG6Letpovdf56j9Jk8ThezKe1Xs38qWfDwtom6lDTj96duF9tm7ziAtUomUOtW1PwdnfWdbsBT0EdD1QcnwALBvw6fDjW25bzAr777rtUrlw5HQD2HmRBMGfOnDRjxgzikKLDhg2TfeAlqEOFSjomwGF2AwMDiQW5bl+OorkjhicolOhpISgqOQRZEGShMSFWqFAhmjNnDn322We0WIQ1HTx4MD179kyK/gkZB31BAARAAARAwBwBeAqao4I2EAABEAABEAABEAABEAABEIiHwK17j9Uenu5OajkphTdrxXji8RhBIffpvMi5Z41tPKL3+vtYeOIpgqBy/cCWxVRPRG67c/cx3YjQe9VtOXxT6S6Pg9sU09W54p/XnVrV9jNpNzbsOxnr9VfEL6uJIMj9SwvPwTZ1Yvd8/WbCQ7WxqOnmEvt719A7sffFuCbUQcCRCHA4UPb6Y8FAEQTfeOMNGX5wypQpZgVBzjPI3kosCLJguHnzZtq/fz917tyZKlas6Eh4sBcQSBKB9u3bS4/B0xcvUvevRlvtMbj35MkkCYLaRY8fP156+3LbF198QXPnztWeRhkEQAAEQAAEEkUAomCisOEiEAABEAABEAABEAABEACB9E7AOXPsP6c4fKctLI+XC5Urll0d6vdtV9VyXIUbtx6qp53EuhqVy6PWlQJ7Ijatrvc8vHZLLwpG3o/1NqxQwpu8DF54yli1AnIqRbPHO/ef0HMNkw5xiIgNy+ZSx+Br+NqEGodbVczZKfa+KG04goCjEZg0aRK999570uvP3d1d5g9csmQJTZ8+nRo0aGCyXfYQZEEwS5YsUhCsXLmy7MOhRdk45CgMBEBAT4A9BtnT79T589Rt9BiKehL3n0/ThFdft+EjKErkInzttdcS7CGonz2mNnToUOmxyDX26uWcgzAQAAEQAAEQSAqB2J9TJmUUXAsCIAACIAACIAACIAACIAAC6YwA57ILfSmq3YuM+0FhQtB0qVeAjp6PkJds3B9KQ9qWiPfyu5r5c3hlsRjKtEged91YV4WYWK24t2y7+0C/h4J53HR9tRW/nK7aqkn5WnisSMkn1x68SSevRpr044a7GiGS63ytt4c+JCi3W7Knz/8jrSjomyPutVkaB+0gYE8EmjZtKgVB9vDj3GNeXl4Wl68IghmEW+2sWbPUvIFnzpwhzkNYrFgxKl68uMXrcQIE0jMBJfQni+4cSnRIz3epSmF9KOzg8HD6fNo02nfipETFYXmV62zBrn///uTh4UEjR46kL7/8kp4/f656ENpifIwBAiAAAiCQvghAFExf9xu7BQEQAAEQAAEQAAEQAAEQsBEBXyGMKeIde7g9Ed5qWu/BxE7DXngeIhwp5wjkcf/eHxLnUBEGMc/7ZR5BcxflNJzT5kUMidCH3fR0tfzPRXeRQzEuC74TrTt9+OwdOnxW12Sx8uhprNefxU6aE7ci9ev2yeGiOYsiCDgmARbxFi1aFO/mFEGQO86cOVMKiMpFLAiytW7dWmnCEQRAwAwBReBjYbDrwM+oTZMm1ECE3/UUXrob9+2jOStXqlexZyF7GHKYXltajx49pDDIeQ5Hjx4tcwz27dvXllNgLBAAARAAgXRCwPK/8tIJAGwTBEAABEAABEAABEAABEAABBJDwMdbLz6FihyDBWzgpcY58tqLcJuz11yWy5q/5So1qJDb4hI9XPT5DB9EP7PY9350bHhQ7pTNLfZaF0PYzYwi3GhizSg+JmSc+ARH41g37+oFSF/DfTH2Rx0E0gsBrSA4TXgxNW7cWN16VFQUKaFDa9WqpbajAAIgYJ4AC4PVqlWToTyXinye/DIaC4KKgGg8Z4s65zlkj8E+ffrQ119/LYVB9iKEgQAIgAAIgEBCCEAUTAgt9AUBEAABEAABEAABEAABEACBlwR8vfVhKi/ciLKJKMjDd6gZKwpyiNITV8yH3uS+TpkykJtLZnr4Ugy8LcRJS2b0BiyYKzZEaL7s+v2E3bU8jqXxlfYi+fRhSisH5qCWVfMpp+M8Fs7jEed548nzIQ90TT6G+6I7iQoIpBMCWkFw4sSJ1Lx5c93OWRC8ceMGVa9encqWLas7hwoIgIB5AizK5c+fnz755BMKDg5WO2XNmlV6B/L55LbXX3+d5s2bR126dKEJEybIUKIDBgxI7mkxPgiAAAiAgAMRgCjoQDcTWwEBEAABEAABEAABEAABEEg5AkaPtIXbr1P9MpY9+hKyMs6pV6VUDtp38ra8TAlTammMXMI7LujGfXmaw46eE+XiPqbi2vpDN3VD5M8ZKwq6OGckp8wZ1fx8h17mNdRd8LISX4jPHGL9mYSnIYc/ZQsKfUANyuahJDgfvpzZ9LBo+zVdo48NvDV1A6ICAnZGQCsIfvPNN9S2bVuTHSxbtky2GcVCk45oAAEQ0BFgb8G1wktw3bp1tH79euk9yF64fn5+un7JWWHv3oULF9KgQYNo0qRJFBYWRmPHjk3OKTE2CIAACICAAxHI6EB7wVZAAARAAARAAARAAARAAARAIMUIBBbIRi6a3Hos3IUaQlkmZTHd6hW0+vJShfS5i6auumBy7cGLEapwyCdZtCuUO1YU5DYfjecgeygeuXyXm01sr8gRGJ+VKJRN7RImcgx+Pve4WrdV4cz1KLomBEfFvL2yUH54Cio4cEyHBLSC4FdffUVcNxqLGQcPHqR8+fKZeBAa+6IOAiBgSoDzBbJX4E8//UTvvvtuigqCymrYy/e3336jUqVK0R9//EHvvPOOcgpHEAABEAABEIiTAETBOPHgJAiAAAiAAAiAAAiAAAiAAAiYJ8BhO7s20gt387bpvdbMX2lda+Wi2cnL09mqzn0b++v6sYfhJ7OP0dng+3Qr6jEt23uD+v9wWNenU4MC5Cw8A7XWw7CffuKaPefu0EuHP3ry7AUt3RNMk5ac1V5mtjy8U4CufdvhMGo5ehf9cyCEwiMf038xToT09Pl/dEkIexuO3pTj6y6KpzJH5FvUWq8mhYlzMsJAID0S6N69O+3evVtufejQodStWzezGP766y/Z3qxZM2JxAwYCIGCfBIoUKUKzZs2iYsWK0aZNm4hDi8JAAARAAARAID4CCB8aHyGcBwEQAAEQAAEQAAEQAAEQAAELBN6sVYB+XX1ZDZP5pxCpygmvvUbl8li4ImHNnesVoOkrTL3+jKPkzpaFWtbyoxU7rqun/j0aTvwyZxwmtGfDwianmlTIS5OXn6e7kU/kuadCBPzox8PSq9DJKSNFP35uco2lhsLCC7Fr40L0+7orahf2Phw975RaNxbmDKxCJf2yGpvN1v/aHUybD4aq5zivYosqPmodBRBITwTee+892rp1q9zy4MGDqVevXma3zx6C7CnI1rJlS7N90AgCIGA/BDhs6YIFC+Tn+dSpU/TKK6/QoUOH7GcDWCkIgAAIgECKE9D/LDTFp8eEIAACIAACIAACIAACIAACIGC/BNxE+NDWtfV5hIbOPiG86W7YZFNtq/laPc7HzYtSjbI54+3P4tnU9ysQr91o7GX3Xc9yxH20xrkBtYJgs5q+UijU9jFX7iM8GLsL7z1r7Up4bCjQuK75ddMV+nbRGV2XLsLLMXNyJC3UzYIKCKQ9Ah999JHMccYr+/jjj+n999+3uMilS5fKc2+++SaVKVPGYj+cAAEQsB8CuXLlkt8BXl5edPv2bSpYsKA82s8OsFIQAAEQAIGUJABRMCVpYy4QAAEQAAEQAAEQAAEQAAGHI/Buw0ImIto3C09Tt8kHaOrqizL8ZvSTF3Humz33zFlW18xUu3xuc6dM2lydM9Gkd8rR4E4lydPDyeQ8z1G1dE5aPrwGVfD3MjmvNJQu4El/Da1O5YplNxH+PNydqHHVfPS/tiUomxWhTVmke7+JP60YWVPOrc3BqMynPd6OeqqtquWHwkNx15nbNOmf8/TmhH0085+L6jkucJjVzq/m17WhAgLpgcCgQYNo+fLlcqsffPABDRgwwOK2b968SX/++ac8z6IgDARAwHEIcCjgffv2kZNTzJ//7DF48uRJx9kgdgICIAACIGAzAhn+E2az0TAQCIAACIAACIAACIAACIAACKRDApfDHlLXb/cSh9s0Zx3qF6BPWxQzdyrZ2liIPHsjih4/fUGFRChPDjGaUONcgpdvPpA5AP3zuOvGCImIJnbM83B1IjchSFqby+/Rk+cUJHjduf9E5hNkMTNv9iyUL7srcZ5Gc/a/eSdp04HYUKHaPuzVuPDzqpTHy0XbjDIIODyB4cOH05w5c+Q+e/fuTV988UWce540aRJNnjyZWBAcN25cnH1xEgRAwH4JFC5cmF68iPn7yK+//koNGjSw381g5SAAAiAAAjYnAFHQ5kgxIAiAAAiAAAiAAAiAAAiAQHokcPNuNP3v95N04uJdk+2zt9/4txGqzwSMlQ09phykU5dMuVYplYNGdg6kHB7OVo6EbiDgGATGjh1LM2fOlJvp3r07jRo1Kt6NVa5cmcLCwmjlypUIHRovLXQAAfsmULRoUXr6NMb7fsyYMdSlSxf73hBWDwIgAAIgYDMC+kQRNhsWA4EACIAACIAACIAACIAACIBA+iLAnmq/9K9I+y9E0N/7QmjPqVsUeT/mgdytyCfpC4aNdxsR9ViOmEm4JmYVoVFrls5FLavko3KFstl4JgwHAmmfwHfffacKgp07d7ZKEORdsShYvHhxCIJp/xZjhSCQZAIXLlyggIAAevjwofQiDg0NpYEDByZ5XAwAAiAAAiBg/wTgKWj/9xA7AAEQAAEQAAEQAAEQAAEQSMMEOB8eh9bkMJmwxBFghmxuWcAwcQRxlaMQmDZtGo0fP15up127dsQCIQwEQAAELBEoXbo0RUVFydP4zrBECe0gAAIgkL4IQBRMX/cbuwUBEAABEAABEAABEAABEAABEAABELBDAj/99BONHj1arrxFixY0depUO9wFlgwCIJDSBMqXL08RERFy2po1a9L8+fNTegmYDwRAAARAIA0RgCiYhm4GlgICIAACIAACIAACIAACIAACIAACIAACRgJz586lYcOGyebGjRvTrFmzjF1QBwEQAAGLBCpVqkTh4eHyfOHChWnr1q0W++IECIAACICAYxOAKOjY9xe7AwEQAAEQAAEQAAEQAAEQAAEQAAEQsGMCixYtokGDBskd1K9fn2bPnm3Hu8HSQQAEUotAtWrVKCQkRE7v5OREx48fJ1dX19RaDuYFARAAARBIJQIZU2leTAsCIAACIAACIAACIAACIAACIAACIAACIBAHgeXLl6uC4KuvvgpBMA5WOAUCIBA3gT179pCfn5/s9PTpUypZsiRduHAh7otwFgRAAARAwOEIQBR0uFuKDYEACIAACIAACIAACIAACIAACIAACNg7gdWrV9NHH30kt1G1alX6448/7H1LWD8IgEAqE/j333+pUKFC6ioaNGhA27ZtU+sogAAIgAAIOD4BiIKOf4+xQxAAARAAARAAARAAARAAARAAARAAARAAARAAARAAARAAARAAgXROAKJgOn8DYPsgAAIgAAIgAAIgAAIgAAIgAAIgAAJpi8CmTZuob9++clEVK1akxYsXp60FYjUgAAJ2S4A9A4sWLaquv1u3bjR//ny1jgIIgAAIgIBjE8jwnzDH3iJ2BwIgAAIgAAIgAAIgAAIgAAIgAAIgAAL2QWDHjh3UpUsXudhy5crR33//bR8LxypBAATsikDjxo3pzJkz6pr79etHn332mVpHAQRAAARAwDEJQBR0zPuKXYEACIAACIAACIAACIAACIAACIAACNgZgT179lDHjh3lqkuVKkWcVxAGAiAAAslF4I033qCTJ0+qw7dq1Yq+//57tY4CCIAACICA4xGAKOh49xQ7AgEQAAEQAAEQAAEQAAEQAAEQAAEQsDMCWkGwRIkStH79ejvbAZYLAiBgjwRatmxJR44cUZdeuXJlWrJkiVpHAQRAAARAwLEIQBR0rPuJ3YAACIAACIAACIAACIAACIAACIAACNgZAa0g6O/vT1u2bLGzHWC5IAAC9kygTZs2dPDgQXUL+fLlI/5egoEACIAACDgegYyOtyXsCARAAARAAARAAARAAARAAARAAARAAATsg4BWECxYsCAEQfu4bVglCDgUgaVLl1LVqlXVPYWEhBB/H925c0dti6/AfTt16kSbN2+OryvOgwAIgAAIpCIBiIKpCB9TgwAIgAAIgAAIgAAIgAAIgAAIgAAIpF8Cu3fvVnMI+vj40Pbt29MvDOwcBEAgVQksXryYGjRooFtDhQoV6NixY7o2SxXux99pQ4cOpQcPHljqhnYQAAEQAIFUJgBRMJVvAKYHARAAARAAARAAARAAARAAARAAARBIfwT44Tl71bDlyZOHduzYkf4gYMcgAAJpisCvv/5Kbdu21a2pefPmtHr1al2buUrdunWJX8HBwfTFF1+Y64I2EAABEACBNEAg00hhaWAdWAIIgAAIgAAIgAAIgAAIgAAIgAAIgAAIpAsCWkHQ29tbCoJZsmRJF3vHJkEABNI2gcaNG9P9+/fp0KFD6kJXrVpF7u7uVLFiRbXNXMHZ2Zm475kzZyhv3rxUpkwZc93QBgIgAAIgkIoEIAqmInxMDQIgAAIgAAIgAAIgAAIgAAIgAAIgkL4IaAXBbNmyyZChWbNmTV8QsFsQAIE0TaBOnTqUOXNm2rVrl7pO9maOiIigevXqqW3GQvHixYnzpF6/fp0OHz5MDRs2JP7hAwwEQAAEQCDtEED40LRzL7ASEAABEAABEAABEAABEAABEAABEAABByagFQTZ62bz5s3EwiAMBEAABNIagf79+9OYMWN0y5ozZw698847ujZjpUOHDrLp9u3b9O233xpPow4CIAACIJDKBCAKpvINwPQgAAIgAAIgAAIgAAIgAAIgAAIgAAKOT0ArCHKo0I0bN1LOnDkdf+PYIQiAgN0S6NKlC/3444+69W/atImaNGmia9NWOCdh+fLlZdPatWtp7ty52tMogwAIgAAIpDIBiIKpfAMwPQiAAAiAAAiAAAiAAAiAAAiAAAiAgGMT2L9/P3Xq1EluMlOmTLRhwwby8fFx7E1jdyAAAg5BoGnTprRgwQJ1L+fPn6fTp09T2bJl1TZjQfm+4/YZM2ZQaGiosQvqIAACIAACqUQAomAqgce0IAACIAACIAACIAACIAACIAACIAACtiOwZMkSmjx5Mp06dcp2g9pgpCNHjlC7du3UkdavX08FCxZU6yiAAAiAQFonUKNGDWKvP7ZixYrJXKj37t2T32U3b940WX7nzp2pcuXKsj04OJhmzpxp0gcNIAACIAACqUMgw3/CUmdqzAoCIAACIAACIAACIAACIAACIAACIAACSSNw/fp16tWrl04MbNeqFQ37/HPyypcvaYMn8WoWKF9//XV1lDVr1lBgYKBaRwEEQAAE7IlASEgIVatWTS550aJF1LFjR1lesWKFGjJU2c8///xD/fr1U6q0cOFCql69ulpHAQRAAARAIHUIwFMwdbhjVhAAARAAARAAARAAARAAARAAARAAgSQSUEQ3xTvQJ3du8smVi5YsX05vtGhBO//5O4kzJP7yc+fO6QTBv//+G4Jg4nHiShAAgTRAIJ/4ocXZs2flSlgQnDJliiy3bNmSVq9erVth8+bNqV69emrbrFmz1DIKIAACIAACqUcAomDqscfMIAACIAACIAACIAACIAACIAACIAACiSTw559/StEtMjKSPD08aNqgQbRl+o+0ZcZ0GtKjBwWHhdFb/frTQlFPabt8+TI1atRInfavv/6icuXKqXUUQAAEQMBeCbi4uFBQUBBlz56dPvzwQxoyZIjcSt++fennn3/Wbat79+5qffPmzdJbUG1AAQRAAARAIFUIZBopLFVmxqQgAAIgAAIgAAIgAAIgAAIgAAIgAAIgkAgCLAgOHDhQXhlQuDAtnzCeAsVRsfLFi5Ov8BrctG8fbdy5kzz+e0EVq9dQTifrkfNn1apVS52DQ+ZVrVpVraMAAiAAAo5AoE+fPsQe0Bwm9O233ybOn7p9+3Z68OAB1a5dW26xsPhevnjxIrHnNBuXWwgvbldXV1nH/0AABEAABFKeAETBlGeOGUEABEAABEAABEAABEAABEAABEAABBJJQCsItq5Xl34eOpSyODubjBZQuBA1rFqFVgpRkIXB6+JhdOM33jDpZ8uGMOGdqOTb4nHnzp1Lr776qi2nwFggAAIgkGIEOnXqROzhd/fuXfIQHtnsHag1FgNZCOR8qW+I71f+Dty1axdduXJFDZ+cS4R0Xrx4sbyMx8mWLRtVqVJFOwzKIAACIAACKUggw3/CUnA+TAUCIAACIAACIAACIAACIAACIAACIAACiSKgFQT7dWhP/UVOq/jstHg43WXYcLr/8CG1b9tWdp8wcWJ8lyX4fEREBJUvX1697pdffqGGDRuqdRRAAARAwN4IvP/++7Rq1Sp12cWFF3b16tWpUqVK8uXj4yPPde3aVYqDfI49BY8dO0Y1a9ak+fPny/Off/45LViwQJZ5jHXr1lHGjMhqpYJFAQRAAARSkABEwRSEjalAAARAAARAAARAAARAAARAAARAAAQSR0ArCI7r14/aCC9Ba00rDPI1EyZMoPbt21t7ebz9+CF4YGCg2m/GjBmql4zaiAIIgAAI2CGB3bt307Jly2jlypVS8NNuoXHjxvK7jo8DBgyQYl+FChXIz89PhhVlAXDDhg109uxZatWqFT0UP85gmzRpErVp00Y7FMogAAIgAAIpRACiYAqBxjQgAAIgAAIgAAIgAAIgAAIgAAIgAAKJI6AIgh5ubjTvq1EUUKhQggcKDg+nvl9/I687K7wHWRRkcTCp9uTJEypWrJg6zJQpU6hly5ZqHQUQAAEQcAQCN27coLVr18rX3r17dVvy9fUlFgbPnz9PO3bsoICAAKpfvz798MMP5OXlRUePHqXx48fTtGnT5HV16tSR4ZV1g6ACAiAAAiCQIgQgCqYIZkwCAiAAAiAAAiAAAiAAAiAAAiAAAiCQGAK2EASVeVkYZGvxyacx4USTKAxyRpZCGoHyu+++o3bt2inT4QgCIAACDklgz5490nOQvQc5dLLWOIdguPiuZW/BDz/8kAYNGiRPb926lTjM6LVr12Sdw4nWqFFDeynKIAACIAACKUAAomAKQMYUIAACIAACIAACKUfg6u1H9O/p2+qEDcrkotzZsqh1FEAABEAgvRLYffYORT16Kl7PKFIcnz3/j3JkdRavLPKY05PLzuSUCTl+0ut7JC3uWxEEeW1zR31JVUuVssky9548Sd2Gj5BjJcVjsGDBgup6vv76a+rcubNaRwEEQAAEHJ0Ai38sDPLrwIEDJtt1E97dY8eOlaFF+WSPHj1o9uzZsl+HDh2k96DJRWgAARAAARBIVgIQBZMVLwYHARAAARAAgbRB4NGT53Tuxn06dT2KHj99TiV9s1IJXw/K7u6cNhZow1Ws2B9CY/84pY44oXd5qhWYQ62jAAIgAALpicC2k7dozcFQ2nk8nJ4+fWHV1guJPyMqFvOiiv5eVKWYN2V1zWzVdegEArYmoBUEE5pD0Jq1LN2ylYa8DGWXGGFQKwh+9dVX1K1bN2umRR8QAAEQcEgC27dvVwVCzrOqWIYMGahZs2YyxyC38XdnUFAQeXt7y1CjHh4eSlccQQAEQAAEUoAARMEUgIwpQAAEQAAEQCA1CLz4j2jGuku0bOd1irz/1OwSXLJkokoB3jS4dQmH8aaDKGj2VqMRBEAgHRE4G3yf1hwOpa1Hwygk/JHFnRfKn51eiNCH4j/xEv+jDHTrzn2Kjn6muyZnDncq4JOdGpTJTtWLZiVfb1fdeVRAIDkIaAXBfh3aU/+OHZNjGvpciILLhDjIlhBhUCsIDhs2jHr27CnHwP9AAARAIL0T4NyDivcg5xJULFu2bPTw4UPxI6XYf5vOmDGDXn/9daULjiAAAiAAAilAAKJgCkDGFCAAAiCQWgSePHtBK/aF0D/iVV0IP51f9SMvB/QMSy2+aXnem3ej6aOfjtHl4CirlpkpYwaaNaASlS7gaVX/tNwJomBavjtYGwiAQHITWH/kJn239BzdjXxC2UQo0HtRT0ymZJGvarn8VLZ4HpNz3BB+5yFdv3lPvCIpOPQeRdzVC4tuwnMwsJAnBYo/Mz54vYjZMdAIAkkhoBUEW9erS1/365eU4eK9VisMDh8+nN599904r6latSqFhobKPp9//jn17ds3zv44CQIgAALplQDnEZwyZQodPHhQRZA5c2Z69izmB0gIIapiQQEEQAAEUowARMEUQ42JQAAEEkvgUugD4t+um7OMQsjw9XYh58zIfWOOzy+brtCsfy6qp6qWzklTepZT6yg4JoHTIkTouxP303N2FTSYk/isuLpkMus5WMQvK80fWMVwhf1VIQra3z3DikEABGxDYNb6S/TL6stUtEA2qlc5P+XMlYOOnr1Jq7eckRPw35vKl/KlOpULkouz9SFBWSQ8G3SLTp0Po9t3YsOB8aBj3ilDDcvmts0GMAoICAIpLQgq0LXC4PhvvqEOnTopp3TH9957j9auXSvbPv30U/rwww9151EBARAAARAwJTBnzhwaMWLEy8gEsedZILx4MfaZRewZlEAABEAABJKLgPX/EkyuFWBcEAABEIiDwK4zt+njGUfi6BFzikMg+ov8N5+0LEplCmaLt7+tOvy1O5jOvPTEYmHys1bFbTW0TcZZ/m+wbpy9J27R0+f/kVOmDLp2VByLwFcLT5sIgu+8UZhaVfGhPF4ucrN3HzyhZXtv0Iy/Y/4B5uHuRJN7QTB2rHcCdgMCIJBeCEQ+fErvTj1EV0Pu0+u1/KlksXx06VoELd92kMJv3Sc3N2cqJbwCyxXPS7m83RKMha/J5V2AXq1QgE5eCKODp25Q8I17cpwZqy8R561tXilfgsfFBSBgJJBagiCvQ/FG5FCinw0eTOIX1NS+c2fjEiksLIyaNm1K/YT3YmBgoMl5NIAACIAACJgS6N69O/n5+RH/mCIiIoIqVKhAJ06ckKFEOR/r3LlzTS9CCwiAAAiAQLIQyDRSWLKMjEFBAARAwAYELt58SBsP3Yx3pGdC6AqPiKa/99yg8zcfUINyeUjksk52m/z3Bdp+JIzOXo2Sr3cbF072ORMywXGxrsviAaFiuYVXZZd6BZQqjg5IYPupW7Roy1V1Z+wZOO2DCtS8sg95uMT+FsjFORNVKOxF/j4edEp4Fs75uLLD5BQ8e+M+7TgerjJ4rWJeKpgr4Q/B1QFQAAEQAIE0TODwpbvUceweunf/CfXqWIWcnJxo4+6LtOfQVXr+/AVVKudHzWqXoAD/nOTu6pTkneT2diefXJ50T/y4JOLuQ+l5vv1YON1/+oKql/BO8vgYIP0SSE1BUKHesEoVCg4PozNXrtD6jRvJN2dOKlVO/6OpTsKDkEXBXLlyKZfhCAIgAAIgYAUBf39/KQbu2bOHzp49SwMHDiT2FIQgaAU8dAEBEAABGxKIfTpow0ExFAiAAAikJoFth8PoV98r1LNhodRcRpqYu7cQKc9eE/mAwh6Sp4cTDWpXMk2sC4tIHgLiB+30zZ8xIeKUGQZ3KkkVi2RXqibHBiLkG7+MNm7pWbophHajfSK8YQvkcKWLIqzvxBXn6ELwfYq6/1R4kLhQifxZ6dOWxVRvRO21X4l1HT4fQRy6jl8sSnpndaKyhbLRK/5e0sOX8xpaYxwVdeHOa3TgQgSdvHxPzl9EzN28aj5qUiGvNUPo+lwWPyRYtu8GnbkWRRfFfh4/fk4+ud2ohAin2qh8bqodmFPXHxUQAAEQSCsEDl6OovenxOToebttRdp97BqdOB1CmTJlpIpl/ahSoA95e7nafLnsOdihSSnae+w67TwQRE+ePKOFm4Jo/9k7NKZrKSqcx93mc2JAxyZgFAT/16NHqm1Y5zE4dChlyJiR2r/1VqqtBxODAAiAgCMR4JysP/30E/3yyy9UsmRJ+uCDDxxpe9gLCIAACNgFAeQUtIvbhEWCQPolsPXkLRr801EVwITe5ck/b6zHz4NHz+l8aBTNWnOZQm89UvtxYcXImpT3ZahE3QkbVnr/cIiOCqGDjQWNXRPr23B02w0VIX7N7yVCh6WE96TtVo2REkrgshB/O43drV7m5elMa76sRVZqbep1XGg0dLvZvINfdi9FebK5UJ+XD6F1F4kKfw5+/rgSBeb31J1qOXqXyWdU24HX+kWngHgFuKhHz+iDmUfo7JWYsHXaMbicM3sW6vW6P42bf1o9xd8btQJzqHVtYf6Oa/T9X+e0TSblOhVy08hOgeQmwhTDQAAEQCCtEDh2/RG9//1eEXbrOVV7pSAdOnFdiHPPqawQAiuVEuGic6SMMBd66wFtP3CFLl65paIZ2KEkta/hq9ZRAIG4CBgFQUWUi+ualDinyzE4bhx1ePPNlJgWc4AACIAACIAACIAACIBAshLImKyjY3AQAAEQsDEBvxwu5Ovtqr6K+3pQ04r5aOn/qlMR4dWjtf3CiwgWQyC7OwTB9PBeuBr+ULfNbsJbNjGCoG4QQyVIzDF83klDa2z1uXDj47C6Rrt997GxSVe/G/mEPpt1lP45EKJr11bYE/LN8XstCoLc91bEY9pwOP6Qw9z3m2Xn4hUEuR97H78/4zAXYSAAAiCQJghci3hKg34+LAVB7+xuIlRoEHm4Z6GWjQKpaW3hsZ1CgiDDyJvTXXoNVn0lNjz5hMVniH84BQOB+AhwCDkOH8fWul5dNa9ffNelxHkWJ4f0iPFY/GzIENq9c2dKTIs5QAAEQAAEQAAEQAAEQCBZCSB8aLLixeAgAAIpRYC9kwa3K0G9Jx9Qpzwn8opZMg4/uGxPMB28eJfOiXxq7GXo7paZioj8agEFPOntegUpq6vpV+SUVRfokgibqNh5EW5QMRZDBvwS69WotCvHj5oXo8IiJKFiRy7fpd82BylV9Rgo5u/dqDDxeLPF+fUHb9INIcRwbriC+dypaeV8Zn99f+JqJP284bI6jrlCzYCcZq8115fbEhtW8ddNV+iYxpPro2ZF4wwlZlz76yIHXOPyecwuK7FrMjuYgzVevaUXBYvl80j0Doe/WYqU8aYsjfWkOybCdYbdiQkr2qq2H5Ut6Ekbj4bRrmOxHiLsPcveqSxGK9alUUF6KMJyPn72gp6I120hAobejqYgTc5L7ssefpVEuNN82V2US9XjuiOh6txKY4tXfamU+MxkEG6w206E079Hw+nA6TvKaYvH4DuPaOm2a7rzxcQ49cvlImfxWePvj3V7YwXK02Lfu87cpholzXsc6gZCBQRAAASSkYBIo0yDfzsh8vnFREi4E/GQSgfko3qVCglhMPZ7NxmXYHbo+lUK06PoZ3Ts1A15nv8sGDb/FH31ZqDZ/mgEgVOnTlGvXr0kiLQmCCp35+1mTcnT3Z2GTJtGvXr3psWLF1Ng6dLKaRxBAARAAARAAARAAARAwO4ImD7xtrstYMEgAAIgEEMgwOApeEXkCTNnN+9G00c/HaPLwbGCHvdjT6WDkXfo4Jk7tESIBWPeLkOvBugFgJ0nbpuIGNo5dh+PFUa07VxuWC63ThS8dvsRmet/VgiNLAr2nHaITl26qw7zVAgpLEzwa9+5OzRerE9rIRHmx9P2cXXObLUoaCmsYpAQS/i1fl8IWQqryDnjtHsrkMuNPmlRTLsUXXnxv9d1/euWyaU7r1SSsiZlDEc+XhHhQ7WWP2fi80jFhNuMef/P3XhFfj547MNnYzxwv+1VjuqUism1x966nDNw5b/B6vTBQvDTioJ9Gvur57SFO/ef0LglZ2n7kTDZzGL4b1uCaEibEtpusjx91SVd2//eCqSWQiRXjMtLhdj/zUJ9XkXlvPY4XngJaq1XM3+Rh7Swtok61PSjdyfuV9smr7gAUVClgQIIgEBqERi+8CxdDIr58UPWrC5Uq1JBKlci4flUk2P97KX4KPopnb8ULofnvyvk9spC/d8okhzTYUw7JsCCYMeOHSkyMjLNeQgasbYRHoxsLAx26NCBvps0iRo3bizb8D8QAAEQAAEQAAEQAAEQsDcCCB9qb3cM6wUBELBI4Cn/dF5jnq5OmlpMMTzyMbUetctEEDR2jBYeTZ+KvGVHNd5uxj7JVb8nxMkNR2/qBEHjXCygnBEejsllSQ2r2Fwj1PAa1x0ItbhUDgm57aUgpHQy5yWY1DUpYzvy8ZohfGjubFl02+X3P3u7WXrx+fiMRbva5XOrgqDSv0qx7EpRHsPuxXgT6hrNVLw9nOmb7mXIWzw0VuzcdVMv31tRj3U5CfMKwVMrCCrXtqnmS/lyxS+G7jt5W7lEhh42CoJ8srTwHGxTJ7/a77qFHxqoHVAAARAAgWQmsP74bdq497qcpZQQArs2L5dmBEFl6y3qlSA/Hy+lSvPWX6EV+2O8B9VGFNI1Aa0gyOE500oOwbhuCguDc0d9Sf+Jv7j2Fh6DnAcRBgIgAAIgAAIgAAIgAAL2SACegvZ417BmEAABswS2nojxNFJO5hP5B402UXj6sKihGIcdbS7CDxYUnmxRj57Syj0huvCEYxadocWDqyrdaWCb4nReE+5wvvBo4hxmin0ozluysoVjH5Bxn1rCC1Hpv+PkLeGBFfOrf17f8j0xD8/y53WnVtV9iPXOecJbK/L+U3X4BTuu05edA9R6CT9Peq+5/pf4PNbPBu8q9QILBVuEVcwhhJ5Afy9V2GQvzAshD6ioCH9qtGNB94hFWMWqlMpBrs6ZlKo82mJNugEdtOKUKYNuZ4+fviC3LLEsd56+TV8vOK3ro6181rEktavuq20yW25jpk9Afk/icKKKFffV5/hU2pUji/j3Hj4hN+G9ymt8pWh22vhSPL5iJvRvyMuQpcr1bV6NnUtpU441hAfjX1uvKVWTI3snar8HOmjWbezcsGwuNcwoX8PXspAJAwEQAIGUJnBRhC+fujzGy/n1uiWofMm04R1o5ODslIma1SlBi9edoDt3YqI2fL/svIiW4C5CTmczdkc9nRFgMU3JIThO5OxTvPDsAUPVUqVo3lejqMuw4eoe2rdvbw9LxxpBAARAAARAAARAAARAQCUAUVBFgQIIgIA9Ezgq8vONW6APGVjLkPuLc9FtPhjrsebl6UxzP6lMebxixcN3RfjAgbOPqaEsOd+ZNo8Ye0NpPaK2HQ9XRUEWGN+qHetVFB9PL5H3R+nv5Z5ZFQX5Og7RyPnNfv2wosxvxm0tKuelJl/s4KK064b8cQVyuNI7DQq9PBtzYOEloaKgrcIqtq3ho4qCvJpVB0OIcwsabc2h2HvC51pX8zF2IVutyWRgB2vwE+K2Np/eDSGkaYVY9sq0hVU2eAXymPz+MxfyU5mP8wku2HmN/hGCd4QQibVCMOfL1NrjJ7EisdLOwrDWCom9WjI/sZa4zOhRuVbk7TwpcnKas7saIZ7P87UQBc2RQhsIgEByErgtfpAw6LfjFHb7IVUunz/NCoIKg+zZXKhaufy0ekvM380ePHxGv2y4Qt/3LKd0wTEdElAEwawiR9+4Dz6gRlWr2B2FgEKFHEoYvHnzJo0YMcLkPvj6+tKwYcNM2pWGQYMGydCvSl05TpgwgTw8Ep/TWhnH1sdjx47RkCFDyMnJicaPH0/FillOa2DruZNrvK+++oqWLVtG3bp1owEDBiTXNCk67vPnMf8GyJQp9keNKboATAYCIAACIAAC6YQARMF0cqOxTRBwFALfr7xAnm6xYUEfCaHhsvjl/DXx0lrO7FmonMEzb9NxvSfh6G6ldYIgX59ZCHvDOwZQs5M7VU+ik9ciUzyPGHskfdujjCoI8to4PxvvS/FMvHUv1kORz9vKEhJWcanIvchmLqzia+Xz0Nj5p1WOa4UXmDlRcNOhm+rSWRyqXco0n6Ct1qRO5KCF/AYx7IbIM6kVBbO5Jf2Pfb5H/DlJiHE43BFzTqrvBeO1nC8zPgs2eArGtRd3l7j3aRyLvXQPn41vBTHnHwnvSxgIgAAIpDSB1eLHC9fF33Wye7lSHZFD0B6sXIk8dFrkFrwcFBOuec+JW7RH5ESuVtzbHpafqmtk8WzJkiXUrl07atSoEXl56aNNpOriEjm5Igh6uLnR7yIMJ4tr9mopJQy+//77dOnSJWrRogVx2WhdunShW7du0TvvvCNzHRrPW1N/+PAhrVmzxqQri2ZxiYIbN26k27djQ7ErA4wdO1YppqnjjBkz6MSJE3JNv/32G40ZMybV1xcREUFbt26V66hZsyblzp3b6jWdPn2afv75Z9l/kshxybkufXxMf1hp9YCp2PHs2bO0cuVKOnDgAO3atUuupECBAtSyZUvq1asXZcsGD/NUvD2YGgRAAARAwEEJxP3UzEE3jW2BAAjYL4Hdx2/Fu3j2AJzW9xWTflduxnoaebg7UWURrtCcsReQv19WOv/ScygoLPY6c/2To61gPg/yyR7rwajM8Vb9ghT0Mm8chzy1tdkyrKKzEI9qitCLnP+Q7c7dx8TemoXzxIYQ5ZCi2pConKvOKDjZck225pXWxvPLqX9PcE7M2oE51WU2KJub/p1YX61zoe6grWSNKKdc5J5AYfGE+BwNnR3zEEYZQzm6iLChz4U3qzXzu4hwdFpLmCypvZIop2ds/kL9mfhr7mLNMBAAARBIaQLrXkY6qFjaj5wy28/3UJUyvqooyMxW7A2BKGjFm+f69esUEhKihqhs1aoV1atXj5o0aUIuLqZ/P7RiyFTtcvLkSRo1ciSxIMjhN+1ZEFRA8h7+nvgdtfjkU/U+2TqU6Llz5+j8+fNUtWpVZVrd8dChQ/TgwQOz4pyuYxyVvHnzEnucca5ENhbMWIiMz4YPH07lk1R5AABAAElEQVT37t2T3bZv304sEqZlK1q0qLo8f39/tZyahePHj6sefosWLUqQKKgVAN2F5623t33+2OLvv/+m/v37m9yGq1ev0tSpU2nLli20dOlSypIl8X93NxkcDSAAAiAAAiAAAgRREG8CEAABhyJQuogXTeldnsw9uL/6UkzjDT969IzGLNGHG9WCCL8brVa116mNyVyoUtL8P+zerGV9eNLELNHWYRXb1vBVRUFez0rxULP/G0XUpa0+FKKWudDGTOhQW69JN6GDVYyegos2X6X3Xius8zg1iq4JReBiyPcY3/U/b7is69Kylp8Ic1uQ8mrC9kY/eUEjF56iLRqvUd1FouJr8IK8mQRP2SKG3JaVA3NQy6r5jFOarRfOk/ZCYpldKBpBAAQchsDGY2Hyh0oeHlmoTDHrPUnSAgB/v+xUJiAfHT8d8+c9h3E/Jv4cKFsInh9x3Z+PP/5YPijftGmTFFs2bNhAy5cvp4IFC0qvsebNm1OJEiXiGiLNnLsbHEydhBdT5P37NNfOPQSNUH1z5aIfPx9M3YaPkMJgVFSU9Noz9kvLdVdXVxl+Ulnj5s2brRIFWahWLDo6Os2LguxpycJg5syZpfetsnZ7PbL3HAux7FlXp04du/yxwN69e3WCYNOmTal69erE3qvz5s0jFgbZu3PdunXye89e7xXWDQIgAAIgAAJpkQBEwbR4V7AmEAABiwRye7tQdo2XT4jIq6f1NGsuHuybEwR5wJuanGQcnvPvncEW59GeiDaT30x7PjnKubKlzq8hbR1WsWoxb2JvMCV/3Nr9ITpRcIMIh6aYmwj5WLGIqfemrdekzOeIR/bC9PbKIr0yeX/sgTd/xzV6u17qhZo7ev6uirqs8M79X1vTh5guzhkp+HbcHrl+3vo8gQcu3iUOUWvO4vvM5hDewJwDlL8H2IJESL4GZfNQAqOimpsabSAAAiBgcwKrX3oJliySW/yZan//fKtaxo/OXgynJ0+eSTbrjtyEKGjFu4TFi8aNG8sXh2lcsWKFfLH3DL/4AXqzZs3ojTfesGK01OkScTWIOnXtJgXBfh3aU9VSpVJnIck4K+9pSI8eNG72bPryyy+lx927776bjDNiaCbAHpLsPcafE2uMPWw5DGtCjXPcPXr0KE3mSeQQrwnNjfjkyRMRpeM5sRicEHv8+LF8byfEU/nZs2d0X/wYgO8RezNmyKCP88EesCzWzpkzh2aLz4/WI5Z/9NC9e3e5RA4vCgMBEAABEAABELAtAev+BmXbOTEaCIAACCSawJT3yuvCT56/cZ+6fLtXHW/6yovUorKP2Yf72bNmobuRT9S+1hbcUiFcYK6sztYuz6b9bB1Wkf/t10QItcu3X5fr5HyIV4WQW0CEubwREU1hmjxxr1XJK/6xaLodW6/JdAbHaWFR6+NWIgfMb7HhOqevuECuwruvY02/VNnoY42o/khT1i7mcthDOhcUqW0yKfvm0IdLW737Bg1oVpTMfT73ipxV8VkJ4aVy6lKMYMnvw8/nHqdvu5eJ7zKcBwEQAIEUJcAhmP89Gi7nLF3UvrwEFVC5vN2ooggjuvtgkGw6/TI8u3Iex/gJ5MiRQ3qgce449hr8559/pEC4atUqKlmypBQHWSRMK2EReUfPI+7QKBFi8syVK1SlVCD179gx/o3aaY+3mzWlfSdP0KZ9+2nUqFHk5+cnxdzU2s5PP/1Eo0ePltOzGMP52SpVqkTs3cfH1DQOsRoaGir5DB06VLcUFlM5ZCp7i3377be6c1w5cuQIff/993T06FE1ZGqVKlXknvr06WOSe45D7rIwZTTOlfjaa68Zm6V32syZM+X9++6772jcuHHE3rosQPJncMCAAcR5HDNmzKi7NiG8r4jPQ9euXeX12pyMLI7xvdLaW2+9RbwvxTgv55QpU5SqeuQQsHzOkr148UKKbtyHcxGy8XcF/+iAvZKNoTk//fRT2rdvH7Vu3Vrei+nTp9O2bdvkdQEBAcShY2vUqCHr2v+xcMgezfz9pIS21Z5nhgcPHtSJg4MHD5Z7NOYN1DJ2hJyqWg4o257AF198Id9bPPKHH35o8mMZJf8qv9/5fQ8DARAAARAghA/FmwAEQMC+CRTz8aByxbLT0fMRciMs+q3Yd4NaVzVNtM4hAy8HR6kb/rJ7KektpDZYKOTOphcjLHRTvY4snU9IuzmhIyHXJ7ZvcoRVbFfNVxUFeV0rD4TS+038ac2hUN0y21b11dWVSnKsSRnbEY/sPfdDzosUeivW827in2dp56lb1FqEZw3M76mG7rwV9ZhevPSW07J4JtqePYvxotO2K2UO96kYPxfh/JGWLJfw7lXWwnk6d56+TTVK5pDCPU996GIEDZhxRHc5e/DtPX+H/ETIUA4zyl59LGy+ViUfrd8XIvuyF2Tn8Xtpat8KVOBlaNHb95/Q7E1B6gN03aCGyvBOAdRp7G61ddvhMGp5bRf1bFJY5rvKKX5EwCL1U5HzkEPYXrx5n+qUyhXnXtXBUAABEAABGxE4J378xBZYLBfly2W/4YsD/XOpouDJS/coSoRxz+qK36cm5m3SqFEjGf6QH4LOnz9figETJkygyZMnS3GQvQe5T2raf8IbafHs32jplq3kI0Js/iAe/Du6fd2vH9Xr05fui9CHA4Wokj9/fgoMDEyVbbPophgLWiwE8ev333+XnlksxKSWXbx4UQp6WkFMWQvnMuSQkcWLF1ea1CN7x/L73GgsXvHr33//lZ8HD4/Y70lLuRE5PKU5u3Xrlpyf18bzcb47xbiNxUS2bt26Kc3ymBDe7DnHezQaj29kouRsVPryvTR37dOnT5UuJkfOFfnee+/R+vXrdeeYDYt9W7dupb/++ksnSLJwyfPwe2bZsmW6Obmtc+fOtHr1aiql8fxl4XHgwIE6ZroJRcXZ2VknCCrnjYLg3bt3dfc6tYVsZZ04pl0CQUFBquA9Y8YME1FQEamNn6m0uyOsDARAAASSnwD+JZb8jDEDCIBAMhP4oGkR6j35gDrLj/9cEN6C+UwEv6I+7rRR7UXyfKNyeTQtCS/mNIT5DBN5znIb2hI+aupdkRxhFVm45bCvilfgGiHqsCi4ThM6lENeFveN/Ue8lkByrEk7viOWR7wZSP2mHdIJ1ftO3ha/Yr8tt8siG5sSPlNWNP/7ZeNl+nX1ZU1LbJEFvjqDtqgNvrndaOn/qqt1Y6G8CAm7ViNQfjrziPzsZc/mTBH3RAijl6Jk8YKeOm/BD384LIcKKJyNfvso5lftH7zhr4qCfJLX0v6rXeT0UpRkodBaKyzW3bVxIfp93RX1Eh5v9LxTat1YmDOwCpX0y2psRh0EQAAEko3AqWsxXtRFC+VKtjlSYuDcOdypaOGcdOHyLTndiav3qHqJHCkxtcPOkSdPHunlw95V7J2jfZUpU0YKhE2aNKFChQqlOIOg48dozM8/y3m/7t+PPA0eUCm+oBSYkPeo5BeMFLkFvxwxghbF4b2VkCWxt5s5IYtFInPGHl4VK1YUIXufSK88zt3GuQLZfvzxR+n9Vbt2bXOXpsk29gzUCoL9hAD7yiuvULDIV7lo0SKZd477sEjeu3dvdQ9LliyRoT+5gUUuFtKtMebKY/Xv35/Kly9PO3fulJ52fO3XX39tIgomhDd/HhcsWCCXwZ6P33zzjSyz16RWZONGHx/9j1w5VLDWG5i9Jg8ciP03sBzI8D8WAxVBMF++fPTZZ59JcY6FE87XxyIfh+9kT0Wj8XXsvchep+zlxwKhMhZfz8KpYsxIEVF5np49e8ocjor3IzPVev8p1xmPLAgyT0XQbdOmDVWoUMHYDXUQsEiAvwtOnjxp8nmyeAFOgAAIgEA6JQBRMJ3eeGwbBByJQDkRBrBYAU9iLyQ2zjG4fO8Naltd73lWyZCvbsSck1TgU3cqYUGMsoZR4Txuum6L/r2uy5mnO2knleQIq9iyhg/9tPKSJMDi4NHLdynopfcDNzYTIUbjsuRYU1zz2fu5V/y9aPmImvTpr8d0QpuyL0WIU+rJefysdXHaeSKc7j+I/RUzz8+hZLU2pltp6jRmtyoSas8pZfYaHCw8/L5ZeFppkkejGPhWo4L0x4YgXR9zlT6N/YXHYgaas9a8AGq85kr4A4iCRiiogwAIJCuBo8Krji13dn1YuWSdNJkGL10styoKHhJ/D4AoaBvQnp6eUqRg7yV+YM+5B1euXEnHjx+XgkP9+vVJefHD+pSw0SL0YpQQAbqJkKaOmEfQEkPea+t6dWnZlq20R3ivrVu3ziah6ubNmydDW1qa19heunRp4pdiHIIyPDxcroW90Xhd9iQKTpw4UdmK9HbUrr1du3ZSvONQoRwiUGuVK1dWq2fOnFHL1hQ+//xz6tu3r+zasGFDKa6uWbNGhhJlhiySKZYQ3uzJqITe1IY2ZSG/WrVqypBmj7lz5yZ+KbZw4UKlaPE4a9Ys9Rz3V34kwLw49CqLddOmTZN7Neb84wvZu5QFZjYOuVq2bFl5DYuJWlNEPG7jELrmwrNq+5srMw9mrozF95PzdMJAIKEEWHhXQign5Fr2JOQ/U819FhIyDvqCAAiAgD0QgChoD3cJawQBEIiXAHsLDpge41nEnX8UuQVbihCimV96RHFbmYLZqHb53LT9SBhXpfjQ47t91Eh4FXau5UcFhecQhyhki3jwhC6GPCAXUS8tBEdLViSv/iHdvPVX6EH0M+pQw4/8crqKX0RmoHsPn1KIEMKyCG8m9ppT7InwahKRVqQ9MYRq5HPaEI3OThnN5klUxmJnqydPXw6mNIrj0+f6tmeirh2Xu2bKlIGcxEux5Air2LJKrCjI8wyafVyZTh5bmQn3qu2QHGvSju+IZfZY/f3jyjR/xzVavT+Urt18QNGPn5vdqoe7E70iwvCWL+Qlz7s42+6vBx4umWjBoKo0WXjwbhKhY43m5elMA9sWlyFA+fMSn2DZRoRALS4+R1/MPaGGJVXGzJk9C71ZryC9UTGPVaIgfz+w1yqPOXbJWRmG2BIjnuN2VKywqcyJIwiAAAgkF4EQkXv3akhM+FBPjyzJNU2KjRsgQohu9nShyMhoOnwhRuxMsclfTlSwYEHx955M0lPGycmJMmfOrJa5rn1xqDs+z21cNvY3XmvJC4bD93F4P37gzZ5bcZXjCgMYFyu+7vnz53IOnodDV/LDTX7gv3HjRvnih5y8d14nr4n7sxcUe//Y0vbs2UMb/t0lw4b279jBlkPbxVicO5FFQbafRX661MxfxTnewsLCiENWspjEYhrfb/aisSfbv3+/XC6HkdQKgtzo5uZGv/zyi823w162WmMRjUVBNg4zqhUFlX5pkbci3tWpU0cVBHm9LE5yGNCfhUcvf0+waKwVHLkP71ERBLnO33n8fl66dCnduHGDm1TLJcIEK8Y5B8+ePUuvvvqqFKf5u9Ma45yHu3btkl0/+ugj+uSTT6y5DH1AQCXAP3wJCQmRYjaHSc6aNf4ILxwud8yYMTIEMX8W2Lu1XLlyNGjQIHipqmRRAAEQcEQCtnvq54h0sCcQAAG7IVC9hLfIteNKIeGP5JrZK2npnmApzmk3MbRDSWp64hYpnkUsQKwVXoX8YuOwilpRgvMVzvrgFe0QujLnGGMhQuv1tGz7deKX0coWzU4/9Ysdq9bA2BCMxr7DfjuhaxrfuxzVDsypa9NWDl+KoPenHtI2mS2zIFrnpSiqdKhfMS+N61pKqVJyhFXM5SnCg2rCQ3LuR8XyC2HV19tVqZo9JseazE7kgI1v1spP/GJ7IETB8zei6JHICchiXR7heeedVTz0FO97rXWvW4D4ZStjgXJsl1L0RHj5XRchOsNFLkPxPJJ8RC7A/OLei+eU0uYPqUasT3OOwixO4qFtpozEgrjRWKhfMbQGsXh+VuQJ5WNJP09yzxIj6nP/RV9Ul3t0z5JZFfuN4yh19kCc0rOcrD568pyCwh7SHZGfkPMJ8g8F8orPeL7srjrxXLkWRxAAARBILgIHRc5VNheXzJTl5Y+WkmuulBq3fIAPbd97iR6KH1ClhrVv317m4Hv06JEaVjA11pEac7IQqPVM4jUYBRZbrIsf7LOxOJYewoYamfkKcaRBlcq0ad9+OnPunPF0oursdfX222+bXPvmm2+atHHD9u3bKa7QkvaUV0sRt3lfxvCa3JZcZhTIsmSx/MOMtMqbQ3GyyMFWuHBhE1SK1yCf4FCsxj37+fmZXGOJA3tTsmjL4UxZlOFwr0rIV37/sreqVmA0GVg0KAImizIsCsJAIKEEOJ/u1q1bZbhgDmf71ltvxTkE5yPlvxdojT8zLE63atVK5urlcLYwEAABEHBEAhAFHfGuYk8gkE4JfNCsCA2dHSumzVx1SXgA+eoEj2xuTvSL8J764vcTdC005h9JWlxaQZDbr4WbT0SvXMMi4tjuZXQ5DZVzxuON23GPZeyfmvXkCKvYpoYvfR0UE+JVu7eW1X20VYvl5FiTxckc9ASLZuULx3gDpsYWWezzFyIwv8xZASESJsR4PPYANmeFculD+5rrY66NRUDkDTRHBm0gkPwEWLBgz4UokY+LPZg4d5M5Yy+N7t27S08nfgD06aefmutm9217z8WIgu5uzna/F2UDhX2z03ZR4agKqWH8kJpzal2+bF3Y6ISsUfHUU7wB2XuPy/y+Vsp85Be3Kf2Vo9LGdaXN3NFcW3zXRkdHE3sx8bUsDrKxx+DHH3+ckC1a1ff69ZgfxlUtHfuDM6sudKBOAYUKSVFQYZ3UrXFuuZo1a5oMw+KJIvooJzlPXNeuXZWqPJrrp+uQRir8PjbaCyWsijjBn52UMkuevzy/9r6mZd78eVeMvfyMpt2jlrPSz1oPP+7PYiH/IIA9kznPIx8V47DG/OIQx1999ZXSbHLk7yk2FhfZqxkGAgklwO9p/vshv89mz54dpyjI73n2alWMw9WyRzB/pidNmiSbOQ8pC97WeBwq4+AIAiAAAvZCwPRvBvaycqwTBEAgXRKI65fyDcvmoYle5+nO3ZhcZewtuOHITXr9lbw6VpxD8M/B1WjZ3mD6Zd1lnZefrqOoPHxk+o9TYx/OafjnsBr09Z9n6Ih4gGcUFpX+9wyhB52EoKF4LCp9EntkrypbWnKEVWxcPg+NX3TGhE9T4alojSXHmqyZF31AAARAAARShgA/tKxatSpNnTpVegzwg21zniH8oIcf2rB9J3KXOapdfJl718nJcf7J5uEaE0buoYVw1ilxL/PkyUP8cnQ7cuQIbdiwQeaPO3/+vLrdunXryhCAxvCIaockFjgfE9tcIQoMESJ/erSNwkuQrXqNGim+/YEDB8o5WQicM2cOBQYGynB4LLixCMzeM3GZIhSxiGytKddwfxbCrTGtsKb0v3r1qlJUj9mzZ5frZ/Hz2LFjantaKSSFt5Ybh3m1tXl7e6tDmmOrCPjcydfXV+2b2ALvh70C+cUCLv85vWPHDpoxY4YUr+fOnUs9evQgf39/s1N8++23xC8YCCSFAHv2sSjIf+4dPHjQoocqewMq3qnsLcghRNlYBHRxcaFx48bJ9y2HXGZBGwYCIAACjkbAcf6F6Wh3BvsBARCQBOqWykl7JzewigaHIFwz8lWr+7IXIb/4B9Nh96IpSIQejX76XHoWZnXNTPlFTkAvd+t+nc8eTj/2qUCc2++CeIh3S4RH5DKP7ZYlI+US4Ql9srvo1rZzQj1dPSkVDqdoLaeEzGPLsIpuwktt18T6CZnebF9brsnsBGgEARAAARBINQI9e/akX3/9VT6I+eGHH+jHH3/UrSUiIoKmTZsm25o1a2ZWNNRdYMeV25ExD+Uj7tpPpIH4cHu8/HvVYxHGGmZ7AqdOnaLNmzfLFz8MVax48eKqEFi6dGmlOVmOLAiwV9BvS/6iVtWrE3vNpSf7beUqOiNyVLElhyemHNjC/1g4Ux5y80PsypUr63oqufl0jYZKzpw5ZQuLSCzsWOMtps2vx0JT3ryWf/CXLVs2un37tgxxykKl4sF2+PBhw0piq2XKlCHOVcki09q1aym5BO3YGa0rJZW3lhPvq0WLFtZNbGUvziPKn3fmxp9JFh6VEKF8b9mjTzFtTkClLSlHft9UqFBBvniuKVOmyOH4/WFJFGTPLc53yd9XlsKUJmVNuDZ9EODvozZt2sjclwsWLLAoCl66dEkFwlEqtNahQwcpCnLbhQsXtKdQBgEQAAGHIQBR0GFuJTYCAiCQWAIsJnJuNX4l1Tg1W3HhiVicPJI6VJq7Pi2GVUyLa0pzNw4LAgEQAAE7IuDl5UV9+/aVuYhWrVpFZ8+epRIlSqg7YE8DxeJ74M5hSF1dXdWHzsp1cR3Ze+X+/fvEDyc9PDxSNYSZkn/38eNn9Ei8XEWOVHu3jOIvSlnEPnhPMNsQ4AeWihC4e/dudVD2sKpfv74UUFioSyljj4tffvlFilPdRoyk5RPGE+fZSw92WoiB0xYvllsdMGCA9NJLyX27ubmpXnXr1q2jtm3bSgHm2rVrMhwz53pj4zx97CWTP39++dKukdsUGzRoEHFIPf4uPHPmDPHD9ldfNf0BJoc3VWzEiBE0ZMgQ4jZ+6H7nzh3iB+yK8fc5t7MwyH3Zq4e/c4cNG6Z0odDQUGKRsGTJkvI7/MMPP5SiIHd477336IMPPpBh/ljgYs9EDinNno3sBatYeHi4DEWt1DlnnmLMQxEE2LutUCKF66Ty1ubs4z/vfvvtN+kt7+zsrP45xMIaGwuoRm8/5sYWGRmp7ofrfJ9YfGV755136JNPPpFlFoo5XCILbizS8T1ge//995P8Zx2L0fznNb9/eG6eQxFNFUGQ54rLS5uFGRZ/ef3sYcjerjAQSAwB/t5aunSpDGnLIUDNmfbzVKBAAV0X9rJVwi4HBQXpzqECAiAAAo5CwP7/ZekodwL7AAEQAAEQAAEQAAEQAAEQkOHFpk+frnoLKg8U+cHnzJkzJSEWHooWLWpC68aNGzIEFIsjygNP9pTgh55NmzY16c8NLKgsX76cdu7cqV6jdOSHQpwnyVwYU6VPchw5goHW7tx9RL55smqb7Lbs5uoMUTCJd++KEJ+2bt0q37vbtm1TR+MH7nXq1KHatWvLoxLKU+2QQoWff/5ZipGRQpjv991EmjNsKHk6+AP+SOGl1+/b8RQljizCxvejheS4FewZxiLbvHnzpEjEYfC09vbbb0vhib8bWYDh70YWo7TWqlUrNSwzP1Tnl2LsfWNOFOQccPxQnR+ys1faW2+9pVwiBR6tKMhrWLNmjTzP6+QXW5UqVaSH4b59++QYvA4OFc3CNudT5DxhHA6Vjb3I+aU1FpIOHTqkNk2ePFkdW218WeDcovxSLLEP/ZPKm0MUco7T8ePHy6WwSKq1YsWKqbn5WOTkfGfmjMU37blRo0ZJXty3ZcuW9Pvvv0uRlYU7o0dUvnz5pNBqbtyEtLEn4sSJE+O8hP/c1v7IR9uZ35MsCLJxmUVhc+817TUog4AlAhUrViT+/HAIUe13mLa/NoSxNpSvtg+Xtf2M51AHARAAAXsmkNGeF4+1gwAIgAAIgAAIgAAIgAAIOBYB9kpRHqivWLFC9YDgh8f88JONPUeMxg8R+SH4ypUrdeIeP6RmUdBcriJ+WMo5jngeRUTUjsvzscdVSlt4pD4v1+17jhNC1M0tJq9gSjO19/lYCGRPIhZHWPhjAYEFQfY2YhGGBXOus8DQvHlzSi1BkDnzmhYLj7msWbPSKfFQttuXo4hFM0c13ht7RV6/eZMCAgJUUS2p+1VCayZkHPaK4feI1liw++ijj3Rinfa8tsx9+UcZ5ry0MmUyn8Oc1zlr1iz5EF47Fpd5HPZyU6xatWpSkNOOz6Li6NGjpYCo9DMeWehikZD5mjP+/tbOY66PuTbtOrTnjSKBdu/akKpJ5d2nTx8aPHiw2b1rc4Fq1xZfWbt2vjccJrRXr14m95TDlXLYUvbQN2facZTzlt6THNrbkrHwyN6KfI8tGYu6jRs3lqf5PcjvCRgIJIUA/wCBjf/cVP7uKBte/k/rFa14USvn2QtXuYbfjzAQAAEQcEQCGcSvHkTGKxgIgAAIgAAIgAAIgAAIgAAIpA0Cjx49kt4h/KCXvUxGjhwp82PxQxoOgfbVV1/pFsrhPtmz5OjRo7K9f//+xA+fOUwee4QooeK2bNmi5jPihz5aD0C+pmzZslJM4Qef0dHRxA86X3/99QSFINUtLJGVoPCH1GHMbvXqGpUKUZ1KBdW6PRe+n7ubfHO707wBr9jzNlJk7Q8fPpSeXOzNxe9dxdhLtlatWqpXoFawUPqkhSPnOOQ8oRy6McDfn+aOHOFwHoMsCHYdPkLmEWTBisXQ1BRklfvO4TT5QTcLWL6+vrKZRTP+TuTwlNzOx/+zdx5gUVxdGD52BERFrKiAKKLYe4u9RY09URN7NLEnRk3UaCyJiUlMzB9N0xh77xq7xqiJvSuxK6AiIiAI9vbfc3GGmWUXlr7lO8+zzMydW99ZlmW+OecYE364Mn+mchhPrs8PanDOOfZsS8w4XOhtIY5y6GYOwWeKxfPnz2U9DjPJghAbe4PzuDwvfpkSoHgd/J7i3w9uz+EqlT4Sm19anU8pb54Xe7rz3yUlfDV7/moFyJTOnW/98TXl/jmfYWp/bvDc2aORWfBY/FAAP1RjSng1th5++IHFmtSem7GxUGY7BDhcKIecZSFw0qRJcmH8ecL5SLXGD80onsscJYIfCmPjv1PaEMZLliyRYZD53Lhx46SozvswEAABELAlAlltaTFYCwiAAAiAAAiAAAiAAAiAgPUT4BvK7FnAXhh8k51vECtPbbPXn6Fx2DFFEBwyZAiNHDlSrcI36pXQahs3bpQeM3xSGzKObwhp26iNM2jHI78jOebMSg8exnrYREQ9zKCZpO6wj588Ezfy2QvSMXU7trHeWEzjkLYsBt64cUOujgVrDgvKIfVq165tFSsuW7as9ERiLyX+HWVvugWTJtqMMKh4CJ4XQkbnzp2l96YpESy9LxiLZYa58lhkM1c8Y7GQ8wJq8wWaswYWAvmVmLHoY9i3uex4HR4elvWQREp5My9DHokxTOp5DnfKXntpZSwe8yslZvieTUlfaGvfBPjzhMVCJUSxIQ3+e8pegBz2mENes4DN3xU5ugTnRVWMwybDQAAEQMAWCSB8qC1eVawJBEAABEAABEAABEAABKycAD/NrdzAVG7qsLiglGmXd+XKFfWQb85rrYTwUOIXG3shKObm5qbsyhtCHJ5u7969FC3yoFmC+RSLyyEYGm4Zc0opl7C7seJmLiF4wkwTYE8HDgfKYf0+/vhjYjGbX5x/zFoEQWV1fGOWwxf27duXzl27Jr3qbCGU6DnxWdJ+xEi5Jv7M+e6770x6xSkssAUBEAABEEg/AoY5NLUj88MF2jye7EXYqlUr+TdXqcdeguY+SKG0wRYEQAAErIUAREFruVKYJwiAAAiAAAiAAAiAAAjYEQH2DhwxYoRuxZx/yZhpvf7YU7B169a6lxI+VPG64j44NJu2vzlz5lCPHj2oXLlyMhQp5xlMTo4qY/NLTlnZ4i6yWbWKRSki4gGFR1q/t+CVGxFyTTV8Y8MFJoeLPbTh8Li7du2SnoKDBw+WYW2tfd1885XFM/aq43Cb1iwMsiDIa7gpQiUqgqC1Xx/MHwRAAASslYCpcLP8fa5ixYoml8V5qNkr3zBXKT98xg/m8INoMBAAARCwVQLIKWirVxbrAgEQAAEQAAEQAAEQAAErJ8CiHIdN5NChvXr1IvbmM2ZffPEFzZ4929gpXVmdOnVo6dKlujJ/f3+aN2+eFGCUEKVKBb6htGrVKpkfSylLr+3m4yE0aYE/fTHkNRo3cx/Vr1mC6lYull7Dp8k489adoFsh92jOiJpUrljKwsylyQTRaZoTGC5E+zXC69HX05MWTp5kdaFE1+z+m8bMnCk5de7Uib77/vs0Z4YBQAAEQAAE0pYA5ygNDQ2VYUQ5TykMBEAABGydAOK22PoVxvpAAARAAATsjkDUg6cUGvmY7j18SqXdXcjZIYvdMUiNBT978ZJOB0RRbsdslD93DnJBuLvUwIo+QCBJBDi8E4cfZLEuV664cJqGnXh7e6tFU6ZMoUqVKqnH2h1j+Y78/PyIw0Z98803dPnyZTp06JAUCS9duiRzy3DYRg5lmt5WslCsaHbobLAc+prwsrNmUTA65rEUBPO5OkIQTO83kwWNN+nLL+mcyJl4ToT8ZW87axIGtYJgp/btIQha0PsKUwEBEACBlBBwdHSMl4c1Jf2hLQiAAAhYOgGIgpZ+hTA/EAABEAABEDCDwLkb0bRk73XaeyqUHj1+rraY9WE1quiJpx1VIEnY+edcOH0y+5TawtEhKzWqUpDeqV+MvAs5qeXYAQEQyHgCpUqVUifBnoBdu3YlFhSTYpkyZSLuh1+ewovpnXfekc21oUmT0l9K6/oUcSZP91x083YU1anmQQeOBVF0zBPK5Zw9pV1nSPsrN+7KcWuUyZ8h42NQyyDAIv+yZcuoqxDalRyD1iAMagXBZo0b0/f/+59lAMUsQAAEQAAEQAAEQAAEQCCJBJBTMInAUB0EQAAEQAAELInAk2cvaPKK89R72mHafviWThDkeRZxdbCk6aZ4Lkcv36Wl/9yQr83HQlLcX0IdFBLegVp78OgZbdp/k96eepC+23CJ2JMQBgIgYBkEqlatSq+99pqczNmzZ6lLly70yy+/0IkTJ+j27dt07tw52rFjh26yd+/epRUrVtD+/fvpwoULdP36dboivJf+/vtv4nCkinFumYyyNjUK0fFzYVSnUnF6+fIlXb0Zm5Mvo+aTknEDb0XJ5q2rFUxJN2hrAwTyFChAS+fPo9JCfLeGHINaQbCMeGgAgqANvAmxBBAAARAAARAAARCwYwJJe3zWjkFh6SAAAvZD4Onzl3Tn3mO6E/WYcmbLQj7uyHmT3KsfFP6QooRXA4dezJcrB2XLkim5XaGdEQL8Xu038zhdECEujVmWzJkkd8Nz0Q+fUah4fyvm4piV8rvoBTDl3K27j+jBK8/D3E5ZyU1cx4y01QeC6S+NGNisUsE0e18VTkBQXfFXEF0LuU8/9q9EAjMMBEAggwmwlx+HDe3QoQOFh4fT0aNH5ctwWpw/UAkhykLgqFGjDKvojtlrkPvMKGtXowgt+zuI9h2+LKfgf/kOVSxdKKOmk+xxr1yPoP8uhJBvyfxU3ct0GNhkD4CGVkcgr4cnLfvtN+r6/vuqMGiJHoM6QdDHh1asXi1DGlsdcEwYBEAABEAABEAABEAABF4RgCiItwIIgAAICAIsriz/5zr9eTiErt2MVpmUFmEXF4jwi7DkERj26wm6deeh2tjHw4Xa1ixCHWsVIRasYCkj8Omis/EEQY/CztRKeJZU885LvkVzGRWsPl3sT4fOhqmD53HJTtsmx3rYqIWvdkbMOU1XRGhStmbVC9MX75R9dcb2N5xL8J/vGpN/UBQdvXKXNh68RSFhce/nI/+F09drL9CYjqVtHwZWCAJWQMDDw4P27dtHM2bMkB6ALA4aWkhICJUsWVIWR0SY9rpzcnKijh070sCBA4n3M8o4l+nr4rN34fYAOYVAIa6dvnibKvhYl7fdXwevyvk3rOCWUSgxrgUScPX1pSU//URfTPuW1u7+mxoPHCRzDJYRHoSWYFpBsKyY6/KVKyEIWsKFwRxAAARAAARAAARAAARSRACiYIrwoTEIgIAtEOBwhGPmnaF7MU/jLcfWQi8+evKC1h4OVtdZrlguKu+RdvnmCuRx0ImCFwPv0TTxmrfjGn3dpwKVK+6izgU7SSPAXph7ToTqGtURN1u/7V2BsiYiuD4TIUe1FnnvCR28GEG1fFy1xdgXBNi7tZJXHvnq2dCDBv5ygs5eiVTZrNt7g95v7kWuVprjS10IdkDAggkcPHjQ7NmxgDd69Gj5un//PrEI+OzZM+kdWLBgQV2ewVatWtGlS5dknYcPH8p63D5v3ryUK5d4qCKzZWRaaFujMK3cc10ND33kzE0qX6oAsXekNdjeY4EUFn6fvIq7Uo96RaxhyphjOhJwK1eOvhn/mRyRhcEen02wCGFQKwjy58HsOXMgCKbj+wJDgQAIgAAIgAAIgAAIpB0By/hPN+3Wh55BAARAIEEC+8+H02ARftGYIMgNvQo5xmvPXoVXRNhA5cXijCm7e/+JWfVMtU/t8uhHT+mHVRfU1+bjt1N7CF1/XoWNe1eE3X1M7/1wlE5dixNXdA1xkCiBX7fEel0oFVvVLkLf96mYqCCo1DfcLtwdaFiEYwMC2bNmplmDqxCLr1r7YxfYaXlgHwQshQALfN7e3lS6dGlyd3fXCYLKHLNnz07FixeXdfz8/MhTeCjlzp3bYgRBnmdxN0dqIYRBxULvRNMhIQxag92JeEBHTt2QU31LPFghPkZhIBCPQHbxe/rN2LHUoVFDihZiPguD5wIC4tVLrwJDQZBzjxYtWjS9hsc4IAACIAACIAACIAACIJCmBOApmKZ40TkIgIAlE7gW+oCG/3oy3hTbvVZUhF7MQ1XEy1j+tFUHbkpRTdtww8S6VFB4xRnaQpEHaPGOWMEgoRCNhu1s5ZjDKr7bxJPYG/PQxbu09VCcl+LzFy9p4IzjtF6wM5XPzlY4pPY6wqIf066jIWq32cRd1rGdfYXXiFqU5J2j5yKI+zX2nk9yZzbcgMPefv52OWp+dg/xe5htjfDgGdSyBDnmyGLDK8fSQAAEMpJA59pFad+ZOxQRGZsP9ih7C5YsSE4izLElG3sJPnnyjGpWKkqtK8Ib3ZKvVUbPLbtPaSkM8jwUj8GfPvmYagqxPj2Nxcgv586VQ7KHIAuCZcvaT+j09GSNsUAABEAABEAABEAABDKGAJ7VzBjuGBUEQMACCPy8+YpuFnyz/9dhVWlsp9LUvFJBk+LIs+f60IvcyeJ913V94SCOQIHcOahV1UI0qVsZ+va9inEnxB6LKrN3BOjKcJA4gTk79Z5pHRsUlWEuE2+ZcI3l/8R6cyRcC2edHbJQY/GeVozfx0v2BSmH2IIACIBAqhPwKeJEIzv6qP1GRz+iLf9cUo8tcWfD7gt08Uoo5cmdkwa/7kl4bsISr5JlzYmFwW8//5yGvPWm9BjsKTwG2WsvvYwFQfZSZG9FCILpRR3jgAAIgAAIgAAIgAAIpDcBeAqmN3GMBwIgYBEEQiIf0d6TcfnY2NNq7kfVqVQR52TNb/0/N+mD1iWJhUWYaQL1y7rRrA+qSg9Bxcvqz39v0pBW3uSSE3+STJPTnzl26a6uoEeD4rrj5B6s2nODBrb0puS+jbefvE3/nAun89ejKVh44jo5ZiUfkbeyrMgd+fZrxSi3GR4tkSLk7uK91+nU1Si6JPp5KvIfVivjSm9UL0z1/fIneWkcIvhv/zA6F3SPgkTY38zCndJDhLUtLeb1Vp2i5F3IeIjbxAbq3diDdhy5pVY7eimS+jVVD7EDAiAAAqlOoEmFAjSwXUn6Zf1l2felq3do1fb/qHNzy/Ni2rLvMvlfiPVof6uRB5UumCPVeaBD2ySQrVhx+ujTT8m9QEEaM3OmfPFKO4rQomlp5gqCN27coC5dulCHDh1o5MiRaTkl9A0CIAACIAACIAACIAACaUIAd2DTBCs6BQEQsHQCv22/ppti/zbeyRYEuaNHj5/TztOh1EJ4GMISJlDRKw91bliMlv8V61nF4uB8kc9uqBAGYeYRCI+KDR/HtUsJwS0l4VcrlMxL/lcjpdfmg0fPaO9/YdTQT58zL7FZPRDv/7GLztKBM2G6qpH3ntBh/3D5WrH7On3brwJVE+OZssu37tP7M49RzP2nuircL79a1IzLqaWrYOTgiRATv1h5nrYdihPulGrnrkURv9btvUHvv+FNfRp7Jjn0akkhLLrlzUGcH5MtJMJ0blFlXGxBAARAIKUEeguBLVT8DVj9d2yEAksUBnccuEon/WNzHtav6k7vNnRP6bLR3s4IZMmTl7oOGkSZsmej0d9PT3NhkAXBnhMmmuUhmDNnTuJcpDNmzKCIiAj68ssv7ezqYLkgAAIgAAIgAAIgAALWTgDhQ639CmL+IAACSSYQ9eApbd4fl9uOvfs61075DatFuxE+0NyL0d3As40FQhZxYIkTeCnS2GlFs8Ku8XNZJt5LXI0c2TPrxLaFf+lDk8bVNL7Hou6bUw/EEwQNa7PgOHjmcWLPPWMWfPcR9fz2kG5thvVY4AsVXr6JGTN6e9pho4KgYdvfNl6hWTuuGhabdVzINadaTxEH1QLsgAAIgEAaEfi4vQ/Vr1RA7V0RBtWCDNz5+0gAHT0VK1gWK5Kbvu3hm4GzwdDWTCCzkxN1HTyEvhZ5BdnYazAtQoneE6FCWRC8FxNjVsjQ119/nUJCQih37ty0ePFiGjBggDVjxtxBAARAAARAAARAAATskAA8Be3womPJIGDvBM7diNYhaFKtEDmlQqKbi4EiPGH4QyqeL04o0A2UyMGde49pmcjpxmEOL4k5Pnz0nIoWdKIyHrmoUbn8xKE3zbE9IlTiLuG1eFp4f4WGP6J8eXLQ6zUKUxsRfjFntqQ9C3Lt9n1aezhYhoO8cjOGHguPsCIFHKl00VzUTNyQNHdOhvPmPINVfV3p2PkIeYpDRAaKcJPJDd9q2L8tH4dFx3kJ8jpTKgpyHyzSbj4QK5SfvRJJHF63UB7zxMY1B4NVbznui429D6v55KEQ4UW340iIDAEae4bo61UXaP24Osqhuv1161XpragUcEjfHs09qLibI917+IxWixC9gbdiiOeXmP157BZdF6FCtcY30Ct75yH2ajx8IYJOaUKwzt8aQF3qFqU8Ttm1TRLdL5TXgc6+qsXv4afPX6ZKbsdEB0YFEAABuyfwbe/yNHT2SemJzTBYGFyw4STVr+pFnu65051PZNQj2nX4mswhyINnFg9cLRtZLd3ngQFti0CmrFmFx+BgyuTgQB9PmiyFwVyOjtSsZo1UWWhSBUEetE+fPjR16lR1/C1btshwosuXL1fLsAMCIAACIAACIAACIAAClkwAoqAlXx3MDQRAIE0IBBuE+WufhJCEhhNi4cLL3ZlYEGRbvCeIxnQsbVgt0WMO2Tj2jzM68YQbXbsZLV/s2dhUiJcTupah7GJMY8beUd+uv6iGFFPqhEY8ovlbr8nXHJE30Vxbsu86/W/1xXjVA4NjiF/bD9+iBpUL0MSuZckxGaJq+1pFVFGQB7kpPMUgCsbDHa/gphCetcbCVEqN8+p5ueeS7zXui3P6jWhbKtFuWQT7eUNsbiulMofi7dfUUzmkPk08qNe0I8SegmwhYQ9p64kQalm5kFonIuaJzquPvXcNc3y+KfL/fTL/jC4XqNqBZofn9MPaS5oSop+HVqGq3nFhS3l+vMYf18S+v9nb8fedgTSyXeJr1nZcyMBL85YQU5P7UIC2X+yDAAiAgDkEZvSvJL2vh/96Ula/GRxFS4NPUu2qntSwuoc5XaRKnbOXQ2mPEATv3Yvz5J71YXUy8XUlVcZEJ/ZFoEvfd4UwmJNGjRlDY376STw0V4DKeHomGwKLgWw9PptA50Xo0DJlytDvv/9ORYsWTbTPgQMHkoeHB/FWsYMHD1Lz5s1p+/btShG2IAACIAACFkqAc8PWrVuXhg8fTjVr1qTatWtb6EwxLRAAARBIOwLG7yyn3XjoGQRAAAQynMANA1GlaDI9+5SF9GhUXNmlTUK8Y1EiKbbleAiNmnUqniBo2MfOoyHUc/pRw2L1ePKKc/EEQfXkq51ftlwxLDJ6/PXai0YFQcPKe06E0qBfTxgWm3VsyN1Q7DKrEzusdEuIp1orKLwuU8O6NyqmdrPh35v0TAhlidn5G/dUsY/rco49rSDIZezp169VCd5VbfvJUHWfd/41CCnapq57PIFY6IQ09s3SxIJhQnZBCOna8KrvNPPQCYJK23fqFyN34fWqmP8rYV85NmdbUHjhau2WwQMH2nPYBwF7IzBs2DB54zw83HjIYHvjkVbrreObjzZ9/hpx1APFDhwLkF6DFwPSlv3z5y9o+/4rtHHnOVUQZA/BBZ/UpvLFcynTwRYEUoXAW2+/TdOmTZN5/1jM4zyAyTVurwiCLOatWLHCLEFQGa9Vq1Zk6Bl44cIFql7d/IfvlL6wBQEQAAEQSF8CLAqyTZ8+nfihDhgIgAAI2CMBiIL2eNWxZhCwcwLBIqSm1txc9Df2tefM2W9SoQA5vPKU4xCCm0ToQnONhZdpBt543Fe714pS79e9yMfDRdcVew5yaFBD49CjSvhH5VxFn7z0cRdfmtDDjzo3jBV8jp6LDdep1DG2vSmEjTV7YvMBKedLFXeh99/wpqEdSunyz/H5c9eiTOaJU9ob2xY08HDjcWGJE8hhEAI2tXIxthCee+z5yvZIhNjceep2opMJCnugq9OxnvEn7DsKr1Ct3bijv9aG3rumcnzmFeE9OXxtQhZ0Rz+nrvXixE7Ddo00ObmCDdZiWNfY8ZOn+jyYDnCLMYYJZXZE4PLlyzRT5P2qVq0arV+/XoSQzEw5cqTsb6wd4Uv2Ut1yZacvu/vRgLbeah/sNbh661latuUspYU4eP5qGC3aeIqOnY69scYDl/TIS+sm1qPShRP+nFYniR0QSCKBN998UycM7jh0OIk9EI3+5RfpHcgegp07d6bZs2eTi4v++7Y5ndaqVYu2bt2qqxoaGkre3t704oX++4GuEg5AAARAAAQylMBLDrH0yvi7KgwEQAAE7JEAwofa41XHmkHAzgloPdJYgEvM8ygxXNy+fT13WrYrSFZdsvs6ta+hF0FM9bFc5BDUejXlcclOi0bWoPyvhMqBLUoQe+1pRbppIuRh4/IFKJPGYWr2jgDdEI2rFqKvhBioWCtx3KmWO3WbmviTcN+K8bTWv00J4f3lpS2it0T+tXe/P6KW/bD+MrG3QlLM1SB/W7CBB2dS+rKnuu6u+puthp6DyWWRLUsmalOnCK3dG3uDd5F4H2tDfBrrN0iEAtWat8iBacxyZs9Cjg5ZVa/COyKkrdZuGgj1ngkIf4XzOcTLF6jtK9BA3Ju945r2tG7/XFBcftHIe09058w5MGRfJIVex+aMiTogYIkEzpw5Q0uWLJEv7fwqV65Mzs7O2iLspyGBPo09qZ6vG20RIZq3H7tN/Fl7LTBcvrw88lGVMoWpVHFXypSIx7WpKbJn4MkLt+nsxRAKDokNm67Ure6Xn77rXY4MH1xRzmMLAqlFgIVBtpEjR9KQb76hr4YMoY6NGsqyxH7MFA8rrN25S4b65LrfffddYk0SPM9hR9nLhAVCxZ49e0ZeXl50/vx5ypkzeXnGlb6wBQEQAAEQSH0C2gc3MmlvqqT+UOgRBEAABCyWAERBi700mBgIgEBaEQiLeqx27eKUTd1Pyc7brxVTRcHAWzF0SeTcMyc/3k6DMIrDhSeeIggq8+E8Z5sPBkvvLS6LiHxMwXcfkrtr3I2G3Sf0Xl2fdIyfG62EyBvXvn5RWvdK9FH6N9we9o8LN+ZdNFc8QZDrlxOegx0bFFPFyhu3Y3OzGPaV0DF//9YKRSERcdcloXb2fq6IQR6725r3c0rZdG9YXBUFLwXdowADrzvD/pPidZs7VzZVFOT8gvyApvI/2C0DUdBU3kwe39kh4d/Zm2F6wXHDPzcNp23yWDsnk5U0J0LF76LW3HLBI0rLA/u2T8CUGMgr55vhCxcutH0IFrZC/u5RqkhJer95CdoswpOvO3Sbzl+NUMVBR8fs5FE0r8gjm5eKFXQh1zxx3yWMLYWFwDt3H9CloAgpBkZG6h8GKV8qH3WrX4SaiIeVYCCQXgRYGOTwbz/88AONEd7J7PU3tk/vBIdfd/IkzViwUOYPTKkYqB2ocOHCxKFDS5curS0mX19fOinGzJs3LqexrgIOQAAEQAAEMoSA1lMQomCGXAIMCgIgYAEEIApawEXAFEAABNKXgFZwMCdvmjmzK5jHgSqWykunLt2V1RfuCaLJ3com2lQbspBDNzarWDBeG/ZEbF27iC5f4HXhoaUVBe/FPFXbVS7tSnkMvPCUk6+VcUtQFIyIeULPNbnk3hIioilrWiG/KgpyG27r6pzdVHWj5RxuVbHsBmExlXJs9QRy5cwqvVuV63TbIMegvnbSjooKobm0Z266EBAlGy7Zez3BDlwc9V8jYoTYZ8oeipCkivF7WhEEuSxH9tQL25I/d9Leg8qceKudk7bc1H6Ihr2zeMAgqe1N9YtyELB0AhwmdN68eTrRr1SpUnTp0iV16oMHDyYnJ+Pew2ol7KQZAfbY61CziHwdvXyXdpy+Q/7COzowOJrOXbwtXzx4jhxZyS2fEzlkzyb3HbJnFZ/JWSki6gFFRt2ncBHa+5kQBg2tetl81LmOOzUsl9/wFI5BIF0IDB8+XI7DwuD8P/+kG2F3aOqgQeRi8LmTSRyvO3KUPvn8CykIcv7A5IQLTWhRDg4OFBgYSH5+fhQTE6NWrVSpEh06dIgKFSqklmEHBEAABEAgYwloRcGMnQlGBwEQAIGMI6C/m5dx88DIIAACIJBuBDiXXcirsIdRyQgZaGqi3RsVV0XBnUdCaEwn/RPDxtppQxbmy5PDZChTw7CMnMutlo+r7DLyvj7soUdBfXhJ7bhF3RL2CLhu4Bm2VYQf8xceY8YsUiNE8nlumxRR8Onzl6QVBd0RetEYZqNluZyzkfLeuXYr6V6aRjt9VdijUTEaNzdWFOQ8lUXym34/eRiE+TQMp6kdJ1rzfuH3utbcxfsyLhgtUfTDZ8TiZ3KsZGG9CNFH5Ob0Fl6yiVm2LEkTJtmr8MbtuPyF+XLr15TYeDgPAtZIICwsTIqBc+fOVW98d+zYUeYNXLp0qbqkfPmEYCRydcEsg0C1knmJX2z8MJR/YBSdvR5NZwPv0TnxN57zDyZkjuLzuLgID11CfL6WENuShZ2ptngACQYCGU2AhcGiRYvKUKK7Dh6i9gGB9NXQoVSznB9lEt7K97Nlo5mLFtOcOXPSTBDUMvD395ehRG/duqUW16xZk/bs2UOenp5qGXZAAARAAAQyjoBWFISnYMZdB4wMAiCQsQSSd8ctY+eM0UEABEAgRQRYgFA8+tjb6onwVtN6Dya3c/bCY28hzhHI/W44EndDwFifdw3EPNdXeQSN1XUzOKfNi3jrrj6EoUsCYoqTyKGYkN00yPV24kIEnbiQUIu4cw+fxvckiDsbfy/snn7eRUSuOJh5BAoKjz5FFGSB+/yNaPIVoV5TwzhfJefafCQ8+1i05XC4pqy4m14w3Hb8tvRKMax/8GKEzgPV3UBoLGogCB+5HCHzZhr2w8cPn5j2RuTzJQvpOXB41QEiN2dq24EL4Wo4VO4b79/UJoz+LIkA58hiIZC9AzlkX44cOahHjx7EIfwOHDhAX331lZwui4Hh4eFSEOSQejDLI5BVeGpX9MojX4azi3rwlAJDH9Cjp3Ge3fzAjjYygWEbHINARhPgz6FixYpRv3796GZICPX89FP52cRi4bZt2+i///5LF0FQ4cA5Bps0aULsUa1YgwYNaPv27fFCjCrnsQUBEAABEEg/AhAF0481RgIBELBcAkl7LN5y14GZgQAIgIDZBAxzsoWkUk42Dh34pibc5pLdQQnOyTA32v0EQi/GPIoLD8qd5naMy6vmYBB2M7O44ZdcMxQfk9JPYoKjYV+3I/W539wNcuUZ1sdxHIHmlfW5m+Yn8l6La5n4Hof27KR5HyfUwjBvJovIlw08F4U+Tj9tuqLrppyni+64ZCFn3fG8XcZ/d9i79MzVhD1avISnLIfiVWzz/mBaI3JyprbN2xWo67J5s3gt9AAAQABJREFUpfihf3UVcAACVkpgyZIl1K5dO/riiy/o4cOHNGDAANqyZYs85rB4iiBYvnx5KQhmz56d3nrrLStdrX1Pm79bVBAhpGuUclVfEATt+z1hLauvVasW7d+/n5o3by6nvHLlSpo+fboUBLksLUKGJsRm165dVLlyZV0Vnsfp06d1ZTgAARAAARDIWAKZM8f935ixM8HoIAACIJC+BOApmL68MRoIgIAFEDC8wXVZ5NcpbuCplNxpvlW3KM3dck02Zw+uswHGQ29yhWxZMpGjQ1bV2yg8AXHS0BvQQ+NpVTivPiRoaKTeAy8pa/E2CL3IOYPa1TTP28OroF7YSWzcSwbiURHh/QYzjwC/z37deEUNv/rXsRCKedOXnB0S9gQ1r3eibq8Vo8U79KKXsbb5RA7JliJn1dZDcaJb7+8O07h3ylB1cVP5lvA8/XHjZbooQtQpxqJjr4YeyqHccihcVxFSNOLVe5dzGo6ad4bGdylDiucri43frLkgPXF1jQ0O2Ov3o84+9PWy8+qZr5edo62CUd9mnlRGeFQqovoD4Q159fZ9uitCm74m3uvmWogQtBVvY27DHsItqyBfkLn8UM86CLAYyK8zZ86Qm5sbffjhh9StWzc1N9bs2bNpypQpcjF8Q569BLkue+2ULFnSOhaJWYIACNgMAc4TyJ9L7M3MHoJ8zDn+ypZNPMd3WkBYt24d9e3bl1ggVOyNN94gFixr1KihFGELAiAAAiCQzgRevIiLcITwoekMH8OBAAhYDAGIghZzKTAREACB9CJg6JG2bO8Nk6EKkzonzqlXwy8fHfYPl021woGxvvIL77jA4NjwjBx29KLY9ykSX1zbLsIyaq2YJmyjQ/bM0jNKyc93/NJdbVXdfmIhPlnkYdGGw5+yBYbcpyYVClIKnA9142sPlu+9rj0U4RchCuqAJHDAwleXxsVp0fYAtdaAn4/TrMFVyDGRELFqgwR28otwtRV98tKpi6bfS0rzoW1K0A4RKld5z/D7cMJ8f+V0vG2PFp7x8gWyl+3gNt70+aL/1Pp7T4ZSM/HiUKZPRWhapX+1QgI7HWu50xrhIXhJkw+Tfxc/MPG7we/5/d83TqDHuFOcw3PAT8fjCsRe3xZeafI7ohsEByCQTgS0YmDWrFmpf//+1Lt3b5m3S5nC77//Lj0F+bhSpUr0ySefUIcOHeTptm3bKtWwBQEQAIF0J8BhQ9999910H9fYgH/88QdNmDBBhl5WzvODE4sXL6Z69eopRdiCAAiAAAikIwGED01H2BgKBEDAYgnAT9piLw0mBgIgkFYEyhbPLYUGpX8WC9jzJ7WsZyO9F1RC/foZhFGcsSku/4jS7tiVu6pwyGUsYHgW0OdyK6LxHGQPxZPXIpXmuu0hEd4xMSstQocpFio8vUYvOKMcptqWc+BdF4KjYuwlVgyeggoOs7a9xPuM3wuKsQDWXXjpcU6o1DBz38duuXLQtPcq6kJ2mhq/eY3C1K+pl9HTraoWphZGvFI5t6EiCPJ6W9dxN9resHDG+xWpXqX8hsVGj7l/FvsSszsiD2a3bw/TrTsP1aocqvRNM+ekNsIOCFggARYD27RpQ2PGjFE9/tavX0/jxo3TCYJz5syhzz//XK7A19eXuM7atWvlce3atYm9BmEgAAIgAAKxBCZNmiQ/R7U83nnnHfrrr7+0RdgHARAAARBIJwIQBdMJNIYBARCwaAIQBS368mByIAACaUGAw3b2aKYX7hbt0XutpWTc6iXzUh6X7GZ1MbBFCV099jD8aO5punAzhsKiH9NaEZZx6E8ndHW6NilO7CmmtT4G6xki2hy8GEGvHP7oifDeWnPwJk1fdUHbzOj+Z13L6Mr3nAildl/sp41HbxGLIi9jnQiJ87tdFcLejlO3Zf+6RokcGObA69/Si9hbDGY+AQ6r2VN43WntZugDaj95P41Z6C/z6AWFx4lX2nra/RzC09SY1fXNJ8NiGjtnWFZH1F03oY70LtQKlUo9Fn0n9y5Hn79dVobNVcq1W9Y3J3crS6O7lTE6rpd7LvrfoMpUvWQebTOT+3mdstN3vSvQL8OqkrcIGWpsXtrGoZHGRcFrIrzoiv03ZDjTDoKtEuJUaTu0Q6l4v4/KOWxBwBoIGIqBTZs2lV4s06ZNo3LlyumWMHfuXJo8ebIs8/LykiH6IiIiaNOmTbKsdevWuvo4AAEQAAEQIOlxPWvWLB2KPn36yPysukIcgAAIgAAIpDkBiIJpjhgDgAAIWAGBTOLD8NXtXSuYLaYIAiAAAqlEgHOJNR2zR/VA4m6/6FOOmlUsmOgIC/cE0cy1l2Q99hL6Z1qjeG3m7Q6kX9brvf5YKNw2+bV4db9cfYHW77sRr9xYAY+3fUr9eCEi+ZO85YR9FHlPL2ywEJItW2Zibytj1rFBMfqkg0+8UzO3XKGF2wLilZsqmD+yBvkK4cUcW33gJn2zPC7fG+dV3PFlfcrKqhAsyQQmrzhPm/bfNNnuXxEWM73ZBt99REF3Hogch1llOFxDEdvkZDUn7j18RpdvxUgxr2wxF1VMfPjkOd2JfkLOIqwo95+Uvtkb8NrtB3T/1e8Dh1rlcMIFcjsYFaVZmG89/h/NrPS73Zt70tBW3vpCHIGAFREYNGiQFPRy5sxJ7du3lyFAa9asaXQF8+bNk2Hw+GThwoXp4MGDst7ChQulF4yrq6vMncVbGAiAAAiAQHwC/v7+1KpVK92JH3/8kdq1a6crwwEIgAAIgEDaEdixYwf169dPDsARMThUPgwEQAAE7I2AcfcAe6OA9YIACNgdARYDOtQvqlv3uLlnpXeVrjCZB51ETjNzbfgbJalOBbdEq7N4NkN4ShnLGcdedt/1q0hcR2scFlErCLap656oxxS3HyA8GHsJ7z1zLeBOXCjQhNr8sStAJwhy3e7CyzG9RauE5mht5z57y5dGdfE1eV3vRD1O9yUVyetAtXxcqVxxlySJdtqJsidklRJ5qKIIZ8vevYrlzJ6Fiov8k5y/MymCILfPI7wHK4s+65XJJ1/cf8E8xgVBrh9y1zg7Fucn9vSDIMiQYFZNoFSpUjR8+HDaunUrTZ06lUwJggsWLFAFwbx586qCIC9e6yUIQdCq3w6YPAiAQBoT8PPzk+GZtcMMGzaMVqxYoS3CPgiAAAiAQBoS0PrGZEK4ojQkja5BAAQsmYD+7rElzxRzAwEQAIFUJvBuU0/afPAWPXj0TO3562XnaN3BYKruk1eEKcxLlTzzkIOJ8IrciMUBY5ZLCBr1KxWgvSdDjZ3WlbHIMb1vRRne85c/r9C9GH1OOB6jiq+rDL2Y2zGbrq32gAWY1eNq0+j5Z+nslUidF6SzUzaqW86NxnYqTfv9w+KFQNT2w/ss0g1qWYI61ipCX4qQo5x3USsuGtYPj9bPWTnPHpmc3/DQpQg6cuEuXRG5BLXG3pPd6hXTFmE/GQQ613anphXy059HQ2jzkRAKEmFdn4qQsWxhIuRrYSHSwZJOIEx4JCrGv4de7s7URuRFbC3yHzo7ZFFOYQsCVkuABcHEjAXB8ePHy2qOjo508uRJtQl7Cx44cEAed+rUSS3HDgiAAAiAgHECLi4uFBgYSKVLl6ZHj2Jzmo8aNYqePHlC3bt3N94IpSAAAiAAAqlG4MWL2P+TucPMmY3fz0m1wdARCIAACFgoAYQPtdALg2mBAAikD4FrIgdbj28OqQKK4ahvNS5OI9qWMixO0+NHT17QheBoevz0BXkWcBShDXMkeTzOJci50DgHYImCTro+bonQjhyp0zlnNnIUgqS5D8dx2MZAwSsi5onMJ8hiZqG8OYTglFPnyaWd7NhF/rRLCFXGjL0al42uKT21jJ1HWcoIPBNvgvtC8HYR19nca5yyEW2vNXvaxgiGuQRDRLe1veuLFSVOYNGiRfTpp5/KinzT5Nq1a7pGLBayaNi2bVuaMWOG7hwOQAAEQAAEEiZQsWJFioyMVCtNmDCB+vbtqx5jBwRAAARAIPUJcISM999/X3Y8ceJE4hyvMBAAARCwNwLwFLS3K471ggAI6Ah4CdGNvevGLvSX3nW6k+IgJCL2CV7D8rQ8Zs9EDpmYEmMBw7uQk3wZ9pNcrzEWAc3NG6iMecsEvxp++Whit7KUT4SAhKUNAfb2TMizNG1Gta1eOScnGNrWNcVqzCewePFiVRDkVleuXNE1joiIUEOHwktQhwYHIAACIGAWgVOnTsmwzSEhsQ/QTZo0iR4/fkwDBw40qz0qgQAIgAAIJJ0AwocmnRlagAAI2B4BiIK2d02xIhAAgSQS4Jxic4ZWpSOX79KGw7fo4H9hagjPsHtx4QOT2C2qCwJ3o2NzsrG4ksuZQ5jmp3Yi/GJKRU/ABQEQAAEQSDsCS5cupbFjx6oDXLhwIV54Jc4lGB4eTg0bNpQvtTJ2QAAEQAAEzCZw6NAhatCgAQUEBMg2nN+VQ4l+8MEHZveBiiAAAiAAAuYTgChoPivUBAEQsF0CEAVt99piZSAAAkkkwDkE+aUY58ND2EWFRvK2S0bWlA0dcyD/WvIIohUIgAAIpC+BZcuW0ejRo9VBz5w5Qw4O8fOSsijIBi9BFRV2QAAEQCBZBPbs2UMtWrSg8+fPy/bff/89PX36lEaOHJms/tAIBEAABEDANAGIgqbZ4AwIgID9EEBGVfu51lgpCIBAEgmwkMUhM2HJJ8AMIQgmnx9aggAIgEB6ElixYgV98skn6pDHjh0jFxcX9VjZ4fIDBw5Q5cqVZT5BpRxbEAABEACB5BHYtm0bVapUSW3MeVqnTJmiHmMHBEAABEAgdQhoRcHU6RG9gAAIgID1EYAoaH3XDDMGARAAARAAARAAARAAgVQlsHLlSho1apTa5/79+8nNzU091u5s2LBBHnbs2FFbjH0bIhAZGUkeHh7k5+dnQ6vCUkDAsgmsX7+eatWqpU5y1qxZNHHiRPUYOyAAAiAAAikn8OLFC7WTzJlxW1yFgR0QAAG7IoBPP7u63FgsCIAACIAACIAACIAACOgJrFq1Shembvfu3eTu7q6vpDnauHEjOTk5Uc+ePTWl2LUVAhyycPjw4XI5MTExFBERYStLwzpAwOIJLF++nNq1a6fOc+7cubocr+oJ7IAACIAACCSLgNZTMBPyxSSLIRqBAAhYPwGIgtZ/DbECEAABEAABEAABEAABEEgWgTVr1tCIESPUthzCrkSJEuqx4c6iRYsoPDyc+vfvb3gKxzZCgL1GT58+ra7mzz//VPexAwIgkPYEfvzxRxowYIA60OLFi3We3OoJ7IAACIAACKSIAETBFOFDYxAAASsmAFHQii8epg4CIAACIAACIAACIAACySWwdu1a1SOM+2APQF9f3wS7Y1HQ2dmZOnfunGA9nLRuAhw+lC1Lliw0depU614MZg8CVkhgzJgx9Pnnn6sz55yvw4YNU4+xAwIgAAIgkDwC8BRMHje0AgEQsC0CEAVt63piNSAAAiAAAiAAAiAAAiCQKAHOXfXhhx+q9VavXk0VKlRQj43tHDhwgM6dOycFwWLFihmrgjIbIfDs2TMpCLq4uND9+/elYGwjS8MyQMBqCHCI5t9//12dL39uDxw4UD3GDgiAAAiAQNIJQBRMOjO0AAEQsD0CEAVt75piRSAAAiAAAiAAAiAAAiBgkgDfWNZ6nCxbtoyqVatmsr5yIl++fFSrVi1q3769UoStDRNgL0EWBdmmT59uwyvF0kDAcgk0a9aMNm/erE6Q99999131GDsgAAIgAAJJIwBRMGm8UBsEQMA2CUAUtM3rilWBAAiAAAiAAAiAAAiAQDwCHCLUUBCsXbt2vHrGCnx8fGj58uVUuXJlY6dRZgMElLChvJSsWbOSk5MTFS9enK5cuUKcaxAGAiCQ/gT8/PzoxIkT6sA7d+4k9iKEgQAIgAAIJJ2AVhRMemu0AAEQAAHbIABR0DauI1YBAiAAAiAAAiAAAiAAAgkS+PPPP2nIkCFqHfYQNFcQVBthx6YJ/P3333J9rq6ulC1bNrnfqlUruV24cKHc4gcIgED6E+DfycDAQMqfP78cfM+ePdS1a9f0nwhGBAEQAAErJ/DixQt1BZkz47a4CgM7IAACdkUAn352dbmxWBAAARAAARAAARAAAXskwCHnBg8erC4dgqCKAjsaArt375ZHhQsXJr5R9vz5c2rTpo0sO3XqFG3atElTG7sgAALpTeDo0aNUsWJFOSznee3SpUuSpsDCIgwEQAAE7JmA1lMwU6ZM9owCawcBELBjAhAF7fjiY+kgAAIgAAIgAAIgAAK2T2DLli00aNAgdaEQBFUU2NEQiI6Opr1798qSQoUKSVGQn6YvX748cV4zNoiCEgN+gECGEtiwYQM1atRIzuHgwYNmC4OcG7R+/frEfwNgIAACIAACRBAF8S4AARCwVwIQBe31ymPdIAACIAACIAACIAACNk9g27Zt0kNQeSoagqDNX/JkL5DDEUZERMj27u7uqqcgF7zxxhuynEXBS5cuyX38AAEQyDgC8+bNo/bt28sJmCsM1qxZU9ZftWpVxk0cI4MACIBABhNQvhPzNCAKZvDFwPAgAAIZRgCiYIahx8AgAAIgAAIgAAIgAAIgkHYEWBAcOHCgDAHJo0AQTDvWttCz4iXIa8mePTtlyZKFlBtnrVu3phIlSshlwlvQFq421mALBP73v/9Rr1695FLMEQbr1KlDVapUoSNHjpASKtgWOGANIAACIJAUAsp3G24DUTAp5FAXBEDAlghAFLSlq4m1gAAIgAAIgAAIgAAIgIAgsGPHDukhyDnh2CAISgz4YYLAkydPaN++ferZbNmy6TwFs2bNqvMWVCtiBwRAIEMJTJ48mYYNGybnYI4wqOSWRQjRDL1sGBwEQCADCUAUzED4GBoEQMBiCEAUtJhLgYmAAAiAAAiAAAiAAAiAQMoJ7Ny5U+YQfPr0qewMgmDKmdp6D+wlGBwcTGXLlpVLZREwc+bMxDkFFWvTpo3cvXjxInILKlCwBQELIDBixAgaP368nEliwmDTpk3l7/nWrVvp2LFjFjB7TAEEQAAE0peA9rsNPAXTlz1GAwEQsBwCEAUt51pgJiAAAiAAAiAAAiAAAiCQIgJ//fWX9BBkzy82CIIpwmk3jZXQoUruQA4daigK+vj4UMuWLSWT7du32w0bLBQErIFAv379aNq0aXKqiQmDffr0kfXmzJljDUvDHEEABEAgVQnAUzBVcaIzEAABKyUAUdBKLxymDQIgAAIgAAIgAAIgAAJaApwjikPDPXr0SBYvXbqUateura2CfRCIR4A9Svm94+TkRG3btlXPszCofZqeTzRv3lye//vvvykyMlKtix0QAIGMJ/Dmm2/S7Nmz5UQSEgY7depE3t7e0uOXf5dhIAACIGBPBCAK2tPVxlpBAARMEYAoaIoMykEABEAABEAABEAABEDASgjwjV0WBB88eCBnvGDBAqpTp46VzB7TzEgC7PUXFBREHB60aNGi6lQ4pJaSk1IpZE/BQoUKSUEQYoJCBVsQsBwCLNyvXLlSTsiUMMiCf5cuXWSduXPnWs7kMRMQAAEQSAcCEAXTATKGAAEQsHgCEAUt/hJhgiAAAiAAAiAAAiAAAiBgmsCePXukIHj//n1ZiUPCNWjQwHQDnAEBDYEtW7bII62XIBewcGAoCrI3oeItyN6FMBAAAcsjUKNGDdq1a5ecmClhkL0F8+fPTyzur1u3zvIWgRmBAAiAQDoQQE7BdICMIUAABCySAERBi7wsmBQIgAAIgAAIgAAIgAAIJE5g3759UhCMiYmRlX/77Tdq2rRp4g1RAwQEgVu3btHGjRupVq1aVK9ePR0TzimofZpeOdmsWTO5y2ICQogqVLAFAcsiULJkSTpx4oSclDFh0M3NjTjcKNu8efPkFj9AAARAwB4IaL/bQBS0hyuONYIACBgjAFHQGBWUgQAIgAAIgAAIgAAIgICFE/jnn3+kIBgdHS1nOmPGDOLwjjAQMJcAC4Jsb7zxRrwmLAoaegpypfr161OZMmWkIHjo0KF47VAAAiBgGQRcXV0pMDCQcubMSSwM9urVSzexzp07U44cOaR4CGFQhwYHIAACNkxAmy8ZoqANX2gsDQRAIEECEAUTxIOTIAACIAACIAACIAACIGB5BPbv3y8FwaioKDm56dOnk2H4R8ubNWZkaQTWrFkjcwQaEwX5Rpn2xpl27oo36n///actxj4IgIAFEjh//jy5u7vLUKEDBgxQZ+jt7a16C3JuQXj+qmiwAwIgYMMEtJ6C/AAUDARAAATskQA+/ezxqmPNIAACIAACIAACIAACVkvgwIEDUhBUbuB+88031LFjR6tdDyaeMQQ4J+C5c+ekmJw7d+54k+AbZYmJgtweBgIgkHEE+O8Bi36JGT9IUr58eeIcoh999JFanb0F2QICAoiFQRgIgAAI2DoBrSho62vF+kAABEDAFAGIgqbIoBwEQAAEQAAEQAAEQMCuCdy4cYNWrVpFluQNxSHgBg0aRBEREfLaTJkyhbp06WLX1wmLTx6BTZs2yYbGvAT5REKegpUqVaLSpUubJUYkb3ZoBQIgkBgBFgS7du1KLVq0oNq1a9PQoUNpwYIFdPv2baNN//zzT1lv9erVNHr0aFmncuXKqpc5hxBlcRAGAiAAArZMQCsKInyoLV9prA0EQCAhAhAFE6KDcyAAAiAAAiAAAiAAAnZH4N69ezRi+HCqW7cujRgxgl5//XX5YpEwI43ztw0ePFgVBCdOnEjdu3fPyClhbCsmsHLlSvm+rlChgtFVmMopqFTm3w/OVwYDARDIGAIsBE6dOpVY2AsODqYNGzbQ+PHjqUmTJjRq1CjauXNnvIktW7ZMepYvXbpU9Rjs1KmTrMfe5/AWjIcMBSAAAjZGAKKgjV1QLAcEQCBZBCAKJgsbGoEACIAACIAACIAACNgiARZK6tapQ6tErjWtsbcgi4MZ5TV45MgRKQiGhYXJaY0bN4769OmjnSL2QSBJBDjsLIvepoxFQe2NM8N6vXr1ouXLlxsW4xgEQCAdCXTr1o3WrVtH33//PdWsWVOOHB0dTStWrKB3332XWrZsST/88INOwOcctG+//Taxx+CQIUOoYcOGOm/B06dPp+MKMBQIgAAIZBwBeApmHHuMDAIgkLEEIApmLH+MDgIgAAIgAAIgAAIgYCEEJk+eTCNHjqR74oZqkfz5aYwQ3RZMnkQ9W7cmZ0dHYg9CDtWZ3sLg0aNHpSB4584dSerjjz+m/v37Wwg1TMNaCfB7uVSpUianzzfKnj9/bvK8p6cn1apVy+R5nAABEEg/Auztx0LgzJkz6bXXXlMH5ryfLALyQy2ffvopHTt2TJ776quvpDC4ceNGWX/AgAGUM2dOeW7hwoVqe+yAAAiAgK0R0D7wBFHQ1q4u1gMCIGAuAYiC5pJCPRAAARAAARAAARAAAZskwGIf3zCdM2eOXB+Lgbt//YV6t2lNNf386NO+fWjD999RaSGCpLcwePz4cSkIKjmiPvroI3lskxcCi7IoAnyjLEuWLBY1J0wGBEAgYQKcI3TRokU0a9YsGUZUqX3//n1Z3rFjR3rvvfdo27ZtpAiDQUFB1KpVKzUcNYuLJ0+eVJpiCwIgAAI2ReDFixfqeiAKqiiwAwIgYGcEIAra2QXHckEABEAABEAABEAABOIIsNcf50bjLYt+676bJsXAuBqxe+7Cc3CDONehUUNVGGSBMC3txIkTUgAMCQmRw7AX4wcffJCWQ6JvEFAJcPjQHDlyqMfYAQEQsB4CLVq0oD/++IMWL15MHTp00E2cBUEWBlkgLFeunCoGzp49m7y9vWXdefPm6drgAARAAARshYDWU5C/68BAAARAwB4J4NPPHq861gwCIGC1BJ6ajuJltWvCxEEABEAgowiwENjlrbekyMdiH4t+ZYQwmJBNFfmXFGEwLUN4spfGoEGDKDg4WE5n9OjRNHTo0ISmhnMgkKoEIAqmKk50BgIZQqBevXoypyCHCeU8oE5OTuo8OJTo2LFjiT3SlVDAV65ckefXrl1LHHoUBgIgAAK2RkArCtra2rAeEAABEDCXQFZzK6IeCIAACIBAxhK4HvWScmZ+QpeC79ODx8/o0dMX9PDJc3r4+LnYf04F8jhQNe885O4amw8kY2eL0UEgPoGImCd0+dZ9qlEqrzwZ8+QlZc2ciRzwbSQ+LJSkOYGVK1fK/IE80FdC6OsoREFzjYXBm6GhdPDgQZo0aRJNmDDB3KZm1Tt9+rT0EFQEwXHjxiGHoFnkUCk1CXBILXgKpiZR9AUCGUegQoUKxK++ffvSqlWraP369cRhQ9mUPLl58uShyMhIdZIzZsygn3/+WT3GDgiAAAjYAgGtKIjwobZwRbEGEACB5BDAbbjkUEMbEAABEEhHAscC7tH6I3coIDiSLlyL+0fd1BSKFHCkqj55ya+YC1X0yE0lCsU9EWyqDcpBIK0IXAt9QAcvRtC//4XRkf/C5TAliuUhoWlTgXzOVDi/M5Ut6kztKrhQ1iyZ0moa6BcEdAR++OEHmj59OuUSHhMLJ09K1DtQ1/jVwU+ffELdP5sgw7Plzp2bPvzwQ2PVklx25swZKQjeuHFDtmXRsXfv3knuBw1AIKUEHj16BFEwpRDRHgQsjICn8IbnUNT8N2vHjh20fft2uY2OjtYJgjztTZs20dWrV6lEiRIWtgpMBwRAAASSTwCiYPLZoSUIgIDtEIAoaDvXEisBARCwMQI/br5Cxy5H0fmrdxNcWS7n7FQwnyNFRD2iiMhHFCxEGH5tpJuyXeXSrtSqWiFqW71wgv3gJAikBoFTAZG0bN9NuhwcQxH3HlPM/afxur16PVbcvn4zdvunqDE9WxYqJATC6mXyU/NKBahEAQfKnQMiYTx4KEgRAc4BOOKjj2i7uBHq7OiYbEGQJ+EiBMVFQlBsPHCQFBhZxJs2bVqK5ufv7y8FQcV7Y8qUKWqupxR1jMYgkAwC/PsCT8FkgEMTELACAlmzZqXXX39dvkKF5zsLhPw6dOgQPXjwQF1Bu3btiB9WgYEACICALRKAp6AtXlWsCQRAwBwCEAXNoYQ6IAACIJBOBPb4h9GiPTeoYdXitHh7QLxRCxdyIU93V8qXJyfly+1IbnlzUnYhpmgt8t4jiox+TKcv3qJL18LoxIUI+Vr4VxC1ql6Q3qhehNxyZdc2wT4IpIhA5P0ntOPUHdpwKJguBt7T9eXslJ18Sxago6duULkyhen+gydCKHwstw/EvmJPRQjc68FR8rVm12UqXjQvvXjxghpXLUrtaxQk91wQCBVW2CaPAIdH+0h4Rpy7cEEKgos+n5wsD0Ht6CwM/vTJx9RTeAxyOFK25AqDPL/BgwdTYGCg7AeCoMSAHxlIgD2HChfGA0UZeAkwNAikC4ECBQrQO++8I1/8e3/kyBHatm0brVixgvgYBgIgAAK2RID/x1QMoqBCAlsQAAF7IwBR0N6uONYLAiBgsQQmrbhIm/dfl/M7fTE2zCIf+JYqSGVKuFHJ4q4ivGLmROefx8WB+OXpnpue139BJ86HUMCNCCEQhtOvG2JopfDiGtbWm1pWLpRoX6gAAgkRuC/yWf7vz8u081iIEPme6ap6ivernxADK/gUlOWVShem/K6Oujp8cOtODIWEiVd4DN0Oi6Y7Yv/ZsxcUdCPWQ3aBEApX7bpINcsXEaJ2Iart5UwGOni8PlEAAoYEOPdf/3796J64uckegqkhCCpj1PTzoyHdutLMpcuSLQyeO3dOCoLXrl2T3U6ePNmqPQSZ98SJExVElCVLFuKQdT4+PlSqVClq3rw5sZcKzLIJsBjA1w0GAiBgPwRy5cpFjRs3lq/x48er+QbthwBWCgIgYOsEtOFDM2dO/P6KrfPA+kAABOyTAP4bt8/rjlWDAAhYEIGHQksZv/Q87TsWG+5TmZprXkdqUMOLfL3clKIkb7MIEbGaXxH5CrgZJca4RjeEyDJhvj8dvhJNn3YsSVkywwMryWDRgK7dfkCfLfHXeQbye9bHKz+VLZGfCrrpc1kaEwQZI+cU5JfWwu4+pOu3o+jq9bt0+dodEcbqKe0+FChf3p5uVLN0Hmril5fKFXfRNsM+CBglwB58nD+JLbUFQWXAoZ0706EzZ+nI2bNSGLx+/TrNnj2bXFwSf4+eP39eCoKct4ltwoQJ1KtXL6Vrq9xyKDoWOrV2VrBRrFy5cjLkKouEMMslgJyClnttMDMQSA8Czs7OVKNGjfQYCmOAAAiAQLoR0IqC8BRMN+wYCARAwMIIQBS0sAuC6YAACNgXgbuPXtLAn0/StaAI3cLL+RamRtU9iUMvppax56CneyXaczSA9h8NpE3/BNG5oGh673UvalQmb2oNg37sgMDBixE0dt4Z1TswVy4Hql7enWpWKJoqq+ewuPyq7MverGWkN+G/J4Lo0tU7dCUgTL6WbCNqVIXD4RamumXypcq46MT2CLAYqIT1TCtBUKH288ejqN3IUSKnayixp1yXLl1o+fLlCQqDFy9elILglStXZDfjxo2jvn37Kl3axHbo0KHET2Gz+Hn06FEKDw8nFgibNWtGO3fulJ6DNrFQG10Ecgra6IXFskAABEAABEDATglAFLTTC49lgwAI6AjAT1qHAwcgAAIgkH4E7jx4SYN+OaUTBB0cslGL+j70RkOfVBUEtatqUM2Tur1RSRZdDbpLE+adpvVHbmmrYB8ETBJYfySYPvj5hBQEs4k4njUrF6c+HaqkmiBobGD2JOzcvCy92ao8eXnECYC7j9+mj347SfP/DjLWDGV2TODevXtSlEsvQZBRc37Bn0V+QRYf2ThHIAuDPBdT9tFHH9Hly5fl6TFjxlD//v1NVbXacl4Tr3PWrFm0f/9+nRfkt99+m+C67t+/L8IJ60MTJ9jg1cnktHv69ClFRETQw4cPzRnCbuo4ODjYzVqxUBAAARAAARAAAdsnoBUFbX+1WCEIgAAIGCcAUdA4F5SCAAiAQJoSCIl5SSP/OEtXA+NyB3IOtm5tKlKVsoXTdGzunL0GxwxoIMd5/PgZfbn4P5q7OzDNx8UA1k3g953XxHslNiRgGZErsGf7ytS4phc5OWZLl4VxXs2ur5ejxnW8deP9vO4SbYCwrWNizweKIMjeemxp7SGoZV1G5F/jfIW5hEDIlpAweOnSJXr8+LGs9/HHH9OAAQPkvi3/YIGJ8yU2bNhQLnPbtm105swZ3ZJZ0Pvss8/otddeo7Jly5K3tze1bt2aVqxYoatneHDy5Enq06cPValSRW335ptv0tdff01RUVGG1YlDnP7000+ybw8PDypZsiRVrlyZfH19iY+HDRsWr409FsBT0B6vOtYMAiAAAiAAArZLQCsKInyo7V5nrAwEQCBhAhAFE+aDsyAAAiCQ6gRuRr+kTxedo/OXQ9W+67D3nvCCKmSQh02tkEY7775ZXe351/WX6aetsSHs1ELsgMArAj+L98bsP2NznjWuU5LaN/alAvn0eQPTCxaHKe3TuRq5F8mtDvnb5ti5qQXYsUsCLMLVrVtXinEKgKlDhhCLdellPNbCyZPU4UwJg8OHDycOH8ohTgcPHqzWt4cdreB26tQpdclhYWHUsmVLmj9/PgUFxXkAc7jRUaNGEXtTGrMZM2ZQu3bt6K+//pLhSZU6hw8fpp9//pl69OhBMTExSjHdvXtXenF+8803MpSpekKz4/RK2NUU2eUuREG7vOxYNAiAAAiAAAjYLIEXL16oa4MoqKLADgiAgJ0RQE5BO7vgWC4IgEDGErge9ZK+XHWRzp6PDdeZT4gqjWp4USlNSMT0nGGBfI70RtMytHFnrPfXgq0BcvjBLfWeWOk5J4xleQSOXrlL88V7I28eB2pV35eKa8S4jJotC+g923KOzECRIzOAwu4+os2nI6hVBdeMmhLGzWACxsS3r4Qg2KxmjXSfGQuDPPaYmTPl2MrctDkG2SutadOmxDn37M0qVqyoLvnGjRvq/i+//KKKgbVq1aLevXtTZGQkff7558QehEuWLKHOnTtT1apV1TYsKk6bNk09HiK4s7fgzZs3ZU5HFhS5Drd97733ZL3ff/+drl6NfZCgRo0ask93d3fKmTOnDFcaHR1NhQunvde+OmkL3oEoaMEXB1MDARAAARAAARBIEQGIginCh8YgAAJWTACioBVfvPSaOrvWr1+/ngICAuRNk6JFi6bX0BgHBGyKwLk7L+jXLVfp+JnYG6BlShWkZrVLiNCL2TN0neVKFqCY+09o94FYL0EWBksUcKLXqxTK0HlhcMsgcD38IQ2ecVxOpkGNEhYhCGrJNKjmQbfuRNM1EYr383knqfioelSucMb+Tmnnh/30IaCIbtr8fSzKdWzUMH0mYGQUHtu9QH4a/PU3FC0ELWWOijDIQpe9WtasWaXoduvWLVUEZE8+FuvYihcvTgsXLqTs2WN/lzmkZ/v27eW5OXPm6ETB77//XpbzD25Tv3599ZgFRBZdGzVqRN27d1fL2UNTsdmzZ1OePHmUQ2wNCEAUNACCQxAAARAAARAAAasmoA0fmjkzAuhZ9cXE5EEABJJNwO4+/fip4fHjx1OnTp2In0Dm3CObNm2i58+fJxuirTc8cOAAffDBBzR9+nT64osvbH25WB8IpAmBIOEhuGTfTTp4PFD2X79mCWrfxDfDBUFlsbUqFqV61b2UQ5q4wJ8u3IwLtaaewI5dEXj05AUN+eWEXHPlcu5UpkR+i1z/Gw1K0+sNS9OLFy9p8hJ/inny0iLniUmlDQH2NOvSpQtZkiCorLSmn58MJWpOjkGljb1sFSFOuW7Xr19Xl/7222+rgiAXsldlqVKl5Plz52I925XKR44ckbvVqlXTCYJc6OjoSCwiagVBLtd6Afbs2ZMWL15Mly9fJu1NIq4HI4IoiHcBCIBASgg8e/aMOL8rh4YePXq0ya44fDTnj+V63333ncl6OAECIAACKSWg/b4HT8GU0kR7EAABayVgV6Lg3r17qVmzZrRgwQI6evQo8dPJnHtk0KBB1KJFCxlayFovZFrO+8mTJ2r3W7Zs0eVqUU9gBwRAwCSBJ+KZg1UHQ2j73ljPhLbNylLdysVM1s+oE69VLU5+peO8A4f+dpLCoh9n1HQwrgUQmLT8HIWEPaQC+Z2lV6sFTMnoFJwcs1El30JUzrcwBV6PoPHL4ryAjDZAoc0QYEGpf//+FikIKpCVHIPOQqBiUzwGFTFMqWdvWyVnoBKBgh/cU8xThF81NB8fH1nEYT+VXDBRUVEyrCif8BMCrLnWt29fUnIGcmjRsWPHUpMmTWQf/PCgNqSpuX3aWr0ffvhBLgmioK1dWawHBNKXAHuG16xZk/iBjqVLl5K/v7/RCcydO1fmeOV6LAzCQAAEQCCtCEAUTCuy6BcEQMCaCNiNKBgcHEw9evRQbxwYXqRLly5R27ZtZdgi5UaDYR17PTbkwU/xmWuPHj2iFStWEN9YuHbtmrnNUA8EbIrAon9DaPmW/+SaWBD087ZMbyueYKPqnpQ7d04516h7j2n8Yr1HhjyBH3ZBYNr6i/TXsRC51gbCizRLFsv/ytC6fikqVNCF9h+/QXP3xebttIuLZceLZA9BFtkUy+iQoco8DLcsDC76fDIVyR/7+c9znjx5smE1uzkODQ1Vv5NzqFA2bdQOY6GctE9yK99NlS23f/r0KW/MMhYdDx06RJMmTSL2MFSM8xbyw4N169aVofOVcnveQhS056uPtYNA6hDo16+f+iDGTz/9FK/Tu3fv0sxXOXjbtGmTpIc84nWGAhAAARBIhIBWFEykKk6DAAiAgM0SsPw7fKmEXslRklh3nN/l3XffhTecBpT2Jg0XP3z4UHM24d1vvvmGRo0aJUOPNmzY0OSTgQn3grMgYL0EVh++Tb+tiX0itnZVT4sWBJlyLucc9Fo1TxX48fPhtPZQsHqMHfsgsMc/jFbujg3lV0e8H0oWd7WKhWfOnEl44cYKDL+u/o/OBEZZxbwxyeQR4LDm1iAIKqtjYXD9d9Oo9CsvuJUrV9LIkSOV03a1/eOPP9T1enl5yX3FY5APtF6DSkUlvCiLiOx5wpY3b171RvPp06eVqmZtc+XKRb1796bVq1dL75T58+fLBwiVxiwYwhA+FO8BEACBlBPgcNEDBw6UHXHqlgsXLug65YcxFBs+fLiya3QbHR1NHJI0KcYCALdj73LDextJ6Qd1QQAEbIOA9qEy7UNntrE6rAIEQAAEzCNgF6JgQECAzCeiRdK0aVOaOnUqDR06VOYp0Z7jkKKdO3eWXxq15fa6r/2DyQwOHz5MzJTDsS5fvpx+/PFH+t///ie9LJctW0a3b99WUXF+Fq1BcNXSwL6tE3j89AXN3xHrIcuCYMPqHlax5PKlClBFvyLqXFf8A1FQhWEnO6sPxIbxy549K1X0KWhVq/bxzEdeHvnknOf/fcOq5o7Jmk+AwztqhSVL9RA0XJGLkxNtEMJgh0YN5Sl7FAb5hvAvv/wi188CH4ftZNOKghxlQvv9kyN6cJhPtpIlS8qt8qN8+fJy9+zZs7R161alOElbFgj54TXOnc35C9nCw8NVb8YkdWZjlR0cHGxsRVgOCIBARhDo06eP+hCH1luQQ2n/9ttvckqce9DwM55PcNSnwYMHU5UqVahcuXLk7e0t8w/y3xNTxvd0hg0bJtuwdzi3q1ChApUoUYLKli2Lh5VNgUM5CNgBAa2nIERBO7jgWCIIgIBRArGP2Ro9ZRuF//zzD7333nvxFjNx4kQqViw2p9eIESNow4YN8kujUpHzlbBgyLHts2TJohTb3Za/pBs+yTdlyhTilymrVauWFAv5fJkyZWjPnj1qVc7jyMLgunXr1DLsgICtEli0N4hu37lPuXLloOoakc0a1ttQhBG9cSuKwiPu09XrUbT5eAi1qhKXb9Aa1oA5Jo8AewkeOhsbJtpPCIJ5XKzvhnCVMoXpWmA47TsRQkfqFqHqJfMmDwZaWSwBDr2p5OSzFkFQC3PqkCHk6+lFX4nvmSwMcr4lvhlqi8Y3Ztmzjx8oO3LkiO574bhx4yh79uxy2blz55YMmAfnlPrwww+JbyKzZwfn+VOsV69eyq7c8k3fgwcPyv33339f3jhu1KgRFShQgDgvNoe9f/z4sRT9lIZ///03cYj7woULk4uLC3G4Un4/8VxPnDghq3HOQSXvoNLOHrcIH2qPVx1rBoHUJ+Ds7EzsBcgPX6xfv15+xrNAt2jRIvUBDP48NzT+TH7nnXfUOsp5fhBk0KBB8jP/448/VorlduHChcR/X0wZh4pmT3MYCIAACEAUxHsABEDAXgnYtCjITxlz6EpD43/w+caDYvxHoF27dvLGgPaGDItZGzdupPbt2ytVbXrLHn4sgt65c0e+Ll68SCziJdW0eWCYP3/Z5xBfSl/8xZ7HyP8qr05S+0d9ENAS4CdE+R+/t956ixo3bkwcnsYSLCz6Ca3aF+ttVbmsOzk5ZrOEaZk9B0eHbFRfCINrt/nLNusOBEMUNJseyRva/FCENZriJchzr+BjnUKw4i3IwuDSfdftWhTkv7+cH61GjRrW+HY0OmcWjbZt20Yu4gbjzI9HUU0/P6P1LL2wd5vWVMbLkwZN/VoNI6r9Hmrp8zd3fizuGRp/F//ss8+oRYsWulNcd/PmzfLmL9805pfW+O98Q+HRpzV+f7NQyOE/2dgDReuFwmX58uWj48eP8640Ps+RLxIyhA+NpQNRMKF3Cc6BAAgkhUD37t2lpzh7YrPHOD+oreQS7NmzJyk5ZpU+2WN8woQJqiDID23zA8j8sMi0adOIH+Tmz3OO8sT3HNhiYmJ0giC3YQ9BfgCEH1DhB0I4hyE/OAIDARCwTwJaT0Ht/Uv7pIFVgwAI2CsBmxUF2RPNmCDYrVs3GiKezuYvhYbGN8y+/fZbXTsOMdq6dWvKls26bugra+Mv3Pw0tL+/v7xJrXj9caglvhnDoTfYOMb+66+/nqxcinyjpWrVquTj4yP7097g4S/eXbp0oU6dOsmnwzmcKN9ccHNzU6aILQikiACL1wcOHJAvfr9xcnr2EOCbhhkpEG47cZsiIh+Jp1AdqXq5uFCcKVpsOjf29XKj/G7OdCcshk5duks7T9+mphWsK5RkOiOTw7FXSsuWLaWnND9Uwk83c2g6azCtl2DpkgWoSAFna5i20TlyHkQWBf89dYf+ORdO9crEhhQ1WtlGC9l76ocffpAvvlnG32c6duyo3jizxmVzDkH2EswlRKUFkyYS5+mzZmNBc9Hnk6n7+M9sWhjka8Q3e319feX3Rb75W7Bg/L8nHEJ09+7dNHbsWNq5c6d6aVlEVLwA1ULNDr8n+O8+57JmL0ND4+/DnIOKvyew8ee0KePwoT169JDfXU3VsadyiIL2dLWxVhBIWwI5c+akjz76iD799FPiB7jZU5y99tjY68/Q+HuMEjqa7+Fo8/Dyw3f8Px8bP8j9wQcfyP3AwEC55R/9+vXTtVFPYAcEQMCuCWhFQXgK2vVbAYsHAbsmkEl8GL60NQKc647/mdca/4P//fffm3UjjMOJrlq1Sm3OXkj169dXj9Nrh59y46eYOW8O38Tg0FKKiJfQHPiLNXtPrV27lvbv32+yKt8c/Pnnn+X5Y8eOyRuFJitrTnCoJb7hzTH9+aXNAaOpluJdJYG4cgMnxR2iA5slwF69q1ev1nkUsCDINwgzSiD8ZP4Z+vtEKDWpW5JqlHe3WvZ7jwXSv0cC5Pxr+OWjGf0rWe1a0nPifLODP4P589jV1VV6svLfpbT6vEyttQ37/ZQaOvTNVuWJhTVrtZu3o2nB2ljPIHt+7y5ZskTeLFO+D3B+MH6inh+gqF27tlVdXhYE+UEjDvO4YPIkq/UQNAb9kHh4q+dnE+Qp9n6wRY9BY+tOqIy/B4aEhMgH84wJiKbacrubN2/SgwcP5INoHB2EH2AzNBYKIyMjZYhRPseh5Pi7A3LoEXl4xOVAXrp0KdWpU8cQH45BAARAIFkEOKwz31tRoghxJ/3799d59ykda8OA8oPOXl5eyim55f/z2FuQH3jiyAhsHP1IGx2BU5fw/4R8P8haHtKTC8EPEACBNCPA0SCU3ORbtmyReUbTbDB0DAIgAAIWSsDmRMGHDx/KMFn8j75ifCNg+/btZnun8RPGLHopxvlMJorQFlrjkBWc8JrzEnJ8/NS0p0+f0rJly+irr75Sn5xT+ue18Hz4STnDJ1pY2OOY/BxSS3niTmlnbMvhFtkzku3SpUvUtGlTY9VkPhVtf+x10KFDB6N1U1LI+vS+fftkPkL2/FKuIa+5dOnS8iYmX5fk5Hc5evSo5BkRESGvGf/TYOwGUUrmj7YZT4CfJGVBnwVC7XuWb/I1a9ZM3gTnfwrT2oLvPqQOk/ZLL7t3O1WJ97ua1uOnZv+h4Q9ozsojsst8eXLQ5on1UrN7m+6Ln1RmYXDNmjWkPLXMn7v8ql69ukWuveWEf+hu1GNx08SBhrxT0yLnaO6kXrx4Sd/+vo946+SYlXZ80YCyZDa3te3V+/fff2WI8B07dqiL4/chRwngV5Eilu3RzEIgC4IsDH4lvAE61n9NXYet7MxYvpxmrlgpl4MbFLZyVa1zHSwKclQRzsXIERmU/zkMt7w6LuPv8IbnknOs7UfpOzlbbsNmag4JnVPayA4S6EOpZ7g17Fu7JsO6STnmfgz7Ts6x7ET8MBw7qX0lZV2m+k5KH4bzVY6V9ZgaI6FyY+eU/rT9K/tJ2SZlbcbmwWMlpQ9Tc1PWY2qMpJQrfSljGWvLOQPNMQ4DrvX64/sYxiIJcf7B2bNnyy7LlSsXr2vOK8jGIiD3qRjfR/n111+VQ3XLwiDfS+GHo/HgsYoFOyBgdwT4/i6nTmLbunWrjPBjdxCwYBAAAbsnYHOioLE8guw1Z+xLpKmrz1/A+el55ek1DrnFoYwUY0GQQ2Qq50ePHk0DBw5UTpvc8tNtjo6OuifXDCuzIPnee+9RUFCQ4Snd8dtvv01TpkwhJf41hwdt1aqVro7hAXv4FSpUSOZT5PCp7BHpqQm7xXlbxowZQyVLlpTeVc2bNyc/EdaKn9JmL0XFOP5/YmMpdc3d8rr5DzOHCEnM+KYghx3htWiNc9DwPxc8f/5HgOfOZswLkt8PfLOeQ5bAbI9AQECAFAZZHGRvAa3xP4PsIfPGG28YDV2mrZvc/Y1Hb9EXi/6jmpWLU+Oa+idak9tnRrZbvtWfrgaEySn8Obke5XfJkZHTsbqxWaBWxEH+PGLjJ5v54QrOZ2spdjPiIXWcHOtd7ik8BLsJT0Frtz/WHKfbodFyGbM+qEoVvSwj52hGcuVoCnzjbMOGDeo0+GEb/rvOL87ZZonGf/v5O0JHcSPvq759LHGKqTKnHiK0+2H//2SY++VCJCxbtmyq9ItOQCApBDhKCedqhIEACIBAUghwehL2zEvM2KOb8/zxd2TOCcshoI2ZVhQ0dl4pY29m9mrWGt8fmTdvnoygpH1YlOvwvQB+kJTDmcJAAATsjwB/Vim5qCEK2t/1x4pBAARiCWS1NRDa/CO8thkzZiQqCHICa0Vc4zb89Fu9evV0T5txuWIsECqCIJdxiM/EREH2VOSwGGwsWLGoZ2jsrce59wy/tBrW42MOB9a1a1eqWLGiPM2ehcaM87cMHjyYWODjEHYJGd+cNnaD2jAJN4c1Taqx9yMLf/zUsfbpQvbs/PHHH9Uwpub0yzfJ+DpzOBFF+ON2fIOT2bG3GPNh0TQ0NDReKFmuy08VcriAAQMG8CHMxgiw2M2iN/9Tyv/w8Yvff2wnTpyQL37fsTjIr9QOi3X8aqQcy9PdNgQIX698qih47fYDiILy6pr/gwWX7t27yxd7crNYzVv+W8Ke1yzC8Cujb8BeCXmgLsotr5O6b807Bd1yqaLgmaB7EAXFxeSQXfzq3bu3zOfD30/Yi56FQn7x31VFIOSHoizB2LufBcGyIn/QmLe7WcKU0mwOP33yCTUaMFCGSOXvjewxaCwPdppNAB2DgCDAoa45hO3169clD35gkk3ZaveVMlNb2VDT1lQ9w3LDMfj/B8M6yTlW/g9JTlvtnBJbl7auqbES68NUO6VcGSM12Rj2rYyh3SrzxhYEFAIc9pojKNWqVUspSnDLXnr8t43/d08opKc2dQr/b1+pkvE0BsYiN/H3GY6KxLlmL1++TIcOHZIiId9z4XsBnIeQo3fAQAAE7I+A9m+d8r3A/ihgxSAAAvZOwOZEwdOnT6vXlIWwtm3bqsfGdlhA6tatG7F4xh5/SmjBbNmyqdUNxbTz58+r53iHc5UkZuzFphjfEDYUBdmziZ+C1wqCHN6SPfc47CF7DnIIDPZ6VGzXrl2qKKidr3Ketyx8sGdUSowFU76prcyNhbak2J07d6RXFgupvB6tENezZ08pqhrrj69fkyZNKDo6Wt4M1Aq+HFqUb1qyy7/i1aAVL3lMFhxZEFXmbTgGi4vauRiex7H1E+CwoZxgnl/sqcsiDG/59409fhcvXixf/A8sh8/j9xJ/FqTUTl2Jkl2UKJo3pV1ZRPsi+V3UeQTcuU81StnGutRFpeMOe5nziz0GOawoexD+/vvv8lVGCB6KQFitWrV0nFXsUJdDYj3q+Ch/Xsd0Hz8tBnTNHfcEuH9Q3PrSYixr67Nq1arEr3HjxhF7DyovfrKeX5yHmT8X+W8th9nKKGMxkMVzvmk49eOPyUXkRLRlcxHft34e/YnML8jeWhwBYdasWba8ZKzNQgnwTX4YCBgS4Bup/ECtckNV2dduuY22nnKOy7TlyrG2TKnLfSj72q22XNtO6Uupq5zT1lfO8VYxpZ52q/SlLVPaaPvQ1jOsy8cJjSw+PFYAAEAASURBVK30o7QzVlfbv1Jf2Zo6l9R+jNXnMbTlyhyNjamc45ys5gqCsnMzf5QqVUqtyZ6A/FB0UsN+8g1/7odf/ODoO++8I/tUQvurA2AHBEDALgloHUTsEgAWDQIgYLcEbEoUZPFI68GnTTBt6gqPHz9eikbsRcShK/bv30/8pVYbxpKFBa1x8mqtcRjCxIxvtinGYoTW+Mv0qFGj1Bx6fI6/tLInHIf8ZOM58I07rSjIOT4Ue//994nzBBmGHW3fvr30gmIxjkN0JNccxE04RVx79OhRkrrh9f6fvfuAj6pKHz7+EFqoIaETNKAICAgosgKigIKLDYW/oNgW99V1bbuw6+q6uta1Y0VXXSv2wipgWcSGiKAoAtLBAIHQSyCUkAT0nefgubkMk8yEzGTunfmdz2eYO7ec8r1hcnOfe86x50WDMO5AnN54DJXuuece54Jdt+vT8uquQ5fasb91vc4JoL0cdM5B9/AfWqYeo704bXIHNnWdTkqenZ0t7icQ7b68J56ABvxt0D84QKj/3/V11113OUEZDc40bdq03BA6BOOaDbvMcUXF+6RG9arlzsNrBzRsUCvQm7qKmZtt5caS3mReq6ef6mMDMjpctAYGP//8c9PLWX8XPfnkk+ZJaBsg1B7WlZFWuHoKNslIjJ6CdWuXDBG9dktBZTD6rgwNtmnQT186nJder+h3pD549MEHH5hX27Ztzby+GtDWG2qVmbTXt6bfX3KJtKtfrzKLjltZJwR6N1waOB8vBx4E017F+vspFjdb49ZACkYAAd8KaIClalX/X9v69gQkWcX1evmkk06Sr776yvTu04eo+/fvb34n6lzIOtKBPkCjD1HblJeXZ+6LaI9nfchap28pKioyPZ/vu+8+u5tzn8VZwQICCCSNgN6DtYmeglaCdwQQSDaBhAoK6kWhO+Xn57s/hlzWoSTcSYcB1RuwGjCySW+CuZMGktxJL1TLShqY0p5tNumwXe6kQS138Eq36VxTGtiy8+Z9/fXXogFMd3LPM6PBCx0iUXs76s1ld9JApwYudZhEDT5W9Iae+xeou5zSlt2/ZDVoqTcd7RN+Ov+f9tZ0Jw2GBhvpdm2jzjuo2zQYaJP2htSb6O4ejO6gru6nfxCoj5avwV+bfvjhB4KCFiOJ3kMFCPX/mA4no/8f9aV/QNqgjL7rwwKRpJnL8pzdiooSIyioAcG0+rUkb9tuyQkMH0qKnkBWVpaMHDnSvLT3oPZm1e/wOXPmmJf21tIHXOzPoj4AEau0Ia/kgY/GGYnRU7Be7ZoOV0Hg/2M0kp6zcEnnq9UHVdyvmjVrms/6bl/6wI1d1nc9zr50BAC7Tpftel2nx+mDLvo9ZV+6rqJJfzfbnzWd30cfNtLvQ+2prw/r6KtPnz5mHx2JIXgkhYqWH3z8888/b272ZWZmyrWBIb5+3rJ/btPg/RLx83XnD5N3A98HO3fvFnUgKJiIZ5k2IYAAAgiUJaD3EXTYUL03ovdTvv/+e/MKPkYfNLZDiC5ZssTc8wjex/1ZH8DWPEkIIJCcAu57mu77lcmpQasRQCBZBRIqKGh71dmT6R5K1K4Lfrdj2dv1GqByB6n0plvwsFn79pXcWNShBvVGXWlJA2D/+Mc/DtjsHtJUn1rTCbSDk459r6+ykg7r5U4aNNNedNqbUAOI7kCk7mef+Ncn7DT42bp1a/fhZS67ewe621/mQb9utBfodl8N3umTfZr03e2tbQ4VELTH6rvesNT5uV599VWzWm+i6zAnwUFhe4wGBHWeJJ0bSdusPye256I7kGj3j+W7Bp30/LiHftHy9KLEvU4/2wuVUOvt/vZYu797X/c293qbr83DHhvu3ebhzjc4D/c2u7/N124LXm+3h3q3+9pj7T52vX52b3Ovd28LXm+3Ba/XG++6Tl+7Azdi7f8ZLUMDzcHfMabwoH825xc6a7SnYKKktPqpJihYtWqVuDQpkkBMpBXTwIcNsOi7Db7YwIv7sy6H+6zl2p8lfdfvSPdnXQ712a637/pzqcvaVu29pb2j9TtKHxrRlz7drL3GNRCjv7s0uD1q1KhImx12vzq1Si4JUgI3YRIh1atT8vu5YM/eqDRJ59nS3yllJf3drq/gkQHKOsYv27788kvR12233eZUWedqjUWAUAOSms4LPNiUTAFBbbMOIzrirDPlibffMfML6joSAggggAACySag18XaU3DMmDFmLuTgexzqsX79etGHjTWVdk9At+m9nSFDhph7IbpMQgCB5BSw94O09QQFk/NngFYjgIBIyR3ABNDQm7sapLNDaE4JDH+lN1LLGkZUewG+9NJLpbZeg0/BF4x6g9gmO6Sm/Rz8PnbsWNP7yL3ePRypDgtl6+veJ9yyBhr1CbdQSYOYOhefzmOoT5frRNrupHPp6Uufjrv66qtFhwUrT3IHCN3HaQ+/N998U3TIUh260yYNyrmTzslog4Lu9bocyVCsul/wOdAb6Zs3h+5B8Oyzzzq9AfUXvg4v8vLLL2s2onMPVlZSb+2ZSPKnwHfffRd2jlJtWapruNDCBAoKpgd6Cq4MtK9B7ZLvP21vZaVIAjGR1kUf1tCXBn79lrZt2yb60qTzH0QzKFi7RsklQfHenwPDg6X4jeeg+rqDgnui1FNQh7PUHoD6kIeXkgaj9ZpE59PVd7tc3gd5ytsm/Tm0vf/Le2y4/XXoL02rV64078n2T706dU2T6SWYbGee9iKAAAKJLxA8sk9ZLdb7MToikr70+kaDgHotrw8f64PR7usQnQtZr9F0H70m0v30eB31RR+6Y/6wsqTZhkByCOj9Q5sICloJ3hFAINkESu4AJkjL9SLw6aefdlrzpz/9yfSu0B4VodINN9xghmorLTDnHqbSHq/DWOlT8Zr0STUdnrNXr152s/OuQQQdfis4uX/pvP/++85mvVjVOXw0sPboo486690Luo8OoTksMIxWWUmHEdOn4PSlQUHtQajDZ7qTzmOlLw0MqoO7Xu79dFl72NngYqin77Q3hM55qEn3uyQw948dykwv0rXeegGvSQO12tsvOOk+OgxauKQ9Z9zzCnbq1Mn8IWB7/7mP1+CpzkXgTkcffbTzUefvqqzUMTBHkAau9akk2yPILtsnlexnvUixy/Zd66nLpW0LtV7311TaNpt3qHc9RlNp22ze7u3mgAT9p7CwpAdgWU2sVbNknpXiBAoK2jan1Y1PUHD06NGi3736h7y+9Psq1LvdHmqbPaasfdzH2f3scfZd15eV9P9EcXGx6Slm323PMX3XdfrzpO8asNGbFe734PX6nafzpejNDd2mSeug393RTLVTS9pVXPyzpJaMvBnNYio1r6qBoW9t2lMYnZ672mNY5x/1S9KfL/vSnx9dtu/uZbtO3zVgrr+z7bs+zKPDreu1kvsBHP051ACp9lyNRdKHAfTa5d1Jk+Tac8+RzMaNY1GMJ/PMD/g/8fbbpm76/5+EAAIIIIAAAvt7+x155JFlUtiHxcvciY0IIJC0AvZeVtIC0HAEEEAgIJBwQcHf//73BwQFNVCkc8j17dvXDDl57LHHSqNGjczNLt1m55kL9dNw3nnnhRwu8IQTTjDDCtpj/vKXv8iECRPMU2q6ToMpGmwLngPQ7q831+xQeDouvk0a6NObjdrzQ9uhw2Jq0EpvCLdq1Urat29v5vKxwTZ7XLh3DZo99NBDcs0118hTTz1lht1wH/Pvf//b3OS79957D3jKzr2P9uyzQcFQvet07H530puK7npqD04bgNMgqk3uG4l6A1IDqd27d7ebD3rXIWH//Oc/H7Bee3PqU4DBQ4looNbdY9Ee5B42VZ9Q1Lq6ex7a/aL9HmqY2GiXQX6RC2hgX4fA0x7FNshvj9ahZi+//HK56KKL7KqI32vVKAkKrt+yUw5vkRbxsV7eMS+/wFQvLU49BbXwaPaK87K11k0fvtChhvU1Y8YMU13tda3DRg8cOFDCzWV7KO2rm1pySVC8NzoBtEOpRzSPcQcCf/55/0MS0czfD3npgznuJ+gjrbP+zv7vf/9r5hNcsWKFc5heR/Xs2VP0WkjfdY7DWCXtIafXYnqt9vcnnpBX7rgjVkV5Lt9Lbr1Ndvz6MJUGR/2UdOhj9/Cytu76YEdp18Z2H95DC9gev1WrllxjhN6TtQgggAACCCCAAAJlCbiDgvrgLwkBBBBIRoGSO4AJ0nodPkKDX08++eQBLdIb//oqTyptaDAN3mlPPhuE0uBiv3795MQTT5QmTZqYnoPLly8vtaj//Oc/ctppp5ngoc1Dd7Y9s3Q5LS3NDMOpQ3GWJ2neU6dOlbPOOsvcSHPfCNRAx4MPPmh8ngjcXHPPifR24Gl0veGsQ3KESu651DSopzcn7I0J/YWqwUabNACoQ3O4k54XGxTcsWOHs0nnN9ShTG3SAK72CNIbjTrEhyYNFupN8f/9738H9XbUdg4fPlxWhhhaTINwoXr0dOjQwRZn3nVi8q5dux6wjg+JKVBWIFBbrDe3NZCsAUH781deidTqJb2tcjfky2+OySxvFp7cf3v+HlOvtNoJ92vDM976vaqBan0gRIOB9veDPtSigUB9HerPZSSNPLxxSU/tosDwoYmQ3PN6NqhfIxGaFNM2aA9WnUtVe+PPmzfPlKXXBjoXsg0Chns6P9oV1ODSjK+/lpnzF8jdL7woN//+smgX4bn8tJ2Lf72u0etFvw0fqj1M9ZotOOmw98kaFNS/QfLy8sy1dri5s62bPnCn/x/1AUL7QJ1eY59zzjnmWkX/ViAhgAACCCCAAAIIlE/AHRQMdc+wfLmxNwIIIOBPgYS8u6s99zSgN3ny5Aqdlblz55obszpHnztpD7iHH37Y9EC06zVwVVp5V111lRlyS2/yatI/7ufMmSP6tL17WE19Ev6mm24yQ3HZfMv7/sILL4gGKXUybh2G9O677xYdttKdtNehBt50aFTtdWeDnxrY07qGusnQOGjIrtdee80MYZqdnS3PPfecGYLVlhFqyFU7L5Duk5GRYXc1Q3t26dJF1FqTOmodNOmNSJ2/UNeFSvrk/D333GM2uX+p6woN6pR241Lbp0OI2iCl3oAnKBhKODHWhQsEaivbtWtnAvXaCyv4/0t5FWrXLPlaXbN+e3kP9+T+2sNq+689BRvUic/woZ6EiUKl9PtNA4H6MIe+1qxZY3LV7y994EEDgcccc0wUSgqfxW+O2v8ghu65N0GCgnuK9joNz6gfux5tTiE+XNCHaqZNm2ZeNpCjPbouvfRS6d27t3nptUq8ko4o8Fzg2mZYoMfgy4HrqKMDw5kP6dc3XtWJebnvfjHFtFML0msVHenBb6lZs2ZmiF17baZzd5f1sJzf2nco9dW/G/RaV+c5jyQoOHHiRLnuuusOKkqH8B0zZox5eOTdd9+NaU/dgwpnBQIIIIAAAgggkGAC9BRMsBNKcxBAIGKBkrvXER/i/R21d5z2mBs/frzcfPPNpQaV3C3RXnTXXnutGarzvvvuczb97W9/Mzdk9clcd9KeG5MCc9xoAEyDcKGSBrU0KKeBBh160wYFdd+FCxeaoOBxxx1nAni6Tm8O33rrrfLAAw+UOb+f7lta0vmqbNJgiM6xqMNoao86HTbV9uDTHiizZs1yAoL2GB3yKVRQMDjApk96h3raW9usN7KDk7t3nt4scift1fnb3/72oPNke8m499VlvUl25ZVXmp6U9he4Dgmqcxpq70C9eRlurq3/9//+n1x//fUmax2SlJRYApEEArVX74ABA8xLe/pGK7VqUtLbaufOQtm0dbc0zihZF61yKjOfLdsKAj2Z9w+9mFab3lbRsNffB5988okJBNrvOu2pqj1A7BCh9vstGuVFkkdmRi1p0rCWbNxSIDsLdA7NA3t8R5KH1/YpLCoZBjWjHj+79vxo8FkfiPn888/Nux2poE+fPuZ6QR+Gimcg0NbTvus1xFuBB53OOPtsuSkw0kFmk8ZyQtADT3ZfP78vCgRotX2a9FpHR3FwD7Pul7bpPJMaVLZJf86SPShoLSJ5//bbbw8ICOr/Rx1BQ3tgvvrqq+ZBQ/274uOPPza9eCPJk30QQAABBBBAAAEE9gvYB9f0U2X/zc05QAABBLwikJBBQcXVL/bBgwdL//795fXXXzc3vnQoLA286Y0uDV5pIKlt27bmKXh9atd2G9+8ebPp/ab56P46pKXOGRh8Y0Zv2Ogf5M8//7y8//775oaH5n3qqaeaQIMGuuxcOzqvnz7R++yzz8rMmTPNMFya/4033ugEBfWz3gDSnnQajHQP/anbgtPOnTtF5/nRG8ra+09fGiyzvefs/jrkkB12yK4r7d3di8+9jw5fpUOIlhYA1X217fo0uN4MCk4alNSn3bWuGqR0p8MOO8wMD6rDiKqne55Fu5/mrZ46f2C3bt3s6gPeddhHHZqqTZs25vwesDHog85RpOdMe+josGgk/wvo0Is6l6c+DKA9ZUMl/T+lgUD9edZ3GyQPte+hrmueniotm9aR3A37e7jmbtju+6Dg6l97PNauVU3cvckO1ShZj9OHQXTYZv2e1+9vTXXr1jW9AbVHlj5sot+H8UxdjsyQT7askRVrtkn71o3iWZWolL14xSYnn4ZJPnyoXttocOazzz4zgUAdKlSTjgSgv6P1dfzxxzteXlvo2LmzGeVAH+i55v4H5JU775CjA9c9iZLWbNokOo+gpngFBDXopNfC7jmhS/PVoeD1ei/ctWppx0e6XueM1mv6SOoUaZ7R3E+vPbSO+l0eraRD9erDbWPHjjVD+epnm3RkA/27RFPwfN52H94RQAABBBBAAAEEShewD0TqHgQFS3diCwIIJLZAlcATEvu7fyR2O8vVur1795rg2qeffuocd9JJJ4kOzVmjRvR7GmiPu5dfftkpSxe056L2cuzevbvTc0/rpcN8fR2YW0dv6mlAyyYNhtn66vBfGoCzw4LafcK933XXXQc82R28vwbRtDdlcNKAnQ7lqcMcaW/E0lJxcbEsXrzY3Owq6yaSDhm6du1a0f31x1NvtGjvwrKOKa3Mstarp768eqOprLqz7UAB7W2qwcClS5ceuCHwqXPgRrIOD6tDMOq8TMG9fg86IAor7vnvEpnwVa7JqVP75nJ237ZRyDV+Wbz43g+yfsMO6XNsU3ngd53iVxGflaw9Y3RYaR0WVL+3bdKHRPThCJ2HVn8+S3sYw+5fme/vf79O/vXqQslIry1Xnt+9MouOSVljXv0mEIDdH/z63cBWcvXAI2NSjpcz1YcltFegXjfYYLRey+h1jQaiNRjopZ/BcJYaWNfAYGYgmDn+odFSP3AN4veUH3gATQOCOo+g/p7SB8iCH0SLRhv1+kp7Iuu1j006R7YGAvVhNHvdqEFJHbVCv5/cSa/NdAQMnefZ9nDW7zMNYAUPte8+ToNYOqee+1rVvT14WYNsWi/9/rQ9DPW6WOfZ1rKqV6/uHKL11OtTTToMv85h7U7aVv0Z14f89KExnS9Tk15f6sN0NunDgjqcvX43X3jhhSF7ymovvWeeeUZ0SHy9zr733nvN/yvNW48fOXKkeXjNPmR4++23m+1ahg77aVOo6xAdfUSvp91p+/btzt8Adr3+PrnkkkvMRx0hQx+IIyGAAAIIIIAAAghELjBq1CjTaUOP0A4UOnUCCQEEEEg2gYTtKViRE6nBp8cff9zMmafD82jSnkc6192///1vp/dfRcpwH/v3v/9ddNhO7SVnk94E0SEuNemNBr1R4L6hYPez7/aJf/2sN3y0J5TeYNAbSzqUYmlJ89V9//jHP0r79u1L282sPzswbJfeXNE5BLWHot7Q1nXa4ypU78DgzPQmTiRzY2mQTm/+xDrpeY52oDHWdSb/gwUeeeQRc/PQbtGeLvpUvQYC9QZf8A1Cu18s37u3aeAEBXNyt0pR8T6pUb1qLIuMWd4/Lt1gAoJawICujWNWTiJlrD+T+tCG+7tXgy/6nak3nPUmuldTtyMbmKptzdstmwOvRoHgoF/TijV5TkBQ23Biu4Z+bcoh1/uCCy4wARzNQAMROpKBBgO1Z6r2/vdj0oeQNGlgUANp2mPQz4FBd0BQRzGI5RyCeg1ng2z23C9YsMD8DrVBPl2vcy6PGDHC/OzYES/0++yiiy46aKh3vU7WQN0111wjN9xwg832kN+3bdsm6mADlDYjrbfOy6dBMR1Zwl6/6e95fTBIkw7JrCNKuNMPP/zgzCF9/vnnO5vy8vKcZV3Q9mtPWn3pHNv6oFFw8E572+q1uO6r8/rpvH826To7rL4dOlWH6Q117R5qXahnNIOH81cbnRPcJi/37LV15B0BBBBAAAEEEPCaQKjrLq/VkfoggAACsRYgKFiKsAbLtGegzu9kh8zUnngaPNP5Ct1PKZeSRcSrtaynn37aBCL1ZnJw0hsN+iotadBQn6h2p6pVq5qntvXJbR3aSJ/uzsnJMUE9HTJRbzToU+jas88+0ew+vrRlHZJVXyQEvCKgN4h1yAcNBB577LGe6PnZo21DaVCvumzbUSw7Ar2U5i3bKN06+PMG/Nwl682p1qFD+3Zq4pXT7ul6aC8SvYGu37/60IQGAuMRnD4UpBbpteTkQI/QqbM3yMq123wdFFyWs9UhyGhQU7q03h/wdFYmwYJ+Lw4ZMsR8P2ZlZSVMi/V7/5tvvjE9w/wcGKzMgKCefH3o6o033jA/B5dffrkJ8OnQ9nqNqT0ANQimQ+Jrrz5dpz0C9YEGHWLptttucwKCOjKE9mjUnmwapNKAnQbmNJhX0Ye69HrYBgT1O3TYsGGm/Ndee82MiKFDzGvvVxsc1vrpdbT21tMeg8FBQe1taJP2GLRJLfR6Xh+q0x6UGtzUBwC1Ldr2P/3pT6acUENKaVk6NYE66ANI06ZNc3og6rzkNij4l7/8xcw9rmXqsv49ob0l77zzTlsN5z3cqBUaENTrbxvU1f/Xes1DQgABBBBAAAEEECifgDsoWJ77oeUrhb0RQAABbwsQFCzj/OhNXPtHv+0xqE8Qr169usI3PYKL1V9EOuyQzpunwUidc6qspD1N9In/nj17mhsSZfXU0wChzlMV77mqymoP2xA4VAENwOj/HS+leoEA2pk9Wshrn+SYai3M9mdQcPHyzZIbmFtOU/f2DaV61SpmmX/KFtCb1faGddl7enPr4BOam6BgztrtcnzHFt6sZAS1ys4peZjm+KMyIjgi8XbRoXESNWmPuvz8fDPEpAYG//33G82Qon5pb2UHBNVFe9fpQwqaNAilwS19gEEDe/Y7S68nNSioyT4UpwHYuXPnmnU6jLz20rRJhxrt16+f+ahBuT//+c92U7nftT7aS0+T9gDU0Tls0uCf9ozTgJ1eI9v6ak9GDZC98sorJlC8KTA3o86TqUlv+HzwwQdmWfNzDxdau3Ztc81tNgb+0eCjJh0pRH+21EXb36JF6O9AHeVDRxDRpPOXr1+/XnT4fm2D1lEf2FMbm5o0aWLyS09Pd86B3RbuXXt4alk2IKiBzzvuuCPcYWxHAAEEEEAAAQQQCCHgDgqGegAsxCGsQgABBBJOgKBgmFOqTzzr8EATJkwQncdG/5jXP+xjlfQGwoMPPig33nijmX9Pe/fpTQm9kaM3JvQp7rZt2/pq/p9YWZEvAl4WOLdHprz71Rop2LPXBNZW5OZJ65bpXq7yQXWb82svQd1wcsfkG3rxIJAkWdErEACuX6+GLA0Es3OPaSktm9XzXcu/nr1atm0vcOrdo72//u85FWehTAEN3mgwR4e7POev15uhRI9u1arMY7yw8YCAYCCgFcshQyNpr7sHXYcOHZygX8eOHc3h2dnZTjbaG9Cd9DpZXxqw0nmvK5L0oTub7Lx59rM+PKf1HDt2rLjro9t1rkENCmrSUT2GDx9ulvWBPhvYDK632SHwj94U0qFENZioc21q7257PrRNpQUFBw4caLMw7xoY1aCgJh1mVIOC0Ur694fOd6NJg67a65CEAAIIIIAAAgggUHEBgoIVNyQHBBDwpwBBwQjOm/a006eQ9VVZSYf11Dl/9EVCAAH/CRzesJacfkIzeffLXFP5BdmbfBUUnDE3V1b82tOqa7sMOet4fw5/6r+fHG/U+NxeLeTlj1fK9wvXBIKCZc83640al9Ric16BzJxbElzo3CZdzuzGz2+JUOIs6TDo2mvMBgbtUKJeDgy6A4L/N2iQPBRi2PjKPEM6BKV7tAkdilOHxXQnfUDNJu0pGJxsD7bc3P2/74K3R/rZfbz2EnzppZcOOFTns9akPfGKi4udofyPO+44M0emBgA//PBDJyioAUKbgoN4OhynDh+qZWjvvlBp586doVabdcEPCNq5F0s9oAIbNOitSc9NRXpiVqAKHIoAAggggAACCCSMAD0FE+ZU0hAEEKiAQEoFjuVQBBBAAIEyBAb/JjMwZ2cVs8finzbK9h2FZeztnU1rNuyQad+tdCp02amJMxeZ0ygWyhQY2mv/z+6ipRtEe7n6Kc2YuyowR1ixU+WL+x3uLLOQeAIaGHz44YdF50veEQjuaGBwUQV7rMVKyR0QHHz66fLwmDGxKirifIODW+EO1N53wS97jI5qEa2kgcbgctzBO/f8L7p8wQUXmKJ1XkANGmrSkT409enT56BRPm644QYzD6I7Tw26RZrc5Qcf477RFLztUD7rnIeadPhUfVCRhAACCCCAAAIIIHDoAjpftk30FLQSvCOAQLIJRO+v92STo70IIIBAGIG2mXVl8Mkt5b9TVgd6NeyTGYHeSwN7twlzVPw3f/n9Ctm7d5+pyP/1PUx6tE3O+djifybiV4MmaalyVqC34MRpa+T7Bet808t1yYrNMn/xegfutO7NpU/HRs5nFhJTQIe8tD0Gd+zYYQKDr9x5h3ipx+CawPCUV993vywOBCyHnDZAHnn6aU+cjEgCYUceeaRT17vvvtvMZe2scC3UrVvX9alk0QbQCgvLfjDGPeffoEAvyiuvvLIkE9eS3rwJDo7p/o/82uvys88+k27dujlz8A0ePNh19P4hRj/++GOz7oorrhCdoy8rK0s0X+2VF9yr8ICDD/FD9erVzZE692B50wMPPCD6IiGAAAIIIIAAAghUXMD9ABdBwYp7kgMCCPhTgJ6C/jxv1BoBBHwicNVvj5CsFvtvlM6ev0Z0GFEvp6mzVknO6v09w5o0TJXfn9rKy9WlbjEUGHFKljSoX0N+WrFJFnr859YyaODdptSaVeXSQBtIySFgA4Ne7DGoPRd1zkMNCA7uf6o8/OS/fXVSjjrqKKe+b7zxhrRv3146dep00Msd1HMOCCzokPiaVq1aZYb9NB9C/KPzZtukvfwyMjIOKkPLtXMd2n31Xec17NKli1n1wQcfmLkF7fb+/fvbRfM+e/Zs57MOx6n1tjeEtHdiLFJmZqbJVg3scKCRlqNPs8+bN0/CBVUjzY/9EEAAAQQQQACBZBYgKJjMZ5+2I4CAFSAoaCV4RwABBGIgUK9WNblyYGsn568Cw3Lu3FXkfPbSQs7a7TL9+5VOlUb0byWN6tVwPrOQXAKZGbXkol+Dav/7cqmsXLPd0wDjJi+UdevznToOC/RyPap55MMBOgey4FsBLwYGNSCoQ5rq0KaD+/WVhx97XKrUiM/3qs6jp8Ny6ssOSZmfn++sy8sLPVSw9ro76aSTzM+FBs3OP/98eeqpp0SDaxs2bDBBrk8++aTUn5vDDjvM2abDds6aNUuWLFkiEyZMkGnTpjnbagRcdLtNWs59990nU6dONeVovadMmSLajlDpvPPOM6u//PJLefXVV83yWWedZYaWde/ftGlT5+Obb74p2rtUhxGdNGmSXH/99c62uXPnmmBcQUGBs+5QF9yB1dtvv12mT58uP/30kyxbtswsb926tdSshw8fLtqOnj17ljr/YakHswEBBBBAAAEEEEDgAAGCggdw8AEBBJJUoErgy/CXJG07zUYAAQQqTeCB8UvNMKJaYKf2zeXsvm0rrexIClq/eZeM/3Sh5G3bbXbv3bWxPDSicySHsk+CC1z2+CxZuHyb1K+fKueffow0Sq/tuRZrQHDZ8pJeuFeefSS9XD13liqvQgsXLpRhw4aZYE+9wDxx8RpKNDgg+FBgeMuq6fEbjlkDeRpkKy1pQOy6664LuTknJ0d0GE47X1+onRYsWCChhhDV3nE2qBh83JAhQ5xhP3VbcXGxXHjhhTJz5szgXZ3PY8eOlb59+zqf7YLW7bjjjrMfzftzzz0nAwYMOGDdihUrQh6vO2nwrlmzZqJzE9r04osvyimnnCKPPvqoU9fFixdLrVq17C4yfvx40V6HmnRoUu1N6U4ayOzVq1epQb3//Oc/8tvf/tZ9iFkObtNrr70mvXv3Pmg/ViCAAAIIIIAAAghEJqBD1OvDYJrmzJkj6enpkR3IXggggEACCdBTMIFOJk1BAAHvCugwoq0y65kKzl+8TuYuKf+8QrFqXW6gd9W4j+c7AcHfdGwo911yTKyKI1+fCVx6yv4h/fLz98jELxZLQeFeT7UgOCD45/9rS0DQU2eo8ivjhR6DwQHB0fffH9eAYCRnoVq1aqXupnPuaaDsqquukoYNG4bcr7T58nRYUA1Ihpq/MHhuQJ17T3vvPfjgg+IeTtRd4MaNG90fnWWtl3uoUC3v5JNPdrbbhdatW4sG13TIUXfSQONjjz0maWlp7tUhl+08iXajux12/kC7Td8bNGgg//3vf0sNjpZmp22ywUL1OP74493ZsowAAggggAACCCBQAQE7hHwFsuBQBBBAwJcC9BT05Wmj0ggg4EeBn9btlBtfmi+5G3aZ6p/Rr510adcsrk1ZkZsnEz9bLLsL9g9p2uWodHno/3WReqlV41ovCveWwITv1so9ry0ylWp1eEZgXrSjJbVG6QGEyqp9cEDwxgvay5Ae++fuqqw6UI53BXRoTO0xqHO4VWaPwYMCgg88INWalAxZ6V2xyGumw21qIGvv3r2md6AOyVlWUFFz1rnx9Jjt27ebYxo3biypqallFlpUVCTr1q2T3bt3m555Wo67h16ZB4fZqIOl6LCdmzdvNvMK1qxZ0xyhPzdaVx3OVF/h2hWmmIM2a1vWrl1r7DR/fTo93BPqKwPD0OowrO7g40EZswIBBBBAAAEEEEAgrMAVV1whkydPNvv9+OOPET0QFjZTdkAAAQR8JkBQ0GcnjOoigIC/BYIDg/17HyXdO7WIS6OW5WwxAcGiov09v9pm1ZdHLu8cmEdw/43RuFSKQj0r8Pb0NfLQ24tN/Ro1rCMnHd9K2rduFJf6btq6W76YuUKyV2425R/WrI5cflorGXhcfIPsccGg0DIFKjswGBwQDDVkqB0SUoeb/Mtf/lJm/dmIAAIIIIAAAggggAAC0RO4/PLLxc6HPW/evMA0GfWjlzk5IYAAAj4RYPhQn5woqokAAokh0KZ5Xbl/RCdp2bSOadCn05bJ17NXV3rjFmZvkvcC87DZgGBWi3py3+8C88UREKz0c+GXAof1ypSrz2ljqrt5yy557+MF8vHX2bLn16ByZbXju/lr5eXxs01AMDW1mlx2RmsZO6o7AcHKOgE+K0f/yH/77bfl6KOPlh2B3m2X3HqbaOAuFimSgKCWu2fPHjO3nw5VeX9gWFESAggggAACCCCAAAIIVI6AjhZhE8OHWgneEUAg2QSq3h5IydZo2osAAgjEUyCjXg3pHZi3L69gr2Sv2Sk5a/JkZ0GxNEyrLbVSq8e0ahrA+eybFTJlRrbYi+HeXRvLTUPaSVaT2jEtm8z9L9C1dQPZVyUwIftP20xj1m3Ml+zVeVKlSoo0b1w3Zg1cv3mXaDBwciCIvmDpBtm372fp062F3H1JBxnQuYnUqMYzTjHDT4CMdVjIQYMGyezZs2X5ihXy4ddfy0nHHiuNA/O8RSsdEBA89RR56OGHS51DUOe9W7hwoQlWXnfddaJDcYaa+y5adSMfBBBAAAEEEEAAAQQQ2C8wYcIEWRH4m0DTNddcY4aL37+FfxFAAIHkESAomDznmpYigICHBOrXqi6nHNNYGqWnypK1O2X5qjxZlL1Bivf9Egiu1AvMGxT9IMfiFZvl/S8WB3pYbTESWvY1gZ5fo84+SjRQSUIgEoHj26RLx1ZpsmFHkazfUhCY66tIfgoMRbtyzbbA3Fsp0iRjfy/YSPIqa5/t+YWyJDA86JTvVsoXgSB27rrtUrCnWNod0VD++n/t5coBh0t6ndgG0cuqH9v8JaCBwaFDh0pubq7MmTs3qoFBd0BwyMCB8siTT0pK3XqlAmkQ8MMPP5Rp06bJO++8IyNHjpS8vDzp169fqcewAQEEEEAAAQQQQAABBCouMH78eCcoeO211xIUrDgpOSCAgA8FmFPQhyeNKiOAQGIJrNlaIE9PWiGTZ64zDWvSqLZ0ad9CslqkS+OMivfe27KtQH5YuFa+/zHXgTujZwu5IjAHW4v0Ws46FhAor8DrX62WVz/LkS3bCp1Dj8jKkA5tmkqnIxtLlZRAt8II0/b8PbJi7TZZvT5f1m/KFx2i1J0apKXK+ae0kt/3yXSvZhmBcgv89a9/lXHjxklm48Yy/qHRUr/OoQey838dknRxYEjS/zvnHHn48ccjqs+YMWNk9OjRcvjhh8sLL7wg2nvwwgsvlHvvvTei49kJAQQQQAABBBBAAAEEyi8wYsQI+eKLL8yBixYtktq1K37Ppfy14AgEEEAgvgIEBePrT+kIIICAI/Det2vlf9+vl7nL8px1LZrVk1YtG0qHIxqXK0C4a3ex/Lhsg6zM3SorA8M72tT28PpyySmHy2ldm9pVvCNQIYH12/bIC4HA4NfzN8nmvJLgYL26NeTIrMZydOBnNzjl5e+WvEAQUF/bAssaECws3Bu8m/lcp04N6dWpsVz12yzJzCCIHRKJleUWeP755+XOO+8MzO/aVJ644W9ydKtW5c7DHRA877zz5KGHHipXHk899ZSZWzAzM1PGjh1rAoPam1GDhSQEEEAAAQQQQAABBBCIvsCll14qX375pcl48eLFUqsWf2NGX5kcEUDA6wIEBb1+hqgfAggkncC8nO3y+bxNMv7rNbI7MO+gTdpT6rDmDSQ9MPdgncDwo/UCwZI9gUBK/s5Cyd8VeAXed+3eIzsC7zt3FdvDAsOR1pJBPVrISR0ay1HND71HjJMhCwiUIjB/1XaZtypfPpm9URZk7593sJRdw65u1zpDTu/eVAZ1ayp1alYNuz87IFBeAR268/rrrw98l9aRV+68o1yBwYoGBG1dn332WfnXv/4lzZo1k7feekv69Okj5wR6HD4eYY9Dmw/vCCCAAAIIIIAAAgggEF7AHRRcsmSJpKamhj+IPRBAAIEEEyAomGAnlOYggEBiCUyavV7GBYKDG7bukc2BIRp//vmXiBp4WLM60rdLYznhqAzpHpgDjoRAPAQ+mbtBJny7Tr5buH8ey7LqkFqzmmQ0SJXu7TJk+ImZ0ropw7iU5cW26AjYwGD9evXk5UDPwaMPPyxsxms2bZKr77tfdMjQQ+khGFyA7bXYqFEjmThxovTq1UvOOOMM0Z6EJAQQQAABBBBAAAEEEIiewCWXXCJTp041GS5dulR07nESAgggkGwCBAWT7YzTXgQQ8LXAhsBQjWsDAcJ1v74XFe87sD1VqshpXZpKG3oEHujCp7gLbNheKOsD82euD7xvCAS4dbbBFhmpZkjQzIa1pF6tanGvIxVITgEnMFi/vrzy4IPSvlHDUiEWBQKBl9x6m+wIzCUYjYCgLUiHD7311lslPT1dJk+eLN27d5cBAwbIc889Z3fhHQEEEEAAAQQQQAABBCoocPHFF8tXX31lclm2bJnUqFGjgjlyOAIIIOA/AYKC/jtn1BgBBBBAAAEEEEAgigI2MKhZPvDPW+Scrl0Pyv2Tb2fKTU8+GfWAoC3o1VdflZtvvlnqBXot6tPLxx57rPTt29fMN2j34R0BBBBAAAEEEEAAAQQOXeCiiy6SadOmmQyys7OlWjUeTj10TY5EAAG/CqT4teLUGwEEEEAAAQQQQACBaAgMHTpURo8ebbK64a5/yf0TJkpKxv4egzp/4D0vviTXPvBAzAKCWrA+tazzC+7YsUNOPPFEWbBggUyZMkWGDx8ejSaSBwIIIIAAAggggAACSS/wyy8lU7JUCYy0REIAAQSSUYCegsl41mkzAggggAACCCCAwEECCxculMsvv1zWrFkjLVu2lNP69ZNx48dLfiBQp2nkyJEyatSog46L5ooXX3xRbr/9djO/ybx586Rt27ZmONFx48ZFsxjyQgABBBBAAAEEEEAg6QQuuOACmTFjhmn38uXLpWrVqklnQIMRQAABegryM4AAAggggAACCCCAQECgQ4cOMmnSJBP82759u7zwyismIJiZmSlvvfVWzAOCehIuu+wyM4xoYWGhqY/erPjuu+/knHPO4RwhgAACCCCAAAIIIIBABQToKVgBPA5FAIGEEaCnYMKcShqCAAIIIIAAAgggEC2B/Px80Z6Dq1evFh1etLLTE088IQ8++KApVgODRxxxhAkS/u9//6vsqlAeAggggAACCCCAAAIJIXD++efLN998Y9qycuVKYQjRhDitNAIBBMopQE/BcoKxOwIIIIAAAggggEDiC9SvX1969OgRl4Cg6l577bWmx6Iua0BwyZIlJkjZv39/2bdvn64mIYAAAggggAACCCCAwCEKEBA8RDgOQwAB3wsQFPT9KaQBCCCAAAIIIIAAAokooPMX3nDDDaZp7dq1E51jcNmyZXLKKadIUVFRIjaZNiGAAAIIIIAAAgggEDMBO3woAcGYEZMxAgj4QICgoA9OElVEAAEEEEAAAQQQSE6Ba665Rm655RbT+GOOOUZ++OEH0aGO+vTpI7t27UpOFFqNAAIIIIAAAggggEAFBFJSuCVeAT4ORQABnwvwDejzE0j1EUAAAQQQQAABBBJb4IorrpA77rjDNPK4444z86CsXbvWBAZ17kMSAggggAACCCCAAAIIhBegp2B4I/ZAAIHEFyAomPjnmBYigAACCCCAAAII+FxgxIgRcu+995pW6FyHX331lWzatElOOukk2bJli89bR/URQAABBBBAAAEEEEAAAQQQQKAyBAgKVoYyZSCAAAIIIIAAAgggUEGBCy+8UEaPHm1y0WDgZ599Jtu2bTOBwY0bN1Ywdw5HAAEEEEAAAQQQQCCxBegpmNjnl9YhgEBkAgQFI3NiLwQQQAABBBBAAAEE4i4wdOhQefzxx009Tj31VJk0aZKZW7BXr16yZs2auNePCiCAAAIIIIAAAggg4FUBgoJePTPUCwEEKlOAoGBlalMWAggggAACCCCAAAIVFDjnnHPk6aefNrkMHDhQJk6cKMXFxaKBwVWrVlUwdw5HAAEEEEAAAQQQQCCxBapUqZLYDaR1CCCAQBkCBAXLwGETAggggAACCCCAAAJeFDj99NPl+eefN1UbNGiQjBs3zizrsKLLly/3YpWpEwIIIIAAAggggAACcRWgp2Bc+SkcAQQ8IkBQ0CMngmoggAACCCCAAAIIIFAegf79+8vLL79sDjnvvPOcwGC/fv1k6dKl5cmKfRFAAAEEEEAAAQQQSBqBlBRuiSfNyaahCCBwkADfgAeRsAIBBBBAAAEEEEAAAX8I9OnTR958801TWQ0Mfvjhh2Z5wIABsnDhQn80gloigAACCCCAAAIIIFAJAvQUrARkikAAAc8LEBT0/CmigggggAACCCCAAAIIlC7Qs2dPeffdd80OZ555pnz++edmWYcY/fHHH0s/kC0IIIAAAggggAACCCCAAAIIIJBUAgQFk+p001gEEEAAAQQQQACBRBTo1q2bfPDBB6Zpp5xyisyYMcMsn3322TJ79uxEbDJtQgABBBBAAAEEEECgXAL0FCwXFzsjgECCChAUTNATS7MQQAABBBBAAAEEkkvgmGOOkcmTJ5tGa+9BGww899xzZebMmcmFQWsRQAABBBBAAAEEEAgSICgYBMJHBBBISgGCgkl52mk0AggggAACCCCAQCIKtGvXTqZMmWKaduyxx8rixYvN8tChQ53eg4nYbtqEAAIIIIAAAggggEA4gSpVqphd7Hu4/dmOAAIIJKIAQcFEPKu0CQEEEEAAAQQQQCBpBVq3bi3Tp0837W/fvr3k5ORIzZo15YILLpCpU6cmrQsNRwABBBBAAAEEEEhuAdtTMCWFW+LJ/ZNA6xFIbgG+AZP7/NN6BBBAAAEEEEAAgQQUyMzMlFmzZpmWZWVlyYQJE6RRo0ZyySWXyOeff56ALaZJCCCAAAIIIIAAAghEJkBPwcic2AsBBBJTgKBgYp5XWoUAAggggAACCCCQ5AIaBJw3b55RGDhwoNxyyy1yxBFHyGWXXebMPZjkRDQfAQQQQAABBBBAIIkEbE9BgoJJdNJpKgIIHCRAUPAgElYggAACCCCAAAIIIJAYAvXr15clS5aYxowcOVIGDRokXbp0kSuuuEI++uijxGgkrUAAAQQQQAABBBBAIAIBgoIRILELAggkvABBwYQ/xTQQAQQQQAABBBBAIJkFUlNTZcWKFVKtWjV59NFHpVevXtK7d2+56qqrZOLEiclMQ9sRQAABBBBAAAEEEEAAAQQQSCoBgoJJdbppLAIIIIAAAggggEAyCqSkpEh2drZkZGTIU089JW3btpXTTz9drrvuOnn33XfDkjzyyCMyY8aMsPuxAwIIIIAAAggggAACXhWgp6BXzwz1QgCByhQgKFiZ2pSFAAIIxFDg+eefl6ysLHnnnXdiWApZI4AAAgj4WWD27NnSqlUreeGFF6RBgwYybNgwGTVqlLz99ttlNkt7GD799NNl7sNGBBBAAAEEEEAAAQT8IMCcgn44S9QRAQRiJUBQMFay5IsAAghUosD06dPlzjvvNCWuXbu2EkumKAQQQAABvwl8+eWX0rlzZ3njjTekqKhILr/8cvnb3/4mr7/+eqlN+cMf/iBTpkyR8ePHl7oPGxBAAAEEEEAAAQQQ8LKA7Smoo2iQEEAAgWQV4BswWc887UYAgYQSeOyxx5z22ItcZwULCCCAAAIIBAm8//77Zl5BDfLl5uaa3oI33XSTvPLKK0F77v947rnnmoWXXnop5HZWIoAAAggggAACCCDgFwF6CvrlTFFPBBCIhQBBwViokicCCCBQiQJvvfWWfPPNN06JOmcUCQEEEEAAgXACr732mpxxxhkyadIkmTVrltx2221yyy23mKFFg4/t2LGj9O/fX3T40bJ6FAYfx2cEEEAAAQQQQAABBLwiYB+iJijolTNCPRBAIB4CBAXjoU6ZCCCAQBQFPvroowNyIyh4AAcfEEAAAQTKEHjqqafkggsukKlTp4r+Phk9erTccccd8p///Oegoy688EKzjqDgQTSsQAABBBBAAAEEEPCBAEFBH5wkqogAAjEXqBbzEigAAQQQQCBmAitXrjRzPLkL0KDgli1bpGHDhu7VLCOAAAIIIBBS4P7775f69eubQGBBQYE888wzcuWVV8revXvl6quvdo459dRTpU+fPqJzEmpg0AYJnR1YQAABBBBAAAEEEEAAAQQQQAABTwvQU9DTp4fKIYAAAmULTJkyxezwm9/8xtlxz5498umnnzqfWUAAAQQQQCCcwM033yx//etfZf78+aa34MsvvywaLHz88ccPOHTo0KHmM70FD2DhAwIIIIAAAggggIAPBOgp6IOTRBURQCDmAgQFY05MAQgggEDsBL744guTufbecKfPPvvM/ZFlBBBAAAEEwgr86U9/Eg0OLlu2TP75z3/KuHHj5KGHHjIve/DZZ58t3bt3l3nz5jG3oEXhHQEEEEAAAQQQQMAXAnYuwZQUbon74oRRSQQQiIkA34AxYSVTBBBAIPYCubm5ztCh/fv3dwps166d6SmYk5PjrGMBAQQQQACBSAT+8Ic/yF133SX6O+S6664z8wxqb8H77rvPOZzegg4FCwgggAACCCCAAAI+EqCnoI9OFlVFAIGYCRAUjBktGSOAAAKxFZg+fbopoG/fvtKmTRunsI4dO8q+ffvMjVxnJQsIIIAAAghEKHDppZfKgw8+KOvWrZOLL75YvvrqK3nqqadMsFCzOP/886Vr1670FozQk90QQAABBBBAAAEEvCVgewx6q1bUBgEEEKgcAYKCleNMKQgggEDUBWbMmGHyPPnkk817jRo1zHvnzp3l8MMPlwkTJkS9TDJEAAEEEEgOgWHDhsmYMWNk69atMnDgQJkzZ44899xzZlhRFRgxYoSBYG5Bw8A/CCCAAAIIIIAAAj4QoKegD04SVUQAgZgLEBSMOTEFIIAAAtEX2Lt3rwQHBWvVqmUKqlmzppx11lmyaNEiZ5/o14AcEUAAAQQSXWDQoEHyzDPPyK5du8w8gtnZ2fLyyy/L3//+dxk8eLD06dPH9BbkIZRE/0mgfQgggAACCCCAQGIIEBRMjPNIKxBAoGICBAUr5sfRCCCAQFwEdOhQHdatR48ectRRR5k62KBg1apV5eyzzzbr9OYtCQEEEEAAgWABnYv2xBNPlHfeeSd40wGftZfg2LFjpbi4WI488kiZO3euvPHGG/LXv/7V6S347LPPHnAMHxBAAAEEEEAAAQQQQAABBBBAwJsCBAW9eV6oFQIIIFCmwDfffGO2n3TSSc5+NiioY+N36NDBDPf20UcfOdtZQAABBBBAwAro74nc3Fy5/vrrze+Mxx57zHy2293vOnft22+/bVZ16dJFPvnkExk3bpyMHz9etDfhvHnzZMqUKe5DWEYAAQQQQAABBBBAwHMC9BT03CmhQgggEAcBgoJxQKdIBBBAoKIC2lNQk51PUJdtUDAlZf9Xuw4hqmnixInmnX8QQAABBBCwAo8//rj88MMPcvXVV5vhQR9++GHTc1CDhF988YXdzXk/4YQT5P333zefBwwYIK+99pqZu3bjxo1m3ZNPPunsywICCCCAAAIIIIAAAl4WsPdNvFxH6oYAAgjESoCgYKxkyRcBBBCIkYDOFTh79mzR3hqdO3d2StG5BDXp8KGadAjRdu3amfmgzAr+QQABBBBAwCXQsGFDufHGG+XDDz+UK664Qpo0aWKGEx0xYoTpAfjSSy9JYWGhc4T+zvnss8/M54suukgeffRR0Z7rLVq0kJkzZ8qsWbOcfVlAAAEEEEAAAQQQQMBrAvQU9NoZoT4IIBAPAYKC8VCnTAQQQKACAtOmTTNHu3sJ6orgnoK6TgOD8+fPNzdr9TMJAQQQQACBYIFOnTrJLbfcIpMnT5Y777xTunXrZuYOvO222+TUU081wb+8vDxzWJs2bWTGjBlmeeTIkfKPf/xD1q5daz4/8cQTwVnzGQEEEEAAAQQQQAABzwgQFPTMqaAiCCAQRwGCgnHEp2gEEEDgUARsUNA9n6DmU6NGDZOdzilok871lJqaanp+2HW8I4AAAgggEEogPT1dfve738m7774rL774onmwZPXq1fLII49I79695Z577pFVq1aZnoFz5swxWei66667zix//vnnDFkdCpZ1CCCAAAIIIIAAAp4ScN838VTFqAwCCCBQCQIEBSsBmSIQQACBaAlobwwNCuqwoDq/kzsFDx+q27KysswQcG+//bYsXLjQvTvLCCCAAAIIlCpwyimniPb803kEr7zySmnQoIEZjlofSLn22mvl22+/laVLl5rjx4wZI+edd55Z1h6H69atKzVfNiCAAAIIIIAAAgggEC8BegrGS55yEUDASwIEBb10NqgLAgggEEZAA4J79+6V4KFD9TDtEagpeMJsHUJUkwYGSQgggAACCJRHQOcR1CFCP/nkExk9erT069fPCRSee+65ZtgfmnUpAABAAElEQVTR2rVry7hx40SHFt2+fbsMGzbM9CgsTznsiwACCCCAAAIIIIBAZQnQU7CypCkHAQS8KEBQ0ItnhTohgAACpQjYoUNDBQVtT8HgoKDuq70K33nnHW7SluLKagQQQACBsgU08Dd06FB56aWXTADwkksuER1a9F//+pfUrVvXvH766SeTiQ4xqr0Ls7Ozy86UrQgggAACCCCAAAIIVKKA7SlYiUVSFAIIIOA5AYKCnjslVAgBBBAILaC9L7744gszJGiooKCdUzA4KKi5aW/BnTt30lswNC1rEUAAAQTKIdC9e3cTDJw8ebLceOONkpGRYX7HuJ+41iGr77rrLtm9e3c5cmZXBBBAAAEEEEAAAQRiLxDqvknsS6UEBBBAwBsCBAW9cR6oBQIIIBBW4OOPP5b8/HzR+ZxCpdJ6Cuq+Z555pjRs2FAmTJgQ6lDWIYAAAgggUG6BFi1ayNVXXy36++nRRx+VPn36OHnUqlVLRowYIdrDkIQAAggggAACCCCAgJcE3A+zeale1AUBBBCoDAGCgpWhTBkIIIBAFAQ+/fRTk0uoXoK6wQYFq1atelBp2otj0KBBZvhQ7W1IQgABBBBAIJoCgwcPlrFjx8qbb75pAoH79u2Tvn37RrMI8kIAAQQQQAABBBBAoEICdvhQgoIVYuRgBBDwuQBBQZ+fQKqPAALJIaA3VzUo2KhRI+ndu3fIRtugYGkXtxoU1PTJJ5+EPJ6VCCCAAAIIVFSgZ8+esmjRIlm2bFlFs+J4BBBAAAEEEEAAAQRiIlDafZOYFEamCCCAgMcECAp67IRQHQQQQCCUwHfffScaGNRegnXq1Am1i9gn3kobG/+4444TvVmbnZ0d8nhWIoAAAggggAACCCCAAAIIIIAAAokqYO+bEBRM1DNMuxBAIBKBapHsxD4IIIAAAvEV6NGjh4wcOVLOOuussBUJNXyoPUiHdSMhgAACCCCAAAIIIIAAAggggAACySpAUDBZzzztRgABFSAoyM8BAggg4BOBUaNGRVTT0noKRnQwOyGAAAIIIIAAAggggAACCCCAAAIJKEBPwQQ8qTQJAQTKLcDwoeUm4wAEEEDA2wIFBQXeriC1QwABBBBAAAEEEEAAAQQQQAABBOIkQE/BOMFTLAIIeEKAoKAnTgOVQAABBKInsGfPnuhlRk4IIIAAAggggAACCCCAAAIIIIBAAgjQUzABTiJNQACBCgsQFKwwIRkggAAC3hKgp6C3zge1QQABBBBAAAEEEEAAAQQQQACB+AsQFIz/OaAGCCAQfwGCgvE/B9QAAQQQiKoAQcGocpIZAggggICHBG6++WYZOHCgeX300UcH1eziiy822z7++OODtrECAQQQQAABBBBAAAEVYPhQfg4QQCCZBQgKJvPZp+0IIJCQAgQFE/K00igEEEAAgYBATk6OLFq0yLyefvrpg0x++OEHs2379u0HbWMFAggggAACCCCAQHIL0FMwuc8/rUcAgf0CBAX5SUAAAQQSTICgYIKdUJqDAAIIIBBSYO7cubJgwYKQ21iJAAIIIIAAAggggEBpAvQULE2G9QggkAwCBAWT4SzTRgQQSCqBoqKipGovjUUAAQQQSF6BN95445Aarz0J7ZPih5QBByGAAAIIIIAAAgj4TsBe/6WkcEvcdyePCiOAQNQE+AaMGiUZIYAAAt4QKC4u9kZFqAUCCCCAAAIxEmjevLnJ+ZVXXpEdO3ZEVMrKlSvliiuukA4dOkjnzp2lY8eOMnz4cJk9e3ZEx7MTAggggAACCCCAAAIIIIAAAn4XICjo9zNI/RFAAIEgAYKCQSB8RAABBBBIOIEBAwbI4Ycfbto1ceLEsO2bOXOm9OnTRyZPniy7du0y++v79OnT5dxzz5X33nsvbB7sgAACCCCAAAIIIOBvAdtTkOFD/X0eqT0CCFRMgKBgxfw4GgEEEPCcAEFBz50SKoQAAgggEGUBHfLpd7/7ncn1xRdfLDP3n3/+WW699VZnn4svvlief/55GTVqlLPu5ptvjrjHoXMQCwgggAACCCCAAAK+FCAo6MvTRqURQCBKAgQFowRJNggggIBXBAgKeuVMUA8EEEAAgVgKDB482GS/bNkymTVrVqlFaW/ARYsWme1Dhw6Vu+++W/r37y8jR46Um266yazXXoP0FiyVkA0IIIAAAggggEBCCRAUTKjTSWMQQKCcAgQFywnG7ggggIDXBQgKev0MUT8EEEAAgWgINGzYUIYMGWKyeuONN0rNcvny5c42nUPQnYYNG+Z8/Omnn5xlFhBAAAEEEEAAAQQST4DhQxPvnNIiBBAovwBBwfKbcQQCCCDgaQGCgp4+PVQOAQQQQCCKAjoUqKZ33nlH8vLyQua8atUqZ72dh9CuyMjIkDp16piPOTk5djXvCCCAAAIIIIAAAgksoEPRkxBAAIFkFeAbMFnPPO1GAIGEFSAomLCnloYhgAACCAQJdOvWTY466iiz9t133w3auv+jfSJcP5V1A8i9X8iMWIkAAggggAACCCDgawF7vcfwob4+jVQeAQQqKEBQsIKAHI4AAgh4TYCgoNfOCPVBAAEEEIilwIgRI0z2L730kujcgMHpsMMOc1atW7fOWdaFnTt3OscE9yI8YEc+IIAAAggggAACCPhewAYFfd8QGoAAAghUQICgYAXwOBQBBBDwogBBQS+eFeqEAAIIIBArgUGDBpms3cOEustyB/vee+899yaZOHGi8zkrK8tZZgEBBBBAAAEEEEAgcQXoKZi455aWIYBAeAGCguGN2AMBBBDwlQBBQV+dLiqLAAIIIFBBgfr164udWzBUVieffLLYwOBzzz0nTzzxhCxYsEDeeustuemmm5xDhgwZ4iyzgAACCCCAAAIIIJB4AranIEHBxDu3tAgBBCIXICgYuRV7IoAAAr4QKCoq8kU9qSQCCCCAAALREhg+fHipWVWrVk1uu+02Z/uDDz4oZ5xxhtxwww3OultuuUUaNmzofGYBAQQQQAABBBBAIHEFCAom7rmlZQggEF6AoGB4I/ZAAAEEfCVQUFDgq/pSWQQQQAABBCIVqFq1ashdO3XqJF26dAm5TVf2799fxo8fL0cfffQB+zRv3lyeeeYZueKKKw5YzwcEEEAAAQQQQACBxBOgp2DinVNahAAC5ReoVv5DOAIBBBBAwMsCBAW9fHaoGwIIIIBARQTGjh1b6uHu+QFD7XTsscfKpEmTZPfu3bJx40ZJT0+XtLS0ULuyDgEEEEAAAQQQQCCBBVJS6CeTwKeXpiGAQBgBgoJhgNiMAAII+E2AoKDfzhj1RQABBBCoTIHatWtLq1atKrNIykIAAQQQQAABBBDwgAA9BT1wEqgCAgjEXYCgYNxPARVAAAEEoiuwZ8+e6GZIbggggAACCCCQNAIzl209oK21a1aVI5vVlVo1Qg/desDOfEAAAQQQQAABBDwsYIOCHq4iVUMAAQRiLkBQMObEFIAAAghUrgA9BSvXm9IQQAABBBDws0BB0T75ZskWmb5kq0yZs1HydxaHbE6t1KrSMC1VGjWoKVWr15RGaTWkSmDPqoHht2pVF+lzdLr85qj0kMeyEgEEEEAAAQQQ8JJAlSp6FUNCAAEEklOAoGBynndajQACCSxAUDCBTy5NQwABBBBAIEoCq7cUyCtTVskXszc4gcAGgaBf105NJLVGNalX8xcp2itSUFgsi5ZvkR07iyR3zy7J3bArZA3Gfb5CTu6WKdcPaSdN63CjLSQSKxFAAAEEEEAgrgK2pyBBwbieBgpHAIE4CxAUjPMJoHgEEEAg2gJ6katDiKampkY7a/JDAAEEEEAAAZ8L7Pv5Fxk7JUfe/GKVbN9RLN3aZ0irzHRJy0gXvYZYmL1J1m3aIT9u2Sm7dxc5ra1WrapkpNeWxhl1JK1eqixZsUm2bDkwQDh11hrJ2VggJ3VtLkNPaCrN6hIcdABZQAABBBBAAIG4C9hgoH2Pe4WoAAIIIBAHAYKCcUCnSAQQQCDWAtpbkKBgrJXJHwEEEEAAAX8JTJq9Xl6bslqW5uQHgnup8uFdJ8g73+fJxKkrZGtettOYmjWrSWazNDmseZo0DgQCm6QHAoH1D3zYqM/xWbJ1e4EsXrFZVuTmSe7abfJzIOCYs3qreX323Wo5/5QjZPrcXPnzWUdKm+Z1nfxZQAABBBBAAAEE4iFAT8F4qFMmAgh4TYCgoNfOCPVBAAEEoiCgQcH0dOb1iQIlWSCAAAIIIJAQAnePWyITp+U6bdm0dY+c+c9p5nOdOjWlTevG0qJJPWnVooFkNq3n7FfWQkZaLenV9TDz2rmrSLJzt8qynMBreaC34fp8efT1OebwPwaCkFefdYQM6ZFZVnZsQwABBBBAAAEEKkUgJTAnMgkBBBBIVgGCgsl65mk3AggktIAOH0pCAAEEEEAAAQT2Bnrv/eGJH2TB8m0ORkpKFWkZCP5pT8Cs5g0kq0Was+1QF+rWqSFd2jUzr01bd8uC7I0yY1aOyU7nI7z/zcUyd0W+XHvmEdK4fs1DLYbjEEAAAQQQQACBQxagp+Ah03EgAggkkABBwQQ6mTQFAQQQsALaU5CEAAIIIIAAAsktsGrzbrnske9l565iA9G0SV3p0r6FtMtqKBrEi1VqnFFb+ma0kr7dW8mcxetlWiA4uGPHHpn07VqZv3K76TV46jFNYlU8+SKAAAIIIIAAAiEFCAqGZGElAggkmQBBwSQ74TQXAQSSQ4CgYHKcZ1qJAAIIIIBAaQKzAz0D//j4LLO5eZM6ckz7TOnWoXlpu8dsfdf2zaR1oFfil4HA4IIl6yV3wy7550vzpc6VXaVH24yYlUvGCCCAAAIIIIAAAggggAACBwswgPLBJqxBAAEEfC/A8KG+P4U0AAEEEEAAgUMW+GndTicgeFSrdDm3f6e4BARtA9Lqp8qgfu3k9L7tzKp9+36R219bKItzd9hdeEcAAQQQQAABBGIuQE/BmBNTAAII+ECAoKAPThJVRAABBMorUFhYWN5D2B8BBBBAAAEEEkBAA4IX3f+tacnxHZvIOf07SoNAUM4LSXsNDhrQwVQlb3uh3Pr6Qlmbx5DnXjg31AEBBBBAAIFkEqhSpUoyNZe2IoAAAgcIEBQ8gIMPCCCAQGIIEBRMjPNIKxBAAAEEECiPgAYE//biPHPIwB4tZcBJR0v1alXLk0XM9+14ZGMnMJizdqfc/voi2VW4L+blUgACCCCAAAIIIGB7CqakcEucnwYEEEheAb4Bk/fc03IEEEhgAYYPTeCTS9MQQAABBBAoReDxD7Nl7cbdcuGAVtL9uCNL2Sv+q92BwbnL8uTWNxbEv1LUAAEEEEAAAQQSXsAGBekpmPCnmgYigEAZAgQFy8BhEwIIIOBXAXoK+vXMUW8EEEAAAQQOTeDLBZvl2/mbpdORDaR751ay9+dDy6eyjnIHBqfN2SSf/rixsoqmHAQQQAABBBBIcgGCgkn+A0DzEUhyAYKCSf4DQPMRQCAxBQgKJuZ5pVUIIIAAAgiUJvDfGWvMpv4ntJKNu34pbTdPrdfAYKejm5s6vfP1/vp7qoJUBgEEEEAAAQQSSoCeggl1OmkMAggcogBBwUOE4zAEEEDAywIEBb18dqgbAggggAAC0RV4Zcoq00vwtBMypVb9tOhmHuPcenc9XGrXriFzlmyVj2atj3FpZI8AAggggAACCCCAAAIIJLcAQcHkPv+0HgEEElSAoGCCnliahQACCCCAQJDA4twd8sLHK6RO7epyVJuWQVu9/zE9LVV6dD3MVJTegt4/X9QQAQQQQAABPwvQU9DPZ4+6I4BAtAQICkZLknwQQAABDwkUFBR4qDZUBQEEEEAAAQRiJfD5/I2yu2Cv9OqaKfXrpcaqmJjme0LnlpLVMl0WLt8m7327NqZlkTkCCCCAAAIIJK+AnUvQvievBC1HAIFkFiAomMxnn7YjgEDCCuzevTth20bDEEAAAQQQQKBEYMaireZDWlrdkpU+XGp/RGNT6/8yt6APzx5VRgABBBBAwB8CtqdgSgq3xP1xxqglAgjEQoBvwFiokicCCCAQZwGCgnE+ARSPAAIIIIBAJQgsW7dLlubkm5JaNq1fCSXGrojMJvvrv2xVvnz3U17sCiJnBBBAAAEEEEhaARsUpKdg0v4I0HAEEAgIEBTkxwABBBBIQIE9e/YkYKtoEgIIIIAAAgi4Bb6Yt8F8bNqkntRKre7e5Lvlpo3qSO3aNUy9Zy0nKOi7E0iFEUAAAQQQ8JEAQUEfnSyqigACURcgKBh1UjJEAAEE4i9AT8H4nwNqgAACCCCAQKwFvlqwxRTR/NdedrEuL9b523b8sGxbrIsifwQQQAABBBBIQgF6CibhSafJCCBwkABBwYNIWIEAAgj4X4CgoP/PIS1AAAEEEECgLIFN+YXO0KGZgZ6CiZAym+5vx/zsbVJY/HMiNIk2IIAAAggggIAHBegp6MGTQpUQQKDSBAgKVho1BSGAAAKVJ2Cffqu8EikJAQQQQAABBCpTYEFg7j2b6tdJtYu+fs9skmbqv+/nX2TGkv29IH3dICqPAAIIIIAAAp4S4F6Jp04HlUEAgTgJEBSMEzzFIoAAAggggAACCCCAAAKHKrBhe+GhHurZ42q75kWc+RPzCnr2RFExBBBAAAEEfC6QksItcZ+fQqqPAAIVEOAbsAJ4HIoAAggggAACCCCAAAIIxENgYwIGBevUqu5QLnL1hHRWsoAAAggggAACCFRAwPYUZPjQCiByKAII+F6AoKDvTyENQAABBBBAAAEEEEAAgWQT2LgtAXsK1qrmnMbCIuYUdDBYQAABBBBAAIGoCBAUjAojmSCAgM8FCAr6/ARSfQQQQAABBBBAAAEEEEg+gU3b9yRco/Wp/Zo19wcG9xQTFEy4E0yDEEAAAQQQ8IgAPQU9ciKoBgIIxEWAoGBc2CkUAQQQQAABBBBAAAEEEDh0gdqpJb3qDj0X7x1Z69d5BQuL9nmvctQIAQQQQAABBHwtQE9BX58+Ko8AAlESICgYJUiyQQABBLwi0KBBA69UhXoggAACCCCAQIwEGtWv6eRcvHevs+z3hVq/zitYWJQ4bfL7OaH+CCCAAAIIJJoAPQUT7YzSHgQQKI8AQcHyaLEvAggg4AOBvn37+qCWVBEBBBBAAAEEKiLQJK2Gc/jmbQXOst8Xdu4qMk1gTkG/n0nqjwACCCCAgPcEbE9B79WMGiGAAAKVJ5CYY85Unh8lIYAAAp4TeOyxx2Tnzp2eqxcVQgABBBBAAIHoCTSsV9JTcHPe7uhlHMecior3yY4d++dKLGJOwTieCYpGAAEEEEAgMQVsUDAlhX4yiXmGaRUCCEQiwDdgJErsgwACCPhAYNSoUZKTk2NqWrduXR/UmCoigAACCCCAwKEKNE0rCQpu2bbrULPx1HGbtpYEN49sWc9TdaMyCCCAAAIIIOB/ATtsqH33f4toAQIIIFB+AYKC5TfjCAQQQAABBBBAAAEEEEAgrgJtmpc8ALQ1QXoKbnYFN9sdTlAwrj9gFI4AAggggEACCtieggQFE/Dk0iQEEIhYgOFDI6ZiRwQQQMBfAp9++qk899xzTqXtxa+zImgh3HbdPdw+4bZHkkck+0SjHHcepf1B4N5H6xUqhdsn3HbNM9w+4bZHkkck+5RVjjUqa59Iyohkn3BlRJJHJPtEoxx3HtZIy3Yn9z7u9e7lcPuE2655hdsn3PZI8ohkn7LKsUZl7RNJGdHaJ1w9IiknGnm4y7FGus6dolGOV/LQdoWrS1nbrVFZ+0RSRrT2CVePSMo51DzSBzwgVVMbSGHhXtkSmFewYYNaWpxv0+a8krkRX3vyXnnhb1/6ti1UHAEEEEAAAQS8J2CvI+2792pIjRBAAIHYCxAUjL0xJSCAAAKVLpCVlVXpZVIgAggggAACCFSuQNG2HKnVrIEpdNX67f4PCm4rGT507/b9Q6JXriilIYAAAggggEAyCBAUTIazTBsRQKA0AYKCpcmwHgEEEPCxwNChQyU3N9e04FB7H7ib75U8tE7h6hJuuzuP0v4QKE8ebqfg5XD5hNvurmtw3vZzNPIoqxxrFI1yopFHWXWtLJPgcqyRXe9+D9fmcNs1r2jsE+883Ebh6hJueyQm0cgjHuW4nbR8m6LRnmjkofUJl0+47RXNwxrFupxI7aNRj4qY7NsR+F3frIup7oJlG+TY9s1s1X33viXQSzBn9VZT732FO2Vf3krftYEKI4AAAggggIC3Bey1m72m9HZtqR0CCCAQGwGCgrFxJVcEEEAgrgKjR4+Oa/kUjgACCCCAAAKxF/hk7ga55cX5pqDVa7bJ8tw8OaJleuwLjkEJ837aIPv2/Wxy7nlcljz25IoyS7E39craqbR97I3A0ra784zGPl7JQ9sVri52uzVyW9hlu4/9HPwebrvuH419opGHrXu4vMrabq3K2idabY4kn3D1qKw83OVYI13nTl6sq7t+7uVY19Uaxboc2yavlFOeelgj2wb3e7h8wm3XvMLtE257JHlEsk9FyrFGFclD62hTuHzCbdd8orFPNPMYPnx4RHWyBrwjgAACiShAUDARzyptQgABBBBAAAEEEEAAgYQXOOWYJpLVoq7krN1p2jp/2UZfBgWLi/eJ9nS06ZwTmktKSor9yDsCCCCAAAIIIBAVARtg5DojKpxkggACPhXgLy2fnjiqjQACCCCAAAIIIIAAAsktUDWlipwdCKDZtPinjbJlW4H96Jv3HwMBwfz8Paa+XY5Kl/6dm/qm7lQUAQQQQAABBPwnYHtV+q/m1BgBBBCouABBwYobkgMCCCCAAAIIIIAAAgggEBeBc0/IlCYZqaZsHX5zfiAw6LekPRxtOrdnC7vIOwIIIIAAAgggEFWBnJwckx9BwaiykhkCCPhMgKCgz04Y1UUAAQQQQAABBBBAAAEErEC91KpyVo+SQNr071fKyjXb7WbPvy9avknWrttf3/at0uSM45p5vs5UEAEEEEAAAQT8KzBo0CDp37+/fxtAzRFAAIEKChAUrCAghyOAAAIIIIAAAggggAAC8RQY0S9LjmnTwKnC1FkrnGUvL2wPDBn66fRsp4r0EnQoWEAAAQQQQACBGAmMGTNGjjnmmBjlTrYIIICA9wUICnr/HFFDBBBAAAEEEEAAAQQQQKBUgZrVU+Rvg9s529es3S5ffr9/eCxnpQcX3v9yiezcWWhq1qNTIxl8QkmPRw9WlyohgAACCCCAAAIIIIAAAr4XICjo+1NIAxBAAAEEEEAAAQQQQCDZBdpl1pV/XdbJYfD6MKIff50tq9dsM/VtFxg29M4LOzh1ZwEBBBBAAAEEEEAAAQQQQCA2AgQFY+NKrggggAACCCCAAAIIIIBApQoM6NJUhvRp6ZT5v6+WyLpNO53PXllYv3mX/DAv16nOvy7pKGm1qzufWUAAAQQQQAABBBBAAAEEEIiNAEHB2LiSKwIIIIAAAggggAACCCBQ6QI3BoYRrV93f4Bt27YCeWfSPFm5Znul16O0AnfvKZYXx33vbH5u1PFyeMNazmcWEEAAAQQQQAABBBBAAAEEYidQ5ZdAil325IwAAggggAACCCCAAAIIIFDZAkPumSFrNu42xdaoUU3OPqW9tG3VsLKrcUB5OYG5Dl+fOMdZd+eITvLbrk2dzywggAACCCCAAAIIIIAAAgjEVoCgYGx9yR0BBBBAAAEEEEAAAQQQiIvAza8ulE+/X2fKTqlSRU7t3UaO79giLnWZtXCdTJ661Cn7qnPayIh+Wc5nFhBAAAEEEEAAAQQQQAABBGIvQFAw9saUgAACCCCAAAIIIIAAAgjEReCNabny9Ps/yZ7Cfab8zh1ayGk9j5Dq1atWWn0+/jrbmUOwakoVefiPXaVH24xKK5+CEEAAAQQQQAABBBBAAAEE9gsQFOQnAQEEEEAAAQQQQAABBBBIYIF5OdtlzAfZMndZnmll86b1pUfXw6R960Yxa3VBYO7AuUs2yPxlG2TT5p2mnKMOry9PBgKCabX3z3kYs8LJGAEEEEAAAQQQQAABBBBAIKQAQcGQLKxEAAEEEEAAAQQQQAABBBJL4NMfN8ibU9fIvJ/2Bwdbtmggnds1ky7tojev35ZtBTJ36XpZEAgI7txV6ACe0bOF3Hb+0c5nFhBAAAEEEEAAAQQQQAABBCpfgKBg5ZtTIgIIIIAAAggggAACCCAQN4F//y9bxn680im/SeO60rJZA2nRpJ60DgQK69ap4WyLZGFrIBC4ZlO+rFqXLwsDAcG9e392DktPqykjTmslF5zY0lnHAgIIIIAAAggggAACCCCAQHwECArGx51SEUAAAQQQQAABBBBAAIG4CSxYnS/jZqyRj6avPagOjRrWkZbNG0jrzAaSWiP0UJ8btu6U1eu2y4bNOyQ/f89BeWgw8JxeLeS8npnSuH7Ng7azAgEEEEAAAQQQQAABBBBAoPIFCApWvjklIoAAAggggAACCCCAAAKeECgrOHgoFSQYeChqHIMAAggggAACCCCAAAIIVI4AQcHKcaYUBBBAAAEEEEAAAQQQQMCzAhocfP+7dTJj4WZZv/ngnn/hKt6yaR3pf1wTegaGg2I7AggggAACCCCAAAIIIBBHAYKCccSnaAQQQAABBBBAAAEEEEDAawIzl22VL+Zvkk9nbZD8ncUhq5dWr7oc366hHN+mgXTOSpM2zeuG3I+VCCCAAAIIIIAAAggggAAC3hEgKOidc0FNEEAAAQQQQAABBBBAAAFPCRQW/yy7i/bK7sJ9UlAYWC7cG5hnsKq0bUEQ0FMnisoggAACCCCAAAIIIIAAAhEIEBSMAIldEEAAAQQQQAABBBBAAAEEEEAAAQQQQAABBBBAAAEEEPCzQIqfK0/dEUAAAQQQQAABBBBAAAEEEEAAAQQQQAABBBBAAAEEEEAgvABBwfBG7IEAAggggAACCCCAAAIIIIAAAggggAACCCCAAAIIIICArwUICvr69FF5BBBAAAEEEEAAAQQQQAABBBBAAAEEEEAAAQQQQAABBMILEBQMb8QeCCCAAAIIIIAAAggggAACCCCAAAIIIIAAAggggAACCPhagKCgr08flUcAAQQQQAABBBBAAAEEEEAAAQQQQAABBBBAAAEEEEAgvABBwfBG7IEAAggggAACCCCAAAIIIIAAAggggAACCCCAAAIIIICArwUICvr69FF5BBBAAAEEEEAAAQQQQAABBBBAAAEEEEAAAQQQQAABBMILEBQMb8QeCCCAAAIIIIAAAggggAACCCCAAAIIIIAAAggggAACCPhagKCgr08flUcAAQQQQAABBBBAAAEEEEAAAQQQQAABBBBAAAEEEEAgvABBwfBG7IEAAggggAACCCCAAAIIIIAAAggggAACCCCAAAIIIICArwUICvr69FF5BBBAAAEEEEAAAQQQQAABBBBAAAEEEEAAAQQQQAABBMILEBQMb8QeCCCAAAIIIIAAAggggAACCCCAAAIIIIAAAggggAACCPhagKCgr08flUcAAQQQQAABBBBAAAEEEEAAAQQQQAABBBBAAAEEEEAgvABBwfBG7IEAAggggAACCCCAAAIIIIAAAggggAACCCCAAAIIIICArwUICvr69FF5BBBAAAEEEEAAAQQQQAABBBBAAAEEEEAAAQQQQAABBMILEBQMb8QeCCCAAAIIIIAAAggggAACCCCAAAIIIIAAAggggAACCPhagKCgr08flUcAAQQQQAABBBBAAAEEEEAAAQQQQAABBBBAAAEEEEAgvABBwfBG7IEAAggggAACCCCAAAIIIIAAAggggAACCCCAAAIIIICArwUICvr69FF5BBBAAAEEEEAAAQQQQAABBBBAAAEEEEAAAQQQQAABBMILEBQMb8QeCCCAAAIIIIAAAggggAACCCCAAAIIIIAAAggggAACCPhagKCgr08flUcAAQQQQAABBBBAAAEEEEAAAQQQQAABBBBAAAEEEEAgvABBwfBG7IEAAggggAACCCCAAAIIIIAAAggggAACCCCAAAIIIICArwWq+br2VB4BBBBAAAEEEEAAAQQQ8IDAVwu3yBtTV0njtFQZcWqWtG5S2wO1ogrJLFC092eZMHOdvB949Tw6Q4b3bikN6tRIZhLajgACCCCAAAIIIIBA0gtU+SWQkl4BAAQQQAABBBBAAAEEfCqwfP0uKe2CPiWlimRmpEqNagwQEsvTu3B1vlz20HdOEbVTq8ln9/aRAD8JgbgJPP/ZSvnP+9lO+Sd0aiSPX97F+cwCAggggAACCCCAAAIIJJ8APQWT75zTYgQQQAABBBBAAIEEEZi+eIuMenpO2Nak1qwqR2TWk7+c00aOyUoLuz87lE9g3Iy1Bxywe89emblsq/Rom3HA+tI+PDRxmewp2mc2t21RT4b2yixtV9YnqEAsfgbGf73mAK1v52+W4n2/SPWqRKsPgOEDAggggAAC/7+9+4Cvo7gXPf63rS5ZVrOaZVvFHXewjU1sQmxiEiAG34QLppcQMC/kkXBDybuQm5CXl5AQMDeQkBAuJUASMBgwoZcABjeEwb03SZZkFatbcnkzR9o9u6sj6Uh7JFbSbz6fk7M7u7M7+52VPpH//GcQQAABBPqRAEHBfjTYPCoCCCCAAAIIIIBA3xJoVP/AH0xpOHpcNu+ulOt+t07OnJYq/++KSWSxBQMX5DlTcobIylX2AMyYzLggW4s8/94BOX6ieSwn5iUQFAxaru+c2B3vwMScBHmn/JCJlKqyhgkImhxsIIAAAggggAACCCDQLwWYR6hfDjsPjQACCCCAAAIIINBfBd7PL5G/qGkFKaET+Ob0dDl1XHNW4CA1Z+h15+ZKUhxrt4VOmCt1ReD6hTkyrGVty/i4cPnxt8d15TK0QQABBBBAAAEEEEAAgT4kQKZgHxpMHgUBBBBAAAEEEECgfwv85vqpkpseYyLU1h+XHYeq5ZF/7pFDh+vN+j+9skvOOy1d0hOizDo2ui6gs68eumGa1KmMzDC1zRqOXbekZegEclRAcPmds6WitlESYiJkALOGhg6XKyGAAAIIIIAAAggg0EsFyBTspQNHtxFAAAEEEEAAAQQQcApkJUfJsKRo8zNmWJyce2qGLzCQlzXYdvranRW2fXbcC8SotRsJCLp35AqhFUiMJSAYWlGuhgACCCCAAAIIIIBA7xUgU7D3jh09RwABBBBAAAEEEEAgKAE9peVt3x4r19+/zjx/e2GNue3c0MvbvfBJgazfVSnbD1b7sgxjY8IkT62TN35EvFx11kgZHN19f0pU1R+Tu57eZHbrCnW/6bkJvv3H39sv+buaA5o5abHyg/NG+eqrVZv/tLS56IwsmTMu2Xfsl8u3SXFFg3k9Y+OHF4yREcnRsutQrdy3YrvsLKiR6pomGarWXhs7fLD8aNFoSXNkUzY0npDbn/zCuETA77TEKLlj8diAx04q21v+ssF2zFhPUFfuVN7/+1H7cePk6Igw+a9LxrcbeNxTXCsvrCmUrQeqZZd6nqMqezFTZYyNVUHhs6emyrwJKcbleuT7lXVF8uHmMtm094iUVR719eW8WRkqWJ0uQ+Mj5Y4nN0l94zFfX65U4zytZZyNzt338g7ZX1Ln2x2nnuGGhbnGIdv3Xc9slqq6Jl/drLHJcslXsmzHrTuhfL93FtXKy+sKZfO+ajlS26Q+jVJb1/w80VGDJFb9nMSroNx3vjJMzj8tw9eN7ngHNu6vkj+/ucf6mK22zxif0qn1KkurjsqzHx6ULeraO9R7Wd9wXLLUz9z4kYPlrIlD232XPlVrmD7x7j5fHwaq3z/3XT3Z93P20Gu7ZPPeKjlS1SiD1ZSm+nfKhbMz5ewpaa36SwUCCCCAAAIIIIAAAgiEXqD7/pIPfV+5IgIIIIAAAggggAACCHRRYLwjU3CvCh4FKsWVDfKDP30uewqqbYcr1T/ir68ql/Vby+W59w/IL66aJF8Z3xx0s50Ygp3oiEHy8ReHzStNyRliBgVXrCqQgpYgUf72CjMoWFjeYGszPS/RDAq+82mxVKlgn7Nsnp4mZUeOyg3L1tsO6alW9efDDaXy51tOkwnD483jx1REydo384BlQ6/f1mZQUJ3XXvsGFcRr7/iPLhglKYMjLXfzbz79wQF54Pnt/oqWrX0qAKw/b6wpkjOnpcpPL54gOquxO0vT8ZO+4OmHn5XabnNABWAfXrFT/rxyt7x2zzx5Z/0h8/iM0UmtgoLvqzEwpr7dsq+qzaDgO+uLpenYCd+1ytS72lZQMFTv9+Hqo3L179ZJiXrv2ipNNSd8711Rab2UTPIHY1VcuN0x7so7UFRR3+41dR91UPk7c4a11V1b/b82H5Y7//KFaWoc1L8X9OfVVYWyQE1BfPfFgYPU+w/X2fqzYU+l3Pjgp2INgFt/p7wxtUR+feUkpjg1oPlGAAEEEEAAAQQQQKCbBJg+tJtguSwCCCCAAAIIIIAAAl4S0EEaa4mPDrfu+rZ1ZtCFP1vVKiDoPFEHLX70x89kg8r+6o6i1+gLD/P/qVKqgjxGsQZhdD8aWwJBxUfswZlslR3XUdlXWid3PeXPSHSerwMY97+001ntyf1fvbA9YEDQ2dn380tk6R/yndUh31/6cL44A4LWm+gA3rKVPWsbqvdbrx3ZUUDQ+qx6O0tlpPaW8s9PD8l/PLKhVUDQ2f+31h2SK1RgNJhy7ws7bAFBZ5t/fVYiK9YWOqvZRwABBBBAAAEEEEAAgRALkCkYYlAuhwACCCCAAAIIIICAFwXe21hi61aGWn/QWe5TGVzWTB497ej5atrDkUNjpLq+SV75pMiWGfWLv22Vv982y3mZkOwnJ0SaGWKHVTafLrUqGGNkgxk32a+ysEZlxEqxmprSWrLVNIdGuWvJKaIzl3RZttyfSff5niPm81wwL0smj4yXtzaUyKrP/VmKG3ZUSIWaElKvy6ZLZPhA+d75eb5t6/88+eY+qWtonjbSWu/cVqTyn5dNkCMtU0zq49Y+pSRGyhI1jWZbJS6ydTC3oLxelqvsTWsZraZ5/dqUob6pRvVUsa+vLjIPb1HPvWprmZlJaR4I0YaeyvJzx5qV89TUpaePTfJlKH6yrUJeW10oKz44GKI7BneZUL3ff1LTdFqD0/ruOcMGy7RRCZKupo6NVVmY+mdHB6z1lKZl1Y0yUY2HUbrjHRibFd/qvdQ/yzojszNFZ8L+xpFtGqWeZ+HMDElUGbCr1FSw21XGplF01uDbn5fI/MmpRlXA7x3qndDltPFJon82C9U7u3pjme33zYPq988FMzMDtqcSAQQQQAABBBBAAAEEQiNAUDA0jlwFAQQQQAABBBBAAAHPCuip+375zFZb/+a2rLdnVOq16KxTOSbER8gTP5xhW1Pv2gU5cutjn5vTAu4rqrEFl2rUmmO3P/GFmb1nXDvY7wUqcHTRnCzf6XpdPmPaSD0dpC77SlpPebqnpKY5KOjIFBym2htl7gQ9zWnzVKdPvLVX9LSFuuSr4JQuv/7uFDnzlObpHc89NUN+/o+t8spHBb5j+n8KyhrMoKDOYrxmfrZ5zNh4RQXdggkK6vPPa1lbzmj7+xf9WVTpSdFy6bzhxqGgvu9VWYLW8t3zcuU6NVbWotdYvPa+tWbV/SoAY6y5aFaGaOOhV3fZrnTFwmy56Rv+QOo3pqfLN09Lk5t/3/0Zi0ZHQvF+G9faZAmK6bq7Lp+g1khsXi/QOKej71C/A3ptTOd7qbODOxsU/JtaQ7BGrY1oFP174KlbZ/rWf9R1N6o1HXVWqjUI/RsVaP/apNQOp/78+VUT5etT/WsH6nVAF9zxvnEr333175A4tRYjBQEEEEAAAQQQQAABBLpHgKBg97hyVQQQQAABBBBAAAEEelzggVd2SnyMP5OsXmXW7VFruOl13KxFZ6NNyUmwVsnbX9gzCe+5YqItIKhPDlMpTnf9+3g5b9OHZobPpgNVZnBJT8+4VmUSdbVkJKopFuc0t05PipINLRcqU9fVZW/LWoI6C2uIClaUq+zAfSpTUJfiCn+moF7TT5/TUdGZVDqDzQgIGufPHJ1oCwqW+AKO/kwv4zyvfK/Z5DfPU2tHOgOCup86U23xmcPNYM7BNtaUDMUz6XUnjaKngf2eCiQ5yyy1fuAMFax18744r9nefijeb+P65Srzz1qGqUBuXylvqWk8reWWC0ebAUGj/tZFo+XVTwpFT9+ri/45LFRrGrbnMCE3wRYQ1O0GR4f5MgfXbfG/L3tV4N+aVanPoyCAAAIIIIAAAggggEDoBAgKhs6SKyGAAAIIIIAAAggg8KUKfPyFf9rLtjqiM3/++8bprQ7vLW4OrukDcbHhMmNUYqtzdEVSXITkqsCTMR3gvhJ/u4ANulg5zDK96ZHq5sylvWoNQF2GqoChziTUwYi9xc11xZX+NQUzUjpeT9Do1uLZw4xN83v88HjR04kaZYyaGtKrpbym0QzQ6j5eZOm3s88LJg81g4I6IKrb6vEMZdHXtJaFszJ8wWRrnbE9Z3zPBQVD+X7npMfaAu1LH/xUTp+UIudMSxMdUE5omWrWeM7e9F3YMs2u7rMO6J49xZ/ZZzyHDrifOztTnn/PP2XtgcPtBwXnTWzO1DWuYXx/VWUYWoOCFTX+LEXjHL4RQAABBBBAAAEEEEAgdAIEBUNnyZUQQAABBBBAAAEEEPC0wMS8BFl2/VTfmmfOju5vCbjp+no1rd8vnrNPN2o9v9QSgLO2GxITJt9SaxB2teiAilEyLdlXxrScOutRlxFpMZKppkvU6/0ZU4oeVgFCo2Sr48GWGZZ7Gm30VIx3LB5r7Hr6+4Bl3HRHX1tfLJta1m9zdrzSEXDRbUMdFCws9wdn9f1zLWs7OvszohPBW2fbzu5b39Ouvt/GPS9R07v+y5JRpwOsH20o9X30OUlqPcy5k4bKN6enyZTshA6n1TSu64VvY2pd3Re9rmdbGbd5jnHVa3aePiapzUdoK4swQq3RSUEAAQQQQAABBBBAAIGeEyAo2HPW3AkBBBBAAAEEEEAAgW4VSFUZdInxkeY9itQ/1FdZAkHnq6yt2MjA63UVl/sz/nSQ46UP/WvqmRcMsNHQ2DyFoD6kA0w/+fa4AGd1virTsiagbl2npio80DJVaK7K1DKChoUqQ0mXipZ1AvV2TpBBQZ0JpadE7c2lwBGEy99WrtZKDO6J6ptOBHdiJ84qUtNIWkucmiKyrdLWu9jW+W7qQ/F+G/efrqbCvOfqifJ/1TqdRsDaOKa/dQbrig8O+j56qt57Lp8o01Qbr5eKWnuWZ5Lld4mz7ymOYwVl9nF3nh8X1fZ74DyXfQQQQAABBBBAAAEEEOg+Af6feffZcmUEEEAAAQQQQAABBHpUYNn3pqqAWKx5zx2FNXLZr1eb+w+/sku+NSNTAsXBEgdHijVLyGzUwUZMG0HGDpp1eNgI+hknHq4+KjrIqUtuepwYQcOa2iY5poKY1uBMTqrfwGgf6DtWZTb29uIMznTmebojKBcZbg86D+qhmOsJ9Q60V0L9futpNb86MVXe21gir64/pNZGLJemY62DrIfVWpc3LFsv/3PrTBmvpt31comL8q9HqvtZ23Csze7WNNin+RxiWcu0zUYcQAABBBBAAAEEEEAAgS9doPf/FfylE9IBBBBAAAEEEEAAAQS8KTA6M06mqOkx9TSbuuig34o1hXLhrMxWHc7LiJU9BdVm/X9deUqbUweaJ6mN1CFR1t2QbQ8d4s941Bc9qDICG1S2oC65qTGSobIijeKcLjPbEhg1zgn0HRVhD2AFOqen6xoDBJba64MeN2uZMSFZFqmM0GBKTlpcMKd16pwsy7SvumHxEf+0rp26UCdOrlfZqjq7tb3SHe93uIp46uCgse7edhWEX7WtTD7YeFg27qq0defXy7fLYzefaqtra6ez70Bb1+lsvX6eGJXRZwTYy9oZuyIV7LSWkUODn7LX2o5tBBBAAAEEEEAAAQQQ6FkBgoI9683dEEAAAQQQQAABBBDoUYGbzs2T6+9fZ97zoZd3qmzBjFYBv1GZsfKWeZb4jhvBDkt1j23qaT2jVBaiEQhcv9sfZNGZgjrLTa93poNBa3aU2/qVpdYE7E1lcFy4maVpTIcabP+T1ZSthoNus0+tuzh/clrAbNBgr+nmPGeG59rtFXLt/OyAl2wIYvpSPcWrUarVVLg6YBZhqdPHdDCuo9IT7/cYFYTXn6vOGunr0+WWLN29HfTRzTvQ0bN35vhQFWzf19JXnYWrbfUzOcsbnxbbqob34PqQthuzgwACCCCAAAIIIIAAAp0S8P+F1almnIwAAggggAACCCCAAAK9QWBK9hAZPSLe7KpeY/DF1YXmvrFxWl6isen7vvvxTbKtoONgi61RiHeSLdmC61uyHXWQyJj2UgcwdFmzrTkTUm/HxYb3unUCMy0BFR2IWbvT/zz6mToqY9UYG6VErTF4+xNfGLs9/h0VMdCXbWbcWK9xeKiywdi1fX+8vcy2H2gnI9mfEaoDwP/MtwejdJvH3t4bqKmtrqffb73upQ7WGiXQ1KLGMf3t9h2wXsvN9inZ/t8V+joPrtzZ6nLrd1WYgUN9UD9ntsrepSCAAAIIIIAAAggggID3BQgKen+M6CECCCCAAAIIIIAAAq4EdLagtTyk1hbU6/BZy6SRQ2Te1FSzSgdgrv7tGrn7mS2y9WC16CkajVJR2yjrVOBq4/4qo6pbvtMtU4Ru39d8r0zLNIUj0poDEZssWYSZKfYsQf2cDY0nzI+zo9ZjwUzbqM+xtjG2T5y0exr1xnd719ZZbNZyyx8+k6c/OCCFFQ2+cdJtdWDtU/WcpVX2aRt1u7suHm9tLu/nl8iie1bJy+uKfOcbXWs6flJ2q0zCNzcU+zLubI1CuLNk/gjb1S69d41sPuB/V2rVNLB/UYG8v72933ZeoJ0cFVyzlmUv7pA/v7XXZ6Of5SdPbZKPvzhsPUUqqxt972Zdy3Sz+mAo3+/X8g/Ji2oa3nw1HsVqXPR9tLEeJz0+2nfJb9bYpjRNtbzLts627Lh5B/SPsvGe2b/9P7P6NseOt3539TthLTcuzLXuyppNZfLDxz73/QcCel3PF9R/UPD93+fbzrlYjbcze9N2AjsIIIAAAggggAACCCDgGQGmD/XMUNARBBBAAAEEEEAAAQS6R2D22CTJGBotRaX1vhvobLTlnxTIRXOybDf8PxeNk3PVemhGVpMODL6mggD6o4t1mkq9r9crfOSm6XqzW0qGZRpQ3RddRrYEAvV2jlo7UActjGO+46n2INKjb+2Rv7y6Rx9qVQ6pdQrP/PG7Zv0wle20/M7Z5n6gjbm3+s8PdFzX6WxM63V1XbyaIvTNe+bpzVblmvnZsnJVofkc2v+B57f7Ps6Trz8/r9V0nDmq35cvzJYnX99rnq6f7Z6nNpv7zo3Hb50p47IGO6tDsn/pvBHyuOqL8R7p9+3q3671vT8DVVaZUR/MzRafPswWPNTX+pMKauuPUZISIuWEej/0mpm66GzJa+9b69teff9837f+n1C93w+v3C3atzPlyrOz2z3dzTuQv7tClj74abvX1wf/9VmJnKk+1vK1U9Pll5efYlalquzcRXOzZMUHB826jzaUiv4EKjpz97oFOYEOUYcAAggggAACCCCAAAIeFCBT0IODQpcQQAABBBBAAAEEEAi1wE3n2bMF/6gCG85swSEx4fLoLTNkuCM7y+iLNfim6w6U1hmHuuU7M0B2VW6GP+inp2h0lpz03jeNYUZilOhgXzBlf0ngYNQNKsPrynOCD87sLa0N5nZdOidGrff4y2sm26bP1BfS7481IDjzlOQOr5+tMkMXnzm83fPuXjJBEgdHtnuOPhiq97ussnW2Zns310G2RWodz/ZKKN6B9q7fmWO3nD9K5kxO6bBJTFSYPLh0mujxpiCAAAIIIIAAAggggEDvECAo2DvGiV4igAACCCCAAAIIINChQGRE2/84v2BymuiMKqPojKs3P2u9PtvYYXHyj9tOl9suHicpif7zjXbW77r6Y9bdkG9nqmCZs+isOKPkqUxBZ7Ee18eiIkI7OYp1nTjnvd3sX3XWSHlABVhGZsS1exk9XWWgEqYy8JaekysrfnqGzJqYIlEdBGrKqpsCXSZkdXMnJMuzP5ktOcNaZyMmxEfIkgUjRWfHBVNuu3CM3KqyWAPZX3r2SDl9TJLExwY3zm7fbz3dps5KDKZMUxm6P79qoty+eGwwp0tX34HI8LZ/7oO6seOkaPV75HfXTPH9DtAZrs6iswP1O/biXXNkWm6C87BvPyq8a//UENnFdgE7QSUCCCCAAAIIIIAAAgi0EhhwUpVWtVQggAACCCCAAAIIIIAAAkpA/7VQcqRB9qmpRxuajosOPg2ODpPhau2+hNgIjLpBQK9Nt0Wt43ikrkkGDhigPiKxKsiXqaZTTVEZcaoqqKLXgdxXUiflNY2ig1k62JOuAr0ZidESPijIiwR1p/ZP0hmpOwtr5Eh9k4zJjJPElvdGr8l3w7L1ZuObF4+RS+e1nRWo38WCinopKKtX/R8oo1UAVb+Luui1/OqbTkiUClhFhA1Qn0GiA0yBAonmDdVGV9/vSrWu5sGyBt9am3odPz0m+l7JcREqazFcEtW3/lnpagnVO9DV+1vb6efbVlgtR5VvtgrK6ylGKQgggAACCCCAAAIIINA7BQgK9s5xo9cIIIAAAggggAACCCCAQK8W6GxQsFc/LJ1HAAEEEEAAAQQQQAABBDwg0LU5PTzQcbqAAAIIIIAAAggggAACCCCAAAIIIIAAAggggAACCCCAAALBCRAUDM6JsxBAAAEEEEAAAQQQQAABBBBAAAEEEEAAAQQQQAABBBDotQIEBXvt0NFxBBBAAAEEEEAAAQQQQAABBBBAAAEEEEAAAQQQQAABBIITICgYnBNnIYAAAggggAACCCCAAAIIIIAAAggggAACCCCAAAIIINBrBQgK9tqho+MIIIAAAggggAACCCCAQN8RCBs0oO88DE+CAAIIIIAAAggggAACCHhQYMBJVTzYL7qEAAIIIIAAAggggAACCCDQhwWOnzgpdUePm08YFxUmA4gLmh5sIIAAAggggAACCCCAAAKhFiAoGGpRrocAAggggAACCCCAAAIIIIAAAggggAACCCCAAAIIIICAxwSYPtRjA0J3EEAAAQQQQAABBBBAAAEEEEAAAQQQQAABBBBAAAEEEAi1AEHBUItyPQQQQAABBBBAAAEEEEAAAQQQQAABBBBAAAEEEEAAAQQ8JkBQ0GMDQncQQAABBBBAAAEEEEAAAQQQQAABBBBAAAEEEEAAAQQQCLVAWKgvyPUQQAABBBBAAAEEEEAAAQQ6Fqg7elxKqo5KRXWjpCdGSYb6ULomsPlAlQwcMEBShkRIUlyk2u7adWiFAAIIIIAAAggggAACCPRlgQEnVenLD8izIYAAAggggAACCCCAAAJeEThcfVSefG+/vLG+WMorj5rduvobOXLDwlxzn43gBRqPnZC5t75rNhikIoIzT0mWi87Ikjnjks16NhBAAAEEEEAAAQQQQACB/i5ApmB/fwN4fgQQQAABBBBAAAEEEOgRgWc+PCgPLt8ux0+0/u8ydaZgXyr7y+rloy1l5iPNnzRUUodEmvuh3IgIGyg6EGi46u+Pvzjs+0wbmyS/uPwUSY6LCOUtuRYCCCCAAAIIIIAAAggg0CsFCAr2ymGj0wgggAACCCCAAAIIINCbBJat3Cl/fXNfm10emRLT6ljT8ZOyv7TOrA8PHygjkqPNfetGRW2jlFc3+araO8/apju383dXyv3PbTNvkZUU3W1BQX2T1OQoKSqtN+9nbORvK5clv14tz/x4lppWeghUPwAAGlVJREFUlMCg4cI3AggggAACCCCAAAII9E8BgoL9c9x5agQQQAABBBBAAAEEEOghgRfXFLYKCMbFhsuiOZkyY1SSTMkeIjGRg1r15rmPC2yBNX3CSz89Q9ISWmcV6ilJjaBjQnyEvP6zua2u15crXrhzjuwurpV1uyrk7Q0lsmF7hfm4lVWNct2y9bL8ztlmHRsIIIAAAggggAACCCCAQH8UGNgfH5pnRgABBBBAAAEEEEAAAQR6QkDPFPrgip22W6UkRsrfbp8lN587Smar6S0DBQR1g2PHT9ja6Z2/fnCgVR0VIgMGiOSlx8q/q3UEH1k6XS77eraNpaCkTt7bWGqrYwcBBBBAAAEEEEAAAQQQ6G8CBAX724jzvAgggAACCCCAAAIIINBjAq+uL5Ka2uZpPfVNR2bEyd9umy0pg7u2vt6KDwvMtfN67CF64Y2+/808uXnxGFvPH1q527bPDgIIIIAAAggggAACCCDQ3wQICva3Eed5EUAAAQQQQAABBBBAoMcEHl65y3avey47ReKiWk8VajupnZ2Go8flrc9L2jmDQ4bApfOGy8jMOGNX9hXVyHo1vSgFAQQQQAABBBBAAAEEEOivAgQF++vI89wIIIAAAggggAACCCDQrQIfbC6TwxVHzXvoANWYYf4glXmgkxtPvbu/ky367+lXzR9pe/iHXiVb0AbCDgIIIIAAAggggAACCPQrgbB+9bQ8LAIIIIAAAggggAACCCDQQwJrd5Xb7nT1AnuAynawEzvb91XJ/rJ6GZEc3YlW/lM37q+SlesPyRb1vaewRgaqBflyVbBy/Ih4WTQjQ0Zbsuv8rexbeq3EZz88IOt2VsimPUekuqZJ8oYPlvNnZcg509LtJwext6e4Vl5YUyhbD1TLroIaOaoyIjNTY2Rs1mA5e2qqzJuQEsRVWp/y9Wlp8qu/bxWdYanLNmVHQQABBBBAAAEEEEAAAQT6qwBBwf468jw3AggggAACCCCAAAIIdKtAUVmD7fpnT02z7XdmJzxsoOSowJ0OCOry1/f3yx2Lx3bmEnJSBfL+9NYeeTTA2nobd1WK/vxDZSHqtfj01Jttler6Y3LTHz+TbXuP2E7Rffut+jz+5l757jdybcfa23n6gwPywPPbW52yTwUs9eeNNUVy5rRU+enFEyQmsnNTr4YNHCBfm54mr35c6Lt+07ET0tB4QqIimDSnFTgVCCCAAAIIIIAAAggg0OcF+Euozw8xD4gAAggggAACCCCAAAJfhkDB4XrztnGx4aIDVG7K5WeNMJuvXFUoTcdVlK8T5e5nNwcMCDovsWz5dvndyzuc1b59HVhccu/qVgFB68l6ytQ384utVW1u/+qF7QEDgs4G7+eXyNI/5Durg9of5sioLKrwj0tQF+AkBBBAAAEEEEAAAQQQQKCPCBAU7CMDyWMggAACCCCAAAIIIICAtwRKK/2ZgslDIl13bv7kVIlqyZTTGW8r1xcFfc29pXXy+mr7+ekp0bJETWl60ddGSFKCvX/Pvr1fDlf710M0bvT6Z4ekpNz/XLr+W18ZJncsGS93XjpBzpgy1Hfqui32qVON9tbvgvJ6Wf7+AWuVjFZTmH7v/Dz5/oWjZaGaitRatqhpSldtLbNWBbWdkWh/tgJH/4O6CCchgAACCCCAAAIIIIAAAn1AgOlD+8Ag8ggIIIAAAggggAACCCDgPYEqtc6eUdIcgSmjvjPfg1Sm4QUqAKcDdro8/e4BuWBmZlCXuHf5Ntt508YmyYPXT5XwQc3Zi0vPyZXv/venskOtM2iUZa/skp9dMsHY9X0/7Jh6VAcC9TqERtHbyz8pkF89u9WoavP7XpUlaC3fPS9XrluQY62Si87IkmvvW2vW3b9ip8wZl2zuB7ORnhBlO00HIykIIIAAAggggAACCCCAQH8UIFOwP446z4wAAggggAACCCCAAALdKlBR22i7fqojMGU72ImdJXP9a/3tK6qRHWrNvWBK/rYK22n3XDbBDAjqA9ERg+RnS+wBwA82lNra6MzBQ5YpUXWmoTUgaJy8+PRhkjE02tht83vNJn/WX17W4FYBQd1wosocXHym/5kPFte2eb22Dgx12BcSFGyLinoEEEAAAQQQQAABBBDo4wIEBfv4APN4CCCAAAIIIIAAAggg0PMCkWGDbDdtbDph2+/qTpoKcE0ZnWg2f/L95qxBsyLARllNoxw/4V9/cPKoREkZbJ9SUzfLTY+V4epjlLqGY3LM0q7IMe3m4q9kGae2+p5zSkqrOmtFuaNPF81r+1oLJjdPSarb6+fQbTtTmhz2USoASkEAAQQQQAABBBBAAAEE+qMA04f2x1HnmRFAAAEEEEAAAQQQQKBbBWLU2n96uk8jGFdcYV+Hz83NLztrhGzY0Zz599baQ3LHv41t93IH1XqC1pKT4Q/8Wev19sj0GDlwyJ+NV6j6PSK5OevPOe1m9tAYZ3NzP6uljVnh2Djg6NNr64tlk2XqUuvplZZpWHW9bpsUF2E9pd3tYsvajvrEzKSOsxjbvSAHEUAAAQQQQAABBBBAAIFeKkBQsJcOHN1GAAEEEEAAAQQQQAABbwskDomQwxVHfZ0scQSm3PR87vgUiYsNl5raJl/Q8aW1Re1eTgf2rGVofNsBtZR4ewZhkZpq0x8UtF9nSEzbf07GRrV9TPelwJF1mL+tXPLtyx5au2zbrndk/tkOBtg55LDPSrKvMRigCVUIIIAAAggggAACCCCAQJ8UYPrQPjmsPBQCCCCAAAIIIIAAAgh82QKpif6MtIqqzk152V7fBwwQ+Y5lus2n321/CtEhMeG2y1XXH7PtW3dq6uzHhkT720aF26fdVN3ocnEGHztzoViVhdmZcqiyOTBrtCFT0JDgGwEEEEAAAQQQQAABBPqbQPv/+WZ/0+B5EUAAAQQQQAABBBBAAIEQCQxTU2hu3l3pu1rD0eOig3GDo0PzJ9hFZ2TJY//c47v2ocP1snFvVZu9znJM8+lcG9Da8JAjq3CEpa1+HmspPmIPtlmPdbSd55jCdMaEZFk0K6OjZr7jOWlxQZ1nnLSjsNrY9H2nDrFnQ9oOsoMAAggggAACCCCAAAII9GGB0PxF2oeBeDQEEEAAAQQQQAABBBBAoCsCw1Ls01T+Y9VBuWZ+dlcu1aqNXlNv5inJsmZTme+YscZgqxNVRWaivR9rtpTJsRMnJUyteWgttSpwuWXPEbMqPGyg6LURjZLlWItv3a5K+frUNOOw7buh8bht37mTrPpvXXNxn1rHcP7kNHF0ydms0/s6ELvq88Nmu6iWtR7NCjYQQAABBBBAAAEEEEAAgX4kwPSh/WiweVQEEEAAAQQQQAABBBDoOYEFk1JtN3umg2k+bScHsXPFWSODOEt8wb+URH92nM5a/PtHB1u1feydvb41Co0D2Zn2jLxhyfbg4qsfF0qdulagsnp7eaBqW93Y7CHmfolaY/D2J74w90O18dzH9uecO9k+JqG6D9dBAAEEEEAAAQQQQAABBHqDAEHB3jBK9BEBBBBAAAEEEEAAAQR6ncBoFVSbmJdg9ruqpklWbW3O7DMrXWzMGJUoCfERQV3h5kWjbec98Px2eeTNPVJQXi/7D9fJfS/tkCdf32s75wffGmXbj44YJF+f6Z/is+nYCbnk3tWyv6zePK+splF+s2KHfLSh1Kxra+Oui8fbDr2fXyKL7lklL68rktKqo3LyZPPhpuMnZbfKJHxzQ7E0qnt2pjz9jn29xevPyelMc85FAAEEEEAAAQQQQAABBPqUANOH9qnh5GEQQAABBBBAAAEEEEDASwJLv5krSx/81OzST/5nozz2oxmSbVmrzzzYhY1LzhohD6/Y2WHLhWqaz4eH7pKiUn8A79GVu0V/AhWdxaeDjs5yk3qeN9YUmdV6PcPv/HyV6KlGddGBwmBLTmqMXL4w2xaM1Ne756nNbV7i8VtnyriswW0eNw6o2VHlJ09tEh2INYpet3CEY11E4xjfCCCAAAIIIIAAAggggEB/ECBTsD+MMs+IAAIIIIAAAggggAACX4rAqXmJMjw91rx3XcMxuexXq2XLwWqzzs3Gv50+LOjmy26YJukp0R2eP1JlOP72mkkBz0tPiJLbHBl++kQdDLQGBC89O7ipTW9YmCtXdiJ7b29pbcB+WSt1ZuH3H8mXd9YfslbL0m/k2vbZQQABBBBAAAEEEEAAAQT6mwBBwf424jwvAggggAACCCCAAAII9KjAbd8ea7ufDp5de99a+V+PfCaPv7dfNh+osq3lZzu5ZcfIxHMeGxwdJvOmBrdOns6Se+6O2bJobpZERQ5yXkpiosJkyYKR8ux/zJKh8f41CJ0nLj49Ux794YyAAUa9duHNi8fI5V8d4WwWcD9s4ABZek6urPjpGTJrYkrAflkbllX7M/+s9Xq60dfyD8nP/r5VvvXzj2TdFvuahrMnpciE4fHWJmwjgAACCCCAAAIIIIAAAv1OYMBJVfrdU/PACCCAAAIIIIAAAggggEAPCuiA1d2Pb2rzjvffOE1mj01q83h3HKisbZRdaq0+XcZkDhYdYOxs0Wv8bSuo9q31Ny4rXmItwca9pXUSFzVI1YWJXo8w2FLfeFz2ldRJuVqfUGf96bbpKtiYkRgt4YMGBLzMwrs+kMqqxoDH9FSoj37/1DbbBmxEJQIIIIAAAggggAACCCDQBwU6/1dfH0TgkRBAAAEEEEAAAQQQQACB7hQ4Z1q6pA2Jkjuf2CjllUdb3aqowr/WX6uD3VSREBshp+ZFuLp6hFpLcNLIIQGv0dV1E3UQMJh1A603rbasHWit19OY3nhOHgFBKwrbCCCAAAIIIIAAAggg0G8FCAr226HnwRFAAAEEEEAAAQQQQKAnBablJshL/3mGvLWhWF5ZWyQbdx+RhqPHfV04XB04y60n+9db79XQeMKcfnWQmo40NTlKzp6eJhfMypRhSR2vodhbn5t+I4AAAggggAACCCCAAAKdFWD60M6KcT4CCCCAAAIIIIAAAgggECIBvZhDTcMxiVLZcW1NjRmiW/Xpy1TVH/NNM4phnx5mHg4BBBBAAAEEEEAAAQRcChAUdAlIcwQQQAABBBBAAAEEEEAAAQQQQAABBBBAAAEEEEAAAQS8LjDQ6x2kfwgggAACCCCAAAIIIIAAAggggAACCCCAAAIIIIAAAggg4E6AoKA7P1ojgAACCCCAAAIIIIAAAggggAACCCCAAAIIIIAAAggg4HkBgoKeHyI6iAACCCCAAAIIIIAAAggggAACCCCAAAIIIIAAAggggIA7AYKC7vxojQACCCCAAAIIIIAAAggggAACCCCAAAIIIIAAAggggIDnBQgKen6I6CACCCCAAAIIIIAAAggggAACCCCAAAIIIIAAAggggAAC7gQICrrzozUCCCCAAAIIIIAAAggggAACCCCAAAIIIIAAAggggAACnhcgKOj5IaKDCCCAAAIIIIAAAggggAACCCCAAAIIIIAAAggggAACCLgTICjozo/WCCCAAAIIIIAAAggggAACCCCAAAIIIIAAAggggAACCHhegKCg54eIDiKAAAIIIIAAAggggAACCCCAAAIIIIAAAggggAACCCDgToCgoDs/WiOAAAIIIIAAAggggAACCCCAAAIIIIAAAggggAACCCDgeQGCgp4fIjqIAAIIIIAAAggggAACCCCAAAIIIIAAAggggAACCCCAgDsBgoLu/GiNAAIIIIAAAggggAACCCCAAAIIIIAAAggggAACCCCAgOcFCAp6fojoIAIIIIAAAggggAACCCCAAAIIIIAAAggggAACCCCAAALuBAgKuvOjNQIIIIAAAggggAACCCCAAAIIIIAAAggggAACCCCAAAKeFyAo6PkhooMIIIAAAggggAACCCCAAAIIIIAAAggggAACCCCAAAIIuBMgKOjOj9YIIIAAAggggAACCCCAAAIIIIAAAggggAACCCCAAAIIeF6AoKDnh4gOIoAAAggggAACCCCAAAIIIIAAAggggAACCCCAAAIIIOBOgKCgOz9aI4AAAggggAACCCCAAAIIIIAAAggggAACCCCAAAIIIOB5AYKCnh8iOogAAggggAACCCCAAAIIIIAAAggggAACCCCAAAIIIICAOwGCgu78aI0AAggggAACCCCAAAIIIIAAAggggAACCCCAAAIIIICA5wUICnp+iOggAggggAACCCCAAAIIIIAAAggggAACCCCAAAIIIIAAAu4ECAq686M1AggggAACCCCAAAIIIIAAAggggAACCCCAAAIIIIAAAp4XICjo+SGigwgggAACCCCAAAIIIIAAAggggAACCCCAAAIIIIAAAgi4EyAo6M6P1ggggAACCCCAAAIIIIAAAggggAACCCCAAAIIIIAAAgh4XoCgoOeHiA4igAACCCCAAAIIIIAAAggggAACCCCAAAIIIIAAAggg4E6AoKA7P1ojgAACCCCAAAIIIIAAAggggAACCCCAAAIIIIAAAggg4HkBgoKeHyI6iAACCCCAAAIIIIAAAggggAACCCCAAAIIIIAAAggggIA7AYKC7vxojQACCCCAAAIIIIAAAggggAACCCCAAAIIIIAAAggggIDnBQgKen6I6CACCCCAAAIIIIAAAggggAACCCCAAAIIIIAAAggggAAC7gQICrrzozUCCCCAAAIIIIAAAggggAACCCCAAAIIIIAAAggggAACnhcgKOj5IaKDCCCAAAIIIIAAAggggAACCCCAAAIIIIAAAggggAACCLgTICjozo/WCCCAAAIIIIAAAggggAACCCCAAAIIIIAAAggggAACCHhegKCg54eIDiKAAAIIIIAAAggggAACCCCAAAIIIIAAAggggAACCCDgToCgoDs/WiOAAAIIIIAAAggggAACCCCAAAIIIIAAAggggAACCCDgeQGCgp4fIjqIAAIIIIAAAggggAACCCCAAAIIIIAAAggggAACCCCAgDsBgoLu/GiNAAIIIIAAAggggAACCCCAAAIIIIAAAggggAACCCCAgOcFCAp6fojoIAIIIIAAAggggAACCCCAAAIIIIAAAggggAACCCCAAALuBAgKuvOjNQIIIIAAAggggAACCCCAAAIIIIAAAggggAACCCCAAAKeFyAo6PkhooMIIIAAAggggAACCCCAAAIIIIAAAggggAACCCCAAAIIuBMgKOjOj9YIIIAAAggggAACCCCAAAIIIIAAAggggAACCCCAAAIIeF6AoKDnh4gOIoAAAggggAACCCCAAAIIIIAAAggggAACCCCAAAIIIOBOgKCgOz9aI4AAAggggAACCCCAAAIIIIAAAggggAACCCCAAAIIIOB5AYKCnh8iOogAAggggAACCCCAAAIIIIAAAggggAACCCCAAAIIIICAOwGCgu78aI0AAggggAACCCCAAAIIIIAAAggggAACCCCAAAIIIICA5wUICnp+iOggAggggAACCCCAAAIIIIAAAggggAACCCCAAAIIIIAAAu4ECAq686M1AggggAACCCCAAAIIIIAAAggggAACCCCAAAIIIIAAAp4XICjo+SGigwgggAACCCCAAAIIIIAAAggggAACCCCAAAIIIIAAAgi4EyAo6M6P1ggggAACCCCAAAIIIIAAAggggAACCCCAAAIIIIAAAgh4XoCgoOeHiA4igAACCCCAAAIIIIAAAggggAACCCCAAAIIIIAAAggg4E6AoKA7P1ojgAACCCCAAAIIIIAAAggggAACCCCAAAIIIIAAAggg4HkBgoKeHyI6iAACCCCAAAIIIIAAAggggAACCCCAAAIIIIAAAggggIA7AYKC7vxojQACCCCAAAIIIIAAAggggAACCCCAAAIIIIAAAggggIDnBQgKen6I6CACCCCAAAIIIIAAAggggAACCCCAAAIIIIAAAggggAAC7gQICrrzozUCCCCAAAIIIIAAAggggAACCCCAAAIIIIAAAggggAACnhcgKOj5IaKDCCCAAAIIIIAAAggggAACCCCAAAIIIIAAAggggAACCLgTICjozo/WCCCAAAIIIIAAAggggAACCCCAAAIIIIAAAggggAACCHhegKCg54eIDiKAAAIIIIAAAggggAACCCCAAAIIIIAAAggggAACCCDgToCgoDs/WiOAAAIIIIAAAggggAACCCCAAAIIIIAAAggggAACCCDgeQGCgp4fIjqIAAIIIIAAAggggAACCCCAAAIIIIAAAggggAACCCCAgDsBgoLu/GiNAAIIIIAAAggggAACCCCAAAIIIIAAAggggAACCCCAgOcFCAp6fojoIAIIIIAAAggggAACCCCAAAIIIIAAAggggAACCCCAAALuBAgKuvOjNQIIIIAAAggggAACCCCAAAIIIIAAAggggAACCCCAAAKeFyAo6PkhooMIIIAAAggggAACCCCAAAIIIIAAAggggAACCCCAAAIIuBMgKOjOj9YIIIAAAggggAACCCCAAAIIIIAAAggggAACCCCAAAIIeF6AoKDnh4gOIoAAAggggAACCCCAAAIIIIAAAggggAACCCCAAAIIIOBOgKCgOz9aI4AAAggggAACCCCAAAIIIIAAAggggAACCCCAAAIIIOB5AYKCnh8iOogAAggggAACCCCAAAIIIIAAAggggAACCCCAAAIIIICAOwGCgu78aI0AAggggAACCCCAAAIIIIAAAggggAACCCCAAAIIIICA5wUICnp+iOggAggggAACCCCAAAIIIIAAAggggAACCCCAAAIIIIAAAu4ECAq686M1AggggAACCCCAAAIIIIAAAggggAACCCCAAAIIIIAAAp4XICjo+SGigwgggAACCCCAAAIIIIAAAggggAACCCCAAAIIIIAAAgi4EyAo6M6P1ggggAACCCCAAAIIIIAAAggggAACCCCAAAIIIIAAAgh4XoCgoOeHiA4igAACCCCAAAIIIIAAAggggAACCCCAAAIIIIAAAggg4E7g/wOegdq2hYwWWwAAAABJRU5ErkJggg=="
-    }
-   },
-   "cell_type": "markdown",
-   "id": "848ba742-7443-4123-8115-061da9823309",
-   "metadata": {},
-   "source": [
-    "# Self RAG\n",
-    "\n",
-    "Self-reflection can enhance RAG, enabling correction of poor quality retrieval or generations.\n",
-    "\n",
-    "Several recent papers focus on this theme, but implementing the ideas can be tricky.\n",
-    "\n",
-    "Here we show how to implement self-reflective RAG using `Nomic`, `Mistral`, and `LangGraph`.\n",
-    "\n",
-    "We'll focus on ideas from one paper, `Self RAG` [here](https://arxiv.org/abs/2310.11511).\n",
-    "\n",
-    "This can run fully locally (e.g., on a laptop).\n",
-    "\n",
-    "![Screenshot 2024-02-15 at 3.33.46 PM.png](attachment:e3e60bc2-6033-4d66-af6d-1dfafce5cb9f.png)\n",
-    "\n",
-    "### Embeddings\n",
-    "\n",
-    "We'll use Nomic's recently released [v1](https://blog.nomic.ai/posts/nomic-embed-text-v1) and [v1.5](https://blog.nomic.ai/posts/nomic-embed-matryoshka) embeddings.\n",
-    "\n",
-    "Simply: \n",
-    "\n",
-    "(1) Clone [`llama.cpp`](https://github.com/ggerganov/llama.cpp):\n",
-    "\n",
-    "```\n",
-    "git clone https://github.com/ggerganov/llama.cpp\n",
-    "```\n",
-    "\n",
-    "(2) Download GGUF weights for Nomic's embedding model(s), allowing them to be run locally: \n",
-    "\n",
-    "* https://huggingface.co/nomic-ai/nomic-embed-text-v1-GGUF\n",
-    "* https://huggingface.co/nomic-ai/nomic-embed-text-v1.5-GGUF\n",
-    "\n",
-    "(3) Add to `llama.cpp/model` directory.\n",
-    "\n",
-    "(4) Build llama.cpp:\n",
-    "```\n",
-    "cd llama.cpp\n",
-    "make\n",
-    "```\n",
-    "\n",
-    "### LLM\n",
-    "\n",
-    "(1) Download [Ollama app](https://ollama.ai/).\n",
-    "\n",
-    "(2) Download a `Mistral` model from various Mistral versions [here](https://ollama.ai/library/mistral) and Mixtral versions [here](https://ollama.ai/library/mixtral) available.\n",
-    "```\n",
-    "ollama pull mistral:instruct\n",
-    "```\n",
-    "\n",
-    "(3) Set `local_llm` to the model downloaded."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "d35ebcdc-8510-4ce5-a0e9-8db714944c68",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "! ollama pull mistral:instruct"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 43,
-   "id": "32d50725-04c1-487c-89cc-a119ec708c17",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# Ollama model name\n",
-    "local_llm = \"mistral:instruct\"\n",
-    "\n",
-    "# Local embedding model paths (downloaded above)O\n",
-    "embd_model_path = \"/Users/rlm/Desktop/Code/llama.cpp/models/nomic-embd/nomic-embed-text-v1.Q4_K_S.gguf\"\n",
-    "# embd_model_path = \"/Users/rlm/Desktop/Code/llama.cpp/models/nomic-embd/nomic-embed-text-v1.f16.gguf\"\n",
-    "# embd_model_path = \"/Users/rlm/Desktop/Code/llama.cpp/models/nomic-embd/nomic-embed-text-v1.5.f16.gguf\"\n",
-    "# embd_model_path = \"/Users/rlm/Desktop/Code/llama.cpp/models/nomic-embd/nomic-embed-text-v1.5.Q4_K_S.gguf\""
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "ba68a46d-b617-4fdc-9113-fabdcf736feb",
-   "metadata": {},
-   "source": [
-    "## Indexing\n",
-    "\n",
-    "First, let's index a popular blog post on agents. \n",
-    "\n",
-    "For local, we can use Nomic's recently released [v1](https://blog.nomic.ai/posts/nomic-embed-text-v1) and [v1.5](https://blog.nomic.ai/posts/nomic-embed-matryoshka) embeddings.\n",
-    "\n",
-    "We'll use the [llama.cpp](https://github.com/ggerganov/llama.cpp) integration.\n",
-    "\n",
-    "We'll use a local vectorstore, [Chroma](https://python.langchain.com/docs/integrations/vectorstores/chroma)."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "c3bb9060-ad74-4470-9991-2ba167b6b8d8",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from langchain.text_splitter import RecursiveCharacterTextSplitter\n",
-    "from langchain_community.document_loaders import WebBaseLoader\n",
-    "from langchain_community.vectorstores import Chroma\n",
-    "from langchain_community.embeddings import LlamaCppEmbeddings\n",
-    "from langchain_nomic.embeddings import NomicEmbeddings\n",
-    "\n",
-    "# Load\n",
-    "url = \"https://lilianweng.github.io/posts/2023-06-23-agent/\"\n",
-    "loader = WebBaseLoader(url)\n",
-    "docs = loader.load()\n",
-    "\n",
-    "# Split\n",
-    "text_splitter = RecursiveCharacterTextSplitter.from_tiktoken_encoder(\n",
-    "    chunk_size=500, chunk_overlap=100\n",
-    ")\n",
-    "all_splits = text_splitter.split_documents(docs)\n",
-    "\n",
-    "# Embed and index\n",
-    "embedding = LlamaCppEmbeddings(model_path=embd_model_path, n_batch=512)\n",
-    "\n",
-    "# Index\n",
-    "vectorstore = Chroma.from_documents(\n",
-    "    documents=all_splits,\n",
-    "    collection_name=\"rag-chroma\",\n",
-    "    embedding=embedding,\n",
-    ")\n",
-    "retriever = vectorstore.as_retriever()"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "3d3339b9-5f30-4d54-bfc9-9091c6035955",
-   "metadata": {},
-   "source": [
-    "## State\n",
-    "\n",
-    "Every node in our graph will modify `state`, which is dict that contains values (`question`, `documents`, etc) relevant to RAG."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 44,
-   "id": "90fb1dc6-c482-483a-8441-39965c401beb",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from typing import Dict, TypedDict\n",
-    "\n",
-    "from langchain_core.messages import BaseMessage\n",
-    "\n",
-    "\n",
-    "class GraphState(TypedDict):\n",
-    "    \"\"\"\n",
-    "    Represents the state of our graph.\n",
-    "\n",
-    "    Attributes:\n",
-    "        keys: A dictionary where each key is a string.\n",
-    "    \"\"\"\n",
-    "\n",
-    "    keys: Dict[str, any]"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "b89b2f21-b6b3-42db-a826-ff9d9aaec443",
-   "metadata": {},
-   "source": [
-    "### Nodes and Edges\n",
-    "\n",
-    "Every node in the graph we laid out above is a function.\n",
-    "\n",
-    "Each node will modify the state in some way.\n",
-    "\n",
-    "Each edge will choose which node to call next."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 45,
-   "id": "5324ea49-5745-47b5-a0a5-bf58c8babe46",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "import json\n",
-    "import operator\n",
-    "from typing import Annotated, Sequence, TypedDict\n",
-    "\n",
-    "from langchain import hub\n",
-    "from langchain.prompts import PromptTemplate\n",
-    "from langchain_community.vectorstores import Chroma\n",
-    "from langchain_core.messages import BaseMessage, FunctionMessage\n",
-    "from langchain_core.output_parsers import StrOutputParser\n",
-    "from langchain_core.pydantic_v1 import BaseModel, Field\n",
-    "from langchain_core.runnables import RunnablePassthrough\n",
-    "from langchain_community.chat_models import ChatOllama\n",
-    "from langchain_core.output_parsers import JsonOutputParser\n",
-    "\n",
-    "### Nodes ###\n",
-    "\n",
-    "\n",
-    "def retrieve(state):\n",
-    "    \"\"\"\n",
-    "    Retrieve documents\n",
-    "\n",
-    "    Args:\n",
-    "        state (dict): The current graph state\n",
-    "\n",
-    "    Returns:\n",
-    "        state (dict): New key added to state, documents, that contains retrieved documents\n",
-    "    \"\"\"\n",
-    "    print(\"---RETRIEVE---\")\n",
-    "    state_dict = state[\"keys\"]\n",
-    "    question = state_dict[\"question\"]\n",
-    "    documents = retriever.get_relevant_documents(question)\n",
-    "    return {\"keys\": {\"documents\": documents, \"question\": question}}\n",
-    "\n",
-    "\n",
-    "def generate(state):\n",
-    "    \"\"\"\n",
-    "    Generate answer\n",
-    "\n",
-    "    Args:\n",
-    "        state (dict): The current graph state\n",
-    "\n",
-    "    Returns:\n",
-    "        state (dict): New key added to state, generation, that contains LLM generation\n",
-    "    \"\"\"\n",
-    "    print(\"---GENERATE---\")\n",
-    "    state_dict = state[\"keys\"]\n",
-    "    question = state_dict[\"question\"]\n",
-    "    documents = state_dict[\"documents\"]\n",
-    "\n",
-    "    # Prompt\n",
-    "    prompt = hub.pull(\"rlm/rag-prompt\")\n",
-    "\n",
-    "    # LLM\n",
-    "    llm = ChatOllama(model=local_llm, temperature=0)\n",
-    "\n",
-    "    # Post-processing\n",
-    "    def format_docs(docs):\n",
-    "        return \"\\n\\n\".join(doc.page_content for doc in docs)\n",
-    "\n",
-    "    # Chain\n",
-    "    rag_chain = prompt | llm | StrOutputParser()\n",
-    "\n",
-    "    # Run\n",
-    "    generation = rag_chain.invoke({\"context\": documents, \"question\": question})\n",
-    "    return {\n",
-    "        \"keys\": {\"documents\": documents, \"question\": question, \"generation\": generation}\n",
-    "    }\n",
-    "\n",
-    "\n",
-    "def grade_documents(state):\n",
-    "    \"\"\"\n",
-    "    Determines whether the retrieved documents are relevant to the question.\n",
-    "\n",
-    "    Args:\n",
-    "        state (dict): The current graph state\n",
-    "\n",
-    "    Returns:\n",
-    "        state (dict): Updates documents key with relevant documents\n",
-    "    \"\"\"\n",
-    "\n",
-    "    print(\"---CHECK RELEVANCE---\")\n",
-    "    state_dict = state[\"keys\"]\n",
-    "    question = state_dict[\"question\"]\n",
-    "    documents = state_dict[\"documents\"]\n",
-    "\n",
-    "    # LLM\n",
-    "    llm = ChatOllama(model=local_llm, format=\"json\", temperature=0)\n",
-    "\n",
-    "    # Prompt\n",
-    "    prompt = PromptTemplate(\n",
-    "        template=\"\"\"You are a grader assessing relevance of a retrieved document to a user question. \\n \n",
-    "        Here is the retrieved document: \\n\\n {context} \\n\\n\n",
-    "        Here is the user question: {question} \\n\n",
-    "        If the document contains keywords related to the user question, grade it as relevant. \\n\n",
-    "        It does not need to be a stringent test. The goal is to filter out erroneous retrievals. \\n\n",
-    "        Give a binary score 'yes' or 'no' score to indicate whether the document is relevant to the question. \\n\n",
-    "        Provide the binary score as a JSON with a single key 'score' and no preamble or explanation.\"\"\",\n",
-    "        input_variables=[\"question\",\"context\"],\n",
-    "    )\n",
-    "\n",
-    "    # Chain\n",
-    "    chain = prompt | llm | JsonOutputParser()\n",
-    "\n",
-    "    # Score\n",
-    "    filtered_docs = []\n",
-    "    for d in documents:\n",
-    "        score = chain.invoke(\n",
-    "            {\n",
-    "                \"question\": question,\n",
-    "                \"context\": d.page_content,\n",
-    "            }\n",
-    "        )\n",
-    "        grade = score[\"score\"]\n",
-    "        if grade == \"yes\":\n",
-    "            print(\"---GRADE: DOCUMENT RELEVANT---\")\n",
-    "            filtered_docs.append(d)\n",
-    "        else:\n",
-    "            print(\"---GRADE: DOCUMENT NOT RELEVANT---\")\n",
-    "            continue\n",
-    "\n",
-    "    return {\"keys\": {\"documents\": filtered_docs, \"question\": question}}\n",
-    "\n",
-    "\n",
-    "def transform_query(state):\n",
-    "    \"\"\"\n",
-    "    Transform the query to produce a better question.\n",
-    "\n",
-    "    Args:\n",
-    "        state (dict): The current graph state\n",
-    "\n",
-    "    Returns:\n",
-    "        state (dict): Updates question key with a re-phrased question\n",
-    "    \"\"\"\n",
-    "\n",
-    "    print(\"---TRANSFORM QUERY---\")\n",
-    "    state_dict = state[\"keys\"]\n",
-    "    question = state_dict[\"question\"]\n",
-    "    documents = state_dict[\"documents\"]\n",
-    "\n",
-    "    # LLM\n",
-    "    llm = ChatOllama(model=local_llm, temperature=0)\n",
-    "    \n",
-    "    # Create a prompt template with format instructions and the query\n",
-    "    prompt = PromptTemplate(\n",
-    "        template=\"\"\"You are generating questions that is well optimized for retrieval. \\n \n",
-    "        Look at the input and try to reason about the underlying sematic intent / meaning. \\n \n",
-    "        Here is the initial question:\n",
-    "        \\n ------- \\n\n",
-    "        {question} \n",
-    "        \\n ------- \\n\n",
-    "        Formulate an improved question:\"\"\",\n",
-    "        input_variables=[\"question\"],\n",
-    "    )\n",
-    "\n",
-    "    # Chain\n",
-    "    chain = prompt | llm | StrOutputParser()\n",
-    "    better_question = chain.invoke({\"question\": question})\n",
-    "\n",
-    "    return {\"keys\": {\"documents\": documents, \"question\": better_question}}\n",
-    "\n",
-    "\n",
-    "def prepare_for_final_grade(state):\n",
-    "    \"\"\"\n",
-    "    Passthrough state for final grade.\n",
-    "\n",
-    "    Args:\n",
-    "        state (dict): The current graph state\n",
-    "\n",
-    "    Returns:\n",
-    "        state (dict): The current graph state\n",
-    "    \"\"\"\n",
-    "\n",
-    "    print(\"---FINAL GRADE---\")\n",
-    "    state_dict = state[\"keys\"]\n",
-    "    question = state_dict[\"question\"]\n",
-    "    documents = state_dict[\"documents\"]\n",
-    "    generation = state_dict[\"generation\"]\n",
-    "\n",
-    "    return {\n",
-    "        \"keys\": {\"documents\": documents, \"question\": question, \"generation\": generation}\n",
-    "    }\n",
-    "\n",
-    "\n",
-    "### Edges ###\n",
-    "\n",
-    "\n",
-    "def decide_to_generate(state):\n",
-    "    \"\"\"\n",
-    "    Determines whether to generate an answer, or re-generate a question.\n",
-    "\n",
-    "    Args:\n",
-    "        state (dict): The current state of the agent, including all keys.\n",
-    "\n",
-    "    Returns:\n",
-    "        str: Next node to call\n",
-    "    \"\"\"\n",
-    "\n",
-    "    print(\"---DECIDE TO GENERATE---\")\n",
-    "    state_dict = state[\"keys\"]\n",
-    "    question = state_dict[\"question\"]\n",
-    "    filtered_documents = state_dict[\"documents\"]\n",
-    "\n",
-    "    if not filtered_documents:\n",
-    "        # All documents have been filtered check_relevance\n",
-    "        # We will re-generate a new query\n",
-    "        print(\"---DECISION: TRANSFORM QUERY---\")\n",
-    "        return \"transform_query\"\n",
-    "    else:\n",
-    "        # We have relevant documents, so generate answer\n",
-    "        print(\"---DECISION: GENERATE---\")\n",
-    "        return \"generate\"\n",
-    "\n",
-    "\n",
-    "def grade_generation_v_documents(state):\n",
-    "    \"\"\"\n",
-    "    Determines whether the generation is grounded in the document.\n",
-    "\n",
-    "    Args:\n",
-    "        state (dict): The current state of the agent, including all keys.\n",
-    "\n",
-    "    Returns:\n",
-    "        str: Binary decision\n",
-    "    \"\"\"\n",
-    "\n",
-    "    print(\"---GRADE GENERATION vs DOCUMENTS---\")\n",
-    "    state_dict = state[\"keys\"]\n",
-    "    question = state_dict[\"question\"]\n",
-    "    documents = state_dict[\"documents\"]\n",
-    "    generation = state_dict[\"generation\"]\n",
-    "\n",
-    "    # LLM\n",
-    "    llm = ChatOllama(model=local_llm, format=\"json\", temperature=0)\n",
-    "\n",
-    "    # Prompt\n",
-    "    prompt = PromptTemplate(\n",
-    "        template=\"\"\"You are a grader assessing whether an answer is grounded in / supported by a set of facts. \\n \n",
-    "        Here are the facts:\n",
-    "        \\n ------- \\n\n",
-    "        {documents} \n",
-    "        \\n ------- \\n\n",
-    "        Here is the answer: {generation}\n",
-    "        Give a binary score 'yes' or 'no' score to indicate whether the answer is grounded in / supported by a set of facts. \\n\n",
-    "        Provide the binary score as a JSON with a single key 'score' and no preamble or explanation.\"\"\",\n",
-    "        input_variables=[\"generation\", \"documents\"],\n",
-    "    )\n",
-    "\n",
-    "    # Chain\n",
-    "    chain = prompt | llm | JsonOutputParser()\n",
-    "    score = chain.invoke({\"generation\": generation, \"documents\": documents})\n",
-    "    grade = score[\"score\"]\n",
-    "\n",
-    "    if grade == \"yes\":\n",
-    "        print(\"---DECISION: SUPPORTED, MOVE TO FINAL GRADE---\")\n",
-    "        return \"supported\"\n",
-    "    else:\n",
-    "        print(\"---DECISION: NOT SUPPORTED, GENERATE AGAIN---\")\n",
-    "        return \"not supported\"\n",
-    "\n",
-    "\n",
-    "def grade_generation_v_question(state):\n",
-    "    \"\"\"\n",
-    "    Determines whether the generation addresses the question.\n",
-    "\n",
-    "    Args:\n",
-    "        state (dict): The current state of the agent, including all keys.\n",
-    "\n",
-    "    Returns:\n",
-    "        str: Binary decision\n",
-    "    \"\"\"\n",
-    "\n",
-    "    print(\"---GRADE GENERATION vs QUESTION---\")\n",
-    "    state_dict = state[\"keys\"]\n",
-    "    question = state_dict[\"question\"]\n",
-    "    documents = state_dict[\"documents\"]\n",
-    "    generation = state_dict[\"generation\"]\n",
-    "\n",
-    "    llm = ChatOllama(model=local_llm, format=\"json\", temperature=0)\n",
-    "\n",
-    "    # Prompt\n",
-    "    prompt = PromptTemplate(\n",
-    "        template=\"\"\"You are a grader assessing whether an answer is useful to resolve a question. \\n \n",
-    "        Here is the answer:\n",
-    "        \\n ------- \\n\n",
-    "        {generation} \n",
-    "        \\n ------- \\n\n",
-    "        Here is the question: {question}\n",
-    "        Give a binary score 'yes' or 'no' to indicate whether the answer is useful to resolve a question. \\n\n",
-    "        Provide the binary score as a JSON with a single key 'score' and no preamble or explanation.\"\"\",\n",
-    "        input_variables=[\"generation\", \"question\"],\n",
-    "    )\n",
-    "\n",
-    "    # Prompt\n",
-    "    chain = prompt | llm | JsonOutputParser()\n",
-    "    score = chain.invoke({\"generation\": generation, \"question\": question})\n",
-    "    grade = score[\"score\"]\n",
-    "\n",
-    "    if grade == \"yes\":\n",
-    "        print(\"---DECISION: USEFUL---\")\n",
-    "        return \"useful\"\n",
-    "    else:\n",
-    "        print(\"---DECISION: NOT USEFUL---\")\n",
-    "        return \"not useful\""
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "bdf3826c-b668-4f0b-bf83-81c40baaaf02",
-   "metadata": {},
-   "source": [
-    "## Build Graph\n",
-    "\n",
-    "This just follows the flow we outlined in the figure above."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 46,
-   "id": "5605dee4-b2df-46ae-a640-cc2ed90c21a6",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "import pprint\n",
-    "\n",
-    "from langgraph.graph import END, StateGraph\n",
-    "\n",
-    "workflow = StateGraph(GraphState)\n",
-    "\n",
-    "# Define the nodes\n",
-    "workflow.add_node(\"retrieve\", retrieve)  # retrieve\n",
-    "workflow.add_node(\"grade_documents\", grade_documents)  # grade documents\n",
-    "workflow.add_node(\"generate\", generate)  # generatae\n",
-    "workflow.add_node(\"transform_query\", transform_query)  # transform_query\n",
-    "workflow.add_node(\"prepare_for_final_grade\", prepare_for_final_grade)  # passthrough\n",
-    "\n",
-    "# Build graph\n",
-    "workflow.set_entry_point(\"retrieve\")\n",
-    "workflow.add_edge(\"retrieve\", \"grade_documents\")\n",
-    "workflow.add_conditional_edges(\n",
-    "    \"grade_documents\",\n",
-    "    decide_to_generate,\n",
-    "    {\n",
-    "        \"transform_query\": \"transform_query\",\n",
-    "        \"generate\": \"generate\",\n",
-    "    },\n",
-    ")\n",
-    "workflow.add_edge(\"transform_query\", \"retrieve\")\n",
-    "workflow.add_conditional_edges(\n",
-    "    \"generate\",\n",
-    "    grade_generation_v_documents,\n",
-    "    {\n",
-    "        \"supported\": \"prepare_for_final_grade\",\n",
-    "        \"not supported\": \"generate\",\n",
-    "    },\n",
-    ")\n",
-    "workflow.add_conditional_edges(\n",
-    "    \"prepare_for_final_grade\",\n",
-    "    grade_generation_v_question,\n",
-    "    {\n",
-    "        \"useful\": END,\n",
-    "        \"not useful\": \"transform_query\",\n",
-    "    },\n",
-    ")\n",
-    "\n",
-    "# Compile\n",
-    "app = workflow.compile()"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "105ae1b5-6963-4186-bb83-6d6cb96d095f",
-   "metadata": {},
-   "source": [
-    "## Run\n",
-    "\n",
-    "Trace for below run: https://smith.langchain.com/public/928651fd-85b3-49ff-b481-bd28417645e5/r"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 47,
-   "id": "26a64f7d-0c14-4e31-a67f-63021dee626e",
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "\n",
-      "llama_print_timings:        load time =     149.49 ms\n",
-      "llama_print_timings:      sample time =       0.00 ms /     1 runs   (    0.00 ms per token,      inf tokens per second)\n",
-      "llama_print_timings: prompt eval time =      17.39 ms /    12 tokens (    1.45 ms per token,   690.01 tokens per second)\n",
-      "llama_print_timings:        eval time =       0.00 ms /     1 runs   (    0.00 ms per token,      inf tokens per second)\n",
-      "llama_print_timings:       total time =      17.39 ms /    13 tokens\n"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "---RETRIEVE---\n",
-      "\"Node 'retrieve':\"\n",
-      "'\\n---\\n'\n",
-      "---CHECK RELEVANCE---\n",
-      "---GRADE: DOCUMENT RELEVANT---\n",
-      "---GRADE: DOCUMENT RELEVANT---\n",
-      "---GRADE: DOCUMENT RELEVANT---\n",
-      "---GRADE: DOCUMENT RELEVANT---\n",
-      "\"Node 'grade_documents':\"\n",
-      "'\\n---\\n'\n",
-      "---DECIDE TO GENERATE---\n",
-      "---DECISION: GENERATE---\n",
-      "---GENERATE---\n",
-      "\"Node 'generate':\"\n",
-      "'\\n---\\n'\n",
-      "---GRADE GENERATION vs DOCUMENTS---\n",
-      "---DECISION: SUPPORTED, MOVE TO FINAL GRADE---\n",
-      "---FINAL GRADE---\n",
-      "\"Node 'prepare_for_final_grade':\"\n",
-      "'\\n---\\n'\n",
-      "---GRADE GENERATION vs QUESTION---\n",
-      "---DECISION: USEFUL---\n",
-      "\"Node '__end__':\"\n",
-      "'\\n---\\n'\n",
-      "(' In a LLM (large language model)-powered autonomous agent system, LLM '\n",
-      " 'functions as the agent’s brain, complemented by several key components. One '\n",
-      " 'of these components is memory. Memory can be defined as the processes used '\n",
-      " 'to acquire, store, retain, and later retrieve information. There are several '\n",
-      " 'types of memory in human brains:\\n'\n",
-      " '\\n'\n",
-      " '1. Sensory Memory: This is the earliest stage of memory, providing the '\n",
-      " 'ability to retain impressions of sensory information (visual, auditory, etc) '\n",
-      " 'after the original stimuli have ended. Sensory memory typically only lasts '\n",
-      " 'for up to a few seconds. Subcategories include iconic memory (visual), '\n",
-      " 'echoic memory (auditory), and haptic memory (touch).\\n'\n",
-      " '2. Short-Term Memory (STM) or Working Memory: It stores information that we '\n",
-      " 'are currently aware of and needed to carry out complex cognitive tasks such '\n",
-      " 'as learning and reasoning. Short-term memory is believed to have the '\n",
-      " 'capacity of about 7 items (Miller 1956) and lasts for 20-30 seconds.\\n'\n",
-      " '3. Long-Term Memory (LTM): Long-term memory can store information for a '\n",
-      " 'remarkably long time, ranging from a few days to decades, with an '\n",
-      " 'essentially unlimited storage capacity. There are two subtypes of LTM:\\n'\n",
-      " '   * Explicit / declarative memory: This is memory of facts and events, and '\n",
-      " 'refers to those memories that can be consciously recalled, including '\n",
-      " 'episodic memory (events and experiences) and semantic memory (facts and '\n",
-      " 'concepts).\\n'\n",
-      " '   * Implicit / procedural memory: This type of memory is unconscious and '\n",
-      " 'involves skills and routines that are performed automatically, like riding a '\n",
-      " 'bike or typing on a keyboard.\\n'\n",
-      " '\\n'\n",
-      " 'We can roughly consider the following mappings in an LLM-powered agent '\n",
-      " 'system:\\n'\n",
-      " '\\n'\n",
-      " '* Sensory Memory: Input data from sensors\\n'\n",
-      " '* Short-Term Memory: Active processing of information, temporary storage for '\n",
-      " 'complex tasks\\n'\n",
-      " '* Long-Term Memory: Stored knowledge and experiences that can be accessed '\n",
-      " 'and used to learn new tasks or make decisions.')\n"
-     ]
-    }
-   ],
-   "source": [
-    "# Run\n",
-    "inputs = {\"keys\": {\"question\": \"Explain how the different types of agent memory work?\"}}\n",
-    "for output in app.stream(inputs):\n",
-    "    for key, value in output.items():\n",
-    "        # Node\n",
-    "        pprint.pprint(f\"Node '{key}':\")\n",
-    "        # Optional: print full state at each node\n",
-    "        # pprint.pprint(value[\"keys\"], indent=2, width=80, depth=None)\n",
-    "    pprint.pprint(\"\\n---\\n\")\n",
-    "\n",
-    "# Final generation\n",
-    "pprint.pprint(value['keys']['generation'])"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "4d40a430-be4a-4d9c-98f9-46c5eb3066e8",
-   "metadata": {},
-   "outputs": [],
-   "source": []
-  }
- ],
- "metadata": {
-  "kernelspec": {
-   "display_name": "Python 3 (ipykernel)",
-   "language": "python",
-   "name": "python3"
-  },
-  "language_info": {
-   "codemirror_mode": {
-    "name": "ipython",
-    "version": 3
-   },
-   "file_extension": ".py",
-   "mimetype": "text/x-python",
-   "name": "python",
-   "nbconvert_exporter": "python",
-   "pygments_lexer": "ipython3",
-   "version": "3.9.16"
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 5
-}