From 4d7a42a65e2c7cbe18f613adcdd04175a0e5ff66 Mon Sep 17 00:00:00 2001
From: Vadym Barda <vadym@langchain.dev>
Date: Fri, 2 Aug 2024 11:05:17 -0400
Subject: [PATCH] langgraph: remove FewShotExamples managed value (#1195)

---
 examples/learning.ipynb                      | 477 -------------------
 libs/langgraph/langgraph/managed/few_shot.py | 105 ----
 libs/langgraph/tests/test_pregel.py          | 228 ---------
 libs/langgraph/tests/test_pregel_async.py    | 183 -------
 4 files changed, 993 deletions(-)
 delete mode 100644 examples/learning.ipynb
 delete mode 100644 libs/langgraph/langgraph/managed/few_shot.py

diff --git a/examples/learning.ipynb b/examples/learning.ipynb
deleted file mode 100644
index 2588a7d28..000000000
--- a/examples/learning.ipynb
+++ /dev/null
@@ -1,477 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "markdown",
-   "id": "51466c8d-8ce4-4b3d-be4e-18fdbeda5f53",
-   "metadata": {},
-   "source": [
-    "# Get/Update State\n",
-    "\n",
-    "When running LangGraph agents, you can easily save good threads and use them in the future.\n",
-    "\n",
-    "**Note:** this requires passing in a checkpointer.\n"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "7cbd446a-808f-4394-be92-d45ab818953c",
-   "metadata": {},
-   "source": [
-    "## Setup\n",
-    "\n",
-    "First we need to install the packages required\n"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 1,
-   "id": "af4ce0ba-7596-4e5f-8bf8-0b0bd6e62833",
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "\n",
-      "\u001b[1m[\u001b[0m\u001b[34;49mnotice\u001b[0m\u001b[1;39;49m]\u001b[0m\u001b[39;49m A new release of pip is available: \u001b[0m\u001b[31;49m23.3.1\u001b[0m\u001b[39;49m -> \u001b[0m\u001b[32;49m23.3.2\u001b[0m\n",
-      "\u001b[1m[\u001b[0m\u001b[34;49mnotice\u001b[0m\u001b[1;39;49m]\u001b[0m\u001b[39;49m To update, run: \u001b[0m\u001b[32;49mpip install --upgrade pip\u001b[0m\n"
-     ]
-    }
-   ],
-   "source": ["!%pip install --quiet -U langgraph langchain langchain_openai tavily-pythonvily-python"]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "0abe11f4-62ed-4dc4-8875-3db21e260d1d",
-   "metadata": {},
-   "source": [
-    "Next, we need to set API keys for OpenAI (the LLM we will use) and Tavily (the search tool we will use)\n"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 2,
-   "id": "c903a1cf-2977-4e2d-ad7d-8b3946821d89",
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "OpenAI API Key: ········\n",
-      "Tavily API Key: ········\n"
-     ]
-    }
-   ],
-   "source": ["import getpass\nimport os\n\nos.environ[\"OPENAI_API_KEY\"] = getpass.getpass(\"OpenAI API Key:\")\nos.environ[\"TAVILY_API_KEY\"] = getpass.getpass(\"Tavily API Key:\")"]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "f0ed46a8-effe-4596-b0e1-a6a29ee16f5c",
-   "metadata": {},
-   "source": [
-    "Optionally, we can set API key for [LangSmith tracing](https://smith.langchain.com/), which will give us best-in-class observability.\n"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "95e25aec-7c9f-4a63-b143-225d0e9a79c3",
-   "metadata": {},
-   "outputs": [],
-   "source": ["os.environ[\"LANGCHAIN_TRACING_V2\"] = \"true\"\nos.environ[\"LANGCHAIN_API_KEY\"] = getpass.getpass(\"LangSmith API Key:\")"]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "21ac643b-cb06-4724-a80c-2862ba4773f1",
-   "metadata": {},
-   "source": [
-    "## Set up the tools\n",
-    "\n",
-    "We will first define the tools we want to use.\n",
-    "For this simple example, we will use a built-in search tool via Tavily.\n",
-    "However, it is really easy to create your own tools - see documentation [here](https://python.langchain.com/v0.2/docs/how_to/custom_tools) on how to do that.\n"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 1,
-   "id": "d7ef57dd-5d6e-4ad3-9377-a92201c1310e",
-   "metadata": {},
-   "outputs": [],
-   "source": ["from langchain_community.tools.tavily_search import TavilySearchResults\n\ntools = [TavilySearchResults(max_results=1)]"]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "01885785-b71a-44d1-b1d6-7b5b14d53b58",
-   "metadata": {},
-   "source": [
-    "We can now wrap these tools in a simple ToolNode.\n",
-    "This is a prebuilt node that extracts tool calls from the most recent AIMessage, executes them, and returns a ToolMessage with the results.\n"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 2,
-   "id": "5cf3331e-ccb3-41c8-aeb9-a840a94d41e7",
-   "metadata": {},
-   "outputs": [],
-   "source": ["from langgraph.prebuilt import ToolNode\n\ntool_node = ToolNode(tools)"]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "5497ed70-fce3-47f1-9cad-46f912bad6a5",
-   "metadata": {},
-   "source": [
-    "## Set up the model\n",
-    "\n",
-    "Now we need to load the chat model we want to use.\n",
-    "Importantly, this should satisfy two criteria:\n",
-    "\n",
-    "1. It should work with messages. We will represent all agent state in the form of messages, so it needs to be able to work well with them.\n",
-    "2. It should work with OpenAI function calling. This means it should either be an OpenAI model or a model that exposes a similar interface.\n",
-    "\n",
-    "Note: these model requirements are not requirements for using LangGraph - they are just requirements for this one example.\n"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 3,
-   "id": "892b54b9-75f0-4804-9ed0-88b5e5532989",
-   "metadata": {},
-   "outputs": [],
-   "source": ["from langchain_openai import ChatOpenAI\n\nmodel = ChatOpenAI(temperature=0)"]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "a77995c0-bae2-4cee-a036-8688a90f05b9",
-   "metadata": {},
-   "source": [
-    "After we've done this, we should make sure the model knows that it has these tools available to call.\n",
-    "We can do this using the `.bind_tools()` method, common to many of LangChain's chat models.\n"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 4,
-   "id": "cd3cbae5-d92c-4559-a4aa-44721b80d107",
-   "metadata": {},
-   "outputs": [],
-   "source": ["model = model.bind_tools(tools)"]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "e03c5094-9297-4d19-a04e-3eedc75cefb4",
-   "metadata": {},
-   "source": [
-    "## Define the nodes\n",
-    "\n",
-    "We now need to define a few different nodes in our graph.\n",
-    "In `langgraph`, a node can be either a function or a [runnable](https://python.langchain.com/v0.2/docs/concepts/#langchain-expression-language-lcel).\n",
-    "There are two main nodes we need for this:\n",
-    "\n",
-    "1. The agent: responsible for deciding what (if any) actions to take.\n",
-    "2. A function to invoke tools: if the agent decides to take an action, this node will then execute that action.\n",
-    "\n",
-    "We will also need to define some edges.\n",
-    "Some of these edges may be conditional.\n",
-    "The reason they are conditional is that based on the output of a node, one of several paths may be taken.\n",
-    "The path that is taken is not known until that node is run (the LLM decides).\n",
-    "\n",
-    "1. Conditional Edge: after the agent is called, we should either:\n",
-    "   a. If the agent said to take an action, then the function to invoke tools should be called\n",
-    "   b. If the agent said that it was finished, then it should finish\n",
-    "2. Normal Edge: after the tools are invoked, it should always go back to the agent to decide what to do next\n",
-    "\n",
-    "Let's define the nodes, as well as a function to decide how what conditional edge to take.\n"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 77,
-   "id": "3b541bb9-900c-40d0-964d-7b5dfee30667",
-   "metadata": {},
-   "outputs": [],
-   "source": ["# Define the function that determines whether to continue or not\ndef should_continue(state):\n    last_message = state[\"messages\"][-1]\n    # If there is no function call, then we finish\n    if not last_message.tool_calls:\n        return \"end\"\n    # Otherwise if there is, we continue\n    else:\n        return \"continue\""]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "ffd6e892-946c-4899-8cc0-7c9291c1f73b",
-   "metadata": {},
-   "source": [
-    "## Define the graph\n",
-    "\n",
-    "We can now put it all together and define the graph!\n"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 154,
-   "id": "812b4e70-4956-4415-8880-db48b3dcbad2",
-   "metadata": {},
-   "outputs": [],
-   "source": ["from typing import Annotated, TypedDict\n\nfrom langchain_core.messages import (\n    AIMessage,\n    AnyMessage,\n    HumanMessage,\n    SystemMessage,\n    ToolMessage,\n)\n\nfrom langgraph.graph import END, StateGraph, START\nfrom langgraph.graph.message import add_messages\nfrom langgraph.managed.few_shot import FewShotExamples\n\n\nclass BaseState(TypedDict):\n    messages: Annotated[list[AnyMessage], add_messages]\n    examples: Annotated[list, FewShotExamples]\n\n\ndef _render_message(m):\n    if isinstance(m, HumanMessage):\n        return \"Human: \" + m.content\n    elif isinstance(m, AIMessage):\n        _m = \"AI: \" + m.content\n        if len(m.tool_calls) > 0:\n            _m += f\" Tools: {m.tool_calls}\"\n        return _m\n    elif isinstance(m, ToolMessage):\n        return \"Tool Result: ...\"\n    else:\n        raise ValueError\n\n\ndef _render_messages(ms):\n    m_string = [_render_message(m) for m in ms]\n    return \"\\n\".join(m_string)\n\n\n# Define a new graph\nworkflow = StateGraph(BaseState)\n\n\ndef _agent(state: BaseState):\n    if len(state[\"examples\"]) > 0:\n        _examples = \"\\n\\n\".join(\n            [\n                f\"Example {i}: \" + _render_messages(e[\"messages\"])\n                for i, e in enumerate(state[\"examples\"])\n            ]\n        )\n        system_message = \"\"\"You are a helpful assistant. Below are some examples of interactions you had with users. \\\nThese were good interactions where the final result they got was the desired one. As much as possible, you should learn from these interactions and mimic them in the future. \\\nPay particularly close attention to when tools are called, and what the inputs are.!\n\n{examples}\n\nAssist the user as they require!\"\"\".format(\n            examples=_examples\n        )\n\n    else:\n        system_message = \"\"\"You are a helpful assistant\"\"\"\n    output = model.invoke([SystemMessage(content=system_message)] + state[\"messages\"])\n    return {\"messages\": [output]}\n\n\n# Define the two nodes we will cycle between\nworkflow.add_node(\"agent\", _agent)\nworkflow.add_node(\"action\", tool_node)\n\n# Set the entrypoint as `agent`\n# This means that this node is the first one called\nworkflow.add_edge(START, \"agent\")\n\n# We now add a conditional edge\nworkflow.add_conditional_edges(\n    # First, we define the start node. We use `agent`.\n    # This means these are the edges taken after the `agent` node is called.\n    \"agent\",\n    # Next, we pass in the function that will determine which node is called next.\n    should_continue,\n    # Finally we pass in a mapping.\n    # The keys are strings, and the values are other nodes.\n    # END is a special node marking that the graph should finish.\n    # What will happen is we will call `should_continue`, and then the output of that\n    # will be matched against the keys in this mapping.\n    # Based on which one it matches, that node will then be called.\n    {\n        # If `tools`, then we call the tool node.\n        \"continue\": \"action\",\n        # Otherwise we finish.\n        \"end\": END,\n    },\n)\n\n# We now add a normal edge from `tools` to `agent`.\n# This means that after `tools` is called, `agent` node is called next.\nworkflow.add_edge(\"action\", \"agent\")"]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "bc9c8536-f90b-44fa-958d-5df016c66d8f",
-   "metadata": {},
-   "source": [
-    "**Persistence**\n",
-    "\n",
-    "To add in persistence, we pass in a checkpoint when compiling the graph\n"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 115,
-   "id": "6845ed6a-d155-4105-9160-28849877248b",
-   "metadata": {},
-   "outputs": [],
-   "source": ["from langgraph.checkpoint.sqlite import SqliteSaver\n\nmemory = SqliteSaver.from_conn_string(\":memory:\")"]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 155,
-   "id": "79d29875-8aa8-434c-9f20-1c58346a6249",
-   "metadata": {},
-   "outputs": [],
-   "source": ["# Finally, we compile it!\n# This compiles it into a LangChain Runnable,\n# meaning you can use it as you would any other runnable\napp = workflow.compile(checkpointer=memory, interrupt_before=[\"action\"])"]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "e8aff75b-563e-42b1-969b-742201514fc3",
-   "metadata": {},
-   "source": [
-    "## Preview the graph\n"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 156,
-   "id": "c9ab60eb-679b-4eef-9e64-5ffbf3dffc70",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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sPv/8cz786CMSExLo1LMvgydPp3Ov8EoztgvrkHw2lp9WL2Pnhm8wGY1ERExhzpw5tGvXTuvSxI1JeAlxIyaTicjISBYteo8tWzbj5lmHzn360/f+cbTr3lN6KFqwzLRU9m/bzP6fNnHy9yiCgoP5+9/+xqxZs/Dx8dG6PHFzEl5C3Ipz586xevVqVq5aRczJk9RvGED3oSPp2m8QIR27SIvMAlxMSuDwrkj2bd1IzOGDeHh6Mm7sWCZNmkTv3r2vOzu+qLEkvIS4XceOHWP16tWsWbuW+PPncfesQ4ew3nTqHU6nXuF4+tTVukQB6EtLOXloP0d2RXJkVyTJ5+Jwc3NnxMgRTJo4kcGDB+NgpfNc1gISXkLcjdOnT7Nlyxa2bNnKrt27MOj1BLVsTcvOobTqEkrLLt3KbzUiqldJcRGxRw8Tc+gApw4f5NSRgxQXFtK6TRuGDxvG0KFD6dmzJzqd9U0QXAtJeAlRVQoKCoiMjCQyMpLde/Zy9I8jGAwGGgQ2JqRzKC06dKZJ63YEhrTC0clZ63ItmmIykZoYz/mTx4mL/oMzRw4Sd/wYBoOegMBAevfuTZ/evRk8eLB0cbdOEl5CVJf8/Hz27dvHnj172L17N4cO/U5eXi62dnYENGlGUKu2BLdqS1CrNjRs0lxaaDdQVJBPyvmzJJw5xfmT0cSfOkH8qRMU5udjb29Py1at6NWzJz179qR37940atRI65JF9ZPwEsJcFEXh7NmzHDlyhCNHjnD48BEOHzlM+qVLALi6ueMf3JQGQU1o2KSZ+jgwGJ8G/nh4eWtcffUqLS4mPSWZ9JRkLpyL48L5s6TGnyXl/Fky0lIBcHJ2pl27dnTp3JlOnTrRqVMn2rVrh5OTk8bVCw1IeAmhtUuXLhETE8Pp06c5c+YMMadOcerUKRLi4zEajQA4Ojnh2zAAHz9/vOv7Ude/ET71G+Dh7YOHlzce3j54etfF1cND41dTWWlxMbmXs8jJyiAnM4O8y1lkZ1wiIzWFjJQLZKalkJGWQk5WZvnP+NStR8uWLWndqiUtWrSgZcuWhISEEBwcjL293AhDABJeQtRcpaWlxMfHk5SURFJSEgkJCSQlJZGYmERiUiIXki+Qn59X6Wd0Ogc8vb3x8PLG2d0dBydnnF3dcXJ1xcnZBUdnZ1w96uDg6IjDFS0We50OR2eX69ZhNBgoLiwo/95kNFKYn09JUSElRUUUFeRTmJ9LaXExpcVFFObmkp9zmcuZGRQXFlbal4OjI/Xq1SMwsDFBjQMJCAggICCAxo0bl3/18vKqwndRWCkJLyEsWUlJCRkZGaSnp3Px4kUyMjLKl5ycHAoLC8nNzSUnJ5f8ggIKCwvIzs6moKAAfam+fD9FxUWUFBdf9xi2trZ4eHhWWlfHqw4uLi64uLjg6eGBp6cnrq6uuLq64unpiZeXF76+vtStW5e6detSr1496tevj7u7zBcpqoSElxCisgULFrBy5UpOnTqldSlC3Ijcz0sIIYTlkfASQghhcSS8hBBCWBwJLyGEEBZHwksIIYTFkfASQghhcSS8hBBCWBwJLyGEEBZHwksIIYTFkfASQghhcSS8hBBCWBwJLyGEEBZHwksIIYTFkfASQghhcSS8hBBCWBwJLyGEEBZHwksIIYTFkfASQghhcSS8hBBCWBwJLyGEEBZHwksIIYTFkfASQghhcSS8hBBCWBwJLyGEEBZHwksIIYTFkfASQghhcSS8hBBCWBwJLyGEEBZHwksIIYTFkfASQghhcSS8hBBCWBwJLyGEEBZHwksIIYTFkfASQghhcSS8hBBCWBwJLyGEEBZHwksIIYTFkfASQghhcSS8hBBCWBwJLyGEEBZHwksIIYTFkfASQghhcSS8hBBCWBwJLyGEEBZHwksIIYTFkfASQghhcSS8hBBCWBwJLyGEEBZHwksIIYTFkfASQghhcSS8hBBCWBwJLyGEEBZHwksIIYTFkfASQghhcSS8hBBCWBwJLyGEEBZHwksIIYTFkfASQghhcSS8hBBCWBwJLyGEEBZHwksIIYTFkfASQghhcSS8hBBCWBwJLyGEEBZHwksIIYTFkfASQghhcSS8hBBCWBwJLyGEEBZHwksIIYTFkfASQghhcSS8hBBCWBwJLyGEEBZHwksIIYTFkfASQghhcSS8hBBCWBwJLyGEEBZHwksIIYTFkfASQghhcSS8hBBCWBwbRVEUrYsQQmhj586d3H///RiNxvJ1paWlGAwGXFxcytfZ2NgwZcoUFi9erEWZQlzNYK91BUII7YSFhWFra0tOTs41z+Xn51f6fvjw4eYqS4ibktOGQtRi9vb2TJ48GZ1O95fbeXp6MmjQIDNVJcTNSXgJUctNnDgRvV5/w+d1Oh2TJk26acAJYU4SXkLUct27d6dRo0Y3fF6v1zNx4kQzViTEzUl4CVHLlXXGuFHLys/Pj7CwMDNXJcRfk/ASQtzw1KGDgwNTp07F1lY+KkTNIl3lhRAAtGzZktOnT1+z/siRI3Ts2FGDioS4IYP8OSWEACAiIuKaU4dNmjSR4BI1koSXEAJQTx0aDIby73U6HdOnT9euICH+goSXEAJQW1mdO3fGxsYGUHsZTpgwQeOqhLg+CS8hRLmpU6diZ2eHjY0NHTt2pHnz5lqXJMR1SXgJIco9+OCDmEwmFEVh6tSpWpcjxA1JeAkhyvn5+dGnTx9sbGwYP3681uUIcUMyMa8QopIpU6ZgY2ODv7+/1qUIcUMSXkKISsaOHYuTk5PWZQjxl2SQshBWymAwkJKSQmJiIvHx8SQnJ5ORkUFWVhZZWRlkZV0iKyuToqIiTCYTOTl5f/6ckdJSAy4ujgA4Ozvh5OSInZ0d3t4+eHvX+3PxxtvbG39/fwIDAwkMDCQoKKjSfcCEqCYGCS8hLFxxcTEnT57k+PHjnDhxgujoP4iJiSY5+RIGg3qTSQcHWxo21OHrq+DtbfxzAW9vcHYGOzvw8FD3Z2cHDg5QVFS2f/Wx0QhZWWWLLVlZ9mRl2ZCUZCQ3t2J8WN26nrRo0YK2bTvRtm1b2rRpQ7t27ahXr5653xphvSS8hLA0CQkJ/Pbbb+zfv599+3byxx/H0euNODra0rq1jjZtSmnTRiEoCAIDISgI/PygOqcnzM6GxERISFCXmBg4eVJHdDRkZqpzJgYF+dOjRx/uvbc7PXr0oEOHDtjby5ULcUckvISo6QoKCoiMjGTr1q38+ONGzp+/gE5nS8eO9nTvXkr37tCpEzRrpraaapq0NIiOhv37Yf9+O/bts+HyZQPu7s707z+AIUOGM2TIEBo3bqx1qcJySHgJURNlZ2ezfv161qxZwc6du9HrDXTurGPIkFIGDoR77lFP91kiRVFbZjt3wtatdkRGQkGBkdatmzF27CQmTpxIy5YttS5T1GwSXkLUFHq9no0bN7JixTK2bv0RGxsTQ4fC6NFGBg8GX1+tK6weJSWwezds2gTffGNPSoqBTp1aM2nSDKZOnYqvtb5wcTckvITQ2sWLF1myZAmffPIBaWnpDBhgx4QJBkaPBk9PraszL5NJbZGtXm3Dt9/aUVhow4MPPsicOXMJDQ3VujxRc0h4CaGV+Ph4FiyYz8qVK3Fzg1mz9Dz8MMilH1VREaxaBR9+qOOPP/R069aZl15awLBhw7QuTWhP7uclhLklJyfz8MN/JySkOTt3ruTDD/UkJel5800Jris5O8P//R8cOaJn927w9T3K8OHDCQsLZceOHVqXJzQm4SWEmej1et577z1atmzO5s3/4+23DZw4oWfWLMvtfGEuPXvCxo1GDhwAb+8jDBgwgBEjhpKQkKB1aUIjctpQCDPYs2cPf/vb/xEff5aXXjLy5JPqQGBxZyIj4ZFH7ElOtuOVV15n7ty5MmasdpHThkJUJ0VReOutt+jTpzdBQWc5ftzIs89KcN2t8HCIjjbw4oslPP/8M4SH9yYlJUXrsoQZSctLiGqSnp7O5Mnj2b17F4sWGfnb37SuyDpFR8PYsfbk5nqyevW39O3bV+uSRPWTlpcQ1SExMZEePe7h9Ok9/PqrBFd1atcODh0y0KdPNgMG9Oerr77SuiRhBnKSWIgqlpCQQHh4Lzw80ti2TY/MR1v93N1hzRojL74IM2fOwGAwMHPmTK3LEtVIwkuIKnTp0iV69+6Or28GP/2kx9tb64pqlwULAEzMnj0LDw8Pxo4dq3VJoprINS8hqojBYGDQoP4kJOzj4EEJrqvl5MD8+WpnixEjqvdYjz1mw7JlTuzff4jWrVtX78GEFuSalxBVZd68eRw4sJfvvpPgutquXdC8OSxcCHp99R/v3XcV2rXT88AD91FcXFz9BxRmJ+ElRBU4f/48//3vf3jrLSMdOmhdTc1z5Aikp6uPbWyq/3g6HaxdayApKZ4PP/yw+g8ozE5OGwpRBSZMeJA//thAdLQena56j2UwwA8/wB9/QEYGuLlBq1bccCLfgwfVQb25uRAWBkOGwL59EBenjjebOLHy9seOwa+/Qnw8tGwJvXurX6904ACcOgX29jB5srrttm1w+jS0aQNjxkCdOuq2kZGwdKk6TyHA3LnQuTOMHEm1t1Bfegk++MCNuLh4fHx8qvdgwpwMKEKIu5KSkqLY2tooa9agKEr1LgYDSrduKHDt0rw5Slxc5e2feQbFxqbydqNHo4wbpz6uU6diW6MR5fnnUWxtK29vb4/yxhsoJlPFto88oj7n7Izy3XcoLi6Vf6Zx44paRo68fr1HjlT/+5Wbi+LlZa+8++67VfcLFzWBXk4bCnGX1q1bh6urHfffX/3HWrhQbfUADBsGTzwBXbqo38fGwosvVmz77bfw9tvqzR9tbGDUKOjeHdavV5+72v/+B6+9pt6WxMcHZs+GRo3Ult6//w3ffHPtzxQXw9ix0Lat2qIKClLXJyTAW2+pj4ODwd+/4meCgqBDB/PM5+juDvffb+Tbb1dX/8GEeWkdn0JYusGD+ysTJtgq1d2KUBSUzz5DmTkTZe7cinX5+RUtny5dKta3bauus7VF2b+/Yv2XX1a0fjw91XUlJSi+vhWtsfx8db1ejxIYqK5v1aqi9VXW8gKUUaMq9h0bW7G+W7eK9YsWVaxfv77636crl40bUWxtbZSLFy/e9e9a1BjS8hLibsXGnqJDB5NZjjV7tnr96L334OJF+P57eOGFiufz89Wvej3ExKiP770XunWr2GbaNKhfv/J+z56FS5fUx/37qy2qzEy1e3vZ7bNiYiAt7dqaHnmk4nGzZlC3rvo4M/POX2dV6tgRTCaFs2fPal2KqEIySFmIu6AoCikp6TRqZJ7jGQzq6bjvvlN78F3d3aqsJ19iIhiN6uPr1daokRp+ZWJjKx6vW6cu13PhAjRoUHnd1TOIuLioX8uOrzV/f7CzsyE5OVnrUkQVkvAS4i4YjUZKS/Vmux/XuHGwYYP6uHdv9TpWeLh63SkuDmz/PJfi7l7xM1lZlfdRWAgnTlRed2UPyY4doWvX6x/fyenadY6Olb+3rWHnc+zswNHRlsLCQq1LEVVIwkuIu2Bvb0+9enVISblc7cdKTq4IrjFjKreOsrPVr2UtL19ftdt8Tg7s368GWFm39B071NOCV2rSpOKxuzssWVLx/YkTanf8wMDrj9G6lXFbV25jMs8Z1nKZmVBYaKSRuZrHwixq2N9IQliegIAAzp+v/uNcuFDxOC+v4pThxx+r471AHctVpmxe2vx89ZrXkiXq9bFp067dd0iIOvYKYM8eWLtWPe2XkKCODQsKUltkpaV3VvuV9y87flxtJV5Za3WKj1e/SnhZFwkvIe5Snz4D2bSp+u8u2b59RZfz7dvVLujBwWqHibKbCGdmVlxr+te/1AHDoIbFQw+pXeF9fCAgQF1/ZYvonXfU61WKAhMmgJ+f2iLLyVH3/9lnd34TzZCQisfz5qlTRZV1+a9umzZBw4a+NG/e3DwHFGYh4SXEXRo7diyxsaVER1fvcZyd1VOFZZ/BCXKDr2cAACAASURBVAlqi+utt+A//1HXFRaqM1qAeupw926YNQuaNlWDb/Jk2Lu3okfglWHUrx/89ps6bszeXt23gwMMHAgrV1busXi7+vRRT3WWcXBQW4/msG6dA6NHP4htTbsYJ+6KTA8lxF1SFIWWLZtyzz0JrFhR/Rd0TCb1VFhJidqiudFn8v796hRNjRtfOyA4OFjdR0iIOs3T1YqL1R6IzZpV7WDitDQ1FENCqPZptAC2bIERI2zYv38/oaGh1X9AYS4yq7wQd8vGxoY33niHVatMHDpU/ceztVVP57Vq9dc9+15+Wd3GxQWeflq9XlVSos6UkZioblN2netqTk7qHYqruheln586G4c5gstohGef1fHAA6MkuKyQtLyEqCI9e95LUdFh9u7VX7dLubmtWqWeJizj5KR+oJfdksTeHo4eBWu93dVrr8Err9hz/PhJud5lfaTlJURVWbFiDQkJLsyeXTP+W02aBF98UXGNrLhYDS4bG3Uc17Zt1htcO3bAvHk2vPvuIgkuKyUtLyGq0ObNm7nvvpG8/rrCs89qXU2F3FxISVEH7AYGXjuw2JocPQr9+9szZMg4VqxYpXU5onoYJLyEqGIffvghc+fO5ZVXlErzDorqd+gQDB5sT6dOYWzcuAWXsrmqhLUxyAwbQlSxOXPm4ODgwMMP/52sLIW3364YhyWqz08/wfjx9oSF9Wfdug041YQLj6La1IyT80JYmYceeojly1fw6aeO9O9vT2qq1hVZL5NJ7Vk5bJgN998/gfXrN0pw1QISXkJUk0mTJrF//0HS0gLo3FnH999rXZH1OXcOBg+24803dSxe/DHLli3H4U6nAREWRcJLiGrUrl07Dh78g0GDJjB6tA2jR9uTlKR1VZZPr4c334R27exITW3K3r37+Nvf/qZ1WcKMJLyEqGYeHh4sW/YVP//8MydONKJ1azteflmdM1DcHkVR72XWsaOOV15x4PnnX+HIkeN06dJF69KEmUl4CWEm4eHhHDsWw7/+NZ/333enSRMdr79ecfdj8dc2b4auXe0ZN86Gtm3vIzr6JM899xw6c0zXIWoc6SovhAby8vJYvHgxb775KiZTMRMmGHjiCXU6J1GhuBi+/hr++18dx47pGTCgH2+99Q6dbzSvlagtZJyXEFrKyspiyZIlfPzx+yQlpTJkiC0zZxoZPvz6dy2uLQ4dguXLYdkyO0pL7Zg4cRJz5sylU6dOWpcmagYJLyFqAqPRyA8//MDHH3/Ajh2/4upqy6hRRiZOVOjf3zwT2Wrt5En1JpirVzsQG1tK8+aNmT37UWbOnImPj4/W5YmaRcJLiJomMzOTdevW8dVXS/ntt4M4O9vSo4fCiBEmRo1Sb3FiDQoL1fuH/fwzbNzoSExMCf7+9Rg7diLjxo0jLCwMmyvvlilEBQkvIWqy+Ph4tmzZwtatm/jll18oKCgmJMSBHj1K6dEDune/+a1RaoqLF2HfPjWwfvtNx6FDRvR6hU6d2jJkyH0MHTqU7t27y00jxa2Q8BLCUpSUlLB792527tzJ3r07OXjwEPn5RdSpY0+HDja0aaOnbVt1pvi2bUGrM23FxeoNLk+ehOPH4eRJW44ds+f8+VJsbW1o06YFPXr0pWfPngwaNAhfX19tChWWTMJLCEtlMBg4duwYL7/8MjExMdSv782JEyfJzlb73ru729O4sR1BQXoCA00EBkK9euDtrQabt7e6ODmps8zfaA5bgwHy8tRpmLKy1CUzs+JxcjIkJNiQmKgjIQFSU0sB0OnsCAlpQps2nWjXrj1du3ale/fueHh4mOstEtZLwksIS7ZixQqmTp3KokWLmDt3LgDJycnExMQQHx9PQkICiYmJxMefISkpkYyMbPLzi/5yny4udjg42JCdbfjL7Rwc7PH29sDf35/GjZsSGBhEcHAwgYGBtGjRghYtWsgYLFFdJLyEsFSbN29m1KhRPPfcc8yfP/+Wf660tJSsrKzypbi4mNLSUgoKCgAoKCigtLQULy8vAHQ6HW5ubtja2uLt7V2+vP/++zz33HPV8tqEuAkJLyEsUVRUFOHh4YwZM4Zly5Zp0ivPx8eH999/n8mTJ5v92KLWk/ASwtLExcURFhZGaGgo69evx16jm4V5e3uTn5/Prl27uPfeezWpQdRaEl5CWJKUlBTCwsLw9fUlMjISV1dXzWqpX78+ly5dwsfHh8OHDxMYGKhZLaLWMciACiEsRG5uLsOHD0en07Fp0yZNgwso74yRm5vL0KFDyZcZhoUZSXgJYQFKS0t54IEHuHTpEtu3b6devXpal1QeXnq9njNnzjB27FiMRqPGVYnaQsJLiBrOZDIxZcoUoqKi2LJlC41ryPxQV96x2GAwsH37dl588UUNKxK1iYSXEDXck08+yffff893331Hhw4dtC6n3JXhBWrIvvHGG3z++ecaVSRqEwkvIWqwV199lQ8//JCVK1fSv39/rcup5OrwKvPwww+zc+dOM1cjahsJLyFqqBUrVvDSSy+xcOFCxo4dq3U513B0dLzuepPJxKhRozh37pyZKxK1iYSXEDXQ5s2bmTFjBi+++GL5tE81zV+FV0FBAUOGDCEnJ8fMVYnaQsJLiBomKiqK8ePHM3369Nua9sncbhReoPZAPH/+PA8++KD0QBTVQsJLiBokLi6OkSNH0q9fPz7++GOty/lLfxVeoPZA3LZtG88//7yZKhK1iTbzygghrpGSksLAgQMJCgpizZo1mk37dKtuFF46nQ69Xk9wcDCzZ88mIiLCzJWJ2qBm/+8QopYomz3DxcWFrVu3aj57xq1wcHDA1tYWk8mEra0tiqKgKArDhg3j2WefpXv37lqXKKyYnDYUQmNXzp6xZcsWvL29tS7pljg4OJQHV79+/Vi5ciVt2rTB399fgktUO5mYVwgNmUwmxo8fz/bt29m5c2eNGoR8M2+//TZGo5GIiAgaNWpUvu71118nNTUVZ2dnjSsUVkxmlRdCS48//jifffYZ27Zto1evXlqXc9dSUlIIDAxk9erVjBs3TutyhPWSWeWF0ErZ7BnLly+3iuAC8Pf3Z8CAASxbtkzrUoSVk/ASQgNls2csWrSoRs6ecTemTZvGTz/9RFpamtalCCsm4SWEmV05e8Zjjz2mdTlVbvTo0bi5ubFq1SqtSxFWTK55CWFGUVFRhIeHM3HiRJYsWaJ1OdVm9uzZ7N+/n+joaK1LEdZJOmwIYS6xsbH07NmT0NBQ1q9fX+MHId+NPXv20KtXL44cOULHjh21LkdYH+mwIYQ5lM2eERwcbBGzZ9ytnj17EhISIh03RLWR8BKimpXNnuHq6sqWLVssYvaMqjB58mRWrVqFXq/XuhRhhSS8hKhGljp7RlWYOnUqGRkZ/Pjjj1qXIqyQXPMSoppY8uwZVaVfv374+Pjw7bffal2KsC5yzUuI6vLkk0+yadMmfvjhh1oZXKCO+frhhx/IyMjQuhRhZSS8hKgGCxYssLrZM+7EuHHjcHBw4Ouvv9a6FGFl5LShEFVsxYoVTJ06lffee88qByHfrqlTp3L69GkOHDigdSnCeshpQyGqkrXPnnEnpk2bRlRUFMePH9e6FGFFJLyEqCJRUVGMHz+e6dOnM3/+fK3LqTH69etHYGAgK1eu1LoUYUUkvISoArGxsYwcOZJ+/frx8ccfa11OjWJra8uUKVNYvnw5RqNR63KElZBrXkLcpZSUFHr06IGfnx87duyoNYOQb0dsbCwhISH89NNPDBw4UOtyhOWTuQ2FuBu5ubn06dOH0tJSdu/eXasGId+u7t2707RpU1asWKF1KcLySYcNIe5UaWkpY8aMqZWzZ9yJadOm8d1335Gdna11KcIKSHgJcQdMJhOTJ0/m0KFDbNmyhcaNG2tdUo03ceJEANatW6dxJcIaSHgJcQdk9ozb5+npyciRI2WmeVElJLyEuE1ls2esWLGiVs+ecSemTZvGnj17OHv2rNalCAsn4SXEbVi+fDnz5s1j0aJFPPDAA1qXY3EGDRqEn58fy5cv17oUYeEkvIS4RZs3b2bmzJkye8ZdsLe3Z9KkSSxbtgzp6CzuhnSVF+IWREVFER4ezsSJE1myZInW5Vi0EydO0LZtW3bu3Env3r21LkdYJhnnJcTNxMbGEhYWRrdu3Vi/fj329vZal2TxOnfuTKdOnVi6dKnWpQjLJOO8hPgrKSkpDBw4kCZNmrBmzRoJrioybdo0vv76a/Lz87UuRVgoCS8hbiA3N5fhw4fj6urKli1bZNqnKjRp0iRKSkr4/vvvtS5FWCg5bSjEdZSWljJs2DBiYmL47bffZBByNbj//vspKipi27ZtWpciLI+cNhTiajJ7hnlMmzaNHTt2kJSUpHUpwgJJeAlxFZk9wzxGjBiBt7e33OdL3BEJLyGuILNnmI+DgwPjx4/niy++0LoUYYEkvIT4k8yeYX7Tpk3jzJkzREVFaV2KsDASXkJQMXvGSy+9JLNnmNE999xD27ZtZbJecdskvEStFxUVxfjx45kxYwYvv/yy1uXUOhEREaxZs4aSkhKtSxEWRLrKi1pNZs/QXkpKCoGBgXzzzTeMHj1a63KEZZDpoUTtlZKSQo8ePfDz82PHjh0yCFlDAwcOxN3dne+++07rUoRlkHFeonbKzc1l2LBhMntGDREREcHmzZvJyMjQuhRhISS8RK1TWlrKmDFjSE9PZ8uWLXh7e2tdUq33wAMP4ODgwNdff611KcJCSHiJWkVmz6iZXF1dGTVqlNykUtwyCS9Rq8jsGTVXREQE+/fv5/Tp01qXIiyAhJeoNRYsWMBHH30ks2fUUAMGDKBRo0YyXZS4JRJeolYomz1j4cKFMntGDWVra8uECRP46quvkE7Q4mYkvITV27Rpk8yeYSGmT59OQkICe/bs0boUUcPJOC9h1aKioggPD2fSpEl89tlnWpcjbkHHjh0JDQ2V35f4KzLOS1iv2NhYRowYQb9+/Vi8eLHW5YhbFBERwdq1aykqKtK6FFGDSXgJq5SSksLAgQNp0qQJa9askWmfLMiUKVMoLCzkhx9+0LoUUYPJaUNhdXJzc+nduzd6vZ7du3fLIGQLNHToUOzt7SXAxI3IaUNhXcpmz8jIyJDZMyxYREQEP/74IxcvXtS6FFFDSXgJq3Hl7BmbN2+W2TMs2OjRo3F1dWXt2rValyJqKAkvYTVk9gzr4ezszOjRo2W6KHFDEl7CKrzyyisye4aViYiI4NChQxw/flzrUkQNJOElLN7y5ct5+eWXZfYMK9O3b18CAgJYtWqV1qWIGkjCS1g0mT3Detna2jJlyhS++uorjEaj1uWIGka6yguLJbNnWL+YmBhat27Njh07CA8P17ocUXMYJLyERYqNjSUsLIxu3bqxfv16GYRsxbp27Uq7du344osvtC5F1BwyzktYHpk9o3aJiIjg22+/JT8/X+tSRA0i4SUsSk5ODsOGDcPV1ZUtW7bg6uqqdUmimk2aNImSkhI2btyodSmiBpHThsJiFBcXM2jQIM6dO8fevXtlEHItMnLkSAwGA1u3btW6FFEzyGlDYRlMJhMREREcO3ZMZs+ohSIiIti2bRsXLlzQuhRRQ0h4CYsgs2fUbvfddx8eHh6sWbNG61JEDSHhJWo8mT1DODk5MXbsWJkuSpST8BI12ueff868efNk9gxBREQER48e5dixY1qXImoACS9RY23atImHH36YefPmyewZgl69etGkSRNpfQlAwktobMGCBSQmJl6z/sCBA0yYMIEZM2bw8ssvm78wUePY2NgwefJkVq1aJdNFCekqL7Rz6dIlGjVqhLe3Nz///DNt27YF4OTJk/Tq1YsePXrI7Bmikri4OFq0aMGPP/7IoEGDtC5HaEe6ygvtLFmyBEVRyMzMpEePHuzevZuUlBSGDRtG8+bNZfYMcY1mzZrRrVs3OXUopOUltGE0GgkMDCQlJQVQZxC3tbWlYcOGuLq6snv3bry9vTWuUtREixcv5p///CdpaWm4u7trXY7QhrS8hDa+//57UlNTy783mUwYjUYSExOZPHmyBJe4oQkTJmA0Glm/fr3WpQgNSctLaKJPnz7s3bv3hhfen332Wd58800zVyUsxejRo8nPz2f79u1alyK0IS0vYX6nTp1i9+7df9lj7D//+Q+zZs2SXmXiuiIiIoiMjCQpKUnrUoRGJLyE2S1evPimHTEURWHp0qW88sorZqpKWJIRI0bg5eXFqlWrtC5FaEROGwqzys/Px8/Pj4KCghtuY2dnR7169Zg/fz6zZs3C1lb+xhLXevjhh9m1axcnTpzQuhRhfnLaUJjX8uXLKS4uvu5zOp0Od3d3XnvtNc6fP89DDz0kwSVuKCIigpMnT3L48GGtSxEakJaXMKtWrVpx+vRprvxnV3YKcebMmbz66qvUq1dPq/KEhQkJCWHYsGEsXLhQ61KEeUnLS5jPzp07OXXqVHlw6XQ6bGxsuP/++zl9+jSffvqpBJe4LZMmTWLVqlXo9XqtSxFmJuElzObDDz/E1tYWOzs7AHr06MGhQ4f49ttvadKkicbVCUs0bdo00tPTpct8LSSnDYVZpKSkEBgYiNFoJCQkhIULFzJ06FCtyxJWoFevXjRq1IjVq1drXYowHzltKMzjs88+o379+ixdupQTJ05IcIkqExERwYYNG8jOzta6FGFG0vKyAPn5+eTn51NYWEhxcTFFRUUYjUZyc3MByMnJwWQyUVBQQGlpKaWlpeVd0fPy8jAYDNfdb2FhISUlJbdUg5ubGzqd7rrPOTs74+TkBECdOnWwsbEp397R0REXFxe2b9/Offfdh5OTE56entja2lKnTh1cXV1xdHS83bdEiHLZ2dk0aNCAjz76iJkzZ2pdjjAPg4RXNcvKyrrhkpeXR25uLrm5uRQU5FFQkEtOzmXy8vIoKCikoKCI7Oz8Wz6Ws7MdTk622NuDu7vaqHZyAmfn62+v0ym4ud3ar//yZRvA5rrP5eaC0QiKAtnZpj/XGTAab23f9vZ2uLs74+npjqurK66ubnh4eOLh4Y2rqxtubm54eHhQp04dvL298fHxwdvbGy8vL7y9vfH29sbNze2WjiWs0wMPPEB2djY7duzQuhRhHhJetys9PZ1Lly5x8eJFUlNTuXTpEqmpqVy8eJH09DSystLJysokKyubrKw8rn57dTpbvL3t8fa2wd0dPDxMeHgYcHVVcHUFT09wcwNXV3Xx8qp47OYGOp361cYG6tRR9+nuDjX1ziFFRVBcDHo95P+Zw5cvlwUdFBSoS35+5e/z8iAnBwoKbCkstCMnx5bsbMjMNJKdfW1L0sHBHm9vD7y9vfD29sHb25f69RvQoEED6tWrh7+/P/Xr16d+/fo0aNAAV1dXM78Tojp99913jBs3joSEBBo1aqR1OaL6SXiVKS0tJTk5mcTERBITE0lISCApKYmUlGTS0i6QlpbGpUuZ6PUVc+3pdLb4+urw87PBz8+Ar68Bb2+uWXx8Kh7LHRzunskEWVk3X9LSbElLs+fSJYX09MpdqV1dnfDz88HPz4/69QMICGhMYGAggYGBBAQEEBgYiJ+fHzY2129tipqlpKQEPz8/XnjhBZ5++mmtyxHVr/aEl16vJz4+nri4OOLi4khISCAxMZGkpPMkJMSTlpZZ3kpydLQlMFBHQIBCw4al1K8P/v7g6wt+furi6wsyJMly6PVw6RKkpanLpUuQkqJ+TU2F5GR7EhJsSEszYDKV/TvQ0ahR/T8DrRlBQUEEBgbSvHlzmjdvToMGDTR+VeJKs2bN4vDhwzLjRu1gXeFlMBjKAyo2NvbP5RRxcaeIj0/BYFBbTb6+Oho3tiEgQE9goELjxhAQAIGB6lc/P41fiNCMXg/JyZCUBAkJkJioPk5KsiMhwZ74eAMFBeq/Izc3Z5o1C6JZs9Y0b96CZs2alQebn/wjMrtffvmF8PBwoqOjadu2rdbliOplueGVkpLCyZMnOXHiBCdPnuDEicMcPhxNUVEpAF5e9jRpYkOTJnqaNIEmTaB1a2jXTr2uJMSdunwZzp1TlxMn4ORJW86dc+D0aT35+WqweXi40Lx5c1q3bk+XLl1o06YN7du3x9fXV+PqrZfJZCIoKIiIiAhee+01rcsR1avmh5der+fo0aMcPHiQY8eOER19hOPHj5OTo3YFDwhwpG1bA+3aGWnXDkJCoFkztaODEOakKGqrLS4OYmLg2DE4ftye48cVcnLUUAsI8KVt2460a9eRjh07EhoaStOmTTWu3Ho888wzfP3115w/f16uV1q3mhVeiqIQGxtLVFQUBw8eJCpqL3/8EU1xcSkeHva0b29D27Z62reHtm3VVlRZjzsharKEBDh+XF2OHbPh+HEdMTEG9HoTPj4ehIbeS2hod0JDQ7nnnntkjsc7dPToUTp27MiePXsICwvTuhxRfbQNL4PBwKFDh4iMjGTXrl+IijrA5ct5ODjY0qGDI6GhRdxzD4SGqi0quTuGsCZFRXDkCERFwcGDNkRF6YiLU097N2niz7339qZv33DCw8OldXYb2rVrR69evVi8eLHWpYjqY97wMplMHD16lF9++YXIyO3s2rWLvLxC/P0d6ddPT7duJkJDoWNHkEkXRG2UmQkHD6qB9ttvduzZAwUFRgID/QgPH0y/fmqYyVimG3vjjTd45513SE1NxcHBQetyRPWo/vDKy8tj69atrF+/ju3bfyQzM5e6dXX07WskPNxEv37QsmV1ViCE5SotVYMsMhIiI+3Zv99ESYmJFi2CGDFiDKNHj6ZHjx5y084rJCYmEhwczIYNGxg5cqTW5YjqUT3hlZ6ezsaNG1m//lt27IhEr9fTu7eOkSNLCQ+H9u3VGSKEELenqAj27oWff4bvv9dx6pSe+vW9ue++MYwePYbw8HCZKxLo3bs3/v7+rFmzRutSRPWouvAqLi5m3bp1LF36Kbt27cXBwYaBA2H0aCMjR6qzTAghqlZMDKxfD+vX6/j9dz3u7i6MHj2W2bMfqtUdFj799FOeeOIJ0tLS8JSxMdbo7sMrJiaGJUuW8NVX/yM3N4+RI22YNMnIkCHqfHxCCPNISlKD7MsvdRw5oqdNm+Y89NAcIiIi8KplY0cuX75MgwYN+PTTT5k2bZrW5Yiqd2fhpSgKGzdu5N1332bXrt8IDnZg1qxSZswAmTFHCO0dOgSffWbD6tW2GAy2jB8/gWee+RetW7fWujSzGTVqFIWFhWzbtk3rUkTVu/3w2rBhA/PmPU909Enuu8+ORx4xMmCAZXdjP3YMzp5VHw8YoP3kudHR6kBXgP79wcPj5j/z66/qzA86HYwYUa3laepO3pvaLC8PVq2CDz5Qx5WNGTOKBQtep2Ut6CX1zTffMGHCBBITE2nYsKHW5YiqZUC5RUePHlX69u2p2NigPPigrXLsGIqiWMfy2GMooC7Hj2tfz+OPV9Rzq+9z167q9u7u2tdfFUt2NsqTT6L88MPdvzeyoBiNKOvWobRvr1N0OjvlscfmKNnZ2Yo1KyoqUjw9PZWFCxdqXYqoevqbtpcMBgOvvvoq99zTheLi/ezbB2vXmmjXrlpTVdRiu3ZB8+awcKE6Ua64e7a2MGYMHD6sZ/FiI2vXfkq7di3Zvn271qVVGycnJ0aPHs3KlSu1LkVUg78Mr5ycHIYPH8zrr7/Mm28a2LvXQLdu5ipN1FZHjkB6uvr46iEVTzwB+/api0w6cfvs7GDWLDhxQk/37pcYMmQwb775ptZlVZvJkydz6NAhTp48qXUpoord8P672dnZ9O0bRnp6LLt3G+nSxZxlVY3cXPjmG3X277w89R5cPXpAv35/Pc4sPh62bFF/rn17GDXq+tdWjEb1g3bXLrh4UZ1vMTwcrj69vndvxXWasWMr98JcswZKStQbVd7qeMoDB9RrXNnZ0L37rf/crUhJUccQHT+uftCFhMCDD4KLy7Xb3urrP3AATp1S7/Y8ebL6/m7bBqdPQ5s2aougbI7KyEh1UG6ZX35R76g8cqT6HqWnqz8Ham1ldd3OMeDOfyfHjqnvfXy8Ori+d2/LHGRft656BuWDD+Cpp57n4sU0Fi5cpHVZVS48PJyGDRuyZs0aXnnlFa3LEVXpeicTDQaD0rt3DyUgQKfEx2t/vv5Olp07Uby9K66PXLmMH1952yuveb3yCoqbW+XtW7RASUys/DOxsSiNGl27bzc3lMWLK287fXrF8wkJlZ/z8lLXd+xYse5G13VMJpSnnrr2mMOHo4SE3P01ry+/RPHwuHb/9eqhHDp056//kUfU55ydUb77DsXFpfLPNG6MEhenbjty5PV/Z0eO/PV7czvHuJPfidGI8vzzKLa2lfdrb4/yxhvq70brf/N3unz9NYqdnY3y7rvvKtboqaeeUpo2baqYTCatSxFVR8/11r733nuKo6Nld8oICFA/XJo0UT90Fi5E6dev4kNn+fKKba8ML0Dp2RPliSfU0CpbN3duxfbnzlXsH1C6d0cZNqzyB+YXX9z5B+WNPqDXrKlYb2OjftD36FG59jsNr3371H2W7XvgQJS+fSs+rBs0QCkqurPXXxYsNjbq/kJD1fczKKhi+9mz1W3nzkXx969YHxSE0qEDyqlTf/3e3M4x7uR3smRJxfY+Puq+rgzvtWu1/zd/N8ubb6I4OemU06dPK9bm999/VwBl3759Wpciqs614WUwGJSAAD/ln//U/j/UnS6pqRUfKtOno5SUqOuLi1H+/W+U//0P5cSJiu2vDK/Royvvp+zDu0ePivWTJlVsv2hRxfqYGBQHB3W9lxdKVtadfVDe6AO6deuK9Vu3Xv+D9U7Dq1cv9edtbVH276/83tjYqCHw66939vrLggVQRo2q2D42tmJ9t24V6xctqli/fn3lOm8WXrd6jNv5nZSUoPj6quvq1EHJz1fX6/UogYHq+latLLv1ZTCgknDZcgAAIABJREFUtGypUx5++O+KNWrTpo0yZ84crcsQVefa3oZHjx4lKSmN6dOvfsZy1K+vXq8A+PJL9ftRo2DJEnjoIZgxQ72r8vVMnFjx2M8Pym6rdPlyxfpfflG/OjrC//1fxfqWLdXraWXbHzxYJS8HUHvdlV3r8fGBQYMqnps58+7uDq0oFbXecw+VOuW88YZ6be38eejTR113N6//kUcqHjdrpl57AXU29apS1cc4exYuXVIf9+8PxcXqvnJyYNgwdX1MDKSl3XnNWrOzg4gIPZs2rde6lGoxceJE1q5di166r1qNa8LrwoULADRpYvZaqoyNDSxdql68B/XD9/vv4bHHIDgYBg+uGJR8taCgyt87Oalfi4vVr+fPQ2qq+rhvX3Bzq7z9lQOET5y4dv+KUvl7g+Fmr0aVlKR2kAA1RK4cFG5rC3dzh4zk5IrX5+9f+TlX18qdVe729V99j8WyDhdlr60q3O4xbvY7iY2teLxunRqGZcsnn1Q89+d/HYvVrBmkpKRjMpm0LqXKTZkyhYyMDKseGlDbXNPbsH79+oD6H9GSuyKPGqX2CFuyBLZuhd9/r/jw2rZNfT46+tqfu7pX3dW9Ev381Fks9Prrf1glJ1c8vt50clf/4VcWGjdzZYBcvQ+DARITb20/13NlAOXm/vW2d/v6r57wvDpmZrndY9zsd6LTVTzu2BG6dr3+fsr+0LFUycng6+tllbdXady4Md27d2flypUMK2suC4t2zb/SDh06ULeuJ6tXa1FO1VAUtaUSE6OeIjxwQD3Ns3YtBAaq2xw/fmeneZyd1Q+wsn2cO1f5+Y0bKx6XDeS+Mnhycioep6Tc+iDcunUrTg3+/jtc+cfxvn3qUIA75eVVcWrt2DG1m3iZzZvV1uiIEbBp0529/ivdyq1wrtzmThoBt3KM2/mdXHkWwt1d/YOobHniCXjhBfjsM3WogKVSFFi9WseAAUO0LqXaTJ48mQ0bNpCfn691KaIKXBNejo6OPProE7z9tj0JCVqUdPc2bFBDauBA9XpQUZH6wX/ffWrLAdS/ku/0Ni3h4RWPH31UPa2UkQGvvqoGJqin9jp3Vh9fOQ5o6VK1pZSVBQ8/fHvHHT1a/ZqSAnPmqK2ksuPerQcfVL+mp6uPDx2Cw4fhpZcgIUENsbKW1O2+/tt15c1vjx9Xx2PdrEV4u27ndxISUvFa9uxR/wgyGtX3JSxMDfeOHdUbR1qq//0Pjhwx8vjjT2pdSrWZMGECBoOBDRs2aF2KqArX68ZRXFystG0borRtq1MyM7XvCXW7i9GodvPmz95kjo5qd+uynnCg9jq8skdd2fqr5zYs62odHFyxrrQUZezY649H4s+u1LGxFdtHR6s1lD3v4YFiZ4fi6VnR5fxWehsmJ1ceh2Vnp/YEtLdHadr07nobZmai+Pnd+DWNG3fnr//KnoAxMTd/fyMjr93ntm1//d7c7jFu93cSGVl5KEDd/2/vzuOiqv7Hj7+GmQHZF1FRxB1RXFPBzAoRSXNfU8uyLK2sbLPdlk+72WZ9s01LK/OX5pKpuS9ZpqJFmAtu7IKC7AjMdn9/XAVRVFTgAvN+Ph73Mdudue/RGd5zzn2fc3xLK1ENhrIVmrVt27oVxdlZrzz77LNKXTd48GBlwIABWochrl/5cxs6OTmxdu0m8vMbEhZmJC7uuvJjtXNwUFsKTz6pdvMUF8O//6q/jH194f33r6+1YjSqszA891zZX/BOTjBqlFqo0KZN6f0dO8KiRWrVI6hdfJ07w/bt0LZtxY/r7692gd5wg3rbalWXoFm5Uq2Cux4+PhAdrbbuzj/H4+IC06fDggWl913t+79aYWHqjBjnODpeX7doea72/yQ8HHbsgO7d1UKgjAw1rshIWLiQWjtt2urVMHSogf79B/H2229rHU6Vu+uuu9i4cSMnT57UOhRxvS6X2hITE5WuXTsovr5GZfFi7X8hXstmMqHExaFERaGkpFTNWJz0dHXcmNl85X0PH0bJyLj+Y6allW3dVOZWXKy2TI4cKR0jV1nv/2q21FQ1DpOpaj8jV/t/UliotvrOnKnauKpyKyxUZ2vR6VAefvghxWQyKfbgzJkzioeHh/LJJ59oHYq4PuYrrudVUFDAU089yddfz2XECAfef99Ky5bVlVqFEJVt3Tp44gkjqamOfPzx/3FvbR7UeQ0mTpxIbGwsO3fu1DoUce0qvhjlhg0bmDbtYeLj43nkESvPPFPa5SJqlthYGDOm4vv/8IPaZSbqtqgoeOUVPWvXWhkxYggff/x/NDtXfmtH1q9fT//+/YmNjaXt1fTbi5rEUuEBHZGRkcTEHOS99z5i4UJfWrbUM22ajkOHqjI+cS1MJrWEvaLb+aXxom6x2dSW1u236wkNhaysLmzevJlly1baZeICiIiIwM/Pj0W1eTyQoMItr/MVFhYyd+5cPvroPeLikrn1VgNTplgYNar2D9QUoi44cQK+/RbmzjWSkGChT5+bef75Gdx2/rxiduyJJ55g9erVHD58GF1FBgaKmqbi3YblsdlsbNiwga+++oJff/0Vd3cH7rnHzJ13qrMQyGdCiOpTWKi2shYscGDVKgUvLw8mTnyAyZMnExQUpHV4NUpUVBShoaHs3r2bkJAQrcMRV+/6ktf50tLS+Pbbb5k37wuOHUskIMCRYcNMjBihLthnuOSyl0KIa5WdrZa7L1/uwNq1OgoLbYSF3czkyQ8zcuRInC6cK0uUaNeuHQMGDODjj+veIpx2oPKS1/n++ecfVqxYwfLli9m37xD16zsyZIiFIUNs9OlTOuO7EOLqHTmirna9YoWeLVts6HR6+vS5lZEj72DYsGH4nZtGRlzW66+/zmeffUZKSgoG+XVd21RN8jrf0aNHWb58OStWLGHXrr0oikLXrkbCw02Eh6utMnf3qoxAiNotKQk2b4bNm3Vs2WIkKcmEm5szAwbczvDhIxk0aBBeXl5ah1nrHDt2jMDAQH777Tf69++vdTji6lR98jpfVlYW27ZtY8uWLWzevJb9+4+g1+sICTEQHm6iZ08IDS2df1AIe2OzqUMddu+GP/+ELVuMHD1qpl49I7169SQ8/Db69u1LaGgoxvOnQhHX5MYbb6Rt27Z89913Wocirk71Jq8LnTp1ii1btrBlyxa2bdtAbGwciqLQrJkjoaEWQkNthIaqU/JcuG6UEHXBiRNqolI3A1FRkJtroV49R7p370p4+G2Eh4dz0003UU9KeSvdp59+ygsvvMDJkydxdXXVOhxRcdomrwvl5OSwe/fus9tOdu/+i7S00+j1Otq3N9K1q5mOHRU6dVLnprPTYSqiFjq3EvZ//6nryP33nwN//20gOdmEg4OOoKBWhIbeTGhoKKGhoXTp0kVaVtUgPT0df39/FixYwPjzl1EXNV3NSl7lSUpKKkloMTHR/PffvyQnq5Nqenoa6NjRQMeORSUJrV07mflDaMdiUZdKOXDg/ERl4NAhK2azgsGgp23b5nTs2I2uXbvRs2dPevTogcf5C4yJajVw4EAcHBxYtWqV1qGIiqv5yas8OTk5HD16lP3797N3714OHIgmJiaGU6eyAXBycqB1az0dOlho1UqhVStKtpYtZfyZuH4nTqgJqnSmEj3Hjzty4ICJwkJ1ye7GjX3p0KETwcGd6NChA8HBwXTv3h1nZ2eNoxfnW7hwIffeey8pKSk0bNhQ63BExdTO5HUpycnJHD58mKNHj3LkyBGOHj3MkSMHOHo0geJidXlcT08DgYF6mjUz0ayZQvPmEBBQujVurPGbEJozmSAlRa3yS0iAxET1elKSnuPHDRw/bsZkUpd49vFxp02b1gQGdiAwMJDAwEDatGlDUFAQnueWvhY1WkFBAX5+fsycOZOpU6dqHY6omLqVvC7FZrORlJTE0aNHS7bExEQSE4+TmJhAaqq6HgaorbaAACMBAQoBASaaNYOGDdUKSD8/9bq/vxSQ1EaKoq4UfeoUpKZCWpp6PSUFkpMhKclAYqKOtDQLNtu5z4ORgAA/AgICCAhoQ8uWLUsSVJs2bah/rctxixplwoQJxMXF8eeff2odiqgY+0heV2IymUhOTiYpKYnExEQSEhJISkoiKSme5OR4Tp1K59SpbM7/p3Jx0ePnZ8DPT6FhQzP+/goNGqgDsL291csLN4cKT4MsKqKoCDIzIStLvbxwS02FU6f0pKYaSEtTOHXKjMVS+n/o5GSkQQNvmjRpgr9/c5o1a0Hz5s0JCAigWbNmBAQE0Fia4nbht99+Y+DAgRw+fJjAwECtwxFXJsmroiwWC6dOneLkyZOkpqZy6tQpUlNTOXnyJCdPnuTEiQQyMk6RmZlFZmYuJpPlotfw9DTg46Onfn3w8bHi6WnB3V1txbm6gqenOmDb1VXdvLxKH3NzU2+rr1N7E2FxMZw5o1bf5eer0xsVFKhbXh7k5Kj3n3+7oADy83VkZRnIzHQgM1MhM9PKmTPWi17f1bUePj6e+Ph407hxAA0bNsbPz4/GjRvTsGFDmjRpQsOGDfHz88NHpnoRZ1ksFvz9/XnssceYMWOG1uGIK5PkVVXy8/PJzMwkKyuLzMzMcrecnBzy8rLIz8+loCD/7O0C8vPPUFhouuIxXF31ODo64OgIrq4O6HSlCc7Dw4Zer143Gm24uV38hx7A2fnKKwHk5alVdOXJyjIAagVMYaGOoiIdFov6HIDsbBuKAvn5Vsxm2xXfk6enK66uzhQUFOHn1xB/f39cXT1wc/PA29sbHx+fMtuF9zk6Ol7xGEKUZ9q0aWzcuJEDBw5oHYq4MkleNZXNZiMnJ4fc3FwKCgooKCggJycHRVHIzlarKvPy8rBYLBQVFVFYWIjFYiHvbNbIzi7t5jxz5gzFl1i0Kzc3C6tVLWbJzMzCwcEBL6+yhQb16rni7Fz+AE53d/eSeeGcnJxwcXHBwcGhpFjBw8MDvV6Ps7Mz9erVw2g04ubmhl6vx8PDAw8PD9zc3HB1dS0pF8/Pz2fs2LGsW7eOTz75RE6ii2qxY8cOevfuTXR0NF26dNE6HHF5krxEqTFjxuDg4MBPP/2kdSgoisLcuXPp1asXHTt21DocYQcURaF169bccccdvPvuu1qHIy6v4ispi7rPZDLVmG43nU7H5MmTJXGJaqPT6Rg7diyLFi1CftPXfNLyEiWio6OxWCz06NFD61CE0ERMTAxdunThzz//5KabbtI6HHFp0m0ohBDn69ixI+Hh4Xz66adahyIuTboNRe1UUFDApEmTSExM1DoUUceMGzeOn376CculSmxFjSDJS9RKaWlp7Nq1i86dO7Nt2zatwxF1yPjx48nIyGDLli1ahyIuQ5KXqJVat27N3r17efzxx+nUqZPW4Yg6pHXr1vTo0YNFixZpHYq4DDnnJYQQF/joo4947bXXOHnypCwCWjPJOS8hhLjQuHHjKCgoYO3atVqHIi5BkpcQQlygcePG3HLLLdJ1WINJ8hIlxo8fz5o1a7QOo9IcP36cxx9/nPz8fK1DEbXQ+PHjWblyJbm5uVqHIsohyUuU2LBhAwkJCVqHUWni4uJYuHAhXbt2lZJ6cdXGjBmDzWbj119/1ToUUQ5JXqKEi4tLjZkeqjJERESwb98+Ro8ejb+/v9bhiFrG29ubyMhI6TqsoaTaUAghLmHhwoXcd999nDhxAl9fX63DEaWk2lAIIS5l+PDhODo6snTpUq1DEReQ5CWEEJfg6urKoEGDpOuwBpLkJezaxo0bmT17NjbblVd5FvZp/PjxbN++neTkZK1DEeeR5CXs2qFDh3jmmWfo16/fJVebFvZt4MCBeHl5sXjxYq1DEeeR5CXs2qOPPsrOnTvp168fTk5OWocjaiBHR0eGDx8uXYc1jFQbCiHEFWzcuJHIyEhiY2Np27at1uEIqTYU59hsNvLy8rQOQ4gaKTw8HD8/P3766SetQxFnSfISABw5cgQPDw9iYmK0DkWIGkev1zNmzBh+/PFHrUMRZ0nyEgCYTCYAjEajxpHUPPPmzeO3337TOgyhsfHjx3Po0CH+/fdfrUMRSPISZ3l5eXH//ffj7e2tdSg1zl9//cWgQYN48skntQ5FaOjGG2+kefPm0nVYQ0jBhhAV8PPPP5Ofn8+9996rdShCQ88++yxLly7l6NGj6HQ6rcOxZxZJXkIIUUF79+6lR48e7N69m5CQEK3DsWdSbSiEEBXVvXt3AgMDpeuwBpDkJYQQV+GOO+5g8eLFSKeVtiR5CVEJ3nnnHQ4cOKB1GKIajB07lqSkJP766y+tQ7FrkryEuE5nzpzhl19+oXv37nz77bdahyOqWKdOnQgODpauQ41J8hLiOrm4uPDHH38wY8YMWrdurXU4ohqc6zq0Wq1ah2K3pNpQABAbG8uOHTu47777tA5FiBovNjaWdu3asXXrVsLCwrQOxx5JtaFQbd++nccff1zrMISoFYKCgujcubN0HWpIkpcA1Il59Xq91mEIUWuMHTuWJUuWYLFYtA7FLknyEoC64N6KFSu0DqPOslgszJgxg9OnT2sdiqgk48aN4/Tp02zZskXrUOySnPMSohocPXqU8PBwrFYrS5YsoXfv3lqHJCpBjx496Nq1K3PnztU6FHsj57yEqA5t2rRh3759DBkyhICAAK3DEZVk7NixLFu2rGRVBlF9pOUlhBDXKCkpiebNm7Nq1SoGDhyodTj2RFpeQghxrQICArjxxhul6lADkryEEOI6jB07lhUrVlBYWKh1KHZFkpcQNURGRgZvv/02ZrNZ61DEVbjjjjsoKChg3bp1WodiVyR5CVFD7Nmzh7feeosbb7yRxMRErcMRFdS4cWN69+4tXYfVTJKXAGDhwoUyNZTGBgwYQHR0NMHBwTRo0EDrcMRVGDt2LCtXrqSgoEDrUOyGJC8BqOOQdu3apXUYdi8wMJDvv/8eZ2dnrUMRV2HMmDGYTCbWrFmjdSh2Q5KXAMDR0RFvb2+twxCiVmrQoAF9+vSRrsNqJOO8hBCiEsydO5fHHnuMkydP4uHhoXU4dZ2M8xKiNomOjubHH3/UOgxRjhEjRmC1Wvn111+1DsUuSPISohbZuHEjEyZMYMyYMTKuqIapX78+ERERLFmyROtQ7IIkLyFqkenTp7N+/Xp8fX2lqKMGGj16NOvWrSM3N1frUOo8SV6i2owfP56IiAitw6j1+vXrx+eff651GNcsIyOD+fPnl9z29vZm5syZ2gVUiYYPH47FYpGqw2ogyUsIUa0efPBBFi9eXHL7rrvuolOnThpGVHnq169PeHg4P//8s9ah1HkGrQMQQtgXm82GTqcruf1///d/GkZT+UaPHs2TTz5JQUEBrq6uWodTZ0nLSwBQWFhY0k//zTff0K1bN9zd3QkNDb2oeiosLIz169fz2GOPERAQQEBAANOnTy8zJ5/NZuP111+nU6dONGvWjFdffRWbzVat78lerVq16poHnH/99df06dOHBg0acPfdd7Nt27aSx6xWKx9//DHBwcEln43ly5eXef6VPhvTpk1j69at/PHHH/To0YPU1FTCw8NZsGBBhV8DoFevXheNqXrxxRd58MEHy9x3pc9yVRg5cqQMWK4OihCKojz//PNKt27dlPfff1/R6/XK2LFjlaVLlyrTpk1TdDqdsmzZspJ9PTw8lKZNmyo33XSTMmfOHGXy5MkKoMyaNatknzfffFNxdnZWPvjgA2Xx4sVKjx49FCcnJ6Vv375avD27Mm7cOMVgMCivvvrqVT3v22+/VQwGg/LSSy8py5YtU4YOHap4eHgoGRkZiqIoyowZMxSDwaDMmDFD+eWXX0r+3+fPn1/yGlf6bKxfv17p1q2b0rlzZ+Xbb79V8vLyFC8vL+Xdd9+t8GsoiqI4Ojoqn3zyyUXvu0+fPiW3K/JZrip9+/ZVxo4dW+XHsWNmSV5CURQ1eXXu3Fnx9PRUJk6cWOaxMWPGKIGBgSW3PTw8lO7duys2m63kvp49eyr9+vVTFEVR0tPTFb1er8yePbvk8czMTMXV1VUJDw+v2jciFJvNpsyZM0d56623Kvwcs9msODs7K88//3zJfYWFhUpAQIAya9YsJSkpSTEajRe95p133qn4+fkpJpNJUZQrfzYURVGGDx+uDBw4sOR2ecnrSq9xpeSVnZ1doc9yVfnss88UFxcXpaCgoMqPZafM0m0oAOjZsychISHk5OQQEhLC3r17S7bg4GCOHDlCRkZGyf7h4eFlzlsEBgaWdDvu27cPq9XKiBEjSh739vamf//+1feG7JhOp+Phhx/mxRdfrPBzDh8+TGFhIWPGjCm5r169eiQkJDB9+nSio6Mxm83cfffdZZ53zz33kJaWxrFjx0ruu9xno6Ku9zWio6Mr/FmuCqNGjaK4uFiWSalCUrAhALXENycnh3nz5vHoo4+Wu098fDy+vr4AF816Xq9evZJzWocPHwbAz8+vzD7+/v5kZWVVduiiEhw8eBBQl/c437kEEhcXh06no0mTJmUeb9q0KQApKSm0a9cOuPxno6Ku9zXi4+MBKvRZrgqNGjWid+/eLF26tMyPOFF5pOUlSpybmHf79u0UFBRctHXr1q1k3/N/FV/o3B+0CxNVfn5+FUQtKoOnpycAOTk5Ze7PysrCbDZTv359FEUhOzu7zOOnT58GoFWrViX3Xe6zUVEVeQ2TyVTmdmZmJsrZqVqv5rNcVUaPHs3KlSspKiqq8mPZI0leokT79u0BWLlyJS4uLiXb4sWLeeihhyq8wm/Xrl0B9Q/HOYqisHv37soPWlyTBQsWkJycXHI7KCgInU53UZViREQEDz30EMHBwQBs3bq1zONbt27Fzc2NFi1aVPjYOp3uuitP3dzcyMzMLLlts9mIjY0tuV1Zn+XrMWbMGAoKCli/fn2VH8seSfISJQIDAxkzZgzffPMNc+bMISsriw0bNjBt2jSaNm2Kk5NThV7H39+f8ePH8+yzz/LPP/+QmJjIlClT2L9/fxW/A1ERxcXFzJo1i06dOpUMFg4ICGDs2LG89NJLzJ8/n9OnTzN79mxiYmJ44YUX6Nq1KwMGDGD69Ons3LmT4uJiVq5cyQcffMBDDz10Va0tNzc3Dh48yPbt2695fsYbbriBuXPnsmvXLuLj43nkkUdITU0tebyyPsvXw8/Pj169erF06dIqP5Y9kuQlyvjqq68YNGgQjz/+OD4+PkycOJFx48bx8ssvX9XrfPPNN/To0YPevXvTokULDh48yN13310pXUri+jg5OREVFcXEiRPL/H/MmTOHW265hQceeABfX19efPFFPvjgA9q0aQPA999/X/J/6uzszOTJk3n44YeZNWvWVR3/nnvuISsri1tvvfWaW+MfffQRPj4+3HjjjbRp04bs7GyeeOKJMu+nsj7L12PUqFGsXLnyoi5Ocf1kPS9RLpPJREpKCi1atLiuhFNYWEhOTs5FxRui5iosLCQlJYVmzZrh6OhY7uNpaWm0bNnymo+hKApZWVn4+PhcT6ikpKTg6emJm5vbJfeprM/ytUhOTqZZs2asWrWKgQMHVuux6ziLJC9R4r///iM6OrpajtWpUye6dOlSLccSV7Z69Wq7qwQdPHgwXl5eVX6cXr16ERwczLx586r8WHbEIqXyAoC9e/fy9ddfs3Pnzmo53uTJkyV51SALFiwoGeJgL3r27FktyWvUqFG8/fbbfP755+W2ZMW1kZaXAOD1119n0aJFJeN9hPj888+ZOHEiLi4uWodSq8XHx9OyZUvWr19PZGSk1uHUFRYp2BBCXOT48eO89NJLdO3alb1792odTq3WokULbrjhhosmMRbXR5KXAGDChAnSJy9KtGrViv/++4/27dtjNBq1DqfWGzFiBMuWLZOVFSqRdBsKIUQV279/Px07dmTHjh306tVL63DqAuk2FEKIqtahQweCgoKk67ASSfISQohqMGzYMJYtW6Z1GHWGJC8hxDU5c+YM33zzDXLmoWJGjBjBsWPH2Ldvn9ah1AmSvIQQ12Tbtm08+OCDREZGlplXUJSvZ8+eNG3aVFpflUSSlxDimtx+++3s2LEDRVGqZaLb2k6n0zF06FA571VJpNpQCCGqycaNG4mMjOTYsWNl1kATV02qDYVq+/btREZGyvkLIapQWFgYPj4+rFixQutQaj1JXgKAjIwMNm7ciMVi0ToUIeoso9HI4MGDpeuwEkjyEgC4uLjg7e0tyUtUqvj4eFlJ+AIjRoxgx44dUuRynSR5CQD69+9PZmYmzs7OWoci6pBFixYxYMAApk6dSlFRkdbh1Aj9+/fH2dmZX3/9VetQajVJXkKIKvPCCy+waNEiEhISZI7Es5ydnenfv790HV4nqTYUQohq9sMPPzBp0iROnjyJt7e31uHURlJtKIQQ1W3w4MHodDp+++03rUOptSR5CSFENfPy8iIsLEy6Dq+DJC8hhKZ27tzJoUOHtA6j2o0YMYK1a9dKIcs1kuQlhNDUrFmz6NatG7Nnz9Y6lGo1bNgwCgoK2Lx5s9ah1EqSvAQANpuNrKwsrFar1qEIO/PTTz/x/PPP213rq0mTJnTr1o2VK1dqHUqtJNWGAoC4uDhatWpFVFQUPXr00DocIezC66+/zhdffEFKSgo6nU7rcGoTqTYUKoPBAIDZbNY4EiHsx7Bhw0hNTWXPnj1ah1LrSPISAHh4eDBlyhR8fX21DkUIu9GlSxdatmwpXYfXQLoNhRA13rp16wgNDa2TA3qnTZvG1q1biYmJ0TqU2kS6DYUQNZvZbObRRx+lY8eOrFu3TutwKt3QoUPZt28fcXFxWodSq0jyEkLUaEajkT179jB48GCOHDmidTiVLiwsDG9vb+k6vErSbSiEEBq78847SUtLkzFfFSfdhkIIobWhQ4fy+++/c/r0aa1DqTUkeQkhhMZuv/129Ho9a9eu1TqUWkOSlxCiTli5cmWtXQlJvsJwAAAgAElEQVTc09OTsLAwOe91FSR5iRLz58/n4MGDWochxFVLSkpi/Pjx9OrVq9Z+hocOHcqaNWsoLi7WOpRaQZKXKPH000+zbds2rcMQ4qoFBASwb98+XFxcSEtL0zqcazJ06FAKCgrYunWr1qHUCgatAxDa2LRpEwkJCVgsFmw2Gzk5OZhMJlauXEl8fDwAubm5WK1WnnrqKYKCgrQNWIgraNWqFVu3bq21cwQ2a9aMLl26sHLlSvr37691ODWelMrbqeeee4733nuvZE5DvV6PzWZDp9OVfPnNZjOOjo6cPn0aFxcXLcMVwi689tprzJ07l6SkpFqbhKuJlMrbq0mTJgFgsViwWCwUFxdjNpsxmUwUFxdTXFyMg4MDAwcOlMQlRDUZOnQoKSkp/PPPP1qHUuNJ8rJTQUFBdOvWDQeHS38EbDYbY8aMqcaohKg6VquVNWvWaB3GZXXr1o0WLVpI1WEFSPKyY1OmTLls14Rer2fgwIHVGJEQVWfDhg0MHjyYcePGkZmZqXU4lzRo0CB++eUXrcOo8SR52bFx48aVnPO6kF6vp1+/fnh4eFRzVEJUjQEDBrBu3Tr27dtHXl6e1uFc0tChQ4mOji4pnBLlk+Rlxzw9PRk5ciRGo7Hcx6XLUNQ1kZGR7Nu3j+bNm1f4OX///XcVRnSx8PBwvLy8WLVqVbUet7aR5GXnJk2aVO7qyYqiMGTIEA0iEqJqXe4874WWLFnCLbfcUq0Dn41GI7fddpuc97oCSV52rm/fvjRp0qTMfQ4ODtxyyy2yqrKwa2lpaUyZMoXCwkJGjhxJYWFhtR176NChbN26lezs7Go7Zm0jycvOOTg4cP/995fpOnRwcOCOO+7QMCohtHH69Gn27NkDqL0SBQUFKIrC0aNHeeqpp6otjkGDBgHIRL2XIclLcN9995WZ0NRqtTJ8+HANIxJCG19++SW9evVi6NChrF27tqRL3WKx8MUXX7Bo0aJqicPLy4tbbrlFug4vQ5KXoGXLlvTq1Qu9Xo9OpyMkJOSirkQh7MELL7zA66+/zurVq7lw8iGdTseUKVOIi4urlljOTdRrMpmq5Xi1jSQvAahjvhRFQa/XS5ehsFuKorBmzRr0en25jxUXFzNq1KhqSSjDhw8nJyeH7du3V/mxaiNJXgKA0aNH4+TkhMViYcSIEVqHI4QmZs+ezZ9//lluBS6o833u27ePV155pcpjad68OZ07d5auw0uQiXlFiUmTJrFnzx5iYmK0DkWIanfw4EG6du1aoVaVTqfjt99+q/LZ319++WW+//574uLiZKLesmRiXlHqvvvuY+zYsVqHIYQm3nzzTUwmE3q9vtxuw/PpdDruvPNOUlNTqzSmoUOHkpCQID8oyyEtrzrIarWSnp5Ofn4+OTk55OTkkJ+fT15eHnl5eeTk5ABQWFhIUVFRyfMKCwvJzMzExcUFd3f3MlNHnbttMBhwd3fHy8sLd3d33N3dcXNzw8PDAx8fH7y8vKr9/QpRWdLS0ti+fTsbNmxgxYoVpKen4+joWLLu3fmMRiM9e/Zk69atV0x2FWWxWDh58iRZWVlkZ2eTmZnJY489RmRkJIGBgRQVFZWMNzObzeTn56PT6cp87zw8PNDr9bi7u+Pt7V1m8/HxqSvjNy2SvGqZkydPEh8fT1xcHAkJCaSmppKenk5qSiKnTqWRnn6a9NPZF1VKARgNDrg5O+Dtqn7RHA3g6lT6uJNRwcVRfV72GR2KUtpNkX0GFAXMVoX8IoWsvPLPCTgaDTTw9aZRo4b4NW5Kg4Z+NGrUCH9/f1q2bEnLli1p0aIFbm5ulfivIkTls9lsREdHs2HDBtauXcuOHTswmUzUq1ev5EefTqfjjTfe4KWXXqrw6yYmJhIbG8uhQ4c4fvw4yclJpKTEk5iYSFraaazWskmyXj0HvLwMuLjoMBrh3FfHwUHB01PBaoXc3NJOtKws9TInx0ZWlgWbrezfAmdnR5o29cPfP4CAgFYEBATQunVrgoODadeuXW35ASrJqybKycnhwIED7N+/n4MHDxJ76ABxx48QF59EYZHaH2/Q6/Cvb8TfBxq4WfDztNHIExq4g58XNPIENyfwdAEPZ3CvB07lT2F4zfKKIP/sllsIp/PhVA6k50FaNpzMgfR8PWm5BpJP2ziVXZrwfH08admyBS1bt6V9+2CCg9Wtbdu2ODo6Vm6gQlSCwsJCtm/fzsaNG1m9ejUHDx4sqdDdtm0bvXv3LrN/UVER0dHRREVFsXfvXvbv/4dDh46Qn6+2nHx9jQQG6vD3N+Pvr9CsGfj7Q9Om4O0NXl7qpbPz9cWdm6smtKwsOH0akpMhMRFSUiAlRU9iooEjRywUFloB8PPzoX37YDp16kZISAghISG0bdu2pp1zk+SltYSEBHbv3s3u3bv5N3ovB/bvJyX1FACu9Qy0DzDSrlERLRsotGgALRtAiwYQ4AOGyumpqDYFxRCfDnHppZdx6Q7sT9Fz/KQFi1XBoNfTplUAHTp1pVv3EEJDQwkJCcHT01Pr8IUo49SpU2zatIkNGzYQGxvLggULiI6OZsuWLezcuZ19+w5gNlvx9jbSowd07mwmKAjat1e3+vW1fgelbDZISIDYWDhwAA4dguhoI//+a8VksuHp6UL37t25+eZw+vbtS69evbT+kSnJqzoVFRXx119/sX37dqJ272b3rr84lZGJQa8jOMCJbs2KCPaHDk2hvT+08IWa9WOn6hSb4VAqHEyB/cmwP0VHVJyB5AwzOp2OoDbNCbnxZkJDe9KnTx86dOhQ034JCjtjtVr5/fff+e2339i0aS3R0f+h00GPHkZuuslESAj06AGBgVpHeu1MJvj3X4iKUrdt2wzExVlwdXXi5ptvJiKiP0OHDiUoKKi6Q5PkVZUsFgtRUVFs3ryZzRvXs+OvnRQVm2jRyJGercyEtlIIaQ3dWpQ99yRKpWZD1HHYfQyi4gzsOgo5BRYaNfAhPCKS8PC+9O3blzZt2mgdqrADJpOJjRs3smzZUn75ZSkZGTm0b+9Iv34mIiIgLEzt7qvLjh+HTZtg0yYdmzcbSE8306FDICNHjmPUqFF06dKlOsKQ5FXZzpw5w6ZNm1iy+Cd+XfkL2bn5NPI2cmtbC/06KkR2Urv+xLWxKWrr7M/DsHG/gfX71GTWopk/Q4ePYsiQIfTp0+eSi2wKcS1iYmKYO3cuP/wwn+zsPHr0MDJqlJmRI2t3y+p6Wa3wxx+wdCksX24kOdlM+/ZteOCBh7n77rtp0KDK/thJ8qoMubm5LF26lGVLl7Bx4ybMZjO3BhsZ3s1E/84Q1FjrCOsusxV2HYVV/8CKv43Epphp1MCHocNHMmbMHURERFzV+k1CnFNcXMwPP/zAV199xu7d/xAY6Mj995sYPx6aNdM6uppHUWDXLliwQMePPzpQVKRj+PBhPPzwo/Tp06eyDyfJ61opisK2bdv49ttvWPrzEqwWM/07w/DuVoZ0g/pSCa6JgymwYi8s32sk6qiZAH8/7p00mYkTJ9K6dWutwxO1QF5eHl9++SUfffQep0+fZtQoeOABG3362M856Ot15gwsXgxff21kxw4zvXr14IUXXmHw4MGVda5aktfVysnJ4YsvvuCrLz7jeHwSoYGO3HuzifE3gZeL1tGJ88Wmwvzf4bs/DKRmWbn15l48/uR0hg0bJq0xcZHCwkI+/PBDPvzwPUymMzz4oIWnngJZYOH6/PUXvPOOA6tW2ejYsR1vvfVeZazSLsmrolJSUpg9ezZffjEHna2Y+8MsTApTKwNFzWa1wdp/4eutDvz6t0KbVs2Z/uyL3HPPPTg5SaWMgCVLlvDss0+SkXGS6dMtPPYY+PhoHVXdsm8fvPGGAz//bCMysi8fffQpwcHB1/pykryu5NSpU7z66it8M28evh46Hr/NzEMR6sBfUfvEpsL7q3V8/4cOb29vXn71daZMmSIFHnYqISGB++67m61bt3PPPQ68/bZNWlpVbPt2eOIJIzExVp56ajpvvPHGtYwZk+R1KcXFxcyePZu33vwf7o5mXhth5u6bK3+WCqGN1GyYtQo+2+hAm9at+fDjT6t8hnBRs/z4449MnfogTZua+OYbE6GhWkdkP2w2+PprmD5dT2BgexYuXEz79u2v5iUkeZVn27ZtTLr3btLSTvDMQCvPDJZxWHXV0ZPwzCI9K6KsDBk8kK/nfkOjRo20DktUIZPJxEMPTWH+/AU8+qiOmTOV656CSVybo0dhwgQjMTE6vvpqHhMmTKjoUyV5nc9isfDaa6/x7rvvMKSbjk/vsdJU+r3twpYDMHmegTyLO98u+IGBAwdqHZKoAnl5eYwaNZydO39n0SILgwZpHZGwWOCFF+CDD+Cdd97lueeeq9DTJHmdlZaWxohhQ4iJ+YcP77TyYITWEYnqllcEj87X8f0fCk888QTvv/+BVCXWIVlZWURG9iEl5SCrV5vp1k3riMT5Pv0UnnhCx+OPP86HH350pd0leQHEx8cTGdEHffEJlj9upr2/1hHVPlsPQlYBGPUw+Aato7k+P+6A+7/WM2LkaBZ89z1Go5zorO3MZjMDBvTjyJG/2LrVTKtWWkdUefbtU7vfACIiwMND23iux//7f3DXXTpmzXqfp5566nK7SvKKjY2lX98wGtTLZO0zZhrW4v94LYW8DHuOq0uv5M7TOprrt+UADP1QT3jEbSz5ebmU1NdyDzxwP4sXf8cff1jo3FnraK5NTg7873/Qty8MHlx6/xNPwOzZ6vWYGOjUSZv4KsuHH8Izz+hYseKXy40Hs9h1n0h+fj7DhgykqdtpNr8giUuUCg+Gjc9b+X3LBp5++rK/AEUN98svv/DNN9+waFHtTVy//67OofjRR2Aufx3YOuOpp+C++2DSpHvIzMy85H52nbymPvwQmaeSWDrNIrNjiIv0bANzH7Dw2WdzWLRokdbhiGtQVFTEU089xoQJ+lpdnPHPP5Cerl6/cHalJ55QZ7H46y+oKzOgffSRgqNjAS+/POOS+9jtyMylS5ey8McfWfOMQhNvraOpfDGJ6nmo+HRo1wRubadenm/XUXUNLYMD3NVb3Xf9PnUgb4emMDKk/Cmvdh1VXzv7DPQKhCG1/BzX5YwOhYf76Zj68BRuu+026tekFQTFFc2ZM4f09FRmzrRW2zEtFvj1V4iOhowMcHNTF58cMQLKW1P1xAnYuBH++w/0eggKgjvuAJez373Nm2H37tL9t2xRuxCHDFFnAUlPVxeRBPW5Lhd8Z61WNfn9/jucPAkdO6pdj/4XnNvftUtdhNJggLvugvh4WL9efe0OHWDkyOpb7sXdHWbONHPvvV/yxBNPEljO1P12ec5LURQ6tG/LDb7HWTjVpnU4lcqmwCs/wzu/qNfPMejhjdHw3JDSX26PzIc5G8DZERY+AhM+gzOm0uc094VNL0Lrs8OeFAWm/wgfril7zEE3wNE0NenVlXNe58svgtZPG7n/4em8/fbbWocjrkLXrh246aaDzJlTPX/mrFbo3VtNBBcKDITffivbOlqwAKZNg9zcsvs2aKDu2707DB2qJsML/fMPdO16+XNeR49CeDgkJ5d9rpsbvPcePPxw6X2PPAJz5oCzMyxcCBMmqBPsntO8ubqOV3W17mw2aNbMkcmTX+TVV1+98GH7POe1Y8cODsYe5ZlBdStxAXyzFd5aoSau+m4wORya+oDFCi/8BEvK+VIVmWH0x9AxAKb1hxZnl+BJyICZ531pFu8qTVw6HQzpBje1hdX/qImrrnKrBw/3NTP/m6+xWqvvF7y4PgcOHODffw8wfnz1/T7/6KPSxDVwoJpYundXbx85Ai+/XLrvzp3quZ3cXPX7FBkJffqAg4PamhoyBIqKoGXLspMDt2gBXbpwxYHVcXFqC+tc4urVS43JxQXy82HqVJg//+LnFRXB6NFqC23aNPV4AAkJMHPm1f+bXCsHBxg3zsSPP5YTJHZ6zmvdunW0aeJI1+ZaR1K5TBZ4abF63csFEj6Brx6AuI+h2dnerteWqS2o8ykKDO0Ou16H2ffAhhdKH4tJKr3++rLS62uegZVPw5+vwtcPVM37qUlG94TUkxlER0drHYqooJ07d+LubuDmm6vvmJ6eMGmS+kd/9Wo1mW3bVtqVd/hw6b7PPqt+9xwc1PNV69erXYKPPKImMycnNRHOnq3ue85HH6ldkkFBl49lxgxIOvv9/fhj2LFDjWnvXjg3leBTT0FWVtnnKYra2jt37A0bSh+Libm2f5drNWAAHD4cX27hhl0mr30x/9Kjed0r2Tl2Ek6d7X6I6Ki2qE7nQ04hDOyq3n8wBdJyLn7u1H6l19s0Al939frpPPXSbC1tXdV3g9vOq9qa1Ac863jBS7A/uDjp2bdvn9ahiApKTEykWTN9ta7BNXkyzJun/tE/eRJ++UVNIufk56uXigJRUer1kBDo2bN0n3fegexsteUUFnbtsWzZol46OcH995fe366d2pUIauI6F8f5pk4tvd6mDfj6qtdPn772eK5F87MNjMTExIses8uCjeys07R3rXun+o6cLL2+dLe6lSclExpfcOK1wQXDBFzO/jKznu1ZTTpdej2sPTic9wfBQad2Teacoc5y0IG3u56sC3+mihorLS2NRo2q99SAxaJ2rS1bpp6TurCX41wiTU5Wu+fg4vXCXF2vP464OEg9+2OzTx/1HNf5Bg+GdevU6/v3w223lX28QYOyt8+1HKu717zx2VXoT5w4QdeuXcs8ZpfJy83dg/xyWh+1nVFfer1rc+hxiVkE6pWz+oDTBZ+EC2dFOn8JGPMFH2CLFRIzKh5nbZV3xoa7u7vWYYgK8vX1ZefO6u1cGjMGVqxQr996Kwwfrp53Gj1aLZ449706P5lcWKxRGfz8wGhUx4SlpFz8+PkFHN7lVFtfOCZfq1nSMs7+XWlwYTbFTpNXULtgNi7bBJiuuG9t0qph6XX3emXPRe1PVgsPmtUvfynzK3Wt+LqrXYM5Z2BvnFoQcq719ddRdV7AuiwuHXLPWGjXrp3WoYgKCggIICmp+lpeycmliWvkSFi6tPSx7Gz18tz3zNtb7YrLyFDPIxUXlyaM1avV814dO8JDD6mtpPO/n7YKvCVnZ7USMSpKLcE/fpwyU2KtXFl6vbwZOaqzq/VyzvUWBgQEXPSYXZ7zioiIICbeRFy61pFUrqDG0K2Fev2Pw/DTTrWrLyEDev8PWjwOXV9UCzuuxYge6uWJLHh0PuQWQkYevLm8MqKv2X7ZCx7uLvTo0UPrUEQFdenShcxMc7UVGZzfwsnLK+0y/Pzz0hbE+a2sO+5QL9PT1et79sDff8Mrr6iVfatXl7aKzl+r8b//1FbclVpsffuWXn/kEbXaMSMD3nwTDh5U7w8Lo0ZPULx1KzRu7FvuMkV2mbwiIyNp3KgBn6zVOpLK9/5d6vkqRYFxn4LfVGj1hNpiMujhq/vB8Rrb22/eUdp9+PlG8JkCDR+GzQdKx4LVRRYrfLbRyPg7J1CvXj2twxEV1LNnT1q2bEp1TY7SuXPp+asNG9QS95Yt1eKHcwt1nz5det7ojTfU7j1QW0IhIWpZ/d9/q/eNGaOOGYOylYWvvqqOGStvLNn53nhD7a4EWLsW2rZVz2WdK9evXx/mzq05razy/PSTI+PG3Y2unCDtMnkZDAZenPEqX2zWE3NxEUutFh4MO/4H3VuqySojT01WkZ1g4VR1yqNr5e+tltPf0EK9bbWphR8rn4aIDpUSfo30wRpIzFB47rnntQ5FXAWdTsddd93L/PlG8vKq/njOzmpX4bnJIBIS1JbOzJkwa5Z635kz6owZoM6OER2tzrxx/sIFLi4wfbo6gPmcsDC1K/IcR0eu+J6MRnWW9ueeUysMz3FyglGj1EKNNtfx96CqbdgABw6YuPPOO8t93C5n2ACw2Wzc1q8vibE72PuGGfc6+IO6yAxH0tTSd+dyijSux8kc9TxXmzrc4gLYfQxuecOBd96ddaUlGkQNlJmZSVBQK+67L4f33queY9ps6tRKxcVqi6kixQ4mkzoGrF49aNasbDfh+dLS1IQYFFQ24VVERgacOqW2wAw1vNrBYoFu3Yy0aXM7y5b9Uu4udpu8QC2/vKFLR25tncOiR2wY9Fd+jrAfCRlwyxtGut4YyS8rV5XbdSFqvi+++IJp0x5h505bjT6/I0q98Qa8844jBw7E0uLcFB9l2XfyAti2bRuDB91ORHsTPz1qxamK1x2MTYUxsyu+r8kCnS4utCnXD1Ohc7Nrj62y1IX3eDgVImca8Wnclk1bfsfHx6fqDyqqhNVqpX//fsTG/smuXeaLxlWJmmX5chg9Wsfs2Z/w6KOPXmo3Sw1vPFa9sLAw1m/YxMDbb2PwB0UsfsyCdyUMErwUkwWOn6r4vlDx/YuvsYqwstX297jnOAz50EiLwM6sWbsB7/IGwohaQ6/X8/PPy7npphCGDUtg0yZzrV5tuC7bvRvuvlvPgw8+cLnEBdjxOa8LRUdHM3hgfxzMWXz/kJmw9lpHJKqbTYH3V8PLPzsQHh7Oz0tX4Hbh1ASi1jp27Bi33tqLhg2zWbPGXDJ7g6gZ1q2D0aP1hIX1Y8WKVRguf2LOPmeVL0/Xrl35d98BuvceQMTbOl5arBY8CPuQkAGR7xp4+WcDb771Lmt+Wy+Jq45p3bo1O3ZEUVTUjJtuMnLggNYRiXPmz4chQxwYOXI8y5f/eqXEBdhpqfyl1K9fn+UrVvLZnM+ZvaEewc8Zy11CRNQd+UUwYwm0f1bPCVNzdvy1k2eeeQYHrebDEVWqefPm/PHHLvz9uxESoufLL7WOyL7l5sLEiQ5MmqRj+vTnmD//O4wVLKOUbsNLSElJ4cUXnueHhQvpHWTgvXFmbqzBYyLE1TFb4fs/4OWfDZyxODHjlf/x2GOP4XipGmVRp5jNZl577TVmznyXwYN1fPmllXImcRBV6I8/YOJEA/n57syb9x2DBw++mqdLt+Gl+Pv7s+C779m5cxeKT3d6vQp93jKwJvrimaJF7ZFfBB+vhTZPG3lwnp5hYx/gyLF4nn76aUlcdsRoNPLWW2+xefMWoqMb0batgVmz1PFWomqlpMCECQ7ceiu0axdBTMzBq01cgLS8KmzLli3Meu9d1q7bQMdmBp7sb2ZMT3WyW1HzxafD3K3w+SYDxVYD9z8whSeffPJSY0iEHTlz5gwzZ85k1qyZNG2q8M47JkaM0G4m9boqJ0dd5+y99/Q0atSYWbNmM/L8aUOujozzuloxMTG8P+s9Fi9ejFFvY0yojUlhCr3b1uw5wuxRoQmWRcG32w1s2W+loa8PUx99nKlTp1K/fn2twxM1TGJiIs8//yw//bSYoCADzz1n5s47r34mC1HWqVPqSs5z5hgAJ55/fgZPPvkkTheuu3J1JHldq8zMTBYuXMi3877in3//I7CJI3eEmhjeXZ1XUBKZNgpNsPE/WLFXx9IoPWeKFQYNGsh9kx5g4MCBFapiEvbt4MGDzJz5Lj/+uJAmTRx48EEz996LlNZfpT17YO5cHd9954C7uwePPz6dRx55BE9Pz8p4eUleleHff/9lwYIFLF+6mPjEFAIaODHshmKG94Bbgq59FndRMafzYU00/LLXgbUxOgpNNkJ73MCYsXcxYcIEGjZseOUXEeICCQkJfPzxx3z33Tfk5uYzaJCOBx6wMmBAzZ8bUCuZmfDjjzBvnpHoaDPt27dh6tTHmTRpEi7nlmOuHJK8Ktv+/ftZtWoVv674mR279uLs5MBNgdA70MrNQRDWvuyKx+LqFRTDX0fUFtbGA478E2dGp9NxY89QxtwxjtGjR+Pv7691mKKOKC4uZuXKlXz11Rw2bdqGl5eewYOtDBmiMHAguFbhjDy1wenT6tpjS5YYWL/ehoODgSFDhjJlyoNERERU1ZygkryqUlxcHJs2bWLz5k1s2bSBtFOn8XQ1cEuQQkhLK6GtIaQ11JexsJd19KQ6u3vUMdgVZyTqqAWrDTq0D6RvvwH07duX8PBwPGTOH1HF4uLiWLp0KUuX/j927fobNzc9/fvbiIiwERFRuhxKXWaxqNM4bdoE69cb2LHDSr16jtx++0BGjhzNkCFDcHd3r/IwJHlVo/3797Nlyxb++GM7u3f+SVyCuvRq6yb1CG1homtzGx2bQrA/NPe1v/NmJgscOgEHUmBfEuyN1xN1XEdmngWjQU/njkGE9gojLCyM8PBw6Q4UmkpJSWH58uX89tsqfv/9d/LzC2nWzJG+fc3cfLNCSAgEB9f+Lsb8fNi7F6KiYNs2Pdu2QV6elaZNG9Cv3+0MGTKMAQMGVHa34JVI8tJSeno6UVFRREVFsXvXX/wb/TcpqekAuDnrae+vp6O/iXZNoIUvtGigbg1rcQPDYoXkTIjPUMvXj52Egycc+O+EkWOpJixWBYNBT2DrFnQPuZGQkFBCQkK44YYbZBVjUWOZzWZ27959tqdlHXv2/E1BQRGurnpuuMGBkBAznTpB+/bqwpBeXlpHXL7ERIiNhUOH4J9/ICrKiYMHTVitCn5+9end+1YiIiKJiIigbdu2WoYqyaumyc7O5sCBA+zfv5/9+/dzYH8MsYcOkpJ6CqvVBoCLk54WDQ209LXQ2MuKn6ea0Bp5gp8XNHCHhp7g41p9rbdiM2QWQHoupGbDqVx1S81SL5OyjMRn6EjOMGOxqh+5ek6OtGzRlOAOXQju0JEOHToQHBxMUFCQDBgWtZrVamX//v3qD9Pdu9mzZwcHDx6msFAdBd2okZHgYGjZ0kxAAAQEQNOm6mWTJlWT3KxWSE9XBwknJ6uJKjkZkpLg8GFHYmOt5OdbAfD19aRz5y6EhPQiNFT9ARkQUMF1i6qHJK/awmw2k5SURHx8fJktNSWJtLQU0tMzSD+djc1W9r/TtZ4eN2cH3J0d8HQGD2cbbo5W6mP9F1EAAAGMSURBVBnVROigA8/zWvtORrX77tynwmqD3MLSx/OK9eQX68kr0pFXCNkFNvKLrJjMtjLHdXI00sDXm8aNG9OwUWP8mzajRYsWZbbGUnss7IjNZiMhIYHY2FgOHDhAbGws8fFHSU5OICEhhYKCopJ9HRx0eHsb8PZ2wMsLvL2teHmp6wE5OpYWiTg6gtlc+n3NyVFXcrZadWRlGcjKciArC7KyrOTmll1PqGFDb5o2bULTpi0JDAyiXbt2JZuvr2+1/JtcB0ledYnVaiU9Pb1ky8zMJD8/n7y8PPLy8sjNzSUnJ4e8vDxMZ+fBMZtN5Odml7xGYeEZjEbH0vFQOh3ePg1KHnd1dcXd3R13d3c8PDzw9PQsue3t7U2DBg3w8/PDq6b2iwhRQ2VnZ5OcnExKSgpZWVkXbbm5uQAUFZ2hsDD/7PVCHB2dSiaS9vLyRadzwGAw4OXlhbe3N97e3iXXGzRoQJMmTWjatGlt74aX5CWEEKLWkYl5hRBC1D6SvIQQQtQ6kryEEELUOv8fM7O4ln6hh3sAAAAASUVORK5CYII=",
-      "text/plain": [
-       "<IPython.core.display.Image object>"
-      ]
-     },
-     "execution_count": 156,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": ["from IPython.display import Image\n\nImage(app.get_graph().draw_png())"]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "2a1b56c5-bd61-4192-8bdb-458a1e9f0159",
-   "metadata": {},
-   "source": [
-    "## Interacting with the Agent\n",
-    "\n",
-    "We can now interact with the agent. Between interactions you can get and update state.\n"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 118,
-   "id": "cfd140f0-a5a6-4697-8115-322242f197b5",
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "{'messages': [AIMessage(content='', additional_kwargs={'tool_calls': [{'id': 'call_yQrJa8CEOfKBdpVl80jzWf5h', 'function': {'arguments': '{\"query\":\"weather in San Francisco\"}', 'name': 'tavily_search_results_json'}, 'type': 'function'}]}, response_metadata={'token_usage': {'completion_tokens': 21, 'prompt_tokens': 94, 'total_tokens': 115}, 'model_name': 'gpt-3.5-turbo', 'system_fingerprint': 'fp_c2295e73ad', 'finish_reason': 'tool_calls', 'logprobs': None}, id='run-3764f79e-17b4-4aa3-bbe4-4f92b11ca52c-0', tool_calls=[{'name': 'tavily_search_results_json', 'args': {'query': 'weather in San Francisco'}, 'id': 'call_yQrJa8CEOfKBdpVl80jzWf5h'}])]}\n"
-     ]
-    }
-   ],
-   "source": ["thread = {\"configurable\": {\"thread_id\": \"1\"}}\nfor event in app.stream(\n    {\"messages\": [HumanMessage(content=\"what's the weather in sf?\")]}, thread\n):\n    for v in event.values():\n        print(v)"]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 120,
-   "id": "4479f8ae-7c46-4117-8ca2-0a9c2ef9785b",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "{'messages': [HumanMessage(content='what's the weather in sf?', id='1cfd0c2f-9b60-48da-8938-408fd6aeda13'),\n",
-       "  AIMessage(content='', additional_kwargs={'tool_calls': [{'id': 'call_yQrJa8CEOfKBdpVl80jzWf5h', 'function': {'arguments': '{\"query\":\"weather in San Francisco\"}', 'name': 'tavily_search_results_json'}, 'type': 'function'}]}, response_metadata={'token_usage': {'completion_tokens': 21, 'prompt_tokens': 94, 'total_tokens': 115}, 'model_name': 'gpt-3.5-turbo', 'system_fingerprint': 'fp_c2295e73ad', 'finish_reason': 'tool_calls', 'logprobs': None}, id='run-3764f79e-17b4-4aa3-bbe4-4f92b11ca52c-0', tool_calls=[{'name': 'tavily_search_results_json', 'args': {'query': 'weather in San Francisco'}, 'id': 'call_yQrJa8CEOfKBdpVl80jzWf5h'}])]}"
-      ]
-     },
-     "execution_count": 120,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": ["current_values = app.get_state(thread)\ncurrent_values.values"]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 122,
-   "id": "1a0cdb78-40c6-4550-8c27-8f1b02d9e678",
-   "metadata": {},
-   "outputs": [],
-   "source": ["current_values.values[\"messages\"][-1].tool_calls[0][\"args\"][\n    \"query\"\n] = \"weather in San Francisco, Accuweather\""]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 126,
-   "id": "652f699a-89bc-4277-b37a-c3d94b835df5",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "{'configurable': {'thread_id': '1',\n",
-       "  'thread_ts': '2024-04-20T01:13:15.108790+00:00'}}"
-      ]
-     },
-     "execution_count": 126,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": ["app.update_state(thread, current_values.values)"]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 127,
-   "id": "e2b29825-a108-4d40-b377-22e8f4629d64",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "StateSnapshot(values={'messages': [HumanMessage(content='what's the weather in sf?', id='1cfd0c2f-9b60-48da-8938-408fd6aeda13'), AIMessage(content='', additional_kwargs={'tool_calls': [{'id': 'call_yQrJa8CEOfKBdpVl80jzWf5h', 'function': {'arguments': '{\"query\":\"weather in San Francisco\"}', 'name': 'tavily_search_results_json'}, 'type': 'function'}]}, response_metadata={'token_usage': {'completion_tokens': 21, 'prompt_tokens': 94, 'total_tokens': 115}, 'model_name': 'gpt-3.5-turbo', 'system_fingerprint': 'fp_c2295e73ad', 'finish_reason': 'tool_calls', 'logprobs': None}, id='run-3764f79e-17b4-4aa3-bbe4-4f92b11ca52c-0', tool_calls=[{'name': 'tavily_search_results_json', 'args': {'query': 'weather in San Francisco, Accuweather'}, 'id': 'call_yQrJa8CEOfKBdpVl80jzWf5h'}])]}, next=('action',), config={'configurable': {'thread_id': '1', 'thread_ts': '2024-04-20T01:13:15.108790+00:00'}}, parent_config=None)"
-      ]
-     },
-     "execution_count": 127,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": ["app.get_state(thread)"]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 128,
-   "id": "f0aad8a6-056e-42ca-bbc2-b45f768da75f",
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "{'messages': [ToolMessage(content='[{\"url\": \"https://www.weatherapi.com/\", \"content\": \"{\\'location\\': {\\'name\\': \\'San Francisco\\', \\'region\\': \\'California\\', \\'country\\': \\'United States of America\\', \\'lat\\': 37.78, \\'lon\\': -122.42, \\'tz_id\\': \\'America/Los_Angeles\\', \\'localtime_epoch\\': 1713575495, \\'localtime\\': \\'2024-04-19 18:11\\'}, \\'current\\': {\\'last_updated_epoch\\': 1713574800, \\'last_updated\\': \\'2024-04-19 18:00\\', \\'temp_c\\': 16.1, \\'temp_f\\': 61.0, \\'is_day\\': 1, \\'condition\\': {\\'text\\': \\'Sunny\\', \\'icon\\': \\'//cdn.weatherapi.com/weather/64x64/day/113.png\\', \\'code\\': 1000}, \\'wind_mph\\': 16.1, \\'wind_kph\\': 25.9, \\'wind_degree\\': 300, \\'wind_dir\\': \\'WNW\\', \\'pressure_mb\\': 1015.0, \\'pressure_in\\': 29.97, \\'precip_mm\\': 0.0, \\'precip_in\\': 0.0, \\'humidity\\': 67, \\'cloud\\': 0, \\'feelslike_c\\': 16.1, \\'feelslike_f\\': 61.0, \\'vis_km\\': 16.0, \\'vis_miles\\': 9.0, \\'uv\\': 4.0, \\'gust_mph\\': 20.6, \\'gust_kph\\': 33.1}}\"}]', name='tavily_search_results_json', id='8c7e9af3-6569-4982-a83d-be1ec02f828a', tool_call_id='call_yQrJa8CEOfKBdpVl80jzWf5h')]}\n",
-      "{'messages': [AIMessage(content='The current weather in San Francisco is sunny with a temperature of 61.0°F (16.1°C). The wind speed is 25.9 km/h coming from the west-northwest direction. The humidity is at 67%, and there is no precipitation at the moment.', response_metadata={'token_usage': {'completion_tokens': 59, 'prompt_tokens': 476, 'total_tokens': 535}, 'model_name': 'gpt-3.5-turbo', 'system_fingerprint': 'fp_c2295e73ad', 'finish_reason': 'stop', 'logprobs': None}, id='run-7d652a0d-cb00-4857-b9fb-d3e37b0a6d23-0')]}\n"
-     ]
-    }
-   ],
-   "source": ["for event in app.stream(None, thread):\n    for v in event.values():\n        print(v)"]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 129,
-   "id": "84748206-975e-4a33-a178-d43df683298c",
-   "metadata": {},
-   "outputs": [],
-   "source": ["chkpnt_tuple = memory.get_tuple({\"configurable\": {\"thread_id\": \"1\"}})\nconfig = chkpnt_tuple.config\ncheckpoint = chkpnt_tuple.checkpoint\nmetadata = chkpnt_tuple.metadata\n\n# mark as \"good\"\nmetadata[\"score\"] = 1\nmemory.put(config, checkpoint, metadata)"]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 130,
-   "id": "ce7fa228-8c37-4001-afd4-0001b268e1db",
-   "metadata": {},
-   "outputs": [],
-   "source": ["examples = list(memory.search({\"score\": 1}))"]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 131,
-   "id": "0543a501-b4cb-4890-8236-3350ebee5af9",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "[CheckpointTuple(config={'configurable': {'thread_id': '1', 'thread_ts': '2024-04-20T01:13:36.933600+00:00'}}, checkpoint={'v': 1, 'ts': '2024-04-20T01:13:36.933600+00:00', 'channel_values': {'messages': [HumanMessage(content='what's the weather in sf?', id='1cfd0c2f-9b60-48da-8938-408fd6aeda13'), AIMessage(content='', additional_kwargs={'tool_calls': [{'id': 'call_yQrJa8CEOfKBdpVl80jzWf5h', 'function': {'arguments': '{\"query\":\"weather in San Francisco\"}', 'name': 'tavily_search_results_json'}, 'type': 'function'}]}, response_metadata={'token_usage': {'completion_tokens': 21, 'prompt_tokens': 94, 'total_tokens': 115}, 'model_name': 'gpt-3.5-turbo', 'system_fingerprint': 'fp_c2295e73ad', 'finish_reason': 'tool_calls', 'logprobs': None}, id='run-3764f79e-17b4-4aa3-bbe4-4f92b11ca52c-0', tool_calls=[{'name': 'tavily_search_results_json', 'args': {'query': 'weather in San Francisco, Accuweather'}, 'id': 'call_yQrJa8CEOfKBdpVl80jzWf5h'}]), ToolMessage(content='[{\"url\": \"https://www.weatherapi.com/\", \"content\": \"{\\'location\\': {\\'name\\': \\'San Francisco\\', \\'region\\': \\'California\\', \\'country\\': \\'United States of America\\', \\'lat\\': 37.78, \\'lon\\': -122.42, \\'tz_id\\': \\'America/Los_Angeles\\', \\'localtime_epoch\\': 1713575495, \\'localtime\\': \\'2024-04-19 18:11\\'}, \\'current\\': {\\'last_updated_epoch\\': 1713574800, \\'last_updated\\': \\'2024-04-19 18:00\\', \\'temp_c\\': 16.1, \\'temp_f\\': 61.0, \\'is_day\\': 1, \\'condition\\': {\\'text\\': \\'Sunny\\', \\'icon\\': \\'//cdn.weatherapi.com/weather/64x64/day/113.png\\', \\'code\\': 1000}, \\'wind_mph\\': 16.1, \\'wind_kph\\': 25.9, \\'wind_degree\\': 300, \\'wind_dir\\': \\'WNW\\', \\'pressure_mb\\': 1015.0, \\'pressure_in\\': 29.97, \\'precip_mm\\': 0.0, \\'precip_in\\': 0.0, \\'humidity\\': 67, \\'cloud\\': 0, \\'feelslike_c\\': 16.1, \\'feelslike_f\\': 61.0, \\'vis_km\\': 16.0, \\'vis_miles\\': 9.0, \\'uv\\': 4.0, \\'gust_mph\\': 20.6, \\'gust_kph\\': 33.1}}\"}]', name='tavily_search_results_json', id='8c7e9af3-6569-4982-a83d-be1ec02f828a', tool_call_id='call_yQrJa8CEOfKBdpVl80jzWf5h'), AIMessage(content='The current weather in San Francisco is sunny with a temperature of 61.0°F (16.1°C). The wind speed is 25.9 km/h coming from the west-northwest direction. The humidity is at 67%, and there is no precipitation at the moment.', response_metadata={'token_usage': {'completion_tokens': 59, 'prompt_tokens': 476, 'total_tokens': 535}, 'model_name': 'gpt-3.5-turbo', 'system_fingerprint': 'fp_c2295e73ad', 'finish_reason': 'stop', 'logprobs': None}, id='run-7d652a0d-cb00-4857-b9fb-d3e37b0a6d23-0')], 'agent': {'messages': [AIMessage(content='The current weather in San Francisco is sunny with a temperature of 61.0°F (16.1°C). The wind speed is 25.9 km/h coming from the west-northwest direction. The humidity is at 67%, and there is no precipitation at the moment.', response_metadata={'token_usage': {'completion_tokens': 59, 'prompt_tokens': 476, 'total_tokens': 535}, 'model_name': 'gpt-3.5-turbo', 'system_fingerprint': 'fp_c2295e73ad', 'finish_reason': 'stop', 'logprobs': None}, id='run-7d652a0d-cb00-4857-b9fb-d3e37b0a6d23-0')]}}, 'channel_versions': defaultdict(<class 'int'>, {'__start__': 1, 'messages': 6, 'start:agent': 2, 'action': 5, 'agent': 6, 'branch:agent:should_continue:action': 4}), 'versions_seen': defaultdict(<function _seen_dict at 0x10832bce0>, {'__start__': defaultdict(<class 'int'>, {'__start__': 1}), 'agent': defaultdict(<class 'int'>, {'start:agent': 2, 'action': 5}), 'action': defaultdict(<class 'int'>, {'branch:agent:should_continue:action': 4}), '__interrupt__': defaultdict(<class 'int'>, {'messages': 4})})}, parent_config={'configurable': {'thread_id': '1', 'thread_ts': '2024-04-20T01:13:35.392072+00:00'}})]"
-      ]
-     },
-     "execution_count": 131,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": ["examples"]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 157,
-   "id": "336a70d3-d8c7-4310-a373-df2be3320030",
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "{'messages': [AIMessage(content='', additional_kwargs={'tool_calls': [{'id': 'call_amNrYLgitup6hCDiHUYKwodH', 'function': {'arguments': '{\"query\":\"weather in Los Angeles, Accuweather\"}', 'name': 'tavily_search_results_json'}, 'type': 'function'}]}, response_metadata={'token_usage': {'completion_tokens': 25, 'prompt_tokens': 296, 'total_tokens': 321}, 'model_name': 'gpt-3.5-turbo', 'system_fingerprint': 'fp_c2295e73ad', 'finish_reason': 'tool_calls', 'logprobs': None}, id='run-6b852685-e84f-48e7-b8d0-5b0a44ac9776-0', tool_calls=[{'name': 'tavily_search_results_json', 'args': {'query': 'weather in Los Angeles, Accuweather'}, 'id': 'call_amNrYLgitup6hCDiHUYKwodH'}])]}\n"
-     ]
-    }
-   ],
-   "source": ["thread = {\"configurable\": {\"thread_id\": \"7\"}}\nfor event in app.stream(\n    {\"messages\": [HumanMessage(content=\"what's the weather in la?\")]}, thread\n):\n    for v in event.values():\n        print(v)"]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 158,
-   "id": "ab245e7c-e47d-45e4-8b79-361fab359e76",
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "{'messages': [ToolMessage(content='[{\"url\": \"https://www.weatherapi.com/\", \"content\": \"{\\'location\\': {\\'name\\': \\'Los Angeles\\', \\'region\\': \\'California\\', \\'country\\': \\'United States of America\\', \\'lat\\': 34.05, \\'lon\\': -118.24, \\'tz_id\\': \\'America/Los_Angeles\\', \\'localtime_epoch\\': 1713576177, \\'localtime\\': \\'2024-04-19 18:22\\'}, \\'current\\': {\\'last_updated_epoch\\': 1713575700, \\'last_updated\\': \\'2024-04-19 18:15\\', \\'temp_c\\': 17.8, \\'temp_f\\': 64.0, \\'is_day\\': 1, \\'condition\\': {\\'text\\': \\'Partly cloudy\\', \\'icon\\': \\'//cdn.weatherapi.com/weather/64x64/day/116.png\\', \\'code\\': 1003}, \\'wind_mph\\': 4.3, \\'wind_kph\\': 6.8, \\'wind_degree\\': 250, \\'wind_dir\\': \\'WSW\\', \\'pressure_mb\\': 1014.0, \\'pressure_in\\': 29.94, \\'precip_mm\\': 0.0, \\'precip_in\\': 0.0, \\'humidity\\': 65, \\'cloud\\': 50, \\'feelslike_c\\': 17.8, \\'feelslike_f\\': 64.0, \\'vis_km\\': 16.0, \\'vis_miles\\': 9.0, \\'uv\\': 5.0, \\'gust_mph\\': 10.3, \\'gust_kph\\': 16.6}}\"}]', name='tavily_search_results_json', id='6477bd73-bdf1-46c5-ab0a-cc808b1a183e', tool_call_id='call_amNrYLgitup6hCDiHUYKwodH')]}\n",
-      "{'messages': [AIMessage(content='The current weather in Los Angeles is partly cloudy with a temperature of 64.0°F (17.8°C). The wind speed is 6.8 km/h coming from the west-southwest direction. The humidity is at 65%, and there is no precipitation at the moment.', response_metadata={'token_usage': {'completion_tokens': 60, 'prompt_tokens': 679, 'total_tokens': 739}, 'model_name': 'gpt-3.5-turbo', 'system_fingerprint': 'fp_c2295e73ad', 'finish_reason': 'stop', 'logprobs': None}, id='run-a604c04e-c3c3-4545-804f-a89aee6bf516-0')]}\n"
-     ]
-    }
-   ],
-   "source": ["for event in app.stream(None, thread):\n    for v in event.values():\n        print(v)"]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "9ab115de-9b11-4e8b-8ace-c23e1369300b",
-   "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": "3.11.9"
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 5
-}
diff --git a/libs/langgraph/langgraph/managed/few_shot.py b/libs/langgraph/langgraph/managed/few_shot.py
deleted file mode 100644
index 6dcab2aa8..000000000
--- a/libs/langgraph/langgraph/managed/few_shot.py
+++ /dev/null
@@ -1,105 +0,0 @@
-from contextlib import asynccontextmanager, contextmanager
-from typing import (
-    TYPE_CHECKING,
-    Any,
-    AsyncGenerator,
-    AsyncIterator,
-    Callable,
-    Dict,
-    Generator,
-    Generic,
-    Iterator,
-    Optional,
-    Sequence,
-    Union,
-)
-
-from langchain_core.runnables import RunnableConfig
-from typing_extensions import Self
-
-from langgraph.channels.manager import AsyncChannelsManager, ChannelsManager
-from langgraph.managed.base import ConfiguredManagedValue, ManagedValue, V
-from langgraph.pregel import Pregel
-from langgraph.pregel.io import read_channels
-from langgraph.pregel.types import PregelTaskDescription
-
-if TYPE_CHECKING:
-    from langgraph.pregel import Pregel
-
-# Metadata filter can be a dict (static) or a function (dynamic) that takes a
-# RunnableConfig and returns a dict. Functions are used for filtering on
-# metadata values that are only available at runtime.
-MetadataFilter = Union[Dict[str, Any], Callable[[RunnableConfig], Dict[str, Any]]]
-
-
-class FewShotExamples(ManagedValue[Sequence[V]], Generic[V]):
-    examples: list[V]
-
-    def __init__(
-        self,
-        config: RunnableConfig,
-        graph: Pregel,
-        k: int = 5,
-        metadata_filter: Optional[MetadataFilter] = None,
-    ) -> None:
-        super().__init__(config, graph)
-        self.k = k
-        self.metadata_filter = metadata_filter or {}
-
-    @classmethod
-    def configure(
-        cls, k: int = 5, metadata_filter: Optional[MetadataFilter] = None
-    ) -> ConfiguredManagedValue:
-        return ConfiguredManagedValue(
-            cls,
-            {
-                "k": k,
-                "metadata_filter": metadata_filter,
-            },
-        )
-
-    @property
-    def metadata_filter_dict(self) -> Dict[str, Any]:
-        if isinstance(self.metadata_filter, Callable):
-            return self.metadata_filter(self.config)
-        else:
-            return self.metadata_filter
-
-    def iter(self, score: int = 1) -> Iterator[V]:
-        for example in self.graph.checkpointer.list(
-            None, filter={"score": score, **self.metadata_filter_dict}, limit=self.k
-        ):
-            with ChannelsManager(
-                self.graph.channels, example.checkpoint, self.config
-            ) as channels:
-                yield read_channels(channels, self.graph.output_channels)
-
-    async def aiter(self, score: int = 1) -> AsyncIterator[V]:
-        async for example in self.graph.checkpointer.alist(
-            None, filter={"score": score, **self.metadata_filter_dict}, limit=self.k
-        ):
-            async with AsyncChannelsManager(
-                self.graph.channels, example.checkpoint, self.config
-            ) as channels:
-                yield read_channels(channels, self.graph.output_channels)
-
-    @classmethod
-    @contextmanager
-    def enter(
-        cls, config: RunnableConfig, graph: "Pregel", **kwargs: Any
-    ) -> Generator[Self, None, None]:
-        with super().enter(config, graph, **kwargs) as value:
-            value.examples = list(value.iter())
-            yield value
-
-    @classmethod
-    @asynccontextmanager
-    async def aenter(
-        cls, config: RunnableConfig, graph: "Pregel", **kwargs: Any
-    ) -> AsyncGenerator[Self, None]:
-        async with super().aenter(config, graph, **kwargs) as value:
-            value.examples = [e async for e in value.aiter()]
-            yield value
-
-    def __call__(self, step: int, task: PregelTaskDescription) -> Sequence[V]:
-        return self.examples
diff --git a/libs/langgraph/tests/test_pregel.py b/libs/langgraph/tests/test_pregel.py
index 01d02a1f6..7095a5b39 100644
--- a/libs/langgraph/tests/test_pregel.py
+++ b/libs/langgraph/tests/test_pregel.py
@@ -54,7 +54,6 @@ from langgraph.graph import END, Graph
 from langgraph.graph.graph import START
 from langgraph.graph.message import MessageGraph, add_messages
 from langgraph.graph.state import StateGraph
-from langgraph.managed.few_shot import FewShotExamples
 from langgraph.prebuilt.chat_agent_executor import (
     create_function_calling_executor,
     create_tool_calling_executor,
@@ -3394,233 +3393,6 @@ def test_state_graph_w_config(snapshot: SnapshotAssertion) -> None:
     assert app.config_schema().schema_json() == snapshot
 
 
-def test_state_graph_few_shot() -> None:
-    from langchain_core.language_models.fake_chat_models import (
-        FakeMessagesListChatModel,
-    )
-    from langchain_core.messages import AIMessage, AnyMessage, ToolMessage
-    from langchain_core.prompts import ChatPromptTemplate
-    from langchain_core.tools import tool
-
-    def filter_by_source(config: RunnableConfig) -> Dict[str, Any]:
-        """This function is a trivial example that demonstrates that passing
-        a Callable to metadata_filter works as expected.
-        """
-        return {"source": "loop"}
-
-    class BaseState(TypedDict):
-        messages: Annotated[list[AnyMessage], add_messages]
-
-    class AgentState(BaseState):
-        examples: Annotated[
-            Sequence[BaseState],
-            FewShotExamples[BaseState].configure(k=1, metadata_filter=filter_by_source),
-        ]
-
-    # Assemble the tools
-    @tool()
-    def search_api(query: str) -> str:
-        """Searches the API for the query."""
-        return f"result for {query}"
-
-    tools = [search_api]
-
-    prompt = ChatPromptTemplate.from_messages(
-        [
-            (
-                "system",
-                """You are a nice assistant.
-Some examples of past conversations:
-{examples}""",
-            ),
-            ("placeholder", "{messages}"),
-        ]
-    )
-
-    model = FakeMessagesListChatModel(
-        responses=[
-            AIMessage(
-                content="",
-                tool_calls=[
-                    {
-                        "id": "tool_call123",
-                        "name": "search_api",
-                        "args": {"query": "query"},
-                    },
-                ],
-            ),
-            AIMessage(content="answer"),
-        ]
-    )
-
-    def agent(state: AgentState, config: RunnableConfig) -> AgentState:
-        # begin: testing code
-        assert state["examples"] == config["configurable"]["expected_examples"]
-        # end: testing code
-        formatted = prompt.invoke(state)
-        response = model.invoke(formatted)
-        return {"messages": response}
-
-    # Define decision-making logic
-    def should_continue(data: AgentState) -> str:
-        # Logic to decide whether to continue in the loop or exit
-        if not data["messages"][-1].tool_calls:
-            return "exit"
-        else:
-            return "continue"
-
-    # Define a new graph
-    workflow = StateGraph(AgentState)
-
-    workflow.add_node("agent", agent)
-    workflow.add_node("tools", ToolNode(tools))
-    workflow.set_entry_point("agent")
-    workflow.add_conditional_edges(
-        "agent", should_continue, {"continue": "tools", "exit": END}
-    )
-    workflow.add_edge("tools", "agent")
-
-    with SqliteSaver.from_conn_string(":memory:") as saver:
-        app = workflow.compile(checkpointer=saver)
-
-        first_messages = [
-            _AnyIdHumanMessage(content="what is weather in sf"),
-            AIMessage(
-                content="",
-                id=AnyStr(),
-                tool_calls=[
-                    {
-                        "name": "search_api",
-                        "args": {"query": "query"},
-                        "id": "tool_call123",
-                        "type": "tool_call",
-                    }
-                ],
-            ),
-            ToolMessage(
-                content="result for query",
-                name="search_api",
-                id=AnyStr(),
-                tool_call_id="tool_call123",
-            ),
-            _AnyIdAIMessage(content="answer"),
-        ]
-        actual = app.invoke(
-            {"messages": "what is weather in sf"},
-            {
-                "configurable": {
-                    "thread_id": "1",
-                    "expected_examples": [],
-                },
-            },
-        )
-        expected = {"messages": first_messages}
-        assert actual == expected
-
-        # get first checkpoint
-        chkpnt_tuple_1 = saver.get_tuple({"configurable": {"thread_id": "1"}})
-        config = chkpnt_tuple_1.config
-        checkpoint = chkpnt_tuple_1.checkpoint
-        metadata = chkpnt_tuple_1.metadata
-
-        # not needed in application code, only for testing
-        hiscored = list(saver.list(None, filter={"score": 1}))
-        assert hiscored == []
-
-        # mark as "good"
-        metadata["score"] = 1
-        saver.put(config, checkpoint, metadata)
-
-        # not needed in application code, only for testing
-        hiscored = list(saver.list(None, filter={"score": 1}))
-        assert len(hiscored) == 1
-        assert hiscored[0].checkpoint["channel_values"]["messages"] == first_messages
-
-        second_messages = [
-            _AnyIdHumanMessage(content="what is weather in la"),
-            AIMessage(
-                content="",
-                id=AnyStr(),
-                tool_calls=[
-                    {
-                        "name": "search_api",
-                        "args": {"query": "query"},
-                        "id": "tool_call123",
-                        "type": "tool_call",
-                    }
-                ],
-            ),
-            ToolMessage(
-                content="result for query",
-                name="search_api",
-                id=AnyStr(),
-                tool_call_id="tool_call123",
-            ),
-            _AnyIdAIMessage(content="answer"),
-        ]
-        assert app.invoke(
-            {"messages": "what is weather in la"},
-            {
-                "configurable": {
-                    "thread_id": "2",
-                    # below is only for testing purposes, not part of few shot api
-                    "expected_examples": [{"messages": first_messages}],
-                }
-            },
-        ) == {"messages": second_messages}
-
-        # get first checkpoint
-        chkpnt_tuple_2 = saver.get_tuple({"configurable": {"thread_id": "2"}})
-        config = chkpnt_tuple_2.config
-        checkpoint = chkpnt_tuple_2.checkpoint
-        metadata = chkpnt_tuple_2.metadata
-
-        # not needed in application code, only for testing
-        hiscored = list(saver.list(None, filter={"score": 1}))
-        assert len(hiscored) == 1
-
-        # mark as "good"
-        metadata["score"] = 1
-        saver.put(config, checkpoint, metadata)
-
-        hiscored = list(saver.list(None, filter={"score": 1}))
-        assert len(hiscored) == 2
-
-        assert app.invoke(
-            {"messages": "what is weather in ny"},
-            {
-                "configurable": {
-                    "thread_id": "3",
-                    # below is only for testing purposes, not part of few shot api
-                    "expected_examples": [{"messages": second_messages}],
-                }
-            },
-        ) == {
-            "messages": [
-                _AnyIdHumanMessage(content="what is weather in ny"),
-                AIMessage(
-                    content="",
-                    id=AnyStr(),
-                    tool_calls=[
-                        {
-                            "name": "search_api",
-                            "args": {"query": "query"},
-                            "id": "tool_call123",
-                            "type": "tool_call",
-                        }
-                    ],
-                ),
-                ToolMessage(
-                    content="result for query",
-                    name="search_api",
-                    id=AnyStr(),
-                    tool_call_id="tool_call123",
-                ),
-                _AnyIdAIMessage(content="answer"),
-            ]
-        }
-
-
 def test_conditional_entrypoint_graph_state(snapshot: SnapshotAssertion) -> None:
     class AgentState(TypedDict, total=False):
         input: str
diff --git a/libs/langgraph/tests/test_pregel_async.py b/libs/langgraph/tests/test_pregel_async.py
index 883dc2c40..9b3a5ec1f 100644
--- a/libs/langgraph/tests/test_pregel_async.py
+++ b/libs/langgraph/tests/test_pregel_async.py
@@ -14,7 +14,6 @@ from typing import (
     List,
     Literal,
     Optional,
-    Sequence,
     Tuple,
     TypedDict,
     Union,
@@ -52,7 +51,6 @@ from langgraph.errors import InvalidUpdateError
 from langgraph.graph import END, Graph, StateGraph
 from langgraph.graph.graph import START
 from langgraph.graph.message import MessageGraph, add_messages
-from langgraph.managed.few_shot import FewShotExamples
 from langgraph.prebuilt.chat_agent_executor import (
     create_function_calling_executor,
     create_tool_calling_executor,
@@ -3242,187 +3240,6 @@ async def test_conditional_graph_state() -> None:
     )
 
 
-async def test_state_graph_few_shot() -> None:
-    from langchain_core.language_models.fake_chat_models import (
-        FakeMessagesListChatModel,
-    )
-    from langchain_core.messages import (
-        AIMessage,
-        AnyMessage,
-        ToolCall,
-        ToolMessage,
-    )
-    from langchain_core.prompts import ChatPromptTemplate
-    from langchain_core.tools import tool
-
-    def filter_by_source(config: RunnableConfig) -> Dict[str, Any]:
-        """This function is a trivial example that demonstrates that passing
-        a Callable to metadata_filter works as expected.
-        """
-        return {"source": "loop"}
-
-    class BaseState(TypedDict):
-        messages: Annotated[list[AnyMessage], add_messages]
-
-    class AgentState(BaseState):
-        examples: Annotated[
-            Sequence[BaseState],
-            FewShotExamples[BaseState].configure(k=1, metadata_filter=filter_by_source),
-        ]
-
-    # Assemble the tools
-    @tool()
-    def search_api(query: str) -> str:
-        """Searches the API for the query."""
-        return f"result for {query}"
-
-    tools = [search_api]
-    tools_by_name = {t.name: t for t in tools}
-
-    prompt = ChatPromptTemplate.from_messages(
-        [
-            (
-                "system",
-                """You are a nice assistant.
-Some examples of past conversations:
-{examples}""",
-            ),
-            ("placeholder", "{messages}"),
-        ]
-    )
-
-    model = FakeMessagesListChatModel(
-        responses=[
-            AIMessage(
-                content="",
-                tool_calls=[
-                    {
-                        "id": "tool_call123",
-                        "name": "search_api",
-                        "args": {"query": "query"},
-                    },
-                ],
-            ),
-            AIMessage(content="answer"),
-        ]
-    )
-
-    async def agent(state: AgentState, config: RunnableConfig) -> AgentState:
-        # begin: testing code
-        assert state["examples"] == config["configurable"]["expected_examples"]
-        # end: testing code
-        formatted = await prompt.ainvoke(state)
-        response = await model.ainvoke(formatted)
-        return {"messages": response}
-
-    # Define decision-making logic
-    def should_continue(data: AgentState) -> str:
-        # Logic to decide whether to continue in the loop or exit
-        if tool_calls := data["messages"][-1].tool_calls:
-            return [Send("tools", tool_call) for tool_call in tool_calls]
-        else:
-            return "exit"
-
-    def tools_node(tool_call: ToolCall, config: RunnableConfig) -> AgentState:
-        output = tools_by_name[tool_call["name"]].invoke(tool_call["args"], config)
-        return {
-            "messages": ToolMessage(
-                content=output, name=tool_call["name"], tool_call_id=tool_call["id"]
-            )
-        }
-
-    # Define a new graph
-    workflow = StateGraph(AgentState)
-
-    workflow.add_node("agent", agent)
-    workflow.add_node("tools", tools_node)
-    workflow.set_entry_point("agent")
-    workflow.add_conditional_edges(
-        "agent", should_continue, {"continue": "tools", "exit": END}
-    )
-    workflow.add_edge("tools", "agent")
-
-    async with AsyncSqliteSaver.from_conn_string(":memory:") as saver:
-        app = workflow.compile(checkpointer=saver)
-
-        first_messages = [
-            _AnyIdHumanMessage(content="what is weather in sf"),
-            AIMessage(
-                content="",
-                id=AnyStr(),
-                tool_calls=[
-                    {
-                        "name": "search_api",
-                        "args": {"query": "query"},
-                        "id": "tool_call123",
-                    }
-                ],
-            ),
-            ToolMessage(
-                content="result for query",
-                name="search_api",
-                id=AnyStr(),
-                tool_call_id="tool_call123",
-            ),
-            _AnyIdAIMessage(content="answer"),
-        ]
-        assert await app.ainvoke(
-            {"messages": "what is weather in sf"},
-            {"configurable": {"thread_id": "1", "expected_examples": []}},
-        ) == {"messages": first_messages}
-
-        # get first checkpoint
-        chkpnt_tuple_1 = await saver.aget_tuple({"configurable": {"thread_id": "1"}})
-        config = chkpnt_tuple_1.config
-        checkpoint = chkpnt_tuple_1.checkpoint
-        metadata = chkpnt_tuple_1.metadata
-
-        # not needed in application code, only for testing
-        assert [c async for c in saver.alist(None, filter={"score": 1})] == []
-
-        # mark as "good"
-        metadata["score"] = 1
-        await saver.aput(config, checkpoint, metadata)
-
-        # not needed in application code, only for testing
-        hiscored = [c async for c in saver.alist(None, filter={"score": 1})]
-        assert len(hiscored) == 1
-        assert hiscored[0].checkpoint["channel_values"]["messages"] == first_messages
-
-        assert await app.ainvoke(
-            {"messages": "what is weather in la"},
-            {
-                "configurable": {
-                    "thread_id": "2",
-                    # below is only for testing purposes, not part of few shot api
-                    "expected_examples": [{"messages": first_messages}],
-                }
-            },
-        ) == {
-            "messages": [
-                _AnyIdHumanMessage(content="what is weather in la"),
-                AIMessage(
-                    content="",
-                    id=AnyStr(),
-                    tool_calls=[
-                        {
-                            "name": "search_api",
-                            "args": {"query": "query"},
-                            "id": "tool_call123",
-                        }
-                    ],
-                ),
-                ToolMessage(
-                    content="result for query",
-                    name="search_api",
-                    id=AnyStr(),
-                    tool_call_id="tool_call123",
-                ),
-                _AnyIdAIMessage(content="answer"),
-            ]
-        }
-
-
 async def test_conditional_entrypoint_graph() -> None:
     async def left(data: str) -> str:
         return data + "->left"